01-introduction.ipynb

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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        }
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      "outputs": [
        {
          "output_type": "error",
          "ename": "ModuleNotFoundError",
          "evalue": "No module named 'preamble'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-6209498111fc>\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mpreamble\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'matplotlib'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'inline'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'preamble'",
            "",
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
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            "actions": [
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                "action": "open_url",
                "actionText": "Open Examples",
                "url": "/notebooks/snippets/importing_libraries.ipynb"
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      ],
      "source": [
        "from preamble import *\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "hide_input": false,
        "id": "7Wo59v7Q86T-"
      },
      "source": [
        "## Introduction\n",
        "### Why Machine Learning?\n",
        "#### Problems Machine Learning Can Solve"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sM5eBHos86T_"
      },
      "source": [
        "#### Knowing Your Task and Knowing Your Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ozKxmxRk86T_"
      },
      "source": [
        "### Why Python?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fy5yDNeD86T_"
      },
      "source": [
        "### scikit-learn\n",
        "#### Installing scikit-learn"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gu3rYcwy86T_"
      },
      "source": [
        "### Essential Libraries and Tools"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-7glxunX86T_"
      },
      "source": [
        "#### Jupyter Notebook"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cevJ22PI86T_"
      },
      "source": [
        "#### NumPy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "uuid": "e2b8e959-75f0-4fa9-a878-5ab024f89223",
        "id": "MdogRixn86UA",
        "outputId": "d755fe31-050a-41e5-df61-d85271a5ba05"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "x:\n",
            "[[1 2 3]\n",
            " [4 5 6]]\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "\n",
        "x = np.array([[1, 2, 3], [4, 5, 6]])\n",
        "print(\"x:\\n{}\".format(x))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cM7Id6KG86UA"
      },
      "source": [
        "#### SciPy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mUadR_IR86UA",
        "outputId": "5274e881-b2af-422a-bb0f-2c0994663daa"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "NumPy array:\n",
            " [[1. 0. 0. 0.]\n",
            " [0. 1. 0. 0.]\n",
            " [0. 0. 1. 0.]\n",
            " [0. 0. 0. 1.]]\n"
          ]
        }
      ],
      "source": [
        "from scipy import sparse\n",
        "\n",
        "# Create a 2D NumPy array with a diagonal of ones, and zeros everywhere else\n",
        "eye = np.eye(4)\n",
        "print(\"NumPy array:\\n\", eye)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HLMnQjrB86UA",
        "outputId": "cd350221-fe32-4f60-f26c-00e1ce8ca326"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "SciPy sparse CSR matrix:\n",
            "   (0, 0)\t1.0\n",
            "  (1, 1)\t1.0\n",
            "  (2, 2)\t1.0\n",
            "  (3, 3)\t1.0\n"
          ]
        }
      ],
      "source": [
        "# Convert the NumPy array to a SciPy sparse matrix in CSR format\n",
        "# Only the nonzero entries are stored\n",
        "sparse_matrix = sparse.csr_matrix(eye)\n",
        "print(\"\\nSciPy sparse CSR matrix:\\n\", sparse_matrix)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6lob988s86UA",
        "outputId": "f99de0e7-cc09-4c8e-cd75-814edd7ec671"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "COO representation:\n",
            "   (0, 0)\t1.0\n",
            "  (1, 1)\t1.0\n",
            "  (2, 2)\t1.0\n",
            "  (3, 3)\t1.0\n"
          ]
        }
      ],
      "source": [
        "data = np.ones(4)\n",
        "row_indices = np.arange(4)\n",
        "col_indices = np.arange(4)\n",
        "eye_coo = sparse.coo_matrix((data, (row_indices, col_indices)))\n",
        "print(\"COO representation:\\n\", eye_coo)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jNRYo0sD86UA"
      },
      "source": [
        "#### matplotlib"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "uuid": "30faf136-0ef7-4762-bd82-3795eea323d0",
        "id": "U4XSgQ1386UA",
        "outputId": "b6227325-5728-4645-e8d5-0962ba3662f2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x1be867b9748>]"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
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\n",
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Generate a sequence of numbers from -10 to 10 with 100 steps in between\n",
        "x = np.linspace(-10, 10, 100)\n",
        "# Create a second array using sine\n",
        "y = np.sin(x)\n",
        "# The plot function makes a line chart of one array against another\n",
        "plt.plot(x, y, marker=\"x\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5jFkbDhK86UB"
      },
      "source": [
        "#### pandas"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "uuid": "ad1b06f7-e03a-4938-9d59-5bb40e848553",
        "id": "FkbC66k686UB",
        "outputId": "ac2bbd9a-6fdc-41e0-e7e2-b8e29e0de460"
      },
      "outputs": [
        {
          "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>Name</th>\n",
              "      <th>Location</th>\n",
              "      <th>Age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>John</td>\n",
              "      <td>New York</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Anna</td>\n",
              "      <td>Paris</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Peter</td>\n",
              "      <td>Berlin</td>\n",
              "      <td>53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Linda</td>\n",
              "      <td>London</td>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Name  Location  Age\n",
              "0   John  New York   24\n",
              "1   Anna     Paris   13\n",
              "2  Peter    Berlin   53\n",
              "3  Linda    London   33"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "# create a simple dataset of people\n",
        "data = {'Name': [\"John\", \"Anna\", \"Peter\", \"Linda\"],\n",
        "        'Location' : [\"New York\", \"Paris\", \"Berlin\", \"London\"],\n",
        "        'Age' : [24, 13, 53, 33]\n",
        "       }\n",
        "\n",
        "data_pandas = pd.DataFrame(data)\n",
        "# IPython.display allows \"pretty printing\" of dataframes\n",
        "# in the Jupyter notebook\n",
        "display(data_pandas)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eBryGbzo86UB",
        "outputId": "8887c6b8-a12d-4175-f04c-9991ed889c4e"
      },
      "outputs": [
        {
          "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>Name</th>\n",
              "      <th>Location</th>\n",
              "      <th>Age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Peter</td>\n",
              "      <td>Berlin</td>\n",
              "      <td>53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Linda</td>\n",
              "      <td>London</td>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Name Location  Age\n",
              "2  Peter   Berlin   53\n",
              "3  Linda   London   33"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Select all rows that have an age column greater than 30\n",
        "display(data_pandas[data_pandas.Age > 30])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dq7vyai286UB"
      },
      "source": [
        "#### mglearn"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GNbg-cvR86UB"
      },
      "source": [
        "### Python 2 versus Python 3"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z1uiiefG86UB"
      },
      "source": [
        "### Versions Used in this Book"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sxJ8las386UB",
        "outputId": "8751d057-8113-4c64-de92-e7359faedbc2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Python version: 3.7.6 (default, Jan  8 2020, 20:23:39) [MSC v.1916 64 bit (AMD64)]\n",
            "pandas version: 1.0.3\n",
            "matplotlib version: 3.1.3\n",
            "NumPy version: 1.18.1\n",
            "SciPy version: 1.4.1\n",
            "IPython version: 7.13.0\n",
            "scikit-learn version: 0.24.dev0\n"
          ]
        }
      ],
      "source": [
        "import sys\n",
        "print(\"Python version:\", sys.version)\n",
        "\n",
        "import pandas as pd\n",
        "print(\"pandas version:\", pd.__version__)\n",
        "\n",
        "import matplotlib\n",
        "print(\"matplotlib version:\", matplotlib.__version__)\n",
        "\n",
        "import numpy as np\n",
        "print(\"NumPy version:\", np.__version__)\n",
        "\n",
        "import scipy as sp\n",
        "print(\"SciPy version:\", sp.__version__)\n",
        "\n",
        "import IPython\n",
        "print(\"IPython version:\", IPython.__version__)\n",
        "\n",
        "import sklearn\n",
        "print(\"scikit-learn version:\", sklearn.__version__)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vOdoLpBn86UB"
      },
      "source": [
        "### A First Application: Classifying Iris Species\n",
        "![sepal_petal](https://github.com/khoshmard/introduction_to_ml_with_python/blob/main/images/iris_petal_sepal.png?raw=1)\n",
        "#### Meet the Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "Wv3qDLKv86UC"
      },
      "outputs": [],
      "source": [
        "from sklearn.datasets import load_iris\n",
        "iris_dataset = load_iris()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "i2q-WuZC86UC",
        "outputId": "d6c801f8-f929-4195-c915-adbb99fd05bf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Keys of iris_dataset:\n",
            " dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename'])\n"
          ]
        }
      ],
      "source": [
        "print(\"Keys of iris_dataset:\\n\", iris_dataset.keys())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "rCTue3hT86UC",
        "outputId": "884f2e78-6da7-4e43-c373-dbfb842e5491"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            ".. _iris_dataset:\n",
            "\n",
            "Iris plants dataset\n",
            "--------------------\n",
            "\n",
            "**Data Set Characteristics:**\n",
            "\n",
            "    :Number of Instances: 150 (50 in each of three classes)\n",
            "    :Number of Attributes: 4 numeric, pre\n",
            "...\n"
          ]
        }
      ],
      "source": [
        "print(iris_dataset['DESCR'][:193] + \"\\n...\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ngRMLkoe86UC",
        "outputId": "d293a29a-d501-42d0-d47a-670eec0a5fe2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Target names: ['setosa' 'versicolor' 'virginica']\n"
          ]
        }
      ],
      "source": [
        "print(\"Target names:\", iris_dataset['target_names'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lA8BAWrY86UC",
        "outputId": "b8a6fcea-c733-4d10-c5c0-036a4e14d49f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Feature names:\n",
            " ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n"
          ]
        }
      ],
      "source": [
        "print(\"Feature names:\\n\", iris_dataset['feature_names'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SktuXqGI86UC",
        "outputId": "164424ca-b546-4313-ed69-3ef1305080c6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Type of data: <class 'numpy.ndarray'>\n"
          ]
        }
      ],
      "source": [
        "print(\"Type of data:\", type(iris_dataset['data']))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qwuD1Xgb86UC",
        "outputId": "44a26d88-2948-428e-923b-b99368580f18"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Shape of data: (150, 4)\n"
          ]
        }
      ],
      "source": [
        "print(\"Shape of data:\", iris_dataset['data'].shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gXajDoy686UC",
        "outputId": "e4a72427-cf7f-48ae-c9a7-4061b9d98244"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "First five rows of data:\n",
            " [[5.1 3.5 1.4 0.2]\n",
            " [4.9 3.  1.4 0.2]\n",
            " [4.7 3.2 1.3 0.2]\n",
            " [4.6 3.1 1.5 0.2]\n",
            " [5.  3.6 1.4 0.2]]\n"
          ]
        }
      ],
      "source": [
        "print(\"First five rows of data:\\n\", iris_dataset['data'][:5])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "g9ZFftqs86UD",
        "outputId": "ee6febcd-0f8e-4515-9c4c-6abffd8b7d96"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Type of target: <class 'numpy.ndarray'>\n"
          ]
        }
      ],
      "source": [
        "print(\"Type of target:\", type(iris_dataset['target']))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bcsmlyy386UD",
        "outputId": "0ed46846-5c41-49b0-b680-0e9a1d339d71"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Shape of target: (150,)\n"
          ]
        }
      ],
      "source": [
        "print(\"Shape of target:\", iris_dataset['target'].shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EtGtSWST86UD",
        "outputId": "7321a929-d2ae-4aee-d54e-e3b9cf1fd2c4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Target:\n",
            " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
            " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
            " 2 2]\n"
          ]
        }
      ],
      "source": [
        "print(\"Target:\\n\", iris_dataset['target'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oIUqAacY86UD"
      },
      "source": [
        "#### Measuring Success: Training and Testing Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rkZ7qGfd86UD"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    iris_dataset['data'], iris_dataset['target'], random_state=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b_4bAn5486UD",
        "outputId": "cddd3fe9-28b8-429a-a935-5d55a1702f3a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "X_train shape: (112, 4)\n",
            "y_train shape: (112,)\n"
          ]
        }
      ],
      "source": [
        "print(\"X_train shape:\", X_train.shape)\n",
        "print(\"y_train shape:\", y_train.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oWls8UMI86UE",
        "outputId": "eaf59c8d-2914-4f1e-f821-cebbd55245b7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "X_test shape: (38, 4)\n",
            "y_test shape: (38,)\n"
          ]
        }
      ],
      "source": [
        "print(\"X_test shape:\", X_test.shape)\n",
        "print(\"y_test shape:\", y_test.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N4ZEUSW886UE"
      },
      "source": [
        "#### First Things First: Look at Your Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JClVuQuP86UH",
        "outputId": "9411d648-e2a5-4368-ab27-d98aa42f51b3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000001BE868F9C88>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001BE869714C8>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001BE869A5D48>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001BE869DFE08>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000001BE86A18E48>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001BE86A4FEC8>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001BE86A88F88>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001BE86AC8088>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000001BE86ACEC48>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001BE86B06D88>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001BE86B71188>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001BE86BAA208>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000001BE86BE22C8>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001BE86C1A388>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001BE86C54408>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001BE86C8C3C8>]],\n",
              "      dtype=object)"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
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\n",
            "image/png": 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JjidEB2TFyrM8y2EOu9ZtYAOTmMQ1XMMKVlBMMQCVVHKRi6Q4li1s4RM+YSlLPYJKCBFOa1nLv/iX63VLv4e11PIUT3GSk651n/Ips5jFdVwXljy3hwYaeJ7nOcAB17qNbGQsY1nIwmYDuUn3SyGEaKFTnPKo0Dmd4Qzv8m5Q+3iP9zwqdE5rWMNxjrc5j8HYyU6PCp3TQQ7yGZ9FJA9CiJZZxzqPCp3TVrbyDM+4KnT11HORiwBUUEE99QCc5Szv8E7kMiy6tWKKPSp0Tmc5y9u8HdQ+VrPao0LntJa1HOVom/PYUWxko0eFzmk3u9nJzma3l0qdEEK0UKCT6x72YMUacHuN9lmZctrBjlbnrSUCfY5I5UEI0TL+yq1G8zmfu17XUOOR7v56L3ubPU8JEQqBriV72et62NDafXSla1VbP6dU6oQQooWqqfabZsPW7M2SDRsWLH7Tm96MhUug40QqD0KIlvF3/tFobNhcr+3YPdLdX9uwBXUzLURbBbqWNNAQ8FoIxve2jjq/6YGux51NoM8SzDVZKnVCCNFCzoAE0HhzpNEA5JBDAgkBt48mmku4xG/6AAaEJJ/NCXQc988ohOg4/JVNjSaFFFflLYYYj3T311lkNXueEiIUAl1nMslsdgy6CVPAeWRDfa2qp56TnKSEkpDuNxiBPksw9wVSqRNCiBYaxzgSSeQc5zjDGc5xjtOcppJKruKqoPZxFVf5jDCZSioTmBDqLPs0gxnEEuu13oyZmcyMSB6EEC0zm9keQYwaaKCEEs5yFitWiimmnHLiiXdF440iymMi5jnMkQi3IiLGMY5MMn2m+bsONuXv+5pCChOZ2OY8Oq1hDb/iV/yJP/Ff/Bd/4S+c41zI9t+cWczyGUE7llhmMKPZ7aVSJ4QQLdRAAzZsHjdWUURhwhR0t8U88riDO8gmGwCFIpdclrEsYk/QM8lkKUsZxCDXun704z7uC9iSKIRoPwMYwD3c45r+pIQSNJpMMkkmmXTSqaOOSirJIou+9CWHHBSKTDJZyELGM76dP4XoLqKJZilLGc1oV/TGHvTgVm4N+nuYSy6LWEQOOYBxvRzBCJaxzONhRVt8yqd8wAfUUutaV0ghy1nebBfRUOlDH+7jPo+WyUEMYilLySKr2e1lSgMhhGih7WzHgoUe9MCOHY3GhAmFYh3rmMKUoJ4+jnYsFVQQTTTxxEcg95760pdlLKOaauzYJcy5EJ3AUMeyne28wiseoc4THIsJEz/lp6SRRh11WLCQQoq00ImISyONRSxq0/dwlGMJx/XSjp31rPeZVkEFO9nJVKaG7HiBDGYwD/AAlVRiwtSiSqtU6oQQooWKKHL93nTemDLKqKW2Ra1tKaSELG+tFaqnnUKIyLnABb9zV9mxU0EFaaQR51iEaE+h+B6G43rpnM/RH/drfqS05gGrdL8UQogWCnRRiXUsQggRboFu/BSqQzwwEqKjiyeeaKL9pneWciQtdUII0UITmch61rsiXrrLJ99jrF1A58/D229DQQHExcHMmTBnDpjl1CxEh1BfDx9+CBs2gMUCY8bADTdARkZ75wyAMYzhXd71OeZnKENJI60dciW6nGPHjGvV8eOQng5XXw2TJ7d3rkImhhjGMpZtbPNKM2GKWPCytgrbnYNS6mrgIcfLYcD9Wus3HWm9gZeAOOA/tNZrwpUPIYQItWyyWcACVrGKBhpc6wczmGu5NridnDoFDz0EFRWN6w4cgK1b4ec/B5N0pBCiXVmt8OijsG9f47pjx2DdOvjd7yAnp71y5hJPPItYxAu84FGxyyGHBSxox5yJLmPHDvjtb8HmmAPx+HHYtQu+8Q1YtKh98xZCX+NrnOMchRS61kURxS3cQgYd4yFOc8JWqdNafwB8AKCU2gq4V9weAh4B9gLvNkkTQogObyITGc5wPudzaqllEIO4lEuD38GLL3pW6Jx27oQtW2BqZAZlCyH8WL/es0LnVFYGr7wC3/9+5PPkwzCG8QiPsIc9VFJJb3qTS67fsXZCBM1uhxUrGit07latgrlzoWfPyOcrDOKJ59t8m8McppBCEkjgMi7rVMHDwt7HRyk1CDirta5yWz0a+J7WWiulKpVSyVrrynDnRQghQimFFKYzveUbNjQYLXL+bNwolToh2tuWLf7TNm3qMJU6MG5IpzClvbMhuprjx+HsWd9pWsPmzXDTTZHNUxgplCuybGcUicc4XwfeaLIuSmvtHIxyEUhvupFSaolSaodSasf58+fDnUchhIgs7T0eL6g0IURkNDT4T7PbI5cPIdpLc99zKQcdSiRG438No2Lnzv1MmQKUN91Ia/0U8BRAfn6+3OEIITodu25gw6FnOVt1hOzEgVw+dDFmUwxERcHYsbBrF2dz4MilEGuBvH0QXwdM6ByDsoXo0iZMMLpD+zJxovFvSYkx5sj5/h49XG85z3kOc5hooskjz+80J7XUsp/9WLAwmMH0xHd3Njt2DnGIEkrIJJOhDJUuliK8Bg0yvtOlpb7TneUAjBa9XbuM69ukSZCaGpk8hlgRRRRSSDzx5JHX4mjWddTxAR9QSinDGMblXB6mnHoLa6VOKdUTqNdaN/027FVKTcEYU5eitfYxsEQIITqvY+e389zJX1KebDceXVHAms/fYVGvhxjaczoNd3yLV0cVsGt04zOumHr4xpZLGHd55C4CQgg/rrwSPvoIjh71XJ+UBAsXwt//Dv/4R2NrhckEt9yC/bZbWcUqtrHNFSE3mmjmM59JTPLY1W528xqvUU+9a904xvFNvukRRfc853mGZyihxLUum2zu5d5OE8RBdEJRUXD33fDHP3r3ILn6aujb1/j96afhnXca37N8Odx5J9x4Y2Tz2wZWrLzIixzggGtdHHHcxm3kkhvUPjazmSd50lWeP+Ij/s7f+SW/JIussOTbXbgf8dwIvOV8oZT6s+PX3wO/wQiQ8tsw50EIISLuxRP/aVTo3FQkaV48/TvsuoGPBx9n19f6QEqyMYVBTAz1vXqw8u44zkT7eSoqhIic2Fj4zW/g1luhTx/IzISrroL//m84eRJWrvTsfma3w8qV7N28gq1s9ZjyxIqVf/JPTnLSte4sZ3mFVzwqdAC72MXHfOx6rdE8x3MeFTqAc5zjeZ4P8YcWookZM+DXv4b8fKPVbsgQ+M534P77jfQPPzSmO3Cv9Nls8MwzsHdv++S5Ff7FvzwqdGC0ur3AC5R7dyj0UkUVf+WvXuW5jDJ+z+9Dmld/wtpSp7Ve0eT1dx3/ngJmh/PYQgjRXnYWvk5pqu/xOBeT7Ww+8iKbh5yAmBivsOh2YCtbuZHO84RTiC4rIQFuu834cffkk343aXjvHZji/WRfo9nCFvpitG5sYQt2fI9J2sQm5jIXgGMc4yy+g1UUUcQJTtCf/sF8GiFaZ9Qo48eX997zv93778Po0eHJUwhZsbKd7T7TbNjYwQ7mMCfgPt7mbaxYfaaddCzOsh8uMsNtJ6HUa+2yP6077jw3XeEziK7pXOUxYxZOP87UFFJJld/0C1wIQ66EECFz7pzfpJjzF/2muZftQE//q6jCipVoops9H1zgglTqRPsJFMwwQDnpSGqo8ZjnsalgrsnnCPxZiygKe6VORtgKIUQb2LFjw3MOn/4ZYwJuMzBtDD1oDKigHYuTv0AJQogOoq//mzNL32y/aTnk+Py9qXTSiSa62fe5p2u035YCIcImQFmgf+d42JDkWJzcr8fQfBkEGMAAj+3d96FQDGZw2zPajIAtdY5gJrcD04FeQC2wD/gX8JLW2v/jKCGE6MKqqeY93mM3u6mnnr70ZR7zGM5wcvvMoffWJzid4f3kL/tCNOMmzqeWLF7hFSqooJ56FIp44skii8lMbodPJIQI2o03wvbt3sEjlCL+xlvBR1cuM2am0jj/5GQms4ENHi0E1VRTSSVVVPELfsFkJjOHOQxkIMc57rXPIQwhk0ze5V22sY0aasgmm1nMYgISRVdEwPz5cOCA93qTCa67LvL5aYUoopjGNN7kTSqowIoVhSKBBHrSk3zym93HtVzLm7xJNdU0OIL8KxQmTOSS276BUpRS7wP3AquBqzEqdbnAIxgdi95SSt0Q9hwKIUQHY8XKcpazla2uQdEnOckzPOMaaH1v7mNcUhqPctuud1ks9wwzBkxnkYXVsYDxZM+ChRhiiCc+op9HCNFCo0bBAw9ASkrjupQU+N73yBv5Ta7lWo9Q6GmkcRd3edzYpZHGYha7oldWUkk55cQTTxJJVFPNx3zMS7zEndzpMSGyQjGc4dzBHTzP86xjHTXUAEY3sFd5lQ1sCPMfQQhg8mRYsgQSExvXpafDj38Mg8PfOhUqWWRhweJ1TU4gIahpDWKIIY88j3UajRkzM5gRljw3Fail7g6tdUmTdVXALsfPH5VSmWHLmRBCdFB72EMxxV7rNZrVrCaXXDKT+/Pjyas4cmYjp8oL6JU6gmFDrnC990M+JJlkEkl0tdTFEksttexgB9OYFsmPJIRoqSuvhOnTG1spcnON4EfAbGYzjWkUUkg00QxggM855QYzmId4iKMcZTnLSSTR63372c8c5rCEJZznPKWUkulYjnOcL/jCZ/Y+5mMmM9nVjVOIsLn+eiMy7IEDEB0NI0YY0yF0Ih/xEWmkkUwyVqyYMBFDDBe4QAEFjCHwsIrjHOcsZ+lPf2qowYaNOOKIIYZP+ZQZzAh7WfRbqWtaoVNKpbi/X2td5qPSJ4QQXd4RjvhNK6KIWmpdrW2X9pzGpT09K2g2bBRSCIAJE3FNoqoc4YhU6oToDGJiYIzvm71YYhnGsGZ3YcKEGTPKsfhyhCP0pS9ZjsV9vT9VVHGGM2EPziAEYEwBMnZse+eiVSqocEWYjXIs7o5wpNlK3VEa57NMIMEjrZrqiJTFZqNfKqX+DfhPjPF0zs7jGhgUxnwJIUSHFagrRhRRmJs5tZowEU2036AGTSt5QoiuLYaYgOn+zjnNdQsLptuYEN1dNNEolFeAFKdgu18GEomyGMyUBv8O5EmrnBAiUuqoYyc7OcEJkkhiAhPoRa/2zpbLOMaxiU1kHCplyCcnia2xcTovnWMz+zEy9jJXF4uLZYVsO/Iy5xvOkmnKZuLghaRlDsaEiTGM8Tsvzlgan3auZz3rWIcVK6MYxY3cKJU+Idqqvh7Wr4d9+yA+Hq64wugy1hZnzxoTMZ8/b0QEvOoqSEsDoJhitrOdKqroRz/GM95j7Gwf+pBDjsd8dM4xPXXUcZSjVFPNPvZxnvNkkUUeeRRTTDnlxDkWd33oQzb+I3EKEbTKSlizBo4fN8bLzZ0LffqE9hgXLsBHH8GpU8b8rVddBdmN318rVj7ncw5zmBhiGMtYBoWofSmeeEYwgn3so4Ya6qnHhIkEElzHas5lXMa7vIsFC9VUY8dONNEkkkhf+rrKYi21bGc7pzhFMslMYIJHxGs7dvaznwMcQKEYxSiGM9xvK767YCp1R8Ex+lYIIcKshBKe5Eku0hhcdwMbuIEbmM70dsxZowEMYO7L5xn86k7Xurx1JVx4s4jev10C6XD08GqesS2nPt355O8r1pbs5M6SOxk+fD7XcA3HOU4Jns/LpjDFFRDhUR71GC9zhCOsZS3/j/9HOulh/5xCdEkVFfDTn8JXXzWue+89uOkmuPvu1u1z82b4wx/A5ja9yapV8OijfDa8hLd4y9UKsItdrGUt93M/mTSGJriFW/g//o866tBoyiijllrSSWcDGyihBIUiiii+5Es2sIFMMjFjpoQSEkhwBV2JJ54FyBytIgSOH4ef/9woN05vvgnf+x7Mnh2aY+zfD7/8JdTVNa57/XV46CGYMIEaaljOck5z2pW8mc1czuXMZ35IsjCb2XzKp9TRmIcqqpjEJC7hkma3TyWVPPJ4n/c91ldTzR3cARhBjJaznAoa/5af8ik3czOTmYwVK8/yLIc57ErfxjZGMYo7uMPnuFx3wcxT9zCwSSm1Qin1uPMniO2EEKLFVrHKo0IHxhPrt3nbqwLUXooPrWfEq3uJxkwUJkwozESRU2Sl7Nn/pqGhnr/XPk292bMrhzVK84rtRaz1NaSQwoM8yE3cxChGMYEJLGEJN3MzAO/xns8ACP2Ou7EAACAASURBVOWUs5zlEfmcQnRJzz3nWaFzeuMNo+WupWpr4bHHPCt0ADU11P/v73hLv+nVresiF1nFKo91/enPj/gRc5lLFllEEUUOOcQTTymlgHEubKDBtb9SSkkggWyyUSiyyWYe8/gRPwrqRlSIZj3+uGeFDoypPJ54Ai6GYGYzux3+5388K3QAViv87/+CxcL7vO9RoXP6jM84xKG25wGjV0wGGaSSSjzxJJJIFlkUUeTz2E1VUskBDpBNNkkkEUccKaSQRRaf8ikAr/GaR4UOjDL9Oq9TTjmf8ZlHhc6pgAK/PXvcBVOpWwF8AmwBdrr9CCFESFVQ4Xfgv0azm90RzpFvpZ/8E8D11NyM2fUELXljAV9++Q4X432Pl6uObeDLw+8CRh/7aUzjTu7km3zTI2T5etb7Pb5z2gQhRAvZ7bAhQKj/tWtbvs+tW42KnQ91xcfp+YXvG98jHPG6wUsllbnMJc2xRBNNJZUelUI7dtfvGk0VVcQQQzrpZJDBVVxFCikI0WYnT8LRo77TrFb47LO2H2P/fqPLsi9VVbBzJ7vY5XfznSGoktRRx372Y8JEMsn0oAfppLvGwQU6vtMe9tBAAzHEkEYamWSSQgpRRHGSkxzmsM+5JsEo03vYE/CzBPM5g+l+adNa/yCI9wkhRJtYsPgdqAxGX/SOQNdU+00z2RqospQSKHJxrbXCf6KDexeQpmzY/KYJIQKw2cBi8Z9e7b9st2YbO3ZianyXV42mjjqfFTD3c51zImN/3NM7yjlSdBFVVYHTW1NeWrgPe1UlFvyX2VB85+up93hY0ppjBLpmA149kHxtH2gfweQhmJa6tUqpJUqpXkqpDOdPENsJIUSLOLs++BOqQdFBq6+Hgwfh8GGju4lDTJ7boGm7Np7+O5Kr+2cxtN8cTH7qpgoY1PvyZg89GP+TtrqHNBdCtEBMDFx6qfG73W60sLlX8kaONP6trDS6Yp461fw+8/L8JsVEJ3J2iO9WsxRS6EEPn2nu57pEEj3SmgZMcE8PdN4QIqBTp4zvfGVl47qBAyHBEZ6/ocEoL/X1jenO8tIWw4f7n9NOKUx5I+lPf7+bh+K+IJlkj/GtrTlGoPfEE08eeSSRFHD7gQxsUx6CqdTdhmNcHY1dL3cEsZ0QQrRIFFHMYY7PtL70JZfcyGVm9WpYvBh+8hP44Q9hyRLYbXT/HDjzLmpyUoyLW3091Fuh3gINDUQvXERaj0FMLhvqc7fjSwfQI3t4s4e/lVv9hkB2jrsTQrTCwoVQXg6FhVBUZHQxO3HCuHmdOROefhruussIprJsGfz4x3A6wJiaAQNg6lSfSXHXLyArxffN2BzmeM2H5TSd6a7omLGOxcl9G+fkxgBJJDEV3/kQwq8zZ4zr3LJlxnf+rrvgqaeMSlxcHMyfD+fOGQFTioqM8ahFRTB0KOSG4JqclgbXXus7bcYM6NOHucz1Gf0xjTQmManNWVAo5jLXZ1oOOYxmdLP7GOxYfJnFLOKJZza+A8sMZCBDGMJsZvucoDyeeGYwo9k8NFup01oP9PEjc9QJIcJiClO4hVtcT82iiWYSk1jCkmYjP4XM1q3GIHD3weFnz8Kvfw0nTxJj0fS2ZXOxTxL2KONCU50eQ32vTAaXGE/l50/8LfMuTCCpzujlnmiJYk7ZWBZM+H1QWcgmm5/xM4/JStNI417u5QquCNEHFaIbKi2F6Ggwu41AiY01xgi9+CK8/bbxu9MXXxjR/9xbKJr6wQ+M6JlJjifxGRlw552Y7rqbJSxhEpNcN2uZZHILtwSsgGWQwTKWuUKZ96Y3OeQQRxxRRBFPPDnk0IteKBS55HI/95NGWlv+MqK7sVrhkUeMHinu6959F154wXh94YLRwu0sLyaT8bqqymjtDoV77oHbb3dNAUJyMixYYETYBIYxjMUsdgX/MWFiNKNZxjKPqUHaYhzjuJ3bySEHADNmxjOe+7nfZ0XLl8UsZhrTXA9hMsjgJm5yVeZmMIObudkVpTaWWKYwhXu5F4WiF71YylJX5VChGMYwlrLUb6u+O6W1//ErAEqpbwMva63LHa/TgYVa678G9QlDID8/X+/Y0TEaB5V6Laj3aR3aUMLBHjfU2utzBHPc9vq/6Izy8/PpKGUoWBpNLbXEENPsZN4h99BDcMBPMJJrrjHmznn+eQCsJjtWsyah3vH0PDPTeNJvMiqgdruNuuoy4hIzMJla9zmqqMKKVaYxaEedsQwJP/7t36C42Pi9oQGUMsqr3W6M70lO9r3dgw/CrFmB993QADU1kJjoOgc42bBRTz3xxAc155STc6xxHHHYsFFOOWmkYcZMHXUoVKeZZFzKUQezfj388Y++0+Li4M9/NlrwrFZjCILdbnyvleP7+7OfwaS2t5S5OMtgQoLfLpm11GLGHHRFqzVqqCHasbRGAw1YsPgt68Hc3zjHETYt20qpnVrrfF/bBHOHcZ/W+glXRrS+oJS6D4hYpa4zkgqHEG2jUCSQ0D4HLywMnOYW6S7abiLa/QF+SYnHjaHJZCYhuW0TAAfqhy+EaAGLpbFCB543jjab0frgr1IX6Lzgvj8/25sdS0u539SZMXuM/Wk64bgQLRLoO11XBwUFja3WSnlXtE6cCG2lzmTyX/4cQtUyF0hb7z2iiAq4j2Dub1rzoCaYvkwmpZSrmqmUigJHB24hhOiKegTo5pCZGTg9IQHiw3/REUK0QkwMpPgJ9x8VZaT7E6jcC9EZBfpOKwX9+ze2yrV0exFxwTwyWg38Qym1HCO+21Lgg7DmSggh2tPVV8P//Z/vtHnzoEcP7G+s4oz9NHXUodGYMdODHiTNucFzrE4ApZSyjnUc4QixxDKOcUxhSli7lQjRrSkFc+fCP//pnWY2G90r9+71TouNNYKoBOkCF1jPeg5xCDNmUkmliipqqaUf/biCK+hDH6/tCilkPesppphUUpnCFMYwxut9FVSwnvUc5CBmzIxmNNOZ3mm6YYoO4oorjLFzTSf+BpgyBYYMgbFjYZePedqSkuDy5iM5h0oZZaxjHYc5TAwxjGUs05jWouvlu7zLR3xEJZWkk871XM8smulS7aaWWjawgQIKaKCBXHKZwYwOMy9kMHcePwGWAPdjROP+EHg6mJ0rpRYBdwJRwLe01kWO9c8BI4Ba4Cmt9d9bnHMhhAiX664zIn2tWdO4zmSCO+6A0aOpp57HH9Rc/3gdZqsxUNyKlfXjqjDfkc28IA5xhjM8wRMec8+c4hQHOci93Os3Kp4Qoo1uvdWI4LdtW+O66GhYuhQmT4Zf/coIjuIUH29EwPTXwtdECSX8hb9QhTHHVxll1FBDDDFkkUUJJexlL4tZzFAao+TuZS8v8qJrrs4SSjjKUU5xiuu53vW+csr5M3/2mPfqNKcpoIBlLJOKnQhecjI8/DD87nfGWFCnoUONsXRgBCt59FHjmui+3U9/ajzsiICznOUv/MXjellEEQc5yBKWBHW9fJzH2cQm1+saaljBCk5zmm/xrWa3t2Dhr/yVYhq7b5/jHHvYw3f5bsDpmCKl2Uqd1toOLHf8BE0p1Qe4Qmt9pZ+3fEtrfaQl+xRCiIgwmeCBB4xQzjt3Gjd8kycbXS+B53meTVdEs3vcYEZvrCS+uoFjIxP4alg8CfyDedzQ7CHe4z2fk4ke5jAFFPh8Oi+ECIGYGCPi3+HDRqtcQgJMm9ZYafv97431hw9DaqqR1oIu1atZ7arQ1VNPDTUevyeSiA0b7/AOP+SHgDFJ+Vu85arQuVvPeqYwxRX9bg1rfE5kXEQR29jGdKa36M8hurmxY+Fvf4NNm4ypPi69FC67rDE9PR0ee8yY0uf4cSOy69SpEavQAXzABz6vl0c5yh72MJ7xAbcvppjNbPaZ9j7vcxM3NTvGbRObPCp0TuWU8wmfcBM3Bdw+EvxW6pRS7wBPAR9ora1N0gYBdwGFWutn/exiHhCllPoYOAB8X2vd4EjTwAtKqVLgO1rrE237GEIIEQb9+hk/TexwTNVZmxzF1qs9Q4jXUMN5zgecILyBBg5y0G/6PvZJpU6IcBsyxPjxZfRo46cV9rHP9XvTG9Faal2ThRdTTCml9KAHpzjls6IGRqS8/ex3zVO1n/1+j11AgVTqRMvFx8OV/tpgMLotjxtn/ESY8/vvz372N1upW8tanw9MwIhKu4ENzGumj417ufaVh45QqQsUKOU+YDrwhVJqu1LqPaXUJ0qp48AKYGeACh1ADhDjaKmrAW50S/uh1noq8DvAZyxVpdQSpdQOpdSO8+fPt+QzCSGEEEJ0eM4bTX83nE3fF8x7hehOgikP4S4zHaVM+q3Uaa3PaK1/rLUeDCwAfgX8AMjTWl+ltX6rmX1fBNY7fv8EYwydc99ljn8/A3r6Of5TWut8rXV+Vpb/J95CCOGLBQvb2c5a1nKUo17pGs0XfMFa1rKb3Vixeu/k5El46y1jItbSUtfqfHxOEQMYoZADtdKBEe54hOOUWE89lVRSRRUNGJ0ZRjIymI8ohGgJi8WYl2vVKqMrWTPz9LZWHnmu35uGX3e+dpb51azmAhfoS1+fY3Js2KhyLOcxHnAHOj/IuUO0Sl0dfPKJUTZ8BQpqRwpFLrl+092/85VUspnNrGMdpzntWj+b2X7nhjRj9mjdLqSQdaxjK1s9Wtrdy3WgPNRTz052spa1HOJQRCt8QYVo01oXAoUt3PcmjNY+gDGAa4SlUipFa12hlBoGlLdwv0IIEdAXfMFLvEQdjRG9BjCAxSwmgQQucpFneMbjpJ9EEndxFwMYYEyA+sQT8NFHjTt9+mkjUMrNN3Mnd/IZn1FJpcdxFSqoAdcAc5nLZjZ77KOccnLJZRSjWvfBhRC+HTwIv/kNVFQ0rhs4EH7xC2OMUAjNYx5HOEIVVcQQQwIJrkApMcTwFV9hw4YZM+/wDu/xHrdwCzdwAy/xkusmsMKxJJHEWtayjnVMZSpXciUHOEAFFR7H7UMfJhHCOcNE97BnjxEopbq6cd2wYfDznwcdHCjcruEajnLUqzvzIAZxGcb4v81s5k3edD0cBRjDGBaykF70YgpTPAKlOM1jHgkkYMHCczzHYQ670t7iLW7lVkYzmqlMZTe7vcbVpZHmiqB5lKM8z/OucbQAl3AJ93APyQSefy8UgpmnrlW01nuAWqXUOmAC8JlS6meO5JeVUp9hRNF8KFx5EEJ0P9VU8wIveFTowHj69gZvALCSlR4VOoAqqniO54wWu/fe86zQgVHRe/552LuXGGJYznLGMIZYYjFjJossfsAPuIZrgspnAQUkOhYzZqKJJpVUKqjgHOda/wcQQniyWLwrdGAEffjTn0J+uCyyeIAHmMY0ssgijzzmMIfxjKeccuzYMWPG5LgFa6CBlawknXSWsYxRjCKWWCxYyCCDNIxxuxrNRjZyiEN8j+8xgxlkk00venE1V3M/90vkS9EylZXw2996VugAvvwSnnyyffLkQw45fJ/vM5WpZJJJH/pwPddzH/dhxsxJTvI6r3tU6AD2sIe1rAXgAR7gW3yLHHJIIIE+9GEJS7iDOwBjugP3Ch0YrW4v8zIXuEAccSxjGXOZSy96kU02M5nJAzxAGmmuSqF7hQ6MqNav8VoY/zqNgptMqZW01v/eZNVvHOu/Fs7jCiG6r53spJ56n2l72csMZniduJ2qqKKAAsa9/77/A6xeDaNHE088/8F/tDqfW9mKGTPppPtMm8/8Vu9bCOFm0ybvCp3Tnj1w9izk5IT0kBlkeAVOqKLKVe6b0mje5E0e4AEGMpBneRYLFp/73sIWJjGJGxyLEK22fr3vOerAKDcXLxoRYDuAHvTg63zdZ9pWtvrt5riZzcxhDgBfcyxNWbGyk50+t2+gge1sZy5ziSeeuY6lqc/53GeEToCDHKScctcDmnAJW0udEEK0B38R5MA4OZ/hTMDtyyn3GD/npaSktVlzsWJ1hTz3mwchRGgEKs9aB04PoTLKsGMPmO4U6Dwm5wcRMs2VjbIy/+kdSKAyUUFFs+Paaqjx+zAYApfHYPKg0UHto62ardQppaYppT5SSh1SSh1TSh1XSh0Le86EEJ2bxQK1vp9aBau2qoQGm++n1f70dIu9pNEeN1GxxDKYwR4Tldqxe5zwe9Mb+vd3vbbEgNX9wfqAAS3Kjy/RRJNJpt/03vT2XFFfH/BvacdOLbUBbxiF6LbcyrMXpSA7u/l9BCiDlrqLWOqMG7amZdGCxRWEqSc9iSba7yH60tf1ey96uX63ORZfaUK0ifv1TGtoaGgMIBQXBz19xjL0y1JbTr2l0v8b6uu9u3q2UDnlXi1i7mWi6XW/Jz39BklxSnIs/gRT5gK9xzlEwz2PtdR6dRdtq2C6Xz4DPAjshBAfXQjR9RQVGROZbt9uXBxGjDACjIwMPirboY+Xo197lYTT5TTERlM1czyD7/wV8Un+K0JOYxjD+7zPCU5QQw0ajRkzySQzm9lkkME4xrGWtVRSSQMNKBSJJDLUsXDTTRy1HOT96xSFAxRKw4gDmus/iCL7uuva8tdxuYIrWMUqr/WxxDKRicaLM2eMv+XWrcaYvqFD4fbbYYwxh50VKx/yIVvYQi21pJLKdKZzBVc0exETotsYPx769jWi2TpVVxutELGxsGQJTJkCd98NmU3OMQHK4JmjGyl97o+k7C0EoGh0JhvuGsTZwUmuMXMWLEQRRR55fI2vMYEJPoM1RBPNzdzsej2d6XzGZ5znvKtSaMZMBhlcwRUh/xOJbmrKFOjRwwgkVFlpXLOjo40Jx+fPN+avC0LRgTWUv/A4KQdOopWiIn8oOXf9hKy+Y403lJcb5eizz8BqNeZ/XbgQpk0LOqurWc0bvEE55SgUfenLMpYxgAFMYQrrWc95zrsqfM5x6sGUlyiimMEM3uM9r7QkkpqdBw8gl1yyyHJFqXU3kYmuyc23sIW1rKWUUmKJJZ98ruXakIyHDab75UWt9fta63Na61LnT5uPLIToesrK4KGHYNu2xqd9Bw/Cf/yHMfA6CF9+8Cfi/7SChNNGV4Yoi5XU1Vs4/otF2O22ZrY2Ts5xxGHF6mqBs2HDitXVnz2VVOqpdz0l02isWEkkERMmvprcm//7RW8KBxvPvbSCA2Oi+evvB1DRLzR94qcwhWu5ljjiXOuyyeY+7jPyWVEBDz8MmzcbN5MAhw7Bo4/CPmMS1Fd4hbWsdV3ELnKRd3nX54VJiG7LZIJf/hJGOaLKVlcblbWYGKMSZ7PBhg1GeXNvjQtQBks+fZPahx8k5fPjoDU2bSXr89Nc//AmYopKKKSQYxyjggrs2CmggCd4gru5m/GMdwVJAUghhR/xI4/xNhYslFLq0UJnw8YFLlBN21o6hHCJjoakJKMFzXnNttmMnjZNH3D4UXxkA7afP0TKAeOhidKa1O1fUvHwMi6WHDP2/bOfwdq1RoUO4KuvjIibGzYEdYw1rOFv/M3VxVGj+YqveJRHKaOMOOIwY/aYmsjZwu1rqhBfZjGLK7mSGGJc63rTmyUs8ZqaxJcooriP+xjEII91k5nsGvu6gQ38k39SilGNsmBhIxt5hmdCMvWB35Y6pZRz2vi1Sqk/AK9D46hdrfWuNh9dCNG1/OtfxsDqpmw2eO01eOSRgJs3NNRjevWfPtOSDxdzYvsqBk76ZsB9HOQg5zhHNtlYsWLHTjTRmDDxMR8zhjFsYAMZZJBKKjZsRBGFGTPHOc4JTvAxH2NLT4LURLDUGV20YuOoUrCRjUFHuGzObGYzjWmc4hRxxNGHPo2JH3zge7yD3Q6vvkrxyB7sxfd8Qp/xGbOY5XoyKES3l5lpRMAsKjJCtcfGgrnJLdDZs/Dxx3D99cbrAGXwzOt/IbXOuIE0unsZN2QxdXZGv3GUY98xynIllSSTjAkTF7nINrbxI35EGWUUUEA66YxmtNchVrISjSaaaFdXMuVY/sE/GMc4r22EaLFdu+DECbjkEqPy1dBgPOyIijKu2dde611Omih9bTmpVu+OfDEVdZx856+k9rvGs5Xc3SuvwPTpvtPcvM7rPtfXUcdrvMZQhlJPPTnkuB7oOq/7H/ERQxjS7DEUimu4hpnM5DSnSSChxV2dM8hgGcs4z3kqqCCHHFe3Ths2PuZjn9sd4xiHOWz0FGqDQP9Tf2zy2n22XQ3MbtORhRBdz/79/tMKCprdvPzMl8SV+g8gUr1vCzRTqTtG45DfpuNXLnKRAxxwRZWLcixNtz/unFbTpLy6n7jvPxSc4/y8OFrjfNq3L2A+rFj5iq8YzvAQ5FCILiQrywh25O9Gdd++xkpdgDIYfboEHF2cmz5hH7DPc0xRPfWuFvljHONKrmy2G+UJTrh+NzXpVNV0OhYhWs39mh0T45l28SKcOtXsOPL4/f6vRab9B6Gyn/+NT50yumam+e8BY8PmEUSoqUMcckWUVSiPljaA4xxHo4MekhBPvO9rcgtkORZ3ZzkbMEDaMY6Fr1KntZ4FoJQapLX2+B9TSg3yvZUQoltLTPSfluR/ELJTTEIqNUo1dgNpwpTY/OSdgbpJKBQpBJ5MNc6x+AtN7N5dMqwC/S0TE5vNRzDdRYTodsxmo5XO4icAk3u5C1AG7TFmqPMdZqAu0fNBkfvNZLDlsumNqbtAwVaEaJFA15lg0oGGhFio8D0tgk6Mh4QAPUaiooyALAGYMRNFlN+gIs5rtj9xxHWIMebNlf1Q3FsEM6bOV1+oyMyiJ4ToeGpr4d134b/+Cx5/3PNp9syZ/rcLlOaQnN6PitEDfScqRa+ZC5vdxzjG+T2BX8qlDGWo3y4VZsxcxmUBB0UHM2A6JJr5W45kJLHEYsVKOeWUUspFLtJAA5lk0o8AT0eF6K5MJt/dvWpr4dw5KCyEt96CqqqAZVDlNQZ+MmHyOOPsm9X4hN6M2aOC5qvb5CEO8Sqv8gIvsJGNWLAwmcl+j30Zl/lNE6JFZswwhhf4kpdntGw3wzrzcpQd4iqspJ6pJfVsHbFVNtAQM3MezJrlf+PJk5ut1AEBW7BmMSvgdTnSXZWLKOIN3uB5nmcNa6jEaLnPIIOB+L6/MWFiDGPafGy/lTql1HCl1M1AqlLq624/d0GkHlULITqUkhJ44AF46iljYtI1a+CnP4VnnzXSL7/c943Q8OHwjW8EdYhe9/+Suh5NWvWUovqeW0nvOaLZ7TPI4AZu8KrYpZLqmrj0m3zTa7yZCRMLWEACCcxiFv3xDoM+jnGRu6GaNAnmzfNeP3gwLFzoipp1jnNUUUUttVRSyTnOMY1pHeLJpBAd0p13GtEwnUpLjbF2JhMcPgzPPGOc5wYMgLnekwwzeDBDlv0PF8c3jtNxduM+Oj6NwquHE000CkU66a6yOJnJ5JLrsavXeZ2neIrtbGcve3mDN3iMx7iWa30+mMkii/u4r+1/AyHAqLQtWeJdsUtPh29/O6hdDLn2ARJrIPVcHXFVNuIqraSdqSXOnMTg6Xca16xbb/XesFcvuOeeoI7xbb5NMt49dfLIYw5zGM5wpjDFK703vZmHj+tomGxmM4/xGBvZSAEFfMAH/J7fU0QRAN/gG16fQ6G4iZtCMjG50n66OSmlbgTmAzcAb7slVQIrtdbeMXnDJD8/X+/YsSNShwtIqdA2Umq9oF2OG6xg8xesYD9HMMcN5b66uvz8fEJShv7wB//Rqn73O2P6AoA9expDF48fD1OnNjvY2l1dTRmFnzyL7cv9kJpK1pULyRk4qUVZLaaYbWyjmmr60pd88j26P1RTzTa2UUwxqaQykYkefeAbaKCAAg5y0NWC19b+7q1SUACffmp0Fxs3zggBHR1NHXX8J/9JDTVUU40NG9FEk0giWWTxMA9LxS6EQlaGRMdgsRjl6tNPjYdTSUne4dsnTjSCO/kpg3a7jRPbV1G1dQ0AatIUvpqQzQXTRbLIIoEEvuIrYohhLGO9xukc4hBP8ZTP7E1gAgtYwEd8xEY2YsfuCn0eqGtmRyflqIP66iv46CNjfNuQITB7dlBDJgB4+WX0ylewVJehq6vQCqKSUomJT0V9/0G48krjfYcPwyefGK3geXnGA+AgWumcaqnlTd6kgAJiiWUWs5jBDI/3HOUoe9iDBQvDGMZoRkesu3IFFfyaX/ucL7YPfXiQBwFjovMd7OAUp0gmmQlM8JhftzlKqZ1a63xfaYHG1L0FvKWUmqK13hz00YQQXVN9vRHa25/16xsrdWPGuOZSa424hAyGX//vcH2rd0EvenEjN/pNTySRWfjvFhJFFGMcS7saNaoxFLub/eynnnrMmL1CNpdRxglOMIABEcqkEJ1MbCxcdZURqMFfF7Pt240bUD9l0GQyG9F43YI3BT8bJ+xmt9+0PexhAQuY51iECKt+/YJuNfOybh1KmYhLyoSmc8muW9dYqRsyxPhppXjiWehY/BnsWNrD53zus0IHRpdMZ1TuBBK8KqOhEsyj89uUUk3/gheBHY6KnxCiO7DZjB9/amoilxdBHb4HpgebLoTAc166prQ2WueCbbFoIQt+grWAazqWppEvhehwApWhuu5zHQpUnoNJD4VgzhaxwBjgsONnNJAB3KOUeiyMeRNCdCQJCTAoQOBbH0+yuzytjUnVd++Gysrm3x9CgZ5GRhPtc0ygEKKJkX7a1mw2I/JfeXnYDh2oDPenvytMuxAdQklJ47x27gJd+7vRfUGg8pxAQou6WLZWMGeMS4HZWmsbgFLqSeBD/j975x0fVZX+4efMTCa9EBICofdIkxIgIFIEASkqKthYRVGxoa5tLWDBimV/tnXVtSy6dkURBUUBDUWUKtKR3tNIL5OZOb8/TiaTyVQgPefJZz7M3DP33neGOfee95z3/b5wPuC/8JRGo2k4XHWVKuBbORe3dWulotWY2LkTXnpJCSyAqvEzcSJcc413NbEqpDnN6UMfjyFcwximSxpoNIEweLCarNpbVrlJX5jQ0AAAIABJREFUSkhPh9xcSEiAv/9dtd9zj6u4ShWQTDIrWEEGGS7bBUKHXGrqDhYL/OtfKpTSce/v2hXuvVf1kcmTYd069zIh0dEwfnyNm1tbtKc9SSSxgx1ubaMYVSO5fYGs1LUEKhaqCAcSpZQ2qIG1RI1GU3cYMABmz3bGxQcHw6hR8PTT6nljITsbHnvM6dCBuvF9+SV89VWNmXEFVzCa0USgwsNiiWUSkxjL2BqzQaOp15hM8MQTMHasEm3IzFSD0+bNIbJMpW7vXnjkEe+17U6TYIK5lVtJJrl8Va4tbbmRG2tHlEmj8cQbb8Dy5a6TuTt3wqOPgs2mJj2eegrOPltNaBoMarLk2WehadPas7sWuJZrGc7w8knVeOKZwpRqy6GrTCArdc8Bm4QQPwMCGAo8LYQIB36qRts0Gk1dJDlZPSwWNSAyNMKcjx9/hIICz20LF8LFF9fI92LEyGhGcz7nl6tfajSaUyQyEm69VZU6uPZadW2rTGamUv4dNapKTx1FFFdwBVOYgg2b7sOaukVOjlqh88TRo0pMKCUFunRRkyOlpereZzTWqJl1hSCCmMAExjO+Vu7JfkcdUsp3gMHA12WPIVLKt6WUBVLK+6rbQI1GU0cxmxunQwdw6JD3tsxM7w5fNSEQejCo0ZwpmZmeHToHvvr9GWLAoPuwpu5x9KhvgbSDB11fBwU1WoeuIrV1Tw50RGYA0oEsoJMQopElz2g0Gk0FvEmggxJXCAvz3q7RaOomTZr4HpD66vcaTUMkLs53jrjuE3UKv+GXQoi5wOXAVigvwCCB1Gq0S6PR1FGOcYzlLGcvewkhhH70YwhDGtcs8/nnU7pwPisH2VmfDMUhgvZ7Jectk7RIGV2zM5U//wyLF0NamhJyuPhiVSBZo9EEhtUK332nCiOfPKnKszRp4ponHBICw4axgx2kksoJTtCEJgxmMH1R/W0DG1jNak5ykgQSGMpQkkiqpQ+l0VQB8fHQvz/8/rt7W1SUyp0DTnKS5SxnBzswYKAXvRjOcMIom+AsLVWpCcuXq0iWs86CSy/1rahdiTTSWM5ydrMbM2b60IehDCWYRpTP74dAcuouBrpKKU85Q1gIcQ1wLWAErpZSHinb3gN4A5Wjd4uUcvOpHluj0dQ8BznIG7yBBWeI0nd8xy52cSM3NpqaSvbmzXj35d7sLvwD7Gqua2NfwZbBUdzc/NyaKyYwb54SZ3GQmQmbNsFtt8EYrZ6n0fhFSnjmGZUbBGqlPS9PFSVPTITQUJVz949/8GvkFr7E2d9yyGE/+znBCSSSZSxzadvFLi7jMlJIqelPpdFUHTNnwpNPKnEUBzEx8PDDEBxMFlm8yqvk4Szrs4xlbGUrt3M7ofZgJaSyYYNz/xUr4LffYM4c6NbNrwnHOMa/+JdL/dUf+IEd7OBmbm5ck8o+CMSp2wsEcYpKl0KIlsAwKeVID81PAFeiVv5eBy46lWNrNJra4Tu+c3HoHOxmN1vZSk8aR02arWxld4s8sLeD/ALl2IWGUBoczCJ+5BY6Vb8R6ekwf77ntnnzYMQIlfeo0Wi8s3Gj06EDtcresqUqmtykiZogGTiQ0mADi5jj8RA/8RN27BhxX6H/ju/oRz896NTUX6Kj4fnnYcsW2LdPKVr276/y54ClLHVx6Byc4ARrWMOIdeGuDp0Di0Xdq+bO9WvCYha7OHQODnCATWyiP/1P/XM1QAJx6gpR6pdLqeDYSSnv8LPfGMBYtt824K6yMggAsVLKQwBCiOhTN1vT0BHi89o2QVOJEkrYwx6v7dvY1micum1sU08MBoiKdGnbwx5KKKn+kJD1693rBTrIz4dt26B37+q1QaOp73gKKwMVbllUBL16QXAw+9hFEUUe31qAEkZylBapSBFF7GOfLlGgqf/06KEelSi/H3pgK1sZsdZHTt727WplPDLS61vs2NnOdq/t29imnboyAnHqvil7nCoJgFlKObIsL+8iwDGtXDFGy2O8lhDiJuAmgDZt2pzG6TUaTVUiyv4knh0JQfUX3K4r+PqsNfY9+Ctw3liVSTWaU6EK+pG/Pt+Yro2axoev37cBA35//n76oEBgwIANm9d2jSKQkgbzgM+ANVLKeY5HAMfOAX4pe74MOKtCm93L84rnfUtKmSylTI7X6joaTa1jxkxXunptbyyrdOD8rDarhfyCE+QWHKOkOBeALnQ5tVW6bdvg009hwQKVExcoAwZ4F2SJjlaJ6BpNY2ffPvj8c5V7evSoe3uZ0INHunVTYhBABzoQQQQGq51Oq9IY+PE+zlp6DFOJjfCyv8pYsFBCCYc5TDbZVfWJNJo6Rfm9v6QEsk4qsaHSUmfboEHed+7VCyLcV7grIhB0pzsATben0/PT7SQt2EVoRqE6BL3O/EM0EAJRv5wIvACYgfZCiN7AHCnlhX52XQ3cWPa8N7CvQluWEKIVyqHLOWWrNRpNrTCe8RzggFsYUg96+HT4GhpJJBF3ws6m6ENgdKxcFhCWe5JR5hsgJICDWCzw9NOuuQbvvQc33gjjx/vfv0kTuPpqeP991+1CwA03lOc7aDSNErsdXnsNfvrJue3995Xi3jXXOLf17AlDh0JqJUHvkBCYPr38pQkTlx4djOHRx4g64bz+nfveX5ycfRsFXVuxiEUASCRZZFFEEbHE8h3fsYhFjGc8wxleHZ9Wo6k1RtpHsH3ft2TZK0xKZmbS2tCWAR0GQG8zDBkCK1e67hgaCtddF9A5zrcMp8Xct+mwNqt8m3xvL5um96PnxMYzoeyPQMIvHwMGAD8DSCk3CSHa+9up7H1FQoifgQzgYyHEw1LKp4BHgU9Qi7K3nZ7pGo2mpmlBC/7O3/mFX9jLXkIJpS99GcCARqN8CZB+7E8y8vbSxAYF4SANYC6ByNxSNqW9R/sU/4nffPSRe/K43Q5vvaVW2QKRer7sMmjXzlnSoE0bmDgRkrSMuqaR8/33rg4dqBzUL76Arl1h4EDn9rvvVvmny5apfNSkJLjoIiWYUoGeL3xPyYlI8hGUUooJE3G5EXR5Zhm8/TaJpkRWs7o8/yee+PJVe4nkW76lXdmfRtNQiFq0kjs+TGflUMH2s8Bog15/wKDV+wh+4E9IToZ774U+fVQfKyhQq+AXXqgUZgMg69PX6bo2FzsG7EgEYLAbGPj2n6QlraZF53Or90PWEwJx6qxSyhzhGvPqJTvfFSnlvZU2PVW2fTMwJCALNRpNnSKWWCYxqbbNqFV+P/A5NIXwAvWoyLqo3VxoLcFo8hGCKSX8+KP3tiVL4OabAzMmOVk9NBqNkyVLfLdVdOoMBhg1Sj28sXcv/PUXwWV/LmRlwbp1JKWkkEQST/Kk19zj3/hNO3WahsUPPxBRAGMXS8Yudm8jOVn1sfPPV4/TIGRJKgKBsbLGrJRkLvlIO3VlBDK1vkUIcRVKybKzEOJVVGilRqPRNEpyfUSNlwTZKSnyE1VeWqoUv7yRleW9TaPR+MdXfuqp5K468NcnKxwzx8f1wVebRlMv8dU3quBeZrdbMed4Vp4FVA6fBgjMqZsJdEeVM/gYyAXuqk6jNBqNJiByc9XDC3nZhzi6ZyVWq3t9mzOhpamd17bYAjOhEXG+D2A2Q6tW3tsDCb1sgBQWWklPL0F6K9VQh8nMLCEvr7S2zWg82O3KkSopq7SUlwfZZWIkpaUqrMvuUYft9PpX27a+VfoqHLMVzr5twYIVa/nrlriGdGp8k51tISdH9yssFvV7t1r9v9cbR47A4cNVZ5MDX/2pCu5lBoOJgrbeBRNFh44ur7PI4igeRJEaAX7DL6WUhcDDZQ+NRqOpfXbsUKIi28tq15x1lkq4LsslyzjyJ3/98xYStmdhtEr+ijZxcvjZDLrtf1Vy+v5dp7J8/2ryQ9xvsMNs5yJEAPNll1wCr7zivj0iAsaMqQIr6w+ZmSW8+OIuli1Lw2qVtGwZyo03tmfChMDyLWqTFSvSef31PezenY/BACkpTbn33q60aRNW26Y1XL7+Wj2yspTjZjKpwa4QShHWZlM15o4fV+qVTZs6HbKgIJXLc6rEx8OwYfDzz+5t3bq5qM2OYASv8Ao55JTLsAcRRCKJDMKHEqCmnM2bs3nppd1s3qxWNvv0ieGuuzrTvXsjK21cUqLudcuWQXGxUjaeOBEmT/ZfjsPBTz/BM8/AsWPqdUIC3HMPTJhQNTZecgls3uxeNzUoSNlaBRgvmQz/97rb9tLwYFqPuQGAHezgLd4qd+giieQiLmICVfQ56wFenTohxEJ85M4FoH6p0Wg0Vc/BgzB7tnOGHpRzN3s2vPgi1sRmHH3wbyQed7aH5VgJW7Ce1dYrGXznx2dsQlhkM2bEPshnR1/iUIwKowy1GBlROJBz+s8M7CCjRkFhIXz2mXO1sUMHmDlTKVs2EiwWOzffvIF9+5zJiUeOFPHYY9uQEiZOrLuO3e+/Z3HPPX+ULwjZ7bB6dSY33riOjz9OITbWXLsGNkQ+/RQ+/FA9Ly2FQ4fUFx8SogaRjrDmli2VI5aZqZy8hAS1ejdjhhIXOh1uv12dY/lypxOZkqK2VyCddHLJdamrZcVKGmmUUFL5qJpK/PVXPrfcsoGSEudK68aN2dxyywbef38A7dq5l49osDz7LKxf73ydkwP/+58SGwlEOXLdOvj731UfcHDiBDzwgHIQz62CXLQ+fdQ55s1zhiG3bKn6WhXVme484kZ2FOQS9MmXmHNVKGZB+2ZE3z6LqKbtSSONp3kaC5byffLI43/8jyCCGEPjmCj1tVL3Qo1ZodFoNIHy9deuDp2DkhL46is2JRyi2XHPA6e4X/7EMiMXc0jUGZvRomU/7mz5AenH/6So+CQt2vQlKNh3vR03LrwQxo6FAweUvLOvkMwGyo8/nnBx6Cry9tv7mDChBSLQGeka5u2393qM8MvMtDB//mFuuKFxhtFWG8XF8NVXztc5Oc4Qy6IiNUniKBaenQ0tWqiVb4sFZs2C/v0DX93whNmsJl2uvVatesTFqVXASnzFVxgwEEQQEoko+7Nh4zM+407uPH0bGgHvv7/fxaFzUFho43//O8CsWd1qwapaYPduV4euIt99p9SPIyN9H+Pll10dOgd2u4oUqQqnDmD4cHWsffvUyvnpTpz4IGnCPVjH3Er6/nWYQiJo3bpPedunfOri0FVkAQu0Uyel/MVbm0aj0dQa27Z5b9u+HUvWXq/NIfk2Dm1bQse+l1WZOfHNz7BGjtkMnTtXjTH1kD/+8F6U+ciRIjIyLMTHn0Ix9xrkjz+8i174atOcJgcPKsfNQXGFXFm7XYV/OZy6ojJhBSEgOFitbFTV5EBUVHlRck9kkKFOXfZXkX0uJXs1ntD9qgxHeoEnLBbYs0eV4vDFX395b9u//7TM8orRCJ06Ve0xK2EKCvWodOmrX2WRhR17oyi71PA/oUajaVhE+FgNi4iAsFDv7QLCYltXvU2a0yYy0nvAiNEoCA83em2vbaKivNvuq01zmoRXCrszVBjCVHbYjJV+N/5WNKqQIIK8toUQUmN21Fd0vyrD170OAvtNh/q4H4Y0nN+ir35lwtQoHDoIrE6dRqPR1Ci55LKGNRzkIJFEkkwyHSlTuBo5EnbuVLkzBWVhe+Hh6gY3YgStOo5GLrsT4SEjOL19BP3aldWn2rYNXntNzVY2bapCqirWqSopgaVLVYFwkwnOOUc9Kg4k169X+TWFhdC9O4weXaODx4bAhAmJvP/+Abcce4ARI+IJC6u529SePfl8+eVhjhwpom3bcC67rJVPwZMJExL54IMDHtvGj2/h8lpKycqVGSxefJyiIhvJyU246KKWREQ04ttwcbEScdi0SfWxIUNg8GDXPlaRli2VGNKOHep1ZKRz5c5gUPlupaUq3MxgULlDVqta1ZgzR9XImjFDrY5XZtMmJUaRl6dET8aMUTlHp8ARjrCGNYQTTgYZGDG6rdSNYMQpHbMxMmFCItu37/TYVrlf+eLo0SK++OIwe/bkk5AQwiWXtCQp6cxD72uMQYPgjTdUmHFOjvotm83qd9mpE3Ts6P8YY8cqoRW73RmqbDCox3nnOd/3xhsqV7WgQOXBzZpVr+qfjmQke/EcpdMNZ7juUY6yhjVkkUUCCQxiEHH4UauuR4j6IB2dnJws161bV9tmACDE51V6PCkn18p5AyVQ+wKlNj5HVX+G+khycjJ1pQ/54xjHeIM3KMA1z2p02R9FRUpt60ClwXSbNjB/PoSF8eu/ryHx67Uujl1BjImwx16gbbcxsHAhPPSQe67BlVcqwZW8PHj4YffwlAED4MEH1SrAG2/AokWu7XFxSmUsIeHMvoRGxocfHuCll3a7OHZt2oTx1lv9iIurmdDLJUuOM3v2Vmw2pxFms4HnnuvFkCFxHvtQYaGV22/fWK7Q5+DKK1tzzz1dXbbNmbONb75xldlu2TKU//ynH82aNZwZ84DJzVV96dAh1+2DBsE//uHdsTt8WA04s7JUuGVamuqvCQnKeTt+XK3aBQWpiRkplcMYVLZ6VuE6Uc5777nm6gHExKi+3DKwEgRrWctnfIZEYsPGYQ5jw+ayStCTnjzcyIXEA7kX2WyS++77g9TUDJfto0Y14+mne2Iw+A+j3bDhJHfeuYmiIuc13mCABx5I4pJL6lHu8muvwb//7aosaTLB3LlwwQX+9y8shKFD3evFRUerCcmYGKWkWTl3z2CAJ55Q98R6wrM8yyY2uWyLIYZneZYYYljPej7hE2QFDUgTJqYxjSSSatrc00YIsV5K6dHj9urU1SX1S+3UaafuTNBOXf1y6v7Fv7zGx9/P/TSbv1INwoqKXFfqQkNh2jS49FIA9m76iuML30LkF0K79vS8+mkiosqUFFNSPNe3E0I5asuWuQ/yHNx1FzRrppxCTwwapAarmlNi7958vvvuGLm5Vs4+O5rRo5tjNtdMyExhoZULLlhBQYG7oEBcnJlvvx1CSsoAj33IZpP88ks6a9ZkEhJiZPToBHr0cF3hWb06gzvu2OS2L6iVh8cf7141H6Q+8Z//qMkVT9xzjyof4I2iItVH9+5Vq+ytWsHWrfDJJ6oPW62QkeEUVDIY1AqHI0Tzb39z9tHdu9X5PNG3Lzz2mN+PUkQRc5hDKc56anbs5JKLBQtd6cpIRpJCit9jNXQCvRdJKfnttyx++SUdIWD48GYMGBAb0DmklEyatJrDh90LVpvNBhYvPpfoaO8hsnWG3FylcFlUpJ47VuoiI9Vkw5tv+s8TXbAA3nlHOXU5ZZNP0dHKmbvmGtV/brzR875hYbBlS9V+pmrmd37nJ37CgoXe9GYc4zBjpphinuAJj+qz0UTzEA9hpO6G+lfEl1On1S81Gk2dIZtsnwnPG9nImNRUdSMLC3OdbQdYsaLcqevQexIdek9yP8jPP3svWC4lvP++Gix6IzXV90rcb7+pwWRw3RT3qKt06BDBzJm1IxizalWmR4cOICPDwvr1J73uazQKzjuvGeed18zre5YsOeG17ccfT/Doo90CWn1oUKSm+m7z5dSFhsL48e7bFi92vj5xwjnglVKFnjny7H76yenUrVjh/TwbN6pVQD8h1VvZ6uLQARgwEEMMAFdyJR3QSqinghCClJSmpKS4q4v6Y9u2XI8OHagSKsuWpTFpUj0oAr9mjQonNpkgtpJDe/w47NoFXbt63teB4/cdG+t+jNRUdRxvFBbCL7/47ot1jAFlf5XZxjav5URyyGEf++hE9Yq81ARa/bKM2loJ02gC/e01hhXHygOjyliweC5n4KCiGp43HHWsvFFY6P8cvs5jt6sbsXbq6g0VQ7ROp/1Mjm+x2LHZZONz6s60H/vbx1dqSWmF64yvc0mpQjr94E1KPdB2TdVS3f25xvDVRyCwfuLrPcXF/s9ROWyzntJY+qjf2BYhRGchxBdCiG1CiL2OR00Yp9FoGhdNaUosZbOJpVbIL4Ci4vJA8M50hrPP9n6AivLOUipJ6DVrID3duX34cGd+jSfGjnWew2ZTIZ6Fhc5BYu/ezvNIqUJj8vNVaAyoBHZ/qmWaOkX//k28pnCZzQb69DmzYvADB3oPG+vbN4agoMahzOaCt35cVKRWxtLSfO9/4oTq27t2qdc9ezpX4iwWtbpR0bGT0vm6Z4UyJL4k4Vu39liHrjKd6ewmiOLAjJl2tPN7DE3V0b17tE/V3JSUwMI4ax1f97rQUNdVuowM1R+2bXP93fv6fffpo4THHDj6iGN/k8l9Rbye4quPBhFEe9rXsEXVQyB3kveAfwNWYATwPvBBdRql0WgaJwYMjLGOUgO2A/tVgd/Dh+HgATrmxtOVrjBpkucaUZGRqg1U+OSttyrBhaefhhtugJdeUoO9yEi46CLPBiQlqVCTKVOUo7a/zIajR1VRVYMBxo1TKpixsUqs5cgRFcKyf7+6sdajxHKNokWLUC691LN4wtSpbc44/2bcuBZ06BDutt1kEtx8cwAKdg2RK65wVaG0WFR/OnFChYzdeCO8+KL7SllJCTz3HNx0k+rb994Ld9yh+uv556v+ePCgc2DqGKQ6VDCNRrj7bufxBg6ELl3c7RNC5d4FQDzxJONZKfA8ztNlDGqY0FAj06d7HqSPHducDh3qyaRbmzZqEtITU6aokgRWqyoiPn266g8PPAC33OKsT3fRRZ7vlxER6n55113KQay8si2luhd6UoqthzSlqcewTFCKtKH4KP1QjwjEqQuVUi5FiaockFI+BpznZx+NRqM5Lfr9dzPTXsqh9UGJACIKYPgPFqbf9SeiqFjlsz37rJI/N5mc5QbmzlVtxcXw6KNqcOdASiWs8N//qtdz5ihp8yZlKzChoUpJ7KOP1OtDh9RxHTV+hFBiLKWlynErKFBhnOHhTpW+kBCV47dPFxeuj9x3X1f+/vfOJCaqAXjbtmE88EASt9565nkWISFG3nqrH5de2pLwcCMGAwwYEMu//92Xvn3PbBWw3tKxo1KX7NdPvT52TPWhVq2U4yWlyud5+23X/d54A1audB2E7t+v+vyBA6pPOlbihXDm1Qmhjt+8uXNVHdS55syBCy9UA10h1OTOI48oQaUAmcxkxjGuPI+uGc2YzGRGMcrPnprq4Jpr2vHoo93o2DEcIaBZs2BuvbVj/RMluvNOmDpVKSuDWj2+447y3HE++EDliFbsD0ePKoGfwkKIj1f3xnPPVf3CZFJiXs8+C4mJkJnpPQRzz55q/Wg1zaVcynjGl0cDNaMZl3KpUtVuIPgtaSCEWAWcC3wBLAOOAM9KKf1kZ1YdNaF+WdfVJeu6fYGi1S/dqYmcunqjfllUpOrFleUBSHANmLj1VhUe6YslS5QMtCeCg5UQSsWCrHa7u3z6vfc6w7qkdFUYGzVKDQz/9z/ntorviYlRzqO3eD5NnUdKiaikKleVfcjT8Rs1S5fCyy97bgsKgnnzlMOVk6MUbiuXIgE1MHWsxINa4a+YT9SqlbPY8tChqo97onJ/Pw0k0muoV2OnNu5FDaa/Vf5tlpQoBcsiz6IwzJjhP3xy5kz47jvPbUKoydC2bU/P3jpMfe6jvtQvAxl13AWEAXcA/YC/AddWnXkajUZTRmamy0DM7ZJ7+LD/Y/h6T0mJWmmriCfnq+IxKg8GDh92P0fF92RnO0staOol1T0AbBADzKqk4qp6ZUpLnfl1x497duhAOXQVBVBKS93bHfi6RlTB/019HSw2VBpMf6v8ObKyvDt0ENj90pfSs5Tw+++B2VbPaKh91K9TJ6VcK6XMB3KBO6SUl0gp11S/aRqNptERG+tbxKR5c//HaOZdWp6gIHdZZ0/4KlnQvLnv9shI91ILGo3GO776k9HoDD2Lj/fudDlCyyq+rtweyPk0mvpCTIxvleVA7pctfZR2EAJ69Tp1uzS1hq86dQAIIZJRYimRZa9zgOullOt97qjRaBonubnw+eewapWaLe/XDyZPdt487HaV0P3990rcIC5OhVz+7W/KGRo+HL75Bk6eVKt2RqNK9E5M9J40XpHhw+HDDz2vlg0bpnJu/DF+vOcQTiFU7l3TpjB/vvtqAKjwUGPdKWK6eXM2//3vfv74I4fo6CAmTmzB1Ve3rbHC3o0Jq9XOxx8f4ptvjpKVZaF79yimTWvXePPmAmXoUBVimZ/v3FZcrK4BISFK2CQyUuWxOnKAmjRRbRaLyiEqKlL9MyNDDWajopyr/maza8h1A1H0a8gcP17Mu+/uIzU1HSEEw4fHc9117WjWrJGJzkgJP/ygajBmZCjxlIsvVgI/oaEwYgR89ZX7/TIhQbX549FHVYhl5RVwIVQ/8lcHT1OnCOSu/i5wq5SynZSyHXAbysnziRCinRDihBDiZyHEkkptjwkh/ihru9vbMTQaTT2joECpby1YoG5AOTnqhnHffc5QkGuvVYIkWVnOAdkzzyhFO1ASzNnZKsnbUfMtO1s5dYGUCoiIgNmznSIoDvr3V4p6gTB6tFIGqxiaGRyscvrOOkutBj7wgKs9QiinsQ6pX65Zk8lNN60nNTWDnJxSDh4s5F//2sO99/6Bv3xqzanzwAN/8vLLu9m3r4CcnFJWr87k5pvXk5qa7n/nxkxoqBImcZQPKCpS1wsp1QB1wwY1sN24Ub2221XIZl6eEnMoKFCDWaNRDWz371erdk2aqH7booU6rtmsVDP16kOdJi2tmOuuW8v8+UfIyLCQnl7C558f5vrr15GZ6aeuWkPjzTfh9deVAFdeHmzdCk89pSZFQZUlyMlxv1+2bOlZ9bIyLVt6XrmWUjmPmnqF35U6IE9KucLxQkq5Ugjhp3pvOT9KKad6abtHSvlTgMfRaDT1ge+/9xzHn58Pn32mFLjWe1nk/+gjuP129W+LFmo2vrhYDc7CwtTgbcsW6NHDvx3dusE776hz5eRA587Q/hTr0Fx3HUycqAaSZjMkJ7uu8vXvD++9p87N7mKPAAAgAElEQVSRnw/du/sOZakFXnllN1aru/O2enUmv/6ayeDBcbVgVcNkw4aT/Pyzu/Nmt8PLL+9m6ND4WrCqHpGUpJQu169X5UcMBuWQ5eU5V9yKitTzli3Vvzk56n0mkzMs02xWX7rRqJQyW7VSxzQaVR92CKlo6iwffHCA9HR35+348WI++uggM2d2rgWraoFjx9QKnSc++ADOO08JdjVv7rxfGo3qPrVvH2za5LtOHcCiRWqVLzhYqcJKqY5hMql79j33aNGvekQgTt3vQog3gY9RYnSXAz8LIfoCSCk3+Nh3hBBiBTBfSvl/ldrmCiFOAvdKKTedhu0ajaau4c1hA1i3Ts0gesNigS+/dIomBAe75wusXRuYUwfqpjRwYGDv9UZcnKp95Y3gYBg8+MzOUU2kp5ewa1e+1/aVKzO0U1eFrFyZ4bXtwIFCDh0qpHVrnWvpE6NRrYTn5zv7fmGh63sKC9UKeUiIKmFgMLjn2RkMqt6do8j4KF1WoD6xalWm17aVKzMaj1O3fr17/TgHeXnw66+q/A54vl+uW+ffqXOs+BkM7jXpCgqUUMoplPbQ1C6BOHWOX8SjlbYPRjl53mrWHQO6ACXAAiHEUinl5rK2V6SUjwkhOqPCO8+tvLMQ4ibgJoA2bdoEYKZGo6l1fOWSOWb/fBHiJ1/C3/6ackwm3+peQUF69rUq0d93FWE0KifN22C2ogPnS9WwDuW1ak4NX32pUfUjf/c7f4XBA7lf+nuPLyEWTZ0jEPXLET4eXouQSylLpJQFUkor8C3Qo0JbVtm/u33s/5aUMllKmRwfr8NWNJp6wTnn+G674grvA7HwcJXH1slHsWdfx2+kHD9ezH33/cHkyb/y+ONbyc9XhZWbNDHTt2+M1/1GjvShElqJ7GwLH398kNde+4uffjqB1Wo/ZTu3bMnhjTf28NZbe9mzx/sKYn1l1CjviordukXRvHkIGzac5Prr13LFFb/y3nv7sNud3+OyZSeYOvU3rrpqDV99FYAUeUMlLAzOPtv5unIebcUQ6M6dvV9P+vSpets0NYLj2pSTY2Hfvnz27SsgN7fUpS0Q8vOtzJmzlcmTf+Xuuzdx9Gih/53qEgMHep+ciItT7UlJ3vcfMsT/OaZM8d4WG6v7UT3Dr1MnhEgQQrwjhFhc9rqbEGJ6APtVDFw/B9hToS2q7N84Alst1Gg09YHzzlO5ZZVJSIDLL4cuXWDcOPd2IeCOO9Ss4U03eV6xGz8eOnSoepvrMR9+eICkpO95/fU9LFp0jOee20mHDt+xcqXK7br77i5ERLhfYi++OJFevbw7fBVJTU1n/PiVvPjiLv773/088MCfXH75GtLSiv3vDNjtkocf/pNp09by9tv7eOutvVx++RpefHFn4B+0HtClSyRXXNHabXtYmJF77+3CrbeuZ+jQ5Xz66SEWLjzG7bdvpEePJWRlWZg4cSUTJ67iq6+OsGDBUaZO/Z0hQ5adlvPcILj+eqczFxbmfF6xXEhUFLz4omfZ9shIJZakqZdMmdKKQ4cK2bu3gOzsUrKzLezZk8+RI4VMnhxY3vKvv2bSseMi5s7dyaJFx3jzzb2cddYPzJu3v3qNr0qaNIGpHmQpjEZVWNxgUOJfFZVdHVxwge8JUgcpKSrX3dM57r//1G3W1CrCnwJamTP3HvCwlPJsIYQJ2Cil7Olnv3HAE6jwy5VSyvuFEK9KKWeW5ej1QDmVD0gpf/F1rOTkZLlu3brAP9VpIMTn1Xp8b0g5OaD31XX7AqU2PkdVf4aqJtDv5Ew+R3JyMtXdh8qxWOCnn1xLGlxwgasS1yefwMcfqxy7Vq3g5ptdbyxHj8LChbB7N0RHw8iRdTZ3rbbIz7fStu23FBe7D/zj44PZv1/Jth89WsQnnxxi8+ZsoqODmDAhkVGjmgVUkDcnp5Rx41ZQUuJ+jsGDm/LKK/5ncT/99BDPP+/ZgXvuuV6cd17gM++1SaB9aPnyNBYuVCUNunWL4sor27BxYzZXXum5vGv79uHs2+e5WP1NN3Xg//7PT05MQyU9Hb79Vqn9hYerlYm0NCWW0qMHTJigtuXnwz//CampSpY9OVmVQHAoXmrqFIH0owcf3MzLL+/GZpPlSvtK3FTwj38k8eijHiYOK9Gx4yKOH3efeDKbDezbN46YGD+hi3WJTZtU7lt6OrRtq377FSc4jx1TfWXnTnWfHTny1KNa/vtf+OIL1Z/at1eiZf36VenH0FQNQoj1Uspkj20BOHVrpZT9hRAbpZR9yrZtklLW2J1GO3V1375A0U6dOw3OqdPUCM89t4PHH9/mtf2LLwZxwQVnNrD97LNDPPecZ4dMCPjuuyF+60ZdddUar4ItgTqGdYEz6UNjx6ayYoVnIZXiYhvBwQaPTnZF51yjaQgE0o86d17M0aNFHtvatAlj+/axPvdftuwEEyeu8tr+4INJzJrVzb+xGk0dxJdTF0joY4EQoilKFAUhRAqQU4X2VSu15Qxp6g414TRpNDXNsWO+wx8PHjzz/JHMTIvXNinh5MlSv06dr2NkZXlva0j4+py+5lUd+ZEaTWMiL6/UR5v/PnHggO9rn79rp0ZTXwlERuhu4BugoxBiFfA+MLNardJoNBqNT4YM8V6OQAgYPvzMBaa6dXOGzNpsEovFXl60PDzcSOvWHnI5fBzjVNoaEj16RJc/l9LVkTObPa/SAbRtq0sgaBof7do5xXAq95cOHcI97OHK8OHxPkurDRnS9EzM02jqLIGoX24AhqFKGMwAulcoTaDRaDSaWuDSS1vRpo3nQf/AgbF07XrmDtO558bRpk0YR44UsXt3Hnv25LN7dz4ZGSVMmdKKsDD/wR7XXNPW4wArONjgUVikITJ79lmYzQYsFjslJbayhx2bTXL11W0wGt2dOiHg3nu71IK1Gk3t8uCDSUgJJSWu/QXg4YfP8rt/+/YRnHOO50mvli1DufLKtlVqr0ZTVwhE/XIyECql3ApcDHzqKDyu0Wg0mtpj+fLh9OkTU67qbjQKRoxoxsKFAUhZB4jJJLDbZflsuWPmPCgosDpgffo04bnnerms6nXuHMErr/ShQ4cIH3s2HJo2DaZTpwg35y0mJoirr27L66/3pWlTp3BDdHQQTz7ZQw8+NY2SpKQomjcPdpkMMhigRYsQOnUK7Jrx9deDGTUqobzmnRBw9tkxLFs2rDpM1mjqBIHk1M2WUn4uhBgCjAFeAP4NDKxWyzQajUbjk+bNQ1i58jwOHChg1648zj47xm+O26mwenUme/cW0Lp1GFarWllyhAt+/PFBrrmmLSEh/p274cObMWxYPPv3F2I0Cq8rjA2Vr78+gpQqDLOw0IrVKomMNCGE4J139vHhhwOZOrUtv/6aSWmpnSFDmmLwFT+m0TRg3n9/P7GxwcTGBpOfr/LrIiKCAPjww4M88oh/kZOQEBMLFpxDRkYxGzdm06lTBO3bN45JJE3jJRCnrkxQlvHAv6WUC4QQj1WfSRqNRqM5Fdq2DadtW/+5JqfKn386NbFMJgOmCneMvDwrBw4U0rVrpIc93RFC0L591dtYH9iyJbf8eeWQ1Z0787BY7JjNBgYN0rk+Gk3F647DmfPUFghxcSGcf76HWoYaTQMkkKnAI2V15aYAi4QQwQHup9FoNJp6TExMkM/26Gjf7RqFr+8xPNxIUJD/moEaTWPBVw05f9ckjaYxE8hK3RRgLPCClDJbCNECuK96zdJoNBpNIGzZksOXXx7m2LFiOnQIZ/Lk1lW2IjZmTHNeffUvLBb34uP9+zehefOqC/X0x969+Xz++WH27SsgMTGUSy9tSffu0f53rEXS00v44ovDbNqUzaFDBQQFGSgtVfmJYWFGYmKCmDChdUCF4DWahsSxY0V8/vlhtm3LJTbWzEUXJTJwoFqpvvDCRLZsySEnp7S8hEFkpIno6CAmTkysTbM1mjqNX6dOSlkIzK/w+hhwrDqN0mg0Go1/5s8/zDPP7CgXMVm37iRff32U55/v5VX97VSIjTXz2GPdeOSRrVitTl3xxMQQZs+uueK9K1akc//9myktddhwkoULj/LQQ2cxaVLLGrPjVNi7N5+bblpPdnYpUkqKi+1kZFgwGgVBQQYKCqzYbJJLLqmb9ms01cW2bbnceusGlzqMS5acYPr09txyS0fGjk3gsce2cvy4s55cQYGVyEgTo0Y1qw2TNZp6gQ6j1Gg0mnpITk4pL7ywy614tcVi56mntmO3+6hqfQqMHt2cb745h9tu68jll7fmsce68cUXg0lM9F+jriqw2SRPPbW9gkOnkBJeeGGnz0LFtckLL+wiO1vZlp+vxFEcIjORkSZatw4lISGE997bX7uGajQ1zLPP7nBx6By8884+9u8v4JNPDhESYqRt2zBiY800bWqmXbswTCYDX3xxuBYs1mjqB9qp02g0mnrIL7+kewyLBEhLK2HTpuwqO1ezZiFcd1177ruvKxMmJGI219ytY+PGk2RkWDy2lZTYSU3NqDFbAiUnp5S1a7PKXztCyAwGgcmkVuocAhBLl56oMgdco6nrlJba2bYt12v7jz+e4McfTwBKVCghIYRmzUIIDTWVt2s0Gs9op06j0WjqId4cukDb6wv+PkdJic1ne21gsdhdVlArO20V26xWqZ06TaPB7ueyZLHYffZ5RxFyjUbjTiBCKRqNRlPzHDkCf/0F0dHQqxc00rpdFoudNWsyKS62kZwcS2ysUoZLSYlFCOUgFBZaKS2VBAcbCAkxEh5upFcvp4jIunVZfP/9cZo1C+H669thMp3ad2m3S9avP0lmpoWkpEjatXMXYklNTSc1NZ02bcKYOrVNldVZO/vsGMLCjBQWujtvBgOkpNS9MgDx8arY+F9/5VNYaMVisWO1SoSwY7VCXp6F8HADZrORtm3D2bw5h65dI/jtt5PY7ZIBA2KJiqoHKn/HjsGuXRAVBWef3Wj7qCZwgoMNJCaGcPRoscf2QYOaUlBg5dChwxQX2zh+vBghVE3O4GAjgwc7+7vVamfevP0cO1bMmDEJ9O/vfi3YvTuPPXsKSEgIpk+fJm7tJ09aWLs2i+BgIykpsQQH+6+7WSfRfVGDduo0Gk1dw2KBl1+GlSudSxrNmsH990OXLrVrWw2zdOkJnnpqO7m5KnwvKEhw1VVtmDmzM61ahTFyZDPeemuvy+x1WJiRJ5/sQViYicJCK2PHrmDDhpPlX+WsWX/y2mt9mTKldUA27NqVxz/+sZlDh4rKtw0fHs8TT/QgNNRIRkYxY8asYMeOvPL2Bx74k3nz+ldJfajwcBPTp7fn1Vf/cmu77LJWNZbbd6pMn96OSy/9lYICK1K6rs7l5trIzS1ACLBYrEyZ8is5OaXlA9fgYAM33dSBa69tV2v2+8RigVdegRUrnB8sPh7uuw+SkmrXNk2d57bbOjFr1ha3fOAhQ+Lo27cJLVqEMHfuDrKynPmyGRkW4uLMXHVVGwC++uowN9/sFFt55pkd9O4dww8/DCUiwkRubikPPvgnv/3mDIPu2DGc558/mzZtwgD497/38P77+8vzdaOiTPzjH0mMGVOP6trpvqipgHblNRpN3eLdd11vUABpafD441BYWHt21TB79uTz0ENbyh06gNJSybx5B/jyy8PY7ZK//sonJsZcJsABISFGmjQxs3WrylmZOvV31q8/6fJVFhTYuOmm9Rw86P+7LCmxMXPmRheHDuDnn9N57rkdAFxyya8uDh2onLKrrvrNoxjC6XDtte2YM6c7XbtGYjYbaNcujPvu68p993WtkuNXB2+8sReLxY7BINwGrw6khMOHSzh2rJjCQlv591xSYufVV/9i6dI6mj80bx6kprr20fR0mDMH8vNrzy5NvWDMmOa89FJv+vSJITjYQIsWIdx8cweef74XAHPn7uDkSVcBJCEgM9PCyy/v5vjxYq6/fp3b9WXTpmyuuGINAI8/vs3FoQPYs6eAO+/ciN0u+eabo7zzzj4XAabcXCuzZ29h507X61mdRvdFTQX0Sp1Go6k7FBXB0qWe2/Ly4OefYdy4GjWptvjii8PYbJ69gU8+OURiYij79xcSG2suD8l0sHp1Jtu357JsmWenoLTUzrPPbuf11/v5tOGnn9LIzPQsUvL998e56KJENmw46bG9sNDGP/+5k0ce6e7zHIEyblwLxo1rUSXHqm7y860sXZqG0SgwGgU2m82rY1dYaCMsTIV8lZbaycsrJTJShV5+8skhRo5MqCmzA6O4GH780XNbfr7qoxMm1KhJmvrHOefEeS278sEHBxBCOXKVefvtfRQW2rzm3a1Ykc7WrTmkpqZ7bD90qIiVKzP45JODHtvtdvj880PMmlVzJVtOG90XNZWot06dEJ/XtglVQl3/HHXdvtog0O9EysnVbEkDJDMTSkq8tx89WnO21DIHDnhfSTt4sNDvSttvv2W6lQGoyJ49BQHY4P09paWSVasyvDorALt2Nc6Z4r178yktdQ46fX1HADabDaNROXYVB6uBrKbWOFlZajDpjSNHas4WTYMkP9+7+FFubim7d3u/rlitkjVrsnz2uUOHCv1eX+sFui9qKqHDLzUaTd2haVMIDvbe3qJ+rNRUBY68D0+0bh1K69a+c8kGDmxKUJCHqe4yOnRwFzs5FRuCggSDB8d5nE130LlzhN9zNEQ6dIhw+e59fUdAuUMHuJSLaN3a+/dfa8TG+u6jLXUxdc2ZERHhXawkMjLI53XFZBLlIlLeaN06zM/1tQ72O0/464uJiTVni6ZOoJ06jaYxkZ8P770H114LU6bAE08oxay6QmgonHee57aICBgxombt8UFhoZVXX93NBRes4Nxzl3PHHRv5449Tqw23bVsud9+9iaFDlzNmTCovvbSrvJj25MmtkFJy/Hgxu3blsXNnHocPF1JcbOOKK9qQktKUtm09Dz4GD27KWWdFMXx4M+x2icVip7jYRnGxDatVYjIJHnzwLABOnCjmySe3cd55PzN8+M/Mnr2lfKb6/PMT3EI7HYwZ05w+fZrQp0+Mx/bQUCP33FN3c96qil278rjvvj8YOnQ5o0en8vzzO7Fa7YwY0Qyr1U5RkffQSweFhTZKSuyYTIKICGcAzRVXBCZmU6OEhMCoUZ7bwsNh+PDAj7V/Pzz7rLoWTZ0Kb7wB2VVXX1FTP5k6tS1SqlDIig8plQDRffd18Vorc8iQOLp3j+bccz2HdrZqFcqQIXFcfrnnvmUwqGsvQEGBlVdecV7j77xzI3/+mVM1HzJQ7Hb45hu45RaYPBnuuUflnIPqi+ef73m/8PA6db/U1AzV5tQJIdoJIU4IIX4WQiyp1JYohFgmhFgthPByd9BoNFWKxQKzZsFXX8HJkypsY+1aePBB2Lattq1zcv31MGSI6/JGfDw8+iiE1Y0ZVKvVzm23bWTevAOkp5dQVGRj9epMZsxY71J02hebN2dzww3rSE3NoLDQRmamhf/97yAzZqynpMRGixYhhIcrFTebTdUyy8+3UlRko2fPaAwGwcsv96ZTJ9dZ6+TkJsyZo/LYbrutAyaTcKmDZrdL+vdvQqtWoWRlWbjuurV8/fVRcnOt5OdbWbz4ONddt5YjR4oIDjby2mt9aNXKdVVw2LB47r9fOWxffTWYpKRIl/bo6CA+/HCgi4PSENmxI5fp09exfHk6hYU2srIsfPrpIW68cT0pKbFYrdKvQwfqp26zybKyBwKz2cBtt3Vk1Kg6lk/n4Lrr4NxzXftoXBw88oiafAmE/fuVou3q1epalJsLixbBAw9ogYdGzqBBTT32GylVKZfExDDefjvZ7fpy9tkxfPrpIAAee6w7AwbEurR36BDOyy/3xmAQXHxxS66/vp3LinpkpInHH+9OUlIUFoudW27ZwPvvO6/xq1ZlctNN67zmEVcLr78Ob7+tQilLSmD3bnj+eVi4ULVPm3bmfVHTYBAykDvO6RxYiHbAk1LKqR7aXgE+ATYD30oph/s6VnJysly3bl2lY+hcL03VEmgOXFXn1NVEjl5ycjLrnnkGXn3V8xt69YInnzzt41cLjjp1dbDuzpIlx3nooS0e23r1iubdd/v7Pcatt27g9989O4CPPNKNggIrL764CymVMyelKldgMhkYPjyeF144u/z9mzZlc+xYER07RtCli9PBuvzyX9mzp4CCAis5OaUEBRlo2jQIg8HASy/1ZvPmbN59d79HGy65pCUPPaRW8xx16jIySkhKiqJ9e/fQzZ9/TiM1NZ127cKrtE5dXSE5OZnK96G7795EamqGx/fv319AUZENu11isylnzVGnDlSYmBBKGVOJQijn+4knujNjRsf6Uafu6FE1yDydPjp3Lqxa5blt2jS45JIqMVFTt/DUjyoTH7+AjAzPAk0tWoRw9OhEQE2u/fe/+zl+vJjzz09g4ED3OnW7duWxZ08+zZuHeKxTl5VlYd26LMxmAykpTQkJUaGfixYd45FHtnq0oU+fGP7zn2Sfn6FKOHIEbr3Vc1JuRISKunGEX55JX9TUK4QQ66WUHn+A1T2NOkIIsQKYL6X8vwrbewF3SimlECJPCBEppaxHGrIaTT1k40bvbZs3q5U8s+dQu1qhZcs6m5+zenWm17bNm3PIz7f6XKWyWu0+V/RWrcqgqEiJBQghytUQvZ2/d+8Yevd2DYNMSysuF0MJDzcRHu5qz+rVGWze7D2UqOI5DAZB//6xXt8LMHx4M4YPb+bzPQ2NX3/1/DsoLlZOtNlswGAQGAyOWXQjUqpC5Eaj+6DLaBTs2VNQPxw6UDk7p5u3s36997YNG7RT14jJyvLs0AGkpTmFtEwmAzfc0MHnsbp0iXSZ6KpMbKyZ0aPd69J569sAGzdmU1RkIzS0mguVb9zoXWUpPx927lQTsnBmfVHTYKhOV/4Y0AUYAYwSQvSq0GaUziXCHMBt+kQIcZMQYp0QYl16umdpWo1GcwqYfMzhGI3qoQkIb/kcoCZITSbfyhgGg/D5nuBgg89z+GoL9D1m85mfo7ETFOT5O/K3Sil8qDhU+0CxruBrAimonji1mmrBV/+oKbz1baC8VEm14+ueDbqfaNyotru2lLJESlkgpbQC3wI9KjRX1KuNAtwyo6WUb0kpk6WUyfHx8dVlpkbTeBgyxHvb4ME169RlZ8P8+fCf/6g6O5XKGFixsoENLGABP/ET2e6XiFrFketksdhJTy/hxInicoGTIUPiykN40tKKufvuTUyYsJI779zI4cNKgMRgEIwY4X1Va9SoBM4/33s+la82BzExZpKT3cONKh7DV85WIOdo7Hj7jsxmA4mJIYDKlSstVatzVqu9LG9O1aSrnP4gBNx0k++Vh3rJjh0qVOy992BrWUibr+uRr7ZqIossfuRHFrCAP/gDG95l9TVnjsViZ/HiY7z44k7ee28faWlOaf42bbwr+3bq5F+1tyrwdf0bNiy+Zia9UlKUY2ezqTz49HTIyVHiKfHx0LVhCVEVU8xqVrOABaxgBYXUk9ISdYhqC7+sFFJ5DlAxmWezEGIQKqcuSkqZW112aDSaMvr3VwnVDuUsB02bwjXX1Jwda9eqfBpLhRCbjz5SSpytWpFNNm/yJuk4V+iXsIQpTCGZGshjCIABA2Jp1y6MxYuPl2/LyoLoaCvTp7cH4JtvjjJt2u+UlKi6Y8uXw7x5B3jzzX5cfnlrZs7sxKZN2S7hRACjRydw7rlxSAk//HDcLWerZctQZswIbOB/771dmTFjPTk5pS7bp0xpRffu0XTqFMGyZWls2uTqNHfpEsHf/tY2sC+jEXPzzR1ZuzaLo0dda0UNHRrHOec05corfysvIO8QqxFCOfUWi3LwgoMN5eGZU6e25ayzomr2Q1QnUsIrr8DSpc5tX32lnLYbboBNm9xrT/btC8OG1aiZv/Ir85mPRP0frWAFLWjBDGYQgRabqGpOnCjmlls2uNSDe/PNvTz6aDcuuKAFCxeeQ48e7kW1hYAFC86pERtTUmIZN645ixYdd9keF2fmjjs61YgNxMQoBcs33nANw8zOVrl2DShv7jCHeZu3yccpkvQ933M919ORjrVoWf2iOoVSxgFPACXASinl/UKIV6WUM4UQrYD3gVDgUSnlEl/H0kIpmpqgwQulrFunZvhWrYJffoGiIujZEy64AKKjT/vYp0RRkSqn4KlgaqdO8M9/8i7vsg13NU4DBh7iIWLwLKFfk+zenccVV6whL89ark4ZFmaiSZMgLr20FQ8+mETr1t+Rm1vqtm9YmJEjRyZiNhvIzrbw5ZdHWLs2i7AwI2PHNmfUqITyQb7NJvnhh+MsWXKCkhIbKSlNmTSp5SnlXKWlFfP554fZvDmH6OggJkxowdChzugHi8XOd98dY9myNGw2ydChcVx4YSJhYQ1bufJU8SbwkJtbyvz5R/jtt0yCg42MHp3AmDHNmT17CwsWHOHo0WLy80uxWh2CKBAcbMRuVyt4oaFG+vSJ4eabO3L11Q3MkV62DF56yXPbjBnKefvhB5U7FBSknL1hw/yHnVUh6aTzHM+VO3QV6U1vpuKm9aY5A5KTkxk8+D8e85KDggQLFw5h7twdvPLKLpd5PyHUT+Tee5N46qmeNWKrlJJly9JYtOg4+flW+vVrwmWXtfJa5qXKKS5WSrMZGUodtrRUCaNER0NSkve+Vc+QSOYylwzcRaciiGAWszBVuwRI/aFWhFKklIuARZW2zSz79zDgpRiVRqOpNgwGtVp37rm1c/5Vqzw7dAB//UXBwe1sb7PdY7MdO+tZz0hGVqOBgbFw4TGEEERFBbk5WN9/f5zWrUM9OnSgapK9/fZebr21EzExZqZPb1++ulcZo1EwblwLxo07/aLrzZqFcNtt3meWzWYDkya1ZNKkuilKU9eJigpi2rR2TJvWrnxbQYGVZcvSCA010bFjBLt25VFQYC1vt9lUrUCjUamZ/vLL8LEtqR8AACAASURBVAanFgrATz95b1u6FMaPh0svVY9aYh3rPDp0AH/yJyWUEIyPAs+aU8JqlV5FSEpLJYsXH2f+/COYTEaPvv2XXx6uMadOCMHIkQmMHFlLoehr1kBBgarfGlopJHXvXvXoUP/Dtfez36NDB5BPPjvYQQ+XDC6NNxrgXUSj0dRZcnwXbi3JTfc6wAIooKCqLTotsrO9q7OVlNhdwoo8cfy4F8dW0yDIz7ditTp/x44QTAcVI2RKS+0UFtprzLYaxVd/z60bWRe+rik2bBRRVIPWNHxsNt+1G7OzLS4TIJXJz29EuY5+7pd1pQ+dKf7u63Xlvl8f0E6dRqOpOZKSvLeZzUS17ekzh6UNbarBqFOnRw9nuKrVaqekxFY+UG/TJowJExLLa8FKqfKpKg5kRo+u2pnfnJxS9uzJp7DQ+2BIU3PExweTkKBWd6RUeXMV//8d4bVSqoLH6ekN1Mn3JeRQR0QefF1TYoghigaU41gHMJsNREer6AYpJSUlNqxW56RGjx7RdOzovAfY7bI8HxWgc+dGlOPoq48EBTWIVTqAVrRC4F1NtK7c9+sD2qnTaDQ1R/fu6uGJceMwRTZhOMM9NieQQE9qJuzGH+PHtyA6OogDBwrYvTufvXsL+OuvfE6etDB9env69m1C794xlJYqh89iUf+Wlto566wohgypGkXf/Hwrs2dvYcyYVC6/fA1jxqzgxRd3UlraQFd+6gkGg2DatHZkZZWwe3c+RUXWMufe2V5SYqeoyEZWloWePZeQkrLU7wpvvePiiz2XLjCZYNKkmrfHA33oQ1Pci1YDjGQkBj1MqlKEgKlT25CRUVJ+7dy9O59Dhwpp0yaMYcPiefjhJGw2SVGRjeJiO8XFqq/Y7ZLZs8+q7Y9QcyQlOevQVWbsWFVovAEQQ4xXEbTudKcFp59+0NjQVyuNRlOzzJoF553nrLETEQGXXw7TpgEwnOFMZCKRqIKxAkFPejKDGRipGzW8HKtvFespCSEQgvJZ55SUpoSFudobGmpk4EDvZQZOlfvu+4PFi4+Xh/oVFdn4+ONDPPfczio7h+b0UP8nokzt0kBwsCivGmKx2Mrz6oKC1Cren3/mMHLkL7Vqc5XTpg3MmaNEkBy0bw+PPOK6rRYJIohbuIVudCtfLYgmmklMYhCDatm6honNJqlcik4IZ2im1YpbuQ9Q193S0uoR96uzPPQQjBrlvF+Gh8PkyTB9eu3aVcVcxmUMY1h5/moQQQxiEFdzdS1bVr+oNvXLqkSrX2pqgkahflmXKChQOQNxcR5n823YOMlJwsr+6hKffHKQF17YBSgnzm5Xym1CCFq3DuXVV/swadLqsgGKmmkOCTFgMql5tM8+S6FDhzMLI9qyJYdp09Z6bDOZBN9+O4S4OC3wUFWcSh+y2yUTJqwkLa0EKSWlpbKsKL2BvLxS9u/Px2AQHsVRXn+9L9de266Kra8DpKerUXkz7/UZa5sCCiiiiCY0qTMTSA2Nfv2SiY5+gdxca3nfqFjMe+7cnsyatYXNm1U+mb1sedvRV/r1a0Jq6ojaMb42KSxUteri4z2vfjcQLFjIIYcoorRAkRdqRf1So6lv6ImCGiY8XD28YMRIHHE1aFDgbNvmTFB3OGoODh0qYv36k+U5VCaTgYgI1/ds25Z7xk5dRRsqY7VKdu3K005dLZGeXlJef1AIgdnsXJawWOxI6dmhA1i1KqNhOnXxVRNyXJ2El/1pqg+LxU5ursr9rdw3QF3X9u1zCmNU7id79zZS0YywMPVo4JgxE0/dv1bUVXT4pUaj0ZwivuoUhYYaad485LT3rwobquocmtMjMtJEUJDnxP/gYINL2G5lEhJ8/3Y0mvqM0Sh81syOjTUTGem9DmdkpF6L0Gi8oZ06jUajOUUuvDDRLSfEwQUXNGfAgFjatPE8q5qQEExKihJmUEXMf6VLl8X06vUDjz221UUJzhdDh8bTpInnwU+XLhEkJTWMJPr6xi+/pDNr1hYKC22cOFGMxeL6/xkVFURiYkh58fGSEjsWix2bTRIUZODOO+tGrplGUx2YTIJzzvEcgWE2G7jgghZccklL7HZZLiZUVGSjpMSO3S654orWNWyxRlN/0E6dRqPRnCIdOkRw//1d3Wace/aM5o47OiOE4Nlne7qtlkVFmZg7txcGg2Dt2kwGDVrGwoXHOHKkiD17Cnj++Z0MGrQsIMfObDYwd24vIiJcZ66bNQuuseK8Glf++c9d3HPPH6SmZiCEKjS/b18BRUWqtpbRKJg1qxuzZp2FzSbLhCFkuYN30UWJxMXplTpNw+ahh86ifXvXMFez2cCcOd2JjTUzc2YnpHQKpzieCwG33NK+lqzWaOo+eh1bo9FoToPJk1szZEgcP/xwgry8Uvr2bcKgQU3La5B16RLJggXnsGTJcQ4cKKRVq1DGjm1OWJi67M6cual8sF+Rbdtyee21v7jrri5+bejbtwkLF57D998f59ixYjp2jOD88xMwm/V8XU2za1ceH310sPy1yWSgXbsw8vOthIcbuf32Towd25y4uGAuvngVPXpEc+JEMYWFNsxmAwkJIWzfnktaWjHNmmnHTtNwiY8P5pNPUvj55zS2bcslNtbMBRe0KJ8E+8c/NmM0CoKDDeXKviaTEqJ64IGtvPtu/9o0X6Ops2inTqPRaE6TFi1CmTatndf20FAjF13U0m271Wpny5Ycr/t9+eXhgJw6gMjIICZP1iFJtc1PP51w2yaEIDIyCLsdRo9u/v/s3Xd8VGXWwPHfMzNJJoH0SqihGEqoCaAICKK4sIhi3dVVd8VesK29rXVtuGJbxbWX9bXs2nVFQRBBOkQ6JJBAEkgjpCczmfv+Mckkw5QMyZSU882Hj8l97tw5MXPLueUcEhKMbN9eTn5+LTqdolevULv5LRZYtqyICy+Uv6fo2vR6xYwZicyYkegwtmJFsW2epqqYTZYtK/RLfEJ0RnI6Vwgh/MxstuCum0y368XUBTRdUXClqSF8a7fWSuN40d01NLhel9yNCdHdSVInhBA+tGnTET77LK+xzYH1gMRoNDBwoPWZEotFw2xufr4K4PTTHc9eu2KxaKxdW8rnn+eRmVnm/V9AeMRV8YeKChMVFSY++eQAxcV15OfXYDZbXCZvrpYjRHcxblw0YN221ddbCwlZLNZt4/jxMbb5TCYLK1YU8fnneWRnVwYkViE6Ern9UgghfKCwsJZbb93Czp0VtmmDBvXgH/8YQ3JyKI88ksaFF/7qcOUmMdHInXcO9eg9cnKquOWWLeTmVtumjRoVyTPPjJaWBn6Wnh7NKafEs3x5EWC9IrdnTyW1tQ0EBem4++6t3HHHb/TuHYrBoCgoqCUqKsjuFsyzz052KCAhRHfz5JMjSU9fQl1d81U5s1nDaNTxxBPWIlAbNx7h7rt/o6Sk3jbPtGnxPPpoGkajNI4X3ZNcqRNCCB+4445Mu4QOICuriltv3QJAaWk9/fuHERqqRymFTqeIjLSWuy8trXe2SDsWi8ZNN222S+gAMjOPct99W733iwiPPfnkSBYsGMyAAWEcPFhDfb2F4GAdOp3CZLJebThwoBqjUUefPqHU11uoqjIzeLC1muo99wwL9K8gRMD973+HGreJzdOavv/hh8OUl5u45ZbNdgkdwE8/FfHcc3v8GKkQHYtcqRNCCC/bsaOcrVvLnY7t3VvJxo1H+Pjjg0REBBER4dhr7j//ab1QyurVJRw8WON0bO3aUnJyqujfX676+JPBoOPSSwdw9tnJ9O37ta0K6bG3Wh4+XEf//j0IDw8iIcFaCVAIYfXGG/vR6ZTTK26vvppNdHQwVVWOlYMBvvoqnwULBtuqDAvRnciVOiGE8LK8POfJVpP9+6soKqpr8+s9mceTZQjfyM2tsSuccmxRnLq65iSvsLDOoUG5EN1ZcbHrbWNxcZ3bbVttrYXi4tbvdBCiK5KkTgghvKx//zC340OGhNOrl+teZAMGtH6Fzd17KAX9+rmPQfjOgAE97HoFKvuq7HZXIJKTjdJXUIgWEhNdbxuTkkLd3oEQFqYnISHEF2EJ0eHJnkQI4VWFhbU89NA2pkxZxokn/sitt25m166K1l/YhQwZEk5GRrTTsREjIhg5MpI//rGf0/GQEB3z5jn2tjvWhAkxDBrk/OBmypQ4+vSRpM7f6uoaeOmlvZxzzip0OusVuYYGDYOhZYKnSEpqPmj9wx+cfw6E6K6uu26Qy7EFCwYza1YS4eEGCgtr2b27gh07ytm/v4qKChPz5vWWQimi2/J5UqeUulUptfKYaW8ppdYopX5SSl3k6xiEEP5x9KiJ+fPX8+WXBdTUNGA2a6xYUcwVV6xn9+7uldj9/e8jmTAhxm7amDFRPPPMaAD++Me+XHppf7urNPHxISxcOJrkZPum1M4opXjuuTGMGBFhN/3kk2P5299GeOE3EMdD0zRuvnkzb765n9LSegYM6EFYmN5WICU4WIfBoKN//zCCg3UEB+u49NL+/PGP0mhciJauvnoQV189kKCg5kvcwcE6FiwYzKWXDiAsTE98fAgVFWZb37qamgYqK82kpoYHKmwhAs6nT5IqpUKA0S6GL9Y0ba8v318I4V+ffHKQgoJah+k1NQ288cY+nnhiVACiCozo6GBefnkcWVmV5ORU06dPKCec0HzAoZRiwYIhXHJJf7ZsKSMszEB6ejR6vXKzVHu9eoXy9tsT2LGjnIKCWgYO7OHRrZvC+9asKWXduiO2n5VSnHBCODU1ZkBx992pnH9+X7ZsOUp1tZnRo6OIjpa2E0I48+yzY7jnnqF8+OEBdDrFRRf1IyrKur6sXl1CdnYVgwb1pLramtgZjXqCgnS89lo2s2YloY6951mIbsDX5YGuAN4GHj5muga8o5QqAW7QNC3Hx3EI4XdKfRyQ5Wna+V593+OxZk2Jy7Fffy31YyQdx6BBPRk0qKfL8ejoYKZNS2jXewwbFsGwYRGtzyh8Zs0a55/v0FDrbnbq1ARCQvQOV2+FEM7FxRm54YYhDtNb7kuOrXJ54EAN+fm19O7d+t0OQnQ1Prv9UikVBJyiadpSJ8O3aZo2CXgSWOji9VcppdYrpdYXFRX5KkwhhBe5e5YhNFSecxBdl9Hofnfa2rgQwjOyrgnhnC8/+ZcAHzgb0DSttPG/K4EkF/Ms1jQtQ9O0jPj4eN9FKYTwmpkzE9s01lllZVXy/PN7ePzxHXz7bYFDP7LWmM0Wliw5zOOP7+C553Z3u4IyXcnMmdZdWWWlmaysSnbvriAvrxqz2cKYMVEkJLiu6CeEsGexWHj99Wxmz/6Z2bN/5u2399vGzjjD6WEjAOnp0cTGSvVL0T358vbLVGCMUuoaYIRS6kZN014AUEpFaJpWrpRKBcp8GIMQwo9mzerF0qWFrFhRbDd98OCezJ+fEqCofOPNN/fx0ktZtp//85883nprP6+8ku7Rs1IVFSauu24jO3Y0J3LvvZfL/PkpXHut6+pvomNKSelBXFwwmzY1P1dXVWWmtLSehx4aHsDIhOhc6ustTJq0lB07ym3Tli8v4pVXsvj55+kMGmTdn7z++j6710VHB3HHHan+DleIDsNnSZ2maXc2fd9Y/fJjpdS9mqY9BryvlIrG+mzdtb6KQQjhX3q94plnRvPDD4f54YdC6ustnHxyLHPmJHep2y937iy3S+iaZGVVsWjRHo+qT770UpZdQtfk9df3cdJJsYwZE+WVWIV/bN58hJUriwkO1tHQoKFpoNMp9HrFAw9s56yz+gQ6RCE6hdtv32KX0DXJzDzKvfdu5cknR3HttYMYPz6aL77Ip6zMRFpaJOee21uu0oluzdeFUgDQNG1y47ePNf58pj/eVwjhfzqdYubMJNvtaF3R118XuBz7/vvD3HPPMLcNpTVN45tvXC/jq6/yJanrZBYt2mtL5HQ6+8p7e/dWkpVV6bZgjhDC6quvXG8b//vfPJ580lpFOSMjhowMKTwkRBN5mlQIIY7T0aMml2P19RZqaxvcvt5k0qiudj1Pebm5zbGJwDhypN7t+KFDjq0+hBCOqqpcb//cbTeF6O4kqRNCiOM0enTzVbSmJE7TrE1wBw7sQUREkNvXBwfrGDbMdZPc0aMjvROo8Jni4jq2by+nvNya4E+ZEudy3tBQPenpcuVViJaqq81s315OQUGN3fSW/TyPNXSoNBcXwhW/3H4phBBdyaxZSbzyShabNpVRU2M9c6zXK2Jjg3n88TSPlnHllQO57bYtNOaCNgkJIZx5ZrK3QxZeUl5u4tFHd/DTT4VYLNYEfc6cXtx88xBeeGEvRUV1Dq+56KJ+GI2yuxUCwGLRePnlLD766IDtytuECTHcf/8wevUK5f77h3HuuatpaLDfOBoMigcekKJDQrgiV+qEEOI4mc0a9fUWDIbmZ6f0eoXBoPP41smpU+N54omRpKT0AECng6lT43jttYxWr/SJwLn11i0sXWpN6MB6pfY//8njqad2sXz5NNLTo23P1PXsaeDqqwfy/PNjAxixEB3LK69k8dZb++1upVy7tpTrr9+E2Wzh9NOTeO21DLsG4n36hPLWW+OZOlVaXAnhipw6FEKI4/Tll/lUVTXQp08YDQ0aFouGwaBQSvHOO/s599zeKKVaXc6MGYnMmJFIcXEdRqOenj1lk9yRbdlSxubNzrvwfPNNAdddN4gVK6ZTXFxLUVE9Q4b0xGCQc6dCNNE0+Oijg07HcnOrWbq0kJkzk7jwwr5ceGFf9u2rRKdT9O/fw8+RCtH5yN5GCCGOU8sm4Xq9IihIZ0vi8vNrqag4vkIncXEhktB1Au6aw1ss1iqXAHFxRoYNi5CETohj1NdbqKx0vX3cudN+HUtJ6SkJnRAekj2OEEIcp7g4172QwsL0hIV1nZ58opm7v7sn40J0d3q9QufmyDM+XtYhIdpKkjohhDhOc+cmuzwwmTOnl1yh6aKmTIkjNjbY6djw4RFuq/YJIazFTk45xflzcSEhOmbN6rr9TYXwNTnyEEKI4zRgQA/uu2+4XaEUgPT0aG64YXCAohK+FhSk4+mnRxEZaV/IJjnZyGOPeVb1VIju7u67h3HCCT3tphmNOv7+95FERTk/aSKEaJ08xCGEEG0wd24yJ58cy/ffH6aiwsy4cVFkZMQEOizhY6NGRfHVV5NZsuQwhw7VMnBgD6ZNi5ers0J4KCYmmPfem8iqVSVs315OTEwwM2cmStVfIdpJacc2SeqA4uLitAEDBgQ6DCE6rf379yPrkBBtJ+uQEO0n65EQ7bNhwwZN0zSnZxE7xZW6AQMGsH79+kCH0WnV1DTYziqnpPRg+nQ5q9zdZGRkyDokRDt4Yx0qLKxlyZLDVFc3kJERzdix0V6KTojOwZ/7IrPZwvLlRWRlVZGYGMLppycSFtYpDnuFcEkptdHVmHy6u7jMzDJuuWULR4+abNOSk4289NI4+vYNC2BkQgjRfXz00QEWLtxNQ4P17phXX4WTTorlmWdGERIi1VKF8Kb8/Bquv34jBw7U2KYtWrSHhQtHy8kU0WXJ5ZouzGSycPvtmXYJHVj7aN1779YARSWEEN3Lrl0VPP30LltC12T16hJefTU7QFEJ0XXdd99Wu4QOoLzczO23Z1JfbwlQVEL4liR1XdjPPxdTUlLvdGz79nJ273bdSFcIIYR3fP55Hq4eX//883w6w7PtQnQW2dmVZGYedTpWVmbip58K/RyREP4hSV0XVlxc165xIYQQ7Vdc7PzkGsDRoyZMJknqhPAWd+sbQFGRHPuIrkmSui4sNdV1I1ydDgYP7ulyXAghhHe42xYPHNiD4GDZFQvhLYMH93ToIdrS0KERfoxGCP+RPUkXNnp0FGPGRDkdmz27FwkJRj9HJIQQ3c+8eb2JiHBel+yyywb4NxghuriYmGDmzOnldCwtLYL0dCmUIromSeq6uGefHc2ppyaga/xLBwfrOOec3txzz7DABiaEEN1ETEww//xnOsOGNV+xi44O4vbbU/n9750ffAoh2u7OO4dy/vl9bFfBdTo45ZR4/vGPMQGOTAjfkZYGXVxERBBPPTWK4uI6Cgvr6NMnlIiIoECHJYQQ3UpqajjvvjuR3NxqqqrMDB7ck6AgOa8qhC8EBem4886hXHvtIA4erCE+PoT4+JBAhyWET0lS103ExYUQFycbNCG8RamPPZpP0873cSSiM+nXT/qDCuEvERFBDB8uJ7JF9yCnCYUQQgghhBCiE5OkTgghhBBCCCE6MUnqhBBCCCGEEKITk6ROCCGEEEIIIToxSeqEEEIIIYQQohOTpE4IIYQQQgghOjFJ6oQQQgghhBCiE5OkTgghhBBCCCE6MUnqhBBCCCGEEKITk6ROCCGEEEIIIToxSeqEEEIIIYQQohOTpE4IIYQQQgghOjFJ6oQQQgghhBCiE5OkTgghhBBCCCE6MUnqAqS62kxmZhn79lUFOhQhhBB+sndvJZmZZdTVNQQ6FCECqqLCxJYtZRw4UB3oUIToEgyBDqC70TSN117bx3vv5VBdbd2pDx8ewd/+NpyBA3sGODohhBC+sH17OQ8/vJ29eysBiIgwcPnlKfzpT/0DHJkQ/mWxaCxatIdPPjlIXZ0FgLFjo/jb30bQu3dogKMTovOSK3V+9u67OSxenG1L6MC6s7/22o1UVZkDGJkQQghfKC6u4/rrN9oSOoDycjPPPbeHL77ID2BkQvjfyy9n8f77ubaEDmDTpjKuu24jJpPFzSuFEO5IUudHDQ0a77+f63SspKSer78u8HNEQgghfO2//82josL5Sbu3397v32CECCCLBT766IDTsby8Gn78sdDPEQnRdUhS50elpfWUlNS7HN+9u8KP0QghhPCHPXsqXY7l5FRTXy9XJ0T3YDJZ7O5UOpYcBwnRdpLU+VFEhAGj0fX/8oQEox+jEUII4Q/x8SEux6KigggOll2x6B4MBoVer1yOJybKcZAQbSV7Ej8KCdEze3Yvp2MGg+LMM52PCSGE6LzOPjsZnYu97Tnn9PZvMEIEkF6vmDYt3ulYaKieWbOS/ByREF2HJHV+dvPNQ8jIiLabFhKi45FH0ujVS6o+CSFEVzNkSDh33z2MoCD7KxRTp8ZxxRUDAxSVEIFxzz3DGDEiwm5ajx56nnxyJBERQQGKSojOT1oa+FlYmIFXXkln06YjbN5cRmRkEKedlhiwDVlBQQ06nZJbHoQQoo00TSM/v5aQEB1xcc5vtZw3rzennBLPDz8cprq6gQkTYhg+PMLpvEJ0ZZGRQbz11njWri1l+/ZyYmNDOO20BMLCHA9JCwpqUEqRlCTHKEK0RpK6ABk7NpqxY6Nbn9FH1qwp4bnn9tge4B8+PILbbjuB0aOjAhaTEEJ0Nj/8cJiXXtrLgQM1AIwbF8Uddwxl8GDHvqMxMcFccEFff4coRIejlGLixFgmTox1Or52bSnPPbeb3butxyjDhoVzyy0nMG5c4I6bhOjofH77pVLqVqXUymOmpSmlViqlflFKjfJ1DMLetm1HuemmzXYV2bZvL+f66zeyb19VACMTQojO45dfirn77t9sCR3Axo1lXHPNBoqL6wIYmRCd186d5dx00yZbQgewY0cFN964iaws15VkhejufJrUKaVCgNFOhh4B/ghc0Pi98KN33snBbNYcptfWWvjgA+d99IQQQth74419aI6bUsrKTHz66UH/ByREF/DuuzmYTI4rVl2dhffeywlAREJ0Dr6+UncF8LaT6TGaph3QNC0PiPRxDOIYv/121OXY1q2ux4QQQjRzvy0t92MkQnQdsl4J0TY+S+qUUkHAKZqmLW3lfZ3GoJS6Sim1Xim1vqioyCcxdlfR0cEux6KipPKUEEJ4QralQnifrFdCtI0vr9RdAnzgYszi4nsbTdMWa5qWoWlaRny8854mom3mzk12OXbmma7HhBBCNJNtqRDeJ+uVEG3jy6QuFbhWKfUdMEIpdWOLsVKlVB+lVDIg9/v52Xnn9WHGjASH6XPnJkvjTyGE8ND8+SkOfUcBLr98ABMmxAQgIiE6v3nzejNzZqLD9N//vhdnntkrABEJ0Tn4rKWBpml3Nn3fWP3yY6XUvZqmPQY8CHwIKOB6X8XQlVRXmwkK0hEU1P48XK9XPPnkKDZtOsKKFcXodHDqqQmMGCGPNwohhKeMRj3//Oc4Vq8uYc2aUkJCdJxxRhKDBjm2M/BEXV0DmmZdrhDdlU6nePzxkVx4YV+WL7c+fjN9egIjRx7/MYqmaVRXNxAaqkenU94OVYgOxS996jRNm9z47WONP2cCk12/QjRZtaqYV17JZvv2coKCFDNmJLJgwWASEtrfiDPQvfKEEKKzU0oxaVIckybFtXkZ2dmVPP/8XlatKsZigfHjo7nhhsFyok10a6NHR7Wrd+6HH+by/vu5FBTUEhFh4Oyze3PNNYMIDvZ5Ny8hAkI+2R3Y6tUl3HzzZrZvt1Z7Mpk0vvvuEFddtYGqKnOAoxNCCNFehw7VcuWVG1i50prQAaxbd4Srr97Anj0VgQ1OiE5q8eJsnnlmNwUFtQCUl5t5550c7rnntwBHJoTvSFLXgS1enG3bybd08GANX31V4P+AhBBCeNUHH+Ry9KjJYXptrYW33trv/4CE6OSqqsy8+67zfnY//VTErl1yskR0TZLUdVAmk8Vtr5YNG474MRohhBC+sHGj6225bOeFOH7bt5dTU9PgclzWK9FVSVLXQen1CqPR9Z+nZ0+/PA4phBDCh9xty2U7L8Txa229kfVKdFWS1HVQOp3id79z3V5AWg8IIUTnN3u26xLt7saEEM4NGxZBSkoPp2NGo47p06X3seiaJKnrwK6/fjADBzpumP7wh76MHy89kIQQorObM6eX04PMceOiuPjifgGISIjO78EHhxMebn9FTq9X3HffcMLDgwIUlRC+JdegO7Do6GDefXcC3313Hc6y+AAAIABJREFUiHXrjhAWpud3v0ti3DhpQyCEEF2BTmftG/rLL8UsXVpIQ4PGlCnxnHpqAnq99NUSoi3S0iL59NNJfPZZHnv3VpKUZOTss3vTr19YoEMTwmckqfOh4uJarr56I8uXF1Ffb2HAgDDuvXc4F17Y1+NlhIToOeus3px1Vm8fRio6oiqq+J7v2cIW6qhjCEM4ndPpi+Pnp446lrCEDWygllpSSOF0TieFlABELoQ4Vnm5iX/9ax/ffXeI6moziYlGzGYLRUX1JCSEcM45vbn//uHSIFkEzDrW8TM/U0ghMcRwMicziUko7D+TBzjAEpawhz2EEMIYxnA6p9MD57c8tsWBA9W89lo2y5cXoZRi2rR4rrpqIMnJoQDU11t48819fPFFPkeOmBg5MpLLLx/AxImxtmXExARz+eWyDxRWGhqrWMUv/EIppSSSyFSmkk66x8soo4wlLCGTTCxYGMYwZjKTBBJ8GLnnlKZpgY6hVRkZGdr69esDHcZxqa01k5b2va1HShOl4IUXxvKXv8iGRrhWRx3P8zyHOWw3PYggruM6u8SugQZe4iVyybWbV4+e+cznBE4gIyODzrYOdXRKfezRfJp2vo8jEf7QnnWorq6Bv/xlHbt3VwJw5Eg9hw7VohT06xdGWJj1/Ors2Uk8/HCa12IWwlM/8APf8Z3D9ClM4SzOsv2cQw6v8Aom7NtwJJHEjdxICCFu38eT9aigoIZLL13LkSP27xEXF8w770wgPj6EG27YxJo1pXbjOh088cQoTj21Yxxgi47lv/yXX/jFYfosZjGDGa2+voIKFrGIMsrspocSygIWEI9/ntVUSm3QNC3D2Zg8U+cjCxfucUjoADQNHn98ZwAiEp3JetY7JHQAJkz8j//ZTcsk0yGhA2uyd+y8Qgj/++67Q7aETtM0iorqGr+H4uJ623zffHOIrKzKgMQouq8aaviRH52OrWSl3UHs93zvkNABHOIQG9nolXjefTfHIaED67rywQe5rFlT6pDQAVgs8NJLe70Sg+haSillFaucjv3Ij9TieLx+rF/4xSGhA+v6s5Sl7Y7RGySp85ElSw65HMvPr+HQodY/QKL72sUut2Mamkfz5pBDDTVejU0IcXxWry6xfV9b20BDQ/P6W1VlpuUdM6tWlSCEP2WT7TRRA+sta7vZDYAFi+17Z3binRPWLdeXY61aVeJ2PCenmvx82ecJe3vYY3fc1FI99WST3eoyWjsu6wjkmTofCQ52nS8rhdsedEIY3KyaQQTZPePgbl6FQo/eq7EJ35DbObuulvsDpeyfTzrmR0JCZN8g/MvdPgSs+xwAHToMGFwmgE3ztZe746eQEF2r64i714vuqbXPeGvj4P7z7cnr/UE++T5y8cX9XY4NHRpBVFSwH6MRnc0YxrgcG81otz+3NJzhBCOfNSEC6fTTE23fG416u4PO8PAgW6Kn1yt5Hkj43SAG0ZOeTsdCCGEYw2w/j2KUy+W42xcdj5bri7Ox005zPT52bBRxce6f6xPdz3CGu0zKIohgEINaXYa7z7e7YzZ/kqTORy67bAAnnujYSy40VM8LL4wNQESiM0kjjZGMdJgeQwxncIbdtCEMYQITHOYNJ5w5zPFZjEIIz0yeHMcZZzQfiCYlGVEKDAZFQkLzAeiCBYPlgFT4nQED53Gew10dCsVZnIURo23aLGYRjWNbpVGMYgQjvBLPRRf1Y/jwCMf3GBXJBRf05YQTwrn0UscT5xERBm6/PdUrMYiuJZRQzuZsh0quevScy7ke3dE0gQlOk79e9GIa07wVartI9UsfslgsPP/8Xj74IJeqKjNjx0bz0EMjGDTI+RkxIVqyYCGTTDayERMmhjCEEzmRMBz77GhobGMbG9hADTUMZCAncRLhhAPtq9wnnPP27ZJy+2XH1t51yGLRWLq0sLGlQQMpKT2oq2vg4MGaxpYGfRgzJsqLEQtxfAooYBWrbC0NJjHJaQudaqr5lV/Zwx6CCWYsYxnFKHQeXCfwdD2qrW3gq68KGlsawLRp8cyZk2x3lXvVqmK+/LKAsrJ60tIiOf/8PiQkGN0sVXR3BzjAKlbZWhpMYhJJJHn8ejNmNrLR1tJgOMMZz/hWq756k7vqlx3jJtAuymyGuLgQBg/uSWWlmYEDezg8P9HEYtH49NODfPllAUeO1DNyZCSXXNKfsDA9b7+dw/r1pYSFGZg1K4k//KEvISHynFRXp0PHmMav1igUaY1fQoiOp6Sklkce2c66dUdoaNBITjbyzDOjue++4YEOTQjAesXhXM5tdb4wwji18ctXDhyo5v33c2xVLvPyahg7NoqUlOaT4pMmxTFpUpzLZaxbV8r77+eSlVVJYqKRc8/tzaxZvXwWs+j4+tKXC7mwza83YGBC41dHJEmdj1gsGrfdtsWuSlN+/iGWLSvilVfGMWJEpN389967lSVLmkvYFxTU8u23BQDo9c1npl54YS+//FLMyy+Pw2CQu2eFEKKjq6ysZ8iQ/1FR0VxgIje3mgsvXM2rr6Yzf/7AAEYnRMeSlVXJlCnLqKgw26Z9990hVq8uYfXqU+nfv/Um5998U8CDD26j6Wa0goJaNm8uY/fuSm66aYivQhcioCQr8JEVK4qclt2tqWnghRfs+6hs2nTELqFrkp9fS05OtcP0jRvL+OGHQu8FK4QQwmeuv36TXULXRNPg7ru3BiAiITqu22/PtEvomhw9auKuu35r9fUmk4XnntuDs6eL3n8/R1oeiC5LkjofWbGi2OXY+vVHqK5u3mD9/LPzeSsrzdTVWTCZLE6WX9T+IIUQQvjcjz+63l6XlNRRU9Pgx2iE6NjWrHHfp641W7cepbS03umYxQIrV7o+PhOiM5OkzkdcPTvX2pg3li+EEEII0Rm19/jp2F6QbVmGEJ1Rq0mdUipDKXWLUupppdTDSqkLlFKOtfqFnenTXfcamjgxhrAwQ6vzhocbMBr1BAU5/pncLV8IIUTHMWuW6+pq8fEhhIZK4SshmrgrfjJ1quuxJiNHRhIX57w/q04Hp5wS3+bYhOjIXCZ1Sqk/K6U2AncDocAuoBCYDCxRSr2tlOrnnzA7n0mTYp1uOHr2NLBggf1DuiNHRnLmmY4Vmfr0CWPgQMfy9SedFCtJnRBCdBLPPz+W6GjHg0ydTrFoUcdoWitER/H006OIjHRsFB0TE8zjjzv2bz2WXq/4619T0Tk5wp0/P0XaHoguy131yx7AyZqmOX2iVCk1BhgC5PoisM5Op1M89dQoPv88j6+/LqCiwsy4cdFcfHE/+vZ1TNQeeGA46enRfPFFPmVlJtLSIvnTn/phNOr54INc1q0rpUcPA7/7XRLz5vVGr5f7B4QQojMIDdWTlTWLiy5aw8qVxZhMGv37h/Hcc2M44wzPeyQJ0R3079+DtWtncNddv9mefzvllHj+/vc0kpMdj5+cOe20RBITjfz737ns3dvc0mDaNDkhLroul0mdpmkvuXuhpmmbvR9O55WbW82HH+aybVs5sbHBzJ2bzLRpCZxzTh/OOadPq69XSjFnTjJz5iQ7jP31r6m+CFm0QTbZrGIVJZQQTzxTmOK0OasQQuTn1/DhhwfYsqWMyMggbrppCF99NbnVZ36E8BYNjU1sYgMbqKGGFFKYzGSiiQ50aG5FRwczc2YiRqPe1nw8Ksr5LZWujBwZyciRrV/ZE6IjqaGG1axmO9tRKEYykolM9KjBeat96pRSKcCNwICW82uaNrcdMXcpW7aUccMNm+wqmK1YUczFF/fjlltOCGBkwptWs5r/8B80rHWSD3CATWzij/yRcYwLcHRCiI5k164Krr56A5WVzZWOV60qYc2aUu6/XxqOC//4N/9mIxttP+eSy1rWch3X0YuO2Yi7osLElVduYO/eStu0TZvK+PbbQyxenG5Xk0CIrqSaal7iJQ7T3OZsH/vYxCau4ZpWEztPql9+BuwHXgAWtvgnGj311C6nJanffz+X7OxKJ68QnU0NNXzBF7aEromGxmd8hgnHHlRCiO5r4cJddgldk88/zyczsywAEYnuZg977BK6JjXU8DVfByAiz3zwQa5dQtdk584K/u//DgQgIiH84yd+skvomhzgAKtZ3errPUnqajVNe17TtGWapi1v+teGWLukgwer2bWrwuW4NAnvGnaww2XiVk01WWT5OSIhREdVVlbPxo2uEzfZLwh/yCTT5dgudlFHnR+j8dyPP7peP9yNCdHZuVtn3Y018eQa9iKl1IPA99C8BdA0zfH0TzdkNmutjDs2DhedTwPumwO3Ni6E6D5kvyA6Anf7JQ2tw+633K0/JpOsO6LrsuD68+3J+urJlbqRwJXAEzTfevmMZ+F1ff37h9GnT6jL8cmTW++pIjq+VFLRuVhdgglmEIP8HJEQoqOKiwshNTXc5bjsF4Q/DGOYy7H+9CcMzypJ+tvJJ8e6HJN1R3RlQxnqcszd+tzEk6RuHjBQ07RTNE2b3vjvVM9D7NqUUtx00xCn/VBmzEhg1Kgo/wclvC6CCKYz3enYTGZiRPreCCGaLVgwGIPBscrliSfGcNJJrg9ahfCWEYxwesLRgIHZzA5ARJ655JL+xMc7FoRISjJy8cX9AxCREP4xnemE43hCMIYYJjO51dd7cvvlFiAKa+Nx4cT06Qn885/pvP32frZvLycmJpizzkrmD3+Q3uxdySxmEU88v/ALJZSQQAJTmMJoRgc6NCFEBzNxYiyLF6fz1lv7ycw8SmRkEHPm9OLii/tLSwPhFzp0XMEVLGc561lva2kwgxkduhVPQoKRN98cz5tv7mP58iKUUkyfHs+f/zyAmJjja2sgRGcSTTQ3ciNLWco2tqFDRxppzGAGPenZ6us9SeoSgZ1KqXXYP1MnLQ1aSEnpwahRkRgMipiYYEaOjESvV7z+ejZvv51DVZWZ8eNjuO++YfTp0/5bHrZvL+ezz/IoLq4jNTWcefN6k5AgV4t8LaPxSwghnNmypYwvvsjnyJF6kpKMFBXVceRIPQ0NGkFBOoKDrbd1HD5cy6efHmTv3kri40OYN683Q4dGBDh60dUEEcRpjV+dSUSEgbo6C6Wl9SilqK1tICLC/pB182brulZWVk9aWiTz5vUmOtq7Sd+vv5bwzTcFVFaaSU+PZu7cZMLDg7z6HkK0FEMM5zV+HS+lae4f6FZKneJsemsVMJVSacBioAHYC1yuNb6ZUuotYBhQAyzWNO0Dd8vKyMjQ1q9f7zbOQNqzp4JrrtnI0aP21RFNJotDWd6ePQ38739TGDOm7Y0/P/wwl4ULd9PyTxcebuCll8YxfLgcFAhHGRkZdOR1qDNS6mOP5tO08wOyPOFdnqxDr7++j3/+01oJt6rKzJ49lWiaRnCwDp3OenVu9uxe3H//MG64YRPV1c0PvisFd901lHPP7eO7X0KIAPNkPSorq2fixB85eLDGbvqAAWGsWXMaPXsaWLw4m8WLs+3GY2KCefXVdFJSengl1iee2Mknnxy0m5acbOS11zJITJST6CIwlFIbNE1zenXBk2fqcoE1LVoZrAVyPHjdLk3TJmmaNqXx52MDuFjTtGmtJXSdwd//vtMhoSstrWPr1qMcmzNXVpq5+uq2Fw4tLKzl2Wd3Oyy3osLMo49ub/NyhRBCtN3+/VW2hA4gJ6eappOmJlPzBvubbwq47roNdgkdgKbBM8/sorS03j8BC9FB/fWvmQ4JHcD+/dXceWcm2dmVDgkdQGlpPU89tdMrMaxfX+qQ0AHk59eyaNEer7yHEN7mSVL3MdjV2GxonOaWpmkts5w6oGXHSA14Ryn1pVKqUz/1WlBQQ2bmUYfpJSXWHXNDg+OV0G3bjlJe3rYd9w8/FGJxUfF09+5K9u2ratNyhRBCtN333zc3jK2vt1BX15y0aZpmS/AsFo1t25z3NjWZNJYulcfXRfe2ZIlj8+Um3313iP/9z/X4unVHKClpf/89d++xdGmhtFYQHZInSZ1B0zRbBtL4vUc3LSul5iqltgIJQEmLods0TZsEPIm1RYKz116llFqvlFpfVFTkydsFRE2N874RFot1B+7s9lZNs16x8+b7eTouhBDC+1pue52dzGu5K2jaPzhTXd22fYMQXUV9veuEqb7e0upxTm1t+xMud+9hNmuS1IkOyZOkrkgpZSuKopQ6Cyj2ZOGapn2haVoakAfMaTG9tPG/K4EkF69drGlahqZpGfHx8Z68XUAMGNCDhATH0rtND9Lq9Y5VzpKSjCQnt61Yyvjxrp/Fi44OYsiQ1qvjCCGE8K6W2+bQUL3dtl8p6z8AnU4RF+e4z2gycaK0OxDd28iRrmsDjB4dxYQJMS7H+/QJJTm5/c+7uXuPtLQIwsI8qTMohH95ktRdA9yjlMpVSuUCdwJXtfYipVTLvVY51qIoTWMRjf9NBcqOK+IORqdTXHONYx+YhIQQwsL0tofjmygFd9yR2ub3GzUqiilTnDffvPLKgQQFefInFUII4U0nnRTLuHHNfUlbFlIwGHS2Nga9ehl54AHnTWRPPTXBbdNyIbqDRx5Js1WJbSkkRMejj45g0qRYxo513gP42msHeaVlyBlnJDJ4sONJcp0Op8d8QnQErWYAmqZlaZp2IjAcGNFY/CSrtdcBv1NKLVdKLcfaFiFTKXVv49j7SqmVwL+Au9oafEcxd24yTzwx0rYzjowM4vLLU9i8+XSmTo23NaDt3TuU554bw9VXt2+D8OSTo7j88uZ+LYMH9+SRR0ZwwQUdt++MEEJ0ZUopFi0ayyWX9CcyMojERCPjxkWTmGhEr1cEB+s49dQEVq06lcsuS+HBB4czcKC1Sl9sbDBXXTWQxx9PC/BvIUTgTZwYy3//O4nhwyNsV7nT0iL58svJjBkTjU6nWLRoDBdf3M/W5mD48AieeWYUZ5zh9Oav4xYSoufVV9M599zehIXpARg7NooXXxzHiSfK1XTRMblsaaCU+hPwgaZpTm8cVkoNAno13kLpU95saVBWVs+XXxaQk1NFcnIoc+cmu7wVprLSzNdfF7BnTwUJCUbmzk0mKcn9ZX2LRbO7OrdnTwXffHOI8nIT6enRnHZaIgDff3+ILVuOEh5uYM6cXgwc6Py2ydpaM//4xx6WLSskLMzAVVelMHt2ssv385ktW2D1auuDIRMmwLhxzfcTHWM/+9nCFkyYSCWVEYzAhIn1rCeffMIJZwITiMH17Q3Cu6SlgfdJS4Pu5XjXIYtFo7LSxNNP7+aXX4qJiQkmIyOaX38tpbrazKRJcQwa1JP9+6tITDTSr18omZlHsVhg8uQ4Jk2K9c+2XVjl5sLSpVBRAcOGwdSpEOyfRtcHOchGNlJPPYMZzEhGoseaSGSRRSaZWLAwjGEMZSg6j26y6pg8XY/MZjN//vMGvv22AKWsJ88XLx6HwdB822NOThVffVVASUkdo0dHccYZSRiNett4RYWJr74qICurksRE6zFcW1sR+O1YqyvLybGuY1VVMHw4TJ7st3XMn0opZS1rqaCCZJJJJx0jzZ+7OurYyEYOcpBwwskggzic34HnjLuWBu6SupuAy4ENjf+KACMwGDgF63N1d2ma5vPart5K6n777SgLFmyioqL5QXSjUcczz4x2OPOyd28l11230a68tMGgeOyxNGbMSPTo/d54Yx8vv2x/UbN371AsFo2Cglq76TffPIQ//cm+EOjBg9VMmbKMwkL7Sk5nntmLDz88yaMY2s1igWefhRUr7Kenp8M990CQfRPO//AfVrHKbloSSVQ2fjXRoeNCLiSddJ+FLppJUud9ktR1L8e7Dm3bdpTTT19ha3dTW9uAxWLdjxgMivp6CwaDjtTUcIqL6ygvN5GcHEpEhHWbOmlSLAsXjpZb6v3hs8/gzTftq9kkJ8Njj0Gsb6/KfMu3/MiPdtN605sruZIv+IKN2LdASiWVv/AXDHTOZ7o8WY8qK80kJHzhUKykRw89xcVzMRoNfPzxAZ56apfdn6x371BefTWdpCQju3dXcP31GzlypLkQe1CQ4vHHRzJ9eoJXfyfhgU8/hbfftp/Wpw88/jhEOb+VtjPayEY+5EMsLZoGRBDBNVxDAgkUUcQrvMJRmqvmKxQXcAHjGe/Re7SpT52maYuAccC/gXhgRuPPecAlmqad64+EzlssFo377ttql9CBtUrSffdtdai29Le/bXPoF2Q2azz44DYqKux70jmzY0e5Q0IHsHZtKRs2HHGY/txze8jOtm9UfsUV6x0SOoAvvyzg3//2pFWgFyxb5pjQAWzYAF99ZTdpG9scEjqATDLJI89umgULH/ERFTgv7S2EEJ3ZX/6yzpbQmc0WWysas1mz7W/MZgtZWZUcPWpC0yA/v8ZWOXPVqhL+/e/cgMTereTmOiZ0APn58NprPn3rfexzSOgA8sjjDd5wSOgAdrGLn/nZp3EF2uTJS51Wn6yqamDq1GXk5dXw9NO7HP5keXk1PPmktU/dAw9ss0vowNoy5MEHt1FVJRVm/WrfPseEDuDgQZ+vY/5USSUf8ZFdQgdQTjn/x/8B8Amf2CV0ABoan/AJ5ZS3Owa3pwA1TWvQNG2Jpml/0zTtak3TbtY07VVN0zrdnmbz5jLy8hybWQKUlZlYubK5oGdWViU7dzpPNmprLfz4Y+t9hL7+usBhmsWiUVFhorzc5LSkdcvXWCwWVq8ucZinyeuv7281Bq9YutTjsfU4nn0zY6aeeqqpdhhroIEtbGl3iEII0ZEcOlTL9u3NO2iz2X5737LXaE1NA5bGCZqG3UnDr75y3I8IL1u2zDGha/Lrr1DtuO/yFmf7zCab2dym13UFW7e6PrjdvPko33xT4LJf7y+/FLNuXQl791Y6Ha+ubmDZMukF6VfLlrkeW70a6trfV7Aj2MIWzDg/YZBDDlmNX8400MAmNrU7hm5zX8exV+iOVV5ucvp9a/MezzwWi4amYfvn+JrmGGtrLQ4HAscbg1dUOt8wOhurwTFpbjpjceyZiybOkj0hhOjMSkrq7LbxrnIGZ2Mte9z5bTvfnbnbx1ksPk3q3O3/TJjQcP7Bcbav7UpcPRYE1vXD3fGcxQKHD7tPEloeawk/cLeOmc1Q0zU+z60dzx7B8S6943m9J7pNUjdiRITTnnFNxoxpvqf3hBPCCQ3Vu5x39OjW7/9tubwmBoOOoCAdISE6p7GMHh1p+z4szOC2KEtGhut+dV41zHnpbcD6oGsLAxjgMIsBAwpFsIt+9c5eI4QQnVlqajjh4c3PPOmO2dO2rDGl1yt0LWZoue/xZF8j2umY/ZidxESI8V1BrxRSXI4lkIDC+TFLV99v9uzp+nnByMggu2OlYyUkhDB5chwhIa4Pb929XviAu+PI5GSI7Bp/D3frpREjwxlOGK57VLvbHniq2yR1cXEhnHNOb6djp5+eyIABPWw/9+hh4KKL+jmdd8KEGI92tLNm9aJPn1CH6fHxIU6rbfbvH8bMmfaleG++eYjTZffoYeC++9ysJN40dy6EOv4eBAXBOefYTZrEJHpiX8VTh46Ixq9jDWAAQ3D+OwohRGdlMOiYP795Bx0UZN+IvKnNDVgPQpuq6vXoYbA1NQ4O1vHnPw/wT8Dd2cknWws2OHPhhY4ZuReNZzxROB5PKBTncZ5dxbwmBgxMZ7rPYuoI3B3fPPLICKZNS+CEE5xXDL/88hSiooL5wx+ct3iaNCmWESO6RhLRaUydak3enLnwQpeV1DubIQxxmZhNYxphhLlcd/vTn1Ta3sO6SatbK6VUiFLqIqXUPUqpB5r+tfudA+D221O58soUIiOt1cV69jTwpz/146GHRjjMe801A1mwYDCxsdYrTKGhes47rw8LF4726L1CQ/UsXpzOjBkJtqty/fqF8eKLY/nHP8bYEj6DQTFzZiKLF6c7NNu88cYhPPzwCFs/OqVg2LAIvvtuCsnJrrN9r2qqAJbWon9Saio8/DAMsu+3F0EE13ItQxlqO8OYTDK3cAvncz6RWDekQQQxkYnMZ77LM5FCCNGZPfbYSG6+eQgREUHodDqMRkWPHnqMRj1BQToiI4MYNKgnycmhpKaGk5YWadsvpKVF8OKLYxk2zPFkmPCy4GDrPm7KFNA3XiVNSoIFC+C003z61qGEch3XMYIRtn1hIolcwiWcxElcy7UMZrBt/n7040qupC9duyft7bcP5dZbB9udDAkKUtx9dyrXXz8EvV7x8svjmD07yTZPcrKRe+8dxnnnWRP0G24YzPXXD7IdP4WF6bnggj489dQo//9C3V1IiHUdO/nk5pMkyclwyy0wvWudoJjPfCYykSCseUYEEcxlLqdh3ZZMZzpnc7bteNiAgfGM5wqu8MrxsMuWBrYZlPoOOIq1rYGtHJGmaQvb/e4e8mafOrBWHDtyxERkZJBDInWshgaN0tJ6IiIMhIS4viXTYtH4+editm07SmxsCGeckUhUVDCFhbV88UU+R46YmDw5lhNPjEUphaZplJTUExamt52ZdWfPngrCw4Na7ZPnUxUV1hvWPbhUXkMNZsyEY23ITkMDljW/UnFwB6E94wieMgPCw9sdkgULS1nKFrYQRhi/5/f0w/lV1u5MWhp4n7Q06F7asg5ZLBqrVhWzZMlhkpONnHdeX0wma8GsIUPC7fYveXk1fPNNASaTxu9+lyQJXSDU1Fj/RUfbXz04dAhWrrQWdBg71v0tm21USy311Du9q6Waattz6Z/zOQUUkEwyZ3O229u5OqLjWY92767gX//KRqdTXHnlQAYNcrw6V11tprq6gZiYYKd95Fo7hsvMLGP16hKCgnScfnoifft2rv+fnUpJCfzwAxw5AuPHu+13HCgaGrvYxT72EUYYYxnrdJ1sTT311FBDOOFO+0pasFBBBaGEOn08KY88trENhWIkI0mi+U6+NvWpa/HirZqmpbmdyce8ndR5W0lJHddfv8mu2lJIiI6ZMxMSsdtLAAAgAElEQVT55ptDdg++jxkTxfPPj/Eokesyiovh/vshr0Vbg5AQuPNOyHD6ufRIOeXczd2UYF8ldBazuIzL2rzcrkiSOu+TpK57Od51qLzcxA03bLKrghkUpLj//uHMnt3Lbt5//GM3779vX1R69uwk/va3EdLwONA++gjef9++os348XDXXQ59Wn1pHet4nucx0Vw8J5hgbuZmxjHOb3G0lyfrkaZpPPbYDj77LN9u+vnn9+HOO4d6JQ6z2cLdd//GsmVFtmlKwZVXDuSqqwZ65T1EC0uWwMsvQ0OLVhVDh8KDD0KPHq5f50c11PAv/kUOzS3D9Og5j/M87iHXXhYsfMzHrGOd3fTJTOZszgba2KeuhVVKqZHtjrQLe/zxnQ7lc8vLTTz99C7q6ux7rWzeXMbzz+/1Z3iB9/zz9gkdWM94PvWU+6pIrVjEIoeEDqzNXH/jtzYvVwgh2mvhwt12CR1Y+2Q99NB28vObq70tXVrokNABfPPNIT755KDP4xRubN0K773nWKJ03Tr42LOTMN5gxszLvGyX0IH1asCLvOiyunRn9eWXBQ4JHcDHHx/k22+90+bj3Xdz7BI6sP6ZFy/OZu3aUq+8h2iUlwcvvmif0AHs3AlvvBGYmJz4gi/sEjqwthr4iI8optjFq7xrDWscEjqAlax02+akicukTin1m1IqE5gMbFRK7VJKZbaYLoDS0np+/rnIYXpTM1lnpXOtt9h0rY2wS0VFsMVFL7raWvi5bU1U66lnJztdjn/N121arhBCtFd1tZklSw47HWto0Oz6z332WZ7T+VobE37w/feux5Ys8VsYy1jmso1BNdVdrhm5+3XCMdlr23u4Xo6sd17244+u+7osX94h+tTVUeeyT5yG5rfekGtZ63JsDWtafb27ewDntCGebufIkXqnTTCbesyZzY6D1dUN1NQ0EBTUDYqPlpW5b9J0xH3fDlcqqaSBBpfj5bhuXiqEEL5UUWGmvt71ibvi4uaDmJKSepfzuRsTfuBu/9TGfVdbtHaVwF9XEfyl5fpxrJIS7yQA7pbj7v1FG5SVuR6rr7c+xxriWBXen2qpddk4HKCCCr/E4e7YtZLW72xzmVVompajaVoO8GjT9y2ntSXYrqhPn1C7fkRNjEa93X9b6t3b+Wu6pN693a+sgwe7HnMjiihCcdJqoVFX7+MjhOi44uJCiI93vd0bPrz5wfthw1wXjGo5nwgAd/unIf5rx5OG+7IGI3Cs4N2ZuSsS5K11wh/vIRoNdPOMYnw8RAT+/3c44baKlM70wUXbEy9z9z6exODJpSK7rYVSSg+ke/C6biEkRM8f/+hYbTEqKoiePQ1Ok7fLLuuP6mAVf3wmLAxmz3Y+NmBAmwul6NBxKqc6HQsmmHM4x+mYEEL4ml6vuOSS/k7HkpKM/O53zZXMLrqon9MqzDodLpch/GT2bOs+zJlzz/VbGCMZSW+c99ntRz+G4p3iIR3FJZf0t7WCaikoSHHxxd5ZJy67bIDTwovW1gddu2WE3516KsTGOh+bN8+nvSA9pUPHNKY5HYsk0m/FiKYxzWlrAwMGpjCl1de7e6bubqVUBTBKKVXe+K8CKAQ+b0fMXc6VV6Zw5ZUp9OxpTeCUgsmT4/j668mMGxdtmy86Oohbbz2Bc87xT8bfYVx2mXUHaGxsx6AUTJgADz3UrpX5Ei5hBjNs/UAAYonlTu4kjrj2Ri2EEG120UX9uPHGwba+qADjx0fzyivj7O7gGDiwJ4sWjWHgwOYKcL17h/LEE6Ps9h8iAOLjHXuyxsbCTTfBiSf6NZQHeZDBDLYd8CkUJ3ACD9Ap2wa7lZYWyTPPjKJfv+aEesCAMJ59dgypqe1vhQTWY7SHHx5h1yYqNTWcF18cS3Ky67uARBuEhcGjj8KIFteIIiLgL3+BOR3nSa8pTOH3/N6uTcggBnEt1xKCf24PTSGFy7jM7hg2kUQu53KScdHAvQVPWhr8XdO0u9sdaTu01tLAYtH45Zdi8vJq6N+/BxMnxrgsA22xaKxZU0pOThXJyaFMnhyHTqfIyqpk8eJsTCYLF13Uj4yMmOOOc926ElauLKZ371DOPruP7exrQUENFRVmUlJ6dPzn6CwW2LTJWq0oOdnaR8RbZ1FqaqCgAKKiIMb6/7eGGraylXrqGcQgWy+OXHLJIYce9CCNNKd9PJrUUst2thNJJIOw7nxNmNjGNiqooC99bbdjFlLIHvYQRBBppFlXXrMZ1q619lAZMABGui72asHCTnZSQgnxxHMCJzjtQdLRSEsD75OWBt2Lu3UoJ6eKNWtKMRr1jB0byXvv5VJQUMspp8QzeHBPli4tJDExhHnz+hAa2pzQbdhwhD17KkhIMDJlShx5eTVYLBopKT26z90cnUF5OXz7LVRXWxuSl5TAgQPWpG/8+Oam5cdoub8ZwQh60Jy4H+AA+9lPGGGkkebRQaMFC7/yK3vZSyqpjGe8bf9jwsR2tlNOOb3pjREj2WRjxEgaaRgJYI/bFo5nX/T661k8/PB2lIKHHkrjsstSvB6PxaKRnV1FcLDOLokUTmzdCvv2WY/fJkxoW0uPwkJr1fM+fSDY9XGdK0UHNlGyZSm6kFD6nHgeYeEJx/X6BhrYyU5KKSWRRIYwxOHKmAkThRQSRhjRHP+JtQoqeId3KKaYEYzgPM477mVoaBzmMDp0JGD/O7apT51Syu21Rk3TNh53lG3kLqnbt6+Km2/eTF5ec2WogQN78NxzYxzOthQU1HDTTZvJzq6yTUtONhIZGcRnn+XZFTyZNi2eL788GZ0HCU1lpZnbbtvChg3ND05HRgbx1FOjSE/vRGda8/OtZyXzW1SFSk6GBx6w/tfLNrKRj/nYrkxzGmnUUstemts+hBLKxVzs8S0mWWTxDu9QRfPfOYUUIom0KwlrwMC8/BOZeM9nUNqihPHgwdbfOSrKbrmHOczrvE4pzfMmksh85hPD8Z8E8CdJ6rxPkrruxdk6ZLFoPProDr74wrrNLC6u4+DBGvR6hV5vbWFgMChSU8MxGHT07Gng0UfTGD48gptv3mzX8iA+PoSFC0fL8zwdzXffwWuvgclkPQGYn299TjwhwXrXSXy8tQ/rgAG2lzT1mlrPejSsx1gGDJzN2YxjHO/wjl31ZiNGLuIihuO6qXkRRbzO63ZFURJIYD7zqaCCt3iLSirR0CillAYaiCUWPXpCCOECLmA0o73//+c4ebovio7+jLIy+xYOsbHBFBef5avQhCtHj1qvtO3a1TwtKgruvRdSU/0SQkNDPdsXXUPUT80VKhuCDdRfO58TZlzj0TIOcYg3eMPuGC6JJK7gCqKIcvNKz33GZ7zHe3ZtRowY+Tt/pz/euXW4rX3qFjb+ewlYAywGXmv8/nmvRNZOFovGbbdtsUvoALKzq7jrLsc+ZXfd9ZtdQgfw229H+fe/c+0ahAP89FOR02U488QTO+0SOrC2NLj11s1UVJhcvKoDeuIJ+4QOrD8//rjX3+owh/mQDx367vzMz2xgg920Gmp4m7c9qvxTQw1v8qZdQgeQSSY/8qPdNLPFxCc173Iw9JieNHv3wrPP2k2yYOFN3rTbGDT9Hu/ybqtxCSG6nvfey7EldPX1Fg4cqEbTNMxmCyaThsWiUV9vISvLuj2qrDRzxx2Z3HlnpkMPu6KiOm6+ebPbqpnCz/bsgX/+05rQARw6ZK3WV1HRXNGvqMh6wNvirPAKVrCOdbaEDqy95j7lU97jPYd2PLXU8g7vcJSjTsPQ0HiTNx2qXBZSyBuNX037x3LKqaGGeuo5gvW4pI46PuCDTlMlMz39e4eEDqzVYKdOXRaAiLq5F16wT+jA+vl/5BG/tSPY9cljdgkdgL7ejPH5xRze13qpfwsWh4QOrImet47hSijhXd516BtZSy0P8qBX3qM17qpfTtc0bTqQA4zTNC1D07R0YCzQIbpnr1tXSm5utdOx7dvL2bGjeae5c2c527Y5lgotKqpD03DaluCjj1pv/Hr0qIkffnDej6iqqoHvvjvU6jI6hO3bYf9+52O5udZxL/qVXx0++BoaVY1fLXeGYL0c7kmfkE1sopZah+mVVFJDjX0bhKpKNEsDv05ycpvT5s3WHXijvex1uUM80PglhOheWjYHP3SoebujadidKKyuNtt6k1ZVmV3uM0pL6/npp0IfRSuO27ffNrfkqauz9lZtcrRFAlZYCBuaT0auYpXTxTXdPumMGbPTpsMA2WRTiPPPRTbZlNFcMr7lyc9aam37vAYa3PbA6kg2bXKe3AKsWtU5EtMuo6gI1jn/XFJeDqucf9a9LfjbH51OV5pG0f/ea/X1u9ntkNA1ySGHPNrfm/BN3nQ4dm1STjnb2Nbu92iNJw8DDdU0zXbJStO0rcAY34XkuZY7UWcKCprHXc3b1E/O2V2oR4+2fpWtuLjOtozWYujQihwbqNsp9O6BRsudUJOmnc+xyV6TprOOx7vclsu2S+rM1p4kZa6uurf4f9Lae3sSmxCia2m5Xzn2Ctux+5S6Ouu2x2SyuL0a12n2Gd1By/2i+ZgeVsf+fLg5UXe1P2iggTpcX9lwtf9yt39poMHWX8uCxeGgsuU+z9VBbUfjrtRDg+v2tMIXiovd/0FaO3b0AovFTEhplesZiluPobVjNFfr3vEown0c2f/P3nnHSVXdffi5M7OF7SxbgaUsfYGlLR2kKEVFRbB34ovRaIwlJqbY4ptETdBEE2PU5NVYYhdQUZAiRRGkd5a2wMKyhe29zH3/ODvlzr13ZrYXzjOf/ezOPXPPPTM7557zO+X75XiTr+ELf4K6g4qivK4oynRFUaYpivIacLClC+YPffuGek13VxNLTg4zfI1DzMRo61xcnO+Ny4mJwZqN757062d83XZHkg8J315624amEE+87pgVKwqK87cnDhGVhuYLYj+DgoINN4uJ+k268UaD5haL8NjzkW9DyiaRSDoX7u2KZzvgqXPiULwMDLR4bTPc2y1JG+Pe7nmKOng+d9tTZ9YeWLFqxFI8MWtnvLU/NmxOBWgLFqxYdem+ytXeMNGdAYStgaQV6d4dbF58lZu5b2iExWKjvLv5njclyXcZvNUhBcVnH88ffPkjp5La5Gv4wp+gbhGwH/gZ8ABwoP5Ym5OaGkVqqrFZ4JQpMfTp47p59uoVwtSpepn7hIRgLBbFUC3znnv66Y55EhJi4+qrjf1j4uKCmDWr6V+UViE5GUaYbKJOTfVuHtkIJjBBp/aloBBGGOHoJYvDCPPLJySVVMMNr+GEE0qoVqkyJJQASyATvzUYhbroIqdCJ4jKarbJdQhDdOpEEomk83PLLa7OREJCkLMdURQ0PlsREQHYbOLeExMTxLXXGg+i9e0byuTJ0o6l3XDZZS6Fv4AACHULyNyFtPr1g2Eug/BpTDPMLpBALuZiw7RQQknD2Lc1iSSSMW6DU0nVBGthuAYa3Nu8YIIZxzjDPNobl1+eaJp2zTUXmCVUWxMZCdOnG6d17y5UMFuD+fMND9cFBdB97p0+T08mmSSM77sppDSLDdYd3KEbVHEQT3yzCaV4w2dQp6pqpaqqL6iqenX9zwuqqrab9SF//vMIJkxwdb4VBaZNi+Xpp4fqXvv008OYPj1WM4I6a1Y8Tz01lNBQ1z8iMNDCXXcl88ADA/0qw/3392fBgh7YbK6MBw8O5+WXRxuayrZbfvELYQbu+IAUBcaMEcebmSii+B/+h264DCkDCWQhC5nDHE3FSCSRH/NjvySfAwjgLu6iJ64bvxUrM5nJ9VyvySNaiWZR9M+JiRviykBRREB37726vO/gDgbi+k4oKAxlKDdxk9/vWyKRdB7mzevOT3/an5AQK1arheTkUIKCLAQEWAgIULDZFCIiApyzb8nJobz88mieeCKFSy9N0KwQGTEikhdfHGlqxyNpA3r0gN/+VihcAsTHi2AuJkb4bIEY9HzsMc1paaRxOZdr2puudGURi7iaq5nKVM0MWjzx3MVddMHcH+12btcoQCsopJDCbdzGYhY7O6zhhBNBBOGEOwc4Y4hhMYuJoGMoqy5bNoUhQ/SDuyNHRvLuu63rDygB7r4bZszQLj8YPLjJXsMNYdDcn1Fy85XUBrtsFCriI7E+9gRd4/1T4FzEIgYwwPlcQWE4w7mRG5uljF3owqM8quurxhLLczzXLNfwhTdLgw9UVb1OUZS9oN/5p6pqy88j1uPLpw7g1Knyep+6EJ/GkRs35rJ9ewHDhkVwySVihKu62s7HH5+mstLOwoU9iIgQSyuOHy/l6NFS4uODGTFC3CBLS2t5660MSkpqWbiwp3OJ5fnzVRw5Ukp0dCADBzaPQWabkJUlfhISWsTKwB0VlVOcoooqkkhyNmqllHKGM4QSqgnQjCg8f5yMs98RHBjBgP5zsVrF/+4MZyillEQSnY3ZyaJ9rMv9kC6WLszr+WNCA+stJ06dEmvHe/USDbYXcsnlPOeJI67dWxk4kJYGzY+0NLiw8FaHystr2bevmKAgC8OHR7Jy5TnOnKlgxow4unUL5NChEiIjA+jePZjf//4gBQU1LF7cF0VR+PbbPAYMCOPyy1v2XisxID9fiIB16SJWqpgtM7Pb4dAhoXw5aJBQv8zMFLYG0dGwe7d43ciRIq96znOeLWwhiCCmMEXT2SujjEwyCSGERBI5ylGqqKIvfTXBVxVVHOEIduwMYABllJFHHrHEagZFAbLIoogiEkkkkEBOcYpggulFL8MtDW2Bez06fbqcgweL6dYtiFGjonQDGt9+m8Mtt2xBURTee28848bFtkWRJQ727IFt20Q/6eKL9WvMS0pg716xfnbkSGH90cxUlueTc3gT1uBQEgdNw2LxsjTUhBxyyCfftA9XQAEnOUkIIfSnf4O9iFVUPuADTnOaSfUPT3LJ5QxnCCecZJIbVD8b61OXqKpqlqIohvOFqqqe9LsETcSfoM4fCgqqefTRvRr7gZSUCJ57LpWEBK0xZ1lZLb/5zT42bXIpLSUnhzJkSDh/+9tRKivFRneLBWbPTuDjj/X/NEnLYrfXsnTrr/k+Oh17fX2IqAjghuA7GDjwct3rX/9+Mfu6nsFeXz+Dq2CWZQ6zUn7WiqVuG2RQ1/zIoO7Coql16Be/2M0LLxzBbledugNBQRYGDw7DarWSnBzKn/6USu/eck9di6Oq8Prr8MUXLunryEh44AGxOsVfPv8c/vMflypmcDDcdhvMm8cKVrCe9U6hklBCWchC3b6aQxziPd5zqlZasTKJSVzJlfzADyxnuVPROYAAZjObGcxo2vtvQ9LS0vj22y08+eQBVq/OdtaFpKQuPPNMKoMGiQHxuXM3aNIVRSzLXLZsShuV/AKmogKWLIGtbuqpSUnw6KMuPYYPP4T33xcDHyCWKi9eDDNntn55G0kddXzMxxo7kiiiuJmb6Yt/xvfnOMd/+I9GrdaxoiuIIKqp5j3eYy97ndeIIYbbuI3u+Dew1yifOlVVs+r/vBgIVFX1pPuPX1duZ/zmN/t0fnIHDhTz0EO7dK99+ukDmoAOYPfuQn7/+0POgA5Ee/DVV+d48EF9HpKWZe225/iumyugAyjuUsMbda9TeF6rMvTh9l+xp5sroAOoDIIvrCs5eHZtK5VYIpFciKxYcZYlS9Kx211qy6oKlZV2jh4Vqm7Hj5fx05/u1HmmSlqATz6Bzz7TehkVFQlP1mxjuwkd27fDq69qbQ4qK+HVV9m78z+sZa1GebKMMt7hHc7hsso5z3mnabiDOurYyEY+4iM+5EONRU8NNXzBF+xhT8PfcztiyZJ0vv46WyOqePp0BT/96U4qKup46KFdunRVhc8/z+Kxx/zzD5Y0I3//uzagAzh9Gp58UqjAbtgAb73lCugAysrgr3/V+9u1Y1axiq1s1SjIFlLI67zul09yDTW8xms6+5H97OdjPgaEOfke9miukUcer/O6zre5Mfgzp9gH+KeiKMcURflAUZSfKorSLiwNGsLx46Vs3Wos55ueXsqOHa5gLyenkrVr9RL+2dmVqKpq2Oi+/770KWtN7PZaNnXZYZhWbVPZeuxdzbHdFuNG0G6B9aekebhEImk5fv3rfc6/PRfHlJW5Ov5nz1ayYUPLS4Rf8Hz+ufHxmhpYudK/PD77zDSp6rOPDI/XUcdmNjuff8/3TjsCT9ay1tTzahOb/CtjO8RuV/n88yzDtPz8alatOsebb2aYnv/qqydaqGQSQ/LzYeNG47TcXPj+e/O6oKpiNrwDUEutqb9kFVWmHpLu7GMfRRh7LO5mNznksAPjfmsxxexmt/8FNsEfoZTHVVWdCQwDNgGPANu9n9X+OHnS2KTcKD0zs8LQjLy62runnd3oJEmLUFmWT2mwcWMIkFunHW0tCTMf/S5SzI1OJRKJpKl481RVVaiudgV2p055b6skTaSqCs6fN08/46cJ8dmzpklBZ83zd/ey8uZrVUqpaVDnyw+rPVNTo3r1aTx5spzSUnMzuuLips9mSBrAuXPeferOnvVeZ/ytT21MKaVUUGGanodv03tv9bKOOjLIMB3E8XW+v/gM6hRF+a2iKF8Cq4D+wM/Bh3JFOyQpKcRHumtzc48eXQwFfRz+KJ57Q0FIVltaSQVIAsGh0YRVmpvZxFi1FgPhpeabUCPUjqEIJpFIOibx8cGmaYoCgYGue5mvtkrSRIKCNHY1OhLN5fT9fV1Vgnn+7tLp3mTUQwk1FU9oDvn1tiIgQPGqCp6UFKJRI/ckPDzANE3SAsTHG3d6HSQkeK8z/tanNiaUUIIxv097ihI19DUWLPSil6nlgb/X8IU/UcgCoBuwGvgEWO62367D0L9/GKNHG5sX9usXypgxXZ3P4+ODmTZNr7KUkBCMoiga/yEHCxYYe9VJWgaLxcak8lGGaQF1CuOTtTYDw+3DDF9rUWFa0i3NXj6JRCJx4G6x49k/cu/AJiQEc9FFHbfD3mG4XC+kBQgvujlz/Mtj3jzTpMB5CwyPW7AwkYnO5xOYYNrJm85006BuMpP9K2M7xGJRuPRSYxP0yMgA5syJ59Zbzf28Fi3q0zIFkxjTrRtMNvm+desGEyea1wWhbtNyZWtGAghgAsZ2GUEEMZaxPvNIJdXUNsThJzkK435rGGGMpOk72/xZfjkaIZayFZgF7FUUpUMu6P7jH4frzMoHDgzjhRdGoni0tI89lsK4cdrRtqFDI3n44QGaUSZFgRkz4njppQ63zbDDc3HaLxh7vi+K28qAsEort3M7UTFa4/hrR/+BoecTsLi9NrAaZldNJ6XHJa1UYolEciFy5ZU9uO++/hpzchDql/37C7XLXr1CePHFkU6TckkLsnAhzJ2rjbDDw4Unq78zC2PHwo9+BIGBrmOBgfCjHzE87Q4u4iKNFHoXunATN5GIK3+H6l0IrtlZBYWJTOQ6rmMBCzQ2CDZszGFOs3T+2pKf/3wQ06drB84TEoJ58cWRhITY+OtfRzF1qnZwQ1HgkkviePbZVnPTkji4914Y5RGMJCbCE0+IgZAZM+CGG7SWIMHB4rwhQ+gozGUuoxmtGUwJJ5xFLCIc3zZlAQRwJ3fqbBIGM5hruAaAq7maFFI06dFEcyd3EkggTcXU0sD5AkUZBkwFpgFpwGlgo6qqjzf56n7ibmlQXW3nwIFibDaFlJQIZyN59mwFWVmVJCV1IS7OfAoVYP/+IjIyyunePZhRo7p6fe3WrefZvDmf5ORQ5s1LRFEUioqq+MMfDlFYWMuPf9yX0aPFP7CoqIajR4VPXd++bSRLXV0NR46IxqV/f1ejde6c2NTavbsYXfFCQd5RzhccoVvXAXSN6Q+AHTunOY0du3YKOT9frJmOjRXT8M3EaU6zmc3EEcd0prsSMjKEF0pyspDMBbIyt7H7+FLCAqKYkHYvtgCxlDabbEopJYEEQhGvPX1+NzszPyfYFsKk/rcRFmT+WdRQQyaZWLCQRJKzgc4nnwIK6EY3p7lrc1BCCTnkEEEEsYgGT0Ulk0yqqSaJpEZXemlp0PxIS4MLC886dOZMBefOVdKrVwixsVo/puPHSykoqGHAgDBKS2tYuzaXxETR3jzxxH7y86tYvDiZkBCrrn2RNAO5ucJrNTFRtBNr1wrvrFmztB3PU6dEWkSEmG1wD9CKi+HkSWF10KuXJvsiisgjjyii6FYSCDt3ioRRo4TqX31bm9dNdfrUTWUq6aSTTz5DGapZQllDDYc5TBVVJJNMJZWkk0488fShD4c5jB07AxnobMsAKqhgO9sJIIAxjCGffEooIZ54wgjTlPkMZ6ikkh708LrMzBt55FFEEbHENtrI3L0ebdqUx9KlZ0hODuXuu/vpfOp++CGPn/xEKIu//voYRozw3l+TeMFudylRDhhg7Ml4+jQUFkLv3qJOePLtt/DNN9CvH1x3nd54PCcHvvpK1KMrrnD20dzx+T3MyxN1Ny5OLP30IJ981rCGMMKYzWyvyxnN8NWHO8ABVrGKbnTjJm7SXcO9P5xEEjZsuvTVrOYMZ0gjjeEM113jHOfIJJNwwhnAgAZ54TXKp87t5C+A9QiRlB9UVW31XaqOoG7ZsjP87W9HKSgQRUhMDObee/uzcuU5Nm3KQ1XFd+zii+P57W+HEBracFNCB9XVdp555hArVmRRW6vWl6Mr11zTg5dfPu7czB4ebmPRoj5kZ1fx6adnnBuAhw+P5He/G9q6+yM+/xzefRdK66VX4+OFZ87q1a5Gx2KBqVPFCEqwtkJVlObywb7H2RedhaqAosLQ/ASGpd7MVyHrKaQQENPE82rmkPbSZiFl6xCIGTkSHnwQujb+xltNNY/wCJlkOjeJBxHEQxkLGfuXTXC83qogKAiuuIL9tnRClq7CVim+E2W9YrA88CAr+x/jJMJ5w4aN8YznSq70+wawhS18yZdOGduudOVSLmUXuzjIQVRUFBRSSeVarm10I5A/pykAACAASURBVAmiQf+ET9jBDqcEdj/6MZGJfMVXzg26XejCxVysDXL9RAZ1zY8M6i4sHHUoP7+aJ57Yz+bNQgzDalWYPTueX/96CLm5VTz22D727y9GVVUyMsopK6vFYgFFUYiLC+Jf/0pjypRYw/bld78b6nNQUuKFsjJ48UWhyKeqYjCzuFgMbioKhITAfffBHXcIX62PPhIeXAA9e8L994sO77//LTqnNfXdnYED4cEHqeoRw0d8xC52OduAQQzieq4nvLAO/vIX2CHU7UqUMrZPDeGr+/pTGlxDHnnYsWPBgoLCMIbxS36p6RBWUMEf+ANHOeps/2KJ5REeoRfawPI//Iev+ZoaapyvjSCCSCKxYmUsY5nPfLLJ5j3eIwuxayaQQC7iIuYwx2/D42KK+S//5QhHALGMdDSjWchCAmjYHre0tDS++24rV1/9LRs25DltPrp378Lbb49j/Hgx0Hr77Vv4739PO9XGbTaF22/vw6uvGvZlJd747jt47TWXQFBUlJhlnj5dPM/KghdegEOHxPOAADEAsnixGAwpLYW77xb9SEfMkJgorA4GDxbPV60Sno3FxeJ5bKw4Z6xYtniGM7zP+5xFCAzpvocVFfC3v8GmTeIaiiI8Ix94wBlg/pbfOvtfIPp2N3ADCzBe7uxJKaW8x3sc5rCz/o5gBNdyLUEEUUcdv+AXZJDhvEYggSxmMRdzMSDsCT7lU01/+DIuYxzjADjOcZawhPOIz9pxj3iUR5vUT3SnSUFdEy46DHgVqAOOAj9S6y9Wn/YKoAD3qKrq1XQlLS1N/ctfvuKBB/RecKdPlxMXF0RQkLazPm1aLEuWjGh0+X/3uwMsX65Vt6qutpOVVaEzh83JqSQoyEpkpPbmlpgYzMcfT/K6KbjZ+PZbePZZ/XHHLFqgxwzP1KnwyCOaQ69t+R8OR2sVfqoDoDjGRkxYH81xJesci/9czMAjHtfr3x+ef76RbwIe4RGOcUxzLLisjl/efYKhRT01DeChhEJCMwvBpv3fl4SpvP2PyVRHdtEcn8Y0ruAKn2U4wAH+zb91x/PII5JIXSM2lKEsYpHPfM14n/d1crm11JJPPrHE6hreG7iBNBrWsHXmoK6tgiEZ1F1YOOrQbbdt5cCBYl36zJmx7N9fTHZ2FQAZGWUUFAjfJqtVISBAtAPBwVYWL+7L6tV625x+/UL5738n6GYsJH7y5JPOoIrz50VnFcRgpqMNVBRYtEgMSHoSEiLaRiNbg9hY3np1Krut+3RJvejF/Q+fFKtkgDLKya/v1B2eGsc/HglzdhIDCHDe08cwhkdwtcOP8zjppOvyDyOMV3jF2f59zue8zduAWM1RS60z/0QS6YJo+8YylgMcoIwyXZ7zmOfXAKGKyl/4C2fQqxiOYQw3cqPPPNxJS0ujd+8X+eqrc7q0sDAbx45dxl/+ks4TT+w3PP+554bz8MODG3TNC5rDh8WyYs++vqLA//4vDBoEP/mJmGXzZP58EfzdeqvwZfQkMlLYHezcCU8/rU+32WDJEsr7xvMszxp+Dy/ncmYwA37/e9iyRZ9HSgo88wx/4k8aOxDn20DhKZ5iGMa6Ce68xEvOwX53hjOc27md3/AbDnJQl27Bwku8hB07L/Kixn/SUYY7uZNkkrmHewzfZwopPE7zLHBslPl4M3BYVdVJqqpOrX/uXoCngRuB6+r/9slbb+n/ERUVdZSW1pKfX61L27Aht9HS0Pn51axYodeCKSysprxcXNOB3a5SUFDD+fNVutdnZVWyZo2fRqZN5dNP9ccqKsRPkYFk/6ZNYolIPVmZP+gCOoDScKi01VJT7fYlralFLStlw3SDjsfRo7C3ceagJZRwnOO646PXFRNaVEuum6SsqqpYzxdCXR3uqs927ASV1jJg9SldPt/zPVXo/0+erGe97lg11VRSaWhAeYADjZaiLaHE0LekjDKqqTYsr1H5JBJJy7NjR4FhQAewdOkZMjPFrI+qqhQWuha11NWpOAZQKypqefNNfXsGcOxYGd9/70VuX2LOqVOugA60tgV2u2tFiaqKFS1GlJaK2TsD8mtz2VP2vWFa3b7dVB1xBXullDj/Tt6URVSOq4/i3iHcxS5nm5JFlnMmTFcsSlnNaufzr/jK+bda/3BQgMtzdz3rKXErizsb2Ygd3zZMRzhiGNAB7GSnqS+XGbW1KqtXG/eLSktref75w7z00lHT859/3vgzkpiwfLmxJYGqwrJlsHmzcUAHYrb66FHXSi9Piorg7beN+58gjMk//5xtbDMMdKD+e3g2U29u7uDAAUhPN/WJU1H5P/7P+Fw3MsgwDOhA+Mud5SyHMTZKt2PnNV5jE5t0AZ2jDOtZz5d8afo+D3LQL1uEptJiQZ3HMs0qxF48B9Gqqp5WVfUMoFUuMeHoUX1nuqqqrv63/sakqsbn+ENGRplzSYz2enbNdQFqauzY7aphGQCOHGlcGRrMSYMva1WV9rc7qioawXqy8g8YZltTP7hZU+vm31FdDSpkJZqMJmdk+FFgPe7T6u4kZojy1+BqGCuD7ASX1NbfrFznOM6PydA3ZFVUOafEveFYpuJODeLrXI1+AEFFNTzHH3LIMbxJeLveOc6Z+hdJJJKWw1ubUlFhd7YDlZV2PFfBOJ6qKpSVmXsVtVqb0dnwbHdqPT5j939AqclnXFtrmpadAGq1sd9gTEaZ854NUO32N6qd+FOuNtj93l1HnbOjeYQjXu/r7itYHEu/PPMDNOWopNLUF6uIIsrxPfDtrW2zYyebhg1cV1TUGvavHOzeXaQZEPHEaBBf4gVv/bETJ7ynV1aKCQBvHsz79xv3P92u7+07VEwxlafSvXrh1WQc9erv1th+nQMVlV3s8jrIcZazXvPIIosTnPB6jaOYD1Y0Fy26LlBRlCsVRdkHxIHmU7eY/O1+7l2KomxTFGVbbm4u8fFButc4VMJsNuPgIiGhcetX4+L013K/jmMZDeC0N2juMjQYI/ETx0bYAJM17zGujdqRIcaKX9b6emS1uC3frM83qtCkEsY0TpK7N8YyxkXdxPXc98MF1lioCbKIBbxuyxMdy1pKYvSfuwWLX5u7jTbOOq7tuSHWQVcat48w0mRMw9v1Iojwey+ERCJpPszaBhDtgsPL1PHbHXcNFG9L8r152km84NnueIo4aP8BxnlYraZpkYUYi0sAJTFBmvbJ5va3gkJhTIDmufvf8QgxiO50Ny5TPe7CKo7llYZvQVMOm6kAQxBBfu3x8SUG1lCxsKAgq1fbs169QujSxbx+hIQ0XBjjgsabMF5MjPd0i8W3emX37t77fDExpv0cEN/DgG7eFWcDYhK8Com4iweZ4a0MIHQMvPWrIusfZkQR5RS4M8NXHW8OTD8lRVE+UxRludmPP5mrqrpcVdVhwBnA3cjCbvK3+7mvqqqapqpqWmxsLAsW6P3OQ0OtBARY6NpVfxMeNCiclJTGqTP17BmiszMAiIoKxGZTCA933dhtNgsREQGGZQgJsTJ3bvMpQnpl7lz9sdBQ0UgZqRgNHiwUjupJ7ncJ8SX6hiK0FGx1FoKC3PIICoQuwUzYbBDUdevm3BjbUOKJNzRV/eGSSOpsFo0xo1VVKE+IAIvVPabDggXVAumXJOnyGcYwnSKYEUZeJUGIRtvo5tGDHiShv54/xBDDAAbojocSigWLYePt7nMkkUhajylTYkwDu4kTo537qm02i0aoy2JRnMqWNpuFSy6JM8yja9cAZszw3jGQmJCSAklu9+FItw6YQyjF8feMGcZ52Gymad3zg+gdYtzBLUjrR3A3l1etezuRPbgr5/q4vjPundM+9HG2ef3p7wzwPAkgQLMf3L2N8uzsug9cjmSkxhLBnbGMNR2kdCeFFFM59370Iw7j77IZgYEWhg837hzbbAoPPzyQK64w7+Rfe23j2toLFqO+oXvatGk60TwnEyfC+PGavqKGwEC4807v3o5z5zKOcaYidWmkETBgiFA0NyI+HkaOJBmTdGA+882vX89gBpsOvieRxCAGmdY/gJu52WvfawITuIIrTOtUd7rrxI5aAm8zdX8Glnj58YqiKO53kmLAbf0e+Yqi9FQUpTv4tyB74cIeLFzYQzPCY7NZePTRQSQnazvavXqF8MwzegnRhvDUU0MZOFAbAHTvHswTT6ToRllvuimJGTO0N7bwcBvPPZdKRETDlKEazZVXwsUXa4/ZbPDTn4qRFHd69oSf/1xzSFEs3J74K6LLtMFpj/OBzK+5XCO1raAwJeoKxp7zuLl27Qq/+Y3paKY/PMVTukCmODaQYz+fT3CQtiEYlHARReO1G6brAm3kPXgrdT20wXRverOQhX6VYWL9w33UxoqVBSwgAW2+McRwK7f6la8ZN3CDbgQnmmiu53rdDWIkI8WmYolE0urYbBaef34EMTHa+2TfvqH8+c8jeO65VKeZeHJyKIGBFhRFcc7c2WwKTz2Vwksvjda1L127BrBkyQid6JekAfzqVy4Z9IQEMbCpKGK1iqMNu/RSWLIEJngM3gUEiPbyl7/Uz06EhMCvfsXNgYt0QUwEEdxhuxPlt49BtBgMDiecEELI7xnKyp8PpxvdUFCwYnUGYdFE8zAPa/L6Bb/QrSYJIIB7uEfjZbeIRQxiECDaY0c7EUaY8/ye9ORWbuVGbtSJew1mMJdxma9P03n9RSzSDYgmkNBgkRQHH344kR49tO18YKCFJUtG0LNnCG+9NcFwUH7kyCheeWVMo655wTJpElx7rXamWlFEn3HGDOHP+Otf6+0HBg2Ce+4Rf7/8sn5GLzBQiKNERIg65en7aLEIgZVRo4gmmpu4Sfc9HMQgLqfenPyXv9T3VWNiRJ/SYuEpnjKcFR7LWGYxy+fHYMHCHdyhm22LJdbZh3uap3UD9woKs5hFKqkMZjBzmaubbZ/EJMYznggiWMxiXb8tkkiNIFJL0pLql1cBD9U/PQI8DixSVfX3iqKkAi8j5ljuVVVVL2vphrtP3cmTZXz77XlsNoXp02OJiwumttbOhg15ZGaW06dPKFOmxDSLepiqqnz/fT5HjpQQHx/MjBlxBAZayM+vZt26HCoq6hg/PpoBA8Qo1vbtYhN9dHQgM2fG0aVLGzTOJ08K353gYFHJunYVsszr1om100OHitEXz6Up9dTVVXPw8FJyy04SE5JEyuAFWK2BFFLIPvZhx04KKWJ00W4XG9NPnhSeIhMmmC/1RKz1zyefMMK0FScvT6zd7t7dWa5P+ISd7CSSSG7ndjGtXVYmZHPz8sR7GCbUjrKObKRw70asYRH0nHgdIeFxVFHFPvZRQglJJNGPfkZF8koOORziEFasDGUoUURRRx372c95zhNHHEMY0iB/ETNUVA5zmCyyiCKKYQwjgABKKGEf+6iiioEMbPT0vVS/lOqXkqbhXodqaux8800uWVkV9OsXxsSJ3ZxtTnl5LWvX5lBQUMPQoREcP17K6tU5dOsWyM9+NsBpWWDWvlzwFBUJWfT4ePNlkt6oq4MffhDKz927i9Uq778v2qYf/UgEe0VFos3KzIQ9e6BLF9H5dV/VsnMnbNsmyjFrlngNYh/ZIQ6RTTbRRDOUoa5OXE2NsFPIyYFevcga0510y1ECCKAPfViPsAZKIYVpTDMsvh07X/M1xzhGLLHMYx4VVGDHrlvJcoADbGITVqzMZCb55FNMMT3owQAGODufFVSwhz1UUEFf+ppudfBGDTXsZz8FFJBIIgMZ2Ki2z70evf32STZtyiMhIZj77x9AdLT2/71ixVn++tcjKIqYwZs1q5VWPnVGcnKEGIndDuPG6X2FKyvhjTeEWub8+WIGzx27XdiA7Nol6tUdd4iA0J3MTKGSabWK/qDHskyf30O7XdTdzExhmTBunG6SYBWrWMtaQgjhFm7xOoNnRC21HOAA5zlPPPEMZrDue7yUpWxnOxFEcAu3kIh25riQQvaznzrqGMIQ3bLLcsr5jM84z3kGMYgZzGiWfqKDpvrUDQD+CKSAawG2qqoN+ySbgHtQJ/HCpk3CJ+RcvVTwwIHCp27NGiE7W1srgr05c+D225s0o9YQHI3UJjZRQQUWLAxjGAsyxxP28puwr141LCYGbrgBZs/WZ5KdLUaLdu0SG2ojI8Xo05VXtsp76OjIoE4GdZKm0ZnrULugsFDc47dsEff4sDBxf7/+erxuwvLGli2io3rmjAj2ampEXkFBYvbt8svh5pu1g5yqKhQwly6FkhLx+rFjxayFt/1HLcAJTrCUpU71yTjimMc8Ukhp1XI0J7IetUM2bBCechVuC+p69RJ9x1bqJ/rDNraxkpVOhde+9GUBC3RBV2enqZYG/wf8A6gFZgD/Ad5qvuJJmoUdO+BPf3IFdADp6cI8ctUqlxJYZaWQsf3nP1utaCtZydd8TUX9Clw7dvbU7eS1k7/Fvt/N8ycvT5hPrveQ7K+qElPw7saXRUXw+uvwxRet9C4kEolE0iLY7fDYYy7TcBAqlO++C++917g8DxyAP/xBBHQgPOsyM8VPdTWUl4uZhzff1J734Yfw1lsioANRnq1b4fHHRWDYSuSQw6u8qrETyCGHN3jDq8qeRNIgKiuFb6N7QAdCHX3mzLYpkwF72MN7vKex7DjBCV7hFVPbjgsRf0LwLqqqrlEURVFV9STwpKIoG4EnWrhskobw0Ud6SdjKStEwWa16daLVq+Gmm8TyzBakiio2sUmfUFzMmbgaDg+GIZ5ejx9+qJ36X7/e3Efl44/Fem6T5aQSSVvj7wxcc+cnZ/QkHYatW81l0ZcvhwULxOxaQ/jkE1ebWF4u2kMQxwoLxfJLgBUrxKqPsDAR7C1bZpzf6dPC02vKlIaVo5FsYIPGnsCBHTvrWEdf+rZKOSSdnPvuM7csOH1aDIYktv1M2BrWGB4vo4wtbOESLmnlErVP/OkJVyqKYgGOKIpyn6IoV0MD5Y4kLc8RA0NORyNm5FNXVwfH9UbfzU0OOcaG3/VlO93LYFnNqVOicXWQnm5+gbw8KCgwT5dIJBJJ+8bbPb6sDM6ebXieh92MhD3bQPfnVVUuz9Zz51wzdEYYtbMtxGmNta+WTDJbrRySTs7+/d7Tl/sldt+i2LFrZqw9OcUp07QLDX+CugeAEOB+YAxwK3B7SxZK0ggiDSSCrVbtb0+iGuYv0xhM/UOs9WpdJQZ7OkNCtOu4jd6bg4AAvWqTRCKRSDoO3u7ximJsy+ML9/bNsw30fO64fkSE9/173srZzHiz3/HHmkci8QtfdWvQoNYphxfM7J0cmFluXIj4DOpUVf1BVdVShC3B/aqqLlBV9fuWL5qkQVxiMPUcGiqWJXoqFAH07Qv9Gq4I2VCiiaY//fUJEeEE1MDInQYnzZypXU558cXmDe3kyeYeKxKJRCJp/0ybZq6cPGpU4wRK3NvEsDBtG+LekR0yBHrUe8xFRZn7rNpsMH16w8vRSMZi7vfqLU0iaRBPeNlJFRTUqt95b4xjnGmarA8ufAZ1iqKkKYqyF9gD7FUUZbeiKNIopL2xYAGM8fi3WCxC2cuzQYyOhoe1/jgtyXVcpzEOB7AFh3Fz3hxCKj2CtSFDhLeJO927w7336gO75GT4n/9pgRJLJBKJpNWIioKHHtIHdt27iz0/jWHePGFTAKItTEgQbUhkpAjyQOyre+AB7Xn33CO8XN2x2cTr6n3oWoORjGQyk3XHU0k1PC6RNIpJk/T2BSDqzEsvtX55TJjDHJ01lYLCXObShz5tU6h2iD+WBnsQXnIb659PAV5WVTW1FcoHSEuDBrF3r/DWCQoSG7p79RJ7BNasET51gwaJmbCGbjpvIrXUspvdZJBBNNGkkSamzM+epXbDOuwVZQSmjoHRo81n5XJz4ZtvhCrakCHCw6SdC6TYsVNJJUEEYaVxvoXVVFNHndflB77ozDLS7d3SoK2QQinNS2euQ+2GggLhqVpUJFaSTJzo1fvUFLtdtBNhYXDokPC+slohNRVOnIDz58VqlcmTtV541dVij12XLkIU5ehRMas3Y0ajA7oKKrBiJZBGeO4BZzjDDnZgx04qqY0WSKmpf7ibmDeVCiqwYdOZSntD1qN2yoYN8OSTwiNy8GChRN6ILTqVVKKgEETz9zFVVNJJ5whHCCSQUYzSecSB7z5THXVUUUUwwab+ceWUE1D/aG94szTwR/2yxBHQAaiquklRFKkf2l4ZPlz8OCgthUceEQ1UTY3Lp+73v2/VgGgLW1jPevLJJ4ggCilkMpNZ2X0N+27YRx11dKeUOQQzlKHGmcTGCpWyDoAdO2tZyyY2UUopXejCRCYyhzl+B3f55PMZn7Gf/dixk0QSc5jDYAa3cOklEomkDejaVaw6aSx2u7BA+PJLERhGRAh15Ftuce2jSzUYjy4qgn//W3i91tSImbrrrxdS740knXS+5EtOcxoFhaEM5Qqu0K1a8UYWWXzJlxzmMCoqmWRyOZc3aGaihBI+4zP2sIdaaoknntnMZgQjGvGuBAc5yEpWkkkmFizO9xZN681kSpqR3Fx47TXxu65OCBe98go8+qjfWRznOCtYQQYZKCgMYhDzmEcCzWcY78h3EMb7/Hz1mWqpZRWr2MxmKqgggggmM1ljDr6LXaxiFTnkYMPGCEZwBVd0mH2s/szUvYAQSvkvoALXAwXAxwCqqu5o4TLKmbqmcNVVxopdF10kKm0rsI51fIHWT86OnVJKiUC7SVdBYRGLOrS5KsAylrGRjbrjoxnNTdzk8/xyylnCEooo0hxXUFjMYgYysEHl6cyjo3Kmzhg5U9e8dOY61Gn4+99h5Ur98UsugfvvNz6npgYefNClgOnOgw+KWboGcoxj/JN/YkcrFR9BBA/zsLmAmBv55PMCLzj9XR0EEMB93EcPevjMo4YaXuAFctBbAt3CLYxkpM88PEknndd4DRVt3zGKKB7iIZ8zgbIetTPsdqFbkJ2tT7vuOjF754PTnObv/J1aajXHQwjhIR4iipYX5fOnz/Q2b7OLXbpzpzKVq7iKHezgXd7VpccTzwM80G5m7ZpqPj4SGIjwpXsSGAJMApYAf26mMkpago0bzSWYN20S/iMtTA01rGWt7ngZZRRTrLM7UFH5mq9bvFwtSQklfMd3hmk72UkuuT7z+J7vdTcnEJ/PalY3uYwSiUTSqcjNhVWrjNPWrDHutAJ8+61xQAdi1s/HwLcRX/O1LqADKKaYzWz2K4+NbNQFdCDa1HWs8yuPnew0DOgcZWwMq1ilC+gACilkK1sblaekDfnvf83rxrJlwuPRB2tYowvoQARahj7FLYCvPlM22YYBHcB3fEcJJazC+P6RTTZ72NOs5W0pfC6/VFW14cNUkvbBOi83frsd1q4VQiotyDnOGTZM1QgfuiqqdGuvT3OaGmrazahIQznJSeqoM0xTUTnOccN14O6c4ITXNBUVBS/S252A9j4TJpFI2hEHD5oHYKoKBw5AfLw+zZtPV1aW2H8XE9OgohzH3APW273d3zy8pfl7rWyyKaPMr1lDB3bsnMTEJL6+XNOZ7nd+knbAd8YD0IDYX7pzp9h76gVv3zN/v6tNxVef6RjHTNPrqOMgB8kjz/Q1xznOGNq/RqQ/6pfxiqL8S1GUL+ufpyiKcmfLF03SZHx56rSCklcwxnYDjoDEaJNqAAGNFhVpD/gSNPFH8MTsc3OkdfaATiKRSBqEL79Ss/QQL8sFFUWIpjQQb/d4fwWvvL3OW/vg7+usWBs8cGrB4lXwxd9ySdoRRpZX7vhhJ+Lt/94UgbeG4KvP5GtZcDjhpqIp0Hrvo6n4s/zyDWAl0L3+eTrCkFzS3rn1VnPj8fBwIZjSwsQSSy966Y47RgeNKspoRnutXO2dvvSlK10N07rQhSEM8ZmHtxGh0YxudNkkEomkUzJihBBaMSIyUvjdGTFzpnme48f7DhYN8HaP9vf+7a0N8HfGwNvrhjO8UYqc3vJMw3Cbj6Q9c8cd5mmJiUIJ0wftob/iqwwppJgGZtFEM4hB5iJ9dJx+lz895xhVVT8AsUBcVdVaMFlbJmlfREfDz36mtwgICBCbX1tJ/fI6rtMpBwUTzFSm6mbkEkjgMi5rlXK1FBYs3MiNumWlNmzcyI1+jY4OZrChF1EPejCXuc1WVolEIukU2GzC687TricoyNgDz0Hv3nDbbfrj8fGweHGjijKb2SSRpDs+kYl+i4CNYYyhQmV/+nMRF/mVR096MpvZuuMxxHAlV/qVhydzmUt35xi/iylMabCAl6QdMHiwUIf1JDgYnn3WryymM51kknXHR9U/WgNffaZAArmBG7B57DoLIogbuRELFq7iKkN1WrPvfHvEH/XLb4CFwNeqqo5WFGUC8KyqqgZuhS2DVL9sIvv2wT//KTbD9u0LP/mJaMhakXLK2cY2MskkggjGMY444sggg53spJJK+tOfkYzssHvpPCmmmK1sJYccoolmPONNZ/DMOM5xdrKTaqoZyEBSSW3U59MRFceae0+dVL+UNIWOWIcuSPLy4OuvxX64hASYPdu/PXHHjol95iUlwgd1+vRGLb10UEste9hDOukEEMBIRurMk32honKYw+xlL3bsDGEIwxjW4JUspznNdrZTTjnJJDOa0Y32zQMh1uJ4b0EEMYpRfvvnyXrUTvn+e3jjDcjPh5QUuPdeYSPlJ3XUsZ/9HOQgVqykksoABrT6VhFffaZ88tnKVvLJJ444xjFOo8JeRRU72MEJThBKKGMYQ096tup78EVTfeoeApYD/RRF+RaIBa5pxvJJmotjx4SS0c6dYtRyyhQhhDJsGLz0kvalOz7i/JvPE3u0EFWBnEHRJN75GL1TWmZJ5lGOspOdnOEMEUQQRBATmMAudrGTnVRRRQklxBDTaHPV9kYEEVzCJU3KI7n+IZFIJJJ6vv0WPv4Yjh8Xe35mz4aFC0W798MPQvn5zBlhnrxuneioZMBD+QAAIABJREFUWq0waRLcdBPExenz7NdP/DSAXHJZyUr2I8RWhjGMOcwhhhh2sIP3eI8ssrBiZS97+TE/JpFE0klnNavJIIMQQkgjjVnM0q3uUFAYXP9oCkn1j+YigADG1D8kLYzdDp9+KrwX8/LEgPz8+Q2z2jh3Dt59V/gVA4wbBzfeKPwYAaqrRV2qqhJ/l5WJ/mQDgjpHIJeKgQ9kM3GSk3zN1xrz8TnMcW7nOc5x/syfySYbFZUwwriO65jHPGce0UR7Xe0URBAT6x8dEZ8zdQCKotiAQYACHFZVtaalC+aOnKnzg+PH4Ze/FJXSnfh4eP55zWbYjN3LqX38VwRWaCWXK8KshP/pFbr3m9KsRdvCFj5EO6uholJFlW5zqxUrd3FXg0c0Jd7piKOjcqauaciZuualI9ahTslXX8HLL+uPT5wI3buLYA9E5zQzU3SKo6Jcs3XdusELL4hjTSCffF7kRUop1RwPI4yLuIjXeV1naxBCCD/mx3zERzpbgD704Sf8pEPvJ/cHWY8ayF//Kiw5PFm0CK6+2vf5eXnCb7HIQ+4/LEz0DRMSxHacHQaW0430aWwJMsjgFV7RWSfEE8/93E8xxdzLvYbWCotYxBVc0VpFbXGa5FOnKMq1QBdVVfcD84H3FUXpGDsGLyTef18f0IFYcunh3XPunSW6gA6gS2kdGW893azFqqOOr/hKd7yccvLIc1obuL9+JQbmsRKJRCK5sKmpgXfeMU7buFG0gw4KCkRAB1BYCLX1nb3z5+Gzz5pclPWs1wV0AKWU8g7vGPrUlVPOG7xh6POWQQYHONDkckk6EZmZxgEdmPf5PFm2TB/QAZSWwiefiO05RgEdwNtvu+pQG7OSlYYBWzbZbGc7/+AfhumAblKhM+PPkNBjqqqWKIoyBZgDvAn8o2WLJWkwO3f6nRZ5It/0peHHTEwoG0kWWZRQojteSaXmtzvHOU4NrToZLJFIJJL2TkaGcQcVoLISiotdzz1Nkyvc/FK9tZd+kk66aVohhYbHVVTyMW9/D3O4yeWSdCJ2GZtlA+L7fehQ0/LYudN7XcjNFYFlG2PHzlGOmqank+7VD6+UUlPv4M6GP3vqHJ/E5cA/VFVdpijKky1XJEmjCAoSjZoRgdoN0XabeSxf5yWtMZiJejg2zxptorXWPyQSSfvB3+WmctmnpMXwVLZ0R1G0Ss+eqs/uz73l4yfeBKu8iUN4S+ssImGSZiLQh5iNP99jb3kEBvrOw1cZWgEFBRs208F+Gzavy5YVlAumT+lPD/6Moij/BK4DViiKEuTneZLWZIqXfXBTp2qeFg0z3zBdPnJQc5UIEOudE0nUHXcYQRr5hqSS2un3FUgkEomkgfTqJX6MCAkR+4McuJsqWyxak3GPNrExjGSkaZqZ/LmCQm/MladbS/5d0kGYMMHciiM+Hgb6YSHh7bt+0UUwebJ+AMRB//7aOtVGKCiG9h4ORjGKsYw1TY/BD/XbToI/PefrEObjc1VVLQSigUdatFSShuOuZOTOuHGi4rox7J6/U5AYrHtpXq8QRt35ku54U7mGa3SqXkEEMYIROs+QrnTt8D51EolEImkh7r1XeGh5smiREHaw1o/IR0W5ZhliY12+rCNGwKxZTS7GFKYYBmi96c3P+blz4NKdJJL4GT/T+bYCXMRFzapQKekEREQIr0TPoCswUNQDf7yGL70UhhqYag8YAFdeCT16iP6jJ6Ghwv6qnXAplxJNtO74SEaSQgp3cqehZZQVKw/xUGsUsV3gl/plWyPVL/2kogJWr4bt28XoztSpYgbPoOKXl+Sw++2fE7DnEFgUakemMvLmZwkO0Vea5qCAAr7jOzLJJJJIxjOePvRhP/vZwQ6qqKI//RnPeMPGUNI0OqLimFS/bBpt9X476/LLjliHOi05OfDFF1pLg5R6U+9Tp2DFCmFpEBcnOsYZGSLYmzxZtIs2f3ae+KaGGnawg33sQ0FhKEMZzWgCCKCQQj7gAw5yEBs2JjKRK7kSGzZKKGEzmzWWBk21LegoyHrUCI4cEZYGubnC0uCyy4TSq7/U1MD69cKLzm6H8eOFqqX70sp9+4SoXmGhCPguvdQ/f8dWpIIKtrBFY2kwnOHOJc3VVPMv/sVWtlJHHX3py93cbbharCPTVJ86SUehSxe44grx4866deKGkJMDSUkwfz4hvXsz0TIZagLFKJBtEtS03JrjrnTlci7XHR9W/5DoqaKK9axnJzupoYYBDGAmM4nFf+8YiUQiabecOwcffSTEGgICRNC1YIGYJfBGXJyYmQNhFv7JJ0L6vbYWxoyBa64x9qJrZgIIYHz9w5MooriLuwzPCyec2cxu6eK1Cac4xTrWcYpThBLKWMYyiUkXzJ6mFuH0afGTmysG6bOzGxbUlZWJwY6TJ0FVxZLKkhIxIOJg2DDx047pQhem1z+MCCSQe+ofbYWKypb6RxFF9KQn05jWajZdMqjr7LzxhmjwHOTni5k8i0U7g/fpp2IU509/EiObkjalhhpe4RVOc9p57Ad+YC97uZd7O93Ik0QiucA4cwZ+8QvRuXTw4YewbRs884wYpPRFeTk8+qjo8Dr46ithsvznP4t9R5JWI510/sW/nEqDRRSxjGUc4xh3cEfbFq6j8u9/w9Klruf5+ULR8r77/FtGXFwMjzwiAkEHn3/u6u+5B3aSJvMRH7GFLc7nBzjAQQ5yMzd73YfbXEg1is5Mbq4I1jwpKBDLUTz9R7KyRGWXtDk72KEJ6BxUUil9/CQSScfnvfe0AZ2DEyfENgJ/+PJLbUDnoKhI61knaRU+4zND6fh97OMYx9qgRB2c7GzhM+eJqsKbb0J1tT7Nk2XLtAGdg7w84/6hpNFkkaUJ6ByoqHzO54belc2NDOo6M9u3i8rvSVmZCOiMLBC2bm35ckl84s2E9iAHW7EkEolE0gL88IN5mr/tkLc8vKVJmp0CCsgiyzR9P/tbsTSdhB9+MO7DgZiBSzf3SnTibf+irCPNird+WyGFnOVsi5dBLr/szJgpI5nJ13o7R9Kq+PJckTScC13oQyJpV3hra/xth5ojD0mz4KtdkjZFjcDqYx+iP99x2d9rNXx9x1uj7yb/o52ZceOMbwqhoeK40Z6FyZNbvlwSn3gTjxnO8FYsiUQikbQAEyeap02a5F8e3l7nLX9JsxNFlFdLBtluNYJx48wDr27dYJAfvsLe6oi/9UziF8MYZhq4xRBj6l/ZnMigrjMTFQW33KI/Hh0tbgaeIzh9+wqpXEmbM5KRDGCA7ng44cxhThuUSCKRSJqRG24wFmlISRFy6/4wa5axAXNcHFx3XdPKJ2kwV3EVgQTqjo9lrFfTdYkJ3brBTTfpj1sswr/O10wewLx50KeP/niPHnDVVU0uosRFLLFMY5ruuBUr85nfKjN1cvllZ2fhQlGhHR4nSUnC8qB7d+Hzs3WruEFMnCgCOn8UxyQtjhUrd3InW9jCLnZRTTUDGMBUphJJZFsXTyKRSJpGbCy88AJ89pnY/x0YKHxV58zR+md5IygIfv970b59953w40pLEx3ZSHmfbG360IcHeICNbOQkJwkllDTSGM3oti5ax+W66yA5Wai65uZCr17CNHyAftDXkJAQoSb75ZdCFdbhU3fZZRAe3rJlvwCZxzx605stbKGYYnrQgylMoQc9WuX6MqhrILXUspWtuo52BAY2AHY7rFkD33wjxEmGDhWVsTlklr//XhhF5ueLCn/FFdC1q1Cv3LFDeP7UN5CHx4SxeUxPCgglnnimEkIS4WKk9IYbml4WP8jJ+IHs5a9iO36auq4RhM65mr4Tbmxyvvnks5GNHOc4wQQzhjGkkdYp1u/bsDG5/iGRSCTtijNnYPlyOHxY2OBccokw9va2h8eTqCi49Vbx0xBOnBDB4PHj4tqOAE5RXD8NQEVlBzvYxjbKKacPfZjKVGLwYb5cViaMzrcIxbt9E8J597IC8kLKiSGGq7jK0MOuOSijjE1s4hCHsGIllVQmMpEAAlrkev4SRxwLWdimZeh0pKWJHzO2b4cXXxT1ISRE9Ad/8hPX0s2QEDHAv9DL/+Xbb+Hrr4X5eP/+oq/aq5f/ZSwuFv3PbdvAZhNbeebOFQMvfpJBBpvYRC65RBPNJCZpViwVUsgmNnGUo07z8XGMc3og+tM/Tyed7/iOAgqII44pTGn2WeTh9Y+2QFHNlHWamrGijAdeAOqAbaqqPuiW9iRwNVAALFdV9XlveaWlpanbvCn4tBJ11PEv/kU6WsWhCCK4l3vphttSElWF554TFcWdsDD4wx+Mp8P95c034eOP9ccDA3USt+tu6ckX13QBi6uRU1C4hVsYwYjGl6EBnNq5HPV/f4elRit1XLzgEobe8adG53uOc/ydv1NBheZ4Kqncyq1SUMSNtLQ02kMdagj+Cps0N/4KpbRV+fyluQVfLnShmY5Yh1qMQ4fg8cf1Cspz5sC997bstXfsELNzNTVi4PTsWVGOrl1dyznj40X727WrX1n+l/+yne2aY0EEcTd3m+8TKy0VHnmnTgGQQw4llJLdO4hX/5hMZZjoaF7Ltc0e5BRTzN/4G/nka473pjd3c3ebB3bekPWomfnyS+H3WOdhJTF6NLz9tn95vPaaGCRxJzAQnngChvsRnBQWijKcO6c9PmgQ/O//+hXYbWc77/EeKtqYZD7zmcIU8sjj7/ydErQ2KIMZzCIWAfjsn29gA8tZrklXULiJmxjFKN/vs52gKMp2VVUNo/yWnM44CcxUVXUqEKcoiuc342FVVaf7CujaE7vZrfvCgLjB6rzDduzQB3QgGoI33mh8Ic6e1ZqJO8jJgSNHNPK3JWHwVepZnReQispSlhr6yTQ3qmqn4p8v6AI6gIhP13D+zN5G5/05n+sCOoA97OEwhxudr0QikUi88PrrxpY4K1f6J7PeWFQVXn1VBHQgZgcc5SgocB3PzoYPPvAry2Mc0wV0AFVU8RmfGZxRz/LlzoCumhpKKQUg/mQVkz4773zZUpZSjR9+Yg1gDWt0AR3ASU6yFWlLdEHx7LP6gA5EH3TVKt/nnzypD+hATBC89pp/ZfjgA31AB2IW/+uvfZ5eQw3LWKYL6AC+4AvKKecrvtIFdACHOMRe9vrsn5dRxgpW6NJVVJaxjBpqfJazI9BiQZ2qqudUVXXc9WtBF0E8qyjKakVRWt5ivZnYi3kAsoc92gPffWee0c6dxg2iP2zebOxbUloqGjS3mboDQ6HOCpSV6l5eQgkZZDSuDA0g9+R2Qs4WGieqKtnfL21UvtVUew3cdP8PiUQikTSdvDzvgZvRYGZzceKEGNh0UFamTS91a+s2b/YrS2/t+nGOU0aZcaJbG19CsaY7OnRzsfPvGmr4lub9TBrUF5F0XtLTxYC+GUYTAJ54q68ZGWKZtS+81TU/7gfHOEY55YZpNdRwgANev/N76x9m7GEPBzhALbWG6aWUcoITPsvZEWjxjUeKoqQCMaqqurvyvaiq6hjgHuAlk/PuUhRlm6Io23Jzc1u6mH7hzQ1eN8LgbVmrqoplI40qhI/z3K5rt+iPudMqM3V240rkKkTjyqDWP8zw9r+SSCQSSSPxtWWjsW1bY67t7bmfbYu3dgS8tCVu79MzD0ud9nlzt7Xe2jfZ9l1A+PqO+1MXm6M+eyuHH+f7+s7WUefzO++rf97oet7BaNGgTlGUaOBvwJ3ux1VVza//fcTsXFVVX1VVNU1V1bTY2NiWLKbfDGGI/2neNrUOHSo2rjYGs3xDQsTmVLe1y4MPgqIifOk86EIX+tK3cWVoALF9xlIZa66w1C2tcfL8QQSRTLJpegopjcpXIlGUD/36kUguSGJjobcXYYFx41ru2n37am0QPNs29+djx/qVpbd2PYkkwjFpv9zy93zN4TTXcxs2JtG8fmDeyizbvguIIUO87xud40f/yltfNTERevZsWh5+1MN+9CMI4313VqwMqX+Y4U/6QAaaCugFEdQq/eHWoMWCOkVRbMDbwCOqqp7zSIuo/x1DB1LgHMMYw03TQQTpvcMmTIDUVH0mgYFw222NL0TfvkJlzJOYGFH53FS/uhbCtD1RQh3Mg8u4rFU2U1ssNpQ7foRqoEZWdHEa8cmNN4g1ew/96MdQhjY6X4lEIpF4YdEiY4+s8eP9E1ZoLBaLuLajPYmIEErPIOTZHYOaERF++9QNYhCDGaw7bsXK5VxufuL8+SLARfQBQhADtUWxAWy82qWaeTEXO9Oai0u4hDDCdMfjiGMCE5r1WpJ2zn33Gau99uvnXe3SwaBBQrXWE0XR1jVvXH+9sYVIjx5CAdMHQQQxF+PXzWAGEUQwhzmGgV8SSYxilM/+eRRRTGe64TUu5VLToLKj0ZLqlzcCLwL76w/9CrhJVdWfKoryT2AYIqh8VFXV9d7yai/qlyA2T3/DN+xkJ9VUM5CBzGQmccTpX1xdDcuWwbp1Yu3/sGGikiWbzzD5hd0uNqSvXOmyNLj6atHAfPyx1tLg6qvZFn7YKeGaQAJTmdrqo3mn93xB0advEnzsDDXR4Vhmz2HA3J9isTQtpj/DGdaxzmlpMJrRTGNau1b/ags6ouKYnA1rGlL9snnpiHWoRTl0SLQ36ekuS4N58/wzRG4qu3fDp58KCffwcDFDV1AAtbVi1uCaaxpkHVRLLRvZyDa2UUEFvenNTGaaK186OH9efAb1lgY7JwTxzsIqcqJriCKKuczlMi5ryjs1JZ981rKWgxzEipURjGA60wlFvzKnPSHrUQvw5Zfwyitw+rTwGp45E37zGwgO9u98u11YczgsDQYMEH3KoQ0YHM/Oho8+clkaTJok+rsGkwpm7GMfG9lIDjlEE81kJms8DrPJZi1rOcIRAgl09vccAZk//fPtbOc7viOffOKI4yIu6nCTAN7UL1ssqGtO2lNQh90uPOI2bhRiJyNHisbMYIlji7JihZCrLSgQQd1994mpeInEgI7YkMqgrmnIoK556Yh1qF1QUwMbNrjEFMaNg+nTfRuMFxcLw+UDB0T7mpgIWVligHT4cJg9W5ond0BkPWoEa9bA//2fGETo3Vt40LmvBCsoEIGdY4Dl4othROtYVklaH29BXYdZ+tgusNthyRIR0DnYvl1Upj/+0W9PnCbz2GNan7qTJ0WZnn0WLr20dcogkUgkEok3qqvhySdh3z7Xsa1bxYzA00+bzyScPQu//rVYiQJC4a+4WCzxio0Vq1FWrBDtbpzBKhmJpLPwzDPwn/+4np88KRQln3hCzEifOiXqSrFLcZVvvoFrr4Vbb2314kralhZXv+xUbNmiDegcnD3rv8ljU3EsefGktlaYPEokEolE0h748kttQOfg8GHh82bGv//tCujKy10d1qIiqKj3Js3NbZrnq0TS3jlzxrhvWVcnBvHtdrHs0j2gc/Dhh8KSQHJBIYO6hmAU0PmT1py88455WkGBWBoqkUgkEklb461d3LDB+HhFBfzwg+t5qYfPqqcXXXXzGntLJO2Gd94xtwQoK4OlS40HTRy0Vr9U0m74f/buOz6O8k78+Gd2tVp1yWpWcTc2uBt3Y9wwYAw2BkwzobeQkJBLuFwuyeV3l9z90nPkCCUYfpQjxJCATYyNDe5V7g13W7axLclFva22aOf3x2rHWu3M7spaaVfy9z0vv9DOszPzrNjRzDPP83y/0qhrDbs9cFlHzE/0PqU00vICKIQQQkRCoGtmQ4P+eqfT91ra8qa2+evGxivOdSpE1KvXT8itqawMXG50jokuSxp1rTFyZOCyUEK/ttUttxiXWa2eiENCCCFEpAW6Zl5/vf76lBRP6h6vljldm7++7jpPtD8huqJA6QBiYjwRKvPyjN9jdI6JLksada1x882evBstWSzw4IMdU4eZM+Gaa/TL7rvvypOaCyG6FEmiLiJu7lz9/FVJSXDPPcbbPfLI5YekSUmXI2VarZ7X4MlZ99BD4a2vENFkwgTjvI933OEJzvfww/odCkOGwKhR/utFlyaNutaIj/dE25o50/OzyeQ5af7v/+3YdAIffugJ5+x9QpmRAd/9ricCkhBCCBENMjPht7+FKVM8DTOLBSZN8gR5yM013m7MGPj5zz03pmazJ0HyxIkwcKDnujt8uCcwWKCeQCG6gg8+gDlzLj+w79YNnnnGcy8KnnzE//Zvnl5rRfE8RLn7bk90TJPc4l9tumxKA1VV2bKljPXrL6GqMHVqFjfckIHJ1MYhkmlp8Pzznn+RkpAAP/yhp0HpTT5+002Rq08oamthzRpPsthu3Yx7PYUQohM7ebKWpUtLKC93MGRICnfckUtCQpe91AaXmwv//M+t327kyNAbbXv3wpYtl5OPjx8fPAm6wwGbNnkCTcTFeXLnDRzY+nqKiCks9JxrFRUOhg1LZdasnK53rsXEeB6CBDJ2rOdfW+zYAf/v/3nm6Q0e7MmFl5nZtn2KDtclk483Nqr88If72LCh1Gf9lCmZ/Pa3w4mJ6eRPL1avhpdf9p1Mnprq6THs1Sty9TJy5oznSVLzSb2K4uldvPnmyNXrKtIZE77K0MCOEWqycEk+Hvwc+tvfzvK73x31+dPcvbuVN94YTY8eMjQ+7FQVXnrJk5eruSFDPPnxrFb97aqr4ac/9eT8au7uu+GJJ9qjpqJJuK5FH354hj/84ZjPuZabG8cbb4wmL0/mWbbKr34F77/vuy4+HhYsgNGjI1MnYShQ8vFO3rrR98kn5/wadAAbNpSyaFFRBGoURpWV8Oqr/pE2q6o8Db1o9Mor/lGaVBVeey149CYhhOgEiott/P73R/3+NF+4YOfXvz4SmUp1dRs3+jfoAA4e1M/n6vW//+vfoANYvNizrYhq587V+zXoAEpKGvjNb+Rca5Vjx/wbdOCJtP6jH3V8fUSbdMlG3bJlJVdU1ils2uQZYqLn2DFPsspoUlzsSZiux+WSPCpCiC5h+fLzhimltm0rp6wsQHh/cWXWrDEu02vsgSclwvr1xtutXdumKon29/nn5w0zSBUUlFFRIbkLQ/bOO8Zlge7fRFTqko26mhqDRk+Qsk4hWB66urqOqUeogtWnpqZj6iGEEO2opsZpWKaqUFvbya890ShQHi+ja6XLFTh/nuR6jXqBziW3G+rq5FwLWXV14PKyso6phwiLLtmou/76NMOykSONyzqFwYONyxIToXfvjqtLKHr1uhyCWs+QIR1XFyGEaCeBri1ZWVaZU9ceAkWdNrpWxsYapwUCuSZ1AoHOte7drTKnrjUmTjQus1ol110n0yUbdY880puEBP/IVwkJZh55JMoaPa01fLjxReeee4wnhkeK1Qrz5umXDRkCI0Z0bH2EEKIdTJmSxaBBybplTz7ZB7O5jZGXhb/ZsyFZ53ceEwP332+83QMP6Of2ys6GGTPCVz/RLqZOzeLaa/XPtaee6tv2KOdXkwcf9Hzv9TRPpSA6hS4W+9WjT59E/vzn0bz88nF27qwAYPTobnzvewPo2zcxwrULg//zf+Dddz3zCex2yMryRO2aPTvSNdM3b56ncbd4MVy65Pl5+nSJMtbFSLRKcTUzmRRefXUUf/zjcb744jx2u5v8/HieeKIPd90l6VvaRVaWJ3Lf22/Dnj2eca7XXedJXh4oPcH48fDjH8Nf/wqnT3vyeU2YAE89JTexnYDZrPDaa6P44x+P8cUXF3A43PTsGc8TT/TlzjvzIl29ziUmBv7+d/j+92HfPmhs9Iz6uucezzkiOpUumdKgOe/Y66SkLth+dTg8EYqSk9s9yWQRRRzgAG7cDGYwvQnQ43nunCcAitPpyaU3dKhnvdvtmUMXH+8ZAiM6TEekNJBGXeckKQ1C05pzyOFwU1/vIjXVgqLXIyTCz2bzzJfT67kLpLra86CxpsYTQKW62jN0c+zYVl9XbdjYzW4qqKA73RnJSCxYWlefLi7c1yI518KottYTkTwnx9PYa6WiQ6uo3LUKzDFkTZpLdu825s4TugKlNOiCLR1fXbIx5xUb2yGNo8UsZjObtderWc31XM985mNqOYL3L3+Bv/3t8uuPP/Ykg/3xj8Fi8eTTE0KILiw21kSsPLjqWPFXOI8qJQVWrvSkCvKGL128GPr1g1/8wlMegkIKeYd3aKBBW7eCFTzLs3Sn+5XVTQQl51oYJSUFjoFgoLHRweHfPkFqwSG8Z4v9w2UcuHMqQ5/+Y3jrKALqknPqRPjsZa9Pg85rD3sooKDFyj2+DTqvnTsD5wwSQgghIqGkxJNLtWU+ipMn4a23QtqFEyfv875Pgw6giio+4INw1VSIqHRsye9JLTjktz51yXpObl0YgRpdvaRRJwLazvbQy1atMt7RypVhqpEQQggRJmvWYJj0bPNmz7DOIA5zmFr0UyEUU8xZzralhkJENWWVcb7I+lX/6MCaCGnUiYCMLlS6ZZWVxjuqqgpTjYQQQogwCXTdcjpDyv1aQ+B8q3VEWf5YIcIottL4+22qlFzEHUkadSKgnvQMvSxQ7p9AZUIIIUQkDBhgXJaRAenpQXcR6DppwkQeEpFRdF22AT0My9zX9OvAmghp1ImAJjNZN3qXCRPTmOa78o479MNBKwrc1zUj4gkhhOjEpk71pEbQM29eSBEwe9GLa9B/cDmWsaQQWrAVITqjlHlPoupEHm20Wsid82wEanT1kkadCCiHHJ7maXLJ1dZlkcXjPE4f+vi+OTsb/vM/fXvlsrPhxRdh9OiOqbAQQggRKqsVfvlLGDHickLylBR48slW5X59nMcZxSjMmAGIJZbJTOZu7m6PWgsRNXoOm4XrX1/Elpumravt1x3Lz39JRv6wCNbs6tPl89SJ8CmlFDdusshCIUg+mAsXPHn08vPbPYeeCK4tuYEk/5xoDclTJzqtsjLPHLrcXE8KnitQTz1VVJFOOlasYa5g5yfnUdelqm7KivZjjrHSLWdQpKvTZV0U4akuAAAgAElEQVTVeepE+GSSGfqbu0teHiGEEJ1IRobnXxskNC1CXG0UxURmj5GRrsZVTbpQhBBCCCGEEKITk0adEEIIIYQQQnRi0qgTQgghhBBCiE5MGnVCCCGEEEII0YlJo04IIYQQQgghOjFp1AkhhBBCCCFEJyYpDcLp3Dk4cQLS0mD4cMnPJoQQQgRSWQn790NsLFx/vScZuBDiMlWFgwehtBR69YJ+/SJdIxGlpFEXDnY7/Pd/Q0HB5XXZ2fCjH8GAAZGrlxABSFJx0R4i9b0KNel5KPXrqgnUo87778PixeByeV4nJsJzz8HUqZGtlxDRoqgIfvlLOHv28roRI+Bf/gWSkyNXLxGV2q0rSVGU8YqibFEUZaOiKC+1KMtTFGVNU/nN7VWHDvPmm74NOoCLF+HnPwebLTJ1EkIIIaLVl1/C3/9+uUEHUFfneUBaWBi5egkRLdxu+MUvfBt0APv2wf/8T2TqJKJae44P/Bq4SVXVyUC2oijDmpX9K/BvwK1N/+28amth7Vr9supq2LChY+sjhBBCRLulS/XXqyosW9axdREiGu3cCSUl+mU7dsCFCx1bHxH12q1Rp6rqeVVVG5peuoDGZsXDgQJVVWuBGkVR/PqQFUV5VlGUnYqi7Lx06VJ7VbPtSkvB6TQuLy7uuLoIIYQQnUGga6PRjawQV5NA54Gqyv2l8NPukTwURRkOZKqqeqjZarOqqmrTz1VAt5bbqaq6QFXVMaqqjsnKymrval65zEywWIzL8/M7ri5CCCFEZxDo2piX13H1ECJaBToPFEXOE+GnXRt1iqKkA68AT7Uoat5rlwJUtmc92lVSEtx0k35ZSgpMmdKx9RFCCCGi3ezZ+usVBW6/vWPrIkQ0Gj3auOE2dix0796x9RFRrz0DpcQAfwF+qKrq+RbF+xVFmagoSiKQoqpqdXvVo0M8/TRMmuS5GHl17w7/8R8QFxexagkhhBBR6ZZb4P77fUe6JCXBiy9C//6Rq5cQ0cJkgp/9DHr39l0/YgR873uRqZOIau2Z0uA+YCzwG8XT2Pkx8JCqqt8FtgOf42lUuhRFuUtV1U/bsS7ty2r1pC8oKrqcp27YMMlTJ4QQQhh5+GGYM8cTzU/y1AnhLz8f/vQnOHQILl3y5Knr2zfStRJRSrk8tS1CFVCUbcCMpqApujIzM9U+ffp0XKWE6GJOnz6NnENCXDk5h4RoOzmPhGibXbt2qaqq6vYaRTT5uKIo/YALgRp0AH369GHnzp0dVCshoscJTrCXvThwMIABjGQkFgIE5jEwZsyYLnUOuXFziEMc5CAqKkOaFlP7x34SV6mudg4JEQlyHomu7jzn2cEOqqgin3zGMY5EEsO2f0VRdhuVRbRRB9wDLNYrUBTlWeBZgF69enVknYSICh/zMVvZqr3ezW42spHneI4EEiJYs8hy4eJd3uUIR7R1O9nJdVzH4zxOTMT/rAkhhBDiarONbXzMx6h4RkHuZS/rWc+zPEse7R+tNNKPtecAS/QKOk1KAyHawRGO+DTovIop5gu+iECNokcBBT4NOq8jHKGAggjUSAghhBBXs2qqWcQirUHnVUstf+fvHVKHiDXqFEXJARyqqpZFqg5CRKtd7LqisqvBbgxHHgQsE0IIIYRoD/vYR6NPxrbLznKWi1xs9zpEsqduLvCPCB5fiKjVQINhmR2735Ogq4kN2xWVCSGEEEK0h0D3baGUh0PEGnWqqr6hquorkTq+ENGsH/0ClikohuVd3TVcY1jWH8lvJYQQQoiOFei+LZ54cslt9zpEek6dEELHeMaTRprfehMmbuGWCNQoekxlKlb8c1lZsTKNaR1fISGEEEJc1frTnwEM0C2bwYwrilzeWhImTogolEAC85nP7/k9F7mIikoSSdzHfQF7qq4GWWTxbb7NcpZzlKMADGQgt3M7WYQeVGk/+1nPeoopJo00JjKRG7lR0iJcgUMcYi1rOcc5UkllPOOZwhTMmCNdNSGEEAIVlQIK2Mxmyiknhxxu5EZGMzrkfVRSySpWsZ/9uHEzmMHczM1kkw3AEzzBcpazgx000EAGGUxjGhOZ2F4fy4c06oSIQjXU8Ct+RS21Woh+Bw4WspDudGcc4yJcw8jKJ5+neRoHDgBiiW3V9gUU8AmfaK8vcYklLKGYYh7kwbDWtavbwQ4+4iPtdSmlLGMZ5zjHIzwSwZoJIYQQHktYwkY2aq/PcpaFLKSKKm7ipqDb11LLq7xKBRXaut3s5jCHeYEXyCKLWGKZy1xmMxsHDuKI69DpMtKoEyIK/Z2/U0stgM8fBDduPuTDq75R59XaxhyAEycrWKFbtpOdTGMaOeS0tWpXhUYaWc5y3bJ97GMa0+hJzw6uVfRRlNDCWavqfe1cEyGEuPqUU84mNumWrWY1N3ADccQF3MdmNvs06Lxs2FjDGh7gAW2dGTPxxLet0ldAGnVCRAEbNnaykxJKSCONfewzfG8xxbhxyzDBK3SOc9RRZ1h+hCMhN+qKKGI3u2mggb70ZQQjOmTcfLQooYRqqg3Lj3BEGnVCCCEi6jjHDaOG27FzkpMMZjBOnOxlL6c5TTzxjGa0FuBELz+ul3cqSKRJo06ICCuhhDd4Q+uZA7jABcOGm6lpEVcmJsifvVAbZatY5dPjt41trGUtz/EcySS3qY6dRbDfZbByIYQQor2Fcq2qpprXeZ1LXNLWr2Mds5nNNKYF3Ee0XOvkzlCICPuQD30adACJJOLCpftkScL2t00PepBBhm6ZCRNDGRp0H2c5qzuE8wIXWMrSNtexs8ghh+501y1TUBjBiA6ukRBCCOFrMIMNH9gmk0x/+vMP/uHToPNaylLOc56RjDTcf7Rc66RRJ0QElVBCEUV+61NJxYIFN26f9XHE8QzPdFT1uiQFhXnM0/0Dfzu3k0pq0H3sYpdh2T724cTZpjp2JvOYpzu3cSYzSSc9AjUSQgghLosnnrnM9QtaYsbMPObhwsUBDhhuv4tdjGOcbi66HHKYzvSw1/lKREd/oRBXKRs23fUmTOSTT3e6c4ELOHEykIHMZz6ZZHZwLbuegQzkB/yATWzS5jGOZ3zIvaD11BuWuXDhxHnVzK3rRz9+wA/YzGaKKCKFFMYz3jBfjxBCCNHRJjCBXHLZylbKKKM73bmBG8gll2qqaaTRcNt66rFg4RmeYTe7+YqvaKSRwQxmHON0c+dGgjTqhIigPPKIJVYLzd+cCRP3cz996RuBmnV9WWRxN3df0bZ96ctuduuWdac7CSS0pWqdTiaZzGVupKshhBBCGOrdtLSUTDIZZFBGme52fegDeObcj29aopEMvxQiguKIYwpTdMsGMEAadFFqFKMM5+Xdwi0dXBshhBBCXCkFxfDanUVWwPl00UR66oSIsJnMxIqVDWyghhpiiWU0o5nN7JD34cTJOtaxi11aeP0ZzKAHPdqx5lcvK1a+xbdYwhIOcAA3bjLJZCYzO80f/3A6xjHWsY5iikkmmQlMYCITJUqrEEKIqLGDHWxhCxVUkE02k5nMMIYBMIYxgCeydSmlmDEzjGHcyZ2dZjqFNOqEiDAFhelMZwpTqKOOeOJb9QekkUbe4i0KKdTWfcVXHOYw3+Sb0tvXTtJI41Eexd60JJPsNwn7arCXvXzAB1qk1lpqWcxiznCG+cyPcO2EEEIIWMYy1rJWe11LLSc5yVzmMpnJgKdhN5rR1FCDtWnpTOQxqhBRwoyZFFJa/UToIAd9GnReLlx8zufhqp4wYMVKCilXZYPOjZulLNVNvbGLXbqRXYUQQoiOVEkl61inW7aCFdixa68VFFJI6XQNOpBGnRCd3mEOG5ad4hQNNHRgbcTVpIQSKqk0LA/03RRCCCE6wlGO6j58BLBj5yQnO7hG7UMadUJ0coHmLSlNixDtIdicOZlTJ4QQItKCXYvMmDuoJu1LrrhCdHLeSb56ruXaTjmEQHQOueSSTbZumYLCcIZ3cI2EEEIIX4MZTIxBGJEkknSTindG0qgTopO7lmt1Iy4mkNCqCJpCXIm7uEv3Yjmd6WSSGYEaCSGEEJclksgd3OG3XkExvIZ1Rl3jUwhxFVNQeIiHGMQgdrKTBhroRz9u5Ea60S3S1RNd3EAG8k/8E5vYRBFFJJPMeMYzhCGRrpoQQggBwGQmk0uultIgiywmM5me9Ix01cJGGnVCdAEmTIxuWoToaDnkcC/3RroaQgghhKFrmpauSoZfCiGEEEIIIUQnJj11QrTCfvazhS2UU6513V/HdbrvPcIRNrGJi1ykG92YxKRWBY6wY2cjG9nHPuzYGchApjFN5imFSSGFrGc95zlPKqlMYEKrejpduNjMZnaxiwYa6EtfpjOdHHLasdbhV08961nPV3yFGzeDGMR0ppNCSqSrJoQQQoQkGu6ZVFR2spOtbKWGGvLIYypT6UvfkPdRTTXrWMchDqGgMIxhTGUqiSQG3VYadUKEaBWrWMEK7XU55RzlKHdzN5OY5PPeLWxhEYt83ltIIbdxGzdzc9BjOXGygAV8zdfauq1sZR/7eJ7nO13DIdrsYQ9/5a9a3ppyyjnFKc5xjrnMDbq9Gzfv8A5HOaqtK6ecr/iK53iOXvRqt7qHUz31vMIrXOSitm4jG/mKr/gu3yWV1AjWTgghhAguWu6ZFrGIAgq01+WUc5CDPMIjIT3Ur6aal3nZJ//rGtZo1+QEEgJuL8MvhQhBDTWsZKVu2ed8jh279tqOnWUs033vSlZSQ03Q4+1hj88fJy8bNr7gixBrLfQ00shnfKabiHQTmyilNOg+DnHIp0Hn5cBh+P8+Gm1ms0+DzquSStaxruMrJIQQQrRSNNwznee8T4POS0XlMz7DjTvoPtay1qdB53WJS2xiU9DtpVEnRAiOcYxGGlFRqaOOKqqoow4VFTt2CinU3nuSkz6NvOYaaeQIR4Ie7xCHrqhMBHeOc1RTrVumoob0+z3IQcOyQgoN///rOcYxlrOcNayhnPKQtwuHQJ810GcUQgghOtoJTrCc5axmtc/1MhrumQIdp4IKSigJuo9A191QPkfEhl8qivIo8BhgBr6hqmpRpOoiRCgcOCil1OdpSxVVZJHl8z4FJeB+TG18lhJs/6L9Bfp/EOr/HwcO3uZtTnBCW7ec5cxhDlOY0uY6CiGEEF2BEydv8zbHOa6tW8EKbud2pjM94Lad6Z6prfcWEempUxQlH5iqquoMVVWnSYNORLsBDKCccr/uczduKqn0CZHbn/7EE6+7nxhiDAOrNDeUoVdUJoLrSU/DuWIKSki/30DvuYZrsGINuo8v+MKnQQeensIlLOEsZ4NuHw6BPscwhnVIHYQQQohAvuRLnwYdeK6Xy1jG13wdFfdMgY6TTjp55LVpH6Hkfo3U8MuZgFlRlNWKovxJURRzhOohurBqqimhBCfOK9peReUCFyillHOcM4w8lEACxRRrry1YmMMc3acqt3FbSBGMRjKS/vT3W59EEjOZ2YpPIVoyYWIuc3V7TKcznXTSg+7jOq7T/sC6cOHEiYqKFSuzmR10exWV7WzXfnbixIVLK/eWtbdJTCKXXL/16aQzjWkdUgchhBAikEDXxO1sD+s9kwsXJZTozm0LJJtsJjMZ8Ey1ceLEjRszZu7irpB62qYxjQwy/NbnkusXkE9PpIZfdgdiVVWdoSjKb4C50CxUIKAoyrPAswC9enWOSHIiOlRRxSd8wmEOo6ISTzyTmcwt3BJyN/x+9rOMZZRRBoAVK7HEkkkmtdTiwkUMMSSTjBWrX/CTcYwjgww2spGLXCSddG7kxpB66cDTo/c0T1NAAXvYgwMHAxnIFKbQjW6t+4UIP8MZzvM8z0Y2UkwxqaQykYkhp5wwYWIWszjNaU5xCjduUkhhBjPIJz/o9i5c2LBRSy3VVGs9wLHE0o1u1FLbps8Xqjji+DbfZjObtZQGgxnMZCaTRFKH1EEIIYQw4sZNHXWG5TXUhO2eaT3rWcta7Ro8gAHMY17IaRFu4iaOcpRDHMKFi3jimca0kO/9kknmBV5gIxs5yEFMmBjGMCYxyXAEWHORatRVAeubfl4DjGn5BlVVFwALAMaMGeMfpk4IHY00soAFXOCCts6GjS/5EoBbuTXoPk5wgvd53yc6Yg01lFJKNtl+J7eConsj379puVIWLExpWkT49W5aroQNGwtYQB11ZJOtrV/DGjLIYDzjA25vwUIssX5PAh04uMQl3Sd17SWeeG5uWoQQQohoYsJEHnk+I6Ka60EPoO33TJvZzGd85rPuOMf5M3/mh/ww6LQKN27e4i0uctHnPnEPe0glNaRRPACJJHJb09JakRp+uQW0R+IjgVMRqofoYg5y0KdB19xGNuLAEXQfa1nrF+4+llisWHV7UEYyMqQhe6Lr2MlOwwiaa1ijmy6hpXAEWxFCCCG6upu4SXd9AglMYEKb9+/GzVrW6pZVUsludgfdx1GOUoR+iJAtbKGBhjbVMRQR6alTVXWvoig2RVHWAaXAS5Goh4hO5znPBjbwNV+TSCJjGcsYxuje6JZSygY2cJKTxBGHgoKKqvteGzbKKNOdQ9ScUZCKDDKop54yynDiJJZYJjCB+7n/yj6oMFRCCRvYwBnOkEQS4xjHKEZFTWPnLGdx46accuqpx42bWGJJJ50yyrBhC5gk1IkTO3bSSNMdfhlKrjwhhBCiM3DjZjvb2cUubNjoS1+mMMUveriRkYyknHIWsYgqqlBQ6ElPnuIpUkhpc/1qqAk4h+4c5wBPHuLNbGY/+3HjZhCDtOkKgQKcOXBwkYv0on2nk0UspYGqqv8cqWOL6HWKUyxggU9wk5OcpJBCHuRBn/cWUcTrvO7z9KOGGuzYdcc/KyghBSlJJpl66v3W11KLHTvppGtz6o5znD3sYRzjWvMxRQCFFPIWb2nfgQtcoLBpiZYGdCKJFFHk8z1toIFiisknn1hiA24fQ4w2Pj6RRFy4UFCIafqTLPPZhBBCdAUqKn/hL+xnv7buPOfZzW6e4zl60jPoPi5xiQ1swIqVdNJRULBjZwUreJqnMdO2eItxxBFDjE/AsuaSScaOndd4zac3rphi9rCH7/AdkkkOeIyOuK5L8nERVZawRDda5U52cprTPus+53O/7uwEEmigARs2v30MYlBIT3T0GmiNNFJFFYkkYsJELLGYMKGi8hmfhTSsU4TG6Duwne0dFuo/GBs2w6iq3knbgSgojGWs9rMFi8828pBACCFEV3CMYz4NOi87dpayNKR9LGc5tdT6XS+Pc1x3361lxcr1XK9bpqAwhjEUUKA7vLKcctaylpGMNHygO4ABHTJNRxp1ImpUUhnwpv0AB7Sf7dg5xjHAcxPtTT3gxk066dix+2ybTTbzmMdZzrKa1Wxko2FX+43c6BcFsYEGkkjSjT5kw0YhhSF/TmGsnHLDMekAX/FVB9bG2CEO6T4ZVFBooMFwvl1zM5lJf/rjwEE11dRSixs3c5jj8+TyDGd4kzd5lVfZyc6wfg4hhBCiPTW/d2upkELdh/DNqagc5KBhebjuC7zXXjt27ZqsonIf95FJZsDjfMVXxBPPwzxMDDHUUUc11TTQQCaZrR5ldIELrGUt61jXqukYERt+KURrtQw+4cJFEUU+3eXVVJNKKtOZTh/6UEstPejBdVzHQhb6nJSf8RlzmKPlFfEyY+ZRHuU0pznMYcyYMWFiBStCrptoH9Hye1ZRte+Fdz6cgqKb+86It8fXjRsVFTduFBSfJ30LWMAa1mivN7KRXvTiv/ivoEM8hRBCiEgLdt0OpTzQe8J1X2DGjAWLdk32xmcI5VrrrYMFi3Zf4L2uxxAT8vBQFZVFLKKAAm3dMpYxjWncwR1Bt5eeOhE10kgjjzzD8sEM1n62YqWOOt3xz1VUkUEGU5jC7dzOcIazjnV+T1ncuPkH/+BrvtY9Xh/6MItZ3MqtjGe84Ulpxdqm1AXisnTSySHHsNyb8DvSvMMjFRTMTYu3QZdFVkjDfFeyksMcJo44UkklhRQUFBaxiCKK2MIWnwad1xnO8Gf+HN4PJIQQQrSD5vduLfWlb8CgYuB5ADqIQYbl4bov+IzPOMlJ4oknlVRtjtxCFlJOecDPMYQh2LHzHu/hwEEyyaSSSgIJnOc8f+NvIdVhO9t9GnTgaeitZW1IPZLSqBMdpoEGjnGMCip81tdQQznluHEzm9m685GGM9yn4eTAoSX8Vpst4LnRbvnl38Y2w3oFKvNKJplbuEW37HZuD5q/RIRuDnN0G9AjGUkf+oT1WE6clFIaMNRwOeV+wylnMUu38WnCxMM87LPOgYNSSn2GBKuoht87FZXtbOdzPjesU8vwynbslFJqOM9PCCGEiITrmpaWLFhC6n0CuI3biCceFRUXLhppBDwP30cyss11dOBgF7u01y5c2iicRhrZyU5u4Aa6091v2xRSuImb2Mtew3uJoxz1uff13g+XU+7zvq1sNaxjKPeqMvxStDs3bl7hFXawAydOFBT60pfHeZx1rOM4x1FRSSedmczkeZ5nPet9UhpMZKLPPr1jnc2Y/XrrzJj9bsIDzXEKZf4TwM3cTDbZbGYzZZSRTTaTmRzwCZJovWu5lu/wHdaylrOcJYkkxjI2LLlovNy4WcUqNrIRGzZiiGEkI5nLXG3e5CEOsYxlWt7DvvRlLnPpQQ9iiOHX/Jr3eI9d7MKBg3zyeZAHGcYwwHNR+JzP2cY27NiJJZaxjNUSkNZRZ1i/Kqq0hxZ6vBcOO3aWsITd7MaJk3jimchEZjKzzdHAhBBCiLYyYeIJnmAzm9nJTi2lwXSmB00x5ZVLLjOYwSd8Qjnl2n3kXdwVNDBZKLzBz2qppYYardEYRxzd6EYVVcQTr92felMaDGYwU5lKGmlUUWW4fxVVmx70Oq+zla3a/XBvevMDfkA22QHvRwPt30sadaLdvczLPk8fVFQKKeRn/Ix88rVha+WUs5CFPMqjfr0dLaWRRgwxOHD45S5rpNFvGGceeYYBOAIN+WxpeNMi2ldPevIoj7bb/j/nc9axTnvtwsVOdlJOOd/m2xRSyDu84zNW/xSneIM3+D7fJ5104ojjm3zT8Bgf87FPYBMHDjazmRpqeJRHySabi1zU3TaPPC40LXrSSAPgfd7nCEe09TZsrGENduzczd0h/S6EEEKI9mTGzJSm5Uoc4ADLWIYVq9YQtGHjLd7ih/wwpHRVgSSRhBu3XwC9Bhq4yEWyyQY8EdZnNS0t5ZNvuH8LFrLJ5hVeYQtbtPUqKqc5zc/4Ga/yKvnkGzbeQmkAy/BL0a5qqGEHO/zWu3HjxKnbG7GKVUH3a8IUcN5Syzlu05mu+z4rVr9eQNG12bCxmc26Zd6ciGtZqzv5OtC2zZVT7jOUo7n97OciF5nGNN3yeOKZwATu5V7D3rZbuZWznPVp0DW3la0Be/qEEEKIzmI1q3WvybXUBhyyGKqWnQPNeUeFBTOIQbrDMwHGM16bWqGniiq+5EumMlW3LmbMTGVq0DpIT51oV4c5rHVjN+c9ORtoIJVUn7IznOFjPqaIIm345QhG+LzHho0EEkgiiTrqtP2ZMZNFFkc5Sgkl1FBDD3owmcnczd2sZCW11AKQQw73ci/d6NYeH11EqWKKA849O8MZv5yIzXkD67hw8QmfsIUt2LHThz7MZz696c1ZzgaMyPU1XzOOcTTQwEpWaiGd88jjPu4jpWn5Ft/iXd7VvrMWLMxgBvdwT8DGZSONFFGkO49BCCGE6CzcuDnLWRpppJZa7NhRUEhoWoyC3bVGDTWYMJFIItVUa/PprFjJIosSSoLuw4SJZ3iGj/iI4xwHPNfs8YxnDnPYxz7D5ObgmfJxO7fzDb7BUpZqvYbppHMXdwXsCfSSRp1oVxlkBCxv+fTDgYMyyiigQHtacYQjnOAE85invc/StGSTjRs39dRjxkw88VRSyQEOaEPUznKWnezkGZ7hp/yUYoqJJTbksdyiawk2TCOxaTGa8OwdpvFv/JtP428veznAAX7Ej0I6BsAUpjCRiRRTjBWrX/CVG5uWfeyjgQZGMII44kL+HEIIIURnZsKEBQvFFGuNLfDMKfc+4G+rOOJopJF66jFhQmla3LipoSbk62kaaXyTb2oB1rLJ1uoX7H7Y28ExkpEMZzhFFGHCRB55AXsSm5Phl6Jd9ac/mWT6rffOo2vZS1dJJXHE+X2BCyjwSUweQwyjGKXty5sY3DvRteVJ7sTJp3yKBQu96S0NuqtYDjk+yb2bs2JlOMMZy1jD7ccwhpWs1O3Nc+Hibd6mH/1IJ113+xRSuJZrtdfe72SgVA4jGMF4xmsNOvCEUPYGdWkp0GcUQgghOhNv/riWGmjQHuC3hRUrJkxabjpvww6gnnqyyGrV/tJJpw99fO5Fe9Nbm5vXkoKiBVEDz31tT3qST37IDTrPdkKEWQ01FFDARjZykYu8wAs+N6Pg6aGbwAQsWLR13mGaLRt6XvvY5/P6Du7w6462YSOVVN1kkcUUc4hDbGQjW9kaMPqg6NrmM9/vexZDDA/xEHHEMZWpukMXJzGJoQz1mejcUgkl1FHHwzxMPPHYsFFDDTZsWLHyMA+HJTKlBQuP8AgWLNoxGmggiSQe4qE2718IIYSINBUVGzbd+7oUUrjEpTYfo5563Li1xmNj06KikkYa5zgX8r6cONnLXjawgUIKfcq+x/f8HsYqKNzLvWHpbJDhlyKsNrOZJSzxmUc3jnG8wissYQmFFJJOOndyJz3pyQUusIc92LGTTTYf87HhU4mWc/MSSOAFXuAgB7WEkdVU6+byUFGpoII3eENrSH7Kp9zFXWENlS86h2yy+RE/Yg97KKaYNNIYwxgt2agFC0/xFMc5zmEOE0MMIxhBD3oA6D4xbK6RRhJI0IZ0eMfRxxEX1mGRiSQSSyyupkVBIZ74sAxHEUIIISLNOz89iywaaPCZU2fBohu3obUaadR66F1Q2ZAAACAASURBVLzH9A7BNGMOes33OsUp3uM9bR48eKJ5P8VTJJFEf/rzGq/xKZ9yghOkkspd3EUverX5M4A06kQYneY0n/KpX4CI7Wwnhxzd3oPudOc2btNeb2KTYRh3vXxwZsw+aQZOc1q3UVdDDXbsPkPivIEu8smXoWpXoVhiGc94w3IFhYFNS0vXc702EbqlDDJII43/5r+poIIkkrSyKqp4l3f5F/6lVUMq9DTSyDu8Qx11WmMU4BKX+IAPeJ7n27R/IYQQItJMmLiWaznCEeKblubCkSs4mWQUFOzYMTUtXmWUhXSPaMfO27ytBT7zOstZ/sbfeJInAU+E6/nMb3Od9cjwSxE2BRQYRvwLNFytmmotstAc5vicTF6DGax7c91SH/r4RcoET6JnvXHXKqpuI1B0DQ4cPpGswmU2s3XniioozGc+X/M1xRTrbnuJS5zghM8670OH1jjMYb+cOl6nOMV5zrdqf0IIIUQ0msUsrFj91vekpxZfwauBhlan9HHiNLx/tWI1vNY2t499WoNORdWGb0Lg63U4SU+dCJtAX1i9spOc5HVe1wKgJJHEbGbzLb7FWtZyhjMkkMBYxrYqYeVDPERverOd7dRSSw96YMeu+wchWL1F51RPPUtYwl724sJFOulMZ3rYchLGEstv+S1v8ib72IcTJznkcD/3M5axfvM/W/J+5/azny/4ggtcQEFhCEO4kzsNg6w0Z5SgtPkxAgVfEUIIITqDfPL5Dt9hDWs4znFiieV6rmc607UpNaWUsoQlHOYwKiq55HIbtzGEIUH37416mU021VTjwIEJEwkkkExy0OsteK65btxUUUU99Vp+uySStH2EI6hLINKoE2GTQ47fpFCvlhNAK6jgF/zCJ2x8LbV8yIfEEKN1U18JM2amNC1ef+APhnlGjJJFis7JjZsFLPCZ2FxOOZ/wCY00ciM3huU4CSTwPb6nWxbsO5VDDgc5yPu8rz3JU1E5wAHOcY4XedEwsmXzfRhRUAyjbAkhhBCdTS65fINv6JbVUcdrvEY11dq6Ekp4l3d5mqd9Ik7rSWpaaqnVHYUTygPSHHIopRQHDm1dI41UUYUJU6sjaF4JGX4pwmYSk4gxeE4wlak+rz/mY8M8YJ/xWdjrZtTTZ8HCDdwQ9uOJyDnEIcNIVatZHZZJ1cHkkGOY+Lsf/ehJT1axSne4RyWV7GBH0GP0p78WuKWl4QwPqbdPCCGE6Oy2sc2nQeelorKKVUG3N2M2fOCbSCJjGBN0H1ashlM9VFTD++Nwkp46ETbZZPM4j7OIRZRTDngmhN7KrYxkpM97jYJMgOem9lM+pYQSEklkLGMNJ8J60ycUUkg88YxiFMMY5heEYixjqaWW1azWGpPppHM/9wdNCCk6l1OcMiyroYYyysLSi+XGzRKWsIlN2LHTj348yINar/Q3+AYf8REHOahF0rqWa5nPfFy4fPIu6n2GUIYcP8mTLGShdj4pKIxgBPdxX5s/nxBCCNEZBLrun+a0dg0+wxkKKKCccrLJ5gZu0K7ZM5hBAw1sZjNOnICnd/BBHgw6cgbgDGfIJJNLXMKOXTtmMskkksh5zoctyqURadSJsLqO6/hX/pUznMGFi1700s0tYnSCePODbGCDFjBlP/uZwhTu5E6f95ZSyqu86jMh9gAHGMtYHuABv31PZzo3cANnOEMssfSiV5sjEIroE+iPr4LilzPxSv07/+7zcOISl9jDHn7CT7iO64gnnsd5nHLKKaWUjKYFPE/trFgNg6OEmpIghRS+yTe5xCUqqCCb7HYfsy+EEEJEk0DX/TjiUFDYxjY+5mNthEwhhWxnO4/wCEMZqiUAv4mbOMc5Ekn0y4UcSAIJ1FNPI42YMWuNugYacODokFRDMvxShJ0JE33owzVco9ugA7iFW3TXN9KIFatfBMwNbKCIIp91S1mqG+FoBzsMewKtWBnAAHrTWxp0XdQoRhn+vx3AAFJIafMxvJO1W3Lg4E3e9FmXTjoDGejTI6yg+EXsai5QmZ4sshjIQGnQCSGEuOoEGh45hjHYsOmm3GqkkUUs8pmWkUACAxnYqgYdQG96a/ek3px3CgoqKg4cunP1wq3NjTpFUcYoivJ9RVF+pyjKLxRFuV9RFJnM0QkUUcQHfMBf+SsXuQh4ehBOcIICCjjOccMQr1fiLGcpoICDHGQiE/1OQhVViz7kwEEttVoEIcAnoqATJwc5aHgso+iD5ZSzjW3sZrfhnD7RuaWTzj3cg4pKJZWUUkoddXSjG/dyr897v+ZrCijgMIdblfZgAxsAz3fW27vs/Z4WUeSTeLSQQgoo4BjHfM6nWcwijzxcuKijTvuu38RN9Kd/W34FQgghRIeqpJLtbGcXu6invl2OUUYZ29jGHvb4jHQZyEAmM5lGGqmggjLKaKCBnvTkVm7lEIe0tAUNNFBLrbZ9NdWGQf5a4wQn6EY34PKoMzduTJiII84wWF84XfHwS0VRHgdeAE4Bu4CjQBxwI/AjRVEOAD9TVfVMGOopwuwlXmI727WbzM/4jPGMp5FGny9eDjk8yZNtCrpgw8Z7vOeTmyuVVB7jMaYylZWs1OYk7WEPlVT6NLhMmEgnXRvjDJ4TJlCDs/l7ve9fzGK2slXbzoqVe7iH0Yy+4s8mopMTJxe4oI1rr6OOWGK1icp11PEu7/qMw+9GNx7n8ZCezrlw4caNC5e2rpFGTJgwY8aJkwoqeId3fPLVZZPNkzxJJpnEEUcuuZzgBA4cKCgkkNDqp4NCCCFEJC1lKRvYoD0ctWBhNrOZxKSw7N+Nm0/4xOe+1YqVe7mX67ke8OSnO8957f6vjjoyyMCCBRcunDgpo8znum3BQiaZPuuulAsXMcRovXPeesYQgxlzWI4RTFt66hKBSaqqzlNV9Zeqqr6lquorqqq+oKrqaOAlYEB4qinC6RM+YRvbfBpFKiob2chRjvq89zzneZd323S8j/nYL9lyFVW8zduMYAQ/4Sf8nJ/zGI9pT1Gac+OmjDJ601tbZ8Xq87qllpEHN7LRLzm6HTsf8qFhkmjROZVTzhu8gQsXZszaH9QLXOB3/A6Aj/jIb2J1BRW8zdsh/eEdyEDd97lxE0cc3ejG//K/ft+ti1zUzqc1rGEXu0gggW5004ZONu85F0IIIaLZDnawjnU+o12cOFnM4rD0gAGsY53ffasdOwtZyHnOs499LGaxFmUyhhhMmDjKUd7kTfrT369B561nJZX0o1+b69if/pRSquWn8957OHBQRx155LX5GMFccaNOVdVXVVW1BSjfq6rq6ivdvwgvN26t12INa3TLVVTdBIvFFHOa0zibltaoppr97Pc7Dnjy0jUfJmkUNAI8yZ5b1m0WszBjBsBic2Fyef6g9KQnwxjm897NbNbdr4pKAQWt+ETCy4693dMDeJOAtsZiFhvW6wQnKKSQwxzWLa+iigMcCHqMdNK1757JpRJru3wxSyGFr/naMLrlec5TSCFb2KJb3kgjW9katA5CCCFEpDW/ljXvoQJaf3+lqmCzgdt3OoTR9dKNm61sZQlLDOuwne2UUaYFSVObLeC5v6ygonX11FFOOVasumVmzNRR1+ZjBNPm6JeKovQFvgv0ab4/VVXvNNpGdBwXLlaykq1spY460kijggotKk9LevOKnDhZyEItTcE1XMPt3E5PegY9fhVVqKjUNi0uXCgoJJJICik+J1INNVpXeA01OHBgwkQiiSSTrB3f6xqu4Z+23UDdwreIO1mEaonBNnkMvR9/kJg03692oBM2HCfz1eQgB/mSLymiiBhiGMlI7uAOkkkO2zE+4iO+5EvqqMOMmYEM5AVe0MarB3KJS4ZlKiqFFAYcutvye6anmmoGVmYz+t2vGLyxnBinm/P9Etj6YF/OT7BynvNB66iXU6c1dRBCCCEirYwy7Nipplp7OB9HHKmkUkZZaDtRVVi0CJYuhbIySE2FWbPggQdwmxUqqTTctLxpUVG1eWzgCVZixowNG2WUkUqq1mvmjeHgTTpeTrmW2qAtv4d00qmhhjrqcOPGgoVkkoknngoqwhKoLZBwRL/8FDgN/An4Q7N/hhRF6aMoygVFUdYpivJlGOogDHzIh6xmtfaEoJJKbT6QHm/vg5cLFxe5SAkl2pON4xzndV4PadJnOunUUacdF9AaeS3zhaWSihUrccSRRRb55JNLLimkoKDQne6+O9+6ldxfvs01JxV60IOezhwGrjmH9cf/AXbfXr8ssgzrGKhM+DrAAd7lXS0SqQsXO9nJ67ze6l5cI+/zPotZrH1nG2nkMIf5CT8JaWhkoDlpJkwMZnDAyKd+3zO999i7cf9P9jF2TQ2JTotnOPDJRh74VSHDttYZJgX3yiU34DzVUOoghBBCRFoiiZRS6jPaqoEGLnGJVFJD28nbb8N773kadABVVfDhh/CnP2HCFDByZDbZZJDhd2+rouLChRUr3elOBRU4cGDBos2xb6CBKqrCkrs2m2wUFFJIIZdc8sknm2ziiceMuXNEvwQaVFV9WVXVtaqqrvf+C2G7laqqTlNV9dYw1EHoKKGEvez1W59Cik+0PkALvdryBKylVjsBmnPgYC1rg9YhlljDBqQLl0/eDgsWJjJR972JJPqHeV+40PN0p6WiIljv+xWczGTd/cYQww3cEOATiOa+5EvdXq6LXGQ3u9u8fxcuVrFKt6yCClawIug+7uIuLFh0y4YwhB70YAQjdMszyTRMdN/cxPUOMs/pjD5XVWYuLCeffK7hGt1tezctRt9JCxbGMz5oHYQQQohI8+Zka8mNWwtOFlBlJSxbpl+2di0UFwe8Xk5kIv3oZzgCpxvdSCbZ8MFzI41hySE3lKGGo4lGMYpEEtt8jGDCkXz8fxRF+XfgS7jcTFdVNdgd3nRFUTYCi1RVfSkM9RAtnOSk7vo00nDh0ubYgadxM4tZNNDgk3/L23WsJ5QJsOc5TxJJxJbWMHxFEbknG6jpFsOeW7OwX9ub05xmIAO1989iFjZs7GCH1hjMJJOHedg3aXRdHZw61fJwlx04ALdefl4wkYnU2cq4uHoh+Xsv4rKaOTe5L+PGf5csRXrqQmHDFjCoTCGFbW6MnOAEduwkVroYt6Kcnsds1KaZ2T2jG6eHJPIVXzGb2QH3kUwyL/Iif+SPWmoBBYUBDOBFXgTgPu6jkUYOcEA7B3rQg4d52C9Hop6EA4WYyaSccq33UEEhiSRST9VDTQ2PJD/Cp0Wvkbx8I92K6qnMTaDqtgnc2esxAG7kRmqoYQMbtH2kkcb93N+maLNCCCFER6mhhu41icx45TBDtlThNivsuiWdTd8cTHlMCFMJjhwBl8EoHFWFAweYlHcrZZSxghXUU4+CQjbZPMZjZJCBDRsZZGjDML0SSMCChUIKySSTMsp85ul7A5ud5jRDGNKm30MMMTzDM/yFv2j3SgoKIxjB3dzdpn2HXoe2GwY8AtwEWpeM2vTaSAkwEE8j8B+KoqxWVXV/8zcoivIs8CxAr169wlDNq49PI6iFTDJ5gic4y1kUFKYwhXjiAU9DrJRSMslkEYsMG4eB9t/8PRnHy7nrZwew1l8OXjFuZRVrn3ITN9d3H2bM3Md93MItFFFEIon6icItFoiJMf5DkNDiqUtlJTf/eBmNRVU4AAU31o3nUKZ8Di8OAkUSkQfjjeRkFITE+/1pixRS6P51A0//9BQJ1ZePM3pVJavnZ1M/P7RjXOKSljbAhYt44rFgoZxy8sjDipXHeIxSSjnPeVJJDWmOqCYhAStWcsjBjgMVN7HEeoYvx8SA1Uritj184zcFOF1OXCiYcRL7+XZ4cR9MnoyCwu3czlSmcprTxBFHX/qG1KgUQgghokG3S428cM9Wksu8HQUKvY8Vc+Pn1az4ZDhBbxVb3q+1lJhINdUc4hBxxKE0LS5c7GEP13ItVqykkkoyydRSixs3SSQRQwxWrNoQSG8e5EYaiSFGG9UTyv1sKLLJ5gf8gLOcpYoq8sjr0Ie04bh7uBvop6rqVFVVpzf9C9SgQ1VVu6qqdaqquoClwFCd9yxQVXWMqqpjsrKkJ+VKDGGIYSSeDDIYzGBu4zZmMtPnhjyHHIYylBxy/Ic8NhOozCuLLG5+5QjWejegNPsH094+Sf9L+uOt00hjCEPoQx/9+U+xsXBDgGGTU6f6vv7gAygqwoyJeOK1Pwxs2ABbJdJgKCxYGM5ww3Jvrpi2yCOPB16v8GnQec1YeJE7zwX/zl3kIl/wBQBJJJFGGlas1FHHYhb7vDeTTIYytHUNOoBp0wDPU7i4ZhcMACZO9DwkeOUVcLmwYCGeeGKxeCJ6vfaaz5zPRBIZwhD6018adEIIITqVB//9MMllnmua0uyOLftULXe8fMJ4Q6+hQyHTYL5ZYiKMGcNyllNKKaYW93C72MVhDmv5hk2YSCGFNNK0oZ+jGMUgBmkNt1hitQe94Bme2Ze+V/4L0NGTngxlaIePugnHHcQ+aEqwFCJFUZqP55sEYUpkIXzEEcf93O8X/MSKlQd50DhYxLFjnnHMR48ylrEMYQhu3NQ3LW7c9Kc/U5gStA6l5/aSc8qmeySLaqZ6k8E46lA88QTk6eT9uOceGNRiXtSGDcb7CVQmfMxmtu5k3+lMpw992n6A0lKuP2TFpPONSSGZfhvOBd2F3jxSr1OcChhFK2TXXQfz5qGiYsNGHfWeIZS5ufDkk7B/v2eit566Oti16/Lr0lLPHNBt28DRuvQNQgghRCR13/G17v2kgomc1cFTBGEywfe/D9YWnRAxMfC976FaY7XrelxZPb3Xf03+tiJMDs/D3z3sYRCDmMAEv13nkstt3IYVa8D74a7yQDUcwy+7A0cURdmB75y6QCkNJiuK8p9N79+kquq2MNRD6BjBCPLIYxvbqKSS7nRnPOP1w6qWlsKvf+1p1DUxDxjA0B/fzL7MfVpScG+IeaNgFM05G2pQULBgwd20KChaYBZ3g2Gqw+AyMuCll2DdOs8cusRETw/K4MH+77Ub58CjocG4TPhIJZXv8312s5tCCoknnlGMCt9TLrudeOLoSU8qqMCOHRMmUkkjkYSQ/l8Fy2vX2rx3Ro49NpHV43bRe+1JrHUuioakkjR9CnPj0zAfDfB9A8/ncLthwQJYvvxywJ+UFPjud2G8BEoRQggR/RSnixhimrIQe2Zhee/xFHuI19thw+D11+HLLz3B7rKzYeZMyMlBxY1TdTDurf2M+v/s3Xd8VFXaB/DfmZqekAaEEHroHQQpgihFBTsWFl3FXdbFuta1l9fVXbuiLGtZxcWGYkEFRQVEAQWkiBAgJJRAei+TTDIz5/3jyZ2ZO3MnM0mG1OfrJx/I3Jk7Z2RuOec853m+yoGoX+hVHXkA390yGLUTaS3c5bgcIzESe7AHVliRilSMxmjnveoIjEASkrAd21GMYiQiEWfgDMQ0bl6qTQtGp+6Rxr5ASrkWwNogvDcLQAIS/CaXAODVoQOAmvTfIZ76FYbnxqmmkddhHRKQ0GA4HgAk9BqH49GhMJVVQ1f/n7uYUdMD/hyaQkOplsl55zX8vJEjgd27tbeNGtW8NnQyZphxZv1/Qde9O5CYCEN+vnapiQD+rQZgADZhk+a2WMQGJa1wKUrxFt5C3WATMgYPctuyGxFIwKyhE3yv+RQCGDEC+PRTYK3HabC8HHj6aQrd7N68mjmMMcbYadevH0RaGvQQ8AoAHKmdaVpTfDywYIHXwzroMGFNPsZ8oS6jFVphx/lP78eppfOgVDIaUP+fz7dAPM7H+YG3qZ0JxnzjCQC/uJUy2A7geBD2y1rS4cNeHToAqEQFuqaXodsh71Cyn/CT390ajKGwLbjS9YBbxtmy8QPRY/A5TWpuo111FSVX8ZSUBJx7Lv3doV16gbUgnQ5YuFB727BhwGj/6/ZSkeqznMBszA5KmMV2bPeZHnkrtsIRHQlc6CNY4bzz6OL15Zeux9xLc9TVAV9rlG7g7ydjjLHWpHUduvNOQK/3fjw0FLjjjsD24cewL7SznRtsEnHrtjd6fx1VMGbqPgJUhb7s9Y+ND8K+WUvJ1k5V70y1nlON3IHqpCaFKAxo1wPPuw0Z+UWo+3YdwvOrYA03wjpmCAb+7d/Na3NjDB4MPPEE1bbbu5cSrUydClx9NfD558A33wAlJUDPnsDFFwMzZ7Zc25ja9OlASAiwahWQkQFERgLnnEMjeAFkKRUQWIRFWI/12IEdsMCCZCRjBmZgOIYHpYkNffcrUYka1CDsuutcnbfsbAonmTsXuOgiWjtXVARUVwPFxfSnEPRZY2OBnPoRyaIiYOVK4Kef6DUjRtD/B881o4wxxtjpsnEj8MknwPHjQEwMhUZecQUNlk+aRNElzzxDpaaEoGUwDz8M9OpFr6+spGvZpk10vRs4ELjySmDsWL9vbbfXIiK/ChIG2GGHA7J+TlAHPfTQ5ead1o/engSjU2eQUjqDZqWUtUIIU0MvYG1Qjx6aDxtgRB3qUJLknXJWMzxOy9q16Ld6D4DucIRI6OwC2FEFPP8yHfQtZfBg4PHHaVZE6Rw8/TTdMCuysoClS4GKCkq4wlrHxIn04/5v1QgmmDC3/j8J6TspUBM1FMIZgQhXeuS5c+nH4aBZSGcDTbQo/IhbZjApKfyyuhpISKCL4N//DuS5XbD27gUOHACefJIuiowxxtjp9OWXtP5bUVoKfPghcOwY8MAD9Ni0afTjea0DKPrkwQeBTLfyWAcP0v3Y/ff7XUOu15tQnRiF0Pxy7WLmvFTBKRjhlwVCCGeckRDiIiDAKRzWdgwYoHmTGIEI5KVGIy/VO7HKFEzxv9+6Opodq6fKarhzJ5CW1qTmNovSScjMVHfo3H30UcPJVVjLCEL9wGB36ADgDJzhM1HQJEzyDvH0vMj5egygdXjh4bRgPE9jBLKuDvjgg0a2mDHGGGuk2lrVPZzKL78A6enqx7Sua1u3qjt0CimB994LqBn2edrr4BwGPbrPWRTQPjqDYMzU3QjgXSHEK/W/nwQVI2ftzd//DvzrXzSCUi9k4Ajg3nNhxmZY65ObGmHELMzC8KIk4Jv3aHYrMZFCFpOT1fs8ftx3ancA2LMn8FCygweBDRtoBmPwYArJ81e0siF79/reVlVFJ6thXiUUWWdRV0ed/p07KcRkyhQKFRECMYjB9bge71e+jgpLHuBwQISEYkL4DJxrPNf/vm02wGIBoqPVx4deT8fS8eMNZ/ps6LvLGGOMNUZmJvDttzQL178/3c9FRdHjFRV0zSovp06eXk/XLpOJ7uEG+E5MAqDh69XRo3QNjNauWawYdOHd2F+Qg6ivfoSw05q82qgQ6G65HXE9grOsoiNodqdOSpkBYKIQIgKAkFJWNL9ZrFXExVE4Yno6rQFKSgIGDMA4AMMxHYdwCBISAzAAYQeOAY8toVAxxeef06LYs9zq14WENPyeoaENb1e89556duKnn4DPPqMwtK5dA/2EasFqG+t4qquBhx5SJw/asIHWYd55J6DTIfXtrXjws0NITwWqQ4HeR8vRJWQj8ORM34VUFTodXRATEoAuXej9dDoapBDC/3fP33eXMcYYC8QXXwBvvOFK2LVlC91f/eMfdK2pqaF7QvcEJ2VlNAAZyH1SQ9crIeha6IcQOgz704uouPQ48n/fCH1IGFJGXQCjKdz/+3ciTQ6/FEIsFEI4Xy+lrHTv0Akh+gkhAojPY23OgAEUG+02+mKGGSMwAiMxEmEyFHjpJXWHDqADfulSmoFQJCcDfftqv48QNPvhT2amdrhZQQHw+usBfCAfJk2itPNaevQA+vVr+r5Z+/bxx5rZYPHjj/Rz8CDwySfQO4BBB4HRu4EupQByc4G33vK/f53O9d03GChBSni4K9z0rLPoGPTFfeCEMcYYa4r8fHWHTlFWBrz6KtC7N0UuaWWsLCgAhg71/x4NXa/Gj2/UAHpkbC/0O+s69D7jCu7QaWjOTF0cgN1CiF8B/AqgAEAIgP4ApoHW1f292S1sg+ywIwMZqEUt+qAPwtEOvlgZGXTw9uzpHSIZCKuVCnzb7ZTgISfH9/N++olmIGpr6YC/6SZKiFJVpX7uokX+ZzQA4IcffG/bsYP2G96Ef4PoaGDxYip46X5CCwmhAtDMt9JS6tiEhVGIqq/1Yc15C5QiC1mIQAR6o/dpWRuH7GwKdYyPV4eQbNrk+zWbNjU8O7x1K333/Y0+/vGPtKY0N1f9+MyZwJgx9J2cOpU6ke569vSu5dPc45sxxljns3mz6/6nutp1j2c00vVp9266vyor8+7YxcUB+/cDffrQ7xYLsG8fDVQOH+66Bg4aRFmfP/uMZv3sdrrP6toV+NOfVLvMQx7ykY9YxKIHtBP4Md+a3KmTUr5Uv45uBoDJAEYAqAaQBuAaKeWJ4DSxbUlDGj7CRyhHOQDAAAOmYzrmYE4rt8yHggJaJ+c+6zBuHIVJRkQEto+NG2lGrLKSfrfZ6O8xMd7PrawEXnjBVRPObAbmz6d0t998Q7NuXboAs2b5j8NWuM/8eZKSOpJN6dQBwJw5QGoqJaUoLKT0u3PmUKeUeXM4gP/+l4pmK4W1ExKAv/0taOsPbbBhNVZjJ3ZC1hc2TEACFmJh8E7y1dXAiy8CP//suqD17Qvcey9l0mroO2exNLzdbg+sUxcbS234/ntacxAWRrNzSopnIYC77qJRzs2b6Xs+ZgwwY4YrnCUYxzdjjLHOyWKha0tuLq0jV0RG0rW9pISuNykp1LFT1tRFRdHjSsTWp59SQhVlLXhUFA2aK7N0kybRPWB+Pt1H2O3AqFEUwgmgClV4D+/hEA45m9AbvbEQCxEDjXtNpqlZa+qklHYA39b/dHgFKMAKrHDWbgPoBvQ7fIdoRONMnNmKrdMgJaWMPe5RC37nTrqZfPBB//tIS6Pnus9kCUH1s4xGdWdKOTH07Kl+bOVKBeM+9QAAIABJREFUmgnxnF0I1PDhdDLQ0qMH3Rw3R9++wI03Nm8fncUnnwBr1qgfKyig79ny5c3/twDwFb7CDuxQvwUK8Dpex324D2aYm/0eeOUVYNs29WOZmcCjj9LM7bBhlNlLy4gRdCHyNZvXp0/gHaqwMGDePPrRIgSle9ZK+RyM45sxxljnNXAgRazY7erHKyro/m7CBLqeVVbSzJynYcNoDZ7nsoPycuC552iQNDEReOwx6vC5R7l8/z1tu/pqrw4dABzDMazACtyG24L0YTu+4MdMdWDbsE3VoXO3GZtbuDUB2LvX+4ZPsWOHd9iXli+/9I611utplq60VP14WRkd/GaNm+7PPw+szVomTfK9Lu/qq5u+X9Y4DgctqNZSU0OZs5rJCiu2Y7vmtkpUYhd2Nfs9UFTku5RFTg6wfTsVRTVqlCyIiQHOP59GH90HLxRCtNx3MhjHN2OMsc6rokL7Wge41nhffrn29jFjKBO550CvQkq6h/zuO9/RLV99hTx7tleHTpGFLBzF0QY+AHPHnbpGyEe+z20FKHCGirUZJ0/63iYlcOpU0/cRF0cjLvVT54iKoul5X2uN0tKAu+8GLruMYqhXrXKF7/ljMNCsg5LqPSODRoGuuIJmAB9+mPZ7zTU0WtRQaBxrOouFQjF8ycqiP4uLaYH1ggUUevvUU1SkNABlKHOWztDS0DEYsJwc74EKd6dOUUrnJ56gWWKA1gxOmgT8858UPmw2U3bM2Fj6bBkZNJJ59dVUNF2xfj2tz7z0UlpbunZt89uvCMbxzRhjrPM6dYpm04xGCq20WuneLCqKZury8+n6lZoKHDlCa+gOHqTB/bvuon00dC06ebLha1F5OYorfAxO1suDRr1WpikYdeo6jThoTD3Xi0Xs6Unk0Bz+Uv0HUgogMZHqiGgZPpzW89TU0E3u0qU0IuOpvJw6A4fqR2Ly8ykkMyMDuO8+/21wOIBnn6XXpaTQDatOB6xYAbzzjmt9UVkZxXXv20ft8jX6xJomLIxO9OXl2tu7daNRv3vvVRfN3raNFls/8wytWWxAFKJggMHnjHhDx2DAEhNpBNJXx04ZqBg8mFI619bS9809U2pdHX2e4mL6TMp38sMPaVH4qFHeZTiysihENTeXkgQ1VzCOb8YYY51X1650va6rc90zCUHX+fBwGsB/6inXgKSyVvzQIUr2tXo17aPCRzWzbt1c11Qt4eGICk9qsImxaP6yjs6i2TN1QgizEGKBEOJ+IcTDyk8wGtfWTMRE6Hz8L5uESS3cmgCMHUsHlJaRIwPLknfBBf63hYTQSeC881zT9QopKdxNq7Dktm3aaeM9bd8OHDhAfxfClWmxqIg6ep6OHKEMhCy4dDpg9mztbUYjJb9Zt07doVPU1NDsrB8hCMFYjG30tkZJTKQ0ylri4tQzbQBdxDxLX2zdSvUcAfV30m6nAYvycrrYaVmzhr67zRWM45sxxljnlZDgSm4ihPoezuGga5v7tdv9OWlptLb8/PO19y0EbTv3XN+Jw2bNQg9jL/RGb83NiUjEAASYVI8FJfzycwAXAbABqHL76XC6ozuuwlWqRA0CAmfiTJyFNlg3Sqej0MQkj1GQgQOpgHIgRo2icEn3WS+9HrjqKu/aIwMGADffrC40WVtL6+y0OnUA8PXXlFnz5Zcpy2Ztrfdzdmmso3I46ERUW6sdxqn1GtZ8V18NTJ+uPvFHRNDsXGIizcj5EuC/yYW4EEMwRPVYJCKxCIsQiiAVhL/1Vu/6OomJdLwo33W7nTpvS5fSDNu+fa7nNvQ5Dx+mZCXumcTcORzAb781r/1AcI5vxhhjndeBAxR+qdPRvVRdHf0ZGkozdZ99RiGZvqxbR522yy9XlzYym4G//IWus/HxFJUVGal+7dSpwMKFAICFWIieUK9TT0QiFmFR24uCa8OCEX6ZLKVso/n8g28MxmAohiINaahFLfqjf9ueGk5OBpYto6QKBQWU3GHw4Mbt48ILgbPPphtVh4NmCLTKGQBUY2vyZErUUFtLo0AP+5i4LSwEPvqI1igBFLr56ae0likqyvU8f6nhPWcHA3kNaxqDgdLlX3klxdZHRND3QUmO09D/9wDDYc0wYxEWIRvZzjp1gzAIeuiD8AHqRUVRSMnhw7QmLi4OGD3adVGyWikT5v79rtesXUsXr1tuafizCOG/mGqwQoODcXwzxhjrnEwmuldzOFyzcEK4Bsz9lYtSrv3XXkvRW7t30/Vt7Fh1FuixYynnwc6dtP58yBBVNEkMYnAbbsNRHHXWqeuP/tyha6RgdOq2CiGGSyn3+X9qx2CGGaMwqrWbETidjm5YmyMykjp2gVDqbSlSUoATHmULq6spe6bnGqtjx4D//Y+SSiimTPHOrqTT0clGSpo59DR1amBtZU3Towf9eJoyxfcslufMrh9J9f+dVqmp9ONp9Wp1h07x3Xd0cZo61XeZjTPOoOf4Wn8YGuqqRRcMwTi+GWOMdT5Dh9KAIKC+l7LbaXDzkksoR4GvtfT1M20AaHD03HN9v5fJRAnHGtCn/j/WNE0OvxRC7BNC/AZgCoBdQohDQojf3B5nrcFqBfbsoSl1h8P1+M6dlLQhLc3/PnJyKFQuJyc4bVqyRB2SCdCi2thY7RmLTZvUbR80SHttX+/eQL9+3o+fey7VEmMt7+yzKc2xp+Rkylba0jIzqZPpWX7Dnw0b6E+Hg7J+Vle7Eqts3Ejfr5kz6feyMkqYUldHs87XX08XryVLvAcchKCaiP5m8hhjjLFgsdmA33+nZQTuy1wOHXLVl7Xb6XnKWjqjkTp8f/+79uD5RRepB0WtVooa2b/fu+4daxHNmambG7RWsOD4/HPg/fddKf3j44GLL6Y1Qe4pZQcOBN5807tQdEUFFS3euZNuYIWgGYXbb1eHQzbWkCHUhrVrKeNlbCy1R0k04UlJqeseyveXv1BnYcMGmrofPJgSs+h0FNO9fz/NEJ59tneiC9ZylPITP/5IBUmtVmDcOOoAhYW1XDuysqjwaWamq10zZwKLF2tfnDxVVVFHsLjYNcCg19O6OyXL18CBFE5SUUHHS0EBdV6V5CWTJgEvvEDfz1On6PHzztMeiGCMMcZOh82bKXdBWRn9HhUFXHcdDYBXVVEIZW2tqyMmBF3vTCbafvHFVCv4lVcomiomhmboLrzQ9R5r11KSsMpK+j0uju7b+H6sRQnZUL2mQHYgxP+klNf4e6w5xo0bJ3fu3Bms3XVMmzdT2n9Phw6ps/MpUlNpAay7hx6iURZPI0bQOrdgWrMGeOMN7W39+wPPPx/c9+vkxo0bh05zDFmtNBumlWHykktoJs2fv/4V+OEH7W2LF9N+LrxQOxnKxRcDTz7ZuDazNq85x5AQHwX0PCnnN2n/jLUXnepa1BakpdFMm+e9vhDAY4/RoOT116ujoxRdugA//+x/Dfgvv1D5H096PQ2u9u3b9PYzL0KIX6WU47S2BSP7pSqFnBBCDwQj7zhrFM8OGuAKCdOaBj98mKbiFZmZ2h06gDL1KTMewXLOOdq1S4SgJByMNdXmzb5LBqxb50rf3BBfoSM6HY1oLlvmO7vlN99oZ2RljDHGWtKaNdo1WaWkbb/+6vu1VVWu2b2GaN1/AnQd/fLLwNrJgqLJ4ZdCiPsA3A8gVAihrKAUAGoBvBaEtrHGyMqim82SElf4pcNBB67DQduUkEq9nm5OX3yRZjV0OgoNs9m863EpTpwI7mhLeDhlH3zjDRrlcTgoacqCBcCECcF7H9b5ZGXRd720lBZ32+20plPJsqpkidyxg7KtHjtGocqzZ1NNHaXwalISdQ6VdM5hYfS8wkJ6jZS0b6UDqNPRsVVdTe/RvXtrfHrGGGMdhZQ0ULhuHV1XevWiaJBA75M8k9S5O36cQi/1etf9okK5T0xL8594zt97sBbT5E6dlPIpAE8JIZ6SUt4XxDaxpggLo4PPfQpdmS1QUtUCrhtRIWj2TUk5m51NRaOTk7U7dlqzas2VkEC1S5R6c81Zt8eYIiEByM2lUUaFxUI/PXrQeoD162l9gKKyEvjPf4CjR6nWYkICPT8szHW8KCHMyra6OvXxZrfT76GhtJ6AMcYYa47//IfWqyn276efv/6V1mj7k5BAA51aEhNpgL2uzns2T0ma0ieATJQJCa615lrvwVpMMMIvPxJCjPH46SeECEa5BBYos9k7JtpzHZ0n98QVoaH0fK2p9pQUSnZyuoSEcIeOBU9iomu22pPDQcfK//6nvX39euDkSfXFUhmxVJx3HpUt8BXSEhPDdRIZY4w1T04OzdBpWblSncXSl4Y6fnPmUA4DX7k1pKSIlea+B2sxweh4LQMwBsBvoPDL4QD2AogTQtwopVwfhPdgng4fpmxGhYWUCbKoyLsulpQ06+a5PkgIuuksLXXNQoSHUwimZy2SpCSaTWOsMdLTgZ9+ohHAsWMpc6lWkXhfamvp9YcPA9HRwIwZQNeu6uf8/juF7kpJGbaGDaPH09Io9DE3Vz3QERpKAwjbtjW8TmDHDgpvOXGCLqjKBc9goIxhw4YB339Pncf8fPUFMSSE2qvM8jHGGGNN8euvvjtcFRWUCG/48Ib3MWECcPXVwNtvuzI1R0bSUpdp04Bbb6VEKJ5rxIWga9727f4zWM6aRWGWX33laq9eD1xzDTByZEAflQVHMDp1xwDcIKXcDwBCiCEA7gbwfwA+AeCzUyeEuAPApVLKKUFoR+exfDmVCFAOnl9+oQOyVy+aJbBY6IB0OFydPSVlbUgIHdgWC3UIlRmI0lI60KdMoRNAdjbdGI8d63/GjzF3y5erw0W+/JJO7A89FNgMVmEh8MAD6jqJH3xAYZEzZ9L3+rnnqGyCYs0aivu/8066EIWFUR3DqioauDCbXbXhzOaG399odNWTu+giurAajXRxjImh5+j1FHLSpQsdY3Y7HT+RkdrZZhljjLHG8JXjQBFIeR6Hg66ler0ri6VeT4/Z7fQevqJOpPR/vQTomrd4MWWE3rnT+3rJWkww7jwGKR06AJBSHgAwWkrZYLpEIYQZAHfhG+vUKVoLpHUQZmXRTXNMDM0WKCGNkZGU4CEpiWrE6XTqdXYKpSD4uHF0cI4fzzenrHF+/lndoVPs3Qt88klg+/j3v70L30tJ3/uCAuDbb9UdOsWPP1L45KRJ9LtOR9/9mBhXh65/fwqd9BXnL4R6VLJ7d2DuXEqi4n6BmjyZ/jQYaAYxKYneC6CBkJCQwD4rY4wxpmXCBN8dt/h4YNAg//v4/ntg0ya6VkVH04/RSBEr69bRtcxXtma7HRg9OvD2duumfb1kLSYYd+yHhBD/FkJMq/9ZBuBwfafNR85vAMCfAKwIwvt3Lv/9r3Y9EYOBDsyqKko8omS1HD3adbOpkNJ1k+twuLIemc2+F7syFojvv2/aNkVZGY30AXRBqa52rRuQki5ODe1nwwbK0qoURa2tpX0oGTD//Gc6Lv76V+3aO3/4A10s/Rk1CjjrLO/HlaKujDHGWHN06UIhjJ70eirs7T7obrEABw7QmnB3GzbQn1LSvWFNjWtSYMMGYNcu34P3QtASCNZuBCP88joASwDcDlpT9xOAu0AdurO1XiCEMAKYJqV8VQjxuI/nLAawGABSUlKC0MwOoqRE+3GlVIHFQh06ISjByR130OzdunW0/iclBdiyhdYb5eW5RmiMRlfoJmNN5bkmM9BtiooKV9hwWZnr4hMSQrNr5eWBvccFF9D3fN8+2l94OIVuDhxI28eOpQL3X3xB5Qni4mhBd6CjkkLQsTV+PLBxIw2mDBkCzJsXWKeQMcYY8+fSS4F+/YCvv6ZIlZQUus4oWSkdDkr89eWXrvI7gwYBt91G2Z7Lyym7c2Gh635Pr6frVHk5dfLMZnVNY2U9nV5P75ma2vKfmzVJszt1UspqAM/V/3iq9PGyawC852e/r6G+3t24ceN8rBTthCZMoIPbk1Ivq0sXVxy23U614F56CbjnHtdzjx+nm12dTr3GKS8POFuzH85YYAYNokQlvrb5060bzayVlqofr6mhdZ79+9OFKDtb+/UDB9Ls3EMPUcfQPXPXhg3UeVNGPnv1onV6TaXT0ULzadOavg/GGGOsISNH+k448uGHwOrV6scOHgQefJDWt8fH0yC+O7ud7vfGj6fByE2baGDfM3rFZOJEJ+1Ms8MvhRCThRDfCiEOCyEylR8/LxsI4K9CiK8BDBVC3NLcdnQa8+drrwdSEjV4LqytraWMRO58ZVNSkqsw1lRz57pqH7oTgr67/ijZWLXodPR9vuQS7YQrJhNlrdyyhWaltXz1lWs0kzHGGGuvamsp2kRLURHwww/a+RMA1/3ewoW+66rOnq19PWdtVjDW1L0J4HkAUwCMd/vxSUp5r5RytpRyDoD9UsqlQWhH56DTAe+9R9PqtbV0g2q304GXnKz9mqNH1b+XlNAMhvuNcUgIPVZUdPrazjq+hATgiSeAoUNdj/XsSdkslZIDAK2Lu+ceYNEi4LHHKJEKQKEeoaE0uui+QDwigr6fx49TVsvHHqNZO0X//sCjj1JIiuf33Z3F4rvDxxhjjLUXRUUUWllTQ7Nxx45RKZ7SUhq8P3oUKC6ma6d7FkuTiZKAlZTQRMD771NpLKXzZzbT4Om//tUqH4s1XTDW1JVJKX1UR/SPyxk0waef0o1vaqqrTEFWFt2wao2qeI7CxMXRySAlxRVjrczw+RqxYSxQfftS2G9pKcXpJySot7/+unp0sbCQFmvffjtlXtXrXRlcbTYayFA6eMr3c+hQWhNXVEQXL/d1bA19h3U62i9jjDHWnkVF0cC+Z3KUwkJaxhAfTxnNQ0NpcNXX/V5yMoVwFhRQWGb//pzBuZ0KRqduoxDiGVBNOmdck5RyVxD2zTwdPw588w393WBwHZxRUXQgh4d7T7XPmaP+ffZsV0Yjz3DN2bOD32bWOWmlNM7OpgXdnqQE3nqLas1NmkTlCYRQx/ibTN5rPrU6cNOn08JxJWumu8mTXaU+GGMsCIT4KKDnSRlACDpjgQoP973NYqE16nPmuCJh/N3vJSR4D8KydiUYnboJ9X+Oc3tMApgRhH0zT9u30592O2UKtNloqjw6mkZsrFbXCIsQwLXXqsPeAODcc4GMDHU9MSGAyy4DzjyzZT4H65x27KAOnPv312SiGebSUhpsuPFGCpE8dMj1upAQ4K67Aqt9Ex0N3Hsv8MwzFJaiSE2lfTPGGGPtRUUFrY8rKKDlB5Mn03WzoIA6aiEh6mudEFQ/9eBB4IYbKJTys89c+RSEAM4/HzjnnFb5OOz0CUb2S06X2JJ0OkqfnpurTnhiNFLc9H330YFuNNKMh1Z6dSHo5nbuXOCXXyi0beJEGtVh7HTS6WgEMSdH/f1V4v6VguHPPAPs2QOkp1MnbfLkhkclPY0fD7z9NvDTT1QaYcAAyuLlKwkLY4wx1tbs3Qv84x/qTtvKlcDjj9OAvl5P4ZMWCz1Hr6dBUr3eVX/u+utpxu6XXyg5yhln+M7BwNq1ZnfqhBBdATwJIElKeZ4QYgiAM6WUbza7dczbiBHeHTqA1i5VVlL4WqA3rsnJfGCzljVypPb312ajzpd7PZxRo+inqcLCgFmzmv56xhhjrLVYrZSsxL1DB9DA/XPP0bry/v2BI0foehcWpn6ee+RV9+6UHZp1aMHIfvk2gG8AKAWhDoMKkbPTISPDd6IHk8mVAenUqRZtFmMBOXLE9/c3NLTtfW8dDlrH2tbaxRhjrGP7+WcarNdy5Ajd6/3pT+rMlopzzgmsNqy7wkIgM9O7E8najWCsqYuXUq4SQtwHAFJKmxDCHoT9Mi0lJRRSGRJCMxt2Ox3QMTE0qnP77ZT1CKD07jfeSKlqGWsLSkoouYnZTEXElTV1MTGu73TPnq3dSrJ5M7BiBY2KAnQ8/eUvVKyVMcYYO51KS/1vHzWKZuw+/5zWoUdFUYfOM6lYQwoLgVdeAXbvpiia0FBg3jxgwQJXCCdrF4LRqasSQsSBkqNACDERQFkQ9su0KOFpERHq8gVVVXRgRkS4DsKjR4FHHgGWLqVFs4y1Nl/fX4DWgaaktHybtOzeTeEt7mGiyvH00ku0/o8xxhg7XQYM8L3NYKCBRoAGQm++uWnvYbcDDz2kjkaprgZWraKlPH/4Q9P2y1pFMLrgdwBYA6CfEGILgHcA3BKE/TIto0dTHTBPJSU0QuM5qlJTA3z1Vcu0jTF/hg/3HRIye3bbKTewerX3uj+AZsP5eGKMMXa6DRlCNVm1zJoVnJqrP//se3nBF1/QNY+1G8HIfrlLCDENwEAAAsAhKWVds1vW3mRmUsrY9HQKJZs5k6a/g51tT6cDHnsMWLaMDkYpaXFsZKTvG+ItW2hdUH4+zYRceKHvEwXr2OrqqM7hxo00GjdiBC2ebsnMpw89BCxfTt9Lh4PCLs87j8pvBKqigi44P/9Mv0+YQN/ryMjgtPHIkaZtY4wxxoLlgQdoGc2vv7pKWM2eDfz5z8HZf0PXM4uFassqM4KszWtyp04IcamPTalCCEgpP2nqvtudPXuA//s/umEGaNRj/37gwIGmT4k3JDqaSheUlVFMdbdu9D55ed7PLS6m5CnKuqBTp+hG+PbbGxdzzdo/u52+p3v2uB47eZLWjj31FNCrV8u0IzISuPtuWp9WUkKhwUptxUBUVAD33KMeXTx2jAqWP/10cGb7unShC5qWQGrlMcYYY8314IPArl30d72eruPr1lHE1k03NX//Xbr43iYEX+/ameaEX85r4Gdu85vWjrz+uqtD5279espWebpER9ONuNmsnbrdZqNOnedNrpTAm29qt5l1XNu2qTt0ispK4H//a/n2REXR97cxHToAWLNGO1wkO5u2BcPMmb63cZkExhhjp9vhw3QfCVAHS6dzRX+9+WZwslROn07JyrSMH99wp4+1OU3u1Ekpr2/gZ1EwG9mmnToFZGX53q6Eh51ul1xCBcTdWSx0QHrWLgEo8+CBAy3TNtY2NPRd3LGDBgHag23bmratMS66iAqeuxMCmD8fGDs2OO/BGGOM+bJqle9tNTWuDl9zREUBd93l3bFLSQnOTCBrUcHIfskaopVs4XQwGID77wcOHqSpeqORZuI++KD128baho7y790Sn0OvB+69l1JE//orHV+TJwM9epz+92aMMcb8XescjuC8z8SJwH//S0sxSkoo6+YZZ3A5g3aIO3XN1aMH/fjKHuQ5e3a6DRrkyi5YWEgjPVoHfmQk19vqbCZMoHVnWsaOpY5LezBxou/Z8QkTgvteAwfSD2OMMdaSLr8ceP997W2+lt00VVQUMLdzrZzqiLgb3lRFRbSGx+EA/vQnGtn3dM45QP/+Ld82RXw8hYt5EgK47jrfcdSsY5o8mbJdegoLA665puXbU1lJgyGNTZl80UVA9+7ej3frRtsYY4yx9m7wYFdCOynpflOZvbvuOu2lNaxTOx3ZLwGg42a/PH6c0rHv30+/d+0KLFxIWffcSxqce27DyRZayh/+QMko1q2jkga9egHz5gEjR7Z2y1hL0+uBhx+mOms//EAlDYYPp/WYLRlWWFkJvPYa8NNPtI4vPBw4/3z6rgYS7hEVBTzzDB1vSlmPiROpNEMw6vYwxhhjbcEzz1D5gl9/pcyXJhMwZw5wC5eDZt6aE281r4FtEkDH69SVlFDNkPJy12N5ecDzz9N6trvvbr22NWTKFPphzGSiTtwll7ReG554Qp2kp6oK+OgjoLYWuOGGwPYRFUV17RpT244xxhhrT/7xDyrjk5rqeiw9HXjjDWDx4tZrF2uTmtypk1JeH8yGtAtff63u0CmkpJvSYK/nYayj+f1331lX160DrrgieAXEGWOMsfYqLQ3Yt0972zffAFddFZy6rKzDCEpmBCHEBQCGAnAWnJJSPh6Mfbcphw/73paeTp07pYYIY8zboUO+t9XWUnjzsGEt1x7GGGOsLWronrOuDsjMBEaNarn2sDav2YlShBDLAVwJ4BYAAsB8AL2au982qaH1OpGR3KFjzB9/a9541JExxhjj6yVrtGBkv5wkpbwWQImU8jEAZwLoGYT9tj3nnut72znntFw7GGuvJk8GQkO1tw0YQAVPGWOMsc5u4kRKJKalb1/6YcxNMDp11fV/WoQQSQDqAPQJwn7bnmHDKIbZ09ChwIIFLd8extqb0FBKKGQ2qx+PiwP+9rfWaRNjjDHW1oSEAPfc4329jI0F7ryzddrE2rRgrKn7UggRA+AZALtAmS/fCMJ+26YFCyiT5I8/AjU1FM88ZgyHXjIWqHHjKHPXxo1AYSGV2TjrLO8LF2OMMdaZjR4NvPkmXS8LCiiaZdo0vl4yTcHo1D0tpbQCWC2E+BKULKUmCPttu1JSqKYWY6xpoqOprhxjjAWJEB8F9Dwp55/mljAWRFFRwEUXtXYrWDsQjPDLbcpfpJRWKWWZ+2MMgMUCFBVRdkzGWGAcDprJq672/1zGGGOss+LrJUMzZuqEEN0A9AAQKoQYDcp8CQBRAMKC0Lb2r6AAeP114JdfqEPXvTtw9dXA9Omt3TLG2ra1a4HVq+kYMhgowcqf/8zZvhhjjDF3X39NtZILCgC9Hpg0ia6XMTGt3TLWwpoTfjkbwHUAkgE87/Z4OYD7/b1YCDEMwGsA7ACOAFgkZQeayqquBu6/H8jLcz2WkwM8/zytv5s2rfXaxlhb9uWXwGuvuX632YAffgBOnKDjR69vvbYxxhhjbcXatcDy5a7f7XbK+XD8OPDiizQoyjqNJodfSilXSCnPBnCdlPJst5+LpJSfBLCLQ1LKSVLKqfW/j2tqW9qkH35Qd+jcrVrVsm1hrL2w2WjEUcvRozTrzRhjjHV2Dofv6+WJE8A2XgnV2QRjTd0WIcSbQoh1ACCEGCKEuMHfi6SUdW6/WgFkBaEtbceBA763ZWUbEi2EAAAgAElEQVQBFRUt1xbG2ovcXKCkxPf2tLSWawtjjDHWVuXlUb4GX/h62ekEo1P3FoBvACTV/34YwO2BvFAIcaEQ4ncAiQCKPLYtFkLsFELsLCgoCEIzW1hkpO9tRiOno2VMS0REw+VBGjquGGOMsc4iPLzh62VERMu1hbUJwejUxUspVwFwAICU0gZaJ+eXlHKNlHIYgFMA5npse01KOU5KOS4hISEIzWxhM2b43jZ1KmAytVxbGGsvYmKoLo8WITjJEGOswxPio4B+WCcXFQWMH6+9TYiG70NZhxSMTl2VECIOVHQcQoiJAMr8vUgI4T5VVQ6gY+Vh7dcPuOYa78dTUoDrr2/59jDWXixZAiQmqh8TAvjrX70fZ4wxxjqrv/wF6NZN/ZgQwOLF3o+zDi8YaXHuALAGQD8hxBYACQAuD+B1c4QQd9T/PR3A+iC0pW2ZP59GUTZupFp1Q4dSanajsbVbxljblZgIvPoqJRtKT6fRyBkzgB49WrtljDHGWNuRkAAsXQps3gwcPkzXy7PPBpKTW7tlrBU0u1MnpdwlhJgGYCCoVt0hjyQovl73OYDPm/v+bV7v3jwzx1hjmc3ArFn0wxhjjDFtZjMwcyb9sE6t2Z06IUQIgCUApoBCMH8UQiyXUtY0d9+MMcYYY4wxxhoWjPDLdwBUAFha//vVAP4HYH4Q9s0YY4wxxhhjrAHB6NQNlFKOdPt9oxBibxD2yxhjjDHGGGPMj2B06nYLISZKKX8GACHEBABbgrBfxhhjrM3itPLtU6D/blJywBFjrP0IRqduAoBrhRAn6n9PAZAmhNgHQEopRwThPRhjjDHGGGOMaQhGp25OEPbBGGOMMcYYY6wJglHS4HgwGsIYY4wxxhhjrPF0rd0AxhhjjDHGGGNNx506xhhjjDHGGGvHuFPHGGOMMcYYY+0Yd+oYY4wxxhhjrB3jTh1jjDHGGGOMtWPcqWOMMcYYY4yxdow7dYwxxhhjjDHWjnGnjjHGGGOMMcbaMe7UMcYYY4wxxlg7xp06xhhjjDHGGGvHuFPHGGOMMcYYY+2YobUbwBhjjDHW1gjxUYfYn5Tzg/q+jLG2iWfqGGOMMcYYY6wd404dY4wxxhhjjLVj3KljjDHGGGOMsXaMO3WMMcYYY4wx1o5xp44xxhhjjDHG2jHu1DHGGGOMMcZYO9YqnTohxAQhxFYhxI9CiBdaow2MMcYYY4wx1hG01kzdcQAzpJRTASQKIYa3UjsYY4wxxhhjrF1rleLjUspct19tAOyt0Q7GGGOMMcYYa+9adU2dEGIEgHgp5QGNbYuFEDuFEDsLCgpaoXWMMcYYY4wx1va1WqdOCBEL4BUAN2htl1K+JqUcJ6Ucl5CQ0LKNY4wxxhhjjLF2orUSpRgArARwt0coJmOMMcYYY4yxRmitmbr5AMYD+JcQYpMQ4sxWagdjjDHGGGOMtWutlSjlfQDvt8Z7M8YYY4wxxlhH0iqdOsYYY4x1bkJ8FNDzpJx/mlvCGGPtH3fqTrPMzEqsXZuLykobxoyJwYwZiTAYAo96ra114KWXDmPDhgKEhelx4419MXNmt9PYYsZYW5KWVo716/NgtdoxYUIcpk6Nh04nnNuLiqz44osc5ORUo2/fCFxwQXdERLStU7uUEj//XIyffiqEXi8wY0YiRo2Kae1mMcbasO3bi7F5cwGEAKZPT8TYsV0a9fqqKhu++ioHGRmV6No1BPPmJSEhwezc7nBIbNlSiG3bimAy6TBzZlcMHRod7I/BWItpW1f+DmbFimNYuvSI8/ePPz6JAQMi8O9/j0FMjMnv67OzLZg6dRNyc2ucj339dS4uvjgJ77478bS0mTHWdrzwwmG8++4J5++rVp3EqFExWLp0NEJD9di6tRB33/0brFaH8zmvv56JV18dg4EDI1ujyV7q6hy466692LKlyPnYe++dwLx53fHww0MghGjg1YyxzsZul7jvvn3YsCHf+dj772dh1qyueOKJYapBLV+OHKnEkiW7UFxc63zsjTeO4p//HI6zzkpATY0dt922B7/+WuLcvnLlCVxxRTLuuWdQcD8QYy2kVevUdWSHDlWoOnSK9PRKvPRSekD7uOGGX1UdOsVnn2Vj1aqsZreRMdZ2bdtWpOrQKfbsKcUbb2SipsaOBx74XdWhA4DS0jo89NDvLdVMv95//4SqQ6f44oscfP01Jz9mjKmtXn1S1aFTrF+fhzVrsgPax8MP/67q0AEU+fTgg7/DYrHhrbeOqTp0ilWrTmLzZq6NzNon7tTVczgkDhwox2+/laKuznWTlJFRiXffPY4dO7xvSjydPGnBu+8ex5YthfjyS9eJx2q1w2KxweGQAIBvvslDba3D127q2+PA1q2FPre//nqm3/Ywxk6/9PQK7N5dAovFprk9P78Gu3aVIC/Pe4AGAGpq7NizpxSHD1eoHv/iC983L19+mYMffihARQW9Z22tAxaLDTYbnVcyM6tw4EC58/mVlTbs2lWCzMzKRn22xigtrcWuXSU4ccLi1VZfGtrGGOuclPOCzWbD0aOVOHasEjabTbWtIYcPV+DwYe1zncVix8aNBap7NF/v776/hs7xjLUVHH4JYOvWQvzznweRnU03XbGxJixa1BtvvHEUP/9cBEl9MfTpE44PP5zoFXNdW+vAZZdtxaZNBc6Om9msQ0yMEWVldaipoRstnU4gLs6E+HgzamrsMJl896ktFgdsNulze1lZXXM+MmOsmQ4dqsBjj+133jyEhelxzTW98Oc/9wVA6zmeeCIN33+fB4cD0OmAs85KwMMPD0FUlBEA8M47x/Df/x5DZSXdLPTtG46HHhqC4cOjGzzGy8rqUFZWB5vNgezsGlRVuW42oqON6NYtBKWltXA4JJYty8AHH5xwnoeGDInCo48OQd++EUH5/2CzOfDss4exZk22c7BqzJgYPPbYUHTvHur3czDGmLvS0locOFCG6mrX4HdRUR3CwnTo0yc8gNc3fF5Rzp8NbQeAAwfK8fjjB3DkCJ3jw8P1+OMfe2PRoj6BfAzGWlynn6nLyKjEXXf95uzQAUBxcS0WL/4VP/1U6OzQAcDRo1U477wfnaPhiquv/hkbNuQ7O3QAUFVlx7FjFlRX252PORwSBQVW6PVw3tT5EhFhQGKi2ef2xi4YZowFT2lpLZYs2aUaDbZY7PjPfzLx/vsUMvnQQ7/j22+pQwcADgewaVMB7r33NwDAZ5+dwssvH3F26ACaYbvllt0oKLBi5EjfiURGjIjGiBHRyMqqVnXoALohycurweDBUXj77WN4++1jzg4dQDcqS5bsCtqo8wsvpOPjj0+qog927SrFTTftht0u/X4Oxhhzl55eoerQKSwWB44cKdd4hdqgQZENDprT+dP3eWnkyGgUF9fi5pt3OTt0AN3XLVuWwctfWJvV6Tt1H36Y5RUKabXaUVVl05wpKyqqxZtvHnX+XlhYg+++y9PYs4SUQF2d9z6khKoD6Mtttw3QfDw8XI8HHhjs9/WMsdNjzZpsnyO9K1ceR2ZmJTZv1g6f3rGjBAcPluOdd45rbq+stOHTT0/hsst6IDbWO6GSTgcsWtQHFosdBoN2wgC9XqCqyubsYHoqLKzF2rXNX89WUVGHzz47pbntxAkLfvihAH/8Yy8Yjd7tDA/X4+qrU5rdBsZYx5Ke7jtM/PffK3xuU0RFGXHllT01t02YEIthw6KxaFFv6DTugKOjjbj88mR8/vkplJdrD3ytXHkcUvq/h2OspXW68MvNmwvw/vsncOyYBUlJIcjKqvZ6jjLyrXXQOhwOPPvsITz++AHU1TmQlBQKq9UOvV59dlBearNJ2Gw0WycEEB9vgsVix1137cXBgxWIjDRg7twkzJ+fjE8/PYUvvshGSUkdhg+PxrXX9sL+/WVYseK4s3PYpYsR7703AcnJYQF/5rVrc/DxxyeRk1ODPn3CsXBhCiZNig/49YwxNffRW095eVbs21cGADh+vBIlJXWw26kzFhNjRJ8+EThwoNxr7Zm79PQKxMWZ8Z//jMWzzx7C9u3FkJLCM2+6qT8mTozDqlVZSE4ORXp6JSorbZCS3iM62ojExBDs3l2KkhLfIUbp6f5vjgCgttaOyy7bho0b81Fb60BMjAm33tofDz44BFlZ1bBaaT1fUVEtamrsMBgo9DwmxogjRyoxY0YiXnppNF5+OR0HD9J7jh4dg7/9LRU9ewZ+Hvvpp0K8994JHD1ahe7dQzB/fjLOO697wK8PxPr1uVi16iROnapGr15hWLAgBWedlRDU92gI123TFuj/F6Yt2N+rYOxv1aosPP30IRw/XoXwcAMuuaQHnnlmBAwGHex2ny9zDrbb7RKrVmV53TMNG0az/7fc0h/r1uXg++/zYbNJ6HTAkCGReOaZEQCAceNi8fzzo/DKK0ec5/MzzojFHXekIjExpMGOZXZ2DSwWO8LDO90tNGvjOtU38oMPTuDZZw87fy8osOLkSQvMZr2qdonZrAcAr1TbDocDVqvE8eMWGI3UiTt0qAJWq4TJ5FDVn9OaiZMSKCioRXl5HcxmHYQQyM+34qWX0vHii4dV77lhQz7WrDmFkyerYTDooNdLCCFgtTqwZMlu7NkzE2Fh/v/5Xn45XTUjUFBgxfbtxbj//kG49NJkv69njHlLTAzxuS0iwoCePUNx6FA5Kivdw6+B4uI61NSUIzk5DF26GH12upT99+kTjldfHYOysjpYrXbV+yYmmpGZWYWqKhvcT1VlZXU4edKCPn3CYTbrvLJjKrp29f0Z3KWmrlMNfhUVWfHII/tx8mQ1Hn98KCoq6nDypGu7zWZHbq4d1dV2Zwj5GWfEYuXKCSgstEKnE5ozkA35+OOT+Oc/Dzp/Lyiw4rffypCZWYWbburfqH358tprmXjtNVcCqoICK3buLMGdd6byjCJjQbR0aTruu2+fc/DbYrHjtdcysX17MbZsmdHga5VznWfJgw0b8rF5cwFeeGEUzjwzDldeuQ3ffpvnfI2UwIEDFTjjjO/x+++zAQBTpsRjypR45OfXwGTSqUpNNXR+jIoyIDRU35SPzthp1WnCLy0WG5Yty/B6vEsXE4qKrKp1cuHhBphMOug9jlmbjU4M7p03nU5Ap/MOs2xoZr62Vqo6jBaLDQcPVqCqSj08deJENWpqHBCC3kd5yalT1XjmmUP+PjJyc2uwcqV2iNcrrxyB1drAcBhjzKcLL0yCXq8d+njhhUmIjjapOnTuKGwSuOSSHprbdTrvbcrsm7uoKIMz+yUAVceupKQOKSmhmDOnm+Z7mEw6zJ3rf5brP/85ohnNAABvv30MUVEGVbZgdxUVNvTtq05qEB9vbnSHrqbGjlde8S4PAwDvvHMc+fnaWUUbo7i4Fv/971HNbf/+dwZnvWMsiJ566qDmPdKePaX49NOTaKh0pRDArl0lmiUPbDaJl19OR3FxLT77LFv1GmWfaWnlWLtWnfkyMTHEq3bwRRclaYZn0rYeAdXKY6yldZpO3a5dpbBY7LDZHDh50oIjRypx4gTNuMXHm2GxqG/A/vznvkhIMMNul6iro0yUUsr6xbcSNhs97nBImM30v7GuzgGr1YHaWkeDnTopgaKiGuTl1aCw0Opcm+OeMEFKCavVDiml5r4Cqe+0dWuhM0mDp/JyG/buLfO7D8aYt5SUMDz22FDU1Tlw7FgVMjIqkZ9fg4kTY3HTTf3wwguHfd6YCEFFxf/0p76YNi0elZU25OXVoKDACofDgQceGIz+/V2ZKY8ercJrr2Xi5ZfT68Mw6YTwwQdZzrVqDofrRwgBvV5g9epTuOOOVIwaFY3c3GocOVKJY8eq4HBIPPnkMFUn8eDBcixbdgSvvHIEv/1W6nz87be1B4UAOt+9/HI6oqKMCAlRj4Dp9QI9e4Zi507vOlCNtXt3qerc6M5ul9i61X+5GX+2bSvymW3YYrEH5XMwxug+R7nnUd9f0fYPP8zye//044+0Xrm62o7jx+n8m5tbAykl0tMr8a9/pTWYt0CrhrCn3r3D8cgjQ70SrkydGo+//rWf39cz1ho6TfilTgeUldXi6FGLaq1ccXEtUlJC8eijQ6DX61BX58DkyfHQ6wWOHKnA/v0VsFhsMBp1KC+vg9WqLjVgt7tm3RoaXfKUk1MNff1UYEOlC3x/Hv9v5u85vkahGGP+rVuXgyNHKkDlkyRqauxYty4X998/CHq9cI4Ou9+gKOcIo1EHm82B8nIbDAYBg0HUH69ClY57+fIMvPGGawbpnXeOY8KEWLzwwijo9cLZkXNnt0s4HBJ6vUB+fg2++y4fRUW19R0+Chn/9ts8TJ+eCAB46qk0rF7tSnby9tvHMGtWVzzxxDCfs5EKo1EHg4HSjFdV2Zxr6qKiDBBCBGU0218bgvEe/s6F/trAGAuMck6kwW/l5Chhs9H5xN+xpqwdzs6uVtX+LC+vQ35+DQYOjHQuofEl0OP5ggu6Y8qUeHz/fR4qK20YO7aLV0krxtqSTnNbP2ZMDE6csHglP5FSIiurGueem4grr+yJhQt7oU+fcDz66H7k59ciIcGMXr3CkZQUitBQvWpESWG3S2dYptmsg8mk89vB07vFdup0VOvJPUZbCIGQED2EEJr7uvRS7dAtd2edleAzO15srAmjRvlO6csY8+377/PqO1uivlOmg04nkJtbgz/+cQfuu2+QarDHPfxHCOCBB4Zg+fJM7N5dipAQPeLizOjSxQSdTmDp0iPYv78MO3YUqzp0il9+Kcabbx7F1VeneGXuVdTVOXDFFclYuHA7CgpoHZvBIJw3My+9lI6dO4uxfn2uqkOnWL8+Dx9/fBK33OJ7vZrJpMPtt6ciJYWSnYSHGxAXZ0Z0tNF53poxI7Ex/1s1jRoVgy5dtEvAmEw6TJ3a/KRPkyfHOyMuPEVFGTBuHJeQYSwYwsMNMBiEZiK6ujqH37X+QgA9e4apOnQKu10iJ6cG99032Oe9DwDcc8/AgNsbHW3EpZcm49pre3OHjrV5HaZTl5FRiTVrslUZ5aSUOHKkEunpFfjii2yvxCcKnU7gww9PIjOzEocPVyAzsxK//eYdmqjXe8/GKeclz8d9hT16vk55/7Awvdcat/79IxAe7j3ilJoaidtuG4D8/BocOFDuVadKERtrwpIl3mECOh1w990DVWsDGQu2zMxKHDpUEVD5jvbGPXzH4aCZMeUm5ZdfihEVZcQllyRpvnb27G7o2zcca9ZkO19fVlarOo7XrMl2btfyxRfZWLs212vdr0KvF/jkk5PYu5dCKaWUznbS78CLL6bj8899v8fnn2fjyitTMGRIlOb2u+5KhRAC99wzULMm1DXX9HJ2+JrDZNLhrrsGas6mLVnSD126NG6NnpaoKCNuvdW7hIxOB9x550C/I/+MscDYbBLx8drHbESEATU1Da/1l5LOTcoAlZSuHwD1mXituOEG7QLhEyfG4qyzmj/YxFhb1O7DL3Nza3DZZVuxd2+pc1p+/PhY3H33QLz2WqZzkb/SYTKZdM7ZNiHgHM159dUMrFhB60dCQijU0rNAuJQCZrOAyaSH1Urr5kwmgcpKu1enTqfz7tgpYQdC0OgPlUIQiIkxISrKgOHDoxEebkBJSS2GD4/GggUpqKy04aGH9mPXrhKEhuoxb1533Hprf9x5515s3VoEKYHQUD0uu6wHbrllgFdYwbXX9saAAZH1JQ2q0a9fBK66qiePOLHTZvfuEvzznweRkVEFgLI03nxzf5x/fnDTz7em4uJaOBwSdXVSNeJM5xMdiotr8dFHk/Cvfx3E0qVHUFJSi6goA268sR8eeWQoamsdqKy0ISvLgqKiWuc+DAYdevUKQ0lJnc/BGuX98/JqYDbrnWtSFEYjzRympVVASooCcN8uhIDRKFBYaG2ww11SUgsA2LdvNm688Vd8/PFJVFfb0bWrGY8/PhQLF/YGAEycGIcVK8bj/fezcPBgORISzLj44h44++zg3TjNnt0NSUmh+OADKmmQlBSK+fOTMWFCXNDe48ore6Jv33B89NFJnDxpQe/e4bjqqp4NFilmrCNpidIRdrtEbKwZRqMOOTk1qKmh+6C4OBO6dQtFcXGt330UFlqh1wtnlBRA91UU9i5w6lQ1li0biyFDovH00wdRVFSLiAgDrrkmBc8+O+o0f0LGWk+779TNmvWD8+YRoI7Utm1FuOKKbRg40DXCrCzINZl0qlFlSkjigN3u6oFVVdmRnV0NvV6o6pCEhOhRWalDjx6hznICNpsDv/9e7tUunU7A4ZAwGoWz/IHyXnq9QI8eIQDUHbCLLuqhmRHvk08muX0+iQULflHVyaqutmPlyhOQEvjb31K9Xn/mmXE488zg3fww5ktWlgW33roH1dWu0db8fEqBHxNj7DD1EYcMicKWLd7FxW02ibAwHZKSKAnJvfcOwr33DvJ6nsmkg9VqR2Gh1eP1DmRmViE+3oTevcPwyy/FPt9/+vQEvPZaJoxGHYwe0YlCAAsWpOCll9JRU+Mdcl5bKzFiRDSio004fFi7HtPQoa7z5/LlY7F8+VjN5wHAgAGRePjhIT63B8Pw4dEYPnz4aX2P8eNjMX587Gl9D8Y6M5OJ1twCQGSkd1h1IAPOY8d2wbp1lCzOfQbfbpcwmYCRI2kfN9/cHzffHJySJ4y1B+06/m7duhxVh05hs0lYLHbVSHdYmAFhYQavpCQ2m4TZrEd0tCscQK8X9aUO1CNGXboYER1tVNWHUwrtesZvG4066HRQPS4EjaDHxZnh2aFLSgrBeedppx93t2VLoc/Cx6tXn/SZJY6xlvDhh1mqDp1CSjhnwjuCkSOjfSboSEgw+wz1dpebq52KX0qJPXtKMX9+T+fNjzshgEWL+uCii3qgT59wjT0AkybFITU1ymdook4nMGpUDBYsSEFIiPdlwGAQuPba3n4/A2OMNYYQwMKFvTS39esXjmnTEnyGlQOAwQAMHx7lM7lRTIwRen27vrVlrMna9TdfSWvrSZmOr6hQF/YdMCAc8fEmZ0dLpxOIjzcjNTXCcxdITDSjSxcjIiLopspgEJg7NwlffTUFo0e7wnGSkkKwYsU4nH9+Nzgc0lnSoEePEHzwwQT06+fad3i4Hrfe2h8vvjgKcXF0syUE3YAtXz7WKy24loMHK3xuq6mh9OqBKC6uxcsvp2P+/G244optWL48A+Xl2oWQGQtUQ9/PtDTvGe32KienBv37R6hm/XU6gYQEM6KijKr6cVoqK22wWCjsSEqoslgajQLp6ZVISDBj+fKxzlFnAOjRIxT/+McwTJlCM56bNk3DGWfEOjuYBoPA7NndsGbNZBQUWJGQEIK4OHUn02zWIzU1AsePU4HypUtHY9CgSOf2vn3D8fzzIzF8eMuFaDscEh9/fBLXXbcdl1yyBY8+uh+ZmdqDV4yx9u3663vjppv6ISaGZup0OuDssxOwbNkYZ1ZfXxwO4NgxCwYMiFCtdRVCIDaWQjh9DZgx1tG16/DLnj1DNR9X1q55Lt7X6XSYOzcJTz01DAcPVqB//0g899whbNxYoLEPgZEjY/DOO2cgN7cGsbEm5xq7118fh/z8GtTUOJCcHIrt24uRk1ODQYMiUV1th9Gog9msR1paJfbtm4309Ark51sxfnyss02XXdYDp05VIzLS2KhivPHxZp/bhICzs9iQoiIrrr9+B7KzXSe+zMyj2LAhH2++OU4zJIKxQPhaAE/bfH9325v4eDPCww0YOjQaVqsddXUS4eGUrTYsTK/KZKslLIxSdyuhke4Te3Y7nOeE1NRIvPnmeOf5pmfPUFUHLT4+BBs3Tkd2tgWZmVUYMiTa+dqoKDoHpqSEoWfPUFRV2WE0CueNkPLvMXp0F6xcOQHZ2dVwOCSSk5uf3KQxpJT4+9/3qYoJZ2VV47vv8rBs2Rhe08ZYByOEwPXX98Ef/tALOTnViI42qop/6/WoLxXjTa+nwfjQUAOGDIlCTY2tPuxdD51OB4NBODuLjHU27Xqm7oYb+qrWvCn0ekrdHRvrfRN58cU9EB8fgilTEtCtWwguvth3aYCLL+6BkBA9evcO90qakpgYgpSUMOh0As89dxh1dRJ6vQ4REUbnTdOaNdlISyvHgAGRmDw5XtXJpIQI4Y3q0AHAzJldERamfcM4YUIsunfX7ui6W7HiuKpDp8jMrMKqVScb1R7G3DV8PGlng2yP5s1Lcob/mM16REQYnJ2tCy7o7lxH64tOp0PXrnR+8ix54HBITJ2qXgOrnG98hXUmJYVhypQE1fkkLMyAmTO71r+HQESEwXluMhgE5s1L8thHaIt36ABgx44SVYdOUVPjwIsvprd4exhjLcNkovsg9w4dAK/7LXexsSacf353GI10LgwJMSAiwghd/Ql5xoxEHphmnVa769SlpZXj9dcz8dZbR5GbW4M33hjnFbYYFWXA9df39nrt5Zcn44IL1Bn4Jk+Ox6JFvb2yV86Z0w1XXdXTb3tOnLDg6FHfIY+bNnnPAjZHRIQBTz89wqtj17dveMCJCn74wXebNm3yvrliLFATJ8Zh8eK+XsfTzJld8Yc/aK+jaI/69AnHAw8MBgAUF1tRUGBFVZUNY8bENFjbTWG3S0RFGTVro8XHm2Gx+KmJEqA770zFsGHqkgQmkw6PPTYU3bqFBOU9mquhc85vv5U5s3AyxjoOh0Ni8+YCLF+egVWrslBW5lr+4atmJG3TIyHBjCeeGOYVjTVkSJRmYirGOot2E37pcEg88sh+Z8YjAFi2LAPXXdcbGRnn4fnnDyMjoxJDhkTjttsGICLCgMzMSmenavr0BPTt6712DgCWLOmPefOSsGFDPurqHJgyJR6DBmnXZmoLJk6Mw9q1U7F+fS4KCqwYODASZ52V4DNxA2MtafHivjj//G7YsCEfVqsDkyfH+6x11t5RSQABoPG1+PR6gSFDolFcbEVZme87CgsAACAASURBVA16PZCQEOI3dLMxoqKMeOut8fj552Ls21eG6GgjZs3qGpTabowx1hTFxbW4+eZdqsy7L72UjiefHI5p0xKcYezu5VoMBqGKgDjnnK4YO7YL1q/PQ2lpHYYNi8KZZ8YFlKSKsY6q3XTqPvwwS9WhA2jd3FtvHcOIEdF4/PFhXq/p2zfCZ0fOU8+eYfjjH3s3ul0pKWHo2zccmZnas3Vnn53Q6H0GIiLCgEsvTW7Sa6dPT8C7757Q3BbM2lKs80pODuvQ2ROPHq3CP/6RBp1OqEIed+0qxdKlR/yOFuv1AlOmxGPz5kLExpq9QsWnTw/eeUMI0abLmkyfnugz7HvkyGjugLYhLVHHjHV8Tz6Z5lVKxWp14P7/Z+/O46Oo08SPf6pz33cg4Q5yY0CInIJcsngrKqMIKjKe6/7mcGbVmdmZnd84u87O+JvLdR0vdhwVFU8Q8ULw4A4I4T7CEQggCSHkvuv3R6WLrnRVdYf0leR594sXpL7dVd80VfWtp47n+dlOPvzwCiZOTGPVqtOm5VqmTLlQFic5OZJ58zzfUSVEd9Fpbr98//1im7aTAeyJux//eLB+f7erG27IDskrfnff3V+vo+UqJyeO2267uEBRiO5kxYqTlhnaVq48RWOj59snH3nkEhIS3M+rjRuXysyZPTraxU5j3LhUZsxwP5kUHe3gBz8YFIQeCSH8palJu+3STH19C6tWneb3v881fa4uOTmCp57yb61KITqzTnOlrrTU+rmKtgV8A23ChDSWLLmcV18tYs+eClJTI7nxxmyuuy7L84eDIDU1kv/933G8/noRX31VgsOhMGNGJrff3kceMBbCC3b7nJqaZj0Lrp2cnHj+8Y9xvPpqEVu2lBEXF86cOT259dbehIV1r1uInnrqUt57r5gPPzzF+fONjB6dzIIFfb2+00II0Tk0N6u2JQtKS+sZMCCe9eun88QTO1m37iyKAldckc5//VduUJI5CdFZBC2oUxQlG/gQGA7Eq6pqW9hp6NAENm0qM20bNiz4V8OGDk3kySfdbwENVampkTzyyCU88ojnpA5CCKOhQxP56KPTpm3Z2dGmV+DM9O4dy+OPy4P9DofCLbf05pZb5E4BIbqyyEgH8fHhVFWZH/I5j+cGDIjnjTcmBrJrQnR6wbz9sgyYCWz05s13391fTyHuKirK4VWWSiGE8JXrrsuyLEdy99395WF9IYQwoShYHrP17RvrtzwEQnQHQQvqVFWtU1X1nLfvHzculSefHGlIw52TE8ef/zya/v3j/NJHIYQwk5gYwXPPjSE3N0mflpQUwQ9/OEiuNgkhhI3778/h3nv766WZFAUmTEjlf/5nDOHhnSbVgxAhJ2SfqVMU5X7gfoC+ffsCMHt2T2bN6sGhQ1VERDgYMECCOSFEcOTkxPPyy5dTXFxLZWUjOTnxbnWThBBCGDkcCg8/fAn33NOfoqIaUlIi6dEjNOpmCtGZKara/vpKPu2AoqwFZtk9U5eenq72798/YH0Soqs5evQosg0JcfFkGxKi42Q7EqJjtm7dqqqqanoGOWSv1Lnq378/+fn5we6GCEHnOMdXfMURjhBNNGNbXw4/3Vl8iEOsZz1nOUsGGVzBFfSnv1+W5Ut5eXmyDXVCTTSxiU3sYAeNNDKYwVzBFSSQ4LNl1FPPBjawk52oqAxnOJOYRCySZc6VbEMi2AopZB3r9PFnMpMZwADbz7TQwja2sZWt1FLLAAYwhSmkkhqgXhvJdiRExyiKss2qzW9BnaIo44E/As1AvqqqP3Jp+3fgZqAvEAV8oijKz1RV3eSv/oiu5zSneZZnqaFGn3aIQ+xlLwtZiIJvk1WsYx3v8Z7+czHF7GAHt3M7Yxnr02UJ0UwzL/ESBzmoTzvOcfLJ55/5Z58clNVTz3M8x3GO69OKKGIrW3mER4hDbnEXIhRsYAPv8i4q2t1VzvHnNm5jHONMP6Oi8jqvs53t+rQTnCCffB7iIbLJDkjfhRCB4c8HQI4BM1RVnQJkKorStmLko6qqpqiqGquq6kwJ6ER7fciHhoDOqYAC9rPfp8uqoYYP+dBtuorK+7xPI40+XZ4Q3/KtIaBzOs95PuMznyxjPesNAZ1TCSWsZa1PliGE6Jg66ljBCj2gc1JRWc5yGjCv43uQg4aAzqmWWtPxTAjRufktqFNV9bSqqnWtPzahXbFz9TtFUT5XFGW0v/oguq4GGmwDtwIKfLq8vey1DNxqqaWQQp8uTwi7ddhX6/dOdvp9GUKIjtnHPsvArY4605M/YL8NH+QgddRZtgshOh+/p2pTFCUXSFdVdY/L5L+oqjoWeAj4q8Xn7lcUJV9RlPySkhJ/d1N0Mmrry0oLLT5fnp1mt3MWQnSM3Trsq/U7kNuQEOLiXOz4Y/c5FVW2cSG6GL8GdYqipALPAItdp6uqWtb6t/npJa3teVVV81RVzcvIkGKUwiiKKHLIsWwfznCfLm8wgwkjzLIvl3CJT5cnhN06PIxhPlmG3Xx8vQ0JIS7OIAYRbpECIYIIBjHItM1u++5Pf0mGJEQX47egTlGUcOBV4Keqqp5u05bY+nc6nSQDpwg913ANEUS4TR/IQEYwwqfLSiSR6Uw3bZvNbKKI8unyhMgjj170cpseQwyzme2TZUxmMmmkuU23W9+FEIEVTzwzmGHadhVXEUOMadtwhpsGfOGEcy3X+rSPQojg82dAdRtwOdqzcwBPAPNVVf0X4PeKooxECyof92MfRBfWn/48wiOsYQ2FFBJDDGMYw5VcaXlVrSPmMIdMMvWU0plkMoUpXErbHEBCdFwkkTzIg6xlLdvZTiONDGEI05lOJpk+WUYccfo2tJOdtNDCcIYzgxkkk+yTZQghOm42s8kkk2/4hlJKySCDKUwhl1zLzzhwcC/38jVfk0++XtJgBjPoTe8A9l4IEQh+C+pUVV0KLG0zeUNr2wP+Wq4ITeWUs4ENFFNMAgmMZ7xP6rulkEI22dRRRzTR9Ka36dU7XxnT+hLCG2c4wwY2UEIJaaQxgQlkkeX152OI4erWl5ViitnIRs5xjh70YCITSSfd62UkkMANrS8hROhooIH3eI988mmhhVxyuZd723XbZAQRzGh9CSG6Nrn1UfjdMY7xAi8YMm1tYQvXcR3TmHbR8y2jjGd5lnLK9Wnb2c5EJnILt3Sky0J02G528wqvGJIYbGAD85nPaHyT9Hczm1nGMj0hwj72sZ71LGIRgxnsk2UIIQKvjjoe4zG+4zt9WjHFbGADT/FUl7qSrijLvHqfqt7m554I0bn5PfulEO/wjmnq5JWsNARk7fUhH5p+fgMbpMSACKommljGMresdC208DZvW6Ynb48aaniP99wy3DXSyDKWSWY7ITqxpSw1BHRO5ZSzhCVB6JEQItRJUCf86gxnOMlJ0zYV1bQwqjcaaWQXuyzbL3a+QvjCIQ5RRZVpWx117GNfh5exm92WtRPPcY5jHOvwMoQQwbGVrZZtdvUlhRDdlwR1wq/qqbdtv9grFs00216J8LRcIfzJ03rtiyt1nuYh24AQnZfVCRvQ7gQQQoi2JKgTfpVFFvHEW7Zf7HM/0UTThz6W7UMYclHzFcIXcsixzMCqoPikruFgBqOgmLZFEeWTRERCiOCw20f0pW8AeyKE6CwkqBN+FU44/8Q/mbYNZ3iHDjznMMf0oLYXvWzTPAvhb/HEcyVXmrZNYpJPkhxkkEEeeaZt05lONNEdXoYQIjjmM59IIt2mhxHGfOYHoUdCiFAn2S+F301kIjHEsIY1nOSkXtKgoymWhzCE+7mfz/mcwxwmmmjGMIY5zPFrWQMhvHEN15BEkl5XKpVUJjOZKUzx2TJu4zYyyWQ96ymnnEwyuZIrGcc4ny1DCBF4vejFr/gVL/ESRzmKikpverOABQxneLC7J4QIQRLUCZ8rpJACCvRiySMZyejWV1vHOc42tlFHHZdwCbnktisg609/8sgjlVSiiWYsY4khhjOcYQtbqKKK3vRmLGNx4GAFK9jOdiKJZDrTuYIrfPmr+9x61rOa1TTQQC65XM/1cgUGLcnOfvbryXJGMIKhDLW8HdFf8snnUz6lllqGMpS5zCWGGL09l1zqqKOEEtJJJ5dcQx8rqeRFXmQf+4gggjnM4Tqu83r5DhyMZjSNNHKOc2SSyQhG+PR3FEK0XzPN7GQnBzhABBH0oQ+nOU011WSRhQMHxRQTSyxZZHGCE9RTbxgHBzKQ/+A/9Hme5zxb2MIbvEE66YxjHIkkAlBCCVvYQiWVZJNNHnn6vqiYYvLJp446BjCAy7jMbyc+66lnG9sooohYYrmcy+lJT78sSwhhpKiq6vldQZaXl6fm5+cHuxvCC2/zNhvZaJjWj37cz/1EEWWYvpKVrGGNYVoWWTzIg8QR53FZlVTyHM+5pX0eyEAOc9iQ6j2WWE5xikoqDe8dwQj+jX/z6ncLtCd50i3DZxJJPMVTpJDSrnnl5eXRVbahZpp5hVfYzW7D9OEM527utnyWzdf+xJ/c1vU44niSJ8kii0IKeZmXDQlLIojgHu5hCEM4znF+yk/dEp70pS9/4k9e9WEXu3iVVw2JE2KI4T7uk+dufKwrbUPCv+qo42/8jeMcB6CKKsopJ554EkjgDGcA7RbqaqqppJIkkkggAYBssnmABwzj4AEOsIQlhgQqUURxL/dyjnO8yZuGMS+BBB7gAXaxi4/52NC/DDJ4iIf0gNBXyijjOZ6jjDJ9moLCDdyg36Fgth1JnTohvKcoylZVVU2fvZBn6oTP7GKX20EuaMXHV7PaMO0wh90COoBTnGIVq7xa3od86BbQNdPMl3zplvmvkELDQOO0m918wideLS+QPuMz05IN5znPszwbhB6Fjo1sdAvoAPawx3T984fNbDZdVjXV/JW/0kILS1nqth420shSltJEE//Bf5hmsCyiiGV4Psipp16fl6taanmd193q1wkhAuMzPtMDuiaa9HqqVVRRSinNra9SSvUTjec5rwdsJzlpGAebaOJ1XnfLiFlPPa/wCstY5ra9V1LJ3/m7W0AH2lW9Fazw3S/c6j3ecxtnVVSWs5xSSn2+PCGEkQR1wmfs6urkYzwzZ/febWzzWDi5kUZ2sMNteg01gHZw7aqOOtTWV1trWWu7rGAwC3id9rI3gD0JPW3XJW/bfMnuRMARjrCLXfqBXFtVVLGTnfrZejOf87nHPuxil2XZglJKKaLI4zyEEL7nuh9yjkmgBTi11Oo/O8cls/e6joP72W9Z99J5S6eZgxy0LH9QQIFPSqs4VVNtWX9TRWUb23y2LCGEOQnqhM+4Dlae2uze20ADzTTbLqup9dWWcxBsG7zZXbWoo852WcFg16cmmjwGvV1Ze9azYPRBReU8520/X0ml7TrpzcGWp/U2UN+FEMLIddtru6+22+5d3+s6Dtpt6yqq5XjQ0voy00yzbS289moboLblGrAKIfxDgjrhMwMY4HWb3Xv70MfjQ9wxxJBFltt053N7bVNBh9vkBMohx3ZZwTCQgZZtaaTh6MabbnvWM3+xq4MYTzyXcqnl/5GCwnCGuz1j6qof/Tz2wa4cSDjhtnUchRD+47ofarudu45FbZ//dR23XMfB/vS3rUlptS9JIMFyLM0k06tn172VQoptqZZQHGeF6Gq675Gh8LmJTDQtNO7AwUxmGqY5M1a2paAwi1leLe8qrnKbFk00CSQQS6xhejLJhBHmNjBGEsn3+J5XywukecyzHKhv5uYA9ya0TGe66XcTSSTTmBaQPsxlrts65nQN15BMsmVZgcu4jHTSTddf0A70HuABj33oRS9GMtK0bTKTfXrAJoTw3ixm6WNNNNF6YBVGmCHJVRJJepAXTri+T1FQDPuHNNIYwxjTZU1lKoMYZNp2LdeSSaZp22xmt/O3smc2zjtlky1ZeYUIAAnqhM8kksjDPMwwhukDWh/6sJjFbleeYojhIR4yXNHIIou7uMvrnX8uuSxkIT3oAWgD5ihG8Rt+w0Qm6gNpKqncwz0sZrEh21cvevELfmE56AVTBhn8nJ/Ti176tEQSuYu7vA56u6pMMnmQBw3rVA45PMiD+rrgbwkk8Gt+bTiDHkcct3Ebc5kLaIHfbGbrwVUMMcxghn4S4V7udaupmEQSP+fnplehzdzJnUxlql7mIoEEruZqruVan/2uQoj2GcQgFrOYXvRCQSGTTPrTn2yyiSeefvSjL31JJJEssuhPf3rQAwWFLLK4m7vdatHNYx4zmKGXKYgjjlnMYi5zWcxiJjJRv9KXTDI3ciOzmc1DPMRoRutXBTPJZAELTEsMddREJjKPeaSRBmiB6ljG8gAPBCwrsRDdmZQ0EH5RTz1NNHl1taDte1toYQ97OMYx4ohjDGM8pl6uoooIIgxXcBpppJ56Yok13Ap3hjNEE+3zdM7+UEopG9lINdWMYYzlGVlPumo6duezK6614QLpHOdYz3oqqWQ0oxnCELerwc00U0MNscRaHtgc5ShxxJFBxkX1o4kmaqkljrig3JrrrMlVTDGJJDKGMV3uSmFX3YaEf1VTTTjhRBGlj0nObaOaaiJbX96Oma77kyKK2Mc+wggjl1zSSKOOOtP9QEPry+xumotxgAMc4hCRRDKa0aSTrrepqFRTTRRRbrd/SkkDITrGrqSBFB8XfmF3n7/de6uo4nme5yQn9fZVrGIe8xjLWMt5mA1UEa2vtkLxypyZz/mcT/hEf/h8C1sYxCDu5V6/FY7tbIIVzIFWGP493tP/f/LJpx/9+D7fN/QrjDC9/pQVu+fjvBFOuMdl+Ms5zvE3/mZIWf4RH7GABXLLlej2XIO0tmOS67jl7ZgZRhixxPIqr7KTnfr0T/mUaUzjOq4z/ZwzeOyoBhpYwhIOclCf9gmfcDVXM4MZgHb7qK+CRyGE9+T2SxFS3uM9Q0AH2pnJN3nTMkV8V3SEI3zMx27ZxA5ykM/4LEi9Ek6nOW0I6JyOcYyVrAxSr4LjLd5yq0HVSCOv8ZpkvBPCD77ma0NA57SWtexhj1+X/RmfGQI60K7MfcRHHOWoX5cthLAnQZ0IGTXUmA5UoN2SGagaZKFgM5svqk0Exha2WKbv3sY2y9pQXU0ZZW4HeE4NNLCd7QHukRBdXzDHBxmbhAhdEtSJkFFDjW39Naviq11RJZWWbdVU29YDEv5n9//jfHalO7AqeuzUnbZZIQLFbv9j19ZRLbTYbvOyvQsRXBLUiZCRQortffjdqe6W3e/am96WNYtEYNj9/6SRFtRn/QIpk0zb54B60zuAvRGie7Dbrvy5zTlwGDIyB3LZQgjPJKgTISOMMK7kStO2NNLIJTfAPQqeCUywDAymMz3AvRFt5ZFnmT11BjO6TdAdRRSTmWzalk02Qxka4B4J0fVNZ7rpPiaKKKYwxa/LdiZDaSuWWCYwwa/LFkLYk6BOhJTpTOcarjEUYR3CEB7kwW6V8TGJJB7kQfrRT5+WTDLf43vdKrgNVc46i6618uKJ5yZuYjzjg9izwJvDHGYyU6+Vp6AwkpHcx31BKa8gRFc3mMEsZCGppOrTetGL+7jPUFrAH0YxinnMI4kkfVo/+vEAD3SKMkFCdGVS0kAEVQ017GIX9dQzkIFkk80MZjCFKZRSSiyxhsHDTDHFFFJINNFcyqXEEEMjjexkJ1VU0Zve5JADaFkLD3KQCCK4lEttawKVUqrXABrBCL8OWCoqBznIaU6TRBLDGU4vevEv/AtllNFAA5lkykGyD5l95+05cZBBBjdxEy/zMlVUMZ7xXMEVhve00MI+9lFCCemkM5Sh7S7CW0UVu9hFI40MZnDACqx7y4GDq7mamczkLGeJJz5o5RWE6IycScIaaCCHHKqp5jSnSSSRPvRhP/tRURnMYM5whlJKySCDx3iMUkoJI4wkktjNbr7iK3rRy3DCyReaaGIPeyinnJ705Gf8jBJKiCTSEFwKIYJHgjoRNFvZytu8TSON+rRccpnPfCKIIIss28830sirvMpuduvT3ud9JjOZjWzUC1ODdiYxiSQKKDC890ZuZCITDfNVUXmf91nPej0hyfu8zxzm+OXWxwoqeImXKKZYn5ZAAotYRF/6yoDpB56+c288wzOsYY2+juxnP2/yJn/lrySQQCmlvMiLhnT/qaSymMVeB2Yb2MAHfGDIpplHHvOYF3IBfiSRHrdZIYRRPvm8wzs00kgzzZRSSjjhpJJKJZVUU00aaSgonOUs0USTTDIA6aTzfb5PJZU8wzOGRCV96cu93OuTenHHOc4SllBBhT4tm2wWs9jjSVchROCE1lGB6DZOc5o3eMMQ0AEUUMDnfO7VPD7mY0NAB1BHHa/xmlsGsJ3sZA1rDNOaaOJd3uU4xw3TN7KRdawzZJhsppmVrOQAB7zqW3u8wRuG4AK0DGYv87Lb9yN8o6Pf+WY28wVfuGUhraCCX/ALAF7hFbf6bWWUsYQlXmUvLaKId3nXrTxCPvl8yZcePy+ECG2nOc2bvKnvc8ooo5FGaqmljDIqqKCZZkoo4SxnaaKJKqr0DJSllLKEJfrdAq6KKGIZyzrcxyaa3AI6gJOcZClLOzx/IYTvSFAngmITmywPbDey0eNBbzPNpjVxnGUR2qZdrqaaWmrdSiaoqGxik2HaBjZYLncjG2371V5nOWsZKDpvuxO+5Yvv/A3esGw7wQn2s5+TnDRtL6WUQxzyuIwNbLDcDuzWUSFE5+A6DjbRRD31elsVVXpbM82GMimuAdxhDlNOuen8nbdLdsQe9rgFdE6HOEQJJR2avxDCdySoE0FhN9BUUeWxeHMDDYbbK52aaQZw+7xzuvNvu77Y9e0c52z71V6eBlxfL0/45ju3qwWlolJEUYf64Ok9HT1QE0IEn+t23HZscj0Bqba+zN7b3Poyo6JaBmTe8rQ/lDFKiNAhQZ0ICrtnb9JI85iwwvW5AlfOz7X9fAQRKCiEmzxG2pOetj972++LkUmmbeIMeUbJ93zxnWeSadkWRpjHVP5265g3/ZD1QojOz3U/0HZsCiNML1ugtL6cXMe3iNaXmXDCO5wN025fo6CEXOImIbozCepEUIxjnGXRYqtada4UFKYy1W16DDFEEumW1TKeeOKIc6vtE0GEW6IUq+WHEWZZk+tiJZDAGMaYtvWgB0MY4tPlCd9854tZbFmLbjjD6UMfy8Auhxzb4uVOk5hkebBmtu4LITqX8YzXx8EwwvRSPoAh23IkkYa6pa7ZZUcxyjLwupzLDfO8GIMYRDbZpm2XcZkkShEihEhQJ4IimWS3mjpRRDGHOUxiklfzmMpUZjGLSCL1aT3pyaM8Sn/669PCCGMWs5jPfL2WFmhXBBezmDTSDPMdwQhu4RbDYJhMMndxF73o1d5f1aO5zCWPPEM2wxxypM6XH3X0O88hh8UsNgRdCgo55PBLfgnAndzJSEYazrYPYxh3c7dXfUwnnUUsMmQ/jSGG67mesYz1ah5CiNDVdhxMIYVEEkkiiWSSSSONeOLJIINUUkkggRRSiCZarwe5kIXcz/2GmqZhhDGe8dzADR3uo4LCYhYbSiQoKIxhDLdya4fnL4TwHUVVPWdhC7a8vDw1Pz8/2N3o1I5whHOcowc9/BKYXCwVlROcoI46+tJXP2t5gANsZzsppDCd6aa3TTrVUstxjhNDjOEKyClOUUklWWTpZzbrqaeIIiKJpC99La+2gFYyoYgiwgijL339HmCd5zzf8R3JJOu39zXSyAEO0EgjOeTY1sprppkDHKCOOvrTnxRS9La8vDw62zZUQgnHOU488VzCJX75/g9ykH3sI4ssxjLWdn0w00wzr/AKZZRxLdeaXp0ro4xSSklrfbVVTjlHOUokkQxikNvVuRZaOM5xGmmkL30NJzFCyXd8RzHFJJLIQAa2+7sMdZ1xGxKB5zrWJpJIIYVEEMElXMIxjlFFFX3oQwYZ+mfajoN11OljQRppFFFECy30pS8VVHCWs6ST7lbu5jSnqaCCnvS86LqqxzlOCSWkkmo4OQpwhjOUU67XTD3MYSKIYDCDva7xabYdKYp3WTpV9Tav3idEV6YoylZVVfPM2qROXRdXSil/5++c4pQ+bSADuYu7bAtvB4qCYgjEaqnl1/yaoxzVp73Ga9zP/ZZX8GKIYTCD3aZntb5cRRHFIAZ51bcIInxewNVOUuvLaSc7WcYyaqgBLtz+eT3Xux0wH+AAS1mqJ/BQUBjHOOYyt93FroOtkUbe5E12sENPDpBCCgtZ6HUNOU9qqeU1XmMf+/RpX/AFd3O318+ItP3OX+Il0+88tfXVVgstfMAHbGCDnhQhnnjmMY/hDNff58BhOAsfauqp5zVeYw979GnppHMXd1netiVEV9N2rD3PeRppJIUUmmmmnHLiiSeWWBQULuVS7uAO/Xlv13EwiijDWDCAAfq/rU4OgXanijfP65qppJJXeIUjHNGn9aIXd3O3vv/KJJMMMljOctaxTt9vxRHHbdzGSEZe1LKFEL7ht0sPiqKMVxRlvaIoXyuK8sc2bdmKonzR2j7LX33o7lpo4SVeMgR0AIUUhmx9mad52hDQgVZ77lmepYyy4HQqCL7jO17lVT2gA+2q0Fd8xTd8Y3hvOeUsYYkhI6OzVMNqVgesz76yghVsZ7sh29s5zvECL1BHnU+W8RZvGQI60M5Cv8RLlpnkXPniO/+SLw0HRqBlfjWrbxfK3uEdQ0AH6IXXpc6i6A5aaOFlXtbH2iqqqKSSOur0K/UNNOh16FRUCihgOcuD3PMLXuVVQ0AHUEwxS1himPZ168t1v1VNNf/gH5zhTED6KoQw58/7yY4BM1RVnQJkKopyqUvb48AvgNmtfws/OMAByxoy+9gXcvVlKql0Ozh0chYK7y42sMEyuFjHOsPPm9lsefC8nvVutflCWT31bGGLaVsttWxjW4eXUU65ZS26MsrcCtqb8cV33vb/w4xSKwAAIABJREFU0amJJp/XQ/SXSirZznbTtgoqKKAgwD0SIvAOcMAQ0LjWkauhxrAvd23LJ99nJ6o64hSnKKTQq7a2JxWdmmmW+plCBJnfgjpVVU+rqurcWzWB4Qg1F9igqmoVUKkoSoLbDESHeTrbf5azAeqJd05xyvZguO0Vx67M7v+ulFLDVSy791ZRFRIHDd5y3rJkxRcnIs5y1ra4vTdXyTr6nTfSaFtrrrNcqTvHOdttNtT2MUL4Q9v13LVOatsac65tjTRynvP+76AHnvarzv1RCy22d8zI9i5EcPk9tZ6iKLlAuqqqrpdgwtQLGVrOg0tGhwufu19RlHxFUfJLSkLrilJn4ak+jdV9+cGSRZbUbGtl93+XTrrhmTq798YTb8j4GeqSSLJ94N41ucDFSiPNNomHN3WdOvqdRxBhWmexPX0IBSmk2CawCbV9jBD+0HY9d03s1bbGnGtbBBEhURLA037VuT9y4DB9PthJtnchgsuvQZ2iKKnAM8DiNk2uV+0Swf2Utaqqz6uqmqeqal5GRscP5LqjwQy2LJI8lKE+OUD2pQQSGMYw07ZwwpnL3AD3KHgmMtEywG1bK28c4ywDoUlM6lRlEaKI4nIuN22LIcayvlx7JJNs+UB/KqmMYITHefjiO7eqeRhOOBOY4PHzoSCBBEYz2rQtkURyyQ1wj4QIvLZjbTzx+r9jiTXsy13b8sgLiZNuWWRZJgVr23YFV5i+L4wwt5qvQojA8meilHDgVeCnqqqebtNcoCjKREVR4oBEVVUr/NWP7syBg3u51+0K1yVcwh3cEaRe2XuURw2ZvgCiieZhHrY9Q9jV9KAHC1hgqJUXRhhTmeo2qCaTzCIWGQrSKiiMZzyz6Hx5iK7nekYz2nB2O4UU7uM+nx0AzWOeW/mBTDJZzGKvsoX64ju/kiuZzGRDABhPPHdxV6e5UgdwC7cYsnWCdmb/+3zf6zTnQnRmzrHWme01nngSSCCaaFJJJZ10IokklVQ92+UoRvmkjpyvLGCB29jbi14sYpFh2pTWl+t+Mo44FrLQ8iSyECIw/FanTlGUO4C/gJ514Algvqqq/6IoSm/gFSAG+JWqqp/azUvq1HWcWZ26WmrZzGYUFCYwwe/1ryqo4DSnSSRRT7tsV6duBztIIYVpTLOtUxfKznFOr/lzMQfq7a1Td5CD1FLbJerUHeMYBRSQRhoTmHBRVxxXs5rv+I4JTCCHHLf2AxxgP/vpSU/yyLuoOnVW37m3znOeIxwhkkgGM7jTruvOOnVJJJFDjtSpE93SNrZRTDGDGEQSSXzCJ8QSy7VcSzHFVFNNb3oTRxwnOEEccQGpHXuc49RSSx/6EEOM5ftOcIIznCGNNNtSKhVUcJjDlvU1rUidOiE6xq5OnRQf76Ze53VWsUpPShFJJHOZy03c5PNlNdLI+7xPPvl6FrB+9GMqU1nFKv0h7CiimMY0ruIqn/ch0Oqo4y3eYic79YfkhzKU27ndcPtNoHSmA9JmmlnOcjaxSU8q0JvezGe+12eC17Oev/JX6qnXp2WRxdM8TTTRNNLIO7zDNrbpiT5yyGE+822fdRPdV2fahkTgVVDBUpZykIOAlnykhhocOFBQiCGGhSxkGtP4iI9Yxzp9/M0iizu4wy91HU9wgqUs5Tu+A7Tn+KYylTnMCcqJFwnqhOgYu6Cu8zxsI3zmcz5nOcsNWQYbaOAN3mATm3y+vBWsYBObDGmdCynkaZ42ZN2qp55P+IT1rPd5HwJtKUspoMCQ9Wwf+/hf/jd4neokPuET1rHOkCXuBCf4G3+jgQaPnz/Pef7IHw0BHWjZUx/ncQDe5V3yyTdkbjzMYV7ghU5VAkIIERqWsEQP6Mooo5JKmltfoN0Z8yIv8jqvs5a1hvH3FKd4nud9nqm4mmqe53k9oAPtJOtqVrOWtT5dlhAi+CSo64ZWstKy7X3e9+myaqhhM5tNpzfQYDqIfcmXPu1DoJVQYllv7yhHOcaxAPeo82igwbJ+23nOW9ZEc/UCL1jW+DvOcY5xzLLe3Xd8x372e99hIUS3V0ghxzmu/1xJpf7vFlr0k3vNNPMZn5nOo4oqtrLVp/3awhZqqDFta1tAXAjR+UlQ1w3Z1ZnxdX2ss5w1XHFxcp6lNKtJdpaztrXKQt13fGdbB6071dtrr3LK3a6wufLmu3M9uGpLRTXcBnyxyxBCCKfTGHPBtd2/uI4HtdRazsfX+562/XJVQYVlwCeE6JwkqOuG4oizbHPN5ucLiSSa3rfvzJxllmkwnvhOmywC8Fh36GKSaXQXCSTYZp/05rvzlCV1MINt2+X/RwjRHm2fw22b1Ml1DLRLKOLr53nt5hdFVEiUUxBC+I4Edd3QFKZYtvk6BX4SSW7pzkELLB04DCn7nSYwoVNnzuvT+jKTRhqDGBTgHnUeMcQwilGmbZFEMpaxHudxD/dYrj9ppHEpl1oGdvHEW9awE0IIM0MZagigXMc11+LjCoplHc5wwsnDNPfBRRvHOMuTZOMY16lPngoh3ElQ1w3dwR2mBYMnMIFruMbny5vHPPrS1zAthRTu5E63Mgq55HbK2mptmdXsSSGFRSzqVMXAg+FmbnYrhBtDDHdzt+1VZqd+9OMWbnEL7GKI4d/5dwBu53a3NOIJJHAv90ptNSFEu4QRxiIW6XdppJNOFFEoKHrgpKAwhzk8xEMMYYjh81FEsZCFPr9Sl0oqd3KnXirIaTjD/TLWCyGCS0oadGOFFLKWtThwMJOZeuBVW1VKRUkhCekDiE24EJiUUIKKSgYZF3UlbSc72cEOsshiKlOJIIIaatjFLuqpZyAD9ZTOjTRSSimxxHq8nbGZZs5whhhiApKOvoYaznOeZJJt6/200MIBDnCGM6SSyjCGeVXY2h86Yzr2QxxiD3tIIYXxjG93HcUyyniJlyijjLGM5VZuNbSrqBzkIKc5TRJJjGCE25nrFloooYQwwizrDO5iF6WUMp7xtuuDlebmBsqKdxIeHU9K5hDPHxBB0Rm3IeEfdTVlVJwpJC61D3GJPTnDGUC7E2A3uymnnB70oJ561rCGaKK5mZsNJ/qKKOIoR4kjjpGMNARe9dRTRhkJJBBPvOXYa7d/ch0Xo4lmJzuppZYBDLC8kyQQpKSBEB1jV9JArr13YwNbX04N9ZXsf+GnJK7Jx9HYTElEGJXT8gi//yFWRn2uP8SdTjrXcz0jGOHVcppo4k/8iW/5Vn+AfBWreJRHySKLcYwzvN+ZbrmWWhQUBjOYW7nV9FmndazjMz6jiipAqzV2K7d6Xc+sPeqp5z3e03+PCCLII48buMH06o4DB0NbX8J7KiqrWc2XfKmvA/vZz63c6nXQXk457/IuZziDisoOdpBEErOYZbgVanDry8wOdrCSlXpioV704mZupj/9AdjIRv7KX/XEBw4c5JLLL/ml17/r/k/+StjSN4kqqwbg1LDepD/8SzL7md+iJYQInqbGWva9/K/Ef76RsPomasLgqyvS+fLBYTTFRZJJJjdwA1OZyla2sopVlFMOaGVu5jJXD6j6tr5cNdPMKlaxgQ3Ut75Aew7PgYN00rmO6xjJSAoo4EM+NN0/BXJcFEKEDrlSJ3S7nlpA0vrdhmkqKrsnJrH6CeNBpgMHD/OwfoBr5/f83jRVcyKJPMuzhqsja1hjWnIhjTR+wk8MwdNmNvMWb5nO96f89KKumth5kRfZxz636WMYw3zm+3RZvtaZrjKsZjWrWOU2PZ10fsJPPD4H0kSTWw1EpznM8er23gMc4AVecMtiGkUUP+JH1FPPD/iBaUrw8YznMR7zuIxDX75E1NPPuE1vSIyhx38vIz6pl8mnRLB0pm1I+Meuv9xP0udbAG1sbGzN7Hx0VCKrfjMJ0G7FnMMc03Eshhge5VHLk1Mf8AFf8zWg3a3iPCkVQQQ96AFoJ6Ou4Ro+4iPT/dN0pvMxH7vNO5FEfsJPTJ9jDyS5UidEx0jxceHR2eKdJG1wr63WQguDN5wl/sR5t+neFC+toMKytlgFFXzKp/rPzTRb1qg7y1kKKNB/VlH5gi8s55uPbw++iik2DegAvuVb2zIRwnuNNPIVX5m2lVLKTnZ6nMcudpkGdABf8ZVX5TLWsMa0LEU99axjHS/yomWNJ08lE5yalr1hOj2yopaiT1/0+HkhROBUnD1C4hcXTk66buP9d1SQevCsPv0DPjCdRy21bGCDaVsNNYa2Kqr0fVAjjXpNVxWVD/jAcv+0ghXm/ffDuCiECC0S1AkAzh3aDCZXbVtaB470wnK3Nrt6YE772Gd7gHuAAxf6wDn9dhEzrsuro862pp43fWuPE5ywbFNRKabYp8vrrs5xjmqqLdu9+X+1e08NNZzjXIfmcZzjtutDM82c5KTt/Bsbqokrsl5/Ww4dsGwTQgRe2ZGtKC0XTuS0DarSD1zYr9iNTVb7jtOcNtR0baDB0O56MuosZ03n0UKL7f7N1+OiECK0SFAnAIhMyjCd7nwkuzYpyq0tnniP87VKLuHkmgQllljbzJCuy4sk0i2jV3v71h6e6vf5enndVRxxtkl4vPme7d6joHh1+5HdPBJIsJ2HguKx1l14RAyNcdbrL0n2yYGEEIEVldzD8HPb/VRNyoXt2a7+m1UG37ZjTNukWq5jo9XYp6DYJpSScUqIrs2roE5RlBRFUUYoipKjKIoEgl1Q79xrqO3hfiDpwEF5ZiRncnu4tbVNcGImhxzLh7MdOLie6/WfY4nlUi61fK9rDZ8wwhjDGNP32tUCulhDGGKZhTOTTK+eLRSeOTPBmQkjzKs6dWMZa5lldAQjvDqwGc94y7ZxjGMucy3bM8n0uAxFcVAza6Jle9pVd3jsoxAicLIumUL1gAtjmWuQVZ0Uzsm8bP1nu/HHat+SQQYDGKD/3LbWnevPVmOvgmJZ605B8WrMFkJ0XpYBmqIoSYqi/ExRlJ3ARuBvwFvAMUVRlimKMj1QnRT+53CEk/j4b2lINCYXaUqM49jjt6M6jGclxzCGiVgflLr6MT92u7LhwMF85rtdybuZm/WyBk5hhPE9vuf2cPm1XEs/+hmmKSjcxE1kkeVV37wVRhh3cZdb8pUEEljAgk5dLD3UzGWu2/9fGGHcwR0ey1uAlhDgDu5wS6jSk562wZirqUw1DS6nM51hDONKrjQNMKOJ9jr75aA7/52Kkcb1V1UUqhbdRtagKV7NQwgROGk/eYq6NO2EjQMHYTioiwtj1eO5tERqJ5LGMY5FLHLLeqygMJvZbjU4Xd3O7aSSCmhBXTzxKCikkaaPMaMZzSIWmZ4AncY07uEet5OM/hoXhRChxTL7paIonwGvACtUVS1v0zYWWAjsVFX1JX93MhSzX9ZTTxNNXhVD7kzq685z7KtXaTx5lIjsfvSbupCo6CROcpJd7KKFFkYwot11bhpoYAUrKKSQFFK4kRstr+C10MIe9lBEEbHEMoYxJJJo+l4Vlf3sp+j8LiKj4hkVPd7jrW8dUU8929hGGWVkkskoRrW7flowdLbMfS20sItdHOIQSSQxjnEeb4Ftq5JKvuVbqqiiN70ZwYh21wk8whH2sY8wwhjFKD0DnVMBBbxT9xpN9dUMTrqcBSxo1zJUtYXjOz6ksmAdSkwsWVNvJ6WH1KoLRZ1tGxLt10AD9dTrwZSZxoZqjn7zKg1Fhwjv0ZuIK2dyIFZ7Tu5SLqVXYyZUV0NCAoVhRznAASKIYBSjyMD8MQdXTTRRQAGnOEUyyWSTzUEO0kILwxhmOJF5lKPsZa/b/sk5Lh7mMNFEM5rRerAYbJL9UoiOsct+KSUN2qmUUpaznL3sRUUlm2zmMIfhDA9217qnr7+GpUvhxAkIC4OJE+HeeyHd/lm+7qazHZBuYhOrWU0ZZYQTzmVcxvVcH/R03AalpfDyy7BhAzQ3Q+/ecPvtMHVqsHsm/KCzbUPCe5VUspzlFFBAM82kk85VXOXV7d66hgb4xz/g00+htlZ7Lvb66+HWW8EhT604SVAnRMd0uPi4oii5QH/X96uq+q5PeteJVFPN//A/nOdCev+TnGQJS/g+32cIcoY9oNatg9///sLPzc3wzTdQWAh//jNEWz+sLkLXOtbxHu/pPzfRxBa2cJKT/IAf2CbTCZi6OvjFL+CkS5bLEyfgD3/QDuCuuCJ4fRNCeK2RRp7jOb7jO31aKaUsZSkttHj/fPYf/gAbN174+fx5ePVVqKrSTjQKIYSfeTw6UhTlZeBl4Bbg+tY/1/m5XyFpE5sMAZ2TisrnfB6EHnVzb5jX+eLUKVi7NqBdEb7RTDOrWW3aVkwxe3CvpRgUa9caAzpXVuulECLkFFBgCOhcfc7npvXg3Bw5YgzoXK1cCRUVHeihEEJ4x5srdRNUVZV7C9Ger7FylKOoqJIwI1Cqq+HYMev23bthzpzA9Uf4RBllVGB9AHSYw5bZMQNqj01wWVQElZWQ0L5nAIUQgWc3rp/lLOc575aky43d/qCxEQ4ehLHtuJVTCCEugjf3MW1QFEWCOnDLfOgqmmgJ6AIpIkL7YyVe6vF0Rp62o5B5pi7OJkFSRARE2dSgE0KEDLtx3YHDtuacztN4Y7e/EEIIH/EmqPs7WmC3X1GUAkVRdiqKUuDvjoUiu4emrWrDCD+JjLR/bmnGjMD1RfhMAgkMZrBpm4JiWZsw4OzWr8mTtfVTCBHy7Mb1EYzwLqgbPx5iLU44ZWfDEHneXgjhf94EdS+jlS+Yw4Xn6a63/UQXNYQhXIF7INGb3sxmdhB61M0tWgR9TEor3H47DBoU+P4In5jLXLfbnZx1lkIlLTeDBmnrWVu9e0tSBCE6kZ705FqudZueTjo3cqN3M4mOhh/9yP3ukfh4+PGPQZG7eIQQ/ufNM3VFqqou93tPOombuInRjOZbvqWBBoYwhEu5tN31r4QPJCfDH/+olTXYvVs7Uzp9Ogy0Lu4qQl8aafyUn7KNbRRRRBxxXM7lbjXigm7+fO0M/Zo1qDXVKMNHaOUM5CqdEJ3KdKYzhCFsZSs11NCf/lzGZe2rQTp+PDz3HHz2GZSUaCd4rroKEs1rrAohhK95E9TtUxTldWAFUO+c2B1LGjj1b311G6dPa6mZN2wAVYW8PFiwAPr29X4eX30F776rZQlLToZ/+ietfk9HD4AjI2HmTO2Pn7XQwtd8zTrWcY5zpJPOVKYykYl+X3Z3E0UUE1tfoaqWWj4eWMC2gceoo44BRDCLvpa3j16UsjJt2/vmG6ivh8su04LJwa3LaGmB996Djz7S6ub17g033giz5c4BIbxWW0v2ax+RvWaNVoJg6FCYF64lN/G0DbrKyNDaAqCYYj7hE/azHwcOcsllDnNIISUgyxdChB6PxccVRVliMllVVTVg9xiFUvHxbufsWe22kvJy4/TYWHj6aejVy/M8Pv4Ynn3Wffq4cVqtr07iXd5lPevdps9kJldzdRB65D0pnOxbjTTyDM9QTLFhuoLCYhYzlKEdX0h1tbbtnT5tnB4RAf/5n9pB5V/+Ap+blFNZsADmzet4H4ROtqEuqrkZHn8c9u83TlcU+OEPYelS+20wCE5ximd4hvoL59kBSCaZH/JD4gndRGFSfFyIjrErPu7xmTpVVReZ/JGHRrqL5cvdAzqAmhp45x3Pn29shNdeM2/bvBn27u1Y/wKkjDI2sMG07Uu+pJrqAPdIBFMBBW4BHWg1Kz/hE98s5NNP3Q8mQdum3nhDK3a+2rymH2+/rW2jQgh7mza5B3Sg3ZXy5z/bb4NBsprVbgEdQDnlpicehRDdgzfFx/+uKEqyy88prQXJRXewY4d12/btnj9/7Bicdy/Y3q55hIBDHLIsQttEk22tI9H1HOCAZdtxjlNLbccXYrdtbN+ubZtWd1rU1cG+fR3vgxBdnd12duIENDW1/3N+Zrf/2Y9JgCqE6Ba8yX6Zq6qqfqlGVdVzwGX+65IIKXb1trypxRXtIR20p/YQ4emB+XY9UC86Pbv/bwcOwr16XNkDu20jOrrLbFtCBJXdduJwWGeuDOL2Zbf/kbFIiO7Lm6DOoSiK/uStoiipeJdgRXQFU6ZYt02d6vnzvXvDgAHmbYpiX2suhAxjGFGYB7EJJDAQybjZnYxmtGXbCEYQQYRlu9c8bXvjxlknGsrI0JI9CCHs2W1no0dDmEVma2/GPz+x2/9cJufchei2vAnqngbWK4ryG0VR/i+wHvgv/3ZLhIzZs2HUKPfpgwbBTTd5N4+HHzYvzHrPPdrBZycQRRS3ciuONptMOOHcxm1S0qKbGchAJjHJbXoSSVzvqzKekyaZn/To1QvuuAMSEuCBB9yvJERGwiOPaFcZhBD2rMay5GT45S9h8mT3Nuc2GCQzmUk22W7ThzHMtpi6EKJr85j9EkBRlOHADEABVququsffHXMl2S+DrKlJS+e8fr2WQn3cOJg2rX3lCEpLYdUqKCyE1FQtWOyEVxJOcYqNbOQsZ8kgg0lMIoPQD0wlc59/7GMfW9lKLbXkkMMEJhCLyQmMi9XSAhs3arUYGxq0dOozZ0JMzIX3HD6sZZg9cwb69YM5cyAry3d9EIBsQ13e9u3wxRdQWQnDhmlld5KSvNsGg6CBBraylb3sJYwwRjGKXHLdTjyGGsl+KUTH2GW/tLyNUlGUeFVVqwBagzi3QM71PaKL+OYbWLECTp6E7Gy47joqp4zmi2nn2TVNpQWV4VQyg2pSzO7dr6zU6tF9842WIWzMGLjlFopKv6WycDnRhcU0nkuAwiYGD74EhyOAd/KeOAHLlmmDd1SUdtvNLbeYX0W0kEUWN3OzHzsZ+r47vIGSt/+H6F2FNMdF0Tx9KoNu/CkRUXG+W8iePdp6tH+/dsZ81iy47jrrW6Euwj728SVfcopTJJPMBCYwnvEoWDxD01Z9PUM/KGDomm+08gMjzsKt/WCgy62469drGWSLi7VA69pr4corve+kw6FdsZvkflVQl5OjXQ0XQhidOaPt8/PzL2xLt96qBWuuGhq0E46FhVBRoZ2wPHNGe5/dNnjoEPz2t1BQAED5qH4s/9lIDl/iIJFEwgmnnHIiiGA0o5nGNGLwTTAYSWTI1/IUQgSW5ZU6RVFWA9uBD4CtqqpWt07PAaYD84AXVFV92+Lz2cCHwHAgXlXVJpe2/wWGAbXA86qqvm7XSblSFyDLlsE//mGYVB0Lf3m6L2d7GR8KTySR/8P/IZnkCxNra+Ff/1XLeOmipqmCcsppDjceLJ+fMZaRP3zRt7+DlaNH4bHHtD66GjgQnnrKu6QvnZivrjIU711N4789TliDMSNcxYi+DH1yGWFhPnhIf8sW7UCppcU4feJEeOKJjs8f2MIW3uItt4ymE5nILdzieQbNzdqtWTt3GqdHRMBvfgPDh2slP/7+d/fP3n57wAoUC9+RK3WdzJkz8Oij7tmXs7Ph97/Xbl8GLYPsr38N27YZ3xceDr/6lfnjBwBHjsBtt+mlQ5poooUW6mPD+dOyyewZUIOKSjLJet24XvTin/nnbp3MRK7UCdExF1WnTlXVmcBq4AFgt6IoFYqinAVeBXoCd1sFdK3KgJnARov2O1VVneYpoBMBUlkJb77pNnndFIWztcXaQayLCipYwxrjmz//3C2gU9UWHCdOEl9a5zbvpC+28t1h89pvPvf66+4BHWhnZtescZ8uTJW/8he3gA4gcXcRRze95ZuFLFniHtABbNgAu3d3ePbNNLOSlaYlKjaykTOc8TyTTZvcAzrQrk7/4x/alTurOlZvv21f5kMI0XHLlplvZydPwkcfXfh52zb3gA60xw7MTso4/e53ekCnotKCts+Kqmni6t8V6PuX85zX/11MMVvZenG/jxBCeGB787Wqqh+pqnqnqqr9VVVNVFU1TVXVSaqq/lZVVZOKnIbP1rWWPzBtBl5RFGWFoij9Lrbzwod27NBuQWlj7zC0M5k17gHRnrZ35G7Z4vaexoYaHE0tRFU3u7UBnM33UaFmT+zOsMvZd6/U15aTuLvIsr1289qOL+T0ae02WSubN3d4Ecc5ThXmd42rqOxlr+eZ2K0zu3dr20K9e3FgQDtY/PZbL3oqhLhodtuo61hlMm7pDh2C8nLzNpcars6Azqn/jgr93yqqoVC427gphBA+Eqwnah9VVXUS8Du07JpuFEW5X1GUfEVR8ktKSgLbu+7IIlOe4ryYYfKYkdsD2abz0D6oWj2m5MNnpGzZZQKULIFeURxhqFY1m8A336OneYR3/BlMT4kEvEo0YPc9KIrn9VrWOSH8y24bc90+PW2LVu02+wC1zUdcn9MN9UQmQojOKyh7F1VVy1r//gbtVk6z9zyvqmqeqqp5GZ0k7X2ndtllptm8Li1AG9RMkonkkmucYPIgeURkDM0RYdTHmxyMKwo9JnhZFqGj7BJN2LUJXWRUAhVjLrFsT5g8p+MLycyES6yX4Yv/q970JoUU0zYFhZGM9DwTu35cdpmWITbOInFMVBSMlbTjQviVt/t8u/eNGAGJieZt48bp/2wbqB0Yn2Joc32Gzm3cFEIIHwlKUKcoSmLr30MAi3sbREDFxMD3v+929nHSeugTM8jtbGUGGUxjmnEe06fDSOMBsaI4UAddQkWme8BYcfNM0npd6pPue3TnnVophbZGj+40BdBDQY9Fj9GYEO02/fzkkfQd46MA/b77zBPXXHONMbPkRXLg4GZuNq0teBVXWQZ8BmPGmK838fGwaJHWf5PtCUXR2q0CPiGEb9x6q5YUpa1Bg7RyBU4jR8KMGe7vi43VtmErTzwBKdq+QkHR9yfVKZGsemK0/nMyyfqVukEMsi0cLoQQHeFtnbowoAcuJRBUVbV+uEb7TASwChgLbAP+L3CFqqq/VRRlBZCC9mzdQ6qq7rKbl2S/DKA9e2DlSi0Fe3Y2XHst9SMuYSMbKUB7+Hs4w5nEJPN6XA0N8OmnWkmDhgbtisS113Lm/EHSlcmqAAAgAElEQVTOLH+JsMKjNKclE3vVjeRMCHDx1nPn4MMPteeZoqJg6lQtVX5ERGD7EQS+zNxXXnKQE8ufJWzXPlriY4i48ioumXGfb8tTnDihldY4cEBLKz5zplaCwoeKKeYbvuEkJ0kiiYlMZBjDvJ9BS4uWZGftWqiq0s7q33CDdrXRae9ebZ1zLWkw0osrgSLkSPbLTqiyUkuKsmWLdsvlpElaQBfd5sSUqsKXX8Lq1dq2PHQo3Hgj9DS9keiC0lL4wx+0OnZA+cThrHp0OKfTG0khhUgiKaFEL2kwjnGEW1eS6hYk+6UQHWOX/dJjUKcoyr8AvwK+A/1pYFVV1YDdQyBBXYjav1+rwXX8OGRkwNVXQ57petZxLS1aYdg1ay4Uh73hBu0Zq/ff14LR2FjtauGsWe17ZmnTJq1489mzWvHmG27QzuZ2IXJA6gdNTfD//h988omWFGXQIPjRjyDXZde4a5cxqLvmGu3qsFNJCXzwgZZJMzoapk2Dq67yybODwrdkG+pi9u7Vtt+9e7WTfLNmadtvdDQUFWnjyqFD2tX35GQtgGto0Eoc3HADpKUF+zfolCSoE6JjOhrUHQLGq6p61h+d84YEdSHo66+1M5Rt15+FC7XaPb729NPamdS2HA739PcTJmi3xtgls3B6/XX31PMOBzz+uDafLkIOSH2spQVuvhkOHjROj4iAZ57Rrip++in893+7byP3368VUj9xQlvPKiqM7WPGaDXwJJlKSJFtqAvZtAkeeMA94/OAAVqNzCef1NpUVSuBUFurPVvnvAqfkgL/9V/Qo0fg+97JSVAnRMdcVJ06F8cBKaokLmhqguefdz9YBS1IKivz7fJ27jQP6E6e1M6otrVxI2z1ohbQmTOmtfloadF+P7NaaUKAtp63DehAq1P3299CXR28/LL5NvL3v2u3eL3yintAB1rNrA0Bqt8oRHfkDNraOnIEfv7zC22VlRfqm1ZUXChTcu6ctg8QQogQYhnUKYryY0VRfgwcBtYqivKEc1rrdNFd7dtnXTy5udkntcQM1q93n9bSohV+ratzK4xu+Zm2Nm82P+gG7VabAwfa10/RfaxaZd1WVKQ9U9pamNhNfb1WQ8tuO/Fm/RVCtF9dHRQWmrepKhw9euHn6mpje5VLfUs58SKECDF2D24ktP5d1PonsvUPaAlORHdlFkS58vUVLk/JfMzavUgAFPDfQ3QdntYvT+tOS4v9PLxZf4UQ7We3baqqcduz2w5lfBBChBjLK3Wqqv5aVdVfA3uc/3aZtjdwXRQhZ9gwSEgwb1MUuPxy3y7PpR6QzuHQyjBERZknlTD7TFuXX2793F1yMgwe3L5+iu7DLAW6U3a2Vu7ArCwDaM/d5eVpz85Z8Wb9FUK0X2yslhDLjMMBvXpd+Llt6RHXn8eP933fhBCiA7x5pu4JL6eJ7iIyEu6+2zwguukmLROmL112mXmg2KOHllGwrVGjvBtwW0s2uHHWEpMMhMLKPfdAnz7u08PC4NFHtQPHBQvMP/u972lJFxYudE+tDtpJk8mTfdpdIYSLxx7TttW2srLg17++0JaQcOHkTHz8he01Ph7uCHBJHiECQFGWefVHhCbLo1ZFUa4GrgF6KYryF5emRKDJ3x0TIW72bEhP19I+O0saXHONlpLd1xRFy2a5cqVWR6iyEoYPh7lztcDrnXe0ZCpxcVpJg+uv9z5z4P33awWtP/5Ye46uXz8tMB0tBWKFDed699vfakl86ushJwd++MMLAZmzztWKFVpJg549tayXzvacHC2r67vvwo4d2pXnK6/UPtcNaicKETTTpsGLL8If/6glPIqM1DLW/tu/aYHcf/6ntl0ePKhlxIyP15KjNDZqJxnnzjUvbC6EEEFkdyniJLAVuKH1b6dK4Ef+7NT/b+/Ow6MqrweOf89kDyEJEAKELewIArK7gCKodamC+77UWre6r22tFbUqtVV/ta1aK4r7ghaLSxVRQUCrILIoKLvsS2QN2Sfv7493JpnJ3JlMyExmJpzP88yTyb0zd84s9773vfe956gwvPMOvPyybWi6dYPrrrNpln/7W1sby+WyxbUnTbIp+595xmbvatvWnkk48cTGxzBkSOghZL62b7edsjVroHVr2yns18+mlp41y2YYGzDAFoZ1GtqZnAydOtmzI3v32iEybdrYYZK3NDJvz7hx9qYio6ICnnoKPvrIZkodORJuuMF+7+EqKrId7R9+sN/xuHEN72gvXgwzZ8Lu3XYo7Ukn2QMRXkuW2JID69bZ39LFF9vHeK1ebdertWvtgYXBg21W1KwsOz8ryx4AyM21CRX69w8sLD5yZOizxp07w403Nux9KdVcLV1qtxu7dkHPnvZAYX0jP8rK4J//tM9zu6FLF5vQpKjIrtctW9oDjy6XrSHZpo3dJnTsaGtC9uljD6h06ABPPmkPGnbubLcVpaV2/T7uODsCRCml4lg4depSjDGVTRSPI61TV8dvf2sLFvsSsTvTdZN/JCfbo4u+QyVF4Ior7HKawvLlcM89tvH1MsY2olu3+j+2bVvbEa3bkP/rX7Yj6ysnxx5R7dQpOnE3I01WY6uiwtZvW7vWf3pODrz5pv/1KsGsWQO//71/pjmw9Q8vvji8OF5+ObBcRVYW3H+/PTM7bZqtBVd3fTnnHJg40XYIzzgjMBlCWpotmZGbazuEH37oP791a/ubdBoWrBKa1qmLstdft+utr8xMu8726uX8nLIye2DFW9qmstIeSBKxZ9+8teaSk+20qio7vWtX2LLFrv8FBXZbs2eP7QDm5NhyOSJ2e5XqyQ933nlwwQXRe/8HCa1TF9/0u4h/B1SnTkSWisgS4GsRWVL3FrVoVWjLlwd26MAeUaxyGBVb6dAfNwaee865Tk80PPmkf4cObLr3uXMD49uxw9bx8rViRWCHDmwj/MwzkY1VNc7f/hbYoQP7XU2cGN4y/vnPwA4dwNSpznUJ69qwwbn+YHGxXXZ1tT1w4JT9dOpUG/+vfuWc3a683M5bujSwQwe2RuNzz9Ufo1Kq1ubNznXfSkps+xHM44/XbhOqq2vbQGPsuuo9aF1VVdvWVFTYbURVlZ2/dWttiZ59++z/1dV2+1BUVPtar70GGzc27n0qpVQUhbrw6OfAqcAHntuFntv7wJvRD005ClbwtKFp/auqmqZDtGGDf90fL+9Ou9PO++ef++9Qf/ZZ8OV/841tiFV8mDkz+LxwCsIXFdkDF8HMmVP/MkI95vvvYfr04L8ZY+DFF/135upasiT0a3z5ZW2RYqVU/ebODd5erVplz6o5+fjj2vv1lajxMsb/IGN5eW17Y0xtsXGwnUrf5YZqi5RSKsaCXlNnjPkRQESOMsb4pmL7jYjMA+6LdnDKQd0zXo3h1KGKtGBnA70NuFNDXlVVO0wm1DK8z3c6Q6liI9R3Fc5OV31nj8M5u1xfh2rv3tDzy8rqryEXzm8yWEkDpZS/A13vnUaiNFR9del8pzXV6BallDoA4aQIbCEio7z/iMiRQIsQj1fRdPzx4T82WA0277wLL2x8PPXp2tVef1RXRob9m5kZOK9//9oOHdgEFcEUFtoEMSo+hEqc06NH/c/v0MGWqggmnGQpoX4v7drZa3BCZZc86STn36VXQUHoOHr1CqxvpZQKLtT6lJfnXD4EbOITL6cSBU5E/MvVeK+3c5qXmur/f6hti1JKxVg4nbpfAv8QkXUisg54Arg8qlGp4E44wfmi8ZSU0J24uoYNCy9pRWMlJzvX62rZ0u4c163T5fT4kSNt7a66RILXAlOxccstzh0ab/22+ojYZChOv+VBg8Lr1AV7nHfZ2dlw5pnOz+3Xz6Y2v+mm4PE9+qgtS9CzZ+B8l0t/k0o11KGH2jbJyUUXBS9Rc9tttQdgXC7/x/keuHG5ajtnLpd/IqO2bWsPMqal+R9UatOm9v7gwZoBUykV1+rNflnzQJFsz+P3RDekQJr9so6yMltP59NP7fj/vDzb8GVnw1/+YpM1iNiskJMm2Sx9X31VOyTshBPg//6vaWP+/HNb92f16tqSBiecYDMizp5dW9Lg3HOdO3BlZfZC9U8+scNGDznEZkPUenJhadLMfatW2aQoS5faIZc9etidr9Gjw1/GggU2acnKlTYb3bhxNjOl7xncUCoq7PNnzrRJEHr1grPO8i9i/49/2N/Uzp12x3DcOLjvvtrXeOIJ+Pvfa6+xycmxdeW85S9KSuzzP/20tqTBuecGljVQzYJmv4yyigrbHnjLkPTsaQ++hCoJAjaR1sSJtpRPdbVdl91u22akp9sDSt51uF07216Wl9t2KCkJfvrJrvMtW9q2paystvzKzp12vT/+eLv9CHf7o4LS7JfxTb+L+Bcq+2XQTp2IXGSMeUlEHIuAGWMejWCMIWmnrpG2bYMZM2ytuM6dbWcqKwumTLEXmovYekAXXBB+0W6VUJrdDml1Ncyfb5OSgN3xGz7c//e7bl1tp653bxg7NvLDIlessAcaSkpsp27MGL2WrplqdutQItu3z67b3rqnBQV2XXS7YehQOPJI22Fzu+02Yv58284ddZQdIt6QUS0qorRTF9/0u4h/oTp1oYqPe/d+HCpBq4Tx+ef27J1vMpGpU20Hb8eO2mmLFtnpb73lfw2BUvGmqgoefNCezfOaOdPuzN11l/39vvuurW3oPWg1e7Y9U/zAA3YHMBLq1sKbNcvWv3vwwYYVWldKhW/dOjtSZc8eu35v22bPsOXn29Eqn3xih1HfdZetGfntt7XPnTkTRo2yIwf0AKZSqpkJulUzxvzTc/dPxph7696aKD7VGKWldphl3eyQq1fbejt1z9KuXGmvF1Iqnn3wgX+Hzuvrr+28bdv8O3ReP/0UuuZVQ6xY4VwLb/NmrVOnVDQ9/nhtXbni4tosztu317Z1y5bBPff4d+i85s61Q6aVUqqZCedQ1bciMk9EJonIySKSE/WoVGT873/OJRCKi+0Or9PQW6eCykrFk08+CT1v1qzgJQmWLLHXyUQzhnnzNPW5UtGwcaO9Zterbr1J3zI9c+cGX86sWRENSyml4kG9nTpjTE/gfGAptiD5YhFZFO3AVATs3+883bfQal2RrIOnVDSUlISeF2q+MaHnRyKGqiotPq5UNNRd77xtmdP/oQ6sNEWNVqWUamL1dupEpBNwFDAaGAx8BziMO1JxJ1gWvrQ0e6G40zUFffpENyalGqt//9Dz+vULPr9NG/905gcqVIbLwkKbSU8pFVldu9okX151S+J4SxOAXQ+D0Qy1SqlmKJzhl+uBm4D/GmOOMMacYox5KMpxqUgoLLRZwOrKz/cvuOqVkmLrjCkVz844I3BnDuy000+3WTB793Z+7jnnhF+kOJRjjnGu8ygC553X+OUrpQKlpfnXmMzNrT04mZFR26lLSYEbb3QuQZCVBaedFv1YlVKqiYXTqRsMvABcICJfiMgLIvLLKMelIuWWW+xOsPfoZps2cP318Ne/+u+Udu1qa3LpEUwV7zp2tFntBg2ynSgRe//BB21tRpcL7r0Xfvaz2vICBQVwww1w0kmRiSEtzb7emDG1RY4LC+HOO50PpCilIuPMM+Hqq2sPTvbsadstb1bbfv1svcljj4X77689sy9iM+ROmmQLjiulVDMTVvFxEckCRmGHYF4EGGNMYXRDq5XQdeq2brUXZXvrWNWtpXWgdu+2yRp27YLu3W39ndRUWLwYFi6090eNsp01sPV6SkpsnS7f19+50/6fm9v4mFTcirsaW3v32gx0RUW2MzRqlH99t7Iym0Xym29seYDLL3c++1ZWZq+T8x125auy0l7f1qJF9GpTVVTYm++wMNXsxN06FGtVVTYZ1w8/2ALdY8bYwt7RsmWLLU1SUmIvEygttVlo8/PhuOPsNqC62nlbUFpq13+nM/yqSWmduvim30X8O9A6dd4nLwDSgM+BucDRxpgfIxtiM1W3Vtbbb9ujivfd17gdwP/9D/78Z7vD6vXii7ZB/f772mmvv26Ho/3iF3bImdN1PlpPSzW1r7+2R8t9k4m8/LI9qt6xoy25cdFFtWnLAd55B666yp5t81XfTlpKSu2ZtGhJTXUe5qVUc7V7t60Dt2FD7bSXXrLr59ixkX+9//wHnn3WtqWVlbBpkz1L16GDbdtefRVuvx0OP9z5+cEO+iilVDMSzimjk4wxA4wxVxljXtQOXZjWrXOulbVqFUyZcuDL3bcPHnnEv0MH9ojlxx8HPn7aNLsTrVQ8KC2Fhx8OzA5ZVASPPWbv33yzf4cO7Hr0z3/a37lSKraeesq/Qwf2LNnjj8OOHZF9rTVrYPLk2rbUW4+urMzWngTbHj7ySGCJA6WUOoiEU9Igwlvog8THHwevlTV7dmCnLFxz5zqnS9+71+4wOy3XqbOnVCx8/rn9nTpZsQK++sq/DpUvY+zReqVU7OzbZ0eLOKmujnxhb9/2q7LSf/uxb19tO1tebmtEKqXUQSoCF3cpR3XPNPgqLz/wenC7dztPd7v9/4bzHKWaWn2/xXXrQs+PROFwpdSBKy4OrA/nK9Ltje/y6rZvxvgfPNW2Til1ENNOXbQES6kONkvXgV5TF2y56en2QnCna3t69Tqw11Iq0kLVQUxJsckWQl2fNmhQxENSSjVA27Y2MUowodq+A+G7vNRU/4RHKSn+ib+0rVNKHcSCdupE5IxQt6YMMiGNHWvLBzg588wDz8Q3ZIhNtlJXq1b+NXu8MjPhlFMO7LWUirRDD4VDDnGed9JJNpNdsLID2dnwS62molRMJSf714rzVVBgMzFH0rhxtQm96mZqbtWq9n7PnrZ9VEqpg1SoM3Wnhrj9PPqhJbjMTHjggdpaWmA7eddcA8cff+DLFYGJE20KeG8HLivLpnyfNMlmA/Pq29dmFMzPP/DXUyrSfv97W7zbWwQ8MxPOOsv+hsHWoBs/3v+MXffuNruepiRXKvYmTIDLLqs9YycCI0bAH/8Y+WyzWVm1bSnYDl7nzvaWnW3bwVGjbLsYrbIlSimVAIKWNDDG/KIpA0kou3fbhA9lZXDYYXaHE2yNuFWr7JHEI4+0Ry3vv9/Wktu/vzb9cmNlZ8ONN9q6d1u22MZu2DDboI0aBZs32x1ib4HVigp7YfuOHbYhHDbMNoSbNsGCBfZ5hx+unb+Dxd699vdbUgIDBzqf+Y2mli3hyivttTDr19uCwBdc4H+W+aGHbMr077+Hdu3s77aurVttYhVjYORIaN/ef35pqU2csGePHcI1YEB035dSB5MzzoBTT4Vt22zHK1St023b4Msv7bo6fHhtoXCw26L33rN1Ki+4wH97tHw5LFtml3/HHTZRSkmJfX5lpW3TcnL8y/W43Xa7sHmzbXNHjLBnF5VSqpkLt/j4KUB/oOYwuTHmvnqeUwC8C/QDsowxVT7zDgWeAgS4xhizJNSy4qr4+PvvwzPP2JTKXiNG2I7bypW10zIz4c47YfDgyMfw7bfw4IP2gnWv7t3tkcq6DeuqVbUdS6+OHe21B7Nn115kLgJnn23rg6lmp6bg60cf2XTkvllSR4ywO0xNVWvt/ffhd7+zBxu8WrWyxcbDvR7nueds3Uff3++ECbYmI9gyHg8/7J8pr29fuPtu53qNStVDi48foBdfhDff9F9XTz3Vnum76CJY4tP8i9jO4l132TZu0aLaeWlpcP31cPTRwV9r40bbDm7fXjutbVu45x7o0iWS70odIC0+Ht/0u4h/oYqP15soRUSeAs4Frsd2ws4GuobxujuBcYBT7uP7gfOBczz3E8PKlbZWlm+HDmD6dHsU0ldJiT3bEOm6OaWlgR06sLV8Hn/cf1pVlR224tuhA3v086WX/LOGGQNvvBE8VbVKfD/+CH//e2DZi6++gtdea5oYiosDO3Rgf6PXXRfeMubMsfUX6/5+p02Dzz6zZyInTQosnfD997ZDq5RqGl98AVOnBq6r06fbs/VL6hzPNQbeesseEPXt0IHNGv3YY/asXzAPPeTfoQN7Nu+hh4KXGFJKqWYinOyXRxpjLgF2GWPuBY4AHMZC+TPGlBljdgWZ3doYs8EYswkIkUYrzsyYEdgwuN12R9WphEFZmT0bFklz5wZ26Ly+/toWcfZasKC2OKuvvXttbE717j74IDJxqvjj9PsNZ14kTZkS2KHz2rgRvvmm/mWE+o1+8IGtk+X02wY71EsLFCvVNEKtq1984TzdGPjkE+d5brcdbeBk2bLAguhemzbB0qXBY1FKqWYgnE6d93B3iWdIZSXQLYKv6xiDiFwpIgtEZMGOHXFS/9y3w+TlPWvndjvvFEc6dqcYvIzx78QFe6z3TE3dM47g3AlUzUOo387evcE7W5G0aVPo+fXVqYPQv9Gffgo93+3WWlZKNZVQ25y6IwZ8hdoWBVu/Q71WqOcppVQzEU6n7l0RyQX+DCwE1gGNHatVHeR+DWPM08aYYcaYYW29CT9irbAwcFpKSm19OKfMW90a2/9twPJSUvwvQHeKF+y1CeB8DVWw56jEF+q7LSio/V1E08CBweeJhJeSvGuI0d+FhaHfZ2amJgRSqqnUty4GE6qOa7Bl1td2hdpuKKVUMxBOp+5hY8xuY8xb2Gvp+gJ/bOTr7hSRTp4zfw7jFuPUSScFplR3uWz2LafMX/n5NgtmJI0YYROdODn+eP8kEIce6lyMNTfXNpp1U0+7XHDaaZGLVcWXn/0s+I7UhAlNE8O55/rXlvI1aFB4O14TJjgfQBGxpRBGjarN/FrXySc3TedVKWXXx7q1U8Guq2ed5fycpCS48ELnednZtgasky5dgh8UGjSoNku1Uko1U+F06moGvhtjyo0xe3ynBSMiKSIyExgEfCgix4jIXZ7Z92DP9k313E8M+fk2i5ZvpyorC267zTZCvp2kvn3hvvsin1HQ5bLL7d+/dlpyMpx4onNh5rvvtg2ddyfY5bI7tvff798RzcuD3/zGuROomofWreHee/3LA2RmwiWX2N9PU3C54Pnn/WMQsaVBnn46vGUccgjcfrut++jVpg3ceiv062fXufvugz59auenptoDFprdVamm07u3zaybl1c7rVUruOUW+MMfbAkD3zYyK8tOv/lmewDI9yBqt252vQ6Vvfb22+2BVG975y3Xc8cdkX1fSikVh4KWNBCR9kBH4CXgAmzmS4Bs4CljTN8miZA4K2ngtXq1TTbSs2ftkf99+2yGwdxc6NQp+jFs2mSzBnbuXFsENpht2+z1fQUFduce7DV1K1faHe1evZyPqKpmISCN9Jo1Njtkjx6xK+i9eDGsXWvLfhzI0Ci32/5+jbE7j041IDdssEmMunbVUgaqUbSkQSNUV9t1tbo6cF0tLrbJjTIy7Fk433aopMRuq7KyGnZpQFGRrWPZrl3ws/YqJrSkQXzT7yL+hSppEKoi58+Ay4BOwKM+0/cCv4tYdImqR4/AaS1b2iGPTaVjx+BDMetq187efCUn27Me6uATD0ORBg2ytwOVlGTPiIfSubNz4XKlVNNxufzPnPvKyrJ165xkZh5Ym5qX5392UCmlDgJBO3XGmOeB50XkTM/1dEoppZRSSikVtw7WM47hjLebJyKTReS/ACLST0QcLt5SSimllFJKKdXUwunUPQd8CHhz5a8AbopaREoppZRSSimlwhZOpy7PGPMGnnpyxpgqwB3VqJRSSimllFJKhSWcTt1+EWkDGAAROZxEqi2nlFJKKaWUUs1YqOyXXrcA04EeIjIPaAsEqRqqlFJKKaWUUqop1dupM8YsFJFjgD7YWnU/GGMqox6ZUkoppZRSSql61dupE5F04FpgFHYI5hwRecoYUxbt4JRSSimllFJKhRbO8MsXgH3A3zz/nw+8CDSv4g5KKaWUUkoplYDC6dT1McYM8vn/UxFZHK2AlFJKKaWUUkqFL5zsl994Ml4CICIjgXnRC0kppZRSSimlVLjCOVM3ErhERNZ7/u8CLBeRpYAxxgyMWnRKKaWUUkoppUIKp1N3YtSjUEoppZRSSil1QMIpafBjUwSilFJKKaWUUqrhwrmmTimllFJKKaVUnApn+KVSSimllFJKxYzI1FiHEFK48RkTnapweqZOKaWUUkoppRKYduqUUkoppZRSKoFpp04ppZRSSimlEph26pRSSimllFIqgWmnTimllFJKKaUSmHbqlFJKKaWUUiqBaacugqqrDTt2lFNa6o51KEopVcO7bSopqYp1KMqH222/l/JybTOUUko1jtapi5A33tjA88+vY9u2clJTXRx/fDtuvbU32dkpsQ5NKXUQ+89/NjF58lo2by4jOVkYOzaf227rQ+vWqbEO7aBljOGFF37k5ZfXs3NnBenpLk4+uQM33dSLzExtlpVSSjWcth4R8Mor63n00RU1/1dUVPPee1tYs6aY558fgcslMYxOKXWwevvtTfzxj8tr/q+qMsyYsY1Vq4p5+eWRpKToYI1YeOqpNUyevLbm/7Kyav79701s3FjKE08MiWFkSimlEpW26I1UWVnNc8+tdZy3fPk+5s4tauKIlFLKDrn07Tj4WrNmPzNnbmviiBTA/v1VvPLKesd5X321k8WLdzdxREoppZoD7dQ10vr1JezaVRl0vjbQSqlY2L69nC1byoLOX7x4TxNGo7x++GFfyOuutc1QSil1ILRT10gtWyYjIUZX5uToNXVKqaaXlZVMUlLwjVN2to6+j4Xc3NBtgl6HrZRS6kBop66R8vPTGT68teO8pCThxBPbN3FESillO3XHHNPWcZ4InHJKhyaOSAF0755F374tHeelp7sYNy6/iSNSSinVHOih2gi4665DuPrqr/2GOrlcdnp+fnoMI1NKHcxuu603q1YVs359Sc00Ebj11t507doihpEd3O69tz/XXLOQnTsraqalpAj33tufli31TJ1SKr6JTA3rccacHeVIGqe5vA8v7QN83MgAACAASURBVNRFQMeOGbzxxhF88MFWli/fS6tWqZx6agc6dcqseYzbbUIOhVJKqWCMMRhDgzPp5uen89prh/Phh1tZunQPOTkpnHJKBwoLtUMXSz16ZDFt2pG8994WVq4sJj8/jVNPLSA/P43qaqMZk5VSSjWYduoiJCMjidNP78jpp3esmbZ3byVPPLGa99/fQkmJm4EDc7jyyu4cfnibGEaqlEoU27eX8cQTq5kxYxuVldUMH96aq6/uzsCBuWEvIzXVxamnFnDqqQVRjFQ1VIsWyZxzTmcANmwo4a9/Xcmnn26nutpw1FF5XHNND3r3dh6mqZRSStUV1WvqROQxEZkjIn+tM32KiHwpIrNE5IJoxhArFRXVXH3117z55kZKSmymsyVL9nDDDd8wb56WOVBKhbZvXyVXXLGAd9/dQkVFNcbYlPdXX72Qb7/VzJXNxfbtZfzylwv46KNtVFUZqqthzpwirrhiAWvX7o91eEoppRJE1Dp1IjIEaGGMGQ2kisjwOg+50BgzxhjzSrRiiKWPPtrGihXFAdOrq+Gpp1bHICKlVCKZNm0TmzcHliSoqKjmX/9aE4OIVDS89toGv2vrvEpK3EyZsq7pA1JKKZWQonmm7ghgpuf+TOBwn3kGeEFE3hGRrlGMIWa++mpn0HnLl+9j377gte2UUmr+/F1B54XavqjEMn9+8O9Sv2ellFLhimanLhfY67m/B2jlM+9WY8yRwJ+AR5yeLCJXisgCEVmwY8eOKIYZHRkZSUHnJSUJqalaTUIpFVyobUioeSqx6PeslFIqEqKZKGU3kO25n+35HwBjzE7P37kiMsnpycaYp4GnAYYNG2aiGGdUnHhie958c6PjvDFj2pKWpo21Uiq4E09szyefbA86TzUPJ57YnoULdwedp5Q6+DS3VPuqaUTzdNEXwDjP/eOA/3lniEi2528ffDp7zclhh+Vy/vmdA6Z36JDOTTf1ikFESqlEcuyxbR136rt3b8GVV3aPQUQqGk47rYAjjwzMiDxgQA4XXdQlBhEppZRKRFE7U2eMWSgiZSIyB1gMrBeRu4wxDwAvi0gr7LV110Qrhli79dY+jB7dlvff38K+fVUMGZLLaacVaHFZpVS9RIT77+/PCSe0Y8aMbZSXuzn88DacfHIHHZbXjCQnu3jsscP45JPtfPLJdtxuw+jRefzsZ+11mL5SSqmwRbVOnTHmxjqTHvBMPzWarxsvysrcLFq0m2++2U1xcRUiMHhwK/r1C79T9+GHW7nnnu9YsWIfqakuxoxpy9//Ppi8vPSwlzFt2ibefHMjW7aU0r17Fhde2IVjj80/kLeklGpCIsLRR7fl6KPbBn1MtNfv6mrDa69t4O23N1FUVE7fvtlcdlkhI0a0DnsZmzaVMnnyWubOLSIpSRg7Np/LLy+kTZu0iMUZCe+8s5mpUzeycWMJhYUtOP/8Lhx/fLuILHv9+mJOP/0Lvv12L9XVhrZt07j//v788pfd2b27gm++2cWiRbtxuw1ZWckMHdqKgoKMiLy2Ukqp5k+Lj0eJ22244YZv/K6VmDVrB59//hP/+MdgBg9uFeLZ1vTpm7nooi9xu+0lheXl1bzzzhbmz9/F4sUnkJVV/9f36KMreOWV9TX/L1q0m0WLdnP77X0499zA4aFKqcTRFOv33Xd/y4cfbqv5/6uvdrJgwU7++MdDOeGE+q/52ry5lF/8Yr5f2v7XX9/A3LlFPP/8cHJzUyMSZ2P94x+reO65dTX/L1myhyVLlrJlSymXXFLYqGXv3FlB//4fUVJSVTNt27Yyrrrqa3burGDu3J/YtKm0Zt706ZuZO7eIKVOGa8dOKaVUWHRsR5R8+ul2x4vfKyqq+cc/wqtT9/vfL63p0PnaurWMhx/+vt7nb95cymuvrXec99RTqykrc4cVh1Iq/jTF+r1s2V6/Dp1XdTX87W+rqK6uP4fVlCnrHOuwbdpUyhtvOCeTampFReW88MKPjvP+9a+17N9f5TgvXNde+7Vfh87LGLj33mV+HTqvnTsrtE6dUkqpsGmnLkrmzSsKOm/Rot317iSUlVWxevX+oPNnzAjc0arriy9+orraed6+fVUsXtwsc9QodVBoivV77tzg27EtW8pYvbq4UcsINa8p/e9/PzkeQAMoLXXz9dfBawaGY86c4O9z/343brdzBzxePh+llFLxT4dfRklycvD+sssFLpfU+3wReyTXSUpK6OfbZYR+TEqK9umVSlRNsX5H4jVCLSOc7VhTCLW9tvMbF2dSUujnizjP1220UrXCTfMfLi0HoGIlWiUrtMWIknHjgicqOPLIvHqz1yUnuzj00Jyg8888s1O9MRx9dNugO015eakMGpRb7zKUUvGpKdbv445rR5D+Bj16tKCwsEVYywhm3LjIJCFprKOOakN6unNzmJOTwrBh4SeFcXLaaQVB5+XmpuByOb92qHZEKaWU8qWduigZObK1Y42p3NwUbrihZ1jL+NvfDnPs/PXrl81119W/jFatUrn++sCaeElJwp139q336LFSKn41xfrdpUsml11WGDA9Pd3FHXf0DWsZl15aSGFhZsD0gQNzOOOMjo0NMSJatkzh5pt7B3RgXS64444+jS4t8Oijg2jbNjDTZ1KS8MwzQxkwIPAAXrduLbj00sJGva5SSqmDhw6/jBIR4b77+jNqVF5NnbrBg3M577zO5OeHV45g+PA2zJt3LPfc8x0LF+4mPd3FhAkd+cMf+tU7XMjrggu60KdPS7+U5+ed15nevVs25u0ppeJAU6zfv/51TwYNyq0paXDIIdmcd15nunat/ywd2DNdU6aM4K23NjJnji1pMG5cPuPHF5CWFj/19s48sxM9emQxdeoGNm0qpbCwBeec05l+/bIbvezU1CTWrz+FSy75io8+2kZVlaFnzyyefHIII0a04ZRTCvjPfzbz8cfbqK6G0aPzOPPMTmFlOFZKKaWgGXfq9u6t5LXXNjBr1nYAjjmmLeef34WVK4t57bX1rFtXQkFBOuec05mjjsqLSgxVVYaPPtrGBx9spbTUzaZNJQwf3jrsTh1Anz7ZvPbaEY2KY+jQVgwdWn8JBaUiobTUzRtvbGDmzG1UVFRz1FF5XHBBF/Ly4qsmWXOxf3+V5+amuLiKkhL/pBtr1hTz8svrWbJkDzk5Kfz85x047bSCeq/r9TVqVB6jRgXfTi5fvpeXX17PDz/sIy8vldNP7+hX7iArK5lLLy2M+zNPhx2Wy2GHHfiwVWMM77+/lf/8ZxO7dlXSokUSK1bsY/36UjIzXfTrl80ppxRQVuZm2LBWdOpkz2Cmpydx7rmdtcyMUkqpA9YsO3V791Zy+eXzWbeupGbaihXFPP/8j5SWumuGJa1du595837ixht7cfHFXSMaQ1VVNUcc8THff7+vZtq8eT9x0kmf8eqrh3PSSR0i+npKxYOyMjdXX/013323t2ba6tX7+eCDrTz77HDatw//gIaq3+TJa3nyydoSKWvX7mfWrO1MnNifk0/uwJIlu7n22oWUldWmyVy0aDfz5+/kgQcGRCSGuXOLuO22xVRVmZoY5s/fxdKle7j11j4ReY1Ecf/9y5k+fTMAu3aV17RBKSmC223XhYyMJPr2zWbNGrtePPPMMLp3z4pl2EoppZqBZnlN3SuvrPfr0IE9grp06R7HeklPPrmaPXsqIxrDU0+t9uvQeVVWGu64Y0lEX0upeDF9+ma/Dp3X9u3lTJ68NgYRNV9FReU8/fSagOnV1fDYYyuorKzmscdW+nXovD78cBsLFzYuTT/Y7epf/vJDTYfO16uvbmDduuBlWZqb777bU9OhA9i0qazmfmWlqanpV1rqZufOcgD27q3iiSfCq1uqlFJKhdIsz9R9+un2gGn797uprjbs21cZcMF6RUU18+YVcfLJkTt7Nm3apqDz1qzZT1FRGXl5etZCNS+zZu0IOu/TT7dz112HNGE0zducOUVBa6vt2lXJ7Nk7WLp0T9Dnf/rpdoYMadyw7FWritm4MbBwtu9r/OIX3Rr1GonC97dfVlZFZWVtZ7q6GkSoScSyc2clrVvbduizz3bgdhtNXKWUarBIl3mItGil7lfOmuWZumC13SL9nKZcnlKJwIT44es6EVmhPms7v77nRyKG6L9GomjIe63vu1NKKaUaqll26o45pm3AtMzMJFwuccwmlpIiHHlkm4jGMH588FTdhYWZepZONUtHHx247nk5rZfqwI0alUeQ8mbk5KRwzDFtQ2ZujMT30bNnFgUFwbdlY8YcPN/50UfXJpJJT0/2KxzucuFXLqFVq9Sa+6NG5elZOqWUUo3WLDt1F17YhU6dMvymuVxC374tad06NeDxV1zR3a+RjYRf/7oHPXsGXvyenCw8+GBkEhQoFW/Gjy+gd+/A333r1qlcccXBMQyvqeTnpzvWkBOBG2/sRWqqi5tu6uVYY+3YY9syfHjjCmqD3a7efHNvx87lGWd0PKgSgAwcmOtXm7RDh9rObnKyIJ5eXXp6Enl5tr3Jykrmmmt6NG2gSimlmqVmeU1dbm4qzz03nJde+pFZs3ZgjD1ifNFFXfnhh328+up61q3bT0FBBuec05mxY/MjHkNysosvvhjL7bcv4b33tlBW5qZv35ZMnNifMWMi/3pKxYPMzGSefnoYr7yy3q+kwcUXd9XMl1Fw7bU96dWrJVOnbmDr1jK6d8/iggu6MGKE7bANGdKKKVOG88ILP7JkyW5yc1P5+c87cOaZnSIWw7HH5vPMM8N46aX1fP/9XvLy0pgwoSOnnnrwZfi9777+DBmSy/Tpm9m5s4IRI1qzalUxmzaVkpmZRK9eLWnVKpXSUjfDh7fikksK6dIlsDC7Ukop1VDNolM3ffpm/u//VrBlSxmdO2dw5519GTeuHddf34vrr+/l99iSkip++GEf69eXUFxc5anrVMUDDyzn3Xe34HYbjj46j4kT+7N69X4eeGA5q1cX07ZtGtdc052zz+7MpEnfM23aJsrK3Iwc2Yb77uvP3LlF3HrrYoqKKkhNdXH66QU899ww3G5DWZmb8vJqSkrctGyZTFFROVOnbmTx4t1kZ9u6UUcf3ZbPPy9i+vTN7NpVyYABOZx9difKytzcc893zJ+/k/T0JM4+uzO33dabxYv3MG3aJnbsKKdPn5acc06nmppH4SgtdTN9+mY++2wHLpdw7LFt+fnPCxyP6ivVEFlZyVx5ZXeuvLJ7rEOJKrfbza9/vYi3395EeXk1vXrZYtJDh9aeAZs8eQ2TJn3Pjh0VtG6dwu239+Gaa3rWzP/vfzdz6aXz+emnCkSEww7L4bPPxpCZmQJAdbXhgw+28tFH2ygvr2bkyNacfnpHsrNTapYxY8YW3nhjA6Wl1WRn/0RhYUZNpw6gsrKaVav2sXJlMS1bJjNiRCu/4X4VFdW8994WPv10O263YfToPMaP70hGRm1h8OeeW8u//rWWXbsq6NOnJX/4Qz+/JCutW6fSvn06e/ZUkpeXSkFBes2ZqXB99tkO3n13C3v2VDJoUA5nn905IKlVPNq4sYSJE5fxxRdFpKUlUVVVzZo1xVRWQlqaUFZWTWWlQQS2bCmlstJeUzdz5jb++MdlVFYakpOFYcNaMXRoa9xuw6hReYwfX0BmZrNoopVSSjUBSYQLtocNG2YWLFjgOO/uu7/lscdW+F2kLgL33ts/oEbSo4/+wB/+8F3ABe2ZmUkBBXvT0lxUVFT7Pba6uprs7BSKi/0fa4yhtDQwbXhSkgRcEO9yCf36ZftdbwF2qM6WLWV1Hmuzy5WXVwc8Njc3BajdacrISOLxxw9j8OD6s9kVF1dx1VVf88MP/iUXBg3K4YknhpCWlhTkmSpRDRs2jGDrkDow3bq9x/r1/qVTkpKE118/gtNP78ivfrWAZ58NLONw7rmdeeWVw3nyyVVce+03AfNTUoT9+8eTlJTMbbct5rPPivzmd+yYweTJw8jLS2P48I9YsGB3wDLOOacTr79+BB9/vI2zz/4iYBty7LH5vPvuKMrL3Vx33Td8843/Mnr3zuLpp4eRlZXMJZd8yVtv+WfzTU4WJk8exllndWbRot1cf/03lJb6bxevvbYHl18e3pDbhx/+njfe2Og3LTc3haefHho3Qzid1qHly/cyduxs9u61JXHqtiMNlZICAwfabXivXlk8/fRQWrZMqedZSiUOp/UoVhkc4znjYrxntYy0cL+LeP+tRDo+p9cVka+NMcOcHp/Qp2W2by/j8cdXBnTSjIEHH/yekpKqmmn2bNz3AY+tqKimqKiipoaQ9/m7d1cG7KS43bB9u/9jAccOnX28obrOrKoqw7Jl/nW8ysrcfPLJdr94AZYt28u+ff7TjDGsXbuf7dvL68Tg5qGHvneMo66XXvoxoEMHsHjxnoCdN6VUoHvu+TagQwd2nb/66q/ZubOCKVPWOT73jTc2sH59MTfeuMhxfmWl4fjj5zJz5raADh3Apk2lPP30Gr74osixQwcwdepGysqquOGGbwI6dGBLDUyfvpl//3tTQIcOYMWKYl566Ufmz//JcZtQVWW47TZbb/Ohh5YHbCvB1urcvDl4uQOvpUv3BHTowG6DH310Rb3Pj6Xrr/+mpkPnW8LgQFVWwk8/2YN7K1cW8+KLPzZ6mUoppQ4OCT22Y/LktY5Fb8F2lF56aX3NELBXX11PWVngjoe3zpPbbXC57Jmv6mqDMYEpqr2vVVVlSE0Vz/3wj8x6l1dVZaisdJOSYs+I7dtX6flbVTPcxp79c9c8zzuSyRvvrl2V5Of7X6O0Zs1+Vq8upkeP0Ee2P/poW8h5F1zQJez3pNTB6JVX1gedV1RUzu9+tzjg4I+XMXD33UuprAw+SuKLL35ixozg6+mMGdt4773NQecbA1dcsYB16wI7nl7PPbc2ZIKoGTO2sWBB8ALlO3aU88YbG1i92rnAeHU1zJy5jUsuKQy6DAi9Pfryy53s3VvpN9w0nixYsLPmfrC2qKE2bSqjTRu7bZ8xYxvXXtuznmcopVRii/czk/Een1dCn6mrb6iL79Hj+h7r24FryIjUumfiwuX2Cce7DP+zhaGDCLbD6HRUvq6KiuCPKS9v3PAhpQ4G9Z2V2bOnMuT8ukO46zIm9HpaUVFNWVnoGLxnkIIpL68O4zVCx1nfa4Rafm0cwV/DmMicAYsW3+LvkbqSwXfbH87np5RSSkGCd+rOO68zwa7FT0oSzj67tlbcmWd2dKwF5D07l5wsfs+18+o+1n++vR9+vN5YXS6b1trLWzvPt4aey+UiNdWFiPi9R2+8Tkeu8/JSHdPJ13XEEcFr8h15ZF7QeUopK1QG2xYtkrnrrv4hn3/77X2D1pgD6N69Rcj19Igj2nD99aFT4f/5zwNp0yb4mbiTT25fz7agDRMmFASd36JFEhdf3LUmPb/zMurfnoR6TO/eWbRpE7/JUnr3bllzvyFtQSi+ZXdCfT9KKaWUr4Tu1PXvn8O4ce0c551+ekcKCmqzQRYUZDqm8U5NdZGaKjWdJbCdr9RUFykp/p3AlBQhNdVVp1OXFHLnrO48EQJSu7dokUzXrpkBhdE7dswIyEbpLaDevn3gjs611/YkObn+r/TSSws9iVb85eencf75net9vlIHu7///TBatHAevX7zzb049NAchg1zrgPXv38Ohx+ex4UXOq9rIvDee0cyfnwB3bq1CJifkZHElVd257LLujvW3QQ45JCW9OmTwx139HE88NWlSyZXX92D88/vQn5+4LYkJyeFSy8t5KyzOgctYH7ttT1JS0sKOjxw3Lj8kMXPvUaPzmPIkNyA6S4XXHddfA89vP/+/jXtQaQSTHXubL/z7OxkxzqESimllJOE7tQBTJt2BNde24PWrVNJShLatk3j1lt78/zzIwIe+9xzw7n99j60bZtGUpLQunUq11zTg4ULj+Ooo9qQluYiOVkYNCiXDz8czQMPDKBDh3SSkoScnBQuu6wb33//M8aOzSc9PYnkZOGQQ7J5552juOCCzjWdQBHo0aMFCxeOZcCAHJKS7Nm2Vq1SmTRpAO+8M4qRI1uTnu4iPz+Nyy8v5IsvxnHVVT1o3z6d9HQXw4a14uWXR/LqqyPp27clyclCRkYSxx/fjm+/PYGbbupNp04ZpKW5GDgwh0ceGcRppwU/qu6rY8cMnn12OCef3J4WLZLIzk5m/PgCnn12eFwfFVcqXmRlpfLtt8cHrN8PPTSAe+89FIAvvxzHGWd0rDkrn5aWxKmndmDJkhMAeOGFw7niikK/g0QtWyYxe/Yx9OiR46n5N5Rzz+1Mq1YpZGYmMXZsPpMnD6NPH3uGaNu2Uxg0KKfm4FFysnDccfksW3YiANdd14t//GMI3bq1IClJaNEimdNOK+DLL8eSnOyidWtb03PChAKys5Np0SKJk05qz3PPDadjxwwA5swZw7nndiY7O4WkJKFTpwz+9KcBTJxoz0aedloBjz46iEGDckhLc9GpUwbXXdeTBx8cENZn6XIJjz8+mMsvLyQ/P430dBcjR7bmySeHxv3IgZNPLuD11w+nX79skpOFnJxkMjJcQUeQhNKyZRKjR7eiRYskTjzRfgedO2sNO6WUUuFJqJIGVVXVzJy5nXnzikhOFsaNa8dRR7VpcD2kcO3cWcHbb29i1api8vPTGD++I926teCrr3YyY8ZWSkttnbqf/axdg47S7t9fxXvvbWHRIlun7pRTOjBgQE5U3oNSoCUNouWNNzbwzDNrKC52c/jhrfnd7/qSl1d7Jn7btjKmTdvEjz+W0LlzBhMmdKSgICOGESeu7dvLePvtzaxbt58OHdKZMKFjk3Z6fNeh9etLePvtTWzdWka3bi2YMKGjY029/furePfdLSxevJucHDs6YubMbZSUuBk1Ko/f/rYvubnBh68q1dxoSYPwJEpiDhVdDS1pkDCdujlzvuTXv17IkiV7/OaNG5fPQw8N8Bs+GQnLlu3luusWsndvbUkBlwv69m3JsmX+5QB69szin/8cWtNoh7JlSylXXfU1mzf716Q7GIo1q9jRTl3knXXW5/z3v1v9puXkpPDRR0fTv38OX321k1tuWeSX0CQ11cWf/jSA0aPbNnW4CW3hwl3cdNMiv4RXycnCgw8OYOzY4Nc3RpJ3Hfroo23cffe3ftkuMzOTePzxwRx2WO0w0s2bS7nyyq/ZutVbomAfxcVVJCe7aq7hbtUqldmzx9SbsVip5kI7deHRTp2CZlyn7oUX1gV06AA+/nh7wI5VJNx773d+HTqAvXur+Pe/N+F2+2ckW7WqmKeeWh3Wch95ZEVAhw7g6afXsGJFYO04pVT8eeutjY7bnT17KvnVr77G7Tbcc893ARkqKyqqmThxmWY1bIDqasPEid8FZDCuqjLce+93AfU9o2n//iruv39ZQPmCkhI3Eyd+55e58i9/+aGmQ1dUVE5xcZUn7uqa7MW7dlVw5ZV6sEWpWBCZGtGbUrGWMJ26998P3nH773+3RPS1VqzY51h7ac+eSowhoCC4jaH+juX+/VV89tmOoPOj0TlVSkXe5Mlrg85bunQ3s2fvYMeOcsf5e/ZU8vnngUXFlbPFi3c7HggD2L/fzZw5TfdZzp69I2h5nI0bS1m61B543Levkrlza+P66acKv8f6lkKYP38XVVXayVdKKdU4CdOp278/+NFY7xHQaL+W9+iqb4PsVVJSVW9tudJSd8i6dpF+H0qp6Ai1rlZX2+u/DvT5yt/+/aFr5TXlZ1nfa3nn193WB6srCrY90TO3SimlGithOnVDh7YKOi9Y6vAD1bdvNi1aBCY+ycy005xSmQ8Z0qrehC15eWkUFga/sH/YsODvUSkVP0aPDp6VsW3bNI47rp1jXUyw1+YOGaLrergGDMgJKO3iK1TbEGmhttFpaS4OPdQmvGrbNo3OnWsT4tRtM3yvAe/YMYPMTOfyGEoppVS4EqZTd/nl3UhLCwy3VasUzjknsP5cY2RkJHHppYUB03NzU2nXLt2vcDjYYuS/+lV4SU6uusq5YHCvXllNdsG/Uqpx7ryzT9AacTfd1Iu8vDTOPtt5u3TqqQWaAbMBcnJSgtbPPPHE9hQWBtbyi5bu3bM44QTn2qgXXtiF7GybLEtE/Lb1BQXpNZ18EfG5D3fc0SfKUSullDoYJEynrk+fljz55JCaI6UuF4wZ05ZnnhlGfn56Pc9uuMsv78add/ahoMAuOzs7mV/8opBZs8ZwwgntarKXDRyYw9/+Njjso8XHH9+Ohx8eSK9eNttZerqL8eMLeOqpoaSkJMzXodRBLTs7lVmzxjBiROuasy5t26YxadIAbrqpNwC33NKb66/vWZPqvnXrVK66qjt33XVIzOJOVNdd15Obb+5Fu3b2s2zVKoVf/rIbEyf2a/JY7ruvP5dfXkhuru3AtW+fzi239A4own7iie2ZNGkAPXtmkZzs4tBDs+ncOZOMjKSa5z3yyCCuuEKzHiullGq8hClp4JsCt6zMjcslIYfkRIoxhtJSN+npSX5DZiorq6mqMjUN9IEoKakiLS0p6DAtpSJFSxpET1lZFcXFVX716XwF24aohovlZ1l3HaquNpSVucnISKp36L3vtr6kpIqysuqgZ3qVas7iqaRBpEWyREJz+UxU4zTLOnUisgP4MdZxNEAe0FzT2+l7S0xDgIV1piXK+02EOBMhRkiMOOM1Rqd1KJbi9XOKJf1MAsXbZxJv65FXvH1OThIhRkiMOBMhRnCOs6sxxrHYbUJ06hKNiCwI1otOdPremo9Eeb+JEGcixAiJEWcixBgP9HMKpJ9JIP1MwpMIn1MixAiJEWcixAgNj1Mv4lJKKaWUUkqpBKadOqWUUkoppZRKYNqpi46nYx1AFOl7az4S5f0mQpyJECMkRpyJEGM80M8pkH4mgfQzCU8ifE6JECMkRpyJECM0ME69pk4ppZRS1a4NyQAAED9JREFUSimlEpieqVNKKaWUUkqpBKadOqWUUkoppZRKYNqpU0oppZRSSqkEpp26CBKRQ0XkPBEZHutYIkFEOnj+iohMEJHfet5fcqxjiwQROU1EMmMdR1MSkRYi0klEsmIdi1IqfCLSX0T61pk2MlbxxCMR+XWsY4il5t5mK9VYzX0fSBOlNJKIfGCMOVFEbgLGAe8BRwGbjDG/iW10jSMinxhjxorIX4FS4BPgMGCYMeac2EbXeCKyGfgR2AZMA6YbY3bFNqroEJGxwN3AXs8tG2gJPGiMmRnL2HyJyE3GmP8TkUHA3wADJAO/McbMiW10logcCvwR+xm6sDHuBu4xxiyJZWy+ROQ8Y8xrItIFeARoD+zCfpbLYhudlSifZayJyCNAO6AKaANcbozZ4d1Gxza62BCROdjfC4B4/vYHvjXGHB2bqGKrubfZkZII20ZIjO1jAn2Wcb8PFJH9H2OM3hpxAz7x/J0NuHymz411bBF4bzN9//pM/zTWsUXo/X3q+dsNuBWYBXwIXBvr2KLwXucCmXWmtQDmxTq2OjF516cZQE/P/bx4ihOYA3SoM60AmBPr2IJ8lv8BjvLc7wPMjnVsifZZxvrm+50BAz3bquHe7/hgvAG3AFOAMT7T/hvruGL8mTTrNjuCn1Pcbxs9McX99jGBPsu43weKxP6PnpJvvH4i8gLQA0jDHh0DSI9dSBHzvIg8A2wQkZewHdeBwILYhhVZxpi12CNMj4hIO2B8jEOKhnJgAPClz7QBQFlswgmqteeIWmtjzCoAY0yRiMTbkAKp5/94kCEi3YE8Y8w8AGPMDyISb8PuE+GzjLVkEUk1xlQYY5aIyOnAS9gzUwclY8yjIpIKXCEiVwOvxDqmOHBQtNkRkCjbRoj/7WOifJaJsA/U6P0f7dQ1nveahruxQ2PwjNW9O2YRRYgx5kUR+Rj4GXboTzLwjDFmcWwji5hJdScYY7aROEUpG+Ii4DciMgk7jMMNLAEuiWlUgaYBo4F3RCTXGLNbRFoC38Y4Ll9XA38XkVbUDon5Cbg2plEF+h67HfqhzmcZT0OME+WzjLWbgVxgO4AxZpeInAacHdOoYswYUwE8ISJPAxcDzaVtOiAHQZsdKYmwbYTE2D4mymeZCPtAjd7/0WvqlFJKKaWUUiqBxdvpUaVUE/JcUB/3EiFOEfltrGMIRyLEmQgxKqWal0TZ7iRCnIkQIyTMvkXYMerwS6UOEiLSH3AbY773mRx316EEifPVWMXjRESGAhuwQ2F+jr2W9rmYBuUgUeL05UlLPznWcSilmi/PtvFwoBU2o+T/SJztztxYBxCGuIsxEfaBGrv/o8MvD1IiMga4zRjz83CmR+D1JgArjCfFrYjM8rxOyAu4PXV3/tXYeESkLfCiMebExiwnUSVKSvREiFNEJmMvVi8H2gKbsSmS840xV8YyNl+JEKempW+eROQyYIYxZnM9j5sCvGuMeTOc6RGI63fGmAc99ws9r3FoGM+7CdhpjHmhka9/HbDfGBPXB1aaOxF5DJvYbiawB5ve/jjszvQNsYzNV5BkIwJ8YIw5vqnjcZIIMULC7Fs0OkY9U6eaygTgXaChdUtuAf7V2Bf3rBhbROQob4amg8wwY8wxACIyEJgqIrfHOCYniRBnT58YlxpjzvLc/zS2YQVIhDinYbPzTTHGzAIQkf8aY06KaVSqsS7DXtwfslMXA78DHmzIEzyFuy8HhkTg9Z8F5hHnZ8sPAkMdDhpNE5HPYhJNcMXYM4iC/8GvgTGLKFAixAiJsW/R6Bj1mro4Jbbq/XsislhEvhWRcz3Th4rIbBH5WkQ+9JzJQkRmicj/icjnnseP8Ewf4Zn2jedvnwbG8KyIzPc8f7xn+mUi8m8R+UBEVorIwz7P+aWIrPDE8y8R+buIHAmcBvxZRBaJSA/Pw88Wka88jx8dJIwzgQ88y04Skb+IyFIRWSIi13umrxORB0XkCxFZICJDPJ/NarGprr3eBi4M9/03M8li039jbNHS04GJxF9K9ESI0/dg2O987sdbqum4j9MY8yhwJbY0zGtiMzqqOCIihSLyvYg879nuvikimZ55Ae2RiJwFDANe9mzvM0TkD5525FsReVpEwv4N1tPm/aluGyIimSLyhifW10XkSxEZJjbrXYYnppc9i0/ytFPficgMEclwCGEssNAY481u3VNEZnra5oUi0kNExnhifMMTyyQRudAT21Jvm2eMKQHWedtnFTMLROQpETlLRE7w/H0SWBjrwOpYDpxujDnWGDPWczuW+IozEWKEBNm3aHSMsSiwp7ewihCeiR126P0/B0gBPgfaeqadCzzruT/L+3jgaOzwJbDDCpI9948D3vLcH4MdelL3dWumY49oXuS5nwuswBZrvAxY44kpHfgR6IwtirkOaO2JdQ7wd8/zpwBn+bzOLOARz/2TqVMs1TO9G/C1z//XAG/5vJ/Wnr/rgGs89x/DpqltiR1ytt3n+R2BpbH+bmP0exqBHXbnOy0JOC/WsSVanNgNbFKdaanAabGOLRHj9IktGfgFMCnWsejN73spxB6B9xYWfha4LYz2aJjPMlr73H8RONVz369d8HnMFOCsMF4joA3xxPZPz/1DsUOZhnn+L67zvqqAwzz/v4GnvasTy73A9T7/f4ndiQXb/mVi283dQAfssL5NwL2ex9wI/J/P8+8Cbo3193qw34DB2NIAv/P8HRzrmBxi7ACkOkxPjnVsiRSjJ55E2LdodIw6/DJ+LQX+IiJ/wnay5ojIodhG6iPPgc4kYIvPc14FMMZ8JiLZIpKL7dw8LyK9sA1zSgNiOAE4TURu8/yfDnTx3P/YGLMHQESWAV2BPGC2MWanZ/pUoHeI5f/b8/drbANbVwdgh8//xwFPGc8RU+/reEz3/F0KZBlj9gH7RKRMPPU+sDWeCkK/5ebJGPOVwzQ38FoMwgkqEeI0xnznMK2C2t9gXEiUOL0867UOS4tPG0ztsPWXgBuwIyhCtUe+jhWRO7AdoNbAd8A7Ybxun3pew6kNGQX8FcAY862ILAmx/LXGmEUOy/DVAXs2ArE1ozoaY6Z5ll/mmQ4w3xizxfP/amCG5/lLgWN9lrcd6BsiJtUEjDHfAN/EOo5QvL8nh+lVTR1LMIkQIyTMvkWjY9ROXZwyxqwQm53pZOAhEZmBvf7kO2PMEcGe5vD//cCnxpjTxV4YPqsBYQhwpjHmB7+JIiOxyRe83NjfUkOHdXmX4X1+XaXYjqRvPMEy+3iXVV0ntmqfZad7lqmUUip8Tm2LELo9AkBE0oEnsGfLNojIRPy36yGfXs9rOLUhDWmH6rZjTsMvfduhUMuu2+74tkm+7Zu2Q0qpqNBr6uKUiBQAJcaYl4C/YC/S/gFoKyJHeB6TIjb9qZf3urtRwB7PmbQc7FAQsMMmG+JD4Hrv9Q8iMriex38FHCMircReXH6mz7x92LOGDbEC/yOnM4CrPctGRFo3cHm9sRfvK6WUCl8Xb7sDnI9NVx6qPfLd3ns7REUikoUdVhmu+to8J3OBczyP7wcM8JlXKSINGa0C9ixdTwBjzF5go9hszohImvf6wgbQdkgpFRXaqYtfA4CvRGQRdgz+Hz1Dp84C/iQii4FFwJE+z9klIp8DTwG/9Ex7GHumbx526EpD3I8drrlERL71/B+UMWYT9jq8L7Gpgpdh0wWDPX18u9iEKz2CLKLu8vYDq0Wkp2fSM8B6TzyLgQsa+H6OBd5r4HOUCsmTJOHdA3hegYg4pmz3JIEY5rn/O5/phZ51MZzl3yQilzQ0LoflXCciv2jsclRCWw5c6hnK2Bp4sp72aArwlKf9KsdmMF6KTVY1P9wXDaPNc/IEtiO4BLgTe421tx16Gtt+vBzsyQ7+i71O3eti4AbP8j8H2jdgWQBHYdtHpeolNjFdvZeNiMgUsUmKGrr8q53aCd+2RkQOE5GTfeZN9LksJ9SyRUQ+EZHshsblsKyZItKqsctp7rROXTMhYdZ9a4I4sowxxZ6zadOwF7VPa8TyTsemH/59BGL7DBhvjNnV2GUp5SVRqO3ouz6LSLExJsszvZAwamt51r+FwJDGXtvgORMxzxhT35l61QyF+5uLFyKSBKQYY8o8BxA/Bnp7OogHusxpwB3GmJWNjG0wcIsx5uLGLEcdPMLdt5MI13b0Xe/F1p0cZoy5zjNvIjbp0F/qWcYpwHHGmJsjEM+lQCdjzAONXVZzpmfqVKRN9Byd/RZYiz0ye8A8HcJ1jQ1KbPHxR7VDd/CRGJUHEZH3xdaawfOcP3ju3y8iV9Q5EpohNqX/EhF5Hc+1PaJp2JVqqExgrufM3jRsZuQD7tB5/AabMKWx8oC7I7AclYCkicuDiEi+iHztuT9IRIyIdPH8v1ps+Y+as26eGBaLyBfArz3TUoH7gHM9MZzrWXw/T1u5RkSCFWy/EPiPTzyXeN73YhF50TNtiog8KSKfepZ1jNhSWss9HVWv6dih3yqUWKfw1Jve9Ka3aN6IXXmQ32AbxmzskLMPPdM/xWb1K/RZ9i0+rz8QTcOuN73pTW/N6kZsyoN852mDrvO0Qxdis5V/4Zk/EXsmEOxQ5WM89//s0z5dhqc8lc9zPve0G3nAT9iz43Vf+0egped+f+w1snm+78MT92vYJETjgb3Yy49c2Iy0h/ksbyXQJtbfYzzf9EydUqq5WwocJ7ZQ8WhjEwj5pkpfBPwe6OTznJryIIC3PEgOMNVzdu0x6i8IOgfbKRyFvZYzy3NUttDUySjredxLntdcgm1cg1lrwkvDvgOc07Abe/YNPGnYjTHlQN007L7LPWjLgSilVATVLQ8yivrbI1/HisiXIrIUOyKjvnboc+x1nEdjcx4cDYzGtk81RCQHyDXGzPZMerGe5b5njCk3xhRh24d2Do9pbWx5KTyxvul5PMa/JNU7xvbalgLbjDFLjTHV2A5poc/jtB2qh5Y0UEo1ayZ25UHmY4fOrAE+wh7R/BW2IxbOawajadiVUioxNXV5kDnYTlxX7FDIOz2vWTe5V6iSUU6cylrVVSUiLk8HrbElqUDboXrpmTqlVLMmMSoPYux1PBuw6dX/h21cb6POEVKPz7DDYhCRQ7FDML00DbtSSjUPTV0e5DPgImClp3O1E3uAc57vg4wxu4E9njYPPO2RQwwN8QPQ3XP/Y+AcEWkDDS9J5bl2sD0RyLHQnGmnTinV3MWyPMgc7HCSEs/9Tjh36p7EDs9cAtyBrfnopWnYlVKqeWjS8iDGmHWeu595/s4FdhvnpHG/AP7hSZTie0bsU2xiFN9EKeF4D3vtNsaY74AHgNme9/hoA5YDMBT4n2lkNufmTksaKKWUD4mT8iCNJZqGXSml4oYkWHmQxhKbUfoFY8zxEVjWX4HpxpiPGx9Z86Vn6pRSqnnSNOxKKaViwhizBfiXRKD4ODYTp3bo6qFn6pRSSimllFIqgemZOqWUUkoppZRKYNqpU0oppZRSSqkEpp06pZRSSimllEpg2qlTSimllFJKqQSmnTqllFJKKaWUSmDaqVNKKaWUUkqpBPb/Q7C+kqNo+l4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1080x1080 with 16 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# create dataframe from data in X_train\n",
        "# label the columns using the strings in iris_dataset.feature_names\n",
        "iris_dataframe = pd.DataFrame(X_train, columns=iris_dataset.feature_names)\n",
        "# create a scatter matrix from the dataframe, color by y_train\n",
        "pd.plotting.scatter_matrix(iris_dataframe, c=y_train, figsize=(15, 15),\n",
        "                           marker='o', hist_kwds={'bins': 20}, s=60,\n",
        "                           alpha=.8, cmap=mglearn.cm3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OzPiCfFr86UH"
      },
      "source": [
        "#### Building Your First Model: k-Nearest Neighbors"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fjP2HlWD86UH"
      },
      "outputs": [],
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "knn = KNeighborsClassifier(n_neighbors=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7-sg4gP386UH",
        "outputId": "295a6579-9d86-46bb-94f7-0baad39abe51"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "KNeighborsClassifier(n_neighbors=1)"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "knn.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PxlCGO0C86UI"
      },
      "source": [
        "#### Making Predictions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Nx84YltN86UI",
        "outputId": "9977d23b-db42-45ad-a571-cf3c93e6e834"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "X_new.shape: (1, 4)\n"
          ]
        }
      ],
      "source": [
        "X_new = np.array([[5, 2.9, 1, 0.2]])\n",
        "print(\"X_new.shape:\", X_new.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yA9znL1086UI",
        "outputId": "d70e3c96-ed0d-4579-a100-60abac4eb6b4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Prediction: [0]\n",
            "Predicted target name: ['setosa']\n"
          ]
        }
      ],
      "source": [
        "prediction = knn.predict(X_new)\n",
        "print(\"Prediction:\", prediction)\n",
        "print(\"Predicted target name:\",\n",
        "       iris_dataset['target_names'][prediction])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zMFDQXWQ86UI"
      },
      "source": [
        "#### Evaluating the Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lQ287O6H86UI",
        "outputId": "2246e587-ce5f-4e20-e52b-68a1d4bf8518"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Test set predictions:\n",
            " [2 1 0 2 0 2 0 1 1 1 2 1 1 1 1 0 1 1 0 0 2 1 0 0 2 0 0 1 1 0 2 1 0 2 2 1 0\n",
            " 2]\n"
          ]
        }
      ],
      "source": [
        "y_pred = knn.predict(X_test)\n",
        "print(\"Test set predictions:\\n\", y_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_7y7T2l386UI",
        "outputId": "f8e2ff57-b6e9-4192-deff-41d8746d3f95"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Test set score: 0.97\n"
          ]
        }
      ],
      "source": [
        "print(\"Test set score: {:.2f}\".format(np.mean(y_pred == y_test)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5l8kXggw86UI",
        "outputId": "51df7b5d-50b0-4872-f00f-58ffe490728e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Test set score: 0.97\n"
          ]
        }
      ],
      "source": [
        "print(\"Test set score: {:.2f}\".format(knn.score(X_test, y_test)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VZNUcUCR86UI"
      },
      "source": [
        "### Summary and Outlook"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yM_iCUs-86UI",
        "outputId": "5de9dbe7-5f45-45a2-b42c-cf76bab06c4c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Test set score: 0.97\n"
          ]
        }
      ],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    iris_dataset['data'], iris_dataset['target'], random_state=0)\n",
        "\n",
        "knn = KNeighborsClassifier(n_neighbors=1)\n",
        "knn.fit(X_train, y_train)\n",
        "\n",
        "print(\"Test set score: {:.2f}\".format(knn.score(X_test, y_test)))"
      ]
    }
  ],
  "metadata": {
    "anaconda-cloud": {},
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.6"
    },
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}