[인공지능과빅데이터]_21011910_박지호(최종).ipynb

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      "gpuType": "T4",
      "authorship_tag": "ABX9TyP+L2fBip8k8IGsnGALGNaR",
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    "kernelspec": {
      "name": "python3",
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    "language_info": {
      "name": "python"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/parkjh21c/python_practice/blob/main/%5B%EC%9D%B8%EA%B3%B5%EC%A7%80%EB%8A%A5%EA%B3%BC%EB%B9%85%EB%8D%B0%EC%9D%B4%ED%84%B0%5D_21011910_%EB%B0%95%EC%A7%80%ED%98%B8(%EC%B5%9C%EC%A2%85).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from google.colab import files"
      ],
      "metadata": {
        "id": "cjKHqjV4nynG"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Uploaded = files.upload()\n",
        "Uploaded = files.upload()\n",
        "\n",
        "train_df = pd.read_csv('train_data.csv')\n",
        "test_df = pd.read_csv('test_data.csv')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 135
        },
        "id": "2M9ZRBsToGx4",
        "outputId": "3a04b1ed-ec8b-4952-f4c5-773a3c247710"
      },
      "execution_count": 3,
      "outputs": [
        {
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              "\n",
              "     <input type=\"file\" id=\"files-d0096cd4-45e5-400f-8063-a07400fa0143\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-d0096cd4-45e5-400f-8063-a07400fa0143\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
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              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving train_data.csv to train_data.csv\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
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              "\n",
              "     <input type=\"file\" id=\"files-c16e928f-18fc-48d1-b6eb-d5b339eaf4f7\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-c16e928f-18fc-48d1-b6eb-d5b339eaf4f7\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving test_data.csv to test_data.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "train, test의 형태 확인"
      ],
      "metadata": {
        "id": "sRwQJKR_2Vxx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('----------------- train -----------------\\n')\n",
        "print(train_df.head(5))\n",
        "print('\\n')\n",
        "print('----------------- test -----------------\\n')\n",
        "print(test_df.head(5))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FKHoH2Zl1CFL",
        "outputId": "14a24c37-7e9b-433e-e082-288991434e40"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "----------------- train -----------------\n",
            "\n",
            "   age  sex  chest pain type  resting bp s  cholesterol  fasting blood sugar  \\\n",
            "0   51    1                3           110          175                    0   \n",
            "1   50    0                4           110          254                    0   \n",
            "2   46    1                4           110          236                    0   \n",
            "3   59    1                3           130          318                    0   \n",
            "4   61    0                4           145          307                    0   \n",
            "\n",
            "   resting ecg  max heart rate  exercise angina  oldpeak  ST slope  target  \n",
            "0            0             123                0      0.6         1       0  \n",
            "1            2             159                0      0.0         1       0  \n",
            "2            0             125                1      2.0         2       1  \n",
            "3            0             120                1      1.0         2       0  \n",
            "4            2             146                1      1.0         2       1  \n",
            "\n",
            "\n",
            "----------------- test -----------------\n",
            "\n",
            "   age  sex  chest pain type  resting bp s  cholesterol  fasting blood sugar  \\\n",
            "0   64    1                3           125          309                    0   \n",
            "1   51    1                4           128            0                    1   \n",
            "2   55    1                3           136          245                    1   \n",
            "3   42    1                4           136          315                    0   \n",
            "4   56    1                4           120            0                    0   \n",
            "\n",
            "   resting ecg  max heart rate  exercise angina  oldpeak  ST slope  target  \n",
            "0            0             131                1      1.8         2       1  \n",
            "1            1             125                1      1.2         2       1  \n",
            "2            1             131                1      1.2         2       1  \n",
            "3            0             125                1      1.8         2       1  \n",
            "4            1             148                0      0.0         2       1  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "정형 데이터  \n",
        "'target' column은 class를 표현하는 label 데이터이므로 분리"
      ],
      "metadata": {
        "id": "JLfhbYwD0d3p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = train_df.drop(['target'], axis=1)\n",
        "y = train_df['target']"
      ],
      "metadata": {
        "id": "NF8OtbXW0Zke"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# label 제거하고 information 출력\n",
        "print(X.info())\n",
        "print(y.info())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ROM9kq54pFth",
        "outputId": "1c0fc174-a706-4f7b-b2a2-964ad06f00e7"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 952 entries, 0 to 951\n",
            "Data columns (total 11 columns):\n",
            " #   Column               Non-Null Count  Dtype  \n",
            "---  ------               --------------  -----  \n",
            " 0   age                  952 non-null    int64  \n",
            " 1   sex                  952 non-null    int64  \n",
            " 2   chest pain type      952 non-null    int64  \n",
            " 3   resting bp s         952 non-null    int64  \n",
            " 4   cholesterol          952 non-null    int64  \n",
            " 5   fasting blood sugar  952 non-null    int64  \n",
            " 6   resting ecg          952 non-null    int64  \n",
            " 7   max heart rate       952 non-null    int64  \n",
            " 8   exercise angina      952 non-null    int64  \n",
            " 9   oldpeak              952 non-null    float64\n",
            " 10  ST slope             952 non-null    int64  \n",
            "dtypes: float64(1), int64(10)\n",
            "memory usage: 81.9 KB\n",
            "None\n",
            "<class 'pandas.core.series.Series'>\n",
            "RangeIndex: 952 entries, 0 to 951\n",
            "Series name: target\n",
            "Non-Null Count  Dtype\n",
            "--------------  -----\n",
            "952 non-null    int64\n",
            "dtypes: int64(1)\n",
            "memory usage: 7.6 KB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1J0yz4SVpvEw",
        "outputId": "871f8d5c-de64-4257-c8e0-d2b6c8d7ee1d"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 238 entries, 0 to 237\n",
            "Data columns (total 12 columns):\n",
            " #   Column               Non-Null Count  Dtype  \n",
            "---  ------               --------------  -----  \n",
            " 0   age                  238 non-null    int64  \n",
            " 1   sex                  238 non-null    int64  \n",
            " 2   chest pain type      238 non-null    int64  \n",
            " 3   resting bp s         238 non-null    int64  \n",
            " 4   cholesterol          238 non-null    int64  \n",
            " 5   fasting blood sugar  238 non-null    int64  \n",
            " 6   resting ecg          238 non-null    int64  \n",
            " 7   max heart rate       238 non-null    int64  \n",
            " 8   exercise angina      238 non-null    int64  \n",
            " 9   oldpeak              238 non-null    float64\n",
            " 10  ST slope             238 non-null    int64  \n",
            " 11  target               238 non-null    int64  \n",
            "dtypes: float64(1), int64(11)\n",
            "memory usage: 22.4 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "test_df, train_df 모두 결측값이 없음을 확인"
      ],
      "metadata": {
        "id": "BT-U_ka3zmWE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "aTEaUw5pm4DX",
        "outputId": "2b633379-7063-4fd9-f3a3-e6c9711d8f31"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              age         sex  chest pain type  resting bp s  cholesterol  \\\n",
              "count  952.000000  952.000000       952.000000    952.000000   952.000000   \n",
              "mean    53.754202    0.761555         3.235294    132.358193   209.544118   \n",
              "std      9.391389    0.426357         0.939435     18.593297   103.180220   \n",
              "min     28.000000    0.000000         1.000000      0.000000     0.000000   \n",
              "25%     48.000000    1.000000         3.000000    120.000000   186.000000   \n",
              "50%     54.000000    1.000000         4.000000    130.000000   229.000000   \n",
              "75%     60.000000    1.000000         4.000000    140.000000   270.000000   \n",
              "max     77.000000    1.000000         4.000000    200.000000   603.000000   \n",
              "\n",
              "       fasting blood sugar  resting ecg  max heart rate  exercise angina  \\\n",
              "count           952.000000   952.000000      952.000000       952.000000   \n",
              "mean              0.218487     0.684874      139.697479         0.378151   \n",
              "std               0.413437     0.872203       25.973149         0.485181   \n",
              "min               0.000000     0.000000       60.000000         0.000000   \n",
              "25%               0.000000     0.000000      120.000000         0.000000   \n",
              "50%               0.000000     0.000000      140.000000         0.000000   \n",
              "75%               0.000000     2.000000      160.000000         1.000000   \n",
              "max               1.000000     2.000000      202.000000         1.000000   \n",
              "\n",
              "          oldpeak    ST slope      target  \n",
              "count  952.000000  952.000000  952.000000  \n",
              "mean     0.922689    1.612395    0.525210  \n",
              "std      1.096045    0.608366    0.499627  \n",
              "min     -2.600000    0.000000    0.000000  \n",
              "25%      0.000000    1.000000    0.000000  \n",
              "50%      0.600000    2.000000    1.000000  \n",
              "75%      1.600000    2.000000    1.000000  \n",
              "max      6.200000    3.000000    1.000000  "
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>chest pain type</th>\n",
              "      <th>resting bp s</th>\n",
              "      <th>cholesterol</th>\n",
              "      <th>fasting blood sugar</th>\n",
              "      <th>resting ecg</th>\n",
              "      <th>max heart rate</th>\n",
              "      <th>exercise angina</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>ST slope</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>952.000000</td>\n",
              "      <td>952.000000</td>\n",
              "      <td>952.000000</td>\n",
              "      <td>952.000000</td>\n",
              "      <td>952.000000</td>\n",
              "      <td>952.000000</td>\n",
              "      <td>952.000000</td>\n",
              "      <td>952.000000</td>\n",
              "      <td>952.000000</td>\n",
              "      <td>952.000000</td>\n",
              "      <td>952.000000</td>\n",
              "      <td>952.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>53.754202</td>\n",
              "      <td>0.761555</td>\n",
              "      <td>3.235294</td>\n",
              "      <td>132.358193</td>\n",
              "      <td>209.544118</td>\n",
              "      <td>0.218487</td>\n",
              "      <td>0.684874</td>\n",
              "      <td>139.697479</td>\n",
              "      <td>0.378151</td>\n",
              "      <td>0.922689</td>\n",
              "      <td>1.612395</td>\n",
              "      <td>0.525210</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9.391389</td>\n",
              "      <td>0.426357</td>\n",
              "      <td>0.939435</td>\n",
              "      <td>18.593297</td>\n",
              "      <td>103.180220</td>\n",
              "      <td>0.413437</td>\n",
              "      <td>0.872203</td>\n",
              "      <td>25.973149</td>\n",
              "      <td>0.485181</td>\n",
              "      <td>1.096045</td>\n",
              "      <td>0.608366</td>\n",
              "      <td>0.499627</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>28.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-2.600000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>48.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>186.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>54.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>130.000000</td>\n",
              "      <td>229.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>60.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>270.000000</td>\n",
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              "      <td>2.000000</td>\n",
              "      <td>160.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>77.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>603.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>202.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.200000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"train_df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 320.56368830195714,\n        \"min\": 9.391389393231831,\n        \"max\": 952.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          53.75420168067227,\n          54.0,\n          952.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 336.32099346623767,\n        \"min\": 0.0,\n        \"max\": 952.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.7615546218487395,\n          1.0,\n          0.42635680515189506\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"chest pain type\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 335.56623502561007,\n        \"min\": 0.9394352049826188,\n        \"max\": 952.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          952.0,\n          3.235294117647059,\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"resting bp s\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 306.3163184431004,\n        \"min\": 0.0,\n        \"max\": 952.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          132.35819327731093,\n          130.0,\n          952.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cholesterol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 309.343706639397,\n        \"min\": 0.0,\n        \"max\": 952.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          209.5441176470588,\n          229.0,\n          952.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fasting blood sugar\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 336.50058113932613,\n        \"min\": 0.0,\n        \"max\": 952.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.2184873949579832,\n          1.0,\n          0.41343705897474936\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"resting ecg\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 336.30317642156825,\n        \"min\": 0.0,\n        \"max\": 952.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.6848739495798319,\n          2.0,\n          0.8722031671270918\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max heart rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 298.9615822800313,\n        \"min\": 25.973149484873723,\n        \"max\": 952.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          139.69747899159663,\n          140.0,\n          952.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"exercise angina\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 336.4384640395487,\n        \"min\": 0.0,\n        \"max\": 952.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.37815126050420167,\n          1.0,\n          0.4851805372769529\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"oldpeak\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 336.19672651156634,\n        \"min\": -2.6,\n        \"max\": 952.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.9226890756302522,\n          0.6,\n          952.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ST slope\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 336.06788660394324,\n        \"min\": 0.0,\n        \"max\": 952.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          952.0,\n          1.6123949579831933,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"target\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 336.37980010815494,\n        \"min\": 0.0,\n        \"max\": 952.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.5252100840336135,\n          1.0,\n          0.49962652506241556\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "metadata": {
        "id": "31eQ7Q1qH0Il"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(train_df['max heart rate'])\n",
        "plt.title('max heart rate')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "id": "QuKg-en-QCQh",
        "outputId": "e212d801-15e7-49d2-bb1b-32ec64acbf81"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'max heart rate')"
            ]
          },
          "metadata": {},
          "execution_count": 51
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(11,11))\n",
        "sns.heatmap(train_df.corr(), cmap='Blues' , annot=True)"
      ],
      "metadata": {
        "id": "NVxOE2wl0VXB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "53d5b4c2-115b-4622-8c18-4c17b86f2435"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 52
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1100x1100 with 2 Axes>"
            ],
            "image/png": 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il49g5eUv2yYQCmHtFYD4iLCPOkZudhYkkjxovdMLKZVIcGndHHjVaw1jW+diz/eHxEW/RlJiPHwDCmJLT98Abl5l8OTBHYX75Obk4NmjMPi+E49CoRC+AZXxOEzxPlmZGTh7dC8srO1gZmGtMI04PhbXzp+AVznVNczycnMQ8+whHMuoJlYPLJiG5cPaY6MaYzUvNwfxzx/BzjtAtk0gFMLOJwCxTx983DGysyDJy4O2mnrMP8brVy8RHxeHKlULprQYGBqirF953L75cRdf8vLycPjgPmRkpMOvfAAAICEhHndu34KpmTl6d+uIxvVqol/vrrhx/aoqiqE4X2/PoU+AbJtAKIStTwBinnzcOXyfVJIHqUQCDS35xr+GtjZiHt/7kux+lqLqVCvPAMQ/+7Q69f0GVdyjO9g7oQsOzeiPa1sWICstuVjz/rHycnOQ+OIRrL0CZNsEQiGsvAIQH/Hxn0WpJA/aeoaybVKJBJfWzoG3mr433vVfOI9qIRCUjse/EOc4/8u1adNG7vmKFStgaWmJe/fu4cyZMxAIBFi6dCl0dHRQpkwZvHr1Cn379pWlnz9/PgIDAzFjxgy5Yzg6OiI8PBxeXoXnqGRlZRUaCieV5EEg1Cjm0pU+aSlJkEjyYGgiP3TK0MQMMa+eKdwnRZwAQxP5oXWGxqayYa8pb/41fG+YqKGJKVIS81/z9KuAHSv/QujO9ajd7FtkZ2Vgz5pFAIDkxMJDg4tTZkoypBIJ9IxM5LbrGZkgMfLFZx83OTYSd47vhX+j1qjYrANiIsJxesNCaGhqwqdGwy/M9afJSssvo46h/DkQGZogOfrjevRv71kJXSMzuR9RYaHbIBAK4RHcojiz+9GS3sSGkYJ4TSoiblKTxZBI8mBkKr+PkYkZol7Kx/jxfduwbeXfyMrMgI29E0ZM/ROaWvK9YUt+m4ibF04hOzsL/lVqovuQcV9arCJlvIlVXQWxKi6GWA0IaY1KzTog+mk4Tq1fCKGmJnxLOlZT38Tqe8N1dQxNkBT9cWW8uuMf6BqbfTW9y4rEvxkVZWZuLrfdzMwC8fGxSvd99DAcvbt1RHZ2FnR19TBrzl9wc/cAALx6mf83WrpoPoaNGAMvHx/s27MLg77riQ1bdyudP11c3p7D94dc6xp9/Dl8n5aOHizdfHFz/waY2DhCx8gETy+fROyTBzC0tC2ObH+SoupUHUMTpMR8XJ16Z29+nWr1Tp1q7VMRduWrQ9/MGqnxkbi7bw3OLvkZdYf9VuK/UbLflFFkaCK3/VPKeGv3SugYmcH6nQthD0K3QiDUgGftb4oxt5/nv3Ae6b+FPc7/cg8fPkTHjh3h5uYGIyMjuLi4AACeP3+OsLAwlC9fXm4xkypVqsjtf/PmTRw/fhwGBgayh4+PDwDIDfl+18yZM2FsbCz3yI0uuavxVJitkxs6DxmP47s3YnTHBvip1/9gbm0LQxMzCErpVUGpVApLZw8EtekJS2cPlK3dFGWCG+POCfUMK/wSD45uwYvrpxHU+0dZj0/ii0d4eGo3KncaXmLn6MKJQxj8bT3ZIy83V6XvV7VOCCb8uQqjZy6Atb0TFv/6U6G50+37DMNPc1di0E+zEBP5CpuXzVNpnlTh/VgtV6cpypbSWL19aDOeXj2Jut9NKNQ7qU4H9+1B7aCKskdubs5nH8vZxQVrN23HijWb0KZdB0yeOA5PHj8CAEglUgBA6zbt0aJla3j7lMHI0ePg7OKKPbu2F0tZ1CW4xyhIIcWmcV2xesj/cO/4brhWrg1BCc7dLi5hb+vUXj/KxaljhWDYlasKYzsX2PsFoXqfiUh8/hCxjxSPhvma3T+yBS+un0KN3uNlZUx48QgPT+5Glc4l972hSv+F80ilC3uc/+VatGgBZ2dnLF26FHZ2dpBIJChXrhyys7M/av/U1FS0aNECv/5aeEEeW1vFV6HHjRuHkSNHym2zqvWDwrT/NfqGxhAKNWS9xG/l9yqbK9zH0MSs0MJhKUmJsl7At73XKUmJMDazeOeYibB39ZA9rxTcCJWCGyFZnACRSAcQCHB8zyaY26h2nqWOoREEQmGhxZXSk8VFLqb0MfSMzWBq5yS3zczWCU+unv3sY34ukX5+GTNT5M9TVoq4UM/e+8KObUfY0W2oNXAqTOxcZdvjHt9FVmoS9k/uJdsmlUhwc9cKPDy5G00nLS/eQgAIqFITbl4Fq7vn5OQ3PpLFCTCRi60EOLopXhHVwMgEQqEGkhPlYzxZnAAjU/kY19M3gJ6+AaztHOHmXQ7DOjbCtfMnUbV2I1kaY1NzGJuaw9bRBfoGRpg1tj+adegpl5/iovsmVt9fYOlLY1XfxAxm78WqqZ0THqsjVg3exGqyfKxmpoiha6R8EaE7R7bh9uEtaDR0OswcXJWmLWm16tRDWb/ysudvv+MS4uNhYWkl256QEAcvL1+lx9LS0oajU/4QV98yZXHv7m1sWr8G4yZMhrmlJQDA1V1+xXAXVzdERUYWS1k+5O05zHjvHGYkf/gcKmNkaYumI2chJysTOZnp0DM2w/FlM2FoYfOlWf5kRdWpmR9Rp4Yf346w0G2oNWAqjO2Ux6mBhQ209Y2QGvdabjhxSdB+U8as9xbJykwRF+qhfd+DY9vxIHQrag+cBhN7+e+NzNQk7P25YJE+qUSCmzuXI/zkLjSftKJYy/Ah/4XzqBZcVVtt+Jf/F4uPj0dYWBh++ukn1K9fH76+vkhMLKi8vL29cfv2bblh1ZcvX5Y7RoUKFXD37l24uLjAw8ND7qGvr/iWMCKRCEZGRnIPDp3Jp6mlBUd3L4TfKuiBl0gkCL91FS7eZRXu4+pVDuG3r8htC7t5GS7e5QAA5tZ2MDIxR/itgjSZ6Wl49vAeXN+keZeRiRlEunq4fjYUWlra8H5nLqoqaGhqwdLZEy/v35Btk0okeHn/Bmzclf+AVcbWswzEUfJDvcTRr2BoblXEHqoj1NSCiYMHYh4WzBd/u2CLeRG3hwHyh2LfP7wJNfv/DDMnT7nXnCrXRcMxf6HB6Hmyh46xGbzrtUKt/qq55YaOnj6s7BxlDzsnVxibmuPBzYLYykhPw5Pwe3DzKRxbQH6MO3t44/478SiRSHD/5hW4K4jHt6SQAlIpcnOK7imUSiUAoDTNl9DQ1IKVsydeFHOs2niUQeL7sRqlnljV0NSCuZMHIsMK5vlKJRJEht2ApatPkfvdObwFtw5sQMPBU2FRxG1k1ElfXx+OTs6yh5u7B8wtLHD50gVZmtTUVNy9fQt+/p/2w1oikcoa4nZ29rC0tMKziKdyaZ4/ewZb25JZ7E3ZObRyK/ocfiwtkQ70jM2QlZaC1/euwal8tS8+5qd6W6fGhsvXqbEPbxZ56yWgoE6t0e9nmL5XpyqSLo5DdnoKdL7ggsPn0tDUgqmjB6LD5c9j/vdG0efxQehW3D+0EcH9Jxf63nCuXBchY/5Co9HzZA9dYzN412uN4P5TVFaWovwXziP9t7DH+V/M1NQU5ubmWLJkCWxtbfH8+XOMHTtW9nqnTp0wfvx4fPfddxg7diyeP3+O33//HQBkQ3wGDRqEpUuXomPHjhgzZgzMzMzw6NEjbNy4EcuWLYOGxtfVINbX1Ya7o6XsuYu9Ocp72SMxOR0vohTf7qmk1WnRAev+mg4nDx84efri5J7NyM7KQNV6zQAAa/+cCmNzS7To0h8AULv5t5g3YTCO7dqAshWr49qZo3jx+AHa9x8DIP9c1W7+LQ5vXQVLW0eYW9ti/4ZlMDYzh1+VWrL3PbV/G1y9y0Gkq4uwm5exa9UCtOjaH3r6hoUzWcwCGrVG6PLfYeXiCStXb9w8ugO5WZnwrZHfs3h02W/QNzVHUJv83tW83BwkvH7+5v+5SE2MQ+zzx9AS6cLEOv/HqX/DVtg+cySu7NsIj0rBiHkahrsn96NO92EqL48iXnVa4vL6P2Dq6AEzJy88PLkLudmZcKnaAABwae0c6Bqbw69FdwDAg6Nbce/AOlTpNgr6ZtayHkBNkQ40RboQ6RtBpG8k9x5CoSZ0DE1haO1QImUSCASo/0177Nu0ElZ2jrCwtsWutUthYmaBwGoF97WdPX4wAoNqo17zbwEADVt2xIo/psLFwweuXmVxdNdGZGdmokaD5gCA2KhXuHz6KMoGVoWBkQkS42NwcOsaaIlE8KuUv5jT7SvnkCxOgIunL0Q6enj9/Am2/jMfHr7lYWGtujmXASGtcXRZfqxau3rj5pE3sVozP1aPLM2P1eptFcdqmrhwrAY0aoVtM0biyt6N8KgcjOg3sVpXTbFapl4rnFk9B+bOnrBw9sL947uQm5UFj6D8+danV/4OPRNzVHxza6nbh7fgxt41CO45BgZmVshIyh9NoCnShZaOLgAgKy0FaQkxSH/zWtKbuf26RqZquR2OQCBAh87dsGLpIjg6OcPO3gGL/p4HC0sr1K7bQJZu4Hc9UadeA7Tr0BkA8Pe8OQiqUQs2NnZIT0/DoQN7ce3KJcxbsFR23C7de2HJovnw9PKBl7cP9u3ZiWcRT/DL73NLrHxl67fCmVVzYO7kCUsXL9w9ln8OPd+cw1NvzmGlN+cwLzcH4sg3cZqXi3RxPOJf5Mep0ZvV3V/duwqpVApjawckx77Gle0rYGztAM/qJTsP/y3POi1x5U2daurshUdv6lTnN3Xq5XX5dWq55vl1aljomzq1q+I6NTcrA/cObYB9+erQMTJFWlwUbu/5BwYWtrD2qaCWMnrVaYlL6/6AmZMnzJy8EP6mjK5vynhx7WzoGpujfIseAID7R7fi7v61qNZtNPTMrGWjDjRFOtAq4ntDoKEJHSNTGJXQ98b7/gvnkf472HD+FxMKhdi4cSOGDh2KcuXKwdvbG/PmzUOdOnUAAEZGRtizZw8GDBiAgIAA+Pn5YeLEiejUqZNs3rOdnR3Onj2LH374AY0aNUJWVhacnZ3RuHHjEr1n5ceqUMYZh5cV/BidNSp/cbQ1uy/gu0lr1ZUtORVq1kdqshj7NyxDsjgBDq4e6D9htmzodWJctNycMlcfP3QbMQn71y/F3nVLYGnrgN4/zISds5ssTf1WnZGdlYlNi2YhIy0Vbr5+6D9htmzlbAB4/vAeDmxcjqzMDFjbO6F9/9GoXKdxiZTZs0ptZKQk4eLONUhPToSFoxuaj5gmG/6akhAjNx8rTRyPzZMHyZ7fOLQNNw5tg523H1qN+Q1A/m2AmgyaiPPb/sGV3etgZGmDmh36w7tavRIp0/scK9RCVloS7h1Yh8zkRBjbu6Fmv8myIXfpibFyZXxy9gAkebm48M8vcsfxDemIsk06lWjelWncpguyMzOwZv4vSE9LhWeZ8hg2+Q+52IqNeoXU5CTZ88q1GiAlKRG71i1DcmI8HN08MWzyH7IFw7S0tPHw7k0c3b0J6akpMDIxg2fZAIydtUT2OdDSFuH0oV3YtOxP5OZkw9TCGhWC6qBJ264qLe/bWL20cw3SkhJh6eiGFu/HqlA+Vjf9XBCr1w9uw/WD+bHa+ofCsXr5TazW6tgf3kHqiVXXSrWRmZqMG3vXICM5EWYObmgweIpssam0xFi5Oijs1D5IcnNxYqn8fd/9m3ZCQPMuAIAXty7g7Jo/ZK+dWvFroTQlrVuPPsjMyMCMqZOQmpIM/8AK+HPBEohEBbH76sVziN8ZiZWQEI/JP41FXFwsDAwM4eHlhXkLlqJqUA1Zmo5duiM7Oxt//P4LkpOS4Onljb8WLYeDo/xwfFVye3MOr79zDhsNeeccJsRC8M5wzvSkBOyeMUT2/M7RbbhzdBtsPP3QZGT+ucrOSMPVnSuRJo6DSM8QzoE1UPF/3SFU0+2MHANrISs1CfcOfmKdurJwnVqmcScIBEIkvY7A88vHkJ2Rlr/glHcgyjbtXOSt5FTNqUIwslKTcGf/WmQmJ8LEwQ3B/afIhjHnl7HgPD4+ux+SvFyc+2em3HHKNO6Ick06l2jeP9Z/4TzSf4dAKpVK1Z0J+nqsW7cOPXv2RFJSEnR1dYvtuLqBg4vtWF+rHWsnqTsLKheemKruLKhUdIpqhgB/TULci39u8NfmZmzShxOVcimZEnVnQeUGVf+65lGrwsLzTz+cqBRLzsxTdxZUTuOdC2n/VnmSf39TYUbTr28KSlF0a01UdxY+Ssbpkp8eoGrscf6PW716Ndzc3GBvb4+bN2/ihx9+QLt27Yq10UxERERERFSaseH8HxcVFYWJEyciKioKtra2+PbbbzF9+nR1Z4uIiIiIiOirwYbzf9yYMWMwZswYdWeDiIiIiIg+hLejUhv+5YmIiIiIiIiUYMOZiIiIiIiISAkO1SYiIiIiIioNOFRbbfiXJyIiIiIiIlKCDWciIiIiIiIiJdhwJiIiIiIiIlKCc5yJiIiIiIhKA6FA3Tn4z2KPMxEREREREZESbDgTERERERERKcGh2kRERERERKUBb0elNvzLExERERERESnBhjMRERERERGREhyqTUREREREVBoIuKq2urDHmYiIiIiIiEgJNpyJiIiIiIiIlGDDmYiIiIiIiEgJznEmIiIiIiIqDXg7KrXhX56IiIiIiIhICTaciYiIiIiIiJTgUG0iIiIiIqLSgLejUhv2OBMREREREREpwYYzERERERERkRIcqk1ERERERFQacFVtteFfnoiIiIiIiEgJNpyJiIiIiIiIlGDDmYiIiIiIiEgJznEmIiIiIiIqDXg7KrVhw5lKxI61k9SdBZVr1WWyurOgcn8sGKXuLKhUPVczdWdB5RxM9dSdBZV7mpym7iyonL+lvrqzoHKvEzPUnQWV09L4d/8A9rP998dpek6eurOgcldfpqo7C0RfBQ7VJiIiIiIiIlKCPc5ERERERESlAW9HpTb8yxMREREREREpwYYzERERERERkRIcqk1ERERERFQacFVttWGPMxEREREREZESbDgTERERERERKcGh2kRERERERKUBV9VWG/7liYiIiIiIiJRgw5mIiIiIiIhICTaciYiIiIiIiJTgHGciIiIiIqLSgLejUhv2OBMREREREREpwYYzERERERERkRIcqk1ERERERFQa8HZUasO/PBEREREREZESbDgTERERERERKcGh2kRERERERKUBh2qrDf/yREREREREREqw4UxERERERESkBBvOREREREREREpwjjMREREREVFpIBCoOwf/WexxJiIiIiIiIlKCDWciIiIiIiIiJThUm4iIiIiIqDTg7ajUhn95IiIiIiIiIiXYcCYiIiIiIiJSgkO1iYiIiIiISgOuqq02bDjTV+n0gW04tnMDksUJsHdxR5s+I+DsWabI9NfPHcP+DcuQEBMFS1sHtOg6AGUrBslel0qlOLBxOc4f2YOM9BS4+vjh2+9GwcrOUZbmxeMw7F6zEC8ePYBAKIR/UG206jEEIl09lZb1U9So4I4R3RqgQhkn2Foao92IJdhz4pa6s/XRbhzdjasHtiItKQGWTm6o22UgbNx8FKaNexWB89tXIybiEZLjo1G7Yz9UCGld5LEv7d2Es1tXILBhS9TpPEBVRZAjlUqxd/0ynD2yBxlpKXDzKY+OA+TjSpGT+7bhyM71SE5MgIOLB9p9NwIuXgXxfebQLlw+dQQvHochMyMdv687CD0DQ4XHysnJxm+j++Ll00cY98c/cHTzKtYyKiKVSrFm+QIc3LMdaSkpKOMXgMGjxsPe0bnIfTatWY6zJ0Px8tlTaItEKOMXgF4DhsPByUWWZt6sKbh+5SIS4mKho6eHMuX80WvAcDg6u6q8TO+6cngXLu7bjNSkBFg7uaNR98Gwc1ccp7EvI3Bq60pEPX2IpLhoNOgyAFWatJFL8/z+LVzYtxlRTx8iVRyPNiMmw7tSjZIoioxUKsXudUtx+vBupKelwMO3PDoPHAPrD8Tq8X1bcWj7OiQlJsDR1QMd+42Eq1dZ2etr5v+C+zevQJwQC5GOHtx9/dCm+0DYOroAAFKTk7Bs9iS8jHiMtOQkGJqYIqBqLbTqNgC6evqqLDKkUik2/LMIR/btQFpqCnzK+aP/iB9h5+BU5D53b17Fjk2r8Tj8PhLj4zB26mxUq1lXLk3LuhUU7tu93zC06tC9WMugzN3je3Dz8FZkJCXCzMENNToOgJWrt8K0Ca+f4cquNYh7/hCp8TEIavcd/Bq0kksjkeTh6p51eHThGNKTE6FnbAbv6g0R2KwjBCX0Q/3SoZ04u2cTUpMSYOPkjiY9h8DBw7fI9HcvnMCxzf9AHBsFcxsHNOjUF16B1WSvZ2Vm4Oj6JXhw5SwyUpJhYmWLqo1boXLDb2Rp9iydgye3ryIlMR7aOrpw9CqLBp2+g6V90XFSnK4d2YVL+7cgLSkBVo7uaNBtEGyLqG/iXkbgzLZViIp4iOS4aNTrPACVGst/L14/ugc3ju1BUmw0AMDCwRnVW3aBm38VlZflYwW7maKhpzmMdDTxMikLm29G4llipsK0AXaGCPG2gKW+NjSEAsSkZiP0YTwuvUgq4VwTcag2fYWunQnFjn/mI6RdT4z+fTnsXDywcMpIpIgTFaZ/+uA2Vs+ZjGr1m2P07BXwq1ILy38dh9fPnsjShO5Yh1P7tqJd/1EY8csSaIt0sWjqSORkZwEAkhLisGDycFjaOmDEr0vQf8JsRD2PwLq/ZpRImT+Wvq4It8NfYfjMTerOyicLu3gCpzYuQbWWndF58t+wcHTD9t/HIz1ZrDB9blYWjC1tUfPbXtAzNlN67KgnYbh9Yh8sHEu2gXVk+zqc2LcVHQeMxujflkKko4O/fi6IK0WunD6KbSv+QrP2vTBuzgrYu3rgr5/l4zs7KxNlAqsipG23D+Zhx8oFMDazKJbyfKwt6/7B7q0bMGTUT5i7ZC10dHXx08gByM4quty3r19Bi9bt8cfiNZjxx2Lk5uZi/Ij+yMxIl6Xx8C6DkT9OwZJ1OzB99kJIpVKMH9EfeXl5JVEsAMC988cRum4Rarbuil7TFsHKyQ0bfxmLtCTF9U9OViZMrGxRp0Mf6JsojtOcrExYObkhpMcQVWZdqYPb1iJ07xZ0GTgGP/6+HNo6upg7cbjSWL18+ig2L5uHFh17Y8LclXBw9cTciSOQLE6QpXH28EGPYeMxZcFGDJ88F5BKMXficEjenDOBUICAqsEY/NMsTFu8CT2H/4T7Ny5j7d+/qrrI2LFxFfZu34D+I37ErAWroKOji8ljBiFbSZkzMzPh6u6FfsPGFpnmn22H5R5DxkyCQCBAUHB9VRRDoceXT+L8liWo2LwzWv/0F8wdXbH/z5+QUVR9mp0JI0sbVGnVE7pGpgrT3Dy4BfdO7EONjgPRbvISVG3TCzcPbcXdY7tVWJICd84dx6E1C1GnbTf0m7kY1s7uWDvzB6QW8dl7HnYHW+dNQ4W6TdD/lyXwqVQDG3+fiOgXT2VpDq1egEc3L6P1oB8xaPZKVGvSBvv/mYcHV87K0ti6euF/A8Zg0OyV6PLjr5BCijUzxkAiUX29c//CCRxfvxg1WnVB96kLYenkhs2zxhVd32RnwdjKFrXb9YZ+Ed+LhmYWCG7XG92m/o1uU/6GU5kAbP9jEuJeRqiwJB+vor0R2vhZY9+DWMw89gSvkjIxpIYzDEQaCtOnZefhYFgcfj/5FNNDH+PCMzG6VrSDr5VqL7wRKcKG83/E1q1b4efnB11dXZibm6NBgwZIS0sDACxbtgy+vr7Q0dGBj48PFixYINuvV69eKF++PLLe/CDOzs5GYGAgunX78A/6z3Viz0ZUb9gC1eo3g42jK9r1Gw1tkQ4uHNurMP3JvVvgE1gV9Vt2go2DC5p16gsHVy+cPrANQH6vw8m9W9CobTf4VakFexcPdBn6E5IS4nH70mkAwN0rZyHU0ETbviNhbe8EZ09ftOs/CjcvnEBs5EuVlfVTHT57D5MX7MXu46Wnl/mta4e2o1ztxihbKwTm9s5o0H0oNLVFuHPqkML0Nm7eCO7QF97V6kBTU6vI42ZnZuDA4l/RoOdw6Ogp7pVVBalUimN7NqPxt93hX7UWHFw80H34BCQlxOHmhdNF7nds1ybUaNQCQQ2awdbJFR0HjIa2SIRzRwviu9437RHStitcvcsWeRwAuHv1PO7fuITWPQYXW7k+RCqVYueWdejQrS+CatWFq4cXRv00DfHxsTh3+liR+02bsxANm/4Pzm4ecPP0xsgfpyAmOhIPw+7L0jT9X1v4BVSEta09PLx90b3vYMTGRCE66nVJFA0AcOnANgTUbQr/2o1h6eCMJr2GQ1Mkws2TBxWmt3P3Qf1O/VA2qG6RceoeUAV12vWCd+Waqsx6kaRSKUJ3b0Kzdj0QUC0YDq4e6DViIsQJcbh+4VSR+x3ZuQG1Qr5BjQbNYefkii4Dx0BbJMLZIwWxGty4JbzKBcLC2hbOHt5o2aUfEuKiERcTCQDQNzBCnaat4eLpC3MrW/j6V0adpm3w8N5NlZd5z9b1aNe1D6rWrAMXdy8MGzcFCXGxuHjmRJH7VaxaA517D0K1WvWKTGNqZiH3uHj2JMoFVIKNnYMKSqLYrSM74FOzCbxrNIKpnTNqdR4CTW0Rws4eVpjeysUb1dr2gUeVOtDQUhyn0Y/vwyWgGpzKV4GhhTXcKtaCfZkKiIkIU2VRZM7v24IK9ZoisE4TWDm4oHmfEdDSFuH6iQMK0188sB0e/lVQo0UHWNo7o177XrB19cSlQztlaV6E30VAcAhcywbA1MoGlRo0h42zO149fiBLU6lBc7j4+sPUygZ2rl6o164XkuNjII6JUnWRceXANpSv0wR+wY1hYe+MkJ7DoCUS4XYR34u2bt6o2/E7+AbVLfI8elQIgntAVZjZOMDM1gHB3/aCto4uXj+6rzB9SavnaY6zEWJceJaEqJRsbLgeiew8Cao7myhM/zAuHTdfpyAqJRtxaTk4/jgBr5Iz4W7x9YwGpP8ONpz/AyIjI9GxY0f06tUL9+/fx4kTJ9C6dWtIpVKsW7cOEydOxPTp03H//n3MmDEDEyZMwKpVqwAA8+bNQ1paGsaOzb/6Pn78eIjFYsyfP18lec3NycGLx+HwKl9Jtk0oFMKrfCVEhN1VuM/T8Dvwfic9APgEVkVE2B0AQHz0aySL4+HlX1n2uq6+AZw9y+DpmzS5OTnQ1NSCUFjwkdDSFgEAntwvfY3Ur01ebg6iIx7CqUzBEEeBUAinsoGIfHzvi459bM18uPpXgXNZxcMnVSU++jWSE+Ph418Qe7r6BnDxKoMnb+Lqfbk5OXj+OAze78SiUCiEj38lWSx+rGRxAtb9/St6DJ8AbZHO5xXiM0S9foXE+DgEVq4q26ZvYAjvMn54cOfjPyvpaakAAEMjI4WvZ2ak4/D+XbCxtYellc2XZfoj5eXmIPJpOFzKycepa7kKePXwy+JUneKiXyMpMR6+AQVxp6dvADevMnjyoOhYffYoDL7vxapvQGU8LiJWszIzcPboXlhY28HMwlphGnF8LK6dPwGvcoFfUKIPi458hcSEOJSvKB+nXr7lEHa3+Op0cUI8rl44gwZNWxbbMT8kLzcHcc8fwsE3QLZNIBTC3jcA0U8+v3Fk7e6LVw9uQBydf7E4/sUTRD+6C8dylT6w55fLzc3B66fhcPOrKNsmFArh5lcRL8MVf/ZePLwHNz/5et/DvzJehhf8VnD0Kouwq+eQnBALqVSKp3evIz7yJdzLKy5TdmYGbpw4CBMrWxhZWBVDyYqWl5uDqIhwuJSVr2+cy1bA60fFU99IJHm4f/44crIyYadkultJ0RAATiY6CItJk22TAngQkwZXs49rCHtb6sPaQIRHcekfTvxvJRCWjse/EOc4/wdERkYiNzcXrVu3hrNz/hxEPz8/AMCkSZMwe/ZstG6dP0fG1dUV9+7dw+LFi9G9e3cYGBhg7dq1qF27NgwNDTF37lwcP34cRkX82P1SaSlJkEjyYPjekEdDEzPEvHqmcJ8UcQIMTeSHnhkam8qGE6a8+dfQ+L00JqZIScx/zdOvAnas/AuhO9ejdrNvkZ2VgT1rFgEAkhPjv7xg/3EZKcmQSiTQMzaR265nZIrEyBeffdywCycQ8+wROk386wtz+OmS3sSO0XuxamRiVmTMpCaLIZHkFdrH0MQM0S+ff/R7S6VSrP5zOmo1bglnT1/ER0d+Yu4/X2JCHADA1NRcbrupqbnstQ+RSCRYPG8WyvgFwMXNU+61vds3YfnCP5CZkQEHJxdMn7sYWkX0rBS39JQkSCUS6L9XV+gbmSL+9efHqbolvYlHRXGX9KFYNS0c31Ev5evi4/u2YdvKv5GVmQEbeyeMmPonNN87Z0t+m4ibF04hOzsL/lVqovuQcV9aLKXECfnlMnkv/8afEKcf49ihPdDV00NQcNE91MUtMzW/Pn1/yLWuoSnEXzBCKqBxO2RnpmPzxO8gEAghlUpQuWV3eFZVfdnSk/M/ewbvf/aMTRH3SnHdmCpOUJj+3aHdTXsOwZ6lczBnYHsINTQgEAjR4rvv4eLrL7ffpcO7cGTdYuRkZcLczhHdfpyldKRTcXhb3+gpqG8SvrC+iX3xFGsnD0VuTja0dXTRctgkWNgXvQZFSTEQaUJDKEByVq7c9pSsXFgbiorcT0dTiBlNvaAlFEAilWLjjSg8eKfxTVRS2HD+D/D390f9+vXh5+eHkJAQNGrUCG3btoW2tjYeP36M3r17o2/fvrL0ubm5MDY2lj0PCgrCqFGjMHXqVPzwww+oWVP5cMOsrCzZ0O63srOzoK1ddKWobrZObug8ZDx2rpyPvWsXQyAUonaztjA0MSuxRVHo06TEx+DE+oVoPXomNLW1Vf5+l04cwoaFv8meD5jwm5LUqnVi71ZkZaQjpE1Xlb/XscP78NdvU2XPJ8/68tEmf8+ZgYgnj/H7gpWFXqvbqCkCK1dDQnwctm1YhZkTRmP2wlXQFn299cfX5sKJQ3JziIdM/F2l71e1TgjKBFZBUkIcDu9Yj8W//oSxsxbLRu0AQPs+w9CiQy9Ev36B7asWYvOyeeg8cHSx5eHkkf1YOGe67PlPM+cV27GVCT2wG8ENmnzV328f6/GVU3h08Tjq9R4DMztnxL14gvObF0Pf2Axe1RuqO3uf5eLBHXj58B46jp4GYwtrPLt/C/tX/AlDU3O4v9O7Xb5mfbj7VUSKOB7n9m7Glj+noNfkv6BVAt8tqmBm64Ae0xchKz0NYZdOY/+S39Bx/OyvovH8ObJyJZgZ+hgiTSG8LfXRxs8acWnZePhf7nUmtWDD+T9AQ0MDR44cwblz53D48GH89ddfGD9+PPbs2QMAWLp0KapWrVpon7ckEgnOnj0LDQ0NPHr06IPvN3PmTEyePFluW+cBo9Bl0JgP7qtvaAyhUEPWS/xWfq+yucJ9DE3MCi0clpKUKOtdedt7nZKUKLeIUoo4EfauHrLnlYIboVJwIySLEyAS6QACAY7v2QRzG7sP5puU0zU0gkAoRHqSWG57/sqtiheq+ZDoiEdITxZj3aRBsm1SiQQvw2/jRuhuDF22F0Kh4sVGPkf5KjXh8s6c49ycbAD5Q6bfjatkcQIcXD0L7Q8ABkYmEAo15BZXAvLj+/2ePWXCbl/Fk7A7GNpWfrXfX7/vg8q1G6L78AkffawPqVazDnzK+Mme52TnlzsxMR5mFpay7YmJ8XD3ULyi77sWzJmBS+dO4bf5K2BpVXg4r76BIfQNDGHv6AyfsuXxbZOaOHfqGOo0bFIMpVFOz9AYAqGw0MI8acmJhXqhv2YBVWrC7Z1V2nNycgDkx6aJXB2YUOQq7LJYTZSP1WRxAozeG22gp28APX0DWNs5ws27HIZ1bIRr50+iau1GsjTGpuYwNjWHraML9A2MMGtsfzTr0FMuP1+iSo3a8CpTTvY8Jzu/zOLEBJiZF8RpUmI8XD8iTj/G3VvX8OpFBEZN/KVYjvexdAzy69OMZPk4zUj5/PoUAC5uW46Axu3gUaUOAMDMwRWpCTG4fmCzyhvOekb5n733FwJLS0qEQRGL7hmYmClO/+ZvkJOdhdCNy9Hh+ynwqpC/0raNszuinj3Gub2b5RrOOnoG0NEzgLmtAxw8y+DX3v/Dg8un4VdDdQu+va1v0hXVNyZfVt9oaGrB1NoeAGDj6oWop2G4emgHQnoN/6LjfqnUrFzkSaQwEsk3PwxFmkjOzC1ir/zh3LFp+Z/pl0lZsDESIcTbAg/jPn6k1r8KO3TU5t85AJ0KEQgEqFGjBiZPnozr169DW1sbZ8+ehZ2dHZ48eQIPDw+5h6trwerEv/32Gx48eICTJ0/i4MGD+Oeff5S+17hx45CUlCT3aNd32EflU1NLC47uXgi/dVW2TSKRIPzWVblGy7tcvcoh/PYVuW1hNy/DxTv/R5S5tR2MTMwRfqsgTWZ6Gp49vAdX73J4n5GJGUS6erh+NhRaWtpy81Hp82hoasHaxRMv7l2XbZNKJHhx7wZs3T9v3pVTmQB0nbYYXaYslD2sXb3gU60eukxZWKyNZgDQ0dOHla2D7GHr6AojU3OEvROrGelpiAi/BzcFcQXkx7eTuzfC3olFiUSCsFtXFcZiUdr1HY7xc1fhx7kr8ePclRg4Mb/3u/foyfimS7/PLKFienr6sHNwkj2cXN1ham6BG1cuytKkpaUi7N5t+JQrX+RxpFIpFsyZgXOnjuGXP5d+1EJKUqkUkObfcqskaGhqwdbVCxF3rxXkQSJBxJ3rsP8K5gd+LB09fVjZOcoedk6uMDY1x4ObBXGXkZ6GJ+H34OZTdKw6e3jj/nuxev/mFbgriVUppIBUitw3jXWFaaQSAFCa5lPp6unD1t5J9nB0cYOpmQVuXbskS5Oelorw+3fgXbboOP0UR/fvgruXL1w9VH8LuHdpaGrBwskTrx7ckG2TSiR4ff8GrN2KvnXTh+RmZxUaYSUQCgGp9LOP+bE0NbVg5+qFp3cKPnsSiQRP7lyDg5fiz56jZxm59ADw+NYVOLy5XVpebi4kebmFyiQUCiGVSIrOjFQK6QdiuDhoaGrBxsULz977Xnx29zrsPIq3vpFKpMgroXpUmTwp8FycCe93VsQWAPC20sfThI/vPRYA0BSy8Ugljz3O/wEXL15EaGgoGjVqBCsrK1y8eBGxsbHw9fXF5MmTMXToUBgbG6Nx48bIysrClStXkJiYiJEjR+L69euYOHEitm7diho1amDOnDkYNmwYateuDTc3N4XvJxKJIHpvWKW2dtG3/3hfnRYdsO6v6XDy8IGTpy9O7tmM7KwMVK3XDACw9s+pMDa3RIsu/QEAtZt/i3kTBuPYrg0oW7E6rp05ihePH6B9//weboFAgNrNv8XhratgaesIc2tb7N+wDMZm5vCrUkv2vqf2b4OrdzmIdHURdvMydq1agBZd+0NPv+RWav4QfV1tuDsW9J642JujvJc9EpPT8SJK8e0rvhYVQlrj0NLfYeXqBRs3b1w/vAM5WZkoWyu/V+rgklkwMLVAzW97AchfOCX+zdy2vLwcpCbGI+bZY2jr6MDE2h7aunqwcHCRew8tbR3oGhgW2q4KAoEA9Vq0w4HNq2Bl6wBzazvsWb8UxmYW8K9WEFd/ThgK/2rBqNOsLQCg3v/aY/Wf0+Hs4QNnzzI4vmczsjIzEdSgmWyfpMR4JCfGy1Z0f/3sMUS6ejCztIG+oRHMLOUXyxLp6AIALGzsYariBW0EAgFaftsZG1cthb2jM6xt7bFm2d8wN7dE9XdWIh47rC+qB9fDN206AgD+nj0DJ44ewMSZc6Grp4+E+Px5pvoGBhCJdBD56iVOHTuECpWDYGxiirjYaGxeuwLaIhEqB5XcatRVmrTBnsWzYOvqDTt3b1w6uB05WZkoX7sxAGD3wl9gaGqBuh36AHizUNObOb95ublISYxDdMQjaOnowswmv8cnOzMDiVGvZO+RFBuJ6IhH0DEwhHERi2gVJ4FAgPrftMe+TSthZecIC2tb7Fq7FCZmFgisFixLN3v8YAQG1Ua95t8CABq27IgVf0yFi4cPXL3K4uiujcjOzESNBs0BALFRr3D59FGUDawKAyMTJMbH4ODWNdASieBXKQgAcPvKOSSLE+Di6QuRjh5eP3+Crf/Mh4dveVhY26q0zC3adsKWNctgZ+8EK1s7rF+xEGYWlqhas44s3YSR/VCtVl00a9UBAJCRkY7IVwXzS2MiX+HJozAYGhrB8p38pqel4tzJI+g5YKTKyqBM+YatcOKf2bB09oSlqzduH92JnOwseNXI7xk+vuJ36JuYo0rrngDy4zQxMr8+leTmIk0cj7gXj6El0oWxVf6oKufyVXF9/0YYmFnB1M4ZcS8e4faR7fCu0UhxJopZULNvsWPhL7Bz84a9hw8u7N+GnKxMBL757G3/eyaMzCzQoGP+1LKqTVpj5ZQROLd3MzwDq+HOuWN4/SQcLb77HkD+BSRnX38cXrcYmtoimFhaI+LeTdw8dRghXQcAABKiX+Pu+RNwL18JekbGSI6PxZndG6ClLYJnYFXFGS1GlZq0wf4ls2Dj6gVbN29cOZT/vegXHAIA2LfoVxiYWqB2+94A3tQ3r57J/p+SGIfoZ4+graMr62E+uWk53Pwrw8jcCtmZGbh37hieP7iJdqNnqrw8H+PYw3h0q2SHZ4kZeJaYgboe5hBpCHH+mRgA0L2iHcSZudh1NwYAEOJljmfiTMSmZkNLQ4Cy1gao6mSCDTdKbm0PorfYcP4PMDIywqlTpzB37lwkJyfD2dkZs2fPRpMm+UMf9fT08Ntvv2H06NHQ19eHn58fhg8fjszMTHTp0gU9evRAixYtAADfffcd9u3bh65du+LUqVNyQ7qLS4Wa9ZGaLMb+DcveDHv1QP8Js2VDrxPjovOvgr/h6uOHbiMmYf/6pdi7bgksbR3Q+4eZsHMuaNjXb9UZ2VmZ2LRoFjLSUuHm64f+E2bLzcF7/vAeDmxcjqzMDFjbO6F9/9GoXKdxsZfvS1Qo44zDywp672eNagMAWLP7Ar6btFZd2foo3lXrICMlCed3rEZ6UiIsndzQ6vvpsiGwKfGxELyzCmNqYjzWTRooe3714FZcPbgVDt7l8e049c0vflfD1p2RlZmB9QtmIT0tFe6+5TF4knxcxUa9Qmpykux5pVoNkJosxt71y5CcmD+se/Ck2XILN50+uBP7N66QPZ/zY/5w9K5Df0RQ/YIGtrp827knMjMzMG/WFKSmpqCsXyCmzl4gNw858tVLJIvFsuf7dm4GAPwwpLfcsUb+OAUNm/4P2iJt3Ll5DTs3r0VqSjJMzMxRzr8i5ixaDRNTxdM0VKFMUF2kpyTh1NaVSEtKhLWzO9r/MFM2/DM5PkYuTlMS47F8fH/Z84v7tuDivi1w8i2PLj/NAQBEPgnDuumjZGmOrs1feNCvViO06P/hKSzFoXGbLsjOzMCa+b8gPS0VnmXKY9jkP5TGauVaDZCSlIhd65YhOTEejm6eGDb5D9m0Ai0tbTy8exNHd29CemoKjEzM4Fk2AGNnLZHFs5a2CKcP7cKmZX8iNycbphbWqBBUB03aqn5+fqsO3ZGZkYEFs6chLTUFvn4BmPjrfLn5yFGvXyL5nSkkj8LuYcKI72TPVyzIP4d1Q1pg2NiCKUinjx2CVArUqhei8nIo4l65NjJSknBl91qkJyfA3MEdTYdOhd6bBcNSE2LkelrTxQnYPrXgtnW3Dm/DrcPbYOvlhxajZgEAqnccgCu7VuPM+r+RkSKGnrEZfIObokLzTiVSpnLV6yItWYzjW/5BqjgRNs7u6DL2V9lQ7aQ4+c+ek3c5tBkyHsc2rUDoxuUws7FHh1FTYO1YMGKu7bAJCN2wFNvnT0dGagqMLa1Rr0NvVGr4DQBAU0sbzx7cwoUD25CRmgIDY1M4+5ZH7ynzCi08pgq+1eogI0WMM9tWIS0pEVZO7vh29AzZ92J+fVNwHlMT47HqpwGy55f3b8Hl/Vvg6FMeHcfPBgCkJ4uxb/EspIkTINLVh6WTK9qNngmXd4amq9PVV8kwEGmgeRlLGIk08TIpC/PPPkdKVv59s031tPDueABtTSE6BNjARFcLOXlSRKdkYeXlV7j6Klk9BfgKcO0d9RFIpSUwBof+8w7ejVV3FlSuVZfJH05Uyv2xYNSHE5VinqYG6s6Cyrma//vLePrZv7++cTXS/3CiUs7SoPQvuPUh+x9GqzsLKmVn9O8/h+k5eerOgspdfZmq7iyo3ILWpWc6jl6bFR9O9BVI39ZL3VkodpzjTERERERERKQEG85ERERERERESnCOMxERERERUSnAOc7qwx5nIiIiIiIiIiXYcCYiIiIiIiJSgg1nIiIiIiKi0kBQSh6f4e+//4aLiwt0dHRQtWpVXLp0SWn6uXPnwtvbG7q6unB0dMSIESOQmZn5eW/+EdhwJiIiIiIiIrXZtGkTRo4ciUmTJuHatWvw9/dHSEgIYmJiFKZfv349xo4di0mTJuH+/ftYvnw5Nm3ahB9//FFleWTDmYiIiIiIiNRmzpw56Nu3L3r27IkyZcpg0aJF0NPTw4oViu9bfe7cOdSoUQOdOnWCi4sLGjVqhI4dO36wl/pLsOFMRERERERUCggEglLxyMrKQnJystwjKytLYZmys7Nx9epVNGjQQLZNKBSiQYMGOH/+vMJ9qlevjqtXr8oayk+ePMH+/fvRtGnT4v+jv82Tyo5MRERERERE/zkzZ86EsbGx3GPmzJkK08bFxSEvLw/W1tZy262trREVFaVwn06dOmHKlCmoWbMmtLS04O7ujjp16nCoNhEREREREZUO48aNQ1JSktxj3LhxxXb8EydOYMaMGViwYAGuXbuG7du3Y9++fZg6dWqxvcf7NFV2ZCIiIiIiIio2AsFnLlldwkQiEUQi0UeltbCwgIaGBqKjo+W2R0dHw8bGRuE+EyZMQNeuXdGnTx8AgJ+fH9LS0vDdd99h/PjxEAqLv3+YPc5ERERERESkFtra2qhYsSJCQ0Nl2yQSCUJDQxEUFKRwn/T09EKNYw0NDQCAVCpVST7Z40xERERERERqM3LkSHTv3h2VKlVClSpVMHfuXKSlpaFnz54AgG7dusHe3l42T7pFixaYM2cOAgMDUbVqVTx69AgTJkxAixYtZA3o4saGMxEREREREalN+/btERsbi4kTJyIqKgoBAQE4ePCgbMGw58+fy/Uw//TTTxAIBPjpp5/w6tUrWFpaokWLFpg+fbrK8siGMxERERERUSlQWuY4f47Bgwdj8ODBCl87ceKE3HNNTU1MmjQJkyZNKoGc5eMcZyIiIiIiIiIl2HAmIiIiIiIiUoJDtYmIiIiIiEqBf/NQ7a8de5yJiIiIiIiIlGDDmYiIiIiIiEgJDtUmIiIiIiIqDThSW23Y40xERERERESkBBvOREREREREREqw4UxERERERESkBOc4ExERERERlQK8HZX6sMeZiIiIiIiISAk2nImIiIiIiIiU4FBtIiIiIiKiUoBDtdWHPc5ERERERERESrDHmUpEeGKqurOgcn8sGKXuLKjciIG/qzsLKrV1zUR1Z0HlUjJz1J0FlfsvXItPzv73n0c/E2N1Z0Hlzj8RqzsLKlXO3lDdWVC5mNR//2fxUWSyurNA9FVgw5mIiIiIiKgU4FBt9eFQbSIiIiIiIiIl2HAmIiIiIiIiUoINZyIiIiIiIiIlOMeZiIiIiIioFOAcZ/VhjzMRERERERGREmw4ExERERERESnBodpERERERESlAUdqqw17nImIiIiIiIiUYMOZiIiIiIiISAkO1SYiIiIiIioFuKq2+rDHmYiIiIiIiEgJNpyJiIiIiIiIlGDDmYiIiIiIiEgJznEmIiIiIiIqBTjHWX3Y40xERERERESkBBvOREREREREREpwqDYREREREVEpwKHa6sMeZyIiIiIiIiIl2HAmIiIiIiIiUoJDtYmIiIiIiEoDjtRWG/Y4ExERERERESnBhjMRERERERGREhyqTUREREREVApwVW31YY8zERERERERkRJsOBMREREREREpwYYzERERERERkRKc40xERERERFQKcI6z+pTKHueIiAgIBALcuHFD3VkpFitXroSJiYm6s0FEREREREQKsMe5CHXq1EFAQADmzp2r8vdq3749mjZt+kXHWLlyJYYPHw6xWFw8mfrK3D62G9cPbkV6UiLMHd0Q3GkgrN28FaaNfxWBSzvXIPbZQ6TEx6Bmh37wb9iqULrUxDic37ocz25fQW52Foyt7FC/10hYuXipujgK3Ti6G1cPbEVaUgIsndxQt8tA2Lj5KEwb9yoC57evRkzEIyTHR6N2x36oENK6yGNf2rsJZ7euQGDDlqjTeYCqilAsalRwx4huDVChjBNsLY3RbsQS7DlxS93ZUujMge04vmsDUsQJsHNxR6vew+HsWabI9DfOHcfBDcuQEBsFC1sHNO/SH2UqBgEA8nJzsX/DUty/dgEJ0a+ho6cPr/KV0KxLfxibWQAAEmIicXjLKjy6cw3J4ngYm1qgYnAjNGjTDZpaWiVSZgCQSqXYvGoxQvfvQFpqKnzK+qPPsLGwdXAqcp97t65h9+Y1ePrwPhLj4zBq8u+oUqNOkemXzJ2Bo3u3o/uAkWjWppMKSvHxrhzehQv7NiM1KQHWTu5o1H0w7N0VfzZjX0bg5NaViHr6EElx0WjYZQCqNGlTwjku7MyB7Tixa+M7sToMTkpi9ea54ziwYTkSY6NgYWuP5l36w/edWD0gi9VI6Ojpw7N8JTTr0k8WqwDw8kkY9q5ZjBePHkAoFKJ8tdr4pscgiHT1VF5eID9Oly6cj107tiA1JQV+/oEY8+NEODm7FLnPts0bsX3rRkS+fgUAcHPzQK/vBqB6zWBZmgF9uuP61cty+7Vq0w4//PSzKorxSRr7WOCbctYw0dXCs8QMLL/wAo/i0j+4Xw1XU4yo44pLz8SYdexJCeT044Sf2osHoduRkZwIU3tXVGzbD+Yuir/7H509iIhLxyCOfAYAMHP0gH+LbrL0krxc3Nq7Bq/vXkFqfBS0dfRh7e0P///1gJ6xeYmV6WMEu5mioac5jHQ08TIpC5tvRuJZYqbCtAF2hgjxtoClvjY0hALEpGYj9GE8Lr1IKuFcF61FOWt8G2gHMz0tPIlPx9+nniIsJu2D+9XxMMePIZ449yQBPx8Il3vN0VQHfYKcUN7OCBpCAZ4lZGDKwXDEpmarqhhEpbPH+d9GV1cXVlZW6s7GV+vhpZM4s2kpKn/TBe0mzYeFoxv2/DEe6clihelzs7NgZGmDoDa9oGdsqjBNZloKts8cCaGGJloMn4ZOU5egRru+EOkZqLAkRQu7eAKnNi5BtZad0Xny37BwdMP235WUMSsLxpa2qPltL+gZmyk9dtSTMNw+sQ8Wjq4qyHnx09cV4Xb4KwyfuUndWVHq+tlQ7Fo5HyHtemDkb8tg5+yBJVO/R0pSosL0Tx/cxto/JqNK/Wb4/vfl8KtSC//M+hGRz/N/pGZnZeLVk3A0atsdI39bjh5jpiPm9XMs/2Ws7BjRr55DKpXg236j8MMfa/C/nkNw7vAu7F+/pETK/NauTatwYMdG9B02DjPmr4RIRwfTxw5BdnZWkftkZWbAxc0TvYf88MHjXzpzHA/v34GpuWVxZvuz3Dt/HEfXLUKt1l3Re9oiWDm5YeMvY5FWxHnOycqEqZUt6nboA30T5Z/NknL9bCh2r/wbjdr1wAhZrI76QKxOQdX6zTDy92UoV6UW/pk1Xi5WXz55iIZtu2PEb8vQY8w0xL5+jhW/jJMdIykhDosmj4SFjT2G/bIIfSf8hqgXT7Fx/swSKTMArFm5HJs3rMUPP07CstUboauri+GDvkNWVtFxamVtjUFDRmDlui1YuW4LKlapijEjBuPJ44dy6f7X+lvsO3JS9hg8fJSqi/NB1V1N0b2KA7bciMSY3Q8QkZCBnxp5wEhHeR+JpYE2ulW2x72olBLK6cd5dvUUru9YhnJNOqLxmD9hYu+K4wsmIjNFrDB9zKPbcK5YG/WHzkSjkb9Dz9QSxxdMRLo4DkD+b4OEF49RrnEHNB7zJ2r2+REpMa9wevHUEizVh1W0N0IbP2vsexCLmcee4FVSJobUcIaBSENh+rTsPBwMi8PvJ59ieuhjXHgmRteKdvC10i/hnCtW28Mc/Wo6Y+3llxi4+TaexKVhRgtfmOgqj0trQxH61nDC7dfJhV6zNRLhj9Zl8SIxE6N23kO/jbew7sor5ORJVFWMr4pAICgVj3+jr7bhLJFIMGvWLHh4eEAkEsHJyQnTp0+XS/PkyRPUrVsXenp68Pf3x/nz5+VeP3PmDGrVqgVdXV04Ojpi6NChSEsruMK1YMECeHp6QkdHB9bW1mjbti0AoEePHjh58iT+/PNP2cmPiIhQmE8XFxdMnToVHTt2hL6+Puzt7fH333/LpZkzZw78/Pygr68PR0dHDBw4EKmpqbLX3x+q/fPPPyMgIABr1qyBi4sLjI2N0aFDB6SkKP5SO3HiBHr27ImkpCRZfn/++WdMmTIF5cqVK5Q+ICAAEyZMkJW1ZcuWmDx5MiwtLWFkZIT+/fsjO7vgip1EIsHMmTPh6uoKXV1d+Pv7Y+vWrQrzogo3Dm9H2eDG8K3ZCGZ2zqjTdQg0tUW4f+aQwvTWrt6o0a4vPKvWgYam4l646we2wMDMEvV7fQ9rN28YWdrAqVxFGFvZqbIoRbp2aDvK1W6MsrVCYG7vjAbdh0JTW4Q7pxSX0cbNG8Ed+sK7Wh1oFlFGAMjOzMCBxb+iQc/h0NEzVFX2i9Xhs/cwecFe7D7+dfYyv3VyzyZUa9ACVeo1g42jK9r2GwUtkQ4uhe5TmP70vq3wCayCei07wdrBBU069oG9qxfOHNgOANDVN0D/SX8goEY9WNk7wcWrLFr3GYGXj8OQGBsNAPANrIqOg3+Ed0AVmNvYoVzlmqjzTQfcunCyxMotlUqxf/sGtO7cG5Vr1IGzmycG/zAFifGxuHz2RJH7BVapgQ69BqJKzbpKj58QF4MV83/D0HFToamp/kFRFw9sQ0DdpvCv3RiWDs5o2ms4NEUi3Dx5UGF6O3cf1O/UD2WD6ir9bJakU3s2o1qD5qhSrylsHF3Qpt/3H4xV78AqqNuyo1ysnpWL1TmyWHX2KotWfYbLxeq9K+egoaGJ1n1HwMreCU4evmjb73vcunAScZEvVV5mqVSKTetXo2fffgiuWx+eXt6YNPUXxMXG4NTx0CL3q1W7LqrXqg0nZxc4ObtgwODh0NPTw51b8vWRjo4OzC0sZQ99A/VcdH1Xi7JWOBoeh+OPEvAyKRNLzj1HVq4E9TyL7k0VCoBhwS7YdD0S0SlfV09d2PGdcA8KgVu1hjC2dULl9oOgqS3Ck/NHFKav3n00PIObwdTBDUY2jqjSaQikUgmiw24CALR19VFv8DQ4VagFI2sHWLj6oOK3/ZHw4hHSEmJKsmhK1fM0x9kIMS48S0JUSjY2XI9Edp4E1Z1NFKZ/GJeOm69TEJWSjbi0HBx/nIBXyZlwtyiZkR0f0ibAFgfuxuDwg1g8T8zAnyeeIitXghDfojuMhAJgbEMPrLn0EpFJhS909azmiEvPxFh2/jkex6UjMjkLFyISIc7IVWVRiL7ehvO4cePwyy+/YMKECbh37x7Wr18Pa2truTTjx4/HqFGjcOPGDXh5eaFjx47Izc3/0Dx+/BiNGzdGmzZtcOvWLWzatAlnzpzB4MGDAQBXrlzB0KFDMWXKFISFheHgwYMIDs4fivXnn38iKCgIffv2RWRkJCIjI+Ho6FhkXn/77Tf4+/vj+vXrGDt2LIYNG4YjRwoqdqFQiHnz5uHu3btYtWoVjh07hjFjxigt/+PHj7Fz507s3bsXe/fuxcmTJ/HLL78oTFu9enXMnTsXRkZGsvyOGjUKvXr1wv3793H5csGQsuvXr+PWrVvo2bOnbFtoaCju37+PEydOYMOGDdi+fTsmT54se33mzJlYvXo1Fi1ahLt372LEiBHo0qULTp5U/Y/1vNwcxD57CAffQNk2gVAIhzKBiHp8/7OP+/TGBVi5eOHggmlYMbw9Nv08CHdPHiiOLH+yvNwcREc8hFOZCrJtAqEQTmUDEfn43hcd+9ia+XD1rwLnshU+nJg+Wm5ODl4+DodX+YqybUKhEF7lKyEi/K7CfSLC78CzfCW5bT4BVRARdqfI98lMS4NAIICuftE/yjPT06BnaPSJJfh8MZGvIE6IR/kKVWTb9AwM4OFbDuH3bn/RsSUSCf76ZSK+adcVji7uX5rVL5aXm4PIp+FwLSf/2XQtVwEvH37ZZ7OkvI3Vd2MvP1Yr4lkRsfos/K5cbAOAd0AVRIQpTg8UjtXc3BxoaGpCKCz4maGlLQIAPLn/ZXHyMV6/eon4uDhUrhok22ZgaIiy5crj9q0bH3WMvLw8HDm4HxkZGfAr7y/32qH9exFStzo6tf0GC+bNQWZGRnFm/5NpCgVwM9fDrdcFF9ilAG5HpsBbSc9jW39bJGXm4tjD+BLI5cfLy81BwotHsPEOkG0TCIWw9g5AXMSDjztGdhakeXnQ1i/6onFORjogEEBbV/0XPgBAQwA4mejIDWOWAngQkwZXs49rCHtb6sPaQPRRQ/RVTVMogKelPq6/LBg2LgVw/WUSfG2K/pt3ruwAcUYODt6PLfSaAEAVZ1O8EmdiRgsfbO5ZEfPalkN1V8UjDImKk/ov5yuQkpKCP//8E/Pnz0f37t0BAO7u7qhZs6ZculGjRqFZs2YAgMmTJ6Ns2bJ49OgRfHx8MHPmTHTu3BnDhw8HAHh6emLevHmoXbs2Fi5ciOfPn0NfXx/NmzeHoaEhnJ2dERiY3zgzNjaGtrY29PT0YGNj88H81qhRA2PH5g+n9PLywtmzZ/HHH3+gYcOGACDLA5DfQz1t2jT0798fCxYsKPKYEokEK1euhKFhfoXftWtXhIaGFup1BwBtbW0YGxtDIBDI5dfAwAAhISH4559/ULlyZQDAP//8g9q1a8PNzU1u/xUrVkBPTw9ly5bFlClTMHr0aEydOhU5OTmYMWMGjh49iqCg/B8gbm5uOHPmDBYvXozatWsXyk9WVlahoXC52VnQfPOj6VNkpiRDKpFAz8hEbruekQkSI1988vHeSo6NxJ3je+HfqDUqNuuAmIhwnN6wEBqamvCp0fCzj/s5Mt6W0dhEbruekekXlTHswgnEPHuEThP/+sIc0vvSUpIgkeTB8L2huIbGpoh59UzhPiniBBi+N6ze0MQMKeIEhelzsrOwd+1CBNZsAB09xT98YyNf4syBbWjRbeBnlOLziBPzf2Abm8r3YhmbmEGc8GU/vndtXAUNDQ00adXhi45TXNJTkiCVSKD/3pQPfSNTxL/+/M9mSSqIVfkyGBibIebVc4X7pIgTYFAoVk2Vxuq+tYsQULO+LFY9y1XA7pXzcXznBtRq1hbZWZnYt3bxm+OrvpEWH5c/PNfsnTnXAGBmbo74+Dil+z56GI6+3TsiOzsburp6+HX2PLi6e8heD2nSDDa2drCwtMKjh2H4+885ePYsAr/Onlf8BflIhiJNaAgFSHqvx02ckQt7Yx2F+/hY6aO+lzlG7fr8i9CqkpWW/72o8953v46hCVKiP27Ewo1dK6FrbCbX+H5XXk42buz+B84Vg6FVQvPuP8TgzXlMzpI/jylZubA2LPo3lI6mEDOaekFLKIBEKsXGG1F48BFziFXNSCe/PInpOXLbE9Nz4Giqq3CfsraGaOxriQGbFF9gM9HTgp62BtpXsMPKiy+w7PxzVHYywcQmXhi98x5uv/66phyowr91GHRp8FU2nO/fv4+srCzUr19fabry5cvL/m9rawsAiImJgY+PD27evIlbt25h3bp1sjRSqRQSiQRPnz5Fw4YN4ezsDDc3NzRu3BiNGzdGq1atoKf36ZXn2wblu8/fXVTs6NGjmDlzJh48eIDk5GTk5uYiMzMT6enpRb6fi4uLrNH8tnwxMZ8+lKhv377o1asX5syZA6FQiPXr1+OPP/6QS+Pv7y+Xj6CgIKSmpuLFixdITU1Fenq67CLAW9nZ2bILDe+bOXOmXI81ADTuORRNeg3/5PyrilQqhZWLJ4La5Pe8Wzp7IP5VBO6c2FfiDWdVSImPwYn1C9F69ExoamurOzv0ifJyc7F69iRIpVK0/e57hWnE8bFYMm0U/IPqIKjhNyrLy+nQA1jyxwzZ83HT56rkfZ6E38f+HRvx68K1/FFQihQVqzZOrug45EfsXvk39q9bAoFQiFpN28DQxEwl5/fg/j34ddrPsuez5y367GM5u7hg9cbtSEtNxbGjhzBl4o9YuGyVrPHcsk07WVoPTy9YWFhicL9eePniORwci14g72uioynEkGAXLDr7HClZeerOTrG7d3gLnl87hXpDZ0JDq/B3oCQvF2dX/AJIgcrtBqkhh8UrK1eCmaGPIdIUwttSH238rBGXlo2HX0Gv86fQ1RLihwbumHv8KZIzFQ+7flt7nHuaiO03owAAT+LSUcbGAM3LWv8nGs6kPl9lw1lXV/FVqPdpvbOK7NsvYokkf2GA1NRU9OvXD0OHDi20n5OTE7S1tXHt2jWcOHEChw8fxsSJE/Hzzz/j8uXLxXprqIiICDRv3hwDBgzA9OnTYWZmhjNnzqB3797Izs4usuGs9d4KuQKBQFa2T9GiRQuIRCLs2LED2trayMnJkc3l/hhv52Lv27cP9vb2cq+JRIqvfo4bNw4jR46U27bsyutPzHk+HUMjCITCQotkpSeLi1z462PoGZvB1E7+B46ZrROeXD372cf8XLpvy5gkltuenpz42WWMjniE9GQx1k0q+EEglUjwMvw2boTuxtBleyEUKl5ohD5M39AYQqFGoR64lKREGJoonk9oaGKGlKT30osTCvVa5+XmYtXsiUiIjcLAyX8q7G1OSojDwklD4epdDt/2Vz7t40tVCgqGp0/BWgk5OfnzIJMS42FqXtCblyROgIv7569If//2dSSLEzCwU3PZNokkD6sXz8X+7Rvw97o9n33sz6VnaAyBUFhoIbC05MRCvdBfq4JYlS9DalLh2HvL0MQMqYViNVFhrK6ePQmJsdEYMHluoVitUKshKtRqiBRxArRFOoBAgJN7N8PcuvjXkqhVux7Kliu4mP42ThMS4mBhWbDIXEJ8PDy9Fa+I/paWljYcnZwBAD5lyuLe3TvYtGENxv40WWH6sn7576vOhnNKVi7yJFIYv7fgkomuJsQZOYXS2xiJYG0owtgGBVMi3l7P2NQ9EEO331XrnGeRfv73YuZ73/2ZKWLoGCn/7N0P3Y57R7ei7uBpMLUvvCjm20ZzWkIM6g2d8dX0NgNA6pvzaCSSP4+GIs0iG5JA/vDn2LT88/wyKQs2RiKEeFvgYZziUSUlJTkzvzymevK/aU31tJCQXji+bI11YGOkgynNClZOfxuXBwZURa91NxCbmo3cPAmeJ8hPj3iemIlytqVjLRcqvb7KhrOnpyd0dXURGhqKPn36fNYxKlSogHv37sHDw6PINJqammjQoAEaNGiASZMmwcTEBMeOHUPr1q2hra2NvLyPuwp74cKFQs99fX0BAFevXoVEIsHs2bNlc702b978WWVSpqj8ampqonv37vjnn3+gra2NDh06FLowcfPmTWRkZMi2X7hwAQYGBnB0dISZmRlEIhGeP3+ucFi2IiKRqFCjWlP784bmaWhqwdLZEy/v34BbheoA3jQA79+AX70Wn3VMALD1LANxlPxwL3H0Kxial/zq5hqaWrB28cSLe9fhUbGgjC/u3YB//c/rSXQqE4Cu0xbLbTu8fDZMbRxRuVk7Npq/kKaWFhzcvfDw9lX4Vc1fG0EikeDhrauo2UTxbcFcvMrh4a2rqN28oLcq/NYVuHgXNErfNprjIl9i4OQ/oW9oXOg44vhYLJw0FA5u3ugwaJzcHFJV0NXTh+47DSKpVAoTM3Pcvn4ZLh75P27S01Lx6P4dNGrx+bddCm7QFH7vzJsGgOljhyC4QVPUbfz5n/UvoaGpBVtXL0TcvQbvSjUA5H82I+5cR6VG/1NLnj6VfKzWAvA2Vq+hRpPCt+kDAGevsnh46xqC5WL1Mly8y8qev200x0W+xIAiYvWttw3ui6H7oKWlDS//SkWm/Vz6+vrQ15ePU3MLC1y+eAFe3vnfx2mpqbh75xZaf/tpUwGkUimysws3Pt8KD8ufc2tuob5V4HMlUjyJT4efrSEuP8+fTyoA4GdriAMK5om+SsrEiB3y8/Q7VrCDrpYQKy6+RHxa0eUtCRqaWjBz9EBU+E04+OeP6pNKJIgOvwmvWs2L3O/e0a24d2gz6gycAnMnz0Kvv200p8S+Rr0hMyHSL7n1IT5GnhR4Ls6Et5U+bkbm95wKAHhb6ePkY8VTJRQRIH9+sbrlSqR4GJuGAAdjnHuaf/FOACDAwQi7b0cXSv8iMQPfbbgpt61HVUfoamtg4emI/EazRIqwmDQ4mMpPQXAw0UF0StEr5hMVh6+y4ayjo4MffvgBY8aMgba2NmrUqIHY2FjcvXsXvXv3/qhj/PDDD6hWrRoGDx6MPn36QF9fH/fu3cORI0cwf/587N27F0+ePEFwcDBMTU2xf/9+SCQSeHvn/xB0cXHBxYsXERERAQMDA5iZmRX5A/Xs2bOYNWsWWrZsiSNHjmDLli3Yty9/tVIPDw/k5OTgr7/+QosWLXD27FksWvT5Q8iK4uLigtTUVISGhsqGXr/tze7Tp4+sIX/2bOEe1ezsbPTu3Rs//fQTIiIiMGnSJAwePBhCoRCGhoYYNWoURowYAYlEgpo1ayIpKQlnz56FkZGRbA66KgU0ao3Q5b/DysUTVq7euHl0B3KzMuFboxEA4Oiy36Bvao6gNr0AvFlU5PXzN//PRWpiHGKfP4aWSBcmb3o6/Bu2wvaZI3Fl30Z4VApGzNMw3D25H3W6D1N5eRSpENIah5b+DitXL9i4eeP64R3IycpE2Vr5ZTy4ZBYMTC1Q89uCMsa/mZ+Yl5eD1MR4xDx7DG0dHZhY20NbVw8WDi5y76GlrQNdA8NC2782+rracHcs+AHqYm+O8l72SExOx4soxbfPUYfaLdpjw18z4OjuAydPX5zcuwXZWRmoUi//nuzr502DkZkFmnfpDwCo1awt/p44BCd2b4RvhSBcPxuKF48f4Nv+owHkx+rK3yfg1ZNw9P7xV0gkEiS/mU+sZ2AETS0tiONjsWDiUJhaWqNF90FIfac3xsi06JVzi5NAIEDT1h2xfd1y2No7wsrGHhtXLoSpuSUqv3Nf5imjB6BKjTpo3LI9ACAzIx1RrwrmBcdEvkLEozAYGBrDwtoGhsYmMHxvnr+mpiZMzMxh5+hSAiVTrGqTNti9eBZsXb1h5+6NSwe3IycrE+VrNwYA7F74CwxNLVC3Q/5F3rzcHMS+fPbm/7lISYxDVMQjaOvowszGvsj3UaXgFu2w8a+ZcHT3hpOnL04VitXpMDazQLMu/QDkx+qCiUNlsXrjbChePg6Ti9VVv0/Ayyfh6PPjr5BI8grFKgCc2b8NLj7lINLRQ9jNy9i7eiGadekHXSWLNRUXgUCA9p26YeWyxXB0coadvQOWLJgHC0srBNctmAY2uF9P1K7bAN926AwAWDBvDoJqBMPa1hbpaWk4fGAvrl25hLkLlgLI71U+fGAfqtcMhpGJCR6Fh+HP2b8isEIleHopvr9wSdlzNwaDazrjcXw6HsWmo1lZS4g0hTj+ZuGvIbWcEZ+eg/VXXyMnT4oXYvn7Aqdl5198f3+7unjXbYkLa/+AmZMnzJ29EHZiF3KzMuFarQEA4Pzq2dA1MUfANz0AAPeObMXt/WtRvfto6JtbIyM5//tCU6QDLZEuJHm5OLN8JhJfPEZwv4mQSiWyNNp6BkXehaOkHXsYj26V7PAsMQPPEjNQ18McIg0hzj8TAwC6V7SDODMXu+7mT98L8TLHM3EmYlOzoaUhQFlrA1R1MsGGG5FqLEWBbTciMbq+Ox7GpOJBTCpa+9tCR1MDh95c0Bld3x3xadlYceEFcvKkiHivJzn1zVSCd7dvvf4aP4Z44vbrFNx8lYRKTiao5mKKUTtLx6KNX0z910T+s77KhjMATJgwAZqampg4cSJev34NW1tb9O/f/6P3L1++PE6ePInx48ejVq1akEqlcHd3R/v2+T/iTExMsH37dvz888/IzMyEp6cnNmzYgLJl86+ojxo1Ct27d0eZMmWQkZGBp0+fwsXFReF7ff/997hy5QomT54MIyMjzJkzByEhIQDy5w/PmTMHv/76K8aNG4fg4GDMnDkT3bp1+7I/0HuqV6+O/v37o3379oiPj8ekSZPw888/A8jvwa9evToSEhJQtWrVQvvWr18fnp6eCA4ORlZWFjp27CjbFwCmTp0KS0tLzJw5E0+ePIGJiQkqVKiAH3/8sVjLUBTPKrWRkZKEizvXID05ERaObmg+YppsGHNKQozcnLk0cTw2Ty4Yonzj0DbcOLQNdt5+aDXmNwD5t6xqMmgizm/7B1d2r4ORpQ1qdugP72r1SqRM7/OuWgcZKUk4v2M10pMSYenkhlbfT5cNB02Jj4VAUHDhJjUxHusmFSwIdfXgVlw9uBUO3uXx7bjfSjz/xalCGWccXlZwAWPWqPxezDW7L+C7SWvVla1CAmvUR2qSGAc3LkeyOAH2rh747qffZb1riXHRcnHp6uOHLsMn4cCGpdi3bgksbR3Qc8wM2DrlL9SXlBCLu5fPAABmf99T7r0GTp4Hj3KBCL95GXFRLxEX9RJTvpPv2Z6z7bQqiyvnf+27IyszE4v/mIH01BT4lAvAj7/Mg/Y7CwBGv36J5HemHzwOu4fJowrq8NWL8tdaqN2oOQaN+bmksv7JygTVRVpKEk5uXYm0pERYO7ujww8zYfDms5kUHyP32UxJjMfy8QXlvLBvCy7s2wIn3/Lo+tOcEs8/kB+raUliHNq4Qharfd+JVbHCWJ2IAxuWYf+6pW9idfp7sZp/EXb2973k3mvA5D/hUS5//Yvnjx7g0KZ/kJWZASt7J7TtNwqV6oSURJEBAF179EZmRgZ+mTYJqSkpKB9QAXP/XiI3IurlixcQvzOMPTEhAZMnjEV8XCwMDAzh7umFuQuWomq1/NFAWlpauHzxPDauX43MjAxYWdugTv2G6NXn43+fqMq5p4kw0tFEh0BbmOhqISIhA9MPP0LSmyG+FvrakEjVnMlP4FwxGFmpSbi9by0yUxJhau+GOgOnQPfNUO30RPnvxUdn9kOSm984fle5Jh3h17Qz0sXxeHX7IgDg4K/y0/jqDZ0Ba8/y+BpcfZUMA5EGmpexhJFIEy+TsjD/nbnopnpaeHfinramEB0CbGCiq4WcPCmiU7Kw8vIrXH1V+P7H6nDyUTyMdTXRraojTPW08CQuHeP3PpBNIbAyFEH6iXF59mki5p18ig4V7DCwlgteijMw5WA47kZyfjOplkAq/dRwpXe5uLhg+PDhcitnf22kUik8PT0xcODAQnOPe/ToAbFYjJ07d6o0D/POPFXp8b8G2hr//kuAIwb+ru4sqNTWNRPVnQWVczD+uDUkSrOb0WJ1Z0HlzHT+/Yv+1XCz+HCiUq7vppsfTlSKlbP/9885jUlV77D2kvAo8utohKvS4UHV1J2Fj2bXf7u6s/BRXi9SPHWtNPtqe5ypeMTGxmLjxo2IioqSu3czERERERGVLrzzhPqw4fwvZ2VlBQsLCyxZsgSmpqVjFVgiIiIiIqKvCRvOXygiIkLdWVDqQyPxV65cWTIZISIiIiIiKqXYcCYiIiIiIioFOFRbfVR7A1AiIiIiIiKiUo4NZyIiIiIiIiIl2HAmIiIiIiIiUoJznImIiIiIiEoBznFWH/Y4ExERERERESnBhjMRERERERGREhyqTUREREREVBpwpLbasMeZiIiIiIiISAk2nImIiIiIiIiU4FBtIiIiIiKiUoCraqsPe5yJiIiIiIiIlGDDmYiIiIiIiEgJNpyJiIiIiIiIlOAcZyIiIiIiolKAc5zVhz3OREREREREREqw4UxERERERESkBIdqExERERERlQIcqq0+7HEmIiIiIiIiUoINZyIiIiIiIiIlOFSbiIiIiIioFOBQbfVhjzMRERERERGREmw4ExERERERESnBodpERERERESlAUdqqw17nImIiIiIiIiUYMOZiIiIiIiISAk2nImIiIiIiIiU4BxnKhHRKTnqzoLK1XM1U3cWVG7rmonqzoJKte06Rd1ZULn5i8eoOwsqZ6Cloe4sqJw4699fpz6MSlV3FlSutpepurOgUjl5UnVnQeXKWOmrOwsqV83JUN1ZoHfwdlTqwx5nIiIiIiIiIiXYcCYiIiIiIiJSgkO1iYiIiIiISgEO1VYf9jgTERERERERKcGGMxEREREREZESHKpNRERERERUCnCktvqwx5mIiIiIiIhICTaciYiIiIiIiJRgw5mIiIiIiIhICc5xJiIiIiIiKgV4Oyr1YY8zERERERERkRJsOBMREREREREpwaHaREREREREpQBHaqsPe5yJiIiIiIiIlGDDmYiIiIiIiEgJDtUmIiIiIiIqBbiqtvqwx5mIiIiIiIhICTaciYiIiIiIiJRgw5mIiIiIiIhICc5xJiIiIiIiKgU4xVl92ONMREREREREpAQbzkRERERERERKcKg2ERERERFRKSAUcqy2urDHmYiIiIiIiNTq77//houLC3R0dFC1alVcunRJaXqxWIxBgwbB1tYWIpEIXl5e2L9/v8ryxx5nIiIiIiIiUptNmzZh5MiRWLRoEapWrYq5c+ciJCQEYWFhsLKyKpQ+OzsbDRs2hJWVFbZu3Qp7e3s8e/YMJiYmKssjG85ERERERESlwL91Ve05c+agb9++6NmzJwBg0aJF2LdvH1asWIGxY8cWSr9ixQokJCTg3Llz0NLSAgC4uLioNI8cqk1ERERERETFJisrC8nJyXKPrKwshWmzs7Nx9epVNGjQQLZNKBSiQYMGOH/+vMJ9du/ejaCgIAwaNAjW1tYoV64cZsyYgby8PJWUB2DDmYiIiIiIiIrRzJkzYWxsLPeYOXOmwrRxcXHIy8uDtbW13HZra2tERUUp3OfJkyfYunUr8vLysH//fkyYMAGzZ8/GtGnTir0sb7HhXAIiIiIgEAhw48YNlb7PiRMnIBAIIBaLVfo+RERERERERRk3bhySkpLkHuPGjSu240skElhZWWHJkiWoWLEi2rdvj/Hjx2PRokXF9h7v4xznYtajRw+IxWLs3LlTts3R0RGRkZGwsLBQX8ZKuUen9yH82HZkpiTC2M4VgW36wczZS2HaJ+cP4dnlY0iOfAYAMHX0QLlm3YpMf23z33hy7iD8W/aBZ53/qawM75NKpdi7fhnOHtmDjLQUuPmUR8cBo2Bl56h0v5P7tuHIzvVITkyAg4sH2n03Ai5eZWSvnzm0C5dPHcGLx2HIzEjH7+sOQs/AUOGxcnKy8dvovnj59BHG/fEPHN0U/40+x5kD23F81wakiBNg5+KOVr2Hw9mzTJHpb5w7joMbliEhNgoWtg5o3qU/ylQMAgDk5eZi/4aluH/tAhKiX0NHTx9e5SuhWZf+MDbL/1wlxETi8JZVeHTnGpLF8TA2tUDF4EZo0KYbNN/Mffla1KjgjhHdGqBCGSfYWhqj3Ygl2HPilrqz9VGuHdmFS/u3IC0pAVaO7mjQbRBs3X0Upo17GYEz21YhKuIhkuOiUa/zAFRq3FouzYXdGxB+5QziI19AS0sEO88yqN2hD8xtlX8OitPFQztxds8mpCYlwNrJHc16DoGDh2+R6e9cOIFjm/+BODYKZjYOaNSpL7wCq8len9ihnsL9GnX+DjVbdJA9D7t2ASe2rUb08yfQ1NaGi68/Oo2aWnwFe+Py4Z04v3ezrHyNuw+BvYficwYA9y6cxIkt/0Acl1+++h36wjOwquz11KQEhG5Yiie3riIzPRXOPuUR0n0wzG0dZGkSol/j6LpFeBF2B7m5OXAvXxmNewyGgbFZsZfvLalUiu1rluD4wZ1IT0uFV5ny6DH4B9jYOynd78ieLdi/dS2SEuPh6OaJbgNGwd27LAAgNvo1RvZoqXC/wT/OQNVaDXDqyF4snTNFYZr5Gw7C2EQ1Zb4VuhvXD25FelIiLBzdENx5IKzdvBWmjX8VgYs71yA24iFS4mNQs0M/BDRqVShdamIczm1Zjme3ryA3OwvGVnao32skrF2L77vhU9w5vgc3D21FRlIizB3dUKPjAFi5Ki5jwqtnuLJ7DWKfPURqfAyC2n+H8g3ky7hubHekxscU2rdMneao1XmQSsrwIRcP7cCZPZuQKk6AjbM7mvUcqrz+OX8CoZtXyOqfkM7fydU/E9rXVbhfSOd+qPlNB4WvqdqVw7twYV9BHdSo+2DYF/G9EfsyAie3rkTU04dIiotGwy4DUKVJG7k0z+/fwvl9mxH19CFSxfFoO2IyvCvVKImifBUEpWSSs0gkgkgk+qi0FhYW0NDQQHR0tNz26Oho2NjYKNzH1tYWWlpa0NDQkG3z9fVFVFQUsrOzoa2t/fmZLwJ7nN+RnZ2tkuNqaGjAxsYGmpq8TvE5Xlw7jVs7l6FM445oMGouTOxdcXrRRGSmiBWmj310G04VglF70AzUHf4bdE0scHrhRGSI4wulfXXrPOIjwqCjwh9zRTmyfR1O7NuKjgNGY/RvSyHS0cFfP49ETrbi+R8AcOX0UWxb8Reate+FcXNWwN7VA3/9PBIp4kRZmuysTJQJrIqQtt0+mIcdKxfIGp7F6frZUOxaOR8h7Xpg5G/LYOfsgSVTv0dKUqLC9E8f3MbaPyajSv1m+P735fCrUgv/zPoRkc+fAMgv06sn4WjUtjtG/rYcPcZMR8zr51j+S8FiEdGvnkMqleDbfqPwwx9r8L+eQ3Du8C7sX7+k2Mv3pfR1Rbgd/grDZ25Sd1Y+yf0LJ3B8/WLUaNUF3acuhKWTGzbPGoe0Is5rTnYWjK1sUbtdb+gX8Rl78eAWAht8g66T5qHdD79AkpeLLb+ORXZmhiqLInP73HEcXLMQddp2Q/+Zi2Hj7I7VM39AahFleh52B1vnTUOFuk0w4Jcl8K1UAxt+n4joF09laUYv2ir3aNl/NAQCAcpUCZaluXvxFLb/PRMV6jTGwF+Xos/keShfo36xl+/u+eM4snYRglt3Q9/pi2Dt5I71v/xQ5Dl7EX4X2+dPQ0CdJug7YzG8K9bA5jkTEfOmfFKpFJtnT4Q4JhLtv5+CvjMWw9jCCutmjpads+zMDKyfOQYQCNBl/O/oMelP5OXmYNNvP0EqkRR7Gd/at2U1Du/ehJ5DxuLnuSsg0tHFrJ+GIltJnXrh5BGsXzIXrTr3wdS/VsPJ1ROzfhqKJHECAMDcwhp/rdsv92jd5Tvo6OrBv1J1AEC14AaF0vhVrAYfvwoqazQ/vHQSZzYtReVvuqD9pPkwd3TD7jnjkZ4sVpg+NzsLxpY2CGrbC3rGpgrTZKalYNuMkRBqaOKbEdPQedoS1GzfFzr6Biopw4c8unwS5zcvQcUWndFmwl8wc3DFvrk/IaPIMmbC0MIGVVv3LLKMrcf/ia6/r5M9mo2YAQBwr1RLVcVQ6va5YziweiHqtumOAb8sgY2zO1bNGKO0/tkybyoq1m2KAb8shW/lmlj/2wREPy+of8Ys3ib3aNV/TH79UzVY4TFV7d754zi6bhFqte6K3tMWwcrJDRt/GVv090ZWJkytbFG3Qx/oF/H5yc7KhLWTG0J6DFFl1qkEaWtro2LFiggNDZVtk0gkCA0NRVBQkMJ9atSogUePHkHyzvdKeHg4bG1tVdJoBv7jDec6depg8ODBGD58OCwsLBASEgIAuHPnDpo0aQIDAwNYW1uja9euiIuLk+23detW+Pn5QVdXF+bm5mjQoAHS0tLw888/Y9WqVdi1axcEAgEEAgFOnDhRaKj22yHVoaGhqFSpEvT09FC9enWEhYXJ5W/atGmwsrKCoaEh+vTpg7FjxyIgIOCD5Tp79izKly8PHR0dVKtWDXfu3JG9tnLlSpiYmGDnzp3w9PSEjo4OQkJC8OLFiyKPl52djcGDB8PW1hY6OjpwdnYuco6CKoSf2AnXoBC4VG0AIxsnVPh2IDS0RYi4eERh+qpdR8G9ZjOYOLjByNoRlToMgVQqQUz4Tbl0GeJ43Ni2GFW6fg+hsGQvakilUhzbsxmNv+0O/6q14ODige7DJyApIQ43L5wucr9juzahRqMWCGrQDLZOrug4YDS0RSKcO7pXlqbeN+0R0rYrXN/0mBTl7tXzuH/jElr3GFxs5Xrr5J5NqNagBarUawYbR1e07TcKWiIdXArdpzD96X1b4RNYBfVadoK1gwuadOwDe1cvnDmwHQCgq2+A/pP+QECNerCyd4KLV1m07jMCLx+HITE2/+qkb2BVdBz8I7wDqsDcxg7lKtdEnW864NaFk8Vevi91+Ow9TF6wF7uPl45e5reuHNiG8nWawC+4MSzsnRHScxi0RCLcPnVIYXpbN2/U7fgdfIPqQqOIXv9vx8yEX3AILBxcYOXsjqbfjUZyfAyiIx6qsigy5/ZtQcV6TVGhThNYObigRZ8R0NIW4dqJAwrTXziwHR7+VVCzRQdY2jujfvtesHX1xMVDO2VpDE3M5B4PrpyDS5kAmFnbAQDy8vJwYNV8NOrcD5UbfgMLO0dYObigXFCdYi/fhf1bEVi3KQLqNIalgwua9R4OLZEIN04eVJj+0sHt8PCvjOot2sPS3hl12/WErasnLh/OL19C1Eu8enQfTXoNh527DyzsHNG013DkZGfj7vljAPIb3+LYaPyv3xhYO7nB2skN/xvwA14/DcfTu9eLvYxAfp16cOdGfNOhFyoG1YaTqyf6jfoZ4vg4XD1XdB1wYMd61GnSEsGNWsDe2Q09h4yFSKSDU4f3AACEGhowMbOQe1w9dwJVatWHjq4eAEBbpCP3ulCogXs3r6B2yDcqKSsA3Di0HWWDG6NMrUYws3dG3W5DoKktwv3Tij+L1q7eqNGuL7yq1oGGpuLP4rX9W2BgZokGvb+HtZs3jCxt4FSuIoyt7FRWDmVuH9kB31pN4FOjEUztnBHcJb+MD84eVpjeytUbQd/2gUeVOhAWUUZdQxPoGZvJHs9uXYSRpS1svfxUWZQindu3BZXqN0OFum/rn5HQ0tbBteOK65/zB7bBI6AKan7TAVYOzmggq392yNK8X//cv3IWrmUL6p+SdvHANgTUbQr/2o1h6eCMpr2GQ1Mkws0i6iA7dx/U79QPZYPqQrOI8+gRUAV12vWCT+Waqsw6lbCRI0di6dKlWLVqFe7fv48BAwYgLS1Ntsp2t27d5IZ6DxgwAAkJCRg2bBjCw8Oxb98+zJgxA4MGqW70yH+64QwAq1atgra2Ns6ePYtFixZBLBajXr16CAwMxJUrV3Dw4EFER0ejXbt2AIDIyEh07NgRvXr1wv3793HixAm0bt0aUqkUo0aNQrt27dC4cWNERkYiMjIS1atXL/K9x48fj9mzZ+PKlSvQ1NREr169ZK+tW7cO06dPx6+//oqrV6/CyckJCxcu/KgyjR49GrNnz8bly5dhaWmJFi1aICcnR/Z6eno6pk+fjtWrV+Ps2bMQi8Xo0KHo4Tvz5s3D7t27sXnzZoSFhWHdunUqX+79LUluDsQvH8HKy1+2TSAUwtorAPERYUr2LJCbnQWJJA9a71w1l0okuLRuDrzqtYaxrXOx5/tD4qNfIzkxHj7+lWTbdPUN4OJVBk/C7ijcJzcnB88fh8Hbv7Jsm1AohI9/JTwtYp+iJIsTsO7vX9Fj+ARoi3Q+rxBFyM3JwcvH4fAqX1Eun17lKyEi/K7CfSLC78CzfCW5bT4BVRChpFyZaWkQCATQVdIbkpmeBj1Do08sASmSl5uDqIhwuJStINsmEArhXLYCXj+6V2zvk5WRBgDQ0Vc8vaA45ebmIPJpONz95GPV3a8iXoYrLtOLh/fg5ldBbpuHf2W8KCK2U8UJCL9+ARXrNpVti3wajuSEOAiEAiwY+x1m9W+L1TPHyvVaF4e8N+VzLSd/zlzLVcDLh4rL9/LhPbiWqyi3za18JVn63DffJZpaBVfzBUIhNDW18PzN5zUvJxsQQO5iiaaWNgQCAV58Yl31sWKjXiMpMR7lAqvItunpG8DNuywePbitcJ/cnBxEPHyAsgHydWrZgMp4dF/xPk8f3sezJ+GoHVL0tJ4zofshEumgSk3FQ/a/VF5uDmKePYRjmUDZNoFQCIcygYh6fP+zj/v0xgVYuXjhwIJpWD6sPTb+PAh3TypuwKlaXm4OYp89hL1vgGybQCiEg28Aor+gjO+/x6OLx+Fdo5Fahr7m5ubg9ZNwuBWqfyrgxUPF9cmL8Htwf+/z6eFfGc8/UP9UeKf+KUmfUwfRhwkEpePxqdq3b4/ff/8dEydOREBAAG7cuIGDBw/KFgx7/vw5IiMjZekdHR1x6NAhXL58GeXLl8fQoUMxbNgwhbeuKi7/+bHDnp6emDVrluz5tGnTEBgYiBkzZsi2rVixAo6OjggPD0dqaipyc3PRunVrODvnN7j8/AquVOrq6iIrK6vI8fjvmj59OmrXrg0AGDt2LJo1a4bMzEzo6Ojgr7/+Qu/evWVXWSZOnIjDhw8jNTX1g8edNGkSGjZsCCD/woCDgwN27Ngha/zn5ORg/vz5qFq1qiyNr68vLl26hCpVqhQ63vPnz+Hp6YmaNWtCIBDIyl2UrKysQsvN5+Zky/3Q+lhZacmQSiTQMZQfdiUyNEFy9MuPOsbtPSuha2QGa68A2baw0G0QCIXwCG7xyXkqDkmJ+cMAjd4bhmRkYobkxMJDygEgNVkMiSSv0D6GJmaIfvn8o99bKpVi9Z/TUatxSzh7+iI+OvLDO32CtJQkSCR5MHw/n8amiHn1TOE+KeIEGBoXLlfKm+GS78vJzsLetQsRWLMBdPT0FaaJjXyJMwe2oUW3gZ9RCnpfekoSpBJJoSGQ+kamSHhd9IiVTyGVSBC6diHsvcrC0tG1WI6pTHpyEiQSCfTfL5OxKWJfKf5MpYoTYPBeegNj0yKHVl4/dRgiHT34VikYCpoYk/+ZO751FRp3HQhTSxuc3bsZ/0wZgaF/rIaeQfFc7Hl7zt7Pr76xKeKKOGep4oRCfw8DY1OkvfksWtg5wdjCCsc2LkOz3iOgraODC/u3IjkhFqlv6jV7zzLQFukidMNS1GvfO3+EzcZlkEokSFUwZaY4iN/Um8am8vWIsakZkoqoU1Pe1Knv72NkaobXLxXXVScP7Yadoyu8ypQvMi8nD+1GUJ2QYr8o+VZGSv73oq6Ridx2PSMTiCM//7OYHBuJO8f3IiCkNSo164Dop+E4tX4hhJqa8K3R8Atz/WkyU9+WUT4WdY1MIY76uO/+D4m4fh5Z6anwLuGyvfW2/lFUn8S9VlL/mHxC/XPyEEQ6enLTRErS2zqoUB1rZIr4YvreoH+XwYMHY/BgxSMhT5w4UWhbUFAQLly4oOJcFfjP9zhXrCh/5e7mzZs4fvw4DAwMZA8fn/wFDB4/fgx///+zd9/hURRvAMe/l957SO+N3nvvHaUoRRQpgoggiIAi0qsIiIiodKRXEekdpPcOSQiEnp5LJSHlfn8ELrlwCS0hwO/9PM89T25vdm8muze7s/PObDkaNWpEmTJl+PDDD5k3bx6xsdorrGcpWzb7xOvk5ARARETWpBWBgYFPNWK1NWq1yTkWwMbGhoCAAK5ezb5Dq6enR5Uq2XfYixcvjpWVlUaanLp37865c+cICAjgq6++YudO7WFST2ibfv7I6j+fK+8F7drutdw5+x81en2P7uOGe+yd6wQf3ESVjwa9trvMJ/bv4OtOjdWvjIz01/K92uzfvI7Uh8k06/BJkeXhVWSkp/PX9NGoVCo+6PON1jTK6EjmThhCuRr1qdGk8MIlRcHateRXou6G8t6XI4o6KwXm7P5tlK3dCP0c461UmSoA6rX9mFLV6uLs7U+7L4YBCi6/gUMLctLV0+PDQWOJCbvLtD5tmdy9JbeunMe3XFUUOln1qamFFR0GjiL4zFGm9GzN1M/eIyU5EUdPPxSKgrnsOLx3O5+1q6d+ZaQXfp36KDWFo/t35BuCHXz1Avfv3CzUMO3ColKpsPfwpUaHHth7+FK6fktK1W3Opf3ah9i87a4d2oFb6cqYWtkWdVYKzZn92yhbu7FG/SOEeHn/9z3OpqaavVWJiYm0adOGH3/88am0Tk5O6OrqsmvXLo4cOcLOnTv59ddfGTFiBMePH8fL68V6SPRzhLE9acBlFuLEKS+rYsWK3Lx5k23btrF79246duxI48aNWbdundb0w4cPZ/DgwRrLJu5//h7RnAxNLVDo6JCSoHlzIjVBiVGuO9G5Be7dQODu9dTpNx4r5+x9ExVymdTEOLaOzQ6NV2Vmcv6fhQQf2ETL0QteKq/5KVu1Np45xhynp2VNRBevjNGYnCteGYOrl5/WbZhZWKGjo0t8rl7YBGUMFtbPPwFN4MXT3Ai8xFcfaM68+eM3n1GlXhM+HTTyubeljam5JTo6uk/1FifExWKexwWKuZUNCXFPlyt3r3VGejpLpo8iJjKMfmN/0drbHBcTxe+jv8IroDQf9h32SmUR2UzMLVHo6JCcq2cjKT4WU6v8f4vPY9eSXwk5d5wuI6ZjbmP/ytt7HiYWlujo6Dw1SU1SXOxTx94TZlY2T/XuJMbFPtVrBBB69QJR9+/QceAozW08/r3au2ZH7+jpG2BdzIm4qKdn/H1ZT/ZZ7vwmxcVilk/5cv8/EuNiNSbpcfL2p8/kuaQkJ5KRno6phRULRn6Jc45Z+X3KVqb/zGUkx8eho6uLkakZM774AKtiTgVStorV6+BbPLtOTXtcp8bFxmCVo06Ni43Bw0f7jNDmj+vUJxFAT8THxmBl/XRddeLQXlJTU6jdKO+w1/3b/8HD2x8vv7xnRX5VxuZZ58Xck2QlxyvznBTreZha2WDjrDkDubWzOyGnD7/0Nl+WkdmTMmoeiw/jY5/qhX4ZCdHh3Lt6jqb9fnjlbb2sJ/WP1vokv/pH+Wr1z+v0pA56qo6Nj32qF1o8v7dlVu130f99j3NuFStW5PLly3h6euLr66vxetLIVigU1KpVi7Fjx3L27FkMDAz4+++siRkMDAzIyMh45XwEBARw8uRJjWW53+clZ8hCbGwsQUFBlCiRfRJPT0/n1KlT6veBgYEolUqNNLlZWFjQqVMn5s2bx+rVq1m/fj0xMdrDaA0NDbGwsNB4vUyYNoCOnj5Wrr5EBGdPoqTKzJroy9ZT+yMpICsU++rO1dTuOwYbd82GqHuVBjQZ9iuNh85Sv4wsbQho2I46fce+VD6fxcjElGJOruqXk5sXFta2BF44rU7zMDmJ0KAreAeU1roNPX193H0CCLyQve8yMzMJvHAarzzW0aZj70GMmLmE72cu5vuZi+k36icAeg0dy3sff/6SJdTMp6uPP8EXs8uWmZlJ8IXTePprn7DM0780wTn+FwBBF07hmaNcTxrNUQ/u8sXonzE1t3xqO8roSOaMGoCrdwCdvxyOjo5UcQVFV08fR09/bl3JntxJlZnJrctncfbN+zFjz6JSqdi15FeCTx+m0/CpBdaweh56evo4eflz49IZ9bLMzExuXDqDq7/2Mrn5ldRIDxBy4RRuWo7tM/u24eztj6OHj8ZyZy9/9PT1NcKlM9LTUUaFY2Xn8CpF0qD7uHyhlzX32c3LZ3HN49Fwrn4luZmrfDcvntaa3sjEDFMLK6If3OXBjSD8Kz39OBgTC0uMTM24efksSfFK/CvlPe/HizA2McXB2U39cnH3xtLalsvnss+TD5MSuRF4Gd/i2id+0tPXx9OvOFdyrJOZmcnlc6fwLfH0Ogd2bKJitbpY5HGjKOVhMif+21Povc26evoU8/DjztVz6mWqzEzuXj2Ho8/LN9gdfUsSmysMWhl2D3PbYi+9zZelq6ePvYcf93KV8d7Vczi8QhmfCDy8C2MLS9zLPF8kX2HQ09PH2dufGxefrn/c/LSfK938tdQ/F0/jrrX+2Yqztz9Onr4Fm/EXkF0HZedZlZlJ6KW86yAh3mRyVZnLl19+SUxMDF26dOHkyZOEhISwY8cOevToQUZGBsePH2fSpEmcOnWK27dvs2HDBiIjI9WNTk9PTy5cuEBgYCBRUVEak3K9iAEDBrBgwQKWLFlCcHAwEyZM4MKFC891l2ncuHHs2bOHS5cu0b17d+zs7Gjbtq36c319fQYMGMDx48c5ffo03bt3p3r16nmGgs+YMYOVK1dy7do1goKCWLt2LY6OjlhZWb1U2V6Uf/223Dy6g9ATe4gPu8OZtXNIf5SCZ7XGAJxYNoOL/y5Rp7+2ex2Xty6jcpevMLVxICU+lpT4WNJTsx6VYmhqgaWTh8ZLR0cPI3NrzB1cteahoCkUChq26ci2NUu4cPw/7oWGsGTmeCxt7ChXPXss5C8jv2L/luye/Ybvd+Lwzn85tncrD+6EsuqPaaSmpFCjcSt1mrjYaO7cCCLyQdYF0P1bIdy5EURSQjwANvaOOHt4q18Oj3sY7BxdsLYrmAukem06cWz3Zk7u20b43VDWzZ3Oo9SHVG2Y1VOzYtYENi/LfkB9nVYfcO3ccfZvWkX43VtsX72QOyHXqN0i65m/GenpLJ42krshgXQdNIrMzEziY6OJj41WT1aU1Wj+Cis7B9p8+iWJ8Up1mjeNqbEBZf1dKOvvAoCniy1l/V1wc3yz78BXbtGB8/u3cum/nUTfu8XOxbNIS02hTN2sJxJs+eNHDqzOjtjISE8j/NZ1wm9dJyM9jYTYKMJvXSc2/J46za4lv3LlyB5afzEcAyMTEpUxJCpj8n0sW0Gq2epDTu/dwtkDO4i8d4vNC2byKDWFivWaA7D+t8nsWjlPnb56i/ZcP3+Sw5vXEHnvNnvXLub+jSCqNWursd2U5CQuHz+gMSnYE0YmplRu3IZ96xZz/fxJou7f5t8FMwEoVb1egZavessPOLNvC+cPZpVv68KZpKWkUK5e1j7bOGcKe1bNV6ev2rw9IRdOcnTLGqLu3ebAuiXcvxFElabZ5bty7AChV84RG36fwFOHWT55GAGVa+GTY4K/c/u3czf4CjHh97lwaBfrfxlL9RYdsHvGc+pflkKhoHnbzvyzaiFnjh3kzs3r/DF9DFa2dlSqmf0/nfxdP3ZtWqN+36LdR+zf/g//7drMvds3WTz7R1JTH1K3SWuN7Yffv0PgpbPUb573pGDHDu4iIyODmg1bFHwBcynfrD1XDmzj6uFdxNy/zf6lv5KemkKJ2k0B2DXvJ46sW6hOn5GeRuTtECJvh5CRnk6SMorI2yEow+9nb7NpO8JvXOPU5lUow+8TeGwflw9spUzDopkLpEyTdlz7bzuBR3YR++A2/y2fTdqjVPWY5L0LpnF8wyJ1+oz0NKJuhxB1O4TM9HSSYqOJuh1CXMR9je2qMjMJPLwL/xqN0cnx/NeikFX/bObsge1E3L3Fv/N/zqp/6mfVP+tmT2Lniuz6p0aLDgSfP8Hhf3PUPyGBVGum+bzqlOQkLh07QKWGrShq1Vp04Oy+rVw4uJOoe7fYtugX0lJTKPu4jt30+xT25aiDsiaivE5Y6HUy0tNJiI0iLPQ6MWHZ541HKQ/VaQCUkQ8IC71OXJTmM4CFKGj/96HauTk7O3P48GG+/fZbmjZtSmpqKh4eHjRv3hwdHR0sLCw4ePAgM2fOJD4+Hg8PD6ZPn06LFlknyt69e7N//34qV65MYmIi+/bte6kZqLt27cqNGzcYMmQIKSkpdOzYke7du3PixIlnrjtlyhQGDhxIcHAw5cuX599//9V4npmJiQnffvstH330Effu3aNOnTosWJB3eLK5uTlTp04lODgYXV1dqlSpwtatW19bb55bxTqkJsVxZdtyUuJjsXTxpvbnY9UThiXHRmrcULhxeBuZGekcWzRFYzslmnWhVIuPXkuen0eT9l1JTXnIijlTSU5KxKdEWfqPno6+QfbD4iPD7pEYH6d+X7lOYxLjlWxeMZ/42Kyw7v6jp2tMGPbf9o1sXZV9wTTj+6xp+T/56ntqNHo9J9EKtRqRGKdk+6oFxCtjcPHypc8P09Thr7FR4Rr7zKt4GT4eNJptK+exZflc7J1c6TFsEk7u3gDExURy+eQhAKZ/00Pju/qNnYVv6QoEnT9JVNhdosLuMq5Pe400M9bn/YivolCxpAc75w9Uv586pAMASzcdo8/oZUWVrWcqUb0+DxOUHFq/hKS4WIq5+/Dh0EnqkLv46AiN/ZoYG82SH75Qvz+5dS0nt67FrXhZuoyYDsC5PVmP/Vk1aYjGd7XoPUTdIC9MZWo2IDleyd61i0hUxuLo4cMn3/2oDpWMi4rQGJfrHlCaDwaMYM/qhexetQBbRxe6DBmHQ67JzC4d2QcqFWVqaZ9ZuVnXvujo6LJ+zhTSH6Xi4luCHj9Mw9isYGcTL1WjAcnxcRxYt5hEZSwOHj589N0UzB5PxhcfHaEemwzg5l+Kdl+OYN/ahexbvRAbRxc6Dh5HsRzlS1RGs2vZ7yTGxWJubUOZ2k2p2/5jje+NfnCHvavn8zAxASt7B2q/35VqLT8o0LLl1urDbqSmpLBw1iSSExPxL1WOoeN/wSBHnRrx4B4JOUKcq9drQkJcLOuXzSUuJhp3H3+Gjv8Fy1yh2gd2/ouNXTFKV6yW5/cf2LGJyjXrY1rA+1Abv6r1eJgQx4mNS0mKi8XezZs2X09Qh2onxGju1yRlNKvHZD+i5ez29Zzdvh7ngDK0/zYr6sjBK4AWX47i6PpFnNy0HAt7R+p06UtAjcKZHfxZfKvUIyUhjlP/LCM5PgY7Nx9aDhyPyeNQ7cQYzfomWRnD+vHZkwpd2LmeCzvX4+RfhveGZk8Ce/fqWRJjIgio1fT1FSYPZWo2JCk+jj1rFpOojMHJ04duw3PUP9ERGtda7gGl+XDAD+xevZBdq+Zj6+jCR0PH4+CuWf9cPLIXVCrK5lH/vE4lazQgKSGrDkqKy6qDOn87WR1eHhetWccmxEazYERf9ftjW9ZybMta3EuU5ZMfZgDw4EYgyyZmnzN2P74RX7ZOU9r8HwzRklDtoqNQqVSqos6EeD5NmjTB0dGRpUuXvvQ2Fi9ezKBBg1AqlQWXsecwYlvQa/2+otDQ6/nHGb+tUtLfvDH4BemDT8YVdRYK3ew/3/2LCjP9ou1Feh3SMt/9U7e/deE3QIva8Qfahzy9K9Iy3v3j1NWicGZPf5OkpL/6EMQ3XbfKhRMNUxjKjd5T1Fl4LufHNirqLBQ46XF+QyUnJ/PHH3/QrFkzdHV1WblyJbt372bXrl1FnTUhhBBCCCGE+L8iDec3lEKhYOvWrUycOJGUlBQCAgJYv349jRs3LuqsCSGEEEIIIcT/FWk4v6GMjY3ZvXt3gW+3e/fudO/evcC3K4QQQgghhChcMsS56Mis2kIIIYQQQgghRD6k4SyEEEIIIYQQQuRDQrWFEEIIIYQQ4i0gj6MqOtLjLIQQQgghhBBC5EMazkIIIYQQQgghRD4kVFsIIYQQQggh3gISqV10pMdZCCGEEEIIIYTIhzSchRBCCCGEEEKIfEjDWQghhBBCCCGEyIeMcRZCCCGEEEKIt4A8jqroSI+zEEIIIYQQQgiRD2k4CyGEEEIIIYQQ+ZBQbSGEEEIIIYR4C0ikdtGRHmchhBBCCCGEECIf0nAWQgghhBBCCCHyIaHaQgghhBBCCPEWkFm1i470OAshhBBCCCGEEPmQhrMQQgghhBBCCJEPaTgLIYQQQgghhBD5kDHOQgghhBBCCPEWkCHORUd6nIUQQgghhBBCiHxIw1kIIYQQQgghhMiHhGoLIYQQQgghxFtAHkdVdKTHWQghhBBCCCGEyIc0nIUQQgghhBBCiHxIqLYQQgghhBBCvAUkUrvoSMNZvBbNfOyKOguFztXapKizUOgSUtKKOguFavafw4o6C4Wu/+dTizoLhS547/SizkKh+/vK/aLOQqH7NyiiqLNQ6Jp42xZ1FgpVeHJKUWeh0HlamhZ1FgqdjZlBUWdBiDeChGoLIYQQQgghhBD5kIazEEIIIYQQQgiRDwnVFkIIIYQQQoi3gDyOquhIj7MQQgghhBBCCJEPaTgLIYQQQgghhBD5kFBtIYQQQgghhHgLSKR20ZEeZyGEEEIIIYQQIh/ScBZCCCGEEEIIIfIhodpCCCGEEEII8RaQWbWLjvQ4CyGEEEIIIYQQ+ZCGsxBCCCGEEEIIkQ8J1RZCCCGEEEKIt4BEahcd6XEWQgghhBBCCCHyIQ1nIYQQQgghhBAiH9JwFkIIIYQQQggh8iFjnIUQQgghhBDiLSCPoyo60uMshBBCCCGEEELkQxrOQgghhBBCCCFEPiRUWwghhBBCCCHeAhKqXXSkx1kIIYQQQgghhMiHNJyFEEIIIYQQQoh8SKi2EEIIIYQQQrwFJFK76EiPsxBCCCGEEEIIkQ9pOAshhBBCCCGEEPmQhrMQQgghhBBCCJEPGeMshBBCCCGEEG8BeRxV0ZEeZyGEEEIIIYQQIh/ScBZCCCGEEEIIIfIhDecXFBoaikKh4Ny5c6+0HU9PT2bOnFkgeXrdFi9ejJWVVVFnQwghhBBCiP8rCsXb8XoXyRjnd8DixYsZNGgQSqWyqLNSYFQqFZuWz+O/nZtITkrAt0RZuvYbhoOzW77r7duyjh0blhMXG4Obly9dPh+Ml38p9edLZ0/h6vlTKGMiMTQywadEGTp82g8nN08AEuPjmD99NHdDQ0iKj8Pcypry1erQrtsXGJuYFmaRUalULF0wh+3/biApIYGSZcrTf8gIXNw88lxn9dIFHD6wh7u3bmJgaEjJMuXp+cUgXN091WlmTR3H2VPHiYmKxMjEhJKly9Hzi0G4eXgVanm0UalUrFnyJ3u2/k1SYiLFS5Xjs4Hf4eTqnuc6Vy6cYdOapdwMvkpsdBRDxk6jaq36eaafO3MSuzdv4NMvBtOqw0eFUIq8ndn1Dye2riUpLoZibj407vYlTj7FtaaNuhvKofVLCAsNJj4qnIZdv6By8/YaaY5tWknQqUNEP7iDvr4hzn4lqdf5M2yd8v8dvAlqVfTh626NqVjSHSd7Szp+PZd/918o6mw9F5VKxeJ5c9j6z3oSExMoXaY8A4f9gKt73r/FFUvmc2j/Hm7fuonh499iny+zf2dh9+/RtX0LreuOmjiNeo2aFkpZ8nJhzybObl9Hclwsdm7e1O3aDwfvAK1po++FcnzjUiJDg0mIjqB2588p37TdU+kSY6M4snYBty6eIv1RKpbFnGnUczAOXv6FXRytgv/bTODeDaTEx2Ll4kWFDp9j66G9jCFHtnPr5F7iHtwCwNrNlzKtu2mkv7RtOXfO/EeyMhIdXb2sNK26YeupfZuFoajOjTklxscx9qtPUEZH8svKnZiYmRdY+Y7t+JtD/64mURmDo4cPrXt8hatviTzTXzq6n91rFqKMDMPW0ZWmXfsQUKG6RpqIu7fYuWIuN6+cJzMzg2IuHnT5ZixWdg4AbJw7nZBLZ0iIicLAyBj3gFI0++hz7F3yPi8VNJVKxfqlc9m3bSPJSYn4lyxLjwHf4viMPOzatJYt65YRFxuNu7cf3foNwScge7+G37/Livm/EHT5PGlpaZStVJ1P+w3B0tq2sIukQaVSsWzB7xrXN18O+f6Z1zdHDuzh7q1QDAwNKVGmnMb1TUJ8HMsW/M6ZE0eJDA/D0sqaGnUb8Mln/TAtwGNSiJykx1moZWRkkJmZWdTZAGD7+mXs2byWj/sN4/tpCzAwMmbmqEGkPUrNc52T/+1mzfxZtOnSi5EzF+Pq5cfMUV8Tr4xRp/HwLU73gSMYN2cVg8bOBJWKmaMGkZmRAYBCR0H5anXp/8NUJvy5mh6DfuDquZMs++3Hwi4ya5cvYtO6lQwY8gMz5y7DyNiYHwZ/waPUvMt88ewp2rTvxM9/LmXSz3+Snp7OiK/7kvIwWZ3GN6Akg78fx9zlfzNx+u+oVCpGfN2XjMdlfp3+Wb2EbX+vovfA4UyavRhDIyMmfjeAR/ns19SUh3h6+9FrwLfP3P6JQ/sIvnoJa1v7gsz2c7l6bD/7VvxJrXYf8+n437F392bN1OEkxcVqTZ/2KBXLYk7U69gLU0sbrWnuXLtAhcbv8cnoWXT8dgqZGems/fE7HqU8LMyiFAhTY0MuBt1j0OTVRZ2VF7Zq6SL+XrOCQd+OZPb85RgZG/PdoL75/hYvnD3Fex06M3v+MqbOmktGejrDBvbl4ePfor2DI2u37NV4fdq7H8YmJlStUft1FQ2A4BMHOLR6HlXe+5hOo2dj6+bNphkjSI5Xak2f/igVS3tHanzQExNLa61pUpISWD9pMDq6erz39QS6TphL7U69MTI1K8SS5O32mYOc/3s+pZp1ocnQX7By9uLg76NISVBqTR95/SLuFetRv/9kGn09DRMrew7+PopkZZQ6jbm9CxU/6Euzb3+j4cCpmNo4cPD3kaQkxr2mUhXduTGnJbMm4erpW+Blu3hkL9v++p0GHT6l35S5OHr4sHjSMBLzqENvB15izazxVGrQkn5T5lGiSm1W/DSS8Ns31Wmiw+4xb/RX2Dm70Wv0z/SfOp/6HT5BT99AncbF25/2fYcxcMYSun8/FVSweOJQMjNf3zly89q/2PnPanp+9R1jZy7E0MiYH0d8le+58diBXSyfN5N2H3/GhNl/4e7tx48jviLu8X5NSXnIjyMGoEDB91PmMHr6PDLS05g++pvXfq23bvliNq1bQf8hI/h57lKMjI0ZObhfvnXqpbOnad2+EzP+/IuJP/9BRno6I77+gpSHWee/6KhIoqMi+ezLwfy+dB1fjxjHqWOHmTll7Osqlvg/JA3nPGRmZjJ16lR8fX0xNDTE3d2diRMnqj+/ceMGDRo0wMTEhHLlynH06FGN9devX0+pUqUwNDTE09OT6dOn5/t9SqWSzz77DHt7eywsLGjYsCHnz59Xf37+/HkaNGiAubk5FhYWVKpUiVOnTrF//3569OhBXFwcCoUChULBmDFjAEhNTWXIkCG4uLhgampKtWrV2L9/v3qbT0KuN23aRMmSJTE0NOT27dvExsbSrVs3rK2tMTExoUWLFgQHB7/6P/U5qVQq9mxaTauO3SlfvS6uXr70/HoUypgozh47mOd6uzaupE6z96jVuDXO7l583G8YBoaGHN61WZ2mbvO2+JeugJ2DEx6+AbT9+HNiosKJingAgKmZBfVbtsfTrwS2xZwoUa4K9Vt2IPjK+by+tsDKvHHtcjp3602NOg3w8vVnyA8TiI6O5Mh/e/Ncb8KM32nS8n08vH3x9gtg8PfjiAh/QHDgVXWalu9/QJnylXBwcsE3oASf9u5PZEQY4WH3C7VMualUKrZuWEn7rr2oUqs+Ht5+9P92HLHRkZw8vD/P9SpUrUXnnv2oWrtBvtuPiYpg4eyf+Gr4ePT0Xn8wzalt6ylbvwVl6jbHzsWDZj0Gom9oyMWDO7Smd/IOoEGXPpSo0QBdfX2taT4cNpkydZth5+pJMQ8fWvYZSnx0BOGhr+/3+LJ2Hr7C2Dmb2bTv7ehlfkKlUrFh9TI+7tGbWnUb4OPnz7ejJxIVFcmhg3n/FqfM/IPmrd/H09sXH78Aho0cT0TYA4KvXQFAV1cXG1s7jdfhA3up16gZxiYmr6t4AJzbsYFSdZtTsk5TbFw8aNBtAHoGhlz9T/ux6uAVQK2OvfGvVh9dPe3H6pmtazGzsadxr29w8A7Awt4R99KVsCzmXJhFyVPQ/o1412yGV/UmWDq6U6njl+gZGHLz2C6t6at3G4pvnVZYu3pj4eBG5S4DUGVmEhGUXfd7VK6PQ0B5zOwcsXTyoHy7z0hLSSbu3k2t2yxoRXlufGL/1g0kJyXQtF3BR/Mc3rKWyo1aUalBC4q5evLeZ4PRNzDi9L5tWtMf2bYev/JVqfNeZ4q5etC4U0+cvPw4tuNvdZrdqxbgX6EazT/ui7OXH7aOLpSoXAuzHDeAqjRug1fJclgXc8TZ25/GnXoSFx1BbERYgZdRG5VKxfa/V/F+l55UqlEPd28/+g4dgzI6itNHDuS53rYNK2jQvC31mrbBxcObHgO+w9DQiAM7/gUg+PJ5IsMf0OebUbh5+eLm5cvnQ8ZwM/gqV86dei1lA+3XN9/8MJ7o6EiO/rcvz/XGz5jz1PVNZPgDggOz6lRPb19+mDidarXr4eTiRvlKVfm0T3+OHz5ARnr66ypekXhyvf+mv95F0nDOw/Dhw5kyZQojR47kypUrrFixAgcHB/XnI0aMYMiQIZw7dw5/f3+6dOlC+uMf6unTp+nYsSOdO3fm4sWLjBkzhpEjR7J48eI8v+/DDz8kIiKCbdu2cfr0aSpWrEijRo2Iicm6c9i1a1dcXV05efIkp0+f5rvvvkNfX5+aNWsyc+ZMLCwsePDgAQ8ePGDIkCEA9O/fn6NHj7Jq1SouXLjAhx9+SPPmzTUawcnJyfz444/Mnz+fy5cvU6xYMbp3786pU6fYtGkTR48eRaVS0bJlS9LS0grhP/20qPD7xMVGU6J8FfUyE1MzvP1LcuPaJa3rpKelcet6ICXKZa+jo6NDifJVCAnUvk5qykMO796MnYMzNnYOWtMooyM5c3Q//qUrvEKJni3s/j1io6OoUKWaepmpmTkBJctw7dLzNzySkxIBMLew0Pp5ysNkdm79B0cnF+yLOb5apl9QxIN7KGOiKVuxqnqZiZkZviVKE3Tl4ittOzMzk1+njOK9jp/g5unzqll9YRnpaYSFBuFZqqJ6mUJHB49SFbl//UqBfU/qwyQAjEwlDK2wPLh/j5joKCpWyQ73NDMzp0SpMly5+Pw30JISn/wWLbV+HnTtCteDrtGyzdMhz4UpIz2NiFvBuJXMrtMUOjq4lqxAWMjVfNbM381zxyjm6c+2ORNYMLATq8Z8yeUD2hs8hS0jPY3YO9dx8C+vXqbQ0aGYf3miQ6893zYepaLKzMDARPtvLSM9jZAj29E3NsXK5fUMeynqc+P92zfZvGohPb8ehY5OwV4+pqencf9GED5lKmnk06dMRe4EX9a6zp2gK/iUrqSxzK9cFe4EZaXPzMwk8Owx7JxcWTxxKJN7t+OPEV9w5eShPPPxKOUhZ/Zvx7qYE5Z2xQqgZM8WGZa1X0tXyHFuNDXDp3gpgq9qPzemp6VxM/gapSpo7tdSFapw/fE6aWlpKFCgn6N3XV/fAIVCh8DL5wqnMFo8ub4pr+X65uqlF6hTk/KvU5+kMTE1Q7cIbp6L/w9yZGmRkJDAL7/8wuzZs/n0008B8PHxoXbt2oSGhgIwZMgQWrVqBcDYsWMpVaoU169fp3jx4syYMYNGjRoxcuRIAPz9/bly5Qo//fQT3bt3f+r7Dh06xIkTJ4iIiMDQ0BCAadOmsXHjRtatW0efPn24ffs2Q4cOpXjxrPGSfn5+6vUtLS1RKBQ4OmY3hG7fvs2iRYu4ffs2zs7O6jxv376dRYsWMWnSJCCrYp0zZw7lypUDIDg4mE2bNnH48GFq1qwJwPLly3Fzc2Pjxo18+OGHBfI/zk9cbDQAFlaa4avmVjbqz3JLjFeSmZmBhbXmOhZWNoTdvaWxbN+W9axf/BupKQ9xdHHn6/G/oJerx2/uT6M4f+wgjx6lUq5qbT4dMPxVi5Wv2JiscEDrXOOOrK1t1Z89S2ZmJn/OmkrJMuXx9PbT+GzzhtUs+P1nUh4+xNXdk4kz/0Q/j17OwqJ8vO9yj62ytLJBGaN9vz6vf1YtQVdXlxbtOr/Sdl5WckIcqszMp8JYTS2sibl/p0C+Q5WZyZ5lv+PiXwp7t9c/Pv3/RWz049+iTa7foo0tsdHPd5xmZmby28yplC5bAS8fP61ptm3agLunN6XKln+l/L6ohwnxqDIzMbaw0lhuYmGF8sHLH6vxkQ+4tG8z5Zu1p3KrzoTfDOLgit/R0dOjRK0mr5jrF/MoKauMhuZWGsuNzK1IiLj7XNu4sGkxRhY2OASU11h+/9IJji2ZSnpaKsYW1tT7YjyGZnlfyBekojw3pqU9Yt5Po/igR39sizkSFV6wEUvJ8XFkZmZq9AQDmFlaE3X/ttZ1EpUxmFo9nT7hcWh3UrySRykPOfjPShp36kmzrp8TdO4EK6ePoueoGXiVLK9e7/iOjexY/iePUlOwc3aj+4if0MsjuqKgKfPYrxb57NeEx/vVMtc6llY2PLiTtV99i5fG0MiIVQtn07F7P1SoWL1wNpmZGa98zn0ReV3fWFnbEPuc+ci6vvnp8fWN9mECccpYVi6eR4s27bV+LkRBkB5nLa5evUpqaiqNGjXKM03ZsmXVfzs5OQEQERGhXr9WrVoa6WvVqkVwcLDWcaXnz58nMTERW1tbzMzM1K+bN28SEhICwODBg/nss89o3LgxU6ZMUS/Py8WLF8nIyMDf319jmwcOHNBY18DAQKMsV69eRU9Pj2rVsu8M2traEhAQwNWrz9cbkZqaSnx8vMYr33E6+3fQ/8OG6ldhh9hUq9+Mkb8sYejkOTi4uPPnjz88NT6s02cD+WHmYr78YSoRD+6xZv6sAs3D3p1baNekuvqVXgBl/m3GJEJvhPDd2KlPfdagaUtmL1zN1NkLcXHzYPLIofmOLSoI/+3Zxiet66hfhbVfbwRdZevfq+g3dMw7GxoEsGvJr0TdDeW9L0cUdVbeKbu3b6FVg2rqV0H8Fmf9NJHQkOv8MEH73AipKSns2bmNFq+5t7kwqVQq7D18qdGhB/YevpSu35JSdZtzaf+Wos7aC7u6ay13zh6kVq8R6OborQMo5leWJsNm0WjQTzgWr8TRxT/mOW76Vb1J58YNS37Hyc2T6g2aF2oeCpLq8TjeEpVrUqvVhzh5+lKv7UcEVKzBiV3/aqQtV6cx/X6cR6/RM7FzcmP1zLGkPXpUKPk6vHc7vdrWU78Ka79aWFnz1YjJnD3+H5+1q0ef9g1JTkzE07c4OjqFd67ct3ML7ZvUUL8KonxzZkzm1o3rfDdWe52anJTI6KEDcPf0pmuvvq/8fULkRXqctTA2Nn5mmpy9dU8u1l92soXExEScnJw0xh8/8eSxT2PGjOGjjz5iy5YtbNu2jdGjR7Nq1SratdN+4ZWYmIiuri6nT59GV1dX4zMzs+zJWoyNjQu8sTF58mTGjtWcnKF7/2H0yGNyp/JVa+PtX1L9/klIeLwyBisbO/XyBGUMbt7aZ2c1s7BCR0eX+NgYjeXxyhgsct3lNDE1w8TUDAdnN7wDSjOwS1POHD1AtXrZs9paWttiaW2Lk5snpmYWTP2uL60699DIz6uoXrs+xUuWUb9/coKOjY3Gxi57YqvY2Gh8fJ89Y+ucGZM4ceQgP81eiH2xp8POTc3MMTUzx8XNg+KlyvJhi9ocObiX+k20z/JbECrXqItf8dLq92lpWWWMi43G2jb7/xinjMHT5+Vn3b168Szxyhj6fdRavSwzM4O//pzJ1g0r+W35v/msXTBMzC1R6OiQnGsSm6T42Kd6RF7GriW/EnLuOF1GTMfc5vVPfPYuq1mnPiVK5fgtPj5OY2Oisc35W4yJxsfv2b/FWdMmcezwQX7+Y1GewyEO7ttFaspDmrZs84q5f3HG5hYodHR4mGsisOR4ZZ4Tfz0PUysbbJw1ZwC2dnYn5PThl97myzIwzSpjaq4GbUqCEiPz/Mt4be8Gru1ZR71+E7SGYOsZGmFu7wz2zth6Fmfr+N7cPLaTEk06FmQRgDfr3Hjtwmnu3Qrh9PtZY1JVqAD4umsLWnb8lPe79n6lsppYWKKjo/PURGCJcbGYWWmfPNHMyoYk5dPpzR8fxyYWlujo6mLv4qmRxt7FnVvXNEOgjUzMMDIxw87JFTf/kkzs+R5XTv5HuVp5d6C8rIrV6+BTPHvm6/TH5/94ZYzGuTFeGYN7HvvV/PF+jVNq7tc4ZYxGVFeZStWZsehvEuKU6OjqYmpmzpddmmPvWHhRINVq1yfgOa5vlLExePs++9w/Z8ZkThw5yNTZC7HTcn2TnJzEyG/6YWJiyshJM15bpEBReof7CN540nDWws/PD2NjY/bs2cNnn332wuuXKFGCw4c1LxYOHz6Mv7//U41YgIoVKxIWFoaenh6enp55btff3x9/f3++/vprunTpwqJFi2jXrh0GBgZP9WRXqFCBjIwMIiIiqFOnzgvlPT09nePHj6tDtaOjowkMDKRkyZLPWDvL8OHDGTx4sMayE7eT8kxvZGKKUY5HPalUKiytbbl2/pT6pPEwOYkbQVeo11J7CI6evj4evgFcvXCKCjXqAVk3Mq6eP0XDVh/k+d0qVKBSkZ7P+G2VKuuGSH5pXpSJiSkmucpsbWvHuVPH8fHLCsdPSkok8MpFWrXNOzxepVLx+8+TOXJwLz/+ugBHZ9dnfrdKpQJVdgOhsBibmGo8wkulUmFlY8vFsyfxfHwzIDkpketXL9G0TYeX/p66jVtSJse4aYCJ3w2gbuOWNGj+ehomunr6OHr6c+vKWfwqZ0WbqDIzuXX5LBWbvP/S21WpVOz+azbBpw/T+ftpWBVzKqgsi8dMTE0xMdU8Tm1s7Thz8ji+/tm/xauXL9Kmfd6NI5VKxa/TJ3PowF5m/LYAp3x+i9s2/U2NOvWxstbeIChMunr6FPPw487Vc3hXzKrjVZmZ3L16jrINX/734uhbktgwzTBoZdg9zG1fzzjRnHT19LF28yU86DwuZWsAqCf68q3TOs/1ru1Zx9Wda6j7xThs3LWH2OemUqnISC+c+T/epHPjF8MnaURmhQZfZfEvExn24+/YO7q8cln19PRx9vbnxsUzlKxSW53PG5fOUK2Z9g4CN/+ShFw6Q80c5bh+8TRujx+zpaenj4tPcaJyDUGIenAXK3vt85oAoMoqe0Yhzeui7dxoaW3L5XMn8Xh8Ezk5KZGQa5dp1Er7uVFPXx8vv+JcPneSyjXrA1n/r8vnTtGkzdPXDOaWVgBcPneSeGUsFavXLdhC5ZDX9c35UyfU1zfJz319M4WjB/cy5df5ODo/fZwlJyXyw+B+6OvrM+rHmRg8Hu4oRGGRhrMWRkZGfPvttwwbNgwDAwNq1apFZGQkly9fzjd8+4lvvvmGKlWqMH78eDp16sTRo0eZPXs2c+bM0Zq+cePG1KhRg7Zt2zJ16lT8/f25f/8+W7ZsoV27dpQqVYqhQ4fywQcf4OXlxd27dzl58iQdOmRVqJ6eniQmJrJnzx7KlSuHiYkJ/v7+dO3alW7dujF9+nQqVKhAZGQke/bsoWzZsurx2bn5+fnx/vvv07t3b/7880/Mzc357rvvcHFx4f33n68BYGhoqB6r/YSBwfOH6igUChq914ktqxdTzNkNOwcn/lk2DysbOyrkqOynj+hPhRr1aNg6q+Jt0rYLC38ej6dvcbz8S7H7n1U8SkmhVuOsC6XIsHuc/G83pSpUw8zCitjoCLavW4q+oSFlKmddXF08dYR4ZQyefiUwNDLh/u0brFs0G98SZbFzKLxGi0KhoO2HXVm1ZB4ubh44OLmwdP5v2NraU7NOQ3W67wb2pmbdhrzXoQsAv02fxP7d2xg1eSbGJqbEPB6faWpmhqGhEQ/u3eXg3h1UrFIDSytroiLDWbNsIQaGhlR5zY/AUSgUtGzfhQ3LF+Dk4kYxRxdWLf4da1t7quR4LvO4oV9QtVZ9mrftBGRNaBZ2L/vCJ+LBPUKvB2JmbomdgyPmllbqi4In9PT0sLKxxVnLM0gLS+UWHdg6dyqOXv44eQdwasffpKWmUKZuMwC2/PEjZtZ21OvUC8iaXCjq3i313wmxUYTfuo6BkTHWDlkXCLuW/MrVo3tpN2gsBkYmJD7uXTA0MUXf4M2+QDA1NsDHLbt3wdPFlrL+LsTGJ3MnTPvjZd4ECoWC9p0+Zvniubi6uePo7MKiub9hZ2dP7brZv8Uh/T+jdr1GtP0w67c466eJ7Nm5jfFTf8HENMdv0dQMQyMj9Xr37tzmwrnTTJrx2+stWA7lm7Vn9/xpFPP0w8ErgPO7/iY9NYUStbOibnbN+wlTa1tqftATyDo+Yx6PM81ITydJGUXk7RD0DY2xcsiaQ6N803asnzSYU5tX4VulLuE3A7l8YCsNPh1YJGX0r9+WE8t/xsbdDxt3f4IO/EP6oxS8qjUG4Piy6Rhb2lK2TXcAru5ex+Wty6jebSgmNg48jM86RvUMjdA3NCY9NYUrO1fjUqYaRhY2pCbFc/2/zTyMi8at/OupS4vy3FjMSfNGUGJ81iO4nFw9C+w5zrVafcj6OVNw9vHH1acER7au41FqCpXqZ4WHr5s9CQsbe5p+lNW7XbNFB+aPHcShf9cQULE6F47s5X5IIG17f6PeZp02nVg9cxyeJcriXaoCwedOEHj6CD1HzwQgJvw+F4/sw7dcZUwtrIiPjuTgPyvRMzDEv0K1p/JYGBQKBc3bdWbjyoU4OLtRzNGZdX/9gZWtHZVq1lOnm/RdPyrXrE/T97Ju4LVo/xF/ThuLl18JfAJKsf3vVaSmPKRe0+ybQwd2/ouLmyfmltYEX73Isj+m07xdF5zzeX5yYZTvyfWNs5u7xvVNjTrZT8sYPrAPNes2pE2HrLlK5jzj+iY5KZERX39BamoKQ0dNJDkpieSkrE4aSytrrR1VQrwqaTjnYeTIkejp6TFq1Cju37+Pk5MTffs+37iJihUrsmbNGkaNGsX48eNxcnJi3LhxWicGg6xKZevWrYwYMYIePXoQGRmJo6MjdevWxcHBAV1dXaKjo+nWrRvh4eHY2dnRvn17dTh0zZo16du3L506dSI6OprRo0czZswYFi1axIQJE/jmm2+4d+8ednZ2VK9endat877jDrBo0SIGDhxI69atefToEXXr1mXr1q2vdTKp5h0+5lHKQ5bOnkJyUiJ+JcsycOzPGo2FyLB76pM3QJU6jUmIi+Wf5fOJj43GzduPgWN/Vk+Koq9vQPDl8+zetJrkxAQsrGzwK1We76bOVU/KoW9gyH87/mH1/F9IT3uEtZ0DFWvUp8UHnxR6mT/s2oOUlIfMmjqOxMQESpWpwPjpczTuoD64d5d4pVL9fsvGNQB8O6CXxrYGfz+OJi3fx8DQgEvnz7BxzTISE+KxsrGldLlKzPjjL6xyhem9Du93+pTUlBT+/HkSyYkJFC9dnu+nzMIgx34Nv3+X+Dil+n1I4BXGDsn+7f31x88A1Gvami+HjXldWX+mEtXr8zBByaH1S0iKi6WYuw8fDp2E6eOwwfjoCI1hEYmx0Sz54Qv1+5Nb13Jy61rcipely4isx9ed25MVZr5q0hCN72rRe4i6Qf6mqljSg53zsxtNU4dk3ehbuukYfUYvK6psPZfOn2T9FmdMyfotlilbgckzf9f4Ld6/e5e4HGGimzZk/RYH9+upsa2hP4yneevsm47bNv+NfTEHKlerWcilyJtf1Xo8TIjjxMalJMXFYu/mTZuvJ6hDtRNiIlDkGAOZpIxm9Zgv1e/Pbl/P2e3rcQ4oQ/tvfwKyHlnV4stRHF2/iJOblmNh70idLn0JqNGQouBesS6piXFc2rqMlPhYrFy9qdt3HEYWWWVMjo1Eocie5iXk8FYyM9I5smiyxnZKNu9C6RZdUejokBBxlyML95CaGI+BqQU27n40/OpHLJ1eXyOkqM6Nr0OZmg1Jio9jz5rFJCpjcPL04dPhP6pDtZXREShyzObtHlCajgN+YPfqhexaNR9bRxc+GjoeB/fsEPuSVevwXu+vObhxBVsW/YqdsxtdBo/Fs3hWKLGevgG3rl3kyLb1pCQmYGpljWfxsvQZ/+tTE5UVptYfdiM1JYWFsyaRnJiIf6lyDJvwi8a5MeL+PRJynBur12tCfFws65fOJS42Gg9vf4ZN+EUjVPvB3VusWfQbiQnx2Ds48V7nHrRoX/CPEnuWD7p2JyXlIb9OHa++vhn31PXNHY06dcvGtQB8O0Az8vPr78fSpOX7XA+8SuDjJ3L06qQZLbNo7RYcnF49EuJNpSOx2kVGoVKpVEWdCfHuOxgU8+xEbzlX69f7LNaikJDyeh5JVlROPXhze0ILSv/Pn5487l0TvHd6UWeh0P195fU+h70oRCS8289iBWji/fpvYL5O4ckpRZ2FQudpafrsRG85GzODZyd6y/nYP3t+ozdFk9nHijoLz2VX/+rPTvSWkVm1hRBCCCGEEEKIfEiothBCCCGEEEK8BSRSu+hIj7MQQgghhBBCCJEPaTgLIYQQQgghhBD5kIazEEIIIYQQQgiRDxnjLIQQQgghhBBvAYUMci4y0uMshBBCCCGEEELkQxrOQgghhBBCCCFEPiRUWwghhBBCCCHeAjoSqV1kpMdZCCGEEEIIIYTIhzSchRBCCCGEEEKIfEiothBCCCGEEEK8BWRW7aIjPc5CCCGEEEIIIUQ+pOEshBBCCCGEEELkQxrOQgghhBBCCCFEPmSMsxBCCCGEEEK8BWSIc9GRHmchhBBCCCGEECIf0nAWQgghhBBCCCHyIaHaQgghhBBCCPEWUCCx2kVFepyFEEIIIYQQQoh8SMNZCCGEEEIIIYTIh4RqCyGEEEIIIcRbQEcitYuM9DgLIYQQQgghhBD5kIazEEIIIYQQQgiRDwnVFkIIIYQQQoi3gEIhsdpFRXqchRBCCCGEEEKIfEjDWQghhBBCCCGEyIc0nIUQQgghhBBCiHzIGGchhBBCCCGEeAvIEOeiIz3OQgghhBBCCCFEPqTHWbwW5yPjijoLhe5mfFJRZ6HQves3Oc30dYs6C4UueO/0os5CofNr+E1RZ6HQrVs6qqizUOhquRoVdRYK3a245KLOQqEy19cv6iwUumP3Y4o6C4UuMjG9qLNQ6MY18yvqLIi3gDSchRBCCCGEEOItoCOx2kVGQrWFEEIIIYQQQoh8SMNZCCGEEEIIIUSR+u233/D09MTIyIhq1apx4sSJ51pv1apVKBQK2rZtW6j5k4azEEIIIYQQQrwFFIq34/WiVq9ezeDBgxk9ejRnzpyhXLlyNGvWjIiIiHzXCw0NZciQIdSpU+cl/6PPTxrOQgghhBBCCCGKzIwZM+jduzc9evSgZMmS/PHHH5iYmLBw4cI818nIyKBr166MHTsWb2/vQs+jNJyFEEIIIYQQQhSY1NRU4uPjNV6pqala0z569IjTp0/TuHFj9TIdHR0aN27M0aNH8/yOcePGUaxYMXr16lXg+ddGGs5CCCGEEEIIIQrM5MmTsbS01HhNnjxZa9qoqCgyMjJwcHDQWO7g4EBYWJjWdQ4dOsSCBQuYN29egec9L/I4KiGEEEIIIYR4CyjeksdRDR8+nMGDB2ssMzQ0LJBtJyQk8MknnzBv3jzs7OwKZJvPQxrOQgghhBBCCCEKjKGh4XM3lO3s7NDV1SU8PFxjeXh4OI6Ojk+lDwkJITQ0lDZt2qiXZWZmAqCnp0dgYCA+Pj6vkHvtJFRbCCGEEEIIIUSRMDAwoFKlSuzZs0e9LDMzkz179lCjRo2n0hcvXpyLFy9y7tw59eu9996jQYMGnDt3Djc3t0LJp/Q4CyGEEEIIIcRb4C2J1H5hgwcP5tNPP6Vy5cpUrVqVmTNnkpSURI8ePQDo1q0bLi4uTJ48GSMjI0qXLq2xvpWVFcBTywuSNJyFEEIIIYQQQhSZTp06ERkZyahRowgLC6N8+fJs375dPWHY7du30dEp2mBpaTgLIYQQQgghhChS/fv3p3///lo/279/f77rLl68uOAzlIs0nIUQQgghhBDiLaDzrsZqvwVkcjAhhBBCCCGEECIf0nAWQgghhBBCCCHyIQ1nIYQQQgghhBAiHzLGWQghhBBCCCHeAjLCuehIj7MQQgghhBBCCJEPaTgLIYQQQgghhBD5kFBtIYQQQgghhHgLKORxVEVGepyFEEIIIYQQQoh8SMNZCCGEEEIIIYTIh4RqCyGEEEIIIcRbQEcitYuM9DgLIYQQQgghhBD5kIazEEIIIYQQQgiRD2k4CyGEEEIIIYQQ+ZAxzkIIIYQQQgjxFpDHURWdF+pxVqlU9OnTBxsbGxQKBefOnSuUTI0ZM4by5csXyrZz6t69O23bts03Tf369Rk0aFCh58XT05OZM2cW+vcIIYQQQgghhHgxL9TjvH37dhYvXsz+/fvx9vbGzs7ulTOgUCj4+++/NRqwQ4YMYcCAAa+8bfHuuLBnE2e3ryM5LhY7N2/qdu2Hg3eA1rTR90I5vnEpkaHBJERHULvz55Rv2u6pdImxURxZu4BbF0+R/igVy2LONOo5GAcv/8Iujlandv7D8S1rSIyLwcHdh6af9sfZp7jWtJF3Qzm4bjFhN4OJiwqn8cdfULVFB400t69e4NiWNYTdDCZRGU2Hr8cSULnW6yjKczu18x+O5SqzSz5lPpCjzE20lPl1O75jI4f/Xa3Of6seA3D1LZFn+kvH9rN3zSKUkWHYOLrS9KPe+Feorv58VOeGWtdr2rUPtdt0Vr8PPHOM/ev/Ivz2DfQMDPAsUY6PhowvuII9g0qlYvG8OWz9Zz2JiQmULlOegcN+wNXdI891ViyZz6H9e7h96yaGhoaULFOePl8Ows3DC4Cw+/fo2r6F1nVHTZxGvUZNC6Usr6JWRR++7taYiiXdcbK3pOPXc/l3/4WiztZTDm3bwL5/VpKgjMHZ04d2vQbh4Vcyz/Tnjuxj+8r5xESGYefkSuuP+1KyUg315xeOHeDIzn+4GxJIcmI830xbiIuXn8Y2osLusWnJb9y8doH0tDSKl69G+88GYW5lU2jlzE2lUrHurz/Zu20jSYmJBJQqS8+vvsPJxT3Pda5eOMPmtUu5EXwNZUwUg0f/RJVa9TXSnDi0l92bN3Az+BqJCXFM/n0Znj7az0cF6ej2vzmwaRWJyhicPHx4r+dA3Pzyrm8uHN3HrlULiY0Mw9bRhRYf96V4xepa0/49dzrHd22idff+1G71oXr5vRtBbFv2B3dDAtHR0aF0tbq0+vRLDI1NCrx8AP9tW8/ejSuJV8bg4ulDh8++zvdYPXtkL1tXzicmIgx7J1fafPIFpXIcq+ePHeDwjo3ceXysDp2+CNdcx+qRnf9w+r9d3LkRROrDZCYv3YaJqXmhlE+b/4frm+CDm7m2dwMp8bFYuXhR8YPPsfXQXsaQI9sJPbGXuAe3ALBx86VMm24a6S9tXc7tM/+RrIxER1cvK03rbth6Fv7vUPx/e6Ee55CQEJycnKhZsyaOjo7o6RVOpLeZmRm2traFsm1RcFQqFenp6YX+PcEnDnBo9TyqvPcxnUbPxtbNm00zRpAcr9SaPv1RKpb2jtT4oCcmltZa06QkJbB+0mB0dPV47+sJdJ0wl9qdemNkalaIJcnblaP72LP8D2q3/4SeE/6gmLs3q6Z8R1JcrNb0aakpWBVzon7nzzDN40I0LTWFYu7eNOv+Zt6EunJ0H7uX/0Gd9p/Q6znLbF3MiQb5lPl1unhkH9uX/k79D7rRd/KfOHr48Nfkb0nMI/+3Ay+xbtYEKjZowRdT5lKici1WThtF+J2b6jRD/1in8WrbdygKhYKSVeuq01w+fpANv02mYv3m9PtxHp+NnUXZWo0Kvbw5rVq6iL/XrGDQtyOZPX85RsbGfDeoL49SU/Nc58LZU7zXoTOz5y9j6qy5ZKSnM2xgXx4+TAbA3sGRtVv2arw+7d0PYxMTqtao/bqK9kJMjQ25GHSPQZNXF3VW8nT28B7+WTybZh27M/in+Th7+DJ3/Dck5HGc3rx2kWU/j6Vqo1Z8M20BZarWYdHU73lw+4Y6zaOUh3gVL0PrT/pq3UZqykP+HDcYhULBF2N+YcDEOWSkpzF/8ndkZmYWSjm1+XfNX2zfuJpeXw1n/KxFGBoZM2X4AB49yvs4TU15iLu3Pz37D8snTQoBpcvR5bP+hZFtrc4f3svmJb/R+MNPGfDjPJw8fFgwcUie9c2twEusmjmeyg1b8tXUeZSqWoelU0cQlmM/PnHp+EFuB13BwlqzMyQ+Jor54wZj6+jCl5N+p8eIqYTfDWXtb1MKpYxnDu3h70WzadaxB0OnLcDZ05ffxw0mQZn3sfrXjLFUb9SaodMXUqZqHRb8OJz7tzSPVe8SZXnvky/y/N5HqakUr1CNJh0+KfAyPcv/w/XN7TMHOff3fEo170LTob9g5eLFgTmjSElQak0fEXwR90r1aDBgMo0HT8PY2p4Dc0aRrIxSpzEv5kLFD/vS/LvfaDRoKiY2DhyYM5KUhLjXVKqipVC8Ha930XM3nLt3786AAQO4ffs2CoUCT09PIKsXunbt2lhZWWFra0vr1q0JCQlRr/fo0SP69++Pk5MTRkZGeHh4MHnyZAD1Ntq1a6exzdyh2k9CqqdNm4aTkxO2trZ8+eWXpKWlqdM8ePCAVq1aYWxsjJeXFytWrHju8OexY8dib2+PhYUFffv25dGjR3mmjY2NpVu3blhbW2NiYkKLFi0IDg7WSLN+/XpKlSqFoaEhnp6eTJ8+XePziIgI2rRpo87r8uXLn5nH/fv3U7VqVUxNTbGysqJWrVrcunVL4/+T06BBg6hfv776fUJCAl27dsXU1BQnJyd+/vnnp8LQly5dSuXKlTE3N8fR0ZGPPvqIiIgIjTwoFAq2bdtGpUqVMDQ05NChQ8/M+6s6t2MDpeo2p2Sdpti4eNCg2wD0DAy5+t8OrekdvAKo1bE3/tXqo6unrzXNma1rMbOxp3Gvb3DwDsDC3hH30pWwLOZcmEXJ04lt6ynfoCXl6jXH3tWDFj0HoWdoyPkD27Wmd/YpTqOPPqdUjQbo5VFGn/JVqd+xJwFV3sxGx/FcZW5ZAGV+nY5sWUulhi2pWL8FxVw9afPZ1+gbGHJm/zat6Y9t24BvuarUbtMZexcPGnXqiZOXH8d3bFSnMbey0XhdO3UEz5LlsXHIOi4zMjLYtmQ2Tbt+TpUm72Hn7EYxV09K16j/GkqcRaVSsWH1Mj7u0ZtadRvg4+fPt6MnEhUVyaGDe/Ncb8rMP2je+n08vX3x8Qtg2MjxRIQ9IPjaFQB0dXWxsbXTeB0+sJd6jZphbFI4vVuvaufhK4yds5lN+968XuYnDvy7muqN21C1YSsc3bz44PMh6BsacWLPFq3p/9uyjuIVqtKw7Uc4uHrSostnuHj5c2jbBnWayvWb06xjD/zLVta6jdBrF4mJDKNL/+9x9vDB2cOHLgNGcDfkGtcvnimUcuamUqnY9vdK2n3Uk8o16+Hh7Ue/YWOJjY7i1OEDea5XvmotOvX4giq1G+SZpk7jlnT4uDdlKlQtjKxrdWjzGqo2ak3lBi1xcPOkbZ9vMDAw4tTerVrTH96yDv/yVan3fheKuXrStHMvnL39Obr9b410cdGRbFo4i84Df0AnV2fI1dNH0NXT4/3PvsbexR033xK06z2YS8cPEPXgboGXcf+/q6jZpA3VG2Udqx0/H4qBoRHH9m7Wmv7A5rUUr1CNRm0/wtHVk1Yf9cbVy5//tq1Xp6lSvznNO/bAv5z2YxWgfpuONGn/CZ7+pQq8TM/y/3B9E7hvI941m+FdvQmWTu5U7vglegaG3Dy2S2v6Gp8Oxa9OK6xdvbFwcKNKlwGoMjMJDzqvTuNRuT6OAeUxs3PE0smDCu0+Iy0lmbj7N7VuU4iC8twN519++YVx48bh6urKgwcPOHnyJABJSUkMHjyYU6dOsWfPHnR0dGjXrp36rvKsWbPYtGkTa9asITAwkOXLl6sbyE+2sWjRIo1tarNv3z5CQkLYt28fS5YsYfHixSxevFj9ebdu3bh//z779+9n/fr1zJ07V6PRl5c9e/Zw9epV9u/fz8qVK9mwYQNjx47NM3337t05deoUmzZt4ujRo6hUKlq2bKluxJ8+fZqOHTvSuXNnLl68yJgxYxg5cqRGXrt3786dO3fYt28f69atY86cOfnmNT09nbZt21KvXj0uXLjA0aNH6dOnzwtNDjB48GAOHz7Mpk2b2LVrF//99x9nzmhewKSlpTF+/HjOnz/Pxo0bCQ0NpXv37k9t67vvvmPKlClcvXqVsmXLPnceXkZGehoRt4JxK1lBvUyho4NryQqEhVx96e3ePHeMYp7+bJszgQUDO7FqzJdcPqC9wVPYMtLTeHAzCM/SFdXLFDo6eJWuyL3gK0WSp8L2pMxeWsp89y0oc/rj/PuUqaRepqOjg0+ZStwN0p7/O8FX8C5TUWOZb7kq3Am6rDV9ojKGoLPHqNSgpXrZg5tBxMdEodBRMOe7Pkzt+wF/Tf5Oo9e6sD24f4+Y6CgqVskO+TQzM6dEqTJcuXg+nzU1JSUmAmBuYan186BrV7gedI2WbZ4OQxTPJz0tjbshQfiX1TxO/ctWJjSP4y406BJ+uRrExctXJTTw0gt9rwIFevrZF/b6BgYoFDrcuPZ6bjJEhN1DGRNN6YrZjVsTUzN8ipci+Oqbe6NDm/S0NO7dCMI31370LVuJW3nsx1tBlzXSA/iXq6KRPjMzk9W/TqTue51xcPN6+nvT09DV00NHJ/tSUc/AEMi6OVKQ0tPSuBMSpHEzRn2sBmov482gSwTkPlYrVHuhY7Uo/b9c38TeuY5DQHn1MoWODg4B5Ym6ee35tvEoFVVmBoYm2sPnM9LTCDmyHX1jU6xcnj6OhShIzx1rbWlpibm5Obq6ujg6OqqXd+igOcZw4cKF2Nvbc+XKFUqXLs3t27fx8/Ojdu3aKBQKPDyyx8DZ29sDYGVlpbFNbaytrZk9eza6uroUL16cVq1asWfPHnr37s21a9fYvXs3J0+epHLlrEp0/vz5+Pn55btNAAMDAxYuXIiJiQmlSpVi3LhxDB06lPHjx2ucLACCg4PZtGkThw8fpmbNmgAsX74cNzc3Nm7cyIcffsiMGTNo1KgRI0eOBMDf358rV67w008/0b17d4KCgti2bRsnTpygSpUqACxYsIASJfIepxQfH09cXBytW7fGx8cHIN/0uSUkJLBkyRJWrFhBo0ZZIZ2LFi3C2Vnz7mPPnj3Vf3t7ezNr1iyqVKlCYmIiZmbZIT7jxo2jSZMmeX5famoqqblCNtMepaL/+IT7Ih4mxKPKzMTYwkpjuYmFFcoHd154e0/ERz7g0r7NlG/WnsqtOhN+M4iDK35HR0+PErXyLlthSE6IQ5WZiWmusCtTC2ui7798Gd9kb3uZk+PjyNSWf0trIu/d1rpOojIGs1zpzSyt8wy1PHtwJ4ZGJpSoWke9LDbiAQD71i2h+Sf9sLZ35PDmNSwa9zVf/fwXJmYWr1Ks5xIbnRUuZ22jOZzG2saW2Ojo59pGZmYmv82cSumyFfDy0V5Pb9u0AXdPb0qVLf9K+f1/lpQQR2ZmxlPjis0trYm4d0vrOgnKGMwtc6W3siFBGfPc3+vhXxIDIyP+XfoHrbr2QaVSsWXZH2RmZhAf+3zHyKuKi8n6HksrzePU0toW5WvKQ0FJfrwftdUfL1TfWFmTmGM/HvhnBbq6utRqqX2uCN/SFdmy5DcO/LOSWi0/4FFqCtuXzwUgQVmw/8M8j1Urm/yPVSvNMppbWhP/AsdqUfp/uL55lJRVRiNzK43lRuZWxIc/X9TC+U2LMbKw0Wh8A9y/dIKji6eSnpaKsYU19fqNx9BM+43Yd43Mql10Xvk5zsHBwXTp0gVvb28sLCzUvcm3b2dV5t27d+fcuXMEBATw1VdfsXPnzpf6nlKlSqGrq6t+7+TkpO6lDQwMRE9Pj4oVs3tzfH19sbbWPv4jp3LlymGSIwywRo0aJCYmcufO05XW1atX0dPTo1q1aupltra2BAQEcPXqVXWaWrU0J2CqVasWwcHBZGRkqLdRqVL2neDixYtjZWWVZx5tbGzo3r07zZo1o02bNvzyyy88ePDgmWV74saNG6SlpVG1avadd0tLSwICNCdROH36NG3atMHd3R1zc3Pq1asHZO/LJ57cnMjL5MmTsbS01HjtWvr7c+f3dVCpVNh7+FKjQw/sPXwpXb8lpeo259J+7eGLQrxuZ/dvo2ztRugbGKiXqTJVANRr+zGlqtXF2dufdl8MAxRcPpZ3+Omr2L19C60aVFO/CmJeg1k/TSQ05Do/TPhR6+epKSns2bmNFtLb/FYys7Tm02/GceXUYYZ3bcqIT1rwMCkRV29/dArpgu/Qnm10f6+u+pWeUfjzb7zN7oYEcnjLej78cnieF+EObl50/HI4//27hlEfN2Ni73bYFHPCzNIGheKVLx9FIXmXrm+u7lrLnTMHqf3ZCHT1DTQ+K+ZXlqbfzqLRoJ9wLFGJo4t+zHPctBAF5ZVn92rTpg0eHh7MmzcPZ2dnMjMzKV26tHqccMWKFbl58ybbtm1j9+7ddOzYkcaNG7Nu3boX+h59fc2xHAqF4rVOMlLUFi1axFdffcX27dtZvXo1P/zwA7t27aJ69ero6OigUqk00ucc//08kpKSaNasGc2aNWP58uXY29tz+/ZtmjVr9tSYb1NT03y3NXz4cAYPHqyxbP7p+y+UnyeMzS1Q6OjwMNdEGcnxyjwnxngeplY22Dhrzqxq7exOyOnDL73Nl2VibolCR+epSbGS4mOf6tF8V7ztZTaxsERHW/7jYvOcNdjMyuap3uXEuNineoUAQq9eIOr+HToOHKW5Deusbdu7Zkfu6OkbYF3MibioZw9NeRk169SnRKky6vdpaVn1QWxMNLZ29urlsTHR+Pg9e0bTWdMmcezwQX7+YxH2xbRHGh3ct4vUlIc0bdnmFXP//83U3BIdHd2neosT4mIxt9I+Aae5lQ0JcbnSK2NeeDbsgPJVGTFnNYnxSnR1dTE2NWd0r/fV4/ULWqUadfEtXlr9/slxGqeMxto2e9KruNhoPH2KZmbhl2XyeD9qrT9epL5RZqcPvXaBpPhYpnzRUf15ZmYGW5bM4dCWdXw3J2vCu/J1mlC+ThMSlDEYGBqhUCj4b/MabBycCrKIeR+rypj8j9VcE4clxMVi8QZMHvk8/h+ubwxMs8qYu0GbkqDEyDz/Ml7bs4Gru9dR/8sJWkOw9QyNMLd3xtzeGTuv4mwZ35sbR3dSsmlHLVsTomC80i3D6OhoAgMD+eGHH2jUqBElSpQgNvbpsEMLCws6derEvHnzWL16NevXrycmJqty1NfXJyMj41WyQUBAAOnp6Zw9e1a97Pr161rzktv58+d5+PCh+v2xY8cwMzPDzc3tqbQlSpQgPT2d48ePq5c9+R+ULFlSnebwYc3K6fDhw/j7+6vDzNPT0zl9+rT688DAQJRK5TPzWqFCBYYPH86RI0coXbo0K1asALJC3nP3QOd8xra3tzf6+voaY8jj4uIICgpSv7927RrR0dFMmTKFOnXqULx48ecaI66NoaEhFhYWGq+XCdMG0NXTp5iHH3euZpdHlZnJ3avncPR5/nD13Bx9SxIbphkmpAy7h7ltsZfe5svS1dPHycuf0MvZY85VmZmEXjqLSz6P4Xib5Vdm17egzHqP83/jUnb+MzMzuXHpDK7+2vPv5ldSIz1AyIVTuGmZkObMvm04e/vj6OGjsdzZyx89fX2icoSzZ6Sno4wKx8rO4VWKlCcTU1Nc3NzVLw8vH2xs7ThzMrseTEpK5Orli5QsUy7P7ahUKmZNm8ShA3uZNns+Ts6ueabdtulvatSpj5X123EB/KbS09fH1cef4IvZ55vMzEyCL5zOcyIkT//SBF84rbEs6MIpPANKa03/LGYWVhibmhN88TSJcbGULqTJCo1NTHF0cVO/XD28sbKx5dLZ7PNeclIiIdcu41eicOfmKGh6+vq4ePtzPdd+vH7xDB557EcP/1Ia6QGCL5xSp69QtykDpy3kq5/mq18W1nbUfa8zvUb89NT2zK1sMDQ24fyRvegZGDw1Dv5V6enr4+bjT9AFzTIGXTiNZ4D2Mnr5lybo4imNZYHnT770sfq6/b9c31i7+WpM7KXKzCQ88Dx2XtofPQlwdfc6ruxYRd2+Y7Fxf/awy6ztqshMf7FOo7eVjuLteL2LXqnhbG1tja2tLXPnzuX69evs3bv3qZ7GGTNmsHLlSq5du0ZQUBBr167F0dFRHZrs6enJnj17CAsLe66GrjbFixencePG9OnThxMnTnD27Fn69OmDsbHxM8cBPHr0iF69enHlyhW2bt3K6NGj6d+//1PjmwH8/Px4//336d27N4cOHeL8+fN8/PHHuLi48P777wPwzTffsGfPHsaPH09QUBBLlixh9uzZDBkyBMhq5Ddv3pzPP/+c48ePc/r0aT777DOMjY3zzOPNmzcZPnw4R48e5datW+zcuZPg4GD1OOeGDRty6tQp/vrrL4KDgxk9ejSXLmVPjmFubs6nn37K0KFD2bdvH5cvX6ZXr17o6Oio/z/u7u4YGBjw66+/cuPGDTZt2sT48a/vubD5Kd+sPVcObOPq4V3E3L/N/qW/kp6aQonaWc913TXvJ46sW6hOn5GeRuTtECJvh5CRnk6SMorI2yEow7N7vcs3bUf4jWuc2rwKZfh9Ao/t4/KBrZRpWDQ9XFVbdODcvq1cOLiTqHu32LboF9JSUyhbrzkAm36fwr5V89XpM9LTCA+9TnjodTLS00mIjSI89DoxYffUaR6lPFSnAYiLfEB46HXiosJfb+HyUK1FB86+YJnDQq8TlqPMYbnK/DrVbPUhp/du4eyBHUTeu8XmBTN5lJpCxcf5X//bZHatnKdOX71Fe66fP8nhzWuIvHebvWsXc/9GENWatdXYbkpyEpePH9CYFOwJIxNTKjduw751i7l+/iRR92/z74KZAJSqXq/QypqTQqGgfaePWb54LkcO7uPG9SCmjB2BnZ09tetmP4d6SP/P2Lh2pfr9rJ8msnv7FkaMnYKJqSkx0VHEREeRmpKisf17d25z4dxpWr7X/rWU51WYGhtQ1t+Fsv4uAHi62FLW3wU3xzcnaqJem04c272Zk/u2EX43lHVzp/Mo9SFVG2YdXytmTWDzsj/U6eu0+oBr546zf9Mqwu/eYvvqhdwJuUbtFtn7Iykhnns3gwm7EwpAxP3b3LsZrDF++cTeLYQGXSYq7B6nDuxgybRR1G3dkWL5PEO5ICkUClq068LGFQs5dfQAt29e5/epY7C2taNyrezfyoRhX7DjnzXq9ykPkwkNCSQ0JBCAyLD7hIYEEhURpk6TGB9HaEggd29nTcr34M4tQkMCUcZkPy6noNVu3ZGTe7Zwev92Iu6GsnHeDB6lPqRSg6xnn6/+daJ6/DFArVYfEHTuBAf/XU3EvVvsWrOIeyGB1GieNfzB1NwSR3dvjZeOnh7m1jbY59hHR7Zt4N6NICLv3+Ho9r/ZtOAXmn/UG+NCeM5x/TadObr7X07s20bY3VDW/jmNR6kPqdawFQDLfhnPvzmO1XqtP+Tq2ePs/Wcl4XdvsW3VAu6EXKNOi+wx20kJ8dzNeazeu83dXMdqfGw0d28GE/Ug61zy4NYN7t4MJikhvsDLmNv/w/VNQIO23Diyg5vH9xAfdodTa+aQ/igFr2qNATi2dDoXNi1Wp7+6ax2XtiyjykcDMbV14GF8LA/jY0lLzerkSk9N4cK/S4i6eY2kmAhibl/nxPKZPIyLxq3Cm/kUEfHueKVQbR0dHVatWsVXX31F6dKlCQgIYNasWRqPQTI3N2fq1KkEBwejq6tLlSpV2Lp1q7phOn36dAYPHsy8efNwcXEhNDT0pfLy119/0atXL+rWrYujoyOTJ0/m8uXLGBkZ5bteo0aN8PPzo27duqSmptKlSxfGjBmTZ/pFixYxcOBAWrduzaNHj6hbty5bt25Vh5JXrFiRNWvWMGrUKMaPH4+TkxPjxo3TmJ160aJFfPbZZ9SrVw8HBwcmTJignkxMGxMTE65du8aSJUuIjo7GycmJL7/8ks8//xyAZs2aMXLkSIYNG0ZKSgo9e/akW7duXLyYPevljBkz6Nu3L61bt8bCwoJhw4Zx584d9f/H3t6exYsX8/333zNr1iwqVqzItGnTeO+99571ry90flXr8TAhjhMbl5IUF4u9mzdtvp6gDmVKiIlAkePWVpIymtVjvlS/P7t9PWe3r8c5oAztv826k+7gFUCLL0dxdP0iTm5ajoW9I3W69CWgRkOKQskaDUhOiOPgusUkxcXi4OFDp28nq8N446MjNMaUJcRGs2BE9jNUj29Zy/Eta3EvUZaPf5gBwIMbgSyfOESdZvfjC44ydZrSpm/ezyh9XUrWaEBSQhwHcpS5c44yxz2jzMe2rOXY4zJ/8rjMr1OZmg1Ijleyd+0iEpWxOHr48Ml3P6pDIeOiNPPvHlCaDwaMYM/qhexetQBbRxe6DBn31Gy2l47sA5WKMrW0H4vNuvZFR0eX9XOmkP4oFRffEvT4YRrGZgV/IZuXzp/0ICXlITOmjCMxMYEyZSsweebvGBhmR5bcv3uXuBxhlJs2ZDVOBvfrqbGtoT+Mp3nr99Xvt23+G/tiDlSuVrOQS/HqKpb0YOf8ger3U4dkXbAv3XSMPqOXFVW2NFSo1YjEOCXbVy0gXhmDi5cvfX6Ypg69jo0K17jB7FW8DB8PGs22lfPYsnwu9k6u9Bg2CSd3b3WayycPseq3yer3S2eMAaBpxx4075S1fyPu3WHL8rkkJ8ZjY+9I4w6fUK9Np9dQ4mxtOnYjNeUh82dOIjkxkYDS5fhu0iwMckRAhT+4R0KcUv3+RtBVxg/NrmeW/vkzAHWbtOKLoWMAOH3sIH9MG6dOM2vSCAA6fNybD7r1KZSylKvVkKR4JbtWLyRBGYOzpy89R/yk3o/KXPWNR0BpOg8cyc6VC9ixYh52Tq58Mmwijjn24/O4c/0qu9csIjXlIfYu7rTr8w0V6zUr0LI9UbF2IxLjlWxdOZ94ZQyuXr70HTldHXodGxWOIkenhlfxMnT7ejRbV8xj8+Njtde3k3H2yC7jpZOHWDF7kvr9khmjAWjesQctOvcC4PCOjWxfs0idZtYPWdcPH/X/nmoNn76BWZD+H65v3CvWJTUxjktbl5ESH4uVqzf1vhiHkUVWGZNjIzWO3euHt5KZkc6RhZM1tlOqeRdKt+yKQkeH+PC7hJ7YQ2piPAamFti4+9Fw4I9YOnkgRGFSqHIPjn1H3L17Fzc3N3bv3q2eSVpkS0pKwsXFhenTp9OrV69C/75fD7/7z9azMHzlKQPeeO9o5I2aoe67P+FNLU+7Zyd6y/k1/Kaos1Do1i0d9exEbzkn8/xvfL8LbsUlF3UWCpVxjkld31XBysSizkKhi0x89yfbG9fs+ULC3wTdV74dj9Rb3OXtGhbzPN6ZK/29e/eSmJhImTJlePDgAcOGDcPT05O6desWddbeCGfPnuXatWtUrVqVuLg4xo3Lulv+JMRcCCGEEEII8WaTx1EVnXem4ZyWlsb333/PjRs3MDc3p2bNmixfvvyp2bj/n02bNo3AwEAMDAyoVKkS//33H3Z2737vkxBCCCGEEEK8inem4fzkUUpCuwoVKmjM5C2EEEIIIYQQ4vm8Mw1nIYQQQgghhHiXSaB20Xn3Z8IRQgghhBBCCCFegTSchRBCCCGEEEKIfEiothBCCCGEEEK8BXRkVu0iIz3OQgghhBBCCCFEPqThLIQQQgghhBBC5EMazkIIIYQQQgghRD5kjLMQQgghhBBCvAVkiHPRkR5nIYQQQgghhBAiH9JwFkIIIYQQQggh8iGh2kIIIYQQQgjxFlBIrHaRkR5nIYQQQgghhBAiH9JwFkIIIYQQQggh8iGh2kIIIYQQQgjxFpBI7aIjPc5CCCGEEEIIIUQ+pOEshBBCCCGEEELkQxrOQgghhBBCCCFEPmSMsxBCCCGEEEK8BXRkkHORkR5nIYQQQgghhBAiH9JwFkIIIYQQQggh8iGh2kIIIYQQQgjxFpBI7aIjPc5CCCGEEEIIIUQ+pOEshBBCCCGEEELkQ0K1hRBCCCGEEOItoJBY7SIjPc5CCCGEEEIIIUQ+pOEshBBCCCGEEELkQxrOQgghhBBCCCFEPmSMs3gtElIyizoLha6cvWlRZ6HQxT9KK+osFCpl6rtdPoC/r9wv6iwUunVLRxV1FgrdB5+MK+osFLoVS34o6iwUuvTMd/vcGJqUUtRZKHTWRvpFnYVCZ/N/UMa3ifR6Fh353wshhBBCCCGEEPmQhrMQQgghhBBCCJEPCdUWQgghhBBCiLeAPI6q6EiPsxBCCCGEEEIIkQ9pOAshhBBCCCGEEPmQUG0hhBBCCCGEeAvoSKR2kZEeZyGEEEIIIYQQIh/ScBZCCCGEEEIIIfIhodpCCCGEEEII8RaQUO2iIz3OQgghhBBCCCFEPqThLIQQQgghhBBC5EMazkIIIYQQQgghRD5kjLMQQgghhBBCvAUUChnkXFSkx1kIIYQQQgghhMiHNJyFEEIIIYQQQoh8SKi2EEIIIYQQQrwF5HFURUd6nIUQQgghhBBCiHxIw1kIIYQQQgghhMiHhGoLIYQQQgghxFtAJtUuOtLjLIQQQgghhBBC5EMazkIIIYQQQgghRD6k4SyEEEIIIYQQQuRDxjgLIYQQQgghxFtARwY5FxnpcRZCCCGEEEIIIfIhDWchhBBCCCGEECIfEqothBBCCCGEEG8B6fUsOvK/F0IIIYQQQggh8iEN5yISGhqKQqHg3LlzRZ0VIYQQQgghhBD5kFDt16B79+4olUo2btyoXubm5saDBw+ws7Mruoy9Ra4d+JdLu9bzMD4WG1cvqnb8AnvPAK1pgw5tJ+T4HpT3bwFg6+5Lhfc/1Uh/6+xhAv/bSsyd66QmJdBm+K/YuPm8lrI8oVKp2LR8Hv/t3ERyUgK+JcrStd8wHJzd8l1v35Z17NiwnLjYGNy8fOny+WC8/EupP186ewpXz59CGROJoZEJPiXK0OHTfji5eQKQGB/H/OmjuRsaQlJ8HOZW1pSvVod23b7A2MS0wMp3aNsG9v+zigRlDM6ePrTrNRB3v5J5pj9/ZB/bVi4gNjIMOycXWn/clxKVagCQkZ7OtpXzuHrmGDHhDzAyMcWvbGVaffw5ljbZv6G7NwLZvPRP7ly/ho6ODmWr1+O97l9iaGxSYOXK6eTOjRzdvIbEuBgc3H1o/ukAXHyL55n+yrED7F+7CGVUGDaOrjTq3Bu/CtXUnyfGxbBn5TxuXDhNSnIiHsXL0uzT/tg6uarTxITfZ/fyP7gTeIn09DR8ylaheff+mFnaFEoZc7uwZxNnt68jOS4WOzdv6nbth4O39t9i9L1Qjm9cSmRoMAnREdTu/Dnlm7Z7Kl1ibBRH1i7g1sVTpD9KxbKYM416DsbBy7+wiwNkHav7/lmZ41gdhEc+x+q5I/vYvnI+MZFh2Dm50vrjvpR8fKwCXDh2gCM7/+FuSCDJifF8M20hLl5+GtuICrvHpiW/cfPaBdLT0ihevhrtPxuEudXr2Y/Pq1ZFH77u1piKJd1xsrek49dz+Xf/haLO1lOObv+bA5tWkaiMwcnDh/d6DsTNr0Se6S8c3ceuVQuJjQzD1tGFFh/3pXjF6lrT/j13Osd3baJ19/7UbvUhACGXzzJvzCCt6b+c/Aduvnl/98s6tuNvDv27mkRlDI4ePrTu8RWu+XzPpaP72b1mIcrIMGwdXWnatQ8BFTTLGHH3FjtXzOXmlfNkZmZQzMWDLt+MxcrOAYCNc6cTcukMCTFRGBgZ4x5QimYffY69i3uBl0+bc7s3cXrbOpLiYrB396bBx/1w9NZex0bdC+Xohr+ICL1OfHQ49bp8TsVm7fPc9onNqzm8biEVmrSlftcvCqsIL+VFzi0Rd0M5sHYxD24GERcVTtNP+lGtRYfXnGNNJ3du5EiO/Ld4jnPjvsfnRtt8zo0hOc6NzXOdGzfPn8HNS2dIiI3GwMgYV/9SNO7cG7vXdKy+TjKpdtGRHudnePToUaFsV1dXF0dHR/T05N7Fs9w8dYCT6+dRrtVHtBn+K9Yu3uz+dSQPE5Ra04cFX8Crcj2aDZpMy6HTMbG2Y9evP5CkjFKnSX+UgoNvKSq27fGaSvG07euXsWfzWj7uN4zvpy3AwMiYmaMGkfYoNc91Tv63mzXzZ9GmSy9GzlyMq5cfM0d9TbwyRp3Gw7c43QeOYNycVQwaOxNUKmaOGkRmRgYACh0F5avVpf8PU5nw52p6DPqBq+dOsuy3HwusbGcP72HT4t9o2rE7X/80H2cPX+aOH0JCXKzW9DevXWTZz+Oo1qgVg6fNp3TVOiyaOoIHt28A8Cg1hbs3gmnywad8/dN8ug+bQOT92yycMly9jbiYKP4YOxg7RxcGTvmD3iN/IuzOTVbNnlxg5crp8tF97Fr2B3Xbd6P3xD9wcPdhxZRvScqjjHeCLrNh9gTK129B70l/ElCpFmtmjCLizk0g60bKmumjUEY8oNM34+g96U8s7YqxfPJQHqU8zPo/pDxkxeRhoFDw8YhpdB/9Cxnpaaz+6QdUmZmFUs6cgk8c4NDqeVR572M6jZ6NrZs3m2aMIDleqTV9+qNULO0dqfFBT0wsrbWmSUlKYP2kwejo6vHe1xPoOmEutTv1xsjUrBBLku3s4T38s3g2zTp2Z7D6WP3mGcfqWKo2asU30xZQpmodFk39Xn2sQtZ+8ipehtaf9NW6jdSUh/w5bjAKhYIvxvzCgIlzyEhPY/7k78h8DfvxRZgaG3Ix6B6DJq8u6qzk6fzhvWxe8huNP/yUAT/Ow8nDhwUTh5CYxz68FXiJVTPHU7lhS76aOo9SVeuwdOoIwnLswycuHT/I7aArWFhr3uT28C/NiLkbNF5VGrXCppgTrj55NxBe1sUje9n21+806PAp/abMxdHDh8WThuVZxtuBl1gzazyVGrSk35R5lKhSmxU/jST89k11muiwe8wb/RV2zm70Gv0z/afOp36HT9DTN1CncfH2p33fYQycsYTu308FFSyeOJTMzIwCL2Nugcf3c3DVXKq37UrXsb9h5+bNhmn51DepqVjaO1H7w56YPONGYtiNQC7u34Kdm1ch5PzVvOi5JT01BetiTjTs/Blmb8CNt8tH97Fz2R/Ua9+NPhP/wNHdh+XPODeunz2BCvVb0OfxuXF1rnPj6umjiH18buzz+Ny4LMe5EcDJy5/3Ph9Gv2mL6PrdFFCpWDbl29dyrIr/H9JwzqV+/fr079+fQYMGYWdnR7NmzQC4dOkSLVq0wMzMDAcHBz755BOiorIbYuvWraNMmTIYGxtja2tL48aNSUpKYsyYMSxZsoR//vkHhUKBQqFg//79T4Vq79+/H4VCwZ49e6hcuTImJibUrFmTwMBAjfxNmDCBYsWKYW5uzmeffcZ3331H+fLl8y3Ts/KemZnJ1KlT8fX1xdDQEHd3dyZOnKj+/MiRI5QvXx4jIyMqV67Mxo0bX2uY+ZW9f+NXqzl+NZpi5eROjS790TUw5PqRnVrT1+0xjOL1WmPj5oOloxs1Px4IqkzCrp1Xp/Gp1ohyLT/CuXiF11KG3FQqFXs2raZVx+6Ur14XVy9fen49CmVMFGePHcxzvV0bV1Kn2XvUatwaZ3cvPu43DANDQw7v2qxOU7d5W/xLV8DOwQkP3wDafvw5MVHhREU8AMDUzIL6Ldvj6VcC22JOlChXhfotOxB85XxeX/vCDv67huqNW1O1YUsc3Tzp8Pk36BsacWLPFq3p/9uyjoAKVWnQtgsOrp606PIZLl7+HN62AQBjUzP6jp5B+VoNKebijod/Kdp9Noi7IYHERoYDcOXUEXR19Wjf+2uKubjj7luCDz7/hgvHDhD14G6Ble2JY1vXUaFBS8rXb469qyeteg1C39CQcwe2a01/YvsGfMtVoWabTti7eNCgYw+cvPw4uXMjADFhd7l3/Soteg7C2ac4ds5utOw5iLRHj7h8dC+QdYGhjAzn/c+H4eDujYO7N+9/8S33bwZx8/LZAi9jbud2bKBU3eaUrNMUGxcPGnQbgJ6BIVf/26E1vYNXALU69sa/Wn109fS1pjmzdS1mNvY07vUNDt4BWNg74l66EpbFnAuzKGoH/l1N9cZtqNqwFY5uXnzw+ZBnHqvFK1SlYduPNI7VQ4+PVYDK9ZvTrGMP/MtW1rqN0GsXiYkMo0v/73H28MHZw4cuA0ZwN+Qa1y+eKZRyvqydh68wds5mNu1783qZnzi0eQ1VG7WmcoOWOLh50rbPNxgYGHFq71at6Q9vWYd/+arUe78LxVw9adq5F87e/hzd/rdGurjoSDYtnEXngT+gk+smt56+PubWtuqXibklV04eplKDFigKoTvo8Ja1VG7UikoNWlDM1ZP3PhuMvoERp/dt05r+yLb1+JWvSp33OlPM1YPGnXri5OXHsR3ZZdy9agH+FarR/OO+OHv5YevoQonKtTDLcZOrSuM2eJUsh3UxR5y9/WncqSdx0RHERoQVeBlzO7NjA6XrNadUnWbYunjQ+NOv0DMw5NJB7fWNo3cAdTv3JqB6ffTyqG8g68bWtj9/pHGPQRiZmBdW9l/ai55bnH2K07jr55Su2TDPevZ1Orp1HRW15P9sHvk//pznxpY9B+Hy+NzY6vG58dLjcyNApUat8ShRFit7R5y8/GnQsQfx0REoH18jCFEQpOGsxZIlSzAwMODw4cP88ccfKJVKGjZsSIUKFTh16hTbt28nPDycjh07AvDgwQO6dOlCz549uXr1Kvv376d9+/aoVCqGDBlCx44dad68OQ8ePODBgwfUrFkzz+8eMWIE06dP59SpU+jp6dGzZ0/1Z8uXL2fixIn8+OOPnD59Gnd3d37//fd8y/KsvAMMHz6cKVOmMHLkSK5cucKKFStwcMgK04qPj6dNmzaUKVOGM2fOMH78eL799ttX+fe+kIz0NKJvX8c5oLx6mUJHB+fi5Ym8ee35tvEolcyMDAxeUw/W84gKv09cbDQlyldRLzMxNcPbvyQ3rl3Suk56Whq3rgdSolz2Ojo6OpQoX4WQQO3rpKY85PDuzdg5OGPzOPQuN2V0JGeO7se/dMHcREhPS+NuSBB+ORoNOjo6+JetxK2gy1rXuRV0Gf+ylTSWBZSvSmig9vQAKUlJKBQKjB/v1/T0NHT19NDRya7W9A0MAbhx9eJLl0ebjPQ0HtwMwqt0RfUyhY4OXqUrcjf4itZ17gZfwau0Zhm9y1ZWp09PSwPQ6O1R6Oigp6fP7cf7NyPtEShAVz/74khP3wCFQsGdPI6BgpKRnkbErWDcSmYfJwodHVxLViAs5OpLb/fmuWMU8/Rn25wJLBjYiVVjvuTyAe2NgYL25FjNeexlHauVCc3jWA0NuqRxbAMUL1+V0Bf4/6enpaFAgV6O/ahvYIBCocONa29uA/VNlJ6Wxr0bQfjm2oe+z6hvfHPVN/7lqmikz8zMZPWvE6n7XmccnqNX8sqpwyQnxFO5QYuXLEne0tPTuH8jCJ8ymmX0KVORO8Hay3gn6Ao+ueobv3JVuPO4jJmZmQSePYadkyuLJw5lcu92/DHiC66cPJRnPh6lPOTM/u1YF3PC0q5YAZQsbxnpaYSHBuNeUrOOdS9VgQch2uvY57V36Wy8ylXFo1TFZyd+zV7m3PImKahzo89znhvzOu89SnnIuQM7sLJ3wtLW/pXKJEROEieshZ+fH1OnTlW/nzBhAhUqVGDSpEnqZQsXLsTNzY2goCASExNJT0+nffv2eHh4AFCmTBl1WmNjY1JTU3F0dHzmd0+cOJF69eoB8N1339GqVStSUlIwMjLi119/pVevXvTokRVePGrUKHbu3EliYmKe25s9e3a+eXdycuKXX35h9uzZfPrppwD4+PhQu3ZtAFasWIFCoWDevHkYGRlRsmRJ7t27R+/evZ9ZloKQmhiPKjMTIwvNME8jcyviwu881zZO/70IY0ubIutd1iYuNhoAi1xhVeZWNurPckuMV5KZmYGFteY6FlY2hN29pbFs35b1rF/8G6kpD3F0cefr8b9oXKQDzP1pFOePHeTRo1TKVa3NpwOGUxCSEuLIzMzA3Epzn5lZ2hBx77bWdRKUMU+N0TW3siYhRwh6TmmPUtmy7A/K126E0eNx2X6lK7Jp8Wz2bVxJnVYf8Cg1hS3L/ny8fe3/05eVnBCHKjNTo2cGwNTSmqj72o/LRGUMppa5/yfWJD0uo52zO5Z2xdi7aj6ten2NgZERx7auIz4mksTYrDQufiUxMDRmz8p5NOzUC5VKxd5V81FlZpJYwGXM7WFC1m/R2MJKY7mJhRXKB8/3W9QmPvIBl/Ztpnyz9lRu1Znwm0EcXPE7Onp6lKjV5BVznb/sYzXXsWdpTcS9W1rXSVDGYP7UsWqT57GqjYd/SQyMjPh36R+06toHlUrFlmV/kJmZQXwev3+hXfLjfZj7t2hmaU1kHvVNojLm6fRW1iTm2IcH/lmBrq4utVo+31jRU3u34F++Cpa2Bd+gTI6PI1NLfWNmaU3U/bzLaPpUHWytHoKQFK/kUcpDDv6zksadetKs6+cEnTvByumj6DlqBl4ly6vXO75jIzuW/8mj1BTsnN3oPuKnfHt0C8KT+sbE0kpjuYmFNbGvUN8EHttPxK3rfDTq11fMYeF4mXPLm+RJ/nOf6551btRW3sTnODcmxGrWuyd3/cPuFXNJS03B1smNj7+f+kb0whc0HRnkXGSk4axFpUqad77Onz/Pvn37MDN7uscyJCSEpk2b0qhRI8qUKUOzZs1o2rQpH3zwAdbW2sf05ads2bLqv52cnACIiIjA3d2dwMBA+vXrp5G+atWq7N27l7w8K+9KpZLU1FQaNWqkdf3AwEDKli2LkZGRxnfmJzU1ldRUzXG66Y9S0Xvc+/c6XdyxhpunD9Bs0I/o5rhb+bod279DYwzxgFHTCvX7qtVvRskKVYmLiWLn3yv488cf+G7qn+oeWIBOnw2kTeeehN+/w4Ylv7Nm/iy69htaqPkqCBnp6fw1fTQqlYoP+nyjXu7o7kWXAd+zafFvbF0+F4WODnVadsDcyqZQQicLmq6eHh8OGsu/86YxrU9bFDo6eJeuhG+5qqhQAWBqYUWHgaPYtnAmJ3b8jUKhoHTNhjh6+qFQvJ0BRCqVimKeftTokHVD0N7Dl5h7oVzav6XQG85FxczSmk+/Gce6udM5tHUdCoUOFWo3wtXbXy6I3gB3QwI5vGU9X02d91x1R1x0BEHnTvLR4DGFnreC8mROhBKVa1Lr8YRnTp6+3Am6zIld/2o0nMvVaYxP2cokxEZzePMaVs8cS+9xs9E3KLpz6stIiI5g/4rfaT90MnpvWd7/n+U8N/6Ux7nxiTK1GuFduhKJyhiOblnD+l/G0WPMLNnfosBIw1kLU1PNmYUTExNp06YNP/749ORJTk5O6OrqsmvXLo4cOcLOnTv59ddfGTFiBMePH8fL68UmntDP0Sv45IT9KpPFPCvvN248PRnKq5o8eTJjx47VWNbwkwE0+nTgC2/L0MwChY4OKfGak0qkJCgxtsh/EoxLu9Zzcedamn41ERvXop0ApHzV2nj7Z8/Sm/Y49CheGYNVjlmhE5QxuHlrn0nYzMIKHR1d4nPdYY1XxmBhbauxzMTUDBNTMxyc3fAOKM3ALk05c/QA1eo1VaextLbF0toWJzdPTM0smPpdX1p17qGRn5dham6Jjo4uCUrNfZYYF5PnjMHmVjYkxmmWK0EZ+1T6J43m2Mhwvhg7U93b/ETFOk2oWKcJCcoYDAyNQKHgwOY12DoU7HhZE3NLFDo6T03MkxQXm+fkLGZWNk9NjpIYF4tpjvRO3v70mTyXlOREMtLTMbWwYsHIL3HOcUz4lK1M/5nLSI6PQ0dXFyNTM2Z88QFWxZwKsIRPMzbP+i0+zDUxT3K8Ms+Jv56HqZUNNs6as55aO7sTcvrwS2/zub9bfazmOvbiYjG3stW6jrmVDQlPHat5H9t5CShflRFzVpMYr0RXVxdjU3NG93ofmwI+Vt91Jo/3Ye7fYuIzfotPpVdmpw+9doGk+FimfJE9pCkzM4MtS+ZwaMs6vpujOVHaqX3bMDG3oGTlWgVRpKeYWFiio6W+eVYZk56qg2Mxf/xbNbGwREdXF3sXT4009i7u3LqmObTFyMQMIxMz7JxccfMvycSe73Hl5H+Uq6X9hntBeFLfJMcpNZYnx8e+dH0THnqd5Hgly0d/qV6myszkbtBFzu3ZxFfzN6Ojo/sq2X5lL3NueZM8yX/uc92zzo3PKq+ztz+f5zo3zs91boTsY9XWyRVXvxJM7d2Wa6cOUbpmwwIqofh/93Z2UbxmFStW5PLly3h6euLr66vxetLIVigU1KpVi7Fjx3L27FkMDAz4+++sSTgMDAzIyHj1Wf0CAgI4efKkxrLc7180735+fhgbG7Nnz548v/PixYsaPcjP+s7hw4cTFxen8arXRfvsss+iq6ePrbsvDwKzJ65SZWbyIPAc9l55z1x6aedaLmxbSZP+47HzeD2PtMmPkYkpxZzd1C9ndy8srW25dv6UOs3D5CRuBF3Bu3hprdvQ09fHwzeAqxey18nMzOTq+VP4BGhfB8i6I6tSqccJaU2jyro5k1+a56Wnr4+rjz/BF09r5DP4whk8cjw2KycP/1IEX9CcFCnowkk8A7LTP2k0Rz24S9/RP2NqbplnHsytbDA0NuHc4b3o6xvgX077JE0vS1dPHycvf0JzTMilyszk5uWzuObxGCNXv5LcvKRZxpsXT2tNb2RihqmFFdEP7vLgRhD+lZ6+IDexsMTI1Iybl8+SFK/Ev1LecycUBF09fYp5+HHn6jn1MlVmJnevnsPR5+UfvePoW5LYMM3J25Rh9zAvhJDX3PI+Vk/jmcex6ulfmuALpzWWBV04hWc+v8H8mFlYYWxqTvDF0yTGxVK6Su2X2s7/Kz19fVy8/bmeax9ev5h/fZMzPUDwhVPq9BXqNmXgtIV89dN89cvC2o6673Wm14ifNNZTqVSc3reNivWaoVtIT8nQ09PH2dufGzkmjsvMzOTGpTO4+Wkvo5t/SUJy1TfXL57G7XEZ9fT0cfEpTlSusOeoB3exstc+HwYAqqzzSUYBnCvyo6unj4OnH3euaNaxd66cw8kn70fF5ce9ZHk+mfAnH4/7Xf1y8PKnePWGfDzu9yJvNMPLnVveJE/yf/MVz403nvPcGKDl3Kj+XpUKlUpFelrhPB2nKCkUb8frXSQN5+fw5ZdfEhMTQ5cuXTh58iQhISHs2LGDHj16kJGRwfHjx5k0aRKnTp3i9u3bbNiwgcjISEqUyLqY9PT05MKFCwQGBhIVFaXubXxRAwYMYMGCBSxZsoTg4GAmTJjAhQsX8g0le1bejYyM+Pbbbxk2bBh//fUXISEhHDt2jAULFgDw0UcfkZmZSZ8+fbh69So7duxg2rSsMOO8vtfQ0BALCwuN16uEaZds2I6gw9u5fmw3yge3ObbqN9JTU/GtkRXG+d/iaZzeuEid/uLOtZzdvJRanwzCzKYYD+NieBgXQ1qOxxakJiUQcycE5YOs8WFx4XeJuRPCw7jnH6f4KhQKBY3e68SW1Ys5d/w/7oZeZ+GMcVjZ2FGhel11uukj+rN381r1+yZtu/Dfjk0c2bOFB3dCWT5nKo9SUqjVuDUAkWH32Lp2CbeuXyM6IozrVy/w55QR6BsaUqZy1nNmL546wuHdm7l3K4So8AdcOHmYZXOm4luiLHYOBdNrWbdNR47v3szJfdsIvxvK+rnTeZT6kKoNWwKwYtZE9fhjgDqtPuDauePs37SK8Lu32LF6IXdDAqnVIusZnBnp6SyZNpI7IdfoOmikeixofGy0RmP/0Nb13L0RSOT9OxzatoG/58+kZdc+GJsW/Myp1Vt+wJl9Wzh/cAeR926xdeFM0lJSKFcvayb+jXOmsGfVfHX6qs3bE3LhJEe3rCHq3m0OrFvC/RtBVGnaVp3myrEDhF45R2z4fQJPHWb55GEEVK6FT47JqM7t387d4CvEhN/nwqFdrP9lLNVbdMDuGc//Lgjlm7XnyoFtXD28i5j7t9m/9FfSU1MoUTsrkmHXvJ84sm6hOn1GehqRt0OIvB1CRno6ScooIm+HoAy/n73Npu0Iv3GNU5tXoQy/T+CxfVw+sJUyDdsUenkA6rXpxLEcx+q6p47VCWxe9oc6fe5jdfvqhdwJuUbtFtnPi01KiOfezWDC7oQCEHH/NvduBmuMXz6xdwuhQZeJCrvHqQM7WDJtFHVbd6TYG/bMUVNjA8r6u1DW3wUATxdbyvq74Ob48lEGBa12646c3LOF0/u3E3E3lI3zZvAo9SGVHk/UtfrXiWxfPledvlarDwg6d4KD/64m4t4tdq1ZxL2QQGo0z3rGuKm5JY7u3hovHT09zK1tnnp+ccilM8REPKBKo1aFWsZarT7k1N7NnDmwnYi7t9g0/2cepaZQqX5zANbNnsTOFfPU6Wu26EDw+RMc+ncNkfdus2ftYu6HBFK9WfZz1Ou06cSlI/s4uWcz0WH3OLb9bwJPH6Hq4zopJvw+B/5ezr0bgSijwrkdeIlVP49Fz8AQ/xzP2C0sFZu15+KBbVw+tIvo+7fZ89evpKWmUKpOnRxGFwABAABJREFUVn2zfe5UDq3VrG8iboUQcSuEjIw0EmOjibgVgjL8HgAGxibYuXpqvPQNjDA2M8fO1bPQy/O8XvTckpGeRljodcJCr5ORnk5CTBRhodeJCbtXJPmvkSv/Wx7nv3we+a+W69y4/znOjctynRtjw+9z6J8V3L+R9SzrO0GXWffLOPQNDPArX/jHqvj/IaHaz8HZ2ZnDhw/z7bff0rRpU1JTU/Hw8KB58+bo6OhgYWHBwYMHmTlzJvHx8Xh4eDB9+nRatMg6affu3Zv9+/dTuXJlEhMT2bdvH56eni+cj65du3Ljxg2GDBlCSkoKHTt2pHv37pw4ceKl8w4wcuRI9PT0GDVqFPfv38fJyYm+fbN6iC0sLPj333/54osvKF++PGXKlGHUqFF89NFHGuOeC5NX5XqkJMZzbvNSHsbHYuPqTeP+4zB+PGFYUmwkihwzKQce3EJmejr7503S2E65lh9RvvXHANy5cIzDS39Wf3Zw4Y9PpSlszTt8zKOUhyydPYXkpET8SpZl4NifNcYhR4bdIzE+Tv2+Sp3GJMTF8s/y+cTHRuPm7cfAsT+rJwzT1zcg+PJ5dm9aTXJiAhZWNviVKs93U+eqJyLTNzDkvx3/sHr+L6SnPcLazoGKNerT4oNPCqxsFWo1IilOyY5VC4lXxuDi5UvvH6apw1mVUeEaN168ipfh40Gj2LZyPluXz8PeyZUewybi5O4NQFxMJJdPZoXuTv+mp8Z3fTH2F3wfzwh++/o1dqxeRGrKQ4q5uPPB50OoXL9ZgZUrp1I1GpAcH8eBdYtJVMbi4OHDR99NUU9yFh8dgUInu4xu/qVo9+UI9q1dyL7VC7FxdKHj4HEUyzFjb6Iyml3Lfs8KqbS2oUztptRtr3k8Rj+4w97V83mYmICVvQO13+9KtZYfFEoZc/OrWo+HCXGc2LiUpLhY7N28afP1BHXoZEKMZpmTlNGsHpMdFnl2+3rObl+Pc0AZ2n+b1XPn4BVAiy9HcXT9Ik5uWo6FvSN1uvQloMbrCa2rUKsRiXFKtq9aoD5W++Q4VmO1Hquj2bZyHluWz318rE5SH6sAl08eYtVv2c8PXzpjDABNO/ageaes4zfi3h22LJ9LcmI8NvaONO7wCfXadHoNJX4xFUt6sHN+9jCbqUOyJstauukYfUYvK6psaShXqyFJ8Up2rV5IgjIGZ09feo74KUd9E6ExB4BHQGk6DxzJzpUL2LFiHnZOrnwybCKOOfbh8zq5ZwseAaUp5uJRYOXRpkzNhiTFx7FnzWISlTE4efrw6fAf1eGsyugIjfOge0BpOg74gd2rF7Jr1XxsHV34aOh4HNyz65uSVevwXu+vObhxBVsW/YqdsxtdBo/Fs3jW5KZ6+gbcunaRI9vWk5KYgKmVNZ7Fy9Jn/K9PTeZUGAKq1edhQhxH//6L5LhY7N29affNRPXEUwnRkRr7NTE2muWjs+eBOb19Hae3r8M1oCwfDv/pqe2/qV703JIQG8287z9Xvz+6ZQ1Ht6zBo0Q5uo2cUST5T4qPY38e+Y/Tcm5s//jcuPfxubFTrnNjgjKanTnOjWVznRv1DAy4fe0ix7et52FSImaW1rgXL0uPMb8+NVGZEK9CoVKpVM9OJt5UTZo0wdHRkaVLl76271y+fDk9evQgLi4OY2Pj51pn0p6QQs5V0avt9u5XzvGPCjc8r6gpU9/t8gHEprz7ZfS2NH12orfcB5+MK+osFLoVS34o6iwUuvRXmMPkbRD98N0Lk83NzODd74N6R6NuNXSt5FrUWXhuY3YGF3UWnsuYpn5FnYUC9+7/2t8hycnJ/PHHHzRr1gxdXV1WrlzJ7t272bVrV6F+719//YW3tzcuLi6cP3+eb7/9lo4dOz53o1kIIYQQQggh3mbScH6LKBQKtm7dysSJE0lJSSEgIID169fTuHHjQv3esLAwRo0aRVhYGE5OTnz44YdMnDixUL9TCCGEEEIIId4U0nB+ixgbG7N79+7X/r3Dhg1j2LBhr/17hRBCCCGEEOJNIA1nIYQQQgghhHgL6Lyrz3p6C8jjqIQQ/2PvrsOjOPo4gH/v4u7unqAhwV0ChCJFipUWl0LxAoVSvEhLoUBbKFqkQHCH4FLcPSQkSCAh7naRu/ePhAtHLkeguRzh/X763PP09mb3ZpjNzc7Ob2aJiIiIiEgBdpyJiIiIiIiIFGCoNhERERERUSXASG3V4YgzERERERERkQLsOBMREREREREpwFBtIiIiIiKiSkDIUG2V4YgzERERERERkQLsOBMREREREREpwI4zERERERFRJSCoJP99iD///BPOzs7Q1tZGvXr1cPXq1VLTrl69Gk2aNIGJiQlMTEwQEBCgMH15YMeZiIiIiIiIVGbbtm0YP348ZsyYgZs3b6JmzZpo27Yt4uLi5KY/c+YMevfujdOnT+PSpUtwcHBAmzZtEBUVpbQ8suNMREREREREKrN48WIMGTIEAwYMQJUqVfDXX39BV1cX69atk5t+8+bNGDFiBHx9feHt7Y01a9ZALBbj5MmTSssjO85ERERERERUbkQiEdLS0mReIpFIbtrc3FzcuHEDAQEB0m1CoRABAQG4dOlSmb4vKysLeXl5MDU1LZf8y8OOMxERERERUSUgFFSO1/z582FkZCTzmj9/vtwyJSQkoKCgAFZWVjLbraysEBMTU6Z/l++//x62trYyne/yxuc4ExERERERUbmZMmUKxo8fL7NNS0tLKd+1YMECBAUF4cyZM9DW1lbKdwDsOBMREREREVE50tLSKnNH2dzcHGpqaoiNjZXZHhsbC2tra4X7/vrrr1iwYAFOnDiBGjVqfHB+y4Kh2kRERERERJWAqkOwy/p6H5qamvD395dZ2Ov1Ql8NGjQodb9ffvkFc+bMQXBwMGrXrv2h/6RlxhFnIiIiIiIiUpnx48ejX79+qF27NurWrYslS5YgMzMTAwYMAAD07dsXdnZ20nnSP//8M6ZPn44tW7bA2dlZOhdaX18f+vr6SskjO85ERERERESkMj179kR8fDymT5+OmJgY+Pr6Ijg4WLpgWGRkJITC4mDpFStWIDc3F1988YXMcWbMmIGZM2cqJY/sOBMREREREVUCAsF7xkFXIiNHjsTIkSPlfnbmzBmZ98+ePVN+ht7COc5ERERERERECrDjTERERERERKQAO85ERERERERECnCOMxERERERUSXwvo96ovLDEWciIiIiIiIiBdhxJiIiIiIiIlKAodpERERERESVwCf8NKqPHjvOVCG+beii6iwoXXRytqqzoHTVjY1UnQWlehyToeosKN2BsDhVZ0HpGtlrqzoLSrdlw4+qzoLSfdnvJ1VnQeniLi1TdRaU6mXSp98u2ph8+r832hpqqs4C0UeBodpERERERERECnDEmYiIiIiIqBIQMlZbZTjiTERERERERKQAO85ERERERERECrDjTERERERERKQA5zgTERERERFVAkJOcVYZjjgTERERERERKcCOMxEREREREZECDNUmIiIiIiKqBPg0KtXhiDMRERERERGRAuw4ExERERERESnAUG0iIiIiIqJKQAjGaqsKR5yJiIiIiIiIFGDHmYiIiIiIiEgBdpyJiIiIiIiIFOAcZyIiIiIiokqAj6NSHY44ExERERERESnAjjMRERERERGRAgzVJiIiIiIiqgSEDNVWGY44ExERERERESnAjjMRERERERGRAgzVJiIiIiIiqgSEXFZbZTjiTERERERERKQAO85ERERERERECjBUm4iIiIiIqBJgpLbqcMSZiIiIiIiISAF2nImIiIiIiIgUYMeZiIiIiIiISAF2nN+TQCDA3r17VZ0NIiIiIiL6PyMUCCrF61PExcEqkfXr12Ps2LFISUlRyvGfPXsGFxcX3Lp1C76+vkr5jg8lkUiwasXv2Lt7BzLS01HDtxa+/2EGHJ2cS91n5/at2L0jCK+iowAALm7uGDx0BBo2biqT7u6dW1jxx1I8uHcXampCeHh5Y9nyNdDW1lZmkUqQSCTY+vdfOH5oDzIz0uFdrSa+GfcDbO0dS93nwZ0b2LNtIyLCQpCcmIDJcxahfuMWMmk6t/CTu2+/YWPQpVe/ci3Du0gkEqxe8Qf27Smsx+o1a2HSD9MV1uOu7UHYvbO4Hl1d3TFw6HCZehw+uB9u3bgms1+Xbj3w/Y8zlVEMKYlEgt2bVuF08F5kZWbAs0oN9B/5PaztSq8zADh+YAcO7/wHqcmJcHD1QN/hE+DmVRUAEB8bjfH9O8vdb+QP81CvSQDOHT+I1Ytny03zx9ZgGBmb/qdyKfL434MIPbUbOWnJMLZzQa1uw2Dm5CU3bcTFYDy/dgqpr54DAEwc3FG9Q1+Z9PePbMaLm/8iKyUeQjX1wjTt+8LMWf4xK4JEIsHOjStx6sheZGZkwKtqDQwcPRk2Cuo15O5NHNyxCU8eP0JKUgLGz1iIOo2ay6S5ev4UThzcjaePHyEjPRXzV/wDZ7eKKeel4D04uz8IGSlJsHFyQ6eBY+Dg4VNq+ruXTuN40Dokx8fAzNoO7b76Bt5+9eWm3bNqEa4c348O/UeicfvuAICIB7eweuZYuem/nf8XHNxL/+6K1MjPDeP6BsCviiNsLIzQY9wqHDhzV9XZKjOJRIKVy3/HnqK2saZvLUye+u62cef2N35T3dwxeNgINCr6TY2OikKnzwLk7rtg4W8IaBNY7uVQ5P+lbVy14g/se+Map0xt444gRL9Rj4PebhsH9cPNt9vGL3pgspLbxrdJJBIs/2MZdu/cgfT0NPjW8sPU6TPhpKB8b1q7ehWWLVmEPl/1xaQpU6Xbd27fhiOHDyLk4QNkZmbi30vXYGhoqKRSELHjXGnk5eV98L65ubnQ1NQsx9xUvI3r12Dbln8wY8582NrZY+XyZRg9Ygi27T4ILS0tuftYWVnj29Hj4eDoBAkkOLR/HyaMHYlNQbvg5u4BoLDTPObboeg/cCgmfD8V6urqCAt9BKGw4oMx9gRtwMHdWzFm8mxY2dhiy7oVmDXpW/y+fic0NeWXMScnBy5ungho9zkWTJ8gN83fu47JvL955QL+WDgbDZq2KvcyvMum9Wuxfes/mD57Hmzs7LFq+TKM/XYotu46UGo9WlpZ4dtR42Dv6AQAOHRgLyaNG4mNQbvg6uYhTfd51+4YOnyk9L22to5yCwPg0I6NOLZ/G4Z+NwMW1rbYtXElfvlxNBas3FZqnV0+exxbVi3BgFGT4eZVFcF7g/DLj6Pxy+odMDI2hZm5FX7ffFhmn9NH9uLwrn9Qs3ZDAED9pgGo4S/biVm1eDbycnOV2mmOvHkOd/asgX+Pb2Hq7IXHZ/bh3IrpaDd1JbQNjEukjw+/B0e/ZjBz8YGahgYendiFcyumo+3kP6FrbA4AMLCwg98X30DPzBoFeSKEndmHcyumod201dDWN1JaWRQ5sH0jgvduw/CJM2FhbYsdG/7CgimjsHDN9lLrVZSTDUdXTzRv2wmLZ08qJU0OvKrVRP1mAVj921xlFkHGnQuncHDDn+gydDwc3KvgwqEdWDt3AiYs/Qf6RiYl0j8PvY+gJXPQ9ssh8PFvgNvnT2LTL1Mx6pfVsHZ0lUl7/8o5RIY9hKGJucx2J89qmLpqt8y2Y9vWIuLeTdi7eZd/IT+Qno4W7oVFYeO+S9i2eKiqs/PeNvy9BkFb/8HMOfNhZ2ePFX8uw6jhQ7B9T+lto6WlNUaOGQ9HRydIJBIcPLAP340Zic3bCttGK2trBJ88J7PPnp3bsWnDOjRs3KQiiiX73f8vbeOWfzB9zjzpNc6YEUMRtFtx2zhi9Dg4vG4b9+/FxKJrHFd32bZx2IjitlGrAtrGt/29djW2bt6EOfMWwM7OHn/+vhTDhw7Cnv2HSy3fa/fv3cXOHUHw9Cx5kzEnJxsNGzVBw0ZNsGzJImVln0iqUoRqN2/eHKNGjcLYsWNhYmICKysrrF69GpmZmRgwYAAMDAzg7u6OI0eOSPcpKCjAoEGD4OLiAh0dHXh5eWHp0qXSz3NyclC1alUMHVrcUEZERMDAwADr1q1TmJ+EhAR06dIFurq68PDwwP79+2U+v3//Ptq1awd9fX1YWVnh66+/RkJCgvTz4OBgNG7cGMbGxjAzM0OHDh0QEREh/fzZs2cQCATYtm0bmjVrBm1tbWzevBkDBgxAamoqBAIBBAIBZs6cKTd/M2fOhK+vL9asWQMXFxfpyOm7vtfFxQUAUKtWLQgEAjRv3lz62Zo1a+Dj4wNtbW14e3tj+fLlCv+NypNEIkHQ5o0YOOQbNGvRCh6eXpg5ZwES4uNw9vSJUvdr0qwFGjVpBkcnZzg5uWDEqLHQ1dXF/Xt3pGmW/LoAPXt/hX4Dh8DN3QNOzi5o3bZdhd9okEgkOLBzC3p8PRj1GjeHs5snxkyZjaSEeFw5f6bU/fzrNUKfQd+ifpOWpaYxMTWXeV25cBbVfGvD2tZeCSUpnUQiwbYtGzFgyDA0LarHGUX1eO70yVL3a9KsBRoW1aOjkzOGjyyqx7uyo0La2towM7eQvvT09ZVenuC9QejUayD8GzSDo4sHhk2YiZTEBNy4eLbU/Y7s2YLm7TqjaZuOsHNyxYBRk6GlpY1zxw4AAIRqajA2NZd53bh4BnWbtIK2ji4AQFNLW+ZzoVAND+9cR7O2nZRa5rAze+HasC1c6reGkbUj/Ht8C3VNLTy9fFxu+vp9J8K9SXuY2LvC0MoBtXuPgkQsRlxY8d+gU+3msPLyhb65NYxsnODbZTDycrKQGvVUqWUpjUQiwZE9W9Hly4Go3bAZnFw9MGLSLCQnJuD6hdLr1bduI/QcMBx13hrVelOTgM/Q7ashqF6rrjKyXqrzB7ejbqsOqN3iM1g5OKPz0O+gqamN66cOy01/4dBOePrWRbPPe8PS3hlteg2CrasnLgXvkUmXmhiP/euWodeYHyFUl70Pr66hAQMTM+lL18AID69dgH+LdhB8RCF8xy48xKzlB7H/dOUZZX5NIpFg6+aNGDTkGzQv+k2d/dMCxMfH4cyp0tvGps1boPHrttHZBd8WtY337hb+XaqpqcHc3ELmdfrUSQS0CYSurl5FFQ/A/0/bGLS5sG0seY2juG1s9GbbKL3GUdw26iu5bXybRCLB5k0bMWTYcLRoGQBPL2/8NP8XxMfF4dTJ0s9TAMjKzMSU7ydixqyfYGhU8kbqV337Y9CQoahRs6aysv9REggqx+tTVCk6zgCwYcMGmJub4+rVqxg1ahSGDx+O7t27o2HDhrh58ybatGmDr7/+GllZWQAAsVgMe3t77NixAw8fPsT06dPxww8/YPv27QAg7Yxu2LAB+/btQ0FBAb766iu0bt0aAwcOVJiXWbNmoUePHrh79y4+++wz9OnTB0lJSQCAlJQUtGzZErVq1cL169cRHByM2NhY9OjRQ7p/ZmYmxo8fj+vXr+PkyZMQCoXo0qULxGKxzPdMnjwZY8aMQUhICFq0aIElS5bA0NAQr169wqtXrzBhgvy7qAAQHh6OXbt2Yffu3bh9+3aZvvfq1asAgBMnTuDVq1fYvbtwtGDz5s2YPn065s6di5CQEMybNw/Tpk3Dhg0bylp9/0l01EskJiSgbr0G0m36BgaoWr0G7t25o2DPYgUFBTgWfAjZ2VmoXsMXAJCUlIj79+7CxNQMg/r2RmDLxhg26GvcvnVDGcVQKPZVFJKTElDDv550m56+ATx9qiH0Qfld0KUkJeLG5fMI+KxzuR2zrF7XY52367FaDdy7e7tMxygoKMDx4MPIzs5G9RqyDeXRwwfRtkVDfPlFJyxfthg52dnlmf0S4mOikZqciGpvdIJ09fTh6lUV4Y/uyd0nPy8Pzx4/QlXfOtJtQqEQVX3rIDxE/j5PH4fg+ZMwNGv7eal5OX/yMLS0tFG3cekXif9VQX4ekl+Ew8rTV7pNIBTC0tMXic8ele0YuSJIxAXQ1DUo9TsiLgZDQ0cPxnYu5ZHt9xYXE4WUpERU85OtVzfvqngcUvk6V/l5eYh6Egb3Gv7SbUKhEO41/PE87IHcfZ6HPZBJDwCeNevIpBeLxdj2+1w07dQLVg7vrquH1y8gKz0NtVu0+8CS0NuiSmkbq1WvIe0Ev0tBQQGOHilsG2vU9JWbJuThA4SFhuDzLl+UR7bfy/9T2yj/Gud2mY5ReI1T2DZWe7ttPHIQbZo3RO9unfBnBbSNb4t6+RIJCfGoV7+hdJuBgQGq16iJu3duKdx33k+z0bRpM9Rv0FBhOqKKUmlCtWvWrIkff/wRADBlyhQsWLAA5ubmGDJkCABg+vTpWLFiBe7evYv69etDQ0MDs2bNku7v4uKCS5cuYfv27dJOrK+vL3766ScMHjwYvXr1wvPnz3Hw4MF35qV///7o3bs3AGDevHlYtmwZrl69isDAQPzxxx+oVasW5s2bJ02/bt06ODg4ICwsDJ6enujWrZvM8datWwcLCws8fPgQ1apVk24fO3YsunbtKn1vZGQEgUAAa2vrd+YxNzcXGzduhIWFhXTbu773dVozMzOZ75gxYwYWLVokzYuLiwsePnyIlStXol+/kvOARCIRRCKR7DaxxjvDcUqTWDRab2pmJrPd1NQciYnxCvcNfxyGQX17IzdXBB0dXfyy+He4urkDAKJevgAArP7rD4wZNwme3t44dGAfvh06AFt37lc4t6i8pSQlAgCMTWTDbI1MzJCclCBvlw9y6ugB6OjqokFT5XWwSiOtR1PZkE5TMzMkJiouY/jjMAzp1xu5ubnQ0dHFz4uWwaWoHgGgbbv2sLaxhbmFJcIfh+LPpYvx/Pkz/LxoWfkXpEhKcmGdGZWoM1OkFn32tvS0FIjFBSX2MTQxRfTL53L3OXt0P2wdXOBZpUapeTl7dD8aNG8LTS3lzcvPzUyDRCyG1lsh2doGxkiPe1mmY9zdvx7ahqaw8vKV2R59/youb/gF+Xki6BiaoNnwOdBSUZh2atHfopGx7O+NkYmZtM4rk6z0VIjFBSVCsvWNTBAfFSl3n4yUpJLpjU2QkZIkfX923xaoqamh0Wfd3t5druunDsHTtw6MzCzfswRUmte/qWZvt41m5khMeHfbOODrorZRVxcLfytuG9+2b89OuLi6oaZvrfLJ+Hv4v2obzd5qG03NkFSGtnFw3zfaxsXLZOqxTbv2sLEtahvDQvHH0sWIfPYMPy9WXtv4toSic9HMXPY8NTMzk4nGfNuRw4cQEvIQW7btVGr+iN5HpRlxrlGj+KJRTU0NZmZmqF69unSblZUVACAuLk667c8//4S/vz8sLApDU1atWoXISNkLhe+++w6enp74448/sG7duhIN0LvyoqenB0NDQ+n33rlzB6dPn4a+vr705e1dOJ/rdVj048eP0bt3b7i6usLQ0BDOzs4AUCJvtWvXfmdeSuPk5CTTaX6f731TZmYmIiIiMGjQIJky/fTTTzJh3m+aP38+jIyMZF6LFy4oc96DDx1Aswb+0ld+/ofP73ZydsY/23Zj3aZt6NajF2ZNn4InEeEAAIlYAgDo2q0nOnbuCi/vKhg/cQqcnF1wYN9uRYf9z84eP4xe7RpJX/n5+Ur9vtdOHtmPpgHtSp0XVp6CDx9Ai4b+0td/KaOTszM2Bu3G2o1B6Nq9J2ZP/wFPi+oRADp364H6DRvD3cMTgZ91xIw583H21Am8fFH6uf2+LpwKxuAuzaSvggqos1xRDi6dOaowBPtxyF1Ev3iq9DDt/yrk+A68uHUOjQZNhZqG7FQIS48aaD1pGVqNXQhrb39cWv8zctJTKiRf508eQf9OTaWv/IKK+VuszF5GhOLCoV3o/u2UMoVdpybGIez2NdRu2b4CcvfpOnLoAJrU95e+/mvbuGX7bqz/Zxu+6N4LM6cVt41vysnJQfCRQ/i8c9lukPxX/xdt46EDaN7AX/r6r23jpm27sXZTELr2KGwb36zHLl+80Ta274iZP83HmXJuG9926OB+1K9dS/r6kPLFvHqFXxbMxfyfF37woMunTFhJXp+iSjPirKGhIfNeIBDIbHvdeL8OOw4KCsKECROwaNEiNGjQAAYGBli4cCGuXLkic5y4uDiEhYVBTU0Njx8/RmDgu1eLlJeX19+bkZGBjh074ueffy6xn42NDQCgY8eOcHJywurVq2FrawuxWIxq1aohNzdXJr2e3ofPJZK3b1m/900ZGRkAgNWrV6NevXoyn6mpqcndZ8qUKRg/frzMthyxhty08jRp3hJVqxffnHidv6TERJhbFI9WJCUlwNNT8cqsGhqa0oUzfKpUxcMH97BtyyZMmTYLZkU3Flzc3GT2cXZxRcyrV2XO74eo26gZPKsURxfk5RZeAKUkJ8HUrPiGR2pyIlzcy2fV3Qd3byLqxTNMmF72mxj/RZNmLVG1WnE95uUV1WNSAszfuKmTlJgIDy/FiwW9WY/eVari4YP72LZ1Eyb/OEtu+tfnz8sXkbB3ULzCdVn51W8Cd++q0vevy5OanATjN0bRU5OT4OTmKfcYBobGEArVkJqcJLM9LTkJxiYlb9pdPX8KIlEOGrf6rNR8nQneBydXT7goWCG5PGjqGUIgFEL0Voc2Jz0F2gYlF5h606NTu/Ho5E40G/GT3BBsdS1tGFjYAha2MHP2xuE5Q/D08jH4tO4h52jly79BU7h7v/G3+LpeUxJhYvZmvSbCuZR6/ZjpGhhBKFRDRmqyzPaM1GTol7KQnL6xacn0KcXpnz26i8y0ZCwYXlw/YnEBDm1YjvOHdmLy8m0y+14/fQS6BoaoUrtReRTp/1bT5i1RTU7bmPh225iYAE+v928bt27ehKnTZX9TTx4/ipzsHLTvWPpUkfL0f9E2vnWNkye9xnmrbUxKhIdn2dtGnypVEfLgvvQaRx5ltI1va96iJapXLw4Xzy36TU1MSITFG+dpYmIivLzll+/hwwdISkxEr+7FUZcFBQW4cf0agrZuxrVb90q9BiVSpkrTcX5fFy5cQMOGDTFixAjpNnkjpAMHDkT16tUxaNAgDBkyBAEBAfDx+fALUD8/P+zatQvOzs5QVy/5z5uYmIjQ0FCsXr0aTZoUrk55/vz5Mh1bU1MTBQUFH5Svsnzv6wWx3vwOKysr2Nra4smTJ+jTp0+ZvktLS6vEHUJJtriU1CXp6enJdPwlEgnMzM1x7epleHoX1k1GRgYe3LuLbt17lfm4ACAWS6QXG7a2drCwsMTzZ7KLEEU+f46GjZS7cqiOrh50dGXLaGJqjrs3r8K16GIgKzMDYSH3Efh593L5zhOH98HN0wcu7hVz8V9qPV65LL2oy8zIwIP7d9H1PetRIpEgN7f00Zaw0MI5t2bmFqWmeV/y6szIxAwPbl+TdpSzMzPwJPQBWrWXPzqjrqEBZw9vPLx9DbUbNgdQeLPvwe3raN2pZD2fPboffvWawtBYfsc0JzsLV/89iR79R8j9vDypqWvAxMEdsWF3YFejcC7e64W+3Jt0KHW/Ryd3IuTYdjQdPhumjh6lpnuTRCJBwX8YTXsf8urV2NQM929dkz4qKiszAxGPHqB1h4qf4/lfqWtowM7VE+H3bqBq3cLfNbFYjPB7N9EwsIvcfZw8qyL83g3po6UA4PHd63DyLLxxVKtpG7hXl50Dve6niajVtE2JOcwSiQQ3Th+BX7O2UJPTJlLZKfpN9Xqjbbz/gW3j65tGb9q3dxeaNm8BE1Plrdb/pv/rtlHONc77to1isUR6s0GesEfl3za+TU9PH3p6xQuQSSQSmJtb4MqVS/D2KS7fvbt30L1nb7nHqFe/PnbuPSCzbcbUKXB2dcWAQUPYaSaV+WRbMQ8PD2zcuBFHjx6Fi4sLNm3ahGvXrklXjgYKQ7kvXbqEu3fvwsHBAYcOHUKfPn1w+fLlD15V+dtvv8Xq1avRu3dvTJo0CaampggPD0dQUBDWrFkDExMTmJmZYdWqVbCxsUFkZCQmT55cpmM7OzsjIyMDJ0+eRM2aNaGrqwtdXd0y7VuW77W0tISOjg6Cg4Nhb28PbW1tGBkZYdasWRg9ejSMjIwQGBgIkUiE69evIzk5ucTIsjIIBAL06tMX61b/BQdHJ9ja2eOvP5fB3MISzVoUP2tyxNABaN4yAD16FXbw/1y2GA0aNYG1tS2ysjJx9MhB3Lx+FcuWr5Ye96t+A7Hqrz/g4ekNTy9vHDqwF8+fPcGCX5covVxvl7HjF19ix6Y1sLVzhGXRIzdMzS1Qr3Fzabpp44ehfpMWaN+lsDHNzs7Cq6gX0s/jXkXhSXgoDAwMYWFlI92elZmBi2ePY8Bw5ddXaQQCAXp+2Rfr16yU1uOq5YX12LRF8eM/Rg4bgGYtAtC9qB6XL1uMBo2awsrGBlmZmThWVI9Liurx5YtIHDtyCA0bN4WhsTHCw0KxdNHPqOVXGx5yHl9RnuUJ7NwL+4LWwdrOARZWtti56S8Ym5nDv2Ezabr5k0egdsPmaN2pcHSuXZcvsWrRLLh4+MDVqyqO7g2CSJSNpq1lO5+x0S8Qev8WJsxeUmoeLp87joKCAjRsWTELLnk274yrm3+DqaMHTB09EXZ2H/Jzc+BSr/Dv8Mo/i6BjZIYaHfsDAEJO7MSDw/+gft+J0DW1QnZa4SimupY2NLR0kC/KwcNj22BXvR60DU0hykxD+L8HkZ2aCAffxhVSprcJBAK069Ibe7cU1qultR12rP8LJmbmqN2ouF5/mjQcdRq1QNvPC+s1JzsLMdHFf4vxMdF4FhEKfQMjmFsWrhmRkZaKhPgYJBfNW3z1onBeu7GJmUzUQnlr3KEHdvw5H/Zu3nBw98b5QzuRK8qGf1End9vvc2FkaoHAPoVPmWjU/gusnDEa5w5sg7dffdy5cApREaHoOqxwQUo9AyPoGcjOQReqq8PAxBQWbz3rOuL+TSTFvUKdVh9nmLaejibcHIo7Ec52ZqjhaYfktCy8iElWsKfqCQQC9O7TF2tX/wUHJyfp46gsLCzRvGVx2zh8SGHb2LN34W/qH0sXo2Hj4rYx+PBB3Lh+Fb+vWC1z/BeRz3HrxnUs/XNlhZbrTf8vbWOvPn3x9+ritnGl9BqnuG38tugap/sb1zgNGzWFlbWNzDXO0jfaxqNFbaORkTHCH4diya8/o5a/cttGeeXr83VfrF65Ak6OTrCzL3wclYWlJVq2Kj5Phwzsh5atWqN3n6+gp6cPDw/ZGxk6urowNjKW2Z4QH4+EhAS8KJpyGP44DLq6erCxsYGRsXGFlI/+v3yyHedhw4bh1q1b6NmzZ2Hj0rs3RowYIX1k1aNHjzBx4kSsXbsWDg4OAIDly5ejRo0amDZtmtxQ67KwtbXFhQsX8P3336NNmzYQiURwcnJCYGAghEIhBAIBgoKCMHr0aFSrVg1eXl5YtmyZzKOfStOwYUN888036NmzJxITEzFjxoxSH0n1NqFQ+M7vVVdXx7JlyzB79mxMnz4dTZo0wZkzZzB48GDo6upi4cKFmDhxIvT09FC9enWMHTv2g/6NPkTf/oORk52NeXNmICM9DTVr+WHp8lUyI9tRLyKRklx8oZOUlIhZP05GQkI89PUN4O7piWXLV6Neg+Jwwd5f9UNubi5++3UB0lJT4eHphd//Wqu0ECZFuvTqh5zsbCxf9BMyM9LhU90X03/+Q2bOVUz0S6Slpkjfh4c+xLRxxY9UW7d8MQCgRduOGDO5OFTr31NHIZEATVq2VX5BFPi6/yDkZGdjwU8zkJGejhq+fljyp2w9vnzxAikpxfWYnJSEWdMmI7GoHt08PLFk+WrpCp0aGhq4duUSgrZsRE52NiytrNG8VWsMHPyN0svTvntfiHJysG7ZPGRlZMCzak1MnLNUps7iXkUhPS1F+r5+s9ZIT03Grn9WITUpEY5unpg4ZymM3grVPnvsAEzNLVHNT3aKhEyao/tRu2Fz6OnLX6W6vDn6NYUoIxX3D/+DnLRkGNu7ouk3s6FtWDginpUcD4GgeGZTxIXDEBfk4+Lf82WOUyWwN6q16wOBUIj0uJe4uO4kRBlp0NQzhKmjB1qO/hlGNk4VUiZ5OvboC1FONtYsKaxXr2o1MXneMpl6jX0VhfQ3/hafhIVgzsTic27Tyt8AAE1bt8fwiTMBADcun8Nfv86Wplk2byoAoNtXQ/BFX+U9Q7hmo5bITEvB8W3rkJ6SBFtndwycuhAGRaHXKQlxMvXm5FUNvcZMw7Gta3F0y2qY29jj60lzSzzDuSyunTwEJ69qsLRTXX0q4lfFCcfWjJG+/2VCYbTIpv2XMXTGP6rKVpn1G1DUNs6egfT0NPjW8sOyt9rGly8jZX5Tk5ISMePHyUiIL/xN9fD0xO8rVqN+A9lQ+v17d8PSyrrE9or2/9I2ZmdnY/6cwrZR/jXOC5lrnOSkpBLXOEuXr0a9Bm+1jZuL28YWrVpjwBDlt41vGzBoCLKzszF75nSkp6ehlp8/lq9co7DtL4sd24Pw1/I/ir+nb+FNhdk/zcfnXbqWtlul9zE90u//jUAikUhUnQn69KW+R6h2ZRWdXLGPeFAFa2Plrdr8MXgck6HqLCjdgbC4dyeq5Lr4WKk6C0r3PDVL1VlQui/7/aTqLChd3KWKW91YFV4mffrtoo3Jp90uAoC2xqcfGq1diYYSN1x/8e5EH4F+tR1UnYVy96kuekZERERERERULirR/RUiIiIiIqL/XwzUVh2OOBMREREREREpwI4zERERERERkQIM1SYiIiIiIqoEhFxVW2U44kxERERERESkADvORERERERERAqw40xERERERESkAOc4ExERERERVQKc4aw6HHEmIiIiIiIiUoAdZyIiIiIiIiIFGKpNRERERERUCfBpVKrDEWciIiIiIiIiBdhxJiIiIiIiIlKAodpERERERESVgICx2irDEWciIiIiIiIiBdhxJiIiIiIiIlKAHWciIiIiIiIiBTjHmYiIiIiIqBLgqKfq8N+eiIiIiIiISAF2nImIiIiIiIgUYKg2ERERERFRJcDHUakOR5yJiIiIiIiIFGDHmYiIiIiIiEgBhmoTERERERFVAgzUVh2OOBMREREREREpwI4zERERERERkQIM1SYiIiIiIqoEuKq26nDEmYiIiIiIiEgBdpyJiIiIiIiIFGCoNlWIFZeeqjoLSqeh9umHzlx6kqLqLChVM08TVWdB6Vq7mqk6C0r3PDVL1VlQunyxWNVZULq4S8tUnQWls2wwWtVZUKpu3w1RdRaULjs3X9VZULr07DxVZ0Hpjn1bX9VZoEqAHWciIiIiIqJKgOHCqsN/eyIiIiIiIiIF2HEmIiIiIiIiUoCh2kRERERERJUAH0elOhxxJiIiIiIiIlKAHWciIiIiIiIiBRiqTUREREREVAkwUFt1OOJMREREREREpAA7zkREREREREQKsONMREREREREpADnOBMREREREVUCfBqV6nDEmYiIiIiIiEgBdpyJiIiIiIiIFGCoNhERERERUSUg5AOpVIYjzkREREREREQKsONMREREREREpABDtYmIiIiIiCoBrqqtOhxxJiIiIiIiIlKAHWciIiIiIiIiBdhxJiIiIiIiIlKAc5yJiIiIiIgqAQEfR6UyHHEmIiIiIiIiUoAdZyIiIiIiIiIFGKpNRERERERUCfBxVKrDEWciIiIiIiIiBdhxJiIiIiIiIlKAodpERERERESVgJCraqsMR5zLSCAQYO/evarOxn/m7OyMJUuWqDobRERERERElQZHnMvo1atXMDExUXU2/rNr165BT09P1dl4byFnDuD+8V3ITkuGib0L6vccDgtnL7lpk6Of49aBTUiMDEdGUhzqfjEUVVt1lkmTl5OFm/s34fmdi8hJT4WpgxvqdR8GC2fPCiiNfA9OH8CdYzuRnZoMU3tXNOo9HJYu8suYFP0c1/dtQkLkY2QkxqFBj6GoHtBFJo1YXIAbBzYj/PIpZKUlQ9fIFF4NW6NW+94QfCQrSwR6m6NTNSsY62jgeXI21l5+gfCErHfu18jFBOOau+Dq8xT8cupJBeS0bO6e3I9bwTuRlZoMcwdXNO0zAlau8uswMeoZruzdhPhnj5GeGIfGvYbBt02XEukykhNwccdaPL93Hfm5IhhZ2qLVwPGwcqmYc1UikWD/5tX499h+ZGWmw92nBvqMmAQrWweF+50+tBNHd29GanISHFzc0XvYeLh4VpV+vumPBQi5cx0pSfHQ0taFm091dOs3AjYOziWOlZGWilmjv0ZKYjyWbj0GXX2Dci3jpeA9OLs/CBkpSbBxckOngWPg4OFTavq7l07jeNA6JMfHwMzaDu2++gbefvXlpt2zahGuHN+PDv1HonH77tLtUU/CcOSfv/AyIhRCoRDV6jVF+37fQktHt1zL9trlo3tw/sA2ZKQkwdrJDR0GjIa9e+llvH/pDE5sX4eU+BiYWdujTZ+h8KolW8a4l89xbMsqPH14B2JxASztnND7u1kwNrcCAOxdtQgR928iPSkBmto6cPSqirZfDoOFnaNSyvg2iUSClct/x57dO5CRno6avrUweeoMODo5l7rPzu1bsXN7EF5FRwEAXN3cMXjYCDRq3BQAEB0VhU6fBcjdd8HC3xDQJrDcy/FfNfJzw7i+AfCr4ggbCyP0GLcKB87cVXW2yiTA0wztq1jCSEcdkcnZ2HgtCk8Ss+Wmre1ghE7VLGFloAU1IRCblovDIfG48DRZJp2toRZ6+dnA21IfQiEQnSrC0rPPkJiVVxFFKpNAHwt8XtQ2PkvOxtpLkWVuG8e3cMXV5yn4+WREBeS0bDpWs0L3WrYw1dXAk8Qs/HnuKULjMt+5X3N3M/zQ1gMXnyRh5pEwmc8cTLQxuIEjatgaQk0owPOkbMwODkN8Rq6yikHEEWcAyMt794+ltbU1tLS0KiA3ymVhYQFdXeVcmCnLk+tncXXXavi2/xKdfvgdpvauOLZsGrLTUuSmz88VwcDcBv6dB0DHUP7NjvP/LEX0o1to2n8COv+4HHY+tXB06Q/ITElQYklKF3HtLC7tWAX/Dn3Q9cffYebggsNLf1RQxhwYWlijbpfSy3gneAcenjmERr1HoMesVajXbSDuHN2JB6f2K7EkZdfQxQT96tpjx+1XmLT/EZ4lZePHNu4w1FZ8P89CXxN969jhYUx6BeW0bB5fPYvz21ajTqev0HPGHzBzcMX+xVORpeA8NbKwRoMvBkLXSH4d5mSmY9e88RCqqaPTuJ/Q56dVaNxzCLT19JVYElnBu/7ByYM78NWISfjh17XQ1NbBkuljkZcrKnWfa/+ewPY1y9Cx9yBMW7Ie9i4eWDJ9HNJSkqRpnNy90X/MVMxeHoSxs5YAEgmWTB8LcUFBieNtWDYP9s7uyige7lw4hYMb/kRA934Y9fNq2Di5Ye3cCchITZab/nnofQQtmYPaLT/D6F9Wo2rdJtj0y1TERJa8gXP/yjlEhj2EoYm5zPa0pASsmT0eZtZ2+HbeCgyY+gtiXz7Djj8XKKWM9y6ewpGNK9CiWz+MWLAK1k5uWD9vUqlljAy9j+3L5sC/xWcYsWA1fOo0xpaF0xAb+VSaJjEmCqtnjIa5rQMGzfgNI39Zg+bdvoa6hqY0jZ2rJ7p+MwljFm9A/x9+ASTA+rkTIRaXrGNl2PD3GgRt/QdTfpyJ9f9sg7aOLkYNHwKRqPRz19LSGiPHjMemrTuxccsO1K5bH9+NGYmI8McAACtrawSfPCfzGjZ8JHR1ddGwcZMKKdf70tPRwr2wKIydv03VWXkv9ZyM0cffFnvuxuDHw2GITM7B9y1dYaglv43IzM3H/vuxmBX8GD8cDMO5iCQMbeCA6jbFN9os9TUxra07olNFmHs8Aj8cDMPee7HIK5BUVLHeqaGLCfrXtcf2268wcX8InidlYVpbjzK1jf3q2n90bWMzdzMMa+yEf669xIjt9/AkIRPzOvrAWEdxeawMtDCkkSPuRaeV+MzGUAu/da2KF8k5mLD3IYYF3cXm61HIKxArqxhEAD7CjrNYLMb8+fPh4uICHR0d1KxZEzt37gRQePc4ICAAbdu2hURS+COXlJQEe3t7TJ8+XXqMNWvWwMfHB9ra2vD29sby5culnz179gwCgQDbtm1Ds2bNoK2tjc2bNwMA1q1bh6pVq0JLSws2NjYYOXKkdL83Q7Vzc3MxcuRI2NjYQFtbG05OTpg/f740bUpKCgYPHgwLCwsYGhqiZcuWuHPnjsJyf//99/D09ISuri5cXV0xbdo0mQ79zJkz4evri02bNsHZ2RlGRkbo1asX0tOLfyDT09PRp08f6OnpwcbGBr/99huaN2+OsWPHStO8HaotEAiwZs0adOnSBbq6uvDw8MD+/cUdq4KCAgwaNEhaH15eXli6dKnCspS3Byf3wLNRIDwatoGxjSMa9h4JdU0tPL50TG56C2dP1Ok2CK51mkFNXaPE5/m5Ijy/dQG1uwyEtUd1GFraolaHr2BoYYtHZw8puzhy3T2+B96N28GrURuY2DqhSZ9RUNfUQugF+WW0dPZC/S8Gw71uc6hplCwjAMRGhMDZtz4ca9SFgbkVXP2bwK6KH+KehSqzKGXWsaolToQl4HR4El6m5mDVxUiI8sVo6WFW6j5CATCmqTO23XqF2PSP667y7aO7UbVpIKo0aQNTOye06FtYhyH/HpWb3srFC416DIFnveZyz1MAuHl4B/RNLRAw6DtYuXrB0MIajtX8YWRpq8yiSEkkEpzcvw3te/SHb/2msHdxx8Bx05GSlIBbl8+Vut/xvVvRpG0nNAroAFtHF3w1YhI0tbRw4fhBaZqmgZ3hWa0WzK1s4OTuhc5fDUNSQiwS4l7JHOvM4d3IykxHmy5fKqWM5w9uR91WHVC7xWewcnBG56HfQVNTG9dPHZab/sKhnfD0rYtmn/eGpb0z2vQaBFtXT1wK3iOTLjUxHvvXLUOvMT9CqC57gRhy4yLU1NXx+eBxsLBzhIO7D7oMGY/7V84i4dXLci/jhUM7ULtVe/i3aAdLe2d0GjweGprauHH6iNz0F4/sgodvXTTp1AuW9k4I6DkQNi4euHy0uIwngtbCs1Y9BH71DWxdPGBmbQef2o2g/8ZNoDoBHeFSpSZMLK1h6+qJgJ4DkZoYh+S4mHIv49skEgm2bt6IQUO+QfMWreDh6YXZPy1AfHwczpw6Uep+TZu3QOMmzeDo5AwnZxd8O2osdHV1ce9uYRuupqYGc3MLmdfpUycR0CYQurofZzTXsQsPMWv5Qew/XTlGmV9r52OO0+FJOPckGdGpIvx95SVEBRI0czeVmz4kNhPXX6QhOk2EuIxcHA1NwIuUbHhZFtdLd19r3IlKQ9CtV3ienI24jFzcfJmGNFF+RRXrnTpWs8KJ0AScfpyIlyk5WHmhsG1s5am4bRzbzAXbbkYjNr30G0Oq0M3XBkcexOHYo3hEJmdj6ZmnEOWL0dbHstR9hAJgcmt3bLr6Eq9SS5ZnQH0HXH2egjWXIhGRkIVXaSJcfpaMlOyPpx6VSSCoHK8P8eeff8LZ2Rna2tqoV68erl69qjD9jh074O3tDW1tbVSvXh2HD8tvu8vLR9dxnj9/PjZu3Ii//voLDx48wLhx4/DVV1/h7NmzEAgE2LBhA65du4Zly5YBAL755hvY2dlJO86bN2/G9OnTMXfuXISEhGDevHmYNm0aNmzYIPM9kydPxpgxYxASEoK2bdtixYoV+PbbbzF06FDcu3cP+/fvh7u7/BGOZcuWYf/+/di+fTtCQ0OxefNmODs7Sz/v3r074uLicOTIEdy4cQN+fn5o1aoVkpKS5B4PAAwMDLB+/Xo8fPgQS5cuxerVq/Hbb7/JpImIiMDevXtx8OBBHDx4EGfPnsWCBcUjFOPHj8eFCxewf/9+HD9+HP/++y9u3rz5zn/zWbNmoUePHrh79y4+++wz9OnTR5pXsVgMe3t77NixAw8fPsT06dPxww8/YPv27e88bnkoyM9DYmQ4bL19pdsEQiFsvH0R9+TRBx1TIi6ARCyG2hsjIwCgpqmJuIiH/yW7H6QgPw8JkY9h7+Mr3SYQCmHn44vYJyEffFwrNx9EPbqNlNjCC/HEF08QG/4ADtVq/9cs/2fqQgFczXRxN7r4xo8EwL1X6TIXOW/7oqYNUnPycepxYgXksuwK8vMQ9/wxHKrUkm4TCIWwr1ILMREfXodPb1+GpbMnjiz/CWvH9ETQzG/x4Kz8zo4yJMRGIzU5ET6+daTbdPX04epZBU8e3Ze7T35eHp6Hh8KnZvE+QqEQPr51EBEqfx9RTjYunDgIcytbmBaF+QJAdORTHAxah4HjpkMoLP/mKj8vD1FPwuBew18mr+41/PE87IHcfZ6HPZBJDwCeNevIpBeLxdj2+1w07dQLVg4uJb83Pw9q6uoyZVLXLIxoevbo3n8qk7zvin4SBrfqsmV0q+6HF4/ll/FF2EO4VZMto0fNOnhRVEaxWIzQW5dhbmOP9XMnYv6QLvhr6nA8vHa+1Hzk5mTj5plgmFjawMi89Avm8hIV9RKJCQmoW6+BdJu+gQGqVa8h7QS/S0FBAY4eOYTs7CzUqOkrN03IwwcICw3B512+KI9sUxE1oQAuprp48Eq2jXjwKh3u5mWLmqtqrQ9rQy08is0AAAgA+NoZIiZdhEktXfHnF1UwM9Ad/vaGSijBh1EXCuBmpou7b4yySgDcjU6Hp0XpkUbdfW2QmpOHkx9Z26guFMDDQg+3XqZKt0kA3HqZCh/r0svTp449UrLzEBwSX+IzAYC6TiaISsnBvI7e2D7AH8u+qIaGLpV/OuX/u23btmH8+PGYMWMGbt68iZo1a6Jt27aIi4uTm/7ixYvo3bs3Bg0ahFu3bqFz587o3Lkz7t+Xf61RHj6qOc4ikQjz5s3DiRMn0KBBYWPn6uqK8+fPY+XKlWjWrBns7OywcuVK9O3bFzExMTh8+DBu3boF9aI7+jNmzMCiRYvQtWtXAICLiwsePnyIlStXol+/ftLvGjt2rDQNAPz000/47rvvMGbMGOm2OnWKL/zeFBkZCQ8PDzRu3BgCgQBOTk7Sz86fP4+rV68iLi5OGtr966+/Yu/evdi5cyeGDh0q95g//vij9P+dnZ0xYcIEBAUFYdKkSdLtYrEY69evh4FBYdjR119/jZMnT2Lu3LlIT0/Hhg0bsGXLFrRq1QoA8Pfff8PW9t0jU/3790fv3r0BAPPmzcOyZctw9epVBAYGQkNDA7NmzZKmdXFxwaVLl7B9+3b06NHjncf+r0QZaZCIxSXCkXUMjZEa++KDjqmhrQsLVx/cObwVxtYO0DY0xtNrZxH/5BEMLGzKI9vvJae0MhqYIOU/jD75BvZAbk4Wtk8fCoFACIlEjDqd+8GjXsv/muX/zEBLHWpCAVLfujuckp0POyNtuft4W+qhlacZJuz78I6osmSnv65DY5ntuobGSHn1YecpAKTFv8L90wfh27YrarfvhdinYTi3ZQWE6urwadT6P+b63VKTCy/CDI1lR3gMjE2ln70tIy0FYnEBDE1k9zE0NkXMy+cy204f2oVd6/+EKCcb1naOGDdnKdSLIijy8nKxeuF0fDFgJMwsrZEQG11exZLKSk+FWFwgM0oKAPpGJoiPipS7T0ZKUsn0xibIeCMM/ey+LVBTU0Ojz7rJPYZ7NT8c2vAnzu7bikaffYFcUQ6CN68CAKSnlO+Fb1ZaKsRisdwyJkSXXkY945Lp04tCuzPTUpCbk41z+7YioOdAtO0zDGG3r2LroukYOH0xXKr4Sve7cnQvjm5eiVxRDsxtHdB/6kKolxJhUZ4SEwqn3ZiZyY7SmZqZIzGh5MX4m8Ifh2HA172RmyuCjq4uFv72O1zd5N9I37dnJ1xc3VDTt5bcz+nDGGipFbYRObJtRGpOPmyMSp82p6MhxO9dq0BdTQixRIL1V6NwP6aw42yorQ4dDTV0qGqJnbdjEHQrGjVtDTGmmTPmHY/AozLMuVW2123j2yOnqdl5sDMupW200kMrT3N8t7fib/y/i6F2YXmS35o/npyVBwcTHbn7VLUxQKCPBYZvk38T0VhXA7qaaujpZ4v1V15gzaVI1HE0xvR2npi49yHuRX9coepUdosXL8aQIUMwYMAAAMBff/2FQ4cOYd26dZg8eXKJ9EuXLkVgYCAmTpwIAJgzZw6OHz+OP/74A3/99ZdS8vhRdZzDw8ORlZWF1q1lLwhzc3NRq1Zxo9S9e3fs2bMHCxYswIoVK+Dh4QEAyMzMREREBAYNGoQhQ4ZI0+fn58PIyEjmmLVrF4+6xcXFITo6WtrhfJf+/fujdevW8PLyQmBgIDp06IA2bdoAAO7cuYOMjIwSjXV2djYiIkpfqGHbtm1YtmwZIiIikJGRgfz8fBgayt4FdXZ2lnaaAcDGxkZ6F+bJkyfIy8tD3bp1pZ8bGRnBy0v+wkRvqlGjhvT/9fT0YGhoKHN3588//8S6desQGRmJ7Oxs5ObmwtfXt9TjiUSiEnPI8nNF0hGVj0HT/hNwftNv2DblawiEQpg5uMOlTjMkRoarOmvlJuL6OYRfOY2WgybB1NYJCS+e4NL2ldAzMoVnQ+V3usqTtroQo5o6468LkUgXVcz8yI+BRCKBpbMHGnQrbEQsnNyRFPUM988cUkrH+fKZo/jnz5+l70dN/7Xcv+NN9Zq3RZVadZGalIBje7Zg5c8/YvIvK6GhqYXdG1bAxsEZ9Vt8fIstKfIyIhQXDu3C6F9Wl7oIn5WDC3p8OwUHNyzH0S2rIRAK0ahdN+gbmUIg+OgCwUqQiAvnEfrUbohGRQue2Ti740XYA1w9fkCm41yzSQDcatRGenIiLhzcjm1LZmHI7D+goakp79Af7MihA5g3Z6b0/ZI/VnzwsZycnbFl+25kZGTg5PGjmDltClat3Vii85yTk4PgI4cweMjwD/4uKl85eWJMPRQGLQ0hqloboI+/LeIzRAiJzZSGjt58kYbgR4U3ViKTc+BhoYtWnmYfRcf5fWmrCzG6qQtWXHj+SbSNOhpCfB/ghiWnnyItR37Y9etf1YtPk7H7TuG0jycJWahirY8OVa3+LzrOH8n6ru8krz+gpaUld82o3Nxc3LhxA1OmTJFuEwqFCAgIwKVLl+Qe/9KlSxg/frzMtrZt2yr1KUgfVcc5I6PwruChQ4dgZ2cn89mb/8hZWVm4ceMG1NTU8Pjx4xL7r169GvXq1ZPZX01NTeb9mytL6+jIv+tVGj8/Pzx9+hRHjhzBiRMn0KNHDwQEBGDnzp3IyMiAjY0Nzpw5U2I/Y2Njuce7dOkS+vTpg1mzZqFt27YwMjJCUFAQFi1aJJNO4625rAKBAGLxf18IQdFxg4KCMGHCBCxatAgNGjSAgYEBFi5ciCtXrpR6vPnz58uMUgNAq76jENBvTCl7lE5L3xACoRDZabKL2GSnpUDHUP48p7IwtLDBZ+N/QZ4oB3k5WdA1MsXpNfNhYG79wcf8UNqllTE9udRFo8riyq618A3sAfe6zQEApvYuyEiKw60j21XecU4X5aNALIHRW4uDGOuoIyW75GJ91oZasDLQwuQAN+m21w3Htn61MHr3A5XOedYxeF2HKTLbs9JS/lMd6hmbwtRWdgViE1tHRNy48MHHVMS3bmO4elaRvn+9zkJaShKMTYsXuEpPSYKDq/xVvfUNjSEUqiEtWXZqSlpKEgxNZG8o6urpQ1dPH1a2DnD1qoYxvdvg5qWzqNesDR7dvYGo5xG48flpAIAEhetajOvTDp/16IfP+wzBf6VrYAShUK3EIlkZqcnQN5b/+6JvbFoyfUpx+meP7iIzLRkLhhdH5IjFBTi0YTnOH9qJycsLF2jybdIavk1aIz0lCZpa2hAIBPj34HaYWpVv1IuuoRGEQuF7lzEzpWR6g6JzWdfQCEI1NVjYOcuksbBzxPO3Qs21dfWhrasPcxt7OHhWwdyBnfDw2r+o2ahsN6rLqmnzlqhWvfgmcG5u4e9BYmIizC2KQ8OTEhPg6VX6auIAoKGhCQfHwkgynypV8fDBPWzdvAlTp8u2ayePH0VOdg7ad/y8vIpBRdJFBYVtxFsLYhlpq5eIVHqTBEBs0arKkck5sDPSQseqVgiJfYJ0UQHyxRJEpebI7BOVKoKXxccxP/112/j2wllGOhpIkbPq9+u2cUpA8U2d123j9v5+GLXrvkrbxrScwvKY6MpeZ5roaiApq2S+bIy0YW2ojdntiwd9XpfnyPB6GLj5NuIzcpFfIEZkkuzq6pHJOahmU75PXKD/Rl5/YMaMGZg5c2aJtAkJCSgoKICVlZXMdisrKzx6JH9qZkxMjNz0MTHKW0fjo+o4V6lSBVpaWoiMjESzZs1KTffdd99BKBTiyJEj+Oyzz9C+fXu0bNkSVlZWsLW1xZMnT9CnT58yf6+BgQGcnZ1x8uRJtGjRokz7GBoaomfPnujZsye++OILBAYGIikpCX5+foiJiYG6urrMvGdFLl68CCcnJ0ydOlW67fnz5wr2KMnV1RUaGhq4du0aHB0LL7RTU1MRFhaGpk2bvtex3nThwgU0bNgQI0aMkG5TNHIOAFOmTClxB2jZxQ8LOVZT14CZoztehd6Bk29DAIWjHa9Cb8OneccPOuabNLS0oaGlDVFmOqIf3kTtLgP/8zHfl5q6BswdPRD16DacaxWXMTrkNqq26PTBx83PFZUY8RIIhYBE9auH5osleJKYheo2BrgWWTj3SQCguo0BjsiZ0xSVmoNxe2TD0Hr72UJHQ4h1V14iMVO1jxFRU9eApZMHXoTchqtfcR2+DLmNGi0//Dy1dq+C5BjZv52UmCgYmClnjqi2rh6031jgSCKRwMjEDI/uXIdjUUc5OysTT8IeotlnXeUeQ11DA07uXgi5ex21GhT+jovFYoTcuY6W7UufByqBBJBIkF/UWR8+ZZ7Myt3PHodg/dK5mPTzClhY25V2mPeirqEBO1dPhN+7gap1m0jzGn7vJhoGlnw0GAA4eVZF+L0bMo+Wenz3OpyKHrVVq2kbuFeXnR+87qeJqNW0DWq3aFfieAZFnddrpw5BXVMTHjXKdw0CdXUN2Lp64sm9m6hSpzGAwjI+uX8T9drKL6ODZxVE3L+Jhm/UV/i9G3AoKqO6ugbs3LyR8NY0hIRXL2FsIXsRI0NSWMcFZXiSxfvS09OTuSEukUhgZm6Oa1cuw8u7sKOckZGB+/fuolv3Xu91bLFYgry8khf5+/buQtPmLWBi+uE3cUm+ArEET5OyUNXaADdeFs73FaBw3vLxsLJPZxBAAA01gfSYTxKzYGMoO9plY6CFhMyPY7HJfLEEEYlZqG5riKtvtI01bA1wJKTkPM+o1ByM3S27VsGX/nbQ1hBi3eUXKm8b88USPI7PhK+9ES4WPRZMAMDX3hD778WWSP8iORtDt8quQdC/ngN0NNWw4t9nhZ1msQShcZmwN5ENXbc31v7oFkb7fyevP1DZn1D0UXWcDQwMMGHCBIwbNw5isRiNGzdGamoqLly4AENDQ/Tr108a637p0iX4+flh4sSJ6NevH+7evQsTExPMmjULo0ePhpGREQIDAyESiXD9+nUkJyeXqLw3zZw5E9988w0sLS3Rrl07pKen48KFCxg1alSJtIsXL4aNjQ1q1aoFoVCIHTt2wNraGsbGxggICECDBg3QuXNn/PLLL/D09ER0dDQOHTqELl26yISIv+bh4YHIyEgEBQWhTp06OHToEPbs2VMi3bv+7fr164eJEyfC1NQUlpaWmDFjBoRC4X96Zq+Hhwc2btyIo0ePwsXFBZs2bcK1a9fg4lJywZvX5IVh/Jcw7aqtuuD8hsUwc/SAhbMnHpzah3yRCB4NCkdNz63/FbrGZqjduTCctSA/DymvCufuFRTkIyslEYkvIqChpQPDotWIox7eKOwUWNkjLT4a13evg5GVPTxUNBJbo3UXnPl7ESycPGDh4oV7J/YiL1cEz6Jw3NPrfoWesRnqdi0uY3JRGcX5+chMSURCURlfr7jsVKMebh0Ogr6pJUxsnZDwIhz3ju+GV6M2Kinj2w48iMPIxk6ISMxCeHwW2le1gJa6EKeLFjcZ1cQJiVl52HIjGnkFErxIkR0lyMwtDEt7e7uq+LbtihNrfoWlswesXLxw5/ge5Ity4NO48N/7+OqF0DMxQ8MvCm/OFOTnIalojmlBfj4yUxIQH1lYh8ZWhXXo26YLds0bj+sHg+Bepylin4biwdnDaPEB0RsfQiAQoFWnnji0bT0sbR1gbmWDff+shrGpOWrVL74ht2jqSNRq0AwtOxR2Jlt37o11v82Bs7s3XDyr4sS+IOTm5KBRQAcAQHxMFK79ewJVa9WDvqExkhPjELxzEzS0tFC9duH6FpY29jJ5yUgrvIi0sXcu1+c4N+7QAzv+nA97N284uHvj/KGdyBVlw7+ok7vt97kwMrVAYJ/C9Skatf8CK2eMxrkD2+DtVx93LpxCVEQoug6bAADQMzCCnoHs1CChujoMTExlnl988chuOHlVg6a2DsLvXsfhTSsQ2GcodPTKf8SkUfvu2LV8AWzdPGHv5oOLh3ciV5QD/+aFYfA7/5gHQ1MLtPmycBS/YbtuWDNrLM4f2A4vv/q4e/EUoiNC0XnId9JjNunYE9uWzIazTw24Vq2Fx7evIvTGRQycsQQAkBQbjXsXT8O9Zm3oGRojLTEe5/ZthbqmFjxr1SuRx/ImEAjQu09frF39FxycnGBnZ48Vfy6DhYUlmrcsfg7z8CED0LxlAHr2LrzZ/sfSxWjYuAmsrW2RlZWJ4MMHceP6Vfy+YrXM8V9EPsetG9ex9M+VSi/Lf6Wnowk3Bwvpe2c7M9TwtENyWhZexMh/JNnH4EhIAoY1dMDTpCxEJGQh0KewjTgbURjNMqyhA5Kz8rD9duHoUseqlniamIXYjFxoCAWoaWeIRq4mWH+1+Obj4YeF7c6juEyExGSghq0BatkbYu7xj+eZxwfux2JUE2dEJGTicXwWOlS1hJa6EKeKbhiMauqMpMxcbC61bcwHoP7RtI27br/CxFZueByXgUdxGeha0wba6mo4WnSTfGIrNyRm5mLd5RfIK5Dg2VsjyRlFIehvbt95Kxo/tPXAveh03IlKRW1HY9R3NsGEj3CetzIIUDlitUsLy5bH3NwcampqiI2VvaESGxsLa2v50aDW1tbvlb48fFQdZ6BwYreFhQXmz5+PJ0+ewNjYGH5+fvjhhx8QHx+PQYMGYebMmfDz8wNQuCL0sWPH8M0332Dbtm0YPHgwdHV1sXDhQkycOBF6enqoXr26zCOZ5OnXrx9ycnLw22+/YcKECTA3N8cXX8gfHTEwMMAvv/yCx48fQ01NDXXq1MHhw4elK6QePnwYU6dOxYABAxAfHw9ra2s0bdq0RDjBa506dcK4ceMwcuRIiEQitG/fHtOmTZMbyqDI4sWL8c0336BDhw4wNDTEpEmT8OLFC2hry19QoiyGDRuGW7duoWfPnoUXIr17Y8SIEThypOJW9nWt3Qw5GWm4dXATstOSYWrvijajZksX08pMipeZF5iVmoT984pveNw/sQv3T+yCtUd1tBtfOH8zNzsTN/auR2ZKArR0DeBUqxH8P+8HoZpq/iTc6jRDdnoqru//B1lpSTCzd8Nno+dAt6iMGUlxMjdAslKSsHtO8ePS7h7bhbvHdsHGszo6TvgFANCw93Bc37cR57f8iez0FOgamcKn6Wfw66Ccx/q8r4tPk2GorY5etWxgrKOBZ0nZmHssXLoYjLmeJsSqHxwvM4+6hXV4de8mZKYmw8LBFR3H/SQN1U5PioNAWFyHmSmJ2DbzW+n7W8G7cCt4F2y9qqPr9wsBFD6yqt2303Fp19+4tn8zDC2s0aT3N/BqUHELvAV2+wq5OdnY9McCZGVmwKNKDYyZ9Rs03rgZFh8TJe3YAkCdJgFIT03Gvs1rkJacCAdXD4yZ9Zt0wTANDU08fnAHJ/ZvQ1ZGOgyNTeFR1ReTf1lVYiEyZavZqCUy01JwfNs6pKckwdbZHQOnLpSOBKckxMn8vjh5VUOvMdNwbOtaHN2yGuY29vh60lxYO7q+1/e+CA/Bie1/Q5STDQs7R3QZ+h38mrUt17K9Vr1hS2SmpeLk9vXISEmCjbMb+k35WRqqnZIYVxiNUsTRqxp6jPoRJ7atw/GgNTCztsOXE+fAyrH4hmmVuk3Qacg4nNu7BYf+/h3mtg7oPX4WnL2rAwDUNTTx/NE9XDyyCzkZ6dAzNoGzdw0MnfN7iYXKlKXfgMHIyc7GvNkzkJ6eBt9afli2fJXMhdzLl5FIeSMsPSkpETN+nIyE+Hjo6xvAw9MTv69YjfoNGskce//e3bC0si6x/WPkV8UJx9YU32z7ZULhonWb9l/G0Bn/qCpb73TleQoMtdTQrYY1jHTU8Tw5G7+cKp77aq6nKRNApaUuRP+69jDV1UBugRjRaSKsuBCJK89TpGmuv0jDuqtR6FTVEn1r2+FVmghLzz1DWPzHM7/54tNkGGmro5efLYx1NPA0KRs/HXss0zZKPoLIsbI6G54IIx119K3nABNdDTxJyMLUg4+k07IsDbTeOxDuwtNkLDv7FL38bDGiiTNepmRjdnCYzCrsVLloamrC398fJ0+eROfOnQEURkedPHlS5vHAb2rQoAFOnjwp08c7fvy4dIFpZRBIKtNfH72XzMxM2NnZYdGiRRg0aJBK87Lg1MdzN1dZXoeDfcouPUlRdRaUqpnnp/84i5oWRu9OVMkl5nwcYZfKlF8O61t87AK9K37NiYpm2WC0qrOgVN2+++9rEXzssnM//WcHp8tZe+RTc+zb+qrOQpkdD0lQdRbKpLWP+bsTvWHbtm3o168fVq5cibp162LJkiXYvn07Hj16BCsrK/Tt2xd2dnaYP38+gMKprs2aNcOCBQvQvn17BAUFYd68ebh58yaqVaumjCJ9fCPO9OFu3bqFR48eoW7dukhNTcXs2bMBAJ9/zoVLiIiIiIjo49SzZ0/Ex8dj+vTpiImJga+vL4KDg6URu5GRkdLoXgBo2LAhtmzZgh9//BE//PADPDw8sHfvXqV1mgF2nD85v/76K0JDQ6UhD//++y/Mzd/vjg8REREREX18hJ9wgOPIkSNLDc2W98Si7t27o3v37iUTKwk7zp+QWrVq4caNG6rOBhERERER0SdF+O4kRERERERERP+/OOJMRERERERUCVSWx1F9ijjiTERERERERKQAO85ERERERERECjBUm4iIiIiIqBIQMFJbZTjiTERERERERKQAO85ERERERERECjBUm4iIiIiIqBLgqtqqwxFnIiIiIiIiIgXYcSYiIiIiIiJSgB1nIiIiIiIiIgU4x5mIiIiIiKgSEHKKs8pwxJmIiIiIiIhIAXaciYiIiIiIiBRgqDYREREREVElwMdRqQ5HnImIiIiIiIgUYMeZiIiIiIiISAGGahMREREREVUCAkZqqwxHnImIiIiIiIgUYMeZiIiIiIiISAF2nImIiIiIiIgU4BxnIiIiIiKiSoBTnFWHI85ERERERERECrDjTERERERERKQAQ7WJiIiIiIgqASGfR6UyHHEmIiIiIiIiUoAdZyIiIiIiIiIFGKpNFSItp0DVWVC66jZ6qs6C0lWzM1B1FpQqr0Ci6iwoXWxWjqqzoHQGGhqqzoLSPcv89OvxZVK2qrOgdN2+G6LqLCjVrkWrVZ0FpfPv01PVWVA6a7NP//qmMmGgtupwxJmIiIiIiIhIAXaciYiIiIiIiBRgx5mIiIiIiIhIAc5xJiIiIiIiqgw4yVllOOJMREREREREpAA7zkREREREREQKMFSbiIiIiIioEhAwVltlOOJMREREREREpAA7zkREREREREQKMFSbiIiIiIioEhAwUltlOOJMREREREREpAA7zkREREREREQKsONMREREREREpADnOBMREREREVUCnOKsOhxxJiIiIiIiIlKAHWciIiIiIiIiBRiqTUREREREVBkwVltlOOJMREREREREpAA7zkREREREREQKMFSbiIiIiIioEhAwVltlOOJMREREREREpAA7zkREREREREQKMFSbiIiIiIioEhAwUltlOOJMREREREREpAA7zkREREREREQKsONMREREREREpADnOBMREREREVUCnOKsOhxxJiIiIiIiIlKAHeePxJkzZyAQCJCSklJqmvXr18PY2LjC8vTazJkz4evrW+HfS0RERERE9DFgqDZVChHnDyHs1G7kpCfDyNYFvl2HwdTJU27ap5eO4vm1U0iLeQ4AMLZ3R7X2fWXSX9/yG55fOyWzn5W3HxoPm6W8Qrzh6tG9uHBgGzJSk2Dt6IZ2A0bB3t2n1PQPLp/Bqe1/IyU+BmbW9gj4cgg8a9WXfi7KycaJLavw6PoFZKenwdjSBvUCu6BO607SNAdWL8aTezeQnpwITW0dOHhWRcCXQ2Fh56jUsr4p7NxBPDq5G9lpyTCxc4H/F8Ng5uwlN234hWA8u3oKKa8K69HUwR01O/aVphcX5OPuwU2IfnAdGYkx0NTWg5VXTdT8vD90jcwqrExvun/6AO4c3Yns1GSYObiiUe/hsHSRX76kqOe4vn8T4p8/RkZiHBr0HIoaAV1k0mye3A8ZiXEl9q3SvAOa9PlWKWV42+Wje3D+wDZkpCTB2skNHQaMVniu3r90Bie2r5Oeq236DIXXG+cqAMS9fI5jW1bh6cM7EIsLYGnnhN7fzYKxuRUAYO+qRYi4fxPpSQnQ1NaBo1dVtP1ymNLO1X+P7MKpvVuRlpIEO2c3dBs8Dk4eVUpNf+viKRzeugZJcTGwsLFHx6+Ho6p/A+nndy6fxYWje/EiIhRZGWmYuOhv2Lt4yBzj4rF9uPHvcbx4EgZRdhbmbzoCXT0DpZRPntsn9uPGkZ3ITE2ChaMrWnw1Atau3nLTJkQ9w6XdGxH3LBxpibFo1nsY/Np2LfXYVw9uw4Wd61CrdWc07zNcWUV4J4lEgq1//4Xjh/YgMyMd3tVq4ptxP8DWvvTz6MGdG9izbSMiwkKQnJiAyXMWoX7jFjJpOrfwk7tvv2Fj0KVXv3Itw7sEeJqhfRVLGOmoIzI5GxuvReFJYrbctLUdjNCpmiWsDLSgJgRi03JxOCQeF54my6SzNdRCLz8beFvqQygEolNFWHr2GRKz8iqiSB+kkZ8bxvUNgF8VR9hYGKHHuFU4cOauqrNVJl19bdC7jj1M9TQREZ+B305GICQmQ27adlUtMbWdbJsiyhej1ZIL0vdNPczQuaYNvKz0YaSjgf4bbiI8PlOpZXiXQG9zdKpmBWMdDTxPzsbayy8QnpAlN209J2N0rWEFawMtqAkFeJUmwoEHcTgXkSRNY6Stjq9q26GmnQH0NNXxMCYda6+8REyaqKKKpFqM1VYZdpzpo/fi1r+4u3cNanX/FqZOnnh8dj/Or5yONlP+graBcYn08eH34ODXFGYuPhCqayDs1C6c/2s6Wn//J3SMiztUVt5+qN17rPS9UF2jAkoD3L94Gkc3rUCHwWNh5+6Dy4d34Z/532Pk4g3QNzIpkT4y9D52LvsJAb0Hw9OvAe6dP4mgX6dj2IKVsHJwAQAc3bgcTx/cQtdvf4CxhTUi7l7HoXVLYGBiBu/ajQAANi6eqN64FYzMrJCdmYYzOzdg07xJGPv7ZgiFakov9/Mb53BrzxrU6fktzJy8EHpmH04vn44O01bKrce48Htw8m8Gf1cfqKlr4OGJXTi9fDo+++FP6BqbIz9XhKQXEagW2AvGdi7IzcrAzV2r8O/KOWg7aYnSy/O28GtncWn7KjT5ahSsXLxw98ReHFryI3rNWQ0dw5Lly8/NgYG5NVz9G+PS9lVyj9l16lJIxGLp+6So5zj02w9wq91EWcWQce/iKRzZuAKdBo+Dg4cPLh7eifXzJmHsbxtLPVe3L5uD1r2HwMuvAe5eOIktC6dhxIJVsHIsPFcTY6KwesZo+Ldoh5bd+0NLRxdxL59BXUNTehw7V0/UbBwAY3MrZGek4dTODVg/dyK++2NLuZ+rN8+fxJ6//0CPYRPg7FkFZw5ux4rZ4zH1960wMC5ZxqeP7mHj4lno8NUwVK3dEDfOHcfan6dgwsJ1sHVyBQDk5mTD1acGajVsiaAVP8v93lyRCN616sG7Vj0c/GdluZbpXUKvnMG5oFVo1W8UrF29cfPYHuz+dSr6L1gLXXnnqkgEIwsbeNZpijNbFec15kko7p05BPOi3yZV2hO0AQd3b8WYybNhZWOLLetWYNakb/H7+p3Q1NSSu09OTg5c3DwR0O5zLJg+QW6av3cdk3l/88oF/LFwNho0bVXuZVCknpMx+vjb4u8rLxGemIVAbwt839IVE/eHIk2UXyJ9Zm4+9t+PRXSqCPliCWrZGWJoAwek5eTj3qt0AIClviamtXXH2fAk7LoTi+y8AtgbayOvQFKhZXtfejpauBcWhY37LmHb4qGqzk6ZtfQyx8jmrvj1RDgevkpHDz9bLP6iGnqvu4GUUm5UZIjy8eXa69L3b9eMjoYa7kal4VRoPCa3lT/AUJEaupigX117rLoYicfxWWhf1RI/tnHH6N0PkZZT8jzNEOVj150YRKWKkC8Ww9/BCN82dkJqdh7uRBeep5NauaJALMHPJ58gO7cAHapZYkZbd4zdEwJRvrjEMYnKC0O1K5BIJMLo0aNhaWkJbW1tNG7cGNeuXSs1/fr16+Ho6AhdXV106dIFiYmJMp+/DqFeuXIlHBwcoKurix49eiA1NVUm3Zo1a+Dj4wNtbW14e3tj+fLlMp9///338PT0hK6uLlxdXTFt2jTk5ZV+ZzkiIgKurq4YOXIkJBLlN6aPz+yFc4O2cK4XAENrR/h1HwE1TS08v3Jcbvq6X0+AW+P2MLZzhaGVA/x7joJEIkbc4zsy6YTqGtA2NJG+NHX1lV4WALh0aAf8Wn6GWs3bwdLeGR0Gj4OGphZunTkiN/2VI7vhXrMuGnXsBQs7J7TsORA2Lh64enSvNM2LsAfwbdoWLlV9YWJpjdoBHWDt5IaoiEfSNLUDOsDZpyZMLK1h6+KJlj0GIi0xDilxMcouMgAg9PReuDVoC9f6rWFk44g6Pb+FuqYWnlySX48N+02ER9P2MLF3haG1A+p+WViPsaGF9aipo4eWI3+Co18TGFrZw9zFG/7dv0HSi3BkJpUcpVW2e8f3wKdJO3g3agMTWyc0/WoU1DW18OjCMbnpLV280KD7YLjXbV7qTRsdA2PoGplKX8/vXoGhhQ1sPKsrsyhSFw7tQO1W7eHfovBc7TR4PDQ0tXHjtPxz9eKRXfDwrYsmnXrB0t4JAUXn6uWje6RpTgSthWetegj86hvYunjAzNoOPrUbyXTE6wR0hEuVonPV1RMBPQciNTEOyUo4V88cCELD1h1Rv1V7WDu4oMewidDU0sblUwflpj97cAe8a9VDq85fwtreGe2/HAJ7F0/8e2RXcf6bByKwxwB41qxd6vc279gDrbt+DWfPquVepne5eXQ3qjULRNUmbWFm54SAfqOhrqmF++eOyk1v7eqFpr2GwKt+c6gruMGYm5ONIyt/RsCAsdDWrbjRc3kkEgkO7NyCHl8PRr3GzeHs5okxU2YjKSEeV86fKXU//3qN0GfQt6jfpGWpaUxMzWVeVy6cRTXf2rC2tVdCSUrXzsccp8OTcO5JMqJTRfj7ykuICiRo5m4qN31IbCauv0hDdJoIcRm5OBqagBcp2fCy1JOm6e5rjTtRaQi69QrPk7MRl5GLmy/T5HbEPybHLjzErOUHsf905Rhlfq1XbTscuBeDw/dj8SwxCwuPhyMnT4wO1axK3UciAZKy8qSv5Lc62EcfxmH9pUhcf56i5NyXTceqljgRloDT4Ul4mZqDVRcjIcoXo6WH/MiwBzEZuBqZiqjUHMSm5+Lww3g8T86Gj1XhNZqNoRa8LPWx6tILRCRkITpNhNUXX0BTTYjGLiVvdhKVJ3acK9CkSZOwa9cubNiwATdv3oS7uzvatm2LpKSkEmmvXLmCQYMGYeTIkbh9+zZatGiBn376qUS68PBwbN++HQcOHEBwcDBu3bqFESNGSD/fvHkzpk+fjrlz5yIkJATz5s3DtGnTsGHDBmkaAwMDrF+/Hg8fPsTSpUuxevVq/Pbbb3LLcPfuXTRu3Bhffvkl/vjjDwgEyo0XEefnIeVlOCw9a0q3CYRCWHr4IvF5aJmOkZ8rglhcUKJjnBB+HwenfYWj877BzR3LIcpMK9e8y81Lfh6in4bBtbq/dJtQKIRrdX+8DHsod58Xjx/CtbpsaKB7zTp4GfZA+t7BsypCb1xEWlI8JBIJnj64hcRXL+FWQ/6Fe25ONm6fCYaxpQ0MzS3LoWSKFeTnIelFOKy9fKXbBEIhrLx8kfDsUek7vnmMXBEkBQXQVBDOmpedBQgE0NSpmJsgrxXk5yH++WPY+fhKtwmEQtj7+CI2IqTcviP8yml4NWqj9L87oOhcfRIGt7fOVbfqfnjx+IHcfV6EPYRbNX+ZbR416+BF0bkqFosReusyzG3ssX7uRMwf0gV/TR2Oh9fOl5qP3Jxs3DwTDBNLGxiV87man5eHFxFh8Hzj70QoFMKzRm08C5Vfxqdh9+H11t+Vd616eBZ6v1zzpiwF+XmIffYYjlWKf1MEQiEcq9bCqwj5v0FldWrTH3CpWRdOVeWHMlek2FdRSE5KQA3/etJtevoG8PSphtAH5de5SklKxI3L5xHwWedyO2ZZqAkFcDHVxYOikWKgcOTxwat0uJvrlukYVa31YW2ohUexhWHBAgC+doaISRdhUktX/PlFFcwMdIe/vaESSkDqQgE8rQxkOrgSANcjU1DVtvR/cx1NNewcWge7htbF/M5V4GJWtvpWBXWhAK5murgbLXue3nuVLnPDRpHqNgawNdTCw6LzVEOtsP3LKygeWZYAyBNL4G1VsW2/qggqyX+fIoZqV5DMzEysWLEC69evR7t27QAAq1evxvHjx7F27VrUqVNHJv3SpUsRGBiISZMmAQA8PT1x8eJFBAcHy6TLycnBxo0bYWdnBwD4/fff0b59eyxatAjW1taYMWMGFi1ahK5dC+ejubi44OHDh1i5ciX69Suci/Xjjz9Kj+fs7IwJEyYgKChI+t2vXbx4ER06dMDUqVPx3XffleO/TulEmWmQiMXQNpC9i6htYIz0uJdlOsb9g+uhY2gKS09f6TYrb3/Y1mgIPVMrZCS+woNDm3Bh1Uy0GLMQAiWGLWelpUIiFpcIc9UzMkFCVKTcfTJSkuSmz0gtnpf22YBROLB6MRaP6AmhmhoEAiE6Dv0Ozj41Zfa7emwfjm9eiTxRDsxsHdD3h18UjiCVF2k9vhUGqm1gjPTYstXj7X3roWNkKtP5flNBXi5u7/8bTv5NoaFTsRcSORmF5dMxlK0nHUMTpMSUrXzv8uzWJYiyMuDVqHW5HO9dstJSIZZzruobmSAhuvRzVc+4ZPr0onM1My0FuTnZOLdvKwJ6DkTbPsMQdvsqti6ajoHTF8Oliq90vytH9+Lo5pXIFeXA3NYB/acuLPdzNTM9FWJxAQyMZUfoDIxNERf1XO4+6SlJJUK4DYxMkJZS8gboxyg7vfBc1TUyltmua2iC5FcvPvi4oZfPIO55OL6c/vt/zGH5SEkqjNAyNpGtWyMTMyQnJZTb95w6egA6urpo0LT0EWplMNBSg5pQgNS3Ql1Tc/JhYyQ/DB0AdDSE+L1rFairCSGWSLD+ahTuF82nNdRWh46GGjpUtcTO2zEIuhWNmraGGNPMGfOOR+BRnGrnyX5qjHQ0oC4UICkzV2Z7UmYunEx15O4TmZSNBcFhCI/PhL6WOnrXscOKL2vi679vID4jV+4+qmSgpV54nmbLnqcp2fmwM9IudT9dDSFW9qwODTUhxGIJ1lx+Ie18R6XkID5DhD7+dlhZNHrdoaolzPU0YaJbMVPu6P8XO84VJCIiAnl5eWjUqJF0m4aGBurWrYuQkJASHeeQkBB06SK7UFCDBg1KdJwdHR2lnebXacRiMUJDQ2FgYICIiAgMGjQIQ4YMkabJz8+HkZGR9P22bduwbNkyREREICMjA/n5+TA0lL3bGRkZidatW2Pu3LkYO3aswrKKRCKIRLILNOTn5crMYawooSd24MWtf9Hs23lQe+P7HfyaSv/fyNYZRjYuODp3COLD78uMblcWV4L34OXjh+g98ScYmVvhechdHF63FAYmZjIjhjUat4JbdX+kpyTi4sHt2LF0NgbO+h0amhVfN+/j4bEdiLx5Di1Hz5epx9fEBfm4sG4BIAHq9KiYRbMq2qPzR+FQrTb0jFWz8Fl5eD1f26d2QzRq3x0AYOPsjhdhD3D1+AGZjnPNJgFwq1Eb6cmJuHBwO7YtmYUhs//46M/V/0fpiXE4s2UFuk6cD3UV1c/Z44exYvFc6fsf5y+rkO89eWQ/mga0K3XO9McmJ0+MqYfCoKUhRFVrA/Txt0V8hgghsZl4Hchy80Uagh8V3lyITM6Bh4UuWnmaseP8EXjwKl0myuBedBo2D/DH5zVtsOaC/Jt9lVF2nhgT9z2CtoYQ1W0M0K+OHWLTRXgQk4ECCbDw1BMMb+SEDX1qokAswd3oNNx8mfqJjnHSx4Qd509YRkbhXeTVq1ejXr16Mp+pqRWOql66dAl9+vTBrFmz0LZtWxgZGSEoKAiLFi2SSW9hYQFbW1ts3boVAwcOLNGxftP8+fMxa5bs6tSNvxyJJn1GvXcZtPQMIRAKkZMuu+pnTnoKtA0Vz2UJO70boSd3ocnwOTCyVbxQjb65NTT1DJGREK3UjrOuoREEQqHMaDEAZKYmQ99Y/rw0fWNT+emLRgLzckU4GbQWvb6bDU+/wtWLrZ3cEPM8AhcPbpfpOGvr6kNbVx9mNvaw96iCnwd9jkfX/kX1Rspd1EZaj2kpMtvLUo8hJ3fj4YmdaDHyJ5jYlazH153mzKQ4tBw9r8JHmwFAW7+wfNlpsvWUnZZcYhT6Q6QnxiIq5DbajPjx3YnLia6hEYRyztWMd5yrmSkl0xsUnau6hkYQqqnBws5ZJo2FnSOeP7ons+31uWpuYw8HzyqYO7ATHl77FzXL8VzVMzCCUKiG9LdGiwtHleXfoDAwNkX6W2VMT02GYSn/Jh8bHYPCczUrNUVme1ZaMnTlLPhWFrHPwpGVloLNM4pvWknEYrwMu4fbJ/dj9JqDSl+AsG6jZvCsUk36Pi+3cN5nSnISTM0spNtTkxPh4i5/pfv39eDuTUS9eIYJ0xeUy/HeR7qoAAViCYy0ZS/jjLTVS4zuvUkCILZoZDIyOQd2RlroWNUKIbFPkC4qQL5YgqjUHJl9olJF8LIoW1gtlV1qdh7yxRKY6snebDLV00RiZtlWMC8QS/A4LgP2xqWP3qpSuii/8DzVkT1PjXXUkZJdehklAGLSCwdgniVlw85YG11qWONBTDgA4EliNibufwRdDSHUhUKkifIxv4MXIkpZqZuovHCOcwVxc3ODpqYmLlwofmRAXl4erl27hipVSj72xMfHB1euXJHZdvny5RLpIiMjER0dLZNGKBTCy8sLVlZWsLW1xZMnT+Du7i7zcnEp7IBcvHgRTk5OmDp1KmrXrg0PDw88f17yrqWOjg4OHjwIbW1ttG3bFunp6SXSvDZlyhSkpqbKvBr0GPbufyQ5hOoaMLZ3R3xY8Zw0iViM+Md3YOZU+sVP6MldCDm2DY2GzYSJo0ep6V7LSklAblY6tA2Ve/Grrq4BWxdPPL1/U7pNLBbjyf2bsPeU//gbB48qMukBIOLuddgXLSpUkJ8PcUF+iXmvQqFQZkXmEiQSSCQS5CtYCK68qKlrwNTBHTFhxQu0ScRixIbdgbmz/EfgAMDDEzvxIDgIzYfPgpmcenzdaU6Pj0aLkXOhpaeauXhq6hqwcPJAVMht6TaJWIyokNuwciv90U1lFXrhOHQMjeBYve5/PlZZqatrwNbVE0/ulTxXHTzkL2jl4FkFEW+dq+H3bsCh6FxVV9eAnZs3Et4KCU549RLGFqUvhgOJBJBIUFDO56q6hgYc3DwRdveGdJtYLEbY3Rtw9pJfRhfPagi7d11mW+ida3D2qiY3/cdGTV0DVs4eePHwlnSbRCzGi4e3YeNW+iO4FHGs4ouvf1qJr2avkL6sXDzhXb8lvpq9okJW7dfR1YONnaP05eDsChNTc9y9eVWaJiszA2Eh9+FVtUa5fOeJw/vg5ukDF/eKX7m4QCzB06QsVLUuXvNBgMJ5y6U95kceAQTSOaMFYgmeJGbBxlB29NzGQAsJmR9fGHBlly+WICw2Hf6OxtJtAgD+jsZ4EF22NVeEAsDVXO+jrZ/8onOquo3seVrdxgCh7xHBIASgISw5npyVJ0aaKB/WhlpwNdPFtciU/57pSkAgqByvTxFHnCuInp4ehg8fjokTJ8LU1BSOjo745ZdfkJWVhUGDBuHOHdkVn0ePHo1GjRrh119/xeeff46jR4+WCNMGAG1tbfTr1w+//vor0tLSMHr0aPTo0QPW1tYAgFmzZmH06NEwMjJCYGAgRCIRrl+/juTkZIwfPx4eHh6IjIxEUFAQ6tSpg0OHDmHPnj0lvud1GQ4dOoR27dqhXbt2CA4Ohr5+yYUYtLS0oKUl2/D+lzBtj+adcX3LbzBxcIeJkyfCz+5Dfm4OnOoFAACubV4MHSMzVOtQOGc79OROPDyyGXW/ngA9UyvkFI0CqmtpQ11LB/mibDw8uhV2NRpC29AEmQkxuHfgb+ib28DKW/mL2jRo3x17ViyArasX7Ny9cfnwLuSJclCrWSAAYPef82Foao6A3oXh9fXadcX62eNw8eB2eNSqj/sXTyH6SRg6Di2cZ66tqwcnn5o4tnkl1DW1YGxhhWcP7+DOuWNo+3XhM1STYqPx4NIZuNWoDV1DI6QlxuP8/q3Q0NSCR6168jNazrxadMblf36DqaMHzJw8EXpmH/JFOXCpX1iPlzYugo6xGXw79QcAPDy+E/cO/4OG/SZCz8xKOpqrrqUNDS0diAvycX7tfCS/iEDTYdMhkYilaTR19aFWQY8Xe6166y44s24RLJw9YOnihXsn9iIvVySdk3xq7a/QMzFDva4DABQu0pRcNFdYnJ+PzOREJERGQENbB0aWttLjSsRihF44Ds8GARCqKb8D8qZG7btj1/IFsHXzhL1b4eOockU58G9eeK7u/GMeDE0t0ObLwnO1YbtuWDNrLM4f2A4vv/q4e/EUoiNC0XlI8ZoITTr2xLYls+HsUwOuVWvh8e2rCL1xEQNnLAFQeK7eu3ga7jVrQ8/QGGmJ8Ti3byvUNbXgqYRztXnHXtj8+1w4unvD0cMHZw9sR64oG/VatgcA/LN0DozMLNDxq28AAM06dMeyaSNxat9WVPVviJvnT+BFxCP0/KZ4TYjM9DQkJ8QitWgubVzR+gWGxqYwNCkcyU5LTkRaShISXkUBAF49fwItHV2YmFtBz0C5N4D82nbF0dW/wtLFE9auXrh1bA/yRDmo2qQNACB41S/QNzFH4+4DARSeq4lFZSgoyENGciLinkdAU1sbxlZ20NTRhbm9s8x3aGhqQ0ffoMT2iiIQCNDxiy+xY9Ma2No5wrLocVSm5hao17i5NN208cNQv0kLtO/SCwCQnZ2FV1HFN3biXkXhSXgoDAwMYWFlI92elZmBi2ePY8Dw8RVWprcdCUnAsIYOeJqUhYiELAT6WEBLXYizRc+7HdbQAclZedh+u3A1+o5VLfE0MQuxGbnQEApQ084QjVxNsP5q8ToMhx/GYWRjJzyKy0RITAZq2Bqglr0h5h6PUEkZy0pPRxNuDsWRBc52ZqjhaYfktCy8iElWsKdqBV2PwtR2XngUm46QV+no4W8HHQ0hDt2PBQD82M4T8Rm5WPnvMwBA/waOeBCdhqiUHOhrqeHLOvawNtTCwXux0mMaaKvDykAL5vqF112ORfOlkzJzkaSCZ3EfeFB4TkUkZiE8Pgvtqxaep6cfF65DMKqJExKz8rDlRuEgUJfqVohIzEJMmggaakL42RuiqbsZVl8sXlujgbMx0nLyEZ9ROB98QF17XItMkT6uikhZ2HGuQAsWLIBYLMbXX3+N9PR01K5dG0ePHoWJScnwuPr162P16tWYMWMGpk+fjoCAAPz444+YM2eOTDp3d3d07doVn332GZKSktChQweZx00NHjwYurq6WLhwISZOnAg9PT1Ur15dOk+5U6dOGDduHEaOHAmRSIT27dtj2rRpmDlzptwy6Ovr48iRI2jbti3at2+Pw4cPQ09PuSFcDrWaQJSRiofBm5GTlgwjO1c0HjZLumBYVnK8zGjrkwtHIC7Ix+X1suFzPm17o0rglxAIhEiNfobIa6eQm51ZuHCYVy1U/axPhXS2qjVsgcy0FJze8TcyUpJh7eSGryb/LA1/TU2Ig0BQHAzi6FUN3UZNxalt63AyaC1Mre3Qa8Js6TOcAeCLMdNwcutq7P5jLrIz0mFkYYWWvQahdutOAApvXDx/dBeXj+xCdkY69I1M4ORTA4NmL5P7PF5lcPJvClFGKu4d+gc56ckwsXNF8xGzpaHMhfVYXO7w84chzi/sHL+pWrveqP5ZH2SlJCLqXmFURvDPo2XStBw9D1Ye5TOqVFbudZohJz0V1/f9g6y0JJg7uOGzMXOgW1S+jKQ4mfM0KyUJu+aMlL6/e2wX7h7bBRvP6ug08Rfp9pcht5CRFAevRm0qrjBFqjdsicy0VJzcvh4ZKUmwcXZDvynF52pKYhwEQtlztceoH3Fi2zocD1oDM2s7fDlxjvQZzgBQpW4TdBoyDuf2bsGhv3+Hua0Deo+fBWfvwkdsFZ6r93DxyC7kZKRDz9gEzt41MHTO70o5V/0at0JGWgoOb12DtJQk2Lu445tpi6Sh18kJsTJldPGujr7jZuDwltU4uHkVLGzsMej7+dJnOAPA/WvnseWPedL3GxbPAAAE9hiAdr0GAQAuHN2L4O1/S9Ms+7EwzPnLkT+gXsvPyr2cb/Kq1xzZ6am4tGcjslKTYeHoii7fzYVe0b9veqLs32JGciI2zyh+WsON4J24EbwT9l410H3KQqXm9b/o0qsfcrKzsXzRT8jMSIdPdV9M//kPmfnIMdEvkfZG2Hp46ENMG1f8HOB1yxcDAFq07Ygxk4unIP176igkEqBJy7bKL0gprjxPgaGWGrrVsIaRjjqeJ2fjl1NPpc/GNdfTxJtPjNRSF6J/XXuY6mogt0CM6DQRVlyIxJU3VnW+/iIN665GoVNVS/StbYdXaSIsPfcMYfEf9/xmvypOOLZmjPT9LxO6AQA27b+MoTP+UVW23ulUaAKMdTUwuJETTHU1ER6fge92PpA+YsrKUAviN+rQQEsd37f1gKmuJtJF+QiNzcA3W+/gWWJxlEFjN1NMbVcckTe7Y2HU07qLz7HuovyFHZXp4tNkGGqro1ctGxjraOBZUjbmHguXLmxnrqcpU0YtDSGGNHCAqa5m4XmamoNl557h4tPiGyAmOhroV9ceRtqFId9nw5Ow807FPFqT/r8JJBXxIF5SipkzZ2Lv3r24ffu2qrPyTj8cDlN1FpSuus2nPwcsNP7Tnj9kqF2xI7qq4GD4cc6FK08GGp/+yqrP0j7ujkx5aOZk8e5EldzcU+GqzoJS7Vq0WtVZUDr/Pj1VnQWlszb79K9vdg5Q/WP0yupOZOUYWa/pWPrjQysrznEmIiIiIiIiUoAdZyIiIiIiIiIF2HGuxGbOnFkpwrSJiIiIiKgcCCrJ6xPEjjMRERERERGRAuw4ExERERERESnAjjMRERERERGRAnyOMxERERERUSUg+FQnEFcCHHEmIiIiIiIiUoAdZyIiIiIiIiIFGKpNRERERERUCQgYqa0yHHEmIiIiIiIiUoAdZyIiIiIiIiIFGKpNRERERERUCTBSW3U44kxERERERESkADvORERERERERAqw40xERERERESkAOc4ExERERERVQac5KwyHHEmIiIiIiIiUoAdZyIiIiIiIiIFGKpNRERERERUCQgYq60yHHEmIiIiIiIiUoAdZyIiIiIiIiIFGKpNRERERERUCQgYqa0yHHEmIiIiIiIiUoAdZyIiIiIiIiIFGKpNRERERERUCTBSW3U44kxERERERESkADvORERERERERAqw40xERERERESkAOc4ExERERERVQac5Kwy7DhThVATfvp/5Vl5BarOgtLFZeSpOgtKVcVST9VZUDpno0+/jJejk1SdBaUz0dZQdRaUzsZEW9VZULrs3HxVZ0Gp/Pv0VHUWlO7G5m2qzoLS1ejRXdVZIPooMFSbiIiIiIiISAGOOBMREREREVUCAsZqqwxHnImIiIiIiIgUYMeZiIiIiIiISAGGahMREREREVUCAkZqqwxHnImIiIiIiIgUYMeZiIiIiIiISAF2nImIiIiIiIgU4BxnIiIiIiKiSoBTnFWHI85ERERERET00UtKSkKfPn1gaGgIY2NjDBo0CBkZGQrTjxo1Cl5eXtDR0YGjoyNGjx6N1NTU9/5udpyJiIiIiIjoo9enTx88ePAAx48fx8GDB3Hu3DkMHTq01PTR0dGIjo7Gr7/+ivv372P9+vUIDg7GoEGD3vu7GapNRERERERUGfwfx2qHhIQgODgY165dQ+3atQEAv//+Oz777DP8+uuvsLW1LbFPtWrVsGvXLul7Nzc3zJ07F1999RXy8/Ohrl727jBHnImIiIiIiKjciEQipKWlybxEItF/OualS5dgbGws7TQDQEBAAIRCIa5cuVLm46SmpsLQ0PC9Os0AO85ERERERERUjubPnw8jIyOZ1/z58//TMWNiYmBpaSmzTV1dHaampoiJiSnTMRISEjBnzhyF4d2lYceZiIiIiIioEhBUkv+mTJmC1NRUmdeUKVPklmny5MkQCAQKX48ePfrP/3ZpaWlo3749qlSpgpkzZ773/pzjTEREREREROVGS0sLWlpaZUr73XffoX///grTuLq6wtraGnFxcTLb8/PzkZSUBGtra4X7p6enIzAwEAYGBtizZw80NDTKlLc3seNMREREREREKmFhYQELC4t3pmvQoAFSUlJw48YN+Pv7AwBOnToFsViMevXqlbpfWloa2rZtCy0tLezfvx/a2toflE+GahMREREREdFHzcfHB4GBgRgyZAiuXr2KCxcuYOTIkejVq5d0Re2oqCh4e3vj6tWrAAo7zW3atEFmZibWrl2LtLQ0xMTEICYmBgUFBe/1/RxxJiIiIiIiqgQE/8ePowKAzZs3Y+TIkWjVqhWEQiG6deuGZcuWST/Py8tDaGgosrKyAAA3b96Urrjt7u4uc6ynT5/C2dm5zN/NjjMRERERERF99ExNTbFly5ZSP3d2doZEIpG+b968ucz7/4Kh2kREREREREQKcMSZiIiIiIioEvg/j9RWKY44ExERERERESnAjjMRERERERGRAgzVJiIiIiIiqgwYq60yHHEmIiIiIiIiUoAdZyIiIiIiIiIF2HEmIiIiIiIiUoAd50+MQCDA3r17VZ0NIiIiIiIqZ4JK8t+niIuDKVF8fDymT5+OQ4cOITY2FiYmJqhZsyamT5+OvLw8tGjRQuH+p0+fRvPmzSsmsx+5x/8eROip3chJS4axnQtqdRsGMycvuWkjLgbj+bVTSH31HABg4uCO6h36lpr++rY/8ORiMHy7DIFn88+VVoZ3uXl8H64e3oHM1CRYOrghoO+3sHHzlps24eUznN+1ATHPHiMtIRYt+wxH7cCuMmlunTiA26cOIDU+FgBgbu+Ehp2/gmvNukovS1k1dTVBaw8zGGqr42WqCNvvvMLz5By5aX1tDdDWyxwWeppQEwoQl5GLk48TcfVFagXnunRXju7B+QPbkJGSBGsnN7QfMBr27j6lpr9/6QxObl+HlPgYmFrbo22fofCsVV/6+bSe8n8j2vYZhsadepV7/stKIpFg16ZVOH1kL7IyM+BZpQYGjPoe1naOCvc7vn8HDu38B6nJiXB09UDfERPg5lVV+nls9EtsWbMUYQ/uIC8vDzX866PfiAkwMjFTdpFk3D25H7eCdyIrNRnmDq5o2mcErFzl/34kRj3Dlb2bEP/sMdIT49C41zD4tulSIl1GcgIu7liL5/euIz9XBCNLW7QaOB5WLp7KLk6ZXDu2F5cObkdGahKsHN0Q2G8U7Nzl//7EvXyGszvW49XTMKQmxKLN1yNQr123Cs7xu0kkEqxa8Qf27d6BjPR01PCthUk/TIejk3Op++zaHoTdO4IQHR0FAHB1c8egocPRsHFTaZrhg/rh5o1rMvt1+aIHJv84UxnFeC+BPhb4vJoVjHU08Cw5G2svRSI8Ieud+zVyMcH4Fq64+jwFP5+MqICclk1XXxv0rmMPUz1NRMRn4LeTEQiJyZCbtl1VS0xtJ/t3KsoXo9WSC9L3TT3M0LmmDbys9GGko4H+G24iPD5TqWUoL4383DCubwD8qjjCxsIIPcatwoEzd1WdrTL5ws8Wfeo5wExfE4/jMrDoWDgevkqXm7Z9dStM7yD72yPKF6Ppwn+l7wc3dkLrKpawMtBCXoEYj2Iy8Ne5p3gQLf+YROWFHWcl6tatG3Jzc7Fhwwa4uroiNjYWJ0+eRGJiIgIDA/Hq1Stp2jFjxiAtLQ1///23dJupqakqsv3Ribx5Dnf2rIF/j29h6uyFx2f24dyK6Wg3dSW0DYxLpI8PvwdHv2Ywc/GBmoYGHp3YhXMrpqPt5D+ha2wuk/blnYtIeh4KHSPV/luHXD6D01tWos2A0bBx88H14N3Y/ssUDP5lHfSMTEqkz8sVwcjSBl51m+LU5r/kHtPA1BxNewyCibUdIAHunz+G3b/NQP+fVsDc3lnJJXo3fztDdKtuha23X+FZUjZaupthVCMnzDwejgxRQYn0mbkFCA5NQGy6CPliCapbG+Brf1uki/IREqf6C597F0/hyMYV6DR4HOw9fHDp8E5smDcJY37bCH05dRgZeh87ls1B695D4OnXAHcvnMSWhdMwfMEqWDm6AAAmrdwls8/jW1ewd+VCVKnXtMTxKtLBHRtxbN82DJswAxZWtti5cSV+njoaP6/aBk1NLbn7XD57HJtXL8GAUZPh7lUVwXuD8PPU0Vi4ZgeMjE2Rk5ONn6eOgqOLB35YsBwAsHPjX1g04zvMXLIOQmHFBEg9vnoW57etRvOvR8Ha1Qu3j+/F/sVT0WfeGugaGpdIn58rgpGFNdxrN8H5oJVyj5mTmY5d88bDzrsmOo37CToGRkiJjYK2nr6SS1M2Dy6dxvF//sJnA8fCzt0bV47sxpYF32PEovVyf3/yRTkwsbSBT72mOP7PChXkuGw2rV+L7Vv+wfQ582BrZ4+Vy5dhzIihCNp9AFpa8s9TSysrjBg9Dg6OTgCAQ/v3YuLYkdgUtAuu7h7SdJ937Y5hI0ZK32tp6yi3MGXQ0MUE/evaY+XFSDyOz0SHqpaY1tYDo3Y9QFpOfqn7Wehrol9dezyM+bg6HS29zDGyuSt+PVHYyerhZ4vFX1RD73U3kJKVJ3efDFE+vlx7Xfpe8tbnOhpquBuVhlOh8Zjc9uO4aVVWejpauBcWhY37LmHb4qGqzk6ZBfhYYEwrN/wcHIYH0enoVccOS3tWR49V15BcWj3m5KP7qqvFG96qyMikbPx67DGiUnKgpS5E7zr2WNazBrr9dRUp2fKPSVQeGKqtJCkpKfj333/x888/o0WLFnByckLdunUxZcoUdOrUCZqamrC2tpa+dHR0oKWlJbNNU1OzxHFzc3MxcuRI2NjYQFtbG05OTpg/f36p+bh37x5atmwJHR0dmJmZYejQocjIKL5b279/f3Tu3BmzZs2ChYUFDA0N8c033yA3N1eaRiwWY/78+XBxcYGOjg5q1qyJnTt3lu8/mAJhZ/bCtWFbuNRvDSNrR/j3+Bbqmlp4evm43PT1+06Ee5P2MLF3haGVA2r3HgWJWIy4sDsy6bJSEnBr10rU+3oCBGqqvYd0/cgu1GjeDtWbBsLczgltB4yBhpYW7p07Kje9jasXWvQeCp8GLaCmoSE3jbtfA7j51oOptT1MbezRtPtAaGrrIDo8RJlFKbOWHma48CwFl5+nIiY9F1tvvUJugRgNnYzlpn+ckIU70emISc9FQmYeTkckISotB27muhWb8VJcPLQDtVu1h1+LdrC0d0bHweOhoamNm6ePyE1/6cguuPvWReNOvWBp74SAngNh4+KBK0f3SNMYGJvKvEKuX4BLVV+YWtlWVLFKkEgkCN4ThM97D4R/g2ZwdPXANxNnIiUxATcuni11vyO7t6BFYGc0a9MRdk6uGDBqMrS0tHH26AEAwOMHdxAf+wpDv5sOBxd3OLi4Y9iEmXj6OAQPb18v9bjl7fbR3ajaNBBVmrSBqZ0TWvQdBXVNLYT8K/9v0crFC416DIFnveZQU5f/t3jz8A7om1ogYNB3sHL1gqGFNRyr+cPIUnX1+KbLh3eiVovP4Ns8EBb2zmg/aCw0tLRw+2yw3PS2bt4I6DMM1Rq2LLXMqiaRSBC0eSMGDBmGZi1awcPTCzPnLEBCfBzOnj5Z6n5NmrVAoybN4OjkDEcnZwwfNRa6urq4f092ZE9bWxtm5hbSl76+6m+CdKxmhROhCTj9OBEvU3Kw8kJk4YirZ+kRG0IBMLaZC7bdjEZsuqgCc/tuvWrb4cC9GBy+H4tniVlYeDwcOXlidKhmVeo+Egnwv/buO6ypew8D+Juw91SWTBkuFHDvWbfWUfceWOus2w63dbVu66i4a6tWrVq17tFbN4q4ZQqooGwIe5z7BzWaglQlcEh8P/fheeSXk/CeCz3J9/xWQnqO/OvfhdnJBy+x/Uok/COSSjm98p269ADz1h/FkfOq0cv8Sr96lXA4MBpH775AeHw6lpwIRmZuPrrUtH7rcwQACWk5r7/+9Xs89eAlbjxJwvOkTITHpWP12VAY6mrCtaJBKZ9N+SCRqMaXOmLhXEoMDQ1haGiIQ4cOIStLeW9Ga9aswZEjR7Bv3z48fvwYu3fvhpOTU5HHpqWloV27djAzM8ONGzfw22+/4cyZMxg3bpzCcWfPnsXDhw9x4cIF/Prrrzh48CDmzZsnf3zx4sXYuXMnNm7ciPv372PSpEkYOHAgLl58+4dkZcnLzUFiVAis3L3kbRKpFBXdvRD/5NG7vUZ2FoT8PGjrG8nbhPx8XP95BTxa9YCJjaOyY7+XvNwcxDwJglN1H3mbRCqFY3UfPA95oJSfkZ+fh4dXziMnKxO2btWU8poloSEBHEx18fiNnmIBwKOXaXA2f7dC2KOCAawMdd5pGGJpy83NwfOwILh41pa3SaVSVPb0QVTw/SKfExX0AJVr1FZoc61VF5FBRR8vS0pAUMBV+LTsqLzgHyA25jmSE+NRw/v1kH99A0NUrlIdwQ/vFvmc3JwchAc/QnXvuvI2qVSK6t51EfLPc3JyciCBBFpar28YamlpQyKR4vH926VzMv+Sl5uDlxHBsK/mLW+TSKWoVM0bMaEffsMp/PZVVHRyx5/rF2LLxD7YM3cs7l8s+oZKWcvLzUF0eBCcayhef5xr+OBpsHKuP2J4/uwp4uPiUK9+Q3mboZERqnvWxN3A2+/0Gnl5eTh14jgyMjJQo2YthcdO/nkUbVs0Qr+eXfHjmhXIzMhQZvz3pimVoLKFPu48T5G3CQDuPE+Fe4W3F/W9vGyQnJmDs8HxZZDy3WlKJXC3MlIocAUA/pFJqG5r/Nbn6WlrYP+oujgwqh4Wd6sGZ4vycWP1Y6UplaCKtRGuhyfK2wQAN54kwtOu+N/joTH1cWRsfXzfszqci7lBrimVoJuXDVIzcxH8suhh/ETKwqHapURTUxPbt2+Hr68vNm7cCB8fHzRv3hx9+/ZFzZo1P/h1IyMj4ebmhiZNmkAikcDR8e1F3y+//ILMzEzs3LkTBgYFd+HWrVuHLl26YOnSpbCyKrhrq62tja1bt0JfXx/Vq1fH/PnzMW3aNCxYsAA5OTlYtGgRzpw5g4YNCz6AuLi44O+//8amTZvQvHnzQj83Kyur0M2C3OxsaBbRg/5fstNSIOTnQ+dfQ7J1jUyR+vLpO73GnSPboWtsDisPL3nbo7P7IZFqwK151/fOpGzpqckQ8vOh/68hkQbGZkh4HlWi146NCsfP8yYgNycb2rp66DZxDiztxL1RAACGOprQkEqQkqU4fDA1KxdWRkUPoQQAXU0pFnV0h5ZUgnxBwJ7bMXhUDoZpp6ckIz8/v9CQbEMTM8Q9jyzyObKkBBiaFj5elpxY5PEBF09CR1cf1eqJO0w7KbHgA7axqeL0BmNTcyQnFv3hOzUlCfn5eTD513NMTM0RHVWwFoFrlRrQ0dXFnq3r0HvoGAgQsHfrOuTn5yEpoWw+1GekFlxv9P41JFvf2BRJ0R/+32JKbDTunT8Kr3Y9UKdTX7wID8Jfv2yAVFMTVRt/UsLUJfPq+vPvv10DEzPElfD6I6b4uDgAgLmF4vQcc3MLJMTHFfvckOAgjBzcD9nZ2dDT08fSFWvgUtlV/njbDp1gY2sLywoVERL0GOtWr0DkkydYumKN8k/kHRn9c01NylC8piZn5MDOVLfI51SxMkBrd0tMOVT+bpCY6GlBUypBQlq2QntCWjYczYseFh+ZkIElJ4IQEpsGQx1N9Ktrhw39a2HQtpuIlWUX+RwqXab6//we/9VjnJCWA8e33NSISMjAwmOPEfJSBkMdTQyobw+/Qd7o53cDL1Nf/x4bu5pj4afVoKslRZwsG+P33EFyxtunJBApA3ucS1HPnj3x/PlzHDlyBO3bt8eFCxfg4+OD7du3f/BrDh06FLdv34aHhwcmTJiAU6dOvfXYhw8folatWvKiGQAaN26M/Px8PH78WN5Wq1Yt6Ou/voA1bNgQMpkMUVFRCAkJQXp6Oj755BN5L7qhoSF27tyJ0NCiFxBZvHgxTExMFL4u7St6Hm5pe3j6N0QF/IXGI76Bxj89WQlRIQi+eAT1BnwJibqOJfmHuU0lDP1uIwbNXQuvVl1w/KfvEfcsQuxYHywrNx+Lz4Zi6fkwHLn/Ej09reBWToZql7ZbF/5EzSZtoPUBN6BK4tK5ExjRrbn8Ky+3dD6YGJuaYcI3ixFw7X8Y2b05RvVohXSZDE6uVSCVqvZ/p4IgoIKjKxr2HIYKjq6o0aIjqjdrj3sXjokdTW2cOPYHWjSsLf/KLcHfqaOTE3btPYgtu/agR+8+mD/7a4SFhsgf7/5ZbzRo1ASubu5o36kL5i5cjAvnzuBpVNE3ycojXU0pJjRzxoZLEUgtYk0JVXQ/OhUnHrxESGwabj9NxteHHyIpPQef1rIROxq9h3vPUvDnvRcIfpmGgKhkzDh4H4kZOejurTi15WZEEgZt9YfvzgBcDUvAom5VYaZfPqeOKJtERb7UEXucS5muri4++eQTfPLJJ5g1axZGjhyJOXPmYOjQoR/0ej4+PggPD8eff/6JM2fOoHfv3mjTpk2pzTl+NR/62LFjsLOzU3jsbYurfPXVV5g8ebJC2+ILH9ZzoW1gDIlUiqzUJIX2zNQk6BoVXrTmTY/OHcSjs/vRfMxCmNo5y9vjQu8jU5aMo3OHyduE/HwEHtqCoIuH0XnO1g/K+qH0jUwgkUqR/q+exrSURBiYFn+O/0VDUwtmVgW/N2tnd8SEP8bNk7+j3fAvS/S6JSXLykVevgBjHcVLkJGOZrGL2AgAYtMK7lw/Tc6CtbEO2nlYIjhO3A+s+sYmkEqlhXqLZcmJMDQteuE5Q1NzyJKKOL6IxZiePLyDuOdR6D1xtvJCvyOfBk1Rucrrla9z/1n/ICUpAWZv9OalJCXAwaXoxXaMjE0hlWogOSlBoT05KUFhxWzP2g2wYtvvSE1OglRDAwaGRhjbrz0qWJdNr6yeUcH1JiMlSaE9PSWp0IiQ92Fgag5zW8UVx81sHRB689JbnlF2Xl1//v23m1bM32551LRFK1T3fD2aK+efv9OE+DhYVqggb09IiIebe9Grhb+ipaUtXxysarXqeHj/Hvb+sgtfzZpX5PGvfu7TqEhUsi9+ZfnSkvrPNdVUT/GaaqKnVeRCWtbGOrAy0sFXbV73pL+6j7xvqA/GH7iHF6ni9dImZ+QgN1+AuYHijUJzA23Ep73b4k95+QKCX8pQ6S097lT6ktL/+T3+q6A1N9BCwjuOAsjLFxAUI0MlM8WRBpk5+XiamImniZm49zwV+z+vi661rLHjiuqOlKHyjz3OZaxatWpISyvZ0FJjY2P06dMHmzdvxt69e3HgwAEkJCQUOq5q1aoIDAxU+HmXLl2CVCqFh8frLRsCAwOR8cb8rKtXr8LQ0BD29vaoVq0adHR0EBkZCVdXV4Uve3v7IvPp6OjA2NhY4etDhmkD/xR+9q548cbCXq8W+rJwevuHn0dn9+PhyT1oNnoezB3cFB5zrNsS7aavRdtpa+Rfeibm8GjVA81Gz/+gnCWhoakFayd3RDwIkLcJ+fmIuB8AW1flzkcW8gXk5Yg/ZC1PACKTMuHxxkIeEgAeFQ0QnvDuc5YlKJjfJDZNTS3Yurgj7O4teVt+fj7C7t2CvVv1Ip9j714NYfduKbSF3r0JB/fCx986fxy2Lu6wcXIt9Fhp09M3gLWtvfzLztEFJmYWuH/79XY86WkyhD66D7eqnkW+hqaWFpzdqig8Jz8/H/dv+8O1iOcYmZjCwNAI92/fQEpSInwalM3wdA1NLVR0dEPUw9vyNiE/H08f3oZ15bdvK/ZfrF2rITFGcWpJUswzGFlU/ODXVBYNTS3YOLvjyX3F60/4/QBUKgfrIbwrAwMD2Ds4yr+cK7vCwtISN65flR8jk8lw/+4deNbyeq/Xzs8XkJP99mIt6FHBehsWlhXeekxpy80XEBqfDs835v9KANS0NUJQbOF5n8+SM/HlwfuYcuiB/Ms/Mhn3olMx5dCDdy5OS0tuvoCgF6mo7WAqb5MAqO1givtvzOMujlQCuFgaIC5N/Pe8j1VuvoBHMamo6/T6xqMEQF1HM9x99u6/x8oVDRD3H4W2RCKBlgbLGipd7HEuJfHx8ejVqxeGDx+OmjVrwsjICP7+/li2bBk+/fTD9wpesWIFbGxs4O3tDalUit9++w3W1tYwNTUtdOyAAQMwZ84cDBkyBHPnzkVsbCzGjx+PQYMGyec3AwUrdY8YMQLffvstnjx5gjlz5mDcuHGQSqUwMjLC1KlTMWnSJOTn56NJkyZITk7GpUuXYGxsjCFDhnzwubwr9xbdcH33Spg7uMHcwR1BFw8jNzsTzvXbAACu/bwceiYWqNllKADg4Zn9uH/8ZzQYPA365lbISCnoSdHU0YWWjh50DIyhY6C4KIVEQxO6xmYwtqpU6udTlDodeuL4T8tg7ewOGxcP+J/8HTlZmfBs1g4AcGzjUhiaWaJ5nxEAChb0eTXkOi83B6mJcXgREQJtXT15D/PFvVvgUqsujC0qIjszAw8un0Pko0D0nvb2VdjL0rngeAyuY4uIxAxEJGagpasFdDSkuPLPYjBDatsiKTMXh++/BAC0c7dARFImYmXZ0NKQoLqVIeo7mOLX29HF/JSy06hTLxxcvwR2ld1hV7lgO6rsrEz4tGgPANi/bhGMzSugbX9fAEDDDj2xZd6XuPTHPrj7NMDdy+fwPPQxPvWdovC6melpuHf1ItoP+qLMz6koEokE7bv3xaFft8LK1h4VrW2xf+dGmFpYonaj12seLJo5BnUatUDbrr0BAB169MemH+bB2a0qKntUx4nf9yArMwPN23aWP+fiqT9gZ+8EIxMzBD+8i583Lkf77v1ga1928/K92vXAGb8fUNHJDVbOHgg8/TtyszJRtUlbAMDpzd/DwMwCjT4bDqDgv7+Ef+ax5+XmIi0pDrGRodDS0YPpP6ufe7XtjgOLJsP/6B641m2GF+GPcf/icbQcMrHMzqs4DTp+hsMbl8LGxR22lavg+p8HkJOZiVrNC64/h9YvgZG5JVr3HQmg4Jxjn766/uQiNSEOMU8Krj/m1nZv/TllSSKRoO+Awdi2eRPsHRwLtqP6cQ0sK1RE85at5ceNHTUMLVq1Qa++AwAAP65ZgUaNm8HK2gbp6Wk4+edR3PK/jtXrNwMo6FU++ecxNGrSDCYmpggJfoxVPyyFd+06cHMveq/vsvLHvRcY39QJoXFpCI5NR+fqFaGjKcW5oII1AsY3c0JCWjZ233yOnDwBUUmZCs9Py84FoFmoXSx7/J/hmw4eePQiFQ+jU9G7th30tKQ4du8FAODbDu6IlWVj0/+eAACGNnTA/ecpeJaUCUMdDfSvWwnWxjo4eveF/DWNdDVhZaQDS8OCm/kO/8yXTkjLLjQPt7wx0NNGZfvXN2ec7CxQ090OiSnpiIopem2M8uDX608xu3MVPIxJxYN/tqPS1ZLi6J0YAMCczh6ITc3G+ovhAIARjR1x73kKohIzYKSjiYEN7GFtrIMj/7zX62pJMayRI/4XHIc4WTZM9bXwmY8tKhjp4OyjWNHOs0yJ31/w0WLhXEoMDQ1Rv359rFy5EqGhocjJyYG9vT18fX3x9ddff/DrGhkZYdmyZQgODoaGhgbq1q2L48ePF7nHqb6+Pk6ePImJEyeibt260NfXR8+ePbFixQqF41q3bg03Nzc0a9YMWVlZ6NevH+bOnSt/fMGCBahQoQIWL16MsLAwmJqawsfHp0Tn8T4cfJohS5aMe8d/RmZKIkwruaDZ6PnQNS64g5meGAuJ5PX5h146jvy8XFzeplggVmvfDzU6DCiTzO+raoMWyEhNwt8HdiAtOREVHSqj17RF8j1UU+JfKszHliXGY8e3rwupG8d/w43jv8G+Sk30+2Y5gILhpcc2LUNaUgJ09AxQwcEZvacthpOn4krOYrn5LAWGOhroXK0CjHU08TQ5C+suRcrn25npayH/jeO1NaXo62UNUz0t5OQJeJGahe03nuHmO961Lm2ejVohLSUZZ/dthywpATZOlTH4q6Xy4a7J8S8V/jt18KiBXuO/xZm9W3F6jx8srO3Qf9oC+R7Or9y9fA4QBNRs3KpMz6c4nXsNRlZmJrauWYR0mQzu1Wth+sLVCns4v3z+DKnJSfLvGzT/BCnJiTiw6yckJ8bD0cUd0xeuVhiqHf00Avu2/QhZagoqWNmga99h6NCjf1meGtzqNUdGajKuH9qFtOREVLB3QZdJC+VDtVMTXkLyxiiHtKR47J07Vv59wIkDCDhxALYenugx43sABVtWdRg7G1cObMONI7thXMEaTfuNhkfD8vE7rd6wJdJTknFx/3bIkhJh5VgZ/WcugeE/+9unxCuec2piPDZ//bn8+yvH9uHKsX1wrFoLg2etKPT6Yhk0dAQyMjKweMEcyFJTUcvbB6vX/6QwzehZVBSSEl8XHYkJCZj37UzExcXC0NAIru7uWL1+M+o3bAQA0NLSwo1rV7Bn905kZmSgopU1Wrb+BMN8R5f5+f3b5fBEmOhqoq+PLUz1tBCekIGFp4KR/M/0F0sDbQjCv3c2Lr/OPY6Dqb4WRjZ2hLm+NkJiZZiy/758iykrYx3kv3E6RjqamNHODeb62kjNysXjFzKM/jUQT+Jfj2JqUtkc33R4fYNjfpeCkSRbL0dg6+XyPUfdp5ojTvm9vtm2bGpPAMCuI1cxas7PYsX6T2cexsJUXwujmjrBwkAbQS9l+HLfXfmNCitjXcXfo64mvurgDgsDbaRm5uJRTCp8d91G+D+/x/x8AY4WeujoWR2melpIzsjBw+hUfP7zbYSXg102SL1JBFW6ipLSDR06FElJSTh06FCp/pxZJ4JL9fXLAyfzt68GrS5uPlXvrR5auJiKHaHUOZuo/z6XV58Xnrqibsx01X8RnM7V1H9RpxG/3hY7Qql68R5Tb1TVzd17xY5Q6mr27iV2hFJ37avCu8SUV0/iy8eokP/iZKF+6wtwMgARERERERFRMThUm4iIiIiISAVIOMlZNCycP3Il2VOaiIiIiIjoY8Ch2kRERERERETFYI8zERERERGRCpBwpLZo2ONMREREREREVAwWzkRERERERETF4FBtIiIiIiIiFcCR2uJhjzMRERERERFRMVg4ExERERERERWDhTMRERERERFRMTjHmYiIiIiISAVwOyrxsMeZiIiIiIiIqBgsnImIiIiIiIiKwaHaREREREREKoFjtcXCHmciIiIiIiKiYrBwJiIiIiIiIioGh2oTERERERGpAK6qLR72OBMREREREREVg4UzERERERERUTFYOBMREREREREVg3OciYiIiIiIVACnOIuHPc5ERERERERExWDhTERERERERFQMDtUmIiIiIiJSAdyOSjzscSYiIiIiIiIqBgtnIiIiIiIiomJwqDYREREREZEKkHBdbdGwx5mIiIiIiIioGOxxpjKRly+IHaHU3XwqEztCqQuJThE7Qqlq4GAkdoRSZ26oLXaEUhcryxU7Qqkz19USO0Kp09XSEDtCqUvNyBE7QqmytjAQO0Kpq9m7l9gRSt2dfb+JHaH0fdVc7ASkAtjjTERERERERFQM9jgTERERERGpAk5xFg17nImIiIiIiIiKwcKZiIiIiIiIqBgcqk1ERERERKQCOFJbPOxxJiIiIiIiIioGC2ciIiIiIiKiYnCoNhERERERkQqQcKy2aNjjTERERERERFQMFs5ERERERERExeBQbSIiIiIiIhUg4braomGPMxEREREREVExWDgTERERERERFYOFMxEREREREVExOMeZiIiIiIhIFXCKs2jY40xERERERERUDBbORERERERERMXgUG0iIiIiIiIVwJHa4mGPMxEREREREVExWDgTERERERERFYNDtYmIiIiIiFSAhGO1RcMeZyIiIiIiIqJisHAmIiIiIiIiKgYLZyIiIiIiIqJicI4zERERERGRCpBwQyrRsMeZiIiIiIiIqBgsnImIiIiIiIiKwaHaREREREREKoDbUYmHPc5ERERERERExWCPcznWokULeHl5YdWqVWJHASBuntC/jyHo3EFkpibCxNYZXj0+h7mje5HHhl85iYgb55ASEwEAMK3kihqdBisc7//LSkTcOKfwPKsqPmjy+bzSO4n31MzFDJ+4WcBYVxNPk7OwLzAaEYmZRR7rZWuEdh6WqGCgDQ2pBC9l2TgbHI/rUcllnLp4XWpYoZe3Lcz1tRAWn44f/wrH45dp//m8Fq4W+LqdGy6HJWDun0EKj9mb6WJkQwfUtDWGhlSCiIQMzD8RhFhZdmmdxlv5nzqMq8f2QZacACuHymg7ZBzsKlcp8tjYp09wcf92xIQHIznuBT4Z+AXqdeipcEzkwzu4cmwfYsKDIUuKx2eT5sGjTuOyOJViCYKAn7dswIk/DiItNRXVPL0wdurXsLN3fOtz9u7agssXz+JpxBNo6+igqmctDP/iS1RycAIApKYk4+ctG3Dr+hXEvoiBiakZGjZriUEjx8DA0KiMzqxA8F9H8ejcQWSmJMLUzhk+n30OC0ePIo8NvXwCT66fQ3J0wfXG3N4Vnl0GKxx/7/huRN76H9KTYiHV0Cw4pvNgWDgV/Zql4capQ7h89PXfZoch42HnWvTfJgA8uHoR53/bhqS4GFhYV0Lrvr5w864vf1yWnICzv25G6J2byEyXwbFKTbQfMg4WNpXkxxz1W4Hwe7eQmhgPbV09VHKvjjZ9fWFp51Cq5/qKIAhYv24NDu7/DampKfDy9sE3s+fC0dHpnZ6/ZfNPWLNqOQYMHIzpX30jb9+/by/+PH4UDx/cR1paGv535QaMjY1L6SyKp+7X1PZVLNG1hhVM9bQQkZiBLVejEBKXXuSx9R1N0aOmFayNdKAhlSA6JQt/3H+Jv0IT5MeY6GpiYB071LIzgoG2Jh7EpGLLtaeISckqq1Mq5DMfWwyobw8LQ20Ev5Rh+akQPIhOLfLYTp5WmN1Z8b/brNx8NPv+f/LvRzZxxCfVKsLKSAc5efl4FCPDxr/Ccf950a9ZnjT2qYxJg9vAp5oDbCqYoPekn/DHhTtixyKSY4+zmsvOLvs3OmWLCvgf7hzyQ9V2/dB6yiqY2Drj702zkZmaVOTxsSF3Ye/TDM3GLkKLid9D38wSf2+cjYykeIXjrKr4oNO8nfKveoOmlcHZvJvadsbo6WmFY49isfhcGJ4lZ2J8Y0cY6mgUeXxadh5OPI7DDxfD8d3ZUFyNSMKg2raoWtGgjJO/XXNXC3zexBE/33iKMfvuIiwuDYu6VIWpXvH376yMdODb2AF3n6cUeszGWAcre1RHVGImph56gM/33MFu/2fIycsvrdN4qwdXzuPM7o1o2mMQRizciIoOLtizZCbSkhOLPD4nKxNmFW3Qsu9IGJiaF3lMdlYmrBxc0G7o+NKM/t72796OI/t/wbip32DlT7ugq6eHWZPHIDvr7R8+7wXcROcefbBi0058t3Ij8nJz8c2kL5CZkQEAiI+LRXxcLEaOnYwNu/Zj0jfz4X/1ElYtKdubWZG3/sLt3/1QvX0/tJ22GqZ2zri4/u3Xm5fBd+FQuzlajl+MNpN/gJ5ZBVxcPxvpSXHyY4wq2sGn12i0n/kjWn+5DPrmVri4fhYyU8vmxtb9K+dx6ueNaN5jMEZ9txHWDpWxe8mMt/5tRgXdx4F1C+HdogNGLdoEj9qNsXfFbLyMCgdQUJDuXT4biS+j0WfKfIxatAkmlhXx8+JpyM7MkL+OjbM7un4+HWN+2IYBM5cAgoCfl8xAfn5emZz3ti2b8evuXfh2zlz8/Os+6Onp4YtRI5BVzN/pK/fu3sH+3/bA3b3wzY3MzAw0atwUI3xHl0bsd6bu19RGzmYYUq8SfrsdjelHHuFJQga+besKY92iz0+WlYsDgTH4+lgQphx+iPMh8RjbxBG1bF/feJve2gVWRtpYejYM0w4/RGxaNua0c4WOpjgfh9tUrYCJrStjy99PMGTrTYS8kGF1H0+Y6Wu99TmyzFx0WHNZ/tXtx6sKj0cmZOCHU8Hov8Ufo36+jejkTKzpUxOmem9/zfLCQE8Hd4Oe4cvFe8WOQlQkFs7l1NChQ3Hx4kWsXr0aEokEEokEoaGhGDFiBJydnaGnpwcPDw+sXr260PO6deuG7777Dra2tvDwKHjTv3z5Mry8vKCrq4s6derg0KFDkEgkuH37tvy59+7dQ4cOHWBoaAgrKysMGjQIcXFxb83z5MmTMvn/IvjCITg1bAen+m1gbO0An15joKGtg4hrp4s8vt6gqajcpBNM7VxgbGWP2n3GQxDy8TI4UOE4qaYWdI3N5F/a+oZlcTrvpJWbBS49ScLViGTEpGbj14BoZOflo5GjaZHHB8elI/B5KmJSsxGXloPzoQl4lpKJypb6ZRu8GD29bPDn/Zc49SgWkYkZWH0hHFm5+WhXteJbnyOVADM/ccWu608RnVz4w+6wBva4HpEEvyuRCI1LR3RKFq4+SURSRm5pnkqRrv15AF4tO6JW8/aoUMkRHYd/CU0dHQRePFHk8baVq6B1/89RvWFLaGoW/YHG1aseWvQejip1m5Rm9PciCAIO/bYbfQf7omHTlnB2dceUbxcgPj4WV/53/q3PW7BiPT7p+CkcXVzh4uaByV/PR+yLaAQ/fgAAcHJxxbffLUf9Js1hY2cPr9r1MGTUOFy7dBF5uWX3+3x8/hBcGrWDS4NPYGLjgDq9x0JTWwfhV4u+3jQcMg1uTTvBrFLB9aZuv/EQ8vPxIuj19caxTgtYe3jB0NIaJjaO8O4+EjmZ6Uh+Hl4m53Tl+H74tOwIrxbtUaGSEzqN+BJaOjoIeMvf5rUTB+Faqy4ademDCnaOaNl7GGyc3XDj1CEAQELMUzwLeYiOw7+EXeUqsLS1R6fhXyInOxv3rrweyVO7dWc4Vq0J0wrWsHF2R8vew5AS/xJJsS9K/ZwFQcDuXTvh+/kXaNmqDdw9qmDh4mWIffkS586eKfa56Wlp+GrGNMyZtxDGJiaFHh84eChG+I5CzVq1Siv+O1H3a2qX6hVxJigO50MS8DQ5Ez9djkRWbj5auVkUefz9GBmuRybjWXImXqRm4/iDWEQkZqCqVcF7u42xDjwqGuKnK1EIjUvH85QsbL4cBW0NKZo4m5Xlqcn1q1cJhwOjcfTuC4THp2PJiWBk5uajS03rtz5HAJCQlvP6Kz1H4fFTD17ixpMkPE/KRHhcOlafDYWhriZcy9GN9Lc5dekB5q0/iiPn2ctM5RML53Jq9erVaNiwIXx9fREdHY3o6GhUqlQJlSpVwm+//YYHDx5g9uzZ+Prrr7Fv3z6F5549exaPHz/G6dOncfToUaSkpKBLly7w9PTErVu3sGDBAsyYMUPhOUlJSWjVqhW8vb3h7++PEydO4MWLF+jdu/db89jb25f6/w/5uTlIehqCiu6vP6BIpFJUdPNCfMTjd3qN3Ows5OfnFSqM40Lu4eisgTi5aDRu/bYeWWmF776LQUMCOJjqKgy3EwA8epkGZ/N3K4Q9KhjAylDnrUPaypqmVAK3CgYIePq6h00AEPA0GVWt337DYkDdSkjKyMGJh7GFHpMAqOdohmdJmVjUpQr2DauNNZ/VQCMRPgDl5eYgOjwIzjV8XueTSuFcwwdPgx+UeZ7SFPP8GRLj4+BV9/WwXQNDI3hU88TDe4HFPFNRWpoMAGBkXLgwefMYfQNDaGiWzayivNwcJEaFwMrDS94mkUph5eGFuPBH7/Ya2VkQ8vOgo1/08PK83ByEXj4BLT0DmNo5KyN28Xk+4G/zafADONeordBWuWYd+fG5OQUf1DW1tBVeU1NTC1GP7xX5mtmZGbh98SRMK9jAxKJCic7pXTx7+hRxcbGo36CRvM3IyAieNWvhTmBAsc9dtHA+mjVrjgYNGxV7nJjU/ZqqKZXAxUIfd94YXiwAuBudCo93LAA9bYxga6yDBy8KrjVaGgUrKr3Zey4AyMkXUMWq7G+ca0olqGJthOvhr0d+CABuPEmEp93bh/7raWvg0Jj6ODK2Pr7vWR3Oxdwg15RK0M3LBqmZuQh+KVNmfKKPEuc4l1MmJibQ1taGvr4+rK1f33mcN+/1sEVnZ2dcuXIF+/btkxe4AGBgYAA/Pz9oaxd8qNm4cSMkEgk2b94MXV1dVKtWDc+ePYOvr6/8OevWrYO3tzcWLVokb9u6dSvs7e0RFBQEd3f3IvOUtqy0FAj5+dA1Unzj1jUyRerLp+/0GveOboeesTkqunvJ26yq1IZtzUYwMLeCLD4a94/twqWf5qLlxO8hkRY9HLqsGOpoQkMqQUqW4h3+1KxcWBnpvPV5uppSLOroDi2pBPmCgD23Y/DoHea6lQVj3YJzSvzXnfHE9BzYm+kV+ZzqNkZoX7UCvth7t8jHTfW1oK+tgT4+tth+LQp+VyJR18EUszu4Y9qhB7hbhvO50lOTIeTnw8BE8e/UwNgM8c+jyixHWUhMKBiFYmam2OtjamaOxIT4op5SSH5+Pjat+R7VPL3g5OJa5DHJSYn4dftmdOjSo2SB30O2/HpjqtCua2SKlBfvdr0JPLIdusbmCsU3ADy/dx1Xti9Dbk4W9IzN0HzMAugYvv2mgbK89W/TxAxxb/nblCUlwLCI42VJBXNFLW0dYGJZEef2+KHTiEnQ1tXF1eP7kZIQi9TEBIXn3Th9GGd++Qk5WZmwsLHHwK+XQeMtIyyUKS6uoDC0sFT8O7WwsJCPpCrKn8eP4eHDB/hl7/5SzVdS6n5NNfrnfTD5Xz3dSRm5sDPRfevz9LWk2NTHE1oaUuTnC/C7GiUvvp8lZSJWloUBte2w6Z/e687VK8LSQLvYodGlxVRfC5pSSaEe44S0HDhaFF0MRyRkYOGxxwh5KYOhjiYG1LeH3yBv9PO7gZepr6fmNXY1x8JPq0FXS4o4WTbG77lT6P9LInp/LJxVzI8//oitW7ciMjISGRkZyM7OhpeXl8Ixnp6e8qIZAB4/foyaNWtCV/f1m029evUUnhMYGIjz58/D0LDwXdfQ0FC4uxe9EFdRsrKyCs0hy83JVuidKCuPz/yGqID/ofnYRdB44+fb+zST/9vE1gkmNs44+Z0vYkPuKfRuq5Ks3HwsPhsKHU0pPCoYoKenFeLSshFcTnqd34eelhQz2lTGqvPhSMks+s3+1W4Ml8MTcTAwBgAQFpeOataG6Fzdqkw/5Kmz86eOYe33C+Xfz1u2tsSvuX7FYkSEheCH9duLfDw9TYY508bDwckFA0aIO4/0fTw8/Ruibv2FluMXK1xvAKCiW020nbEGWbIUhF05iSvblqLNlOWFinRVoKGpiV5fzsMfm3/A96O6QSKVwqVGbbjWqgcBgsKxno1bw6VGbciSEnDl2D4cWD0fw+augaa2ct8Pjh09ggVz58i/X7dh03u/Rkx0NJYt+Q6bNm+Fjs7bb1Kqoo/lmpqRk49phx9BV0sKTxsjDKlrhxepWbgfI0OeAHx/LgxfNHbEjgG1kJcv4M7zFNx6mgxV2d3n3rMU3Hv2enTcnWcp2DuqLrp722LTX0/k7TcjkjBoqz9M9bTwqZcNFnWriuE7AgrdaCHVxO2oxMPCWYXs2bMHU6dOxfLly9GwYUMYGRnh+++/x7Vr1xSOMzB4/3ksMpkMXbp0wdKlSws9ZmNj816vtXjxYoWecQBo0n8cmg54/wWOdAyMIZFKkZmquIhNZmoSdI2LHz4WdP4gHp89gKZfLICJbfFDIg0traFtYAxZ3HPRC2dZVi7y8gUY6yj+52mko/nWDzxAwRCv2LSCN8WnyVmwNtZBOw9LBMdFlmbcd5KSWXBO/76rb6avhYT0wgvY2ZjowtpYF/M7vV6Y59UbxZ9f1Mfw3bcRK8tGbl4+IhMyFJ4bmZiJGjZluwqzvpEJJFJpocWW0lISC/X0qZr6TVrAo5qn/PucfxYcTEyMh7nl6yG3SYkJcHH97xts61csxvXLf2HZuq2wrGhV6PH09DTMmjIG+voGmLVoxVvnf5cGbfn1JkmhPTM1qdCol397dPYgHp7ZjxZjFxY5BFtTRxdGFWxhVMEWls5VcGyBL8KunEK1tr2LeDXleevfZnIiDN+yKJ2hqTlk/3G8rYs7Pl/8EzLTZcjLzYWBsSn8Zo2FrYvi34CuviF09Q1hYVMJldyqYplvNzzy/xs1GrVS0hkWaNGyFTw9X1+7s3MK/k7j4+JRocLrOb/x8fHwqFL0auIPHtxHQnw8+vZ6PcohLy8PN/1vYM+vu3Ej4C40NMQdkfSKul9TU/95HzT510JnpnqaSMp4e/EnAIhJLbhx/yQhA3amuuhe0xr3Y0IAAGHxGZh25BH0taTQlEqRkpWLxZ09ECrCDeak9Bzk5gsw/9fv0NxACwnvuIJ5Xr6AoBgZKv1rlEFmTj6eJmbiaWIm7j1Pxf7P66JrLWvsuKJeI6CIyhoL53JMW1sbeXmvVx+9dOkSGjVqhDFjxsjbQkND//N1PDw88PPPPyMrK0t+F/3GjRsKx/j4+ODAgQNwcnKC5lvmE/47z9t89dVXmDx5skLbwvMfVrxJNbVgWskVsUF3YOfZEAAg5OcjNjgQlZt0euvzHp89gEdn9qHJ5/Ng5uD2nz8nPSkO2emp0DUu+oNkWcoTgMikTHhUNEDgP1tSSAB4VDTAxdCE4p/8BgkK5jeVB7n5AoJj0+BVyQSX/5nPJQHgVckYR+4WXigoKjEDo35VnC87tL499LQ1sOF/Two+4OULePwyDZXMFIftVTLVxYvUst1aRENTCzbO7nhy/5Z8uyghPx9P7gWgTttPyzSLsunrG0Bf//XNOEEQYGZhiUD/66jsVlCApKfJ8PjBXXTq1uutryMIAjasXIIrf53DkrV+sLa1K3RMepoM304eAy0tLcxeugraZdzrp6GpBTN7V7wICkSlmq+vNy8eB8KtWee3Pu/hmf14eGofmn0xH+bvcL0peF0B+bml3/vz6m8z/H6AfJE5IT8f4fcDULdttyKfU8mtGsLv3UKDN7ZHC7t7E5XcqhU6VveftSPio58iOiwILXsNe2sWQRAgCAJyc5S/24OBgSEMDF6PmBIEAZaWFXDt2hVUqVoVQMEN4rt3AtGrT78iX6N+gwbYf+gPhbY533wFJxcXDBvhW26KZkD9r6m5+QLC4tPhaWOEG5EF87glKJi3/GcR87PfRgpAq4j3wfScfAD5sDbWgYuFPvbceq6c4O8hN1/Ao5hU1HUyw1/BBdNcJADqOprht5vP3uk1pBKgckUDXP6PzwYSiQRaGlzWiKikWDiXY05OTrh27RqePHkCQ0NDuLm5YefOnTh58iScnZ2xa9cu3LhxA87Oxfem9u/fH9988w1GjRqFmTNnIjIyEj/88AOAgospAIwdOxabN29Gv379MH36dJibmyMkJAR79uyBn58fNDQ0CuUxNzeHVFr4Qqyjo1NomFtJhmm7tegG/19WwszeFWaO7gi5eBi52ZlwrN8GAHBj9wromVigRuchAIDHZ/fjwZ+7UW/QVBiYWyEzpeBDhaaOLjR19JCblYEHJ3+FXc1G0DU2Q1pcDO7+sQ2GljawquLz1hxl6VxwPAbXsUVEYgYiEjPQ0tUCOhpSXIlIAgAMqW2LpMxcHL7/EgDQzt0CEUmZiJVlQ0tDgupWhqjvYIpfb0eLeBaKDtyOxrTWlRH8UoZHL2XoUcsGupoaOPnPh6BprSsjPi0bW69GISdPwJN/9XrIsgpu2rzZvj/gOb5u54a7z1MR+CwZdRxM0cDJDFMPlf2CXPU79MSRTctg4+wB28oeuH7iIHKyMlGzeXsAwJENS2BkZomWfUcCKFi0KfZpxD//zkVqYhxinoRAW1cP5tYFRWV2ZgYSYl5/gEqKjUbMkxDoGRrBxLJwb21ZkEgk6NZrAPbs2AxbewdY2dhhl9+PsLCogIZNW8qP+2riKDRq1gpdevYFAKxfvggXzvyJ2YtXQU/fAAnxBfNMDQwNoaOji/Q0Gb6Z9AWysjIxbfZ3SE9LQ3pawRx9E1OzMitaPFp2w7WfV8Lc3g0Wju54fKHgeuP8z/Xm6q7l0DexQM2uQwEAD0/vx73jP6PBkGkwsLBCxhvXGy0dPeRmZeLBqb2wrVEfeibmyJKlIOR/R5GRHA9777JZLb1hx89waONS2Lq4w7ZyFVz78wByMjPh1bwdAODQ+iUwMrdE63/+Nuu374EdCybhyrF9cPNqgHtXzuN5WBA6j3x9Q/TB1YvQNzaBiUVFvIwKx4mdP8KjTmNUrlkHAJD44jnuX70AF886MDA2QUpCHC4d+RVa2tpw86pfOKSSSSQSDBg0GJs3bYCjgyPsKlXCj2tXo0LFimjVuo38ON/hQ9Cq9SfoN2AgDAwM4eam2GOup68PUxNThfa42FjExcUhKrLghnBIcBD09Q1gY2MDE1PTUj+3V9T9mvrH/ZcY18QRofHpCIlNR6fqFaCjKcX5f4rM8U0dEZ+eg19uFhS93T2tEBqfjpiULGhpSOFTyRjNXC2w+fLrG/cNnUyRkpmLWFk2HM31MKxeJdyITEKgSMPQf73+FLM7V8HDmFQ8eJ6KvnXtoKslxdE7BUPl53T2QGxqNtZfLFiBf0RjR9x7noKoxAwY6WhiYAN7WBvr4Mg/7/W6WlIMa+SI/wXHIU6WDVN9LXzmY4sKRjo4++jdbziIxUBPG5XtX49kcrKzQE13OySmpCMqpujt8z5GEpWZXKB+WDiXY1OnTsWQIUNQrVo1ZGRk4NGjRwgICECfPn0gkUjQr18/jBkzBn/++Wexr2NsbIw//vgDX3zxBby8vODp6YnZs2ejf//+8nnPtra2uHTpEmbMmIG2bdsiKysLjo6OaN++vbw4/nee8PBwODk5lfb/DbD3boosWTIenNiNzJREmNi5oMnn8+RDJ9MTY+U3AAAg7NKfyM/LxdXtSxRep2q7fqjWvj8kEimSnz9B5I1zyM5IK1g4zMMb1TsOKJNFa97FzWcpMNTRQOdqFWCso4mnyVlYdykSqf980DHT18Kbu2pqa0rR18sapnpayMkT8CI1C9tvPMPNZ+VjpXAAuBgSDxM9TQyubw8zfS2ExaXjm6OP5MPuKhrpQBD+40X+5VJ4ItZcDEdfH1uMaeqEp0kZmH8iCPejy/5DULWGLZGWmoyL+7cjLTkRVo6V0XfGYvkiS8nxLyGRvL7RlJoYjy3fvJ6/e/XYb7h67Dc4VK2JQd+uAABEhz3Gz99NlR9z5ueNAICaTduiy+jpZXFaRfpswFBkZmZg7bIFkMlSUd3TG/OXr1foIY5+FoXkpNcfdI4d+g0AMGP8SIXXmvT1PHzS8VOEPH6Ixw8KFi0a0aeLwjHbfjsGK5vCPdSlwcGnGbJkybh3/GdkpiTCtJILmn8xXz41pOB68/r3GHLpOPLzcnF562KF16nevh9qdBwAiVSKlBdP8eT6WWTJUqBtYAxzBze0mrgUJjaOZXJO1Ru2RFpKMi7s3w5ZUsHfZv+ZS2BoUjDCJjn+JSRv9MrZu1dHj7Hf4PxvW3Fu71aYW9uhz+T5qGj/+iZtalI8Tv28AbLkRBiZmaNmk7Zo1mOg/HFNbW1EPrqLa38eQEaaDIYmZnCoUhPD5q4ts+kLw0b4IiMjA/PnzkZqagq8fWpj/SY/hRu7T6OikJT0fh/If9u3BxvXr3v9cwYPAADMX7gYn3Yvu8Xs1P2aejk8Eca6mujrbQNTPS08ScjAd6dCkPzPlCVLA23kv3F+OlpS+Da0h7m+NrLz8vE8ORNr/noi75EHADM9LQypVwkmugVDvi+GJGD/P/O5xXDmYSxM9bUwqqkTLAy0EfRShi/33ZUvGGZlrKtwjka6mviqgzssDLSRmpmLRzGp8N11G+HxBUPN8/MFOFrooaNndZjqaSE5IwcPo1Px+c+3Ea4C6534VHPEKb+J8u+XTS0Y9bLryFWMmvOzWLGI5CSC8L6XVVIHu3fvxrBhw5CcnAw9vaJX4FSmr48HlfrPEFtSMfOP1UVIdPkpxEvDwPplU5yJqbGjpdgRSt2OW++2ArYq87As/eu22HrWqiR2hFLXddNVsSOUKmP9sl8UtKxFxZT/RdNK6s6+38SOUOoyAtb990HlRHJG/n8fVA6Y6Knf9AD2OH8kdu7cCRcXF9jZ2SEwMBAzZsxA7969y6RoJiIiIiKikuOq2uJh4fyRiImJwezZsxETEwMbGxv06tUL3333ndixiIiIiIiIyj0Wzh+J6dOnY/p08eZEEhERERERqSr1G3xOREREREREpETscSYiIiIiIlIBnOIsHvY4ExERERERERWDhTMRERERERFRMThUm4iIiIiISBVwrLZo2ONMREREREREVAwWzkRERERERETF4FBtIiIiIiIiFSDhWG3RsMeZiIiIiIiIqBgsnImIiIiIiIiKwaHaREREREREKkDCkdqiYY8zERERERERUTFYOBMREREREREVg4UzERERERERUTE4x5mIiIiIiEgFcIqzeNjjTERERERERFQMFs5ERERERERU7iUkJGDAgAEwNjaGqakpRowYAZlM9k7PFQQBHTp0gEQiwaFDh977Z7NwJiIiIiIiUgUSFfkqJQMGDMD9+/dx+vRpHD16FH/99RdGjRr1Ts9dtWoVJCXYz4tznImIiIiIiKhce/jwIU6cOIEbN26gTp06AIC1a9eiY8eO+OGHH2Bra/vW596+fRvLly+Hv78/bGxsPujns8eZiIiIiIiIlCYrKwspKSkKX1lZWSV6zStXrsDU1FReNANAmzZtIJVKce3atbc+Lz09Hf3798ePP/4Ia2vrD/75LJyJiIiIiIhUgERF/rd48WKYmJgofC1evLhE5x4TE4OKFSsqtGlqasLc3BwxMTFvfd6kSZPQqFEjfPrppyX6+RyqTURERERERErz1VdfYfLkyQptOjo6RR47c+ZMLF26tNjXe/jw4QflOHLkCM6dO4eAgIAPev6bWDgTERERERGR0ujo6Ly1UP63KVOmYOjQocUe4+LiAmtra7x8+VKhPTc3FwkJCW8dgn3u3DmEhobC1NRUob1nz55o2rQpLly48E4ZARbOREREREREJJIKFSqgQoUK/3lcw4YNkZSUhJs3b6J27doACgrj/Px81K9fv8jnzJw5EyNHjlRo8/T0xMqVK9GlS5f3ysnCmYiIiIiISAWUYDcllVe1alW0b98evr6+2LhxI3JycjBu3Dj07dtXvqL2s2fP0Lp1a+zcuRP16tWDtbV1kb3RDg4OcHZ2fq+fz8XBiIiIiIiIqNzbvXs3qlSpgtatW6Njx45o0qQJfvrpJ/njOTk5ePz4MdLT05X+s9njTEREREREROWeubk5fvnll7c+7uTkBEEQin2N/3q8uCcSqZXMzExhzpw5QmZmpthRSg3PUT3wHNWDup+jup+fIPAc1QXPUT18DOdIqkkiCB9achOVTykpKTAxMUFycjKMjY3FjlMqeI7qgeeoHtT9HNX9/ACeo7rgOaqHj+EcSTVxjjMRERERERFRMVg4ExERERERERWDhTMRERERERFRMVg4k9rR0dHBnDlzoKOjI3aUUsNzVA88R/Wg7ueo7ucH8BzVBc9RPXwM50iqiYuDERERERERERWDPc5ERERERERExWDhTERERERERFQMFs5ERERERERExWDhTERE9I7y8vLw119/ISkpSewoREREVIZYOBMRiYgFmGrR0NBA27ZtkZiYKHYUIiIiKkMsnEmthISE4OTJk8jIyAAAfCyLxqvLef7vf//DwIED0bBhQzx79gwAsGvXLvz9998iJ1OOpUuXYu/evfLve/fuDQsLC9jZ2SEwMFDEZPQ+atSogbCwMLFjlJo7d+4U+XX37l0EBwcjKytL7IhKsWvXLjRu3Bi2traIiIgAAKxatQqHDx8WOZny7N+/H71790aDBg3g4+Oj8PUxePVZQNWp+3tjZGRkkZ9jBEFAZGSkCImIisbCmdRCfHw82rRpA3d3d3Ts2BHR0dEAgBEjRmDKlCkip1OOoUOHIi0trVD7kydP0KxZMxESKdeBAwfQrl076OnpISAgQP7hPDk5GYsWLRI5nXJs3LgR9vb2AIDTp0/j9OnT+PPPP9GhQwdMmzZN5HQfzszMDObm5u/0pQ4WLlyIqVOn4ujRo4iOjkZKSorCl6rz8vKCt7d3oS8vLy9UqVIFJiYmGDJkCDIzM8WO+sE2bNiAyZMno2PHjkhKSkJeXh4AwNTUFKtWrRI3nJKsWbMGw4YNg5WVFQICAlCvXj1YWFggLCwMHTp0EDue0kyYMKHI9rS0NHTs2LGM0yjfx/De6OzsjNjY2ELtCQkJcHZ2FiERUdG4jzOphcGDB+Ply5fw8/ND1apVERgYCBcXF5w8eRKTJ0/G/fv3xY5YYt7e3khJScHPP/+Mhg0bAgB27NiBCRMmoFWrVvj9999FTlgy3t7emDRpEgYPHgwjIyP57zAgIAAdOnRATEyM2BFLTE9PD0FBQbC3t8fEiRORmZmJTZs2ISgoCPXr11fZ4b87dux452OHDBlSiknKhlT6+p6zRCKR/1sQBEgkEnkRpqoOHz6MGTNmYNq0aahXrx4A4Pr161i+fDnmzJmD3NxczJw5E3369MEPP/wgctoPU61aNSxatAjdunVTuN7cu3cPLVq0QFxcnNgRS6xKlSqYM2cO+vXrp3COs2fPRkJCAtatWyd2RKWoXLkyBg4ciHnz5snb0tLS0L59ewAFvbWq7GN4b5RKpXjx4gUqVKig0B4REYFq1aoV2WlAJAZNsQMQKcOpU6dw8uRJVKpUSaHdzc1NPgRP1V2/fh1ff/01WrRogSlTpiAkJAR//vknVqxYAV9fX7Hjldjjx4+L7Dk3MTFRm3nAZmZmiIqKgr29PU6cOIGFCxcCKCi4VLnYUodi+H2cP39e7Ail6rvvvsPq1avRrl07eZunpycqVaqEWbNm4fr16zAwMMCUKVNUtnAODw+Ht7d3oXYdHR21+ZAeGRmJRo0aASi4aZeamgoAGDRoEBo0aKA2hfOpU6fQtGlTmJmZ4csvv0RqairatWsHTU1N/Pnnn2LHKzF1fm+cPHkygIIbkLNmzYK+vr78sby8PFy7dg1eXl4ipSMqjIUzqYW0tDSFC+4rCQkJ0NHRESGR8mlpaeH777+Hvr4+FixYAE1NTVy8eFHe+6zqrK2tERISAicnJ4X2v//+Gy4uLuKEUrIePXqgf//+cHNzQ3x8vHy4ZEBAAFxdXUVOpzx5eXk4dOgQHj58CACoXr06unbtCg0NDZGTKUfz5s3FjlCq7t69C0dHx0Ltjo6OuHv3LoCC4dyvpsSoImdnZ9y+fbvQeZ44cQJVq1YVKZVyWVtbIyEhAY6OjnBwcMDVq1dRq1YthIeHq826GEBBj/OJEyfQsmVLSKVS/Prrr9DR0cGxY8dgYGAgdrwSU+f3xoCAAAAFN4/v3r0LbW1t+WPa2tqoVasWpk6dKlY8okJYOJNaaNq0KXbu3IkFCxYAKLh7mZ+fj2XLlqFly5Yip1OOnJwczJw5Ez/++CO++uor/P333+jRowe2bNmiFvO4fH19MXHiRGzduhUSiQTPnz/HlStXMHXqVMyaNUvseEqxcuVKODk5ISoqCsuWLYOhoSEAIDo6GmPGjBE5nXKEhISgY8eOePbsGTw8PAAAixcvhr29PY4dO4bKlSuLnFB50tPTERkZiezsbIX2mjVripRIOapUqYIlS5bgp59+kn+QzcnJwZIlS1ClShUAwLNnz2BlZSVmzBKZPHkyxo4di8zMTAiCgOvXr+PXX3/F4sWL4efnJ3Y8pWjVqhWOHDkCb29vDBs2DJMmTcL+/fvh7++PHj16iB1PqWrWrImjR4/ik08+Qf369XH06FHo6emJHUsp1Pm98dXonWHDhmH16tUwNjYWORFR8TjHmdTCvXv30Lp1a/j4+ODcuXPo2rUr7t+/j4SEBFy6dEktPqzXqlUL6enp2LVrFxo0aABBELBs2TLMmTMHw4cPx/r168WOWCKCIGDRokVYvHgx0tPTARQMm5w6dar8hgiVfx07doQgCNi9e7d8MbD4+HgMHDgQUqkUx44dEzlhycXGxmLYsGFvHQaqysPuAeDy5cvo2rUrpFKp/CbA3bt3kZeXh6NHj6JBgwbYtWsXYmJiVHpRu927d2Pu3LkIDQ0FANja2mLevHkYMWKEyMmUIz8/H/n5+dDULOgj2bNnDy5fvgw3Nzd8/vnnCr17qsbb21thfYFXIiIiULFiRYWi+datW2UZTek+pvfGkJAQhIaGolmzZtDT05OvG0FUXrBwJrWRnJyMdevWITAwEDKZDD4+Phg7dixsbGzEjqYUI0aMwJo1awoNPQsICMCgQYNw7949kZIpV3Z2NkJCQiCTyVCtWjV5ryypBgMDA1y9ehWenp4K7YGBgWjcuDFkMplIyZRnwIABiIiIwKpVq9CiRQv8/vvvePHiBRYuXIjly5ejU6dOYkcssdTUVOzevRtBQUEAAA8PD/Tv3x9GRkYiJ1O+9PR0yGQyVKxYUewo9I7eXAjsv8yZM6cUk5QddX5vTEhIQK9evXD+/HlIJBIEBwfDxcUFw4cPh5mZGZYvXy52RCIALJyJ1EJWVpbazOUGgKioKACQb91EqsPc3BxHjx6VL0r0yqVLl9ClSxckJCSIlEx5bGxscPjwYdSrVw/Gxsbw9/eHu7s7jhw5gmXLlqnN3qrqrFWrVjh48CBMTU0V2lNSUtCtWzecO3dOnGBKlpSUhOvXr+Ply5fIz89XeGzw4MEipaIPpa7vjR/DziikHjjHmdTCnTt3imyXSCTQ1dWFg4ODWhSWu3btwsaNGxEeHo4rV67A0dERq1atgrOzMz799FOx45VIbm4u5s2bhzVr1sh7JQ0NDTF+/HjMmTMHWlpaIiekd9G5c2eMGjUKW7ZskW9ldO3aNYwePRpdu3YVOZ1ypKWlyXsnzczMEBsbC3d3d3h6eqr8sFCgYE66lZUVhg8frtC+detWxMbGYsaMGSIlU54LFy4UmpsOAJmZmSq/fdErf/zxBwYMGACZTAZjY2OFIa8SiYSFs4r4GN4bP4adUUg9sHAmteDl5SX/UPBqEMWbHxK0tLTQp08fbNq0Cbq6uqJkLKkNGzZg9uzZ+PLLL/Hdd9/J51Gamppi1apVKl84jx8/HgcPHsSyZcvkK4VfuXIFc+fORXx8PDZs2CByQnoXa9aswZAhQ9CwYUP5B7rc3Fx07doVq1evFjmdcnh4eODx48dwcnJCrVq1sGnTJjg5OWHjxo1qMTVk06ZN+OWXXwq1V69eHX379lXpwvnNm6wPHjxQ2AM3Ly8PJ06cgJ2dnRjRlG7KlCkYPnw4Fi1aVOSuE+oiLy8PK1euxL59+4pcrE/VR7l8DO+NH8POKKQmBCI1cOjQIcHDw0Pw8/MT7ty5I9y5c0fw8/MTqlatKuzZs0f4+eefhUqVKglTpkwRO+oHq1q1qvD7778LgiAIhoaGQmhoqCAIgnD37l3BwsJCxGTKYWxsLBw/frxQ+7FjxwRjY2MREpWeGzduCDt37hR27twp3LhxQ+w4SpOfny9EREQI6enpQnBwsHDkyBHhyJEjQnBwsNjRlGrXrl3Ctm3bBEEQBH9/f8HS0lKQSqWCrq6usGfPHnHDKYGOjo4QFhZWqD00NFTQ0dERIZHySCQSQSqVClKpVJBIJIW+9PX1hS1btogdUyn09fXl7xPqbNasWYKNjY3www8/CLq6usKCBQuEESNGCBYWFsLq1avFjldiH8N7Y4cOHYRvv/1WEISCzzdhYWFCXl6e0KtXL6Fnz54ipyN6jT3OpBa+++47rF69Gu3atZO3eXp6olKlSpg1axauX78OAwMDTJkyBT/88IOIST9ceHg4vL29C7Xr6OggLS1NhETKpaOjU2ifSqBgv1VVXv31TU+fPkW/fv1w6dIl+dzKpKQkNGrUCHv27Ck0TE3VCIIAV1dX3L9/H25ubmq1N/WbBg4cKP937dq1ERERgUePHsHBwQGWlpYiJlMOe3t7XLp0Cc7Ozgrtly5dgq2trUiplOPVHsYuLi64fv06KlSoIH9MW1sbFStWVJv9xtu1awd/f3+V3+v3v+zevRubN29Gp06dMHfuXPTr1w+VK1dGzZo1cfXqVUyYMEHsiCXyMbw3Llu2DK1bt4a/vz+ys7Mxffp0hZ1RiMoLFs6kFu7evQtHR8dC7Y6Ojrh79y6AguHc0dHRZR1NaZydnXH79u1C53nixAlUrVpVpFTKM27cOCxYsADbtm2TD83KysrCd999h3HjxomcTjlGjhyJnJwcPHz4UL7H8ePHjzFs2DCMHDkSJ06cEDlhyUilUri5uSE+Ph5ubm5ixykz+vr68PHxETuG0vj6+uLLL79ETk4OWrVqBQA4e/Yspk+fjilTpoicrmReXT//vVCWOurUqROmTZuGBw8ewNPTs9BcWHVZcyAmJka+ir+hoSGSk5MBFKy3oOr7HAMfx3tjjRo1EBQUhHXr1sHIyAgymQw9evRQq51RSD2wcCa1UKVKFSxZsgQ//fST/A5sTk4OlixZgipVqgAAnj17BisrKzFjlsjkyZMxduxYZGZmQhAEXL9+Hb/++isWL14MPz8/seOVWEBAAM6ePYtKlSqhVq1aAAq2MMrOzkbr1q3Ro0cP+bEHDx4UK2aJXLx4EZcvX5YXzUDBfNm1a9eiadOmIiZTniVLlmDatGnYsGEDatSoIXacUjF58uQi218tRujq6opPP/1Uvo+1qpk2bRri4+MxZswY+XxRXV1dzJgxA1999ZXI6ZTrwYMHRc6LVYei0tfXFwAwf/78Qo9JJBKV32/8lUqVKiE6OhoODg6oXLkyTp06BR8fH9y4cUMt5sd+DO+NAGBiYoJvvvlG7BhExWLhTGrhxx9/RNeuXVGpUiXUrFkTQEEvdF5eHo4ePQoACAsLw5gxY8SMWSIjR46Enp4evv32W6Snp6N///6ws7PD6tWr0bdvX7HjlZipqSl69uyp0KZuW27Y29sjJyenUHteXp7KD4F9ZfDgwUhPT0etWrWgra0NPT09hcdVfaEeoOCD7K1bt5CXlye/CRIUFAQNDQ1UqVIF69evx5QpU/D333+jWrVqIqd9fxKJBEuXLsWsWbPw8OFD6Onpwc3NTS2KkFfCwsLQvXt33L17FxKJpNCikupQVH4MveoA0L17d5w9exb169fH+PHjMXDgQGzZsgWRkZGYNGmS2PFK7GN4b/xYdkYh1cd9nEltpKamYvfu3QgKCgJQ0JPXv39/GBkZiZxMOTIyMiAIAvT19ZGeno579+7h0qVLqFatmsLcbiq/Dh8+jEWLFuHHH39EnTp1AAD+/v4YP348ZsyYgW7duokbUAl27NhR7ONDhgwpoySlZ9WqVfjf//6Hbdu2wdjYGACQnJyMkSNHokmTJvD19UX//v2RkZGBkydPipz2w4WEhCA0NBTNmjWDnp4eBEFQ2K1AlXXp0gUaGhrw8/ODs7Mzrl+/jvj4ePk6GOoyAuRjdOXKFVy5cgVubm7o0qWL2HHoHUilUrXfGYXUAwtnUivqPOyubdu26NGjB0aPHo2kpCRUqVIFWlpaiIuLw4oVK/DFF1+IHbFE5syZg+HDhxc5V11dmJmZIT09Hbm5udDULBjw8+rfBgYGCseqQ8+surKzs8Pp06cL9Sbfv38fbdu2xbNnz3Dr1i20bdsWcXFxIqX8cPHx8ejduzfOnz8PiUSC4OBguLi4YPjw4TAzM8Py5cvFjlhilpaWOHfuHGrWrAkTExNcv34dHh4eOHfuHKZMmYKAgACxI36QNWvWYNSoUdDV1cWaNWuKPVbVF8362MTGxuLx48cACjoG3lzYTtUdPnwYM2bMwLRp01CvXj0AwPXr17F8+XLMmTMHubm5mDlzJvr06aOyC7ySeuBQbVILRQ27e/NupToMu7t16xZWrlwJANi/fz+srKwQEBCAAwcOYPbs2SpfOB8+fBjfffcdmjdvjhEjRqBnz55qNzRr1apVYkcoE6Ghodi2bRtCQ0OxevVqVKxYEX/++SccHBxQvXp1seOVWHJyMl6+fFmocI6NjUVKSgqAguGV/76BpyomTZoELS0tREZGKiw82KdPH0yePFktCue8vDz5aCRLS0s8f/4cHh4ecHR0lBcnqmjlypUYMGAAdHV15e8XRZFIJGpVOO/atQsbN25EeHg4rly5AkdHR6xatQrOzs749NNPxY5XImlpaRg/fjx27twpH36voaGBwYMHY+3atWqxR/fHsDMKqQlxdsEiUq7OnTsLn376qRAbGysYGhoK9+/fF/73v/8J9erVE/766y+x4ymFnp6eEBERIQiCIPTq1UuYO3euIAiCEBkZKejp6YkZTWlu3boljB8/XrC0tBRMTU2F0aNHC9evXxc7Fr2HCxcuCHp6ekKbNm0EbW1t+T6yixcvVpv9OPv37y84OzsLBw8eFKKiooSoqCjh4MGDgouLizBw4EBBEATh119/FWrXri1y0g9jZWUl3L59WxAExT3jQ0NDBQMDAzGjKU2TJk2E33//XRAEQejXr5/Qvn174e+//xYGDx4sVK9eXdxw9F7Wr18vWFpaCgsXLhT09PTkf6/btm0TWrRoIXK6khs1apTg4uIiHD9+XEhOThaSk5OFY8eOCZUrVxZGjx4tdjyl0NXVFR4+fFio/eHDh4Kurq4gCIIQHh6uNp91SHWxcCa1YGFhIQQGBgqCIAjGxsbCo0ePBEEQhLNnzwpeXl5iRlMaT09PYfXq1UJkZKRgbGwsXL58WRAEQfD39xesrKxETqdc2dnZwoEDB4TOnTsLWlpagqenp7Bq1SohKSlJ7GgllpubK/z222/C/Pnzhfnz5wv79+8XcnJyxI6lNA0aNBCWL18uCIJi0XXt2jXBzs5OzGhKk5qaKowcOVLQ1tYWpFKpIJVKBW1tbcHX11eQyWSCIAhCQECAEBAQIG7QD2RoaCgEBQXJ//3qd3jjxg3B3NxczGhKc+LECeHAgQOCIAhCcHCw4OHhIUgkEsHS0lI4e/asyOnofVStWlV+E+TNv9e7d+8KFhYWIiZTDgsLC+H8+fOF2s+dOydYWlqWfaBS4OXlJQwZMkTIysqSt2VnZwtDhgyRf4b7+++/BScnJ7EiEgmCIAgcqk1qQV2H3b1p9uzZ6N+/PyZNmoTWrVujYcOGAIBTp07B29tb5HTKJQgCcnJykJ2dDUEQYGZmhnXr1mHWrFnYvHkz+vTpI3bED3L//n107doVMTEx8tWYly5digoVKuCPP/5Qi+2b7t69i19++aVQe8WKFVVyvm9RDA0NsXnzZqxcuRJhYWEAABcXFxgaGsqP8fLyEildyTVt2hQ7d+7EggULABQM683Pz8eyZcvQsmVLkdMpx5tDQl1dXfHo0SMkJCTAzMxMbRZAU/dt014JDw8v8j1QR0cHaWlpIiRSrvT09CK30qxYsSLS09NFSKR8H8POKKQeWDiTWqhRowYCAwPh7OyM+vXrY9myZdDW1sZPP/0EFxcXseMpxWeffYYmTZogOjpavpcjALRu3Rrdu3cXMZny3Lx5E9u2bcOvv/4KHR0dDB48GD/++CNcXV0BAGvXrsWECRNUtnAeOXIkqlevDn9/f5iZmQEAEhMTMXToUIwaNQqXL18WOWHJmZqaIjo6Gs7OzgrtAQEBsLOzEylV6TA0NJR/yFMny5YtQ+vWreHv74/s7GxMnz4d9+/fR0JCAi5duiR2vBLLycmBnp4ebt++rXCzStULyH9T923TXnF2dsbt27cLLSx54sQJhTn6qqphw4aYM2cOdu7cKV9ROiMjA/PmzZPfQFd1jRo1Qnh4uMLOKL169VLYGWXQoEFiRiQCwFW1SU2cPHkSaWlp6NGjB0JCQtC5c2cEBQXBwsICe/fuRatWrcSOSP/B09MTjx49Qtu2beHr6yvfLuZNcXFxqFixosruT6qnpwd/f/9CC2Tdu3cPdevWRUZGhkjJlGfq1Km4du0afvvtN7i7u+PWrVt48eIFBg8ejMGDB2POnDliRyyxli1bFtsree7cuTJMUzqSk5Oxbt06BAYGQiaTwcfHB2PHjoWNjY3Y0ZTCxcUFv//+u8JNSHXzsWyb5ufnh7lz52L58uUYMWIE/Pz8EBoaisWLF8PPzw99+/YVO2KJ3Lt3D+3atUNWVpb87zUwMBC6uro4efKkyi+4mJOTgypVquDo0aNqcaOD1BsLZ1Jb6jbsTt0tWLAAw4cPV7teyTfVqlULK1euLHQj59y5c5g4cSLu3r0rUjLlyc7OxtixY7F9+3bk5eVBU1MTeXl56N+/P7Zv317oZogqmjRpksL3OTk5uH37Nu7du4chQ4Zg9erVIiWjd7VlyxYcPHgQu3btUrue5lfUfdu0N+3evRtz585FaGgoAMDW1hbz5s3DiBEjRE6mHOnp6di9ezcePXoEAKhatSoGDBgAPT09kZMph52dHc6cOcPCmco9Fs5EVC7Mnz8fU6dOLbS1RkZGBr7//nvMnj1bpGQl82p7IgD4+++/MX36dMydOxcNGjQAAFy9ehXz58/HkiVL0LFjR7FiKl1UVBTu3r0LmUwGb29vuLm5iR2p1M2dOxcymYzbpagAb29vhISEICcnB46OjoX2Ub9165ZIyZTH0NAQR48eRYsWLRTaL1y4gC5duiA1NRVhYWHw8vJSuE6psvT0dMhkMlSsWFHsKPQeFi1ahKCgIPj5+UFTk7NIqfxi4UxE5YKGhgaio6MLfeCJj49HxYoVVXYvbqlUqjDq4dUl91Xbm9+r6jm+SV1vgLyLkJAQ1KtXDwkJCWJHof8wb968Yh9XhykFAwYMwJUrV7B8+XLUrVsXAHDjxg1MnToVjRo1wq5du7Bnzx788MMP8Pf3Fzltyb18+VK+GGiVKlVQoUIFkRN9uCNHjrzzsV27di3FJGWje/fuOHv2LAwNDeHp6VnoRtbBgwdFSkakiIUzEZULUqkUL168KPRh59y5c+jTpw9iY2NFSlYyFy9efOdjmzdvXopJyoa63gB5F7t27cKMGTPw/PlzsaMQQSaTYdKkSdi5cydyc3MBAJqamhgyZAhWrlwJAwMD3L59G4BqrwKfmpqKMWPG4Ndff5Wvf6GhoYE+ffrgxx9/hImJicgJ359UKn2n49TlhuuwYcOKfXzbtm1llISoeCyciUhUr+ahJycnw9jYWKF3Ni8vDzKZDKNHj8aPP/4oYkp6V+p6A+RNPXr0UPheEARER0fD398fs2bNUoveSlIfMpnsrdumqYM+ffogICAAa9eula8yfeXKFUycOBFeXl7Ys2ePyAmJSF2wcCYiUe3YsQOCIGD48OFYtWqVQu+AtrY2nJyc1GbLDXX2Md0A+XfviFQqRYUKFdCqVSu0bdtWpFREHycDAwOcPHkSTZo0UWj/3//+h/bt26vFXs7/lpSUBFNTU7FjEH10OAOfiEQ1ZMgQAAV7cTZu3JgLg6ioVatWyW+AzJs3T61vgKj7sEFvb+8idyOQSCTQ1dWFq6srhg4dipYtW4qQjt5VWloalixZgrNnz+Lly5eFtvF71Qut6iwsLIocjm1iYgIzMzMREinX0qVL4eTkhD59+gAo2N/4wIEDsLGxwfHjx9VmS7X9+/dj3759iIyMRHZ2tsJj6rBYH6kHfkIlonJBHeb3fsw+phsgUVFRkEgkqFSpEgDg+vXr+OWXX1CtWjWMGjVK5HQl1759e2zYsAGenp6oV68egIJFpe7cuYOhQ4fiwYMHaNOmDQ4ePIhPP/1U5LT0NiNHjsTFixcxaNAg2NjYqO3WjN9++y0mT56MXbt2wdraGgAQExODadOmYdasWSKnK7mNGzdi9+7dAIDTp0/jzJkzOHHiBPbt24dp06bh1KlTIicsuTVr1uCbb77B0KFDcfjwYQwbNgyhoaG4ceMGxo4dK3Y8IjkO1SYiIqW5desWtLS04OnpCQA4fPgwtm3bhmrVqmHu3LnQ1tYWOWHJNW3aFKNGjcKgQYMQExMDd3d31KhRA8HBwRg/frzKrxzu6+sLBweHQkXHwoULERERgc2bN2POnDk4duyYyq7G/DGs/m5qaopjx46hcePGYkcpVa+2FsvKyoKDgwMAIDIyEjo6OoW2wVPFnks9PT0EBQXB3t4eEydORGZmJjZt2oSgoCDUr18fiYmJYkcssSpVqmDOnDno168fjIyMEBgYCBcXF8yePRsJCQlYt26d2BGJALBwJiIiJapbty5mzpyJnj17IiwsDNWqVUOPHj1w48YNdOrUCatWrRI7YomZmZnh6tWr8PDwwJo1a7B3715cunQJp06dwujRo1V+CKyJiQlu3rwJV1dXhfaQkBDUrl0bycnJePToEerWrYvU1FSRUpbMx7D6u7OzM44fP46qVauKHaVU/dfWYm9SxYX7bG1tsX//fjRq1AgeHh5YuHAhevXqhcePH6Nu3bpqsQe3vr4+Hj58CEdHR1SsWBGnT59GrVq1EBwcjAYNGiA+Pl7siEQAOFSbiKjMfAxzR4OCguRb2/z2229o3rw5fvnlF1y6dAl9+/ZVi8I5JycHOjo6AIAzZ87I91GtUqUKoqOjxYymFLq6urh8+XKhwvny5cvQ1dUFAOTn58v/rYoEQSjyv8XAwECYm5uLkEj5FixYgNmzZ2PHjh2FetbViSoWw++jR48e6N+/P9zc3BAfH48OHToAAAICAgr9N6qqrK2tkZCQAEdHRzg4OODq1auoVasWwsPDwf49Kk9YOBMRlZGPYe6oIAjyRYjOnDmDzp07AwDs7e0RFxcnZjSlqV69OjZu3IhOnTrh9OnTWLBgAQDg+fPnsLCwEDldyY0fPx6jR4/GzZs3UbduXQAFf6d+fn74+uuvAQAnT55Uyb1/X63+LpFI4O7u/tbV39XB8uXLERoaCisrKzg5OUFLS0vhcVUctvwxWrlyJZycnBAVFYVly5bJtxOLjo7GmDFjRE6nHK1atcKRI0fg7e2NYcOGYdKkSdi/fz/8/f0Lbf9HJCYO1SaicuFjWAH2Y5g72qpVK9jb26NNmzYYMWIEHjx4AFdXV1y8eBFDhgzBkydPxI5YYhcuXED37t2RkpKCIUOGYOvWrQCAr7/+Go8ePcLBgwdFTlhyu3fvxrp16/D48WMAgIeHB8aPH4/+/fsDKJgL/GqkhCr5mLa/+68hzKrcU/vqBsi7SEhIKOU0VFLh4eGws7OTr4GxZ88eXL58GW5ubmjfvn2huepEYmHhTETlQr9+/YpdAXbixIkiJVOej2Hu6J07dzBgwABERkZi8uTJ8g/n48ePR3x8PH755ReREypHXl4eUlJSFLa7efLkCfT19QvNm6XyJTc3F7t375bf5CHVs2PHDvm/4+PjsXDhQrRr105+0+PKlSs4efIkZs2ahUmTJokVk97Rx7DmAKkHFs5EVC58DCvAWllZ4fvvv8fgwYMV2nfu3Ilp06bhxYsXePDgAZo3b47Y2FiRUpaOzMxMaGhoFBouSuVXdnZ2kaM/Xq1crMreXIyIVFvPnj3RsmVLjBs3TqF93bp1OHPmDA4dOiROMHpnUqkUMTExhQrniIgIVKtWDWlpaSIlI1LEOc5EVC6YmZmpzaI8b6POc0fflJSUhP379yM0NBTTpk2Dubk5Hjx4ACsrK9jZ2Ykdj/5DcHAwhg8fjsuXLyu0v1pQSx16f+rVq4eAgAC1Lpzz8vKwcuVK7Nu3D5GRkcjOzlZ4XF2GMJ88eRJLly4t1N6+fXvMnDlThET0riZPngygYIHM2bNnKyxil5eXh2vXrqn8+yGpFxbORFQufAwrwH777bdwdnbGunXrsGvXLgAFc0c3b94snzs6evRofPHFF2LGLJE7d+6gdevWMDU1xZMnT+Dr6wtzc3McPHgQkZGR2Llzp9gR6T8MHToUmpqaOHr0aJHTJtTBmDFjMGXKFDx9+hS1a9eGgYGBwuM1a9YUKZnyzJs3D35+fpgyZQq+/fZbfPPNN3jy5AkOHTqkFvtUv2JhYYHDhw9jypQpCu2HDx9W6cX6/vrrLzRq1Aiamur7UT0gIABAwU25u3fvyuc4AwVrDtSqVQtTp04VKx5RIRyqTUTlgre3N0JDQyEIAleAVWFt2rSBj48Pli1bBiMjIwQGBsLFxQWXL19G//791WJxMHVnYGCAmzdvokqVKmJHKTVSqbRQm0QiUate9cqVK2PNmjXo1KkTjIyMcPv2bXnb1atX1Wa9ge3bt2PkyJHo0KED6tevDwC4du0aTpw4gc2bN2Po0KHiBvxAb5v3q46GDRuG1atXw9jYWOwoRMVS39tYRKRSunXrJnaEMqPOc0dv3LiBTZs2FWq3s7NDTEyMCInofVWrVk1ttg57m/DwcLEjlLqYmBh4enoCAAwNDZGcnAwA6Ny5c6GV/VXZ0KFDUbVqVaxZs0a+on3VqlXx999/ywtpVfQx9Wtt27ZN7AhE74SFMxGVC6q8Ncq7+hjmjuro6CAlJaVQe1BQECpUqCBCIuVYs2bNOx87YcKEUkxS+pYuXYrp06dj0aJF8PT0LDT6Qx16hdR5bvMrlSpVQnR0NBwcHFC5cmWcOnUKPj4+uHHjBnR0dMSOp1T169fH7t27xY6hdOo4TYJIlXGoNhFRGWncuDE0NTUxc+bMIueO1qpVS6RkyjNy5EjEx8dj3759MDc3x507d6ChoYFu3bqhWbNmWLVqldgRP4izs7PC97GxsUhPT4epqSmAggXRXm1Fpep7jr8axvzvv091usHzyoMHD4pcOKtr164iJVKemTNnwtjYGF9//TX27t2LgQMHwsnJCZGRkZg0aRKWLFkidsQPVtTNubdR1Rs9UqkUHTp0+M+bHOqwbzyRqmDhTESiMTc3R1BQECwtLWFmZlbs3XV1WAH2Y5g7mpycjM8++wz+/v5ITU2Fra0tYmJi0LBhQxw/frzQIkyq6JdffsH69euxZcsWeHh4AAAeP34MX19ffP755xgwYIDICUvm4sWLxT7evHnzMkpSesLCwtC9e3fcvXtXPrcZeH2zQJ1uDrxy9epVXL58GW5ubujSpYvYcUpEKpX+Z2+sqt/okUql6N27N/T09Io9jsOcicoOC2ciEs2OHTvQt29f6OjoYMeOHcUeO2TIkDJKVXrq1q2LlStXokmTJmJHKXV///037ty5A5lMBh8fH7Rp00bsSEpTuXJl7N+/H97e3grtN2/exGefffZRzJ9VdV26dIGGhgb8/Pzg7OyM69evIz4+HlOmTMEPP/yApk2bih2RivFfN3fepKo3et62tzERiYeFMxFRGTl37hy+/fZbtZ47+jHQ19fHxYsX5Xtxv3L9+nW0aNEC6enpIiX7cHfu3EGNGjUglUpx586dYo9Vh62aLC0tce7cOdSsWRMmJia4fv06PDw8cO7cOUyZMkW+TQ6phqSkJGzZsgUPHz4EULDA3YgRI2BiYiJysg/3Ma2qTaQqWDgTUbmTmZlZaM6hOhSV6jp39GNaOAso6K189uwZ/Pz84OPjA6Cgt3nUqFGws7PDkSNHRE74/t7s3Xo1DLaojweq/Hf6JjMzM9y6dQvOzs6oXLky/Pz80LJlS4SGhsLT01Mlb358rPz9/dG+fXvo6uqiXr16AApW98/IyJAviKaK2ONMVP5wVW0iKhfS0tIwY8YM7Nu3D/Hx8YUeV4cP6+fPnxc7QqlYuXLlOx0nkUjUonDeunUrhgwZgjp16shHDeTm5qJdu3bw8/MTOd2HCQ8Pl696/jEMNa9RowYCAwPh7OyM+vXrY9myZdDW1sZPP/0EFxcXsePRe5g0aRK6dOmCzZs3Q1Oz4GNtbm4uRo4ciS+//BJ//fWXyAk/zPnz52Fubi52DCJ6A3uciahcGDt2LM6fP48FCxZg0KBB+PHHH/Hs2TNs2rQJS5YsUfkFl0j9BAUF4eHDh5BIJKhSpQrc3d3FjqQUf/31Fxo1aiQvQl7Jzc3F5cuX0axZM5GSKc/JkyeRlpaGHj16ICQkBJ07d0ZQUBAsLCywd+9etGrVSuyI9I709PQQEBBQaNHFBw8eoE6dOio7euDKlSuIj49H586d5W07d+7EnDlzkJaWhm7dumHt2rVqt7UYUXnGwpmIygUHBwfs3LkTLVq0gLGxMW7dugVXV1fs2rULv/76K44fPy52xA/ysc0dfdO/VypWR+p4jm+bWxkfH4+KFSuqxeiPoiQkJPzn6v6qJikpCfv370doaCimTZsGc3Nz3Lp1C1ZWVrCzsxM7nlJYWVlh165daNu2rUL7yZMnMXjwYLx48UKkZCXToUMHtGjRAjNmzAAA3L17Fz4+Phg6dCiqVq2K77//Hp9//jnmzp0rblCij4hU7ABEREDBh9ZXQySNjY3l2081adJEZYfaAYCXlxfi4uLk//b29oaXl1ehr3+v0KzKdu7cCU9PT+jp6UFPTw81a9bErl27xI6lVOp8jq/m3P9bfHy8Wmwn9qaQkBCcPHkSGRkZajcs9s6dO3B3d8fSpUvxww8/ICkpCUDBvr9fffWVuOGUqE+fPhgxYgT27t2LqKgoREVFYc+ePRg5ciT69esndrwPdvv2bbRu3Vr+/Z49e1C/fn1s3rwZkydPxpo1a7Bv3z4RExJ9fDjHmYjKBRcXF4SHh8PBwQFVqlTBvn37UK9ePfzxxx8wNTUVO94H+9jmjq5YsQKzZs3CuHHj0LhxYwAFW1ONHj0acXFxmDRpksgJS05dz7FHjx4ACnrPhw4dqjAENC8vD3fu3EGjRo3EiqdU8fHx6N27N86fPw+JRILg4GC4uLhgxIgRMDMzw/Lly8WOWGKTJ0/G0KFDsWzZMhgZGcnbO3bsiP79+4uYTLl++OEHSCQSDB48GLm5uQAALS0tfPHFF1iyZInI6T5cYmIirKys5N9fvHgRHTp0kH9ft25dREVFiRGN6KPFwpmIyoVhw4YhMDAQzZs3x8yZM9GlSxesW7cOOTk5WLFihdjxPpijo6P83xEREcXOHX3zWFW1du1abNiwAYMHD5a3de3aFdWrV8fcuXNVtqh8k7qe46utewRBgJGREfT09OSPaWtro0GDBvD19RUrnlJNmjQJWlpaiIyMRNWqVeXtffr0weTJk9WicL5x4wY2bdpUqN3Ozg4xMTEiJCod2traWL16NRYvXozQ0FAABXut6+vri5ysZKysrBAeHg57e3tkZ2fj1q1bmDdvnvzx1NTUQlsaElHpYuFMROXCm8VGmzZt8PDhQ/k8Z3WZ+9uyZcsi544mJyejZcuWajF3NDo6usheyUaNGiE6OlqERMqnrue4bds2AICTkxOmTp2qdsOy33Tq1CmcPHkSlSpVUmh3c3NDRESESKmUS0dHBykpKYXag4KC5KNg1Im+vj48PT3FjqE0HTt2xMyZM7F06VIcOnQI+vr6aNq0qfzxO3fuoHLlyiImJPr4cI4zEZVLTk5O6NGjh9oUzcDHMXfU1dW1yHl3e/fuhZubmwiJlE/dz3H69OkKf6cRERFYtWoVTp06JWIq5UpLSyuyRzIhIUFtVinu2rUr5s+fj5ycHAAFQ/AjIyMxY8YM9OzZU+R09F8WLFgATU1NNG/eHJs3b8bmzZuhra0tf3zr1q2FFkQjotLFVbWJqNw4e/YsVq5ciYcPHwIAqlatii+//BJt2rQROVnJvJo7evjwYbRv377IuaMeHh44ceKEWBGV5sCBA+jTpw/atGkjn/976dIlnD17Fvv27UP37t1FTlhy6n6Obdu2RY8ePTB69GgkJSXBw8MD2traiIuLw4oVK/DFF1+IHbHEOnbsiNq1a2PBggUwMjLCnTt34OjoiL59+yI/Px/79+8XO2KJJScn47PPPoO/vz9SU1Nha2uLmJgYNGzYEMePH1ebm3XqLjk5GYaGhtDQ0FBoT0hIgKGhoUIxTUSli4UzEZUL69evx8SJE/HZZ5+hYcOGAICrV69i//79WLlyJcaOHStywg83bNgwAMCOHTvQu3fvQnNHnZyc4OvrC0tLS7EiKtXNmzcL3QCZMmWKWq0crs7naGlpiYsXL6J69erw8/PD2rVrERAQgAMHDmD27Nnyc1Zl9+7dQ+vWreHj44Nz586ha9euuH//PhISEnDp0iW1GgJ76dIlBAYGQiaTwcfHR+VvRBIRiYWFMxGVC5UqVcLMmTMxbtw4hfYff/wRixYtwrNnz0RKpjzz5s1T+7mjpPr09fXx6NEjODg4oHfv3qhevTrmzJmDqKgoeHh4ID09XeyISpGcnIx169YpFJVjx46FjY2N2NFKTVJSkkrvUkBEJCYWzkRULhgaGuL27dtwdXVVaA8ODoa3tzdkMplIyZQnIyMDgiDI51ZGRETg999/R7Vq1dRqrlp+fj5CQkLw8uVL5OfnKzzWrFkzkVIpV15eHg4dOiTvfa1evTq6du1aaDilKqpZsyZGjhyJ7t27o0aNGjhx4gQaNmyImzdvolOnTmq1IrM6W7p0KZycnNCnTx8AQO/evXHgwAFYW1vj+PHjqFWrlsgJiYhUCwtnIioX+vfvD29vb0ybNk2h/YcffoC/vz/27NkjUjLl+Rjmjl69ehX9+/dHREQE/v32IpFI1GLl8JCQEHTq1AlPnz6Fh4cHAODx48ewt7fHsWPHVH6Y7/79+9G/f3/k5eWhVatWOH36NABg8eLF+Ouvv/Dnn3+KnFA5kpKScP369SJv8Ly51ZiqcnZ2xu7du9GoUSOcPn0avXv3xt69e7Fv3z5ERkaq1WJvRERlgYUzEZULCxcuxA8//IDGjRsrzHG+dOkSpkyZAmNjY/mxEyZMECtmiXwMc0e9vLzg7u6OefPmwcbGptAq4q/2ClZlHTt2hCAI2L17N8zNzQEUrIw+cOBASKVSHDt2TOSEJRcTE4Po6GjUqlULUmnBBhzXr1+HsbExqlSpInK6kvvjjz8wYMAAyGQyGBsbK/ydSiQSJCQkiJhOOfT09BAUFAR7e3tMnDgRmZmZ2LRpE4KCglC/fn0kJiaKHZGISKWwcCaicsHZ2fmdjpNIJAgLCyvlNKXjY5g7amBggMDAwEJD7tWJgYEBrl69WmjP2MDAQDRu3FgtphUABT3roaGhaNasGfT09N66nZoqcnd3R8eOHbFo0aIit6VSB7a2tti/fz8aNWoEDw8PLFy4EL169cLjx49Rt27dIvd4JiKit9MUOwAREQCEh4eLHaHUubq64tChQ+jevTtOnjyJSZMmAQBevnyp0KOuyurXr4+QkBC1Lpx1dHSQmppaqF0mk6nF1jDx8fHo3bs3zp8/D4lEguDgYLi4uGDEiBEwMzPD8uXLxY5YYs+ePcOECRPUtmgGCrbB69+/P9zc3BAfH48OHToAAAICAtT6v08iotIiFTsAEdHHYvbs2Zg6dSqcnJxQr149+ZD0U6dOqfQ2Rnfu3JF/jR8/HlOmTMH27dtx8+ZNhcfu3LkjdlSl6Ny5M0aNGoVr165BEAQIgoCrV69i9OjR6Nq1q9jxSmzSpEnQ0tJCZGSkQmHZp08ftdhrHADatWsHf39/sWOUqpUrV2LcuHGoVq0aTp8+DUNDQwBAdHQ0xowZI3I6IiLVw6HaRERlSB3njkqlUkgkkkKLgb3y6jF1WRwsKSkJQ4YMwR9//AEtLS0AQG5uLrp27Yrt27er/Dxua2trnDx5ErVq1YKRkRECAwPh4uKCsLAw1KxZU2WHoh85ckT+79jYWMyfPx/Dhg2Dp6en/Pf4ijrcACEiIuVi4UxEVMbUbe5oRETEOx/r6OhYiknKVnBwMB49egQAqFq1qtoMfzUyMsKtW7fg5uamUDj7+/ujXbt2iI+PFzviB3l1o+q/qPINniNHjqBDhw7Q0tJSuFFQFN4cICJ6PyyciYjKyNvmjg4fPlxt5o4uXrwYVlZWGD58uEL71q1bERsbixkzZoiUjN5Vx44dUbt2bSxYsABGRka4c+cOHB0d0bdvX+Tn52P//v1iR6S3kEqliImJQcWKFYu9UaDKNweIiMTCwpmIyoXIyEjY29sX6nkVBAFRUVFwcHAQKZnyDB48GC9fvoSfnx+qVq0q78k7efIkJk+ejPv374sdscScnJzwyy+/oFGjRgrt165dQ9++fVV2EbjJkye/87ErVqwoxSSl7969e2jdujV8fHxw7tw5dO3aFffv30dCQgIuXbqk8vtUExERfQiuqk1E5YKzszOio6NRsWJFhfaEhAQ4OzurRe/IqVOncPLkSVSqVEmh3c3N7b2GO5dnMTExsLGxKdReoUIFREdHi5BIOQICAt7pOFUecv9KjRo1EBQUhHXr1sHIyAgymQw9evTA2LFji/zdEhERfQxYOBNRufC2eb4ymQy6uroiJFK+tLS0Ire/SUhIgI6OjgiJlM/e3h6XLl0qtC/3pUuXYGtrK1Kqkjt//rzYEcpETk4O2rdvj40bN+Kbb74ROw6VwIQJE+Dq6ooJEyYotK9btw4hISFYtWqVOMGIiFQUC2ciEtWrIbASiQSzZs1SKCzz8vJw7do1eHl5iZROuZo2bYqdO3diwYIFAArOOT8/H8uWLUPLli1FTqccvr6++PLLL5GTk4NWrVoBAM6ePYvp06djypQpIqdTvqioKAAFNwzUgZaWltpsG/axO3DgQJELhDVq1AhLlixh4UxE9J5YOBORqF4NgRUEAXfv3oW2trb8MW1tbdSqVQtTp04VK55SLVu2DK1bt4a/vz+ys7Mxffp0hbmj6mDatGmIj4/HmDFjkJ2dDQDQ1dXFjBkz8NVXX4mcTjlyc3Mxb948rFmzRr41k6GhIcaPH485c+YU2tpI1QwcOBBbtmzBkiVLxI5CJRAfH1/k1mjGxsaIi4sTIRERkWrj4mBEVC4MGzYMq1evhrGxsdhRSlVycjLWrVuHwMBAyGQy+Pj4qOXcUZlMhocPH0JPTw9ubm5qMxQdAL744gscPHgQ8+fPR8OGDQEAV65cwdy5c9GtWzds2LBB5IQlM378eOzcuRNubm6oXbs2DAwMFB5X9cXPAGD79u0YOnRoofbc3FzMmjULixcvLvtQSlajRg2MHj0a48aNU2hfu3YtNmzYgAcPHoiUjIhINbFwJqJyKSUlBefOnUOVKlVQpUoVseOU2JtzR93c3MSOQyVgYmKCPXv2oEOHDgrtx48fR79+/ZCcnCxSMuUobtqARCLBuXPnyjBN6TA2Nka7du3w008/wczMDADw+PFj9O/fH/Hx8Xjy5Im4AZVg69atGDduHKZNm6YwbWL58uVYtWoVfH19RU5IRKRaOFSbiMqF3r17o1mzZhg3bhwyMjJQp04dPHnyBIIgYM+ePejZs6fYEUuEc0fVh46ODpycnAq1Ozs7K0w1UFUfw0JoAQEBGDhwIDw9PbFt2zYEBQVh+vTp6NatG9avXy92PKUYPnw4srKy8N1338nXVXBycsKGDRswePBgkdMREakeqdgBiIgA4K+//kLTpk0BAL///jsEQUBSUhLWrFmDhQsXipxOOV7NHSXVNm7cOCxYsABZWVnytlcFyr+HxVL5VLlyZVy6dAk9evRA+/btMWnSJPj5+WH37t1FzgtWNbm5udi5cyd69OiBp0+f4sWLF0hJSUFYWBiLZiKiD8QeZyIqF5KTk2Fubg4AOHHiBHr27Al9fX106tQJ06ZNEzmdcuTm5mLr1q04c+aM2s4dVVc9evRQ+P7MmTOoVKkSatWqBQAIDAxEdnY2WrduLUY8+gDHjh3Dnj170LBhQwQFBWHLli1o3ry5Sm+b9oqmpiZGjx6Nhw8fAijYR52IiEqGhTMRlQv29va4cuUKzM3NceLECezZswcAkJiYqDb7ON+7dw8+Pj4AgKCgIIXHitrDmsqPf/dC/nvqgLpsR/Wx+Pzzz7Fjxw589913mDx5Ml68eIHhw4fD09MTGzZsQO/evcWOWGL16tVDQEAAHB0dxY5CRKQWuDgYEZUL69evx8SJE2FoaAgHBwcEBARAKpVi7dq1OHjw4Ecx75KIykaNGjWwe/du+YiBV3788UfMmDFDvs2YKtu3bx+++uorTJo0qcgRLjVr1hQpGRGRamLhTETlhr+/P6KiovDJJ5/A0NAQQMFwSlNTUzRu3FjkdESkLrKyst66Rdrjx4/h4eFRxomUTyotvIyNRCKBIAiQSCTIy8sTIRURkepi4UxE5Up2djbCw8NRuXJlaGpyNgkR0YeIiIgo9nEO4SYiej8snImoXEhPT8f48eOxY8cOAAVzgF1cXDB+/HjY2dlh5syZIickInWyf/9+7Nu3D5GRkcjOzlZ47NatWyKlIiKi8orbURFRufDVV18hMDAQFy5cUFgMrE2bNti7d6+IyYhI3axZswbDhg2DlZUVAgICUK9ePVhYWCAsLAwdOnQQO57S7Nq1C40bN4atra28B3rVqlU4fPiwyMmIiFQPC2ciKhcOHTqEdevWoUmTJgorTFevXh2hoaEiJiMidbN+/Xr89NNPWLt2LbS1tTF9+nScPn0aEyZMQHJysodVYngAAAvbSURBVNjxlGLDhg2YPHkyOnbsiKSkJPmcZlNTU6xatUrccEREKogTCImoXIiNjUXFihULtaelpXGrJipX1qxZU2S7RCKBrq4uXF1d0axZM2hoaJRxMnpXkZGRaNSoEQBAT08PqampAIBBgwahQYMGWLdunZjxlGLt2rXYvHkzunXrhiVLlsjb69Spg6lTp4qYjIhINbFwJqJyoU6dOjh27BjGjx8P4PW+xn5+fmjYsKGY0YgUrFy5ErGxsUhPT4eZmRmAgv3G9fX1YWhoiJcvX8LFxQXnz5/n/s7llLW1NRISEuDo6AgHBwdcvXoVtWrVQnh4ONRl6Zfw8HB4e3sXatfR0UFaWpoIiYiIVBuHahNRubBo0SJ8/fXX+OKLL5Cbm4vVq1ejbdu22LZtG7777jux4xHJLVq0CHXr1kVwcDDi4+MRHx+PoKAg1K9fH6tXr0ZkZCSsra0xadIksaPSW7Rq1QpHjhwBAAwbNgyTJk3CJ598gj59+qB79+4ip1MOZ2dn3L59u1D7iRMnULVq1bIPRESk4riqNhGVG6GhoViyZAkCAwMhk8ng4+ODGTNmwNPTU+xoRHKVK1fGgQMH4OXlpdAeEBCAnj17IiwsDJcvX0bPnj0RHR0tTkgqVn5+PvLz8+Vb3u3ZsweXL1+Gm5sbPv/8c2hra4ucsOT8/Pwwd+5cLF++HCNGjICfnx9CQ0OxePFi+Pn5oW/fvmJHJCJSKSyciYiI3oO+vj7++usv1KlTR6H9xo0baN68OdLT0/HkyRPUqFEDMplMpJREwO7duzF37lz5Aou2traYN28eRowYIXIyIiLVw8KZiMqN/Px8hISE4OXLl8jPz1d4rFmzZiKlIlLUqVMnxMTEwM/PTz6HNCAgAL6+vrC2tsbRo0fxxx9/4Ouvv8bdu3dFTktvk5mZiTt37hR5venatatIqUpHeno6ZDJZkQswEhHRu2HhTETlwtWrV9G/f39EREQUWpxHIpHIt1IhEltMTAwGDRqEs2fPQktLCwCQm5uL1q1bY9euXbCyssL58+eRk5ODtm3bipyWinLixAkMHjwYcXFxhR5Tl+vN3LlzMXv2bEilisvZJCcnY/To0fj1119FSkZEpJpYOBNRueDl5QV3d3fMmzcPNjY2hbagMjExESkZUdEePXqEoKAgAICHhwc8PDxETkTvys3NDW3btsXs2bNhZWUldpxSYW9vD3t7e/z8889wcXEBAFy4cAGDBw+GtbU1rl+/LnJCIiLVwsKZiMoFAwMDBAYGwtXVVewoRKTmjI2NERAQgMqVK4sdpdQkJibi888/x4kTJ7B8+XIEBQVh9erVmDZtGubNmydfGI2IiN4Nr5pEVC7Ur18fISEhLJyp3MvLy8P27dtx9uzZIufHnjt3TqRk9K4+++wzXLhwQa0LZzMzM+zbtw9ff/01Pv/8c2hqauLPP/9E69atxY5GRKSS2ONMRKK5c+eO/N+hoaH49ttvMW3aNHh6esrnjr5Ss2bNso5HVKRx48Zh+/bt6NSpU5HTClauXClSMnpX6enp6NWrFypUqFDk9WbChAkiJVOutWvXYubMmejWrRtu3rwJDQ0N/PLLL6hVq5bY0YiIVA4LZyISjVQqhUQiKbQY2CuvHlOXxXpIPVhaWmLnzp3o2LGj2FHoA23ZsgWjR4+Grq4uLCwsFG5+SCQShIWFiZhOOdq3bw9/f39s3LgRn332GTIyMjB58mRs374d8+bNw/Tp08WOSESkUlg4E5FoIiIi3vlYR0fHUkxC9O5sbW1x4cIFuLu7ix2FPpC1tTUmTJiAmTNnFlp1Wl188skn2LFjB2xtbRXajx07hpEjRyI6OlqkZEREqomFMxER0XtYvnw5wsLCsG7dukLDtEk1mJub48aNG2o9x7k4cXFxsLS0FDsGEZFKYeFMROXC4sWLYWVlheHDhyu0b926FbGxsZgxY4ZIyYgUde/eHefPn4e5uTmqV69eaH7swYMHRUpG72rSpEmoUKECvv76a7GjlKr//e9/2LRpE0JDQ7F//37Y2dlh165dcHZ2RpMmTcSOR0SkUriqNhGVC5s2bcIvv/xSqL169ero27cvC2cqN0xNTdG9e3exY1AJ5OXlYdmyZTh58iRq1qxZ6ObHihUrREqmPAcOHMCgQYMwYMAABAQEICsrCwCQnJyMRYsW4fjx4yInJCJSLexxJqJyQVdXFw8fPoSzs7NCe1hYGKpVq4bMzEyRkhGRumnZsuVbH5NIJGqxpZi3tzcmTZqEwYMHw8jICIGBgXBxcUFAQAA6dOiAmJgYsSMSEakU9jgTUblgb2+PS5cuFSqcL126VGhxGyKikjh//rzYEUrd48eP0axZs0LtJiYmSEpKKvtAREQqjoUzEZULvr6++PLLL5GTk4NWrVoBAM6ePYvp06djypQpIqejj52Pjw/Onj0LMzMzeHt7F7so2K1bt8owGVHRrK2tERISAicnJ4X2v//+Gy4uLuKEIiJSYSyciahcmDZtGuLj4zFmzBhkZ2cDKBi+PWPGDHz11Vcip6OP3aeffgodHR35v7maNpV3vr6+mDhxIrZu3QqJRILnz5/jypUrmDp1KmbNmiV2PCIilcM5zkRUrshkMjx8+BB6enpwc3OTFytERPTuBEHAokWLsHjxYqSnpwMAdHR0MHXqVCxYsEDkdEREqoeFMxER0XtwcXHBjRs3YGFhodCelJQEHx8fhIWFiZSMqLDs7GyEhIRAJpOhWrVqMDQ0FDsSEZFKYuFMRET0HqRSKWJiYlCxYkWF9hcvXsDe3l4+1YCIiIjUB+c4ExERvYMjR47I/33y5EmYmJjIv8/Ly8PZs2cLrQpPRERE6oE9zkRERO9AKpUCKNjn999vnVpaWnBycsLy5cvRuXNnMeIRERFRKWLhTERE9B6cnZ1x48YNWFpaih2FiIiIyggLZyIiohJKSkqCqamp2DGIiIiolEjFDkBERKRKli5dir1798q/79WrF8zNzWFnZ4fAwEARkxEREVFpYeFMRET0HjZu3Ah7e3sAwOnTp3HmzBmcOHECHTp0wLRp00ROR0RERKWBq2oTERG9h5iYGHnhfPToUfTu3Rtt27aFk5MT6tevL3I6IiIiKg3scSYiInoPZmZmiIqKAgCcOHECbdq0AQAIgoC8vDwxoxEREVEpYY8zERHRe+jRowf69+8PNzc3xMfHo0OHDgCAgIAAuLq6ipyOiIiISgMLZyIiovewcuVKODk5ISoqCsuWLYOhoSEAIDo6GmPGjBE5HREREZUGbkdFREREREREVAz2OBMREX2ABw8eIDIyEtnZ2QrtXbt2FSkRERERlRYWzkRERO8hLCwM3bt3x927dyGRSPBq4JZEIgEALhBGRESkhriqNhER0XuYOHEinJ2d8fLlS+jr6+P+/fv466+/UKdOHVy4cEHseERERFQKOMeZiIjoPVhaWuLcuXOoWbMmTExMcP36dXh4eODcuXOYMmUKAgICxI5IRERESsYeZyIioveQl5cHIyMjAAVF9PPnzwEAjo6OePz4sZjRiIiIqJRwjjMREdF7qFGjBgIDA+Hs7Iz69etj2bJl0NbWxk8//QQXFxex4xEREVEp4FBtIiKi93Dy5EmkpaWhR48eCAkJQefOnREUFAQLCwvs3bsXrVq1EjsiERERKRkLZyIiov9w584d1KhRA1Jp0TOcEhISYGZmJl9Zm4iIiNQL5zgTERH9B29vb8TFxQEAXFxcEB8fr/C4ubk5i2YiIiI1xsKZiIjoP5iamiI8PBwA8OTJE+Tn54uciIiIiMoSFwcjIiL6Dz179kTz5s1hY2MDiUSCOnXqQENDo8hjw8LCyjgdERERlTYWzkRERP/hp59+ki8GNmHCBPj6+sq3pCIiIiL1x8XBiIiI3sOwYcOwZs0aFs5EREQfERbORERERERERMXg4mBERERERERExWDhTERERERERFQMFs5ERERERERExWDhTERERERERFQMFs5ERERERERExWDhTERERERERFQMFs5ERERERERExWDhTERERERERFSM/wOLqG+LKt6iqgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense"
      ],
      "metadata": {
        "id": "BhrFeTn8nwBF"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "모델 training 용 (952개의 데이터 중 80%) -> train_X, train_y   \n",
        "\n",
        "test용 (952개의 데이터 중 20%) -> test_X, test_y\n"
      ],
      "metadata": {
        "id": "wobl8JuFhYOL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "위 히트맵을 보고 'max heart rate' column을 삭제하고 모델을 돌렸으나, 오히려 accuracy가 낮아져서 포함하고 진행함"
      ],
      "metadata": {
        "id": "6EdcPYyjY1lf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# X = X.drop(['max heart rate'], axis = 1)\n",
        "# test = test.drop(['max heart rate'], axis = 1)"
      ],
      "metadata": {
        "id": "_AyMNA9RYOU3"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_X, test_X, train_y, test_y = train_test_split(X, y,test_size=0.2 ,random_state = 42)\n",
        "\n",
        "test = test_df.drop(['target'], axis=1)\n",
        "label = test_df['target']"
      ],
      "metadata": {
        "id": "oBTrJvVgOZ_h"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(Dense(32, activation='relu', input_dim=11))\n",
        "model.add(Dense(1, activation='sigmoid'))"
      ],
      "metadata": {
        "id": "1LaWSoMSOtiS"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='adam',\n",
        "              loss='binary_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "history = model.fit(train_X, train_y, epochs = 150,\n",
        "                    batch_size = 10,\n",
        "                    validation_data=(test_X, test_y),\n",
        "                    )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YzoORQxDlSSi",
        "outputId": "542cfbad-28f2-4d90-dcfc-1e71550fc6b9"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/150\n",
            "77/77 [==============================] - 1s 6ms/step - loss: 6.0007 - accuracy: 0.3758 - val_loss: 1.9369 - val_accuracy: 0.3927\n",
            "Epoch 2/150\n",
            "77/77 [==============================] - 0s 6ms/step - loss: 1.2449 - accuracy: 0.5545 - val_loss: 0.6953 - val_accuracy: 0.6806\n",
            "Epoch 3/150\n",
            "77/77 [==============================] - 0s 6ms/step - loss: 0.8897 - accuracy: 0.6689 - val_loss: 0.5920 - val_accuracy: 0.7225\n",
            "Epoch 4/150\n",
            "77/77 [==============================] - 0s 6ms/step - loss: 0.6643 - accuracy: 0.7346 - val_loss: 0.4671 - val_accuracy: 0.8063\n",
            "Epoch 5/150\n",
            "77/77 [==============================] - 0s 6ms/step - loss: 0.5994 - accuracy: 0.7411 - val_loss: 0.4756 - val_accuracy: 0.7853\n",
            "Epoch 6/150\n",
            "77/77 [==============================] - 0s 6ms/step - loss: 0.5464 - accuracy: 0.7714 - val_loss: 0.6264 - val_accuracy: 0.6649\n",
            "Epoch 7/150\n",
            "77/77 [==============================] - 0s 6ms/step - loss: 0.5283 - accuracy: 0.7687 - val_loss: 0.4312 - val_accuracy: 0.8168\n",
            "Epoch 8/150\n",
            "77/77 [==============================] - 0s 5ms/step - loss: 0.5666 - accuracy: 0.7451 - val_loss: 0.5726 - val_accuracy: 0.7225\n",
            "Epoch 9/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.5269 - accuracy: 0.7700 - val_loss: 0.4368 - val_accuracy: 0.8220\n",
            "Epoch 10/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4838 - accuracy: 0.7792 - val_loss: 0.3967 - val_accuracy: 0.8482\n",
            "Epoch 11/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4587 - accuracy: 0.7989 - val_loss: 0.4185 - val_accuracy: 0.8325\n",
            "Epoch 12/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.5263 - accuracy: 0.7661 - val_loss: 0.5626 - val_accuracy: 0.7539\n",
            "Epoch 13/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4632 - accuracy: 0.8003 - val_loss: 0.3793 - val_accuracy: 0.8325\n",
            "Epoch 14/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4653 - accuracy: 0.8081 - val_loss: 0.4389 - val_accuracy: 0.8220\n",
            "Epoch 15/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4440 - accuracy: 0.8173 - val_loss: 0.4494 - val_accuracy: 0.8168\n",
            "Epoch 16/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4398 - accuracy: 0.8042 - val_loss: 0.4447 - val_accuracy: 0.8220\n",
            "Epoch 17/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.5052 - accuracy: 0.7740 - val_loss: 0.4473 - val_accuracy: 0.8429\n",
            "Epoch 18/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4342 - accuracy: 0.8134 - val_loss: 0.3862 - val_accuracy: 0.8429\n",
            "Epoch 19/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4232 - accuracy: 0.8147 - val_loss: 0.5023 - val_accuracy: 0.8010\n",
            "Epoch 20/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4453 - accuracy: 0.8121 - val_loss: 0.3909 - val_accuracy: 0.8325\n",
            "Epoch 21/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4444 - accuracy: 0.8121 - val_loss: 0.3738 - val_accuracy: 0.8534\n",
            "Epoch 22/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4822 - accuracy: 0.7898 - val_loss: 0.5177 - val_accuracy: 0.7487\n",
            "Epoch 23/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4927 - accuracy: 0.7963 - val_loss: 0.4188 - val_accuracy: 0.8063\n",
            "Epoch 24/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4977 - accuracy: 0.7766 - val_loss: 0.4948 - val_accuracy: 0.7539\n",
            "Epoch 25/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.5146 - accuracy: 0.7727 - val_loss: 0.3917 - val_accuracy: 0.8325\n",
            "Epoch 26/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4394 - accuracy: 0.8173 - val_loss: 0.4391 - val_accuracy: 0.7801\n",
            "Epoch 27/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4241 - accuracy: 0.8226 - val_loss: 0.3700 - val_accuracy: 0.8534\n",
            "Epoch 28/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4452 - accuracy: 0.8055 - val_loss: 0.4019 - val_accuracy: 0.8115\n",
            "Epoch 29/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.5165 - accuracy: 0.7674 - val_loss: 0.3772 - val_accuracy: 0.8429\n",
            "Epoch 30/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4642 - accuracy: 0.8042 - val_loss: 0.3656 - val_accuracy: 0.8534\n",
            "Epoch 31/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4240 - accuracy: 0.8331 - val_loss: 0.3699 - val_accuracy: 0.8534\n",
            "Epoch 32/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4208 - accuracy: 0.8239 - val_loss: 0.3686 - val_accuracy: 0.8534\n",
            "Epoch 33/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4271 - accuracy: 0.8121 - val_loss: 0.3839 - val_accuracy: 0.8482\n",
            "Epoch 34/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4568 - accuracy: 0.8068 - val_loss: 0.4406 - val_accuracy: 0.7853\n",
            "Epoch 35/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4871 - accuracy: 0.7898 - val_loss: 0.3719 - val_accuracy: 0.8534\n",
            "Epoch 36/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4245 - accuracy: 0.8187 - val_loss: 0.4735 - val_accuracy: 0.7801\n",
            "Epoch 37/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4263 - accuracy: 0.8239 - val_loss: 0.3707 - val_accuracy: 0.8482\n",
            "Epoch 38/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4467 - accuracy: 0.8187 - val_loss: 0.5761 - val_accuracy: 0.7382\n",
            "Epoch 39/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4312 - accuracy: 0.8279 - val_loss: 0.3941 - val_accuracy: 0.8482\n",
            "Epoch 40/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4249 - accuracy: 0.8147 - val_loss: 0.4755 - val_accuracy: 0.8272\n",
            "Epoch 41/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4780 - accuracy: 0.8029 - val_loss: 0.3834 - val_accuracy: 0.8377\n",
            "Epoch 42/150\n",
            "77/77 [==============================] - 0s 5ms/step - loss: 0.4236 - accuracy: 0.8239 - val_loss: 0.3662 - val_accuracy: 0.8482\n",
            "Epoch 43/150\n",
            "77/77 [==============================] - 0s 6ms/step - loss: 0.4164 - accuracy: 0.8213 - val_loss: 0.3768 - val_accuracy: 0.8482\n",
            "Epoch 44/150\n",
            "77/77 [==============================] - 0s 5ms/step - loss: 0.4501 - accuracy: 0.8134 - val_loss: 0.4375 - val_accuracy: 0.8010\n",
            "Epoch 45/150\n",
            "77/77 [==============================] - 0s 5ms/step - loss: 0.4318 - accuracy: 0.8239 - val_loss: 0.3916 - val_accuracy: 0.8168\n",
            "Epoch 46/150\n",
            "77/77 [==============================] - 0s 6ms/step - loss: 0.4618 - accuracy: 0.8016 - val_loss: 0.3772 - val_accuracy: 0.8429\n",
            "Epoch 47/150\n",
            "77/77 [==============================] - 0s 6ms/step - loss: 0.4174 - accuracy: 0.8160 - val_loss: 0.6195 - val_accuracy: 0.7435\n",
            "Epoch 48/150\n",
            "77/77 [==============================] - 0s 6ms/step - loss: 0.4703 - accuracy: 0.7898 - val_loss: 0.3693 - val_accuracy: 0.8377\n",
            "Epoch 49/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4629 - accuracy: 0.8029 - val_loss: 0.3701 - val_accuracy: 0.8429\n",
            "Epoch 50/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4312 - accuracy: 0.8095 - val_loss: 0.4385 - val_accuracy: 0.7906\n",
            "Epoch 51/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4357 - accuracy: 0.8160 - val_loss: 0.5023 - val_accuracy: 0.7644\n",
            "Epoch 52/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4239 - accuracy: 0.8226 - val_loss: 0.3740 - val_accuracy: 0.8325\n",
            "Epoch 53/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4357 - accuracy: 0.7950 - val_loss: 0.4416 - val_accuracy: 0.8220\n",
            "Epoch 54/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4796 - accuracy: 0.7898 - val_loss: 0.4136 - val_accuracy: 0.8063\n",
            "Epoch 55/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4358 - accuracy: 0.8173 - val_loss: 0.4194 - val_accuracy: 0.8272\n",
            "Epoch 56/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4231 - accuracy: 0.8410 - val_loss: 0.4130 - val_accuracy: 0.8115\n",
            "Epoch 57/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4017 - accuracy: 0.8371 - val_loss: 0.3768 - val_accuracy: 0.8377\n",
            "Epoch 58/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4114 - accuracy: 0.8279 - val_loss: 0.3744 - val_accuracy: 0.8377\n",
            "Epoch 59/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4762 - accuracy: 0.8042 - val_loss: 0.3793 - val_accuracy: 0.8429\n",
            "Epoch 60/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4335 - accuracy: 0.8252 - val_loss: 0.3694 - val_accuracy: 0.8377\n",
            "Epoch 61/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4111 - accuracy: 0.8357 - val_loss: 0.5129 - val_accuracy: 0.7853\n",
            "Epoch 62/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4298 - accuracy: 0.8252 - val_loss: 0.3823 - val_accuracy: 0.8325\n",
            "Epoch 63/150\n",
            "77/77 [==============================] - 0s 3ms/step - loss: 0.4155 - accuracy: 0.8265 - val_loss: 0.5454 - val_accuracy: 0.7382\n",
            "Epoch 64/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4710 - accuracy: 0.8173 - val_loss: 0.3776 - val_accuracy: 0.8377\n",
            "Epoch 65/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4197 - accuracy: 0.8344 - val_loss: 0.4577 - val_accuracy: 0.7853\n",
            "Epoch 66/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4249 - accuracy: 0.8200 - val_loss: 0.4636 - val_accuracy: 0.8010\n",
            "Epoch 67/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4456 - accuracy: 0.7989 - val_loss: 0.3806 - val_accuracy: 0.8325\n",
            "Epoch 68/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4774 - accuracy: 0.7989 - val_loss: 0.4187 - val_accuracy: 0.8063\n",
            "Epoch 69/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4354 - accuracy: 0.8121 - val_loss: 0.4096 - val_accuracy: 0.8168\n",
            "Epoch 70/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4276 - accuracy: 0.8108 - val_loss: 0.3872 - val_accuracy: 0.8220\n",
            "Epoch 71/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4304 - accuracy: 0.8108 - val_loss: 0.3657 - val_accuracy: 0.8325\n",
            "Epoch 72/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4033 - accuracy: 0.8213 - val_loss: 0.3984 - val_accuracy: 0.8220\n",
            "Epoch 73/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4180 - accuracy: 0.8226 - val_loss: 0.4428 - val_accuracy: 0.7958\n",
            "Epoch 74/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4359 - accuracy: 0.8187 - val_loss: 0.3679 - val_accuracy: 0.8429\n",
            "Epoch 75/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4417 - accuracy: 0.8147 - val_loss: 0.3674 - val_accuracy: 0.8377\n",
            "Epoch 76/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4254 - accuracy: 0.8187 - val_loss: 0.4475 - val_accuracy: 0.8063\n",
            "Epoch 77/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4597 - accuracy: 0.8108 - val_loss: 0.4730 - val_accuracy: 0.7853\n",
            "Epoch 78/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4475 - accuracy: 0.8173 - val_loss: 0.4314 - val_accuracy: 0.7958\n",
            "Epoch 79/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4543 - accuracy: 0.8042 - val_loss: 0.4914 - val_accuracy: 0.7696\n",
            "Epoch 80/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4264 - accuracy: 0.8108 - val_loss: 1.0265 - val_accuracy: 0.5864\n",
            "Epoch 81/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.5588 - accuracy: 0.7792 - val_loss: 0.4264 - val_accuracy: 0.8115\n",
            "Epoch 82/150\n",
            "77/77 [==============================] - 0s 5ms/step - loss: 0.4376 - accuracy: 0.8095 - val_loss: 0.5937 - val_accuracy: 0.7330\n",
            "Epoch 83/150\n",
            "77/77 [==============================] - 0s 5ms/step - loss: 0.4172 - accuracy: 0.8292 - val_loss: 0.3640 - val_accuracy: 0.8534\n",
            "Epoch 84/150\n",
            "77/77 [==============================] - 0s 5ms/step - loss: 0.4426 - accuracy: 0.8055 - val_loss: 0.3713 - val_accuracy: 0.8377\n",
            "Epoch 85/150\n",
            "77/77 [==============================] - 0s 5ms/step - loss: 0.4214 - accuracy: 0.8016 - val_loss: 0.3731 - val_accuracy: 0.8429\n",
            "Epoch 86/150\n",
            "77/77 [==============================] - 0s 6ms/step - loss: 0.4885 - accuracy: 0.8003 - val_loss: 0.4060 - val_accuracy: 0.8377\n",
            "Epoch 87/150\n",
            "77/77 [==============================] - 0s 5ms/step - loss: 0.4871 - accuracy: 0.8055 - val_loss: 0.4253 - val_accuracy: 0.8115\n",
            "Epoch 88/150\n",
            "77/77 [==============================] - 0s 6ms/step - loss: 0.4459 - accuracy: 0.7989 - val_loss: 0.3648 - val_accuracy: 0.8429\n",
            "Epoch 89/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4211 - accuracy: 0.8213 - val_loss: 0.3785 - val_accuracy: 0.8429\n",
            "Epoch 90/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4275 - accuracy: 0.8134 - val_loss: 0.3994 - val_accuracy: 0.8377\n",
            "Epoch 91/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4387 - accuracy: 0.8173 - val_loss: 0.5338 - val_accuracy: 0.8063\n",
            "Epoch 92/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4419 - accuracy: 0.8147 - val_loss: 0.4711 - val_accuracy: 0.7801\n",
            "Epoch 93/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4352 - accuracy: 0.8279 - val_loss: 0.3760 - val_accuracy: 0.8429\n",
            "Epoch 94/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4036 - accuracy: 0.8252 - val_loss: 0.7178 - val_accuracy: 0.6911\n",
            "Epoch 95/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4360 - accuracy: 0.8160 - val_loss: 0.4454 - val_accuracy: 0.8115\n",
            "Epoch 96/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4335 - accuracy: 0.8068 - val_loss: 0.3676 - val_accuracy: 0.8482\n",
            "Epoch 97/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4024 - accuracy: 0.8305 - val_loss: 0.4808 - val_accuracy: 0.8115\n",
            "Epoch 98/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4334 - accuracy: 0.8213 - val_loss: 0.5721 - val_accuracy: 0.7435\n",
            "Epoch 99/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4534 - accuracy: 0.8226 - val_loss: 0.4046 - val_accuracy: 0.8010\n",
            "Epoch 100/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4079 - accuracy: 0.8279 - val_loss: 0.4271 - val_accuracy: 0.8115\n",
            "Epoch 101/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4145 - accuracy: 0.8239 - val_loss: 0.3680 - val_accuracy: 0.8377\n",
            "Epoch 102/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4382 - accuracy: 0.8252 - val_loss: 0.3664 - val_accuracy: 0.8377\n",
            "Epoch 103/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4054 - accuracy: 0.8187 - val_loss: 0.3959 - val_accuracy: 0.8272\n",
            "Epoch 104/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4158 - accuracy: 0.8147 - val_loss: 0.4061 - val_accuracy: 0.8220\n",
            "Epoch 105/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4029 - accuracy: 0.8226 - val_loss: 0.4172 - val_accuracy: 0.8220\n",
            "Epoch 106/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4298 - accuracy: 0.8187 - val_loss: 0.3844 - val_accuracy: 0.8377\n",
            "Epoch 107/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4201 - accuracy: 0.8318 - val_loss: 0.3662 - val_accuracy: 0.8377\n",
            "Epoch 108/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.3982 - accuracy: 0.8160 - val_loss: 0.4810 - val_accuracy: 0.8010\n",
            "Epoch 109/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4009 - accuracy: 0.8397 - val_loss: 0.3650 - val_accuracy: 0.8272\n",
            "Epoch 110/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4314 - accuracy: 0.8226 - val_loss: 0.5489 - val_accuracy: 0.7853\n",
            "Epoch 111/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4315 - accuracy: 0.8239 - val_loss: 0.3782 - val_accuracy: 0.8325\n",
            "Epoch 112/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4412 - accuracy: 0.8147 - val_loss: 0.3619 - val_accuracy: 0.8377\n",
            "Epoch 113/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4308 - accuracy: 0.8226 - val_loss: 0.3951 - val_accuracy: 0.8377\n",
            "Epoch 114/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4405 - accuracy: 0.8213 - val_loss: 0.4401 - val_accuracy: 0.8115\n",
            "Epoch 115/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4010 - accuracy: 0.8200 - val_loss: 0.4740 - val_accuracy: 0.8220\n",
            "Epoch 116/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4446 - accuracy: 0.8213 - val_loss: 0.3794 - val_accuracy: 0.8272\n",
            "Epoch 117/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4490 - accuracy: 0.8081 - val_loss: 0.4159 - val_accuracy: 0.8272\n",
            "Epoch 118/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4423 - accuracy: 0.8147 - val_loss: 0.4219 - val_accuracy: 0.8010\n",
            "Epoch 119/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.3997 - accuracy: 0.8331 - val_loss: 0.3706 - val_accuracy: 0.8377\n",
            "Epoch 120/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4620 - accuracy: 0.8200 - val_loss: 0.4176 - val_accuracy: 0.8063\n",
            "Epoch 121/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.3996 - accuracy: 0.8331 - val_loss: 0.4008 - val_accuracy: 0.8325\n",
            "Epoch 122/150\n",
            "77/77 [==============================] - 0s 5ms/step - loss: 0.4285 - accuracy: 0.8357 - val_loss: 0.3779 - val_accuracy: 0.8534\n",
            "Epoch 123/150\n",
            "77/77 [==============================] - 0s 6ms/step - loss: 0.4307 - accuracy: 0.8239 - val_loss: 0.3656 - val_accuracy: 0.8377\n",
            "Epoch 124/150\n",
            "77/77 [==============================] - 0s 5ms/step - loss: 0.4219 - accuracy: 0.8226 - val_loss: 0.3815 - val_accuracy: 0.8429\n",
            "Epoch 125/150\n",
            "77/77 [==============================] - 0s 6ms/step - loss: 0.4038 - accuracy: 0.8449 - val_loss: 0.3855 - val_accuracy: 0.8325\n",
            "Epoch 126/150\n",
            "77/77 [==============================] - 1s 7ms/step - loss: 0.4151 - accuracy: 0.8279 - val_loss: 0.4456 - val_accuracy: 0.8115\n",
            "Epoch 127/150\n",
            "77/77 [==============================] - 1s 7ms/step - loss: 0.4112 - accuracy: 0.8279 - val_loss: 0.4610 - val_accuracy: 0.7853\n",
            "Epoch 128/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4417 - accuracy: 0.8173 - val_loss: 0.3687 - val_accuracy: 0.8482\n",
            "Epoch 129/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.3971 - accuracy: 0.8410 - val_loss: 0.3787 - val_accuracy: 0.8325\n",
            "Epoch 130/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4247 - accuracy: 0.8121 - val_loss: 0.4131 - val_accuracy: 0.8272\n",
            "Epoch 131/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4916 - accuracy: 0.7832 - val_loss: 0.3881 - val_accuracy: 0.8272\n",
            "Epoch 132/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4265 - accuracy: 0.8265 - val_loss: 0.3810 - val_accuracy: 0.8482\n",
            "Epoch 133/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4089 - accuracy: 0.8200 - val_loss: 0.4280 - val_accuracy: 0.8168\n",
            "Epoch 134/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.3975 - accuracy: 0.8371 - val_loss: 0.3624 - val_accuracy: 0.8482\n",
            "Epoch 135/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4204 - accuracy: 0.8160 - val_loss: 0.5527 - val_accuracy: 0.7749\n",
            "Epoch 136/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4419 - accuracy: 0.8213 - val_loss: 0.4434 - val_accuracy: 0.8010\n",
            "Epoch 137/150\n",
            "77/77 [==============================] - 0s 5ms/step - loss: 0.4533 - accuracy: 0.8042 - val_loss: 0.3773 - val_accuracy: 0.8325\n",
            "Epoch 138/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4451 - accuracy: 0.8068 - val_loss: 0.3789 - val_accuracy: 0.8377\n",
            "Epoch 139/150\n",
            "77/77 [==============================] - 0s 5ms/step - loss: 0.4175 - accuracy: 0.8410 - val_loss: 0.3820 - val_accuracy: 0.8429\n",
            "Epoch 140/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4309 - accuracy: 0.8279 - val_loss: 0.3806 - val_accuracy: 0.8482\n",
            "Epoch 141/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4035 - accuracy: 0.8200 - val_loss: 0.4584 - val_accuracy: 0.8063\n",
            "Epoch 142/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.3960 - accuracy: 0.8331 - val_loss: 0.3738 - val_accuracy: 0.8482\n",
            "Epoch 143/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4028 - accuracy: 0.8160 - val_loss: 0.4310 - val_accuracy: 0.8220\n",
            "Epoch 144/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4296 - accuracy: 0.8134 - val_loss: 0.3930 - val_accuracy: 0.8115\n",
            "Epoch 145/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4104 - accuracy: 0.8121 - val_loss: 0.3920 - val_accuracy: 0.8220\n",
            "Epoch 146/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4141 - accuracy: 0.8305 - val_loss: 0.3681 - val_accuracy: 0.8325\n",
            "Epoch 147/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4595 - accuracy: 0.7976 - val_loss: 0.3792 - val_accuracy: 0.8429\n",
            "Epoch 148/150\n",
            "77/77 [==============================] - 0s 5ms/step - loss: 0.4341 - accuracy: 0.8108 - val_loss: 0.3700 - val_accuracy: 0.8325\n",
            "Epoch 149/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.3993 - accuracy: 0.8384 - val_loss: 0.3599 - val_accuracy: 0.8377\n",
            "Epoch 150/150\n",
            "77/77 [==============================] - 0s 4ms/step - loss: 0.4172 - accuracy: 0.8173 - val_loss: 0.3888 - val_accuracy: 0.8220\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_loss, test_accuracy = model.evaluate(test, label)\n",
        "print(f\"Test Loss: {test_loss:.4f}, Test Accuracy: {test_accuracy:.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S2bWK0iFnUEY",
        "outputId": "95b6038c-728b-4f11-a17a-a80b6b5fd35d"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "8/8 [==============================] - 0s 2ms/step - loss: 0.4108 - accuracy: 0.8193\n",
            "Test Loss: 0.4108, Test Accuracy: 0.8193\n"
          ]
        }
      ]
    }
  ]
}