MultiOutputExample.ipynb

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        "<a href=\"https://colab.research.google.com/github/fatimaAfzaal/Multiple-grade-prediction-Multi-Target-Problem-/blob/main/MultiOutputExample.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "source": [
        "# Student Grade Prediction Project"
      ],
      "metadata": {
        "id": "uQhasmlRUq5s"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## STEP 1:\n",
        "### Problem definition\n",
        "We have to define the problem and set goal that we we have to do"
      ],
      "metadata": {
        "id": "DeDXMTDaUy51"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## STEP 2:\n",
        "### Data Collection and Exploration\n",
        "This is the step where we have to collect the data required. As in our project we get the dataset from kaggle(https://www.kaggle.com/datasets/dipam7/student-grade-prediction)\n",
        "\n",
        "Also in this step we will explore data to get insight and use visualizations as well if needed to understand data"
      ],
      "metadata": {
        "id": "RSCqaLqSWm26"
      }
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    {
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        },
        "id": "7Y0-k9ksdQ2a",
        "outputId": "75cc3e5a-7687-4529-c4b8-ece3327e4d97"
      },
      "outputs": [
        {
          "output_type": "stream",
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          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Data Collection"
      ],
      "metadata": {
        "id": "2jY1KmmPYzkY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.utils import shuffle\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "df = pd.read_csv('/content/gdrive/MyDrive/Colab Notebooks/'+\"student.csv\")\n",
        "\n",
        "df"
      ],
      "metadata": {
        "colab": {
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          "height": 423
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        "id": "4aOCc4F_deIL",
        "outputId": "b2aa316e-c895-4120-fb8b-8bda2b499f89"
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      "execution_count": null,
      "outputs": [
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              "    school sex  age address famsize Pstatus  Medu  Fedu      Mjob      Fjob  \\\n",
              "0       GP   F   18       U     GT3       A     4     4   at_home   teacher   \n",
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              "..     ...  ..  ...     ...     ...     ...   ...   ...       ...       ...   \n",
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              "\n",
              "     ... feespaid  ecactivities  internet  freetime goout health absences  G1  \\\n",
              "0    ...       no            no        no         3     4      3        6   5   \n",
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              "394  ...       no            no       yes         2     3      5        5   8   \n",
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              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-606d6dc4-c869-4983-a9df-d5de5e532e68 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Exploration"
      ],
      "metadata": {
        "id": "DgK1zQrrgtmw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('Total number of students: ',len(df))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o8ntAuSgdrXE",
        "outputId": "bad88cf0-2ea7-467f-90ef-e574a269c743"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total number of students:  395\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Parameter are: \",df.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UrBtOSjRg8Rq",
        "outputId": "561ec45b-f06b-4946-dd96-e243cb652e49"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Parameter are:  Index(['school', 'sex', 'age', 'address', 'famsize', 'Pstatus', 'Medu', 'Fedu',\n",
            "       'Mjob', 'Fjob', 'guardian', 'traveltime', 'studytime', 'failures',\n",
            "       'Ssupport', 'Gsupport', 'feespaid', 'ecactivities', 'internet',\n",
            "       'freetime', 'goout', 'health', 'absences', 'G1', 'G2', 'G3'],\n",
            "      dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6PYLBN8nhAbb",
        "outputId": "1b62c267-953e-44f4-fbaf-3a88fe0a0a33"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 395 entries, 0 to 394\n",
            "Data columns (total 26 columns):\n",
            " #   Column        Non-Null Count  Dtype \n",
            "---  ------        --------------  ----- \n",
            " 0   school        395 non-null    object\n",
            " 1   sex           395 non-null    object\n",
            " 2   age           395 non-null    int64 \n",
            " 3   address       395 non-null    object\n",
            " 4   famsize       395 non-null    object\n",
            " 5   Pstatus       395 non-null    object\n",
            " 6   Medu          395 non-null    int64 \n",
            " 7   Fedu          395 non-null    int64 \n",
            " 8   Mjob          393 non-null    object\n",
            " 9   Fjob          395 non-null    object\n",
            " 10  guardian      395 non-null    object\n",
            " 11  traveltime    395 non-null    int64 \n",
            " 12  studytime     395 non-null    int64 \n",
            " 13  failures      395 non-null    int64 \n",
            " 14  Ssupport      395 non-null    object\n",
            " 15  Gsupport      395 non-null    object\n",
            " 16  feespaid      395 non-null    object\n",
            " 17  ecactivities  395 non-null    object\n",
            " 18  internet      395 non-null    object\n",
            " 19  freetime      395 non-null    int64 \n",
            " 20  goout         395 non-null    int64 \n",
            " 21  health        395 non-null    int64 \n",
            " 22  absences      395 non-null    int64 \n",
            " 23  G1            395 non-null    int64 \n",
            " 24  G2            395 non-null    int64 \n",
            " 25  G3            395 non-null    int64 \n",
            "dtypes: int64(13), object(13)\n",
            "memory usage: 80.4+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "PDQ89gKWhH7P",
        "outputId": "519f1273-ecc4-42ff-f5b8-839e27f47b66"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              age        Medu        Fedu  traveltime   studytime    failures  \\\n",
              "count  395.000000  395.000000  395.000000  395.000000  395.000000  395.000000   \n",
              "mean    16.696203    2.749367    2.521519    1.448101    2.035443    0.334177   \n",
              "std      1.276043    1.094735    1.088201    0.697505    0.839240    0.743651   \n",
              "min     15.000000    0.000000    0.000000    1.000000    1.000000    0.000000   \n",
              "25%     16.000000    2.000000    2.000000    1.000000    1.000000    0.000000   \n",
              "50%     17.000000    3.000000    2.000000    1.000000    2.000000    0.000000   \n",
              "75%     18.000000    4.000000    3.000000    2.000000    2.000000    0.000000   \n",
              "max     22.000000    4.000000    4.000000    4.000000    4.000000    3.000000   \n",
              "\n",
              "         freetime       goout      health    absences          G1          G2  \\\n",
              "count  395.000000  395.000000  395.000000  395.000000  395.000000  395.000000   \n",
              "mean     3.235443    3.108861    3.554430    5.708861   10.908861   10.713924   \n",
              "std      0.998862    1.113278    1.390303    8.003096    3.319195    3.761505   \n",
              "min      1.000000    1.000000    1.000000    0.000000    3.000000    0.000000   \n",
              "25%      3.000000    2.000000    3.000000    0.000000    8.000000    9.000000   \n",
              "50%      3.000000    3.000000    4.000000    4.000000   11.000000   11.000000   \n",
              "75%      4.000000    4.000000    5.000000    8.000000   13.000000   13.000000   \n",
              "max      5.000000    5.000000    5.000000   75.000000   19.000000   19.000000   \n",
              "\n",
              "               G3  \n",
              "count  395.000000  \n",
              "mean    10.415190  \n",
              "std      4.581443  \n",
              "min      0.000000  \n",
              "25%      8.000000  \n",
              "50%     11.000000  \n",
              "75%     14.000000  \n",
              "max     20.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6e299d99-a63a-45fb-b2d9-6719ad750eaf\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>Medu</th>\n",
              "      <th>Fedu</th>\n",
              "      <th>traveltime</th>\n",
              "      <th>studytime</th>\n",
              "      <th>failures</th>\n",
              "      <th>freetime</th>\n",
              "      <th>goout</th>\n",
              "      <th>health</th>\n",
              "      <th>absences</th>\n",
              "      <th>G1</th>\n",
              "      <th>G2</th>\n",
              "      <th>G3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>395.000000</td>\n",
              "      <td>395.000000</td>\n",
              "      <td>395.000000</td>\n",
              "      <td>395.000000</td>\n",
              "      <td>395.000000</td>\n",
              "      <td>395.000000</td>\n",
              "      <td>395.000000</td>\n",
              "      <td>395.000000</td>\n",
              "      <td>395.000000</td>\n",
              "      <td>395.000000</td>\n",
              "      <td>395.000000</td>\n",
              "      <td>395.000000</td>\n",
              "      <td>395.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>16.696203</td>\n",
              "      <td>2.749367</td>\n",
              "      <td>2.521519</td>\n",
              "      <td>1.448101</td>\n",
              "      <td>2.035443</td>\n",
              "      <td>0.334177</td>\n",
              "      <td>3.235443</td>\n",
              "      <td>3.108861</td>\n",
              "      <td>3.554430</td>\n",
              "      <td>5.708861</td>\n",
              "      <td>10.908861</td>\n",
              "      <td>10.713924</td>\n",
              "      <td>10.415190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.276043</td>\n",
              "      <td>1.094735</td>\n",
              "      <td>1.088201</td>\n",
              "      <td>0.697505</td>\n",
              "      <td>0.839240</td>\n",
              "      <td>0.743651</td>\n",
              "      <td>0.998862</td>\n",
              "      <td>1.113278</td>\n",
              "      <td>1.390303</td>\n",
              "      <td>8.003096</td>\n",
              "      <td>3.319195</td>\n",
              "      <td>3.761505</td>\n",
              "      <td>4.581443</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>15.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>16.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>17.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>18.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>14.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>22.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>20.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6e299d99-a63a-45fb-b2d9-6719ad750eaf')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-6e299d99-a63a-45fb-b2d9-6719ad750eaf button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6e299d99-a63a-45fb-b2d9-6719ad750eaf');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-6e950de6-0a42-478a-a4b7-030e14deff4c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6e950de6-0a42-478a-a4b7-030e14deff4c')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6e950de6-0a42-478a-a4b7-030e14deff4c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['G3'].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SH9RQ67RhMZR",
        "outputId": "b642f9ab-2da3-4233-ed5d-1dde963c1ed5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    395.000000\n",
              "mean      10.415190\n",
              "std        4.581443\n",
              "min        0.000000\n",
              "25%        8.000000\n",
              "50%       11.000000\n",
              "75%       14.000000\n",
              "max       20.000000\n",
              "Name: G3, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 8))\n",
        "correlation_matrix = df.corr()\n",
        "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')\n",
        "plt.title('Correlation Heatmap')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 811
        },
        "id": "IAihkrfdXGKX",
        "outputId": "899c3d41-ed9e-4061-d6b6-96cd7b76c38a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-94-6688a53f2a0d>:2: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  correlation_matrix = df.corr()\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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sjUlIRYdQaqu/OlDRIVRpkU3tKjqEUktMregIyqZ5taSKDqFMDlxxqugQSs3bpaCiQyiTqAsVHUHZtIjIr+gQymTXCZOKDqHU4uOzKjqEMinIV1d0CGXy6/v2FR2CQRV5H3now2eL/ODz+PHjy+VHt/ft20f79u311nXq1Il33nmnzPt+GKkICSGEEEIIIR5q3LhxjBw5Um/do1qDSiomJgY3Nze9dW5ubqSlpZGdnY2FhUW5vM+DpCIkhBBCCCGEeKiSdIOrbKQiJIQQQgghRCWgMFFUdAhPhLu7O7GxsXrrYmNjsbW1fWKtQSCzxgkhhBBCCCEqUNOmTdmyZYveuk2bNtG0adMn+r7SIiSEEEIIIUQloDSuHC1CGRkZXLp0Sff31atXiYqKwtHREV9fX8aNG8ft27eZP38+AK+++iq//PILo0eP5sUXX2Tr1q38+eefrFmz5onGKS1CQgghhBBCiHJz+PBh6tatS926dQEYOXIkdevW5ZNPPgEgOjqaGzdu6NIHBASwZs0aNm3aRO3atfn++++ZOXMmnTp1eqJxSouQEEIIIYQQoty0bt2ah/1U6dy5cw1uc+zYsScYVVFSERJCCCGEEKISUJhIZ67yJLkphBBCCCGEqHKkRUgIIYQQQohKoLJMllBZSIuQEEIIIYQQosqRFiEhhBBCCCEqgf/qD6pWFGkREkIIIYQQQlQ5UhESQgghhBBCVDnSNU4IIYQQQohKQCZLKF/SIiSEEEIIIYSocqRFSAghhBBCiEpAJksoX9IiJIQQQgghhKhypEXoP8CxRQMCRw3Drl4E5p6uHO7zOrErt1R0WAB0a25Oi1qmWJgpuHKngIUbs4lPURebPtjbiA4NzfF1N8LeWsm0ZZkcv5RfJJ27o5JerSyo5mOMUgHRiSqmr8gkOV1TJeM/uHUBe9fPIiM1AXefUJ4a+BFegbWKTX/60Hq2LZ9MSsJtnNz8aN/3ParVaqV7fcKwUIPbte/3Ps07DwNg5+ppXDyxnZib5zAyMmHsL4dKFKtGo+Hg+p85vf8vcrPT8AioR+u+47F38X/odid2L+DYtllkpSfg7BlKy14f4eZXeIwF+bnsWfk1F46tQV2Qj09Ic1r3HY+ljXORfWVnJrPou55kpsby0pcHMbOwBSAzLY7dK74m7tYpUhNuULvFICJ7fVCi47pn45olrFq6gNTkJHwDghn6ykiCq4cbTLtlwwp2bV3PretXAAgIDmHA4FeLTT9zyjdsWb+cQcPfpkuPAY8VV3GO71rA4a2zyEqLx9krlDZ9Psbdr/iyc+HYOvatnUxa0m3sXfxp0f09AmoUlp1LxzdyYs8i4m6eJicrhYHvL8fVO6zIfu5cPcbeNT8Sc/0ESoUSF+8wer06C2NT8zIdz56NC9m+eg7pqQl4+IbQa8gH+AYbPp6YW5fY8NfP3Lp6huSEOzw9aAwtnxqsl2bvpkXs27yYpITbALh7BdO+92uE1YksU5z3tKqppG6QAnMTuJmgYd0hNUkZD9+mQTUFTUOVWFtAbDKsP6LiTpJ+Gi8naFNbiZeTAo0GYpJh4XYVBSqws4LIGkr83RRYm0N6Npy6pmHXGTXq4r/eHmn7ukVsXDmPtJREvP2qM2DYGAKq1TSY9s7NS6xa9CvXr5whKT6afkPfo1235/XSqFUqVv85jQO71pCWkoidgwtNWz9Nl74voVCUz1Px1rWU1A2+m//xGtYeUpOU/vBtGlRX0CysMP/XHVZxJ1E/jbfz3fx3Lsz/BVu1+X8/IyUM62SEu6OC39YWEJtctuPp0sSMZjVNsDBTcPWOisVbcx56zQryMqJdfVN8XY2ws1YyY1UWJy4XFJt+QFvtNXHJjhy2H8srW7AGVJZrrvjvkBah/wAjK0vSTpzn1FsTKjoUPR0bmdGmnhkLN2XxzYJ0cvPgrX5WGBsVv42ZiYLb8SoWbc4uNo2zvZJRA62JSVLxw6IMvpiXzrp9OUUuMFUl/lMH17Jx8f/R6ukRvDJ+KW4+Ifzx43Ay0xINpr956ShLpo+ibmRfXhm/jJC67Vn0yxvE3bqgSzPqh116y9MvfAkKBeH1O+rSqAryCG/QmQatn3mseI9uncnxXb/Tut+n9HvnT0xMLVj523AK8nOL3ebisbXsXvF/NOw0ggEjl+LkGcLK6cPJSi88xt0rJnL19DaeGjKZXiPmk5kWx9o5bxrc39bFH+HsEVJkvaogDwtrRxq2fw1nT8OVwYfZt2szv8/8iT7PvshXk+bgFxDM/33yLqkpSQbTnz15jGYt2/PRVz8z4dvfcHJ2ZeIn75CUGF8k7aF9O7h0/jQOjkUrdqV1/uhadi6bSJNOIxj4/jJcPENZ9uswvXy9352rR1k3fxQ1mvTlufeXE1SzHatmjSDhTmHZyc/LwjOwHi2efq/Y971z9RjLpw3HL6QFz478i2dG/U3tyOdAWbZLUtS+daz84xs69H6dd778C0/fEGb83yukpxo+nrzcbBxdfejyzLvY2BvOVztHN7o88y7vfPEX73zxJ8E1GjP3+zeIuXWpTLECNAtT0Ki6grWH1MzepCK/AAa2McLoIdkQ7qugQ10lO0+pmbFeRWyKhoFtjLA0K0zj5QQDWxtxJVrD7A0qZm1QcfiCGs3dez5nWwUKBaw9pGbaWhWbjqmpV01B21qlz//Dezbw97zv6dbvFT745n94+1fn5y9eJy3VcNnPy83B2c2LXs+9jW0xeb9h+Rx2bPyLZ4aNZfykpfR6/m02rpjLtrX/K3Wc92sWrqBRiII1B9XM2qDN/+celf9+CjrWU7LjpJrpa1XEJGt47oH893bWnscr0RpmrVcxc52KQ+cL8/9+7esqSc8un5vx9g1MaVXXlMVbcvh+USa5+Rpe72VZgmuWmj+35Txy/7WCjPH3MCIlowy15YeoLNfciqY0VlTY8l9UZSpC69evp0WLFtjb2+Pk5ES3bt24fPmy7vW9e/dSp04dzM3NadCgAcuXL0ehUBAVFaVLc+rUKZ566imsra1xc3Nj0KBBJCQkVMDR6IvfsJML4ycRu2JzRYeip219M9btz+HEpQJux6uZuzYTO2sldaqZFLvN6asFrNydw/GLRZ/o3NOjhTmnrxSwbEcOt+JUJKSoOXG5gPSs8n2yU1ni379xLvVa9qNuiz64eAbTbdAETEzNObZ7icH0Bzb/TnBEC5p3HoaLZxBte72Nh184B7cu0KWxtnPRW84f20pASGMcXHx0adr0fIumHYfi5lW9xLFqNBqO75xPgw6vEhjRDmfPENoP/JrMtDiunCq+/EbtmEuNJv0Ib9QHR/dg2vSdgLGJOWcPao8xNzudMweW0KLHGLyrNcHVJ4L2z0wk5toxYq5F6e3r5J7/kZudRt02LxZ5H1tHb1r2+pDQhj0xNbcu8XHds2b5Itp2eprW7bvh7RvAsNdHY2pmxvZNqw2mf+O9T+nYtQ/+gdXx8vHn5TfHoVGrOXX8sF66pMR45v72AyNGjcfIuPwa8o9un0NEs/7UaNIHJ/dg2vWfgLGpOaf3Gy47x3bMxz80kgbthuPoHkSzru/g6h3O8V1/6NKENexJk85v4FO9abHvu3PZROq0HETDDi/j5FENR7dAqtftgrGxaZmOZ8faeTRu05dGrXvh7h1Mn2HjMTEz59COpQbT+wbVpPtz71G3WfHvXaN+G8LqtsTFww8XD3+eGvA2puaWXL94vEyxAjQKUbLrtJoLtzXEpcCK/WpsLCDUu/gbjiYhSo5d1nD8qoaENFhzSE1+AdQJLNymYz0jDl3QsPeshvg0SEyHMzc1qO7ev16O1rDqgJorMRpSMuHCbQ37z6oJ9Sn9jc7mVb/TvH1vmrXtiadPEANf/ggTM3P2bl1uML1/cAR9Bo+kYYvOGJsY/k69cv44tRu2pmb9lji7elG/aQfCazfl2qVTpY7zfo1Dlew6pebCLW3+L9+nxsaSh+ZD01AlRy9pOH7lbv4fVJOvgrpB9+V/fSMOntew54yG+NS7+X+jMP/vCfZUEOihYNPR8qlYtK5ryoYDuZy8UsCdBDW/b8jGzkpBraDivzPOXCtgzb7ch7YCAdhZKejb2px567KLHEd5qSzXXPHfUmUqQpmZmYwcOZLDhw+zZcsWlEolvXr1Qq1Wk5aWRvfu3alZsyZHjx7l888/Z8yYMXrbp6Sk0LZtW+rWrcvhw4dZv349sbGx9O/fv4KO6N/N2U6JnbWSc9cLv1xz8uBqtIoAz9LfyCmAiCATYpNVvNnXim9et2X0c9bUDi7+i7I0Kkv8qoI87lw/TWBYs8L3UCoJDG/KrctRBre5eTmKwPBmeuuCajQvNn1GagIXT+6gbmSfUsV4v7SkW2Slx+NTvfD9zSxscPOtVaTCco+qII+4W6f1tlEolXhXb6rbJv7WadSqfL00Dm6B2Dh4EnO9cL9JMZc4tHEqHQZ+DeXUteaegvx8rl46T0TtBrp1SqWSiDoNuXi+ZDduubk5FKgKsLa21a1Tq9VM+WEC3XoPxMcvsNziVRXkEXezaL76Vm9G9LVjBreJuRqFT4h+BccvtAXRxZw7Q7LSE4m5fhxLGycW//gM0z9sxl8/Pc/ty4cfvfFDFBTkcfvqGapHFManVCqpFtGkXCotAGq1imN715KXm41ftdpl2pe9FdhYKLgaU3gzlpsPtxPBy9lw2VQqwcMRvW0ArsZq8L67jaUZeDsryMzRMLS9Ee/2MmJwOyN8HtGQaGaiILv4RtmHKsjP58aVs4TVanxfrErCajbmyvkTpdspEBhSm3MnDxB75zoAt66d59K5Y9So27zU+7zH3lqb/1cezP8EdHn5oGLzP8Zw/r/Q0YiRvY0Y0t4IHxf9fVmZQ7fGSpbvVZFfDi0TTrYK7KyUnL+pf826FqMiwOMhTSoloAAGd7Zgy5E8YpKeTC2oslxz/w0URooKW/6LqswYoT599G/iZs+ejYuLC2fOnGH37t0oFApmzJiBubk54eHh3L59m5deekmX/pdffqFu3bp89dVXevvw8fHhwoULVK9e9Kl4bm4uubn6V5Z8jRoTxX+//mlrpf3ApGXqf2mmZ6p1r5WGjZUCc1MFnRqZs3J3Dst25hDub8zLPS2ZtCiDi7fKp627ssSflZ6MRq3CytZJb72VrTMJ0VcNbpORmlAkvbWtMxlphls3j+9djqmZFWH3dYsrraw0bZcvSxv997e0cSYr3fD7Z2dqj9HCwDYpcdpjzEyLR2lkohvrc4+FtRNZd49LVZDHht9H0bz7+9g4eJKaeLPMx3O/tLQU1GoVdg6Oeuvt7B25c+t6ifaxcO5UHBydiahTWJlaueQPjJRGdO5evg9d7uVr0XPhRFLcFYPbZKYnFBlzZWlTmMclcS/f96/7hcgeo3HxDuPsweUsnTKU58euxsHV//EORBebNv+t7fSPx8bOibg7hj8LJRV94wI/jx9IQX4epuaWDH33J9y9g8u0T2sL7b+ZD/RIyszRYF3MMClLM1AqFWTkaB7YBpxttN9LDncbMlvWVLL5mJrYFA01/ZU839aI39aqDI4/crCGhtUVbI4q3U1uRnoyarUK2wfz3t6JmNvXSrVPgE69XiQnO5NP3+6JQmmERq2ix7Nv0Lhl11Lv8557eZz5QI+qjByN7tw86F7+ZxrKf1v9/G9VS8mmo2pikzXUClAyqJ0R09aodOOPejRVcuSimugk7ZitsrK10t5XpGfqx5aepdG9VlrtG5qiUsOOqPIfE3RPZbnmiv+eKlMRunjxIp988gkHDhwgISEB9d0RoTdu3OD8+fPUqlULc/PCq0+jRo30tj9+/Djbtm3D2rpod5nLly8brAhNnDiRCRP0x+08q3DkOaPy6+P/b9EwzISBHS11f09d8ojRvqV07+vwxKV8th7RVjJvxakI8jImso4ZF29llWq/lT3+J+nY7iXUbNINYxOzRyd+wIn9q1g9fzzcvTZ3Gz6tnKMrub1rvsfRLYiQBk9XWAwPs+Kv+ezbtZmPv5qCqak2r69cOsf6lX/y1aQ55TY4vKJpNNrv3prNBlCjifYBlat3ODcv7OP0gSW06D6qIsMzyMXTn5ETl5CTlcGJgxtZNO0DXvt47mNVhiL8FHRtWHhD+r8dT+YG7F4xOXpJ230OICZZTYC7EXWClGw9rn+jaWOhHU909qaGY5f/XV2FjuzdyMFda3nx7Yl4+gRx89p5/przLXaO2kkTHkeEv4Juje7L/+1POP8varvPwQP5H6WmUYgCU2MFu0+XvnWlQYgxz7QrrLFNW/Fkrh0+rkpa1zHl64WZ5bpfueaWnvI/2jJTUapMRah79+74+fkxY8YMPD09UavVREREkJdXsiccGRkZdO/ena+//rrIax4eHga3GTduHCNHjtRbt9Wx/uMHXwmcuJTPtejCqXbuDW60tVKSlll4wbGxUnIrrvQXoIxsDSqVhuhE/X1EJ6oI9i59ca6s8VvaOKBQGhWZGCEzLQFrO8MVbms75yLpM9ISsLYtmv76hcMkxlyl76s/PnZsACG12+A9vhbJd7NWpdJ+3rLSE7GyddWly0pPwNmr6MxiABZW2mPMfmAAf9Z9rRNWti6oVfnkZqfptQplZyRiefe4bl88QGL0BS69t0H74t2RyzM/bkqD9q/QuPNbpTrGe2xt7VEqjUhN1h8cnpqShP0DrUQPWr10ISuX/MEHn0/GL6Dw5vrc6eOkpSbz5ou9devUahV/zP6ZdSsX8/Msw2NfSuJevj44MUJWeiJWBmbaA7Ay0HKXlV6YxyVhZavtI+ToHqS33sE9iPTkOyXeT9HYtPmf8cDECOmpicUOxi8pY2NTnN39APAOrMHNy6fYvf4P+g7/tMT7uHBbw+37PvfGd+/Jrcwh475WIStzBTHJhiskWbmgVmuwNlege7qg24f274y7LRwJafr7SEjVYGuptwprCxjU1ohbCRpWHyz9Tbm1jQNKpRFpD+Z9StnyfunvP9Kp5ws0bNEZAC+/aiTFR7N+6ezHrghduKXht4T78v/ud7yVhX7+W5cg/60M5X+2fv7Hpz6Q/2ka7O7mv7+bAm9n+PAZ/S5rL3U24uQ1DSv2PfpcnLxSwLWYwsqD8d2bYxsrBWn3jX2xsdROJlBaQV7GWFsq+GxY4UNgI6WCXpFmtK5ryqezS1eBqazXXPHfUyVKQWJiIufPn2fGjBlERmqnPN29e7fu9ZCQEP744w9yc3MxM9M+iT10SH8q4Hr16rFkyRL8/f0xLuFgZTMzM93+7vmvdovLzafIFJepGWpCfI11X2LmphDgYcSuqFJ2RAdUam2fZzdH/QuIm6OSpNTSX8gra/xGxqZ4+tXgytl9hNZrD4BGrebK2f00avucwW18gupw9ew+mnQYolt35cxevIPqFEl7bNffePjVwN3n8WdQAzCzsMbMwhrN3cZWjUaDpY0Lty7uw+VuxScvJ4PYGyeIaP5sscfo6l2Dmxf3EViz8BhvXdxPrRbaY3TxroHSyISbF/YRXLsTAMlxV0hPvoO7n/a4nhr6EwX5hXc8sTdPsnXRh/R+4w/snHxLdXz3MzYxISA4hFMnjtCwqXY6abVazenjh+nYtfjxVSuX/MHyP+cxbsKPBFXTrwxGtulMzfu6yQFM/ORdItt0plX7snUPMjI2xdWnhjbPahXm680L+6gd+bzBbdwD6nDzwn7qtR6qW3fj/F48/OuU+H1tHb2xsnMlOU6/u1pK3DX8w1o+9nHcY2xsildAOBdP7yeiYTtAm/+XTh+geUfDZau01Bo1BQWP100orwDyHrhnTM/WEOCuIDZFe+Nqaqyd8e3IRcM34mo1RCeBv7uC87cL0wS4KTh0Qfv9kZIJaVkanGz0b9adbBVcunPfDfLdSlB0soaVB8o27sPYxATfwDDOnTxInUZt78aq5tzJg7R+6vFmlbxfXm4OigdmElQqlbpWxcfaV3H576YgNvm+/HeGw4/I/wB3Bedv3Zf/7goOnX8g/23189/RRsHlu/m//rCabfcNW7OxUPB8OyP+3q3mdkLJWuVy8yFXr7KlITVTTYiPMbfjtWXT3BT83Y3YfaL0XdoOns3n/A39iRRe72XJobP57D9T/AQFj1JZr7niv6dKVIQcHBxwcnJi+vTpeHh4cOPGDcaOHat7feDAgXz44Ye8/PLLjB07lhs3bvDdd98B6LqjjBgxghkzZvDss88yevRoHB0duXTpEosWLWLmzJkYGZVtMGJZGFlZYhVceCNnGeCNbe1Q8pJSybkZXWFxbT2SS5emZsQnq0hIVdO9hQWpGWqi7pvd5e3+VkRdzGfH3d8jMDMBF4fCvHSyU+LtakRmtlo33/+mQ7kM727JxVumXLhRQHiAMTWDTPhxUfk2rVeW+Jt0HMryWWPx9I/AK6AW+zfPIz83mzrNta0Iy2aOwcbBlfZ9tF2OGrcfxNxvBrN3w2yq12rNqYNruHPtNN0Hf6a339zsDM4c3kDHAWOKvCdAauIdsjNTSU2KRqNWEXPjLACOrr6Ymhvu9K5QKKjdcjCHN03D3tkfG0cvDqz/CStbVwIj2uvSLf91KIER7al194a8TquhbP7fWFx9InDzrcXxHfMoyMsmrJH2GM0sbAhv3Ic9K7/G3NIOU3Nrdi77Anf/OrjfvUm3c9av7GRnan+ww9EtSK8VKf629jjyc7PIzkwi/vZZbpll4e0b8IgzAV17PsOvP35BYHAowdXDWbdiMbk5ObRq3w2AqT98hoOTC88OeQ2AlX//zl8LZvLGe5/i4uZBSrL2ibq5uQXmFpbY2NphY2un9x5GxsbYOTjh6e33yHgepV7rF9i4YAxuvhG4+9bi6I555OdlE95Ym68b/hiNlZ2brrta3VaD+funQRzZOpuAGq04f3QtsTdP0W5AYdnJyUwhLTmazNQ4AF2Fx8rWGStbFxQKBfXbDmP/up9x8QrFxSuMMweXkRR3ha4v/lSm42nVZQiLpn2Ad2ANfINqsmvd7+TlZNOwVS8A/jd1HHaOrnR55l1AO8FC7C3t7KGqgnxSk+K4fe0sZuaWuhagtYt+JKR2JA7OHuRmZ3Js7xqunD3ES2OnlylWgIPn1bSooSQpXU1KhobWtZSkZ8O5+26yn2+j5Nwtje7mfP95NT2aKIlOUnAnUUOjECUmxui6wQHsO6emVYSS2BRt60btACVONvD3Fe2Nn40FDGpnRGqmhs3H1HpTPz84Zqmk2ncfxNxfPsYvKBz/4Ai2rllAXm42zdr0AGDOTx9h7+RKr+e0La8F+flE6/K+gJSkOG5ePYeZuSWuHtrPas0GLVm3ZCaOzu54+ARx8+p5Nq/+Q7fPsjpwTk1kxN38z7yb/1lw7mZhXg5qp+TcTQ2HLmjX7TunpmdTJXcStfnfOFSJiRFEXbkv/8+oaVVLSWzy3fwPVOJsC3/v0uZ/2gO9sfIKtNsmZ2hIL34W6EfafiyPTo3MiEtRk5iqplszM1IzNXozwr3R25ITl/PZeVx7HTM1ARf7wsqmk60SLxclWTkaktM1ZOVol/up1NrKXlxy+VYkKss1t6IplNI1rjxViYqQUqlk0aJFvPXWW0RERBASEsJPP/1E69atAbC1tWXVqlW89tpr1KlTh5o1a/LJJ58wcOBA3bghT09P9uzZw5gxY+jYsSO5ubn4+fnRuXNnlGX87YuysqsfQdMtv+v+Dv9O+wOQN+cv5cSwcRUVFhsP5mJqomBgJ0sszRRcvl3Az39n6s3d72JvhLVF4Qpfd2NGPlPYBN+vrbYP9L5Tecxfp716HL+Yz8KN2XRuYkb/thbEJquZviKTy7fLt893ZYk/olEXstKT2L78ZzLS4nH3CeO5d2fousalJt3RG1/iE1yP3i99x7Zlk9i69EccXf155o1fcPXWH+d26uAaNGiIaGS45WHb8p84vne57u/fJmhvNoe8Pw//0MYGtwGo13Y4BXnZbPvrk7s/qFqf7i/P0BuDlJpwQ1dRAahWtwvZGUkcXP8zmWnxuHiF0f3lGXoD91v0GIdCoWTd3LdRqfLwDWlBqz6flCAH9S3+vpfu//G3TnPh6Gq2uLqXqBta08j2pKWm8PeCGaQkJ+EXWI2xE37QdY1LiI9FcV+r8KZ1yygoyGfS/32ot58+z75I34HDHzv2xxVST5uv+9b+pP1BVe8wer46E6u7Xd3SkqPhvng9A+rRefB37Fs7ib2rf8DexZ/uw6bg7FlYdi6f2sqmhYXfO+vmaSsdjTu/QdOntL/rVK/1UFT5eexYNpGcrFRcPEPp/dps7J3L1jJXp+lTZKQlseHvX0hPScDTL5ThY3/D5u5nITkxWu8mIi05nh8/6Kv7e8eaOexYM4fAsIa8/vFcADLSklj06zjSUuIxt7TB06c6L42dTvWa+jMvlsbesxpMjDV0bajE3BRuxGtYuF2lNz2xg7XibkVFe1N35oYGSzM1rWoqsTbX/qDnwu0qvQrMwfMajJVqOtRVYmGmTbNgm4rku/d9Ae4KnGy0yzs99a9fn//v4dMoF6dB806kpyWzatGvpKUk4O0fwpsfTsXWXjuBQlKCft6nJMfx5fuFrUWbVs5n08r5VAuvz6jPZgHwzLCxrFw0hf/NmEh6WhJ2Di5EduhD176vlCrGB+09o8HUWEO3xnfzP07Dgm2PyP/rGqzM1LSufV/+b9PP/wPnNRgbqelYvzD//9hamP9PyubDeZgaK3i2nfndHyRVMXVZlt41y9leiZVF4Tn3dTPi7b6FD656t9Le8xw4k8cfG0tZKy6lynLNFf8tCo3G0E98iQULFvDCCy+QmpqKhUUxU8iUwhqToj/iWFms/upARYdQpUU2tXt0on+pxNSKjqBsmlcz/KOQlcWBK06PTvQv5e1Suhvzf4uoC49O82/WIqL03Z/+DXadqLzTJMfHV76B/PcryK/cXc9+fd++okMwaG+DhhX23s0OH3p0okqmSrQIlcT8+fMJDAzEy8uL48ePM2bMGPr371+ulSAhhBBCCCHEv4NUhO6KiYnhk08+ISYmBg8PD/r168eXX35Z0WEJIYQQQgghngCpCN01evRoRo8eXdFhCCGEEEIIYZD8jlD5+m/O5SyEEEIIIYQQDyEtQkIIIYQQQlQCMn12+ZIWISGEEEIIIUSVIy1CQgghhBBCVAIyRqh8SYuQEEIIIYQQosqRipAQQgghhBCiypGucUIIIYQQQlQCCukaV66kRUgIIYQQQghR5UiLkBBCCCGEEJWAQiltGOVJclMIIYQQQghR5UhFSAghhBBCCFHlSNc4IYQQQgghKgGFUiZLKE/SIiSEEEIIIYSocqRFSAghhBBCiEpAKdNnlytpERJCCCGEEEJUOdIi9A9b/dWBig6h1Lp90LiiQyiTtnOGVHQIZfLO7lcrOoRSi2zuUNEhlMnX0zMqOoQyad/ZqaJDKLWUzMp9mTI2UlV0CGWSmGVW0SGUSft6mRUdQqkpFZqKDqGMpOVC/PtV7iuMEEIIIYQQVYRMllC+pGucEEIIIYQQosqRFiEhhBBCCCEqAYVS2jDKk+SmEEIIIYQQosqRFiEhhBBCCCEqARkjVL6kRUgIIYQQQghR5UhFSAghhBBCCFHlSNc4IYQQQgghKgGlkXSNK0/SIiSEEEIIIYSocqRFSAghhBBCiEpAJksoX9IiJIQQQgghhKhypCIkhBBCCCGEqHKka5wQQgghhBCVgEIpbRjlSXJTCCGEEEIIUeVIi5AQQgghhBCVgEyWUL6kRUgIIYQQQghR5UhFyIDt27ejUChISUmp6FCEEEIIIYQAtC1CFbX8F1XKrnFDhw5l3rx5vPLKK0ybNk3vtREjRjB16lSGDBnC3LlzKybActStuTktapliYabgyp0CFm7MJj5FXWz6YG8jOjQ0x9fdCHtrJdOWZXL8Un6RdO6OSnq1sqCajzFKBUQnqpi+IpPkdM2TPJwiHFs0IHDUMOzqRWDu6crhPq8Tu3LLPxqDIYsOnWPevlMkZmRT3c2RMZ0bUdPLpdj0aTl5/LLtKFvP3SA1OxcPO2ve79iQyGreAMzafZIt565zLTEVM2Njanu78E67+vg72z2xY+geaUFkbTMszBRcvl3Awg2ZxCUXX3aq+RjTsbE5vm7G2NsombokneMX9cvOb2MdDW67ZGsWGw/mlCrOQ1sXsG/DLDJSE3DzCaXzsx/hFVir2PRnDq9n+/LJpCTcxtHNj3Z93qNarVZ6aeLvXGbLku+4ceEQapUKZ88g+r32E3ZOngDM/2YQ1y8c0tumXqsBdB00oVTHYEi/Tna0a2yNlYWS81dzmbk0iZiEgmLT92xrS6Oalni6mJBXoOHCtVwWrEkmOt7wNmOHu1I31IJv58Rx+HR2qeM8tmMBhzbPIjMtHhevUNr1/xgP/+Lz//zRdexZPZnUxNs4uPrTssd7BEYU5v+eNT9z/sga0pJjMDIywc23BpHd38UjoLYuzf71v3Ll1A7ibp3FyNiEN787XKrYD2+7V3bicfMJpdOzH+MV8LCys44dK+6VHX/a9XmP4Jr6ZSch+jJblnxbWHY8guj72s+6snOPRqNh0U8vcfnULvq9PoWQuu1LdQyRNRTUCVRgZgK3EmHDETXJGQ/fpl6wgsYhCqzNIS4FNh5TE51kOG3/SCVBHgr+3q3i4h3tupr+Cro1MvwcdPIKFVm5pToUg/ZvXsCutbPJSE3A3SeUboM+xCfI8DmKvXWRLUt/5va106Qk3KHLwLE07zyk/IJ5hC1r/2Tdst9JTUnE178az730PoHVIwym3bFxGXu2reH2jcsA+AeF0ef51/XSH963le3rl3Dtyjky01OZ8MMCfANDnlj8m9f8xbrlf5CanIiPfzWef/k9gqrXMJh2+8bl7Nm2hlvXr9yNP5S+g17XpS8oKGDJgl85cWQvcTG3sbS0Jrx2Q/oPfgMHp+KvhVUxdlG5VdoWIR8fHxYtWkR2duENQE5ODgsXLsTX17cCIys/HRuZ0aaeGQs3ZfHNgnRy8+CtflYYGxW/jZmJgtvxKhZtLv7GyNleyaiB1sQkqfhhUQZfzEtn3b4cClRP4CAewcjKkrQT5zn1VvndgJbVhtNX+X7TIV5pWZv/vdSd6m4OvL5wM0mZhvM0X6Xi1T82ciclg2/7tmb56734pFtTXG0sdWmO3IhhQMNQ5r/QhWnPdaBArea1hZvIzitaSS0PnRqb07a+GQs2ZPJ/89PIzdfw1gCbh5YdUxMFt2JV/G9TZrFp3v85WW+ZtyYDtUbD0fN5pYrz9MG1bPrz/2jZfQQvfbIUN58QFk4aTmZaosH0Ny8dZen0UdRp0ZeXPllGSN32/DnlDeJuX9ClSYq7wbyvB+LsHsig9+fz8qcriOz2OsYmZnr7qtuyH+9+v0u3tO/7fqmOwZCn29jyVAtbZi5J4sOfYsjJ0/DBS66YPOTRU1igORv2pPPRzzF8+VssRkbw4ctumJkWfQrXJdIGyuGZxbkja9m+dCJNu4xg0NhluHqH8vcvw8hMN5z/t68cZfWcUUQ07cvgccsJrtWO5dNHEH+nMP8dXf1p1/8Thn64imdHLsTOyYu/fnmRrPTCO3VVQT7V63WmduSzpY799KG1bPpzIpHdRzD842W4eYfyv0nDHlp2ls24V3aWE1KnHX9OGVF82Xnvd14av9Jg2QE4uHkeULYnpE1CFTSopmD9ETXztqjJL4ABLZUYPeTKHOajoF1tBbtPa5i9SU1sioYBLZVYFg2RhtUNx3f2poafVqr0livRGq7Hacq1EnRi/1rWLvyatj1HMOKzJbj7hjD325fIKOYc5efl4ODiQ6f+I7G2cy6/QErgwO6NLJr9Iz2eeYlPf/gDH//qfD/hTdJSDNcwz506QpPIToz5fBoffT0HR2c3vvv0DZIT43Rp8nKyqRZeh36D33zy8e/axP9mT6LHgOFM+GE+PgHV+O7Tt4qP/6Q2/rFf/MrH38y6G/+bJN2NPy83h+uXz/N0/xf57IffeXPc18TcvsGkL0dJ7OI/pdJWhOrVq4ePjw9Lly7VrVu6dCm+vr7UrVtXt06tVjNx4kQCAgKwsLCgdu3a/P3333r7Wrt2LdWrV8fCwoI2bdpw7do1vdc//fRT6tSpo7du0qRJ+Pv7l/dh6Wlb34x1+3M4camA2/Fq5q7NxM5aSZ1qJsVuc/pqASt35xR5kn+/Hi3MOX2lgGU7crgVpyIhRc2JywWkZ/2zrUEA8Rt2cmH8JGJXbP7H37s4v+8/Q++61ehZpxpBLvZ81LUp5iZGLI+6ZDD98qhLpOXk8mP/ttT1ccXL3poGfu6EuBe2nkwd2IEetYMJdnUgxN2Rz55uQXRqJmeiDd8QlFW7huas3astB7fjVcxZnYm9tZI61U2L3eb0lXxW7Mom6kLxZSctU6O31K5myoXrBSSkFt/S9DD7N82lbmQ/6rTog4tnMF2fn4CJqTlRu5cYTH9w8+8ER7SgWedhuHgG0abn23j4hXNo6wJdmm3LJhFcsxXt+72Ph284jq6+hNRpi5Wtk96+TEwtsLZz0S1mFtalOgZDukTasHRzKodPZ3MjOp8pixJwsDWmYYRlsdtMnBnHjsOZ3IrN53p0PlMXJeLiYEygt/458/M0oVsrW379M6HMcR7eMoeazfpTs2kfnD2C6fCMNv9P7TOc/0e3zScgPJJGHYbj5B5Ei+7v4OYTTtSOP3Rpwhp2xy+0GfbOPjh7VqN173Hk5WQQf/u8Lk3zbm/RoO1QXDyrlzr2A5vmUDeyP3Waa8tOl3tlZ4/h2A9tmU9QjUiadhqOs0cQrXu+g4dvOIe3Fsa+ffmPBNVsSbu+o3G/W3aq12lXpOzE3DjL/o2z6T70q1LHD9CwmoI9ZzVcvAPxqbD6oBobC6juVXwFq1F1BcevaDh5TUNiGqw/oqGgAGoF6G/jaq9Nu+ZQ0c9mgQoycwoXtQb8XOHE1fK9BuxZP48GrftRv2VvXL2C6TH0U0zMzDmyY6nB9N6BNXnq2fep1aQrxibFf1c9CRtXLKBlx55EtnsaL59ABr82DlMzc3ZtWWkw/Ssjv6Btl374Bobg4e3PCyM+QqPRcObEQV2aZm260mPAS9So1eiJx79+xUJadexJy/bd8fINZOhrYzE1M2fn5lUG07866nPademLX2B1PL39GfbGh6jVGs4c17aUW1pZM/qzX2jcogMe3n4Eh9Rk0Cvvc+3yORLjYyT2CiRd48pXpa0IAbz44ovMmTNH9/fs2bN54YUX9NJMnDiR+fPnM23aNE6fPs27777L888/z44dOwC4efMmvXv3pnv37kRFRTF8+HDGjh37jx6HIc52SuyslZy7XtgtJicPrkarCPAsfY9GBRARZEJssoo3+1rxzeu2jH7OmtrBxVeuqpJ8lYqz0Yk0DijsBqNUKGgc4MmJW/EGt9l+4Sa1vFyZuG4/bX9YTJ9pK5i5+wQqdfGVg4xcbQuKnYWBx7hldK/snL1WWKHJydVw9U4BgV7l1xvWxlJBzSATdp8o3SNkVUEe0ddPExDeTLdOoVQSENaUW1eiDG5z60oUAWHN9NYF1mjOrcva9Bq1mksntuPo5s+CH4fx/bvNmPVlf84dK1rRPrV/Fd+904Rpn3Rny5Lvyc8tffey+7k6GuNga8zJi4X7y87RcOlGLtX8Sn6+Lc21X88ZWYXlyNREwVvPOTN7WRKp6aWrfN6jKsgj9uZp/EL18983tBl3rhwzuM2dq1H4hTTVW+cf1oI7V6OKfY8TexZjZmGDi3f5dQnSlZ0w/dj9w5px+7Lh2G9diSIgXD/2wBotdGXtXtlxcvNn4Y/D+GFkU2Z/1Y/zD5Sd/Nxsls8cRefnPsHarvTdbOytwNpCwbXYwspHbj7cSQQvJ8PbKJXg7gBXY/UrLNfiNHg5Fd6kGBtBj8ZKNh5Vk1mCHqs1/RTkq+DcrfKrCBUU5HHn2mmCaxTmuVKpJDi8KTcuRZXb+5SHgvx8rl0+R41ajXXrlEol4bUbcen8iRLtIzcvB5WqACvrJ9fduTi6+Gs31K1TKpXUqN2QS+dPlmgfubna+K1tbItNk52ZgUKhwNKq/B4aVebYxX9DpRwjdM/zzz/PuHHjuH79OgB79uxh0aJFbN++HYDc3Fy++uorNm/eTNOm2i/jwMBAdu/ezW+//UarVq349ddfCQoK4vvvvwcgJCSEkydP8vXXX5c5vtzcXHJz9W8SVQW5GBk/+mbI1kp7UUvL1L/ZSc9U614rDRsrBeamCjo1Mmfl7hyW7cwh3N+Yl3taMmlRBhdvVUD/uH+R5KxcVBoNTtbmeuudrMy5lpBqcJvbyekcSommS81Afnm2PTeT0vhq3QEKVGpebVWnSHq1RsO3Gw9Rx8eVYFeHcj8GW2vtDfSDZSctU41dGcrOg5rWNCMnT8OxUnaLy8pIRqNWYf3A03YrW2cSYq4a3CYjNaHI03lrW2cyU7WtI5npieTlZrF33Qxa93ybdn3e4/KpXfw19U0GvzcPvxDtk9mIxt2wc/LE2t6VuFsX2LLkOxJjrtF/xM+lOpb72dto+x8+WFFJzVDpXnsUhQKG9HDg3NUcbsYUVmiHPO3AhWu5ZRoTdE/23fy3snkg/22cSIq5YnCbzLQELG31uyxZ2jqRmabfOnX55DZWzx5Jfn421rYu9H1zNpbWhseXlca9slO0LDiRWEzsGakJWNnox25l61RM2XmHtn3e4/LpXfz16xsMGjVfV3Y2/jkR76C6hNQp3Zgg3Xvf/Yp5sKKSmavRvfYgS1NQKhVFuq9l5oCTTeHf7esouJWo0Y0JepTaAQrO3NCUa/forPQU1AY+39Z2TsRHG/58V5T0u7Ha2uuXUTs7R2JuXSvRPv6a9zP2Ds7UqP3kW38elJ6mjd/uwfjtHYm+db1E+/hz/i/YOzoTXkz8eXm5LJ7/C00iO2JhWX6Vicoce0WRH1QtX5W6IuTi4kLXrl2ZO3cuGo2Grl274uxceKG7dOkSWVlZdOjQQW+7vLw8Xfe5s2fP0rhxY73X71WaymrixIlMmKA/9qV++zE07Fi0xalhmAkDOxZ2m5m65BGjZUvp3m3wiUv5bD2ivZreilMR5GVMZB0zLt7KeiLv+1+m1oCjlQUfd22KkVJJuIcTcelZzNt32mBFaOK6/VyKS2bu0KfK5f0bhZvyXGcr3d+//JVeLvt9lOa1zDh4Jq9CxpYVR6PRVj6q12lLk45DAXD3DePm5WMc2bFIdzNbr9UA3TZu3iFY27nwx/dDSYq7gaPr440xbFHXipf6Fl7E/29W3ENSl8yLvRzxcTdl/JTCbhz1wy2oEWzOmB+jy7z/J82nemMGj1tOdmYyJ/b8yapZ7/Dc+38VqXT9mxSWnXY07jAU0JadW5eP6srOhagtXDu3n5c+XvbY+6/hq6Bz/cIHEX/uLluLXnGCPcHPVcHsTSXbv5cTONspWHXwycRTFaxZMpeDuzcy5ovfMDEt/1b+J2313/M4sGsTY7/8FVMD8RcUFDDlmw9Ao2HIa2MqIMLiVebYxb9Dpa4IgbZ73BtvvAHAlClT9F7LyNBWJtasWYOXl5fea2ZmJf+yUiqVaDT6XQby8x89yH3cuHGMHDlSb917Uww/yT1xKZ9r0YU3sPcGtdtaKUnLLLzTtLFSciuu9HeeGdkaVCoN0Yn6+4hOVBHsXemLQ5k5WJphpFCQmKH/mDYxMwdnawuD27hYW2BspMTovqc0Ac52JGRkk69SYWJU2Aowcd1+dl68xezBnXGztTK0u8d2/FIeV2cXdqE0NtbebD1YdmytlNwsQ9m5X7C3Me5ORsxYUfoKu6W1AwqlUZGB05lpCcUOlLa2cy4yGD4jLQGru+ktrR1QGhnj4hmsl8bZI4ibF48UG8u9WeqS464/dkXo8JksLv5Q+Ije5G7+29koSUkvzG87ayOu3Xl069kLvRyoF27Bp1NjSUot3D4i2Bw3J2PmfO6jl37UEBfOXs3ls19jHytui7v5/+DECJnpiVjZGs5/K1tnsh5o/clKK5re1MwSU1c/HPDDM6AOMz/tyKm9f9O40yuPFWNx7pWdomUhEetiYre2cyYzXT/2zLTEImXH2SNIL42zexA3L2nLzrVz+0mOv8G3bzfUS/P3r2/iU60Bg9//vdiYL97RcCep8Dpyb0IEK3P9ViErMwWxKYa7qGXlgVqtKTIxgpU53PvK8ndV4GANI3vqPzXu3UzJzQRYuF2/wlM7QEFMsoaY5GJDLxVLG3uUBj7fGamJ//hECI9iczfWBwfnp6YmYevw8Mr7uuW/s2bJXN7/bCo+/tWeZJjFsrHVxp/6YPwpSdg9Iv61y/5gzdJ5jJ7wC74G4tdWJMaRGB/N2M+nlnuLSmWOXfw3VPr2tc6dO5OXl0d+fj6dOnXSey08PBwzMzNu3LhBcHCw3uLjo72ZCAsL4+DBg3rb7d+/X+9vFxcXYmJi9CpDUVFRj4zNzMwMW1tbvaW4bnG5+RCfotYt0YlqUjPUhPgWVk7MTSHAw4ird4qfgvdRVGq4FqPCzVG/i46bo5KkUg54/y8xMTIizMOJg9cKn7qrNRoOXo2mlrfh8QC1fVy5kZSG+r7ycT0xDRdrC10lSKPRMHHdfraev8H05zvh5WBjcF+lkZv3QNlJUJGaoSbUv3Dcl7kpBHgac+V26cvO/ZrXNuN6dEGZKuVGxqZ4+NXg2tl9unUatZqr5/bjHVjH4DbegXW4el96gKtn9uIdVEe3T0//CBIf6FqXFHutyPTH94u9cQ4AazvXxz6OnFwNsYkFuuVWbD7JaQXUrFbYv8nCTEGwrxkXrz98PNULvRxoFGHJ59NiiU/SP1fLt6Uy+odoxvxYuADMW5nMr4sff9INI2NT3HxqcOO8fv7fOL8Pz8C6BrfxDKjD9fP634/Xz+3FM6DOQ99Lo1FTUFC6LpSG3Cs7Vx8oO9fO7sMryHDs3oF1uHZWP/arZ/fqypq27NQkMdZQ2dE+SGv21Mu8PH4lL32yXLcAdBgw7pETJ+QVQHJG4ZKQpn0w5e9a2EpkagyeTnC7mNOpVkNMMvi76Xdx9XNVcDtR+/2z75yGmRvUzNpYuABsOa4pMnGCiTGE+ijKfZIEAGNjUzz9a3D5dGGeq9VqLp/Zj29wnXJ/v7IwNjHBPyhUb6IDtVrN2ROHCA4pfjr2tUvnserPmYwa/zMBweH/RKgGFcZf+JMAarWaMycOExxSs9jt1iydz8o/ZzFq/GQCqhWN/15FIjb6JqM/m4K1rb3E/i+gNFJU2PJfVOmbAIyMjDh79qzu//ezsbHhvffe491330WtVtOiRQtSU1PZs2cPtra2DBkyhFdffZXvv/+e999/n+HDh3PkyJEivz/UunVr4uPj+eabb+jbty/r169n3bp12NoWPzCvPGw9kkuXpmbEJ6tISFXTvYUFqRlqou6bEe7t/lZEXcxnxzHtTYaZCbg4FOaDk50Sb1cjMrPVut8I2nQol+HdLbl4y5QLNwoIDzCmZpAJPy56Mt3xHsbIyhKr4MIn8JYB3tjWDiUvKZWcmxXTBWhQk3A+XrGbcA8nIjydWXDwLNn5BfSorW1l+Gj5LlxtLHmrXX0A+tcPYfGhc3yz4SDPNgzlelI6s/ac5NmGYbp9frXuAOtOXWHSgLZYmZmQkKFtGbQ2M8H8YXMql9KWQzl0aWZOXJK27PSItCAlQ03UhcKb0XefseHYhTy2H9XenD9Ydpzt75adHA3JaYU3UOamUD/ElL+3lr0bZZMOQ1kxeywefhF4BtTi4OZ55OdmU7t5bwCWzxqDjb0r7fpopz1t1H4Q878dzL4Ns6lWqzWnD67hzrXTdB38mW6fTTsNY8lvI/Gt3gD/kMZcPr2LC8e3Mfj9+YB2iuRTB1ZTrWZLLKztib11gU2LJ+JbvQFuPuUzoH/trnR6tbMjOr6AuKQCBnS2JzmtgEOnCvPso1dcOXQqmw17tC3Bw3o70ryuFd/OiSM7V42djfY5VVa2hvwCDanpaoMTJCQkFxSpNJVUg3YvsG7+GNx8I/Dwr8WRrdr8j2iizf+180Zjbe9Gyx7a/K/XZjCLfxzEoc2zCYxoxbkja4m5cYoOA7X5n5ebxYH10wiq1RYrWxeyM5OJ2rGAjJRYQup21r1vWtIdcjJTSUu+g1qtIu6m9jvc3sUXU/OStZQ27vACK2ePwcM/Aq+AWhzYPI/8vMKys2LWaGwc3GjbWxt7w3aD+f27QezfOJvgmq04fWgtd66dosugwrLTpOMwlk5/F99qDfEPbczlU7u4cGIbg97Tlp17Mww+yM7REwcXnyLrH+XQRQ3NwhUkZWhIzYSWEUrSs+HC7cKKybOtlFy4reHIJe26gxc0dGukICYJ7iRpaFhdgYlx4Yxv92aCe1BqpvY97hfmo0CpgFPXn8yMoc07D2HJjHF4BUTgHViTvRvnk5ebTf2WvQD467cx2Dq40am/tudEQUEecbe1v8ujKsgnLTmOO9fPYmZuiZOb3xOJ8Z6OPZ5j5uRP8Q8OJ7BaDTauWkhuTjYt2nUHYMakT7B3cqXfIG0PlDVL57J84W+8MvILnF09SE3WtjaamVtibqHt5p6RnkpSfAzJSdqJdqLvaMe82Dk4YedQvq1inXsMZMbkCQQEhxFYrQYbVi0iNyebyPbdAPjtx/E4OLnSf/AIbfxL5rF04XReHfU5zq4epNyN3/xu/AUFBfzy9ViuXz7Hux//gFqt0qWxtrbD2KT8JliqzLGLyq/SV4SAh1ZIPv/8c1xcXJg4cSJXrlzB3t6eevXq8cEHHwDg6+vLkiVLePfdd/n5559p1KgRX331FS+++KJuH2FhYUydOpWvvvqKzz//nD59+vDee+8xffr0J3pcGw/mYmqiYGAnSyzv/ijmz39n6o3JcLE3wtqicIWvuzEjnyls/u3XVtuda9+pPOav096EHb+Yz8KN2XRuYkb/thbEJquZviKTy7f/+cEedvUjaLqlsDtJ+Hfa83Jz/lJODBv3j8cD0KlGAMlZOfy6I4qEjGxC3ByZOrA9Tne7xkWnZaJQFD4ZcbezYupz7flu4yH6/bYSV1tLBjYK44VmhT+s99cR7dTBw+dv0HuvCU8311WwytOGAzmYmip4vrMVluYKLt0q4KfF6Xplx9lBibVlYaOwn4cxowYWfpb6t9PekO49mcu8NYV3UA3DzFAo4ODZsj/hr9GoC1kZSexY8TMZafG4+YQx8J0Zuq4zaYl39PLaJ7gevV76jm3LJrFt2Y84uvrTf8QvuHoVTsMcWq8DXQd9yp6109nwvy9xcg+g32s/4VtNW3E1Mjbh6tm9HNw8j7zcbOwcPQit15HIbq+V+XjuWbktDTNTBS/3dcLSQsn5qzlMnBFH/n31FTcnE2ysCluIOjbTthJ++rq73r6mLkpgx+Hif9upLELrdyErPYk9q38iKz0eF68w+o6YqevqlpYcjUJRWEa8AuvR9YXv2L1qErtX/YC9iz89X56imwZbqTQiKfYKp2csIzszGXMre9x9a/LMyAU4exZ2Xdmz+idOHygcZzP//3oC0P/t+fhW1x+zWZwaDbWx71jxE5l3y86zb8/UdY1LTdKP3Se4Hj2Hf8f25ZPYtuyHu2VnSpGy0+X5T9mzbjobF32Bk1sAfV/7Cd9qDR4zZ0tm/zkNJkbwVH0l5qZwMwH+3KlGdV99194a7p9c8uxNbde4yAgFVuYK4lK025Tm939qByi4cFtD7pP5OTNqNelCZnoyW5b+RHpqAh6+YQx9f7ru852aqH+O0pPjmfJxb93fu9fNZve62QSENmT4B/OfTJB3NW7RkfTUZJb/bxqpyYn4BlRn5PifsbPXds9KjI/Ri3XbuiUUFOQz5Rv9cSc9BrxEz2e1XUCjDu5k1s+F44Sn3b2+3Z+m3OKP7EBaWjJLF07Xxf/e+Mm6+JMSYlHe13176/qlFBTk88vX+mOWez4znF7PvkxyYhzHDu4E4ON3ntdLM/aLXwmrWV9iryD/1WmsK4pC8+DgF/FEvfZtSkWHUGrdPijZDcq/Vds5/9wvlD8J79x6taJDKLXI5uU/O94/adWKGxUdQpm07/xkn6Y/Seb/7M/JlLtbMf+i2URKoZpf5b7p8rR9Mg8R/glKhXRXr0hNQv/5qdBL4srQbhX23oFzV1fYez8p/4kWISGEEEIIIf7rZPrs8iW5KYQQQgghhKhypCIkhBBCCCGEqHKka5wQQgghhBCVgEyWUL6kRUgIIYQQQghR5UiLkBBCCCGEEJWAtAiVL2kREkIIIYQQQlQ5UhESQgghhBBCVDnSNU4IIYQQQohKQH5HqHxJbgohhBBCCCGqHKkICSGEEEIIUQkolIoKW0pjypQp+Pv7Y25uTuPGjTl48OBD00+aNImQkBAsLCzw8fHh3XffJScnp1TvXRJSERJCCCGEEEKUq8WLFzNy5EjGjx/P0aNHqV27Np06dSIuLs5g+oULFzJ27FjGjx/P2bNnmTVrFosXL+aDDz54YjFKRUgIIYQQQohKQKFUVtjyuH744QdeeuklXnjhBcLDw5k2bRqWlpbMnj3bYPq9e/fSvHlzBg4ciL+/Px07duTZZ599ZCtSWUhFSAghhBBCCPFQubm5pKWl6S25ubkG0+bl5XHkyBHat2+vW6dUKmnfvj379u0zuE2zZs04cuSIruJz5coV1q5dS5cuXcr/YO7F9MT2LIQQQgghhPhPmDhxInZ2dnrLxIkTDaZNSEhApVLh5uamt97NzY2YmBiD2wwcOJDPPvuMFi1aYGJiQlBQEK1bt5aucUIIIYQQQlR5CkWFLePGjSM1NVVvGTduXLkd2vbt2/nqq6+YOnUqR48eZenSpaxZs4bPP/+83N7jQfI7QkIIIYQQQoiHMjMzw8zMrERpnZ2dMTIyIjY2Vm99bGws7u7uBrf5+OOPGTRoEMOHDwegZs2aZGZm8vLLL/Phhx+ifAK/oSQtQkIIIYQQQlQClWX6bFNTU+rXr8+WLVt069RqNVu2bKFp06YGt8nKyipS2TEyMgJAo9E8Zk6VjLQIiRJrO2dIRYdQJltfmFfRIZSJ8cTXKzqEUktMqegIykaVr6roEMokM+vJXED+CUal/O2Kfwt7W6OKDqFMElMrOoKycbWuvM9781SmFR1Cmag1lfuzK8pu5MiRDBkyhAYNGtCoUSMmTZpEZmYmL7zwAgCDBw/Gy8tLN86oe/fu/PDDD9StW5fGjRtz6dIlPv74Y7p3766rEJU3qQgJIYQQQgghytWAAQOIj4/nk08+ISYmhjp16rB+/XrdBAo3btzQawH66KOPUCgUfPTRR9y+fRsXFxe6d+/Ol19++cRilIqQEEIIIYQQlUBpfs+nIr3xxhu88cYbBl/bvn273t/GxsaMHz+e8ePH/wORaVWu3BRCCCGEEEKIciAtQkIIIYQQQlQCjztpgXg4aRESQgghhBBCVDnSIiSEEEIIIUQlUNnGCP3bSW4KIYQQQgghqhypCAkhhBBCCCGqHOkaJ4QQQgghRCUgkyWUL2kREkIIIYQQQlQ50iIkhBBCCCFEJSAtQuVLWoSEEEIIIYQQVY5UhIQQQgghhBBVjnSNE0IIIYQQojKQ3xEqV5KbQgghhBBCiCpHWoSEEEIIIYSoBBQKmSyhPEmLkBBCCCGEEKLKkYpQMRQKBcuXL6/oMIQQQgghhABAoVRW2PJf9J/pGjd06FDmzZtXZP3FixcJDg6ugIjKR7fm5rSoZYqFmYIrdwpYuDGb+BR1semDvY3o0NAcX3cj7K2VTFuWyfFL+UXSuTsq6dXKgmo+xigVEJ2oYvqKTJLTNeUW+6JD55i37xSJGdlUd3NkTOdG1PRyKTZ9Wk4ev2w7ytZzN0jNzsXDzpr3OzYkspo3ALN2n2TLuetcS0zFzNiY2t4uvNOuPv7OduUW8+NybNGAwFHDsKsXgbmnK4f7vE7syi0VFs/9ujY3p3nNwrKzaNOjy077hub4uGnLzm/LMzlhoOy4OSrp2fJu2VFCTKKKGY9RdjQaDYc2/MyZA3+Rm52GR0A9WvYej72L/0O3O7lnAVHbZ5GVnoCTRyiRvT7CzbeW7vWC/Fz2rvqai1FrUBXk4xvSnJa9x2Np46xLc+viPg6sn0xSzAWMTS0Jrd+Txk+9g9JI+1V4+9IBju+aR9yNk+TlZGDn4kfd1sOAiBId2/0GPOVA+6Y2WFooOX81h+l/JRATX1Bs+l7t7Wlc2xIvV1Py8jWcv5rDH6uSuBNXeA4mvOFBjWoWettt3JPG9D8THju+ezQaDYc2/szZu+fD3b9k5+PUngVE7Sg8Hy166p+PM/sXc/HYauJvnyE/N5MXPzuImYWt3j7WznmNxDvnyM5IxMzCDu9qTWnSZRRWdm4liv3o9gUc2DSLzLR4XL1DaT/gYzz9axWb/tyRdexaNZnUxNs4uPrTutd7BEW0Mph2w8JPiNq1mLZ9x9Gw3VDd+iVTXyX21jmy0hMxt7TDP7QprXq9h419yWK+3/FdCzi8dRZZafE4e4XSps/HuPsVH/+FY+vYt3YyaUm3sXfxp0X39wioURj/peMbObFnEXE3T5OTlcLA95fj6h2mt4+/fh7E7UsH9dbVbDaAdgM+e+z4o3Yu4PAWbf67eIXSpu/HeDwk/y8cW8ee1YXxR/Z4j8C78atU+exZPYmrp3eSmngTM3NrfEOaEdljFNYGykNBfh7/+74f8bfP8fyYosdZGtvWLWbD8vmkpiTi41+dZ4ePJqCa4c/+7RuXWbnoV65fPktifDQDXhhF++7P6aXJyc5k+cKpHDuwjfS0ZHwDQhjw4vsEVKtR5lgftGP9IjavnEtaSgJeftXp/+I4/KvVNJj2zs1LrFk8hRtXzpIUf4c+Q9+nbddBRdKlJMayfMEkzhzbTV5uDi7uPjw/4nP8gso//p3r/8eWVffiD6Hvi+PwDzYcf/Td+G9ePUNS/B16DxlNmwfiX/vnVNb9/aveOldPfz6etKrcYxeV23+qete5c2eio6P1loCAgIoOq9Q6NjKjTT0zFm7K4psF6eTmwVv9rDA2Kn4bMxMFt+NVLNqcXWwaZ3slowZaE5Ok4odFGXwxL511+3IoUJVf7BtOX+X7TYd4pWVt/vdSd6q7OfD6ws0kZRqOK1+l4tU/NnInJYNv+7Zm+eu9+KRbU1xtLHVpjtyIYUDDUOa/0IVpz3WgQK3mtYWbyM4rerP+TzGysiTtxHlOvTWhwmIwpEMjM1rXNWPRpiy+XZBOXj680ffhZcfURMGtOBV/Pqzs2CkZ+aw1sUkqJi3O4Ku52rKT/xhl59i2mZzY/Tut+nxKn7f+xNjUgtUzhlOQn1vsNhej1rJn5f/RoMMI+r2zFGfPEFbPGE5WeqIuzZ6VE7l2ZhudBk2m5+vzyUyLY/28N3WvJ9w5x+qZL+MbEkn/d5fR8fkfuHpmK/vWfq9LE3P9GE4eIXQaMpkBo1YQ2rA3W/43hrS4AyU/QKBnOzu6tLRl+p8JfPDjHXLzNHz8qgcmxsX37Q4PNmf9rjTG/Xibz6ZGY2Sk4OPX3DEz1d9m0940hn90Xbf8viKxmD2WTNT2mZzc/Tste39Knzf/xMTUgtUzH34+LkWtZc8q7fno+85SnDxDWD1zOFkZhbHk5+fgExJJvbavFLsfr6DGdHj+R54dvY5OgyeTmniDDb+/XaK4zx5ey9YlE2nedQRDP1iGq3cof/40jMw0w/lx6/JRVs4eRa1mfRn6wXKq1W7H0mkjiL99oUjaC1GbuHP1ONZ2rkVe8w1pQo/hk3jp0/X0fPknkhNusnxGyWK+3/mja9m5bCJNOo1g4PvLcPEMZdmvw/TK9P3uXD3KuvmjqNGkL8+9v5ygmu1YNWsECXcK48/Py8IzsB4tnn7voe8d0bQ/L32+W7e06DH68eM/spYdyybS5KkRPD96GS5eoSyd+pD4rxxlzdxRRDTty/NjlhNcqx0rZxTGX5CXQ9zNMzTp/BrPj15K9+G/kBx3lRW/vWZwf7tWfIOVgfNTWod2b+DPOT/Qvf/LfPzdQrz9qzHpsxGkpSQZTJ+Xm4Ozmxe9B72Fnb2zwTTzpnzGmRMHGPb253z642LCazfhxwmvkZwYV25xAxzZs56l876lS79XGfv1Yrz9Qvjly1dJTzV8LvJzc3By9abHc29jW0zsWRlpfP/xEIyMjHn9g6l89OMyeg95D0srW4PpyxT/3vUsm/8tT/V9ldFf/4mXX3WmfvlKsfFr896bpwe+U2z8AB4+wXw5fZtuefez+eUeu6j8/lMVITMzM9zd3fUWIyMjVqxYQb169TA3NycwMJAJEyZQUFD4ZPbixYu0bNkSc3NzwsPD2bRpk95+t2/fjkKhICUlRbcuKioKhULBtWvXntjxtK1vxrr9OZy4VMDteDVz12ZiZ62kTjWTYrc5fbWAlbtzOH6x+MpBjxbmnL5SwLIdOdyKU5GQoubE5QLSs8qvNej3/WfoXbcaPetUI8jFno+6NsXcxIjlUZcMpl8edYm0nFx+7N+Wuj6ueNlb08DPnRB3R12aqQM70KN2MMGuDoS4O/LZ0y2ITs3kTHTZbgTLIn7DTi6Mn0Tsis0VFoMhbeqZsX5/DicuF3AnQc28u2WndnDxZefM1QJW78kx2IJ4T/dIc85cKWD5zrtlJ1XNycsFZJSw7Gg0Gk7smk/99q8SENEOZ88Q2j3zNZlpcVw9VXweHt8xl/DG/Qhr1AdH92Ba9ZmAsYk55w4tASA3O52zB5fQvPsYvKs1wdU7grYDJhJz7Rgx16MA7c27k0cIDTuOwM7ZD6+gRjTr+h6n9iwkLycDgPrtXqVx57fx8K+HnbMvtSMH4xMSSVrsnhId3z1dW9mxZGMKh05lcf1OHj//EYeDnRGNaloWu82X02LYfjCDWzH5XL+Tx5QFcbg4mhDoY6aXLjdPQ0q6Srdk55b+c6s7H+2058PJM4S2z3xNVlocV08/5Hzs1J6P0IZ9cHQLplXvCZiYmHPu4BJdmtqRQ6jX9mXc/GoXu5/aLYfi7lcHGwcv3P3rUbfNy8TeOI5K9eiHG4e2zKF28/7UatYHZ49gOj07ARNTc07uW2Iw/ZFt8wkMj6Rxx+E4ewTR8ul3cPMJ5+iOP/TSpafEsmnx53R74TuURkU/Lw3bDcUrsA52Tl54B9WjSceXuHM1qkQx3+/o9jlENOtPjSZ9cHIPpl3/CRibmnN6v+H4j+2Yj39oJA3aDcfRPYhmXd/B1Tuc47sK4w9r2JMmnd/Ap3rTh763sak5VrYuusXM3PqxYgc4sm0OEU37E9GkD04ewbQfoI3/VDH5f3T7fPzDImnYfjhO7kE07/YOrj7hRO3Uxm9mYUPfN+YQUq8Ljm6BeAbUoW2/j4m9eZq0pDt6+7p6egfXz+2hVc8xjx13cTatWkBkh140b9cDT59Ann/lQ0zNzNmzdYXB9AHVatBvyLs0atEJY5Oi5SQvN4ej+7fSd9DbVK9RH1cPX55+5lVc3L3ZvuGvcosbYMvq+TRr14embXri4RPEMy9/jKmpBfu2LjeY3i84gt6DR9Gg+VMYm5gaTLNx+WwcnNwYNOJz/KvVxNnNm7DazXBx9ynX2AG2rZ5P03Z9aNKmFx7eQQx46RNt/NuWFRt/z0GjqP+Q+AGUSiNs7Z11i7WtQ7nHXhEUSkWFLf9F/6mKkCG7du1i8ODBvP3225w5c4bffvuNuXPn8uWXXwKgVqvp3bs3pqamHDhwgGnTpjFmTPl9uZaWs50SO2sl564XVthy8uBqtIoAz9L3aFQAEUEmxCareLOvFd+8bsvo56wfeoP8uPJVKs5GJ9I4wFO3TqlQ0DjAkxO34g1us/3CTWp5uTJx3X7a/rCYPtNWMHP3CVTq4rtyZeTmAWBnYVZsmqrI6W7ZOf9A2blWHmUnUFt2RvSx4v9et+X956yp9RhlJy3pFlnp8fhUa6ZbZ2Zhg5tvLV2F5UGqgjzib5/Gu3rhNgqlEu9qTXXbxN86jVqVr5fGwTUQa3tPYu+mURXkYWyiX1aMTMxRFeQSf+t0sTHn5aRjZGJT4mN0dTLGwc6YExcKW9aycjRcvJ5L9QDzEu/H0kL79ZyRpd/cFtnAmtlf+vHDWG8GdnPA1KT0F6f0u+fD+4Hz4epbS5dvD9Kdj2r658OrWtNitymJnKwULh5bhbtfXYwMVEAejCHmxmn8QvVj8A9txu0rxwxuc/tKFH6h+hWEgPAW3L5SGLNGrWb1nPdp3GEYLp7VHhlzdmYKZw6twivw0TE/GH/czdP4PFCmfas3I/qa4fhjrkbhE6Ifv19oC6KvRRlM/zDnD69i2geN+X1iN3av+p78vOJbgYuLP/bmafxC9OP3Cyk+/uhrUfg9EL9/aAvuXC0+/tzsDFAo9LpUZqYlsGnRx3Qe/A3GpiX/PD1MQX4+1y+fJaxWY906pVJJWK3GXD5/olT7VKtVqNUqTEz1b9RNTc25dDaqLOHqKcjP5+aVs4TWaqJbp1QqCa3VmCsXjpd6vycPb8c3qAYzvx/FmGGtmPh+f/Zs/rs8QtZTUJDPzStnCKmpH39IzSZcK0P8APExN/jwlbZ8+kZn5v00hqSE6LKGK/6D/jNjhABWr16NtXXhk62nnnqK5ORkxo4dy5AhQwAIDAzk888/Z/To0YwfP57Nmzdz7tw5NmzYgKen9sb9q6++4qmnnipzPLm5ueTm6ncvURXkYmT86Bt3WyvtzU1apn5FID1TrXutNGysFJibKujUyJyVu3NYtjOHcH9jXu5pyaRFGVy8Vfb+cclZuag0Gpys9S9STlbmXEtINbjN7eR0DqVE06VmIL88256bSWl8te4ABSo1r7aqUyS9WqPh242HqOPjSrDrf+MpT3nRlZ2sB8pOVhnLjqW27HRsbM6q3Tms2JlDWIAxL/WwZPLiDC6VoOxkpWsrwhY2TnrrLaydyUo3PM4lJzMZjVqFpfUD29g4kxx3VbdfpZFJkTEoljZOZKVp9+sT0oITu+Zz8dhqgmo/RVZ6Aoc3TdWL60GXotYRd/MkQU1efuSx3eNgo+1/mJKunx+p6SrsbR7SN/E+CgW80NuJs1dyuBld2NKw60gG8ckFJKcW4OdpxvNPO+Llasq3s2NLHN/9ijsfliU4HxbWRbdJuXs+Hse+Nd9xas8CCvKzcfOtTZcXpz067gxtDFa2D8Rg60Ri7BWD22SmJWBlq9+NxsrWicy0wuPcv3EGSiNj6rcZ/ND3377sW45uX0B+XjaeAXXo+/qjY75f9r0y/WC+2ziRFFdM/OkJeuPd7qXPSnu88WGh9bth4+CJtZ0rCXfOs3vldyTHXaX7sF8eP/4H89/GiaSH5L/B+IspZwX5uexa+R2h9btiZqG9rms0Gjb8MZZazZ/B3bcmqYm3Shzzw2Skp6BWq7C1d9Rbb2vvSMzta6Xap7mFFUEhtVj910w8vAOxtXPk4O71XL5wAtdybFXJSE9GrVZhY6d/LmzsnIi5/fifx3sS4m6xa+OftO02iE69h3P90mn+mv01RsYmNGndo6xh62SmJd/N+wfit3ci9k7p4/erVpPnX/8cV09/0pITWPf3r0z6ZAgffL8McwursoZdsf6jkxZUlP9URahNmzb8+mvh4DgrKytq1arFnj17dC1AACqVipycHLKysjh79iw+Pj66ShBA06YP71ZQUhMnTmTCBP2xI/Xbj6Fhx7FF0jYMM2Fgx8JuM1OXZJRLDA+6dxt84lI+W49oK2m34lQEeRkTWceMi7eynsj7PopaA45WFnzctSlGSiXhHk7EpWcxb99pgxWhiev2cykumblDy15hrewahpnwbIf7ys7SJ1R27haeE5fy2Xav7MSrCPQ0JrK2GZcMlJ3oS+s5t+v/2H63Sb3rsMe7YSxPviEtaNrtfXYs+ZTN/xuDkZEpDTq8RvTVw6AoemG5fWk/Wxd/QOt+nxOf4lfsfiPrW/PygMIbvIm/xZQ51uF9nfFxN+WjyfpdgjbvS9f9/0Z0PslpBXz6hiduTsbEJhY/EcM9F46uYseS8bq/u5ag0vGk1Wk9jLBGfUhPvsPhTVPYsmgsXV6c9o//VkbM9VMc2TafIeOWPvK9G3UYRq1mfUlNusOeNb+wet4Y+r7+W6X4fY+azQbo/u/sGYKVrQtLpgwlJeEG9s6+FRhZIZUqn9Wz3waNhnb9C6+hx3b8Tl5uJo06Fj/u7N/kxbc/Z94vE3h/eCeUSiN8A0Np1KIT1y+frejQHkmjVuMbVIMeA7Xj33wCwrhz8xK7N/5VrhWhJ6VG3Ujd/738QvCrVpPxr3fi2L4NNG3buwIjE/82/6mKkJWVVZEZ4jIyMpgwYQK9exct+ObmJWtWV96tfWs0hX3x8/Mf3R983LhxjBw5Um/de1MMd0E4cSmfa9GFNzn3BrXbWilJyyx8smxjpeRWXOlbbTKyNahUGqIT9fcRnagi2Lt8ioODpRlGCgWJGTl66xMzc3C2tjC4jYu1BcZGSozue9IR4GxHQkY2+SoVJkaFT9InrtvPzou3mD24M262lfzJTjkotuxYPlB2LMun7MQ8UHZikrQVaUNcfCOx612DgEBt1zJVgbY7Y3Z6Ila2hQOdszMScPI0POuTuZUDCqWR3kB87T4SsLz7hN/SxgW1Kp/c7DS9VqGs9ERdGoA6rV6gdsuhZKXFYWZpR1rSbfav/QFbR/0ntLcvH2TN7Ndp3mMsoQ16Er+5+CeTh05lcvF6YVk3vjshgr2NESlphXllZ2PEtdt5xe7nnmF9nKhfw5JPfrpDUurDz9fF69oKqbuLSYkqQv7hbfRmdivufGRlJOD8iPOR/cD5yMoo+sS/JCysHLCwcsDeJQAH1yB+/7I1sdejcPevW+w2ltbaGB6cGCErLbFIq889VrbOeq0/AJn3pb956TCZ6Yn8+mEb3esatYptS77m8Nb5vPbl1vve3xFLa0cc3QJwcg/i1w9acedqFF6Bxcf84DErlEZFJhbISk/Eqpg8tLIp2kr3YPkuDfe7Y7hS4q+XuCKki//B/E9/eP4bjP+B49VWgt4hLekO/d6ap2sNArh5YT/RV6OY/K7+bGILvu1DWIPudB70dYnif5C1jT1KpVGRiRHSUpKKtFQ8Dld3H97/Yia5OdlkZ2Vg7+jCb9+NwcXNu9T7fJC1jQNKpVGRiQXSUxMfOpHAo9g6uODhHai3zt0rgKj95Tse1srW4W7ePxB/SmKZ8v5Blla2uHr6ER9zo9z2Kf4b/vPta/Xq1eP8+fMEBwcXWZRKJWFhYdy8eZPo6MK+o/v379fbh4uLdsrn+9NERUU98r3NzMywtbXVW4rrFpebD/Epat0SnagmNUNNiG/hDaa5KQR4GHH1zqNveIqjUsO1GBVujvpddNwclSSlFj8e53GYGBkR5uHEwWuF+aXWaDh4NZpa3oanz67t48qNpDTU91U2ryem4WJtoasEaTQaJq7bz9bzN5j+fCe8HEo+buO/rNiy46dfdvzLoexcj1Hh5qBfdlwdlCSlGS47xqZWWNr5YOfsh52zHw5uwVjauHDr4j5dmrycDGJvnMDdr47BfRgZm+LiVYPb922jUau5dWm/bhsX7xoojUz09pscd4WMlDu4PbBfhUKBlZ0bxibmXDq2Bmt7D1y8w3Wv3750gDWzXqVp11HUaDKAR8nJ1RCTUKBbbsXkk5xaQM3qhZV+CzMF1fzMuHA15yF70laCGtWy4tMpd4hLKkHFxks7/uD+CtfDmJpb686F3vm4pH8+4m6cKJJv99w7H/dvo1GruX1pf7HblJRGoy1HKtXDK4xGxqa4+9bg+nn9GK6d31dsZcQrsA7Xz+t/t187txevQG3MEY178OKHK3nhg+W6xdrOlUYdhtH/zZkPCfpuzAWPruTeH7+rTw1uXtCP/+aFfXgUUwF0D6jDzQv68d84vxcP/zolfl9D4m9rWyesbIv/aYMHGRmb4uZTgxsPxH/jIfF7+NfhxgPxXz+/F8+AOrq/71WCUuKv0/eNuVhY6Xd7btP3IwaNXcGgMcsZNGY5vV6dDkDXF36kebd3Sxz/g4xNTPALCuPsicJpxdVqNWdPHCQopPjpwEvKzNwCe0cXMjPSOB21jzqNDE/ZXhrGJib4BIZx/mThzJZqtZrzJw8QWL34iUoeJSikDrF3rumti4u+jqOLR6n3aYixsQk+geFcOKUf/4VT+/EvQ/wPys3JIiHmJrb2JS/n/1YyWUL5+k+1CBnyySef0K1bN3x9fenbty9KpZLjx49z6tQpvvjiC9q3b0/16tUZMmQI3377LWlpaXz44Yd6+wgODsbHx4dPP/2UL7/8kgsXLvD9998X847lZ+uRXLo0NSM+WTs7V/cWFqRmqIm6b0a4t/tbEXUxnx3HtBdhMxNwue9G1clOiberEZnZat3vvGw6lMvw7pZcvGXKhRsFhAcYUzPIhB8XlV+XqkFNwvl4xW7CPZyI8HRmwcGzZOcX0KO2tsXuo+W7cLWx5K129QHoXz+ExYfO8c2GgzzbMJTrSenM2nOSZxsWPpX+at0B1p26wqQBbbEyMyEhQ9u6Zm1mgrlJxRRlIytLrIILn6JaBnhjWzuUvKRUcm5W3MDMbUdz6dzEjLhkFYmparo115ad+2eEe6ufFccvPVB27B8oOy5GZOYUlp3Nh3J58W7ZuXizsOxMXlyysqNQKKgVOZgjW6Zh5+KPraMXB9f/hJWtKwER7XXpVkwbSmBEe2q2eB6A2q2GsnXRWFy8I3D1rcWJXfMoyMsmtKG2pdfMwoawRn3Ys/JrzC3tMDG3ZteyL3Dzq6NXwTq2bRa+oS1QKJRcObmJo9tm0HHQjyiV2uO+fWk/a2a9Rq3IQQTV7EhWmnYMTUFeOsamJa94r9mRSp+O9kTH5xOXmM8zXRxJTlVx8GRh98HxIzw4cCKT9bvSABjez4nIetZ8PTOWnByNbjxRVo6avHwNbk7GRNa35uiZLNKz1Ph5mjK0lxOnL2Vz/U7Jb8Lvp3c+nO+ejw0/YWnrSkCNwvOx8rehBES0p2bzu+ej5VC2LtaeDzcf7fnIv+98AGSlxZOVnkBqgvYJbGL0BUzNrLB28MDc0p7YG8eJu3kSD//6mFnakpp4k0PrJ2Pr5Iu736NbVhq2e4E188bg7huBh38tDm+dR35uNjWbamNYPXc0NvZutOo5CoD6bQbzvx8GcXDzbIIiWnH28Fpirp+i80Dt7+dYWDtgYa1/4600MsHK1hknd+2T8TtXjxN9/STeQfUxt7QlJf4Gu1ZNxt7FF8+AkrUG3VOv9QtsXDAGN98I3H1rcXSHNg/DG2vj3/DHaKzs3GjRXRt/3VaD+funQRzZOpuAGq04f3QtsTdP6f3+T05mCmnJ0WSmaqdnvjeGzsrWGStbF1ISbnD+yCr8w1thbmlPwp3z7Fw2Ea+ghrh4hT5W/PXbvMD6P+7G71eLo9u1+V+jiTb+dfNHY23vRuTTo+4e72D+nDyIw1tmE1ijFeeOriX2xik6PKONX6XKZ/Wst4i9eYZer/yGRqMi8+7nz9zSDiNjU2wdPfViMDHTdgm2d/bFxsH9seJ/UIfuzzH75/H4B4cTUK0Gm1ctJC83m+ZtnwZg1uSPcXBypffz2in5C/LzuXNLOx6qoCCf5KQ4blw9j7m5Ba4e2mvCqWN7QaPBzcuf+Oib/DV/Eu5e/jS7u8/y0q7bYOZP+QjfoHD8g2uydc0f5OZm06RNTwDm/fwB9o5u9HjubV3s0bcuA6AqyCclMY6bV89hZm6pi71tt0F899Fg1i+dQb2mnbh+6SR7Nv/Ns6+MNxhDWbTpNpg/pnyIb2AN/IJrsn3t79r4W2vjn//LB9g7uvL0wHe08RfkE3M3/oKCfFKT4rh1TRu/i7s2/mXzvyOiQSscnT1JTY5n7Z9TUCqNqN9CutMLff/5ilCnTp1YvXo1n332GV9//TUmJiaEhoYyfPhwQNvtbdmyZQwbNoxGjRrh7+/PTz/9ROfOnXX7MDEx4X//+x+vvfYatWrVomHDhnzxxRf069fvica+8WAupiYKBnayxNJMweXbBfz8d6be7/242BthbVG4wtfdmJHPFHYl6NdW+1R636k85q/T3oQdv5jPwo3ZdG5iRv+2FsQmq5m+IpPLt8vvh4Q61QggOSuHX3dEkZCRTYibI1MHtsfpbte46LRMvf707nZWTH2uPd9tPES/31biamvJwEZhvNCs8Mfs/jpyHoDh8zfovdeEp5vrKlj/NLv6ETTd8rvu7/DvPgDg5vylnBg2rkJiAth0r+x0tMTibtmZskS/7DjbG2H1QNl5Z0Bh2enbRnuu9p/K4/f1d8vOpXwWbcqmY2Mz+rW1IC5ZzczHLDt12wynIC+b7X9/Ql52Gh4B9en20gy9Gd3SEm+QnZms+7tanS7kZCRxcMPPZKXH4+wZRrfhM/S61TR/ehwKhZL1895GVZCHT0gLWvX+RO+9b5zbyZEt01AV5OHsGcpTQ6fgF9ZS9/q5w8spyM/m6NbpHN06XbfeyqEmgY2/KfExLt+SipmpklcGOGNloeTclRy+mBZDfkFhi6ebkzG2VoUVz84ttD8M/Nlb+jd7vyyIY/vBDApUGmqGWNC1tR1mpgoSU1TsP57Jkg3JlEWd1sPJz8tmx9+fkJeThrt/fboNL3o+cu47H8F1upCdmcShh5yP0/sXcXjTFN3fK37VVqLa9P+K0Ia9MTYx58rJTRza+DMFedlY2rjgExJJh/avYWRc/JS494Q16EJWRhK7V/909wdVw+j/5kxd16y0pGgU94398g6qR/cXv2PXyknsXPEDDi7+9H51Ci5e1UucVyam5lw4tpHdq38mPzcLazsXAsIj6dHl9YdO42tISL0uZGcksW/tT9ofVPUOo+er98WfHK03ds0zoB6dB3/HvrWT2Lv6B+xd/Ok+bArOnoXxXz61lU0LC7931s3TtpI07vwGTZ96EyMjE26c38ex7fPJz8vCxt6D4NodadTp9ceKHSCkvjb/9675iaz0eFy8wuj9emH86cn6+e8ZWI8uQ79jz+pJ7Lkb/9MvFcafkRLL5ZPa7oe/f60/BqXfW/PxqdaYJ6lhi06kpyWz4n+/kpaSiE9ACG9//Iuue1ZSQgyK+7pupyTH8/moZ3V/b1zxOxtX/E71GvV5//MZAGRnZbDsj19ITozFytqOek3b0nPgCIyNy2+WVoD6zTuTnpbM6sVTSU9JwMs/hBEf/qqLPTkhRu9cpCbH8X+j++v+3rJqHltWzaNaeAPemTAb0E5R/fL7P7JywWTW/f0bTq5e9B06mkaRXcs1doD6zTqTkZbEmj+n3I0/lNc/mKbr2pecEK13v5CaFMfXowvvv7asmsuWVXMJDm/A25/OASAlKZa5k8eQlZ6Cta0DgaH1GPnlAmxs9SfEqIwUBsa0itJTaO4f+CKeuNe+TanoEErtB4+pFR1CmWx9YV5Fh1Amayc+3o96/ptUD7Gr6BDKZNdDxghVBi3aVd4flra1rtzdMXJL11j3r1HZJ6gK88qs6BBKLU9VuZ9VqzWV+7PbsfbjPdz4pyR/afhHhv8JDh/++uhElUwl/4oTQgghhBBCiMdXuR83CCGEEEIIUVX8RyctqCjSIiSEEEIIIYSocqRFSAghhBBCiEpAUdkH7v3LSG4KIYQQQgghqhxpERJCCCGEEKIS+K/+sGlFkRYhIYQQQgghRJUjFSEhhBBCCCFElSNd44QQQgghhKgMFNKGUZ4kN4UQQgghhBBVjrQICSGEEEIIUQnIZAnlS1qEhBBCCCGEEFWOVISEEEIIIYQQVY50jRNCCCGEEKIyUEobRnmS3BRCCCGEEEJUOdIiJIQQQgghRCWgUMhkCeVJWoSEEEIIIYQQVY60CAkhhBBCCFEZyBihciUVoX9YZFO7ig6h1N7Z/WpFh1AmxhNfr+gQyqTLuMYVHUKp7Zp6vKJDKJMBz/pXdAhlcjO2oiMoPZW6oiMoG7WmoiMom9ycyn0AGw5U3tuclKTsig6hTCysTCs6hDLpWLtyxy9KRqqVQgghhBBCiCqn8j4qEUIIIYQQogpRKGWyhPIkLUJCCCGEEEKIKkdahIQQQgghhKgMFNKGUZ4kN4UQQgghhBBVjlSEhBBCCCGEEFWOdI0TQgghhBCiMpDJEsqVtAgJIYQQQgghqhxpERJCCCGEEKISUMhkCeVKclMIIYQQQghR5UiLkBBCCCGEEJWBjBEqV9IiJIQQQgghhKhypCIkhBBCCCGEqHKka5wQQgghhBCVgEIpbRjlSXJTCCGEEEIIUeVIi5AQQgghhBCVgUImSyhPlbZF6Nq1aygUCqKioh6arnXr1rzzzjv/SExCCCGEEEKIyuGxWoRat25NnTp1mDRp0hMKp/S2b99OmzZtSE5Oxt7eXrd+6dKlmJiYVFxgj+Hg1gXsXT+LjNQE3H1CeWrgR3gF1io2/elD69m2fDIpCbdxcvOjfd/3qFarle71CcNCDW7Xvt/7NO88DICdq6dx8cR2Ym6ew8jIhLG/HCrXY+oeaUFkbTMszBRcvl3Awg2ZxCWri01fzceYjo3N8XUzxt5GydQl6Ry/mK+X5rexjga3XbI1i40Hc8o1/q7NzWle0xQLMwVX7hSwaFM28SnFxx/sbUT7hub4uBlhb63kt+WZnLiUXySdm6OSni0tqOZjjFIJMYkqZqzIJDldU67xP4pjiwYEjhqGXb0IzD1dOdzndWJXbvlHYyipDvWNaRhqhIUpXItVs3x3AYlpxedXgLuClrWM8XJWYmulYP7GPM5cL/7cldb+zQvYvW627nPb7fkP8Q4q/nN76uB6Ni/9Sfe57dh/FCG1Cz+3GakJbPjzey6d2kNOVjr+IQ3o+vyHOLv7A5CVkcLWZb9w6dQeUhKjsbJxJKx+O9r3fgtzS5vHjl+j0XB448+cO/gXudlpuPvXI7LXeOxc/B+63am9Czi+YxbZ6Qk4eYTSvMdHuPoWHveZ/Yu5FLWahNtnyM/NZOiEg5hZ2Opev3P5AKt+G2Jw373e/AtXn5qPjP3YjgUc2jyLzLR4XLxCadf/Yzz8i8/780fXsWf1ZFITb+Pg6k/LHu8RGKHNe5Uqn92rJnH19E5SEm5iZmGNX0gzWvYYhbW9m24fsTdOs3PFd8RcP4lCaUT1Oh1p3XsspuZWj4z3+K4FHN06i6z0eJw9Q2nV52Pc/YqP92LUOvavnUxa0m3sXfxp3v09/MMLy4pGo+HAup84tV977jwD6tGm36fY3z13ty4eYOmUwQb3PWDkX7j51iIt8RZzP29X5PV+7yzGw7/OQ49Ho9FwaOPPnD1QWHZa9h6ve//inNqzgKgds8i6W3Za9PwItwfKzsVjq4m/W3Ze/Ey/7ACsnfMaiXfOkZ2RiJmFHd7VmtKkyyis7NwefLuHalfXiIYhRpibwvU4DSv3Pvx7BaBxmJLICGOsLSAmWcPqfQXcSijcxtEGnmpkjJ+rEiMjuHhbzap9BWTed3l6vr0xHo5KrMwhJw8u3VGz4VAB6dmPFX4RT+KaNaizJU0iTPXWnbmaz5QlmWUL1oBODU1oHGaMhRlcjVGzdGceCanFn49ADyWt65jg5aLAzkrJnHW5nL6m0r2uVMJTjUwI9TXCyVZBdp6Gi7fUrN2fT1rWP3u9Ff9O5doipNFoKCgoKM9dlpmjoyM2No9/c/BPO3VwLRsX/x+tnh7BK+OX4uYTwh8/DiczLdFg+puXjrJk+ijqRvbllfHLCKnbnkW/vEHcrQu6NKN+2KW3PP3Cl6BQEF6/oy6NqiCP8AadadD6mXI/pk6NzWlb34wFGzL5v/lp5OZreGuADcZGxW9jaqLgVqyK/20q/gv2/Z+T9ZZ5azJQazQcPZ9XrvF3aGRG67pmLNqUxbcL0snLhzf6Wj06/jgVf24u/mrmbKdk5LPWxCapmLQ4g6/mprNuXw75qmI3eWKMrCxJO3GeU29N+Off/DG0qm1EsxpGLN+dz5QVeeTnw4tPmTz0XJgYK4hO0rBib9GKaHk5eWAt6/73NW16jOD1CUtw9wlh7ncvkVHM5/bGxWP8+et71G/Zh9c/W0pYvXYsnPwmsXc/txqNhgWT3yAp7ibPvT2F1z9bip2TJ3O+eZG83CwA0lPiSEuJo/Mzo3nzy5X0fukrLp7YxbJZH5XqGI5vn8mpPb8T2ftTer35J8amFqyZNZyC/Nxit7kUtZZ9q/6P+u1H0OftpTh6hLBm1nCyMwqPuyA/B5+QSOq2fcXgPtz86jLo4116S2ijftg4euPiHfHIuM8dWcv2pRNp2mUEg8Yuw9U7lL9/GUZmuuG8v33lKKvnjCKiaV8Gj1tOcK12LJ8+gvg72rwvyMsh7uYZmnR+jcFjl9LjpV9Iir3Kst9e0+0jIyWWv35+AXtnX557/0/6jJhBQvRF1v0+7pHxXji6ll3LJ9K48wieeW8Zzl6hrJg2jKxi4o2+epT180cR3qQvz763nMCa7Vg9awSJ0YXf8Ue2zCBq5++06fcpA97Vnrvl04bpzp1HQF2GfbZbb6nRpB+2Tt5FKpq9Xp+rl87Vp8Yjjylq+0xO7v6dlr0/pc+bf2JiasHqmY8uO3tW/R8NOoyg7ztLcfIMYfXM4WTdV3by75adesWUHQCvoMZ0eP5Hnh29jk6DJ5OaeIMNv7/9yJjvF1nTiKbhRqzYW8Cvq/LJz9cwtNPDv1dqBijp0siYrVEFTFmZT0ySdhsrc+3rJsYwtJMpGg3MWp/P9DX5GClhcAcT7u/QdCVaw6Jt+UxaksfCrfk42ih4tm3ZHto+qWsWwOmr+YybmqpbZq/OKlOshrSpY0yLmsYs2ZnHT0tyyMvX8FI3s0fED3cS1SzbZfh73tQYvJyVbD6Sz49/5zBvQx6u9gpeeMrUYPpKQamsuOU/qMRHNXToUHbs2MHkyZNRKBQoFArmzp2LQqFg3bp11K9fHzMzM3bv3s3ly5fp0aMHbm5uWFtb07BhQzZv3qzb1wcffEDjxo2LvEft2rX57LPPdH/PnDmTsLAwzM3NCQ0NZerUqQZju3btGm3atAHAwcEBhULB0KFDgaJd4/z9/fniiy8YPHgw1tbW+Pn5sXLlSuLj4+nRowfW1tbUqlWLw4cP673H7t27iYyMxMLCAh8fH9566y0yM8vvacj+jXOp17IfdVv0wcUzmG6DJmBias6x3UsMpj+w+XeCI1rQvPMwXDyDaNvrbTz8wjm4dYEujbWdi95y/thWAkIa4+Dio0vTpudbNO04FDev6uV2LPe0a2jO2r05HL+Yz+14FXNWZ2JvraRO9eK/gE5fyWfFrmyiLhR/85qWqdFbalcz5cL1AhJSy/dpf5t6Zqzfn8OJywXcSVAzb20mdtZKagcXf7E6c7WA1XtyOG6gFeie7pHmnLlSwPKdOdyKU5GQqubk5QIyKuDpVPyGnVwYP4nYFZsfnbgCNY8wZuuxAs5cVxOTpGHx9nxsLRWE+xX/FXbhlpqNhws4fa38W4Hu2bN+Hg1a9aN+y964egXz9NBPMTE158jOpQbT7904n2o1WxDZZRiunkG07/M2Hv5h7N+8EIDE2GvcvHycp4eMxzuwJi4eATw9ZDwFebmc2LcGADfv6gx88ydC67bByc2XoPAmdOj7DueitqFSPd6DKI1Gw8nd86nX7lX8a7TDySOENgO+Jistjmuniy8TJ3fNJaxxP0Ib9sHBLZiWvSdgbGLOuUOF31e1IodQt83LuPnWNrgPI2NTLG1cdIuZpT3XTm8hpEFvFCXoA394yxxqNutPzaZ9cPYIpsMz2u/MU/sMf2ce3TafgPBIGnUYjpN7EC26v4ObTzhRO/4AwMzChn5vziG0fhcc3QLxDKhDuwEfE3vjNGlJdwC4fGo7SiNj2g8Yj6NbIB5+tejwzAQuRm0gOe76Q+M9tn0OEU37E964D07uwbTtNwFjU3POHDAcb9SO+fiFRlK/7XAc3YNo2uUdXLzDOb5LG69GoyFq53wadXyNoJrtcfYMpeNz35CZGseVk5t1eWxl66JbzK3suXJqC+GNiuaxuaW9Xlojo4fflGs0Gk7smk/9dq8SENEOJ88Q2j6jLTtXH1J2ju+cS/jdsuPoFkyr3hMwMTHn3MHCfKgdOYR6bV/Gzc9w2QGo3XIo7n51sHHwwt2/HnXbvEzsjeOoVCV/8NG8hhHbj6s4e0NNbLKGv3YWYGMBYb7Ff680jzDi8Hk1Ry+qiU/RsGJPAfkFUL+69m7dz1WBgzUs2VVAbLKG2GQNf+8swNNZQaBnYZ7vPa3iZryGlEy4Eadh5wkVPq6KMv1W5pO6ZgEUFEBalka3ZOeW//UqspYJm4/kc/qaiugkDYu25mFrqSAioPia0LkbatYfzOfUVcNPEnPyYPrqXI5fVhGfouFGrLbS5ONqhL21jLURj1ERmjx5Mk2bNuWll14iOjqa6OhofHy0N9Rjx47l//7v/zh79iy1atUiIyODLl26sGXLFo4dO0bnzp3p3r07N27cAOC5557j4MGDXL58Wbf/06dPc+LECQYOHAjAggUL+OSTT/jyyy85e/YsX331FR9//DHz5s0rEpuPjw9Llmi/RM+fP090dDSTJ08u9lh+/PFHmjdvzrFjx+jatSuDBg1i8ODBPP/88xw9epSgoCAGDx6MRqP9oF++fJnOnTvTp08fTpw4weLFi9m9ezdvvPFGSbPvoVQFedy5fprAsGa6dQqlksDwpty6HGVwm5uXowgMb6a3LqhG82LTZ/w/e/cdHUX1NnD8uyWb3ntPSEISSAKhB4j0IqJ0UKkKigoiYkFelWJDf1awi9IUFaVL70jvPbSEBNJ779nd948Nu9lkAyEEY+R+ztnDYfbOzDNPZmfmzr1zJzeDa+f3ER45rEFivhMHaynWFlIuxekOriWlamKTKmjm3nBjdFiaSQj1M+LAudrvQNaHfWX8V27oLixLyiAuWYmvW/3jlwAhzYxIzVYyZZg5H75gxWujLQi7zYnqQWdnKcHKTEJ0oq5CU1oO8elqvJ0b7w5VRUUZSXEX8WsZoZ0mlUrxaxlBfPQZg/PER5/VKw8QENJVW76iXPN7kRsZ6y1TZqTgxrVTtcZSUpSPsakFMtnd7Zv5WQkU5afjHqA7lhibWuLkGUbqDcPboKwoIz3xIu7++scrj4CIWuepixtRuyktyiGw3dA7llVWlJEafxHvIP0YvII6k3T9tMF5kmLP4B2on3uf4K4kxdYec1lxAUgk2m5ZyooyZDIjveFr5UaapoDEmJO3jTct4SKezfXj9WzemeQ4w/Emx53Bs7l+vN5BXUmJ08Sbl5lAUV663jKNTS1x9m5V6zJjL+ympDCH4I41zwN//fg8i96K4M8FT3D9wp27x97adzyq7zted953qs4jkUpxv8d9p6Qoh2un/8LFO/yOFbhbbC0154+YJP3jSkK6Gi8nwxfIMim42UuIrjKPGk23Ni9HzTxymQQ1UFHlurxCCWo1tR6vTBXQyk/KzTQ1qnrWL+7XOeuWAE85H75gxeynLXm8tynmJg1bibCzlGBlLuFagi5xJWVwM03V4Md5EwWo1PenMvePkEga7/MfVOe9y9raGoVCgZmZGS4uLri4uCCTaWrp77zzDn369MHPzw87OztatWrF5MmTCQkJISAggHfffRc/Pz82bNgAQMuWLWnVqhW//vqrdvkrVqygY8eO+Pv7AzBnzhw+/fRThg4diq+vL0OHDuXll1/m+++/rxGbTCbDzk7z3IiTkxMuLi5YW1vXui0DBgxg8uTJBAQEMHv2bPLy8mjfvj0jRoygefPmzJw5k0uXLpGamgrA/PnzGT16NNOnTycgIIDOnTuzcOFCli9fTklJ7c+klJaWkpeXp/cpL6t5wV6Un41apcTcyl5vurmVAwW5GQaXXZCbUaO8hZUDBXmGy589tA6FsTnBVbrF3U9WFppdK69Q/258XqEKa/OG+zFFhBpTUqbmdAN3i7OqjDGvSD/+/CKV9rv6sDSTYKKQ0LejCVFxFXz1ZyFnrpXzzCAz/D1u0/7/ALMw1fxbUKx/0iooVmNh2ngH5qL8HFQqJRbW1X6H1vZ3+N061CifX1ne0dUXa3tXdvz5OcWFuVRUlPH3pkXkZaWQn5NucJmF+dns2fAt7buPrMc2aJZpaqG/DaaWDhTlG96GkkLN8crUsto8Fg4U1zJPXVw+thqP5l2xsHG5Y9nigspjZrUYzC3tKazlGFiYl4FZtdybWdVevqK8lL/XfUJw20cwNrUAwKt5JwrzMji240eUFWWUFOXy9/pPK5dv+O8DUFyZM7Nq8ZpZ2lNUy/qL8jMws3SoUf5WvLf+dnezzItHVuEV1BXLKjk2Mjaj66A3GDBhAY89+z1uzdqy8acpd6wMafed6uu3qMO+Y1H3eW7n8KZPWPR/4SyZ04mC7CQenvB1nee1rDx21DiulNR+XDEzBplUYvhYZKaZ52a6ivIK6NdehpFM01Xu4Q5yZFKJdp239GsnY85YBW+NMcbGQsIvO+vfjfd+nbNA8zzQ8i2FLPyjgHV/l+DvKeeFYeYNel1sWZm//Oq5LVJrv2sIchk8EqHgzDUlpfev17TQhDTIrfl27drp/b+goIC5c+eyadMmkpOTqaiooLi4WNsiBJpWocWLF/P222+jVqv57bffmDFjBgCFhYXExMQwceJEnnnmGe08FRUVt63g1FVYmO6hTGdnzYOVoaGhNaalpaXh4uLC2bNnOXfuHCtW6LqdqdVqVCoVsbGxBAcHG1zP/PnzmTdP/9mLoU/NZtjTc+95G+7W6QOrCe00UO9Oc0Pq0ELB6P66h4W/+jP/vqynui5hxhyLKtO7+1Yf7YONeKKPmfb/36wpuMfIDLt14jgXXc6ek5pKcUK6kmZuciJbGROd0PD9rpua1n5ShkTq7uou3dqwldx/M5nciCdf/JK1i9/i/Rc6IZXK8GsZQfOwSNQGbl6WFBfw82fP4eTmT8/BU+64/Gun/uLvNXO0/3/4qe8aMvx6K8hJIeHqAXqP+byxQwE0Ayf89dNLqFHT+3HdMdzBLYCHx33IntUfsn/DZ0ilUsK7jdVUWP7ld0vzc1K4efkAD0/4Qm+6qYUdbXo8pf2/s1cYhblpnNr9E81CdIMoXD31F/tW6/adR55u/H2ndfeJBHcYRn52Eid2fM2u399gwNPfGexa2aqZlEFddJc8y3fcn6vgohL4bXc5j3U2IqKFDLUazl1XkZihqvEb3n9eyYmrKmwsoGe4nBEPyVm+o27dW/+pcxbAySu6XCVlqEhMV/LOM1Y095Rz5Wb9ngsPD5AxvJuum/xPmxq2V4chUimM7atZ5+q/m+55RbxQtWE1SEXI3Fx/tJxXX32VHTt28Mknn+Dv74+pqSnDhw+nrEy34z3xxBPMnDmTU6dOUVxcTHx8PKNGjQI0FSmARYsW1XiW6FYr1L2oOorcrQOmoWkqlUobz+TJk5k2bVqNZXl5edW6nlmzZmkrd7esPVHz+RgzS1skUlmNgREK8zKwsHaoUR7AwtqhRvmCvAwsrGqWv3H1BJkpsQx/7v5dZJyNLiN2se6AKJdrcmhlLiWvUFdLsTKXEp/WMKMC+HvIcbGXsWj9vZ8AzkWXE5esq7zdejjTykw/fkszKQn3EH9BsRqlUk1Kpv4yUrKU+DVgl8GmLOqmivg1umPFrZ+8halE726hhamE5Mz79/zPnZhZ2iCVyijIrfY7zM28w+82o0Z5yyrl3X1bMvXdtZQU5aOsKMfcyo7v5o3C3Vf/4fXS4kKWffIMChMznpz2JTL5nbsEebfowfAqo3MpKzR5Li7IxNzKSTu9OD8DezfDN3hMzDXHq+JqD/kXF2Rgaml4u+/kyok1GJvZ4N2iZ53Km1pUHjOrxVCYn1mjxe0WcyuHGi0lRXk1y2sqQdPJy0pi5LRl2tagW4LbP0pw+0cpzMvASGEKEgkndy/FxsGT2phW5qz6wAhF+Zk1WqluMTPQKldUZfvMLB1106yd9Mo4utccMfTS0dWYmNvgG3LnHDt7t+Lm1UN603xa9NAb2U277+Tr7ztFBRk43GnfKaiWh4KarV91YWpui6m5LTaOvtg6+fHz+91JvXEGF5/wGmUv3VQRn647rshlmnNUjeOKiYTkLMPHlaJSUKputRjpH4uqPuMZnaTms1VlmBmDSq3p4vXG4wqy8lU1lldUqiYzD9Jzypn5uDGejppnh+7knzpnGZKZqyK/SIWjjZQrN+9c3pCoOCWfpep61NyK39JUQn6VXFqYSUjKuPfjvFQKY/sosLWQ8t2GEtEaJGjdVbVSoVCgVN75B3Xw4EEmTJjAkCFDCA0NxcXFhbi4OL0yHh4edOvWjRUrVrBixQr69OmDk5PmYOrs7IybmxvXr1/H399f7+Pr61trbECd4rtbbdq0ISoqqkYs/v7+2vUaYmxsjJWVld7HSFGzRUYmV+Dm3ZLrlw5rp6lVKq5fOoKHX2uDy/b0a01slfIA16MOGSx/ev8qXL1b4uJpeDjthlBaBuk5Ku0nOUNJboGKIB/dxZmJAnzd5FxPbJiRBbu0MuZGckWDHORLy6vFn6kit0BFoLeucmKiAB9XGbFJ9Y9fqYIbKUqcbfUr9E62UrLyGu+i/t+krBwy89TaT1q25uFcf3fd4crYCDwdJdxIbbycyeUK3Hxacj3qiHaaSqXietQRPP1bG5zH078VMVXKA0RfPGSwvImZJeZWdmSkxJEYe4GgcN3d+ZLiApZ+PBGZ3Igx078xeFwxRGFigbWDt/Zj6+yPmaUjidd0x5KykgLS4s/h7G14G2RyBY7uLUmM1j9eJUYfqXWe21Gr1Vw5sYbmbQfV+fkOmVyBs2dLbl7Rj+HmlcO4Nat5EQzg5tuaG1f0c3/j8iHcfHUx36oEZafdYMSLSzG1sK01BnMrBxQm5lw5uRmZkTHeQV1uG6+TR0vir+nHG3/1MK4GLtoBXH1aE39NP96bVw7hUjmktZW9B2ZWjnrLLC0pIPXG2RrLVKvVRB1bQ1D7wXXKcUbiJcytHPWm1bbvJERX23du3nnfSWigfacqtVpzLFAqDd/pL6uArHzdJy1HTX6RmmZu+scVD0cJN9MMV0SUKkjKVONXZR4J4Ocm5aaByktRqaYS1MxVgrmp5sH+2txqxLrdCGlV/VPnLENsLCSYm0rILaz/8be02nE+NVsz+FFAlS7ixkbg5SS95+P8rUqQo42U7/8qoej+Nz4JTchd3YL28fHh6NGjxMXFYWFhoW0xqS4gIIA1a9bw6KOPIpFIePvttw2WHT16NHPmzKGsrIzPP9dvrZg3bx7Tpk3D2tqa/v37U1payokTJ8jOzq7RygLg7e2NRCJh48aNDBgwAFNTUywsLGqUq4+ZM2fSqVMnpk6dyqRJkzA3NycqKoodO3bw1VdfNcg6OvWdwLqf3sDNJwR33zCO7FxGeWkxrbtoHhxe++NMLG2d6D3sFQA69h7L0v+N49C2xTQP686FY5tIirvIo+Pe0VtuaXEBUSe20XfUTIPrzc1Morgwl9ysZNQqJSk3LwFg5+RVp/di3M6u4yUM6GxCWpZmZLRBkabkFKg4c1V3onr5cUtOXy1j7ynNkcnYCByrVBIcbKR4OMkoLFGTXaWiYKKAtoEKVu2+f13J9pwqpX8nY9KylWTmqhjYxZTcApXe6DrTRphzNrqcfafLdPHb6OK3t5bi4SijsESlfUfQzuOlPP2oGdcSFFyLr6CFr5xQPyMWrLx/XRtqIzM3w9xf16pp5uuBVasgyrJyKYlP/sfjqc3BCxX0DJeTkasmK19N33Zy8orUeu8FmjTAiItxKg5HaSrGCjnYW+m6yNhZSnC1k1BUqia3gQZ87NJ/PKsXzcLNNwSPZqEc2racstJi2kYOAWDV9zOxsnWm70jNMatz33H8OH8cB7YsIbBVN84d3UxS7EUGP6XrfnXh2FbMLO2wsXclNeEqm1Z8QHDbXgSEai60b1WCyktLeHLy/ygtLqC0WLPvmFvZIZXWvdVcIpEQ2nUcp3Z/h7WDD5Z27pzYvhAzKyd8WvbWlvvrhwn4tuxNSJcxAIRGTmDvH2/g6BGCk2cY5w8so7ysWG+gg6L8dIryM8jN0Nwyzkq5ipGxORY2rpiY2WjLJUYfIT8rgaAOI+4m9bTr9RRbls/E2SsEV58wTu7WHDNDOmli2LzsdSxsnHlokOaY2abHOFZ+PpbjOxfTLKQbl09uJuXmBfo8qTlmKpXlbFg0jbT4KIY8/z1qlZLCXM1zMCbm1sjkmptep/b+gnuzcIyMzbhx+RD71v6Phwa9gomZlYEodcK7P8WOX2fi7BmCs1cYZ/Yto6KsmBYdNfFu/+V1zK2d6fKoJt7W3cax+suxnNqzGJ8W3bh6ajNp8RfoNeod7d+u9UPjOL79W2wcvbGy8+DI5gWYWzvRLLS33roTrh0hLzOBlp2G14jr0rG1SGVGOHpoWnFizu0g6uhqej3+3m23RyKREBY5jpO7NPuOlZ07x7Zp9h3fKvvOhu8n4BvSm9DKfafVQxPYvVKz7zh7hnFuv2bfCWpfZd/J0993MpOvojA2x8JWs++k3jxLWvx5XH3aYmxmRW5mPMe3LsDK3gsXb8MVS0MOXlTSo5WMzFw12QVqereRkV+saT265en+RkTdUHLkkmbawQtKhkXKScyQkpCupnNLGQo5nLyquyHXJkBKeo6awhI1nk5SBnaUc+iikozK9xN5OErwcJBwI1XzwL6dlYTebeRk5qlrrYTVxf04ZxkbwYDOJpy+Wk5eoRpHGymDHzIhPVvFpbiGrWDtP1dOr7ZGpOeqycpT0b+DEXlFar0R4SY/asyFWCUHL2jWrZCDg3WV47yVBDd7CUWlkFOgRiqFcX0VeDhK+WlzKVIJWFY+d6pp4WvQTfhnSETXuIZ0VxWhV199lfHjx9OiRQuKi4tZsmSJwXKfffYZTz/9NJ07d8bBwYGZM2eSl5dXo9zw4cOZOnUqMpmMwYMH6303adIkzMzM+Pjjj3nttdcwNzcnNDRUbyjsqtzd3Zk3bx5vvPEGTz31FOPGjWPp0qV3s3m1CgsLY9++fbz55ptERkaiVqvx8/PTduVrCCEdBlCUn8XedV9SkJeOi2cwo19epO1ik5uVpNfv2dO/DUOf+YQ9a79g95rPsXPy4fGpX+HkoT8M9oVjm1CjJqTDIwbXu2fdQs4eWqf9//fzNBdw419bhk9QzSHO78a2oyUoFBLG9DfHzERCdEIFC1fm6z3P42ArxcJM96P2dpXzypO6C4qRvTSVsUPnS1m2SXf12j7YGIkEjl26f/18dxwrRWEk4cm+ZtoXwn69ulA/fhsZ5qa6CV4ucqaP0lXAh/fQHHGPXCjj562aStvZ6HJ+31FM347GjOhpSlq2ih/XFxKT+M+/SMi6bQgRu37W/r/FJ/8HQPzyNZybeOd3o/xT9p1VopBLGBpphEnlC1WXbC3X+1vYW0kxN9FdRHg4Snl2oK7FdmCE5k74yatK/tzXMP0iQjsOoDAvm11rFlKQm4GrVzDjX/1B+7vNyUrW68/tFRDOyOc+ZufqBexY9Tn2zt48+dKXOFf53ebnpLP5t48ozM3EwsaB8C6D6D5I9y6bpLgoEmLOAfD56/304nnlk53YOrrf1Ta06j6J8rJi/l49m7KSPFx82jJg4iK95wnzMm9SUpit/b9/6wGUFGZxYvuXlS8HDWbAxEV63ZuiDv/OyZ26h9c3fKu5EO4+8gO9CtOV46tw9g7H1qnZXcUd1FZzzDy4cSFF+ek4ugczfMqP2q5jednJSKpcMLg3a8MjT33Cgb++4MBfn2Hj6MPgZ7/G0U2T+4KcVGLO7wZg+fxBeusa+dJyvJprjocpN85xaPOXlJcWYufcjD5PzKNlx8F3jLd5mwEUF2ZxZMvCyhfABjNo8o/anOVXi9fVtw39xn3C4U1fcGijJt6BE7/G3lW3r7Tt9QwVZcXsXjlb80LVZm0ZNPnHGs+CXjyyClffcOyc/QzGdmz7N+RnJyGVyrB1akb/8Z8T0Lr/HbepdeW+s2+Vbt8ZOOnO+05xYRbHt+n2nYGT9Pedi0d+58QO3b6zvnLf6THyA4LaD0VuZML18zs4vv1LKsqKMbN0xDMwkj69n9dWWOti/3klCjkM7iLXvlB16Tb944qdpQSzKiOknY9VYW5SQa82cixNITlLzdLt5XovS3WwltC3realoDkFavaeVXLwom6h5RXQwltGr3AJRnLIL4ZrCSr2ni2/pwvz+3HOUqnBzUFGx5aal7TmFqi5FFfOxoMl9/xsbnV7zlSgMJIwvJsCU4XmhaqLNpZWO85L9Eas83SS8vwgE+3/B3XR/P2PX65g5Z4yrM0lhPhqLnVfGWmqt75v15fojRooPJgkarWhR3CF++XXA0033fsOZN+50L+YXN6076IMmHVvFdPGtP+bs40dwj1pG9qEX74HxKf+ux/kvx3LBhxlsjGUNfFnEcrLm+45CyA9o+k+FJ+TdfuXnP7bmZo37ePmJ8+b3blQIyj+dX6jrdv0yX/PDdKG0rSvDAVBEARBEARBEOpBVIQEQRAEQRAEQXjgiPF6BUEQBEEQBKEJkIjBEhqUyKYgCIIgCIIgCA8c0SIkCIIgCIIgCE2BtGkPIPNvI1qEBEEQBEEQBEF44IiKkCAIgiAIgiA0BRJp433q4euvv8bHxwcTExM6duzIsWPHbls+JyeHKVOm4OrqirGxMc2bN2fz5s31WnddiK5xgiAIgiAIgiA0qJUrVzJjxgy+++47OnbsyBdffEG/fv24cuUKTk5ONcqXlZXRp08fnJycWLVqFe7u7ty4cQMbG5v7FqOoCAmCIAiCIAiC0KA+++wznnnmGZ566ikAvvvuOzZt2sTixYt54403apRfvHgxWVlZHDp0CCMjIwB8fHzua4yia5wgCIIgCIIgNAUSSaN9SktLycvL0/uUlpYaDLOsrIyTJ0/Su3dv7TSpVErv3r05fPiwwXk2bNhAREQEU6ZMwdnZmZCQED744AOUSuV9SSWIipAgCIIgCIIgCHcwf/58rK2t9T7z5883WDYjIwOlUomzs7PedGdnZ1JSUgzOc/36dVatWoVSqWTz5s28/fbbfPrpp7z33nsNvi23iK5xgiAIgiAIgtAUSBuvDWPWrFnMmDFDb5qxsXGDLV+lUuHk5MQPP/yATCajbdu2JCYm8vHHHzNnzpwGW09VoiIkCIIgCIIgCMJtGRsb17ni4+DggEwmIzU1VW96amoqLi4uBudxdXXFyMgImUymnRYcHExKSgplZWUoFIr6B18L0TVOEARBEARBEIQGo1AoaNu2Lbt27dJOU6lU7Nq1i4iICIPzdOnShejoaFQqlXba1atXcXV1vS+VIBAVIUEQBEEQBEFoGprQe4RmzJjBokWLWLZsGZcuXeL555+nsLBQO4rcuHHjmDVrlrb8888/T1ZWFi+99BJXr15l06ZNfPDBB0yZMqXB0led6BonCIIgCIIgCEKDGjVqFOnp6cyePZuUlBRat27N1q1btQMo3Lx5E2mVZ548PT3Ztm0bL7/8MmFhYbi7u/PSSy8xc+bM+xajqAgJgiAIgiAIQlMglTR2BHdl6tSpTJ061eB3e/furTEtIiKCI0eO3OeodETXOEEQBEEQBEEQHjiiRegflpnb2BHUX2QX28YO4Z5k5jR2BPdm/zdnGzuEeot8oVVjh3BPNrxr+OVvTYXCpOke6h0dzRo7hHuiVKobO4R7IjdqWnefq+vRrrEjqL+yCvPGDuGeFJXL7lxIuHv1eFZHqJ3IpiAIgiAIgiAIDxxRERIEQRAEQRAE4YHTdPtLCIIgCIIgCMKDRNK0u6v+24gWIUEQBEEQBEEQHjiiRUgQBEEQBEEQmgKpaMNoSCKbgiAIgiAIgiA8cERFSBAEQRAEQRCEB47oGicIgiAIgiAITYEYLKFBiRYhQRAEQRAEQRAeOKJFSBAEQRAEQRCaAolow2hIIpuCIAiCIAiCIDxwREVIEARBEARBEIQHjugaJwiCIAiCIAhNgXiPUIMS2RQEQRAEQRAE4YEjWoQEQRAEQRAEoSkQw2c3KNEiJAiCIAiCIAjCA0e0CAmCIAiCIAhCUyCGz25Q/+psLl26FBsbm3teTvfu3Zk+ffo9L0cQBEEQBEEQhP+GBm8RmjBhAjk5Oaxbt66hF31He/fupUePHmRnZ+tVoNasWYORkdE/Hs+dqNVqjm39kotH/qS0OA9X3zZ0Hz4HG0ef28537sAKTu/5iaL8DBzcgnhoyFs4e4dpv68oL+Xgho+4enoTqopyPAO70H34HMwsHWosq7gwm98/GUxhbirPvH8MY1MrAArz0jiw/iPSEi6Qm3GTVl3H8tj4/6s1puO7V3B4208U5Gbg7BlE/yfewr1ZWK3lo05sZe+6BeRkJGLn7E2vYa8SENZNr0x6Ugy7Vn/CzavHUSmVOLj5MeL5hVjbuwGw/H9juXH1uN48bbqN4pGx826bP9Dk/vi2L4k6qsv9Q0PvnPvzB1dwZq8m9/auQUQOeQtnL/3cH/rrI66d2YSyohyvwC48NFQ/9wnXDnN06wKyUq4iV5gR1HYwHR+ejlSm+TkmRh/l7P5lpN08T1lJAdaO3oR3n4ilW987bted9Gkrp32QDFMFxKWqWHeggsw8da3lfV0kPBQmx91BipW5hOXby4i6obrnOBqCXdd2NHtlItZtQjBxc+LEsBdI3bCrscPSejTSlMhWxpgaS4hJrODXbYWkZdeeuwBPOX07muDlLMfGUso3q/M5e61cr8z3b9gZnHf17iK2HytpsNgHdDKmc6gRpsYSYpOUrNxdQnpO7bH7ucvo1VaBl5MMawspi/4q4lxMRa3lR/U0oWuYgtX7Sth7uuye4+0eJiXcX4KJEcSnq9l8XEVW/u3naddcQudgKRamkJoNW04oScrUL+PhAD1aSXF3kKBWQ0o2rNitpEKpX04mhYn9ZLjYSfh+cwWp2XWPvUcrKW0DpJgo4Ga6mo1HlHeMvUOglM4tK2PPUrP5mIrETM3v2MYcXh5m+Hy3cl8FUTd0v/fWfhIiWsiwt4LSMoi6oWLTsbv7fXcLlRLuV5n7DDVbjqvIKrj9PO0CJEQE6XK/9aSSpCz9Mu72lbm31+X+17263I+KlOJsK8HcBIrLIDZFza6zKgqK7yp8Pfu2/s7ODUvJy8nA3bs5I5+ehU9AqMGySfHRbFr5NTevXyIrPYlhE16j5yNja5TLyUxl3YoviDp9gLLSEhxdPBkz5V28/VrWP9BaHNj+G7v/WkJ+bgZuXoEMnfB/ePsbjj85Ppqtq74i/noU2RlJDB47k24DasZ/y871P7Lp9y94qP8Yhox/455jPbJzBfs3L6YgNwMXzyAGjn0TT7/arxnOH9vKztULyclIxN7Zm36jXiGwle6aoSA3g60rPyX6wkFKivLxCWzHwLFv4uDioy2Tn5PO1t8/JvriYUqLC3Fw9aH7Y88R0v7ez61C0/WvbhFqKHZ2dlhaWjZ2GDWc2v0jZ/f/TPcRcxkx/Q+MFKZs+H4SFeWltc5z7fRmDqz/kPb9pjBqxhrs3QLZ8MMkivJ1Z/AD6+cTe3EPD49fwJApyynMS2PzkhcNLm/3yrdwcA2sMV1ZUYaphR3tez+Pg1vQbbfj4rHN7PjjQx56dArPzF6Ds2cgv34xicK8TIPl46NPseaHV2jddTjPzF5LYHhv/vh6KmmJV7VlstJusuyjJ3FwacbY15bz7Nz1RA58AbmRsd6ywh8awcuf7td+eg9/7bax3nJ6z4+cO/Az3YbNZdi0P5ArTNm46A65P7OZgxs+pF2fKYyYvgYHt0A2LtLP/cEN84mL2kO/sQsY/IIm91uX6XKfkXSZjT8+i1dgJCNfXkvfMZ8RG7Wbw5s/1ZZJuXEae9dA+o1fwKhX1hPUfii7fptJcsy+Om1bbbq1ktG5pYx1B8r5en0Z5eXw9MNGyGW1z2Mkl5CcpWb9ofLaCzUSmbkZeeeucGHanSu+/7R+HU3o2daYFdsK+XB5HqXlaqaNsrxtrhVGEhJSlfy2o7DWMq99ma33WbapAJVazakr916ZuKV3OwXdwhWs3FXCp78XUlqu5oUhZreN3dhIQmK6ij/23LkyFuYnx8dVRk5Bw1SoO7eQ0CFQwqZjKn7apqS8Akb3kCG7zdmthbeEvm2k7Duv4ofNSlKy1YzuIcOsyuHFwwGe7CHjerKan7Yq+XGLkuNXVKgN3DfoHS4lv7j2Gwq16dpSSsdgKX8dVbJocwXlFTC2txz5bWJv6SOhXzspe88q+X5jBSnZMLa3DHMTzfe5RfDxH+V6n91nlJSWq4lO1MUYESylV7iMAxeUfL2+guU7KohOurtt6BwsoUNzCZuPq1i8Q5P7J++Uey8JfcKl/H1BxaKtSlJz1DxZLffu9vBkd03uF29T8tM2JSeu6uc+Lk3N6oNKvtmoZNUBJbYWEoZ3uc1OegcnD25lzbKPGTDiOd74aCUe3oF89f5z5OcaPo+Vl5Zg7+TBoNEvYWVT8yYjQFFBHp++PR6ZTM4L//cNb32+lqHjX8XM3Krecdbm9OEtrPv5f/Qb9jyvfPAnbt6BfP/h5NrjLyvG3smDgU9Mx7KW+G+5GXOew7v+xM2reYPEeu7IZjb/+hE9B09hyjurcfEKZOnHz1BQyzXDjWun+eObV2n30DCmvLOG4Da9WPHFi6QmaK4Z1Go1v3wxlez0eMZM/5op767BxsGNJR89TVlpkXY5q354g/SUOMZM/5ppH6ynZbs+/P7VyyTFRTXIdv1jJJLG+/wH1bsitGrVKkJDQzE1NcXe3p7evXvz2muvsWzZMtavX49EIkEikbB371727t2LRCIhJydHO/+ZM2eQSCTExcVppy1duhQvLy/MzMwYMmQImZm6H0VcXBxSqZQTJ07oxfHFF1/g7e3N9evX6dGjBwC2trZIJBImTJgA1Owa5+Pjw3vvvce4ceOwsLDA29ubDRs2kJ6ezqBBg7CwsCAsLKzGug4cOEBkZCSmpqZ4enoybdo0Cgtrv2i5HbVazdm/l9Ouz3M0C+mFg1sgvZ/8iMK8NK5f2FnrfGf2LaVlpxG06DAMOxd/egyfh9zIhEvHVgNQWpxP1NHVdB00E4+ATjh5htD78fmkxJ0mJe6M3rLOH/yN0uI8wns8XWM9VnYePDTkTYLaD0ZhYnHbbTmyYynhkSNo3XUYjm7+PDJmHkYKE84cWG2w/LGdP+Mf0pXO/Sfi6OZHj8Ev4erdguO7V2jL7Fn7Bf6h3eg94jVcvVpg5+RFYOuemFvZ6y3LSGGKhbWj9mNsevtYQZP7c/uX07b3c/hW5r7X45rcx94m92f3LaVFxxEEV+a+2zBN7i8f1+X+0rHVdHm0MvceIfQcVZn7G2cAiD6zGXvXQNr3nYK1gzfufh3o/MirXDj4K2UlmtuobXs9R8f+L+Hq0wZrBy9aRY7DMzCSpGv31trRJUTO7tMVRN1QkZKlZuXecqzMJLTwrv0wcDVBxfYTFVyM+3e0AlWVvu1vrs75gtT1tf/NGkuv9iZsPlTC2WvlJKYrWbKxEBsLKa2bK2qd5+L1ctbvL+bM1dornXmFar1PqwAFV29UkJHbcH+f7uEKth0t5fz1CpIyVPy8rRhrcwlhfrV3IIiKq2DT4dLbtgIBWJtLGN7dhGVbilE2UMgdg6Tsv6DiaoKatBxYd1iFpRkEedZ+0o4IknIqWs3Z62oy8mDTMRXlSgj3083Tt62MY1fUHIxSk54LmfkQdVNdI25/NwnNXCXsOHX3G9QpWMrf51RciVeTmgNrDig1sXvVHnvnYCknr6k4E6OJa+MRpSZ2f83vWK2GghL9T7CXlItxasoq/zwmCugZLmXNASXnY9VkF0BqDlxJuLuKUIdAKfsvqriaqMn9+iMqLE0hyKP2+DsFSjkdo+ZsbGXuj6sor4DWzarkvo2M41fVHLqkJj2vMvfx+rk/ekVNYqam4peQAYcuqfBwAGk9r9V2bVxO517DiOgxGFdPPx5/9m0UClMO715nsLy3fwhDx71Cuy4PIzcy/Lvevm4xtvbOjJ3yLj4BoTg4exDcqjOOLp71C/I29m5aTkTP4XTsPgQXDz9GTJyNQmHC0b1rDZb38gvlsdGv0qbzAOTy2o9LpSVF/PLVG4x8Zi6mDVSBO7h1Ge26j6DtQ0Nxcvdn0IS5GBmbcHLfGoPlD29bTkBoVyIfmYiTux99hr+Em08wh3f8CkBmShzxMWd5bPwcPJqF4ujqy2Pj51BeVsrZw5u0y7l57QwRfUbj6ReGnZMnPQY9j4mZJYlxFxtku4SmqV4VoeTkZJ544gmefvppLl26xN69exk6dChz5sxh5MiR9O/fn+TkZJKTk+ncuXOdlnn06FEmTpzI1KlTOXPmDD169OC9997Tfu/j40Pv3r1ZsmSJ3nxLlixhwoQJeHt7s3q15oL0ypUrJCcns2DBglrX9/nnn9OlSxdOnz7NI488wtixYxk3bhxjxozh1KlT+Pn5MW7cONSVt6BiYmLo378/w4YN49y5c6xcuZIDBw4wderUu00fAHlZCRTlp+PZXJcfY1NLnL3CalRYblFWlJGWcFFvHolUikfzCO086QkXUSnL9crYOjfD0tZNezEOkJUSzfHt39DnyY/uqZavrCgj+cZFfFvox+QbHEHCdcPbkXD9DL7B+vtFs5ZdSIjRlFerVESf24udsw8rPp/Ipy935qf3R3L5dM0L3gtH/uKT6Z34bvaj7Fr9KeWld+4Xoc19gIHcV8lR9e1MT7yIR/XcB0Ro57mV+6plbJ2aYWHjRmplGWVFWY1WLZmRCcqKUtITaj8Yl5XkozCxvuO21cbOUoKVmYToRN2VRGm5phuRt/MD0TD8j3GwlmJtIeVSnK5CU1KqJjapgmbuDdcb2dJMQqifEQfO1d6KebfsrSRYm0u5Eq+r0JSUQVyKEl/X+t9tB5AA4/qbsutkGSlZDVMLsrEAS1MJ11N0F/Cl5ZCYAR4Oho9rUim42mm6UlUVm6LWzmNmrJm/sETNU31lzBgqY3xvGZ6O+ssyN4GBHaWsO6SpjNwNWwvN3/B6sv5vMjFdjaej4dhlUnC1l3A9WRe7GrieXPs8rnbgaifhVLRuPX6uEiQSsDKDqY/JmTFMzoiHZFiZ1T1+G3NN7mOr5z4T3O8296mGcz+ht4yXh8gY10uG520aLUwUEOItIT4DVHffMEdFeTnx1y8RFNapSqxSgsI6cv3q2btfYKXzJ/bi5deSHz99hZkTuzH/tZEc3Lmq3surTUVFOQmxUTQP0Y8/IKQTN67VP36AVYvfIzj8IQJDI+41TAAqKspIiruIf0vd8qRSKf4tIrgZfcbgPDejz+LXUn/9/qFdia8sX1GhOdZWPbdKpVLkRgpuXD2lneYV0JrzR7ZQVJCDSqXi3JFNVJSX0Sy4Q4Ns2z9GKm28z39QvStCFRUVDB06FB8fH0JDQ3nhhRewsLDA1NQUY2NjXFxccHFxQaGo/U5DVQsWLKB///68/vrrNG/enGnTptGvXz+9MpMmTeK3336jtFRz4j916hTnz5/nqaeeQiaTYWen6T/v5OSEi4sL1ta1XzgOGDCAyZMnExAQwOzZs8nLy6N9+/aMGDGC5s2bM3PmTC5dukRqaioA8+fPZ/To0UyfPp2AgAA6d+7MwoULWb58OSUlhruDlJaWkpeXp/cpr+x6VZSXDoCZpX4Lh5mlA0X5GQaXV1yYjVqlxPQ28xTmpSOVGWmf9bnF1MKeojxNGWVFGdt+foUuj76Gpa1brTmqi6ICTUwW1VpqzK0cKMg1vB0FuRk1WnYsrBworCxfmJ9JWWkRh7Yswq9lJKNf/omg8N78+c2L3LhyTDtPSMeBDJ70P8a+uowuA57l/JENrP3x9TvHnK/JffU8mlrUnvuSytybWVSbx9JBm9eifMO5N7PU5d4zsCspcae5dnojKpWSgtxUTuz4Ri+u6qLPbCEt/jzeIYPuuG21sTDV/FtQrftOQbEaC9P/ZnN3Y7Gy0BxW8wr1L/bzClVYmzdcriNCjSkpU3O6AbvFWZlrYs8v1N9P8ovU2u/qq3d7BUoV7DvTcPFaVHYHK6x2/6OgRK3d56szMwapVHOhXVVhCdrfgm1lw3K3MCmnolX8ukdJcpaasb1k2FXpZT0oQtM6k1zt+ZY6xV65roJqp4+CKnEYil0mldR4DqagWK3NRXVtAqSk5aiJT9dtr62lBAkQGSpjywklf+xTYmoM4/rIb9utTT9+zb+F1eIvLKk9llu5LzCUexP93D8UKuV0jIrf9mpyP6anDLtqDf69WkmZOULGa8PkWJtL+OPvu6yNVirIz0alUmJprX98t7S2Jy/H8DmhLjLSEti//Q8cXb2Y+tZ3RPYdyZ+LP+LI3vX1XqYhhXn3J/5ThzaTGHeJgY9Pv8cIdYryc1AZuGawsLa/7TWDhbV+TdjCyp78yvKOrr7Y2Luy/c/PKS7MpaKijL83LiI3K4X8HN159fEpn6NUVvD+CxHMmdiKdUvmMvqlL7F39m6w7ROannrdnmzVqhW9evUiNDSUfv360bdvX4YPH46trW29A7l06RJDhgzRmxYREcHWrVu1/x88eDBTpkxh7dq1PP744yxdupQePXrg4+Nz1+sLC9M9lOfs7AxAaGhojWlpaWm4uLhw9uxZzp07x4oVuu5barUalUpFbGwswcHBNdYxf/585s3TPL9gaWmJs7MzMrkCuVzBwEnf3XXMDeXQpk+xc/YjsN1jjRbD7ajVmgvI5q170qnvBABcvIKJjznNyX2/4x2ouXvTptso7TzOHoFYWDvyy6cTyEq7iZ2Tl/a780f+YtPPc7T9yx+Z2Hi59wrsSsTA19i3ei47f5uJTKagXZ/nSY49YXBIzMToI+xe+X90H/EuVg7+dV5Paz8pQyJ1D0wv3dpwF5+Cvg4tFIzub679/1d/3uFJ9wbSJcyYY1FlNR7cvxvtAuU83ktXY/hufdFtStefp5OU7q0VfPRr/boS3xLiI2FgB93v5Le997Dxt3GrkfzUNU33OYCUbBW+LjJa+0nZfUZFh0AJCrmEAxfr1roV6ivh0U66VrUVu+9P7FXJZRDqq+l+V5UEkMskbDlWQUxl69Kqv5W8NkKOj4uEGAPPCoV4S3ikfZXc77vPuY/WdJ+Dark/q9uWQ5dUnL6u6XL5UIiUQZ2k/P73v6cbr1qlwsuvJYOefAkAT99gkuKjObD9Tzp1r/+NrX9CdmYya5d9yPP/twgjhfGdZ2hEMrkRT077kjU/vcV7z3dCKpXh1zKC5mGRVN2Td65eSElRPk/PXIyZpS1RJ3fx+9cv88ybv+Di2TDPPwlNT70qQjKZjB07dnDo0CG2b9/Ol19+yZtvvsnRo0cNlpdKb/Vd1u2S5eV3//C1QqFg3LhxLFmyhKFDh/Lrr7/etvvb7VQdRU5SeeQ1NE2l0hxUCwoKmDx5MtOmTauxLC8vrxrTAGbNmsWMGTMAKCwsJCsriz8OGyGXK1AqNRemRfmZmFs5aecpys/Awb1mpQrA1NwWiVRGcb7+A4VF+RnaUcnMrRxRKcspLc7Ta5koLsjEzEpTJvHaUTKTrxL96jbNl5V/lx/fjqBd78l07F9zG2tjZqGJqfpDjoV5Ne/g3GJh7VBjIIWCvAzMK8ubWdgilclxdNO/8Hdw9SP+2slaY7k1Sl122g29ilDz1j1w9w0jp/L6VFmhyX1xtdwXF2Rg72Y49yaVuS8q0I+7OD9Dm1czS8O5L8rX5R6gdbenaPXQBIry0jA2syYvK5Ejmz/Dyk6/33hizDE2LX6BLoPeIKjdYJJT6t4FKuqmivg1usqPrPL6y8JUovdQt4WphOTMf8+FQ1N0NrqM2MW6rmRyuebYYWUuJa9Qd7FoZS4lPq1hLh79PeS42MtYtP4Ow3PdwfnrFcSl6JYhl2litzSXkFek208szSQkptc/dj93ORZmEt6ZqLulL5NKGBJpTPdwBXMX1207riao+T5DF8etARzMTfVbVixMJKRkG+4jVVQKKpUacxMJVLlMMjfRtZjeanFJz9VfRkaeGuvK7mM+zhI8HODNx/W7DD7TX8b5ODXrD+v/rq7Eq0nM0O0nt1peLEzQa+GxMOG2sStVNVu7LEwlNVqWQDMohJEMzsTox3LrGFB1+4pKNR8bcwy6mqgmMbNK7ivjNzfRz715HXJvYSj3Jfq5z6g2mmVGrrpG173iMs0nK19NRq6S6YPluNurSDT8zH2tLCxtkUplNQYWyM/NrHUghLqwsnXE1aOZ3jQXd1/OHGnY5xrNrRo+/oTrURTkZfHp/43UTlOplFy/fJID23/j459PIZXefXdZM0sbpAauGQpyM297zVC9taggLxPLKuXdfVvy4ntrKSnKR1lRjrmVHd/OHYW7r2Z0vszUmxzZuYJpH2zA2SMAAFevIG5cOcGRnb8y+Km5d70tjUX9Hx20oLHUu8O6RCKhS5cudOnShdmzZ+Pt7c3atWtRKBQolfonTEdHTcfq5ORkbavRmTNn9MoEBwfXqEgdOXKkxnonTZpESEgI33zzjbZ73i23uuFVX39DaNOmDVFRUfj71/2uvLGxMcbGmjspVlZWuLq64hCnObir1WrMLB1JuHYYx8qKT1lJAak3zxHS5QmDy5PJFTh5tCT+2mGahfbWLEelIuHaEcK6jgbA0aMlUpkR8VcP499K07UwO+06+dlJuHi3BuDhCQupKNeduVLjz7P79zcZOvUXrO0NV+pqI5MrcPVuSdylwwSF62KKvXyE9j1GG5zHo1lrYi8dpmOf8dppsVGH8PBrrV2mm08ImSmxevNlpcZph842JPXmZQAsrJ30phubWGBsYoG68qZW1dw7VMt9y4jac+/o3pLEa4dpFlIl99FHCO2in/uEa4fxC9PlviAnCefK3N8ikUgwt9a0Okaf3oSFjSuOHi203ydGH2XT4ueJeOQVWnYaxd0qK4fMcv0LibwiNf7uUpKzNL8PYyPwdJRwJEpUhO5FaRmkl+nnMLdARZCPEQmVFR8TBfi6ydl3umGe5+nSypgbyRXa5ddXaTmU6l3sq8ktVBHoKScxXVORNlGAj4uMA+fq36p47FI5V27qD6TwwhAzjl8q50hU3W+KlVVAWbU6U36xGl9nCamVF98KObg7wIlrhi/GVSpIztIMDV91cABfFwnHr2j+jjmFmt+LvZX+Bbudpa61ZOsJFXuqPH5haSphTC8Zqw6oSMyoue6yCmoMi51fpKaZq5SUymHVjY3A3VHC8auGf5NKFSRnqmnmKuFyvGYdksrYj12pOU8bfylXEtQUVdvt4tM089pb6Sq8pgpN17WcWuqktebeRUJqTpXc28PJO+Tex0XClSoj2Pk667ZZm3tL/dzbW0luO6rdrWtDTWX+7h4UkhsZ4dksmCvnj9KqQ8/KWFVcOX+Ubv0NnxPqwi+wNalJcXrT0pJvYOfoWu9lGiKXG+Hh24KrF44S2r4XoIn/2sWjdO1bv/gDQjrx+v/0B1r47bu3cHLzpddjE+tVCdLEqsDNpyUxF4/Qom1vbawxUUfo1NvwNYOXfytioo7Qpb/umiHmwiE8/VvXKGtipum7mpESR2LsBXoP09zYLS/TXPNIqvW8kEhl2l4owoOpXhWho0ePsmvXLvr27YuTkxNHjx4lPT2d4OBgSkpK2LZtG1euXMHe3h5ra2v8/f3x9PRk7ty5vP/++1y9epVPP/1Ub5nTpk2jS5cufPLJJwwaNIht27bpdYu7JTg4mE6dOjFz5kyefvppTE11t8a8vb2RSCRs3LiRAQMGYGpqioXFnUcRq4uZM2fSqVMnpk6dyqRJkzA3NycqKoodO3bw1Vdf3fXyJBIJrR4ax4kd32Hj4IOlnTtHty7E3MpJe6ENsO7bCTQL6U1Y5BgAWnebwM7f3sDJMwRnrzDO7ltGRVkxwR00FUJjU0tadBzGwQ0fYWJmjcLEgr/XvoeLT2tcfFoDYO2gX9kpLtS89MLO2U+vJSM98RIA5aVFFBdmkXLzEjK5UY2Wmk59JrB+8Ru4eofg5hvGsZ3LKC8tplUXTUzrfpqJpY0TvYa9AkCH3mNZ/vE4Dm9bTEBYdy4e20RS3EUeGfeOdpkR/Say+vsZeDVvh09gR2Iu7ufq2T2Me205oBle+8LRjQSEPoSphQ2pCVfZsXI+Xs3b4exZczjw6rkPixzHyV3fYe3og5WdO8cqc+9bJffrv9PkPrSrJvetuk1g9+9v4OgRgpNXGOf2a3If1F6X++AOutwbmViwf+17OHu31lZCAU7v+QmvoK5IJFKun9/BqT2L6Dv2c+2JJTH6CJt+ep6wyLH4hfbVPk9WVqxGYVr/ARMOXqigZ7icjFw1Wflq+raTk1ek1nsv0KQBRlyMU3E4SnOBrZBTeTGoYWcpwdVOQlGpmtx76+V0z2TmZpj76/ZlM18PrFoFUZaVS0l8ciNGBruOlzCgswlpWUoyclUMijQlp0DFmau6ysTLj1ty+moZe09prlKNjcDRVndx4WAjxcNJRmGJmuw83d/IRAFtAxWs2n1/urHtPV1Gvw7GpOWoyMxVMbCzMbmFar0R4aYONeNcTDl/n9VUYhRG4Giju8Cwt5Li7iilqERNdr6aohLNpyqlSnPBe7t3K9XF0csqIkOkZOWryClU0z1MSn4R2ooCwNheUi7Hqzl+VTPt8GUVgyOkJGVKSMpU0zFIqmk5ua6b53CUim5hUlKzNS0crZpJcbCCVfs18eZVS39ZhWbe7AI1+XV8l82RSyoeCpWSmacmu0BNz9YyTew3dXGM7yPj0k21tqJz6JKKIV1kJGaoScxUExEsRSGH09H6ebSzBG9nCSt21awsZ+bDpZsqHm4v468jmqG1e4fLyMirOZDB7Ry7oqJry8rcF1TmvhguV6lgjukh5XKCWlsxPXJFxaBOUpKzNLnvECjFSI62Gxxo/j7dQqSk5lTm3leKvSWsuq7ZRjd7cLOTEJ+upqRM88xT91ApWflqEgxUQuui18BxLP/6Lbz8WuDjH8ruTb9QWlpMpx6DAVj25f9hY+fMoNGabm4V5eUkJ8QAoKwoJyczjfjYyxibmOHkqjku9Rw4lk/eGsfWNYtoE9GPG9HnObhzFU9MnlOvGG+n+yPj+PXbN/Fs1hJv/xD2bfmFstJiOnbTxL/im1lY2zox8ImXNfFXlJNaJf7c7FQS4y6jMDHD0cULE1NzXD0D9NahMDbF3MKmxvS71aX/eFYvmoW7bwgezUI5tH05ZaXFtH1I83jEn9/PxMrWmX4jNT1qIvqN48cPxnFgyxICW3Xj3JHNJMZeZPDTulcnnD+2FXNLO2zsXUmJv8qmFR/Qom0vAkK7AJrniOydvVi/dA79H38dMwsbLp3aRczFQ4yd8e09bc8/zkA3eqH+6lURsrKy4u+//+aLL74gLy8Pb29vPv30Ux5++GHatWvH3r17adeuHQUFBezZs4fu3bvz22+/8fzzzxMWFkb79u157733GDFihHaZnTp1YtGiRcyZM4fZs2fTu3dv3nrrLd59990a6584cSKHDh3i6af1h312d3dn3rx5vPHGGzz11FOMGzeOpUuX1mcTawgLC2Pfvn28+eabREZGolar8fPzY9Sou79bf0ubnpOoKCtmz5+zK1/q2ZZHn12kN/JJbsZNbUUFICB8AMUFWRzb+iWFeek4ugfz6LOL9F7Y2XXQLCQSKVuWvoRSWYZXYFe6DZt91/Gt/FT3zFZ6wkWuntqItb0b0z7arVeuZYcBFBVksW/9lxTkpePsGcyT0xdpm7nzMpO0XQ0BPP3bMOSZT9iz9gv2rP0cOycfRk75Cid3XR/doDZ9eGTsXA5u/oFtv72PvYsvI55fiFdAW0DTJzj20iGO7VxGWWkx1nauBLXpS+TA5+u0beE9NLnfu2o2ZZW5H/iMfu7zMqvlvvUASgqyOLbtS4ry03FwC2bgJP3cd3lMk/uty15CWVGGZ2BXug3Vz/3Ny39zctd3KCvKcHAL4uEJX+Md/JD2+8sn1lFRXsyp3T9wavcP2ukOHu2IfPynOm2fIfvOKlHIJQyNNMKk8oWqS7aW6z1jYm8lxdxEdyHh4Sjl2YG6AU8GRmi6j568quTPfY37biHrtiFE7PpZ+/8Wn2he+Bu/fA3nJs5qrLAA2Ha0BIVCwpj+5piZSIhOqGDhyny9XDvYSrEw053QvF3lvPKk7kbEyF6aPkqHzpeybJOu1tk+2BiJBI5duj/Pfe08UYZCLuGJXiaYGku4nqTkm7VF+rHbSDE31cXu5SzjpeG6PlVDu2melj8aVcYv2xvuRa+GHIpSo5CrGdix8qWkaWpW7FHqDbVsayGpfE+NZt+OuqHG3FhF91ZSLEw0L/X8dY9S78H/o1fUyGUq+raVYmqsKfPLbiXZ99YbUc+BiyqM5PBohEwb+y87K6ioGrulBLMqv8mLcZrYe7aWYWEKKVlqft6lrDFoQbi/lLwiDD7vA7D2oJL+7WSM7ilDDcSlqPl5Z8Vdjbp26JIaI7maR9rrXgj769475P6mGjNjFd1Cq+R+r378x66okUtV9AnX5X7FHl3uKyo0w6N3C9VUAvOLISZZzYGLqnoPy962S3/y87LZuPIb8nMycPcJZMqb32Jlo3moPzsjRa81ITc7jQ9f13Ub2/XXMnb9tYyAFu2YPm8xoBli+9nXPmfDigVsWfU99k7uDJ/wOh0iH6lfkLcRHvEwBXnZbF31VeULYYOY/MZ32ncEZWck68Wfl53GJ7OGa/+/Z+NS9mxcil9wO6bOXtrg8VUV1mkAhfnZ7FqzkPzcDFy9gpnw2g/aa4bcTP1YvQPCGfn8x+xctYDtf36OvbM3o6d/ibOH7pohPyedLb9+REFuJpY2DrTuMogeg3XXAzK5EeNe+Z7tf3zGz5+/QFlJEfbOXgx7dr7ei1mFB49ErTb0erh/t3fffZc///yTc+fONXYod+3LTU0u3Vq2Df8OuH9UZk5jR3Bv7uYZoX+byBdaNXYI92TDu4cbO4R7ojBpuGG7/2mOjncxpvO/kFLZdI/5AHKjpv08QkTL+z8oxf1SVtG07/wXld/bkPuNbXjHf2f+i/f+1mjrNu1e/66i/1b/zr9yLQoKCrhw4QJfffUVL774YmOHIwiCIAiCIAhCE9WkKkJTp06lbdu2dO/evUa3OEEQBEEQBEEQhLpqUv0lli5d2mDP/AiCIAiCIAhCUyKGz25YTapFSBAEQRAEQRAEoSE0qRYhQRAEQRAEQXhgieGzG5TIpiAIgiAIgiAIDxxRERIEQRAEQRAE4YEjusYJgiAIgiAIQlMgBktoUKJFSBAEQRAEQRCEB45oERIEQRAEQRCEpkAq2jAaksimIAiCIAiCIAgPHNEiJAiCIAiCIAhNgHihasMSLUKCIAiCIAiCIDxwREVIEARBEARBEIQHjugaJwiCIAiCIAhNgUS0YTQkkU1BEARBEARBEB44okVIEARBEARBEJoAtWgRalCiIvQP6xKQ1dgh1NtHPxQ0dgj3RFmubOwQ7smoJ3waO4R62/Du4cYO4Z489nZEY4dwT4r2X2rsEOrNxEjV2CHck9yipn2aVaub9ghV895uuscetapp7/tN3fC/ujV2CMI/QFQrBUEQBEEQBEF44DTtW1WCIAiCIAiC8KAQ7xFqUKJFSBAEQRAEQRCEB45oERIEQRAEQRCEJkAMltCwRDYFQRAEQRAEQXjgiBYhQRAEQRAEQWgKxDNCDUq0CAmCIAiCIAiC8MARFSFBEARBEARBEB44omucIAiCIAiCIDQFYrCEBiWyKQiCIAiCIAjCA0e0CAmCIAiCIAhCE6AWgyU0KNEiJAiCIAiCIAjCA0dUhARBEARBEARBeOCIrnGCIAiCIAiC0BSIwRIalMimIAiCIAiCIAgPHNEiJAiCIAiCIAhNgBoxWEJDEi1CgiAIgiAIgiA8cBq9IqRWq3n22Wexs7NDIpFw5syZ25aPi4vTK7d3714kEgk5OTn3PVZBEARBEARBaCxqibTRPv9Fjd41buvWrSxdupS9e/fSrFkzHBwcblve09OT5OTkO5b7L9i+aTV/rVlBbnYWXr7+TJg8A//mLQyW3bVtPft3byXhxnUAfP0DGTXuuVrL//j1/9i1dR1jJ73EgEGj7ts2jOhnTa+OFpibSrkSW8qPa7JIyaiotfzgnlZ0CDXDzdGIsgo1V+NKWbEpm+R0w/O8McmJ8CBTPl6SxomLxQ0e/6iHbekdYYmZqZQrsSX88GcGKbXEAjCktw0dW5nh7qSgrFzNldgSfvkri6S0cm2ZeVNdaRlgqjff9oN5/PBHRr3jPLJzBQe2LKYgNwMXzyAGjnkTD7+wWstfOLaVnWsWkpORiL2zN31HvkJgq27a7wtyM9j2x6dEXzhISVE+PoHteGTMmzi4+ABQVJDD7rVfEX3hIDmZyZhb2hHcthe9h07DxMyy3ttR1aORpkS2MsbUWEJMYgW/biskLVtVa/kATzl9O5rg5SzHxlLKN6vzOXutXK/M92/YGZx39e4ith8raZC468quazuavTIR6zYhmLg5cWLYC6Ru2PWPxgD/vX3n4PZf2btxCfm5Gbh6BTJk/P/h5W94e1ISotn255ckxEaRnZHEY2Nn8tDD4/TK7Fq/iPPHd5CeFItcYYJPQGseeWIGTm6+9xzr8d0rOLztJwpyM3D2DKL/E2/h3qz23Eed2MredQvIyUjEztmbXsNeJSCsm16Z9KQYdq3+hJtXj6NSKnFw82PE8wuxtnfTlkmIOc2etV+QeP0cEqkUF89gnnz5R4wUJncV/4k9t+JPx9kziH5PvI277+3i38K+9bfi96HXsFfxD9WPPyM5hl2rP9bF7+rH8Oe/1IsfNDdRf1/4DDEX9jPiha8JDO99V7HXZuKT3jzaxwULcxnnL+fx6bfRJCTXfmwY3N+VwQ+74uJkDEDszSKWrrzJ0VPZ2jIL3wslPNRGb751W5P59NvoBolZL/7RPjza1wVLcznnL+XxyTfXSEiu/dw4+GFXBj/shquziS7+329w5GSWXrmWgVY8O9aHFoFWqFRqrl0vYMac85SV1X5MfhDjF5qeRq8IxcTE4OrqSufOnetUXiaT4eLi0qAxlJWVoVAoGnSZ9+rw/p38/ONCJk55Df/mLdmyYSUfzn6ZT7/7DWubmhdzl86fpvNDvWkeHIqRkYK/Vv/C/NnT+fjrFdjZO+qVPX54H9FXLmJrd38rk4/1sOLhrlZ883sGaVkVjOxnw/8948QrHydRXktdIriZCdsO5hMTX4ZMCo8PsOHNZ5155eMkSsvUemUHRFqC2vByGsLgXtYMeMiKr1akk5ZVweMDbHn7OVemz0+gvMLwilv4m7B1fx7RN0uRSSU8OdCOt593Yfr8BL34dxzKY+Vm3Ymy9B4OxuePbmbLbx/x2Pi5ePqFcWjbcpZ+8gzTP9qMhZV9jfI3r53mj29fpc+Ilwls3Z1zhzfy64IXeeGdVTh7NEetVrNiwVSkMjmjX/oaY1MLDm5dypL/Pc1L8zeiMDYjPyeNvJw0+j/+Oo5ufuRkJrFh6Vzys9N44sUF9d6WW/p1NKFnW2OWbiokI0fFYw+ZMm2UJXMX5VKhNDyPwkhCQqqSg+dKeX6o4Qvq177M1vt/SDMjxg4w59SVsnuO+W7JzM3IO3eF+KWrabfq6398/fDf23fOHN7Chl/+x7Cn5+DlH8r+LT+z6MPJvP7pRiyta25PWWkxdk6ehHXsx4ZfPjK4zOuXjtOlzxN4+oWiUlaweeUCfvjwGV773waMTczqHevFY5vZ8ceHDBgzF/dmrTi6cxm/fjGJF97bgrmB3MdHn2LND6/Qc+gMAsK6c+HYRv74eirPzF6Nk3tzALLSbrLsoydp3XU43Qa9iLGJBelJ0ciNjLXLSYg5za9fPEOXh5+l3xNvIZXJSI2/guQu7/ZePL6ZHX/M5+Ex83D3bcWxncv47YuJPP/u1lrjX7voVvw9uHD0L/74egqT3l5jIP5hdHtsGgoTCzKSrunFf8uxncuggZ+VeHKoB8MeceODBVdITi1h4mgfPp0bwtipJykrN3zMT8ss5bvlsSQkFSORSOjf04n5/9eCp18+TVx8kbbchm3J/PTrDe3/S0ob/gJ89DBPhg905/0vLpOcWsKk0T589k4oY144Xmv86RllfLfsVvzwcC9n5r/ZkqennyT2pib+loFWfDovlF9W3eSLH6KpUKoJ8LVArWrYE3BTj19omhq1nWvChAm8+OKL3Lx5E4lEgo+PD1u3bqVr167Y2Nhgb2/PwIEDiYmJ0c5TvWtcdXPnzqV169Z607744gt8fHz01jt48GDef/993NzcCAwMBCA+Pp6RI0diY2ODnZ0dgwYNIi4uTjvf3r176dChA+bm5tjY2NClSxdu3LjB/bBp3e/07PcY3XsPxMPLl4kvvI7C2Ji9OzYaLD/11bn0fWQYPs2a4+7pw7MvzkKtUnHh7Am9clmZ6Sz9/jOmvDIHmfz+1oMHRFqyZmcuJy4WczO5nK9/z8DWSk77kNovHub/mMa+E4UkpJZzI7mcb37PxNFWTjMP/Yqqt5sRA7tZ8e09tKLcySPdrFm9PYfjF4q4kVTGl7+kYWsto0No7fG//10Ke48VkJBSzo2kMr5ekYajnRHNPPVP5KVlanLyldpPcWn9D8gHty6jXbcRtH1oKE7u/jw2YS5GChNO/r3GYPlD25cTENqVyAETcXLzo/ewl3D1CebIzl8ByEyNIz7mLI+Nn4NHs1AcXX15bPwcKspKOXd4EwDOHs158sWFBIX3wN7ZC78WnegzfDqXz+xBqay9xayuerU3YfOhEs5eKycxXcmSjYXYWEhp3bz2GxYXr5ezfn8xZ66W11omr1Ct92kVoODqjQoycv/5u4Lp2/7m6pwvSF2/8x9f9y3/tX1n3+ZldOwxnA7dh+Di4c+wiXMwMjbh+D7D2+PlF8qjo18lvPMA5HLD+9Yzb/xA+26a5bl5B/H4c++Tk5FMQmzUPcV6ZMdSwiNH0LrrMBzd/HlkzDyMFCacObDaYPljO3/GP6QrnftPxNHNjx6DX8LVuwXHd6/Qltmz9gv8Q7vRe8RruHq1wM7Ji8DWPfUqJttXfkj7XmPpMuBZnNwDcHBpRsv2DyM3urubgUd3LCE8ciStu2jiH3Ar/oOG4z++azl+LSOJ6DcJB1c/ug+ejqtXC07s/kVbZu+6z/ELfYhew1/HpTL+5q171ahYpdy8xJHti3l0wgd3FfOdjHzUneV/3uTAsSxibhTx/hdXsLczJrJT7TcNDx3P4sjJbBKSS4hPKmbRLzcoLlHSMlD/ZkxJqYqsnHLtp6i4ljs692DEY+4s/+MGB45mEhNXyHufX75j/AePZ3LkZBYJycXEJxXzw89xFJcoaRFopS0zbZIfq/5K5JdV8cTeLCI+sZjdB9JrvSH4oMb/j5FIG+/zH9SoW7VgwQLeeecdPDw8SE5O5vjx4xQWFjJjxgxOnDjBrl27kEqlDBkyBJWqYS9Udu3axZUrV9ixYwcbN26kvLycfv36YWlpyf79+zl48CAWFhb079+fsrIyKioqGDx4MN26dePcuXMcPnyYZ599Fomk4UfvqCgvJzb6CiGt2mmnSaVSQlq359qVC3VaRmlpCRXKCiwsdAcDlUrF15/NY+DQJ/H0btbgcVflZCfH1krO+Wu6Ju3iEjXRN0sJ8K55d682ZiaaXbSgSPf3VxhJmDbagcVrs8jNvz8XsE72cmyt5Zy7qou/qETNtRulNPete/cRM9Nb8euf9CLbWbD4fW8+e8ODJwfaojCq335UUVFGUtxF/FpGaKdJpVL8WkYQH33G4Dzx0Wf1ygMEhHTVlq8o11Qkqt6FlUqlyIwU3Lh2qtZYSoryMTa1QCa7twq2g7UUawspl+J0FZqSUjWxSRU0c2+4yrulmYRQPyMOnCttsGU2Jf+1faeioozE2Ciah+hvT0BIJ25cO1vv5VZXUpQPgJmFdb2XoawoI/nGRXxb6HpCSKRSfIMjSLh+xuA8CdfP4Bus33OiWcsuJMRoyqtVKqLP7cXO2YcVn0/k05c789P7I7l8WlfRLszLJPH6Wcwt7Vgy/3E+e7kLy/43hpvXTtYv/mD9+H2CO5MYc7r2+Fvo7zvNWnbVbu+t+O2dffj184l8NiOCxR+M4Mpp/RsF5aXFrPvxFfqPno2FtX5vh3vh6myCvZ2CE2dztNMKi5Rcuppfo1JTG6kUekU6YmIi4+KVfL3v+nZz4q+fO7FsYRsmj/XBWNGwl19uziY42Blz/Iyu1buwSEnU1TxCgqxuM2ct8V/OA8DG2oiWQVZk55bx7f9as2F5BF/Ob0VYi7ot80GJX2i6GrVrnLW1NZaWlnrd3YYNG6ZXZvHixTg6OhIVFUVISEiDrdvc3Jwff/xR2yXul19+QaVS8eOPP2orN0uWLMHGxoa9e/fSrl07cnNzGThwIH5+fgAEBwffdh2lpaWUlupfZJWVlaJQ3L4ikJeXg0qlxNpWvwuctY0dSQl1a4H6dek32No5ENJaV5nasPoXZFIZ/R8dWadl3AsbSxlAjYpKboFS+92dSCQwfpAtl2NLiE/RXRSPf8yWq3Gl9+WZoFtsK2PMydevwOTm3138Tw2159L1EuKTdfHvP1lAenYF2bkVeLsZM+YxO9ydFHy8OPWu4yzK1+wrFtW6/VhY25ORHGtwnoLcDMytHGqUz8/VtK45uvpibe/Kjj8/Z9BTczEyNuXQtmXkZaWQn5NucJmF+dns2fAt7bvf+75lZaG5QMgr1N938gpVWJs33I2HiFBjSsrUnG6EbnH/Bv+1faewlu2xtLYnLcnw9twtlUrF+p8/wqd5OK6eAfVeTlFBNmqVskb3Q3MrBzJSbpf7an8rKwcKK3NfmJ9JWWkRh7Ysovvgl+g17FViLuznz29eZNyry/AO7EB2ejwAf2/4it4jXsfZK5jzh9bzy6cTmDzvL+ydfe4q/prx2JOZcr32+C319x1zK/ta4p9Oz2GvEnNxP39+O5WxryzHO7ADANv/mI+HXziBrRvmmaBb7G2NAMjO0T8eZOWUYWd7+9ayZt5mfPtRaxQKKcXFSt6cH6XXLW7H3+mkpseTkVWGn485z43zxdPdlLc+vNRg8d+KMTtHv0U8u07xm/Pdx+Ha+P/v/Yva+N1dNDf+nn7Ch68Xx3AttpD+PZ354r1WjJty4rbP7zxI8f+T1PfhBvyDrNGfEaru2rVrzJ49m6NHj5KRkaFtCbp582aDVoRCQ0P1ngs6e/Ys0dHRWFpWa84uKSEmJoa+ffsyYcIE+vXrR58+fejduzcjR47E1dW11nXMnz+fefPm6U17duprTH5xZoNthyHr/1zO4f07efuDr7WVruvRl9m64Q8++GLJfWnF6hpuzjPDdRW3D39Ku+dlPj3EDk8XBXO+TtFOa9vClJb+Jsz8PPmel19VZFsLnh2lO0nP/z7lNqXrZtJwBzxdFLy1IElv+s7DujuFN5PLyc6rYO5UN5zt5aRm3nu3snslkxvx5ItfsnbxW7z/QiekUhl+LSNoHhaJ2kBPgpLiAn7+7Dmc3PzpOXjKXa+vQwsFo/uba///1Z/5tyndcLqEGXMsqqzWZ46Eu/dP7zv/tLVL3iMl/hpT5vzc2KHUoFZrzpXNW/ekU98JALh4BRMfc5qT+37HO7CDtkybbqNo3VVz09HVqwWxlw5z5sBqeg17pVFih6rx96JjnwmAJv6EmFPa+K+e2UXc5SM88/bae15fn26OvPq8rjI7892L9V7WzcRinp5+CnNzOT06O/DmS4G8+OY57cX4X9t155PrN4rIzCpjwXthuLmYkJRSv0Fa+nRz4rUpzbX/f/2d8/cQfxFPvXQCCzM53bs48ubLgbw46yxx8UXa64X1W5PZvEtzs+7a9QLahtnwSB8Xvl9ev5sMTT1+4b/jX1cRevTRR/H29mbRokW4ubmhUqkICQmhrKxud22lUinqamfc8vKazwyYm5vr/b+goIC2bduyYsWKGmUdHTXN70uWLGHatGls3bqVlStX8tZbb7Fjxw46depkMJZZs2YxY8YMvWlRNwvuuA1WVjZIpTJys/VHPcnNycKmWitRdRvX/MqG1b/wf+8uwNvXXzv98sWz5OVm8+LTQ7XTVColvyz+ki0bVvLlT4b70NfViagirn2ma/0ykmsOPtaWUr1WFWsLGXFJd/5bPjXEljYtTJn7TSpZubr5Q/xNcLaXs+RdT73yr4x35FJsKe98e/etKgDHLxRy7YbuhCSvjN/GUkZOXpX4LWXEJd45/onD7Gnb0ozZC5P04jfk2g1N3lwcje66ImRmqdlXCnIz9aYX5GZiYW24X7WFtQOFeRk1yltWKe/u25Kp766lpCgfZUU55lZ2fDdvFO6+LfXmKy0uZNknz6AwMePJaV8ikxvdVfwAZ6PLiF2s2+5bubcyl5JXqMudlbmU+LSGqbX4e8hxsZexaP2df4//Vf+Ffacq81q2Jz83Eyubex8YZs2S94g6vY8XZi/Dxv7eBuwxs7BFIpVRkKcfa2Fexh1yX+1vlZeBeWV5MwtbpDI5jm7+emUcXP2Ir+z6ZmHtVDmtZpm8rLrfXLoVf814MrGwuk38+fr7TmFeZo34HVz99GNz8SM+WhN/3OUjZKff5OOX2uuVWfXti3gGtGPca3WvoB44lkXUFV13TSMjTUu0rY2CzGzdNYOdjYJrsbc/TlRUqEmsrNBcjSkgKMCC4QPd+KSWUeGirmpu9ni41r8idOBYJlFXdc8AK7TxG5GZrTtH2dooiL5eh/grR8a7ElNAcIAlIx5z5+Ovr2mXFRdfqDfPjYQinB3r3s39vxa/8N/xr6oIZWZmcuXKFRYtWkRkZCQABw4cuKtlODo6kpKSglqt1t4JuNO7iQDatGnDypUrcXJywsqq9r6j4eHhhIeHM2vWLCIiIvj1119rrQgZGxtjbKz/Q1Moan+Q+xa5kRG+/oFcOHeS9hGaoUVVKhUXz56g7yPDap1vw+pfWPfHMmbN+xy/AP1ue5E9+hNapZscwPzZLxPZoz/dej9yx5jupKRUTUmp/kV8dl4FoQEm3EjSbLOpsQR/L2N2HL79Hf+nhtjSIcSMed+mkp6lv8x1e3LZfUz/oPjJq24s25DNyaj6N3GXlKpJqR5/bgWhzU21FR9TYwkB3sZsP5B322VNHGZPhzBz5nyVRFrWnSs2Pu6alsmqFa66kssVuPm05HrUEVq01XQVUalUXI86Qsfeow3O4+nfipioI3TuN147LfriITz9W9coe2s444yUOBJjL9Br6DTtdyXFBSz7eBIyIwVjpn+D0R26fNamtAzSy6p3oVQR5GNEQmXFx0QBvm5y9p1umOd5urQy5kZyhXb5D6L/wr5TfXvcfVtw7eIRQtr30m5P9MWjdOn7RL2Xq1arWbv0fS6c2MXzby3F3snjnmOVyRW4erck7tJhgiqHfVarVMRePkL7HoZz79GsNbGXDtOxjy73sVGH8PBrrV2mm08ImdW61mWlxmmHnrZxcMfSxonMVP0ymalx+IdG3nX8sZcOa4etVqtUxF06TLueY2qNP+7SETr2nqCL/9IhPJpVjT+0Rmya+N0B6Pzws7SOHKH3/Q9zH6XPqFkEhPWoc/wAxcVKEqsNWJCZVUbbMBuiYzUXzWamMoKbW7Ju6931QJBIJNoLe0MCfC2066svQ/FnZJXSrpWtXvwtmluxbnOSoUXUSiLRVQyTU0tIzyzFy11/kCBPN1OOnMw2NPsDEX9j+q++z6ex/KuyaWtri729PT/88APR0dHs3r27RovKnXTv3p309HT+97//ERMTw9dff82WLVvuON/o0aNxcHBg0KBB7N+/n9jYWPbu3cu0adNISEggNjaWWbNmcfjwYW7cuMH27du5du3aHZ8Tqq9HBj/Onm0b2LdrM4nxcSz+5mNKS0ro1nsgAN989g6/LftWW37Dqp/585dFTJ72fzg6u5KTnUlOdiYlxZqmeUsrazy9/fQ+Mrkca1t73Dy878s2bN6fz5Be1rRtYYqnixFTnnAgO6+C4xd0faffmuxEvy667ogTh9oR2caChSsyKC5VYW0pxdpSqm1hys1XEZ9SrvcByMiuqFFpuleb9uUyrK8N7ULM8HI14sUxTmTnKjl2Xhf/nCmu9I/UVZwnjbDnoXYWLFieRkmJGhtLGTaWMu1gCM72cob3taGZhwJHOzntQsx4cYwTF6OLuVGHljJDuvQfz4l9f3LqwDrSkmLYsGweZaXFtI0cAsCq72ey/Y/PtOU79x3HtfMHOLBlCelJ19m19iuSYi/SqfeT2jIXjm3l+qVjZKXFc+nULpZ+PJHgtr0ICO0CaC5kl348kbLSYoY8/R6lxQXk56STn5OOSnXvlYtdx0sY0NmEMH8j3BxlPDXQgpwCFWeu6nL08uOWdG+ju4A2NgIPJxkeTppnuBxspHg4ybC10j/MmSigbaCCA2cbd5AEmbkZVq2CsGoVBICZrwdWrYIw8ay9u21D+6/tO90GjOfonlUc/3sdqYkxrFn8DmUlxbTvptme376ZxebfP9eWr6goIzHuEolxl1BWlJOblUZi3CUyUnTPYq5Z8i6nDm5k9NT/YWxqRl5OOnk56ZSX3dt7pzr1mcCpv//k7MG1pCfFsPmXuZSXFtOqi6bVft1PM9m1+lNt+Q69xxJz8QCHty0mI/k6+9Z/SVLcRdr31FWcIvpN5OLxLZz6+w+yUm9wfPcvXD27h3Y9NH8fiURCRL+JHN/1M1EntpKVeoM96xaQmXKd1l2H31X8Hfs8xen9f3D20FoykmPYvGIu5WW6+Nf/9Dq71+jib99rHDEX93Nk+2IykmPYt+FLkuIu6FWcOvWdSNSt+NMq4z+3h7bdNRVZC2tHnNyb630ArO3csHXU7yVQH3/8lcj4kZ506WBHM28z3prenMysUvYf0bVkffFOKEMH6H6jk8f60KqFFS5OxjTzNmPyWB/CQ6zZvk/TPdzNxYTxIz1p7meBi5MxXTrY8eb05py5kEvMjaIaMdyLPzckMn6UF1062NPM25y3ZgTVjP+9MIY+onsn0+RxvrRqaV0ZvzmTx/kSHmrD9r267u2/roln+KPudO/sgLurCZNG++DtYcbGHQ3bRb2pxy80Tf+qFiGpVMrvv//OtGnTCAkJITAwkIULF9K9e/c6LyM4OJhvvvmGDz74gHfffZdhw4bx6quv8sMPP9x2PjMzM/7++29mzpzJ0KFDyc/Px93dnV69emFlZUVxcTGXL19m2bJlZGZm4urqypQpU5g8efI9brVhEZG9ycvNYdWKReRkZ+HdLIA35n2m7RqXkZ6q996HHVvWUlFRzhcfvqm3nGFPPM3wJyfdlxjvZMOePIwVEp4dbq99Ien8RWl67xBytjfC0lx3Qdq3s6ZSNPcF/a4n3/yewb4T+k3b99u6XbkYK6RMHuWAuamUy9dLeO+7FL0hN53t5ViZ6wZP6N9VM5LUO9P0X/731Yo09h4roEKpJjTQlEe6W2OskJCZo+TI2UJWb6v/nanQjgMozMtm15qFFORm4OoVzPhXf9B2scnJSkYi1e0rXgHhjHzuY3auXsCOVZ9j7+zNky99ibOHrr92fk46m3/7iMLcTCxsHAjvMojug57Xfp8UF0VCzDkAPn+9n148r3yyE1tH93pvD8C2oyUoFBLG9DfHzERCdEIFC1fm6z3P42ArxcJMt13ernJeeVJXKR3ZS9P99dD5UpZt0u077YONkUjg2KXGHSTBum0IEbt0XXlafPJ/AMQvX8O5ibP+kRj+a/tO64iHKcjLYtuqr8jPycDNO4hJb3yv7bqXnZmMRKp7RjIvO53P/09XAdi3aQn7Ni2hWXB7Xnh7KQCHd64E4Nt3J+ita9Tk97QVrPpo2WEARQVZ7Fv/JQV56Th7BvPk9EXa3OdlJuk9z+np34Yhz3zCnrVfsGft59g5+TByylfaygBAUJs+PDJ2Lgc3/8C2397H3sWXEc8vxCugrbZMxz7jqSgvZcfKDykuzMXZM5DRMxZj5+R1d/G3H0BRfhb71i+ksDL+J176Uds1LjcrWe8c5enfhsGTPmHvui/Ys/azyvi/rhH/gDFzObjlB7b//h72zr4Mf34hXgHtaqz/fvh1TQKmJjJeeyEAC3M55y/l8uq8i3rvsHFzMcHaSteN08baiDenB2Jvp6CwsIKYG4W8MveCdvS5igoV7VrZMuJRd0xMZKRllLLvcAbL/ohv8PhXrI7HxETG61Oba+KPyuWVOef14nd3McWmSvy21ka89XKQLv64QmbMOc+JKqO3/bkhEWOFlBcn+WFlaUR0bAEvzz5X7259/9X4/zFisIQGJVFXf6BGuK9OXc28c6F/qY9+aNrPUyjLm3Y3qFFP+DR2CPW2c29OY4dwTx57O+LOhf7FivY33OhU/zQTo6b95vfcorqNMvlvpVY37YuuHz7d39gh1Ju6gV8bItydA391a+wQDMo6f3ePjDQku9Cujbbu++Vf1SIkCIIgCIIgCIJh4hmhhiWyKQiCIAiCIAjCA0dUhARBEARBEARBaHBff/01Pj4+mJiY0LFjR44dO1an+X7//XckEgmDBw++r/GJipAgCIIgCIIgNAFqJI32uVsrV65kxowZzJkzh1OnTtGqVSv69etHWlrabeeLi4vj1Vdf1b5K534SFSFBEARBEARBEG6rtLSUvLw8vU9pae2vovjss8945plneOqpp2jRogXfffcdZmZmLF68uNZ5lEolo0ePZt68eTRr1ux+bIYeURESBEEQBEEQhCZALZE22mf+/PlYW1vrfebPn28wzrKyMk6ePEnv3r2106RSKb179+bw4cO1bt8777yDk5MTEydObPDcGSJGjRMEQRAEQRAE4bZmzZrFjBkz9KYZGxsbLJuRkYFSqcTZ2VlvurOzM5cvXzY4z4EDB/jpp584c+ZMg8RbF6IiJAiCIAiCIAjCbRkbG9da8blX+fn5jB07lkWLFuHg4HBf1mGIqAgJgiAIgiAIQlMgaRovOXZwcEAmk5Gamqo3PTU1FRcXlxrlY2JiiIuL49FHH9VOU1W+VFgul3PlyhX8/PwaPE7xjJAgCIIgCIIgCA1GoVDQtm1bdu3apZ2mUqnYtWsXERERNcoHBQVx/vx5zpw5o/089thj9OjRgzNnzuDp6Xlf4hQtQoIgCIIgCILQBKibUBvGjBkzGD9+PO3ataNDhw588cUXFBYW8tRTTwEwbtw43N3dmT9/PiYmJoSEhOjNb2NjA1BjekMSFSFBEARBEARBEBrUqFGjSE9PZ/bs2aSkpNC6dWu2bt2qHUDh5s2bSKWNW7ETFSFBEARBEARBEBrc1KlTmTp1qsHv9u7de9t5ly5d2vABVSMqQoIgCIIgCILQBKibyGAJTUXT6WgoCIIgCIIgCILQQESLkCAIgiAIgiA0AWqJaMNoSCKbgiAIgiAIgiA8cESLkCAIgiAIgiA0AWrEM0INSVSE/mFHr9s3dgj11rt/040doLBI3dgh3JP41DuX+bdSmDTtQ03R/kuNHcI9MYsMbuwQ6q3Z5d2NHcI9OVPi1tgh3JPomxWNHcI9efL5yMYOod5Ky5r2OcvISFywC/9+omucIAiCIAiCIAgPnKZ9m1YQBEEQBEEQHhBisISGJbIpCIIgCIIgCMIDR7QICYIgCIIgCEITIF6o2rBEi5AgCIIgCIIgCA8cURESBEEQBEEQBOGBI7rGCYIgCIIgCEITIN4j1LBEi5AgCIIgCIIgCA8c0SIkCIIgCIIgCE2AGD67YYlsCoIgCIIgCILwwBEtQoIgCIIgCILQBIhnhBqWaBESBEEQBEEQBOGBIypCgiAIgiAIgiA8cETXOEEQBEEQBEFoAsRgCQ1LZFMQBEEQBEEQhAeOaBESBEEQBEEQhCZADJbQsO57i5BarebZZ5/Fzs4OiUTCmTNn7vcq9XTv3p3p06f/o+sUBEEQBEEQBOHf7b63CG3dupWlS5eyd+9emjVrhoODw31Zz969e+nRowfZ2dnY2Nhop69ZswYjI6P7ss6Gdnb/Ck7s/omivHQc3IPoMextXLzDai1/9fQWDm9eQF5WIjaOPnR99FV8W3bTfh99djvnDv5OWvxFSopyePK1dTh5BNdYTlLsaQ5t+pyUG+eQSqQ4egQz5LmfkCtM6hz76X0rOL7zJwrz0nF0D6LXyLdx9ak99iuntnBw4wJyMxOxdfLhoUGv0ixEF/vBTV9y5eQm8rJTkMmMcPZqSeSjL+Pq20pb5sjWb7l+YR9pCZeQyY148ZMTdY63OrVazfHtX3Lp6J+UFufh4tOGh4bOwcbR57bzXTi4gjP7fqIoPwN71yC6Dn4LZy/ddkcdWcm10xtJT4yivLSQp985hrGpld4yNi95nsykyxQXZGJsao1HQASdBryCubXzXcV/YvuXXD6miz9yyBys7xT/oRWc3fcTxZXxdxn0Fk7V4o8+s5GMyvgnzNOPPynmKH99P97gsoe8+CdOnqF13obqBnQypnOoEabGEmKTlKzcXUJ6jqrW8n7uMnq1VeDlJMPaQsqiv4o4F1NRa/lRPU3oGqZg9b4S9p4uq3ecR3au4MCWxRTkZuDiGcTAMW/i4Vf7vn/h2FZ2rllITkYi9s7e9B35CoGtdPt+QW4G2/74lOgLBykpyscnsB2PjHkTBxcfAIoKcti99iuiLxwkJzMZc0s7gtv2ovfQaZiYWdZ7O+6WXdd2NHtlItZtQjBxc+LEsBdI3bDrH1t/bTb/tY61q1eSk52Fj68fzzz/Is0Dax73ALZv3cieXTu4eSMWAD//5owZP1GvfE52FsuWLOLMqRMUFhbQMiSMZ557ETd3j3uO9diuFRzc+pN233l49Ft4NKt937l4fCu71y7Q7ju9R7xK8zDdvjP36SCD8/UZ8RpdHp6o/f/Vs3vZt+EbUhOuIDcyxjuwPU+8+HW9tqFbqJRwPwkmRhCfoWbLcRVZBbefp12AhIggKRamkJoNW08qScrSfT+2pwwfZ/273ievqdh8Qvf7d7WDXq2kuNpJUANJmWp2nVGRmlO3uBvjfLtz5WzirxyiIC8NhcIMV99wuj72KnbOfnULuorGPGfdoqwoY/XCkWQmX2bE9LU4uBv+nRlydv8KTu3+iaL8dBzcguh2h/xfO7OFI1Xy3+XRV/FpoZ//84d+J70y/0+8ug7Havm/cGglV05uJC3hIuWlhUz+4DjGZoa3TXhw3PcWoZiYGFxdXencuTMuLi7I5fp1r7Ky+l+A1IWdnR2Wlv/cxUF9XTm1mb/XzqdTvyk8+dpaHN2CWPvtRIryMw2WT4o9xZblr9Cy03BGv7YOv9Be/PXTFDKSrmrLlJcV4dasDV0fe7XW9SbFnmbdd5PwDuzKEzP+5PFXVtEqcjRI675rXD65mb1r5hMxYApj31iLk0cQq76aSGEtsSdeP8XGJa8QEjGccbPW4R/Wi3U/TCG9Sux2Tj70GjmbCW/+xRMzfsXa3p0/v3qaonzd2VJZUU7zNv1pFflEnWOtzZm9P3L+wM88NHQuw178AyOFKRt/nERFeWmt80Sf2czBvz6kXZ8pDJ++Bnu3QDb+OImiAt12l5eX4BkYSZuek2tdjrtfR/qM+ZwnXt9Cv3ELyM28ybafX7qr+M/u/ZELB38mcuhchrz4B3KFKZt+unP8h//6kLa9pzDspTXYuQay6adJFFeJv6Iy/vBa4nf2Dmfs2/v1PkEdRmBp54GjR8hdbUNVvdsp6BauYOWuEj79vZDScjUvDDFDLqt9HmMjCYnpKv7YU3LH5Yf5yfFxlZFTUHvFqi7OH93Mlt8+osegKbwwbzUunoEs/eQZCvIM7/s3r53mj29fpe1Dw3jhnTUEt+nFrwteJDVBs++r1WpWLJhKVlo8o1/6mhfeWYO1vRtL/vc0ZaVFAOTnpJGXk0b/x1/nxfc3MPSZD7h2bj9rf3rrnrblbsnMzcg7d4UL0+b9o+u9nQP79rB40bc8/uQ4Pvvye3ya+THv7Znk5GQbLH/h3Fkiu/Xk3fmf8dGnX+Hg4Mjct14nMyMd0Pw95r87m9TkJP5v9rt8/uX3ODo5M+f/XqWkpPieYr1wbDPbVn5I98emMHnOGpw9A/nls0m17zvRp1j1/Su0iRzOc3PXEhTem9+/nKrddwBe+Xy/3mfQU++DREJw277aMlEntrHmx5m07jqU5+at4+lZvxLacWC9tqFzsIQOzSVsPq5i8Q4l5RXwZA8ZstucPlp4SegTLuXvCyoWbVWSmqPmyR4yzIz1y52KVvHZ2grtZ+cZ3W/VSA5PdpeRWwSLtytZtkNJWblmmrQOvYYa63zr7NmSPk/OZ9yszQx5/idAzdpvJqJSKe8cdDWNec665fCmjzG3drrr2K+e2sz+dfPp2H8Kj7+6Fgf3INZ/V3v+k2NPsXX5K7ToNJwnXl1Hs9BebPxpCpnJ1fLv24bOj9ae//KyYryDI2nf57m7jvnfRC2RNtrnv+i+btWECRN48cUXuXnzJhKJBB8fH7p3787UqVOZPn06Dg4O9OvXD4ALFy7w8MMPY2FhgbOzM2PHjiUjI0O7LJVKxfz58/H19cXU1JRWrVqxatUqAOLi4ujRowcAtra2SCQSJkyYANTsGufj48N7773HuHHjsLCwwNvbmw0bNpCens6gQYOwsLAgLCyMEyf0WxcOHDhAZGQkpqameHp6Mm3aNAoLCxssV6f2LiGk80hadhqGvYs/vUbOQ64w4eKR1QbLn963HJ+gSNr1moSdix+dH5mOk0cLzu7/RVsmuP1gOvWfimfziFrX+/fa+bR+aCzt+zyLvWsAds7NaB4+ALlcUefYT+xaQmjnkYRGDMPB1Z8+j8/DSGHChcOGYz+1Zzm+LSLp0GcS9i5+dH10Os6eLTizr2rsj+Id1BkbB08c3ALoPnQWZSUFpCde0ZbpMnAa7XpOwNGteZ1jNUStVnNu/3La9noO35Be2LsF0vPxjyjKSyP24s5a5zv791JadBxBUPth2Dn7023oPIyMTLh8TLfdrSLH06bnszh7t6p1Oa0emoCLd2ssbd1x8WlDeI9nSb15FqWyvM7xnz+wnDa9nsOnZS/sXQPpMUoTf9xt4j+/fynBlfHbOvvz0NB5yI1MuHxcF39Y5HjCezyLs5fh+GVyBWaWjtqPsZkNcRd3EdhuKBJJ/fsxdw9XsO1oKeevV5CUoeLnbcVYm0sI86u9ETsqroJNh0tv2woEYG0uYXh3E5ZtKUZ5b/UgDm5dRrtuI2j70FCc3P15bMJcjBQmnPx7jcHyh7YvJyC0K5EDJuLk5kfvYS/h6hPMkZ2/ApCZGkd8zFkeGz8Hj2ahOLr68tj4OVSUlXLu8CYAnD2a8+SLCwkK74G9sxd+LTrRZ/h0Lp/Zg1J5+21vSOnb/ubqnC9IXV/7PvZPW7/2T/r2H0Cvvg/j6eXD81NfxtjYmF3btxgsP+P1NxkwcBDN/Pzx8PRiykuvolapOXf2NABJiQlcuRzFc1OnE9A8CHcPL56bMp2ysjL27919T7Ee3raUNg+NIDxyGE7u/gwcpzlunt5v+Lh5dMfP+Id0pcvDE3F086Pn0Jdw9W7Bsd0rtGUsrR31PpfP7MY3qCN2Tp4AKJUVbPntA/qOeI32PR7HwcUXJ3d/Qjo8XK9t6BAoZf9FFVcT1aTlwPojKixNIcij9t9+p0App2PUnI1Vk5EHm46rKK+A1s305ylXQmGJ7lNWZdd2sAIzYwn7zqvIzIf0PPj7ggoLUwnW5neOu7HOt6GdR+Hh3x5rew+cPFsSMWA6+TnJ5GUl3jnoKhr7nAVw4/LfxF89SMTA1+8qdoDTe5cQEjGSFh01+e85QpP/qKOG839m33K8gyJp21OT/4gB03E0kP+O/afidZv8h3efQLvez+Jyh20THiz3tSK0YMEC3nnnHTw8PEhOTub48eMALFu2DIVCwcGDB/nuu+/IycmhZ8+ehIeHc+LECbZu3UpqaiojR47ULmv+/PksX76c7777josXL/Lyyy8zZswY9u3bh6enJ6tXa35AV65cITk5mQULFtQa1+eff06XLl04ffo0jzzyCGPHjmXcuHGMGTOGU6dO4efnx7hx41Cr1YCmVat///4MGzaMc+fOsXLlSg4cOMDUqVMbJE/KijLS4i/i2byzdppEKsWreWeS404bnCcl9gyegfo/eO+griTHnanzeovyM0m5cRYzS3tWfv44P7zZmT8XjiExpu5dzJQVZaTGX8Q7qFrsQZ1Jum449qTYM3hXi90nuCtJsYZjV1aUce7gSoxNLXH0CKxzbHWVn5VAUX46HgG6bTA2tcTJK4zUG7XHlJ54UW8eiVSKe0BErfPURUlRDtdO/4WLdzgyWd26dN6K3716/J53jt/dXz9+j3uM/0bUbkqLcghsN7Tey7C3kmBtLuVKvO7Kp6QM4lKU+LrepkmoDiTAuP6m7DpZRkrWvdWCKirKSIq7iF9L3b4slUrxaxlBfPQZg/PER5/VKw8QENJVW76iXFP5lRvpbo9LpVJkRgpuXDtVaywlRfkYm1ogkz2449+Ul5cTE32VsNZttdOkUimtWrflyuWoOi2jrLQUpbICCwtL7TIBjBS6G0NSqRS5kRFRURfqHWtFRRlJNy7SrIXu9yeVSmnWIoKEmDMG54mPOaNXHsA/pAsJtexrBbkZXDu3j/DIYdppyTeiyM9ORSKR8N3cIXzyciS/fPaMXqtSXdmYg6WphNgUtXZaaTkkZoK7g+GKkFSq6dJWdR6A2FQ1HtXmCfGW8MpQGZMfltGzlVSvNTgzD4pK1bRuJkUqBbkMWvtJSc9Vk3OH+5ONdb6trry0iKija7Cy98DSxuWu5m3sc1ZRfgb7Vr1Nr8c/Qm5U9y70t+JIS6iZf8/b5D857kyNCqZ3UFdS7iH/TZkaSaN9/ovu61nT2toaS0tLZDIZLi66H3pAQAD/+9//tP9/7733CA8P54MPPtBOW7x4MZ6enly9ehVvb28++OADdu7cSUSE5sfQrFkzDhw4wPfff0+3bt2ws7MDwMnJSe8ZIUMGDBjA5MmaZt/Zs2fz7bff0r59e0aMGAHAzJkziYiIIDU1FRcXF+bPn8/o0aO1LUsBAQEsXLiQbt268e2332JiYvhAUFpaSmmpfjN1eZkxRgr9PgDFhdmoVUrMLO31pptZ2pOVdt3gsgvzMzCzdKhRvigvw2B5Q3Iz4wE4suUrIge9jqNHMJeOrWPN1xMY88ZGbJ187riM4gJN7ObVYje3tCcrpZbY8zIws6oWu5U9hdVijzm/h42LZ1BeXoyFlSPDX1yMmYVdnbevroryNd1gTKvn38KBonzD+Syp/JuZWtScJyct9q5jOLzpEy4cXEFFeTHOXq0Y8PR3dZ5XG3+1WEwt6xB/tW02rWf8t1w+thqP5l2xuMsTe1VW5pr7M/mF+hdL+UVq7Xf11bu9AqUK9p259y65Rfk5qFRKLKz1c2hhbU9GsuEcFuRmYF5t37ewtic/V/N3cnT1xdrelR1/fs6gp+ZiZGzKoW3LyMtKIT8n3eAyC/Oz2bPhW9p3H2nw+wdFfl4uKpUKG1tbvenWNrYkxN+s0zKWLfkBWzt7WoVrKlMenl44Ojrx85IfeeHFGRibmPDXulVkZqSTnWW4G09dFOVrfn8WVtWOm1YOt913DJUvqOWYf+bQOhQm5nrd4rLTNcf8vRu+pt+omdg4uHNo2xKW/m8cL36wFTMLmzpvg4Wp5t/Caj1RC0vUWNRybWxmDFKphIISdbV5wMFSd5F14YaK3EIoKFbjZCOhV2sp9pZS/jyguXlRVgHLdykZGSkjsqWmhpRVAL/uUaLWX3QNjXW+veXs/hUc2PAJ5WVF2Dr5MvSFJcjuogcGNO45S61Ws3vlLFp2ehwnz1DyshLuKvbb5T871XD+i2rJf/VrBkGoj0a5fdi2bVu9/589e5Y9e/ZgYWFRo2xMTAzl5eUUFRXRp08fve/KysoIDw+/6/WHhekeyHN21jyQHhoaWmNaWloaLi4unD17lnPnzrFiha4LglqtRqVSERsbS3Cw4QcE58+fz7x5+v3nB4yew8Axc+865vtBrdacVEI7j6JlJ81dQyePFsRfPczFo6vp+ugrjRkens07Mm7WOooLszl38A/++mk6o1/7s0al625dPfUX+1bP0f7/kbuodNwvrbtPJLjDMPKzkzix42t2/f4GA57+zmD3smun/uLvNbr4H36q8eMHKMhJIeHqAXqP+fyu5msXKOfxXqba/3+3vqihQwPA00lK99YKPvq14bq0NjSZ3IgnX/yStYvf4v0XOiGVyvBrGUHzsEiDF3glxQX8/NlzOLn503PwlH8+4P+Q1X/8yoF9e3jvo89QVLYAyeVyZr71Dl8t+JgxowZpWpjC29KmXQe4wwV3Yzu9fzVhnQZiVKV18dYxP/KRybRop+mWPvjp+Xz2SjeiTmylXffHa11eiLeER9rrbkT8tu/un2upq9MxuuSm5aopKFExtqcMWwsV2QWaFqBHO8iIz1Cz5pAKqQQigqQ83k3GT9uVVNy/0O5ZULvH8ArsQmFeOqf2/MTmJdMZOf03vVbg6v5N56zzB3+mvLSQ8J7PNloMDzr1PXQ7F2pqlIqQubl+J96CpWF0BwABAABJREFUggIeffRRPvrooxplXV1duXBB0wVh06ZNuLu7631vbFz7waM2VUeRu3WhaWiaSqXSxjd58mSmTZtWY1leXl61rmfWrFnMmDFDb9rSvTXjNTW3RSKV1XhQsCg/E3NLw6PsmRu421+Un1mjpeV2zK0cAbBz0R+xxtbFj/zspDotw9RCE3v1gREK8zNr3PnWrdehxp20orya5RXGZiicvLHFGzff1vw4ty8XDq2iY787P8R5Oz4teuiNkqOs0LQOFOdnYm6le/CzqCADBzfDlVyTyr9Z1YEFbs1T/c5VXZia22JqbouNoy+2Tn78/H53Um+cwcWnZkXfu0UPhhuKv0A//uL8DOzvFH+1v1txQQam9Ygf4MqJNRib2eDdouddzXf+egVxKbphpuQyze/P0lxCXpHugsjSTEJiev2vcPzc5ViYSXhnou6Gi0wqYUikMd3DFcxdfIehrqoxs7RBKpVRkKufw4LcTCysDefQwtqhxl3MgtxMLKuUd/dtydR311JSlI+yohxzKzu+mzcKd9+WevOVFhey7JNnUJiY8eS0L5HJm8bomPeLpZU1UqmUnGz9gRFyc7Kxtbt9S/K61StZ/edvvPP+J/j46h8P/QOa88VXiygsLKCiogJraxtem/4C/gH176ZrZqn5/VUfGKEwL+O2+47B8gaOszeuniAzJZYRz+nflLC01hzzHd38tdPkRgpsHT3JzUy+bcxXE9UkZup+f/LKOpG5CRRUaRUyN5GQkm24llhUCiqVGgsTCVVrkppl1F6zTMzQfGdrISG7QE2ItwRrC1i8Q9e9dc1hFa8NkxHoLuHizdqX1Vjn21uMTS0xNrXE1skHV59WfDurA9HndhDUtvYBK/5N56zE6KOk3jjDD7P0R3hbtXA4AeED6fV4zeu4qm6X/9ryaVZL/mu7xhCEu/GvGAKiTZs2XLx4ER8fH/z9/fU+5ubmtGjRAmNjY27evFnje09PzUOgt+7gKZUNfyuoTZs2REVF1Vi3v7+/dr2GGBsbY2Vlpfep3i0ONA+cO3m2JP7qYe00tUpF/NXDuBq4EAZw8W1N/NUjetNuXjmEq0/rOm+XlZ0H5tZOZFdrFs9Ji8PK1r2WuWrG7uzZkptX9GO/eeUwbs0Mx+7m25obV/Rjv3H5EG6+t49drVZRUXHvXZoUJhZYO3hrP7bO/phZOpIQrduGspIC0m6ew9nbcEwyuQJH95Z686hVKhKjj9Q6T13dumurVBre1triT7xWLf74O8ef2EDxq9VqrpxYQ/O2g+r8bNMtpeWQkavWflKyVOQWqgj01N2nMVGAj4uM2OT6/76PXSrnw18K+WiF7pNToGLXyTK+WXv3rVByuQI3n5Zcj9LtyyqViutRR/D0b21wHk//VsRE6e/70RcPGSxvYmaJuZUdGSlxJMZeICi8l/a7kuICln48EZnciDHTvzF4XHnQGBkZ4effnHNndc9SqVQqzp05RWBQi1rnW/Pn7/zx2y/Mefcj/JvXXrkxN7fA2tqGpMQEYqKv0iGic61l70QuV+Dm3ZLYS7rfn0ql4vqlI3j4tTY4j6dfa73yADEXD+FhYN85tX8Vrt4tcfHSH07b1ScEmVxBZorumK+sKCcnMxFre7fbxlxWAdkFuk96HuQXq/F10d2dVsjB3V5XcalOpYLkLPBx0b+j7essIaGWeQCcK3s73qosGcmo0UKqVgNquNPN8sY63xqirgxceYfz2r/pnNV10JuMmLGOES+vZcTLa3nk6e8B6DP6Mzr2f/mO88vkCpw8WhJ/re75d/VpTfy1mvl3ucf8CwI0UotQdVOmTGHRokU88cQTvP7669jZ2REdHc3vv//Ojz/+iKWlJa+++iovv/wyKpWKrl27kpuby8GDB7GysmL8+PF4e3sjkUjYuHEjAwYMwNTU1GBXu/qYOXMmnTp1YurUqUyaNAlzc3OioqLYsWMHX331VYOso033p9i+YibOXiG4eIVxat8yysuKadFR89D5tl9ex9zaWdtdLbzbOFYtHMvJ3YvxbdmNK6c2kxp/gV6j3tEus6Qwh7zsZApz0wC0FR5zKwfMrRyRSCS07TmRI1u+xNE9CEf3YKKOrSUr7TqPPL2wzrG36/UUW5ZrYnf1CePk7mWUlxYT0kkT++Zlr2Nh48xDgzSxt+kxjpWfj+X4zsU0C+nG5ZObSbl5gT5PamIvKy3i6Nbv8AvribmVI8WF2ZzZt4KCnFQCw/tr15uXlURJYS552UmoVErS4i8BYOPohcKkDkMHVZJIJIRFjuPkru+wdvDBys6dY9sWYmblhG/L3tpyG76fgG9Ib0K7jAE0o73tXvkGjh4hOHuGcW6/5m8W1F43UEBRXjpF+RnkZmieUchMvorC2BwLW1dMzGxIvXmWtPjzuPq0xdjMitzMeI5vXYCVvRcu3nXr9imRSAjtOo5TuzXxW9q5c2K7Jn6fKvH/9cMEfFv2JqQy/tDICez9QxO/k2cY5w9o4q860EFRvn78WSlXMTI2x8JGE/8tidFHyM9KIKjDiDrn/Xb2ni6jXwdj0nJUZOaqGNjZmNxCtd6IcFOHmnEuppy/z2oeaFcYgaON7t6OvZUUd0cpRSVqsvPVFJVoPlUpVZBXpCYtu34DJ3TpP57Vi2bh5huCR7NQDm1bTllpMW0jhwCw6vuZWNk603ekpmW4c99x/Dh/HAe2LCGwVTfOHd1MUuxFBj+l60J74dhWzCztsLF3JTXhKptWfEBw214EhHYBdJWg8tISnpz8P0qLCygt1rRmmVvZIZXe24ASdSUzN8PcX9cibubrgVWrIMqycimJv33rwv0yaMgIFnz2If4BgQQ0D+Kv9aspKS2hVx/NceOLT+Zjb+/A2KeeAWDNn7/x689LmfH6mzg5uZCdpRme38TUFFNTTXfNg/v3YmVtg6OjEzfiYvnx+6/o0KkL4W3a31OsEf0msPbHN3DzCcHdN4wjOzTHzfCumt/fmkUzsbJ1ovdwzXGzY5+xLP1oHIe2LiagVXcuHN1EUtxFHh3/jt5yS4oLiDq+jb6jZtZYp4mpBe26P86e9V9iZeeCjb0bB7cuBqBl+/41yt/JsSsquraUkpWvIqdATfcwKfnFcDlB9zsb00PK5QQ1J65pph25omJQJynJWRKSMtV0CJRiJIezsbdafTTd8K4lqSkuA2cbzXDbN9I0I9MBXE9R0zscHm4n5fhVFRKgcwspKjXEpd65z2JjnG9zM+K5cnoz3kFdMDW3oyA3hRM7f0BuZIJvlffh1EVjnrMsbfUrzEYKMwCs7b3q/GxoePen2PHrTJw9Q3D2CuPMvmVUVMn/9sr8d6nMf+tu41j95VhO7VmMT4tuXD21mTQD+c/PTqYwTz//ZpX5ByjMS6coL4Ocym3LqNw2S1tXTMxt6hT7v4FaLbrGNaR/RUXIzc2NgwcPMnPmTPr27UtpaSne3t70798faeX7bN59910cHR2ZP38+169fx8bGhjZt2vB///d/ALi7uzNv3jzeeOMNnnrqKcaNG8fSpUsbJL6wsDD27dvHm2++SWRkJGq1Gj8/P0aNGtUgywcIbDOA4oIsDm9eqHnBm0cwg5/7Udv0m5edDFXGcHfzbUP/cZ9wePMXHNr4GTaOPjw68WscqgwlHXNhNzt+naX9/5Zlmrs1HftPJeLhFwFo030CyvIy9q2dT0lRLo5uQQx9fjE2DrV3+asuqO0AivKzOLhxIUX56Ti6BzN8in7skiqxuzdrwyNPfcKBv77gwF+a2Ac/+7V2GGypVEZW6nUuLlpLcWE2JuY2uHiF8viMFTi4BWiXc3Djwv9n777Do6jWB45/dze9995JSAIBEnpvoQuCNEUFQfReFf2pWLCLeu9Fr6gotisCgooo0nvvvYVeQwkQEtJ73Z3fHwu7bLIJAYIQeT/Psw9k98zsO2fnnJkz58wZjuycb/h75scDABj64kyC6reqcfwAsZ2foqy0iI1/vkdpcS4+Ic3o+9QUk3HbuRlJFBcYh92Ex/ahqCCT3SsnX30oXDR9n5piMszgyI7Z7FltfFDhwu/0B6QuQ/9DVIuBWFjacObQanavmkx5aRF2jp4ERnage7dnb+oG2iZX49801xh/n9E3jr+4IJM9q4zx9xltGv/R7bPZu8YY/6Kr8Xce+h+TBtOJ3X/iHRyHq1dYjWOuzpo9pVhZqBgWb4OttYozyVq+nV9oMvbfw0WNva1xvwry1vDiYGMDeGAn/R3bO4+W8suqGz9b6FY0atWHgtws1s77ivycdHyDonni1R8Mw5uyMy+juu6ZXEERcQx95lPWzP2S1X9+gbt3MI++OBnvAGO5zctOY9lvn1CQk4GDiwdx7frTuf+zhs+Tzx3lYuJBAL54vadJPK9MXIOrZ816c2+Xc7MY2qz92fB3g4n6uvjCzHkcHP1mVYvdUe07dSEnN5vffp5OVlYWoWH1eP/DT3Bx1Q+NS0u7YvJ7LF+6iPLyMv77n/Em63n40REMe3wkAFmZmUyb8p1+iJ2rG53jezB02PDbjjWmZR8K8jJZv2Ay+Tlp+ARG8/jLUwz7Tk5mMqrrHooTFN6UQf+YyLp5k1g77wvcvEN45IWvTfYdgMM7l6Kg0KjVA2a/t8fQ11BrNMz/cRxlpcUEhDXhidd+wtbe+aa3YdsxBUsLhQdaqLGxgqQ0hVkbtCbT0rs6qK4+I0jfQDmapGBnraNTIzUONvoHqs7aoDVMuqDVQaiPmpaR+h6mnEJ9w2rzYeNKM/Jg9iYdHWPUjOquQVEgJUv/3fk1KOp343irsbQiOXEPCRtmUFyUi52jO/71mjP0pd8qTRxQE3frmFUb6jfVx7Fj+VdXH8IeTf9//miII6/COYNvaFN6jpjI9qXG/O87+hvcfY35f+bwOtb8Zsz/FTP1+d+y5/O0vnq+c2jrbHatNF68njv5MQC6DZtgaISJ+49KUW40x4qoTd+tuNsR3DqLe6LZfOsKCuv2rq6rw+GfPZt3t0O4LR1b107v8t1i16HmT3y/14Qdv73n9dxtCSnVDzm7151OuodnHqgBD9e6e+AqKa3DlT5gaVm3ey7G3Nrjte64U4nn79p3R9QLvmvffafcE/cICSGEEEIIIcRfSRpCQgghhBBCiPtO3e0zFkIIIYQQ4j6iULeHHN5rpEdICCGEEEIIcd+RHiEhhBBCCCHqAOkRql3SIySEEEIIIYS470iPkBBCCCGEEHWA9AjVLukREkIIIYQQQtx3pCEkhBBCCCGEuO/I0DghhBBCCCHqABkaV7ukR0gIIYQQQghx35EeISGEEEIIIeoARZEeodokPUJCCCGEEEKI+440hIQQQgghhBD3HRkaJ4QQQgghRB0gkyXULukREkIIIYQQQtx3pEdICCGEEEKIOkB6hGqX9AgJIYQQQggh7jvSI/QXC/Asv9sh3LLsgrq9u2jUdfsqilZ3tyO4dZ6ednc7hNtiY1mHMx8IO77ubodwy85Edb3bIdyWK/OO3+0QbkuAT92u9/fuz7nbIdwyrVa52yHcFmubur3vgMPdDsAs6RGqXdIjJIQQQgghhLjvSENICCGEEEIIcd+p6/2WQgghhBBC3BcURYbG1SbpERJCCCGEEELcd6RHSAghhBBCiDpAJ5Ml1CrpERJCCCGEEELcd6QhJIQQQgghhLjvyNA4IYQQQggh6gB5jlDtkh4hIYQQQgghxH1HeoSEEEIIIYSoA2T67NolPUJCCCGEEEKI+470CAkhhBBCCFEHyD1CtUt6hIQQQgghhBD3HWkICSGEEEIIIe47MjROCCGEEEKIOkAmS6hd0iMkhBBCCCGEuO9Ij5AQQgghhBB1gEyWULukR0gIIYQQQghx35EeoZt07tw5QkND2b9/P7GxsXf0u7aumsWGJdPJy0nHNyiSh554i6DwxmbTplw8zco5k7l49ihZ6ck8OHwcHXuPMEmzbfVstq/5ncz0SwD4+IfTbeCzRMd2uO1Y96z/le0rp5Kfk4Z3YBQ9h72Lf6j5WAGO7lnOxoVfkp1+CTfvEOIHvUp4o04madIvJ7J27qckndyNTqvFw7ceg5+djLO7n0k6RVGY/dXTJB7ezJDnviEyrttNx79vw6/sXD2Vgtw0vAKi6Pbwu/iFVB3/8b3L2bz4S3IyLuHqFULnh16lXkwns2lXznqPhM2/03Xwm7SIH2l4f+63z5B68TiFeRnY2DkTEtWGTg+9iqOL903Hv3/jr+xeo4/f0z+K+KHv4ltN/Cf2LWfrEmP8Hfu/StjV+LXaMrYsnsTZI5vITr+Ata0DwZFt6dj/FRyuiy016QibFk4k5fwhVGoN9WN70HngG1jZ2N90/ACdG6uJC1dhYwkX0hSW7daRmVf9Ms3rq2gbrcbBFlKzYPkeLckZpmkCPKBLEzX+HioUBVKy4Nd1Wsq1puk0ahjdU4OPm4r/LSsnNeuWNqPWy+3ahVM4tHs1aclnsbCyISQilgeGjcXLL/TWAryBZYsXMH/u72RnZRISWo+nn32B+pHRZtOuWrGE9WtXk3T+LAD1wuvz+BOjTdJnZ2UyY/oUEvbtoaAgn4YxjXn6mRfw8w+4I/HXhFv75oS9MhrnpjHY+HmxZ9BzpC5ae8e/V1EUdq+czNGdcygpysU3tCkdB76Pi2dItcsd2vorCRumUpiXjrtvFB0eegfvIOM+VV5WwrbFn3AqYSna8jKCItvRceD72Dl6GNJcPLWdnSu+JDPlJBZWdkQ1G0Cr3i+h1hhPAxRFIWHjNI7u+IO8rGRs7V1p1vlR2vZ51mxcezf8ys5Vxnqz+8Pv4ldNvX9873I2LdLXO27X6s1G5uvNFb/q6834Iab1pnGbS5n5yRCuXDzOqLcX4B1ofh+9WQ+0taZtjBW2NirOXNLy+9oi0rJ1Vaav56+hW3Nrgrw1ODuo+WFhAQcTy03SPN7TltYNrUzeO3qujG/nFdZKzNfr286G9o2tsLVWcSa5nFmrqo8/PEBD9xY2BPlocHFQ8/38Ag6cLquUzsdNzUOdbIkItECtgssZWn5YWEBWnlKr8fduZUXrhhbYWqs4e1nLnPUlpOdU/R1hfmq6NrUi0FONs4OaqUuLOHTGtHLv1dKKuPoWuDio0GrhQpqWZdtLOZ9adb6I+4f0CN2jErYvZ9Ev/6X7wOd46d9z8AuKZMrH/yQvJ8Ns+tKSIty8AunzyMs4uniYTePs5k2fR17mpX/N4aV//UF4w1b89NnzpFw8fVuxHtm9jNV/TKBDvzE89e58vAOi+G3SaApyzcd64fQ+5k95hdj2g3n6vQVExsbzxzdjuHLppCFN5pUkZnzyKB4+YQx/9Weefn8RHfo+h4WldaX17VozA26jq/jYnmWsmzuBdg+MYeRb8/EKiOKPr6qO/2LiPhZNe4XGbQcz8q0FRDSJZ973Y0i7Lv5rTiasJvnsARycvSp9FhTZmv5PTeLp8SsY8I+vyEq/wIIpL950/Mf3LmPDvAm06TOG4W/o4//z69EU5JmP/9KZfSyZ/goxbQYz4s0FhDeOZ8EPY0hL1sdfXlrMlQtHad3rWUa8MY/+T39NZupZ5v/PeDKUn53KnMmjcPEI4rHX/mDQmCmkXz7F8p/fvOn4Ado2UNEyUsXSXTqmrtRSVg6PddGgqaaGahCsokdTNRsP6fhhmZaULIXHumiwu24XCfCAR7toOHNZYeoKLT8u17L7hA7FzHG1W5yavKLbO6jfiXJ75thu2nUfxgsf/sY/35yCVlvODx8/TUlx7Z9Ebdm4nmlTvuORR0fw+eT/ERJWjw/eHUd2tvlW4eGDB+jQqSsfTficTz77Gg8PT8a/8zoZ6WmA/sR6wkfvkXo5mbfe+4gvJv8PTy9v3n/rVYqLi2o9/prS2NuRe/AEh//vg7/0e/ev/5GDW36m06DxDPq/P7CwsmXJlKcoLyupcplTCcvYuuhjmncfw5CX5uHhF8mSKU9ReF353rpoAueOrqfn8C8Z8NxMCnKvsGLGC4bP05OPs+THfxAU2YGhL8+nx+Ofc/boOrYv+8zku7Ys/DfHdv5J237jePT15fR+8tsqL6gc27OMdX9OoH3fMYy6Wm/+Prn6enPh1Fdo0m4wo95eQERsPHOrqDdP7K+63rxm/bz/Vvv5rejWwopOsdbMXlvExFn5lJYpjBloj4Wm6mWsLVVcStPy+7rq9+cjZ8t48/tcw2v60tovvz1aWtOlqTWzVhfy31/zKCmF/xtSs/hnr6k6fg8XNa886kBKppbPZ+fzrxl5LN9eXOli0u2Kb2pJxyaWzFlfwhd/FFFaBs/0t71h/MnpOv7cWHUZupKtY+7GEv47q5Cv5haRmavwTH9b7G1qN/6/iqKo7trr76hONYTy8vJ47LHHsLe3x9fXly+++ILOnTvz0ksvAZCVlcWIESNwdXXFzs6O3r17c+rUKZN1zJ07l4YNG2JtbU1ISAiffWZ6IFCpVCxYsMDkPRcXF3766ScAQkP1V2Hj4uJQqVR07tz5TmwqG5fNoFWXwbTs/BA+AeEMGv0+ltY27N44z2z6oHqN6PfYq8S17YOFhZXZNA2bdSE6riOevsF4+obQ++EXsbKx4/ypA7cV687V04nrMJTYdoPw9Aunz+MfYGllQ8LWuWbT7147k3oNO9Cm51N4+Naj84CX8A1qwJ51vxjSbFjwBfUadSR+8Ov4BDXAzSuI+rHx2Du5m6wrJekYO1ZNo9/I/9xy/LvXTqdJu6E0bjsID99weg7Tx39ou/n4966fSViDDrTqoY+/44Mv4R3YgH0bfzFJl5edyurfP6LvqImoNZaV1tMifiT+YbE4u/sTUK8prXs8TfLZBLTaylfjqrNn7XQatR1Kozb6+Ls/oo//cBXx71s/k9AGHWjZ/SncferRvp8+/oSr8VvbOjLkhelENeuDm3cYfqGxxD/8LqlJR8jNTAYg8fAG1BoLuj38Pm7eYfgGN6b7Ix9wKmElWVfO31T8AK2i1Gw+rOPkRYUr2bBguw5HO4gKrLribROlZt9phQNnFNJzYekuHWVaiKtnXKZHMw27TihsPaqQlgMZeXA0SUFb4UJguJ+KMF8Vq/fd3hXCO1Fun37jB1p00q/PLziKR575N9npl7l49uhtxWrOwvlz6NGrD/E9ehMYFMKzz7+MtbU1a1ctN5t+7Otv06dvf8LqhRMQGMSYF19F0SkcPLAfgORLFzlx/CjPPP8SEfWj8A8I4pkxL1FaWsrmDetqPf6aSlu5iZPvTyJ14Zq/7DsVReHg5pk06/YMoTHxePhFEv/IJxTkXuHs4arjOLDxJxq0GkJ0y0G4+YTTadAHWFjacHy3vnyXFOVxbNdc2vUbR0BEa7wCYuj68ARSzu0n5XwCAKcTluHuG0mLHmNw9gjGv15L2j7wKoe3zqK0OB+AzNREjmybTe9R3xDasCtO7gF4BcQQ2qCd2bh2rbmu3vQLp9ejH2BpacPBbebrnT3rZhLW0LTe9AlqwN4NFerNrFTW/P4R/Z40X28CJB7eyLljW+k6aFy1eX6zusRZs3JnMYcSy0lO1zFzRSHODiqahJuPA+DouXKWbCvh4OnyKtMAlGshr1AxvIqqPm+/ZV2bWbN8RzEHT5dzKU3HT8sKcHZQExtRdfxHzpazaEsxB05Vfdzp396GI2fKmb+xmItXtKRn6ziYWE5eYe32BnWMtWTV7lIOn9VyOUPHr6uLcbZX0Sis6sFLx85rWbajtFIv0PX2nSzn5AUtGbkKKZk6FmwuwdZahZ9HNS0scd+oUw2hsWPHsnXrVhYtWsTq1avZvHkz+/btM3w+cuRI9uzZw6JFi9i+fTuKotCnTx/KyvQFfO/evQwdOpRHHnmEQ4cOMX78eN59911DI6cmdu3aBcCaNWu4fPky8+aZP8G5HeXlpVw6e5T6MW0M76nVaiJiWt92o+UanU7L/m3LKC0pIjiiyS2vR1teyuXzRwiNbmt4T6VWExLdlkuJ+80uc/FMAqEN2pi8F9awPRfPJACg6HScPrgBd+8QZn0xms/HtmHaf4ZwYr/pyUJZSRELfnyFXo+9h4Oz5y3Hn5J0hOCoCvFHteXSGfPxXzqTQHCUafyhDdpz6Wr817ZhyfTXaNV9NJ5+ETeMo6ggm6O7F+MfFoemioN/VfGnXqgcf1BUW5KriD/5bALBkabxh0S3J/lsgtn0AKVF+aBSYW3rZPhejcYSldpYhVhY6i+vXUrcW+P4AVwcwNFWxZkU40G1pAwupUOAh/mGkFoNvm5wNsX0QHw2RTEsY2etX76gWGFUDw1jB2p4opuGwAq7ir0N9G2lZsE2LWW3cYXzryi3AMWF+vGCdg7OtbZOgLKyMhJPn6RxbDPDe2q1miaxzThxvGaNrtKSErTachwcHA3rBLC0Mjby1Go1FpaWHD16uBajv/flZl6kMC+NwAhjWbW2dcQ7qLGhwVKRtryUtEtHCKhvWr4DItoYlkm7eASdtswkjatXGA4ufqReTaMtL63Um66xtEFbXkLaxSMAnD+6Hif3AM4f3cDP/47n5393Zf0f71BUkG02rpSkI4SYq/erqnfOJBBSg3pz8U+v0bKaerMgN50Vv7xL31H/xcKq9i7puzurcHZQczzJ2KApLoVzKVpCfG//hDkiwIIJzzjy7kgHHo63wd6mdq+uezjrh4YdP28a/9nLWkL9bv0uCBUQU8+S1CwtLwy257/POfH6Yw7VNg5vhbuTCmd7NScvGCvh4lI4n6ojxKf2TlU1amgbY0lRiUJyei13af1FdHfx9XdUZxpCeXl5zJgxg4kTJxIfH09MTAzTp09Hq9XvyKdOnWLRokX8+OOPdOjQgSZNmvDrr79y6dIlQw/P559/Tnx8PO+++y7169dn5MiRPP/883z66ac1jsPTU38W5e7ujo+PD25ublWmLSkpITc31+RVVnrjy0AFednodFocnE17Pxyd3cnNTq9xrOZcTjrJW6Oa88aIOOZO+5CRL3+FT0D4La+vMD8LRaet1FPj4OROfq75WPNz0rF3NB0GZO/kTkGOPn1BXgalJYVsWz6FejEdePSlaUTGdWfOd89z/sQuwzKr/phAQL04ImNv/p6gG8Vv5+ROQRXxF+SmY+9kJv7r0u9YNQW1xoJmXUZUXNzEhvmf8vmLsXz1aityMy8z6Jlvbyr+omvxO5rGb+9Yffx2FeKvbnvLy0rYtGAi0c0ewNrWAYCg+q0pyE1n1+of0ZaXUlyYw6aFn11df9pNbYPD1XOZggojM/KLFRxszS9jZw1qtb6RY7JtxeBgqz/BcNWHSqfGavad1jFrvZbLmQrD4zW4ORqX6d9Gzd5TOi5n3lTYldzJcnuNTqdj4c+fEFI/Dt/AGzewb0Zebg46nQ4XV1eT951dXMnKrFnmzJj+A65u7jSJ0zemAgKD8PT04ufpP5Kfl0dZWRnz5vxGRnoaWZnmh1D9XRXm6cuFbYWyauvgQWGe+f2juEBfvu0cKizj6EHh1fJamJeGWmNpuEhxjZ2juyFNYGR7Us7t59T+Jeh0WvJzUtmz+luTuHIyLpCXlUziwZXED/uErg9PIO3iEeb/8H+Vt6WKerO6eiffXL3paKbeVFvQvKv5elNRFJbOeIPYjo/gG9zIbJpb5WSnPx2q2MuRV6DDyf72Gi3HzpXz84pCJv9ZwMLNxYQHWPDsQDtUtdgWuhZjboHp6ertxu9or8LGSkXPljYcOVvOV38WkHCqjH8MsCMioPZ6VBzt9DFWyv/C289/gAYhGj75pz2fPmdPp1hLvl1QREHxba9W/A3UmckSzpw5Q1lZGS1btjS85+zsTGRkJADHjh3DwsKCVq1aGT53d3cnMjKSY8eOGdL079/fZL3t2rVj0qRJaLVaNJra7SadMGECH3xgOgb9kaff5dF/vler33MzPP1CGDthLsWF+RzctYrZ37/Fs+/+dFuNodqmKPqKvH5sPK26jwTAJyiai4n72LtxNsGRLTmZsJZzx3fw9Lvz72Kk5qWcP8ze9TN54s15qG5wpGvZfTSN2w4mJzOZrUu/ZsmMcQx+7n83XO6votWWsXjqiygodHvEuC97+EXQe8THrJ/7MZsXfY5arSau03D9zdk3iD0mREXflsZrML9tuDNX5a6Fse+UfvgcQEqWjlAfDbH11KxL0NEyUoWVhYotR+rGta750/9FyoVTjHn/57sdSiVz/5jFlo3r+dcnn2N1tQfIwsKCce98yNdffsrjD/fX9zDFNaNp85ZQu6Nq7jl7HWCOB+jeagrAA6O/v2uxBEW2p03f19g4dzxrfhuHRmNF8+7PcvnsHlBdLYuKDm15KfHDPsbFUz8EvMvQfzFn0iAyUs7g7hN2R2NMOX+YPetmMvKtquvNvet/prS4gDa9/nnb39c8ypJh3YxXWr5bUHDb66zK3hPGYWfJ6ToupRfwwWgnIgI0Jj0gN6NFtCWP9rAz/P3t3PzbjtOca7/EwdNlrNurv5B78YqWev4WdIi15tTFW7vXqVl9C4Z2MfZS/rD4zt4zePqilk9nF2Jvo6JNQ0tG9rLhizlF5N/mfaGi7qszDaG/ikqlQqlwJ/W14R03680332Ts2LEm7605cuPGlr2jC2q1hvwKN1jn5WTgVMUN1TVlYWGFh08wAAFhDbmQeJgtK35h8FPjb2l9dg6uqNSaSjfI5udm4OBkPlYHZw8KKlwBLcjNwN7Zw7BOtcYCD996Jmk8fOpx4bR+2NW54zvISkvi0xdbmKT587sXCIxozojXanaiWFX8hbkZla5eXmPv5FHpqmfBdekvnN5DQV4G373dxfC5otOyfu4n7Fk3k2f/bbw3ws7BDTsHN9y8Q3H3qcd3b3Ui+WwC/mFxNYrf9lr8FSZGKMirPv7CCvGb2159I+glcjOTGfp/Mwy9QddEt+hHdIt+FOSmY2llCyoVe9f9hItHYLUxn7yo8L/rhiRcuxHW3hbyr7tC52CjIiXL/EGqsAR0OuXq8BJjGnsbDAe2/KvH1bQKMw6l5yo4Xz1/CPFWEeABbz9iWi6f7qXh0DmFhdtr3kC6k+UWYN70f3F0/0aee28GLu4+t72+ihydnFGr1WRnmU6MkJOdhWs1Pd8AC+b+ztw5v/HhvycSEmpabsMj6jPp6ykUFORTXl6Os7MLr730HOERkbW+DfeShgUQVAxJ3+ov1mjLSwEoysvA3sl4k39RfjrufuZnPLOx15fvwnzTfaooz9ira+foiU5bRklRrkmvUGFehknPb2ynUTTpOJLC3CtY2zmTm3mJHcs+x8lNX17tnDxRqy0MjSAAV2/9b5mbedmkIVRVvVldveNgrt7Mq1xvfvuWab257s9P2L12Js/9Zx3nT+zg0pkEPn3etDfopwmDaNiyH31HfmL2u805lFjGuZTK9ZCjnYrcAmOd4Wiv5uKV2r1Yk5GjkFeow9Pl1htCB0+Xce6ycVrNa/E72avJLTCu83bjzy9S0GoVLmeYruNyhpbwgFs/hTx8tpzzqdfnv77J5WinIve6XiFHOzWX0m4//0vLIT1HIT1H4XxqCW8Pt6N1AwvW7L2187u76e86acHdUmcaQmFhYVhaWrJ7926CgoIAyMnJ4eTJk3Ts2JHo6GjKy8vZuXMnbdvqxy1nZGRw4sQJGjRoAEB0dDRbt241We/WrVupX7++oTfI09OTy5cvGz4/deoUhYXGKx7XrnReG5JXHWtra6ytTcdlW1pVf0Ml6Bsr/qENOHVkBzEt4gH9kJjTR3bSrsewGy5/M3SKjvKrB+hbobGwwje4IWePbTdMW63odJw7tp3mXR83u0xAWCznju2gVbeRhvfOHttGQFisYZ1+IY3ISD1rslxm6jmc3f0BaNv7H8R2GGLy+Q/j+9H94TeJaNyFmtJYWOET1JDzJ7ZTP/a6+E9sp1ln8/H7h8Vy/sQOkyldzx3fhv/V+GNa9Sfkunt2AP6YPJqGrfrTqM3AqoO52hOmvYnfQ2NhhXdgQ5JObCeiiTH+pBPbietkPn6/UH38zboa4z9/fBt+obGGv681grKunOfhF2di6+BaeUVXXTuRObTtTzSW1gRHmb+5+prSciitcPEyr0gh1FtF6tWGj5UF+HvAnlPmG0I6HVzOhFAfFScuGtOE+qjYfUKfj9kFkFuo4O5k2lhyc1SRmKz/e8UeHeuvu33H0VbF4/Ea/tyi41L6zV0pvFPlVlEU5v/0bw7vWcuz7/yEu9edmXba0tKSeuH1OXhgH63btgf08R9M2EeffgOqXG7enNn8+fuvvP+vTwivX3Xjxt5e35BOvnSRxNMneXTEqFqN/15jo4BNOeR46C88KYqCnaMnF09tx8Nf3/ApLc4nNekgDduY3z80FlZ4+jfk0qnthMUYy/fF0zto1O4xADwDGqLWWHLx1HbqNe4JQNaVM+RnJ+MdHGuyPpVKhb2zfgr80/uX4uDii2eA/vjoE9IUna6cnPQknD30x9jstHMAlR5ZcK3ePHfctN48f3w7TauoN/3CYjl3vEK9eaz6evP3r0YT09pYb3Z7+B06PviS4fP8nCv8/tVo+j/1BX6hN3eva0kZlFSYVjonX0dkkAWX0vR1sI0VhPho2HLg1o+R5rg4qLC3VVUaxnYzSsqoNC32tfivNXxsrCDUV8PmhFufmUGr098n5e1merHI201NZs7txV9icpFKIadAR0Sghkvp+vVaW0Kwt5qth2q/saJSGRtf4v5WZxpCjo6OPPHEE7z22mu4ubnh5eXF+++/j1qtRqVSERERQf/+/Xn66af53//+h6OjI2+88Qb+/v6G4XCvvPIKLVq04KOPPuLhhx9m+/btfP3113z7rfG+jK5du/L111/Tpk0btFot48aNw9LSeFOgl5cXtra2rFixgoCAAGxsbHB2rt2blgE69XmC2d+/RUBYQ4LqNWLz8p8pLS6iRaeHAPjt2zdxdvOizyMvA/obtVMvJgKgLS8jJ/MKl84dw9rGztADtGz2F0Q26YCrhy8lRQXs37aUM8d28/QbP9xWrK26j2LRtHH4hsTgH9qYnWtmUFZaRJN2+oPXwqmv4+jqTdeBrwDQIn4EP08czo5V0whv1Ikju5eRfO4wfYZ/aFhn6x6jmffDywRFtCAkqhWJhzdz8uB6hr86EwAHZ0+zEyQ4u/nh6ll9j0RFLeJHsXTGOHyCYvANacyedTMoKykyHHyX/PQ6ji7edBqgj79ZlxH89vlwdq2ZRr2YThzbs4yU84fp9ag+flsH10oNB7XGEnsnD8NV1eSzB7h8/hAB9ZphY+dEdloSmxd/iYtnEH6hNesNuqZ5/CiWzxyH99X4916NP6a1Pv5lM17HwcWbjv318TftMoLfvxjO7jXTCIvpxPG9y0hJOkz3q/FrtWUsmvJ/XLlwlIee/R+KTktBjv4+Aht7ZzRXZzfbt+EX/MPisLS24/zxbWyc/1869n8FGzsnM1FWb+dxHR1i1GTm6cguUOjcWE1eIRy/YDxQDo9Xc/yCwu6T+ve2H9cxoI2a5AwVyRkKraLUWGog4Yxxme1HdXRqrCY1S9+71CRMjYcT/LlZf6DNrTCqo7Rcv2xWvkLeLYzUuBPldt70j9i/bRmjXpmMta0dudlX7zWxc8SyFm8WB+j/0BC+/PxjwiMiiagfxeKFcykuKSa+ey8AJk2cgLu7B8NHPa2Pbc5vzPr5J8a+/jZeXj6Ge4lsbG2xtdUPO9q6eQNOzi54enpx/txZfvzf17Rs3Y64pi3MxvBX0NjbYR8eZPjbLjQApyZRlGbmUHzhcjVL3jqVSkXjDiPYu/Z7nD1DcHLzZ9eKr7B38iI0xnif48LvRxIW041G7fUNiiadRrJu9ht4BsTgFdSYg5tnUF5aRFQLffm2tnUkuuUgti76BBs7ZyxtHNg8/194B8fic11DaP/6qQRFtUelUnPm0Gr2rZ9Cj+FfoFbrT3ADI9ri6d+A9X+8Rbv+b6EoOjbP+4iQ6Ha4eVd+ZlXLbqNY8tM4fION9WZpaRGN2+rjWjxdX292fkhf7zTvOoJZnw1n52p9vX909zIunz9Mr8dqXm86u5k2yCyt9V27rp5BOLnefi/p+v0l9GplQ1qWjoxcHQ+0tSEnXzF5rs4Lg+05cLqMTQn6xpGVJXi6GIf6ujur8fdUU1iskJWnYGUJfdrYkHCqjNwCHR7OagZ0tCU9W8ex8ze+MHoz1u0toU8ba9KytKTn6OjX3pacfB0J180I9+JQexJOlbFxvz5+a0vwdDU2ctyd1QR4aSgo0hmeEbR6dwlP9bPj1EUrTiaV0yDUgkb1LPlidu0Ox9uUUEaP5lakZevIzFXo09qKnAKFQ2eM+fTcABsOntGy5aB+m6wswdPZmP9uTmr8PRQKihWy8xWsLKB7CysOnyknt1A/iqBDY0uc7VUk3GCmv3uVchuPC7kbvvnmGz799FNSUlJo0qQJkydPNrnN5XpTpkxh5syZHD6sn0ynWbNm/Oc//6kyfW2oMw0h0E928Mwzz9C3b1+cnJx4/fXXuXDhAjY2+pOB6dOn8+KLL9K3b19KS0vp2LEjy5YtMzRkmjZtyh9//MF7773HRx99hK+vLx9++CEjR440fMdnn33GqFGj6NChA35+fnz55Zfs3WucBcvCwoKvvvqKDz/8kPfee48OHTqwYcOGWt/W2Da9yc/NZOWfX5OXnY5fcBRPvfE/HK8OH8vKuIxKbSwMuVlpfPHWYMPfG5dOZ+PS6YRFt+C5d38CID83k9nfvUludho2do74Bdbn6Td+oH4j06twN6thiz4U5mWyceFXFOSm4R0YzbAXfzQMjcvJvIxKZayoAsObMuCpiWxYMIn18z/HzSuEoWO+wcu/viFNVNPu9Hl8PFuX/8Cq2f/C3TuUwc9+RVBE89uK1Zzo5n0ozM9ky5Kvrj4YMJqhL/xo6OnIrRB/QL2m9HtyIpsXTWLTws9x9Qxh4DPf4Hld/DdiaWXDyf2r2LJkMmUlhTg4exLaoAP9+zyHhaX5aZSrEtVMn/9bl3xFYV4anv7RDB5zXfxZpvH7hzXlgVET2bJ4ElsWf46LZwgD/vENnn76+POzU0k8pB++N3OC6T11Q1+cSVB9/X14KecPsm3ZZMpKCnDzDqP7sA9o2GrATcV+zbajClYWCn1bqbGxgqQrCr+u15pMc+3qoLr6jCD9wfnoeQV7ax2dm6hxsNE/UHXWeq3JDbA7TyhYaHT0aKbG1lqf5pd1WrLuzHD6O1Jut6/5HYDvPhpp8l0P//NfhgZWbWnfqQs5udn89vN0srKyCA2rx/sffoKLq35oXFraFZOZApcvXUR5eRn//c9409geHcGwx/XxZmVmMm3Kd/ohdq5udI7vwdBhw2s17pvl3CyGNmuNw2cbTHwLgAsz53Fw9K09C6sm4ro8RXlpERv+fI/Solx8Q5vR9+kpJjO65WYkUVRgHJ4YEduH4vxMdq2cTGFeGh5+0fR9aorJw1LbPfgmKpWaFTNeRFteSmBkezoNNL0XNen4Jvau/R5teSkeflH0HvkNwdEdDZ+r1Gr6PPkdmxf8iwXfPo6FlR3BkR3oOewNs9sS3Vxf72xebKw3H75Bvfng6IlsulZveoUw6CbrzTttze5SrC1VDOtui621isRLWr6dV2DyvBwPZ7VhQhaAYG8NLw41Dhse1Fl/AWDHkVJ+WVmEooC/h5pWDeywtVaRk69w/Hw5S7bV/nN4Vu0qwcpSxaM97bCzVpF4qZzJf5rG7+miwcHW+EaQjwVjHzHGP6SrPv7th0uZuVx/pejAqTJmrSqiV2trhna1JTVLxw8LC0i8VLsbsHZfGVaWKh7uYq1/IOxlLf9bVFQ5/22MB4YgLw3PDzTe6/VQB31Z2nWsjFlrStAp4OWqZlQfGxxsVRQUKSRd0fHV3CJSMuvGvaF12e+//87YsWP5/vvvadWqFZMmTaJnz56cOHECL6/KzwHbsGEDw4YNo23bttjY2PDJJ5/Qo0cPjhw5gr+//x2JUaVUvCGmDikoKMDf35/PPvuM0aNH3+1wamTx3rp5BQIgu6BOtZsrKau7WQ9Q6dk3dcnl1Lqd+XFRdzuC2xPuknq3Q7hlZ6K63u0Qbsvpecfvdgi3xcmhbl19rmjv/py7HcIt02rr7OkZANY2dfucYdILDjdOdBdsPXqHruTVQLsGN5cnrVq1okWLFnz99deAfrh1YGAgL7zwAm+8Yf4iy/W0Wi2urq58/fXXjBhR/Sy8t6pO7aX79+/n+PHjtGzZkpycHD78UN+lXnEmOCGEEEIIIUTtKSkpoaTE9J4zc/fDA5SWlrJ3717efNPYy65Wq+nWrRvbt2+v0fcVFhZSVlZW7aNqbledeY7QNRMnTqRJkyZ069aNgoICNm/ejIfH7c/IJIQQQgghhDBvwoQJODs7m7wmTJhgNm16ejparRZvb2+T9729vUlJSanR940bNw4/Pz+6dbv150XeSJ3qEYqLizO5X0cIIYQQQoj7xd2cLMHcY2HM9QbVho8//pjZs2ezYcMGw1wAd0KdaggJIYQQQggh/npVDYMzx8PDA41GQ2qq6T2qqamp+PhUP8vjxIkT+fjjj1mzZg2NGze+5Xhros4NjRNCCCGEEOJ+pFPu3utmWFlZ0axZM9auXWuMXadj7dq1tGnTpsrl/vvf//LRRx+xYsUKmjev/ZmCK5IeISGEEEIIIUStGjt2LE888QTNmzenZcuWTJo0iYKCAkaN0j9Qe8SIEfj7+xvuM/rkk0947733mDVrFiEhIYZ7iRwcHHBwuDOz+ElDSAghhBBCCFGrHn74YdLS0njvvfdISUkhNjaWFStWGCZQSEpKQn3ds+m+++47SktLGTx4sMl63n//fcaPH39HYpSGkBBCCCGEEHXA3Zws4VY8//zzPP/882Y/27Bhg8nf586du/MBVSD3CAkhhBBCCCHuO9IjJIQQQgghRB2gKHWrR+heJz1CQgghhBBCiPuO9AgJIYQQQghRByg3OY21qJ70CAkhhBBCCCHuO9IQEkIIIYQQQtx3ZGicEEIIIYQQdYCujk2ffa+THiEhhBBCCCHEfUd6hIQQQgghhKgDZPrs2iUNob9Ywsm7HcGts9Bo73YIt8XFSXO3Q7gtujo8U4xWW4eDB3IK63ZVmVDsd7dDuGVX5h2/2yHclvCBUXc7hNty7PdjdzuE2+LpZXe3Q7hlJSW6ux3CbbG1q9vHXHF/kKFxQgghhBBCiPtO3b7MKYQQQgghxH1CniNUu6RHSAghhBBCCHHfkR4hIYQQQggh6gBFps+uVdIjJIQQQgghhLjvSI+QEEIIIYQQdUBdnkH2XiQ9QkIIIYQQQoj7jjSEhBBCCCGEEPcdGRonhBBCCCFEHaAoMllCbZIeISGEEEIIIcR9R3qEhBBCCCGEqAPkgaq1S3qEhBBCCCGEEPcdaQgJIYQQQggh7jsyNE4IIYQQQog6QIdMllCbpEdICCGEEEIIcd+RHiEhhBBCCCHqAJksoXZJj5AQQgghhBDivnNPN4Q6d+7MSy+9dEe/IyQkhEmTJlWbZvz48cTGxt7ROIQQQgghhKiOoqju2uvvSIbGVaBSqZg/fz4DBgy4K9/fqZGauHoqbCzhQrrC8t06MvOrX6Z5hIo2UWocbCE1C1bs1ZKcaZrG3x26NFHj765CUSAlC2Zt0FKuBWd76NBQTYi3CgcbyCuCw+cUNh/VodPdXPwdGqqIDVNhbQkXM2DlXh1ZN4i/abiKVpH6776SDav267icaT7t0A5q6vmq+HOLllPJ+vcahajo29J8m/7LhVoKS24c94HNv7Jn3VQKc9Pw8I+iy6B38QluXGX6k/uXs33Zl+RmXsLFM4T2/V4ltGEnw+enD6zi4NbZXLlwhOLCbB59bQFeAdEm65gzeTiXTu8yea9R24eJf/jDG8a6b91UCvPS8PCLotMNYj2VsJwd18Xart+rhDQwxqooCjuXf8XhHXMoKcrFL7QpXYaMx8UzBICLp3Yy75sRZtf98Ng5eAc1JjfjIj99FF/p8yEv/Y5vSGy123NNlyZqmkWosbGCpDSFJTu0ZOZVv0zLSDVtG17d9zMVlu3ScSlDP27AxR5eHmRpdrnfN5Zz9LxxfEFsPRVtGmhwd4KSUjh6XsfSXTXb+Xev+5XtK6eSn5OOd2AUvYa9g39Y1b/H0T0r2LDgS7LTL+HmHUz8oFeJaNzJJE1aciJr504k6eRudFotHn71GPLsVzi7+xnSXEzcz/r5k7h05iAqtRqfwGgefflHLK1sahT3NbvW/srWFfr4fQKj6P3YOwRUE/+R3StYN18fv7t3MN2GvEr96+If/2SU2eW6D3mNdr1HG/4+eWADGxd9S+rFE1hYWhMc2YJhL3xTbayKorB75WSO7tTvq76hTek48H3DvlqVQ1t/JWHDVArz0nH3jaLDQ+/gHWTcxvKyErYt/oRTCUvRlpcRFNmOjgPfx87Rw5Dm4qnt7FzxJZkpJ7GwsiOq2QBa9X4JtcZ4GFUUhYSN0zi64w/yspKxtXelowt0z642vJvm1r45Ya+MxrlpDDZ+XuwZ9Bypi9bW7pfcIkVR2LtmMsd3z6G0KA/v4DjaD3gfZ4+Qapc7sv1XDm6aRlF+Om4+UbR98G28Ao2/0eb573Pp9HYKc69gaW2Hd1AcLXu9gotX2G3F27mxmqYRav0xN01h6a4b1zst6qtp20Bf76Rk6Y/TyVfrHWd7eOkh8/XOnE3lHE1SsLWCge01eLuosLWGgmI4cVHH2gQdpWU1j71bUw3NIzXYWsH5VIWF28rJyK1+3FTraDUdGlnoY89UWLy9nIvpxmXcHKF3SwtCvNVoNHDqoo7F28vJLzauw91JRe+WGoK91WjU+vWs2VfOmcs3N2brbpzvALRvoCLcT42PK2h18Olc7U3FLf5epCF0D2kbraJlfRULd+jILlDo3EjNo100fLdUi7aKc7IGQSq6x6lZtlt/AtgqUr/Mt0uMDQB/d3i0s4atR3Ws3KNDp4C3i8owztTDSYVKBct268jMU/ByUfFASzWWFmrWJNS8JdQ6SkXzCBVLdunILoCOMWoe7qhmygpdlfFHB6qIb6JixV6F5EyFFhEqHu6o5oflukoNmBb1zV+NOHZB4UyKaUXWt4W+Eq9JI+jEvmVsmj+BrkM/wCekCfs3zGD+d6N54u0V2Dm6V0qffHYfy2e+Qru+Ywlr2IXjexezeOoYHn11Hh5+9QEoKy3EL6wp9eN6s2b2O1V+d0ybobTp83+Gvy2sbKuN9eS+ZWxeoI/VO7gJCRtnsPD70Qx/y3ysl8/uY8XMV2jbdyyhDbpwYt9ilkwdw7BX5+Huq49179opJGz6me6PfYyzewDbl33Jgu9H8/gby7CwtMY3NI7RH24xWe+OZV9y4dR2vAIbmbz/0HM/4eYTbvjbxt6l2u25pn1DNa2i1czfqiU7T6FrnIbh3Sz4ZmE55VXsOw1DVPRsrmbxDi2X0hVaR2sY3k3D5IXlFBRDTiF8+ofpWUWz+mraNVRz+pLxgN0mWt+YWrVXy8U0BSsLcHGo2ZWvI7uWsfqPj+nz+Hj8w5qwc80MZk16iuf+tRx7p8q/x4XT+5j3wyt0HTiWiMadObxrCX988zxPvzcXL3/975F5JYkZnzxKbPvBdOr/AtY2DqQln8bC0tqwnouJ+5k16Wna9f4HPYe9g1qjIfXCCVSqm+vkP7xrGSt//5i+w/Xx71g9g18+f4rn/7McBzPxJ53ex5//e4Vug8ZSv0lnDu1YwuzJz/PP9+fiHaCP/5UvNpssc/rgJhb+9A7RzXoY3ju6ZyWLZrxH/MCXCY1uhU6r5cqlUzeMd//6Hzm45WfiH/kYR7cAdq38kiVTnuKR15aa5M/1TiUsY+uij+k0aDzeQU04uHkGS6Y8xbDXlxvKzNZFEzh/bCM9h3+Jla0Dm+d/xIoZLzDw+d8ASE8+zpIf/0Gz+GfoNuwT8nNS2Th3PDpFS7t+4wzftWXhv7lwYitt+43D3ac+xUXZuI8bfMPtulkaeztyD57gwk9zaf5n9Y3Hv9qBTT9yZNsvdBoyAUfXAPau/orl055m8MtLqvyNEg8uY8fST2g/YDxegY05vHUmy6c9zdBXlmHroP+NPPwbEh7bFwcXP0oKs9m79huWTXuKR15fjVqtuaVY2zVQ0ypKzYJtWrLyFbo00fB4Vwu+WVxe5TGrYbCKHs3ULN2p5WKGQusoDY931fD1onIKSyC3ECb+WaHeidA3nE4l6+sdBThxQWFdgo7CEgU3RxV9Wmiwbali3taanZR3bKyhTQMNf24qJytPoVszDaN6WjJpXqnhhL+iRqFq+rSyYMHWci6mKbRtqGFUL0s+/7OUgmKwtIBRvaxIydTx43L9NnRvpmF4D0u+X1TGtVrziR4WpOcqTF1WRpkW2jXUMKK7JRPnlJJfVKPw79r5DoBGreLYBR0XM1TEhf09ezlEzd3TQ+MAdDodr7/+Om5ubvj4+DB+/HjDZ9nZ2Tz11FN4enri5ORE165dOXDggOHzxMRE+vfvj7e3Nw4ODrRo0YI1a9ZU+V0hISEAPPTQQ6hUKsPf1/z888+EhITg7OzMI488Ql7eDS4b3aSWkWo2H9Fx8pLClWxYuEOHoy1EBVRdUFtHqtmfqHDgrEJ6LizdraOsHGKvK9w9mmrYfVJh2zGFtFzIyIOjFxRDZZN4WWHxTh1nUhSyC+DkJYUdx3REBd5cBdEiQsXWYwqnkiEtB5bs0sdf37/q9bSsr+LAGYVD5xQycmHFXoXycmgcarqMl4s+7dLdlWvIcq3+itq1l06BYC84eLZmV6f2bZhOTNuhNGw9CHefcOKHfoCFlQ1Hdsw1m37/xpmERHWgefxTuPnUo+0DL+EV0IADm38xpIluMYDWvZ4nsH6bar/bwsoGeydPw8vaxqHa9Ps3TCemzVAatNLH2nWIPtajO83HmrBxJsFRHWjWVR9rmz4v4XldrIqikLBpJi17PEu9Rt3w8Iuix2P/pSDnCmcO6cuKxsLKJEYbexfOHF5Lg5YDUalMfycbOxeTtBqN+SujFbWOVrPpoI4TFxRSs2HeFi2OdhAVVPW+0zZazd5TOhISFf3+tkNLmRbiwtVXtw3yi01f0UFqjpxTKC2/Gq8VdI1TM2+LlkNnFbLyITUbTlys2b6zY/VPxHUYQmz7QXj6hfPA4x9gaWVDwhbzv8euNT8THtOetr1G4+lXjy4DXsQ3uAG71/1qSLN+/iTCG3Wi25DX8A1qgJtXEJGxXU0aVqt+/5gW8cNp1+cfePlH4OETRsMWvbGwtKpR3NdsX/kTTTsOIa7DILz8w+k7Qh///s3m49+5Wh9/u976+LsO1Me/67r4HZ09TV7HE9YRGtUKN69AALTacpb/9h96DHmNFl0ewcMnFC//cGJa9q42VkVROLh5Js26PUNoTDwefpHEP/IJBblXOHu46nr9wMafaNBqCNEtB+HmE06nQR9gYWnD8d36bSwpyuPYrrm06zeOgIjWeAXE0PXhCaSc20/K+QQATicsw903khY9xuDsEYx/vZa0feBVDm+dRWmx/hJ2ZmoiR7bNpveobwht2BUn9wC8AmKIrOGJ4c1IW7mJk+9PInVh1dt9NyiKwuGtM4nr8gwhDeJx942k89CPKcy7wvmjVcd6aPMMoloMIbL5QFy9w2k/YDwWVjac2DPPkCa65VB8Q1vg6OqPh39Dmnd/kYKcy+RnXbrleFtFq9l0SMeJi/pj7oJtV+udao59raPV7DutI+GMQnoOLNlZud65/nhUUAxRgWqOnlcou1rvFJfCnlM6Lmcq5BTA2RSF3Sd1BHnV/JjbtqGG9QlajiXpSMlSmLOxHEc7aBBc9Wld+xgNu0/o2HdKx5VshYVbyykth2b19Q3JYG8Vrg7w56ZyUrMUUq+u199DRZifPjY7a/BwVrPpgJaULIWMXIUVe8qxslTh7Vrz+O/W+Q7AxsM6dp5QuJJdN2cd0Cl37/V3dM83hGbMmIG9vT07d+7kv//9Lx9++CGrV68GYMiQIVy5coXly5ezd+9emjZtSnx8PJmZ+n7S/Px8+vTpw9q1a9m/fz+9evWiX79+JCUlmf2u3bt3AzB9+nQuX75s+Bv0jaoFCxawZMkSlixZwsaNG/n4449rbTtd7MHRVsXZFOOeVlIGlzLA38N8xaBWg68bJssAnE1VCPAwVloBHioKihVGdtPw8kMaRsRrCPQwt0Yja0sVRTXoTbk+fgdbFedSTeNPztBfoakqfh9XfbzXO3dFwd/duM0WGujfSs2qfToKiiuupbJGwSrKtHC8Biez2vJSrlw4QmD9tob3VGo1QfXbcvncfrPLpJxNIDDStIETHNWey+cSbhxcBSf2LOb7t1rx84S+bFn8GWWlVZ81actLuXKxcqyB1cR6+VxCpcZYcFR7Uq7GmptxkcLcNJN1Wts64h3cpMp1nj28juKCbKJbDar02eIfn2XKO22Y8+Uwzhyu2VAdVwdwtFNx5rLxSFVSBpfSFAI9ze/7GjX4uqtMhmIowJnLVS/j6wa+bir2nTZ+Tz1ffW+okx08/6AFYwdZMKSjBie7G8etLS/l8vkjhDYw/T1Co9tw8UyC2WUunkkgNLqtyXthDdtxMVGfXtHpOH1wA27eIfz6xWg+e7ktU/89lOP7jSeRBbkZXDpzAHtHN6ZPeITPX27HjP8+TtKpvTcO+jrl5aUknz9C2HXxq9Vqwhq0McRT0YXEBJP0AOEx7bh42nz6/Jx0Th3cSFwH475y+fxR8rJSUalUfD/+ISa+3IFfPn+a1Isnq403N/MihXlpBEZU2FeDGhsaLBVpy0tJu3SEgAplJiCijWGZtItH0GnLTNK4eoXh4OJH6tU02vLSSr0ZGksbtOUlpF08AsD5o+txcg/g/NEN/PzveH7+d1fW//EOBff8Ubb25GVdpCgvHf9wY51jZeOIZ2BjUpMOmF1GW15KevIRk2VUajX+9dpwJSnB7DJlpYWc3DsPR9cA7J19bilWFwf9MfdMimm9czG96jpErQY/N1WlIWBnLhuPuRWZq3cqcrCF6CAV56/U7EzT1RGc7FQkJleIPU2psjGlUYOfh4rT1y2jAInJxgaYhVqFAiY9SuVafeMuxFu/IxeWQFq2jrgIDZYWoFZBy0gN+UUKl9JrFv+9dr4j7m/3fBXduHFj3n//fSIiIhgxYgTNmzdn7dq1bNmyhV27djFnzhyaN29OREQEEydOxMXFhT///BOAJk2a8M9//pOYmBgiIiL46KOPqFevHosWLTL7XZ6engC4uLjg4+Nj+Bv0PVM//fQTMTExdOjQgeHDh7N2bfUneiUlJeTm5pq8ysvMty4cro6IqniiX1Cs4FDFkH87a1CrVeQXKxWWAQcbfcXgerWDoWMjNfsTdfy2QcvlTIXHu2pwq6LzwdVBPwxtX2LNh8XZ21QRf4li+KxS/Fb6+CsOX9PHb/y7W6yKixmK4Z6gG2kSquJoklLl8IDrFRVkoei0lYaV2Tm6U5CXbnaZgrx0k3sHrqUvzDWfvipRzfrSc/inDH5+Ji26/4Pjuxey4ufXbinWqr67sIpYC66mL8xLM7xX03Ue2fEnQVHtcXQxnoBYWtvRvv8b9Bn5JQ/+43/4hTVjydQxNWoMOdjq99X8CvtOfrHxs4rsrPXDGyoOw8gvqrq8NI1QcyVb4UKasby4OqpQAR0aaVi+R8sfG7XYWsOI7hZoblA7Fubrf4+KQ8jsnTzIzzGfd/k56ZWGzDk4eVBwNX1BXgalJYVsWz6Feg078NjLU4mK68acb1/g/An9/WRZaRcA2LToa+I6DGHYy1PwDWrIL5+NJCP1XPVBXx9/3q3FbzZ9FftKwrYFWNnYmwyLuxb/hkXf0LHvMzz64nfY2Dvx039HUJifXU28+n3VtsK+auvgQWEVZbX4WplxqLCMo4dh/y7MS0OtscTa1skkzfVlIDCyPSnn9nNq/xJ0Oi35OansWf2tSVw5GRfIy0om8eBK4od9QteHJ5B28QgzvKvcpL+doqu/g23F/HbwoOhqPlVUXJiNotNWXsbRvdLvenT7LKa/34yf3m/GhZOb6TN6KhqLm+sFvebaMbLyMRfsbaqud9RqlfnjdBWjmuPqqUnLVkzuw7lmYHsNbz1iwSuDLCkpg0XbazYszvFanVlkus78IqXqOtPmWp1ZeZlr67uQpu9h6dVCg6VGP1SuT0sLNGoVjnbG9U5dXoavm4r3R1jxwUgr2sdomL6yjOLSGoV/T53v1EWKcvdef0f3/D1CjRub3rTr6+vLlStXOHDgAPn5+bi7m1aeRUVFJCYmAvoeofHjx7N06VIuX75MeXk5RUVFVfYIVSckJARHR8dKcVRnwoQJfPDBBybvdR74Ll0Hv0dMsIoHWhjPtH7beGdu1rs2cmnfaX13MkBKlo5QHw2x9dSsO2Da2HG01Y+vPXZBYX9i1Xt9wyAVvZoZK8Y/ttzkrAo1FO4HwV4qpq2u2fr93cHDWcXiGt7ofjc1avuw4f8efpHYO3ky95uRZKcn4eIRdBcjq1pedgpJx7fQe+Qkk/dtHdxo2mWU4W/voMYU5Fxh37qphMWYTqLQKFRFv9bGMf2/rrvzN6paaPTj4zcdNN0vVICFRsXyXeUkXr3K++cmLa8NsSDER0Vi8l9b8yuKPr76sV1p3WMkAD5B0VxI3M/ejbMJjmxpSNO008PEttf3tPgGNeDsse0kbJlL/KBX/tKYq7N/81wat+6L5XW9Kdfi7/DAP2nQvCcAA56cwOevdOLonhU07/wIAAe3L2bxzPcNB98HRn//1wZ/naDI9rTp+xob545nzW/j0GisaN79WS6f3QPX7stSdGjLS4kf9jEunqEAdBn6L+ZMGsQVS/C6iZvg64rT+xezecF4w9+9nvjujn5feFw//CPaUpiXxsHN01k762X6PTOrynuPrtcoREXfVsZ6Z9b6v7DeOWT+eLRyj5aNB/WTD8THaujZTH//S0VN6qkZ0M54ujZz1Z3ZmQqKYda6Mvq3taRNQw2KAgfP6LiUrjM5CX6wrQUFxQo/LCmnXAvNI/X3CH27sJQ8M4Ma7tXzHSGgDjSELC1N7zFQqVTodDry8/Px9fVlw4YNlZZxcXEB4NVXX2X16tVMnDiR8PBwbG1tGTx4MKWlNbxsUYM4qvPmm28yduxYk/c+W6CviE9eUriUYawMLK7WEfY2plfG7W1UpGSZPxkrLAGdTrl6NUS5bhkMV02uXTFPrzCTTHqOUmn4j4MtDO+q4WK6wpIbNCROJesnN7hGc13811/lsbdWkVrFONzCUn38dhWOYdfnQYiXfszy2AGml+cHtlVzIR1mbTCNs0moPr9SsqoN38DW3hWVWkNhXoZpbHkZ2Dua70+3d6x8BbowLwM7p9vrf/cJbgJAdtp5sw2h6mKt6rvtqojV/mp6O0dP43vOXiZpPP0rz/51bOdcbOxdCI3pesPt8Q5uQtLJbZXeP3FB4VJ6ueHva/uOgw0mPTwONlS772t1la/COtiqKvUsATQIVmGpgYQKvZx5V6+OpuUYv6ewRP9ysa9u68DOQf975Oea/h4Fuek4OJv/PRycPSiokD4/Nx37q+ntHFxRayzw9As3SePhW48LV4e+OVz9nTx8K6fJzbxcfdDXx+94a/GbTW9m/zt/cg8ZKWcZ8swXJu87Ouv3ueu30cLSClfPQHIyjPFHxnbBP6wxmTn6v7Xl+nq7KC8DeyfjvlqUn467n+mMjNfYXCsz+aYxF+WlG8qMnaMnOm0ZJUW5Jr1CFctVbKdRNOk4ksLcK1jbOZObeYkdyz7HyU1/75OdkydqtYWhEQTg6l0PgCyLv2dDKKhBVwZeN7ObVnv1N8rPwK7ib+RbxW9k54JKraGo0m+UUak328rGESsbR5w9QvAKbMLMD1tz7sgawmMfuGGsJy4qXLyu3rG42iayr1Dv2NtA6g2OuRVHOdjbVO6dBv3N/ZYaOHDG/PH02j1EGbkKRSVanuxpwabDukrrOpak48IV43mLhUZ/xu9gqzLUYdf+vpxp/rsKi6/VmabnCxXXcfqSwmdzSrGz1t8TUlwKbw6zIjNPv956viqiAtV89EspJVf36UXbygn3syQuQsOmg5UbOffi+Y4Q19zzQ+Oq0rRpU1JSUrCwsCA8PNzk5eGhrzy3bt3KyJEjeeihh2jUqBE+Pj6cO3eu2vVaWlqi1dbO1Qpra2ucnJxMXteuXJWWQ1a+8ZWWqz8pC/Ux9rJYWeh7OKoad6vTweVMCPEx7QoP9VYZuuGzCyC3UMHd0TSNu5OKnALj3462MKKrhstZCot23viqScX403P1XewhXqbx+7nrx/1WFX9KFoR4m8YW7KUyTIG8/bjCjyt1TF1lfAGsPaBUmjjB0kJ/k2tNJ0kA/UQAXoENuXByu+E9Rafjwsnt+IbEmV3GJzSWCyd3mLyXdGJbjaeJrkrapWMA2Dt5mv1cY2GFV0BDLpyqeay+IbFcOFU5Vp+rsTq5B2Dn5GmyzpLifFLPH6i0TkVROLprHlEtBtRoEoT0S8fMbktpOWTmGV9pOZBXqBDma6yOrC3B31NlMoztelodXM5QCPM17jsqINTH/DJNw9WcuKhUGoZ54eqYfHcn43psrfTDMLJvMI2rxsIK3+CGnDtm+nucPb6DgLBYs8sEhMVy9rr0AGePbiOgXqxhnX4hMWSknDVJk5l6zjB1touHP44uXmSkmqbJuC5NTVhYWOEX3NAkHp1Ox5ljOwzxVBRYr3L8iUe2ERBeOf2+zX/iG9wQnyDTBrVvSAwaCyuTbdSWl5GdcckkfmtbB9y9g3H20L9cvcOxc/Tk4nX7amlxPqlJB/EJNh+vxsIKT/+GXKpQZi6e3mFYxjOgIWqNpcl6s66cIT87Ge8K61WpVNg7e2NhacPp/UtxcPHFM6ABAD4hTdHpyslJN444yE47B4BrOX9LVtb2ht/H2SMYV69wbB09uJRorHNKi/NJu3AQ76AmZtehsbDCw6+hyTKKTkdy4g68gmKr/X4FBZ22Zhc2Kx1zc/TH3DAfY71jZam/x6Sqekeng+RMhbAKx9wwH5XZoW9xVdQ75lzrzTA3JLe0zLTOvJKtkFuoUM/PtM4M8FSRVMV9RlodJKcrhF9Xz6qAen5qs8sUlugbQWG+Kuxt9Y0xAEsLfaAVh0kpinEbKsV/j53v1HUKqrv2+juqsw2hbt260aZNGwYMGMCqVas4d+4c27Zt4+2332bPnj0AREREMG/ePBISEjhw4ACPPvroDXtxQkJCWLt2LSkpKWRl1bBboZbsOqGjfUM19f1VeDnDgDZq8opMb/p/vIua5hHGnXHHCR1N66loHKrCwwn6tFBjaYGhWxhg+3EdLeqriA7U9650bqTG3RESrl6lcrSF4fEacgoV1uzXYWetv8pS1b09Vdl9SqFtAxXhfuDpDP1a6eM/ed1UxcM6qWkWbox/10mF2DAVjYJVuDtCr2YqLC2MM74VFOsbWde/AHIKlEoVW3SgCrUKDp+veUMIoGnnURze/gdHd80nMyWRtXPGU1ZaRINWAwFY+cvrbFn8mSF9XKcRnD+2mb3rppGZmsj25ZNJvXCYJh0eN6QpLsjmysVjZKboh2lmXTnLlYvHKMjVj5PPTk9i58pvSL1wmJyMiyQeWsvKX8bhX6+F2Z4Yw3d3HsWR7X9w7Gqs6+eMp/y6WFf98jpbr4s1ttMIko5tZt96faw7lk/mynWxqlQqYjuOYPeq7zhzeC3pySdY/cvr2Dt7Edaom8l3Xzy1g9yMizRsXXk64GO75nNi7xIyUxPJTE1k9+rvObpzrkmeVGfHMR0dG6mJDFDh5QIPtdOQVwjHk4y/5RPdNbSMNFZZ247paBqhpkmYCg9n6NtajZUF7K9wU7Kbo342pH2nKpf9jDz9Ab53Cw2BnsbvTs+tfFOuOa27j2Tfpjkc2DqftORElv0ynrKSIpq00/8eC6aOY+1c4+/RsttwEo9sYfvKaaRfPsPGhZNJPneEFl0fM6Rp03M0R3YvZ9+mP8hMPc/udb9w8sB6mnd5FND/Zm16jmb32p85umcFmannWb/gSzJSzhDb/uamam7TcyR7N84h4Wr8S3/Wxx/XXh//vCnjWPOnMf5W3Ydz+vAWtq2YRtrlM6xfoI+/5XXxAxQX5XN090qadhxS6TttbB1o3vkR1i+czOnDW0i/fIYlP+uHEDds0avKWFUqFY07jGDv2u85e2QdGZdPsPa3cdg7eREaY9xXF34/kkNbjDM4Nuk0kqM753B893wyUxPZOE9fZqJa6LfR2taR6JaD2LroEy6d3sGVi4dZ9/tbeAfHmjSw9q+fSsblE2SmnGLP6m/Zt34K7Qe8bZi6OTCiLZ7+DVj/x1ukXTrKlYuH2fjn+9QvrP3eII29HU5NonBqoq8r7EIDcGoShU2gb+1+0U1SqVTEtBvB/nXfc/7oOjJTTrJhzhvYOXoR3MD4Gy39cRRHthlnGmzU4QlO7J7Dyb0LyLqSyJaFH1BWWkT9Zg8BkJt5gYQNP5B26Qj52cmknt/PmlkvYWFhTWBkx1uOd+cxHR1i1NS/Vu+0vVrvXDCW/eHxGlrUN9Y7O66vd5ygbyv9Mbdib7Org/6inrlJEsL99M/b83TWP3cowl9F35Yakq7oanzCvu2Ili6xGqKC1Hi7qhjSyYK8Qv0z0K4Z3duS1tHG2Lcc1tI8Uk1cuBpPZxX921lgZQH7Thov/jaNUBPoqcLNEWLrqXm0qyVbD2tJv9prnnRFR1EpDO5ogY+bCncnFb1aaHB1VHHiQs2Hnt2t8x3QT47j7QLOdvrGm7eL/mV5z4+REndCnf3ZVSoVy5Yt4+2332bUqFGkpaXh4+NDx44d8fbW3536+eef8+STT9K2bVs8PDwYN24cubm51a73s88+Y+zYsUyZMgV/f/8b9iDVpm3HFCwtFB5oYXyo5KwNpnPquzqorg4l0xf8o0kKdtY6OjVS42Cjf8DYrA1ak+Fpu04oWKh1dI9TY2utT/Preq3hQaehPircHfWvlyoMQfvot5pfytxxXMFSA72b6eO/kA5/bDJ9hpCLA9heNxTu2AX90LgOMSrsbVRcydYvU5MraBU1CVVx8pJi6K6vqcimfSjKz2T7sq/0D1QNiGbAMz8aho/lZl023gcA+IU2pdeIiWxfNoltSz7HxTOEfqO/MTxDCCDx8DpWz3rT8PfyGS8D0KrX87Tp/QIajSVJJ7azf8NMykoLcXTxJbxJD1r2fK7aWOs37UNRQSY7ln9FQW4anv7R9P/nj4YhJHlZl02eJeMb2pSeIyayfakx1r6jvzE8QwigWfzTlJcWse739/QPVA1rRv9//lhp3P2RHX/iGxqH29XhPhXtWvUteVnJqNUaXL3C6PXEF0TEVn1ie70tR3RYWkC/Nhr9vn9F4Zc1ps8QcnVUYWdjPOAdOadgb62ja6zG8HDAn9dqK92AGxeuJreQKu/3mb9VS6/mGh7rqkEBzqUo/LymvEZThTZs2YfC/Ew2LpxMfm4a3oHRPPrSFMPQstyMZJMpxgPDm/LQ0xNZP38S6+d/gZtXCEPHfG14hhBAVNPuPDB8PFuX/cDK3/6Nu08oQ579iqCIZoY0rbo/QXlZCat//5iighy8AyN5bOw03Lxu7t6ymJZ9KMjLZP2CyeTnpOETGM3jLxvjz8lMRqU2xh8U3pRB/5jIunmTWDvvC9y8Q3jkha8NzxC65vDOpSgoNGplfshSj6GvodZomP/jOMpKiwkIa8ITr/2Erb1ztfHGdXmK8tIiNvz5HqVFufiGNqPv01NM9tXcjCSKCowXsSJi+1Ccn8mulZOvPoQ4mr5PTTEZdtXuwTdRqdSsmPEi2vJSAiPb02ngeybfnXR8E3vXfo+2vBQPvyh6j/yG4GjjSbhKrabPk9+xecG/WPDt41hY2REc2YHHNpmfLe12ODeLoc3anw1/N5j4FgAXZs7j4Og3q1rsL9Gko/432jz/fUqLc/EObkqvUT9U+o2KC42/Ub3GfSjOz2Lvmq+uPvQ2mt6jfjD8RhoLa1LO7uHw1pmUFOVi6+COT0hzHnz2t0qTLNyMrUev1jutrqt31pk+Q8itYr1zXn/M7dxYY3ig6q/rqql3zDxktFyrb3D0bK5Co9Y/e+hYko4tR2rekNh0UIuVBTzUzgKbqw9Unb6yzGSSIDdHlcnED4fO6rC3KadbMwscbfW96tNXlpkMT/N0VtGzuQW21pCdr7D+gJath40rLSyBn1aW0b2ZBU/1tkSt1vdQ/bKmnJTMml+EvFvnO6BvHDUJMx4n/9Fbfyo8c622xjP33U1/12ms7xaVovxd54G4N91Mw+Jec21ccl3l4nRrD927V9Tlyu9KWt2+QSIitM5eMwLMD7epK65k3jjNvSx8YNU9vHXBsd+P3e0QbktB4Z2fFOFOKSmp2zf329rV7WPuu8PuzXr/zxrcvnCnDG5Vhw8mVbg3f2UhhBBCCCGECem+qF1/v6adEEIIIYQQQtyANISEEEIIIYQQ9x0ZGieEEEIIIUQdIEPjapf0CAkhhBBCCCHuO9IjJIQQQgghRB2gU+r2DL73GukREkIIIYQQQtx3pCEkhBBCCCGEuO/I0DghhBBCCCHqAJksoXZJj5AQQgghhBDiviM9QkIIIYQQQtQB0iNUu6RHSAghhBBCCHHfkR4hIYQQQggh6gCd9AjVKukREkIIIYQQQtx3pCEkhBBCCCGEuO/I0DghhBBCCCHqAEVR3e0Q/lakR0gIIYQQQghx35EeISGEEEIIIeoAmT67dklD6C/WPqbsbodwyzIKre92CLclI+duR3B7Sorrbu1nYVm3u/Lr+lCE00nldzuEWxbgU7cPU8d+P3a3Q7gt0Q9H3+0QbsuR2XU3/21tNXc7hNvi5iKDjsS9T/ZSIYQQQgghxH2nbl9qE0IIIYQQ4j4hzxGqXdIjJIQQQgghhLjvSI+QEEIIIYQQdYBMllC7pEdICCGEEEIIcd+RhpAQQgghhBDiviND44QQQgghhKgDZGhc7ZIeISGEEEIIIcR9R3qEhBBCCCGEqANk+uzaJT1CQgghhBBCiPuO9AgJIYQQQghRB8g9QrVLeoSEEEIIIYQQ9x1pCAkhhBBCCCHuOzI0TgghhBBCiDpAp7vbEfy9SI+QEEIIIYQQ4r4jPUJCCCGEEELUATJZQu36y3qEzp07h0qlIiEh4a/6SiGEEEIIIYQwS3qE7mEbls9m1aIZ5GZnEBBcn4dHjyM0opHZtMkXTrN49necP3OUzLTLDBn5KvF9HzdJo9NqWfLH9+zcvJTc7AycXT1p0/lB+gx+GpVKdce3Z8eaX9m8bBr5Oen4BEbRd/jbBNZrbDZt6sVTrJ03mUvnjpCdnkyfR9+gXa8n7lhsCZt+Zc/aqRTkpuHpH0WXwe/iG2I+NoCT+5ezdcmX5GZewsUzhA79XyWsYScAtNoyti6ZxNkjm8jJuIC1jQNBkW3p0P8VHJy9K62rvKyU3z4bQtql4zw+bgFeAdE3Hb+iKOxeNZljO+dQUpSLT0hTOg58HxfPkGqXO7z1VxI2TqUwLx133yjaD3gH7yDjdh/d8Tun9i8h7dJRykoKePLDXVjbOpmsY9n0Z8lIPk5RfgbWts4ERLShdZ9XsDezrdXp1EhNXD0VNpZwIV1h+W4dmfnVL9M8QkWbKDUOtpCaBSv2aknONE3j7w5dmqjxd1ehKJCSBbM2aCnX6j9/uIMab1cV9jZQVApnUxTWHtCRX1SzuPes/5XtK6eSn5OGd2AUPYe9i39o1fvO0T3L2bjwS7LTL+HmHUL8oFcJb9TJJE365UTWzv2UpJO70Wm1ePjWY/Czk3F29zNJpygKs796msTDmxny3DdExnWrWdAV3Im8H95VQ4i3ab2y95SOZXuMA9x93SC+iRpfNxUKkJyhsDZBR2p2zeLeu+FXdq7Sl1uvgCi6P/wuftXk/fG9y9m06EtyMi7h5hVC54depV6FvL9mxa/vkbD5d+KHvEmL+JGVPi8vK2XmJ0O4cvE4o95egHfgzZdbcxRFYe+ayRzfPYfSojy8g+NoP+B9nD1Cql3uyPZfObhpGkX56bj5RNH2wbfxCjTmxeb573Pp9HYKc69gaW2Hd1AcLXu9gotXWK3EfTPc2jcn7JXRODeNwcbPiz2DniN10dq/PA5FUdi3ZjIn9hjzum3/G+f10e2/cmizMa/b9Hsbz+vyesv890lOvJrXVnZ4BcfRoqdpXm9f/G9Sz+8jK/UULl71eOiF+XUm/ozLxzm4cQqp5/dRXJCFg6s/US0fJqbdiBrHvm/jr+xebSy78UOrP+ae2LecLYv1ZdfVK4ROA14lLMZYdrcumczxvUvJy0pBrbHEO6ghHR58Gb/QJpXWVV5Wyi+fDiHt4nFGvFl7ZVfUTXKP0D1qz9aV/DnjM/oO+Sdv/fc3AkLqM/lfz5Gbk2k2fWlJMR7e/jz02Is4uXiYTbNywXQ2rprDI6Pf4P1J83jo8RdZtfAn1i/77U5uCgAHdyxj2axP6DpgDGM+nItPUCQ/ffo0+bkZZtOXlRbj6hlIz6FjcXA2vz215cTeZWycP4HWvcfw+Ovz8fSPYt63oynMMx9b8pl9LP3pFWLaDObxcQsIbxzPoiljSE8+CUB5aTFXLhylda9nefz1efR76muyrpxl4f+eNbu+zQv/i72z121tQ8KGHzm05Wc6DhzPoBf+wNLKliU/PkV5WUmVy5xOWMbWxR/TvPsYBr80D3e/SJb8+BSF+cbtLisrJjCyA027/rPK9fjXa0X3x79g2OvL6TniS3Iyklj584s3FX/baBUt66tYtlvHtNVaysrh0S4aNNXUUA2CVHSPU7PpsI4pK7SkZis82kWDnfV1sbnDo501nLmsMG2llqkrtew5qTMZWnDuisLcrVq+XaLlzy1aXB1UDG6nqVHcR3YvY/UfE+jQbwxPvTsf74Aofps0moIq9usLp/cxf8orxLYfzNPvLSAyNp4/vhnDlUsnDWkyryQx45NH8fAJY/irP/P0+4vo0Pc5LCytK61v15oZwO1dxLhTeQ+w77SOz+eXG15rEoyNIEsL/W+TUwjTVmmZsVpLaZn+PXUNNunYnmWs+3MC7fuOYdRb8/EKiOL3yVXn/cXEfSyc+gpN2g1m1NsLiIiNZ+73Y0i7Lu+vObF/NclnD+BQTblcP++/1X5+qw5s+pEj236h/YDx9H/udyyt7Fg+7elqy3LiwWXsWPoJTePH8NDzc3H3jWT5tKcpuq4se/g3pNPgfzNk7FJ6j5qCgsKyaU+h02lrfRtuRGNvR+7BExz+vw/+8u++3sFNP3J0+y+06z+eB5/9HQsrO1ZOrz6vzxxcxs5lnxAXP4b+Y+bi5hvJiumV87rDoH8z6OWl9Bw1BRSFFdMr53X9ZgMJa9y7zsWfcekINvbudBryCQNfWkxs53+yZ9UXHN3+a43iPr5nGRvmTqDtA2MY8ab+mDtn8mgKqjjmXkrcx+Jpr9Co7WCeeHMBEU3imf+/MaQlG8uuq3cI8Q+/x8h3FvPoK7NwdvdnzuQnKcyrfM60cf6dKbt/FUW5e6+/o1ptCK1YsYL27dvj4uKCu7s7ffv2JTEx0STN8ePHadu2LTY2NsTExLBx40bDZ1lZWTz22GN4enpia2tLREQE06dPN3x+4cIFhg4diouLC25ubvTv359z584ZPh85ciQDBgxg4sSJ+Pr64u7uzpgxYygrKzOkKSkpYdy4cQQGBmJtbU14eDhTp041fH748GF69+6Ng4MD3t7eDB8+nPT0dMPnf/75J40aNcLW1hZ3d3e6detGQUFBbWYjAGsW/0y7bgNp23UAfoH1ePQf72BpbcO2dQvMpg8Jj2HQiLG0aN8LC0tLs2nOnDhAkxadadSsIx5e/jRr050GTdpw7vThWo+/oq0rZtC88xCadRyIl384/UeOx9Lahr0b55lNHxDWiN7DXqNx6wewsLS6o7HtXT+dmDZDiWk9CHffcLo9/AEWVjYc3j7XbPp9G2YSEt2BFt2ewt2nHu36voRXYAMSNv0CgLWtI4Ofn05k0z64eYfhFxpL1yHvknrhCLmZySbrOntkI+ePb6XTgHG3HL+iKBzcPJNm8c8QGhOPu18kXR/5hMLcK5w9sqbK5Q5s+okGrYYQ1WIQbt7hdBr4AZaWNhzfZdzuJh2eoGnXf+AdXPmqmiFNx5H4BMfi6OqPT0hT4rr8g9SkA2i1ZVUuU1HLSDWbj+g4eUnhSjYs3KHD0RaiAqo+I24dqWZ/osKBswrpubB0t46ycogNMy7To6mG3ScVth1TSMuFjDw4ekFBe92sOztPKFzKgJxCuJgO247pCPCgRifjO1dPJ67DUGLbDcLTL5w+j3+ApZUNCVvN7zu7186kXsMOtOn5FB6+9eg84CV8gxqwZ90vhjQbFnxBvUYdiR/8Oj5BDXDzCqJ+bDz2Tu4m60pJOsaOVdPoN/I/Nw60Gncq7wHKtFBQbHyVlhs/83ACO2sVGw/pyMiDtFzYdFiHg60KZ/sbx71rzXSatBtK47aD8PALp9ej+v334Dbzeb9n3UzCGnagVQ993nd88CV8ghqwd8MvJunyslJZ8/tH9HtyImqN+bo08fBGzh3bStdBt15uzVEUhcNbZxLX5RlCGsTj7htJ56EfU5h3hfNHqy7LhzbPIKrFECKbD8TVO5z2A8ZjYWXDiT3G+jW65VB8Q1vg6OqPh39Dmnd/kYKcy+RnXarVbaiJtJWbOPn+JFIXVr1Nd5qiKBzZNpPYLs8Q3CAeN99IOg25cV4f3jKDyBZDqN9Mn9ft+uvz+uReY15HVcjrZmbyuk2/t2nQ5jEcXQPrXPz1mw+iTb+38A1riZNbIOFxD1K/6UOcO7K6RrHvWTedxu2G0qjNIDx8w+kxTF9vHq6i7O5dP5PQBh1o2f0p3H3r0b7fS3gHNmD/dWW3QYt+hES1xcUjEA+/CLoMepPS4nzSLp0wWdeZI/qy23lg7ZZdUXfVakOooKCAsWPHsmfPHtauXYtareahhx5Cd91cf6+99hqvvPIK+/fvp02bNvTr14+MDP1VgHfffZejR4+yfPlyjh07xnfffYeHh743oKysjJ49e+Lo6MjmzZvZunUrDg4O9OrVi9LSUsP6169fT2JiIuvXr2fGjBn89NNP/PTTT4bPR4wYwW+//cZXX33FsWPH+N///oeDgwMA2dnZdO3albi4OPbs2cOKFStITU1l6NChAFy+fJlhw4bx5JNPcuzYMTZs2MDAgQNRarmZXF5WRtKZY0Q3bmV4T61WE92oFWdOHLzl9YZFNuH4oZ2kJp8H4OK5E5w+vp+Gce1uO+bqlJeXknzuCOEN2xjeU6vVhDdoQ9LphDv63TeiLS8l9cIRgiPbGt5TqdUER7bl8rn9Zpe5fC6B4Mg2Ju+FRLUn+WxCld9TUpQPKpXJsLKC3HRWz36XXiP+i4WVzS1vQ17mRQrz0giIMG6Dta0jXkGNST1vPiZteSlpl46YLKNSq/GPaFPlMjVRXJjNqf2L8QmOQ1PFSWRFLvbgaKvibIqxHJWUwaUM8PcwfzKuVuuHVV2/DMDZVIWAq8vYWUOAh4qCYoWR3TS8/JCGEfEaAqvpYLSxgphgFRfSQXeDYq0tL+Xy+SOERpvmYUh0Wy4lmt93Lp5JILSB6b4T1rA9F88kAKDodJw+uAF37xBmfTGaz8e2Ydp/hnBiv+mJTVlJEQt+fIVej72Hg7Nn9YFW407l/TUxwSpeGajhn701dG2ixuK6jraMXCgsUYgNU6NWg4UGYuupSctRyL7BtSVteSkpSUcIMZf3Z8znffKZBEKiTPM+tEF7Ll3Ne9Dn/+KfXqNl99F4+kWYXU9BbjorfnmXvqNur9yak5d1kaK8dPzDjXFa2TjiGdiY1KQDZpfRlpeSnnzEZBmVWo1/vTZcSUowu0xZaSEn987D0TUAe2efWt2GuuJaXvvVq5DXAY25coO89quQ1343yut9tZ/X91r8pSX5WNs63zDua2W30jE3qi3JZ6sou2cTCK5QdkMaVH3M1ZaXcmDL71jbOuIZEGl4vyA3nZW/vssDI/+LZS2X3b+STrl7r7+jWr1HaNCgQSZ/T5s2DU9PT44ePWpobDz//POGdN999x0rVqxg6tSpvP766yQlJREXF0fz5s0BCAkJMazr999/R6fT8eOPPxruZ5k+fTouLi5s2LCBHj16AODq6srXX3+NRqMhKiqKBx54gLVr1/L0009z8uRJ/vjjD1avXk23bvqx9GFhxjG7X3/9NXFxcfznP8YrrNOmTSMwMJCTJ0+Sn59PeXk5AwcOJDg4GIBGjczfs3M78vOy0Om0ODmbXgF2dHEn5dK5W15vz4eepLiogPEvDkCl1qDotPQf9jytOj5wmxFXrzAvG51Oi0OFK9oOzu6kXT57R7/7RooKslB0WuwqxGbn6E5m6hmzyxTkpmPn6FEpfWFeutn05WUlbF40kahmD2Btqy8HiqKw8pc3aNzuEXyCGpGTcfGWt6EwLw0AW8cK2+DgUWVMxVe329ah8jLZV27+N9m+dCKHt/5KeVkR3kFN6PPk9zVe1sFW/29Bsen7BcUKDlUcq+ysQa1WkV+sVFgGPBz19YOrPqvp2EjNmv06UrMVGoWoebyrhv8t05rcAxPfRE3z+iqsLFRcTFeYvfHGw4UK8/V5WLGnxsHJnYwU8/tOfk469hX2HXsndwpy9L9TQV4GpSWFbFs+hc4DXqLroFdJPLKZOd89z/BXZhIc2RKAVX9MIKBeHJGxt3ZPkCHWO5T3AIfP68gpgPwiBS8XFfGxatwd1czZor8wVloOM9dqGdpBQ4eG+hZSZj7MWq+94RCMqvLe3rGavM9Nx97Jo1L6glxjGdmxagpqtQXNu5q/10FRFJbOeIPYjo/gG9yI7PRbL7fmFF0trxXLpa2DB0VXy3lFxYXZZsuyraM72WmmZfno9lnsXPEZ5aWFOHuG0mf0VDQWd7bH/V5VbV7n32ReO7iTUzGvd8xi97W89gil15O1m9f3Uvyp5/dz5uByejxx43q/KP/WjrmV6s0KZRcg8dB6Fk8bS1lpEQ5Ongx5YRp2Dm6Avuwun/kGsR0ewSf49o654u+lVhtCp06d4r333mPnzp2kp6cbeoKSkpJo0KABAG3aGFv1FhYWNG/enGPHjgHw7LPPMmjQIPbt20ePHj0YMGAAbdvqrxocOHCA06dP4+joaPKdxcXFJsPvGjZsiEZjvOzo6+vLoUOHAEhISECj0dCpk/mbYw8cOMD69esNjbbrJSYm0qNHD+Lj42nUqBE9e/akR48eDB48GFdXV7PrKykpoaTEdKxuaakOK6vKY/3/Cnu3rWLX5mU8+eIE/ALrceHcCeZM/xRnN/2kCaL2abVlLJn2IigK8UON4+H3b/yZ0pICWvao+t6bqpzct5iNc983/P3ATTQ67pTYzqOJbjmIvKxk9qz+hrWz36DPk9+bnYQjJljFAy2MndG/1aDRcSuuffW+0/ohXAApWTpCfTTE1lOz7oCxp3rbMR37z4CzvYqOMWr6t1Yze9Nf/9Q6RdF/Z/3YeFp1HwmAT1A0FxP3sXfjbIIjW3IyYS3nju/g6Xdv/ubqvyrvAfYnGlszV3IU8ot1DO+qwdVBR1a+vgeoX0sNF9IV5m3ToVZBmyg1j3TSMHWVcTKLv0rK+cPsWTeTkW/Nq3LymL3rf6a0uIA2vW6+3Jpzev9iNi8Yb/i71xPf1cp6qxIe1w//iLYU5qVxcPN01s56mX7PzDJ7/9nfzemExWy9Lq97jLjDeR3bD/9wfV4f3jyddb+9TN9/3npe36vxZ6acZM3PY4jr+hwBEXd2dMmNBNZvxRNvLqCoIIuDW/5g8dSXeOz1Odg7urNvg/6Y26pn7ZTdu6m2RyHdnJu/J/Wbb77h008/JSUlhSZNmjB58mRatmxZZfo5c+bw7rvvcu7cOSIiIvjkk0/o06fP7QRdrVptCPXr14/g4GCmTJmCn58fOp2OmJgYk6Fr1enduzfnz59n2bJlrF69mvj4eMaMGcPEiRPJz8+nWbNm/Ppr5ZvxPD2NQ0MsK9wfo1KpDA0yW1vbar8/Pz+ffv368cknn1T6zNfXF41Gw+rVq9m2bRurVq1i8uTJvP322+zcuZPQ0NBKy0yYMIEPPjC9GXTEM28x8rl3qo3DwdEVtVpDbo7pjYN52RlVToRQE/N+/oKeA0bRon0vAPyDI8hMu8yKedPuaEPIztEFtVpTaWKE/JyMOz4Rwo3Y2ruiUmsorBBbYV5GpavH19g7Ve5pKczLqNRLpG8EvURuZjJD/m+GoTcI4MLJHVw+m8CXL5v2KP766SCim/ej1/DK++A1IQ26mMzspi3Xl6+ivAzsnYw3gBbmp+PhZ342HJur2339DbLXlqm4HTVha++Krb0rLp6huHrV4+d/dyb1fAI+IXGV0p68pHApw3iWa3H1vNzeBvKv65mwt1GRkmW+wi8sAZ1OwcFGBSjXLYOhp+LarG/puabrSM9RcLIzXV9Rqf6VmaeQnqPlpQEW+LvruGT+3l0A7Bz0eVjx5vz83Awcqth3HJw9KKiw7xTkZmB/tRzYObii1ljg4VvPJI2HTz0unN4LwLnjO8hKS+LTF1uYpPnzuxcIjGjOiNd+rjLmvyrvzbmUrv/M1UFFVr5CTLAKZweYttrY4Jy3XcdrgzRE+qs4klT1uqrK+4Jqyq2Dk0elK8jXp79weg8FeRl8+1YXw+eKTsu6Pz9h99qZPPefdZw/sYNLZxL49HnTcvvThEE0bNmPviOrLrfmBDXoysDrZuvSaq+W5fwM7K4ry0X56bj7VlGW7VzMluUiM3WSlY0jVjaOOHuE4BXYhJkftubckTWEx97ZUQH3gqDoriaz6BnqTTN57XazeZ2fge0N8vqXj1pz/uga6jW5tby+F+PPSj3N8qlPEtlyKHFdzU8GVJGtw60dcyvVm2bSW1nbYeUVjCvB+IXGMuX9Hhza+iete/2TpBM7SD6TwOf/Z1p2f/5kEA1a9KPPEzdXdkXN/P7774wdO5bvv/+eVq1aMWnSJHr27MmJEyfw8qo8YcW2bdsYNmwYEyZMoG/fvsyaNYsBAwawb98+YmJi7kiMtXaPUEZGBidOnOCdd94hPj6e6OhosrKyKqXbsWOH4f/l5eXs3buX6GhjofX09OSJJ57gl19+YdKkSfzwww8ANG3alFOnTuHl5UV4eLjJy9n5xuNSQT+MTafTmUzQcL2mTZty5MgRQkJCKn2Hvb3+7l2VSkW7du344IMP2L9/P1ZWVsyfb/7K7JtvvklOTo7J69GnXrthnBaWlgSFRXP80C7DezqdjuOHdhEWWfX0kjdSWlKMSm36k6vVasNV6DvFwsIKv5CGJB4x/vY6nY7EozsICo+9o999IxoLK7wDG5J0crvhPUWnI+nkdnzNnMQD+IbEknRyh8l7509swy801vD3tUZQdtp5Bj//E7b2pr2GXQa/w/A3FjJ83AKGj1vAQ8/o9/MHRn1Bu74vVxuzlY0Dzh7Bhperdzh2jp5cPG3chtLifK4kHcQ7ONbsOjQWVnj6NzRZRtHpuHR6R5XL1NS1/enaSV1FpeWQlW98peVCXpFCqI/xSpOVhX7Gt2snzxXpdHA5E0J8TK9OhXrrh7YBZBdAbqGCu6NpGncnFTnV3INyrTPAQlP9lS+NhRW+wQ05e8w0D88d245/PfP7TkBYLOeOme47Z49tIyAs1rBOv5BGZKSaDlPJTD2Hs7s/AG17/4N/vL+Ip99bYHgBdH/4zRtOnPBX5b053leLwLXGkqWm8ixEigIoxt+gKhoLK3yCGnLuuGnenz++Hf8w83nvFxbLueOmeX/u2Db8r+Z9TKv+jH5nEU++vcDwcnD2olWP0Tz8fz8C0O3hd3jynYWGz4c+ry+3/Z/6go79qy+35lhZ25uWZa9wbB09uJRojLO0OJ+0CwfxDjI/YYnGwgoPv4Ymyyg6HcmJO/AKiq32+xUUdFWU078bK2t7nNyDDS+Xq3mdXDGvLx7E6wZ5ffn0reX1tcbL3yH+rNRTLPtxJBFN+9O8x0s13o5rZff8iQpl98R2/EKrKLuhsSRVKLvnj5kec83GrOgMMccPfYcn3l7IE28t4Im3FjDoOX3Z7Tf6Czo8ePNl935VUlJCbm6uyaviyKfrff755zz99NOMGjWKBg0a8P3332NnZ8e0adPMpv/yyy/p1asXr732GtHR0Xz00Uc0bdqUr7/++k5tUu31CLm6uuLu7s4PP/yAr68vSUlJvPHGG5XSffPNN0RERBAdHc0XX3xBVlYWTz75JADvvfcezZo1o2HDhpSUlLBkyRJDI+mxxx7j008/pX///nz44YcEBARw/vx55s2bx+uvv05AQMANYwwJCeGJJ57gySef5KuvvqJJkyacP3+eK1euMHToUMaMGcOUKVMYNmwYr7/+Om5ubpw+fZrZs2fz448/GiaB6NGjB15eXuzcuZO0tDSThtz1rK2tsbY27Ua2sqrZw0m69RvOT1+/S3C9BoSEx7Bu6a+UlhTRtkt/AKZ/9Q4u7l489Nj/AfoJFi5f1A8R1JaXk515hQtnj2NtY4eXbxAAjZp3ZPncH3Hz8ME3sB4Xzp5gzZJfDOu8k9r1eoK5U97EPzSGgLBGbFs1k9KSIpp1fAiAOf8bh5OrNz2HjtVvT3kpVy5d254ycrOukHz+GNY2drh7B9dqbM26jGLFL+PwDorBJ7gx+zbMoKykiIatBwKwfObrOLh40+HBVwBo2nkEf3w5nD1rpxHWsBPH9y0jNekw3R/5UB+vtowlU/+P1AtHeeif/0NRtBTk6sds29g5o7GwwsnN9Hkwltb6LgoXjyAcXW/uhlqVSkXjDiPYu/Z7nD1CcHLzZ9fKr7Bz8iK0ofEekkX/G0loTDcatdM/X6pJx5Gs+/0NPANi8A5szMHNMygrLSKqxUDDMoW5aRTmpZOTngRAxuWTWFnb4+Dqi42dC6lJB7hy4RC+Ic2wtnMiJ+MCu1d8iZN7ED7B5g9q5uw6oaN9QzWZeTqy8xU6N1aTVwTHLxrPlB/voub4RYU9p/Tv7Tiho39rNZczVSRnKLSMVGNpgWEYHMD24zo6xahJzdb3cDQJVePuCH+e0TfW/NzBz03FhTSF4lJwdVTRuZGazDyl2pP6a1p1H8WiaePwDYnBP7QxO9fo87BJO30eLpz6Oo6u3nQdqN93WsSP4OeJw9mxahrhjTpxZPcyks8dps/wDw3rbN1jNPN+eJmgiBaERLUi8fBmTh5cz/BXZwLg4OxpdoIEZzc/XD1vfgaqO5H3rg76YXinkhWKSsHbRT/d9vkr+pnpAM6kKHSLg97N1ew+qUMFtG2gRqfAudQb533LbqNY8tM4fINj8A1pzJ51MygtLaJxW33eL57+Oo4u3nR+SJ/3zbuOYNZnw9m5Wp/3R3cv4/L5w/R6TJ/3tg6u2DqYXrBQayyxd/LA3SfMkMfXu1ZuXT2DcLrJcmuOSqUipt0I9q/7Hmf3YBzdAtiz+ivsHL0IbmAsy0t/HEVIg240bPsYAI06PMHGOW/i6R+DZ2AjDm+dSVlpEfWb6evX3MwLnDm4HP+Idtjau1KQk0rCxilYWFgTGNnxtuO+WRp7O+zDgwx/24UG4NQkitLMHIovXP5LYlCpVDRsO4KE9d/j5BGMo2sAe83k9bIfRxHSsBsN2ujzOqb9E2z68008AmLwDNDndXlpEfWbGvP67NW8trma1wfN5HVuxnnKSgopyk9HW1ZMRrL+FgEXr3o1upfobsafmXKS5VNH4R/Rjpj2Iw33qapUGmyv3pNTneZdR7Fs5jh8gmPwDW7MnvX6Y25MG33ZXfqTvux2HKAvu826jGD2F8PZvWYaYTGdOL5nGSlJh+lxteyWlhSyY8X3hDfuir2TJ0UFWezf+Cv52alENtWPgKl4zLW6jWPu3XY3R8aZG+n0/vvvM378+EppS0tL2bt3L2+++abhPbVaTbdu3di+fXul9ADbt29n7NixJu/17NmTBQsW3HbsVam1hpBarWb27Nn83//9HzExMURGRvLVV1/RuXNnk3Qff/wxH3/8MQkJCYSHh7No0SLDzHBWVla8+eabnDt3DltbWzp06MDs2bMBsLOzY9OmTYwbN46BAweSl5eHv78/8fHxODk5VQynSt999x1vvfUWzz33HBkZGQQFBfHWW28B4Ofnx9atWxk3bhw9evSgpKSE4OBgevXqhVqtxsnJiU2bNjFp0iRyc3MJDg7ms88+o3fvW38OQFWat+tJXm4Wi2d/R252OgEhkbzw9rc4uehvMMxMv4zquvl9s7Ou8O/XHjH8vXrRTFYvmklEg2a88qF+evBHRr/Botnf8NuUCeTlZuLs6kmH7oN4YPCdHzPbuHUfCvKyWDvvK/Jy0vENimbkaz8YhsblZFxGpTL2VuVlpfHNu8YT8i3Lp7Fl+TRCo1rw1FszazW2yGZ9KMzPZNvSryjMS8PTP5qBz/1o6HbPyzKNzS+sKX1GTmTrkklsXfI5Lp4hPPj0N3j41QcgPzuVxEPrAPj5E9NG5pD/m0lgRCtqW2znpygrLWLjn+9RWpyLT0gz+j41xWQ8d25GEsUFxl7a8Ng+FBVksnvlZArz0vDwi6bvU1NMhtMc2TGbPau/Mfy98Dt9I6rL0P8Q1WIgFpY2nDm0mt2rJlNeWoSdoyeBkR3o3u3Zm7oxeNsxBUsLhQdaqLGxgqQ0hVkbtCbTXLs6qK4+p0Z/FDiapGBnraNTIzUONvqHes7aoDW58X/XCQULtY7ucWpsrfVpfl2vJevqRAnl5RAVqKJTIzVWFpBXBImXFbYc0Zl8d1UatuhDYV4mGxd+RUFuGt6B0Qx78UfD0LicTNN9JzC8KQOemsiGBZNYP/9z3LxCGDrmG7z86xvSRDXtTp/Hx7N1+Q+smv0v3L1DGfzsVwRFNK9xft6MO5H3Wh2E+qhpGanvYcop1DesNh82rjQjD2Zv0tExRs2o7pqrD7vVf/f1w/SqEt1cn/ebF3919aGM0Tz8grHc5lbI+4B6TXlw9EQ2LZrEpoWf4+oVwqBnvsHzury/FzTp+BTlpUVsnv8+pcW5eAc3pdeoHyqX5UJjWa7XuA/F+VnsXfPV1YcjR9N71A+GsqyxsCbl7B4Ob51JSVEutg7u+IQ058Fnf6t04/xfwblZDG3WGodwNpioPwZfmDmPg6PfrGqxWtf4al5vvS6ve1bI67xM03ozrHEfigv0eV10Na97jvrBMLRMY2FNyjl9XpcWG/O67zOmeb153ruknN1t+HvB1/rj3dDX1uDo6n9Px3/u8CqKCzJJTFhMYsJiw7odXPx4+PUbPxg3qrn+mLt1ibHsDn6+wjH3utEr/vWa0vfJiWxeNInNiz7H1TOEh/75DZ5Xj7lqtYbMlDMs3DGfooIsbOxd8A1uxLCxv+JRxeyP4ta8+eablRoqFS/4X5Oeno5Wq8Xb2/Th6t7e3hw/ftzsMikpKWbTp6Sk3EbU1VMpd/euq/vO+kM1fFz9PSijsG7fUJuRU7efH1xUzf0X97q8gr/+oY21KcS/Vm+n/Mudu1R+40T3qACfup33GVl//aQbtSn6YfMjHuqKI7OP3e0Q7ltuLnX7mPtU/N2OwLwvF9+9c4EX+9V8soTk5GT8/f3Ztm2byURpr7/+Ohs3bmTnzp2VlrGysmLGjBkMGzbM8N63337LBx98QGpq6u0FX4W6vZcKIYQQQggh7ikeHh5oNJpKDZjU1FR8fMwPR/Tx8bmp9LVBGkJCCCGEEEKIWmNlZUWzZs1Yu9Y4XFKn07F27VqTHqLrtWnTxiQ9wOrVq6tMXxvq9pgDIYQQQggh7hN16YaWsWPH8sQTT9C8eXNatmzJpEmTKCgoYNSoUQCMGDECf39/JkyYAMCLL75Ip06d+Oyzz3jggQeYPXs2e/bsMcwgfSdIQ0gIIYQQQghRqx5++GHS0tJ47733SElJITY2lhUrVhgmREhKSkJ93cQYbdu2ZdasWbzzzju89dZbREREsGDBgjv2DCGQhpAQQgghhBB1gq4O9QgBPP/88zz//PNmP9uwYUOl94YMGcKQIUPucFRGco+QEEIIIYQQ4r4jPUJCCCGEEELUAXXpHqG6QHqEhBBCCCGEEPcdaQgJIYQQQggh7jsyNE4IIYQQQog6QLmrsyWo7uJ33xnSIySEEEIIIYS470iPkBBCCCGEEHVAXZs++14nPUJCCCGEEEKI+440hIQQQgghhBD3HRkaJ4QQQgghRB0gzxGqXdIjJIQQQgghhLjvSI+QEEIIIYQQdYBOZkuoVdIjJIQQQgghhLjvSI/QX2zzQcu7HcIt69a04G6HcFu8HOp2u3/lzrpbXLs0v9sR3J4P3t1+t0O4LY8+2+Fuh3DL9u7Pudsh3BZPL7u7HcJtOTL72N0O4bY0fCT6bodwy8IHhtztEG6LvafT3Q7h9sTPudsRmCX3CNWuun1mKIQQQgghhBC3QBpCQgghhBBCiPtO3R1rI4QQQgghxH1EhsbVLukREkIIIYQQQtx3pEdICCGEEEKIOkAnXUK1SnqEhBBCCCGEEPcdaQgJIYQQQggh7jsyNE4IIYQQQog6QNHd7Qj+XqRHSAghhBBCCHHfkR4hIYQQQggh6gBFJkuoVdIjJIQQQgghhLjvSI+QEEIIIYQQdYBO7hGqVdIjJIQQQgghhLjvSENICCGEEEIIcd+RoXFCCCGEEELUATJZQu2SHiEhhBBCCCHEfUd6hIQQQgghhKgDdNIhVKukR0gIIYQQQghx35EeISAlJYUJEyawdOlSLl68iLOzM+Hh4Tz++OM88cQT2NnZ8cMPPzBr1iz27dtHXl4eWVlZuLi41HosnRuriQtXYWMJF9IUlu3WkZlX/TLN66toG63GwRZSs2D5Hi3JGaZpAjygSxM1/h4qFAVSsuDXdVrKtabpNGoY3VODj5uK/y0rJzXr1rdl7bI/WD7/Z3KyMwgKieCxp18jrH6M2bQbV81n6/qlXEpKBCCkXjSDHn/OJP2e7evYsGIu584cpyAvhw8+/5WgsMhbD7Aa65f/zsoFM8nJziAwpD7Dnnqd0AjzsV9KSmTR7O84n3iMjLTLPDzqFbr1e8wkTXFRAQtmfcv+nevJy80iKDSSh598jdCIhrUWc3ychhaRGmys4PwVhUXbysnIrf7SUatoNR1iLHCwhZQshSXby7mYblzGzRF6t7Qg2EuNRgOnLulYvL2cgmLjOh7vZoGvmxp7GyguhdPJOlbuLiev6Na3ZeOK2axZ9BO52en4B9dn6JNvEhLRyGza5AunWfr7NySdOUZmWjKDRr5G1weGV0qXnZHKgl8ncXT/FkpLivH0CeTxMR8RXK/2foPrjX40mH7dfXCw13DoeC6ffXeai5eLq0w/oJcvA3r74uNlDcDZpEJ++j2JnfuMhfCrfzUirpGLyXILVlzms+9O33KcBzb/yp51UynMTcPDP4oug97FJ7hxlelP7l/O9mVfkpt5CRfPENr3e5XQhp0Mn58+sIqDW2dz5cIRiguzefS1BXgFRJusY83v73HhxDbyc69gZWWHb2gc7R98FTfvere8Hdd7oK01bWOssLVRceaSlt/XFpGWXfWcs/X8NXRrbk2QtwZnBzU/LCzgYGK5SZrHe9rSuqGVyXtHz5Xx7bzC24q1c2M1TSPUhjp/6S7tDev8FvXVtG2gNpTb5bt1JGfoy62zPbz0kKXZ5eZsKudokoKtFQxsr8HbRYWtNRQUw4mLOtYm6Cgtq3nsiqKwb81kTuyZQ2lRHt7BcbTt/z7OHiHVLnd0+68c2jyNovx03HyiaNPvbTwDjfvclvnvk5y4ncLcK1ha2eEVHEeLnq/g4hVmSLN98b9JPb+PrNRTuHjV46EX5tc88Nvg1r45Ya+MxrlpDDZ+XuwZ9Bypi9b+Jd99Iy49+uHWbzAaFzdKzp/hyvRvKU48UWV61z4P4dL9ASw8vNDm5pK3czPpv01DKdPvBO6DH8djiGldWnLpAufGPnVHt+Ma+449cYx/EI2TC2WXzpM1Zxpl56uu6xw698G+Q08sXD3QFuRStH8HOYtmQflN7NTivnPf9widOXOGuLg4Vq1axX/+8x/279/P9u3bef3111myZAlr1qwBoLCwkF69evHWW2/dsVjaNlDRMlLF0l06pq7UUlYOj3XRoKnmV2oQrKJHUzUbD+n4YZmWlCyFx7posLM2pgnwgEe7aDhzWWHqCi0/Ltey+4QOc/fbdYtTk1d0+/2uO7esYva0L+j/yNOM//wXAkPq89kHL5CbnWk2/fHDe2ndoSfjPvqedz6ZjpuHNxPHP09WxhVDmtLiIiIaxDJkxAu3HV91dm9ZyR/TP6ff0H/w7sRZBIREMOnDMVXGXlpSjIe3PwOH/x/OLh5m08z45kOOHtzJ6Bc/YvwXv9OgSWu++OBZk+27HR0aaWjTQMPCbeV8t7iMsjKFkT0tsdBUvUyjUDV9WlqwLqGcbxaVkZKpX8beRv+5pQWM7GmFosDUFWX8sLQMjRpGdLdEdd16zlxWmL2+jElzS5m1rgw3RxXDupo/EauJvVtXMG/Gp/QZ8gxvfPI7AcGRfP3vZ8jLyTCbvqykGHevAPo/9iJOVeR/YX4un737BBqNBc+99S3vfDGfgU+8ip290y3HWZ1HBwYw6AE/Jn53in++lkBRsY7PxsdgZamqcpkrGSV8P/MsT43dz9OvJLDvUDYT3mpASKCdSbpFKy/T/4kdhtd3P5295ThP7FvGpvkTaN1zDI++Nh9PvyjmfzeawjzzeZ18dh/LZ75Cw9aDeey1BdRrFM/iqWNITz5pSFNWWohfWFPaP/hqld/rHdiQ7o9OYMSby3jo2amAwvxvR6PTaatcpqa6tbCiU6w1s9cWMXFWPqVlCmMG2ldbFqwtVVxK0/L7uupb70fOlvHm97mG1/Slt9cIatdATasoNUt3avlxRTml5fB4V4tq6/yGwSp6NFOz8aDWcLHq8a7GOj+3ECb+WWbyWn9AS0mZwqlkfd2uACcuKPy2QcvXi8pZuF1LmI+avi2rySQzDm76kaPbf6Fd//E8+OzvWFjZsXL605SXlVS5zJmDy9i57BPi4sfQf8xc3HwjWTH9aYryjfuch3/D/2fvvuObqP8Hjr8ymu69W7oLhZYNsvdWQEARFQRkiAMnoIgLBQX150DFxdcBKCgyRBmyQfYeZRa6W9pC915J7vdHICU0LaUU0tjP8/HIQ3P53PHO9ZO7e99nHN0f/oCHX9nAwAn/A0li08+TK9WPJu0eIrjl/bcV851S2NqQFxnFmRffu6f/7q3Yd+6J+7gpZKxeRsLrUylNiKXRGx+gcHA0Xr5rb9wen0jGqmXETXuKtO8/w6FzT9wem2BQrjQpnugpj+lfSbOn3Yuvg3XbLjiNGE/ePyu58tFMyi4n4D71TeR2xo/Z1u274ThsDHn/rCTt/ZfJXvYtNu264Pjg6HsS770kaSWTvf6LGnwi9Nxzz6FUKjl69CijRo2iWbNmBAcHM2zYMDZs2MDQoUMBePnll3n99dfp1KnTXYulY1M5e85ouZgscTUH1h7QYm8DTf2qvnjq3FTO8WiJU7ESGXmw4bCWcg20CalYZ0A7BYejJPadk0jPhcx8OJcoobnpBmmoj4xgbxlbj9/507q2/LWMHgOG073vg/j6BTPu2VmoLK3Ys/1vo+WfnvY+fR54BP/gMLwbBTJh6ltIksS5yMP6Ml16D2bYo08R0bLDHcdXna3rltG9/wi69h2Gj18wTzz9JipLK/bt+Mto+aDGETwy/hU6dBuI0qJyAlBWWsLxgzsYOfYlmkS0w8PbnwcfewZ3r0bs2ryyTmLuGqFg1ykN5xO1XMmWWLlbjb01NPOv+ifetbmCo1Fajl/Skp4j8dc+NeVqaNdEdzEU4CHD2Q5W71FzJVviSrbEqt1qfNxkBPtU1K/9ZzUkpUvkFELiVYndkRr8PGTIq6621dq+fild+j5M597D8fYL4bEpb6NSWXNgx1qj5QNCm/PQuOm073o/SguV0TJb1v6Es6snY6fOJbBxC9w8G9GsVRfcvfxqF+QtjBrqy9KView9nEVMQhEfLIjC1cWS7p2MJ2oA+49kcfBYNsmpJSSlFPO/XxMoLtEQEWZvUK6kVEtWTrn+VVRc++Th+K6fad5lFBGdHsbVK5S+o95DqbLi7MHVRsuf+HcpgU27077vZFy8Qugy+GU8GoVzas+v+jLN7htOp0HP49ekc5X/bosuj9Io9D4cXRvh4RdB5wdeJj8nlbysy7X+Ltf1bmPJ5kMlnI5Rk5KhZemmIhztZLQKrTo5PxevZv3+UiKj1VWWAVBrIL9I0r+Kq77er5GOzeTsPq0l6voxf7/mlsf8Ts3kHI/WcjJWIiMX1h/S6I75obrfuiTpWnhufDX1k3MuQaL82tcrKYOjl7SkZknkFkJcmsSRi1r8PWr+o5UkibP7l9K69zMEhPfFxTuMno98SFH+VRLObatyvTN7lxB23yM0afcQzp6hdB32LkqVFRePrdGXadphFN5B92Hv7IubbwTt+r9EYW4qBdkV9aPz0DcJ7zwGe+e78xuuSvrm3VycvYArf1X9HU3BefBD5G7fRN6uLZRdTuTKD1+iLSvFsfdAo+Wtm4RTHHWW/H07UadfoSjyOHn7d2EVatjLQtJo0ORmV7zy8+7F18G+zxAK92+n6OAu1GnJ5Py+CKmsDNvOfYyWtwwKozQ2iuKje9FkpVN6IZKio/tQBYTek3gF89WgE6HMzEy2bNnC1KlTsbW1NVpGJqvl1dxtcrIDe2sZsWkVGXdpOVzOgEZuxmOQy8HbRXcSu1FcmqRfx8ZSt35hicSEAQqmPaRgfD8Ffu6G27K1giEd5azdrzup3gl1eTnxMReIaNnxhljlhLfqQHRUZI22UVpWgkajxtbO+N2su0VdXk5CzHma3RR7s5Ydialh7DfTajVotRosVIYX6SqVFdHnT95JuAA424O9jYyYlIoEtrQcktOlKi9sFHLwcZURfcM6Erpubf7uunWUChkSGHSfVGt0F1oBnsYPHdYqaBUiJ/GqVKsBnerycpJiz9O0ZcUNB7lcTtOWHYm9eOr2N3jN6aO78A+J4IdPpzNzUk/mvzqKfdtW1Xp71fH2tMLVRcXRUzn6ZYVFGs5fzK+U1FRFLoe+3d2xslJwNsqwn9SAnh6s+6UTS75sy9NjA7FU1e4wrlGXcTXpLH5NuuiXyeRy/Jt0ITX+hNF10uJO4hdmmOAENO1GavzJWsUAUF5axLlDa3BwbYS9k1ettwPg6ijD0U7OhcSKhKakDOLTNAR6315rhzGNGymZ/4w9bz9px6N9rbC1qv35oeKYf9PvNkPCz73qY76Pi4zYVMMfV2yqVOV5wtsFvF1kHI+u+gaXnTU085eRcLXmP9r87GSK8zPwCamoDyore9wbteRqovHfqkZdRkbKWXxCK9aRyeX4hHTmauJJo+uUlxVx8fga7J0bYet4Z/XjP0uhxCq4MUWnj1cskySKTp/AqnG40VWKL57DKrgxViG6xMfCwwvbNvdReOKIQTmVly8h3y4n6MvFeL8wE6Wru7HN1S2FEgu/YEpuPOdKEiVRkaiCmhhdpTQuCpVfMBbXEh+FqwdWEW0oOXvcaHlzJkmme/0XNegxQtHR0UiSRFiY4R0QNzc3Skp0ffmnTp3KRx99VKvtl5aWUlpqeMtQXa5AaWFZqazdte5IhTf1zCgokbCzNr59G0uQy3VJzo0KS8DNQXdSdLbTLevZUs7W47rWgpZBcsb2VfDdhoq+6MM6yzl2SUtqlq6P+Z3Iz89Bq9Xg4ORisNzR0YW05PgabWPlkq9wcnYjotXdbf25WUEVsTs4uZB2Ob5W27SytiUkrCXrV/6Ad6NgHBxdOLx3EzEXI/GogxYJe2vd37rgpi6Nurpj/OLIxhIUclnldYol3J10F9aJ6VrK1TDwPgVbj2pABgPbK1HIZfp/87qB7RV0aqZAZSEj8aqWpVtr1ye7ID8brVaDvaOr4Xd0dCXtcu27gGVcTWbPlj/oM2QsAx+aTEL0WVb+9BEKpQWdeg2r9XaNcXXWtTxk55QZLM/KKcPF2XiL1XXBATZ8+1FrVCo5xcUa3px/jvikiu5XW3encyU9iYysMkICbXlmXBB+vta89eH5246zuDAbSavBxt5wX9vYu5J1NdboOoX5GdjYu1UqX5SXcdv//qk9y9j79yeUlxXh7BHEQ8/9jEJZ/f65FQcbXd3NLzKs1/mFWhxs7+ym1vl4NaculZOZp8XNUc7QblY8+5CCT38rrNUFgt21JKrwpmFjhSVUmWBVHPNvXkfCzdH4Om1C5KTnSAZj/657qJuCpo1kWChlRCVr+ftAze+CFefr/ubWdob1x9rOjeKCdKPrlBTlIGk1RtZxJTfd8Pd97uByjmz6FHVZEY5uQQya+OMd14//KoWDAzKFAnVujsFyTW42Kh/j55j8fTtR2DvgP+dTQIZMqSRny3qy1v6uL1MSfYHUbz+hPCUZhbMLbg8/gf97nxI342mkkjsYBHoLcjt7ZAoF2vxcg+XavFwsPH2NrlN8dC8KW3s8XpkLMpAplBTs2UL+lnszdkwwXw06EarK4cOH0Wq1jBkzplIiczvmz5/Pe+8Z9iPuNeJtej/8Ds0DZQzpUHEn97ddd9433pjrDVrHL+m6zwGkZWsJ8lLQOkTOjpNaOoTJUCll7D17513i6sKG1Ys5vHcLM9//HgtV5aTRHE18aS5LFr7Hq5MHIpcr8A9uSoduA0mIuf0L2FbBcoZ1rfjp1jbpuJWiEvhtRzkPdrGgc7gCSYLIWC2XMyqPL9tzWsPRi1qc7KBPGyWP9FCydGv13YzuJUmrxT8kgmGjXwLAL6gZKUnR7N2y8o4Tof493ZnxbGP9+5lzz9Z6W4mXi5n48nFsbZX07uLGmy+F8cKbkfpkaN2WNH3Z2IQiMrPK+OL9lvh4WZGSVvVEDPVR0/YP4h/WlcK8dI7v/JGNP7/MqJd/M3qjqCrtm1rweL+KO0Xfri28G6ECcCyq4neWkqHlckYh701yoHEjBReTbn38bhEoY0jHilap5TvvzjH/RkqFbizg7tPGj+2bj2r4NxJcHWT0ba1gYDs5G48YLxt9ch371r6rfz9g3Ld3I2S90NZD8Q3tQlF+Omf2/MyO315hyNPLb6t+CFWzDm+J64jHuPLjQoovXUDl5YPHk8/imj2azDXLASg8ebRihcQ4ki9dIPjrX3Do3IPcnZtNFLlxlo3DsR/4ENkr/kdZQjRKNy+cRk7AftDD5G8y3tXXXGn/o2N1TKVBJ0KhoaHIZDKiogxnVQkO1s1MY21dRVNMDc2aNYtp0wwHFn6yRncivJgs8X1GxYnw+kBeW2souOF6xs5KRlq28UpfVKr7QejuHlaUsbWqaB0ouHbTJj3XcBsZeRKO18ZgB3rKaOQGbz5m2HXkqUEKTsdL/HXg9hIke3sn5HJFpckFcnOzcHB2rWItnX/W/sKG1Yt5dc43+AU2rrbs3WBXRex5OVk4OFUfe3U8vPx49f0fKC0pprioACcXd77/ZCbuno1ue1vnE7UkpVe0NigVumzXzlpmMNGFnZWM1Czjf7uiUtBor7cY3bCOtYyCG+6mR6dIfLaqDBtL3bMLSsrg9cdUZOVrK22vqFQiMw/Sc8qZ+Zglfu66sUO3w87eGblcUWlihPzczConQqgJB2d3vBsFGyzz8g3i5ME77+e/93AW56Iqul9YWOhucDg7qcjMrrh4dnFScSmuoNptqdUSl68lNBdjCmja2I6RQ3z4pIpZ4c5d1DXpNvK+/UTI2tYZmVxRaWKEovxMbO2N72tbezeK8jMqlbdxuP2/jaW1PZbW9jh7BOId2IpvZ3UgOnIrTdsNqfE2TseUE59W+ThqbyMjr7Ci7tnbykm+WreJR2auRH6RFnenmiVCUckSyRkVNwf0x3yriuP09fdXbnnMN1xuayUz2MZ14f4yLBRwKtb4ceD6GKLMPIniUg0TByrZfUZrdFv+zfrgccPMbhq17hhUXJCJjYOHfnlxQQYu3s0qrQ9gZeOETK4wmBjh+jasb6pzKit7VFb2OLoF4uHXil/ndiLh3DZCWg02uu2GTJOXh6TRoHR0MliucHRGnWN86le3UePJ272d3B2bAChLikduaYXnlJfI/PM3o/2gtEWFlKUmY+HlU+ffweDfKchH0miQ2xt2jZc7OKLJyzG6jsPgxyg6vJuiAzsAUKckkmdpidPjT5O/ec1/t1+XcMca9BghV1dX+vfvz8KFCyksrPs7iZaWljg4OBi8rt/NKlNDdkHFKz0X8oslgjwrujeolODrhtEuDQBaLaRmQZCXYZeIIC+Zfp2cQsgrknB1MCzjYi8j99pX3nRUy/cbNfrX8p26k+aqvVp2nLz9ViKlhQWBIU0NJjrQarWcjzxCaFjV0/JuXLOEdX/8wPTZXxEUarxf892mtLAgIKQZ5yvFfpiQamKvKUsra5xc3CksyOPsyQO07tDz1ivdpEwNWfkVr6s5uoHbwT4VP2dLC2jkLiOxij7/Gi2kZEqE3LCODAjxkZNoJHkpKtUlQcHeMmyt4UJi1fXieitkdbN0VUVpYYFfcDOiTh/SL9NqtUSdPkRwk1a3v8FrQsJacyUl3mDZ1dQEXNy9a73N64qLNVxOK9G/4pN0LTXtWjrpy9hYK2jWxL7SeJ9bkclkqCyqPkw3DtL1fc3MKquyTFUUShUefhEkXTygXyZptSRdPIB3YBuj63gFtSbp4kGDZYlR+/EObH3b//6NJABJ0l9c11RpOWTkaPWvtEwtuQVawvwr7vFZqSDQS0F8at0mQk52MmytZeQV1uwYWdUxP9ir4u+rstCN6azqBoJWCylZEsE3HfODbzjm36hNqJyoZImiGnRsuP67rWrGOpWlLQ6uAfqXk0co1vZupMRU1IeykgLSkyPx8Df+W1UoVbj5RJAaXbGOpNWSEnMQD//W1cYncfv1o8HQqCmJvYRNixt+tzIZNs1bU3LpnNFV5JaWSDclB5L2el023s1SZmmFytMHdbbxGVTrjEZNeVIsVmE3PDJBJsOySQvK4i4aXUWmsgTJ8Ld4q+8jCNDAW4QAvvnmG7p27Ur79u159913admyJXK5nCNHjnDhwgXatWsH6J41lJaWRnS07s7s6dOnsbe3x9/fHxcXl+r+iRo7dEFL9+ZysvK15BRK9GopJ78ILiRVHKzG9pVzIUniyEXdsgMXtAzvLCclU0ZKpkTHpnIsFHAytmKdA+e09Gwp50q2rnWpVbAcNwdYtUd3kMi7aQbYMrVu3ewCqdbPghkwbAw/fPEugaHhBDeOYMu65ZSWFNOtr24Wvv8teAcnVw8eGfs8ABvWLGbt8u95etr7uHl4k5utu+tsaWWDlbWu6aogP5es9DSys3T9z1NTEgBwdHbF0bn2rQU36z90DD99NZvA0HCCGkewbd1yykqL6drnQQB+/OJtnF09eOgJ3TTe6vJyUpJ1YyrU6nKys66SGBeFlZU1Ht7+AJw5sR8kCU/fQNJTk1i5dAFevoF0ubbNO7XvrIberRRk5kpkF0j0a6sgv1jXenTdxEEWnEvQcPC8btm+Mxoe7q7kcoac5HSJLhEKVEo4drHigrFtY934gsISCT8POUM6Ktl/VkPGtecTNXKX0chNRsIVieJSCRcHGf3aKsnMk6pMwm6l75BxLP36LfxDwgkMbcGODb9SWlpMp97DAVjy1Rs4uXgybIyum5u6vJzUZN3zpzTqcnIyr5IUdwFLKxv9/u8zZCyfvDWOTWv+R9vOA0mIPs2+bat4/OnZtYrxVv5Yd5nxo/xITi0m9UoJk0cHkJlVyp6DFa0pC+a0YPfBDNZsTAXg6bGBHDyWxZWMUmysFfTv4UGb5o5Mf/cMAD5eVvTv4c6BY9nk5ZcTEmjLCxODOXkml5iE2k3j3LbXBLYsm4mnf3O8/Fty/N8llJcVE97xIQA2//oato6edBs6HYA2Pcex6suxHNvxE0ERPYk6vpErSWfo++gc/TZLCnPIy06lMFc3NXz2Vd3YD1sHN2wd3MnNSCLqxEYCmnbF2taFgtw0jm5bhNLCiqDw278xcLOdJ0oZ1NGK9GwtmXlaBnexIrdA4lR0RevcCyNtORVdzu6TugtrlQX6sXEAro5yfN3lFJVIZOdLqCzggc5WnLxUTl6hbozQ8B7WZORoOZ9Q+y6gh87rjvmZ+RI5BRK9WymMHPMV1475ut/twfNahndRkJIlcTlDolMzORZKOBljeBHobKeb+XHZjsoJYKiPDDsruJwpUaYGDycZ/dsoSLyq1d8guxWZTEZEl3Gc3PkdDm4B2Ds34tjWL7Gx9yAgvJ++3MYfJhAY0Y/wzrpnqzXvNp7dq2bh1qg57o1acGbfUtRlxTRpOwKAvKwk4iL/wbdxV6xsnSnMvULkv/9DqbTEL6yHfrt5mQmUlxZRXJCBpryEzBRdN2Mnj5C7OpZIYWuDbai//r1NUCMcWjWlLCuXkqTUu/bv3kr2hjV4PTeDkpiLlMRE4fzACOSWVuTu2gKA19RXUWdlkPHbzwAUHDuI8+CHKI2PpuTSBSy8fHF7dDwFxw7pEwr3J56i4NhByjOuonR2xe2RsUhaDfn7dt3175O/Yz0uY6dSlhhDWXw0dr0HI7e0pPDgTgCcxz6PJjeLvL913fhKzhzFrvcQypLjKIuPRunuheOQxyg5faxSgmTubk5ghTvT4BOhkJAQTpw4wbx585g1axbJyclYWloSHh7OjBkzeO655wD47rvvDMb79OihOyD//PPPPPnkk3USy/5zEiqlxJCOcqxUuqmIl+3UGExz7Wwnu/a8CN0P4VyChK2lll6t5NhZ6R6ounynxmAw7aEoCaVCy4B2cqwtdWV+3aEhu/peOnekY7cB5Odms/a378jNzsQ/qAnTZn+F47XuZZnpachkFRceO/9ZjVpdztcfzzTYzrBHn2L4408DcPLwbn78quJv8N0nb1QqUxfu6zaQ/Lxs/vrtW/JyMvELCuOltxfqu8ZlZaQhk1fEnpOdztzpj+vfb/nrF7b89QtNItrx6tz/AVBcVMCfvy4kO/MKtnaOtO3ch+Gjp6JU1v55Ozfac1qDSgnDuyr1D1RdvLncYMY3F3sZNjcMwj4dp8XWSk3ftkrsrSE1S2LxlnKDuuPmKGNAOyXWlpBTILHrlIZ9Zys2Wq6G8AAFfdvIsFBCfjFcStay61R5penZa6pd10Hk52WzfsU35Odk4BsYxtQ3v9Xv/+wMw7qTm32VD18bpX+/fd0Stq9bQuPw9rz83k+AbortKa9+zt/LvuCfVd/j6uHLyCdfo0P3u9PNZvmaZKytFLz6XGPsbJWcPp/LjPfOUlZecQLz8bLC0aHi7+/kaMGbL4fh6qKisFBNTEIh0989o599Tq3W0r6VM48M9cXKSsHVjFL+PZDBkj+Sah1nWNsHKC7I4sDGL3UPVG3UjOHP/IDtta5uedmpcMO+9glqy6Bxn3Bg4wL2r/8MJ/dAhk76GjefipmcYs7sYOvyWfr3/yx5BYCOg56n8/0voLBQkRJzlJO7llBSnIeNvSu+Ie0Z9fJvlSZuqI1tR8qwtJDxeH9rrC1lxFzW8M2aQoPfgpuj3GAikQBPBS+NstO/f7iXrlv0wbNl/Lq5GEkCXzc5HcNtsLaUkVsgcSFBzfr9JZUeSn079p3TYqGEoR0V+mP+rzvUBr8d3e+2ot6cTZCwsdTSq6VC/0DVZTs0lSZQaBMqJ68IYlIrXzSpNbqbHAPby1DIdTfDzidqb3ucaMsek1GXFbPvz9mUleThGdCWgRMWGYzjyc9KpKSwontWcMsHKCnM5ti2LynOz8DVuxkDJyzSd41TKC1Jiz/KmX1LKSvJw9rOFa/A9gx55jeDSRb2rHmbtLiKGc7WLtQl76Ne3Ya9s/EB9XXBsV1zOm//Rf8+/Np5KGnpGiInzapqtbsu/8C/KBwccRs1DoWTM6XxsSTPfxPNtQkULFzddU2K1+jGAUm4PfokShdXNHm5FBw7SMbvi/VllK5u+Lw4C7m9PZq8XIqjzpL41stobprE4G4oPr6fHDsHHAY/isLeifLL8WR8/YF+AgWli5tBd7e8TauRJAnHIY+jcHRBU5BHyZmj5K777a7HKpg3mSRSy3tqzrL6M4D8dvVre/dmibkX1Frz7gm6+ZD53rfo3d7UEdyZ994+fOtC9djoZ7ubOoRaO3vu7l903U3uHja3LlSPWVvf+bTjphTxmPHxSuYg9KFAU4dwR2zd784Dq++VRgvr5jl/de2170x3LfbxM3c2dr4+Mu8rQ0EQBEEQBEEQhFoQiZAgCIIgCIIgCA2O+fa1EQRBEARBEIQGRCtGtNQp0SIkCIIgCIIgCEKDI1qEBEEQBEEQBMEMiDnO6pZoERIEQRAEQRAEocERiZAgCIIgCIIgCA2O6BonCIIgCIIgCGZAqxVd4+qSaBESBEEQBEEQBKHBES1CgiAIgiAIgmAGxFwJdUu0CAmCIAiCIAiC0OCIFiFBEARBEARBMAOSGCNUp0SLkCAIgiAIgiAIDY5IhARBEARBEARBaHBE1zhBEARBEARBMANaMVtCnRItQoIgCIIgCIIgNDiiRUgQBEEQBEEQzICYLKFuiRYhQRAEQRAEQRAaHNEidI+lpxeZOoRak8vM+y5EmUZl6hDuSE5WsalDqLUyta2pQ7gjklZr6hDuSGmZ+f52NRrzjR2gtNS86461tcLUIdyR0IcCTR1CrUWviTd1CHfEq5ubqUO4I41MHYBwT4hESBAEQRAEQRDMgOgaV7dE1zhBEARBEARBEBoc0SIkCIIgCIIgCGZANAjVLdEiJAiCIAiCIAhCgyNahARBEARBEATBDIgxQnVLtAgJgiAIgiAIgtDgiERIEARBEARBEIQGR3SNEwRBEARBEAQzIEmia1xdEi1CgiAIgiAIgiA0OKJFSBAEQRAEQRDMgFZMllCnRIuQIAiCIAiCIAgNjkiEBEEQBEEQBEFocETXOEEQBEEQBEEwA2KyhLolWoQEQRAEQRAEQWhwRIuQIAiCIAiCIJgBSUyWUKdEi5AgCIIgCIIgCA2OaBESBEEQBEEQBDMgWoTqlkiEgLS0NObPn8+GDRtITk7G0dGR0NBQnnjiCcaPH09JSQmzZ89my5YtJCYm4u7uzvDhw5k7dy6Ojo53NbYHOlnSpYUF1pYy4lI0rNhRQnqOtsryIb4K+rZT4e+hwNFOzv/WFREZo66y/KN9rOjWUsXqf0vYdaKsTmPftmEl/6z9ldzsTPwCG/PElBmENIkwWnbXlrXs27mB5IRYAAJDmjJy7HP68mq1mtXLviXy2H6upl3GxsaO8Fb3MWrc8zi7utdp3AD/bvqdbX8vJi8nA9+AJoyaOIvAxi2Mlk1JimbDiq9JjD1PVnoKDz/5Kn0Gj61ULifzCmuXLeDcib2UlZbg7uXHE1PnEhBifJ/cqcFdrejaQoW1pYzYFDW/by2utu6ENlLQ7z4r/DwVONnJ+X5tIZHR5QZlxg6yoVNzlcGyc3HlfL26sE5j37vlN3as+5n83Ax8/MN46Mk3CAg1vv9Tk6LZtGohSbHnyM5IYfjYmfR8oPL+v27bXz+w4fcF9Bj0BCPGv16ncd9o0phAhg7wwt5WyenzeXzyzSWSU4urLD/8fm+G3++Dt6cVAHGJRSz+PYGDx7IMykWEOTBlbCDhYQ5otRKXYguYNvs0ZWVV/22rI0kSR7Z8xflDKyktzsMrsC09HpqNk3tgteud2beMk//+SFF+Bq7eTek2/C08/VvqPz93cAWXTqwn/fI5yksLmTjnMJbWDka3pVGXsfrLUWSmXuCRl//EzbdZrb7LdUO66o5r1+v+8i23rvv977PC30tX97/7s5BTN9V9AC8XOSN6WtPYT4lcBqmZGhb9VUh2fu0vTPq1VdA+TIG1ChKuSPy1X01mXvXb69RMTvcWSuysIS1LYt0BNckZFeu42MP9HZQEespRKOBSspZ1B9QUlFRsw9VBxv0dFAR4ylHIddvZdlxNbGrNv4skSRzf9hVRR1dSVpyPZ0AbugybjaNbYLXrnTuwjNN7fqK4IAMXr6Z0Hvom7n4VdWfvn7NJiTlAUd5VLFQ2eAS04b6B03HyCAYgM/UCkf/+jysJxykpzMbO2ZemHR6leddxNY7dGKcBQ3EZOhKFkwulCbFc/fkbSmKiqizv/MAInPoPRunmgSYvj/xDe8j47Sekcl3dcR35BG6PGB6LSi8nET9t8h3FeSdcurUnePokHNs2x8rHg6MPP8eVv7ebLJ7quA9/CM9HR2Ph4kJxTDSJX35O0YXzxgsrFHiPGYfrgPuxcHejJCmRy99/S96RQ/c2aKFWsrKyeOGFF1i3bh1yuZyHH36YL774Ajs7uyrL19V1eYPvGhcbG0ubNm3YsmUL8+bN48SJExw4cIDXXnuN9evXs23bNlJSUkhJSeGTTz7hzJkzLF68mE2bNjFp0qS7Glu/9ip6tlGxYnsJn/5eSGm5xHMjbFAqql7H0kLG5XQtf+wsqbrQNS1DlAR6K8gpqN0FVHUO7dnKbz8tYNijk3nvs6X4BTXmk3dfJC8ny2j5C6eP0an7QF5//1ve/vhHXNw8+eTdF8jKvApAWWkJCTFRPDhqInM++4UXZn1E2uVEFnwwvc5jP7ZvE2uW/B8PPPIMr3+0gkYBYSz84BnyczONli8vLcHVoxHDxryEg5Ob0TJFBXl8+vZ4FAolz73xDW99/icPjZ+Bja3xC8M71b+DJb3aWPL71iL+b1k+ZeXw/EjbauuOykJG8lUNf2yr+mId4GxcObO+ydW/flpfVKexnzjwD2t/+ZiBDz/L9Hkr8QkI4/sPn656/5cV4+rRiCGPv4x9Ffv/usSY0xzYvhIf/yZ1GvPNxjzsx8ghvnzyzSWmzDhBcYmGz+a0QGUhq3Kd9IwyvlsSx6SXjzP5leMcj8xm/psRBPnb6MtEhDnw6XstOHIymynTjzN52nHWbEi5ozuEJ3f9wOm9v9DjoXd5+IU/sFBZs/6HyajLS6tcJ/rkRvat+5D2/acy8uU1uPqEsf6HyRQVVPyNystL8AvrTts+T98yhgMb/g9bR49af4cbDehgSe+2lizfWsTHy/IpLYMXH6m+7uuOmxp+r6buuznJmT7ajrQsDZ/9XsD7S/L550AJak3tY+3RUkHncAV/7VPz7d/llKklJgy0qDbWFkFyHuioZPsJNV//VU5qlsSEQRbY6vJnLJQwYZDuZsUP/5Tz/fpyFAoYO8CCG2vf+AFK5HL4cWM5X/9VTlqWxLj+FthZ1zz+yN0/cO7Ar3Qd9i4PPrsCpcqGzT8/VW3diY3cyKGNH9Gm71SGTV2Ni3cYm35+iuIb6o6bbwTdH/6Ah1/ZwMAJ/wNJYtPPk9FqdTs78/JZrGxd6fnIRzz08jpa93qao1s+59yBZTUP/ib2nXviPm4KGauXkfD6VEoTYmn0xgcoHIxfVNl37Y3b4xPJWLWMuGlPkfb9Zzh07onbYxMMypUmxRM95TH9K2n2tFrHWBcUtjbkRUZx5sX3TBrHrTj37kujZ18gdclPnJ8ykaKYaBp//BlKJyej5X0nTcFtyDASv/qcs08+QfrfawmZOx/r0Mb3NnChVsaMGcPZs2fZunUr69evZ/fu3UyZMqXK8nV5Xd7gE6HnnnsOpVLJ0aNHGTVqFM2aNSM4OJhhw4axYcMGhg4dSvPmzVm9ejVDhw4lJCSEPn368MEHH7Bu3TrU6qpbW+5UrzYqNh8q5XSsmpQMLb9sLsbRVkbLkKob8s7Fq9lwoLTaViAAR1sZI3tZseSfYjR1nwex6a/l9BwwnB79huLrH8yTz76OytKK3dvWGS3/zPS59H1gJAHBTfBpFMik599Eq5U4d+oIADa2drw2ZyEdu/XHu1EAoWEtGPv0q8THXCAzPa1OY9++fild+j5M597D8fYL4bEpb6NSWXNgx1qj5QNCm/PQuOm073o/SguV0TJb1v6Es6snY6fOJbBxC9w8G9GsVRfcvfzqNPbrere1ZNPBEiJjdHVnycZCHO3ktAq1qHKdc3Fq1u8rMXon/EZqNeQVSfpXcWndNtPv2rCUzn1G0rHXCLwahfDIpHdQqaw4tOtPo+X9Q1rw4JgZtO3yAEql8f0PUFpSxK8LX2fUU+9ifZcS0OseedCXpX8ksPdQJjHxhbz/+QVcXSzp3qnqRG3fkUwOHssiObWYpJRiFv0ST3GJhvCwilhfnBzCqnWX+XVVEnGJRSRdLmbH3nTK1bX7G0iSROSepbTr+wxBzfvi6hNGn8c+oijvKnFnt1W53qndiwnv+AhN73sYF89Qej70HhYWVlw4vFpfplX38bTtMwXPgFbVxpBwYTdJF/fRechrtfoON+vTzpJ/DpYQGa3mcrqWxdfqfuvGVdf9s3Fq/t5bwqlLVdf9Yd2sOBur5s9/S0i+qiEjR0tkjJr8otrX/y4RCnae1HA+UUtatsTKf9XY20B4QNWn5m7NFRyJ0nL8kparORJ/7VNTpoZ2TXTZU4CnDGc7WLVbzZVsiSvXtuvrJiPYR5cK2ViCm6Oc3ac0pGVLZOZJbDqqRmUhw9O56mT9RpIkcXb/Ulr3foaA8L64eIfR85EPKcq/SsK5quvOmb1LCLvvEZq0ewhnz1C6DnsXpcqKi8fW6Ms07TAK76D7sHf2xc03gnb9X6IwN5WC7MsANGn/MJ2HvoF3cAccXPwIbfMgTdqOIP7s1hrFbozz4IfI3b6JvF1bKLucyJUfvkRbVopj74FGy1s3Cac46iz5+3aiTr9CUeRx8vbvwio0zHA/aTRocrMrXvl5tY6xLqRv3s3F2Qu48lfVf6P6wPORR8nYsI7MTRspSYgn8bP/Q1tSiuv9Q4yWd+k/iLTlS8k7dICy1BQy/l5L7qEDeI56/B5HfvdpJclkr7vh/PnzbNq0iR9++IGOHTvSrVs3vvrqK37//XdSUlKMrlOX1+UNOhHKzMxky5YtTJ06FVtbW6NlZDLjJ4Xc3FwcHBxQKu9O70JXBxmOtnKikir+oCVlEJ+mIci7mtuFNSADxg2yZvuxMtKy6j4LUpeXEx9zgYhW9+mXyeVyIlrdR3TU6Rpto7S0BI1GjZ191ResxYUFyGQybGyNN53Whrq8nKTY8zRt2Um/TC6X07RlR2Ivnqr1dk8f3YV/SAQ/fDqdmZN6Mv/VUezbtqouQq7E1VGOo52cqISb6k6qhiCfO6+vjf2UfPicA+9MtOexftbYWtXswqkm1OpykuPO0aS54f5v3LwTCZdqv/8BVv30Ps3a9CCsRec7DbNaPp5WuLlYcuRktn5ZYZGGcxfzaN60ZgmYXA59u7tjZaXg7AXdhZOTowURTR3Izi3j249b8/fSznw1vxUtw2uf1OVnJVOUn06jxl30yyyt7fHwb8mVhJNG19Goy0i/fNZgHZlcjm/jzlWuU5Wi/Az+XfU2fR/7CKWFVW2+ggG3a3X/wk11P+4O674MaB5iwZVsDS+MtOXj5xx4bYxdtTcWbsXZHhxsZMSkVByDS8shOV3C38P4b0ohBx83GdE3rCMBMSla/TpKuQwJDFqq1BqQJAj01J3yi0ohPUdLm8YKLJQgl0GHMAUFxRKXM2p2sZOfnUxxfgY+IRW/J5WVPe6NWnI10fhvVaMuIyPlLD6hFevI5HJ8QjpzNfGk0XXKy4q4eHwN9s6NsHX0qjKestICLK1r2VVdocQquDFFp49XLJMkik6fwKpxuNFVii+ewyq4MVYhusTHwsML2zb3UXjiiEE5lZcvId8uJ+jLxXi/MBPlXejK/V8jUyqxaRJG3rEb9qUkkX/8KHYRzY2uI7ewQFtm2L1fW1qKXYuWRssLtVNaWkpeXp7Bq7S06hbgmjhw4ABOTk60b99ev6xfv37I5XIOHap518baXpc36EQoOjoaSZIICzO8g+Pm5oadnR12dnbMnDmz0noZGRnMnTu32mY7MF5hNOqaVRgHW92fJr/Q8KSUXyTpP6utfvep0Gjh35N1Oybouvy8HLRaDY5OLgbLHZ1cyM023r3pZn8sXYiTixvhrToY/bysrJQVSxfSqfsArG3qLhEqyM9Gq9Vg7+hqsNze0ZW8nIxabzfjajJ7tvyBu7c/z7/1Hd0HjGLlTx9xcNdfdxpyJQ62uguivCLDJDe/SKv/rLbOxZWz9J9CvvyjgLW7Swj1U/Lcw7ZUcb/gthXm3Z39f3z/Ri7Hn2fIYy/fYYS35uKsa5XKzjFsXcjOKdN/VpXgAFu2/NGNHWt6MOO5JrzxwVnik3RdD329dInCxMcDWbc5lenvnuZiTAEL3m9FI+/b6M90g6L8dACs7Q33t42dG0X5xvd3SWE2klaDtV3N1zFGkiR2rJhFRKfH8PAzPv7rdunrfuFNdb/wzuq+va0MK5WMgR2sOBun5stVhZy8VM6U4TY0blS7G1P21rp4CooNj/EFxRJ21sZjtbEChVxmdJ3r20tK11KuhkH3KbBQ6LrKPdBBiUIuw96mYrs//lOOt4uM2eNUvPekim7NFfy8uZySGp4Wiq/9rW+uB9Z2bhQXpBtdp6Qox2jdsbZz1W/vunMHl7Pk3XYsfbcdyVF7GDTxRxRVtPheSThBbOQ/hHUYVbPgb6JwcECmUKDOzTFYrsnNRunkbHSd/H07yfhjKf5zPqXJsg0Ef7WE4rORZK39XV+mJPoCqd9+QvL8N7ny41dYuHvh/96nyKxq93ttKJSOTsgUStTZhl3py7OzsHBxMbpO3tFDeD7yGJa+jUAmw77dfTh374mFi6vR8uZM0kome82fPx9HR0eD1/z58+/o+6SlpeHhYdg1WqlU4uLiQlpazXr81PS63BgxWYIRhw8fRqvVMmbMmEqZbl5eHoMHDyY8PJx333232u3Mnz+f994z7Id738DX6ThoVqWy7cOUPNa34uD43V91O+7iOj8POb1aq/hoed0Obq9L61ct4dCerbz+wbeoVJaVPler1Xz98RsgSYx/tnKiWh9JWi3+IREMG/0SAH5BzUhJimbvlpV06jXsjrZ9XzMLHu9fMY7kmzUFd7S96hyLqri4T8nQcjldw5ynHGjipyQq8e51E70T2Zmp/LnkQ559439YGKlPd6p/Tw9enVox5ui1OTVr9TQm8XIRE146ip2Nkl5d3XnzlTBemHWK+KQifev0X5tS2bj9CgCXYgto19KJwf29+H5p3C23f/H4Ov5dPVv/fvDE72od6506ve8XyksLadPn9k9c193XzILRA26o+6vvTt2/nj5ERpez45junJB8VUOIr5LurS25lHzr43WrEDnDu1accpduqb4Lam0VlsDyHeUM62JB5wgFkgSRsVouZ2i5sWfLg12UFJZILFqvRq2B9mEKxvW34Ju/ysg3MlQq+uQ69q19V/9+wLhv70r814W2HopvaBeK8tM5s+dndvz2CkOeXo7SwvA3nJV2kW2/TKVNn+do1LjrXY3pRtbhLXEd8RhXflxI8aULqLx88HjyWVyzR5O5ZjkAhSePVqyQGEfypQsEf/0LDp17kLtz8z2LtSFI+uoLAmbMJGLJckCi9HIKGZs24FZFVzqhdmbNmsW0aYbj3CwtjZ9XX3/9dT766KNqt3f+fBWTX9yG27kuN6ZBJ0KhoaHIZDKiogxnhQkO1s1MY21teNcmPz+fQYMGYW9vz59//omFRfXdIoxVmNcXGW8ROh2rJj6t4iSuVOhOvfa2MvJu6INub6Mb1FtbIb5K7GxkzJlU0YqikMsY0d2SXm1UvPvTnV9I2Ds4IZcryL1pYoTcnCwcnau/O7Pxz1/ZsGYJr723EP/AyoMcdUnQLDLTU3l97jd12hoEYGfvjFyuqDQwPz83s8qJEGrCwdkd70bBBsu8fIM4efDO+2lHRpcTn5qvf399oLWDjZy8woq6Ym8jJ/nqHYzsNiIzV0t+kRZ3JzlRiXe+PVuHut//ybHnKMjL4tM3Ku4Wa7UaYi8cY++W3/i/X44jl9e+u+new5mcu1hxwaOy0LXYOjtZkJldcXvd2UlFdGz1vy+1WuJyqm6ik6iYApo1tueRB335v68v6bcVn2R4EyMhuQhP95oleIHhvQ1mdtOoddsszs/E1qHijlxRQQZuPsZnbrOydUYmVxgMbr++jo19zf9Gl6MPcSXhJItmGXZdWfXlSBq3GULfx6o/gUI1dd/2prpve2d1v6BYQqORSM003EZqpobQRjU7jZ5P1JJ0taI+XD/G21nLyL+hhcfOWkZqFV2Wi0pAo73eYmS4zo3biL4s8enKMmwsQSvpugfOelxFVr5uuyHeMpr6yZn7axml1/Kxv/erCfWxoE1jBbsjK+8r/2Z98PAzUncKMrG5oe4UF2Tg4l1F3bFxMlp3igsysb6p7qis7FFZ2ePoFoiHXyt+nduJhHPbCGk1WF8m+0o0//w4kbAOo2jT51mj/2ZNaPLykDQalI5OBssVjs6oc7KNruM2ajx5u7eTu2MTAGVJ8cgtrfCc8hKZf/4GRsZTaIsKKUtNxsLLp9axNgTq3BwkjRqls2Hrj4WzC+VZxidcUufmEPP2LGQWKpSODpRnZOA75VlKU42PMRFqx9LSssrE52bTp0/nySefrLZMcHAwXl5eXL161WC5Wq0mKysLL6+qu8PC7V+XG9OgEyFXV1f69+/PwoULeeGFF6ocJwS6jHPgwIFYWlry999/Y2V16/7sxiqMQml8oGRpOZTm3njglMgt1BLmp+Ryuu6EY6WCQC8FeyNr36Xt8PnySnfunxthw5Hz5Rw8Vzd3KJUWFgSGNOVc5BHadeoFgFar5VzkUfo98EiV621Ys5R1K39mxrtfEmSkX/b1JOhKahKvv/8tdg5OdRLvzbH7BTcj6vQhWnXoo4896vQheg6q/aDLkLDWXEmJN1h2NTUBF3fvOwkX0NWdm6cGzi3QEhagJPla0mylgkBvBXtO3llf3ps52cmwtZaRW1g3Y82USgsaBYVz8cwhWtzXF9Dt/0tnD9FtQO32f+PmnXjtY8OJFn777i08fILo++CkO0qCAIqLNVwuNrxwzMgqpX0rZ6LjdEmLjbWC8CYOrN14eydlmQwsriVWqVdKSM8sxd/XxqCMn481B48Zv1i7mcrKDpVVxc0DSZKwsXcnOfqAfsrqspICriZGEtHZ+P5WKFW4+0aQHH2AoOb9dNvRarkcfZDmXcbU+Lt1G/YmHQa9pH9flHuV9T9Mpv+Yz/D0r36CheuqrPv+Sn3iY6WCoDus+xqtbnymp4thXfF0kZOVW7O6X1YOWQaHWN1kIyE+clKzdLFaWkAjdxmHLhgfp6PRQkqGRKi3nPMJun9XBoT4yDlwrnLyUnTtKwd7y7C11iVjABZKXRJ287W6JFFlN1eVpS0qS9sbykpY27uREnMQV5+KupOeHEnTjo8Z3YZCqcLNJ4LU6IMEhlfUnZSYg4R3rr7uSEj65Asg+8olNv4wgcZth9F+wMvVrntLGjUlsZewadGGgqMHdMtkMmyatyZn899GV5FbWiLdtAMl7fW6YJioXieztELl6UPe7vo5XXV9IanVFF2MwqFte3L37dEtlMmwb9uOq3+urn7d8jLKMzJAocCpRy+yd+24BxHfWzfXu/rK3d0dd/dbj4nr3LkzOTk5HDt2jHbt2gGwY8cOtFotHTt2rHK92lyXG9OgEyGAb775hq5du9K+fXveffddWrZsiVwu58iRI1y4cIF27dqRl5fHgAEDKCoq4tdff9WP9wHdH1qhuLMLqarsOlHGwA6WXM3RkpmrZUgXS3ILJYMZ4Z5/yIbImHJ2n9KdYVUW4O5UMYbI1UGOr7ucohKJ7HyJohLd60YarW4WsKvZdTdxwqBho/nfF+8RFNqM4MYRbF73O6UlxXTvp2um/v7z2Ti7ejBq3FQANqxewprli3hm+lzcPLzJydb1F7eyssHK2ga1Ws3Cj14nIeYCr7z9GVqtRl/Gzs4RZS3uAlSl75BxLP36LfxDwgkMbcGODb9SWlpMp97DAVjy1Rs4uXgybIzuIk5dXk5qcgwAGnU5OZlXSYq7gKWVDR7e/gD0GTKWT94ax6Y1/6Nt54EkRJ9m37ZVPP70bKMx3Kmdx0sZ1MmSq9kaXd3pak1ugdZgRrgXH7HlVHQ5/157fpSlBbg7VdRlV0c5jdwVFJZoyc6XsLSAB7pYceJiOXmFEu5Ocob3sCI9W8v5+LrrFtdr8DiWf/smfsERBIQ2599/fqWstJiOPYcDsOybWTg6ezDk8VcA3QQLV27Y/7nZV7gcfwGVlQ3uXv5YWdvi7WfYuqiytMbWzqnS8rqy8u/LjH/Un6SUYlKvlDD5iUAys0rZc7BiHMSC91uy+0AGazbokqOnxwVx8FgWV9JLsLFW0r+nB21aODFtdkVXu+Vrkpg0OpDouAIuxRVwfx8vAhrZ8NaH52oVp0wmo2X3cRzb/h2OboE4uPhyePOX2Dh4EBTRT1/u7++fJKh5P1p0fQKAVj2eZMeK13Fv1BxPv5ZE7llCeVkxTe97SL9OUV46RfkZ5GbomgozUy+isrTFztkbKxsn7J0N74pbqHQJnqOrP3ZO1d8FrM6OY6U80NmS9GwNGblahnbT1f2TN8wI99IoW05euqnuO99U9z0UFBZr9c8I2nqklMlDbbiUrOJioprwICUtQiz4/Pfat6LvP6uhd2sFGXm643P/dgryi+BcQsWxeNL9FpyN13DwvG7Z3jMaRvZQkpwhJzldomtzBSolHL9YkQi1bSwnPUeisETC30POkE5K9p3RkHHtZlviVS3FZTCyh5IdJzWUq+G+MDnO9jKikmp2HpDJZER0GcfJnd/h4BaAvXMjjm39Eht7DwLCK+rOxh8mEBjRT5/oNO82nt2rZuHWqDnujVpwZt9S1GXFNGk7AoC8rCTiIv/Bt3FXrGydKcy9QuS//0OptMQvrAeg6w73z48T8G3clebdntSPdZPJFFjbGR9DcivZG9bg9dwMSmIuUhIThfMDI5BbWpG7awsAXlNfRZ2VQcZvPwNQcOwgzoMfojQ+mpJLF7Dw8sXt0fEUHDsEkm4fuj/xFAXHDlKecRWlsytuj4xF0mrI37erVjHWBYWtDbah/vr3NkGNcGjVlLKsXEqSUk0W182urFxB4OtvUnjxAkXnz+ExchRyKysyN20AIHDWW5SlZ5Dyg657r02zcFRu7hRFX0Ll5o73kxORyWRc+a32U6oL90azZs0YNGgQTz31FN999x3l5eU8//zzPPbYY/j46M4Tly9fpm/fvixdupQOHTrU6XV5g0+EQkJCOHHiBPPmzWPWrFkkJydjaWlJeHg4M2bM4LnnnuPw4cP6mStCQ0MN1o+LiyMwMPCuxLbtaBkqpYzH+1pdezCghm/+LDKYDcjNSY6tdUXi4++p4KWRFXftHuqpy5APnSvj1y23frZQXenYvT95edmsWb6I3OxM/IOaMGP2Fzg66brGZWVcQS6viHvHpjWo1eUs/MjwAZfDH5vMiMenkJ15lROHdwPw9stPGJR5/f1vadaiXZ3F3q7rIPLzslm/4hvyczLwDQxj6pvf4nAt9uyMNGSyithzs6/y4WsV3a62r1vC9nVLaBzenpff+wnQTbE95dXP+XvZF/yz6ntcPXwZ+eRrdOg+mLth6+FSVBYyRg+wwdpSRsxlNV+vLryp7iiwta5Y4O+l5OVHK1oLRvbWdQ09eKaMXzYVoZXAx01BxwjdgypzCyTOx5ezft+dPUvlZm06309BXjabVi289kDbpjz9+nf6ZwRlZ6Qa7P+87Kt8Mmuk/v3O9YvZuX4xIc3a8/w7i+susNuwbHUSVlYKXnu+CXa2Sk6fy2X67NOUlVfchPD1ssbJoSKBd3a04K1XmuLqoqKwUE1MfCHTZp/m6A2zz638+zKWKjkvTA7Bwd6C6LgCXnknkpS02v+2W/eaTHlZMf+ueoeykjy8AtsxZPL/DMZi5GUmUlJYEUdo6wcoLsziyOavKMpPx82nGUMm/8+ga9zZg79zdOvX+vd/fav73fYeNc8gYaprW67X/YE22Fyr+1+tMqz77k4K7G6q+9Meq6j7j/TR1f0DZ8pY+o9u/M+pS+Us31LMoE6WjOpjzZVsLYv+KiTmcu0r/+5IDSoljOiqxOraA1V/3lxuEKuLvcxgZsbTcVpsrdT0a6fE3hpSM3Xr3PiwVHdHGQPbK7G2hJwCiZ2nNOw7U7HRolJYvLmc/u2UTL7fArkcruZI/LpNTVpWze82t+wxGXVZMfv+nE1ZSR6eAW0ZOGGRQd3JzzKsO8EtH6CkMJtj276kOD8DV+9mDJywSN81TqG0JC3+KGf2LaWsJA9rO1e8Atsz5Jnf9JMsxJ/ZQklhFjEn1xFzsuKRDHZOPjz6Wu1aW/IP/IvCwRG3UeNQODlTGh9L8vw30VybQMHC1R20FUmibhyQhNujT6J0cUWTl0vBsYNk/L5YX0bp6obPi7OQ29ujyculOOosiW+9jCY/t1Yx1gXHds3pvP0X/fvwT94AIGnpGiInVR6/bCrZO7ejdHTC58nJ1x6oeolLM6ejztbVJZWHp8Hz0+QqFT4Tn8LSxwdtcTG5hw4QP28umsK7N2bWVLR38Ny4+mrZsmU8//zz9O3bV/9A1S+//FL/eXl5OVFRURQV6Y7Hx48fr7PrcplkLm1s/xEvLDDtMwTuxJhB5l1VCsrufHpeU/pzS/UPOq3PBveputupOZg3e7+pQ7gjI5/qYeoQai0qynQXjXXB2cW8Zwhzcqq71nZTGPbH/aYOodai18SbOoQ74tWt9uNq64N2O/eZOgSjnnjTdOOefv3gvze+rcG3CAmCIAiCIAiCOZD+gy1CptSgnyMkCIIgCIIgCELDJBIhQRAEQRAEQRAaHNE1ThAEQRAEQRDMgBjaX7dEi5AgCIIgCIIgCA2OaBESBEEQBEEQBDMgaevumY+CaBESBEEQBEEQBKEBEomQIAiCIAiCIAgNjugaJwiCIAiCIAhmQCueI1SnRIuQIAiCIAiCIAgNjmgREgRBEARBEAQzIKbPrluiRUgQBEEQBEEQhAZHtAgJgiAIgiAIghmQxBihOiVahARBEARBEARBaHBEIiQIgiAIgiAIQoMjusYJgiAIgiAIghkQXePqlmgREgRBEARBEAShwREtQoIgCIIgCIJgBrSS1tQh/KeIROgeU5ebcwWWmTqAO6KVzDt+a1uVqUOotaJyhalDaNAsLMy37ltamfdpytrGvOu+i5N5dxyxdXcwdQi15tXNzdQh3JG0vRmmDkEQbsm8j3CCIAiCIAiCIAi1YN632gRBEARBEAShgRCTJdQt0SIkCIIgCIIgCEKDI1qEBEEQBEEQBMEMiBahuiVahARBEARBEARBaHBEi5AgCIIgCIIgmAFJEi1CdUm0CAmCIAiCIAiC0OCIREgQBEEQBEEQhAZHdI0TBEEQBEEQBDOg1WpNHcJ/imgREgRBEARBEAShwREtQoIgCIIgCIJgBsT02XVLtAgJgiAIgiAIgtDgiERIEARBEARBEIQGR3SNEwRBEARBEAQzIElisoS6JFqEBEEQBEEQBEFocESLkCAIgiAIgiCYATFZQt0SLUKCIAiCIAiCIDQ4IhESBEEQBEEQBKHBEV3jgLS0NObPn8+GDRtITk7G0dGR0NBQnnjiCcaPH4+NjQ1PP/0027ZtIyUlBTs7O7p06cJHH31E06ZN72psQ7pa0a2lCmtLGbEpapZvKSY9p+qBcqGNFPS/zwp/LwVOdnK++7OQU9Hllcp5ucgZ0dOaxn5K5DJIzdSw6K9CsvPrrsl124aV/LP2V3KzM/ELbMwTU2YQ0iTCaNldW9ayb+cGkhNiAQgMacrIsc/py6vValYv+5bIY/u5mnYZGxs7wlvdx6hxz+Ps6l5nMV+3e9NvbF+3mLycDHwDwhg5cRaBoS2Mlk1NimbDiq9JijtHVnoKD41/jd6DxxqU2fjHN/yz6luDZR4+gby9YF2dx37dwPss6NhMibUlxKVpWbO7jIzcqv++wd5yerW2wNddhqOtnJ//KeVsvEb/uVwO93ewoKm/AlcHGcVlEpeStWw8WE5eUe3rzcFty9iz8ScKcjPw8mvKkLFv4hfSssrypw9vYtvqL8nJuIyrZwADH51OWKue+s8LcjPYtOJTos/so6Qon8Cw9gwZ+yZuXoH6Mvk56Wz6/f+IPnuA0uJC3LwD6fXgMzS/b0Ctv8fNJo0JZOgAL+xtlZw+n8cn31wiObW4yvLD7/dm+P0+eHtaARCXWMTi3xM4eCzLoFxEmANTxgYSHuaAVitxKbaAabNPU1ZWuwG0p/Ys4/iOHynKT8fNpyk9H34br4Cq9/+lk/9wcOMX5GVdxsk9kK5DZxAYXrH/o09t4fT+30lPOktJUQ6Pz1iLe6NmBts4s38FUcfWczX5LOWlhTw97wiWNg61it+Y+zuq6BShxNpSRlyqhpU7S6uv+z5y+rRV4ecux9FOzo8bijkdqzEoM6iDijZNlDjZydBoICldw8YDZSRcubOByz1byGkTIsPKApIyJP45oiWroPp12jeW0bmpHDtruJINm45pSDGsJvi6Qu9WcnxdZUgSpGXD8l0a1Ne+VrdwGaE+crycQaOF/1utqfwP3cLxf5dxZOuPFOal49GoKX1HvY13YNV1J+r4P+xd9wW5mZdx9gik5/AZBDevqDv71n/FhWMbyM9OQ66wwNM/gu4PvoJPUKtK21KXl/Hr/z1CevIFxs1ai6dfs0pl7pRtj4HY930QhYMT5ZcTyF75E+UJ0VWWt+v1ALbdB6J0dkNTmEfxiYPk/r0c1JXPwfeC+/CH8Hx0NBYuLhTHRJP45ecUXThvvLBCgfeYcbgOuB8LdzdKkhK5/P235B05dG+DvgWXbu0Jnj4Jx7bNsfLx4OjDz3Hl7+2mDuueEl3j6laDbxGKjY2lTZs2bNmyhXnz5nHixAkOHDjAa6+9xvr169m2bRsA7dq14+eff+b8+fNs3rwZSZIYMGAAGs3tnzxqakAHS3q3tWT51iI+XpZPaRm8+IgtSkXV61hayLicruH3bVVfcLk5yZk+2o60LA2f/V7A+0vy+edAif4EWRcO7dnKbz8tYNijk3nvs6X4BTXmk3dfJC8ny2j5C6eP0an7QF5//1ve/vhHXNw8+eTdF8jKvApAWWkJCTFRPDhqInM++4UXZn1E2uVEFnwwve6CvubY/k38ufT/uH/kM7z20R/4BjThmw+eJj8302j5stIS3Dwb8eDol3Fwcqtyu95+oXywaKf+9cqcpXUe+3W9Wyvp1kLJ6t1lfLm6hLJyiaeGWFZbd1QWkJKp5c89xk/aKiX4usnZdqycz1eVsGRzGR5OMibcr6p1nJEHN7Jx+Uf0GT6VqXNW4+UfxuL/e4qCPOP7OuHSCf74ZgbtezzM1DlraNa2L8sWvMCV5IsASJLErwueJzs9iSde/pqpc9fg5ObDzx9NpKy0SL+dVYteJz0tnide/poX5/1FRPv+/L7wFVLiz9X6u9xozMN+jBziyyffXGLKjBMUl2j4bE4LVBayKtdJzyjjuyVxTHr5OJNfOc7xyGzmvxlBkL+NvkxEmAOfvteCIyezmTL9OJOnHWfNhpRanxgvHt/InrXz6ThoKo/N+BM336b89d0kivKN7//UuONsWjqd8E4jeXzGWoJb9GX9j1PJTL2oL1NeVoRPUFu6DJ1R5b9bXlZMQLPu3Nf/mVrFXZ2+bS3o0cqClTtL+fyPYsrK4Zlh1rc8bqZkaFn1b2mVZa7maFn9bykfLy/iy9XFZOVJPDPMGlur2sfapZmMDk1kbDyi5aetGsrVMLq3AkU1Z+Zwfxn928jZfUbL/zZpuJIjMbq3AhvLijK+rjC6l4LYVImfNmv4cbOGoxe1SDdUE4VcxvkkLUeja1d3LhzdyK7V8+kyeCrjZv2Ju29TVn41icIq6s7lmOOs+2k6LbqMZPystTRu1Zc/v59KekpF3XH2DKTvo+/w5FvrGD19OY6uvqz8aiJF+ZXPG//++TF2jh61ir0mrNt2wWnEePL+WcmVj2ZSdjkB96lvIrcznrBbt++G47Ax5P2zkrT3XyZ72bfYtOuC44Oj71qM1XHu3ZdGz75A6pKfOD9lIkUx0TT++DOUTk5Gy/tOmoLbkGEkfvU5Z598gvS/1xIydz7WoY3vbeC3oLC1IS8yijMvvmfqUIT/iAafCD333HMolUqOHj3KqFGjaNasGcHBwQwbNowNGzYwdOhQAKZMmUKPHj0IDAykbdu2vP/++yQlJREfH3/XYuvTzpJ/DpYQGa3mcrqWxRsLcbST07qxRZXrnI1T8/feEk5dqvoO1LBuVpyNVfPnvyUkX9WQkaMlMkZN/h3c1b/Zpr+W03PAcHr0G4qvfzBPPvs6Kksrdm8z3gLyzPS59H1gJAHBTfBpFMik599Eq5U4d+oIADa2drw2ZyEdu/XHu1EAoWEtGPv0q8THXCAzPa3O4gbYuX4pnfs+TKfeI/BuFMKjT72DSmXNgZ1/Gi0fENqc4WOn067r/Sgtqk4K5HIFDk5u+pedg3Odxn2j7i0t2HasnLPxGlKzJH7fUYaDjYzmQVVfDV5I1LLpcDln4oxnxCVlsGh9KadiNKTnSCRe0SVNfh4KnOyqvsCvzr5NS2jf6xHa9XgID99Qhj35LhaWVhz7d43R8gc2L6Vxi250HzwJD98Q+o98CZ/AZhzYuhyAzLR4kmJO8eD42TQKboG7dxAPjp9NeVkppw5s0G8n8dJJOvcfg19IS1w8/Og97FmsbOy5HH+2Vt/jZo886MvSPxLYeyiTmPhC3v/8Aq4ulnTvVHWivO9IJgePZZGcWkxSSjGLfomnuERDeFjFhdeLk0NYte4yv65KIi6xiKTLxezYm065una/3RO7fqZ551GEd3wYV69Q+jzyHkqVFecOrTZa/uS/Swlo2p12fSbj4hVC5wdexr1ROKf2/Kov0+y+4XQc9Dz+TTpX+e+26fUk7ftNwSug8p3+O9WjtQVbjpRxJk5DaqaWZVtLcLSV0SK46g4Q5xM0bDxYVqkV6EbHL6q5mKQhM08iLUvL2j2lWFvK8HGrJsO6hQ5hcvac1XLxssTVHPjroBZ7a2jaqOrfU6cwOSdiJE7FSWTkwYYjWsrV0Dq4Yp0BbRUcuSix/7xEeh5k5sO5JAnNDY1X/57RcihK4mpO7erO0R0/07LrKFp0fhg371AGPP4eFiorzuw3XneO7VxKUHh3OvSfjKt3CN2GvoynXzgndlXUnfD7hhLYtAtObn64+TSm98OzKCspIP1ylMG2Ys/+S/z5ffR6aGatYq8J+z5DKNy/naKDu1CnJZPz+yKksjJsO/cxWt4yKIzS2CiKj+5Fk5VO6YVIio7uQxUQetdirI7nI4+SsWEdmZs2UpIQT+Jn/4e2pBTX+4cYLe/SfxBpy5eSd+gAZakpZPy9ltxDB/Ac9fg9jrx66Zt3c3H2Aq78tc3UoZiMVtKa7PVf1KAToczMTLZs2cLUqVOxtbU1WkYmq3xCKiws5OeffyYoKAg/P7+7Epubo66LxoUEtX5ZSRnEpWoI8ql9j0YZ0DzEgivZGl4YacvHzznw2hg7WoVWnVzdLnV5OfExF4hodZ9+mVwuJ6LVfURHna7RNkpLS9Bo1NjZV91dpriwAJlMho2t3R3HfJ1aXU5S7DnCWnTSL5PL5YS16ET8xVN3tO30tETefLoP7z4/iCVfziQrI/VOwzXKxV6Gg62MS8kVF3UlZZB4VUuAZ93+5K1UoJUkiktv/2JKrS4jJf4soREVF8xyuZzQ8M4kRp80uk5i9ClCIgwvsENbdCPpWnn1tS4oSouK2+NyuRylhYqEi8f1y/wbt+b0wX8oKshBq9USeXAD6vIygpt1uO3vcTMfTyvcXCw5cjJbv6ywSMO5i3k0b1qz7l9yOfTt7o6VlYKzF/IAcHK0IKKpA9m5ZXz7cWv+XtqZr+a3omV47bqUadRlXE0+i1+TLvplMrkcvyZdSI0/YXSd1PiT+N2U4AQ07UZa/MlaxVDXXB103TovJhnW/YQrWgK96q7uK+TQpbkFxaUSKRm1a0p3sgV7axlxaRW/ndJyuJwJvm7GEyG5HLxdMFgHIO6KRKNr69hYQiM3GYUlEk/2U/DKCAXj+irwqzoHv20adRlpiWcJCDOsOwFNu5ASZ7zupMSdJKCpYd0JDO9GStzJKv+NU3tXYGltj3ujMP3ywrwMNi97m8FPfoyF6g6a46qjUGLhF0xJVGTFMkmiJCoSVVATo6uUxkWh8gvG4lrio3D1wCqiDSVnjxstfzfJlEpsmoSRd+xIxUJJIv/4UewimhtdR25hgbaszGCZtrQUuxZVd3UUhP+CBj1GKDo6GkmSCAsLM1ju5uZGSUkJAFOnTuWjjz4C4JtvvuG1116jsLCQsLAwtm7dikpVdQtAaWkppaWGXS006lIUSssq1qjgYKs7qeUVGmbg+YVa/We1YW8rw0olY2AHK/7eW8Kfu0sID1QyZbgNC34vMLh4rq38vBy0Wg2OTi4Gyx2dXEhNTqjRNv5YuhAnFzfCWxm/MC0rK2XF0oV06j4Aa5u6S4QK87LRajU4OLkaLLd3cuVKSlyttxvQuAVPPDcXD59A8rIz+GfVtyx4ZzxvfPonVtbGk/DasrfR1Y/8YsOLpYIiSf9ZXVAqYHBnFScvaSitRRf4onxdPbFzMNzXdo6upKca39cFuRnYORpe0dk5uJKfmwGAu3cQTq7ebFn5OcMnvIuFpTX7Ny0hNyuN/Jx0/TqPTf2c37+exgfPdUauUGKhsmLMS1/h6hlw+1/kJi7OumNCdo7hTsnOKdN/VpXgAFu++782qFRyios1vPHBWeKTdF36fL10F30THw/k659iuBRXyKA+nix4vxXjph6tdvyRMcWF2UhaDTb2hvvfxt6V7CuxRtcpys/Axt6tUvnCvIzb+rfvFn3dv6l1O7/ozo6b14UHKhg/0AoLC8grlPhmbTGFJbXblp217r83r19YImFXxfW9jSXI5TIKSqSb1gE3e933c752OOzRQs62E1qu5Ei0CJTzRB8F32/U3HL8UU0UF1yrOw6V605WFXWnMC8D25vqjq2RuhNzeifrfppGeVkxdg7uPPLCT9jY6c4lkiTxz9LXad39MbwCWpCbmXznX8YIuZ09MoUCbX6uwXJtXi4Wnr5G1yk+uheFrT0er8wFGcgUSgr2bCF/i/GeBHeT0tEJmUKJOtuwS2F5dhZW/v5G18k7egjPRx6j4NRJSlMuY9+2Pc7de+qyb6FeEWOE6laDToSqcvjwYbRaLWPGjDFIZMaMGUP//v1JTU3lk08+YdSoUezbtw8rK+Nnrfnz5/Pee4b9WNv1m8l9A16vVPa+ZhaMHlAxFuCb1XVwtjLi+qVAZHQ5O47pvlvyVQ0hvkq6t7bkUnJR1SvfI+tXLeHQnq28/sG3qFSVk0a1Ws3XH78BksT4Z+9e14i6FNGmu/7/fQPCCGjcgtnPDeTEgc107vPQHW27TWMFI3tWXGD/uKHqcQ51RS6HsQN0/+bq3WW3KH3vKJQWjH7xK9b8+BbvP9sJuVxBSERnmrTszo2njm2rv6SkKJ+JM3/Cxt6Zc8e28/vXr/DUm7/i5Wf8jm9V+vf04NWpFeu8NqdmrZ7GJF4uYsJLR7GzUdKrqztvvhLGC7NOEZ9UpG+d/mtTKhu3XwHgUmwB7Vo6Mbi/F98vrX2ibq7aNVEyqnfFMWLRuttLBm9XdLKG//u9CFsrGZ0jLHhykBWfryymoPjWFybNA2QMvq/iovK3f+/O+NLrnRiOR+u6zwGkZWsJ8lLQOkTOjlP1u3uLX5OOjJ+1luLCbCL3/sG6H19mzGsrsbV35fiuXygrLaTjwKdNHWYllo3DsR/4ENkr/kdZQjRKNy+cRk7AftDD5G8y3l2wPkn66gsCZswkYslyQKL0cgoZmzbgVkVXOkH4r2jQiVBoaCgymYyoKMP+x8HBwQBYW1sbLHd0dMTR0ZHGjRvTqVMnnJ2d+fPPP3n8ceN9aGfNmsW0adMMls342viJOjK6nPjUfP376wN7HWzl5BVWnDDtbeUkX639CbSgWEKjkUjNNNxGaqaG0EZ1Ux3sHZyQyxXk3jQxQm5OFo7OrlWspbPxz1/ZsGYJr723EP/AyoM0dUnQLDLTU3l97jd12hoEYOvgjFyuIC/HcMBvfk5mpVaiO2Fj64CHTwDpaYl3vK1z8Ro+u1JxW/l63bG3lhncGbez0Q0Iv1NyOYztr8LZTs53f5fUqjUIwMZeV09unhihIDezUqvPdXaObhTkGt5BLsjLxP6G8r5BEbzw/p+UFOWjUZdj6+DCt+8+im+QbgbCzCuJHNy2jBfn/Y1nI10d8/ZvSkLUUQ5uW87wCe/e1vfYeziTcxeP6t+rLHQXu85OFmRmVySJzk4qomOrv8GhVktcTtX9LaNiCmjW2J5HHvTl/76+pN9WfFKhwToJyUV4ut+6lflm1rbOyOSKShMjFOVnYuNgfP/b2LtRlJ9RqbxtFeXvtjNxahKuVBzLlApdFmBvIzOYydDeRs7l9DtPPMrUkJErkZErkXCllDfH2tApXMm2Y7f+EVy8LHH5huOu8lpOZGsFBTe0CtlayUjLNp5YFZWCVithZyWDG1J73TZ07wuunWIy8gy3kZEr4WBDnbC2u1Z38irXnarqgq2DG4U31Z1CI+VVljaoPAJwJgCfoNb8b/YATu9bRadBT5MYdZCU2JN89qLhDJ6/fPQw4fcN5YHxH9XBtwNtQT6SRoPc3tFgudzBEU1ejtF1HAY/RtHh3RQd2AGAOiWRPEtLnB5/mvzNazCYqeIuU+fmIGnUKJ0Ne2VYOLtQnmV8wiJ1bg4xb89CZqFC6ehAeUYGvlOepTQ15V6ELAgm06DbPF1dXenfvz8LFy6ksLDw1ivcQJIkJEmq1PXtRpaWljg4OBi8quoWV1oO6Tla/Ss1U0tugZYw/4rkxEoFQd4K4lLURrdRExotxKdp8HQxHODr6SInK7du7hQqLSwIDGnKuciK/slarZZzkUcJDTM+BTXAhjVL+fuPH5k++wuCGodX+vx6EnQlNYnX5nyNnYNTncRrELvSAr/gcC6eqZgyVKvVcvHMQQKb1N3A7tKSIjLSknBwuvOpv0vLITNP0r+uZEvkFUo0blTxN7a0AH8P+R1P9Xs9CXJ3kvP9uhKK7qDxSalU4RMYQczZg/plWq2WmHMH8Q9tbXQd/9BWxJw7aLAs5sx+/IyUt7Kxx9bBhYy0eC7HnaFZ274AlJfprjplMsPDn0yuQKrFYNDiYg2XU0v0r7jEIjKySmnfqmIyDBtrBeFNHDhzbbxPTclkYHEtsUq9UkJ6Zin+voZXs34+1qRdvf0/hEKpwqNRBEmXDuiXSVotSRcP4B3Yxug63oGtSbpkuP8To/bjFdj6tv/9ulBaXpGYZOTqJjHILdTS2M+w7gd4yolPq/uWEJmsIvm6lTI1ZBdUvNLzdN1Xg7wq1lcpdTO+Xc4wftGs1UJqFgR6Gf6bQZ4ykq+tk1MIeUUSrvaGZVwdZOTe3mmuSgqlCi//CBKiDOtOQtQBfIKM1x2foNYkXjCsOwnn9+MT1Lraf0uStGjUupsAfUe9xfg3/2L8G2sZ/8ZaHn5uEQBDJ31O9wdfuYNvdBONmvKkWKxuPF/JZFg2aUFZ3EWjq8hUlnDT8UPSXn9fd12Sa0JSqym6GIVD2/YVC2Uy7Nu2o+DsmerXLS+jPCMDFAqcevQiZ9+euxytcLskrdZkr/+iBt0iBLpxP127dqV9+/a8++67tGzZErlczpEjR7hw4QLt2rUjNjaWFStWMGDAANzd3UlOTubDDz/E2tqaBx544K7FtuNYKQ90tiQ9W0NGrpah3azJLdBy8oYZ4V4aZcvJS+X8e0J3orC0AHfniosAV0c5jTwUFBZr9c8I2nqklMlDbbiUrOJioprwICUtQiz4/Pe66443aNho/vfFewSFNiO4cQSb1/1OaUkx3fvpmtm//3w2zq4ejBo3FYANq5ewZvkinpk+FzcPb3KydXcOraxssLK2Qa1Ws/Cj10mIucArb3+GVqvRl7Gzc0RpUXeTPfQeMo5fv34T/+AIAkJbsGvjL5SWFtOp13AAli58AycXDx4c/TKgG6Cflhyj///crKskx1/A0soGdy9df+w/l35C8/Y9cXHzITc7nY1/fI1crqBdt/vrLO4b7Yksp287C9JzJbLytAzqYEFekWQwI9zTQy05E6dh3xldYq1SgptjxQnbxUGGj6uMolLIKZCQy2HcABWN3OX8uLEUuQzsrzWaFpViMCNVTXUdNJ7V/5uFb1BzGgW3YP+WpZSVFtOuxwgAVn4/EwdnTwaO0rWsdh44jh/mjWPvPz8T1qonkQc3cjnuLMMnVnRBPX14E7b2Lji5epOWdJENy+YR3q4vjVt0BXTjiFw9/flr8WwGPfYaNnZOnD++nZiz+xk77dvKQdbCyr8vM/5Rf5JSikm9UsLkJwLJzCplz8GKO+IL3m/J7gMZrNmgu+P69LggDh7L4kp6CTbWSvr39KBNCyemza7oard8TRKTRgcSHVfApbgC7u/jRUAjG976sHbTfrfpNYGty2fi6dccT/+WnPx3CeqyYsI76rprbvn1NWwdPek6VDdNfeue41j91ViO7/yJwPCeXDy+katJZ+j76Bz9NksKc8jPTqUwTzf1ffZVXZc9Gwc3bB10iX9hXjpFeRnkZOhaRDNSL6KytMXe2RsrW6dafZfrdp8sZ0B7Fek5WrLyJB7opCK3UOJ0bMUNpOeGWxEZq2FvpO5YqrIAd8eKxNjFQY6vm0RhiUROgYRKCf3vU3EmVk1ekYStlYzuLS1wtJVxMrr2N6YOR2npFiEnK19LToFEr5Zy8ovhQnJFIvREbzkXkiWOXtItOxilZVgnOalZMlIyJTqEybFQou8GB3DggpaezeVcydG1LrUKkuNqD6tiK36kDjZgrQJHG11C5+mkW55VAOU1+Ert+0xg49KZeAU0xzugJUd3LqG8tJjmnXV1Z8Pi17B38qTHcF3dadd7HL9/PpYj234iuHlPLhzdSFriGQaM0dWdstIiDm76jtCWfbB1cKe4MJsT/y6jIOcKYW0H6WJ28TGIQWWpuyng5OaPvbPXbez5W8vfsR6XsVMpS4yhLD4au96DkVtaUnhwJwDOY59Hk5tF3t+6GStLzhzFrvcQypLjKIuPRunuheOQxyg5faxSgnQvXFm5gsDX36Tw4gWKzp/DY+Qo5FZWZG7SzZ4ZOOstytIzSPnhOwBsmoWjcnOnKPoSKjd3vJ+ciEwm48pvy+557NVR2NpgG1oxzskmqBEOrZpSlpVLSdLdmYBI+G9r8IlQSEgIJ06cYN68ecyaNYvk5GQsLS0JDw9nxowZPPfcc+Tk5LBnzx4WLFhAdnY2np6e9OjRg/379+PhcfeeY7DlcCkqCxmjB9pgYykj5rKar1YVGjzvx91JgZ11xQJ/LyXTHqvoLvZIH92V6oEzZSz9Rzf+59SlcpZvKWZQJ0tG9bHmSraWRX8VEnO57vqsd+zen7y8bNYsX0Rudib+QU2YMfsLHK91L8vKuIL8hkGYOzatQa0uZ+FHhuOnhj82mRGPTyE78yonDu8G4O2XnzAo8/r739KsRbs6i71dl0EU5GWx4Y+vyc/JwDewKc+98Z3+GUHZGakGswnmZl3lo9ce0b/fvm4x29ctJjS8PS+9+zMAOVlXWPzFTIryc7BzcCa4aVumfbAMewfDrgt1ZedJNSoLGSN7qrBW6R6o+r/1pQZ1x9VBhq1Vxffw85Dz7LCK8W7DuurGAB25oGbFzjIcbWU0D9IdMqaPMuw2+u1fJcSk3P7JvmWnByjMz2b7mi/Jz83A278ZT766SN81Ljcz1aDlJqBxG0Y9+39sW/UFW1Z+jqtnAGNe/grPRhVjdPJz0vln+UcU5GZi7+RG667D6D38Wf3nCqUF46Z/z5Y/PuOXz5+jrKQIV09/Hp4y3+DBrHdi2eokrKwUvPZ8E+xslZw+l8v02acpK6+4WPX1ssbJoSKBd3a04K1XmuLqoqKwUE1MfCHTZp/m6A2zz638+zKWKjkvTA7Bwd6C6LgCXnknkpS02o3Yb9L2AYoLszj4z5cU5qXj7tuMYU//oJ8QIT/bcP97B7Vl4LhPOLBhAfvXf4aTeyBDJn2Nq3fF/o89s4Ntv83Sv9+0VHenvsPA5+l0/wsAnN73O4c3L9SXWf3VGAD6PT5fn4TV1vbj5agsZDza21L3IOpUDd//XWxQ990c5dhZVdRXfw8Fzz9UUadHdNe13B8+X87ybaVoJfBwljPhASvsrGUUFkskXtXy5epi0rJqf5G7/7yEhVJi8H1yrFSQmC6xfJfG4KaCs53s2jOCdHXnXKKEjaWWni3k2FnpHqi6fJfGYNKFw1ESSrmW/m3kWFvqyizbqSH7hntdvVrIaRVc8bedcr/ut710u4aEq7fuxtW0/QMUFWSxb/2X1x6o2oyRz/+g7+qWn52K7IZjvG9IW4ZM/IQ9fy9gz9+f4eweyIinv8bdR1d35HIFWWmx/HXwT4oLs7GydcI7oAWPT1uGm8+9f5ZN8fH95Ng54DD4URT2TpRfjifj6w/0EygoXdwMurvlbVqNJEk4DnkchaMLmoI8Ss4cJXfdb/c8doDsndtROjrh8+Tkaw9UvcSlmdNRZ+uOJyoPT4NB93KVCp+JT2Hp44O2uJjcQweInzcXTeHdGa9cW47tmtN5+y/69+GfvAFA0tI1RE6aVdVq/ylisoS6JZOke9hxVeDZ/8sxdQi1Nn7ovW3er2t5pda3LlSPbdlf+zvPptap7V2a5vYeWfC+eXcPefy5uknwTOFSdP26ELtdrm7mXfe9Pcz7fumgPx+5daF66spZ8x6fk7a3fswmWVuDy6NuXcgE+j1+9NaF7pJtv7W/dSEz06DHCAmCIAiCIAiC0DCZ960eQRAEQRAEQWggajOpj1A10SIkCIIgCIIgCEKDI1qEBEEQBEEQBMEMaMVkCXVKtAgJgiAIgiAIgtDgiBYhQRAEQRAEQTAD/9UHm5qKaBESBEEQBEEQBKHBEYmQIAiCIAiCIAgNjugaJwiCIAiCIAhmQBKTJdQp0SIkCIIgCIIgCEKDI1qEBEEQBEEQBMEMiAeq1i3RIiQIgiAIgiAIQoMjEiFBEARBEARBEBoc0TVOEARBEARBEMyAmCyhbokWIUEQBEEQBEEQGhzRIiQIgiAIgiAIZkDSiskS6pJoERIEQRAEQRAEoeGRhP+EkpISafbs2VJJSYmpQ6kVEb9pmXP85hy7JIn4TcmcY5ckEb8pmXPskiTiF4TrZJIkiVFX/wF5eXk4OjqSm5uLg4ODqcO5bSJ+0zLn+M05dhDxm5I5xw4iflMy59hBxC8I14mucYIgCIIgCIIgNDgiERIEQRAEQRAEocERiZAgCIIgCIIgCA2OSIT+IywtLZk9ezaWlpamDqVWRPymZc7xm3PsIOI3JXOOHUT8pmTOsYOIXxCuE5MlCIIgCIIgCILQ4IgWIUEQBEEQBEEQGhyRCAmCIAiCIAiC0OCIREgQBEEQBEEQhAZHJEKCIAiCIAiCIDQ4IhESBEEQBEEQBKHBEYmQIAiCIAiCIAgNjkiE/gOio6PZvHkzxcXFAIgZ0YWaKisrIyoqCrVabepQasUc409KSiI5OVn//vDhw7z88sssWrTIhFHdPnPc9wB9+vQhJyen0vK8vDz69Olz7wNqYIqLiykqKtK/T0hIYMGCBWzZssWEUQmC0FCJRMiMZWZm0q9fP5o0acIDDzxAamoqAJMmTWL69Okmju6/LzExsdpXfVZUVMSkSZOwsbEhIiJCH+8LL7zAhx9+aOLobs2c4x89ejQ7d+4EIC0tjf79+3P48GHefPNN5syZY+Lobs2c9z3Arl27KCsrq7S8pKSEPXv2mCCihmXYsGEsXboUgJycHDp27Minn37KsGHD+Pbbb00cXe1kZ2frv1N9ptVqq1xe389ZkiQRFxenv/FSVlbGihUrWLp0KRkZGSaOTjBnIhEyY6+88gpKpZLExERsbGz0yx999FE2bdpkwshqLigoiODg4Cpf9VlgYCBBQUFVvuqzWbNmcerUKXbt2oWVlZV+eb9+/VixYoUJI6sZc47/zJkzdOjQAYA//viD5s2bs3//fpYtW8bixYtNG1wNmOu+j4yMJDIyEoBz587p30dGRnLixAl+/PFHfH19TRzlrRUWFvL222/TpUsXQkNDzeqYCXD8+HG6d+8OwKpVq/D09CQhIYGlS5fy5Zdfmji62klMTGTChAmmDqNKeXl5jBo1CltbWzw9PXnnnXfQaDT6z9PT0+v1OSsqKoqgoCBCQ0Np1qwZcXFxdOnShUmTJvHss8/SrFkzLl26ZOowBTOlNHUAQu1t2bKFzZs306hRI4PljRs3JiEhwURR3Z6XX37Z4H15eTknTpxg06ZNvPrqq6YJqoZOnDhh8P567J999hkffPCBiaKqmbVr17JixQo6deqETCbTL4+IiCAmJsaEkdWMOcdfXl6OpaUlANu2bePBBx8EoGnTpvpW3frMXPd969atkclkyGQyo13grK2t+eqrr0wQ2e2ZPHky//77L2PHjsXb29vgb2AOioqKsLe3B3TnsIceegi5XE6nTp3q7XkrLy+v2s/z8/PvUSS18/bbb3Pq1Cl++eUXcnJyeP/99zl+/Dhr1qxBpVIB9btL/cyZM2nVqhXr1q3jp59+YvDgwTRp0oQDBw6g1Wp55JFHmDNnDr/88oupQxXMkEiEzFhhYaFBS9B1WVlZ+gut+u6ll14yuvzrr7/m6NGj9zia29OqVatKy9q3b4+Pjw//93//x0MPPWSCqGomPT0dDw+PSssLCwvN4sLKnOOPiIjgu+++Y/DgwWzdupW5c+cCkJKSgqurq4mjuzVz3fdxcXFIkkRwcDCHDx/G3d1d/5lKpcLDwwOFQmHCCGvmn3/+YcOGDXTt2tXUodRKaGgoa9euZcSIEWzevJlXXnkFgKtXr+Lg4GDi6IxzcnKqtm5LklSv6/7atWtZsmQJvXr1AmD48OEMHjyYoUOH8vfffwPU6/j379/Pli1baNGiBe+//z5ffPEFixYtwsLCAoDXX3+dxx9/3MRRCuZKdI0zY927dzfolyyTydBqtXz88cf07t3bhJHdufvvv5/Vq1ebOoxaCQsL48iRI6YOo1rt27dnw4YN+vfXT4I//PADnTt3NlVYNWbO8X/00Ud8//339OrVi8cff1yfUP/999/6LnP1mbnu+4CAAAIDA9FqtbRv356AgAD9y9vb2yySIABnZ2dcXFxMHUatvfPOO8yYMYPAwEA6dOigrzNbtmyhTZs2Jo7OOHt7e+bPn8+OHTuMvur7RCfp6ekEBATo37u5ubFt2zby8/N54IEHDCavqI8KCgr0dd7W1hZbW1u8vb31n/v5+XHlyhVThSeYOdEiZMY+/vhj+vbty9GjRykrK+O1117j7NmzZGVlsW/fPlOHd0dWrVpV70/2N3eXkCSJ1NRU3n33XRo3bmyiqGpm3rx53H///Zw7dw61Ws0XX3zBuXPn2L9/P//++6+pw7slc46/V69eZGRkkJeXh7Ozs375lClTjLbw1jfmvO+BWw5qHzdu3D2KpHbmzp3LO++8w5IlS8yivtxs5MiRdOvWjdTUVINW9b59+zJixAgTRla1tm3bAtCzZ0+jnzs5OdXrrmX+/v6cP3/eYByQvb09W7ZsYcCAAfV2v1/n4+NDYmIi/v7+gO7a58ZW6fT0dINjqSDcDplUn3+9wi3l5uaycOFCTp06RUFBAW3btmXq1KkGd0vqszZt2hg0yUuSRFpaGunp6XzzzTdMmTLFhNFVTy6XV+pOIEkSfn5+/P777/X67jhATEwMH374oUHdmTlzJi1atDB1aDVizvGr1Wp27dpFTEwMo0ePxt7enpSUFBwcHLCzszN1eLdkzvv+5gum8vJyioqKUKlU2NjYkJWVZaLIqnbzcTI6OhpJkggMDNR3D7ru+PHj9zq8WomOjiYmJoYePXpgbW1dr7uX/e9//6OoqKjKrtxXrlzhu+++Y/bs2fc4spp58cUXSU1NZeXKlZU+y8/Pp3///hw5csRgAoX65JlnnqF9+/ZMnjzZ6Ocffvghe/bsMWipFoSaEomQYFLvvfeewXu5XI67uzu9evWiadOmJoqqZm6++3099tDQUJRK0dgqGJeQkMCgQYNITEyktLSUixcvEhwczEsvvURpaSnfffedqUNscC5dusSzzz7Lq6++ysCBA00dTiU3HyerU18vxq/LzMxk1KhR7Ny5E5lMxqVLlwgODmbixIk4Ozvz6aefmjrE/5zs7GxSUlKIiIgw+nl+fj7Hjx+vssWrvouLi8PKyspsbgAL9YtIhMzY9algbyaTybCyssLf399sJk0QTOPq1atcvXq10vMlWrZsaaKIbo85xj98+HDs7e358ccfcXV15dSpUwQHB7Nr1y6eeuops5kG1hz3fXWOHj3KE088wYULF0wdyn/auHHjuHr1Kj/88APNmjXT1//Nmzczbdo0zp49a+oQKykpKWHbtm0MGTIE0E0hX1paqv9coVAwd+5cg+nk65Pi4mK2b99eZfxKpZI5c+bU2/jNff8L9Zu4bW3Grk8HCxVTX97YtcDCwoJHH32U77//vl4dIG41FemN6tssQtdn2KmJ69Mi10fHjh1j/PjxnD9/vlLfdplMVm+7SFxnzvHv2bOH/fv366etvS4wMJDLly+bKKqaM+d9Xx2lUklKSoqpw7il4OBgjhw5UmmGwZycHNq2bUtsbKyJIqsZc3zsw+LFi9mwYYP+QnzhwoVERERgbW0NwIULF/Dx8dHPgFffLFmy5Jbxe3t719v4zX3/C/WbSITM2J9//snMmTN59dVX9bNNHT58mE8//ZTZs2ejVqt5/fXXeeutt/jkk09MHG2FW01FeqP6dlE1fPhwg/cymczgYvDG71XfYr/RxIkTadKkCT/++COenp71tm9+Vcw5fq1Wa7RuJCcn65+vUp+Z876Hyjczrk9ysnDhQrOYkjo+Pt5o/SktLSU5OdkEEd0ec3zsw7Jly3jttdcMli1fvlz/ANtff/2Vr7/+ut5eiIv4BaFqIhEyYx988AFffPGFQZ/2Fi1a0KhRI95++20OHz6Mra0t06dPr1eJ0M6dO/X/Hx8fz+uvv86TTz6pn1zgwIEDLFmyhPnz55sqxCrd2A1o27ZtzJw5k3nz5hnE/tZbbzFv3jxThVgjsbGxrF69mtDQUFOHUivmHP+AAQNYsGCBfspdmUxGQUEBs2fP5oEHHjBxdLdmzvsejN/McHd3p0+fPvV6fMqNCdzmzZtxdHTUv9doNGzfvt1gVrD66vpjH64/P8scHh/LIHMAAB9CSURBVPsQHR1tMBGIlZUVcnnF00c6dOjA1KlTTRFajYj4BaEakmC2rKyspPPnz1dafv78ecnKykqSJEmKi4uTrK2t73VoNdanTx9p+fLllZYvW7ZM6tmz570P6DZERERIe/bsqbR89+7dUtOmTU0QUc0NGzZMWrVqlanDqDVzjj8xMVEKDw+XmjVrJimVSqlTp06Sq6urFBYWJl25csXU4d2SOe97cyaTySSZTCbJ5XL9/19/qVQqqUmTJtK6detMHeYtnT59WvLw8JAGDRokqVQqaeTIkVKzZs0kT09PKTo62tThGWVlZSVduHChys/Pnz8vWVpa3sOIbo+IXxCqJlqEzFjTpk358MMPWbRokX68QXl5OR9++KF+xrXLly/j6elpyjCrdeDAAaOzZFU3VWZ9ERMTg5OTU6Xljo6OxMfH3/N4bscPP/zA+PHjOXPmDM2bN680BW99Ht8E5h2/n58fp06dYsWKFfrppydNmsSYMWP0fd7rM3Pe9zeTjIytrK+ut0YHBQVx5MgR3NzcTBxR7TRv3pyLFy+ycOFC7O3tKSgo4KGHHqrXj31o1KgRZ86cISwszOjnkZGRlcY81ScifkGohqkzMaH29u3bJ7m6ukru7u5S3759pb59+0oeHh6Sq6urdODAAUmSJGnp0qXSxx9/bOJIq9akSRPp1VdfrbT81VdflZo0aWKCiGque/fuUv/+/aW0tDT9srS0NGnAgAFSjx49TBjZrf3999+So6NjpTvL1+8413fmGn9ZWZkUHBwsnTt3ztSh1Jq57vsbLVmyRGrevLlkaWkpWVpaSi1atJCWLl1q6rCEeurFF1+UwsPDpeLi4kqfFRUVSeHh4dKLL75ogshqRsQvCFUT02ebufz8fJYtW8bFixcBCAsL0z+g0Rxs3LiRhx9+mNDQUDp27AjoJny4dOkSq1evrtdjJqKjoxkxYgQXL17Ez88PgKSkJBo3bszatWvr9RiKwMBAhgwZwttvv12vWwyrYs7x+/r6sm3bNpo1a2bqUGrFnPc9wGeffcbbb7/N888/r58cYe/evXz99de8//779XLA9Zdfflnjsi+++OJdjOTO/fzzz9jZ2fHII48YLF+5ciVFRUWMHz/eRJFV7cqVK7Ru3RqVSsXzzz9PkyZNAIiKimLhwoWo1WpOnDhRb38PIn5BqJpIhP4Dzp07R2JiImVlZQbLzaWLSlJSEt9++63++R3NmjXjmWee0ScX9ZkkSWzdutUg9n79+tX7rjb29vacPHmSkJAQU4dSK+Yc/7x587h48SI//PCDWT5415z3Pei6lr333nuMGzfOYPmSJUt49913iYuLM1FkVavpJAgymazeT5/dpEkTvv/++0oTI/z7779MmTKFqKgoE0VWvbi4OJ599lm2bt1q0KWyf//+fPPNN/oZzOorEb8gGCcSITMWGxvLiBEjOH36tH4aZ3OZvvm/pqSkBEtLy3qfAF03fvx4unfvXu/HYVXFnOMfMWIE27dvx87OjhYtWmBra2vw+Zo1a0wUWc2Y874H3YxTZ86cqdRie+nSJVq0aEFJSYmJImsYrKysuHDhAoGBgQbL4+PjadasGcXFxaYJrIaysrKIjo4GIDQ0FBcXFxNHdHtE/IJgyPxuRwp6L730EkFBQfppUw8dOkRWVla9my77Vvbs2cP3339PbGwsK1euxNfXl19++YWgoCC6detm6vCqpNVq+eCDD/juu++4cuUKFy9eJDg4mLfffpvAwEAmTZpk6hCr1KRJE2bNmsXevXtp0aJFpQHv9b17jTnH7+TkxMMPP2zqMGrNnPc96C6e/vjjD9544w2D5StWrKBx48Ymiqrh8PDwIDIyslIidOrUqUoPia2PXFxc9M/tM0cifkEwJFqEzJibmxs7duygZcuWODo6cvjwYcLCwtixYwfTp0/nxIkTpg7xllavXs3YsWMZM2YMv/zyC+fOnSM4OJiFCxeyceNGNm7caOoQqzRnzhyWLFnCnDlzeOqppzhz5gzBwcGsWLGCBQsWcODAAVOHWKXqutqYQ/cac4/fnJn7vl+9ejWPPvoo/fr1048R2rdvH9u3b+ePP/5gxIgRJo7w1pKTk/n777+Ndon+7LPPTBRVzcycOZMVK1bw888/06NHD0DXLW7ixImMHDnSrG7iCYJg/kQiZMacnZ05fvw4QUFBhISE8MMPP9C7d29iYmJo0aIFRUVFpg7xltq0acMrr7zCuHHjsLe359SpUwQHB3PixAnuv/9+0tLSTB1ilUJDQ/n+++/p27evQewXLlygc+fOZGdnmzpEQRCMOHbsGJ9//jnnz58HdGP7pk+fTps2bUwc2a1t376dBx98UH+sad68OfHx8UiSRNu2bdmxY4epQ6xWWVkZY8eOZeXKlfoxclqtlnHjxvHdd9/pHwUhCIJwL4iucWasefPmnDp1iqCgIDp27MjHH3+MSqVi0aJFZjNwMCoqSn9X8EaOjo7k5OTc+4Buw+XLl43ODKfVaikvLzdBRII5CAoKqnYsWX1vUfkvaNeuHb/++qupw6iVWbNmMWPGDN577z3s7e1ZvXo1Hh4ejBkzhkGDBpk6vFtSqVSsWLGCuXPncurUKaytrWnRogUBAQGmDk0QhAZIJEJm7K233qKwsBDQddMaMmQI3bt3x9XVlRUrVpg4uprx8vIiOjq6Un/xvXv31vtkLjw8nD179lQ6ga9atape3lmeNm0ac+fOxdbWlmnTplVbtj52rzH3+K97+eWXDd6Xl5dz4sQJNm3axKuvvmqaoG7hv7Lvr9NoNKxdu1bfIhQREcGDDz6IQqEwcWS3dv78eX777TcAlEolxcXF2NnZMWfOHIYNG8azzz5r4ghrpkmTJvppkAVBEExFJEJmbODAgfr/Dw0N5cKFC2RlZeHs7Gw2s5c99dRTvPTSS/z000/IZDJSUlI4cOAAM2bM4O233zZ1eNV65513GD9+PJcvX0ar1bJmzRqioqJYunQp69evN3V4lZw4cULfUmUO48duZu7xX/fSSy8ZXf71119z9OjRexxNzfxX9j3onv81ePBgkpOT9U+qnz9/Pn5+fmzYsKHeTwtua2urHxfk7e1NTEwMERERAGRkZJgytBrRaDQsXryY7du3c/XqVbRarcHn9b1rnyAI/y1ijJBgUpIkMW/ePObPn68f02RpacmMGTOYO3euiaMzLjY2Vt+9ac+ePcyZM4dTp05RUFBA27ZteeeddxgwYICpwxTMTGxsLK1btyYvL8/UofynPfDAA0iSxLJly/RT72ZmZvLEE08gl8vZsGGDiSOs3vDhwxk8eDBPPfUUM2bM4K+//uLJJ59kzZo1ODs7s23bNlOHWK3nn3+exYsXM3jwYLy9vSvdtPv8889NFJkgCA2SJAj1QGlpqXT27Fnp0KFDUn5+vqnDqZZcLpeuXLmifz9q1CgpLS3NhBHdvgkTJkh5eXmVlhcUFEgTJkwwQUS3x9zjN+ajjz6SAgICTB3GLZn7vrexsZEiIyMrLT958qRka2trgohuT0xMjHTq1ClJknT7/Omnn5ZatGghPfTQQ1J8fLyJo7s1V1dXacOGDaYOQxAEQZIkSRItQoJJTJw4sUblfvrpp7scye2Ty+WkpaXh4eEBgIODAydPnqz3Y5pupFAoSE1N1X+H6zIyMvDy8kKtVpsospox5/jbtGljcBdckiTS0tJIT0/nm2++YcqUKSaM7tbMed+D7jkk69evp0uXLgbL9+3bx9ChQ8nKyjJRZA2Dj48Pu3btEuODBEGoF8QYIcEkFi9eTEBAAG3atMHcc3Fzij8vLw9JkpAkifz8fKysrPSfaTQaNm7cWOkCtz4x9/hB17XpRnK5HHd3d3r16kXTpk1NE1QN/Bf2PcCQIUOYMmUKP/74o/7BjIcOHeKZZ57hwQcfNHF0NZOTk8OqVauIiYnh1VdfxcXFhePHj+Pp6Ymvr6+pw6vW9OnT+eKLL1i4cKHZjGUVBOG/SyRCgkk8++yz/Pbbb8TFxTFhwgSeeOIJfX/9+k4mk1U6gZvLCd3JyUkfv7E7sjKZjPfee88EkdWMuccPMHv2bFOHUCv/hX0P8OWXXzJ+/Hg6d+6MhYUFoJu5b9iwYSxYsMC0wdVAZGQk/fr1w9HRkfj4eJ566ilcXFxYs2YNiYmJLF261NQhVmvv3r3s3LmTf/75h4iICP3f4Lo1a9aYKDJBEBoi0TVOMJnS0lLWrFnDTz/9xP79+xk8eDCTJk1iwIAB9TqxkMvl3H///VhaWgKwbt06+vTpg62trUG5+nhC//fff5EkiT59+rB69WqD5FOlUhEQEICPj48JI6yeucZ/OxMgODg43MVIas9c931VoqOjDR6oauyZYPVRv379aNu2LR9//LHBg5z379/P6NGjiY+PN3WI1ZowYUK1n//888/3KBJBEASRCAn1REJCAosXL2bp0qWo1WrOnj2LnZ2dqcMy6lYn8uvq8wk9ISEBf3//ep1wVicuLo7AwECziV8ul98yVkmSkMlkaDSaexRV7Zh73anqOUgymQwrKytCQ0MZNmxYvW2hdnR05Pjx44SEhBgkQgkJCYSFhVFSUmLqEAVBEMyG6Bon1AvXLxQlSar3F4L1OcGpTmRkpMH706dPV1m2ZcuWdzucO9KnTx8mTJjAk08+ib+/v6nDuaWdO3eaOoQ6ExAQwJ49e/j++++JjY1l5cqV+Pr68ssvvxAUFES3bt1MHWK1Tpw4wfHjx9FoNPrnCF28eBGFQkHTpk355ptvmD59Onv37iU8PNzE0VZmaWlptIXx4sWLuLu7myCi26dWq9m1axcxMTGMHj0ae3t7UlJScHBwqLc3wARB+G8SLUKCydzYNW7v3r0MGTKECRMmMGjQIORyuanD+8+5Mdmsjjm0SixYsIDFixdz5swZevfuzaRJkxgxYoS+u6Jw96xevZqxY8cyZswYfvnlF86dO0dwcDALFy5k48aNbNy40dQhVmvBggXs2bOHn3/+Wd8NMTc3l8mTJ9OtWzeeeuopRo8eTXFxMZs3bzZxtJVNnjyZzMxM/vjjD1xcXIiMjEShUDB8+HB69OhR78c5JSQkMGjQIBITEyktLeXixYsEBwfz0ksvUVpaynfffWfqEAVBaEBEIiSYxHPPPcfvv/+On58fEydOZMyYMbi5uZk6rP+0hISEGpcNCAi4i5HUnePHj7N48WJ+++03NBoNo0ePZuLEibRt29bUoRmIjIykefPmyOXySi1zN6vvrXFt2rThlVdeYdy4cQZds06cOMH9999PWlqaqUOslq+vL1u3bq3U2nP27FkGDBjA5cuXOX78OAMGDCAjI8NEUVYtNzeXkSNHcvToUfLz8/Hx8SEtLY1OnTrxzz//VBqrWN8MHz4ce3t7fvzxR1xdXfX1Z9euXTz11FNcunTJ1CEKgtCAiERIMAm5XI6/v3+lZ6rcrD5OOCDUP+Xl5XzzzTfMnDmT8vJyWrRowYsvvsiECRPqxViWG589VV3LnDm0xtnY2HDu3DkCAwMNEqHY2FjCw8Pr/RgVOzs71q9fT69evQyW79q1i6FDh5Kfn09sbCytW7e+rUku7rV9+/Zx6tQpCgoKaNu2Lf369TN1SDXi6urK/v37CQsLM6g/8fHxhIeHU1RUZOoQBUFoQMQYIcEkxo0bVy8uUBuyX375he+++464uDgOHDhAQEAACxYsICgoiGHDhpk6vBopLy/nzz//5Oeff2br1q106tSJSZMmkZyczBtvvMG2bdtYvny5qcMkLi5OP34jLi7OxNHcGS8vL6KjowkMDDRYvnfvXrN4qPCwYcOYOHEin376Kffddx8AR44cYcaMGfpnPB0+fLheP/Bz+/btbN++natXr6LVarlw4YK+ntfHh1DfSKvVGk32k5OTsbe3N0FEgiA0ZCIREkxi8eLFpg6hQfv222955513ePnll/nggw/0FyZOTk4sWLCg3idCx48f5+eff+a3335DLpczbtw4Pv/8c4MHko4YMUJ/oWtqN3Y1NJduh1V56qmneOmll/jpp5+QyWSkpKRw4MABZsyYwdtvv23q8G7p+++/55VXXuGxxx5DrVYDoFQqGT9+PJ9//jkATZs25YcffjBlmFV67733mDNnDu3bt8fb29vsbigNGDCABQsWsGjRIkDXClpQUMDs2bN54IEHTBydIAgNjegaJwgNUHh4OPPmzdP317/ePeXMmTP06tWrXo6NuJFCoaB///5MmjSJ4cOHV3ooI0BhYSHPP/98vZ3l79y5cyQmJlJWVmaw/MEHHzRRRDUjSRLz5s1j/vz5+m5MlpaWzJgxg7lz55o4uporKCggNjYWgODgYLOZrczb25uPP/6YsWPHmjqUWklOTmbgwIFIksSlS5do3749ly5dws3Njd27d+Ph4WHqEAVBaEBEIiQIDZC1tTUXLlwgICDAIBG6dOkSLVu2pLi42NQhVishIcFsW1ZiY2MZMWIEp0+fNhgrdP3Ofn0fI3RdWVkZ0dHRFBQUEB4ebjaJhLlzdXXl8OHDhISEmDqUWlOr1axYscJgjNOYMWOwtrY2dWiCIDQwYo5iQWiAgoKCOHnyZKXlmzZtolmzZvc+oNtkrkkQwEsvvURQUBBXr17FxsaGs2fPsnv3btq3b8+uXbtMHV6NJSYmkpSURIsWLbCzs7vltOxC3Zg8eXK9GPd2J5RKJWPGjOHjjz/mm2++YfLkySIJEgTBJMQYIUFogKZNm8bUqVMpKSlBkiQOHz7Mb7/9xvz58+vt2AhnZ+caj4fIysq6y9HU3oEDB9ixYwdubm7I5XLkcjndunVj/vz5vPjii5w4ccLUIVYrMzOTUaNGsXPnTmQyGZcuXSI4OJhJkybh7OzMp59+auoQ/3OmTZum/3+tVsuiRYvYtm0bLVu2rNQt9LPPPrvX4d2WJUuW4ObmxuDBgwF47bXXWLRoEeHh4fz2229mfZNDEATzIxIhQWiArt+BfeuttygqKmL06NH4+PjwxRdf8Nhjj5k6PKNufFBkZmYm77//PgMHDqRz586ALsHYvHlzvR+wr9Fo9LNjubm5kZKSQlhYGAEBAURFRZk4ult75ZVXsLCwIDEx0aD18NFHH2XatGkiEboLbk6OW7duDcCZM2cMlpvDxAnz5s3j22+/BXS/2YULF7JgwQLWr1/PK6+8Ih6ZIAjCPSXGCAlCA1dUVERBQYFZDVJ++OGH6d27N88//7zB8oULF7Jt2zbWrl1rmsBqoHv37kyfPp3hw4czevRosrOzeeutt1i0aBHHjh2rdHFb33h5ebF582ZatWpV6TlCLVu2pKCgwNQhCvWYjY0NFy5cwN/fn5kzZ5KamsrSpUs5e/YsvXr1Ij093dQhCoLQgIgxQoLQAL3//vv659nY2NiYVRIEsHnzZgYNGlRp+aBBg9i2bZsJIqpeZGQkWq0WgLfeeks/nmbOnDnExcXRvXt3Nm7cyJdffmnKMGuksLAQGxubSsuzsrKwtLQ0QUSCObGzsyMzM/P/27u/kKrvP47jz3POOv0R01IPua1tNsPKM0WTzlFq4cIRtdaybpo3K3SsDXQ1GUQR2YgNF0JtbdG2hCApSqgNhkSLkqyck1KzY+HSXMwWZrW5EdXp7CI8v9+ZzWygn3P6vh5Xnu/3XLw4IHzf3/fn83kDcPjwYfLy8gAYM2ZM2B/SIiJPHhVCIha0f/9+kpOTycnJ4Ysvvgj747L/KS4ujkOHDg24fujQIeLi4gwkGlxGRkbwN161ahUvv/wyAMnJybS1tdHT08O1a9d45ZVXTMYckjlz5rB79+7gZ5vNxv379ykvLyc3N9dgMokEeXl5FBYWUlhYyMWLF4Ozg1pbWwcM6RURGW7aIyRiQU1NTbS2trJnzx62bNnC+++/T15eHgUFBbzxxhsPfeMfTsrKyigsLOTYsWN4PB4A6uvrqamp4auvvjKcbqDY2Fg6OjpwuVx0dnYGu0P9Jk6caCjZ4ysvL2fevHn89NNP3Llzhw8//JDW1lZ6e3upq6szHU/C3Pbt21m/fj2//PIL1dXVwRcXjY2NLF++3HA6EbEa7RESEerq6qiqqmL//v3cvn2b33//3XSkR6qvr2fbtm34fD4Apk+fTnFxcbAwCidvv/02u3fvJjExka6uLp599lkcDsdDv9s/5DOc3bp1i88//zxkDsx7771HYmKi6WgiIiJDpo6QiBAVFcXYsWNxOp388ccfpuMMicfjYc+ePaZjDMnOnTvJz8+nvb2d4uJiioqKgifHRZK7d+8yf/58duzYwbp160zHkQh148YNvvnmm5CXGCtXroyozqiIPBnUERKxqI6ODqqqqqiqquLChQvMnTuXN998k2XLlhETE2M63qC6uroGvf/cc8+NUJLHt2LFCrZt2xaRhRBAQkICJ0+eZOrUqaajSASqra1l0aJFxMTEkJWVBTxYFnfz5k2+++674P45EZGRoEJIxIK8Xi8NDQ2kpaVRUFDA8uXLeeaZZ0zHGjK73T7ozBS/3z+Caaxl9erVjB49mk8++cR0FIlAL730EtnZ2Xz55ZfB5aF+v593332XkydP0tLSYjihiFiJlsaJWNC8efPYtWsXM2bMMB3lP/nngMm7d+9y5swZKioq2Lx5s6FU1nDv3j127drFkSNHmDlzJlFRUSH3KyoqDCWTSNDe3s6BAwdC9sg5HA7WrFkTchqhiMhIUCEkYkGRXiykp6cPuJaVlcXTTz/Np59+Sn5+voFUT67m5mbcbjd2u51z586RmZkJwMWLF0O+N1iXTgQgMzMTn89HSkpKyHWfz/fQ/2sRkeGkQkjEoq5cucK3335LV1cXd+7cCbkXqW/1U1JSaGhoMB3jiZORkUF3dzcul4vLly/T0NAQlvOaJDw1NzcH/y4uLqakpIT29na8Xi8Ap0+fZvv27VpuKSIjTnuERCzohx9+4PXXX2fKlCm0tbXhdrvp7OwkEAiQmZnJ0aNHTUcc1D+P9w4EAnR3d7Nx40ba2to4e/asmWBPqLi4OL7//ns8Hg92u53ffvuNhIQE07EkQvTv6XvU44bNZtP+PhEZUeoIiVjQ2rVrKS0tpaysjOjoaKqrq3G5XBQUFDB//nzT8R4pNjZ2wDKsQCDA5MmT2bt3r6FUT66lS5cyd+5cEhMTsdlsZGVlRfQcJBlZHR0dpiOIiDyUOkIiFhQdHc3Zs2d58cUXmTBhAidOnCA1NZWmpiYWL15MZ2en6YiDOn78eMhnu91OQkICycnJPPWU3u8Mh5qamuAcpE2bNv3r8d8lJSUjnEwi0fnz5wcsy7XZbCxatMhgKhGxGj0xiFhQVFRU8AEkMTGRn3/+mdTUVAB6enpMRhsSm81GTk7OgKLn3r171NbWahbJMOjvFDY2NlJSUhKxc5DErEuXLrFkyRJaWlpClsv1d3i1NE5ERpLddAARGXler5cTJ04AsGDBAj744AM2b97MypUrgxuYw1lubi69vb0Drt+6dYvc3FwDiayjsrJSRZD8ZyUlJSQlJXHt2jXGjRvHuXPnqK2tJSsri2PHjpmOJyIWo46QiAVVVFTQ19cHQFlZGX19fezbt4+pU6dGxIlxgUDgoUc1X79+fcBcGxEJH6dOneLo0aPEx8djt9txOBzMnj2bjz/+mOLi4gEzwkREhpMKIRGL8fv9XLlyhbS0NODBMrkdO3YYTjU0/fOBbDYbb731FqNHjw7e8/v9NDc3k5OTYyqeiDyC3+8PdhTj4+P59ddfSUlJ4fnnn+fChQuG04mI1agQErEYh8PBq6++is/nIzY21nScxxITEwM86AhFR0czduzY4D2n04nX66WoqMhUPBF5BLfbTVNTE0lJSXg8HsrLy3E6nezcuZMpU6aYjiciFqNCSMSC3G43ly5dIikpyXSUx1JZWQlAQkICGzduZNy4cQB0dnZy8OBBpk+fTnx8vMmIIjKI9evX8+effwKwadMmXnvtNebMmUNcXBz79u0znE5ErEbHZ4tYUE1NDWvXruWjjz5i5syZA/bVjB8/3lCyocnLy2Pp0qW888473Lx5k2nTpjFq1Ch6enqoqKhg1apVpiOKyBD19vYyYcKEh+77ExEZTiqERCzIbv/fgZH///DRfwhBuB9hGx8fz/Hjx0lNTeXrr7/ms88+48yZM1RXV7NhwwZ8Pp/piCIiIhLmtDROxIIqKyuZPHkyDocj5Pr9+/fp6uoylGro/vrrr+CG68OHD5Ofn4/dbsfr9XL58mXD6URERCQSqCMkYkEOh4Pu7m5cLlfI9evXr+NyucK+I5SWlkZhYSFLlizB7XZTU1NDdnY2jY2NLFy4kKtXr5qOKCIiImFOA1VFLOjf5vD09fUxZswYA4kez4YNGygtLeWFF17A4/GQnZ0NPOgOZWRkGE4nIiIikUAdIRELWbNmDQBbt26lqKgoeOoaPJjvUV9fj8PhoK6uzlTEIbt69Srd3d2kp6cH9zz9+OOPjB8/nmnTphlOJyIiIuFOe4RELKR/ansgEKClpQWn0xm853Q6SU9Pp7S01FS8xzJp0iQmTZoUcm3WrFmG0oiIiEikUUdIxIJWrFjB1q1bw/6YbBEREZHhokJIREREREQsR4cliIiIiIiI5agQEhERERERy1EhJCIiIiIilqNCSERERERELEeFkIiIiIiIWI4KIRERERERsRwVQiIiIiIiYjl/A8vkQJT4ac/+AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x=\"age\",data=df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "_ea6jToPhoWW",
        "outputId": "4c082d9a-8010-4d28-b2ba-e01706449e55"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='age', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 95
        },
        {
          "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": [
        "df['G3'].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yljRgVwdkgZa",
        "outputId": "9b51dffa-c974-4e48-c8f5-429bd4142343"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    395.000000\n",
              "mean      10.415190\n",
              "std        4.581443\n",
              "min        0.000000\n",
              "25%        8.000000\n",
              "50%       11.000000\n",
              "75%       14.000000\n",
              "max       20.000000\n",
              "Name: G3, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## STEP 3:\n",
        "### Data Preprocessing\n",
        "In this step we will perform task as handling missing values and encode catagorical data\n",
        "\n"
      ],
      "metadata": {
        "id": "C06Lai1jbmQw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Handling missing values"
      ],
      "metadata": {
        "id": "rqVrV3dIYMwA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df=df.dropna()\n",
        "print((df.isnull()).sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "abM8kte7YRQG",
        "outputId": "3447d472-c243-41d8-b61d-47a0351abb03"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "school          0\n",
            "sex             0\n",
            "age             0\n",
            "address         0\n",
            "famsize         0\n",
            "Pstatus         0\n",
            "Medu            0\n",
            "Fedu            0\n",
            "Mjob            0\n",
            "Fjob            0\n",
            "guardian        0\n",
            "traveltime      0\n",
            "studytime       0\n",
            "failures        0\n",
            "Ssupport        0\n",
            "Gsupport        0\n",
            "feespaid        0\n",
            "ecactivities    0\n",
            "internet        0\n",
            "freetime        0\n",
            "goout           0\n",
            "health          0\n",
            "absences        0\n",
            "G1              0\n",
            "G2              0\n",
            "G3              0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Encoding catagorical variables"
      ],
      "metadata": {
        "id": "DcWrCBFqlkhP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#from sklearn.preprocessing import LabelEncoder\n",
        "#le=LabelEncoder()\n",
        "#for col in df.columns:\n",
        "#  df[col]=le.fit_transform(df[col])"
      ],
      "metadata": {
        "id": "LYQJNDMulZaF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder()\n",
        "\n",
        "# List of column names you want to encode\n",
        "columns_to_encode = [\"internet\", \"ecactivities\", \"feespaid\", \"Gsupport\", \"Ssupport\", \"guardian\", \"Fjob\", \"Mjob\", \"Pstatus\", \"famsize\", \"address\", \"sex\", \"school\"]\n",
        "\n",
        "# Apply LabelEncoder to selected columns using .loc\n",
        "for column in columns_to_encode:\n",
        "    df.loc[:, column] = le.fit_transform(df.loc[:, column])\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nnV6fBjcLi43",
        "outputId": "9dfdcea7-255f-4668-94ca-a4c54cbbd30f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-99-827a75d26753>:9: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df.loc[:, column] = le.fit_transform(df.loc[:, column])\n",
            "<ipython-input-99-827a75d26753>:9: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
            "  df.loc[:, column] = le.fit_transform(df.loc[:, column])\n",
            "<ipython-input-99-827a75d26753>:9: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df.loc[:, column] = le.fit_transform(df.loc[:, column])\n",
            "<ipython-input-99-827a75d26753>:9: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
            "  df.loc[:, column] = le.fit_transform(df.loc[:, column])\n",
            "<ipython-input-99-827a75d26753>:9: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df.loc[:, column] = le.fit_transform(df.loc[:, column])\n",
            "<ipython-input-99-827a75d26753>:9: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
            "  df.loc[:, column] = le.fit_transform(df.loc[:, column])\n",
            "<ipython-input-99-827a75d26753>:9: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df.loc[:, column] = le.fit_transform(df.loc[:, column])\n",
            "<ipython-input-99-827a75d26753>:9: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
            "  df.loc[:, column] = le.fit_transform(df.loc[:, column])\n",
            "<ipython-input-99-827a75d26753>:9: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df.loc[:, column] = le.fit_transform(df.loc[:, column])\n",
            "<ipython-input-99-827a75d26753>:9: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
            "  df.loc[:, column] = le.fit_transform(df.loc[:, column])\n",
            "<ipython-input-99-827a75d26753>:9: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df.loc[:, column] = le.fit_transform(df.loc[:, column])\n",
            "<ipython-input-99-827a75d26753>:9: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
            "  df.loc[:, column] = le.fit_transform(df.loc[:, column])\n",
            "<ipython-input-99-827a75d26753>:9: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df.loc[:, column] = le.fit_transform(df.loc[:, column])\n",
            "<ipython-input-99-827a75d26753>:9: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
            "  df.loc[:, column] = le.fit_transform(df.loc[:, column])\n",
            "<ipython-input-99-827a75d26753>:9: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df.loc[:, column] = le.fit_transform(df.loc[:, column])\n",
            "<ipython-input-99-827a75d26753>:9: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
            "  df.loc[:, column] = le.fit_transform(df.loc[:, column])\n",
            "<ipython-input-99-827a75d26753>:9: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df.loc[:, column] = le.fit_transform(df.loc[:, column])\n",
            "<ipython-input-99-827a75d26753>:9: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
            "  df.loc[:, column] = le.fit_transform(df.loc[:, column])\n",
            "<ipython-input-99-827a75d26753>:9: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df.loc[:, column] = le.fit_transform(df.loc[:, column])\n",
            "<ipython-input-99-827a75d26753>:9: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
            "  df.loc[:, column] = le.fit_transform(df.loc[:, column])\n",
            "<ipython-input-99-827a75d26753>:9: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df.loc[:, column] = le.fit_transform(df.loc[:, column])\n",
            "<ipython-input-99-827a75d26753>:9: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
            "  df.loc[:, column] = le.fit_transform(df.loc[:, column])\n",
            "<ipython-input-99-827a75d26753>:9: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df.loc[:, column] = le.fit_transform(df.loc[:, column])\n",
            "<ipython-input-99-827a75d26753>:9: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
            "  df.loc[:, column] = le.fit_transform(df.loc[:, column])\n",
            "<ipython-input-99-827a75d26753>:9: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df.loc[:, column] = le.fit_transform(df.loc[:, column])\n",
            "<ipython-input-99-827a75d26753>:9: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
            "  df.loc[:, column] = le.fit_transform(df.loc[:, column])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "le=LabelEncoder()\n",
        "df[\"internet\"]=le.fit_transform(df[\"internet\"])\n",
        "df[\"ecactivities\"]=le.fit_transform(df[\"ecactivities\"])\n",
        "df[\"feespaid\"]=le.fit_transform(df[\"feespaid\"])\n",
        "df[\"Gsupport\"]=le.fit_transform(df[\"Gsupport\"])\n",
        "df[\"Ssupport\"]=le.fit_transform(df[\"Ssupport\"])\n",
        "df[\"guardian\"]=le.fit_transform(df[\"guardian\"])\n",
        "df[\"Fjob\"]=le.fit_transform(df[\"Fjob\"])\n",
        "df[\"Mjob\"]=le.fit_transform(df[\"Mjob\"])\n",
        "df[\"Pstatus\"]=le.fit_transform(df[\"Pstatus\"])\n",
        "df[\"famsize\"]=le.fit_transform(df[\"famsize\"])\n",
        "df[\"address\"]=le.fit_transform(df[\"address\"])\n",
        "df[\"sex\"]=le.fit_transform(df[\"sex\"])\n",
        "df[\"school\"]=le.fit_transform(df[\"school\"])"
      ],
      "metadata": {
        "id": "smUBImmZa_Wu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b87cd580-41f4-4c0c-e0f0-ca653f8a0fa6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-100-f94a1fa6793b>:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df[\"internet\"]=le.fit_transform(df[\"internet\"])\n",
            "<ipython-input-100-f94a1fa6793b>:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df[\"ecactivities\"]=le.fit_transform(df[\"ecactivities\"])\n",
            "<ipython-input-100-f94a1fa6793b>:5: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df[\"feespaid\"]=le.fit_transform(df[\"feespaid\"])\n",
            "<ipython-input-100-f94a1fa6793b>:6: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df[\"Gsupport\"]=le.fit_transform(df[\"Gsupport\"])\n",
            "<ipython-input-100-f94a1fa6793b>:7: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df[\"Ssupport\"]=le.fit_transform(df[\"Ssupport\"])\n",
            "<ipython-input-100-f94a1fa6793b>:8: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df[\"guardian\"]=le.fit_transform(df[\"guardian\"])\n",
            "<ipython-input-100-f94a1fa6793b>:9: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df[\"Fjob\"]=le.fit_transform(df[\"Fjob\"])\n",
            "<ipython-input-100-f94a1fa6793b>:10: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df[\"Mjob\"]=le.fit_transform(df[\"Mjob\"])\n",
            "<ipython-input-100-f94a1fa6793b>:11: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df[\"Pstatus\"]=le.fit_transform(df[\"Pstatus\"])\n",
            "<ipython-input-100-f94a1fa6793b>:12: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df[\"famsize\"]=le.fit_transform(df[\"famsize\"])\n",
            "<ipython-input-100-f94a1fa6793b>:13: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df[\"address\"]=le.fit_transform(df[\"address\"])\n",
            "<ipython-input-100-f94a1fa6793b>:14: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df[\"sex\"]=le.fit_transform(df[\"sex\"])\n",
            "<ipython-input-100-f94a1fa6793b>:15: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df[\"school\"]=le.fit_transform(df[\"school\"])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "FTHT9zPQoPp7",
        "outputId": "75b2c400-82e1-4beb-d1b9-50b0d9b0569f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   school  sex  age  address  famsize  Pstatus  Medu  Fedu  Mjob  Fjob  ...  \\\n",
              "0       0    0   18        1        0        0     4     4     0     4  ...   \n",
              "1       0    0   17        1        0        1     1     1     0     2  ...   \n",
              "2       0    0   15        1        1        1     1     1     0     2  ...   \n",
              "3       0    0   15        1        0        1     4     2     1     3  ...   \n",
              "4       0    0   16        1        0        1     3     3     2     2  ...   \n",
              "\n",
              "   feespaid  ecactivities  internet  freetime  goout  health  absences  G1  \\\n",
              "0         0             0         0         3      4       3         6   5   \n",
              "1         0             0         1         3      3       3         4   5   \n",
              "2         1             0         1         3      2       3        10   7   \n",
              "3         1             1         1         2      2       5         2  15   \n",
              "4         1             0         0         3      2       5         4   6   \n",
              "\n",
              "   G2  G3  \n",
              "0   6   6  \n",
              "1   5   6  \n",
              "2   8  10  \n",
              "3  14  15  \n",
              "4  10  10  \n",
              "\n",
              "[5 rows x 26 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cca9ec7f-c3dd-4b23-8285-d3d01ecb58fb\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>school</th>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>address</th>\n",
              "      <th>famsize</th>\n",
              "      <th>Pstatus</th>\n",
              "      <th>Medu</th>\n",
              "      <th>Fedu</th>\n",
              "      <th>Mjob</th>\n",
              "      <th>Fjob</th>\n",
              "      <th>...</th>\n",
              "      <th>feespaid</th>\n",
              "      <th>ecactivities</th>\n",
              "      <th>internet</th>\n",
              "      <th>freetime</th>\n",
              "      <th>goout</th>\n",
              "      <th>health</th>\n",
              "      <th>absences</th>\n",
              "      <th>G1</th>\n",
              "      <th>G2</th>\n",
              "      <th>G3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>15</td>\n",
              "      <td>14</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>16</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 26 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cca9ec7f-c3dd-4b23-8285-d3d01ecb58fb')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-cca9ec7f-c3dd-4b23-8285-d3d01ecb58fb button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-cca9ec7f-c3dd-4b23-8285-d3d01ecb58fb');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-33067655-52ed-450b-9195-7dad99cb047c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-33067655-52ed-450b-9195-7dad99cb047c')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-33067655-52ed-450b-9195-7dad99cb047c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Finding Correlation between Attributes"
      ],
      "metadata": {
        "id": "weRFJ3UfobQe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "most_correlated_G2 = df.corr().abs()['G2'].sort_values(ascending=False)\n",
        "most_correlated_G2 = most_correlated_G2[:9]\n",
        "\n",
        "most_correlated_G3 = df.corr().abs()['G3'].sort_values(ascending=False)\n",
        "most_correlated_G3 = most_correlated_G3[:9]\n",
        "\n",
        "# Combine the most correlated features for both G2 and G3\n",
        "most_correlated = most_correlated_G2.index.union(most_correlated_G3.index)\n",
        "\n",
        "# Select these features in your DataFrame\n",
        "df = df.loc[:, most_correlated]\n"
      ],
      "metadata": {
        "id": "H5kNlEZto0Ub"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "rsKO9TIWo7LS",
        "outputId": "ce1795e8-7113-4d1d-df7c-a51b21ba8fd6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Fedu  G1  G2  G3  Medu  age  failures  goout  traveltime\n",
              "0     4   5   6   6     4   18         0      4           2\n",
              "1     1   5   5   6     1   17         0      3           1\n",
              "2     1   7   8  10     1   15         3      2           1\n",
              "3     2  15  14  15     4   15         0      2           1\n",
              "4     3   6  10  10     3   16         0      2           1"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6929c1f7-1408-48c2-bc88-60f74528be94\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Fedu</th>\n",
              "      <th>G1</th>\n",
              "      <th>G2</th>\n",
              "      <th>G3</th>\n",
              "      <th>Medu</th>\n",
              "      <th>age</th>\n",
              "      <th>failures</th>\n",
              "      <th>goout</th>\n",
              "      <th>traveltime</th>\n",
              "    </tr>\n",
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              "      <td>17</td>\n",
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              "      <td>3</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>15</td>\n",
              "      <td>4</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>3</td>\n",
              "      <td>16</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
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              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-6929c1f7-1408-48c2-bc88-60f74528be94 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6929c1f7-1408-48c2-bc88-60f74528be94');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "\n",
              "\n",
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              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
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              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8063bc9d-cb65-4360-9be6-9aa6bdd8653e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data spliting\n",
        "In this step we will split data in to testing and training data"
      ],
      "metadata": {
        "id": "E7ptr0O5pafN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.multioutput import MultiOutputRegressor"
      ],
      "metadata": {
        "id": "VuQaUOA4Fxzq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.drop(['G2', 'G3'], axis='columns')\n",
        "y = df[['G2', 'G3']]\n"
      ],
      "metadata": {
        "id": "n6V4zagYF1Vv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n"
      ],
      "metadata": {
        "id": "LF5buVO1pTk4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "34FME4sopjVL",
        "outputId": "eb9e9aa9-93f9-436a-c157-f2dc598334aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Fedu  G1  Medu  age  failures  goout  traveltime\n",
              "329     4  14     4   17         0      3           2\n",
              "175     3  10     4   17         0      4           2\n",
              "161     2   5     3   15         2      4           2\n",
              "144     1   5     2   17         3      5           1\n",
              "219     2   9     2   17         0      3           1"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5da5176d-8589-4e7c-9711-4e35dda9da92\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Fedu</th>\n",
              "      <th>G1</th>\n",
              "      <th>Medu</th>\n",
              "      <th>age</th>\n",
              "      <th>failures</th>\n",
              "      <th>goout</th>\n",
              "      <th>traveltime</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>329</th>\n",
              "      <td>4</td>\n",
              "      <td>14</td>\n",
              "      <td>4</td>\n",
              "      <td>17</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>175</th>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>4</td>\n",
              "      <td>17</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>161</th>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>15</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>144</th>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>17</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>219</th>\n",
              "      <td>2</td>\n",
              "      <td>9</td>\n",
              "      <td>2</td>\n",
              "      <td>17</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5da5176d-8589-4e7c-9711-4e35dda9da92')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-5da5176d-8589-4e7c-9711-4e35dda9da92 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-5da5176d-8589-4e7c-9711-4e35dda9da92');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-52e5fa04-073e-4450-a000-10a5bddef083\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-52e5fa04-073e-4450-a000-10a5bddef083')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-52e5fa04-073e-4450-a000-10a5bddef083 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model Selection and Model Training\n",
        "Firstly we will select the right algorithm according to our requirement and then we will train data on model"
      ],
      "metadata": {
        "id": "R8WS3MgVZ3ym"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lr = LinearRegression()\n",
        "model = MultiOutputRegressor(lr)\n",
        "model.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "5Zsp628gpxBj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117
        },
        "outputId": "5ef20fd7-8b5d-4a21-f860-c0f718b14132"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MultiOutputRegressor(estimator=LinearRegression())"
            ],
            "text/html": [
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See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MultiOutputRegressor(estimator=LinearRegression())</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MultiOutputRegressor</label><div class=\"sk-toggleable__content\"><pre>MultiOutputRegressor(estimator=LinearRegression())</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model Evaluation or Testing"
      ],
      "metadata": {
        "id": "FKNJDFSoaXK2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(model.score(X_test, y_test))                 #Near to 1, good predicts target"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L_4rs64Qai7H",
        "outputId": "ef496662-4885-4c04-9656-bebc4e0ac9f7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.7182368272632518\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = model.predict(X_test)"
      ],
      "metadata": {
        "id": "YwqQhzVrqAGN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Hhvajy9xqEu7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "rFDvuAJk6N1Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "TFcbCVlJ6Ngt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tvRxhwlQ6NcY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "LK5f0UCO6NYS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "lsSj_wm46L9O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0lsyi9k-6L2A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "J9KaJi8h6Lyr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KHcl_KcT6Luv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "b9BW4S496Lo8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "grxzv9cA6LYW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "user_input = {}  # Dictionary to store user inputs\n",
        "\n",
        "for col in X.columns:\n",
        "    user_input[col] = int(input(f\"Enter value for {col}: \"))\n",
        "\n",
        "# Create a DataFrame for user input\n",
        "user_input_df = pd.DataFrame([user_input])\n",
        "\n",
        "# Make predictions on user input for G2 and G3\n",
        "predicted_grades = model.predict(user_input_df)\n",
        "\n",
        "print(\"Predicted G2 Grade:\", predicted_grades[0, 0])\n",
        "print(\"Predicted G3 Grade:\", predicted_grades[0, 1])\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "Pwi-DOgyqHRx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f3056fa5-0226-4b25-848c-b5153272d178"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter value for Fedu: 1\n",
            "Enter value for G1: 1\n",
            "Enter value for Medu: 1\n",
            "Enter value for age: 1\n",
            "Enter value for failures: 1\n",
            "Enter value for goout: 1\n",
            "Enter value for traveltime: 1\n",
            "Predicted G2 Grade: 5.001413331209447\n",
            "Predicted G3 Grade: 6.045580510601643\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "id": "LibTCEqMtP86",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "b28f8e88-a2b2-4410-dcf0-7e21d94b4e59"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Fedu  G1  G2  G3  Medu  age  failures  goout  traveltime\n",
              "0     4   5   6   6     4   18         0      4           2\n",
              "1     1   5   5   6     1   17         0      3           1\n",
              "2     1   7   8  10     1   15         3      2           1\n",
              "3     2  15  14  15     4   15         0      2           1\n",
              "4     3   6  10  10     3   16         0      2           1"
            ],
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              "\n",
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              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6ffc63b2-547f-44a0-bd08-fcefc651515e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "IQTAKvyU6Fbv"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}