Outlier_Removal.ipynb

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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import warnings\n",
        "warnings.filterwarnings (\"ignore\")"
      ]
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      "source": [
        "df=pd.read_csv(\"cars.csv\")\n",
        "df.head()"
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              "   symboling normalized-losses         make fuel-type   body-style  \\\n",
              "0          3                 ?  alfa-romero       gas  convertible   \n",
              "1          3                 ?  alfa-romero       gas  convertible   \n",
              "2          1                 ?  alfa-romero       gas    hatchback   \n",
              "3          2               164         audi       gas        sedan   \n",
              "4          2               164         audi       gas        sedan   \n",
              "\n",
              "  drive-wheels engine-location  width  height engine-type  engine-size  \\\n",
              "0          rwd           front   64.1    48.8        dohc          130   \n",
              "1          rwd           front   64.1    48.8        dohc          130   \n",
              "2          rwd           front   65.5    52.4        ohcv          152   \n",
              "3          fwd           front   66.2    54.3         ohc          109   \n",
              "4          4wd           front   66.4    54.3         ohc          136   \n",
              "\n",
              "  horsepower  city-mpg  highway-mpg  price  \n",
              "0        111        21           27  13495  \n",
              "1        111        21           27  16500  \n",
              "2        154        19           26  16500  \n",
              "3        102        24           30  13950  \n",
              "4        115        18           22  17450  "
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              "      <td>ohc</td>\n",
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 205 entries, 0 to 204\n",
            "Data columns (total 15 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   symboling          205 non-null    int64  \n",
            " 1   normalized-losses  205 non-null    object \n",
            " 2   make               205 non-null    object \n",
            " 3   fuel-type          205 non-null    object \n",
            " 4   body-style         205 non-null    object \n",
            " 5   drive-wheels       205 non-null    object \n",
            " 6   engine-location    205 non-null    object \n",
            " 7   width              205 non-null    float64\n",
            " 8   height             205 non-null    float64\n",
            " 9   engine-type        205 non-null    object \n",
            " 10  engine-size        205 non-null    int64  \n",
            " 11  horsepower         205 non-null    object \n",
            " 12  city-mpg           205 non-null    int64  \n",
            " 13  highway-mpg        205 non-null    int64  \n",
            " 14  price              205 non-null    int64  \n",
            "dtypes: float64(2), int64(5), object(8)\n",
            "memory usage: 24.1+ KB\n"
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        "df[\"horsepower\"].value_counts()"
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              "68     19\n",
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              "?       2\n",
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              "175     1\n",
              "106     1\n",
              "Name: horsepower, dtype: int64"
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        "df[\"normalized-losses\"].replace(\"?\",np.nan,inplace=True) "
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            "text/plain": [
              "   symboling normalized-losses         make fuel-type   body-style  \\\n",
              "0          3               NaN  alfa-romero       gas  convertible   \n",
              "1          3               NaN  alfa-romero       gas  convertible   \n",
              "2          1               NaN  alfa-romero       gas    hatchback   \n",
              "3          2               164         audi       gas        sedan   \n",
              "4          2               164         audi       gas        sedan   \n",
              "\n",
              "  drive-wheels engine-location  width  height engine-type  engine-size  \\\n",
              "0          rwd           front   64.1    48.8        dohc          130   \n",
              "1          rwd           front   64.1    48.8        dohc          130   \n",
              "2          rwd           front   65.5    52.4        ohcv          152   \n",
              "3          fwd           front   66.2    54.3         ohc          109   \n",
              "4          4wd           front   66.4    54.3         ohc          136   \n",
              "\n",
              "  horsepower  city-mpg  highway-mpg  price  \n",
              "0        111        21           27  13495  \n",
              "1        111        21           27  16500  \n",
              "2        154        19           26  16500  \n",
              "3        102        24           30  13950  \n",
              "4        115        18           22  17450  "
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              "      <td>fwd</td>\n",
              "      <td>front</td>\n",
              "      <td>66.2</td>\n",
              "      <td>54.3</td>\n",
              "      <td>ohc</td>\n",
              "      <td>109</td>\n",
              "      <td>102</td>\n",
              "      <td>24</td>\n",
              "      <td>30</td>\n",
              "      <td>13950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>164</td>\n",
              "      <td>audi</td>\n",
              "      <td>gas</td>\n",
              "      <td>sedan</td>\n",
              "      <td>4wd</td>\n",
              "      <td>front</td>\n",
              "      <td>66.4</td>\n",
              "      <td>54.3</td>\n",
              "      <td>ohc</td>\n",
              "      <td>136</td>\n",
              "      <td>115</td>\n",
              "      <td>18</td>\n",
              "      <td>22</td>\n",
              "      <td>17450</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e9be5e88-06aa-4cdc-a06e-78da17818dbe')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-e9be5e88-06aa-4cdc-a06e-78da17818dbe 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-e9be5e88-06aa-4cdc-a06e-78da17818dbe');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"horsepower\"].replace(\"?\",np.nan,inplace=True)"
      ],
      "metadata": {
        "id": "KP_c1zEYTcyu"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"horsepower\"].isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RXumYK9-Tjiw",
        "outputId": "cbd3b59d-4b67-4172-c63c-f218fd247f61"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.impute import SimpleImputer\n",
        "si=SimpleImputer(missing_values=np.nan,strategy=\"mean\")\n",
        "df[[\"normalized-losses\",\"horsepower\"]]=si.fit_transform(df[[\"normalized-losses\",\"horsepower\"]])"
      ],
      "metadata": {
        "id": "LCv-a1mBUypI"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "490DSzDDVgKg",
        "outputId": "068eeb20-0b5f-47fe-9199-85c5bf83a142"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   symboling  normalized-losses         make fuel-type   body-style  \\\n",
              "0          3              122.0  alfa-romero       gas  convertible   \n",
              "1          3              122.0  alfa-romero       gas  convertible   \n",
              "2          1              122.0  alfa-romero       gas    hatchback   \n",
              "3          2              164.0         audi       gas        sedan   \n",
              "4          2              164.0         audi       gas        sedan   \n",
              "\n",
              "  drive-wheels engine-location  width  height engine-type  engine-size  \\\n",
              "0          rwd           front   64.1    48.8        dohc          130   \n",
              "1          rwd           front   64.1    48.8        dohc          130   \n",
              "2          rwd           front   65.5    52.4        ohcv          152   \n",
              "3          fwd           front   66.2    54.3         ohc          109   \n",
              "4          4wd           front   66.4    54.3         ohc          136   \n",
              "\n",
              "   horsepower  city-mpg  highway-mpg  price  \n",
              "0       111.0        21           27  13495  \n",
              "1       111.0        21           27  16500  \n",
              "2       154.0        19           26  16500  \n",
              "3       102.0        24           30  13950  \n",
              "4       115.0        18           22  17450  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-07e7aad2-1d8b-4947-9f6a-1e75629a8616\">\n",
              "    <div 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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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>symboling</th>\n",
              "      <th>normalized-losses</th>\n",
              "      <th>make</th>\n",
              "      <th>fuel-type</th>\n",
              "      <th>body-style</th>\n",
              "      <th>drive-wheels</th>\n",
              "      <th>engine-location</th>\n",
              "      <th>width</th>\n",
              "      <th>height</th>\n",
              "      <th>engine-type</th>\n",
              "      <th>engine-size</th>\n",
              "      <th>horsepower</th>\n",
              "      <th>city-mpg</th>\n",
              "      <th>highway-mpg</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>122.0</td>\n",
              "      <td>alfa-romero</td>\n",
              "      <td>gas</td>\n",
              "      <td>convertible</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>64.1</td>\n",
              "      <td>48.8</td>\n",
              "      <td>dohc</td>\n",
              "      <td>130</td>\n",
              "      <td>111.0</td>\n",
              "      <td>21</td>\n",
              "      <td>27</td>\n",
              "      <td>13495</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>122.0</td>\n",
              "      <td>alfa-romero</td>\n",
              "      <td>gas</td>\n",
              "      <td>convertible</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>64.1</td>\n",
              "      <td>48.8</td>\n",
              "      <td>dohc</td>\n",
              "      <td>130</td>\n",
              "      <td>111.0</td>\n",
              "      <td>21</td>\n",
              "      <td>27</td>\n",
              "      <td>16500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>122.0</td>\n",
              "      <td>alfa-romero</td>\n",
              "      <td>gas</td>\n",
              "      <td>hatchback</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>65.5</td>\n",
              "      <td>52.4</td>\n",
              "      <td>ohcv</td>\n",
              "      <td>152</td>\n",
              "      <td>154.0</td>\n",
              "      <td>19</td>\n",
              "      <td>26</td>\n",
              "      <td>16500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>164.0</td>\n",
              "      <td>audi</td>\n",
              "      <td>gas</td>\n",
              "      <td>sedan</td>\n",
              "      <td>fwd</td>\n",
              "      <td>front</td>\n",
              "      <td>66.2</td>\n",
              "      <td>54.3</td>\n",
              "      <td>ohc</td>\n",
              "      <td>109</td>\n",
              "      <td>102.0</td>\n",
              "      <td>24</td>\n",
              "      <td>30</td>\n",
              "      <td>13950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>164.0</td>\n",
              "      <td>audi</td>\n",
              "      <td>gas</td>\n",
              "      <td>sedan</td>\n",
              "      <td>4wd</td>\n",
              "      <td>front</td>\n",
              "      <td>66.4</td>\n",
              "      <td>54.3</td>\n",
              "      <td>ohc</td>\n",
              "      <td>136</td>\n",
              "      <td>115.0</td>\n",
              "      <td>18</td>\n",
              "      <td>22</td>\n",
              "      <td>17450</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-07e7aad2-1d8b-4947-9f6a-1e75629a8616')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\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-07e7aad2-1d8b-4947-9f6a-1e75629a8616 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-07e7aad2-1d8b-4947-9f6a-1e75629a8616');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KxqboMshV5sK",
        "outputId": "2e093498-98c7-4c19-d7b6-35c750d94082"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 205 entries, 0 to 204\n",
            "Data columns (total 15 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   symboling          205 non-null    int64  \n",
            " 1   normalized-losses  205 non-null    float64\n",
            " 2   make               205 non-null    object \n",
            " 3   fuel-type          205 non-null    object \n",
            " 4   body-style         205 non-null    object \n",
            " 5   drive-wheels       205 non-null    object \n",
            " 6   engine-location    205 non-null    object \n",
            " 7   width              205 non-null    float64\n",
            " 8   height             205 non-null    float64\n",
            " 9   engine-type        205 non-null    object \n",
            " 10  engine-size        205 non-null    int64  \n",
            " 11  horsepower         205 non-null    float64\n",
            " 12  city-mpg           205 non-null    int64  \n",
            " 13  highway-mpg        205 non-null    int64  \n",
            " 14  price              205 non-null    int64  \n",
            "dtypes: float64(4), int64(5), object(6)\n",
            "memory usage: 24.1+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Spliting the data into X (Input) & Y (Output)**"
      ],
      "metadata": {
        "id": "UNrcJzTuUEbR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x=df.iloc[:,:-1]\n",
        "x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 537
        },
        "id": "mXhMxiVuUiwC",
        "outputId": "8b2b3e91-7863-46e7-a373-39fbe9d1cc83"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     symboling  normalized-losses         make fuel-type   body-style  \\\n",
              "0            3              122.0  alfa-romero       gas  convertible   \n",
              "1            3              122.0  alfa-romero       gas  convertible   \n",
              "2            1              122.0  alfa-romero       gas    hatchback   \n",
              "3            2              164.0         audi       gas        sedan   \n",
              "4            2              164.0         audi       gas        sedan   \n",
              "..         ...                ...          ...       ...          ...   \n",
              "200         -1               95.0        volvo       gas        sedan   \n",
              "201         -1               95.0        volvo       gas        sedan   \n",
              "202         -1               95.0        volvo       gas        sedan   \n",
              "203         -1               95.0        volvo    diesel        sedan   \n",
              "204         -1               95.0        volvo       gas        sedan   \n",
              "\n",
              "    drive-wheels engine-location  width  height engine-type  engine-size  \\\n",
              "0            rwd           front   64.1    48.8        dohc          130   \n",
              "1            rwd           front   64.1    48.8        dohc          130   \n",
              "2            rwd           front   65.5    52.4        ohcv          152   \n",
              "3            fwd           front   66.2    54.3         ohc          109   \n",
              "4            4wd           front   66.4    54.3         ohc          136   \n",
              "..           ...             ...    ...     ...         ...          ...   \n",
              "200          rwd           front   68.9    55.5         ohc          141   \n",
              "201          rwd           front   68.8    55.5         ohc          141   \n",
              "202          rwd           front   68.9    55.5        ohcv          173   \n",
              "203          rwd           front   68.9    55.5         ohc          145   \n",
              "204          rwd           front   68.9    55.5         ohc          141   \n",
              "\n",
              "     horsepower  city-mpg  highway-mpg  \n",
              "0         111.0        21           27  \n",
              "1         111.0        21           27  \n",
              "2         154.0        19           26  \n",
              "3         102.0        24           30  \n",
              "4         115.0        18           22  \n",
              "..          ...       ...          ...  \n",
              "200       114.0        23           28  \n",
              "201       160.0        19           25  \n",
              "202       134.0        18           23  \n",
              "203       106.0        26           27  \n",
              "204       114.0        19           25  \n",
              "\n",
              "[205 rows x 14 columns]"
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              "      <th></th>\n",
              "      <th>symboling</th>\n",
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              "      <th>0</th>\n",
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              "      <th>2</th>\n",
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              "      <td>122.0</td>\n",
              "      <td>alfa-romero</td>\n",
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              "      <td>19</td>\n",
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              "      <th>3</th>\n",
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              "      <td>24</td>\n",
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              "      <td>54.3</td>\n",
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              "      <td>115.0</td>\n",
              "      <td>18</td>\n",
              "      <td>22</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>gas</td>\n",
              "      <td>sedan</td>\n",
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              "      <td>front</td>\n",
              "      <td>68.9</td>\n",
              "      <td>55.5</td>\n",
              "      <td>ohc</td>\n",
              "      <td>141</td>\n",
              "      <td>114.0</td>\n",
              "      <td>23</td>\n",
              "      <td>28</td>\n",
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              "    <tr>\n",
              "      <th>201</th>\n",
              "      <td>-1</td>\n",
              "      <td>95.0</td>\n",
              "      <td>volvo</td>\n",
              "      <td>gas</td>\n",
              "      <td>sedan</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>68.8</td>\n",
              "      <td>55.5</td>\n",
              "      <td>ohc</td>\n",
              "      <td>141</td>\n",
              "      <td>160.0</td>\n",
              "      <td>19</td>\n",
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              "      <th>202</th>\n",
              "      <td>-1</td>\n",
              "      <td>95.0</td>\n",
              "      <td>volvo</td>\n",
              "      <td>gas</td>\n",
              "      <td>sedan</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>68.9</td>\n",
              "      <td>55.5</td>\n",
              "      <td>ohcv</td>\n",
              "      <td>173</td>\n",
              "      <td>134.0</td>\n",
              "      <td>18</td>\n",
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              "      <td>sedan</td>\n",
              "      <td>rwd</td>\n",
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              "      <td>145</td>\n",
              "      <td>106.0</td>\n",
              "      <td>26</td>\n",
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              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>68.9</td>\n",
              "      <td>55.5</td>\n",
              "      <td>ohc</td>\n",
              "      <td>141</td>\n",
              "      <td>114.0</td>\n",
              "      <td>19</td>\n",
              "      <td>25</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>205 rows × 14 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2de2f754-2324-4623-b899-05a2a50467ea')\"\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y=df.iloc[:,-1]\n",
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GZpYVzJZVoK-",
        "outputId": "c9260d5e-d497-4661-913e-450eb00f40c6"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      13495\n",
              "1      16500\n",
              "2      16500\n",
              "3      13950\n",
              "4      17450\n",
              "       ...  \n",
              "200    16845\n",
              "201    19045\n",
              "202    21485\n",
              "203    22470\n",
              "204    22625\n",
              "Name: price, Length: 205, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Outlier is the observation that lies away at abnormal distance from other values in random sample of population.** \n",
        "\n",
        "Outliers are the measure of variability.\n",
        "\n",
        "They ruin the accuracy of the model. hence it is necessary to remove them.\n"
      ],
      "metadata": {
        "id": "v9gosWsrWPCL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(data=x,x=y)\n",
        "plt.grid()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "MqGPCFsuVuD_",
        "outputId": "720d928f-de00-4b69-9b5a-1786d5fa264e"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here we have taken only price into consideration, but looking at dataset we need to consider the make also."
      ],
      "metadata": {
        "id": "yelZJB1lXO8o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(data=x,x=y,y=\"make\")\n",
        "plt.grid()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "0FAt5IL_XDXL",
        "outputId": "6cde67d1-0a34-4b13-ba05-0df8dd85e8b7"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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joe1VpTvWarU6rNriiRVcKhNzRXkyPndwZ3x6vb5aPiMh3EmSRSFEpZXWzWnretbr9dU+9YOrJ4Ivi8FgIDo6+s9u9jiGDx9eI2cckPhcJyMjQ24FED6vetfiEkIIIYQQPk1aFoUQopJe7KvH31ryqjHVpcBkZU5mUTf0S3116DSu7+I0K1p+wTPxuUN1x1dghjnbDC4vVwhPkWRRCOFVbPcFgvff76XTKPiXsmqMu85fPcmiopbvyfiqS/XHZy1/F+F2vvS3xdtIN7SXi4yMZMaMGZ6uhhBuY7svMDo6Wv3DLoQQVSV/WypPkkUhhBBCCFEq6YYWQngtb/vfv60+Vqu12PylQtjYXxueuIZNJhNGo7FGT31Umfi87e+JL5Fk0QeYTCamTZvGmjVr0Gq1TJ48mWeffRZFUWjevDkTJkzg2LFjfPjhh4SFhZGUlES3bt2YMGECn332GTfeeCPvvfcet912G1arlQYNGvDWW28xdOhQADp16sRvv/3G6dOngaL1pu+9914uXrxY4rQkBQUF6jxvADk5OUDRdCmumobCVk5NnLYDJL6y5Obmqo9jYmJcVidXMplMmEwmzEr1TgtUkqKlFA1/Ptaq99+59hxah39rmuqOL99iAYr+RnryGl65cqXHzu0OVYkvNze3WueArQp3fj84ew7vfKeEg9TUVMaPH8++ffs4cOAAcXFxNG3alIkTJwKwbNkyXnjhBZ5++mmWLVvG6NGj6d69O4888ggvv/wyc+bMYcyYMfzwww8oisLdd9/Njh07GDp0KBcvXuTIkSMEBgby448/cvPNN7Nz505uv/32UuevW7x4MYmJicW2b9u2zeVz3mVmZrq0PG8j8RWXl5dXDTWpHseuH+72cxb9cS/6kjx6/YhqncfSE/G5U3XFZ/8ZCe+0fft2t87RWhnu+H5w9u+tJIs+ICIigmXLlqEoCq1bt+a7775j2bJlarJ4//338+ijjwKwcOFC3nrrLW6//XZ1Teg5c+bQrVs3fvvtNxo1akRkZCTvvPMOAJ9//jmdO3emUaNG7Nixg5tvvpkdO3bQq1evUuszb9484uPj1ec5OTlEREQQFRVFSEiIS2I2Go1kZmbSt2/fap/U2RMkvtLl5+eTnJwMQFpaWpVWXHE1g8HA2LFj0Wq1aDQaWv03HX+re1uHC0x/dXG2/u+6aps659j1wz0SnztUd3wGo0VttUpNTXX7NWw0GsnKyqJ379419u9LZeIzGAyMGjUKgAEDBnjtZOnu/H6w9QyWR5JFH9C1a1eHIf7dunVj6dKlmM1mADp06KC+1rBhQwDat29fbNvZs2dp1KgRvXr14vHHH+f3339n586dREZGqsni+PHj+eKLL5g9e3ap9dHpdOh0umLbtVqtyy/s6ijTm0h8xdnfgxQUFORVf9A1Gg0mkwlFUVAUBX+r0e3JlL/d/XBF56++6T88EZ87VVd8GqzqdeyJa9hoNKLVagkODq6Rf18qG599t7Mv/O11Rx2dLV9GQ9cA9h+2LaksaZvFYgGKEsl69eqxc+dONVmMjIxk586d7N+/H6PRSPfu3d0YgRBCCCG8lbQs+oC9e/c6PP/yyy9p2bIl/v6VW3lAURR69uxJRkYGP/zwA3fddRe1atWioKCAd955h9tuu43atWu7oupCVJherycjI0N9LIQQriB/WypPkkUfkJ2dTXx8PI8++igHDx4kKSmJpUuXVqnMyMhInnzySW677TaCgoIAuPvuu0lLS2PWrFmuqLYQlaIoild1PZelwGR16BZ21zlLeuxKRSOuPROfO1R3fAVmlxcpXMCX/rZ4G0kWfcCYMWPIz8/njjvuwN/fn8cff5y4uLgqldmrVy/MZjORkZHqtsjISDIyMhy2CSFKNzfT4NHpj2xrRLuaVmsmLs7z8VWXmh6fEK4myaKX27Fjh/r4rbfeKvb6yZMni227erLg5s2bF9vWqVOnYttmzJghSwsKIYQQwoEki8Ln2C8Gf/V222ThOp0ORVFksXjhUrZ7nkwmE1lZWaSnp7t9Yt+SrnNX82R87uDO+OTeOFET1Ly/AqLGsy0G74yMjAy5R0W4jO2eJ1vXpV6v98j0G9U9mbCn46tuNT0+IVxNps4RQgghhBClkpZF4dNmDtQS8OdVXGiy8srHzi8qL4QQQojySctiFZw8eRJFUTh06JCnq1Ki2NhYBg8e7OlqVJrt3sT8/Pxig3FsAjQQoFHUn6uVd7wQQgghyibJolDt2LEDRVH4448/PF0VoOgm9AcffJDo6OgSB7Q4IyYmpkrHCyGEENc6SRZ9UGFhoaerIIQQQohrhCSLTrBYLCxZsoQWLVqg0+lo2rQpzz//vPr6L7/8wj333EOtWrXo2LEje/bscTj+3//+Nz179iQwMJCIiAgee+wxrly5AsD8+fO58847i52zY8eOPPvss8Bf3cnPP/88jRs3pnXr1gB899139O7dm8DAQMLCwoiLiyM3N7fMOBYvXswNN9xAYGAgHTt25IMPPgCKutTvueceAOrWrYuiKMTGxlb+TXMB+65jW3dyfn6+QyuhdC8LIYQQ1UsGuDhh3rx5rFq1imXLlnHXXXdx+vRpfvzxR/X1BQsW8Morr9CyZUsWLFjAiBEj+Pnnn9FoNBw/fpz+/fvz3HPP8d577/H7778zbdo0pk2bRnJyMqNGjWLx4sUcP36cm266CYAffviBb7/9lg0bNqjn+OyzzwgJCSEzMxOAK1eu0K9fP7p168b+/fs5e/YsEyZMYNq0aaSkpJQYx+LFi3n//fd5++23admyJZ9//jkPP/ww9evX56677mLDhg08+OCDHD16lJCQkFKnnCkoKFDneQPIyckBiqajcNVqCEajEZPpr8EqMTExJe5XaNGiVYr+z2NRrEDJ58/NzfWq+eJs71NNXT1C4vNtEp9vk/h8mzvjc/YcilWaZsp0+fJl6tevzxtvvMGECRMcXjt58iQ33HAD7777LuPHjwfg8OHD3HLLLRw5coSbb76ZCRMm4O/vzzvvvKMe9+9//5tevXpx5coV9Ho9nTp14sEHH+Tpp58Gilobs7Ky+PLLL4GilsWtW7eSnZ1NQEAAAKtWrWLOnDn8+uuv1K5dG4AtW7YwaNAg/ve//9GwYUNiY2P5448/2LRpEwUFBdSrV4/t27fTrVs3tS4TJkwgLy+PtWvXsmPHDu655x4uXrxIaGhoqe9JQkICiYmJxbavXbvWpfO/5eXlkZycXOY+cXFx6jxpRqORlStXlrjfuHHjqn1uOiGEEMKX5OXlMXLkSC5dukRISEip+3lPU4uXOnLkCAUFBdx7772l7tOhQwf1cXh4OABnz57l5ptv5ptvvuHbb78lLS1N3cdqtWKxWDhx4gRt2rRh1KhRvPfeezz99NNYrVbWrVtHfHy8wznat2+vJoq2enXs2FFNFAF69OiBxWLh6NGjNGzY0OH4n3/+mby8PPr27euwvbCwkM6dO1fgHSlqabWvX05ODhEREURFRZV5sVWE0Whk8+bN6vO0tDR1JQSDwcDYsWMBqHd2DQHaopbFQtNf/+/x9/cHwGw2AzBgwACvmpzbaDSSmZlJ3759a+SkwBKfb5P4fJvE59vcGZ+tZ7A8kiyWw5kEw/7DtC29ZbFYgKLuz0cffZTHHnus2HFNmzYFYMSIEcyZM4eDBw+Sn5/Pr7/+Wqzb1T4prAzbvYybN2+mSZMmDq/pdLoKlaXT6Uo8RqvVuvTCtl/GLCgoSP0sNBqN2kXth4KftWg/P7tGcluSWF11cxVvrZerSHy+TeLzbRKfb3NHfM6WL8liOVq2bElgYCCfffZZsW5oZ3Tp0oXDhw/TokWLUve5/vrr6dWrF2lpaeTn59O3b18aNGhQZrlt2rQhJSWFK1euqInk7t278fPzUwfA2Gvbti06nY7s7Gx69epVYpm2lsurEy0hhBBCXLskWSyHXq9nzpw5zJ49m4CAAHr06MHvv//ODz/8UGbXtM2cOXPo2rUr06ZNY8KECdSuXZvDhw+TmZnJG2+8oe43atQonnnmGQoLC1m2bFm55dr2Hzt2LAkJCfz+++9Mnz6d0aNHF+uCBggODmbmzJk88cQTWCwW7rrrLi5dusTu3bsJCQlh7NixNGvWDEVR+Pjjj7n//vsJDAwkKCioYm+YC2k0GjZs2IBGo1G7oCtq/fr16PX6Sh8vhBBCXOtk6hwnPP300zz55JMsXLiQNm3aEBMTw9mzZ506tkOHDuzcuZNjx47Rs2dPOnfuzMKFC2ncuLHDfkOHDuX8+fPk5eU5tepKrVq1+PTTT7lw4QK33347Q4cO5d5773VIQK+2aNEinn76aRYvXkybNm3o378/mzdv5oYbbgCgSZMmJCYmMnfuXBo2bMi0adOcirG6KIqCXq8nMDDQoUvaXqGp6F5F28/VyjteCCGEEGWTlkUn+Pn5sWDBAhYsWFDstasHk4eGhhbbdvvtt7Nt27YyzxEaGlrqKiOlTYXTvn17srKySi3z6uMUReHxxx/n8ccfL/WYp59+Wh2V7Qte+bhmTp0ghBBCeAtpWRRCCCGEEKWSlkXhc/R6PRkZGcW2W61WDAYDBQUF6HQ6tes5Pz+/3DKtVqs60bj9sc7URbq4hRBC1GSSLAqfoyhKqVMaKYrC8OHD3VaXjIwMr5q/UQghhHA16YYWQgghhBClkpZF4TNs3czgXPfvhMF+aJ28wo0mK+9usv55nIJWU3rZRhO8u8lS6XoJIYQQvkRaFr1IZGQkM2bMqPTxKSkpZa7p7OsMBgPR0dFER0eXOnLcnlYDWo3i9M9fx5W3b9Xq5S327NnDww8/zJ49ezxdFSGEEF5MkkUhrkEGg4GkpCTOnj1LUlKSTyW5Qggh3EuSRSGuQenp6Zw/fx6A8+fPs379eg/XSAghhLeSZNFDrly5wpgxYwgKCiI8PJylS5c6vH7x4kXGjBlD3bp1qVWrFvfddx8//fSTwz4pKSk0bdqUWrVqMWTIEPXL395zzz1HgwYNCA4OZsKECcydO5dOnTo57PPuu+/Spk0b9Ho9N998MytWrHB5vK5mMBjIz88v9mPfQnb15OiuYl+uwWDwuVa5U6dOsX79ejUOq9XK+vXrOXXqlIdrJoQQwhvJABcPmTVrFjt37iQjI4MGDRowf/58Dh48qCZysbGx/PTTT3z00UeEhIQwZ84c7r//fg4fPoxWq2Xv3r2MHz+exYsXM3jwYLZu3cozzzzjcI60tDSef/55VqxYQY8ePUhPT2fp0qXq8n62fRYuXMgbb7xB586d+frrr5k4cSK1a9dm7NixJda9oKBAnZMQICcnBwCj0YjR6JoVVWzl2JeXm5urPo6JiSm3DJNFi1Zx7v9DVsUKFPz5WIu1jEEqJosFKCyxHrm5uWg05f9alRSfO1itVpKSkool0haLhaSkJJ599lmXDNDxVHzuIvH5NonPt0l8rj9XeRRrdTW/iFLl5uYSFhbG+++/z0MPPQTAhQsXuP7664mLi2Pq1Km0atWK3bt30717d6CoqzAiIoLU1FQeeughRo4cyaVLl9i8ebNa7vDhw9m6dSt//PEHAF27duW2225zWC/6rrvuIjc3l0OHDgHQokULFi1axIgRI9R9nnvuObZs2cIXX3xRYv0TEhJITEwstn3t2rXUqlWrSu9NWfLy8khOTnZ6/7i4OLRarVP7Go1GVq5c6dRx9vtebdy4cdX6HlTVhQsXWLduXamvjxgxgnr16rmxRkIIITwlLy9PzSdCQkJK3U9aFj3g+PHjFBYWcuedd6rb6tWrR+vWrQE4cuQIGo3G4fWwsDBat27NkSNH1H2GDBniUG63bt3YunWr+vzo0aNMmTLFYZ877rhDXU/6ypUrHD9+nPHjxzNx4kR1H5PJRJ06dUqt/7x584iPj1ef5+TkEBERQVRUVJkXW0UYjUYyMzPp27evmrjl5+eryWJaWhp6vb7YcQaDQW0R9buQgp/WuZZFP9Nf/2fyu5iKXxlT5/gZLWrrYWpqKgCjRo0CYMCAAU5N0l1SfO5gtVo5evQohw4dwmL5a/ofPz8/OnfuzKhRo1zWsuiJ+NxF4vNtEp9vk/hcx9YzWB5JFq9htm7dVatWOSSmAP7+/qUep9Pp0Ol0xbZrtVqXX9j2ZZpMJnV7UFBQiUmZRqNR91PwQ7E6l/godg3sitVY5nEKVkwmi1qP0urrjOp4z8ozffp0JkyY4LDNz8+P6dOnExAQ4NJzeSI+d5L4fJvE59skPtecwxkywMUDbrrpJvW+Q5uLFy9y7NgxANq0aYPJZHJ4/fz58xw9epS2bduq+9i/DvDll186PG/dujX79+932Gb/vGHDhjRu3JhffvmFFi1aOPzY39coapYmTZoQExOjtiAqikJMTAyNGzf2cM2EEEJ4I2lZ9ICgoCDGjx/PrFmzCAsLo0GDBixYsAA/v6LcvWXLlkRHRzNx4kTeeecdgoODmTt3Lk2aNCE6OhqAxx57jB49evDKK68QHR3Np59+6tAFDUUtSBMnTuS2226je/furF+/nm+//ZYbb7xR3ScxMZHHHnuMOnXq0L9/fwoKCjhw4AAXL1506Gr2Bnq9noyMDPVxeYwmAOduyTXadUPbPy693MrXyxsMHz6cbdu2ce7cOcLCwpwaMCSEEOLaJMmih7z88svk5uYyaNAggoODefLJJ7l06ZL6enJyMo8//jgDBw6ksLCQu+++my1btqhNxl27dmXVqlU888wzLFy4kD59+vDUU0+xaNEitYxRo0bxyy+/MHPmTAwGA8OGDSM2NpZ9+/ap+0yYMIFatWrx8ssvM2vWLGrXrk379u2rtJJMdVEUxan7AW3sl+SriKJl/5wf91XRenkDvV7P9OnTefPNN5k6darPJLlCCCHcT5JFDwkKCmLNmjWsWbNG3TZr1iz1cd26dVm9enWZZTzyyCM88sgjDtuefPJJh+dPP/00Tz/9tPq8b9++tGjRwmGfkSNHMnLkyArHIHxbt27d6Natm6erIYQQwstJsliD5eXl8fbbb9OvXz/8/f1Zt24d27dvJzMz09NVqzb2XcIVYbVa1bkjdTqd0yOCpUVOCCFETSfJYg2mKApbtmzh+eefx2Aw0Lp1azZs2ECfPn08XbVqU1qXsNVqLbbSytUJYmUSv9JWb9Hr9S6ZgkYIIYTwNEkWa7DAwEC2b9/u6Wp4BYPBoA4OcoeMjAyfu49RCCGEKIlMnSOEEEIIIUolLYvC69l3Ibuie/fBB0CjAZMJNnzouK0q7MurCFfHJ4QQQriStCx6SGRkpEemp/HUeavC1oUcHR1d6j2CFaHR/PVT0raq/HhDfM7as2cPDz/8MHv27HHbOYUQQvgeSRaFuAYZDAaSkpI4e/YsSUlJbk1ShRBC+BZJFoW4BqWnp3P+/HmgaCnJ9evXe7hGQgghvJUkix5ksViYPXs29erVo1GjRiQkJKivZWdnEx0dTVBQECEhIQwbNozffvtNfT0hIYFOnTqxZs0amjdvTp06dRg+fDiXL19W97ly5QpjxowhKCiI8PBwli5dWqwOa9as4bbbbiM4OJhGjRoxcuRIzp49W61xV4XBYCA/P7/CP/YtZ1bnF2epEPtyy6unwWDAaDRiMBjc3qp36tQp1q9fj/XPClutVtavX8+pU6fcWg8hhBC+QQa4eFBqairx8fHs3buXPXv2EBsbS48ePbj33nvVRHHnzp2YTCamTp1KTEwMO3bsUI8/fvw4mzZt4uOPP+bixYsMGzaMF198keeffx4oWhFm586dZGRk0KBBA+bPn8/Bgwfp1KmTWobRaGTRokW0bt2as2fPEh8fT2xsLFu2bCm13gUFBer8hAA5OTlqWUaj0SXvja0co9HIlStX1O2uWMPYYtEACkVL+tkWerZtq0q5f5XnbD1Xrlzp8Dw3NxdNVUfalMFqtZKUlKQmijYWi4WkpCSeffZZlwywsf/8aiKJz7dJfL5N4nP9ucqjWK/+1hBuERkZidlsZteuXeq2O+64g969e3Pvvfdy3333ceLECSIiIgA4fPgwt9xyC/v27eP2228nISGBl19+mTNnzhAcHAzA7Nmz+fzzz/nyyy/Jzc0lLCyM999/n4ceegiACxcucP311xMXF8fy5ctLrNeBAwe4/fbbuXz5MkFBQSXuk5CQQGJiYrHta9eupVatWlV5W0qUl5dHcnKyy8qLi4tDq9ViNBrVZM22rSrsy6uscePGVct7aHPhwgXWrVtX6usjRoygXr161XZ+IYQQ3iMvL4+RI0dy6dIlQkJCSt1PWhY9qEOHDg7Pw8PDOXv2LEeOHCEiIkJNFAHatm1LaGgoR44c4fbbbwegefPmaqJofzwUtToWFhZy5513qq/Xq1eP1q1bO5zzq6++IiEhgW+++YaLFy9isViAom7wtm3blljvefPmER8frz7PyckhIiKCqKioMi+2ijAajWRmZtK3b19MJpOaLKalpVV6pZWxY8cCcOmP99BoFEymv/6fZNtWFSaTVW0VTE1NLbOeRqORrKwsevfujdlsZtSoUQAMGDCgWifztlqtHD16lEOHDqmfNYCfnx+dO3dm1KhRLmtZtH1+VU3CvZHE59skPt8m8bmOrWewPJIsetDVF4GiKA5f4NV9/JUrV+jXrx/9+vUjLS2N+vXrk52dTb9+/SgsLCz1OJ1Oh06nK7E+rr6wtVqtQ/ISFBRUqWRKo9FgMpnK2KOs15xnO0V59TQajWi1WoKDgx3qVR3v4dWmT5/OhAkTHLb5+fkxffp0AgICXHoud8TjSRKfb5P4fJvE55pzOEMGuHihNm3a8Ouvv/Lrr7+q2w4fPswff/xRamvf1W666Sa0Wi179+5Vt128eJFjx46pz3/88UfOnz/Piy++SM+ePbn55pu9enCLcI0mTZoQExOjJuGKohATE0Pjxo09XDMhhBDeSFoWvVCfPn1o3749o0aNYvny5ZhMJqZMmUKvXr247bbbnCojKCiI8ePHM2vWLMLCwmjQoAELFizAz++v/x80bdqUgIAAkpKSmDRpEt9//z2LFi2qrrAqTa/Xk5GRoT6uKltDnn1DY5mNjhUst6JcHZ8zhg8fzrZt2zh37hxhYWEuGTgkhBCiZpJk0QspikJGRgbTp0/n7rvvxs/Pj/79+5OUlFShcl5++WVyc3MZNGgQwcHBPPnkk1y6dEl9vX79+qSkpDB//nxef/11unTpwiuvvMLf/vY3V4dUJYqiuPQ+vpKW5KvMMn2u4ur4nKHX65k+fTpvvvkmU6dOdVuSKoQQwvdIsugh9lPg2GzatEl93LRpU7W1qSQJCQkO8zICzJgxw2Epv6CgINasWcOaNWvUbbNmzXI4ZsSIEYwYMcJhmwyQvzZ069aNbt26eboaQgghvJwki+KaYN/Va2O1WtX5InU6nUtGAdufTwghhKgJJFkU14TSunpdPaeh1Wp1alUW2whog8GgPq7O5NWV9Hq919ZNCCGE60myKIQLGQwGoqOjy91Pq9USFxfH8OHDfW4VgoyMDLffYymEEMJzZOocIYQQQghRKmlZFDWerWsY3NuF2jsG/Ev5DbP9L633MLBNo242Qdb68o/1BJMR/vX3oscyAMo77NmzRx3NLgOVhBDVyYu+joRNZGQknTp1KnX9ZlEx9l3D7uxC9deARltyYqpQtN1fq+CHbZ+/krCyjvUEs93SiAUFBdW6frUon8FgICkpiXPnzpGUlETnzp1lUJUQotpIsuiFPvzwwxq9hJEQomrS09M5f/48AOfPn2f9+vXq2udCCOFqkix6oXr16nm6CjVWeaOUXVm+9NaK6nDq1CnWr1+v3g5gtVpZv349ffr0oUmTJh6unRCiJpJk0QvZd0OvWLGCZcuW8euvv1KnTh169uzJBx98AEDz5s2LTcTdqVMnBg8eTEJCAikpKYwbN65Y+c888wwJCQkldncPHjyY0NBQUlJSSq1fQUGBOsULQE5ODgBGo9FlI3tt5biivNzcXPWxO5e1s5o1KKWMIVPQOvxb9NgKGNXtCt7TDa1gAYqm+DGZTOV+Lq78/LyRp+KzWq0kJSUVu2/UYrGQlJTEs88+65J7cuXz820Sn29zZ3zOnkOSRS924MABHnvsMdasWUP37t25cOECu3btcvr4mJgY+vfvrz7fsWMHo0ePpkePHlWq1+LFi0lMTCy2fdu2bS6/ly0zM7PKZeTl5bmgJhV3U51Hyr2doHntWPVx0S/tyj+3j/OqWxGMAUY++7NuO3fudLpurvj8vJm747tw4QIHDx4stt1isXDw4EHS0tJc2jMhn59vk/h8mzvic/b7UZJFL5adnU3t2rUZOHAgwcHBNGvWjM6dOzt9fGBgoDqY4/jx40ydOpUXXniBvn37Vqle8+bNIz4+Xn2ek5NDREQEUVFRhISEVKlsG6PRSGZmJn379q1y0pSfn09ycjIAaWlp1ToQwGAwqPeOncx9D01A6S2LzWvHcvJKCtY/WxNNxr9ai05eSfaqAS4F+Rb1ca9evQgNDS1zf1d+ft7IU/FZrVaOHj3KoUOHsFj++kz8/Pzo3Lkzo0aNclnLonx+vkvi823ujM/WM1geSRa9WN++fWnWrBk33ngj/fv3p3///gwZMqTCrXeXLl1i4MCBDBgwoNja0JWh0+nQ6XTFtmu1Wpdf2K4o07ZCChStl12do6E1Gs1fK7IoYC2nK9mKUU0WrXajoYu2e0+yaF83jUbj9GdSHdeEN/FEfNOnT2fChAkO2/z8/Jg+fToBAQEuPZd8fr5N4vNt7ojP2fJlUm4vFhwczMGDB1m3bh3h4eEsXLiQjh078scffwBFXxBX37t09f0HZrOZmJgYQkJCWLlypcNrzhwvhPAuTZo0ISYmRm1BVBSFmJgYGjdu7OGaCSFqKkkWvZxGo6FPnz4sWbKEb7/9lpMnT5KVlQVA/fr1OX36tLpvTk4OJ06ccDj+iSee4LvvvmPTpk3Ful+vPt5sNvP9999XYzRCCFcYPnw4YWFhAISFhbl14JYQ4toj3dBe7OOPP+aXX37h7rvvpm7dumzZsgWLxULr1q0B6N27NykpKQwaNIjQ0FAWLlyIv7+/enxycjIrVqxg48aNKIrCmTNngKKu2KCgIHr37k18fDybN2/mpptu4tVXX1VbLWsSvV5PRkaG+thdzCawn2jbnt+f281GKxbb4796y8s81hPsG6BLugVBuJder2f69OnqCi4yIbcQojpJsujFQkND+fDDD0lISMBgMNCyZUvWrVvHLbfcAhQNNDlx4gQDBw6kTp06LFq0yKFlcefOnZjNZv72t785lGubOueRRx7hm2++YcyYMWg0Gp544gnuuecet8boDoqiuG3VFnu2pftKotVCizjI+juU1PNf1rGe5q7lEkXZunXrJsv8CSHcQpJFL7Rjx44SH18tJCSE9PR0h232qzikpKSUOV+iVqtlxYoVrFixorJVFUIIIUQNJ8miEC5k3+VdFpPJRFZWFunp6Wg0Rb+GVqtVnexcp9N5bQuedHkK4T5Wq7XCK0/ZZmQwGAx/zc5Qwt8XvV7vtX9nhHeRZFEIF3K2y9s26lyv1ztMXeDqSc2FEL7NYDAQHR1doWO0Wi1xcXEMHz68zBkuMjIyPHKLjvA9MhpaCCGEEEKUSloWhRBCCB9w8xgFPye+tf3/nND/5tEK5j8fW4xWflxTnbUTNZkkix4QGxvLH3/8waZNmzxdFSGEEC5mf5+hK+8L9NOAnxPLgPr9mSD6aZWrVoJynI7LarWSn5/v8nqKmkeSRQ947bXXiq2cIoQQomawv8/Qm+8LLCgoUCd09+Z6Cs+TZNED6tSp4+kqOKWwsNDla80KIYQQwrfIABcPiI2NZfDgwQBs3bqVu+66i9DQUMLCwhg4cCDHjx932P+LL76gU6dO6PV6brvtNjZt2oSiKBw6dAgomk8xNDTU4RjbPjbHjx8nOjqahg0bEhQUxO2338727dsdjmnevDmLFi1izJgxhISEEBcX5/LYhRDiWmIwGMjPz6/0j/20Oa7ukarolDzi2iUtix525coV4uPj6dChA7m5uSxcuJAhQ4Zw6NAh/Pz8yMnJYdCgQdx///2sXbuW//znP8yYMaPC58nNzeX+++/n+eefR6fTsXr1agYNGsTRo0dp2rSput8rr7zCwoULeeaZZ0otq6CgQJ2vC4rWpIai6WDKmqahImzluKo8byPx+TaJz7dVd3y5ubnqY1eu2+1n0uIfUH4bjz9ah3+LWIFCh/3sF3HIzc1V53z1dnJ9uv5c5fGNK6MGe/DBBx2ev/fee9SvX5/Dhw/Trl071q5di6IorFq1Cr1eT9u2bTl16hQTJ06s0Hk6duxIx44d1eeLFi1i48aNfPTRR0ybNk3d3rt3b5588skyy1q8eDGJiYnFtm/bts3l8wRmZma6tDxvI/H5NonPt1VXfHl5edVSbo/AcWg12vJ3/FM3zV/JoNFq5HtWlrrv9u3bfW6eV7k+q87Za1WSRQ/76aefWLhwIXv37uXcuXNYLBYAsrOzadeuHUePHqVDhw4Oq2bccccdFT5Pbm4uCQkJbN68mdOnT2MymcjPzyc7O9thv9tuu63csubNm0d8fLz6PCcnh4iICKKioggJCalw3UpiNBrJzMykb9++DpNW1xQSn2+T+HxbdceXn59PcnIyAGlpaVVa9chgMKgtgF8Yk/FXnGtZ7KYZyx5TKmaKWo7Mpr+6sP39/VEUhXfeeYfx48cDMGDAAJ8Z4CLXp+vYegbLI8mihw0aNIhmzZqxatUqGjdujMVioV27dhQWFpZ/8J/8/PyK3ctyddPyzJkzyczM5JVXXqFFixYEBgYydOjQYuepXbt2uefT6XTodLpi27Varcsv7Ooo05tIfL5N4vNt1RWfbYk9gKCgoColYRqNRi3PoiiA89PbmDGqyaLFbtocs9ms1s3GFz9rX6xzRbgjPmfLl2TRg86fP8/Ro0dZtWoVPXv2BODf//63wz6tW7fm/fffp6CgQE3Q9u/f77BP/fr1uXz5MleuXFGTPdvgF5vdu3cTGxvLkCFDgKKWxpMnT1ZDVEIIIYSoSWQ0tAfVrVuXsLAwVq5cyc8//0xWVpZD9y7AyJEjsVgsxMXFceTIET799FNeeeUVAHW085133kmtWrWYP38+x48fZ+3ataSkpDiU07JlSz788EMOHTrEN998o5YrhBDCtfR6PRkZGWRkZFSpC7q66XQ6n6in8DxJFj3Iz8+P9PR0vvrqK9q1a8cTTzzByy+/7LBPSEgI//znPzl06BCdOnViwYIFLFy4EED95a5Xrx7vv/8+W7ZsoX379qxbt46EhASHcl599VXq1q1L9+7dGTRoEP369aNLly5uiVMIIa4liqIQGBhIYGCgS1dFsZiKlu1z5geK7+uueoqaR7qhPaCgoEC9V6RPnz4cPnzY4fWr7z/s3r0733zzjfo8LS0NrVbrMOXN4MGD1bkbbexHTDdv3pysrCyH16dOnerwXLqlhRDCe/242rl5FrVaK3fHwY9rrBhLSBKFqChJFt3IZDJx7Ngx9uzZw6OPPur0catXr+bGG2+kSZMmfPPNN8yZM4dhw4b5zMg1IYQQQvguSRbd6Pvvv6d79+7cc889TJo0yenjzpw5w8KFCzlz5gzh4eE89NBDPP/889VY02uH1Wr1yCoGttGNBoPBYeSk1WpVJzzX6XSV6hrS6/XSpSREDWG7/7EiTCYTWVlZpKenqxNtl/S3Re5TFM6SZNGNOnXqVKnJWmfPns3s2bOroUbCYDAQHR3t9vNqtVri4uIYPny4y2fpz8jIkFZnIWoI232FFWH7m6LX6x2mRvG1SbeF95ABLkIIIYQQolTSsijEn/zGNQOte7pv/f781fMb1ww/7LqhjRasyUWr6ijjmqJonfz/nNGKJfk/Lq+nEEIIIcniNWTHjh3cc889XLx4kdDQUE9Xp1y2+wlNJpN77sPTKs4nZ1Wk/Nmor2j91Mc2trGLitbP6fpYkTkzy2J/b6rc0ymEEBUj3dDCa5lMJh588EGio6M9MghF1By2e1PlWhJCiIqTZFEIIYQQQpRKuqHdJDIykvbt2+Pv709qaioBAQE899xzjBw5kmnTpvHBBx/QsGFDkpKSuO+++zCbzcTFxZGVlcWZM2do2rQpU6ZM4fHHH1fLLKkrrVmzZurk2lu2bGHGjBn8+uuvdO3albFjxzrse/78eaZNm8bnn3/OxYsXuemmm5g/fz4jRoyo1vfCWfaTk1dXa5B9uVarFV/tnHTHe1URJpMJo9FYbGogT/GG90QIIXyVJItulJqayuzZs9m3bx/r169n8uTJbNy4kSFDhjB//nyWLVvG6NGjyc7ORqvVcv311/OPf/yDsLAwvvjiC+Li4ggPD2fYsGEAnD59Wi37ypUr9O/fn27dugHw66+/8sADDzB16lTi4uI4cOAATz75pEN9DAYDt956K3PmzCEkJITNmzczevRobrrpJu64445S4ygoKFDn6wLIyckBiqZrcNU0MEaj0SHJiImJcUm5ZdGa/PELcM+vhBZ/h39trFgoUPfRFLufsTQWExT++dgd75WzVq5c6ekqFJObm6vOPVdZtuvc1dMeeQuJz7dJfL7NnfE5ew7FevXacqJaREZGYjab2bVrFwBms5k6derwwAMPsHr1agB10u09e/bQtWvXYmVMmzaNM2fO8MEHHzhst1qtPPjgg2RnZ7Nr1y4CAwOZP38+GRkZ/PDDD+p+c+fO5aWXXipzgMvAgQO5+eabeeWVV0qNJSEhgcTExGLb165d69J5vPLy8khOTnZZeeWJi4tzmJPME4xGo5pgVaQ+9seJso0bN07mmxNCCIq+Z0eOHMmlS5cICQkpdT9pWXSjDh06qI/9/f0JCwujffv26raGDRsCcPbsWQDefPNN3nvvPbKzs8nPz6ewsJBOnToVK3f+/Pns2bOHAwcOqJO3HjlyhDvvvNNhP1uro43ZbOaFF17g73//O6dOnaKwsJCCgoJyv0jnzZtHfHy8+jwnJ4eIiAiioqLKvNgqwmg0snnzZvV5Wlpataw2YDAY1O75FOtn+F3V0lddtPgzht6sJgsjZnW7/ajmVLKcb1m0mtXWstTUVI+vzGA0GsnKyqJ3794eT8Ch6HMeNWoUAAMGDKjypOVGo5HMzEz69u3rFfG5msTn2yQ+3+bO+Gw9g+WRZNGNrv7QFUVx2Ga7B9FisZCens7MmTNZunQp3bp1Izg4mJdffpm9e/c6lPH++++zbNkyduzYQZMmTSpUn5dffpnXXnuN5cuX0759e2rXrs2MGTMoLCws8zidTodOpysxPlde2Pb3ZAYFBVXLqiQajUbt7vZTzCi4t6HdiBmj/TyLdsmiEZPTyaJVsWD5M47qeq8qwmg0otVqCQ4O9oo/5vbdzq68Tl19zXsbic+3SXy+zR3xOVu+JIteavfu3XTv3p0pU6ao244fP+6wz549e5gwYQLvvPNOsW7rNm3a8NFHHzls+/LLL4udIzo6mocffhgoSlKPHTtG27ZtXRmKEEIIIXyYTJ3jpVq2bMmBAwf49NNPOXbsGE8//TT79+9XXz9z5gxDhgxh+PDh9OvXjzNnznDmzBl+//13ACZNmsRPP/3ErFmzOHr0KGvXriUlJaXYOTIzM/niiy84cuQIjz76KL/99ps7wyyTRqNhw4YNZGRkeLxbVfg2vV5PRkaGXEtCCFEJkix6qUcffZQHHniAmJgY7rzzTs6fP+/Qyvjjjz/y22+/kZqaSnh4uPpz++23A9C0aVM2bNjApk2b6NixI2+//TYvvPCCwzmeeuopunTpQr9+/YiMjKRRo0YMHjzYnWGWSVEU9Ho9gYGB7llxw2gtWm7PTT9AqdtLe63UsowyTq0siqIQGBjovmtJCCFqEOmGdpMdO3YU22abD9Ge/eD05OTkYqOBFy9eDBSNri5vIPvAgQMZOHCgw7Zx48apj+vVq8emTZvKqfm1w51rK1u0WogrOqellKkLrMnZbr6DUgghhChOWhaFEEIIIUSppGVRXNNs97K5m8lkIisri/T0dIeRularVZ3wXKfTVarLVO7JEzWJ1Wp1+Qo8thkQPLHCkCt+x8vjyvj0er3cuiEkWRTXNtu9bO5mmzVfr9cXm7pAJowW4i8Gg4Ho6GiXlqnVaomLi2P48OE1chUQV8aXkZHh8am4hOdJN7QQQgghhCiVtCwKIYQol313sKe6Jv3H3gXaqq+y5P9nO4n/2Luw2E2E7w5WoxlL6r8B8Bt7F4oL4rlaleMzmjH/WUfhWd7weweSLFbayZMnueGGG/j6669LXILPFSIjI+nUqRPLly+vlvKFEMJZ9t3BHuua1Pq7JLmyrYykaP1R8Nz9eIqL4ilWbhXjk1kYvIdX/N4h3dBCCCGEEKIMNT5ZLG+dYyGEEEIIUTqPJouRkZFMnz6dGTNmULduXRo2bMiqVau4cuUK48aNIzg4mBYtWvDJJ5+ox3z//ffcd999BAUF0bBhQ0aPHs25c+ccypw2bRozZszguuuuo1+/fgD88MMPDBw4kJCQEIKDg+nZs6fDWsvvvvsubdq0Qa/Xc/PNN7NixQqHuu7bt4/OnTuj1+u57bbb+Prrr4vFU17dPvjgA9q3b09gYCBhYWH06dOHK1eulPkemUwmpk2bRp06dbjuuut4+umnHSbjLigoYObMmTRp0oTatWtz5513OkwAnpKSQmhoKJ9++ilt2rQhKCiI/v37c/r0aXUfRVGK/TRv3rzMegkhrl0Gg4H8/Hy3/NhPm1PeQgTCNezfZ3d+1hW5JoxGo1fWzdXxuXraqMry+D2LqampzJ49m3379rF+/XomT57Mxo0bGTJkCPPnz2fZsmWMHj2a7OxsCgsL6d27NxMmTGDZsmXk5+czZ84chg0bRlZWlkOZkydPZvfu3QCcOnWKu+++m8jISLKysggJCWH37t3q/FNpaWksXLiQN954g86dO/P1118zceJEateuzdixY8nNzWXgwIH07duX999/nxMnTvD44487xPHHH3+UWbfTp08zYsQIlixZwpAhQ7h8+TK7du0q949famoq48ePZ9++fRw4cIC4uDiaNm3KxIkTAZg2bRqHDx8mPT2dxo0bs3HjRvr37893331Hy5YtAcjLy+OVV15hzZo1+Pn58fDDDzNz5kzS0tIAHBLHK1eu0L9/f7p161ZqnQoKCtR5wgBycnKAoulgXDUNha2cmjitBUh8vu5ajC83N1d9HBMT4/Y6AWhN4BdQ9TYO7Z/tJFoPtJdYsWK2q4dSDXWoanwWE9j+wnvqs3bGypUrPV2FanV1fLm5uQ7z8rqCs3/DFKsH/6sWGRmJ2Wxm165dAJjNZurUqcMDDzzA6tWrAThz5gzh4eHs2bOH7du3s2vXLj799FO1jP/+979ERERw9OhRWrVqRWRkJDk5ORw8eFDdZ/78+aSnp3P06NFic9oBtGjRgkWLFjFixAh123PPPceWLVv44osvWLlyJfPnz+e///2vOuHx22+/zeTJk9UBLs8991yZdcvNzeXWW2/l5MmTNGvWzOn35+zZs/zwww/qCKi5c+fy0UcfcfjwYbKzs7nxxhvJzs6mcePG6nF9+vThjjvu4IUXXiAlJYVx48bx888/c9NNNwGwYsUKnn32Wc6cOeNwPqvVyoMPPkh2dja7du0q9UbahIQEEhMTi21fu3atzBEoRA2Vl5dXbPlRd4uLiyvxb7gvMRqNahLgrfHY11F4j3Hjxrn8OzYvL4+RI0dy6dIlQkJCSt3P4y2LHTp0UB/7+/sTFhZG+/bt1W0NGzYE4OzZs3zzzTf861//IigoqFg5x48fp1WrVgDceuutDq8dOnSInj17lvhLeeXKFY4fP8748ePV1joo6v6tU6cOAEeOHKFDhw4OK2Nc3fJWXt2ioqK49957ad++Pf369SMqKoqhQ4dSt25ddu3axX333afu/8477zBq1CgAunbt6jBUvlu3bixduhSz2cx3332H2WxW47YpKCggLCxMfV6rVi01UQQIDw/n7Nmzxeo5f/589uzZw4EDB8occTVv3jzi4+PV5zk5OURERBAVFVXmxVYRRqORzMxM+vbt65V/TKtK4vNt12J8+fn5arKYlpbmtpWCDAYDY8eOBSDVfAw//6p/bWnxY7RfS9ZYfsLo7qlzLGb18WrLTygW14+Grmp8FrNJbcFKTU31ulWhjEYjWVlZ9O7du8b+/tniM5vNaj4wYMAAl4+GtvUMlsfjyeLVH7SiKA7bbImSxWIhNzeXQYMG8dJLLxUrJzw8XH1cu3Zth9fKenNtXSurVq3izjvvdHjN39/5X+Ly6ubv709mZiZffPEF27ZtIykpiQULFrB3715uu+02Dh06pO5vS5CdOae/vz9fffVVsbraJ60lvcdXNyi///77LFu2jB07dtCkSZMyz6vT6dDpdMW2a7Val//iVkeZ3kTi823XUnz2y8YFBQW5bQoPjUajntuqWFFcmNwZsbg/WbQ7nxFLtU7dU9n4rIoV85/vuTs/a2cZjUa0Wi3BwcE18vfPPj7737vq+o51hseTxYro0qULGzZsoHnz5hXqt+/QoQOpqanqB2CvYcOGNG7cmF9++UXN3q/Wpk0b1qxZg8FgUP+H9eWXX1a4boqi0KNHD3r06MHChQtp1qwZGzduJD4+nhYtWpR4zN69ex2ef/nll7Rs2RJ/f386d+6M2Wzm7Nmz9OzZ06n3oiR79uxhwoQJvPPOO3Tt2rXS5QghhBCi5vGpqXOmTp3KhQsXGDFiBPv37+f48eN8+umnjBs3DrPZXOpx06ZNIycnh+HDh3PgwAF++ukn1qxZw9GjRwFITExk8eLFvP766xw7dozvvvuO5ORkXn31VQBGjhyJoihMnDiRw4cPs2XLFl555ZUK1W3v3r288MILHDhwgOzsbD788EN+//132rRpU2bM2dnZxMfHc/ToUdatW0dSUpI6uKZVq1aMGjWKMWPG8OGHH3LixAn27dvH4sWL2bx5s1Pv6ZkzZxgyZAjDhw+nX79+nDlzhjNnzvD77787dbwQ4tqg1+vJyMggIyPDc92SRjNWF/0ALiurMueu7vNXpXyMpX+fCvfyit87fKxlsXHjxuzevZs5c+YQFRVFQUEBzZo1o3///vj5lZ73hoWFkZWVxaxZs+jVqxf+/v506tSJHj16ADBhwgRq1arFyy+/zKxZs6hduzbt27dnxowZQFEz/D//+U8mTZpE586dadu2LS+99BIPPvig03ULCQnh888/Z/ny5eTk5NCsWTOWLl3qcK9iScaMGUN+fj533HEH/v7+PP7448TFxamvJycn89xzz/Hkk09y6tQprrvuOrp27crAgQOdek9//PFHfvvtN1JTU0lNTVW3N2vWjJMnTzpVhhCi5lMUxePdka5ags5Pq4W4tphT/43ZgyPaLdW0pJ63xCeqzht+78DDyaL9fIA2JSUo9vfXtWzZkg8//LBCZUJRV7T9SOWrjRw5kpEjR5b6eteuXR3uK7y6XuXVrU2bNmzdurXU8ktiH8tbb71V4j5arZbExMQSRycDxMbGEhsb67Bt8ODBat0jIyNl7jIhhBBClMqnWhaFEEJcW2zdcK5kMpnIysoiPT3d5fPWlcdqtarz1Op0OofZLlzFlfF520ho4RmSLAohvI7ValVXLtDr9dXyhSp8Q3V0w9kmItbr9R4ZTVvd89F6Oj5R8/jUABchxLXBYDAQHR1NdHS01yx3JYQQ16pKJ4t//PEH7777LvPmzePChQsAHDx4kFOnTrmsckKIa5N9gijJohBCeFaluqG//fZb+vTpQ506dTh58iQTJ06kXr16fPjhh2RnZ6tL9QkhRGVYLJYSHwshhHC/SrUsxsfHExsby08//eRw8+v999/P559/7rLKCSGuTZcvXy7xsRBCCPerVLK4f/9+Hn300WLbmzRpwpkzZ6pcKSGEEEII4R0qlSzqdLoSF58+duwY9evXr3KlxF8KCwt9qlwhhBBC1CyVShb/9re/8eyzz6rD8xVFITs7mzlz5jisalLTRUZGMn36dGbMmEHdunVp2LAhq1at4sqVK4wbN47g4GBatGjBJ598oh7z/fffc9999xEUFETDhg0ZPXo0586dcyhz2rRpzJgxg+uuu45+/foB8MMPPzBw4EBCQkIIDg6mZ8+eHD9+XD3GttqMzeDBgx0m427evDmLFi1izJgxhISEEBcXR+/evZk2bZrDcb///jsBAQF89tlnLn63hBBCCOGLKjXAZenSpQwdOpQGDRqQn59Pr169OHPmDN26deP55593dR29WmpqKrNnz2bfvn2sX7+eyZMns3HjRoYMGcL8+fNZtmwZo0ePJjs7m8LCQnr37s2ECRNYtmwZ+fn5zJkzh2HDhpGVleVQ5uTJk9m9ezcAp06d4u677yYyMpKsrCxCQkLYvXs3JpOpQnV95ZVXWLhwIc888wwAe/fuZdq0aSxduhSdTgfA+++/T5MmTejdu3ep5RQUFKiTygJqK7PRaFT/A1FVtnJcVZ63kfjKZn9tm0wmr3uf5PPzbRKfb5P4XH+u8ijWKqz1tnv3br755htyc3Pp0qULffr0wWq1XjMT6EZGRmI2m9m1axcAZrOZOnXq8MADD6gjws+cOUN4eDh79uxh+/bt7Nq1y2HZwf/+979ERERw9OhRWrVqRWRkJDk5ORw8eFDdZ/78+aSnp3P06NESJ1iNjIykU6dOLF++XN02ePBgQkNDSUlJAYpaFjt37szGjRvVfQwGA40bN+btt99m2LBhAHTs2JEHHnhATShLkpCQUOLygmvXrq32yWbFteHSpUu8//77ADz88MPUqVPHwzUSQoiaJy8vj5EjR3Lp0iVCQkJK3a9SLYsvv/wys2bNokePHvTo0UPdbjabefjhh1m3bl1livVJHTp0UB/7+/sTFhZG+/bt1W0NGzYE4OzZs3zzzTf861//IigoqFg5x48fp1WrVgDceuutDq8dOnSInj17Vnkm/ttuu83huV6vZ/To0bz33nsMGzaMgwcP8v333/PRRx+VWc68efOIj49Xn+fk5BAREUFUVFSZF1tFGI1GMjMz6du3b41cgUDiK9t//vMfNVns3r07zZo1c3UVq0Q+P98m8fk2ic91Shp/UpJKJ4v16tVj/Pjx6jaz2czw4cP5/vvvK1Okz7r6g1QUxWGbrZXVYrGQm5vLoEGDeOmll4qVEx4erj6uXbu2w2vlLXXl5+fH1Q3EJTUtX10uwIQJE+jUqRP//e9/SU5Opnfv3uV+Met0OrXb2p5Wq3X5hV0dZXoTia9k9uvZajQar32P5PPzbRKfb5P4XHMOZ1QqWdy8eTNRUVHUqVOHoUOHYjKZGDZsGD/++CP/+te/KlPkNaFLly5s2LCB5s2bV2hx9w4dOpCamorRaCzxg61fvz6nT59Wn5vNZr7//nvuueeecstu3749t912G6tWrWLt2rW88cYbTtdLCCGEEDVfpUZD33777WzYsIFHHnmEjz76iAcffJCjR4/yr3/9i0aNGrm6jjXG1KlTuXDhAiNGjGD//v0cP36cTz/9lHHjxmE2m0s9btq0aeTk5DB8+HAOHDjATz/9xJo1azh69CgAvXv3ZvPmzWzevJkff/yRyZMn88cffzhdrwkTJvDiiy9itVoZMmRIVcMUosqCg4NLfCyEEML9Kr02dO/evVm9ejUPPvggJ06cYOfOner9eaJkjRs3Zvfu3ZjNZqKiomjfvj0zZswgNDQUP7/SP4qwsDCysrLIzc2lV69e3HrrraxatUptZXzkkUcYO3YsY8aMoVevXtx4441OtSrajBgxAo1Gw4gRIxxW5BHCU+x/H8r63RBCCFH9nO4LfeCBB0rcXr9+fUJDQ4mLi1O3ffjhh1WvmQ/YsWNHsW0nT54sts3+fsKWLVuW+f6UVCYUdUXbj6K2p9VqWbFiBStWrCi13JLqZXPu3DkMBoPDPahCeJL9f1rkPzBCCOFZTieLpU1dYZs0Wvgeo9HI+fPneeqpp+jatStdunTxdJWEAIoSxIyMDPWxEEIIz3E6WUxOTq7OeggP2L17N/fccw+tWrXigw8+8HR1qp3VasVgMFS5DNuE5DqdrtJzitomna7CNKc1mqIo5c4CIIQQwj0qNRpa1AyRkZHXVLJiMBiIjo72dDWAolsH4uLiKCgoICAgwNPVEUIIIUpV6WTxgw8+4O9//7u6jJ09+9VHhBBCCCGE76pUsvj666+zYMECYmNjycjIYNy4cRw/fpz9+/czdepUV9dRXOPsu4/1er1LlpPUjh4GFZjrUq2L0YTp/b8DoHl4GIq2Er9CJhOkbyx/PxeqjvcQYM+ePbz55ptMnTqVbt26qc8nTZrkkvKFEEJ4XqXmpFixYgUrV64kKSmJgIAAZs+eTWZmJo899hiXLl1ydR290o4dO1AUpULzGVaGoihs2rSp1NdPnjyJoigcOnSo3LKc2TclJYXQ0NAK17M62bqPo6Ojq3zPoUqjQdFqK/HzV3KoaCtXRmWS1KqqjvfQYDCQlJTE2bNnSUpK4tKlS+rzN9980+kF6oUQQni3SiWL2dnZdO/eHShaiu7y5csAjB49+ppZF7p79+6cPn1aHSXuqSQrIiKC06dP065dO5eUFxMTw7Fjx1xSlqjZ0tPTOX/+PADnz58nMTFRfX7hwgW5HUUIIWqISjVxNGrUiAsXLtCsWTOaNm3Kl19+SceOHTlx4sQ1M2AiICDAK1ar8ff3d2k9AgMDvXoUalVaxeyPtVqtuKYjtuKsVitYrRiNRgwGQ4WWfqwsl7XI/unUqVOsX79e/X23Wq0O68JbrVYOHjzI//73v3LXGhdCCOHdKvUt1bt3bz766CM6d+7MuHHjeOKJJ/jggw84cOBAqZN3e7vIyEjat2+Pv78/qampBAQE8NxzzzFy5EimTZvGBx98QMOGDUlKSuK+++5jx44d3HPPPVy8eJFDhw4xbtw4APVesGeeeYaEhARWrFjBsmXL+PXXX6lTpw49e/ZUp6lp3rw5M2bMYMaMGWo9OnXqxODBg0lISFC3nT59Wj1neHg4S5YsYejQoUBR1/INN9zA119/TadOnbh48SLTpk1j27Zt5Obmcv311zN//ny1fgC//PILTzzxBHv37qVly5a8/fbbdOvWDShqIZ0xY0aZ3esFBQXq9DEAOTk5QNG8ja7qerSVYzQauXLliro9JibGJeVrTWb8KjEI2QrYItRCpRJOi8lMvsnEypUrWblyZSVKqJrc3NwqJahWq5WkpKRy/2NotVpZsWIFixYtctk9kt7C/vqsiSQ+3ybx+TZ3xufsOSr1jbFy5UosFgtQtN7xddddx+7du/nb3/7m0ze2p6amMnv2bPbt28f69euZPHkyGzduZMiQIcyfP59ly5YxevRosrOzHY7r3r07y5cvZ+HChep6zUFBQRw4cIDHHnuMNWvW0L17dy5cuMCuXbsqXK+nn36aF198kddee401a9YwfPhwvvvuO9q0aVPivocPH+aTTz7huuuu4+effyY/P99hnwULFvDKK6/QsmVLFixYwIgRI/j555+dTiAWL15MYmJise3btm2jVq1aFY6vLJmZmeTl5bm0TIDRtULV5RIrwogRW3r3sDaocmXU0uH+FPEv27dvr9Ln5GwXs9Vq5euvvyYtLY169epV+nzeLDMz09NVqFYSn2+T+HybO+Jz9vu1Usmin58fhYWFHDx4kLNnzxIYGEifPn0A2Lp1K4MGDapMsR7XsWNHnnrqKQDmzZvHiy++yHXXXcfEiRMBWLhwIW+99Rbffvutw3EBAQHUqVMHRVEcuoSzs7OpXbs2AwcOJDg4mGbNmtG5c+cK1+uhhx5iwoQJACxatIjMzEySkpJKXN4vOzubzp07c9tttwFFrZdXmzlzJgMGDAAgMTGRW265hZ9//pmbb77ZqfrMmzeP+Ph49XlOTg4RERFERUUREhJS0fBKZDQayczMpG/fvphMJnVS+LS0tEqv6GEwGBg7diwAawov40fFmxatdv8Le9+Yi0LFk0VLYSGBgYGMHj2aHj16EBQUVOEyKspgMDBq1CgABgwYUKVbDaxWK0ePHuXQoUPqfxpLoigKnTp1YtSoUTWyZdF2fVbmPwzeTuLzbRKfb3NnfLaewfJUKlncunUro0ePVm9mt6coCmazuTLFelyHDh3Ux/7+/oSFhdG+fXt1W8OGDQE4e/asU0lR3759adasGTfeeCP9+/enf//+DBkypMKtOrYuYvvnpY1onjx5Mg8++CAHDx4kKiqKwYMHq4ORbOzjDA8PV2NyNlnU6XTodLpi27VarcsvbK1W65BoBAUFVTrR0Wg06sopiqJUqgvZvuPVSOW6oa2KAiYTWq2WoKAggoODK1FKxdi3Grvic5o+fbr6H5jSKIrC1KlTa/Sk49VxzXsTic+3SXy+zR3xOVt+pUZDT58+nWHDhnH69GksFovDj68milD8TVMUxWGbLWkpqzXFXnBwMAcPHmTdunWEh4ezcOFCOnbsqN4P6OfnV+y+r6reo3Dffffxn//8hyeeeIL//e9/3HvvvcycOdNhn6rEJARAkyZNiImJUa8fRVFo166dw/MuXbqo/xkRQgjhuyqVLP7222/Ex8erLW2iqCu6pERZo9HQp08flixZwrfffsvJkyfJysoCoH79+pw+fVrdNycnhxMnThQr48svvyz2vKT7FW3q16/P2LFjef/991m+fLlHBlGImm/48OGEhYUBEBYWxjPPPOPwvEuXLp6snhBCCBepVDf00KFD2bFjBzfddJOr6+OzmjdvTm5uLp999hkdO3akVq1aZGVl8csvv3D33XdTt25dtmzZgsVioXXr1kDRqPKUlBQGDRpEaGgoCxcuxN/fv1jZ//jHP7jtttu46667SEtLY9++ffzf//1fifVYuHAht956K7fccgsFBQV8/PHHZSaWvkCv15ORkaE+dgmTicpM8mQ1mkp8XNFzu1t1vId6vZ7p06erK7jUqVNHfT5p0qQSb1MRQgjheyqVLL7xxhs89NBD7Nq1i/bt2xfrvn3sscdcUjlf0r17dyZNmkRMTAznz5/nmWeeoU+fPnz44YckJCRgMBho2bIl69at45ZbbgGKBoqcOHGCgQMHUqdOHRYtWlRiy2JiYiLp6elMmTKF8PBw1q1bR9u2bUusR0BAAPPmzePkyZMEBgbSs2dP0tPTqzX26qYoisvnfjSu+XuVy7At+1cZ7r7PpjreQyi6f9b+nlrbc6PRyJYtW1x+PiGEEO5XqWRx3bp1bNu2Db1ery57Z6Moik8mizt27Ci27eTJk8W22d9jePX9hm+99RZvvfVWueXahISEFEvkbKN1rz7HlClTSiyjefPmDvV46qmn1BHd5e0LEBoa6rAtNjaW2NjYUusshBBCiGtLpZLFBQsWkJiYyNy5c/Hzq9Rtj0K4nX1XLBQl4vaTi5fEfh/bCHD755WdEsZkMvHFF18AFJsH01vo9foaN+WNEEKIiqtUslhYWEhMTIwkisKnXN0Vm5+f77IVYSpKq9USFxfH8OHDvXYVgoyMDK9e+lEIIYR7VCrbGzt2LOvXr3d1XYQQQgghhJepVMui2WxmyZIlfPrpp3To0KHYzfqvvvqqSyonhLvoRo8DTfFBJ1ajkcL3i1aPCXh4HIqLBqbYStE9PK5y/2OrLiYjBWuSPV0LIYQQXqRSyeJ3332nLlv3/fffO7wm9ziJ6mK1WjEYDEA13E+n0ZabCCra8vdxlq3milZbqVVgqktlphO6Flx97QkhxLWkUsniv/71L1fXQ9hRFIWNGzcyePBgT1fFqxgMBqKjowG5n06419XXnv3yiUIIUdPJXzwvdPr0aerWrevpagghhBBCSLLojRo1auTpKng9W5egq8qwWq1e1R3sKfZzblblPTaZTBiNRgwGAyYPrFjjaq643oQQwldJsugBkZGRdOjQAb1ez7vvvktAQACTJk0iISEBcOyGLiwsJD4+ng0bNnDx4kUaNmzIpEmTmDdvHlarlcTERN577z1+++03wsLCGDp0KK+//joAa9as4bXXXuPo0aPUrl2b3r17s3z5cho0aAAUTRh+zz33sH37dubMmcPhw4fp1KkTycnJ6pKEJSkoKHCYnzAnJwcAo9HosmlgbOXYl5ebm6s+dvWUN1qTCb+AgGLbrYAtUi24LKHUXvWvt7CYTBT++dgV73FNXJc8NzeX2rVrA3jttEdVVdLvX00i8fk2ic/15yqPJIsekpqaSnx8PHv37mXPnj3ExsbSo0cP+vbt67Df66+/zkcffcTf//53mjZtyq+//sqvv/4KwIYNG1i2bBnp6enccsstnDlzhm+++UY91mg0smjRIlq3bs3Zs2eJj48nNja22DJsCxYsYOnSpdSvX59JkybxyCOPsHv37lLrvnjxYhITE4tt37ZtG7Vq1arK21JMZmam+jgvL8+lZdsbVUtLSWNXjIAt3RmppcR9qmKEl2WLxlpaal5651rbt29Xr3P767Mmkvh8m8Tn29wRn7Pfq5IsekiHDh145plnAGjZsiVvvPEGn332WbFkMTs7m5YtW3LXXXehKArNmjVzeK1Ro0b06dMHrVZL06ZNueOOO9TXH3nkEfXxjTfeyOuvv87tt99Obm4uQUFB6mvPP/88vXr1AmDu3LkMGDAAg8FQ6qjPefPmER8frz7PyckhIiKCqKgoQkJCqvCu/MVoNJKZmUnfvn3VqZny8/NJTi6a1iUtLa3Ko1INBoO6vGJaobXEKWysdv/pWmt0bcviCC2sMxYlpN7CUmhVB2+kpqZW+j02Go1kZWXRu3dvt6+DXR0MBgOjRo0CYMCAAWg0mmLXZ01S0u9fTSLx+TaJz3VsPYPlkWTRQzp06ODwPDw8nLNnzxbbLzY2lr59+9K6dWv69+/PwIEDiYqKAuChhx5i+fLl3HjjjfTv35/777+fQYMGqV/2X331FQkJCXzzzTdcvHgRi8UCFCWZbdu2LbEu4eHhAJw9e5amTZuWWHedTqcufWdPq9W6/MK2L9P+3regoKAqj4bWaDRqmf6KUmIiaD+VjBHXJYv2ZXpTsmhVFPU9qcp7bDQa0Wq1BAcH14g/5vajn7Varfq8Oq55byLx+TaJz7e5Iz5ny/eq+YCvJVd/QIqiqMmcvS5dunDixAkWLVpEfn4+w4YNY+jQoQBERERw9OhRVqxYQWBgIFOmTOHuu+/GaDRy5coV+vXrR0hICGlpaezfv5+NGzcCRcs1llYX29yFJdVFCCGEENceaVn0ASEhIcTExBATE8PQoUPp378/Fy5coF69egQGBjJo0CAGDRrE1KlTufnmm/nuu++wWq2cP3+eF198kYiICAAOHDjg4UiqRq/Xk5GRoT4Wwl2uvvZqwghvIYRwliSLXu7VV18lPDyczp074+fnxz/+8Q8aNWpEaGgoKSkpmM1m7rzzTmrVqsX7779PYGAgzZo1w2KxEBAQQFJSEpMmTeL7779n0aJFng6nShRFqb6JuE3GElcvsdqNFLO6cGSaFUCrxWos+bweY/KmTnHvUa3XnhBCeDlJFr1ccHAwS5Ys4aeffsLf35/bb7+dLVu24OfnR2hoKC+++CLx8fGYzWbat2/PP//5T8LCwgBISUlh/vz5vP7663Tp0oVXXnmFv/3tbx6OyDs5sx6ybY1oV7BotRAXR8H7yTV2+gchhBA1gySLHrBjx45i2zZt2qQ+tp8YeeLEiUycOLHEcgYPHlzmkoAjRoxgxIgRDtvsy46MjHR4DtCpU6di24QQQghx7ZJkUVyz7O9DK43ValUnINfpdOoAoKoymUxkZWWRnp7uVesM28cLRdMVVYbtnr6qrOBS1nuv1+td9lkIIYQom/d8SwnhZs7eh+bqicbhr1nz9Xq9V039kJ+f75KVW7RaLXFxcQwfPrxautkzMjLkHkIhhHATmTpHCCGEEEKUSloWhRAlqjXmcdBUrtVTixUwUGv0YxgrOZW51VhI/pqidc4DRz+GoijkrX6tUmUJIYSoPEkWhagBrFYrBoMBcOH9fBotijagUocqfyaLijaAktfGqWB5laxHtbwvQghxjZFuaAEUjdBWFIU//vjD01URlWAwGIiOjiY6OlpNjoS8L0II4QqSLAohhBBCiFJJsljNIiMjmTZtGtOmTaNOnTpcd911PP300+pchgUFBcycOZMmTZpQu3Zt7rzzTod5GBMSEujUqZNDmcuXL6d58+bqc5PJxGOPPUZoaChhYWHMmTOHsWPHOszBWFBQwGOPPUaDBg3Q6/Xcdddd7N+/H4CTJ09yzz33AFC3bl0URSE2NrY63g7hBgaDgfz8/Er92Le+edt8m/b1cTZGaU0UQoiqk3sW3SA1NZXx48ezb98+Dhw4QFxcHE2bNmXixIlMmzaNw4cPk56eTuPGjdm4cSP9+/fnu+++o2XLlk6V/9JLL5GWlkZycjJt2rThtddeY9OmTWoCCDB79mw2bNhAamoqzZo1Y8mSJfTr14+ff/6ZiIgINmzYwIMPPsjRo0cJCQkpc1qSgoICh7n4cnJygKLpYFw1TYqtnJq6uomr48vNzVUfu2LqGwCtyYhfQOXuFdT8uYihpgqLGVrtjtVixWoyYpv1sTIx5ubmumxOS7k+fZvE59skPtefqzySLLpBREQEy5YtQ1EUWrduzXfffceyZcvo168fycnJZGdn07hxYwBmzpzJ1q1bSU5O5oUXXnCq/KSkJObNm8eQIUMAeOONN9iyZYv6+pUrV3jrrbdISUnhvvvuA2DVqlVkZmbyf//3f8yaNYt69eoB0KBBA0JDQ8s83+LFi0lMTCy2fdu2bS6fkzAzM9Ol5XkbV8WXl5fnknLsDQ80oNWYq1RGjOZypY81Wo2stC9Hg/q8MrZv3y7XZwVJfL5N4vNt7ojP2e8OSRbdoGvXrg6jMLt168bSpUv57rvvMJvNtGrVymH/goICdX3n8ly6dInffvuNO+64Q93m7+/PrbfeisViAeD48eMYjUZ69Oih7qPVarnjjjs4cuRIheOZN28e8fHx6vOcnBwiIiKIiooiJCSkwuWVxGg0kpmZSd++fb1q0mpXcXV8+fn5JCcXrV2dlpaGXq+vVDkGg4GxY8cCkG4Mwk/RVaocDVZiNJdZbwrGVNmpc0yF6uP1pmCsVqvaMpiamupUjAaDgVGjRgEwYMAAl03kLdenb5P4fJvE5zq2nsHySLLoQbm5ufj7+/PVV1/h7+/v8FpQUBAAfn5+xe4d83TTu06nQ6crnkRotVqXX9jVUaY3cVV89kvqBQUFVTop0mg0alkmxa/K096YUCo/z6LdcUYUUBS1bs7GaN/tLNdnxUl8vk3i823uiM/Z8mWAixvs3bvX4fmXX35Jy5Yt6dy5M2azmbNnz9KiRQuHn0aNGgFQv359zpw545AwHjp0SH1cp04dGjZsqA5WATCbzRw8eFB9ftNNNxEQEMDu3bvVbUajkf3799O2bVsAAv68N81srlq3oxBCCCFqFmlZdIPs7Gzi4+N59NFHOXjwIElJSSxdupRWrVoxatQoxowZw9KlS+ncuTO///47n332GR06dGDAgAFERkby+++/s2TJEoYOHcrWrVv55JNPHLp7p0+fzuLFi2nRogU333wzSUlJXLx4Ue36rl27NpMnT1bvTWzatClLliwhLy+P8ePHA9CsWTMUReHjjz/m/vvvJzAwUG3dFN5Pr9eTkZGhPhZF5H0RQoiqk5ZFNxgzZgz5+fnccccdTJ06lccff5y4uDgAkpOTGTNmDE8++SStW7dm8ODB7N+/n6ZNmwLQpk0bVqxYwZtvvknHjh3Zt28fM2fOdCh/zpw5jBgxgjFjxtCtWzeCgoLo16+fw5fjiy++yIMPPsjo0aPp0qULP//8M59++il169YFoEmTJiQmJjJ37lwaNmzItGnT3PTuCFdQFIXAwEACAwNdt0qJyYjVWFjpH6BKx9vKsJWDqeK3X1TL+yKEENcYaVl0A61Wy/Lly3nrrbdKfC0xMbHE0cU2kyZNYtKkSQ7b5s+frz7WaDQkJSWRlJQEgMVioU2bNgwbNkzdR6/X8/rrr/P666+Xep6nn36ap59+2um4RM1WlXWYtVotxMWRt+Z1l9xja1sjWgghhPtJslgD/Oc//2Hbtm306tWLgoIC3njjDU6cOMHIkSM9XTUhhBBC+DhJFmsAPz8/UlJSmDlzJlarlXbt2rF9+3batGnj6ap5hNVqrfDKHVarVZ1oXKfTldtlqdfra2S3pv09flVhMpnIysoiPT290hNhl/WZyP2HQgjhPpIsVjP7pfuqS0REhMNI52udwWAgOjq6Ws+RkZHhsjn7vIntHr+qsnU96/X6Kk394OpJtIUQQlScDHARQgghhBClkpZF4TPsu5ed7QYOHpOIoil/fWOrsZDLa54pOmZ0Ioq2+DFWUyGXVz/jknoJIYQQvkJaFqugefPmLF++3NPVqLSEhAQ6derk6Wo4zda9HB0d7fQ9iYomAEWrc+Lnr+RQ0ZZyTClJZ2Xq5Ul79uzh4YcfZs+ePZ6uihBCCB8gyeI1QlEUNm3a5OlqCA8zGAwkJSVx9uxZkpKSfCK5FUII4VmSLApxDUlPT+f8+fMAnD9/nvXr13u4RkIIIbydJItliIyMZNq0aUybNo06depw3XXX8fTTTzus02zzyCOPMHDgQIdtRqORBg0a8H//939qedOnT2fGjBnUrVuXhg0bsmrVKq5cucK4ceMIDg6mRYsWfPLJJw7l7Ny5kzvuuAOdTkd4eDhz587FZDKpr5fUHd6pUycSEhLU1wGGDBmCoijqc5s1a9bQvHlz6tSpw/Dhw7l8+XIl3i33MhgM5Ofnl/hj31pW0mdVWfZl2Z/fV1rnTp06xfr169U4rFYr69ev59SpUx6umRBCCG8mA1zKkZqayvjx49m3bx8HDhwgLi6Opk2bMnHiRIf9JkyYwN13383p06cJDw8H4OOPPyYvL4+YmBiH8mbPns2+fftYv349kydPZuPGjQwZMoT58+ezbNkyRo8eTXZ2NrVq1eLUqVPcf//9xMbGsnr1an788UcmTpyIXq9Xk8Hy7N+/nwYNGpCcnEz//v3x9/dXXzt+/DibNm3i448/5uLFiwwbNowXX3yR559/vtTyCgoK1PnvAHJycoCi5NgVq3XYyrL/FyA3N1d9bP+elkVrKsAvwIkBLlj+OgYLit1zG4vpr5hLO39ubq5T8wqWFF91slqtJCUlFUueLRYLSUlJPPvssy4dmOPu+NxN4vNtEp9vk/hcf67ySLJYjoiICJYtW4aiKLRu3ZrvvvuOZcuWFUsWu3fvTuvWrVmzZg2zZ88GitZ9fuihhwgKClL369ixI0899RQA8+bN48UXX+S6665Ty1u4cCFvvfUW3377LV27dmXFihVERETwxhtvoCgKN998M//73/+YM2cOCxcuxM+v/Mbh+vXrAxAaGkqjRo0cXrNYLKSkpBAcHAzA6NGj+eyzz8pMFhcvXlzi8oTbtm1z+bx4mZmZ6uO8vLwKHz808DRazbly9zNajaz88/GDmv+g1RSfG9AY+Nc+pdm+fXuF3gP7+KrThQsXOHjwYLHtFouFgwcPkpaWRr169Vx+XnfF5ykSn2+T+HybxFd1zn6vSrJYjq5duzq0uHTr1o2lS5diNpuL7TthwgRWrlzJ7Nmz+e233/jkk0/Iyspy2KdDhw7qY39/f8LCwmjfvr26rWHDhgCcPXsWgCNHjtCtWzeHOvTo0YPc3Fz++9//0rRp0yrF17x5czVRBAgPD1fPXZp58+YRHx+vPs/JySEiIoKoqChCQkKqVB8bo9FIZmYmffv2VSd1zs/PJzk5GYC0tLRSV/EwGAyMHTsWgA+MTfFTyl/tw2rXarjB1AxF0RXbx2I0qK2Gqamp6vkNBgOjRo0CYMCAAU5Nal1SfNXJarVy9OhRDh06hMXyV6upn58fnTt3ZtSoUS5vWXRnfO4m8fk2ic+3SXyuY+sZLI8kiy40ZswY5s6dy549e/jiiy+44YYb6Nmzp8M+V3/wiqI4bLN9Ydt/oZfHz8+vWPeis03LJdWnvHPrdDp0uuLJlFardfmFbV+m/X2aQUFBpSZlGo1G3dek+KM4cWuu1W4fI34lHmNV/NVy7c9v3+1c0fegOt6z0kyfPp0JEyY4bPPz82P69OkEONFVXxnujM8TJD7fJvH5NonPNedwhgxwKcfevXsdnn/55Ze0bNnS4b4/m7CwMAYPHkxycjIpKSmMGzeuyudv06YNe/bscUgGd+/eTXBwMNdffz1Q1M18+vRp9fWcnBxOnDjhUI5Wqy2xNVRcO5o0aUJMTIz6HxJFUYiJiaFx48YerpkQQghvJi2L5cjOziY+Pp5HH32UgwcPkpSUxNKlS0vdf8KECQwcOBCz2ax2hVbFlClTWL58OdOnT2fatGkcPXqUZ555hvj4ePV+xd69e5OSksKgQYMIDQ1l4cKFxZLZ5s2b89lnn9GjRw90Oh1169atct3cTa/Xk5GRoT52htVU6Nx+xsISHztTVmXq5SnDhw9n27ZtnDt3jrCwMKcHCgkhhLh2SbJYjjFjxpCfn88dd9yBv78/jz/+OHFxcaXu36dPH8LDw7nllltc0mLTpEkTtmzZwqxZs+jYsSP16tVj/Pjx6iAZKLqH8MSJEwwcOJA6deqwaNGiYi2LS5cuJT4+nlWrVtGkSRNOnjxZ5bq5m6IoTt0PaK+k5fnKPWZNxY6pTL08Ra/XM336dN58802mTp3q9cmtEEIIz5NksRxarZbly5fz1ltvFXutpITrypUrXLx4kfHjxxd7bceOHU6VcfX9h7169WLfvn2l1jEkJIT09HSHbVe3ag4aNIhBgwY5bEtISCg2/c6MGTOYMWNGqecSvq9bt25069bN09UQQgjhIyRZdBGLxcK5c+dYunQpoaGh/O1vf/N0la5Z9t3CzrJarerckTqdrtyRwdIiJ4QQ4lohyaKLZGdnc8MNN3D99deTkpLi1MTMonpUtlvY1XNEClERVqvVa1YDso36NxgMDrMQ1BTujK+i/xEtj16vd+k0V0I4QzKaMpTUbVya5s2bu3RpOSHEtcVgMBAdHe3pagBFt9/ExcUxfPjwGrlKhi/Hl5GR4TP3SIuaQ6bOEUIIIYQQpZKWRSFqCPtuTOmq8m13jn0df23xie/dxQ8z8AN3jn0NC8XnlPV17ozPbCxgb+pjQOU/V/syPEn+xly7JFmswZo3by6jm68h9t2Y0lXl2/y1Oi9IFovqodTYZNH98Xn6c60q+Rtz7ZJuaCGEEEIIUSpJFr2U2Wyu0PrQQgghhBDVQbqhXSQyMpJ27doBsGbNGrRaLZMnT+bZZ59FURQuXrzI448/zj//+U8KCgro1asXr7/+Oi1btgQgJSWFGTNmsHr1aubOncuxY8f4+eefOXnyJLNnz+aHH35Aq9Vyyy23sHbtWpo1awbAP//5T5599lm+++47goKC6NmzJxs3blTrlZeXxyOPPMI//vEP6taty1NPPeWwAs2vv/7Kk08+ybZt2/Dz86Nnz5689tprNG/e3H1vnnA5T0/BYjKZMBqNNXrqFVfHZ/+ZycwKwp799eDM73Z1/f55+u+K8BxJFl0oNTWV8ePHs2/fPg4cOEBcXBxNmzZl4sSJxMbG8tNPP/HRRx8REhLCnDlzuP/++zl8+DBarRYoSuxeeukl3n33XcLCwqhXrx6dOnVi4sSJrFu3jsLCQvbt26feVLx582aGDBnCggULWL16NYWFhWzZssWhTkuXLmXRokXMnz+fDz74gMmTJ9OrVy9at26N0WikX79+dOvWjV27dqHRaHjuuefo378/3377LQEBASXGWVBQoM4bBpCTkwOA0Wh02TQUtnJ8bVoLZ1VHfLm5uepjb1nzeeXKlZ6uQrWqtvhM+fgFaKunbCfY7umz/VvTuDM+q905/DBX7pymfPVhRX63q/P3Lzc312PzCcv3g+vPVR7FKv+FdYnIyEjOnj3LDz/8oCZzc+fO5aOPPiIjI4NWrVqxe/duunfvDsD58+eJiIggNTWVhx56iJSUFMaNG8ehQ4fo2LEjABcuXCAsLIwdO3bQq1evYufs3r07N954I++//36JdWrevDk9e/ZkzZo1QNH/Ths1akRiYiKTJk3i/fff57nnnuPIkSNqnQsLCwkNDWXTpk1ERUWVWG5CQgKJiYnFtq9du1YmtvagvLw8kpOTPV0N4QJxcXHqfyKFbzMajWrSVtnP1b4MbzFu3Dj5e18D5OXlMXLkSC5dukRISEip+0nLogt17drVYSqBbt26sXTpUg4fPoxGo+HOO+9UXwsLC6N169YcOXJE3RYQEECHDh3U5/Xq1SM2NpZ+/frRt29f+vTpw7BhwwgPDwfg0KFDTJw4scw62ZenKAqNGjXi7NmzAHzzzTf8/PPPBAcHOxxjMBg4fvx4qWXOmzeP+Ph49XlOTg4RERFERUWVebFVhNFoJDMzk759+9bIL83qiC8/P19NFtPS0jy6JKHRaCQrK4vevXvX2M/P1fEZDAZ1TfcfzB3R+Hvu8/PDTFu/bzhs6Vhjp85xV3xmy1+9MIctnfC3VHw0tMlsUFvxUlNTy/3drq7fP4PBwKhRowAYMGCAx0ZDy/eD69h6BssjyaIXCQwMLDZvVXJyMo899hhbt25l/fr1PPXUU2RmZtK1a1enflGvvtAURVEHzuTm5nLrrbeSlpZW7Lj69euXWqZOp0OnK/4HT6vVuvzCro4yvYkr47O/NykoKMij01oYjUa0Wi3BwcE18vOrjvg0Go36GVoVjVckaRb8vaIe1cUd8dmXb8G/UlP1WJW/rg1nfrer6/fPvtvZG/42e0MdqpM74nO2fBkN7UJ79+51eP7ll1/SsmVL2rZti8lkcnj9/PnzHD16lLZt25ZbbufOnZk3bx5ffPEF7dq1Y+3atUBRq+Fnn31W6fp26dKFn376iQYNGtCiRQuHnzp16lS6XCGEEELUHNKy6ELZ2dnEx8fz6KOPcvDgQZKSkli6dCktW7YkOjqaiRMn8s477xAcHMzcuXNp0qRJmWvBnjhxgpUrV/K3v/2Nxo0bc/ToUX766SfGjBkDwDPPPMO9997LTTfdxPDhwzGZTGzZsoU5c+Y4Vd9Ro0bx8ssvEx0dzbPPPsv111/Pf/7zHz788ENmz57N9ddf75L3RbiHXq8nIyNDfSx8l9lYUP5O1ciKGXRF9aiJLYvujM/+s6zs5+rp68FG/sZcuyRZdKExY8aQn5/PHXfcgb+/P48//rg6TU1ycjKPP/44AwcOpLCwkLvvvpstW7aU2QRcq1YtfvzxR1JTUzl//jzh4eFMnTqVRx99FCgaVPOPf/yDRYsW8eKLLxISEsLdd9/tdH1r1arF559/zpw5c3jggQe4fPkyTZo04d5773XZvYfCfRRFkRUVaghPL+2m1WrpGBfH3tTHa+SIU0/F5+nPtarkb8y1S5JFF9JqtSxfvpy33nqr2Gt169Zl9erVpR4bGxtLbGysw7aGDRs6zJlYkgceeIAHHnigxNdOnjxZbNuhQ4ccnjdq1IjU1NQyzyGEEEKIa5cki0II4QXsu/g8zWQykZWVRXp6usfm0qtO7ozParWq89LqdLpigxgrSrp/hSfUvL8CQngJq9VKfn5+sS8K+WMvSuJNXXy2rlm9Xl8jR5u6Oz6Zj1D4OkkWXWTHjh2eroLwMgaDgcGDBxfbnpGRUSNba4QQQtRMMnWOEEIIIYQolTRvCOFiVqsVg8GAwWAo8XWDoWg1BllpUwghhC+QZPEaFhsbyx9//MGmTZs8XZUaxWAwlDl/ZkxMDIA6rZIQQgjhzaQbWqgiIyOZMWOGp6shhBBCCC8iyaKbFRYWeroKwktIN7QQQghfIN3Q1SwyMpJ27dqh0Wh4//33ad++PUlJScyaNYtdu3ZRu3ZtoqKiWLZsGdddd516TIcOHdDr9bz77rsEBAQwadIkEhIS1HJfffVVkpOT+eWXX6hXrx6DBg1iyZIlBAUFAZCQkMCmTZscJuFevnw5y5cvL3Gy7tjYWHbu3MnOnTt57bXXgKLlBps3b15s34KCAnU6GICcnBygaDoKV62GYCvHF1ePyM3NdWo/k8nkk/E5w5c/P2dIfL5N4vNtEp/rz1UeSRbdIDU1lcmTJ7N7927++OMPevfuzYQJE1i2bBn5+fnMmTOHYcOGkZWV5XBMfHw8e/fuZc+ePcTGxtKjRw/69u0LgJ+fH6+//jo33HADv/zyC1OmTGH27NmsWLGiUnV87bXXOHbsGO3atePZZ58FoH79+iXuu3jxYhITE4tt37Ztm8vnE8vMzHRpee6Ql5fn9L6+GF9FSHy+TeLzbRKfb3NHfM5+X0my6AYtW7ZkyZIlADz33HN07tyZF154QX39vffeIyIigmPHjtGqVSsAOnTowDPPPKMe/8Ybb/DZZ5+pyaL9vYXNmzfnueeeY9KkSZVOFuvUqUNAQAC1atWiUaNGZe47b9484uPj1ec5OTlEREQQFRXlsjWljUYjmZmZ9O3b1+cmBc7Pzyc5ORkoSuotFgsA/v7+AJjNZgA0Go1PxucMX/78nCHx+TaJz7dJfK5j6xksjySLbnDrrbeqj7/55hv+9a9/qd3F9o4fP+6QLNoLDw/n7Nmz6vPt27ezePFifvzxR3JycjCZTBgMBvLy8qp9tQCdTodOpyu2XavVuvzCro4yq5vJZFIf2xJF+CtJtFEUxSfjqwiJz7dJfL5N4vNt7ojP2fIlWXSD2rVrq49zc3MZNGgQL730UrH9wsPD1cdXf4CKoqiJx8mTJxk4cCCTJ0/m+eefp169evz73/9m/PjxFBYWUqtWLfz8/IoNoKip93cIIYQQovpIsuhmXbp0YcOGDTRv3rzSS7599dVXWCwWli5dip9f0YD2v//97w771K9fnzNnzmC1WtWF6+0Hu5QkICCgWOuXEEIIIa5tMnWOm02dOpULFy4wYsQI9u/fz/Hjx/n0008ZN26c04laixYtMBqNJCUl8csvv7BmzRrefvtth30iIyP5/fffWbJkCcePH+fNN9/kk08+KbPc5s2bs3fvXk6ePMm5c+cculCF8/R6PRkZGaxfv77E19evX8+GDRtkfWghhBA+QZJFN2vcuDG7d+/GbDYTFRVF+/btmTFjBqGhoWorYXk6duzIq6++yksvvUS7du1IS0tj8eLFDvu0adOGFStW8Oabb9KxY0f27dvHzJkzyyx35syZ+Pv707ZtW+rXr092dnal47yWKYpCYGAger2+xNf1ej16vV5t8RVCCCG8mTRtVLMdO3YU29ayZUs+/PDDCh1z9ZJ8TzzxBE888YTDttGjRzs8nzRpEpMmTXLYNn/+fPVxSkqKw2utWrViz549pdZLCCGEENceSRaFqCZ6vZ5NmzapE5jrdDoURUGv1zuMmHY3q9WKwWCo1nPY4svPz1cnKbfFX52kxVYIIVxPkkUhqomiKNSqVavapzKqKIPBQHR0dLWeQ6vVEhcXx/Dhw92aGGdkZBAYGOi28wkhxLVA7lkUQgghhBClkpZFIa5hTwxdgVZTfIL1qrIqZvL4hqlDlvHaP6YDMGPoCgKq4VxGUwHLPpji8nKFEEIUkWRRiBrA/j7Eity3p9XoCNCWPGq7SvXBRB4Q4P9XchhQTeeqqMq+V0IIca2SbuhrXEpKCqGhoZ6uhqgi232I0dHR1T54xdfJeyWEEBUjyaIQQgghhCiVdEMLUcOU11pm//rV64f7IvsYnGkplNZEIYSoGEkWfcAHH3xAYmIiP//8M7Vq1aJz585kZGRw+PBh5s+fz9dff43RaKRTp04sW7aMLl26qMe++uqrJCcn88svv1CvXj0GDRrEkiVLCAoKcjjHpk2bmDVrFr/++iu9evXi3XffJSIiosT6FBQUqHMHAuTk5ABgNBoxGo0uidlWjqvK8zaujs82lyFATEyM08cVWvIJQOuSOtizYnb41/bYiuun0Sm05KuPKxI7FL1vlVl2Ua5P3ybx+TaJz/XnKo8ki17u9OnTjBgxgiVLljBkyBAuX77Mrl27sFqtXL58mbFjx5KUlITVamXp0qXcf//9/PTTTwQHBwPg5+fH66+/zg033MAvv/zClClTmD17NitWrFDPkZeXx/PPP8/q1asJCAhgypQpDB8+nN27d5dYp8WLF5OYmFhs+7Zt21w+p2BmZqZLy/M2roovLy+vUsfl1/0Ok9b1yaJNTsjX6uNLIQfQVsO5jIGV/4O6ffv2Kl2zcn36NonPt0l8Vefsd4dirQn9UDXYwYMHufXWWzl58iTNmjUrc1+LxUJoaChr165l4MCBJe7zwQcfMGnSJM6dOwcUDXAZN24cX375JXfeeScAP/74I23atGHv3r3ccccdxcooqWUxIiKCc+fOERISUtlQHRiNRjIzM+nbt2+1JBie5ur48vPzGTp0KABpaWmlrksNRd2wY8eOBWDG0DfRVctoaDOXQr4i8Hw7XkmfDMCs4SsJ0Lp+6pwCo4HlH0wFIDU1tczYoSj+UaNGAUW/D5WZxFuuT98m8fk2ic91cnJyuO6667h06VKZ39/SsujlOnbsyL333kv79u3p168fUVFRDB06lLp16/Lbb7/x1FNPsWPHDs6ePYvZbCYvL4/s7Gz1+O3bt7N48WJ+/PFHcnJyMJlMGAwG8vLy1BYVjUbD7bffrh5z8803ExoaypEjR0pMFnU6HTpd8S99rVbr8gu7Osr0Jq6Kz36VlKCgoDITII1Go+7vhwalGv8MKPg7PK6Oc/nxVzzlxQ44dDtX9f2X69O3SXy+TeJzzTmcIaOhvZy/vz+ZmZl88skntG3blqSkJFq3bs2JEycYO3Yshw4d4rXXXuOLL77g0KFDhIWFUVhYCMDJkycZOHAgHTp0YMOGDXz11Ve8+eabAOo+QgghhBBlkZZFH6AoCj169KBHjx4sXLiQZs2asXHjRnbv3s2KFSu4//77Afj111/V7mWAr776CovFwtKlS/HzK/p/wd///vdi5ZtMJg4cOKC2Ih49epQ//viDNm3auCE64Qp6vZ6MjAz1sSidvFdCCFExkix6ub179/LZZ58RFRVFgwYN2Lt3L7///jtt2rShZcuWrFmzhttuu42cnBxmzZrl0AXXokULjEYjSUlJDBo0iN27d/P2228XO4dWq2X69Om8/vrraDQapk2bRteuXUvsghbeSVGUyt17Zyoof6dKsCpFo6ALzX+VX1hN56poDJV9r4QQ4lolyaKXCwkJ4fPPP2f58uXk5OTQrFkzli5dyn333UejRo2Ii4ujS5cuRERE8MILLzBz5kz12I4dO/Lqq6/y0ksvMW/ePO6++24WL17MmDFjHM5Rq1Yt5syZw8iRIzl16hQ9e/bk//7v/9wdqvCA6lpTWavVEhcXx5sbn1C3LZf1m4UQwidJsujl2rRpw9atW0t8rXPnzuzfv99hm21ErM0TTzzBE0884bBt9OjR6uPY2FhiY2MBeOCBB1xQYyGEEELUJJIsimua1WottqKH1WpVpwbS6XQoiuLy89pG73pi5ir7e/aqi8lkIisri/T0dMzmoi7p6nov7ck9iEII4XqSLIprmsFgIDo62u3ntXXTFhQUEBAQ4NZzu+OePduqAIGBgTV6agshhLgWyNQ5QgghhBCiVNKyKMSfnr/vDQL8dRSaCliwdVrRtv5vEKBx7aojheYCErY/Uf6OQgghhBeQZNEHNG/enBkzZjBjxgxPV8VtrFYrRqMRg8GARqOp9nvdAAL8deg0jve8BWiKb/Mm9vdc6vV6t7xPQgghri3SDS28UkFBAStXruTBBx8sNgBF/MV2z2V0dLS8T0IIIaqFJIvXKLPZjMVi8XQ1hBBCCOHlJFl0kw8++ID27dsTGBhIWFgYffr04cqVK0RGRhbrXh48eLA696HN5cuXGTFiBLVr16ZJkybqGs82r776Ku3bt6d27dpEREQwZcoUcnNz1ddTUlIIDQ3lo48+om3btuh0OrKzs50+v7vZTyljMBjIz8+vlh/71jh3TWNjtVodutldVX8hhBCiOsg9i25w+vRpRowYwZIlSxgyZAiXL19m165dFUpOXn75ZebPn09iYiKffvopjz/+OK1ataJv374A+Pn58frrr3PDDTfwyy+/MGXKFGbPns2KFSvUMvLy8njppZd49913CQsLo0GDBpWKp6CgQJ2HECAnJwcomi7FNmVKVdknujExMS4pszwFlny0ihbzn0vVAZgVM2bF5PLzmEwmVq5cycqVK11Wbm5uLhqNd/xK264DV10P3kbi820Sn2+T+Fx/rvJ4xzdLDXf69GlMJhMPPPAAzZo1A6B9+/YVKqNHjx7MnTsXgFatWrF7926WLVumJov2rYPNmzfnueeeY9KkSQ7JotFoZMWKFXTs2LFK8SxevJjExMRi27dt20atWrWqVLZNXl6eS8qpiF8jfkCr1Tr88vwSftDl8wRW1x+A7du3u+z9d5XMzExPV6FaSXy+TeLzbRJf1Tn7XSvJoht07NiRe++9l/bt29OvXz+ioqIYOnQodevWdbqMbt26FXu+fPly9fn27dtZvHgxP/74Izk5OZhMJgwGA3l5eWoCERAQQIcOHaocz7x584iPj1ef5+TkEBERQVRUFCEhIVUu31ZmcnIyAGlpadW2MofBYGDs2LEA3Pi/zui0egpMf7Wa3ni6CzoXT51TYDQQGBjI6NGj6dGjB0FBQZUuy2AwMGrUKAAGDBhQ7ZNtO8toNJKZmUnfvn1r5KTcEp9vk/h8m8TnOraewfJIsugG/v7+ZGZm8sUXX7Bt2zaSkpJYsGABe/fuxc/Pr1h3dEVbnk6ePMnAgQOZPHkyzz//PPXq1ePf//4348ePp7CwUE0WAwMDi02tUpnz63Q6dLriCZRWq3XZhW2/qklQUFC1JUEajUZdes8fDf5WDf7Wv7qd/a3++Ftd+2viT9E5tVotQUFBBAcHV7os+25nV77/ruKNdXIlic+3SXy+TeJzzTmcIQNc3ERRFHr06EFiYiJff/01AQEBbNy4kfr163P69Gl1P7PZzPfff1/s+C+//LLY8zZt2gDw1VdfYbFYWLp0KV27dqVVq1b873//c6pezp5fCCGEENcmaVl0g7179/LZZ58RFRVFgwYN2Lt3L7///jtt2rShdu3axMfHs3nzZm666SZeffVV/vjjj2Jl7N69myVLljB48GAyMzP5xz/+webNmwFo0aIFRqORpKQkBg0axO7du3n77bedqlvv3r2dOr+76XQ64uLi6NevX7V1QdcEer2ejIwM9bEQQgjhapIsukFISAiff/45y5cvJycnh2bNmrF06VLuu+8+jEYj33zzDWPGjEGj0fDEE09wzz33FCvjySef5MCBAyQmJhISEsKrr75Kv379gKJ7Il999VVeeukl5s2bx913383ixYsZM2ZMuXV75JFHnDq/uymKglardeuqJIXmonsVC+3uWbR/7OrzuIKiKF5zn6IQQoiaSZJFN2jTpg1bt24t8TWtVsuKFSscRi1f7eTJk+We44knnuCJJxzXGx49erT6ODY2tsS5E505/7ViwSfTim/bWnybK9Tk+2yEEELULHLPohBCCCGEKJW0LIprmv09fzYWi0WdTkCn0zndDW61WtXJyss7zmQy8cUXXwCQn5/vdF3d1SUvhBBC2EiyKK5pJd3zl5+fr869WF20Wi1xcXEMHz7c6amSMjIy5P5EIYQQbifd0EIIIYQQolTSsii8ntVqxWAwAO7vin2j1xJ0/s6t4FJgLmDaztlOHWf2M/Mdx0nq9RL+Fn+nyrTnyfdECCHEtUVaFoXXMxgMREdHEx0drSZI7qLz16HTOPljlxyWd1zAn0sIBlSgTHuefE9cISUlhf79+5OSksKePXt4+OGH2bNnj8M+pW0vS2WOEUIIUTZJFj0gMjKSGTNmuP28iqKwadMmt59XCHuXLl0iPT0di8XCunXreO211zh79ixJSUlq4mswGEhKSiq2vSyVOUYIIUT5JFkUQrhVQkICFosFKOpOv3DhAgDnz59n/fr1AKSnp3P+/Pli28tSmWOEEEKUT5JFN4uNjWXnzp289tprKIqCoiicPHmSnTt3cscdd6DT6QgPD2fu3LmYTCYAVq9eTVhYmDoti83gwYMdJt5+6623uOmmmwgICKB169asWbNGfa158+YADBkyBEVR1OfHjx8nOjqahg0bEhQUxO2338727dur902oAoPBQH5+frX+2LdIWa1WD0breH772H211ezgwYP88MMPJb5mtVpZv349Bw8eZP369Wrstu2nTp0qtdxTp05V+BghhBDOkQEubvbaa69x7Ngx2rVrx7PPPguA2Wzm/vvvJzY2ltWrV/Pjjz8yceJE9Ho9CQkJPPTQQzz22GN89NFHPPTQQwCcPXuWzZs3s23bNgA2btzI448/zvLly+nTpw8ff/wx48aN4/rrr+eee+5h//79NGjQgOTkZPr374+/f9GgitzcXO6//36ef/55dDodq1evZtCgQRw9epSmTZuWGENBQYFD4mqbk9BoNDo9DUx5bOUYjUauXLmibo+JiXFJ+c7KJx+Nn3OrrZj8zA6P/e2eX82smP/6t4z/suXz1xyMpcWem5uLRuNdv8r2n5+NxWLh+eefL/M42z62lkf77UlJSTz77LPFBvNYrVaSkpKKJfZlHVNVJcVXk0h8vk3i823ujM/ZcyhWTzedXIMiIyPp1KkTy5cvB2DBggVs2LCBI0eOqF9qK1asYM6cOVy6dAk/Pz+mTJnCyZMn2bJlCwCvvvoqb775Jj///DOKotCjRw9uueUWVq5cqZ5n2LBhXLlyhc2bNwNF9yxu3LiRwYMHl1m/du3aMWnSJKZNK3mpu4SEBBITE4ttX7t2LbVq1aro21GuvLw8kpOTXV6uM+Li4pxems9oNKrvf0WOc7bM0owbN65a3ndXO3HihHr9VtaIESOoV6+ew7YLFy6wbt26Ch0jhBCi6Pt15MiRXLp0iZCQkFL3867miGvUkSNH6Natm0PrR48ePcjNzeW///0vTZs2ZeLEidx+++2cOnWKJk2akJKSQmxsrHrMkSNHiIuLcyi3R48evPbaa2WeOzc3l4SEBDZv3szp06cxmUzk5+eTnZ1d6jHz5s0jPj5efZ6Tk0NERARRUVFlXmwVYTQayczMpG/fvphMJjVZTEtLQ6/Xu+QcpTEYDOqk3J2Pt0ande58Baa/Wls7/3IzOk0ZU+coZg7deJROv7TG31rG1DlGg9pqmJqaqsZuMBgYNWoUAAMGDPC6ybrtPz9b0myxWNi1axeXL18u9Tg/Pz+CgoK4fPmyQ0uhn58fnTt3ZtSoUSW2LB49epRDhw45tEiWdUx1xFeTSHy+TeLzbe6Mz9YzWB5JFn1E586d6dixI6tXryYqKooffvhBbTGsipkzZ5KZmckrr7xCixYtCAwMZOjQoRQWFpZ6jE6nQ6crnghptVqXX9hardbhiz4oKKjaEyONRqPeL6qxatCUMQ+iPbPdfhqLf9nH/dn17G8tez+z9a+62Mdu3+1cHe+7q1xdtwULFjB37txS9/fz82PBggXMnz8fs9nssH369OkEBASUeNz06dOZMGFCsbLKOsYVvPm9dwWJz7dJfL7NHfE5W74McPGAgIAAhy/CNm3asGfPHoeWlN27dxMcHMz111+vbpswYQIpKSkkJyfTp08fIiIiHMrYvXu3w3l2795N27Zt1edardbhvLZ9YmNjGTJkCO3bt6dRo0acPHnSVaEK4aBLly7ccsstJb6m/H979x4WVbX+Afw7MMMMCsNdLiqCgXhDxVQCA0kmLyhBFxWlwkuQCIYPSppl4qkeyDimVprWCc1QTvmIqKC/uAgZBzUNvABOSBKaEqYokFxmhvX7w8M+DDBIMAPM+H6eh+eZ2XvtNeudtZHXvfZei8fDggUL4ObmhgULFnD/SWjZbmdnp7LewYMH/+1jCCGEdA0li33AwcEBZ86cQXl5Of7880+sWLEC169fx8qVK3HlyhWkpqZi48aNiI6Ohp7e/7po0aJFuHHjBr744gssXbpUqc6YmBjs2bMHO3fuRGlpKbZs2YJDhw5hzZo1Sp+blZWFyspKVFdXAwCcnZ1x6NAhFBYW4sKFC1i0aFG7hwv6mkgkQmpqKlJTUzU+BN1Wo6IRjfIu/igau3xc03+HrJv+Rp2t9eV30lOxsbHcea2np8fdT2hhYcE9xBMUFAQLC4t22zvTnWMIIYQ8Gg1D94E1a9YgJCQEo0ePRn19PXfjf0xMDMaPHw9zc3MsW7YM77zzjtJxJiYmePHFF5GWltbuIZXAwEBs27YNCQkJiIqKgqOjIxITE+Hj48OV+ec//4no6Gh88cUXGDx4MMrLy7FlyxYsXboUnp6esLS0xNq1a7t8D0Nv4fF4fXZPXkdL7anjOIFAgDCXMKzMXdutJ9768jvpKRMTEwQFBSE5ORlBQUFwcXHBZ599hoiICC7xFYlEWLlyZbvtnenOMYQQQh6NksU+MGLEiHbLkTk4OODs2bOPPPb3339HcHBwh/cMhoeHIzw8XOWx/v7+8Pf3b/e52dnZStsiIiIe2Q5CemLx4sVYvHgx997Dw6NdGQ8Pjw63d6Y7xxBCCOkcJYtaorq6Gjk5OcjJycGOHTv6ujk6rWWI9+9ijHHzTwqFwk6fwJXL5cjOzkZycnKX50ikK2WEEEL6AiWLWsLNzQ3V1dX48MMP4eLi0tfN0Wmth3gZY11eLYXH46lM6Nomkt3RlXaIRCK1TxNDCCHk8UbJopagJ5T7RkNDAwICAtRer0AgQFhYGIKCgtQ6S39qaqrW3stICCGkf6KnoQkhhBBCiEp0ZZGQVloPO7cdUv7Udx2E+t2b4LlR3oTI7PiH9UxfB74BH5dQjU9810K/uWf/Z2tUNCEyK75HdajS9vugIW5CCHn8ULLYidZrKZeXl8PR0REFBQWYMGFCXzeNaEjrYee2D7kI9Q0g4vd8NRAh3wB8fT5XJ1+v/17gb/t90BA3IYQ8fvrvXylCCCGEENLnKFkkhBBCCCEq6WyyuHv3btjZ2bVbui4gIIBbKm/nzp144oknYGBgABcXF+zbt6/L9SsUCixduhQjR45ERUUFGGOIjY2Fvb09hEIh7Ozs8MYbbwAAPv30U4wdO5Y79vDhw+DxePj888+5bRKJhFuxpaysDAEBAbC2toaRkREmT56MzMxMpc+/desW5syZA0NDQzg6OmL//v1wcHDA1q1buTL37t3Da6+9BisrK4jFYkyfPh0XLlzg9sfGxmLChAnYt28fHBwcuJU1amtru/w96LKGhgal6Wpar93dn7RuV0NDA+rr69X209VpgwghhOgunb1ncd68eVi5ciVOnjwJX19fAMDdu3dx4sQJpKenIyUlBVFRUdi6dSskEgmOHTuGJUuWYMiQIXjmmWc6rbuxsRELFy5EeXk5Tp06BSsrKxw8eBAff/wxkpOTMWbMGFRWVnKJ2bRp0/DGG2/g9u3bsLKyQm5uLiwtLZGTk4Ply5dDJpMhPz8f69atAwDU1dXBz88PH3zwAYRCIb7++mv4+/tDKpXC3t4eAPDqq6/izz//RE5ODgQCAaKjo1FVVdXuOzA0NMTx48dhYmKCXbt2wdfXF7/88gu3Hm9ZWRkOHz6MY8eOobq6GvPnz0d8fDw++OCDTuNvmTMQALc8oEwmU9s0MC31qHNama6oq6vjXrddW7ieNULAE3SrXgWvWek177/vW2/vrnr2v77Q5HrIdXV1XZ5AvK/6r7dQfNqN4tNuFJ/6P+tRdDZZNDMzw+zZs7F//34uWTx48CAsLS3xzDPPwMvLC4sXL8aKFSsAANHR0Th9+jQSEhI6TRbr6uowZ84cNDY24uTJkzAxMQEAVFRUwMbGBhKJBAKBAPb29pgyZQoAYOzYsTA3N0dubi5eeukl5OTkYPXq1di2bRsA4OzZs5DJZPD09AQAjB8/HuPHj+c+87333kNKSgqOHDmCyMhIXLlyBZmZmfjpp58wadIkAMCXX34JZ2dn7pgff/wRZ8+eRVVVFTcJdEJCAg4fPoyDBw8iLCwMANDc3Iw9e/bA2NgYAPDKK68gKyur02QxLi4OmzZtarf9+++/x4ABA1Qe1x0ZGRlqre9RHjx4oHJf0fB7EAj+6la9rX8hfx5WBYHgYdJZMOx2t+pTVbcmZWZm/u3+7e3+620Un3aj+LQbxddznf3Na01nk0UACA4ORmhoKHbs2AGhUIikpCQEBQVBT08PJSUlXMLUYurUqVwCp8rChQsxZMgQZGdnKz0ZOm/ePGzduhXDhw/HrFmz4OfnB39/f/D5fPB4PHh7eyMnJwcSiQTFxcVYsWIFNm/ejCtXriA3NxeTJ0/m/hDX1dUhNjYWaWlpuHXrFuRyOerr61FRUQEAkEql4PP5mDhxIvf5Tk5OMDMz495fuHABdXV1sLCwUGp/fX09ysrKuPcODg5coggAtra27a5QtvXWW28hOjqae19TU4OhQ4dixowZEIvFnR7bVTKZDBkZGXj22We5xKo31NfXIzExEQCQlJQEAAgJCQEAuP1mBRG/e6uvNMqbuNcTfxsEvoCPgmG34fabFfRZz+4GaZA3clf89u7dq9ZlARsaGhAcHAwA3G0PXdFX/ddbKD7tRvFpN4pPfVpGBh9Fp5NFf39/MMaQlpaGyZMn49SpU/j44497VKefnx+++eYb5OfnY/r06dz2oUOHQiqVIjMzExkZGVixYgU++ugj5ObmQiAQwMfHB7t378apU6fg5uYGsVjMJZC5ubmYNm0aV9eaNWuQkZGBhIQEODk5wdDQEC+99BKampo6alKH6urqYGtri5ycnHb7TE1NuddtT0Qej9fuPs+2hEJhh0vWCQQCtZ/YmqizM3K5nHttZGSktI3P9MHvZmInb3WcPtPjEkR9ptftOlvwmT7XRiMjI7VOb9N62Lk7fdHb/dfbKD7tRvFpN4pPPZ/RFTr7gAvwcBLhF154AUlJSThw4ABcXFy4q3GjRo1CXl6eUvm8vDyMHj260zrDw8MRHx+P5557Drm5uUr7DA0N4e/vj+3btyMnJwf5+fm4dOkSgIf3LRYXF+O7776Dj48PAMDHxweZmZnIy8vjtrW0Y/HixXj++efh6uoKGxsbpeX+XFxcIJfLUVBQwG27evUqqqurufcTJ05EZWUl+Hw+nJyclH4sLS27/B0SQggh5PGm01cWgYdD0XPnzkVRURFefvllbntMTAzmz58PNzc3SCQSHD16FIcOHWr31HFHVq5cCYVCgblz5+L48eN4+umnsWfPHigUCri7u2PAgAH45ptvYGhoiGHDhgEAxo0bBzMzM+zfvx/Hjh0D8DBZXLNmDXg8HqZOncrV7+zsjEOHDsHf3x88Hg8bNmxQuto3cuRISCQShIWFYefOnRAIBFi9ejUMDQ25FTYkEgk8PDwQGBiIzZs3Y8SIEbh58ybS0tLw/PPPc/c6EmUikYibjFskEik9Ddyo6PqV3bZaD0M3ypug0Gvm6pSrYQUXTWn7fRBCCHn86HyyOH36dJibm0MqlWLRokXc9sDAQGzbtg0JCQmIioqCo6MjEhMTla7wdWbVqlVobm6Gn58fTpw4AVNTU8THxyM6OhoKhQKurq44evQod88gj8eDl5cX0tLS8PTTTwN4mECKxWK4uLhg4MCBXN1btmzB0qVL4enpCUtLS6xdu7bdfQVff/01li1bBm9vb9jY2CAuLg5FRUXcH3Qej4f09HS8/fbbWLJkCW7fvg0bGxt4e3vD2tq6J1+pTuPxeCqHcdW1pF5kdjwEAgHCngjDyqwP+/UTfZ19H4QQQh4POp8s6unp4ebNmx3uCw8PR3h4uMpjW89f5+Dg0G6evejoaKUHPQIDAztty+HDh9u17e7du+3KOTg4IDs7W2lbRESE0ntbW1ukp6dz72/cuIGqqio4OTlx24yNjbF9+3Zs3769w/bExsYiNjZWaduqVauwatWqTuMghBBCyOND55NFXZWdnY26ujq4urri1q1bePPNN+Hg4ABvb+++bppOaT0M2xOMMW5uSqFQCIVCgezsbCQnJ3d57sKuoKFiQggh6kbJopaSyWRYv349fv31VxgbG8PT0xNJSUk6/WRYX1DnMGzrOQpbhp5FIhH12X8xxjS6YkzbhL3l/t7uaHn6vKGhgXstEol6VCchhPRXlCxqqZkzZ2LmzJl93QxC1KahoQEBAQF93YwuEQgECAsLQ1BQEJf4p6am0v2dhBCdpNNT5xBCCCGEkJ6hK4uEPIZaD/n2x+HTz2Yuh1BfvcPzjXIZIr7//GH9M5ZDyO9+/Qoew0Uw/NN3Gd448bm6mqhSf+8vQohuo2RRx/B4PKSkpDzyyWzyeGs95Nsfh0+F+gKIepDMPbJ+fs/ql/MYgCYI9Xvnn9D+3l+EEN1Gw9CEEEIIIUQlurJIyGNOE08gy+VyyGQypaeF/0472s5p2l+1bqcmn+TWZN2EEPIolCz2I7t370ZsbCxu3LgBPb3/XfQNCAiAhYUFvvrqK+zcuRMJCQm4fv06HB0d8c477+CVV17psD5PT094eXnhww8/5Lbdvn0bdnZ2yMrKgre3N6qrqxEVFYWjR4+isbER06ZNw/bt2+Hs7KyynY2NjdwUJAC41WVkMpnaViNpqac/r27SE30dX11dHfd6wYIFGvuc3bt3d+u4+mYZBDz1DkMreEzptZzX/YS0pa765v/1nya/x9bq6urUOjdnR/r6/NQ0ik+7UXzq/6xHoWSxH5k3bx5WrlyJkydPwtfXFwBw9+5dnDhxAunp6UhJSUFUVBS2bt0KiUSCY8eOYcmSJRgyZAieeeaZdvUFBwdj8+bNiI+P526I//e//w07Ozt4eXkBABYvXozS0lIcOXIEYrEYa9euhZ+fH4qLi1XO/xcXF4dNmza12/79998rzSWoDhkZGWqtr7/pq/gePHjQJ5/bVZeHMQgE6l3zuvU/iucHN0Eg6PnVyxL7Hlfxt2VmZqr990wV+v3TbhSfduuN+Lr6t4DHtGW85zERGBgICwsL/Otf/wLw8MrMpk2bcP36dXh5eWHMmDFKV2vmz5+Pv/76C2lpaQCUH3BpuYqYnZ3NJYeenp7w9vZGfHw8SktLMWLECOTl5cHT0xMAcOfOHQwdOhR79+7FvHnzOmxjR1cWhw4dij///BNisVgt34NMJkNGRgaeffZZnZy0uq/jq6+vx0svvQQASEpKUvvKLzKZDNnZ2Zg+fXqX42toaEBISAgA4NMZr0MkMFBrmxrlMrye9ikAYNecyB4/Df3zYBlGlzNE/d/D38e9e/dqbAWdhoYGBAcHAwAOHjyo8Qdc+vr81DSKT7tRfOpTU1MDS0tL3L9/v9O/33RlsZ8JDg5GaGgoduzYAaFQiKSkJAQFBUFPTw8lJSUICwtTKj916lRs27atw7qsrKwwY8YMJCUlwcvLC9euXUN+fj527doFACgpKQGfz4e7uzt3jIWFBVxcXFBSUqKyjUKhEEKhsN12gUCg9hNbE3X2J30VX+v7CI2MjNSefMhkMggEAhgbG3c5Pj6fz7WLDz3wmXqnh5G3qk+f8dRSPx96XJs18T1yn9Nq2Lk3zxn6/dNuFJ926434ulo/PQ3dz/j7+4MxhrS0NFy/fh2nTp3irih0R3BwMA4ePAiZTIb9+/fD1dUVrq6uamwxIYQQQnQZJYv9jEgkwgsvvICkpCQcOHAALi4umDhxIgBg1KhRyMvLUyqfl5eH0aNHq6wvICAADQ0NOHHiBPbv36+UeI4aNQpyuRxnzpzhtt25cwdSqbTTOgkhhBDy+KBh6H4oODgYc+fORVFREV5++WVue0xMDObPnw83NzdIJBIcPXoUhw4dQmZmpsq6Bg4ciMDAQGzYsAElJSVYuHAht8/Z2RkBAQEIDQ3Frl27YGxsjHXr1mHw4MFas0Yv6R6RSITU1FTudX/TqFD/U4CNclmHr7uj5WnoRkXXpgXqqf7eX4QQ3UbJYj80ffp0mJubQyqVYtGiRdz2wMBAbNu2DQkJCYiKioKjoyMSExPh4+PTaX3BwcHw8/ODt7c37O2VH99MTExEVFQU5s6di6amJnh7eyM9PV2n7wMhDx+E6s+rgET8n2aX0GtZ9q+7BAIBwsLCsDrrX2pqUef6e38RQnQbJYv9kJ6eHm7evNnhvvDwcISHh6s8tqOH22fPnq1ykmMzMzN8/fXX3WsoIYQQQnQeJYukx1oS0ZbJudVBJpPhwYMHqKmp0cmrnBRfe4wx7Nu3T2NtYoxxUz4JhUJu7tHukMvl+OGHH7B7927uSeWmpiadmSSYzk/tRvFpt96Mr+Xv9qNmUaRkkfRYbW0tAGDo0KF93BJCCCGE/F21tbUwMTFRuZ8m5SY91tzcjJs3b8LY2LhHV2taa5no+/r162qb6Ls/ofi0G8Wn3Sg+7UbxqQ9jDLW1tbCzs1NaZrgturJIekxPTw9DhgzRSN1isVgn/zFoQfFpN4pPu1F82o3iU4/Orii2oHkWCSGEEEKISpQsEkIIIYQQlShZJP2SUCjExo0bO1yDWhdQfNqN4tNuFJ92o/h6Hz3gQgghhBBCVKIri4QQQgghRCVKFgkhhBBCiEqULBJCCCGEEJUoWSSEEEIIISpRskg0JjY2FjweT+ln5MiR3P6GhgZERETAwsICRkZGePHFF/HHH38o1VFRUYE5c+ZgwIABGDRoEGJiYiCXy5XK5OTkYOLEiRAKhXBycsKePXs0Es8PP/wAf39/2NnZgcfj4fDhw0r7GWN49913YWtrC0NDQ0gkEpSWliqVuXv3LoKDgyEWi2Fqaoply5ahrq5OqczFixfh5eUFkUiEoUOHYvPmze3a8t1332HkyJEQiURwdXVFenq6xuNbvHhxu/6cNWuWVsQXFxeHyZMnw9jYGIMGDUJgYCCkUqlSmd48Hz/77DM4ODhAJBLB3d0dZ8+e1Xh8Pj4+7fpv+fLlWhHfzp07MW7cOG6SYg8PDxw/fpzbr81915X4tLnvOhIfHw8ej4dVq1Zx27S9Dx8Vn9b3ISNEQzZu3MjGjBnDbt26xf3cvn2b2798+XI2dOhQlpWVxc6dO8eeeuop5unpye2Xy+Vs7NixTCKRsIKCApaens4sLS3ZW2+9xZX59ddf2YABA1h0dDQrLi5mn3zyCdPX12cnTpxQezzp6ens7bffZocOHWIAWEpKitL++Ph4ZmJiwg4fPswuXLjAnnvuOebo6Mjq6+u5MrNmzWLjx49np0+fZqdOnWJOTk5s4cKF3P779+8za2trFhwczC5fvswOHDjADA0N2a5du7gyeXl5TF9fn23evJkVFxezd955hwkEAnbp0iWNxhcSEsJmzZql1J93795VKtNf45s5cyZLTExkly9fZoWFhczPz4/Z29uzuro6rkxvnY/JycnMwMCAffXVV6yoqIiFhoYyU1NT9scff2g0vmnTprHQ0FCl/rt//75WxHfkyBGWlpbGfvnlFyaVStn69euZQCBgly9fZoxpd991JT5t7ru2zp49yxwcHNi4ceNYVFQUt13b+/BR8Wl7H1KySDRm48aNbPz48R3uu3fvHhMIBOy7777jtpWUlDAALD8/nzH2MHnR09NjlZWVXJmdO3cysVjMGhsbGWOMvfnmm2zMmDFKdS9YsIDNnDlTzdEoa5tMNTc3MxsbG/bRRx9x2+7du8eEQiE7cOAAY4yx4uJiBoD99NNPXJnjx48zHo/Hfv/9d8YYYzt27GBmZmZcfIwxtnbtWubi4sK9nz9/PpszZ45Se9zd3dnrr7+usfgYe5gsBgQEqDxGm+KrqqpiAFhubi5jrHfPxylTprCIiAjuvUKhYHZ2diwuLk5j8TH28I9V6z9ebWlTfIwxZmZmxr788kud67u28TGmO31XW1vLnJ2dWUZGhlJMutKHquJjTPv7kIahiUaVlpbCzs4Ow4cPR3BwMCoqKgAA58+fh0wmg0Qi4cqOHDkS9vb2yM/PBwDk5+fD1dUV1tbWXJmZM2eipqYGRUVFXJnWdbSUaamjt1y7dg2VlZVKbTExMYG7u7tSPKamppg0aRJXRiKRQE9PD2fOnOHKeHt7w8DAgCszc+ZMSKVSVFdXc2X6KuacnBwMGjQILi4uCA8Px507d7h92hTf/fv3AQDm5uYAeu98bGpqwvnz55XK6OnpQSKRaDS+FklJSbC0tMTYsWPx1ltv4cGDB9w+bYlPoVAgOTkZf/31Fzw8PHSu79rG10IX+i4iIgJz5sxp1w5d6UNV8bXQ5j7k9+hoQjrh7u6OPXv2wMXFBbdu3cKmTZvg5eWFy5cvo7KyEgYGBjA1NVU6xtraGpWVlQCAyspKpV+clv0t+zorU1NTg/r6ehgaGmooOmUt7emoLa3bOmjQIKX9fD4f5ubmSmUcHR3b1dGyz8zMTGXMLXVoyqxZs/DCCy/A0dERZWVlWL9+PWbPno38/Hzo6+trTXzNzc1YtWoVpk6dirFjx3Kf3RvnY3V1NRQKRYdlrly5orH4AGDRokUYNmwY7OzscPHiRaxduxZSqRSHDh3SivguXboEDw8PNDQ0wMjICCkpKRg9ejQKCwt1ou9UxQdof98BQHJyMn7++Wf89NNP7fbpwu9fZ/EB2t+HlCwSjZk9ezb3ety4cXB3d8ewYcPw7bff9loSR9QnKCiIe+3q6opx48bhiSeeQE5ODnx9ffuwZX9PREQELl++jB9//LGvm6IRquILCwvjXru6usLW1ha+vr4oKyvDE0880dvN/NtcXFxQWFiI+/fv4+DBgwgJCUFubm5fN0ttVMU3evRore+769evIyoqChkZGRCJRH3dHLXrSnza3oc0DE16jampKUaMGIGrV6/CxsYGTU1NuHfvnlKZP/74AzY2NgAAGxubdk/Dtbx/VBmxWNyrCWlLezpqS+u2VlVVKe2Xy+W4e/euWmJu2d9bhg8fDktLS1y9epVrV3+PLzIyEseOHcPJkycxZMgQbntvnY+WlpbQ19fv9fg64u7uDgBK/def4zMwMICTkxOefPJJxMXFYfz48di2bZvO9J2q+DqibX13/vx5VFVVYeLEieDz+eDz+cjNzcX27dvB5/NhbW2t1X34qPgUCkW7Y7StDylZJL2mrq4OZWVlsLW1xZNPPgmBQICsrCxuv1QqRUVFBXefjoeHBy5duqSUgGRkZEAsFnPDMx4eHkp1tJRpfa9Pb3B0dISNjY1SW2pqanDmzBmleO7du4fz589zZbKzs9Hc3Mz9w+Hh4YEffvgBMpmMK5ORkQEXFxeYmZlxZfpDzDdu3MCdO3dga2vLtau/xscYQ2RkJFJSUpCdnd1uKLy3zkcDAwM8+eSTSmWam5uRlZWl0fg6UlhYCABK/ddf4+tIc3MzGhsbtb7vHhVfR7St73x9fXHp0iUUFhZyP5MmTUJwcDD3Wpv78FHx6evrtztG2/qQnoYmGrN69WqWk5PDrl27xvLy8phEImGWlpasqqqKMfZwqgR7e3uWnZ3Nzp07xzw8PJiHhwd3fMtUAjNmzGCFhYXsxIkTzMrKqsOpBGJiYlhJSQn77LPPNDZ1Tm1tLSsoKGAFBQUMANuyZQsrKChgv/32G2Ps4dQ5pqamLDU1lV28eJEFBAR0OHWOm5sbO3PmDPvxxx+Zs7Oz0tQy9+7dY9bW1uyVV15hly9fZsnJyWzAgAHtppbh8/ksISGBlZSUsI0bN6pl6pzO4qutrWVr1qxh+fn57Nq1aywzM5NNnDiROTs7s4aGhn4fX3h4ODMxMWE5OTlKU1c8ePCAK9Nb52NycjITCoVsz549rLi4mIWFhTFTU1OlpyDVHd/Vq1fZP/7xD3bu3Dl27do1lpqayoYPH868vb21Ir5169ax3Nxcdu3aNXbx4kW2bt06xuPx2Pfff88Y0+6+e1R82t53qrR9Oljb+7Cz+HShDylZJBqzYMECZmtrywwMDNjgwYPZggUL2NWrV7n99fX1bMWKFczMzIwNGDCAPf/88+zWrVtKdZSXl7PZs2czQ0NDZmlpyVavXs1kMplSmZMnT7IJEyYwAwMDNnz4cJaYmKiReE6ePMkAtPsJCQlhjD2cPmfDhg3M2tqaCYVC5uvry6RSqVIdd+7cYQsXLmRGRkZMLBazJUuWsNraWqUyFy5cYE8//TQTCoVs8ODBLD4+vl1bvv32WzZixAhmYGDAxowZw9LS0jQa34MHD9iMGTOYlZUVEwgEbNiwYSw0NLTdP0D9Nb6O4gKgdK705vn4ySefMHt7e2ZgYMCmTJnCTp8+rdH4KioqmLe3NzM3N2dCoZA5OTmxmJgYpXne+nN8S5cuZcOGDWMGBgbMysqK+fr6cokiY9rdd4+KT9v7TpW2yaK292Fn8elCH/IYY6xn1yYJIYQQQoiuonsWCSGEEEKISpQsEkIIIYQQlShZJIQQQgghKlGySAghhBBCVKJkkRBCCCGEqETJIiGEEEIIUYmSRUIIIYQQohIli4QQQgghRCVKFgkhhLRTXl4OHo/HrWFLCHl80QouhBBC2lEoFLh9+zYsLS3B5/P7ujmEkD5EySIhhBAlTU1NMDAw6OtmEEL6CRqGJoQQHefj44PIyEhERkbCxMQElpaW2LBhA1quFTg4OOC9997Dq6++CrFYjLCwsA6HoYuKijB37lyIxWIYGxvDy8sLZWVl3P4vv/wSo0aNgkgkwsiRI7Fjx47eDpUQogE0tkAIIY+BvXv3YtmyZTh79izOnTuHsLAw2NvbIzQ0FACQkJCAd999Fxs3buzw+N9//x3e3t7w8fFBdnY2xGIx8vLyIJfLAQBJSUl499138emnn8LNzQ0FBQUIDQ3FwIEDERIS0mtxEkLUj4ahCSFEx/n4+KCqqgpFRUXg8XgAgHXr1uHIkSMoLi6Gg4MD3NzckJKSwh1TXl4OR0dHFBQUYMKECVi/fj2Sk5MhlUohEAjafYaTkxPee+89LFy4kNv2/vvvIz09Hf/5z380HyQhRGNoGJoQQh4DTz31FJcoAoCHhwdKS0uhUCgAAJMmTer0+MLCQnh5eXWYKP71118oKyvDsmXLYGRkxP28//77SsPUhBDtRMPQhBBCMHDgwE73GxoaqtxXV1cHAPjiiy/g7u6utE9fX7/njSOE9ClKFgkh5DFw5swZpfenT5+Gs7Nzl5O5cePGYe/evZDJZO2uLlpbW8POzg6//vorgoOD1dZmQkj/QMPQhBDyGKioqEB0dDSkUikOHDiATz75BFFRUV0+PjIyEjU1NQgKCsK5c+dQWlqKffv2QSqVAgA2bdqEuLg4bN++Hb/88gsuXbqExMREbNmyRVMhEUJ6CV1ZJISQx8Crr76K+vp6TJkyBfr6+oiKikJYWFiXj7ewsEB2djZiYmIwbdo06OvrY8KECZg6dSoA4LXXXsOAAQPw0UcfISYmBgMHDoSrqytWrVqloYgIIb2FnoYmhBAd5+PjgwkTJmDr1q193RRCiBaiYWhCCCGEEKISJYuEEEIIIUQlGoYmhBBCCCEq0ZVFQgghhBCiEiWLhBBCCCFEJUoWCSGEEEKISpQsEkIIIYQQlShZJIQQQgghKlGySAghhBBCVKJkkRBCCCGEqETJIiGEEEIIUen/AT54ls8PfxzbAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As we can see there are outliers present in the dataset.\n"
      ],
      "metadata": {
        "id": "0y7PrhNHYJa5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x[(x.make==\"dodge\")&(y>10000)]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "CScQxdchY1fS",
        "outputId": "7a081fb3-6ecf-47b8-851c-11790396c55d"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    symboling  normalized-losses   make fuel-type body-style drive-wheels  \\\n",
              "29          3              145.0  dodge       gas  hatchback          fwd   \n",
              "\n",
              "   engine-location  width  height engine-type  engine-size  horsepower  \\\n",
              "29           front   66.3    50.2         ohc          156       145.0   \n",
              "\n",
              "    city-mpg  highway-mpg  \n",
              "29        19           24  "
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              "      <th>fuel-type</th>\n",
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              "      <td>145.0</td>\n",
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              "      <td>145.0</td>\n",
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              "                                                     [key], {});\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x.drop(29,axis=0,inplace=True)"
      ],
      "metadata": {
        "id": "1lf1yRZPZdBa"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(data=x,x=y,y=\"make\")\n",
        "plt.grid()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "KD4UaZekZvqC",
        "outputId": "ac4f96e2-45a4-49b1-87fc-82a1d09f9b1f"
      },
      "execution_count": 31,
      "outputs": [
        {
          "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": [
        "x[(x.make==\"honda\")&(y>12000)]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "1b8vpr4YZ79O",
        "outputId": "f9c21129-f114-45e5-c3ee-afc7ee1f4e61"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    symboling  normalized-losses   make fuel-type body-style drive-wheels  \\\n",
              "41          0               85.0  honda       gas      sedan          fwd   \n",
              "\n",
              "   engine-location  width  height engine-type  engine-size  horsepower  \\\n",
              "41           front   65.2    54.1         ohc          110       101.0   \n",
              "\n",
              "    city-mpg  highway-mpg  \n",
              "41        24           28  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cc30a235-07fe-477f-93e6-f4b5fb3fe45c\">\n",
              "    <div 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>symboling</th>\n",
              "      <th>normalized-losses</th>\n",
              "      <th>make</th>\n",
              "      <th>fuel-type</th>\n",
              "      <th>body-style</th>\n",
              "      <th>drive-wheels</th>\n",
              "      <th>engine-location</th>\n",
              "      <th>width</th>\n",
              "      <th>height</th>\n",
              "      <th>engine-type</th>\n",
              "      <th>engine-size</th>\n",
              "      <th>horsepower</th>\n",
              "      <th>city-mpg</th>\n",
              "      <th>highway-mpg</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>0</td>\n",
              "      <td>85.0</td>\n",
              "      <td>honda</td>\n",
              "      <td>gas</td>\n",
              "      <td>sedan</td>\n",
              "      <td>fwd</td>\n",
              "      <td>front</td>\n",
              "      <td>65.2</td>\n",
              "      <td>54.1</td>\n",
              "      <td>ohc</td>\n",
              "      <td>110</td>\n",
              "      <td>101.0</td>\n",
              "      <td>24</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cc30a235-07fe-477f-93e6-f4b5fb3fe45c')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-cc30a235-07fe-477f-93e6-f4b5fb3fe45c 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-cc30a235-07fe-477f-93e6-f4b5fb3fe45c');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x.drop(41,axis=0,inplace=True)\n",
        "sns.boxplot(data=x,x=y,y=\"make\")\n",
        "plt.grid()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "ixTBG528c9OF",
        "outputId": "cc4711ba-8158-418b-ab6a-72829dd81841"
      },
      "execution_count": 34,
      "outputs": [
        {
          "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": [
        "x[(x.make==\"isuzu\")&(y>20000)]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "nRMRv5fbdNaL",
        "outputId": "7466bb16-cde3-4712-8059-f183563f503c"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    symboling  normalized-losses   make fuel-type body-style drive-wheels  \\\n",
              "45          0              122.0  isuzu       gas      sedan          fwd   \n",
              "\n",
              "   engine-location  width  height engine-type  engine-size  horsepower  \\\n",
              "45           front   63.6    52.0         ohc           90        70.0   \n",
              "\n",
              "    city-mpg  highway-mpg  \n",
              "45        38           43  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-43be1dcd-7e55-4e18-b8fd-d3ecea63f726\">\n",
              "    <div 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>symboling</th>\n",
              "      <th>normalized-losses</th>\n",
              "      <th>make</th>\n",
              "      <th>fuel-type</th>\n",
              "      <th>body-style</th>\n",
              "      <th>drive-wheels</th>\n",
              "      <th>engine-location</th>\n",
              "      <th>width</th>\n",
              "      <th>height</th>\n",
              "      <th>engine-type</th>\n",
              "      <th>engine-size</th>\n",
              "      <th>horsepower</th>\n",
              "      <th>city-mpg</th>\n",
              "      <th>highway-mpg</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>0</td>\n",
              "      <td>122.0</td>\n",
              "      <td>isuzu</td>\n",
              "      <td>gas</td>\n",
              "      <td>sedan</td>\n",
              "      <td>fwd</td>\n",
              "      <td>front</td>\n",
              "      <td>63.6</td>\n",
              "      <td>52.0</td>\n",
              "      <td>ohc</td>\n",
              "      <td>90</td>\n",
              "      <td>70.0</td>\n",
              "      <td>38</td>\n",
              "      <td>43</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-43be1dcd-7e55-4e18-b8fd-d3ecea63f726')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-43be1dcd-7e55-4e18-b8fd-d3ecea63f726 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-43be1dcd-7e55-4e18-b8fd-d3ecea63f726');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x.drop(45,axis=0,inplace=True)\n",
        "sns.boxplot(data=x,x=y,y=\"make\")\n",
        "plt.grid()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "lYbZle_3dSWS",
        "outputId": "1791bb3a-a96a-4c79-b40e-d7f70737729d"
      },
      "execution_count": 36,
      "outputs": [
        {
          "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": [
        "x[(x.make==\"mitsubishi\")&(y>13000)]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "id": "TPwHmaMAdXGa",
        "outputId": "d47633de-bfff-4ca8-8219-d55c547c7b68"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    symboling  normalized-losses        make fuel-type body-style  \\\n",
              "83          3              122.0  mitsubishi       gas  hatchback   \n",
              "84          3              122.0  mitsubishi       gas  hatchback   \n",
              "\n",
              "   drive-wheels engine-location  width  height engine-type  engine-size  \\\n",
              "83          fwd           front   66.3    50.2         ohc          156   \n",
              "84          fwd           front   66.3    50.2         ohc          156   \n",
              "\n",
              "    horsepower  city-mpg  highway-mpg  \n",
              "83       145.0        19           24  \n",
              "84       145.0        19           24  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-826b1c41-c396-4e93-91b4-d9846e61ced6\">\n",
              "    <div 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>symboling</th>\n",
              "      <th>normalized-losses</th>\n",
              "      <th>make</th>\n",
              "      <th>fuel-type</th>\n",
              "      <th>body-style</th>\n",
              "      <th>drive-wheels</th>\n",
              "      <th>engine-location</th>\n",
              "      <th>width</th>\n",
              "      <th>height</th>\n",
              "      <th>engine-type</th>\n",
              "      <th>engine-size</th>\n",
              "      <th>horsepower</th>\n",
              "      <th>city-mpg</th>\n",
              "      <th>highway-mpg</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>83</th>\n",
              "      <td>3</td>\n",
              "      <td>122.0</td>\n",
              "      <td>mitsubishi</td>\n",
              "      <td>gas</td>\n",
              "      <td>hatchback</td>\n",
              "      <td>fwd</td>\n",
              "      <td>front</td>\n",
              "      <td>66.3</td>\n",
              "      <td>50.2</td>\n",
              "      <td>ohc</td>\n",
              "      <td>156</td>\n",
              "      <td>145.0</td>\n",
              "      <td>19</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84</th>\n",
              "      <td>3</td>\n",
              "      <td>122.0</td>\n",
              "      <td>mitsubishi</td>\n",
              "      <td>gas</td>\n",
              "      <td>hatchback</td>\n",
              "      <td>fwd</td>\n",
              "      <td>front</td>\n",
              "      <td>66.3</td>\n",
              "      <td>50.2</td>\n",
              "      <td>ohc</td>\n",
              "      <td>156</td>\n",
              "      <td>145.0</td>\n",
              "      <td>19</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-826b1c41-c396-4e93-91b4-d9846e61ced6')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-826b1c41-c396-4e93-91b4-d9846e61ced6 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-826b1c41-c396-4e93-91b4-d9846e61ced6');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x.drop([83,84],axis=0,inplace=True)\n"
      ],
      "metadata": {
        "id": "4p3E7-Xxdkj3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(data=x,x=y,y=\"make\")\n",
        "plt.grid()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "6KBaIWlFd2O8",
        "outputId": "5a4af498-be5b-4cdf-ed01-5edd3d12d8cc"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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UPhraVnm6Yy0Wi92qLa5YwaUsMZeWK+OrDJUZn16vr5D3SIjKJMmiEKLMiurmtHY96/X6Cp/6wdkTwRfHYDAQERHxZzd7NMOHD6+SMw5IfM6TnJwstwIIj1exa3EJIYQQQgiPJi2LQghRRi/20eNtKXzVmIqSY7IwNyW/G/qlPjp0Gud3cZoVLT/gmvgqQ0XHl2OGuXsMTi9XCFeRZFEI4Vas9wWC+9/vpdMoeBexakxlnb9ikkVFLd+V8VWUio/PUvIuotJ50neLu5FuaDcXFhbGzJkzXV0NISqN9b7AiIgI9YtdCCHKS75byk6SRSGEEEIIUSTphhZCuC13+9+/tT4Wi6XA/KVCWNl+NlzxGTaZTBiNxio99VFZ4nO37xNPIsmiBzCZTEyfPp0NGzag1WqZMmUKzz77LIqiEBoaysSJEzl79izvv/8+QUFBxMfH07VrVyZOnMgnn3zC7bffzjvvvEOnTp2wWCzUq1ePN954g6FDhwLQvn17fvnlFy5evAjkrzf94IMPcvXq1UKnJcnJyVHneQPIyMgA8qdLcdY0FNZyquK0HSDxFSczM1N9HBkZ6bQ6OZPJZMJkMmFWKnZaoMLkL6Vo+POxVr3/zrnn0Nr9W9VUdHzZeXlA/nekKz/Da9euddm5K0N54svMzKzQOWDLozL/Pjh6Dve8UsJOUlISEyZM4PDhwxw9epTo6GgaN27MpEmTAFixYgUvvPACzzzzDCtWrGD06NF069aN8ePH8/LLLzN37lzGjBnDt99+i6Io3H///ezbt4+hQ4dy9epVTp06ha+vL6dPn+bOO+9k//793HPPPUXOX7d06VLi4uIKbN+zZ4/T57xLSUlxannuRuIrKCsrqwJqUjHO3jq80s+Z/+We/0fyzK0jKnQeS1fEV5kqKj7b90i4p71791bqHK1lURl/Hxz9vpVk0QOEhISwYsUKFEWhRYsWfP3116xYsUJNFh9++GEee+wxABYtWsQbb7zBPffco64JPXfuXLp27covv/xCgwYNCAsL46233gLg008/pUOHDjRo0IB9+/Zx5513sm/fPnr27FlkfebPn09MTIz6PCMjg5CQEMLDwwkICHBKzEajkZSUFPr06VPhkzq7gsRXtOzsbBISEgDYtGlTuVZccTaDwcDYsWPRarVoNBqa/7QFb0vltg7nmP7q4mzx07sVNnXO2VuHuyS+ylDR8RmMeWqrVVJSUqV/ho1GI6mpqfTq1avKfr+UJT6DwcCoUaMA6N+/v9tOll6Zfx+sPYMlkWTRA3Tp0sVuiH/Xrl1Zvnw5ZrMZgLZt26qv1a9fH4A2bdoU2Hb58mUaNGhAz549eeKJJ/j111/Zv38/YWFharI4YcIEPvvsM+bMmVNkfXQ6HTqdrsB2rVbr9A92RZTpTiS+gmzvQfLz83OrL3SNRoPJZEJRFBRFwdtirPRkytvmfrj881fc9B+uiK8yVVR8Gizq59gVn2Gj0YhWq8Xf379Kfr+UNT7bbmdP+O6tjDo6Wr6Mhq4CbN9sa1JZ2La8vDwgP5GsU6cO+/fvV5PFsLAw9u/fz5EjRzAajXTr1q0SIxBCCCGEu5KWRQ9w6NAhu+eff/45zZo1w9u7bCsPKIpCjx49SE5O5ttvv+W+++6jRo0a5OTk8NZbb9GpUydq1qzpjKoLUWp6vZ7k5GT1sRBCOIN8t5SdJIseID09nZiYGB577DGOHTtGfHw8y5cvL1eZYWFhPPXUU3Tq1Ak/Pz8A7r//fjZt2sTs2bOdUW0hykRRFLfqei5Ojsli1y1cWecs7LEz5Y+4dk18laGi48sxO71I4QSe9N3ibiRZ9ABjxowhOzubzp074+3tzRNPPEF0dHS5yuzZsydms5mwsDB1W1hYGMnJyXbbhBBFm5dicOn0R9Y1op1NqzUTHe36+CpKVY9PCGeTZNHN7du3T338xhtvFHj9woULBbbdPFlwaGhogW3t27cvsG3mzJmytKAQQggh7EiyKDyO7WLwN2+3Thau0+lQFEUWixdOZb3nyWQykZqaypYtWyp9Yt/CPufO5sr4KkNlxif3xomqoOp9C4gqz7oYvCOSk5PlHhXhNNZ7nqxdl3q93iXTb1T0ZMKujq+iVfX4hHA2mTpHCCGEEEIUSVoWhUebNUCLz5+f4lyThVc+dHxReSGEEEKUTFoWy+HChQsoisLx48ddXZVCRUVFMWjQIFdXo8ys9yZmZ2cXGIxj5aMBH42i/tyspOOFEEIIUTxJFoVq3759KIrCH3/84eqqAPk3oQ8ZMoSIiIhCB7Q4IjIyslzHCyGEENWdJIseKDc319VVEEIIIUQ1IcmiA/Ly8li2bBlNmzZFp9PRuHFjnn/+efX1H374gQceeIAaNWrQrl07Dh48aHf8f/7zH3r06IGvry8hISE8/vjj3LhxA4AFCxZw7733Fjhnu3btePbZZ4G/upOff/55GjZsSIsWLQD4+uuv6dWrF76+vgQFBREdHU1mZmaxcSxdupTbbrsNX19f2rVrx3vvvQfkd6k/8MADANSuXRtFUYiKiir7RXMC265ja3dydna2XSuhdC8LIYQQFUsGuDhg/vz5rFu3jhUrVnDfffdx8eJFTp8+rb6+cOFCXnnlFZo1a8bChQsZMWIE33//PRqNhnPnztGvXz+ee+453nnnHX799VemT5/O9OnTSUhIYNSoUSxdupRz585xxx13APDtt9/y1VdfsW3bNvUcn3zyCQEBAaSkpABw48YN+vbtS9euXTly5AiXL19m4sSJTJ8+ncTExELjWLp0KRs3buTNN9+kWbNmfPrpp/z973+nbt263HfffWzbto0hQ4Zw5swZAgICipxyJicnR53nDSAjIwPIn47CWashGI1GTKa/BqtERkYWul9unhatkv9/njzFAhR+/szMTLeaL856narq6hESn2eT+DybxOfZKjM+R8+hWKRppljXr1+nbt26vPbaa0ycONHutQsXLnDbbbfx9ttvM2HCBABOnjzJXXfdxalTp7jzzjuZOHEi3t7evPXWW+px//nPf+jZsyc3btxAr9fTvn17hgwZwjPPPAPktzampqby+eefA/kti7t37yY9PR0fHx8A1q1bx9y5c/nxxx+pWbMmALt27WLgwIH873//o379+kRFRfHHH3+wY8cOcnJyqFOnDnv37qVr165qXSZOnEhWVhabN29m3759PPDAA1y9epXAwMAir0lsbCxxcXEFtm/evNmp879lZWWRkJBQ7D7R0dHqPGlGo5G1a9cWut+4ceMqfG46IYQQwpNkZWUxcuRIrl27RkBAQJH7uU9Ti5s6deoUOTk5PPjgg0Xu07ZtW/VxcHAwAJcvX+bOO+/kxIkTfPXVV2zatEndx2KxkJeXx/nz52nZsiWjRo3inXfe4ZlnnsFisfDuu+8SExNjd442bdqoiaK1Xu3atVMTRYDu3buTl5fHmTNnqF+/vt3x33//PVlZWfTp08due25uLh06dCjFFclvabWtX0ZGBiEhIYSHhxf7YSsNo9HIzp071eebNm1SV0IwGAyMHTsWgDqXN+CjzW9ZzDX99f8eb29vAMxmMwD9+/d3q8m5jUYjKSkp9OnTp0pOCizxeTaJz7NJfJ6tMuOz9gyWRJLFEjiSYNi+mdalt/Ly8oD87s/HHnuMxx9/vMBxjRs3BmDEiBHMnTuXY8eOkZ2dzY8//lig29U2KSwL672MO3fupFGjRnav6XS6UpWl0+kKPUar1Tr1g227jJmfn5/6Xmg0GrWL2gsFL0v+fl42jeTWJLGi6uYs7lovZ5H4PJvE59kkPs9WGfE5Wr4kiyVo1qwZvr6+fPLJJwW6oR3RsWNHTp48SdOmTYvc59Zbb6Vnz55s2rSJ7Oxs+vTpQ7169Yott2XLliQmJnLjxg01kUxLS8PLy0sdAGOrVatW6HQ60tPT6dmzZ6FlWlsub060hBBCCFF9SbJYAr1ez9y5c5kzZw4+Pj50796dX3/9lW+//bbYrmmruXPn0qVLF6ZPn87EiROpWbMmJ0+eJCUlhddee03db9SoUSxevJjc3FxWrFhRYrnW/ceOHUtsbCy//vorM2bMYPTo0QW6oAH8/f2ZNWsWTz75JHl5edx3331cu3aNtLQ0AgICGDt2LE2aNEFRFD788EMefvhhfH198fPzK90FcyKNRsO2bdvQaDRqF3Rpbd26Fb1eX+bjhRBCiOpOps5xwDPPPMNTTz3FokWLaNmyJZGRkVy+fNmhY9u2bcv+/fs5e/YsPXr0oEOHDixatIiGDRva7Td06FCuXLlCVlaWQ6uu1KhRg48//pjff/+de+65h6FDh/Lggw/aJaA3W7JkCc888wxLly6lZcuW9OvXj507d3LbbbcB0KhRI+Li4pg3bx7169dn+vTpDsVYURRFQa/X4+vra9clbSvXlH+vovXnZiUdL4QQQojiScuiA7y8vFi4cCELFy4s8NrNg8kDAwMLbLvnnnvYs2dPsecIDAwscpWRoqbCadOmDampqUWWefNxiqLwxBNP8MQTTxR5zDPPPKOOyvYEr3xYNadOEEIIIdyFtCwKIYQQQogiScui8Dh6vZ7k5OQC2y0WCwaDgZycHHQ6ndr1nJ2dXWA/66Titvs5cl7pzhZCCFHdSLIoPI6iKEVOaaQoCsOHD6+Q8yYnJ7vVXI1CCCFEZZBuaCGEEEIIUSRpWRQew9rNDI51CU8c5IW2kE+40WTh7R2WP/dR0GqKLsdogrd35Dm1XkIIIYQnkZZFNxIWFsbMmTPLfHxiYmKxazp7OoPBQEREBBEREUWOHLel1YBWoxT689c+hb/+14/z6yWEEEJ4EkkWhRBCCCFEkSRZFEIIIYQQRZJk0UVu3LjBmDFj8PPzIzg4mOXLl9u9fvXqVcaMGUPt2rWpUaMGDz30EN99953dPomJiTRu3JgaNWowePBgrly5UuA8zz33HPXq1cPf35+JEycyb9482rdvb7fP22+/TcuWLdHr9dx5552sWbPG6fE6m8FgIDs7u8CPbTfwzZOjl4VtGY6cUwghhKhqZICLi8yePZv9+/eTnJxMvXr1WLBgAceOHVMTuaioKL777js++OADAgICmDt3Lg8//DAnT55Eq9Vy6NAhJkyYwNKlSxk0aBC7d+9m8eLFdufYtGkTzz//PGvWrKF79+5s2bKF5cuXq8v7WfdZtGgRr732Gh06dODLL79k0qRJ1KxZk7FjxxZa95ycHHWeQoCMjAwAjEYjRqNzVlSxlmNbXmZmpvo4MjKyxDJMeVq0SsH/D1kUC5Dz52MtlmIGpJjy8oBch8+ZmZmJRlPyr1Vh8VUlEp9nk/g8m8Tn2SozPkfPoVic0fwiSiUzM5OgoCA2btzIo48+CsDvv//OrbfeSnR0NNOmTaN58+akpaXRrVs3AK5cuUJISAhJSUk8+uijjBw5kmvXrrFz50613OHDh7N7927++OMPALp06UKnTp3s1ou+7777yMzM5Pjx4wA0bdqUJUuWMGLECHWf5557jl27dvHZZ58VWv/Y2Fji4uIKbN+8eTM1atQo17UpTlZWFgkJCQ7vHx0djVarLbDdaDSydu3aYvcpbF9HjBs3rkKvgRBCCOEsWVlZaj4REBBQ5H7SsugC586dIzc3l3vvvVfdVqdOHVq0aAHAqVOn0Gg0dq8HBQXRokULTp06pe4zePBgu3K7du3K7t271ednzpxh6tSpdvt07txZXU/6xo0bnDt3jgkTJjBp0iR1H5PJRK1atYqs//z584mJiVGfZ2RkEBISQnh4eLEfttIwGo2kpKTQp08fNZnLzs5Wk8VNmzah1+sLHGcwGNQWUa/fE/HSFmxZ9DL99f8jr6tJeBUzdY6XMU9tKUxKSirynKNGjQKgf//+Dk3cXVh8VYnE59kkPs8m8Xm2yozP2jNYEkkWqzFrt+66devsElMAb2/vIo/T6XTodLoC27VardM/2LZlmkwmdbufn1+hSZlGo1H3U/BCsRRMBBWbxnTFYix0H/V1LJhMeSWes7D6OqIirpk7kfg8m8Tn2SQ+z1YZ8TlavgxwcYE77rhDve/Q6urVq5w9exaAli1bYjKZ7F6/cuUKZ86coVWrVuo+tq8DfP7553bPW7RowZEjR+y22T6vX78+DRs25IcffqBp06Z2P7b3NQohhBCi+pKWRRfw8/NjwoQJzJ49m6CgIOrVq8fChQvx8srP3Zs1a0ZERASTJk3irbfewt/fn3nz5tGoUSMiIiIAePzxx+nevTuvvPIKERERfPzxx3Zd0AAzZsxg0qRJdOrUiW7durF161a++uorbr/9dnWfuLg4Hn/8cWrVqkW/fv3Iycnh6NGjXL161a6r2R3o9XqSk5PVxyUxmgAK3pJrtOmGtn1cdBnOrZcQQgjhSSRZdJGXX36ZzMxMBg4ciL+/P0899RTXrl1TX09ISOCJJ55gwIAB5Obmcv/997Nr1y61ybhLly6sW7eOxYsXs2jRInr37s3TTz/NkiVL1DJGjRrFDz/8wKxZszAYDAwbNoyoqCgOHz6s7jNx4kRq1KjByy+/zOzZs6lZsyZt2rQp10oyFUVRFIfuB7QqaZm+/H0sFJZQlkZp6yWEEEJ4EkkWXcTPz48NGzawYcMGddvs2bPVx7Vr12b9+vXFljF+/HjGjx9vt+2pp56ye/7MM8/wzDPPqM/79OlD06ZN7fYZOXIkI0eOLHUMQgghhKj6JFmswrKysnjzzTfp27cv3t7evPvuu+zdu5eUlBRXV63C2HYJF8VisajzROp0OpRi5lm8uWwhhBCiupFksQpTFIVdu3bx/PPPYzAYaNGiBdu2baN3796urlqFKaxL2GKx2K2yoigKer2+1EmjIyu16PV6h5NPIYQQwhNIsliF+fr6snfvXldXw+UMBoM6MKiiJScny/2LQgghqhSZOkcIIYQQQhRJWhaF27PtRi5vN++QR8A6h7bJBNveL7i9tGzLKQtnxieEEEI4m7QsukhYWJhLpqdx1XnLw9qNHBER4dB9g8XRaOx/itpe2h93iU8IIYRwNkkWhRBCCCFEkSRZFEIIIYQQRZJk0YXy8vKYM2cOderUoUGDBsTGxqqvpaenExERgZ+fHwEBAQwbNoxffvlFfT02Npb27duzYcMGQkNDqVWrFsOHD+f69evqPjdu3GDMmDH4+fkRHBzM8uXLC9Rhw4YNdOrUCX9/fxo0aMDIkSO5fPlyhcZdHgaDgezs7FL92HbtWsq3WEuhbMt0tH4GgwGj0YjBYJCuZyGEEG5NBri4UFJSEjExMRw6dIiDBw8SFRVF9+7defDBB9VEcf/+/ZhMJqZNm0ZkZCT79u1Tjz937hw7duzgww8/5OrVqwwbNowXX3yR559/HshfEWb//v0kJydTr149FixYwLFjx2jfvr1ahtFoZMmSJbRo0YLLly8TExNDVFQUu3btKrLeOTk56vyEABkZGWpZRqPRKdfGWo7RaOTGjRvq9sjIyHKVm5enAawDSCyAdfFn2+2lLfOvckpbv7Vr19o9z8zMRFPemyDdgO37VxVJfJ5N4vNsEp/zz1USxWKpiLYWUZKwsDDMZjMHDhxQt3Xu3JlevXrx4IMP8tBDD3H+/HlCQkIAOHnyJHfddReHDx/mnnvuITY2lpdffplLly7h7+8PwJw5c/j000/5/PPPyczMJCgoiI0bN/Loo48C8Pvvv3PrrbcSHR3NypUrC63X0aNHueeee7h+/Tp+fn6F7hMbG0tcXFyB7Zs3b6ZGjRrluSyFysrKIiEhwSllRUdHq+trG41GNVmz3V5atuWU17hx4yrkGgohhBA3y8rKYuTIkVy7do2AgIAi9/P8JgwP1rZtW7vnwcHBXL58mVOnThESEqImigCtWrUiMDCQU6dOcc899wAQGhqqJoq2x0N+q2Nubi733nuv+nqdOnVo0aKF3Tm/+OILYmNjOXHiBFevXiUvLw/I7wZv1apVofWeP38+MTEx6vOMjAxCQkIIDw8v9sNWGkajkZSUFPr06YPJZFKTxU2bNpV62T2DwcDYsWMBuPbHO2g0+S2IJtNf/0+y3V5aJpNFbQ1MSkpyqH5Go5HU1FR69eqF2Wxm1KhRAPTv379KTOpt+/6VNQl3ZxKfZ5P4PJvE5zzWnsGSSLLoQjd/CBRFUZO1yjj+xo0b9O3bl759+7Jp0ybq1q1Leno6ffv2JTc3t8jjdDodOp2u0Po4+4Ot1Wrt5h308/MrdTKl0WgwmUwl7FXS6yUc/efhjtbPaDSi1Wrx9/e3q1tFXENXqmrx3Ezi82wSn2eT+JxzDkfIABc31LJlS3788Ud+/PFHddvJkyf5448/imztu9kdd9yBVqvl0KFD6rarV69y9uxZ9fnp06e5cuUKL774Ij169ODOO+9068EtQgghhKh80rLohnr37k2bNm0YNWoUK1euxGQyMXXqVHr27EmnTp0cKsPPz48JEyYwe/ZsgoKCqFevHgsXLsTL66//HzRu3BgfHx/i4+OZPHky33zzDUuWLKmosMpMr9eTnJysPi4P2wbGoh6Xp8yycGZ8QgghhLNJsuiGFEUhOTmZGTNmcP/99+Pl5UW/fv2Ij48vVTkvv/wymZmZDBw4EH9/f5566imuXbumvl63bl0SExNZsGABq1evpmPHjrzyyiv87W9/c3ZI5aIoitPu4ytqWb7yLNdXXs6MTwghhHA2SRZdxHYKHKsdO3aojxs3bqy2NhUmNjbWbl5GgJkzZ9ot5efn58eGDRvYsGGDum327Nl2x4wYMYIRI0bYbZMB8kIIIYSwkmRRVHm23by2LBaLOl+kTqezG0hTnnMJIYQQVYkki6LKK66btzRzGloslhJXW3FkNRaLxcKNGzcwGo1kZ2cXOlJbr9c7JXkVQgghykuSRSEcZDAYiIiIcGqZ77zzTqHJYnJystzHKIQQwi3I1DlCCCGEEKJI0rIoqjzb7mNnde/2igTvMv72mE2QujX/cc8h4KUpuN1WRdRfCCGEcJQki24oLCyM9u3bF7l+sygd2+5jZ3XvemtAoy1r0vbXaHNvjYK3Wk7ho9Arov5CCCGEoyRZdEPvv/9+lV7CSAghhBCeQ5JFN1SnTh1XV6HKcmS0siPHVsRUlLZl2p6rPHUWQgghykuSRTdk2w29Zs0aVqxYwY8//kitWrXo0aMH7733HgChoaEFJuJu3749gwYNIjY2lsTERMaNG1eg/MWLFxMbG1tod/egQYMIDAwkMTGxyPrl5OSo8xMCZGRkAGA0GjEajeUL/k/WcpxRXmZmpvo4MjKy3OUBWMwalDKOD1OwAMY/H2tRUP4sMw/IHxldVD0zMzPRaNz/19aZ7587kvg8m8Tn2SQ+55+rJO7/V6caO3r0KI8//jgbNmygW7du/P777xw4cMDh4yMjI+nXr5/6fN++fYwePZru3buXq15Lly4lLi6uwPY9e/aUat5CR6SkpJS7jKysLCfUxN4dtcaX+VaB/F/OtQA0qfl3tRyjj5FP/txelL179zr9GlckZ7x/7kzi82wSn2eT+MrP0b+Pkiy6sfT0dGrWrMmAAQPw9/enSZMmdOjQweHjfX191cEQ586dY9q0abzwwgv06dOnXPWaP38+MTEx6vOMjAxCQkIIDw8nICCgXGVbGY1GUlJS6NOnT7nv38zOziYhIQGATZs2lXmVFYPBwNixYwG4kPkOGp+ytSyajH/1N//3xka8tfmtiabcPLXVMCkpSa2nwWBg1KhRAPTv398jBrg48/1zRxKfZ5P4PJvE5zzWnsGSSLLoxvr06UOTJk24/fbb6devH/369WPw4MGlblm6du0aAwYMoH///gXWhi4LnU6HTqcrsF2r1Tr9g+2MMm0nvfbz8ytzsqXRaNSyLApYKNtoaIvNqGcLRixYy7RgraptPW27nSviGlckT6tvaUl8nk3i82wSn3PO4QiZlNuN+fv7c+zYMd59912Cg4NZtGgR7dq1448//gDAy8sLy00jLW6+/8BsNhMZGUlAQABr19p3cTpyvBBCCCGqN0kW3ZxGo6F3794sW7aMr776igsXLpCamgpA3bp1uXjxorpvRkYG58+ftzv+ySef5Ouvv2bHjh0Ful9vPt5sNvPNN99UYDRCCCGE8DTSDe3GPvzwQ3744Qfuv/9+ateuza5du8jLy6NFixYA9OrVi8TERAYOHEhgYCCLFi3C29tbPT4hIYE1a9awfft2FEXh0qVLQH4Xp5+fH7169SImJoadO3dyxx138Oqrr6qtllWJXq8nOTlZfewMZhMUNYm2Y8daH1vUbmlzwSWigYqpvxBCCOEoSRbdWGBgIO+//z6xsbEYDAaaNWvGu+++y1133QXkDzQ5f/48AwYMoFatWixZssSuZXH//v2YzWb+9re/2ZVrnTpn/PjxnDhxgjFjxqDRaHjyySd54IEHKjXGyqAoitMHhRS2LF9Z7N8GpiKSRKuKqL8QQgjhKEkW3dC+ffsKfXyzgIAAtmzZYrfNOloXIDExsdj5ErVaLWvWrGHNmjVlraoQQgghqjhJFoVwkG13cHlYLBZu3LjB/v37CQ8PL3Q0mnQ3CyEg//uitKs4WWdtMBgMf83gYLGoiynodDoURUGv16MoZV3jXlQnkiwK4SBndgdbp0Tw9fWt0lM/CCHKx2AwEBERUapjtFot0dHRDB8+vNgZLpKTk+UWF+EQGQ0thBBCCCGKJC2LQgghhAe4c4yClwN/tb3/XDDgztEK5j8f5xktnN5QkbUTVZkkiy4QFRXFH3/8wY4dO1xdFSGEEE5me5+hM+8L9NKAl7bksrz+TBC9tMpNK03ZT/dlsVjIzs52ej1F1SPJogusWrWqwMopQgghqgbb+wzd+b7AnJwcIiMjAfeup3A9SRZdoFatWq6ugkNyc3Px8fFxdTWEEEII4UIywMUFoqKiGDRoEAC7d+/mvvvuIzAwkKCgIAYMGMC5c+fs9v/ss89o3749er2eTp06sWPHDhRF4fjx40D+fIqBgYF2x1j3sTp37hwRERHUr18fPz8/7rnnHvbu3Wt3TGhoKEuWLGHMmDEEBAQQHR3t9NiFEKI6MRgMZGdnl/nHdtocZ/dIlXZKHlF9Scuii924cYOYmBjatm1LZmYmixYtYvDgwRw/fhwvLy8yMjIYOHAgDz/8MJs3b+a///0vM2fOLPV5MjMzefjhh3n++efR6XSsX7+egQMHcubMGRo3bqzu98orr7Bo0SIWL15cZFk5OTnqfF2QvyY1gNFoLHaahtKwluOs8tyNxOfZJD7PVtHxZWZmqo+t3bzO4GXS4u1TchuPN1q7f/NZgFy7/WwXccjMzESj8YyUQD6fzj9XSTzjk1GFDRkyxO75O++8Q926dTl58iStW7dm8+bNKIrCunXr0Ov1tGrVip9//plJkyaV6jzt2rWjXbt26vMlS5awfft2PvjgA6ZPn65u79WrF0899VSxZS1dupS4uLgC2/fs2UONGjVKVa+SpKSkOLU8dyPxeTaJz7NVVHxZWVkVUm5333FoNY7Py9pV81cyaLQY+Ya1Re67d+9ep39/VzT5fJafo59VSRZd7LvvvmPRokUcOnSI3377jby8PADS09Np3bo1Z86coW3btnYrenTu3LnU58nMzCQ2NpadO3dy8eJFTCYT2dnZpKen2+3XqVOnEsuaP38+MTEx6vOMjAxCQkIIDw8nICCg1HUrjNFoJCUlhT59+lTJSaslPs8m8Xm2io4vOzubhIQEADZt2lSuFZkMBoPaAviZMQFvxbGWxa6asRw0JWEmv+XIbPqrC9vb2xtFUXjrrbeYMGECAP379/eYAS7y+XQea89gSSRZdLGBAwfSpEkT1q1bR8OGDcnLy6N169bk5uaWfPCfvLy8CtzLcnPT8qxZs0hJSeGVV16hadOm+Pr6MnTo0ALnqVmzZonn0+l06HS6Atutq5I4U0WU6U4kPs8m8Xm2iorPusQegJ+fX7mSMI1Go5aXpyiA49PbmDGqyWKezbQ5ZrNZrZuVJ77Xnljn0qiM+BwtX5JFF7py5Qpnzpxh3bp19OjRA4D//Oc/dvu0aNGCjRs3kpOToyZoR44csdunbt26XL9+nRs3bqjJnnXwi1VaWhpRUVEMHjwYyG9pvHDhQgVEJYQQQoiqREZDu1Dt2rUJCgpi7dq1fP/996Smptp17wKMHDmSvLw8oqOjOXXqFB9//DGvvPIKgDra+d5776VGjRosWLCAc+fOsXnzZhITE+3KadasGe+//z7Hjx/nxIkTarlCCCGcS6/Xk5ycTHJycrm6oCuaTqfziHoK15Nk0YW8vLzYsmULX3zxBa1bt+bJJ5/k5ZdfttsnICCAf/3rXxw/fpz27duzcOFCFi1aBKD+ctepU4eNGzeya9cu2rRpw7vvvktsbKxdOa+++iq1a9emW7duDBw4kL59+9KxY8dKiVMIIaoTRVHw9fXF19fXqaui5Jnyl+1z5AcK7ltZ9RRVj3RDu0BOTo56r0jv3r05efKk3es333/YrVs3Tpw4oT7ftGkTWq3WbsqbQYMGqXM3WtmOmA4NDSU1NdXu9WnTptk9l25pIYRwX6fXOzbPolZr4f5oOL3BgrGQJFGI0pJksRKZTCbOnj3LwYMHeeyxxxw+bv369dx+++00atSIEydOMHfuXIYNG+YxI9eEEEII4bkkWaxE33zzDd26deOBBx5g8uTJDh936dIlFi1axKVLlwgODubRRx/l+eefr8CaVh8Wi8UlqxhYRzcaDAa7kZMWi0Wd8Fyn05Wpa0iv10uXkhBVhPX+x9IwmUykpqayZcsWdaLtwr5b5D5F4ShJFitR+/btyzRZ65w5c5gzZ04F1EgYDAYiIiIq/bxarZbo6GiGDx/u9Fn6k5OTpdVZiCrCel9haVi/U/R6vd3UKJ426bZwHzLARQghhBBCFElaFoX4k9e4JqCtnO5brz9/9bzGNcELm25oYx6WhPxVdZRxjVG0Dv5/zmghL+G/Tq+nEEIIIcliNbJv3z4eeOABrl69SmBgoKurUyLr/YQmk6ly7sPTKo4nZ+Wk/Nmor2i91MdW1rGLitbL4fpYkDkzi2N7b6rc0ymEEKUj3dDCbZlMJoYMGUJERIRLBqGIqsN6b6p8loQQovQkWRRCCCGEEEWSbuhKEhYWRps2bfD29iYpKQkfHx+ee+45Ro4cyfTp03nvvfeoX78+8fHxPPTQQ5jNZqKjo0lNTeXSpUs0btyYqVOn8sQTT6hlFtaV1qRJE3Vy7V27djFz5kx+/PFHunTpwtixY+32vXLlCtOnT+fTTz/l6tWr3HHHHSxYsIARI0ZU6LVwlO3k5BXVGmRbrsViwVM7JyvjWpWGyWTCaDQWmBrIVdzhmgghhKeSZLESJSUlMWfOHA4fPszWrVuZMmUK27dvZ/DgwSxYsIAVK1YwevRo0tPT0Wq13Hrrrfzzn/8kKCiIzz77jOjoaIKDgxk2bBgAFy9eVMu+ceMG/fr1o2vXrgD8+OOPPPLII0ybNo3o6GiOHj3KU089ZVcfg8HA3Xffzdy5cwkICGDnzp2MHj2aO+64g86dOxcZR05OjjpfF0BGRgaQP12Ds6aBMRqNdklGZGSkU8otjtbkjZdP5fxKaPG2+9fKQh456j6aAvczFiXPBLl/Pq6Ma+WotWvXuroKBWRmZqpzz5WV9XPu7GmP3IXE59kkPs9WmfE5eg7FcvPacqJChIWFYTabOXDgAABms5latWrxyCOPsH79egB10u2DBw/SpUuXAmVMnz6dS5cu8d5779ltt1gsDBkyhPT0dA4cOICvry8LFiwgOTmZb7/9Vt1v3rx5vPTSS8UOcBkwYAB33nknr7zySpGxxMbGEhcXV2D75s2bnTqPV1ZWFgkJCU4rryTR0dF2c5K5gtFoVBOs0tTH9jhRvHHjxsl8c0IIQf7f2ZEjR3Lt2jUCAgKK3E9aFitR27Zt1cfe3t4EBQXRpk0bdVv9+vUBuHz5MgCvv/4677zzDunp6WRnZ5Obm0v79u0LlLtgwQIOHjzI0aNH1clbT506xb333mu3n7XV0cpsNvPCCy/wj3/8g59//pnc3FxycnJK/EM6f/58YmJi1OcZGRmEhIQQHh5e7IetNIxGIzt37lSfb9q0qUJWGzAYDGr3fKLlE7xuaumrKFq8GUMv1pOKEbO63XZUcxKpjrcsWsxqa1lSUpLLV2YwGo2kpqbSq1cvlyfgkP8+jxo1CoD+/fuXe9Jyo9FISkoKffr0cYv4nE3i82wSn2erzPisPYMlkWSxEt38piuKYrfNeg9iXl4eW7ZsYdasWSxfvpyuXbvi7+/Pyy+/zKFDh+zK2LhxIytWrGDfvn00atSoVPV5+eWXWbVqFStXrqRNmzbUrFmTmTNnkpubW+xxOp0OnU5XaHzO/GDb3pPp5+dXIauSaDQatbvbSzGjULkN7UbMGG3nWbRJFo2YHE4WLUoeeX/GUVHXqjSMRiNarRZ/f3+3+DK37XZ25ufU2Z95dyPxeTaJz7NVRnyOli/JoptKS0ujW7duTJ06Vd127tw5u30OHjzIxIkTeeuttwp0W7ds2ZIPPvjAbtvnn39e4BwRERH8/e9/B/KT1LNnz9KqVStnhiKEEEIIDyZT57ipZs2acfToUT7++GPOnj3LM888w5EjR9TXL126xODBgxk+fDh9+/bl0qVLXLp0iV9//RWAyZMn89133zF79mzOnDnD5s2bSUxMLHCOlJQUPvvsM06dOsVjjz3GL7/8UplhFkuj0bBt2zaSk5Nd3q0qPJteryc5OVk+S0IIUQaSLLqpxx57jEceeYTIyEjuvfderly5YtfKePr0aX755ReSkpIIDg5Wf+655x4AGjduzLZt29ixYwft2rXjzTff5IUXXrA7x9NPP03Hjh3p27cvYWFhNGjQgEGDBlVmmMVSFAW9Xo+vr2/lrLhhtOQvt1dJP0CR24t6rciyjDJOrTiKouDr61t5nyUhhKhCpBu6kuzbt6/ANut8iLZsB6cnJCQUGA28dOlSIH90dUkD2QcMGMCAAQPsto0bN059XKdOHXbs2FFCzauPylxbOU+rhej8c+YVMXWBJSG9ku+gFEIIIQqSlkUhhBBCCFEkaVkU1Zr1XrbKZjKZSE1NZcuWLXYjdS0WizrhuU6nK1OXqdyTJ6oSi8Xi9BV4rDMguGKFIWf8jpfEmfHp9Xq5dUNIsiiqN+u9bJXNOmu+Xq8vMHWBTBgtxF8MBgMRERFOLVOr1RIdHc3w4cOr5CogzowvOTnZ5VNxCdeTbmghhBBCCFEkaVkUQghRItvuYFd1TXqPvQ+05V9lyfvPdhLvsfeRZzMRfmWwGM3kJf0HAK+x96E4IZ6blTs+oxnzn3UUruUOv3cgyWKZXbhwgdtuu40vv/yy0CX4nCEsLIz27duzcuXKCilfCCEcZdsd7LKuSa23U5Ir68pIitYbBdfdj6c4KZ4C5ZYzPpmFwX24xe8d0g0thBBCCCGKUeWTxZLWORZCCCGEEEVzabIYFhbGjBkzmDlzJrVr16Z+/fqsW7eOGzduMG7cOPz9/WnatCkfffSResw333zDQw89hJ+fH/Xr12f06NH89ttvdmVOnz6dmTNncsstt9C3b18Avv32WwYMGEBAQAD+/v706NHDbq3lt99+m5YtW6LX67nzzjtZs2aNXV0PHz5Mhw4d0Ov1dOrUiS+//LJAPCXV7b333qNNmzb4+voSFBRE7969uXHjRrHXyGQyMX36dGrVqsUtt9zCM888YzcZd05ODrNmzaJRo0bUrFmTe++9124C8MTERAIDA/n4449p2bIlfn5+9OvXj4sXL6r7KIpS4Cc0NLTYegkhqi+DwUB2dnal/NhOm1PSQgTCOWyvc2W+16X5TBiNRresm7Pjc/a0UWXl8nsWk5KSmDNnDocPH2br1q1MmTKF7du3M3jwYBYsWMCKFSsYPXo06enp5Obm0qtXLyZOnMiKFSvIzs5m7ty5DBs2jNTUVLsyp0yZQlpaGgA///wz999/P2FhYaSmphIQEEBaWpo6/9SmTZtYtGgRr732Gh06dODLL79k0qRJ1KxZk7Fjx5KZmcmAAQPo06cPGzdu5Pz58zzxxBN2cfzxxx/F1u3ixYuMGDGCZcuWMXjwYK5fv86BAwdK/PJLSkpiwoQJHD58mKNHjxIdHU3jxo2ZNGkSANOnT+fkyZNs2bKFhg0bsn37dvr168fXX39Ns2bNAMjKyuKVV15hw4YNeHl58fe//51Zs2axadMmALvE8caNG/Tr14+uXbsWWaecnBx1njCAjIwMIH86GGdNQ2EtpypOawESn6erjvFlZmaqjyMjIyu9TgBaE3j5lL+NQ/tnO4nWBe0lFiyYbeqhVEAdyhtfngms3/Cueq8dsXbtWldXoULdHF9mZqbdvLzO4Oh3mGJx4X/VwsLCMJvNHDhwAACz2UytWrV45JFHWL9+PQCXLl0iODiYgwcPsnfvXg4cOMDHH3+slvHTTz8REhLCmTNnaN68OWFhYWRkZHDs2DF1nwULFrBlyxbOnDlTYE47gKZNm7JkyRJGjBihbnvuuefYtWsXn332GWvXrmXBggX89NNP6oTHb775JlOmTFEHuDz33HPF1i0zM5O7776bCxcu0KRJE4evz+XLl/n222/VEVDz5s3jgw8+4OTJk6Snp3P77beTnp5Ow4YN1eN69+5N586deeGFF0hMTGTcuHF8//333HHHHQCsWbOGZ599lkuXLtmdz2KxMGTIENLT0zlw4ECRN9LGxsYSFxdXYPvmzZtljkAhqqisrKwCy49Wtujo6EK/wz2J0WhUkwB3jce2jsJ9jBs3zul/Y7Oyshg5ciTXrl0jICCgyP1c3rLYtm1b9bG3tzdBQUG0adNG3Va/fn0ALl++zIkTJ/j3v/+Nn59fgXLOnTtH8+bNAbj77rvtXjt+/Dg9evQo9Jfyxo0bnDt3jgkTJqitdZDf/VurVi0ATp06Rdu2be1Wxri55a2kuoWHh/Pggw/Spk0b+vbtS3h4OEOHDqV27docOHCAhx56SN3/rbfeYtSoUQB06dLFbqh8165dWb58OWazma+//hqz2azGbZWTk0NQUJD6vEaNGmqiCBAcHMzly5cL1HPBggUcPHiQo0ePFjviav78+cTExKjPMzIyCAkJITw8vNgPW2kYjUZSUlLo06ePW36ZlpfE59mqY3zZ2dlqsrhp06ZKWynIYDAwduxYAJLMZ/HyLv+fLS1ejPZqxoa87zBW9tQ5eWb18fq871DynD8aurzx5ZlNagtWUlKS260KZTQaSU1NpVevXlX2988an9lsVvOB/v37O300tLVnsCQuTxZvfqMVRbHbZk2U8vLyyMzMZODAgbz00ksFygkODlYf16xZ0+614i6utWtl3bp13HvvvXaveXs7/ktcUt28vb1JSUnhs88+Y8+ePcTHx7Nw4UIOHTpEp06dOH78uLq/NUF25Jze3t588cUXBepqm7QWdo1vblDeuHEjK1asYN++fTRq1KjY8+p0OnQ6XYHtWq3W6b+4FVGmO5H4PFt1is922Tg/P79Km8JDo9Go57YoFhQnJndG8io/WbQ5n5G8Cp26p6zxWRQL5j+veWW+144yGo1otVr8/f2r5O+fbXy2v3cV9TfWES5PFkujY8eObNu2jdDQ0FL127dt25akpCT1DbBVv359GjZsyA8//KBm7zdr2bIlGzZswGAwqP/D+vzzz0tdN0VR6N69O927d2fRokU0adKE7du3ExMTQ9OmTQs95tChQ3bPP//8c5o1a4a3tzcdOnTAbDZz+fJlevTo4dC1KMzBgweZOHEib731Fl26dClzOUIIIYSoejxq6pxp06bx+++/M2LECI4cOcK5c+f4+OOPGTduHGazucjjpk+fTkZGBsOHD+fo0aN89913bNiwgTNnzgAQFxfH0qVLWb16NWfPnuXrr78mISGBV199FYCRI0eiKAqTJk3i5MmT7Nq1i1deeaVUdTt06BAvvPACR48eJT09nffff59ff/2Vli1bFhtzeno6MTExnDlzhnfffZf4+Hh1cE3z5s0ZNWoUY8aM4f333+f8+fMcPnyYpUuXsnPnToeu6aVLlxg8eDDDhw+nb9++XLp0iUuXLvHrr786dLwQonrQ6/UkJyeTnJzsum5JoxmLk34Ap5VVlnNX9PnLUz7Gov+eisrlFr93eFjLYsOGDUlLS2Pu3LmEh4eTk5NDkyZN6NevH15eRee9QUFBpKamMnv2bHr27Im3tzft27ene/fuAEycOJEaNWrw8ssvM3v2bGrWrEmbNm2YOXMmkN8M/69//YvJkyfToUMHWrVqxUsvvcSQIUMcrltAQACffvopK1euJCMjgyZNmrB8+XK7exULM2bMGLKzs+ncuTPe3t488cQTREdHq68nJCTw3HPP8dRTT/Hzzz9zyy230KVLFwYMGODQNT19+jS//PILSUlJJCUlqdubNGnChQsXHCpDCFH1KYri8u5IZy1B56XVQnQrzEn/wezCEe15FbSknrvEJ8rPHX7vwMXJou18gFaFJSi299c1a9aM999/v1RlQn5XtO1I5ZuNHDmSkSNHFvl6ly5d7O4rvLleJdWtZcuW7N69u8jyC2MbyxtvvFHoPlqtlri4uEJHJwNERUURFRVlt23QoEFq3cPCwmTuMiGEEEIUyaNaFoUQQlQv1m44ZzKZTKSmprJlyxanz1tXEovFos5Tq9Pp7Ga7cBZnxuduI6GFa0iyKIRwOxaLRV25QK/XV8gfVOEZKqIbzjoRsV6vd8lo2oqej9bV8Ymqx6MGuAghqgeDwUBERAQRERFus9yVEEJUV2VOFv/44w/efvtt5s+fz++//w7AsWPH+Pnnn51WOSFE9WSbIEqyKIQQrlWmbuivvvqK3r17U6tWLS5cuMCkSZOoU6cO77//Punp6epSfUIIURZ5eXmFPhZCCFH5ytSyGBMTQ1RUFN99953dza8PP/wwn376qdMqJ4Sonq5fv17oYyGEEJWvTMnikSNHeOyxxwpsb9SoEZcuXSp3pYQQQgghhHsoU7Ko0+kKXXz67Nmz1K1bt9yVEn/Jzc31qHKFEEIIUbWUKVn829/+xrPPPqsOz1cUhfT0dObOnWu3qklVFxYWxowZM5g5cya1a9emfv36rFu3jhs3bjBu3Dj8/f1p2rQpH330kXrMN998w0MPPYSfnx/169dn9OjR/Pbbb3ZlTp8+nZkzZ3LLLbfQt29fAL799lsGDBhAQEAA/v7+9OjRg3PnzqnHWFebsRo0aJDdZNyhoaEsWbKEMWPGEBAQQHR0NL169WL69Ol2x/3666/4+PjwySefOPlqCSGEEMITlWmAy/Llyxk6dCj16tUjOzubnj17cunSJbp27crzzz/v7Dq6taSkJObMmcPhw4fZunUrU6ZMYfv27QwePJgFCxawYsUKRo8eTXp6Orm5ufTq1YuJEyeyYsUKsrOzmTt3LsOGDSM1NdWuzClTppCWlgbAzz//zP33309YWBipqakEBASQlpaGyWQqVV1feeUVFi1axOLFiwE4dOgQ06dPZ/ny5eh0OgA2btxIo0aN6NWrV5Hl5OTkqJPKAmors9FoVP8DUV7WcpxVnruR+Ipn+9k2mUxud53k/fNsEp9nk/icf66SKJZyrPWWlpbGiRMnyMzMpGPHjvTu3RuLxVJtJtANCwvDbDZz4MABAMxmM7Vq1eKRRx5RR4RfunSJ4OBgDh48yN69ezlw4IDdsoM//fQTISEhnDlzhubNmxMWFkZGRgbHjh1T91mwYAFbtmzhzJkzhU6wGhYWRvv27Vm5cqW6bdCgQQQGBpKYmAjktyx26NCB7du3q/sYDAYaNmzIm2++ybBhwwBo164djzzyiJpQFiY2NrbQ5QU3b95c4ZPNiurh2rVrbNy4EYC///3v1KpVy8U1EkKIqicrK4uRI0dy7do1AgICityvTC2LL7/8MrNnz6Z79+50795d3W42m/n73//Ou+++W5ZiPVLbtm3Vx97e3gQFBdGmTRt1W/369QG4fPkyJ06c4N///jd+fn4Fyjl37hzNmzcH4O6777Z77fjx4/To0aPcM/F36tTJ7rler2f06NG88847DBs2jGPHjvHNN9/wwQcfFFvO/PnziYmJUZ9nZGQQEhJCeHh4sR+20jAajaSkpNCnT58quQKBxFe8//73v2qy2K1bN5o0aeLsKpaLvH+eTeLzbBKf8xQ2/qQwZU4W69Spw4QJE9RtZrOZ4cOH880335SlSI918xupKIrdNmsra15eHpmZmQwcOJCXXnqpQDnBwcHq45o1a9q9VtJSV15eXtzcQFxY0/LN5QJMnDiR9u3b89NPP5GQkECvXr1K/MOs0+nUbmtbWq3W6R/siijTnUh8hbNdz1aj0bjtNZL3z7NJfJ5N4nPOORxRpmRx586dhIeHU6tWLYYOHYrJZGLYsGGcPn2af//732Upslro2LEj27ZtIzQ0tFSLu7dt25akpCSMRmOhb2zdunW5ePGi+txsNvPNN9/wwAMPlFh2mzZt6NSpE+vWrWPz5s289tprDtdLCCGEEFVfmUZD33PPPWzbto3x48fzwQcfMGTIEM6cOcO///1vGjRo4Ow6VhnTpk3j999/Z8SIERw5coRz587x8ccfM27cOMxmc5HHTZ8+nYyMDIYPH87Ro0f57rvv2LBhA2fOnAGgV69e7Ny5k507d3L69GmmTJnCH3/84XC9Jk6cyIsvvojFYmHw4MHlDVOIcvP39y/0sRBCiMpX5rWhe/Xqxfr16xkyZAjnz59n//796v15onANGzYkLS0Ns9lMeHg4bdq0YebMmQQGBuLlVfRbERQURGpqKpmZmfTs2ZO7776bdevWqa2M48ePZ+zYsYwZM4aePXty++23O9SqaDVixAg0Gg0jRoywW5FHCFex/X0o7ndDCCFExXO4L/SRRx4pdHvdunUJDAwkOjpa3fb++++Xv2YeYN++fQW2XbhwocA22/sJmzVrVuz1KaxMyO+Kth1FbUur1bJmzRrWrFlTZLmF1cvqt99+w2Aw2N2DKoQr2f6nRf4DI4QQruVwsljU1BXWSaOF5zEajVy5coWnn36aLl260LFjR1dXSQggP0FMTk5WHwshhHAdh5PFhISEiqyHcIG0tDQeeOABmjdvznvvvefq6lQYi8WCwWAo1/HWSch1Ol2p5hHV6/XVZt5RZ1IUpcRZAIQQQlSOMo2GFlVDWFhYgSl3qiKDwUBERIRLzp2cnCxJjxBCCI9W5mTxvffe4x//+Ie6jJ0t29VHhBBCCCGE5ypTsrh69WoWLlxIVFQUycnJjBs3jnPnznHkyBGmTZvm7DqKas62G7m83bra0cOgFHNcAliMJkwb/wGA5u/DULQlHG8yYdzwj7JWsUI48xoKIYSoXso0J8WaNWtYu3Yt8fHx+Pj4MGfOHFJSUnj88ce5du2as+volvbt24eiKKWaz7AsFEVhx44dRb5+4cIFFEXh+PHjJZblyL6JiYkEBgaWup4VydqNHBERUa57DwHQaFC02lL+/JUcKtqSjy9tMloZnHoNhRBCVCtlShbT09Pp1q0bkL8U3fXr1wEYPXp0tVkXulu3bly8eFEdJe6qJCskJISLFy/SunVrp5QXGRnJ2bNnnVKWEEIIITxfmZpAGjRowO+//06TJk1o3Lgxn3/+Oe3ateP8+fPVYsAEgI+Pj1usVuPt7e3Uevj6+rr1gIyytIrZHmOxWKjoDljb34Gi6msymTAajRgMBkwmUwXXqGzXTQghhIAyJou9evXigw8+oEOHDowbN44nn3yS9957j6NHjxY5ebe7CwsLo02bNnh7e5OUlISPjw/PPfccI0eOZPr06bz33nvUr1+f+Ph4HnroIfbt28cDDzzA1atXOX78OOPGjQNQ7wVbvHgxsbGxrFmzhhUrVvDjjz9Sq1YtevTooU5TExoaysyZM5k5c6Zaj/bt2zNo0CBiY2PVbRcvXlTPGRwczLJlyxg6dCiQ37V822238eWXX9K+fXuuXr3K9OnT2bNnD5mZmdx6660sWLBArR/ADz/8wJNPPsmhQ4do1qwZb775Jl27dgXyW0hnzpxZbPd6Tk6OOpUMQEZGBpA/b6PRaCz7m2DDWo7RaOTGjRvq9sjIyHKVqzWZ8fIp3TEWwBqVFkpMNvNMZqzpX0n1Xbt2bekq4wSZmZmlWpu8LGzfv6pI4vNsEp9nk/icf66SlOkvxtq1a8nLywPy1zu+5ZZbSEtL429/+xuTJ08uS5FuISkpiTlz5nD48GG2bt3KlClT2L59O4MHD2bBggWsWLGC0aNHk56ebndct27dWLlyJYsWLVLXa/bz8+Po0aM8/vjjbNiwgW7duvH7779z4MCBUtfrmWee4cUXX2TVqlVs2LCB4cOH8/XXX9OyZctC9z158iQfffQRt9xyC99//z3Z2dl2+yxcuJBXXnmFZs2asXDhQkaMGMH333/vcAKxdOlS4uLiCmzfs2cPNWrUKHV8xUlJSSErK8tp5Y2uEaguk+goI0asKd3ftX4lHm+soaPyU0DH7d271+nvU1FSUlIq5TyuIvF5NonPs0l85efo39cyJYteXl7k5uZy7NgxLl++jK+vL7179wZg9+7dDBw4sCzFuly7du14+umnAZg/fz4vvvgit9xyC5MmTQJg0aJFvPHGG3z11Vd2x/n4+FCrVi0URbHrEk5PT6dmzZoMGDAAf39/mjRpQocOHUpdr0cffZSJEycCsGTJElJSUoiPjy90eb/09HQ6dOhAp06dgPzWy5vNmjWL/v37AxAXF8ddd93F999/z5133ulQfebPn09MTIz6PCMjg5CQEMLDwwkICChteIUyGo2kpKTQp08fTCaTOin8pk2bSr2ih8FgYOzYsQBsyL2OF6VrWrTY/M9rozETheKTxbzcXDXxTkpKKrS+RqOR1NRUevXqVerktSwMBgOjRo0CoH///hV+q4Ht+1cZ8VU2ic+zSXyeTeJzHmvPYEnKlCzu3r2b0aNHc+XKlQKvKYqC2WwuS7Eu17ZtW/Wxt7c3QUFBtGnTRt1Wv359AC5fvuxQUtSnTx+aNGnC7bffTr9+/ejXrx+DBw8udauOtYvY9nlRI5qnTJnCkCFDOHbsGOHh4QwaNEgdjGRlG2dwcLAak6PJok6nQ6fTFdiu1Wqd/sHWarV207z4+fmVOtHRaDTqfYGKopT6nkXbu3CNlNwNbVEU9XxF1ddoNKLVavH396+ULzvbVuOKeJ+KUpnncgWJz7NJfJ5N4nPOORxRptHQM2bMYNiwYVy8eJG8vDy7H09NFKHgRVMUxW6bNWmxdsGXxN/fn2PHjvHuu+8SHBzMokWLaNeunXo/oJeXV4EBQeW9R+Ghhx7iv//9L08++ST/+9//ePDBB5k1a5bdPuWJSQghhBDVS5mSxV9++YWYmBi1pU3kd0UXlihrNBp69+7NsmXL+Oqrr7hw4QKpqakA1K1bl4sXL6r7ZmRkcP78+QJlfP755wWeF3a/olXdunUZO3YsGzduZOXKlS4ZRCGEEEKIqqFM3dBDhw5l37593HHHHc6uj8cKDQ0lMzOTTz75hHbt2lGjRg1SU1P54YcfuP/++6lduza7du0iLy+PFi1aAPmjyhMTExk4cCCBgYEsWrQIb2/vAmX/85//pFOnTtx3331s2rSJw4cP83//93+F1mPRokXcfffd3HXXXeTk5PDhhx8Wm1h6Ar1eT3Jysvq4XEwmSju5k8VoKvRxcedwN069hkIIIaqVMiWLr732Go8++igHDhygTZs2BbpvH3/8cadUzpN069aNyZMnExkZyZUrV1i8eDG9e/fm/fffJzY2FoPBQLNmzXj33Xe56667gPyBIufPn2fAgAHUqlWLJUuWFNqyGBcXx5YtW5g6dSrBwcG8++67tGrVqtB6+Pj4MH/+fC5cuICvry89evRgy5YtFRp7RVMUxWkDMsq7DJ912T9P48xrKIQQonopU7L47rvvsmfPHvR6vbrsnZWiKB6ZLO7bt6/AtgsXLhTYZnuP4c33G77xxhu88cYbJZZrFRAQUCCRs47avfkcU6dOLbSM0NBQu3o8/fTT6ojukvYFCAwMtNsWFRVFVFRUkXUWQgghRPVSpmRx4cKFxMXFMW/ePLy8ynTboxCVxrYL1pbFYrGbXLwwFosFg8FAbm4uPj4+6PV6u/8cOeLmeS4BdbR0Za3gUhZliVUIIUTVU6ZkMTc3l8jISEkUhUcoqgs2Ozu73CvClJVWqyU6Oprhw4e77SoEycnJ0nUthBCibKOhx44dy9atW51dFyGEEEII4WbK1LJoNptZtmwZH3/8MW3bti0wwOXVV191SuWEqCy60eNAU3ByUovRSO7G/NVjfP4+DsVJE6RaS9H9fVzZ/sdWUUxGcjYkuLoWQggh3EiZksWvv/5aXbbum2++sXtN7nESFcV6/yBUwP10Gm2JiaCiLXkfR1lrrmi1pV5RpiKVdlqh6uLmz54QQlQnZUoW//3vfzu7HsKGoihs376dQYMGuboqbsVgMBAREQHI/XSict382bNdPlEIIao6+cZzQxcvXqR27dquroYQQgghhCSL7qhBgwauroLbs3YJOqsMi8XiVt3BrmI752Z5rrHJZMJoNLr11ECl4YzPmxBCeCpJFl0gLCyMtm3botfrefvtt/Hx8WHy5MnExsYC9t3Qubm5xMTEsG3bNq5evUr9+vWZPHky8+fPx2KxEBcXxzvvvMMvv/xCUFAQQ4cOZfXq1QBs2LCBVatWcebMGWrWrEmvXr1YuXIl9erVA/InDH/ggQfYu3cvc+fO5eTJk7Rv356EhAR1ScLC5OTk2M1PmJGRAYDRaHTaNDDWcmzLy8zMVB87e8obrcmEl49Pge0WwBqpFpyWUGpv+tdd5JlM5P752BnXuCquS56ZmUnNmjUB3Hbao/Iq7PevKpH4PJvE5/xzlUSSRRdJSkoiJiaGQ4cOcfDgQaKioujevTt9+vSx22/16tV88MEH/OMf/6Bx48b8+OOP/PjjjwBs27aNFStWsGXLFu666y4uXbrEiRMn1GONRiNLliyhRYsWXL58mZiYGKKioti1a5fdORYuXMjy5cupW7cukydPZvz48aSlpRVZ96VLlxIXF1dg+549e6hRo0Z5LksBKSkp6uOsrCynlm1rVA0thY1dMQLWdGeklkL3KY8RbpYtGmtoqXrpnXPt3btX/Zzbfj6rIonPs0l8nq0y4nP076okiy7Stm1bFi9eDECzZs147bXX+OSTTwoki+np6TRr1oz77rsPRVFo0qSJ3WsNGjSgd+/eaLVaGjduTOfOndXXx48frz6+/fbbWb16Nffccw+ZmZn4+fmprz3//PP07NkTgHnz5tG/f38MBkORoz7nz59PTEyM+jwjI4OQkBDCw8MJCAgox1X5i9FoJCUlhT59+qhTM2VnZ5OQkD+ty6ZNm8o9KtVgMKjLK27KtRQ6hY3F5j9dm43ObVkcoYV3jfkJqbvIy7WogzeSkpLKfI2NRiOpqan06tWrwNRanshgMDBq1CgA+vfvj0ajKfD5rEoK+/2rSiQ+zybxOY+1Z7Akkiy6SNu2be2eBwcHc/ny5QL7RUVF0adPH1q0aEG/fv0YMGAA4eHhADz66KOsXLmS22+/nX79+vHwww8zcOBA9Y/9F198QWxsLCdOnODq1avk5eUB+Ulmq1atCq1LcHAwAJcvX6Zx48aF1l2n06HT6Qps12q1Tv9g25Zpe++bn59fuUdDazQatUxvRSk0EbSdSsaI85JF2zLdKVm0KIp6TcpzjY1GI1qtFn9//yrxZW47+lmr1arPK+Iz704kPs8m8Xm2yojP0fLdaj7g6uTmN0hRFDWZs9WxY0fOnz/PkiVLyM7OZtiwYQwdOhSAkJAQzpw5w5o1a/D19WXq1Kncf//9GI1Gbty4Qd++fQkICGDTpk0cOXKE7du3A/nLNRZVF+vchYXVRQghhBDVj7QseoCAgAAiIyOJjIxk6NCh9OvXj99//506derg6+vLwIEDGThwINOmTePOO+/k66+/xmKxcOXKFV588UVCQkIAOHr0qIsjKR+9Xk9ycrL6WIjKcvNnryqM8BZCCEdJsujmXn31VYKDg+nQoQNeXl7885//pEGDBgQGBpKYmIjZbObee++lRo0abNy4EV9fX5o0aUJeXh4+Pj7Ex8czefJkvvnmG5YsWeLqcMpFUZSKm4jbZCx09RKLzUgxixNHplkAtFosxsLP6zImd+oUdx8V+tkTQgg3J8mim/P392fZsmV89913eHt7c88997Br1y68vLwIDAzkxRdfJCYmBrPZTJs2bfjXv/5FUFAQAImJiSxYsIDVq1fTsWNHXnnlFf72t7+5OCL35Mh6yNY1op0hT6uF6GhyNiZU2ekfhBBCVA2SLLrAvn37CmzbsWOH+th2YuRJkyYxadKkQssZNGhQsUsCjhgxghEjRthtsy07LCzM7jlA+/btC2wTQgghRPUlyaKotmzvQyuKxWJRJyDX6XTqAKDyMplMpKamsmXLFrdaZ9g2XsifrqgsrPf0lWcFl+KuvV6vd9p7IYQQonju81dKiErm6H1ozp5oHP6aNV+v17vV1A/Z2dlOWblFq9USHR3N8OHDK6SbPTk5We4hFEKISiJT5wghhBBCiCJJy6IQolA1xjwBmrK1emqxAAZqjH4cYxmnMrcYc8nekL/Oue/ox1EUhaz1q8pUlhBCiLKTZFGIKsBisWAwGAAn3s+n0aJofcp0qPJnsqhofSh8bZxSllfGelTIdRFCiGpGuqEFkD9CW1EU/vjjD1dXRZSBwWAgIiKCiIgINTkScl2EEMIZJFkUQgghhBBFkmSxgoWFhTF9+nSmT59OrVq1uOWWW3jmmWfUuQxzcnKYNWsWjRo1ombNmtx777128zDGxsbSvn17uzJXrlxJaGio+txkMvH4448TGBhIUFAQc+fOZezYsXZzMObk5PD4449Tr1499Ho99913H0eOHAHgwoULPPDAAwDUrl0bRVGIioqqiMshKoHBYCA7O7tMP7atb+4236ZtfRyNUVoThRCi/OSexUqQlJTEhAkTOHz4MEePHiU6OprGjRszadIkpk+fzsmTJ9myZQsNGzZk+/bt9OvXj6+//ppmzZo5VP5LL73Epk2bSEhIoGXLlqxatYodO3aoCSDAnDlz2LZtG0lJSTRp0oRly5bRt29fvv/+e0JCQti2bRtDhgzhzJkzBAQEFDstSU5Ojt1cfBkZGUD+dDDOmibFWk5VXd3E2fFlZmaqj50x9Q2A1mTEy6ds9wpq/lzEUFOOxQwtNsdqsWAxGbHO+liWGDMzM502p6V8Pj2bxOfZJD7nn6skkixWgpCQEFasWIGiKLRo0YKvv/6aFStW0LdvXxISEkhPT6dhw4YAzJo1i927d5OQkMALL7zgUPnx8fHMnz+fwYMHA/Daa6+xa9cu9fUbN27wxhtvkJiYyEMPPQTAunXrSElJ4f/+7/+YPXs2derUAaBevXoEBgYWe76lS5cSFxdXYPuePXucPidhSkqKU8tzN86KLysryynl2Brua0CrMZerjEjN9TIfa7QYWWtbjgb1eVns3btXPp+lJPF5NonPs1VGfI7+7ZBksRJ06dLFbhRm165dWb58OV9//TVms5nmzZvb7Z+Tk6Ou71ySa9eu8csvv9C5c2d1m7e3N3fffTd5eXkAnDt3DqPRSPfu3dV9tFotnTt35tSpU6WOZ/78+cTExKjPMzIyCAkJITw8nICAgFKXVxij0UhKSgp9+vRxq0mrncXZ8WVnZ5OQkL929aZNm9Dr9WUqx2AwMHbsWAC2GP3wUnRlKkeDhUjNdbaa/DGVdeocU676eKvJH4vForYMJiUlORSjwWBg1KhRAPTv399pE3nL59OzSXyeTeJzHmvPYEkkWXShzMxMvL29+eKLL/D29rZ7zc/PDwAvL68C9465uuldp9Oh0xVMIrRardM/2BVRpjtxVny2S+r5+fmVOSnSaDRqWSbFq9zT3phQyj7Pos1xRhRQFLVujsZo2+0sn8/Sk/g8m8Tn2SojPkfLlwEuleDQoUN2zz///HOaNWtGhw4dMJvNXL58maZNm9r9NGjQAIC6dety6dIlu4Tx+PHj6uNatWpRv359dbAKgNls5tixY+rzO+64Ax8fH9LS0tRtRqORI0eO0KpVKwB8/rw3zWwuX7ejEEIIIaoWaVmsBOnp6cTExPDYY49x7Ngx4uPjWb58Oc2bN2fUqFGMGTOG5cuX06FDB3799Vc++eQT2rZtS//+/QkLC+PXX39l2bJlDB06lN27d/PRRx/ZdffOmDGDpUuX0rRpU+68807i4+O5evWq2vVds2ZNpkyZot6b2LhxY5YtW0ZWVhYTJkwAoEmTJiiKwocffsjDDz+Mr6+v2rop3J9eryc5OVl9LPLJdRFCiPKTlsVKMGbMGLKzs+ncuTPTpk3jiSeeIDo6GoCEhATGjBnDU089RYsWLRg0aBBHjhyhcePGALRs2ZI1a9bw+uuv065dOw4fPsysWbPsyp87dy4jRoxgzJgxdO3aFT8/P/r27Wv3x/HFF19kyJAhjB49mo4dO/L999/z8ccfU7t2bQAaNWpEXFwc8+bNo379+kyfPr2Sro5wBkVR8PX1xdfX13mrlJiMWIy5Zf4BynW8tQxrOZhKf/tFhVwXIYSoZqRlsRJotVpWrlzJG2+8UehrcXFxhY4utpo8eTKTJ0+227ZgwQL1sUajIT4+nvj4eADy8vJo2bIlw4YNU/fR6/WsXr2a1atXF3meZ555hmeeecbhuETVVp51mLVaLURHk7VhtVPusbWuES2EEKLySbJYBfz3v/9lz5499OzZk5ycHF577TXOnz/PyJEjXV01IYQQQng4SRarAC8vLxITE5k1axYWi4XWrVuzd+9eWrZs6eqquYTFYin1yh0Wi0WdaFyn05XYZanX66tkt6btPX7lYTKZSE1NZcuWLWWeCLu490TuPxRCiMojyWIFs126r6KEhITYjXSu7gwGAxERERV6juTkZKfN2edOrPf4lZe161mv15dr6gdnT6IthBCi9GSAixBCCCGEKJK0LAqPYdu97Gg3sP+YOBRNyesbW4y5XN+wOP+Y0XEo2oLHWEy5XF+/2Cn1EkIIITyFtCyWQ2hoKCtXrnR1NcosNjaW9u3bu7oaDrN2L0dERDh8T6Ki8UHR6hz4+Ss5VLRFHFNE0lmWernSwYMH+fvf/87BgwddXRUhhBAeQJLFakJRFHbs2OHqaggXMxgMxMfHc/nyZeLj4z0iuRVCCOFakiwKUY1s2bKFK1euAHDlyhW2bt3q4hoJIYRwd5IsFiMsLIzp06czffp0atWqxS233MIzzzxjt06z1fjx4xkwYIDdNqPRSL169fi///s/tbwZM2Ywc+ZMateuTf369Vm3bh03btxg3Lhx+Pv707RpUz766CO7cvbv30/nzp3R6XQEBwczb948TCaT+nph3eHt27cnNjZWfR1g8ODBKIqiPrfasGEDoaGh1KpVi+HDh3P9+vUyXK3KZTAYyM7OLvTHtrWssPeqrGzLsj2/p7TO/fzzz2zdulWNw2KxsHXrVn7++WcX10wIIYQ7kwEuJUhKSmLChAkcPnyYo0ePEh0dTePGjZk0aZLdfhMnTuT+++/n4sWLBAcHA/Dhhx+SlZVFZGSkXXlz5szh8OHDbN26lSlTprB9+3YGDx7MggULWLFiBaNHjyY9PZ0aNWrw888/8/DDDxMVFcX69es5ffo0kyZNQq/Xq8lgSY4cOUK9evVISEigX79+eHt7q6+dO3eOHTt28OGHH3L16lWGDRvGiy++yPPPP19keTk5Oer8dwAZGRlAfnLsjNU6rGXZ/guQmZmpPra9psXRmnLw8nFggAt5fx1DHorNc6s8018xF3X+zMxMh+YVLCy+imSxWIiPjy+QPOfl5REfH8+zzz7r1IE5lR1fZZP4PJvE59kkPuefqySSLJYgJCSEFStWoCgKLVq04Ouvv2bFihUFksVu3brRokULNmzYwJw5c4D8dZ8fffRR/Pz81P3atWvH008/DcD8+fN58cUXueWWW9TyFi1axBtvvMFXX31Fly5dWLNmDSEhIbz22msoisKdd97J//73P+bOncuiRYvw8iq5cbhu3boABAYG0qBBA7vX8vLySExMxN/fH4DRo0fzySefFJssLl26tNDlCffs2eP0efFSUlLUx1lZWaU+fqjvRbSa30rcz2gxsvbPx0M0/0WrKTg3oNH3r32Ksnfv3lJdA9v4KtLvv//OsWPHCmzPy8vj2LFjbNq0iTp16jj9vJUVn6tIfJ5N4vNsEl/5Ofp3VZLFEnTp0sWuxaVr164sX74cs9lcYN+JEyeydu1a5syZwy+//MJHH31Eamqq3T5t27ZVH3t7exMUFESbNm3UbfXr1wfg8uXLAJw6dYquXbva1aF79+5kZmby008/0bhx43LFFxoaqiaKAMHBweq5izJ//nxiYmLU5xkZGYSEhBAeHk5AQEC56mNlNBpJSUmhT58+6qTO2dnZJCQkALBp06YiV/EwGAyMHTsWgPeMjfFSSl7tw2LTarjN1ARF0RXYJ89oUFsNk5KS1PMbDAZGjRoFQP/+/R2a1Lqw+CqSxWLhzJkzHD9+nLy8v1pNvby86NChA6NGjXJ6y2JlxlfZJD7PJvF5NonPeaw9gyWRZNGJxowZw7x58zh48CCfffYZt912Gz169LDb5+Y3XlEUu23WP9i2f9BL4uXlVaB70dGm5cLqU9K5dTodOl3BZEqr1Tr9g21bpu19mn5+fkUmZRqNRt3XpHijOHBrrsVmHyNehR5jUbzVcm3Pb9vtXNprUBHXrCgzZsxg4sSJdtu8vLyYMWMGPg501ZdFZcbnChKfZ5P4PJvE55xzOEIGuJTg0KFDds8///xzmjVrZnffn1VQUBCDBg0iISGBxMRExo0bV+7zt2zZkoMHD9olg2lpafj7+3PrrbcC+d3MFy9eVF/PyMjg/PnzduVotdpCW0NF9dGoUSMiIyPV/5AoikJkZCQNGzZ0cc2EEEK4M2lZLEF6ejoxMTE89thjHDt2jPj4eJYvX17k/hMnTmTAgAGYzWa1K7Q8pk6dysqVK5kxYwbTp0/nzJkzLF68mJiYGPV+xV69epGYmMjAgQMJDAxk0aJFBZLZ0NBQPvnkE7p3745Op6N27drlrltl0+v1JCcnq48dYTHlOrafMbfQx46UVZZ6ucrw4cPZs2cPv/32G0FBQQ4PFBJCCFF9SbJYgjFjxpCdnU3nzp3x9vbmiSeeIDo6usj9e/fuTXBwMHfddZdTWmwaNWrErl27mD17Nu3ataNOnTpMmDBBHSQD+fcQnj9/ngEDBlCrVi2WLFlSoGVx+fLlxMTEsG7dOho1asSFCxfKXbfKpiiKQ/cD2ipseb4Sj9lQumPKUi9X0ev1zJgxg9dff51p06a5fXIrhBDC9SRZLIFWq2XlypW88cYbBV4rLOG6ceMGV69eZcKECQVe27dvn0Nl3Hz/Yc+ePTl8+HCRdQwICGDLli12225u1Rw4cCADBw602xYbG1tg+p2ZM2cyc+bMIs8lPF/Xrl3p2rWrq6shhBDCQ0iy6CR5eXn89ttvLF++nMDAQP72t7+5ukrVlm23sKMsFos6d6ROpytxZLC0yAkhhKguJFl0kvT0dG677TZuvfVWEhMTHZqYWVSMsnYLO3uOSCFKw2KxuM1qQNZR/waDwW4WgqqiMuMr7X9ES6LX6506zZUQjpCMphiFdRsXJTQ01KlLywkhqheDwUBERISrqwHk334THR3N8OHDq+QqGZ4cX3JyssfcIy2qDpk6RwghhBBCFElaFoWoImy7MaWryrPdO3Y13tqCE99XFi/MwLfcO3YVeRScU9bTVWZ8ZmMOh5IeB8r+vtqW4UryHVN9SbJYhYWGhsro5mrEthtTuqo8m7dW5wbJYn49lCqbLFZ+fK5+X8tLvmOqL+mGFkIIIYQQRZJk0U2ZzeZSrQ8thBBCCFERpBvaScLCwmjdujUAGzZsQKvVMmXKFJ599lkUReHq1as88cQT/Otf/yInJ4eePXuyevVqmjVrBkBiYiIzZ85k/fr1zJs3j7Nnz/L9999z4cIF5syZw7fffotWq+Wuu+5i8+bNNGnSBIB//etfPPvss3z99df4+fnRo0cPtm/frtYrKyuL8ePH889//pPatWvz9NNP261A8+OPP/LUU0+xZ88evLy86NGjB6tWrSI0NLTyLp5wOldPwWIymTAajVV66hVnx2f7nsnMCsKW7efBkd/tivr9c/X3inAdSRadKCkpiQkTJnD48GGOHj1KdHQ0jRs3ZtKkSURFRfHdd9/xwQcfEBAQwNy5c3n44Yc5efIkWq0WyE/sXnrpJd5++22CgoKoU6cO7du3Z9KkSbz77rvk5uZy+PBh9abinTt3MnjwYBYuXMj69evJzc1l165ddnVavnw5S5YsYcGCBbz33ntMmTKFnj170qJFC4xGI3379qVr164cOHAAjUbDc889R79+/fjqq6/w8fEpNM6cnBx13jCAjIwMAIxGo9OmobCW42nTWjiqIuLLzMxUH7vLms9r1651dRUqVIXFZ8rGy0dbMWU7wHpPn/XfqqYy47PYnMMLc9nOacpWH5bmd7sif/8yMzNdNp+w/H1w/rlKoljkv7BOERYWxuXLl/n222/VZG7evHl88MEHJCcn07x5c9LS0ujWrRsAV65cISQkhKSkJB599FESExMZN24cx48fp127dgD8/vvvBAUFsW/fPnr27FngnN26deP2229n48aNhdYpNDSUHj16sGHDBiD/f6cNGjQgLi6OyZMns3HjRp577jlOnTql1jk3N5fAwEB27NhBeHh4oeXGxsYSFxdXYPvmzZtlYmsXysrKIiEhwdXVEE4QHR2t/idSeDaj0agmbWV9X23LcBfjxo2T7/sqICsri5EjR3Lt2jUCAgKK3E9aFp2oS5cudlMJdO3aleXLl3Py5Ek0Gg333nuv+lpQUBAtWrTg1KlT6jYfHx/atm2rPq9Tpw5RUVH07duXPn360Lt3b4YNG0ZwcDAAx48fZ9KkScXWybY8RVFo0KABly9fBuDEiRN8//33+Pv72x1jMBg4d+5ckWXOnz+fmJgY9XlGRgYhISGEh4cX+2ErDaPRSEpKCn369KmSfzQrIr7s7Gw1Wdy0aZNLlyQ0Go2kpqbSq1evKvv+OTs+g8Ggrun+rbkdGm/XvX9emGnldYKTee2q7NQ5lRWfOe+vXpiTee3xziv9aGiT2aC24iUlJZX4u11Rv38Gg4FRo0YB0L9/f5eNhpa/D85j7RksiSSLbsTX17fAvFUJCQk8/vjj7N69m61bt/L000+TkpJCly5dHPpFvfmDpiiKOnAmMzOTu+++m02bNhU4rm7dukWWqdPp0OkKfuFptVqnf7Arokx34sz4bO9N8vPzc+m0FkajEa1Wi7+/f5V8/yoiPo1Go76HFkXjFklaHt5uUY+KUhnx2Zafh3eZpuqxKH99Nhz53a6o3z/bbmd3+G52hzpUpMqIz9HyZTS0Ex06dMju+eeff06zZs1o1aoVJpPJ7vUrV65w5swZWrVqVWK5HTp0YP78+Xz22We0bt2azZs3A/mthp988kmZ69uxY0e+++476tWrR9OmTe1+atWqVeZyhRBCCFF1SMuiE6WnpxMTE8Njjz3GsWPHiI+PZ/ny5TRr1oyIiAgmTZrEW2+9hb+/P/PmzaNRo0bFrgV7/vx51q5dy9/+9jcaNmzImTNn+O677xgzZgwAixcv5sEHH+SOO+5g+PDhmEwmdu3axdy5cx2q76hRo3j55ZeJiIjg2Wef5dZbb+W///0v77//PnPmzOHWW291ynURlUOv15OcnKw+Fp7LbMwpeacKZMEMuvx6VMWWxcqMz/a9LOv76urPg5V8x1Rfkiw60ZgxY8jOzqZz5854e3vzxBNPqNPUJCQk8MQTTzBgwAByc3O5//772bVrV7FNwDVq1OD06dMkJSVx5coVgoODmTZtGo899hiQP6jmn//8J0uWLOHFF18kICCA+++/3+H61qhRg08//ZS5c+fyyCOPcP36dRo1asSDDz7otHsPReVRFEVWVKgiXL20m1arpV10NIeSnqiSI05dFZ+r39fyku+Y6kuSRSfSarWsXLmSN954o8BrtWvXZv369UUeGxUVRVRUlN22+vXr282ZWJhHHnmERx55pNDXLly4UGDb8ePH7Z43aNCApKSkYs8hhBBCiOpLkkUhhHADtl18rmYymUhNTWXLli0um0uvIlVmfBaLRZ2XVqfTFRjEWFrS/Stcoep9CwjhJiwWC9nZ2QX+UMiXvSiMO3XxWbtm9Xp9lRxtWtnxyXyEwtNJsugk+/btc3UVhJsxGAwMGjSowPbk5OQq2VojhBCiapKpc4QQQgghRJGkeUMIJ7NYLBgMBgwGQ6GvGwz5qzHISptCCCE8gSSL1VhUVBR//PEHO3bscHVVqhSDwVDs/JmRkZEA6rRKQgghhDuTbmihCgsLY+bMma6uhhBCCCHciCSLlSw3N9fVVRBuQrqhhRBCeALphq5gYWFhtG7dGo1Gw8aNG2nTpg3x8fHMnj2bAwcOULNmTcLDw1mxYgW33HKLekzbtm3R6/W8/fbb+Pj4MHnyZGJjY9VyX331VRISEvjhhx+oU6cOAwcOZNmyZfj5+QEQGxvLjh077CbhXrlyJStXrix0su6oqCj279/P/v37WbVqFZC/3GBoaGiBfXNyctTpYAAyMjKA/OkonLUagrUcT1w9IjMz06H9TCaTR8bnCE9+/xwh8Xk2ic+zSXzOP1dJJFmsBElJSUyZMoW0tDT++OMPevXqxcSJE1mxYgXZ2dnMnTuXYcOGkZqaandMTEwMhw4d4uDBg0RFRdG9e3f69OkDgJeXF6tXr+a2227jhx9+YOrUqcyZM4c1a9aUqY6rVq3i7NmztG7dmmeffRaAunXrFrrv0qVLiYuLK7B9z549Tp9PLCUlxanlVYasrCyH9/XE+EpD4vNsEp9nk/g8W2XE5+jfK0kWK0GzZs1YtmwZAM899xwdOnTghRdeUF9/5513CAkJ4ezZszRv3hyAtm3bsnjxYvX41157jU8++URNFm3vLQwNDeW5555j8uTJZU4Wa9WqhY+PDzVq1KBBgwbF7jt//nxiYmLU5xkZGYSEhBAeHu60NaWNRiMpKSn06dPH4yYFzs7OJiEhAchP6vPy8gDw9vYGwGw2A6DRaDwyPkd48vvnCInPs0l8nk3icx5rz2BJJFmsBHfffbf6+MSJE/z73/9Wu4ttnTt3zi5ZtBUcHMzly5fV53v37mXp0qWcPn2ajIwMTCYTBoOBrKysCl8tQKfTodPpCmzXarVO/2BXRJkVzWQyqY+tiSL8lSRaKYrikfGVhsTn2SQ+zybxebbKiM/R8iVZrAQ1a9ZUH2dmZjJw4EBeeumlAvsFBwerj29+AxVFUROPCxcuMGDAAKZMmcLzzz9PnTp1+M9//sOECRPIzc2lRo0aeHl5FRhAUVXv7xBCCCFExZFksZJ17NiRbdu2ERoaWuYl37744gvy8vJYvnw5Xl75A9r/8Y9/2O1Tt25dLl26hMViUReutx3sUhgfH58CrV9CCCGEqN5k6pxKNm3aNH7//XdGjBjBkSNHOHfuHB9//DHjxo1zOFFr2rQpRqOR+Ph4fvjhBzZs2MCbb75pt09YWBi//vory5Yt49y5c7z++ut89NFHxZYbGhrKoUOHuHDhAr/99ptdF6pwnF6vJzk5ma1btxb6+tatW9m2bZusDy2EEMIjSLJYyRo2bEhaWhpms5nw8HDatGnDzJkzCQwMVFsJS9KuXTteffVVXnrpJVq3bs2mTZtYunSp3T4tW7ZkzZo1vP7667Rr147Dhw8za9asYsudNWsW3t7etGrVirp165Kenl7mOKszRVHw9fVFr9cX+rper0ev16stvkIIIYQ7k6aNCrZv374C25o1a8b7779fqmNuXpLvySef5Mknn7TbNnr0aLvnkydPZvLkyXbbFixYoD5OTEy0e6158+YcPHiwyHoJIYQQovqRZFGICqLX69mxY4c6gblOp0NRFPR6vd2I6cpmsVgwGAwVeg5rfNnZ2eok5db4K5K02AohhPNJsihEBVEUhRo1alT4VEalZTAYiIiIqNBzaLVaoqOjGT58eKUmxsnJyfj6+lba+YQQojqQexaFEEIIIUSRpGVRiGrsyaFr0GoKTrBeXhbFTBYnmDZ4Bav+OQOAmUPX4FMB5zKacljx3lSnlyuEECKfJItCVAG29yGW5r49rUaHj7bwUdvlqg8msgAf77+SQ58KOldplfVaCSFEdSXd0NVcYmIigYGBrq6GKCfrfYgREREVPnjF08m1EkKI0pFkUQghhBBCFEm6oYWoYkpqLbN9/eb1wz2RbQyOtBRKa6IQQpSOJIse4L333iMuLo7vv/+eGjVq0KFDB5KTkzl58iQLFizgyy+/xGg00r59e1asWEHHjh3VY1999VUSEhL44YcfqFOnDgMHDmTZsmX4+fnZnWPHjh3Mnj2bH3/8kZ49e/L2228TEhJSaH1ycnLUuQMBMjIyADAajRiNRqfEbC3HWeW5G2fHZ53LECAyMtLh43LzsvFB65Q62LJgtvvX+tiC86fRyc3LVh+XJnbIv25lWXZRPp+eTeLzbBKf889VEkkW3dzFixcZMWIEy5YtY/DgwVy/fp0DBw5gsVi4fv06Y8eOJT4+HovFwvLly3n44Yf57rvv8Pf3B8DLy4vVq1dz22238cMPPzB16lTmzJnDmjVr1HNkZWXx/PPPs379enx8fJg6dSrDhw8nLS2t0DotXbqUuLi4Atv37Nnj9DkFU1JSnFqeu3FWfFlZWWU6Lrv215i0zk8WrTICvlQfXws4irYCzmX0LfsX6t69e8v1mZXPp2eT+DybxFd+jv7tUCxVoR+qCjt27Bh33303Fy5coEmTJsXum5eXR2BgIJs3b2bAgAGF7vPee+8xefJkfvvtNyB/gMu4ceP4/PPPuffeewE4ffo0LVu25NChQ3Tu3LlAGYW1LIaEhPDbb78REBBQ1lDtGI1GUlJS6NOnT4UkGK7m7Piys7MZOnQoAJs2bSpyXWrI74YdO3YsADOHvo6uQkZDm7kW8AW+V1rzypYpAMwevhYfrfOnzskxGlj53jQAkpKSio0d8uMfNWoUkP/7UJZJvOXz6dkkPs8m8TlPRkYGt9xyC9euXSv277e0LLq5du3a8eCDD9KmTRv69u1LeHg4Q4cOpXbt2vzyyy88/fTT7Nu3j8uXL2M2m8nKyiI9PV09fu/evSxdupTTp0+TkZGByWTCYDCQlZWltqhoNBruuece9Zg777yTwMBATp06VWiyqNPp0OkK/tHXarVO/2BXRJnuxFnx2a6S4ufnV2wCpNFo1P290KBU4NeAgrfd44o4lxd/xVNS7IBdt3N5r798Pj2bxOfZJD7nnMMRMhrazXl7e5OSksJHH31Eq1atiI+Pp0WLFpw/f56xY8dy/PhxVq1axWeffcbx48cJCgoiNzcXgAsXLjBgwADatm3Ltm3b+OKLL3j99dcB1H2EEEIIIYojLYseQFEUunfvTvfu3Vm0aBFNmjRh+/btpKWlsWbNGh5++GEAfvzxR7V7GeCLL74gLy+P5cuX4+WV//+Cf/zjHwXKN5lMHD16VG1FPHPmDH/88QctW7ashOiEM+j1epKTk9XHomhyrYQQonQkWXRzhw4d4pNPPiE8PJx69epx6NAhfv31V1q2bEmzZs3YsGEDnTp1IiMjg9mzZ9t1wTVt2hSj0Uh8fDwDBw4kLS2NN998s8A5tFotM2bMYPXq1Wg0GqZPn06XLl0K7YIW7klRlLLde2fKKXmnMrAo+aOgc81/lZ9bQecqbQxlvVZCCFFdSbLo5gICAvj0009ZuXIlGRkZNGnShOXLl/PQQw/RoEEDoqOj6dixIyEhIbzwwgvMmjVLPbZdu3a8+uqrvPTSS8yfP5/777+fpUuXMmbMGLtz1KhRg7lz5zJy5Eh+/vlnevTowf/93/9VdqjCBSpqTWWtVkt0dDSvb39S3bZS1m8WQgiPJMmim2vZsiW7d+8u9LUOHTpw5MgRu23WEbFWTz75JE8++aTdttGjR6uPo6KiiIqKAuCRRx5xQo2FEEIIUZVIsiiqNYvFUmBFD4vFok4NpNPpUBTF6ee1jt51xcxVtvfsVRSTyURqaipbtmzBbM7vkq6oa2lL7kEUQgjnk2RRVGsGg4GIiIhKP6+1mzYnJwcfH59KPXdl3LNnXRXA19e3Sk9tIYQQ1YFMnSOEEEIIIYokLYtC/On5h17Dx1tHrimHhbun52/r9xo+GueuOpJrziF275Ml7yiEEEK4AUkWPUBoaCgzZ85k5syZrq5KpbFYLBiNRgwGAxqNpsLvdQPw8dah09jf8+ajKbjNndjec6nX6yvlOgkhhKhepBtauKWcnBzWrl3LkCFDCgxAEX+x3nMZEREh10kIIUSFkGSxmjKbzeTl5bm6GkIIIYRwc5IsVpL33nuPNm3a4OvrS1BQEL179+bGjRuEhYUV6F4eNGiQOveh1fXr1xkxYgQ1a9akUaNG6hrPVq+++ipt2rShZs2ahISEMHXqVDIzM9XXExMTCQwM5IMPPqBVq1bodDrS09MdPn9ls51SxmAwkJ2dXSE/tq1xlTWNjcVisetmd1b9hRBCiIog9yxWgosXLzJixAiWLVvG4MGDuX79OgcOHChVcvLyyy+zYMEC4uLi+Pjjj3niiSdo3rw5ffr0AcDLy4vVq1dz22238cMPPzB16lTmzJnDmjVr1DKysrJ46aWXePvttwkKCqJevXpliicnJ0edhxAgIyMDyJ8uxTplSnnZJrqRkZFOKbMkOXnZaBUt5j+XqgMwK2bMisnp5zGZTKxdu5a1a9c6rdzMzEw0Gvf4lbZ+Dpz1eXA3Ep9nk/g8m8Tn/HOVxD3+slRxFy9exGQy8cgjj9CkSRMA2rRpU6oyunfvzrx58wBo3rw5aWlprFixQk0WbVsHQ0NDee6555g8ebJdsmg0GlmzZg3t2rUrVzxLly4lLi6uwPY9e/ZQo0aNcpVtlZWV5ZRySuPHkG/RarV2vzw/BB9z+jyBFfUFsHfvXqddf2dJSUlxdRUqlMTn2SQ+zybxlZ+jf2slWawE7dq148EHH6RNmzb07duX8PBwhg4dSu3atR0uo2vXrgWer1y5Un2+d+9eli5dyunTp8nIyMBkMmEwGMjKylITCB8fH9q2bVvueObPn09MTIz6PCMjg5CQEMLDwwkICCh3+dYyExISANi0aVOFrcxhMBgYO3YsALf/rwM6rZ4c01+tprdf7IjOyVPn5BgN+Pr6Mnr0aLp3746fn1+ZyzIYDIwaNQqA/v37V/hk244yGo2kpKTQp0+fKjkpt8Tn2SQ+zybxOY+1Z7AkkixWAm9vb1JSUvjss8/Ys2cP8fHxLFy4kEOHDuHl5VWgO7q0LU8XLlxgwIABTJkyheeff546derwn//8hwkTJpCbm6smi76+vgWmVinL+XU6HTpdwQRKq9U67YNtu6qJn59fhSVBGo1GXXrPGw3eFg3elr+6nb0t3nhbnPtr4k3+ObVaLX5+fvj7+5e5LNtuZ2def2dxxzo5k8Tn2SQ+zybxOeccjpABLpVEURS6d+9OXFwcX375JT4+Pmzfvp26dety8eJFdT+z2cw333xT4PjPP/+8wPOWLVsC8MUXX5CXl8fy5cvp0qULzZs353//+59D9XL0/EIIIYSonqRlsRIcOnSITz75hPDwcOrVq8ehQ4f49ddfadmyJTVr1iQmJoadO3dyxx138Oqrr/LHH38UKCMtLY1ly5YxaNAgUlJS+Oc//8nOnTsBaNq0KUajkfj4eAYOHEhaWhpvvvmmQ3Xr1auXQ+evbDqdjujoaPr27VthXdBVgV6vJzk5WX0shBBCOJski5UgICCATz/9lJUrV5KRkUGTJk1Yvnw5Dz30EEajkRMnTjBmzBg0Gg1PPvkkDzzwQIEynnrqKY4ePUpcXBwBAQG8+uqr9O3bF8i/J/LVV1/lpZdeYv78+dx///0sXbqUMWPGlFi38ePHO3T+yqYoClqttlJXJck159+rmGtzz6LtY2efxxkURXGb+xSFEEJUTZIsVoKWLVuye/fuQl/TarWsWbPGbtTyzS5cuFDiOZ588kmefNJ+veHRo0erj6OiogqdO9GR81cXCz+aXnDb7oLbnKEq32cjhBCiapF7FoUQQgghRJGkZVFUa7b3/Fnl5eWp0wnodDqHu8EtFos6WXlJx5lMJj777DMAsrOzHa5rZXXJCyGEEFaSLIpqrbB7/rKzs9W5FyuKVqslOjqa4cOHOzxVUnJystyfKIQQotJJN7QQQgghhCiStCwKt2exWDAYDEDld8W+1nMZOm/HVnDJMecwff8ch44ze5n5mnPE93wJ7zxvh8q05cprIoQQonqRlkXh9gwGAxEREURERKgJUmXReevQaRz8sUkOSzrO588lBH1KUaYtV14TZ0hMTKRfv34kJiZy8OBB/v73v3Pw4EG7fYraXpyyHCOEEKJ4kiy6QFhYGDNnzqz08yqKwo4dOyr9vELYunbtGlu2bCEvL493332XVatWcfnyZeLj49XE12AwEB8fX2B7ccpyjBBCiJJJsiiEqFSxsbHk5eUB+d3pv//+OwBXrlxh69atAGzZsoUrV64U2F6cshwjhBCiZJIsVrKoqCj279/PqlWrUBQFRVG4cOEC+/fvp3Pnzuh0OoKDg5k3bx4mkwmA9evXExQUpE7LYjVo0CC7ibffeOMN7rjjDnx8fGjRogUbNmxQXwsNDQVg8ODBKIqiPj937hwRERHUr18fPz8/7rnnHvbu3VuxF6EcDAYD2dnZFfpj2yJlsVhcGK39+W1j99RWs2PHjvHtt98W+prFYmHr1q0cO3aMrVu3qrFbt//8889Flvvzzz+X+hghhBCOkQEulWzVqlWcPXuW1q1b8+yzzwJgNpt5+OGHiYqKYv369Zw+fZpJkyah1+uJjY3l0Ucf5fHHH+eDDz7g0UcfBeDy5cvs3LmTPXv2ALB9+3aeeOIJVq5cSe/evfnwww8ZN24ct956Kw888ABHjhyhXr16JCQk0K9fP7y98wdVZGZm8vDDD/P888+j0+lYv349AwcO5MyZMzRu3LjQGHJycuwSV+uchEaj0eFpYEpiLcdoNHLjxg11e2RkpFPKd1Q22Wi8HFttxeRltnvsbfP8ZmbF/Ne/xfyXLZu/5mAsKvbMzEw0Gvf6VbZ9/6zy8vJ4/vnniz3Ouo+15dF2e3x8PM8++2yBwTwWi4X4+PgCiX1xx5RXYfFVJRKfZ5P4PFtlxufoORSLq5tOqqGwsDDat2/PypUrAVi4cCHbtm3j1KlT6h+1NWvWMHfuXK5du4aXlxdTp07lwoUL7Nq1C4BXX32V119/ne+//x5FUejevTt33XUXa9euVc8zbNgwbty4wc6dO4H8exa3b9/OoEGDiq1f69atmTx5MtOnF77UXWxsLHFxcQW2b968mRo1apT2cpQoKyuLhIQEp5friOjoaIeX5jMajer1L81xjpZZlHHjxlXIdXe28+fPq5/fshoxYgR16tSx2/b777/z7rvvluoYIYQQ+X9fR44cybVr1wgICChyP/dqjqimTp06RdeuXe1aP7p3705mZiY//fQTjRs3ZtKkSdxzzz38/PPPNGrUiMTERKKiotRjTp06RXR0tF253bt3Z9WqVcWeOzMzk9jYWHbu3MnFixcxmUxkZ2eTnp5e5DHz588nJiZGfZ6RkUFISAjh4eHFfthKw2g0kpKSQp8+fTCZTGqyuGnTJvR6vVPOURSDwaBOyt3hXAt0WsfOl2P6q7W1ww93otMUM3WOYub47Wdo/0MLvC3FTJ1jNKithklJSWrsBoOBUaNGAdC/f3+3m6zb9v2zJs15eXkcOHCA69evF3mcl5cXfn5+XL9+3a6l0MvLiw4dOjBq1KhCWxbPnDnD8ePH7VokizumIuKrSiQ+zybxebbKjM/aM1gSSRY9RIcOHWjXrh3r168nPDycb7/9Vm0xLI9Zs2aRkpLCK6+8QtOmTfH19WXo0KHk5uYWeYxOp0OnK5gIabVap3+wtVqt3R96Pz+/Ck+MNBqNer+oxqJBU8w8iLbMNvtp8ryLP+7PrmdvS/H7mS1/1cU2dttu54q47s5yc90WLlzIvHnzitzfy8uLhQsXsmDBAsxms932GTNm4OPjU+hxM2bMYOLEiQXKKu4YZ3Dna+8MEp9nk/g8W2XE52j5MsDFBXx8fOz+ELZs2ZKDBw/ataSkpaXh7+/Prbfeqm6bOHEiiYmJJCQk0Lt3b0JCQuzKSEtLsztPWloarVq1Up9rtVq781r3iYqKYvDgwbRp04YGDRpw4cIFZ4UqhJ2OHTty1113FfqaoihERkbSoUMHIiMj1f8kWLc3bNiwyHIbNWpU6mOEEEI4RpJFFwgNDeXQoUNcuHCB3377jalTp/Ljjz8yY8YMTp8+TXJyMosXLyYmJgYvr7/eopEjR/LTTz+xbt06xo8fb1fm7NmzSUxM5I033uC7777j1Vdf5f3332fWrFl25/3kk0+4dOkSV69eBaBZs2a8//77HD9+nBMnTjBy5MgCgwtcTa/Xk5ycTHJycoV3Qd8sx5xDjsnBH3OOw8fl/tllnVuKMm258pqUV2xsrPq59vLyUu8nDAoKUgfxDB8+nKCgoALbi1OWY4QQQpRMuqFdYNasWYwdO5ZWrVqRnZ2t3vg/e/Zs2rVrR506dZgwYQJPP/203XG1atViyJAh7Ny5s8AglUGDBrFq1SpeeeUVnnjiCW677TYSEhIICwtT91m+fDkxMTGsW7eORo0aceHCBV599VXGjx9Pt27duOWWW5g7d67D9zBUFkVRXHZPXmFL7TnjOK1WS3SLaGbsn1umEW+uvCblVatWLYYPH86WLVsYPnw4LVq04PXXX2fatGlq4qvX65kxY0aB7cUpyzFCCCFKJsmiCzRv3rzAcmShoaEcPny4xGN//vlnRo0aVeg9g1OmTGHKlClFHjtw4EAGDhxY4Lypqal226ZNm1ZiPYQoj6ioKKKiotTnXbt2LbBP165dC91enLIcI4QQoniSLHqIq1evsm/fPvbt28eaNWtcXZ0qzdrFW1oWi0Wdf1Kn0xU7AtdkMpGamsqWLVscniNRWsqEEEK4giSLHqJDhw5cvXqVl156iRYtWri6OlWabRevxWJxeLUURVGKTOhuTiTLwpF66PV6p08TI4QQonqTZNFDyAhl1zAYDERERDi9XK1WS3R0NMOHD3fqLP3Jyckeey+jEEII9ySjoYUQQgghRJGkZVEIG7bdzjd3Kb/24Dx03mWb4DnHlMv01Bfzy+k1D42Phq+5SvyDc/HOK9//2XLMuUz/5MVylVGUm6+HdHELIUT1I8liMWzXUr5w4QK33XYbX375Je3bt3d11UQFse12vnmQi87bB72m/KuB6DQ+aLw1apkaL/dt4L/5ekgXtxBCVD/u+1dKCCGEEEK4nCSLQgghhBCiSFU2WVy7di0NGzYssHRdRESEulTeG2+8wR133IGPjw8tWrRgw4YNDpdvNpsZP348d955J+np6VgsFmJjY2ncuDE6nY6GDRvy+P+3d+9RUZXrH8C/AzMMIAygIBcFwUC8oWIqgYEkkxeUoE7eolIzSBTDhZFmmXqsBcf4lVppejqhFcopl3gD/QkiZBzUNPACNClpWEqYNxjlMgPP7w9/7MMIgyQzwIzPZy3Wmtnvu995n3k38Kz97v3u118HAHzyyScYOnSosO/u3bshEonw2WefCdvkcrnwxJaysjKEh4fD0dERVlZWGD16NLKzszU+/+rVq5gyZQosLCzg4eGB7du3w93dHevWrRPq3Lp1C6+++iocHBwgk8kwfvx4nD59WihftWoVRowYga+++gru7u7CkzWqq6vb/T0Ys9raWo3lapo/u7s7ad6v2tpa1NTU6OynvcsGMcYYM15Ge83itGnTsGjRIhw5cgQhISEAgBs3buDgwYPIzMxEeno64uLisG7dOsjlcuzfvx9z585F37598dRTT7XZdl1dHWbNmoVLly7h6NGjcHBwwM6dO/HRRx8hLS0NQ4YMQUVFhZCYjRs3Dq+//jquXbsGBwcH5OXlwd7eHrm5uZg/fz5UKhUKCgqwbNkyAIBSqURoaCjef/99SKVSfPnllwgLC4NCoYCbmxsA4OWXX8aff/6J3NxcSCQSxMfHo7KyssV3YGFhgQMHDsDGxgabN29GSEgIfv75Z+F5vGVlZdi9ezf279+PmzdvYvr06UhKSsL777/fZvxNawYCEB4PqFKpdLYMTFM7ulxWpj2USqXw+v5nC9dQHSQiyUO12yBq1Hgt+v/3zbc/rBr671jo83nISqWy3QuId9X4dRaOz7BxfIaN49P9Zz2I0SaLdnZ2mDx5MrZv3y4kizt37oS9vT2eeuopBAYGYs6cOViwYAEAID4+HseOHUNycnKbyaJSqcSUKVNQV1eHI0eOwMbGBgBQXl4OJycnyOVySCQSuLm5YcyYMQCAoUOHomfPnsjLy8Pzzz+P3NxcLFmyBOvXrwcAnDhxAiqVCgEBAQCA4cOHY/jw4cJnrlmzBunp6di7dy9iY2Px008/ITs7Gz/88ANGjRoFAPj888/h5eUl7PP999/jxIkTqKysFBaBTk5Oxu7du7Fz505ER0cDABobG7F161ZYW1sDAF566SUcPny4zWQxMTERq1evbrH90KFDsLS01Lrfw8jKytJpew9y9+5drWXF/W9BIrnzUO02/4X8sV8lJJJ7SWdhv2sP1Z62tvUpOzv7L49vZ49fZ+P4DBvHZ9g4vo5r639ec0abLAJAZGQkoqKisHHjRkilUqSmpmLmzJkwMTFBaWmpkDA1GTt2rJDAaTNr1iz07dsXOTk5GneGTps2DevWrUP//v0xadIkhIaGIiwsDGKxGCKRCEFBQcjNzYVcLkdJSQkWLFiAtWvX4qeffkJeXh5Gjx4t/CNWKpVYtWoVMjIycPXqVajVatTU1KC8vBwAoFAoIBaLMXLkSOHzPT09YWdnJ7w/ffo0lEolevXqpdH/mpoalJWVCe/d3d2FRBEAnJ2dW5yhvN9bb72F+Ph44X1VVRVcXV0xYcIEyGSyNvdtL5VKhaysLDz99NNCYtUZampqkJKSAgBITU0FAMyePRsA4PurA8zFD/f0lTp1vfB65K+9IZaIUdjvGnx/dYApdexqkFp1nXDGb9u2bTp9LGBtbS0iIyMBQLjsoT26avw6C8dn2Dg+w8bx6U7TzOCDGHWyGBYWBiJCRkYGRo8ejaNHj+Kjjz7qUJuhoaH4+uuvUVBQgPHjxwvbXV1doVAokJ2djaysLCxYsAAffPAB8vLyIJFIEBwcjC1btuDo0aPw9fWFTCYTEsi8vDyMGzdOaOuNN95AVlYWkpOT4enpCQsLCzz//POor69vrUutUiqVcHZ2Rm5ubosyW1tb4fX9B6JIJGpxnef9pFJpq4+sk0gkOj+w9dFmW9RqtfDayspKY5uYTCF+yMRO3Ww/UzIREkRTMnnoNpuIyVToo5WVlU6Xt2k+7fwwY9HZ49fZOD7DxvEZNo5PN5/RHkZ7gwtwbxHh5557DqmpqdixYwe8vb2Fs3GDBg1Cfn6+Rv38/HwMHjy4zTZjYmKQlJSEZ555Bnl5eRplFhYWCAsLw4YNG5Cbm4uCggKcPXsWwL3rFktKSvDtt98iODgYABAcHIzs7Gzk5+cL25r6MWfOHDz77LPw8fGBk5OTxuP+vL29oVarUVhYKGy7cOECbt68KbwfOXIkKioqIBaL4enpqfFjb2/f7u+QMcYYY482oz6zCNybip46dSqKi4vx4osvCtsTEhIwffp0+Pr6Qi6XY9++fdi1a1eLu45bs2jRIjQ0NGDq1Kk4cOAAnnzySWzduhUNDQ3w8/ODpaUlvv76a1hYWKBfv34AgGHDhsHOzg7bt2/H/v37AdxLFt944w2IRCKMHTtWaN/Lywu7du1CWFgYRCIRVqxYoXG2b+DAgZDL5YiOjsamTZsgkUiwZMkSWFhYCE/YkMvl8Pf3R0REBNauXYsBAwbgypUryMjIwLPPPitc68g0mZubC4txm5uba9wNXNfQ/jO792s+DV2nrkeDSaPQploHT3DRl/u/D8YYY48eo08Wx48fj549e0KhUOCFF14QtkdERGD9+vVITk5GXFwcPDw8kJKSonGGry2LFy9GY2MjQkNDcfDgQdja2iIpKQnx8fFoaGiAj48P9u3bJ1wzKBKJEBgYiIyMDDz55JMA7iWQMpkM3t7e6NGjh9D2hx9+iFdeeQUBAQGwt7fH0qVLW1xX8OWXX2LevHkICgqCk5MTEhMTUVxcLPxDF4lEyMzMxNtvv425c+fi2rVrcHJyQlBQEBwdHTvylRo1kUikdRpXV4/Ui81JgkQiQfRj0Vh0+B/d+o6+tr4PxhhjjwajTxZNTExw5cqVVstiYmIQExOjdd/m69e5u7u3WGcvPj5e40aPiIiINvuye/fuFn27ceNGi3ru7u7IycnR2LZw4UKN987OzsjMzBTe//bbb6isrISnp6ewzdraGhs2bMCGDRta7c+qVauwatUqjW2LFy/G4sWL24yDMcYYY48Oo08WjVVOTg6USiV8fHxw9epVvPnmm3B3d0dQUFBXd82oNJ+G7QgiEtamlEqlaGhoQE5ODtLS0tq9dmF78FQxY4wxXeNk0UCpVCosX74cv/zyC6ytrREQEIDU1FSjvjOsK+hyGrb5GoVNU8/m5uY8Zv+PiPT6xJj7E/am63sfRtPd57W1tcJrc3PzDrXJGGPdFSeLBmrixImYOHFiV3eDMZ2pra1FeHh4V3ejXSQSCaKjozFz5kwh8d+zZw9f38kYM0pGvXQOY4wxxhjrGD6zyNgjqPmUb3ecPv104nxITXU7PV+nVmHhoc/utT9hPqTih2+/QUQ4A8L/hMzD6wc/01UXteru48UYM26cLBoZkUiE9PT0B96ZzR5tzad8u+P0qdRUAvMOJHMPbF/csfbVIgJQD6lp5/wJ7e7jxRgzbjwNzRhjjDHGtOIzi4w94vRxB7JarYZKpdK4W/iv9OP+NU27q+b91Oed3PpsmzHGHoSTxW5ky5YtWLVqFX777TeYmPz3pG94eDh69eqFL774Aps2bUJycjIuX74MDw8PvPPOO3jppZdabS8gIACBgYH4xz/+IWy7du0aXFxccPjwYQQFBeHmzZuIi4vDvn37UFdXh3HjxmHDhg3w8vLS2s+6ujphCRIAwtNlVCqVzp5G0tROd366SUd0dXxKpVJ4PWPGDL19zpYtWx5qv5pGFSQi3U5DN4hI47Va9PAJaVNbNY3/HT99fo/NKZVKna7N2ZquPj71jeMzbByf7j/rQThZ7EamTZuGRYsW4ciRIwgJCQEA3LhxAwcPHkRmZibS09MRFxeHdevWQS6XY//+/Zg7dy769u2Lp556qkV7kZGRWLt2LZKSkoQL4v/973/DxcUFgYGBAIA5c+bg/Pnz2Lt3L2QyGZYuXYrQ0FCUlJRoXf8vMTERq1evbrH90KFDGmsJ6kJWVpZO2+tuuiq+u3fvdsnntte5fgSJRLfPvG7+R/FUn3pIJB0/e1nq1uEm/rLs7Gyd/55pw79/ho3jM2ydEV97/xeIyFDmex4RERER6NWrF/71r38BuHdmZvXq1bh8+TICAwMxZMgQjbM106dPx507d5CRkQFA8waXprOIOTk5QnIYEBCAoKAgJCUl4fz58xgwYADy8/MREBAAALh+/TpcXV2xbds2TJs2rdU+tnZm0dXVFX/++SdkMplOvgeVSoWsrCw8/fTTRrlodVfHV1NTg+effx4AkJqaqvMnv6hUKuTk5GD8+PHtjq+2thazZ88GAHwy4TWYS8x02qc6tQqvZXwCANg8JbbDd0P/2EeFwZcIcf977/dx27ZtenuCTm1tLSIjIwEAO3fu1PsNLl19fOobx2fYOD7dqaqqgr29PW7fvt3m/28+s9jNREZGIioqChs3boRUKkVqaipmzpwJExMTlJaWIjo6WqP+2LFjsX79+lbbcnBwwIQJE5CamorAwEBcvHgRBQUF2Lx5MwCgtLQUYrEYfn5+wj69evWCt7c3SktLtfZRKpVCKpW22C6RSHR+YOujze6kq+Jrfh2hlZWVzpMPlUoFiUQCa2vrdscnFouFfolhAjHpdnkYdbP2TEmkk/bFMBH6rI/vUficZtPOnXnM8O+fYeP4DFtnxNfe9vlu6G4mLCwMRISMjAxcvnwZR48eFc4oPIzIyEjs3LkTKpUK27dvh4+PD3x8fHTYY8YYY4wZM04Wuxlzc3M899xzSE1NxY4dO+Dt7Y2RI0cCAAYNGoT8/HyN+vn5+Rg8eLDW9sLDw1FbW4uDBw9i+/btGonnoEGDoFarcfz4cWHb9evXoVAo2myTMcYYY48OnobuhiIjIzF16lQUFxfjxRdfFLYnJCRg+vTp8PX1hVwux759+7Br1y5kZ2drbatHjx6IiIjAihUrUFpailmzZgllXl5eCA8PR1RUFDZv3gxra2ssW7YMffr0MZhn9LKHY25ujj179givu5u6Bt3fBVinVrX6+mE03Q1d19C+ZYE6qruPF2PMuHGy2A2NHz8ePXv2hEKhwAsvvCBsj4iIwPr165GcnIy4uDh4eHggJSUFwcHBbbYXGRmJ0NBQBAUFwc1N8/bNlJQUxMXFYerUqaivr0dQUBAyMzON+joQdu9GqO78FJCF/6vfR+g1PfbvYUkkEkRHR2PJ4X/pqEdt6+7jxRgzbpwsdkMmJia4cuVKq2UxMTGIiYnRum9rN7dPnjxZ6yLHdnZ2+PLLLx+uo4wxxhgzepwssg5rSkSbFufWBZVKhbt376Kqqsooz3JyfC0REb766iu99YmIhCWfpFKpsPbow1Cr1fjuu++wZcsW4U7l+vp6o1kkmI9Pw8bxGbbOjK/p//aDVlHkZJF1WHV1NQDA1dW1i3vCGGOMsb+quroaNjY2Wst5UW7WYY2Njbhy5Qqsra07dLamuaaFvi9fvqyzhb67E47PsHF8ho3jM2wcn+4QEaqrq+Hi4qLxmOH78ZlF1mEmJibo27evXtqWyWRG+cegCcdn2Dg+w8bxGTaOTzfaOqPYhNdZZIwxxhhjWnGyyBhjjDHGtOJkkXVLUqkUK1eubPUZ1MaA4zNsHJ9h4/gMG8fX+fgGF8YYY4wxphWfWWSMMcYYY1pxssgYY4wxxrTiZJExxhhjjGnFySJjjDHGGNOKk0WmN6tWrYJIJNL4GThwoFBeW1uLhQsXolevXrCyssLf/vY3/PHHHxptlJeXY8qUKbC0tETv3r2RkJAAtVqtUSc3NxcjR46EVCqFp6cntm7dqpd4vvvuO4SFhcHFxQUikQi7d+/WKCcivPvuu3B2doaFhQXkcjnOnz+vUefGjRuIjIyETCaDra0t5s2bB6VSqVHnzJkzCAwMhLm5OVxdXbF27doWffn2228xcOBAmJubw8fHB5mZmXqPb86cOS3Gc9KkSQYRX2JiIkaPHg1ra2v07t0bERERUCgUGnU683j89NNP4e7uDnNzc/j5+eHEiRN6jy84OLjF+M2fP98g4tu0aROGDRsmLFLs7++PAwcOCOWGPHbtic+Qx641SUlJEIlEWLx4sbDN0MfwQfEZ/BgSY3qycuVKGjJkCF29elX4uXbtmlA+f/58cnV1pcOHD9PJkyfpiSeeoICAAKFcrVbT0KFDSS6XU2FhIWVmZpK9vT299dZbQp1ffvmFLC0tKT4+nkpKSujjjz8mU1NTOnjwoM7jyczMpLfffpt27dpFACg9PV2jPCkpiWxsbGj37t10+vRpeuaZZ8jDw4NqamqEOpMmTaLhw4fTsWPH6OjRo+Tp6UmzZs0Sym/fvk2Ojo4UGRlJ586dox07dpCFhQVt3rxZqJOfn0+mpqa0du1aKikpoXfeeYckEgmdPXtWr/HNnj2bJk2apDGeN27c0KjTXeObOHEipaSk0Llz56ioqIhCQ0PJzc2NlEqlUKezjse0tDQyMzOjL774goqLiykqKopsbW3pjz/+0Gt848aNo6ioKI3xu337tkHEt3fvXsrIyKCff/6ZFAoFLV++nCQSCZ07d46IDHvs2hOfIY/d/U6cOEHu7u40bNgwiouLE7Yb+hg+KD5DH0NOFpnerFy5koYPH95q2a1bt0gikdC3334rbCstLSUAVFBQQET3khcTExOqqKgQ6mzatIlkMhnV1dUREdGbb75JQ4YM0Wh7xowZNHHiRB1Ho+n+ZKqxsZGcnJzogw8+ELbdunWLpFIp7dixg4iISkpKCAD98MMPQp0DBw6QSCSi33//nYiINm7cSHZ2dkJ8RERLly4lb29v4f306dNpypQpGv3x8/Oj1157TW/xEd1LFsPDw7XuY0jxVVZWEgDKy8sjos49HseMGUMLFy4U3jc0NJCLiwslJibqLT6ie/+smv/zup8hxUdEZGdnR59//rnRjd398REZz9hVV1eTl5cXZWVlacRkLGOoLT4iwx9DnoZmenX+/Hm4uLigf//+iIyMRHl5OQDg1KlTUKlUkMvlQt2BAwfCzc0NBQUFAICCggL4+PjA0dFRqDNx4kRUVVWhuLhYqNO8jaY6TW10losXL6KiokKjLzY2NvDz89OIx9bWFqNGjRLqyOVymJiY4Pjx40KdoKAgmJmZCXUmTpwIhUKBmzdvCnW6Kubc3Fz07t0b3t7eiImJwfXr14UyQ4rv9u3bAICePXsC6Lzjsb6+HqdOndKoY2JiArlcrtf4mqSmpsLe3h5Dhw7FW2+9hbt37wplhhJfQ0MD0tLScOfOHfj7+xvd2N0fXxNjGLuFCxdiypQpLfphLGOoLb4mhjyG4g7tzVgb/Pz8sHXrVnh7e+Pq1atYvXo1AgMDce7cOVRUVMDMzAy2trYa+zg6OqKiogIAUFFRofGL01TeVNZWnaqqKtTU1MDCwkJP0Wlq6k9rfWne1969e2uUi8Vi9OzZU6OOh4dHizaayuzs7LTG3NSGvkyaNAnPPfccPDw8UFZWhuXLl2Py5MkoKCiAqampwcTX2NiIxYsXY+zYsRg6dKjw2Z1xPN68eRMNDQ2t1vnpp5/0Fh8AvPDCC+jXrx9cXFxw5swZLF26FAqFArt27TKI+M6ePQt/f3/U1tbCysoK6enpGDx4MIqKioxi7LTFBxj+2AFAWloafvzxR/zwww8tyozh96+t+ADDH0NOFpneTJ48WXg9bNgw+Pn5oV+/fvjmm286LYljujNz5kzhtY+PD4YNG4bHHnsMubm5CAkJ6cKe/TULFy7EuXPn8P3333d1V/RCW3zR0dHCax8fHzg7OyMkJARlZWV47LHHOrubf5m3tzeKiopw+/Zt7Ny5E7Nnz0ZeXl5Xd0tntMU3ePBggx+7y5cvIy4uDllZWTA3N+/q7uhce+Iz9DHkaWjWaWxtbTFgwABcuHABTk5OqK+vx61btzTq/PHHH3BycgIAODk5tbgbrun9g+rIZLJOTUib+tNaX5r3tbKyUqNcrVbjxo0bOom5qbyz9O/fH/b29rhw4YLQr+4eX2xsLPbv348jR46gb9++wvbOOh7t7e1hamra6fG1xs/PDwA0xq87x2dmZgZPT088/vjjSExMxPDhw7F+/XqjGTtt8bXG0Mbu1KlTqKysxMiRIyEWiyEWi5GXl4cNGzZALBbD0dHRoMfwQfE1NDS02MfQxpCTRdZplEolysrK4OzsjMcffxwSiQSHDx8WyhUKBcrLy4XrdPz9/XH27FmNBCQrKwsymUyYnvH399doo6lO82t9OoOHhwecnJw0+lJVVYXjx49rxHPr1i2cOnVKqJOTk4PGxkbhD4e/vz++++47qFQqoU5WVha8vb1hZ2cn1OkOMf/222+4fv06nJ2dhX511/iICLGxsUhPT0dOTk6LqfDOOh7NzMzw+OOPa9RpbGzE4cOH9Rpfa4qKigBAY/y6a3ytaWxsRF1dncGP3YPia42hjV1ISAjOnj2LoqIi4WfUqFGIjIwUXhvyGD4oPlNT0xb7GNoY8t3QTG+WLFlCubm5dPHiRcrPzye5XE729vZUWVlJRPeWSnBzc6OcnBw6efIk+fv7k7+/v7B/01ICEyZMoKKiIjp48CA5ODi0upRAQkIClZaW0qeffqq3pXOqq6upsLCQCgsLCQB9+OGHVFhYSL/++isR3Vs6x9bWlvbs2UNnzpyh8PDwVpfO8fX1pePHj9P3339PXl5eGkvL3Lp1ixwdHemll16ic+fOUVpaGllaWrZYWkYsFlNycjKVlpbSypUrdbJ0TlvxVVdX0xtvvEEFBQV08eJFys7OppEjR5KXlxfV1tZ2+/hiYmLIxsaGcnNzNZauuHv3rlCns47HtLQ0kkqltHXrViopKaHo6GiytbXVuAtS1/FduHCB/v73v9PJkyfp4sWLtGfPHurfvz8FBQUZRHzLli2jvLw8unjxIp05c4aWLVtGIpGIDh06RESGPXYPis/Qx06b++8ONvQxbCs+YxhDThaZ3syYMYOcnZ3JzMyM+vTpQzNmzKALFy4I5TU1NbRgwQKys7MjS0tLevbZZ+nq1asabVy6dIkmT55MFhYWZG9vT0uWLCGVSqVR58iRIzRixAgyMzOj/v37U0pKil7iOXLkCAFo8TN79mwiurd8zooVK8jR0ZGkUimFhISQQqHQaOP69es0a9YssrKyIplMRnPnzqXq6mqNOqdPn6Ynn3ySpFIp9enTh5KSklr05ZtvvqEBAwaQmZkZDRkyhDIyMvQa3927d2nChAnk4OBAEomE+vXrR1FRUS3+AHXX+FqLC4DGsdKZx+PHH39Mbm5uZGZmRmPGjKFjx47pNb7y8nIKCgqinj17klQqJU9PT0pISNBY5607x/fKK69Qv379yMzMjBwcHCgkJERIFIkMe+weFJ+hj5029yeLhj6GbcVnDGMoIiLq2LlJxhhjjDFmrPiaRcYYY4wxphUni4wxxhhjTCtOFhljjDHGmFacLDLGGGOMMa04WWSMMcYYY1pxssgYY4wxxrTiZJExxhhjjGnFySJjjDHGGNOKk0XGGGMtXLp0CSKRSHiGLWPs0cVPcGGMMdZCQ0MDrl27Bnt7e4jF4q7uDmOsC3GyyBhjTEN9fT3MzMy6uhuMsW6Cp6EZY8zIBQcHIzY2FrGxsbCxsYG9vT1WrFiBpnMF7u7uWLNmDV5++WXIZDJER0e3Og1dXFyMqVOnQiaTwdraGoGBgSgrKxPKP//8cwwaNAjm5uYYOHAgNm7c2NmhMsb0gOcWGGPsEbBt2zbMmzcPJ06cwMmTJxEdHQ03NzdERUUBAJKTk/Huu+9i5cqVre7/+++/IygoCMHBwcjJyYFMJkN+fj7UajUAIDU1Fe+++y4++eQT+Pr6orCwEFFRUejRowdmz57daXEyxnSPp6EZY8zIBQcHo7KyEsXFxRCJRACAZcuWYe/evSgpKYG7uzt8fX2Rnp4u7HPp0iV4eHigsLAQI0aMwPLly5GWlgaFQgGJRNLiMzw9PbFmzRrMmjVL2Pbee+8hMzMT//nPf/QfJGNMb3gamjHGHgFPPPGEkCgCgL+/P86fP4+GhgYAwKhRo9rcv6ioCIGBga0minfu3EFZWRnmzZsHKysr4ee9997TmKZmjBkmnoZmjDGGHj16tFluYWGhtUypVAIA/vnPf8LPz0+jzNTUtOOdY4x1KU4WGWPsEXD8+HGN98eOHYOXl1e7k7lhw4Zh27ZtUKlULc4uOjo6wsXFBb/88gsiIyN11mfGWPfA09CMMfYIKC8vR3x8PBQKBXbs2IGPP/4YcXFx7d4/NjYWVVVVmDlzJk6ePInz58/jq6++gkKhAACsXr0aiYmJ2LBhA37++WecPXsWKSkp+PDDD/UVEmOsk/CZRcYYewS8/PLLqKmpwZgxY2Bqaoq4uDhER0e3e/9evXohJycHCQkJGDduHExNTTFixAiMHTsWAPDqq6/C0tISH3zwARISEtCjRw/4+Phg8eLFeoqIMdZZ+G5oxhgzcsHBwRgxYgTWrVvX1V1hjBkgnoZmjDHGGGNacbLIGGOMMca04mloxhhjjDGmFZ9ZZIwxxhhjWnGyyBhjjDHGtOJkkTHGGGOMacXJImOMMcYY04qTRcYYY4wxphUni4wxxhhjTCtOFhljjDHGmFacLDLGGGOMMa3+D3kkG4ohU3BrAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x[(x.make==\"plymouth\")&(y>10000)]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "gHnIuyVveGjd",
        "outputId": "48ce525d-a805-4c6b-aeb8-31c61b930089"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     symboling  normalized-losses      make fuel-type body-style drive-wheels  \\\n",
              "124          3              122.0  plymouth       gas  hatchback          rwd   \n",
              "\n",
              "    engine-location  width  height engine-type  engine-size  horsepower  \\\n",
              "124           front   66.3    50.2         ohc          156       145.0   \n",
              "\n",
              "     city-mpg  highway-mpg  \n",
              "124        19           24  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0370339e-8954-4c66-a4a8-c4639e364e93\">\n",
              "    <div 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>symboling</th>\n",
              "      <th>normalized-losses</th>\n",
              "      <th>make</th>\n",
              "      <th>fuel-type</th>\n",
              "      <th>body-style</th>\n",
              "      <th>drive-wheels</th>\n",
              "      <th>engine-location</th>\n",
              "      <th>width</th>\n",
              "      <th>height</th>\n",
              "      <th>engine-type</th>\n",
              "      <th>engine-size</th>\n",
              "      <th>horsepower</th>\n",
              "      <th>city-mpg</th>\n",
              "      <th>highway-mpg</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>124</th>\n",
              "      <td>3</td>\n",
              "      <td>122.0</td>\n",
              "      <td>plymouth</td>\n",
              "      <td>gas</td>\n",
              "      <td>hatchback</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>66.3</td>\n",
              "      <td>50.2</td>\n",
              "      <td>ohc</td>\n",
              "      <td>156</td>\n",
              "      <td>145.0</td>\n",
              "      <td>19</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0370339e-8954-4c66-a4a8-c4639e364e93')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-0370339e-8954-4c66-a4a8-c4639e364e93 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-0370339e-8954-4c66-a4a8-c4639e364e93');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x.drop(124,axis=0,inplace=True)\n",
        "sns.boxplot(data=x,x=y,y=\"make\")\n",
        "plt.grid()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "iNSCAR2yeVO9",
        "outputId": "caee9d8c-26dc-4375-e9ac-b9e5c8ddca74"
      },
      "execution_count": 45,
      "outputs": [
        {
          "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": [
        "x[(x.make==\"toyota\")&(y>15000)]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "wzcuEtM4eatc",
        "outputId": "6fb8f373-407d-4bc1-8f07-66e9f5755e82"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     symboling  normalized-losses    make fuel-type   body-style drive-wheels  \\\n",
              "172          2              134.0  toyota       gas  convertible          rwd   \n",
              "178          3              197.0  toyota       gas    hatchback          rwd   \n",
              "179          3              197.0  toyota       gas    hatchback          rwd   \n",
              "180         -1               90.0  toyota       gas        sedan          rwd   \n",
              "181         -1              122.0  toyota       gas        wagon          rwd   \n",
              "\n",
              "    engine-location  width  height engine-type  engine-size  horsepower  \\\n",
              "172           front   65.6    53.0         ohc          146       116.0   \n",
              "178           front   67.7    52.0        dohc          171       161.0   \n",
              "179           front   67.7    52.0        dohc          171       161.0   \n",
              "180           front   66.5    54.1        dohc          171       156.0   \n",
              "181           front   66.5    54.1        dohc          161       156.0   \n",
              "\n",
              "     city-mpg  highway-mpg  \n",
              "172        24           30  \n",
              "178        20           24  \n",
              "179        19           24  \n",
              "180        20           24  \n",
              "181        19           24  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-50c1ef6f-dfc3-4cfc-a0af-666bf9d1d07d\">\n",
              "    <div 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>symboling</th>\n",
              "      <th>normalized-losses</th>\n",
              "      <th>make</th>\n",
              "      <th>fuel-type</th>\n",
              "      <th>body-style</th>\n",
              "      <th>drive-wheels</th>\n",
              "      <th>engine-location</th>\n",
              "      <th>width</th>\n",
              "      <th>height</th>\n",
              "      <th>engine-type</th>\n",
              "      <th>engine-size</th>\n",
              "      <th>horsepower</th>\n",
              "      <th>city-mpg</th>\n",
              "      <th>highway-mpg</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>172</th>\n",
              "      <td>2</td>\n",
              "      <td>134.0</td>\n",
              "      <td>toyota</td>\n",
              "      <td>gas</td>\n",
              "      <td>convertible</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>65.6</td>\n",
              "      <td>53.0</td>\n",
              "      <td>ohc</td>\n",
              "      <td>146</td>\n",
              "      <td>116.0</td>\n",
              "      <td>24</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>178</th>\n",
              "      <td>3</td>\n",
              "      <td>197.0</td>\n",
              "      <td>toyota</td>\n",
              "      <td>gas</td>\n",
              "      <td>hatchback</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>67.7</td>\n",
              "      <td>52.0</td>\n",
              "      <td>dohc</td>\n",
              "      <td>171</td>\n",
              "      <td>161.0</td>\n",
              "      <td>20</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>179</th>\n",
              "      <td>3</td>\n",
              "      <td>197.0</td>\n",
              "      <td>toyota</td>\n",
              "      <td>gas</td>\n",
              "      <td>hatchback</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>67.7</td>\n",
              "      <td>52.0</td>\n",
              "      <td>dohc</td>\n",
              "      <td>171</td>\n",
              "      <td>161.0</td>\n",
              "      <td>19</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>180</th>\n",
              "      <td>-1</td>\n",
              "      <td>90.0</td>\n",
              "      <td>toyota</td>\n",
              "      <td>gas</td>\n",
              "      <td>sedan</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>66.5</td>\n",
              "      <td>54.1</td>\n",
              "      <td>dohc</td>\n",
              "      <td>171</td>\n",
              "      <td>156.0</td>\n",
              "      <td>20</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>181</th>\n",
              "      <td>-1</td>\n",
              "      <td>122.0</td>\n",
              "      <td>toyota</td>\n",
              "      <td>gas</td>\n",
              "      <td>wagon</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>66.5</td>\n",
              "      <td>54.1</td>\n",
              "      <td>dohc</td>\n",
              "      <td>161</td>\n",
              "      <td>156.0</td>\n",
              "      <td>19</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-50c1ef6f-dfc3-4cfc-a0af-666bf9d1d07d')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-50c1ef6f-dfc3-4cfc-a0af-666bf9d1d07d 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-50c1ef6f-dfc3-4cfc-a0af-666bf9d1d07d');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x.drop([172,178,179,180,181],axis=0,inplace=True)\n",
        "sns.boxplot(data=x,x=y,y=\"make\")\n",
        "plt.grid()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "1Ym9q35Nektr",
        "outputId": "9965660e-5df8-40da-869f-9a3bbe06c3f9"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**All Outliers are removed.**"
      ],
      "metadata": {
        "id": "kFHfK-qFe18N"
      }
    }
  ]
}