1) Regression Models/Elastic Net Regression/ElasitcNet_Regression.ipynb

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            "--2024-11-15 15:29:00--  https://media.githubusercontent.com/media/pradeep-016/ML_Course/refs/heads/main/1)%20Regression%20Models/Elastic%20Net%20Regression/gym_members_exercise_tracking.csv\n",
            "Resolving media.githubusercontent.com (media.githubusercontent.com)... 185.199.109.133, 185.199.108.133, 185.199.110.133, ...\n",
            "Connecting to media.githubusercontent.com (media.githubusercontent.com)|185.199.109.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 65136 (64K) [text/plain]\n",
            "Saving to: ‘gym_members_exercise_tracking.csv’\n",
            "\n",
            "gym_members_exercis 100%[===================>]  63.61K  --.-KB/s    in 0.02s   \n",
            "\n",
            "2024-11-15 15:29:00 (2.78 MB/s) - ‘gym_members_exercise_tracking.csv’ saved [65136/65136]\n",
            "\n"
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      "source": [
        "!wget \"https://media.githubusercontent.com/media/pradeep-016/ML_Course/refs/heads/main/1)%20Regression%20Models/Elastic%20Net%20Regression/gym_members_exercise_tracking.csv\""
      ]
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    {
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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",
        "from sklearn.preprocessing import OneHotEncoder,StandardScaler\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import ElasticNet\n",
        "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score"
      ],
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        "id": "rekJ-aL2Q9VM"
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      "execution_count": 2,
      "outputs": []
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      "source": [
        "df = pd.read_csv('/content/gym_members_exercise_tracking.csv')\n",
        "df.head()"
      ],
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        "outputId": "0e5e2f08-dd62-4e4b-8b7c-85e5093c3f64"
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      "execution_count": 3,
      "outputs": [
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              "   Age  Gender  Weight (kg)  Height (m)  Max_BPM  Avg_BPM  Resting_BPM  \\\n",
              "0   56    Male         88.3        1.71      180      157           60   \n",
              "1   46  Female         74.9        1.53      179      151           66   \n",
              "2   32  Female         68.1        1.66      167      122           54   \n",
              "3   25    Male         53.2        1.70      190      164           56   \n",
              "4   38    Male         46.1        1.79      188      158           68   \n",
              "\n",
              "   Session_Duration (hours)  Calories_Burned Workout_Type  Fat_Percentage  \\\n",
              "0                      1.69           1313.0         Yoga            12.6   \n",
              "1                      1.30            883.0         HIIT            33.9   \n",
              "2                      1.11            677.0       Cardio            33.4   \n",
              "3                      0.59            532.0     Strength            28.8   \n",
              "4                      0.64            556.0     Strength            29.2   \n",
              "\n",
              "   Water_Intake (liters)  Workout_Frequency (days/week)  Experience_Level  \\\n",
              "0                    3.5                              4                 3   \n",
              "1                    2.1                              4                 2   \n",
              "2                    2.3                              4                 2   \n",
              "3                    2.1                              3                 1   \n",
              "4                    2.8                              3                 1   \n",
              "\n",
              "     BMI  \n",
              "0  30.20  \n",
              "1  32.00  \n",
              "2  24.71  \n",
              "3  18.41  \n",
              "4  14.39  "
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              "      <td>56</td>\n",
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              "      <th>2</th>\n",
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              "      <td>68.1</td>\n",
              "      <td>1.66</td>\n",
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              "      <td>1.11</td>\n",
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              "      <td>2.3</td>\n",
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              "      <td>1.70</td>\n",
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              "      <td>0.59</td>\n",
              "      <td>532.0</td>\n",
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              "      <td>28.8</td>\n",
              "      <td>2.1</td>\n",
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              "      <td>18.41</td>\n",
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              "      <td>1.79</td>\n",
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       127.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Height (m)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.12771989460721703,\n        \"min\": 1.5,\n        \"max\": 2.0,\n        \"num_unique_values\": 51,\n        \"samples\": [\n          1.92,\n          1.96\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Max_BPM\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11,\n        \"min\": 160,\n        \"max\": 199,\n        \"num_unique_values\": 40,\n        \"samples\": [\n          173,\n          199\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Avg_BPM\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14,\n        \"min\": 120,\n        \"max\": 169,\n        \"num_unique_values\": 50,\n        \"samples\": [\n          161,\n          130\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Resting_BPM\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7,\n        \"min\": 50,\n        \"max\": 74,\n        \"num_unique_values\": 25,\n        \"samples\": [\n          52,\n          62\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Session_Duration (hours)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3430334803899634,\n        \"min\": 0.5,\n        \"max\": 2.0,\n        \"num_unique_values\": 147,\n        \"samples\": [\n          1.9,\n          1.81\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Calories_Burned\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 272.64151645225877,\n        \"min\": 303.0,\n        \"max\": 1783.0,\n        \"num_unique_values\": 621,\n        \"samples\": [\n          1122.0,\n          494.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Workout_Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"HIIT\",\n          \"Strength\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fat_Percentage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.259418834269056,\n        \"min\": 10.0,\n        \"max\": 35.0,\n        \"num_unique_values\": 239,\n        \"samples\": [\n          26.1,\n          21.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Water_Intake (liters)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6001719365613234,\n        \"min\": 1.5,\n        \"max\": 3.7,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          1.8,\n          3.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Workout_Frequency (days/week)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2,\n        \"max\": 5,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Experience_Level\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BMI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.660879393631086,\n        \"min\": 12.32,\n        \"max\": 49.84,\n        \"num_unique_values\": 771,\n        \"samples\": [\n          22.67,\n          23.94\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 554
        },
        "id": "N0WgA2BXSbiw",
        "outputId": "ca1ffe85-35b7-4b7f-f0d1-18b62321e02a"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Age                              0\n",
              "Gender                           0\n",
              "Weight (kg)                      0\n",
              "Height (m)                       0\n",
              "Max_BPM                          0\n",
              "Avg_BPM                          0\n",
              "Resting_BPM                      0\n",
              "Session_Duration (hours)         0\n",
              "Calories_Burned                  0\n",
              "Workout_Type                     0\n",
              "Fat_Percentage                   0\n",
              "Water_Intake (liters)            0\n",
              "Workout_Frequency (days/week)    0\n",
              "Experience_Level                 0\n",
              "BMI                              0\n",
              "dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gender</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Weight (kg)</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Height (m)</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Max_BPM</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Avg_BPM</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Resting_BPM</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Session_Duration (hours)</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Calories_Burned</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Workout_Type</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fat_Percentage</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Water_Intake (liters)</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Workout_Frequency (days/week)</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Experience_Level</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BMI</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "nOphCk5qShV5",
        "outputId": "7ffb0f02-e2f1-4923-dabb-70d6d9798234"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 973 entries, 0 to 972\n",
            "Data columns (total 15 columns):\n",
            " #   Column                         Non-Null Count  Dtype  \n",
            "---  ------                         --------------  -----  \n",
            " 0   Age                            973 non-null    int64  \n",
            " 1   Gender                         973 non-null    object \n",
            " 2   Weight (kg)                    973 non-null    float64\n",
            " 3   Height (m)                     973 non-null    float64\n",
            " 4   Max_BPM                        973 non-null    int64  \n",
            " 5   Avg_BPM                        973 non-null    int64  \n",
            " 6   Resting_BPM                    973 non-null    int64  \n",
            " 7   Session_Duration (hours)       973 non-null    float64\n",
            " 8   Calories_Burned                973 non-null    float64\n",
            " 9   Workout_Type                   973 non-null    object \n",
            " 10  Fat_Percentage                 973 non-null    float64\n",
            " 11  Water_Intake (liters)          973 non-null    float64\n",
            " 12  Workout_Frequency (days/week)  973 non-null    int64  \n",
            " 13  Experience_Level               973 non-null    int64  \n",
            " 14  BMI                            973 non-null    float64\n",
            "dtypes: float64(7), int64(6), object(2)\n",
            "memory usage: 114.1+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (10,8))\n",
        "sns.histplot(df,x = 'Calories_Burned', kde = True, bins = 30)\n",
        "plt.title(\"Calories Distribution\")\n",
        "plt.xlabel('Calories Burned')\n",
        "plt.ylabel('Frequency')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "id": "4AI4QWEaSjv7",
        "outputId": "07f481a1-c393-4e43-d953-3566c78caa83"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "id": "B3OGW3CHTHTy",
        "outputId": "3ccbf9cb-6672-4b3a-ac03-e2f6e6672769"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Age  Weight (kg)  Height (m)     Max_BPM     Avg_BPM  \\\n",
              "count  973.000000   973.000000   973.00000  973.000000  973.000000   \n",
              "mean    38.683453    73.854676     1.72258  179.883864  143.766701   \n",
              "std     12.180928    21.207500     0.12772   11.525686   14.345101   \n",
              "min     18.000000    40.000000     1.50000  160.000000  120.000000   \n",
              "25%     28.000000    58.100000     1.62000  170.000000  131.000000   \n",
              "50%     40.000000    70.000000     1.71000  180.000000  143.000000   \n",
              "75%     49.000000    86.000000     1.80000  190.000000  156.000000   \n",
              "max     59.000000   129.900000     2.00000  199.000000  169.000000   \n",
              "\n",
              "       Resting_BPM  Session_Duration (hours)  Calories_Burned  Fat_Percentage  \\\n",
              "count   973.000000                973.000000       973.000000      973.000000   \n",
              "mean     62.223022                  1.256423       905.422405       24.976773   \n",
              "std       7.327060                  0.343033       272.641516        6.259419   \n",
              "min      50.000000                  0.500000       303.000000       10.000000   \n",
              "25%      56.000000                  1.040000       720.000000       21.300000   \n",
              "50%      62.000000                  1.260000       893.000000       26.200000   \n",
              "75%      68.000000                  1.460000      1076.000000       29.300000   \n",
              "max      74.000000                  2.000000      1783.000000       35.000000   \n",
              "\n",
              "       Water_Intake (liters)  Workout_Frequency (days/week)  Experience_Level  \\\n",
              "count             973.000000                     973.000000        973.000000   \n",
              "mean                2.626619                       3.321686          1.809866   \n",
              "std                 0.600172                       0.913047          0.739693   \n",
              "min                 1.500000                       2.000000          1.000000   \n",
              "25%                 2.200000                       3.000000          1.000000   \n",
              "50%                 2.600000                       3.000000          2.000000   \n",
              "75%                 3.100000                       4.000000          2.000000   \n",
              "max                 3.700000                       5.000000          3.000000   \n",
              "\n",
              "              BMI  \n",
              "count  973.000000  \n",
              "mean    24.912127  \n",
              "std      6.660879  \n",
              "min     12.320000  \n",
              "25%     20.110000  \n",
              "50%     24.160000  \n",
              "75%     28.560000  \n",
              "max     49.840000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-208b0b9d-98c6-4233-97ac-174706a00dcc\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Weight (kg)</th>\n",
              "      <th>Height (m)</th>\n",
              "      <th>Max_BPM</th>\n",
              "      <th>Avg_BPM</th>\n",
              "      <th>Resting_BPM</th>\n",
              "      <th>Session_Duration (hours)</th>\n",
              "      <th>Calories_Burned</th>\n",
              "      <th>Fat_Percentage</th>\n",
              "      <th>Water_Intake (liters)</th>\n",
              "      <th>Workout_Frequency (days/week)</th>\n",
              "      <th>Experience_Level</th>\n",
              "      <th>BMI</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>973.000000</td>\n",
              "      <td>973.000000</td>\n",
              "      <td>973.00000</td>\n",
              "      <td>973.000000</td>\n",
              "      <td>973.000000</td>\n",
              "      <td>973.000000</td>\n",
              "      <td>973.000000</td>\n",
              "      <td>973.000000</td>\n",
              "      <td>973.000000</td>\n",
              "      <td>973.000000</td>\n",
              "      <td>973.000000</td>\n",
              "      <td>973.000000</td>\n",
              "      <td>973.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>38.683453</td>\n",
              "      <td>73.854676</td>\n",
              "      <td>1.72258</td>\n",
              "      <td>179.883864</td>\n",
              "      <td>143.766701</td>\n",
              "      <td>62.223022</td>\n",
              "      <td>1.256423</td>\n",
              "      <td>905.422405</td>\n",
              "      <td>24.976773</td>\n",
              "      <td>2.626619</td>\n",
              "      <td>3.321686</td>\n",
              "      <td>1.809866</td>\n",
              "      <td>24.912127</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>12.180928</td>\n",
              "      <td>21.207500</td>\n",
              "      <td>0.12772</td>\n",
              "      <td>11.525686</td>\n",
              "      <td>14.345101</td>\n",
              "      <td>7.327060</td>\n",
              "      <td>0.343033</td>\n",
              "      <td>272.641516</td>\n",
              "      <td>6.259419</td>\n",
              "      <td>0.600172</td>\n",
              "      <td>0.913047</td>\n",
              "      <td>0.739693</td>\n",
              "      <td>6.660879</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>18.000000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>1.50000</td>\n",
              "      <td>160.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>12.320000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>28.000000</td>\n",
              "      <td>58.100000</td>\n",
              "      <td>1.62000</td>\n",
              "      <td>170.000000</td>\n",
              "      <td>131.000000</td>\n",
              "      <td>56.000000</td>\n",
              "      <td>1.040000</td>\n",
              "      <td>720.000000</td>\n",
              "      <td>21.300000</td>\n",
              "      <td>2.200000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>20.110000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>40.000000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>1.71000</td>\n",
              "      <td>180.000000</td>\n",
              "      <td>143.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>1.260000</td>\n",
              "      <td>893.000000</td>\n",
              "      <td>26.200000</td>\n",
              "      <td>2.600000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>24.160000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>49.000000</td>\n",
              "      <td>86.000000</td>\n",
              "      <td>1.80000</td>\n",
              "      <td>190.000000</td>\n",
              "      <td>156.000000</td>\n",
              "      <td>68.000000</td>\n",
              "      <td>1.460000</td>\n",
              "      <td>1076.000000</td>\n",
              "      <td>29.300000</td>\n",
              "      <td>3.100000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>28.560000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>59.000000</td>\n",
              "      <td>129.900000</td>\n",
              "      <td>2.00000</td>\n",
              "      <td>199.000000</td>\n",
              "      <td>169.000000</td>\n",
              "      <td>74.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1783.000000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>3.700000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>49.840000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
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              "      display:flex;\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    }\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a82adfb7-f6f9-4147-8cc5-d83e6fd0783a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 331.9992638143134,\n        \"min\": 12.180927866987108,\n        \"max\": 973.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          38.68345323741007,\n          40.0,\n          973.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Weight (kg)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 321.4294680767086,\n        \"min\": 21.20750049840718,\n        \"max\": 973.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          73.85467625899281,\n          70.0,\n          973.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Height (m)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 343.47859876018913,\n        \"min\": 0.12771989460721703,\n        \"max\": 973.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1.7225796505652622,\n          1.71,\n          973.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Max_BPM\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 295.1061974835412,\n        \"min\": 11.525685962379304,\n        \"max\": 973.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          179.88386433710176,\n          180.0,\n          973.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Avg_BPM\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 303.46721076685174,\n        \"min\": 14.345101439108843,\n        \"max\": 973.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          143.76670092497432,\n          143.0,\n          973.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Resting_BPM\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 325.4799587540758,\n        \"min\": 7.327059908996752,\n        \"max\": 973.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          62.223021582733814,\n          62.0,\n          973.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Session_Duration (hours)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 343.6108861976696,\n        \"min\": 0.3430334803899634,\n        \"max\": 973.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1.2564234326824255,\n          1.26,\n          973.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Calories_Burned\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 476.4571555402799,\n        \"min\": 272.64151645225877,\n        \"max\": 1783.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          905.4224049331963,\n          893.0,\n          973.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fat_Percentage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 336.41406276433855,\n        \"min\": 6.259418834269056,\n        \"max\": 973.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          24.97677286742035,\n          26.2,\n          973.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Water_Intake (liters)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 343.1841388270036,\n        \"min\": 0.6001719365613234,\n        \"max\": 973.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2.626618705035971,\n          2.6,\n          973.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Workout_Frequency (days/week)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 342.93710988231015,\n        \"min\": 0.9130469699343143,\n        \"max\": 973.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          973.0,\n          3.3216855087358685,\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Experience_Level\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 343.42489165603297,\n        \"min\": 0.7396927419551071,\n        \"max\": 973.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          973.0,\n          1.8098663926002057,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BMI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 335.8375670064553,\n        \"min\": 6.660879393631086,\n        \"max\": 973.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          24.91212744090442,\n          24.16,\n          973.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "numerical_features = [\n",
        "    'Age', 'Weight (kg)', 'Height (m)', 'Max_BPM', 'Avg_BPM', 'Resting_BPM',\n",
        "    'Session_Duration (hours)', 'Fat_Percentage', 'Water_Intake (liters)',\n",
        "    'Workout_Frequency (days/week)', 'Experience_Level', 'BMI'\n",
        "]\n",
        "categorical_features = ['Gender', 'Workout_Type']\n"
      ],
      "metadata": {
        "id": "obLpl_xmTN5J"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for feature in numerical_features:\n",
        "    fig, ax = plt.subplots(1, 2, figsize=(12, 4))\n",
        "    sns.histplot(df[feature], kde=True, ax=ax[0], bins=20)\n",
        "    ax[0].set_title(f'{feature} Distribution')\n",
        "    sns.boxplot(x=df[feature], ax=ax[1])\n",
        "    ax[1].set_title(f'{feature} Boxplot')\n",
        "    plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2998
        },
        "id": "hwUdgkk_TqCH",
        "outputId": "239201d8-71b9-49ce-902a-636d8d39ca40"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 2 Axes>"
            ],
            "image/png": 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HDq1waJ7SfbLGGOPZ2dm3fd9UJVdFlEolmjRpUunlQ0JCykxr3rx5rY8df+3aNcjlcjRr1sxiupeXF5ydna3+3iSi6uMxQ/XxmKFiPGYA2rZti7CwMISFhWHkyJH47rvvMHjwYEybNs08VGxV28uvv/4aubm5uHz5MlauXFnmB59St7b/wcHBkMvlt23/a+t1p6ph0d4IqdVqdO3aFe+99x6WLl2KoqIi/PzzzwBKGgSZTIatW7eafwX89+OLL74wr+ejjz7C6dOn8eabbyIvLw8vvfQSWrdujevXrwMAdDod9u3bhz/++ANjx47F6dOn8dhjj6F///4wGo1W25+KeuKuqGGojtJOuv795VnRl3NF+1beF+h7772HqVOn4p577sF3332Hbdu2YceOHWjdunWdDclS3dev9N61ihpqa/67WOO1lslk+OWXX3Dw4EFMmjQJN27cwDPPPIPOnTubD7BKle7Tv3sFro6ioiJcunSpTKNb3VwV+ffZF2up6mtujXXfqi4+20R0ezxmqDoeM5TFY4bbu++++5Cfn48jR46UyVEZe/bsMf+gcObMmUpv1xonJ6husGhv5Lp06QKg5BIzoOQXNyEEgoKCzL8C/vtx6zAdbdu2xVtvvYV9+/bhzz//xI0bN7Bs2TLzfLlcjvvuuw8ff/wxzp8/j3nz5mHXrl3mS+ZuFRAQAAC4ePFimXkXLlyAu7t7mU416sL//vc/AMDAgQPN01xcXJCenl5m2ar84vjLL7+gX79++PrrrzFq1CgMGDAAYWFhZdZblS/VgIAAxMXFlbks78KFC+b51tCyZUsAQFRUVLXXERAQUOG/del84J8zIbe+LtX5dfeuu+7CvHnzcOzYMaxevRrnzp3DDz/8YLFM6T6V/opcXb/88gvy8vIs3jfVyWXtRvXy5ctlpl26dMmiw6bKvr+r+t40mUxltp+QkID09HSrvTeJqHbwmKFyeMxQFo8Zbq+4uBjAP1dpVKW9vHnzJiZPnowBAwZg8ODBeO211yrc11vXd+XKFZhMpgpHminNUpnXnT8A1C4W7Y3E7t27y/21svQeltLLXh555BEoFArMmTOnzPJCCPM9SZmZmeYvmFJt27aFXC43/9KXmppaZnsdOnQAgAov/fX29kaHDh2watUqiy/bs2fPYvv27XjwwQcrsbfWtWbNGnz11Vfo0aMH7rvvPvP04OBgHDp0CIWFheZpmzZtKjPMze0oFIoyr/PPP/+MGzduWEwrPegor8G/1YMPPgij0WgxzBhQMsSXTCbDAw88UOl8t+Pr6ws/Pz8cO3as2ut48MEHceTIERw8eNA8LScnB8uXL0dgYCBCQ0MBwHz/3L59+8zLGY1GLF++vNLbSktLK/NaV/R+DA8Ph16vr1EP5adOncKUKVPg4uJy23FXK5OrtDf4yvz7V8b69est3mNHjhzB4cOHLd4bwcHBuHDhgvlSPaBkn27tybkq2Uo/vwsXLrSY/vHHHwMABg0aVKX9IKLawWOG6uMxQ/l4zHB7mzZtAlAyXCxQtfbyueeeg8lkwtdff43ly5dDqVTi2WefLfczvGTJEou/Fy9eDAC3/Xeu7OtelfcdVR2HfGskJk+ejNzcXDz88MNo2bIlCgsLceDAAfz4448IDAw0d8ARHByMd999F9OnT0d0dDSGDRsGR0dHREVFYd26dZgwYQJee+017Nq1C5MmTcKjjz6K5s2bo7i4GP/73/+gUCgwfPhwAMDcuXOxb98+DBo0CAEBAUhMTMTnn3+OJk2aWHRmcasPP/wQDzzwAHr06IFnn33WPHyLXq83j/9cW3755Rc4ODigsLAQN27cwLZt27B//360b9/efDlgqfHjx+OXX37B/fffj5EjR+Lq1av47rvvLDpouZPBgwdj7ty5ePrpp9GzZ0+cOXMGq1evRtOmTS2WCw4OhrOzM5YtWwZHR0fY29uje/fu5d7zNmTIEPTr1w8zZsxAdHQ02rdvj+3bt2PDhg2YMmVKlfLdydChQ7Fu3ToIIar1C+u0adPw/fff44EHHsBLL70EV1dXrFq1ClFRUfj111/Nl3y3bt0ad911F6ZPn47U1FS4urrihx9+KHMQeDurVq3C559/jocffhjBwcHIysrCl19+CScnpzIHdjt27MCQIUMqvU9//vkn8vPzzR0D7d+/Hxs3boRer8e6devg5eVVo1w6nQ6hoaH48ccf0bx5c7i6uqJNmzbVHo6uWbNm6N27N1544QUUFBRg4cKFcHNzwxtvvGFe5plnnsHHH3+MgQMH4tlnn0ViYiKWLVuG1q1bW3TYVJVs7du3x7hx47B8+XKkp6ejT58+OHLkCFatWoVhw4ahX79+1dofIrIuHjNUDo8ZqobHDCVKjxmAkh+rNm7ciL1792LUqFHmKxIq216uWLECv//+O1auXGnu22bx4sUYM2YMli5dihdffNFi21FRUXjooYdw//334+DBg/juu+/wxBNPmH8sKE9lX/eqvO+oGuqgh3qyAVu2bBHPPPOMaNmypXBwcBBqtVo0a9ZMTJ48WSQkJJRZ/tdffxW9e/cW9vb2wt7eXrRs2VJMnDhRXLx4UQghRGRkpHjmmWdEcHCw0Gq1wtXVVfTr10/88ccf5nXs3LlTDB06VPj4+Ai1Wi18fHzE448/Li5dumReprzhW4QoGRqkV69eQqfTCScnJzFkyBBx/vx5i2UqGnKloqE+bqd0XaUPrVYrmjRpIgYPHiy++eYbkZ+fX+7zPvroI+Hr6ys0Go3o1auXOHbsWIXDt5Q31E1+fr549dVXhbe3t9DpdKJXr17i4MGD5Q63tWHDBhEaGiqUSqXFa3br8C1ClAzF8corrwgfHx+hUqlESEiI+PDDD4XJZLJYDoCYOHFimVwVDU1zq+PHj5cZBqT0+YMGDSqzfHn7dfXqVTFixAjh7OwstFqt6Natm9i0aVOZ5169elWEhYUJjUYjDAaDePPNN8WOHTvKHb6ldevW5WZ9/PHHhb+/v9BoNMLT01MMHjxYHDt2zGK5iIgIAcDivVyRW4dvUalUwsPDQ9xzzz1i3rx5IjExscxzbn1/VjbXgQMHROfOnYVarbYYYm3cuHHC3t6+3HwVDfn24Ycfio8++kj4+fkJjUYj7r77bovhXkp99913omnTpkKtVosOHTqIbdu2lft+qyjbrUO+CSFEUVGRmDNnjggKChIqlUr4+fmJ6dOnl/mMVeU9RETWxWOG2+MxgyUeM1TvmAF/D6HXsmVLMW/evDJDKt6pvYyNjRV6vV4MGTKkzLYefvhhYW9vLyIjI4UQ/7xnz58/L0aMGCEcHR2Fi4uLmDRpksjLy7N4bnn/npV93St631HNyYRgjz5EVH333XcffHx8zPfw1XdTpkzBvn37EB4ezvuziIiIrIjHDNKYPXs25syZg6SkpBp3skvS4D3tRFQj7733Hn788ccGMeRHSkoKvvrqK7z77rs23fgSERHVRzxmIKoe3tNODVpGRgby8vJuu8zt7jemO+vevbtFxzr1mZubW6WHWSMiooaFxwy1j8cMRNXDop0atJdffhmrVq267TK8Q4SIiIh4zEBEtor3tFODdv78ecTFxd12mbCwsDpKQ0RERLaKxwxEZKtYtBMRERERERHZKHZER0RERERERGSjeE87AJPJhLi4ODg6OrL3RyIikpwQAllZWfDx8YFczt/XrYFtPRER2ZrKtvcs2gHExcXBz89P6hhEREQWYmNj0aRJE6ljNAhs64mIyFbdqb1n0Q7A0dERQMmL5eTkJHEaIiJq7DIzM+Hn52dun6jm2NYTEZGtqWx7z6IdMF8m5+TkxIaciIhsBi/jth629UREZKvu1N7zRjkiIiIiIiIiG8WinYiIiIiIiMhGsWgnIiIiIiIislEs2omIiIiIiIhsFIt2IiIiIiIiIhvFop2IiIiIiIjIRrFoJyIiIiIiIrJRLNqJiIiIiIiIbBSLdiIiIiIiIiIbxaKdiIiIiIiIyEYppQ5ARA1LTEwMkpOTrbIud3d3+Pv7W2VdRERERET1EYt2IrKamJgYtGzVCnm5uVZZn87ODhciIli4ExEREVGjxaKdiKwmOTkZebm5GP3fD2HwD67RuhJirmL1+68jOTmZRTsRERERNVqSFu379u3Dhx9+iPDwcNy8eRPr1q3DsGHDzPOFEJg1axa+/PJLpKeno1evXli6dClCQkLMy6SmpmLy5Mn47bffIJfLMXz4cHz66adwcHCQYI+ICAAM/sFoEtJa6hhERERERPWepB3R5eTkoH379liyZEm58z/44AMsWrQIy5Ytw+HDh2Fvb4+BAwciPz/fvMzo0aNx7tw57NixA5s2bcK+ffswYcKEutoFIiIiIiIioloj6Zn2Bx54AA888EC584QQWLhwId566y0MHToUAPDtt9/CYDBg/fr1GDVqFCIiIrB161YcPXoUXbp0AQAsXrwYDz74IBYsWAAfH5862xciIiIiIiIia7PZId+ioqIQHx+PsLAw8zS9Xo/u3bvj4MGDAICDBw/C2dnZXLADQFhYGORyOQ4fPlzhugsKCpCZmWnxICIiIiIiIrI1Nlu0x8fHAwAMBoPFdIPBYJ4XHx8PT09Pi/lKpRKurq7mZcozf/586PV688PPz8/K6YmIiIiIiIhqzmaL9to0ffp0ZGRkmB+xsbFSRyIiIiIiIiIqw2aLdi8vLwBAQkKCxfSEhATzPC8vLyQmJlrMLy4uRmpqqnmZ8mg0Gjg5OVk8iIiIiIiIiGyNzRbtQUFB8PLyws6dO83TMjMzcfjwYfTo0QMA0KNHD6SnpyM8PNy8zK5du2AymdC9e/c6z0xERERERERkTZL2Hp+dnY0rV66Y/46KisLJkyfh6uoKf39/TJkyBe+++y5CQkIQFBSEmTNnwsfHxzyWe6tWrXD//ffjueeew7Jly1BUVIRJkyZh1KhR7DmeiIiIiIiI6j1Ji/Zjx46hX79+5r+nTp0KABg3bhxWrlyJN954Azk5OZgwYQLS09PRu3dvbN26FVqt1vyc1atXY9KkSbjvvvsgl8sxfPhwLFq0qM73hYiIiIiIiMjaJC3a+/btCyFEhfNlMhnmzp2LuXPnVriMq6sr1qxZUxvxiIiIiIiIiCRls/e0ExERERERETV2kp5pJyK6k4iICKusx93dHf7+/lZZFxERERFRXWHRTkQ2KTM1CQAwZswYq6xPZ2eHCxERLNyJiKjOJCQkICMjQ+oYVEl6vR4Gg0HqGERlsGgnIpuUl50JABj0nxlo0a5zjdaVEHMVq99/HcnJySzaiYioTiQkJGDM2CdRVFggdRSqJJVag+/+9y0Ld7I5LNqJyKa5+QSgSUhrqWMQERFVSUZGBooKC5DXtA9MWr3UcWqFPC8duqh9yAu6Byads9RxakSenwFE7kVGRgaLdrI5LNqJiIiIiGqJSauHyd5d6hi1yqRzbvD7SCQl9h5PREREREREZKNYtBMRERERERHZKBbtRERERERERDaKRTsRERERERGRjWLRTkRERERERGSjWLQTERERERER2SgW7UREREREREQ2ikU7ERERERERkY1i0U5ERERERERko5RSByCi6omJiUFycrJV1uXu7g5/f3+rrIuIiIiIiKyHRTtRPRQTE4OWrVohLzfXKuvT2dnhQkSEZIW70SRwMyMPCZkFSMkpQH6RCWnwg/vg1xBZ6Ajn5Bz4uuigUvDiICIiIiJqXFi0E9VDycnJyMvNxej/fgiDf3CN1pUQcxWr338dycnJdVq0CyFwMyMfZ+MyEJmUg4Ji0y1LOMC+dV/EFgOxp+KglMsQ6uOEjn7OcLZT11lOIiIiIiIpsWgnqscM/sFoEtJa6hhVIoRAZHIOjkSlIjGrwDxdp1LAx1kLT0ct7NQKxFw4icO7tiC0/yjkqPTIyi/G6esZOHsjA10CXdEt0BUKuUzCPSEiIiIiqn0s2omoztzMyMPeS0lIyCwp1hVyGVoYHBHq7QRvZy3ksn+K8IILGcg6ug4tBj+A9ne1x/W0PBy7loaY1FwciUpFZFI2hrTzgZNOJdXuEBERERHVOhbtRFTrcguLsf9KCs7fzAQAqBQydPBzRkc/F+jUijs+XyaTwc/VDk1cdLicmI09F5OQnF2IH47G4qH2PvDSa2t7F4iIiIiIJMGinYhqjRACZ+Mysf9Ksvme9VBvJ/Rq5gY7ddW/fmQyGZobHOGt12LjqTgkZxdi7YnreLijL7z1OmvHJyIiIiKSHLtiJqJakZlXhLUnbmDXhUQUFJvg7qDGo52boH+ooVoF+785alV4tLMf/Fx0KDIKbDgZh+Tsgjs/kYiIiIionmHRTkRWF5ktx3eHr+F6Wh6UchnuCXHH41394eNsvbPhaqUcg9v5wMtJi4JiEzacjENuYbHV1k9EREREZAtYtBOR1STnGuE5ci5OpCpRZBTw0Wsxurs/Ovq7QF4LPb2rlXIM7eADFzsVsguKsfVcPExCWH07RERERERSYdFORDUmhMBPx2IxZVsSdEGdIJcJ3BPijuGdm9T6mOpalQKD2npDKZchNjUPhyNTa3V7RERERER1iUU7EdVIbGounvzmCN745TRyiwTyb0QgzKuo5Oy6rG7GUXdz0OC+Vp4AgKPRqYjPzK+T7RIRERER1TYW7URULSaTwKoD0Ri4cB/+vJwMjVKOJ9s5ImH1f+EowdDpLb2c0MLLEQLAH+cTUGwy1X0IIiIiIiIrY9FORFUWmZSNx5YfxKyN55BbaES3QFdsefluDGvpAAjpiuU+IR7QqRRIySnE0eg0yXIQEREREVkLx2knokorKDbiqz+j8OnOyygsNsFercC0B1pidPcAyOUyHI+VNp9OrUDfFh7YcjYe4dFpCPV2gl73z2n/iIgIq23L3d0d/v7+VlsfEREREVF5WLQTUaXsupCAub+dR3RKLgDg7hB3zH+kLZq42EmczFKIpwPOuuoQm5qHvy4nY1A7b2SmJgEAxowZY7Xt6OzscCEigoU7EREREdUqFu1EdFuRSdmY93sEdl5IBAB4Omow/cGWGNbBF7I66miuKmQyGe4J8cCawzG4kpSN62m5yMvOBAAM+s8MtGjXucbbSIi5itXvv47k5GQW7URERERUq1i0E1G5bmbkYdHOy/jp2HUYTQJKuQzP9g7C5PtC4KCx7a8OdwcN2vjqceZGBv68nIyQv6e7+QSgSUhrSbMREREREVWFbR95E1GdS8spxNK9V7HyQDQKi0s6lbu3pSfefLAVmnk6SJyu8u5q6ooL8ZlIzCqAG+pPbiIiIiKif2PRTkQAgLwiExbtvIwv90Uiq6AYANAt0BVv3N8CXQJdJU5XdXZqJdo3ccaxa2mIgYfUcYiIiIiIqoVFO1EdiYmJQXJyslXWZc1e0I0CcOw8BC9uTkJGQQIAINTbCa/f3wJ9m3vY5H3rldUpwAWnrqcjx6iFLqSH1HGIiIiIiKqMRTtRHYiJiUHLVq2Ql5tr1fVmZ2dX+7kmIXAxPgt/xangGvYfZBSYEOhmh6kDWmBwW2/I5fW3WC+lUynQwc8ZR6PToO/5GISQOhERERERUdWwaCeqA8nJycjLzcXo/34Ig39wjdcXcWQvtqz6FPn5+dV6fmxqLv68nIyk7AIAMhRnpWBS36Z49ZGeUCnkNc5nSzr6uSA8OgUar2ZIN1nnSgciIiIiorrCop2oDhn8g63Se3lCzNVqPS8ttxB/XU5GZHIOAECtlKO5QyE2f/QcBjx7sMEV7ACgUytgQDpuwhWxReyQjoiIiIjqFxbtRI2A0SQQfi0Nh6NSYBKATAa089Wje5AbUq5dwO/FhVJHrFU+SEWcSY80aJGcXQB3B43UkYiIiIiIKoVFO1EDl5RVgD8iEpCYVQAACHCzwz0hHnC1V0ucrO7oUITcSwdh37I3TsSko3+oQepIRERERESV0vCuhSUiAIAQAkeiUvHD0RgkZhVAo5RjYGsDhrb3aVQFe6nMo+sBABcTspBXZJQ2DBERERFRJfFMO1EDlF9kxNZz8biWUtJbfVN3e9zb0hP2msb7kS+MuwAHWSGyTWqcj8tE5wAXqSMREREREd0Rz7QTNTBpuYX44WgsrqXkQiGXoX8rAwa3827UBXspH1XJjxinr6fDxPHfiIiIiKgeYNFO1IDczMjDT8dikZFXBCetEo918UOojxNksvo/5ro1eCryoFHKkZlfbL4KgYiIiIjIlrFoJ2ogrqflYu3xG8gvMsHgpMHILn7wcGQv6f+mkAmE+jgBAM7cyJA4DRERERHRnbFoJ2oArqflYsPJOBSbBPxd7TC8UxNeDl+BNj56AEB0Sg5yCoolTkNEREREdHs2XbQbjUbMnDkTQUFB0Ol0CA4OxjvvvAPxr3tRhRB4++234e3tDZ1Oh7CwMFy+fFnC1ER1KzEzHxtPlRTsAW52GNLOGyqFTX+0JeVqr4a3XgshgIibmVLHISIiIiK6LZs+sn///fexdOlSfPbZZ4iIiMD777+PDz74AIsXLzYv88EHH2DRokVYtmwZDh8+DHt7ewwcOBD5+fkSJieqG3kmBTacikORUaCJiw6D23pDyYL9jkovkT93M9PiR0AiIiIiIltj00f3Bw4cwNChQzFo0CAEBgZixIgRGDBgAI4cOQKg5Cz7woUL8dZbb2Ho0KFo164dvv32W8TFxWH9+vXShieqZTK1DmcLXJFbaIS7gxqD27Fgr6zmno5QKWRIzy1CXAZ/4CMiIiIi22XTR/g9e/bEzp07cenSJQDAqVOn8Ndff+GBBx4AAERFRSE+Ph5hYWHm5+j1enTv3h0HDx6scL0FBQXIzMy0eBDVJwKA+4OvIFeoYK9RYGgHX2iUCqlj1RtqpRwhno4AeIk8EREREdk2my7ap02bhlGjRqFly5ZQqVTo2LEjpkyZgtGjRwMA4uPjAQAGg8HieQaDwTyvPPPnz4derzc//Pz8am8niGrBdbjBrkVPyCAwqK03HNjpXJW19Cop2q8kZsNo4iXyRERERGSbbLpo/+mnn7B69WqsWbMGx48fx6pVq7BgwQKsWrWqRuudPn06MjIyzI/Y2FgrJSaqffEZ+YiBBwCgmToD3nqdxInqJ18XHezVChQUm3AtJUfqOERERERE5bLp03Ovv/66+Ww7ALRt2xbXrl3D/PnzMW7cOHh5eQEAEhIS4O3tbX5eQkICOnToUOF6NRoNNBqOX031T2GxCVvPxUNAhpyIffDu1EzqSPWWXCZDiMERJ2PTcTEhC009HKSORERERERUhk2fac/NzYVcbhlRoVDAZDIBAIKCguDl5YWdO3ea52dmZuLw4cPo0aNHnWYlqgt/Xk5CRl4RNChCyrYlkMmkTlS/tfj7EvnIpBwUFpskTkNEREREVJZNn2kfMmQI5s2bB39/f7Ru3RonTpzAxx9/jGeeeQYAIJPJMGXKFLz77rsICQlBUFAQZs6cCR8fHwwbNkza8ERWdj0tF2fjSjpNa444XCrgJd01ZXDUQK9TISOvCJHJ2Wjp5SR1JCIiIiIiCzZdtC9evBgzZ87Eiy++iMTERPj4+OA///kP3n77bfMyb7zxBnJycjBhwgSkp6ejd+/e2Lp1K7RarYTJiayr2GjCHxGJAIC2vnrob+RKnKhhkMlkaGFwxJHoVFxKYNFORERERLbHpot2R0dHLFy4EAsXLqxwGZlMhrlz52Lu3Ll1F4yojh2JTkVGXhHsNQr0auaGszekTtRwtPAqKdqvpeQgr8gInYpD5xERERGR7bDpe9qJCEjPLcTxa+kAgL7NPTkeu5W52qvh4aCBSZQM/0ZEREREZEtYtBPZuD8vJ8MoBPxd7RDsYS91nAapuVdJz/GX4rMkTkJEREREZIlFO5ENu5aSg8jkHMhkwD0h7pCxu/ha0dxQ0ov89fQ8ZOcXS5yGiIiIiOgfLNqJbJQQAvuvpAAA2vs6w81BI3GihstJq4KPvqTzysuJPNtORERERLaDRTuRjbqUkI2k7AKoFXJ0C3KVOk6D18yz5BL5q0kcSo+IiIiIbAeLdiIbZDQJHIwsOcveOcAFOjU7n6ttwR4lRXtceh5yC3mJPBERERHZBhbtRDboXFwGMvKKYKdWoKO/s9RxGgUnnQqejhoIAJHJPNtORBXLz8/HpUuXkJ+fL3UUIiKqQ1J9/7NoJ7IxRpPA0eg0AEDXQFeoFPyY1pXSs+1XOfQbEd1GTEwMJkyYgJiYGKmjEBFRHZLq+5/VAJGNibiZieyCYtirFWjj4yR1nEaldEi92NQ8FBabJE5DRERERMSincimlJxlTwVQci+7kmfZ65SrvRrOOhWMQiA6hZfIExEREZH0WBEQ2ZCLCVnIzC+GnVqBNr56qeM0OjKZDMHmXuR5iTwRERERSY9FO5GNEELg+LWSe9k7+jnzXnaJlF4iH52ci2ITL5EnIiIiImmxKiCyEdEpuUjJKYRaIUdbnmWXjJeTFvZqBQqNJsSm5kkdh4iIiIgaORbtRDYi/O+z7G18naBRcVx2qchkMjT14CXyRERERGQbWLQT2YD4zHzcSM+DXAZ08HOWOk6j1+zv+9ojk3JgEkLiNERERETUmLFoJ7IBp2PTAQAhBkc4alXShiH4OuugUcqRV2TEzYx8qeMQERERUSPGop1IYnmFRlxKLLkMu30T3stuCxRyGQLdSjqki0rm0G9EREREJB2l1AGIGrtzcRkwmgQ8HTXwctJKliMiIsIm1mErgtztcTEhC9HJOejdzF3qOERERETUSLFoJ5KQSQicvpEBAGjXRA+ZTFbnGTJTkwAAY8aMsdo6s7PrfwduAW52kMmAlJxCZOQVQa/jbQtEREREVPdYtBNJKDo5B1n5xdAq5WhhcJQkQ152JgBg0H9moEW7zjVaV8SRvdiy6lPk59f/+8C1KgV89DrcSM9DdHIO2rODQCIiIiKSAIt2Igmdul5ylj3UxwlKhbRdTLj5BKBJSOsarSMh5qqV0tiGIHd73EjPQxSLdiIiIiKSCDuiI5JIWm4hYlJzAQDtmjhLG4bKFeRe0hnd9bQ8FBabJE5DRERERI0Ri3YiiZz++yx7oJsd75e2US52Kuh1KhiFQGxartRxiIiIiKgRYtFOJIEiownnb5bcS96eZ9ltlkwmQxCHfiMiIiIiCbFoJ5LAlcRsFBab4KRVIsDNTuo4dBuB7iX/PlHJORBCSJyGiIiIiBobFu1EEjgXV3KWvbWPNMO8UeX5uuigUsiQW2hEYlaB1HGIiIiIqJFh0U5Ux9JzC3EjPQ8A0MpbmmHeqPKUcjn8Xf85205EREREVJdYtBPVsYibWQAAf1c7OGrZAV19UNqLPIt2IiIiIqprLNqJ6pAQMHdA19rHSeI0VFmBf3dGl5hVgJyCYonTEBEREVFjwqKdqA4l5suQXVAMjVKOpn+fvSXbZ69RwuCkAQBEp/BsOxERERHVHRbtRHUoOqfkI9fCyxFKBT9+9Unp2fboZI7XTkRERER1h1UDUR2Rax0Ql1vykWvtzUvj65vSoj0mNRcmjvxGRERERHWERTtRHbFr1QcmyODuoIaHo0bqOFRFBicNdCoFCo0mpBRwmD4iIiIiqhss2onqiEO7/gCAUG8njs1eD8lkMgS6lQz9djOPX51EREREVDd45ElUB2IziqDxagYZBFp68dL4+irw784D4/P5owsRERER1Q0W7UR14M+YfACAl05Ap1ZInIaqy9/VDjIAWUVyKJw8pY5DRERERI0Ai3aiWiaEwJ8xeQAAPzuTxGmoJrQqBbz1WgCArmlnidMQERERUWPAop2olp2ITUdCjhGmwjx461i013ell8jrgrtKnISIiIiIGgMW7US1bMOJGwCA3EsHoeQnrt4rHfpNG9AOBcUc+42IiIiIahdLCKJaVGw0YdPpmwCAnIi9Eqcha3B3UEOnEJCrtDiXVCB1HCIiIiJq4Fi0E9Wi/VdTkJJTCCeNHPnRJ6WOQ1Ygk8lg0Jbc5nD8Jot2IiIiIqpdLNqJalHppfE9m2gBk1HiNGQtXn/3TRB+swBC8BJ5IiIiIqo9LNqJakleoRHbzsUDAO4J0EmchqzJoBUQxiIk5BgRmZwjdRwiIiIiasBYtBPVkp0XEpBTaEQTFx1auKmkjkNWpJQD+bFnAQC7LyRKnIaIiIiIGjIW7US1ZMPJOADAQ+19IJPJJE5D1pZ39RgAYM/FJImTEBEREVFDxqKdqBak5xZiz8WSM7DDOvpKnIZqQ15kSdF+OCoFOQXFEqchIiIioobK5ov2GzduYMyYMXBzc4NOp0Pbtm1x7Ngx83whBN5++214e3tDp9MhLCwMly9fljAxEbDlbDyKjAItvRzR3OAodRyqBcWpN2CwV6DIKLD/SrLUcYiIiIiogbLpoj0tLQ29evWCSqXCli1bcP78eXz00UdwcXExL/PBBx9g0aJFWLZsGQ4fPgx7e3sMHDgQ+fn5Eianxm7DyZJe44d24Fn2hqyztwYAsPsi72snIiIiotqhlDrA7bz//vvw8/PDihUrzNOCgoLM/y+EwMKFC/HWW29h6NChAIBvv/0WBoMB69evx6hRo+o8M9HNjDwcjkoFADzUwUfiNFSbOnlrsPlKLnZfSIIQgn0XEBEREZHV2fSZ9o0bN6JLly549NFH4enpiY4dO+LLL780z4+KikJ8fDzCwsLM0/R6Pbp3746DBw9WuN6CggJkZmZaPIis5bdTcRAC6BboCl9nDvXWkLX20ECrkiM+Mx8X4rOkjkNEREREDZBNF+2RkZFYunQpQkJCsG3bNrzwwgt46aWXsGrVKgBAfHzJGNgGg8HieQaDwTyvPPPnz4derzc//Pz8am8nqNEx9xrPs+wNnkYpQ89gdwC8RJ6IiIiIake1ivamTZsiJSWlzPT09HQ0bdq0xqFKmUwmdOrUCe+99x46duyICRMm4LnnnsOyZctqtN7p06cjIyPD/IiNjbVSYmrsriRm4VxcJpRyGQa19ZY6DtWBfi09AXC8diIiIiKqHdUq2qOjo2E0GstMLygowI0bN2ocqpS3tzdCQ0MtprVq1QoxMTEAAC8vLwBAQkKCxTIJCQnmeeXRaDRwcnKyeBBZQ+lZ9j7NPeBir5Y4DdWFvs09AADh19KQkVskcRoiIiIiamiq1BHdxo0bzf+/bds26PV6899GoxE7d+5EYGCg1cL16tULFy9etJh26dIlBAQEACjplM7Lyws7d+5Ehw4dAACZmZk4fPgwXnjhBavlIKoMIQQvjW+E/FztEOLpgMuJ2dh3OQlD2vPfnoiIiIisp0pF+7BhwwAAMpkM48aNs5inUqkQGBiIjz76yGrhXnnlFfTs2RPvvfceRo4ciSNHjmD58uVYvny5OceUKVPw7rvvIiQkBEFBQZg5cyZ8fHzMWYnqysnYdMSk5sJOrUD/UMOdn0ANRr+WnricmI3dFxNZtBMRERGRVVWpaDeZTABKznAfPXoU7u7utRKqVNeuXbFu3TpMnz4dc+fORVBQEBYuXIjRo0ebl3njjTeQk5ODCRMmID09Hb1798bWrVuh1WprNRvRrUrPsg8INcBObdOjKZKV9W3hgeX7IrH3YhJMJgG5nEO/EREREZF1VKuyiIqKsnaOCg0ePBiDBw+ucL5MJsPcuXMxd+7cOstEdKtiowmbTpcU7UM7+Eqchupa10BXOGiUSMkpxOkbGejg5yx1JCIiIiJqIKp9OnDnzp3YuXMnEhMTzWfgS33zzTc1DkZUnxy4moLk7EK42qvRO6R2r0Ah26NSyHF3iDu2nI3H7guJLNqJiIiIyGqq1Xv8nDlzMGDAAOzcuRPJyclIS0uzeBA1NutPloyaMKitN1SKan2sqJ7r16Jk6Lc9HK+diIiIiKyoWmfaly1bhpUrV2Ls2LHWzkNU7+QXGbHtbDwAYCh7jW+0+rQoGfrt1PUMJGUVwMNRI3EiIiIiImoIqnVKsLCwED179rR2FqJ6aWdEInIKjfB11qGTv4vUcUgiBictWvs4AQD2XkqSOA0RERERNRTVKtrHjx+PNWvWWDsLUb204e9L44d28GGv4Y3cvS1LLpHfzUvkiYiIiMhKqnV5fH5+PpYvX44//vgD7dq1g0qlspj/8ccfWyUcka3LyC3CnoslZ1XZazz1beGJxbuuYN+lJBQbTVCyfwMiIiIiqqFqFe2nT59Ghw4dAABnz561mCeT8UwjNR5bzt5EodGEll6OaOHlKHUcklgHP2c426mQnluE4zHp6BbkKnUkIiIiIqrnqlW0796929o5iOqlDSc5Njv9QyGXoU9zD2w4GYfdFxNZtBMRERFRjfHaTaJqis/Ix6GoFADAkPbeEqchW1E69NvuC7yvnYiIiIhqrlpn2vv163fby+B37dpV7UBE9cVvp+IgBNA10AVNXOykjkM2ok9zD8hkwIX4LMSl58HHWSd1JCIiIiKqx6pVtJfez16qqKgIJ0+exNmzZzFu3Dhr5CKyeWtPlPQa/xAvjad/cbFXo6OfM47HpGPPxSQ80d1f6khEREREVI9Vq2j/5JNPyp0+e/ZsZGdn1ygQUX1wIT4TETczoVLIMLgtL40nS/1aeOJ4TDp2X0xk0U4kkYKCAhQUFJj/zszMtPo2rl27ZvV1UsPB90f9xH83uh2p3h/VKtorMmbMGHTr1g0LFiyw5mqJbM66v8+y92vhCRd7tcRpyNb0a+mJj3Zcwv4rySgoNkKjVEgdiajRmT9/PubMmVOr25g3b16trp+I6h4/12SLrFq0Hzx4EFqt1pqrJLI5RpPAhhMlvcY/0omXxlNZod5O8HDUICmrAEeiUnF3iIfUkYganenTp2Pq1KnmvzMzM+Hn52fVbcyYMQMBAQFWXSc1HNeuXWMBWA/xc023I9XnulpF+yOPPGLxtxACN2/exLFjxzBz5kyrBCOyVYciUxCfmQ+9ToV+LT2ljkM2SC6XoV8LD/x07Dp2X0hi0U4kAY1GA41GU6vbCAgIQPPmzWt1G0RUt/i5JltUrSHf9Hq9xcPV1RV9+/bF5s2bMWvWLGtnJLIpa4+XXBo/qJ03L3umCpUO/bbnIod+IyIiIqLqq9aZ9hUrVlg7B1G9kFdoxNazNwEAj3TkpfFUsV4h7lDKZYhMzkF0cg4C3e2ljkRERERE9VCN7mkPDw9HREQEAKB169bo2LGjVUIR2art5+ORU2iEv6sdOge4SB2HbJiTVoUugS44FJmK3RcT8bR7kNSRiIiIiKgeqlbRnpiYiFGjRmHPnj1wdnYGAKSnp6Nfv3744Ycf4OHB+zepYSq9NH5YR1/IZDKJ05Ctu7elJw5FpuKPiAQ83YtFOxERERFVXbXuaZ88eTKysrJw7tw5pKamIjU1FWfPnkVmZiZeeukla2cksgmJWfn483ISAOBhXhpPlTAg1AsAcCgyFWk5hRKnISIiIqL6qFpF+9atW/H555+jVatW5mmhoaFYsmQJtmzZYrVwRLZk48k4mATQ0d8ZQbw/mSoh0N0eLb0cYTQJ7LzADumIiIiIqOqqVbSbTCaoVKoy01UqFUwmU41DEdkaIQR+PBoLABjeqYnEaag+Gdi65Gz71rPxEichIiIiovqoWkX7vffei5dffhlxcXHmaTdu3MArr7yC++67z2rhiGzFidh0XE7MhlYlx0MdfKSOQ/XI/W1KivZ9l5OQU1AscRoiIiIiqm+qVbR/9tlnyMzMRGBgIIKDgxEcHIygoCBkZmZi8eLF1s5IJLkfj5ScZR/U1gdO2rJXmRBVpKWXIwLc7FBYbMKei0lSxyEiIiKieqZavcf7+fnh+PHj+OOPP3DhwgUAQKtWrRAWFmbVcES2ILugGL+dLrmq5LGufhKnofpGJpPh/tZe+GJfJLadi8egdt5SRyIiIiKieqRKZ9p37dqF0NBQZGZmQiaToX///pg8eTImT56Mrl27onXr1vjzzz9rKyuRJH4/HYfcQiOautujayDHZqeqG/D3fe27LiSioNgocRoiIiIiqk+qVLQvXLgQzz33HJycnMrM0+v1+M9//oOPP/7YauGIbEFpB3Qju/pxbHaqlo5+zvB01CC7oBgHrqRIHYeIiIiI6pEqXR5/6tQpvP/++xXOHzBgABYsWFDjUES2Yt/JSzgekw6FDAhRpuL48YxqrSciIsLKyag+kctlGNjaC/87dA1bz8ajX0tPqSMRERERUT1RpaI9ISGh3KHezCtTKpGUxI6WqGGIiYnBiP9+DLuOQ5B18SDC/m9ejdeZnZ1thWRUH93fpqRo3xGRgPdMAgo5r9ogIiIiojurUtHu6+uLs2fPolmzZuXOP336NLy92ckSNQw3E5KgaXEPACCsVxd4h62t9roijuzFllWfIj8/31rxqJ7pFuQKZzsVUnMKcTQ6FXc1dZM6EhERERHVA1Uq2h988EHMnDkT999/P7RarcW8vLw8zJo1C4MHD7ZqQCKpHL6RD4WdHlqFQOc2rSCvwZnRhJirVkxG9ZFKIUdYKwN+Cb+OTafjWLQTERERUaVUqSO6t956C6mpqWjevDk++OADbNiwARs2bMD777+PFi1aIDU1FTNmzKitrER1avOVHABAkL2pRgU7Uakh7X0AAJvPxKPIaJI4DRERERHVB1U6024wGHDgwAG88MILmD59OoQQAErGIR44cCCWLFkCg8FQK0GJ6tLZGxm4kFwEYSxCkKOQOg7ZqKp2MGhnEtBr5EjNKcTKrYfQ2bvkiiV3d3f4+/vXRkQiIiIiqueqVLQDQEBAADZv3oy0tDRcuXIFQgiEhITAxYXjV1PDsfJANAAg58Jf0AX1kjYM2ZzM1JION8eMGVPl57rcNwFOXR7C9GVrkbLpIwCAzs4OFyIiWLgTERERURlVLtpLubi4oGvXrtbMQmQTkrMLsPFkHAAgK/w34AEW7WQpLzsTADDoPzPQol3nKj03pUCGPQmAvk1fjB7YCynXr2L1+68jOTmZRTsRERERlVHtop2oofrhSAwKjSaEuKpw7eYlqeOQDXPzCUCTkNZVeo6vEDiReQ0ZeUUo0PvDUKWeRYiIiIioseHhItG/FBlN+N+hawCAB0PsJU5DDZFMJkMLgyMA4GJClsRpiIiIiMjWsWgn+petZ+ORkFkAD0cNejbR3vkJRNXQwqukaL+WkoMCo8RhiIiIiMimsWgn+pdVf3dA90Q3f6gUHOaNaoervRoeDhqYBHAjl1/DRERERFQxHi0S/e1kbDqOXUuDSiHD6O7sEIxqV+nZ9lgW7URERER0GzxaJPrb0j1XAAAPtfeFpxMvjafa1dzgAABILpBD4eghcRoiIiIislUs2okAXEnMwrZzCQCAF/o2lTgNNQaOWhWaOOsAAPZt+kmchoiIiIhsFYt2IgBL90QCAAaEGtDM01HiNNRYhPo4AQAc2g2ASQiJ0xARERGRLWLRTo1ebGouNpy8AQB4sV8zidNQY9LM0wFKmYDK2QvnEguljkNERERENohFOzV6n+26gmKTQO9m7ujg5yx1HGpEVAo5/OxNAIA/onIlTkNEREREtohFOzVq11Jy8Mvx6wCAV/qHSJyGGqOgv4v2Q9fzkZ7Ls+1EREREZIlFOzVqi3ZegdEk0Ke5BzoHuEodhxohZ7VAYUIkikzAL+HXpY5DRERERDamXhXt//d//weZTIYpU6aYp+Xn52PixIlwc3ODg4MDhg8fjoSEBOlCUr1xOSEL606UnmVvLnEaaqxkMiDr5BYAwOrDMTCZ2CEdEREREf2j3hTtR48exRdffIF27dpZTH/llVfw22+/4eeff8bevXsRFxeHRx55RKKUVJ/835YLMImSHuN5LztJKefcbtipZIhKzsFfV5KljkNERERENqReFO3Z2dkYPXo0vvzyS7i4uJinZ2Rk4Ouvv8bHH3+Me++9F507d8aKFStw4MABHDp0SMLEZOsOXk3BzguJUMhl+O8DLaWOQ42cKMpH34CSMdu/PXhN4jREREREZEvqRdE+ceJEDBo0CGFhYRbTw8PDUVRUZDG9ZcuW8Pf3x8GDBytcX0FBATIzMy0e1HgYTQLvbY4AADzRzR/BHg4SJyIC7m9mDwDYdSEBsansSZ6IiIiISth80f7DDz/g+PHjmD9/fpl58fHxUKvVcHZ2tphuMBgQHx9f4Trnz58PvV5vfvj5+Vk7NtmwH4/G4syNDDholHg5jD3Gk21o4qTE3SHuMAngm/1RUschIiIiIhth00V7bGwsXn75ZaxevRpardZq650+fToyMjLMj9jYWKutm2xbWk4hPth2AQAwtX9zuDtoJE5E9I/n7m4KoOSHpYzcIonTEBEREZEtsOmiPTw8HImJiejUqROUSiWUSiX27t2LRYsWQalUwmAwoLCwEOnp6RbPS0hIgJeXV4Xr1Wg0cHJysnhQ4/DBtgtIzy1CSy9HPNkjQOo4RBbuDnFHSy9H5BYa8d1h3ttORERERDZetN933304c+YMTp48aX506dIFo0ePNv+/SqXCzp07zc+5ePEiYmJi0KNHDwmTky06cDUZ3x8puapi7tA2UCps+u1PjZBMJsOEe0rOtq88EI38IqPEiYiIiIhIakqpA9yOo6Mj2rRpYzHN3t4ebm5u5unPPvsspk6dCldXVzg5OWHy5Mno0aMH7rrrLikik43KKSjGf389DQAY3d0f3YJcJU5EVL7B7Xzw4baLuJmRj5+PxWJsj0CpIxERERGRhOr9qcZPPvkEgwcPxvDhw3HPPffAy8sLa9eulToW2Zj3t15AbGoefJ11mP5gK6njEFVIrZTjhb7BAIAlu6+ioJhn24mIiIgaM5s+016ePXv2WPyt1WqxZMkSLFmyRJpAZPN2nE8wj309/5G2cNDUu7c9NTIju/jh891XEZ+Zj5+O8mw7ERERUWNW78+0E93OzYw8vP7LKQDAs72DcE9zD4kTEd2ZVqWwONvOe9uJiIiIGi8W7dRgFRQbMWnNCaTnFqGtrx7/vb+l1JGIKu2xrn7w1msRn5mPlQeipY5DRERERBJh0U4NkhACb607i/BraXDSKrH48Y5QK/l2p/pDq1Lg1QEtAABLdl1Bak6hxImIiIiISAqsYqhB+urPKPwcfh1yGfDZE50Q6G4vdSSiKnu4oy9CvZ2QVVCMRTsvSx2HiIiIiCTAop0anF/Dr2Pe5ggAwIxBobyPneothVyGGYNKRjv436FruBCfKXEiIiIiIqprLNqpQdl6Nh5v/D0e+/jeQXimV6C0gYhqqFczdwxsbYDRJPDm2jMwmYTUkYiIiIioDrFopwZjw8kbmLjmOIwmgeGdmuDNB1tBJpNJHYuoxmY/1Br2agWOx6Tjh6OxUschIiIiojrEop0ahP8djMaUH0/CaBJ4pKMv3h/eFnI5C3ZqGLz1Okz9u1O6+VsicCM9T+JERERERFRXWLRTvVZsNGH2xnOYueEchACe6O6PBY+2h1LBtzY1LON6BKCDnzOy8osx9e8fqIiIiIio4WNlQ/XWjfQ8PP7lIfMY1m/c3wLzhrXhGXZqkJQKORY+1gF2agUOR6Vi+b5IqSMRERERUR1g0U71jhAC60/cwIOf/omj0Wlw0CixbEwnvNi3Ge9hpwYt0N0es4e0BgAs2H4R+68kS5yIiIiIiGobi3aqVyKTsvHUiqOY8uNJZOQVoV0TPX5/qTfub+MtdTSiOvFolyYY3qkJjCaBiWuOIyYlV+pIRERERFSLlFIHIAKAmJgYJCdXfNYwNc+ItRHZ2HY1F0YBKOXAyFAHDG2hQ8q1i0i59s+y7u7u8Pf3r4PURHVPJpNh3sNtcCUxC6euZ+CplUfw0396wN1BY/Vt3elzWVX8bBIRERFVHYt2klxMTAxatmqFvNyyZwyVbk3g2HEwHNr1h1xVUpTkXj2KtF1fYX7qDcwvZ306OztciIhgcUANllalwBdju+CRz/cjMikHT359BN9PuAt6ncpq27jd57K6+NkkIiIiqjoW7SS55ORk5OXmYvR/P4TBPxgmAdzIlSMqW46kgn/u4HBVmxDqbITBvz3Qb3G560qIuYrV77+O5ORkFgbUoHnptfhufHeM/OIQzt/MxBNfHsKKp7vC01FrlfXf+rmsKX42iYiIiKqHRTvZBJlKi2L3ZjhX6Iro5BzkF5tKpgMIcrdHez9n+Lno2NEc0b809XDAd+O7YcxXh3EuLhOPfH4AK57qihCDo9W2YfAPRpOQ1lZbHxERERFVDYt2kkxiVj7+OJ+IXw6lwu+lNTiUrAKQBQCwUyvQxkePNr5OcNRa75JfooampZcTfn2hJ8Z9cwTRKbl46LP9mP1QKEZ28eOPXES1xN/fH8uXL+dVI0REjYxU3/8s2qnOCCFwNSkb288nYMf5BJyISTfPkynVsFcKNPdxQbC7A7z12hqNtx4REVHjvNZYB1FdCHCzx68v9MTLP5zEX1eS8d9fz2DL2Xi8PTgUTT0cpI4HIQRMouT/rfW5smandtbscI+d7TUOWq0WzZs3lzoGERHVMam+/1m0U60ymgSOx6Rhx9+FelRyjsX89k30aO1sxIJXxmHS7E/gF+JRo+1lpiYBAMaMGVOj9fxbdna21dZFVFvcHDT49pluWLbvKj7ZcQl7LiZh/5V9eLijL567u6lVL5kvJYRAXpERmXnFyMgrQkZ+ETLzipCRV4Ts/GIUGk0oMppQZBQA1Aj47ya8ebII4thVmArzYMxKQnFmMoxZyShKuY6Cm5dQlHwNMBnvuG1rdWpn7Q732NkeERERWRuLdrI6o0ngcGQKfjt9E9vPxSMlp9A8T62Qo0ewG/qHGhDWygAvvRbHjx/H/ORrsMaVvHnZmQCAQf+ZgRbtOtdoXRFH9mLLqk+Rn59f82BEdUAul+HFvs3wQBtvzP3tHHZfTMJPx67jp2PX0d7PGQ+28ULPYHe08naEUiG/8woBFJuAlOwCZOb/XZjn/V2Y/12glxTklSdTqCBTqCDX2EHp6AaNzy37IBNwUQl4aAV87UzQq0SZ7wZrdmpnzQ732NkeERER1QYW7WQVRpPA0ehU/H76JracvYnk7H8KdSetEv1aeqJ/qAF9mnvUyT3qbj4BNe48KyHmqpXSENWtIHd7rHi6G8KvpeKLvZH4IyIBp2LTcSo2HQCgUcoR4GYHPxc76O1U0KoUKP77jHih0YSM3CJcS0xHk5d/wIbrauB6zG2356BRQq9TwUmnhF6rgl6ngqNWBbVSDpVCBpVCjrN/bcXPi+fi0dc/QstOdyG/yIis/GJkFxQjK78IydmFiM/MR2GxCSmFMqQUAhcyFdDrVGjm4YBW3o5wq4Wx6Euxwz0iIiKyVSzaqUaiknPw07FY/Bp+HYlZBebpznYq3N/aCw+29UaPYDeoKnlWj4isp3OAK5Y/6YrErHxsPRuPXRcSEX4tDVn5xbiUkI1LCbe/9UOhLbkfXq2Q/1OU61Rw0pUU5iXFuRJK+Z0/30qYYMrNgEZuMj/X4GS5jBAC6blFiMvIQ1RyDqJTcpGRV4TwmDSEx6TB39UOHf2doajayX0iIiKieo1FO1VZYbEJW87exPdHYnAoMtU83UmrxMDWXhjc3gc9WagT2QxPRy2e7BGIJ3sEwmQSiE3LRXRKLuLS85CRV4SCIhNUShnUCjmUchkctCpkJcZiwtiReH72IgQ1D62TnuhlMhlc7NVwsVejtY8eRUYTopNzcDEhC5FJOYhJzUVMai4clSrYtewNIVi9ExERUcPHor0RqWkPyZkFJmy7moMtV3KRnl8yjrpcBnTw0iAsyA6dvTVQKYxAdizOnIqt9HrZSztR3ZHLZQhws0eAm/1tlzt+PBHFKdehlkOyoeNUCjlCDI4IMTgiI68IJ2PTcT4uE1nFJngMnYapv1/Hi6lFCHFTV3sb/P4hIiIiW8eivZGoSQ/JSr0BTt1HwL7NvZCrSu4pLc5KQfaprcg+vQNRWclYZ4WM7KWdiCqi16nQp7kH7mrqiu0HT+JKthrXoMV/d6Yg++wupO3+Bqbc9Gqvn98/REREZKtYtDcS1ekhOasIuJipQEyOHAIlZ9qc1SaEOJqQHX8CW/d/z17aiahOaZQK+BTewL4v/w9tnvsQ6WpPOLS5F27t+qGTazF87Kp2yTy/f4iIiMjWsWhvZCrTQ3JmXhEORqbgYnwWSg9/A1zt0DXQFT7OWshkMoSnXAbAXtqJSBrG7BS0dy6Gd2s/7IhIQEp2IQ4mq9Daxwn3hHhAraxcnxr8/iEiIiJbx6KdzPIKjTgSnYoz1zNg/LuDpyB3e3QLdIWXXitxOiKisgxOWozq6odDV1MRHpOGc3GZuJ6Wh0FtveHhWHtDxBERERHVFRbthGKTCSdi0nEsOg2FxpIO5vxcdegV7A6DE4t1IrJtSrkcvUPcEehuh+3nE5CRV4Sfw2MxsLUXgj0cpI5HREREVCMs2hsxIQSiUnKw71IyMvKKAAAejhr0Cna7Y8/SRGSbajpKRKn62Kt6Exc7PNHNH5vP3kRsah42nb6JHsFu6BrgIlkP+EREREQ1xaK9kUrPLcSeS0m4llLSm7y9WoFezdzR0suRB7dE9VRNRomoSH3rVV2rUmBYe1/su5yEU9czcPBqClKzC9E/1ACFnN9tREREVP+waG9kTAI4Ep2KI1GpMJoE5DKgo78LugW6VrrjJiKyTdUZJaIi9blXdblchr4tPOFmr8GeS4m4mJCFgmIjBrX1hlLB7zkiIiKqX1i01wJrXZ4KAO7u7vD397fKutTezbEzXonMohQAgL+rHfq28ICLndoq6yci21CZUSLupCH0qt62iR5OOiU2nb6J6JRcbDgVhyHtfPgDJREREdUrLNqtzNqXp+rs7HAhIqJGhXt+kRErTmbCa+wCZBbJoVMpcE+IO1rwUngiauAC3OwxrIMvNpy6getpeVh/8gaGdvCBRqmQOhoRERFRpbBotzJrXp6aEHMVq99/HcnJydUu2s9cz8DUn07icmIOZDI5/O2MuL9zU+jUPGAlosbB10WHRzo2wfqTN3AzIx/rTtzAwx19WbgTERFRvcCivZZY4/LUmig2mrBk91Us3nUZxSYBZ60cl76bheFTp7NgJ7JB1uitvT72+F5XvPRaDO/UBGtPXEdCZgE2nb6Joe19pI5FREREdEcs2huguPQ8vPT9CRy7lgYAeLCtF0Y2Feg356jEyYjoVpmpSQCAMWPGWG2d9a3H97ri4ajBsA6++PX4dVxPy8PWc/HwkjoUERER0R2waG9gdpxPwOu/nEJ6bhEcNEq8O6wNhnbwwYkTJ6SORkTlyMvOBAAM+s8MtGjXuUbrqs89vtcVg5MWQ9r5YMPJOFxNykE2y3YiIiKycSzaG4jCYhP+b8sFfLM/CgDQ1lePz57oiAA3e4mTEVFluPkEsMf3OuLnaoeBbQzYciYeCXCB893Wu8qBiIiIyNpYtDcASVkFeHF1OI5Gl1wO/0yvIPz3gRbsZImIqAIhno4oaGnCzguJ0PcchYTiNKkjEREREZWLg9XWc6di0zFk8V84Gp0GR40Sy8d2xttDQlmwExHdQRtfPZogGQBwsdAZNzPyJE5EREREVBaL9nrs1/DrePSLg4jPzEdTD3usn9QLA1rz/kwiosoKQBJyLx2EgAybTt9EVn6R1JGIiIiILLBor4eEEFiw7SJe/fkUCotNCGvlifUTeyHYw0HqaERE9YoMQPKmj2AvK0JuoRG/nb6JIqNJ6lhEREREZiza65nCYhOm/nQKn+2+AgCY1K8Zlo/tAietSuJkRET1kyjKRxtNKnQqBZKyCvDH+QQIIaSORURERASARXu9kpFXhHHfHMG6EzeglMvwwfB2eG1gC8jlMqmjERHVa1q5EYPaekMuAy4lZuP09QypIxEREREBYNFebyRm5uPRZQdwMDIFDholvnmqK0Z29ZM6FhFRg+HrokOvZu4AgH2XkxCfyfHuiYiISHo2PeTb/PnzsXbtWly4cAE6nQ49e/bE+++/jxYtWpiXyc/Px6uvvooffvgBBQUFGDhwID7//HMYDAYJk1vXgVMX8N3mRMRnG+Gqk2PG3c5wyI7F8eOxlV5HRERELSYkImoYOvo5Iy49D1eTcrD5zE080c0fWhVH4yAiIiLp2HTRvnfvXkycOBFdu3ZFcXEx3nzzTQwYMADnz5+Hvb09AOCVV17B77//jp9//hl6vR6TJk3CI488gv3790ucvuYyU5OgdPHB/x0thNLJiKL0eJxZNgMjZidUe53Z2dlWTEhE1LDIZDL0DzUg+UgsMvKKsP18Aoa084ZMxtuQiIiISBo2XbRv3brV4u+VK1fC09MT4eHhuOeee5CRkYGvv/4aa9aswb333gsAWLFiBVq1aoVDhw7hrrvukiK21SRnF8Br9PtQ2LvAUSlwd6grdO8trda6Io7sxZZVnyI/n5d7EhHdjkapwKC23vjxWCyiknNwPCYdnQNcpI5FREREjZRNF+23ysgo6RjI1dUVABAeHo6ioiKEhYWZl2nZsiX8/f1x8ODBCov2goICFBQUmP/OzMysxdTVk5xdgDPwh8JeCQdZEUb1aA47dfX/uRJirloxHRFRw+bhqEGf5h7YdSERB64mo4mLDgYnrdSxiIiIqBGqNx3RmUwmTJkyBb169UKbNm0AAPHx8VCr1XB2drZY1mAwID4+vsJ1zZ8/H3q93vzw87OtDt1Scwqx9vgNFEOJgrhLaK9NrlHBTkREVdfGxwnNPBxgEsDWc/Ecv52IiIgkUW+K9okTJ+Ls2bP44Ycfaryu6dOnIyMjw/yIja18h261LT23EGuPX0dekRH2yEfiTzOhlHG8YCKiuiaTyXBfK084aJRIzy3C3ktJUkciIiKiRqheFO2TJk3Cpk2bsHv3bjRp0sQ83cvLC4WFhUhPT7dYPiEhAV5eXhWuT6PRwMnJyeJhCzLzirD2xA3kFBrhZq9GG8TAVJAjdSwiokZLq1JgYOuS0UjOxWXicmKWxImIiIiosbHpol0IgUmTJmHdunXYtWsXgoKCLOZ37twZKpUKO3fuNE+7ePEiYmJi0KNHj7qOWyO5hcVYe+IGsvKL4WKnwsMdfaGCUepYRESNXhMXO3T5uyO6nRGJyMovkjgRERERNSY2faP0xIkTsWbNGmzYsAGOjo7m+9T1ej10Oh30ej2effZZTJ06Fa6urnBycsLkyZPRo0ePetVzfGGxCRtPxSEjrwiOWiUe6dgE9hqb/qchImpU7mrqhpjUXCRmFeCPiEQM6+DDYeCIiIioTtj0mfalS5ciIyMDffv2hbe3t/nx448/mpf55JNPMHjwYAwfPhz33HMPvLy8sHbtWglTV43RJLD5zE0kZBZAq5Lj4Q6+cNCyYCcisiUKuQz3t/aCQi5DTGouztzIkDoSERERNRI2XR0KcecO2LRaLZYsWYIlS5bUQSLrEkLgj4gEXEvNhVIuw9D2vnCxV0sdi4iIyuFir0avYDfsu5yMPy8nw9/VDs52/M4mIiKi2mXTZ9obugNXU3AhPgtyGTCorTe89BwDmIjIlnXwc0YTZx2KTQI7zifAVIkfl4mIiIhqgkW7RM7FZeDYtTQAwH2tDAh0t5c4ERER3YlMJkP/UANUChniMvJxMiZd6khERETUwLFol0Bsai52XUgEAHQLdEWot20MOUdERHfmpFPhnhAPAMCByBSkZBdInIiIiIgaMhbtdSwtpxC/n7kJkwCaGxxwV1NXqSMREVEVtfZxQoCbHYwmge3nE2A08TJ5IiIiqh0s2utQXpERG07FoaDYBC8nLfq3MnDIICKiekgmkyGslQEapRyJWQU4Fp0qdSQiIiJqoFi01xHT30O7ZeQVwUmrxOB23lAq+PITEdVXDhol+rYouUz+SHQq0gr5IywRERFZH6vGOvLXlWRcT8uDSiHDkPY+sNfY9Gh7RERUCS0Mjmjm6QCTAI6lKAAFv9uJiIjIuli014GIm5k4EZsOABgQ6gV3B420gYiIyCpkMhn6tfCATqVAZpEczr1HSx2JiIiIGhgW7bUsITMfO//VU3wzTweJExERkTXZqZW4r5UnAMCp2yO4kFwocSIiIiJqSFi016KcgmJsOn0TRpNAkLs9e4onImqggj0c4G9vRFFyDLRK3ttORERE1sOivZaYBLD5zE1kFxTDxU6Fga3ZUzwRUUPW0cWIm9++gkBnldRRiIiIqAFhjzm15FSaAnHZ+VAr5BjSzgcapULqSEREVIuUcgDGYqljEBERUQPDM+21wKH9QERmlxTp97fxgou9WuJEREREREREVB+xaLeyC8mFcO3/PACgR1M3BLnbS5yIiIiIiIiI6isW7VYUn5GPDw6kQaZQwVdnQtdAF6kjERERERERUT3Got2KriRmI79YoDAxCl3citnxHBEREREREdUIi3Yr6h3ijvn3uiFp7bslHRIRERERERER1QBLSysLcFahOCNB6hhERERERETUALBoJyIiIiIiIrJRHKediIiIiKiWyPMzpI5Qa+R56Rb/rc8a8r8T1X8s2omIiIiIrEyv10Ol1gCRe6WOUut0UfukjmAVKrUGer1e6hhEZbBoJyIiIiKyMoPBgO/+9y0yMngGt77Q6/UwGAxSxyAqg0U7EREREVEtMBgMLAKJqMbYER0RERERERGRjWLRTkRERERERGSjWLQTERERERER2SgW7UREREREREQ2ikU7ERERERERkY1i0U5ERERERERko1i0ExEREREREdkoFu1ERERERERENopFOxEREREREZGNYtFOREREREREZKNYtBMRERERERHZKBbtRERERERERDaKRTsRERERERGRjWLRTkRERERERGSjWLQTERERERER2SgW7UREREREREQ2ikU7ERERERERkY1i0U5ERERERERko1i0ExEREREREdkoFu1ERERERERENopFOxEREREREZGNYtFOREREREREZKNYtBMRERERERHZKBbtRERERERERDaqwRTtS5YsQWBgILRaLbp3744jR45IHYmIiIiIiIioRhpE0f7jjz9i6tSpmDVrFo4fP4727dtj4MCBSExMlDoaERERERERUbU1iKL9448/xnPPPYenn34aoaGhWLZsGezs7PDNN99IHY2IiIiIiIio2pRSB6ipwsJChIeHY/r06eZpcrkcYWFhOHjwYLnPKSgoQEFBgfnvjIwMAEBmZmaN82RnZwMArl8+h4K83BqtKyHmKgAgPvoSrtrb2cy6bDlbY1iXLWdrDOuy5Wy2ui5bzmbNdSVdjwJQ0g7UtD0pfb4QokbroX+UvpbWaOuJiIisobLtvUzU8yOCuLg4+Pr64sCBA+jRo4d5+htvvIG9e/fi8OHDZZ4ze/ZszJkzpy5jEhERVVlsbCyaNGkidYwG4fr16/Dz85M6BhERURl3au/r/Zn26pg+fTqmTp1q/ttkMiE1NRVubm6QyWQSJquczMxM+Pn5ITY2Fk5OTlLHqbGGtD/cF9vUkPYFaFj7w30pnxACWVlZ8PHxsVI68vHxQWxsLBwdHetFWw/w82GruC+2qyHtD/fFNll7Xyrb3tf7ot3d3R0KhQIJCQkW0xMSEuDl5VXuczQaDTQajcU0Z2fn2opYa5ycnOr9G//fGtL+cF9sU0PaF6Bh7Q/3pSy9Xm+FNFRKLpfX26sW+PmwTdwX29WQ9of7YpusuS+Vae/rfUd0arUanTt3xs6dO83TTCYTdu7caXG5PBEREREREVF9U+/PtAPA1KlTMW7cOHTp0gXdunXDwoULkZOTg6efflrqaERERERERETV1iCK9sceewxJSUl4++23ER8fjw4dOmDr1q0wGAxSR6sVGo0Gs2bNKnOJf33VkPaH+2KbGtK+AA1rf7gvRBVrSO8p7ottakj7AjSs/eG+2Cap9qXe9x5PRERERERE1FDV+3vaiYiIiIiIiBoqFu1ERERERERENopFOxEREREREZGNYtFOREREREREZKNYtNuoJUuWIDAwEFqtFt27d8eRI0duu3x6ejomTpwIb29vaDQaNG/eHJs3b66jtHdW1f1ZuHAhWrRoAZ1OBz8/P7zyyivIz8+vo7QV27dvH4YMGQIfHx/IZDKsX7/+js/Zs2cPOnXqBI1Gg2bNmmHlypW1nrMyqrova9euRf/+/eHh4QEnJyf06NED27Ztq5uwd1Cdf5dS+/fvh1KpRIcOHWotX1VUZ18KCgowY8YMBAQEQKPRIDAwEN98803th72D6uzL6tWr0b59e9jZ2cHb2xvPPPMMUlJSaj/sHcyfPx9du3aFo6MjPD09MWzYMFy8ePGOz/v555/RsmVLaLVatG3b1qa+l8k2NKT2nm092/raxLbeNtt6gO09UPvtPYt2G/Tjjz9i6tSpmDVrFo4fP4727dtj4MCBSExMLHf5wsJC9O/fH9HR0fjll19w8eJFfPnll/D19a3j5OWr6v6sWbMG06ZNw6xZsxAREYGvv/4aP/74I9588806Tl5WTk4O2rdvjyVLllRq+aioKAwaNAj9+vXDyZMnMWXKFIwfP94mGsCq7su+ffvQv39/bN68GeHh4ejXrx+GDBmCEydO1HLSO6vqvpRKT0/Hk08+ifvuu6+WklVddfZl5MiR2LlzJ77++mtcvHgR33//PVq0aFGLKSunqvuyf/9+PPnkk3j22Wdx7tw5/Pzzzzhy5Aiee+65Wk56Z3v37sXEiRNx6NAh7NixA0VFRRgwYABycnIqfM6BAwfw+OOP49lnn8WJEycwbNgwDBs2DGfPnq3D5GTLGlJ7z7aebX1tY1tvm209wPa+Ttp7QTanW7duYuLEiea/jUaj8PHxEfPnzy93+aVLl4qmTZuKwsLCuopYJVXdn4kTJ4p7773XYtrUqVNFr169ajVnVQEQ69atu+0yb7zxhmjdurXFtMcee0wMHDiwFpNVXWX2pTyhoaFizpw51g9UA1XZl8cee0y89dZbYtasWaJ9+/a1mqs6KrMvW7ZsEXq9XqSkpNRNqGqqzL58+OGHomnTphbTFi1aJHx9fWsxWfUkJiYKAGLv3r0VLjNy5EgxaNAgi2ndu3cX//nPf2o7HtUTDam9Z1vPtr4usa23XWzvS1i7veeZdhtTWFiI8PBwhIWFmafJ5XKEhYXh4MGD5T5n48aN6NGjByZOnAiDwYA2bdrgvffeg9ForKvYFarO/vTs2RPh4eHmy+oiIyOxefNmPPjgg3WS2ZoOHjxose8AMHDgwAr3vT4xmUzIysqCq6ur1FGqZcWKFYiMjMSsWbOkjlIjGzduRJcuXfDBBx/A19cXzZs3x2uvvYa8vDypo1VZjx49EBsbi82bN0MIgYSEBPzyyy82+dnPyMgAgNu+/xvy559qriG192zrG+5nnW29bWhIbT3A9r46lFZbE1lFcnIyjEYjDAaDxXSDwYALFy6U+5zIyEjs2rULo0ePxubNm3HlyhW8+OKLKCoqkvxLqjr788QTTyA5ORm9e/eGEALFxcV4/vnnbeKSuaqKj48vd98zMzORl5cHnU4nUbKaW7BgAbKzszFy5Eipo1TZ5cuXMW3aNPz5559QKuv312BkZCT++usvaLVarFu3DsnJyXjxxReRkpKCFStWSB2vSnr16oXVq1fjscceQ35+PoqLizFkyJAqXwpZ20wmE6ZMmYJevXqhTZs2FS5X0ec/Pj6+tiNSPdCQ2nu29WzrbRHbetvF9r7qeKa9ATCZTPD09MTy5cvRuXNnPPbYY5gxYwaWLVsmdbRq2bNnD9577z18/vnnOH78ONauXYvff/8d77zzjtTR6G9r1qzBnDlz8NNPP8HT01PqOFViNBrxxBNPYM6cOWjevLnUcWrMZDJBJpNh9erV6NatGx588EF8/PHHWLVqVb37Bf78+fN4+eWX8fbbbyM8PBxbt25FdHQ0nn/+eamjWZg4cSLOnj2LH374Qeoo1Mg0pPaebb3tY1tvOxpSWw+wva+O+v2zUwPk7u4OhUKBhIQEi+kJCQnw8vIq9zne3t5QqVRQKBTmaa1atUJ8fDwKCwuhVqtrNfPtVGd/Zs6cibFjx2L8+PEAgLZt2yInJwcTJkzAjBkzIJfXn9+avLy8yt13JyenevvL+w8//IDx48fj559/LnMpUH2QlZWFY8eO4cSJE5g0aRKAksZQCAGlUont27fj3nvvlThl5Xl7e8PX1xd6vd48rVWrVhBC4Pr16wgJCZEwXdXMnz8fvXr1wuuvvw4AaNeuHezt7XH33Xfj3Xffhbe3t8QJgUmTJmHTpk3Yt28fmjRpcttlK/r8V/TdR41LQ2rv2dazrbc1bOttG9v7qqs/34iNhFqtRufOnbFz507zNJPJhJ07d6JHjx7lPqdXr164cuUKTCaTedqlS5fg7e0tacEOVG9/cnNzyzTWpQcoQojaC1sLevToYbHvALBjx44K993Wff/993j66afx/fffY9CgQVLHqRYnJyecOXMGJ0+eND+ef/55tGjRAidPnkT37t2ljlglvXr1QlxcHLKzs83TLl26BLlcfsdGxtbY8mdfCIFJkyZh3bp12LVrF4KCgu74nIb2+SfrakjtPdv6hvVZZ1tvexpSWw/Y9uffZtt7q3VpR1bzww8/CI1GI1auXCnOnz8vJkyYIJydnUV8fLwQQoixY8eKadOmmZePiYkRjo6OYtKkSeLixYti06ZNwtPTU7z77rtS7YKFqu7PrFmzhKOjo/j+++9FZGSk2L59uwgODhYjR46UahfMsrKyxIkTJ8SJEycEAPHxxx+LEydOiGvXrgkhhJg2bZoYO3asefnIyEhhZ2cnXn/9dRERESGWLFkiFAqF2Lp1q1S7YFbVfVm9erVQKpViyZIl4ubNm+ZHenq6VLtgVtV9uZUt9Shb1X3JysoSTZo0ESNGjBDnzp0Te/fuFSEhIWL8+PFS7YJFtqrsy4oVK4RSqRSff/65uHr1qvjrr79Ely5dRLdu3aTaBbMXXnhB6PV6sWfPHov3f25urnmZW7/L9u/fL5RKpViwYIGIiIgQs2bNEiqVSpw5c0aKXSAb1JDae7b1bOtrG9t622zrhWB7XxftPYt2G7V48WLh7+8v1Gq16Natmzh06JB5Xp8+fcS4ceMslj9w4IDo3r270Gg0omnTpmLevHmiuLi4jlNXrCr7U1RUJGbPni2Cg4OFVqsVfn5+4sUXXxRpaWl1H/wWu3fvFgDKPErzjxs3TvTp06fMczp06CDUarVo2rSpWLFiRZ3nLk9V96VPnz63XV5K1fl3+Tdbasirsy8REREiLCxM6HQ60aRJEzF16lSLxkUq1dmXRYsWidDQUKHT6YS3t7cYPXq0uH79et2Hv0V5+wHA4vNc3nfzTz/9JJo3by7UarVo3bq1+P333+s2ONm8htTes61nW1+b2NbbZlsvBNt7IWq/vZf9HY6IiIiIiIiIbAzvaSciIiIiIiKyUSzaiYiIiIiIiGwUi3YiIiIiIiIiG8WinYiIiIiIiMhGsWgnIiIiIiIislEs2omIiIiIiIhsFIt2IiIiIiIiIhvFop2IiIiIiIjIRrFoJ5LAnj17IJPJkJ6eLnUUm9C3b19MmTKlTrY1c+ZMTJgwQZJtV9Vdd92FX3/9VeoYRERUDWzrLbGtLx/beqoMFu3UaCUlJeGFF16Av78/NBoNvLy8MHDgQOzfv7/Wt92zZ0/cvHkTer2+1rdVSiaTmR/29vYICQnBU089hfDw8DrLUNEBzNq1a/HOO+/U+vbj4+Px6aefYsaMGbW+LWt46623MG3aNJhMJqmjEBHVS2zr2dbbOrb1VBks2qnRGj58OE6cOIFVq1bh0qVL2LhxI/r27YuUlJRa37ZarYaXlxdkMlmtb+vfVqxYgZs3b+LcuXNYsmQJsrOz0b17d3z77bc1Wm9hYWGNnu/q6gpHR8caraMyvvrqK/Ts2RMBAQG1vq07qcxr9sADDyArKwtbtmypg0RERA0P23q29VJiW09WI4gaobS0NAFA7Nmz547LPfvss8Ld3V04OjqKfv36iZMnT5rnnzx5UvTt21c4ODgIR0dH0alTJ3H06FEhhBDR0dFi8ODBwtnZWdjZ2YnQ0FDx+++/CyGE2L17twAg0tLSzOv65ZdfRGhoqFCr1SIgIEAsWLDAIktAQICYN2+eePrpp4WDg4Pw8/MTX3zxRaX3GYBYt25dmelPPvmkcHR0FKmpqUIIIWbNmiXat29vscwnn3wiAgICzH+PGzdODB06VLz77rvC29tbBAYGCiGE+Pbbb0Xnzp2Fg4ODMBgM4vHHHxcJCQlCCCGioqIEAIvHuHHjhBBC9OnTR7z88svm9aempoqxY8cKZ2dnodPpxP333y8uXbpknr9ixQqh1+vF1q1bRcuWLYW9vb0YOHCgiIuLu+1r0Lp1a/HZZ59ZTOvTp4+YPHmyeP3114WLi4swGAxi1qxZFstcu3ZNPPTQQ8Le3l44OjqKRx99VMTHx5d5Pf7t5ZdfFn369LHYzsSJE8XLL78s3NzcRN++fYXJZBKzZs0Sfn5+Qq1WC29vbzF58mSL9Tz99NNizJgxt90vIiIqi239P9jWs62n+o1n2qlRcnBwgIODA9avX4+CgoIKl3v00UeRmJiILVu2IDw8HJ06dcJ9992H1NRUAMDo0aPRpEkTHD16FOHh4Zg2bRpUKhUAYOLEiSgoKMC+fftw5swZvP/++3BwcCh3O+Hh4Rg5ciRGjRqFM2fOYPbs2Zg5cyZWrlxpsdxHH32ELl264MSJE3jxxRfxwgsv4OLFizV6LV555RVkZWVhx44dVXrezp07cfHiRezYsQObNm0CABQVFeGdd97BqVOnsH79ekRHR+Opp54CAPj5+Znv2bp48SJu3ryJTz/9tNx1P/XUUzh27Bg2btyIgwcPQgiBBx98EEVFReZlcnNzsWDBAvzvf//Dvn37EBMTg9dee63CvKmpqTh//jy6dOlSZt6qVatgb2+Pw4cP44MPPsDcuXPNr4fJZMLQoUORmpqKvXv3YseOHYiMjMRjjz1WpderdDtqtRr79+/HsmXL8Ouvv+KTTz7BF198gcuXL2P9+vVo27atxXO6deuGP//8s8rbIiJq7NjW/4NtPdt6quek/tWASCq//PKLcHFxEVqtVvTs2VNMnz5dnDp1yjz/zz//FE5OTiI/P9/iecHBweZfvR0dHcXKlSvLXX/btm3F7Nmzy51366/vTzzxhOjfv7/FMq+//roIDQ01/x0QEGDxK6zJZBKenp5i6dKlldpfVPDre15engAg3n//fSFE5X99NxgMoqCg4LbbPHr0qAAgsrKyhBDln3UQwvLX90uXLgkAYv/+/eb5ycnJQqfTiZ9++kkIUfLrOwBx5coV8zJLliwRBoOhwiwnTpwQAERMTEyZbffu3dtiWteuXcV///tfIYQQ27dvFwqFwuJ5586dEwDEkSNHzK9HZX5979ixo8UyH330kWjevLkoLCysMPeGDRuEXC4XRqOxwmWIiKh8bOtLsK1nW0/1G8+0U6M1fPhwxMXFYePGjbj//vuxZ88edOrUyfyL96lTp5CdnQ03Nzfzr/UODg6IiorC1atXAQBTp07F+PHjERYWhv/7v/8zTweAl156Ce+++y569eqFWbNm4fTp0xVmiYiIQK9evSym9erVC5cvX4bRaDRPa9eunfn/ZTIZvLy8kJiYWKPXQQhhXl9VtG3bFmq12mJaeHg4hgwZAn9/fzg6OqJPnz4AgJiYmEqvNyIiAkqlEt27dzdPc3NzQ4sWLRAREWGeZmdnh+DgYPPf3t7et30t8vLyAABarbbMvH+/rreuKyIiAn5+fvDz8zPPDw0NhbOzs0WeyujcubPF348++ijy8vLQtGlTPPfcc1i3bh2Ki4stltHpdDCZTLc9S0REROVjW1+CbT3beqrfWLRTo6bVatG/f3/MnDkTBw4cwFNPPYVZs2YBALKzs+Ht7Y2TJ09aPC5evIjXX38dADB79mycO3cOgwYNwq5duxAaGop169YBAMaPH4/IyEiMHTsWZ86cQZcuXbB48eIa5S29HK+UTCarcW+jpY1RUFAQAEAul5sb91L/vlStlL29vcXfOTk5GDhwIJycnLB69WocPXrU/FrUtPOa8pT3Wtya+9/c3d0BAGlpaZVaV1Ve1+q+Zn5+frh48SI+//xz6HQ6vPjii7jnnnssnpuamgp7e3vodLpK5yEion+wrWdbX9G62NZTfcGinehfQkNDkZOTAwDo1KkT4uPjoVQq0axZM4tHaaMAAM2bN8crr7yC7du345FHHsGKFSvM8/z8/PD8889j7dq1ePXVV/Hll1+Wu91WrVqVGX5m//79aN68ORQKRS3s6T8WLlwIJycnhIWFAQA8PDwQHx9v0TCdPHnyjuu5cOECUlJS8H//93+4++670bJlyzK/hpf+Wv/vMwq3atWqFYqLi3H48GHztJSUFFy8eBGhoaFV2TULwcHBcHJywvnz56v0vFatWiE2NhaxsbHmaefPn0d6ero5j4eHB27evGnxvMq8ZkDJr+tDhgzBokWLsGfPHhw8eBBnzpwxzz979iw6duxYpcxERFQxtvVs68vLw7aebBmLdmqUUlJScO+99+K7777D6dOnERUVhZ9//hkffPABhg4dCgAICwtDjx49MGzYMGzfvh3R0dE4cOAAZsyYgWPHjiEvLw+TJk3Cnj17cO3aNezfvx9Hjx5Fq1atAABTpkzBtm3bEBUVhePHj2P37t3mebd69dVXsXPnTrzzzju4dOkSVq1ahc8+++y2na1UR3p6OuLj43Ht2jXs2LEDI0aMwJo1a7B06VI4OzsDAPr27YukpCR88MEHuHr1KpYsWVKpYUj8/f2hVquxePFiREZGYuPGjWXGYw0ICIBMJsOmTZuQlJSE7OzsMusJCQnB0KFD8dxzz+Gvv/7CqVOnMGbMGPj6+pr/bapDLpcjLCwMf/31V5WeFxYWhrZt22L06NE4fvw4jhw5gieffBJ9+vQxd3Rz77334tixY/j2229x+fJlzJo1C2fPnr3juleuXImvv/4aZ8+eRWRkJL777jvodDqLYWr+/PNPDBgwoGo7S0REbOvZ1lca23qyeVLdTE8kpfz8fDFt2jTRqVMnodfrhZ2dnWjRooV46623RG5urnm5zMxMMXnyZOHj4yNUKpXw8/MTo0ePFjExMaKgoECMGjXKPISHj4+PmDRpksjLyxNCCDFp0iQRHBwsNBqN8PDwEGPHjhXJyclCiNsPA6NSqYS/v7/48MMPLTIHBASITz75xGJa+/btywxZUhH8a/gVrVYrgoODxbhx40R4eHiZZZcuXSr8/PyEvb29ePLJJ8W8efPKHQbmVmvWrBGBgYFCo9GIHj16iI0bNwoA4sSJE+Zl5s6dK7y8vIRMJrvjMDB6vV7odDoxcODAcoeB+bd169aJO32lbd68Wfj6+lp09HLrtoUQYujQoeZsQtx5GBghhHj77beFwWAQer1evPLKK2LSpEllOqe5dTvr1q0T3bt3F05OTsLe3l7cdddd4o8//jDPv379ulCpVCI2Nva2+0VERGWxrWdbX4ptPdV3MiFuc2MIEVEDIoRA9+7d8corr+Dxxx+XOs4d/fe//0VaWhqWL18udRQiIqJ6gW09NUS8PJ6IGg2ZTIbly5eX6bXVVnl6epa57JCIiIgqxraeGiKeaSdqAN577z2899575c67++67K3WfGhEREdkutvVEjReLdqIGIDU1FampqeXO0+l08PX1reNEREREZE1s64kaLxbtRERERERERDaK97QTERERERER2SgW7UREREREREQ2ikU7ERERERERkY1i0U5ERERERERko1i0ExEREREREdkoFu1ERERERERENopFOxEREREREZGN+n99ViHTQ1E9UAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 2 Axes>"
            ],
            "image/png": 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kJCQgISEBjx49QkBAAG7evIn79+8DyF+3r776Kl555RVpehsbGwwZMqTM8W7cuBFt2rSBhYWFtOyEhAR07twZeXl5OHr0qFr5gQMHwsLCQvrcpk0bAPm3hgHAw4cPcfToUYwcORIuLi5q0z59y/OwYcOQlZWl9mqtv//+G7m5uXj77befG/OjR48AQC0O4H/73qRJk9SGT548udA8nn4OMicnB48ePYKnpyfMzc3VblEbMGAA9PX1sXbtWmnYnj17kJCQoBanubk5zpw5gwcPHhQbt0qlwu7du6Vb3YD/rb+C9Xzs2DE0b94cXbp0wbFjxwDk3yIXFhYmlRVC4J9//kHv3r0hhFDbbgEBAUhOTpbqsHHjRvj4+MDb21utXMeOHQGgUM/Oz/vOPa1g3SckJBRb35IyNjZ+bi/y5ubmAIBt27aVudM2pVKJESNGlLj8sGHDYGJiIn3u378/HBwcpH2souzcuRM6OjqF9uEPP/wQQohCPeh27txZrXMfPz8/mJqaSt9HoorG44Wy4/ECjxeKU5OPFxYtWiR9H/766y906NAB77zzDjZv3iyVKU1bOXPmTNSrVw+BgYEYN24c2rVrV2i6Ak+/xq7gEbTs7Gzs37+/yPJ5eXnYu3cv+vbtC3d3d2m4g4MDBg8ejOPHjyMlJaVE9a4u2BFdJZPJZGjZsiWOHj0KlUqFEydOwNbWFp6engDyG+GFCxcCgNQYFzTCd+/ehVwul8oWsLe3h7m5Oe7evas23M3Nrdg4Vq9ejbS0NCxZsgRvvfWW2riC+Xh5eakNVygUcHd3L7ScytKgQYMin50q8Gx9IyIiIITAl19+iS+//LLIaeLj41GrVi3cvXsXLVq0KDT+2XVQGjdv3sSlS5fUDgyeXfbTnm1YC36MHz9+DOB/jXH9+vWfu1xvb280b94ca9euxahRowAAa9euxauvvlpo3ymOeOY5o4J979keSotaPxkZGZg7dy5WrlyJ+/fvq80rOTlZ+t/c3By9e/dGUFAQ5syZI8VZq1YtqSEDgPnz5yMwMBDOzs5o2rQpevTogWHDhqn9iAcHB+Phw4dqjbCdnR3q1KmDY8eO4d1338WxY8fQoUMHtG3bFhMnTsStW7cQHh4OlUolNcIPHz5EUlISli9fjuXLlxe5bgq2282bNxEeHl7i7fu879zTCtZXeXSOk5aWBltb22LHDxw4EL///jveeecdfPbZZ+jUqRPeeOMN9O/fX+0A/Hlq1apVqg6c6tSpo/ZZJpPB09Ozwl/VcvfuXTg6OqqdMADyE6OC8U979vsI5H8nC76PRBWNxwtlx+MFHi/weKGwV155Ra0jurfeeguNGzfGhAkT0KtXLygUilK1lQqFAitWrEDz5s2hr6+PlStXFhmLXC5X2wYAULduXQAotu1/+PAhnjx5UuR+4+PjA5VKhejoaNSrV69Eda8OmLRrQOvWrfHvv//i8uXLOHHihNo7VVu2bImPP/4Y9+/fx/Hjx+Ho6FhoRy/pl/N5Pb+2atUKoaGhWLhwIQYMGABLS8sy1aW4WPLy8qCjo1OmeZbVs/UtuHL40UcfFXsFoaSNUkkUdLrx9PK7dOmCTz75pMjyBT9YBYpbX882iCUxbNgwvP/++7h37x6ysrJw+vRp6eDueaysrADkN/xOTk6lXi4ATJw4EStXrsTkyZPh7+8PMzMz6R25z17NHTZsGDZu3IiTJ0+iQYMG2L59O8aNG6eWMA4YMABt2rTBli1bsHfvXnz33XeYN28eNm/ejO7duwPIPzNcu3Zt+Pr6qs2/devWOHDgADIyMhASEoLp06ejfv36MDc3x7FjxxAeHg5jY2M0btwYwP/2mbfffrvYM9sFr1BTqVRo0KABfvzxxyLLOTs7q30u6Xeu4KDL2tq6yPElde/ePSQnJz93HzcwMMDRo0dx6NAh/Pfff9i9ezf+/vtvdOzYEXv37i3Rd7giepjWht+V8vw+EpUVjxcqBo8X/ofHC/lq4vGCXC5Hhw4d8PPPP+PmzZtlSoD37NkDIL9jwps3bz73BCC9HCbtGvD0+1dPnDihdstQ06ZNoVQqcfjwYZw5cwY9evSQxrm6ukKlUuHmzZvSGS8AiIuLQ1JSElxdXUscg6enJ+bPn4/27dujW7duOHDggHRWrWA+169fVzsAyM7Oxu3bt9XOXltYWBTqIRbIPxP39LSaeKVGwfL19PSee8YdyK/zzZs3Cw2/fv16oWFF1Tk7OxsxMTFqwzw8PJCWlvbCZZdUQX3CwsJeWHbQoEGYMmUK1q1bh4yMDOjp6WHgwIEvnM7b2xsAcPv2bTRo0EAaXrDvRUZGqp31LGr9bNq0CYGBgfjhhx+kYZmZmUXuJ926dYONjQ3Wrl2LFi1a4MmTJxg6dGihcg4ODhg3bhzGjRuH+Ph4NGnSBF9//bXUCP/3339q35UCbdq0wcqVK7F+/Xrk5eWhZcuWkMvlaN26tdQIt2zZUjoAKujpNi8v74XbzcPDAxcvXkSnTp1KtH8/7zv3tNu3b0Mulxc6SCutNWvWAECxB6AF5HI5OnXqhE6dOuHHH3/EN998gy+++AKHDh1C586dy/27++z3TAiBiIgItffJV8TviqurK/bv34/U1FS19X7t2jVpPJG24fFC5eDxAo8XaurxQm5uLoD8O/OA0rWVly5dwuzZszFixAiEhobinXfeweXLl2FmZqa2DJVKhVu3bqnFeePGDQAo9r3qNjY2MDQ0LHK/uXbtGuRyuXSyo6q+tq+0+Ey7BjRr1kx6Nuf+/ftqZ86VSiWaNGmCRYsWIT09Xe19qwU/Mj/99JPa/ArO3D19q09J+Pn5YefOnQgPD0fv3r2l1zZ07twZCoUCv/zyi9pZ2z/++APJyclqy/Hw8MDp06eRnZ0tDduxY0ehVzgZGRkBQJE/xBXF1tYW7du3x7Jlywo1kED+rTcFevTogdOnT+Ps2bNq459+fqqAh4dHoefLli9fXujM+YABA3Dq1CnpLOTTkpKSpB/KkrKxsUHbtm2xYsUKREVFqY179uy6tbU1unfvjr/++gtr165Ft27dSnQmtmnTplAoFDh37pza8ILG7pdfflEb/uy+CORfAXg2nl9//bXQ+gHy3xv+1ltvYcOGDVi1ahUaNGiglrzl5eWp3SIH5G9XR0dH6TUpcXFxOH/+fJH7f8FtbPPmzYOfn5/UkLRp0wYHDhzAuXPnpDIFsffr1w///PNPkQc7T+8zAwYMwP379/Hbb78VKpeRkYH09PRCw4v7zj0tJCQE9erVK9TolcbBgwcxZ84cuLm5Pfc5y8TExELDGjVqBADS+i3v7+7q1avVnrPftGkTYmJipH0MqJjflR49eiAvL6/QFaQFCxZAJpOpLZ9IW/B4oXLweIHHCzXxeCEnJwd79+6FQqGQTu6VtK3MycnB8OHD4ejoiJ9//hmrVq1CXFwcPvjggyKX9fT8hBBYuHAh9PT00KlTpyLL6+jooGvXrti2bZvaLfRxcXEICgpC69atYWpqCkAzvxmawCvtGqBQKNC8eXMcO3YMSqUSTZs2VRvfsmVL6azj041ww4YNERgYiOXLlyMpKQnt2rXD2bNn8eeff6Jv377o0KFDqWN59dVXsW3bNvTo0QP9+/fH1q1bYWNjg6lTp2LWrFno1q0bXnvtNVy/fh2LFy9G8+bN1Tr9eOedd7Bp0yZ069YNAwYMQGRkJP76669CzzJ5eHjA3NwcS5cuhYmJCYyMjNCiRYsKv41m0aJFaN26NRo0aIDRo0fD3d0dcXFxOHXqFO7du4eLFy8CAD755BOsWbMG3bp1w/vvvw8jIyMsX74crq6uau/oLKjze++9h379+qFLly64ePEi9uzZU6iR+/jjj7F9+3b06tULw4cPR9OmTZGeno7Lly9j06ZNuHPnTqlvafrll1/QunVrNGnSBGPGjIGbmxvu3LmD//77D6GhoWplhw0bhv79+wOA9AzYi+jr66Nr167Yv38/Zs+eLQ1v1KgR3nrrLSxevBjJyclo2bIlDhw4gIiIiELz6NWrF9asWQMzMzP4+vri1KlT2L9/v3Qr3bOGDRuGX375BYcOHcK8efPUxqWmpsLJyQn9+/dHw4YNYWxsjP379yM4OFj6juzcuRP6+vpF7v+enp6wt7fH9evXMXHiRGl427Zt8emnnwKAWiMMAN9++y0OHTqEFi1aYPTo0fD19UViYiLOnz+P/fv3S4nu0KFDsWHDBrz33ns4dOgQWrVqhby8PFy7dg0bNmyQ3nn8rKK+c3p6egDyG8EjR45g3LhxRa6rouzatQvXrl1Dbm4u4uLicPDgQezbtw+urq7Yvn079PX1i5129uzZOHr0KHr27AlXV1fEx8dj8eLFcHJykn57yvu7a2lpidatW2PEiBGIi4vDTz/9BE9PT4wePVoqUxG/K71790aHDh3wxRdf4M6dO2jYsCH27t2Lbdu2YfLkyYXmTaQNeLzA4wUeL6jHyeOFlz9eAPKfow8KCsLNmzfx2WefSQlwSdvKr776CqGhodJdAH5+fpg+fTqmTZuG/v37q93NoK+vj927dyMwMBAtWrTArl278N9//+Hzzz8v9jn/gmXs27cPrVu3xrhx46Crq4tly5YhKysL8+fPl8o1atQIOjo6mDdvHpKTk6FUKtGxY8fn9ulTJVVKH/VUyNSpUwUA0bJly0LjNm/eLAAIExMTkZubqzYuJydHzJo1S7i5uQk9PT3h7Owspk6dqvaKEiHyXyfSs2fPIpeNZ94BKUT+60J0dXXFwIEDpde2LFy4UHh7ews9PT1hZ2cnxo4dKx4/flxofj/88IOoVauWUCqVolWrVuLcuXOFXuFSsAxfX1+hq6tbqte5FLzCZePGjUWOL3gFynfffVfk+MjISDFs2DBhb28v9PT0RK1atUSvXr3Epk2b1MpdunRJtGvXTujr64tatWqJOXPmiD/++KPQK1zy8vLEp59+KqytrYWhoaEICAgQERERRb7OJjU1VUydOlV4enoKhUIhrK2tRcuWLcX3338vvU/1efGjiNfFhIWFiddff12Ym5sLfX194eXlVehdl0IIkZWVJSwsLISZmZnIyMgoct0UZfPmzUImk0mvmCmQkZEhJk2aJKysrISRkZHo3bu39C7wp2N8/PixGDFihLC2thbGxsYiICBAXLt2rdjX/QghRL169YRcLhf37t0rVIePP/5YNGzYUJiYmAgjIyPRsGFDsXjxYqlM//79RY8ePYqtz5tvvikAiL///lsalp2dLQwNDYVCoShy3cTFxYnx48cLZ2dnoaenJ+zt7UWnTp0KvSc1OztbzJs3T9SrV08olUphYWEhmjZtKmbNmqX23tSSfud27dolAIibN28WW58Cz77CRaFQCHt7e9GlSxfx888/q71WrcCzr3w7cOCA6NOnj3B0dBQKhUI4OjqKt956q9Brh4r77rZr107Uq1evyPiKe+XbunXrxNSpU4Wtra0wMDAQPXv2LPRKIiFe/nfl2Ve+CZH/ffzggw+Eo6Oj0NPTE3Xq1BHfffed2iuQhCh6ewlR/CuriCoSjxd4vMDjhf/h8cLLHy8AEPr6+qJRo0ZiyZIlhdrAF7WVISEhQldXV+01bkIIkZubK5o3by4cHR2l739gYKAwMjISkZGR0rvq7ezsxIwZM9ReE1lQ92f34fPnz4uAgABhbGwsDA0NRYcOHcTJkycL1fG3334T7u7uQkdHp9q+/k0mBHvVISrOqlWrMGLECNy+fbvY5260VW5uLhwdHdG7d2/88ccfJZ4uLy8Pvr6+GDBgQInPuL+sxo0bw9LSEgcOHCjVdLm5ubCyssLcuXNLdbZZW/Xt2xcymQxbtmzRdChERFQKPF7g8UJlqirHC8OHD8emTZukZ+ap7PhMO1E1tXXrVjx8+BDDhg0r1XQ6OjqYPXs2Fi1aVCk/sufOnUNoaGip4wTyn8n+4IMP8Prrr1dAZJUrPDwcO3bsqLQDHyIiIoDHC1UNjxdqJl5pJ43JyMgo1GnIsywtLUv1DujyVhXPnJ85cwaXLl3CnDlzYG1tjfPnz2s6pCKFhYUhJCQEP/zwAxISEnDr1q3nPn9NREQ1E48XKgaPF6ii8Up7+eGVdtKYv//+Gw4ODs/9O3nypKbDrHKWLFmCsWPHwtbWFqtXr9Z0OMXatGkTRowYgZycHKxbt44NMBERFYnHCxWDxwtEVQevtJPGxMTE4MqVK88t07RpU1hYWFRSRERERKRteLxARDUdk3YiIiIiIiIiLcXb44mIiIiIiIi0lK6mA9AGKpUKDx48gImJCWQymabDISKiGk4IgdTUVDg6OkIu5/n18sC2noiItE1J23sm7QAePHgAZ2dnTYdBRESkJjo6Gk5OTpoOo1pgW09ERNrqRe09k3YAJiYmAPJXlqmpqYajISKimi4lJQXOzs5S+0Qvj209ERFpm5K290zaAek2OVNTUzbkRESkNXgbd/lhW09ERNrqRe09H5QjIiIiIiIi0lJM2omIiIiIiIi0FJN2IiIiIiIiIi3FpJ2IiIiIiIhISzFpJyIiIiIiItJSTNqJiIiIiIiItBSTdiIiIiIiIiItxaSdiIiIiIiISEsxaSciIiIiIiLSUkzaiYiIiIiIiLSUrqYDINJmUVFRSEhIKJd5WVtbw8XFpVzmRURERERENQOTdqJiREVFwdvHBxlPnpTL/AwMDXEtPJyJOxERERERlRiTdqJiJCQkIOPJEwz59DvYuXi81LzioiKxdt7HSEhIYNJOREREREQlxqSd6AXsXDzgVKeepsMgIiIiIqIaiB3REREREREREWkpJu1EREREREREWopJOxEREREREZGWYtJOREREREREpKWYtBMRERERERFpKSbtRERERERERFqKSTsRERERERGRlmLSTkRERERERKSlNJq0L1myBH5+fjA1NYWpqSn8/f2xa9cuaXz79u0hk8nU/t577z21eURFRaFnz54wNDSEra0tPv74Y+Tm5lZ2VYiIiIiIiIjKna4mF+7k5IRvv/0WderUgRACf/75J/r06YMLFy6gXr16AIDRo0dj9uzZ0jSGhobS/3l5eejZsyfs7e1x8uRJxMTEYNiwYdDT08M333xT6fUhIiIiIiIiKk8aTdp79+6t9vnrr7/GkiVLcPr0aSlpNzQ0hL29fZHT7927F1evXsX+/fthZ2eHRo0aYc6cOfj0008xc+ZMKBSKCq8DERERERERUUXRmmfa8/LysH79eqSnp8Pf318avnbtWlhbW6N+/fqYOnUqnjx5Io07deoUGjRoADs7O2lYQEAAUlJScOXKlWKXlZWVhZSUFLU/IiIiIiIiIm2j0SvtAHD58mX4+/sjMzMTxsbG2LJlC3x9fQEAgwcPhqurKxwdHXHp0iV8+umnuH79OjZv3gwAiI2NVUvYAUifY2Nji13m3LlzMWvWrAqqERERERFVZ3FxcUhOTtZ0GKQhZmZmhXIQooqk8aTdy8sLoaGhSE5OxqZNmxAYGIgjR47A19cXY8aMkco1aNAADg4O6NSpEyIjI+Hh4VHmZU6dOhVTpkyRPqekpMDZ2fml6kFERERE1d+1a9cwbtx4qFR5mg6FNERPocRfa1YzcadKo/GkXaFQwNPTEwDQtGlTBAcH4+eff8ayZcsKlW3RogUAICIiAh4eHrC3t8fZs2fVysTFxQFAsc/BA4BSqYRSqSyvKhARERFRDREdHQ2VKg+ZtZogz8xJ0+FQJZNnJgO3jiA5OZlJO1UajSftz1KpVMjKyipyXGhoKADAwcEBAODv74+vv/4a8fHxsLW1BQDs27cPpqam0i32RERERETlTSiMoTKy1nQYRFQDaDRpnzp1Krp37w4XFxekpqYiKCgIhw8fxp49exAZGYmgoCD06NEDVlZWuHTpEj744AO0bdsWfn5+AICuXbvC19cXQ4cOxfz58xEbG4tp06Zh/PjxvJJOREREREREVZ5Gk/b4+HgMGzYMMTExMDMzg5+fH/bs2YMuXbogOjoa+/fvx08//YT09HQ4OzujX79+mDZtmjS9jo4OduzYgbFjx8Lf3x9GRkYIDAxUe687ERERERERUVWl0aT9jz/+KHacs7Mzjhw58sJ5uLq6YufOneUZFhEREREREZFW0Jr3tBMRERERERGROibtRERERERERFqKSTsRERERERGRlmLSTkRERERERKSlmLQTERERERERaSkm7URERERERERaikk7ERERERERkZZi0k5ERERERESkpZi0ExEREREREWkpJu1EREREREREWopJOxEREREREZGWYtJOREREREREpKWYtBMRERERERFpKSbtRERERERERFqKSTsRERERERGRlmLSTkRERERERKSlmLQTERERERERaSkm7URERERERERaikk7ERERERERkZZi0k5ERERERESkpZi0ExEREREREWkpJu1EREREREREWopJOxEREREREZGWYtJOREREREREpKWYtBMRERERERFpKSbtRERERERERFqKSTsRERERERGRltLVdABERFVRVFQUEhISymVe1tbWcHFxKZd5EREREVH1wqSdiKiUoqKi4O3jg4wnT8plfgaGhrgWHs7EnYiIiIgK0WjSvmTJEixZsgR37twBANSrVw/Tp09H9+7dAQCZmZn48MMPsX79emRlZSEgIACLFy+GnZ2dNI+oqCiMHTsWhw4dgrGxMQIDAzF37lzo6vJ8BBFVjISEBGQ8eYIhn34HOxePl5pXXFQk1s77GAkJCUzaiYiIiKgQjWa2Tk5O+Pbbb1GnTh0IIfDnn3+iT58+uHDhAurVq4cPPvgA//33HzZu3AgzMzNMmDABb7zxBk6cOAEAyMvLQ8+ePWFvb4+TJ08iJiYGw4YNg56eHr755htNVo2IagA7Fw841amn6TCIiIiIqBrTaNLeu3dvtc9ff/01lixZgtOnT8PJyQl//PEHgoKC0LFjRwDAypUr4ePjg9OnT+PVV1/F3r17cfXqVezfvx92dnZo1KgR5syZg08//RQzZ86EQqEocrlZWVnIysqSPqekpFRcJYmIiIiIiIjKSGt6j8/Ly8P69euRnp4Of39/hISEICcnB507d5bKeHt7w8XFBadOnQIAnDp1Cg0aNFC7XT4gIAApKSm4cuVKscuaO3cuzMzMpD9nZ+eKqxgRERERERFRGWk8ab98+TKMjY2hVCrx3nvvYcuWLfD19UVsbCwUCgXMzc3VytvZ2SE2NhYAEBsbq5awF4wvGFecqVOnIjk5WfqLjo4u30oRERERERERlQON99bm5eWF0NBQJCcnY9OmTQgMDMSRI0cqdJlKpRJKpbJCl0FERERERET0sjSetCsUCnh6egIAmjZtiuDgYPz8888YOHAgsrOzkZSUpHa1PS4uDvb29gAAe3t7nD17Vm1+cXFx0jgiIiIiIiKiqkzjt8c/S6VSISsrC02bNoWenh4OHDggjbt+/TqioqLg7+8PAPD398fly5cRHx8vldm3bx9MTU3h6+tb6bETERERERERlSeNXmmfOnUqunfvDhcXF6SmpiIoKAiHDx/Gnj17YGZmhlGjRmHKlCmwtLSEqakpJk6cCH9/f7z66qsAgK5du8LX1xdDhw7F/PnzERsbi2nTpmH8+PG8/Z2IiIiIiIiqPI0m7fHx8Rg2bBhiYmJgZmYGPz8/7NmzB126dAEALFiwAHK5HP369UNWVhYCAgKwePFiaXodHR3s2LEDY8eOhb+/P4yMjBAYGIjZs2drqkpERERERERE5UajSfsff/zx3PH6+vpYtGgRFi1aVGwZV1dX7Ny5s7xDIyIiIiIiItI4rXumnYiIiIiIiIjyMWknIiIiIiIi0lJM2omIiIiIiIi0FJN2IiIiIiIiIi3FpJ2IiIiIiIhISzFpJyIiIiIiItJSTNqJiIiIiIiItBSTdiIiIiIiIiItxaSdiIiIiIiISEsxaSciIiIiIiLSUkzaiYiIiIiIiLQUk3YiIiIiIiIiLcWknYiIiIiIiEhLMWknIiIiIiIi0lJM2omIiIiIiIi0FJN2IiIiolLIzMzEjRs3kJmZqelQiIioEmnq959JOxEREVEpREVFYcyYMYiKitJ0KEREVIk09fvPpJ2IiIiIiIhISzFpJyIiIiIiItJSTNqJiIiIiIiItBSTdiIiIiIiIiItxaSdiIiIiIiISEsxaSciIiIiIiLSUkzaiYiIiIiIiLQUk3YiIiIiIiIiLcWknYiIiIiIiEhLMWknIiIiIiIi0lJM2omIiIiIiIi0FJN2IiIiIiIiIi2l0aR97ty5aN68OUxMTGBra4u+ffvi+vXramXat28PmUym9vfee++plYmKikLPnj1haGgIW1tbfPzxx8jNza3MqhARERERERGVO11NLvzIkSMYP348mjdvjtzcXHz++efo2rUrrl69CiMjI6nc6NGjMXv2bOmzoaGh9H9eXh569uwJe3t7nDx5EjExMRg2bBj09PTwzTffVGp9iIiIiIiIiMqTRpP23bt3q31etWoVbG1tERISgrZt20rDDQ0NYW9vX+Q89u7di6tXr2L//v2ws7NDo0aNMGfOHHz66aeYOXMmFApFoWmysrKQlZUlfU5JSSmnGhERERERERGVH616pj05ORkAYGlpqTZ87dq1sLa2Rv369TF16lQ8efJEGnfq1Ck0aNAAdnZ20rCAgACkpKTgypUrRS5n7ty5MDMzk/6cnZ0roDZEREREREREL0ejV9qfplKpMHnyZLRq1Qr169eXhg8ePBiurq5wdHTEpUuX8Omnn+L69evYvHkzACA2NlYtYQcgfY6NjS1yWVOnTsWUKVOkzykpKUzciYiIiIiISOtoTdI+fvx4hIWF4fjx42rDx4wZI/3foEEDODg4oFOnToiMjISHh0eZlqVUKqFUKl8qXiIiIiIiIqKKphW3x0+YMAE7duzAoUOH4OTk9NyyLVq0AABEREQAAOzt7REXF6dWpuBzcc/BExEREREREVUFGk3ahRCYMGECtmzZgoMHD8LNze2F04SGhgIAHBwcAAD+/v64fPky4uPjpTL79u2DqakpfH19KyRuIiIiIiIiosqg0dvjx48fj6CgIGzbtg0mJibSM+hmZmYwMDBAZGQkgoKC0KNHD1hZWeHSpUv44IMP0LZtW/j5+QEAunbtCl9fXwwdOhTz589HbGwspk2bhvHjx/MWeCIiIiIiIqrSNHqlfcmSJUhOTkb79u3h4OAg/f39998AAIVCgf3796Nr167w9vbGhx9+iH79+uHff/+V5qGjo4MdO3ZAR0cH/v7+ePvttzFs2DC197oTERERERERVUUavdIuhHjueGdnZxw5cuSF83F1dcXOnTvLKywiIiIiIiIiraAVHdERERERERERUWFM2omIiIiIiIi0FJN2IiIiIiIiIi3FpJ2IiIiIiIhISzFpJyIiIiIiItJSTNqJiIiIiIiItBSTdiIiIiIiIiItxaSdiIiIiIiISEsxaSciIiIiIiLSUkzaiYiIiIiIiLQUk3YiIiIiIiIiLcWknYiIiIiIiEhLMWknIiIiIiIi0lJM2omIiIiIiIi0FJN2IiIiIiIiIi3FpJ2IiIiIiIhISzFpJyIiIiIiItJSTNqJiIiIiIiItBSTdiIiIiIiIiItxaSdiIiIiIiISEuVKWl3d3fHo0ePCg1PSkqCu7v7SwdFRERERERERGVM2u/cuYO8vLxCw7OysnD//v2XDoqIiIiIiIiIAN3SFN6+fbv0/549e2BmZiZ9zsvLw4EDB1C7du1yC46IiIiIiIioJitV0t63b18AgEwmQ2BgoNo4PT091K5dGz/88EO5BUdERERERERUk5UqaVepVAAANzc3BAcHw9raukKCIiIiIiIiIqJSJu0Fbt++Xd5xEBEREREREdEzypS0A8CBAwdw4MABxMfHS1fgC6xYseKlAyMiIiIiIiKq6cqUtM+aNQuzZ89Gs2bN4ODgAJlMVt5xEREREREREdV4ZXrl29KlS7Fq1SqcOXMGW7duxZYtW9T+Smru3Llo3rw5TExMYGtri759++L69etqZTIzMzF+/HhYWVnB2NgY/fr1Q1xcnFqZqKgo9OzZE4aGhrC1tcXHH3+M3NzcslSNiIiIiIiISGuUKWnPzs5Gy5YtX3rhR44cwfjx43H69Gns27cPOTk56Nq1K9LT06UyH3zwAf79919s3LgRR44cwYMHD/DGG29I4/Py8tCzZ09kZ2fj5MmT+PPPP7Fq1SpMnz79peMjIiIiIiIi0qQyJe3vvPMOgoKCXnrhu3fvxvDhw1GvXj00bNgQq1atQlRUFEJCQgAAycnJ+OOPP/Djjz+iY8eOaNq0KVauXImTJ0/i9OnTAIC9e/fi6tWr+Ouvv9CoUSN0794dc+bMwaJFi5Cdnf3SMRIRERERERFpSpmeac/MzMTy5cuxf/9++Pn5QU9PT238jz/+WKZgkpOTAQCWlpYAgJCQEOTk5KBz585SGW9vb7i4uODUqVN49dVXcerUKTRo0AB2dnZSmYCAAIwdOxZXrlxB48aNCy0nKysLWVlZ0ueUlJQyxUtERERERERUkcqUtF+6dAmNGjUCAISFhamNK2undCqVCpMnT0arVq1Qv359AEBsbCwUCgXMzc3VytrZ2SE2NlYq83TCXjC+YFxR5s6di1mzZpUpTiIiIiIiIqLKUqak/dChQ+UdB8aPH4+wsDAcP3683Of9rKlTp2LKlCnS55SUFDg7O1f4comIiIiIiIhKo8zvaS9PEyZMwI4dO3D06FE4OTlJw+3t7ZGdnY2kpCS1q+1xcXGwt7eXypw9e1ZtfgW9yxeUeZZSqYRSqSznWvxPVFQUEhISymVe1tbWcHFxKZd5ERERERERUdVSpqS9Q4cOz70N/uDBgyWajxACEydOxJYtW3D48GG4ubmpjW/atCn09PRw4MAB9OvXDwBw/fp1REVFwd/fHwDg7++Pr7/+GvHx8bC1tQUA7Nu3D6ampvD19S1L9V5KVFQUvH18kPHkSbnMz8DQENfCw5m4ExERERER1UBlStoLnmcvkJOTg9DQUISFhSEwMLDE8xk/fjyCgoKwbds2mJiYSM+gm5mZwcDAAGZmZhg1ahSmTJkCS0tLmJqaYuLEifD398err74KAOjatSt8fX0xdOhQzJ8/H7GxsZg2bRrGjx9foVfTi5OQkICMJ08w5NPvYOfi8VLziouKxNp5HyMhIYFJOxERERERUQ1UpqR9wYIFRQ6fOXMm0tLSSjyfJUuWAADat2+vNnzlypUYPny4tCy5XI5+/fohKysLAQEBWLx4sVRWR0cHO3bswNixY+Hv7w8jIyMEBgZi9uzZpatUObNz8YBTnXoajYGIiKimqow3xdy9e7fc50naLyYmRtMhkBbg979m0tR2L9dn2t9++2288sor+P7770tUXgjxwjL6+vpYtGgRFi1aVGwZV1dX7Ny5s8RxEhERUfVWGW+K+frrryt0/kSkvfj9p8pUrkn7qVOnoK+vX56zJCIiIiq1ynhTzBdffAFXV9dynSdpv9OnT2PFihWaDoM0jN//munu3bsaOWFTpqT9jTfeUPsshEBMTAzOnTuHL7/8slwCIyIiIiqrin5TDJB/p1/dunUrdBmkfXhbNAH8/lPlKlPSbmZmpvZZLpfDy8sLs2fPRteuXcslMCIiIiIiIqKarkxJ+8qVK8s7DiIiIiIiIiJ6xks90x4SEoLw8HAAQL169dC4ceNyCYqIiIiIiIiIypi0x8fHY9CgQTh8+DDMzc0BAElJSejQoQPWr18PGxub8oyRiIiIiIiIqEaSl2WiiRMnIjU1FVeuXEFiYiISExMRFhaGlJQUTJo0qbxjJCIiIiIiIqqRynSlfffu3di/fz98fHykYb6+vli0aBE7oiMiIiIiIiIqJ2W60q5SqaCnp1douJ6eHlQq1UsHRURERERERERlTNo7duyI999/Hw8ePJCG3b9/Hx988AE6depUbsERERERERER1WRlStoXLlyIlJQU1K5dGx4eHvDw8ICbmxtSUlLw66+/lneMRERERERERDVSmZ5pd3Z2xvnz57F//35cu3YNAODj44POnTuXa3BERERERERENVmprrQfPHgQvr6+SElJgUwmQ5cuXTBx4kRMnDgRzZs3R7169XDs2LGKipWIiIiIiIioRilV0v7TTz9h9OjRMDU1LTTOzMwM7777Ln788cdyC46IiIiIiIioJitV0n7x4kV069at2PFdu3ZFSEjISwdFRERERERERKVM2uPi4op81VsBXV1dPHz48KWDIiIiIiIiIqJSJu21atVCWFhYseMvXboEBweHlw6KiIiIiIiIiEqZtPfo0QNffvklMjMzC43LyMjAjBkz0KtXr3ILjoiIiIiIiKgmK9Ur36ZNm4bNmzejbt26mDBhAry8vAAA165dw6JFi5CXl4cvvviiQgIlIiIiIiIiqmlKlbTb2dnh5MmTGDt2LKZOnQohBABAJpMhICAAixYtgp2dXYUESkRERERERFTTlCppBwBXV1fs3LkTjx8/RkREBIQQqFOnDiwsLCoiPiIiIiIiIqIaq9RJewELCws0b968PGMhIiIiIiIioqeUqiM6IiIiIiIiIqo8TNqJiIiIiIiItBSTdiIiIiIiIiItxaSdiIiIiIiISEsxaSciIiIiIiLSUkzaiYiIiIiIiLQUk3YiIiIiIiIiLaXRpP3o0aPo3bs3HB0dIZPJsHXrVrXxw4cPh0wmU/vr1q2bWpnExEQMGTIEpqamMDc3x6hRo5CWllaJtSAiIiIiIiKqGBpN2tPT09GwYUMsWrSo2DLdunVDTEyM9Ldu3Tq18UOGDMGVK1ewb98+7NixA0ePHsWYMWMqOnQiIiIiIiKiCqeryYV3794d3bt3f24ZpVIJe3v7IseFh4dj9+7dCA4ORrNmzQAAv/76K3r06IHvv/8ejo6O5R4zERERERERUWXR+mfaDx8+DFtbW3h5eWHs2LF49OiRNO7UqVMwNzeXEnYA6Ny5M+RyOc6cOVPsPLOyspCSkqL2R0RERERERKRttDpp79atG1avXo0DBw5g3rx5OHLkCLp37468vDwAQGxsLGxtbdWm0dXVhaWlJWJjY4ud79y5c2FmZib9OTs7V2g9iIiIiIiIiMpCo7fHv8igQYOk/xs0aAA/Pz94eHjg8OHD6NSpU5nnO3XqVEyZMkX6nJKSwsSdiIiIiIiItI5WX2l/lru7O6ytrREREQEAsLe3R3x8vFqZ3NxcJCYmFvscPJD/nLypqanaHxEREREREZG2qVJJ+7179/Do0SM4ODgAAPz9/ZGUlISQkBCpzMGDB6FSqdCiRQtNhUlERERERERULjR6e3xaWpp01RwAbt++jdDQUFhaWsLS0hKzZs1Cv379YG9vj8jISHzyySfw9PREQEAAAMDHxwfdunXD6NGjsXTpUuTk5GDChAkYNGgQe44nIiIiIiKiKk+jV9rPnTuHxo0bo3HjxgCAKVOmoHHjxpg+fTp0dHRw6dIlvPbaa6hbty5GjRqFpk2b4tixY1AqldI81q5dC29vb3Tq1Ak9evRA69atsXz5ck1ViYiIiIiIiKjcaPRKe/v27SGEKHb8nj17XjgPS0tLBAUFlWdYRERERERERFqhSj3TTkRERERERFSTMGknIiIiIiIi0lJM2omIiIiIiIi0FJN2IiIiIiIiIi3FpJ2IiIiIiIhISzFpJyIiIiIiItJSTNqJiIiIiIiItBSTdiIiIiIiIiItxaSdiIiIiIiISEsxaSciIiIiIiLSUkzaiYiIiIiIiLQUk3YiIiIiIiIiLcWknYiIiIiIiEhLMWknIiIiIiIi0lJM2omIiIiIiIi0FJN2IiIiIiIiIi3FpJ2IiIiIiIhISzFpJyIiIiIiItJSTNqJiIiIiIiItBSTdiIiIiIiIiItxaSdiIiIiIiISEsxaSciIiIqBRcXFyxfvhwuLi6aDoWIiCqRpn7/dSt1aURERERVnL6+PurWravpMIiIqJJp6vefV9qJiIiIiIiItBSTdiIiIiIiIiItxaSdiIiIiIiISEsxaSciIiIiIiLSUkzaiYiIiIiIiLQUk3YiIiIiIiIiLaXRpP3o0aPo3bs3HB0dIZPJsHXrVrXxQghMnz4dDg4OMDAwQOfOnXHz5k21MomJiRgyZAhMTU1hbm6OUaNGIS0trRJrQURERERERFQxNPqe9vT0dDRs2BAjR47EG2+8UWj8/Pnz8csvv+DPP/+Em5sbvvzySwQEBODq1avQ19cHAAwZMgQxMTHYt28fcnJyMGLECIwZMwZBQUGVXR0iIqqCoqKikJCQUC7zsra2houLS7nMi4iIiAjQcNLevXt3dO/evchxQgj89NNPmDZtGvr06QMAWL16Nezs7LB161YMGjQI4eHh2L17N4KDg9GsWTMAwK+//ooePXrg+++/h6OjY6XVhYiIqp6oqCh4+/gg48mTcpmfgaEhroWHM3EnIiKicqPRpP15bt++jdjYWHTu3FkaZmZmhhYtWuDUqVMYNGgQTp06BXNzcylhB4DOnTtDLpfjzJkzeP3114ucd1ZWFrKysqTPKSkpFVcRIiLSWgkJCch48gRDPv0Odi4eLzWvuKhIrJ33MRISEpi0ExERUbnR2qQ9NjYWAGBnZ6c23M7OThoXGxsLW1tbtfG6urqwtLSUyhRl7ty5mDVrVjlHTEREVZWdiwec6tTTdBhEREREhdTI3uOnTp2K5ORk6S86OlrTIREREREREREVorVX2u3t7QEAcXFxcHBwkIbHxcWhUaNGUpn4+Hi16XJzc5GYmChNXxSlUgmlUln+QVON9CQ7F/eTMpD8JAfp2XmQywClng6sjRWwN9WHoUJrv2ZERERERKTltDabcHNzg729PQ4cOCAl6SkpKThz5gzGjh0LAPD390dSUhJCQkLQtGlTAMDBgwehUqnQokULTYVONUCeSuB6XCrC7icjJjnzuWWdLQxgL5cDshp5YwsREREREb0EjSbtaWlpiIiIkD7fvn0boaGhsLS0hIuLCyZPnoyvvvoKderUkV755ujoiL59+wIAfHx80K1bN4wePRpLly5FTk4OJkyYgEGDBrHneKoQQuQn6yciHiEtK1cabm2sgLWxEkZKXQgh8CQ7D/GpWUhMz0b04wxEQxeOoxbh1L0MNG4sIJPJNFgLIiIiIiKqKjSatJ87dw4dOnSQPk+ZMgUAEBgYiFWrVuGTTz5Beno6xowZg6SkJLRu3Rq7d++W3tEOAGvXrsWECRPQqVMnyOVy9OvXD7/88kul14Wqv9TMHOwPj0dUYv6roQwVOmjkbA5vexOY6OsVOU1yRg6uxaTg/N1HgJUzvjuZhMvJ5/HV6/VhbcxHNIiIiIiI6Pk0mrS3b98eQohix8tkMsyePRuzZ88utoylpSWCgoIqIjwiSVTiE+wOi0VGTh505DK8UtsSTVzMoavz/FvezQz00MLdCrY5sVi9YTOsWg3C7iuxOHf3MZYNbYKmrpaVVAMiIiIiIqqK+JAt0QtEpsqx5cJ9ZOTkwcZEiSEtXPCKm+ULE/an6cmB5GN/YX5na9S1M0ZCWhYGLT+Njef45gIiIiIiIioek3aiYgghYNZ6MEIf59+Q4utgigFNnWBhqCjzPN0s9LBlXCt0q2ePnDyBjzddwu/HbpVXyEREREREVM0waScqghACay+nwrzVYABACzdLdPaxLdXV9eIYKXWxeEgTvNvWHQDw1X/hWHQo4gVTERERERFRTcSknagICw9GYPO1dABAQ4tcvOpuVa49vsvlMnzW3RtTutQFAHy35zpWnbhdbvMnIiIiIqLqgUk70TPWnrmLH/bdAAAkHvwdniaqClmOTCbDpE51MLlzHQDAzH+vYuuF+xWyLCIiIiIiqpqYtBM95dC1eHy5NQwAMMDXGKnBWyt8me93qoPhLWsDAD7edBFnbydW+DKJiIiIiKhqYNJO9P9uxqViQtB5qATQv6kTBtYzrpTlymQyTO/lix4N8june3fNOdx9lF4pyyYiIiIiIu3GpJ0IQFpWLt77KwTp2Xl41d0S37zeoFyfYX8RuVyGH95sBD8nMzx+koMxq0OQkZ1XacsnIiIiIiLtpKvpAIg0TQiBz/65hMiH6bAzVWLh4CZQ6Fb++SwDhQ5+H9YMPX45jutxqZi+LQzfvdmw0uOgkstRATfjU/EwNQuPn+QgO1cFlRAw0NOBib4u7Ez14WRhAEMFf2qJiIiIqGx4JEk13p8n72DHpRjoymVYPKQJrI2VGovF1lQfv7zVCG//fgYbQ+7hFTdLvNnMWWPxUGEqlUDwg0zYvjkL/97Tg7gX+8JpnMwNUM/RFHXsTKAjr7w7OIiIiIio6mPSTjVayN3H+HpnOADg8x4+aOpqqeGIgJYe1pjcuS5+3HcDX24Lg5+TObzsTTQdVo0nhMD+8Hh8uysckQ/TYeDeFAKAhaEeapkbwMJIAQM9HchkQEZ2HpIzcnA/KQMJadm4l5SBe0kZOHXrEVq4WcHbwQTySnz8goiIiIiqLibtVGMlP8nBhKDzyMkT6OnngBGtams6JMn4Dp4IvpOIYzcTMG5tCLZPaA0jJb+umnLv8RNM3XwZx24mAACM9GR4cHwT3uzbGz6+dZ47bUpmDsIfpODS/WSkZOZiX3gcLt9PRmcfW1hp8K4OIiIiIqoa2BEd1VhfbgtDTHIm3KyNMK+fX6V2PPciOnIZFgxsBDtTJSIfpmPG9iuaDqnG2hRyD91+OoZjNxOg0JVjXHsPLOtli6TDK2Gi9+LpTfX10MLdCsNb1kYrTysodOSITclE0NkoXIh6DCEqvg5EREREVHUxaacaafvFB9h+8YGUHBtr4VVsa2MlfhnUGDJZfuK498qLn52m8pOVm4fPt1zGRxsvIi0rF81cLbB3clt80s0bhnql/+nU05Gjmasl3n7VBW7WRlAJ4OjNBJxO0IVMYVABNSAiIiKi6oBJO9U4scmZmLblMgBgQgdPNHI212xAz9HC3Qpj2rgDAKZuvoyEtCwNR1QzJD3Jxtu/n0HQmSjIZMCULnXx97v+qG1t9NLzNtHXQ28/B7SvawO5DHiQIYf94HlIeMJX/BERERFRYUzaqUZRqQQ+3nQRKZm5aOhkhgkdPTUd0gt90KUuvOxM8Cg9G59vvgzB+6kr1P2kDPRfegrBdx7DRF8XK4Y3x6ROdcq113eZTIaGzuZ4s6kzlHIBhZ07PjuQgJtxqeW2DCIiIiKqHpi0U42y+tQdHLuZAH09OX4c2Ah6Otr/FdDX08GPAxtCT0eGvVfj8M/5+5oOqdq69/gJBiw9hYj4NDiY6WPTey3Rwcu2wpZnb6aPDvY5yE64i8QMFQYuP40rD5IrbHlEREREVPVof8ZCVE7uJKTj293XAOS/3s3DxljDEZVcPUczTO5cFwAwa/sV3Hv8RMMRVT8xyRkY/NsZ3E/KgLu1Ef4Z27JSXrVnpAvErf0MHhZ6SEzPxlvLT+Pqg5QKXy4RERERVQ1M2qlGUKkEPtt8CZk5KrT0sMLQV101HVKpvdvWHU1czJGalYupvE2+XMWnZGLwb2cQlfgErlaGCBr9KhzNK69zOFVmKma2s0QTF3OkZOZi2IqzuJOQXmnLJyIiIiLtxaSdaoR1wVE4fSsRBno6+PYN7Xq9W0np6sjx/ZsNodSV49jNBPwdHK3pkKqFh6lZeOu307idkA4nCwMEjX4V9mb6lR6HkUKOlSNegY+DKRLSsvD2H2cQl5JZ6XEQERERkXZh0k7V3oOkDMzdmX9b/McBXnCxMtRwRGXnbmOMj7p6AQC+/i8cD5IyNBxR1fYkOxcjVwUj8mE6HM30sW70q6hViVfYn2VmoIc/RzaHq5Uh7j3OwNA/ziDpSbbG4iEiIiIizWPSTtWaEAKfb7mMtKxcNHExR2DL2poO6aWNbO2GxrxN/qXlqQQmrbuAy/eTYWmkwNrRr8LZUvMndGxN9PHXqBawM1XiRlwaRqwKRmYOXwdHREREVFMxaadqbfP5+zh8/SEUunLM79+wXF/bpSk6chm+6+8Hha4cR248xKaQe5oOqUqas+Mq9ofHQ6Erx2/DmsGtHN7BXl6cLQ2xemQLmBno4UJUEj7ZdIknZ4iIiIhqKCbtVG3Fp2Zi9o6rAID3O9WBp23V6S3+RTxtTfDB//cmP3vHVcQm89nn0lhx/DZWnbwDAFgwoBGaulpoNqAieNmbYMnbTaArl2H7xQdYeDBC0yERERERkQYwaadqa8a2K0jOyEE9R1OMaeuu6XDK3eg2bmjoZIbUzFx8sYW3yZfUgfA4zPkv/2TOZ9290dPPQcMRFa+lhzVm96kPAPhh3w3suhyj4YiIiIiIqLIxaadqaeflGOwKi4WuXIb5/f2gp1P9dnVdnfxb/vV0ZDhwLR5bQ+9rOiStFxGfivfXh0II4K1XXPBuFTiZM7iFC4b/f18MUzZcRNj9ZM0GRERERESVqvplMlTjJaZnY/q2MADA2PYeqOdopuGIKo6XvQne71QHADBz+1XEp/I2+eIkZ+Rg9OoQpGXlooWbJWb3qVdlXv03racP2ta1QUZOHsasPodHaVmaDomIiIiIKgmTdqp2Zv97BQlp2ahja4wJHT01HU6Fe7edB+o5miI5IwfTtoTxNvki5KkE3l9/AbcT0lHL3ACLhzSpUndf6OrI8etbjeFubYQHyZmYtP4CcvNUmg6LiIiIiCpB1TlqJSqB/VfjsDX0AeQy4Ls3G0Kpq6PpkCqcno4c37/ZELpyGfZejcO/l/jc87O+33sdh68/hL6eHMuGNoWVsVLTIZWamYEelg5tCkOFDk5EPMIP+25oOiQiIiIiqgRanbTPnDkTMplM7c/b21san5mZifHjx8PKygrGxsbo168f4uLiNBgxaVJyRg4+33IZADC6jTsaOZtrNqBK5ONgKt1VMGNbGBJ4+7Rkx6UHWHI4EgAwr58f6tequo9L1LUzwbx+fgCAJYcjsTssVsMREREREVFF0+qkHQDq1auHmJgY6e/48ePSuA8++AD//vsvNm7ciCNHjuDBgwd44403NBZrZk4ewh9mQ8/WHak5QHYub1+tTF/tuIr41Cy4Wxvhgy51NR1OpRvX3hPe9iZ4/CRHeqa/prsWm4KPN14CALzb1h19GtXScEQvr3dDR4xq7QYA+GjjRdx6mKbhiIiIiIioIulqOoAX0dXVhb29faHhycnJ+OOPPxAUFISOHTsCAFauXAkfHx+cPn0ar776amWHiujEJ/ji0CM4jvgFe2MAxETCQE8H1iYKOJgawMXSEA5m+pDLq0bnV1XJkRsPsTHkHmQyYH5/P+jrVf/b4p+l0M2/Tb7PohPYeTkWOy/HoEcD7X2dWUVLepKNMatDkJGThzZ1rPFJN+8XT1RFfNbdG5fvJePsnUS891cItoxrBSOl1v+cExEREVEZaP2V9ps3b8LR0RHu7u4YMmQIoqKiAAAhISHIyclB586dpbLe3t5wcXHBqVOnnjvPrKwspKSkqP2VBwHAwVgHuamPoCvL7wwsIycP0YkZOHsnEZvO38Nvx2/h4LV4xKWwl+/ykpqZg6n/5F9NDfSvjWa1LTUckebUr2WGce09AABfbg1DYnq2hiPSjPyO50IRlfgEThYG+GVQY+hUo5NlejpyLBzSGLYmStyIS8On/1xiB4RERERE1ZRWJ+0tWrTAqlWrsHv3bixZsgS3b99GmzZtkJqaitjYWCgUCpibm6tNY2dnh9jY5z/nOXfuXJiZmUl/zs7O5RJvXTsTLOphi/uLA9HHOQfvtXPHoObO6OhlCy97E+jrypGZo8Ll+8lYHxyNDeeiEfkwjQfbL+nbXdfwIDkTzpYG+KSbl6bD0bgJHT1R184Yj9KzMWP7FU2HoxE/7ruOIzf+1/GchZFC0yGVO1sTfSwe0gS6chl2XIrBihN3NB0SEREREVUArU7au3fvjjfffBN+fn4ICAjAzp07kZSUhA0bNrzUfKdOnYrk5GTpLzo6upwiVqfU1YGdqT4aOJmhWz17jG7jjtcb10JdO2PoyGSISc7Ejksx+PtcNB4kZVRIDNXdyYgErD2Tf/fFvH5+MFTwFmGlrg6+f7MhdOQy/HvxAfZcqVmdle0Oi8GiQ//reK6eY9XteO5FmtW2xLSePgCAb3aG4+ztRA1HRERERETlTauT9meZm5ujbt26iIiIgL29PbKzs5GUlKRWJi4urshn4J+mVCphamqq9lcZ5HIZXCwN0b2+A0a0qo1mrhbQ05EhLiULG0PuYeflGCRn5FRKLNVB8pMcfLjxIgBgSAsXtPSw1nBE2sPPyRxj2roDAL7YUnN6k78Zl4oPN+TvE6Nau1WLjudeJLBlbfRp5Ig8lcD4oPOI56M3RERERNVKlUra09LSEBkZCQcHBzRt2hR6eno4cOCANP769euIioqCv7+/BqMsGSOlLlp5WiPQvzbqO5pCBuBmfBrWnLqL4DuJUKl4y/yLfLktDDHJmahtZYjPe/hoOhyt836nOqhrZ4yEtCx8sqn6P/OckpmDMWtCkJ6dh1fdLTG1e/XpeO55ZDIZ5r7RAF52JniYmoWxa8/zzRVERERE1YhWJ+0fffQRjhw5gjt37uDkyZN4/fXXoaOjg7feegtmZmYYNWoUpkyZgkOHDiEkJAQjRoyAv7+/RnqOLysjpS46+dhhcAsXuFgaIk8InIx8hA0h0UjhRfdibQu9j+0XH0BHLsOPAxux5+wi6Ovp4Je3GkOhK8fBa/FYdfKOpkOqMCqVwAfrQ3E7IR2OZvpYNLgJdHW0+uetXBkqdLF0aFOY6Osi5O5jzPy3ZvZlQERERFQdafVR7b179/DWW2/By8sLAwYMgJWVFU6fPg0bGxsAwIIFC9CrVy/069cPbdu2hb29PTZv3qzhqMvG2liJvo0c0dXXDgpdOeJSsnAgRg+mr7wOVTW/Qlpa95MyMG1r/nvIJ3b0RBMXCw1HpL287U3xxf/fhTB35zWEx5TPmxK0zc8HbuLAtXgodOVYNrQZrIyVmg6p0rlZG+GXtxpDJgOCzkRh3dkoTYdEREREROVAq5P29evX48GDB8jKysK9e/ewfv16eHh4SOP19fWxaNEiJCYmIj09HZs3b37h8+zaTCaTwcfBFG+3cIGrlSFUkMGiwyh8dewxHtWQZ5JfRKUS+HBDKFIzc9HI2RwTOnhqOiStN8zfFZ28bZGdp8LEdReQkZ2n6ZDK1Y5LD/DzgZsAgLmvN0ADp+rb8dyLdPCyxUdd89+gMH1bGELusmM6IiIioqpOq5P2mspEXw99GjqisWUuVDlZCI3NQo9fjrFnaACLD0fg9K1EGCp08NPARjXqFuiykslkmN/fD7YmSkTEp2H2jquaDqncXIh6LHU8N7KVG/o1ddJwRJo3rr0Hute3R06ewHt/nUccO6YjIiIiqtL4ILCWkslkcDdW4b/vpqD55KW4n5KFt347jSld6mJsOw/I5TJNh1jpTkQk4Md9NwAAs16rh9rWRhqOqOqwMlbixwGNMHTFGaw7G4XGzuYY0NxZ02G9lHuPn2D06hBk5arQydsWX/RkZ4RA/m/H9282xK2H6bgel4r3/grB+jGvQqmro+nQiIiqFVl2GuTpCZoOgyqZPDNZ0yFQDcSkXcvlJNzF/M7W2HRHF1su3Md3e64jNDoJPwxoCFN9PU2HV2likzMxad0FqAQwsJkz3mxWtRNOTWhdxxqTO9XFgv03MG1rGOrYGaNxFe0PIDUzB6NWnUNCWhZ8HEzx81uNoVMDT2QVx0ipi+XDmqL3r8dxISoJM7dfwdw3/DQdFhFRteDs7Ay5XAf6988D989rOhzSAD2FEmZmNfdxPKp8TNqrAAM9OX4c0BAt3CwxffsV7Lsahz4LT2Dp203hZW+i6fAqXE6eChOCzuNRejZ8HEwxq089TYdUZU3s6IkrD5Kx92ocxv51HtsntoKtib6mwyqV7FwVJgRdwPW4VNiaKPFHYDMY8+0Bhbha5XdMN2JVMNadjYa3vSkCW9bWdFhERFWet7c31q0LQnIyr7jWVGZmZrCzs9N0GFSD8Ei3ipDJZBj0igt8HU3x3poQ3E5IR99FJzC/vx96N3TUdHgVat6uazh39zFMlLpYMqQJ9PV4m29ZyeUy/DCgIfouOoHIh+kYv/Y81r7zKhS6VaNvgDyVwIcbL+LIjYfQ15Pj98BmcDQ30HRYWqu9ly0+7eaNb3ddw6x/r6CWuQE6+/Igg4joZdnZ2TFpI6JKUzWO1Eni52SOfye2RitPK2Tk5GHiuguYs+MqcvJUmg6tQmw+fw+/H78NAPh+QEM+x14OTPT1sHxYM5godRF85zFmbL8CUQVeKyiEwJfbwvDvxQfQ05Fh6dtN4edkrumwtN67bd0xqLkzVAKYuO4CLt/jlSEiIiKiqoRJexVkZazEnyNewXvt8l9/98fx23j79zN4mFq9Xgt3MiIBn/5zCQAwtr0HAupV3df5aRsPG2P8NKgRZDJg3dkoLD4cqemQXui7PdcRdCYKMhmwYGAjtPey1XRIVYJMJsOcvvXRpo41MnLyMPLPYNx7/ETTYRERERFRCTFpr6J0deT4rLs3lr7dBEYKHZy5nYhevx7D+ajHmg6tXNyMS8W7f4UgJ0+gp58DPv7/d09T+enkY4cZvXwB5CfEG89Fazii4i0+HCGdWPjm9Qbo5Ve9Hwkpb3o6ciwe0gTe9iZ4mJqFESuDkfwkR9NhEREREVEJMGmv4rrVd8C2Ca3hYWOEuJQsDFx2CmtO360StzsXJz41E8NXBiM1MxfNXC3ww5sNa+Qr7irD8FZueLetOwDg038uYcelBxqOSJ0QAvN3X8P83dcBAJ9198Zbr7hoOKqqyURfDyuGN4edqRI349MQuPIs0rJyNR0WEREREb0Ak/ZqwNPWGNsmtEb3+vbIyRP4cmsYPtp4CZk5eZoOrdQKXuV1PykDbtZGWD6sGTueq2CfdvOWnnmevD4Uu8NiNR0SAEClEpi2NUy6wv5pN2/pkRAqG0dzA6we2QLmhnoIjU7CyFXByMiuer8TRERERDUJk/Zqwlipi8VDmmBqd2/IZcA/5++h35KTiE6sOs+upmflYvjKYFy+nwwLQz2sHN4clkYKTYdV7cnlMnz9egO83rgWclUC44POY/P5exqNKSdPhcl/h2Lt/z/D/s3rDTC2PRP28uBlb4I1I1vARKmLs7cTMWbNOWTlMnEvjdw8FRLTs3E7IR2X7yfjfNRjnLn1CJcf68Cyy3tIeML1SUREROWHr3yrRmQyGd5t54EGtcwwYd0FXHmQgt4Lj+PnQY3Rrq6NpsN7rrSsXIxcFYyQu49hqq+LNaNasKf4SqQjl+G7/n7QkcuwKeQepmy4iEdp2XinjRtkssp9NOFxejYmrDuPExGPoCuXYcHARtX+tYaVrYGTGVaNbI6hf5zFsZsJGL/2AhYNaQylLu9qeVZ2rgrxqZmISc5EbHIm4lOznvNYgQ5MmvTC4wwm7URERFR+eKW9GmrpaY0dE1ujoZMZkp7kYPjKs/h+z3Vk52rna+Eep2djyG+ncfZ2IkyUulg9qgXq1zLTdFg1jq6OHPP7+WF4y9oAgK93huPzLZcr9XWC4TEp6LPoBE5EPIKhQge/DWvGhL2CNHW1xO/DmkGhK8f+8DiMWnWOz7gjvx+FhLQsnLuTiE0h97D0aCT+OX8fJyMf4VZCurSO9HRksDZWwM3aCF72JqhfyxR1TPKQdCII5vo8+UFERETlh1faqylHcwP8/a4/Zv17BevORmPhoQgcuh6PHwc0gpe9iabDk9x9lI6Rq4IR+TAdFoZ6WD2yBRo4MWHXFLlchhm9feFkYYCvd4Zj3dlohMek4te3GsPZ0rDCliuEwOpTd/H1znBk56rgZGGA34Y1g4+DaYUtk/JP8K0IbI4xa87heEQChvx2GiuGN4eVsVLToVW6x1ky3L75EDfj05CaqX7ywlipCwczfdib6cPOVB8Whnow0NMpdBfKvZsJ2H88CDZGH1Zm6ERERFTN8Up7Naavp4O5b/hh0eAmsDDUy79d/tfjWHYkEnkqzfcuf+bWI/RddAKRD9PhYKaPje/5M2HXAjKZDO+0cccfgc1gqq+L0Ogk9PjlGDaci66QtxLcSUjH0D/OYsb2K8jOVaGDlw22T2jNhL2StK5jjXWjX4WlkQIX7yXjzWWncD8pQ9NhVYroxCdYF5YKx9HLcTBOD+ejkpCamQtduQy1rQzR3ssGw1vWxqjWbujRwAFNXCxQy9wAhgrdSn9shIiIiGouXmmvAXr6OaB5bQt8tvkyDl6Lx9xd17At9AFm96mHZrUtKz0elUpg6dFI/LD3BvJUAn5OZvhtWDPYmepXeixUvI7edvhvUhtMWn8BF6KS8MmmS9hy/j6m9fJBPceXP7mSkpmDZUci8fux28jKVUGpK8en3bwxolVtJkSVrKGzOTa8649hf5zBrYfpeH3RCSwe0kQjvw8VLTtXhX1X47A+OArHIxIgBKBn6QgdmYCHjQnq2pvA1dIQujo8p01ERETagUclNYStqT7+CGyGb99oAFN9XVyNSUH/pacw5e9QxKdkVloc0YlPMGzFWczffR15KoG+jRzx9xh/JuxaytnSEBve9cfU7t7Q15Pj1K1H6PnLcYwPOo/zUY/LdOU9PiUTP+y9jrbzD2HRoUhk5arQytMKeya3xcjWld/xHeXztDXGP+Naoq6dMeJTszBo+WmsOH67Qu6u0IRbD9Mwd2c4/OcewPig8zh2Mz9h97NT4OH279CrVg66N3CAh40xE3YiIiLSKrzSXoPIZDIMesUFnX3t8N3u69gQEo3NF+5jz5VYDPXPvwXUxqRinmXNys3D6pN3sWD/DTzJzoNSV47ZfephQDNnJmlaTk9HjnfbeaBHAwd8t+c6tl98gP8uxeC/SzHwcTBF9/r26OBlCy97Eyh0Cyc7QgjcSkjHmVuJ2H0lFiciEqTHMzxtjfFJgBe6+NpxP9ACDmYG2DKuFT795xJ2XIrB7B1XcSE6Cd++0QBGyqrXXGTm5GF3WCzWnY3CmduJ0nBbEyXebOaEgc1ckHD3Gv6dcgS68vc1GCkRERFR8areURi9NGtjJeb198PgFi6Yvv0KLkYnYemRSKw8cRsDmztjdBv3cut0LDtXhe0XH+CXAzcR9f/vjH/FzRLz+vnBja90q1KcLQ3xy1uNMba9B34/dhv/XnqA8JgUhMek4Md9N6DQkaO2tSGsjZUw0NNBjkogITUL0Y+fFOrYq5mrBUa1dkMXXzte1dQyRkpd/PpWYzRxscA3O8Px78UHuHI/Gd+92RBNXS00HV6J3IxLxbqz0dh84R6SnuQAAOQyoL2XLQY1d0ZHb1tpv0u4q8lIiYiIiF6MSXsN1tDZHFvGtsSBa/FYdCgCodFJWH3qLv46fRet69jgjca10MHbFmYGeqWed3TiE2w+fx/rg6MQk5x/+72tiRIfBXihfxMnyOW8qlpV+TiY4ocBDTGtpw/2Xo3FvqtxOHs7ESmZubgRl4YbcWmFplHoytHIyRxt61qjRwMHuNsYayByKimZTIaRrd3g52SGCUEXcCshHW8uPYnhLd0wuUsdmOqX/jehomXm5GHn5RisOxuF4DuPpeGOZvoY0NwZA5s7w8HMQIMREhEREZUNk/YaTi6XoYuvHTr72OLUrUdYfCgSxyMScPTGQxy98RC6chmaulqgWW0L+DmZw8PGCA5mBjBU5L/uSAiBtKxcxCZn4lpsKi7fT8bRGw9xLTZVWoaNiRIjW7khsKUrDBXc5aoLCyMFBjZ3wcDmLhBCIDoxA1GJT5CQloWs3DzoyuUwN9SDk4UhalsbQqnLd1dXNc1qW2LP5LaYteMKNp+/jxUnbmP7xQf4OKAu+jVx0vhdEkIIXHmQgk0h97D5/D2k/P8dHTpyGTp52+KtFi5oW8cGOjxJSERERFUYMygCkH9lraWHNVp6WOPuo3T8E3IPO8NiERGfhjO3E9WeBwUAPR0ZdOQy5KkEcvIKd1QlkwGtPKzxRpNa6NHAAfp6TNiqM5lMBhcrQ7hYVdy73EkzzAz18OOARujTqBZmbr+C2wnp+PSfy1h29BYmdvRELz9H6FVy8h716Am2hd7H1tD7iHyYLg2vZW6At15xxpvNnNm5JREREVUbTNqpEFcrI0zp6oUpXb1wJyEdp289wrm7j3E9NhW3HqYhPTsPOXnqybqxUheetsbwcTDFq+6WaFPHBpZGCg3WgojKU7u6Ntg9uQ1Wn7yLxYcjcOthOj74+yLm776Ot15xQf+mTnA0r5jbz1Uqgcv3k7E/PA77w+MRHpMijVPqytHZxw4Dmjujjac1H70hIiKiaodJOz1XbWsj1LY2wqBXXADk346akZOHx09yoFIJyOUyWBjq8bZ3ohpAqauD0W3dMegVZ6w+dRerTt5BTHImftx3Awv230AzVwsE1LNHK09reNmZlDmBzlMJXItNQfDtRATfeYwztxORkJYljZfLgFae1nitoSO61beHiRY+Y09ERERUXphpUanIZDIYKnSZpBPVYCb6ehjfwRPvtHHDzssx+Ds4Gqdv5SfYBZ3AmRnooX4tU/jYm8LFyhD2pvowUupCX08Hhor8x2WSM3KQkpGD5Iwc3E/KwK2H6Yh8mIZbD9ORkZOntkxjpS7a1rVGZx87tPey5Z08REREVGMw8yIiojJR6urg9cZOeL2xEx4kZWDvlVgcvP4Q5+4kIjkjByciHuFExKMyzdtEqYsmrhZ4xc0SzVwt0MjFnJ0ZEhERUY3EpJ2IiF6ao7kBhrdyw/BWbsjJUyE8JgVXH6TgRlwa7j1+gvjULGRk5+FJTi4ysvOvopsa6MFUXw+mBnqwM1HC09YYHjbG8LA1houlIXt9JyIiIgKTdiIiKmd6OnL4OZnDz8lc06EQERERVXmafckuERERERERERWr2iTtixYtQu3ataGvr48WLVrg7Nmzmg6JiIiIiIiI6KVUi6T977//xpQpUzBjxgycP38eDRs2REBAAOLj4zUdGhEREREREVGZVYuk/ccff8To0aMxYsQI+Pr6YunSpTA0NMSKFSs0HRoRERERERFRmVX5juiys7MREhKCqVOnSsPkcjk6d+6MU6dOFTlNVlYWsrKypM/JyckAgJSUlJeOJy0tDQBw7+YVZGU8eal5Pbx3GwAQEhIizfdlyOVyqFSql55PRcxPG+d1/fp1ANq5LQHtXGc1ZV7avG9o6zor7/lp8/c8LS3tpduTgumFEC81H/qfgnVZHm09ERFReShpey8TVfyI4MGDB6hVqxZOnjwJf39/afgnn3yCI0eO4MyZM4WmmTlzJmbNmlWZYRIREZVadHQ0nJycNB1GtXDv3j04OztrOgwiIqJCXtTeV/kr7WUxdepUTJkyRfqsUqmQmJgIKysryGQv917glJQUODs7Izo6Gqampi8bapXD+rP+rD/rz/q/fP2FEEhNTYWjo2M5RUeOjo6Ijo6GiYkJ2/qXxPqz/qw/68/6l0/9S9reV/mk3draGjo6OoiLi1MbHhcXB3t7+yKnUSqVUCqVasPMzc3LNS5TU9MauSMXYP1Zf9af9a+pyqv+ZmZm5RANFZDL5eV+1wL3ddaf9Wf9ayrWv/zqX5L2vsp3RKdQKNC0aVMcOHBAGqZSqXDgwAG12+WJiIiIiIiIqpoqf6UdAKZMmYLAwEA0a9YMr7zyCn766Sekp6djxIgRmg6NiIiIiIiIqMyqRdI+cOBAPHz4ENOnT0dsbCwaNWqE3bt3w87OrtJjUSqVmDFjRqHb72sK1p/1Z/1Zf9a/Zta/Jqnp25r1Z/1Zf9af9a/c+lf53uOJiIiIiIiIqqsq/0w7ERERERERUXXFpJ2IiIiIiIhISzFpJyIiIiIiItJSTNqJiIiIiIiItBST9lKYO3cumjdvDhMTE9ja2qJv3764fv36C6fbuHEjvL29oa+vjwYNGmDnzp2VEG35K0v9V61aBZlMpvanr69fSRGXryVLlsDPzw+mpqYwNTWFv78/du3a9dxpqsu2B0pf/+q07Yvy7bffQiaTYfLkyc8tV532gaeVpP7VaR+YOXNmobp4e3s/d5rquu2rO7b1bOvZ1rOtL8C2vma19YD2tvdM2kvhyJEjGD9+PE6fPo19+/YhJycHXbt2RXp6erHTnDx5Em+99RZGjRqFCxcuoG/fvujbty/CwsIqMfLyUZb6A4CpqSliYmKkv7t371ZSxOXLyckJ3377LUJCQnDu3Dl07NgRffr0wZUrV4osX522PVD6+gPVZ9s/Kzg4GMuWLYOfn99zy1W3faBASesPVK99oF69emp1OX78eLFlq+u2rwnY1rOtZ1vPth5gW19T23pAS9t7QWUWHx8vAIgjR44UW2bAgAGiZ8+easNatGgh3n333YoOr8KVpP4rV64UZmZmlRdUJbOwsBC///57keOq87Yv8Lz6V9dtn5qaKurUqSP27dsn2rVrJ95///1iy1bHfaA09a9O+8CMGTNEw4YNS1y+Om77moptPdt6tvVs69nWv19s2eq2D2hre88r7S8hOTkZAGBpaVlsmVOnTqFz585qwwICAnDq1KkKja0ylKT+AJCWlgZXV1c4Ozu/8GxtVZGXl4f169cjPT0d/v7+RZapztu+JPUHque2Hz9+PHr27Flo2xalOu4Dpak/UL32gZs3b8LR0RHu7u4YMmQIoqKiii1bHbd9TcW2nm0923q29c9THfeBmtzWA9rZ3uuW69xqEJVKhcmTJ6NVq1aoX79+seViY2NhZ2enNszOzg6xsbEVHWKFKmn9vby8sGLFCvj5+SE5ORnff/89WrZsiStXrsDJyakSIy4fly9fhr+/PzIzM2FsbIwtW7bA19e3yLLVcduXpv7VbdsDwPr163H+/HkEBweXqHx12wdKW//qtA+0aNECq1atgpeXF2JiYjBr1iy0adMGYWFhMDExKVS+um37moptPdt6tvVs61+kuu0DNbmtB7S3vWfSXkbjx49HWFjYc59xqM5KWn9/f3+1s7MtW7aEj48Pli1bhjlz5lR0mOXOy8sLoaGhSE5OxqZNmxAYGIgjR44U25hVN6Wpf3Xb9tHR0Xj//fexb9++Kt3BSlmVpf7VaR/o3r279L+fnx9atGgBV1dXbNiwAaNGjdJgZFSR2NazrWdbz7a+JqnpbT2gve09k/YymDBhAnbs2IGjR4++8AySvb094uLi1IbFxcXB3t6+IkOsUKWp/7P09PTQuHFjREREVFB0FUuhUMDT0xMA0LRpUwQHB+Pnn3/GsmXLCpWtjtu+NPV/VlXf9iEhIYiPj0eTJk2kYXl5eTh69CgWLlyIrKws6OjoqE1TnfaBstT/WVV9H3iaubk56tatW2xdqtO2r6nY1rOtB9jWs61nW1+T23pAe9p7PtNeCkIITJgwAVu2bMHBgwfh5ub2wmn8/f1x4MABtWH79u177rNB2qos9X9WXl4eLl++DAcHhwqIsPKpVCpkZWUVOa46bfviPK/+z6rq275Tp064fPkyQkNDpb9mzZphyJAhCA0NLbIRq077QFnq/6yqvg88LS0tDZGRkcXWpTpt+5qGbT3b+mexrWdbz7a+Zrb1gBa19+XarV01N3bsWGFmZiYOHz4sYmJipL8nT55IZYYOHSo+++wz6fOJEyeErq6u+P7770V4eLiYMWOG0NPTE5cvX9ZEFV5KWeo/a9YssWfPHhEZGSlCQkLEoEGDhL6+vrhy5YomqvBSPvvsM3HkyBFx+/ZtcenSJfHZZ58JmUwm9u7dK4So3tteiNLXvzpt++I826Nqdd8HnvWi+lenfeDDDz8Uhw8fFrdv3xYnTpwQnTt3FtbW1iI+Pl4IUfO2fXXGtp5tPdt6tvVPY1tfc9p6IbS3vWfSXgoAivxbuXKlVKZdu3YiMDBQbboNGzaIunXrCoVCIerVqyf++++/yg28nJSl/pMnTxYuLi5CoVAIOzs70aNHD3H+/PnKD74cjBw5Uri6ugqFQiFsbGxEp06dpEZMiOq97YUoff2r07YvzrMNWXXfB571ovpXp31g4MCBwsHBQSgUClGrVi0xcOBAERERIY2vadu+OmNbz7aebT3b+qexra85bb0Q2tvey4QQonyv3RMRERERERFReeAz7URERERERERaikk7ERERERERkZZi0k5ERERERESkpZi0ExEREREREWkpJu1EREREREREWopJOxEREREREZGWYtJOREREREREpKWYtBMRERERERFpKSbtVKPNnDkTjRo10nQYVA4OHDgAHx8f5OXlFVumJm7v4cOHo2/fvsWOX7p0KXr37l15ARERVbKa+NtfXbGtLxrb+uqPSTtplaVLl8LExAS5ubnSsLS0NOjp6aF9+/ZqZQ8fPgyZTIbIyMhKjrJ0XvRDWhSZTFbor3Xr1hUTYDXxySefYNq0adDR0dF0KIXMmjULb7/9tqbDKNLIkSNx/vx5HDt2TNOhEFENwbY+H9v60mNbXzZs66s+Ju2kVTp06IC0tDScO3dOGnbs2DHY29vjzJkzyMzMlIYfOnQILi4u8PDwKPVyhBBqBwvaaOXKlYiJiZH+tm/fXmS5nJycSo5M+xw/fhyRkZHo16+fpkMp0rZt2/Daa69pOowiKRQKDB48GL/88oumQyGiGoJt/f+wrS85tvVlx7a+6mPSTlrFy8sLDg4OOHz4sDTs8OHD6NOnD9zc3HD69Gm14R06dAAAZGVlYdKkSbC1tYW+vj5at26N4OBgtbIymQy7du1C06ZNoVQqcfz48ULLj4yMhLu7OyZMmAAhBB4/foxhw4bBwsIChoaG6N69O27evCmVL+oWrJ9++gm1a9eWxv/555/Ytm2bdBb96bo9j7m5Oezt7aU/S0tL3LlzBzKZDH///TfatWsHfX19rF27FgDw+++/w8fHB/r6+vD29sbixYvV5nf27Fk0btwY+vr6aNasGbZs2QKZTIbQ0FAAwKpVq2Bubq42zdatWyGTydSGbdu2DU2aNIG+vj7c3d0xa9YstYMimUyG33//Ha+//joMDQ1Rp06dQgchV65cQa9evWBqagoTExO0adMGkZGROHr0KPT09BAbG6tWfvLkyWjTpk2x62r9+vXo0qUL9PX11YZ/++23sLOzg4mJCUaNGqV2IAgAwcHB6NKlC6ytrWFmZoZ27drh/Pnz0viRI0eiV69eatPk5OTA1tYWf/zxBwBg06ZNaNCgAQwMDGBlZYXOnTsjPT1dKh8dHY0rV66gW7du+Oijj9Tm99NPP0Emk2H37t3SME9PT/z+++/S5xdt1+joaAwYMADm5uawtLREnz59cOfOnWLXVXBwMGxsbDBv3jxpWO/evbF9+3ZkZGQUOx0RUXlhW/8/bOv/h20923p6DkGkZQYPHiy6du0qfW7evLnYuHGjeO+998T06dOFEEI8efJEKJVKsWrVKiGEEJMmTRKOjo5i586d4sqVKyIwMFBYWFiIR48eCSGEOHTokAAg/Pz8xN69e0VERIR49OiRmDFjhmjYsKEQQoiLFy8Ke3t78cUXX0jLfu2114SPj484evSoCA0NFQEBAcLT01NkZ2cLIYTa9AUWLFggXF1dhRBCpKamigEDBohu3bqJmJgYERMTI7Kysl64DgCILVu2FBp++/ZtAUDUrl1b/PPPP+LWrVviwYMH4q+//hIODg7SsH/++UdYWlpK6yc1NVXY2NiIwYMHi7CwMPHvv/8Kd3d3AUBcuHBBCCHEypUrhZmZmdrytmzZIp7+mTh69KgwNTUVq1atEpGRkWLv3r2idu3aYubMmWqxOzk5iaCgIHHz5k0xadIkYWxsLG2Le/fuCUtLS/HGG2+I4OBgcf36dbFixQpx7do1IYQQdevWFfPnz5fml52dLaytrcWKFSuKXV9+fn7i22+/VRv2999/C6VSKX7//Xdx7do18cUXXwgTExO17XXgwAGxZs0aER4eLq5evSpGjRol7OzsREpKihBCiBMnTggdHR3x4MEDaZrNmzcLIyMjkZqaKh48eCB0dXXFjz/+KG7fvi0uXbokFi1aJFJTU6XyCxculPbn7du3CzMzM5GbmyuEEKJv377C2tpafPrpp9K6ASBu3rwphBAv3K7Z2dnCx8dHjBw5Uly6dElcvXpVDB48WHh5eUn7WWBgoOjTp49UXzMzM7Fs2TK1dZWeni7kcrk4dOhQseuYiKg8sa1nW8+2nm09lRyTdtI6v/32mzAyMhI5OTkiJSVF6Orqivj4eBEUFCTatm0rhMj/QQIg7t69K9LS0oSenp5Yu3atNI/s7Gzh6OgoNQgFDfnWrVvVllXQEJ84cUJYWFiI77//Xhp348YNAUCcOHFCGpaQkCAMDAzEhg0b1KZ/2tMNuRDqP6QlBUDo6+sLIyMj6W/Lli1SQ/7TTz+plffw8BBBQUFqw+bMmSP8/f2FEEIsW7ZMWFlZiYyMDGn8kiVLSt2Qd+rUSXzzzTdqZdasWSMcHBzUYp82bZr0OS0tTQAQu3btEkIIMXXqVOHm5iYdDD1r3rx5wsfHR/r8zz//CGNjY5GWllZkeSGEMDMzE6tXr1Yb5u/vL8aNG6c2rEWLFoW219Py8vKEiYmJ+Pfff6Vhvr6+Yt68edLn3r17i+HDhwshhAgJCREAxJ07d4qdZ5cuXcTChQuFEEI8fvxYyOVyERwcLFQqlbC0tBRz584VLVq0EELkN9y1atWSpn3Rdl2zZo3w8vISKpVKGp+VlSUMDAzEnj17hBD/2/82b94sjI2Nxfr164uM08LCQjpAICKqaGzr2dazrc/Htp5KgrfHk9Zp37490tPTERwcjGPHjqFu3bqwsbFBu3btpGfdDh8+DHd3d7i4uCAyMhI5OTlo1aqVNA89PT288sorCA8PV5t3s2bNCi0vKioKXbp0wfTp0/Hhhx9Kw8PDw6Grq4sWLVpIw6ysrODl5VVovhVhwYIFCA0Nlf66dOkijXu6Hunp6YiMjMSoUaNgbGws/X311VdSxz3h4eHw8/NTu6XM39+/1DFdvHgRs2fPVlvO6NGjERMTgydPnkjl/Pz8pP+NjIxgamqK+Ph4AEBoaCjatGkDPT29IpcxfPhwRERESLdHrlq1CgMGDICRkVGxcWVkZBS6XS48PFxt2wGF6xwXF4fRo0ejTp06MDMzg6mpKdLS0hAVFSWVeeedd7By5Uqp/K5duzBy5EgAQMOGDdGpUyc0aNAAb775Jn777Tc8fvxYmjYlJQVHjhyRnnEzNzdHw4YNcfjwYVy+fBkKhQJjxozBhQsXkJaWhiNHjqBdu3YASrZdL168iIiICJiYmEjjLS0tkZmZqdZp05kzZ/Dmm29izZo1GDhwYJHr0MDAQG0bEhFVJLb1+djWs61nW08loavpAIie5enpCScnJxw6dAiPHz+WftgcHR3h7OyMkydP4tChQ+jYsWOp511UY2BjYwNHR0esW7cOI0eOhKmpaYnnJ5fLIYRQG1ZencXY29vD09NTbdjDhw8BqNcjLS0NAPDbb78VarhK07tqSeqSlpaGWbNm4Y033ig0/dMN6bONtEwmg0qlApDfYDyPra0tevfujZUrV8LNzQ27du164bOB1tbWag1oSQUGBuLRo0f4+eef4erqCqVSCX9/f2RnZ0tlhg0bhs8++wynTp3CyZMn4ebmJj1zp6Ojg3379uHkyZPYu3cvfv31V3zxxRc4c+aMFLuvry+cnZ2l+bVv3x6HDx+GUqlEu3btYGlpCR8fHxw/fhxHjhyRDiZLsl3T0tLQtGlT6VnHp9nY2Ej/e3h4wMrKCitWrEDPnj2LPIhKTExUm4aIqCKxrc/Htp5tPcC2nl6MV9pJK3Xo0AGHDx/G4cOH1V7/0rZtW+zatQtnz56VOqbx8PCAQqHAiRMnpHI5OTkIDg6Gr6/vC5dlYGCAHTt2QF9fHwEBAUhNTQUA+Pj4IDc3F2fOnJHKPnr0CNevX5fma2Njg9jYWLUGsKCzlwIKheK57xN9WXZ2dnB0dMStW7fg6emp9ufm5ibV5dKlS2qdszzd0U9BXVJTU9U6Vnm2Lk2aNMH169cLLcfT0xNyecl+Tvz8/HDs2LHnHvC88847+Pvvv7F8+XJ4eHioXVkpSuPGjXH16lW1YT4+PmrbDihc5xMnTmDSpEno0aMH6tWrB6VSiYSEBLUyVlZW6Nu3L1auXIlVq1ZhxIgRauNlMhlatWqFWbNm4cKFC1AoFNiyZQuA/I58+vTpo1a+Xbt2OH78OA4cOCDt2+3bt8e6detw48YNaVhJtmuTJk1w8+ZN2NraFipjZmYmLdPa2hoHDx5EREQEBgwYUGjdR0ZGIjMzE40bN37ueiYiKk9s60uObT3berb1NZwm780nKs6KFSuEgYGB0NXVFbGxsdLwP//8U5iYmAgAah2GvP/++8LR0VHs2rVLrXOaxMREIcT/nnN7/Pix2nKefk4tNTVVtG7dWrRq1UrqXKRPnz7C19dXHDt2TISGhopu3bqpdU5z9epVIZPJxLfffisiIiLEwoULhYWFhdpzbl9//bVwcXER165dEw8fPiz2+a6n4QWd0xQ8m1bgt99+EwYGBuLnn38W169fF5cuXRIrVqwQP/zwg1Q3a2tr8fbbb4srV66I//77T3h6eqrN69GjR8LIyEhMmjRJREREiLVr1wpHR0e159x2794tdHV1xcyZM0VYWJi4evWqWLdunVqHPkXFbmZmJlauXCmEyH9W0MrKSuqc5saNG2L16tVS5zRC5D9v5uzsLBQKRaFOZ4ryyy+/iKZNm6oNW79+vdDX1xcrVqwQ169fF9OnTy/UOU3jxo1Fly5dxNWrV8Xp06dFmzZthIGBgViwYIHavPbu3SsUCoXQ0dER9+/fl4afPn1afP311yI4OFjcvXtXbNiwQSgUCrFz506Rk5MjzM3NRUhIiNq8EhMThVwuFzo6OiI8PFwIkf88oY6OjtrzgkK8eLump6eLOnXqiPbt24ujR4+KW7duiUOHDomJEyeK6OhoIYT6c5YxMTHC29tb9OvXT+Tk5EjLWblypXB3d3/heiYiKk9s69nWs61nW08lw6SdtFJBg+Xt7a02/M6dOwKA8PLyUhuekZEhJk6cKKytrYVSqRStWrUSZ8+elcaXpCEXIr/Ba9mypWjbtq1IS0sTiYmJYujQocLMzEwYGBiIgIAAcePGDbV5LFmyRDg7OwsjIyMxbNgw8fXXX6s15PHx8aJLly7C2NhYAChRr52lbciFEGLt2rWiUaNGQqFQCAsLC9G2bVuxefNmafypU6dEw4YNhUKhEI0aNRL//PNPoXlt2bJFeHp6CgMDA9GrVy+xfPly8ey5vd27d4uWLVsKAwMDYWpqKl555RWxfPny58b+dEMuRH7vvV27dhWGhobCxMREtGnTRkRGRqpN8+WXXxbqzbU4jx49Evr6+moHA0LkH0RZW1sLY2NjERgYKD755BO17X3+/HnRrFkzoa+vL+rUqSM2btwoXF1dCzXkKpVKuLq6ih49eqgNv3r1qggICBA2NjZCqVSKunXril9//VUIIcT+/fuFk5NTkfE2bNhQ2Nvbq8Uvk8nEoEGDCpV90XaNiYkRw4YNk/Z9d3d3MXr0aJGcnCyEKNw50oMHD0TdunXFgAEDpJ5tu3btKubOnVvM2iUiqhhs69nWs63Px7aeXkQmxDMPthBRjXDnzh24ubnhwoULhd4/qw1GjRqFhw8fFnrva3E+/vhjpKSkYNmyZeUeS1paGmrVqoWVK1cW+YxfUSZNmoTc3NxC71rVNleuXEHHjh1x48YNtdvsiIio6mNbX3Js60mbsSM6ItIqycnJuHz5MoKCgkrciAPAF198gcWLF0OlUpX4mbsXUalUSEhIwA8//ABzc3OpZ9iSqF+/fpl67a1sMTExWL16NRtxIiKqNGzrKxfb+qqPV9qJKtk333yDb775pshxbdq0wa5duyolDm09+96+fXucPXsW7777LhYsWKDRWArWkZOTE1atWoVOnTppNB4iIqoa2NY/H9t6otJh0k5UyRITE5GYmFjkOAMDA9SqVauSIyIiIqLyxLaeiMoTk3YiIiIiIiIiLcX3tBMRERERERFpKSbtRERERERERFqKSTsRERERERGRlmLSTkRERERERKSlmLQTERERERERaSkm7URERERERERaikk7ERERERERkZb6P5LUukPhRrvGAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def cap_outliers(df,column, up):\n",
        "    upper = df[column].quantile(up)\n",
        "    df[column] = np.where(df[column] > upper, upper, df[column])"
      ],
      "metadata": {
        "id": "ZMn1w09WT_1X"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cap_outliers(df, 'Weight (kg)', up =0.99)\n",
        "cap_outliers(df, 'Calories_Burned', up = 0.99)\n",
        "cap_outliers(df, 'Fat_Percentage', up = 0.99)\n",
        "cap_outliers(df, 'BMI', up = 0.99)"
      ],
      "metadata": {
        "id": "ST0-UfQaVXEU"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Calories_Burned'] = np.log1p(df['Calories_Burned'])"
      ],
      "metadata": {
        "id": "cQJwW4M0Vfl8"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.drop('Calories_Burned', axis = 1)\n",
        "y = df['Calories_Burned']"
      ],
      "metadata": {
        "id": "qjVXAlwGV4fx"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)"
      ],
      "metadata": {
        "id": "Px7T_3juWB85"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "numeric_transformer = StandardScaler()\n",
        "categorical_transformer = OneHotEncoder(drop = 'first')"
      ],
      "metadata": {
        "id": "IY5-KZbFWMbU"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "preprocessor = ColumnTransformer(transformers = [\n",
        "    ('num', numeric_transformer, numerical_features),\n",
        "    ('cat',categorical_transformer, categorical_features)\n",
        "    ])"
      ],
      "metadata": {
        "id": "TM54Sz5jWj7d"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "elasticnet_pipeline = Pipeline(steps = [\n",
        "    ('preprocessor',preprocessor),\n",
        "    ('model',ElasticNet(alpha = 0.1, l1_ratio = 0.5, random_state = 42))\n",
        "])"
      ],
      "metadata": {
        "id": "EpVosfQnXDn3"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "elasticnet_pipeline.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "id": "DP6yMzqeXZxk",
        "outputId": "3a9150e4-ac92-4c45-ed34-4039a893a978"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('preprocessor',\n",
              "                 ColumnTransformer(transformers=[('num', StandardScaler(),\n",
              "                                                  ['Age', 'Weight (kg)',\n",
              "                                                   'Height (m)', 'Max_BPM',\n",
              "                                                   'Avg_BPM', 'Resting_BPM',\n",
              "                                                   'Session_Duration (hours)',\n",
              "                                                   'Fat_Percentage',\n",
              "                                                   'Water_Intake (liters)',\n",
              "                                                   'Workout_Frequency '\n",
              "                                                   '(days/week)',\n",
              "                                                   'Experience_Level', 'BMI']),\n",
              "                                                 ('cat',\n",
              "                                                  OneHotEncoder(drop='first'),\n",
              "                                                  ['Gender',\n",
              "                                                   'Workout_Type'])])),\n",
              "                ('model', ElasticNet(alpha=0.1, random_state=42))])"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
              "                 ColumnTransformer(transformers=[(&#x27;num&#x27;, StandardScaler(),\n",
              "                                                  [&#x27;Age&#x27;, &#x27;Weight (kg)&#x27;,\n",
              "                                                   &#x27;Height (m)&#x27;, &#x27;Max_BPM&#x27;,\n",
              "                                                   &#x27;Avg_BPM&#x27;, &#x27;Resting_BPM&#x27;,\n",
              "                                                   &#x27;Session_Duration (hours)&#x27;,\n",
              "                                                   &#x27;Fat_Percentage&#x27;,\n",
              "                                                   &#x27;Water_Intake (liters)&#x27;,\n",
              "                                                   &#x27;Workout_Frequency &#x27;\n",
              "                                                   &#x27;(days/week)&#x27;,\n",
              "                                                   &#x27;Experience_Level&#x27;, &#x27;BMI&#x27;]),\n",
              "                                                 (&#x27;cat&#x27;,\n",
              "                                                  OneHotEncoder(drop=&#x27;first&#x27;),\n",
              "                                                  [&#x27;Gender&#x27;,\n",
              "                                                   &#x27;Workout_Type&#x27;])])),\n",
              "                (&#x27;model&#x27;, ElasticNet(alpha=0.1, random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
              "                 ColumnTransformer(transformers=[(&#x27;num&#x27;, StandardScaler(),\n",
              "                                                  [&#x27;Age&#x27;, &#x27;Weight (kg)&#x27;,\n",
              "                                                   &#x27;Height (m)&#x27;, &#x27;Max_BPM&#x27;,\n",
              "                                                   &#x27;Avg_BPM&#x27;, &#x27;Resting_BPM&#x27;,\n",
              "                                                   &#x27;Session_Duration (hours)&#x27;,\n",
              "                                                   &#x27;Fat_Percentage&#x27;,\n",
              "                                                   &#x27;Water_Intake (liters)&#x27;,\n",
              "                                                   &#x27;Workout_Frequency &#x27;\n",
              "                                                   &#x27;(days/week)&#x27;,\n",
              "                                                   &#x27;Experience_Level&#x27;, &#x27;BMI&#x27;]),\n",
              "                                                 (&#x27;cat&#x27;,\n",
              "                                                  OneHotEncoder(drop=&#x27;first&#x27;),\n",
              "                                                  [&#x27;Gender&#x27;,\n",
              "                                                   &#x27;Workout_Type&#x27;])])),\n",
              "                (&#x27;model&#x27;, ElasticNet(alpha=0.1, random_state=42))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;preprocessor: ColumnTransformer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for preprocessor: ColumnTransformer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;, StandardScaler(),\n",
              "                                 [&#x27;Age&#x27;, &#x27;Weight (kg)&#x27;, &#x27;Height (m)&#x27;, &#x27;Max_BPM&#x27;,\n",
              "                                  &#x27;Avg_BPM&#x27;, &#x27;Resting_BPM&#x27;,\n",
              "                                  &#x27;Session_Duration (hours)&#x27;, &#x27;Fat_Percentage&#x27;,\n",
              "                                  &#x27;Water_Intake (liters)&#x27;,\n",
              "                                  &#x27;Workout_Frequency (days/week)&#x27;,\n",
              "                                  &#x27;Experience_Level&#x27;, &#x27;BMI&#x27;]),\n",
              "                                (&#x27;cat&#x27;, OneHotEncoder(drop=&#x27;first&#x27;),\n",
              "                                 [&#x27;Gender&#x27;, &#x27;Workout_Type&#x27;])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">num</label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;Age&#x27;, &#x27;Weight (kg)&#x27;, &#x27;Height (m)&#x27;, &#x27;Max_BPM&#x27;, &#x27;Avg_BPM&#x27;, &#x27;Resting_BPM&#x27;, &#x27;Session_Duration (hours)&#x27;, &#x27;Fat_Percentage&#x27;, &#x27;Water_Intake (liters)&#x27;, &#x27;Workout_Frequency (days/week)&#x27;, &#x27;Experience_Level&#x27;, &#x27;BMI&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">cat</label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;Gender&#x27;, &#x27;Workout_Type&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;OneHotEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>OneHotEncoder(drop=&#x27;first&#x27;)</pre></div> </div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;ElasticNet<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.ElasticNet.html\">?<span>Documentation for ElasticNet</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ElasticNet(alpha=0.1, random_state=42)</pre></div> </div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train_pred = elasticnet_pipeline.predict(X_train)\n",
        "y_test_pred = elasticnet_pipeline.predict(X_test)"
      ],
      "metadata": {
        "id": "uA_tKwb0bvH3"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mse_test = mean_squared_error(y_test, y_test_pred)\n",
        "mae_test = mean_absolute_error(y_test, y_test_pred)\n",
        "r2_test = r2_score(y_test, y_test_pred)"
      ],
      "metadata": {
        "id": "oTA-7XZfczFZ"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Test Set Evaluation:\")\n",
        "print(f\"  Mean Squared Error (MSE): {mse_test}\")\n",
        "print(f\"  Mean Absolute Error (MAE): {mae_test}\")\n",
        "print(f\"  R² Score: {r2_test}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "BgAQVRPgdKcV",
        "outputId": "82b60741-4974-4c24-801e-29465d4968c0"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Set Evaluation:\n",
            "  Mean Squared Error (MSE): 0.017127841686765812\n",
            "  Mean Absolute Error (MAE): 0.10209873895986953\n",
            "  R² Score: 0.8526218743732435\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (8,5))\n",
        "plt.scatter(y_test, y_test_pred,edgecolors = 'blue', alpha = 0.7)\n",
        "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'r--')\n",
        "plt.xlabel('Actual Calories Burned')\n",
        "plt.ylabel('Predicted Calories Burned')\n",
        "plt.title('Actual vs. Predicted Calories Burned')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "nahCRyWtdMoI",
        "outputId": "6bf85cbf-894d-4767-efdf-e9ac1bbd7852"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import joblib\n",
        "joblib.dump(elasticnet_pipeline, 'elasticnet_pipeline.pkl')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "0TCaB0MNdgFC",
        "outputId": "aa253c2f-aa77-43b5-b6e7-1eeab2f74100"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['elasticnet_pipeline.pkl']"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1PtlEN6peBkP"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}