RI_Analysis.ipynb

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      "authorship_tag": "ABX9TyPumioTLdiHMZdDh3X49hvp",
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/tonywangs/open-police-project/blob/main/RI_Analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PtmPKyLeZycG",
        "outputId": "d643efc1-b448-48e8-f710-4b11cddd80bd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import math\n",
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "metadata": {
        "id": "hWSdHlRDaUrB"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv(\"/content/drive/MyDrive/police_project.csv\")"
      ],
      "metadata": {
        "id": "c0Qjbs07Z_7D"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "YjxdRZBcaZB_",
        "outputId": "44ca5828-d1d2-47a0-b4af-8f719c5353b5"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    stop_date stop_time  county_name driver_gender  driver_age_raw  \\\n",
              "0  2005-01-02     01:55          NaN             M          1985.0   \n",
              "1  2005-01-18     08:15          NaN             M          1965.0   \n",
              "2  2005-01-23     23:15          NaN             M          1972.0   \n",
              "3  2005-02-20     17:15          NaN             M          1986.0   \n",
              "4  2005-03-14     10:00          NaN             F          1984.0   \n",
              "\n",
              "   driver_age driver_race     violation_raw violation  search_conducted  \\\n",
              "0        20.0       White          Speeding  Speeding             False   \n",
              "1        40.0       White          Speeding  Speeding             False   \n",
              "2        33.0       White          Speeding  Speeding             False   \n",
              "3        19.0       White  Call for Service     Other             False   \n",
              "4        21.0       White          Speeding  Speeding             False   \n",
              "\n",
              "  search_type   stop_outcome is_arrested stop_duration  drugs_related_stop  \n",
              "0         NaN       Citation       False      0-15 Min               False  \n",
              "1         NaN       Citation       False      0-15 Min               False  \n",
              "2         NaN       Citation       False      0-15 Min               False  \n",
              "3         NaN  Arrest Driver        True     16-30 Min               False  \n",
              "4         NaN       Citation       False      0-15 Min               False  "
            ],
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            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "source": [
        "# @title violation\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "import seaborn as sns\n",
        "data.groupby('violation').size().plot(kind='barh', color=sns.palettes.mpl_palette('Dark2'))\n",
        "plt.gca().spines[['top', 'right',]].set_visible(False)"
      ],
      "cell_type": "code",
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "9XjD5W0DbFiP",
        "outputId": "56648368-574e-4d01-d6e5-95f8eb9dd19b"
      }
    },
    {
      "source": [
        "# @title driver_age\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "data['driver_age'].plot(kind='hist', bins=20, title='driver_age')\n",
        "plt.gca().spines[['top', 'right',]].set_visible(False)"
      ],
      "cell_type": "code",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "2HWZBZx6aoqY",
        "outputId": "c1f30c05-9f0f-4891-d64d-bdc38d37e52a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate descriptive statistics for numerical fields\n",
        "descriptive_stats = data.describe()\n",
        "\n",
        "# Specifically focus on driver age\n",
        "driver_age_stats = descriptive_stats.loc[:, ['driver_age']]\n",
        "\n",
        "descriptive_stats, driver_age_stats"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LAAytqGAaean",
        "outputId": "0b239dbb-6d1d-4667-a47c-0102ac22f228"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(       county_name  driver_age_raw    driver_age\n",
              " count          0.0    86414.000000  86120.000000\n",
              " mean           NaN     1970.491228     34.011333\n",
              " std            NaN      110.914909     12.738564\n",
              " min            NaN        0.000000     15.000000\n",
              " 25%            NaN     1967.000000     23.000000\n",
              " 50%            NaN     1980.000000     31.000000\n",
              " 75%            NaN     1987.000000     43.000000\n",
              " max            NaN     8801.000000     99.000000,\n",
              "          driver_age\n",
              " count  86120.000000\n",
              " mean      34.011333\n",
              " std       12.738564\n",
              " min       15.000000\n",
              " 25%       23.000000\n",
              " 50%       31.000000\n",
              " 75%       43.000000\n",
              " max       99.000000)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Calculate the frequency of each violation\n",
        "violation_counts = data['violation'].value_counts()\n",
        "\n",
        "# Generate a bar chart for the violations\n",
        "plt.figure(figsize=(10, 6))\n",
        "violation_counts.plot(kind='bar', color='skyblue')\n",
        "plt.title('Frequency of Traffic Violations')\n",
        "plt.xlabel('Violation Type')\n",
        "plt.ylabel('Frequency')\n",
        "plt.xticks(rotation=45)\n",
        "plt.grid(axis='y', linestyle='--')\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "9vKraE3ebWvr",
        "outputId": "30f3d78a-b34d-43f8-bcd6-9656b63d609e"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the proportion of stops that led to a search\n",
        "search_proportion = data['search_conducted'].value_counts(normalize=True)\n",
        "\n",
        "# Generate a pie chart for the proportion of stops that led to a search\n",
        "plt.figure(figsize=(7, 7))\n",
        "search_proportion.plot(kind='pie', labels=['No Search', 'Search Conducted'], autopct='%1.1f%%', startangle=140, colors=['lightgreen', 'lightcoral'])\n",
        "plt.title('Proportion of Stops Leading to a Search')\n",
        "plt.ylabel('')  # Hide the y-label as it's not needed for pie charts\n",
        "\n",
        "plt.show()\n",
        "\n",
        "search_proportion\n"
      ],
      "metadata": {
        "id": "OODdjc2cay0A",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 652
        },
        "outputId": "647b98ff-40d7-430b-8d1f-06b06c4a4e9c"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False    0.965163\n",
              "True     0.034837\n",
              "Name: search_conducted, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Filter the dataset for stops that resulted in a search\n",
        "search_data = data[data['search_conducted'] == True]\n",
        "\n",
        "# Calculate the frequency of each search type\n",
        "search_type_counts = search_data['search_type'].value_counts()\n",
        "\n",
        "# Calculate the proportion of searches that resulted in an arrest\n",
        "arrest_rate = search_data['is_arrested'].value_counts(normalize=True)\n",
        "\n",
        "# Calculate the proportion of searches that were drug-related\n",
        "drug_related_rate = search_data['drugs_related_stop'].value_counts(normalize=True)\n",
        "\n",
        "search_type_counts, arrest_rate, drug_related_rate\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H7goBY6zbnim",
        "outputId": "c141cde5-596f-4751-9b14-0e349e8d39c3"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(Incident to Arrest                                          1219\n",
              " Probable Cause                                               891\n",
              " Inventory                                                    220\n",
              " Reasonable Suspicion                                         197\n",
              " Protective Frisk                                             161\n",
              " Incident to Arrest,Inventory                                 129\n",
              " Incident to Arrest,Probable Cause                            106\n",
              " Probable Cause,Reasonable Suspicion                           75\n",
              " Incident to Arrest,Inventory,Probable Cause                   34\n",
              " Incident to Arrest,Protective Frisk                           33\n",
              " Probable Cause,Protective Frisk                               33\n",
              " Inventory,Probable Cause                                      22\n",
              " Incident to Arrest,Reasonable Suspicion                       13\n",
              " Inventory,Protective Frisk                                    11\n",
              " Incident to Arrest,Inventory,Protective Frisk                 11\n",
              " Protective Frisk,Reasonable Suspicion                         11\n",
              " Incident to Arrest,Probable Cause,Protective Frisk            10\n",
              " Incident to Arrest,Probable Cause,Reasonable Suspicion         6\n",
              " Incident to Arrest,Inventory,Reasonable Suspicion              4\n",
              " Inventory,Reasonable Suspicion                                 4\n",
              " Inventory,Probable Cause,Protective Frisk                      2\n",
              " Inventory,Probable Cause,Reasonable Suspicion                  2\n",
              " Incident to Arrest,Protective Frisk,Reasonable Suspicion       1\n",
              " Probable Cause,Protective Frisk,Reasonable Suspicion           1\n",
              " Name: search_type, dtype: int64,\n",
              " False    0.65801\n",
              " True     0.34199\n",
              " Name: is_arrested, dtype: float64,\n",
              " False    0.744994\n",
              " True     0.255006\n",
              " Name: drugs_related_stop, dtype: float64)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy import stats\n",
        "import numpy as np\n",
        "\n",
        "# Filter data for relevant columns\n",
        "search_data_gender = data[['search_conducted', 'driver_gender']]\n",
        "\n",
        "# Calculate search rates for male and female drivers\n",
        "search_rates = search_data_gender.groupby('driver_gender').mean()\n",
        "\n",
        "# Perform a chi-square test for independence\n",
        "contingency_table = pd.crosstab(search_data_gender['driver_gender'], search_data_gender['search_conducted'])\n",
        "chi2, p_value, _, _ = stats.chi2_contingency(contingency_table)\n",
        "\n",
        "# Calculate confidence intervals for the proportion of searches\n",
        "# For simplicity, we'll use a normal approximation which is reasonable given a large sample size\n",
        "def proportion_confidence_interval(count, n, confidence_level=0.95):\n",
        "    proportion = count / n\n",
        "    z = stats.norm.ppf((1 + confidence_level) / 2)\n",
        "    margin_of_error = z * np.sqrt((proportion * (1 - proportion)) / n)\n",
        "    return (proportion - margin_of_error, proportion + margin_of_error)\n",
        "\n",
        "male_search_count = contingency_table.loc['M', True]\n",
        "female_search_count = contingency_table.loc['F', True]\n",
        "total_male_stops = search_data_gender['driver_gender'].value_counts()['M']\n",
        "total_female_stops = search_data_gender['driver_gender'].value_counts()['F']\n",
        "\n",
        "male_ci = proportion_confidence_interval(male_search_count, total_male_stops)\n",
        "female_ci = proportion_confidence_interval(female_search_count, total_female_stops)\n",
        "\n",
        "search_rates, chi2, p_value, male_ci, female_ci\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CCwGxkTAbsTA",
        "outputId": "e3ee461d-acbc-4824-8f68-d0da5f7efaa5"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(               search_conducted\n",
              " driver_gender                  \n",
              " F                      0.020033\n",
              " M                      0.043326,\n",
              " 260.02171993380176,\n",
              " 1.6973902964935056e-58,\n",
              " (0.04173507967942759, 0.04491727742368076),\n",
              " (0.018242186089027893, 0.021824165831349805))"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import classification_report, accuracy_score\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "# Prepare data for logistic regression\n",
        "# Drop rows with any missing values in the relevant columns to simplify the analysis\n",
        "logistic_data = data[['search_conducted', 'driver_gender', 'driver_age', 'driver_race']].dropna()\n",
        "\n",
        "# Encode categorical variables\n",
        "logistic_data = pd.get_dummies(logistic_data, columns=['driver_gender', 'driver_race'], drop_first=True)\n",
        "\n",
        "# Separate predictors and response\n",
        "X = logistic_data.drop('search_conducted', axis=1)\n",
        "y = logistic_data['search_conducted']\n",
        "\n",
        "# Standardize the age column\n",
        "scaler = StandardScaler()\n",
        "X['driver_age'] = scaler.fit_transform(X[['driver_age']])\n",
        "\n",
        "# Split data into training and test sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Fit logistic regression model\n",
        "logistic_model = LogisticRegression(max_iter=1000)\n",
        "logistic_model.fit(X_train, y_train)\n",
        "\n",
        "# Predict on test set\n",
        "y_pred = logistic_model.predict(X_test)\n",
        "\n",
        "# Model evaluation\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "model_coef = pd.Series(logistic_model.coef_[0], index=X.columns)\n",
        "\n",
        "accuracy, model_coef\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "17jzZlSNbxnO",
        "outputId": "f182384a-157c-4281-e6de-0bc287239797"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.9605759739882715,\n",
              " driver_age             -0.328875\n",
              " driver_gender_M         0.787001\n",
              " driver_race_Black       0.955582\n",
              " driver_race_Hispanic    0.878768\n",
              " driver_race_Other      -0.393518\n",
              " driver_race_White       0.176154\n",
              " dtype: float64)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import beta\n",
        "\n",
        "# Total number of stops and number of searches conducted\n",
        "n_stops = len(logistic_data)\n",
        "n_searches = logistic_data['search_conducted'].sum()\n",
        "\n",
        "# Prior parameters for Beta distribution (using a relatively uninformative prior)\n",
        "alpha_prior = 1\n",
        "beta_prior = 1\n",
        "\n",
        "# Posterior parameters are the prior parameters plus the observed successes and failures\n",
        "alpha_posterior = alpha_prior + n_searches\n",
        "beta_posterior = beta_prior + n_stops - n_searches\n",
        "\n",
        "# Generate posterior distribution\n",
        "posterior = beta(alpha_posterior, beta_posterior)\n",
        "\n",
        "# Calculate the 95% credible interval for the posterior\n",
        "credible_interval = posterior.interval(0.95)\n",
        "\n",
        "# Mean of the posterior distribution\n",
        "posterior_mean = posterior.mean()\n",
        "\n",
        "credible_interval, posterior_mean\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IE2G3IV1b2Wt",
        "outputId": "b0cab828-4eb4-4010-d675-0e3c98764448"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((0.03583795761697916, 0.03836228336953696), 0.037089937873773446)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "# Filter and prepare data for Multinomial Logistic Regression\n",
        "multinomial_data = data[['stop_outcome', 'driver_age', 'driver_gender', 'driver_race', 'violation']].dropna()\n",
        "\n",
        "# Encoding categorical variables\n",
        "label_encoder = LabelEncoder()\n",
        "multinomial_data['stop_outcome_encoded'] = label_encoder.fit_transform(multinomial_data['stop_outcome'])\n",
        "multinomial_data = pd.get_dummies(multinomial_data, columns=['driver_gender', 'driver_race', 'violation'], drop_first=True)\n",
        "\n",
        "# Separate predictors and response (encoded stop_outcome)\n",
        "X_mn = multinomial_data.drop(['stop_outcome', 'stop_outcome_encoded'], axis=1)\n",
        "y_mn = multinomial_data['stop_outcome_encoded']\n",
        "\n",
        "# Standardize the age column\n",
        "X_mn['driver_age'] = scaler.fit_transform(X_mn[['driver_age']])\n",
        "\n",
        "# Split data into training and test sets for Multinomial Logistic Regression\n",
        "X_mn_train, X_mn_test, y_mn_train, y_mn_test = train_test_split(X_mn, y_mn, test_size=0.2, random_state=42)\n",
        "\n",
        "# Fit Multinomial Logistic Regression model (using 'multinomial' option in Logistic Regression)\n",
        "multinomial_model = LogisticRegression(multi_class='multinomial', solver='lbfgs', max_iter=1000)\n",
        "multinomial_model.fit(X_mn_train, y_mn_train)\n",
        "\n",
        "# Predict on test set\n",
        "y_mn_pred = multinomial_model.predict(X_mn_test)\n",
        "\n",
        "# Model evaluation\n",
        "mn_accuracy = accuracy_score(y_mn_test, y_mn_pred)\n",
        "mn_classification_report = classification_report(y_mn_test, y_mn_pred, target_names=label_encoder.classes_)\n",
        "\n",
        "mn_accuracy\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4MukwIJ4b5qw",
        "outputId": "a9170336-b415-4cc9-cc41-a893f9539282"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.889334030076061"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mn_classification_report\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "id": "dmWVEACoc1IO",
        "outputId": "8c03149c-3cb4-4d65-b613-bd3c3fcc995b"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'                  precision    recall  f1-score   support\\n\\n   Arrest Driver       0.00      0.00      0.00       507\\nArrest Passenger       0.00      0.00      0.00        69\\n        Citation       0.89      1.00      0.94     15317\\n             N/D       0.00      0.00      0.00       116\\n       No Action       0.00      0.00      0.00       112\\n         Warning       0.00      0.00      0.00      1102\\n\\n        accuracy                           0.89     17223\\n       macro avg       0.15      0.17      0.16     17223\\n    weighted avg       0.79      0.89      0.84     17223\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert stop_date to datetime and set as index\n",
        "data['stop_date'] = pd.to_datetime(data['stop_date'])\n",
        "time_series_data = data.set_index('stop_date')\n",
        "\n",
        "# Aggregate data by month for trend analysis\n",
        "monthly_stops = time_series_data.resample('M').size()\n",
        "\n",
        "# Plot the trend of traffic stops over time\n",
        "plt.figure(figsize=(12, 6))\n",
        "monthly_stops.plot(title='Monthly Traffic Stops Over Time')\n",
        "plt.xlabel('Month')\n",
        "plt.ylabel('Number of Stops')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "I9QI4poqc45d",
        "outputId": "ef528360-22a7-4e8c-d62a-75dd127f71fb"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
        "\n",
        "# Selecting features and target for the ML model\n",
        "features = ['driver_age', 'driver_gender', 'driver_race', 'violation']\n",
        "X = data[features].dropna()\n",
        "y = data.loc[X.index, 'search_conducted']\n",
        "\n",
        "# Encoding categorical variables\n",
        "categorical_features = ['driver_gender', 'driver_race', 'violation']\n",
        "one_hot_encoder = OneHotEncoder()\n",
        "\n",
        "preprocessor = ColumnTransformer(transformers=[\n",
        "    ('cat', one_hot_encoder, categorical_features)],\n",
        "    remainder='passthrough')\n",
        "\n",
        "# Split data into training and test sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Random Forest Classifier Pipeline\n",
        "rf_pipeline = Pipeline(steps=[\n",
        "    ('preprocessor', preprocessor),\n",
        "    ('classifier', RandomForestClassifier(random_state=42))\n",
        "])\n",
        "\n",
        "# Train the Random Forest model\n",
        "rf_pipeline.fit(X_train, y_train)\n",
        "\n",
        "# Predict on test set\n",
        "y_pred_rf = rf_pipeline.predict(X_test)\n",
        "\n",
        "# Model Evaluation\n",
        "accuracy_rf = accuracy_score(y_test, y_pred_rf)\n",
        "precision_rf = precision_score(y_test, y_pred_rf)\n",
        "recall_rf = recall_score(y_test, y_pred_rf)\n",
        "f1_rf = f1_score(y_test, y_pred_rf)\n",
        "\n",
        "accuracy_rf, precision_rf, recall_rf, f1_rf"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CC9ClGk3dRxb",
        "outputId": "06c99977-5cb0-4c1b-e760-e9f43e8f972c"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.9604598502003135, 0.0, 0.0, 0.0)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import seaborn as sns\n",
        "\n",
        "# Extract the 'driver_age' column, dropping any missing values\n",
        "driver_ages = data['driver_age'].dropna()\n",
        "\n",
        "# Define the number of samples and the sample size\n",
        "n_samples = 1000\n",
        "sample_size = 50\n",
        "\n",
        "# Initialize a list to store sample means\n",
        "sample_means = []\n",
        "\n",
        "# Generate samples and compute their means\n",
        "for _ in range(n_samples):\n",
        "    sample = np.random.choice(driver_ages, size=sample_size, replace=True)\n",
        "    sample_means.append(np.mean(sample))\n",
        "\n",
        "# Plot the distribution of sample means\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.histplot(sample_means, kde=True, color='blue')\n",
        "plt.title('Distribution of Sample Means (Driver Age)')\n",
        "plt.xlabel('Sample Mean Age')\n",
        "plt.ylabel('Frequency')\n",
        "plt.axvline(x=np.mean(sample_means), color='red', linestyle='--', label='Mean of Sample Means')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "# Display the mean of sample means and the population mean\n",
        "np.mean(sample_means), driver_ages.mean()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        },
        "id": "DYqc-2MMd4sp",
        "outputId": "2a7a78d6-bfe8-4061-8555-5990d539ded4"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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IWY4cOULp0qUzvB7Vq1d33H+18uXLZzhGsWLFMoylud55IiMj8fBI/19/ZufJKl9fX1555RWioqI4fvw4X375JXfccQdfffUVTz75pGO/hIQEXnvtNcfYj9DQUMLCwoiNjU03VibNtc8zJCQEgHLlymXYfr3nfu2/J8Ctt956w3/P/fv3YxgGkZGRhIWFpfuKiori9OnTN3wtrhYWFkb79u2ZM2cOCxcuJDU1lQcffDDT854/f56SJUtmOG9cXFy68+7evZtu3boREhJCcHAwYWFhjmLp2texbNmyGcYvXfuz8vLLLxMUFESjRo2IjIxk6NChjktos8LIwficuLg4gAw/85UqVcrS4++++25CQkKYN2+eY9u8efOoU6cOt956KwAHDhzAMAxGjhyZ4TVNm43y2n/PrJ4fzHF0lSpVwtfXlwMHDnDgwAEqV65MQEAAs2fPduyX9ntVpUqVdI8vXrx4hiJ0//79/Pjjjxnytm/f/rp50157rYUmYg3N2ici+e7o0aOcP38+wxuLq/n7+7NmzRpWrVrFkiVL+PHHH5k3bx5t27bl559/ztKnxld/cu8smb1hSU1NzfIn2bmV2Xly8obW2SIiIujZsycPPPAANWvW5KuvvmLatGl4eXnx1FNPMXXqVIYNG0aTJk0ICQnBZrPRs2fP6xbImT3P62131nO32+3YbDaWLl163fOk9aRkVe/evRk8eDAnT57knnvucfS0Xu+8JUuWTPcG/GppY9RiY2Np1aoVwcHBjBo1isqVK+Pn58e2bdt4+eWXM7yOWflZqV69On/++Sfff/89P/74I19//TVTpkzhtdde44033sj0uaVNJHKzAv560iaDuPb/gKz+zvr6+jrGOU2ZMoVTp06xbt063nrrLcc+aa/FCy+8QIcOHa57nJye/8KFC3z33XckJiZet2CfM2cOb775ZrYLHLvdzp133slLL7103fvTisQ0aa99aGhots4jIs6hQkpE8l3axACZvblJ4+HhQbt27WjXrh3vv/8+b731Fq+88gqrVq2iffv2Tv8Udv/+/eluG4bBgQMH0q13VaxYsetOOHDkyBFuueUWx+3sZKtQoQLLly/n4sWL6T6hT7tM6+qFTnOjQoUK7Ny5E7vdnq5XytnnAfOSwdtuu439+/c7Lo1bsGAB/fv3Z/z48Y79EhMT82yK9Gv/PQH27dt33QkM0lSuXBnDMKhUqVKGN6050a1bN4YMGcJvv/2Wrvfkeuddvnw5zZo1u+Gb+dWrV3P27FkWLlyYbvKSQ4cO5SpnYGAgPXr0oEePHiQlJXH//ffz5ptvMnz48EynHk9bHyy7505NTWXOnDkEBARkWGcrO3r06MH06dNZsWIFUVFRGIbhuKwPcPw+ent7O3p0nGXhwoUkJiby8ccfZyhi/vzzT1599VXWrVtH8+bNHb9XBw4cSNfjdfbs2QxFaOXKlYmLi8ty3rTXPq1XWUTyly7tE5F8tXLlSkaPHk2lSpXo06dPpvv9888/GbalLWx7+fJlAMcaL856Iz5jxox0YzYWLFjAiRMnuOeeexzbKleuzG+//UZSUpJj2/fff59hauzsZOvYsSOpqalMnjw53fYPPvgAm82W7vy50bFjR06ePJnuDX1KSgoffvghQUFBtGrVKtvH3L9/P9HR0Rm2x8bGsmHDBooVK+boTfH09MzQc/Thhx9mmLbbWRYvXpxuDMymTZvYuHHjDV/P+++/H09PT954440MWQ3DuOnMiNcKCgri448/5vXXX+fee+/NdL+HHnqI1NRURo8eneG+lJQUx89RWg/T1dmSkpKYMmVKtnJd7drn5OPjQ40aNTAMI91sgdcqU6YM5cqVY8uWLVk+V2pqKk8//TRRUVE8/fTTBAcH5zh3+/btKV68OPPmzWPevHk0atQoXaFSsmRJWrduzaeffsqJEycyPP7MmTM5PvesWbO45ZZbeOyxx3jwwQfTfb3wwgsEBQU5ehfbtWuHl5cXH3/8cbpjXPv7DubPwYYNG/jpp58y3BcbG5thZsytW7cSEhJCzZo1c/xcRCTn1CMlInlm6dKl7N27l5SUFE6dOsXKlStZtmwZFSpU4Ntvv73hIpujRo1izZo1dOrUiQoVKnD69GmmTJlC2bJlHZ9iV65cmaJFi/LJJ59QpEgRAgMDady4cbbGOVytePHiNG/enIEDB3Lq1CkmTJhAlSpVGDx4sGOfRx55hAULFnD33Xfz0EMPcfDgQWbNmpVu8ofsZrv33ntp06YNr7zyCocPH+b222/n559/5ptvvmHYsGEZjp1Tjz76KJ9++ikDBgxg69atVKxYkQULFrBu3TomTJhwwzFrmdmxYwe9e/fmnnvuoUWLFhQvXpxjx44xffp0jh8/zoQJExxv/jt37szMmTMJCQmhRo0abNiwgeXLl6dba8qZqlSpQvPmzXn88ce5fPkyEyZMoESJEpleNgXmv9uYMWMYPnw4hw8fpmvXrhQpUoRDhw6xaNEiHn30UV544YVs5ejfv/9N92nVqhVDhgxh7NixbN++nbvuugtvb2/279/P/PnzmThxIg8++CBNmzalWLFi9O/fn6effhqbzcbMmTNzdWnjXXfdRXh4OM2aNaNUqVJERUUxefJkOnXqdNOfiS5durBo0SIMw8jQC3v+/HnHWnHx8fEcOHCAhQsXcvDgQXr27HndojE7vL29uf/++5k7dy6XLl3ivffey7DPRx99RPPmzalduzaDBw/mlltu4dSpU2zYsIGjR4+yY8eObJ/3+PHjrFq1KsPkMGl8fX3p0KGDY0HmUqVK8cwzzziWcLj77rvZsWMHS5cuJTQ0NN3r9uKLL/Ltt9/SuXNnBgwYQP369bl06RJ//PEHCxYs4PDhw+l6wJYtW8a9996rMVIiVsnnWQJFpBBIm0o47cvHx8cIDw837rzzTmPixInpptlOc+001CtWrDC6dOlilC5d2vDx8TFKly5t9OrVK8O0wN98841Ro0YNw8vLK9104zeafjmz6c+//PJLY/jw4UbJkiUNf39/o1OnTtedxnv8+PFGmTJlDF9fX6NZs2bGli1bMhzzRtmunf7cMAzj4sWLxrPPPmuULl3a8Pb2NiIjI41x48alm/7bMMxpoIcOHZohU2bTsl/r1KlTxsCBA43Q0FDDx8fHqF279nWnaM/q9OenTp0y3n77baNVq1ZGRESE4eXlZRQrVsxo27atsWDBgnT7njt3znHuoKAgo0OHDsbevXszZM9s+vy0n5Frp8Tv37+/ERgY6LidNv35uHHjjPHjxxvlypUzfH19jRYtWhg7duy47jGv9fXXXxvNmzc3AgMDjcDAQKNatWrG0KFDjT///POGr0dWpv43jMxf388++8yoX7++4e/vbxQpUsSoXbu28dJLLxnHjx937LNu3TrjjjvuMPz9/Y3SpUsbL730kvHTTz8ZgLFq1SrHfpn9Dlz78/fpp58aLVu2NEqUKGH4+voalStXNl588UXj/PnzN3wOhmEY27ZtMwDj119/Tbc9ber1tK+goCAjMjLS6Nu3r/Hzzz9f91iZ/Wyn3Xf19Odpli1bZgCGzWYz/v777+s+9uDBg0a/fv2M8PBww9vb2yhTpozRuXPndD+fWf13Mwzz9x8wVqxYkek+06ZNMwDjm2++MQzDnKZ/5MiRRnh4uOHv72+0bdvWiIqKMkqUKJFuOn/DMP8vGD58uFGlShXDx8fHCA0NNZo2bWq89957RlJSkmO/qKgoAzCWL19+08wikjdshuECo5NFRESc5PDhw1SqVIlx48Zlu/dIsq9du3aULl36uosiS+ZiY2MpVqwYY8aM4ZVXXsn244cNG8aaNWvYunWreqRELKIxUiIiIpJjb731FvPmzcvx9PmFQUJCQoZtEyZMAKB169bZPt7Zs2f53//+x5gxY1REiVhIY6REREQkxxo3bpxu8hXJaN68eUybNo2OHTsSFBTE2rVr+fLLL7nrrrto1qxZto9XokQJx1pcImIdFVIiIiIieei2227Dy8uLd999lwsXLjgmoBgzZozV0UQkFzRGSkREREREJJs0RkpERERERCSbVEiJiIiIiIhkk8ZIAXa7nePHj1OkSBHNfiMiIiIiUogZhsHFixcpXbo0Hh6Z9zupkMJcpbxcuXJWxxARERERERfx999/U7Zs2UzvVyEFFClSBDBfrODgYIvTiIhInkhOhqlTzfbAgeDtbW0eERFxSRcuXKBcuXKOGiEzmrUP88UKCQnh/PnzKqRERAqqS5cgKMhsx8VBYKC1eURExCVltTbQZBMiIiIiIiLZpEJKREREREQkm1RIiYiIiIiIZJMmmxARERFxI4ZhkJKSQmpqqtVRRNySp6cnXl5euV72SIWUiIiIiJtISkrixIkTxMfHWx1FxK0FBAQQERGBj49Pjo+hQkpERETEDdjtdg4dOoSnpyelS5fGx8cn15+oixQ2hmGQlJTEmTNnOHToEJGRkTdcdPdGVEiJiEjh4OsL339/pS3iZpKSkrDb7ZQrV46AgACr44i4LX9/f7y9vTly5AhJSUn4+fnl6DgqpEREpHDw8oJOnaxOIZJrOf30XESucMbvkX4TRUREREREskk9UiIiUjgkJ8Ps2Wa7Tx/w9rY2j4iIuDX1SImISOGQlAQDB5pfSUlWpxERN3Xy5EnuvPNOAgMDKVq0qNVxcqRixYpMmDDB6hhuT4WUiIiIiOSpAQMGYLPZeOyxxzLcN3ToUGw2GwMGDMj/YDnwwQcfcOLECbZv386+ffuuu098fDzDhw+ncuXK+Pn5ERYWRqtWrfjmm2/yOW3esdls2Gw2fvvtt3TbL1++TIkSJbDZbKxevdqacPlEhZSIiIiI5Lly5coxd+5cEhISHNsSExOZM2cO5cuXtzBZ9hw8eJD69esTGRlJyZIlr7vPY489xsKFC/nwww/Zu3cvP/74Iw8++CBnz57N57R5q1y5ckydOjXdtkWLFhEUFGRRovylQkpERETE3V26lPlXYmLW972qyMl03xyqV68e5cqVY+HChY5tCxcupHz58tStWzfdvna7nbFjx1KpUiX8/f25/fbbWbBggeP+1NRUBg0a5Li/atWqTJw4Md0xBgwYQNeuXXnvvfeIiIigRIkSDB06lOTk5Bvm/Pjjj6lcuTI+Pj5UrVqVmTNnOu6rWLEiX3/9NTNmzLhhL9q3337LiBEj6NixIxUrVqR+/fo89dRT/Otf/3LsM3PmTBo0aECRIkUIDw+nd+/enD592nH/6tWrsdls/PTTT9StWxd/f3/atm3L6dOnWbp0KdWrVyc4OJjevXunW6C5devWPPnkkzz55JOEhIQQGhrKyJEjMQwj0+ccGxvLI488QlhYGMHBwbRt25YdO3bc8HUC6N+/f4bi+IsvvqB///4Z9v3777956KGHKFq0KMWLF6dLly4cPnzYcf/mzZu58847CQ0NJSQkhFatWrFt27Z0x7DZbPzvf/+jW7duBAQEEBkZybfffuu4/9y5c/Tp04ewsDD8/f2JjIzMUOg5kwopEREREXcXFJT51wMPpN+3ZMnM973nnvT7VqyYcZ9c+Ne//pXuje0XX3zBwIEDM+w3duxYZsyYwSeffMLu3bt59tln6du3L7/88gtgFlply5Zl/vz57Nmzh9dee40RI0bw1VdfpTvOqlWrOHjwIKtWrWL69OlMmzaNadOmZZpv0aJFPPPMMzz//PPs2rWLIUOGMHDgQFatWgWYb/bvvvtuHnroIU6cOJGheEsTHh7ODz/8wMWLFzM9V3JyMqNHj2bHjh0sXryYw4cPX7cwe/3115k8eTLr1693FCMTJkxgzpw5LFmyhJ9//pkPP/ww3WOmT5+Ol5cXmzZtYuLEibz//vv873//yzRL9+7dHQXa1q1bqVevHu3ateOff/7J9DEA9evXdxSXANHR0axZs4aHH344w3Pt0KEDRYoU4ddff2XdunUEBQVx9913k/T/Y1YvXrxI//79Wbt2Lb/99huRkZF07Ngxw2v4xhtv8NBDD7Fz5046duxInz59HDlHjhzJnj17WLp0KVFRUXz88ceEhobe8DnkiiHG+fPnDcA4f/681VFERCSvxMUZBphfcXFWpxHJtoSEBGPPnj1GQkJCxjvTfrav99WxY/p9AwIy37dVq/T7hoZm3CcH+vfvb3Tp0sU4ffq04evraxw+fNg4fPiw4efnZ5w5c8bo0qWL0b9/f8MwDCMxMdEICAgw1q9fn+4YgwYNMnr16pXpOYYOHWo88MAD6c5ZoUIFIyUlxbGte/fuRo8ePTI9RtOmTY3Bgwen29a9e3ej41Wv4dVZM/PLL78YZcuWNby9vY0GDRoYw4YNM9auXXvDx2zevNkAjIsXLxqGYRirVq0yAGP58uWOfcaOHWsAxsGDBx3bhgwZYnTo0MFxu1WrVkb16tUNu93u2Pbyyy8b1atXd9yuUKGC8cEHHxiGYRi//vqrERwcbCQmJqbLU7lyZePTTz/NNC9gLFq0yJgwYYLRpk0bwzAM44033jC6detmnDt3zgCMVatWGYZhGDNnzjSqVq2aLtPly5cNf39/46effrru8VNTU40iRYoY3333Xbpzvvrqq47bcXFxBmAsXbrUMAzDuPfee42BAwdmmvlqN/p9ymptoB4pEREREXcXF5f51//3FjicPp35vkuXpt/38OGM++RCWFgYnTp1Ytq0aUydOpVOnTpl6DE4cOAA8fHx3HnnnQQFBTm+ZsyYwcGDBx37ffTRR9SvX5+wsDCCgoL47LPPiI6OTnesmjVr4unp6bgdERGR7vK5a0VFRdGsWbN025o1a0ZUVFS2nmfLli3566+/WLFiBQ8++CC7d++mRYsWjB492rHP1q1buffeeylfvjxFihShVatWABmew2233eZolypVioCAAG655ZZ02659TnfccQc2m81xu0mTJuzfv5/U1NQMWXfs2EFcXBwlSpRI93ofOnQo3eudmb59+7Jhwwb++usvpk2blu7yxavPceDAAYoUKeI4fvHixUlMTHSc49SpUwwePJjIyEhCQkIIDg4mLi7uhq9HYGAgwcHBjuf/+OOPM3fuXOrUqcNLL73E+vXrb5o/N7SOlIiIFA6+vpB22Y+vr7VZRJwtMND6fbPoX//6F08++SRgFkPXivv/Ym3JkiWUKVMm3X2+//+7O3fuXF544QXGjx9PkyZNKFKkCOPGjWPjxo3p9ve+Zr04m82G3W532nO5EW9vb1q0aEGLFi14+eWXGTNmDKNGjeLll192XOrWoUMHZs+eTVhYGNHR0XTo0MFxqdv1noPNZnP6c4qLiyMiIuK6M+xlZXr3EiVK0LlzZwYNGkRiYiL33HNPhsvx4uLiqF+/PrPT1vK7SlhYGGCOtzp79iwTJ06kQoUK+Pr60qRJkxu+HpD++d9zzz0cOXKEH374gWXLltGuXTuGDh3Ke++9d9PnkRMqpEREJNuio6OJiYnJl3OFhoY6Z0YvLy/o3j33xxGRXEkbF2Oz2ejQoUOG+2vUqIGvry/R0dGOXpprrVu3jqZNm/LEE084tmWl9+Rmqlevzrp169JNlrBu3Tpq1KiR62PXqFGDlJQUEhMT2b9/P2fPnuXtt9+mXLlyAGzZsiXX50hzbUGZNubo6t65NPXq1ePkyZN4eXlRsWLFHJ3vX//6Fx07duTll1/O9Bzz5s2jZMmSBAcHX/cY69atY8qUKXTs2BEwJ6fIyd+ZsLAw+vfvT//+/WnRogUvvviiCikREXEN0dHRVKtWnYSE+Jvv7AT+/gHs3RvlVtMji0jmPD09HZfKXe9Nd5EiRXjhhRd49tlnsdvtNG/enPPnz7Nu3TqCg4Pp378/kZGRzJgxg59++olKlSoxc+ZMNm/eTKVKlXKV7cUXX+Shhx6ibt26tG/fnu+++46FCxeyfPnybB2ndevW9OrViwYNGlCiRAn27NnDiBEjaNOmDcHBwZQvXx4fHx8+/PBDHnvsMXbt2pXusr/cio6O5rnnnmPIkCFs27aNDz/8kPHjx1933/bt29OkSRO6du3Ku+++y6233srx48dZsmQJ3bp1o0GDBjc93913382ZM2cyLZL69OnDuHHj6NKlC6NGjaJs2bIcOXKEhQsX8tJLL1G2bFkiIyMdMxleuHCBF198EX9//2w979dee4369etTs2ZNLl++zPfff0/16tWzdYzsUCElIiLZEhMTQ0JCPN26zSIsLO/+QAGcORPFokV9iYmJyX0hlZICixaZ7W7dzB4qEbFEZm+404wePZqwsDDGjh3LX3/9RdGiRalXrx4jRowAYMiQIfz+++/06NEDm81Gr169eOKJJ1h67RivbOratSsTJ07kvffe45lnnqFSpUpMnTqV1q1bZ+s4HTp0YPr06YwYMYL4+HhKly5N586dee211wCz12TatGmMGDGCSZMmUa9ePd577z3uu+++XOVP069fPxISEmjUqBGenp4888wzPProo9fd12az8cMPP/DKK68wcOBAzpw5Q3h4OC1btqRUqVJZOp/NZrvh7HgBAQGsWbOGl19+mfvvv5+LFy9SpkwZ2rVr5/hZ+Pzzz3n00Ucd0+S/9dZbvPDCC9l63j4+PgwfPpzDhw/j7+9PixYtmDt3braOkR22/58Bo1C7cOECISEhnD9//qa/2CIihd22bduoX78+jz66lYiIenl6rhMntvHZZ/Ud0/HmyqVLV6ZujovLk7EfInkpMTGRQ4cOUalSJfz8/KyOIy6qdevW1KlThwkTJlgdxaXd6Pcpq7WBPo4TERGXl90Zs67HIyGBOv/f3r59O/brXDLitPFYIiJS4KmQEhERlxUXdwKw0bdv31wfKwC49P/tZs2bc70RXhqPJSIiWaVCSkREXFZiYixg0KbNZCIjm+TqWD7JCTC1OQD/GriWJO/0PVJOHY8lImKR601jLnlDhZSIiLi8YsWq5Ho8lnfSJUc7PLwOyT4aIyUiIjnnYXUAEREREck6zRMmknvO+D1SISUiIiLiBry9vQGIj8+fNdxECrK036O036uc0KV9IiJSKKR6+rC4y1RHW8TdeHp6UrRoUU6fPg2Ya/PYbDaLU4m4F8MwiI+P5/Tp0xQtWvS6i0JnlQopEREpFOye3myvM8DqGCK5Eh4eDuAopkQkZ4oWLer4fcopFVIiIiIibsJmsxEREUHJkiVJTk62Oo6IW/L29s5VT1QaFVIiIlIoeNhTqHzgJwAOVumA3UN/AsV9eXp6OuWNoIjknP6KiIhIoeCZcpk+X3YG4M3hcdh99CdQRERyTrP2iYiIiIiIZJM+jhMREbeSmgonTsDhw3D0KMTFQUICJCaa9wUFmV9FikCpUlCxIkREQFYnuI2KisrD9FeEhoZSvnz5fDmXiIg4nwopERFxeYYBf/8NGzfCvn1wozH2ly/D2bNme9cu87uPD1QrB6/c4BxxcScAG3379nVW7Bvy9w9g794oFVMiIm5KhZSIiLgsw7ABPVm9ujHnzl3Z7ucHFSqYX8WKgb+/+eXhYfZQXbwIFy6YPVZHjpg9VgcOXnn88uVQ+w4oXvzKtsTEWMCgTZvJREY2ydPndeZMFIsW9SUmJkaFlIiIm1IhJSIiLunUKVi9+i6gN+fOgacn1K4NDRual+pltg5paGj624ZhHuvQLmCduW3TZvhlC9StC61aQXDwlf2LFatCRES9vHhKIiJSgKiQEhERl5KSAmvWwLp1YLeHAuepXv0MnTpVITAw+8ez2SA8HMoVx1FIVb4F/vgLtm2DnTvN4iw0VH8SRUQk6/RXQ0REXMa5c/Dll3DmjHm7dOlojh9vQvXqXxAYWCVXx0719GHJPZMBeLC+Dw2OwYoVEB0NGzaAv39n4K5cPgMRESksVEiJiIhLiI6GefMgPt6cda9jR0hJ+ZWFC4875fh2T282NxrquF2+PAwYAAcOwNKlcO5cIPATW7cepUoVc8yViIhIZrSOlIiIWG7nTpgxwyyiIiJg8GCoXj3vz2uzQWQkPPYYVK68F7Bz5EhZPvkEjh3L+/OLiIj7UiElIiKWWr8eFi0y14CqXt3sJbp68gdnsdlTqXh4NRUPr8ZmT013n48P3H77VqAFgYGXuHABpk41x1CJiIhcjwopERGxzJYtsGyZ2W7aFLp3N4uavOCVksiA6W0YML0NXimJmey1nrZtf6NqVbOw++478yslJW8yiYiI+7K0kFqzZg333nsvpUuXxmazsXjx4nT3G4bBa6+9RkREBP7+/rRv3579+/en2+eff/6hT58+BAcHU7RoUQYNGkRcXFw+PgsREcmJnTthyRKz3awZ3Hln5lOa5ydv7xR69IA2bczb27bBnDnmQr8iIiJpLC2kLl26xO23385HH3103fvfffddJk2axCeffMLGjRsJDAykQ4cOJCZe+SSxT58+7N69m2XLlvH999+zZs0aHn300fx6CiIikgNRUZD22VnDhtCunaVxMrDZoGVL6N3b7CE7dOjKGC4RERGwuJC65557GDNmDN26dctwn2EYTJgwgVdffZUuXbpw2223MWPGDI4fP+7ouYqKiuLHH3/kf//7H40bN6Z58+Z8+OGHzJ07l+PHnTPLk4iIONexY/D11+ZCuXXqwD33uEZP1PVERkL//hAQAMePwxdfwPnzVqcSERFX4LJjpA4dOsTJkydp3769Y1tISAiNGzdmw4YNAGzYsIGiRYvSoEEDxz7t27fHw8ODjRs3Znrsy5cvc+HChXRfIiKS9y5dgq++MscfVa0K997rukVUmtKlYeBAcwKMs2fNYio21upUIiJiNZctpE6ePAlAqVKl0m0vVaqU476TJ09SsmTJdPd7eXlRvHhxxz7XM3bsWEJCQhxf5cqVc3J6ERG5VmoqzJ8PFy5AiRLQrRt4uOxfofRCQ2HQIDP3hQvmZX4XL1qdSkRErOQmf8Kca/jw4Zw/f97x9ffff1sdSUSkwFu2DI4cMccc9ewJvr5WJ8qe4GDo1w+KFoVz52DmTI2ZEhEpzFy2kAoPDwfg1KlT6bafOnXKcV94eDinT59Od39KSgr//POPY5/r8fX1JTg4ON2XiIjknT/+gLQrrrt1M3t48pvd05uf27/Lz+3fxe7pnaNjpBVTRYrAmTMwaxYkZjaTuoiIFGguW0hVqlSJ8PBwVqxY4dh24cIFNm7cSJMmTQBo0qQJsbGxbN261bHPypUrsdvtNG7cON8zi4hIRufPX5nmvHlzqFbNmhypnj6sb/Yi65u9SKpnzherKlbMLKYCAuDEiStjvkREpHCxtJCKi4tj+/btbN++HTAnmNi+fTvR0dHYbDaGDRvGmDFj+Pbbb/njjz/o168fpUuXpmvXrgBUr16du+++m8GDB7Np0ybWrVvHk08+Sc+ePSldurR1T0xERABzZr5vvzXXYCpT5sraTO4uNBT69gVvb3Nq9KVLzecqIiKFh5eVJ9+yZQttrvqr+txzzwHQv39/pk2bxksvvcSlS5d49NFHiY2NpXnz5vz444/4+fk5HjN79myefPJJ2rVrh4eHBw888ACTJk3K9+ciIiIZbd4Mf/0FXl7Qtau1k0vY7KlEnNgGwImIehgenrk6XkQEPPAAzJ0LW7eaxdUddzgjqYiIuANLC6nWrVtj3OAjPJvNxqhRoxg1alSm+xQvXpw5c+bkRTwREcmFs2fNCSYA7rzTmnFRV/NKSeTR/zUC4M3hcST7BOb6mFWrms9t2TL4+WdzVr/IyFwfVkRE3IDLjpESERH3ZbfD4sWQkgKVKkHDhlYnyjtNmkDduualfQsWQEyM1YlERCQ/qJASERGn27IFjh41pzjv0sX1F93NDZsNOnWCChUgKcksppKTrU4lIiJ5TYWUiIg4VVwcrFxpttu1g5AQa/PkB09Pc7xUQACcOmVOPiEiIgWbCikREXGqZcvMWfpKl4b69a1Ok3+KFDGLKYDff4cdO6zNIyIieUuFlIiIOM3hw7Bzp9nu2NHaWfqscMst0KqV2V6yxFy0V0RECqZC9idORETySmoq/PCD2W7QwFw3qjBq2dKcYCM5Gb7+Wov1iogUVCqkRETEKX77zeyBCQiAtm2tTpOR3dOb1a3+w+pW/8Hu6Z1n5/HwgPvvB39/c7zUL7/k2alERMRClq4jJSIiBcOlS/Drr2b7zjvNIsLVpHr6sLr16/lyrqAgcya/BQtg7Vq49VYoWzZfTi0iIvlEPVIiIpJra9aYE0xERMDtt1udxjXUrAm1apnrSy1erCnRRUQKGhVSIiKSK2fPmutGgdkb5aprRtkMO2GndxN2ejc2w54v5+zY0eydOnsWli/Pl1OKiEg+USElIiK5smIF2O0QGWlOsuCqvJITGPpxLYZ+XAuv5IR8Oae/P9x3n9netAmOHMmX04qISD5QISUiIjn2998QFWX2QrVvb3Ua1xQZCXXrmu3vv4eUFGvziIiIc6iQEhGRHDEM+Plns12nDpQsaWkcl3bnnRAYCDExsH691WlERMQZVEiJiEiOHDkSwtGj4O0NbdpYnca1+ftDhw5me80aiI31tTaQiIjkmgopERHJARtbt0YA0KgRFClicRw3UKsWVK5sLtC7dm05q+OIiEguqZASEZEc6MbZswH4+EDTplZncQ82mzmLn5cXHD8eDDxsdSQREckFFVIiIpItdjvAGwDccQcEBFgax60ULw6tWqXdGsfFi/ozLCLirrysDiAiIu5l+fJiQCV8fFJo0sR9/ozYPb1Z1+QFR9sqTZrAli2JnD9fiv/97+rCSkRE3Ik+ChMRkSxLTYVPPzXHRt1222n8/CwOlA2pnj4su2scy+4aR6qnj2U5PD2hSZOjAHz5ZUn+/NOyKCIikgsqpEREJMu+/BIOH/YD/qFWrdNWx3Fb5ctfAL4nNdXGs89anUZERHJChZSIiGRJaiqMHp12axw+PnYr42SbzbBTNPYwRWMPYzNcIfuzeHnZWboUliyxOouIiGSXCikREcmShQth3z4IDk4BJlsdJ9u8khMYNrESwyZWwis5weo4wAH69DF79YYNg8uXrU0jIiLZo0JKRERuyjDgrbfMds+eZ4A4S/MUFIMGnSQ8HA4cgClTrE4jIiLZoUJKRERu6qefYPt2CAyEHj00NspZAgPtjBljtkePhnPnrM0jIiJZp0JKRERuKq036rHHoGjRVGvDFDD9+0PNmmYR9fbbVqcREZGsUiElIiI39Ouv5pePDzz3nNVpCh4vL3jnHbM9cSJER1ubR0REskaFlIiI3NDYseb3gQOhdGlrsxRUHTtC69bmhBOvvmp1GhERyQoVUiIikqnff4elS8HDA156yeo0BZfNBu++a7ZnzTJfdxERcW0qpEREJFPvvWd+79EDbrnF2iy5ZffwYlODJ9jU4AnsHl5Wx8mgYUPo2dOcIfHll61OIyIiN6NCSkREris6GubNM9svvmhtFmdI9fLlh04f8UOnj0j18rU6znW9+SZ4e8OyZbB6tdVpRETkRlRIiYjIdU2YAKmp0K4d1K1rdZrC4ZZb4JFHzPYrr5i9UyIi4ppUSImISAaxsfDf/5rtF16wNIrzGAYBl84QcOmMS1cor74Kfn6wfr05Pk1ERFyTCikREcngs88gLg5q1YIOHaxO4xzeyfG89F5JXnqvJN7J8VbHyVTp0vDkk2b71VfBbrc2j4iIXJ8KKRERSScpyVzPCOD5580Z5SR/vfwyFClizt63cKHVaURE5HpUSImISDpffgnHj0NEBPTqZXWawik09MrixyNHmmPVRETEtaiQEhERB8OA8ePN9tNPg69rTm5XKDz3HBQvDnv3wuzZVqcREZFrqZASERGHlSvhjz8gIACGDLE6TeEWHHxlEeQxYyAlxdo8IiKSnuutSCgiIpaZMMH8PmAAFCtmZZLCISoq6ob3N2vmQUhILfbv9+Lttw/TseM/OTpPaGgo5cuXz9FjRUTk+lRIiYgIAPv3w/ffm+2nn7Y2S0EXF3cCsNG3b98s7P1vYCwjRyYycmRDIPvT+Pn7B7B3b5SKKRERJ1IhJSIiAEyaZH7v1AmqVrU2S16we3ix/fb+jraVEhNjAYM2bSYTGdnkhvsmJXnw5ZcpXL5cjbZtD1ClyrlsnevMmSgWLepLTEyMCikRESdSISUiIsTGwtSpZnvYMCuT5J1UL18Wd51mdYx0ihWrQkREvZvu17QprFoFO3dWolmzSnhohLOIiOX0X7GIiPDFF3DpEtSsCe3aWZ1GrtW4Mfj5QUwM7NljdRoREQEVUiIihV5KypXL+oYNK8AL8BoG3kmX8E66ZM7z7kZ8feGOO8z2L7+APfvDpERExMlUSImIFHLffgtHjkCJEtCnj9Vp8o53cjyvjA3ilbFBeCfHWx0n29QrJSLiWlRIiYgUch9+aH4fMgT8/a3NIpnz8zOLKYA1a9yuU01EpMDRZBMiIgVEdHQ0MTEx2XrM/v1+rF5dA09Pg2bNdrFtW/JNH3OztY8k79xxB/z2G5w5Y/ZK1axpdSIRkcJLhZSISAEQHR1NtWrVSUjI7iVrHwM1SE39mk6dumfrkXFxF7N5LsktPz+zmPrlF7NXqkaNAjymTUTExamQEhEpAGJiYkhIiKdbt1mEhVXP0mMuX/Zk9uxapKRA5863Ubr01iw9bv/+H1i1aiSJiYm5iSw51Lix2St1+jRERZnFlIiI5D8VUiIiBUhYWPUsrUsEsGGDOWNfyZJQr96tWe7ZiInRpX1W8vc3i6k1a8yv6tXVKyUiYgVNNiEiUggZBmzebLYbNdIbcXdzxx3g4wOnTsHevVanEREpnFRIiYgUQvv3w7lz5pib2rWtTpM/DA9Pdtd4kN01HsTw8LQ6Tq6k9UoB/PqrZvATEbGCLu0TESmENm0yv9eta/ZsFAYpXn7M7z7f6hhOkzaD34kTcOAAREZanUhEpHBRj5SISCFz9iwcPGi2Gza0NovkXEAANGhgtrWulIhI/lMhJSJSyKT1Rt16KxQrZm0WyZ2mTcHTE44ehcOHrU4jIlK4qJASESlELl+G7dvNdqNGlkbJd95Jl3j9DRuvv2HDO+mS1XGcIigI6v3/JI1r1libRUSksFEhJSJSiOzcCUlJUKIE3HKL1WnEGZo1Aw8Ps0cqOtrqNCIihYcKKRGRQsIwrlzW17ChpjwvKEJC4Pbbzfavv1qbRUSkMFEhJSJSSBw6BDEx5ix9depYnUacqUULszA+cACOH7c6jYhI4aBCSkSkkEjrjbr9dvD1tTaLOFexYlfWA1OvlIhI/lAhJSJSCMTGwr59ZruwTTJRWDRvbn7fuxdOnbI2i4hIYaBCSkSkENi82RwjdcstEBpqdRrJC2FhUKOG2V671tosIiKFgQopEZECLjkZfv/dbBfm3ijDw5N9kR3ZF9kRw8PT6jh5okUL8/uuXeZ4OBERyTteVgcQEZG8tWsXJCRA0aIQGWl1GuukePkxp/cSq2PkqfBwc6Hlfftg3Tro0sXqRCIiBZd6pERECrCrpzxv0MBcb0gKtpYtze87dsC5c9ZmEREpyPQnVUSkAPv7bzh5Ery8oG5dq9NIfihTBipXNovodeusTiMiUnCpkBIRKcA2bza/164NAQHWZrGad9IlRrwVyIi3AvFOumR1nDyVNlZq+3aIi/O2NIuISEGlQkpEpIC6eBH27DHbDRtam8VV+CTH45Mcb3WMPFehgvmVmgo7d5ayOo6ISIGkQkpEpIDauhXsdihXDiIirE4j+S2tVyoqKhQoaWkWEZGCSIWUiEgBlJpqFlJQuKc8L8xuucUcL5Wa6gE8a3UcEZECR4WUiEgBFBUFcXEQFATVq1udRqxgs12ZwQ+GEhtbMNfOEhGxigopEZECKG3K8/r1wVPvnwutyEgoUSIeKMLcubq8T0TEmVy6kEpNTWXkyJFUqlQJf39/KleuzOjRozEMw7GPYRi89tprRERE4O/vT/v27dm/f7+FqUVErHXihDntuYeHWUhJ4WWzQd26JwGYOzeM8+ctDiQiUoC4dCH1zjvv8PHHHzN58mSioqJ45513ePfdd/nwww8d+7z77rtMmjSJTz75hI0bNxIYGEiHDh1ITEy0MLmIiHXSeqNq1IAiRazN4koMmweHK7TicIVWGDaX/vPnVJUqxQJ7uHjRiylTrE4jIlJweFkd4EbWr19Ply5d6NSpEwAVK1bkyy+/ZNP/v0swDIMJEybw6quv0qVLFwBmzJhBqVKlWLx4MT179rQsu4iIFRITPdm1y2xrkon0Urz9mTZgtdUx8p3NBvAWMIv334enn4bAQItDiYgUAC79kVzTpk1ZsWIF+/btA2DHjh2sXbuWe+65B4BDhw5x8uRJ2rdv73hMSEgIjRs3ZsOGDZke9/Lly1y4cCHdl4hIQbB3bygpKRAeDmXLWp1GXMdcypZNJCYGPvvM6iwiIgWDSxdS//73v+nZsyfVqlXD29ubunXrMmzYMPr06QPAyZPmdd+lSqVfbLBUqVKO+65n7NixhISEOL7KlSuXd09CRCTfeLBnTyhg9kaZPREiAKkMHHgKgHHjQFe/i4jknksXUl999RWzZ89mzpw5bNu2jenTp/Pee+8xffr0XB13+PDhnD9/3vH1999/OymxiIiVOhEX54u/P9SqZXUW1+OddIkXx4Xx4rgwvJMuWR0n33Xq9A/lypmTkXzxhdVpRETcn0sXUi+++KKjV6p27do8/PDDPPvss4wdOxaA8PBwAE6dOpXucadOnXLcdz2+vr4EBwen+xIRcX9PAVC3Lnh7WxzFRQXGxxAYH2N1DEt4exu8/LLZfucdSEqyNo+IiLtz6UIqPj4eD4/0ET09PbHb7QBUqlSJ8PBwVqxY4bj/woULbNy4kSZNmuRrVhERKx065Afcic1m0LCh1WnEVf3rX+b4uehomDnT6jQiIu7NpQupe++9lzfffJMlS5Zw+PBhFi1axPvvv0+3bt0AsNlsDBs2jDFjxvDtt9/yxx9/0K9fP0qXLk3Xrl2tDS8iko/mzzfHRpUvf56iRa3NIq7L3x9efNFsv/kmJCdbm0dExJ259PTnH374ISNHjuSJJ57g9OnTlC5dmiFDhvDaa6859nnppZe4dOkSjz76KLGxsTRv3pwff/wRPz8/C5OLiOSfCxfgu+9KAFCz5hmgqKV5xLUNGWJe2nfokNkr9a9/WZ1IRMQ9uXSPVJEiRZgwYQJHjhwhISGBgwcPMmbMGHx8fBz72Gw2Ro0axcmTJ0lMTGT58uXceuutFqYWEclfM2ZAfLwnEEWZMhetjiMuLjAQx1ipMWPUKyUiklMuXUiJiMiNGQZMnpx2a7KmPJcseewxKFXK7JWaMcPqNCIi7kmFlIiIG1u+HP78EwIDUwG9I74Rw+bBsdINOFa6AYatcP/5CwhI3yulGfxERLKvcP8lERFxc2m9UZ07nwXiLM3i6lK8/fnv4M38d/BmUrz9rY5juSFDzF6pw4fVKyUikhMqpERE3NRff8F335nt7t3PWBtG3E5AAPz732ZbvVIiItmnQkpExE1NmmSOkerQASpVumx1HHFDQ4aY60odOQLTp1udRkTEvaiQEhFxQ+fPw+efm+1nn7U2i7vwTo5n2ISKDJtQEe/keKvjuAR/f/VKiYjklAopERE39PnnEBcHNWrAXXdZncZNGAZFzx+h6PkjZleeAPDooxARAdHRMG2a1WlERNyHCikRETeTkmJe1gcwbBia8lxy5epeqTffVK+UiEhWqZASEXEzixebY1pCQ6FvX6vTSEEwePCVXqkvvrA6jYiIe1AhJSLiZj74wPz+2GNmb4JIbvn7w/DhZvutt+Cy5i4REbkpFVIiIm5k40ZYvx68veGJJ6xOIwXJ4MFQujT8/bd6pUREskKFlIiIG0nrjerd27wUS8RZ/Pyu9Eq9+SYkJlqbR0TE1amQEhFxE9HRsGCB2daU5zlgs3E6rAanw2poho5MDB4M5cvDsWPw8cdWpxERcW0qpERE3MTkyZCaCm3awO23W53G/SR7BzDlid1MeWI3yd4BVsdxSb6+8NprZnvsWHOKfRERuT4VUiIibiAuDj77zGyrN0ryUr9+UKUKnDkDEydanUZExHWpkBIRcQPTpsH58xAZCZ06WZ1GCjJvb3jjDbM9bhycO2dtHhERV6VCSkTExaWmXukZeOYZ8ND/3DninRzPE1Nq8sSUmngnx1sdx6X17Am1apnF+3vvWZ1GRMQ16c+xiIiL+/57OHAAihaF/v2tTuPGDIOSZ/ZQ8sweMAyr07g0Dw8YPdpsT5wIp05Zm0dExBWpkBIRcXFpU54PGQJBQdZmkcKjSxdo2BAuXTInnhARkfRUSImIuLCtW+GXX8DLC5580uo0UpjYbDBmjNn++GNzoV4REblChZSIiAt75x3ze48eULastVmk8LnzTmjZEpKSrlzqJyIiJi+rA4iIyPXt339lAd6XX7Y2i7i/qKioHD2uX79A1qypyhdfGHTqtIdy5S7fcP/Q0FDKly+fo3OJiLgTFVIiIi7qvffMORE6dYLata1OI+4qLu4EYKNv3765OMoPpKbeQ9euvwMP33BPf/8A9u6NUjElIgWeCikRERd04oS5dhTAv/9taZSCw2YjNqSCo11YJCbGAgZt2kwmMrJJjo5x5ow/ixYB9OHBB+tRvHhiJvtFsWhRX2JiYlRIiUiBp0JKRMQFTZhgjktp2hSaN7c6TcGQ7B3AhGGHrY5hmWLFqhARUS9Hj42IgL17ISrKxq5dNejRw8nhRETckCabEBFxMefPwyefmG31RomraNPG/L53Lxw7Zm0WERFXoEJKRMTFfPwxXLgANWua46NEXEFYGNx+u9lescLaLCIirkCFlIiIC0lMNC/rA3jpJfDQ/9JO45WcwOD/NmTwfxvilZxgdRy31Lq1+TN56BD89ZfVaURErKU/0SIiLmT6dDh1CsqVg169rE5TsNgMO2WOb6HM8S3YDLvVcdxS0aJQv77ZXrnSnFVSRKSwUiElIuIiUlLg3XfN9gsvgLe3tXlErqdlS/Nn89gxc7yUiEhhpUJKRMRFfP21eblUiRIwaJDVaUSuLygIGjc226tWgV2deyJSSKmQEhFxAYYBb79ttp96CgIDrc0jciPNmoGfH5w5Azt3Wp1GRMQaKqRERFzAsmWwfTsEBMCTT1qdRuTG/PyurG+2erV5WaqISGGjBXlFpNCJjo4mJiYmX84VGhpK+fLlb7pfWm/Uo4+al/aJuLpGjeC338x1z7ZuvXK5n4hIYaFCSkQKlejoaKpVq05CQny+nM/fP4C9e6NuWExt3GiONfHygueey5dYhdalgFCrIxQY3t7QqhUsWQK//gp161qdSEQkf6mQEpFCJSYmhoSEeLp1m0VYWPU8PdeZM1EsWtSXmJiYGxZSb7xhfu/b15z2XPJGsk8g4148Y3WMAqVuXVi/Hs6dM3unIiOtTiQikn9USIlIoRQWVp2IiHpWx2DTJli6FDw94ZVXrE4jkj2entCmDSxcaBZU5cp5Wh1JRCTfaLIJERELjRplfu/TB6pUsTaLSE7UqgWlSsHly7BjRymr44iI5BsVUiIiFtmyxRxf4uEBr75qdZqCzys5gQHTWjNgWmu8khOsjlNg2GzQtq3Z3rWrJBBhaR4RkfyiS/tERCyS1hvVu7fGluQHm2Gn4pFfHG1xnshIc3zf3397ACOtjiMiki/UIyUiYoFt2+C779QbJQWDzQbt2qXdeoS///axMo6ISL5QISUiYoHRo83vPXtC1arWZhFxhgoVoFy584A3n3xS2uo4IiJ5ToWUiEg+27EDFi82P8VXb5QUJA0bHgfgp5+KsXOnxWFERPKYCikRkXyWNjaqRw+onrdLWYnkq9DQBGAehmHTdP4iUuCpkBIRyUc7d5pr7thsMFJj8qVAGomnp8H338O6dVZnERHJOyqkRETyUdrYqO7doUYNa7MURkneASR5B1gdo4Dbz733ngVgxAgwDIvjiIjkERVSIiL5ZNcuWLDAbKs3Kv8l+wTy1ohLvDXiEsk+gVbHKdAeffQEvr6wZg389JPVaURE8oYKKRGRfJLWG/Xgg1CrlrVZRPJSqVLJDB1qtocPB7uW7RKRAkiFlIhIPti9G+bPN9uvvWZtFpH8MHw4FCkC27df+dkXESlIVEiJiOSD114zx4rcfz/Urm11msLJKyWR3nM60XtOJ7xSEq2OU+CFhsILL5jtkSMhOdnaPCIizqZCSkQkj+3ZE+CYqS9t6nPJfzZ7Krfu/4Fb9/+AzZ5qdZxC4dlnISwM9u+HadOsTiMi4lwqpERE8tiUKREA9O0LNWtaHEYkHxUpYs7cB/DGG5CQYG0eERFnUiElIpKnWrBhQwheXvD661ZnEcl/jz0G5crBsWMwZYrVaUREnEeFlIhIHjHXz3kTgEcegVtusTSOiCX8/K58iDB2LFy4YGkcERGnUSElIpJHjh4NBlrg62vn1VetTiNinX79oFo1OHsWxo+3Oo2IiHOokBIRyQOGAZs3lwage/czlCljcSARC3l5XVlH7f334cwZa/OIiDiDCikRkTwQFQUxMQHARQYMOGl1HBHLPfAA1K8PcXHw1ltWpxERyT0VUiIiTma3w6pVabfep1gxTbXtCpJ9Ann9Pwav/8cg2SfQ6jiFjs12pYCaMgWio63NIyKSWzkqpP766y9n5xARKTD++ANiYsDXNwV43+o4Ii7jzjuhTRtISoIxY6xOIyKSOzkqpKpUqUKbNm2YNWsWiYlaHV5EJE1qKqxebbZvv/0UoCnKRNLYbFfGSk2dCocOWZtHRCQ3clRIbdu2jdtuu43nnnuO8PBwhgwZwqZNm5ydTUTE7WzbBrGxEBQEtWqdtjqOXMUrJZHu87vTfX53vFL0IaBVmjUze6ZSUuDNN61OIyKSczkqpOrUqcPEiRM5fvw4X3zxBSdOnKB58+bUqlWL999/nzOajkdECqHkZFizxmy3aAFeXoa1gSQdmz2VmnsWUHPPAmx2jVuzUtq6UtOng0YLiIi7ytVkE15eXtx///3Mnz+fd955hwMHDvDCCy9Qrlw5+vXrx4kTJ5yVU0TE5W3aZM5IVrSoOTuZiFxf06Zw113qlRIR95arQmrLli088cQTRERE8P777/PCCy9w8OBBli1bxvHjx+nSpYuzcoqIuLTLl2HdOrPdqhV4elqbR8TVvfGG+X36dDh40NosIiI5kaNC6v3336d27do0bdqU48ePM2PGDI4cOcKYMWOoVKkSLVq0YNq0aWzbts3ZeUVEXNKGDZCQAKGhcNttVqcRcX133AF3321O0KIZ/ETEHeWokPr444/p3bs3R44cYfHixXTu3BkPj/SHKlmyJJ9//rlTQoqIuLL4eLOQAmjdGjy0Qp9IlqT1Ss2cCQcOWJtFRCS7vHLyoP379990Hx8fH/r375+Tw4uIuJW1a811ccLDoUYNq9OIuI9GjaBjR/jhB3Na9OnTrU4kIpJ1OfrcdOrUqcyfPz/D9vnz5zNd/wuKSCFy8SJs3my227Y118kRkaxLm8Fv1izYt8/SKCIi2ZKjQmrs2LGEhoZm2F6yZEneeuutXIcSEXEXa9aYM4+VKwdVqlidRm4k2TuAN4fH8ebwOJK9A6yOI/+vYUPo1Ansdo2VEhH3kqNCKjo6mkqVKmXYXqFCBaKjo3MdSkTEHZw7Zy7AC+qNcgs2G8k+gST7BOofy8Wk9UrNng1//mlpFBGRLMtRIVWyZEl27tyZYfuOHTsoUaJErkOJiLiDX34xP0WvXBkqVrQ6jYj7atAA7r3X/H0aPdrqNCIiWZOjQqpXr148/fTTrFq1itTUVFJTU1m5ciXPPPMMPXv2dGrAY8eO0bdvX0qUKIG/vz+1a9dmy5YtjvsNw+C1114jIiICf39/2rdvn6XJMEREcuPMGUj7PKlNG2uzSNZ4plym6+IBdF08AM+Uy1bHkWuk9Up9+SXs3WtpFBGRLMlRITV69GgaN25Mu3bt8Pf3x9/fn7vuuou2bds6dYzUuXPnaNasGd7e3ixdupQ9e/Ywfvx4ihUr5tjn3XffZdKkSXzyySds3LiRwMBAOnToQGJiotNyiIhca9UqMAyoVg3KlLE6jWSFhz2FOjumU2fHdDzsKVbHkWvUqwddumislIi4jxxNf+7j48O8efMYPXo0O3bscPQUVahQwanh3nnnHcqVK8fUqVMd264em2UYBhMmTODVV1+lS5cuAMyYMYNSpUqxePFip/eOiYgAHD8OUVFmW71RIs4zciR88w3MnQujRsEtt1idSEQkczkqpNLceuut3Hrrrc7KksG3335Lhw4d6N69O7/88gtlypThiSeeYPDgwQAcOnSIkydP0r59e8djQkJCaNy4MRs2bMi0kLp8+TKXL1+5rOPChQt59hxEpOBZtcr8ftttULKktVlEXFFU2icN2WSzQZMmldmwIYSXXjrDiBF/3/QxoaGhlC9fPkfnExHJjRwVUqmpqUybNo0VK1Zw+vRp7HZ7uvtXrlzplHB//fUXH3/8Mc899xwjRoxg8+bNPP30047Ffk+ePAlAqVKl0j2uVKlSjvuuZ+zYsbyRtpy6iEg2HDkCBw6Ahwe0amV1GhHXEhd3ArDRt2/fXBylOfArX38dzNdf3wscv+He/v4B7N0bpWJKRPJdjgqpZ555hmnTptGpUydq1aqFLY+mkbXb7TRo0MAx7qpu3brs2rWLTz75hP79++f4uMOHD+e5555z3L5w4QLlypXLdV4RKdgMA9I+J6pbF4oXtzaPiKtJTIwFDNq0mUxkZJMcH+fbb+M4eTKI2rW30aTJsUz3O3MmikWL+hITE6NCSkTyXY4Kqblz5/LVV1/RsWNHZ+dJJyIigho1aqTbVr16db7++msAwsPDATh16hQRERGOfU6dOkWdOnUyPa6vry++vr7ODywiBdrBgxAdDZ6e0LKl1WlEXFexYlWIiKiX48e3a2euKbV3bynuvrsUAVo/WURcUI4nm6hSpYqzs2TQrFkz/rxmZb59+/Y5JrWoVKkS4eHhrFixwlE4XbhwgY0bN/L444/neT4RKTyu7o1q2BCCg7P+2JyOF8mO/DiHSH6pXBkiIuDECdi4UZO6iIhrylEh9fzzzzNx4kQmT56cZ5f1ATz77LM0bdqUt956i4ceeohNmzbx2Wef8dlnnwFgs9kYNmwYY8aMITIykkqVKjFy5EhKly5N165d8yyXiBQ+UVHmmzofH2jePGuPcc54keyJi7uYb+dyN8neAbz7wmlHW1yXzWb+ns2fD5s2QdOmoAtJRMTV5KiQWrt2LatWrWLp0qXUrFkTb2/vdPcvXLjQKeEaNmzIokWLGD58OKNGjaJSpUpMmDCBPn36OPZ56aWXuHTpEo8++iixsbE0b96cH3/8ET8/P6dkEBGx26/M1HfHHRAYmLXHOWu8SFbs3/8Dq1aN1Bp6N2KzER8YZnUKyaLq1SE0FGJiYPPmrH+AISKSX3JUSBUtWpRu3bo5O8t1de7cmc6dO2d6v81mY9SoUYwaNSpf8ohI4fPHH+abOT8/aJKDeii340WyIiZGl/ZJwZLWK7V4Mfz2GzRuDNd8bisiYqkcFVJXL5ArIlKQpabC6tVmu1kzs5gS9+SZcpkOP5kztv7U4X1SvXStmKurVcv8/YuNhd9/h0aNrE4kInKFR04fmJKSwvLly/n000+5eNG8Jv/48ePExcU5LZyIiNW2bzffxAUG6k2cu/Owp9BoyxQabZmChz3F6jiSBZ6e5vgogHXrzA82RERcRY4KqSNHjlC7dm26dOnC0KFDOXPmDADvvPMOL7zwglMDiohYJSUF1qwx282bmxNNiEj+qlsXgoLgwgXYudPqNCIiV+SokHrmmWdo0KAB586dw9/f37G9W7durFixwmnhRESstG2b+eatSBFo0MDqNCKFk5fXlbGJa9eak7+IiLiCHI2R+vXXX1m/fj0+13w8W7FiRY4dy3wFchERd5GcDL/+arZbtDDfzImINRo0MH8f//kH9uwxx06JiFgtRz1Sdrud1OtcqHz06FGKFCmS61AiIlbbsgXi4iAkxLy0SESs4+NjztoH5lgpw7A2j4gI5LCQuuuuu5gwYYLjts1mIy4ujv/85z907NjRWdlERCyRlGS+WQNo2VK9USKuoFEj83fx5Ek4fNjqNCIiOSykxo8fz7p166hRowaJiYn07t3bcVnfO++84+yMIiL5atMmuHQJihWD22+3Oo2IAAQEXOkdXr/e2iwiIpDDMVJly5Zlx44dzJ07l507dxIXF8egQYPo06dPusknRETczeXLV96ktWplTr8sBUOKtz8TnjnkaIv7adLEvOz2wAE4fdrqNCJS2OX4ghUvLy/69u3rzCwiIpbbuBESEqBECahd2+o04kyGzYPYohWtjiG5UKwYVK9uTjixfv2VcVMiIlbIUSE1Y8aMG97fr1+/HIUREbFSYiJs2GC2W7UCjxwvWS4ieaVpU7OQ+uMPqFXL2+o4IlKI5aiQeuaZZ9LdTk5OJj4+Hh8fHwICAlRIiYhb2rDBLKbCwqBmTavTiLN5pibRdsUrAKxs9yapnlph2R2VKQMVKsCRI7BrV5jVcUSkEMvR563nzp1L9xUXF8eff/5J8+bN+fLLL52dUUQkz8XHw2+/me3WrdUbVRB5pCbTbMN7NNvwHh6pyVbHkVxIW6B3z54wQMuuiIg1nPZWITIykrfffjtDb5WIiDtYv96c9rxUKXMMhoi4rltvhdBQSE72BB6xOo6IFFJO/czVy8uL48ePO/OQIiJ57tIlc8pzgDZtwGazNo+I3JjNdqVXCoaRrA5GEbFAjsZIffvtt+luG4bBiRMnmDx5Ms2aNXNKMBGR/LJhAyQnQ0SE+Um3iLi+226D5cuTSUgoz7Jlds3gJyL5LkeFVNeuXdPdttlshIWF0bZtW8aPH++MXCIi+SI+HjZvNtutWqk3SsRdeHlBrVqn2by5DDNnluSVV/T7KyL5K0eFlN1ud3YOERFL/PabOTYqPFy9USLupnr1GDZvDmHfviCWL4c777Q6kYgUJpqXSkQKrYQEcwFegJYt9Wm2iLvx80sFPgfgvfeszSIihU+OeqSee+65LO/7/vvv5+QUIiJ5buNGszeqZEmoVs3qNJLXUrz9+ejxXY62FBQf4OHxND//bGPHDrj9dqvziEhhkaNC6vfff+f3338nOTmZqlWrArBv3z48PT2pV6+eYz+bPt4VEReVlOThWDdKvVGFg2Hz4ExJrbRc8Byhfftz/PxzccaPhxkzrM4jIoVFjgqpe++9lyJFijB9+nSKFSsGmIv0Dhw4kBYtWvD88887NaSIiLPt2lWSy5chLAxq1LA6jYjkxsMPn+bnn4szdy688445A6eISF7L0Rip8ePHM3bsWEcRBVCsWDHGjBmjWftExA0U4Y8/SgLqjSpMPFOTaL36dVqvfh3P1CSr44gT1agRT/Pm5jIGU6ZYnUZECoscFVIXLlzgzJkzGbafOXOGixcv5jqUiEjeGsrly16UKKHeqMLEIzWZ1r+8Qetf3sAjVSu4FjTDhpnfP/nEnEhGRCSv5aiQ6tatGwMHDmThwoUcPXqUo0eP8vXXXzNo0CDuv/9+Z2cUEXGa+HgPwLz8uGVL8NDcpSIFQpcuUKECxMTA7NlWpxGRwiBHY6Q++eQTXnjhBXr37k1ysvmpnpeXF4MGDWLcuHFODSgi4kwLFoQCoQQHJ1Krlp/VcUTECaKiogDo1q0kEyaU5e23E6hbN8rpl+2GhoZSvnx55x5URNxWjgqpgIAApkyZwrhx4zh48CAAlStXJjAw0KnhREScKT4eZswoBUC9eifx8KhobSARyZW4uBOAjb59+/7/lmDgKAcPFqFBg5eAFU49n79/AHv3RqmYEhEgh4VUmhMnTnDixAlatmyJv78/hmFoynMRcVmffgrnznkDB6lS5TxQ0eJEIpIbiYmxgEGbNpOJjGwCwLp1CezeXYTy5b/m7rsPOu1cZ85EsWhRX2JiYlRIiQiQw0Lq7NmzPPTQQ6xatQqbzcb+/fu55ZZbGDRoEMWKFdPMfSLichIS4N130269hYfHUCvjiIgTFStWhYgIcx3LNm1g926Ijg7B27seoaEWhxORAitHw6yfffZZvL29iY6OJiAgwLG9R48e/Pjjj04LJyLiLF98ASdPQkTEZWCm1XFEJI+UKAG33mq2N260NouIFGw5KqR+/vln3nnnHcqWLZtue2RkJEeOHHFKMBERZ0lOvtIb1a/fKUBTXxdGKV5+fPbIJj57ZBMpXppopCC74w7z+44dmgpdRPJOjgqpS5cupeuJSvPPP//g6+ub61AiIs705ZcQHQ2lSsF99521Oo5YxPDw5HiZhhwv0xDDw9PqOJKHKlY0f9+Tk2HbNqvTiEhBlaNCqkWLFsyYMcNx22azYbfbeffdd2nTpo3TwomI5JbdDmPHmu1nnwU/P8PaQCKS52w2aNzYbG/aBKmp1uYRkYIpR5NNvPvuu7Rr144tW7aQlJTESy+9xO7du/nnn39Yt26dszOKiOTY4sWwdy+EhMDjj8OBA1YnEqt4pibR+LeJAGy84xlSPX0sTiR5qXZtWLECLlww/w+oWdPqRCJS0OSoR6pWrVrs27eP5s2b06VLFy5dusT999/P77//TuXKlZ2dUUQkRwzjSm/UU09BcLC1ecRaHqnJ3LX8Je5a/hIeqRonV9B5eUGDBmb7t9+szSIiBVO2e6SSk5O5++67+eSTT3jllVfyIpOIiFMsXw5btoC/Pzz9tNVpRCS/NWgAa9fC0aPm1zVzZImI5Eq2e6S8vb3ZuXNnXmQREXGqt94yvz/6KISFWZtFRPJfUBDUqmW21SslIs6Wo0v7+vbty+eff+7sLCIiTrNhA6xeDd7e8PzzVqcREaukTYW+Z485XkpExFlyNNlESkoKX3zxBcuXL6d+/foEBgamu//99993SjgRkZxKGxv18MNQrpy1WUTEOuHhUKECHDkCmzdDu3ZWJxKRgiJbhdRff/1FxYoV2bVrF/Xq1QNg37596fax2WzOSycikgN//AHffWdOgfzyy1anERGrNW5sFlJbt0LLlmZPtYhIbmWrkIqMjOTEiROsWrUKgB49ejBp0iRKlSqVJ+FERHLi7bfN7w8+CLfeam0WEbFe1apQtCjExsLOnVC/vtWJRKQgyFYhZRjpF7JcunQply5dcmogEZHcOHgQ5s4128OHW5tFXEuKlx/T+q9ytKXw8PCARo3g559h40aoV8/ssRYRyY0cTTaR5trCSkTEau++C3Y73HMP1K1rdRpxJYaHJ4crtuZwxdYYHp5Wx5F8VreueUnfmTNw6JDVaUSkIMhWIWWz2TKMgdKYKBFxFcePw7RpZlu9USJyNT8/qFPHbG/caGkUESkgsn1p34ABA/D19QUgMTGRxx57LMOsfQsXLnReQhGRLHr/fUhKgubNoUULq9OIq/FITab+1s8A2Fr/UeyemnGgsGnc2Jy5b98++OcfKF7c6kQi4s6yVUj1798/3e2+ffs6NYyISE6dPQuffGK2R4ywNou4Js/UJDotfRKA7XUGqJAqhEqUgMhI2L/f7JW65x6rE4mIO8tWITV16tS8yiEikiuTJ8OlS+alO3ffbXUaEXFVjRubhdT27dCmjXnJn4hITuRqsgkREVdw8SJMnGi2hw/XbFwikrlbboGwMPMy4O3brU4jIu5MhZSIuL3PPoNz58xLdh54wOo0IuLKbDZzKnSATZvMWT5FRHJChZSIuLXLl2H8eLP98svgqVmtReQmbr/dvKTv3Dlz4gkRkZxQISUibm36dDhxAsqWhYcftjqNiLgDb2+oX99sayp0EckpFVIi4rZSUuCdd8z2Cy+Aj4+1eUTEfTRsaF7md/gwnDpldRoRcUfZmrVPRMSVzJ8Pf/1lTmn8yCNWpxFXl+rly+xe3zvaUriFhECNGrB7N/z2G3TpYnUiEXE36pESEbdkt8Nbb5ntYcPgmnXBRTKwe3ix/9ZO7L+1E3YPfY4o5lToAH/8YS6fICKSHSqkRMQtLVkCu3ZBkSIwdKjVaUTEHZUtC6VLQ2oqbN1qdRoRcTcqpETE7RjGld6oxx+HYsWszSPuwSM1mTrbp1Fn+zQ8UpOtjiMuwGa70iu1ebNZUImIZJUKKRFxO7/8Yo5p8PWFZ5+1Oo24C8/UJLp+M5Cu3wzEMzXJ6jjiImrWhKAgiIuDPXusTiMi7kSFlIi4nbTeqEGDIDzc2iwi4t48PaFBA7P9229mj7eISFaokBIRt7JlCyxbZr75efFFq9OISEHQoIH5f8rx43D0qNVpRMRdqJASEbcydqz5vVcvqFjR0igiUkAEBkLt2mZbC/SKSFapkBIRt7FnDyxcaLaHD7c2i4gULGmTTuzZAxcuWJtFRNyDCikRcRvvvGN+79bNXEhTRMRZwsOhQgVzjNSmTVanERF3oEJKRNzC4cMwe7bZVm+UiOSFO+4wv2/bBsmaIV9EbkJLu4uIW3j3XXONlzvvhIYNrU4j7ijVy5evHvzK0Ra51q23QtGiEBsLO3dC/fpWJxIRV6YeKRFxeSdOwBdfmO0RI6zNIu7L7uHFnprd2VOzO3YPfY4oGXl4QKNGZnvjRk2FLiI3pkJKRFzeBx/A5cvQtCm0amV1GhEpyOrWBR8fOHMGDh2yOo2IuDIVUiLi0v75Bz7+2GyPGAE2m7V5xH152FOosXs+NXbPx8OeYnUccVF+fnD77WZbU6GLyI2okBIRlzZ5MsTFwW23QceOVqcRd+aZcpmHFjzEQwsewjPlstVxxIWlTYW+bx+cPWttFhFxXSqkRMRlxcXBxIlmW71RIpJfSpSAyEizranQRSQzKqRExGV99pl5aV+VKvDgg1anEZHCJK1Xavt2SEiwNIqIuChNWyQiLiE6OpqYmBjH7aQkG2PH1gR86NXrCDt2OOf6mqioKKccR0QKtltugZIl4fRp2LoVKle2OpGIuBq3KqTefvtthg8fzjPPPMOECRMASExM5Pnnn2fu3LlcvnyZDh06MGXKFEqVKmVtWBHJsujoaKpVq05CQvxVWwcDnwF/M3p0JKNHO3d1zLi4i049nogULDabOVPo4sXmpBMVK+raYhFJz20Kqc2bN/Ppp59y2223pdv+7LPPsmTJEubPn09ISAhPPvkk999/P+vWrbMoqYhkV0xMDAkJ8XTrNouwsOrY7TBvXk0uXoQmTaB27d+cdq79+39g1aqRJCYmOu2YIlIw1aoFK1bAxYtw4EAxq+OIiItxi0IqLi6OPn368N///pcxY8Y4tp8/f57PP/+cOXPm0LZtWwCmTp1K9erV+e2337jjjjusiiwiORAWVp2IiHr88Yf5xiUgAFq3LoePTzmnnSMmRpf2iUjWeHqaY6WWL4edO0sB6pUSkSvcYrKJoUOH0qlTJ9q3b59u+9atW0lOTk63vVq1apQvX54NGzZkerzLly9z4cKFdF8i4hoMA9auNduNG5sLY4o4Q6qnD4u7TGVxl6mkeuoHS7Kmfn3z/6Fz5/yBe6yOIyIuxOV7pObOncu2bdvYvHlzhvtOnjyJj48PRYsWTbe9VKlSnDx5MtNjjh07ljfeeMPZUUXECfbtMwd3+/hAo0ZWp5GCxO7pzfY6A6yOIW7Gz88spszPZ1+0Oo6IuBCX7pH6+++/eeaZZ5g9ezZ+fn5OO+7w4cM5f/684+vvv/922rFFJOcMA3791Ww3bGi+gRERsVrjxmCzGUBrdu8OsDqOiLgIly6ktm7dyunTp6lXrx5eXl54eXnxyy+/MGnSJLy8vChVqhRJSUnExsame9ypU6cIDw/P9Li+vr4EBwen+xIR6x0/HsSxY+DlBRriKM7mYU8hct8SIvctwcOeYnUccSMhIVClyj8AzJypWYFFxOTShVS7du34448/2L59u+OrQYMG9OnTx9H29vZmxYoVjsf8+eefREdH06RJEwuTi0hObN9ufgBSrx4EBVkcRgocz5TL9PmyM32+7IxnymWr44ibue220wCsWFGUv/6yOIyIuASXHiNVpEgRatWqlW5bYGAgJUqUcGwfNGgQzz33HMWLFyc4OJinnnqKJk2aaMY+EbfTiGPHgvHwMNduERFxJSVKJAA/Ybd34IMP4MMPrU4kIlZz6R6prPjggw/o3LkzDzzwAC1btiQ8PJyFCxdaHUtEsm04ALfdZl5GIyLiesYB8MUXcPasxVFExHIu3SN1PatXr05328/Pj48++oiPPvrImkAikmv79vkDXQGDZs20TouIuKoVVK0az59/BvDxx/Dqq1bnEREruX2PlIi4v//9zxwbVbnyOUJDLQ4jInIDffueAsxL+xITLQ4jIpZSISUiltq1C1asKAZA3bqZr/8mIuIK7rzzHOXKmevdzZhhdRoRsZIKKRGx1Jgxaa35FC+uj3dFxLV5e8Ozz5rt8eMhNdXaPCJiHRVSImKZqCj46qu0W6OtjCKFQKqnD0vumcySeyaT6uljdRxxY488AkWLwr59sGiR1WlExCoqpETEMmPGgGFAmzaxwB9Wx5ECzu7pzeZGQ9ncaCh2T2+r44gbK1IEnnrKbL/1lvn/mIgUPiqkRMQSf/4Jc+ea7UceOWFtGBGRbHrmGQgMhN9/hx9/tDqNiFhBhZSIWOKtt8Buh3vvhWrVEqyOI4WAzZ5KxcOrqXh4NTa7BrZI7pQoAUOGmO0331SvlEhhpEJKRPLdgQMwe7bZfu01a7NI4eGVksiA6W0YML0NXima2ERy7/nnwccH1q2DX3+1Oo2I5DcVUiKS7956y5zpqmNHaNDA6jQiIjlTujT8619m+803rc0iIvlPhZSI5KtDh66svTJypLVZRERy66WXwNMTfv4ZtmyxOo2I5CcVUiKSr9J6o+66C+64w+o0IiK5U6kS9O5ttkdrFQeRQkWFlIjkmyNHYNo0s/2f/1gaRUTEaUaMAA8P+PZbcxY/ESkcVEiJSL55+21ISYF27aBpU6vTiIg4R7Vq0LOn2R41ytosIpJ/VEiJSL6IjobPPzfbmqlPRAqakSPBZoPFi9UrJVJYqJASkXwxejQkJ0Pr1tCypdVppDCye3rzc/t3+bn9u9g9va2OIwVMtWrQq5fZVq+USOGgQkpE8tzBgzB1qtkeM8baLFJ4pXr6sL7Zi6xv9iKpnj5Wx5EC6Opeqe3brU4jInlNhZSI5LlRo8yZ+u6+G5o1szqNiEje0FgpkcJFhZSI5Km9e2HWLLOtNxZiJZs9ldLHNlP62GZs9lSr40gBldYrtWgRbNtmdRoRyUsqpEQkT73+OtjtcN990LCh1WmkMPNKSeTR/zXi0f81wisl0eo4UkBVr35lXalXX7U2i4jkLRVSIpJndu6EefPMtnqjRKSweP118PKCpUvh11+tTiMieUWFlIjkmbRFd7t3h9tvtzaLiEh+qVIFBg0y2yNGgGFYm0dE8oYKKRHJE1u3mjNXeXjAG29YnUZEJH+NHAm+vrB2Lfz0k9VpRCQvqJASkTyRtuhu797mmAERkcKkTBl48kmzPWKEOVZURAoWFVIi4nQbNsAPP4Cn55XL+0RECpt//xuCguD33+Hrr61OIyLOpkJKRJxu5Ejz+4AB5lgBEZHCKDQUnn/ebL/yCiQnW5tHRJxLhZSIONXq1bBiBXh7XymoRFyB3dOb1a3+w+pW/8Hu6W11HCkknnsOwsJg/3747DOr04iIM6mQEhGnMYwrxdPgwVChgrV5RK6W6unD6tavs7r166R6+lgdRwqJ4GBzOnQwJ965cMHSOCLiRCqkRMRpfvzRnKHK19ccXC0iIuYHS7feCmfOwDvvWJ1GRJxFhZSIOIXdbg6sBnOmqjJlrM0jci2bYSfs9G7CTu/GZmgKNck/3t5XCqj334ejR63NIyLO4WV1ABEpGObMgZ07ISQEhg+3Oo1IRl7JCQz9uBYAbw6PI9kn0OJE4o6ioqJy9Lhy5aBOnVvZvj2IoUNj+M9/om+4f2hoKOXLl8/RuUQkf6iQEpFcu3z5ytiol1+GEiWszSMi4mxxcScAG3379s3FURoDv/Htt8X59tv2wI5M9/T3D2Dv3igVUyIuTIWUiOTap5/C4cMQEQHPPGN1GhER50tMjAUM2rSZTGRkkxwfZ/nyf/jrr+JERPxK5877sdky7nPmTBSLFvUlJiZGhZSIC1MhJSK5cuECjB5ttl9/HQICLI0jIpKnihWrQkREvRw//r77YPJkOHGiCOfO1aNmTSeGE5F8pUJKRK4rOjqamJiYm+73yScRxMREUKFCInXq7GHbtuyfK6djDkRE3E1ICDRrBr/8AsuWmbP5eWtZMxG3pEJKRDKIjo6mWrXqJCTE32TPksBBAI4c6Uvjxl/n6rxxcRdz9XgREXfQrBn8/jucPw/r1kHr1lYnEpGcUCElIhnExMSQkBBPt26zCAurnul+a9eWZc+eIMLCLtG16whstpwtHrV//w+sWjWSxMTEnEYWEXEb3t5w112wYIFZSNWta/ZUiYh7USElIpkKC6ue6ViAf/6BvXvN9j33BFK6dM7HDMTE6NI+yXt2T2/WNXnB0RaxUo0aUKECHDkCP/8M3btbnUhEsksL8opIjqxaZS7CW7kyVKpkdRqRm0v19GHZXeNYdtc4Uj19rI4jhZzNBnffbX7fswcOHrQ6kYhklwopEcm2Eydg1y6z3b69tVlERNxVeDg0amS2lyyB5GRr84hI9qiQEpFsW7HC/F67tvlGQMQd2Aw7RWMPUzT2MDbDbnUcEQDatIEiReDcOVi71uo0IpIdKqREJFsOHjS/PDzMNwAi7sIrOYFhEysxbGIlvJITrI4jAoCvL3ToYLbXrYOzZ63NIyJZp0JKRLLMbjcHRQM0aADFilmbR0SkIKhRwxxvmpoKP/wAhmF1IhHJChVSIpJl27fD6dPg56d1T0REnMVmg44dwdMT/voLDhwobnUkEckCFVIikiWXL8PKlWa7VSvw97c2j4hIQVK8uPl/K8D69WWBMEvziMjNqZASkSxZtw4uXTL/2DdsaHUaEZGCp2lTKFUKLl/2AiZZHUdEbkKFlIjc1PnzsGGD2b7zTvPyExERcS5PT+jSBWw2A+jJ6tUhVkcSkRtQISUiN7ViBaSkQIUKULWq1WlERAquiAi47bZTALz9djliY63NIyKZUyElIjd07Bj88YfZ7tDBHBQt4o7sHl5savAEmxo8gd3Dy+o4IpmqX/8EsI8zZ3x44QWr04hIZvSXREQyZRjw009m+/bbzU9KRdxVqpcvP3T6yOoYIjfl5WUAg7DZ1vD55za6dYNOnaxOJSLXUo+UiGTq0KGi/P03eHlB27ZWpxERKUzW0rv3aQAGDYKYGIvjiEgGKqREJBM+bNxYBoBmzSA42OI4IrllGARcOkPApTNa8VTcwtChx6lRA06dgscf14+tiKtRISUimXiKixd9CQoyp+QVcXfeyfG89F5JXnqvJN7J8VbHEbkpX1+DGTPMqwIWLIAvv7Q6kYhcTYWUiGRw7pwn8CoA7dqBj4+1eURECqv69WHkSLM9dCj8/be1eUTkChVSIpLBlCmlgaKUKBHPbbdZnUZEpHAbPtxcCD02Fh5+GFJTrU4kIqBCSkSusWULLFoUCkCzZn/jof8lREQs5e0Ns2dDYCD88guMHWt1IhEBFVIichW7HZ58EgzDBswiPPyS1ZFERASIjIQpU8z266/DunWWxhERVEiJyFWmT4eNGyEgIBV4yeo4IiJylYcfhj59zEv7evc2L/UTEeuokBIRwPyD/O9/m+3Bg08AJ6yMIyIi17DZzF6pW26B6GgYPFhTootYSYWUiADmpSKnT0O1atCr1xmr44g4nd3Di+2392f77f2xe3hZHUckR4KDzWnQ06ZEnzzZ6kQihZcKKRHhjz+u/DGeNAm8vfURpxQ8qV6+LO46jcVdp5Hq5Wt1HJEca9QI3nvPbD//PPz2m7V5RAorFVIihZxhwFNPmdfc338/3Hmn1YlERORmnn4aHnwQkpPhoYcgJsbqRCKFjwopkULuq6/M6XT9/OD9961OI5KHDAPvpEt4J13SwBJxezYbfP453HqruUhv377mzKsikn9USIkUYnFx5mUhACNGQIUK1uYRyUveyfG8MjaIV8YG4Z0cb3UckVwLDjbHSfn7w08/wRtvWJ1IpHBRISVSiL35Jhw7BpUqwYsvWp1GRESyq3Zt+PRTsz1qFCxebGkckUJFhZRIIbVvH4wfb7YnTDAv7RMREffz8MPwzDNX2nv2WJtHpLBQISVSCBkGPPaYOUj57rvh3nutTiQiIrkxbhy0aWNest2lixbrFckPKqRECqHp02HVKvO6+o8+Mgcti4iI+/L2hnnzzLGuBw5Ar16QkmJ1KpGCTYWUSCFz5syVCSZefx1uucXSOCIi4iRhYbBokfkh2Y8/mpf7aYJKkbyjQkqkkHnuOfjnH7jtNnj2WavTiIiIM9WtC7Nnm1caTJkCEydanUik4FIhJVKILFsGs2aZf2D/+1/zUhCRwsLw8GR3jQfZXeNBDA9Pq+OI5Jlu3eDdd832c8/BN99Ym0ekoPKyOoCIZF10dDQxOVy+PiHBxsCBNQBfHnroNF5eR9m27fr7RkVF5TykiItK8fJjfvf5VscQyRfPP2+Olfr0U+jd21x4vUEDq1OJFCwqpETcRHR0NNWqVSchIacLib4F1AWOMm9eDebNu3jTR8TF3XwfERFxPTYbTJ4Mhw+bi/Xeey9s3Ajly1udTKTgcOlCauzYsSxcuJC9e/fi7+9P06ZNeeedd6hatapjn8TERJ5//nnmzp3L5cuX6dChA1OmTKFUqVIWJhdxvpiYGBIS4unWbRZhYdWz9dizZ/1ZuLAahgF33XWZihVX33D//ft/YNWqkSQmJuYisYiIWMnLC776Cpo3hz/+gE6dYN06CA62OplIweDShdQvv/zC0KFDadiwISkpKYwYMYK77rqLPXv2EBgYCMCzzz7LkiVLmD9/PiEhITz55JPcf//9rFu3zuL0InkjLKw6ERH1sry/3Q5LlpgzN1WrBk2aVL7pY2JidGmfFDzeSZd4ZWwQAG8OjyPZJ9DiRCJ5LzgYvv8eGjeGXbuge3fztsbIiuSeSxdSP/74Y7rb06ZNo2TJkmzdupWWLVty/vx5Pv/8c+bMmUPbtm0BmDp1KtWrV+e3337jjjvuuO5xL1++zOXLlx23L1y4kHdPQsRiW7bAsWPg4wP33GN1GhERyW/ly5vFU8uW8PPP8OST8MknWkNQJLfcata+8+fPA1C8eHEAtm7dSnJyMu3bt3fsU61aNcqXL8+GDRsyPc7YsWMJCQlxfJUrVy5vg4tY5MIFWLHCbLdrp8s5REQKq/r1Yc4cs3j67DNzHUERyR23KaTsdjvDhg2jWbNm1KpVC4CTJ0/i4+ND0aJF0+1bqlQpTp48memxhg8fzvnz5x1ff//9d15GF7GEYZifQCYlQZkymq1JRKSw69LFnIACYNQomDTJ2jwi7s6lL+272tChQ9m1axdr167N9bF8fX3x9fV1QioR17VzJ+zfD56ecN994OE2H5uIiEheeeIJOHsWXnsNnnkGiheHvn2tTiXintzirdWTTz7J999/z6pVqyhbtqxje3h4OElJScTGxqbb/9SpU4SHh+dzShHXcfEipA0xbNUKSpa0No+IiLiOV181iyiAAQPMqxdEJPtcupAyDIMnn3ySRYsWsXLlSipVqpTu/vr16+Pt7c2KtEEgwJ9//kl0dDRNmjTJ77giLsEw4LvvIDERSpeGZs2sTiQiIq7EZoP334eHH4bUVHMmvzVrrE4l4n5c+tK+oUOHMmfOHL755huKFCniGPcUEhKCv78/ISEhDBo0iOeee47ixYsTHBzMU089RZMmTTKdsU+koLv6kr4uXXRJn0gaw8OTfZEdHW2RwszDAz7/HGJjzQ/f7r0XfvkF6tSxOpmI+3DpQurjjz8GoHXr1um2T506lQEDBgDwwQcf4OHhwQMPPJBuQV6RwujCBV3SJ5KZFC8/5vReYnUMEZfh7Q3z5sHdd5s9Uh06wNq1EBlpdTIR9+DShZRhGDfdx8/Pj48++oiPPvooHxKJuC7DgMWLdUmfiIhknb8/fPsttGkDv/8Od95pFlXly1udTMT1uXQhJSJZt2kTHDoEXl5w//26pE9ExN1FRUXly3lCQ0P58cfyNG9uXhretq15mV+ZMvlyehG3pUJKpAA4cwaWLzfbd90FJUpYm0fEFXknXeLF98zrXce9cJpkn0CLE4lcX1zcCcBG33yal9zfP4C9e6NYsaI8rVrBwYNXiilNgiySORVSIm4uNRUWLYKUFKhcWQvvityIT3K81RFEbioxMRYwaNNmMpGReTsL8ZkzUSxa1JeYmBjq1SvPqlXQsiXs22cWU6tXa7ytSGZUSIm4udWr4cQJ8zr3Ll3MaW1FRMT9FStWhYiIevl6zgoVcBRTUVHQvj2sXAmhofkaQ8QtaBSFiBs7dMicYQmgc2coUsTaPCIi4v5uucUspiIi4I8/zAko/vnH6lQirkeFlIibio+HhQvNdr16UKOGtXlERKTgiIw0e6JKloTt283xt7GxVqcScS0qpETckGHAN99AXJx5ucXdd1udSERECppq1a5c1rd1q/m35vx5q1OJuA4VUiJuaNMmcyCwpyc88IC5qKKIiIiz1axpzgpbvDhs3KieKZGrabIJETdz5ow/y5aZ7bvu0tS0Illl2Dw4XKGVoy0iV9xszarJk/15/PFINm3yokmTeKZM2U9ISGq2zxMaGkp5rfYrBYQKKRG3UpTly28hNRWqVoWGDa3OI+I+Urz9mTZgtdUxRFxK9tasqg2sYO/eMNq2NYC7gLPZOl/amlUqpqQgUCEl4iYMA2AaFy/6UrQodO2qqc5FRCR3srtm1T//nGHJkqIkJNShePG/6dRpP/7+KVk619VrVqmQkoJAhZSIm5g5syRQDw8PO927e+DnZ3UiEREpKLK6ZlVEhDmT34wZ8M8//vz442306wdBQfkQUsTF6CJxETfw668weXIZAJo2PUrp0hYHEnFD3kmXeHFcGC+OC8M76ZLVcUTcVlgYDBhgrl145gxMmwYXL1qdSiT/qZAScXFHj0L37pCaagNmU716jNWRRNxWYHwMgfH6HRLJrRIlzGIqOBjOnjWLKU2NLoWNCikRF5aYaE5vfuoUVKmSADyqcVEiIuISihc3i6mQEPjnH5g61fwuUliokBJxUYYBTzxhrhlVrBiMH38QiLc6loiIiEOxYjBwoFlUnT9vFlOnT1udSiR/qJAScVFTpph/kDw8YN48KFs2yepIIiIiGYSEmMVUyZIQF2de5nf8uNWpRPKeCikRF7RyJQwbZrbfeQfuvNPSOCIiIjcUFGRe5le6NCQkmLP6RUdbnUokb6mQEnExf/5pjotKSYHeveH5561OJCIicnP+/tCvH1SoAJcvw6xZ8NdfVqcSyTsqpERcyNmz0LkzxMZCkybw+edadFfEWQybB8dKN+BY6QYYNv35E8kLvr7Qpw9UrgzJyTBnjvkBoUhBpAV5RVxEUpLZE3XgAFSsCIsXo0V3RZwoxduf/w7ebHUMkQLP2xt69oSvv4a9e81xvl27mutPiRQk+khOxAUYBjz6KPzyi7nA4XffmYN2RURE3JGXl7kG4m23mX/jFi2CP/5QJSUFiwopERfw2mswfTp4epqf3NWqZXUiERGR3PHwMHuiGjc2b2/YUA4YjWFYmUrEeVRIiVjs009hzBiz/ckncM891uYRKai8k+MZNqEiwyZUxDtZa7KJ5AebDTp0gDZt0ra8ytix5UhNtTKViHOokBKx0LffmovuArz+OjzyiKVxRAo2w6Do+SMUPX8EfSQukn9sNmjZEpo3jwbsfP11GD17mjP7ibgzFVIiFlm71hyMa7ebBdRrr1mdSEREJO/UqBEDPIS3t50FC6BjR7h40epUIjmnQkrEAtu2QadO5qKFnTvDxx9rmnMRESkMvmbSpIMEBZmLz7dpA6dPW51JJGdUSInks717zevFL1wwL3X46itzdiMREZHCoFGji6xaBaGhsHUrNG0K+/ZZnUok+1RIieSjw4ehfXuIiYH69c1pzv39rU4lIiKSvxo0MC9xr1QJDh40i6n1661OJZI9KqRE8snRo9CuHRw7BtWrw48/QnCw1alERESsUbUqbNgADRvC2bPQtq25iK+Iu1AhJZIPjh0zrwP/6y+45RZYtsy8pEFE8pHNxumwGpwOq6FBiSIuolQpWLUK7rvPnMWve3f44ANNrCnuQYWUSB47ccL8lO3AAahY0fyDUaaM1alECp9k7wCmPLGbKU/sJtk7wOo4IvL/AgNh4UIYOtQsoJ57DoYNQ2tNictTISWSh9KKqH37oHx5s4gqX97qVCIiIq7F0xM+/BDee8+8PWkSPPggxGvtbHFhmitMJJeio6OJiYnJsP3kSW8efzyS6Gg/SpVK4sMP9/HPP0n880/OzhMVFZXLpCIiIq7LZoPnnzc/cHz4YVi8GFq0ML+XK2d1OpGMVEiJ5EJ0dDTVqlUnIeHaj8wqAisBP+Awp061pUuXQ045Z1ycVi8UyQnv5HgG/7chAP8dvFmX94m4qO7dISICunUz111s0MC89K9ZM6uTiaSnQkokF2JiYkhIiKdbt1mEhVUHIDbWlyVLIrl0yYfg4EQ6d75IUNCCXJ9r//4fWLVqJImJibk+lkihZBiUPLPH0RYR19W8OWzZAl26wI4d5oRNU6bAI49YnUzkChVSIk4QFladiIh6nDgBS5bApUvmrHz9+vlRpEhtp5wjJkaX9omISOFRoQKsWwcDB8L8+TB4MGzfbs7q5+1tdToRFVIiTnP4MMyda07fGh4OffuaMxGJiIjIFdkd8/vyyxAWFs6UKaX56CP47beLvPPOXxQrduNp/UJDQymvGZ4kD6mQEnGCw4dDWLHCnKq1QgXo2RP8/KxOJSIi4jri4k4ANvr27ZvDI9wLzGLr1mDatw8GugI7M93b3z+AvXujVExJnlEhJZJrg1i27BYMw1yl/YEHdMmBiIjItRITYwGDNm0mExnZJEfHOHfuKD/9dAsXLlTC0/N3mjb9m2rVzmZYY/vMmSgWLepLTEyMCinJMyqkRHLIMGDKlAjgfxgG1KkD994LHlqdTUREJFPFilUhIqJejh4bEQGVKsGiRbB/vwe//lqB8+cr0KkT+Pg4OajITegtn0gOJCVBv37w+ecRANSrd4L77lMRJeLSbDZiQyoQG1KBDB9fi4jb8PeHXr2gfXvzV3nnTvjvf+HkSauTSWGjHimRbDp71lzjYtUq8PQ0SE0dRIMGT2KzRVgdTURuINk7gAnDDlsdQ0ScwGYz15UqWxa+/hpiYuB//4N27eCOO6xOJ4WFPj8XyYa9e6FxY7OICgqCCRMOAFOtjiUiIlIoVagAjz1mjlFOTYWff4ZZs+DSJQ1WlrynHimRLPrpJ+jRA86fh4oV4dtvITn5otWxRERECrWAAPPv89at5t/qv/6Co0erA/209rbkKfVIidyEYcD48dCxo1lENW8OmzZBbeessysi+cQrOYHB/23I4P82xCs5weo4IuJENhs0aABDhkDp0pCU5AVM55lnKnP0qNXppKBSISVyA3Fx5ppQL7wAdru5uvry5RAWZnUyEckum2GnzPEtlDm+BZthtzqOiOSB0FAYNAgaNToGXGbduhBq1oQPPzQv/RNxJhVSIpnYv98csPrVV+DlBZMnw+efg6+v1clEREQkMx4eUKfOKaAOtWpd4sIFePppaNjQvKJExFlUSIlcx7x55iUCu3eba1asXg1Dh2rGZBEREfexly+++JMpUyAkBH7/3fyAdPBgTZUuzqFCSuQqCQnm9dU9e8KFC9CihTl4tVkzq5OJiIhIdnl6wuOPw59/wsMPm+Oe//c/qFIFRo+G+HirE4o7UyEl8v/27DGnNv/sM7Pn6ZVXYOVKs0dKRERE3FepUjBjBqxda/6tv3QJXnsNIiNhyhS4fNnqhP/X3p1HRXWkbQB/mp1mkR0EBYw6gAFxiRJ0EhdQ9GQx6ox+RBP3MRGNyCSOfDNGM+oYo2P8zJjoKCJuUdGBGI/RcYOowUQQFDwKYjBiAGXfl5au748bWlsWaUGa5fmdc0/3XfrWe63TWG/fW1XUETGRoi5PqQQ2bQIGDQKSkwE7O2n41NWrpb5RRERE1DkMHw7ExQEHDkhTmWRlSY/u9+0LbN3KhIo0w0SKurTMTGDMGGDJEumP57hxQFKStI2IOp9yuQ3K5TbaDoOItEgmk+adunkT2LIFcHKS2gPvvw+88ALw2WdAUZG2o6SOgIkUdUlKpfQIn6en9PieXA589RVw/Dgf5SPqrBQGJlj/US7Wf5QLhYGJtsMhIi0zNAQWLADS06Xh0R0dpTtUf/kL0LMnEBwMpKVpO0pqz5hIUZeTmgqMGiUNKlFSIo3gk5QEvPceR+UjIiLqaoyMgIULgYwMYNcuwMtLmkfy//4PcHMDRo+WHgXkY3/0JCZS1GVUVgKffAJ4ewPffw+YmEh9oy5ckJ6NJiIioq7LwACYMQO4ehU4cQJ4/XVpTqpz54DAQOmO1XvvSW0IJef0JjCRoi5ACODIEcDDA1i5UvpFKSAASEkBFi+WhkYlos5PT1GJmbtGYuaukdBTVGo7HCJqp2QyqZ3w7bfSXaoVK6R+VAUFwLZtwIgRQK9eUv/q2FigtlbbEZO2cEwy6tTi46Vnnc+eldZ79gQ2bAD++Ec+xkfU1ciEEq6/xKreExE9jbOz9CPs8uVATAywb5/04+zdu9JTLZs2ATY2wMiRZRg4MBc+PqWwtHz4XGOysbGBs7Pzcy2DmoeJFHVKN29Kf/QOH5bWDQ2BpUulpMqEfcyJiIhIA7q6gJ+ftGzZIk2TEh0t3bXKywMOHzbF4cOmAJQArgD4HsBFAD8AyGnVWIyN5bh58waTqXaAiRR1KtevA+vWSb8YKZXSXafp06W+Ub16aTs6IiIi6uiMjYG33pKWhw+BsLA0vPdeFLp1m4fiYisAL/22hAAAzMyqYW9fBgeHcjg4lMHCogo6z9i5Jjf3BqKipiMvL4+JVDvARIo6hUuXgE8/Bb755tG2CROkSXU9PbUXFxEREXVeenrAkCFlAJZh6tQxMDOzws8/S/NSZWYC9+8DpaWGKC01RHq6teoztraAvb36Ipdr91pIc0ykqMOqqAAOHgS+/FLqCwVId6AmTgRmzsyBk1MWamqAK1eeXww3btx4ficnIiKiFmmL/6cfL8PUFOjfX1oAaYCre/ekPlWZmcCvvwI1NUB2trQ8zsxM6m9lbQ1YWUmv1taAhQUHxmqvmEhRhyIE8NNP0qN7e/cChYXSdgMD4O23pT5QcvlduLt7oLKyos3iKisrbbOyiIiIqGllZdkAZJg+fXobllm/LWBoCPTuLS2A1I4pLJTuVOXkAA8eSK9FRUBpqbRkZKifQyYDLC2lRV/fGcDf8O23ViguBlxcgB49pHYQtT0mUtTuCQFcuwZERUkJVHr6o32urtKcDrNnS7fJAeDKlTxUVlZg4sS9sLX1eK6x3bp1HOfOLUdVVdVzLYeIWkeNPp+dIeoKqqqKAAiMGvUv9O3r+1zL0qQtIJNJd5usrKRpWepUV0tJVX6+tBQUPHr/8KG0XlAAADYAVmHlSvVzOjhIIww6O0sjFDs5qS/du0sTD1PrYiJF7VJZGXD+PPDdd8DRo8AvvzzaJ5dLHTynTwfGjm38dretrQe6dx/0XOPMy+OjfUQdhcLABP/433Jth0FEbcjSsk+HaAsYGkoJUM+e6tuFkO5S5ecDxcXAvXtZSEj4Dj4+f0RhoTnu3gWqqh49Kvjjj42XYW0tJVWOjo8SrMffOzlJjxY+60AYXRETqXbo7t27yMvLa5Oy2nIugqauq7BQF9evm+DaNRPEx5shJcUEtbWPJnoyNFTi5ZdL4OdXhFGjiiCXS3PAXL1a/1zst0RERESdgUwGmJtLCwDY2+cgIWEuFi0ygoeHB4QAior0kJOjj+xsA+TkSEtenj4ePNDHgwcGyM3VR02NjuoO17VrjZenp6eEjY0CdnYK2NoqYGVVhe7dlbC1VcDWtgZ2dtI+Y+PWn4uvI86PxUSqnbl7t23797TVXASPrqsSgAsAr9+W/pCGCO3dwKd+BnAGwFFUV59BbGwlYmObXyb7LREREVFn8ux9vywBOAFw/O3VqYF1Ozx8qIOcHEPk5Bg+5XzFAH79bcl64n0mpDZcoUYRdsT5sZhItTN5eW3Xv+d5zEWgUEi3lu/dk0amuXdPWq5cMUdl5Y/Q0fGAUtnws3jdulXBzq4c3buXwdGxFObmNQAG/7Z80uwY2G+JiBqi97AKUw5NBgAcmnIED/XYYYCIOpbn1/crC0plFioq9FFero+KCgPcuZOGW7eSYG//FnR1HVFeboCKCn0oFLoAuv229Gv0jAYGD2FmVgNz82qYm1ervTc1rVF7hLCjzo/FRKqdaov+PQ1RKqVhxcvLG16Kiup3hKx7zc6WRqERoqEzWwCwgFIp9WmysZHmTLCzkzpIOjoCxsZGAIwAWLfoGthviYgaIlPW4ne3jqveExF1VG3R98vMLA63boVi+PCB8PJyV22vrn40wmBJyaP3devFxVJf95oaPeTn6yE/v/4gPzIZ0K3bo9EI9fTsAfwB9+4ZYFDbN3+fGROpDurhQ2kegupq9VeFovHXJ7dVVLgBuI7XXusNhUJKlFrjJo6+vtRhsUcPaXFyAnR0MrF+/Vz8z//8E337erIjIxEREVEHZGgoLTY2jR+jUEg/vhcWSktBgfr6w4fSelFR3XDvTgAicexYNt58sw0uopUwkWpHsrOBL7/sDuBzxMQ4Q0+vfqJUXS0tylbp42cCoB9ycurvkcmk0fFMTNQXC4tHk8Q9+WpvLyVODY34cuVKLtav/y/MzWuYRBERERF1Yvr60rQ0dVPTPE4I6Y5VXYJVWAhkZeUjPT0Vffo4tH2wLdBpEqktW7Zg/fr1yMnJgbe3N7744gsMHTpU22FppKAACAvrDiAYaWnN+4yenvSrgIHBo0Vf/9Hr4++ffC0rS8fJk/MQEfEVBg92V0uYjI2lZIqIiIiIqLXIZICZmbTUdYfKzv4F6enD4e+foN3gNNQpEqmDBw8iJCQEW7duhY+PDzZt2oSAgACkpqbCzs5O2+E1m709MGXKAxw6FIYhQ96FjY0TDAweJUqPv9a9b8ndnezsEgAx8PSswIsvttplEBERERF1ep3iIauNGzdi3rx5mDVrFvr164etW7dCLpdj586d2g5NIzY2wF/+cg/A/2LgwPsYOhQYMECa+bp3b+mxOTs7qXOekREnTCMiIiIi0pYOf0eqpqYGCQkJCA0NVW3T0dGBv78/4uLiGvxMdXU1qqurVevFxcUAgJKSkucbbDOUlZUBALKyElBTU/Zcy8rLSwUAJCQkqMp9XlJTpbLa4rpyc2/89pqMX34xZlntvKy2Lo9ldd2yDBRVqPsrf/fuRdToqw9/3lGvq6uW1dblsSyWxbKen7o2aVlZWbtoj9fFIBoeilpFJp52RDuXlZUFJycn/PDDD/D1fTSe/tKlSxEbG4sff/yx3mdWrlyJTz5p/rxERERERETUtWRmZqJHjx6N7u/wd6SeRWhoKEJCQlTrSqUSBQUFsLa2hkzDERZKSkrQs2dPZGZmwtzcvLVDpXaC9dx1sK67BtZz18B67hpYz11DW9azEAKlpaVwdHRs8rgOn0jZ2NhAV1cX9+/fV9t+//59ODg0PISioaEhDA0N1bZZWFi0KA5zc3N+ebsA1nPXwbruGljPXQPruWtgPXcNbVXP3bp1e+oxHX64AgMDAwwePBhnzpxRbVMqlThz5ozao35EREREREStpcPfkQKAkJAQzJgxAy+99BKGDh2KTZs2oby8HLNmzdJ2aERERERE1Al1ikRq6tSpyM3Nxccff4ycnBwMGDAAJ06cgL29/XMv29DQECtWrKj3qCB1LqznroN13TWwnrsG1nPXwHruGtpjPXf4UfuIiIiIiIjaWofvI0VERERERNTWmEgRERERERFpiIkUERERERGRhphIERERERERaYiJVDOtXbsWQ4YMgZmZGezs7PDWW28hNTVV7ZicnBy88847cHBwgImJCQYNGoQjR45oKWJ6Fl999RX69++vmuzN19cX3333nWp/VVUVgoKCYG1tDVNTU0yePLneZNDU/jVVzwUFBVi0aBHc3NxgbGwMZ2dnfPDBByguLtZy1KSpp32f6wghMH78eMhkMkRHR7d9oNQizannuLg4jB49GiYmJjA3N8err76KyspKLUVMz+Jp9cw2WOf06aefQiaTITg4WLWtPbXFmEg1U2xsLIKCgnDp0iWcOnUKCoUCY8eORXl5ueqYd999F6mpqTh69CiSk5MxadIkTJkyBYmJiVqMnDTRo0cPfPrpp0hISEB8fDxGjx6NCRMm4Pr16wCAJUuW4Ntvv0VkZCRiY2ORlZWFSZMmaTlq0lRT9ZyVlYWsrCxs2LABKSkp2LVrF06cOIE5c+ZoO2zS0NO+z3U2bdoEmUympSippZ5Wz3FxcRg3bhzGjh2Ln376CZcvX8bChQuho8MmUEfytHpmG6zzuXz5MrZt24b+/furbW9XbTFBz+TBgwcCgIiNjVVtMzExEbt371Y7zsrKSmzfvr2tw6NWZGlpKXbs2CGKioqEvr6+iIyMVO27ceOGACDi4uK0GCG1hrp6bsihQ4eEgYGBUCgUbRwVtbYn6zkxMVE4OTmJ7OxsAUBERUVpLzhqNY/Xs4+Pj/jb3/6m5YjoeXi8ntkG61xKS0tF3759xalTp8SIESPE4sWLhRCi3bXF+HPMM6p7zMfKykq1bdiwYTh48CAKCgqgVCpx4MABVFVVYeTIkVqKklqitrYWBw4cQHl5OXx9fZGQkACFQgF/f3/VMe7u7nB2dkZcXJwWI6WWeLKeG1JcXAxzc3Po6XWKOcy7pIbquaKiAm+//Ta2bNkCBwcHLUdIreHJen7w4AF+/PFH2NnZYdiwYbC3t8eIESNw4cIFbYdKLdDQ95ltsM4lKCgIr732mlqbC0C7a4uxVfAMlEolgoODMXz4cHh6eqq2Hzp0CFOnToW1tTX09PQgl8sRFRWFPn36aDFa0lRycjJ8fX1RVVUFU1NTREVFoV+/fkhKSoKBgQEsLCzUjre3t0dOTo52gqVn1lg9PykvLw+rVq3Cn/70Jy1ESS3VVD0vWbIEw4YNw4QJE7QcJbVUY/V86dIlAMDKlSuxYcMGDBgwALt374afnx9SUlLQt29fLUdOmmjq+8w2WOdx4MABXLlyBZcvX663Lycnp121xZhIPYOgoCCkpKTU+0Vr+fLlKCoqwunTp2FjY4Po6GhMmTIF58+fh5eXl5aiJU25ubkhKSkJxcXFOHz4MGbMmIHY2Fhth0WtrLF6fjyZKikpwWuvvYZ+/fph5cqV2guWnllj9Zyeno6zZ8+y/0Qn0Vg9K5VKAMD8+fMxa9YsAMDAgQNx5swZ7Ny5E2vXrtVm2KShpv5usw3WOWRmZmLx4sU4deoUjIyMtB3OU8mEEELbQXQkCxcuxDfffIPvv/8evXr1Um2/ffs2+vTpg5SUFLz44ouq7f7+/ujTpw+2bt2qjXCpFfj7+6N3796YOnUq/Pz8UFhYqPZLiIuLC4KDg7FkyRLtBUktVlfP27ZtAwCUlpYiICAAcrkcx44d6xB/0Onp6urZ2NgYmzdvVhtwoLa2Fjo6OnjllVcQExOjvSCpxerqedmyZXjhhRewZ88eTJ8+XbV/6tSp0NPTw759+7QYJbVUXT0vXbqUbbBOIjo6GhMnToSurq5qW21tLWQyGXR0dHDy5En4+/u3m7YY+0g1kxACCxcuRFRUFM6ePauWRAHSs/YA6o0CpKurq/pFjDompVKJ6upqDB48GPr6+jhz5oxqX2pqKu7evdto3xrqOOrqGZDuRI0dOxYGBgY4evQok6hOpK6ely1bhmvXriEpKUm1AMDnn3+O8PBw7QZJLVZXz66urnB0dKw3XUlaWhpcXFy0FB21lrp6Zhus8/Dz80NycrLa3+aXXnoJ06ZNU71vT20xPtrXTEFBQdi/fz+++eYbmJmZqZ7D7NatG4yNjeHu7o4+ffpg/vz52LBhA6ytrREdHY1Tp07h2LFjWo6emis0NBTjx4+Hs7MzSktLsX//fsTExODkyZPo1q0b5syZg5CQEFhZWcHc3ByLFi2Cr68vXn75ZW2HThpoqp7rkqiKigrs3bsXJSUlKCkpAQDY2tqq/UpG7VtT9ezg4NDgABPOzs71fiij9q2pepbJZPjoo4+wYsUKeHt7Y8CAAYiIiMDNmzdx+PBhbYdOGmiqntkG6zzMzMzUxh8AABMTE1hbW6u2t6u2WJuPE9hBAWhwCQ8PVx2TlpYmJk2aJOzs7IRcLhf9+/evNxQntW+zZ88WLi4uwsDAQNja2go/Pz/x3//+V7W/srJSLFiwQFhaWgq5XC4mTpwosrOztRgxPYum6vncuXONft8zMjK0Gzhp5Gnf5yeBw593SM2p57Vr14oePXoIuVwufH19xfnz57UULT2rp9Uz22Cd1+PDnwvRvtpi7CNFRERERESkIfaRIiIiIiIi0hATKSIiIiIiIg0xkSIiIiIiItIQEykiIiIiIiINMZEiIiIiIiLSEBMpIiIiIiIiDTGRIiIiIiIi0hATKSIiIiIiIg0xkSIiog5HJpMhOjpa22EQEVEXxkSKiIjqyc3Nxfvvvw9nZ2cYGhrCwcEBAQEBuHjxorZDaxV37tyBTCaDrq4ufv31V7V92dnZ0NPTg0wmw507d7QTYAPWrl0LXV1drF+/XtuhEBERmEgREVEDJk+ejMTERERERCAtLQ1Hjx7FyJEjkZ+fr+3QWpWTkxN2796tti0iIgJOTk5aiqhxO3fuxNKlS7Fz505th0JERGAiRURETygqKsL58+exbt06jBo1Ci4uLhg6dChCQ0Px5ptvqo7buHEjvLy8YGJigp49e2LBggUoKytT7d+1axcsLCxw7NgxuLm5QS6X4w9/+AMqKioQEREBV1dXWFpa4oMPPkBtba3qc66urli1ahUCAwNhYmICJycnbNmypcmYMzMzMWXKFFhYWMDKygoTJkxo1t2kGTNmIDw8XG1beHg4ZsyYUe/YlJQUjB8/HqamprC3t8c777yDvLw81f4TJ07g97//PSwsLGBtbY3XX38dt2/fVu2vuwv2n//8B6NGjYJcLoe3tzfi4uKeGmdsbCwqKyvx97//HSUlJfjhhx/qHbN69WrY2dnBzMwMc+fOxbJlyzBgwAC1Y3bs2AEPDw8YGRnB3d0dX3755VPLJiKihjGRIiIiNaampjA1NUV0dDSqq6sbPU5HRwebN2/G9evXERERgbNnz2Lp0qVqx1RUVGDz5s04cOAATpw4gZiYGEycOBHHjx/H8ePHsWfPHmzbtg2HDx9W+9z69evh7e2NxMRELFu2DIsXL8apU6cajEOhUCAgIABmZmY4f/48Ll68CFNTU4wbNw41NTVNXuubb76JwsJCXLhwAQBw4cIFFBYW4o033lA7rqioCKNHj8bAgQMRHx+PEydO4P79+5gyZYrqmPLycoSEhCA+Ph5nzpyBjo4OJk6cCKVSqXauv/71r/jwww+RlJSE3/3udwgMDMTDhw+bjDMsLAyBgYHQ19dHYGAgwsLC1Pbv27cPa9aswbp165CQkABnZ2d89dVX9Y75+OOPsWbNGty4cQP/+Mc/sHz5ckRERDRZNhERNUIQERE94fDhw8LS0lIYGRmJYcOGidDQUHH16tUmPxMZGSmsra1V6+Hh4QKASE9PV22bP3++kMvlorS0VLUtICBAzJ8/X7Xu4uIixo0bp3buqVOnivHjx6vWAYioqCghhBB79uwRbm5uQqlUqvZXV1cLY2NjcfLkyQZjzcjIEABEYmKiCA4OFrNmzRJCCDFr1iyxZMkSkZiYKACIjIwMIYQQq1atEmPHjlU7R2ZmpgAgUlNTGywjNzdXABDJyclqZe7YsUN1zPXr1wUAcePGjQbPIYQQxcXFwtjYWCQlJQkhhEhMTBSmpqZq/4Y+Pj4iKChI7XPDhw8X3t7eqvXevXuL/fv3qx2zatUq4evr22jZRETUON6RIiKieiZPnoysrCwcPXoU48aNQ0xMDAYNGoRdu3apjjl9+jT8/Pzg5OQEMzMzvPPOO8jPz0dFRYXqGLlcjt69e6vW7e3t4erqClNTU7VtDx48UCvf19e33vqNGzcajPXq1atIT0+HmZmZ6m6alZUVqqqq1B6ta8zs2bMRGRmJnJwcREZGYvbs2Q2Wce7cOdX5TU1N4e7uDgCqMm7duoXAwEC88MILMDc3h6urKwDg7t27aufq37+/6n337t0BoN71P+7rr79G79694e3tDQAYMGAAXFxccPDgQdUxqampGDp0qNrnHl8vLy/H7du3MWfOHLVrWL16dbP+jYiIqD49bQdARETtk5GREcaMGYMxY8Zg+fLlmDt3LlasWIGZM2fizp07eP311/H+++9jzZo1sLKywoULFzBnzhzU1NRALpcDAPT19dXOKZPJGtz25ONvmigrK8PgwYOxb9++evtsbW2f+nkvLy+4u7sjMDAQHh4e8PT0RFJSUr0y3njjDaxbt67e5+uSoTfeeAMuLi7Yvn07HB0doVQq4enpWe/xwsevXyaTAUCT1x8WFobr169DT+/Rf9lKpRI7d+7EnDlznnp9dfEDwPbt2+Hj46O2T1dXt1nnICIidUykiIioWfr166eauykhIQFKpRL//Oc/oaMjPdxw6NChVivr0qVL9dY9PDwaPHbQoEE4ePAg7OzsYG5u/kzlzZ49GwsWLKjXr+jxMo4cOQJXV1e1hKZOfn4+UlNTsX37drzyyisAoOp31RLJycmIj49HTEwMrKysVNsLCgowcuRI3Lx5E+7u7nBzc8Ply5fx7rvvqo65fPmy6r29vT0cHR3x888/Y9q0aS2Oi4iIONgEERE9IT8/H6NHj8bevXtx7do1ZGRkIDIyEp999hkmTJgAAOjTpw8UCgW++OIL/Pzzz9izZw+2bt3aajFcvHgRn332GdLS0rBlyxZERkZi8eLFDR47bdo02NjYYMKECTh//jwyMjIQExODDz74APfu3WtWefPmzUNubi7mzp3b4P6goCAUFBQgMDAQly9fxu3bt3Hy5EnMmjULtbW1sLS0hLW1Nf79738jPT0dZ8+eRUhIyDNff52wsDAMHToUr776Kjw9PVXLq6++iiFDhqgGnVi0aBHCwsIQERGBW7duYfXq1bh27ZrqjhcAfPLJJ1i7di02b96MtLQ0JCcnIzw8HBs3bmxxnEREXRETKSIiUmNqagofHx98/vnnqgb88uXLMW/ePPzrX/8CAHh7e2Pjxo1Yt24dPD09sW/fPqxdu7bVYvjzn/+M+Ph4DBw4EKtXr8bGjRsREBDQ4LFyuRzff/89nJ2dMWnSJHh4eGDOnDmoqqpq9h0qPT092NjYNHi3CQAcHR1x8eJF1NbWYuzYsfDy8kJwcDAsLCygo6MDHR0dHDhwAAkJCfD09MSSJUtaPHFuTU0N9u7di8mTJze4f/Lkydi9ezcUCgWmTZuG0NBQfPjhhxg0aBAyMjIwc+ZMGBkZqY6fO3cuduzYgfDwcHh5eWHEiBHYtWsXevXq1aI4iYi6KpkQQmg7CCIiojqurq4IDg5GcHCwtkPp0MaMGQMHBwfs2bNH26EQEXVK7CNFRETUwVVUVGDr1q0ICAiArq4uvv76a5w+fbrRubeIiKjlmEgRERF1cDKZDMePH8eaNWtQVVUFNzc3HDlyBP7+/toOjYio0+KjfURERERERBriYBNEREREREQaYiJFRERERESkISZSREREREREGmIiRUREREREpCEmUkRERERERBpiIkVERERERKQhJlJEREREREQaYiJFRERERESkof8HGu0dCynE1ocAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(33.9734, 34.011333023687875)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the distribution of driver ages\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.histplot(driver_ages, kde=True, bins=30, color='green')\n",
        "plt.title('Probability Distribution of Driver Ages')\n",
        "plt.xlabel('Driver Age')\n",
        "plt.ylabel('Frequency')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "cequkcdQjYmd",
        "outputId": "715f68c1-744d-4e08-d384-7c875d07ac4e"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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kiSeeOOX5irAZHh7Ohg0bmDt3Lr/++iu//vorkyZNYuTIkUyZMuW8rikizkWBTETkHDRq1Ijff/+dvLy8Sv/Cv337dvv54yUmJp7Ux86dO/H29rbvvDdr1ixGjRrFW2+9ZW9TWFh42hvNJiYm0rdvX/vz/Px8Dh06xMCBA6v8viqc6ZfIYcOGMX78eL788ksKCgpwc3OzTxG7ELNmzaKgoOC0vzgf76KLLuKiiy7ilVdeYfr06QwfPpyvvvqKu+6664I2NDmV0/3sjt9sIigo6JRTA08cXTqf2ho1aoTNZiMxMbHS5hlpaWlkZ2ef9BmrqvP9LDtCWVkZ06dPx9vbm169elW5n5tvvpkpU6Ywf/58EhISMAyj0mexSZMmALi5udlHmBzl22+/pbCwkAkTJhAaGlrp3I4dO3j66adZunQpvXr1sn8Pd+3aVWkE7vDhwyeNYDZt2pT8/Pxzqtfd3Z1rr72Wa6+9FpvNxv3338/HH3/MM88845ARaxExh6Ysioicg4EDB1JWVnbSyNC///1vLBYLV199daXjy5cvr7QObP/+/Xz//ff079/fPrLh4uJy0mjG+++/f9pRlk8++aTSOqoJEyZQWlp60rWrwsfH57RBMDQ0lKuvvpqpU6cybdo0rrrqqpN+IT1fGzdu5OGHHyYoKIixY8eett2RI0dO+h516tQJwD5tsWLXxNPVf76+++67SuuMVq1axcqVKyt9n5s2bcr27dvt00+h/D0tXbq0Ul/nU1tFsK7YVa/C22+/DVBpZ8ILcb6f5QtVVlbGgw8+SEJCAg8++CD+/v5V7qtfv34EBwczY8YMZsyYQffu3SsFnvDwcPr06cPHH3/MoUOHTnr98T+v8zV16lSaNGnCvffey9ChQys9HnvsMXx9fe3TFq+44gpcXV2ZMGFCpT5ONbJ80003sXz5cubOnXvSuezsbEpLS4HyMHc8q9Vq37HzfKbwiojz0QiZiMg5uPbaa+nbty///Oc/2bdvHx07duS3337j+++/5+GHH6Zp06aV2rdr144BAwZU2vYeyu9HVeGaa67hv//9LwEBAbRp04bly5fz+++/V9qC/3jFxcVcccUV3HTTTezYsYOPPvqIXr168be//e2C3198fDy///47b7/9NtHR0cTFxdGjRw/7+ZEjR9pv7vzSSy+dV99//PEHhYWFlJWVcfjwYZYuXcoPP/xAQEAAs2fPJjIy8rSvnTJlCh999BFDhgyhadOm5OXl8emnn+Lv728PMF5eXrRp04YZM2bQokULgoODadeu3RmnQp5Js2bN6NWrF/fddx9FRUW88847hISEVJpSduedd/L2228zYMAARo8eTXp6OhMnTqRt27bk5uba251PbR07dmTUqFF88sknZGdn07t3b1atWsWUKVMYPHhwpdHRC3G+n+XzkZOTw9SpU4Hy7dx37drFt99+y+7duxk2bNh5f3ZO5ObmxvXXX89XX33F0aNH+de//nVSmw8//JBevXrRvn177r77bpo0aUJaWhrLly/nwIEDbNy48byvm5KSwsKFC0/aCKWCh4cHAwYM4Ouvv+a9994jIiKChx56iLfeeou//e1vXHXVVWzcuJFff/2V0NDQSiOnjz/+OD/88APXXHON/bYFR48eZfPmzcyaNYt9+/YRGhrKXXfdRVZWFpdffjkNGzYkKSmJ999/n06dOlX5dgQi4iTM2+BRRKTmVGxxfabt0w2jfNtyHx+fU57Ly8szHnnkESM6Otpwc3Mzmjdvbrz55puGzWar1A4wxo4da0ydOtVo3ry54eHhYXTu3NlYuHBhpXZHjhwx7rjjDiM0NNTw9fU1BgwYYGzfvt1o1KhRpW2xK2pfvHixMWbMGCMoKMjw9fU1hg8fbhw+fLhSn1Xd9n779u3GZZddZnh5eZ20LbdhGEZRUZERFBRkBAQEGAUFBWf8Hlao2Pa+4uHm5maEhYUZl112mfHKK68Y6enpJ73mxG3v161bZ9xyyy1GbGys4eHhYYSHhxvXXHNNpVsKGIZhLFu2zIiPjzfc3d0rbZd+pp/n6ba9f/PNN4233nrLiImJMTw8PIxLL73U2Lhx40mvnzp1qtGkSRPD3d3d6NSpkzF37tyT+jxTbaf6OZSUlBgvvPCCERcXZ7i5uRkxMTHGU089VWkLd8Mo3/Z+0KBBJ9V0uu34T3Sun+Xz3fb++J+3r6+v0bx5c2PEiBHGb7/9dsrXVPy3crpzp7pdwLx58wzAsFgsxv79+0/52t27dxsjR440IiMjDTc3N6NBgwbGNddcY8yaNcve5lz/TjAMw3jrrbcMwJg/f/5p20yePLnSbShKS0uNZ555xoiMjDS8vLyMyy+/3EhISDBCQkKMe++9t9Jr8/LyjKeeespo1qyZ4e7uboSGhhoXX3yx8a9//ct+q4tZs2YZ/fv3N8LDww13d3cjNjbWuOeee4xDhw6dtX4RcW4Ww3DS1b8iIuI0SktLiY6O5tprr+U///mP2eWI1ErZ2dkEBQXx8ssv889//tPsckTESWgNmYiInNV3331HRkYGI0eONLsUkVqhoKDgpGMV6wP79OlTs8WIiFPTCJmIiJzWypUr2bRpEy+99BKhoaGnvGG1iJxs8uTJTJ48mYEDB+Lr68uff/7Jl19+Sf/+/U+5gYeI1F/a1ENERE5rwoQJTJ06lU6dOjF58mSzyxGpNTp06ICrqytvvPEGubm59o0+Xn75ZbNLExEnoxEyERERERERk2gNmYiIiIiIiEkUyEREREREREyiNWQOYrPZSElJwc/Pr9INH0VEREREpH4xDIO8vDyio6OxWs88BqZA5iApKSnExMSYXYaIiIiIiDiJ/fv307BhwzO2USBzED8/P6D8m+7v729yNSIiIiIiYpbc3FxiYmLsGeFMFMgcpGKaor+/vwKZiIiIiIic01ImbeohIiIiIiJiEgUyERERERERkyiQiYiIiIiImESBTERERERExCQKZCIiIiIiIiZRIBMRERERETGJApmIiIiIiIhJFMhERERERERMokAmIiIiIiJiEgUyERERERERkyiQiYiIiIiImESBTERERERExCQKZCIiIiIiIiZRIBMRERERETGJApmIiIiIiIhJFMhERERERERMokAmIiIiIiJiEgUyERERERERk7iaXYDIiZKTk8nMzKy2/kNDQ4mNja22/kVEREREzpUCmTiV5ORkWrVuRcGxgmq7hpe3F9sTtiuUiYiIiIjpFMjEqWRmZlJwrIAh/xhCWKMwh/efkZTB7Fdnk5mZqUAmIiIiIqYzdQ3ZkiVLuPbaa4mOjsZisfDdd9/Zz5WUlPD3v/+d9u3b4+PjQ3R0NCNHjiQlJaVSH1lZWQwfPhx/f38CAwMZPXo0+fn5ldps2rSJSy+9FE9PT2JiYnjjjTdOquXrr7+mVatWeHp60r59e3755Zdqec9ybsIahRHVIsrhj+oIeSIiIiIiVWVqIDt69CgdO3bkww8/POncsWPHWLduHc888wzr1q3j22+/ZceOHfztb3+r1G748OFs3bqVefPm8dNPP7FkyRLGjBljP5+bm0v//v1p1KgRa9eu5c033+T555/nk08+sbdZtmwZt9xyC6NHj2b9+vUMHjyYwYMHs2XLlup78yIiIiIiUu9ZDMMwzC4CwGKxMHv2bAYPHnzaNqtXr6Z79+4kJSURGxtLQkICbdq0YfXq1XTt2hWAOXPmMHDgQA4cOEB0dDQTJkzgn//8J6mpqbi7uwPw5JNP8t1337F9+3YAbr75Zo4ePcpPP/1kv9ZFF11Ep06dmDhx4jnVn5ubS0BAADk5Ofj7+1fxuyDr1q0jPj6eMR+PIapFlMP7P7TzEJ/c8wlr166lS5cuDu9fREREROR8skGt2vY+JycHi8VCYGAgAMuXLycwMNAexgD69euH1Wpl5cqV9jaXXXaZPYwBDBgwgB07dnDkyBF7m379+lW61oABA1i+fPlpaykqKiI3N7fSQ0RERERE5HzUmkBWWFjI3//+d2655RZ7ykxNTSU8PLxSO1dXV4KDg0lNTbW3iYiIqNSm4vnZ2lScP5XXXnuNgIAA+yMmJubC3qCIiIiIiNQ7tSKQlZSUcNNNN2EYBhMmTDC7HACeeuopcnJy7I/9+/ebXZKIiIiIiNQyTr/tfUUYS0pKYsGCBZXmYEZGRpKenl6pfWlpKVlZWURGRtrbpKWlVWpT8fxsbSrOn4qHhwceHh5Vf2MiIiIiIlLvOfUIWUUYS0xM5PfffyckJKTS+Z49e5Kdnc3atWvtxxYsWIDNZqNHjx72NkuWLKGkpMTeZt68ebRs2ZKgoCB7m/nz51fqe968efTs2bO63pqIiIiIiIi5gSw/P58NGzawYcMGAPbu3cuGDRtITk6mpKSEoUOHsmbNGqZNm0ZZWRmpqamkpqZSXFwMQOvWrbnqqqu4++67WbVqFUuXLmXcuHEMGzaM6OhoAG699Vbc3d0ZPXo0W7duZcaMGbz77ruMHz/eXsdDDz3EnDlzeOutt9i+fTvPP/88a9asYdy4cTX+PRERERERkfrD1EC2Zs0aOnfuTOfOnQEYP348nTt35tlnn+XgwYP88MMPHDhwgE6dOhEVFWV/LFu2zN7HtGnTaNWqFVdccQUDBw6kV69ele4xFhAQwG+//cbevXuJj4/n0Ucf5dlnn610r7KLL76Y6dOn88knn9CxY0dmzZrFd999R7t27WrumyEiIiIiIvWOqWvI+vTpw5lug3Yut0gLDg5m+vTpZ2zToUMH/vjjjzO2ufHGG7nxxhvPej0RERERERFHceo1ZCIiIiIiInWZApmIiIiIiIhJFMhERERERERMokAmIiIiIiJiEgUyERERERERkyiQiYiIiIiImESBTERERERExCQKZCIiIiIiIiZRIBMRERERETGJApmIiIiIiIhJFMhERERERERMokAmIiIiIiJiEgUyERERERERkyiQiYiIiIiImESBTERERERExCQKZCIiIiIiIiZRIBMRERERETGJApmIiIiIiIhJFMhERERERERMokAmIiIiIiJiEgUyERERERERkyiQiYiIiIiImESBTERERERExCQKZFLrldpKOXzsMDbDZnYpIiIiIiLnxdXsAkSqIi0/jS3pW0jOSeZg3kHKjDLCvMMY0HQATYObml2eiIiIiMg5USCTWudA7gEmb5hMmVFmP2bBQsaxDKZunkrz4Ob0b9qfUO9QE6sUERERETk7BTKpVXKLcpmxdQZlRhmxAbF0jOhIo4BGeLt5syR5CasOriIxK5G92Xu5vePtNPBvYHbJIiIiIiKnpTVkUmuUlJUwY+sM8ovzCfcJZ3j74XSJ6kKIdwhebl4MaDqA+7veT6OARpTaSvlq61fkFeWZXbaIiIiIyGkpkEmtYBgGP+78kZS8FLxcvRjWdhjuLu4ntQvxDuGWdrcQ5h1GfnE+X239ipKyEhMqFhERERE5OwUyqRXWHVrH5vTNWLBwY5sbCfIKOm1bD1cPhrUbhperFyl5Kfy08ycMw6jBakVEREREzo0CmTg9wzBYdmAZAJfHXU5cUNxZXxPsFcyNbW7EgoVN6ZtYeXBldZcpIiIiInLeFMjE6e05soesgiw8XDzo3qD7Ob8uLiiOAc0GALBg7wJyi3Krq0QRERERkSpRIBOntzplNQAdIzuect3YmXSP7k5D/4aU2EqYv3d+dZQnIiIiIlJlCmTi1LILs9l5eCcA3aK7nffrLRYLVzW9CoBNaZtIK05zaH0iIiIiIhdCgUyc2tqUtRgYxAXGVflGzw38G9ApshMAy3KXgcWBBYqIiIiIXAAFMnFapbZS1qWuA6o2Ona8K+KuwN3FnYySDOjoiOpERERERC6cApk4rW0Z2zhWcgx/D39ahra8oL583X25rNFl5U/6wdHSow6oUERERETkwiiQidOq2MwjPioeq+XCP6oXNbgIfxd/8IVv9n1zwf2JiIiIiFwoBTJxSrmluRzIPYDVYqVLVBeH9OlidaGzb2cApu6ZSmFpoUP6FRERERGpKgUycUoHig4AEOMfg6+7r8P6be7VHLLhcNFhJq2f5LB+RURERESqQoFMnNLB4oNA+c2dHclqscKy8j+/sewNSspKHNq/iIiIiMj5cDW7AJGTWCClKAWAJoFNHN//OvC/1p992fv4v5/+j0ExgxzWdWhoKLGxsQ7rT0RERETqNgUycT6RUGQU4e7iTrRftEO7zs/Kh1LInZsL/eDZec/y7EfPguGY/r28vdiesF2hTERERETOiQKZOJ+/BsUaBzbGxeri0K4L88s38ri046WstKykOKyYK/99JXFeFz41MiMpg9mvziYzM1OBTERERETOiQKZOJ+/slG1TFf8S1h0GD2ievBH8h9sK9vGxS0urrZriYiIiIicjjb1EKdSXFYMjcr/7OgNPU7UvUF3XCwuHMw7yMHcg9V6LRERERGRU1EgE6ey6cgmcAMvqxdh3mHVei1fd1/ahrUF/ncTahERERGRmqRAJk5lVeYqABp4NMBisVT79bo16AbAlvQtHC0+Wu3XExERERE5ngKZOJWVGSsBaODeoEau18CvAdF+0ZQZZaxPXV8j1xQRERERqaBAJk4jpzCHbdnbgPIRsppgsVjoFl0+SrYmZQ02w1Yj1xURERERAQUycSKL9i3Chg0ywdfFt8au2y68HV6uXuQU5bDz8M4au66IiIiIiAKZOI35e+eX/2FPzV7X1epKl6guAKw6uKpmLy4iIiIi9ZoCmTiNtYfWlv9hf81fu2t0VyxY2Ju9l4yjGTVfgIiIiIjUSwpk4hRsho2NqRvLnxyq+esHegbSIqQFAOtS19V8ASIiIiJSLymQiVPYnbWboyVH8bB6wGFzaqiYtrgpbROltlJzihARERGRekWBTJzChtQNADTzbwaGOTU0C26Gn7sfx0qOsePwDnOKEBEREZF6RYFMnEJFIGvp39K0GqwWK50iOwGw/pDuSSYiIiIi1U+BTJzChrQNADQPaG5qHZ0jOwOw+8husguzTa1FREREROo+BTJxCs4wQgYQ5BVEXGAcAOtTNUomIiIiItVLgUxMl3E0g5S8FCxYyteQmaxic48NqRuwGTaTqxERERGRukyBTEy3Ma18u/tmwc3wcfUxuRpoFdoKL1cvcoty2Z212+xyRERERKQOMzWQLVmyhGuvvZbo6GgsFgvfffddpfOGYfDss88SFRWFl5cX/fr1IzExsVKbrKwshg8fjr+/P4GBgYwePZr8/PxKbTZt2sSll16Kp6cnMTExvPHGGyfV8vXXX9OqVSs8PT1p3749v/zyi8Pfr5xaxXTFig01zOZqdaVDRAdA0xZFREREpHqZGsiOHj1Kx44d+fDDD095/o033uC9995j4sSJrFy5Eh8fHwYMGEBhYaG9zfDhw9m6dSvz5s3jp59+YsmSJYwZM8Z+Pjc3l/79+9OoUSPWrl3Lm2++yfPPP88nn3xib7Ns2TJuueUWRo8ezfr16xk8eDCDBw9my5Yt1ffmxc7ZAhn8b3OPHYd3cKzkmMnViIiIiEhdZWogu/rqq3n55ZcZMmTISecMw+Cdd97h6aef5rrrrqNDhw588cUXpKSk2EfSEhISmDNnDp999hk9evSgV69evP/++3z11VekpKQAMG3aNIqLi/n8889p27Ytw4YN48EHH+Ttt9+2X+vdd9/lqquu4vHHH6d169a89NJLdOnShQ8++KBGvg/1XUUg6xjR0dxCjhPhG0GkbyQ2w8aWdAVzEREREakeTruGbO/evaSmptKvXz/7sYCAAHr06MHy5csBWL58OYGBgXTt2tXepl+/flitVlauXGlvc9lll+Hu7m5vM2DAAHbs2MGRI0fsbY6/TkWbiuucSlFREbm5uZUecv4KSgrYnrkdcK4RMvhfQKxY4yYiIiIi4mhOG8hSU1MBiIiIqHQ8IiLCfi41NZXw8PBK511dXQkODq7U5lR9HH+N07WpOH8qr732GgEBAfZHTEzM+b5FAbZmbKXMKCPUO5Rov2izy6mkfXh7rBYrKXkpZBzNMLscEREREamDnDaQObunnnqKnJwc+2P//v1ml1QrHb9+zGKxmFvMCXzcfWgWXL4Nv0bJRERERKQ6OG0gi4yMBCAtLa3S8bS0NPu5yMhI0tPTK50vLS0lKyurUptT9XH8NU7XpuL8qXh4eODv71/pIefPHsgiOplax+lUTFvclLZJ9yQTEREREYdz2kAWFxdHZGQk8+fPtx/Lzc1l5cqV9OzZE4CePXuSnZ3N2rVr7W0WLFiAzWajR48e9jZLliyhpKTE3mbevHm0bNmSoKAge5vjr1PRpuI6Un2ccYfF47UIaYGnqyd5xXnsPbLX7HJEREREpI4xNZDl5+ezYcMGNmzYAJRv5LFhwwaSk5OxWCw8/PDDvPzyy/zwww9s3ryZkSNHEh0dzeDBgwFo3bo1V111FXfffTerVq1i6dKljBs3jmHDhhEdXb4e6dZbb8Xd3Z3Ro0ezdetWZsyYwbvvvsv48ePtdTz00EPMmTOHt956i+3bt/P888+zZs0axo0bV9PfknrFZtjsUwE7RjrPDovHc7W60i68HaBpiyIiIiLieKYGsjVr1tC5c2c6dy6/59P48ePp3Lkzzz77LABPPPEEDzzwAGPGjKFbt27k5+czZ84cPD097X1MmzaNVq1accUVVzBw4EB69epV6R5jAQEB/Pbbb+zdu5f4+HgeffRRnn322Ur3Krv44ouZPn06n3zyCR07dmTWrFl89913tGvXroa+E/XT3iN7yS/Ox8PFg5YhLc0u57Qqpi0mZCZQVFpkcjUiIiIiUpe4mnnxPn36YBjGac9bLBZefPFFXnzxxdO2CQ4OZvr06We8TocOHfjjjz/O2ObGG2/kxhtvPHPB4lA7Du8AyqcFurm4mVzN6TXwa0CIVwiHCw6TkJngtNMrRURERKT2cdo1ZFL37craBWDfydBZWSwWOkR0AGBz+maTqxERERGRukSBTExTEciaBzc3uZKzq1hHVjHNUkRERETEERTIxDSJWYmA84+QAQR7BdPArwEGBlsztppdjoiIiIjUEQpkYhr7CFmI84+Qwf9GybakbTG5EhERERGpKxTIxBQlZSXsy94H1I4RMoC2YW2xYOFA3gGOFBwxuxwRERERqQMUyMQUSTlJlNpK8XT1JNov2uxyzomfhx+NAxsDsCVdo2QiIiIicuEUyMQUx++waLXUno9h+/D2AGzJUCATERERkQtXe34Tljol8XDt2dDjeK3DWuNicSH9aDpp+WlmlyMiIiIitZwCmZiiNm15fzxPV097zbonmYiIiIhcKAUyMcWuI7XjptCn0i7ir90W07dgGIbJ1YiIiIhIbaZAJqaorVMWAVoEt8DdxZ2cohwO5B4wuxwRERERqcUUyKTGldpK2Zu9F6h9UxYB3FzcaBXaCtC0RRERERG5MApkUuOSc5IptZXi4eJBA/8GZpdTJe3Cyqctbs3Yis2wmVyNiIiIiNRWCmRS4yqmKzYNblqrtrw/XpOgJni7eXOs5Bh7juwxuxwRERERqaVq52/DUqvV1h0Wj+didaFNWBtAN4kWERERkapTIJMad/xNoWuziptEJ2QmUFJWYnI1IiIiIlIbKZBJjUvMKp+yWJtHyABi/GMI8AiguKzY/p5ERERERM6HApnUuLoyQmaxWGgb3hbQtEURERERqRoFMqlRZbYy+yYYtT2Qwf+mLe48vJNiW7HJ1YiIiIhIbaNAJjUqOSeZElsJHi4exATEmF3OBYvwiSDUO5Qyo4y9hXvNLkdEREREahkFMqlRFdMVmwQ1qbVb3h/PYrHQLrz8nmR7CrX9vYiIiIicn9r/G7HUKhWbX9SF6YoV2oaVryM7UHQAvEwuRkRERERqFQUyqVF14R5kJwr1DiXCJwIDA1qZXY2IiIiI1CYKZFKj6soOiyequEk0bc2tQ0RERERqFwUyqVHJOckANA5sbG4hDlYxbZEmkF2cbWotIiIiIlJ7KJBJjaoIZLEBsSZX4lgh3iGEuIaAFRYeWmh2OSIiIiJSSyiQSY3JL87nSOERgDqx5f2Jmng1AWBeyjyTKxERERGR2kKBTGrM/pz9APh7+OPv4W9yNY7XxLM8kK05vIaMoxkmVyMiIiIitYECmdSY/bnlgayuTVesEOAaAClQZpQxe/tss8sRERERkVpAgUxqTMX6sRj/ujdd0W5r+ZeZW2eaW4eIiIiI1AoKZFJjKqYs1tURMgC2lX9ZuG8h6UfTza1FRERERJyeApnUmIopi3V6hOwItAlog82w8W3Ct2ZXIyIiIiJOToFMakxd3fL+RFdGXwlo2qKIiIiInJ0CmdQY+whZHdzy/nj9ovsBsDhpMan5qSZXIyIiIiLOTIFMaoRhGPVjUw8g2jua7g26a9qiiIiIiJyVApnUiMMFhyksLQSgoX9Dk6upfje1uQnQtEUREREROTMFMqkRFaNjET4ReLh6mFxN9RvaZigAS5KWcCjvkMnViIiIiIizcjW7AKmdkpOTyczMPOf2i1IXARDqFsq6detO2y4hIeFCS3MKjQIbcVHDi1hxYAXfJHzDuO7jzC5JRERERJyQApmct+TkZFq1bkXBsYJzf1F3YCBsXbaV+Ifjz9o8Pz+/6gU6iZva3MSKAyuYuXWmApmIiIiInJICmZy3zMxMCo4VMOQfQwhrFHZOr1mRu4JNRzfRrls7Lr7i4tO2S1yZyMLPF1JYWOiock0ztM1Qxv82nj+T/+Rg7kEa+DcwuyQRERERcTIKZFJlYY3CiGoRdU5tbdtscBSiI6KJijn9azKTz30apLOLCYjh4piLWbZ/Gd8kfMODPR40uyQRERERcTLa1ENqRE5RDgABngEmV1KztNuiiIiIiJyJApnUiIpA5u/hb3IlNatit8Wl+5dyIPeAydWIiIiIiLNRIJNqZzNs5BXlARDgUb9GyBr4N6BXbC8AZm2bZXI1IiIiIuJsFMik2uUV5WFgYLVY8XX3NbucGqdpiyIiIiJyOgpkUu1yi3KB8umKFovF5Gpq3g1tbsCCheUHlttvkC0iIiIiAgpkUgPsG3rUs+mKFaL9orm00aWApi2KiIiISGUKZFLt6uuGHsfTtEURERERORUFMql2OYX1e4QM/jdtceXBlezL3md2OSIiIiLiJBTIpNrZ15B51t8RskjfSHo37g1o2qKIiIiI/I8CmVS7+r6GrIKmLYqIiIjIiRTIpNpVjJDV90B2fevrsVqsrE5Zzd4je80uR0REREScgAKZVKuSshKOlRwD6vemHgARvhH0adwHgK+3fW1uMSIiIiLiFBTIpFpVjI65u7jj6eppcjXm07RFERERETmeAplUq/p+U+gTVUxbXHtoLXuO7DG7HBERERExmQKZVKu84jwA/Nz9TK7EOYT5hHF53OUAfL1V0xZFRERE6jsFMqlWeUUKZCeyT1vcpmmLIiIiIvWdAplUq4oRMl8PX5MrcR5DWg/BxeLCukPr2JW1y+xyRERERMRECmRSrfKL8wGNkB0v1DuUK5pcAWjaooiIiEh9p0Am1aoikPm6a4TseJq2KCIiIiKgQCbVTJt6nNrgVoNxtbqyIXUDOw/vNLscERERETGJAplUK/umHh4KZMcL8Q6hX5N+gO5JJiIiIlKfOXUgKysr45lnniEuLg4vLy+aNm3KSy+9hGEY9jaGYfDss88SFRWFl5cX/fr1IzExsVI/WVlZDB8+HH9/fwIDAxk9ejT5+fmV2mzatIlLL70UT09PYmJieOONN2rkPdZlRaVFlNhKAE1ZPJWKaYtfbvmy0mdaREREROoPpw5kr7/+OhMmTOCDDz4gISGB119/nTfeeIP333/f3uaNN97gvffeY+LEiaxcuRIfHx8GDBhAYWGhvc3w4cPZunUr8+bN46effmLJkiWMGTPGfj43N5f+/fvTqFEj1q5dy5tvvsnzzz/PJ598UqPvt66pWD/m7uKOu4u7ydU4n+tbX4+HiwfbMraxMW2j2eWIiIiIiAmcOpAtW7aM6667jkGDBtG4cWOGDh1K//79WbVqFVA+OvbOO+/w9NNPc91119GhQwe++OILUlJS+O677wBISEhgzpw5fPbZZ/To0YNevXrx/vvv89VXX5GSkgLAtGnTKC4u5vPPP6dt27YMGzaMBx98kLffftust14naP3YmQV4BnBNi2sAmLZpmsnViIiIiIgZnDqQXXzxxcyfP5+dO8s3Pdi4cSN//vknV199NQB79+4lNTWVfv362V8TEBBAjx49WL58OQDLly8nMDCQrl272tv069cPq9XKypUr7W0uu+wy3N3/N4ozYMAAduzYwZEjR05ZW1FREbm5uZUeUpkC2dkNbz8cKJ+2aDNsJlcjIiIiIjXNqQPZk08+ybBhw2jVqhVubm507tyZhx9+mOHDy3+JTU1NBSAiIqLS6yIiIuznUlNTCQ8Pr3Te1dWV4ODgSm1O1cfx1zjRa6+9RkBAgP0RExNzge+27skv+mvLe90U+rQGNh9IgEcAB/MOsiRpidnliIiIiEgNc+pANnPmTKZNm8b06dNZt24dU6ZM4V//+hdTpkwxuzSeeuopcnJy7I/9+/ebXZLTqRgh04Yep+fh6sHQNkMBTVsUERERqY+cOpA9/vjj9lGy9u3bc9ttt/HII4/w2muvARAZGQlAWlpapdelpaXZz0VGRpKenl7pfGlpKVlZWZXanKqP469xIg8PD/z9/Ss9pLKKTT00ZfHMKqYtzkqYRVFpkcnViIiIiEhNcupAduzYMazWyiW6uLhgs5WvtYmLiyMyMpL58+fbz+fm5rJy5Up69uwJQM+ePcnOzmbt2rX2NgsWLMBms9GjRw97myVLllBSUmJvM2/ePFq2bElQUFC1vb+6TmvIzk3vxr1p4NeA7MJsft31q9nliIiIiEgNcupAdu211/LKK6/w888/s2/fPmbPns3bb7/NkCFDALBYLDz88MO8/PLL/PDDD2zevJmRI0cSHR3N4MGDAWjdujVXXXUVd999N6tWrWLp0qWMGzeOYcOGER0dDcCtt96Ku7s7o0ePZuvWrcyYMYN3332X8ePHm/XW64SKETJNWTwzq8XKLe1uAWDaZk1bFBEREalPXM0u4Ezef/99nnnmGe6//37S09OJjo7mnnvu4dlnn7W3eeKJJzh69ChjxowhOzubXr16MWfOHDw9Pe1tpk2bxrhx47jiiiuwWq3ccMMNvPfee/bzAQEB/Pbbb4wdO5b4+HhCQ0N59tlnK92rTM5fXtFfI2QeGiE7m+EdhvOv5f/ixx0/klOYQ4BngNkliYiIiEgNcOpA5ufnxzvvvMM777xz2jYWi4UXX3yRF1988bRtgoODmT59+hmv1aFDB/7444+qlionKCkroaisfD2URsjOrmNER9qEtWFbxja+3vY1d3W5y+ySRERERKQGOPWURam9KtaPuVnd8HDxMLka52exWLi94+0ATN4w2dRaRERERKTmKJBJtTh+/ZjFYjG5mtphRIcRWC1Wlu5fSuLhRLPLEREREZEaoEAm1cK+fkw7LJ6zKL8ormp2FQBTNpp/rz0RERERqX4KZFIt7DeF9tD6sfNRMW1xysYplNnKzC1GRERERKqdAplUC90UumqubXktQZ5BHMg9wIK9C8wuR0RERESqmQKZVAvdg6xqPF097fckm7xxsrnFiIiIiEi1UyCTaqE1ZFV3e6fbAfg24VtyCnPMLUZEREREqpUCmVQL+xoyjZCdt67RXWkT1obC0kJmbp1pdjkiIiIiUo0UyKRa2NeQeWiE7Hwdf0+y/6z/j7nFiIiIiEi1UiAThyu1lVJQWgBoymJVjew4ElerKysPrmRj6kazyxERERGRaqJAJg5XMTrmYnHB09XT5GpqpwjfCIa0GgLAx2s/NrkaEREREakuCmTicBUbevi6+2KxWEyupva6J/4eAKZummoPuSIiIiJStyiQicNp/Zhj9I3rS7PgZuQV5/HVlq/MLkdEREREqoECmThcxQ6LWj92YawWK2O6jAE0bVFERESkrlIgE4fTlveOc3un23F3cWdNyhrWHVpndjkiIiIi4mAKZOJwFVMWFcguXJhPGNe3vh6Aj9dolExERESkrlEgE4fLL9IaMkeq2Nxj+pbp9g1TRERERKRuUCATh7NPWXTTCJkj9G7Um5YhLckvzueLjV+YXY6IiIiIOFCVAtmePXscXYfUIUdLjgKasugoFouFcd3HAfDeqvewGTaTKxIRERERR6lSIGvWrBl9+/Zl6tSpFBYWOromqcUMw+BocXkg83H3MbmaumNUx1H4e/iz8/BO5u6aa3Y5IiIiIuIgVQpk69ato0OHDowfP57IyEjuueceVq1a5ejapBYqKC3AwADAx02BzFH8PPwY3Xk0AO+ufNfkakRERETEUVyr8qJOnTrx7rvv8tZbb/HDDz8wefJkevXqRYsWLbjzzju57bbbCAsLc3StUgtU7LDo5eqFi9XF5GrqlnHdx/HOineYu3su3yz+hji/OIf2HxoaSmxsrEP7FBEREZEzq1Igs7/Y1ZXrr7+eQYMG8dFHH/HUU0/x2GOP8Y9//IObbrqJ119/naioKEfVKrWApitWH9c8VyyJFozmBkPfHAo/O7Z/L28vtidsVygTERERqUEXFMjWrFnD559/zldffYWPjw+PPfYYo0eP5sCBA7zwwgtcd911mspYz1Rs6KHpio6XmZmJbakNmoNrd1eGXzMcD6uHQ/rOSMpg9quzyczMVCATERERqUFVCmRvv/02kyZNYseOHQwcOJAvvviCgQMHYrWWL0mLi4tj8uTJNG7c2JG1Si2gm0JXs30Q7BpMVmkWKT4pXBxzsdkViYiIiMgFqNKmHhMmTODWW28lKSmJ7777jmuuucYexiqEh4fzn//8xyFFSu2hKYvVr51POwBWHlxJma3M5GpERERE5EJUaYQsMTHxrG3c3d0ZNWpUVbqXWiy/pHyETFMWq08zr2asK1hHblEum9M30ymyk9kliYiIiEgVVWmEbNKkSXz99dcnHf/666+ZMmXKBRcltdex4mOARsiqk6vFlYsaXATAsv3LMAzD5IpEREREpKqqFMhee+01QkNDTzoeHh7Oq6++esFFSe1VMULm66Y1ZNUpPjoeDxcPMo5lsPPwTrPLEREREZEqqlIgS05OJi7u5HsgNWrUiOTk5AsuSmovrSGrGZ6unnSN7grAn8l/apRMREREpJaqUiALDw9n06ZNJx3fuHEjISEhF1yU1E6GYWjb+xp0UcOLcLG4cCDvAMk5+ocQERERkdqoSoHslltu4cEHH2ThwoWUlZVRVlbGggULeOihhxg2bJija5RaorismFJbKaBt72uCr7uvfUOPP/f/aW4xIiIiIlIlVdpl8aWXXmLfvn1cccUVuLqWd2Gz2Rg5cqTWkNVjFfcgc3dxx83FzeRq6oeLYy5m3aF17MraRWp+KpG+kWaXJCIiIiLnoUojZO7u7syYMYPt27czbdo0vv32W3bv3s3nn3+Ou7u7o2uUWkLTFWtesFcwbcPbArA4abHJ1YiIiIjI+arSCFmFFi1a0KJFC0fVIrWcNvQwx2Wxl7ElfQvbM7drlExERESklqlSICsrK2Py5MnMnz+f9PR0bDZbpfMLFixwSHFSu2jLe3OE+YTRLqwdWzK2sHjfYm5ud7PZJYmIiIjIOapSIHvooYeYPHkygwYNol27dlgsFkfXJbWQRsjMc1mjy9iSsYXth7dzKO8QUX5RZpckIiIiIuegSoHsq6++YubMmQwcONDR9UgtVrGph9aQ1bwwnzDahbdjS/oWFictZlg77XYqIiIiUhtUeVOPZs2aOboWqeWOlRwDNEJmlt6NegOw4/AODuUdMrkaERERETkXVQpkjz76KO+++y6GYTi6HqnFKkbIdA8yc4R6h9I+vD0Ai5IWmVuMiIiIiJyTKk1Z/PPPP1m4cCG//vorbdu2xc2t8j2nvv32W4cUJ7WLtr0332WNyndc3Hl4J/tz9hMTEGN2SSIiIiJyBlUKZIGBgQwZMsTRtUgtp009zBfqHUqnyE6sT13P73t/5/aOt2vTHREREREnVqVANmnSJEfXIbVcqa2UorIiQFMWzdancR82p28mOSeZxKxEWoToXoEiIiIizqpKa8gASktL+f333/n444/Jy8sDICUlhfz8fIcVJ7VHxfoxF4sLHi4eJldTv/l7+NM9ujsA8/fOx2bYzvIKERERETFLlQJZUlIS7du357rrrmPs2LFkZGQA8Prrr/PYY485tECpHY6frqgpcubrFdsLT1dP0o+mszl9s9nliIiIiMhpVCmQPfTQQ3Tt2pUjR47g5eVlPz5kyBDmz5/vsOKk9tCGHs7Fy82LS2IuAWDh3oWU2kpNrkhERERETqVKgeyPP/7g6aefxt3dvdLxxo0bc/DgQYcUJrWLtrx3Pj0a9MDP3Y+cohxWH1xtdjkiIiIicgpVCmQ2m42ysrKTjh84cAA/P78LLkpqH+2w6HzcXNzo27gvAEuSl9hv3C0iIiIizqNKgax///6888479ucWi4X8/Hyee+45Bg4c6KjapBbJLykfIdOURefSMbIjET4RFJYWsmjfIrPLEREREZETVCmQvfXWWyxdupQ2bdpQWFjIrbfeap+u+Prrrzu6RqkFjhWXj75ohMy5WC1WBjQdAMCalDVkHM0wuSIREREROV6V7kPWsGFDNm7cyFdffcWmTZvIz89n9OjRDB8+vNImH1J/VIyQ+bppDZmziQuKo2VIS3Yc3sG8PfO4tf2tZpckIiIiIn+pUiADcHV1ZcSIEY6sRWoxrSFzblc2uZLErEQSsxLZnbWbpsFNzS5JRERERKhiIPviiy/OeH7kyJFVKkZqL21779xCvEPoFt2NlQdXMnf3XO4Nuherpcr3hRcRERERB6lSIHvooYcqPS8pKeHYsWO4u7vj7e2tQFbP2AybfQc/bXvvvHo36s2mtE1kHMtg1cFVXNTwIrNLEhEREan3qvRP5EeOHKn0yM/PZ8eOHfTq1Ysvv/zS0TWKk6uYrmjBgpeb1hA6Ky83L66IuwKARfsW2e8dJyIiIiLmcdicpebNm/N///d/J42eSd1XMV3R281b0+CcXOeozkT7RVNUVsTve343uxwRERGRes+hvz27urqSkpLiyC6lFtCGHrWH1WJlYLPyewVuTNtIck6yyRWJiIiI1G9VWkP2ww8/VHpuGAaHDh3igw8+4JJLLnFIYVJ7aMv72qWBfwM6R3Zmfep6fkn8hTHxY8wuSURERKTeqlIgGzx4cKXnFouFsLAwLr/8ct566y1H1CW1iEbIap9+TfqRkJlA2tE0VqesJpZYs0sSERERqZeqFMhsNpuj65BarGJzCG15X3t4u3lzedzl/JL4Cwv3LuTGkBvNLklERESkXtIODHLBKra81whZ7RIfFU+UbxRFZUWsyltldjkiIiIi9VKVRsjGjx9/zm3ffvvtqlzC7uDBg/z973/n119/5dixYzRr1oxJkybRtWtXoHz92nPPPcenn35KdnY2l1xyCRMmTKB58+b2PrKysnjggQf48ccfsVqt3HDDDbz77rv4+v5vzdOmTZsYO3Ysq1evJiwsjAceeIAnnnjigmqvLypGyHQPstrFarFydbOr+XzD5+ws2AkNza5IREREpP6pUiBbv34969evp6SkhJYtWwKwc+dOXFxc6NKli72dxWK5oOKOHDnCJZdcQt++ffn1118JCwsjMTGRoKAge5s33niD9957jylTphAXF8czzzzDgAED2LZtG56engAMHz6cQ4cOMW/ePEpKSrjjjjsYM2YM06dPByA3N5f+/fvTr18/Jk6cyObNm7nzzjsJDAxkzBhteHA2Fdvea8pi7RMTEEOnyE5sSN0Ag6DMKDO7JBEREZF6pUqB7Nprr8XPz48pU6bYw9GRI0e44447uPTSS3n00UcdUtzrr79OTEwMkyZNsh+Li4uz/9kwDN555x2efvpprrvuOgC++OILIiIi+O677xg2bBgJCQnMmTOH1atX20fV3n//fQYOHMi//vUvoqOjmTZtGsXFxXz++ee4u7vTtm1bNmzYwNtvv61Adg60qUft1i+uH9vStlEcVcw3Sd/QLb6b2SWJiIiI1BtVWkP21ltv8dprr1UaqQoKCuLll1926C6LP/zwA127duXGG28kPDyczp078+mnn9rP7927l9TUVPr162c/FhAQQI8ePVi+fDkAy5cvJzAw0B7GAPr164fVamXlypX2Npdddhnu7u72NgMGDGDHjh0cOXLklLUVFRWRm5tb6VEfGYZhHyHTlMXaycfdh65+5f99fLT9I9Ly00yuSERERKT+qNIIWW5uLhkZGScdz8jIIC8v74KLqrBnzx4mTJjA+PHj+cc//sHq1at58MEHcXd3Z9SoUaSmpgIQERFR6XURERH2c6mpqYSHh1c67+rqSnBwcKU2x4+8Hd9nampqpeBZ4bXXXuOFF15wzButxQpKC7AZ5btuaspi7dXGuw3Lti8jLzqPUV+O4tX4Vx1+jdDQUGJjtb2+iIiIyPGqFMiGDBnCHXfcwVtvvUX37t0BWLlyJY8//jjXX3+9w4qz2Wx07dqVV18t/+Wwc+fObNmyhYkTJzJq1CiHXacqnnrqqUqbm+Tm5hITE2NiReaomK7o6eqJi9XF5Gqkqo4dOQY/AnfD3JS5zH1zLiQ69hpe3l5sT9iuUCYiIiJynCoFsokTJ/LYY49x6623UlJSUt6RqyujR4/mzTffdFhxUVFRtGnTptKx1q1b88033wAQGRkJQFpaGlFRUfY2aWlpdOrUyd4mPT29Uh+lpaVkZWXZXx8ZGUlaWuVpWhXPK9qcyMPDAw8Pjyq+s7pDG3rUDYX5hXAIYgpj2O+9H9+RvtwYeiNuVjeH9J+RlMHsV2eTmZmpQCYiIiJynCoFMm9vbz766CPefPNNdu/eDUDTpk3x8XHsL+WXXHIJO3bsqHRs586dNGrUCCjf4CMyMpL58+fbA1hubi4rV67kvvvuA6Bnz55kZ2ezdu1a4uPjAViwYAE2m40ePXrY2/zzn/+kpKQEN7fyX0DnzZtHy5YtTzldUf5HW97XLZ39O5PrkktOUQ4JrgkMaDbA7JJERERE6rQLujH0oUOHOHToEM2bN8fHxwfDMBxVFwCPPPIIK1as4NVXX2XXrl1Mnz6dTz75hLFjxwLl2+o//PDDvPzyy/zwww9s3ryZkSNHEh0dzeDBg4HyEbWrrrqKu+++m1WrVrF06VLGjRvHsGHDiI6OBuDWW2/F3d2d0aNHs3XrVmbMmMG77757Xvdbq6+0w2Ld4mpx5ZoW1wCw8uBKUvJSTK5IREREpG6r0gjZ4cOHuemmm1i4cCEWi4XExESaNGnC6NGjCQoKcthOi926dWP27Nk89dRTvPjii8TFxfHOO+8wfPhwe5snnniCo0ePMmbMGLKzs+nVqxdz5syx34MMYNq0aYwbN44rrrjCfmPo9957z34+ICCA3377jbFjxxIfH09oaCjPPvustrw/B/kl5SNkmrL4PwkJCbWq3xM1C25G+/D2bE7fzI87f+SuzndpfaCIiIhINalSIHvkkUdwc3MjOTmZ1q1b24/ffPPNjB8/3qFb319zzTVcc801pz1vsVh48cUXefHFF0/bJjg42H4T6NPp0KEDf/zxR5XrrK80QvY/+Vnl4XTEiBHVe538/GrtH2BA0wEkZiWSmp/KioMruCTmkmq/poiIiEh9VKVA9ttvvzF37lwaNmxY6Xjz5s1JSkpySGFSO9jvQeamNWSF+YUA9L2/L807Nnd4/4krE1n4+UIKCwsd3veJfNx96N+0Pz/s+IFF+xbRJrQNQV5aTykiIiLiaFUKZEePHsXb2/uk41lZWdp5sJ7RCNnJghoEEdUi6uwNz1NmcqbD+zyTThGd2JS2iX3Z+/gp8SdGtB+BxWKp0RpERERE6roqbepx6aWX8sUXX9ifWywWbDYbb7zxBn379nVYceL8tO193WWxWLim+TW4WFzYc2QPm9M3m12SiIiISJ1TpRGyN954gyuuuII1a9ZQXFzME088wdatW8nKymLp0qWOrlGclGEY2va+jgvxDqF3o94s2LeAObvm0DSoqUZDRURERByoSiNk7dq1Y+fOnfTq1YvrrruOo0ePcv3117N+/XqaNm3q6BrFSZUYJZTaSgFNWazLLo65mHCfcApKC/hx548Ov72FiIiISH123iNkJSUlXHXVVUycOJF//vOf1VGT1BIFtgIA3KxuuLu4m1yNVBcXqwtDWg3h03WfsuPwDjambaRTZCezyxIRERGpE857hMzNzY1NmzZVRy1Sy1QEMo2O1X2RvpH0adwHgDm75pBdmG1qPSIiIiJ1RZWmLI4YMYL//Oc/jq5FapmKQKYt7+uHS2IuoaF/Q4rKivh+x/eauigiIiLiAFXa1KO0tJTPP/+c33//nfj4eHx8Ko+QvP322w4pTpxbQZlGyOoTq8XK4JaD+Xjtx+zL3seKgyvo2bCn2WWJiIiI1GrnFcj27NlD48aN2bJlC126dAFg586dldroPkX1h6Ys1j8h3iFc2fRKfkn8hd/3/E6sfywN/BuYXZaIiIhIrXVegax58+YcOnSIhQsXAnDzzTfz3nvvERERUS3FiXOzBzLdg6xe6RrVlb1H9pKQmcDX277mnvh78HLzMrssERERkVrpvNaQnbhm5Ndff+Xo0aMOLUhqD/saMt2DrF6xWCz8reXfCPIMIqcoR+vJRERERC5AlTb1qKBfwuo3rSGrvzxdPbmxzY24WFzYcXgHKw6uMLskERERkVrpvAKZxWI5aY2Y1ozVX5qyWL9F+UUxoOkAAH7f8zv7sveZW5CIiIhILXRea8gMw+D222/Hw8MDgMLCQu69996Tdln89ttvHVehOC1NWZSu0V1Jzk1mS/oWZm6dyV1d7iLYK9jsskRERERqjfMKZKNGjar0fMSIEQ4tRmoRVyg2igGNkNVnFouFv7X4G0cKjnAw7yDTN09ndOfR2uRDRERE5BydVyCbNGlSddUhtc1fGcxqseLp6mluLWIqNxc3hrUbxqfrPuVwwWFmbZvFre1vxcXqYnZpIiIiIk7vgjb1kHrsr0Dm4+ajdYSCr7svt7S7BTerG3uy9/Bz4s/a9EdERETkHCiQSdX8Fci0fkwqRPpGckPrG7BgYX3qeubtmadQJiIiInIWCmRSNX/lMG15L8drGdqSa1tcC8DyA8v5I/kPkysSERERcW7ntYZMxO64KYsix+sc1ZmisiLm7p7Lwn0L8XT1JIYYABISEqrlmqGhocTGxlZL3yIiIiLVSYFMqqYikGmETE7hooYXUVhayOKkxfy661e6uXYDqm9nVi9vL7YnbFcoExERkVpHgUyq5q8pi75uWkMmp9a7UW+KyopYcWAFq0tXQw/oG9+X5h2bO/Q6GUkZzH51NpmZmQpkIiIiUusokEnVaIRMzsJisdC/SX+sFivL9i+DqyHdms5lLS4zuzQRERERp6FNPaRqtIZMzoHFYqFfXD9albUCYKttK4v2LdLuiyIiIiJ/USCTqqmYsqht7+UsLBYLbWxtYH7588VJi1mwd4FCmYiIiAgKZFIFpbZS8C7/s6Ysyjn7A9pb2wPw5/4/mbt7rkKZiIiI1HsKZHLesouzwVL+Z283b1NrkdqluUtzBjYbCMDKgyv5ZdcvCmUiIiJSrymQyXk7UnwEAE+rJ1aLPkJyfro16Ga/efSalDV8t+M7bIbN5KpEREREzKHfpuW8HS46DICX1cvkSqS26hLVhSGthmDBwqa0TczcOrN8KqyIiIhIPaNAJuctqygLAG+rpitK1XWI6MDNbW/GxeLCjsM7mLZ5GkWlRWaXJSIiIlKjFMjkvFWMkHlaPU2uRGq7lqEtGd5+OO4u7uzL3scXm77gWMkxs8sSERERqTEKZHLejhSVryHzctGURblwcUFxjOo4Ci9XL1LyUpi8YTK5RblmlyUiIiJSIxTI5LxVjJBpyqI4SrRfNHd0ugM/dz8yjmUwacMksgqyzC5LREREpNopkMl5q1hDpk09xJHCfMK4s/OdBHsFk12YzaQNk0jNTzW7LBEREZFqpUAm5y2rWIFMqkegZyB3dLqDCJ8I8ovzmbRhEruzdptdloiIiEi1USCT86YRMqlOvu6+3N7pdhoHNKa4rJjpW6azIXWD2WWJiIiIVAtXswuQ2sUwjP8FMm3qIdXE09WT4R2G88OOH9icvpnvd3xPdmE2vRv1xmKxnPI1CQkJ1VZPaGgosbGx1da/iIiI1F8KZHJejhQeodQov4Gvtr2X6uRqdWVIqyH4e/izdP9SFictJv1oOte1vA4PVw97u/ysfABGjBhRbbV4eXuxPWG7QpmIiIg4nAKZnJf0o+nlfygEV4s+PlK9LBYL/Zr0I9grmJ8TfyYhM4HMY5kMazeMYK9gAArzCwHoe39fmnds7vAaMpIymP3qbDIzMxXIRERExOH0G7Wcl7T8tPI/5Jtbh9QvXaK6EOYdxsxtM8k4lsGn6z7lupbX0Sq0lb1NUIMgolpEmViliIiIyPnTph5yXtKO/hXIjppbh9Q/MQExjOkyhob+DSksLWTG1hl8v+N7SigxuzQRERGRKlMgk/NiHyFTIBMT+Hn4cXvH27kk5hIANqRuYIHrAogxuTARERGRKlIgk/NiX0OmKYtiEherC/2a9GNUx1EEeARw1HIURsPa0rUcLda/FIiIiEjtokAm50VTFsVZNA5szL1d76WRrREASUYS7696nxUHVlBmKzO5OhEREZFzo0Am58UeyDRCJk7A09WT+LJ4+AwCLYEUlRUxd/dc3l/1PmtS1lBqKzW7RBEREZEz0i6Lcl7sUxY1QibO5AD0delLSZMSFu5dSE5RDj8n/sySpCX0jOlJp4hOeLnpRuYiIiLifBTI5LxoUw9xVhaLhfioeDqEd2Bd6jqWJi8lrziP33b/xoK9C2gb1pau0V1p4NcAi8VidrkiIiIigAKZnCdNWRRn5+biRo8GPYiPimdj6kZWpawi/Wg6G9M2sjFtIyFeIbQNb0u7sHaE+YSZXa6IiIjUcwpkcs7yi/M5VnKs/IlGyMTJuVpdiY+Op0tUFw7kHmDtobVszdjK4YLDLElawpKkJYR5h9E8pDnNg5sT4x+Di9XF7LJFRESknlEgk3NWsX7Mw+pBUXGRydWInBuLxUJMQAwxATFc3exqdhzewdb0rew6souMYxlkHMtg2f5luLu40ySoCc2Cm9E8uDn+Hv41Ul9ycjKZmZnV1n9oaCixsbHV1r+IiIhcGAUyOWcV68dCPEJIIcXkakTOn4erBx0iOtAhogMFJQXsOrKLXVnlj2Mlx9ieuZ3tmdsBCPMOo3FgY/wL/MG3eupJTk6mVetWFBwrqJ4LAF7eXmxP2K5QJiIi4qQUyOScVawfC/YIViCTWs/LzYv24e1pH94ewzA4lH+IxKxEdh3exYG8A/bRMwAeg7/N/xuX7LmEbtHd6BrdlTZhbQjzDrugDUIyMzMpOFbAkH8MIayR49ezZSRlMPvV2WRmZiqQiYiIOCkFMjlnFSNkwR7BJlci4lgWi4Vov2ii/aLp3ag3x0qOsS97H0k5SexO283hksMcPHaQmVtnMnPrTPvrgr2CaRXailYhrcq//vWIC4rD1Xruf72GNQojqkVUdbw1ERERcXIKZHLOKtaQKZBJXeft5k2bsDa0CWvDIdshPnnwEyZ8P4Fsn2xWHVzF+tT1JGUnkVWQxbL9y1i2f1ml17tZ3Wga3JSWIS1pF96ObtHd6N6gO1F+Cl0iIiJSmQKZnLOKKYshHiEmVyJSw4qge1h3unTpYj9UUFJAYlYi2zO3k5CRwPbD5evPdmTuoKC0wL4e7fsd39tf09C/IVc3u5rBrQZzedzlZrwTERERcTIKZHLOjl9DJlLfebl52TcIOZ7NsHEg9wA7MnewPXM7G1I3sCplFVvTt3Ig9wCfrvuUT9d9iq+7L73CekEDk96AiIiIOAUFMjln9imL7gpkIqdjtViJDYglNiCWK5teaT+eX5zPsv3L+H7793y34ztS8lKYc3AO3A3fZ37PpYGX0jqsNVaL1cTqRUREpKbp//nlnGlTD5Gq83X3pX/T/nw46EP2P7KfFaNXcE3Da6AU0krSmJUwi4lrJpJ4OBHDMMwuV0RERGqIApmcM60hE3EMq8VKj4Y9eKHzC/AOdPHtgperFxnHMpi+ZTpTN0+1/wOIiIiI1G0KZHJOikqLyC7MBiDII8jcYkTqknzo6teVB7o/QM+GPXGxuLDnyB4+Xvsxv+/5nVJbqdkVioiISDWqVYHs//7v/7BYLDz88MP2Y4WFhYwdO5aQkBB8fX254YYbSEur/C/LycnJDBo0CG9vb8LDw3n88ccpLa38S86iRYvo0qULHh4eNGvWjMmTJ9fAO6o9Km6Q62p1xd/N3+RqROoeLzcv+jftz9huY2kT2gYDg6X7lzJxzUSSc5LNLk9ERESqSa0JZKtXr+bjjz+mQ4fKO5o98sgj/Pjjj3z99dcsXryYlJQUrr/+evv5srIyBg0aRHFxMcuWLWPKlClMnjyZZ5991t5m7969DBo0iL59+7JhwwYefvhh7rrrLubOnVtj78/ZVUyfCvcJ16YDItUoyCuIG9veyM1tb8bX3ZfDBYeZtGESv+76leKyYrPLExEREQerFb9Z5+fnM3z4cD799FOCgv43XS4nJ4f//Oc/vP3221x++eXEx8czadIkli1bxooVKwD47bff2LZtG1OnTqVTp05cffXVvPTSS3z44YcUF5f/cjNx4kTi4uJ46623aN26NePGjWPo0KH8+9//NuX9OqOK9WMRPhEmVyJSP7QKbcX9Xe+nU2QnAFYdXMWENRPYc2SPuYWJiIiIQ9WKQDZ27FgGDRpEv379Kh1fu3YtJSUllY63atWK2NhYli9fDsDy5ctp3749ERH/CxIDBgwgNzeXrVu32tuc2PeAAQPsfZxKUVERubm5lR512fEjZCJSM7zcvLiu5XWMaD+CAI8Asguz+e+m//LDjh8oLC00uzwRERFxAKe/D9lXX33FunXrWL169UnnUlNTcXd3JzAwsNLxiIgIUlNT7W2OD2MV5yvOnalNbm4uBQUFeHl5nXTt1157jRdeeKHK76u2Sc0v/15F+kaaXIlI/dM0uCn3db2P+XvnszplNetT17MraxfXtLiGFiEtzvr6hISEaqkrNDSU2NjYaulbRESkvnDqQLZ//34eeugh5s2bh6enp9nlVPLUU08xfvx4+/Pc3FxiYmJMrKh6Hco/BECUb5TJlYjUTx6uHgxsPpC2YW35YecPZBVk8eWWL2kf3p6rml2Ft5v3Sa/Jz8oHYMSIEdVSk5e3F9sTtiuUiYiIXACnDmRr164lPT2dLl262I+VlZWxZMkSPvjgA+bOnUtxcTHZ2dmVRsnS0tKIjCwfyYmMjGTVqlWV+q3YhfH4NifuzJiWloa/v/8pR8cAPDw88PDwuOD3WFvYA5mfApmImRoFNuLe+HtZtG8Ryw8sZ3P6ZnZl7aJfk350juyMxWKxty3ML5/W2Pf+vjTv2NyhdWQkZTD71dlkZmYqkImIiFwApw5kV1xxBZs3b6507I477qBVq1b8/e9/JyYmBjc3N+bPn88NN9wAwI4dO0hOTqZnz54A9OzZk1deeYX09HTCw8vXP82bNw9/f3/atGljb/PLL79Uus68efPsfQgcyjtuhKzI5GJE6jk3FzeubHolbcLa8MPOH0g/ms6PO39kfep6BjUfdNLU4qAGQUS10D+miIiIOCOnDmR+fn60a9eu0jEfHx9CQkLsx0ePHs348eMJDg7G39+fBx54gJ49e3LRRRcB0L9/f9q0acNtt93GG2+8QWpqKk8//TRjx461j3Dde++9fPDBBzzxxBPceeedLFiwgJkzZ/Lzzz/X7Bt2YpVGyBTIRJxCA/8GjOkyhlUHV7EoaREHcg/wydpP6BzVmb6N+5pdnoiIiJwDpw5k5+Lf//43VquVG264gaKiIgYMGMBHH31kP+/i4sJPP/3EfffdR8+ePfHx8WHUqFG8+OKL9jZxcXH8/PPPPPLII7z77rs0bNiQzz77jAEDBpjxlpyOYRj2EbJov2iyM7PNLUhE7FysLvSM6Unb8Lb8tvs3tmZsZd2hdWxJ30Iza7M68Le8iIhI3Vbr/q960aJFlZ57enry4Ycf8uGHH572NY0aNTppSuKJ+vTpw/r16x1RYp2TW5RLQWkBUD5lMZtscwsSkZP4e/gztM1QumV347c9v5GSl8I2l23wIOwp20MbWxtcrC5mlykiIiInqBX3IRNzVUxXDPAIwMvt1JuciIhzaBTYiLs638X1ra7Hy/ACf9hg28AHqz9gY+pGbIbN7BJFRETkOApkclYpeSmAdlgUqS0sFgvtI9rTv7Q//AIeeJBdmM13O75jwpoJbM3YimEYZpcpIiIiKJDJOai0w6KI1BouuMAqGOA6gH5x/fBy9SLzWCazts3ik3WfsPPwTgUzERERk9W6NWRS83QPMpHazdXiyiWxlxAfHc+KAytYfmA5qfmpfLnlSxr6NaRvXF+aBDUxu0wREZF6SYFMzkojZCJ1g6erJ30a96F7g+4s3b+UVQdXcSDvAP/d9F8aBzbm8saXExMQY3aZIiIi9YqmLMpZ2UfIFMhE6gRvN2+ubHIlD/V4iO4NuuNicWFf9j4+3/A50zdPt/8jjIiIiFQ/jZDJWVUEsmi/aJMrERFH8nX35epmV3Nxw4tZnLSYDakbSMxKJDErkbZhbenXpB+BnoFmlykiIlKnKZDJWdmnLGoNmUidFOAZwN9a/o1LYi5hcdJiNqdvZmvGVrZnbufimIvpFdsLdxd3s8sUERGpkzRlUc5KUxZF6ocQ7xCub30998bfS+PAxpQZZfyR/AcfrPqATWmbtCOjiIhINVAgkzM6WnyU3KJcQCNkIvVFhG8EIzuM5Ka2NxHoGUhecR6zt8/m8w2fczD3oNnliYiI1CmasihnVDE65u3mjZ+7n8nViEhNsVgstA5tTfPg5iw/sJw/kv7gQO4BPlv/GR0jOtLeaG92iSIiInWCApmc0fFb3lssFpOrEZGa5mp15dLYS+kU0Yn5e+ezMW0jG9M2ss2yDS6BTds2Vdu1Q0NDiY2Nrbb+RUREnIECmZyRbgotIgB+Hn4MbjWYrtFdmbNrDgfzDsKVcMeqO+AlYKfjr+nl7cX2hO0KZSIiUqcpkMkZ6abQInK8hv4NGd15ND8v+Jm1BWshBLgVYjxi6Onfk0DXQIdcJyMpg9mvziYzM1OBTERE6jQFMjkj3YNMRE5ksVhoZDRi7ftrafF0C3YZu9hftJ+DmQfp3qA7vRv1xtPV0+wyRUREagXtsihnpC3vReS0iqGdSzvu73o/LYJbYDNsrDiwgvdXvc/qlNXYDJvZFYqIiDg9BTI5I90UWkTOJsQ7hFva38Lw9sMJ9Q7lWMkxfkn8hYlrJrIra5fZ5YmIiDg1TVmUM9IImYicq2bBzYgLjGPtobUs2reIjGMZTNs8jebBzbmyyZWE+YSZXaKIiIjTUSCTM0rJSwE0QiYi58bF6kL3Bt1pH96eJUlLWJWyisSsRHZl7aJbg270adQHLzcvs8sUERFxGpqyKKdVVFpEVkEWoBEyETk/Xm5eDGg2gPu73k/LkJYYGKw6uIoPVn/A+tT1GIZhdokiIiJOQYFMTis1PxUAdxd3gr2CTa5GRGqjEO8QhrUbxm0dbrOvL/thxw98vv5z+98xIiIi9ZkCmZxWxfqxSN9ILBaLydWISG3WJKgJ98bfy5VNrsTdxZ0DeQf4ZO0n/L7nd0rKSswuT0RExDQKZHJaFTss6h5kIuIILlYXLo65mLHdxtImrA0GBkv3L2Xi2onsy95ndnkiIiKmUCCT09IOiyJSHfw9/LmxzY3c3PZm/Nz9yCrIYsrGKczZNYdSW6nZ5YmIiNQo7bIop2W/B5kCmYhUg1ahrWgc2Jh5e+ax7tA6Vh5cyd7svVzf6nqzSxMREakxGiGT07KPkGnLexGpJp6unlzb4lpuaXcLPm4+pB9N59N1n7IpfxNo6aqIiNQDCmRyWpqyKCI1pUVIC+7reh8tgltQZpSxIm8FjID0gnSzSxMREalWmrIop6WbQov8T0JCQq3oszbzcfdhWLthrD20ljmJcyhrWsawxcP4T9B/uKHNDWaXJyIiUi0UyOS0tIZMBPKz8gEYMWJE9V0jP7/a+q5tLBYLXaO74pPtw8yNM8mJzmHo10O5odENPNr2UTxcPBxyndDQUGJjYx3Sl4iIyIVQIJNTKikrIf1o+VQhbXsv9VlhfiEAfe/vS/OOzR3ad+LKRBZ+vpDCwkKH9lsXuOa6wn+APkAv+CbpG75Z8Q3MBI5ceP9e3l5sT9iuUCYiIqZTIJNTOph3EAMDDxcPwn3CzS5HxHRBDYKIauHY0eLM5EyH9leXFOYXQhn0bdkXzxBPFmYvpDCqELeH3egT0Ic4r7gq952RlMHsV2eTmZmpQCYiIqZTIJNT2p+zH4CG/g2xWLTVmYiYI6hBEO3bt6dVUStmbZvF/tz9zMueRw+fHlzZ5EpcrC5mlygiInJBtMuinFJyTjIAMQExJlciIlJ+M+lRHUdxccOLAVh5cCWTNkwipzDH5MpEREQujAKZnNL+3PIRstgATecREefgYnXhyqZXMqztMDxdPTmYd5CP137MrqxdZpcmIiJSZQpkckoVUxZj/DVCJiLOpWVoS+6Jv4dov2gKSguYtnkafyT/gWEYZpcmIiJy3hTI5JQqRsgUyETEGQV6BnJHpzvoEtUFgAV7FzBr2yyKy4pNrkxEROT8KJDJKVWsIdOURRFxVq5WV65tcS3XNL8Gq8XKtsxtfLbuM44UOGBffBERkRqiQCanZB8h06YeIuLk4qPjub3j7fi6+5JxLIPP1n9mn3YtIiLi7BTI5CRHi4+SVZAFaMqiiNQOMQEx3N3lbiJ9IzlWcowpG6ewOW2z2WWJiIiclQKZnKRidMzP3Y8AzwCTqxEROTf+Hv7c0ekOWoW0oswo49vt37I4abE2+xAREaemQCYnqZjqo/VjIlLbuLu4c1Pbm7gk5hIAFu1bxM+JP2MzbCZXJiIicmoKZHISrR8TkdrMYrHQr0k/BjYbCMDaQ2v5etvXlJSVmFyZiIjIyRTI5CQVOyxq/ZiI1GbdGnTjxjY34mJxYXvmdqZunkpRaZHZZYmIiFSiQCYn0ZRFEakr2oS1YUSHEXi4eJCck8x/N/2XIptCmYiIOA8FMjmJbgotInVJ48DGjOo4Ci9XLw7mHeSnwz+Bl9lViYiIlFMgk5NoDZmI1DVRflGM6jgKHzcfDpcehtshqyjL7LJEREQUyKQywzC0hkxE6qQI3whGdRyFt9UbImDMsjEcyjtkdlkiIlLPKZBJJUcKj3Cs5BgADf0bmlyNiIhjhfmEcW3ItZADe/P30ntybw7kHjC7LBERqccUyKSSig09wrzD8HLTIgsRqXsCXANgEkR5RZGYlchlky5jX/Y+s8sSEZF6SoFMKrFPV9T6MRGpy7Lh04s/pWlQU/Zm7+WySZexO2u32VWJiEg9pEAmlVRs6KEt70WkrovyjmLx7YtpGdKS/bn76T25N4mHE80uS0RE6hkFMqmkYsqiNvQQkfqggX8DFt2+iNahrTmYd5A+U/qwI3OH2WWJiEg9okAmlSTnaodFEalfIn0jWXT7ItqFtyMlL4U+U/qQkJFgdlkiIlJPKJBJJfYRMq0hE5F6JNwnnAUjF9AhogOp+an0ntyb9YfWm12WiIjUAwpkUonWkIlIfRXmE8aCkQuIj4on41gGfaf0ZWnyUrPLEhGROk6BTOzKbGUczD0IaMqiiNRPId4hLBi1gMsaXUZOUQ5X/vdK5u6aa3ZZIiJShymQiV3a0TRKbCVYLVai/KLMLkdExBT+Hv78OvxXrm52NQWlBVz75bVM2zTN7LJERKSOUiATu4r1Yw38GuBqdTW5GhER83i7efPdsO+4qe1NlNhKGDF7BC8veRnDMMwuTURE6hgFMrGrWD+mDT1ERMDdxZ0vb/iSx3o+BsAzC59h9A+jKSkrMbkyERGpSxTIxC45R1vei4gcz2qx8mb/N/lw4IdYLVYmbZhE/6n9ST+abnZpIiJSRyiQid3eI3sBaBTQyORKREScy/3d7ueHYT/g4+bDon2LiP8knpUHVppdloiI1AEKZGK368guAJqHNDe5EhER5zOoxSBW3b2KFiEtOJB7gMsmX8bHaz7WujIREbkgTh3IXnvtNbp164afnx/h4eEMHjyYHTt2VGpTWFjI2LFjCQkJwdfXlxtuuIG0tLRKbZKTkxk0aBDe3t6Eh4fz+OOPU1paWqnNokWL6NKlCx4eHjRr1ozJkydX99tzOruyygNZs+BmJlciIuKc2oS1YfXdqxnSagjFZcXc+/O93PrtrWQXZptdmoiI1FJOHcgWL17M2LFjWbFiBfPmzaOkpIT+/ftz9OhRe5tHHnmEH3/8ka+//prFixeTkpLC9ddfbz9fVlbGoEGDKC4uZtmyZUyZMoXJkyfz7LPP2tvs3buXQYMG0bdvXzZs2MDDDz/MXXfdxdy59efeMyVlJfYpi82DNUImInI6/h7+fHPTN/zfFf+Hi8WFr7Z8RaeJnfgz+U+zSxMRkVrIqfc2nzNnTqXnkydPJjw8nLVr13LZZZeRk5PDf/7zH6ZPn87ll18OwKRJk2jdujUrVqzgoosu4rfffmPbtm38/vvvRERE0KlTJ1566SX+/ve/8/zzz+Pu7s7EiROJi4vjrbfeAqB169b8+eef/Pvf/2bAgAGnrK2oqIiioiL789zc3Gr6LtSMpJwkyowyvFy9dA8yEZGzsFgs/L3X3+nduDfDvx3OniN76D25N/+89J88fdnTuLu4m12iiIjUEk49QnainJwcAIKDgwFYu3YtJSUl9OvXz96mVatWxMbGsnz5cgCWL19O+/btiYiIsLcZMGAAubm5bN261d7m+D4q2lT0cSqvvfYaAQEB9kdMTO3emTDxcCIATYObYrXUqo+FiIhpLmp4EevvWc/IjiOxGTZeWvISPT7rwea0zWaXJiIitYRTj5Adz2az8fDDD3PJJZfQrl07AFJTU3F3dycwMLBS24iICFJTU+1tjg9jFecrzp2pTW5uLgUFBXh5eZ1Uz1NPPcX48ePtz3Nzc2t1KKtYP6bpiiIi58ffw58pg6cwqPkg7v/5fjakbiD+k3jGtBjDyKYjcbU69v9qQ0NDiY2NdWifIiJinloTyMaOHcuWLVv480/nmKPv4eGBh4eH2WU4jDb0EBG5MDe1vYnLGl3GqK9H8Vvyb3y4/UM+nP8hzAYyHXcdL28vtidsVygTEakjakUgGzduHD/99BNLliyhYcOG9uORkZEUFxeTnZ1daZQsLS2NyMhIe5tVq1ZV6q9iF8bj25y4M2NaWhr+/v6nHB2rixKzyqcsKpCJiFRdpG8kr3Z4ld/e+Q23IW6UNCjBZZwLXf260t6n/QVPCc9IymD2q7PJzMxUIBMRqSOcOpAZhsEDDzzA7NmzWbRoEXFxcZXOx8fH4+bmxvz587nhhhsA2LFjB8nJyfTs2ROAnj178sorr5Cenk54eDgA8+bNw9/fnzZt2tjb/PLLL5X6njdvnr2P+kBTFkVEHMNiscAmuOnem1hZspJdR3axMm8lKZYUrmt5HSHeIWaXKCIiTsSpd28YO3YsU6dOZfr06fj5+ZGamkpqaioFBQUABAQEMHr0aMaPH8/ChQtZu3Ytd9xxBz179uSiiy4CoH///rRp04bbbruNjRs3MnfuXJ5++mnGjh1rn3J47733smfPHp544gm2b9/ORx99xMyZM3nkkUdMe+81qdRWyt7s8i3vNUImIuIYPi4+3Nr+Vq5tcS3uLu7sz93PxLUTWXlwpW4mLSIidk4dyCZMmEBOTg59+vQhKirK/pgxY4a9zb///W+uueYabrjhBi677DIiIyP59ttv7eddXFz46aefcHFxoWfPnowYMYKRI0fy4osv2tvExcXx888/M2/ePDp27Mhbb73FZ599dtot7+uapOwkSm2leLp60sC/gdnliIjUGRaLhS5RXbiv633EBcZRaitlzq45fLHxC44UHDG7PBERcQJOP2XxbDw9Pfnwww/58MMPT9umUaNGJ01JPFGfPn1Yv379eddYF1RMV2wapC3vRUSqQ6BnILd1uI01KWuYt2ce+3L2MXHtRK5sciXxUfHl0xxFRKRe0m/fog09RERqgMVioVuDbtzb9V5iA2IpLivm58SfmbZ5GjmFOWaXJyIiJlEgE23oISJSg4K9grm94+30b9ofV6sru4/sZsKaCSRkJphdmoiImECBTHQPMhGRGmaxWOjZsCf3xN9DA78GFJUVMXPrTH7d9SultlKzyxMRkRqkQCaasigiYpJQ71Du6HQHPRuW32Zl1cFVfL7+c7IKskyuTEREaooCWT1Xaitl75HyLe+bh2jKoohITXOxutC/aX9uaXcLXq5eHMo/xCdrP2FrxlazSxMRkRqgQFbP7c/ZT4mtBA8XDxr6NzS7HBGReqtFSAvuib+HGP8YisqKmLVtFj8n/qwpjCIidZwCWT1XMV2xSVATbXkvImKyAM8Abu90O71iegGwJmUN/1n/H92zTESkDtNv4PWcfYdFTVcUEXEKVouVK5pcwfD2w/F28yY1P5VP133KniN7zC5NRESqgVPfGFqqX+Lhvzb0CNKGHiJSvyQkVM82847qt1lwM+6Jv4cZW2eQkpfC1E1T6eHXwyF9i4iI81Agq+d2HdGW9yJSv+Rn5QMwYsSI6r1Ofv4F9+Hv4c8dne7gp50/sTFtIyvyVsD1UFBa4IAKRUTEGSiQ1XOasigi9U1hfiEAfe/vS/OOjv+7L3FlIgs/X0hhYaFD+nO1unJdy+uI8oti7q65GB0MRi8dzdwWc2kU2Mgh1xAREfMokNVjZbYy+5oEjZCJSH0T1CCIqBZRDu83MznT4X1aLBZ6NOiBa5YrP+3/iR3soOunXZk5dCZ94/o6/HoiIlJztKlHPZaYlUhxWTHebt7E+MeYXY6IiJxFtEc0fAKtA1qTeSyTK/97Je+ueBfDMMwuTUREqkiBrB7bkLoBgA4RHXCxuphbjIiInJsc+OySz7itw22UGWU8PPdhbv/+dgpKtK5MRKQ2UiCrxyoCWaeITqbWISIi58fTxZMpg6fwzoB3cLG48MXGL7h00qUk5ySbXZqIiJwnrSGrx+yBLLKTqXWIiMj5qdha/1L3S/nwog95cu2TrD20lo4fdeS1Lq/RPax7lfsODQ0lNjbWUaWKiMhZKJDVYwpkIiK1y2m37A8Abobs6GzuW3YfLACWAlVYWubl7cX2hO0KZSIiNUSBrJ46lHeItKNpWC1W2ke0N7scERE5B2fasr/UKGVpzlJ2FOyAftBoUCP6BvbF3ep+zv1nJGUw+9XZZGZmKpCJiNQQBbJ6qmJ0rGVIS7zdvM0tRkREzsvptuwfxjDWHVrHL4m/kFSUxA+5P3BTm5uI8I0woUoRETkX2tSjntJ0RRGRuqlLVBfu7HwnAR4BZBVk8dn6z9iUtsnsskRE5DQUyOqpDWkbAAUyEZG6KNovmjHxY2ga1JRSWymzt8/m58SfKSkrMbs0ERE5gQJZPaURMhGRus3bzZtb29/KpbGXArAmZQ2frf+M9KPpJlcmIiLHUyCrh/KL80k8nAgokImI1GVWi5XL4y5nePvh+Lj5kH40nU/WfsKqg6swjCpswSgiIg6nQFYPbU7bjIFBtF804T7hZpcjIiLVrFlwM+7tei/NgptRZpTx665fmbZ5GjmFOWaXJiJS7ymQ1UOarigiUv/4uvtya7tbGdB0AK5WV3Yf2c1Haz5i7aG1Gi0TETGRAlk9tD51PQCdIjqZW4iIiNQoi8XCRQ0v4p74e4jxj6G4rJifdv7E1E1TOXzssNnliYjUSwpk9ZBGyERE6rdQ71Bu73Q7/Zv0x9Xqyp7sPUxYM4G1eWt1h1IRkRqmQFbPlNpK2Zy+GVAgExGpz6wWKz1jenJf1/toGtSUMqOMtflr4T5YkrpE0xhFRGqIAlk9s/PwTgpLC/F196VpcFOzyxEREZMFewUzvP1whrYZirfVG0LgkdWPcPkXl7M2Za3Z5YmI1HkKZPVMxXTFjhEdsVr04xcRkfK1ZW3D2nJT2E3wB7hb3Vm0bxFdP+3KbbNvIzkn2ewSRUTqLP1GXs+sO7QO0HRFERE5mbvVHebDt32/5bYOtwEwddNUWrzfgid/f1Lb5IuIVAMt3a1nFu5bCMBFDS8yuRIREXFW2UnZPNz6YQYEDOCdbe+w5vAaXl/6OhNXTeSO5ncwtNFQvFy9zrvf0NBQYmNjq6FiEZHaS4GsHsk8lsn6Q+Vb3vdr0s/kakRExNnkZ+UDMGLEiMonmgP9IScsh3e2vcM7q96BP4E1QOm59+/l7cX2hO0KZSIix1Egq0cW7l2IgUG78HZE+kaaXY6IiDiZwvxCAPre35fmHZtXOmczbOws2Mn6/PXk+ebBVeA90JtOvp1o5d0KV8uZf6XISMpg9quzyczMVCATETmOAlk98vue3wHoF6fRMREROb2gBkFEtYg66XgDGnCZ7TI2pm1kSdIScopyWJa7jM2Fm7k09lI6R3XG1apfLUREzof+1qxHft/7VyDTdEUREakiF6sLXaK60DGiI+tT1/NH8h/kFuXyy65f+CP5Dy6OuZj4qHjcXNzMLlVEpFZQIKsn9hzZw54je3C1unJZo8vMLkdERGo5F6sLXaO70imyE+sOrePP5D/JK85j7u65/JH8Bxc1vIju0d3xcPUwu1QREaemQFZPzN8zH4CeDXvi5+FncjUiIlJXuFpd6d6gO12iurAxdSN/7v+T7MJsFuxdwNLkpfRo0IMeDXuYXaaIiNNSIKsnNF1RRESqk6vVlfjoeDpHdWZL+hb+SP6DzGOZLElewvIDy2nt1Rp8za5SRMT56MbQ9YDNsNlHyBTIRESkOlktVjpEdOD+rvdzY5sbifSNpMRWwqajm+BheG3Ta+zL3md2mSIiTkOBrB7YmLqRwwWH8XP3o1t0N7PLERGResBisdAmrA1juozh1na3EuEWAa4wK2kWzd5rxm2zb2NT2iazyxQRMZ2mLNZRycnJZGZmAjBl1xQAOgd1ZvPGzRfcd0JCwgX3ISIi9YPFYqF5SHN8Qnz49LVP6fF4D1ZmrmTqpqlM3TSVq5pdxRMXP0Gfxn2wWCxmlysiUuMUyOqg5ORkWrVuRcGxgvIDtwFNYcnkJcTfF++w6+Tn5zusLxERqdssFgskwUc9P8KINHhz2Zt8ve1r5uyaw5xdc+gW3Y0nLnmCIa2G4GJ1MbtcEZEao0BWB2VmZlJwrIAh/xhCUGwQU1KnUEYZN952I0F3Bl1w/4krE1n4+UIKCwsdUK2IiNQ38dHxfDX0K17JeoW3l7/N5xs+Z3XKam78+kaaBjVlXPdx3NHpDgI8A8wuVUSk2imQ1WFhjcLICMigLLWMAI8AWrdp7ZDpIJnJmQ6oTkRE6qMTp72PjhrN9cHXM2PfDGbuncnuI7t5ZO4j/OP3fzCw4UBuanwTzfybnVPfoaGhxMbGVkfZIiLVRoGsjlt3aB0AnSM7a26+iIiYJj+rfJr7iBEjTt/IDegIdIeC8AK+SfqGb5K+gb3AKmAHYDv9y728vdiesF2hTERqFQWyOuxIyRGScpKwYKFzVGezyxERkXqsML98mnvf+/vSvGPzM7Y1DINDxYfYemwr+wr3YcQZEAc+Vh9aerekuVdzAlwrT2fMSMpg9quzyczMVCATkVpFgawOSygonxbSIqQF/h7+JlcjIiICQQ2CiGoRddZ20UQTTzw5hTmsObSGdYfWcbTkKOvy17Eufx2x/rF0iOhAq9BW+Lj71EDlIiLVQ4GsrnKFxGOJAMRHOW5nRRERkZoU4BnAFXFX0LtRb7Znbmdj6kZ2H9lNcm4yybnJ/Jz4M3FBcTSwNQDlMhGphRTI6qo2UGQUEeARQNPgpmZXIyIickFcra60C29Hu/B25BXlsSltE1sztnIo/xB7juxhD3vgcRj5x0huyruJq5pdRZeoLrha9auOiDg3/S1VV3Ut/9IlqgtWi9XcWkRERBzIz8OPS2Iv4ZLYS8gqyGJrxlY2HdhEZkkmW7O38tyi53hu0XP4uZe3692oN30a9yE+Kh43FzezyxcRqUSBrA7anbcbYinfzCNSm3mIiEjdFewVzKWxl9KssBmfPPoJz/z3GbYUbWHhvoVkF2bbbzwN4OPmw8UxF3Np7KV0a9CNrtFdCfUONfkdiEh9p0BWB32T9A0AjTwa4efhZ3I1IiIiNSQfBscO5sUuL1JmK2Nz+mYW7VvE4qTFLElaQlZBFvP2zGPennn2lzQKaFQezqK60jW6K/HR8QR6Bpr3HkSk3lEgq4NyinPAgNY+rc0uRURExBQuVhc6RXaiU2QnHr7oYWyGja3pW1m0bxErDq5gTcoadh7eSVJOEkk5SczaNsv+2ubBzeka3ZVu0d3oHNWZjhEdCfIKMvHdiEhdpkBWB73S5RXmPDaHhq83NLsUERERp2C1WGkf0Z72Ee15gAcAyC7MZt2hdaxJWcOalDWsTlnNvux9JGYlkpiVyJdbvrS/vlFAI3vA6xjRkU6RnWgc2BiLxWLWWxKROkKBrK7KQf8nISIi9U5CQsJ5tQ8kkH5e/ejXtB80hSNFR0jISWBb9ja2ZW9jZ+5ODhUcso+kfb/je/trfV19aeHfghYBLWjh34Imfk2I843D1833vOsODQ3VDa1F6ikFMhEREan18rPyARgxYoTjO/cEIoGIv75GAuGQTz7rstaxLmtd5fY5QMYJj8PAsdNfwsvbi+0J2xXKROohBTIRERGp9QrzCwHoe39fmnds7tC+E1cmsvDzhfQd+L++y4wyskuzOVxymMOlh8kqyeJI6RGO2Y5BAOWPZpX7cbO4EeASgL+rP/4u/gS4lv+5+FAxc/9vLpmZmQpkIvWQApmIiIjUGUENgohqEeXQPjOTM0/Zd0NOXqtdUFJA5rFMMo5lkHE0o/zrsQxyi3IpMUrILM0kszSz8ovcgafhqnlX0WRdE2IDYonxjyEmIIZI30jCfcKJ8Ikg3CecYK9gXKwuDn1/ImIuBTIRERERB/Fy8yImoDxMHa/UVsqRgiNkFWSVPwqz7H/OLczFZrWRUZhBxsEMVh5cedr+rVgJ8ggiyD2IEI8QgjzKvwa6B9ofAW4BBLgHlP/ZPYCo8CiNvIk4MQUyERERkWrmanUlzCeMMJ+wk87tXL6TL1/9Evwpn+p4/FdfwOevhzfYsHG46DCHiw6zK2/XuV28CBqGNCTSP5IQrxBCvEMI9QolxDvkf8+9Q+1/DvEKwdvNW5uDidQQBbITfPjhh7z55pukpqbSsWNH3n//fbp37252WSIiIlJHFR0tgnzoO/LM699sho0CW8H/HmUFFNoKOWY7RqGtkEJbIUW2ov/92Sgqf6EHHMg/wIH8A+dck4fVA393f3xcfewPb1fvSl8r/hwRGEHjqMb4e/jj5+GHn7sfPu4+eLt54+3mjZerl6ZZipyBAtlxZsyYwfjx45k4cSI9evTgnXfeYcCAAezYsYPw8HCzyxMREZE6zNHr32yGjW0rtvHNW9+AF+D918PrhK8n/tkFimxF5VMoyXBILR5WDzxdPM/8cD3hudWTQL9AwkPDcXdxx93FHTerW/lXF7dKz0885mJ1wcXicsqvVov1lOc0IihmUSA7zttvv83dd9/NHXfcAcDEiRP5+eef+fzzz3nyySdNrk5ERETk3FktVoyjBhw+990nDcOgxCihyFZEkVFEsa2YEqOEEqOk8p+NYkpsJeQcySF1fyp4UL45ScVXt7++/qXIVkSRrYickpzqebMOYMFiD2vHf7ViLf96mnMnHfvrz+5u7nh7eZ82GJ7q6+nC4jm97hzaWi1Wh32/DMNwXF84ri+rxcqwdsMc1l9NUCD7S3FxMWvXruWpp56yH7NarfTr14/ly5ef1L6oqIiioiL785yc8r9gcnNzq7/Ys8jPL78XS8rOFIoLih3ef0ZS+b+WZezNIMknqdb0Xd39q3Zz+lft5vSv2s3pX7Wb039dqL20uPS8fifw+Ot/lZzid/ndB3aTOi2VDoM6EN0kGmxAYfnDwMCGjTLKKLOUnfTVhu2Mx/Pz8zmScgRcKH9Y/3q4UPnY6b5aTvh6FgYGf1Vxzt8ncT7uLu4MfGKg2WXYM8G5BFeL4ch4W4ulpKTQoEEDli1bRs+ePe3Hn3jiCRYvXszKlZV3PHr++ed54YUXarpMERERERGpJfbv30/DhiffIuN4GiGroqeeeorx48fbn9tsNrKysggJCdEc5HOUm5tLTEwM+/fvx9/f3+xyRCrR51OcmT6f4sz0+RRnVZOfTcMwyMvLIzo6+qxtFcj+EhoaiouLC2lpaZWOp6WlERkZeVJ7Dw8PPDwqD+UHBgZWZ4l1lr+/v/7CFqelz6c4M30+xZnp8ynOqqY+mwEBAefUznEr+2o5d3d34uPjmT9/vv2YzWZj/vz5laYwioiIiIiIOIpGyI4zfvx4Ro0aRdeuXenevTvvvPMOR48ete+6KCIiIiIi4kgKZMe5+eabycjI4NlnnyU1NZVOnToxZ84cIiIizC6tTvLw8OC55547aeqniDPQ51OcmT6f4sz0+RRn5ayfTe2yKCIiIiIiYhKtIRMRERERETGJApmIiIiIiIhJFMhERP6/vbuPqbJu4wD+PfL+DgfiHKgoYkwQyIkY8bJaAYKJgVSIHRSF6o8gAbem02huCabN/qAaRCLWgjJXgNCoEe8wIsJAVMJADCKQFhEggsj5PX+0517nsQyf4twE38/G5vldv/s+X7Zrk2v3fe5DREREJBMOZERERERERDLhQEYL7tChQ1i3bh2srKzg6OiI6OhodHd36+yZnp5GcnIy7O3tYWlpiSeeeOKmL+kmWmivvvoqFAoF0tLSpDX2JslpcHAQ8fHxsLe3h5mZGXx8fPD1119LdSEEXn75ZTg5OcHMzAyhoaH47rvvZExMy8Xc3BwyMjLg6uoKMzMzuLm54ZVXXsHvnxXH/iR9qa+vx6ZNm+Ds7AyFQoGSkhKd+nx6cXR0FBqNBtbW1rC1tUVSUhImJyf1kp8DGS24uro6JCcn48svv0RlZSVmZ2exfv16XL16VdqTnp6OsrIynDp1CnV1dfjxxx8RExMjY2pablpbW/H222/j/vvv11lnb5JcfvnlFwQFBcHIyAgVFRW4cOECjh49Cjs7O2nPkSNHkJ2djdzcXLS0tMDCwgLh4eGYnp6WMTktB4cPH0ZOTg7efPNNdHV14fDhwzhy5AjeeOMNaQ/7k/Tl6tWrWL16Nd56660/rM+nFzUaDc6fP4/KykqUl5ejvr4ezz33nH5+AUGkZyMjIwKAqKurE0IIMTY2JoyMjMSpU6ekPV1dXQKAaG5ulismLSMTExPC3d1dVFZWiocfflikpqYKIdibJK89e/aI4ODgP61rtVqhVqvFa6+9Jq2NjY0JExMT8cEHH+gjIi1jGzduFImJiTprMTExQqPRCCHYnyQfAKK4uFh6PZ9evHDhggAgWltbpT0VFRVCoVCIwcHBBc/MK2Skd7/++isAQKlUAgDa2towOzuL0NBQaY+HhwdcXFzQ3NwsS0ZaXpKTk7Fx40adHgTYmySv06dPw8/PD0899RQcHR2xZs0avPPOO1K9r68Pw8PDOv1pY2MDf39/9ictuMDAQFRVVeHixYsAgI6ODjQ2NmLDhg0A2J+0eMynF5ubm2Fraws/Pz9pT2hoKFasWIGWlpYFz2i44O9A9DtarRZpaWkICgqCt7c3AGB4eBjGxsawtbXV2atSqTA8PCxDSlpOPvzwQ5w5cwatra031dibJKdLly4hJycHu3fvxr59+9Da2opdu3bB2NgYCQkJUg+qVCqd49ifpA979+7F+Pg4PDw8YGBggLm5OWRmZkKj0QAA+5MWjfn04vDwMBwdHXXqhoaGUCqVeulXDmSkV8nJyTh37hwaGxvljkKEgYEBpKamorKyEqampnLHIdKh1Wrh5+eHrKwsAMCaNWtw7tw55ObmIiEhQeZ0tNx99NFHKCwsRFFREby8vNDe3o60tDQ4OzuzP4luE29ZJL1JSUlBeXk5ampqcNddd0nrarUa169fx9jYmM7+K1euQK1W6zklLSdtbW0YGRmBr68vDA0NYWhoiLq6OmRnZ8PQ0BAqlYq9SbJxcnLCqlWrdNY8PT3R398PAFIP/u9TP9mfpA8vvvgi9u7di7i4OPj4+GDbtm1IT0/HoUOHALA/afGYTy+q1WqMjIzo1G/cuIHR0VG99CsHMlpwQgikpKSguLgY1dXVcHV11amvXbsWRkZGqKqqkta6u7vR39+PgIAAfcelZSQkJASdnZ1ob2+Xfvz8/KDRaKR/szdJLkFBQTd9RcjFixdxzz33AABcXV2hVqt1+nN8fBwtLS3sT1pwU1NTWLFC989IAwMDaLVaAOxPWjzm04sBAQEYGxtDW1ubtKe6uhparRb+/v4LnpG3LNKCS05ORlFREUpLS2FlZSXdi2tjYwMzMzPY2NggKSkJu3fvhlKphLW1NV544QUEBATgwQcflDk9LWVWVlbSZxn/y8LCAvb29tI6e5Pkkp6ejsDAQGRlZSE2NhZfffUV8vLykJeXBwDSd+YdPHgQ7u7ucHV1RUZGBpydnREdHS1veFryNm3ahMzMTLi4uMDLywvffPMNXn/9dSQmJgJgf5J+TU5OoqenR3rd19eH9vZ2KJVKuLi4/GUvenp6IiIiAs8++yxyc3MxOzuLlJQUxMXFwdnZeeF/gQV/jiMtewD+8KegoEDac+3aNfH8888LOzs7YW5uLjZv3iyGhobkC03L1u8fey8Ee5PkVVZWJry9vYWJiYnw8PAQeXl5OnWtVisyMjKESqUSJiYmIiQkRHR3d8uUlpaT8fFxkZqaKlxcXISpqam47777xP79+8XMzIy0h/1J+lJTU/OHf2smJCQIIebXiz///LPYunWrsLS0FNbW1mLnzp1iYmJCL/kVQvzuK9WJiIiIiIhIb/gZMiIiIiIiIplwICMiIiIiIpIJBzIiIiIiIiKZcCAjIiIiIiKSCQcyIiIiIiIimXAgIyIiIiIikgkHMiIiIiIiIplwICMiIiIiIpIJBzIiIlr2amtroVAoMDY2JncUIiJaZjiQERHRv96OHTugUCigUChgZGQElUqFsLAwHD9+HFqt9i+PDwwMxNDQEGxsbPSQ9o9du3YNSqUSDg4OmJmZkS0HERHpFwcyIiJaEiIiIjA0NITLly+joqICjzzyCFJTUxEZGYkbN2786XGzs7MwNjaGWq2GQqFYsHzXr1+/Zf3jjz+Gl5cXPDw8UFJSsmA5iIhoceFARkRES4KJiQnUajXuvPNO+Pr6Yt++fSgtLUVFRQVOnDgh7VMoFMjJycHjjz8OCwsLZGZm6tyyOD4+DjMzM1RUVOicv7i4GFZWVpiamgIADAwMIDY2Fra2tlAqlYiKisLly5el/Tt27EB0dDQyMzPh7OyMlStX3jJ/fn4+4uPjER8fj/z8/Jvq3377LYKDg2FqaopVq1bhiy++gEKh0Bne/ioTEREtPhzIiIhoyXr00UexevVqfPLJJzrrBw4cwObNm9HZ2YnExESdmrW1NSIjI1FUVKSzXlhYiOjoaJibm2N2dhbh4eGwsrJCQ0MDmpqaYGlpiYiICJ0rYVVVVeju7kZlZSXKy8v/NGdvby+am5sRGxuL2NhYNDQ04Pvvv5fqc3Nz0nu3tLQgLy8P+/fv1znHfDMREdHiYih3ACIiooXk4eGBs2fP6qw9/fTT2Llzp/T60qVLOnWNRoNt27ZhamoK5ubmGB8fx6effori4mIAwMmTJ6HVanHs2DHpNseCggLY2tqitrYW69evBwBYWFjg2LFjMDY2vmXG48ePY8OGDbCzswMAhIeHo6CgAAcOHAAAVFZWore3F7W1tVCr1QCAzMxMhIWFSeeYbyYiIlpceIWMiIiWNCHETZ8N8/Pzu+Uxjz32GIyMjHD69GkAv32+y9raGqGhoQCAjo4O9PT0wMrKCpaWlrC0tIRSqcT09DR6e3ul8/j4+PzlMDY3N4d3330X8fHx0lp8fDxOnDghPZCku7sbd999tzSMAcADDzygc575ZiIiosWFV8iIiGhJ6+rqgqurq86ahYXFLY8xNjbGk08+iaKiIsTFxaGoqAhbtmyBoeFv/21OTk5i7dq1KCwsvOnYO+64Y97vAwCff/45BgcHsWXLFp31ubk5VFVV6VwFu5X5ZiIiosWFAxkRES1Z1dXV6OzsRHp6+m0fq9FoEBYWhvPnz6O6uhoHDx6Uar6+vjh58iQcHR1hbW39tzLm5+cjLi7ups+EZWZmIj8/H2FhYVi5ciUGBgZw5coVqFQqAEBra6vO/n8yExER6Q9vWSQioiVhZmYGw8PDGBwcxJkzZ5CVlYWoqChERkZi+/btt32+hx56CGq1GhqNBq6urvD395dqGo0GDg4OiIqKQkNDA/r6+lBbW4tdu3bhhx9+mPd7/PTTTygrK0NCQgK8vb11frZv346SkhKMjo4iLCwMbm5uSEhIwNmzZ9HU1ISXXnoJAKTbMf+pTEREpF8cyIiIaEn47LPP4OTkhHvvvRcRERGoqalBdnY2SktLYWBgcNvnUygU2Lp1Kzo6OqDRaHRq5ubmqK+vh4uLC2JiYuDp6YmkpCRMT0/f1tWp9957DxYWFggJCbmpFhISAjMzM7z//vswMDBASUkJJicnsW7dOjzzzDPSFTVTU9N/NBMREemXQggh5A5BREREt6epqQnBwcHo6emBm5ub3HGIiOj/xIGMiIjoX6C4uBiWlpZwd3dHT08PUlNTYWdnh8bGRrmjERHR38CHehAREf0LTExMYM+ePejv74eDgwNCQ0Nx9OhRuWMREdHfxCtkREREREREMuFDPYiIiIiIiGTCgYyIiIiIiEgmHMiIiIiIiIhkwoGMiIiIiIhIJhzIiIiIiIiIZMKBjIiIiIiISCYcyIiIiIiIiGTCgYyIiIiIiEgm/wEcQBQ2IXUx0wAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the search rate by gender\n",
        "search_rate_gender = data.groupby('driver_gender')['search_conducted'].mean()\n",
        "\n",
        "# Calculate the search rate by race\n",
        "search_rate_race = data.groupby('driver_race')['search_conducted'].mean()\n",
        "\n",
        "# Plotting the search rates by gender and race\n",
        "fig, ax = plt.subplots(1, 2, figsize=(16, 6))\n",
        "\n",
        "# Search rates by gender\n",
        "search_rate_gender.plot(kind='bar', color='blue', ax=ax[0])\n",
        "ax[0].set_title('Search Rate by Gender')\n",
        "ax[0].set_xlabel('Driver Gender')\n",
        "ax[0].set_ylabel('Search Rate')\n",
        "ax[0].set_ylim(0, max(search_rate_gender) * 1.2)\n",
        "\n",
        "# Search rates by race\n",
        "search_rate_race.plot(kind='bar', color='orange', ax=ax[1])\n",
        "ax[1].set_title('Search Rate by Race')\n",
        "ax[1].set_xlabel('Driver Race')\n",
        "ax[1].set_ylabel('Search Rate')\n",
        "ax[1].set_ylim(0, max(search_rate_race) * 1.2)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "1vIK1hC1k1ts",
        "outputId": "b8e58846-7ccd-47e7-ffa3-bd77525ed835"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x600 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0i5Gh1P3m9v2"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}