medical_cost_analysis.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zOcF5kIyyYbl"
      },
      "source": [
        "# Overview\n",
        "This is a regression problem. We are trying to predict a continuous variable **_charges_**, which is the healthcare insurance cost, based on a variety of features given in the dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JwqhnqGDlDpC",
        "outputId": "dfadb8bc-ee94-41c1-8dea-be3696bd0b18"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: category_encoders in c:\\users\\abdulrasheed nasir\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (2.5.1.post0)\n",
            "Requirement already satisfied: pandas>=1.0.5 in c:\\users\\abdulrasheed nasir\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from category_encoders) (1.5.0)\n",
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            "Requirement already satisfied: pytz>=2020.1 in c:\\users\\abdulrasheed nasir\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from pandas>=1.0.5->category_encoders) (2021.1)\n",
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            "Requirement already satisfied: packaging>=21.3 in c:\\users\\abdulrasheed nasir\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from statsmodels>=0.9.0->category_encoders) (21.3)\n",
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            "Note: you may need to restart the kernel to use updated packages.\n",
            "Requirement already satisfied: seaborn in c:\\users\\abdulrasheed nasir\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (0.12.0)\n",
            "Requirement already satisfied: pandas>=0.25 in c:\\users\\abdulrasheed nasir\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from seaborn) (1.5.0)\n",
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            "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\abdulrasheed nasir\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from matplotlib>=3.1->seaborn) (9.2.0)\n",
            "Requirement already satisfied: packaging>=20.0 in c:\\users\\abdulrasheed nasir\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from matplotlib>=3.1->seaborn) (21.3)\n",
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            "Requirement already satisfied: six>=1.5 in c:\\users\\abdulrasheed nasir\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from python-dateutil>=2.7->matplotlib>=3.1->seaborn) (1.15.0)\n",
            "Note: you may need to restart the kernel to use updated packages.\n"
          ]
        }
      ],
      "source": [
        "%pip install category_encoders\n",
        "%pip install seaborn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "3PzJXcgw-qOi"
      },
      "outputs": [],
      "source": [
        "# imports\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import plotly.express as px\n",
        "import seaborn as sns\n",
        "\n",
        "from category_encoders import OneHotEncoder\n",
        "from sklearn.linear_model import LinearRegression, Ridge\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.svm import SVR\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.model_selection import train_test_split, cross_val_score, RandomizedSearchCV\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
        "from sklearn.preprocessing import MinMaxScaler\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iVZeR6uQK9BN",
        "outputId": "a9046b2d-58fe-40b9-87b2-b6d89f714b83"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1338 entries, 0 to 1337\n",
            "Data columns (total 7 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   age       1338 non-null   int64  \n",
            " 1   sex       1338 non-null   object \n",
            " 2   bmi       1338 non-null   float64\n",
            " 3   children  1338 non-null   int64  \n",
            " 4   smoker    1338 non-null   object \n",
            " 5   region    1338 non-null   object \n",
            " 6   charges   1338 non-null   float64\n",
            "dtypes: float64(2), int64(2), object(3)\n",
            "memory usage: 73.3+ KB\n"
          ]
        }
      ],
      "source": [
        "# read data and print info\n",
        "data = pd.read_csv(\"data/insurance.csv\")\n",
        "data.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Yj-DTT02LXqu",
        "outputId": "92c5e4b0-77c2-485a-d10a-993e5872e0bf"
      },
      "outputs": [
        {
          "data": {
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>bmi</th>\n",
              "      <th>children</th>\n",
              "      <th>smoker</th>\n",
              "      <th>region</th>\n",
              "      <th>charges</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>19</td>\n",
              "      <td>female</td>\n",
              "      <td>27.900</td>\n",
              "      <td>0</td>\n",
              "      <td>yes</td>\n",
              "      <td>southwest</td>\n",
              "      <td>16884.92400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>18</td>\n",
              "      <td>male</td>\n",
              "      <td>33.770</td>\n",
              "      <td>1</td>\n",
              "      <td>no</td>\n",
              "      <td>southeast</td>\n",
              "      <td>1725.55230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>28</td>\n",
              "      <td>male</td>\n",
              "      <td>33.000</td>\n",
              "      <td>3</td>\n",
              "      <td>no</td>\n",
              "      <td>southeast</td>\n",
              "      <td>4449.46200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33</td>\n",
              "      <td>male</td>\n",
              "      <td>22.705</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>northwest</td>\n",
              "      <td>21984.47061</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>32</td>\n",
              "      <td>male</td>\n",
              "      <td>28.880</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>northwest</td>\n",
              "      <td>3866.85520</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age     sex     bmi  children smoker     region      charges\n",
              "0   19  female  27.900         0    yes  southwest  16884.92400\n",
              "1   18    male  33.770         1     no  southeast   1725.55230\n",
              "2   28    male  33.000         3     no  southeast   4449.46200\n",
              "3   33    male  22.705         0     no  northwest  21984.47061\n",
              "4   32    male  28.880         0     no  northwest   3866.85520"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# print first 5 rows\n",
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3-VUHnOXkZXf",
        "outputId": "1f97edcc-a229-44ca-9fab-14de85bebd3b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['female' 'male']\n",
            "['yes' 'no']\n",
            "['southwest' 'southeast' 'northwest' 'northeast']\n"
          ]
        }
      ],
      "source": [
        "# examine unique categories for categorical vars\n",
        "print(data[\"sex\"].unique())\n",
        "print(data[\"smoker\"].unique())\n",
        "print(data[\"region\"].unique())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UuC1LCcPnRQG",
        "outputId": "25d3a552-ce4b-4068-e136-be00327933ce"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "male      676\n",
            "female    662\n",
            "Name: sex, dtype: int64\n",
            "no     1064\n",
            "yes     274\n",
            "Name: smoker, dtype: int64\n",
            "southeast    364\n",
            "southwest    325\n",
            "northwest    325\n",
            "northeast    324\n",
            "Name: region, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# print values count for categorical vars\n",
        "print(data[\"sex\"].value_counts())\n",
        "print(data[\"smoker\"].value_counts())\n",
        "print(data[\"region\"].value_counts())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YRaf_gbylSk-"
      },
      "source": [
        "## Exploratory data analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "ozOZxvSxtiJr",
        "outputId": "c8fa0d0d-172b-4a68-fd39-62927bd1b613"
      },
      "outputs": [
        {
          "data": {
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>1338.0</td>\n",
              "      <td>39.207025</td>\n",
              "      <td>14.049960</td>\n",
              "      <td>18.0000</td>\n",
              "      <td>27.00000</td>\n",
              "      <td>39.000</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>64.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bmi</th>\n",
              "      <td>1338.0</td>\n",
              "      <td>30.663397</td>\n",
              "      <td>6.098187</td>\n",
              "      <td>15.9600</td>\n",
              "      <td>26.29625</td>\n",
              "      <td>30.400</td>\n",
              "      <td>34.693750</td>\n",
              "      <td>53.13000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>children</th>\n",
              "      <td>1338.0</td>\n",
              "      <td>1.094918</td>\n",
              "      <td>1.205493</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>5.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charges</th>\n",
              "      <td>1338.0</td>\n",
              "      <td>13270.422265</td>\n",
              "      <td>12110.011237</td>\n",
              "      <td>1121.8739</td>\n",
              "      <td>4740.28715</td>\n",
              "      <td>9382.033</td>\n",
              "      <td>16639.912515</td>\n",
              "      <td>63770.42801</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           count          mean           std        min         25%       50%  \\\n",
              "age       1338.0     39.207025     14.049960    18.0000    27.00000    39.000   \n",
              "bmi       1338.0     30.663397      6.098187    15.9600    26.29625    30.400   \n",
              "children  1338.0      1.094918      1.205493     0.0000     0.00000     1.000   \n",
              "charges   1338.0  13270.422265  12110.011237  1121.8739  4740.28715  9382.033   \n",
              "\n",
              "                   75%          max  \n",
              "age          51.000000     64.00000  \n",
              "bmi          34.693750     53.13000  \n",
              "children      2.000000      5.00000  \n",
              "charges   16639.912515  63770.42801  "
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# summary statistics\n",
        "data.describe().T"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GzdNVQqClldx",
        "outputId": "5256d5c5-7909-4930-86bb-5212b2f5ddbd"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0    574\n",
              "1    324\n",
              "2    240\n",
              "3    157\n",
              "4     25\n",
              "5     18\n",
              "Name: children, dtype: int64"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# frequencies for children column\n",
        "data[\"children\"].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NxFSMRUOe46X"
      },
      "source": [
        "Judging from the summary statistics\n",
        "- Age has a high standard deviation despite appearing to be normally distributed (mean approx median)\n",
        "- BMI is appears normally distributed\n",
        "- children is a discrete numerical variable and appears skewed (75% have 2 or less children)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aqqcgEysx1IL"
      },
      "source": [
        "### Visualizing the data\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "OPcIg_Yst_1O",
        "outputId": "1c3b4648-97fb-4cd9-f955-cb68653225b9"
      },
      "outputs": [
        {
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            "text/plain": [
              "<Figure size 2000x500 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# select numerical cols\n",
        "num_cols = (\n",
        "    data.select_dtypes(\"number\")\n",
        "    .drop(columns=\"charges\")\n",
        "    .columns\n",
        ")\n",
        "\n",
        "# create 3 new subplots for the barcharts\n",
        "fig, axs = plt.subplots(1, 3, figsize=(20, 5))\n",
        "for idx, ax in enumerate(axs):\n",
        "  col = num_cols[idx]\n",
        "  ax.hist(data[col])\n",
        "  ax.set_xlabel(col)\n",
        "  ax.set_ylabel(\"Frequency\")\n",
        "  ax.set_title(f\"Distribution of {col}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "0eU6f-txvhl_",
        "outputId": "e062cc68-12cf-4b31-da75-d0030bfb0088"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 2000x500 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# create 3 subplots for the boxplots\n",
        "fig, axs = plt.subplots(1, 3, figsize=(20, 5))\n",
        "for idx, ax in enumerate(axs):\n",
        "  col = num_cols[idx]\n",
        "  ax.boxplot(data[col], vert=False)\n",
        "  ax.set_xlabel(col)\n",
        "  ax.set_title(f\"Boxplot of {col}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "My6pj3Nal6WG"
      },
      "source": [
        "From the above visualizations:\n",
        "- There are outliers in the distribution of the BMI (normally distributed)\n",
        "- Children data is skewed to the left i.e. most people have < 3 children\n",
        "- Age is normally distributed"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LDqzZ21MzQg_",
        "outputId": "0f69a3d6-1295-40e4-888f-407cb69753ad"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "99th percentile is 46.40789999999996\n",
            "age         14\n",
            "sex         14\n",
            "bmi         14\n",
            "children    14\n",
            "smoker      14\n",
            "region      14\n",
            "charges     14\n",
            "dtype: int64 observations are above the 99th percentile\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 1324 entries, 0 to 1337\n",
            "Data columns (total 7 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   age       1324 non-null   int64  \n",
            " 1   sex       1324 non-null   object \n",
            " 2   bmi       1324 non-null   float64\n",
            " 3   children  1324 non-null   int64  \n",
            " 4   smoker    1324 non-null   object \n",
            " 5   region    1324 non-null   object \n",
            " 6   charges   1324 non-null   float64\n",
            "dtypes: float64(2), int64(2), object(3)\n",
            "memory usage: 82.8+ KB\n"
          ]
        }
      ],
      "source": [
        "# removing outliers from bmi\n",
        "q_99 = data[\"bmi\"].quantile(0.99)\n",
        "print(f\"99th percentile is {q_99}\")\n",
        "top_one_pct = data[\"bmi\"] > q_99\n",
        "print(f\"{data[top_one_pct].count()} observations are above the 99th percentile\")\n",
        "\n",
        "# remove outliers\n",
        "mask = data[\"bmi\"] <= q_99\n",
        "data = data[mask]\n",
        "data.info()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "MLO_9clB1vDB",
        "outputId": "9d2b1b48-d6be-4c15-a53c-e18030c59896"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Boxplot of BMI')"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# re-plot the box plot for bmi\n",
        "plt.boxplot(data[\"bmi\"], vert=False)\n",
        "plt.xlabel(\"BMI\")\n",
        "plt.title(f\"Boxplot of BMI\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "UPI9FXQFm8w3",
        "outputId": "f3863ba1-5904-4a85-8d75-7f56d2b86166"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 2000x500 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# select the categorical vars\n",
        "categ_df = data.select_dtypes(\"object\").columns\n",
        "\n",
        "# create 3 subplots\n",
        "fig, axs = plt.subplots(1, 3, figsize=(20, 5))\n",
        "for idx, ax in enumerate(axs):\n",
        "  col = categ_df[idx]\n",
        "  df = data[col].value_counts()\n",
        "  ax.bar(\n",
        "      x=df.index,\n",
        "      height=df.values,\n",
        "      width=0.4,\n",
        "  )\n",
        "  ax.set_title(f\"Bar Chart of {col}\")\n",
        "  ax.set_xlabel(col)\n",
        "  ax.set_ylabel(\"Frequency\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T6m-_48G5tGm"
      },
      "source": [
        "Overall the data is well balanced in terms of sex and region."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "MgvbrZXYrp0S",
        "outputId": "80b6c235-deba-48a4-d21a-a8364a8b02f0"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\Abdulrasheed Nasir\\AppData\\Local\\Temp\\ipykernel_18064\\2071478055.py:13: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
            "  fig.show();\n"
          ]
        },
        {
          "data": {
            "image/png": 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JunXriIuLO+V1R48epaSkxJS8RUREnEF1Xnmq88qoziujOk8uVXpyTuQy1LRpUyZPnsyECRPYs2cPt956KzVq1CAlJYX//Oc/PPjggzz++OM0b96cpk2b8vjjj7N//358fX358ssvK7yIbLt27fj22295/fXXqV+/Pk2aNHEs8loRISEhPP3007z44otERkZy22234e7uzi+//EL9+vWZMmUKnTp1IiAggMGDBzNmzBgsFguffPJJpR6Fv+eee1i4cCHDhw/nu+++o3PnzpSWlrJjxw4WLlxIXFzcadd4AXBxcWH27Nn069eP1q1bM2TIEOrVq8eOHTv47bffHL/4X331VWJiYujYsSMPPPCAYwt4Pz8/nn/+eaBskeMGDRpw++2306pVK3x8fPj222/55ZdfeO2118q9rwsWLGDs2LFcf/31+Pj40K9fv3Pe56BBg/j444+Ji4tj4MCBp3zKuHPnTm6++WbuvPNOwsPDcXV15T//+Q+HDh1iwIABjnETJkxg3rx5pKSknHHreoCpU6eyfv16brvtNsdi1Rs2bODjjz+mZs2aPPLII+fMuaIq8v7+lYuLC59++im33nord955J9988w3du3dn3LhxLF68mL59+3LffffRrl078vLy2Lp1K//617/Ys2cPgYGBpuUuIiJiJtV55anOK6M6T3WeXOKqaltYETl/H374oQEYv/zyy2nPd+3a1bjmmmtOOf7ll18aXbp0Mby9vQ1vb2+jefPmxsiRI43ExETHmG3bthk9evQwfHx8jMDAQGPYsGHG5s2bDcD48MMPHeNObF1+sh07dhg33nijY5v1wYMHlxubnp5+2vtISUkpd/yDDz4w2rRpY7i7uxsBAQFG165djeXLlzvOr1mzxujQoYPh6elp1K9f3xg/frxje/aTt2E/0/tgGIZRVFRkTJ061bjmmmsc12nXrp3xwgsvGFlZWad9zcl++OEHo2fPnkaNGjUMb29vIyIiwpgxY0a5Md9++63RuXNnw9PT0/D19TX69etnbNu2zXG+sLDQGDdunNGqVStHnFatWhnvvPNOuTi5ubnG3//+d8Pf398AjODg4HPmZxiGUVJSYtSrV88AjG+++eaU8xkZGcbIkSON5s2bG97e3oafn5/Rvn17Y+HCheXGDR48+LR/Tn+1Zs0aY+TIkca1115r+Pn5GTabzWjUqJFx3333Gbt27So3Njg42OjTp88pMQBj5MiR5Y6d2Nr+1VdfLXf8XO+vYZz+715+fr7RtWtXw8fHx/jxxx8NwzCMnJwcY8KECUZISIjh5uZmBAYGGp06dTKmT59uFBUVnTUPERERM6nOU51XEarzVOfJ5c1iGBewEqOIiIiIiIiIiIicN605JyIiIiIiIiIiUk3UnBMREREREREREakmas6JiIiIiIiIiIhUEzXnREREREREREREqomacyIiIiIiIiIiItVEzTkREREREREREZFq4lrdCVwu7HY7Bw4coEaNGlgslupOR0RERC4RhmGQk5ND/fr1cXHR56YXI9V5IiIicj4qWuepOWeSAwcO0LBhw+pOQ0RERC5R+/bto0GDBtWdhpyG6jwRERG5EOeq89ScM0mNGjWAsjfc19e3mrMRERGRS0V2djYNGzZ01BJy8VGdJyIiIuejonWemnMmOTHFwdfXV0WbiIiIVJqmS168VOeJiIjIhThXnaeFTURERERERERERKqJnpwTEREREakuubmQnAyFheDuDiEh4ONT3VmJiIhIFVJzTkRERESkKm3bBnPmUBwbj2vyTiyG4ThlWCyUhDTD1isKhg+H8PBqTFRERESqgppzIiIiIiJVISWF0uEjsMbHkekTwJLQjmzp1YukwEYU2NzxLC4kNCOViLQk+n74KQEzZlAaFY11zmxo0qS6sxcREREnsRjGSR/VyXnLzs7Gz8+PrKwsLRQsIiIiFaYa4uJnyp/R3LmUPvwIh919mNLlHpaFdabYajvjcFtpMTGJa3gq4WNqF+VhfetNGDr0/K4tIiIi1aKiNYQ2hBARERERcaaXXoJhw1gU2pkeg2ewOLzbWRtzAMVWG4vDu3HzfTNZFNoZhg0riyMiIiKXHU1rFRERERFxlrlzYeJEpkcOYmanAZV+eZ67F0/2GsP+GrV5bOJECAqCBx5wQqIiIiJSXfTknIiIiIiIM6SkUPrwI3zRKuq8GnMnm9FpAF+0iqJ0zMOQkmJSgiIiInIxUHNORERERMQJSoeP4LC7Dy/eZMJacRYLk28aSrqbN6XDR1x4PBEREbloqDknIiIiImK2bduwxscxpcs95Ll7mRIy192LlyPvxRofB9u3mxJTREREqp+acyIiIiIiZpszh0yfAJaFdTY1bGyzzmT6BMDs2abGFRERkeqj5pyIiIiIiMmKY+NZEtrxnLuyVlaRq60sbtxyU+OKiIhI9VFzTkRERETETDk5uCbvZEtQqFPCbwkKwTUpEXJznRJfREREqpaacyIiIiIiZtq1C4thkBTYyCnhkwKDsRgGJCc7Jb6IiIhULTXnRERERETMVFgIQIHN3Snhj7m6lbuOiIiIXNpcqzsBqbjGTy6t7hRELjt7XulT3SmIiMjlxr2sKedZ7JzmmUdJUbnriIiIyKVNT86JiIiIiJgpJATDYiE0I9Up4UMz9mJYLBAS4pT4IiIiUrXUnBMRERERMZOPDyUhzYhIS3JK+Ii0ZEpCw8DHxynxRUREpGqpOSciIiIiYjJbryj6Jq3DVlpsaly3kuKyuNE9TY0rIiIi1UfNORERERERsw0fTkBuJjGJa0wN22vnGgJyM2HECFPjioiISPVRc05ERERExGzh4ZRGRTPhh0/wLsw3JaRPYT5PJXxMaVQ0tGhhSkwRERGpfmrOiYiIiIg4gXXObOrkHeWZlXMvPJhhMHHlXGrnZ2GdM/vC44mIiMhFQ805EREREREnsWAwYEs8o9Z+cf5BDIPRa79gwJZ4LBjmJSciIiIXBTXnREREREScoHT4CA55B/BWpwE8nvApU5a9Xekprj6F+UyJncFjP8znrU4DOOzlT+lwrTcnIiJyOXGt7gRERERERC4727ZhjY9jSr/HWRzejQO+tXl2xftE7tnItK6DWRbWmWKr7YwvdyspptfONYxfPY+AghzG9xrDwlZR7KrVgLe/ng7bt2vdORERkcuEnpwTEREREafYv38/gwYNolatWnh6etKyZUv+97//Oc4bhsGzzz5LvXr18PT0pEePHiQlJZWLceTIEQYOHIivry/+/v488MAD5ObmlhuzZcsWIiMj8fDwoGHDhkybNu2UXBYtWkTz5s3x8PCgZcuWfPPNN8656RPmzCHTJ4BlYZ0BWNAqmuj7Z5JcqxFvfz2dte8M4cX4d7hjSzytDyTS/HAKrQ8kcseWeF6Mf4c1s4fw9tfTSa7ViOj7Z7KwVRQAsc06k+kTALO17pyIiMjlotqbc1d00SYiIiJymcrMzKRz587YbDaWLVvGtm3beO211wgICHCMmTZtGm+//TZz5szhp59+wtvbm+joaI4dO+YYM3DgQH777TeWL1/OkiVL+P7773nwwQcd57Ozs4mKiiI4OJj169fz6quv8vzzz/Pee+85xqxdu5a7776bBx54gI0bN3Lrrbdy66238uuvvzrt/otj41kS2rHc03G/+wdx350v0OOBd1jSIpIOqVuZumwGX33yGLEfjuarTx5j6rIZdEjdypIWkdz8wGzuu/MFfvcPcsQocrWVxY1b7rTcRUREpGpZDMOotlVlMzMzadOmDTfddBMjRoygdu3aJCUl0bRpU5o2bQrA1KlTmTJlCvPmzaNJkyY888wzbN26lW3btuHh4QFATEwMBw8e5N1336W4uJghQ4Zw/fXX89lnnwFlRVuzZs3o0aMHEyZMYOvWrdx///28+eabjuJu7dq13HjjjUyZMoW+ffvy2WefMXXqVDZs2MC11157znvJzs7Gz8+PrKwsfH19nfJ+NX5yqVPiilzJ9rzSp7pTEJErXFXUENXhySefZM2aNSQkJJz2vGEY1K9fn8cee4zHH38cgKysLOrWrctHH33EgAED2L59O+Hh4fzyyy9cd911AMTGxtK7d29+//136tevz+zZs3n66adJS0vDzc3Nce2vvvqKHTt2AHDXXXeRl5fHkiVLHNfv0KEDrVu3Zs6cOee8l0r/GeXkYPj5Mb7XGBZF9DzrUK+iAhpnHsSttJgiq409AfXId/M862vu2BLPtNgZWLKzwcfn3PmIiIhItahoDVGtT85NnTqVhg0b8uGHH3LDDTfQpEkToqKiHI05wzB48803mThxIn/729+IiIjg448/5sCBA3z11VcAbN++ndjYWObOnUv79u3p0qULM2bM4IsvvuDAgQMAzJ8/n6KiIj744AOuueYaBgwYwJgxY3j99dcdubz11lv06tWLcePG0aJFC1588UXatm3LzJkzq/x9EREREbnULV68mOuuu4477riDOnXq0KZNG95//33H+ZSUFNLS0ujRo4fjmJ+fH+3bt2fdunUArFu3Dn9/f0djDqBHjx64uLjw008/OcbceOONjsYcQHR0NImJiWRmZjrGnHydE2NOXOevCgsLyc7OLvdVKbt2YTEMkgIbnXNovpsn2+pezab6YWyre/U5G3MASYHBWAwDkpMrl5eIiIhclKq1OXdFF20iIiIil7Hdu3cze/ZsQkNDiYuLY8SIEYwZM4Z58+YBkJaWBkDdunXLva5u3bqOc2lpadSpU6fceVdXV2rWrFluzOlinHyNM405cf6vpkyZgp+fn+OrYcOGlbv5wkIACmzulXtdBR1zdSt3HREREbm0VWtz7oou2kREREQuY3a7nbZt2/Lyyy/Tpk0bHnzwQYYNG1ahaaTVbcKECWRlZTm+9u3bV7kA7mVNOc9i5zTPPEqKyl1HRERELm3V2py7oos2ERERkctYvXr1CA8PL3esRYsWpKamAhAUVLbJwaFDh8qNOXTokONcUFAQhw8fLne+pKSEI0eOlBtzuhgnX+NMY06c/yt3d3d8fX3LfVVKSAiGxUJoRmrlXldBoRl7MSwWCAlxSnwRERGpWtXanLuiizYRERGRy1jnzp1JTEwsd2znzp0EBwcD0KRJE4KCglixYoXjfHZ2Nj/99BMdO3YEoGPHjhw9epT169c7xqxcuRK73U779u0dY77//nuKi4sdY5YvX05YWJhjZ9iOHTuWu86JMSeuYzofH0oaNSYiLckp4SPSkilp3ESbQYiIiFwmqrU5d0UXbSIiIiKXsUcffZQff/yRl19+meTkZD777DPee+89Ro4cCYDFYuGRRx5h8uTJLF68mK1bt3LvvfdSv359br31VqDsQ9tevXoxbNgwfv75Z9asWcOoUaMYMGAA9evXB+Dvf/87bm5uPPDAA/z2228sWLCAt956i7Fjxzpyefjhh4mNjeW1115jx44dPP/88/zvf/9j1KhRTrt/m18N+m5PwFZafO7BleBWUlwWt4YacyIiIpeLam3OXelFm4iIiMjl6vrrr+c///kPn3/+Oddeey0vvvgib775JgMHDnSMGT9+PKNHj+bBBx/k+uuvJzc3l9jYWDw8PBxj5s+fT/Pmzbn55pvp3bs3Xbp04b333nOc9/PzIz4+npSUFNq1a8djjz3Gs88+y4MPPugY06lTJ0ed2apVK/71r3/x1Vdfce211zrt/ouPZhNwLIeYxDWmxu21cw0Bx3IozsoxNa6IiIhUH4thGEZ1JrBkyRImTJhAUlISTZo0YezYsQwbNsxx3jAMnnvuOd577z2OHj1Kly5deOedd2jWrJljzJEjRxg1ahRff/01Li4u9O/fn7fffhufkx7137JlCyNHjuSXX34hMDCQ0aNH88QTT5TLZdGiRUycOJE9e/YQGhrKtGnT6N27d4XuIzs7Gz8/P7Kyspw2xbXxk0udElfkSrbnlT7VnYKIXOGqooaQC1PpP6OcHAw/P3YEBlOjMJ/o+2eS5+51wXn4FOYT+8FIcty9aJ6RiiU7W1NbRURELmIVrSGqvTl3uVBzTuTSpOaciFQ3NecufpX+M9q0Cdq04cFbn+KNpa+zuMWNTIgZc2FJGAZTYmdwy/bvebTPWN776mXYuBFat76wuCIiIuI0Fa0hqnVaq4iIiIjIZaewEIC9Nesz6eZh3L0lnlFrvzj/eIbB6LVfcPeWeF64+UFSA+qVu46IiIhc2lyrOwERERERkcuKuzsAnsWFLGgVTWDeUcYlfMJVWYeZ3H1opaa4+hTm8/TKudy9JZ5pN97LwlZRtD6QWO46IiIicmlTc05ERERExEwhIRgWC6EZqWyqH8asTneR4e3PsyveJ3LPRqZ1HcyysM4UW21nDOFWUkyvnWsYv3oeAQU5jO81hoWtogAIzdiLYbFgCQmpqjsSERERJ1JzTkRERETETD4+lIQ0IyItiUURPQFY0CqaNcGtmBw/m7e/nk76irnEhnViS1AISYHBHHN1w6OkiNCMvUSkJdMrcS2184+yqkk7JkaN4Hf/IEf4iLRkSkLDsGkzCBERkcuCmnMiIiIiIiaz9Yqi74efMunmYY4n5H73D+K+O18gJCOVgZuW0WXPJgZuXIYLf+7PZsfCrloNWNIikk9b92ZXYMNycd1KiumbtA7bkEFVej8iIiLiPGrOiYiIiIiYbfhwAmbMICZxDYvDu5U7lRzYiBd6/AMAr6ICGmcexK20mCKrjT0B9ch38zxj2F471xCQmwkjRjgzexEREalCas6JiIiIiJgtPJzSqGgm/PAJK5recMZNIPLdPNlW9+oKhfQpzOephI8pjYrG2qKFmdmKiIhINXKp7gRERERERC5H1jmzqVOYyzPfzT3jGK+iAsIP7ab1gUTCD+3Gq6jg9AMNg4nfzaV2UR7WObOdlLGIiIhUBz05JyIiIiLiDE2aYH3rTQYMG8bvvnWY2WkAgGPNuciUjVx9ZP8pa87trnkVCU3aML91DMmBjcAwGL32CwZsjoe5c6FJk+q6IxEREXECNedERERERJxl6FA4dIjHJ04kLH0P/gW5RO7dRLqXP7FhnXi3fX+SAhtRYHPHs7iQ0IxUItKS6Ls9gSHrvyYhuDVHPX3ot+MHeOkleOCB6r4jERERMZmacyIiIiIizvT007BzJ70/+ZTD3gGM6fc4y8I6O3ZxPdmm+mEsiujJpJuHEZO4hqe++5DaeZlw773w1FPVkLyIiIg4m5pzIiIiIiLO9NJL8PHHLIqI4sXuQ8+4OcTJiq02Fod3Y0XTG3hm5VwGfPwxNGtW1ugTERGRy4qacyIiIiIizjJ3LkycyPTIQY415yojz92LJ2PGsN+3No9NnAhBQZraKiIicpnRbq0iIiIiIs6QkkLpw4/wRauo82rMnWxGpwF80SqK0jEPQ0qKSQmKiIjIxUDNORERERERJygdPoLD7j68eNPQCw9msTD5pqGku3lTOnzEhccTERGRi4aacyIiIiIiZtu2DWt8HFO63FOhNeYqItfdi5cj78UaHwfbt5sSU0RERKqfmnMiIiIiImabM4dMnwCWhXU2NWxss85k+gTA7NmmxhUREZHqo+aciIiIiIjJimPjWRLakWKrzdS4Ra62srhxy02NKyIiItVHzTkRERERETPl5OCavJMtQaFOCb8lKATXpETIzXVKfBEREalaas6JiIiIiJhp1y4shkFSYCOnhE8KDMZiGJCc7JT4IiIiUrXUnBMRERERMVNhIQAFNnenhD/m6lbuOiIiInJpc63uBERERERELivuZU05z+JzN8+8igponHkQt9Jiiqw29gTUI9/N86yv8SgpKncdERERubSpOSciIiIiYqaQEAyLhdCMVDbVDzv1dEYqAzctIzJlI1cf2Y8LhuOcHQu7a15FQpM2zG8dQ/JppsaGZuzFsFiwhIQ49TZERESkaqg5JyIiIiJiJh8fSkKaEZGWxKKIno7DDY6mMTl+Nt1S1pPu5U9sWCfebd+fpMBGFNjc8SwuJDQjlYi0JPpuT2DI+q9Z1aQdE6NG8Lt/kCNORFoyJaFh2Hx8quPuRERExGRqzomIiIiImMzWK4q+H37KpJuHUWy1cdfmOJ5d8T6Znr6M6fc4y8I6U2y1nfK6TfXDWBTRk0k3DyMmcQ3jV88j7oNRTLp5GAtaReNWUkzfpHXYhgyqhrsSERERZ1BzTkRERETEbMOHEzBjBjGJa2h49BDjEj7h84goJncfSp671zlfXmy1sTi8Gyua3sDElXOZGjuDwLyj7POvS0BuJowYUQU3ISIiIlVBzTkREREREbOFh1MaFc2kFe/in5/D9MhBzOw0oNJh8ty9mBAzhgO+tRmX8AlZnjUojYrG2qKFE5IWERGR6uBS3QmIiIiIiFyOrE8/RY2CPL6IiDqvxtzJZnQawBcRUfgcy8P69FMmZSgiIiIXAzXnREREREScoPSllznsW4sXuw+98GAWC5O7DyW9Ri1KX3r5wuOJiIjIRUPNORERERERs23bhjU+jik3Dq7QGnMVkevuxctdB2ONj4Pt202JKSIiItVPzTkREREREbPNmUOmTwDLwjqbGja2WWcyfQJg9mxT44qIiEj1UXNORERERMRkxbHxLAntSLHVZmrcIldbWdy45abGFRERkeqj5pyIiIiIiJlycnBN3smWoFCnhN8SFIJrUiLk5jolvoiIiFQtNedERERERMy0axcWwyApsJFTwicFBmMxDEhOdkp8ERERqVpqzomIiIiImKmwEIACm7tTwh9zdSt3HREREbm0qTknIiIiImIm97KmnGexc5pnHiVF5a4jIiIilzY150REREREzBQSgmGxEJqR6pTwoRl7MSwWCAlxSnwRERGpWmrOiYiIiIiYyceHkkaNiUhLckr4iLRkSho3AR8fp8QXERGRqqXmnIiIiIiIyWx+Nei7PQFbafFZx3kVFRB+aDetDyQSfmg3XkUFZx3vVlJcFreGGnMiIiKXC9fqTkBERERE5HJTfDSbgGM5xCSuYXF4t3LnQjJSGbhpGZEpG7n6yH5cMBzn7FjYXfMqEpq0YX7rGJL/suNrr51rCDiWQ3FWDraquBERERFxOjXnRERERETMlJOD6769bK/dmPGrP2ZF0xvIc/eiwdE0JsfPplvKetK9/IkN68S77fuTFNiIAps7nsWFhGakEpGWRN/tCQxZ/zWrmrRjYtQIfvcPwqcwn/Gr57G9djDNU/dAbq6mtoqIiFwG1JwTERERETHTrl1YDIM3Ov+dN5a+zsSVc9lUP4xnV7xPpqcvY/o9zrKwzhRbT332bVP9MBZF9GTSzcOISVzD+NXziPtgFJO6D6X1wZ0EFOQwqfsw3vvqZUhOhtatq/7+RERExFRqzomIiIiImKmwEIC9Nesz6eZhTI2dwd1b4vk8IorJ3YeS5+51zhDFVhuLw7uxoukNTFw5l6lxMwEY32sMqQH1yl1HRERELm1qzomIiIiImMndHQDP4j+bZ9MjBzGz04BKh8pz92JCzBgO+NbmsR/mA+BRUlTuOiIiInJpU3NORERERMRMISEYFgs3pG7l4bVf8HlE1Hk15k42o9MA6men89yK93ir010YFguWkBCTEhYREZHq5FLdCYiIiIjI5ef555/HYrGU+2revLnj/LFjxxg5ciS1atXCx8eH/v37c+jQoXIxUlNT6dOnD15eXtSpU4dx48ZRUlJSbsyqVato27Yt7u7uhISE8NFHH52Sy6xZs2jcuDEeHh60b9+en3/+2Sn37ODjQ0lIM+7Z+A2Znr5M7j70wmNaLLzUfSiZnjW4Z+MySkLDtBmEiIjIZULNORERERFximuuuYaDBw86vn744QfHuUcffZSvv/6aRYsWsXr1ag4cOMBtt93mOF9aWkqfPn0oKipi7dq1zJs3j48++ohnn33WMSYlJYU+ffpw0003sWnTJh555BGGDh1KXFycY8yCBQsYO3Yszz33HBs2bKBVq1ZER0dz+PBhp9677YbraJh9mGld763QGnMVkevuxbSug2mYfRjb9e1MiSkiIiLVT805EREREXEKV1dXgoKCHF+BgYEAZGVl8c9//pPXX3+d7t27065dOz788EPWrl3Ljz/+CEB8fDzbtm3j008/pXXr1sTExPDiiy8ya9YsiorK1lybM2cOTZo04bXXXqNFixaMGjWK22+/nTfeeMORw+uvv86wYcMYMmQI4eHhzJkzBy8vLz744AOn33+mRw2WhXU2NWZss85ketQwNaaIiIhUr2ptzl3R0x1ERERELnNJSUnUr1+fq6++moEDB5KamgrA+vXrKS4upkePHo6xzZs3p1GjRqxbtw6AdevW0bJlS+rWresYEx0dTXZ2Nr/99ptjzMkxTow5EaOoqIj169eXG+Pi4kKPHj0cY06nsLCQ7Ozscl+VVfzz/1jSIpJiq63Srz2bIldbWdxf1psaV0RERKpPtT85dyVPdxARERG5XLVv356PPvqI2NhYZs+eTUpKCpGRkeTk5JCWloabmxv+/v7lXlO3bl3S0tIASEtLK9eYO3H+xLmzjcnOzqagoICMjAxKS0tPO+ZEjNOZMmUKfn5+jq+GDRtW7uZzcnBN3smWoNDKva6CtgSF4JqUCLm5TokvIiIiVavam3NX+nQHERERkctRTEwMd9xxBxEREURHR/PNN99w9OhRFi5cWN2pndOECRPIyspyfO3bt69yAXbtwmIYJAU2ckp+SYHBWAwDkpOdEl9ERESqVrU3567k6Q4iIiIiVwp/f3+aNWtGcnIyQUFBFBUVcfTo0XJjDh06RFBQEABBQUGnLGdy4udzjfH19cXT05PAwECsVutpx5yIcTru7u74+vqW+6qUwkIACmzu5xzqVVRA+KHdtD6QSPih3XgVFZzzNcdc3cpdR0RERC5trtV58RPTHcLCwjh48CAvvPACkZGR/Prrr1U23SEzM/OM0x127NhxxtynTJnCCy+8cF73LSIiInKlyc3NZdeuXdxzzz20a9cOm83GihUr6N+/PwCJiYmkpqbSsWNHADp27MhLL73E4cOHqVOnDgDLly/H19eX8PBwx5hvvvmm3HWWL1/uiOHm5ka7du1YsWIFt956KwB2u50VK1YwatQo592se1lTzrP49M2zkIxUBm5aRmTKRq4+sh8XDMc5OxZ217yKhCZtmN86huTTPH3nUVJU7joiIiJyaavW5lxMTIzj+4iICNq3b09wcDALFy7E09OzGjM7twkTJjB27FjHz9nZ2ZVfj0RERETkMvX444/Tr18/goODOXDgAM899xxWq5W7774bPz8/HnjgAcaOHUvNmjXx9fVl9OjRdOzYkQ4dOgAQFRVFeHg499xzD9OmTSMtLY2JEycycuRI3I83pYYPH87MmTMZP348999/PytXrmThwoUsXbrUkcfYsWMZPHgw1113HTfccANvvvkmeXl5DBkyxHk37+2NAYRmpLKpfpjjcIOjaUyOn023lPWke/kTG9aJd9v3JymwEQU2dzyLCwnNSCUiLYm+2xMYsv5rVjVpx8SoEfzu/+eTfqEZezEASw3t2ioiInI5qNbm3F+dPN2hZ8+ejukOJz8999fpDn/dVbWy0x2sVut5T3dw16eVIiIiIqf1+++/c/fdd/PHH39Qu3ZtunTpwo8//kjt2rUBeOONN3BxcaF///4UFhYSHR3NO++843i91WplyZIljBgxgo4dO+Lt7c3gwYOZNGmSY0yTJk1YunQpjz76KG+99RYNGjRg7ty5REdHO8bcddddpKen8+yzz5KWlkbr1q2JjY09ZdaEqZYsocTFSkRaEosiepblsTmOZ1e8T6anL2P6Pc6ysM6n3cl1U/0wFkX0ZNLNw4hJXMP41fOI+2AUk24exoJWZfcVkZZMiYsV25Il8PDDzrsPERERqRIXVXPuipruICIiInIZ++KLL8563sPDg1mzZjFr1qwzjgkODj6ljvurbt26sXHjxrOOGTVqVNXWdcuXY7OXEpO4hkk3D+PBn/7NuIRP+Dwiisndh5Ln7uUY6lVUQOPMg7iVFlNktbEnoB75bp4UW20sDu/GiqY3MHHlXKbGziAw7yjv33AbMYlrsNlLIT5ezTkREZHLQLU2567o6Q4iIiIicnlKTwcgMD+LSfGzuXtLPNMjBzGz0wCg8mvOTYgZwwHf2oxL+ISrsg8TmJ9VNvjw4Sq/NRERETFftTbnrujpDiIiIiJyeapZEwPY6xfEHVu/5fOIKGZ2GnBBa87N6DSA+tnp3LllOXv86hKcdQhLzZrVfaciIiJiAothGMa5h8m5ZGdn4+fnR1ZWFr6+vk65RuMnl557kIhUyp5X+lR3CiJyhauKGkIuTKX/jMaNo/j1Nyh2cSXLw4ceQ2fTd0eCY825aV3vPeOacyfYSosda84FFOQw6eZhLG0eybdzh+N7LA+bvQTb2Efh1VdNvFMRERExU0VriItqzTkRERERkUteRARWeyk2eylP3jSa+9Z/fcY1587kTGvOvXzT/bz99XTsAK1aOf1WRERExPlcqjsBEREREZHLyoEDAGR61MCnMJ9xCZ8wPXIQE2LGVKgxd7I8dy8mxIzhtS4DGZfwCT6FBWR61Cg7uX+/2ZmLiIhINVBzTkRERETETKWllLhYWXV1O57+7gPHmnMXYkanAXweEcXT3/2TVVe3o8TFFex2kxIWERGR6qTmnIiIiIiImbZuxWYvpXn6HjI9fZncfeiFx7RYeKn7UDI9a9A8PQWbvQS2br3wuCIiIlLt1JwTERERETHT779jAVqk72Fa13srPZX1THLdvZjWdTAt0vdiAdi3z5S4IiIiUr3UnBMRERERMZNhAJDh5ceysM6mho5t1pkML79y1xEREZFLm5pzIiIiIiJmCgig2MXKsrDOFFttpoYucrWVxXWxQkCAqbFFRESkeqg5JyIiIiJipqZNcbWXsiUo1CnhtwSF4GovhVDnxBcREZGqpeaciIiIiIiZDh7EAiQFNnJK+KTA4LI15/bvd0p8ERERqVpqzomIiIiImMlqBaDA5u6U8Mdc3cpdR0RERC5tas6JiIiIiJjp+EYNnsWFTgnvUVJU9o3F4pT4IiIiUrXUnBMRERERMVNpKQYQmpHqlPChGXsxAIqLnRJfREREqpaacyIiIiIiZsrOptjFSkRa0jmHehUVEH5oN60PJBJ+aDdeRQXnfE1EWjLFLq6Qk2NGtiIiIlLNXKs7ARERERGRy0pRETZ7KX23JzDp5mEUW23lTodkpDJw0zIiUzZy9ZH9uJQ9BweAHQu7a15FQpM2zG8dQ/JfNpVwKymm7/YEbPYSKHTOtFkRERGpWmrOiYiIiIiY6ZprKPluFQHHcohJXMPi8G4ANDiaxuT42XRLWU+6lz+xYZ14t31/kgIbUWBzx7O4kNCMVCLSkui7PYEh679mVZN2TIwawe/+QQD02rmGgGM5FGPBds011XiTIiIiYhY150REREREzNSuHS4YlFhcmPDdh6xoegN9dyTw7Ir3yfT0ZUy/x1kW1vmUJ+oANtUPY1FETybdPIyYxDWMXz2PuA9GMenmYSxtHslT331IicUFF8MO7dpVw82JiIiI2dScExERERExU5cuuAA5ru7Uycvk88+fIuJQMp9HRDG5+1Dy3L3OGaLYamNxeDdWNL2BiSvnMjV2BgM3LqN2XiZ5ru7UKC6AyEjn34uIiIg4nZpzIiIiIiJmys2lxMXKf6+9iRaHdnPdgR1MjxzEzE4DKh0qz92LCTFjOOBbm8d+mM8v9Vuwo24TBmyOw6YNIURERC4L2q1VRERERMRMR47gai9ln28dWqTv4YuIqPNqzJ1sRqcBfBERRXh6Cvt8a+NqL4XMTJMSFhERkeqk5pyIiIiIiJlSU7EA0Uk/kunpy4vdh154TIuFyd2HkulZg+ikn7AA7N174XFFRESk2qk5JyIiIiJipvR0ANod2MG0rvdWaI25ish192Ja18G0O7Cj7EBGhilxRUREpHqpOSciIiIiYqZffwUgw8uPZWGdTQ0d26wzGV5+5a4jIiIilzY150REREREzJSRQbGLlWVhnSm22kwNXeRqK4vrYtWTcyIiIpcJNedERERERMyUm4urvZQtQaFOCb8lKKRsQ4jcXKfEFxERkaql5pyIiIiIiJmysrAASYGNnBI+KTC4bEOIo0edEl9ERESqlppzIiIiIiJm8vQEoMDm7pTwx1zdyl1HRERELm1qzomIiIiImCkwEADP4kKnhPcoKSr7pnZtp8QXERGRqqXmnIiIiIiImbp0wQBCM1KdEj40Yy8GQGdzd4IVERGR6qHmnIiIiIiImZo2pdjFSkRaklPCR6Qll+3WGhLilPgiIiJStdScExEREREx04YNuNlL6bs9AVtpsamh3UqK6bs9ATd7KWzYYGpsERERqR5qzomIiIiImCk1FQMIOJZDTOIaU0P32rmGgGM5ZdNaU50zbVZERESqlppzIiIiIiJmKiigxOJCicWFCd99iHdhvilhfQrzeeq7Dx2xyTcnroiIiFQvNedERERERMwUFISrYcfVsBNwLIeJK+deeEzD4OmVc/E/luOITVDQhccVERGRaqfmnIiIiIiImQoLsWPhiKcvk7oP5e4t8Yxa+8X5xzMMRq/9gru3xDOp+zCOePpixwKFheblLCIiItXGtboTEBERERG5rPz2G3YXF5Y278JnbXoTUJDDuIRPuCrrMJO7DyXP3avCoXwK83l65Vzu3hLPtBvv5bM2MbRIT2HA5jis27Y58SZERESkqqg5JyIiIiJipowMXO2lbAkKBWBWp7vI8Pbn2RXvE7lnI9O6DmZZWGeKrbYzhnArKabXzjWMXz2PgIIcxvcaw8JWUQBsCQph0MZvID29Sm5HREREnEvNORERERERM7m4YAGSAhs5Di1oFc2a4FZMjp/N219PJ33FXGLDOrElKISkwGCOubrhUVJEaMZeItKS6ZW4ltr5R1nVpB0To0bwu/+f68slBQZjOX4dERERufSpOSciIiIiYqaWLWHnTgps7uUO/+4fxH13vkBIRioDNy2jy55NDNy4DBcMxxg7FnbVasCSFpF82ro3uwIbnhL+mKvbn9cRERGRS56acyIiIiIiZurRA778Es/i02/YkBzYiBd6/AMAr6ICGmcexK20mCKrjT0B9ch38zxreI+Soj+vIyIiIpc8NedERERERMzUsiUGEJqRyqb6YWcdmu/myba6V1cqfGjGXgzA0rr1eacoIiIiF49KL1Rx//33k5OTc8rxvLw87r//flOSEhEREZGqpzrPJP/5DyUuViLSkpwSPiItmRIXK3z5pVPii4iISNWqdHNu3rx5FBQUnHK8oKCAjz/+2JSkRERERKTqqc4zyc8/Y7OX0jtxDbbSYlNDu5UU03vHGmz2UvjpJ1Nji4iISPWo8LTW7OxsDMPAMAxycnLw8PBwnCstLeWbb76hTp06TklSRERERJxHdZ7JcnMBqJWfRUziGhaHdzMtdK+da6hVkFXuOiIiInJpq/CTc/7+/tSsWROLxUKzZs0ICAhwfAUGBnL//fczcuRIZ+YqIiIiIk7g7DrvlVdewWKx8MgjjziOHTt2jJEjR1KrVi18fHzo378/hw4dKve61NRU+vTpg5eXF3Xq1GHcuHGUlJSUG7Nq1Sratm2Lu7s7ISEhfPTRR6dcf9asWTRu3BgPDw/at2/Pzz//fN73UiGBgQCUWiw8sXoe3oX5ZxzqVVRA+KHdtD6QSPih3XgVnfrk4gk+hfk8ueojSi2WctcRERGRS1uFn5z77rvvMAyD7t278+WXX1KzZk3HOTc3N4KDg6lfv75TkhQRERER53FmnffLL7/w7rvvEhERUe74o48+ytKlS1m0aBF+fn6MGjWK2267jTVr1gBlT+z16dOHoKAg1q5dy8GDB7n33nux2Wy8/PLLAKSkpNCnTx+GDx/O/PnzWbFiBUOHDqVevXpER0cDsGDBAsaOHcucOXNo3749b775JtHR0SQmJjrvacDGjTGAbDdv/AtymLhyLhNixjhOh2SkMnDTMiJTNnL1kf24YDjO2bGwu+ZVJDRpw/zWMSQHNio7YRg8vXIufsdyy+IW5mJp3Ng5+YuIiEiVshiGYZx72J/27t1Lo0aNsJz4xE6Asukgfn5+ZGVl4evr65RrNH5yqVPiilzJ9rzSp7pTEJErXFXUEBVldp2Xm5tL27Zteeedd5g8eTKtW7fmzTffJCsri9q1a/PZZ59x++23A7Bjxw5atGjBunXr6NChA8uWLaNv374cOHCAunXrAjBnzhyeeOIJ0tPTcXNz44knnmDp0qX8+uuvjmsOGDCAo0ePEhsbC0D79u25/vrrmTlzJgB2u52GDRsyevRonnzyyQrdR6X/jAYOpOiLBSxoFc2vdZsyNXYG0yMH8VV4NybHz6ZbynrSvfyJDevElqBQkgIbUWBzx7O4kNCMVCLSkuiVuJba+UdZ1aQdE3sO5/+2reKxH+YzvtcYWh5K5q7N8bgNuBPmz6/MH4mIiIhUoYrWEJXeEGL79u2OTzShbJpA69at+fvf/05mZub5ZSsiIiIi1c7sOm/kyJH06dOHHj16lDu+fv16iouLyx1v3rw5jRo1Yt26dQCsW7eOli1bOhpzANHR0WRnZ/Pbb785xvw1dnR0tCNGUVER69evLzfGxcWFHj16OMacTmFhIdnZ2eW+KiU5GZu9lC1BoSxoFc2rkffweMKnrHx/OCF/7GNMv8fp9NCHPBP1EEubd6HIasOzuJAiq42lzbvwTNRDdHroQ8b0e5zQP1JZOXcEj/0wn2k33svCVlFsCQrBZi+B3bsrl5eIiIhclCrdnBs3bpyjQNm6dStjx46ld+/epKSkMHbsWNMTFBEREZGqYWad98UXX7BhwwamTJlyyrm0tDTc3Nzw9/cvd7xu3bqkpaU5xpzcmDtx/sS5s43Jzs6moKCAjIwMSktLTzvmRIzTmTJlCn5+fo6vhg0bVuymT0hJwQIkHZ+S2iF1KwD/vrY70ffPZFudq3nquw/49v3h/PrGnXzz0Ri++uQxvvloDL++cSffvj+cp777gG11ribq/ln8+9ruAHTcuwWApMBgLAC7dlUuLxEREbkoVbo5l5KSQnh4OABffvkl/fr14+WXX2bWrFksW7bsvBO54hYKFhEREbnImFXn7du3j4cffpj58+eX2/n1UjFhwgSysrIcX/v27atcgOO1aIHNnelLXidy7yamRw5iZsc7mfXfqXz7z4fouz2BdcERPBEzhlvveY3o+2dy6z2v8UTMGNYFR9B3ewLf/vMhZv13KjM73slrXQYSuXcTry59g2OubuWuIyIiIpe2Cm8IcYKbmxv5+WU7Tn377bfce++9ANSsWbPyj/wfd0UuFCwiIiJykTGrzlu/fj2HDx+mbdu2jmOlpaV8//33zJw5k7i4OIqKijh69Gi5p+cOHTpEUFAQAEFBQad8WHriQ9qTx/z1g9tDhw7h6+uLp6cnVqsVq9V62jEnYpyOu7s77u7uFb7fU3h7Q2Ymbfdt49Ztq/giIop07wDiPhhFpqcvY/o9zrKwzhRbbae8dFP9MBZF9GTSzcOISVzD+NXziPtgFJO6D+WLiCju2PotG+qF/XkdERERueRV+sm5Ll26MHbsWF588UV+/vln+vQpW0x9586dNGjQoNIJ5ObmMnDgQN5//30CAgIcx7OysvjnP//J66+/Tvfu3WnXrh0ffvgha9eu5ccffwQgPj6ebdu28emnn9K6dWtiYmJ48cUXmTVrFkVFRUDZwsFNmjThtddeo0WLFowaNYrbb7+dN954w3Gt119/nWHDhjFkyBDCw8OZM2cOXl5efPDBB2fM+4LXIhERERG5yJhV5918881s3bqVTZs2Ob6uu+46Bg4c6PjeZrOxYsUKx2sSExNJTU2lY8eOAHTs2JGtW7dy+PBhx5jly5fj6+vreLqvY8eO5WKcGHMihpubG+3atSs3xm63s2LFCscYp7j2WgxgwuqPSPcO4JBPTabGzmBxixuJvn8mi8O7nbYxd7Jiq43F4d2Ivn8Wi1vcyNS4mRz2qUm6dwATVn9Utr/rtdc67x5ERESkylS6OTdz5kxcXV3517/+xezZs7nqqqsAWLZsGb169ap0ApfqQsEXvBaJiIiIyEXGrDqvRo0aXHvtteW+vL29qVWrFtdeey1+fn488MADjB07lu+++47169czZMgQOnbsSIcOHQCIiooiPDyce+65h82bNxMXF8fEiRMZOXKk46m24cOHs3v3bsaPH8+OHTt45513WLhwIY8++qgjl7Fjx/L+++8zb948tm/fzogRI8jLy2PIkCEmvnN/cfXVFGPBtyifVVe34+G1XzA9chATYsaQ5+5VqVB57l5MiBnDa10GMmbtF6xseh2+RfkU4wJXX+2kGxAREbkC5ObCpk3w009l/83NrbZUKj2ttVGjRixZsuSU4yc/iVZRJxYK/uWXX045V1ULBWdmZp5xoeAdO3acMfcJEyaUWxg5OztbDToRERG5pJlZ553LG2+8gYuLC/3796ewsJDo6Gjeeecdx3mr1cqSJUsYMWIEHTt2xNvbm8GDBzNp0iTHmCZNmrB06VIeffRR3nrrLRo0aMDcuXMdS5cA3HXXXaSnp/Pss8+SlpZG69atiY2NPaX2M1VaGm4YZLl5c8v27/k8IoqZnQZcUMgZnQZQPzudv21bTZabN35FefCX6boiIiJyDtu2wZw5FMfG45q8E4thOE4ZFgslIc2w9YqC4cPh+JP6VaHSzTkoWzPkq6++Yvv27QBcc8013HLLLVit1grHOLFQ8PLlyy/JhYIveC0SERERkYuQGXXe6axatarczx4eHsyaNYtZs2ad8TXBwcF88803Z43brVs3Nm7ceNYxo0aNYtSoURXO9YLt2UORi5VsD28swOTuQy88psXCS92HErlnIwYWPEsKcUtJufC4IiIiV4KUFEqHj8AaH0emTwBLQjuypVcvkgIbUWBzx7O4kNCMVCLSkuj74acEzJhBaVQ01jmzoUkTp6dX6eZccnIyvXv3Zv/+/YSFlS1GO2XKFBo2bMjSpUtp2rRpheJc6gsFi4iIiFxuzKrzrniGgc1eSsPsw4zp9/hZp7J6FRXQOPMgbqXFFFlt7AmoR76b52nH5rp7Ma3rYN7+enrZmnMnfdovIiIiZzB3LqUPP8Jhdx+mVGJTpqcSPqb2tS2xvvUmDDXhg7azqHRzbsyYMTRt2pQff/yRmjVrAvDHH38waNAgxowZw9KlSysU58RCwScbMmQIzZs354knnqBhw4aOhYL79+8PnH6h4JdeeonDhw87dlU93ULBf/3E9UwLBd96663AnwsFV+knrCIiIiLVzKw674pnt2MBMrz8WBbW+ZTTIRmpDNy0jMiUjVx9ZD8u/Nlks2Nhd82rSGjShvmtY0gObFTutbHNOpPh9T6B+VlqzomIiJzLSy/BxIksiojixe5DK7T264lNmVY0vYFnVs5lwLBhZUtJPP2009KsdHNu9erV5Qo2gFq1avHKK6/QufOpxceZnFgo+GQnLxQMOBYKrlmzJr6+vowePfqMCwVPmzaNtLS00y4UPHPmTMaPH8/999/PypUrWbhwYbnicuzYsQwePJjrrruOG264gTfffNP5CwWLiIiIXGTMqvOueP7+FLtYT/lkvsHRNCbHz6ZbynrSvfyJDevEu+37n35KzfYEhqz/mlVN2jExagS/+5fN6ChytbEsrDMDNsdh+8vazCIiInKSuXNh4kSmRw46r7Vf89y9eDJmDPt9a/PYxIkQFAQPPOCERM+jOefu7k5OTs4px3Nzc3FzczMlqRMu64WCRURERC4yVVnnXda8vXG1l7IlKNRx6K7NcTy74n0yPX0ZU4kpNeNXzyPug1FMunkYC1qV1a9bgkIYtPEb8Krczq8iIiJXjJQUSh9+hEUmbcp0VXY6d4x5GGv37k5Zg67Szbm+ffvy4IMP8s9//pMbbrgBgJ9++onhw4dzyy23XFAyV9RCwSIiIiIXGWfWeVcUd3csQNLxKakj1y5gXMInfB4RxeTzmFIzceVcpsbOIDDvKLM63UVSYDAWgEtwUzUREZGqUDp8BIdtXrxo0qZMk7sPpduejdQePgJrXOyFx/wLl8q+4O2336Zp06Z07NgRDw8PPDw86Ny5MyEhIbz11lumJygiIiIiVUN1nklycwEosLlz1+Y4xiV8wvTIQUyIGVOhxtzJ8ty9mBAzhte6DGRcwifcuTmeY67Hn2I8zVOOIiIiV7xt27DGxzHlxsGV/r17JrnuXrzcdTDW+Dg4vqO9mSr95Jy/vz///e9/SU5OZvvxhFq0aEFISIjpyYmIiIhI1VGdZ5LjzbngIwd4dsX7fG7SlJr62ek8t+I9Hu0ztuxgXt6FZioiInL5mTOHTE/f027KdCFim3Um0/M9AmbPhrffNjV2pZtzJ4SEhKhQExEREbkMqc67QCUlGMCjaz4j09OXySZNqXmp+1Ai92zk0TXzMQBLScmFxxUREbnMFC9ewpLmXU67tuuFKHK1saR5FwYsWYrN5OZcpae19u/fn6lTp55yfNq0adxxxx2mJCUiIiIiVU91nkmsVkosLrRI38O0rveaOqVmWtfBtEjfS4nFBVwqXcqLiIhc3nJycN27p9ymTGbaEhSCa0qK4yl5s1T6N/r3339P7969TzkeExPD999/b0pSIiIiIlL1VOeZxMMDq2En06OGc6bUeNTAati1IYSIiMhf7dqFBcOxKZPZyjZlMiA52dS4lW7O5ebm4ubmdspxm81Gdna2KUmJiIiISNVTnWcSi4USFytLWkQ6Z0pNi0hKXKxgsZgaW0RE5JJ35AhQtimTMzg2ZcrMNDVupZtzLVu2ZMGCBacc/+KLLwgPDzclKRERERGpeqrzTNKsGTZ7qVOn1NjspdCsmVPii4iIXLLS0wHwLC50SniPkqKybw4fNjVupTeEeOaZZ7jtttvYtWsX3bt3B2DFihV8/vnnLFq0yNTkRERERKTqqM4ziY8PFnDylBqgRg2nxBcREblk1amDAYRmpLKpfpjp4UMz9pZtylSnjqlxK92c69evH1999RUvv/wy//rXv/D09CQiIoJvv/2Wrl27mpqciIiIiFQd1XkmOXgQqIIpNcevIyIiIscFBFDiYiUiLYlFET1NDx+RlkyJixVbQICpcSvdnAPo06cPffr0MTUREREREal+qvNMsHEjUAVTajZscEp8ERGRS1ZICK72Unrv+IFJNw8zde1Xt5Jieu/4AVd7KYSEmBYXzmPNOREREREROYuSEseUGmc4MaWGkhKnxBcREblk+fhQUu8qahVkE5O4xtTQvXauoVZBNiVXNQAfH1Njn9eTcyIicvFq/OTS6k5B5LKz5xU9SSaVUKsWJbt2O39KTa1apscWERG51Nlq1+LY4cOMX/0xK5reQJ671wXH9CnMZ/zqeRyz2vCoVdOELMvTk3MiIiIiIma65hpc7aX03Z6ArbTY1NBuJcX03Z5QNqXm2mtNjS0iInI5KD6ajUdpMYH5R5m4cu6FBzQMnl45l8D8LDxKiynOyrnwmH+h5pyIiIiIiJl8fbEDAcdynDKlJuBYDvbj1xEREZGT5OTgum8v22s3Jt/Vnbu3xDNq7RfnH88wGL32C+7eEk++qxvbawfjmroHcnNNSxkuoDlXVFREYmIiJVrrQkREROSyojrvAuXkYHexss+3DuNXf4x3Yb4pYU9MqdnnWwe7ixWys02JKyIictnYtQuLYfBG57/jXlrM5rohPJ7wKVOWvV3p38c+hflMiZ3BYz/MZ3NQCO6lJbzReSAWw4DkZFPTrnRzLj8/nwceeAAvLy+uueYaUlPLFrodPXo0r7zyiqnJiYiIiEjVUZ1nEsPA1V7KJ216E5iXyTMmTamZuHIugXlH+aRNTNm0VovlwuOKiIhcTgrLdkrfW7M+k24eRqtDycSFtueW7d8T98FIbtm26pxLTriVFHPLtlXEfjCy7HUh7WmVlswLNz9IakC9ctcxS6WbcxMmTGDz5s2sWrUKDw8Px/EePXqwYMECU5MTERERkaqjOs8kBw5gAfYE1MeCwQCTptQM2BKPBYM9AVdhAfj9d5MSFhERuUy4uwPgWVzIglbRJAS3JjrpJ1Y2vY6UgKt4++vprH1nCC/Gv8MdW+JpfSCR5odTaH0gkTu2xPNi/DusmT2Et7+eTkrAVaxseh3RyT/xfXBrFraKwqOkqNx1zFLp3Vq/+uorFixYQIcOHbCc9GndNddcw65du0xNTkRERESqjuo8k/zxBwBDf/mKdO+afHltdx5P+JSrsg4zufvQSu0a51OYz9Mr53L3lnje6jSA/r+uYNgvX5W7joiIiBwXEoJhsRCakUpY+h4i924iLrQ93Xf9j0zPGrzc7T4aZKXTMXULAzcuwwXD8VI7FnbVasCysM7s86vD4A1LCDiQQ1xIe6KTf+LOzfEYFjAsFiwhIaamXenmXHp6OnXq1DnleF5eXrkiTkREREQuLarzTFJQAMAN+7cxpt/jLA7vxgHf2jy74n0i92xkWtfBLAvrTLHVdsYQbiXF9Nq5hvGr5xFQkMP4XmNY2CqKXbUa8PbX08sGHTtWFXcjIiJy6fDxoSSkGZ32biYq6Uc+j4hiQswYGhxNY3L8bJ5a9RHpXv7EhnXio7Z9yHfzwm6x4GIYeBXl0zwjlZjENdTOP8qqJu2YGDWC3/3qMiV2Bs+teI/40A6UhIZh8/ExNe1KN+euu+46li5dyujRowEchdrcuXPp2LGjqcmJiIiISNVRnWcSmw0DOOLpy7KwzgAsaBXNmuBWTI6fzdtfTyd9xVxiwzqxJSiEpMBgjrm64VFSRGjGXiLSkumVuLb8Pwz8gwCIbdaZPzzfo2ZBNhbbmZt7IiIiVypbryhiZr9Lhpc/k7sPBeB3/yDuu/MFQjJSGbhpGV32bDrjk3NLWkTyaeve7Aps6Dj3UvehRO7ZSMzOtdhG/MP0nCvdnHv55ZeJiYlh27ZtlJSU8NZbb7Ft2zbWrl3L6tWrTU9QRERERKqG6jyTeHlR7GLlm+Zdyj0dd6H/MAAocrXxTfMu3LU5HjdPzyq7JRERkUtGjx54zJjBtK73nrKURHJgI17oUdZcC8w9QofUX/EsPkaBzYMfG11Lhk/N04bMdfdiWtfBZU+vR0WZnnKlm3NdunRh8+bNTJkyhZYtWxIfH0/btm1Zt24dLVu2ND1BEREREakaqvNMEhCAzV7KlqDQ054++R8GXkUFNM48iFtpMUVWG3sC6pHvdvam25agEAZt/AZq1TI9dRERkUvet9+SedLT6yc78QFZZMpGrj6y/5QPyHbXvIqEJm2Y3zqG5MBG5V4b26wzmZ7vERAfD337mppypZpzxcXF/OMf/+CZZ57h/fffNzUREREREak+qvNMZBhYgKS/FPWnk+/myba6V1cqfFJgcNluraWl55WeiIjI5aw4Np4lf3l6/cSac91S1jvWnHu3fX+SAhtRYHPHs7iQ0IxUItKS6Ls9gSHrvz5laYkiVxtLmndhQNxyzF5YwqUyg202G19++aXJKYiIiIhIdVOdZyIPDwAKbO5OCX/M1a3cdUREROS4nBxck3eWe3r9rs1xxH0wipA/9jGm3+N0euhDnol6iEURPdlUP4zE2o3ZVD+MRRE9eSbqITo99CFj+j1OyB+pxH0wirs2xzlibQkKwTUpEXJzTU27Us05gFtvvZWvvvrK1CREREREpPqpzjPJ8SfaPIsLnRLeo6So7Bu73SnxRURELlm7dmExDMfT6yPXLmBq7AwWt7iR6Ptnsji821l3SwcottpYHN6N6PtnsbjFjUyNncHItQuA40+vGwYkJ5uadqXXnAsNDWXSpEmsWbOGdu3a4e3tXe78mDFjTEtORERERKqO6jyTBAdjAKEZqWyqH2Z6+NCMvRiApdG5p82KiIhcUQrLPhgrsLlz1+Y4xiV8wvTIQczsNKDSofLcvZgQM4YDvrUZl/AJ6d4BbKkXWu46Zql0c+6f//wn/v7+rF+/nvXr15c7Z7FYVLSJiIiIXKJU55kkPJwSFysRaUksiuhpeviItGRKXKzYwsNNjy0iInJJcy9bUiL4yAGeXfE+n0dEnVdj7mQzOg2gfnY6z614j0f7jC13HbNUujmXkpJiagIiIiIicnFQnWeSOnWw2UvpsyOBSTcPO+f0mcpwKymmz44EbPZSCAoyLa6IiMhlISQEw2Lh0TWfkenpy+TuQy88psXCS92HErlnI4+umY9hsWAJCbnwuCep9JpzIiIiIiJyFgcPYgA1C3KISVxjauheO9dQsyAHA2D/flNji4iIXPJ8fChpGEyL9D1M63ovee5epoTNdfdiWtfBtEjfS0mjxuDjY0rcEyr95BzA77//zuLFi0lNTaWoqKjcuddff92UxERERESk6qnOM0FaGiUuVtJ8avHEqnmsaHqDKf848CnM58lVH7HPtw5BuX9gS0szIVkREZHLi83fl8zDNVgW1tnUuLHNOpPp8S4BfjVMjQvn0ZxbsWIFt9xyC1dffTU7duzg2muvZc+ePRiGQdu2bU1PUERERESqhuo8k7i6YrOX4nssD1ejlIkr5zIh5gLX6zMMnl45F79juZRYrGXTWl3P63N2ERGRy1pxVg5LWkSauqwEQJGrjSUtIhlwdAfmRj6Paa0TJkzg8ccfZ+vWrXh4ePDll1+yb98+unbtyh133GFyeiIiIiJSVVTnmaRDBwzAryiPxS1u5O4t8Yxa+8X5xzMMRq/9gru3xPPf8K74FeWVTWvt0MGkhEVERC4TOTm4pu5hS1DoOYd6FRUQfmg3rQ8kEn5oN15FBed8zZagEFz3pEBurhnZOlT647bt27fz+eefl73Y1ZWCggJ8fHyYNGkSf/vb3xgxYoSpCYqIiIhI1VCdZ5IOHShxsZLh5U+33et5q9MAHk/4lKuyDjO5+9BKTXH1Kczn6ZVzuXtLPG91GsBdW+I56FOLwPyj2NScExERKW/XLiyGQVJgo9OeDslIZeCmZUSmbOTqI/txKfu4CwA7FnbXvIqEJm2Y3zqG5NPESAoMxmIYkJwMrVublnalm3Pe3t6O9Ufq1avHrl27uOaaawDIyMgwLTERERERqVqq80zi44OLzRXP4kI8Sgqpm3uEJ3qN5tkV7xO5ZyPTug5mWVjns063cSspptfONYxfPY+AghzG9xpN2wOJ+BfkcMzVHRebzfTFqEVERC55hYUAFNjcyx1ucDSNyfGz6ZaynnQvf2LDOvFu+/4kBTaiwOaOZ3EhoRmpRKQl0Xd7AkPWf82qJu2YGDWC3/3/3B39mKtbueuYpdLNuQ4dOvDDDz/QokULevfuzWOPPcbWrVv597//TQd9eiciIiJyyVKdZx5r+/b4f/89n0dEcfeWeH73q0P0/TOZHD+bt7+eTvqKucSGdWJLUAhJgcEcc3XDo6SI0Iy9RKQl0ytxLbXzj5b9w6DncP5v2yoGbInns1bR/H1zHHTtWt23KCIicvEpKQHAs/jP5tldm+N4dsX7ZHr6Mqbf42f8gGxT/TAWRfRk0s3DiEks+4As7oNRTLp5GAtaRQPgUXJ8s6zSUlPTrnRz7vXXXyf3+NzaF154gdzcXBYsWEBoaKh28BIRERG5hKnOM1HDhpRaLNy4Z6NjWmuDrMOM/NsT1MvJYOCmZXTZs4mBG5edMqVmV60GLGkRyaete3OoRi0mrpzLgOPTWu/Y+i2lFgvWBg2q8eZEREQuUhYLBhCakcqm+mGMXLuAcQmf8HlEVIWXlii22lgc3o0VTW9g4sq5TI2dQWDeUWZ1uovQjL0YgMXktCvdnLv66qsd33t7ezNnzhxTExIRERGR6qE6zzzFP/+PTE8/6ub8QZ3j01qf+/Y9uu5ez5SbhvDyTfdTbLXhVVRA48yDuJUWU2S1sSegHvluno5prZ8umIjfsVzG9xpNmwOJ1Mk9wh+efgT8st70neJEREQueVYrJS5WItKScDHsjEv4hOmRg5jZaUClQ+W5ezEhZgwHfGszLuET0r0DaHkomRIXKzar1dS0K92c++WXX7Db7bRv377c8Z9++gmr1cp1111nWnIiIiIiUnVU55kkJwfX5J3MvXEw477/hLu3xLPfrw5RD8xyTGs98u27LG0eyY7ARuS7eWG3WHAxDNrs30bzjFT67EigZkFOuWmtd2+Jp9hiZe71f2PC9x+X7RSndedERET+5O6OzV5Kn+0J3PbrSj6PiDqvxtzJZnQaQP3sdJ5b8R6FVhs2eym4u5/7hZVQ6ebcyJEjGT9+/ClF2/79+5k6dSo//fSTacmJiIiISNVRnWeS4zvF/dyoJROjH2Jq7AweT/iUZul7sNrL1qipcSyPO7Ysx720uNzUGAMotNoc46z2UsZ/P49bdvwAwNPRI9lZ2zk7xYmIiFzyQkIwAL/CXNJqBDK5+9ALj2mx8FL3oXRN2UDd3D/KprWGhFx43JNUujm3bds22rZte8rxNm3asG3bNlOSEhEREZGqpzrPJCftFLegVTR9tycQuXcTfXasofD4Lm9HPX2PbwgRetqd4k5sCNFu/3bcjy8+/X1waxa2iqL54ZRy1xEREZHjfHwosbljKy5katfBFVpjriJy3b14pdt9vP31dIrd3LGZ/OR6pZtz7u7uHDp0qNyaJAAHDx7E1bXS4URERETkIqE6zyTHp7p4Fhdy1+Y4IvduAqDUxcoRLz+e7HpvhXeKe2L1PGrnZmK1l3Dj3k3cuTmenbWDy11HRERE/mQtLiTTowbLwjqbGje2WWcyPd7F71iOqXEBXCr7gqioKCZMmEBWVpbj2NGjR3nqqafo2bOnqcmJiIiISNUxs86bPXs2ERER+Pr64uvrS8eOHVm2bJnj/LFjxxg5ciS1atXCx8eH/v37c+jQoXIxUlNT6dOnD15eXtSpU4dx48ZRUlJSbsyqVato27Yt7u7uhISE8NFHH52Sy6xZs2jcuDEeHh60b9+en3/+uVL3UmnHp9TckLqVSctnOw5/eW13ou+fyeLwbqdtzJ3sxE5xUffP4struzuOv7j8HW5I3VK2v6vJU2pEREQueQcOUOpiZUmLyHP+rq2sIlcbS1pEUupihbQ0U2NXujk3ffp09u3bR3BwMDfddBM33XQTTZo0IS0tjddee83U5ERERESk6phZ5zVo0IBXXnmF9evX87///Y/u3bvzt7/9jd9++w2ARx99lK+//ppFixaxevVqDhw4wG233eZ4fWlpKX369KGoqIi1a9cyb948PvroI5599lnHmJSUFPr06cNNN93Epk2beOSRRxg6dChxcXGOMQsWLGDs2LE899xzbNiwgVatWhEdHc3hw4cv8N06C8OgxMXKfeu/xrW0bO246ZGDmBAzptLTa07sFPdal4EAuJaWct/6JZS4mLtLnIiIyGXhv//F1V7KlqBQp4TfEhSCq70UvvrK1LgWwzCMyr4oLy+P+fPns3nzZjw9PYmIiODuu+/GZrtyN3TPzs7Gz8+PrKwsfH19nXKNxk8udUpckSvZnlf6VHcKptP/K0TM58z/V1RFDVEZzqzzatasyauvvsrtt99O7dq1+eyzz7j99tsB2LFjBy1atGDdunV06NCBZcuW0bdvXw4cOEDdunUBmDNnDk888QTp6em4ubnxxBNPsHTpUn799VfHNQYMGMDRo0eJjY0FoH379lx//fXMnDkTALvdTsOGDRk9ejRPPvlkhfKu9J/Rpk3Qpg2lgGFxYVHLHkyIGVPxN+p0DIMpsTO4Y+u3WAw7VoCNG7UhhIiIyMmGD4d33+XWe15jU/0w08O3PpDIV588Bv/4B8yZc87xFa0hzmvxEG9vbx588MHzeamIiIiIXMScUeeVlpayaNEi8vLy6NixI+vXr6e4uJgePXo4xjRv3pxGjRo5mnPr1q2jZcuWjsYcQHR0NCNGjOC3336jTZs2rFu3rlyME2MeeeQRAIqKili/fj0TJkxwnHdxcaFHjx6sW7fujPkWFhZSeNJmC9nZ2ZW74ROvtVg4VKOWU3aKwzC0IYSIiMhfHX/+rMDmnHVZjx3f2InKP+d2VufVnEtKSuK7777j8OHD2O32cudOnmogIiIiIpcWM+u8rVu30rFjR44dO4aPjw//+c9/CA8PZ9OmTbi5ueHv719ufN26dUk7voZLWlpaucbcifMnzp1tTHZ2NgUFBWRmZlJaWnraMTt27Dhj3lOmTOGFF16o1L2W4+5OKWA1DKftFFcKWLUhhIiISHlt2gBlmzI5g8fxHdRPXMcslW7Ovf/++4wYMYLAwECCgoKwWCyOcxaLRc05ERERkUuU2XVeWFgYmzZtIisri3/9618MHjyY1atXm5226SZMmMDYsWMdP2dnZ9OwYcOKB6hbF7BwxNM5O8Ud8XwPv4IcCAoyNbaIiMgl79ZbMUaMIDQj1SnTWkMz9mIAlltvNTVupTeEmDx5Mi+99BJpaWls2rSJjRs3Or42bNhQqVhX9C5eIiIiIhcZM+s8ADc3N0JCQmjXrh1TpkyhVatWvPXWWwQFBVFUVMTRo0fLjT906BBBxxtOQUFBp9R9J34+1xhfX188PT0JDAzEarWedkzQWRpb7u7ujvr0xFel7N6N3cWFpc27OGWnuKXNu2B3cYHdu02NLSIicsnz9qbExUpEWpJTwkekJZdtyuTjY2rcSjfnMjMzueOOO0y5+BW9i5eIiIjIRcbMOu907HY7hYWFtGvXDpvNxooVKxznEhMTSU1NpWPHjgB07NiRrVu3lqvHli9fjq+vL+Hh4Y4xJ8c4MeZEDDc3N9q1a1dujN1uZ8WKFY4xTvHbb1WzU9zxmllERESO27ULm72UvtsTsJUWmxraraS4LK69FJKTTY1d6ebcHXfcQXx8vCkX79evH7179yY0NJRmzZrx0ksv4ePjw48//khWVhb//Oc/ef311+nevTvt2rXjww8/ZO3atfz4448AxMfHs23bNj799FNat25NTEwML774IrNmzaKoqGwe8Jw5c2jSpAmvvfYaLVq0YNSoUdx+++288cYbjjxef/11hg0bxpAhQwgPD2fOnDl4eXnxwQcfmHKfIiIiIpcCM+u8CRMm8P3337Nnzx62bt3KhAkTWLVqFQMHDsTPz48HHniAsWPH8t1337F+/XqGDBlCx44d6dChAwBRUVGEh4dzzz33sHnzZuLi4pg4cSIjR47E/fhaa8OHD2f37t2MHz+eHTt28M4777Bw4UIeffRRRx5jx47l/fffZ968eWzfvp0RI0aQl5fHkCFDTLnP00pOxgIkBTZySvikwGAsx68jIiIiJzlyBICAYznEJK4xNXSvnWsIOJZT9kNmpqmxK7Tm3Ntvv+34PiQkhGeeeYYff/yRli1bYrOVf1R/zJjz2yb+itvFS0REROQi4Kw67/Dhw9x7770cPHgQPz8/IiIiiIuLo2fPngC88cYbuLi40L9/fwoLC4mOjuadd95xvN5qtbJkyRJGjBhBx44d8fb2ZvDgwUyaNMkxpkmTJixdupRHH32Ut956iwYNGjB37lyio6MdY+666y7S09N59tlnSUtLo3Xr1sTGxp6ySYSpjk/XdfpOcSb/w0BEROSSl56OHSiy2hi/+mNWNL3BlI2ZfArzGb96HsesNtxKi3ExeaZlhZpzJz9lBuDj48Pq1atPWdDXYrFUujl3xe7iJSIiInIRcFad989//vOs5z08PJg1axazZs0645jg4GC++eabs8bp1q0bGzduPOuYUaNGMWrUqLOOMVWtWkAV7BR3/DoiIiJynI8PJS5WloV1JirpRyaunMuEmPN7iMzBMHh65VwCCnJYFtaZPjt+wK1GDXPyPa5CzbmUlBRTL3qyK3YXLxEREZGLgDPrvCtW8+YY4Pyd4po3Nz22iIjIJS01FZu9lLXBrfixUUumxs5gv18dZnYacH7xDIPRa7/g7i3xjO81BsMCt25bBfv2mZp2pdecmzRpEvn5+accLygoKDfNoKKu2F28RERERC4yZtd5V6zoaIqdvFNcsYsrnDR9V0RERIA9e05Z9/XxhE+ZsuxtvAtPrXHOxqcwnymxM3jsh/mOY451X03eMb3SzbkXXniB3NzcU47n5+ebMs3zitnFS0REROQi4+w674rh7Y3NXkrvHT84Zae43jt+wGYvAR8fU2OLiIhc8o4/4OV7LIdnV7zP5xFRPNFrNLds/564D0Zyy7ZV5/zd7FZSzC3bVhH7wUhu2f4943uN5vOIKJ5b8R5+BdnlrmOWCk1rPZlhGFgsllOOb968mZo1a1Yq1oQJE4iJiaFRo0bk5OTw2WefsWrVKuLi4srt4lWzZk18fX0ZPXr0GXfxmjZtGmlpaafdxWvmzJmMHz+e+++/n5UrV7Jw4UKWLl3qyGPs2LEMHjyY6667jhtuuIE333zT+bt4iYiIiFxkzKzzrmhbtmABahVkE5O4hsXh3UwL3WvnGmqd+IfBli3QqZNpsUVERC55x/cteHjNF2R6+jK5+1Dy3L1YE9yKyfGzefvr6aSvmEtsWCe2BIWQFBjMMVc3PEqKCM3YS0RaMr0S11I7/yirmrRjYtQIfvcP4pvmkUTu2ciYtQvKXccsFW7OBQQEYLFYsFgsNGvWrFzhVlpaSm5uLsOHD6/Uxa/oXbxERERELhLOqPOuaMenuvx8VbhTdor75apwrt+/DXbtUnNORETkZPXqYQDtDuxgTL/HHb9/f/cP4r47XyAkI5WBm5bRZc8mBm5chguG46V2LOyq1YAlLSL5tHVvdgX+ua9ArrsX07oO5u2vp5et+1qvnqlpV7g59+abb2IYBvfffz8vvPACfn5+jnNubm40bty40tNAr+hdvEREREQuEs6o865oBQUAzL3+Vt5Y+rrpO8VN6j6srDl37JgJyYqIiFxGvL2xYyHLswbLwjqfcjo5sBEv9PgHAF5FBTTOPIhbaTFFVht7AuqR7+Z5xtCxzTpzxPM9/ApysHp7m5p2hZtzgwcPBsqeROvUqRM2m83URERERESkeqjOM5m7OwbgV5jHpJuHmb5TnF9hbtmn9m5uZmYtIiJy6cvMxO7iwtLmXSi2nr2eyXfzZFvdqyscusjVxtLmXRiwOQ5rVtaFZlpOhZpz2dnZju/btGlDQUEBBcc/Efwr7VoqIiIiculQnecEgYGO3VqfiXqIwLyjjEv4hKuyDjvWvqkon8J8nl45l7u3xDPtxntZ2CqKF+PfodjFFbfatZ14EyIiIpegxERc7aVsCQp1SvgtQSEM2vgN7NhhatwKNef8/f1PuzjwyU4sIFxaWmpKYiIiIiLifKrznCA11bFb66SbhzGr011kePvz7Ir3idyzkWldB7MsrPNZP9F3Kymm1841jF89j4CCHMb3GsPCVlHld2tNTa3CmxIREbkEFBRgAZICGzklfFJgMBaA/HxT41aoOffdd9+ZelERERERuTioznOCgwdP2a11QavoC94pDv6yW+uBA9V3jyIiIhej4x8kFtjcnRL+mKtbueuYpULNua5du5p6URERERG5OKjOcwIXF+DU3VovdKe4U3ZrPX4dEREROe7662HhQjyLC50S3qOk6M/rmKjCG0L8VX5+PqmpqRQVFZU7HhERccFJiYiIiEj1UZ13gRo3Bs68W+t57RR3ut1aj19HREREjmvWDAMIzUhlU/0w08OHZuwt25QpzNzYlW7OpaenM2TIEJYtW3ba81qLREREROTSpDrPJL16VXi31grtFHem3Vp79XJK+iIiIpesAwccmzItiuhpeviItOSyTZn27zc1bqWfhX/kkUc4evQoP/30E56ensTGxjJv3jxCQ0NZvHixqcmJiIiISNVRnWcSu52S4/8wWNAqmlcj7+HxhE+ZsuxtvAsrt4C0T2E+U2Jn8NgP8x27tUakJVPiYnVS8iIiIpewnTsdmzLZSotNDV1uU6akJFNjV/rJuZUrV/Lf//6X6667DhcXF4KDg+nZsye+vr5MmTKFPn36mJqgiIiIiFQN1XkmSUjAZi8lJnGNY7fWJkf2c8fWb7lp93pevmlIhXdrfeq7D6mdl8m/ru3OOx3vxK2kmJjENdjspbB6Ndx1VxXemIiIyEVuz55TNmUyS7lNmfbsMS0unEdzLi8vjzp16gAQEBBAeno6zZo1o2XLlmzYsMHU5ERERESk6qjOM0lGBgCB+VnEJK6h4dFD3P7bShY370JAQW6ldmtNCG7Nzw1acPuvK0kJuIp9/nUJzM8qu84ff1TjTYqIiFyEvL2BUzdlulCnbMrkdeExT1bp5lxYWBiJiYk0btyYVq1a8e6779K4cWPmzJlDvXr1TE1ORERERKqO6jyT5OYCsL5+c55f/i41j+UwPXKQY82589mtNSnwc8YlfMIRDx/W129OuwM7ICen6u9NRETkYhYYCJx5U6aTXdCmTMevY5ZKN+cefvhhDh48CMBzzz1Hr169mD9/Pm5ubnz00UemJiciIiIiVUd1nkmaNsUAfmxwDa0O7uSLiKhym0Gcz26tMzoN4KrsdO7Y+i0/Ngin7YEdWEJCquqORERELg2urmfdlOnEB2SRKRu5+sj+Uz4g213zKhKatGF+6xiSAxudeVMm25mXpjivtCv7gkGDBjm+b9euHXv37mXHjh00atSIQJM7hyIiIiJSdVTnmSQ3lxIXK7fsSOCQTy1e7D70jEMrtFsrgMXC5O5DuTFlA7fs+IESFyu240/oiYiIyHGuro5NmZ6JeojAvKOMS/iEZul7CCjIJXLvJtK9/IkN68S77fuTFNiIAps7nsWFhGakEpGWRN/tCQxZ/zUJwa3J9PThlh0/ODZlejH+nbLfwVZzN2aqdHPuhKKiIlJSUmjatClt27Y1MycRERERqUaq8y5QcTE2eykNsw8zpt/jpqx1A5Dr7sUr3e7j7a+nlx0oKjIlroiIyGXD1fW0mzLdum0V6d4BjOn3+Bk3ZdpUP4xFET2ZdPMwYhJP2pTpmtNsymTyk3MulX1Bfn4+DzzwAF5eXlxzzTWkpqYCMHr0aF555RVTkxMRERGRqqM6zySZmWX/8ajBsrDOpoaObdaZTI8aZT9kZZkaW0RE5JJXqxYGf27KNHLtAm7/bSWLWvagx9DZLA7vdtbd0gGKrTYWh3fj5qGzWdiyB7f/tpKRaxfQa+caAvOzyibC1qxpatqVbs5NmDCBzZs3s2rVKjw8PBzHe/TowYIFC0xNTkRERESqjuo8k+zcSbGLlSUtIs/5D4DKKnK1lcV1scKOHabGFhERueS5uFDiYmWfbx2eX/4u4xI+YXrkICbEjKn0k+x57l5MiBnDa10GMi7hE55fPod9vnUocbGCydNaK92c++qrr5g5cyZdunTBYrE4jl9zzTXs2rXL1OREREREpOqozjPJ0aO42kvZEhTqlPBbgkJwtZfqyTkREZG/2r0bF7sdv2O5+BbmnbIp0/mY0WkAX0RE4VeYj9+xXFzsdti926SEy1R6zbn09HTq1KlzyvG8vLxyRZyIiIiIXFpU55nkyBEsQFJgI6eETwoMxnL8OiIiInKSffuwYuBdXHDOTZkqumP6iU2Zuu5eT528I1gx4PjSH2apdHPuuuuuY+nSpYwePfp4jmWF2ty5c+nYsaOpyYmIiIhI1VGdZxIfHwAKbO5OCX/M1a3smxo1nBJfRETkkuXujgFYDYNXut13ylTWkIxUBm5aRmTKRq4+sh+XshXkALBjYXfNq0ho0ob5rWNIPulDtlx3L6bcNIS3v56OAVjczf0dX+nm3Msvv0xMTAzbtm2jpKSEt956i23btrF27VpWr15tanIiIiIiUnVU55nLs7jQKXE9So7v0mq3OyW+iIjIJcvTEzsWsj18ym3K1OBoGpPjZ9MtZT3pXv7EhnXi3fb9SQpsRIHNHc/iQkIzUolIS6Lv9gSGrP+aVU3aMTFqBL/7BwEnNmV6F99juVi9zNmJ/YRKrznXpUsXNm3aRElJCS1btiQ+Pp46deqwbt062rVrZ2pyIiIiIlJ1VOeZxNUVAwjNMHfKywmhGXvLPue3mbvZhIiIyCWvRQvsLi7lNmW6a3MccR+MIuSPfYzp9zidHvqQZ6IeYlFETzbVDyOxdmM21Q9jUURPnol6iE4PfciYfo8T8kcqcR+M4q7NccCfmzLZXVygeXNT0670k3MATZs25f333zc1ERERERGpfqrzTFBSQomLlYi0JBZF9DQ9fERaMiUuVmzFxabHFhERuaT5+pbblGnk2gWMS/iEzyOimNx9aIV2bC222lgc3o0VTW9g4sq5TI2dQWDeUWZ1uostQSEM2vgN+PmZmnaFm3PZ2dkVGufr63veyYiIiIhI1VOdZzIvL2z2UmIS1zDp5mGOT+5PO7Sii1Ef51ZSTEziGmz2UjB5So2IiMglz83NsSnTXZvjGJfwCdMjB53Xjq157l5MiBnDAd/ajEv4hHTvAHbWPr4pk8lPr1e4Oefv73/WXboMw8BisVBaWmpKYiIiIiJSNVTnmcxqBSAwP4uYxDUsDu9W7vT5LkYN0GvnGgLzs8pdR0RERI47vimT77Ecnl3xPp9HRJ1XY+5kMzoNoH52Os+teI+H/vZE2UGTN2WqcHPuu+++c3xvGAa9e/dm7ty5XHXVVaYmJCIiIiJVS3We+Qwgx82LCd99yIqmN5Dn7nXBi1H7FObz1Hcfku3mRY2ifM7cThUREblCbdgAwMNrviDT05fJ3YdeeEyLhZe6DyVyz0bGrF3w53X697/w2MdVuDnXtWvXcj9brVY6dOjA1VdfbVoyIiIiIlL1VOeZzN2dEosLvkX5uNlLmLhyLpvqh/HsivfJ9PRlTL/HWRbW+bTTXU8sSD3p5mHEJK5h/Op5xH0wikndh9L64E78j+XgUVJEscUFm7t7NdyciIjIRezQIQyg3YEdjOn3eIXWmKuIXHcvpnUdzNtfT8cALGlppsQ9odK7tYqIiIiIyFlcdRWuhp1MjxpM6j6Uu7fEMzV2Botb3Ej0/TNZHN7trOvQwZ+LUUffP4vFLW5katxM7t4Sz6Tuw8j0qIGrYQc92SgiIlKeqyt2LBzx9GVZWGdTQ8c268wRT1/sWMD1vPZXPSM150REREREzNS1K8UuVpa0iKTUpWxduOmRg5gQM6bSn+CfWIz6tS4DASg5HrfYxRX+8sSjiIjIFa9NG+wuLixt3uWcH4RVVpGrjaXNu2B3cYE2bUyNfUHNubMtHCwiIiIily7VeRfAwwObvZR9vnVMXYz684gonlvxHvt8a2OzlzgWvRYREZHjOnTA1V7KlqBQp4TfEhSCq70UOnUyNW6Fn8O77bbbyv187Ngxhg8fjre3d7nj//73v83JTERERESqhOo8k82fjwWITvqRox41TF2MumvKBqKTfirbDGLePOjQ4cJji4iIXC42bcICJP1lt3OzJAUGl/0O3rABIiJMi1vh5pyfn1+5nwcNGmRaEiIiIiJSfVTnmWzXLsA5i1G/0u0+3v56ernriIiIyHGJiQAU2JyzadIxV7dy1zFLhZtzH374oakXFhEREZGLg+o8k2VmAnDEs4aTFqN+l5oFOY7riIiIyHHZ2QB4Fhc6JbxHSVG565hFG0KIiIiIiJipRg2KXawsbR7ppMWoIyl2sYKvr6mxRURELnmNG2MAoRmpTgkfmrEXA6BJE1PjqjknIiIiImKmo0erZjFqPTknIiJS3vEPyCLSkpwSPiItuWzH9Bo1TI2r5pyIiIiIiJlcXatmMWrXCq9QIyIicmUoLsbNXkrf7QnYSotNDe1WUkzf7Qm42UugqMjU2GrOiYiIiIiYyTCAKliMWkRERMo7fBiAgGM5xCSuMTV0r51rCDiWU/ZDerqpsdWcExERERExk90OVMFi1KWlTokvIiJyybJYADhmtTF+9cd4F+abEtanMJ/xq+dxzOS1ZE9Qc05ERERExEwFBVWzGPWxY06JLyIicsk6vubcsrDOBBRkM3Hl3AuPaRg8vXIuAQU5LAvr7JRNmdScExERERExWUkFF6P2Kiog/NBuWh9IJPzQbryKCs75moi0ZEpcrI7psyIiInLcoUO42ktZG9yKZc06cfeWeEat/eL84xkGo9d+wd1b4vkmrDNrgyPKNmU6dMi8nAGtIisiIiIiYqYWLXDdvp3eO35g0s3DKP7LFJiQjFQGblpGZMpGrj6yHxf+bLLZsbC75lUkNGnD/NYxJP9lUwm3kmJ67/ih7B8GLVpUye2IiIhcMn7/HQuQ5e5Nvx3fA/B4wqdclXWYyd2HkufuVeFQPoX5PL1yLndviQfglu2rWR7SvmxTpn37TE1bT86JiIiIiOmmTJnC9ddfT40aNahTpw633noriYmJ5cYcO3aMkSNHUqtWLXx8fOjfvz+H/vJJdGpqKn369MHLy4s6deowbtw4SkpKyo1ZtWoVbdu2xd3dnZCQED766KNT8pk1axaNGzfGw8OD9u3b8/PPP5t+zw7XX0+xxYVaBdnlFqNucDSNjxY+x7f/fIi+2xNYFxzBEzFjuPWe14i+fya33vMaT8SMYV1wBH23J/DtPx/io4XP0eBomiNGr51rqFWQTbHFCjfc4Lx7EBERuRSllf3OfGjdQqx2O59HRPFEr9Hcsv174j4YyS3bVp1zF1e3kmJu2baK2A9Gcsv27xnfazSfR0RhtdsZuW5h2SA9OSciIiIiF7vVq1czcuRIrr/+ekpKSnjqqaeIiopi27ZteHt7A/Doo4+ydOlSFi1ahJ+fH6NGjeK2225jzZqyhlZpaSl9+vQhKCiItWvXcvDgQe69915sNhsvv/wyACkpKfTp04fhw4czf/58VqxYwdChQ6lXrx7R0dEALFiwgLFjxzJnzhzat2/Pm2++SXR0NImJidSpU8cp928z7I7FqFc0vYG+OxJ4dsX7ZHr6Mqbf42Vr1pxmUelN9cNYFNGTSTcPIyZxDeNXzyPug1FMunkYS5tHOhajdi8tdix6LSIiIse5l+2U3vLwLtJqBDqellsT3IrJ8bN5++vppK+YS2xYJ7YEhZAUGMwxVzc8SooIzdhLRFoyvRLXUjv/KKuatGNi1Ah+9w/im+aRdE3ZwLWHd5Vdx83cndMthqHFKsyQnZ2Nn58fWVlZ+Jq8MOAJjZ9c6pS4IleyPa/0qe4UTKf/V4iYz5n/r6iKGuJikJ6eTp06dVi9ejU33ngjWVlZ1K5dm88++4zbb78dgB07dtCiRQvWrVtHhw4dWLZsGX379uXAgQPUrVsXgDlz5vDEE0+Qnp6Om5sbTzzxBEuXLuXXX391XGvAgAEcPXqU2NhYANq3b8/111/PzJkzAbDb7TRs2JDRo0fz5JNPnjP3Sv8ZDRgACxYAcMzVjcRajWh1KJnPI6IqPaXGuzCficen1GyuG0LYH6l/7tY6YAB8/nmFY4mIiFz2hgzB+OgjLMCYfo+zOLxbudMnlpbosmcTTf/4/ZSlJXbVasAPjVvzaeve7ApsWO61t2xbxdtfT8cALPfdBx9+eM50KlpD6Mk5EREREXG6rKwsAGrWrAnA+vXrKS4upkePHo4xzZs3p1GjRo7m3Lp162jZsqWjMQcQHR3NiBEj+O2332jTpg3r1q0rF+PEmEceeQSAoqIi1q9fz4QJExznXVxc6NGjB+vWrTttroWFhRQWFjp+zs7OrtzN7tgBwM9XhXPNoV20OpTM9MhBzOw0oHJxgDx3LybEjOGAb20e+2E++a7u/HJVONfv3+a4joiIiBwXHo4dC1meNVgW1vmU08mBjXihxz+Ask2ZGmcexK20mCKrjT0B9ch38zxj6NhmnTni+R5+BTlYr7nG1LS15pyIiIiIOJXdbueRRx6hc+fOXHvttQCkpaXh5uaGv79/ubF169Yl7fh6MWlpaeUacyfOnzh3tjHZ2dkUFBSQkZFBaWnpaceciPFXU6ZMwc/Pz/HVsGHD0447o7w8AFY1aYtHaTFfRESdV2PuZDM6DeCLiCjcS4v5rknbctcRERGR43x8sLu4sLR5l9MuH3GyfDdPttW9mk31w9hW9+qzNuYAilxtLG3eBbuLC/j4mJm1mnMiIiIi4lwjR47k119/5Ysv/r+9e4+Lskz/OP6ZGWY4iCCKgiQeSkPZBA+VqWmkBFpqbtZKWZZ56CDZZmprmaXZurl20lwt3VZTK7f9ba5aIoYnQtc2E8lEwjyguagkgpwH5vn9gc5KaB6aEcHv+/Wa1zbPc831XIOvHW6uee77/rimS7kgEydOJC8vz/k4eLE7shUWYgCPf/VPjtYL4JVeI359USYT03qN4Fi9Bjz+1T8rJ+GoOSciIlJVQQEejgrSgtu4JX1acOvKHdMLClyaV805EREREXGb+Ph4Vq1axfr162nWrJnzeHBwMGVlZZw4caJK/JEjRwgODnbG/Hz31tPPzxfj5+eHt7c3gYGBWCyWs8aczvFznp6e+Pn5VXlcrHJM+JUVMf32YRe1xtwvKfD04Y+3P4pfWRHlmEBLR4uIiFT1/feYgMzA5m5JnxnYAtOp67iSmnMiIiIi4nKGYRAfH8+nn37KunXraNWqVZXznTt3xmq1kpSU5DyWkZFBVlYWXbt2BaBr1658++23HD161Bmzdu1a/Pz8CA8Pd8acmeN0zOkcNpuNzp07V4lxOBwkJSU5Y1zOZsOKQa7X2de7+TUSru9Orld9rBgu3ylORESk1ispAaDY6ume9B6nfvcWF7s0b40256ZPn85NN91E/fr1adKkCQMHDiQjI6NKTElJCaNHj6ZRo0b4+voyaNCgat98ZmVlcdddd+Hj40OTJk0YP3485eXlVWI2bNhAp06d8PT0pHXr1ixcuLBaPXPmzKFly5Z4eXnRpUsXvvrqK5e/ZxEREZGrwejRo1myZAkffvgh9evXJzs7m+zsbIpPDWb9/f0ZPnw4Y8eOZf369Wzbto1hw4bRtWtXbrnlFgBiYmIIDw/noYceYseOHaxZs4ZJkyYxevRoPD0rB92PP/44e/fuZcKECezevZu//OUv/P3vf+eZZ55x1jJ27Fjmz5/PokWLSE9P54knnqCwsJBhw4a5583Xq0eZ2cKqdj3Ou97NxSrzsLKqXQ/KzB5Qr55Lc4uIiNR6p6abettLzxN4aZw7phcVuTRvjTbnNm7cyOjRo/n3v//N2rVrsdvtxMTEUHjG+hnPPPMMK1eu5JNPPmHjxo0cPnyYe+65x3m+oqKCu+66i7KyMjZv3syiRYtYuHAhkydPdsbs27ePu+66i9tvv53U1FR+//vfM2LECNasWeOMWbZsGWPHjuWll17im2++ITIyktjY2Crf1IqIiIjIhZk7dy55eXlERUXRtGlT52PZsmXOmDfffJN+/foxaNAgevbsSXBwMP/85z+d5y0WC6tWrcJisdC1a1cefPBBhg4dytSpU50xrVq14rPPPmPt2rVERkby+uuvs2DBAmJjY50xgwcPZubMmUyePJkOHTqQmppKQkJCtU0iXMbPD6ub17uxOsrB398t+UVERGqt7GwMoE1OllvSt8k5ULnu6+HDLs1rMowrZ7GKY8eO0aRJEzZu3EjPnj3Jy8ujcePGfPjhh9x7770A7N69m3bt2rFlyxZuueUWVq9eTb9+/Th8+LBzgDVv3jyee+45jh07hs1m47nnnuOzzz5j586dzmvFxcVx4sQJEhISAOjSpQs33XQT77zzDlA53SE0NJSnnnqKP/zhD+etPT8/H39/f/Ly8i5pXZIL0fIPn7klr8jVbP+f7qrpElxOnxUirufOz4rLMYaQX+ei/406d4ZvvmHgQ6+TGhLm8no6HM5g+eJnoVMn2LbN5flFRERqrQ4dsH+7k48jY3kx5kmXp38l8S/E7ViDtf0NkJp63vgLHUNcUWvO5eXlAdCwYUMAtm3bht1uJzo62hnTtm1bmjdvzpYtWwDYsmUL7du3r/LNZ2xsLPn5+Xz33XfOmDNznI45naOsrIxt27ZViTGbzURHRztjfq60tJT8/PwqDxERERGR09NN3b7ejaa1ioiIVNWgAVZHBX0zUrBW2F2a2lZur8zrqIAGDVya+4ppzjkcDn7/+9/TvXt3brjhBgCys7Ox2Ww0+NmbDgoKIjs72xnz8ykJp5+fLyY/P5/i4mJycnKoqKg4a8zpHD83ffp0/P39nY/Q0NBLe+MiIiIiUrfYK/8YcPt6N3bX/tEhIiJS63XvjgEEFuXRNyPFpan7fJ9CYFFe5bTW7q7d8OmKac6NHj2anTt38vHHH9d0KRdk4sSJ5OXlOR8HDx6s6ZJERERE5EpQVnZ51rspK3NLfhERkVorNBS72cJBvyZM2PgB9Upds3GDb2kREzYu4qBfE+xmD3DxDVpXRHMuPj6eVatWsX79epo1a+Y8HhwcTFlZGSdOnKgSf+TIEYKDg50xP9+99fTz88X4+fnh7e1NYGAgFovlrDGnc/ycp6cnfn5+VR4iIiIiIlRUYDdbiMjOdEv6iOw9lX8YVFS4Jb+IiEitVViI1VGBX0khAcX5TFq34NfnNAxeWLeAgOKT+JUUVm7KVJd2azUMg/j4eD799FPWrVtHq1atqpzv3LkzVquVpKQk57GMjAyysrLo2rUrAF27duXbb7+tsqvq2rVr8fPzIzw83BlzZo7TMadz2Gw2OnfuXCXG4XCQlJTkjBERERERuSBWKzZHBf3Sk92y3k2/9GRsjnKw2VyaW0REpNbbswcT4F9WyIp2Pbk/LZH4zb9ihqZh8NTmj7k/LZF/hd+Gf1khJoBM134BV6PNudGjR7NkyRI+/PBD6tevT3Z2NtnZ2RQXFwPg7+/P8OHDGTt2LOvXr2fbtm0MGzaMrl27cssttwAQExNDeHg4Dz30EDt27GDNmjVMmjSJ0aNH4+lZuQjv448/zt69e5kwYQK7d+/mL3/5C3//+9955plnnLWMHTuW+fPns2jRItLT03niiScoLCxk2LBhl/8HIyIiIiK1l7c3BhBQctIt690ElJysnNbq5eXS3CIiIrWejw8AJRYrUXu38Xa3OMYlL2H66lkXPcXVt7SI6QmzefbLpbzdLY5eP3xNicVa5TquUqPNublz55KXl0dUVBRNmzZ1PpYtW+aMefPNN+nXrx+DBg2iZ8+eBAcH889//tN53mKxsGrVKiwWC127duXBBx9k6NChTJ061RnTqlUrPvvsM9auXUtkZCSvv/46CxYsIDY21hkzePBgZs6cyeTJk+nQoQOpqakkJCRU2yRCREREROQXlZTgwESJxeqW9W5KLFYcmKCkxCV5RURE6oyAAABmdR1Mk8JcggqO84/f9OK+b78gacETDNi14bx3tdvK7QzYtYEvFjzB7779gn/8phdBBcdpXJjLrK6Dq1zHVTxcmu0iGYZx3hgvLy/mzJnDnDlzzhnTokULPv/881/MExUVxfbt238xJj4+nvj4+PPWJCIiIiJyTnl5OMxmVod1p29GCi+uW8Af+o75dTkNg0nrFhBYeILVYd3ptzsZy8mTrqlXRESkroiIwACO1W/E9qbXE5eWCMCKtrcSUFzArJUzOZa0gISwbqQFtyYzsAUlHja8ystok3OAiOw99MnYTOOiEyS36MBXzdpx73frAPhPSDuO1W+IAZgiI11ado0250RERERE6pzCQjwcFaQ3bsmdGV8Sl5bIIf8mvNMt7tLynVrvJi4tkVKLB+mNWzBw1wZQc05ERKSqwEDnpkyfRNzBjYd3M7PHg87fwa1zshiSuppb96cyZPtqzPzvpjEHJn5o1IxV7XqwpMOd/BBYuSNrZuBHPPvlUj6JuMO5KZOtUSOXlq3mnIiIiIiIK1mtmIDYzH9zrF5D/u+GXoxLXsI1eUeZ1msEhZ4Xvk6Nb2kRL6xbwP1pibzdLY5BO5OIzdxauRi11equdyAiIlI7lZRgdVTQb9cm7tm5jo8iYqp8ObYnsDlToh8DwKesmJa5/8VWYafMYmV/QFOKbN7VUs7uFkdI/jFeSnqPMrNH5W6tpaUuLVvNORERERERV6pXD4DOh3czpv84VoRHcdivMZOT5tNj/3Zm3PYwq8O6Y7ecu7lmK7fT5/sUJmxcREDxSSb0GcPfI2P4oVEzZq2cWeU6IiIickpWFibAr6yQ7PqBTOs14pyhRTZvdgVde/6cJhOv9hrBbfu+Iajgp8ovyA4ccFXFgJpzIiIiIiKudWqqS46PP6vDugOwLDKWlBaRTEuce1Hr3Wxo1ZlJMU9wqEEwAAnXdyfHZz6BRXnO64iIiMgp+fkAWAyD1257+BfvVr/QO+cACjx9+FPUI//7gszFS0uoOSciIiIi4koeHtjNlmp3xx1qEMwjv5tySevdnFbmYWV1WHfidqzB6qGhvIiISBWnmnM/nfEF2ZlO/w7usW871x7/sdrv4L0NryG5VUeWdujLnsDmVV6bcH13fvKeT6PiPMjLc2nZ+o0uIiIiIuJKRUV4OCpIC25z1tOXst7NmdKCW/Pg9s+huNjlpYuIiNRqnp7YzRY+/9kXZM1OZDMtcS5R+7ZxzKcBCWHdeLfLIDIDm1Ns9cTbXkqbnCwisjPpl57MsG0rq929XuZh5fO2p74g8/JyadlqzomIiIiIuJKHByYg82ffuJ/NBa93c4bMwBaV691YLJdUnoiISJ114kS1L8gG71jD5KT55Hr7Mab/uHOu+5oaEsYnEXcwtfdI+mZUrvu65v14pvYeybLIWOCML8hyc11atppzIiIiIiKuFBQEQLHV0y3pSzxsVa4jIiIipzgcVb4gG715GeOTF/NRRMwF75hut1hZER5F0nU3M2ndAl5LmE1g4QnmdBv8vy/IHA6Xlq3mnIiIiIiIK53aRdXbXuqW9F7lZVWuIyIiIqdUVACVX5AN3rGG8cmLmdnjQd7pFnfRqQo9fZjYdwyH/RozPnkxx+oFkNb01B15Lm7OmV2aTURERETkaufnhwG0yclyS/o2OQcql6/283NLfhERkVrLVnl3eYvjh5mcNJ+PImIuqTF3ptnd4vgoIoaXkt6jee5/Kw9aq0+L/TXUnBMRERERcSWHA7vZQkR2plvSR2TvwW62uPxbexERkVrPXLn/6jMpH5Lr7ce0XiN+fU6TiVd7jSDXuz7PpCyt/ILMxTumqzknIiIiIuJKLVtidVTQLz0Za4Xdpalt5fbKvI4KaNXKpblFRERqvbQ0yk1m2h3bz4zbhv7iGnM+ZcWEH9lLh8MZhB/Zi0/ZuXdBL/D0YcZtD9Pu2AHKTWbYscOlZWvNORERERERV2rcGAcQUHKSvhkprAiPclnqPt+nEFBykgrAEhjosrwiIiJ1QmEhVsNBrld9Vod1r3a6dU4WQ1JX02Pfdq49/iOV99lVcmBib8NrSG7VkaUd+rLnZ7uuJ1zfnVyvdwkoOQmFhS4tW805ERERERFXatwYMFFi8WDCxg9Iuu7mC9od7nx8S4uYsHERJRYr1opyaNLk19cqIiJSl9jtlJktrGrXA7vlf+vCNTuRzbTEuUTt28ZP3n583vZW3u0yiMzA5hRbPfG2l9ImJ4uI7EwG7NrIsG0r2dCqM5NinuBQg2AAyjysrGrXg8E7ErHZXXtnvKa1ioiIiIi4UlYWDrOZ1WHdCSjOZ9K6Bb8+p2HwwroFBBSfZHVYdxxmMxw8+OvzioiI1CUmE1ZHBWnBbZyHBu9YQ+JfR3N9zgHG9B/HLaMX8WLMk3zW9lbKLFa87aWUWax81vZWXox5kltGL2JM/3GEHdtP4l9HM3jHGmeutODWWB3lYDK5tGzdOSciIiIi4kr79+PhqGBzi0j+3bw9ryXM5kf/Jpe+W5xh8NTmj7k/LZEJfcZgmGDgrg2wb59LyxYREan1du7EBGSempI6evMyxicv5uOIGF7pNYKmJ3N4fv37FzStNXrEXF5ct4DXEmYTWHiCOd0GkxnYAtOp67iSmnMiIiIiIq6Ul+f8wyA1JIzAwhOMT17MNXlHmdZrxEVNcfUtLeKFdQu4Py2RGT2H8vfIGDoczqj8w+DECTe9ARERkVrqp58AKLZ6MnjHGsYnL2ZmjwdZHh7FnH+9RtS+bRzzaUBCWLdzTmvtl55cZVrrj36NGZ+8mGP1AkhreuqOvJwcl5at5pyIiIiIiCs1bgxU/mEAMKfbYHLqNWBy0nx67N/OjNseZnVY9ypr4fycrdxOn+9TmLBxEQHFJ5nQZwx/j4wBoMTDVuU6IiIicopReSdci+OHmbJ2Hh9HxHCsXgBr3o8n19uPMf3HnfN3cGpIGJ9E3MHU3iPpm1H5O3jN+/FM7TWCjyNimLp2LmP6j69yHVdRc05ERERExJVuuAEAb3up89CyyFhSWkQyLXEus1bO5FjSAhLCupEW3JrMwBaUeNjwKi+jTc4BIrL30CdjM42LTlRbjBrAq7ys8j/at7+sb0tERKQ2MIAJmz4g19uPI74NeS1hNh9FxFzw3et2i5UV4VEkXXczk9Yt4LU17zCrWxwnvOszYdMiDMC1K86pOSciIiIi4lolJRhAm5wsUkPCnIcPNQjmkd9NoXVOFkNSV3Pr/lSGbF9dbb2bHxo1Y1W7HizpcCc/BIZWS98m50DlHwbFxZfhzYiIiNQudpOZ1scP8VFEDE9v/piZPR68pHVfCz19mNh3DIf9GvPsl0v5MDKWB3asocxkwWZUuLRmNedERERERFypvJxys4WI7Ew+ibij2uk9gc2ZEv0YAD5lxbTM/S+2CjtlFiv7A5pSZPP+xfQR2XsoN1uwlpe7pXwREZHazGY4yLPVY0D6Jj6KiLn0DZlOmd0tjpD8Y9y9ayN5tnr4lxW6qNL/UXNORERERMSVfH2xOirom5HC1N4jf3FtuSKbN7uCrr3g1LZyO30zUrA6KsDX1xXVioiI1Cl2s4V8r3qYgGm9Rpwz7oK/IDOZeLXXCHrs346BCZ/yksrfwy6k5pyIiIiIiCvddhsGEFiUR9+MFFaER7ksdZ/vUwgsyquc1nrbbS7LKyIiUld4OCoIzT/KmP7jqq0xd3ppiR77tnPt8R+rLS2xt+E1JLfqyNIOfdkT2Nx5rsDThxm3PcyslTNx7VYQp2p2Q04RERERkatXcDB2iwdH6jVkwsYPSLru5gtagPp8fEuLmLBxEQf9mhBUeBxbcPD5XyQiInKVMQE5Pv6sDuvuPNbsRDbTEucStW8bx3wakBDWjXe7DCIzsDnFVk+87aW0yckiIjuTfunJDNu2stqmTAnXdyfHZz6BRXkur1nNORERERERVzp5EmtFOX4lhXgYFUxat4CJfcf8upyGwQvrFhBQfJJykwVrRTkUFGhqq4iIyM/YzRZWh3V3LisxeMcaJifNJ9fbjzH9x1U5d6bUkDA+ibiDqb1H0jcjhQkbF7Hm/Xim9h7JsshYyjysrA7rTtyONS6f1mp2aTYRERERkavdDz9gAvzLClnRrif3pyUSv/njS89nGDy1+WPuT0vkX+G34V9WiAlgzx4XFSwiIlJ3eDgqSAtuA8Dozct4LWE2K9r1JPbRd1gRHuVszPmUFRN+ZC8dDmcQfmQvPmWVu6DbLVZWhEcR++gcVrTryWsJsxm9eRkAacGt8XBxYw5055yIiIiIiGuVlgJw3Ks+UXu38Xa3OMYlL+GavKNM6zXioqa4+pYW8cK6Bdyflsjb3eIYnLaW4171aVhy0nkdERER+R8TkBnYnME71jA+eTEzezzo3LH1Ytecm9h3DIf9GjM+eTHH6gXwfeMWlV+QuZiacyIiIiIiruTpCcCfezzEtC/mEVRwnOf6PMXkpPn02L+dGbc9fM4pNafZyu30+b5ySk1A8Ukm9HmKToczaFyYy6Tox5m+9i/O64iIiEhVfiUnmZw0n48iYninW9yvWnNudrc4QvKP8VLSezx593NuqVfNORERERERV2rdGsNkotzDyqfhUcSlJXLIvwmxj77DtMS5zFo5k2NJC0gI60ZacGsyA1tQ4mHDq7yMNjkHiMjeQ5+MzTQuOlH5h8Edj/PbXRuIS0vkkxt6U+7hgWEyYWrduqbfqYiIyBXp6ZSPyfX2Y1qvES5Zc+7VXiPosX87Y05Nb3U1NedERERERFzJ15fy1tcTkZ3JuH5jCSo4zrjkJTTLO8rou5+j6ckchqSu5tb9qQzZvrralJofGjVjVbseLOlwJ0fqN2LSugXEpSWyqWVHxt/1DK8k/oXyNmFYtRmEiIhINQbQ+fBuxvQfxyPbVjI+eTEfRcRc8NISp9ecS7ruZiatW8BrCbMJLDzBjNseZtbKmRjg8qmtas6JiIiIiLiYtU8M/f62hKm9R/JQ3DRmrnqDe7/9gqi92/jj7cP44+2PYrdY8SkrpmXuf7FV2CmzWNkf0JQim7dzWuvSZZNoXJjLJzf0Zvxdz2Art9MvcwvWYQ/W9FsUERG5IjkwkeddH9/Somprzl2MQk+fKmvOPR8zmuPefvgXn8RyxhdrrqDmnIiIiIiIqz3+OAGzZ9M3I4UV4VGM6zeWZRF38Jd//YlZK2eSu/ZdVrXrUW1a6127k4nI3kO/9GQCSk5yzMefuLhX+U/z9gD0+T6FgIJceOKJGn6DIiIiV6YKs5mNrTrxwvr3nWvO/Rqn15x7Yf1fSWxzC3ftTsbi4h1b1ZwTEREREXG18HAqYmKZ+OVikq67mUJPH/7TvD03PbWUXplbGZe8hEE7k3hw++dVpsYYQLHVk/0NQhh71zOsb32z85xvaRHPJ39ARUwslnbtLvtbEhERqQ2sjgraHtvvXHPuVzOZnGvOtT22D6uLG3Og5pyIiIiIiFtY5s2lyQ3teXH9Av7QZ4zz+Lo2XVjXpgsAgQXHuSVrJ972EoqtXvy7+Q3k+DasnswwmLR+AY3LCrHMm3u53oKIiEitYwLaHdvPmP7jLmiNuQtR4OnjXHPOHdScExERERFxh1atsLz9FnEjR3LIr8lZp9Xk+DZkVXjPX85jGDy1+WPidiTCggXQqpWbChYREakbcnz8WR3W3aU5E67vTo7PfAKL8lyaF8Ds8owiIiIiIlJpxAiYNo1xyUv4U8Is6pUWXdTLfUuL+NOa2Tz75VJ49VUYPtxNhYqIiNQNdrOF1WHdsVusLs1b5mGtzGu2uDQvqDknIiIiIuJeL7wA8+dzX2YKSQvjGbBrA9YK+y++xFZuZ8CuDXyxMJ77MlMq75h7/vnLVLCIiEjt5eGoIC24jVtypwW3xkNrzomIiIiI1EIjRmDp3ZvGjz9RuVvr+r+yqk3Xaru1tsk5ULlba+YWAgpyKzd/mDdXU1lFREQukAnIDGzultyZgS2qbOTkKrpzTkRERERcbtOmTfTv35+QkBBMJhPLly+vct4wDCZPnkzTpk3x9vYmOjqazMzMKjHHjx9nyJAh+Pn50aBBA4YPH05BQUGVmLS0NHr06IGXlxehoaHMmDGjWi2ffPIJbdu2xcvLi/bt2/P555+7/P1ekFatsKxJgO++I2DYg8QV7mVGwmyWL36WhL89xfLFzzIjYTZxhXsJGPYg7NpVGa/GnIiIyEUptnq6JW+Jh80tedWcExERERGXKywsJDIykjlz5pz1/IwZM5g1axbz5s1j69at1KtXj9jYWEpKSpwxQ4YM4bvvvmPt2rWsWrWKTZs2MWrUKOf5/Px8YmJiaNGiBdu2bePPf/4zL7/8Mu+9954zZvPmzdx///0MHz6c7du3M3DgQAYOHMjOnTvd9+bPJzwcZs3CmpGOKT8ftm+Hf/8btm/HlJ+PNSMdZs2Cdu1qrkYREZFazNte6pa8XuVlbsmraa0iIiIi4nJ9+/alb9++Zz1nGAZvvfUWkyZN4u677wbggw8+ICgoiOXLlxMXF0d6ejoJCQn85z//4cYbbwRg9uzZ3HnnncycOZOQkBCWLl1KWVkZ77//Pjabjd/85jekpqbyxhtvOJt4b7/9Nn369GH8+PEAvPLKK6xdu5Z33nmHefPmXYafxHn4+kKHDjVdhYiISJ1hAG1yskgNCXN57jY5BzDA5VNbdeeciIiIiFxW+/btIzs7m+joaOcxf39/unTpwpYtWwDYsmULDRo0cDbmAKKjozGbzWzdutUZ07NnT2y2/00xiY2NJSMjg9zcXGfMmdc5HXP6OmdTWlpKfn5+lYeIiIjUDuVmCxHZmecPvAQR2Xso126tIiIiIlLbZWdnAxAUFFTleFBQkPNcdnY2TZo0qXLew8ODhg0bVok5W44zr3GumNPnz2b69On4+/s7H6GhoRf7FkVERKSGeDgq6JeefN6d0S+WrdxOv/Rkt+zWquaciIiIiMgZJk6cSF5envNx8ODBmi5JRERELlC5yUxAyUn6ZqS4NG+f71MIKDlJucn1rTQ150RERETksgoODgbgyJEjVY4fOXLEeS44OJijR49WOV9eXs7x48erxJwtx5nXOFfM6fNn4+npiZ+fX5WHiIiI1A5Ww0GJxcrE9X+jXmmRS3L6lhbx/Pr3KbFYsRoOl+Q8k5pzIiIiInJZtWrViuDgYJKSkpzH8vPz2bp1K127dgWga9eunDhxgm3btjlj1q1bh8PhoEuXLs6YTZs2Ybf/b9rK2rVrCQsLIyAgwBlz5nVOx5y+joiIiNQ9XhV2GhXl8eK6Bb8+mWEwad0CGhbl4+XiqbKnqTknIiIiIi5XUFBAamoqqampQOUmEKmpqWRlZWEymfj973/PtGnTWLFiBd9++y1Dhw4lJCSEgQMHAtCuXTv69OnDyJEj+eqrr0hJSSE+Pp64uDhCQkIAeOCBB7DZbAwfPpzvvvuOZcuW8fbbbzN27FhnHU8//TQJCQm8/vrr7N69m5dffpmvv/6a+Pj4y/0jERERkctkW0hbCmzexKUlEr/540tPZBg8tflj4tISKbB5sS2kreuKPEONNuc2bdpE//79CQkJwWQysXz58irnDcNg8uTJNG3aFG9vb6Kjo8nMrLrjxvHjxxkyZAh+fn40aNCA4cOHU1BQUCUmLS2NHj164OXlRWhoKDNmzKhWyyeffELbtm3x8vKiffv2fP755y5/vyIiIiJXi6+//pqOHTvSsWNHAMaOHUvHjh2ZPHkyABMmTOCpp55i1KhR3HTTTRQUFJCQkICXl5czx9KlS2nbti29e/fmzjvv5NZbb+W9995znvf39ycxMZF9+/bRuXNnnn32WSZPnsyoUaOcMd26dePDDz/kvffeIzIykn/84x8sX76cG2644TL9JERERORyMoA1bW7Bs8LOjqDWjEtewp9Wz7roKa6+pUX8KWE2z365lB3BrfGsKGdNmy4YbqjZww05L1hhYSGRkZE8+uij3HPPPdXOz5gxg1mzZrFo0SJatWrFiy++SGxsLLt27XIO3IYMGcJ///tf1q5di91uZ9iwYYwaNYoPP/wQqJwiERMTQ3R0NPPmzePbb7/l0UcfpUGDBs6B2+bNm7n//vuZPn06/fr148MPP2TgwIF88803GriJiIiIXIKoqCgM49zDV5PJxNSpU5k6deo5Yxo2bOgc051LREQEycnJvxhz3333cd999/1ywSIiIlInlJsthOYfZWrvkbyWMJs1bbowYNdGbtu7jem3D2N1WHfsFus5X28rt9Pn+xQmrv8b/iUFrGndhdg9W5nQZwztj+yh3GzB6uIdW03GL42aLiOTycSnn37qnMpgGAYhISE8++yzjBs3DoC8vDyCgoJYuHAhcXFxpKenEx4ezn/+8x9uvPFGABISErjzzjs5dOgQISEhzJ07lxdeeIHs7GxsNhsAf/jDH1i+fDm7d+8GYPDgwRQWFrJq1SpnPbfccgsdOnRg3rx5F1R/fn4+/v7+5OXluW3R4JZ/+MwteUWuZvv/dFdNl+By+qwQcT13flZcjjGE/Dr6NxIREaklTCYqMJHnXZ8uoxcxaus/GZ+8mBVtbyWguIAeB1I57u3HZ21vJS24NZmBLSjxsOFVXkabnANEZO/hrt1f0rA4n+QWHcj19mXA7i+Z0XMoC276Lf/+y8P4F5/EggEX0E670DFEjd4590v27dtHdnY20dHRzmP+/v506dKFLVu2EBcXx5YtW2jQoIGzMQcQHR2N2Wxm69at/Pa3v2XLli307NnT2ZgDiI2N5bXXXiM3N5eAgAC2bNlSZW2S0zE/n2Z7ptLSUkpLS53P8/PzXfCuRURERERERETkUlkwaFicT9+MFOZ0G0xOvQZMTppPrnd9/hj1CM3yjtE1K40h21djPmOSqgMTPzRqxqq2PTjo34SHv1lFp8MnmdBnDH+PjGHArg00LHZP7+eKbc5lZ2cDEBQUVOV4UFCQ81x2djZNmjSpct7Dw4OGDRtWiWnVqlW1HKfPBQQEkJ2d/YvXOZvp06czZcqUS3hnIiIiIiIiIiLiLhUmExPX/42k625mWWQsKS0imZY4l+c3LOSYTwMSwrqxsNNdFNl8cJhMmA0Dn7Ii2uZk0TcjhcZFJ9jQqjOTYp7gUINgfEuLeH79+1SYTFjcMAH1im3OXekmTpxY5W67/Px8QkNDa7AiEREREREREZGrm0HljNPGhblMWreAiX3HcKhBMI/8bgqtc7IYkrqaW/ennvvOuXY9WNLhTn4IPNXjMQxeWLeAwMITGEZlfpOLa75im3PBwcEAHDlyhKZNmzqPHzlyhA4dOjhjjh49WuV15eXlHD9+3Pn64OBgjhw5UiXm9PPzxZw+fzaenp54enpewjsTERERERERERF3KAesGHzU/g7uT0vkR/8mvNMtDoA9gc2ZEv0YAD5lxbTM/S+2CjtlFiv7A5pSZPOumswweGrzx9yflsiHkbE8sGMNduDc20lcGrOL87lMq1atCA4OJikpyXksPz+frVu30rVrVwC6du3KiRMn2LZtmzNm3bp1OBwOunTp4ozZtGkTdrvdGbN27VrCwsIICAhwxpx5ndMxp68jIiIiIiIiIiJXPg8g16s+k2Oe4M89HmJc8hKmr55FvdKiKnFFNm92BV1LakgYu4KurdaY8y0tYnrCbJ79cikzeg7l5ejHyfWq75a73Gq0OVdQUEBqaiqpqalA5SYQqampZGVlYTKZ+P3vf8+0adNYsWIF3377LUOHDiUkJMS5o2u7du3o06cPI0eO5KuvviIlJYX4+Hji4uIICQkB4IEHHsBmszF8+HC+++47li1bxttvv11lSurTTz9NQkICr7/+Ort37+bll1/m66+/Jj4+/nL/SERERERERERE5BLZzRZWteuB3WJlTrfBPNfnKQakb2LN+6MZsGsD1gr7L77eVm5nwK4NJLw/mgHpm5jQZwx/6fo7yjyslXnNrm/P1ei01q+//prbb7/d+fx0w+zhhx9m4cKFTJgwgcLCQkaNGsWJEye49dZbSUhIwMvLy/mapUuXEh8fT+/evTGbzQwaNIhZs2Y5z/v7+5OYmMjo0aPp3LkzgYGBTJ48mVGjRjljunXrxocffsikSZN4/vnnadOmDcuXL+eGG264DD8FERERERERERFxBaujgrTgNs7nZ24IMWvlTI4lLSAhrBtpwa3JDGxBiYcNr/Iy2uQcICJ7D30yNlfbEOK0tODWPLj9c5fXbDIMN2wzcRXKz8/H39+fvLw8/Pz83HKNln/4zC15Ra5m+/90V02X4HL6rBBxPXd+VlyOMYT8Ovo3EhERqSVMlVs1DHzodVJDwqqdPnNDiOt+OnTWDSG+bNmh6oYQZ+hwOIPli5+tfHIB7bQLHUNcsRtCiIiIiIiIiIiIXKxi69k38LzoDSF+psTD5vJaQc05ERERERERERGpQ7ztpeeNOb0hxMXwKi+71JJ+0RW7W6uIiIiIiIiIiMjFMIA2OVluyd0m5wDuWBtOzTkREREREREREakT7GYLEdmZbskdkb3HLbu1qjknIiIiIiIiIiJ1goejgn7pyVgr7C7Nayu30y89GQ9HuUvzgppzIiIiIiIiIiJSR5iAgJKT9M1IcWnePt+nEFByEpNLs1ZSc05EREREREREROoEB1BisTJh4wfUKy1ySU7f0iImbFxEicWKwyUZq1JzTkRERERERERE6gSH2cLqsO4EFOczad2CX5/QMHhh3QICik+yOqw7DrPl1+f8GTXnRERERERERESkTvBwVLC5RSSrr+/G/WmJxG/++NKTGQZPbf6Y+9MS+TysO5tbRODhqHBdsaeoOSciIiIiIiIiInWCCcjzrEff7zezI6g145KXMH31rIue4upbWsT0hNk8++VSdgS35s6MFPI8fbXmnIiIiIiIiIiIyC8Z8Z/l5Hr78cD9f+S5Pk8xIH0Ta94fzYBdG867i6ut3M6AXRtIeH80A9I3MaHPGIbE/ZFc7/qM/M9yt9Tr4ZasIiIiIiIiIiIiNeDmH3cxpv84Cj19WBYZS0qLSKYlzmXWypkcS1pAQlg30oJbkxnYghIPG17lZbTJOUBE9h76ZGymcdEJNrTqzKSYJzjUIBiAGbc9zKyVM91Sr5pzIiIiIiIiIiJSJxjAcW8/Vod1dx471CCYR343hdY5WQxJXc2t+1MZsn01ZgxnjAMTPzRqxqp2PVjS4U5+CAytkjfh+u785P0eDYvzXT61Vc05ERERERERERGpE+xmC5+3vRW7xVrt3J7A5kyJfgwAn7JiWub+F1uFnTKLlf0BTSmyeZ8zb5mHlc/b3srgHYnYHOUurVnNORERERERERERqROsjgrSgtucN67I5s2uoGsvKndacGse3P75pZZ2TmrOiYiIiIiIiIhInWACMgObnzfuYu+cA8gMbOGW3VrVnBMRERERERERkTqj2Op51uOn15zrsW871x4/hPmMcw5gb8NmJLfqyNIOfdlzlgZfiYfNLfWqOSciIiIiIiIiInWGt720yvNmJ7KZljiXqH3bKPKwUWL1pPIeO+OMKBMNi/MYvGMNw7atrLZbK4BXeZlb6lVzTkRERERERERE6gQDaJOTRWpIGACDd6zhpS/exTBVTkgttPmQENaNtOA2ZAY2p9jqibe9lDY5WURkZ9InYzM+5WXcdGgniX99kinRj7EsMhaANjkHMEC7tYqIiIiIiIiIiJxNudlCRHYmn0TcwejNyxifvJgyswdHfRsysc9QVod1P+tOrqkhYXwScQdTe4+kb0YKz21cROOCXF5LmE1g4QnmdBtMRPYeys0WrI4Kl9as5pyIiIiIiIiIiNQJHo4K+qUnk964JeOTFwPwfzf0YlqvERR6+pz39XaLlRXhUSRddzOT1i3g/rRExicvJtfbj37pyXi4uDEHas6JiIiIiIiIiEgdYQICSk4y5Yt3AZjZ40He6RZ30XkKPX2Y2HcMh/0a8+yXS5nyxTyX3zF3mvn8ISIiIiIiIiIiIrVDOSZMhsFHETGX1Jg70+xucXwUEYPJMCh3+WpzldScExERERERERGROsNkgqO+DZnWa4RLkr3aawTHfAMwuac3p+aciIiIiIiIiIjUDRWAxTD4U9QjF7TG3IUo8PRhetQwLIaBOya2qjknIiIiIiIiIiJ1hInjXvVZHdbdpVkTru9Orld9cMPUVjXnRERERERERESkTnCYzXzWrgd2i9Wlecs8rKxq1wOH2fWtNDXnRERERERERESkTvBwVJAW3MYtudOCW+Phhh1b1ZwTEREREREREZE6wQRkBjZ3S+7MwBZu2a9VzTkREREREREREakziq2ebslb4mFzS14150REREREREREpM7wtpe6Ja9XeZlb8qo5JyIiIiIiIiIidYIBtMnJckvuNjkHMNyQV805ERERERERERGpE8rNFiKyM92SOyJ7D+Vmi8vzqjknIiIiIiIiIiJ1goejgjt3f4m1wu7SvLZyO3fu/lK7tYqIiIiIiIiIiJyL3WSmUXE+fTNSXJq3z/cpNCrOx27SnXMiIiIiIiIiIiLV+fpiNRyUWKxM2PgB9UqLXJO2tIgJGxdRYrFiNSrA19cleU9Tc05ERERERERERGq/l14CwKvCTmDRCSatW/DrcxoGL6xbQGBRHl6np8qeuo6rqDknIiIiIleFOXPm0LJlS7y8vOjSpQtfffVVTZckIiIirjRuHHaTmXKTmRKLlfvTEonf/PGl5zMMntr8MfenJVJqsVJuMldOax03znU1o+aciIiIiFwFli1bxtixY3nppZf45ptviIyMJDY2lqNHj9Z0aSIiIuJCNsNBhWFQv6yYtKDWjEtewvTVsy56iqtvaRHTE2bz7JdLSQtujW9ZMRWGgc3QhhAiIiIiIhftjTfeYOTIkQwbNozw8HDmzZuHj48P77//fk2XJiIiIq50zTV4YmAyHEQc2cOaNl0YkL6JNe+PZsCuDefdxdVWbmfArg0kvD+68nWtuxCRvQeT4cATA0JDXV6yh8szioiIiIhcQcrKyti2bRsTJ050HjObzURHR7Nly5Zq8aWlpZSWljqf5+fnX5Y6RURExAUOHaLcbKHA5k2D0kJiM7eysu2tNCguYNbKmRxLWkBCWDfSgluTGdiCEg8bXuVltMk5QET2HvpkbKZx0QmSW3Rge4gv/Xd/CcBJz3rUKyvGIyvL5SWrOSciIiIidVpOTg4VFRUEBQVVOR4UFMTu3burxU+fPp0pU6ZcrvJERETExTza30D9b3eSFtSaiCN7uDNjM8fqBfDHqEdolneMrllpDNm+GjOG8zUOTPzQqBmrw7pz0L8Jw79eQePCXADSglvzmyN7sbS/wT31uiWriIiIiEgtNXHiRMaOHet8np+fT6gbprCIiIiIm+zYgcVkck5rjdqzjUaFuTy/YSG5XvVZ1a4HCzvdRZHNB4fJhNkw8Ckrom1OFv3TNxFQcrJyYwnMfNH6JmL3bHXmdQc150RERESkTgsMDMRisXDkyJEqx48cOUJwcHC1eE9PTzw9PS9XeSIiIuIOhgEmU5VprT0OpOJfcpLBOxKxOsoxnRkO2M0eeDjKAfh38whOeP9vWiuGUe0SrqINIURERESkTrPZbHTu3JmkpCTnMYfDQVJSEl27dq3BykRERMStTjXU7szYTJufsvhj1CMs6XgXBwKa8vNWmwEcCGjKko538WrUMNr8lMWdGZur5HEX3TknIiIiInXe2LFjefjhh7nxxhu5+eabeeuttygsLGTYsGE1XZqIiIi4k2FgiYwk8NudFzWttdxkrlxjzk1TWc+k5pyIiIiI1HmDBw/m2LFjTJ48mezsbDp06EBCQkK1TSJERESkDtqxo7IB1qwZAT/++IvTWm2OcggNdcuurOeiaa0iIiIiclWIj4/nwIEDlJaWsnXrVrp06VLTJYmIiMjldOgQGAa2CjumP/8ZGjUCX19o1AjTn/+MrcJeOYX1MjbmQM25aubMmUPLli3x8vKiS5cufPXVVzVdkoiIiIiIiIiIuNK4cZCTAydPVv7vuHE1Voqac2dYtmwZY8eO5aWXXuKbb74hMjKS2NhYjh49WtOliYiIiIiIiIhIHaTm3BneeOMNRo4cybBhwwgPD2fevHn4+Pjw/vvv13RpIiIiIiIiIiJSB2lDiFPKysrYtm0bEydOdB4zm81ER0ezZcuWavGlpaWUlpY6n+fl5QGQn5/vthodpUVuyy1ytXLn/2drij4rRFzPnZ8Vp3MbhuG2a8ivc/rfpi7+zhARERH3udBxnppzp+Tk5FBRUVFtx66goCB2795dLX769OlMmTKl2vHQ0FC31Sgiruf/Vk1XICK1weX4rDh58iT+/v7uv5BctJMnTwIa54mIiMilOd84T825SzRx4kTGjh3rfO5wODh+/DiNGjXCZDL9wiulrsvPzyc0NJSDBw/i5+dX0+WIyBVKnxVymmEYnDx5kpCQkJouRc4hJCSEgwcPUr9+fbeM8/R5ICIiUjPc/Tv4Qsd5as6dEhgYiMVi4ciRI1WOHzlyhODg4Grxnp6eeHp6VjnWoEEDd5YotYyfn58G2CJyXvqsEEB3zF3hzGYzzZo1c/t19HkgIiJSM9z5O/hCxnnaEOIUm81G586dSUpKch5zOBwkJSXRtWvXGqxMRERERERERETqKt05d4axY8fy8MMPc+ONN3LzzTfz1ltvUVhYyLBhw2q6NBERERERERERqYPUnDvD4MGDOXbsGJMnTyY7O5sOHTqQkJBQbZMIkV/i6enJSy+9VG3as4jImfRZISKn6fNARESkZlwpv4NNxvn2cxURERERERERERG30JpzIiIiIiIiIiIiNUTNORERERERERERkRqi5pyIiIiIiIiIiEgNUXNORERERERERESkhqg5JyIiIiIiIiIiUkPUnBMREREREREREakhas6JXIAPPviARo0aUVpaWuX4wIEDeeihhwD417/+RadOnfDy8uLaa69lypQplJeXA2AYBi+//DLNmzfH09OTkJAQxowZc9nfh4hcXlFRUYwZM4YJEybQsGFDgoODefnll53ns7KyuPvuu/H19cXPz4/f/e53HDlypOYKFhGX0vhBRESk5tWGMbmacyIX4L777qOiooIVK1Y4jx09epTPPvuMRx99lOTkZIYOHcrTTz/Nrl27ePfdd1m4cCGvvvoqAP/3f//Hm2++ybvvvktmZibLly+nffv2NfV2ROQyWrRoEfXq1WPr1q3MmDGDqVOnsnbtWhwOB3fffTfHjx9n48aNrF27lr179zJ48OCaLllEXETjBxERkSvDlT4mNxmGYVzWK4rUUk8++ST79+/n888/B+CNN95gzpw57NmzhzvuuIPevXszceJEZ/ySJUuYMGEChw8f5o033uDdd99l586dWK3WmnoLInKZRUVFUVFRQXJysvPYzTffTK9evejduzd9+/Zl3759hIaGArBr1y5+85vf8NVXX3HTTTfVVNki4kIaP4iIiNSs2jAm151zIhdo5MiRJCYm8uOPPwKwcOFCHnnkEUwmEzt27GDq1Kn4+vo6HyNHjuS///0vRUVF3HfffRQXF3PttdcycuRIPv30U+eUFRGp2yIiIqo8b9q0KUePHiU9PZ3Q0FDnIAAgPDycBg0akJ6efrnLFBE30fhBRESk5l3pY3KPy3YlkVquY8eOREZG8sEHHxATE8N3333HZ599BkBBQQFTpkzhnnvuqfY6Ly8vQkNDycjI4IsvvmDt2rU8+eST/PnPf2bjxo36Jlykjvv5/8dNJhMOh6OGqhGRy03jBxERkZp3pY/J1ZwTuQgjRozgrbfe4scffyQ6OtrZXe/UqRMZGRm0bt36nK/19vamf//+9O/fn9GjR9O2bVu+/fZbOnXqdLnKF5ErSLt27Th48CAHDx6scgv9iRMnCA8Pr+HqRMSVNH4QERG5Ml0pY3I150QuwgMPPMC4ceOYP38+H3zwgfP45MmT6devH82bN+fee+/FbDazY8cOdu7cybRp01i4cCEVFRV06dIFHx8flixZgre3Ny1atKjBdyMiNSk6Opr27dszZMgQ3nrrLcrLy3nyySe57bbbuPHGG2u6PBFxIY0fRERErkxXyphca86JXAR/f38GDRqEr68vAwcOdB6PjY1l1apVJCYmctNNN3HLLbfw5ptvOgfPDRo0YP78+XTv3p2IiAi++OILVq5cSaNGjWronYhITTOZTPzrX/8iICCAnj17Eh0dzbXXXsuyZctqujQRcTGNH0RERK5MV8qYXLu1ilyk3r1785vf/IZZs2bVdCkiIiJSS2j8ICIiIuei5pzIBcrNzWXDhg3ce++97Nq1i7CwsJouSURERK5wGj+IiIjI+WjNOZEL1LFjR3Jzc3nttdc0sBYREZELovGDiIiInI/unBMREREREREREakh2hBCRERERERERESkhqg5JyIiIiIiIiIiUkPUnBMREREREREREakhas6JiIiIiIiIiIjUEDXnREREREREREREaoiacyIil5HJZGL58uU1XYaIiIiIuJjGeSJyqdScE5Gr0rFjx3jiiSdo3rw5np6eBAcHExsbS0pKSk2XdtEOHDjA0KFDueaaa6hfvz4xMTHs3bu3pssSERERqREa54lIbaPmnIhclQYNGsT27dtZtGgR33//PStWrCAqKoqffvqppku7KGVlZXz99de0aNGCVatWkZKSQmFhIcOHD6/p0kRERERqhMZ5IlLbqDknIledEydOkJyczGuvvcbtt99OixYtuPnmm5k4cSIDBgwAKqclvPvuu/Tr1w8fHx/atWvHli1b2LNnD1FRUdSrV49u3brxww8/VMk9d+5crrvuOmw2G2FhYSxevPgXa3nppZdo2rQpaWlpAHz55Zf06NEDb29vQkNDGTNmDIWFhc74li1b8sorrzB06FD8/PwYNWoUgwYN4pVXXqFjx45EREQwaNAgDh486OKfmoiIiMiVT+M8EamN1JwTkauOr68vvr6+LF++nNLS0nPGnR4cpaam0rZtWx544AEee+wxJk6cyNdff41hGMTHxzvjP/30U55++mmeffZZdu7cyWOPPcawYcNYv359tdyGYfDUU0/xwQcfkJycTEREBD/88AN9+vRh0KBBpKWlsWzZMr788ssq1wCYOXMmkZGRbN++nRdffLHKuYMHD/Lmm2/y6KOP/sqfkoiIiEjto3GeiNRKhojIVegf//iHERAQYHh5eRndunUzJk6caOzYscN5HjAmTZrkfL5lyxYDMP761786j3300UeGl5eX83m3bt2MkSNHVrnOfffdZ9x5551V8n7yySfGAw88YLRr1844dOiQ89zw4cONUaNGVXl9cnKyYTabjeLiYsMwDKNFixbGwIEDz/qeDh48aDRv3tyIj4+/mB+FiIiISJ2icZ6I1Da6c05ErkqDBg3i8OHDrFixgj59+rBhwwY6derEwoULnTERERHO/w4KCgKgffv2VY6VlJSQn58PQHp6Ot27d69yne7du5Oenl7l2DPPPMPWrVvZtGkT11xzjfP4jh07WLhwofMbX19fX2JjY3E4HOzbt88Zd+ONN571PU2fPp1rrrmGWbNmXeRPQ0RERKTu0DhPRGobNedE5Krl5eXFHXfcwYsvvsjmzZt55JFHeOmll5znrVar879NJtM5jzkcjou67h133MGPP/7ImjVrqhwvKCjgscceIzU11fnYsWMHmZmZXHfddc64evXqnTXv4cOHuf766511iYiIiFytNM4TkdrEo6YLEBG5UoSHh7N8+fJLfn27du1ISUnh4Ycfdh5LSUkhPDy8StyAAQPo378/DzzwABaLhbi4OAA6derErl27aN269SVdf+bMmVgslkuuX0RERKSu0jhPRK5kas6JyFXnp59+4r777uPRRx8lIiKC+vXr8/XXXzNjxgzuvvvuS847fvx4fve739GxY0eio6NZuXIl//znP/niiy+qxf72t79l8eLFPPTQQ3h4eHDvvffy3HPPccsttxAfH8+IESOoV68eu3btYu3atbzzzjvnvf6UKVO45pprmD59+iW/BxEREZHaTOM8EamN1JwTkauOr68vXbp04c033+SHH37AbrcTGhrKyJEjef755y8578CBA3n77beZOXMmTz/9NK1ateJvf/sbUVFRZ42/9957cTgcPPTQQ5jNZu655x42btzICy+8QI8ePTAMg+uuu47Bgwdf0PWzsrIwm7VagYiIiFy9NM4TkdrIZBiGUdNFiIiIiIiIiIiIXI3UehcREREREREREakhas6JiIiIiIiIiIjUEDXnREREREREREREaoiacyIiIiIiIiIiIjVEzTkREREREREREZEaouaciIiIiIiIiIhIDVFzTkREREREREREpIaoOSciIiIiIiIiIlJD1JwTERERERERERGpIWrOiYiIiIiIiIiI1BA150RERERERERERGrI/wMoK49irF2XwAAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 1500x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# charges vs smoker bar chart\n",
        "fig, axs = plt.subplots(1, 2, figsize=(15, 5))\n",
        "axs[0].bar(data[\"smoker\"], data[\"charges\"])\n",
        "axs[0].set_xlabel(\"Smoker?\")\n",
        "axs[0].set_ylabel(\"Healthcare cost\")\n",
        "axs[0].set_title(\"Healthcare cost Vs. Smoker\")\n",
        "\n",
        "axs[1].scatter(data[\"smoker\"], data[\"charges\"], s=300, edgecolors='red')\n",
        "axs[1].set_xlabel(\"Smoker?\")\n",
        "axs[1].set_ylabel(\"Healthcare cost\")\n",
        "axs[1].set_title(\"Healthcare cost Vs. Smoker\")\n",
        "\n",
        "fig.show();"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yjK_idDlIE5m"
      },
      "source": [
        "Smokers seem to have a higher healthcare cost"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "zBXFnJuqCD8e",
        "outputId": "f3168800-f7d3-42be-d62d-b6eaa93d8330"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# region vs smoker\n",
        "reg = data[\"region\"].groupby(data[\"smoker\"])\n",
        "reg = reg.value_counts()\n",
        "\n",
        "plt.scatter(x=reg[\"no\"].index, y=reg[\"no\"].values)\n",
        "plt.scatter(x=reg[\"yes\"].index, y=reg[\"yes\"].values)\n",
        "plt.legend((\"Non-smokers\", \"Smokers\"), loc=\"center right\")\n",
        "plt.title(\"Smokers by region\")\n",
        "plt.xlabel(\"Region\")\n",
        "plt.ylabel(\"Number of smokers\");"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        },
        "id": "CbKjjsYgmrLt",
        "outputId": "4c0d8ca3-a6be-426c-9f44-8be771d2b2be"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# get all rows with smoker equals yes\n",
        "smokers = data[data[\"smoker\"] == \"yes\"]\n",
        "\n",
        "# get the region from the df and count values\n",
        "smokers_by_reg = smokers[\"region\"].value_counts()\n",
        "\n",
        "# create a pie chart\n",
        "plt.pie(\n",
        "    smokers_by_reg.values,\n",
        "    labels=smokers_by_reg.index,\n",
        "    shadow=True,\n",
        "    explode=(0.1,0,0,0),\n",
        "    autopct=\"%0.1f%%\",\n",
        ")\n",
        "plt.title(\"Number of Smokers by Region\");"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y4R5k4JZLfq5"
      },
      "source": [
        "Southeast region have the highest number of smokers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "0e2XXNacAldw",
        "outputId": "0fd4a9a8-2fc1-4832-9f69-883c09adeb25"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "mean male bmi is 30.700982008995503\n",
            "mean female bmi is 30.248919330289194 \n",
            "\n"
          ]
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1500x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# bmi vs sex\n",
        "bmi_sex = data[\"bmi\"].groupby(data[\"sex\"])\n",
        "male, female = bmi_sex.get_group(\"male\"), bmi_sex.get_group(\"female\")\n",
        "print(f'mean male bmi is {male.mean()}')\n",
        "print(f'mean female bmi is {female.mean()}', \"\\n\")\n",
        "\n",
        "fig, axs = plt.subplots(1, 2, figsize=(15, 5))\n",
        "\n",
        "axs[0].bar(data[\"sex\"], data[\"bmi\"])\n",
        "axs[0].set_xlabel(\"Sex\")\n",
        "axs[0].set_ylabel(\"BMI\")\n",
        "axs[0].set_title(\"BMI vs. Sex\")\n",
        "\n",
        "axs[1].scatter(data[\"sex\"], data[\"bmi\"], s=200, c=\"yellow\", edgecolors=\"green\")\n",
        "axs[1].set_xlabel(\"Sex\")\n",
        "axs[1].set_ylabel(\"BMI\")\n",
        "axs[1].set_title(\"BMI vs. Sex\");"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rd7FgD70MIiW"
      },
      "source": [
        "There does not seem to be significant difference in BMI distribution among males and females"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2bmAKdN1mR7o",
        "outputId": "66c28bda-5325-455d-b095-02d21c325d46"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Max:  373\n",
            "region\n",
            "northeast    337\n",
            "northwest    373\n",
            "southeast    367\n",
            "southwest    367\n",
            "Name: children, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# region with the most children\n",
        "children_by_reg = data.groupby(\"region\")[\"children\"].sum()\n",
        "print(\"Max: \", children_by_reg.max())\n",
        "\n",
        "# print the whole series\n",
        "print(children_by_reg)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FDBRwMo5GJN1"
      },
      "source": [
        "southeast region has the highest number of children"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "fbGZfyrgvP7z",
        "outputId": "6ea1698e-ac87-400c-88b3-492971e2a8f6"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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rxYReu2bzzBKfZrabqYLM4pW0HaUtVtWSAs81TiqhFzgD6f1bbGQEnrJ0TRX+2ncj0uP6qWybGaMLMs1NkiVnhh7qPgHz3C8kElaSG4/ROK1kqIAzcmAjSDdWVhLrpz740nLzQILZzdrK10ZSby20G0eSm/tyg00BaXyS3ASKIkEs1WyaSOKVRx4ZLYWRILp6YmjT2aTyWOM0EnqxmXEOsZHpQ2xk/EtXTwxjlrxqqiwJSMDOX8+ieszUG5Mx+d51hl/vK18nv3PH+bYz5Kqzt/76lU8tv5J/547zAehnJO2NyTjvd2+a3niK84LYvHgGQtkBqnECMG1b/aiDxm88sMpcbGX3sPwwAMn2Iwl1m3rxMfneN0370N6wtRTmBrHsqgmYNDr1cZCRD6zmWKHEYF7Uj0Z+/fK/0NDKvtmL5oex6c7pxHMDxON10x3Tsf2LJq6ZfSeWFmHq798ynA87a5xkXApGfhfwgfT+Ld6REXjGysq9phd+IH5jWFm5F9dNGUXcbvzCTSZrrRg9CACQFZAS/0+Dcl5ldb3p+wHaPvX6IpGwNrR2Y2ttIypGD6IaJ/r+3+pYM/T8xgMz35PIZ61u8rTojZNEGmwVy03t3QgEJN2bnpEPrOZYwWhekuPT3jdWdS2dKT4hidftXzRRx0tWQMLkEwdj8omDdT+vrK43nQ87a5xGSu7EehDQIzYyAs+obWjjepyCF5JLN+XEXktL3ZSqeimLdUIa7JSU2upYJ+z3UtrsZN9OzpHAGcRGRuAZpcURrscpeCG5dFNO7LW01E2pqpeyWCekwU5Jqa2OdcJ+L6XNTvbt5BwJnEHkkRF4xryKkbB6tByQ4sfRoEgujZqWYC5PZoFHn7Rt0BxPemw0P+yq36wgsTuaH68qzestBb1xWtkBxGOVt+8UObcVdmOLBFqfOBkvTvZN4yu314NAH7GREXhGVkDC7NPM3/ifP6WMOp9MVkBKqHq0FyPl33ddXM71BT11n1pI+zSzW0FpQ3lxcfa4qGF+DG2fV5493PLYu+eeqts/b7/1xmRs2n0Y973+Ge57fSc27TqcUnensroeL398IGG3Xq8ygLvnnoq755r7jRT1OIH4uxgvbN+PLTUN+PmssaYqluvOK9O1wY7vsgJSYk7MsBNbpBapY89qbpR/L5kzFltqGvDC9v2orK43rGmktKk+zixGrNaK3Nc3S6yarWXtGHlfRwRsCNWSwBOspKGseWSs+nAy/4ORbFRPtsrSjpIfA0iVcJtJXK187XYeGZbxGcltWXK4mKGWtlv5WO/cueNL8OJHB7n7jmdsGc0tANz29EcpPpYkYEHfWtQ7vzASBICUfDykvjBq02q+jc4164sGkUfGe4T8ug+xkfEfVjkaLptwPH57yenU38To4XSGWgWrMenl+TBrSy9viFm+D+WzH00eiQvKo4lxWtl164yTcdP5J7qW2ZdHwjM12hw4dtt+4KoJCAT0c+mQ2rLsqjNRlBfm7jvSzL4k7ejNrVmsSDDOk6P0/uMZJ2Pk4AiGDsxBY2sXFq2yzkdEkq9FD/V6WvPxQdy4Sn+9qPtiQWT29RaxkelDbGT8BUleECdylTgJzzHR5LCw6sdvvibJxcICSQ4cUqI2c9KkY/wCZHFn9o0US86i9T+dhm/+4S2mWFdy2pD2lW7zIYhDev8W78gIXIUkL4g690k6wHNMNDksrPrxm69JcrGwoIyDRz6ZupZOWzamY/wCZHFn9lhNPW7SuFtZuZc51pWcNn6LcYE3CPm1wFW8zD3hFDzHxGPcXueZ8bofP5BuY/UiTw5tfig7faXbfAjoEBsZgat4mXvCKXiOice4vc4z43U/fiDdxupFnhza/FB2+kq3+RDQITYyAldQ1yQqzguhsbXL8KXCqCo3g9Mv6/JoX8k7UdfcQTQmO22Zoe2HxS6WlxtJfRjPiZLjyOOlwtwgJCmunLHz0l9RJIhwdhYOtdD7X6E4L4i6lg5UVtcnfOvGC+dqtC8Gn1tWjEBAwuGjnRicF0ZMlvFeTQMAGRWjBuNsgrgLSIAs679wro0lq7YKc4M4edhARPNzmHwdzQ8T9UWz9vwGybpyS8zgd8TLvgLHYakSbSQb5i0D5tW+lfqCpoq30haQfNNQq5a0CiYjhYZZW9rjWeSmLFWFSZRFZgotpymMBNHU1s3FBiNpspPSXRYZemEkiCvOOgEP9VWi14sVRbVk9LlepXXtsXr9svh6OUFfPFRLXkFaFd7N9BJeIF72FfgC5SJD8lJftCAnRZapPa+uuQMLH9+G13YcdMQu1vZnjivBgqllhp8/tKGGuM2Z40rw4DUTEC1I/jo8WpCD5ddMwHKDz/Qu2GZt6d14jObpoI5fnJij66eW6Y6vpCAH108tQ0mB/UcEVn+vNvdtAAr6NiEKLH/oNrV1p2woeMWwHspGkTaXTlNbN/66oQYLppYZxsri2eVEsQQYx50WI18bkRfKSkllQBrj6QLJunL6+phuiG9kBI5BIukszgtiyUWnIpp/7GtRp2XDTrTvVJtGXxvTfqVs1Rap5LuEUmKrHi9JP+r29eztjcnYvKcei57YhqZ2+sR3xXlBbLpjOj6oacANT2xFqybhmtr+Yflh3H/5GTh8tBNDB+ZgYmkRttY2oq65Hb9+5VPTaudWOCELjsvb19lSb0Xzw9jwn+dja22jYWzRxJ7yiGvRKuP5Unz9h8vG4+YnPzSdV0V2rddfJjxmIbmODLNIEZBJknPS+7d4R0bgGCSSzobWbkTzc1AxehDxeWpJpfo8XnaxtO9Em1kByfBYs89oj6eRfKulrLTjJelHfY6evVkBCQFJYtrEAPF42/5FE7KzA4abGMX+upZOBCQJ3znj+MTvK0YPQmV1va1NjNK+nRjWI/5ukz0Jel1LJ7bWNpraRBN7WQEJgYD5fCm+/vzQEct5VWTXRrHBy5deQXIdsZpjJ2LL74iNjMAxWOW/TsuGnWjfb1JnGmhtYvULLx/Z9aHdeeU5h35si3eMkrZHKsf24xrihR/jIR0QGxmBY7DKf52WDTvRvt+kzjTQ2sTqF14+sutDu/PKcw792BbvGCVtj1SO7cc1xAs/xkM6IDYyGYBfnw2zypJ5ypnt2BWLyXhh+34MHZiDM4YXYtV7tahtaMPwogjGDBuIhvauhL/dkjqzYhYjNJLvAeEsbNz1Nc4dWYzivJDhIxb1eNXjLM4LoqHV+F0J7Tl27TVqH7CWCJcY2HLG8ELTsbPYQoLVOo/L28O235HhlfqANuXCvIqReOSdGqZ1z8tWr6+hJNcR5R0ZI9l6OkvOWfH8Zd/9+/fjjjvuwKuvvoq2tjaceOKJ+Pvf/46zzjoLACDLMu666y48/PDDaGpqwuTJk/Hggw/ipJNOImo/01/29bsEj1Ua6bSk0krirMhCSVBXD3ZS6swKqZSTpXifHtpCjrTSe71zjOwFyGS7LBJhRZKsreBsVQWbxRYrSNe53aKcyzmlPrCTcoF23Tthq5fXUBIfAOTXmnQmLYpGNjY24swzz8S0adOwcOFCDBkyBLt27cLo0aMxevRoAMDvfvc7LF26FI899hjKysqwZMkSfPLJJ6iqqkJOjvVXZ5m8kTG6+fgtmFkvFF7kkSmKBNFIKV1lvQmbLTwJfOaPJkZIbz5WaDd2JBcYq3Ps2muWB4c254oZ2k1OUSQIGfbyyNCu89d2HMRtT3+ENpOXmbUURoK495LTTCtSk15XaDbFRpsx0nXvlK1eX0NFHpk4abGRufPOO7Fp0yZs3LhR93NZlnHcccfh9ttvx09+8hMAQHNzM4YNG4YVK1bgyiuvtOwjUzcyfqtsbAXrV7duZvYdPCCM25/ezvTVvLb6L0+pM+t4WWJE/Rjo8JFO/O+bn6Olg/yGWJwXxObFM5AVkCz7Ls4L4RdzxiYepwH0ku6unhgmLV1n+phnUF4IlYunI5SdmjaLh2RZQZF2b/+iKWnuAfbMvqxzaDWmotxsXDWpFBKAilGDMWn0IC6pD1hTLui1Q5LV1klbvb6Gisy+aSK/fvHFF3HhhRfi+9//PtavX4/jjz8eN954I+bPnw8AqKmpQV1dHWbMmJE4p6CgAOeeey4qKyt1NzKdnZ3o7Dy2gFtaWpwfiAc4LVHmDas00mlJpbr9yup65hua1t88pc6s42eJEa0/aDYxQFzevLW2MWG/Wd/1rV2IFuQm9Udr79baRst3VepbuwwlxTwkywqKtFuvHzfnkGRMje09OO/EISl22b2usKZc0EKy7p221etrKIkPMkFyzgNPM/vu2bMn8b7L66+/joULF+I//uM/8NhjjwEA6urqAADDhg1LOm/YsGGJz7QsXboUBQUFiZ/hw4c7OwiPSGe5r1/hWXmaVx92bLIbI3bk7Sx9u3UOye9Z8ao9Xj5xy59urDWr48Q1NHPwdCMTi8UwYcIE/Pa3v8WZZ56JBQsWYP78+Vi+fDlzm4sXL0Zzc3Pi54svvuBosX9IZ7mvX+FZeZpXH3ZsshsjduTtLH27dQ7J71nxqj1ePnHLn26sNavjxDU0c/D00VJJSQnKy5ML6Y0dOxb//Oc/AQDRaBQAcOjQIZSUHHt56dChQzjjjDN02wyHwwiHw84Y7COclij3F7TvyETzwzjU0smk3inOC2JiaZHh5109MezY34xIMAtt3daPbLTyX7VMe+jAHEBCIn2+3rNxEpnygHA2enpj6I3JuufTyHlp5c2D8kJJ/mKJaRYpvfq9FStpMCm81xuLZF2Bxo8s0vJofjjFn6TSeJ5+stsXyfqwWtMCf+Dpy75XXXUVvvjii6SXfW+99Va89957ePfddxMv+/7kJz/B7bffDiD+zsvQoUP7/cu+QGZWfXUTvbf+7VY+NlINLF1ThYc31hDLdhXVEkAmXzZTdZDIcdWqFZbz1TbTqLNIZdVW8lszG7VSer2K1Hbgvd5o1WN6ldVJJbzafkik5ZIEqO8aPOaQFbt9kcj4M00JlE6kRfXrW2+9FZs3b8Zvf/tb7N69G6tWrcJDDz2ERYsWAQAkScKPf/xj/OY3v8GLL76ITz75BD/4wQ9w3HHH4bvf/a6XpvuCTKv66iZG1WNpq/Fq0as+u3RNFf66gXwTU9I3fwCIK4cbVb39cF8jUZ9Nbd24Qef8meNKsPyaCYmbvxkLppalVCU2qwgOpFbVdiKmtRsWvYrUZmjHrhWF8FxvNNXiFfQqq1v5EdCPLZIY1f7pq409N69LdvsiqdLdXytKpxOeJ8R7+eWXsXjxYuzatQtlZWW47bbbEqol4FhCvIceeghNTU0477zz8MADD+Dkk08maj+Tv5FRyHQJHm9IZZf3XTYeh1s7UzL7Hl+Yi7+u34OGNvOstu/ccT56YzLGLHnV9AYhAbhz1ikYOjCHWIps1W9WQEJXT8yyby1Gkm8SmbP2XDtSc9KYpumDBbMq2LzXG+tYzGTCen4E6GOLxQY3r0t2+7KKb6+l2P2VtJBfA8BFF12Eiy66yPBzSZLwq1/9Cr/61a9ctCq9EBI8Okhll4FAcuXj66aMAhCXCRttYtTnb6lpQNWBZsuNhAwgOxDA9yackPidlRTZqt+K0YOwsnIvdQZaI7kpicyZpdq10bmkMU3TBwtmVbB5wzoWM5mwnh9ZYovFBjevS3b7sopvr6XYAnM8fbQkEHiBm7JN0oq+2uN4yK5J+zY6n8UeFhkw6/Gs57DgRj9uVfV2cizpKlX2g+8E7IiNjKDf4aZsk7Sir/Y4HrJr0r6Nzmexh0UGzHo86zksuNGPW1W9nRxLukqV/eA7ATtiIyPodyiyS6Mn3RKOSZ/tnj+vYmTKy6FaAhIwr2IkVR8kdpP0rcVo3Cw+oxmDmb/NYPETDVaxwBPWsdDa6ITP3PSTE9i9Jgi8RWxkBBlDb0xGZXU9Xti+H5XV9ejVvCCifP7yxwdw5dnDIQMpFy7l33ddXG74Ul9WQEoUODQ7vzcmY2XlXowpGWhq9/wpZSl1gNR9kKDuF4i/B/HqjoOYfRqdQmTJnLHYUtOg68Mrzx5hKFGV+z5XQzoGSWO30fzpYTYXvFBiQYmf57Z9iUc37sFzHx6z0yr2jFCft6WmAUvm0I1FG696dmh/pxzPS+Wht2ZY/aFg93xaSNe03svUbtop0Mdz1ZLT9AfVksC6EqxRzhiAvTKxWZ8f7mu0zBsTkOKbGG0OEKs+9FBXj9YeHwllob27N0U2q6YoEsTlZ52AFz86mDKeueNLUn5vZYdVNWMSu3nMxdzxJXjqgy8t5dbh7AC6emNJPlLPj9kYWOOI1uZIKAuh7IBhPzQxbtQHSf4kba4Z3lWZvazqTFt5O9OrT3tNWlS/dgOxkcl8jBKvKX87LZhahoc21Bh+/uMZJ2Pk4AiTbFNP9vn71z7FXzfUGJ5zaslAXDLhBMyrGKlbkdmoD6vMvmur6gz9IAO4bMIJiISzMLwogpOHDMD7+xoAxNUezW3dWLTKPHkdCUaJyPTGoEjNzezWa8sIvblYW1VHnNDPaCxG8UNyrpHtZjFr1I/y2a0zTsLIwXlJ8UqSfJDU7jmnl+Dlj41zpvzlyjMwaGCOrtTZai2SJqizGwt2IJFy+8HO/oDYyPQhNjKZDUnuDbNspbzzQ5DkbglIwM5fzyLaxJBCmhvHKNcIz7wiND61Y7cVPMYlIZ7JluWJgZHtduwyytfCa/6sxmsnjqzm0slY4Em62JkJpEVmX4HALiS5N8xuQur8EDwgyd0Sk+PH8YQ0N47eOHnnYqHxqR277bZNggy2TYxyrp7tduzSa5Pn/FmN104cWc2lk7HAk3Sxsz8hNjKCtIZXXgde7bDmjbGLnTwYTuXGIGnXyfwdfsn5obWDh1128vXwgFeuIZ7nu0W62NmfEBsZQVrDK68Dr3ZY88bYxU4eDKdyY5C062T+Dr/k/NDawcMuO/l6eMAr1xDP890iXezsT3heokBgH7dqmnhd00mvfyX/Q11zh+GLjlbvyAzLDyMmy3hh+37btXTmVYzEPWs+tXxHZl7FSK7+tPKD8txemwejNyYjFpNRkBtEczu/atB6fSn9qcc8sbTIcv4G5YUwsbRI9zMjH/bGZMRkGYW5QTTZGJedd2QAoDgvmLA98cJzczuK80JobO2ifjlXz7cka4CmfZJ3ZMxyDdHGIK/z9eiNydhcXY/KPYehvNg+adQgoppURmuRxN+FuUHE5Lj0ndc10uvrr58RL/umOW5JAL2WGpr1D8Qr+QLJig9F5REJZaGtqzelTeXzwkgwSYZqJS+1Qql2bcT1U8tw5ogi7v5UlBRAqh+AVCUFqbSbheU6qg0zufFDff4yuhiRyrpp5eJm2FEtqWG1SatgMlPEGM09DdrxatsiUeS8tuOgqUpMLy6059PEsBmv7TiIO5/9JEViXhgJ4t5LTrMlFyf1N69rpNfXX68QL/v2A5TFpL048i4771Y/rP0D8QtctCD5q1wlh4beJgaIb3AApFzotH+N0o5z8exyXD+1LCWrbkA6tolxwp8zx5Xo+iFakKO7idGzQUtRJIgLyocy2aPGbA4f2lCDBVPLUuzWHqf2jVF7B5s78NcNNVQbhpKCHFw/tQwlBn5bPLscD14zIeVzhaJIMBFretDadP3UMiwnnEcFo7kv1LGNdLw0/fOCJobNUDZUevmDmtq6cUNfLLFe24zs1MLjGun19TcdEN/IpCluSQC9lhrS9A8g8dXr4AFh3P70dtS1dBq2bfbIyawf0nF29cSwsnIvahvaUFocwbyKkcgKSI770+oraBq5bjQ/DACmftSiHQPpHL55+7cw+Xfr0NCq/yhIOW79T6fhm394i8s3SYPyQqhcPB2h7ACR35RHQw2tXSgeEEY0P35cb0zGpKXrLCuEk1CiE8+kjxL0xmDUDul4Sfvnea2w8xilNyZj8r3rLGO2pCAHsiwbHkdir/LoatGqbYaPMJ1MIZDpUm/S+7d4RyZNoZEA2ik771Y/vPpXbKisrre8kNG898AyzlB2ANdNGZX0u8rqesf9mRWQTM+lkevSbGAUtGMgncNV79UabmLUx62s3MvtcVh9axe21jaiYvQgS7+Zfb6lpoHLJgbQj2dSjGzU+52d8erB81pB27fWDpK4tYohEnuzAhICAcn0PSw7a9rr62+6IB4tpSluSQC9lhqy9u+1PXbPd1K66ZYsVOmHtD+/S9edbsPJ9tzAD7HtRPtW7fkhhUA6xgtPxEYmTXFLAui11JC1f6/tsXu+k9JNt2ShSj+k/flduu50G0625wZ+iG0n2rdqzw8pBNIxXngiHi2lKU5IFY36iebnoK7F/BmtU+Xt4/2HLZ9ja/snlWXLMpnKw8441c/7B+fF36s41GJPbqz3noY6Zb3Z+wU0ct3C3CAkSUZjWw/xeLW+Io3VeRUj8cg7NbaPo6EwEkRPbwybdh3G4dZO6vcxFHhKoKP5YVvrieY9GTvQSul5XiuS1tSAMCAjMX8TS4tMrxkKyjsyh1o6me0lkfnbGbdb1/l0R2xk0hSl7PzCx7cZyjT1ys7TsraqDh09+qofnv2Y9x+j7p/EP/OnxGWmZoX6rPqxQk82GQllmfZX39qFb/7hLceqSKt9YwVtDhY9X5HGaig7YPs4WpraujHvb1uSfscia1XGyFqgUk1HTwxrq+qY1EFOVHkn7UctpXfymmSVNqCkIAffOeM40xQIii0AmK+hJOkL7I7bret8uiNUS2mOk/kFrCrqFkWCWKrKx8Abq/61+SCM2jDzj97ndvPIkNpvhjZvBs+21PbxziNj5ivSWLV7nN08Miw5SwDr/EF6/Rj9lc3SP02MsPZh1o86D43W/25dkxQ7rHxw/dQyLJ5dnmiTNY+MVT8ij4w9RPXrPjJ9IwM4k/GRRKIbzQ9j053TPZFdA8dkqnalnHpfkbNm9qWx3wqecmMjmWbiUVVLBw4f6cRf3tyF5g7yx0hqivOC2Lx4hmlVb9JYtXtcV08Mk5a+YaqCMoNW1trVE8Mpv3iVeKNZHAkilB2wJf1VwxJvLNJdUjnw+p9Os72GaPumQXvtoLmGkthRGAli2b9NwKTRqVmEWemPmX2F/LofYUeqaASJRLeupdMz2TXAT8qp97ndMfGqvMxLbmwk01SPvbK6nnkTAwANrd0JKbMRpLFq97ittY3MmxiAXta6snIv1bdlDTqJ2uz0zxJvLNJdUjmwVRywwLPKt3bcNNdQEjua2roRCEhcNxpOXOczBaFaEujitezP6/7twtMunnJjM7v8KENmxe2q6Lwl4bT92xkvzblerku/SNzT/dqUiYiNjEAXr2V/XvdvF5528ZQbm9nlRxkyK25XRectCaft3854ac71cl36ReKe7temTERsZAS6KLI/oy9GJcSfMzspu/ayf7tY2U+CMsZ5FSMN6/zQtmXmLzs2+20+7PqfdjzzKkZS9RXNj8vmecU3y3hZ5szLdcljTQH2bUz3a1MmIjYyAl0U2R+AlAXLU/bXG5NRWV2PF7bvR2V1PXr75EJu9e8UZvaToCc3tnNTVtoCoOtvuzbLAK48e7jh58o8P7ftSzy8oRp/eH0n7nv9M2zafRhdPbEUm9RxsWn3YWzadVjXZiPs+l8G8PNZY/Hu7sO4dfWHWPCPD/Dwhmq0d/Xq+i+UHcCCqWVEbUsA7p57Ku6eSx/fvTEZm3Ydxn0q//XGZOrx0qwh9Vxs3lOPy88abvg+kFEcGK1zGkjHKBn8v/rfyrhZ7Er3a1MmIlRLAlOclndbtZ3ussPXdhzEnc9+klKFVysRJZF8s8qlaXLMGPVDWmCTxW6pLzGhgl7eE6s+jNDruygShGzSfsIuWMt4tVL+25/+CK0G1dYV1GkDaOLbKJas2tMbL6kP7cYc7RhJIMkjQxLvdu1K92tTOiDk132IjYx9nJD9WeWiUOe3SGfZ4Ws7DpomSbtu8kjMKI8SS757YzL+8uZu/OmNz4n6v3XGybjp/BOxtqqO2N9KP0aS9MEDwni/pgF/XrcrpT+e+W+MoM2BYpXptubro/jzut22bFkwNZ5ckSWHC0l8W8URACw3aQ+gz+zLI3eRkV/s5LEBzDP7kmS5prn+kNqRbtemdEBsZPoQGxn/0V9K0zsxTppcGqR5aHjnLOGZ/8YIXjHCKzcJ6TdWCjT298ZkTL53HVHKfV5rhlceJMnEL16t8/5y/ckESO/f4h0ZgevQlKZPZ5wYJ00uDdI8NLR2kI6LR/4bqz7sxgiv3CS0r3zQ2B9PWGi+iQFFeyTwyoNk5hev1nl/uf70J8RGRuA6/SUPgxPjZPEJaY4T3vY6lVtFjd0Y8TrGSPp3Oj6cbMdvfdH053VsCMgRGxmB6/SXPAxOjJPFJ6Q5Tnjb61RuFTV2Y8TrGCPp3+n4cLIdv/VF05/XsSEgR5QoEDiO3oujTpemt/sSHm19Jr3PY7KMwtygaQVp1jweRr5To/hxXsVIPPJODTd/n1NWjGh+DupajP9iHZQXwlXnluKRd2oce7wUzQ/jnLJiw7nQ/v6M4YVY9V4tahvaUFocwbyKkX1jCRM9ujEj0Ke8onnCVJwXRF1LByqr603jk9RGdSzZjX+aODOC9B0ZlnXOOr7emIxYzHxd8rj+CNxFbGQEjmJWodioUrAMe3kYnJZVsnxuxNzxJVTjVHJYLHx8m6k8WC8PjZHqhdbfa6vq0NFjLjGub+3C+fe/jbnjS4jVPLQcOtKJGx7/ADv2tzBVwL5nzaeYPnYoOnpizDYoHps/Ja7OoaGhtRu3PrUdgHl8ZgUk3D33VEvVkjKHPGTBpHFmhJ5fZJ3PWdY56/hI1qXIA5OeCNWSwDHMJI5WQbecUZZpV1Zpdb6VnJRGhqucxyJBJc2loc7HY3YjJPU3jSRX7ROrTUW6ovbz0jVVhptzK0ji0yiPTFEkiKWqPDI8ZMXqPq3iTG/T6FQeGdbxkcatyAPjL4T8ug+xkfEGO/JNVvmjXVklic1mMlurr9JZbDKDJpcGD7kpy5yqpdhbaxtxoLENd730LxztNP9GR6EgNxst7T2OfKNjh4KcbDxwzURMGjUo8RjLyjcFOdnIygqgobVL93OSeeiNydhcXY/KPYcBxKshk9pgZ11ZxZndR7GkdrCMj2RuCnODWHb1hIQvBf6A9P4tHi0JHMGOfFMtf6QpW08jq9Rrl8RmKzkp7Z8FrGMF4l//k5xj1y+k7Zi1vbW2ERWjB6GyGsSbGABobu+h6s8tmjt6EJCkxE2PxDfNHeZjIZmHrICEyScNxuSTBqd8xmue9fq0Ot7qGNJYNYN1fCRz09TenTSfgvRCqJYEjsBDukjbhl1ZpZdySyf75iU3tWOjcm4mSVrVY+E5Lta2Ml1WzDq+TPeLQGxkBA7BQ7pI24ZdWaWXcksn++YlN7Vjo3JuJkla1WPhOS7WtjJdVsw6vkz3i0A8WhI4BIlE1wha+aPy/L2uuR3FeSE0tnZRy4xJZJlAaoFD2s9pbFLbpn1H4asjHWho7ULxgDCi+ebvHFhJaRUbYjEZL2zfb0tOrkdhbhAxOV7RmlbWW5CTjZbOHupHdk4TzQ9jYmkRKqvrE/MSzQ/jUEunoY+H5Ychy3G1lR52Zb8kvi2MBBHrqy5uVI+IpO4XyTsvvOsQkcax1n+s5zk5FgFfxEZG4AhmEl0S1RKp/JG2Oq9euzRtWN1QaTcxRjbR2mYl3zWS0ir/bu/uxdWPvqfbHmsFZIWm9m5c/ch7iTZpZL1W75V4xXfOOC6ljlRhX5VpPR8r5zz1wZe67fGQ/arn2Yimtm5c/eh7phWirSqxe1W1XhkfbRoBq/iHwXkKosq1/xGqJQF3rKSOeeEstJq88Hn91DIsnl1uux+Sdp2ozqxHYSQIAEnSWauLIa1tVlJuvQtyUSSIxrbUb1hY5eRW9qHPRiD1Buo2Vt+eRUJZaOtKjtPCSBBXnHWCoQRf7jtGO89W+XQKI0Hc2yehtguJDJwmN4x23qzkzyTHsI7TThoBlg0Jbzm7gA4hv+5DbGTchUTqaJXIzSkpsLY6sF1brSjOC2LJRacmHv0AIP562o7U2Uq+q35MdfvT200zxtJUdZYQ3xj1ykCzRdbUd+44H8AxfwzOCyMmy7j5yQ9NH13l52Tj4tNL8OLHB3GE8NsaRVo7YURRIrPv8KIIHtm4x/Ixz+8vPR3v1TQAkFExajDOLismqiR+32XjExLliaVFllXAeVWu5lXNW4syLlmWDeNFeXwGSIaPlO2mG7ArL6d5RCSqZHuPkF8LPIFE6mh2X3RSCqxt166tVjS0diOan5M0DlIJqh2ps5V8V/mssrreMu09TU4cGUCDzrc7Zjaq7aysrrd8/6alowejhgwg3sQAx6S1uaEsXDdlVKIvo02M2s7srAB+cuEpSTaSSIADAQnfOeN4onMAdgm+Fl7VvLUo47I6xiqe7KQb4CEvp5GBOyVnF/BHqJYEXHGr+i4PiaobckunpbR2zvWb3NzJqtq8JLks57k5H+kiIWax0+24Tod1JIgjNjICrrhVfZeHRNUNuaXTUlo75/pNbu5kVW1eklyW89ycj3SRELPY6XZcp8M6EsQRGxkBVxSpo9ETYwnx9y6MkEBWEdqqH5J27dpK2x8NtONj6ZOkj4AEKh9H8+NycDOfGtlIMh8lfdW8SX1j1B9pXzzOY+2LBZa4IUGxMZofNh2Hnfm3wk0/etGfgB2xkckAemMyKqvr8cL2/aisrkcvzYsNnFGkjkDqDVD59/wpZfG6RAafk0hQzfrRQwYwe1wUW2oaEv6xslUGMOe0qG57ksH/q/tTxsEyP7TjU1D7zqpfq/FLiM8ViQ3K53fPPRV3zzWff735VV7CnDUumpAwa8+VAVx46jCsrNyLmadGLd9fUtpYMmcsttQ0JPlBPXaj85TP1T5U/14PPQkwyZrQs5EF0rixil+9Y++6uBx3zz3V8DgZ1vMvA7jy7OF4+eMD2LTrMDbtPkw8ZhI/8qxaTXJ9UMbi9XXXCj/dI5xAqJbSHL/mOLCyi5fdeu1olTYseTH05NJqAlL8Jn/miCLdisSKnBZIlRnTjHPpmio8vLHG8qVbO3k8WOaKR64Rs/6t5lDBTEJtVZkZgGk1acB47j7c16grp84LZeH+y8frzq3eXAYkYPrYodixv4XrGiatjq43Ri3a6tp6PgOSJeQsa4p0zG5f80jH4ofrrh5+vUeQIOTXfWTyRsbvOQ7cqIir146SmXRtVR3+tmlvyvF6/lG3sfdwG/78xudEf/Eb5Vmxkphr+9fDKo/MZRNOwOSTBqdk9mWJC9q54pX91WqM08cMwbqdXxv6yIz5U8rwyEa6uVG4nmFe1WjzmbDkBALsreEkqX1eGJCAw0dTq6Ov+fgAblz1oWlby03yw2jtVmymXVM0Y3Y70y7JWPxy3VXj93uEFWIj00embmREjgNzWP1Dm4eDJs8KSf9O2++nuCCxVWL0L8A+N3bPBZLzwrDmdnFjrnpjMibfu85SNl1ikUNGQc9mmvH7KT71yLT15RdbjSC9f4t3ZNIUmhwH/RFW/9Dm4WC92VnNj1P2+ykuSGy1s5nw6lwg2cesuV3cmKstNQ2WmxP02UFynJ7NNOP3U3zqkWnryy+22kVsZNIUkePAHKfzhPDCqD8385x4hR9scBJlfHbH6aSfnGrbbr4mv8ZGJq4vP9hqF7GRSVNEjgNznM4Twguj/tzMc+IVfrDBSZTx2R2nk35yqm27+Zr8GhuZuL78YKtdRImCNKQ3JiMWk1GYGzRM6a48/3QzxwHrC3gs51mdo+SAqGvu0H3B0Mg/VudpCfSpZlieRJQUxF+arayuTxmHU/ZLiNfDickyXti+35UXJY2YWFqE4rwgGlqNY9jOOzKkL+bqYWdegeT8IlbjNEKZY6MYIcFonSi/r2vpMCwcqiYvlIVgFtDUblzsVWFQXggTS4uS+iAdv9V1i/QFZitYr1Ws65KnDaTwtNXviI1MmmElqwScyanAYheJxI+1Iq3VOUoOiIUGlXJlAHPHl6T4R30eyY0wOyuArp6YxVH6jDs+P6WYoHocRnaYza+Z/cq/O3piuPqR93T7dAtlDs02MUBceURbfVsZZySUhdau1BsvybxOHzsUb1R9ZehDK5S5sRqnEcr4544vMY0RM4zWiZ4k3Qo9PxpR39qFc377BgBjqbUeVtctUkm5k/Jtq/VlZj8vG0jhZWs6IFRLaQSphNPtGxOrxI/lPNpzlq6pwl831OjaLekcr+7HasNoxfVT48nkjPo3sgkqu3huEAsjQd0bi9tSTJI4tspjo1AUCUJG8g3TaJzqz++95DR8uK/RNDYWTC0zzEFjdi5ALlc2QtlwGEnAAfP5opV7+wGzuCYZjx2/0K4BOxsRtyXRIo9MBpApGxkSCWNhbhDLrp6ASaMGubbLdlMmTHuOXfmh+qvxX730L8uv37WQSlZJxmH3kd3gvDBuf+Yj1LV4K8UkieNBeSFULp6OUHYg6bwtNQ2oa25HQ2sXigeEE/lzABCPE4jPy/qfTkv5pkON4o/1P52Wki8HAFFc0c59cV4QSy46FdH8+OMkEvv05otV7k1DcSSIxbPG4FevfEpViVwCUJwXwi/mjI2/m0HwaIiXfJu3HJn1kbgXkmi38+7wgvT+LR4tpQkkEsam9m4EJMnVAGUtdc9yHu05rLYpZAUkVIwehMrqeupNDPraZkFrl2IHLerzKqvrTW/uVr7gBUkc17d2YWttY5IdVj4gHScQH+fKyr1EsaG1Q+mD5FxaGlq7Ec3PScQca+yyyr2pbG3rRktHD9UmBojbXd/ahWhBLnGcscq3af1CuwZY1iVvG0hhvYakC0K1lCb4VUrnpkyY9hxePvNKnsizX7/Ej9N2kJ5X29DG3J4bcmg7fnIrXkl9qAeNjbzk235YA36wIRMRG5k0wa9SOjdlwrTn8PKZV/JEnv36JX6ctoP0vNLiCHN7bsih7fjJrXgl9aEeNDbykm/7YQ34wYZMRDxaShP8KqVzUiasPY/2HBIpdWFuEDFZTlREZhmjHqzvSSjnqiXSgweEARk43NqZ9P/aZ91G9ZDqWjpw+EgHCnKDaDaR6w/LD2PH/ma8uuMgSosjmFcxMuk9FR6Q+LLEIo7NnveTtD8oL4Qrzh6BZW9Xo6G1S/cYs/VEKnHv7IlRPZIsoYhdO/bxIJofxryKkXjknRqmdUFznaIZj915c/oa6qUNXT0xrKzci9qGNi7r20/v3Xj6su/dd9+NX/7yl0m/O+WUU7Bz504AQEdHB26//XasXr0anZ2duPDCC/HAAw9g2LBhxH1kysu+wLG33QF9KZ1XBcBY7WI5j/Yco+O1kEi+SdrR2mKlbtE7V4a18kZrN5BaxZi2XpBWoqlU+F48u5y8EQKsfKmuoqx3rpUC47UdB3GDgexegcQ32sKPJGOgnT+z/uysd5p4ZWG5SlXHsi5or1Mk/djxi5vXUC9sMKq8zrq+3VJCpU2tpVNPPRUHDx5M/LzzzjuJz2699Va89NJLeOaZZ7B+/XocOHAAl1xyiYfWesvMcSV48JoJiBYkf+0YLcjxtIopq10s59GeY3S8lrrmDix8fBte23GQytbCSBCFkaCuLQCd9BoACvraIr0J1jV34IbHt+GGx7elvERIm0hOe3hMjtu/dE0VXUMWKL4s0PhNobmtW3culBuAdpxWc6cHiW8+3Ndo+JlRPNDOnxl21jtp3Cto/5COhLJs2Wi2LliuUyTjseMXN6+hbtugpKDQxjzr+ua5Dnnh+Tcyzz//PLZv357yWXNzM4YMGYJVq1bhsssuAwDs3LkTY8eORWVlJSZNmkTURyZ9I6Pgp6/0eNjlRGZfveM3V9dj0aptltmQzaSPev0C0P3d5HvftFTQqCmKZCOcnU11jhsEJGDnr2dxfcxkVXWZVUpvJaumgWTcNBJ3M6xkw6zrvasnhklL1xk+QgPisu9Nd0zH9i+amKX6pOvC7nXK68y+PHHDhq6eGMYsedV0406zvt2Wj6eN/HrXrl047rjjkJOTg4qKCixduhQjRozA1q1b0d3djRkzZiSOHTNmDEaMGGG6kens7ERn57GLY0tLi+NjcBu/Sul4yISdOicrICEQkAw3MQCZ9NGoXz2JLu0NrbGtBwCdnNUNYjKwsnIvrpsyilubVlWXWaX0VrJqGkjGTSNxN8Ms9uys9621jaabGCAu+97+RZMtqT7purALr2ufH66hbtiwsnKv5bePNOvbK/m4FZ4+Wjr33HOxYsUKvPbaa3jwwQdRU1ODKVOm4MiRI6irq0MoFEJhYWHSOcOGDUNdXZ1hm0uXLkVBQUHiZ/jw4Q6PQpAuuCl9zDT5pB2prR5OSel520nTnh/jxo00BwL/Qhq/dtIR2DmOF55+IzNr1qzE/59++uk499xzUVpaiqeffhq5ublMbS5evBi33XZb4t8tLS1iMyMA4K70MdPkk3aktno4JaXnbSdNe36MGzfSHAj8C2n82klHYOc4Xnj+aElNYWEhTj75ZOzevRsXXHABurq60NTUlPStzKFDhxCNRg3bCIfDCIfDLlgrYIXXs2F1CYGGo50ozgshWpCb0p46vX1xXgiNrV2GyofivCDqWjpQWV1v65n1OWXFiObnUD1qyA0GkJ0VwNGOHl/VyAlIwLyKkVzkm8pcHGhqx8CcLBzpMC5GmFpBOmQplz556EDTqvA0KOO2gqXKsxat7NbuGiGNeaVfdYXt4twQka/tVOVON3hfs9zy2byKkbhnzaeW78iQxDngDwm7Hr7ayBw9ehTV1dWYN28eJk6ciGAwiHXr1uHSSy8FAHz22WfYt28fKioqPLZUwAov2Z5ZIUHSgoN6NLR249antjPbpZAVkHD33HJLGbCa9u4Y0E1XSZu0GrMd5k8pw/3/tzNFvnnPmk+p5Ju0c9He3Yu1VfHHyPEK0sbvesh9x8/7+xaitkmYP6XMcqNGOib1PFlVIra7RmhsAvQrbLOcky6FCGlx8prltM9C2QHMn1Jmqp4kiXMFv1bU9lS19JOf/AQXX3wxSktLceDAAdx1113Yvn07qqqqMGTIECxcuBBr1qzBihUrkJ+fj5tvvhkA8O677xL3kYmqpXSFZ+VZkkq4C6aW6VYPJoVHXofXdhzEnc9+wkWOqwdpNWYrIqEstHf3Qn01UPJMAOYy8uunWm9mWKoxu7FJM8MsjwxANyazfD/ajbedNUJrk1GFbZZzvM5n5QROX7Pc8lmm55HxdCNz5ZVXYsOGDaivr8eQIUNw3nnn4Z577sHo0aMBHEuI9+STTyYlxDN7tKRFbGT8AS/ZHk0lXKukZ8V5Qfx8djnuWfOp5dfoduSEvTEZ7+4+jBue2IrWTuNHKXp93//98RhWkGOa2Rcgk3oPDAXwHzNOweCBYQwdmNpOb0xOeXQEwLZ8041qzGawbIhIKqNbxbNS5Vn7uNPo8QKPau1WfqapsK137qY7puP8+992vXqzF7h1zXLLZ+mY2Tct5NerV682/TwnJwfLli3DsmXLXLJI4BS8ZHs0lXCtZIcNrd1oauuyfGRhV06YFZCQnRWg2sQofTe2deGSiSeYHkcq+z3SFcO44wtMpeVaCeajG/fYlm+6UY3ZDJa/1KzmnSSejao8G8lu7a4REj/TVNjWO3fVe7W+lN86gVvXLLd8FsoOcE2h4AcJu4LnmX0F/QO/VqLmLTvkfT6JfU5WEuYh30xnma6R7U7IUO226ZSkXY1b68UPuH3NygSfeYXYyAhcwa+VqHnLDnmfT2Kfk5WEecg301mma2S7EzJUu206JWlX49Z68QNuX7MywWdeITYyAldQZHtGT1AlWFc9JmlHjdnjWqW/eRUjudhlBY3dCqSySEXqbUU0P0w9jnkVI039CFjbqYzdKwISqPwOWM87r3jm2Sbt+TQx6fZ68QNuXbMyyWdeITYyAldQZHtA6k2FRranbseK684bCcmiv1B2gMqu3piMyup6vLB9Pyqr69FLWJnRbPxGkMoiFam3Ff91Uap/e2MyNu0+jPte/wz3vb4Tm3YdThqTIt80g+QCfMVZ9Ekpebw2KOGY8oq2PaN4VF5ynDUuaphLw+x8dTvqWFLO0bOVtM0rzx5u+E6QrDlfiUnSqtXKuUZ9eCm/1WK0TknXr3aOeV2z7LQjMMZT1ZIbCNWSv3Ajj4y63bnjS/DiRwct+yOxi4ftpDk+Ligfiod/cDZRm+q2zaTehZEg7r3ktKTx6B2vPQ7Ql2+qMZJyksrPJQnQXokUOz7c12jYt5UyTWkDSJU9F0WCONLRgx6dBoz8TzJ/ev4jaYdUok3aHoldr+04iNuf/gitXeaJCY3s0jvOa+m1kW/tXAu0cZYueWTSnbSQX7uB2Mj4D95ZMv+vqg5/37Q35XOlxWVXTUBRXsiyPzO7eOaBUPpZW1WHv5nYzZJbYs3HB3Djqg9Nj1l+zQQAsEzWp82j0tUTw7WPvofKmgbDc9Q5ZV7bcdCyj2mnDMZbnx3W/Yx3LqALyqOJ+d17uBV/emOX6bna/DikOVokmM8dSSypbbVaIzR2Ke0rOWus5ufWGSfjpvNPxNqqOtM+lOO8/laBNl+Rnk/Mzr9u8kjMKI+mTWbfdEdsZPoQG5nMxo0cDU704VSbk+9dZ1pVGoj/FRiLyTh0xPy4aH4Ym+6cnui/qydGnFMmKyAR5baxyvFi9Y0LCVpf9sZkfGPpGzh0xLwqtDo/Dk0uHLO54z3vtDl6lPbX/3Qapv7+Lcv5ieaHseE/zzfNN+OX3DGs+YrUPkmHcfYnSO/f4h0ZQVpDk6PBT3041abVJgZ97VptYgCgrqUzqf+VlXuJc8ootYessNqj2N3EKH2ofbmlpsFyE6P0vbJyb+Ic0huk2dzxnnfaHD1K+ysr9xLNT11LJ1ZW7nV8jfGANV+R2ifpME5BKr6qtSQQ0OJGjgY/5gyxeyxLmzQ5ZQYP9F/hVpb8KcqYWXyrdw7veXcyPxHtsV7nQbHbf7qMU5CK+EZGkNa4kaPBjzlD7B7L0iZNThk/5sRgyZ+ijJllPHrn8J53J/MT0R7r9Zzb7T9dxilIRWxk+il6MkRWabGX2MnRYDVe5fO6lg4URYy/vCTJA6GVOff0xBDNN8/hURgJItY3LyQ2x/PJWH8TUpwXRH6O9ZexA8IBnDG8MPFv0pwyV51bilhMRkFu0LIPwFwWzZIDRq999fxMLC1CUa71+NX5cVhyrij9qecsFpNN5502pwhLfqLcYAA9MZkoVqL5YVdzx9i5BrH4AuifOXL0SMfrv4J4tNQP0ZMBFkbiNx21TDYdpIGsZeWtpJCkMmmSPBB6EuS/oBqRUFYiR4XeJaOprRtXP/oesSw3nk/mVEslSkMrWSXuo50xnHrXawlZtZJTxqwS9vSxQ02LCmpRxm40d/OnxFVLVi8Fm7UPHJsfZV4b23ssz1Xn8TGLMy0ygLnjS5L60641szHT5BShsUuhvTuGpa/uJLrh3z331ESuJdo1RotdeTKLL/RySjk9Tj+S7tJwoVrqZ9DIE+3IgN2GZiFayV9pJL9WOUNIJK6FkaBpnhWzi7LeHJHmbqFBLUXWyykTkOKbmDeqvmLacERCWWhT5TKx2lQa5QUxy/dBK83Vys8B8jxAZtJxZT61827nxkFqFy3LNXHl1M2OZ3oDJ/LIpNNNnRaevueNkF/3ITYyx2CRJ6aT5JAkRwOJD2gkvyUmvonLoa0lyMMGhnDf98/AzU9+iKZ2+s2H3hz1xmRsrq5H5Z7DiMnA6ve/MK3ybYVaigzEpdgrK/eitqENpcURXHVuKdU3MXrtP/b/nYOGti7duTOaW+3vJ5YWYWtto+5xLDJlIwn15j31WPTENsP5khBP8mcURxKAYflh3H/5GTh8tJNLThHFF3XN7fjVy1VotLmRNYor3nlQnEpFQBIvLDmlMgk30lfYgfT+LR4t9SNY5IlulZjnAUlZeRIf0DwaNvMNqQT50JEufH7oCNMmBtCfo6yAhMknDcbkkwajsroeD7xdzdS2giJFvm7KKADx0gXK/wNAZXW9rW8DYjLw+aEjSW2qMZpbvd8bzQWLTFlvbrMCEgKSZDpfMlIzFWs/r2vpRECS8J0zjie2ywzFF5XV9bY3MYBxXPG+DtBI0kn7pokXmvMzDSd87wXiZd9+hBsSZL/jtESZtS8aOayTdphhZiOPPnj4wAhW+5z2qZvx6Jf2WNvPlGuQn8gU34uNTD/CDQmy33FaoszaF40c1kk7zDCzkUcfPHxgBKt9TvvUzXj0S3us7WfKNchPZIrvxaOlfoQiT6xr7qCqRVKUF8LGXV9h0+7DqBg9CJNGDfLsebHdZ9ckPgj0FTAkqV0TLYi/l1FZXZ9iU1wOnWP5eCkvlIX6o10oimSjsc1aTWNkh5EslGXetailyEDqPEwsLbLVR0ACTh42EC9s32/4rgsA5rmn9QEPn1q9pK1tnzW29eYimh8myvJshpUPSLEal5UvrdYZDxucwMk+ebVN6nu/S87Fy779DOUNdYBNzgqQVfd1Ap6Vs83UKxeUx9U3gLVaaMHUMlMlBIlqyQoSKamewkbN0jVVprJpK7TFII1UIQ/19UEbW1rVkvaFax7pAWhj38qnrHNrpDRjiW2zuaCZbyO5sV3FCum4jOaGdJ3xsIEnTiu8eLZt5ft0UC2JR0v9jJnjSvDgNRMQLUj+qrAoEkzcLKxoauvGDY9vw2s7Djphoi7KYtO+mFbX3IGFlLbMHFeCBVPLDD9/o+orLJhaluIjNdGCnIS81symmeNKsPyaCcS+NerrehN7rXhtx8HEBoMWSUrdxBjNw0Mbaiz9ptc+gKRNDJD6wnVTW3eKnJx27o1i322iBTkpmxiW2Laai+unlpnGXaBvbpfr+ERrIws04zKaG9J1xsMGXjjZpxNtm/k+HVJvAOIbmX6L3leTwDGlza9e+pel8sFMeszbVjcrBqur4SqPNwbnhQEJCansxNIiqkq5imR30+7DeKxyL1o7e3XPUyiOBLHkonJEC3Kp+6IZqxXqCtg8/HbG8EKseq8WtQ1tGF4UwSMb9xAVsDSCVZpLIp82azdeQXsdte2D8kKoXDw9IWNnjW2auXi/pgGVew6jV5ZxpL0HkgSMHJSHeRUjk+zg+RjEzri0j8mcin0npMVO9un0ePwoORfya4EpRvJCGvmmW7I83hJB0va21jYatmclN9balBWQMPnEwQhIEpEUuqGtG9GC3MR8sI6ftSKwglIBu2L0IC5+A5CQWFdW19vaxKj7pJXmksinraT1LLbXt3Yl+Yc1tmnmQpHhm8Fbbsw6Lq0dTsa+E9JiJ/t0ejzpLDkXj5YEKXhdcZm1DzePY22Dxbd27OUxPzzssHOcE23ZtZFXKgOn48gr2Swv+9yIfS/ikFc1dV5tpztiIyNIweuKy6x9uHkcaxssvrVjL4/54WGHneOcaMuujbxSGTgdR17JZnnZ50bsexGHvKqp82o73REbGUEKpFWU3aoEa6fCtVPtsbahSLKtiOaHE+fasZe1IrBeu27PA4uNpNgdyzllxRg20HqNWLVpJ478XKmZl31Oxr4TPnKyT7/PuZeIjYwgBaWKshVL5ozFlpoGprLvNCXjlaq2AFIWsfJvdVXarp4YHt24B//1wg48unEPunpiuu0Z5U2Ij608aWxdPbEke5U+zWzSttEbk/t8W27smD7unntqYjy04ycdqxnadpXaTWcOLzD1G0vlZvX5LFj1qRdrdnyqvBR50en0ag5tm3bsuPLs4aZzceXZI/DyxwdS1pfij+c+3I9HN+7Bc9u+pF6/VpDMLUmskPhHfR3atOswNu0+jBe278eWmgYsmcPmW1bszKeXbac7QrUkMMSoinJRJIjLzzrB9bwOJOcZVWaeP+WYhNhsbIWRIK7QGZtRVWUATJV2l66pwkMba1Lq8URCWfjj5eN1/WDHb1bVsLV5RLS5cKzOt5PHQm/O9MgOSOjRHERSfdzMZ7Q+Za0yrReDNHbS2FAUCUKGfs4dIDVerfqzg9k6o8lFRVPRWg1N1WuepFMeGT8jql/3ITYy9lBXUQbib7U3t3Vj0Sq2su92S8abSQStkr4p+VCsEuKRoLb3gvJokk2NrZ1YtOpDwzEqeTGM/po28wGtRJJ2rD+aPBIXlEcT7ZIkfbt1xsm46fwTmf4StDsXZnFDGmukPjVrTwZwzsgidPb04qMvW6jsVCCxw8pfF51egpc/Ts0lQpJUUTmOZ+4Qo/hhSbam9U9ja5fudUivn2VXnYmivLDI7JtmiI1MH2Ijwxc7uQyczIPQ1RPDmCWvmv5VH5CAf/1yJs6//21bkmQFPXtJ8rZov92xapMV2hwyerlvJt+7zjLVPWs+Ibs5bhRY5oHWzyTtDcsPA5AMS1LwyPNhZYNkEluk8MoP5WVOFV79CLxFZPYVOAJNLgOe51qxsnKv5QU8JgO/XUP/WMAIPXtJ8raY2WnHB1poc8ho+44nR7TOlcJqr90cNwos80DrZ5L26lo6Tetq2Z1bEht4vObiVvzZ8QdN7PBcUwJ/IjYyAir8mtehtqGN6Li99WTH0cCSF4SmTbfboM0bw9oX73wXPPKz2D3OybbczA/iZvy5FTv9Mb9Kf0FsZARU+DWvQ2lxhOi4kYPIjqOBJS8ITZtut0GbN4a1L975LnjkZ7F7nJNtuZkfxM34cyt2+mN+lf6CKFEgoELJg2L1HoBZXgerkvETS4tQWV2vWwfK6OW2eRUjcc+aTy3fkfnZ7HK88elXhjbQoDfWiaVFKM4LoqHVRCHU9+almQ/UbbK+2Gc1V1Z9K/mESN6RIcldodQ4isvXZZxbNoiofVK71XEzOC+MaH4ODrUYz3NhJIhYTE7Ijs18TBK7yjsyRn1q/Us7ryQ28HpH5pyy4oR9dS0daDjaieK8EKIFucTxd8bwQgzMycKRDv26Yqyx3huTEYvJKMwNmpaZMOsn3ekvL/uSIjYyAirWVtWho8f4wgRY5zRZ+Pi2FBWFcvTc8SUpReKUCr56clJF8RDKDmD+lDJT1dL8KWXIDWWZ2kB6D9AbqyKLNNvEAEiRXCd9hrgPtG2ySC3N5soI9XiUfEJWqiWS3BV6Mty/vFWNSCiLyj4tVnEjw3hem9q6cfWj7xHFF0nsKrmXzI5RfMUyryQ2zJ9Slqh0zhrbd11cjrVVdbZk2laSerP1Y+YTWvl7JuZX6U/ya1KEaklAjJX0sygSxFKC3BBmOSGMZMlajOSbNHlktDYURYJExTIVe7UXWLuSbgVFAguAWapOa4/Z3JnlEyKdb6vNECtmcaPcvAsjQdMcOHoY+Zj1ZqvNXWMnBYGd3DgALHMCXW+SHkBtq5GdVmkQtPYqY7LyCaC/Hmj6SXfsxk66IeTXfYiNDB9I5I7R/DA23TmdWM6q/mp0YmlRyl/UVhjJKrt6YlhZuRe1DW0oLY5gXsVIhLJTXwdT2zB4QBi3P73d9DHHoLwQfjFnbMrX67xkxNpxybJsaI8dmbseVnOnl09o0qhBlnMdl3C/Sfx4i4bivCA23THdVE6vPPL5w2XjcfOTHxI9ilCfq+dj0scfesfwkiRb2WDWv9V8mKUHUKMn06ZJg5Db920cqU/M1gMAFOYG8b//diYCAQmHj3Zm3CMXJ+XsfoX0/i0eLQmIIJE71rV0EpeQ15aMr6yup94IGJWtD2UHcN2UUVQ2VFbXW76rUd/ahWhBbsr4eMmIFZRxkRyj528We6zmLisgYfJJgzH5pMFU7SrvWDhBQ2s3Vr1XSySL/vzQEapNjHKuno+1sauH0TE0kmSzPqxsMOvfaj5I37HRs5M0DcKq92oTa5TUJ1Y0tXcjOytAdP1JR3jFTiYiVEsCIpyWqdqRRnotFfVS1snTHifG4bRvSGX3pMfp4YX02im/OSl5B9jmww/S9nTA69jxM2IjIyDCaZmqHWmk11JRL2WdPO1xYhxO+4ZUdk96nB5eSK+d8puTkneAbT78IG1PB7yOHT8jNjICIhRZsRES7JWQtypRb6dPkkrbVv0b9aWWgvKkMDeIooj5k9/ivCAONrWnVDBWxkJDND/siDxVkYDzRpmPeRUjiebN6jgj8sJZ6OmJcasMTRLnWlm4Xvwa/W7T7sO47/XPcN/rO7Fp1+GUqu0TS4ss+w9IZNXI9dbDvIqRsHo9IyDFj1MgXXvR/DD1+swkWK9RTkJybXUD8bKvwBISyaOZioGmn4V96haroOSl8CDpn0bB4jXK2D7c12ipHFFDW42YBqdUS8v75oN03mjiSwtP/5AqyowqN+vJxQsjQXT1xNDWlSy318qu1SovQN9fZkVN1ceyqpaU4q1qSOYQANX6zERor1FO2+K0DFzUWhJwQVk4VjfrBVPLbAfvzHElePCaCYhqvk0oigQTF2+FaEEO0SZGz/a65g4sfHwbXtuRXCXYqH+9vkj94jYHmztww+PbqDYxANDc1q3rEx7MHFeC5ddMoM4Zc0H5UOL2SebN6LhCnfjS0tTWjRs4+WfmuBIsmFpmedzB5g78dUNNSow1tXWnyKeb2rpTNjFA6oatrrkDD22owYKpZYb+Wjy7HA9eM8HwW70Si7V35ogi03HpfU4yhzTrM1Pxiw9or61OI76RERhCKuPlLfvTk44C5plXaW23ki9bSVt5yq0Ve4wWogSgOC+En80ag3te3YmG1i5u/Wr7cUq+ySLDpq0STprtVO+43piMSUvXWfqWJsWAEU7EDw2K79b/dBq21jZaSrhpMvvalQjbkbb3J7z0gZsycCG/FtiGVMbLW/ZnJB2laduOVNFK2spbbq3YY/ZZfWsXmtq7HdvEKP04Jd9kkWGTVglXbCWRRRsdt6Wmgci3NCkGjHAifmhQfLe1ttGWxFsPuxJhO9L2/oSXPvCjDFw8WhIYQivj85Psz2+Vd3lgR0JMQzrJsL2oNm63T7+sEy/n2S8+ENDjxzkWGxmBIbQyPj/J/vxWeZcHdiTENKSTDNuLauN2+/TLOvFynv3iAwE9fpxjsZERGEIq4/Wj9NFJqSKLVNwMCTCVrNqVENPY4dQ8sviMxCe8bCWVifOQqfOOH1q8nGc/XisEdPhxjsVGRmCIUm2X5ILrt+qyiu2Afk4MGcCVZ49gbnvJnLHcCkQC8aKWRt6TAcw8dRi21jbi57PI+9W2JxF8pswjaX4Is+PUn22pacCSOcbzoYfiEytbrewgIV7pu9zyuDmnlWBLTUNKfhbS/pSXNGePi3KJHxZYYp/Uv2Zrw6tK1HZiwy95UvyE2bXVqzlmVi11dXWhpqYGo0ePRna2f98ZFqol+5jlS/F7dVmrXC8s9htVg2ZB3f/SNVWW+TtICvoVRoK44qwTUvKPqCsgs1ZPJq0EbdSHXl4ULeqq2jyqTdNgNLeSBKivlNp5IOlPz06z+TTyV1EkCBnJeWSKIkF06uSRMYPUR7S5mPx0rbATG27kSUln/JRHhnoj09bWhptvvhmPPfYYAODzzz/HqFGjcPPNN+P444/HnXfeac9yzoiNDB8SUszmdjS0dqF4QBjR/PSQPvbGZPzlzV340xu7Uj6jTSTFM7nbrTNOwk3nn4SsgMS1XWVMy66agKK8kK5E00i+aZSszSi5nN5xZjJyAPj3KSPx8Ma9hvY/cNWZmH36cYl/m0lNSe2loTcmY/OeelRW16P66yN4dcchy3Os+rPy148mj8T0McMACSmVm0nTEby+ow43riKPIRIf0fjXKtHfA1dNwOzT3d3EsMaGE3GViTgtA3dsI3PLLbdg06ZN+POf/4yZM2fi448/xqhRo/DCCy/g7rvvxocffmjbeJ6IjYyAV94DllwoRqj7BIDJ966zrL7N2j7phYXUT+t/Og3f/MNbTBJiCfFvN2jyw9i1lzWfBW2+F6P+3Mi7wZqbxiqfEqndAFzLLUKC3TxSfhpLf8axzL7PP/88/vKXv+C8886DJB2bxFNPPRXV1dVs1goEDkKT98CqHR6bGG2f8Xb5bWK07ZNC6qeVlXuZ86DIIM8PYwWveWVtn7Q/p+0k6YOlbxq73RgjDXbs8dtYBNZQv9zy9ddfY+jQ1NThra2tSRsbgcAv8Mp7kE75VVjaJz3WjXw2JLY4nc+C13lu5N1wIreNE3a7lVvEju1+zJMiMIf6G5mzzjoLr7zySuLfyublkUceQUVFBT/LBAJO8Mp74FTeDSfzLTiRH8WNfDYktjidz4LXeW7k3XAitw2N3X7LLWLHHr+NRWAN9Tcyv/3tbzFr1ixUVVWhp6cH//3f/42qqiq8++67WL9+vRM2CvoRXT0xrKzci9qGNpQWRzCvYiRC2fayBCh5D+qaOwxlodGCYzV3jF5eU3KN8HxHRnlxM5ofduQdGdJcDr0xGTFZRmFuEE3txmqskoIcXHVuKZa9vRsNrWyqLasXgtV2G71kPrG0CLGYub0SgIE52XjlkwOoOtCsG0tG8dYbky3b1+tvWH4YMVnGC9v3J+LHKv4A+3k3SPowstkoTqziXXsu6RpzA5o1z/NcgTcwya+rq6tx77334qOPPsLRo0cxYcIE3HHHHTjttNOcsNEW4mXf9GHpmio8vLEm6R2KgBTPJ7J4tnWODzMUFQKQfBNVqxAAc1my0o5ddZGR4oO3aolGiWUmUVdzQflQ7NjfYvoipKzz/2ryQllo7epN+VxPGWVkF4kMXe8cdSwZxdv0seZjNKMwEkySRavl6GaKnkgoC3+8fLwtJQxtDJEorW57+iNDSbcEfdUSYD6vbrF0TZVhFXit7Vr8Npb+imOqpXRDbGTSA7OLDgBcP5XPZsYs7wmNzJQ0j0xhJAgAujc3vRwcVu1qb+A07ethJZmlRe1Po7EoGxijm76ZvJsH108tAwDTeOOFOn4+3Ndo2edyGzdI2o1MYSSIe/vy9bC0pbcm/ZJ7xSp+SK4nfhlLf8axjUxLS4t+Q5KEcDiMUChEZ6nDiI2M/+nqiWHMkldN/8IOSMDOX8+y/ZjJKCcHrdxSnWsEkHHuyEEIZEn46kgnGo52ojgvhGhBrmHODyPZZm9Mxubqemyq/hoHmjpQUpiD4kgYgwcee6SytbbRMqcIiSyUVbJrxKC8ECoXT088mjGTqis+ve+y8Tjcmpo3hdUukkdBVhJw3ihjjcViOHTEvMJ2CaOsl8VnRn2RphmI5oex6c7puuc7mVvEChJfkPrZ67H0d0jv39TvyBQWFpqqk0444QT88Ic/xF133YVAQFRAEFizsnKv5U0lJsePu27KKFt9ZQWklNLyldX11GXpswISJp84GJNPHEzUL2k5+6yAhMknDcbkk4zb1WuLtH01rJJdI+pbu7C1thEVowdZStUVnwYCEr5zxvHc7CJ5n0VGcpZep1HGSoI2zkhh8ZlRX6RpBupaOnXP11tjbkLiC1I/ez0WARnUG5kVK1bg5z//OX74wx/inHPOAQBs2bIFjz32GH7xi1/g66+/xn333YdwOIyf/exn3A0WZB6kcl6nZL/9VW7ppJzcDflrpsIyfp4Scz/KqWnor+u5P0O9kXnsscdw//334/LLL0/87uKLL8Zpp52Gv/71r1i3bh1GjBiBe+65R2xkBESQynmdkv32V7mlU3JymrbtyF8zFZbx85SYOyHZd5P+up77M9QbmXfffRfLly9P+f2ZZ56JyspKAMB5552Hffv22bdO4CluPR+eVzES96z51PIdmavOLUVldT1V7SCSMfGUW2rfnakYNRiTRg8yrJnj9DN6Kzk5i2RXD8VHE0uLsGn3YbxbfTihTjJiQDgbPb0x9MbkpDEpdrGm27caj9/fkSGNM/W8Tiwtop5Lo75I0wxE88O+lCDzWM9q/w7OCyfVwNJ7T028N+Mt1BuZ4cOH49FHH8W9996b9PtHH30Uw4cPBwDU19ejqKiIqt17770Xixcvxi233II///nPAICOjg7cfvvtWL16NTo7O3HhhRfigQcewLBhw2jNFlDi5hv7oewA5k8pM1V0TB87FOff/zZTNWcFszHddXE5Fj6+zVASTFKWXk919Je3qi2rUZtJQO3MgdX5WQEpMW47KF6ZO74E5/z2DeKq4Ec7ezDvb1tS1DNqu0hvyup5slIHLfBAtURil3IcSZwZVRZ/aEONaZ4eNe3dvVhbVZcSS1kBCXfPLbdULd0991Rf3sDV8cOynq3SEbBUPhc4C7Vq6cUXX8T3v/99jBkzBmeffTYA4IMPPsCnn36Kf/7zn7jooovw4IMPYteuXfjjH/9I1Ob777+Pyy+/HPn5+Zg2bVpiI7Nw4UK88sorWLFiBQoKCnDTTTchEAhg06ZNxPYK1RI9XlV+Ncvr8UbVV0yVlkmqNSvHAWQbIj1Y8sCY+dPuHNCcbyV9t6IoEsTlZ51ge2OglR7T5LcpigSxVLUZIslJNP8f72Nt1Ve2bFaw2qwC5nlk8sJZuP/71nlkrOZ1wdSyFBskSf/lZpI8MnoSejPZtp9g+UOARfYvcss4h6N5ZPbu3Yvly5fj888/BwCccsopuP7663H06FGMGzeOqi0lod4DDzyA3/zmNzjjjDPw5z//Gc3NzRgyZAhWrVqFyy67DACwc+dOjB07FpWVlZg0aRJR+2IjQ4fXlV+1mVavOrc05ZsYUkirNWur+LI8/mGtim0k7bYzBzyrFpMwbGAIkhSwnfFYTxKrPKq78fGtaO7oMTxXTwpsliWat/S8xCR+AGsfG0mZ1dBUJ1cefQweEMZtT31o+EiLJJaMHpWmAzSPZu3EhKiI7QyOya8BYOTIkYlHSy0tLXjyySdxxRVX4IMPPkBvr/EzcT0WLVqEOXPmYMaMGfjNb36T+P3WrVvR3d2NGTNmJH43ZswYjBgxwnQj09nZic7OY6nejfLeCPShqfzqhCwxlB1IklhbSaPNIK3WrB0Ti/SV9Sau50+7c0BbvdfuzdzqvQ9S9MaUFZAQkCTTTQygLwXWxpIa3tJzs/ghiWEjKbMa0nlVJPBK32bzYxVLtGkG/AaNfNpOTDh9XRSYw5zoZcOGDbj22mtx3HHH4f7778e0adOwefNmqjZWr16Nbdu2YenSpSmf1dXVIRQKobCwMOn3w4YNQ11dnWGbS5cuRUFBQeJHeW9HQIbfpIs8+iGVbbtdJdmoDbtzQHO+3ySo6VyFmcd82Plc7zi/rWc/w3sdC9yD6huZuro6rFixAo8++ihaWlpw+eWXo7OzE88//zzKy+nSx3/xxRe45ZZbsHbtWuTk8JPBLV68GLfddlvi3y0tLWIzQ4HfpIs8+iGVbbtdJdmoDbtz4Lc5pMGtasROSs9Z++JVfZ1nLPUneK9jgXsQfyNz8cUX45RTTsHHH3+MP//5zzhw4AD+93//l7njrVu34quvvsKECROQnZ2N7OxsrF+/Hv/zP/+D7OxsDBs2DF1dXWhqako679ChQ4hGo4bthsNh5OfnJ/0IyFGki0ZPeSXYr9TL0x4zFFvnVYx0dEyKXJUFvb7tzgHN+STHmj3ylxB/v4N1/GrM5MC8589OXNH2z8t+lnb8tp79DI9rjfCjNxBvZF599VVcd911+OUvf4k5c+YgKyvLVsfTp0/HJ598gu3btyd+zjrrLFx99dWJ/w8Gg1i3bl3inM8++wz79u1DRUWFrb4zid6YjMrqerywfT8qq+vRazM5hiJdBJCyoGmkyHZQj2lLTQOWzBlrao/eZ0D8ufVdF5cjlB3AXReXG+aUQN9xAHR9qfVxV08s6d8AcPdc+oKWRv7MCkhYMmesoXJC1jlHDc0cmh2r9HXdeSPjuVcM2rp77qlM49diNCZeMUkaVzSQ9K+2Xw8ZwJVnW39rbNWOnh1+WM/pgtVaMEMGsGTOWMuXtXleq50iXexUQ/xo6Z133sGjjz6KiRMnYuzYsZg3bx6uvPJK5o4HDhyYonDKy8vDoEGDEr+/7rrrcNttt6G4uBj5+fm4+eabUVFRQaxYynScyvUyc1wJHrxmQkrbURfyJRiNSU9WqtgD6FdaVipDq/+tPaagT0oKpCpLlNwc2n718kjMHV+i277Sr54018ifr+04iF+/8qm+gwBEQtZ/RNDModGxCi9/XGfqf6Wt5ddMIK4KrkU7V3bGo4dZXD31wZdMNtP0P3NcCWaUDzWUe//pjV1Y/f4Xlm3NHFeCBVPLDKXleud6uZ7TDau1AKSuf4Vfv/IpAgHJUMqeDpW008VOLdTy69bWVjz11FP429/+hi1btqC3txd//OMf8aMf/QgDBw60Zcy3vvWthPwaOJYQ78knn0xKiGf2aElLpsqv3cj14nblV6sxLbtqAoryQin2kOTWeGhDjeE3Mmaf8+CBq87E7NOPI/InTR4Lbd4VPWjmcM3HB3HjqtRcOFb+1/anyHX3fH0Ea3YcIhgJedyyxKRZfNiZ81tnnISbzj+JaE2Q5Ooh8YGddS8qOZNjltm3/kgnblr9Yco5RnPgVV4uWvxop6N5ZBQ+++wzPProo1i5ciWamppwwQUX4MUXX2RtzhEycSPjda4XJ2AdE0nuB6O/oEg/twPNXNDmsSDJPUIK75hiycnhRNzyzhejQGNrV08MY5a8ShRjZu1m4rpPN2jnIF3mzK92kt6/meXXQDwR3u9//3t8+eWXePLJJ+00JaCANk9IOsA6JpLcD1Y3ECcfAdPMBW0eCyX3CA94xxRLTg4n4pZ3vhgFGltXVu4ljjGzdjNx3acbtHOQLnOWLnYaYWsjo5CVlYXvfve7vvs2JlPJxNwQrGNKlzGS2MkyFl7j5x1TduziOadOxwdJ+6R5jKzazcR1n27QzkG6zFm62GkEl42MwF0yMTcE65jSZYwkdrKMhdf4eceUHbt4zqnT8UHSPmkeI6t2M3Hdpxu0c5Auc5YudhrBVKJAQA/PF+14lKnX2qR9oU1tn1nNGlKsxs86JqvzgPg7MLJs/GKn1ed2IJ0L4NhYaN6RMWpXz99Ach2gM4YXYtV7tdjz9VEcOtKJnOwAOnpixOMw66OupQPFeUE0tNKpgQojQcRiMnpjclLtKauxGK0nkvhggWZe51WMxD1rPqV6R6anJ4b7Xt8JIJ5if9KoQdzWPQ2k1y3exzllt93+aefAizljIV3sNMLWy77pgB9e9nVC0qa8YQ7ol6knqY5sJjFU7PtwX6NlFWESW0nGzzomq/MUVRLt53Zhedufh2pJz9+KvJlVZqztz6k+FNRVo0n6MVtPZvHBMt8s80qqWpIRl9e3dSXXrCtUpQmws+5poFm3PI9zym69VAos/dNep+xeq93Cj3a6olpKB7zeyDgpaWO9MJDcLEku8tdPtd7M0I7fzpjMzmP9nCaPDI+LpNFY1Cg3NbObthOLWtnI2O2DZMNDu8kg2ewazT+QullSKIoEIYN802TG0jVVKX8UaPtqtNgELr9mgq69vDcEpOuW93FO2W0Ea/+016l0yc/iNzvFRqYPLzcybkjaaL8q5SlHDUjAzl/PMnzMZEdSzfL1r9V5rJ9rfz+xtAhbaxu5f22tN5a65nYcPtqFprYuSBJQMWowJo0eZPi1vRNSY+DYXK3/6TR88w9vUcuqi/NC+MWcsYgW5Ca+nt68px6LntiGpnb73+CobTRaT2bzk/B3SwcajnaiOC+UZCuveVU/ph1eFMGYYQPR0N6FwQPCuO2pDy0riZf0jY+nTVpI161VLNAeZ1fayxr/rP2zXHvTIY+Pn+wkvX+Ld2QchEbSxlr6naZMPYlNNMTkuLT0uimjmPoyGj/tmEjPY/1c7/ekx7HC0pZTUmPg2FytrNzLJKuub+1CtCA3aUwBSeK2iVHbaLSezHxq5W9e8xrKDuiul8rqestNDJA8Pl42aSFdt1axQHucnesgid28+6ddozyvD06SLnaqEaolB/GjpI13X2bSUj+OP5Nxw48sUmIFt6Tz6RhPNDb7QVIOkMcC6XF2x+X1+QLvEBsZB/GjpI13X2bSUj+OP5Nxw48sUmIFt6Tz6RhPNDb7QVIOkMcC6XF2x+X1+QLvEI+WHIRU0jaxtAiV1fWuPJPkKUcNSHFpKWtfPCV9Tss/nXj/hfezdaekxsCxuZpXMRKPvFND3Ud+TjYONLZh4+cx7Kw7gi8a2zC8KBfR/BwcauFjr98lokD8HZnH3t2L9/fWIxLKxqUTTsA3ThyMc8qKEc0Po66l0/T8Ehvj4xVHpLFAe5zdeWON/3SIG4E54mVfhyGRBvNSu9i1SQ1v1ZK2L55qBaflnzzf5Hda7UAyt7QYKVB49KHEmTbeeKuW/MDSNVV4aGMNtFfcSCgLf7x8PADghsdTi3aqISkUqgdLHJnZolWwAebrm7Q9u9DGZjrETX/GlVpLAmuUsvDRguSvLaMFOYn8JdoX1OqaO7Dw8W14bcdBV23S2rf8mgm4fmoZtH+0BSSyTYxZX9GCHG6bmIWPb7P0IelxrO3ztNVO30b+LowEkRfKIrZVjXauzPpQpNWkKDebiMY2dfyRUBgJ+vpmpOSR0fuzsa2rN3GTJx0vDTxjWIvT65uXPSUFObh+ahlKfGKngC/iGxmX0JPwuiFJJLXJ68y+rPbzlInSVtammSO3q+bqxdvU379p+ehCTWFuEMuunoBJo4zl3to57Y3JmLT0DeqMvhKAf/zoHDS0dSVl751875uoa7F+CZNnJXDekFa/juaHAcBwjliuCSxxxHqOmbTd7crKTmX2FbiLkF/7DK2krbK63hVJIo1NRhhJRp3oiwbeMlGtr3nK52nbstu3XrzRbGIAoKm9GwFJMrzQ683plpoG6k0MEB/P54eOJMVZ3GYyJYlSCdyPslHS6tdW88NyTWCJI5ZzzNa3G2kotNCkUhCkP+LRkkcIabJ9eMtEWeXBPCtbO1U1lzWOaM+zE6/aeXKzbyexI1nXwwmptvo4r2LPr/Mn8D9iI+MRQppsH94yUVZ5MM/K1k5VzWWNI9rz7MSrdp7c7NtJ7EjW9XBCqq0+zqvY8+v8CfyPeLTkEelSbdQvz5T17IhLVnMMHz/YlX/ynCMSaWheOAvv7PoaMVnGhBFFKM4LoaFVP9srrXR/YmkRVTVq7diMyjTUNbejobULxQPCiObHf88qgb3q3NKk31nNr5oSE1udjlmr90NOHjaQSIWVGwwgKyDhaGev7ufKnMRiMl7Yvp9obCwxzPvalC7XOhb8cn3s74iXfT3Ej9VG1filgJhZQcenPvhSt/ggqWTYbmVtlsrW2rb0kCToKlzUfZNK962KUBqhlthqz9cWzlT3P3d8CR7aUEMtzVZkyFrbrSTJVrY6GbOsxShpUTZChZEgdQFLlhjmfW3y+7WOBb9cHzMZUTSyDz9vZAD/Lga3qtWy2mFFUSSIpZoK0X7NI0OL2WaBtOowCUqlZdrzJehvsmj61c7bLau3o7MnxmSrUzFrtkZ4X1SNKmOTjo0lhnlfm/x6rWPBL9fHTEdsZPrw+0YG8N/Xk17IJVnsMMNIjuuXzL6b99Tjxse3ormjh+rc4rwgNt0xHeff/7Zt2bkZEoBh+WEAErFySNv/m7d/C5N/t45axaSdu96YbCrDVvqTZZmrdNkMu5XGw1kSOnvNL72SBNx32XhEC3Jw+9PbbY+NJYZ5X5v8dq1jwS/Xx/6AkF+nEX6TBHohl2SxwwwjOa5TlbVp2wpIEvUmBgAaWrux6r1aLrJzM2RYy4Gt+l/1Xi2TFFs7d1tqGkw3U0p/JDbxilm7lcatNjFA/NFiU1sXjivMNZ0L0rGxxDDva5PfrnUs+OX6KDiG2MgIUvCLXDKTq9nylCnbPc4peFXK5jmPvNpyK7ZqG9oweGCY6Fg/x3sm4Zfro+AYQn4tSMEvcslMrmbLU6Zs9zin4FUpm+c88mrLrdgqLY74Zj0K4oj58B9iIyNIQZFLGj3dlWCvCi8vO4xwyz47KNWOaVDGNa9iJNH8WB1n1Vc0Py6pZvU/a//R/LCuHNhqvNH8sGsxyxqbaluszlWqy/tlPQriiPnwH2Ijkyb0xmRUVtfjhe37UVldj16SnOeMZAWkhHxUu1iVf991cbnjL7KZ2WEEiX2kvtQ7jtc8ZAUk3D33VOLj1eMKZQdM50cGMGtcFFtrG7Fkzljd40i4e+6puHsunf8VrOy06hdAws9bahqwZI5xOzKAJXPKE+e5EbMka8ToMxnAhacOw7llRaZ9nDuyGL95pQorNtXg57PGmPalHVtXTwyPbtyD/3phBx7duAddJoovwLnri1m7PPp087qo4Mb10YtxpTNCtZQGeCVb9Itc0iyPDEkeFZK2SHKvKJWdafN4WI3tzmc/0c2Fo6YwEsS9BHJybX4Xq3w7egQkYP6UY9XNaSTjdvLY5IWycP/l4wGk5l8xmm9tv0bn+iWPjFH+HTV68u2ABEwfOxQ79rdYjm3pmio8vLEmqR/tnJKOwY7PaH1D26fX1ycv/NbfJN1Cft1Hum9kvM5X4Be5JI9qtqS+pMm9wmMeemMyNlfX4/H39uLVHYcM+9HrQxn/G1V1eHTTXt3zaBe43pis+lF44KozMfv041J+r52nM4YX4vHNtXh/bz0ioWxcOuEEfOPEwVhbVWc6R/8+ZSQe3qg/TsXmC8qjvsrsS+I3UuZPKcP5Y4YZjm3pmir8dUON4fnXT03ezDh1fWHJsUPTp9fXRQXe10e/jMsviI1MH+m8kRH5CvhB6kuW3Cs85sHOXNvNaULan9PxSDIOs280/LoeeM5PQAJ2/noWQtmpbwV09cQwZsmrpt/4qM93aj7tjJekz0y9LmbquOxAev8W78j4GJp8BQJzSH3JknuFxzzYmWu7OU1I+3M6HknGYXaT9ut64Dk/MRlYWblX97OVlXstH1upz3dqPu2Ml6TPTL0uZuq43EBsZHyMyFfAD1If8cp94tS5esc5Of8s+VxY7Um3HC+k8LbHKEZp8ws5NZ88xmvWRqZeFzN1XG4gNjI+RuQr4Aepj3jlPnHqXL3jnJx/lnwurPakW44XUnjbYxSjtPmFnJpPHuM1ayNTr4uZOi43EJl9fYySr6CuuUP3BTnlmSlpvgK/vLjLwxba8618CcTzl8yrGIlH3qkxPU6L2TwodZUqq+sByKgYNRiTRg9KsZXEvsJIELE+CTiAxPgH58XzvdDWRKIdE2k8xmIyXti+n7qWVUyWUZgbRFO7sbqK5B0Zo/XAu84WyUu+vOdHyS2jx7yKkbhnzaeW78go57NcX0h8GM+RxDZekmsa7+siT2hiTHvsxNIi347L74iNjI9R8hUsfHxbytv+tPkK/CTps2sLy/lmvlTo6InhzZ2HLI9TYzYPetLqv7xVrSulJrGvqa0bVz/6nq4M3Cw5F6tqSTsmq3iUAbR39+LqR99L/J61urgRZpsYPZvN+rBT+dxM+g+kSouVObPL/Cllui/6AkAoO4D5U8pMVUvq82mvL6Q+XFtVh46eXt3+1f2wXtN4Xhd5QhNjZnH10IYaX40rHRCqpTSAx43fL5I+u7bwON8ob4u6DcD4ZkSSR+a1HQdxw+PbDO0AgOU6ttLc1EnIC2ehtVP/pmJEUSSIpZqNlho9G4siQTRa+FSvPRqpuxlm64E1Zmhts5IWy4jnymntopsPhQvKh+LhH5xteoxV3JHGnNafvFIXKLEFpH8eGa0tpDFmdeyCqWXU+bEyFSG/7iMTNjKAva/F/SLps2sLj7H0xmRMvvdNw6+91W0ASPG53u/05NBmfShE88PYdOd03fM376nHoie2mT5mIYHlGxkju7Q2Jh6dDAjj9qe3G1ZoNpoXEpluYW4QkiSjsc24UnhxXhCbF8/Q/aaCNWackLQr/bFccJ2WJVs9JuOVukAdWzwedfvhcTmN3wEQ+3JrbaMvXgPwEtL7t3i0lCZkBSSmkvB+Kjlv1xYeY9lS02C6wdC2odeOlZ+s+lCoa+nUtTUrICEgSbY3MQDbTdPILjXqeKysrjfcxCg26M0LiUyXxAcNrd3YWtvINWackLQr/bGeRxLbrOvD7PrCM3WBOrZYr2lqeLRhF1rZNMmxRvEs0EdsZDIcP0n67NrCYyxu+IPmXLtjdQoeY7A6jucYeceM1/43wovY5p26wK++ZcUJv2eaj5xGbGQyHD9J+uzawmMsbviD5ly7Y3UKHmOwOo7nGHnHjNf+N8KL2OadusCvvmXFCb9nmo+cRmxkMgi958Uksl63Ss7bkU2SynPVEmW9Z8qsNuhJJZVn2IMHhAEZONzamfiMRH4azQ8b+p1k3khgeSejhFJCTSpt146VNDZlWcahlk4mSSrrfPPyv5aABMgym5JMsdMoFuua21GcF0JDa5dpGxNLi1BZXc8tdYHSrlXqAhL5MMs7L16/J0MSKwPC2di46ytUlA1GND8Hh1qExJon4mXfDMGq0qyZkkBbSM5JlDf2AX15oZ6ChEXJQ6JiIbWBpMq0tu+540tMZbCAvoKExE4awtkBdPbEqM4pjASpK3xb2aonOVefZxabZ44ooo4ZEtvcUlSp+1owtQwP9cWFnnxd+/9aOwG2itpKO6yKGFIfsvpa6YNWheQX5RLNWo2EstBmolxz83rsd4RqqY/+sJEhkf59uK/R8MYqwX0JNk2+BZabCclNisQGlv7VN62nPvgyReptdFPXg7cc2wxWCbUCqbRd24ZZxWYlNgF7cl3WG55VNWlS1H1Z/dHB+geJFaefkI9PvmyxlbqAdM2wbEhoJfJ+Siuh2MNjrbp9PfYzYiPTR6ZvZEikf8PywwAkIsmxW1/JknwdTCKBtcrdYSXnNrOBRxXf9T+dhvf3Nlhm9jVDsbOuuR2/fuVTw0cHtBTmBrFo2mgMHhDG0PwcJgm11k5SaTtLRW3AWvpuBu0jCKL463tUZEZxJIjNP0uWh5NmBVbL/p2QgyfGAbJrAKkPaTPc0srG/ZRWQo2SOuHGJ7ahmVF16JXtfkTIr/sJJNI/M2mscoxbEmwFEtkkiQTW7B5iNS4rG3hU8d1a24jJJw7G5BMHM7UDHLOzsrqe2yYGiEubxx1fmGibRUKthlbarpxDIxm2E5+0Ul2i+CP4M7ChLVUebmaL3meV1fWOfitHeg0g9SGNr1lk435KK6FGSZ3AuokBvLM9nRFFI9McNySsXuF1NWSnq/h62Za2Ta+k7X5KD+Bkn3bbcmv8fvZzf4sbv12P/YzYyKQ5bkhYvcLrashOV/H1si1tm15J2/2UHsDJPu225db4/ezn/hY3frse+xmxkUlzFOmf0ZNUCXHpazTf/Bi3JNg0kIzN7BGy3XFZ9W+GEz61Y48WrX0kvrYaD0sbPPp1ChJ/k7zCYCaz52mLHfzs53SMm2g++ybEr9djPyM2Mj6nNyajsroeL2zfj8rqevRqdJZKJVggtQKy8u+7556Ku+eaH2OnqqqVjayQjG3+lDJIJp9bjcvMdrP+zeDhUz1Y7dGinLtkTjm21DTghe37saWmAT+fNcYwtwWQPJ7emIyNn32NH6/+EAv+8QEe3rAHvTHZ1D4ZwDdGD8If/+8z3Pf6Tmz87Gts3lOPWeOikHXO0fZLGme9MRmbdh/Gfa/H+9m06zBTTJLE37dOGWLZzhVnD8fLHx+wtTYUW+ysrAvKhzKtld6YjE27DuO+13fiD6/txMMb9uC5bV9yW+skfjaqxE5zDgk8rmVZASlxvbXCb7anK0K15GN4lIXXVrDlnXPBjTwOVn2w2mBHTmqG03ksjOyeO74kJUeIUcVuvWON8pHoxdFtT3+UkgtDkoAFU+J5X1hkqNr+WebYSAJOI3nXQpJHiES9ZGQzjR16YzNT7im2zp8Sz01Cu1bMJPV2x6PXl5d5ZHhfy6xiEbBfAdwp2/2CkF/3ka4bGZYcCaSSZl5ZMN3M40AilaYZF63tSvtrq+rwt017Ddu9dcbJuOn8E12pJK43XiPprvp3ja2dWLTqQ+K/7h+4agJmn35sE3NDX+IvI66fWob/nDkWf3lzF/70xi7qsV03eSRmlEcTYyKdKxLbrJIQGqH49Y2qOjxqMv9WsK4Ns3xGEoAfzzgZIwdHMHRgDs4YXohV79WitqENpcURzKsYSSz91vZp5U+lf15r3avMvk5dyxQ5tlH6BT/b7gfERqaPdNzI+DVHgpp0sNEIVtvTecwKtLlxtHlcvrH0DRw6Yi4BD0jAv345E+ff/zb1tzJaH5L6fP1Pp2Hq798iKgux6c7pTPNjJ6+QGto48SLu4jmB1lmmblAo8Xncm5HO6zqdbSeB9P4t3pHxIbRl4b0gHWw0gtX2dB6zAm1uHPWYttQ0WG5igPgjl9+uYctwqvUhqc9XVu613MQA8ZxKrPNjJ6+QGto48SLu4jmByDYxcKB/N0nndZ3OtvNEJMTzIX7OkUDbtx9zIbDans5jVnArp87e+jamfrT9kfZb20Den5d5hVja8yLuWNryc9ybkc7rOp1t54n4RsaH+DlHAm3ffsyFwGp7Oo9ZwU5OHZpzRw6KMPWj7k/9XytKi8n78zKvEEt7XsQdS1t+jnsz0nldp7PtPBHfyPgQq7LwynNPL/MMOGUjz5eRjWC1nfeYe2MyNlfXo3JPXB58pKMHkgSMHJSX8oKm+hy1fyaWFmFrbSOxvyaWFqE4L0Rc6kACUJwXwoHGNjS0dWNgOIAjnebVtCUJOP2EQhTn1VGXVFB8OLG0qK9sQgeK84JoaDVO+R4JZqE3JmPYwDAOHTF/HEKT00U9P4CEc8uKEc0P41BLpy0ZtDpOSOLdibizeiF8YmkRovlhqndk9Pq3G68kttu9PqTD9daIdLadJ+JlX59iVBbeT2+ik1QtplVmuCUhZLWd17xYyVrVkln1OVYyYCsprVuVtFlQfLhgalmKNJzofAIJNKlqyWh+8kJZaO3qTZE8q/9tJodWxwlALr/lGXfaPs0k+iSVv43Wi914JbGd1/UhHa63RqSz7VYI1VIf6bqRAfydG8BMDgrEJbjqmzBre04sRru2250XUlmr2hYrmxWM/EVyvlEeGbdQbp4Pbaix9Y1HODuAzp7kb41o8siQzE9hJJhy41cStJltFtXH0cY7j7gjiSG1HQumluGpD76kziNjN15Jbed5ffDz9daKdLbdDLGR6SOdNzKAO49aWGyykqHSyDHdlBDysp11XmhlrSxSZloJMwAU5wWx6Y7p2P5FE+paOvDrl/9l+jiHFqtvSwblhfDOHeczSba1BCTg79eejfdrG6GXu8MM0vkpKcjBfZeNx+HWzpT5V8fG4AFhQEbScQCY491O3NFKx9XS9vdrGlC55zBkGSiMhDB4QAjRglzDXFWsEn+jzMJuXh/8dr0lJZ1tN4L0/i3ekfE5WQHJd6XcSWSoNGXoaSSEdn3By3bWeaGVtbJImbX+IhlzQ2s3tn/RhIrRg1BZXc91EwNYP/Kpb+3CqvdquTz2isnA7q+P4icXnkJ9Lun8HGzuQCAg4TtnHJ/ymVVsVFbXM8e7nbij9a1ix9baRkw+aTAmnzTYkb6s1reb1wc/Xm9JSWfb7SJUSwJqvJKN8pAQei1XZGmXVcpMK2GmPZ43NBJqp9qiGbvTMm6v5dSs5/L2i9drVuB/xEZGQI1XslEeEkKv5Yos7bJKmWklzLTH84ZGQu1UWzRjd1rG7bWcmvVc3n7xes0K/I94tCSwRE9CyVPyZyUhBOLvcEwsLSK2Vy2brRg9CJNGxd+RIOlLkZI6IR2NyTIKcrLR3NFDdE5AAn42uxxvfPqVqc1qtP4nGXNhbhAxWUZvTE4cz1PdFOh7R8YsXuZVjMQj79QQj9MISQLyc4N4dOMeFA8II5pPXrMnFiObH6MYIemHh2Q26T2cvDAgAYePpr6vQ9qnHmZ2mEm4SSTz2n6G5YcRk2W8sH1/ylobnBefw0MtmSsx5vF+Sya+I0OKpy/7Pvjgg3jwwQexd+9eAMCpp56K//qv/8KsWbMAAB0dHbj99tuxevVqdHZ24sILL8QDDzyAYcOGEfeR7i/7eo1ZpeWH+qSZPCR/RhJCNaQVra2qH5OolvQqOPOWjpKgVS0B5pWOzVRLJEopZUz/3PYl1lZ9RWQjidx4wdQyonghHSctLFWUzVhOKZ/W649VMmtlK62EWw9a9ZSehJsEJXa0KjDtWlM+15O9G9mZLvBQHAnVkocbmZdeeglZWVk46aSTIMsyHnvsMfzhD3/Ahx9+iFNPPRULFy7EK6+8ghUrVqCgoAA33XQTAoEANm3aRNyH2MiwYyV51Mv3YbfsvdkF2uqiRVP92CyPDCl2paNm8M4jQ1PJmPaCoL0JqVHbQ3qx1TuuKBJEI+VNUotZvhPS+SmKBLH0ktMA0MuntbDcfEhs5bEJsSurNsJog2KF0YYn3W/WPGTlovq1D+XXxcXF+MMf/oDLLrsMQ4YMwapVq3DZZZcBAHbu3ImxY8eisrISkyZNImpPbGTYoKk6bOdxi5aunhgmLV1nmBHWrDI1qWx2/U+n4Zt/eIvLoxO70lEAyAtnYd65pTja6UxmX1rJNw3KY4H7Lz8Dh492Wj7mIP36Wythvv3p7bbt15srovkJZeGHk0fiG6MHY9KouCqElxyY5nEAjayZVsINwNIOVgl3cV4Iv5gzFtGC3JRHRrc/8xFRsU+lLXWspfvjEx6yclH9Oo5v3pHp7e3FM888g9bWVlRUVGDr1q3o7u7GjBkzEseMGTMGI0aMMN3IdHZ2orPz2AWvpaXFcdszEVLJ49baRq6Sv621jaZp7Y2kljSy2ZWVe7m9/2FXOgoArZ29+OYpQ4n8qCextDqPVvJNg4x4RemApC9F1kIqEVUfFy9VYN9+vbkimp+uXpx34pAke3jJgWkkszSyZhYJN0kcsUi461u7EC3ITbSfPK90Mm2aWPM7PGTlbkrT/YznqqVPPvkEAwYMQDgcxg033IDnnnsO5eXlqKurQygUQmFhYdLxw4YNQ11dnWF7S5cuRUFBQeJn+PDhDo8gM/FK8uh0ZWqAr8zXqn8/SEfdkKWmk/3q9ljmx+9rwykbeEu4/VKF3Ct4xJEfri9+wPONzCmnnILt27fjvffew8KFC3HttdeiqqqKub3Fixejubk58fPFF19wtLb/4JXk0enK1ABfma9V/36QjrohS00n+9XtscyP39eGUzbwlnD7pQq5V/CIIz9cX/yA5xuZUCiEE088ERMnTsTSpUsxfvx4/Pd//zei0Si6urrQ1NSUdPyhQ4cQjUYN2wuHw8jPz0/6EdCjyDWNnqpKMK5460W/5/RVJraipE/ma9YHDVZ+8MqPWhtIfMOCW/ZbzRfJ4389W1nmx69rw2kbaPonsYO2PTdizU14xJEfri9+wPONjJZYLIbOzk5MnDgRwWAQ69atS3z22WefYd++faioqPDQwv5BVkBKFLfTLhJFQTBrXBRbahrQy7nS4OVnDTfMFwEAd11cnqghVFldjxe278eWmgb810WnWrZ918XlCGUHTMem9/9GyACuPNv48aWZH5Xzl8wZCyD+zsBzH+7Hoxv34LltX6Kyup7Jt2q/VFbXAwDunmvtGyMbzY5Vxv/yxwcM7e3qieHRjXuw5PlPsOT5T/DPrXRjs/IhAEw7ZYhlO+q5Umz65Uv/wuTRgyDrtK2NNxp7tOcYoZ0rM5+Q9MtiAylZAQlL5oylykNjZgfNeKzaSkesrrGA9Xh5tJEJeKpaWrx4MWbNmoURI0bgyJEjWLVqFX73u9/h9ddfxwUXXICFCxdizZo1WLFiBfLz83HzzTcDAN59913iPoRqyR52JL8sfenlgNHrx0i6Ou74fLxR9VXKxTYvnIX7vz/eUuZrVsXYrDq0nTwlZjk4eOaTAGDqX4ULyodi1OA8PLyxxrQaNolsd+maKsN2eIxNOyckFbwjoSy0d/cm1X+SJCA3mIW2rl5i+0hyFtGOhzRXklXOmwvKh+LhH5xt2j8tZv0WRYKQQSbh1mIWI7RtpSMij4wxaSG/vu6667Bu3TocPHgQBQUFOP3003HHHXfgggsuAHAsId6TTz6ZlBDP7NGSFrGRsY8i13yjqg6Pbtqb8jmPfAUkeU4euGoCZp9undDOiOU69pnJX/Wkzg++XY0/vfF5StskPljz8UHcuMo6l4te27zySVxQHsXm6nrc/387se2LZsO2rp9ahtu/PQYrK/eitqENpcURXHVuKbZ/0YSvjnRg7+E2/PmNz037+nBfo2WuHtKxKShzsraqDn8ziEUZwP/3jVIc6ejB/9u2n6hdhYtOL8EF5cOIpL1GMUua1M5O7o/emIxbVn+Ilz8+aHiMkkyRB1Zr7oGrJuDCvm9oaVIxkK5lZe1nKiKzrz5psZFxA7GR4YOT+QrcyAHDI5+CHR+w5OBQU8Ixn0RvTMaYJa+a/gUckICdv55lmMvGqq9h+WF8daTT8tsRwHpsrP0DEpW8FzAfN60NrLFAEqtdPTFbc0iDU2ufdE2key4UATuk92/fvSMj8Cc0+QpY2nY6B4wd+xTs+IAlB4caK9tpbFtZuddygxGTgZWVe5n7qmsh28QA9PNC2j/tJgYwHzetDayxQBKrdueQBqfWPuma4LF2BZmN2MgIiHAyX4GbOWB458IgPY5HHgde+SRIfWh0nBM5KWjadDonBol/3IgFs+PsziENTq19p48X9B/ERkZAhJP5CtzMAcM7FwbpcTzyOPDKJ0HqQ6PjnMhJQdOm0zkxSPzjRiyYHWd3Dmlwau07fbyg/+CbEgX9mXR4SUvJV1DX3GEojY4y5itQ8pyQvCMzr2IkHnmnxtAOI6zs643J2Fxdj8o9h+PF6XJDGDwghGhBLs4pK0ZvTMaO/c2IBLPQ1t2r24ZZH1b+s6IwN4ie3hg27T6sW2dmYmkRivOCaGjVVyQpNW/qmttx8rCBlsUhAxIwr2Kk7mfx+coxfXRTkJuNlvYeorHS5LnojcmIyTIKc4Noajceq513ZIzGDcTfTXns3Rq8V9OAnGAAHd0xQxuiBfEXxCur65PWNulaisVkvLB9v+41YV7FSNyz5lPLd2SMxkJzzbGKLSCu1IrFZPTGZMOX5bV9KH4gfUcm03OhCNgRGxmPSRfZnJKvYOHj21JugnbzFWQFJNw991RL1ZI6BwxJJWdS+6xk33pyXdo+zPxHQlN7N+b9bUvS77RScbMbjVLz5tanPwIQL4TY2qW/IQPilbeNXhJdW1WHjh7jcwGgub3H9HMFCeRxQyI7Vlq5e+6pRKopLWbjXrqmCg9tqCGaOxnA3PElKS+mK3NmtpZkAO3dvbj60fdSzlOuCaHsAKaPHYq1VV9Rj4XmmqMcaxZbQFx2ffWj75mmL9D2oV4TVj7tD7lQBOwI1ZKHpGP5dSc3XkYbiqJIEEtVeTmspNqFkSBxPgsS2TcJpD4guRmTwrIh0p6rbSMgxW+ARrJdVum7HjRxQ9qv+kZqdjztuJeuqaLeFBn1C8TXNpB6sy+KBNGos6HWXhOs/GGUR4bmmsMy12YxaXRdM1sTfvyjTuAeQn7dh183Mulcft3JR2HqRzxAvErvpFGDkr6uJpHe3n/5GbqPYLR9kci+zZAA/ONH5+AbJw4m9kFXTwyTlr5h+VcuDyQJht8kKTH2o8kj8UVjO0qLI5hXMdLwGwlWCbkEYGBONi4eX4KAJOGM4UU4rjCXOG5I+i2MBLHs3yZgUl+FX5IYue68Ufiisc1y3F09MZzyi1e5bN6U/pW1DSCxlgYPCOP2p7cbxqNyHkkKAj1JO801BzD3IStG1zXlmlLX3I6G1i4UDwgjmu/Px+wC9yC9f4tHSx6RzuXXswKSYzZlBSRMPmkwJp80WPdzUultQJLwnTOON+2LVPZthgzg80NHMOVk6xT5CltrG13ZxADGmxjgWIyNO74Q86eOtmyLVUIuA2jp6MFFpx/PFDck/Ta1dSMQkJAVkFBZXU8UI+OOL8D8qaMs+19ZuZfbJkbpX722FZ9UVtebxqNyHkkKAr1rB62MmvcmRtuH2jYnrymCzEdsZDxClF9ng6ffePmWVuLqtzl1KxZZz6e1j/d4eEiYSfrnbQ9r+27Ep9/WgCC9ERsZjxDl19ng6TdevqWVuPptTt2KRdbzae3jPR4eEmaS/nnbw9q+G/HptzUgSG/ERsYjWOTMrO+m+LmOB227tH4ze/ZOKvs2IyABV51bmiKxVapz643NrhSbFAnxd2SMJLokslb1GAYPCCOaH8ahlk6u0ndtX3XN7Th8tBNN7d2Q5bj0vCA3iGYDuTUQl3vv2N+Mg03tqG/tQkFONpo79JVTRvYk+m/pQMPRThRGQmhq60JBbpBitNYY9U8a21YpCAzXQEuHpURffZ4TMZrpUup0SKVhB7+OT2xkPIJWzsyzUq5fKquytEvjNxI1BIns24zpY4fi/PvfThnD3PElePGjg4ZjU8ZgB/X4jXwxf0oZHupT29BK5vX8V9hX5ZhUMUUqzber5mpu78E9az5ltoenmoylf+BYbBvFo4zkFAR214CVXVYSce3/k3xmNPZMIF1SabDi5/EJ1ZLHkAQHq0ybh7zbKYm43Xat/EYiHZVwTAZrlkdGj4AU38S8UfUV8V+sehJaOzdP0pwdLBcgs/mRkSpxN0Irnafpywn0xu11/2qs0gEs18ij7a4BK7vM+gCM487sM69vek6Qjqk0aPBqfEJ+3YffNzKA+dd1rDJtHvJur6rekrZr5DeWqroAsLm6Hpuqv8ZjlbVo7TRO+Jafk41375yOC/603nYV7t6YjM176nHjE9tMH50U5gbxv/92JgIBSVdWbvWVL81XwqTz8/tLTsfNqz80zLALANH8MDbdOZ25LyNoc+gU5gax7OoJSVJ+O/2zUJwXxObFM5jl7XrrgnUNSIhnev7FnLGJ7NVmc2R2fWL5LJNI51QaJHg5PiG/TiPMpIesMm0e8m6nJOK82jXyG0tV3YrRgzD5pMEIBCQ88PYe0/NaOnrw1Pv7bFfhrhgdv6kGJMl0EwPEM/tmZwUM/WElX6WRt5LOz+dfHTHdxABxmbOdGDOzgYam9m4EJCnlQmu3KjkNDa3d2FrbyHW9sa4BJdNztCDXMi7MYof1s0winVNpkJAO4xNFI30Oq2SSh9TS66q3Tst19Y53S5LL0qdbklWnZcAsffHAqarkdm0g+Yz2OL/FVCaT6b5Oh/GJjYzPYZVM8pBael311mm5rt7xbklyWfp0S7LqtAyYpS8eOFWV3K4NJJ/RHue3mMpkMt3X6TA+8WjJY6yeMZNU+rUj5TSTQcar3obQ0NpF1bfVuFhtM3sfQP37iaVFVFV11RWKB+fF5dmHWuxJYK36VI+NV2VxrVQaMnC41bxMgx4k9hTnhVCYG0RxXgiNrV2GPogEA3jxo/1Y88kBnHFCIY4riiSqiZNUkTaC9h0Zowrbbknhgfh7OjFZRldPDFtrG6ll+TQpGZysVp8uuPWODkkMKXOvrg6eLqRDLImXfT2EVhGghVS1BOjLIM3eNLdS1Ji1QaPEIrXNqE0jmfPc8SWWlYolAAumlqWcryhyjOSj2sJ92jGY9ac3NmV8rHOlnG82XyySe5qx0WBVeZs3ijqNVqFF24eR7FhLQJPbR09pBFjHAalqiaStTMNtqTDpeklX5ZZXsSRUS334dSNjJW8lwak8MiQXdjO5Jk11XRLbaG80Sl96mxR1P0abHSOJsZFtNBssK7+z5goikZoDdBcct3KrOAnpjcNudWuaP0C0kMjyWVMy+Dn3h1N4JRUmWS/pvIn0IpbERqYPP25k7Eo+1ZV+eWf2JbHNSEbKUz5KY49ZX+t/Og1baxtTMvtOLC0yrSAsga6KNskjL5KvtmnPofEPi0xSnRX21y//y5Vil4W5QSz81ii0dPQkMvsue3s3mtv1M/Vqyc/Jxl0XlSceY5H4nHU96sm6lQrui1Zts1R1KejJ8nmlZOgvMmjAeyk0ydynsxzb7VgS8msfY1fyqa70SwJP6S1gLCPlKR+lscesLyO5K2mFZJIq2kZjYJGf0p5D4x8WmaRiT2V1vWsVu5vau3H6CUVJVaFJNzFAXB5/XFGEa8yb2aqVdWcFJAQCEvEmBtCX5fOSafcXGTTgvVSYZO79IFdmxa+xJFRLHsBDpuaU1M2O1M4JmZ5TFZfTQVJIAot9bp1jBxZ5utH5PI8lPd/J1AGZErtO4Aff+MGG/obYyHgAD5maU1I3O1I7J2R6TlVcTgdJIQks9rl1jh1Y5OlG5/M8lvR8J1MHZErsOoEffOMHG/obYiPjAYqcjeXJogRjKSkPrGwz69/Ouaz2GGHVlxO2egGNf+yMyU7M0qK10ekxOhFjtG3S2J0psesEfvCNH2zob4iNjAcoVW4BpAS7ZPD/6n87WT2WxDaj/tXnGkFru5k9RljZqbywNmtcNFHJWYsM4PKzhmPznnq8sH0/Nu06jE27D+OF7ftRWV2P3pj+O/K9MRmV1fWWx6mPfW7bl3h04x489+Gxc6za6Y3J2Pj517j96e0YOjBkqehSlFizxkWxpabB1C4jrjx7uCuFFc8cXoDNKj9sqWnA7L65MoNlfdC0r9fXkjljsaWmIWmeSOJL25aMuH9f/viAZdxYrYl4WyMoRpM52Ll+6UGznpXjzeLJjWs4L2jH7iVCteQhrJVl3ZDt2ZHaLV1ThYc31iTlyghIwPwpZVg823yjQ2OPNh8HiZ007ZhBI8UmrSqsUBgJAoCh9Pu1HQdx29MfoY0iF4tZ7hIrrGSlevbyIBLKQig7kNSu2Vyx5MqxigWrXEV6OYiAZF9IEmB2lbWabxr71WS6zNoMHlJh2jZI4ykd5sQvsn0hv+7DzxsZwN/VY1n6dzKHg2LPG1V1eHTTXsPjHrhqAmafbpwgkEfAGyXHI82fQ2uHOjeOVb6T6WOG4EfnjQJk4M2dh3R9RZNoz8zWW2ecjJvOPxEAUrIKH2rpwAsf7cf6zw/r9i8DKB0UQW09ed0q5bwfTR6J6WOGARIs5fE041K3f0F51FBK39jahUWr7MfSRaeX4JWPDzKvl96YjL+8uQt/emNXymfpnLOEB3aun7TXMdp48jNe5eHRQ2xk+vD7RiaTcCOHA2sfdnP3mPW1/qfTLHPSKDYBsGUHScLEgATs/PUsZAUkW/Nhdz5JfM7ybZjdOHJjXKSYjZ9knF7nTclEWPL0ZMoc+G0spPdv8Y6MgBs0ORzc7sNu7h6zvlZW7iW2ya4dJPf8mAysrNxrez6cPl+xlRa7ceTGuEgxGz/JON1Yc/0NWp9m0hyk61hEQjwBN9zIn8Dah5M5G2obyB6NuJk3orahDYMHhomOdSrXjtPjdTJXi9lxXubUofmM5TgBvU8zaQ7SdSxiIyPghhv5E1j7cDJnQ2lxhOg4N/NGlBZHbM+HW+ez4mSuFrPjvMypQ/MZy3ECep9m0hyk61jEoyWBbRSZXl1zO4rzQo7mT2DN0TCxtAjFeSHmfs36mlcx0jJnSGEkiFhMxsTSIpQUsF8ESJ5KByRgXsVIolwmxXlBTCwt0v3snLJiRPONbdXztVqyGYvJiOab928nl9LE0iImeWg8FoLM7fPMqROQjH1Asl5IbBmUFzKc43SDVBJsRzpMe43JpLwx6ToW8Y2MwBakFZJ55U9Q8kQoJeW1yADmji9J6kOxsaG1i7lfI+66uByh7EDCJqOXcZvaunH1o++ZVt02Q2k3N5RlKbueP6UsUdDTzFdAvG7WN//wlq6s8vevfYpDLcYv/SntK77Wi4XCSDCRS0VvvLSvyCj9zh1fkvKCNY1k2ahuFGn7Vn4lZf6UMjy0oSbFP6TrRb0ejHxc39plOMfpBKkk2K502MynevNCe7yfSdexiG9kBMwoMj2SFx+jBTncZHszx5VgwdQyw88f2lCD13YcpLaRlgVTyxLjmTmuBA9eMwFRi29b6po78NCGGswoH2p6XCSUlfTvgr5cIyS5Y84cceyvbytfAfGX9xY+vi3hMyCeC+ivJputSDhLV1Ku9XNzX26UXM14SNH6IVqQgwVT4zd/bV91OuNQQxILpO2T+JWEM0cU6cYNzXohiT0r3/gdo7nTjov0OCuMfGo0L7TH+5l0HIuQXwuYIJGgFucFseSiUxHN55sDh1QiaCWLNqMgNxsBSUKjSZK3EgOZ9+Y99Vj0xDbTCrgkstv7LhuPw62dGJwXxu3PfIQ6g29H9M4llYbqndcbkzFmyaumihoJwGe/mYVQdsAxmTUARPPDuP/yMxK5YiaWFhFL3Wnl94PyQnjnjvNx/v1vOxpberYCsJ0zqqsnhklL3zD9tildZMBqeK13lvHT5qLxOvcXT/wwFtL7t3i0JGCCRILa0NqNaH4O97LvpBJBK1m0Gc3tPZbHKDJE9fiyAhICkmS6iQHIZLeBgITvnHF8/P0jgk2M+lzFLlKpsPq8qgPNlpsOGXGJ93VTRjkmswaAupZOBKS4HwCgsrqeWB6qnhcSG+tbu7DqvVrHY8vIVrvrZGtto+EmRq+/dIHXemcZf1ZAovIV7fF+Jp3GIh4tCZjwUqZH2iapLNoOerbwGjOtvJPHuV8d6SD2m3KcmzJrp+X3tGPnAe94cas/t+A9d+k2foE1YiMjYMJLmR5pm6SyaDvo2cJrzLTyTh7nDh2YQ+w35Tg3ZdZOy+9px84D3vHiVn9uwXvu0m38AmvERsZH8K426mT1Ui9leqR9K7JoFoojQUvpcGFuEDFZTvEriSSWRnartEeC+tzemIyYLKMw11hqrKYwN4gd+5tQkBu0lBYrEm+1fazjNUMbQyRzPygvhLrm9qRq1LGYuR/UMUMiOSeR3FuhtBWLybprNFEd/cP98ero2740rcJuZ02aXSu8roJslTqBdE6cuCax+MZrf2Yi4h0Zn8C72qjT1Uu9lOmR9h3KDmDu+BLLIot6NLR1ozBiLg9uau/G1Y+8l+JXEvtoZLdZAYloHOpz11bVEcniteO5Z81OomPVEm+a8dIgITWGrOTGMuLvutz69EcAyCpzq/325s5D6OgxVoYp8n5Fcn8DowRbsb29uxdXP/pe4vdKLAEgmj917LGuSbNrhZ4dblZBtkqdoF3vbl6TWK6xfqkqnWkI1ZIP4F1t1M3qpV4uTKu+SapMs6pp1Bj5lcQ+0rwYJNWy1TcfXlW+jViuE0Ms4zWC5YbACovfll8zAQCINjJ6m6miSFBXEWeUC8YIvdijWZNWVZtJ+3QCkrh3Io+MHdvMfOOnqtLpgqh+3YffNzK8q416Ub3US5meUd+kctv1P52GqX9401TtQYKZ7NfMNySfW42jMDeIZVdPwKRRcYWB1fEFudlY9M0TsWx9NZot1FU0Y6UZb11LBxqOdqIwEkJTW1fiv8V5IUQLcoliSN3Wr1/+F/Uc0vpNzbCBIUhSwFRNVhgJYtm/TcCkPuWH4pfBA8K4/entqGvppLLXCL35IFmTdqp4Oy3lJk3vsHnxjMQ3g9rznbomsVxj/VZVOl0Q8us0gabaKIkUjnd7JHgp0zPqm1Ru+9T7+2xvYgBjv1r5xupzknE0tXcjIEnICkiW8mQgLi3PypKYNjGAeQzZHS8NSluV1fVMc0jrNzWHjlhniW5q60YgICVuTMq443J6PpsYQH8+SPxsp4q301Ju0vQOW2sbdft38prEco314rrcnxAv+3oMb8lkpkowafFKos3br05V4uUxbr/EEI9q6k6NxUl5vt12edjh9Vi8iEEW2/w8nkxAbGQ8hrdkMlMlmLR4JdHm7VenKvHyGLdfYohHNXWnxuKkPN9uuzzs8HosXsQgi21+Hk8mIDYyHsNbxpyu1Ut5QyvRtvtU2im/OlWJ1864/RZDLNWo7Ujcgfg7MmbyfDMf8ayebdWXGXbscDoG/HwdY7HNz+PJBMRGxmMUySSQmmeDRTLIuz0gNe9BV0/MVh4EN/IokPpBkWzqHUeKkV95jJNkHFeePQIvf3wg0ceVZw/XVXoYjZuFK84ajj+t/Qz3vf4ZNu0+zHUOu3pieHTjHvzXCzvw6MY96OqJpRyj9u2WmgYsmUM+FkWRc+XZwxN+A+J+IY2BX35nHO6eaz4vS+aUY0tNQ8r8m80pLVZ9mcUgSWyZ9amc68RatvKRMn+A+3lZWK6xTlyXBccQqiWf4Nc8MnrtaCXLNO26LddeuqYKD2+sSbI3IMXzmiyefezmZ2TX3PEleOqDL03zkLghAdVrrygShIxkWa+ZnNzIzjuf/cR0fGqKIkF09sRSqnAXRoK495LTbM8hyXzp+aIwEkSXjl3h7AC6emIpOUUioSy0qo4lzd+iHadZ3Lz40UHT+echITfri8QGqzHoxb7iA8D5HDNWPtKTtvsl/QOvc/ozQn7dR7psZAD+kkG77ZHmLyHNg+B2HgUz+yWd/vT8tbaqzjRXyK0zTsJN55+U5Fenxqm2b+/hVvzpjV3E594642TcdP6JusnQWJO66aGXX4aUpWuqTJP+XT+1DGeOKHIkR456bi4oj8Zl3c3tOHy0C03tXZAAVIwajEmjB1lKzhtbO7Fo1YdE898bk/GXN3fjT298TmTnj6efhLPLihPVwI36IhmnWew3tnZh0SrjtbNgajzBoRtrmdZHbuZlYbnG+qGqdLogNjJ9pNNGxk/Q5piwyoPgdh4FHv35NV8Er7mxk0fEiBLGsXX1xDBmyaumyQkDEjBkQIhI+syCG3Oj7YNmDuyca9YOyxgkk2/+3F7Lbtgg8AbS+7d4R0agC22OCXUeBJb2rM6nhUd/LG24MU5ec2Mnj4gRrGNbWbnXMsNyTCbL38KKG3Oj7YNmDuyca9aOFpIxmM2V22vZDRsE/kZsZAS6sOYzMDrP7TwKPPrza74IXnPjdQ4QNbzz+djBjbmxk7+GV+4bu2uVtX032xF5WfoHYiMj0IU1n4HReW7nUeDRn1/zRfCaG69zgKjhnc/HDm7MjZ38Nbxy39hdq6ztu9mOyMvSPxAlClyCtPaJX14CU/Ie1DV3EL1IqDyTNsqDYNWe+vzemIzNe+r7ZLEyKkYNxtllxdha25io0WNVk4fEfnXeBj3f09jMMs6unhgee7cG7+9tRF4oC5dMOAHnjhqErbWNSXYASLJtYmkR1dwA8bpSE0uLqH1EC2sujHkVI3HPmk+J3pH56kiXIwUxtfPJsh7PKStGND/HsAaTtg+aOTA6l/UdGaN5OmN4IQbmZONIR4/h+STvyPDKicISp7xtEPgbsZFxARLJnd9keUreg4WPbyOqyCvDPA+CWXvqPAprq+pS5MB/easakgTovZZu5CN1f0bMHV+CrIBk6nsSm/XyRVid8/vXPk1RfTy3/UDKOUby0rnjS/DQhhriasn1rV345h/eSvIVzRyT9tPe3Yu1VXXUMRvKDmD+lDJT1dL8KXHVEk+VlYJ2PlnX49qqOnT09Op+phczyhyQjEnGsZhVzp07vsTUZyQ2qNGTv+udP39KXLWk2EXaPgs0PnLKBoG/EY+WHEaR4mr/aqpr7sDCx7fhtR0HiY7xgpnjSvDgNRMQpch4ytJetCAHD14zAQBww+PbdHOaGGnrDpr4aOa4EiyYWmZoz0MbarB0TZWp7wGY2qx3U7Ma54f7GvFXHekqkLpZaGrrTvFHXXMHHtpQgwVTy1L6MLtu68WTka3adqIFOVh+zQQsv2ZCYnOlR3NbN3PMLp5djuunlqX0HZDi0mt13h9SLigfmpKxtzASTBmDej5Z16NynlFOnsJI0LYk+KENNYn+X9txMLGZIMUsbhX5u9m3Ysr5i2eXU68Lt/CDDQJ3EfJrByGRMQ7LDwOQLL+K9lJG2BuTsbm6HotWbUOTQcVkGjv1vrIHgMn3vmnoByv0ZL88ZaQAqB8z6I2zNybjlF+8avvRiGLb+p9OS3oUdcbwQkz+3TrDatBmUmzt4yvtIy7l+K6eGM797RtoNLhh243Zrp4YVlbuRW1DG0qLI5hXMRKh7AB6YzIm37uOuHK0kY/0Htcp42OVz5NIhKP5YWy6c7otCbx6TN/8w1vE5xXmBrHs6gmYNCo1Dw5AJn+XAFT9aiZyQ1lJ9jv9OJxkTorzQvjFnLGmj5sF6Qfp/Vs8WnIQEhmj1UXZD+XdswISAgHJcBMD0NmZFZBSjqmsrmfexMCgbxL/m23jtWOi9b/eOFds0v8mhhbFtq21jUl9VFbXG25i1OdpfaVnq9F4t9Y2Gm5izPogJZQdwHVTRqX8fktNA/EmRm2H1kcKer+jkU/TxBoQX+ss5+n1v7JyL9V5Te3dCEiS4Q2eRP4uA1j1Xm3S3OjFDW9I5qS+tQvRglzPrpECbxGPlhyEp/TPaxmh07JiHuNzSl7M0/e8ZcasY3ZTYswLN2KLdWxOn6eFJY7M+iJtzwuZvFfxJkgfxEbGQXhK/7yWETotK+YxPqfkxTx9z1tmzDpmNyXGvHAjtljH5vR5WljiyKwv0va8kMl7FW+C9EE8WnIQEimu8o7MoRZyia8XsEiRads3k61aoSf7JbGZVkZq952AeRUj8ZtXPuX2jszE0iJUVtcTS7P1JMZaqbtePSE1E0uLUJwXQkOrfpZdIxmzkXTezKdq+2TIKMzNRlO7vixYj0F5IZwxvDD+6LK5HQ2tXSgeEEY0P3XuemMyYjEZhblB03fBBuZk45VPDqDqQDOuOHsEnnp/H/bWt2FgThaOdBgrlvTWB2uag6vOLcWyt3ebPka06lsNqfx9XsVIAiv5YiUzJ732+Cm9hYAvnm5kli5dimeffRY7d+5Ebm4uvvGNb+B3v/sdTjnllMQxHR0duP3227F69Wp0dnbiwgsvxAMPPIBhw4Z5aDkZJFLcu+eeCgBUEl8vIJUVs9qZFZBw91xyiaUWPdkvic00MlIeEvlQdgALpprLjK1QbJs7viTlhU9Fmm3Uvlomr1f5+i9vVZtWslZ8YLSJ0evDqHqxVYVmAFSVufWob+3CqXe9pnuDVs8daSVqGUBLRw8e37wPAPDrVz61tMFsfdBK4IH4vJ9//9tEmxgFq7VJIn/PCWbhzZ2HXFcDkcjMrcbnt/QWAr54qlqaOXMmrrzySpx99tno6enBz372M+zYsQNVVVXIy8sDACxcuBCvvPIKVqxYgYKCAtx0000IBALYtGkTUR9+KBqZjnlkjHDSTjuVmJVLmJ7s0spm0vnhWdF66Zoqw+rB6t8VRYKQYZxHxup8PZarpO5Wx6nHRFoNXd0HS6Vq0pw1PJBgXMmZFyTrQy8GA5pvC83m3Qwa6frSNVV4aGON7kvwrLFuF6u4sxqfU9XoBc6TltWvv/76awwdOhTr16/H1KlT0dzcjCFDhmDVqlW47LLLAAA7d+7E2LFjUVlZiUmTJlm26YeNDJB+mX3NcMJOHpWYzWS/VjZbPd5woqK1ncy+NNJbPVtjMRmHjpgrgNRyYdoqzVZpBfyC1eNFAKaPmowISMAfLj0dxxVFiNeHlQSeZd5pY7M3JuMbS98wLM7pdjoIu2vPjWr0AudIS/l1c3MzAKC4OH7x3rp1K7q7uzFjxozEMWPGjMGIESMMNzKdnZ3o7Dx2gW5paXHYajJIZIpuSBl54ISdPCoxm8l+rWw2+5xVkmtFKDuA+VNHY/7U5N9bSYUrq+uZfaXYSoJaLkxbpZlGJu0lVhJ8ANSbGCC+MWpq78alZ5HHg5UEnmXeaWNzS02DaYVxt9NB2F17Tq1dgb/wjWopFovhxz/+MSZPnoxx48YBAOrq6hAKhVBYWJh07LBhw1BXV6fbztKlS1FQUJD4GT58uNOmCzjgZ6m63+SfbspMeVVa7o84LbV34tx0jXW7lbxFfKc3vtnILFq0CDt27MDq1atttbN48WI0Nzcnfr744gtOFgqcxM9Sdb/JP92UmfKqtNwfcVpq78S56Rrrdit5i/hOb3yxkbnpppvw8ssv46233sIJJ5yQ+H00GkVXVxeampqSjj906BCi0ahuW+FwGPn5+Uk/Av+jSCztPKWWwF592Qwr25zql9UeMxRbhw0MWx4bzQ+nVFom6VPqOzeab28+3UCCeX0qxV+043BCqswy77SxmW6xbmWP38YjcAZP35GRZRk333wznnvuObz99tsoK0su8Ddx4kQEg0GsW7cOl156KQDgs88+w759+1BRUeGFyY5jVIeI5uVaXi/jWr0Aq80NMjgvjE/rWvBBbQMioWx874zjkZ0VwOHWTks7sgISlswpx42r2CsbywCuPNv8USKLb6wksjKAb5cPw5aaBl0faftSagnV1LdCAnDm8CKUFOYSz7WVrNzolQ/ls5njojjS3oP/t+1L03GPP6EA1/7tPeQGs3DWyGJcOuF4/OWtatNzFH+o0wo4iV2FkwxgzmkleOXjg4l/az+/6+JybK1txMMbySXz86eUIZSd+nciSU0rAMdy58gyCiNBDB6Yg2h+Dn4+ayxuWv0hkQ2kaRG0Ni2ZMxaLVhn3oUjj1fmLnBImkKy9K88eQXS+EX5Ib+Em6SIqocFT1dKNN96IVatW4YUXXkjKHVNQUIDc3FwAcfn1mjVrsGLFCuTn5+Pmm28GALz77rtEffhFtUSCngRTqdKrld8ayTl5yaPN2gFAlHNDi5XdJDlDFAnqUx98aXisUT92fUNio5mPSgpyMO74fKz79CtdlQyvuTbyjyRZv9jKA3UeGtL8LDRkByT85aozAaT6WM+HJOMujATR1RNDW1dvyu+vOOuElFw3RgSk+CZGTw5MIrEujATR3RNDa5d+Yj3t8Wawyr6N4lQZ25kjilxPFWEVR1b9L11ThYc31uiOh6WqerqSLmk+FNJCfi1J+rvAv//97/jhD38I4FhCvCeffDIpIZ7RoyUt6bKRocnRYZT/gFe+BLN27ASLmd1WOU2umzwSM8qjOKesGGur6ix9JWn64eEbO3luWLGyT/vXVWNrFxatos/dwhOtzb0xGX95cxf+9MYubn1cUD4UD//gbMtvMPcebuXarxmTRhXjHz86V/ebGJr1zYsHrjoTs08/zvBzWpusvu0DnM3JYhZHVnmkRB6Z9PQD6f3b03dkZFnW/VE2MQCQk5ODZcuWoaGhAa2trXj22WeJNzHpQm9Mxi9fqiK+oCjH/fKlKvT2/Ylh1obe8Sy22L0IG9l994v/sjx3zY66xE2K1FdKPzx80xuTcdcL1nbyxso+RbL7nTOOxzllxfj1K+Rx5BR6Nq9+n+9L92urvkJ7V2/S+Cv6Sisov7vo9OPw5JZ9XPs1Y0tNg+7vadc3L379yqeGMc1ik9mxNNcYOxjFkVH/vK6L6U6m+8EXL/v2d1hyqKjzH5C0oT2epy006NlNknNEOYfUPpniHBLfxPNreJMbxS9zRwON/1n57Zoq089JY4sXMRlYWblX1w4v5sUsZpywiTROWWFZx7yui+lOpvvBVwnx+is88kPwypfgdn4Imv5YbOPVvh/yTPhl7mhw0qa99eZ5Wrzwh17uGC/nxW5+FZ59utWu+jiRRyZOpvtBbGR8AI/8ELzyJbidH4KmPxbbeLXvhzwTfpk7Gpy0aeQg8zwtXvhDL3eMl/NiN78Kzz7dald9nMgjEyfT/SA2Mgzwlq9NLC1CcV7ItKKwFm3peiVfQl1zh+5zUNJS91bt2EVrx8TSIhTlZqOxvcf0PHWuh5KCHMuvxSUAxXkhHGhsQ0NbN/JzstHSod+Huv7QC9v36740OjgvjGEDw549XirMDSLW976PUvtIG4NOzx0thblBxGQZZ490xq6fzS5P8kNxJISddUfwRWMbSosjuOrcUlfnTAJwxdkjsGnXYVTuOQwZQGFuCIW52RiYk40jBvHnFCT5VXjOidE1pqsnhhWbavB/VXUAJFxQPhT/3+RRCGUHTK+lSpqC2oZj82llc3FeEBNLiwDEr9Nd3b3ICQbQ0R0jttnu9Z32fDfk0LzuD37FV0UjnYC3aom3fI1FnmqlWgJSc4voHW9mk1E7PFVLpJJrILkSsxMKkMJIMMkOPRmv9hgvUOTVWjmwWvbttrLKCnXVZoBPZesLyofi0gknmK4dSQKyJAk9Lr7A6JbEnQS1DF4Po3XOgtE1xqjKOxCfwx37W3Tj+MN9jbpy6eljh+KNqq9MbVbi7fHN+wxl7EY280jRQHO+m3JoXvcHN0kL+bUb8NzI8JavkdyQiyJByMiMPDLa/CI0N9zlOps23vlJrFA2cuHsADp79P/Cs0NeOAutncYXXjOUGFwwtQx/3UCeuM0N1LaR5mMxQ9nEOCFn1m5WjTaO6QDJdYnXOoqEsvDHy8enbGJoY5HkjyW9DRAL2uui3es77fleyKFFHpk0hddGhnc5eKv2gPjXpJsXz0BWQPJ9Zt/C3CB+s+ZTNFoki3vnjvMBAJPvfRN1LeQXohId3yZsaG5HQ2sXiiIh/GZNFRpanfvWRAIwLD+M311yOp7bvh+tnT0Ylh/G+OFFWPrqTqrHgwoFudn4y79NwE+e+cj2YxCShGkFOdm46fwTURgJYfsXjTjU0oF3dtcbfv2uR1EkiOunjML/vLkbbd3Wmy9lfbx5+7cw+XdvMvkpEgxgy88vQG4oy3LtsKDM7f2Xn4HDR5OzUffGZGyurseiVduYqmGzkBfOwg8mjURhJIgH3q5GM0O/JNel3piMd3cfxg/+toV5YxiQgJ2/npXIodPVE8OYJa8SJ++j7evjuy7E1D+8xRRHCp/+aiZyQ1kA7F/fac/nfT+hIZ0y+5Lev8U7MoTwLgdPIn9saO3G1tpGVIweRFViXsmjYRezdrSfVVbXm25igGR5H80mRn2uuk89G5zcxADxea5r6UQomIU/X3mmpm+2i2pzew8+P3SEy7scJDeO5o4ejDu+EBWjB+H7Zw1HZXU93vj0a6p+Gtu6EcwOEG1igGPrY9V7tcx+auuO4ZP9zUBfW7xR5jYgSfjOGccnfZYVkBAISK5tYgCgtbMXU08eAgBMmxiA7LqUFZDw+aEjtr7dUqTn100ZBSD+/0490YvJwO9e+9TWJgYAVr1Xm7DX7vWd9nze9xMaeN0f/ITYyBDCW76WaXI4N8bjJ/mxti+7fevJdp2ERaKqhcVmu+N0Y469kCwbwatPq3Z4xJ+6Dafj2Up6T4LaRrvXL9rzM+367zViI0MIb/lapsnh3BiPn+TH2r7s9q0n23USFomqFhab7Y7TjTn2QrJsBK8+rdrhEX/qNpyO55GDIthos/KE2ka71y/a8zPt+u81IrMvIbzLwWdaeXma8ZxTVoxoPt0CJfGFYoOTGM2L1fit2ptXMZLaJ3qQPOqO5oeT7Gfxm2Iz6ZjV42Txk9KnWmbO+6m+1ZpzI770bLEzXtLryLyKkUSxY0RAirfBqz2rvn42u9xWDEhIttfu9Zj2/Ey7/nuN2MgQopSDB5ASfMq/acrB827Pa2jGkxWQcPdc8oqzEsh8odjAesEnPUbPFvX4afu86+JyhLIDVD7Ra0tCvJqvFXfPPTXJflq/KfMRyg4Yzrn2eOicQ4M6BsxijRWSNWcnvuzYQrK2zD4jWTuh7ABR7Bgxf0pZUrFM1vZIfDt/ShlyQ1m2YmDB1GR77V6Pac/PtOu/14iNDAUzx5XgwWsmIKr5qyxakMMklePdntfQjGfmuBIsv2ZCIl+LESWUvlBsIPnLuSgSxPJrJmC5js2FkWCKbVbzYtW39pqkbY/EJyUFObh+allKH0pbi2eXG7ZR2DdePftJ/aadD6M5txon6Rzp9UnaryRBtxI1iY1GWNkuUd53SOPMbG0ZxTDtdWTx7HJcP7UsJU4DEnD91DLTzxbPTt2cKu0ZueSC8qG6cbz8mglEfRn5RFkjeX2KJDUSjO21ez2mPT/Trv9eIuTXDPCWr6WTHI4EmvH0xmRs3lOPTbsP40BTO44ryEVRXgiDB4QQLci1LR1XS7PrW7vQ2NaJgBRAxehBmDRqUIqcXG0zAKZ50UrTi/PiY5lYWoSttY2W7Sk+qayuhwwZhblBDB4QTvKHlY/VbQAyKkYNxqTRgyzt1/NbY1tXYgxmNieyIA8IAzJwuLXTcpxbahqwv7ENr/+rDm1dPYiEsnFBeRRHOrot+9T2q83sO69iJLICEjZX16Nyz2HEZKAoEsLggWEMHUhmI6mfigeEEc3PwcTSIrxf05CU2bc4EkRTezeKB+j3C5DHGUlKBLvXEW1G3XkVI5Nk1UafmbXHI7OvUV9GbSiy8me3fYnWrl6cPbII136jzNLeTMzsm66IPDJ9OLGREQgEAoFA4Cyk92/xaEkgEAgEAkHaIjYyAoFAIBAI0haxkREIBAKBQJC2iI2MQCAQCASCtEVsZAQCgUAgEKQtYiMjEAgEAoEgbREbGYFAIBAIBGmL2MgIBAKBQCBIW8RGRiAQCAQCQdqS7bUBTqMkLm5pafHYEoFAIBAIBKQo922rAgQZv5E5cuQIAGD48OEeWyIQCAQCgYCWI0eOoKCgwPDzjK+1FIvFcODAAQwcOBCSTmnalpYWDB8+HF988YWoxWSB8BUZwk/kCF+RIfxEjvAVGengJ1mWceTIERx33HEIBIzfhMn4b2QCgQBOOOEEy+Py8/N9O5l+Q/iKDOEncoSvyBB+Ikf4igy/+8nsmxgF8bKvQCAQCASCtEVsZAQCgUAgEKQt/X4jEw6HcddddyEcDnttiu8RviJD+Ikc4SsyhJ/IEb4iI5P8lPEv+woEAoFAIMhc+v03MgKBQCAQCNIXsZERCAQCgUCQtoiNjEAgEAgEgrRFbGQEAoFAIBCkLf1mI7NhwwZcfPHFOO644yBJEp5//vmkz3/4wx9CkqSkn5kzZ3pjrIcsXboUZ599NgYOHIihQ4fiu9/9Lj777LOkYzo6OrBo0SIMGjQIAwYMwKWXXopDhw55ZLE3kPjpW9/6VkpM3XDDDR5Z7B0PPvggTj/99ETirYqKCrz66quJz0U8HcPKVyKm9Ln33nshSRJ+/OMfJ34n4ioVPT9lQkz1m41Ma2srxo8fj2XLlhkeM3PmTBw8eDDx8+STT7pooT9Yv349Fi1ahM2bN2Pt2rXo7u7Gt7/9bbS2tiaOufXWW/HSSy/hmWeewfr163HgwAFccsklHlrtPiR+AoD58+cnxdTvf/97jyz2jhNOOAH33nsvtm7dig8++ADnn38+vvOd7+Bf//oXABFPaqx8BYiY0vL+++/jr3/9K04//fSk34u4SsbIT0AGxJTcDwEgP/fcc0m/u/baa+XvfOc7ntjjZ7766isZgLx+/XpZlmW5qalJDgaD8jPPPJM45tNPP5UByJWVlV6Z6TlaP8myLH/zm9+Ub7nlFu+M8jFFRUXyI488IuKJAMVXsixiSsuRI0fkk046SV67dm2Sb0RcJWPkJ1nOjJjqN9/IkPD2229j6NChOOWUU7Bw4ULU19d7bZLnNDc3AwCKi4sBAFu3bkV3dzdmzJiROGbMmDEYMWIEKisrPbHRD2j9pPDEE09g8ODBGDduHBYvXoy2tjYvzPMNvb29WL16NVpbW1FRUSHiyQStrxRETB1j0aJFmDNnTlL8AOI6pcXITwrpHlMZXzSSlJkzZ+KSSy5BWVkZqqur8bOf/QyzZs1CZWUlsrKyvDbPE2KxGH784x9j8uTJGDduHACgrq4OoVAIhYWFSccOGzYMdXV1HljpPXp+AoCrrroKpaWlOO644/Dxxx/jjjvuwGeffYZnn33WQ2u94ZNPPkFFRQU6OjowYMAAPPfccygvL8f27dtFPGkw8hUgYkrN6tWrsW3bNrz//vspn4nr1DHM/ARkRkyJjUwfV155ZeL/TzvtNJx++ukYPXo03n77bUyfPt1Dy7xj0aJF2LFjB9555x2vTfE1Rn5asGBB4v9PO+00lJSUYPr06aiursbo0aPdNtNTTjnlFGzfvh3Nzc34f//v/+Haa6/F+vXrvTbLlxj5qry8XMRUH1988QVuueUWrF27Fjk5OV6b41tI/JQJMSUeLRkwatQoDB48GLt37/baFE+46aab8PLLL+Ott97CCSeckPh9NBpFV1cXmpqako4/dOgQotGoy1Z6j5Gf9Dj33HMBoF/GVCgUwoknnoiJEydi6dKlGD9+PP77v/9bxJMORr7So7/G1NatW/HVV19hwoQJyM7ORnZ2NtavX4//+Z//QXZ2NoYNGybiCtZ+6u3tTTknHWNKbGQM+PLLL1FfX4+SkhKvTXEVWZZx00034bnnnsObb76JsrKypM8nTpyIYDCIdevWJX732WefYd++fUnP8TMdKz/psX37dgDodzGlRywWQ2dnp4gnAhRf6dFfY2r69On45JNPsH379sTPWWedhauvvjrx/yKurP2k99pEOsZUv3m0dPTo0aQdZk1NDbZv347i4mIUFxfjl7/8JS699FJEo1FUV1fjP//zP3HiiSfiwgsv9NBq91m0aBFWrVqFF154AQMHDkw8Ty4oKEBubi4KCgpw3XXX4bbbbkNxcTHy8/Nx8803o6KiApMmTfLYevew8lN1dTVWrVqF2bNnY9CgQfj4449x6623YurUqbryx0xm8eLFmDVrFkaMGIEjR45g1apVePvtt/H666+LeNJg5isRU8cYOHBg0vtoAJCXl4dBgwYlfi/iytpPGRNTXsum3OKtt96SAaT8XHvttXJbW5v87W9/Wx4yZIgcDAbl0tJSef78+XJdXZ3XZruOno8AyH//+98Tx7S3t8s33nijXFRUJEciEfl73/uefPDgQe+M9gArP+3bt0+eOnWqXFxcLIfDYfnEE0+Uf/rTn8rNzc3eGu4BP/rRj+TS0lI5FArJQ4YMkadPny7/3//9X+JzEU/HMPOViClztDJiEVf6qP2UKTElybIsu757EggEAoFAIOCAeEdGIBAIBAJB2iI2MgKBQCAQCNIWsZERCAQCgUCQtoiNjEAgEAgEgrRFbGQEAoFAIBCkLWIjIxAIBAKBIG0RGxmBQCAQCARpi9jICAQCgUAgSFvERkYgEPiKH/7wh5AkKfEzaNAgzJw5Ex9//HHiGOWzzZs3J53b2dmJQYMGQZIkvP3220nHP//88y6NQCAQuInYyAgEAt8xc+ZMHDx4EAcPHsS6deuQnZ2Niy66KOmY4cOH4+9//3vS75577jkMGDDATVMFAoHHiI2MQCDwHeFwGNFoFNFoFGeccQbuvPNOfPHFF/j6668Tx1x77bVYvXo12tvbE7/729/+hmuvvdYLkwUCgUeIjYxAIPA1R48exeOPP44TTzwRgwYNSvx+4sSJGDlyJP75z38CAPbt24cNGzZg3rx5XpkqEAg8QGxkBAKB73j55ZcxYMAADBgwAAMHDsSLL76Ip556CoFA8iXrRz/6Ef72t78BAFasWIHZs2djyJAhXpgsEAg8QmxkBAKB75g2bRq2b9+O7du3Y8uWLbjwwgsxa9Ys1NbWJh13zTXXoLKyEv9/u3aobFochnH4XZxmKBRdcAO6yAy3ICy3pBF0NyC6EtmMoDBDstMJ++x2wl7+M8+Tv/DGX/jO53P2+33qum5oMdAUIQN8nE6nk9FolNFolMlkkt1ul8fjke12++2u3+9nsVhkvV7n+XxmPp83tBhoipABPl5VVWm1Wt8ee/+q6zqn0ymr1SrtdruBdUCT/jQ9AOBfr9crl8slSXK73bLZbHK/37NcLn/czmazXK/X9Hq9354JfAAhA3yc4/GY4XCYJOl2uxmPxzkcDplOpz9uq6rKYDD45YXAp6je7/e76REAAP/DjwwAUCwhAwAUS8gAAMUSMgBAsYQMAFAsIQMAFEvIAADFEjIAQLGEDABQLCEDABRLyAAAxRIyAECxvgBPRbhN2s0qhQAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# bmi vs age\n",
        "plt.scatter(data[\"bmi\"], data[\"age\"])\n",
        "plt.xlabel(\"BMI\")\n",
        "plt.ylabel(\"Age\")\n",
        "plt.title(\"BMI vs. Age\");"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Npkp0V_IGGQS"
      },
      "source": [
        "There does seem to be a linear relationship between bmi and age. However, they are not strongly correlated"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "UarvXnwcFdHN",
        "outputId": "d8f08c4c-a3aa-4387-d07a-b419bb09ce33"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# bmi vs children\n",
        "plt.scatter(data[\"children\"], data[\"bmi\"])\n",
        "plt.ylabel(\"BMI\")\n",
        "plt.xlabel(\"Number of Children\")\n",
        "plt.title(\"Number of Children vs. BMI\");"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "tXYikhSv2yt8",
        "outputId": "dddafe40-df62-439b-a61b-7ed2ff514b3e"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# bmi vs charges\n",
        "plt.scatter(data[\"bmi\"], data[\"charges\"])\n",
        "plt.xlabel(\"BMI\")\n",
        "plt.ylabel(\"Healthcare cost\")\n",
        "plt.title(\"Healthcare cost vs. BMI\");"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "-PQ3RHDuHR-C",
        "outputId": "96c88ac0-1b96-442f-dc77-709cf5c97467"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# bmi vs region vs smoker\n",
        "df = data[[\"bmi\", \"region\", \"smoker\"]]\n",
        "reg = df.groupby(\"smoker\")\n",
        "regions = (\"southeast\", \"southwest\", \"northeast\", \"northwest\")\n",
        "yes = reg.get_group(\"yes\")\n",
        "no = reg.get_group(\"no\")\n",
        "\n",
        "plt.bar(no[\"region\"], no[\"bmi\"], align=\"edge\", width=0.4)\n",
        "plt.bar(x=yes[\"region\"], height=yes[\"bmi\"], width=0.4)\n",
        "plt.legend((\"no\", \"yes\"), title=\"smoker?\", loc=\"upper center\", ncol=2)\n",
        "plt.xlabel(\"Region\")\n",
        "plt.ylabel(\"BMI\")\n",
        "plt.title(\"BMI vs Region vs Smoker\");"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "id": "kMS8aHONuo0E",
        "outputId": "dfb5613b-dc67-48d6-9d25-85bfc760cbea"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\Abdulrasheed Nasir\\AppData\\Local\\Temp\\ipykernel_18064\\2693634663.py:2: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  corr = data.corr()\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "               age       bmi  children   charges\n",
            "age       1.000000  0.115670  0.040913  0.301754\n",
            "bmi       0.115670  1.000000  0.004627  0.200882\n",
            "children  0.040913  0.004627  1.000000  0.071393\n",
            "charges   0.301754  0.200882  0.071393  1.000000\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# correlations\n",
        "corr = data.corr()\n",
        "print(corr)\n",
        "\n",
        "sns.heatmap(corr);"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-VSAplrxvmMz"
      },
      "source": [
        "- From the heatmap, it can be seen that of all the features, age has the most correlation with healthcare cost, followed by bmi. However the correlation is not strong\n",
        "- smoking seems to have the most influence on charges\n",
        "- age and bmi have some correlation with charges"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "msvUuEGtUppB"
      },
      "source": [
        "### Data Preprocessing\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Dgp2StoEJW0A",
        "outputId": "47db2a0a-ecbf-4add-d228-dff59147983e"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "c:\\Users\\Abdulrasheed Nasir\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\category_encoders\\one_hot.py:145: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
            "  for cat_name, class_ in values.iteritems():\n",
            "c:\\Users\\Abdulrasheed Nasir\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\category_encoders\\one_hot.py:145: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
            "  for cat_name, class_ in values.iteritems():\n",
            "c:\\Users\\Abdulrasheed Nasir\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\category_encoders\\one_hot.py:145: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
            "  for cat_name, class_ in values.iteritems():\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex_female</th>\n",
              "      <th>sex_male</th>\n",
              "      <th>bmi</th>\n",
              "      <th>children</th>\n",
              "      <th>smoker_yes</th>\n",
              "      <th>smoker_no</th>\n",
              "      <th>region_southwest</th>\n",
              "      <th>region_southeast</th>\n",
              "      <th>region_northwest</th>\n",
              "      <th>region_northeast</th>\n",
              "      <th>charges</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>19</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>27.900</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>16884.92400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>18</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>33.770</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1725.55230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>28</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>33.000</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4449.46200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>22.705</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>21984.47061</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>32</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>28.880</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3866.85520</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age  sex_female  sex_male     bmi  children  smoker_yes  smoker_no  \\\n",
              "0   19           1         0  27.900         0           1          0   \n",
              "1   18           0         1  33.770         1           0          1   \n",
              "2   28           0         1  33.000         3           0          1   \n",
              "3   33           0         1  22.705         0           0          1   \n",
              "4   32           0         1  28.880         0           0          1   \n",
              "\n",
              "   region_southwest  region_southeast  region_northwest  region_northeast  \\\n",
              "0                 1                 0                 0                 0   \n",
              "1                 0                 1                 0                 0   \n",
              "2                 0                 1                 0                 0   \n",
              "3                 0                 0                 1                 0   \n",
              "4                 0                 0                 1                 0   \n",
              "\n",
              "       charges  \n",
              "0  16884.92400  \n",
              "1   1725.55230  \n",
              "2   4449.46200  \n",
              "3  21984.47061  \n",
              "4   3866.85520  "
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "## encoding\n",
        "ohe = OneHotEncoder(use_cat_names=True)\n",
        "data_encoded = ohe.fit_transform(data)\n",
        "data_encoded.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "bZcC7360Y_YP"
      },
      "outputs": [],
      "source": [
        "# split data\n",
        "features = data_encoded.drop(columns=\"charges\")\n",
        "target = data_encoded[\"charges\"]\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.25, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "HSjmjjBJwkBO"
      },
      "outputs": [],
      "source": [
        "# normalize data\n",
        "scaler = MinMaxScaler()\n",
        "\n",
        "X_train_std = scaler.fit_transform(X_train)\n",
        "X_test_std = scaler.transform(X_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HEltD6KfTpRT"
      },
      "source": [
        "## Training and testing models"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CNn3hNUrT7jk",
        "outputId": "2ef6615a-39a5-415d-f01a-4957a4c0a37f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy score for LinearRegression is 0.747061430655319\n",
            "Accuracy score for Ridge is 0.7471170951663183\n",
            "Accuracy score for SVR is -0.10255047611723964\n",
            "Accuracy score for DecisionTreeRegressor is 0.6955525095739347\n",
            "Accuracy score for RandomForestRegressor is 0.8392419389601529\n"
          ]
        }
      ],
      "source": [
        "# cross validation\n",
        "models = [LinearRegression, Ridge, SVR, DecisionTreeRegressor, RandomForestRegressor]\n",
        "for model in models:\n",
        "  regressor = model()\n",
        "  scores = cross_val_score(regressor, X_train_std, y_train, cv=5)\n",
        "  print(f\"Accuracy score for {model.__name__} is {scores.mean()}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HXPOLEhcXNWy"
      },
      "source": [
        "Random forest regressor have the highest average accuracy score"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "icEtqz7xl4rX"
      },
      "source": [
        "### Tuning the hyperparameters\n",
        "- n_estimators: number of trees in the forest\n",
        "- max_depth\n",
        "- max_features\n",
        "- min_sample_leaf\n",
        "- min_sample_split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VGnnXkVCtHIM",
        "outputId": "1d18b38c-1a39-4760-a2a8-7d61770bd439"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 100 candidates, totalling 500 fits\n",
            "0.8604500436596011\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "{'n_estimators': 400,\n",
              " 'min_samples_split': 6,\n",
              " 'min_samples_leaf': 10,\n",
              " 'max_features': 1.0,\n",
              " 'max_depth': 90}"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# hyper parameter tuning\n",
        "param_grid = {\n",
        "    \"n_estimators\": [n * 100 for n in range(2, 21)],\n",
        "    \"max_depth\": [n * 10 for n in range(1, 11)] + [None],\n",
        "    \"max_features\": [1.0,\"sqrt\"],\n",
        "    \"min_samples_split\": [2, 4, 6],\n",
        "    \"min_samples_leaf\": [1, 2, 5, 10]\n",
        "}\n",
        "\n",
        "model = RandomForestRegressor() # base model\n",
        "rand_search = RandomizedSearchCV(model, param_grid, cv=5, verbose=1, n_iter=100, n_jobs=-1)\n",
        "rand_search.fit(X_train, y_train)\n",
        "\n",
        "print(rand_search.best_score_)\n",
        "rand_search.best_params_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dxNf4iiCo3mj",
        "outputId": "149c0f49-18a8-41b2-c2f4-e726552d3717"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                                               params  mean_test_score\n",
            "84  {'n_estimators': 400, 'min_samples_split': 6, ...         0.860450\n",
            "2   {'n_estimators': 500, 'min_samples_split': 4, ...         0.860371\n",
            "14  {'n_estimators': 600, 'min_samples_split': 4, ...         0.860355\n",
            "11  {'n_estimators': 1200, 'min_samples_split': 4,...         0.860333\n",
            "46  {'n_estimators': 400, 'min_samples_split': 2, ...         0.860290\n"
          ]
        }
      ],
      "source": [
        "cv_results = pd.DataFrame(rand_search.cv_results_)\n",
        "\n",
        "# display top 5\n",
        "top_5 = cv_results.sort_values(\"mean_test_score\", ascending=False)[[\"params\", \"mean_test_score\"]].head()\n",
        "print(top_5)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lrewD_nbxpH6",
        "outputId": "44ce1f47-abbf-4285-fd79-2b0494fd75b7"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(max_depth=90, min_samples_leaf=10, min_samples_split=6,\n",
              "                      n_estimators=400)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(max_depth=90, min_samples_leaf=10, min_samples_split=6,\n",
              "                      n_estimators=400)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "RandomForestRegressor(max_depth=90, min_samples_leaf=10, min_samples_split=6,\n",
              "                      n_estimators=400)"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# training\n",
        "\n",
        "# extract the best param from random search cv\n",
        "n_est, msp, msl, mf, md = rand_search.best_params_.values()\n",
        "\n",
        "# instantiate model with best params\n",
        "model = RandomForestRegressor(n_estimators=n_est,\n",
        "                            min_samples_split=msp,\n",
        "                            min_samples_leaf=msl,\n",
        "                            max_features=mf,\n",
        "                            max_depth=md\n",
        "                          )\n",
        "# train model\n",
        "model.fit(X_train_std, y_train)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PfQUb6rhz6bs",
        "outputId": "19f29a7f-f7a3-40f6-fe19-a0596d42ec45"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "mean for y_train: 13191.228458478348\n",
            "mean for y_test: 13359.88266670695\n",
            "\n",
            "MAE for training data: 2153.8638403501363\n",
            "RMSE for training data: 3909.2979758262777\n",
            "\n",
            "MAE for test data: 2549.4903304641016\n",
            "RMSE for test data: 4737.775538238622\n"
          ]
        }
      ],
      "source": [
        "# testing the model\n",
        "print(f\"mean for y_train: {y_train.mean()}\")\n",
        "print(f\"mean for y_test: {y_test.mean()}\\n\")\n",
        "\n",
        "# metrics using training data\n",
        "y_pred_train = model.predict(X_train_std)\n",
        "mae_base = mean_absolute_error(y_train, y_pred_train)\n",
        "rmse_base = np.sqrt(mean_squared_error(y_train, y_pred_train))\n",
        "print(f\"MAE for training data: {mae_base}\")\n",
        "print(f\"RMSE for training data: {rmse_base}\\n\")\n",
        "\n",
        "# predict\n",
        "y_pred = model.predict(X_test_std)\n",
        "\n",
        "# metrics for test data\n",
        "mae_pred = mean_absolute_error(y_test, y_pred)\n",
        "rmse_test = np.sqrt(mean_squared_error(y_test, y_pred))\n",
        "print(f\"MAE for test data: {mae_pred}\")\n",
        "print(f\"RMSE for test data: {rmse_test}\")  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "haEu-4kUGfwQ"
      },
      "source": [
        "### Conclusion\n",
        "\n",
        "- Random forest regressor performed best with the dataset\n",
        "- SVC had the worst performance\n",
        "- Age and bmi have some correlation with charges"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ESNzVKww__Jr"
      },
      "outputs": [],
      "source": []
    }
  ],
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