2_8_3_Fraud_Detection_using_Anomaly_Detection_GANs.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "----\n",
        "----"
      ],
      "metadata": {
        "id": "1J6lRXaUM2SM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Project Overview: Fraud Detection using GANs (Generative Adversarial Networks)\n",
        "\n",
        "The goal of this project is to develop a robust fraud detection system by leveraging **Generative Adversarial Networks (GANs)**. This innovative approach involves training a **GAN** on the **Credit Card Fraud Detection dataset**, using **anomaly detection** techniques to identify fraudulent transactions.\n",
        "\n",
        "#### Key Steps:\n",
        "\n",
        "- **Data Preprocessing:**\n",
        "  - The dataset is first loaded and explored to understand its structure and identify any issues, such as missing values or imbalances between classes.\n",
        "  - **Feature scaling** is performed using **MinMaxScaler** to ensure all features are on a similar scale, and **SMOTE (Synthetic Minority Over-sampling Technique)** is used to balance the dataset, addressing the issue of class imbalance in fraud detection.\n",
        "\n",
        "- **Exploratory Data Analysis (EDA):**\n",
        "  - Visualizations like histograms, scatter plots, and correlation heatmaps are generated to gain insights into the relationships between features and identify patterns or anomalies in the data.\n",
        "\n",
        "- **Model Building:**\n",
        "  - A **GAN architecture** is designed with two components:\n",
        "    - A **generator** that creates synthetic fraudulent transactions based on random noise.\n",
        "    - A **discriminator** that distinguishes between real and fake transactions. The discriminator is trained using both real data (fraudulent and non-fraudulent transactions) and fake data generated by the generator.\n",
        "  - The GAN is trained adversarially, with the generator trying to fool the discriminator, while the discriminator learns to become more accurate at distinguishing real from fake data.\n",
        "\n",
        "- **Training:**\n",
        "  - The **training process** alternates between training the discriminator and the generator. Over time, the generator improves, producing increasingly realistic synthetic fraud data, which helps enhance the discriminator’s ability to identify fraudulent transactions.\n",
        "\n",
        "- **Anomaly Generation and Detection:**\n",
        "  - Once trained, the GAN can generate synthetic fraudulent data, which can be used for testing or augmenting real-world fraud datasets.\n",
        "  - The trained discriminator is then used to classify new data as fraudulent or not, providing an effective means of detecting anomalies (fraudulent transactions).\n",
        "\n",
        "- **Evaluation:**\n",
        "  - The model is evaluated using performance metrics like **precision**, **recall**, **F1-score**, and the **confusion matrix** to assess its effectiveness in identifying fraud. The results are analyzed to determine how well the model detects fraudulent transactions while minimizing false positives and false negatives.\n",
        "\n",
        "#### Outcome:\n",
        "The result of this project is a **fraud detection system** that uses GANs to both generate synthetic fraud data and detect anomalies in financial transactions. This approach not only addresses the class imbalance problem but also improves detection capabilities by providing more diverse training data."
      ],
      "metadata": {
        "id": "Og2GlEF0M76O"
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    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "_VOXSziEM_Nw"
      }
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "830V6Q1INASw"
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      "execution_count": null,
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Y4KIYwZjNAZG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings  # To suppress warnings"
      ],
      "metadata": {
        "id": "79R2NNSgMx3K"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "warnings.filterwarnings('ignore')  # Ignore all warnings"
      ],
      "metadata": {
        "id": "ZEwxyIkWMqS4"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qjlwYic1Mpv2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "LbanLE1SMppe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "2zm2jKVsNCRJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 📚 Step 1: Importing Libraries\n",
        "- **Overview:** Essential libraries for data manipulation, model building, visualization, and evaluation are imported.\n",
        "- **Key Insights:**\n",
        "  - **pandas & numpy** enable efficient data handling and computation.\n",
        "  - **matplotlib & seaborn** are crucial for data visualization, helping in understanding feature distributions and relationships.\n",
        "  - **sklearn** is used for preprocessing (scaling), data splitting, and model evaluation (metrics).\n",
        "  - **keras** and **tensorflow** facilitate building and training deep learning models.\n",
        "  - **imblearn** provides tools like SMOTE for addressing class imbalance, which is critical in fraud detection tasks.\n",
        "- **Why this is important:** Proper libraries allow efficient workflow in data science and machine learning tasks."
      ],
      "metadata": {
        "id": "soX58R-2NKuu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd  # Import pandas for data handling"
      ],
      "metadata": {
        "id": "Rx22V9sUHPhP"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np  # Import numpy for numerical operations"
      ],
      "metadata": {
        "id": "HuQvIrKEHPj8"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt  # Import matplotlib for plotting"
      ],
      "metadata": {
        "id": "w8E4L3CsHPmo"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns  # Import seaborn for statistical plots"
      ],
      "metadata": {
        "id": "SetL7Ig3HPoz"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler  # Import MinMaxScaler for feature scaling\n"
      ],
      "metadata": {
        "id": "6H8JgwokHHPN"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split  # Import train_test_split for splitting data\n"
      ],
      "metadata": {
        "id": "a35BlQW2HHR9"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report, confusion_matrix  # Import evaluation metrics\n"
      ],
      "metadata": {
        "id": "CQyDFMbJHHU0"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.models import Sequential  # Import Sequential model from Keras for GAN\n"
      ],
      "metadata": {
        "id": "ABXRanXeHHXX"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.layers import Dense, LeakyReLU  # Import Dense and LeakyReLU layers for the neural network\n"
      ],
      "metadata": {
        "id": "KARUlBkFHHhI"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.optimizers import Adam  # Import Adam optimizer for training the model\n"
      ],
      "metadata": {
        "id": "_HCURnOMHHkY"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.over_sampling import SMOTE  # Import SMOTE for handling class imbalance\n"
      ],
      "metadata": {
        "id": "jvef-teSHHm6"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras import initializers  # Import initializers for weight initialization\n"
      ],
      "metadata": {
        "id": "6W5ZOaB6F6d4"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "----"
      ],
      "metadata": {
        "id": "_d62_9vTNOiD"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "eEyagffnJkv1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "RtV7fCcwJkzd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "soK1BxX8NQO7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "cx0ZJNz_NQi6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 📥 Step 2: Load and Inspect Data\n",
        "- **Overview:** The dataset is loaded into a pandas DataFrame for initial inspection and data understanding.\n",
        "- **Key Insights:**\n",
        "  - Inspecting the first few rows (`head()`) helps assess the structure and check for data quality issues like missing values.\n",
        "  - The `info()` function reveals the data types and the presence of null values, which is essential for preprocessing.\n",
        "  - **Why this is important:** This step ensures the dataset is ready for cleaning and feature engineering. Any missing or corrupted data can be identified early."
      ],
      "metadata": {
        "id": "1e7fjkKxNSKA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "file_path = \"/content/creditcard.csv\"  # Specify the file path for the dataset\n"
      ],
      "metadata": {
        "id": "YMxCmsf1Jk6s"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv(file_path)  # Read the dataset into a pandas DataFrame"
      ],
      "metadata": {
        "id": "lBQSuNZJJk_V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "QsFi2_EDJvoz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "udKwVKdmJvrc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Inspect the dataset"
      ],
      "metadata": {
        "id": "EVXJ5Y6MJvwL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Head of the dataset:\")  # Print header for dataset preview\n",
        "print(data.head())  # Display the first 5 rows of the dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-6EO9rh2Jv13",
        "outputId": "5431a2f6-4d9f-47a0-fcaa-9fbfc83949d2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Head of the dataset:\n",
            "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
            "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
            "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
            "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
            "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
            "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
            "\n",
            "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
            "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
            "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
            "2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
            "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
            "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
            "\n",
            "        V26       V27       V28  Amount  Class  \n",
            "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
            "1  0.125895 -0.008983  0.014724    2.69      0  \n",
            "2 -0.139097 -0.055353 -0.059752  378.66      0  \n",
            "3 -0.221929  0.062723  0.061458  123.50      0  \n",
            "4  0.502292  0.219422  0.215153   69.99      0  \n",
            "\n",
            "[5 rows x 31 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "TE6JmCibJ0Ro"
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      "outputs": []
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "xu53mnvmJ0Ut"
      },
      "execution_count": null,
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    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nInfo about the dataset:\")  # Print header for dataset info\n",
        "data.info()  # Display data types and non-null counts"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ajt9exx8J0X-",
        "outputId": "5398cf3b-be10-4649-c08c-14676a20bbb3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Info about the dataset:\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 284807 entries, 0 to 284806\n",
            "Data columns (total 31 columns):\n",
            " #   Column  Non-Null Count   Dtype  \n",
            "---  ------  --------------   -----  \n",
            " 0   Time    284807 non-null  float64\n",
            " 1   V1      284807 non-null  float64\n",
            " 2   V2      284807 non-null  float64\n",
            " 3   V3      284807 non-null  float64\n",
            " 4   V4      284807 non-null  float64\n",
            " 5   V5      284807 non-null  float64\n",
            " 6   V6      284807 non-null  float64\n",
            " 7   V7      284807 non-null  float64\n",
            " 8   V8      284807 non-null  float64\n",
            " 9   V9      284807 non-null  float64\n",
            " 10  V10     284807 non-null  float64\n",
            " 11  V11     284807 non-null  float64\n",
            " 12  V12     284807 non-null  float64\n",
            " 13  V13     284807 non-null  float64\n",
            " 14  V14     284807 non-null  float64\n",
            " 15  V15     284807 non-null  float64\n",
            " 16  V16     284807 non-null  float64\n",
            " 17  V17     284807 non-null  float64\n",
            " 18  V18     284807 non-null  float64\n",
            " 19  V19     284807 non-null  float64\n",
            " 20  V20     284807 non-null  float64\n",
            " 21  V21     284807 non-null  float64\n",
            " 22  V22     284807 non-null  float64\n",
            " 23  V23     284807 non-null  float64\n",
            " 24  V24     284807 non-null  float64\n",
            " 25  V25     284807 non-null  float64\n",
            " 26  V26     284807 non-null  float64\n",
            " 27  V27     284807 non-null  float64\n",
            " 28  V28     284807 non-null  float64\n",
            " 29  Amount  284807 non-null  float64\n",
            " 30  Class   284807 non-null  int64  \n",
            "dtypes: float64(30), int64(1)\n",
            "memory usage: 67.4 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "UM-EVne_J0at"
      },
      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "cooVihoeJ0dv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nSummary statistics of the dataset:\")  # Print header for summary statistics\n",
        "print(data.describe())  # Show summary statistics (mean, std, min, max)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FymaTL3hJ0gl",
        "outputId": "edc7c0d4-5790-43c3-96a8-49ef21c58614"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Summary statistics of the dataset:\n",
            "                Time            V1            V2            V3            V4  \\\n",
            "count  284807.000000  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
            "mean    94813.859575  1.168375e-15  3.416908e-16 -1.379537e-15  2.074095e-15   \n",
            "std     47488.145955  1.958696e+00  1.651309e+00  1.516255e+00  1.415869e+00   \n",
            "min         0.000000 -5.640751e+01 -7.271573e+01 -4.832559e+01 -5.683171e+00   \n",
            "25%     54201.500000 -9.203734e-01 -5.985499e-01 -8.903648e-01 -8.486401e-01   \n",
            "50%     84692.000000  1.810880e-02  6.548556e-02  1.798463e-01 -1.984653e-02   \n",
            "75%    139320.500000  1.315642e+00  8.037239e-01  1.027196e+00  7.433413e-01   \n",
            "max    172792.000000  2.454930e+00  2.205773e+01  9.382558e+00  1.687534e+01   \n",
            "\n",
            "                 V5            V6            V7            V8            V9  \\\n",
            "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
            "mean   9.604066e-16  1.487313e-15 -5.556467e-16  1.213481e-16 -2.406331e-15   \n",
            "std    1.380247e+00  1.332271e+00  1.237094e+00  1.194353e+00  1.098632e+00   \n",
            "min   -1.137433e+02 -2.616051e+01 -4.355724e+01 -7.321672e+01 -1.343407e+01   \n",
            "25%   -6.915971e-01 -7.682956e-01 -5.540759e-01 -2.086297e-01 -6.430976e-01   \n",
            "50%   -5.433583e-02 -2.741871e-01  4.010308e-02  2.235804e-02 -5.142873e-02   \n",
            "75%    6.119264e-01  3.985649e-01  5.704361e-01  3.273459e-01  5.971390e-01   \n",
            "max    3.480167e+01  7.330163e+01  1.205895e+02  2.000721e+01  1.559499e+01   \n",
            "\n",
            "       ...           V21           V22           V23           V24  \\\n",
            "count  ...  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
            "mean   ...  1.654067e-16 -3.568593e-16  2.578648e-16  4.473266e-15   \n",
            "std    ...  7.345240e-01  7.257016e-01  6.244603e-01  6.056471e-01   \n",
            "min    ... -3.483038e+01 -1.093314e+01 -4.480774e+01 -2.836627e+00   \n",
            "25%    ... -2.283949e-01 -5.423504e-01 -1.618463e-01 -3.545861e-01   \n",
            "50%    ... -2.945017e-02  6.781943e-03 -1.119293e-02  4.097606e-02   \n",
            "75%    ...  1.863772e-01  5.285536e-01  1.476421e-01  4.395266e-01   \n",
            "max    ...  2.720284e+01  1.050309e+01  2.252841e+01  4.584549e+00   \n",
            "\n",
            "                V25           V26           V27           V28         Amount  \\\n",
            "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  284807.000000   \n",
            "mean   5.340915e-16  1.683437e-15 -3.660091e-16 -1.227390e-16      88.349619   \n",
            "std    5.212781e-01  4.822270e-01  4.036325e-01  3.300833e-01     250.120109   \n",
            "min   -1.029540e+01 -2.604551e+00 -2.256568e+01 -1.543008e+01       0.000000   \n",
            "25%   -3.171451e-01 -3.269839e-01 -7.083953e-02 -5.295979e-02       5.600000   \n",
            "50%    1.659350e-02 -5.213911e-02  1.342146e-03  1.124383e-02      22.000000   \n",
            "75%    3.507156e-01  2.409522e-01  9.104512e-02  7.827995e-02      77.165000   \n",
            "max    7.519589e+00  3.517346e+00  3.161220e+01  3.384781e+01   25691.160000   \n",
            "\n",
            "               Class  \n",
            "count  284807.000000  \n",
            "mean        0.001727  \n",
            "std         0.041527  \n",
            "min         0.000000  \n",
            "25%         0.000000  \n",
            "50%         0.000000  \n",
            "75%         0.000000  \n",
            "max         1.000000  \n",
            "\n",
            "[8 rows x 31 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "----"
      ],
      "metadata": {
        "id": "Jk7Jz_K3NemU"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "92iiMMg2Nf3e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "WhGMFZFKJ0j1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "aCgcix9_J0nM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "----"
      ],
      "metadata": {
        "id": "1wBk-gKLNgqJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 🔍 Step 3: Exploratory Data Analysis\n",
        "- **Overview:** Visualize the data distribution and explore relationships between features.\n",
        "- **Key Insights:**\n",
        "  - **Histograms:** Understanding the feature distribution helps in detecting skewness, which may require transformation.\n",
        "  - **Scatter plot (Time vs. Amount):** Visualizes potential correlations or outliers between `Time` and `Amount`, aiding in anomaly detection.\n",
        "  - **Correlation Heatmap:** Shows interdependencies between features, useful for feature selection and detecting multicollinearity.\n",
        "- **Why this is important:** EDA helps identify data patterns, outliers, and potential issues, guiding preprocessing and feature engineering decisions."
      ],
      "metadata": {
        "id": "VzJmKKPwNhpo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting histograms for each feature"
      ],
      "metadata": {
        "id": "06hJgLi4Jv4U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.hist(bins=50, figsize=(20, 15))  # Plot histograms of all numeric features in the dataset with 50 bins\n",
        "plt.suptitle('Histograms of Features', fontsize=16)  # Set the title of the histogram plot\n",
        "plt.show()  # Display the histogram plots"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 535
        },
        "id": "cOIpSVW3Jv62",
        "outputId": "5ff761f5-7843-4b18-8885-e1e1a73a4a29"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1500 with 36 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "awlx3dvtJv96"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "JenH5_C0JlDD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Scatter plot of Time vs Amount to visualize data distribution"
      ],
      "metadata": {
        "id": "iVXTXRQNJlF8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Set the size of the scatter plot\n",
        "plt.scatter(data['Time'], data['Amount'], alpha=0.5)  # Plot a scatter plot between 'Time' and 'Amount'\n",
        "plt.title('Scatter Plot of Time vs Amount')  # Set the title of the scatter plot\n",
        "plt.xlabel('Time')  # Set the label for the x-axis\n",
        "plt.ylabel('Amount')  # Set the label for the y-axis\n",
        "plt.show()  # Display the scatter plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 413
        },
        "id": "-R8FaLmaJlJG",
        "outputId": "93facb4d-d6f7-4413-f16d-32cfc6d786e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "TwVjANlpF6p5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "NfhpQprwF8eh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Correlation Heatmap to visualize correlations between features"
      ],
      "metadata": {
        "id": "TXEvujJjF8h3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "correlation_matrix = data.corr()  # Compute the correlation matrix of the dataset\n",
        "plt.figure(figsize=(12, 8))  # Set the size of the heatmap plot\n",
        "sns.heatmap(correlation_matrix, annot=True, fmt='.2f', cmap='coolwarm', cbar=True, linewidths=0.5)  # Plot the heatmap of correlations\n",
        "plt.title('Correlation Heatmap')  # Set the title of the heatmap plot\n",
        "plt.show()  # Display the heatmap"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 759
        },
        "id": "flCfumBMF8lB",
        "outputId": "d4bbc706-bb13-4ffd-caa1-2068aa894b4e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 2 Axes>"
            ],
            "image/png": 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MAgICmD9//nPZ3717l86dO9OyZUvCw8N5//33GTVqFHv27NGYj2KWY4FAIBAIBAKBQCAQVIhEImHLli306NGjXJtPP/2UHTt2cOXKFVXagAEDSElJYffu3RrxS7TQCgQCgUAgEAgEAsF/hNzcXNLS0tSW3NzcStE+deoUbdq0UUtr3749p06dqhT9stDVmLJAIBAIBAKBQCAQCEqxQ8/3lR373BcDmT59ulra1KlTmTZt2ktrP3r0CAcHB7U0BwcH0tLSyM7OxsjI6KWP8TSiQvs30GTG65x/k9M3UjWm39DPgs1n5RrT79VASs93bmtMf8u8qizSXNd7xraHy5HxGtOv5eNA6BGNyTO0ORy5mqUx/eb+xvx+QnOjEwY2lmg8f+68mK8x/U719Ph0SbbG9H9424izGiwfGvhZsPKwxuQZ3gIW79Wc/ph28N36Qo3pf95fh23nNaffrb4OIYc0Js+IlnD0aqbG9Jv5m2j8/lp9THPlz1tNJeyNyNOYfrsAfS7ceqwx/cBq1uyLqJyWlbJoG2DAxjOaK5/7vCnl8+Wa8/+7YAPWHtdc/hnURPPPL23X13T+Efx9Jk+ezKRJk9TSDAwMXpE3L4+o0AoEAoFAIBAIBALBP4hET/LKjm1gYKCxCqyjoyPx8eqNRPHx8Zibm2ukdRb+5WNohw8fXuGgY4FAIBAIBAKBQCAQ/Dto1KgRBw4cUEvbt28fjRo10tgxX9ksxxJJxW8lpk6dygcffIBCocDS0vKfceo5ON97PPHbDlRoY92sATV++gzTGlXJuR9H5IyFxIZuUbNxHzcIr0nBGDjakXbpBjtvX2HBoqWkJstw9ajKW29/hHc1/zL1Y+9FsWXtEqKjbpCUEMeg4A9o322gms2fG1dy4dQh4mJj0DMw4I3AumRKHLh2fh/ZWem4V6tLj+FTsXX0qPBcTu1bw9GdK8hITcLR1Y9uQ7/A1bu2avvZgxsIP7WdhPvXCA4OZuBbb2NipMONOzksXp9AXOLzdUPr1daKId1t+fNQMis2JQFgaixlQGcb6vgZ42yvx0+z5rBxYxgZ6Wk4edajdb9pWNmX739s5DnOH1hOwv0rZKYl0nXUfHxqqw9Svx2xl0vH15F47yLZ2dno6uri4VWV4LHvUdW3Rpm692Pusm71cu5E3iIx4RHDR79Dlx791GzGjehHYsKjUvvq6huCQkEV73p0HDwNa4fy/Qc4f2gNp/cuJyM1EYcqfrQb+D9cPGur2cRG/cXhdZ8SExMDgIGhMe9+MYdq/oHl657cxx+/L0CW8BB7Jzd6D5lIrcCmqu052VlsXj2H8DOHyMxIxbWKC1KTKjy6d52crDRcferRZehUbJ7h/9kDazixe7kq/3Qc/CVVvGqXsuvfSEHj5u14nHAfHR09PPwCKzV/PrgTQV5eHrq6+ji7+9Jr+Oe4+9QqVzf89B52hc3jceID7Bzd6TLwA2rUbabavnvjfP46tZsU2SN0dPWoU9uf3iO+pfkbzhjpQ/QjOVuO5yNLK794bVFHl5oeOthbSsgvhJh4OTvP5JOUWrzP21308XbWYc2aNSxfvpzExES8farRf8SHFZYPm0qUD4ODP6DDU+XDto0rOf9U+ZBS6MDNv/aTm51GFe96tB/07Px54dAazuxT5k/7Kn60G/A/nJ/kz5SkWBZ+0brM/WydqtF11Nznun/j7ynv326j5uMToH7/KhQKTu6cw50LYQwa/Bb9+w/A3Nyc2CQJuy/ISc4o33dXO2joK8XRGsyMJGw8XsitB+o2errQsraEm2d/Z9myZSQlJeHm6UfHwZ/j5l06HxcRcWY3e8Lmkpz0AFsHdzoNnET1Os1V2y+f28ep/et5EH2VrIxUtmzZwo9L9xFxPIzc7DRcvOvRfuBzxP/wGs7sXU5mmjL+bfurx3/Rl2XHv+fgd+jUe2S5usryYSFJCQ9xUJUPTVTb01JkbPxtDtfCT5GdmUGVKs7IkjPIzEjB2a3y76+AWjXI0XEh8spRVfnT8a1nlz/nDq7h1B5l+ePg6keHgV/iUqL8CZ05hJhb59T2MTW3Zsxn8/CowP+/Tu1h+/p5PE58iJ2jG90Hf4B/vWZl2q5b8hUn9odhYmpGXm4ubp4+DBszCZ/y7t+YO4StWcrdqBskJTxiyKj36Nh9QLm+bAsLZV3oQgyMTCgsyMfFvRp9R06u0P+Lp/ayY/08ZE/87zH4A/zrFZf/v83/kjNHtqntY23vSl5uNjlZ6bhXrUu35yifT+9fw7ES5XOXIcXlc1ZGCgc2zyPyygnSk+OY8O6H9O3TCzMzU2LiFfxxsqDC8rN5bR38PaTYWSjLz3sJcnafK1QrP3s01sXbWYq1GcyaPYeNYWGkpSnzT+chz/H8OriGkyWfX4PU808RA4I0+/yKv3eVrKwsTM1tyMnOqPTfb/+4fiXkH4CzhzZw6cnvz8zMTM6dO4e5uXmFmv82dhr7vbJjd8q68dy2GRkZREZGAlC3bl1mzZpFy5Ytsba2xs3NjcmTJ/PgwQNCQ0MB5Wd7atasyYQJExg5ciQHDx5k4sSJ7Nixg/bt22vkfF5ZC21cXJxqmT17Nubm5mppH330ERYWFv+qyuzzYORRhTe2LUZ2+AzH63fn7txV1Fr8DbZti38MOPXtSPUfJ3P7m/kcb9CTPzeEMWPGDHoNHMX0WaG4elblp2kTSUspe8xNXm4udg4u9B0yAQsrmzJtbl65SOtOffnfj8v5ZPpc7t69y5mD6+jy1mTGT1uPvoExK2aOJj+v/HErl07vZMfaH2jdcwLvfL0JJzdfVswcTUaqrNiXvGyq1W7KwoULGTJkCAvX3OfTn+6TmydnygQX9HSf3Z3Cx82Ado0tuBur7ou1hS7WFrqs3JLE0qVLWb36Nz7/chq/Lt6Anr4RmxcGU5Bfvv/5eVnYufjSqu/U8m1yszAwMiUvTzl+6v2Pp+Lh6cM3//uI1JTkMvfJzc3BwdGZwcPHYGllXabN97OXsPS3LaqlT58+ADTuOIbhkzegZ2DE779W7P+1czvZHzaDpl0mEPzlFuxd/Vj3azCZacXxj436izWzhhEbG0v3geN55/M5eFatyfzvPyg3/0TdCGfZrMk0ad2D//38O3UbtGDBD5N4EBOpsglb+TNX/zpJ8PvfMn3OZry9vYm8fJQ6TXox6ssN6BsY8dvPo8ivwP8rZ3eyZ/33tOg2gTFTN+Pg6svqWaPIKOF/EePGjSMl6SEAHfp/WKn508zSnsJC5fjHCVNCcHb3ZfH3Y0hPLe0HwN1bf/Hb3E94s0VPPpoRRs36rVjx80Ti7hePEbdz8qDX8M/5+IfNvDs1lAEDBtC8rjkb9j9m3tZc8goguJM+ujrluo+Xk5RT1wqY/0cuy3bkIpXCqE766D01EOTXpX8yY8YMuvYZyXezf8PR1YeZ0yaSWkH5YO/gQr8KyocbVy7SplNfpv64nE+flA8Xj66jTb/JDPtMmT/Xz3l2/jywcQZNOk9g5BdbcKjix/o5xfnT3NqJd2ce592Zxxk7diz6hqb4BnZGV88QM2snNi94xv2b++T+7Vf+/Xtu/1LCj/xGSEgII0aO5teFv/PW8DHk5RcyoLkUnQqecHo6kJCiYM+F8sd2takj4c6lXcyYMYOWPcbz/bxN1K3ty/If3lbLZyWJvvUXa+d9TIMWvXj/203412/Nqlnv8qhE/snLycbTtx6dBnwIwJYtW7hw6DfaD5rG0E+V5dv6uRXH5/r5nRzcOIMmXSYw4vMt2FfxY/1c9fi/88Nx2g+ajp6eHv1GfEjLjv2Q6uiwe8vKcsuHyBsRLJ31OU1ad2fKz2up06AF80uUDwqFgvnfTyIpPpYJn/1Cj0HjiY6OJj8/l3enhmrk/srIyCD8xCZa9f6QkZ8r8+faX0ZVGJ+rZ3eyb8P3NOs6gdFTlOXP2tmj1MpPAA+/hujp6dFr2Ce8Ny2EGnWbsuDb8v2/czOclb9+SqNWvfj0hzBqv9GKpT++x8N7peeQiDh7gGvhxwGoXe9Nvp29EjfPqnw/5YNy79/c3BzsHZ0ZMGw8luXcv0VE3brGjq2/I5FI8PCpxac/rMfF3Zf53459Dv978tkPGwh4oxVLyvC/Rp3GfLfkIN8tOcjYsWPJTE+m+/BpjJu6Hj0DY1b++OzyeefaH2jVYwITvtqEo5svK38crSr/01MSSE9JoMPATzhy5AhD3hrMN9/9xLgPviOvQMGI9noVlp+eTlJOXy9k4Z/5rNidj1QKIzroqZWfD5LkbDqWz9KlSwkN/Y33P57G6rXK59fqWRXnnytnd7J3/fc0L/n8+qV0/gHNPr+q1W5KYKDy5XTnQZ9q5PfbP6XfcdCnlZZ/APJzs6laqyljx44tV+ffjlRX8sqWv8P58+epW7cudevWBWDSpEnUrVuXKVOmAMo63b1791T2np6e7Nixg3379hEQEMDPP//MsmXLNFaZhVdYoXV0dFQtFhYWSCQStTRTU9NSXY5btGjBu+++y/vvv4+VlRUODg4sXbqUzMxMRowYgZmZGT4+PuzatUvtWFeuXKFjx46Ympri4ODAkCFDSEpK0sh5ub89gOy7sVz/5AcybtwhZsEaHm3ag+d7w1U2nu+P4P7yDcSu2kzG9Sh+CwujX79+9O3TBxc3L4aP+wx9A0OO7v+zzGN4Va3BgBETadisHXp6+mXafDRtDk1bd6GKmzeuHlVJS0sDhQIzSzuc3HzpN+Z70lMSuHZhf7nncmzXKt5o0Zf6zXrh4OJDjxHT0Dcw5PzRzSqbJh2G0aLraOrWrcvChQs5E5FKzMM8fg2Nx9pChzcDTCqMl6G+hA+GO7Lg93gys9UnXbkXl8fMZXGcu5xBaGgob7Qdh5FLG1o28qPj0JlkpiYQdal8/z1rNKdxlw/wCWhbrk2NBj1IT46jS5cuADg4OfP2Ox9iYGjIwb07ytzHp1p1hgaPp0nz1uXG38LCEitrG6ysbbC0smbHjh0YmVjSuNM4HKr40W3ETNJTErj5V/n+n9kXQp0m/Qho3Bs7Zx86DZ6Orr4hESc2qWz2bZiBoYkFAwYMoHPf0dSu35T3py7EwMCQEwe3lql7YPvv+NcNon2PYThV8aL7oAm4eVbn0K51KpuoGxE0atEF35r1sbFzIjw8HDNLe+V96upLz1E/kJ6SwI2L5ft/as9K6jXrS92mvbF38aHL0Ono6Rvy17FNanYPY65x9OhRWnR7GwArO5dKzZ+PE+7TsmVLABycPekbPAV9fUPOHN5Spu7RXavxC2hMq64jcXDxplO/d6niWYNje9aqbAIbd8a3ViNsHVxxcvWhXbt2LFy4gJNnLvPosYINh/IwN5bg71H+L7IVu/K4cKuQ+GQFcY8VhB3Ow8pMShVb9WJ519ZV9OvXj8atuuLg4snwsZ9h8IzyYeCIiTSqoHz4ZNocmj0pH9xKlA+mFvbYV/Gjy5P8eSu8/Pif3R9CQJN+1G7cG1tnHzo8yZ+XTiqvr1Sqg6mFHSbmtmzatImGHcbzOP4OvoGd6TTsZzJSE4is6P71V96/Vcu5fxUKBX8dDuXN9uOoWrUqJ69LkFYZyKW/TjN/5T7MjMDXpfyH9p1HcOSKolSrbEmq2EpYvmIl/fr1o07jXhSaeDPp0+kYGhly9sjmMvc5vvs3fGs3oUWXYBxcvOnQdyIuHjU4sXeNyiawaTfa9hpP1ZrK7ld//vknQR3HUa1OG1X8M54n/o37UTvoSfwHTUdPr3T8L53cRL9+/WjbdTCRNyJo2LwzBoZGnDj4R5m6B7avxb9uI1X50GPQeNw8/Ti4az0A8XH3uHPrMoPf/hzPqv6cO7GXAQMGIJXq8DDmRqXfX45VvImLiwPA3MoBB1dfuo98Uv5UUH6e3reSuk37UqeJsvzs/Jay/Ak/rl7+JD6MpF+/frTsPASfGvUZPO4r9PWNOHWobP8P71xN9TqNadNtBI5VvOgy4F1cvWpwdPfvanYpj+PZuOI7jIxNMTExwaeaP1XcPAke/wkGBgYc2be9TH3vajUYPPJdgpq1RVdPr9zzy8nOYv7P0zA1t8DOzg4nV2+cqngzYPT/nvi/tRz/1zzl/zu4elXnyO51ana6uvqYW9piZmHDpk2baNl9HDUCW+Po5kvfJ+Xz9QrK/xO7V1G/RV8Cm/XC3sWH7sOnoWdgyIUn941DlWoMmjiH6nVbYmVlxeFLkER9Thz4nfWHcjAzhhru5f9EXbknn4u35SSkKHj0WMGmowVYmUpwsS2+58/dlHM3Tk5oaChNO4/FzL01dWv7MXjCs59fp/c+eX49yT9dhjx5fj2Vf+I0/Pxq3mUUly5dAsC3TvNK//32j+oHNK+0/APQuMMwmncdTUBAQLk6gsqhRYsWKBSKUsvKlSsBWLlyJYcPHy61z19//UVubi5RUVEMHz5coz7+q8fQlsWqVauwtbXl7NmzvPvuu4wbN46+ffsSFBTExYsXadeuHUOGDCErSznja0pKCq1ataJu3bqcP3+e3bt3Ex8fT79+/Z5xpBfDsmEdkg6qf2cpcd9xrBrWAUCip4dFPX+SDpwEoACINYCgoCBMjZQ/fqVSKf4BbxB583Kl+JQY/xCZTPlWy8jEAgBDYzNcvWpzLzKizH0KCvJ4GH0VH//i/u5SqRRv/0bciwxXszU2AH19fU6ePKlKy8qRczs6B18Pwwp9e7u/PeevZHLpZvkzxOZnx5OYmIibbxD6upBXAPqGZji6B/Aw+q8K9Z9FYUEe8fevUq9ePVWaVCqlVp1Abt64+lLaRTyIvUd2djY13uis6mpvaGyGi2cAD+6U7X9hQR5x967iWT1IlSaRSvGsHkTsk30y02Q8vBtBZmoiJ0+e5MMRrfnxy2CibkZQvfab3Ll5qUztqFuXqF77TbU0/7qN1Oy9/QKIOHeEZFkCiY9iSUxMJDszDW//xir/q3jVJjYqvMxjFBTk8TDmKl41iv2XSqV41Wiktk9ebjZhC95HLpfjX7+44lJZ+bPIzt/fX82uas2GxNwuWzv6dgTVaqqP8/CtHVSuvb5OAVKplLPnwzG3qwZATj7cT5DjZv/8RayhvjJvZOUWd5nLz8/j3t1rBAUFUdPTlCp2Bujq/APlg5EZzs/In4/KyJ8efkGl9klJUuYfE3N7EmOvU6tRHwyMzHD0CCDu7ovfv6myWDLTEqldX9mV9268An1DM+xcanMv8jwPZeBi+8LyAETH5XLv7lWCgpTnaWMG5sZS/Go1IuZ2eJn7xESGqyqqRVSr3ZiYcvIyQHJyMh4lYvm88fd4Ov7V1eNfZBcUFERM1DXu371J0zY9qV77TaLKKR/u3LpMjQrKh4J8ZW8WPX19CvLziYm6TuPGjdHV1ePOzb8q/f5KiItGJpOhZ2CMQxVl9zxDYzNcvGrzoJzyp7Agj7iYq3jWeLr8bETsneJ9FAo5mWlJbNmyhe8+7Mm2tbMpyM/Ft1ZDom+V4/+tCHxrNVRL8wsI4m4J/+VyOaFzP6dl56E8ir2jNvGKVCqlZp03uH3zSpn6z0vIop8IqPcm8Q9jsbCwUNP3rfUmd8vx/+6tCPxqqV/f6gFBRD8V/9vXzvPZqOZMe6cjiYmJuHgUl6FF5f/fLZ99apT+/WDyJDRRD+Xk56ajq29KfqEusYkK3OyfvxXJ4EndP/upRr/M1AckJibiUzMILwfIyFGg0FH6f7+C/PMw5ipeT91fTz+/8nOzCVuouecXQHJiLKmp6jPcvw76lZV/tB2JnvSVLa8bWndGAQEBfPnll1StWpXJkydjaGiIra0to0ePpmrVqkyZMgWZTKZ6IzRv3jzq1q3Ld999h5+fH3Xr1mXFihUcOnSIW7duVbp/Bg625Mart/7mxiehZ2GG1NAAfVsrpLq65CYof0Bm6oBcAjY2NuiX6AJgYWlNanLZXYb+LsmPEwFw8aqFo2s1VbqphS3pqYll7pOVnoJcXoiphXqXJzNzG9JT1M/P8MmDpOhHcREp6YVYmpc/kXaTQFO8XA1Yva3i8yzIU3b9tba2IdAbrt9Xphub2ZCV9nIt7dmZySjkhVhZWamlW1pak5JcOZ9ZOH1c+T2OOk16q6WbmNuQUY7/WRlKv0zM1eNvYmZDZqpyn5QkZSAUCgXt27fnvf/Nx82rOr9MHYOOri6pKWXHNS0lCXNL9a7S5hY2avYDRn2Ks6sXn45uz//e7QlAmz6T8PB9o4T/tmSkluN/utJ/06f9f2qfPetmYOvsBVAqr1VG/iyye3pcjZmFDWkpZfuenpKE2dO6Fral7K9ePMynw99g+ffKsYhejT7FwLg4H2VkKzAzLvMQpZAAXRvpcfeRssW2iFMRMgoLC7GxsSFOloutuT5ezsaYW1qTUsnlg5N7LexcissHE/PivPY0RfnT2Ozp62tTKk9kpin1Y24cx9rRG2cv5csjEzMbMl/i/s16omtro8zLmTnKdENTG7IzksjMUWBS8fu0Z7L9RDLyJ/HvUk9Kw6pSLt1ToGdsQ3o5sUlPSSqdLy1sS5WbT1Pmvf4i5UOJfYrsbGxsOL7/D5yqeOLjF4C5pTVp5ZQPqSlJmFmq65YsHxxdPLC2dWTz6nkkPLqPXF7I8ePHSXkcT1pK4pPzrbz76/sPuwLQZ8xsjM2K768Ky5+M5yt/qtZuAcC0adNo2yOYc0f/ZNXcyZhZ2pQbn7Qy/Vcvd/b/sQIdHR3qBXVALi9EKlX/qWXxkvfvyaP7iI66SYfuA5DLC9HVVX/OmluWH//y/C9pX71OY4a88w3vTllK4zZ9Adix9nvk8uJeVKYWtmSkPKN8Nn+6TC9dPqh+PzxO4capJXgGKJ+TGdkKTI2er0IrAbo01CX6kVyt/ATwtlXGeVArW5ys4OAVkCuUeaHc+yu9nPvrqfyze71mn19AueXG66BfGflHIChC6z7bU7t28YBwHR0dbGxsqFWrePKDog/5JiQkABAREcGhQ4cwNTUtpRUVFUW1atVKpefm5pKbq/6aT5PTW1c2Jw/vZuXCGar16rXrA9BzePlj0f4OrVs2oVtg8YPm5K2/P6+YjaUuwb3tmDbvAfkF6vunxh3i4fX5qnW3Okq/W9aC5Aw4H/WCjv9DHD20lyXzflatu7i6AWBiblepx1Eoisf+tWjRgjR9X9y8/Lhx+azaeNi/y5kjO1k5fxrywgJ09fQZNHoyoQumc2DTL9g5++DtH/RskQrIykjm23H1UMgLKSjIY/B7i7kdcfilNP9JnK0k1HTToV1AG94ffpEdR5UvxsL3fo1/hzkYGJc9rroiujfRw8FawqJt6uXOX5HFPyBlafnkFcip7maKrvTFp/o/cXg3ISXKhxpPyof2g6eXt8vfIjsjmZ8m1lWt93tnMQCRl/bRsMM7L63v4wjNaoC8ZQCfDLnI9rOa+35obU8JRR0xj16XY20qobabBOOye3I/FxdP/Mmm5dMA5cuof4K8vDzOHNtFl76jX1pLV1eP8Z/+xMr5XzH1PeXcAFevXqV6naaVcj4Xjm9nwzJlXlQoFPQe8SWbQr5hx+opjPpyU6lKxstQq1E3Dmz6GTc3N4w8/DG3smPeV6No1Lr3s3cuh3t3rnJ452o+/WFDpflZElliPKFLf+Hzr+agX86Qgpfh3LEd/L7kK9X6uMnKZ/Gjeze4e/0s3v6Nytv1uWjdsgk96heXX8dvKvPMuR2TMbPxokbjcX9bs1uQLg5WUhZvz+Pe1R1c3Fvsf4v+Sv+PXlfQsCY08YO95XeWeCZZGcl8N15zz6+s9GSmjiqe0HHYhwsrTft10H/d+LtjWQXlo3UVWr2nxpRIJBK1tKIunXK58sd+RkYGXbt25Ycffiil5eTkVOYxZsyYwfTp6j/upk6dyrRp057pX258EgYO6v3cDBxsyU9NR56TS15SMvKCAgzslQ9lk0KQKpStm+4lKnapKY/LndDlWdRt0BRvX2X3oC2/L+Nq+BlA2WWmJBmpSTi5Vy9Tw9jMEqlUp9TEJ+lpMi6l5HHgSrGvRbI2NjaU/DljaaZTaqKnIrzdDLA01+XnT91UaTo6Emp4G9E8cCCDPvCl6LeRnm4BAImJMk7etUf+JD0rXYZdlZebIc7IxAqJVIfkZPUJoFJSHpc74dOzeOPNJqoZkh/Lkpg2+T0AMtNlmFnaq+wy02Q4uJbtv7Gp0q+nJ6DITJdhYqHMX6YWygqyRCJVdsl7kp2dXDy5G3mVKu4+ZWqbW9qWmhAmLVWGxZNWmeoBb6JQyBk85kv8atWnIF85U7WXf2NO7lmhqtBmpiXh6FZe/lH6//QEUJlpSdg6evLWpGUc/XMhESe3suZX5YQO309UjnNdM+c9PHwDQaF4ofxpZmlbyi4tLU3dLlWGuWXZ/VHNLG1LTaiSnpqkso9PVZByo0C1zdrRGwAbW1tiLm+h2pvBAJgaSXgoe/YP/O6N9ajuJmXRn3mkZqpvMzCyQiLRQSaTYV7FSzXOPD0t+ZkTxpRHvQZN8XlSPmz+fRlXnpQPT7ciPU/+zEp/+vrKsHH0pP97y1VphQXKCmd+Xi41GvQotk2XYe/y9+/fmETYeArSkhPYOHcovcctAqwwMVS20uZkyLB28sPEUEJ8yotXsHR1oFMjK76UKuOfZQlp2QosjEGRK8PMovz8Uypfpiap8mWNeq1UMySnPI5n8bfDAWXsTC1KlA/pMuzLKd8qLB/MbUvZ7d+/n7y8HBq1UM4VkJbyGHPLsvOPhaUt6U+1TpYsHwDcvWswddY60lIf83FwO8aMGcMPsxbh6uX/5Hxf/P7yD2zJRz7FL66LujijkPDX8Y006TRGea5pSTi6llM+mJZf/phalI6PTCYDO1QzAz9OeFBufMzL9L+43Im6fpGMtMdMGd9OVcFPSkpi9Yq57Nq2njnLt5Ca8viF7987kTdIS0nm8/eHq/SvX7/OjRs3OLp7HbPXnictpfz4l+d/kX2t+i3wqFrcSFAUf0NjM2Tx91QV2uf6/fBU/DNSZVxKzmPf5eL7sjAvGzDBzt6JmnU/Qaqj/D1naiQh7nH5E7YV0bWRLr6uUpbuyCMtC5x8WtDGudh/eaHS/3sPZBTo2tOnIbjaKPOCQ3n5x6yc+6vo+fXBMo5u18zzy9bZkxGfLi2OT37ZL+xe9PebVumXkX9Myyl3BQKt63L8d6lXrx5Xr17Fw8MDHx8ftcXEpOwJiyZPnkxqaqraMnny5Oc6XsrpcGxaqY+vsW0dRPLpcAAU+fmkXryKbSvlQ0EXqJILp06dIuPJj1W5XM61S+fx8S1/2v2KMDI2wd6xCnu2reP65fN8PmMxdnZ2RF09rbLJyc7g/p1LuPmUPZheV1cfZw9/oq4V7yOXy4m6ehr7KtXJzEW1pGcrWwBKfl/KyFBKVQ9DbkbnlKl/6WYW730bw6Tv76mW2zE5HD2fzv/mPkbPyBl9Y2csrKvw/WeNsLOz4/c/TlH45PmWm53Bo5gInD3qlqn/vOjo6uPg6s9ffxWPO5PL5VwOv4ivX9mfVXgWRsbGODlXwcm5ClcuXcTcwhJbW1uirxePrc7NzuDB3QhcvMr2X0dXHyc3f6JvFO+jkMuJvn6KKk/2sbCpgqmlPSYWtpw6VWz36GE0GWnJePmW/VkR72q1uXH5rFratYjTKns9fQPkhYVY2dpj7+SGk6sXdnZ2ZKYmqlqFc7IziL1ziSredco8hvLzOP7cLXHOcrmcO9dP41atPjYO7rTp+yHjvtrG2GlbsLKyokmHYQB0HvwZXYd88cL5082nTim7q1evqtndvnoG96pla3tUDeBWiXsF4NblUyr7Qjlk5RYvGU+yeIP6dSl88uPJQA9c7aXcS6j4B1n3xnr4e+iwZHseyemlK19SHT0sHaqrrq+xoQ5yuZyIv86+dPmw+0n58OWT8qFkXsvNzuDhM/Kno5u/Wp5WyOXE3DiFa9U3sLZ3Vy22Tj7o6elh4+iNsZm1Sv9RdAROnn///s0vhLRsUBg4IkvJ5sKZIwB4OEjIy8kg8cElXH3q42wDD16id5pUAkZGBji51VC7vwrlci6cP4171Tpl7ufuU4fbT+Wf21dO4f4kLxsamWDr6I6tozvWdi4AWFlZvVj8b5SOf8l9iux2795NQP3mmFlYIZfLuX7pLN7llA9e1Wpx/any4XrEmTLLE3MLa9y9a7Bnzx7u37lKzfotX/r+MjQywc7RTbU4VvHGzs6O/Lws1Y/j3OwMHty5hEs55Y+Orj5O7qXz590bp6niVaeUXdH1fRB9E4CH927hUa0c/6sFcOvyGbW0m5dO4fnE/wbNuvLZj5v4dGYYn/24ESfXqhgZGdGl52A+mz4buVzO1YjzVPWtWab+s6gZUJ8f5q1mxpxVfD83FFd3b6ytranfpDOfzdwASLh15Qye5fjvWS2Am0/5f+PSaTwqiL+1tTU5WemYWSpfohaV/88sn68+VT5fO41did8PspQMZk8bQUpKCn2GfoGOrrIXnIEeVLGTcC+h4hdSXRvpUsNdyvJd+apPdOkZmGBq5aZazGyU+edOyedvjtJ/1wryj7O7v9o+ihLPL2sHd9r0+ZBx07cxdmrlPr88fetj6+CuWuxdfNTGSMPL/X77t+i/aP4p+XwXCEry2ldoJ0yYwOPHjxk4cCDnzp0jKiqKPXv2MGLECNWnPJ7GwMAAc3NztaWou7GxZxXMA/wwdFU2h/l+M4mAkOLW35gl6zD2dMVvxseY+HrhPnYQTn07cvfXlSqbu7NDcA3uh8uQHpj6efFWn75s2LCBTZs38/D+XVYt+oHcnGyatlG+TV/8y1Q2hBZ3wS3Izyfmzi1i7tyiID+fZFkiMXduER93X2UTungmp47sYtyHX2NkbELv3r05uHUBl8/u5tH9W4Qt+gwzS3tqBBZ/13HZjBGc3Fc8E2fTjsM4dziMC8e2kvAgij9WTicvN5vAZj1VNukpiTyMUb4dHjduHHWq5eBokcDEt2x4nFrImYjiJqfp77rQsZmyYMvJVXAvLk9tyc2Tk55ZyL045Y8WI0MpUyc4Y2igw9ChQzm9ZyH3rx8gPfEme1Z/gomFPd4lviu7cd4wwo+uVq3n5WaSEHudhNjrAKTJYkmIvU7a44cqm5zMFLxrt2P7duWMk5cjLvDzjKnkZGfRsm0nAOb8/C1rVi5W7ZOfn8/dqNvcjbpNQUE+j2VJ3I26TdzDWLV8JJfLObRvFy3bdGTYsGGc2LmQW+EHSIi9ybYVn2BmaY9v3WL/18waxrmDxf6/2XYEfx3bwKWTW0iKi2LXmmnk52VTu3EvQNkboVG7YHIyU1m3bh27t6xk9aJveHgvEolEQuNW3QFY8euXbF49R6XbustArvx1kr1/hBIXe5dt6xYRE3WNlh2V3zo0Mjalmn8gm1bN5uaV88gSHhIYGEjsnQis7dyIj73JlmWfYmZpj1+9Yv9X/TicMweK/W/UfjgXjoQRfmILiQ+j2PHbNPJzs6nbROm/mYUdDlWq4ejqy8iRIzl3RDl7pEKhYP+muZWWP/3faKOafe/KxSOs+vVDcnOyeLN5D2XcF0xm+++/qPZp1vEtbkSc4ND2lcQ/uMPujfO5f+cqTdsPAiA3J4sd62YTfTuCx4kPuX/nKrt27SJ4xCA6du6Fo5WE/i31SctScDW6uIwZ3VmfRv7Fsx73aKxHXR8dfj+YR26+AlMjMDVC9akKazMJrevq8kbLIWzYsIELJ3ZB9kM+/3IKOdnZNHtSPiz6ZSrr/2b5sGrxTE4+VT6c2DGf6xd2k/DgJn+GKPNntTrF8V87axjnDxVf3wZtRhB+fAOXTinz5+61T/JnUC9Kkpx4j/z8fFKT7hN1+QCJD2+y+7dPMLWwV/sudNjcYfx1pPz7N/Wp+1cikVC3xVDO7FlIZGQkQX4KCmPXUrvOm7wzvC3p2XDzQfEP4kEtpAT6FHfv0tMFe0vlAmBhIsHeEsyfjHvOK4CYBAVvjx7Bhg0buHRyK9LMO6yYN52srGzeaK7MZ78v/Iyd62apdJt0GMLNS8c5siOEhId32LtpHrF3rtC43WCVTVZGCg+irxP/QDk0oHHjxpzYMY/Lp7aQ8OAm21d+gulT8f/9l2FceCr+Ecc3cPlJ/Pf8Po28MuJf440uJCQkYGPnRFzsHdYs/o683Gwat+oGwPJf/8fm1XNV9q27DOLqX6fY+8dvqvIhOuoarTr2V9mcP7mPm1fOk/golqo16rJt2zZcPPywsnFk44qvycvNrrT7K/buNezt7cnOTMXI1Ir42JtsXf6k/ClRfv7203C18rNh2+FcPBpGxJPyZ+dqZfkT8KT8fJxwj6N/LsC3TmvWr1/PppXfE/Lrx5hb2lJQkE/DFkr/Q+d9zra1s1W6LTq9xbWIExz4cxWPHtxh54YF3Iu6SrMOym89m5hZ4uxWVbW07/022dnZyBIfoVAoWLFgJjk5OTR/cv8umDWddasWqPQL8vOJvnOL6Du3KCgo4LEskeg7t3j0UHn/Ghmb4OrurVp69B9OcnIy6akypFId1i/7htzcbDX//1j7awn/B3Mt4uQT/++y44n/zTsMUMV/y28/c/dWBLKEB9y6cgZdXV0kEimFhQU8un+LjYuVvx+qlyj/l38/glMlyufGHYZx/kgYF5+Uz9tWqZfPOdkZrJwZTF5eNgqFglb1DPC2y8DKIIU+zXRIz4JrMcUvBIM76tGwevFP1m5ButTxlrLhcH6Z5aeVmfJbtS62UoYOHcrxHYuQJh7k+o2bzPmh9PMr9MfhnC3x/GrYTpl/ip5f25/knzpP8o+phR32VarhoOHnV9y9G7z5pnISr9MH13HpzG5+n/+hVuqfPbiOy2d2s/6J/svknyL9hzHXVZ+LuXXrFtevXyclJQVtQaIneWXL64bWdTn+uzg7O3PixAk+/fRT2rVrR25uLu7u7nTo0KFUF7vnocZPnwNwP3Qzl4InY+Bkh5Frcdfl7OhYznUbQ42fJ+Px7lByYh9xecyXJO07rrKJC9uFvp011aZOxMDRjtoR19Gp68eCRYtJTZbh5lmNj6b+quri9TgpXs3X5MeJTPngLdX6rq2r2bV1NX416zH520UAHNylLFxnfKH+fa5Ny/5HYUEe7tXqMeLjJejpF48LliXcIyu9uOtt7YadyEhPZv+mOaSnJuHkVp0RHy9R62p35uB6DmyZz1xg4sSJvDu0H+bm5ly5kcjXC1LUxsc62uphblrBh+WewsvVAF9PIwBGjx5NdnY2GzZMIS0tDRevQHqNW4auXrH/qUn3yc4o9j/+3hU2zh2qWj+yRTlusEaDnrR/63sAoq4c5MSfP6lsQpcrf1i0atdZ1eU4KTEeqaT45k9+nMTHE4NV69s2r2Pb5nXUqFWHr74vrjheCj9PUmI8rdp1pl2zQM7cyGbn6inkZKXh6hPIgPfU/U9OVPe/xhudyEx/zJFtc8hMS8ShSnUGTFyGaYkuhQ3aDKcgP4/z+5ew+bdfQSLBycWDYe9MV3WZe5z0SK27ubdfHUZ98B1/rJ3P1jXzsHdyY/yns3Ap0UV59KTv2bJ6Lstnf05mRhouzk541Qji+sX9/HV8M25VA3lr0lL0Svj/+Kn8U7OB0v9DW+eSkZqIo2t13vpgaZldhkaPHs1fUVkc3LqQXet+xNOvfqXmzyLWLlD2tGjbc4yqi2ByUhwSSXF8PKvVZcg7P7Bzw1x2rP8VO0d3Rn44ByfXqoDycyjxD+9y7ug2MtKTMTG1pH69WhSa1Oatzu4Y6kP0IzkrduVRUOKdmbW5BBPD4nzUyF9Z/I7tqj42f8Nh5ed8CuXg4yLl+8+6s8Ung2XLFjH/lyTcPKvxcYnyQZYUr3Z9kx8n8mWJ8mHn1tXsfFI+fPGkfDjwpHz47qnyYVfoFxQU5OHqE0i/ier5MyWpdP7MynjMsSf5075KdfpNXKbW5RXg0olNODo64h7Qk32/TyE3+8n9O76M+zdT/f4Nm1P2/dthiPL+faPNaPLzshk2bBiD3xrCB+MGYmY+htgkCeuPyFU9OgAsTZUzshfhZAVvtSouj9rWVcbw0l05288qy62tp+S0COzIp58ms2zZPNYtScTDuzojP16symcpMvX841GtLoMmzGRP2Bx2bZiNraM7wybNxfFJ/gG4euEQG5Z8oVoveqG2d91XyAsLqOITSP93S5cPWSXiX71+J7LSH3Psz+L493+3dPyz0mWYm5tz4dR+Du1aj6unL+/9b1655YOPXwCjPviWrWsXsOVJ+TDhqfIhNTmJDSGznnRFtqVJkyZcuR7Jj5/1wcXdjzGfLarU+yuwbk30rPw4vmMR+8Nm4lY1kEHvL30qPurlg38DZf488sdcMtIScXCtzqD3i8sfHV097l4/SeKD28jlco7uXocCBVU8/OgX/IWqC67S/+L71su3DsMnfs/2dfPY/vuv2Dm5M/rjX3F2K76+JQkM6sCWkG+IuHiasycP4+5Vlc+m/4LFk+eLLDEeqaTk/ZvE5+8NU63v2LKWHVvWUr1mXf43Y0Ep/UZN27Bt/TLu3org+0/64uLhy4TPF6pf3xL6xf7P5c/f52Dn5MbbJfyXSKU8uHebM0e2kZ2ZjoW1Pa1bNOdhujF/hn5NTlYa7lXrMfwj9fL56fK/dsNOZKYnc2Bzcfk8/OMlqvg/jL7G/Sjl5J0NGzZk4sSJ9OvXD3NzS6If5RGyR1e9/DRTLz8bVlfeu6M7q48j3nhU+TmfgkLwcJTQuKYeJoajScvIZtYPU0hPVz5/3/pAPf88Tryndn/VbKC8vw5vVeYfR9fqDH7Fz6+9G5QvhuydvbVTP0ypb+fs/dL5B+DswfUc3FqsP3iw8qXhjBkz6NVL/cWe4PVHovinZqV4Ddih56sx7c75Nzl9I/XZhi9IQz8LNp999niUF6VXAyk93yn9YfnKYsu8qizaozF5xraHy5HxGtOv5eNA6BGNyTO0ORy5mqUx/eb+xvx+QnNFxcDGEo3nz50X8zWm36meHp8uKf/TUy/LD28bcVaD5UMDPwtWHtaYPMNbwOK9mtMf0w6+W192j5vK4PP+Omw7rzn9bvV1CDmkMXlGtISjVzOfbfiCNPM30fj9tfqY5sqft5pK2BuhucnF2gXoc+FW5cyaXxaB1azZF1H2fBWVQdsAAzae0Vz53OdNKZ8v15z/3wUbsPa45vLPoCaaf35pu76m84+2ctCj7KEf/wStosv+dJu2or25QCAQCAQCgUAgEAgE/2lEhVYgEAgEAoFAIBAIBFrJaz+GViAQCAQCgUAgEAj+TbyOkzO9KkQLrUAgEAgEAoFAIBAItBLRQisQCAQCgUAgEAgE/yBSXdFCW1mIWY4FAoFAIBAIBAKB4B/kSPU6r+zYza+Hv7JjawLRQvs30PRndTT9WaAtZzX32YmeDXSEvtAX+kJf6At9oS/0hb6W6X+xQnOfTfp2pIHGP7uorUh0RAttZSHG0AoEAoFAIBAIBAKBQCsRFVqBQCAQCAQCgUAgEGglWjuGtmvXruTn57N79+5S244dO0azZs2IiIhg2bJlnDhxgitXrlC9enXCw8Nf6Hhr1qxhwaKlpCbLcPWoyltvf4R3Nf8ybWPvRbFl7RKio26QlBDHoOAPaN9toJrNnxtXcuHUIZzsbRgxcgS1a9XE0tKS873HE7/tQIW+WDdrQI2fPsO0RlVy7scROWMhsaFb1Gzcxw3Ca1IwBo52pF26wc7bV5izYBkZqUk4ufrSbegXuHrXLvcYl87sZt+muSQnPcDGwZ2O/SfhV6e5artCoWDf5nmcOxRGdlY69QPrgqknV8/tIzsrHY9qdekxfAq2jh4VnsupfWs5snNFuX7l5+WyY+1Mrp/fSXpGJlKp8h2MR7V6laofcXIr2dk5SCQSnNx86TliqojPa6Sv6evrWsUZWUomWRkpGrm/NK2v7flT26+vpuOv7fHR9vhre/7X9vhoe/w1nf/fqF+PAWN+pnGANYb6EJOgYNvJAmRp5VcPmtXWwd9dip2lhPwCuJcgZ8+5QpJK7NM9SBdvZynWZjDrlzmEhYWRnp6Gk2c9WvebhpV9+fGPjTzH+QPLSbh/hcy0RLqOmo9P7TZqNgqFglM753DnYhhpaWnUq1ePadOm4eFRvu6/jeMB9V7ZsZtEXHxlx9YEWttCGxwczL59+4iNjS21LSQkhPr161O7tvKGHzlyJP3793/hY+3cuZMZM2bQvf8ops8KxdWzKj9Nm0hayuMy7fNyc7FzcKHvkAlYWNmUaXPzykVad+pL8LuTsXXyYu3atQDkP6M7vZFHFd7YthjZ4TMcr9+du3NXUWvxN9i2baKycerbkeo/Tub2N/M53qAnf24IY8aMGXToPZ53v96Ik5sfy2e+TUaqrMxjxNz6i3ULPqZ+815M/HoT/oGt+W32uzy6f1tlc2THck7uXU2PEVOZMG0diYmJnDmwjq5DPmfCtHXoGRixYubb5OeVPy4j4vQutq/9gTY9y/dr+5rvuR5+iHbt2qGrp4+ljTN2Tl6Vqh9xZif5+fm06jEOR9eqpCUniPi8Rvqavr5te71DdHQ0hQX5jJuyttLvL03ra3v+1Pbrq+n4a3t8tD3+2p7/tT0+2h7/fyL/d+/encY1DdhyLJuFf+aTn69geHs9dHXKDQ+ejlJOXy9k0Z/5hOzJR0cKwzvooVdiZp6HMjmbj+WzdOlSQkN/Y+JH0/htzQb09Y3YvDCYgvzy45+fl4Wdiy+t+k4t1+b8/qWEH/2NadOmsWHDBoyMjAgODiY3V3PjgQX/XrS2QtulSxfs7OxYuXKlWnpGRgZhYWEEBwcDMGfOHCZMmICXl9cLHyskJIR+/frRrE1XXNy8GD7uM/QNDDm6/88y7b2q1mDAiIk0bNYOPT39Mm0+mjaHpq27oGtsR1qBCQMGDABAplexL+5vDyD7bizXP/mBjBt3iFmwhkeb9uD53nCVjef7I7i/fAOxqzaTcT2K38LC6NevH/369sbBxYceI6aib2DI+aObyzzGib2/Ua12E5p3DsbexZt2fSbi7FGDU/vXAMq3Yid2h9Kq2xj8A1vj6FqNlJQUJBIpCrkcJzdf+o/5nrSUBK5dKL+1+fiulTRo0Zf6zXqV6VdOVjrnj2yi88BPOHz4MG16jGfoB/OIu3eDoLZvVZq+sYkF/fv3p13vdxg44Wcy0mTo6OiI+Lwm+pq+vlfO7aVPnz7k5mSSFBdd6feXpvW1PX9q+/XVdPy1PT7aHn9tz//aHh9tj7+m87+Tmy/du3dn4aJF7N+3l/hkBWFHCzAzgupu5VcRVu3N569IOQkpCh49VrDxWAFWphJcbIpbZc7dlHP3kZzQ0FDeaDcOXcc21Avwo8/omWSmJhB1aX+5+p41mtO4ywf4BLQtc7tCoeDikVAatBtHmzZt8PPzY+bMmSQkJLB/f/m6/zYkUskrW143tLZCq6ury9ChQ1m5ciUle02HhYVRWFjIwIEDK9j7+cnLy+Pq1asEBQWp0qRSKf4BbxB583KlHAMgPT0dAAN5xXaWDeuQdPCUWlrivuNYNawDgERPD4t6/iQdOAlAARBrAEFBQVibSlT++/g3IiYyvMxjxESG4+PfSC2tWq3GxNyOAOBxYizpqUn41GykWpfJZDi6+qo0DY3NcPWqXe4xCgryeBB9DR//hqq0p/2KvXuVwsICbB3cSUxMxKdmI+ydvbC0ceLR/VuVpv848YHq+hbpW9q6iPi8JvqavL5FfrRo0ULlb2XeX5rWL1rX5vypzde3aF2T8dfm+LwO8dfm/P86xEeb4/9P5H9jA+W5XYuUIXuotMnNh9hEBW72z1/pMXzSGJP1VONoZuoDEhMT8awehJ8LpGVBgdQMR/cAHkb/9dz6T5MqiyUrLRE33+Lf5mZmZgQEBPDXXy+uK9BetLZCC8quxFFRURw5ckSVFhISQu/evbGwsKiUYyQnJ1NYWIiNjXrXYQtLa1KTy+7y8XeRy+V89913AFgVVGxr4GBLbnySWlpufBJ6FmZIDQ3Qt7VCqqtLboLSt0wdkEvAxsYGgxKtv6bmNmSkqOsUkZGShKmF+vmaWtiSkZqk2l6UVnLd3MpeZaPcbqO2XpKs9BTk8kKVRll+ZaQmoaOrR35ejtrxinypDH2pji4Kufr1NbWwRSKRiPi8Bvqavr5FftjY2Kj5W1n3l6b1S65rY/7U9utbcl0T8df2+Gh7/LU9/2t7fLQ9/v9E/i+qiGbmSMnJLP5Nm5GjwNTo+Sq0EqDzm7pExytbbEvibaPUHN3ZBldb2H4e5AowNrMhK63sc3gestISAaVOSWxsbEhKenFdgfai1d+h9fPzIygoiBUrVtCiRQsiIyM5duwYX3311Uvp5ubmqvrgZ2RkAJCfnw9GL+1ymYQunsnt27efbfgv4dblEwD8+GEHJBIY/uGiStXPTE9myqhA5IWFFBbkU1j4jFr+vwxtj4+26wsqRtvzp7aj6fgLKkbk/4rR9vhoe/w1ja+HNRcvXkTfwAiJRMLJW8/oFvgcdG2ki4OVlCU78rh3dQcX9hT/Bm85YD4Ae8KhaR1oGwBbz770IV8bJDpa3a74r0LrIxkcHMymTZtIT08nJCQEb29vmjdv/uwdK2DGjBlYWFhgYWGBj48PCoWCJUuWqNmkpjwud8Knv0Po4h+JOHecVatWPZd9bnwSBg7qbw0NHGzJT01HnpNLXlIy8oICDOyVvpkUglQBMpmM3PzifTLSZJhaqusUYWppW2rCAeUbTaV9zTeUYxoGTviRid9uxsTMEoC05AS1N5oZqbJSbziLMDazRCrVKfWGNCNNhq2zJxO/3UzP4OkA6OjqqXwo6cuL6hedt6mFLfLCAiRSHWSyEm8mU5NQKBT/2fhou76mr2/RfhmpSSo/ZDKZmr8vc39pWl/b86e2X19Nx1/b46Pt8df2/K/t8dH2+Gs6/yek69CjRw/W743k4BU5eU/q+yaGcgxNSrQ0G0rIyH72R1C6NtTF11XK8l15pGWBk08L2o4IUy0SPUsA7t6XsTccLE3A0x6y0mUYm5d9Ds+DsbkdoNQpiUwmw9b2xXUF2ovWV2j79euHVCpl7dq1hIaGMnLkSCSSlxvsPHnyZFJTU1VLzZo1qVKlimq7XC7n2qXz+PjWeuFjKBQKQhf/yIXTh/n0mwW4uro+134pp8OxadVQLc22dRDJp8OVuvn5pF68im0r5fgIXaBKLpw6dYrHGQqV/5FXT+PuU6fMY7j71CHy6mm1tNtXTuFeNQAAR9dqmFnYkvQoBlsHd+xdfLCxseHR/ZsqzZzsDO7fuVTuMXR19XHxqEHkteLjFPnl5VsfWwd3/Ou1QkdHl9TkeOzs7Ii8eprEuLukyOJwcqv2wvpF+1Tx9EdHRxdrOxdOnVKOSy7ST0l6+J+Nj7bra/r6WttVwczClsirp1V+HD16VOXvy95fmtbX9vyp7ddX0/HX9vhoe/y1Pf9re3y0Pf6azv9m1i4kp2Zx4exRMnMhPVt5btW9rbFxVtoY6EEVOwn3Eiqu0HZtqEsNdykrdueTrOzMiJ6BCaZWbqrF3NYbOzs77t96MveLBPJyMngUE4GzR90K9SvCwqYKxuYldFH2qIyIiKBu3RfX/aeR6khe2fK6ofUVWlNTU/r378/kyZOJi4tj+PDhatsjIyMJDw/n0aNHZGdnEx4eTnh4OHl5eeVqGhgYYG5urlpGjRrFpk2bOH5wOw/v32XVoh/IzcmmaZsuACz+ZSobQuer9i/Izyfmzi1i7tyiID+fZFkiMXduER93X2UTungmp47s4t1PvsPOxpLHj5WfANL3rIJ5gB+Grk4A+H4ziYCQH1T7xSxZh7GnK34zPsbE1wv3sYNw6tuRu7+uVNncnR2Ca3A/XIb0wNTPi7f69GXDhg2EbdxCwoMotq6cTl5uNoHNegKwftFn7F4/S7V/43ZDuHX5OEd3hpDw8A77Ns/jwd0rNGozGACJRELjDkM5+Mdirl08SHzsbSwtLVEo5Eh0dHh0/xYbFn2GuaU9NQJbq3SXzhjByX1rVOtNOg7n3OGNXDi2tUy/DI3NqN+8NzvXzqRly5Yc2LKAVb+8g6OrLyf3rqk0/ezMNNavX8++zfNZO+9DTM1tKCwsEPF5TfQ1fX1rNmhPWFgYBoYm2Dp5Vvr9pWl9bc+f2n59NR1/bY+Ptsdf2/O/tsdH2+Ov6fz/6P4t/vjjD8aNHUvrtu1wsJLQp5ku6dlw/V5xd+SRHfRoWL24ytCtkS4B3lLWH8knN1+BqRGYGqH61I+VmfJbtS62UoYOHcrZvQsxyzjAjes3WfDzJ5hY2ONd4ruyG+cNI/zoatV6Xm4mCbHXSYi9DkCaLJaE2OukPX6oOo96zYdyZs9CDhw4wM2bN/nkk0+wt7enTRv179UK/hto9RjaIoKDg1m+fDmdOnXC2dlZbduoUaPUJo0qenNz9+7d5/74cqdOnXj8+DELFi0hNVmGm2c1Ppr6KxaWyu4Zj5PiVR/sBkh+nMiUD95Sre/auppdW1fjV7Mek79Vjkc5uGsTAHs2L+W3335T2db+6XMA7odu5lLwZAyc7DB6UrkFyI6O5Vy3MdT4eTIe7w4lJ/YRl8d8SdK+4yqbuLBd6NtZU23qRAwc7agdcR2dun7MXTCX9NQknN38GPnxYsyedEFJkcUhkRT7716tLgPGzWTvxjnsCZuNrYM7Q96fi6NrVZVN887B5OVms3nFVHKy0qkfWA87jwZsC/2GnKx0PKrVY8THS9DTN1DtI0u4T2Z6smo9oGFHMtMfs29T2X4BdBn8GRKJlN27d5Cfl0tK4gMUgKm5VaXqh5/YwoEtC5BIwMnNjx7Dp4j4vEb6mr6+bm5uyFIyWTB9gEbuL03ra3v+1Pbrq+n4a3t8tD3+2p7/tT0+2h5/Tef/N+oHYuTYlF5NbTDUh5gEBSv35FNQqNoFazMJxobFrXpvVlfWXEd3Uv805cajys/5FBSAh4OExv56mBiOJjU9m9kzp5CenoazVyC9xi1DV684/qlJ98nOKI5//L0rbJw7VLV+ZMsMAGo06En7t74HoH6b0eTnZTNlyhTS0tIIDAxk2bJlGBgU6/7beR0/n/OqkChKfvNGUCGnb6RqTLuhnwU79Hw1pt85/yZbzhY+2/AF6dlAR+gLfaEv9IW+0Bf6Ql/oa5n+Fytyn234gnw70oBFezQmz9j2mtPWNGcavfnKjv3mqTOv7NiaQOu7HAsEAoFAIBAIBAKB4L/Ja9HlWCAQCAQCgUAgEAi0hddxcqZXhWihFQgEAoFAIBAIBAKBViJaaAUCgUAgEAgEAoHgH0QiWmgrDdFCKxAIBAKBQCAQCAQCrUTMciwQCAQCgUAgEAgE/yDnmzd6Zceuf+TUKzu2JhBdjv8Gm8/Kn230gvRqINX4tOzis0BCX+gLfaEv9IW+0Bf6Qr+kfpOuRzSmf/zP5hy/lqkx/SY1TDSmrWkkUtFRtrIQkRQIBAKBQCAQCAQCgVYiWmgFAoFAIBAIBAKB4B9EIhWTQlUWWjuGtmvXruTn57N79+5S244dO0azZs0IDw/n+++/5/jx4yQlJeHh4cHYsWN57733/vbxFAoF4z/7lXOHwsjOSse9Wl16DJ+KraNHhfud2reGoztXkJGahKOrH92GfoGrd23V9rMHNxB+ajsP7kSQl5eHjq4+zm6+peye5tKZ3ezbNJfkpAfYOLjTsf8k/Oo0V/N33+Z5FKTcYvDA/gQE1MbKyorzvccTv+1AhT5bN2tAjZ8+w7RGVXLuxxE5YyGxoVvUbNzHDcJrUjAGjnakXbrBzttXmLNgGRmpSTi5Vp7/RfGuH1gXTD25em4f2VnpeFSrS4/hU54j/ms58iT+ZfmVn5fLjrUziTi5lezsHCQSCU5uvvQcMbVS/Xet4owsJZOsjBSNxEfT+toef+H/6+3/9fM7Sc/IRPqk+5ZHtXoiPsL//4z/4vnyeucfTZdvNy7sYujQEfTr3x9zM3MuX0/jpwW3iY3LLld75EB3Rg5SP35MbBaDx51TrX88oSr1A6w4fHAzy5YtIykpCVePagwI/gSvajXL1T53Yh9bf19IUsJDHJzc6DN0IrUDm6i2p6bI2Bg6h6vhp8jOzKBBg/r873//w8Oj4nj8G7nYusmzjTREvQPHX9mxNYHWdjkODg5m3759xMbGltoWEhJC/fr1uXDhAvb29qxevZqrV6/yxRdfMHnyZObNm/e3j7d06VJO7l1NjxHTGD9tPfoGxqyYOZr8vNxy97l0eic71v5A654TeOfrTTi5+bJi5mgyUmUqm7y8bMws7SksVI6PGPP5Kpzc/Fg+8201u5LE3PqLdQs+pn7zXkz8ehP+ga35bfa7PLp/W2VzZMdyTu5dTatuozB1qMH69esBKHzGyyAjjyq8sW0xssNnOF6/O3fnrqLW4m+wbVt80zn17Uj1Hydz+5v5HG/Qkz83hDFjxgw69B7Pu19vrFT/e4yYyoRp60hMTOTMgXV0HfI5E6atQ8/AiBUz364w/hGnd7F97Q+06Vm+X9vXfE/EmZ3k5+fTqsc4HF2rkpacUKn+t+31DtHR0RQW5DNuytpKj4+m9bU9/sL/19v/6+GHaNeuHbp6+ljaOGPn5CXiI/z/z/gvni+vd/7RdPl2PfwQGzZsYOiwkUz/eg4DBr9Ldk4hs76qhb5exT8Y78Rk0m3ISdUy/tO/1LbfjMxg4schzJgxg2793yZkdRh1alfnl68mkJbyuEzNyBsRLJn1OU1bd2fqz2up+2YL5n0/idiYSED5MmHejEkkxsfy7uRfmDprLS4uLowYMYKsrKwK/f03ItWRvLLldUNrK7RdunTBzs6OlStXqqVnZGQQFhZGcHAwI0eO5Ndff6V58+Z4eXnx1ltvMWLECDZv3vy3jqVQKAgNDaVlt7HUCGyNk5sv/cZ8T3pKAtcu7C93v2O7VvFGi77Ub9YLBxcfeoyYhr6BIeePFh+/SYdhPE64T8uWLQGwc/akx4ippexKcmLvb1Sr3YTmnYOxd/GmXZ+JOHvU4NT+NSp/T+wOpVW3MRjaBXA/3ZrBgwcDEGNQ8bm6vz2A7LuxXP/kBzJu3CFmwRoebdqD53vDVTae74/g/vINxK7aTMb1KH4LC6Nfv37069v7yXlWnv/+ga1xdK1GSkoKEokUhVyOk5sv/cd8T1pKAtculN/afHzXShqoxV/dr5ysdM4f2YSxiQX9+/enXe93GDjhZzLSZOjo6FSa/1fO7aVPnz7k5mSSFBdd6fHRtL62x1/4/3r733ngJxw+fJg2PcYz9IN5xN27QVDbt0R8hP//Cf/F8+X1zj+aLt+6DPoUd3d3Qjfc5/wNX65dPsDUGbuxsTagaUPbcvUBCgsVPE7JVy2paQVq27ftiePG5T/o168fbzTrhtTUjW++/goDA0OOH/ijTM3929dSs24jOvQchrOrFz0Hjcfdy4+DO5WNMvEP73Hn1mWGjPkcz6r+OLp4MG3aNHJyctixY0eF/gpeb7S2Qqurq8vQoUNZuXIlJXtNh4WFUVhYyMCBA8vcLzU1FWtr6791rNjYWBITE/GpWTy9tqGxGa5etbkXGVHmPgUFeTyMvoqPf/E+UqkUb/9G3IsML2Xn7++vZufj34iYEnYliYkMV9MFqFarMTG3lb48TowlPTVJzV8zMzMAEvQqPlfLhnVIOqg+lXfivuNYNawDgERPD4t6/iQdOKn0H4g1gKCgIKxNJRrx/3FiLDKZDEdXX5VmUfzLO0ZBQR4Poq/h499Qlfa0X7F3r1JYWMDjxAcEBQUBYO/shaWNE5a2LpXif5EfLVq0UPlbmfHRtH7RurbGX/j/+vtv6+CuKp+L/H90/5aIj/D/tfdfPF9e//yj6fItILAxAOfCk9HVd0Kqa02K7AbXbqVR08+8TP0iqjgbsXVlQzYsbcCUD/1wsFNvMVEoCijIuUdQUBBSCThY6pFXANVrv0nUzUtlakbdvEyNgDfV0vzrNCLq1iXVuQHo6emrnZu+vj4XLlyo0F/B643WVmgBRo4cSVRUFEeOFE83HhISQu/evbGwsChlf/LkSdavX8/bb79doW5ubi5paWmqJTo6GgBTCxs1O1MLW9JTE8vUyEpPQS4vLLWPmbkN6SlJpezMzdULDlNzGzJK2JUkIyWpTF8yUpNU24vSnib7GVfcwMGW3Hj14+bGJ6FnYYbU0AB9WyukurrkJii7rWTqgFwCNjY2GJSoLFem/0Xr5lb2Khvldhu19ZIUx189BiX9ykhNQqqji0JeiI1NsT+mFrZIJJJK8b/IDxsbGzV/Kys+mtYvua6N8Rf+v97+6+jqkZ+Xo3a8Il9EfIT/r7v/4vnyeucfTZdvOrp6WFqYApCckg+AVMcceUEaySl5WFvpUx7XbqXz3ewbfDjtMj8tuI2TgyHzv6+DkZGOykZemAHIsbGxIai6CVamOlyJycHc0prUlLK7fKemJGFuqR5/c0sb0pKV9o4uHljbObJp9TwyM9IoyM9nyZIlPHr0iMTEsn+P/5uRSCWvbHnd0OpZjv38/AgKCmLFihW0aNGCyMhIjh07xldffVXK9sqVK3Tv3p2pU6fSrl27CnXHjBnDiRMnVOsPHjzA1dW10v0XlM2ty8rY//hhByQSGP7holfs0X8LbY+/8P/Vomn/M9OTmTIqEHlhIYUF+RQWFjx7p38R4vq+WrTdf21H2+Ov7eVbq5ZN6RoopVOdznz6dlvOx7yYzukLxWNgo6IzuXYrjY3LG9KqiR079j0qZX8pOhsXGz38qjxj3Nsz0NXVY8KnP7Fy3ldMHNICqVSHoKBGNGvWTK23puC/h1a30IJycqhNmzaRnp5OSEgI3t7eNG/eXM3m2rVrtG7dmrfffpsvv/zymZqzZs1i69atqqWocvv0JAIZqUmYWdiVqWFsZolUqlNqn/Q0GWaWtqXs0tLS1LXTZJhalj1+wdTStkxfVG/wnuxX1ts7I3mZkipy45MwcFA/roGDLfmp6chzcslLSkZeUICBvfINmkkhSBUgk8nIza8c/2u+0RaAgRN+ZOK3mzExswQgLTlB7Y1jRqqszFZoKBl/9RiU9MvUwhZ5YQESqQ4yWbE/GalJKBSKSol/kR8ymUzN38q6vprQ1/b4C/9fb/9tnT2Z+O1megZPB0BHV0/lQ0lf/qvxEf6/3v6L58vrnX80Xb5dun6Hg1fkhG6LoHv37qSnZQBgZak8jrwwDamuOVaW+jxOzitTvywyMgu5/zCLKk5GqjSpjikgRSaTkZYl58b9XIwMpORmJmPxVCtsERaWtqQ91XqbliLD3KrY3sO7BtN+Wcfc1UeYtWIvy5cvJyUlRSsbniRS6StbXje0/oz69euHVCpl7dq1hIaGMnLkSCSS4qb0q1ev0rJlS4YNG8a33377XJrW1tb4+/urljp16mBnZ0fU1dMqm5zsDO7fuYSbT0CZGrq6+jh7+BN1rXgfuVxO1NXTuPnUKWV39epVNbvIq6dxL2FXEnefOkSW8AXg9pVTuFdV+mJtVwUzC1s1m4wMZaFln0+FpJwOx6ZVQ7U029ZBJJ8OB0CRn0/qxavYtlKOH9EFquTCqVOneJyhqBT/HV2rYWZhS9KjGGwd3LF38cHGxoZH92+qNIviX94xdHX1cfGoQeRT8S/pVxVPf3R0dLG2c+HUKeW44cS4u6TI4khJelgp8S/y4+jRoyp/K/P6akJf2+Mv/H+9/ffyrY+tgzv+9Vqho6NLanI8dnZ2RF49rfLfya3afzY+wv/X23/xfHm984+myzeHKtXJzAVTW28ePHjIhfPK+VDqB1hRkPcIecFjLK39qFHNnCs30srULwsjQykujkbISlSCJRJddA3dVPFHovTj0l9n8PYt+7NJ3r61uH7prFratYgzeFcrbW9sYoaZhRXR0dFcuXKF1q1bP7e/gtcPra/Qmpqa0r9/fyZPnkxcXBzDhw9Xbbty5QotW7akXbt2TJo0iUePHr1QP3uJRMLQoUM5+Mcirl08yKP7twhb9BlmlvbUCGyjsls2YwQn961RrTftOIxzh8O4cGwrCQ+i+GPldPJyswls1lNlk56SiP8bbTh8+DAA18MPsXbuB+TlZKns1i/6jN3rZ6n2adxuCLcuH+fozhASHt5h3+Z5PLh7hUZtBqv8bdxhKAf/WMztS0fIT4thzpw5AFR3qoJ5gB+Grk4A+H4ziYCQH1TaMUvWYezpit+MjzHx9cJ97CCc+nbk7q8rVTZ3Z4fgGtwPlyE9MPXz4q0+fdmwYQNhG7eQ8CCKrU+d58v4f+3iQeJjb2NpaYlCIUeio8Oj+7fYsOgzzC3tqRFYXIAtfSr+TToO59zhjar4P+2XobEZ9Zv3JjszjfXr17Nv83zWzvsQU3MbCgsLKs3/mg3aExYWhoGhCbZOnpUeH03ra3v8hf+vt/87186kZcuWHNiygFW/vIOjqy8n964R8RH+/yf8F8+X1zv/aLp827HmB+7du8fQflWo73ud6jVbMe3zDsge53LsdHEL7+xvatOrs7NqfcJIL+rUtMDR3oCafuZ893lNCuUK9h9JAMDZwZC3+rji7deVDRs2cOH4dgzzY5k+bRrZ2dk0bt0NgGW//o9Nv81V6bbpMogrf51izx+/ERd7lz/WLSI66hqtOvVX2Zw7sY8bV86T+CiWv84cZuTIkbRp04YmTV7dN10Frx6tHkNbRHBwMMuXL6dTp044OxffcBs3biQxMZHVq1ezevVqVbq7u7tqoqfnZfTo0fwVlcWWFVPJyUrDvVo9Rny8BD394vEAsoR7ZKUnq9ZrN+xERnoy+zfNIT01CSe36oz4eAlmJbqJnDm4ngNb5qvWNyyaDEDL7mNVdimyOCSS4ncP7tXqMmDcTPZunMOesNnYOrgz5P25OLpWVdk07xxMXm42kec38cmo+dDmcwBq/aT8ez90M5eCJ2PgZIfRk8otQHZ0LOe6jaHGz5PxeHcoObGPuDzmS5L2FX+AOS5sF/p21lSbOhEDRztqR1xHp64fcxfMJT01CWc3P0Z+vLhS/N+8Yio5WenUD6yHnUcDtoV+Q05WOh5lxv8+mSXiH9CwI5npj9m3qWy/ALoM/gyJREr4iS0c2LIAiQSc3PzoMXxKpfrv5uaGLCWTBdMHaCQ+mtbX9vgL/19v/3fv3kF+Xi4piQ9QAKbmViI+wv//jP/i+fJ65x9Nl299+vRh2LCRTJvyPmbmZly+lsaHUy+Tl188JtXF0QhL8+KZP+1sDJj2UXXMzfVISc3n0rVUxnz0Fylpym6AuflyAvwt6NftbXbusGTZsoUsnZOEm6cvH0yZp+py/DjxkVr8ffwCGP3Bt2xZu4DNq+dh7+TGO5/Nooq7j8omNTmJ9SGzSEuVYWFlS/8+PRk/fjzayOs4OdOrQqIQo6ifm81nnzEA9SXo1UDKlrOFGtPv2UCHHXq+GtPvnH9T4/4LfaEv9IW+0Bf6Ql/oC/3K1W/S9cizDV+Q43825/i1TI3pN6lhojFtTXO5S8tXduxa2w+9smNrgteihVYgEAgEAoFAIBAItAWpjmihrSy0fgytQCAQCAQCgUAgEAj+m4gKrUAgEAgEAoFAIBAItBLR5VggEAgEAoFAIBAI/kHEpFCVh5gUSiAQCAQCgUAgEAj+Qa71fHXfzq2x5cArO7YmEC20f4Oe79zWmPaWeVW1fpY8MYuy0Bf6Ql/oC32hL/SFvnbpTw3N15j+9KF6fP17gcb0/zdQe6syEqkY+VlZiEgKBAKBQCAQCAQCgUAr0d7XGgKBQCAQCAQCgUCghYgxtJWHaKEVCAQCgUAgEAgEAoFWorWTQnXt2pX8/Hx2795datuxY8do1qwZhw8fZsaMGVy6dAmZTIa9vT3du3fnu+++w9zc/G8fc8MuGW2CLDAxknLjTg6L1ycQl/h84w56tbViSHdb/jyUzIpNSQCYGksZ0NmGOn7GONvr8fMvc9gYtoH09HQ8qtWlx/Ap2Dp6VKh7at9ajuxcQUZqEk6uvnQb+gWu3rVV2/PzctmxdiYRJ7eSnZ2DRCLByc2XniOmqtk9zaUzu9m3aS7JSQ+wcXCnY/9J+NVprtquUCjYt3keBSm3GDywPwEBtbGysuJ87/HEb6t4oLl1swbU+OkzTGtUJed+HJEzFhIbukXNxn3cILwmBWPgaEfapRvsvH2FOQuWlXueL+r/uUNhZGelUz+wLph6cvXcPrKztCf+Rf67VnFGlpJJVkaKiM8riI/QF/pCX5Q/r8r/6+d3kp6RifTJeDyPavVEfF6j+Gj6/nqjfj0GjZ1Fo9pWGOrDvUQF208X8ji9/Ng0rSmlupsEWwsJ+QVwP1HBvouFyNKKbQKrSqjlKcXTURn3jGwFBnpwP0nBrnNyHmeUr+9mB42qS3GykmBmLGHD0UJuPlCvrpgYQusAKQFe2ts2d6Nvu1d2bL+wva/s2JpAa3NBcHAw+/btIzY2ttS2kJAQ6tevT+3atenevTvbtm3j1q1brFy5kv379zN27NgXOmbn5pYsXpfApz/dJzdPzpQJLujpPru7gI+bAe0aW3A3Nlct3dpCF2sLXVZuSWLp0qWs/u03vpwynYUrNqBnYMSKmW+Tn5dbjipEnN7F9rU/0KbneN79eiNObn4sn/k2Gakylc32Nd8TcWYn+fn5tOoxDkfXqqQlJ5SyK0nMrb9Yt+Bj6jfvxcSvN+Ef2JrfZr/Lo/vFk2Id2bGck3tX06rbKEwdarB+/XoACp8RDiOPKryxbTGyw2c4Xr87d+euotbib7Bt20Rl49S3I9V/nMztb+ZzvEFP/twQxowZM+jQu/zzfFH/e4yYyoRp60hMTOTMgXV0HfI5E6at05r49xgxlba93iE6OprCgnzGTVkr4vMPx0foC32hL8qfV+X/9fBDtGvXDl09fSxtnLFz8hLxeY3i80/cX927d6eRvz5/nMxh6c4C8gtgSBtddCuoIbg7SDh7U87SnQWE7i9ARwpD2+iiV2Igo56uhMiHxZXQvRflrNhXSH4BDGqpg04F+nq6EuKTYdcFebk2/ZrqYGmq3V12JVLJK1teN7S2QtulSxfs7OxYuXKlWnpGRgZhYWEEBwdjZWXFuHHjqF+/Pu7u7rRu3Zrx48dz7NixFzpm2J7HnL2cSczDPH4NjcfaQoc3A0wq3MdQX8IHwx1Z8Hs8mdnqs9Tdi8tj5rI4zl3OIDQ0lJbdxmDn05LG9X3pP+Z70lISuHah/NbO47tW0qBFX+o364WDiw89RkxF38CQ80c3A5CTlc75I5swNrGgf//+tOv9DgMn/ExGmgwdHR2V3dOc2Psb1Wo3oXnnYOxdvGnXZyLOHjU4tX8NoHy7d2J3KK26jcHQLoD76dYMHjwYgBiDimPo/vYAsu/Gcv2TH8i4cYeYBWt4tGkPnu8NV9l4vj+C+8s3ELtqMxnXo/gtLIx+/frRr2/vMs/zZfz3D2yNo2s1UlJSkEikKORynNy0J/7+ga25cm4vffr0ITcnk6S4aBGffzg+Ql/oC31R/rwq/zsP/ITDhw/Tpsd4hn4wj7h7Nwhq+5aIz2sSH03fX05uvnTv3p2Fixaxf99e4lNg8/FCzIzBz638Ss/qA4WERylITIX4ZNhyohBLUwnO1sX7nL4u5/iV4gppZJyChBT447QcMyPwq1K+flScgsOX5dyMLbsTqbUZVLGVsOuc5mZ/FmgXWluh1dXVZejQoaxcuZKSvabDwsIoLCxk4MCBpfZ5+PAhmzdvpnnz5qW2PQ8RN7JU/2flyLkdnYOvh2GF+7zd357zVzK5dDO7XJv87HgSExPxqdkIPV0JBYVgaGyGq1dtYiLDy9ynoCCPB9HX8PFvqEqTSqX4+DdS7RN79yqFhQU8TnxAUFAQAPbOXljaOGFp61KudkxkOD7+jdTSqtVqTMztCAAeJ8aSnpqET81iGzMzMwAS9CoMB5YN65B08JRaWuK+41g1rAOARE8Pi3r+JB04qTxPINYAgoKCsH7yJu7p83xZ/x8nxiKTyXB09VVpakv8i/xo0aKFyl8Rn38uPkJf6At9Uf68Sv9tHdxVvx+K/H90/5aIz2sQn3/i/jI2UJ7btajHyB4obXLz4UGiAle752/FM9RX/s3OU6+AWpmWts3NhwcycLF98VbCotbjgvIbcLUC0UJbeWhthRZg5MiRREVFceTIEVVaSEgIvXv3xsLCQpU2cOBAjI2NcXFxwdzcnGXLllWom5ubS1pammrJzMwEIDVd/U1QSnohlublTxTdJNAUL1cDVm8ru2tIEQV5yQBYW9vi5ywhOlFZIJha2JCRmlTmPlnpKcjlhZha2Kqlm5rbkJGi3CcjNQmpji4KeSE2NjbFNha2SCQSld3TZKQkYWpho5ZmamGr8qVov6ePDZD9jBxl4GBLbrz6cXPjk9CzMENqaIC+rRVSXV1yE5Qxy9QBuQRsbGwwKFFZLnmeL+t/0bq5lb1avLUh/kV+2NjYqPkr4vPPxEfoC32hL8qfV+W/jq4e+Xk5ascr8kXER/vj80/cX4ZPfldl5UrJzijWzMgBU6Pnq/RIgA5v6BCTICchRX1beRqZOQpMK24PqpCkNEjJVNAqQKurMYJKRKtzgp+fH0FBQaxYsQKAyMhIjh07RnBwsJrdL7/8wsWLF/njjz+Iiopi0qRJFepu374dqVSqWjp16vS3fbOx1CW4tx2/rHxEfoH6G6vUuENcP9hHtSjkyg9OB3pJSMuG6w/K7mIhqBxuXT4BwI8fdmDKqEAKCzX3wW9tRMRHIBC8KrS9/NG0/5npyUwZFcjGpV9SWJAv4vMU2h4fTePrYc3FixcZ1MqeroFSJJXQUNf5TSn2lhI2Hi2klqeEzwfqqhaphmoZcgWEHSvE2uz1a2kUvBhaXaEF5eRQmzZtIj09nZCQELy9vUt1KXZ0dMTPz49u3bqxePFiFi5cSFxcXLmaXbp0QaFQqJYdO3YAYGGmo2ZnaaZDSlrZhaW3mwGW5rr8/KkbG3/1YeOvPtSsakzn5pZsDRmIT6M5eDdULsamytbkxCQZZ27LKepBnZEqK7MVFMDYzBKpVKfUG8aMNBmmlsVvIuWFBUikOshkxa3EGalJKBQKld3TmFralppwQPlG01a1vSjtaYye0f0jNz4JAwf14xo42JKfmo48J5e8pGTkBQUY2CvfMJoUglQBMpmM3BITSpc8z7/rf8032gIwcMKPTPx2MyZmlgCkJSeoxVsb4l/kh0wmU/NXxEdz8RH6Ql/oi/LnVflv6+zJxG830zN4OgA6unoqH0r6IuKjnfHR9P2VkK5Djx49WL83koNX5OQ9+QlrbCDHyLRY09RQOSvxs+jUQEq1KlJW7i0gLQtu3lewaHuBasnKKVvDxFBCRs4z5SvkUTIs3a3dY2glUukrW143tP6M+vXrh1QqZe3atYSGhjJy5EgkFbxyksuVNa7c3PJntzMwMMDMzEy1mJoqBwHU9jVW2RgZSqnqYcjN6LLvyEs3s3jv2xgmfX9PtdyOyeHo+XT+N/cxekbO6Bs7Y2Fdhe8/a4SdnR2bd55E/uTez8nO4P6dS7j71ClTX1dXHxePGkReO612bpFXT6v2qeLpj46OLtZ2Lpw6pRy3mhh3lxRZHClJD8vVdvepQ+TV02ppt6+cwr1qAADWdlUws7BVs8nIUM6/bv+MrxilnA7HplVDtTTb1kEknw4HQJGfT+rFq9i2Uo7v0AWq5MKpU6d4nKEo8zz/rv+OrtUws7Al6VEMtg7u2Lv4YGNjw6P7N1Wa2hL/Ij+OHj2q8lfER7PxEfpCX+iL8udV+e/lWx9bB3f867VCR0eX1OR47OzsiLx6WuW/k1s1ER8tjY+m7y8zaxeSU7O4cPYombmQnq08t+peVti4KG0M9MDFTsL9xIortJ0aSKnupqzMpjz5BE9eATxOL14SU0vvp68LLjbwIEn0RhRUHlpfoTU1NaV///5MnjyZuLg4hg8frtq2c+dOQkJCuHLlCtHR0ezYsYOxY8fSuHFjPDw8/vax+naw5o1aJrg56/PeEAcepxZyJiJTtX36uy50bKZsbc3JVXAvLk9tyc2Tk55ZyL24PEBZKZ46wRlDAx2GDh3KgT8WczviILK4W2xY9BnmlvbUCGyt0l86YwQn961RrTfpOJxzhzdy4dhWEh5EsXXldPJyswls1hNQTqxQv3lvsjPTWL9+Pfs2z2ftvA8xNbehsLBAZbd+0WfsXj9Lpdu43RBuXT7O0Z0hJDy8w77N83hw9wqN2ihnMpZIJDTuMJSDfyzm9qUj5KfFMGfOHACqO1XBPMAPQ1cnAHy/mURAyA8q7Zgl6zD2dMVvxseY+HrhPnYQTn07cvfXlSqbu7NDcA3uh8uQHpj6efFWn75s2LCBsI1byjzPl/H/2sWDxMfextLSEoVCjkRHh0f3tSf+1y4epGaD9oSFhWFgaIKtk6eIzz8cH6Ev9IW+KH9elf87186kZcuWHNiygFW/vIOjqy8n964R8XlN4qPp++vR/Vv88ccfjBs7ljZt2mFvCT0b65CeBTfuFVc4h7XVoYFvcZWh85tSantJ2XiskLx8ZYuuqSHolujIaGoIjlbF64E+EvyqQK/GUtKz4UaJGYzfaimlftXixig9XXCwVC4AlqbK/82L25Wo7irB3V67uxxLdSSvbHndKH9GIy0iODiY5cuX06lTJ5ydnVXpRkZGLF26lA8++IDc3FxcXV3p1asXn3322QsdZ+eRFMYNtMfESMr1qBy+XvBAbXyso60e5qY6FSio4+VqgK+nEQCjR48mOzubDSunkZaWhke1eoz4eAl6+sXfwZEl3CczPVm1HtCwI5npj9m3aS7pqUk4u/kx8uPFmJXoRtNl8GdIJFLCT2zhwJYFSCTg5OZHj+FTVHYpsjgkkuKCyr1aXQaMm8nejXPYEzYbWwd3hrw/F0fXqiqb5p2DycvNJvL8Jj4ZNR/afA5ArZ+Uf++HbuZS8GQMnOwwelK5BciOjuVctzHU+HkyHu8OJSf2EZfHfEnSvuMqm7iwXejbWVNt6kQMHO2oHXEdnbp+zF1Q9nm+jP+bV0wlJyud+oH1sPNowLbQb8jJStea+Bf57+bmhiwlkwXTB4j4vIL4CH2hL/RF+fOq/N+9ewf5ebmkJD5AAZiaW4n4vEbx0fT99Ub9QIydmtG9iSmG+nAvQcHq/QVqMwhbmUkwNiz+vdvAV/lbd2R79WrElhMFhEcp7er7SmkZUPybuHUd5f+JqQrWHi6ksKS+qQRjAwDlvs7WEoa2Lt63XT3l/xF35Gw7o9zR1Aja1tX6djlBJSFRlPzmjaBCer5z+9lGL8iWeVXZclZzYwF6NtDRuP4OPV+N6XfOv6n18RH6Ql/oC32hL/SFvtD/t+lPDX3GeLGXYPpQPb7+XXOTc/1voPa2zd0Z3uWVHdtr5fZXdmxNIF5tCAQCgUAgEAgEAoFAKxEVWoFAIBAIBAKBQCAQlMn8+fPx8PDA0NCQN998k7Nnz1ZoP3v2bHx9fTEyMsLV1ZUPPviAnJyXnNq6ArS3nV4gEAgEAoFAIBAItBBt+XzO+vXrmTRpEosWLeLNN99k9uzZtG/fnps3b2Jvb1/Kfu3atXz22WesWLGCoKAgbt26xfDhw5FIJMyaNauMI7w82hFJgUAgEAgEAoFAIBD8o8yaNYvRo0czYsQIatSowaJFizA2NmbFihVl2p88eZLGjRszaNAgPDw8aNeuHQMHDnxmq+7LICaFEggEAoFAIBAIBIJ/kOhR3V/ZsZ3mbyA3N1ctzcDAAAMDA7W0vLw8jI2N2bhxIz169FClDxs2jJSUFP74449S2mvXrmX8+PHs3buXBg0acOfOHTp37syQIUP4/PPPNXI+osvx32DRHs1pj22P1s+SJ2ZRLp/XIf5CX+gLfaEv9IW+0H/99E9cy9CYfuMappy/mfxswxekvq/Vs40EpZgxYwbTp09XS5s6dSrTpk1TS0tKSqKwsBAHBwe1dAcHB27cuFGm9qBBg0hKSqJJkyYoFAoKCgoYO3asxiqzILocCwQCgUAgEAgEAsF/hsmTJ5Oamqq2TJ48uVK0Dx8+zHfffceCBQu4ePEimzdvZseOHXz99deVol8WooVWIBAIBAKBQCAQCP5BJFLJKzt2Wd2Ly8LW1hYdHR3i4+PV0uPj43F0dCxzn//9738MGTKEUaNGAVCrVi0yMzN5++23+eKLL5BqYDIs0UIrEAgEAoFAIBAIBAI19PX1CQwM5MCBA6o0uVzOgQMHaNSoUZn7ZGVllaq06ujoAKCpqZu0dlKorl27kp+fz+7du0ttO3bsGM2aNSMiIoLatWsDIJPJCAgI4MGDByQnJ2Npafm3jqdQKBj23hwunwojNzsNZ896tO43DSt7j3L3iY08x/kDy0m4f4XMtES6jpqPT+02aja3I/Zy6fg6Uh9dITU1FWNTS/Jys/GoVpcew6dg61i+PsCpfWs5snMFGalJOLn60m3oF7h611Ztz8/LZcfamVw/v5P0jExVBvOoVq9S9SNObiU7OweJRIKTmy89R0xVs3uaS2d2s2/TXJKTHmDj4E7H/pPwq9NctV2hULBv8zwKUm4xeGB/AgJqY2Vlxfne44nfdqBcXQDrZg2o8dNnmNaoSs79OCJnLCQ2dIuajfu4QXhNCsbA0Y60SzewfKM24z/7lXOHwsjOSte6+Gvr9S2Kt2sVZ2QpmWRlpJTpx39dv35gXTD15Oq5fRrJn+L6vtr4a3v5IPRF+fxv1tf28vOf9N/VsypDx3yOV7WaZWo+uBfF1t8XER11HVliHANGfki7roNK2R3YuYHdW0NJTZFhb2dLVnYumRnpuHn6MOztD/Gu5l+mfuy9O2xcs4S7UTdISnjEW8Hv07H7ADWb/Ts3sX/XZhIT4gDw863G+PHjad68eVmS/2ruje31yo7ttmjzc9uuX7+eYcOGsXjxYho0aMDs2bPZsGEDN27cwMHBgaFDh+Li4sKMGTMAmDZtGrNmzWLJkiW8+eabREZGMm7cOAIDA1m/fr1GzkdrW2iDg4PZt28fsbGxpbaFhIRQv359VWW2yL7k+t9l6dKlhB/9jTb9pjFw0gb09I3YvDCYgvzccvfJz8vCzsWXVn2nlm+Tm4WLVz3efPNNAFp2H8OEaevQMzBixcy3yc8rXz/i9C62r/2BNj3H8+7XG3Fy82P5zLfJSJWpbLav+Z7r4Ydo164dunr6WNo4Y+fkVan6EWd2kp+fT6se43B0rUpackIpu5LE3PqLdQs+pn7zXkz8ehP+ga35bfa7PLp/W2VzZMdyTu5dTatuozB1qKG6AQqf0TvDyKMKb2xbjOzwGY7X787duauotfgbbNs2Udk49e1I9R8nc/ub+Rxv0JP0SzdYunQpJ/eupseIqVoXf22+vj1GTKVtr3eIjo6msCCfcVPWlunHf1l/wrR1JCYmcubAOroO+bzS86e4vq82/tpePgh9UT7/m/W1vfz8p/zv1m8UVdy8SZElMuurd0hLeVymbl5uDnYOLvQZ8i4WVjZl2pw9vpf1IbPo1v9teg0ez6NHj8jKzOSzr+bg5lGV76e+T2o5+rm5Odg7ujBg6AQsy9G3trVnwLAJfPvLSr6ZtZKGDRsyYcIEbt++Xaa94OXp378/P/30E1OmTKFOnTqEh4eze/du1URR9+7dIy4uTmX/5Zdf8uGHH/Lll19So0YNgoODad++PYsXL9aYj1pboe3SpQt2dnasXLlSLT0jI4OwsDCCg4NVaQsXLiQlJYWPPvrohY6lUCgIDQ2lQbtxeNdug52LHx2GzCQzNYGoS/vL3c+zRnMad/kAn4C25drUaNCDNztM4Pz58wB4V2+Ak5sv/cd8T1pKAtculN8aeXzXShq06Ev9Zr1wcPGhx4ip6BsYcv6o8q1LTlY6549sovPATzh8+DBteoxn6AfziLt3g6C2b1WavrGJBf3796dd73cYOOFnMtJk6OjoqOye5sTe36hWuwnNOwdj7+JNuz4Tcfaowan9a1TxPrE7lFbdxmBoF8D9dGsGDx4MQMwzuvu7vz2A7LuxXP/kBzJu3CFmwRoebdqD53vDVTae74/g/vINxK7aTMb1KC6Nn0JoaCj93hqLf2BrrYu/Nl9f/8DWXDm3lz59+pCbk0lSXHQpP/7r+o6u1UhJSUEikaKQyys9f4rr+2rjr+3lg9AX5fO/WV/by89/yv8eA8cy9qMZpKU+Rqqjw7EDpT/FAuBZ1Z9+w9/nzabt0dXVL9Nmz7bVNGvbk6atu3HuxD4GDBiAiakZt65fYuT4TzEwMOTI/u1l7utdtQaDRrxLo2Zt0dXTK9OmXoOm1KkfhKOzG04ubnzwwQcYGxsTHh5ebjz+rUikkle2/F3eeecdYmJiyM3N5cyZM6qGOFBOAlWyPqarq8vUqVOJjIwkOzube/fuMX/+/L/dO/bvoLUVWl1dXYYOHcrKlSvV+mOHhYVRWFjIwIEDAbh27RpfffUVoaGhLzwIOTY2lsTERNx8g1RpBkZmOLoH8DD6r5c7ESBVFsvjx+pvqwyNzXD1qk1MZHiZ+xQU5PEg+ho+/g1VaVKpFB//Rqp9Yu9epbCwAFsHdxITE/Gp2Qh7Zy8sbZx4dP9Wpek/TnxAUJAyNkX6lrYu5WrHRIbj46/e775arcbE3I4A4HFiLOmpSfjULLYxMzMDIKHs8k2FZcM6JB08pZaWuO84Vg3rACDR08Oinj9JB06qtj/WgcTERJo2Kb6+2hR/bb6+RX60aNFC5e/TfvyX9YvWZTIZjq6+Ks3KzJ/i+r7a+Gt7+SD0X62+uH9f3/Lzn/bfqYonNnaO2No5E3Xzcpnaz6IgP5+YqBvUCGig+r9x48bUDHiD2zcuI5VKVf9XBvLCQnbs2EFWVhZ169atFE2BdqK1FVqAkSNHEhUVxZEjR1RpISEh9O7dGwsLC3Jzcxk4cCA//vgjbm5uz62bm5tLWlqaaomOjgbA2Ey9+4OxmQ1ZaUkvfR5ZaYllppta2JCRWrZ+VnoKcnkhpha26vuY25CRotwnIzUJHV098vNynujZqv5mpCZVir5URxeFvBAbm+LYmFrYIpFIVHZPk5GiPLb6udqqfCna7+ljA2Q/I8caONiSG69+3Nz4JPQszJAaGqBva4VUV5fchOJuN2nKceo42j/t078//tp+fYv8sLGxUfO3pB//Zf2S6+ZW9mrXU1xf7Y+/tpcPQl+Uz/9m/ZLr2lh+vgr/zS1skEglpJbj/7NIf3Jscwsb1f82NjaYW1qRmqL83VXy/xflXnQkI/u1ZFjvZkydOpX58+fj4+PzUpoC7UarP9vj5+dHUFAQK1asoEWLFkRGRnLs2DG++uorQPmNperVq/PWW2/9Ld0xY8Zw4sQJ1fqDBw9wdXWtNL+vn9vGgfXF42p7jKncPuWZ6clMGRWIvLCQwoJ8CgsLKlVf29m2bRtfJl5C4aFcH/WocvU1HX9xfV9vbl1Wlj0/ftgBiQSGf7joFXv030LT8df28kHov1p9QcVoe/mp7f7/Uzi7uPPd7FCyszK5d+Mkn376KatXr9a6Sq1EA5+v+a+i9ZEMDg5m06ZNpKenExISgre3t2qms4MHDxIWFoauri66urq0bt0aUH5TaerU8idqmjVrFlu3blUtRZXbrHT1N0pZ6TKMzUu3Ij4L71qteOvTrarF0NSqTLuMVFmZrZQAxmaWSKU6pd7QZaTJsHX2ZOK3m+kZPB0AHV29J3rFb+aUbwRfTN/UsvhNs7ywAIlUB5msODYZqUkoFAqV3dOYWtqWmjChyKei7SX9LYmRvExJFbnxSRg4qB/XwMGW/NR05Dm55CUl07J5c77zeZMPY+HDWDAtVNo9Snjap39n/F+n61vkh0wmU/O3pB//Nf2abyjH3A+c8CMTv92MiZklAGnJCWrXU1xf7Yy/tpcPQl+Uz//l+1fT8X/V/qelylDIFViU4/+zMHty7LRUmep/mUxGWkoyFpbKluCS/78ounp6ODq74unjx4cffoifnx+hoaEvpSnQbrS+QtuvXz+kUilr164lNDSUkSNHIpEoBztv2rSJiIgIwsPDCQ8PZ9myZYDysz4TJkwoV9Pa2hp/f3/VUqdOHezs7Lh/q3hsZm52Bo9iInD2+Pt99vUNTbG0c1ctNo4+WFtbq9nkZGdw/84l3H3qlKmhq6uPi0cNIq+dVqXJ5XIir57Gy7c+tg7u+NdrhY6OLqnJ8djZ2RF59TSJcXdJkcXh5FbthfWL9qni6Y+Oji7Wdi6cOqWMTZF+StLDcrXdfeoQefW0WtrtK6dwrxoAgLVdFcwsbNVsMjIyALDPL1NSRcrpcGxaNVRLs20dRPLpcAAU+fkU3IymZsfW2BWAXQE4FICdnR3HTxRf339z/F+n61vkx9GjR1X+Pu3Hf03f0bUaZha2JD2KwdbBHXsXH2xsbHh0/6ZK82Xyp7i+rzb+2l4+CH1RPv+X719Nx/9V+h/3IBpZ4iNkiXF4+9YqU/tZ6Orp4e7tx/VL51T/nzx5kiuXzlHVrxZyuVz1f2Uil8vJy8urVM1/BInk1S2vGVpfoTU1NaV///5MnjyZuLg4hg8frtrm7e1NzZo1VYunpycA1atXx97e/rmPIZFIGDp0KGf2LCTq8gGSHt5kz+pPMLGwx7vEd2U3zhtG+NHVqvW83EwSYq+TEHsdgDRZLAmx10l7/FBlk5OZQuKDG7Ro0QKAv05u59KZ3aydOwlzS3tqBLZW2S6dMYKT+9ao1pt0HM65wxu5cGwrCQ+i2LpyOnm52QQ26wkoJw6o37w3O9fOpGXLlhzYsoBVv7yDo6svJ/euqTT97Mw01q9fz77N81k770NMzW0oLCxQ2a1f9Bm7189S6TZuN4Rbl49zdGcICQ/vsG/zPB7cvUKjNoNV8W7cYSgH/1jM7Uv/Z++8w6Oo2j58724akEo2FVIJSSBIS6RL6F0FaYJSAwKiqPjpCxbAioD6IghigUAUBAJBeaVIBOlFQAISaighgbTdtN3Uze5+f8RssiRBUUKYcO7rmgtm5pnf+c1zzkx2Zs6c2YcuN5ElS5aU1p1HY+xbBWPj5QFA0PszaRW5wKSd+NV66vt5ETz/NRoE+eMzdTQew/tz7bPVpphriyPxihhBozGDsQ3255Fl7zB27Fg2freCc7/vITXpEhtXzJJM/qVcv+d+30OLdn2Jjo7G2qYBSg+/Sj4edv205Ms4OjpiNBqQKRT3vH2K+q3d/Ev9/CD0xfn5QdaX+vnzfvn/cf2XrPh4NvYODdHrS+jS84lS3c/msOnbpSbdEp2OG9cucuPaRUpKdGSr07lx7SJpKUmmmL5PPMu+2C0c2vM/2nXuw/fff0+eRkNgs0eI/GIhRYWFhPccCMAX/32H9WuWm+lfv3qJ61cvUVJSQlZmBtevXiL1Vrn++jXLOX/2FBlpt7hxPYFPPvmE3377jccffxzBw4uk36EtIyIigpUrVzJgwAA8PT1rpIzJkydz8GwBv6yfQ1FBLp7+oTw17RssLMu/I5OjSqJAm2WaT7txlk1Lx5rm920p/eBw83ZD6PvsRwBcObuHXWtnm2IObI8EwMmlMRNf+wpLq3J9dXoSeZpy/VYd+pOnySR281I0OSo8vYOZ+NqX2FXohjLomVnIZHJ27tyGrriI7IybGAFbeycm3EP9uENb2L1lOTIZeHgHM3j8HFNctjoFmaz83olPYBuenraQXZuW8HP0YpRuPox5eSnuXk1NMeEDIyguKiDhxGZen7QMer0BwCMfl/6bFBXDmYjZWHu4UO/Pi1uAguvJHH9iCs0/mY3vi2MpTE7ljylvoYo9aIpJid6BlUtDAufOwNrdhdzT55n8aEtOXcknZtVcCvM1+Aa2vaf5qen8S7V+y/Lt7e2NOjuP5e88XcmH0NcQFtoWF992bI16v0bap6jf2s2/1M8PQl+cnx9kfamfP++H/x83fIVMJsPLN5AxU2ebugRnZqQir/A0Lzsrg3kzR5vmd/74LTt//JagkFD+8/5XALTr0gdNbhY/rF9BTpYad3d38vILmf/2DHz8m/Kfef81fcNWnZFq6lUJkJWZwZsvl/9u3rZlLdu2rKVZiza89eEXAOTmZLFi8TtkZ6qp38CWkObBrFy5ks6dOyN4eJEZK37zRnBHVvxcc9pT+8KW3/Q1pj+knULy+tssg2pMf6DuouTzI/SFvtAX+kJf6At9oX+3+ofOaWtMv3NzW05czPrrwH9IWFDV49BIgZsvjay1sht9tqHWyq4JJN/lWCAQCAQCgUAgEAgEDyd1osuxQCAQCAQCgUAgEEgF8dmee4fIpEAgEAgEAoFAIBAIJIl4QisQCAQCgUAgEAgE9xGZvO59Pqe2EINCCQQCgUAgEAgEAsF9JOXV0X8dVEN4fLKu1squCcQT2rvgj4S0GtN+JMBN8qPkSV1fjKIs9IW+0Bf6Ql/oC/2HTX/U6zdqTP/7hd58uavG5JnSp+a0BdJBXNAKBAKBQCAQCAQCwX1EDAp17xCZFAgEAoFAIBAIBAKBJBFPaAUCgUAgEAgEAoHgPiIGhbp3SHZQqMcffxydTsfOnTsrrTtw4ABdu3bl9OnTtGrVqtL677//nqeffvquylu7di1frPiK7KxMfPyaEDH1JZoGNa8yNinxGuu/W8nVhEtkpKcyfvILDBo8wixm2oQRZKSnVtpWJpfT2DeEJ8a+iVeTltX6OXNsJ7Gbl5Kluomzmw/9R84kuHW4ab3RaCQ25nOO/xpNQb4Gr8aeqLPzyNdm4+EVdM/1w0LbgK0f8cdjKcjX4BvYhsHj56B09622DIAjsevYt30V2hxVlb50xUVsW7eQ8ye2o9HmIf+ze4ZvYNt7ou9go8NJlkhj94a4uLiwIOJ5fNbuxu4Or6s07NqO5h/PwrZ5UwqTUkiY/wXJUVvMYnymjcZ/ZgTW7i7knrnA9stnWbL8m2p93M6DVr9S16/p9nn68A8UFBQik8nw8A5iyIS5ksqP1PVF/dbt/NfU+f9+6dd0+5F6/sXxVbv1e+HkDsaNm8iIkSOxs7PnUmIxq7ZkkqoqqVa7Vwdbene0RelU+lwsOU1HzC85nL5YaIpxbWjBs4McCQupx38XL2Hjxmg0mlw8/drSc+Q8nFzv7D9u/1pO7F5JXm4GLo2C6T7sbTx8y/1nZ9xg3w8LyEw6SXFxMY899hhvv/02SqXyjroPEmn/GVNrZbst+LbWyq4JJNvlOCIigtjYWJKTkyuti4yMJCwsjJYtW5rmU1JSTNPgwYPvqqzt27czf/58ho8ez8Il3+DrF8D7b/8fOdlZVcYXFRXi5u7JM+On4OjUsMqYjxZ/xdffbuHrb7fw3PRXsbAoPSkMjXgPD+9gVi58Dm2OusptEy+dYv3y1wgLf4oZ720mJLQn3y5+kdSky6aYfdtWcnjXdwyeMJfeT73A9evX0ZfomDZn3T3Xnz5vPRkZGRzbvZ7Hx7zB9HnrsbSux6qFz6ErLqo2r6eP7uCndQvoNeR5XnxvU5W+flr7EefjfqVPnz5YWFrh6OyJi4f/PdP/fX80p04eIzMzE4ACOUS6VStJPd/GPLr1S9R7j3Ew7EmuLV3DI1++j7J3F1OMx/D+NFs0m8vvL+NguyH8b2M08+fPp9/Q6n38m/zXdP1KXb+m2+fpY9vR6XT0GDwNd6+m5GalSyo/UtcX9Vu381+T5//7oV/T7Ufq+RfHV+3X78aNGxk7fiIffLScsRNnUlRsYFaEK5Z36MOZmaPn+x3ZvLkklTeXpBKfUMj/jXOhsZslANaWMt6Y7ILRCF9//TVr1nzLjP+bx9erN2JpXY+Y5RGU6Kr3f/HkdvZtmU+H/tN59vUtuDQKJmZ5BPmaUv+6onw2L5+IDBlr1qzh+++/R6fTMXXqVAwGQ/XGBXUWyV7QDho0CBcXF1avXm22XKvVEh0dTUREhGmZo6Mj7u7upsnGxuauyoqMjGTEiBH06D0AL29fnnvhVaxtbNiza1uV8QGBzRgb8TxdwntiaWlVZYyDgyNODZ1xaujMntjtNGnSBGdXL0IfG8zgCXOxsrbhxP6YKrc9tOtbAlt2IXxgBK6NmtBn2Aw8fZtz5Je1QOndvUM7o+jxxBRCQnty9vguhg0bRlFhHqqU6/dc390rkOzsbGQyOUaDAQ/vIEZO+Yjc7HTOndxdbV4P7lhNu27DCev6FG6NAir5KszXcGLfZgaOep29e/fSa/DzjH3lc1JuXKBT72fvif6q5R+RrFUSFFQ6wnGXXLhuA9etq9b0ee5pCq4lc/71BWgvXCVx+VpSN/+M30vjTTF+L08gaeVGktfEoD1/hW+joxkxYgQjhg+t0seDXr9S16/p9lm/gQMjR46kz9AXGDX9E7S5ahQKhWTyI3V9Ub91O/81ef6/H/o13X6knn9xfNVu/Q4a/R98fHz4YY+W69quxB3fwccrDuNkryAspH61+r+fLyDuQiGpqhJSVSVs/DmHwmIDAd6lv3kDfa1xcbLgiw0qoqKiaNd3GkVOvegYGsyEGQvR5qSTcOaXavVP/hpJi44jaNFhKM4eAfQa+Q4WVjacPbIZgJtXfydXfZO+z35EUFAQQUFBLFiwgLNnz3L06NFqdR80ZHJZrU11Dcle0FpYWDB27FhWr15NxV7T0dHR6PV6Ro0aZVo2ffp0lEol7dq1Y9WqVWbxf0VxcTHx8fF06tTJtEwul/NI61AuXoj/1/uh0+m4mnCRpKQkwsKfQiaTIZfLCQjpSGJCXJXbJCbEERDS0WxZ4COdSbx8GoDMjGQ0OSoCWnSkpKSYm9fP0a1bN7z8W5KYEHdP9cvm1Wo17l5BJk2b+nam8qqizFdASAfTstt9JV+LR68vQenmQ0ZGBgEtOuLq6Y+jswepSZfumX7FfXUsAScdJFZzz8OxQ2tUe46YLcuIPYhTh9YAyCwtcWgbgmr34VIfQLI1dOrUiYa2sip93M6DVL9S1y+br8n2mZlx03R+KGufjspGksiP1PXL5kX91t381/T5v6b1a7L9lM1LOf/i+Krd+m0V2hmAs5cLsbHzxqqeK+m3/uBKUhFNfaq5s38bMhl0bFUfays5lxNLn7paWsgwGkGbm0JGRgbeQZ0o0YPRCH6N7HD3bUXKtVNV6ulLiklLiscnqPx3t0wuxyeoEynXT5likMlQWJQ/NLK2tkYul3Py5Mm/5VtQt5DsBS3AxIkTuXLlCvv27TMti4yMZOjQoTg4OADw7rvvsnHjRmJjYxk6dCjPP/88S5cuvaNuUVERubm55ObmkpSUhF6vx97e3izG0bEh2VmZ/3ofNLk5GAwGCgsLCX1siGm5rb0z2mxVldtos1XYOjibLbN1UKLNUZnWly3L12RjMOhxdnbG1sHZFHOv9CvO2zu5mmJK1zubzVekzFeZRlX7rc1RobCwRFdcaFZemZd7pV+vgXnd2uohV1GlLNZuSorSzMssSlNh6WCH3MYaK6UTcgsLitJLu8XkKcAgA2dnZ6wtq/ZxOw9S/Updv+J8TbRPucIC45/+K3qRyWSSyI/U9SvOi/qte/m/H+f/mtSv6fZTcV6K+RfH1531K87XVP06OtgCkKMtHTjEwtqJksJMcjR6HO3ufIng5W5J5HuN+fZDLyKeasinURncTC997/byjSKKio10b1Nc6t/RmY5BIJdDfStoYOdMXm7V/gvysjAa9NS3N89T/QrbePi2xtKqHge2LqKgoID8/HwWLFiAXq8nIyPjjr4fKOTy2pvqGJIe5Tg4OJhOnTqxatUqunXrRkJCAgcOHODdd981xbz99tum/7dp04a8vDwWLVrEjBkzqtWdP38+77zzDgAKhYImTZoQFRXFlJffrnabf0vr1q2xd3KtMf17yaU/DgGw6NV+yGQw/tUV91Q/T5PFnEmhGPR69CU69PrqByYQCG6nptunoHYR9Vu7SP38L/W/L1LPv+DO1HT99uj+GI+HyhnQeiD/ea43JxL/udatDB2zFqdS30ZG+0fqM22EM8/933ec3v8pAI9Gy/h08XIARnaCXANk5MK9GIm2vl1DBk38jN0b59GmzbfI5XIGDhxISEgIMlnd604r+Gskf4keERHB5s2b0Wg0REZG0qRJE8LDw6uNb9++PcnJyRQVVf8y+uzZs8nJySEnJ4e0tDQUCgUDBw40i8nOzqx2wKe7oaio9O5nWFiY2XJtrhpbx6pHarN1VFYacKD0jqnStL5sWX07R+RyBWq1Gm2Ouvyu37/Qb/FobwBGTV/EjA9iaGDnCEBuVrrZHcGK5d1Oma/b7zBqc9UoPf2Y8UEMQyL+vKlgYWnyUNHLP9Uv229bByX6Eh0FebnmMQqwr2aU46I0FdZu5mVauynR5WgwFBZRrMrCUFKCtWvpncUGepAbQa1WU6Sr2sft1Hb9Sl2/pttnxfZj0Jcg+9N/RS9Go/GBzY/U9UX91u381/T5/37+famJ9iP1/Ivjq3br98z5q+w5ayBq62mefPJJNLlaABxsS7ullRRlYWHTEAc7BdmaOw+upNdDmrqEazd1rN+ZQ2KKjmdH9iW42wqCu60gsOsXfLGp9IbI6p/V/HoWGliDpgDyNGoa2Fftv14DJ2RyBfm55nnKv20b32ZdiJj7C4cPH+bo0aMsWrSItLQ0vLy87uj7QUImk9XaVNeQ/AXtiBEjkMvlrFu3jqioKCZOnHjHioqLi8PJyQlr6+rfDbC2tsbe3h57e3ucnZ0JCQnhxIkTpvUGg4E/4n4nKDjkX/vf/+suFAoFWq3WTD8h/ig+Aa2r3MYnoDUJ8eYvvV8+ewSfpqWfKGro0hg7ByUJ8UexsLCikW9z9u/fT9LVM/gEtP7X+u5egdg5KFGlJqJ088G1UQDOzs6kJl00aRYWaE3lVUWZr4Rz5eWU+fIPCkPp5kNI2x4oFBbkZKXh4uJCQvxRMlKuka1OwcM78B/rl23T2C8EhcLCLCbHArIswafwdsVSso/G4dyjg9kyZc9OZB2NA8Co05HzezzKHqXvv1gAjYvgyJEjZGqNVfq4ndquX6nr13T7vL39NHRpxJEjpe9Vl7XPbNWtBzY/UtcX9Vu381/T5//7/fflXrcfqedfHF+1W79ujZuRVwS2yibcvHmLkydKx/to0dSGQk0SxQXpuHi0oImXtel92L+LXAZ2drbY2DYqn+x8cHFx4cq5I3g2hHpWcCFRS+r103j4talSR2FhhZtXCDculY9XYjQYuHHpCB6+lbdp2LAh9vb2HDlyBLVaTY8ePe7Kt6BuIPkLWltbW0aOHMns2bNJSUlh/PjxpnX/+9//+Oabbzh79iwJCQl88cUXfPjhh7z44ot3VcaECRPYuHEje3/ZQfKN63y97BOKCgvo3nsAAEs++YC1q780xet0Oq5ducy1K5cpKdGRqVZx7cplUm6Zf2LIYDDwa+wO2oR1YNOmTZw88APpN6/ww+p3KC4qILRr6Tu1G1bMYueGT03bde4zhkt/HGT/9kjSb10lNuZzbl47S8dezwCld3w69xvLnh+/5Nzve2jRri/R0dFY2zRA6eF3z/XTki/j6OiI0WhAplCQmnSJjStmYe/oSvPQnibdr+dP4HDsWtN8l/7jOb63+v22qW9HWPhQtq9bSPfu3dm9ZTlr/vsC7l5BHN619p7od+4ziku/bebMmTMAXA5pTA//YIJdPQAIen8mrSIXmDQTv1pPfT8vgue/RoMgf3ymjsZjeH+ufbbaFHNtcSReESNoNGYwtsH+PDtsOBs3biR60xZJ1q/U9Wu6fRbk5bJhwwZiY5ax7vNXsbV3Rq8vkUx+pK4v6rdu578mz//3Q7+m24/U8y+Or9qt321rF3Djxg0Gd7fFx3YfrcL68X9TO5OVq+dEfL5J783JrvTpZGuaf7qfA8F+1iidFHi5W/J0Pwea+Vtz6FT5NuFhDWjqY83YsWM5HvsFlurd/LT3Ihu/fB1bB1cCWvYyxUYvHcepfd+Z5kO7T+CPwxuJP7YFdeoVftk4D11RASEdnjLFnD26mVvX4rhx4wY//vgjL7/8MuPHj8ff3x/Bw4ek36EtIyIigpUrVzJgwAA8PT1Nyy0tLVm2bBmvvPIKRqORgIAAPv30UyZPnnxX+gMGDCAzM5MvVnxFdlYmvv4BvPnux6Yux6qMNOQVngpnZap4bUb5Z4O2xqxna8x6mj/Smnc/WmJafibuBKqMNOZ88CkZSRdYunwpmhwVnt7BTHztS+z+7E6SrU5BJiu/9+AT2Ianpy1k16Yl/By9GKWbD2NeXoq7V1NTTPjACIqLCohZNZfCfA3e3t6os/NY/s7TNaIfFtoWF992bI16n8J8Db6BbZnw2ldYWpU/CVenJ5GnKf92b6sO/cnTZBK7uer9Bhj0zCxkMjk7d25DV1xEdsZNjICtvdM90R8d8X90f6R8SOOpc94AICkqhjMRs7H2cKGel4dpfcH1ZI4/MYXmn8zG98WxFCan8seUt1DFHjTFpETvwMqlIYFzZ2Dt7kLL0+dRtAmWdP1KXb+m22fcoS3s3rIcmQw8vIMZPH6OpPIjdX1Rv3U7/zV1/r9f+jXdfqSef3F81W79Dhs2jPHjI3h79nTs7Oy4eL2Yj1amo6vw+rSbswV2DcpHy7S3VfD8SGcc7RXkFxq4kaLjo5UZ/HG5vHubh4sFT/d3xMF2Mlm5Bbw9Zw552lwa+Yfy1PPfYGFZ7j9HlURBXrn/oNAB5GszObxtCfmaDFwaNeOp578x63KclXaNg1s/ZfPSHBo1asTUqVPNHmpJAVkdHJyptpAZ7+YbNg85fySk1Zj2IwFubPmtmhc37wFD2imE/l/ob7MMqjH9gbqLks+P0Bf6Ql/oC32hL/Trnv6o12/UmP73C735cleNyTOlT81p1zSqORF/HVRDKN9dWWtl1wR14gmtQCAQCAQCgUAgEEgFmbzuDc5UW4hn3QKBQCAQCAQCgUAgkCTiglYgEAgEAoFAIBAIBJJEdDkWCAQCgUAgEAgEgvuJGBTqniEGhRIIBAKBQCAQCASC+4j63edqrWznOV/VWtk1gXhCexdE7as57bHhSH6UPKF/Z30xirLQF/pCX+gLfaEv9B80/UtXam6U48Am3qw7WHPPzkZ3ke7ASmJQqHuHeNYtEAgEAoFAIBAIBAJJIp7QCgQCgUAgEAgEAsF9RCYTzxXvFZJ9h/bxxx9Hp9Oxc+fOSusOHDhA165dOX36NC1btmT16tV8+umnXLp0CXt7e4YPH86yZcvuqjyj0cikV5dw6kA0RQW5NG7Slv7PzKOhm+8dtzvx61qO7lqJNicDt8bB9Bn1No38WprFJF85xflfP+XEid8xGPTIZOAX/ChPTZyH0v3O+kdi17Fv+yq0OSo8vIJ4YuybeDUp19cVF7Ft3UJOH/6BgoJCZDIZHt5BDJkw1yzuds4c20ns5qVkqW7i7OZD/5EzCW4dbpaP2JjPOf5rNAX5Grwae6LOziNfm12lj3+rHxbaBmz9iD8eS0G+Bt/ANgweP+eBz09J9iWeGTWSVq1a4uTkxImhz5O2dfcdPTfs2o7mH8/CtnlTCpNSSJj/BclRW8xifKaNxn9mBNbuLuSeucD2y2dZsvybavfzn/qXev7vV/uUur7U61fq/kX7uXP+z5/Yjkabh/zPAVR8A9tKqn6l3j6F/7rdPr0ae5Kbm0tubi5+fk2YMm06gUHBVWr/vHM7e3bHkph4HYCAgKaMHTfRFF9SUsJ3UZGcOP4bqampNGhQny5dupCW70j88R0U5ufiFdCWgWPm4vwXv59/27OWwztXos1R4e4VTP/Rb9HIv/J+j+oMkydP5sCBAyxbtoxevXrdUfdBIuuDabVWttObX9Ra2TWBZG8NREREEBsbS3JycqV1kZGRhIWF0bJlSz799FPefPNNZs2aRXx8PL/88gt9+/a96/K+/vprju/5lv7PzmP87I1YWtfj+88iKNEVVbvNuePb+SV6Po8Nmk7EW1tw9Qpm/WcR5OWqTTHJV06x/rNJWFtbY2llzZPj3qTfyJlYWddn1cLn0BVXr3/66A5+WreAXkOe58X3NuHhHczKhc+hzSnX/2ntR5w+th2dTkePwdNw92pKblZ6pbiKJF46xfrlrxEW/hQz3ttMSGhPvl38IqlJl00x+7at5PCu7xg8YS69n3qB69evoy/RMW3Ouip9/Bv96fPWk5GRwbHd63l8zBtMn7ceS+t6kshPjycmYevWnA0bNgCg/4vXJer5NubRrV+i3nuMg2FPcm3pGh758n2UvbuYYjyG96fZotlcfn8ZB9sN4X8bo5k/fz79hla/nw9r/u9H+5S6vtTrV+r+Rfu5c/7Px/1Knz59sLC0wtHZExcPf0nVr9Tbp/Bft9tn2flBp9Ox6NMl+Pn7M+ft2WRnZ1Wp/8eZ03QN786H8xex6JPPUCpdmPPWLNQqFQBFRUVcSUhg5KhnWbx0ObPfmstvv/3Gb7u/Y+CYeUx6cyNW1vX47tNJd/z9fPa37eza8BHhT0xnytwY3LyC+O6/k8x+P5exZs0aZDLxLurDjmQvaAcNGoSLiwurV682W67VaomOjiYiIoKsrCzeeustoqKiGD16NE2aNKFly5Y88cQTd1WW0WgkKiqKLgOnEdS6F26Ng3liwkI02elcPPVLtdsdi42kdZcRtOo8FBfPAAY88w4WVjacPrTZFBO7cT6hPZ7lwoUL9Bw8jY69RhM+MIKnpy0gNzudcyerf5p3cMdq2nUbTljXp3BrFMDgCXOxsrbhxP4YAArzNZzYt5n6DRwYOXIkfYa+wKjpn6DNVaNQKExxt3No17cEtuxC+MAIXBs1oc+wGXj6NufIL2tN+Ti0M4oeT0whJLQnZ4/vYtiwYRQV5qFKuV7Jx7/Vd/cKJDs7G5lMjtFgwMM7iJFTPpJEfmxcWpGkacgzzzwDQKJ1tXYB8HnuaQquJXP+9QVoL1wlcflaUjf/jN9L400xfi9PIGnlRpLXxKA9f4Vvo6MZMWIEI4YPrXI/H+b834/2KXV9qdev1P2L9nPn/A8c9Tp79+6l1+DnGfvK56TcuECn3s9Kpn6l3j6F/7rdPsvODwUFBdxMTuL5F17C2tqa2F0/V6n/f6/PZuCgJ/BvEoCXlzcvvjQTg8HI6dOnAGjQoAHvfbiAx7qG07ixF0FBzSgsLMRoNODh3Qw3ryAGRyxAk53Ohd+r//18dNdq2nYdTpsupb+fB415B0srG04d3GwWl3rjPKtWreLDDz+sVuuBRi6rvamOIdkLWgsLC8aOHcvq1aup2Gs6OjoavV7PqFGjiI2NxWAwcPPmTZo1a0bjxo0ZMWIESUlJd1VWcnIyGRkZ+DbrZFpmU9+ORn6tuHn1VJXb6EuKSbkRj1+FbWRyOX7NOpH85zZ5uWpuXTuNXK4gIyODX//3NV++P5brF09iU98OL/+WJCbEValfUlLMzevnCAjpYFoml8sJCOlo2ib5Wjx6fQmZGTfp1KnUh6unP47OHjgqG1WrnZgQR0BIR7NlgY90JvHyaQAyM5LR5KgIaNHR5KNbt24mv7f7+Df6ZfNqtRp3ryCTplTyU4adnR0A6ZZVSppw7NAa1Z4jZssyYg/i1KE1ADJLSxzahqDafbh0P4Fka+jUqRMNbWVV7ue/9S/l/Nd0+5S6ftm8VOtX6v5F+/nr/CvdfMjIyCCgRUdT/lOTLkmifu9HfoR/0T7/aX4qnh8Cg4K5cP4ccrmc1q3bcvHCuSr1b6eoqAi9vgRbW7sq16elppKdnQ2ATX37P/+1o7F/S5KuVL0P+pJibiXG43/b72f/5h1JrrCNrqiAzV/9H3PmzMHFxeVv+RXUXSR7QQswceJErly5wr595d/TiYyMZOjQoTg4OHD16lUMBgMffvghixcvZtOmTWRmZtK7d2+Ki4v/djkZGRkANLBzNlvewN4Zba6qym3ytVkYDXoa2N+2jZ0zeTml22SrSi+sf/tlDQCjpn+Mp29zvv5oIqrU69g6OKPNqUZfk43BoMfWQWm23NbeGW126TbaHBVyhQVGgx5n53Iftg5KZDKZKe52tNkqbB3Mfds6KE1eyrazdVCafDg7O5v5rejj3+hXnLd3cjXLhxTyczsFf3HEWbspKUozL7coTYWlgx1yG2uslE7ILSwoSi/tdpOnAIMMnJ2dsa5wsSzyf3/ap9T1K85LsX6l7l+0nzvnX2Fhia640Ky8Mi9SqN+azo/wX7v+pd4+K54fHB2dyMoq7Wbs6OhEVmbVXY5vZ3XkNzRs6EzrNm2rXJ+ekQZAcJteWNezNS1vYK8kr7rfz5pqfj/bK81yunPDfLwC2kjqndnbkcnltTbVNSQ9ynFwcDCdOnVi1apVdOvWjYSEBA4cOMC7774LgMFgQKfTsWTJEvr06QPA999/j7u7O7/++mu179LGxMSYNAD++9//3lPf+dosFr7YBqOh9LtiTVt2J/63/+HuFUjgI525cu4oJ/ZV3Z3kYeDSH4cAWPRqP2QyGP/qilp29HAh8l+3kXr9St2/1Knp/OdpspgzKRSDXo++RIdeX3JP9WsaqbdP4f/OSL193kuiN67nwL69fLjgY6ysrADY++tuli1dbIrx9fMHoNew/7unZV+M28P188eYMvfh/a0sMEfyl+gRERFs3rwZjUZDZGQkTZo0ITy8dDQ3Dw8PAJo3b26Kd3FxQalUcuNG9R+RPnnyJOfOnTNN/fr1AyBPY/4yel6uGlv7yk/hAOrbOiGTKyq9wJ6nUePs7sekt39g5IyvSj01agpguvPk6ulPtjoFbY66yqd8APXtHJHLFZXuAGpz1dg6lt8pNOhLkMkVqNXlPrQ5KoxGoynudmwdlZUGHCi946g0rS9bVuZDrVab+a3o4271WzzaG4BR0xcx44MYGtg5ApCblW6WDynk53bqGaqUNFGUpsLazbxcazcluhwNhsIiilVZGEpKsHYtvXPZQA9yI6jVaop0Ve/n3fqvS/mvifYpdX2p16/U/Yv2c+f8Kz39mPFBDEMi3gFAYWFp8lDRy4Nav1Jvn8J/3W6f1Z0fsrOzcHJyAij9f0OnKvXLiNkczebo9bz7/nz8/rxoBWjXviOffb6CTxcvIzi4Obm5uQDoigvMts/LVdGgut/PdtX8fs4t349r54+SmXGDj15sR/PmzU2/81988UXGjBlzR++CuonkL2hHjBiBXC5n3bp1REVFMXHiRNNoZ507dwbg4sWLpvjMzExUKhU+Pj7Vas6ZMweVSmWa0tPTUSqVXD9f/m5jUYGWm9dO08i/TZUaCgsrPLxDuH6hfBujwcD180fwbvooDV198Alsj62jK8VF+bi4uJAQfxSAjNTr2DooSbp6Bp+A1lXqW1hY0ci3OQnnjpqWGQwGEuKPmrZp7BeCQmFBQ5dGHDlS6iMj5RrZ6hSyVbeq1fYJaG3yUsbls0fwadoKgIYujbFzUJIQf9TkY//+/Sa/t/u4W313r0DsHJSoUhNRuvng2igAZ2dnUpMumjQLC7SSyE8ZWq0WAFcddyT7aBzOPTqYLVP27ETW0TgAjDodOb/Ho+xR+o6MBdC4CI4cOUKm1ljlft6t/7qU/5pon1LXl3r9St2/aD93zr9/UBhKNx9C2vZAobAgJyvN9PexLP8e3oEPbP1KvX0K/3W7fVZ3frh08QLBzZpjMBg4HXeKoODmVckDsDl6Axu+/455731I08Ags3X169fH1dWNqDWryMzKZMGiT3FxceHqbb+fk6+ewatJ1fugsLDC0yfEbBujwcDV80dp/Oc2XQZMZtq8H5k6dws//PADP/zwAwCzZ8+W1ABRMrms1qa6huQvaG1tbRk5ciSzZ88mJSWF8ePHm9YFBgby5JNP8tJLL3H48GHOnj3LuHHjCA4Opnv37tVqWltbY29vb5ocHBwYN24ch7Z/waW43aQnX2Trqtexc3QlqE153/21n47j+J7vTPPte0/g1IGNnDm8BVXKFXasnYeuuICWnZ8CQCaT0bFPBCf3fEeHDh3Y/cMXrF06k/SbV0hNuoS9oyvNQ3ua9L6eP4HDsWtN8136j+f43k2cPPAD6Tev8MPqdyguKiC06xCg9MX7sPChFOTlsmHDBmJjlrHu81extXdGry8xxW1YMYudGz416XbuM4ZLfxxk//ZI0m9dJTbmc25eO0vHXs+YfHfuN5Y9P37Jud/30KJdX6Kjo7G2aYDSw6+Sj3+rn5Z8GUdHR4xGAzKFgtSkS2xcMUsS+bl8Zh+63ESWLFkCQDOPxti3CsbGq7T3QND7M2kVucCknfjVeur7eRE8/zUaBPnjM3U0HsP7c+2z1aaYa4sj8YoYQaMxg7EN9ufZYcPZuHEj0Zu2VLmfD3P+70f7lLq+1OtX6v5F+7lz/revW0j37t3ZvWU5a/77Au5eQRzetVYy9Sv19in81+32WXZ+qFe/Po0be7F82RIKiwrp1bv0lbxPP17AmsiVJv1N0ev57ts1zHj5/3BzdScrM5OszEwKCkqfwJaUlPDRh++ScPkS//faLIwGI0OHDmX//5Zz/uTPpCVfZMs3/8HO0ZXgtuW/n6MWjee33eW/nzv0Gc/v+6OJO7SFjFtX+Om7eeiKCmj95+9nWwcXXBsH4to4kMDA0gnA09MTLy8vBA8fkn6HtoyIiAhWrlzJgAED8PT0NFsXFRXFK6+8wsCBA5HL5YSHh7Nz504sLf9iuNnbmDx5MscuFLD9uzl/fhg6lKdf+gYLy/LvsGRlJFGgLX+RvvmjA8jTZLJv6xLycjNwa9yMp2d8Y9ZNuV2v8ZToivnt8HfoigqJP/kLMmSAkQmvfYWlVbm+Oj2JPE25fqsO/cnTZBK7eSmaHBWe3sFMfO1L7Cp0cxn0zCxkMjlxh7awe8tyZDLw8A5m8Pg5prhsdQoyWfm9DZ/ANjw9bSG7Ni3h5+jFKN18GPPyUty9mppiwgdGUFxUQMyquRTma/D29kadncfyd56u5ONe6IeFtsXFtx1bo96nMF+Db2BbSeQn4cRmXp+0DHq9AcAjH5f+mxQVw5mI2Vh7uFDvz4tbgILryRx/YgrNP5mN74tjKUxO5Y8pb6GKPWiKSYnegZVLQwLnzsDa3YWWp8+jaBPM0uVV7+fDnP/71T6lri/1+pW6f9F+7pz/nTu3oSsuIjvjJkbA1t5JUvUr9fYp/Nft9unt7U1uroZXX5mBv38T3nn3Q1P344yMdLOneTu2/URJiY6PPiwfZwZg1OgxjH52LGq1imNHS5+sznhhqlnM1tVz0BUX4N00lGdf+drs93Nmxg3yK/x+btFuAPmaTPb+sBRtbgbuXs145pWvq+3GLVlkkn+u+MAgM1b85o3gjkTt++uYf8rYcNjym77G9Ie0Uwj9WtbfZhn014H/kIG6i5LPj9AX+kJf6At9oS/077/+pSvVjyvzbwls4s26gzV3qTG6i3S7z+Z8/FKtle3wf5/VWtk1gbg1IBAIBAKBQCAQCAQCSVInuhwLBAKBQCAQCAQCgVSoi4Mz1RbiCa1AIBAIBAKBQCAQCCSJeEIrEAgEAoFAIBAIBPcTuXiueK8QmRQIBAKBQCAQCAQCgSQRoxwLBAKBQCAQCAQCwX1E89mrtVa23Uuf1FrZNYHocnwX7IvPrzHt8JD6kh/2XejXrr74LJDQF/pCX+gLfaEv9O9WX/PbthrTt2s3kA/W15z/N59W1Ji2QDqILscCgUAgEAgEAoFAIJAk4gmtQCAQCAQCgUAgENxPxKBQ9wzJvkP7+OOPo9Pp2LlzZ6V1Bw4coGvXrnz22We89NJLVW6flpaGq6vr3y5v7dq1LPvia3Ky1TT2DWTUpP/g17RFtfEnDsfy4/fLUaffwtXDm6FjZvBI6GOm9YUF+cR8t4S4Y7+Sp83Bq3EjLO0acev6eQryNfgGtmHw+Dko3X3v6OtI7Dr2bV+FNkeFh1cQT4x9E68mLU3rdcVFbFu3kNOHf6CgoBCZTIaHdxBDJsw1i7udM8d2Ert5KVmqmzi7+dB/5EyCW4eb1huNRmJjPuf4r9EU5GvwauyJOjuPfG12lT7+rX5YaBuw9SP+eKzITwX9kuxLPDNqJK1atcTJyYkTQ58nbevuO+akYdd2NP94FrbNm1KYlELC/C9IjtpiFuMzbTT+MyOwdnch98wFtl8+y5Ll31Sbxwc1P3Wl/Ujdv6jf2s2PyH/t+j9/YjsabR7yP3+8+ga2lZS+yH/d1j99+AcKCwqQyWQ09fZk9oRhtGjic0dtgJ+PnOLN5d8S3rYFn7wy0bTcaDTyZcxOtvx6FG1+AW3DHuXZ6Z/SoYUT1paQrIIdJwxkaavX9nKBjsFy3BuCXT0Z0Qf0XLppHtPAGrq3ltHKT7oXhZqlr9Va2XYvLqq1smsCybaCiIgIYmNjSU5OrrQuMjKSsLAwJk+eTEpKitnUt29fwsPD7+pidvv27cyfP59BI6bw1sfr8PIN5LN3nyc3O7PK+CsX4vjm09l06TmYtz/5njbturF8wUxuJiaYYqJXf0L8qcNEvPwB7yyJoUmTJlw8fYDQ8KeYPm89ltb1WLXwOXTFRdX6On10Bz+tW0CvIc/z4nub8PAOZuXC59DmqE0xP639iNPHtqPT6egxeBruXk3JzUqvFFeRxEunWL/8NcLCn2LGe5sJCe3Jt4tfJDXpsilm37aVHN71HYMnzKX3Uy9w/fp19CU6ps1ZV6WPf6M/fd56MjIyOLZ7PY+PeUPkp4J+jycmYevWnA0bNgCg/4tvdNfzbcyjW79EvfcYB8Oe5NrSNTzy5fsoe3cxxXgM70+zRbO5/P4yDrYbwv82RjN//nz6Da0+jw9qfupC+5G6f1G/tZsfkf/a9X8+7lf69OmDhaUVjs6euHj4S0pf5L9u65fV76TBfWji5U5GVg4vLvyKzBxNtdoAtzIy+ez7rbQJ8q+0bs22PazfdYDZE4azet7LPPnkk7QPtuKno8WsjjWgK4FR3eQo7nAFYmUBadlGfj5hqDZm2GNynBr8xY+eBxyZXFZrU11Dshe0gwYNwsXFhdWrV5st12q1REdHExERQb169XB3dzdNCoWCPXv2EBERcVdlRUZGMmLECDr3fBJPryY8M+VNrKxtOLTnhyrjd//0PSFtOtF38Dg8Gvvz5OjpePs149cd600xVy6cpmO3QQS1CMPZxYO4uDjsnVyRUXoHdOSUj8jNTufcyeqfth3csZp23YYT1vUp3BoFMHjCXKysbTixPwaAwnwNJ/Ztpn4DB0aOHEmfoS8wavonaHPVKBQKU9ztHNr1LYEtuxA+MALXRk3oM2wGnr7NOfLLWqD07tuhnVH0eGIKIaE9OXt8F8OGDaOoMA9VyvVKPv6tvrtXINnZ2chkcowGg8hPBX0bl1YkaRryzDPPAJBoXW06APB57mkKriVz/vUFaC9cJXH5WlI3/4zfS+NNMX4vTyBp5UaS18SgPX+Fb6OjGTFiBCOGD60yjw9yfupC+5G6f1G/tZsfkf/a9T9w1Ovs3buXXoOfZ+wrn5Ny4wKdej8rGX2R/7qtX1a/U4b2Y/70sWTmalEo5Gzd/1u12nqDgbe++I7nnupLIxdns3VGo5Hvd+4n4onedAttQVNvT5588km+WLGCX37ZRXoObD1mwK4eBDWu/qLqSgrs+8PIxZtVr29oB42VMnbc4YJX8HAh2QtaCwsLxo4dy+rVq6nYazo6Ohq9Xs+oUaMqbRMVFUX9+vUZNmzY3y6nuLiY+Ph4OnXqZFoml8tp1rI9Vy+eqXKbK5fO0Kxle7NlIW06msU3CW7F6eP7yFKnk5GaTEZGBvl5uTR9pDMANvXt8PJvSWJCXJVllJQUc/P6OQJCOpj5CgjpaNom+Vo8en0JmRk3Tf5dPf1xdPbAUdmoWu3EhDgCQjqaLQt8pDOJl08DkJmRjCZHRUCLjiYf3bp1M/m93ce/0S+bV6vVuHsFmTRFfsrzA2BnZwdAumWVkiYcO7RGteeI2bKM2IM4dWgNgMzSEoe2Iah2HwagBEi2hk6dOtHQtvSPz730L9pP3fYv6rd28yPyX/v+lW4+ZGRkENCio8l/atIlyeiL/Ndt/Yr16+vphruzEx7KhpxJuF6lNsA3W3bR0N6Wwd06VFp3MyMTdY6Gdi0CAZBZ10Mul3PhSiYZSaVlF+ngphoaOVfa/G9T9nS3RFzPCv5Eshe0ABMnTuTKlSvs27fPtCwyMpKhQ4fi4OBQKX7lypWMHj2aevXq/e0ysrKy0Ov1ODubH3l2js7kZFfd5SY3W4W9Y0OzZfYO5vFPT/oPnl7+/GdyX95+cQgA/Ua8gn9wmCnG1sEZbY6qyjLyNdkYDHpsHZRmy23tndFml26jzVEhV1hgNJj7t3VQIpPJTHG3o81WYetgvr+2DkqTl7LtbB2UJh/Ozs5mfiv6+Df6FeftnVzN8iHyo6y0bcFfHNHWbkqK0szLLUpTYelgh9zGGiulE3ILC4rSS9tqngIMMnB2dsa6wsXyvfIv2k/d9i/qt3bzI/Jfu/4VFpboigvNyivzIgV9kf+6rV9V/TZ0sEUuA3V21V2O4y5e5cd9x3grYkSV69XZuQA4O5TeZJdZ2pR60skp0Jb7zSs0Yvv3f4pXLicXcvKMdG8p8a6zMnntTXUMSY9yHBwcTKdOnVi1ahXdunUjISGBAwcO8O6771aKPXLkCOfPn+fbb7/9S92ioiKKikrfTdBqS99a1+l0YHVvfB/bt53Vy+Zh0JdgYWnF6MmziVr+Dj9HL8atUROatuj01yJ1mEt/HAJg0av9kMlg/KsratmRQEpIvf1I3X9NI/JTu0g9/zXtP0+TxZxJoRj0evQlOvT6Eknp1zQi/7Wr/2/IKyhkzop1vBkxAkc72ypjXHwD+f3336lnbYVMJqPg0vEa8WIwwqaDBga1q3sXZoJ/huRbQkREBJs3b0aj0RAZGUmTJk0IDw+vFPfNN9/QunVrQkND/1Jz/vz5ODg44ODgQEBAAEajka+++sosRpOtxsGx6v4S9o7KSgNG5eaUxzdr1R6j0cAzU95i3uJo/AMfAaBpi04c2L7atI02R13lUziA+naOyOWKSnfotLlqbB3L7+QZ9CXI5ArU6vKnw9ocFUaj0RR3O7aOykoDPpTeEVSa1pctK/OhVqvN/Fb0cbf6LR7tDcCo6YuY8UEMDewcAcjNSjfLx8Oan4r6t1PvL7rfFKWpsHYzL9faTYkuR4OhsIhiVRaGkhKsXUvbagM9yI2gVqsp0lXwc4/8i/ZT9/yL+n1wzg8i//ffv9LTjxkfxDAk4h0AFBaWJg8VvTyo+iL/dVv/r+o3M0eLwQjOjnaVdJPT1dxSZTLz05W0H/d/tB/3f2w7dIL9p+JpP+7/SE5TgUbF4MGDid+1mbyzBzCWFJd6sjRQz7bcbwMbGdqCKu3/bVKz4JufJd7nWC6rvamOIfkL2hEjRiCXy1m3bh1RUVFMnDgRmcy8orRaLRs3bvzbg0HNnj2bnJwc09SiRQsaN25sWm8wGDh/5jf8g6oetr5JYEsu/GH+Qv2500dN8ZZW1hj0epyUrrh6eOPh5Y+Liwua7AyMxtKDs7BAS9LVM/gEtK6yDAsLKxr5Nifh3FEzXwnxR03bNPYLQaGwoKFLI44cKX1vMiPlGtnqFLJVt6rV9gloTUL8UbNll88ewadpKwAaujTGzkFJQvxRk4/9+/eb/N7u42713b0CsXNQokpNROnmg2ujAJydnUlNumjSfJjzU1G/jLKeBK467kj20Tice5i/96Ls2Ymso3EAGHU6cn6PR9mj9P0mC6BxUWkPh0xt6bvq99K/aD91z7+o3wfn/CDyf//9+weFoXTzIaRtDxQKC3Ky0nBxcSEh/qjJv4d34AOrL/Jft/XvVL/XU9JJVWeRqsqkZYBvJV1fD1fWf/gaa99/1TR1bRNCWLMA1r7/Km7Ojng0tCcvJ4uDv53EWJSPoUCLwWAgyN8JF6/Ssq0sSt+fvVn1W3sCwT9C8he0tra2jBw5ktmzZ5OSksL48eMrxWzYsIGSkhKeffbZv6VpbW2Nvb29aZo0aRKbN2/m8K9bSUm+ytovP6S4qIDOPZ4EYNVnbxHz3RLT9j0HjeLsqcPs+jGKlORrbF2/gsQr5+je/2kA6tW3JTAklM1rFnPx7AnU6bcIDQ0l6coZGrp4kZp0iY0rZmHv6Erz0J4m3a/nT+Bw7FrTfJf+4zm+dxMnD/xA+s0r/LD6HYqLCgjtWvpOrk19O8LCh1KQl8uGDRuIjVnGus9fxdbeGb2+xBS3YcUsdm741KTbuc8YLv1xkP3bI0m/dZXYmM+5ee0sHXuVjqQrk8no3G8se378knO/76FFu75ER0djbdMApYdfJR//Vj8t+TKOjo4YjQZkCoXITwX9y2f2octNZMmS0vbXzKMx9q2CsfHyACDo/Zm0ilxg0k78aj31/bwInv8aDYL88Zk6Go/h/bn22WpTzLXFkXhFjKDRmMHYBvvz7LDhbNy4kehNW6rM44Ocn7rQfqTuX9Rv7eZH5L92/W9ft5Du3buze8ty1vz3Bdy9gji8a61k9EX+67Z+Wf1+FbOTNz6PoqG9LSV6A493bQfAnBXr+HzDTwBYW1kS4OVhNtnVr0d9G2sCvDywtLBAJpMxql9XVv4Yy77fz5KQdIsff/yRaVOn0qtXH1wc4IkOcjQFcDG5fEDX0d3lhDUtfxhlaQFujqUTgGMDGW6OYF/fFEKwF3j//S9wPpDIZPJam+oakn6HtoyIiAhWrlzJgAED8PT0rLR+5cqVPPXUUzg6Ov4j/QEDBpCZmcmyL74gN1tNY78gZry9DPs/uxBnqlKRycsbR5Pg1kx65UN+XLeMH9Z+jquHN8//51Ma+QSYYibP/Igt3y1l5eI3yNPm0sjTg4AWHYn//RdOHtyCb2BbJrz2FZZW5d9hUacnkafJMs236tCfPE0msZuXoslR4ekdzMTXvsSuQjeUQc/MQiaTE3doC7u3LEcmAw/vYAaPn2OKy1anmDVun8A2PD1tIbs2LeHn6MUo3XwY8/JS3L2ammLCB0ZQXFRAzKq5FOZr8Pb2Rp2dx/J3nq7k417oh4W2xcW3HVuj3qcwXyPy86d+wonNvD5pGfR6A4BHPi79NykqhjMRs7H2cKHenxe3AAXXkzn+xBSafzIb3xfHUpicyh9T3kIVe9AUkxK9AyuXhgTOnYG1uwstT59H0SaYpcurzuODnJ+60n6k7l/Ub+3mR+S/dv3v3LkNXXER2Rk3MQK29k6S0hf5r9v6cYe28NWWXchkMgK9PZk9fphpUKdUdRby23o9/hXjBvagsKiYD1dFo8kvIDTsUewahTOogy02VpCUAev3GdBX6C3sZAv1Knx20KMhjOmhMM33blvaxk5fM/DTsdILYVsbGb3b1L2us4J/hsxY8Zs3gjuyLz6/xrTDQ+qz5Td9jekPaacQ+nVcf5tlUI3pD9RdlHx+hL7QF/pCX+gLfaFfWV/z27Ya07drN5AP1tec/zefVvx10ANK3pdv1lrZDaZ8UGtl1wR14gmtQCAQCAQCgUAgEEiGOjg4U21R9zpRCwQCgUAgEAgEAoHgoUA8oRUIBAKBQCAQCASC+0jF8XcE/w6RSYFAIBAIBAKBQCAQVMmyZcvw9fXFxsaG9u3b89tvv90xPjs7m+nTp+Ph4YG1tTWBgYFs3769xvyJJ7QCgUAgEAgEAoFAIKjEhg0bmDlzJitWrKB9+/YsXryYvn37cvHiRVxdK387qbi4mN69e+Pq6sqmTZto1KgRiYmJ//hrM38HMcqxQCAQCAQCgUAgENxH8lfNrbWy609852/Htm/fnkcffZTPP/8cAIPBgJeXFy+++CKzZs2qFL9ixQoWLVrEhQsXsLS0vGee74R4QnsXfH+o5q79R3WWSX7Yd6Fft/XFZ4GEvtAX+kJf6Av9uqe/4ucak2dqX/jp95Ia0x/UVlzK/BOKioooKioyW2ZtbY21tbXZsuLiYk6ePMns2bNNy+RyOb169eLIkSNVam/dupWOHTsyffp0fvzxR1xcXBg9ejT/+c9/UChq5jNL4h1agUAgEAgEAoFAILifyOW1Ns2fPx8HBwezaf78+ZUsqlQq9Ho9bm5uZsvd3NxITU2tcreuXr3Kpk2b0Ov1bN++nbfffptPPvmE999/v0bSCOIJrUAgEAgEAoFAIBA8NMyePZuZM2eaLbv96ew/xWAw4OrqyldffYVCoSA0NJSbN2+yaNEi5s6tmW7Wkn2H9vHHH0en07Fz585K6w4cOEDXrl05ffo0RUVFzJo1i5MnTyKTyWjXrh0LFy6kVatWd1We0WjkudeW8Pv+aArzc/EKaMugsXNxdvO943a/7V7LoZ0r0eaocPcKpv8zb9HYv2WluJEdjXQO70NmehIKhSV+waEMHj8Hpfud9Y/ErmPf9lVoc1R4eAXxxNg38WpSrq8rLmLbuoWcP7EdjTYP+Z9DhPsGtpWU/unDP1BQUIhMJsPDO4ghE+aaxd3OmWM7id28lCzVTZzdfOg/cibBrcNN641GI7Exn3P812gK8jV4NfZEnZ1Hvja7Sh//Vj8stA3Y+hF/PJaCfA2+gW0kkZ+S7Es8M2okrVq1xMnJiRNDnydt6+47em7YtR3NP56FbfOmFCalkDD/C5KjtpjF+Ewbjf/MCKzdXcg9c4Htl8+yZPk31e7nP/Uv9fzXlfYp9fOD1PMjdX2pH19S1xft/yE4/2jyTJ+Q8fQPpeeIeTi5Vq+fnHCcE7tXkp50lrzcDB6ftIyAlr3MYoxGI0e2LyH+UBRarRaZTIa7V1NGTH4H74Dq83P66M/siF5KVsZNlO4+DBo1k2ZtugKgL9GxY+MSzscdIDM9GZt6tnQP78Srr75a6QmiFMhf826tlV1/3Jy/FVdcXEz9+vXZtGkTgwcPNi0fN24c2dnZ/Pjjj5W2CQ8Px9LSkl9++cW0bMeOHQwYMICioiKsrKz+tf/bkWyX44iICGJjY0lOTq60LjIykrCwMPz9/enXrx/e3t4cO3aMgwcPYmdnR9++fdHpdHdV3tdff82xX75l0Nh5THprI1bW9fj2k0nodEXVbnP2t+38vOEjuj0xnSlzY3DzCuK7TyehzVVXip02bRrZqlsA9B85E0vreqxa+By64ur1Tx/dwU/rFtBryPO8+N4mPLyDWbnwObQ55fo/rf2I83G/0qdPHywsrXB09sTFw19S+qePbUen09Fj8DTcvZqSm5VeKa4iiZdOsX75a4SFP8WM9zYTEtqTbxe/SGrSZVPMvm0rObzrOwZPmEvvp17g+vXr6Et0TJuzrkof/0Z/+rz1ZGRkcGz3eh4f8wbT562XTH56PDEJW7fmbNiwAQC9rFq7ANTzbcyjW79EvfcYB8Oe5NrSNTzy5fsoe3cxxXgM70+zRbO5/P4yDrYbwv82RjN//nz6Da1+Px/W/NeF9in184PU8yN1fakfX1LXF+3/4Tj/KCytsG/YCCc3fyyt6hHzRQQld/h9qyvOx6VRED2GV/+07cQvX3NyzyoKCwvpO2w6/s0eJTP9Jl/Ofw5NNfm5dukU3y19jfbdnmLm/E20COtB5CcvkvJnfoqLC0m+dp7eQ6byyofRjJ/5GdeuXWPatGnV+hD8O6ysrAgNDWX37vKHGQaDgd27d9OxY8cqt+ncuTMJCQkYDAbTskuXLuHh4VEjF7Mg4QvaQYMG4eLiwurVq82Wa7VaoqOjiYiI4MKFC2RmZvLuu+8SFBRESEgIc+fOJS0tjcTExL9dltFoJCoqiq6PTyW4TU/cvYIYMmkBmux0Lvz+S7XbHfl5NW27DqfNY0NxbRTAoLHvYGllw6kDm83ibiWeY//+/XR/4jkAnFwaMXLKR+Rmp3PuZPVPww7uWE27bsMJ6/oUbo0CGDxhLlbWNpzYHwNAYb6GE/s2M3DU6+zdu5deg59n7Cufk3LjAp16PysZ/foNHBg5ciR9hr7AqOmfoM1Vo1AoTHG3c2jXtwS27EL4wAhcGzWhz7AZePo258gva4HS+jy0M4oeT0whJLQnZ4/vYtiwYRQV5qFKuV7Jx7/Vd/cKJDs7G5lMjtFgwMM76J7Wb03mx8alFUmahjzzzDMAJP5FbxSf556m4Foy519fgPbCVRKXryV188/4vTTeFOP38gSSVm4keU0M2vNX+DY6mhEjRjBi+NAq9/Nhzn9daJ9SPz9IPT9S15f68SV1fdH+H47zT4d+L/DE5GWobl6gddcx5OWkc+VM9b9v/ZqH03nQKwS06l3leqPRyO/7oqjXwOnP/DzPhFeXUFJSjEwm47e9VefnwI7vCGrVhe6PT8StURP6j5hBI7/mHPp5HQD16tsx9c1vaN2xH66efvg0bcXbb79NfHw8t27dqtav4N8xc+ZMvv76a9asWcP58+eZNm0aeXl5TJgwAYCxY8eaDRo1bdo0MjMzeemll7h06RLbtm3jww8/ZPr06TXmUbIXtBYWFowdO5bVq1dTsdd0dHQ0er2eUaNGERQUhLOzMytXrqS4uJiCggJWrlxJs2bN8PX1/dtlJScnk5GRgX/zTqZlNvXtaOzfkuQrcVVuU1JSzK3EeLNt5HI5/s07mm1TXFRA9PKXMRgMhISVd9ewqW+Hl39LEhOq1795/RwBIR3M9ANCOpq2Sb4Wj15fgtLNh4yMDAJadMTV0x9HZw9Sky5JRj8z4yadOpXmsUzfUdmoWu3EhDgCQszvGgU+0pnEy6cByMxIRpOjIqBFR5OPbt26mfze7uPf6JfNq9Vq3L2CTJr3sn5rMj9l2NnZAZD+F6OvO3ZojWqP+ah3GbEHcerQGgCZpSUObUNQ7T5cup9AsjV06tSJhrayKvfz3/qXcv7rQvuU8vmhLuRH6vpSPr6krl82L9p/3T//eAd1oqFbE+ycPFGnXMLdpxW3rp+qUv/vkKNOJj83A21Omik/9erb4RPQCjsHpWl/byfxchyBLTqYLQtq2Znrl6veV8DUndne3v4f+60tZHJ5rU13w8iRI/n444+ZM2cOrVu3Ji4ujp07d5q6ed+4cYOUlBRTvJeXFz///DPHjx+nZcuWzJgxg5deeqnKT/zcKyR7QQswceJErly5wr59+0zLIiMjGTp0KA4ODtjZ2bF3716+++476tWrh62tLTt37mTHjh1YWPz98bAyMjIAsLV3NlvewF6JNkdV5Tb5miyMBv1fbvPz+vkoPf1L9R2UZrG2Ds530M/GYNBX3sbeGW126TbaHBUKC0t0xYVm+rYOpR6koC9XWGA06HF2Ls+jrYMSmUxmirsdbXZp2Wa6DuV5L9vO1kFp8uHs7Gzmt6KPf6Nfcd7eydUsH1LIz+0U/MUZw9pNSVGaeblFaSosHeyQ21hjpXRCbmFBUXppd6M8BRhk4OzsjHWFi2WR/7rRPqV+fpB6fqSuL/XjS+r6FedF+6/b55/6ds6mf/NyM6hv50x+btX6f4f83NLfzUajwSw/dg7OGI1GNNXkR1NFfuwcnNFkV91FWVdcxMcff8zAgQOxtbX9x34Ff80LL7xAYmIiRUVFHDt2jPbt25vW7d27t1KP2Y4dO3L06FEKCwu5cuUKb7zxRo19sgckPspxcHAwnTp1YtWqVXTr1o2EhAQOHDjAu++WvmRdUFBAREQEnTt35vvvv0ev15sa/vHjx6lXr16VujExMSYNgP/+97/31He+NosPprXFaNBTUlLMMy99yeXTe++Zfp4mizmTQjHo9ehLdOj19/b7XzWtL3Uu/XEIgEWv9kMmg/GvrqhlRw8XIv93pqbzI/Xzg9TzI3V9Qe0i2n/tIvX830/0JTqiPpsJRiPvvPNObdv5Z8gk/VzxgULymYyIiGDz5s1oNBoiIyNp0qQJ4eGlo8WtW7eO69evExkZyaOPPkqHDh1Yt24d165dq3JUrjJOnjzJuXPnTFO/fv0AKg3mlJerqvIpFkB9OydkckWV2yjd/Zg6bwshj/YHo5G1n00FYP6M7gB8t+RlvvxgHNoc9R30HZHLFZXu0Glz1Sg9/ZjxQQxDIkoPcIVF6aMv0x3AHNWfdwQfTH1bx/I7qQZ9CTK5ArW6PI/aHBVGo9EUdzu2jspKAzKUeSpbX7aszIdarTbzW9HH3eq3eLT0nZJR0xcx44MYGtg5ApCblW6WDynk53bqGSotMqMoTYW1m3m51m5KdDkaDIVFFKuyMJSUYO1aege2gR7kRlCr1RRVGKdN5F+67VPq5wep50fq+lI/vqSuL9r/w3n+ydeoTf82sHchX6Omvn3V+n+H+vYuAMhkcrP8aHLUyGQy7KrJj10V+dHkqLFzNH9qW3ox+ypZqlusWrVKPJ0VSP+CdsSIEcjlctatW0dUVBQTJ05EJit9Fy8/Px+5XG6aB0zzFUfeup05c+agUqlMU3p6Okqlkmvnyt8NLCzQknz1DI2btK5Sw8LCCk+fEK6dL9/GYDBw9fxRvAPDcHbzodfwV5n27lamztuCk5MTXfqNA2DQM//hiTFvkHT1DD4B1es38m1OwrmjZvoJ8UfxDwpD6eZDSNseKBQW5GSl4eLiQkL8UTJSrpGtTsHDO/CB1S/bprFfCAqFBQ1dGnHkSGkey/SzVbeq1fYJaE1C/FGzZZfPHsGnaemnmhq6NMbOQUlC/FGTj/3795v83u7jbvXdvQKxc1CiSk1E6eaDa6MAnJ2dSU26aNIsLNBKIj9laLVaAFz/YnDw7KNxOPcwf/9F2bMTWUfjADDqdOT8Ho+yR+n7QRZA4yI4cuQImVpjlft5t/7rUv6l2D6lfn6Qen6kri/140vq+qL9P5znn6RLR8hMu4om6xZKj0BSE0/j6dumSv2/g4NzY+rbu2Dr6GbKT2G+lsSE02hz1Kb9rZSfpq25fFt+Lv1xBN+m5ftadjGrSk1k6psrcXJy+sc+BXUHyV/Q2traMnLkSGbPnk1KSgrjx483revduzdZWVlMnz6d8+fPEx8fz4QJE7CwsKB79+7ValpbW2Nvb2+aHBwcGDduHPt/WsGFU3tIS77Ilm/+g52jK8FtywdyWrNoPMd2f2ea79h3PCf3RRN3aAsZt66w7dt56IoKaNPlKQDsHFxwaxyIu1cQEydO5Pi+0tGPjUYjsZuXYu/oSvPQnia9r+dP4HDsWtN8l/7jOb53EycP/ED6zSv8sPodiosKCO06BCgdOCAsfCjb1y2ke/fu7N6ynDX/fQF3ryAO71orGf2CvFw2bNhAbMwy1n3+Krb2zuj1Jaa4DStmsXPDpybdzn3GcOmPg+zfHkn6ravExnzOzWtn6dirdKRemUxG535j2fPjl5z7fQ8t2vUlOjoaa5sGKD38Kvn4t/ppyZdxdHTEaDQgUyhITbrExhWzJJGfy2f2octNZMmSJQA082iMfatgbLw8AAh6fyatIheYtBO/Wk99Py+C579GgyB/fKaOxmN4f659ttoUc21xJF4RI2g0ZjC2wf48O2w4GzduJHrTlir382HOf11on1I/P0g9P1LXl/rxJXV90f4fjvPPkR2fs/Xr6Sg9g4k78B0NHFxpUuG7sps+H0fc/vLft8VFeaQnnyc9+TwAuepk0pPPk5t5y7QfbcPHUqDNZv369cRuWUHkpzOwsLDCYDTQLrzUx7rls9n2fflrfY/1f5YLpw+x96fVpN28ys+blpF89Syd+44GSi9m1yx+haSr8TzzwgIMBj0ZGRlkZGRQXFyM5JDLam+qY0j6HdoyIiIiWLlyJQMGDMDT09O0PDg4mP/973+88847dOzYEblcTps2bdi5cyceHh53VcbkyZM5fqmA/62ZQ2F+Lt5NQ3l25tdYWpZ/xyQz/Qb5mizTfIt2A8jTZPLrD0vR5mTg7tWMZ1/5uspuIpMnT+bUlXz2/PAF29d/jH9wGBNe+wpLq3J9dXoSeRX0W3XoT54mk9jNS9HkqPD0Dmbia19iV0F/0DOzkMnk7Ny5DV1xEdkZNzECtvZOktKPO7SF3VuWI5OBh3cwg8fPMcVlq1OQVXgPwSewDU9PW8iuTUv4OXoxSjcfxry8FHevpqaY8IERFBcVELNqLoX5Gry9vVFn57H8nacr+bgX+mGhbXHxbcfWqPcpzNfgG9hWEvlJOLGZ1yctg15vAPDIx6X/JkXFcCZiNtYeLtTzKj+WCq4nc/yJKTT/ZDa+L46lMDmVP6a8hSr2oCkmJXoHVi4NCZw7A2t3F1qePo+iTTBLl1e9nw9z/utK+5T6+UHq+ZG6vtSPL6nri/Zf988/JboicjOTAahn68RT077BosLv2xxVEgXacv20G2fZtHSsaX7flvkANG83hL7PfgRAWK/J6IoLOHtwDTs3LkUmk+Hu1ZThk98xdTnOVqWY9aL0C2zDsy8sZMfGJWzfsBgXdx8mvLoUjz/zk5OVTvzJXwH4ZNZQAMreno2KijIbqEjwcCEzVvzmjeCOfH+o5lI1qrOMLb/pa0x/SDuF0Bf6/0p/m2VQjekP1F2UfH6EvtAX+kJf6At9Keqv+LnG5JnaF376veYGnxrUVrrP5gq/X/DXQTWEzaj/1FrZNYHkuxwLBAKBQCAQCAQCgeDhRLq3NQQCgUAgEAgEAoFAitTBd1lrC/GEViAQCAQCgUAgEAgEkkRc0AoEAoFAIBAIBAKBQJKILscCgUAgEAgEAoFAcD+RieeK9woxyrFAIBAIBAKBQCAQ3EcKN35ca2XbjPi/Wiu7JhBPaO+CmN8MNab9VDu55Id9F/pC/9/oi88CCX2hL/SFvtAX+vdff93Bmnu2NbqLjC931Zg8U/rUnHaNIxODQt0rxLNugUAgEAgEAoFAIBBIEnFBKxAIBAKBQCAQCAQCSSLZd2gff/xxdDodO3furLTuwIEDdO3aldOnT5ORkcHbb7/NH3/8QYMGDRg3bhwffPABFhZ319vaaDTy/KzPOP5rNAX5GnwC2zB4/FyU7r533O5I7Fr2b1+FNkeFu1cwT4x9E68mLU3rf9uzkbgjP5F2I578/Hxs7Z0pLNDiG9iGwePn/A39dez7U9/DK6iSvq64iG3rFnL68A8UFBQik8nw8A5iyIS5ZnG3c+bYTmI3LyVLdRNnNx/6j5xJcOtws3zExnxuyodXY0/U2Xnka7Or9CH0hX5V+iXZl3hm1EhatWqJk5MTJ4Y+T9rW3dXqAjTs2o7mH8/CtnlTCpNSSJj/BclRW8xifKaNxn9mBNbuLuSeuYDjoy3Njl+pHV9hoW3A1o/447GS9F/T7bOm83P+xHY02jzk8tJ7wL6BbUX+Rf4fmvyI/Nf9/Ojr+XP+91gK83PxCmjLwDFzcXa7s//f9qzl8M6Vpt+3/Ue/RSP/yr6e7mSkw2N9yFElIVdY0rhJGD1HzsPJtXr95ITjnNi9krQbZ8nLzeCJScsIaNXLLMZoNHJ4+xKunowmNzeXtm3bMm/ePHx97+z7QaJw839rrWyboa/UWtk1gWSf0EZERBAbG0tycnKldZGRkYSFhWE0GhkwYAD9+vXj1KlTbNiwga1btzJr1qy7Lu/rr7/m8K7vGDxhHs/P24CVdX1WLZyMrrio2m3OHN3OtnUL6DlkOi+8txkP7yBWLZyMNkdtiikuLiCw5WOEhoYCMHD0f5g+bz2W1vVYtfC5O+qfPrqDn9YtoNeQ53nxvU14eAezcuFzZvo/rf2I08e2o9Pp6DF4Gu5eTcnNSq8UV5HES6dYv/w1wsKfYsZ7mwkJ7cm3i18kNemyKWbftpV/5mMuvZ96gevXr6Mv0TFtzroqfQh9oV+Vfo8nJmHr1pwNGzYAoP+L10nq+Tbm0a1fot57jINhT3Jt6Roe+fJ9lL27mGI8hven2aLZXH5/GQfbDUFz5kKF43eu5I6v6fPWk5GRwbHd63l8zBuS81/T7bOm83M+7lf69OmDhaUVjs6euHj4i/yL/D80+RH5fzjyc3zv9/Qb9SaT3tyIlXU9vvt0EiW66v2f/W07uzZ8RPgT05kyNwY3ryC+++8k8nIr+5o2bRq5mbcAeOyJ17C0rkfM8og76uuK8nFpFESPEXOrjTn+y9fE7fuWefPmsXHjRurVq0dERARFRdXrCuoukr2gHTRoEC4uLqxevdpsuVarJTo6moiICDZs2EDLli2ZM2cOAQEBhIeHs3DhQpYtW4ZGo/nbZRmNRqKiouj+xFSah/bEwzuIEVM+QpOdzrmTv1S73YEda3i023DCuj6FW6MABk+Yh5W1DSf2x5hiuvQbR/igSZw5cwaA4NZd8fAOYuSUj8jNTufcyeqfVh3csZp2ZvpzzfQL8zWc2LeZ+g0cGDlyJH2GvsCo6Z+gzVWjUCjMfFTk0K5vCWzZhfCBEbg2akKfYTPw9G3OkV/WmvJxaGcUPZ6YQkhoT84e38WwYcMoKsxDlXK9kg+hL/Sr07dxaUWSpiHPPPMMAInWVUqa8HnuaQquJXP+9QVoL1wlcflaUjf/jN9L400xfi9PIGnlRpLXxKA9f4Uzz88hKiqKEc9OJeTP41dKx5e7VyDZ2dnIZHKMBoPk/Nd0+6zp/Awc9Tp79+6l1+DnGfvK56TcuECn3s+K/Iv8PxT5Efl/mPKjx80riMERC9Bkp3Ph9+p/3x7dtZq2XYfTpstQXDwDGDTmHSytbDh1cLNZXEriOfbv30/7PlMAcHBuRL8xC9HmpJNwpnp9v5BwOg96haatele53mg0cmpvFO37TqNXr14EBwezcOFC0tPT+eWX6nUfOGTy2pvqGJLdIwsLC8aOHcvq1aup2Gs6OjoavV7PqFGjKCoqwsbGxmy7evXqUVhYyMmTJ/92WcnJyWRkZBDQoqNpmU19O7z8W3Ij4XSV25SUFHPrejwBIeXbyOVymoR05EZCnFlsVkYyOTk5ZsvK9BNvi62of/P6OQJCOpjpB4R0NG2TfC0evb6EzIybdOrUCQBXT38cnT1wVDaqVjsxIc7MN0DgI51JvFy6r5kZyWhyVAS06Gjy0a1bN5Pf230IfaFfnX4ZdnZ2AKRbVilpwrFDa1R7jpgty4g9iFOH1gDILC1xaBuCavdh0/pMBWRkZPBYl06mZVI5vsrm1Wo17l5BJk2p+L8f7aem86N08zGd/8vyk5p0SeRf5P+hyI/I/0OSn8ZBJF0pz09j/5am+dvRlxRzKzEe/2blf1Nlcjn+zTuSXGEbXVEB0V+8jMFgIKBV+VDE1vXscPdtRcq1U1Xq/x1y1Mnk5WbgHVTuwc7OjlatWnHq1D/XFUgXyV7QAkycOJErV66wb98+07LIyEiGDh2Kg4MDffv25fDhw3z//ffo9Xpu3rzJu+++C0BKSsrfLicjIwMAWwdns+W2Dko0ORlVbpOvycZg0Ffaxs7eGU22ymzZ7fPl+s5oc6peV66vNN/G3hntn3raHBVyhQVGgx5n53Iftg5KZDKZKe52tNmqKve1zEvZdrYOSpMPZ2dnM78VfQh9oV+d/u0U/MUZydpNSVGaeblFaSosHeyQ21hjpXRCbmFBUXp5t6dcRem/7q63e3rwj6+K8/ZOrmZ+peD/frSfmsyPwsISXXGhWXllXkT+Rf7ren5E/h+e/Ng6upKXW67ZwF5pNm/uPwujQU8De/NyGtgrzfZ554b5KD39Aahvd1usnXO1+n+H/NyMKnWdnZ1Rqf657n1HLqu9qY4h6e/QBgcH06lTJ1atWkW3bt1ISEjgwIEDpovWPn36sGjRIqZOncqYMWOwtrbm7bff5sCBA6aX96siJibGpAHw3//e25e28zVZzJ0Uapof9+oX91RfIHiY2bp1K29lnMHoWzo/KbVW7dw1l/44BMCiV/shk8H4V1fUsqMHi5rOT54mizmTQjHo9ehLdOj1JfdUX+qI/N8ZqedH5P/OiPzcmXxtFh8+3xajQU9JSTHPvPQll0/vvadlCARVIekntFA6ONTmzZvRaDRERkbSpEkTwsPLR3ObOXMm2dnZ3LhxA5VKxZNPPgmAv79/tZonT57k3Llzpqlfv34AlV7C1+aosHNwqVKjvp0jcrmi0jaaXDVKTz9e/CDGNDWwc6pSQ5ujrvIplrm++Z0oba4aW8fyO4UGfQkyuQK1utyHNkeF0Wg0xd2OraOyyn013YH8czttjsrkQ61Wm/mt6EPoC/3q9G+nnqFKSRNFaSqs3czLtXZTosvRYCgsoliVRffwcD4MaM+ryfBqMtj++T361PTbPT2Yx1eLR0vfGRo1fREzPoihgZ0jALlZ6WZ+H1T/Nd1+ajo/Sk8/ZnwQw5CIdwBQWFiaPFT0IvIv8l8X8yPy/3DmR5udTgP7cs28XJXZvLl/J2RyRaUBoPJyVSjd/Zg6dwshj/YHo5G1n00F4Ku3S3+X/2/li2z8bAx5GnW1+n+H+valv73zNeYe1Go1SuU/1xVIF8lf0I4YMQK5XM66deuIiopi4sSJyGTmj9JlMhmenp7Uq1eP77//Hi8vL9q2bVut5pw5c1CpVKYpPT0dpVLJlfijppjCAi1JV8/gHdCqSg0LCys8fUO4cq58G4PBwJX4o/gFhaF08zFNro0CcHBwMNu+TN8noHW1+o18m5Nwm35C/FHTNo39QlAoLGjo0ogjR0rfO8xIuUa2OoVs1a1qtX0CWpNQYV8BLp89gk/T0n1t6NIYOwclCfFHTT72799v8nu7D6Ev9KvTL0Or1QLgqqtS0kT20Tice3QwW6bs2Ymso3EAGHU6Si5ep0X/nriUgEsJuJWAi4sLBw+Vv3v7IB9f7l6B2DkoUaUmms4Pzs7OpCZdNGk+yP5ruv3UdH78/zw/h7TtgUJhQU5WGi4uLiTEHzXlx8M7UORf5L9O5kfk/yHNT/JFvJqUahYVaEm+esY0fzsKCys8fUK4er78b6rRYODq+aN4B4bR0M2HXsNeZdo7W5k6dwtOTk6Edh8PQPhTs+k+7E1Sr5/Gw69Nlfp/BwfnxjSwd+HGxXIPWq2W06dP06bNP9e974hBoe4Zkt8jW1tbRo4cyezZs0lJSWH8+PFm6xctWsQff/xBfHw87733Hh999BFLlixBoVBUq2ltbY29vb1pcnBwYNy4cez5cQXnft9DatIlolfMws7Rleah5d/F+mb+BA7HrjXNP9Z/HMf3RnPywA+k37zCj6vfobiogNCuQ0wxmuwMUm5coH379gAc3bOeM8d28v2yV7F3dKV5aE9T7Ne36XfpP57jezeZ9H+4Td+mvh1h4UMpyMtlw4YNxMYsY93nr2Jr74xeX2KK27BiFjs3fGrS7dxnDJf+OMj+7ZGk37pKbMzn3Lx2lo69SkeilclkdO43lj0/fsm53/fQol1foqOjsbZpgNLDr5IPoS/0q9O/fGYfutxElixZAkAzj8bYtwrGxssDgKD3Z9IqcoFJO/Gr9dT38yJ4/ms0CPLHZ+poPIb359pnq00x1xZH4hUxgkZjBmMb7M8jy95h7NixbPyu/PjduGKWZI6vtOTLODo6YjQakCkUkvNf0+2zpvOzfd1Cunfvzu4ty1nz3xdw9wri8K61Iv8i/w9FfkT+H478YDQgV1iQlnyRLd/8BztHV4Lblv++jVo0nt92f2ea79BnPL/vjybu0BYybl3hp+/moSsqoHXnpwCwdXDBtXEgbl5BTJw4kbNHNpVuaDRyeNsSbB1cCWhZrh+9dByn9pXrFxflkZ58nvTk80DpIFDpyedNn/+RyWS06TaWYz9/we7du7l48SKvv/46rq6u9Opl/r1awcOBpN+hLSMiIoKVK1cyYMAAPD09zdbt2LGDDz74gKKiIlq1asWPP/5I//7977qMyZMnc+pKPltWzaUwPxefwLZMeO0rLK3KvzOiTr9BvibLNN+ywwC0mix+2bwETY4KD+9mTHjtK+wqdEM5tmcDu7csM83/vHExAK6eTarQTyKvgn6rDv3J02QSu3kpmhwVnt7BTHztSzP9Qc/MQiaTE3doC7u3LEcmAw/vYAaPn2OKy1anIKtwt8YnsA1PT1vIrk1L+Dl6MUo3H8a8vBR3r6ammPCBERQXFRCzai6F+Rq8vb1RZ+ex/J2nK/kQ+kK/Ov2EE5t5fdIy6PUGAI98XPpvUlQMZyJmY+3hQr0/L24BCq4nc/yJKTT/ZDa+L46lMDmVP6a8hSr2oCkmJXoHVi4NCZw7A2t3F3JPn2fyoy05dSXftD++VR6/D+7xFRbaFhffdmyNel+S/mu6fdZ0fnbu3IauuIjsjJsYAVt7J5F/kf+HJj8i/3U/Pw2927F97XsU5ufi3TSUZ1/5GgvLcv+ZGTfI15b7b9FuAPmaTPb+sBRtbgbuXs145pWvq+xmPXnyZA7GF3Bs53L2/7iQxk0e5annvzHTz1ElUZBXrp924yzRS8aa5vdtmQ9A83ZD6DfmIwAe7TUZXXEBc+bMITc3l9DQUL755husrf/i+38PErf1KBX8c2TGit+8EdyRmN/+4gW/f8FT7eRs+U1fY/pD2imEvtB/oPW3WQbVmP5A3UXJ50foC32hL/SFvtCvCf11B2vuUmB0Fxlf7qoxeab0+euYB5XCrcv+OqiGsHlieq2VXRNIvsuxQCAQCAQCgUAgEAgeTupEl2OBQCAQCAQCgUAgkAx3+ISo4O4QmRQIBAKBQCAQCAQCgSQRT2gFAoFAIBAIBAKB4H4iBoW6Z4hBoQQCgUAgEAgEAoHgPlK4bUWtlW0zcGqtlV0TiCe0d8H233U1pj2graXkR8kT+kL/QdYXoygLfaEv9IW+0Bf6lfVX/Fxj8kztC3PWFNeY/rvjrGpMu8aRiTc/7xUikwKBQCAQCAQCgUAgkCTiglYgEAgEAoFAIBAIBJJEdDkWCAQCgUAgEAgEgvuJ+GzPPeOBHBTq8ccfR6fTsXPnzkrrDhw4QNeuXTl9+jTffPMNhw4d4uzZszRr1oy4uLhK8WfOnGH69OkcP34cFxcXXnzxRV5//fW79rR27VqWLv8GTY4KT+8gnhr/Bj4Bj1QbH3f0Z3ZEf05mxk1c3H0YNOoVmrfpalq/c9MyTh3ZSbY6FYWFJe6uzqgyNRTk5+LhFcQTY9/Eq0nLavXPHNtJ7OalZKlu4uzmQ/+RMwluHW5abzQaiY35nOO/RlOQryEstA3Y+hF/PJaCfA2+gW0YPH4OSnffO+73kdh17Nu+Cm2OqkpfuuIitq1byOnDP1BQUIhMJsPDO4ghE+beU/9ejT1RZ+eRr80W+akF/+dPbEejzUP+58nXN7CtJPQdbHQ4yRJp7N4QFxcXFkQ8j8/a3djd4XWkhl3b0fzjWdg2b0phUgoJ878gOWqLWYzPtNH4z4zA2t2F3DMXcHy0Jc/P+sxUH6L93F//NX1+EPri/Cz8i78vtaV/P+q3wMKfy2diKSrIxdOvLT1HzMPJtXr/yQnHObF7JelJZ8nLzeDxScsIaNnLLMZoNHJk+xKu/h7N6GeeZeSIp7FzsCcpHf53tIRMTfW5eayFnOY+cpQOMnQlkJRhZNfJEtS5pevrWUH31goCPEtjpErhzm9qrWybfpNqreya4IG8NRAREUFsbCzJycmV1kVGRhIWFkbLlqUH88SJExk5cmSVOrm5ufTp0wcfHx9OnjzJokWLmDdvHl999dVd+dm+fTvz58+n79BpvPphNJ4+QXz50RQ0Oeoq469dOsW3S1+nfbch/N/8aFqE9WDVJzNISbpsinHx8OWp8W/w2oIYej35HImJiRTka4j4z0o8vINZufA5tNXoJ146xfrlrxEW/hQz3ttMSGhPvl38IqkV9PdtW8nhXd8xeMJcps9bT0ZGBsd2r+fxMW8wfd56LK3rsWrhc+iKi6rd79NHd/DTugX0GvI8L763qUpfP639iNPHtqPT6egxeBruXk3JzUq/p/57P/UC169fR1+iY9qcdSI/99n/+bhf6dOnDxaWVjg6e+Li4S8Z/d/3R3Pq5DEyMzMBKJBDpFu1ktTzbcyjW79EvfcYB8Oe5NrSNTzy5fsoe3cxxXgM70+zRbO5/P4yDrYbgubMBb7++muz+hDt5/75r+nzg9AX52fhX/x9qS39+1W/pw99T/ehbzFq5kYsreoR80UEJbrq/euK83FpFESP4XOrjTnxy9fE7f+WyMhIxk+YzH+Xr2PMuOco1ukZ29sSiztcgfi6yzl2wcBX20tYE1uCQg7jelti+We/Urv6Muzqy/j5REn1IlJAJqu9qY7xQF7QDho0CBcXF1avXm22XKvVEh0dTUREBABLlixh+vTp+Pv7V6mzdu1aiouLWbVqFSEhITz99NPMmDGDTz/99K78REZGMmLECNp3G4J74yYMj5iDlZUNx/ZuqTJ+/47vCG7VmR6PT8StURMGjHiRxn7NOfDzOlNMaOeBBD3SEaWbF6eP/cywYcMo0RWhLylm8IS5WFnbcGJ/TJX6h3Z9S2DLLoQPjMC1URP6DJuBp29zjvyyFii9K3ZoZxQ9nphCSGhP3L0Cyc7ORiaTYzQY8PAOYuSUj8jNTufcyd3V7vfBHatp1204YV2fwq1RQCVfhfkaTuzbTP0GDowcOZI+Q19g1PRP0OaqUSgU98z/2eO7GDZsGEWFeahSrov83Gf/A0e9zt69e+k1+HnGvvI5KTcu0Kn3s5LQX7X8I5K1SoKCSkc47pIL123gunXVmj7PPU3BtWTOv74A7YWrJC5fS+rmn/F7abwpxu/lCSSt3Ejymhi0569w5vk5REVFMeLZqYSE9hTtp46dH4S+OD8L/+LvS23p39/61ePSKJh+YxaSl5POlTO/VOvfr3k4nQe9QkCr3lWuNxqN/L4vinZ9ptG0aVMOnDVS4jqSM6eOsWTlLuzqQ7B39Zcg3/5SQtwVAxnZRtKyjMQcLMHRVoanc+mFWHq2kQ17S7iY/MB1MhXUEg/kBa2FhQVjx45l9erVVOwRHR0djV6vZ9SoUX9L58iRI3Tt2hUrq/Ihvfv27cvFixfJysr6WxrFxcXEx8fTqVMn0zK5XE7TFh1IvHy6ym2uXz5NYIuOZsuCWnaqMr6kREfytXPodDps6tvh4R2MXC4nIKQjiQlxVeonJsQREGKuH/hIZ5N+ZkYymhwVAX96yMxIRq1W4+4VZNK0qW+Hl3/LassoKSnm5vVzBIR0MNvvir6Sr8Wj15eQmXHTlB9XT38cnT1wVDa6J/7LfHTr1s3kV+Tn/vpXuvmQkZFBQIuOJv+pSZcko18xl44l4KSDRJsqZXHs0BrVniNmyzJiD+LUoTUAMktLHNqGoNp92LQ+UwEZGRk81qX8HCHaT904Pwj92tUvm5dq+xT+a//8L3X9+1W/Ss9gbl0/BYB1PTvcfVqZ5v8JOepk8nMzaBlW2tX5yi0jVjZ2OHs+QtKVk9zMMOLl8vefEtr8+TO+oPqHxoKHnAfyghZKuxJfuXKFffv2mZZFRkYydOhQHBwc/pZGamoqbm7m/QvL5lNTU6vdrqioiNzcXHJzc0lKSkKv12Nvb28WY+fgTG62qsrtNdkq7Bycb4tXVoqP/30vb0R0xGDQs3fvXiL+8w0N7JwAsLV3RluNvjZbhe1t+rYOSrQ5KtP6smUV5+2dXE0xpeudzeYrkq/JxmDQmzRM21Twpc1RIVdYYDTocXYu92ProEQmk90T/2U+nJ2dzfyK/Nwf/woLS3TFhWbllXmRin69BubHrq0echVVymLtpqQozbzMojQVlg52yG2ssVI6IbewoCi9vLtXmZa76+11JtqP1M8PQr929SvOS7F9Cv+1f/6Xsv79rF9bBxfyc8s169s5m83fLfm5GQAonRuWllNY+nDKpoEzhXkqtIVGbOv9PS0Z0P9RCxLTDKRn17EnsjJ57U11jAd2j4KDg+nUqROrVq0CICEhgQMHDpi6G9ck8+fPx8HBAQcHB1NXxaioqHteTkDzdkx942sAWrVqxbqlM6t9L+JuuPTHIQAWvdqPOZNC0esl/o7BPUbq+alp/3maLOZMCmXT12+hL9FJTr+m2bp1K9MyzjDLF2b5gl5ir6JIvf0L6jZSb5/C/52R+t8Xqf/9qun6beoBET3hzUmt+P3335HL//0fyIEdFLg6yYjeL61cC+4vD+wFLZQODrV582Y0Gg2RkZE0adKE8PDwv97wT9zd3UlLSzNbVjbv7u5e7XazZ88mJyeHnJwc0tLSUCgUDBw40CxGk6PG3lFZ5fZ2jspKA0ZpclSV4q1t6uPdpAVyuYLhw4cjVyg4vm8zANpcNbbV6Ns6Kitd+JbeESyNb/Fo6TsNo6YvYsYHMTSwcwQgNyvd7I6gNkdd6Q5hGfXtHJHLFZXuMFb0ZeugxKAvQSZXoFaX+9HmqDAajf/Yf9l22hyVyYdarTbz+zDnp6b9Kz39mPFBDEMi3gFAYWFp8lDRy4OqXzH/+hIdBXm55jEKsK9mlOOiNBXWbrcdp25KdDkaDIVFFKuy6B4ezocB7Xk1GV5NLn3iC5CafnudPZztR+rnB6Evzs8P8/El/r48OH+/7mf9anMyqG9frpmvUZvN/12up0P0EYjcns7gwYPJ+nNARlub0gvbwjw1Ng2U2NrI0Bb8td7A9gqCGsuJ/FlHbn7l9VZS//ioGBTqnvFAX9COGDECuVzOunXriIqKYuLEicjuohI6duzI/v370el0pmWxsbEEBQXh5ORU7XbW1tbY29tjb2+Ps7MzISEhnDhxwrTeYDBwOf4YPk1bVbm9b9NWXIo/arbs0h9Hqoy3sLCksV9zjhw5gtFopKSkGIPBQEL8UXwCWlep7xPQmoTb9C+fLdd39wrEzkGJKjURpZsPro0CcHZ2JjXpokmzsEBL0tUz1ZZhYWFFI9/mJJwrL+d2X439QlAoLGjo0ogjR0rfO8xIuUa2OoVs1a1/7L+hS2PsHJQkxB81+di/f7/J78Oen5r27x8UhtLNh5C2PVAoLMjJSsPFxYWE+KMm/x7egQ+s/u35rxiTYwFZluBTWKUs2UfjcO7RwWyZsmcnso7GAWDU6Si5eJ0W/XviUgIuJeBWAi4uLhw8VP7u7cPcfqR+fhD64vz8MB9f4u/Lg/X3637Vr+rWBTx92wBQVKAlNfG0af5u0OkhNx+M1u6osgv4/bfS1wb9PWToirSob/1BY/9QGrnISMq4c/fhge0VNPMuvZjN1lZeb20J43pL/YpWcK94oC9obW1tGTlyJLNnzyYlJYXx48ebrU9ISCAuLo7U1FQKCgqIi4sjLi6O4uJiAEaPHo2VlRURERHEx8ezYcMGPvvsM2bOnHlXPiZMmMDGjRv5bd+PpN28wqZV71FcVED78MEArF0+m5++/68pvmv/Z7lw+hC//rSatJtX2blpGUlX43ms72gAigrz2bZ+MdcvnyYz4xaPhPVg3bp1ZKtv0ci3OT+sfofiogJCuw4BYMOKWezcUD4yc+c+Y7j0x0H2b48k/dZVYmM+5+a1s3Ts9QwAMpmMzv3GsufHLzn3+x7Ski/j6OiI0WhAplCQmnSJjStmYe/oSvPQnibdr+dP4HDsWtN8l/7jOb53EycP/ED6zSuVfNnUtyMsfCgFebls2LCB2JhlrPv8VWztndHrS+6Z/xbt+hIdHY21TQOUHn4iP/fZ//Z1C+nevTu7tyxnzX9fwN0riMO71kpCv3OfUVz6bTNnzpwB4HJIY3r4BxPs6gFA0PszaRW5wKSZ+NV66vt5ETz/NRoE+eMzdTQew/tz7bPVpphriyPxihhBozGDsQ3255Fl7zB27Fg2freCc7/vEe2njp0fhL44Pwv/4u9Lbenf1/qVK1DdusjP371OAwdXmlT4ruymz8cRt/8703xxUR7pyedJTz4PQK46mfTk8+Rm3jKV0zZ8LMd+/oKEhAS6PiJDkbaeR1q3Y8akPmjy4cINg0lvfB8L2gWXX5IMaq+gpb+cTftLKNaBrU3pZPHnmBXWljC2t4XpMz4CwQPfFCIiIli5ciUDBgzA09PTbN2kSZPMBo1q06b0btK1a9fw9fXFwcGBXbt2MX36dEJDQ1EqlcyZM4fnnnvurjwMGDCAzMxMPv/ic3KzVTTyCWbKrBXY/dnlI0uVgqzCC9Z+gW0Y88ICtm9cyrYNn+Hi7sPEV5fg4dUUALlcQdqtaxzfvxWtJosGto4EBARwKy2TtUtfwdM7mImvfYndn11EstXm+j6BbXh62kJ2bVrCz9GLUbr5MOblpbj/qQ8QPjCC4qICYlbNpTBfQ1hoW1x827E16n0K8zX4BrZlwmtfYWlV/v0SdXoSeZry0Z9bdehPniaT2M1L0eSoKvkCGPTMLGQyOXGHtrB7y3JkMvDwDmbw+Dn31L+3tzfq7DyWv/O0yE8t+N+5cxu64iKyM25iBGztnSShPzri/+j+SPmQxlPnvAFAUlQMZyJmY+3hQj0vD9P6guvJHH9iCs0/mY3vi2MpTE7ljylvoYo9aIpJid6BlUtDAufOwNrdhdzT55n8aEtOXck31YdoP/fXf02fH4S+OD8L/+LvS23p34/6tfVoz6+b3qOoIBdP/1CemvYNFpbl/nNUSRRoy/2n3TjLpqVjTfP7tswHoHm7IfR99iMAwnpNRldcwLhx43jmmTG8Mn009vZTuZEO3/6io6T8ehYnOxkNrMt7YLYLLr1yndjPkorEHCz9nI9HQxleLg/0M7m/h7wO7MMDgsxY8bs4gjuy/XfdXwf9Qwa0tWTLb9W82HcPGNJOIfSF/kOtv80yqMb0B+ouSj4/Ql/oC32hL/QfTv0VP9eYPFP7wpw1xTWm/+44q78OekAp3H3vB5z9u9j0HPvXQRLigX9CKxAIBAKBQCAQCAR1CWMdHJypthDPugUCgUAgEAgEAoFAIEnEE1qBQCAQCAQCgUAguJ/IxHPFe4XIpEAgEAgEAoFAIBAIJIkYFEogEAgEAoFAIBAI7iMFv67966Aaol73Z2qt7JpAdDm+C/7zVUGNaS94rp7kR8kT+kL/YdYXoygLfaEv9IW+0Bf6lfVPXVbVmH6bpsq/DnpQEV2O7xkikwKBQCAQCAQCgUAgkCTiCa1AIBAIBAKBQCAQ3EfEZ3vuHeIJrUAgEAgEAoFAIBAIJMkDOSjU448/jk6nY+fOnZXWHThwgK5du3L69Gm++eYbDh06xNmzZ2nWrBlxcXFmsYWFhUydOpWTJ09y/vx5Bg0axA8//PCPff1yUke7ZhbUs4LrqQa2HNShzq0+fd1aW9DCV4GrowydHhLTDGw/pkOVU77Nc4OsaOKpYO3ataxcuZKMjAz8mgTTa+QbeDVpWa32mWM7id28lCzVTZzdfOg/cibBrcNN641GI7Exn3P812gK8jV4NfZEnZ1HvjYbD68gnhj7ptAX+g+NflhoG7D1I/54LAX5GnwD2zB4/ByU7r7VlgFwJHYd+7avQpujqtKXg40OJ1kijd0b4uLiwoKI5/FZuxu7O7yO1LBrO5p/PAvb5k0pTEohYf4XJEdtMYvxmTYa/5kRWLu7kHvmAtsvn2XJ8m+q9fFv8yP1+hX6dfP40hUXsW3dQs6f2I5Gm4dcXnoP3jewraT0Tx/+gYKCQmQyGR7eQQyZMFdS+Ze6f6m3H6m3T6/GnmTnatDm5uLtF8CEKa8QENS8Su2kxKtEr/2GqwkXUaWnMnbyDAY8OdIsJnrtSjZ/v8psmZ+fX5XXDA86+fvW11rZ9cOfrrWya4IH8gltREQEsbGxJCcnV1oXGRlJWFgYLVuWHmwTJ05k5MiRleIA9Ho99erVY8aMGfTq1etf++rcwoItB4r5/IciiksgYoAVForq4/095Bw5V8KyH4v4ZlsRcjlMGmCF5W0dvT/7+n/Mnz+fxx6fxisfbsLRPYiVC59Dm6OuUjfx0inWL3+NsPCnmPHeZkJCe/Lt4hdJTbpsitm3bSWHd33H4Alz6f3UC1y/fh19iY5pc9bh4R0s9IX+Q6M/fd56MjIyOLZ7PY+PeYPp89ZjaV2PVQufQ1dcVGUZAKeP7uCndQvoNeR5XnxvU5W+ft8fzamTx8jMzASgQA6RbtVKUs+3MY9u/RL13mMcDHuSa0vX8MiX76Ps3cUU4zG8P80Wzeby+8s42G4I/9sYzfz58+k3tHofD3L+hX7d1q/J4+untR9xPu5X+vTpg4WlFY7Onrh4+EtK//Sx7eh0OnoMnoa7V1Nys9IllX+p+5d6+5F6+yw7/5ToSnj34xX4+AUwf85McrKzqtQvLirC1d2T0eOm4ejkXO3+Nfb2Y8W3W1nx7VYOHjzIunXrqo19oJHJa2+qYzyQezRo0CBcXFxYvXq12XKtVkt0dDQREREALFmyhOnTp+Pv71+lToMGDfjiiy+YPHky7u7u/9rXnlMlnEs0kJppZOOvxdjXlxHiW/0V7aodxZy8pCcty0hKppHovcU42clprDRP+44f1jBixAhad34KJ7cAnhw/FytrG07sj6lS99Cubwls2YXwgRG4NmpCn2Ez8PRtzpFfSof/NhqNHNoZRY8nphAS2pOzx3cxbNgwigrzUKVcZ/AEoS/0Hx59d69AsrOzkcnkGA0GPLyDGDnlI3Kz0zl3cneVZQAc3LGadt2GE9b1KdwaBVTyVZivYdXyj0jWKgkKKh3huEsuXLeB69ZVa/o89zQF15I5//oCtBeukrh8Lambf8bvpfGmGL+XJ5C0ciPJa2LQnr/Ct9HRjBgxghHDh1bp40HPv9Cv2/o1eXyd2LeZgaNeZ+/evfQa/DxjX/mclBsX6NT7Wcno12/gwMiRI+kz9AVGTf8Eba4ahUIhmfxL3b/U24/U22fZ+aewIJ+U5CQmTX8NK2tr9sb+VKV+k8BmPDvxBTqF98LC0rLa/VMoFDg6OePo5IyLiwsNGzasNlbwcPBAen+8IgABAABJREFUXtBaWFgwduxYVq9eTcUe0dHR0ej1ekaNGlUrvi7fLO9HWKiDpHQD3q5/P4U2VqUvf+cXle+TTlfMjWvn6NSpEz1byGneWIalhZyAkI4kJsRVqZOYEEdASEezZYGPdCbx8mkAMjOS0eSoCGjRkZKSYm5eP0e3bt3w8m9JYkIccrnQF/oPh37ZvFqtxt0ryKRpU9/OVF5VlPkKCOlgWna7r+Rr8ej1JWZeHEvASQeJNlXK4tihNao9R8yWZcQexKlDawBklpY4tA1BtftwqQ8g2Ro6depEQ1tZlT7+TX6kXr9Cv3b1y+Zr8vhSuvmQkZFBQIuOuHr64+jsQWrSJcnoZ2bcpFOnTgAmfUdlI8nkX+r+pd5+pNw+K55/AoKac+nCWeRyOY+0DuPShbNV6v9dUm8lM23sE8yIGM6rr77KrVu3/pVerSGT1d5Ux3ggL2ihtCvxlStX2Ldvn2lZZGQkQ4cOxcHBoUbLLioqIjc31zTl5eUBoM03f19WW2DErv7f05QBj3e05Fpq6RPbMo6cVqPX63F2duZiihFvpYwwfzm29s5os6v+bpc2W4Wtg3lXDFsHJdoclWl92bJ8TTYGQ6m+rYOzKUboC/2HQb/ivL2TqymmdL2z2XxFynyVaZi2qeBLm6NCYWFJvQb25jF6yK2m44a1m5KiNPMyi9JUWDrYIbexxkrphNzCgqL00u5eeQowyMDZ2RnrCjerpZJ/oV+39SvO19TxpSsuNCuvzIsU9OUKC4x/5r88L0pkMpkk8i91/1JvP1JvnxXPPw6ODcnOLv275uDYkOyszCr1/w4BQc2Z9sqbzHrnUyKe/z9u3rzJM888g1ar/ceaAunzwH62Jzg4mE6dOrFq1Sq6detGQkICBw4c4N13363xsn/66Sf69u1rmh8wYIDZhfU/4ckulrg1lLFiq/k7D6cSyp/6JquNFBYbeayZotJ7tgKB4O9x6Y9DACx6tR8yGYx/dUUtOxII6g41fXzlabKYMykUg16PvkSHXl8iKf2aRurnN9F+ale/LtAmrPwJsY9fAE8NeIzu3buzY8cOhg8fXovO/gHyB/a5ouR4oDMZERHB5s2b0Wg0REZG0qRJE8LDw/96w3/JoEGDMBqNpmnbtm0A2NY3f0RvW0+GJv+v9Z7sbEkzbzlf/VRMTp75Out6TshkCtTq0jtXWX+uL9CosXVUUhW2jspKL+yX3rFTmtaXLatv54hcXqqvzVGX3xXNFfpCv27qt3i0NwCjpi9ixgcxNLBzBCA3K93sjnXF8m6nzNftd8Ar+rJ1UKIv0VGQl2seowD7akY5LkpTYe1mXqa1mxJdjgZDYRHFqiwMJSVYu5beAW+gB7kR1Go1RbqqfdxObedf6Ndt/Zo+vpSefsz4IIYhEe8AoLCwNHmo6OVB1a94fjDoS5DJy/++l+kbjcYHNv9S9y/19iP19lnd+ScnOxNHx9K/aznZmTg63bt3Xu3t7fH19eXGjRv3TFMgPR7oC9oRI0Ygl8tZt24dUVFRTJw4Edl96PdtbW2NnZ2dabK1tQUgwLO8H6G1JXi5yrmRbrij1pOdLQnxVfDVT8VkaSp/4keusMTRrRlHjpS+V+dQHwwGA+dOH8UnoHWVmj4BrUmIP2q27PLZI/g0bQVAQ5fG2DkoSYg/ioWFFY18m7N//36Srp7BJ6A1BoOBhHihL/Trpr67VyB2DkpUqYko3XxwbRSAs7MzqUkXTZqFBVpTeVVR5ivhXHk5t/tq7BeCQmFhFpNjAVmW4FNYpSzZR+Nw7tHBbJmyZyeyjsYBYNTpyPk9HmWP0jvQFkDjIjhy5AiZWmOVPu42P1KvX6Fft48v/6AwlG4+hLTtgUJhQU5WGi4uLiTEHyUj5RrZ6hQ8vAMfWP3bzw8NXRqZ/r6X6Werbj2w+Ze6f6m3H6m3z+rOPwkXzxEY3AKDwcDZ0ycJDG5Rpf4/IS8vj6SkJFxcXO6ZpkB6PNAXtLa2towcOZLZs2eTkpLC+PHjzdYnJCQQFxdHamoqBQUFxMXFERcXR3FxsSnm3LlzxMXFkZmZSU5Ojinmn9CjrQXNfOS4O8kY2d2K3Hwj8dfLH8VMHmhFx5Dyi97BnS1pE6Dg+z3FFOmM2NYD23qYPvXT0E5GzzYWPNp9DBs3biTh5A805Bqz3phLYWEBoV2HALBhxSx2bvjUpNu5zxgu/XGQ/dsjSb91ldiYz7l57Swdez0DgEwmo3O/sez58UvO/b6HFu36Eh0djbVNA5Qefvyw+h2Ki4S+0H849NOSL+Po6IjRaECmUJCadImNK2Zh7+hK89CeJt2v50/gcOxa03yX/uM5vncTJw/8QPrNK5V82dS3o3OfUVz6bTNnzpwB4HJIY3r4BxPs6gFA0PszaRW5wKSZ+NV66vt5ETz/NRoE+eMzdTQew/tz7bPVpphriyPxihhBozGDsQ3259lhw9m4cSPRm7ZU6eNBz7/Qr9v6NXl8hYUPZfu6hXTv3p3dW5az5r8v4O4VxOFdayWjX5CXy4YNG4iNWca6z1/F1t4Zvb5EMvmXun+ptx+pt8+y849Nvfp4eHmzcvnHFBUWEt5rIADLPnmP71d/YdIv0em4fvUS169eQl+iI1OdwfWrl0i9Vf4Zz29Xfs65P06RnpbCxfN/8MILLyCXyxk0aBBSwyiT1dpU13jg39SMiIhg5cqVDBgwAE9PT7N1kyZNMnu3tU2bNgBcu3YNX19foPT918TExEoxFUdP/rscPlvC0MessLGC66kGVu0opqRC18KG9jIa2JQ3ko4hpemd+rj5Nzw27i39nI/eAAGN5Hw060m2BGj5+pvPWb0sAw/vYCa+9iV2f3bhyFanIKvwzSifwDY8PW0huzYt4efoxSjdfBjz8lLcvZqaYsIHRlBcVEDMqrkU5mvw9vZGnZ3H8neexlPoC/2HTD8stC0uvu3YGvU+hfkafAPbMuG1r7C0Kj821elJ5GnKv43XqkN/8jSZxG5eiiZHVckXwOiI/6P7I+VDGk+d8wYASVExnImYjbWHC/W8PEzrC64nc/yJKTT/ZDa+L46lMDmVP6a8hSr2oCkmJXoHVi4NCZw7A2t3F1qePo+iTTBLl1ftQwr5F/p1W7+mjq9Bz8xCJpOzc+c2dMVFZGfcxAjY2jtJSj/u0BZ2b1mOTAYe3sEMHj9HUvmXun+ptx+pt09vb29ycrW8PfM5fPybMuvdT0xdjlUZacjk5b+bMzNVzJoxwTT/U8z3/BTzPc1atGHuR5+XxqjSWbpoLprcXOwdHOnQ/lE2btwoPt3zkCMz/pMru4eU/3xVUGPaC56rx5bfqnnx7h4wpJ1C6At9oV+D+tssg2pMf6DuouTzI/SFvtAX+kL/4dQ/dbnqUZPvBW2aVv2+rxTIO/JDrZXdoOPgWiu7JniguxwLBAKBQCAQCAQCgUBQHeKCViAQCAQCgUAgEAgEkuSBf4dWIBAIBAKBQCAQCOoSRpl4rnivEJkUCAQCgUAgEAgEAkGVLFu2DF9fX2xsbGjfvj2//fbb39pu/fr1yGQyBg8eXKP+xKBQAoFAIBAIBAKBQHAf0R77X62Vbdv+8b8du2HDBsaOHcuKFSto3749ixcvJjo6mosXL+L6/+ydd1gU1/rHP7tLU2nK0qWKgGJD0CgaNWpMrLFFYoxGxcRYc2NucjX3/tSUG6Op15ZigZDoVQnoNYmNmNhL1AhGrCgQUJAiZZe67O7vj5WFlcWSiLB4Ps8zj87MO9/57nvOzHBmzpxxcqpzu9TUVHr37o2vry+tWrVi27ZtD8C5cUSD9j749UJhvWl3D7Qz+VHshL7QF/r1py9GURb6Ql/oC32hXx/6cb9q6k1/dHcpf/+8pN70P5rRvN606xtTadA+9thjdOvWjZUrdZ9O0mg0eHh4MGfOHObPn290G7VaTZ8+fZg6dSoHDx6koKCgXhu0osuxQCAQCAQCgUAgEDxEtBJpg03l5eUUFRUZTOXl5bU8VlRUcOrUKQYOHKhfJpVKGThwIEePHq3zt73zzjs4OTkRERFRL7m7HdGgFQgEAoFAIBAIBIJHhCVLlmBnZ2cwLVmypFZcbm4uarUaZ2dng+XOzs5kZWUZ1T506BDr1q1jzZo19eLdGGKUY4FAIBAIBAKBQCB4RFiwYAHz5s0zWGZpafmXdRUKBRMnTmTNmjXI5fK/rHevNMp3aIcPH45KpWLXrl211h08eJA+ffqQmJjI2rVrOXz4MGfPnqVdu3YkJCQYxO7bt49PP/2UX3/9laKiItq2bcsbb7zBhAkT7tvThg0bWP3FGgrz8/Dwbsukl/9OG/8go7EZf1whduNXpF65QG52JhMiXuPpEeMNYrZ/F8XJo7+QmZGGuaUlrs6OZOUUUKIswNUjgBGT/olHm051+jlzfBfxsSvIz72Gg7MXg8PnEdilr369VqslPm4lJ36JobREQWhIMFj7kHQintISBd7+wYycvBC5i/cdf/fR+I3s37EeZWGuUV+qinJ+3LiMxCPbKC0tQyKR4OoZwKgpi4T/Gv49WruRV1AsyreJ+q+v8q0suMSE8eF07tyJli1bcnLMTG5s33vHnLTq0532H83Hun1bytIzSV7yORnRWw1ivGY8j++8CCxdHCk6cwH7bp2YOf8/+t9javkX9UfoC33T1a/v4/f8yR0olMVIpbpOid7+XU1K/2GfP738gxk5edE9+N/AgVv+XTwCa/n/9ectJBz9gez0c0RERPDC5FdobiklJUtD3IEKcgvrbn70Dzajo68MR3splWpIzVLz4zEVOQWG23g5S5kz2uqOPhszihM7GmzfNt2G3FNcRUUFzZs357vvvjMYqfjFF1+koKCA//3vfwbxCQkJBAcHI5PJ9Ms0Gt072lKplIsXL9KmTZu//gNuo1F2OY6IiCA+Pp6MjIxa6yIjIwkNDaVTJ91BM3XqVMLDw43qHDlyhE6dOhEbG8uZM2eYMmUKkyZN4ocffrgvPzt27GDJkiWMCp/Gu59E4+nTlmWL51JYcNNofEV5OU7O7oybOAu7lg5GYy6c/Y2BQ55l0YfrGPLMBC5fvoyqopxX/vUtrp6BrFv2MsrCPKPbpl06zabVbxDadzRz340lKGQA33w2h6z0y/qY/T+u48iebxk5ZRGzFm8iJyeH43s3MXziW8xavAlzy2asX/Yyqora/eWrSDy2kx82LmXgqJnMefc7o75+2PABicd3oFKp6D9yBi4ebSnKzxb+a/h/cvRsUlNTUVeqmLFwoyjfJua/Psu3/4hpWDu3Z/PmzQCoJXWmA4Bm3q3ptv1L8vYd51DoM6Ss+JqOX76H/Mne+hjXZwfT7sMFXH5vFYe6j0Jx5gJr1qwxKA9Tyr+oP0Jf6Juufn0fv+cTfmHQoEGYmVtg7+CGo6uvSek/zPPnsIn/ZObizVhYNmf9spfu6P/MsR38uHEpA0bNYva7sbh6BrB+2UsGvioqSvHv9Diff/45EydOZMtPCpbHllGh0vLSMEvMZHXK4+sm4/DZSlbElfHl92XIpBJeHmaJRY1+pV7OUqYN/etPFAV3xsLCgpCQEPburb6ZrtFo2Lt3Lz179qwVHxgYyO+//05CQoJ+GjFiBE888QQJCQl4eHjUi89G2aAdNmwYjo6OREVFGSxXKpXExMToXzBevnw5s2bNwtfX16jOW2+9xbvvvktYWBht2rTh1Vdf5emnnyYuLu6+/ERGRjJu3Dj6DByOu6cvU2bMx9LSigM/GR+dzLdte8ZPmUvPPoMwN7cwGvPm4uX0GTCM1p5tOHH0F0aPHk1pcSEVFWWMnLIIC0srTh4w7vPwnm/w79SbvkMjcHJvw6Cxc3Hzbs/RnzYAurtvh3dF03/EdIJCBuDi4U9BQQESiRStRoOrZwDh0z+gqCCbc6fqftpzaGcU3fs9S2if0Ti7+9XyVVai4OT+WJq3sCM8PJxBY2YzftbHKIvykMlkwv8t/2dP7GHs2LGUlxWTm5kqyreJ+a/P8rVy7Ey6opW+V0naXa7dXi8/R2lKBuffXIrywlXSVm8gK3Y3Pq9O1sf4/G0K6eu2kPF1HMrzVzgzcyHR0dGMe+EVgkIGmFz+Rf0R+kLfdPXr+/gdOv5N9u3bx8CRM5n02koy/7hA2JMvmIz+wz1/qnH1DGDc9A9QFGRz7tRPdfo/uPNruhn4X1yrXvR++kX6DX+J4OBgPv/8c85eLSfzppZNP1dg21xCB5+6W7Rrfyzn5EU1N/K1ZOZp2fRzOS1tpLR2rG62jOhlzqHfK+vUMAkk0oab7oN58+axZs0avv76a86fP8+MGTMoLi5mypQpAEyaNIkFCxYAYGVlRYcOHQwme3t7bGxs6NChAxYWxttFf5VG2aA1MzNj0qRJREVFUbNHdExMDGq1mvHjx99h6ztTWFhIq1at7jm+oqKCpKQkwsLC9MukUilBnbuRfPH3P+2jikqVitQrF+jQoQMAzVvYIZVK8QvqSVpygtFt0pIT8AsyvCvi37EXaZcTAbiZk4GiMBe/Dj3183l5ebh4BOg1rZrb4OHbqc59VFZWcC31HH5BPfTLbveVkZKEWl3JzZxr+vw4ufli7+CKvdxd+O/QU++jX79+er+ifJuO/4dRvgA2NjYAZJsbldRj36MLuT8bjjqYE3+Ilj26ACAxN8euaxC5e4/o19+UQU5ODo/3rj7HmUr+q+ZF/RH6Qt/09Kvm6/P4lTt7kZOTg1+HnvrjNyv9ksnoP8zz5x/Jupiq/FfNG/N/PTXJYD9SqZQ2QT354zZfzS11T/iOHKm+5pRVwB/ZGryc770JYmWh655UUq5rE1g3Ay9nGcrSRvfWZJMkPDycjz76iIULF9KlSxcSEhLYtWuXfqCoP/74g8zMzAb12CgbtKDrSnzlyhX279+vXxYZGcmYMWOws7P7U5pbtmzhxIkT+jsKdVFzKOv09HTUajW2trYGMbb2rSjIN97l435QFBWg0ajZvn07Xv5dcfFoC4C1rQPKglyj2ygLcrG2M+zKbG0nR1mYq19ftazmvG1LJ32Mbr2DwXxNShQ6X1Ua+m1q+FIW5iKVmaHVqHFwqPZjbSdHIpEI/3ZyvQ8HBwcDv6J8m4b/h1G+NSm9yxnb0llO+Q3D/ZbfyMXczgaplSUW8pZIzcwoz64+dxXduknu4nS7p8af/5rzov4IfaFvWvo15+vj+JWZmaOqKDPYX5UXU9B/2OdPRWGOwTY15437N9yPja0Ditt8Wd26CZuXZ/j3srJEi03zu7xDcwsJ8EwvC1Iy1WTd1DVgW9nqLoaDut3lLq/ggTF79mzS0tIoLy/n+PHjPPbYY/p1+/btq9WrtiZRUVH1+g1aaMQN2sDAQMLCwli/fj0AycnJHDx48E9/z+iXX35hypQprFmzhqAg44M5VVFzKOuAgAAAoqOj/9R+75X09HSen/XRA9G69PthAD58/WkWTgtBrTatLhmm7r++MfX8mLp/U2f79u3MyDnDfG+Y733393IbG6L+CASmS30fv8WKfBZOC+G7Nf9CXakyOf36pr7zP+CJ3owIkegn6QO4vozqY45LKwnfxlfol1XJHjtnWvm/Ha1E0mBTU6PRNmhBNzhUbGwsCoWCyMhI2rRpQ9++fe++4W3s37+f4cOH8+mnnzJp0qS7xi9YsIDCwkIKCwu5ceMGMpmMoUOHGsQUFdzEvo4Bn+6HbVt0DfY5c+Zg18pFv1xZlIe1vfHhrq3t5bUGBNDdEdTFd+j2JADjZ33I3H/H0cLGXuc5P9vgjqCyMM/oUyCA5jb2SKWyWncYa/qytpOjUVcikcoM7r4pC3PRarWPrP+q7ZSFuXofeXl5Bn5F+Zqu//ou35r6t9NMY1RST/mNXCydDfdr6SxHVahAU1ZORW4+T/Tty/t+j/F6BryeAdZqXVxW9u2eGmf+Rf0R+kLfdPXr+/iVu/kw999xjIp4GwCZmbneQ00vjVW/Ic+fNnaOBtvUnDfu33A/iqI8zpxPYe9ZrX4qv9XerPmUGcC6uQRFyd27C4/qbU57LxlfbC+nsLg6vmrbGzfvclEUPDI06gbtuHHjkEqlbNy4kejoaKZOnYrkPu8q7Nu3j6FDh7J06VJefvnle9rG0tISW1tbbG1tcXBwICgoiJMnT+rXazQaks6cxC+g4315qYlWq+XrLz/k9K8H8PD24/Ll6hHoNBoNyUnH8PLrYnRbL78uJCcdM1h2+exRvNp2BsDFwx8bOzm5WWnInb1wcvfDwcGBrPSLes2yUiXpV8/UuQ8zMwvcvduTfK56P7f7au0ThExmRitHd44e1b23l5OZQkFeJgW51x9Z/60cW2NjJyc56Zjex4EDB/R+Rfmatv/6Lt+a+lUolUoAnFRGJfUUHEvAoX8Pg2XyAWHkH0sAQKtSUXkxlQ6DB+BYCY6V4FwJjo6OHDpc/e5tY86/qD9CX+ibrn59H7++AaHInb0I6tofmcyMwvwbODo6kpx0TH/8unr6N1r9hjx/evp1Nsh/1bwx/27eQVy5zf+VpGM4tW5HcTn6SVGqG4um5mi4lubg6SQl7cadG6OjepvTwUfXmL2pMGz83lRoKVRqcLRv1M2Yu2Mig0KZAo36F1lbWxMeHs6CBQvIzMxk8uTJBuuTk5NJSEggKyuL0tJS/fDQFRW6bgm//PILQ4cOZe7cuYwZM4asrCyysrK4edP453bqYsqUKWzZsoWDP//AtfQUor5YSnlZKX0GDgPgi08XsTl6lT6+UqUi7eol0q5eolKlIj8vh7Srl7iRma6P+frLZRzZv5MZr7/LgMFj2LJlC4d3f8O11HNsi3qbivJSQvqMAmDzF/PZtfkT/ba9Bk3k0u+HOLAjkuzrV4mPW8m1lLP0HKgbCVUikdDr6Un8/L8vOffbz9zIuIy9vT1arQaJTEZW+iW2fDEfW3sn2ocM0OuuWTKFI/Eb9PO9B0/mxL7vOHVwG9nXrtTyZdXchtC+YygtLmLz5s3Ex61i48rXsbZ1QK2uFP5v+e/Q/SliYmKwtGqB3NVHlG8T81+f5Xv5zH5URWksX74cgHaurbHtHIiVhysAAe/No3PkUr122lebaO7jQeCSN2gR4IvXK8/j+uxgUv4TpY9J+SwSj4hxuE8ciXWgLx1Xvc2kSZPY8u0XnPvtZ5PLv6g/Ql/om65+fR+/OzYu44knnmDv1tV8/elsXDwCOLJng8noP8zzp1RmRlb6JWK+mI+NvRPtQwbqddfe5v/xwS9yYl+M3v//bvMPoCjI4XraeS5cuMCMGTPwdczDmjTC+0kpKtFyNkWtj50+3JJeHaq/yTP6cXO6+pux4acKyiu02DQDm2YYfOpnX2IlvTvW+I6P4JGm0deEiIgI1q1bx5AhQ3BzczNYN23aNINBo4KDgwFISUnB29ubr7/+mpKSEpYsWcKSJUv0cX379mXfvn337GHIkCHcvHmT1V98RWF+Hp4+/ryx6D/Y2eu6UOTl3kAirb43kH8zh3+99oJ+fse2b9mx7VsCO3Tln//+AoC9O2MBeP+fr+jjvv92CVKpDHfv9kx940tsbnURKcjLRFLjboqXfzDPzVjGnu+WszvmM+TOXkz82wr9gFIAfYdGUFFeStz6RZSVKAgN6Yqjd3e2R79HWYkCb/+uTHnjK8wtqr8DkpedTrEiXz/fucdgihU3iY9dgaIwFzfPQANfAMMmzEcikZJweCt7t65GIgFXz0BGTl4o/Nfw7+npSV5BMavffq6WD5Ef0/dfX+WbfDKWN6etgoFvAdDxI92/6dFxnIlYgKWrI81uNW4BSlMzODFiOu0/XoD3nEmUZWTx+/R/kRt/SB+TGbMTC8dW+C+ai6WLI0WJ53mpWydOXynR/x5Ty7+oP0Jf6Juufn0fv7t2/YiqopyCnGtoAWvblial/zDOn3J9/ovwMpr/Pyip4b9TjyEoFfn8FLscRWEurp7tmPLGVwb+j/+8mb1bV7ECmDt3Ls+PG4etrS2XUvJZ84MdldXtWRxsJbSwqu6BGdZB15V75kgrarLpZ93nfAAOnqnETAZDe9TPZ2AeBlokdw8S3BMSbc3v4gjuyK8XCutNu3ugHVt/Vd898E8yqrtM6At9oW/C+j+aB9Sb/lDVRZPPj9AX+kJf6Av9P6cf92v9vYs6uruUv39eUm/6H81oXm/a9U3hbz812L7tug68e5AJ0ai7HAsEAoFAIBAIBAKBQFAXjb7LsUAgEAgEAoFAIBA0JbRNcHCmhkJkUiAQCAQCgUAgEAgEJol4QisQCAQCgUAgEAgEDxPxhPaBIQaFEggEAoFAIBAIBIKHSEHCvgbbt32Xfg227/pAPKG9D6L21Z/25H6Y/Ch5Ql/oC33T1RejKAt9oS/0hf6jqb/tRP3pj+xW//4FAtGgFQgEAoFAIBAIBIKHiFYivkP7oBCdtwUCgUAgEAgEAoFAYJKIJ7QCgUAgEAgEAoFA8BARn+15cDTKQaGGDx+OSqVi165dtdYdPHiQPn36kJiYyNq1azl8+DBnz56lXbt2JCQkGMRevHiRV155hXPnzlFYWIibmxvPP/88ixYtwtzc/L48abVaIl5fTsLBGMpLi2jdpitPPb+YVs7ed9zu1C8bOB6/DmVhDk6tAxn03P/h5tMJgILcDD7/5wCj2zm3bsvEV/+D3OXO+kfjN7J/x3qUhbm4egQwYtI/8WjTSb9eVVHOjxuXkXhkG6WlZUgkElw9Axg1ZZFB3O2cOb6L+NgV5Odew8HZi8Hh8wjs0tcgH/FxKznxSwylJQo8WruRV1BMibLAqI9HXT80JBisfUg6EU9piQJv/2BGTl5oMuUr/DdN/3ZWKlpK0mjt0gpHR0eWRszEa8NebO7wulOrPt1p/9F8rNu3pSw9k+Qln5MRvdUgxmvG8/jOi8DSxZGiMxfYcfksy1evrdPHX82PqZ8fTF3f1I8vkf875//8yR0olMVIpbo/vr39uwr9h6j/UOu/ZwDP3EP93POdTl/u7MXg5wz1z56I59jezVxLTaJEWcjWrVtZ8fUe/f4edP28cGonFRUV9O7dm0WLFiGXy++o25i4eeZgg+27VafHG2zf9UGjvDUQERFBfHw8GRkZtdZFRkYSGhpKp066Sj116lTCw8ON6pibmzNp0iT27NnDxYsX+eyzz1izZg2LFi26b09r1qzh5M/f8PSExbw4fwvmls3YvDyCSlV5nducO7GDvd8toffQWUz951acWweyeXkExUV5ANi2cmXOskPMWXaIV155Bctm1nTqMQQzCyvsHVxZv+xlVBV16yce28kPG5cycNRM5rz7Ha6egaxb9jLKwjx9zA8bPiDx+A5UKhX9R87AxaMtRfnZteJqknbpNJtWv0Fo39HMfTeWoJABfPPZHLLSL+tj9v+4jiN7vmXklEU8OXo2qampqCtVzFi40aiPR1l/1uJN5OTkcHzvJoZPfItZizdhbtnMZMpX+G+6/n87EMPpU8e5efMmAKVSiHSuU5Jm3q3ptv1L8vYd51DoM6Ss+JqOX76H/Mne+hjXZwfT7sMFXH5vFYe6j+L7LTEsWbKEp8fU7eOv5MfUzw+mrm/qx5fI/53zfz7hFwYNGoSZuQX2Dm44uvoK/Yeo/zDr/8yq+rm0bv3US6f576o36NZ3NHPfi6V9yACiPzXUrygvxTugK4PDXwdg69atBvX1Qef/s88+45tvviE7O5vZs2fXqdkokUgabmpiNMoG7bBhw3B0dCQqKspguVKpJCYmhoiICACWL1/OrFmz8PX1Narj6+vLlClT6Ny5M15eXowYMYIJEyZw8OD93RHRarVER0fTa8gM/LsMxKl1IMOmLENRkM2lhJ/q3O7XnyLp3HscnXqNQe7mx9MT3sbMwoozR2IBkEplWNs50sJWTmxsLAOeeYWczKt06TGE8TM/pKggm3On9tapf2hnFN37PUton9E4u/sxcsoiLCytOHkgDoCyEgUn98fSvIUd4eHhDBozm/GzPkZZlIdMJtPH3c7hPd/g36k3fYdG4OTehkFj5+Lm3Z6jP23Q5+Pwrmj6j5hOUMgAzp7Yw9ixYykvKyY3M7WWj0dd38XDn4KCAiQSKVqNBlfPAMKnf2Ay5Sv8N13/61d/QIZSTkCAboTj3kWQagWplsY1vV5+jtKUDM6/uRTlhaukrd5AVuxufF6drI/x+dsU0tdtIePrOJTnr/BNTAzjxo1j3LNjjPr4q/kx9fODqeub+vEl8n/n/A8d/yb79u1j4MiZTHptJZl/XCDsyReE/kPSf5j1PycrlVFTFmFuacWJ/XXo776lPywCZ/c2PPWsTv9I/AZ9TNfeIxg4aiZ+HXoC8P333+v396Dr57Dn/0HPnj3p0KED77//PqdPn67VW1PwaNAoG7RmZmZMmjSJqKgoavaIjomJQa1WM378+D+lm5yczK5du+jbt+/dg2uQkZFBTk4O3u3C9Musmtng5tOZa1dPG91GXVlB1h9J+NTYRiKV4h0YVmubglydvk1LJzLTLtCt7xismtvg4duJtOQEo/qVlRVcSz2HX1AP/TKpVIpfUE/9NhkpSajVldzMuUZYmM6Hk5sv9g6u2Mvd69ROS07AL6inwTL/jr1Iu5wIwM2cDBSFufh16Kn30a9fP73f2308yvpV83l5ebh4BOg1TaV8hf+m77+mF/tKaKmCNCujstj36ELuz0cNluXEH6Jljy4ASMzNsesaRO7eIzofQIYlhIWF0cpaYtTHX8mPqZ8fTF2/at5Ujy+R/7vnX+7sRU5ODn4deurzn5V+Seg/JP2HVv/bdOKPy9X184876Xe4Tb9TL/5ITjQaD5Cfn2+wTX1dv9q0aYObm5to0D6iNMoGLei6El+5coX9+/frl0VGRjJmzBjs7OzuSyssLAwrKyvatm3L448/zjvvvHPH+PLycoqKivRTamoqAC1sHQziWtg6UFyYa1SjRJmPVqOmuU3tbZS3bVNclAPA5d8P4+Tmi5d/MADWdrVj9fqKAjQaNdZ2hu8KWNs6oCzQbaMszEUqM0OrUePgUO3D2k6ORCLRx92OsiAXaztD39Z2cr2Xqu2s7eR6Hw4ODgZ+a/p4lPVrztu2dDIoT1MoX+G/afuXmZnTrIWtYYwaiur4rJ+ls5zyG4b7LL+Ri7mdDVIrSyzkLZGamVGeresWViwDjQQcHBywrDFsgakcv0L/zvo1503x+BL5v/v5QVVRZrC/Ki9Cv/71H2r9t3VAcSvGxq76/8b0bW77W9jGVo6iDj81PRjO18/1y8HBgZycnDt6aUxoJdIGm5oajXaU48DAQMLCwli/fj39+vUjOTmZgwcP3rUxaozNmzejUChITEzkjTfe4KOPPuLNN9+sM3769OkcPnxYP3/t2jU8PDz+1O8wRqkyn4/mBuvnx83+EoCkUz8xYOTMB7YfQcNw6Xdd3fnw9aeRSGDy6180sKP7Q/hvWEzdv6BpI+pnw1Lf+S9W5LNwWggatRp1pQq1ulLoP0R9U+f04e+JW78YwKCHpUBQ3zTqJnpERASxsbEoFAoiIyNp06bNfXcXBvDw8KB9+/aMHz+eDz74gMWLF6NW1z2M5yeffMK2bdv0U1XjtmowpyqKi/JoYWd8NLXm1i2RSGWUKGpv4+Diw9R/bdNPzaxbAqAqL6Nr72f0scrCvFp3qPT6NvZIpbJad7iURXlY21ffKdSoK5FIZeTlVftQFuai1Wr1cbdjbS+vNSCA7o6jXL++almVj7y8PAO/NX08avoduj0JwPhZHzL333G0sLEHoCg/26A8G2v5Cv+Pjn91pYrS4iLDGBnY1nF6LL+Ri6Wz4T4tneWoChVoysqpyM1HU1mJpZPuDn4LNUi1kJeXR7nKuI/baejjV+g3nvoprl+19es7/3I3H+b+O45REW8DIDMz13uo6UXo149+g9X/ojxsbsUoCqv/b0xfcdvfwoqiXGxu6bbv2p9X/x3Hq/+OY8rfq2+21Pqt9XT9ysvLw9HR0ahuY0SLpMGmpkajbtCOGzcOqVTKxo0biY6OZurUqUj+4shcGo0GlUqFRqOpM6ZVq1YEBQXppy5duuDo6Ejqhep3x8pLlVxPScTdN9iohszMAhfPIFLPV2+j1WhIu3AUj7bdaOXkpZ/krn6Ym5vj5N4Ga9tWAJSVKkm/egYvvy5G9c3MLHD3bk/yuWMGvy056Zh+m9Y+QchkZrRydOfoUZ2PnMwUCvIyKci9Xqe2l18XkpOOGSy7fPYoXm076/Lj2BobOznJScf0Pg4cOKD3e7uPR03fxcMfGzs5uVlpyJ29cHL3w8HBgaz0i3rNxly+wv+j5b9mTKEZ5JuDV5lRWQqOJeDQv4fBMvmAMPKPJQCgVako/C0JeX/de01mQOtyOHr0KDeVWqM+7jc/pn5+MHV9Uz++RP7vnH/fgFDkzl4Ede2PTGZGYf4NHB0dSU46ps+/q6e/0K8n/Qar/1fO4Nm2un563kH/ihF9Tz+dvmWzFshdvJC7eNHS0R2Ali1bGniqr+vX1atXuX79Ol26GNcVNG0adYPW2tqa8PBwFixYQGZmJpMnTzZYn5ycTEJCAllZWZSWlpKQkEBCQgIVFRUAbNiwgS1btnD+/HmuXr3Kli1bWLBgAeHh4ff1HVqJRMKkSZM4suNzLifuJfvaRb6PfBMbeyf8uwzUx2385EVO/vKtfr77wCkkHNrCmaNbyc28wq6Ni1FVlNIpbLSBfn7OH6hUKm5mZ3Dut5/JSr/Eli/mY2vvRPuQ6u/UrlkyxWAkud6DJ3Ni33ecOriN7GtX2Bb1NhXlpYT0GQXoXrwP7TuG0uIiNm/eTHzcKjaufB1rWwfU6kp93OYv5rNr8yd63V6DJnLp90Mc2BFJ9vWrxMet5FrKWXoOnKDPR6+nJ/Hz/77k3G8/06H7U8TExGBp1QK5q08tH4+6/o2My9jb26PVapDIZCZXvsJ/0/Xfa9B4Lv0ay5kzZwC4HNSa/r6BBDq5AhDw3jw6Ry7Va6Z9tYnmPh4ELnmDFgG+eL3yPK7PDiblP1H6mJTPIvGIGIf7xJFYB/rywthn2bJlCzHfbTXqo7Efv0K/aR9fIv93zv+Ojct44okn2Lt1NV9/OhsXjwCO7Nkg9B+S/sOs/46uPmyNfBtVeSmhfav1d9bUf2oiF8/U0I9dybWrZwl7coI+pkRZwPW082RfS9Zt06sXe7eu5tTBbQ+8fv64YSnHjh3j7NmzvPXWWwQHB4sG7SNKo32HtoqIiAjWrVvHkCFDcHNzM1g3bdo0g0GjgoN1T0tTUlLw9vbGzMyMpUuXcunSJbRaLV5eXsyePZvXXnvtvn289NJLHLtQys5vF1JWUoSHXwjj5q7FzLz6+xYFuemUKvP18+27DaFEeZOD25dTXJSDU+t2jJu7lha2ht0szhyOxcXFhXaPjSRu/SLKShR4+3dlyhtfYW5RrZ+XnU6xolq/c4/BFCtuEh+7AkVhLm6egUx940uDriLDJsxHIpGScHgre7euRiIBV89ARk5eqI8ryMtEUuMFcS//YJ6bsYw93y1nd8xnyJ29mPi3Fbh4tNXH9B0aQUV5qd6vp6cneQXFrH77uVo+hL6C0JCuOHp3Z3v0eyZZvsJ/0/T/fMTfeaJj9ZDGryx8C4D06DjORCzA0tWRZh6u+vWlqRmcGDGd9h8vwHvOJMoysvh9+r/IjT+kj8mM2YmFYyv8F83F0sWRTonnkQUHsmK1cR+mcPwK/aZ9fIn83zn/u3b9iKqinIKca2gBa9uWQv8h6j+s+r9q8XO4eQUy9c0a9TPXUN/bP5jxM5exO2Y5u7Z8htzFi0mvGeqf++0XYr76p37+hx9+AOB/X7+LRqN+4PVz7ty5VFRU0Lt3bxYtWoQp0RQHZ2ooJFrx1vY9E7Wv/rQn94Otv9b9Xu9fZVR3mdAX+kJf6Nep/6N5QL3pD1VdNPn8CH2hL/SFflPV33ai/vRHdqt//6ZKTtLxBtu3Y9BjDbbv+qDRP6EVCAQCgUAgEAgEgibFXxwXSFCNeNYtEAgEAoFAIBAIBAKTRDyhFQgEAoFAIBAIBIKHiFY8V3xgiEwKBAKBQCAQCAQCgcAkEYNCCQQCgUAgEAgEAsFDJPvcyQbbt1P70Abbd30guhzfB1/uqT/t6YPEKMdCX+gL/aarL0ZRFvpCX+gL/capv/O0qt70BwebM+ujgnrTX/V3+3rTrm+0YlCoB4bociwQCAQCgUAgEAgEApNEPKEVCAQCgUAgEAgEgoeIViKeKz4oGuU7tMOHD0elUrFr165a6w4ePEifPn1ITExk7dq1HD58mLNnz9KuXTsSEhLq1ExOTiY4OBiZTEZBQcF9e9Jqtbz4t+WcPRJDWWkR7j5dGRC+mJZO3nVuk5F8gpN713Hjj7MUF+UwYtoq/DoPrKV7ZMdyrpzcQt7NfKRSKRIkeAd0ZeTkhchd6tYHOBq/kf071qMszMXVI4ARk/6JR5tO+vWqinJ+3LiM8yd3oFAWI5XqDh5v/wern3hkG6WlZUgkElw9Axg1ZZFB3O2cOb6L+NgV5Odew8HZi8Hh8wjs0tcgL/FxKznxSwylJQo8WruRV1BMibLAqA+hX7/6oSHBYO1D0ol4SksUePsHi/oj9O+qX1lwiQnjw+ncuRMtW7bk5JiZ3Ni+t05dgFZ9utP+o/lYt29LWXomyUs+JyN6q0GM14zn8Z0XgaWLI0VnLmDfrRMz5/9H/3sedP0U58+mff4R5SvK96/oN4XyvVlQTLGyADfPAMZMeQsvv4516icc282OLSu5mXMNRxcvhj//Gu2D++jX74xZxemjuyjIy0JmZk6XTkE888I79O/RmmaWEq5er2RTfCk5BZo69/F4Zwse72JJK1tdmWXmqdl5tIxzKZX6GDMZjO7XjL7BlnXqNHayLpxusH27BAY32L7rg0Z5ayAiIoL4+HgyMjJqrYuMjCQ0NJROnXQH89SpUwkPD7+jnkqlYvz48Tz++ON/2tOaNWtI2P8NA8IX8/zrWzC3bEbc6ggqVeV177e8BEf3APqPW1RnzImfdLq9evXC3MISV88AmtvYY2ZuyfplL6OqqFs/8dhOfti4lIGjZjLn3e9w9Qxk3bKXURbm6WN+2PAB5xN+YdCgQZiZW2Dv4Iajqy/mls0emH7i8R2oVCr6j5yBi0dbivKza8XVJO3SaTatfoPQvqOZ+24sQSED+OazOWSlX9bH7P9xHUf2fMvIKYt4cvRsUlNTUVeqmLFwo1EfQr/+9Gct3kROTg7H925i+MS3mLV4k6g/Qv+e9PuPmIa1c3s2b94MgPourws1825Nt+1fkrfvOIdCnyFlxdd0/PI95E/21se4PjuYdh8u4PJ7qzjUfRSKMxdYs2aNQX19kPVTnD+b9vlHlK8o37+i31TKt7JSxd/e2YC7VwBfLJmOog79lIuniV7+Jj2eGMXfP4ihY2h/1n00l8wa+k6u3oyZ8hZvLotj7uJonnvuOQZ0b8k3P+bw4QYFFSqYPbYFZrI600++QsP/DpSy9BsFy75VcOmPSqaPbIGrQ3WzZewTzejYxrxuEcEjRaNs0A4bNgxHR0eioqIMliuVSmJiYoiIiABg+fLlzJo1C19f3zvq/etf/yIwMJBx48b9KT9arZbo6Ggee2oGfp0G4ugeyNMTl6EszCb5zE91bucT1Jdew16jbecn69Q9vS+a7oNe4ciRIwwcOZNp/1hHibKAoJCBFBVkc+5U3U8zDu2Monu/ZwntMxpndz9GTlmEhaUVJw/EAVBWouDk/liGjn+Tffv2MXDkTCa9tpLMPy4Q9uQLD0y/eQs7wsPDGTRmNuNnfYyyKA+ZTKaPu53De77Bv1Nv+g6NwMm9DYPGzsXNuz1Hf9qgz8vhXdH0HzGdoJABnD2xh7Fjx1JeVkxuZmotH0K/fvVdPPwpKChAIpGi1Whw9QwgfPoHov4I/bvqWzl2Jl3RigkTJgCQdpcb6V4vP0dpSgbn31yK8sJV0lZvICt2Nz6vTtbH+PxtCunrtpDxdRzK81c4M3Mh0dHRjHvhFYJCBjzw+inOn037/CPKV5SvKF+dfnZmCs9OW4iFhRXH9201qr9/57cEdu5F/+FTcXFvw5DwObT2ac/B3Rv1MSG9hxLQsSdyZw9cPfwYNGgQn3++msPHznA9V8PXO4qxs5bS2a/uxujZq5UkpVSSU6AhO1/D94fKKK/Q4u2qe1PSygJ6drQgbl9pnRqmgBZJg01NjUbZoDUzM2PSpElERUVRs0d0TEwMarWa8ePH37PWzz//TExMDKtWrfrTfjIyMsjJycEzIEy/zLKZDS7enclM+fPdBQrzMiguyqGVSxtycnLw69ATq+Y2ePh2IjP9Ih6+nUhLTjC6bWVlBddSz+EX1EO/TCqV4hfUU79NRkoSanUlcmcvvb6Tmy/2Dq5kpV96YPo3c64RFqbLTZW+vdy9Tu205AT8gnoaLPPv2Iu0y4kA3MzJQFGYi1+Hnnof/fr10/u93YfQrz/9qvm8vDxcPAL0mlX1VNQfoX8n/SpsbGwAyL7LzXT7Hl3I/fmowbKc+EO07NEFAIm5OXZdg8jde0S//qYMcnJyeLx39fn5QdZPcf5s2ucfUb6ifP+KflMpXy+/TqReSkQqleLfsQeplxKN6qdeTsS/o6F+YOewOuMtZJVIpVJ+PZGAtUNbAMoqIDVTjY/bvQ3jI5FASIA5FuYSUjJ1XY49nc0wk0m4kFZ5l60FjwqNskELuq7EV65cYf/+/fplkZGRjBkzBjs7u3vSyMvLY/LkyURFRWFra/unveTk5ADQ3MbBYHkLGweKi3L/tG5JkU5Xq9W9R2BtJ7/1rwPKwlz9v0a3VRSg0aj121RhbeuAskC3jbIwF5mZOaqKstv05Q9MXyozQ6tR4+BQnRtrOzkSiUQfdzvKAt2+DXRveapaX7WsyoeDg4OB35o+hH796dect23pZFBfRP0R+nfTv53Su1xxLJ3llN8w3G/5jVzM7WyQWlliIW+J1MyM8uzq7nBFt7qtuTjd7kmcP5tC/anP848o34bVrzkvyrdhy9fGzoGiW+tq/v92FAW52Nymb2Mnp+i2XCad2sebL3Zj7ZKpALh2mYeFlX21TokG2xZ3fkroJpfyyVw7/vOaHc892Zw1/ysmK0/397JtCwmqSi2l5Y1uGKD7QiuRNtjU1Gi0oxwHBgYSFhbG+vXr6devH8nJyRw8eJB33nnnnjVeeuklnn/+efr06XP34BrExcUZ7OfTTz+9r+3vxvkT2/lp0yK0Wt13xTTqB3uHqViRz8JpIWjUatSVKtQPWF/QtLn0+2EAPnz9aSQSmPz6Fw3sSCCoZvv27fwr5wxab938tKwHqy/Onw1LfZ9/RPk2LKJ8mzZuLSUEech4stNAXp38Gz8evATA+YNL8O39ERbNWt2z1o2bGpZEK7CylBDsb87Ewc35bLNS36gVCGrSqJvoERERxMbGolAoiIyMpE2bNvTt2/fuG97i559/5qOPPsLMzAwzMzMiIiIoLCzEzMyM9evX17ndqVOnOHfunH56+umnAShRGL4kX6zIo4Vt7acQd6NNx/68MH8bI1/5Srfg1g0m/R20wrxbd9TyjD7lAGhuY49UKqt1h1FZlIfczYe5/45jVMTbAMjMzG/Tz/1L+tb21Xc6NepKJFIZeXnVuVEW5qLVavVxt2NtL681oEGVp6r1VcuqfOTl5Rn4relD6D9Y/Q7ddO98j5/1IXP/HUcLG3sAivKzDeqLqD9C/276t9PsLn+HlN/IxdLZcL+WznJUhQo0ZeVU5ObzRN++vO/3GK9nwOsZYK27L0hW9u2exPnTFOtPfZ9/RPmK8hXlW1tfUZiH7a11Nf9/Ozb28loDRikKc7G9pX+jUMvhC5UcvlDJkYtqHFzaACCXy7l2cXu1TnMpRcV3frqq1kBOgYb0G2q2HyzjWo6aJ7rqBmIoKtZibiahmWXTexdU8Odo1A3acePGIZVK2bhxI9HR0UydOhWJ5N4r79GjR0lISNBP77zzDjY2NiQkJDBq1Kg6t1u4cCG5ubn6KTs7G7lczh8Xq9/tKi9VkpWaiKvP/Q97bWFlTUtHLzzaPkYLW0cK8zJwdHQkOekYZaVK0q+ewc0zkPSrZ/Dy62JUw8zMAnfv9iSfO6ZfptFoSE46hm9AKHJnL4K69kcmM6Mw/4ZePyczhYK8TFw9/f+0ftU2rX2CkMnMaOXoztGjutxU6RfkXq9T28uvC8lJxwyWXT57FK+2nQFo5dgaGzs5yUnH9D4OHDig93u7D6H/YPVdPPyxsZOTm5WG3NkLJ3c/HBwcyEq/qNesqqei/gj9O+lXoVQqAXBSGZXUU3AsAYf+PQyWyQeEkX8sAQCtSkXlxVQ6DB6AYyU4VoJzJTg6OnLocPX5+a/UT3H+bNrnH1G+onxF+dbWT0s+g7d/ZzQaDZfOHsfbv7NRfe+2nbl81lD/4pmj+ni1Bkoqqielrsc33UOD0ah1FwArC/B2lZFy/f6enkskYCbTtQH+uFFJpVpLgGej7Wh6T2glkgabmhqNukFrbW1NeHg4CxYsIDMzk8mTJxusT05OJiEhgaysLEpLS/UN14qKCgDatWtHhw4d9JO7uztSqZQOHTrQsmXLOvdraWmJra2tfrKzs+PFF1/k+O7PufL7XnKuX2TXN29ibeeEX6fq78rGrHiR0/u/1c9XlBeTnXGe7IzzgG4QqOyM8xTdvA6ARCIhuN8kft3zBb169WLv1tWs+yCC5tYtOXvyJ2ztnWgfMkCvt2bJFI7Eb9DP9x48mRP7vuPUwW1kX7vCtqi3qSgvJaSPrrFu1dyG0L5j2LFxGU888QR7t67m609n4+IRwJE9Gx6YfmlxEZs3byY+bhUbV76Ota0DanWlPm7zF/PZtfkTvW6vQRO59PshDuyIJPv6VeLjVnIt5Sw9B07Q56XX05P4+X9fcu63n+nQ/SliYmKwtGqB3NWnlg+hX7/6NzIuY29vj1arQSKTkZV+iS1fzBf1R+jfVf/ymf2oitJYvnw5AO1cW2PbORArD1cAAt6bR+fIpXrttK820dzHg8Alb9AiwBevV57H9dnBpPwnSh+T8lkkHhHjcJ84EutAXzqueptJkyax5dsvOPfbzw+8forzZ9M+/4jyFeUrylen7+TqS8y6d6koL+WxviMB+HbVAr7/b/Vrd30Hv8D5xMP88kMUN65dZWfMKtKvJvH4U88DUF5Wwg///YzUy4nczLlO+tUkdu7cybSpE3h68Gjc5FImDW5BoVJDYnL1Hc65z7agb7CFfn7E41b4tZbRylaKm1zKiMetaOthxonzur/vyyrg6O8VjHmiGQIBNOJ3aKuIiIhg3bp1DBkyBDc3N4N106ZNMxg0KjhY97Q0JSUFb2/vB+rjpZde4lBSKfH/XUh5aRHuviGMnrkWM/Pq71AU5qZTWpyvn7/xx1lilk/Sz+/fugSA9t1H8fTEDwDoNvAlVBWlHDy4mYqKUq7/cQGASlUZU974CnOLav287HSKFdX6nXsMplhxk/jYFSgKc3HzDGTqG19iU6MbzbAJ85FIpOza9SOqinIKcq6hBaxtWz5Q/YTDW9m7dTUSCbh6BjJy8kJ9XEFeJpIaL6B7+Qfz3Ixl7PluObtjPkPu7MXEv63AxaOtPqbv0AgqykuJW7+IshIFnp6e5BUUs/rt52r5EPr1rx8a0hVH7+5sj36PshIF3v5dRf0R+nfVTz4Zy5vTVsHAtwDo+JHu3/ToOM5ELMDS1ZFmtxq3AKWpGZwYMZ32Hy/Ae84kyjKy+H36v8iNP6SPyYzZiYVjK/wXzcXSxZGixPO81K0Tp6+U6H/Pg66f4vzZtM8/onxF+T7q5XuzsJhP/2887l6BTJ//BTa3uhzn5xrq+wQEM2nOUn7cvIIfNv0HRxcvIv6+HNdb+lKpjOzrKUR+sh2lIp8WNvaEBnekzLwDU8f40MxSwpVrlayKLaZSrZdFbi+jRbPqBTbNJUwa3ALbFhLKKrRcy1Gz6rtig1GNv/ulFI0W+gbf5XtwjZim+PmchkKirfldHMEd+XJP/WlPHwRbf1XfPfBPMqq7TOgLfaEv9BtM/0fzgHrTH6q6aPL5EfpCX+gL/YbS33n6Lu+D/AUGB5sz66OCetNf9Xf7etOubzIunW2wfbf279Bg+64PGv0TWoFAIBAIBAKBQCBoSjTFz+c0FCKTAoFAIBAIBAKBQCAwSUSDViAQCAQCgUAgEAgEJonociwQCAQCgUAgEAgEDxExKNSDQzyhFQgEAoFAIBAIBAKBSSJGORYIBAKBQCAQCASCh0ha8sUG27eXX/19eaAhEF2O74P3N9ffsOlvhZv+sO9CX+gLfaHfUPris0BCX+gLfaH/aOoLBKLLsUAgEAgEAoFAIBAITBLxhFYgEAgEAoFAIBAIHiJiUKgHR6N8h3b48OGoVCp27dpVa93Bgwfp06cPiYmJrF27lsOHD3P27FnatWtHQkKCQWxqaio+Pj61NI4ePUqPHj3u29ehJA1dfCVYmkNGLuw6pSFfWXe8hyP0CJDi0gpsmkn47pCaS9cMY8zN4IlOEi7++l/Wrl1Lbm4uHj6BPD3+LTzadKpT+8zxXcTHriA/9xoOzl4MDp9HYJe++vVarZb4uJWc+CWG0hIFoSHBYO1D0ol4SksUePsHM3LyQuQu3nf8zUfjN7J/x3qUhbm4egQwYtI/DXypKsr5ceMyEo9so7S0DIlEgqtnAKOmLHqg/j1au5FXUEyJssCoj8aen/Mnd6BQFiOV6jpFePt3NSn9+i5fUT+bdn7qy7+dlYqWkjRau7TC0dGRpREz8dqwF5s79G5r1ac77T+aj3X7tpSlZ5K85HMyorcaxHjNeB7feRFYujhSdOYCOy6fZfnqtXX6+Kv5qe/6I+pn086PuL6I8n2U9S+c2klFRQW9e/dm0aJFyOXyO+o2JlKTLzXYvr39/Bts3/VBo+xyHBERQXx8PBkZGbXWRUZGEhoaSqdOuko9depUwsPD76j3008/kZmZqZ9CQkL+lK/QthJ2ntQQ9ZMGlRqe6ytFdocMmssgu0DL7lOaOmMGdpFw9cxOlixZQr8RM3l/RSxdOwUQ+eHLKAvzjG6Tduk0m1a/QWjf0cx9N5agkAF889kcstIv62P2/7iOI3u+ZeSURcxavImcnByO793E8IlvMWvxJswtm7F+2cuoKsrr9JZ4bCc/bFzKwFEzmfPud7h6BrJumaGvHzZ8QOLxHahUKvqPnIGLR1uK8rNrxf0V/0+Onk1qairqShUzFm406qMx5+d8wi8MGjQIM3ML7B3ccHT1NSn9+i5fUT+bdn7q0/9vB2I4feo4N2/eBKBUCpHOdUrSzLs13bZ/Sd6+4xwKfYaUFV/T8cv3kD/ZWx/j+uxg2n24gMvvreJQ91F8vyWGJUuW8PSYun38lfzUd/0R9bNp50dcX0T5Pur6n332Gd988w3Z2dnMnj27Ts3GiFYibbCpqdEof9GwYcNwdHQkKirKYLlSqSQmJoaIiAgAli9fzqxZs/D19b2jnoODAy4uLvrJ3Nz8T/k6fE7L5euQUwjfH9dg0wwC3OvuLnA1C/af1dZ6KluT1nIJ69ZHMW7cODr1Gk1l8za89o+3sbKy4uSBOOM+9nyDf6fe9B0agZN7GwaNnYubd3uO/rQB0N09PLwrmv4jphMUMgAXD38KCgqQSKRoNRpcPQMIn/4BRQXZnDu1t05vh3ZG0b3fs4T2GY2zux8jpyzCwrLaV1mJgpP7Y2newo7w8HAGjZnN+FkfoyzKQyaTPTD/Z0/sYezYsZSXFZObmVrLR2PPz9Dxb7Jv3z4GjpzJpNdWkvnHBcKefMFk9Ou7fEX9bNr5qU//61d/QIZSTkCAbkCo3kWQagWplsY1vV5+jtKUDM6/uRTlhaukrd5AVuxufF6drI/x+dsU0tdtIePrOJTnr/BNTAzjxo1j3LNjjPr4q/mp7/oj6mfTzo+4vojyfZT1hz3/D3r27EmHDh14//33OX36dK3emoJHg0bZoDUzM2PSpElERUVRs0d0TEwMarWa8ePH35feiBEjcHJyonfv3mzfvv1P+0q5Ue2lXAXX88D9L/ZsSM0s54+UJMLCwgCQ24BtcykBnXqSlpxgdJu05AT8gnoaLPPv2Iu0y4kA3MzJQFGYi1+Hnvr5vLw8XDwC9JpWzW3w8O1U5z4qKyu4lnoOv6DqrtlSqRS/oGpfGSlJqNWV3My5pvfv5OaLvYMr9nL3B+K/yke/fv30fm/30djzI3f2IicnB78OPfX5yUq/ZDL69Vm+VfOifjbN/DwM/zW92FdCSxWkWRmVxb5HF3J/PmqwLCf+EC17dAFAYm6OXdcgcvce0fkAMiwhLCyMVtYSoz7+Sn7qu/6I+tn08yOuL6J8H2X9mmXVpk0b3NzcTKpBq0XSYFNTo1E2aEHXlfjKlSvs379fvywyMpIxY8ZgZ2d3TxrW1tZ8/PHHxMTE8OOPP9K7d29Gjhz5pxu1xWW3z2tpUccfTvfKD4fz0ajVODg48EyolLAAKYmpWsybOaAsyDW6jbIgF2s7B4Nl1nZylIW5+vVVy2rO27Z00sfo1jsYzNekRFGARqPWa+i3sa32pSzMRSozQ6vR+a/pRSKRPBD/VT4cHBwM/Nb00ZjzIzMzR1VRZrC/Ki+moF/f5VtzXtTPppef+vYvMzOnWQtbwxg1FNXxFQdLZznlNwz3WX4jF3M7G6RWlljIWyI1M6M8W9etrVgGGomul49ljY49D6p867v+iPrZtPMjri/V24jyfTT1bz//Ozg4kJOTY1RX0LRptKMcBwYGEhYWxvr16+nXrx/JyckcPHiQd95555415HI58+bN089369aN69ev8+GHHzJixIg6t1OpVJiZVaempKSEFi1a/Lkfchc6+UjYdOv/vyRpkNtI6OwtoXkdXebuhUu/Hwbgw9efRiKBya9/8deNNiHqOz/FinwWTgtBo1ajrlShVlealH59I+rnnTH1/Ji6f8GdEeV7Z8T1pWkjyrdh9QWCumi0T2hBNzhUbGwsCoWCyMhI2rRpQ9++fe++4R147LHHSE5OvmPMhx9+SNu2bfVT9+7dAWo9jW1hJan11PZ+MJPBkJ4tkUpl5OXlUVQKV7O1XMvToi3Pw9reeH9ma3t5rQENdHe8dPEduj0JwPhZHzL333G0sLEHoCg/2+COl7Iwr9YdsCqa29gjlcpq3UFTFlX7sraTo1FXIrnlv6YXrVb7p/1XbacszNX7yMvLM/Bb00djy4/czYe5/45jVMTbAMjMzPUeanpprPr1Xb6ifjbt/DxM/+pKFaXFRYYxMrCtY5Tj8hu5WDob7tPSWY6qUIGmrJyK3Hw0lZVYOume0LRQg1QLeXl5lKuM+7idhq4/on427fyI64so38Zcvg+z/hg7/+fl5eHo6GhUtzGilUgabGpqNOoG7bhx45BKpWzcuJHo6GimTp2K5C8WQkJCAq6urneMef311/ntt9/009GjR9FqtXg7V+/bwgzcHOCa8R4T94RUAs2aWeLq2Z6jR6vf61JrNPx28hhefl2Mbufl14XkpGMGyy6fPYpX284AuHj4Y2MnJzcrDbmzF07ufjg4OJCVflGvWVaqJP3qmTr3YWZmgbt3e5LPVe9Ho9GQnFTtq7VPEDKZGa0c3fX+czJTKMjLpCD3+p/238qxNTZ2cpKTjul9HDhwQO/3dh+NLT++AaHInb0I6tofmcyMwvwbODo6kpx0TJ8fV0//Rqtf3+Ur6mfTzs/D9l8zptAM8s3Bq44bjQXHEnDob/jJNvmAMPKPJQCgVako/C0JeX/de1lmQOty3afebiq1Rn3cb37qu/6I+tm08yOuL6J8G3P5Puz6UzPm6tWrXL9+nS5djOsKmjaNukFrbW1NeHg4CxYsIDMzk8mTJxusT05OJiEhgaysLEpLS0lISCAhIYGKigoAvv76a/773/9y4cIFLly4wPvvv8/69euZM2fOHfdraWmJra2twSSRSOjVXkJbN3C0g+GPSVGUwsVr1QNFPd9PSohfdaPX3Ayc7HUTgF0LCU72YNtcN19RCWnZWl5+aQpbtmzh96PbkJVcJXLV25SUlBLSZxQAm7+Yz67Nn+h1ew2ayKXfD3FgRyTZ168SH7eSayln6TlwAoDO69OT+Pl/X3Lut5+5kXEZe3t7tFoNEpmMrPRLbPliPrb2TrQPGaDXXbNkCkfiN+jnew+ezIl933Hq4Dayr11hW9TbVJRX+7JqbkNo3zGUFhexefNm4uNWsXHl61jbOqBWVz4w/x26P0VMTAyWVi2Qu/rU8tHY87Nj4zKeeOIJ9m5dzdefzsbFI4AjezaYjH59l6+on007P/Xpv9eg8Vz6NZYzZ84AcDmoNf19Awl00t20DHhvHp0jl+o1077aRHMfDwKXvEGLAF+8Xnke12cHk/KfKH1MymeReESMw33iSKwDfXlh7LNs2bKFmO+2GvXR2OuPqJ9NOz/i+iLK91HW/3HDUo4dO8bZs2d56623CA4ONqkGrVYrabCpqdFo36GtIiIignXr1jFkyBDc3NwM1k2bNs1g0Kjg4GAAUlJS8Pb2BuDdd98lLS0NMzMzAgMD2bx5M2PHjv1TXk5d1jI4VIqVBaTnwOb9GtQ1PjFrb43Bu6+uLeGF/tWjkzwZrLt/cCZFww+/6hrC245q6BcymH/8I5+1a1eyZU0OXm3a8eLrX2JzqztGQV4mkhrfjPLyD+a5GcvY891ydsd8htzZi4l/W4GLR1t9TN+hEVSUlxK3fhFlJQpCQ7ri6N2d7dHvUVaiwNu/K1Pe+Apzi2rDednpFCvy9fOdewymWHGT+NgVKApzcfMMZOob1b4Ahk2Yj0QiJeHwVvZuXY1EAq6egYycvPCB+vf09CSvoJjVbz9Xy4cp5GfXrh9RVZRTkHMNLWBt29Kk9Ou7fEX9bNr5qS//z0f8nSc6Vr8L8srCtwBIj47jTMQCLF0daeZR3SOnNDWDEyOm0/7jBXjPmURZRha/T/8XufGH9DGZMTuxcGyF/6K5WLo40inxPLLgQFasNu7DFOqPqJ9NOz/i+iLK91HWnzt3LhUVFfTu3ZtFixYheDSRaGt+F0dwR97fXMeLWQ+At8JlbP21/vRHdRf6Ql/oC/2mq/+jeUC96Q9VXTT5/Ah9oS/0hX5T1TdVkq+kNNi+/dr4NNi+64NG/4RWIBAIBAKBQCAQCJoS2sb95qdJITIpEAgEAoFAIBAIBAKTRDyhFQgEAoFAIBAIBIKHiJamNzhTQyGe0AoEAoFAIBAIBAKBwCQRT2gFAoFAIBAIBAKB4CEintA+OMQoxwKBQCAQCAQCgUDwELl4Jb3B9h3QxuO+4letWsWHH35IVlYWnTt3ZsWKFXTv3t1o7Jo1a4iOjubs2bMAhISE8P7779cZ/yAQT2jvg+0n62/Y8RGhpj9sutAX+kJf6DdVffFZIKEv9IW+0P9z+u9sqKw3/YUTRFOmvtm8eTPz5s3jiy++4LHHHuOzzz7jqaee4uLFizg5OdWK37dvH+PHjycsLAwrKyuWLl3KoEGDSEpKwt3dvV48indoBQKBQCAQCAQCgeAhokXSYNP98Mknn/DSSy8xZcoU2rdvzxdffEHz5s1Zv3690fgNGzYwc+ZMunTpQmBgIGvXrkWj0bB3794HkTajiAatQCAQCAQCgUAgEDwilJeXU1RUZDCVl5fXiquoqODUqVMMHDhQv0wqlTJw4ECOHj16T/sqKSlBpVLRqlWrB+b/dhrlO7TDhw9HpVKxa9euWusOHjxInz59SExMZO3atRw+fJizZ8/Srl07EhISasVrtVo+/vhjvvrqK9LS0pDL5cycOZN//vOf9+Vpw4YNrFi9FkVhLq6eAYx88Z94tulUZ3zi8V3sjllBfu415M5eDBk/j3Zd+urX/34inqM/beZaahIlykJefvlltsT9gLIwF1ePAEZM+iced9A/c3wX8bE6fQdnLwaHzyOwhr5WqyU+biUnfomhtESBR2s38gqKKVEWmKR+aEgwWPuQdCKe0hIF3v7BjJy8ELmLd537ADgav5H9O9bXmVdVRTk/blxG4pFtlJaWIZFIcPUMYNSURSaVH1PP//mTO1Aoi5FKdffYvP27ivJtQvqmevxWFlxiwvhwOnfuRMuWLTk5ZiY3tt/5DnOrPt1p/9F8rNu3pSw9k+Qln5MRvdUgxmvG8/jOi8DSxZGiMxfYcfksy1evrbfzv6nmv6nUT1M/v5l6fkw9/6Ze/y+c2snkyVMZF/4ctra2pOdo2XFCw01F3dq9giQEekiR20KlGtJztOw9rSGvxjYyKQwKkdLN33SfzZ2/cq3B9r35mzW8/fbbBssWLVrE4sWLDZZdv34dd3d3jhw5Qs+ePfXL33zzTfbv38/x48fvuq+ZM2eye/dukpKSsLKyeiD+b6dR1oKIiAji4+PJyMiotS4yMpLQ0FA6ddIdNFOnTiU8PLxOrVdffZW1a9fy0UcfceHCBbZv337fLyXv2LGDJUuW8OTomfztve9w8wxk7QcvoyzMMxqfeuk0G1e+Qfd+o/nbv2MJCh3A15/MISv9sj6moqwUn4CuDHnudQDWr1/PwFEzmfPud7h6BrJuWd36aZdOs2n1G4T2Hc3cd2MJChnAN58Z6u//cR1H9nzLyCmLeHL0bFJTU1FXqpixcKPJ6c9avImcnByO793E8IlvMWvxJswtm7F+2cuoKmrfTaoi8dhOfti49I55/WHDByQe34FKpaL/yBm4eLSlKD/bpPJj6vk/n/ALgwYNwszcAnsHNxxdfUX5NiF9Uz5++4+YhrVzezZv3gyA+i69tJp5t6bb9i/J23ecQ6HPkLLiazp++R7yJ3vrY1yfHUy7Dxdw+b1VHOo+iu+3xLBkyRKeHlM/539Tzn9TqJ+mfn4z9fyYev6bQv3fsmULL06ZxrJPviRixpuoKmHCEzJkd2iBeDlJOHlJw/rdar7dq0YmhQkDZJjLqmOeCpHi7y5GCf6zLFiwgMLCQoNpwYIFD3w/H3zwAZs2bWLr1q311piFRtqgHTZsGI6OjkRFRRksVyqVxMTEEBERAcDy5cuZNWsWvr6+RnXOnz/P559/zv/+9z9GjBiBj48PISEhPPnkk/flJzIyknHjxtGt72icW/sxeuoizC2t+HV/nNH4Q7u+IaBTb/oNi8DZvQ1PPzsXd+/2HN6zQR8T8vgInhw9k7YddHc7Bg0aRGif0Ti7+zFyyiIsLK04ecC4/uE93+DfqTd9h0bg5N6GQWPn4ubdnqM/6fS1Wi2Hd0XTf8R0gkIGcPbEHsaOHUt5WTG5makmp+/i4U9BQQESiRStRoOrZwDh0z+gqCCbc6fqflpyaGcU3fs9W2dey0oUnNwfS/MWdoSHhzNozGzGz/oYZVEeMpnMZPJj6vkfOv5N9u3bx8CRM5n02koy/7hA2JMviPJtIvqmfPxaOXYmXdGKCRMmAJBmWaddALxefo7SlAzOv7kU5YWrpK3eQFbsbnxenayP8fnbFNLXbSHj6ziU56/wTUwM48aNY9yzY+rl/G/K+W8K9dPUz2+mnh9Tz7+p1/9hz/8DLy8vDp+DSseRHD+wnXXfJWLTHAI96m6MbvxFQ+JVLTmFcKMA/ndUg30LCa4OuvWW5hDcRsKeU5o6NQR3xtLSEltbW4PJ0rL2RU4ulyOTybhx44bB8hs3buDi4nLHfXz00Ud88MEH7NmzR/8gsr5olA1aMzMzJk2aRFRUFDV7RMfExKBWqxk/fvw96Xz//ff4+vryww8/4OPjg7e3N9OmTePmzZv37KWiooKkpCTCwsL0y6RSKW079CTtcoLRbdKSE/QN1Sr8O/UiLTmxVqy6UgVgUNBSqRS/oJ6kJdet7xd0m37HXqRd1unfzMlAUZiLX4eeVFZWcC31HP369cPDtxNpyQkmpV81n5eXh4tHgF7TqrmNfn/GqPLlF9RDv+x2XxkpSajVldzMuaYvXyc3X+wdXLGXu5tEfppC/uXOXuTk5ODXoac+/1npl0T5NgH9qnlTPX6rsLGxASDb3KikHvseXcj92fCdopz4Q7Ts0QUAibk5dl2DyN17RPc7gQxLCAsLo5W1xOjv/Kv+TTn/TaF+mvL5rSnkx5Tz3xTqf+eQXgBczdJiJ/eluZ0r6Vd/41outJbf+9NVy1vn3tJbD41dW0mQySRczWp0b03eF1qtpMGme8XCwoKQkBCDAZ2qBniq2QX5dpYtW8a7777Lrl27CA0N/Ut5uhcaZYMWdF2Jr1y5wv79+/XLIiMjGTNmDHZ2dvekcfXqVdLS0oiJiSE6OpqoqChOnTrF2LFj79lHfn4+arUaBwcHg+XWtg4oCnONbqMoyMXazjDexk6OoqB2fElxIQD29va19JVG4gGURvSt7eQob/mp2s7aTk6JogCNRuff2s5BH2Mq+jXnbVs66WN06x0M5mtS5atKQ79NDV/KwlykMjO0GsPytbaTI5FITCI/pp5/mZk5qooyg/1VeRHla/r6NedN8fi9ndK7XDEtneWU3zDcb/mNXMztbJBaWWIhb4nUzIzybF23vGIZaCTg4OCg/4Pt9t/5V/2bcv5NvX6a+vnN1PNj6vlvCvXf3s4agOJSXUyzFg6UKXNRlmmxbmZU3ihPhUr5I1v3xBbAuhlUqrWUq+5dQ/DnmTdvHmvWrOHrr7/m/PnzzJgxg+LiYqZMmQLApEmTDLorL126lP/7v/9j/fr1eHt7k5WVRVZWFkqlst48NtqPNwUGBhIWFsb69evp168fycnJHDx4kHfeeeeeNTQaDeXl5URHR+Pv7w/AunXrCAkJ4eLFiwQEGP+uYHl5uX6kr6rkq1SqRtz8b1pc+v0wAB++/jQSCUx+/YsGdvRoUd/5L1bks3BaCBq1GnWlCrW6/r5PJ3j4iOO3YRH5vzPi/HZnTD0/pp7/+qa+y7f/E48zPETKkC5D+cfLT3Iy7a9rDukmxclOQuSe+vtWbkNxv5/PaSjCw8PJyclh4cKFZGVl0aVLF3bt2oWzszMAf/zxh37QNYDPP/+cioqKWg8QjQ069aBo1E20iIgIYmNjUSgUREZG0qZNG/r27Xv3DW/h6uqKmZmZvjEL0K5dO0CX/LpYsmQJdnZ22NnZ4efnh1ar5auvvjKIURblYWPkDj6Ajb281gv7isJcbOxrxzdvoXvaXFBQUEvf2kg8gLURfd0dR7l+fdWy5jb2SKUy8vLyUBbmVd+Va8T6Hbrp3nEeP+tD5v47jhY29gAU5Wcb3BGsub/bqfJ1+x3Gmr6s7eRo1JVIbvmv6UWr1Tba/Jh6/uVuPsz9dxyjInSj68nMzPUeanoR5Wua+k3p+L2dZnd5Xav8Ri6Wzob7tXSWoypUoCkrpyI3H01lJZZOuicoLdQg1UJeXp7BkwaRf9Otn6Z+fjP1/Jh6/k29/p85f5Wfz2qI3p7IM888g6JI91Coxa2nsaXFeVhZy7G2kqAsNSpvwNOhUtq6S4j+SY2iRryyFMxkEoOeLYL6Zfbs2aSlpVFeXs7x48d57LHH9Ov27dtnMO5RamoqWq221lRfjVlo5A3acePGIZVK2bhxI9HR0UydOhWJ5N7vZvTq1YvKykquXLmiX3bp0iUAvLy86tzu9pG/OnToQOvWrfXrNRoNyWeP4dW2i9Htvfy6cDnpmMGyy2eP4uXXuVZs1cn2zJkzhvpJx/Dyq1s/2Zh+W51+K8fW2NjJSU46hpmZBe7e7Tlw4ADpV8/g5del0eu7ePhjYycnNysNubMXTu5+ODg4kJV+Ua9ZVqrU788YVb6Sz1Xv53ZfrX2CkMnMaOXorv+WVk5mCgV5mRTkXm+0+TH1/PsGhCJ39iKoa39kMjMK82/g6OhIctIxff5dPf1F+ZqoflM6fquo6qnjdJfubQXHEnDo38NgmXxAGPnHEgDQqlQU/paEvL/uvSMzoHU5HD16lJtKrdHfeb/+m1L+TbF+mvr5zdTzY+r5N/X679y6HcXlYC1vw7Vr1zl1UjdegI+zhKLcFEoKM/Hw7Yq7HDJy7/z+69OhUgI9JHyzV01BseG6zJta1GotPi6m8YSzLrRIGmxqajTqBq21tTXh4eEsWLCAzMxMJk+ebLA+OTmZhIQEsrKyKC0tJSEhgYSEBCoqKgAYOHAgXbt2ZerUqZw+fZpTp04xffp0nnzySYOntrdz+8hf06ZNIzY2lpMHtnHj2hXiIt+moryUbn1HAfDfz+ezY9Mn+u17Pz2Ri2cOsf/HSLKvX2VP7Eoyrp6l16AJ+pgSZQHXUs9z41oyAHv27GHv1tWkXDjBtiidfkgfnf7mL+aza3O1fq9BE7n0+yEO7NDpx8et5FrKWXoO1OlLJBJ6PT2Jn//3Jed++5kO3Z8iJiYGS6sWyF19TE7/RsZl7O3t0Wo1SGQystIvseWL+djaO9E+ZIBed82SKRyJrx5JuvfgyZzY9x2nDm4j+9qVWr6smtsQ2ncMpcVFbN68mfi4VWxc+TrWtg6o1ZUmkx9Tz/+Ojct44okn2Lt1NV9/OhsXjwCO7NkgyreJ6Jvy8Xv5zH5URWksX74cgHaurbHtHIiVhysAAe/No3PkUr122lebaO7jQeCSN2gR4IvXK8/j+uxgUv4TpY9J+SwSj4hxuE8ciXWgLy+MfZYtW7YQ891Wo7/zUc5/U6ifpn5+M/X8mHr+Tb3+/7hhKX/88Qe9g7TIcrbS/fHhTB3bGUUJXEivbtBOHCClm391I2twNymdfCRsPaymXAUtrHST2a3P9pSr4PQVLYNCGnUzRvAQabTv0FYRERHBunXrGDJkCG5ubgbrpk2bZjBoVHBwMAApKSl4e3sjlUr5/vvvmTNnDn369KFFixYMHjyYjz/++L48DBkyhJs3b7Ji9QoUhbm4eQUy7R9f6rscF+RlIpFUH1Te/sE8P2sZu2OWs3PLZ8hdvHhx3gpcPNrqY5JO/cKWr/6pn6+srCQ+biUSqZTW3kFMfaNufS//YJ6bsYw93y1nd8xnyJ29mPg3Q/2+QyOoKC8lbv0iykoUeHp6kldQzOq3n8PNM9Dk9ENDuuLo3Z3t0e9RVqLA278rU974CnOL6iHG87LTKVbk6+c79xhMseIm8bG3yu02XwDDJsxHIpGScHgre7euRiIBV89ARk5eaFL5MfX879r1I6qKcgpyrqEFrG1bivJtQvqmevwmn4zlzWmrYOBbAHT8SPdvenQcZyIWYOnqSLNbjVuA0tQMToyYTvuPF+A9ZxJlGVn8Pv1f5MYf0sdkxuzEwrEV/ovmYuniSKfE88iCA6uvLyL/Ta5+mvr5zdTzY+r5N/X6P3bsWCZPieAfr7+CjY0N6TlaNvyiRl3jFY6W1hKaWwLoGrnd/HW/6cUnDZsp/zuqJvGqLmb3KQ1aDBvCgkcXibbmd3EEd2T7yfp7IX1EqIytv9af/qjuQl/oC32hL/T/rP6P5sYHEXwQDFVdNPn8CH2hL/SFfl3672yov8G5Fk5o9M/m6uT35Bt3D6onOvo5N9i+6wPxrF4gEAgEAoFAIBAIBCaJ6d7WEAgEAoFAIBAIBAITRKsV3aUfFOIJrUAgEAgEAoFAIBAITBLRoBUIBAKBQCAQCAQCgUkiuhwLBAKBQCAQCAQCwUNE0wS/B9tQiFGOBQKBQCAQCAQCgeAhknA5p8H23aWtY4Ptuz4QT2jvg8hf6k97yhOY/LDsQl/oC32hL/T/nL74LJDQF/pCv6H06/uzlC+9n1dv+mvecqg37fpGK57QPjDEO7QCgUAgEAgEAoFAIDBJxBNagUAgEAgEAoFAIHiIiM/2PDga5Tu0w4cPR6VSsWvXrlrrDh48SJ8+fUhMTGTt2rUcPnyYs2fP0q5dOxISEgxiFy9ezNtvv11Lo3nz5hQXF9+XJ61Wy9R5y0k8FEN5aRHubbry1PjFtHL2vuN2p/Zt4PiedRQX5eDUOpAnw/8PN59OABTkZvDFvwYY3c65dVsmvvof5C531j8av5H9O9ajLMzF1SOAEZP+iUebTvr1qopyfty4jPMnd6BQFiOV6h7Ke/t3ZeTkhQ9MP/HINkpLy5BIJLh6BjBqyiKDuNs5c3wX8bEryM+9hoOzF4PD5xHYpa9+vVarJT5uJSd+iaG0REFoSDBY+5B0Ip7SEgXe/sEm5d+jtRt5BcWUKAuM+mjs+iL/Ql/oP3j9yoJLTBgfTufOnWjZsiUnx8zkxva9deoCtOrTnfYfzce6fVvK0jNJXvI5GdFbDWK8ZjyP77wILF0cKTpzAftunZg5/z/63/Ogj9/6vr4IfXH9/Sv+TfX80CD59wpgzNRFeN7Bf+LxXeyO0fmXO3sxZPw82tXw//uJeI7+tJlrqUmUKAvZtm0byXlePN7FiuaWEpIzVGzYVUx2vqbOffTtakm/rlY42OmOmes5an44VMrZqyp9zAuDW9DO2xynlrI75qEx89ul+uuKfTe6+ptuV21jNMouxxEREcTHx5ORkVFrXWRkJKGhoXTqpDvYpk6dSnh4uFGdv//972RmZhpM7du359lnn71vT2vWrOHUL9/w1POLmfSPLZhbNGPziggqVeV1bnP+5A5+/m4JvYfNYspbW3FqHcjmFREUF+kqsG0rV2YvPcTspYd45ZVXsGxmTaceQzCzsMLewZX1y15GVVG3fuKxnfywcSkDR81kzrvf4eoZyLplL6MsrD5AftjwAecTfmHQoEGYmVtg7+CGo6sv5pbNHph+4vEdqFQq+o+cgYtHW4rys2vF1STt0mk2rX6D0L6jmftuLEEhA/jmszlkpV/Wx+z/cR1H9nzLyCmLmLV4Ezk5ORzfu4nhE99i1uJNJuX/ydGzSU1NRV2pYsbCjUZ9NGZ9kX+hL/TrR7//iGlYO7dn8+bNAKjvcrO+mXdrum3/krx9xzkU+gwpK76m45fvIX+ytz7G9dnBtPtwAZffW8Wh7qNQnLnAmjVrDI7nB3n81vf1ReiL6+9f8W/K54eHmf+Bo27l/2Y2az+o23/qpdNsXPkG3fuN5m//jiUodABff2Lov6KsFJ+Argx57nUAHBwcGBBqxbc7lbwfVUiFCv72nC1md2iH5hdpiP2lhPfWF/LvyEIupKmY9awNbvLqjdIyK4n6QVm3iOCRolE2aIcNG4ajoyNRUVEGy5VKJTExMURERACwfPlyZs2aha+vr1Eda2trXFxc9NONGzc4d+6cfvt7RavVEh0dTdjgGfh3GYhT60CGTVmGsiCbSwk/1bndrz9F0rnXODqFjUHu5sfTz7+NubkVZ47EAiCVyrC2c6SFrZzY2FgGPPMKOZlX6dJjCONnfkhRQTbnTtV9t/7Qzii693uW0D6jcXb3Y+SURVhYWnHyQBwAZSUKTu6PZej4N9m3bx8DR85k0msryfzjAmFPvvDA9Ju3sCM8PJxBY2YzftbHKIvykMlk+rjbObznG/w79abv0Aic3NswaOxc3Lzbc/SnDfp8H94VTf8R0wkKGYCLhz8FBQVIJFK0Gg2ungGET//AZPyfPbGHsWPHUl5WTG5mai0fjV1f5F/oC/360bdy7Ey6ohUTJkwAIM3SqKQer5efozQlg/NvLkV54SppqzeQFbsbn1cn62N8/jaF9HVbyPg6DuX5K5yZuZDo6GjGvfAKQSEDHvjxW9/XF6Evrr9/xb8pnx8eZv6fGjubF+bo8i+Vyfh1v3H/h3Z9Q0Cn3vQbFoGzexuefnYu7t7tObxngz4m5PERPDl6Jm079ASgVatW/Hi4lMTLKq7lqFn/vRJ7GynBARZ1+j+TrOLsFRXZ+Rpu3NSwbX8p5RVafN2r35Q8mFDO5fTKOjVMAS2SBpuaGo2yQWtmZsakSZOIioqiZo/omJgY1Go148eP/1O6a9euxd/fn8cff/y+tsvIyCAnJwfvdmH6ZVbNbHDz6cy1q6eNbqOurCDrjySDbSRSKd7twmptU5ir07dp6URm2gW69R2DVXMbPHw7kZacYFS/srKCa6nn8AvqoV8mlUrxC+qp3yYjJQm1uhK5sxc5OTn4deiJk5sv9g6uZKVfemD6N3OuERam+51V+vZy9zq105IT8AvqabDMv2Mv0i4nAnAzJwNFYS5+t06GN3MyyMvLw8UjQK/5IPNTn/6rfPTr10/v93YfjVm/al7kX+gL/fo5vgBsbGwAyDY3KqnHvkcXcn8+arAsJ/4QLXt0AUBibo5d1yBy9x7Rr78pg5ycHB7vXeP6ZULXF6F/Z31x/W3a54eGyH9LR3fSLtftv22H2/x36kVacqLR+NatW2NmZsb5lOquwqXlWq5erzRonN4JiQS6tbfAwlzClWum3YAV1B+NskELuq7EV65cYf/+/fplkZGRjBkzBjs7u/vWKysrY8OGDff9dBZ0fwwAtLA17G/ewsaB4qJco9uUKPPRatT3tI2ySKd/+ffDOLn54uUfDIC1nQPKwjr0FQVoNGqs7eQGy61tHVAW6LZRFuYiMzNHVVF2S0+u/1dZmPtA9KUyM7QaNQ4O1b/T2k6ORCLRx92OskC3bwPdW56q1tf0WzVv29LJwK8p+K/y4eDgYOC3po/GrF9zXuRf6Av9B3981aT0LldkS2c55TcM91t+IxdzOxukVpZYyFsiNTOjPLu6u2DRrR56Lk63e2r81xehL66/f8V/Uzg/POz829jJkSBBUYe2woh/Gzs5ijp+r6Oj7lunRcWG78sqijXYtbjzCc/dUcaKv7fi83+04oWnW7A6VkFmruHnhfp1vUu3lkaOVitpsKmp0WhHOQ4MDCQsLIz169fTr18/kpOTOXjwIO+8886f0tu6dSsKhYIXX3zxrrFxcXEG+/n000//1D7rolSZz8evBuvnn531JQBJp35iwMiZf1m/WJHPwmkhaNRq1JUq1GrTuqN16ffDAHz4+tNIJDD59S8a2NGjhci/QGC6bN++nX/lnEHrrZuflvVg9ev7+iL0GxZx/m9YTD3/7q0kdPbSNZa02tb8tOHPNzOy8tS8s66AZpYSQgItmTrcmg+/LTJo1B5PqmDC03/ZtqAJ0Gif0IJucKjY2FgUCgWRkZG0adOGvn373n1DI6xdu5Zhw4bh7Ox819hTp05x7tw5/fT007qjpWowpyqKFXm0sK19lx2guXVLJFKZ0W1aOfsw9Z/b9FMz65YAqMrL6Nr7GX2ssjDP6F18gOY29kilslp36JRFecjdfJj77zhGRehGeJaZmd/Sq74zp7sj+Of0re2r7zRr1JVIpDLy8qp/p7IwF61Wq4+7HWt7ea0BB6o8AXTo9iQA42d9yNx/x9HCxh6AovxsA7+N1X/VdsrCXL2PvLw8A781fTQ2fZF/oS/0H47+7TSre9BPQPc01tLZcL+WznJUhQo0ZeVU5ObzRN++vO/3GK9nwOsZYH3rb7+s7Ns9Nc7ri9AX199H+fzf0PlXFOaiRYtNHdo2RvwrCnOxuaWbVaBl/zkN+89p2H44i/z8fABsb3saa9NCSmHxnU94ag3k5Gv4I0vN1n0lpN+oZEA3K4OY0vJG96EWQQPRqBu048aNQyqVsnHjRqKjo5k6dSoSyf0/Jk9JSeGXX3655+7GCxcuJDc3Vz9lZ2cjl8tJvVD97lJ5qZLrKYm4+wYb1ZCZWeDiGWSwjVajIe3CUTzadqOlk5d+krv6YW5ujpN7G6xtWwFQVqok/eoZvPy6GNU3M7PA3bs9yeeO6ZdpNBqSk47hGxCK3NmLoK79kcnMKMy/gaOjI8lJx8jJTKEgLxNXT/8/rV+1TWufIGQyM1o5unP0qO53VukX5F6vU9vLrwvJSccMll0+exSvtp0BcPHwx8ZOTm5WGnJnL5zc/XBwcCAr/aJe86/kp779t3JsjY2dnOSkY3ofBw4c0Pu93Udj0xf5F/pC/+HoV6FU6kbqdFJxRwqOJeDQv4fBMvmAMPKPJQCgVamovJhKh8EDcKwEx0pwrtR1+zt0uPpa1JivL0JfXH8f5fN/Q+Y/+7ou//k51/FqW7f/y8b8++n8qzVQXK6bFCWVJCcnU1lZSaB39QABVhYSfN3MuHqf78NKJRLMTfcLPUYRg0I9OBp1g9ba2prw8HAWLFhAZmYmkydPNlifnJxMQkICWVlZlJaWkpCQQEJCAhUVFQZx69evx9XVlcGDB9/Tfi0tLbG1tdVPdnZ2vPjiixzZ+TmXE/eSfe0iP0S9ibW9E/5dBuq3+++nL3Lql2/1890HTiHx0BZ+P7qV3Mwr7P7vYioqSukUNtpgfwU5f6BSqbiZncG5334mK/0SW76Yj629E+1Dqr9Tu2bJFI7EV48k13vwZE7s+45TB7eRfe0K26LepqK8lJA+owDdwAGhfcewY+MynnjiCfZuXc3Xn87GxSOAI3s2PDD90uIiNm/eTHzcKjaufB1rWwfU6kp93OYv5rNr8yd63V6DJnLp90Mc2BFJ9vWrxMet5FrKWXoO1I30KZFI6PX0JH7+35ec++1nbmRcxt7eHq1Wg0Qme+D5qW//Hbo/RUxMDJZWLZC7+tTy0dj1Rf6FvtCvH/3LZ/ajKkpj+fLlALRzbY1t50CsPFwBCHhvHp0jl+q1077aRHMfDwKXvEGLAF+8Xnke12cHk/KfKH1MymeReESMw33iSKwDfem46m0mTZrElm+/MMnri9AX19/Gevw2pevvnthVbLiVf426km59dXH//Xw+OzZV++/99EQunjnE/h91/vfEriTj6ll6DZqgjylRFnAt9Tw3riUDur/Vh4RZEuRVhrujjKnDrSlQaDh9sfpv9XnP2/JESPXT11H9mtPWwwwHOynujjJG9WuOv5cZx87qtpHbSxnc0wpPlybWwhX8aRrtO7RVREREsG7dOoYMGYKbm5vBumnTphkMGhUcrHtampKSgre3N6C7KxUVFcXkyZORyf58xX/ppZc4er6UXRsWUlZSRGu/EMLnrMXMvPqF9PycdEqU+fr5dqFDKFHc5OD3yykuysGpdTvC56yt1U35zJFYXFxcaPfYSOLWL6KsRIG3f1emvPEV5hbV+nnZ6RQrqvU79xhMseIm8bErUBTm4uYZyNQ3vjToKjJswnwkEim7dv2IqqKcgpxraAFr25YPVD/h8Fb2bl2NRAKunoGMnLxQH1eQl4lEUn3vxMs/mOdmLGPPd8vZHfMZcmcvJv5tBS4ebfUxfYdGUFFeqs9HaEhXHL27sz36vXrJT3379/T0JK+gmNVvP1fLhynoi/wLfaH/4PWTT8by5rRVMPAtADp+pPs3PTqOMxELsHR1pNmtxi1AaWoGJ0ZMp/3HC/CeM4myjCx+n/4vcuMP6WMyY3Zi4dgK/0VzsXRxpCjxPC9168TpKyUme30R+uL6+1f8m+r54WHmPz6uOv9jptadf2//YJ6ftYzdMcvZueUz5C5evDjP0H/SqV/Y8tU/9fPPPPMMc+fOZdKEF7GxbsHldBX/2VxEZY3xnRztpVg3r35qaNtcwtTh1thZSykt15KRXcln/1VwPlXXjUVVqaWthzkDuzfDlGmKgzM1FBJtze/iCO5I5C/1pz3lCdj6q/rugX+SUd1lQl/oC32hL/Qbqf6P5gH1pj9UddHk8yP0hb7Qrz/97SfrT39EqIyX3s+7e+CfZM1bDncPaqT8eqGwwfbdPfD+vxjTmGn0T2gFAoFAIBAIBAKBoClxl3EABfdBo36HViAQCAQCgUAgEAgEgroQDVqBQCAQCAQCgUAgEJgkosuxQCAQCAQCgUAgEDxExKBQDw4xKJRAIBAIBAKBQCAQPESOni9qsH33bGfbYPuuD8QT2vvgQFJxvWn3CWph8qPkCX2hL/SFvtBvnPpiFGWhL/SFfl36O0+r6k1/cLA5n++qN3lmPF1/2vWNFvGE9kEh3qEVCAQCgUAgEAgEAoFJIhq0AoFAIBAIBAKBQCAwSUSXY4FAIBAIBAKBQCB4iIhBoR4cjXJQqOHDh6NSqdi1q3an+4MHD9KnTx8SExNZu3Ythw8f5uzZs7Rr146EhIRa8bt372bRokUkJSVhZWVFnz59+Pjjj/H29r4vTxs2bGDV52soLMjDw9uf8dPexKdthzrjTx6J53///Zzc7Os4u3oyZuJcOob01q8vKsjju2+Wcy7hKKXFSlq3diM3X0mJsgBXjwBGTPonHm061al/5vgu4mNXkJ97DQdnLwaHzyOwS1/9eq1WS3zcSk78EkNpiQKP1m7kFRSbrH5oSDBY+5B0Ip7SEgXe/sGMnLwQuYt3nfsAOBq/kf071qMszDXqS1VRzo8bl5F4ZBulpWVIJBJcPQMYNWWRSeVH5L9p51/oN+36f/7kDhTKYqRSXacpb/+uD0TfzkpFS0karV1a4ejoyNKImXht2IvNHV7Ha9WnO+0/mo91+7aUpWeSvORzMqK3GsR4zXge33kRWLo4UnTmAvbdOjFz/n/0+TKV/Aj9R0NfXF/urn+zoJhiZQFungGMmfIWXn4d69RPOLabHVtWcjPnGo4uXgx//jXaB/cxGrtl7dsc+SkGz4Be5GZeory0CDefrvR/djEtnbzr3EdG8glO/byO7PSzFBflMCxiFX6dBhrEJCfu4czhTRTdSKKgoIBt27bRrl27OjUbI4fPKRts373aWzfYvuuDRtnlOCIigvj4eDIyMmqti4yMJDQ0lE6ddAfz1KlTCQ8PN6qTkpLCM888Q//+/UlISGD37t3k5uYyevTo+/KzY8cOlixZwvBxL/N/H22ktXdbPntnFkUFN43GJ19IZM0nb9F7wDMs/HgjXbr3Y9XSeVxLSwZ0J5NVH8wj90YGs+Z/ysjnZ5KamoqqopxX/vUtrp6BrFv2MsrCPKP6aZdOs2n1G4T2Hc3cd2MJChnAN5/NISv9sj5m/4/rOLLnW0ZOWcSTo2eTmpqKulLFjIUbTU5/1uJN5OTkcHzvJoZPfItZizdhbtmM9cteRlVRXme5JR7byQ8blzJw1EzmvPudUV8/bPiAxOM7UKlU9B85AxePthTlZ5tUfkT+m3b+hX7Trv/nE35h0KBBmJlbYO/ghqOr7wPT/+1ADKdPHefmTd21qlQKkc51StLMuzXdtn9J3r7jHAp9hpQVX9Pxy/eQP1l9M9b12cG0+3ABl99bxaHuo1CcucCaNWsM8mUq+RH6TV9fXF/uTb+yUsXf3tmAu1cAXyyZjqIO/ZSLp4le/iY9nhjF3z+IoWNof9Z9NJfMGvpVnPn1J1Ivn6FFixZcu3qSAeMW89xrWzC3aMbWLyKoVNVdvqqKEhzdA3hi7KI7xrj5duXvf/97nTGNHS2SBpuaGo2yQTts2DAcHR2JiooyWK5UKomJiSEiIgKA5cuXM2vWLHx9fY3qnDp1CrVazXvvvUebNm3o2lVX8RMSElCp7n1Et8jISMaNG0evAc/g5uHLC9P/iYWlFYd//p/R+L0/bCQouCdPjXwR19a+jHx+Jp4+gfy8czMANzL/4Oql35nw8lv4tA3ixOE9PPfcc0ilMq7/cYGRUxZhYWnFyQNxRvUP7/kG/0696Ts0Aif3NgwaOxc37/Yc/WkDoGswH94VTf8R0wkKGcDZE3sYO3Ys5WXF5Gammpy+i4c/BQUFSCRStBoNrp4BhE//gKKCbM6d2ltnuR3aGUX3fs8S2mc0zu5+tXyVlSg4uT+W5i3sCA8PZ9CY2Yyf9THKojxkMpnJ5Efkv2nnX+g37fo/dPyb7Nu3j4EjZzLptZVk/nGBsCdfeCD661d/QIZSTkCAboTj3kWQagWplsY1vV5+jtKUDM6/uRTlhaukrd5AVuxufF6drI/x+dsU0tdtIePrOJTnr3Bm5kKio6MZ98IrBIUMMKn8CP2mry+uL/eun52ZwrPTFmJhYcXxfVuN6u/f+S2BnXvRf/hUXNzbMCR8Dq192nNw90aDuIKbN4iNWsILsz6gtLQUz4BetOk4EEf3QJ56YRnFhdlc+f2nOsvXp31fwoa+hl/nJ+uMaddtJD2enk3Pnj3rjBE8OjTKBq2ZmRmTJk0iKiqKmj2iY2JiUKvVjB8//p50QkJCkEqlREZGolarKSws5JtvvmHgwIGYm5vfk0ZFRQVJSUmEhYXpl0mlUtp1eowrF88Y3ebqpd9p3+kxg2VBwT25eiu+UlUBgLmFBZUqFWlXztOrVy/MzC1IvfgbUqkUv6CepCUnGNVPS07AL8jwAPbv2Iu0y4kA3MzJQFGYi1+HnlRWVnAt9Rz9+vXDw7cTackJJqVfNZ+Xl4eLR4Be06q5jX5/xqjy5RfUQ7/sdl8ZKUmo1ZXczLmmL18nN1/sHVyxl7ubRH5E/hs2P0K/aetXzddn/Zc7e5GTk4Nfh576+p+VfumB6df8rfaV0FIFaVZGZbHv0YXcn48aLMuJP0TLHl0AkJibY9c1iNy9R/Trb8ogJyeHx3tXXyNNKT9Cv2nri+vLvel7+XUi9VIiUqkU/449SL2UaFQ/9XIi/h0N9QM7hxnEazQaNqxaQP9hk7GwtEKj0dDS0Vu/3rKZDS5enclMOW10HwLBn6FRNmhB15X4ypUr7N+/X78sMjKSMWPGYGdnd08aPj4+7Nmzh7feegtLS0vs7e3JyMhgy5Ytd9yuvLycoqIiioqKSE9PR61WY2tr+AFiW/tWFBUY75JRWJCLjb2DYbydA4W34l3cvWkldyHu25VkZ6Wj0ag5dOgQhTezUBTmAGBt64CyINeovrIgF2s7Q31rOznKwlz9+qplJYoCNBo1Dg4OWNs56GNMRb/mvG1LJ32Mbr2DwXxNqnxVaei3qeFLWZiLVGaG9pb/ml4kEolJ5Efkv2HzI/Sbtn7N+fqo/zIzc1QVZQb7q/LyoPSbtTC8dlmroUhmVBZLZznlNwz3WX4jF3M7G6RWlljIWyI1M6M8u/raV6Xl4nR7Tk0jP0K/6eqL68u969vYOVB0a13N/9+OoiAXm9v0bezkFNUoq73b1yGVyugz+AUUt3TMrVoYbNPcxoFihfF9PEpotA03NTUa7SjHgYGBhIWFsX79evr160dycjIHDx7knXfeuWeNrKwsXnrpJV588UXGjx+PQqFg4cKFjB07lvj4eCQS433IlyxZwttvvw2ATCajTZs2ui5VM5Y+kN9mZmbOzH98RNSqd1j06lgAkpKSCOj0OFqaYC27Ty79fhiAD19/GokEJr/+RQM7erQQ+Rc8ytR3/S9W5LNwWggatRp1pQq1uvKB6tc327dv5185Z9B66+anZT1Y/frOj9Bv2vqChmVP3Bfs2LISC0sr/jG5Oy//Y3VDWxI8IjTaJ7SgGxwqNjYWhUJBZGQkbdq0oW/fvnff8BarVq3Czs6OZcuWERwcTJ8+ffj222/Zu3cvx48fr3O7BQsWUFhYSGFhITdu3EAmkzF06FCDmKKCm9je9hS2Cjt7OYrbnt4WFeZhVyPeq017Fn2yiY8jf0IqlTJ9+nRKlAW0cvQAQFmUh7W94d3HKqzt5bUGBNDd0ZTr11cta25jj1QqIy8vD2VhXvVTh0as36Gb7p2J8bM+ZO6/42hhY6/LYX62wR3Zmvu7nSpft9/hrenL2k6ORl2J5Jb/ml60Wm2jzY/If9POv9Bv2vVf7ubD3H/HMSri1k1TM3O9h5peHsTxpa5UUVpcZBgjA9s6Rjkuv5GLpbPhPi2d5agKFWjKyqnIzeeJvn153+8xXs+A1zN0T3wBsrJvz2njzI/Qb9r64vry5/QVhXnY3lpX8/+3Y2MvrzVglKIwF9tb+hKZ7jmZqqICVUU5q97VjXlzfOdK1r3dX79NiSKPFjbG9/EoIQaFenA06gbtuHHjkEqlbNy4kejoaKZOnVrnU1VjlJSU6IeBr0Im0/WP0mg0dW5naWmJra0ttra2ODg4EBQUxMmTJ/XrNRoN58/8SpsA48Om+/p35PzvvxosO594HF8j8bZ2rfBq057du3eTkZJE+5D+aDQakpOO4eXXxai+l18XkpOOGSy7fPYoXm07A9DKsTU2dnKSk45hZmaBu3d7Dhw4QPrVM3j5dWn0+i4e/tjYycnNSkPu7IWTux8ODg5kpV/Ua5aVKvX7M0aVr+Rz1fu53VdrnyBkMjNaObpz9KjuvbGczBQK8jIpyL3eaPMj8t+08y/0m3b99w0IRe7sRVDX/shkZhTm38DR0ZHkpGP6+u/q6f/Ajq+aMYVmkG8OXmVGZSk4loBD/x4Gy+QDwsg/lgCAVqWi8mIqHQYPwLESHCvBuRIcHR05dLj63dvGnB+h37T1xfXlz+mnJZ/B278zGo2GS2eP4+3f2ai+d9vOXD5rqH/xzFF9fFj/sfzjw628uSyWN5fF8sbS75BKpbT2686oV9YCUF6mJCstEVefYKP7EAj+DI26QWttbU14eDgLFiwgMzOTyZMnG6xPTk4mISGBrKwsSktLSUhIICEhgYoK3aBLQ4cO5cSJE7zzzjtcvnyZ3377jSlTpuDl5UVw8L0fSFOmTGHLli0c+eV7MjOusuHL96koL6VX/xEArPvP/xH37Qp9/IBhz5N0+ih7/vcNmRkpbN/0BalXztF/cPXnhU4eiefi2ZPkZGXQtn0w27dvx82rHfatXNgW9TYV5aWE9BkFwOYv5rNr8yf6bXsNmsil3w9xYEck2devEh+3kmspZ+k5cAIAEomEXk9P4uf/fcm5336mQ/eniImJwdKqBXJXH5PTv5FxGXt7e7RaDRKZjKz0S2z5Yj629k60Dxmg112zZApH4jfo53sPnsyJfd9x6uA2sq9dqeXLqrkNoX3HUFpcxObNm4mPW8XGla9jbeuAWl1pMvkR+W/a+Rf6Tbv+79i4jCeeeIK9W1fz9aezcfEI4MieDQ9Ev9eg8Vz6NZYzZ3QDEl4Oak1/30ACnVwBCHhvHp0jq1+lSftqE819PAhc8gYtAnzxeuV5XJ8dTMp/ovQxKZ9F4hExDveJI7EO9KXjqreZNGkSW779gnO//WxS+RH6TV9fXF/uXd/J1ZeYde9SUV7KY31HAvDtqgV8/99P9fp9B7/A+cTD/PJDFDeuXWVnzCrSrybx+FPPA9DCxh5Xj7b6yc3TH2tra7LSEsnPTiH3+kV2f/smLeycaNOx+ruysStfJOHAt/r5ivJisjPOk51xHoCivAyyM85TdPO6PqasuIDsjPNcuXIF0H2q8/z58+Tk5GAqaLWSBpuaGo32HdoqIiIiWLduHUOGDMHNzc1g3bRp0wwGjapqpKakpODt7U3//v3ZuHEjy5YtY9myZTRv3pyePXuya9cumjVrds8ehgwZws2bN1n1+ecUFeTh4RPAq/+3Ut/l+GZuFpIaT4L9Ajsz7bV/s23jarZuWImTqyez/vEJ7l5++pjC/Fy2RH5yqyuynN69e/P7+Sv851+jcfMMZOobX2JzqwtHQV4mEkm1vpd/MM/NWMae75azO+Yz5M5eTPzbClw82upj+g6NoKK8lLj1iygrUeDp6UleQTGr337OJPVDQ7ri6N2d7dHvUVaiwNu/K1Pe+Apzi+rvT+Rlp1OsyNfPd+4xmGLFTeJjV6AozK3lC2DYhPlIJFISDm9l79bVSCTg6hnIyMkLTSo/Iv9NO/9Cv2nX/127fkRVUU5BzjW0gLVtywei/3zE33miY/WQxq8sfAuA9Og4zkQswNLVkWYervr1pakZnBgxnfYfL8B7ziTKMrL4ffq/yI0/pI/JjNmJhWMr/BfNxdLFkaLE87zUrROnr5To82Uq+RH6j4a+uL7cXf9mYTGf/t943L0CmT7/C2xudTnOzzXU9wkIZtKcpfy4eQU/bPoPji5eRPx9Oa419G/H2toae1cf9m5eSHlpEW6+IYx6ZS1m5tXlW5CXTmlxdfne+OMssSsn6ecPbFsCQLvuo3hqwgcAXDn7M/EbF1D1waDXXnsNgNmzZzNnzpw6/QiaJhJtze/iCO7IgaTietPuE9SCrb/W8WLTA2BUd5nQF/pCX+gL/UdU/0fzgHrTH6q6aPL5EfpC/1HW33laVW/6g4PN+XxXvckz4+n6065v9p0tbbB99+tw7w/2TIFG/4RWIBAIBAKBQCAQCJoS4pHig6NRv0MrEAgEAoFAIBAIBAJBXYgntAKBQCAQCAQCgUDwENE0wc/nNBTiCa1AIBAIBAKBQCAQCEwSMSiUQCAQCAQCgUAgEDxE9v5ex0fBHwIDaoyA3xQQXY7vgx2/1d8ocEO6mpv8KHlCX+gLfaEv9B9NfTGKstAX+qar/82BepNnYh/qfRRlU6Upfg+2oRBdjgUCgUAgEAgEAoFAYJKIJ7QCgUAgEAgEAoFA8BARL30+OMQTWoFAIBAIBAKBQCAQmCSNclCo4cOHo1Kp2LVrV611Bw8epE+fPiQmJrJ27VoOHz7M2bNnadeuHQkJCbXit2zZwvvvv8+lS5dwdHRk9uzZvPHGG/ftacOGDaxYvRZFYS5ungGMnvwWXn4d64xPOLabnTEruZlzDUcXL4aNf432wX3063d9t4rTR3dRkJeFzMwcFycHcm8qKC0pwtUjgBGT/olHm0516p85vov42BXk517DwdmLweHzCOzSV79eq9USH7eSE7/EUFqiIDQkGKx9SDoRT2mJAm//YEZOXojcxfuOv/to/Eb271iPsjDXqC9VRTk/blxG4pFtlJaWIZFIcPUMYNSURSbl//zJHSiUxUiluns83v5dhX4T0jf1+in8C/9/xX99HV92VipaStJo7dIKR0dHlkbMxGvDXmzu8Lpfqz7daf/RfKzbt6UsPZPkJZ+TEb3VIMZrxvP4zovA0sWRojMX2HH5LMtXr63Tx+3cb/49WruRV1BMibJAXH8bID+mrm+qx+/t+hKJTt+jbQhDJiymlfOd9U/+soGju9ehLMzB2SOQp8b/H+4+hnnNuHKaX/77D9LS/gC0WFo156V/rMavXWidugnHdrNjS/Xfz8OfN/z7eWeM4d/PXToF8dprr9G5c+c7+m2M7EmsaLB9D+ps0WD7rg8a5RPaiIgI4uPjycjIqLUuMjKS0NBQOnXSHTRTp04lPDzcqM7OnTuZMGECr7zyCmfPnmX16tV8+umnrFy58r787NixgyVLlvDUmBm8/n4Mbl4BfPnBdBSFeUbjUy6d5psVb/JYv1H8fUkMHUL7s/7juWSmX9bHOLp6M3ryW7yxNI6Bz7xMWloapSUKIv6xDlfPQNYtexllHfppl06zafUbhPYdzdx3YwkKGcA3n80hq4b+/h/XcWTPt4ycsohZizeRk5PD8b2bGD7xLWYt3oS5ZTPWL3sZVUV5nb878dhOfti4lIGjZjLn3e+M+vphwwckHt+BSqWi/8gZuHi0pSg/26T8n0/4hUGDBmFmboG9gxuOrr5Cvwnpm3r9FP6F/7/ivz6Pr98OxHD61HFu3rwJQKkUIp3rlKSZd2u6bf+SvH3HORT6DCkrvqbjl+8hf7K3Psb12cG0+3ABl99bxaHuo/h+SwxLlizh6TF1+/gr+X9y9GxSU1NRV6qYsXCjuP4+5PyYur4pH78G+mYW2Dm44+Dii4VFMzZ+FkGlqm79pBM7iN+yhMeHz2La/23FuXUg//0sguKiav2MK6f59uMXycjIYPC42bz05io8/Tqy7sM5df/9fPE00cvfpMcTo/j7BzF0DO3Puo8M/352cvVmzJS3eHNZHHMXR+Pu7s7UqVP15yHBo0mjbNAOGzYMR0dHoqKiDJYrlUpiYmKIiIgAYPny5cyaNQtfX1+jOt988w0jR47klVdewdfXl6FDh7JgwQKWLl3K/TyYjoyMZNy4cTzWbxQurdvwbMRCLCysOL5vq9H4Azu/JbBzL/oPn4qzexuGjJtDa5/2HNy9UR8T0msoAR17Inf2IPH4bsaOHUulqhx1ZQUjpyzCwtKKkwfijOof3vMN/p1603doBE7ubRg0di5u3u05+tMGQHf38PCuaPqPmE5QyABcPPwpKChAIpGi1Whw9QwgfPoHFBVkc+7U3jp/96GdUXTv9yyhfUbj7O5Xy1dZiYKT+2Np3sKO8PBwBo2ZzfhZH6MsykMmk5mM/6Hj32Tfvn0MHDmTSa+tJPOPC4Q9+YLQbyL6pl4/hX/h/6/4r8/ja/3qD8hQygkI0I1w3LsIUq0g1dK4ptfLz1GaksH5N5eivHCVtNUbyIrdjc+rk/UxPn+bQvq6LWR8HYfy/BW+iYlh3LhxjHt2jFEffzX/Z0/sYezYsZSXFZObmSquvw85P6aub8rHb039x4fPZtysVWRnXCD0iYkoCrK5ePqnOvWPx0cS/Pg4uvQag6ObH0NeeBtzCysSDsfqY+I3L8GqhR3PPfccT42eTlDXvsx4aw0Wls3q/Pt5f42/n13c2zAk3Mjfz72r/3529fBjwYIFKJVKLl68WKdfQdOnUTZozczMmDRpElFRUQYNz5iYGNRqNePHj78nnfLycqysDL+z1KxZMzIyMkhLS7snjYqKCpKSkggLC9Mvk0qltO3Qg7TLiUa3Sb2ciH+HngbLAjqFGY2vrFSRkXIOlUqFVXMbXD0DkUql+AX1JC05wah+WnICfkGG+v4de+n1b+ZkoCjMxe+Wh5s5GeTl5eHiEaDXtGpug4dvpzr3UVlZwbXUc/gF9TD43TV9ZaQkoVZXcjPnmj4/Tm6+2Du4Yi93Nxn/cmcvcnJy8OvQU+8/K/2S0G8i+qZeP4V/4f+v+K/v46vmb7WvhJYqSKvj84b2PbqQ+/NRg2U58Ydo2aMLABJzc+y6BpG794jOB5BhCWFhYbSylhj1cTv3k/+q39mvXz99PsT19+Hlx9T1q+ZN+fit0vdpF4bctQ22rdzIuX4Jd9/OZFw9bVRfXVlBZloSPu2q/y6WSKV4twvj2hXdNsVFeVxLSaS4MIcjR47wr+l9WPH2ZFIvJeDfsQepl+7w93NHw/wHdg6rM76yUsXmzZuxsbHR31gzJTTahpuaGo2yQQu6rsRXrlxh//79+mWRkZGMGTMGOzu7e9J46qmniIuLY+/evWg0Gi5dusTHH38MQGZmZp3blZeXU1RURFFREenp6ajVamxtbQ1ibOwcKCrINbq9oiAXGzuH2+LlteKTftvHWxE90WjU7Nu3j4h/rKWFTUsArG0dUNahryzIxfo2fWs7OcrCXP36qmU1521bOuljdOsdDOZrUqIoQKNR6zX029TwpSzMRSozQ6tR4+BQ7cfaTo5EIjEJ/zIzc1QVZQb7q/Ii9E1f39Trp/Av/P8V/w/j+G3WwvDaaK2GIplRWSyd5ZTfMNxn+Y1czO1skFpZYiFvidTMjPJsXXfEYhloJODg4IBljU9NPqjrY9XvdHBwMMiHuP4+nPyYun7NeVM9fqv0W9g6VMcU5tDCxoHiuvSV+Wg1av02BvpFum3yc9IB3RPtp556ilfmf0lr73asei8Cmcz8/v9+vs1L0ql9vPliN96Y2JWoqCjWr19Pq1atjGoKHg0a7Wd7AgMDCQsLY/369fTr14/k5GQOHjzIO++8c88aL730EleuXGHYsGGoVCpsbW159dVXWbx4sf7lemMsWbKEt99+GwCZTEabNm2Ijo5myNTgv/y7auLXvjuvvLWGFYsn0rlzZzaumMesxZtqnUzvl0u/Hwbgw9efRiKBya9/8SDsPjTq23+xIp//Z+/M46Kq+j/+ngHEhU0ZdllFQDFXNLdHRM3cKs0tc0nA1NKs7LGk/Lm0PC5tmku2KEZIIoJmaSZp7ktqoYErKgYKwiDLDLLP/P5ALgwMmgXK4Hm/XveF997v+dzPfM+517uce+78KV3QlJZSWlJMaWmJ0G9A+nWN2L8eLsL/3TH0/cvQMfT2Kbg7hr7/Pqjjg1arkf7dt29f0mhDS/c2XEw4xo3ki/9a39O3G3OWRpOnyiI5LobXXnuNqKgonZs7hoBWK3vYFhoM9fYJLZQNDhUdHY1KpSI0NJRWrVrh7+9/74J3kMlkLF26FLVazbVr10hLS6Nbt24ANb53CxASEkJOTg45OTncvHkTIyMjhg4dqhOjysnEwkqht7y5laLaC++qHGW1eNPGTXFp1Q653IjRo0cjNzLixP6y9w/UuZmY1aBvZqWoNqBB2R27svh2XZ8AYNyMD5n1QQzNzK0AyM1K17ljp87JrHYHr5ym5lbI5UbV7gBW9mVmqUBTWoJMbkRmZoUfdY4SrVZbb/0rHN2Z9UEMI4Lv3LQwNpE8VPYi9A1T39Dbp/Av/P8b/w9y/yotKSY/L1c3xggsahjluPCmElO7Kv8P2ikozlGhKSikSJmFpqQEU9uyk9JmpSDXQmZmJoXF+n1U5V75Ly+nzlFKvzMzM1MnH4/y/791nR9D1zf0/bcm/fLBnNS5mZhZ2pCnyqRZTfpmzZHJjXQGgJLKWpS3TxsAZDK5Tvu0c/RAnXPr/s+fq3gxbdwUG3sX3Fp34H//+x/GxsZs2bJFr6bg0aBeX9COGTMGuVxOREQEYWFhBAUFIZPd/90MIyMjnJycaNSoEd999x09evTAxsamxnhTU1MsLCywsLDA2toaX19fTp48Ka3XaDRcSjiOa2v9Q4S7te7AxYRjOssu/nlUb7yxsQkt3dty9OhRtFotJSVFaDQaEhOO4erZUa++q2dHEqvoX4qv0Ld39sLcUoEy7RoKO1dsnTyxtrYmLfmCpFmQryb5ypkat2Fs3Agnt7Yknq3YTlVfLd19MTIypoWNE0ePlr0XlZF6lezMVLKVN+qtfw9vPxR2rvh27oeRkTE5WTexsbEhMeGY5N/BxUvoG6i+obdP4V/4r8/Ht6r5qRyTYwxZJuBaoFeW7GNxWPfrrrNM0b8nWcfiANAWF5PzewKKfmXv0BkDLQvh6NGj3FJr9fqoyr3y38KmJeaWChITjkm/88CBA1I+HvX/f+s6P4aub+j7b036SeePkpl2hdxbN7Bx8uL6ldO09NDfK9HIuBEOrr5cPVfxPrxWoyHp3FGcWpWVsVK0xNzKlmaWCql9AqSnJpGnysLNq+bz50vxuvm/cOZojfGVf19R0cP7BI7g4VOvL2jNzMwYO3YsISEhpKamMnnyZJ31iYmJxMXFkZaWRn5+PnFxccTFxUmNWqlUsnbtWs6fP09cXByvvvoqUVFRLF++/L58BAYGsnnzZn7b/z03r19my/r3KCrM53H/4QBsXBPCj999KsX3GTyB86cP8+uPG7h5/Qq7tqwm+UoC/3nyeQAKC26zY9Nyki6d5lbGDR7z60dERATZmTdwcmvLtg2LKCrMp0ufEQBErp3LrshPJP1eAydy8c9DHNgZSvqNK8TGrOL61Xh6DBgPlD2Z7jVoEnu//4Kzv+/lZsolrKys0Go1yIyMSEu+yOa1c7GwsqVtl/6S7leLAzkSu1Ga7z14Mif2beHUwW2kX79czVfjpub4+Y8kPy+XyMhIYmNWE7HqDcwsrCktLTEY/zsjlhEQEMCerWv45tOZ2Dt7c2T3RqHfQPQNvX0K/8L/v/Ffl/tXr4HjuPhbNGfOnAHgkm9L+nn44GPrAID3+7PpELpU0rz25Saaujvjs3gOzbw9cJ3+PA6jB3N1xQYp5uryUJyDx+A0cThmPh5MGDWazZs3E7Vlq14f/zb/7bo9SVRUFKaNm6FwcBf//z7g/Bi6viHvv5X1D/ywis2rZ2DX0oeTv4ZjbmWLd6cBkl74xy9wYm+4NP/4E4H8cXAzp49sRZl6mZ0bF1JclE+HXs9Keer+ZDAFeTls2rSJPdvXs/mrd0lLvoRMJpfOn8NXh/BDpfNn/8ETOFfp/PmnqOrnzz9+V3H+nHwlgZCQEG7evMmgQYMwNLTahzc1NOrtO7TlBAcHs27dOoYMGYKjo6POuilTpugMGtWpU9mdoatXr+Lm5gbAN998w3//+1+0Wi09evRg3759Urfjv8uQIUO4desWqz5fRW62EidXH6bNXYv5nS4TWcpU6YPUAO5enZg4cyk7N69kR+QKbOxdCXrjMxycWwMglxtx88ZVThzYjlqVRTMzKzw9Pblx8xYbV76Oo4sPQXO+wPxOF4vsTF19V69OPPfSMnZv+Yyfo5ajsHNl4msrsb+jD+A/NJiiwnxi1i+g4LYKvy6dsXHrxvaw9ym4rcLNqzOBc77EpFHF9xUy05PJU2VJ8x26DyZPdYvY6JWocpTVfAEMGz8XmUxO3OGt7Nm6BpkMHFx8GD55vkH537VrB8VFhWRnXEcLmFk0F/oNSN/Q26fwL/zXx/3r+eD/EvBYxZDG0+e/DUByWAxngkMwdbChibODtD4/KYUTT0+j7cchuL0yiYKUNP6cNg9l7CEpJjXqJxrZtMBrwSxM7W1of/ocRp18WLlGv4/ayL+LiwuZ2XmsWfSc+P/3IeTH0PUNdf+trF9SVEi2MgWAJubNGffq1xibVOhnZSRzW12h79t1CLdVt9j//Wfk5WZg59yGca9+LXU5Bnh8wGRKios4GfslP0R8CjKwc3Rn3PT3az5/9u7EpFeWsiNyJT9uKjt/Dv6v7vlz+o2rhH5y5/zZ3Aq/To+xceNGWreuqCPBo4dMez8fZH3E2fl78b2D/iFDOpuw9bcaXjyqBUZ0MxL6Ql/oC32hL/TrRH+HSd19MmNo8QWDz4/QF/r1Wf/bA3Umz8Q+8NMfdXf+PLiTyb2D6ik//v7wBu0b1rneP9O8L+p1l2OBQCAQCAQCgUAgEAhqomFdngsEAoFAIBAIBAJBPUf0ka09xBNagUAgEAgEAoFAIBAYJOKCViAQCAQCgUAgEAgEBokYFEogEAgEAoFAIBAIHiDbT9bdYF/34mk/o4e27bpAvEN7H4QfrLtr/wn/kRn8KHlCX+gLfaEv9IV+XeiLUZSFvtCvO/1bfx66d+A/pMVjvet0NN+GNlpvfWX16tV8+OGHpKWl0aFDB1auXHnXz6BGRUXxf//3fyQlJdG6dWuWLl3KkCFD6syf6HIsEAgEAoFAIBAIBA8QjfbhTfdDZGQks2fPZsGCBfz+++906NCBJ598kvT0dL3xR44cYdy4cQQHB/PHH38wfPhwhg8fTnx8fC1kTT/iglYgEAgEAoFAIBAIBNX45JNPePHFFwkMDKRt27asXbuWpk2bsn79er3xK1asYNCgQcyZM4c2bdrw3nvv0blzZ1atWlVnHsUFrUAgEAgEAoFAIBA8IhQWFpKbm6szFRYWVosrKiri1KlTDBgwQFoml8sZMGAAR48e1at99OhRnXiAJ598ssb42qDeDgr11FNPUVxczK5du6qtO3jwIH369CEuLo4lS5Zw6NAhlEolbm5uTJ8+nVdffVUnft++fcyePZuEhAScnZ2ZN28ekydPvi8/Wq2WF//7GX8cjKLgdi7Onp0ZPGEB1nZudy13Yu9Gjv68DnWOEjtnHwaNm4eTR3tpfdiyiVy7eEKnTDOLFkyevQbnVu2rykmcOb6L2OiVZCmvY23nyuCxs/Hp6K/jNzZmFSd+jSL/tgrnlo5kZudxW52Ng7M3T096p1b1/bp0AjN3Ek7Ekn9bhZtXJ4ZPno/C/u75ORobwf6d61HnKPX6Ki4qZEfEMs6d3IlKnYdcXnYPxs2rs9CvpH/6yDby8wuQyWQ4uHgzInCBaD+I+n1U6lfkp2G2z5Lsi4wfN5YOHdrTvHlzTo58mZvb99zVc4s+3Wj70VzM2ramIDmVxMWfkxK2VSfG9aXn8ZgdjKm9DblnzmPVtT0vz10h1Ydonw1L39D3rwex/xbk54NMRms3Z96cOhHf1h56NfcdO8U3MTtISUunpLQUZwc7xj01kMH+PfXGL/0ijG2x+2nSzILiogIcXbwZMfltXDxrrt/Tx37mp6iVZGVcR2HvyrBxs2nTqQ8ApSXF/LT5M87FHeRWegqNm5gR4N+TN954Azs7u7vmoz5Sl+9G34vTO99j0aJFOssWLFjAwoULdZbduHEDJycnjhw5Qo8ePaTlb775Jvv37+f48ePVtBs1asQ333zDuHHjpGVr1qxh0aJF3Lx5s3Z/yB3q7RPa4OBgYmNjSUlJqbYuNDQUPz8/Tp06ha2tLeHh4SQkJPDOO+8QEhKi80j76tWrDB06lICAAOLi4njttdeYMmUKP//88335+eqrr/htz7cMmbCQoLc3Y2LahIhPp1BSXP1uRjkJv+0kdvMS+jw1gxfnx2Dn7E3E8ink5WbqxLn5dMfExIRh499i6tvf4N2hD+uWTUWdk6lX99rFP9i0Zg5+/s8y671ofLv059vlr5CWfEmK2b9jHUd2hzM8cAFPPDuTpKQkSkuKeWl+BA4uPrWqP2PhJjIyMji+ZxNPTXybGQs3YWLahPXLplJcVHN+Th/7iR8jljJgxMu88t4Wvb5+3LiEc3G/MnDgQIxNGmFl7YiNg4fQr6R/+vhOiouL6Tf8JeydW5OblS7azwPMv6jfh1u/Ij8Nt332e3oKZnZtiYyMBKBUVqNdAJq4taTr9i/I3HecQ37PcHXlNzz2xfsonugtxTiMHkybD0O49P5qDnUbgerMeb766iud+jCU/DyI9mno+oa+fz2o/Tdw1FO0cnEi41Y2r7//KbdycvXqWpg144WRw/jqf2/z7ceLGBrQiw9Wh3Isrvq7kfuO/y4t927fi9f/F4WjqzdfLpmGqob6vXrxD8JXzuHxvs8ye/EW2vn1I/TjV0i9U79FRQWkXD3HEyOm8/r/opg8ewVXr17lpZdeqjEXAv2EhISQk5OjM4WEhDxsW/+YentBO2zYMGxsbNiwYYPOcrVaTVRUFMHBwQQFBbFixQr8/f3x8PBgwoQJBAYGEhMTI8WvXbsWd3d3Pv74Y9q0acPMmTMZNWoUn3766d/2otVqCQsL4z/DpuPdqT92zt48E7QUVXY65//4pcZyx2I30Ok/o+nYeyQ2jp4MnbAIk0aNiTsUrROXcSORMWPG0HvQC3i06cqoKe/TyLQxJw/E6NU9vPtbvNr3xn9oMLZOrRg4ahaObm05+stGye/hXWH0e3oavl36E39iN6NGjaKwIA9lahLDAxfUqr69sxfZ2dnIZHK0Gg0OLt6MnbaE3Ox0zp6q+W76oZ820K3vaPz6PIudk2c1XwW3VZzcH83QcW+yb98+Bgx/mUmvryL1r/P0fGKC0L+j37SZJWPHjmXgyJmMm/Ex6txMjIyMRPsR9ftI1K/IT8Ntn41tOpCsasH48eMBuGZao10AXKc+R/7VFM69uRT1+StcW7ORtOifcX91shTj/logyes2k/JNDOpzlznz8nzCwsIYM2E6vl36i/bZwPQNff96UPvvi88N573Z08nKycXISM6Pe/WPety5nQ99H++MW0tHWtrbMnboE7Rybcnpc5d04tIzs/hkXQTNmjSmWbNmuHq2x76lJyODF2DSqDG/7dNfvwd/Cse7Q28CngrCzqkVg8fMwsm9LYd/jgCgSVNzpr/zNR17DMLW0R3X1h34v//7PxISErhx40aN+aivaJE9tMnU1BQLCwudydS0+kFWoVBgZGRU7cnqzZs3sbe31/u77O3t7yu+Nqi3F7TGxsZMmjSJDRs2ULlXdFRUFKWlpTqPsSuTk5NDixYtpPna6MedkpJCRkYG7m0qulQ0bmqOk0d7rl+O01umtKSI1GsJuLetKCOTy3Fv04OUKxVltFoNeblKtm7dyqdzn2ZX5CeUFBfi6duDa4n6ta8lxuHp20Nnmddjvbh26TQAtzJSUOUo8WzXg5KSIq4nnaVv3744e7TnWmIccrm81vTL5zMzM7F39pY0Gzc1l7anj3Jfnr7dpWVVfaVcTaC0tASFnSsZGRl4tuuBraMHVtYOpCVfFPp39G9lXKdnz7J2Vq5vpXAS7UfUr0RDrl+Rn7vnx5DbZznm5uYApJvolZSw6t4R5V7d/9szYg/RvHtHAGQmJlh29kW554i0/pYRZGRk8J/euv+/G0J+6rp9Grp++bwh718Pcv91c3LAXtECexsF8Rcu69WujFar5cSZs/x1I41Obb2k5RqNhndXfs1zwwaQlJKqc5Ekl8vxatddqq+qXLsUh1e77jrLvNv3IumS/t8KZQ+6ZDIZFhYW9/QsuH8aNWpEly5d2LOn4gaKRqNhz549Ol2QK9OjRw+deIDY2Nga42uDentBCxAUFMTly5fZv3+/tCw0NJSRI0diaWlZLf7IkSNERkYydepUaVlaWlq1fvV2dnbk5uaSn5+vd7tVX5ROSkoCoJmFtU5cMwsF6hylXo3b6iy0mlLM7lGmdfu+ACxcuJC+T73I74d/IHLtW5hZWKPO1q+tzlZiZqmra2ZZoVtezsxSwW1VNhpNKdbW1phZWksxtaVfed6iua3Ob6u8vaqU+yrXkMpU8qXOUWJkbEJxUYHO9sq9CH0lciNjtHfqV4qxVCCTyUT7EfWrU6Yh1q/IT8Nun1XJv8cZi6mdgsKbutstvKnExNIceWNTGimaIzc2pjC9ortjrlHZX3vbqp7qf37qun0aun7leUPcvx7G/tvcyhK5TEZmdo5ebQB13m36TXiZ/zw3jf8uXsHsoOfp1sFXWv/ttp8wkssZ0OtxSjUa6d1fyYelNaoa6lelp37NLa1RZevvolxcVMhHH33E0KFDMTMzq9FzfcVQPtsze/ZsvvrqK7755hvOnTvHSy+9RF5eHoGBgQBMmjRJp7vyq6++yq5du/j44485f/48Cxcu5OTJk8ycObM206dDvf4asY+PDz179mT9+vX07duXxMREDh48yLvvvlstNj4+nmeeeYYFCxYwcODAf7XdadOmcfjwYWn++vXrODs7/yvNmnisx9Psif4YFxcXNA7tMbey4eslQXTtO6pOtlcbXPyzLDcfvjEImQwmv7G2VvXzVFnMn9IFTWkppSXFlJbW7ge5DV3f0BHtp2FT1/Vr6Bh6+zd0tm/fzryMM2jdyuanpD1UO4JaxtD3L0PYf5s2acw3Hy4gv6CQk3+e47NvInGys6FzOx/OX05i885f2LBsfp37KC0pJmzFbNBqqw1uJKhdxo4dS0ZGBvPnzyctLY2OHTuya9cu6YHhX3/9pXPjomfPnkRERDBv3jzefvttWrduzbZt22jXrl2deazXT2ihbHCo6OhoVCoVoaGhtGrVCn9/f52Ys2fP0r9/f6ZOncq8efN01tXUj9vCwoImTZro3eYnn3zCtm3bpKn84rbqYE55uUq9d5EBmpo1RyY3Qn2PMuVxmZllcS53RqDLyriOmZV+bTMrRbUBE8ru2Cmk9eXLmppbIb+jr87JrLhrmZv5j/XbdX0CgHEzPmTWBzE0M7cCIDcrXee3Vd5eVcp9Vb3DqM7NROHozqwPYhgRXHaAMjI2kTxU9vKo6pfXm5mlAk1piU77KdfXarWi/Yj61SnTUOpX5OfRaZ9VaaLRKylReFOJqZ3udk3tFBTnqNAUFFKkzCLA35//eT7OGynwRgqY3RlkNC29qqf6n5+6aJ+Grm/o+9fD3n+zsnPQaLVYW1XvBVmOXC7H2cEOL3cXnn/6SQJ6+BG2dScAcecukZWjYsT0Nxk+bQ4ASqWS7eEf8v4rT9z5DZmY11C/5nrqV5WTibmV7lPbsovZN8hS3mD9+vUG+XTW0Jg5cybXrl2jsLCQ48eP8/jjj0vr9u3bV23Mo9GjR3PhwgUKCwuJj49nyJAhdeqv3l/QjhkzBrlcTkREBGFhYQQFBSGTVQx1mJCQQEBAAC+88AIffPBBtfL/pB93ixYt8PX1laaOHTtiY2PD1XMV7+YU5qu5fuUMTq066tUwMm6Eg6svSZXKaDUarp4/RkuPjtXiyt/pvfHXeQDSki/i6qlf29WzI4kJx3SWXYo/imvrDmX+bVpibqkgMeEYxsaNcHJry4EDB0i+cgZXz45oNBoSE479Y317Zy/MLRUo066hsHPF1skTa2tr0pIvSJoF+Wppe/oo95V4tmI75b48vP1Q2Lni27kfRkbG5GTdxMbGhsSEY2SkXiU7MxUHF69HVr+8TEt3X4yMjGlh4yS1n3L9bOUN0X5E/erNv6HXr8jPo9M+y1Gr1QDYFuuVlMg+Fod1P9337xT9e5J1LA4AbXExJReSaDe4PzYlYFMCdiVgY2PDocMV/1c/yu3T0PUNff96mPvvtetppClvkZaRSTvvVnq19aHRaCgqLnuSPNi/B99+vJBvPlpA2McLaeXiRJMmTej7VCBTQ75Eo9FwKeG4VF9VcW3dkUtV6vfin0dxa13xW8svZpVp15j+zjqaN2/+t73WN7Tahzc1NOr9Ba2ZmRljx44lJCSE1NRUne/HxsfHExAQwMCBA5k9ezZpaWmkpaWRkZEhxUyfPp0rV67w5ptvcv78edasWcPmzZt5/fXX/7YHmUzGpEmTOLRjLRfi9nIz5QLb1r2FuZUtPp0qBpz69qPJnNgbLs13f2Iyvx+I4vThrWTcuMzO8IUUF+bTodezANxK/4sDP6zBu2N/IiMj+eHb//Hd6v9ibqmgpKSYLn1GABC5di67Ij+RdHsNnMjFPw9xYGco6TeuEBuziutX4+kxYLzkt9egSez9/gvO/r6Xdt2eJCoqCtPGzVA4uLNtwyKKCvNrTf9myiWsrKzQajXIjIxIS77I5rVzsbCypW2X/pLuV4sDORK7UZrvPXgyJ/Zt4dTBbaRfv1zNV+Om5vj5j2RnxDICAgLYs3UN33w6E3tnb47s3ij07+jn5+USGRlJbMxqIla9gZmFNaWlJaL9iPp9JOpX5Kfhts9LZ/ZTnHuNzz77DIA2Di2x6OBDY2cHALzfn02H0KWS9rUvN9HU3RmfxXNo5u2B6/TncRg9mKsrNkgxV5eH4hw8BqeJwzHz8eCx1YuYNGkSm8PXcvb3vaJ9NjB9Q9+/HtT++/Xm75n3yVqaW1pQWlrKsIBeACz67GvWbKz4Msc3MTv47XQC129mkJRyg4jtP7PrwDEG9Sm7kWRpbkYrl5bSNHnkMPLz88lWpqLVaohe/y5Fhfl08y/zEbEmhB3fVXx15D+DJ3D+9GH2/biBm9ev8POW1aRciafXk88DZRez3yx/neQrCYyfuRSNppSMjAwyMjIoKipC8OhSr9+hLSc4OJh169YxZMgQHB0dpeVbtmwhIyOD8PBwwsMrLiRdXV2lgZzc3d3ZsWMHr7/+OitWrKBly5Z8/fXXPPnkk/fl4cUXX+S3C/nsCJtPwe1cXFp34fnXvsLYpGL0tqyMv7itypLmfbsN4bb6Fvu/X4k6NwM75zY8/9pXUjcRI2MTrp47Qsb1S2g0Go7+8h1atDi6tOGZF+ZhficuOzMVmazi3oOrVyeee2kZu7d8xs9Ry1HYuTLxtZXYO7eWYvyHBlNUmE/M+gUU3Fbh4uJCZnYeaxY9h6OLD0FzvqhVfb8unbFx68b2sPcpuK3CzaszgXO+xKRRRX4y05PJq5SfDt0Hk6e6RWz0SlQ5ymq+AIaNn4tMJmfXrh0UFxWSnXEdLWBm0VzoV9KPO7yVPVvXIJOBg4sPwyfPF+1H1O8jU78iPw2zfSaejObNKathwNsAPPZR2d/ksBjOBIdg6mBDkzsXtwD5SSmceHoabT8Owe2VSRSkpPHntHkoYw9JMalRP9HIpgVeC2Zham9D7ulzvNi1PX9cvi3Vh2ifDUvf0PevB7H/rtu8HZlMRms3Z958cSIt7nQ5vqm8hVxe0SuyoLCQD78KJ/1WFqaNTHB1dGDhrCkM6NUNfQzo1Y2P1m/i/OlDnPktFidXH16c+4XU5ThbmarT69LdqxMTZi7jp82fsTNyOTb2rgS+sRKHO/Wbk5VOwqlfAfh47kgAyt+eDQsL0+kGawg0xCelDwuZVivS+XcJP1h3qZrwHxlbfyutM/0R3YyEvtAX+kJf6At9g9TfYeJdZ/pDiy8YfH6EvtD/N/q3/jx078B/SIvHevPj73U3uNWwzgbxbE4vm4/eY2CAOmRMj3rfSfe+aFi/RiAQCAQCgUAgEAgEjwyGe1tDIBAIBAKBQCAQCAwQjVZ27yDB30I8oRUIBAKBQCAQCAQCgUEintAKBAKBQCAQCAQCwQNEjGJUe4hBoQQCgUAgEAgEAoHgAfLd4Yd3CTauV8Pq7iye0N4Hu0/X3TeuBnZoZPCj5Al9oS/0hb7QF/qGqC9GURb6j7L+kXOqOtPv2cacXXF1d/48qGOjOtOua8QjxdpDvEMrEAgEAoFAIBAIBAKDRFzQCgQCgUAgEAgEAoHAIBFdjgUCgUAgEAgEAoHgAaIRXY5rjXo5KNRTTz1FcXExu3btqrbu4MGD9OnTh7i4OJYsWcKhQ4dQKpW4ubkxffp0Xn31VSk2NTWVN954g5MnT5KYmMisWbNYvnz5P/K0ceNGVn3+NbnZSpxcvRkVFIKb52M1xv9x9Gd+jFzFrYwb2Ni78Mz41/Ht3Edv7KYv3+XwL1G0bteTtOSL5N9W4ebVieGT56Owd7urr6OxEezfuR51jhIHZ2+envQOzq3aS+uLiwrZEbGMcyd3olLnIZeXPZR38+pcq/qnj2wjP78AmUyGg4s3IwIX6MRV5czxXcRGryRLeR1rO1cGj52NT0d/ab1WqyU2ZhUnfo0i/7YKvy6dwMydhBOxIj8PIT917d+5pSOZ2XncVmfr9VHf9UX+G3Z+DP34I45v+v1bNi6muewaLe1bYGNjw9Lgl3HduAfzu7yu2KJPN9p+NBeztq0pSE4lcfHnpIRt1Ylxfel5PGYHY2pvQ+6Z8+y8FM9na76u0ce/zY84for9qzL/pH6zctSoVTm4uLVm/Itz8PBqp1f7+l+X2RqxlqTL58nMSGVc0GwGPv28TsyFhN/5aeu3XLt8juwsJWPGjOGXXw9J588jA0Nwvcf5887NFefPT41/Hd9O+s+fI796lyO/RBESEsLkyZNr1KyvhB98eJdgE/7TsAaFqpddjoODg4mNjSUlJaXautDQUPz8/Dh16hS2traEh4eTkJDAO++8Q0hICKtWrZJiCwsLsbGxYd68eXTo0OEf+9m5cyeLFy9m8KjpvLl0M06uXqz5YBqqnEy98VcuxLFhxVv06Pcsby2Non3Xfnz14avc+OtStdjTv+0h6dIZmjVrxtULpxgeuIAZCzdhYtqE9cumUlxUWKOv08d+4seIpQwY8TKvvLcFBxcf1i2birqSrx83LuFc3K8MHDgQY5NGWFk7YuPgUav6p4/vpLi4mH7DX8LeuTW5WenV4ipz7eIfbFozBz//Z5n1XjS+Xfrz7fJXSEuuyM/+Hes4sjtcykdGRgbH92ziqYlvi/w84PzUtf8nnp1JUlISpSXFvDQ/Qq+P+qwv8t+w82Poxx9xfKvZ/+8Hovjj1HFu3boFQL4cQu1qlKSJW0u6bv+CzH3HOeT3DFdXfsNjX7yP4oneUozD6MG0+TCES++v5lC3EfywOYrFixczaGTNPv5NfsTxU+xf/yb/let33tL1OLt58fGiV8jNvqVXv7CwABv7loyeNBPL5tb6YwrycXZvzYRpbwEQExPDkyOnM2fJZhxdvfj8fzWfP1+9EEfYZ2/RPeBZ5iyJ4rGu/Vh3l/Pna5fOYGtrqz/JBoBWK3toU0OjXl7QDhs2DBsbGzZs2KCzXK1WExUVRXBwMEFBQaxYsQJ/f388PDyYMGECgYGBxMTESPFubm6sWLGCSZMmYWlp+Y/9hIaGMmbMGLoHjMChZSvGvjifRo2acPTXrXrj9+0Mp03HXgx4OhD7lh4Me+4VnD3acmDXdzpx2bdusmX9/5j0ymLy8/Np3a4nvl364+DizdhpS8jNTufsqT01+jr00wa69R2NX59nsXPyZHjgAhqZNubkgbIcFNxWcXJ/NEPHvcm+ffsYMPxlJr2+itS/ztPziQm1pt+0mSVjx45l4MiZjJvxMercTIyMjKS4qhze/S1e7XvjPzQYW6dWDBw1C0e3thz9ZSNQdvfw8K4w+j09Dd8u/bF39iI7OxuZTI5WoxH5ecD5qWv/8Sd2M2rUKAoL8lCmJlXzUd/1Rf4bdn4M/fgjjm81+1+/ZgkpagXe3mUjHPfOhaTGkGSqX9N16nPkX03h3JtLUZ+/wrU1G0mL/hn3VydLMe6vBZK8bjMp38SgPneZb6OiGDNmDGNGj9Tr49/mRxw/xf5VG/Wbn3+btOvXmPRSCI1MG3Nwz3a9+h6tfRk7+VUe/8+TGBvrH124fZdejBz/Ml26BwDQs2dPugeMwL5lK8ZMKTt/PlbD+fP+n8Lx6diL/nfOn4eOfYWW7m05+HP18+fo0P8x8ZUlmJiY1JhnwaNDvbygNTY2ZtKkSWzYsIHKPaKjoqIoLS1l3Lhxesvl5OTQokWLWvVSVFREQkICPXv2lJbJ5XK8H+tO0sXTesskXTyN92PddZb5dOjJ1UsV8RqNhrCVb9P/6UAamTZGo9HodE9p3NQcZ4/2XEuM07uNkpIiriedxdO3YjtyuRxP3x5SmZSrCZSWlqCwcyUjIwPPdj2wdfTAytqBtOSLtaZ/K+O6lJ9yfSuFU43a1xLj8PTtobPM67FeXLuTn1sZKahylHi26yHNZ2ZmYu/sLWmK/Dy4/NSl/3Ifffv2lfxW9VGf9cvnRf4bZn4ehP+6Pv6I49vd/Vf2YlUCzYvhWmO9slh174hy71GdZRmxh2jevSMAMhMTLDv7otxzpMwHkGJadkLfwkym18e/yY84fj789m/I+1fl+m3l1Y7EC38il8tp26EbiRfO6NW/H0qKiwHw8fHR+Z1ej3Un6ZL+8+erF0/j3a76+XPl822NRkP4qrfp91QgDs6e/9qnoGFQLy9oAYKCgrh8+TL79++XloWGhjJy5Ei9T1uPHDlCZGQkU6dO/dfbLiwsJDc3l9zcXJKTkyktLcXCwkInxtzKmtxs/V0mcrOVmFvqdsUwt7RGla2U5n/5fj1GRkb4Dx4v6Zg2aaZTxszSGnWOEn3cVmWj0ZRiZqnQLWNhjfrOdtQ5SoyMTSguKrijp5D+qnOUtaIvNzJGqynF2rri95pZKpDJZFJcVdTZZdvW/a0KyUt5ufJtl89bNLfV8SvyU/f5qWv/5T6sra11/Fb2UZ/1K8+L/De8/NS1/7o+/ojj273z06SZ7v/tZqWQa6RXFlM7BYU3dbdZeFOJiaU58samNFI0R25sTGF62f/peUagkYG1tTWmlR4iGcrxzdCPD2L/qihzr/q1sGxBTlZZu7W0bEFulv7z2/tBpcoGwNzcXGd52fmwfn1VthJzq+rnz7mVcrnn+/XI75w/Gzpa7cObGhr1dpRjHx8fevbsyfr16+nbty+JiYkcPHiQd999t1psfHw8zzzzDAsWLGDgwIH/etuLFy9m0aJFABgZGdGqVSvCwsJ4+sWO/1obYFf0F+yIXEUj08b8d9LjTA9ZXSu65eSpspg/pQua0lJKS4opLS2pVf265uKfhwH48I1ByGQw+Y21taov8iP4N4j83x1Dz4+hH3/E8U1QnxH7l+DfkHwlgf0/hTNnyWZksob3Hqjgn1Nvn9BC2eBQ0dHRqFQqQkNDadWqFf7+/joxZ8+epX///kydOpV58+bVynZDQkLIyckhJyeHmzdvYmRkxNChQ3ViVNmZWFjpfyHewkpR7YV3VU4m5lZld+PkRmW3f4uLiiguKmTloikA/BKzmiWvD5DKqHMyq93BK6epuRVyuVG1O4Dq3EwUju7M+iCGEcF3LsqNTe7oVdz5K7tj98/0zawq7kRqSkuQyY3IzKz4veocJVqtVoqripmVotqABuWeANp1fQKAcTM+ZNYHMTQztwIgNytdx6/IT93kp679l5dT5yglH5mZmTp+K/uob/oi/w07P4Z+/BHHt7/vv7SkmPy8XN0YI7CoYZTjwptKTO10t2lqp6A4R4WmoJAiZRaakhJMbcvODZqVglwLmZmZFBbr91GVh73/GvrxQexf/6x+c3NuSYM85eTcwqKGAZ/uB/M7datSqXSWl50P69c3t1JUe3qrysnE4o7/y+d+R517i4UzBvL6uI68Pq4j169fZ+nSpfTr1+9fe37QaLQPb2po1OsL2jFjxiCXy4mIiCAsLIygoCCdOzIJCQkEBATwwgsv8MEHH9Tadk1NTbGwsMDCwgJra2t8fX05efKktF6j0XAx/hhuXvpHTnbz6sDFP4/rLLtw5ijurcvie/UfRchHMcz9cAtzP9zCW8uikMvleLTpSvCbXwFQkK8m+coZXD076t2GsXEjnNzaknj2mI6vxIRjeHj7obBzxbdzP4yMjMnJuomNjQ2JCcfISL1KdmYqDi5e/1i/vExLd1+MjIxpYePE0aNl7xWV62crb9So7erZkcSEYzrLLsUfxfVOfuydvTC3VKBMu4bCzhVbJ0+sra1JS74gaYr81F1+6tp/C5uWmFsqSEw4Jvk4cOCA5Leqj/qmL/LfsPNj6McfcXy7P/+VY3KMIcsEXAv0ypJ9LA7rfrrv9yn69yTrWBwA2uJicn5PQNGv7B1GY6BlIRw9epRbaq1eH/ebH3H8FPtXXRz/L1+Mx9P7MTQaDefOnMDTu+bPAv1djO8M1nThwgWd33kx/hhurfWfP7t7deBifJXz5z+PSufbXfs8xZvLopmzNEqabG1tCQ4O5uuvv/7XngWGS72+oDUzM2Ps2LGEhISQmpqq842p+Ph4AgICGDhwILNnzyYtLY20tDQyMjJ0NOLi4oiLi0OtVpORkUFcXBxnz569Lx+BgYFs3ryZ4/u+Jy3lCpu/fo/Cwny69x0OQNiqt9kesVyK7ztkAmdPH2bPD9+Qdv0KOzev4a/LCfQZVDaYVTNzKxxdWkuTk6sXZmZmJF8+TUbqVdKSL7J57VwsrGxp26W/pPvV4kCOxG6U5nsPnsyJfVs4dXAb6dcvs23DIooK8+nSZwRQNrCCn/9IdkYsIyAggD1b1/DNpzOxd/bmyO6Ntaafn5dLZGQksTGriVj1BmYW1pSWlkhxkWvnsivyE0m318CJXPzzEAd2hpJ+4wqxMau4fjWeHgPK3oeQyWT0GjSJvd9/wdnf93Iz5RJWVlZotRpkRkYiPw84P3Xtv123J4mKisK0cTMUDu7VfNR3fZH/hp0fQz/+iONbzf57DRzHxd+iOXOmbACcS74t6efhg4+tAwDe78+mQ+hSSfPal5to6u6Mz+I5NPP2wHX68ziMHszVFRukmKvLQ3EOHoPTxOGY+XgwYdRoNm/eTNSWrXp91Pf919CPD2L/+nv126RJUxyc3Ahbu5jCgnx693+qzPfy+UR9W/E5zJLiYv66coG/rlygtKSYrFsZ/HXlAjdTk6WYgvzbUgzAoUOH2BW9lgvxx4n6+j2KCvN5/M75c/iqt/mh0vmz/+AJnDt9mL0/fMPN61f4KWoNyZcT+M+T+s+fHV1aY2JigkKhwMPDA0NDvENbe9Tbd2jLCQ4OZt26dQwZMgRHR0dp+ZYtW8jIyCA8PJzw8HBpuaurK0lJSdJ8p06dpH+fOnWKiIiIajH3YsiQIdy6dYtVn69Gla3Eyc2Hl99ei8WdrhtZylSdJ8ce3h2ZPGsJP25axY/frcDGwZUX56zA0aV1jdswMzPDzqUdMesXUHBbhZtXZwLnfIlJo4rvB2SmJ5OnypLmO3QfTJ7qFrHRK1HlKHF08SFozheYV+rmMmz8XGQyObt27aC4qJDsjOtoATOL5rWqH3d4K3u2rkEmAwcXH4ZPni/FZWemIpNV3Dtx9erEcy8tY/eWz/g5ajkKO1cmvrYSe+eK/PgPDaaoMF/Kh1+Xzti4dWN72PsiPw8hP3Xt38XFhczsPNYseq6aD0PQF/lv2Pkx9OOPOL7p9/988H8JeKxiSOPp898GIDkshjPBIZg62NDE2UFan5+Uwomnp9H24xDcXplEQUoaf06bhzL2kBSTGvUTjWxa4LVgFqb2NrQ/fQ6jTj6sXKPfhyHsv4Z+fBD7173rNztHzXtvTsbF3YvZC1ZieadLcGZGmo5+9q0MFsyuGIxp17Zv2bXtW7x9OzP3gy8BSEo8y9L/my7FlJaW8tPm1chkclxa+TI9pNL5c2YqMnnF+bO7d0cmvbKEnZGr+HHTCmzsXQm+x/mzQAAg02ob4nV63bD7dFGdaQ/s0Iitv9Xw4k4tMKKbkdAX+kJf6At9oS/09ejvMPGuM/2hxRcMPj9Cv2HrHzmnunfgP6RnG3N2xdXd+fOgjvq/h2sIhP768LYdGPDwtl0X1PsntAKBQCAQCAQCgUDQkBCPFGuPev0OrUAgEAgEAoFAIBAIBDUhntAKBAKBQCAQCAQCwQOkIX4+52EhntAKBAKBQCAQCAQCgcAgEYNCCQQCgUAgEAgEAsED5Os9D2/bU/rfO8aQEF2O74NTF2/VmXYXrxYGP0qe0Bf6Ql/oC32hL/Sr64tRlIV+fdYP219n8kzyp879GyrikWLtIbocCwQCgUAgEAgEAoHAIBFPaAUCgUAgEAgEAoHgAaLRPGwHDYd6+w7tU089RXFxMbt27aq27uDBg/Tp04e4uDiWLFnCoUOHUCqVuLm5MX36dF599VUpNiYmhs8//5y4uDgKCwvx9fVl4cKFPPnkk/flZ+PGjaxZ+yU5WbdwcffkhWmz8fTy1Rubcu0KURu/4url8yjT05g45VUGP/Ncjdrbo8LYFPY5LT0eIyczlfzbKty8OjF88nwU9m539XU0NoL9O9ejzlHi4OzN05PewblVe2l9cVEhOyKWcfrINvLzC5DJZDi4eDMicIFOXFXOHN9FbPRKspTXsbZzZfDY2fh09JfWa7VaYmNWceLXKPJvq/Dr0gnM3Ek4EVsn/s+d3IlKnYdcXtapwM2rs8iPyM8jkx/nlo5kZudxW52t18ejrm/o7UfoN8z9qyT7IuPHjaVDh/Y0b96ckyNf5ub2u78016JPN9p+NBeztq0pSE4lcfHnpIRt1Ylxfel5PGYHY2pvQ+6Z81h1bc/Lc1dIv0ecP+j6N/T2+aDyI5OV5cfZswuDxy+khd3d9U/+upFju9ehzsnArqUPA8f9H07uur875fIf7Nv0Fteu/QVoadS4KYFvrMXdx6/W8tPVrzMLFy7Eze3ufusjX+x+eNueNvDhbbsuqLddjoODg4mNjSUlJaXautDQUPz8/Dh16hS2traEh4eTkJDAO++8Q0hICKtWrZJiDxw4wBNPPMHOnTs5deoUAQEBPPXUU/zxxx9/28vOnTtZvHgxz44L5oPlG3Bxb82S+a+Tk63/ndrCwgJs7R157oWXsWpufVftyxfPsmfXNhQKBal/nWd44AJmLNyEiWkT1i+bSnFRYY1lTx/7iR8jljJgxMu88t4WHFx8WLdsKuqcTCnmx41LOH18J8XFxfQb/hL2zq3JzUqvFleZaxf/YNOaOfj5P8us96Lx7dKfb5e/QlryJSlm/451HNkdLvnNyMjg+J5NPDXx7Vr3fy7uVwYOHIixSSOsrB2xcfAQ+RH5eWTy88SzM0lKSqK0pJiX5kfo9fEo6xt6+xH6DXf/6vf0FMzs2hIZGQlAqazGdADQxK0lXbd/Qea+4xzye4arK7/hsS/eR/FEbynGYfRg2nwYwqX3V3Oo2whUZ87z1Vdf6ewPhpR/Q99/G0x+jBthae2EtX1Zfr5bEUxJcc36Z0/s5Jeoxfxn2AyC523F1tmHTSuCycut0E+5/AcbP3mBlJQUBo6cyeQ3PsfZoz3ffDqjVvPTpEkTgoODKSys2a+g4VNvL2iHDRuGjY0NGzZs0FmuVquJiooiODiYoKAgVqxYgb+/Px4eHkyYMIHAwEBiYmKk+OXLl/Pmm2/StWtXWrduzf/+9z9at27NDz/88Le9hIaGMmbMGPoOGEZLF3eCX34TU1NT9sf+qDe+lVdbxge9Qs8+T2BsYlKjbkH+bVZ/vJDgmW+Rk5ODs8dj+Hbpj4OLN2OnLSE3O52zp2q+m3vopw106zsavz7PYufkyfDABTQybczJA2W/v+C2ipP7o2nazJKxY8cycORMxs34GHVuJkZGRlJcVQ7v/hav9r3xHxqMrVMrBo6ahaNbW47+shEouzt2eFcY/Z6ehm+X/tg7e5GdnY1MJker0dS6/6Hj3mTfvn0MGP4yk15fRepf5+n5xASRH5GfRyI/8Sd2M2rUKAoL8lCmJlXz8ajrG3r7EfoNd/9qbNOBZFULxo8fD8A10xrTAYDr1OfIv5rCuTeXoj5/hWtrNpIW/TPur06WYtxfCyR53WZSvolBfe4yZ16eT1hYGGMmTBfnDw2wfT6o/PznqZmMfnk16Snn6RowEVV2Ohf++KVG/eOxoXTsPYYOvUZi4+jJkPGLMG7UmNOHo6WY2M2LadzMkueee45+w1/Cp6M/wW99TSPTJrWWHwcXb5YtW0Z6ejq//FKz3/qKVvvwpoZGvb2gNTY2ZtKkSWzYsIHKvaKjoqIoLS1l3Lhxesvl5OTQokWLGnU1Gg0qlequMZUpKioiISGBnj17SsvkcjntOnbl0oX4v/lr9BO69iM6+fXEzqElxcXFWFo7SOsaNzXH2aM91xLj9JYtKSnietJZPH276/jy9O0hlUm5mkBpaQm3Mq5L/m0dPbCydsBK4VSj9rXEODx9e+gs83qsF9cunQbgVkYKqhwlnu16SPOZmZnYO3tLmrXpX2HnSkZGBp7tekj+05IvivyI/DT4/JT76Nu3r+S3qo9HWb983pDbj9C/u74h71/lmJubA5Be8/1tAKy6d0S596jOsozYQzTv3hEAmYkJlp19Ue45Iq2/ZQQZGRn8p3fFOYo4f2gY7fNB5setTU8UDq2waOFI+vWLOLl34PoV/T0ZS0uKSP0rAfc2FW1OJpfj3qYnKXfK5OVmcuPqafJyMjhy5Ajvz/gPX7w/ib8u/VEn+1eHDh3uq+eloOFRby9oAYKCgrh8+TL791eMJx4aGsrIkSOxtLSsFn/kyBEiIyOZOnVqjZofffQRarWaMWPG/C0PWVlZlJaWYm2t23XY0qoF2Vn6u0z8HY4ciCXp8gXGvvASOXd0Gpk21okxs7RGnaPUW/62KhuNphQzS4VuGQtr1NllZdQ5SuRGxmg1uv7NLBXIZDIprirqbCVmlrq/18xSIXkpL1e+7fJ5i+a2On5rw7+RsQnFRQU62yv3IvIj8tPQ81Puw9raWsdvZR+Psn7leUNsP0K/Ye9fVcm/xxmXqZ2Cwpu62y28qcTE0hx5Y1MaKZojNzamML3i3CP3zhdL7G2reqr/+a88b4j7b0PKTzPzMk/NLKzJy82gmYU16twa9NVZaDWlNLPQ/R3NzK3Ju+MpW5kMlD1RffLJJwmc8wWObm35akkQRkbGtb5/WVtbo1Tq16zPiCe0tUe9HuXYx8eHnj17sn79evr27UtiYiIHDx7k3XffrRYbHx/PM888w4IFCxg4UP+bzhERESxatIjvv/8eW1vbGrdbWFgo9cVXq9UAFBcXU1tfutq1fTNhX31Ko0amTBs/mDfnf1RLyg+Gi38eBuDDNwYhk8HkN9bWqn6eKov5U7qgKS2ltKSY0tKSWtWva0R+7o7Ij+DfYOjtR+gL/g3bt29nXsYZtG5l81PSHqqd+8bQ99+6pqHkR6utGL63b9++JBW3xcmtLZfPHiMt+WKdbFPwaFOvn9BC2eBQ0dHRqFQqQkNDadWqFf7+/joxZ8+epX///kydOpV58+bp1dm0aRNTpkxh8+bNDBgw4K7bXLx4MZaWllhaWuLp6YlWq+XLL7/UicnJvnXPAZ9qwtzCCii7SC4qLOT9d14B4MS+aN5+4TE0mrIPUKtzMvXe5QVoam6FXG5U7Q6dOjcTM6uKO5Ga0hJkciMyMyvu6KpzlGi1WimuKmZWimov7Jfd0SyLb9f1CQDGzfiQWR/E0My87PfkZqXr+P2n/hWO7sz6IIYRwYsAMDI2kTxU9iLyI/LTEPNTXk6do5R8ZGZm6vit7ONR0zf09iP0H539qypN7vGJjsKbSkztdLdraqegOEeFpqCQImUWAf7+/M/zcd5IgTdSwKzsdIG09Kqe6mf+DX3/baj5yVOVecrLzaSZhQ15uZmYWdSgb9YcmdxIZwCoco1m0pNwGwBkMrlOfmwdPVCrbtX6/pWZmYlCoV+zPqPRPrypoVHvL2jHjBmDXC4nIiKCsLAwgoKCkMkqhgpMSEggICCAF154gQ8++ECvxnfffUdgYCDfffcdQ4cOvec2Q0JCyMnJkaZ27drRsmVLab1GoyHh9Elae7f7R7+pc7deLF0VzpKVYSxZGcbiz77B2NgYR7e2zHo/BrnciIJ8NclXzuDq2VGvhrFxI5zc2pJ49piOr8SEY1KZlu6+GBkZ08LGiaNHy97LyUi9SnZmKtnKGzVqu3p2JDHhmM6yS/FHcW3dAQB7Zy/MLRUo066hsHPF1skTa2tr0pIvSJr/xr+Htx8KO1d8O/fDyMiYnKyb2NjYkJhwTPLv4OIl8iPy0yDz08KmJeaWChITjkk+Dhw4IPmt6uNR0zf09iP0H539q5zynl62xXolJbKPxWHdr7vOMkX/nmQdiwNAW1xMyYUk2g3uj00J2JSAXQnY2Nhw6HDFu7fi/MFw2+fDyk/SuaNkpl0h99YNbFt6cf3qaZw8OunVNzJuhIOLL0nnK9qcVqMh6dxRWt4pY2ndEjMrW5pZKqT8AKSnXiVPlV3r+9fp06fp1Em/X8GjQb2/oDUzM2Ps2LGEhISQmprK5MmTpXXx8fEEBAQwcOBAZs+eTVpaGmlpaWRkZEgxERERTJo0iY8//pjHH39cisnJyalxm6amplhYWEjTlClTiI6O5sCeHVxPTmL9mmUUFBTgP2AYAGs+WcSmb9ZI5UuKi0m6cpGkKxcpKSnhVmYGSVcuknaj7J2CJk2b4ezaSppc3Dyxt7fnZkoitzKSSUu+yOa1c7GwsqVtl/6S7leLAzkSu1Ga7z14Mif2beHUwW2kX7/Mtg2LKCrMp0ufEUDZwAF+/iPJz8slMjKS2JjVRKx6AzMLa0pLS6S4yLVz2RX5iaTba+BELv55iAM7Q0m/cYXYmFVcvxpPjwFlIzXKZDJ6DZrE3u+/4Ozve7mZcgkrKyu0Wg0yI6Na978zYhkBAQHs2bqGbz6dib2zN0d2bxT5Efl5JPLTrtuTREVFYdq4GQoH92o+HnV9Q28/Qr/h7l+XzuynOPcan332GQBtHFpi0cGHxs5lA0B6vz+bDqFLJe1rX26iqbszPovn0MzbA9fpz+MwejBXV2yQYq4uD8U5eAxOE4dj5uPBY6sXMWnSJDaHr+Xs73vF+UMDa58PKj8Hf1zF5tUzsG3pw8m94Zhb2eLdqaI348ZPXuDE3nBp/vEnAvnj4GbOHNmKMvUyP21cSHFRPu17PSv9jh4DgynIy2HTpk3s+/FrtoYu4mbKJWQyWa3lJy35Im+++Sa2trb37H0paNjU63doywkODmbdunUMGTIER0dHafmWLVvIyMggPDyc8PCKHc3V1ZWkpCQAvvzyS0pKSpgxYwYzZsyQYl544YVqnwSqiSFDhnDr1i0+X/sV2VmZuHq0Zu6iT7FsXjZScmbGTeSyinsDWbeUvP3qC9L8jq0R7NgaQZt2nfi/xWuq6QM4ODhAo+bErF9AwW0Vbl6dCZzzJSaNKsb5z0xPJk+VJc136D6YPNUtYqNXospR4ujiQ9CcLzCv1A1l2Pi5yGRy4g5vZc/WNchk4ODiw/DJ86W47MxU6YPaAK5enXjupWXs3vIZP0ctR2HnysTXVmLv3FqK8R8aTFFhvuTXr0tnbNy6sT3s/Trxv2vXDoqLCsnOuI4WMLNoLvIj8vPI5MfFxYXM7DzWLHqumg+hb/jtR+g3zP0r8WQ0b05ZDQPeBuCxj8r+JofFcCY4BFMHG5o4V3zdID8phRNPT6PtxyG4vTKJgpQ0/pw2D2XsISkmNeonGtm0wGvBLEztbcg9fY4Xu7bnj8u3xflDA22fDyI/JUWF5GSmAGVdip979WuMTSr0szKSyVdX6LftOoQ81S32b/+MvNwM7Fq24blZX+t0U+42YDIlxUWc/OVLdkV+CoCtozujXvygVvPT1a8LX3/9Naam9/guVj1E+1BHZ7rHh7ENDJn24WbToDh18VadaXfxasHW30rrTH9ENyOhL/SFvtAX+kJf6D8E/R0m3nWmP7T4gsHnR+g/XP2w/feO+6dM8qfO/Rsqq3Y+vEuwmUMa1gWtQTyhFQgEAoFAIBAIBIKGgnikWHvU+3doBQKBQCAQCAQCgUAg0Ie4oBUIBAKBQCAQCAQCgUEiuhwLBAKBQCAQCAQCwQNEc49vUwv+PuIJrUAgEAgEAoFAIBAIDBIxyrFAIBAIBAKBQCAQPECWb394l2CvPS1GOX5kiT1dWGfaT3QwNfhh34W+0Bf6Ql/oC32h/+D1xWeBhP6/0f98V53J89Ig8dkeQd0jLmgFAoFAIBAIBAKB4AGiEX1kaw3xDq1AIBAIBAKBQCAQCAySevsO7VNPPUVxcTG7dlXvB3Hw4EH69OlDXFwcS5Ys4dChQyiVStzc3Jg+fTqvvvqqFHvo0CHeeustzp8/z+3bt3F1dWXatGm8/vrr9+Vn48aNrPr8a3KzlTi5ejE6KAQ3z8dqjP/96G52RK4iM+MGNvYuDB//Or6d/yOt/3b1PI7v365TxtjEFNDi4OzN05PewblV+xr1zxzfRWz0SrKU17G2c2Xw2Nn4dPSX1mu1WmJjVnHi1yjyb6twbulIZnYet9XZdaLv16UTmLmTcCKW/Nsq3Lw6MXzyfBT2bnfJKhyNjWD/zvWoc5R6fRUXFbIjYhmnj2wjP78AmUyGg4s3IwIXCP/Cv/Av/DcI/+L4/HDzY6j5t2xcTHPZNVrat8DGxoalwS/junEP5nfp3dmiTzfafjQXs7atKUhOJXHx56SEbdWJcX3peTxmB2Nqb0PumfPsvBTPZ2u+rtFHfc2PaJ9/P/+3jT1IPBNLYX4uju6d6Td6Ic1ta85/SuIJTu1dR3pyPHm5GQwLXo1n+wE6MYmnd3Pm8CZy0uLJycmhqZkVRYX5tV6/50/9RFFREb1792bBggUoFIq76tYnPvn+4V2CzX6mYb1DW2+f0AYHBxMbG0tKSkq1daGhofj5+XHq1ClsbW0JDw8nISGBd955h5CQEFatWiXFNmvWjJkzZ3LgwAHOnTvHvHnzmDdvHl9++eXf9rJz504WL17M4FHTeWtpJE6u3qz+YDqqnEy98VcuxLFhxVv06DeCuUs306FrP7788FVu/HVJJ65tx17878u9PPfi/2FiYsKQcf/llfe24ODiw7plU1HXoH/t4h9sWjMHP/9nmfVeNL5d+vPt8ldIS67Q379jHUd2hzM8cAFPPDuTpKQkSkuKeWl+RK3rz1i4iYyMDI7v2cRTE99mxsJNmJg2Yf2yqRQX1fze8eljP/FjxFIGjHi5xt/948YlnD6+k+LiYvoNfwl759bkZqUL/8K/8C/8Nwj/4vj8cPNjyPn//UAUf5w6zq1btwDIl0OoXY2SNHFrSdftX5C57ziH/J7h6spveOyL91E80VuKcRg9mDYfhnDp/dUc6jaCHzZHsXjxYgaNrNlHfc2PaJ9/P/9nDn9H32fn8dzrmzFp1ISta4MpKa45/8VFt7Fx8iZg1IK7xjh6dObxxx8HIOCZabVev+fifmX58uV8++23pKenM3PmzBo16yNa7cObGhr19oJ22LBh2NjYsGHDBp3larWaqKgogoODCQoKYsWKFfj7++Ph4cGECRMIDAwkJiZGiu/UqRPjxo3D19cXNzc3JkyYwJNPPsnBgwf/tpfQ0FDGjBlDj4DhOLRsxXMv/h+NGjXh6K/b9Mbv27mRNh17MeDpQOxbejDsuZk4e7Rh/65NOnHGxo2wsFJw9NetjBkzhp5PjMfOyZPhgQtoZNqYkwdi9Oof3v0tXu174z80GFunVgwcNQtHt7Yc/WUjUHb37fCuMPo9PQ3fLv2JP7GbUaNGUViQhzI1qdb17Z29yM7ORiaTo9VocHDxZuy0JeRmp3P21J4a83ropw106zsavz7P6v3dBbdVnNwfTdNmlowdO5aBI2cybsbHqHMzMTIyEv6Ff+Ff+Dd4/+L4/HDzY8j5X79mCSlqBd7eZQNC9c6FpMaQZKpf03Xqc+RfTeHcm0tRn7/CtTUbSYv+GfdXJ0sx7q8FkrxuMynfxKA+d5lvo6IYM2YMY0aPrJPzE9E+61H71JZi4+TDkxOWkZeTzuU/f6kx/+5t/ek59HU8OzxRY0ybrsN5/MkZnDx5EoBWbbrVev0Oe/4tevToQbt27fjf//7HH3/8QVxcXI26goZLvb2gNTY2ZtKkSWzYsIHKvaKjoqIoLS1l3Lhxesvl5OTQokWLGnX/+OMPjhw5gr+/f40xlSkqKiIhIYGePXtKy+RyOd6PPc7Vi6f1lrl68TQ+jz2us6xNh54kXdKNv3T2JG8F9+FaYjzXrl0jT5Ut6Xv69uBaYpxe/WuJcXj69tBZ5vVYL67d0b+VkYIqR4lnux6UlBRxPeksffv2xdmjPdcS42pVv3w+MzMTe2dvSbNxU3Npe/oo9+Xp211aVtVXytUESktLuJVxXcq/raMHVtYOWCmchH/hX/gX/g3avzg+P9z8NIT8V/ZiVQLNi+FaY72yWHXviHLvUZ1lGbGHaN69IwAyExMsO/ui3HOkzAeQYgo9e/akhZlMr4/6nh/RPv9e/hWOPqRe/QMA0ybm2Lt2kOb/DbmZKVIPgnLqqv23atUKR0dHg7qg1Wq0D21qaNTbC1qAoKAgLl++zP79+6VloaGhjBw5EktLy2rxR44cITIykqlTp1Zb17JlS0xNTfHz82PGjBlMmTLlb3nIysqitLQUa2trneUWVtbkZiv1lsnNVmJuqRtvbqkb36ZjLybOfJ/A15YBcPXqVUI/moZGU/byi5mFNeoa9NXZSsyq6JtZKlDnKKX15ctuq7LRaMr8m1laSzG1pV953qK5rRRTtt5aZ74y5b7KNaQylXypc5TIjYzRanTzb2apQCaTCf/Cv/Av/Bu0f3F8frj5MfT8Gxmb0KSZhW5MKeTW8BUTUzsFhTd1t1l4U4mJpTnyxqY0UjRHbmxMYXpZt848I9DIwNraGlMT/T6qUp/yI9rn3fV1523IU1VoNjW31pn/p+SpMvQur6v2b21tTUaG/m0KGjb1+oLWx8eHnj17sn79egASExM5ePAgwcHB1WLj4+N55plnWLBgAQMHDqy2/uDBg5w8eZK1a9eyfPlyvvvuuxq3W1hYSG5uLrm5uajVagCKi4tr6VeV4ddrMO39ArB38gDgrbfeIuXKn1w591utbqcuuPjnYQA+fGMQ86d0obS05CE7uj+E/4eL8P9wEf4bNiI/DxeR/7sj8vNwqev8nz+5ndVzOkmTRtSv4AFRry9ooWxwqOjoaFQqFaGhobRq1apad+GzZ8/Sv39/pk6dyrx58/TquLu789hjj/Hiiy/y+uuvs3Dhwhq3uXjxYiwtLbG0tMTT0xOtVlttEKnc7EwsrPSPpGZhpag2YJQqR3+8mUVz5HIjjIyMaGbenMybfwGgzs3ErAZ9MytFtQEB1DlK6W5WeTl1jpKm5lbI5UZkZmaizsmsuCv3L/TbdS17Z2LcjA+Z9UEMzcytynKSla5zR63y9qpS7qvqHbrKvswsFWhKS5Dd8V/Zi1arFf6Ff+Ff+Dc4/+L4/HDz05DyX1pSTH5erm6MEVjUMMpx4U0lpna62zS1U1Cco0JTUEiRMgtNSQmmtmVP+JqVglwLmZmZFFa6p28o+RHts7p+TflX52TQzLxC87YqU2f+7+LRrh/j39wmTU2aNdcbV1ftPzMzExsbm/v2/bDQaB/e1NCo9xe0Y8aMQS6XExERQVhYGEFBQchkFUNNJyQkEBAQwAsvvMAHH3zwtzQ1Gg2FhTWPrhYSEkJOTo40tWvXjpYtW+qUvxh/HHevDnrLu3t14MKfx3WWnT9zDLfW1eONjU1w9mjDL7/8wm11NuZWNmg0GhITjuHq2VGvvqtnRxITjuksuxR/FNc7+i1sWmJuqSAx4RjGxo1wcmvLgQMHSL5yBlfPjv9a397ZC3NLBcq0ayjsXLF18sTa2pq05AuSZkG+WtqePsp9JZ6t2E5VXy3dfTEyMqaFjRNHj5a995ORepXszFSylTeEf+Ff+Bf+Dc6/OD4/3Pw0tPxXjskxhiwTcC3QK0v2sTis+3XXWabo35OsY3EAaIuLyfk9AUW/svcSjYGWhXD06FFuqbV6fdT3/Ij2+ffyr7xxHgf3TgAUFqhJu3Zamr8fGjU2w8rGVZpa2HtWG9emrtr/lStXuHHjBh076tcVNGzq/QWtmZkZY8eOJSQkhNTUVCZPniyti4+PJyAggIEDBzJ79mzS0tJIS0vT6T+/evVqfvjhBy5dusSlS5dYt24dH330ERMmTKhxm6amplhYWEjTlClTiI6O5ti+70lLuULk1+9TWJhP977DAQhb9TbfR6yQyvcdMp6zp4+w54dvSLt+lR2b1/DX5QT8Bz0HQGHBbbZ++zFXL54mM/06Pu17sG3bNppZtMDK2pFtGxZRVJhPlz4jAIhcO5ddkZ9I+r0GTuTin4c4sDOU9BtXiI1ZxfWr8fQYMB4AmUxGr0GT2Pv9F5z9fS/tuj1JVFQUpo2boXBwr3X9mymXsLKyQqvVIDMyIi35IpvXzsXCypa2XfpLul8tDuRI7EZpvvfgyZzYt4VTB7eRfv1yNV+Nm5rj5z+S/LxcIiMjiY1ZTcSqNzCzsKa0tET4F/6Ff+Hf4P2L4/PDzY8h57/XwHFc/C2aM2fOAHDJtyX9PHzwsXUAwPv92XQIXSppXvtyE03dnfFZPIdm3h64Tn8eh9GDubpigxRzdXkozsFjcJo4HDMfDyaMGs3mzZuJ2rJVr4/6nB/RPu8v/3K5EcobF/g5/E2aWdrS6rGK78pGr3qBuAPh0nxRYR7pKedITzkHlA3+lJ5yjtxbN6SYgrxsMq6fp2/fvgD8ceRHzhzfRcTK2bVWvzs2LuXYsWPEx8fz9ttv06lTJ4O6oBWf7ak9jB+2gb9DcHAw69atY8iQITg6OkrLt2zZQkZGBuHh4YSHV+xorq6uJCUlAWV3dUJCQrh69SrGxsa0atWKpUuXMm3atL+9/SFDhnDr1i1Wfb4GVbYSJzdvZrz9ORZWZd1ybinTkMkq7g14eHdk8qwl/LhpJT989xk2Di5MnbMCR5fWAMjkcq7/dYnj+7eTn6fCsoUtHTt25HLSDVYvHIujiw9Bc77A/E53jOzMVB19V69OPPfSMnZv+Yyfo5ajsHNl4msrsXduLcX4Dw2mqDCfmPULKLitwsXFhczsPNYseq5O9P26dMbGrRvbw96n4LYKN6/OBM75EpNGFd8PyExPJk+VJc136D6YPNUtYqNXospRVvMFMGz8XGQyOXGHt7Jn6xpkMnBw8WH45PnCv/Av/Av/DcK/OD4/3PwYav6fD/4vAY9VDGk8ff7bACSHxXAmOARTBxuaODtI6/OTUjjx9DTafhyC2yuTKEhJ489p81DGHpJiUqN+opFNC7wWzMLU3ob2p89h1MmHlWv0+6jP+RHt8+/nv5nD4/y65T0K83Nx9OjCiOlfY2xSkf/szGTy8yryf/OveKJXTZLmD2xbDECbbiN4cvwSAC7H7yU2IkSKObgzFIDmNi0JqsX6nTVrFkVFRfTu3ZsFCxYgeDSRabUN8Tq9bog9XXM35X/LEx1M2fpbDS++1AIjuhkJfaEv9IW+0Bf6Qr8B6u8w8a4z/aHFFww+P0L/7vqf76ozeV4aRJ37N1QWb667vNyLkDGGmzd91PsuxwKBQCAQCAQCgUAgEOhDXNAKBAKBQCAQCAQCgcAgMYh3aAUCgUAgEAgEAoGgoSBe+qw9xBNagUAgEAgEAoFAIBAYJOIJrUAgEAgEAoFAIBA8QMQT2tpDjHIsEAgEAoFAIBAIBA+QDzY9vFGO33muYY1yLJ7Q3gdbjmvqTHvU43KDH/Zd6At9oS/0hb7QF/oNT198Fqhh6287UXf6w7saEfNb3Z0/P9tNvD0pEO/QCgQCgUAgEAgEAsEDRaPVPrSprrh16xbjx4/HwsICKysrgoODUavVd41/5ZVX8Pb2pkmTJri4uDBr1ixycnLua7viglYgEAgEAoFAIBAIBP+K8ePHk5CQQGxsLD/++CMHDhxg6tSpNcbfuHGDGzdu8NFHHxEfH8+GDRvYtWsXwcHB97XdevsO7VNPPUVxcTG7du2qtu7gwYP06dOHuLg4lixZwqFDh1Aqlbi5uTF9+nReffVVvZqHDx/G39+fdu3aERcXd19+tFotL721ghP7oii4rcK1dSeenrwAhb3bXcsd+2UjB3euR52jxN7Zh2ET38G5VXsAbquz2ROzisT4w+TeuoHMyARNaSlarRY3r04Mnzz/nvpHYyPYf0ffwdmbpydV6AMUFxWyI2IZp49sIz+/AJlMhoOLNyMCF+jEVeXM8V3ERq8kS3kdaztXBo+djU9Hf518xMas4sSvUeTfVuHc0pHM7Dxuq7P1+qjv+n5dOoGZOwknYsm/rar1/J87uROVOg+5vOwekptXZ4OqX0PPj6Hn39Dbv2ifDVvf0Nu/0NevX5J9kfHjxtKhQ3uaN2/OyZEvc3P7nhp1AVr06Ubbj+Zi1rY1BcmpJC7+nJSwrToxri89j8fsYEztbcg9c56dl+L5bM3XNbaz+pqfhnL8qbr/Pht07/1395ay/CjsXBn8nG5+4k/EcmxPJNeTEritzmHr1q2s/Ga3lC9Xr04M/xvnz0djN3Kg0vlzVf+/7d1M3NEfSU8+S15eHidOnMDCwuKumvWNdzeWPLRtvzWqlMLCQp1lpqammJqa/mPNc+fO0bZtW06cOIGfnx8Au3btYsiQIaSkpODo6Pi3dKKiopgwYQJ5eXkYG/+9t2Pr7RPa4OBgYmNjSUlJqbYuNDQUPz8/Tp06ha2tLeHh4SQkJPDOO+8QEhLCqlWrqpXJzs5m0qRJ9O/f/x/5+eqrrzgaG84zkxfy0oJITEybsuHDFykuKqyxzJljO9kZsZR+w2cw491o7F282fDhi6hzMwFQZaejyk5n0Lg3mThxIlqNhsZNzXHz6oyJaRPWL5t6V/3Tx37ix4ilDBjxMq+8twUHFx/WLZuKOidTivlx4xJOH99JcXEx/Ya/hL1za3Kz0qvFVebaxT/YtGYOfv7PMuu9aHy79Ofb5a+QlnxJitm/Yx1HdoczPHABTzw7k6SkJEpLinlpfoReH/VZf8bCTWRkZHB8zyaemvg2MxZuqtX8n4v7lYEDB2Js0ggra0dsHDwMqn4NPT+Gnn9Db/+ifTZsfUNv/0K/Zv1+T0/BzK4tkZGRAJTK9EpKNHFrSdftX5C57ziH/J7h6spveOyL91E80VuKcRg9mDYfhnDp/dUc6jaCHzZHsXjxYgaNrLmd1df8NITjT/n+239Epf13ac35Sbr4B9+tnkNX/2eZ9X40bbv0J+xT3fwUFebj5t2ZwWPfAGDr1q138rWQlxdG0si0KeuX3fv8eUfEUvqPmMHM96JxcPFm/bIXdXwVFeXj1f4/TJ8+vUYdQc0sXrwYS0tLnWnx4sX/SvPo0aNYWVlJF7MAAwYMQC6Xc/z48b+tk5OTg4WFxd++mIV6fEE7bNgwbGxs2LBhg85ytVpNVFQUwcHBBAUFsWLFCvz9/fHw8GDChAkEBgYSExNTTW/69Ok8//zz9OjR4769aLVawsLC6Pv0dNp26Y+9izejpy1BlZ3Oud9/qbHc4V3f4Nd3NF36PIutkyfPTF6IiWljTu0v82fX0ovnZ32GT8e+bN++nQEjZvDUhBCuXjjJqCnvk5udztlTNd8NPfTTBrr1HY1fn2exc/JkeOACGpk25uSBMv2C2ypO7o+maTNLxo4dy8CRMxk342PUuZkYGRlJcdV87/4Wr/a98R8ajK1TKwaOmoWjW1uO/rJRysfhXWH0e3oavl36E39iN6NGjaKwIA9lalI1H/Vd397Zi+zsbGQyOVqNBgcXb8ZOW1Jr+R867k327dvHgOEvM+n1VaT+dZ6eT0wwmPo19PwYev4Nvf2L9tmw9Q29/Qv9mvUb23QgWdWC8ePHA3DtHg9uXKc+R/7VFM69uRT1+StcW7ORtOifcX91shTj/logyes2k/JNDOpzl/k2KooxY8YwZvRIve2sPuenIRx/Ku+/z88s23/lRkac2F9Dfn6+k59hwdg5teLJ0WX5ORK7UYrp3PtpBox4Gc92ZefbP/zwAwF3zp8dXLwZc+f8+eypms+fD/70DV11/C+sVm+9B71A36depEOHDjXq1He0Wu1Dm0JCQsjJydGZQkJC/tXvSUtLw9bWVmeZsbExLVq0IC0t7W9pKJVK3nvvvbt2U9ZHvb2gNTY2ZtKkSWzYsIHKvaKjoqIoLS1l3Lhxesvl5OTQokULnWWhoaFcuXKFBQsW/CMvKSkpZGRk0Mq34mK4cVNzWnq056/E03rLlJQUcSMpAc9KZeRyOZ5te/BXYpxObFZGmb5nux4U5Ktp3MSMZubNcfZoz7UqsZX1ryedxdO3u66+bw+pTMrVBEpLS7iVcZ2ePXsCYOvogZW1A1YKpxq1ryXG6fgG8HqsF9culf3WWxkpqHKUeLbrIfno27ev5Leqj/qsXz6fmZmJvbO3pNm4qXmt5V9h5yrVb3n+05IvGkT9NoT8GHL+G0L7F+2zYesbcvsX+nfXL8fc3ByAdBO9khJW3Tui3HtUZ1lG7CGad+8IgMzEBMvOvij3HAGgBEgxhZ49e9LCrOzxr6Hlx9CPP/r23+YKp2rnqTr5aVclP+171XguDJCVlaVTpjw/93v+3Mq3+vmz4J9jamqKhYWFzlRTd+O5c+cik8nuOp0/f/5fe8rNzWXo0KG0bduWhQsX3lfZentBCxAUFMTly5fZv3+/tCw0NJSRI0diaWlZLf7IkSNERkbqXNVfunSJuXPnEh4efl+PriuTkZEBgJmltc5yM0sF6uwMvWVuq7LRaEoxs6haxhp1jlJnmerOvNzImL3bPqdbwOgaY6vpWyp09S2sUWeXlVHnKJEbGaPVlGJtXeHDzFKBTCaT4qqizlbq/613vJSXM7NUSD6sra11/Fb2UZ/1K89bNLfVyXdt5N/I2ITiogKd7ZV7MYT6NfT8GHr+Db39i/bZsPUNvf0L/bvrVyX/HmeMpnYKCm/qbrfwphITS3PkjU1ppGiO3NiYwvSybqN5RqCRgbW1NaaVLpYNKT+GfPypaf9FJpPOS/Xlx7zKea25hQJVDfms0K2eU1XOPc6fLatux/qe2xHUDW+88Qbnzp276+Th4YG9vT3p6ek6ZUtKSrh16xb29vZ33YZKpWLQoEGYm5uzdetWTEzucQetCvX6O7Q+Pj707NmT9evX07dvXxITEzl48CDvvvtutdj4+HieeeYZFixYwMCBAwEoLS3l+eefZ9GiRXh5ef3t7cbExOhs49NPP/33P6YSt1VZLHqxizQ/6Y3PAdj8+VvYOrViwIgZtbo9gS4X/zwMwIdvDEImg8lvrK1V/TxVFvOndEFTWkppSTGlpQ/vpf9/gqHnx9DzX9fUdf3WNaJ9Plx9geBRxtCPP3XNH4d/IGb9QgCd3pUC/Wjq7vO8tYqNjQ02Njb3jOvRowfZ2dmcOnWKLl3KrnP27t2LRqPh8ccfr7Fcbm4uTz75JKampmzfvp3GjRvft8d6/YQWygaHio6ORqVSERoaSqtWrfD399eJOXv2LP3792fq1KnMmzdPWq5SqTh58iQzZ87E2NgYY2Nj3n33XU6fPo2xsTF79+7Vu81Tp05x9uxZaRo0aBBAtZfk1TlKzKz0V3BTcyvkciNpAKiKMplYO7gz8/0YaTI2Kas4I2MTJr66EiNjEylW311SHf0qd9HUuZmYWVXcydOUliCTG5GZWeFDnaNEq9VKcVUxs1Lo/63ldwjvlFPnKCUfmZmZOn4r+6hv+u26PgHAuBkfMuuDGJqZWwGQm5Wuk+9/mn+FozuzPohhRPAigEr1WXFntOwua/2sX0PPj6Hn39Dbv2ifDVvf0Nu/0P/7+lVpco+T78KbSkztdLdraqegOEeFpqCQImUWmpISTG3Lnrw1KwW5FjIzMyksruSnHufH0I8/f2f/RavFvAZtMysFqirntapcJeZ3dNt27serH8Tw6gcxBP634mJfX07NLe9x/pxTdTuZ0nYE9ZM2bdowaNAgXnzxRX777TcOHz7MzJkzee6556QRjq9fv46Pjw+//fYbUHYxO3DgQPLy8li3bh25ubmkpaWRlpZGaWnp3952vb+gHTNmDHK5nIiICMLCwggKCkImqxhqLyEhgYCAAF544QU++OADnbIWFhb8+eefxMXFSdP06dPx9vYmLi6uxrsF8+fPR6lUSlN6ejoKhYIrZ49JMQX5alKunMHFU//L6MbGjXB08+VyQkUZjUbD5bPHcPf2w9rOFWs7V5pZWPPjtx9gYmJC2y79MWlkKuknXzmDq2fHGvWd3NqSeFZXPzHhmFSmpbsvRkbGtLBx4ujRsvdaMlKvkp2ZSrbyRo3arp4dSazkG+BS/FFcW5f91hY2LTG3VJCYcEzyceDAAclvVR/1Td/e2QtzSwXKtGso7FyxdfLE2tqatOQLkua/yb+Htx8KO1d8O/fDyMiYnKyb2NjYkJhwTMq/g4tXva1fQ8+Poeff0Nu/aJ8NW9/Q27/Q//v65ajVagBsi7kr2cfisO7XXWeZon9Pso7FAaAtLibn9wQU/crejTQGWhaWjYx6S132NK++58fQjz933X9vlOlnKW/gcpf8XNaTn/JzYdMmzVDYu6Kwd6W5jRMAzZs31ylTnp97nj9X8X854ViNvgyVhzkoVF2xceNGfHx86N+/P0OGDKF37958+eWX0vri4mIuXLjA7du3Afj99985fvw4f/75J56enjg4OEhTcnLy395uvb+gNTMzY+zYsYSEhJCamsrkyZOldfHx8QQEBDBw4EBmz54tXdGXv/Mql8tp166dzmRra0vjxo1p164dzZo107vNqi9KW1pa8sILL/Dr92s59/te0pIvsuWLuZhb2dKm8wCp3LolgRytNNJbr0EvcHJ/FL8f3Eb69cts/2YRRYX5dOkzAijbqTcsC6a4uIBJkyax/8evOXVwG5fPHiNy7VtYWNnStkvFZ4a+WhyoM5Jc78GTObFvC6fu6G/boKvfuKk5fv4jyc/LJTIyktiY1USsegMzC2tKS0ukuMi1c9kV+UmF74ETufjnIQ7sDCX9xhViY1Zx/Wo8PQaUjXQok8noNWgSe7//grO/76VdtyeJiorCtHEzFA7u1XzUd/2bKZewsrJCq9UgMzIiLfkim9fOrbX874xYRkBAAHu2ruGbT2di7+zNkd0bDaZ+DT0/hp5/Q2//on02bH1Db/9Cv2b9S2f2U5x7jc8++wyANg4tsejgQ2NnBwC8359Nh9Clkva1LzfR1N0Zn8VzaObtgev053EYPZirKzZIMVeXh+IcPAanicMx8/FgwqjRbN68magtW/W2s/qcn4Zw/Km8/25cXbb/akpL8POvyM9PlfPz5EQunKmUn+hVXL8ST88nxksxt9XZ3Lh2jvTriWVlevViz9bVnDq4lbTki0StLTt/btul4vz56yr+/zP4BU7si5L8f1/FP4AqO4Mb187x119/AXDx4kXOnTtHdnY2godHixYtiIiIQKVSkZOTw/r16zEzM5PWu7m5odVq6du3LwB9+/at8aLbzc3tb2+3Xr9DW05wcDDr1q1jyJAhOh/l3bJlCxkZGYSHhxMeHi4td3V1JSkpqVY9vPjii/yeeJttoQsouJ2La+vOTP7vl9ITVYBb6X9xW5UlzbfvPoQ8VRZ7Yj5DlaPEwaUNk+d8KXUTuZF0luTLZwBYt+4iAFFfvl32G1p3InCOrn5mejJ5lfQ7dB9MnuoWsdErUeUocXTxIWjOFzpdRYaNn4tMJifu8Fb2bF2DTAYOLj4MnzxfisvOTEUmq7i34erViedeWsbuLZ/xc9RyFHauTHxtJfbOraUY/6HBFBXmE7N+AQW3Vbi4uJCZnceaRc9V82EI+n5dOmPj1o3tYe9TcFuFm1fnWs3/rl07KC4qJDvjOlrAzKK5QdWvoefH0PNv6O1ftM+GrW/o7V/o69dPPBnNm1NWw4Cy85LHPir7mxwWw5ngEEwdbGhy5+IWID8phRNPT6PtxyG4vTKJgpQ0/pw2D2XsISkmNeonGtm0wGvBLEztbWh/+hxGnXxYuUZ/O6vP+Wkox5+4w1v5JaZi/x0RWGn/Vermx82rE+NeXsbPUZ+xa/NyFPauTHpdNz9nf/+VqC/fkeZ//PFHALZ/8x4aTSmuevNT/fxZrcril+iK8+fAOV/q+D++N5I9W1dL8+Wfl1q8eDHPPvssgkcLmVa8tf232XK87t7eHvW4nK2//f2+4vfLiG5GQl/oC32hL/SFvtAX+vetv8PEu870hxZfMPj8GLr+thN1pz+8qxExv9Xd+fOz3ep9Z9Mambeh6KFt+/3JjR7atusCw20FAoFAIBAIBAKBQCB4pDGILscCgUAgEAgEAoFA0FDQakQn2dpCPKEVCAQCgUAgEAgEAoFBIp7QCgQCgUAgEAgEAsEDRIxiVHuIJ7QCgUAgEAgEAoFAIDBIxCjHAoFAIBAIBAKBQPAAeXtd4UPb9v+CTe8dZECILsf3QV02vP8Fmxr8sO9CX+gLfaEv9IW+0Bf696svPgv0cPXDD9bds60J/5HV+WeBDBWNGBSq1hBdjgUCgUAgEAgEAoFAYJCIJ7QCgUAgEAgEAoFA8AARb33WHvX2HdqnnnqK4uJidu3aVW3dwYMH6dOnD3FxcSxZsoRDhw6hVCpxc3Nj+vTpvPrqq1Lsvn37CAgIqKaRmpqKvb39fXna+0cJft5GNGkE125q+f5ICZm5NafPv70Rvm5ybCxlFJfCX+kadp0oRZlTUWZ4L2NaOcppYQ6fLv+MqKgoVLm5uHp1Yvjk+Sjs3e7q6WhsBPt3rkedo8TB2ZunJ72Dc6v20vriokJ2RCzj3MmdqNR5yOVlD+XdvDoblP7pI9vIzy9AJpPh4OLNiMAFOnFVOXN8F7HRK8lSXsfazpXBY2fj09FfWq/VaomNWcWJX6PIv63CuaUjmdl53FZn6/VR3/X9unQCM3cSTsSSf1uFm4G1n7quX0PPj9Bv2O1H+BfHz/qsX1f1W5J9kfHjxtKhQ3uaN2/OyZEvc3P7nrt6btGnG20/motZ29YUJKeSuPhzUsK26sS4vvQ8HrODMbW3IffMeXZeiuezNV/X+Dv/qf+G1n5ksjJ9l9ZdGDxhAdZ2d9c/sXcjR39ehzpHiZ2zD4PGzcPJo0I/bNlErl08oVOmmUULAt9Yc8/8795Sln+FnSuDn9OT/+hV/HYn/139OrNw4ULc3O7utz7y1pf5D23bS6c2eWjbrgvqbZfj4OBgYmNjSUlJqbYuNDQUPz8/Tp06ha2tLeHh4SQkJPDOO+8QEhLCqlWrqpW5cOECqamp0mRra3vfnnq0NeL7wyV8vr2YohItgU+aYHyXrvvuDnKOnSvl8x+KWb+rGLkcAgeZYFLpufh1pYbog8V89dVXhIV9y+y3FhL+3WZMTJuwftlUiotqfm/39LGf+DFiKQNGvMwr723BwcWHdcumos7JlGJ+3LiEc3G/MnDgQIxNGmFl7YiNg4dB6Z8+vpPi4mL6DX8Je+fW5GalV4urzLWLf7BpzRz8/J9l1nvR+Hbpz7fLXyEt+ZIUs3/HOo7sDmd44AKeeHYmSUlJlJYU89L8CL0+6rP+jIWbyMjI4PieTTw18W1mLNwk6rcB5UfoN+z2I/yL42d91q/L+u339BTM7NoSGRkJQKmsRrsANHFrSdftX5C57ziH/J7h6spveOyL91E80VuKcRg9mDYfhnDp/dUc6jaCHzZHsXjxYgaNrPl3/lP/Dar9GDfC0toRa/sy/YhPp1BSXLN+wm87id28hD5PzeDF+THYOXsTsXwKebm6eXXz6Y6JiQnDxr/FtHe+wadDH9YtrTn/SRf/4LvVc+jq/yyz3o+mbZf+hH1aJf8/ruPw7nBGBC1g5qJNNGnShODgYAoLH94AS4KHT729oB02bBg2NjZs2LBBZ7larSYqKorg4GCCgoJYsWIF/v7+eHh4MGHCBAIDA4mJiammZ2tri729vTSV3+m6H36NK+XcXxrSsrRE7S/BvCm0da1ZZ8PPxfx+SUN6tpa0W1qiD5TQ3EyGk6LiqH3igoarqRrCwsIIeHoaLTwC6PiYD4GzlpCbnc7ZUzXfrTz00wa69R2NX59nsXPyZHjgAhqZNubkgbLfX3Bbxcn90Qwd9yb79u1jwPCXmfT6KlL/Ok/PJyYYjH7TZpaMHTuWgSNnMm7Gx6hzMzEyMpLiqnJ497d4te+N/9BgbJ1aMXDULBzd2nL0l41A2d29w7vC6Pf0NHy79Cf+xG5GjRpFYUEeytSkaj7qu769sxfZ2dnIZHK0Gg0OLt6MnWY47aeu69fQ8yP0G3b7Ef7F8bM+69dl/Ta26UCyqgXjx48H4No9Bl11nfoc+VdTOPfmUtTnr3BtzUbSon/G/dXJUoz7a4Ekr9tMyjcxqM9d5tuoKMaMGcOY0SP1/s5/478htZ8+T89g7Mw1pKecp2vABFTZ6Zz/45ca9Y/FbqDTf0bTsfdIbBw9GTphESaNGhN3KFonLuNGImPGjOE/g1/Ao01XRr34PiamjTmxv4b8/3wn/8OCsXNqxZOjy/J/JLYi/4d2hdHvmbL8O7h4s2zZMtLT0/nll5r91le0moc3NTTq7QWtsbExkyZNYsOGDTp9zKOioigtLWXcuHF6y+Xk5NCiRYtqyzt27IiDgwNPPPEEhw8f/keeLt+oaAGFxZCSocXF9h63FCthalL2N7/KTaS8nOtkZGTg9VgPXG1k5BVo0Rqb4+zRnmuJcXq1SkqKuJ50Fk/f7tIyuVyOp28PqUzK1QRKS0tQ2LmSkZGBZ7se2Dp6YGXtQFryRYPRv5VxnZ49ewJI+lYKpxq1ryXG4enbQ2eZ12O9uHbpNAC3MlJQ5SjxbNdD8tG3b1/Jb1Uf9Vm/fD4zMxN7Z29Js3FTw2k/dVm/DSE/Qv/u+obefoR/cfysz/p13T4BzM3NAUg30SspYdW9I8q9R3WWZcQeonn3jgDITEyw7OyLcs+Rst8JpJhCz549aWEm0/s7/63/htJ+3Nv0ROHggWULR9KvX8TJoz3XL+vXLy0pIvVaAu5te0rLZHI57m16kHKlooxWqyEvV8nWrVv5ZO7T/BT5CSXFhXj69uCvu+W/XZX8t+/FX4m6+W9dpf106NCBP/74Q6+m4NGg3l7QAgQFBXH58mX2798vLQsNDWXkyJFYWlpWiz9y5AiRkZFMnTpVWubg4MDatWuJjo4mOjoaZ2dn+vbty++//17jdgsLC8nNzZWmvLw8ANT5uu/LqvO1mDX5exe0MmBYd2OS0jTczNLVaaUo63oxso8tdpYyDl/QoNWCmaU16hylXr3bqmw0mlLMLBU6y80srFFnl5VR5ygxMjahuKigbN2dWDNLBeocpUHoy42M0WpKsba2roixVCCTyaS4qqizy7ato3vHU/n68mXlPqytrXX8VvZRn/Urz1s0t9XJt6hfw8+P0G/Y7Uf4v7t/cfxs+PtXZfLvcUZqaqeg8KbudgtvKjGxNEfe2JRGiuKU7pcAAMjGSURBVObIjY0pTC87p8ozAo0MrK2tpQcKVX/nv/XfUNpPMwtr6a86V0kzC0XN+uostJpSzCx081S1TOv2fQFYuHAhAU+9yO+HfmDT529hbmmNqgZtdbYS8yq65hYKVHd8l/81s9D9bdbW1iiV+jXrMxqt9qFNDY16Pcqxj48PPXv2ZP369fTt25fExEQOHjzIu+++Wy02Pj6eZ555hgULFjBw4EBpube3N97eFd8369mzJ5cvX+bTTz/l22+/1bvdH3/8kSeffFKaHzJkiM5F9T/h6Z7G2DWX88WPRfyVsIPfd1f8hr5jVwNw7JKGrm2hq6ecA2f/eX+APFUW86d0QVNaSmlJMaWlJf/K+4PWF9ydi3+W9TD48I1ByGQw+Y21tapv6PVr6PkR+g+Xum4/dY2h+69rxPFB8G8Q7efv81iPp9kT/TEuLi5oHdtjbmXDV4uD6NZ3VJ1tU/DoUq+f0ELZ4FDR0dGoVCpCQ0Np1aoV/v7+OjFnz56lf//+TJ06lXnz5t1Ts1u3biQmJta4ftiwYWi1WmnasWMHQLWnsWZNZNWe2urjqR7GeDvL+XpnEbm3wcGzLwMmR0mTvJEVAMk3lBxP1GDeGByby1DnZOq9iwnQ1NwKudyo2h00dW4mCkd3Zn0Qw4jgRQAYGZfdmpTuMOYo79zlq5/6ZlYVdyI1pSXI5EZkZlYMIKDOUaLVaqW4qphZKaoNOFDuqXx9+bJyH5mZmTp+K/uob/rtuj4BwLgZHzLrgxiamVsBkJuVrpPvR7V+DT0/Qr9htx/hXxw/H+X9q3L9VqXJPe7jF95UYmqnu11TOwXFOSo0BYUUKbPQlJRganvnaWMpyLWQmZlJYbH+33m//htq+ykfzCkvNxMzCwV5ucqa9c2aI5Mboa4yAFTVMuVx5e3H5c7IxrcyrmNeg7aZlQJVFV1VrhLzO/VV/ledq/vbMjMzUSj0a9ZnKl9rPOipoVHvL2jHjBmDXC4nIiKCsLAwgoKCkMkqLiwTEhIICAjghRde4IMPPvhbmnFxcTg4ONS43tTUFHNzc2kyMzMDoJVjRbpMTaCljYy/0u/eKJ7qYUxbVznrfiomS122zMS0GWbNXaTJ3LoVNjY2JCYco/yXFeSrSb5yBlfPjnp1jY0b4eTWlsSzx6RlGo2GxIRjeHj7obBzxbdzP4yMjMnJuinpZ6ReJTszFQcXr3qrX16mpbsvRkbGtLBx4ujRsvdmyvWzlTdq1Hb17EhiwjGdZZfij+LaugMALWxaYm6pIDHhmOTjwIEDkt+qPuqbvr2zF+aWCpRp11DYuWLr5Im1tTVpyRckzfrcfuq6fg09P0K/Ybcf4V8cPx/l/aty/ZajVpedHNkWc1eyj8Vh3a+7zjJF/55kHYsDQFtcTM7vCSj6lb1faQy0LISjR49yS63V+zvv139DbT9Xzx1FmXaFnFs3sG3pxfUrZ3BqpV/fyLgRDq6+JJ2reJ9Zq9Fw9fwxWnp0rBZX3n5u/HUegLTki7jcJf+X9eTfxfPu7ef06dN06tRJr6bg0aDeX9CamZkxduxYQkJCSE1NZfLkydK6+Ph4AgICGDhwILNnzyYtLY20tDQyMjKkmOXLl/P999+TmJhIfHw8r732Gnv37mXGjBn37SWgoxE+LnLsmssY7W+M6jacvVZxSzF4sAnd21Sk9OmexnRsJWfzvmIKi7WYNQGzJkif+mluXvatWieFnEmTJrFv+xcU39jH+QsXWL7kLSysbGnbpb+k99XiQGmkN4DegydzYt8WTh3cRvr1y2zbsIiiwny69BkBlA1M4Oc/kp0RywgICGDP1jV88+lM7J29ObJ7o8Ho5+flEhkZSWzMaiJWvYGZhTWlpSVSXOTaueyK/ETS7TVwIhf/PMSBnaGk37hCbMwqrl+Np8eAspEUZTIZvQZNYu/3X3D297206/YkUVFRmDZuhsLBvZqP+q5/M+USVlZWaLUaZEZGpCVfZPPauaJ+G0h+hH7Dbj/Cvzh+1mf9uqzfS2f2U5x7jc8++wyANg4tsejgQ2PnsgcO3u/PpkPoUkn72pebaOrujM/iOTTz9sB1+vM4jB7M1RUbpJiry0NxDh6D08ThmPl4MGHUaDZv3kzUlq16f6doP2X6B35YzeZVM7Br6c2JvRsxt7LFp9MASe/bjyZzYm+4NN/9icn8fiCK04e3knHjMjvDF1JcmE+HXs8CcCv9Lw78sAbvjv2JjIxke9j/iFj1X8ytFJSWFOPnX5H/nyrn/8mJXDhTKf/Rq7h+JZ6eT1Tkv/egSezd9gVnT+0lNfkib775Jra2tgwYUOFX8OhRr9+hLSc4OJh169YxZMgQHB0dpeVbtmwhIyOD8PBwwsMrdjRXV1eSkpIAKCoq4o033uD69es0bdqU9u3b88svvxAQEHDfPo6eLWVEL2MaN4JrN7WE/lxMSWnF+hbmMpo1rnh63L1N2ZXri0Mb6ehsOVD2OZ+SUnCzl9GrnQnNGr9Irjqfj5bMR6XKxc2rM4FzvsSkUcU49pnpyeSpsqT5Dt0Hk6e6RWz0SlQ5ShxdfAia84VOV45h4+cik8nZtWsHxUWFZGdcRwuYWTQ3KP24w1vZs3UNMhk4uPgwfPJ8KS47M1X6IDiAq1cnnntpGbu3fMbPUctR2Lky8bWV2Du3lmL8hwZTVJhPzPoFFNxW4eLiQmZ2HmsWPVfNhyHo+3XpjI1bN7aHvU/BbZXBtZ+6rl9Dz4/Qb9jtR/gXx8/6rF9X9Zt4Mpo3p6yGAW8D8NhHZX+Tw2I4ExyCqYMNTZwretPlJ6Vw4ulptP04BLdXJlGQksaf0+ahjD0kxaRG/UQjmxZ4LZiFqb0N7U+fw6iTDyvX6P+dov2U6ZcUFZKtvA5AU/MWPP/aVxibVOhnZfzF7Ur6vt2GcFt9i/3fr0Sdm4Gdcxuef+0rqcuxkbEJV88dIeP6JTQaDUd/+Q4tWhxd2jB88ryK/Ct18+/m1YlxLy/j56jP2LV5OQp7Vya9XiX/w8ryH30n/139uvD1119janqP7z7VQzSahtf192Eh0zbEjtR1xNvr6u6jzf8LNmXrb6X3DvyHjOhmJPSFvtAX+kJf6At9oV/v9HeYeN878B8ytPiCweenrvXDD9bdpcCE/8jYdqLu/A/valRn2nXN66vUD23bn840e2jbrgsM4gmtQCAQCAQCgUAgEDQUxCPF2qPev0MrEAgEAoFAIBAIBAKBPsQFrUAgEAgEAoFAIBAIDBLR5VggEAgEAoFAIBAIHiBaMShUrSEGhRIIBAKBQCAQCASCB8irK1QPbdsrXjV/aNuuC8QT2vsg4lDdXfs/31tm8KPkCX2hL/SFvtAX+kJf6Nc3fTGK8sPVF6Mc60cjninWGuIdWoFAIBAIBAKBQCAQGCTiglYgEAgEAoFAIBAIBAaJ6HIsEAgEAoFAIBAIBA8QMShU7VEvB4V66qmnKC4uZteuXdXWHTx4kD59+hAXF8eSJUs4dOgQSqUSNzc3pk+fzquvvqoTX1hYyLvvvkt4eDhpaWk4ODgwf/58goKC7suTVqtl6pzP+P1AFAW3c3H27MzQiQuwtnO7a7nf9m7kyK51qHOU2Dv7MPj5eTh5tK8W91xPLb38B3IrPRkjIxPcfbowfPJ8FPZ31z8aG8H+netR5yhxcPbm6Unv4NyqQr+4qJAdEcs4fWQb+fkFyGQyHFy8GRG4QCeuKmeO7yI2eiVZyutY27kyeOxsfDr66+QjNmYVJ36NIv+2Cr8uncDMnYQTseTfVuHm1alW/Z87uROVOg+5vKxTgZtXZ5GfB5gfoS/067O+oe+/de3fuaUjmdl53FZn6/VR3/XF8VO0n3+iX5J9kfHjxtKhQ3uaN2/OyZEvc3P7nrvmvEWfbrT9aC5mbVtTkJxK4uLPSQnbqhPj+tLzeMwOxtTehtwz57Hq2p6X566Qfo9on7r6Vdvns0H3bp+7t5TVr8LOlcHP6anf6FX8diffXf06s3DhQtzc7u63PjLzk5yHtu1Vsy0f2rbrgnrZ5Tg4OJjY2FhSUlKqrQsNDcXPz49Tp05ha2tLeHg4CQkJvPPOO4SEhLBq1Sqd+DFjxrBnzx7WrVvHhQsX+O677/D2vv/BAb766iuO//ItQycuZMo7m2lk2oTwT6ZQUlxYY5n433ayO3IJ/k/PYNqCGOycvQn/dAp5uZnVYl966SWylTcAGDx2NiamTVi/bCrFRTXrnz72Ez9GLGXAiJd55b0tOLj4sG7ZVNQ5Ffo/blzC6eM7KS4upt/wl7B3bk1uVnq1uMpcu/gHm9bMwc//WWa9F41vl/58u/wV0pIvSTH7d6zjyO5whgcuYMbCTWRkZHB8zyaemvg2MxZuqlX/5+J+ZeDAgRibNMLK2hEbBw+RnweYH6Ev9OuzvqHvv3Xt/4lnZ5KUlERpSTEvzY/Q66M+64vjp2g//1S/39NTMLNrS2RkJAClshrTDUATt5Z03f4FmfuOc8jvGa6u/IbHvngfxRO9pRiH0YNp82EIl95fzaFuI1CdOc9XX32l014Nqf08qPbZf0Sl9rm05vpNuvgH362eQ1f/Z5n1fjRtu/Qn7NMq9fvjOg7vDmdE0AJmLtpEkyZNCA4OprCwZr/1Fa1G+9Cmhka9vKAdNmwYNjY2bNiwQWe5Wq0mKiqK4OBggoKCWLFiBf7+/nh4eDBhwgQCAwOJiYmR4nft2sX+/fvZuXMnAwYMwM3NjR49etCrV6/78qPVagkLC6PPsOn4dOqPnbM3w4OXospO5/zvv9RY7tjuDXTuM5pOvUdi4+jJsImLMGnUmD8ORevEpV47y4EDBwh4eioAzW2cGDttCbnZ6Zw9VfPdxEM/baBb39H49XkWOydPhgcuoJFpY04eKMtBwW0VJ/dH07SZJWPHjmXgyJmMm/Ex6txMjIyMpLiqHN79LV7te+M/NBhbp1YMHDULR7e2HP1lo5SPw7vC6Pf0NHy79Mfe2Yvs7GxkMjlajQYHF+9a9T903Jvs27ePAcNfZtLrq0j96zw9n5gg8vOA8iP0hX591jf0/beu/cef2M2oUaMoLMhDmZpUzUd91xfHT9F+/ql+Y5sOJKtaMH78eACumdaYbgBcpz5H/tUUzr25FPX5K1xbs5G06J9xf3WyFOP+WiDJ6zaT8k0M6nOXOfPyfMLCwhgzYTq+XfqL9nmX9vn8zLL2KTcy4sT+Gur35zv1OywYO6dWPDm6rH6PxFbU76FdYfR7ZpqU72XLlpGens4vv9R8Pi5o+NTLC1pjY2MmTZrEhg0bqNwjOioqitLSUsaNG6e3XE5ODi1atJDmt2/fjp+fH8uWLcPJyQkvLy/++9//kp+ff19+UlJSyMjIwKNtT2lZ46bmtPRoT/LlOL1lSkuKuHEtAY82FWVkcjkebXuQUqlMcWE+UZ+/hkajwddvgI6+s0d7riXq1y8pKeJ60lk8fbtLy+RyOZ6+PaQyKVcTKC0t4VbGdXr2LPNh6+iBlbUDVgqnGrWvJcbh6dtDZ5nXY724duk0ALcyUlDlKPFs10Oaz8zMxN7ZW9KsTf8KO1cyMjLwbNdD8p+WfFHk5wHlR+gL/fqsb+j7b136L/fRt29fyW9VH/VZv3xeHD9F+/kn+uWYm5d9bzPdRK+khFX3jij3HtVZlhF7iObdOwIgMzHBsrMvyj1HpPW3jCAjI4P/9NY9PzSU9vMw2mdzhRN/3a1+21Wp3/a9+CtRt35bV6nfDh068Mcff+jVrM9otA9vamjUywtagKCgIC5fvsz+/fulZaGhoYwcORJLy+r9vo8cOUJkZCRTp06Vll25coVDhw4RHx/P1q1bWb58OVu2bOHll1++67YLCwvJzc2VpqSkJACaWVjrxDWzUJCXq9SrcVuVhVZTqreMOqeizK7IxSgcPQAws1ToxJpZWuvE6upno9GUVi9jYY06u6yMOkeJ3MgYraYUa+sKH2aWCmQymRRXFXW2EjNLXd9mlhW+y8uVb7t83qK5rY7f2vBvZGxCcVGBzvbKvYj81H1+hL7Qr8/6hr7/1rX/ch/W1tY6fiv7qM/6lefF8VO0n/vVr0r+Pc54Te0UFN7U3W7hTSUmlubIG5vSSNEcubExhekV3WVz73wC1d62qqf6334eVvtEJkNVg7Y6W4l5lfNmcwsFqju65X/NLHS3bW1tjVKpX1PwaFBvRzn28fGhZ8+erF+/nr59+5KYmMjBgwd59913q8XGx8fzzDPPsGDBAgYOHCgt12g0yGQyNm7cKF0Ef/LJJ4waNYo1a9bQpEkTvdueNm0ahw8fluavX7+Os7Nzrf222+os/vdyZ7SaUkpKihj/6hdcOr2v1vTrmot/luXmwzcGIZPB5DfW1qp+niqL+VO6oCktpbSkmNLSklrVr2sMPT9CX+jXZ/26pq73X8HdEcdPQX1m+/btzMs4g9atbH5KWu3qi/YpEPwz6u0TWigbHCo6OhqVSkVoaCitWrXC399fJ+bs2bP079+fqVOnMm/ePJ11Dg4OODk56TzRbdOmDVqtVu+AU+V88sknbNu2TZrKL26rDuaUl6ukmUX1u4AATc2bI5Mb6S2jsHdn+oKt+HYdDFotG1dMB2DxrAAAwj97jS8+eAF1Tqbeu4xl+lbI5UbV7qCpczMxs6q406YpLUEmNyIzs8KHOkeJVquV4qpiZqWo9sJ+2R27svh2XZ8AYNyMD5n1QQzNzK0AyM1K1/H7T/0rHN2Z9UEMI4IXAWBkbCJ5qOxF5Kdu8iP0hX591jf0/beu/ZeXU+coJR+ZmZk6fiv7qG/64vgp2k9t6VeliUavpEThTSWmdrrbNbVTUJyjQlNQSJEyiwB/f/7n+ThvpMAbKWBWWhaXll7VU/1sP/WhfaLVYl6DtpmVAlWV82ZVrhLzO7rlf9VVekdmZmaiUOjXrM+IQaFqj3p9QTtmzBjkcjkRERGEhYURFBSETFYxTF1CQgIBAQG88MILfPDBB9XK9+rVixs3bqBWq6VlFy9eRC6X07Jlyxq326JFC3x9faWpY8eO2NjYcOVcxbsVhflqUq6cwblVR70aRsaNcHT11Smj1Wi4cu4YLl5+tLBzZcCoN3hp0XamL9hK8+bN6T3oBQCGjX+Lpye+TfKVM7h66tc3Nm6Ek1tbEs8ek5ZpNBoSE45JZVq6+2JkZEwLGyeOHi3zkZF6lezMVLKVN2rUdvXsSGLCMZ1ll+KP4tq6AwD2zl6YWypQpl1DYeeKrZMn1tbWpCVfkDQL8tX/2L+Htx8KO1d8O/fDyMiYnKyb2NjYkJhwTPLv4OIl8lNH+RH6Qr8+6xv6/lvX/lvYtMTcUkFiwjHJx4EDByS/VX3UN31x/BTtp7b0yyk/B7Qt1ispkX0sDut+3XWWKfr3JOtYHADa4mJKLiTRbnB/bErApgTsSsDGxoZDhyvO9UT7rKF93ijTz1LewOUu9XtZT/26eN69fk+fPk2nTp30agoeDer1Ba2ZmRljx44lJCSE1NRUJk+eLK2Lj48nICCAgQMHMnv2bNLS0khLSyMjI0OKef7557G2tiYwMJCzZ8tGEp4zZw5BQUE1djfWh0wmY9KkSRz8cS0X4vZyM+UCW79+C3MrW3w6VwzkFPbhZH7bEy7Ndx84md8PRBF3eCsZNy7zY/hCigvz6djr2bLfZ2mDbUsv7Jy9CQoK4sT+stGPy76xtRILK1vadukv6X21OFAa6Q2g9+DJnNi3hVMHt5F+/TLbNiyiqDCfLn1GAGUDE/j5jyQ/L5fIyEhiY1YTseoNzCysKS0tkeIi185lV+Qnkm6vgRO5+OchDuwMJf3GFWJjVnH9ajw9BoyX8tFr0CT2fv8FZ3/fy82US1hZWaHVapAZGZGWfJHNa+fWmv+dEcsICAhgz9Y1fPPpTOydvTmye6PIzwPKj9AX+vVZ39D337r2367bk0RFRWHauBkKB/dqPuq7vjh+ivbzT/UvndlPce41PvvsMwDaOLTEooMPjZ0dAPB+fzYdQpdK2te+3ERTd2d8Fs+hmbcHrtOfx2H0YK6u2CDFXF0einPwGJwmDsfMx4PHVi9i0qRJbA5fy9nf94r2eZf2uXF1WfvUlJbg519Rvz/9P3vnHRbF1TXw3y69996RJtix99419t6JGjX6Ro1Gk7xqqiXljTXFYNeoiJrEbjT23hU7CgKCLH3p274/FhaWZoMo+eb3PPPAzpx75sy5555p994pXr9dRnLvRrH6DV9B3KNbNO9UVL8tu47i6O6fuX35KPEx95k9ezb29vZ07Fh0PV5dUKlUb2z5t/HWjqEtJCQkhNDQULp3746zs7Nm/Y4dO5BIJGzatIlNm4puIj08PDSTOJmamnL48GGmTp1Kw4YNsbGxYdCgQXz55Zcvbcf48eO5eD+HP9fPIzc7A3ffYEZMX42uXtE88CmSJ2Rnpmp+12rcnWxpCsd2LyczQ4KjW02GT19dZjeO8ePHczUym6O7f2Tf1m/xDmjI2Fm/oKdfpD85MYYsaZH+uk27kSVN4XD4cqTpSTi7BzBu1s9aXTl6Dp+DSCTm2uldHNm1CpEInNwD6DNmnkYuLTkekajo2YaHX32GTFrCoR3LOBj2A7YOHoz8YDmObr4amTY9QsjPy2HnmvnkZktpGNwAO8/G/LHhS3KzpXj6NahU+w8c2IssP480SRwqwNTcSvDPP+gfQb+g/23WX93bb1Xb7+7uTnJaFqs+G1LKjuqgX8ifQvy8iv6Hl8KZ/e5K6PgxALW/Vf+N2bCTGyFzMXCyw6jg5hYgJyqWi70nEvjdXDynjiI3NoGbEz8l6fApjUx82H707azxmz8NA0c7Mq7fYXyjOlyNzNYcjxCf2vqvnd7FXzuL4rPv2GLxmaRdv55+9Rk6eQkHw5ZxYPsP2Dp6MGp6ifrtqa7f8AJ/N2oYzK+//oqBwXO+yyTwr0ak+jfeplcRW05VnauGtRSx64KiyvT3bawj6Bf0C/oF/YJ+Qb+gX9D//07/Xj3/KtPfQ3av2vunqvXvvlh1+vs00qky3VXNxEUpb2zfP8+xfr5QNeKtf0MrICAgICAgICAgICDwb0L5L5yc6U3xVo+hFRAQEBAQEBAQEBAQEBAoD+ENrYCAgICAgICAgICAwD+IMOqz8hDe0AoICAgICAgICAgICAhUS4RJoQQEBAQEBAQEBAQEBP5BQr6QPF+oigj9r90b23dVIHQ5fgl2XlBWme5+jcXVfhY7Qb+gX9Av6Bf0C/oF/YL+/2/6q/ssyvuuyKpMf/cGevx5WV5l+nsFC7cyAkKXYwEBAQEBAQEBAQEBAYFqivBYQ0BAQEBAQEBAQEBA4B9EJXy2p9IQ3tAKCAgICAgICAgICAgIVEve2kmhevXqhUwm48CBA6W2nTx5ktatW3Pt2jUWLVrEqVOnSEpKwtPTk/fee4///Oc/GtkxY8awfv36UjoCAwOJiIh4YXtUKhWT5yzl4t9h5GRL8fCrT58x87F19Kyw3NnDmzmxbw2Z6Uk4ugXQe9QnuNWoo9l+4eh2rp3dQ9yj6+Tn56Ojq4+zu38puZLcOH+Aw+HLSU2Kw8bBg26DZxBQr42WvYd3rtDY2zC4Pph6EXHxMDnZUjz96tNnzLwXsH8Lxwvsd3IrbZcsP4+9W5Zw/cxucnJyEYlEOLn703fs/Eq1383VmeS0LLIz08q0423XX9X+v3NpH9LMLMRi9TMqT78G1ap+q7t/BP3/7vgR8pvQfl9Hf3WPH6F+y9ZvYSjDShSNq6M1dnZ2LA6ZjMfmI5hVMNzVunVjAr+dg2mgL7kx8Txc+COxG3ZpyXhMGob3jBAMHO3IuHEXy0Z1tK4/qzK/OXv4MzBkHh4+tcvVe+3cQfaHrSBFEoedowc9h04nsH5rzfYDO1Zy9ewB0pIT0NHVw9HehqRUKTlZGer4H/0x7j7lx+f1cwc5EKaOT1tHD3oMmUHNAv0KuYz9Ycu4e+0kyYmxGBmZ0q5Nc2bOnImDg0OF/ngbGbPg2Rvb97oF1c9fFfHWvqENCQnh8OHDxMbGltq2du1aGjZsyOXLl7G3t2fTpk1ERETwySefMHfuXFasWKGRXbp0KfHx8ZolJiYGa2trBg4c+FL2rF69mjOHNtFn7AImL9iGvoExa5aMR5afV26ZG+f2sXfLYjr0ncL7X4Tj5O7PmiXjyUxP1sjk5+dgZmmPQqHOgBM/Xo+TewChSyZoyRUn+v5Vtq6aRcM2/Zj2RThBwR3Y+MNUEmIeaGSO7w0tsHc+UxZsRSKRcP7IVnqN/JgpC7aiZ2DEmiUTKrT/+rn97NmymI59JzP1ix1l2rVn8yKun9+HTCajfZ9JOLr5kpGaWKn2d+r3PlFRUSjkMibN21Lp/qlq/VXt/zvX/qZz587o6uljaeOMnZN3tarf6u4fQf+/O36E/Ca039fRX93jR6jf8vVfORHG1cvnSUlJASBHDGsruEcw8nSl0R8/k3zsPKcavsPj5eup/fOX2HZqqZFxGtiNmt/M5cGXKznVuC/SG3eLXX/Or7L81rnfRBzdfElPTeTnRRORlhM/j+9fZePy2TRp25cPF4ZRq2F71nw3jfhi8WPn5Em/MR8za/FOOr4zgejoaHKypEyY+yvO7v6srkB/1P2rbF4xi8Zt+zH96x3UCm7Puu+navTn5+cS9/gOHfu+x/Svwhg9fSmPHz9m0qRJ5Tte4P8Fb+0Nbc+ePbGzs2PdunVa6zMzMwkLCyMkJIRx48axdOlS2rRpg7e3NyNGjGDs2LHs3LlTI29hYYGjo6NmuXTpEqmpqYwdO/aFbVGpVGzYsIF2vd8jMLgDTu7+DJq4CGlaIrcv/1VuuZP719Oo7UAatu6Hg4sPfcYuQN/AkEsniuxr2XU0KYkxtGvXDgA7Zy/6jJ1fSq44pw9txK9OS9r0CMHepQadB0zD2TOQs39t1th7+sAG2veeSFBwBxzd/EhLS0MkEqNSKnFy92fwxEVkpCVy+/KRcu0/tX8djbXs17YrN1vKpePhGJtYMHjwYDr3f5+hU74jMyMZHR2dSrP/1sVDDBgwgLzcLJLioyrdP1Wtv6r932PobI4dO0bHPpMZNX0F8U/u0rzTiGpTv9XdP4L+f3f8CPlNaL+vo7+6x49Qv+XrX7NqEbGZtvj7q2c4bpkBUYYQZVC2To8JQ8h5HMud2YvJvPuI6FWbSQg/iNd/xmhkvD4YS0zodmLX7yTzTiQ3Js9jw4YNDBrxHkEF159Vkd+6DXyf0dO+ITM9GbFYh/PHdpWp98T+TQTUbUH7XuNwcKlB90FTcfUK5OTBLRqZ4BY98K/dDFsHN66fP8iAAQOQy/JQyGX0D5mPnoEhF4+XHZ8nD2zCv25L2hXo7zpoGi5egZw+pNZvZGzGxI9/pV7Trtg7e+HhW5f//ve/RERE8PTp03L9IfDv5629odXV1WXUqFGsW7eO4r2iw8LCUCgUDB06tMxy6enpWFtbl6s3NDSUjh074uHh8cK2xMbGIpFI8KnVTLPO0NgMN+86PHl4vcwycnk+T6Mi8AkqKiMWi6kR1IwnD6+VkgsKCtKS8wlqRnQxueJEP7ympRfAr3YLoh+obUmRxCJNT9LYmyKJJTk5GUc3f43OQvvL24dcnk9c1G18gpqWa1fs4wgUCjkpkjiaN28OgL2zN5Y2TljaulSK/YV2tG3bVmNvZfqnqvUX/q5K/9s6eGjis9D/CTH3q0X9/hv8I+ivWH91jx8hvwnt93X0V+f4Kfwt1G/5+ov70lIOVjKINixTLZZN65F09KzWOsnhU1g1rQeASE8PiwZBJB05o9meogMSiYRWLZtr1lVVfnNw8cbK1glrO2dNPJQk6sF1/Gppx49/neZlysvlMmIf30Ymk2FobIazuz9isRjfWk3L1R/94Bq+tZpqrfOv04LoB2UfK6hfdIlEIszNzcuVeVtRKVVvbPm38dbe0AKMGzeOyMhIjh8/rlm3du1a+vfvj4WFRSn5M2fOsG3bNiZMmFCmvqdPn7J//37efffdCvebl5dHRkaGZomKigLA1MJGS87UwhZpetkfRc6WpqFUKkqVMTO3QZqWVEquZEM0Nbchs5hccTLTksq0JTM9SbO9cF3x3+ZW9hqZwuMp/rts+23LtSszPQmxji4qpQIbmyJ7TC1sEYlElWJ/oR02NjZa9laWf6paf/HfVeF/HV09ZPm5WvsrtKU61G9194+g/98dP0J+q1h/8d9C+/33xU/x30L9lq3fyKTEtZsCMnTKVIuBgy15z7T3mfcsCT0LM8SGBujbWiHW1SUvsag7bqEuR/uSdVY18WlmYYNIJCajnPiRpiVhVvK61sK2lHzElWN8HNIMpVLBsWPHmDB3NSbmVpp9vIx+UwsbpGlld1GW5efx7bff0qNHD0xNTcuUEfj/wVv92Z6AgACaN2/OmjVraNu2LQ8fPuTkyZN8/vnnpWRv3brFO++8w/z58+ncuXOZ+tavX4+lpSV9+vSpcL8TJ07k9OnTmt9xcXG4ubm91rH8k9y/qbb9m5ldEYlgzMyf3rBF/7+oav9nSVOZ924wSoUChVyGQlF1HyyvCqq7fwT9bxYhv71ZhPb770ao3zfLH3/8waeSG6g81b/fTXij5rwyPoGNee/j1SxfMJK6deuycdlMpn3+W6mb1ddBIZexcdkMUKn47LPPKk3vP8lbOi9vteStfkML6smhwsPDkUqlrF27lho1atCmTRstmdu3b9OhQwcmTJjAp59+WqYelUrFmjVrGDlyJPr6+hXu8/vvv2f37t2apfDmtuQkC5npSZhZ2JWpw9jMErFYp1QZaUYyZpa2peQyMjK0dWckY2qp/XStEFNL2zJtKXwaV6tRJwCGTvmGaV/txMTMEoCM1EStJ3aZ6cmlnuCVtl/7KVpxu0wtbFEq5IjEOiQnF9mTmZ6ESqV6ZfsLy2WmJ2nsSE5O1rL3dfxT1fqr2v+2zl5M+2onfUPUCVxHV09jQ3Fb3tb6re7+EfT/u+NHyG9C+32b41+o37enfhVyGTlZJa7ddMC8nFmO854lYeCgvU8DB1tk6VKUuXnkJ6XSrk0bvvZpwsxYmBmrfuMLkJBYss6qJj6l6cmoVErMy4kfM0vbUhM6SdOTSskbGBrjXqMWYrEOAwcOREesw4VjOzX7eBn9menJmFlq3wirb2Znkpr0lDVr1ghvZwXe/hvaQYMGIRaL2bJlCxs2bGDcuHGIRCLN9oiICNq1a8fo0aP56quvytVz/PhxHj58SEhIyHP3aW1tTVBQkGapV68ednZ2REac08jk5mQS8+gG7j51y9Shq6uPs2cQkbeLyiiVSiIjzuHuU6+UXPFPCCmVSh5GnMOjmFxxPHzq8bCYLQAPbp3Fw1dti6ObH2YWtiQlRGPr4IG9iw82NjYkxNzT6Cy0v7x96Orq4+IZyMMS9he3y9UrCB0dXaztXDh7Vj0uRBL/mLTkeNKSnr6y/dZ2rphZ2PIw4pzGjhMnTmjsfV3/VLX+qva/t39DbB08CGrQHh0dXdJTn2FnZ8fDiHMa/zu5+7219Vvd/SPo/3fHj5DfhPb7Nse/UL9vV/0Wl0nXhVQ98MgtUy1p565h0157fKhth+aknrsGgEomQ34vilrdOmAnBzs5OMjBzs6OU6eLxt5WVX5LfPqY1KR4UpOeauKhJJ6+dblfIn7u3zxbpryurh6uXoGcPXsWlUqFXJZfYMf5cvV7+Nbjwa2y9Bcda+HNrCQhmokfh2JlZVWmruqAUql6Y8u/jbf+htbU1JTBgwczd+5c4uPjGTNmjGbbrVu3aNeuHZ07d2bGjBkkJCSQkJCARFJ6XGtoaChNmjShVq1aL22DSCRi1KhRHP39J25fOUpCzH3CfpqDmaU9gcEdNXK/LhzLmcObNb9bdRvNxWNhXD65m8S4SH5f9xn5eTkEt+6rkZGmSQhq1JFjx44BcOfa32xZPp383GyN3Laf5nBg2/eaMi06j+T+zVOc2LeWxKePOLxzBXGPb9Gs43CNvS26juLo7z9z+8pRnsU+wNLSEpVKiUhHh4SY+2z/aQ7mlvYEBnfQ6F1dwv6W3cZw8dgOjf27S9hvaGxGwzb9ycnKYNu2bRzeuZItK2Ziam6DQiGvNPtrNe5CWFgYBoYm2Dp5lbLjbddf1f7ft2UJ7dq148iuVaz/3/s4uvlz5tDmalO/1d0/gv5/d/wI+U1ov/+f40eo3/L1t+g8lPsXwrlx4wYAD4Jcae8dQIC9EwD+X86g7trFGp3Rv2zF2MuNgIWzMPH3xuO9YTgN7Mbjpes0Mo9/WItbyCBcRvbBNMCb2is/Y9SoUWzfVHT9WRX57cCOVaxf9iGm5tYoFAqatOkDwOZVc9nz2/80elt3G8Hd66f5e886nsU94sCOlcQ8iqBVl2EA5OVms3frD0Q9uE6K5Cm1G7Zny5YtpCY/xdUrkJ1rPic/N4dGbdR2/LZqLvu2Fulv1XUE926c5tjedSTGPeLgjpXEPrpFi85q/Qq5jA1LpxPzKILhUxajVCqQSCRIJBLy8/MR+P/LWz2GtpCQkBBCQ0Pp3r07zs7OmvU7duxAIpGwadMmNm3apFnv4eGhmcgJ1DMfh4eHs3Tp0le2Yfz48VyNzGbXmvnkZmfg4deAsbN+QU+/aH725MQnZEtTNb/rNO1OpjSVv8KXIU1Pwsm9JmNn/YJZsW4i549u48iulZrf23+aC0C7d97TyKUlxyMSFT178PCrz5BJSzi0YxkHw37A1sGDkR8sx9HNVyPTpkcI+Xk57Fwzn9xsKQ2DG2Dn2Zg/NnxJbrYUzzLtjyGrmP11m3YjS5rC4fDlSNOTcHYPYNysn7Xs7zl8DiKRmGund3Fk1ypEInByD6DPmHmVar+7uzvJaVms+mxIKTuqg/6q9v+BA3uR5eeRJolDBZiaW1Wr+q3u/hH0/7vjR8hvQvv9/xw/Qv2WrX9YyIe0q100pfF78z4GIGbDTm6EzMXAyQ4jNyfN9pyoWC72nkjgd3PxnDqK3NgEbk78lKTDpzQy8WH70bezxm/+NAwc7ci4fofxjepwNTJbUx9Vkd8Ohv+ISATOHgEMDPmvZmhcapJ2/Hj51Wfk+4vZt305e7ctxc7Rg3Ezl+FUED9isQ7Pnj7m4ok/yJSmYmJqiY+PD/HPUtiwdDrOHgG8O6fIjtTkeETiol6Xnn71GT5lCQfClrF/2w/YOnowZsZyjf701EQiLv8NwPdz+wNQOKvOhg0baNKkCQL/PxGphBHJL8zOC8oq092vsZhdF8oZeFEJ9G2sI+gX9Av6Bf2CfkG/oF/QL+ivZP179fyrTH8P2b0qt3/fFVmV6e/eQI8/L1fd5Fy9gqvFu7kyGT437o3te/NClze276rgre9yLCAgICAgICAgICAgICBQFtX3sYaAgICAgICAgICAgEA1ROgkW3kIb2gFBAQEBAQEBAQEBAQEqiXCDa2AgICAgICAgICAgIBAtUSYFEpAQEBAQEBAQEBAQOAfZOjsJ29s378tcX9j+64KhDG0L4Ewy7GgX9Av6Bf0C/oF/YJ+Qb+g/5/UX9WzKP9xqers791Qp8r1CwgIN7QCAgICAgICAgICAgL/IEql0Em2shDG0AoICAgICAgICAgICAi8FikpKQwfPhxzc3MsLS0JCQkhMzPzhcqqVCq6deuGSCRi9+7dL7Vf4YZWQEBAQEBAQEBAQEDgH0SlUr2xpaoYPnw4ERERHD58mD179nDixAkmTJjwQmV/+OEHRCLRK+33rZwUqlevXshkMg4cOFBq28mTJ2ndujXXrl1j0aJFnDp1iqSkJDw9PXnvvff4z3/+oyW/efNmlixZwoMHD7CwsKBbt25888032NjYvJRNKpWKyXOWcvHvMHKypXj41afPmPnYOnpWWO7s4c2c2LeGzPQkHN0C6D3qE9xq1NFsv3B0O9fO7iHu0XXy8/PR0dXH2d2/lFxJbpw/wOHw5aQmxWHj4EG3wTMIqNdGy97DO1do7G0YXB9MvYi4eJicbCmefvXpM2beC9i/heMF9ju5lbZLlp/H3i1LuH5mNzk5uYhEIpzc/ek7dn61sv/OpX1IM7MQi9XPeDz9Ggj++Rf5x83VmeS0LLIz08q043X1V/f2VdX+Efwv2P869lf3/CPU75ut3+qq38JQhpUoGldHa+zs7FgcMhmPzUcwq2A4qnXrxgR+OwfTQF9yY+J5uPBHYjfs0pLxmDQM7xkhGDjakXHjLvse3GL5ql+Rpifh5O5Pn9Gf4F5B/Fw/f4CDYer4sXXwoPvQGdQsFj83Lx7m7F/biIuKIDsznQkTJhC2c0+V6d+9ezc1a9asyNVvLYNmRr2xfW//zrPSdd65c4fAwEAuXrxIw4YNAThw4ADdu3cnNjYWZ2fncsteu3aNnj17cunSJZycnNi1axd9+vR54X2/lW9oQ0JCOHz4MLGxsaW2rV27loYNG3L58mXs7e3ZtGkTERERfPLJJ8ydO5cVK1ZoZE+fPs2oUaMICQkhIiKCsLAwLly4wPjx41/aptWrV3Pm0Cb6jF3A5AXb0DcwZs2S8cjy88otc+PcPvZuWUyHvlN4/4twnNz9WbNkPJnpyRqZ/PwczCztUSjUGWrix+txcg8gdMkELbniRN+/ytZVs2jYph/TvggnKLgDG3+YSkLMA43M8b2hBfbOZ8qCrUgkEs4f2UqvkR8zZcFW9AyMWLNkQoX2Xz+3nz1bFtOx72SmfrGjTLv2bF7E9fP7kMlktO8zCUc3XzJSE6uV/Xeu/U3nzp3R1dPH0sYZOydvwT//Iv906vc+UVFRKOQyJs3bIrSvf9g/gv8F+1/H/uqef4T6fbP1W531XzkRxtXL50lJSQEgRwxrHcpViZGnK43++JnkY+c51fAdHi9fT+2fv8S2U0uNjNPAbtT8Zi4PvlzJqcZ9+XN7GAsXLqTrgMl88OUOnN0D+HVR+fETdf8qW1bMonHbfnzwVThBDTuw/nvt+MnPzcHLvwHdh8wEYM2aNXTqV3X6BV6NvLw8MjIytJa8vPJj9kU4e/YslpaWmptZgI4dOyIWizl//ny55bKzsxk2bBgrV67E0dHxlfb9Vt7Q9uzZEzs7O9atW6e1PjMzk7CwMEJCQhg3bhxLly6lTZs2eHt7M2LECMaOHcvOnTs18mfPnsXT05Np06bh5eVFy5YtmThxIhcuXHgpe1QqFRs2bKBd7/cIDO6Ak7s/gyYuQpqWyO3Lf5Vb7uT+9TRqO5CGrfvh4OJDn7EL0Dcw5NKJIhtbdh1NSmIM7dq1A8DO2Ys+Y+eXkivO6UMb8avTkjY9QrB3qUHnAdNw9gzk7F+bNfaePrCB9r0nEhTcAUc3P9LS0hCJxKiUSpzc/Rk8cREZaYncvnykXPtP7V9HYy37te3KzZZy6Xg4xiYWDB48mM7932folO/IzEhGR0en2tjfY+hsjh07Rsc+kxk1fQXxT+7SvNMIwT//Ev/cuniIAQMGkJebRVJ8lNC+/mH/CP4X7H8d+6t7/hHq983Wb3XWv2bVImIzbfH3V89w3DIDogwhyqBsnR4ThpDzOJY7sxeTefcR0as2kxB+EK//jNHIeH0wlpjQ7cSu30nmnUg2hoUxaNAgBg3sj4OrD/3GzUfPwJALx8uOn1MHNuJfpyVte4bg4FKDrgOn4eIZyOlDmzUywa1606nfZHxrNQOgc+fONGrTr8r0V2dUStUbWxYuXIiFhYXWsnDhwtc6noSEBOzt7bXW6erqYm1tTUJCQrnlpk+fTvPmzXnnnXdeed9v5Q2trq4uo0aNYt26dVr9vMPCwlAoFAwdOrTMcunp6VhbW2t+N2vWjJiYGPbt24dKpeLZs2fs2LGD7t27v5Q9sbGxSCQSfIo1HkNjM9y86/Dk4fUyy8jl+TyNisAnqKiMWCymRlAznjy8VkouKChIS84nqBnRxeSKE/3wmpZeAL/aLYh+oLYlRRKLND1JY2+KJJbk5GQc3fw1OgvtL28fcnk+cVG38QlqWq5dsY8jUCjkpEjiaN68OQD2zt5Y2jhhaetSbey3dfDQ1G+h/Qkx9wX//Av8U2hH27ZtNfYK7euf84/gf8H+17W/Oueff8I/1d3+qq7f6q6/eF1ZysFKBtGGZarFsmk9ko6e1VonOXwKq6b1ABDp6WHRIIikI2fUdgCxBtC8eXOsTEQaO3xrNSP6Qdm2Rz+8VupG0q9OC6LLuBZWyGUA1KlT1L24MvULvB5z584lPT1da5k7d26ZsnPmzEEkElW43L1795Xs+OOPPzh69Cg//PDDaxzNW3pDCzBu3DgiIyM5fvy4Zt3atWvp378/FhYWpeTPnDnDtm3btAYet2jRgs2bNzN48GD09fVxdHTEwsKClStXVrjvkq/ho6KiADC10B53a2phizRdUqaObGkaSqWiVBkzcxukaUml5MzNzbV1m9uQWUyuOJlpSWXakpmepNleuK74b3Mre41M4fEU/122/bbl2pWZnoRYRxeVUqE1JtnUwhaRSFQt7NfR1UOWn6u1v0JbBP9Uf/8U2mFjY6Nlr9C+/hn/CP4X7H8d+6t7/qlq/1R3+6u6fv8N+o1MSlwbKiCjnM+uGjjYkvdMe595z5LQszBDbGiAvq0VYl1d8hLV3X2zdEApAhsbGwz1tO2QlmO7tIz4MbOw1bqu1RxnVjoAlpaWpY6zMvT/G3iTb2gNDAwwNzfXWgwMyn79P3PmTO7cuVPh4u3tjaOjI4mJiVpl5XI5KSkp5XYlPnr0KJGRkVhaWqKrq4uurvqLsv3796dt27Yv7Mu39ju0AQEBNG/enDVr1tC2bVsePnzIyZMn+fzzz0vJ3rp1i3feeYf58+fTuXNnzfrbt2/zn//8h3nz5tGlSxfi4+OZNWsW7733HqGhoeXue+LEiZw+fVrzOy4uDjc3t8o9wCrk/k217d/M7IpIBGNm/vSGLXo5qtr+LGkq894NRqlQoJDLUCjklaq/qhH882ap7u2rulPd/S/YXzHVPf8I9VsxVV2/1V2/gMDbiJ2dHXZ2ds+Va9asGWlpaVy+fJng4GBAfcOqVCpp0qRJmWXmzJnDu+++q7Wudu3a/O9//6NXr14vbONb+4YW1JNDhYeHI5VKWbt2LTVq1KBNmzZaMrdv36ZDhw5MmDCBTz/9VGvbwoULadGiBbNmzaJOnTp06dKFVatWsWbNGuLj48vd7/fff8/u3bs1S+HNbclB7JnpSZhZlF3BxmaWiMU6pcpIM5Ixs7QtJZeRkaGtOyMZU0vtp3eFmFralmlL4dO+Wo06ATB0yjdM+2onJmaWAGSkJmo9EcxMTy71hLC0/dpPxYrbZWphi1IhRyTWITm5yJ7M9CRUKtVba7+tsxfTvtpJ35DPANDR1dPYUNwWwT/V0z+F5TLTkzR2JCcna9n7/7l9VbV/BP8L9r+O/dU9/wj1+2brt7rrL16/CrmMnKwS14Y6YF7OLMd5z5IwcNDep4GDLbJ0KcrcPPKTUlHK5RjYq9+AmihArILk5GRyZdp2mJVju1kZ8SNNT9K6rtUcp4m6N2VaWlqp46wM/QJvHzVr1qRr166MHz+eCxcucPr0ad5//32GDBmimeE4Li6OgIAAzXxGjo6O1KpVS2sBcHd3x8vL64X3/Vbf0A4aNAixWMyWLVvYsGED48aN0/o+UUREBO3atWP06NF89dVXpcpnZ2drplEvREdH3Vejoq8VWVtbExQUpFnq1auHnZ0dkRHnNDK5OZnEPLqBu0/dMnXo6urj7BlE5O2iMkqlksiIc7j71CslFxERoSX3MOIcHsXkiuPhU4+HxWwBeHDrLB6+alsc3fwws7AlKSEaWwcP7F18sLGxISHmnkZnof3l7UNXVx8Xz0AelrC/uF2uXkHo6OhibefC2bPqcRuS+MekJceTlvT0rbXf278htg4eBDVoj46OLumpz7Czs+NhxDmN/U7ufoJ/qql/rO1cMbOw5WHEOY0dJ06c0Nj7/719VbV/BP8L9v9/zj9C/b7Z+q3u+kvWb3GZdF1I1QOP3DLVknbuGjbtm2qts+3QnNRz1wBQyWSkX4nAtr16jKou4JqnnkA1NUtVZMetc3j4lm27h089HpQVP2VcCxfe7N+4cUP7OCtJ/78BpUr5xpaqYvPmzQQEBNChQwe6d+9Oy5Yt+eWXXzTbZTIZ9+7dIzs7u1L3+1bf0JqamjJ48GDmzp1LfHw8Y8aM0Wy7desW7dq1o3PnzsyYMYOEhAQSEhKQSIrGtPbq1YudO3fy448/8ujRI06fPs20adNo3Lhxhd9CKolIJGLUqFEc/f0nbl85SkLMfcJ+moOZpT2BwR01cr8uHMuZw0UzsbXqNpqLx8K4fHI3iXGR/L7uM/Lzcghu3VcjI02TENSoI8eOHQPgzrW/2bJ8Ovm52Rq5bT/N4cC27zVlWnQeyf2bpzixby2JTx9xeOcK4h7folnH4Rp7W3QdxdHff+b2laM8i32ApaUlKpUSkY4OCTH32f7THMwt7QkM7qDRu7qE/S27jeHisR0a+3eXsN/Q2IyGbfqTk5XBtm3bOLxzJVtWzMTU3AaFQl5t7N+3ZQnt2rXjyK5VrP/f+zi6+XPm0GbBP/8S/9Rq3IWwsDAMDE2wdfIqZcfb7v/q7h/B/4L9r2N/dc8/Qv2+2fqtzvpbdB7K/QvhmhvCB0GutPcOIMDeCQD/L2dQd+1ijc7oX7Zi7OVGwMJZmPh74/HeMJwGduPx0nUamcc/rMUtZBAuI/tgGuDNiAED2b59OzvCd/EsLpKda9V2NGqjtuO3H+ewb2tR/LTsOpJ7N05xfK86fg6FryD20S1adB6ukcnOTCMu6g7P4h4CcOjQIQ7vXEXk3YtVov/x48fcuXNH6/pf4M1hbW3Nli1bkEqlpKens2bNGkxNTTXbPT09UalUFY6PValUL/UNWniLx9AWEhISQmhoKN27d9e6Cd2xYwcSiYRNmzaxadMmzXoPDw/NJE5jxoxBKpWyYsUKZs6ciaWlJe3bt2fx4sUld/Ncxo8fz9XIbHatmU9udgYefg0YO+sX9PSLBlAnJz4hW5qq+V2naXcypan8Fb6s4IPSNRk76xetrhbnj27jyK6iSaq2/6SeYazdO+9p5NKS4xGJip49ePjVZ8ikJRzasYyDYT9g6+DByA+W4+jmq5Fp0yOE/Lwcdq6ZT262lIbBDbDzbMwfG74kN1uKZ5n2x5BVzP66TbuRJU3hcPhypOlJOLsHMG7Wz1r29xw+B5FIzLXTuziyaxUiETi5B9BnzLxqZf+BA3uR5eeRJolDBZiaWwn++Rf5x93dneS0LFZ9NqSUHdXB/9XdP4L/Bfv/P+cfoX7fbP1WV/3DQj6kXe2iKY3fm/cxADEbdnIjZC4GTnYYuTlptudExXKx90QCv5uL59RR5MYmcHPipyQdPqWRiQ/bj76dNX7zp2HgaEed63fQqR/A8lUFdngE8O5H5ednT7/6DJuyhINhy9i//QdsHT0YPUM7fiIu/832Xz7R/JbL5RwKX4FolxhXr6BK1z99+nQA3n//faZOnUp1QqUsv7eowMshUlXU91ZAi50Xqu4Vfb/GYnZdKGdgRCXQt7GOoF/QL+gX9Av6Bf2CfkG/oL+a6d+r519l+nvI7vHHpaqzv3dDnSrXX13p+/6DN7bvXSt8ny9UjXjr39AKCAgICAgICAgICAj8mxDe0FYeb/UYWgEBAQEBAQEBAQEBAQGB8hBuaAUEBAQEBAQEBAQEBASqJUKXYwEBAQEBAQEBAQEBgX8QYRqjykOYFEpAQEBAQEBAQEBAQOAf5J1J997Yvn//seomGnsTCG9oX4KqnuX4kzV5Vab/q3EGtOx1vMr0n/qzDfM3yKpM/2ej9Dh9O7PK9LcINGXo7CdVpv+3Je7cj6w6/X413JFe2Ftl+s0a9+Cng1Wmnve6wJZTVfdsbVhLUZXbX9WzXFZ1/tl9sers79NIh/1Xqy4/dKuvV+1nMa3qWUCr2v8bT1SZeka2hpSbp54v+IpY127JmTvSKtPfvKYZG6ru9MuoNvDjgarTP6krVZ4fNp2suvw/opWoytvvvitV1766N9Cr8vxQ1bMoV3X8VFeUyqo7r/9/QxhDKyAgICAgICAgICAgIFAtEW5oBQQEBAQEBAQEBAQEBKolQpdjAQEBAQEBAQEBAQGBfxDhO7SVxytNCnX27FlatmxJ165d2bu36sbtVTZt27alXr16/PDDDy9dVqVSMXnOUi7+HUZOthQPv/r0GTMfW0fPCsudPbyZE/vWkJmehKNbAL1HfYJbjTqa7ReObufa2T0kxtwmJCSEkWPew8hATHSiij/OyEnOKL96WtfRIchDjJ2lCJkcniQqOXhRQVKxMu8016WGsxgbcxE5uQp0xCIAbtxO59tVD4iNzylX/7ihHowbpn180bHZDJ90UfN71hRfGta1wsXJiHy5igL1RCeq2HNOQUoFw5Ja1RJT012ErYXa/hiJisNXFCRnFMkE+4qo7SXmzJHfCA0NJTFRgqunL8PfnY23X60y9cY9iWT3bz8RFXmHZEk8Q8bNpHOvYaXkjuzbzoHdG0hPSyawZgD9Ry2iSxtPTIxE3IvKZ82uFBKS5OXa37GpKZ2amWJrpX4uFPtMxs6/0rl+L1cjY2+ty4ielty/votff/2VpKQkPL28mfjeFPz8A8rUe/DAPo4eOUx0dBQAPj6+jBo9TiMvl8vZtGEtly5eICEhARMTY1xcXHgW94Q0aRa+bs7MGtWXWjU8ynd+4b7OXuWTVRtp06AW300fp1mvUqn4eecBdv19jszsHOoHNyRH15sHNw6Tl5OBs1cDOgxagJW9Z7m6Yx9e5NKRUBJjbpGVIaHXuyvxqdNRS0alUnF23zIiL28nOSUVsUgMIhHuvsH0GDkfG4fy9QNcOLqZMwdCNe2r27BPcfGuU0puSHMVTVt1Jj0pBrGOHi41Glaq/RGnN5CZmYlIJMLB1Zf+IZ9rtfOS3Dh/gMPhy0lNisPGwYNug2cQUK+Nlt7DO1do8k3D4Ppg6kXExcNVkn/iHl0nPz8fHV19nD38eaeEXFn2H9qhtt/WwYNuQ7Ttv3XxMOeObCMuKoLszHQmTJhA2M49SNOTcHb3p//Yj/HwqV2u/mvnDrJv+wpSJHHYOXrQa9h0Auu31mzfH7aSq2cPkJacgI6uHnVrByLXd+be9RPkZEvx9KtPnzHzXsA/Wzhe4B8nN/9S/pHl57F3yxLuXNqHNDMLsVjdqcnTr0Gl6r9+Zjc5ObmIRCKcPPzpP24+7hX4//r5AxwMK/J/96EzqFnM/zcvHubsX1Xn/+Js//UzzvwVhldgCyRx98nNzsDVpwHdhy/A+jnt99Lfmzl7MJTMdAkObgF0GfpfXLy0jzs28ip///YR0dHRABgbGvDtxx9QP9CvTJ3Hzl1m/c69xCYkIlcocHNyYGivznRr07xM+cU/b2D34eOYmJqTl5eLu6cvw8fPqvD8smvLT0RF3iVZEs/QcTPo3Fv7/HIv4gr7d20kOvIOaalJrFixgq37b3P1ZBh5ORm41mhAtxf0z7lDBf5xDaBzOf658/f3XLx0BZVSASJwrdGYDoM/f25+u3y0KL/1DCmd3x5eP8SN01uRPLlCTk4OOrp6OHsEVHp+2LVrF9+HHubqyTByszNw82lAtxHPz/8XjxbGTxIObgF0Haqd/zcsGUn0/YtaZURiHWrUbFRl7dfZw5+BIfOe2772hxW1r55DtdvXgR3a+c3R3oakVCk5WRk4ufvTZ/QnlZIfHKz0GT5sMK1atUJPT49L/Sfz7I8jFfrEunVjAr+dg2mgL7kx8Txc+COxG3ZpyXhMGob3jBAMHO3IuHGXfQ9usXzVr0jTk3Byr/zzy+7du6lZs2aFdr+t9Bx/+43te8/qwDe276rglboch4aGMnXqVE6cOMHTp08r26a3ktWrV3Pm0Cb6jF3A5AXb0DcwZs2S8cjyy5/I6ca5fezdspgOfafw/hfhOLn7s2bJeDLTkzUy+fk5+NVpxY8//sjIkSPZeTyTH/+UIZOpGNNFD90Kxrp7OYo5d0fBT3/KWHtQho4YxnTVQ6/Ye/enyUp2nlRPVqBSqXgYlYk0U0ZunoLvP6+Nvp6owuN+FJ1F75FnNMvkj65qbb/3MJOvl94r0A8JKSqy80Amh5EdddGtIMI8HERcuKdk9T45G/6SoyOGUR11tezX0xWxLXwfCxcuZMqUKXz2/WbcPP34/vP3yUhLKVNvfl4udg4uDBg5FQsrmzJlLpw6xLa139N78ATmf7eZ8ePH072NPb9se8J/lz8jL1/JnBB7LVtKkpKu4Lf9aXyyLIFPliUQ8TCXD0fb4eqgB4CBnoiPx9tx9tRBFi5cyJBhI9m8ZQu1awUx779zSUtLLVPvzRvXad2mHV8v/IZvvluKra0d8z6dQ3JSEgB5eXlEPnzI4KEj+GH5Krr16MWtW7fQEYvZ9MUM/NydmbrkF1LSK57k5KkkhaW//UF9f+9S29bvPcrWQyeZO3Yg6xZ8gEQi4frp32jX/1OGztiOnr4RO38MQS4rP/5l+dnYufjTfuD8cmUu/bWaayc20qJFC/T0DHFwD8DY1BJdPQM2ff9uhfpvXdjHoW2LaNN7ChPn78TBzZ9N/3uXrIzkUrKTJk0iI0Wdq1r1nlWp9l8+uobc3Fw69nsfr4BGpEriCC3RzosTff8qW1fNomGbfkz7Ipyg4A5s/GEqCTEPNDLH94YW5Jv5TFmwFYlEwvkjW+k58pNKzz9mlvYoFOoJOyZ+sh4n9wBCF08o1/6o+1f5beUsGrXpx7QvwwkM7sCG/2nbn5+Xg6d/A7oNngnAmjVr6DpgEh8uDMPFw5+fFk5EWo7+x/eusmHZbJq268uHi8Ko3bA9od9OI76YfnsnT/qP/ZjZS3YybYH6YcKlEzvpOngmUxZsRc/AiDVLJlTon+vn9rNny2I69p3M1C92qI97ifZx79m8iDvX/qZz587o6uljaeOMnZN3peq/fn4fMpmMjn0n4ejmS0ZKIr8uqtj/W1bMonHbfnzwVThBDTuw/vsS/s/Nwcu/Ad2HVI3/C7lx4S+iHtzAxMSEmAeX6DZiAWM/3o6+vhFbfqi4fUVc3Mfh7Qtp1WsK7/53Fw6uAfz2Q4hW+42NvMqm70YTGxvLhCF9+Hbufwj09Wb24uWkpGeUqdfc1ITR/Xuy+uuP2fjdZ/Ro14KvVq7l3LVbpWSPnb+iWV+rXlMWfL8JN08/vvtsarnnl7y8XOwcXRk46v1yzy95uTm4efkyYuJHABw+fJiLRzfSbcQCxszdjp6BEb8trdg/ty/u46+whbTqOYWQT3dh7xbA1qWl/bN16bsYGBigq2dA2wH/pWWvD9EzMGLXTy+W39oNKD+/yfKz0TcyJT8/H4ChU76pkvywa9cuLhzZSPcRCxj3sdo/W/5Xcf6PuLCPw9sX0brXFMbPU+f/LT+Uzv/1Ww/kvffew8DIlAHjv2LipxuqpP127jcRRzdf0lMT+XlRBe3r/lU2Lp9Nk7Z9+XBhGLUatmfNd9rty87Jk35jPmbW4p10fGcC0dHR5GRJmTA3FGf3gErLD627DuPevXs8e/asXD8Ux8jTlUZ//EzysfOcavgOj5evp/bPX2LbqaVGxmlgN2p+M5cHX67kVOO+/Lk9jIULF9JlwGSmfbmjSuKnOqNSKd/Y8m/jpW9oMzMz2bZtG5MmTaJHjx6sW7dOs+3YsWOIRCIOHjxI/fr1MTIyon379iQmJrJ//35q1qyJubk5w4YNIzs7W1MuLy+PadOmYW9vj6GhIS1btuTixaKnauvWrcPS0lLLjt27dyMSFd2MLViwgHr16rFx40Y8PT2xsLBgyJAhSKXqi/oxY8Zw/Phxli5dikgkQiQSERUV9ULHrFKp2LBhA+16v0dgcAec3P0ZNHER0rREbl/+q9xyJ/evp1HbgTRs3Q8HFx/6jF2AvoEhl07s1Mi07Dqatr3GU79+fX788UciovJ5lqoi7IQcMyOo6V5+Fa0/JOPqQyWJaSoSUlTsOCnHylSEi02RXy7eUxL1TP3Gdu1v0Sxefh8bawNWb47CxtqAVk1tKzx2hUJFSppMs6RnaL+x/ONgPNcj0gE4dl3JH2cVmBuLOHpVgZkxBLiXf8O86YiCa5EqJOnwLBV2nVZgaSrC2bqozLk7Sn7fsY5BgwbRv39/XNy9GfXex+gbGHLyyO9l6vXyDWLQmA9o0qoLurr6Zcoc/GMTrTv1pVWH3ri4edOpUyd+Wb2OA3vDeJIgY9W2ZKzMdWgYZFyu/Vfu5HDtbi4JSXISkuRsP5hObr4SH3f1Pv08DbCz0mX9erX97dp3xNjUki+++AJDQ0MOHyp72t0PZ8+lR8/eeNfwwc3Nnan/mYFSqeL6dfXDBBMTE774ejGtWrfB1dWNi+fP07lzZ+KTUjE2MGDu2AEYGujxx4kL5dquUCr59MdNTOjXBRc77YsylUrFbwdOENK7E22Da+Hj5kRaWhoikRiVUoGdSwBdRy4hKz2RyBvlx79XYBta9JyOT91OZW5XqVRcOb6BRp3e48yZM7TpPZmRM9aQnZlGzQYdkaYlcvdK+frPHVpHg9YDqd+yP3bOPvQc+Rl6+oZcPRWuJRcffZsTJ07QuPNEAMxtXCrVfiMTKwYPHkzHvpMZ9cFy5PJ8RCKRVjsvzulDG/Gr05I2PUKwd6lB5wHTcPYM5OxfmzV6Tx/YQPveEwkK7oCjm5+W/ys7/6QkxtCuXTsA7J296Dt2PnoGhlw8Xo79Bwvs7xmCg0sNugxU23/m8GaNTIOWvenYdzI+tZoB0LlzZ5q07Yujaw0GvjsPfX1Dzh/bVab+4/s3EVC3Be17jcPRpQbdB0/F1SuQkwe3aGSCW/bAv3YzbB3ccHStQXx8PAAW1vY4ufszeOIiMtISuX25/LcNp/avo7GWf+Zr+Sc3W8ql4+H0GDqbY8eO0bHPZEZNX0H8k7s07zSi0vQbm1gwePBgugx4nxFTvyMzIxmxjg4XyvH/qQMb8a/TkrYF/u86cBounoGcPlTk/+BWvenUbzK+VeR/gLSUZ4SvW8iIKYvIycnBK7AF/vU64uAaQO9xS5CmJXLvavnxef7wWuq3GkS9Fur2232Euv1eO13Ufg9vW4ihifpcPnZAL1oE12Hpf2dgaKDPnqNlz3rcoFYAbZs0wNPVGVdHewb36EQND1eu39G+IU9MTuX70C2YGBliYmKCt18tXNy8GTVpbsH55Y8y9Xv7BjF4zH8qPL/UCW5B/+GTCW6qbld///03LXtMKvLP2BfzT72Wg6hb6J/hn6Grb8j14v7ZvpDg9iO4e/cuTbtOoV6r4TTs8C5dR36rzm83K85vzXuUn98AajbqgzQ1np49ewJgY+9WJfnhzz//pFXP9/Cv3wEHN3/eGbdYnf8r8M+5w+uo32og9Qryf4/C+CmR/3X1DAkPD6fDO+/RsHVfPH3rV2p+KGy/3Qa+z+hp35CZnoxYrFNu+zpRrH05uNSg+6Ay8luLovx2/fxBBgwYgFyWh1yWT79xav9XRn5QmXjxww8/aK6Tn4fHhCHkPI7lzuzFZN59RPSqzSSEH8TrP2M0Ml4fjCUmdDux63eSeSeSjWFhDBo0iEED+uPg4lMl8SMgAK9wQ7t9+3YCAgLw9/dnxIgRrFmzptSHgRcsWMCKFSs4c+YMMTExDBo0iB9++IEtW7awd+9eDh06xPLlyzXys2fPJjw8nPXr13PlyhV8fHzo0qULKSllPyEtj8jISHbv3s2ePXvYs2cPx48fZ9GiRQAsXbqUZs2aMX78eOLj44mPj8fNze2F9MbGxiKRSLQaj6GxGW7edXjy8HqZZeTyfJ5GReATVFRGLBZTI6gZTx5e05I1NgB9fX3OnDmjWZcng1iJCnf7it+gFsdQ/WKQ7BIPHa3M1H9v3E6ne0dHnibkEB2Tze37GdQKMK9Qp6uzEbvXNWX76sbMmxmAg51BKRlnB0NA3eW5vo+YFKmKpAyIk6hws3sJ+wuuDXLyi+JJoZCRGn+b5s2LuouJxWIC6zQm8t7NF9ZdHLlMRnTkXQLrNgbUb1JFIhE37meSlaru/pGTqyIyJg9fj9LHWxYiETSra4yBvpgH0eoK0NMVkZeXT2bqfY39KqUKsVhM40aNuXf3xbqa5OXloVDIMTU1K7VNJpPx8OF9/Pz8EIlEmJoYqfUH+XHjYVS5On/ddQhrc1P6tG1aalucJIXkdCmNa/kV/U5OxtY5gKdR6ptqAyMzHD3qan6/CunJsWRnSLB2rIFEIsE7sDmGxma4etchIeYert51iIm8VmZZhTyfp9EReNcsiguRWIx3YDNii5WR5eUQ9uMHKJVKfOp01qyvTPsz059p6tfQ2Az3GnUxNbclukQ7LyT64TWtvADgV7sF0Q/UuSRFEos0PUmTb1IksSQnJ+Po5q/JN5WVfwrlgoKCtOR8yshTWvaXuJDwq9OiTFsUcnXvkDp1irqXicVi/Go3Jep+2bZHPbiOX21t/QF1m5crnxgfRXJyMvqGxji5q7vlF/qnvDqQy/OJi7qNT1BR/Bced2GZ2McRKBRybB08NPnf3tkbSxsnEmLuV5r+FEmcJn4K9VvZuRD9oGzd0Q+vaW5UC/Gr04Lof9D/SqWSzSvn0r7nGPQNDFEqlVrdQw2NzXDxrkvso7Lbl0KeT3x0BF4l2q9nzebERarLZGUkE/f4OlnpEs6cOUP3kOlMmreYm/ciaVQ7kFv3IsvUXRyVSsXFG7d58jRBq4uyUqnk8+W/MqRnR6Ji4zEwKMrzYrGYwLqNeXjvxnP1vygZGRl4FjtWQ2MzXLzqEleRf56U9o9XzeYan2ZlJPP08XXEYh0kEgkXD/1M2LIRxEVe0uS3+Mevnt8K7UiMiaBBgwaadZWZHwpJTU3VOlZ1/NQhroL8Hx8dgVdgSf80I/aRdpmbZ39HIpFw7sg2Dmz7nvy8nErND8Xbr4OLN1a2TljbOWvyeUmiHlzHr4R//Os0L1NeLpcR+/g2MpkMQ2MznD0CEIvF+NZqVin54WWxbFqPpKNntdZJDp/Cqmk9AER6elg0CCLpiPpaVg7EGkDz5s2xNlVfC1ZF/FRnVErVG1v+bbz0DW1oaCgjRowAoGvXrqSnp3P8uPYH1r788ktatGhB/fr1CQkJ4fjx4/z444/Ur1+fVq1aMWDAAP7++28AsrKy+PHHH/nmm2/o1q0bgYGBrF69GiMjI0JDQ1/KNqVSybp166hVqxatWrVi5MiRHDmifgJnYWGBvr4+xsbGODo64ujoiI5O2f158/LyyMjI0CyFb3JNLbTfZJla2CJNl5SpI1uahlKpKFXGzNwGaVqS1rrCG9HkZO0uGJm5KkyNXuyGUAT0aKJL1DP1G9viNAlQH+fP3zagabA1H/z3BnK5itS0fKytyn7CDHD7vpSvf7jLzAU3+XbVA5wcDFm5qB5GRtp+69vdGYDx3fXwcRGz4bAchRIyc3kp+7s20iE6UUliWtH6/OxUVCoFNjbafjS3tCG9hB9fFGlB3ZgX1I2+rtrGjCwxstyibsDpUgWWZhU3ETdHPdZ+4crGr90I6WfN9xskxCWq32I/eJLHs8RUUCmxsbFBJBJha2uFSCTCxsaG1JSyuxyXZN3aX7G2tqFe/QaltmVkpKNUKtmzZw9dmtbH1Ej9cMHa3IzktLKful6794jfj5/n05BBZW5PTlN35bOxMNP6bWphR3ZGkc+NzWy0fr8s2RkFbaeg64uJuU3BX1uyMpI0f8ssK01FpVRoyhRiYm5LZnpRmQPbFmLr7K2xtziVZb+qoH4LUbd5FZnlxGdmWlKZuaTQ7sJypha2Wr/Nrey18k1l5J9COXNz7QdbZhY2SNPLt9/MvKRe21J5DSA7S917o2QPGzMLGzLK8Y80LQmzknZb2JJRwp6Iy8eYPboRC2f0AmDY+99jYmal2W5qYaMVC1p2afyj3UPF1NxG4+/M9CR0dPWQ5ecW6LPV/M1MT6oU/WIdXVRK7fxmZmGLCFG5/peWET9mFv+s/4/8EYpYrEPrbiM0+9U3NNEqY2JmQ1Z5/sksu/2amtuQWdAmUyUxgPqmtEuXLvzvkw/w9/Jg6mffoqerQ3Jaepm6ATKzsmk/YjKthkzkw4VLmTFuGI3rFj202bh7PzpiMR1bNEGhVGrGRhdiYWFNRmrZXSJfFROzkrmq6FhLUp5/ivs0LUntnwt/rQeg25jvsXcNZOfKMaQmRmFsZkOW9NXzG0BOltoOKysrrfWVlR+K87xcXpxC/5g+p0ytJj1p1XMSAC27juLK6T/Z9pO6G3jVtV8bRCLxy7evEvIRV47xcUgzlEoFx44dY8KcXzX5zdS8fP+/TH54WQwcbMl7pq0n71kSehZmiA0N0Le1QqyrS16iuu1k6YBSBDY2NhjoFben8uNHQOClZjm+d+8eFy5cYNcudVcKXV1dBg8eTGhoKG3bttXIFX8a7ODggLGxMd7e3lrrLlxQd4eMjIxEJpPRokULzXY9PT0aN27MnTt3XupgPD09MTMreovl5OREYmLiS+kAmDhxIqdPn9b8jouLe+G3uS9Ch3Yt6R1cdKN35v7rPynp1UwXBysxv+zNp663mHdaFFXt1qMyWtWGuV/dokdHR774KJBJs5//5Pbc5aI35JFRWdy+n8GO0KZMDfGmY2sHzbZ5iyMY2s+N3/6WU99HzKA2uoTuL38ypbLo0USMvaWINQdertyr4GJnwpUrV9A3MEQsFnP7SfkTYz2PpxIZc35IwNhQRJPaxkwaZMPnPz0jLlGONEvJrzuLfOjj5Yo0M5vcvPwX1h+2fSsnjx/j68Xfoq9f+uGDQq4e+6hSqZgzdsBz9WXl5DLvpy18EjIISzPTMmXO3VSPie4z8ytEIhE/zHz3he19Ee5c/IMj2+ajUqltVyoqt86zM1P5enIDVEoFcnk+w//zMw+uH6vUfVQl92+qc883M7siEsGYmT+9YYveLi6d2sP21Z8B6rgfMO5Tdqz5kl1rFjD187BSF3MvS5Y0lXnvBqNUKFDIZSgqOT6rO4d2/sS+7SvQNzDkozGNmfDRqirZT/ExXm3btsVDNwt/bw8u3bzDwydxFZY1NjJk/TfzycnN49LNOyxbvw0XBzsa1ArgbmQU2/f9xbol86rE7rPH97P+x6+rRDeo89uSqfXVE0ABvnXaEXHhT2yd/PAIaEnM/bNEnA9/jpY3x9XTf7JzzQKAUr37KoPszFQWTSl6+Dt0mjp/1mrcGXuXGvy6aBzJz55U+n4rG5/Axrz38WqWLxhJ3bp12bh8BtM+2/ra+U1A4N/KS93QhoaGIpfLcXZ21qxTqVQYGBiwYsUKzTo9vaJHMSKRSOt34Tql8sUHJIvF4lKJTyaTlZJ73f0U8v3332vGZAHk5+czZMgQMtOTMbe016zPTE/CyaPsmdWMzSwRi3VKDXyXZiRzIy2fI7eKjqfw4XDJt5CmhiLiU55vf6+muvi7ifl1Xz4Z2XDniZIYSdFNk66O+uY5/lkuny66zf7fWtC6mS1Wlvo8fJT5XP2FZGYpiHmaTVa2grH/uaRZr6+nPoC0TBXbjyuYM1iXAHcRpoaQkPr8E1b3xmL8XMWsOSgnI1t7m76xFSKRTqm31xlpyVhYVjz+tzyy8vXo168/w8fPIqhuU0QFUzObmyjRMyx6Em1hpkPU09JxVhyFAp4lqy94H8el4+1mQNeWZoTuVL99fRBnACIxycnJREbFolSq8PZwISk5GStrq4pUszM8jPCwrXzx1WK8vEpP3CSXy/n5p5WAetIjU6Oi+E/JkGJjWbqLcmxiMk+TUpjxfVHvB2VB22oy+kPCl8yhfaM6rN59iK+mjMTbxYF8mfrCKTNdgkuNoguFbGkydq5lz9RcETVqt8fJsy4ZqfGErxhNYYRkZSRjZmlPVkYSDm41eRZzBwe38tqXFSKxTqkJQLIykrB19GLE9F85sedHrp/Zzeal7wGwep56lsQ9oVNxqdEQlUr1SvZrbDC3A0BUUL8UvORUt3kRpuXEp6mlbam8oH7jp5av1agTR3avYuiUb7B3qYFCpm7LGamJePoHa5V5lfxjVsyuQrmMDO0JdqTpyZhZlG+/NKOk3iQtvRr9JhYApKWl4VhCv3k5/jGztC01oYo0PQnzQv8Et8PDp+ihqVxWlOsuHg+nXe8JgLoenDzKrt8i/2g/9c/MSMbW2YtxH60m+sE1wn6ei46uXoG+JMwt7TR19eTh9VfSXxgXpha2KBVyROKC/GZZdKymFjaYW9iV659S9Zr+z/lfpKO+bJAVTBS08osQAE78sYLrp3cyddFRALKkyTi4leMf07Lbb2ZGMqbmhf7Rbl8eBUNbPF2duP3wMT7urmXqBvU1g5uT+qGrn5c7UXHxbNi1jwa1Arh25wGp6VL6vjdbc12RlJTE1nU/cOjP3/h29Z+kp6dgXs6ET8+jXuPWWjMkz5nUF1D7w6zY9UNWxsv7J0uajI2jF0M/CCUjNZ7N343GzsVXs83Ewh4rxxpIU5+q87PLq+c3ACMTtR2pqdq9iV4nPwQ2aK+Z4TY95Rm/fD1GbX9GSf8k4Vhe/i/wT2Y5+X/4B79q1snl6jjNTE/SzAyc/OzJK+eHctuvdZFvTM2tsbCyL6kWqKB9lWiPBobGuNeohVisw8CBA7l1ZzEXjoXT/p0JZGaU7/+XyQ8vS96zJAwcStjpYIssXYoyN4/8pFSUcjkG9gW9rRQgVql7HxafULqyzi//Bv6NXX/fFC/c5Vgul7Nhwwa+++47rl27plmuX7+Os7Mzv/322ysZUKNGDfT19bXeiMpkMi5evEhgoHpKaTs7O6RSKVlZWRqZa9euvfS+9PX1NbN5VoS1tTVBQUGapV69etjZ2REZcU4jk5uTScyjG7j71C1Th66uPs6eQUTeLiqjVCqJjDiHvWtNsvLQLNIc9U1zs2ZF4wYM9MDVTsSTxIqDvVdTXQI9xKw5ICO14N40Xw4p0qKlsAtyw7pWiFCP9zQx0SXQz5xbd8ueKbIsjAzFuDga8UySR1x8rmZ5/ER9F+rtVBBOIvV4WBc7ETGSiu3v3lhMTXcx6w7JSSvj3lpHRw8rp0DOni0at6FUKrlz8yI1/MufFr8iRDp6iPSMOXf6OLkyFTl5SlQqFbX9TDGxUseckYGIGm4GmvGwL4pYpB47q/kt1sPYwo+zZ8+iVKowMjJAJIJLFy/gH1D+lOnhYdvY9tsmFnzxNb5+/qW2y+VyFi/8koSEeGrU8OHmzaLxxEqlkosRD6jj41mqnKeTPVu/nsXmL2dqltb1g2hY04fNX87EwcYSHzcnbCzMiElIws3BDm8XB2xsbEh6ehdnz/oA5OVkkhB9XfP7ZdA3NMXSzgM33yYYm9uRkRyLnZ0dj+6cJS8nk9hHN3B0CyD20Q3catQrU4f68zJBPLpTFBcqpZJHd87h7tcQawcPOg6YyaTP/uC9+buwsrIiuO0YANr0m0vbAZ+8sv2FWNi4Ymxuh6mlgyY+c3MyeRJ5ncyMZDx8yrbdw6ceD4vlEoAHt87i4avOJY5ufphZ2JKUEI2tgwf2Lj7Y2NiQEHNPk29eJ/+4F7OrUC4iIkJL7mEJuZL2R5Zhf1m2FN4M3rhRNB5RqVRy/9Z5PP3Ktt3Tty4Pbmnrv3fjrEbe0MgEO0d3zeLoWgM7Ozvy83M0F6+F/imvDnR19XHxDORhCf88jDiHt39DbB08CGrQHh0dXdJTn2FnZ8fDiHNI4h+TlhyPk7vfK+svLOPqFYSOji7Wdi6a+El8qtafKnmKh2/Zuj186vGgrPj5h/zfvP0APvpmF7OXhDN7STizFu9ALBbj4d+YYQU3Enk5mcQ9uo6rd9ntS0dXHyePIB6XaL9Rd87iUkNdxtLWFTNLe0wsbLXyf3RcPOkZmdTyr1Gm7rJQKpXky9QPHru1acbG7xaw/tv5bPhuATXcXTAyMqJbn5HMXLBcfX65cREf//I/K1IRRkYmODi5aRYAc3Nzoooda15OJnGPr+NSkX/cg4i6W9o/7r6NsLb3wMOvCaaW9uTnZWNnZ0fMfbVsWmIUJmZ2JERfx8nr1fNboR32bkFcvVrUo+t184OBkQm2jh7YOnpgZecCgJWVlVYsqOPnBi4V5H8njyAtn6qUSh7fPYe7rzr/Fy52zj6a9vv0yV1A3T2+qtpvalI8qUlPNfm8JJ6+dblfwj/3b54tU15XVw9XL/X1j1KlQi7PV9tx61yl5IeXJe3cNWzaa8+7YduhOannrgGgkslIvxKBbXv1tawu4Jqn/tRnSqb6WrAyzy8CAsV54Te0e/bsITU1lZCQECwsLLS29e/fn9DQUL755puXNsDExIRJkyYxa9YsrK2tcXd3Z8mSJWRnZxMSon7y26RJE4yNjfn444+ZNm0a58+f15pd+UXx9PTk/PnzREVFYWpqirW1damxM2UhEokYNWoUK3/8CRtHD6ztXDm8YxlmlvYEBhd9t+3XhWMJbNiR5p2GA9Cq22jCfpmLi1ct3Lxrc/rgBvLzcghu3VdTRpomQZqexF1DBZMmTWLTvqekZaXwTgtPpDnqt62FjOuqx+1oBefuqNf1bqZLHW8xm47IyJOpMDVSy+Xmg1yhngyqtpcOD+PU8mOHetC+pR0ymZKWjW1ITsnj5LmiJ5A/fFmHE2eT2LlX/XmTKeO8OX0hmYTEXGytDQgZ5olCqeKv4+pu3M4OhrRvZcfFq+qnt23rignyEKFQQoCbGGk23H1SdEM7upMOd56ouHBPbU+PJmJqe4n57W8F+TIwNSywX6a2H9TrmrQfxfbtn1CrVi0MbXz5c+cW8nJzaNmhNwCrl87DytqOASOnAupJn57GPlL/L5eRlpzIk8f3MDA01lxgdOk9gl+XzcezRk28fGtxODaeCe+ORmGWTEqmHgM7W5CaoeBSRNEr40/G23MxIptDZ9R33kO6WnDtXi5JaXKMDMS0qGdMTW8DFoUWPSRo09AE/dzBbN++EFc3D1o0b8rcpf8jJzeXjp26APD9t4uxsbFl9Fh1vO8I28rmjRv4cPZcHOwdSS2YHM3QyAgjIyPkcjmLvv6cyIcPmbfgC+7evcOvv/yIA5k0rFmD7X+dJicvn16t1ZNezftpC/ZW5rw/uCcG+nr4uDlpxbeZsTpwiq8f2rU1ob8fxs3RFhc7aywtLUlOSUEk1iHp6T3O7F2KiYU9NYp9t3DHitH41OlEvdbqMfb5eVmkSYq6dmUkx5IYewdDYwvMrZ0RiUQ0aDOKC4d+oluX9uz7YyW3Lx7A2NSKu1f/wszSnoAGRfo3fDOGgAYdadxBrb9p5zHsDp2Ds2ctXLzqcO6v9cjycqjXop86dizsNG95xo0bx4pVq4GCb8fuXVZp9p87sIqtW7fSPteayIjz6Orqo1IpNe18209zsLCyp+vgGQC06DySn78ezYl9awmo14br5/YR9/gW/capu9GKRCJadB3F0d9/xrYg31haWpKSkoJYR5eEmPuVmn+CGnXkSPgyAO5c/ZuIy0fJz8umYZsi+82t7OlWaH+Xkfz8VTH7z+4j7tEt+hfYD5CdmUZacjwZqepccejQIZQm3vgGNuLSqT3k5+XQpE0fADatnIuFtT29hk4HoE23ESz/fCx/71lHYP3WXDmzn5hHEQyesACAvNxsDu/6hVoN22FuaUeWNBV7e3skERGYmFqSEHOfQzuWYW5pT2BwB41NqxeOJaiYf1p2G0PYL3NxLfDPqRL+MTQ2o2Gb/uzbsoTOHdrx+65VXDi2A0c3f84c2lxp+m9eOMi2bdt4lm1BxOUjmJrboJDLaFTg/99+VMdP9yFq/7fsOpIfvxzN8b1rqVm/DdfO7iP20S0GhGj7PzUpnoy0yve/iZklJmaWFMfU1JS4R9dJTniMQi7j2O9LMbO0x79+UXxu+m40/vU70ai9un016TSWP9Z8hFNB+z3/13pk+TnULWi/IpGIpl1C+Hvn92zduhVzcnn6TMKjmKcYGejTs516mNJny37FzsaKycP7A7B+515q1vDExdEemUzGmSs3OXDiHLPHq/drYWaKRbHhFmP69+S///uZZEkCKqWKDT8tLDi/qMdmr/5hHpY29gwc+T5QcH6JUZ9fFHIZqSkSnjy6h4FR0fklNyebxPgYzT4aNGjAyT0rMDQyw9mrDsfL8M/m70fjV6+Ef9Z+hJNHLZy96nChwD91ivmnWecQTvyxnM6d2nHw4EoSoq+T/OwhhiZW6vxWu0h/+IrR1HiJ/AaQm5WGT53O7NnzAwAPI85zZPdP5OdmVWp+aNGiBYf+XImxqRWOHoEc263ObwHF/LPxW3X+L/RP005j+H3NHG3/5BXFT0riE26d34Nv7da88847bNi4An1DE1w8AzmxN7TS268kx5ybl45gam6NQiHXtK/Nq+ZiYWVPz4L21brbCFYUa19Xz6rb16DxCwB1fvtr9y8EBavzW+2G7dmyZTmIRLh4BrJz7Wfk5+VUSn7IyUwmICCApIJPAoq9XTGvG0B+Sjq5MfH4fzkDQxcHro9VjzuO/mUrHpOHE7BwFjHrwrFt1xSngd242HuiRvfjH9ZSd81i0i7fIv3iDUZ0Dubb7dtRmgXi6FmbUwc2IMvLqdT4efz4MQC2trbY2ZXds+VtRfkv/HzOm+KFb2hDQ0Pp2LFjqZtZUN/QLlmyROsp8MuwaNEilEolI0eORCqV0rBhQw4ePKiZiMDa2ppNmzYxa9YsVq9eTYcOHViwYAETJkx4qf18+OGHjB49msDAQHJycnj8+DGenp4vVHb8+PFcjcxm15r55GZn4OHXgLGzfkFPv2h2xOTEJ2RLi7rm1GnanUxpKn+FLyv4oHRNxs76Raurxfmj2ziyayXLgWnTpjFo0CDMzc15EJ3GuoNWmhs7AGszEcaGRW//mtRUT840vrv22ModJ9Sf85HLwdNBRIsg9VN6lQpqeJmou4Hripk5/yb5sqIbThdHIyzNi7qt2tkYsODDmpib65GWLuPG7XQmfniVtAx1N9w8mZK6QRYM6u2q0e9gJUIkUnej3vSXHHmxtmplJsLYsGh/jf3V9o/roh2Gu07LuRZZ8FbZX8ysQb3Z5JXBsmXLkEgk1KxZky+WrMLCUt2tJUWSgLjYJ5zSUiUsmFH0ofsDv2/kwO8b8Q8K5qMvf1Hvu2VnpBmp7N76E+mpyQQG1iQlrwbvDfXE2FDMvag8FoUmIis2fM7BRhczk6IJscxNdZg82AZLcx2yc5U8iZexKFTCzQe5GhknO12WfjWYPb8r+fXXX/nu2yV4edXgs8+/1sS3RJKo6fYMsH/vHuRyGYu+/lzLL0OHjWTYiFEkJydx/pz6ifC099/TbP9u0250dcT4e7iyfNYEzaROCcmpWv55EUb3aE9uXj5frwlDmp1Dg+CGmDo14e8dX5CXk4GzdzD9Jv2Krl5R/KcnxZCTWRT/z57cYsfyUZrfx3ctBCCwcV+6jFDPPt6w43hk+TmcPLkNWV4OCU/ugAhk+bmMmL5aS3+K5AnZxfTXatydbGkKx3YvJzNDgqNbTYZPX11qIg9Qt99Tt3I4f3AVJ39fgkuNRpVq/61T6zm0YxmIRDi6+tJv3Geadp6WHI9IVPTgzMOvPkMmLeHQjmUcDPsBWwcPRn6wHEc3X41Mmx4h5OflsHPNfHKzpTQMboCtZ2P+2PBlleSfQrb9NBeA9n3eK7I/Sdt+T7/6DJ28hINhyziw/QdsHT0YNV3b/ttX/ibsl080v+VyOQfCVnBQLMbNK4iJc37SdCFLLaHfy78+o6YuZu+25ezZuhQ7Rw9CPlyGU4F+sViHxKePWfv9H2RKUzExsyS4Xi2MbAM4+sfP7Nv6LZ5l+ieGrGL+qdu0G1nSFA6HL0eanoSzewDjZv2s5Z+ew+cgEok5cGAvsvw80iRxqABTc6tK1X/t9C4O71yFSARO7gH0Hzev3Pjx9KvPsClq/+8v8P/oGdr+j7j8N9uryP9lYWpqio2rF3s3ziM3OwM332CG/ke7faVKYrTab1Ajdfs9/vsysjIkOLjVZOh/ftV0OQZo0nEMclk+lw7/wqpNOxCJwMPFiU8nj8PaUn0t8iwpBXGx/Jmbl8c3qzeRmJKKgb4eHs5OLJj2Lh1bNC7T9o4tGvPtmq3cvHKGy2eP4u7lx4z5yzXnl2RJgpZ/0lIkzJ8xXPP7wO6NHNi9Ef+gBsz5Sn1+iXp4m8X/LcrNx44dA+Dg1i9QqVS4+QQzpAz/FM8/gY26kyVN4fgfBf5xrcmQadr+aVzgnwtnNiHLz1V/hkwkAlT0fU9bf1pyDDlZ2vktfEVRfjuxW53fajbuS5fh6vwWeesop//8ViOzd8sSABq16V+p+WHPnj0A7N/yOUqFAnffYIZ9sLqEf7TzW1Dj7mRnpnD8d3X+d3CrybAPivK/jq4ej++c4cJf65HnZ6Orb0ReThYJWQ8xMjGv9PZ7MPxHRCJw9ghgYMh/y29ffvUZ+f5i9m1fzt5t6vY1bqZ2fnv29DEXTxTkN1NLfHx8iH+WwsZl03H2CODdj36ulPzQuHFjfv+96POHDb75GICYDTu5ETIXAyc7jIo96M6JiuVi74kEfjcXz6mjyI1N4ObET0k6XPQJrfiw/ejbWeM3fxoGjnbUuX4HnfoBLF9V4EePAMbN/rlS42f6dPXDgvfff5+pU6ci8P8TkaoqRuX/S9l5oeqepPRrLOaTNS/XvfVl+GqcAS17HX++4Cty6s82zN9Q8XjT1+GzUXqcvv3i431flhaBpgydXXUTRfy2xJ37kVWn36+GO9ILe6tMv1njHvxU9mdzK4X3usCWU1WXioa1FFW5/bsuPH84w6vSt7FOleef3Rerzv4+jXTYf7Xq8kO3+npV7v+q1v/HparT37th1ft/44kqU8/I1pBys+zvzlYG1rVbcubOi32L81VoXtOMDVV3+mVUG/jxQNXpn9SVKs8Pm05WXf4f0UpU5e1335Wqa1/dG+hVeX7Yq1d6aFNl0UN2r8rjp7rSZfS1N7bvg+vrvbF9VwUvNSmUgICAgICAgICAgICAwOshTApVebz0d2gFBAQEBAQEBAQEBAQEBN4GhDe0AgICAgICAgICAgIC/yCqV/i0qEDZCG9oBQQEBAQEBAQEBAQEBKolwqRQAgICAgICAgICAgIC/yCdhl9+Y/s+vDn4je27KhC6HL8EO85XXdeAAU3EVT4L66nbWVWmv2WgCV/8Jn++4Cvy36G6XLqX+nzBV6ShvxU/H6oy9UzsXPWz+H61tepmEfxkiA57rlRd/fZsoFvl/p+3Pr/K9H8+Wp+rD5KeL/iK1Pe15cMfs58v+Ip8O8m4ymcBnfJtWpXpX/mhZZXb//nmqov/ecN1Gf91cpXpX/2xTZXPglvVsyhXdf45cK3q8kPXevrVfhbu6j7LelXr//Ny1cVnr2DdKp/luKr9U9WzKFdXhEmhKg+hy7GAgICAgICAgICAgIBAteStvqEViUTs3r37TZshICAgICAgICAgICBQaahUyje2/Nt4o12OExIS+Oqrr9i7dy9xcXHY29tTr149PvjgAzp06PAmTSuFSqXir/BlXDwWRm62FA/f+vQeMx9bR88Ky537azMn960hMz0JR7cAeo78BLcadTTbL/y9nRtn9/DVkwiys7MxNrMhLzcTZ68GdBi0ACv78vXHPrzIpSOhJMbcIitDQq93V+JTp2Mpu8/uW8bqjzeQmZmJSCTCxd2H0ZP/i7dfrXJ1Xzx9mN2//UhS4lMcnNwZMGoadYJbaranpyWzY8MyIq6dJScrk8aNGzJk4jc0rW2FoR7EJKnYf1FJSmb5vnG3g2Y1xThZiTAzFrH9hIJ7cdrdL0wMoUNdMZs3b+bXX38lKSkJdy9fRo2fQQ2/oLL98uQROzb/wuPIuyQlJjAi5AO6vTNES+avfeH8tX8nksR4APz9fMnVdSX2wQVyczJw8WpAh8EV+x/g2onNXDoSSlaGBDuXANoN+C9OnkX1myZ5wvHdi1nz30tkSLMQi8WgUuHmG0yPkfOxcahY/4WjmzlzIFQTP92GfYqLd51SciqVCjfVXrp2aIqllQ1xyWL2X1KSWoH/3eygWYAYR2swMxIRdlLB/ThtGRMDaFdPpOV/Zw9/3hn1Me4+pe0o5Pq5g+wPW06qJA5bRw96Dp1BzfqtAVDIZezfvow7106SkhiLoZEpx1s3IzbDintX9r2w/wvj/9kTdfz3fnclPnVLx/+ZfcvY+HkYw4aPYPCgIZhZmBOTCH+ek5MiLd8/rWqJCfQQY2shQiaHGImKQ5flJGeotxvpQ7t6Ovg4l4hPTx/GTJyOj39gmXpjoh8RtvlXHj28R1JiAqPGT6P7O4O1ZMI2hxL+2xqtdTNnz2PIkGEYGcDjBCU7T+STlF5+d6X29XWp7a2DnaUYuQKiEhTsPSdDkqZdxsNBjEqlIurMCnaEhZEhzcDdpz59xsx7bn47e3gLxwvym5ObP71Haec3WX4ee7csYdHU/YweM45Bg4Zgbm7Go6cKth7OQZJW/km1VV19WtUzwNpc/dw1PlnB/rO53H5c1LVPVwf6tTXS2L99exjSjAw8/Crf/jFjxjFo8BDMzc2JkajYd1FZYfy0CBIR4CbG1hzkCnX8HLmqJLlYGR0xdA5WH9+PH1lTOKPFgxgZmw9kkZhavn/aNDCgbQNDbCzU5Z9KFOw5lcOtR0Vdf0d0M6Gmpx4qlYrs28sICwtDKs3AyasB7Qc+v31dPlp0fukZUvr88vD6IW6c3srPc66Qk5ODjq4eLh4B9B/7MR4+tcvVfe3cQfZtX0GKJA47Rw96DZtOYEF+ANgftpKrZw+QlpyAjq4eS+1tSEqVkpOVgbO7P33HVG7+WefmSExcIpnSVFw8/Ok/dm6F9l89W2j/U+wc3ek1fDpBxewvzrbVn/Ofv8IwMjFHlp9bZpyV5Mb5AxwOX05qUhw2Dh50GzyDgHptNNtVKhWHd67g4t9h5GRL2R1cH0y9iLh4mJxsKZ6VHP8L39+HNLPg/AV4+DWgzwtc/5w9vJkTxa5/Suq/cHQ7187uYf6j6+Tn56Ojq19wfnm+fw7tUPvH1sGDbkPK8E/4Ci4U+OcnV2eS07LIzkzDyb3q9fcd/fz4PBBWoN/Rgx5DSsRn2DLuXjtJcmIsRkamrHdz5ElcIlnSVJzc/ekz+hPcK7D/+vkDHAwrsr/70BnULGb/zYuHOfvXNuKiIsjOTOfBhAmE7dyDND2pUvxz6+Jhzh3ZhoOVPsOHDUYma0UP2T0u9Z/Msz+OlKsXwLp1YwK/nYNpoC+5MfE8XPgjsRt2acl4TBqG94wQDBztyLhxt0J9Av9/eGNvaKOioggODubo0aN888033Lx5kwMHDtCuXTumTJnypswql9WrV3P28CbeGbOASfO3oWdgzLpvxiPLzyu3zI1z+9i3ZTHt+0xhyufhOLr7s+6b8WRmFI2VkuXl4Fu7FcHB6sHZrfvMZeiM7ejpG7HzxxDksvL1y/KzsXPxp/3A+eXKXPprNZePriE3N5d3hryHf1AwksQ4/vf5FDLSUsos8/DudX75/mNadXiH+d9toX6TtqxYNIPY6IeAOpmvWDgDybNYps79H/O/38LgwYNpWlOfP8/msOawApkchrXTQaeCCNPTFfEsFfZfLv+ibVArHa6d38/ChQvpMziE0PXbqFs7kMXzPyC9HPvz8nKxd3RhyKgpWFrZlCljbWvPkNFT+Op/6/jy+3UYGhpy7/JeGnWeyLCZ29EzMGLnqor9f+/yPo7vWkjTblMYMXsXdi4B7FwVQrZUXb+yvGzCV41DhIh33nkHXT197F39sXJwR0/fkE3fv1uh/lsX9nFo2yLa9J7CxPk7cXDzZ9P/3iUro/RYu8uXL9OvV0cWLFjAotXnkMlhaFtxhf7X14VnaSoOXirf/wNaibl+Tu3/jn0nszI0nAZ1Avhl0USk6WWP+Xt8/yqbls+iSdt+zFi4g1oN27P2u6nExzwAID8/l9jHd+jU9z2mfx3GmBlLuXDhAlePb6TD4AUv7H9ZXkH8Dyo//i/+tZprxzeydu1axowdz/9WbWHk6AnkyxSM6qSHbgX+8XQUc/6ukl/2yVl/WI6OGEZ30kOv4DGgmbH6Qcz3P/3JwoULGTg8hK3bdxAUVJOF82aQnlb2mO/8vDzsHZ0ZNnpSufEJ4OruxU8b/+CnjX9w+fJlRgwfSviJfJaF55IvUzG+pwG6OuXb7+2sw+lbcpbvzOXnP3PREYuY0NMA/WKPMT0cxLzbw4DVq1ezfv1G+o2bx/zvtqFvYMSaJRMqzG/Xz+1nz5bFdOw7malf7MDJPYDQJRPILBYXezYv4s61v9m+fTujx4Sw6NsfGTN+FvkyeH+ASYX2p0qV/H4ih8UbpSzZJOX+EzkT+5jgZFNUaQPaGVG7hp7G/o8+XkDoBnX8VLr9Y99lyfc/EzJpNjI5DH9OfvOwF3HpvpI1BxVsOqJARwzDO+igV+yYuwSL8XMRASCXQ1KagvgkOfky+GCIecX+yVAS/nc2X65J56u16dyNljFloBnOtkWFouPlrNuTyerVq9mwYSPTPlzAxs3q88uun17s/NJuQPntS5afjb6RKfn56vGnI6cuwcXDn58WVpAf7l1lw7LZNG3Xlw8XhVG7YXtCv52myQ8A9k6e9B/7MbOX7KRTnwlER0eTkyVl4se/4uzhX6n5p3mnIdy6dQuRjg6zFm3H2cOPH7+uyP5rbFj2EU3b9WPWojBqN2pP6Df/4emTB6Vkr184wp1rpwDwq92y3DgrTvT9q2xdNYuGbfox7YtwgoI7sPGHqSQU88/xvaGcObSJPmPnM2XBViQSCeePbKXXyI+ZsmBrpcd/586d0dXTx9LGGTsnb/QNjFmz5PnXP3u3LKZD3ym8/0U4Tu7+rFkyXkt/fn4OZpb2KBTq8ZsTP1mvtmNx+f6Jun+V31bOolGbfkz7MpzA4A5s+F8J/+wJ5fShTfQdN59O/d8nKioKhVzG5HlbqlT/+wu24Ozuz+oK4jPq/lU2r5hF47b9mP71DmoFt2fd99rxGff4Dh37vsf0r8JoVhCfYrEOH3y5A2f3AH5dVLH9Wwr0f/BVOEENO7D+e23783Nz8PJvQPchMwFYs2YNHfpOZtqXOyrFP/l5OXj6N6BVl2Hcu3ePZ8+elamrJEaerjT642eSj53nVMN3eLx8PbV//hLbTkUvU5wGdqPmN3N58OVKTjXui7Sa39Aqlao3tvzbeGM3tJMnT0YkEnHhwgX69++Pn58fQUFBzJgxg3PnzpVZ5qOPPsLPzw9jY2O8vb3573//i0xW9DT6+vXrtGvXDjMzM8zNzQkODubSpUsAREdH06tXL6ysrDAxMSEoKIh9+/a9kK0qlYoNGzbQtvd7BAZ3wNHdn4ETFyFNS+TOlb/KLXf6wHoath1IcOt+2Lv48M6YBegZGHL5+E6NTIuuo2nd811u3LgBgFdQG+xcAug6cglZ6YlE3ihfv1dgG1r0nI5P3U7l2n3l+AaMTKwYPHgwvQdPYMqc75DLZICIU0d+L7PcX3u2UKt+M7r2HY2zmzd9h03GwzuAo/u2AfDs6RMe3b/JyIkf4+UbhKOLJ506deKX1Ws5sO8PEtPg93NKzIwgwFVUrv2R8SqO3VRyL7bshmVtBq62ItasWcugQYNo0a4HYhMn5s9fgKGRIcf/2lNmuRq+gQwbO5VmrTuhq6dXpkyDxq2o17A5js7uODq78ejRI3T0DNHTN9T4PzM9kYcV+P/y32up1WwQtZr2x8bJh46DP0NX35BbZ8MBiHt0hYzkODoPX8i+ffto02syI2eEInkaSf1W/ZGmJXK3gvg5d2gdDVoPpH7L/tg5+9Bz5Gfo6Rty9VS4llzCkzt4eXlx8pacI0eOEPcskz/Oq/3vX6H/4fhNFffiyt5e6P/QAv/XbdmXDF1v5s3/DENDQy4c21lmuZP7N+FftyXteo3DwaUG3QZNw8UrkNMHtwBgZGzGe5/8Sr1mXbF39sLdpw65ubmoVErsXQNf2P9eQer4960g/q8e20CTLpPw9fXl5C0VcvvB3Lh6nmWhhzAzhgD38lPgxr/kXItUIklT8SxVxc5TcixNRTjbqH2amKZi2zE5fx/YwKBBg2jWpivGlk58/eUX6BsYcOxwOfHpV5MR496neZuO5cYngI6ODpZWNlha2WBqasqRK3IiohTEp6jYejQfc2MRtbzKv+P5dW8el+4peJaqIj5ZxdajeViZiXG1Kzrm3i30OHlDxoYNG2jXeyLetTugY+HHoImLyEhL5Pbl8p+mn9q/jsZtB9KwdT8cXHzoM3Y++gaGXDqhjovcbCmXjofTc9hHeHh4cPC8jCSdrlw+u4fl6y9iYSqmrk/5x3/rkZyIx3IkaUoSU5X8eSqXvHwVnk7qO3JDfWhWW5/wv7M19pu4t6NJgwAmTq98+0/fBrldH86f+IPQHdfV8eNWfvva8reS649USNLhWRr8flaJpYkIp4JnGAZ6UL+GiEMFD/T+PJXNqnAp7o56HL2Yg6WZmPr++uXqv/FQxq1IGYmpSp6lKNl9PIe8fBXeLkVPLE5ey+P+E3X9Nuo8CR2HjjSoG8CAdwvOLzcrPr8071H++QWgZqM+SFPj6dmzJwC2Dm4MfHce+vqGnD+2q8wyx/dvIqBuC9r3GoejSw26D56Kq1cgJwvyA0Bwyx74126GrYMb184dZMCAAchlecjlMvqHzEdPv/Lyz+0rx+jcuTOpkqfoGxoz6N156Osbce7vCuyv14IOvcfi6OpND439v2nJpaU8I3zt1xgamWJiYoJbjTplxllJTh/aiF+dlrTpEYK9Sw06D5iGs2cgZ//aDKjz2ukDG2jfeyJBwR1wdPMjLS0NkUiMSqnEyd2fwZXYfnsMnc2xY8fo0GcKI6evJP7JXZp1Go40LZHbl8uPn5P719NIS/+CUsfdsutoUhJjaNeuHQD2zl70HTsfPQNDLh4vxz8HC/zTMwQHlxp0Gaj2z5nDRf45dWAD7d9R++fWxUMMGDCAvNwsJAlRVas//rE6PivQf/KAdnx2LYzPQ0XxOfHjX6nXtER8Jqnjs984tf4L5eg/dWAj/nVa0rbA/q4Dp+HiGcjpQ5s1MsGtetOp32R8azUDoHPnzjRqo66n1/UPQIOWvenYdzJKEy9++OEHpNIKurIUw2PCEHIex3Jn9mIy7z4ietVmEsIP4vWfMRoZrw/GEhO6ndj1O8m8E8nNyeU/cBP4/8UbuaFNSUnhwIEDTJkyBRMTk1LbLS0tyyxnZmbGunXruH37NkuXLmX16tX873//02wfPnw4rq6uXLx4kcuXLzNnzhz0Ci4Yp0yZQl5eHidOnODmzZssXrwYU1PTF7I3NjYWiURCjaBmmnWGxma4etfhycPrZZaRy/N5GhWBT7EyYrEYn8BmPHl4TUs2VRJLenq61joDIzMcPeryNOrqC9lYFunJsWRnSMhMf0bz5s0BMDYxo4ZfbcwtrYm8d6PMcpH3bhJYt4nWuqB6zYi8f0NzbAB6euqLLQM9kfrhxOVrSJ5cASBPBnHJ4GJb/gXf89AVQ35+Ps9ib2vsBxCJxDRq3JQHd2++su7iPHsag0QiQaWU4+xZHyjwv2dd4h+X7X+FPJ9nMRF4+BezSyzGw7858QV1ppDng0hEVkYiEokE78Dm6OoZIBKJSXhyB1fvOsREXitX/9PoCLxrauv3DmxGbLEysrwczv75LTY2NsSlFbUljf/LfwH4XHTK8T8iMQ0bNyP6QdmxH/3gGn61mmqt86/TgqgH18qUT0mMJS0tDQADI/OCvxX7/0VIT44lK0NCnYbqrlCRT1XoG5ph41ybmMjLxElUuNm9eHwaFtxb5BR7KaFQyEiNL/KPjliEChG16zXk/t1br2w7QMLTWCaN6s3CT6cBcPNe0VPu3Hx4kqjEw+HFU7ihvvpYs/PUD5BMjcDDQYcnMer4Hz2gBa0CxNiYqvObm3cdokvkqkLk8nziom7jE1RUz2KxGJ+gZpoysY8jUCjk1A1uAcC9aDkmlp4YmjoS/+Q6UfEKvJxfbNSLSATB/nro64l4HK/ucuzuoIuujogrN58gkUjwqdWMzFz18bk4mFe6/Y8SVFjYemNs4UTMoyvEJakf+LwoBgX37oXx42QtQkdHRLJUXR93HstISFaSnK7AxV6XR0/lWjenz/NPo0B99PVERMZpz7aam/kUiUSCV83mBLhCRjbIxerzy+u0L1DnqcSYCBo0aKBZJxaL8avdlKj7ZeeHqAfX8avdTGtdQN3mZcrL5TJiH99GJpNhaGyGs7u/Wn+tppWSf+TyfGIf38bPzw+RSISxsVmR/eXof3z/Ov4l9Je0X6lUsmnFx7TtPopncY8wMDDQbCsZZ6Xsf3hN67oBwK92C83xpkhikaYn4VNwM5IiiSU5ORlHN3+Nzspsv7YOHpr2Ze/sjaWNE89i7uP2Ctc/NYK0r38K5YKCgrTkfIJKXydp+adWCf/UaaGxpdA/vrWaqY/z8W3atm2LW406PHlwrcr0u9eoQ/SD64jFYnyfE5++ZcRndDnxGVcsPo2MzQv0NytTvtB+3zLsjy6jrhRy9QuhOnWKuhe/rn9eB8um9Ug6elZrneTwKaya1gNApKeHRYMgko6cKRIQvjwqUMAbGUP78OFDVCoVAQEBL1Xu008/1fzv6enJhx9+yNatW5k9ezYAT548YdasWRq9vr6+GvknT57Qv39/atdWj4vx9vZ+4f1KJBIATC207w5MLWzJTJOUWSZbmoZSqcDUvGQZGyTxj7XWSdPL/tyHsZkN2Rmv/imQ7Ay1bSqVEhsbGwo/2mNuaUNGejLpaWV3KUlPS8LcUttuc0sbMlLV8o4unljbORK+aQWjJn2CsZX6wUBc9D10zYt8npWrwtTwlc0nKQOin6agUiqwsbFBCThYG6CvJ8bW1pYHDyNfXTnwJOohC2aPJz9PfYXZedjX2Dj5aLabmNmQVY7/c7JSUSkVGJeoX2MzG1KePQLAybMeevpGnNm3DAB9Q2MObV+MSqlAmi7BxNy2XP3ZUrV+kxL6TcxtSSoWPwe2LaRmHfXDh6xcbR1ZuSpMjZ7nhfJJzoAnxfz/FPC2F2GkL8LW1oZ79x+VWU6allSqrZhZ2CAtJ95Sk9TjmH3qdMTAqOghU0X+fxEK49/WxhqAzFz1ic/QxIbcrCQyX8I/IqBbI12inylJLDYGNT87FZVK7R8dsQhHa2OSM3KxsLQmLvbJK9vu4x/IpOmf4OTijp5I/Qbv6NZp1Or+C7r66gcXmdkqzIxf7IZKBLzTQp/H8QoSUtT2F45NDXROA+BxmjWmdipaBIg5clOJqYUNmeXkJk1+s7DVWm9qboPkqTouMtOT0NHVw9JCXacZ2erj0DeyJj87GWm2EnOTiu13thXz4TAzdHUhLx9W/55FQrJaj7mJCJlcRUaBjYW25MrAUI9Ktz8rRy1jZGJDbubLxQ9Al4ZiniSq39iC+oGCXKHSdAHPyFIW/FVhYSpGmqXEwqTiBxYudjrMGW2Bni7k5atYFS4lPkn78xu13KVcBMb3sEHPGP68CEqVOldlSV/vU1OFedDKykprvZmFDc/iHpdZRpqWhFmp/GCrqcdCIi4fY93SD1EqFRw7doyJH6/G1Fy9H1MLGxKflq//RfNPVoY6Dvbs2UOD5t0wNDbVyFek38yytP7i9h/5fQ1iHR0aNO/K75u+1Yw9LaR4nJUkswz7TS1sNbGcmaYd74W/za3steK9suJflp+r0Ve4X2l6UsHf51z/lKwHcxskxfxaKGdubq4tZ2GDJL58/5iZl9Rri7TAD4V/Tc1tNfptbGwwNbfRXGtViX4Lbf0Vxk+p+i0nPqVF8VmvWXdNfJqa25BYTvyUHf9F9hcnO0udjEq+RHod/7wOBg625D3T1pP3LAk9CzPEhgboWVkg1tUlL7HqPnH2T6NS/vsmZ3pTvJEbWtUrPlHZtm0by5YtIzIykszMTORyuVYinDFjBu+++y4bN26kY8eODBw4kBo1agAwbdo0Jk2axKFDh+jYsSP9+/fXeipVnJ07d/L5559rfhd/C1wZZEtT+Wx80QeNR838sVL1VzVO1oacOHYUuSwfpVLJpEmT2LBhAw6eTUhJTau0/ShVsKfYt38b+FmSkS0nPfP1vnd4+tgBQlctBtQ3++MmzSZ01SKOhX+Fg1uQ1k3tq5CTmcrymeo3vUqlgqeP1B/OXv5xN+o06YmTRyAi0at3jujQriUDm4FSoaBPg485frtqnlAqVbDnQpHubvV0SMpQkZiuhNfY5eVTe9jx6wLNbyd39ffpWvb+8NWVFsPHEVoHgrJdXWaPvMKeC6//fckeTXWwtxIRur/82KvhYk5uvoL45Nf7XqyVmQGjB/fU/H70VH3RIcvP5un9Q7jX6vvSOvu21sPRWsTK3XnE3t3HjSNfIhbB2i9ETJi5EoCMHLj5RIWduQiPl3hzXZL27VrRK1hM93o9+GhCJy5Fv7IqnqUoWbhBiqGBiPp+eozsZsx/5oVxbv9Cjoth6aciAjt89+o7KIPKtL+Q7o3E2FuIWHtIQS1PET0bixGL1b0gKhqH+zwSkhV8HpqGkYGI4AADxvUyZcon27l4aJFGJqfrtwAcuAqt60PnerDr/Gse0D+AT1BjJn2ymmXzR1K3bl02Lp3JtC9+K3Uz8DooFOr2rFKpGPTufytFZ8yjCI7v38SsRdsrRV9J7t88DcA3M7siEsGYmT9Vqv4saSrz3g1GqVCgkMtQKKru+6oCFaOUF8Vnv7FC11oBgYp4Ize0vr6+iEQi7t598cHcZ8+eZfjw4Xz22Wd06dIFCwsLtm7dynffFV3MLFiwgGHDhrF3717279/P/Pnz2bp1K3379uXdd9+lS5cu7N27l0OHDrFw4UK+++47pk6dWmpfly9f5vbt25rfXbt2xdPTk8z0ZMwt7TXrM9OTcPKoWaa9xmaWiMU6WhNAqcskY+PkxZjZqzXr5LKyL7izpcnYub7cW2wtG8ztAHUX3eTkZAyd1esz0pIBERaWZV8YWFjaFsgUkZGWjHnBBDYpmQqkj9U3mjk52YyfsQQAUyMR+bhqypgYikhIfb2brfR8S0QiHZKTk7kemY5coSLA3YykpKRy7X8eDRq30pohWV5w0rC09eDK8Q10GqJ+mJElTcbepWz/G5lYIRLrkF2ifrOlyVg7eNFvSqhmnUKez4avezJm9gY8/Bry7fSWBDV2I+npQxzcyosftf6SE0BlZSRxIy2f/VfhxJ5fuH5mNwYGhnT78w/+Xj8QgOPbPsDeI5gR7Tfy7DX9n5FviUis9v9fNxXky6GFvw6SpGTMLG3LLGNmaVtqQglperLmrUZQcDs8fGqjUMjZtW4hqUlPAZDLtF8xV+T/ioiWwI6zkJGayI7lo+g/6SfAClNDEZk5KnKzkrF0CMDUUER8yvP906OJDv6uYkIPyMgoca+qb2yliU+lUsWjePUUyOlpKVhaWb+07QDpmflk5RZNKCUWqW8uXb0CSUuL0aw3NRbxNOn5T3f7ttQj0EOHVbvzSM9S4ejdBivHWliYiJj0jiG7jqunws5MT8Lc0g5pjgpjfXWucvIo2/+a/FbiDVBmRjI30vI4ektJ9IPrbP95Dh98uQOwwNxYTEaWgvycFPSNbTAzFhObqChTfyEKJZqZkGOeKfBw1GHogE6ojALxctJldA9jvl6brGW/oZ76LW1l229iBJm5kJOVjKGpLaYvmN+6NhTj6yJi/WEF0hy4H6vi5yQFrrYi+rbQQV7gAnMTMelZCsxNRKRnKvFy0SXmWcU3EwolSApmQn6SkI2nkw7DB3ZCaVCUV5QK9fnlcUwyOdgzrgN4ORScX16hfRWnMA+mpmpPgCZNT8a8gvxQcsIcaXoS5iXeFhoYGuNeoxZisQ4DBw7k1t3FXPh7Jx36qCcWetX8U4hCLmPXuoUATJo0SfP2qzx5LfvTSusvtD/yzhUyM1JYMKWz5uF9UlISe7cs4dTBDcz5319kZiRjWo79pmXYn1nwRhSgVqNOHNm9iqFTvsHepQaKguuHjNREPP0bFCvzavFv6+zFuI9WE/3gGmE/z0VHV0+jz9xS/RbYzMKWmIfXn3/9U7IeMrTrrVAuIyNDWy49GTOL8v0jzSipN0mjt/BvZkYS9i41EBecvzIzknF2D6g6/enJOHsU6X+Z+M8sLz7XF8WnuFh8ZmaUb3/Z8Z9UZnsxNrEAIC0tDSvX4vKv7p/XIe9ZEgYOJfKAgy2ydCnK3Dzyk1JRyuUY2FfeQ603jepfODnTm+KNjKG1tramS5curFy5kqysrFLbC8fTFefMmTN4eHjwySef0LBhQ3x9fYmOLv3o3M/Pj+nTp3Po0CH69evH2rVrNdvc3Nx477332LlzJzNnzmT16tWlygPMmzePpKQkzZKYmIitrS2PbhdNVpWbk0nsoxu4+9QtU4eurj7OnkFERhSVUSqVRN4+h5d/Q2wcPDSLvYsPFhYWWuXzcjJJiL6uGdP5KljYuGJsboeppQNnz6rHJeRkZxJ5/ybS9BRq+Jf9hrqGf23u3Ligte729fPU8FPLK5SQK1ORK1Mh0jVGpG+GXC4n0McOV//2gHoGXRcbiEt6vcaqo6uPtXMgZ8+eRa5QYaAnxkhfxMUL5/ENKP+zChVhZGyCo7ObZnFx88LOzo6c7DTNxUFeTiYJUddx8irb/zq6+ji4BfHkftF4D5VSyZP7Z3Gp0QgrOw/NYuPog52dHXGPb/D4zjmypMl4BTQh9tEN3GrUK1e/s0cQj+5o63905xw2LjXJzAW/RgPoMW45HYd/g1wuJ2S6+kl9w64f0W7g12r/v2bPHB1dfWyc1P7Pl6s/42NuqOTyxXN4+JYd+x6+9XhQLO4B7t88i6ev+lgNjUywsnVm39alSNMkvL9gI3Z2djy5V3Ssz/N/RcgU6reNKgNHktNyuHz+OADeTiJkeZkkP72Jq3cwLnYiYiQVx2ePJjrUdBez9qCMtDI+gWRsqId/QBBnz54l8mkGKpW6nd+6fhm/gPI/i1URSpWKfJlSs+TmK1AqldQL9MDARH2yN9ADd3sx0c8qvqHt21KPWl46/PRHHikFYzV19U0wsXRHrueGpY0bgTV9sbOz42FBnZkaikhKzSTm0Q08fOqVqVdXVx8Xz0Ae3tbObw8jzuHgWpOsPDC1rUFc3FMuX1KPd/L30CUrLZrczASc3Ori6aTD46cv9/ZHJAIzU1OMLdxIlzvh4upOcD0/jf2mhmBsICIuUVrp9ns5iMhIekx2ejxu3g1wsYXY5+S3rg3FBLiJ2HhEQVrBqS5fDqmZcD9OhUJR1G08wFMPB2sxNhY6xCbK8XbW5VHcy/lHLBJhZmqCkbmrZjG2VOe3mMJcJYL8XPX55VXaV3F0dPWxdwvi6tWisbhKpZL7t87j6Vd2fvD0rcuDW9r54d6Ns2XK6+rq4eqlzj8qpQq5XN0r6EHE+VfOP6C+WdiwdCYpiTG4eNbk5s2i+RjU9p/Dsxz9Xn51uX9L+xX3vZtF9jdq3YvZS8KZtTiM2Ut24OTmi5GREa17jCNk9mpNnJUXmx4+9TRtsZAHt85qjtfRzQ8zC1uSEqKxLbh+sLGxISHmnkZnbs6rt19v/4bYOngQ1KA9Ojq6pKc+w87OjsiIc0jiH5OWHI+jux8xL3L9U0J/ZMQ53IvZVCgXERFRyg73CvwTWYZ/Cm2xtnPFzMKWhxHn1MfpFciJEyeIibyBu2+9KtP/JPIGHr51C/Q/Jz5vlY5PjxLxuXHZTJLLic+Ht85pyZe0v2T8P7h1Fo8y6qrwYUXhpKQa/a/hn9ch7dw1bNprjy+27dCc1HPXAFDJZKRficC2fbExvKJX700k8O/ijc1yvHLlShQKBY0bNyY8PJwHDx5w584dli1bRrNmzUrJ+/r68uTJE7Zu3UpkZCTLli1j166iWQhzcnJ4//33OXbsGNHR0Zw+fZqLFy9Ss6b6CeIHH3zAwYMHefz4MVeuXOHvv//WbCuJgYEB5ubmmsXCwoLRo0fz9+8/cefKURJi7rPj5zmYWdpTs0HRd/lCF43lbLGZ3lp0Hc2l42FcObmbxLhI/lj/Gfl5OQS3LuoyKE2TEP/kLk2aqMdB3jj9G/eu7mff+umYWNhTo9h3/3asGM21E5s0v/PzskiMvUNi7B0AMpJjSYy9Q0aK+o2XSCSiQZtR5GSmsXXrVvaE/cqKRTPR01N/l7BFh94A/Lr0v4RvXK7R27HnMG5dPcvB3zcSH/uY37f+RFTkbdp3L/pW5sXTh7l76xKShFiunj/GmjVrmDxlKm3btMTeAvo0EyPNgbvFZjAe0U5MQ9+i5KOnCw6W6gXA0lT9v7lxUV3UdBPRussYtm/fzqXTB9DNf8bHn8wjJyeHNh16APDj/z5j6/pVmjJymYyoR/eJenQfuVxOaoqEqEf3SXha9HZr6/pV3Ll1Fcmzp8RER+Ll5UV60hMsbF2RPL3HgY2zMbWw1/ruYtjy0Vw9XuT/4HZjuXlmOxHnd5GcEMlf2xcgy8shqGk/jcytc+HER12nd+/eHNu9nK3LJ1OnaW9O/PkjZpb2BBSLnw3fjOHCkSL9TTuP4cqJMK6d3oXkaSR7Nqn112uh1m9qYYe9qx/2rn7o6urSsakd7du3x9fXl2Fd3ZDmoDWD9LDn+d9EVMr/AW7Qqqva//cu/o6dOIqPPl5Abm4Ojduo43jLqrns/a2oW36rbiO4e/00x/as41ncIw7uWEnso1u06DIMUJ+s1/8wnZhHEQx/fzEqlZL+/ftz7sBKHlw7+ML+Lxn/6WXEf/22ozh/8EcePnxI69oidJ5tpXa9xkx7tzPSbLj7pOiGcExnXRoHFKXEnk10qOMtZscJ9WdUTA3VS+GnVAz0YFQnXQYPG8327ds5eWQ/CXHRrP3xW/Jyc2nTUR2fK7/7gt/WFQ0rKB6fCrmMlOTC+IzVyGwMXcHtm1dJfBbPvTs3CQ8PZ+KEcXTp3gdHaxFDO+iTka3i1uOiN5wTexnQolZRp5t+rfRo4KfL5r/yyctXYWYEZkZofQrm2HU5reroMWrUKP7+42cyov4mPuYeXy34CHNLewKDi74HvnrhWK2ZLFt2G8PFYzu4XJDfdq/Tzm+GxmY0bNOfvZsX8+TJE7o01sNGvp8GTXvw/uhGpGcquf6wqAv3tIEmtKlfNKtv71aG+LjqYG0uxtlWTO9Whvi66XLxjvqhU24+nL2Zz4D2xowaNYpjf/xM1pNjXLh6l1++n1Pp9rcMUqEj2UXjVr0YN6CuOn5iitrXyA5iGvkVta9ujcTU8RKx67SCPJn6m9omxeInTwZXI1Wa79D2amXE5AFmPEmQ0b6hEWlSJf/X3p3H1ZT/fwB/3dttcW+rNKmUMpVqYoYINRp7+ZoMhr6yXBQ/xjq+vvYxZDAZy9hiDMoYW2MXwiASwyBLyZJ9T6IkLar374+rUxc1M/fc+028n4/HeYzTufM6n07nc879nPM5n3P6Umnvnf/0MEVLr9JBCTq3kMPFXgZLMynsrPTQuYUcrrVlOJas+n9qmEvRvpkRatvIoFQqceL3JTDN2Y+LFy5hyZwxqvNLvdL6tekfnl8AIC8nE87122HHDtWI3peTj2HlvFHIz8tBk886AQBWR4xHTJnjw2fte+HC2SOIe3l8iN0QgdvXzqP5y+NDft5z7Fg3DzdSz+Jx+j3Ua9wKa9euxZOMe6jl6IFNkVNRkK+940/T1t3w22+/4VDsaty9eQkbln+HgvxcNGnxsvyLJiBm7bzXyn8g5peX5V+M21fPo7l/MABAYWIOWwcXYWrX5f+Qm5uLzIz7oOLi1/az6J/GYXf0XCHft11vXE5KQPyuKDy8dw2/b16Eu9eT0axNTwCq45pvgBIHti1FSuIBpN1Jhbm5OYiKIdHTw4Pbl/HbT9rb/3et/QEtW7bE/i0RWPXjENS0r4s/9q6FifkH8PAq3X+Wv5LfvH0fnDi4QcjftvLN338+atwGBw8eBABcOB2HNQtHoiD/ORp9Vrp9YstuH//euHSuzPbZtAh3ryXDp23p9vk0QIkDW5ci5dQBeHr7Y8OGDTA0UsDKxglbosLwIj9XJ/kf2Dphc+RUFOTlovHL/HWLx2PX+jL7Z0AvXDp3BAd3rsTDsvtnu9L9c9X8l/vnkJlo2kq1fx7e/Svu3bqIzVGq7SjkLxmHXetLy/9pgKr8h3aqyr9306KX+T2Fzzx/lom7Ny4g7a7qVYx79+7Fvi2Lcf3iCdHbpyT/3s0LeJJ2HW5ubnj0SNUTQFqnFkw/doORvQ0AoO60/+DjqJnC/3fz5/WQO9nD7fvRUNStg9qDesCmW3tcn79S+Mz1eVGwDw2CXe9OMHarA8+IKWAMqKQux4BqUKbExERMnz4do0aNwv3792FlZQUvLy8sWfL6M6UdO3bEyJEjMXToUOTn56NDhw6YNGkSpkyZAkD1iouMjAwolUqkpaWhRo0a6NKlC8LCwgAARUVFGDJkCO7cuQNTU1MEBAT8o2djBwwYgMQrz7E1ajLynj9FbZeG6Pvfn6FvUDp64eOHt/A8u7TrVf2m/0JO9hPs37zg5Qur3dF39M9qAzH8eSAaB7ZGCPNHYlQHjuo1P0SXr5ZDpl+an/XoNnKflean3UrGxoVKYf7QFlX3FA/vzvDvpXqGqlGbAXhRkIvkhF+wZe1iSCQS2Dk4o8/gb4Quu4/TH6g90+ns9jEGjJyOLWsXY/PqRfjAxgFDx81Frdqlz5ZmPXmE6Ki5eJqVATOLGvh3185IvKaPDo2lMDIAbqUT1h4sQlGZG0gWxhLIDYGSBzBtq0ugbF367bpdQ9W/z14rxvaXz84aVwPGD+0AT7tMLF++FIvmPoKDkwvGTvkRZi+7QGekP4CkzFW6J4/TMfHr0u2yc8sa7NyyBu6eDfDNDNW+9TTrCX6aF4bMxxmQK4zh4V4Xbo0CcfbwOvy5dyns6nihy+A3bP+c0u1f1+tfeP7sMY7uXIDn2emwsnNHl8HLoTAt/fs+SbuOhO1zUZCbCZmhAsVFhTh/IhYOLl7oNXKZWv7j9Ft4Xubv6+n9LzzPfoyDWxfi2dN01LR3R8+Ry14byKPEycuEqVOnwty8Ou5kAOsPFb+y/YFqpauDTXWgd6vS7d+2oWofOHu9GDuOvxwJ10iCCUM7wNM2E8uWL0LkwnTYOrhhwLilQhejzEf31ba/k2sD9Br6A2J/W4Bd0fNgVbM2+o1aCBt71YBhWU8e4vypOADAnHFfqv0Oe9dNQmFB7t/a/mm3krFhwZv3/4Deqv2/8cv9v0+fPujZszdGDukBU9NBuPUQ+HXfCxSW3T4mEigMS38PbzfVtgkJUH+1zOYE1et8bKpLYG8lRY+gz1FckIkVK1YgPT0d7u7umDT9R6HL8aP0NEikpbmPHz/CuOH9hPkdm9dhx+Z1cPdsgMnhi1SfefQQC2dNRvbTpzA1M0fTJo0ht2mJ4HaWqGYAXH9QjGU78oXuqgBgaSqBwqh0PT6eqnIP7qQ+Mtv6A6rX+QDA4XOFkOmpjm85z3Px07zJ+OHpUzi6NkS/0erHt4yHt5FT5vj2cdP2yMl+jN83LUR21iPYOrghZPRSta5qn/ccB4lEiq5du6Jvv1BMGPMVTE1NcPVuESI25aiVv4a5HhTVSn9gIpdA2V4BU4UEeQWEu+lFiNiYg4s3S+9abozLRTGVlj98+rfIztZd+ceOGgQTExPcTiesiav4+NbYVVWf+rRVP81u+6MIZ6+pPrPnVDEIqoawvkwCK3PVPvcs9wXmRz9V2z5W5lIYlxkEzFQuQUigMcyMpcjNJ9x5WIh567Jx4YbqIsGLQoKLvT7aeFeDiXwAsrJzMe8H1faxreOFzoPU61dmxuv1a9Oi0voVv1VVv9y9O8O/p6p+XU0+gCMxs4XPbF+t+neTll2E48OTR/fVzi9OdRtAOWwmdkYvxI7182FVszZC/7tAOD5IpXp4eO86ouZux7PsJ1CYmMPZ2Rn30x7jl/kjYVdbN8efzStnQqong72TOwaN/0noMvok475a/XWq+wmUw8KxK3pRaflHz4etQ+mAiGU19AnA1pXTcPnsYSSf2PvafpaZob59ars2QPevfsDejQuwZ8M81LCujd5fL0RN+9L8zzqEoiA/F5sjJyPveTYaeTWElaM3tq+ahrzn2Vrf/3fv3okXBfnITL8LAqAwtXhD/uvff55lP8G+TaXff/qN/lkt//iBaOzfUvr9J/qn8QCAVp0GlW6fV/YfR9cGCB78A/ZsWIDdv81DjZq1oRz5yvb5XLV9Nr3cPg4ODsjIzEHElO6wre2GkDFLtZ7/OCsHC74Nhm1tN/QfV5r/6v7j6NoAPYf8gN0bFiA2WpXf9z9v3j/nji/dP7f/+j2kejLYObqj/9jy9x9H1wboMURV/tiX5e/zH/Xynz8Vh99+nijMFxYW4vdNi7BPKkUtp49Eb5+UxDhs+HkivL29sW1b6eshG86aAAC4vWozzoWOh6GNFaq9bNwCQO6NOzjRcSA85oyH4zAl8u48QNLAb/Do9wThM/c3xMLAqjpcJw+HYU0rPD17AVUZEQ8KpS0S0nSEpvfQxuO62/G6NpHipz06i8cgfyAh5fXu3dryqYcC363T3eARk4JlOHnpyV9/UEON6lpg6V6dxWNgO2Btgu6qWo9PJZi+vuLnEcWY2F0POxJ19/f9vKFM59v/21/EDw5Vnql9DHA6Vfwoj+Vp4FID/10ibrCpisz+So4tf+pu/+nsrYchszN1lh/xX3Odl3/qGt3t/9/2lGHADN2N3LlsgiWW7NZZPL4KAGJPixusryLtG+jr/Piz+4zujg8BnxjofP/Udf7mP3X3/aeLtxRbT+iu/J0a6+k8P+aU7vbPQC8Ztp/UXfk7NtL99tmpX1dn+R1eXNJZtq75dU746w/pSPyWTytt3bpQaXdoGWOMMcYYY+x9xINCaU+lPUPLGGOMMcYYY4yJwXdoGWOMMcYYY+x/iIr5GVpt4Tu0jDHGGGOMMcaqJG7QMsYYY4wxxhirmohpXV5eHk2ePJny8vI4n/M5n/M5n/M5n/M5n/M5v5Lz2buLX9ujA0+fPoWZmRmysrJgamrK+ZzP+ZzP+ZzP+ZzP+ZzP+ZWYz95d3OWYMcYYY4wxxliVxA1axhhjjDHGGGNVEjdoGWOMMcYYY4xVSdyg1QFDQ0NMnjwZhoaGnM/5nM/5nM/5nM/5nM/5nF/J+ezdxYNCMcYYY4wxxhirkvgOLWOMMcYYY4yxKokbtIwxxhhjjDHGqiRu0DLGGGOMMcYYq5K4QcsYY4wxxhhjrEriBq0WXblyBXv27EFubi4AgMfbYowxxhhjjDHd4QatFmRkZKBNmzZwdXXFv/71L9y/fx8AEBoailGjRulsvYWFhbh165bO8nUlLS1Np+UOCwvDo0ePdJb/4sULneQWFhbi999/x4oVK7Bv3z4UFRWJytPlNihRVFSEa9euobi4GACQn5+P3377DevXr0daWppW1pGTk4P4+HhER0djw4YNOHXqlNYvFmVlZeHSpUu4dOkSsrKytJr9riEi0ftmRVauXFll/wapqanYv38/rly5UtlF+dte/Vv++eefOHbsGPLz87W2jlu3buH48eM4ceIEMjIytJZbVn5+vlbLzDRz8OBB4aJ+VZOfn4+rV69W6f0oLS0NDx480FrerVu33ni+JaIq+f2TvcOIida7d2/y9/en27dvk7GxMV29epWIiHbv3k0eHh46W++ZM2dIKpWKyoiIiKDWrVtTt27daN++fWrL0tPTycnJSePsp0+fUs+ePcnBwYGUSiXl5+fT4MGDSSKRkFQqJT8/P8rKytI4Pysr67UpMzOT9PX16fjx48LPNBUdHU35+fnC/MKFC8nBwYGkUilZWlpSWFiYxtlEREOHDqWYmBgiIrp9+za5ubmRnp4eWVtbk56eHtWrV4/u3Lmjcb5UKqVWrVrRmjVrKC8vT1RZ3+Ts2bNkY2NDUqmUPD096datW+Tp6UkKhYKMjY3JwsKC/vzzT43zi4qKaPTo0SSXy0kqlZJUKiWJREISiYRq165N27dvF/07LFu2jNzd3YX8ksnd3Z2WL18uOr8i2qi/O3fupNDQUBo9ejRduHBBbdnjx4+pZcuWGme/ePGCJk6cSH5+fvTtt98SEdEPP/xAcrmcDAwMhDqtbfr6+pSSkiI65/jx41RYWCjMx8TEkJ+fH9na2pKXlxf98ssvovJnzJghHDMfP35MrVu3FvZPqVRKAQEB9OTJE43zjY2NKSQkhI4cOSKqnOW5ceMGeXl5kZ6eHgUEBFBWVha1adNG+B3q1KlDly5dErWOiIgI4ZhZdvL19aWTJ0+K/h327t1L7du3J3NzcyHb3Nyc2rdvT7///rvo/IqkpKSIOj8SqY4B3333HUVERFB6errasqysLOrXr5+o/GXLlpFSqaTIyEgiIlq/fj25ubmRk5OTUKe1TVv1Ny0tTW3+9OnTpFQqycfHh7788kuKi4sTlR8VFUVHjx4lIqLc3FwKCQkhPT09kkqlJJPJaODAgaLOm56enjR16lS6deuWqHKWJyMjg7788kuyt7enQYMGUWFhIYWGhgrHn2bNmtG9e/dEr0cqlb72tyAievTokejzV4nY2Fg6fPiwML9o0SL6+OOPKTg4mB4/fqyVdbB3HzdotcDa2prOnDlDRKTWoL169SopFAqdrVfsF+L58+eTXC6nIUOGUK9evcjAwIBmzJghLH/w4IGo/KFDh5KbmxstWLCAWrRoQV988QV5enpSQkICHTp0iDw8PGjChAka57/6Jalso6fsf8XklxzIIyMjycjIiL799lvauXMnTZs2jRQKBS1btkzjfGtra0pKSiIioqCgIGrTpo3wpSYjI4M+//xz6tq1q8b5EomEAgICyMDAgCwsLGjo0KF0+vRpjfNe5e/vT127dqWkpCQaMWIEubu7U7du3aigoIBevHhBvXr1ojZt2micP3bsWHJ3d6eYmBj6/fffyc/Pj2bOnEkXLlygSZMmkaGhIe3Zs0fj/JLG2bhx4yguLo5SUlIoJSWF4uLiaPz48aRQKGjWrFka5/+VM2fOkEQi0fj/X7NmDenp6VGHDh3o008/JSMjI1q9erWwXGz9/eabb8ja2pr+85//kIeHBw0aNIjs7e1p9erV9Msvv5CdnR3NnDlT43wLC4s3ThKJhMzMzIR5TZWtv9u3byepVEpKpZIiIiKof//+JJPJaPPmzRrn16pVixITE4mIqH///tSgQQNKTEyk3NxcOnPmDDVt2pRCQ0M1zpdIJPTRRx+RRCIhNzc3mj17Nj18+FDjvFd9+eWX9Nlnn1FMTAwFBQWRr68vtWjRgu7cuUP37t0jf39/6tSpk8b5s2bNIltbW1q4cKFw4Wjq1KkUGxtLvXv3JrlcTidOnNA4f+XKlSSTyah79+4UFRVFu3btol27dlFUVBQFBweTvr4+rVq1SuP8vyL2/Ltnzx4yMDCgjz76iBwcHMjS0pIOHDggLBdbf3/88UdSKBTUpUsXsrGxoWnTppGlpSVNmzaNwsLCyNTUlJYuXapxfoMGDd44SSQScnd3F+Y1Vbb+HjlyhPT19emzzz6j0aNHU9u2bUkmk9GhQ4c0zndycqJjx44REdF///tfcnR0pM2bN9OFCxdo69at5OrqSqNHj9Y4XyKRkKWlJenp6ZG/vz9t3LiRXrx4oXHeq0JCQsjT05MWLlxIn332GX3xxRdUv359SkhIoKNHj1Ljxo1JqVSKXo9EInnjcefGjRskl8tF5xOpGv87d+4kIqJz586RoaEhjR8/npo2bUp9+/bVyjrYu48btFpgbGxMly9fFv5d0qA9ceIEVa9eXePc8k4YJZObm5uoE56HhwetWbNGmD9y5AhZWVnRpEmTiEj8CdXe3l44Qd+9e5ckEolwR5KIaMeOHVS3bl2N8+3s7KhDhw504MABOnjwIB08eJDi4uJIT0+PoqKihJ9pSiKRCCdUb29v+uGHH9SWL168WNQJ28jIiK5du0ZEqi/Hx48fV1uelJRENWrU0Di/pPzp6ek0e/Zs8vDwIKlUSg0bNqTFixeLuntNpGqQlFyJf/78Oenp6an9DsnJyWRpaalxvo2NDcXHxwvzd+7cIWNjY+Gq+dSpU6lZs2Ya5zs4OFB0dHS5y9evX0/29vYa53fu3LnCqVWrVqLq1yeffELz588X5qOjo0mhUAh3lsXW3zp16gj1NTU1laRSKa1fv15tfZ6enhrnGxsbU4cOHWjlypXCFBUVRXp6ejR9+nThZ5oqW38//fRTGjdunNry6dOnU9OmTTXONzQ0pBs3bhARkaOj42tfrk+ePEk2NjYa55eU/8yZMzR06FCqXr06GRgYUJcuXWjXrl1UXFyscTYRkZWVlXCBKzMzkyQSidpdklOnTpG1tbXG+Y6OjrRr1y5h/tKlS2RpaSl8qR8+fDi1bdtW43wXFxdatGhRucsjIiLI2dlZ4/yRI0dWOPXq1UtU/WrWrJlwQbe4uJhmzpxJxsbGFBsbS0Ti66+bm5twfk9MTCSZTKbW62T58uXk5eWlcb5MJqOAgACaMmWKME2ePJmkUikNHjxY+Jmmytbftm3bUkhIiNryESNGUKtWrTTONzQ0pJs3bxIRkaurq7DdSxw6dIgcHBw0zpdIJHT37l3asmULBQYGkkwmIysrKxo1apRW7mDb2NgIvTcePHhAEomE9u7dKyxPSEggOzs7jfNL9nOpVEoDBw5U2/eHDx9OTZo0IR8fH9G/BxGRQqGg69evExHR5MmT6csvvyQi8ccg9n7hBq0WtG/fnr755hsiUn1Ju3btGhUVFVG3bt2EiqkJQ0ND6tOnj9oJo+w0cOBAUSe8atWqCQeREklJSWRtbU3jxo0TfUI1NDRU624jl8vVurCJvcKXkZFBnTp1opYtW6p1zZXJZHT+/HmNc0uUvTJZo0YN4S58iStXrpCJiYnG+fXr1xcaCO7u7q91kTt69KioCyJlvxCUzQwJCSETExOSy+XUu3dvjfPNzc2FCzkFBQWkp6dHp06dEpZfuHBB1B02ExMT4eIQkaoLskwmo/v37xMR0fnz50XtP0ZGRhV+sTh//jxVq1ZN43yZTEbt27envn37vnHq2LGjqPqlUCiECyIlDhw4QMbGxrRkyRLR9dfIyEit/hoZGal1a7527Zqo/T81NVW4i5CdnS38XJv1t2T//+CDD17r4nrx4kUyNzfXON/V1ZV27NhBRKq7Pa92DT59+jSZmppqnP9q/c3Ly6O1a9dS69atSSqVUq1atYSLj5owMTER9p+SulX2GJeamirq7yuXy9XOL8XFxSSTyYRukGfOnCFjY2ON8w0NDenixYvlLr948SIZGRlpnF9y8a9FixZvnBo1aiSqfpmamtKVK1fUfrZmzRpSKBQUExMjuv5Wq1ZNaLARqbZXcnKyMJ+amipq/09ISKAPP/yQvv32WyoqKhJ+rov6a2NjQ3/88Yfa8uTkZFEXfGvXri1ccLezs3utt0BKSoqoHnav1t979+7RjBkzyMXFRegSvGLFCo3z5XK5cEGNSNXVu6THF5Hq+Cym/CX7uUQiIR8fH7V9v127dvR///d/wvlfLAsLC2Gf8fX1FXoOXL9+XdQ5mL1fuEGrBUlJSfTBBx8I3Tu7du1K7u7uZG1t/doJ65/w8vKixYsXl7v89OnTou+glr0DVuL8+fNkbW1NSqVSVL6tra1aAyc4OFjtAJ+cnCyqwVNi8eLFZGtrS2vXriUi7Z5QV61aRdu2baNatWoJz9uUSE5OFvWFNSoqimrVqkVxcXG0atUqcnd3p3379tHdu3fpwIEDVK9ePerfv7/G+eU9+0JE9OzZM1q+fLmoK6ytW7em0NBQunPnDoWFhZGzs7PaM1+DBw+m5s2ba5zv4+ND06ZNE+bXrVun9gUsKSlJ1P7TvHlzUiqVb+wGVlhYSEqlkvz8/DTOr1evXoXP4Yqtv2/6kkdEdPDgQTI2NqaJEyeKyre2tqZz584J8z4+PmoXji5cuCBq/ydSPac7ZswY+vDDDykhIYGItFt/4+Li6OzZs1S7du3Xnue+ePGiqAbVrFmzyN3dnVJTU2nOnDnUrFkz4Xh/7do1atGihahHBiqqv9evX6dvvvlGVA+Cpk2bChdiIyMjhQuZJaZOnSrqDt4nn3xCP//8szC/f/9+ksvlwp3lixcvimowN2zYsMIuoWPGjKGGDRtqnO/q6kq//vprucvF1l8rK6s3Pke8bt06ksvltGTJElH5lpaWahfsatWqpdYASk1NFbX/E6nu7Hfv3p2aNGki7PvarL9XrlyhrKwscnJyErr3l7hy5YqoC5oTJkygZs2a0ZMnT2jcuHEUGBgoXFjLycmhoKAgateuncb5FdXfuLg46tWrl6gG58cffyz0UNi1axeZmJjQnDlzhOVLliwR1YOmRN++fUX35vorgYGB5O/vT1OnTiV9fX3hPLNnzx5ycXHR6brZu4MbtFqSmZlJ06ZNo27dulH79u1p4sSJoh/IHz58OI0YMaLc5VeuXKEWLVponB8cHExff/31G5clJyeTlZWVqBNqQEAA/fTTT+Uuj4qK0lqXlfPnzwuDCGjzhFp2Ktu4IlJ12RLT5ZiIaM6cOSSXy6latWpkYGCg9ixwp06d1O5caVL+8k6o2nDixAmytLQkiURCVlZWlJycTE2aNKGaNWuSra0tVatW7bWBxv6Jffv2kaGhIXl7e5Ofnx/JZDL68ccfheWzZs0S1eXs7NmzVLNmTbK0tKTOnTvToEGDaNCgQdS5c2eytLQkGxsbtSve/1Tfvn1p8ODB5S5PSUkhR0dHjfO/+OKLcgd2iYuLI4VCIar+tmzZssIuv7/99puoBk9Z+/fvJwcHBxo/fjzp6+trrf6WHUis7L5DpGo4iB20b9iwYaSvr09ubm5kZGREUqlUqMeNGjUSehNoWv6/qr9iuh3v3r2bjIyMyMDAgIyMjOjQoUPk6upK3t7e1LRpU9LT06uwS/5fiY6OJn19fQoKCiKlUknGxsZqDeaffvpJ1CMDJft4vXr1aOTIkRQeHk7h4eE0cuRIql+/PhkbG4t6xrJHjx7lnh+JxD8D37Zt23Kf0V+7di3p6+uLqr++vr5qjwi8KiYmRisNHiLVBZGaNWvS0qVLtV5/S+pw2YsjRETbtm0T1aU8Pz+fOnbsSBYWFtS2bVsyMjIiuVxOLi4upFAoyMHBQdSgaH+n/oppKK5evZr09PTI2dmZDA0NacOGDWRra0tBQUHUvXt3MjAwqLBL/tvk5s2b1KFDB6pfv77aReCvv/6ahg0bVoklY1WJhIhflvq2Sk5Ohqenp87yk5KScOrUKfTt27fc9W/atAmTJ0/WKP/w4cOoX78+zMzM3rg8NjYW1apVQ4sWLTTKf3X7FBQUYNy4cYiLi8PmzZvh5OSkUW55+a/asWMH9PX14e/vLyo/MzMTe/fuxfXr11FcXAwbGxv4+vrCxcVF06IDABYtWoQBAwbA0NBQVE55kpOT4eTkhIsXL6Ju3bowNjZGXl4e1qxZg9zcXLRt2xZ169YVlV9cXIzo6Gjk5+fD398fbdu21eJvAGRnZ2P16tU4duyY8KqDmjVrolmzZujRowdMTU01zs7Pz0dRURHkcrm2iqvm0KFDOHr0KMaPH//G5XFxcVi1ahWioqI0yr98+TL09fXLrUdr166FTCZDUFCQRvmvysjIwIABAxAXF4djx46J2ncA4ObNm2rzxsbGsLS0FOZXrVoFAFAqlaLWc+HCBezYsUN4fVVJ/W3Tpg0kEonGuWFhYRg9erTO9h8AuHHjBk6dOgUvLy84OjoiLS0NEREReP78OTp06ICWLVuKyo+NjcXq1auF+jtgwABhWcnre8r+TTQp/5IlS95YfwcNGgRHR0eNsx88eID8/HzUrl1b44yKbNmyBfHx8fjxxx/fuHzt2rVYtmwZ4uLiNMo/cuQIFAoFPvnkkzcuX7x4MYqLizF06FCN8l+VmpqKnj174uTJk0hOToaHh4eovEOHDqnN29jYwNXVVZifP38+CgoKMHr0aFHr2b17N2JiYl6rvz169IBCodA4t1+/fliwYAFMTExEla8iR44cwbFjx9CsWTP4+PggJSUF4eHheP78OQIDA9GnTx/R68jJyUF4eDj279+Phw8fCq/oK3Ht2jXR62BMG7hBqyV5eXk4d+7cGyt8x44dNcqUSqXw9vZGaGgounfvrvUDo1QqRePGjdG/f3/OLye/ZPsHBwfD2NhY6/nvyvbRZflLtr8uvxgwxhgTp7i4GNnZ2TA1NRV1MYe9PYKDg3Ho0CH07t0bNjY2r/1dR4wYIXodiYmJ0NfXR7169QAA27ZtQ1RUFDw8PDBlyhQYGBiIXgd7D1TuDeJ3Q2xsLFlZWb3WRVXsa2Pi4+OpX79+ZGJiQgqFgpRK5RufeRWTXzJAkK7ydV3+dym/T58+Vbr8VW37/JWCggK1QVW07cWLF5z/Dufz/lO5+Ywx8czMzITxDXSlUaNGtHHjRiJSve7SyMiIgoODydnZucLH7hgrixu0WuDs7EyDBw+mBw8e6CT/2bNnFBkZSX5+fiSRSMjFxYXCw8NFPZ/F+ZzP+RUT+55Jzud8ztdtfkREBLVu3Zq6dev22vP66enp5OTkxPmcz/kiODo6auU1QxUpO+J3eHi4MBhXQkIC1apVS6frZu8ObtBqgYmJiajRjP+J1NRUmjBhAtnb25O+vj4FBgZyPudzvg5UhS/0nM/572v+/PnzSS6X05AhQ6hXr15kYGBAM2bMEJaLfe0N53P++5xf4tdff6WuXbtSTk6O6KzymJiYCK8AatOmDc2bN4+IVINFiXn1Fnu/8DO0WhASEgJfX1+Ehob+T9aXk5ODNWvWYPz48cjMzERRURHncz7n/0MNGzascHlubi4uX77M+ZzP+W9h/kcffYSJEyeiR48eAICjR4+iU6dOGDRoEKZOnYq0tDTY2tpyPudzvggNGjTA1atXQURwdHSEvr6+2vLExERR+QDQqlUr2Nvbo02bNggNDUVKSgqcnZ1x6NAh9OnTBzdu3BC9Dvbuk1V2Ad4FixYtQrdu3XD48GHUq1fvtQo/fPhwrawnPj4ekZGR2LRpE6RSKYKCgrTaiOZ8zn+f8lNSUtC9e/dyR/G9f/8+Ll++zPmcz/lvYf7169fh4+MjzPv4+ODAgQNo06YNXrx4ga+//lrjbM7n/Pc9v0SnTp20klORefPmoWfPnti6dSsmTpwIZ2dnAMDGjRvVfkfGKlTJd4jfCcuXLyeZTEbGxsZUu3ZtcnR0FCaxzzDcvXuXpk+fTi4uLiSRSMjX15ciIyPp2bNnWik753P++5rv5eVFixcvLnf56dOnRXXZ4nzO53zd5dvb279xkLjz58+TtbU1KZVKzud8zq/CcnNzqaCgoLKLwaoIvkOrBRMnTkRYWBjGjRsHqVSqtdz27dtj3759qFGjBpRKJUJCQkS/m5HzOZ/zVXx9fXHp0qVyl5uYmMDPz4/zOZ/z38L8Tz/9FJs3b0bz5s3Vfu7h4YH9+/eLfocu53P++5z/NjAyMqrsIrCqpLJb1O8CCwsLnQwKFRgYSFu3bqXCwkKtZ3M+57/v+UlJSTrJ5XzO53zd5587d46ioqIqXP+UKVM4n/M5X4SS10+WN2lDYWEhzZo1ixo3bkzW1tZkYWGhNjH2d3CDVgu+/vprmj59emUXgzH2D0gkEmrSpAn9/PPP9PTpU87nfM6vYvne3t6cz/mcr4P8Elu3blWbNmzYQBMmTCA7Oztavny5VtYxadIksrGxodmzZ5ORkRF99913FBoaSpaWljR//nytrIO9+7hBqwXDhg0jMzMz8vPzo6FDh9LIkSPVJsbY2yc+Pp769etHJiYmpFAoSKlUvvGZJM7nfM5/O/NDQkI4n/M5Xwf5f2XNmjXUsWNHrWTVqVOHduzYQURExsbGQo/H+fPnU3BwsFbWwd593KDVghYtWpQ7tWzZsrKLxxirwLNnzygyMpL8/PxIIpGQi4sLhYeH0/379zmf8zmf8zmf89/b/PJcvXqVFAqFVrLkcjndvHmTiIhq1qxJp06dEtZhamqqlXWwdx83aBlj7KXU1FSaMGEC2dvbk76+PgUGBnI+53M+53M+57/3+SWeP39OI0aMIFdXV63kubq60rFjx4iIyNfXl77//nsiIlq/fj1ZWVlpZR3s3ccNWsYYK+PZs2e0dOlSql69uk5ee8D5nM/5nM/5nF8V8s3NzdUGaDI3Nyc9PT0yMTGhbdu2aaHERGPHjhXGoVm/fj3JZDJydnYmAwMDGjt2rFbWwd59/NoeDXXp0gUrV66EqakpunTpUuFnN2/e/D8qFWNMU/Hx8YiMjMSmTZsglUoRFBSE0NBQzud8zud8zuf89zJ/3rx5avNSqRRWVlZo0qQJLCwsROcDQHh4uPDvf//733BwcMAff/wBFxcXBAYGamUd7D1Q2S3qqqpv377CyHJ9+/atcGKMvZ3u3r1L06dPJxcXF5JIJOTr60uRkZH07Nkzzud8zud8zuf89zafsaqEG7QihIWFUU5OTmUXgzGmgYCAAJLJZFSzZk0aM2YMXbx4kfM5n/M5n/M5/73PL+vJkyc0e/ZsCg0NpdDQUJo7dy5lZmaKyty2bdvfnhj7O7jLsQhhYWEYNGgQ5HJ5ZReFMfYP6evrY+PGjfj888+hp6fH+ZzP+ZzP+ZzP+WWcPHkS/v7+qFatGry9vQEAc+fOxfTp07F37140bNhQo9xOnTr9rc9JJBIUFRVptA72fpEQEVV2IaoqqVSKBw8e4IMPPqjsojDGGGOMMaY1zZs3h7OzM5YtWwaZTHUPrLCwEP3798e1a9cQHx9fySVkTEVa2QWo6iQSSWUXgTHGGGOMMa06efIkxo4dKzRmAUAmk2HMmDE4efKkqOwDBw7Aw8MDT58+fW1ZVlYWPvroIxw+fFjUOtj7g7sci+Tq6vqXjdrHjx//j0rDGGOMMcaYeKamprh16xbc3NzUfn779m2YmJiIyp43bx4GDBgAU1PT15aZmZlh4MCBmDt3Lpo3by5qPez9wA1akcLCwmBmZlbZxWCMMcYYY0xr/v3vfyM0NBSzZ8+Gj48PAODIkSMYPXo0goODRWWfPXsWM2fOLHd5u3btMHv2bFHrYO8PbtCK1L17d36GljHGGGOMvVNmz54NiUQCpVKJwsJCAKoBqb766iu198dqIi0tDfr6+uUul8lkSE9PF7UO9v7gBq0I/PwsY4wxxhh7FxkYGGD+/Pn4/vvvcfXqVQDAhx9+qJW3e9jZ2SE5ORnOzs5vXH7u3DnY2NiIXg97P/AoxyLwKMeMMcYYY4z9M8OGDcPBgwdx4sQJGBkZqS3Lzc2Ft7c3WrZsiQULFlRSCVlVwg1axhhjjDHGmJq8vDwsXLgQcXFxePjwIYqLi9WWJyYmapydlpaGhg0bQk9PD0OHDkXdunUBABcvXkRERASKioqQmJgIa2trUb8Dez9wg5YxxhhjjDGmpmfPnti7dy+6du0Ka2vr1x61mzx5sqj8mzdv4quvvsKePXtQ0hyRSCTw9/dHREQEnJycROWz9wc3aBljjDHGGGNqzMzMsGvXLvj6+up0PU+ePMGVK1dARHBxcYGFhYVO18fePTwoFGOMMcYYY0yNnZ2d6PfN/h0WFhZo3LixztfD3l3Syi4AY4wxxhhj7O0yZ84cjB07Fjdv3qzsojBWIb5DyxhjjDHGGFPTqFEj5OXloU6dOpDL5a+9N/bx48eVVDLG1HGDljHGGGOMMaYmODgYd+/exYwZM944KBRjbwseFIoxxhhjjDGmRi6X448//sDHH39c2UVhrEL8DC1jjDHGGGNMjZubG3Jzcyu7GIz9JW7QMsYYY4wxxtSEh4dj1KhROHjwIDIyMvD06VO1ibG3BXc5ZowxxhhjjKmRSlX3vV59dpaIIJFIUFRUVBnFYuw1PCgUY4wxxhhjTE1cXFy5y5KSkv6HJWGsYnyHljHGGGOMMVah7OxsrFu3DsuXL8epU6f4Di17a/AztIwxxhhjjLE3io+PR58+fWBjY4PZs2ejVatWOHbsWGUXizEBdzlmjDHGGGOMCR48eICVK1dixYoVePr0KYKCgpCfn4+tW7fCw8OjsovHmBq+Q8sYY4wxxhgDAAQGBqJu3bo4d+4c5s2bh3v37mHhwoWVXSzGysV3aBljjDHGGGMAgNjYWAwfPhxfffUVXFxcKrs4jP0lvkPLGGOMMcYYAwAkJCQgOzsbXl5eaNKkCRYtWoRHjx5VdrEYKxePcswYY4wxxhhTk5OTg+joaERGRuLPP/9EUVER5s6di5CQEJiYmFR28RgTcIOWMcYYY4wxVq5Lly5hxYoV+PXXX5GZmYm2bdti+/btlV0sxgBwg5YxxhhjjDH2NxQVFSEmJgaRkZHcoGVvDW7QMsYYY4wxxhirknhQKMYYY4wxxhhjVRI3aBljjDHGGGOMVUncoGWMMcYYY4wxViVxg5YxxhhjjDHGWJXEDVrGGGOMMcYYY1USN2gZY4wxxhhjjFVJ3KBljDHGGGOMMVYl/T//S3heIoNBSAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "----"
      ],
      "metadata": {
        "id": "fOhu6hHTNlhr"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Tt2Q1gXBKTUZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "QmPbu7mqKTZ5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "G_dP7ZOFKTdB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "3U-AG-JHNnaq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 🧹 Step 4: Preprocessing Data\n",
        "- **Overview:** Scaling continuous variables and splitting the dataset into features and target.\n",
        "- **Key Insights:**\n",
        "  - **MinMaxScaler**: Scaling ensures that features are within a similar range, preventing any one feature from dominating the model due to differences in magnitude.\n",
        "  - **Feature-Target Split**: Separating `X` (features) and `y` (target) is a crucial step for supervised learning.\n",
        "- **Why this is important:** Feature scaling and proper splitting of data are key to training an effective model, ensuring fair weight allocation during training."
      ],
      "metadata": {
        "id": "DAOM5pnDNoU7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Preprocessing"
      ],
      "metadata": {
        "id": "UlkWhNRVKTjQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Normalize the features (Time and Amount) using MinMaxScaler"
      ],
      "metadata": {
        "id": "vpWACbgtKTmS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = MinMaxScaler()  # Initialize MinMaxScaler for scaling features"
      ],
      "metadata": {
        "id": "rm7RUwR9KTpO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data[['Time', 'Amount']] = scaler.fit_transform(data[['Time', 'Amount']])  # Scale 'Time' and 'Amount' columns\n"
      ],
      "metadata": {
        "id": "FJWFGBidKTsN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kVk3FZobKTv2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zNb-6c-IF8nR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Separate features (X) and target (y)"
      ],
      "metadata": {
        "id": "9pX-p-5xF8q4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = data.drop(['Class'], axis=1)  # Drop the target column 'Class' to create features (X)\n"
      ],
      "metadata": {
        "id": "QY8-puEgF8tp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y = data['Class']  # Set the target variable (y) as 'Class'"
      ],
      "metadata": {
        "id": "gMUm_hlTF8wM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "JOvg_ctnNuxR"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "JgTHRIARF6tm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "x4VzXkIpF6yc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "wO6BSPMVNwTi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "----"
      ],
      "metadata": {
        "id": "YVv-n-4NNwlf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ⚖️ Step 5: Handle Class Imbalance with SMOTE\n",
        "- **Overview:** Address class imbalance in the dataset using SMOTE.\n",
        "- **Key Insights:**\n",
        "  - **SMOTE** generates synthetic samples of the minority class to create a balanced dataset, reducing the model's bias toward the majority class.\n",
        "  - Imbalanced classes can severely affect model performance, particularly in fraud detection where fraudulent transactions are rare.\n",
        "- **Why this is important:** Balancing the dataset enhances model accuracy and ensures it learns to identify anomalies without being biased toward the majority class."
      ],
      "metadata": {
        "id": "ngbvh06nNxxQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "smote = SMOTE(sampling_strategy='auto', random_state=42)  # Initialize SMOTE to balance classes\n"
      ],
      "metadata": {
        "id": "U5AZnJVZF7Jk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_res, y_res = smote.fit_resample(X, y)  # Apply SMOTE to the features and target\n"
      ],
      "metadata": {
        "id": "OMDHdnqBF7Mz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "----"
      ],
      "metadata": {
        "id": "fC4T9-yqN5Ld"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "E1V276hdF7Pe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mBjRgPC7KxEw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gTsfcvdnN6uI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "----"
      ],
      "metadata": {
        "id": "AomTlRzsN6_b"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 🧩 Step 6: Split Data into Training and Test Sets\n",
        "- **Overview:** Split the resampled dataset into training and testing sets.\n",
        "- **Key Insights:**\n",
        "  - The **80/20 split** (training/test) ensures that the model is trained on a large portion of the data while being evaluated on unseen data.\n",
        "  - This step is essential for evaluating the generalization ability of the model.\n",
        "- **Why this is important:** Ensures the model's ability to generalize to new, unseen data and prevents overfitting."
      ],
      "metadata": {
        "id": "HzH0tM2lN7_E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X_res, y_res, test_size=0.2, random_state=42)  # Split data into training and test sets\n"
      ],
      "metadata": {
        "id": "wP8WIIx5KxKr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "----"
      ],
      "metadata": {
        "id": "L3O8-cVpOEyN"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Ur2xH7tXF7S_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mX-61RggF7WK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "HOJb_Cl0OGNJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "----"
      ],
      "metadata": {
        "id": "2o2ZJMd-OGgl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 🚀 Step 7: Build the Generator Model\n",
        "- **Overview:** Create a neural network that generates synthetic fraud instances from random noise.\n",
        "- **Key Insights:**\n",
        "  - The generator's **Dense layers** increase in size, allowing it to learn complex representations.\n",
        "  - The use of **LeakyReLU** activations improves learning by avoiding dying neurons compared to standard ReLU.\n",
        "  - **Tanh output** ensures the generated data lies within the same range as real data, which is crucial for the discriminator to correctly classify the fake data.\n",
        "- **Why this is important:** The generator's ability to create realistic data is critical for training the discriminator effectively and generating synthetic anomalies."
      ],
      "metadata": {
        "id": "WSoJG75sOHj1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# GAN Model Setup"
      ],
      "metadata": {
        "id": "cwj50Zp5F7Zv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def build_generator(latent_dim):  # Function to build the generator model\n",
        "    model = Sequential()  # Initialize the sequential model\n",
        "    model.add(Dense(128, input_dim=latent_dim, kernel_initializer=initializers.RandomNormal(stddev=0.02)))  # Add input layer\n",
        "    model.add(LeakyReLU(alpha=0.2))  # Add LeakyReLU activation function\n",
        "    model.add(Dense(256))  # Add a dense layer with 256 neurons\n",
        "    model.add(LeakyReLU(alpha=0.2))  # Add another LeakyReLU activation\n",
        "    model.add(Dense(512))  # Add a dense layer with 512 neurons\n",
        "    model.add(LeakyReLU(alpha=0.2))  # Add LeakyReLU activation\n",
        "    model.add(Dense(X_train.shape[1], activation='tanh'))  # Output layer with tanh activation for data generation\n",
        "    return model  # Return the generator model"
      ],
      "metadata": {
        "id": "TthxwYtYK8Vx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "-----"
      ],
      "metadata": {
        "id": "mVk-5uzXONI5"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9GeRCtqbOOFd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "JZYNMt1OOONa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "AnlBeGmgK8bT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "----"
      ],
      "metadata": {
        "id": "w7tUjoEwOPoC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 🔍 Step 8: Build the Discriminator Model\n",
        "- **Overview:** Create a neural network that classifies data as real or fake.\n",
        "- **Key Insights:**\n",
        "  - **Dense layers** of decreasing size allow the model to learn features and representations from high-dimensional data.\n",
        "  - **LeakyReLU** helps in training by ensuring no neurons die out during backpropagation.\n",
        "  - The **sigmoid activation** is perfect for binary classification, outputting a probability of whether the data is real or fake.\n",
        "- **Why this is important:** The discriminator’s accuracy determines the quality of the synthetic data generated by the GAN, so it’s crucial for effective fraud detection."
      ],
      "metadata": {
        "id": "aPyAAVfQOQny"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def build_discriminator(input_dim):  # Function to build the discriminator model\n",
        "    model = Sequential()  # Initialize the sequential model\n",
        "    model.add(Dense(512, input_dim=input_dim, kernel_initializer=initializers.RandomNormal(stddev=0.02)))  # Add input layer\n",
        "    model.add(LeakyReLU(alpha=0.2))  # Add LeakyReLU activation\n",
        "    model.add(Dense(256))  # Add dense layer with 256 neurons\n",
        "    model.add(LeakyReLU(alpha=0.2))  # Add LeakyReLU activation\n",
        "    model.add(Dense(128))  # Add dense layer with 128 neurons\n",
        "    model.add(LeakyReLU(alpha=0.2))  # Add LeakyReLU activation\n",
        "    model.add(Dense(1, activation='sigmoid'))  # Output layer with sigmoid activation for binary classification\n",
        "    return model  # Return the discriminator model"
      ],
      "metadata": {
        "id": "s7EqM69wK8fU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "----"
      ],
      "metadata": {
        "id": "Dj8YYBWTOV00"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "CYkgRiZhOW0d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5gmDhzvTOXCr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "yzR6tnXjLFCs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "-----"
      ],
      "metadata": {
        "id": "MdSxkAMyOXq5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 🔄 Step 9: Build the GAN Model\n",
        "- **Overview:** Combine the generator and discriminator into a single model for adversarial training.\n",
        "- **Key Insights:**\n",
        "  - **Freezing the discriminator** during generator training ensures that the generator improves without interference from a discriminator that has already learned to classify real vs fake.\n",
        "  - By combining the generator and discriminator, the GAN is able to generate realistic synthetic data while avoiding mode collapse.\n",
        "- **Why this is important:** The GAN structure allows the generator to improve through feedback from the discriminator, producing increasingly realistic data."
      ],
      "metadata": {
        "id": "8H2QIJt0OY2M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def build_gan(generator, discriminator):  # Function to build the GAN model by combining generator and discriminator\n",
        "    discriminator.trainable = False  # Freeze the discriminator during generator training\n",
        "    model = Sequential()  # Initialize the sequential model\n",
        "    model.add(generator)  # Add generator to the model\n",
        "    model.add(discriminator)  # Add discriminator to the model\n",
        "    return model  # Return the combined GAN model"
      ],
      "metadata": {
        "id": "TwMvLnoYLFGR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "FJXLC93VObOp"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "In3Z_VBULFJu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "TMxRyCJ2LFNw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OlLxcgYLOgAL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "-PCoca3POgR2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 🔧 Step 10: Set Hyperparameters\n",
        "- **Overview:** Define key hyperparameters for GAN training.\n",
        "- **Key Insights:**\n",
        "  - **Latent Dimension (100):** The size of the random noise vector that feeds the generator. A higher latent dimension can create more diverse data but requires more computation.\n",
        "  - **Epochs (50):** A balance between sufficient training and computational efficiency. Lower epochs speed up the process but may affect performance.\n",
        "  - **Batch Size (128):** Controls the number of samples used in each training step. A higher batch size typically leads to more stable training.\n",
        "- **Why this is important:** Choosing the right hyperparameters ensures optimal training of the GAN without overfitting or underfitting."
      ],
      "metadata": {
        "id": "UZL3xRPsOhna"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Hyperparameters"
      ],
      "metadata": {
        "id": "RZcWk833LFSU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "latent_dim = 100  # Latent space dimension for the generator"
      ],
      "metadata": {
        "id": "MXo51uAGK8jF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "epochs = 50  # Reduced epochs to 50 for faster execution"
      ],
      "metadata": {
        "id": "JPtsZkKwK8oM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 128  # Batch size for training"
      ],
      "metadata": {
        "id": "dVkbmhnlK8ri"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "half_batch = batch_size // 2  # Half batch size for discriminator training"
      ],
      "metadata": {
        "id": "Y76nAJMmK8x9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7fn-wCXmK81R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "EjJok-PQK85G"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Build the models"
      ],
      "metadata": {
        "id": "qgO1I3bYK88u"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "discriminator = build_discriminator(X_train.shape[1])  # Build discriminator model\n"
      ],
      "metadata": {
        "id": "qx9qs-HFLZ68"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "discriminator.compile(loss='binary_crossentropy', optimizer=Adam(0.0002, 0.5), metrics=['accuracy'])  # Compile discriminator\n"
      ],
      "metadata": {
        "id": "8whxxIKTLZ_A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9AiaCmkULaDb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "generator = build_generator(latent_dim)  # Build generator model"
      ],
      "metadata": {
        "id": "_QFj2zZRLaHX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "705ZUdwOLaLC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gan = build_gan(generator, discriminator)  # Build GAN by combining generator and discriminator\n"
      ],
      "metadata": {
        "id": "r4JonzgLLaPE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gan.compile(loss='binary_crossentropy', optimizer=Adam(0.0002, 0.5))  # Compile the GAN model\n"
      ],
      "metadata": {
        "id": "j-cIej_WLaSf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "----"
      ],
      "metadata": {
        "id": "Lx2D-yNuOrWo"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "wd8SJAnbLud7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "082naD-lLuhv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mR4kPa_ILulz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "----"
      ],
      "metadata": {
        "id": "NDHwxJKaOu2v"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 🎓 Step 11: Train the GAN\n",
        "- **Overview:** Train the GAN by alternating between training the generator and the discriminator.\n",
        "- **Key Insights:**\n",
        "  - **Discriminator Training:** Half the batch is real data, half is generated data. The discriminator’s role is to classify real and fake data.\n",
        "  - **Generator Training:** The generator is trained to improve its output to fool the discriminator into thinking its fake data is real.\n",
        "  - Print losses at regular intervals to monitor the progress.\n",
        "- **Why this is important:** Properly alternating training between the generator and discriminator ensures the GAN improves over time and avoids mode collapse."
      ],
      "metadata": {
        "id": "gFCE0Qk9Ov3t"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def train_gan(epochs, batch_size, half_batch):  # Function to train the GAN model\n",
        "    for epoch in range(epochs):  # Loop through each epoch\n",
        "        # Train Discriminator\n",
        "        idx = np.random.randint(0, X_train.shape[0], half_batch)  # Select a random half batch of real data\n",
        "        real_data = X_train.iloc[idx]  # Get the real data samples\n",
        "        fake_data = generator.predict(np.random.normal(0, 1, (half_batch, latent_dim)))  # Generate fake data from noise\n",
        "\n",
        "        d_loss_real = discriminator.train_on_batch(real_data, np.ones((half_batch, 1)))  # Train discriminator on real data\n",
        "        d_loss_fake = discriminator.train_on_batch(fake_data, np.zeros((half_batch, 1)))  # Train discriminator on fake data\n",
        "        d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)  # Average the losses from real and fake data\n",
        "\n",
        "        # Train Generator\n",
        "        g_loss = gan.train_on_batch(np.random.normal(0, 1, (batch_size, latent_dim)), np.ones((batch_size, 1)))  # Train generator\n",
        "\n",
        "        if epoch % 10 == 0:  # Print progress every 10 epochs\n",
        "            print(f\"{epoch} [D loss: {d_loss[0]} | D accuracy: {d_loss[1]*100}%] [G loss: {g_loss}]\")  # Print losses\n"
      ],
      "metadata": {
        "id": "JID7dszwLuwR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ksBfoW20Luzy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_gan(epochs, batch_size, half_batch)  # Train the GAN for specified epochs and batch size\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CqU8ASJxLu3k",
        "outputId": "f3ed151d-5116-4b62-8cde-69a9498b735c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step  \n",
            "0 [D loss: 0.6867884397506714 | D accuracy: 41.796875%] [G loss: [array(0.6897079, dtype=float32), array(0.6897079, dtype=float32), array(0.2890625, dtype=float32)]]\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step \n",
            "10 [D loss: 0.6893662810325623 | D accuracy: 32.648470997810364%] [G loss: [array(0.6899691, dtype=float32), array(0.6899691, dtype=float32), array(0.31889203, dtype=float32)]]\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step \n",
            "20 [D loss: 0.695582926273346 | D accuracy: 33.58921408653259%] [G loss: [array(0.69632536, dtype=float32), array(0.69632536, dtype=float32), array(0.33184522, dtype=float32)]]\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step \n",
            "30 [D loss: 0.7057914137840271 | D accuracy: 34.783804416656494%] [G loss: [array(0.70666504, dtype=float32), array(0.70666504, dtype=float32), array(0.34501007, dtype=float32)]]\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step \n",
            "40 [D loss: 0.7165799140930176 | D accuracy: 34.87477898597717%] [G loss: [array(0.71735144, dtype=float32), array(0.71735144, dtype=float32), array(0.34660822, dtype=float32)]]\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "----"
      ],
      "metadata": {
        "id": "4CE_pp0xO8GH"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "MEDYUfq8L9vE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-iyLZtJpL9y_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "z08KT2YNL-OE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "qGPXfo1KO9ex"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 🧪 Step 12: Generate Anomalous Data\n",
        "- **Overview:** Use the trained generator to create synthetic anomalies.\n",
        "- **Key Insights:**\n",
        "  - Random noise is passed through the generator, which transforms it into synthetic data that resembles fraudulent transactions.\n",
        "  - This synthetic data can be used for testing or augmenting real-world fraud datasets.\n",
        "- **Why this is important:** Synthetic anomalies help in evaluating and testing fraud detection systems, especially when real fraudulent data is scarce."
      ],
      "metadata": {
        "id": "Z2k7nWlMO-po"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def generate_anomalies(n_samples):  # Function to generate synthetic anomalous data\n",
        "    noise = np.random.normal(0, 1, (n_samples, latent_dim))  # Generate random noise as input for the generator\n",
        "    generated_data = generator.predict(noise)  # Generate fake data (anomalies)\n",
        "    return generated_data  # Return generated anomalous data"
      ],
      "metadata": {
        "id": "CBYLi1jWL-XZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "----"
      ],
      "metadata": {
        "id": "k6AJThi2PDhg"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ASkYZCNLPEn1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "AQqAZRIpPEvo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0KEFs6M8L-f1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "----"
      ],
      "metadata": {
        "id": "SujbwSmlPFku"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 📊 Step 13: Predict Anomalies on Test Data\n",
        "- **Overview:** Use the trained discriminator to classify test data as fraudulent or not.\n",
        "- **Key Insights:**\n",
        "  - **Predictions** are based on the likelihood that a test sample is fraudulent, with a threshold of 0.5 to classify as fraud.\n",
        "  - This step is crucial for assessing the model’s performance on unseen data.\n",
        "- **Why this is important:** The ability to accurately predict anomalies in new data is the core functionality of a fraud detection system."
      ],
      "metadata": {
        "id": "PYaQuzZdPGZ5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def predict_anomalies(test_data):  # Function to predict anomalies using the discriminator\n",
        "    predictions = discriminator.predict(test_data)  # Get predictions from the discriminator\n",
        "    return predictions  # Return the predicted anomaly scores"
      ],
      "metadata": {
        "id": "PwI5wo-BMGq1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "RibpUNUDPLq-"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "xLeVEUBqMG1h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "LU_L5nhZMG63"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kdszCYMeMG_P"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "G7MhbtlkPNFE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 📈 Step 14: Evaluate Model Performance\n",
        "- **Overview:** Evaluate the model using performance metrics like precision, recall, and F1-score.\n",
        "- **Key Insights:**\n",
        "  - **Classification report** gives insights into how well the model is performing on both classes (fraudulent vs. non-fraudulent).\n",
        "  - Helps to understand trade-offs between **false positives** and **false negatives**, especially in fraud detection where misclassifying fraud as normal can be costly.\n",
        "- **Why this is important:** Provides a comprehensive view of the model's strengths and weaknesses, which is crucial for tuning and improving the system."
      ],
      "metadata": {
        "id": "3EgSeYlvPN5r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the model on test data"
      ],
      "metadata": {
        "id": "BYKyuboSMHEZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = predict_anomalies(X_test)  # Get anomaly predictions for the test set\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-uuMc9ZRMHI-",
        "outputId": "e3fcfe4d-0673-4073-cf8c-49a2d875db48"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m3554/3554\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 2ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-VQBE9NiMQVN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KZNO5YM3MQaf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the predictions to a CSV file"
      ],
      "metadata": {
        "id": "Z22VMDKmMQeN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions_df = pd.DataFrame(predictions, columns=['Anomaly_Score'])  # Create DataFrame for predictions\n"
      ],
      "metadata": {
        "id": "X7pi6cJSMQi_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions_df['True_Class'] = y_test.values  # Add true class labels to the predictions DataFrame\n"
      ],
      "metadata": {
        "id": "QzxhmzjEMQnQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions_df.to_csv('anomaly_predictions.csv', index=False)  # Save the predictions to a CSV file\n"
      ],
      "metadata": {
        "id": "w2-xGDTeMQrp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4XH_0zLOMb8w"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nClassification Report:\\n\", classification_report(y_test, predictions > 0.5))  # Print classification report\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p9JQhxHPMcB8",
        "outputId": "a9aa072b-c0a9-4e51-a7e9-5f9b8eab4eea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.66      0.40      0.50     56750\n",
            "           1       0.57      0.80      0.67     56976\n",
            "\n",
            "    accuracy                           0.60    113726\n",
            "   macro avg       0.62      0.60      0.58    113726\n",
            "weighted avg       0.62      0.60      0.58    113726\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "lgphR2-aPTcX"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "CVvBxBqkMcGD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8u7Bs1hjMcK2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "U7hV-znsMcTs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "INLTn4VaPVCy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 🧩 Step 15: Confusion Matrix\n",
        "- **Overview:** Visualize the performance using a confusion matrix.\n",
        "- **Key Insights:**\n",
        "  - The confusion matrix shows true positives (correct fraud detection), false positives (normal transactions wrongly marked as fraud), and so on.\n",
        "  - Plotting the confusion matrix provides a visual understanding of model performance.\n",
        "- **Why this is important:** A confusion matrix helps identify where the model is making errors, which can be useful for fine-tuning and improving the fraud detection system."
      ],
      "metadata": {
        "id": "OOiY2hmGPWGq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cm = confusion_matrix(y_test, predictions > 0.5)  # Generate confusion matrix\n",
        "plt.figure(figsize=(7, 5))  # Set the size for the confusion matrix plot\n",
        "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=['Normal', 'Fraud'], yticklabels=['Normal', 'Fraud'])  # Plot confusion matrix\n",
        "plt.title(\"Confusion Matrix\")  # Set title for the confusion matrix plot\n",
        "plt.ylabel('True Class')  # Set the y-axis label\n",
        "plt.xlabel('Predicted Class')  # Set the x-axis label\n",
        "plt.show()  # Display the confusion matrix plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "oV0eM8NIMcbx",
        "outputId": "7819d676-1b7b-4d7a-f7cc-dd95a8e6a90f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "----\n",
        "----"
      ],
      "metadata": {
        "id": "61xzFhpQPZES"
      }
    }
  ]
}