2_5_9_Emotion_Detection_in_Text.ipynb

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        "# <span style=\"color:blue;\">Project Overview: Emotion Detection Using LSTM</span>\n",
        "\n",
        "This project aims to build a machine learning model using Long Short-Term Memory (LSTM) to classify emotions from textual data. The process includes data loading, cleaning, exploratory data analysis (EDA), model building, training, and evaluation.\n",
        "\n",
        "## <span style=\"color:darkgreen;\">1. Importing Libraries</span>\n",
        "\n",
        "In this step, we import the necessary libraries that will be used throughout the project.\n",
        "\n",
        "- **Pandas**: Used for data manipulation and analysis, particularly for handling datasets.\n",
        "- **NumPy**: A fundamental library for numerical operations in Python.\n",
        "- **Matplotlib & Seaborn**: Libraries for data visualization, helping to create graphs and plots.\n",
        "- **OS**: Provides functions for interacting with the operating system, like file path handling.\n",
        "- **Scikit-learn**: Specifically, `LabelEncoder` is imported for converting categorical labels into numerical form.\n",
        "- **TensorFlow Keras**: We import several components for building and training the LSTM model, including layers for embedding and dropout.\n",
        "- **Pickle**: This library is used to save the model and encoding objects after training.\n",
        "\n",
        "---\n",
        "\n",
        "## <span style=\"color:darkgreen;\">2. Loading the Datasets</span>\n",
        "\n",
        "In this step, we define a function to load datasets from specified paths. We are working with three datasets: training, testing, and validation.\n",
        "\n",
        "- **Dataset Structure**: Each dataset is assumed to be in a CSV format with two columns: `text` (the input text) and `emotion` (the corresponding emotion label).\n",
        "- **Loading Data**: The function reads each dataset, applying UTF-8 encoding for proper text handling, and returns the loaded data for further processing.\n",
        "\n",
        "---\n",
        "\n",
        "## <span style=\"color:darkgreen;\">3. Data Inspection</span>\n",
        "\n",
        "This step involves inspecting the loaded datasets to understand their structure and contents.\n",
        "\n",
        "- **Dataset Overview**: By utilizing `head()` and `info()`, we examine the first few entries and summary statistics (including data types and counts).\n",
        "- **Missing Values**: We check for missing values in each dataset to ensure data integrity and determine the need for cleaning.\n",
        "\n",
        "---\n",
        "\n",
        "## <span style=\"color:darkgreen;\">4. Data Cleaning</span>\n",
        "\n",
        "In this crucial step, we focus on cleaning the datasets to prepare them for analysis.\n",
        "\n",
        "- **Handling Missing Values**: Rows containing any missing values are dropped from the datasets to maintain accuracy in the analysis.\n",
        "- **Saving Cleaned Data**: After cleaning, the datasets are saved as CSV files for easy access and future reference.\n",
        "\n",
        "---\n",
        "\n",
        "## <span style=\"color:darkgreen;\">5. Exploratory Data Analysis (EDA)</span>\n",
        "\n",
        "EDA is performed to uncover insights from the datasets, focusing on the distribution of emotions and text characteristics.\n",
        "\n",
        "- **Emotion Distribution**: We analyze the frequency of different emotions in the datasets, helping to understand class imbalance issues.\n",
        "- **Visualization**: Various plots (count plots, box plots) are generated to visualize the distribution of emotions and text lengths.\n",
        "- **Top Frequent Words**: A word count analysis is conducted to identify the most common words in the training data, excluding stop words.\n",
        "\n",
        "---\n",
        "\n",
        "## <span style=\"color:darkgreen;\">6. Model Building</span>\n",
        "\n",
        "In this stage, we will build the LSTM model architecture to classify emotions based on the textual data.\n",
        "\n",
        "- **Model Design**: The model will include embedding layers, LSTM layers for sequence processing, and dense layers for output classification.\n",
        "- **Model Compilation**: We will compile the model, specifying the optimizer, loss function, and evaluation metrics.\n",
        "\n",
        "---\n",
        "\n",
        "## <span style=\"color:darkgreen;\">7. Training the Model</span>\n",
        "\n",
        "Training the model on the training dataset will allow it to learn the patterns associated with each emotion.\n",
        "\n",
        "- **Fitting the Model**: The model will be trained on the training dataset, with validation data used to monitor performance during training.\n",
        "- **Callbacks**: We may implement callbacks to save the best model based on validation accuracy.\n",
        "\n",
        "---\n",
        "\n",
        "## <span style=\"color:darkgreen;\">8. Evaluating the Model</span>\n",
        "\n",
        "After training, we will evaluate the model's performance using the test dataset.\n",
        "\n",
        "- **Performance Metrics**: Key metrics such as accuracy, precision, recall, and F1 score will be calculated to assess the model's effectiveness.\n",
        "- **Confusion Matrix**: A confusion matrix will be generated to visualize the performance across different emotion classes.\n",
        "\n",
        "---\n",
        "\n",
        "## <span style=\"color:darkgreen;\">9. Conclusion and Future Work</span>\n",
        "\n",
        "Finally, we will summarize the findings and discuss potential future improvements.\n",
        "\n",
        "- **Summary**: A recap of the model's performance and key insights gained from the analysis.\n",
        "- **Future Improvements**: Suggestions for enhancing the model, such as hyperparameter tuning, utilizing advanced NLP techniques, or expanding the dataset."
      ],
      "metadata": {
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OUfhu49TF3Aa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nS3SvJYdF3FI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mQK30izvF3H7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color:blue;\">1. Importing Libraries</span>\n",
        "\n",
        "In this step, we will import the necessary libraries that will be used throughout the project.\n",
        "\n",
        "- **Pandas**: Essential for data manipulation and analysis, allowing us to load, transform, and manage our datasets effectively.\n",
        "- **NumPy**: A fundamental package for numerical operations in Python, useful for handling arrays and mathematical functions.\n",
        "- **Matplotlib**: A powerful library for creating static, animated, and interactive visualizations in Python.\n",
        "- **Seaborn**: Built on top of Matplotlib, it provides a high-level interface for drawing attractive statistical graphics.\n",
        "- **OS**: A module that allows us to interact with the operating system for tasks like file handling and path management.\n",
        "- **Scikit-learn**: A comprehensive library for machine learning in Python; here, we will use `LabelEncoder` for converting categorical emotion labels into numerical form.\n",
        "- **TensorFlow and Keras**: A powerful combination for building deep learning models; we will use Keras for defining and training our LSTM model. We will specifically import:\n",
        "  - **layers**: To define the architecture of the LSTM model.\n",
        "  - **Sequential**: For building the model in a sequential manner.\n",
        "  - **Dropout**: To prevent overfitting during training.\n",
        "- **Pickle**: A library for serializing and deserializing Python objects; useful for saving the trained model and label encodings for later use.\n",
        "\n",
        "Make sure to have all these libraries installed in your environment before proceeding with the next steps."
      ],
      "metadata": {
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      }
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================="
      ],
      "metadata": {
        "id": "j8QbkdIPF3KT"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Import Libraries"
      ],
      "metadata": {
        "id": "GLgxUF4fF3Ma"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================="
      ],
      "metadata": {
        "id": "J964GUNBF3PD"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd  # Import pandas for data manipulation and analysis"
      ],
      "metadata": {
        "id": "_zwQobS9F3Tz"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np  # Import NumPy for numerical operations"
      ],
      "metadata": {
        "id": "GqKHk2-gF3bq"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt  # Import Matplotlib for plotting graphs"
      ],
      "metadata": {
        "id": "_ZV-MdXOF3eh"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns  # Import Seaborn for advanced visualizations"
      ],
      "metadata": {
        "id": "DX5g0tSCF3ha"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os  # Import os for operating system functionalities"
      ],
      "metadata": {
        "id": "D2sjRD_JF3jj"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder  # Import LabelEncoder for label encoding"
      ],
      "metadata": {
        "id": "9l0zRu_TF3pq"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.preprocessing.text import Tokenizer  # Import Tokenizer for text tokenization"
      ],
      "metadata": {
        "id": "EazRJ5BzF3se"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.preprocessing.sequence import pad_sequences  # Import pad_sequences for padding sequences"
      ],
      "metadata": {
        "id": "wF_gRGZkF3u-"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import Sequential  # Import Sequential model type"
      ],
      "metadata": {
        "id": "ATyD5Nl9F3xO"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.layers import Embedding, LSTM, Dense, Dropout  # Import layers for the LSTM model"
      ],
      "metadata": {
        "id": "tPOsmrQtF31u"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle  # Import pickle for saving models and encoders"
      ],
      "metadata": {
        "id": "2tCbpzA3F34V"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer  # Import CountVectorizer for text vectorization"
      ],
      "metadata": {
        "id": "OsHjHtNZnzek"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from wordcloud import WordCloud  # Import WordCloud for creating word clouds"
      ],
      "metadata": {
        "id": "KcKhPCrhn29A"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KpTgJhRhF36Z"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings  # To suppress warnings"
      ],
      "metadata": {
        "id": "osrjRHzoq29O"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "NrZqA7gaq3B1"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "warnings.filterwarnings('ignore') # Suppressing warnings"
      ],
      "metadata": {
        "id": "imKaCUWJFjqZ"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "C6EWs1ndrATk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ZQes1xWKrAXe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "YV9y0isaFjzT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color:blue;\">2. Loading the Datasets</span>\n",
        "\n",
        "In this step, we will define a function to load our datasets from specified file paths.\n",
        "\n",
        "- **Function Definition**: We will create a function that takes a file path as an argument and loads the dataset using Pandas. The function will read CSV files containing the text and emotion columns.\n",
        "- **Dataset Structure**: Each dataset is assumed to have the following structure:\n",
        "  - **text**: The textual content from which emotions will be extracted.\n",
        "  - **emotion**: The corresponding label for the emotion expressed in the text (e.g., happiness, sadness, anger).\n",
        "- **Data Loading**: The function will apply UTF-8 encoding to ensure proper handling of special characters in the text. After loading, the function will return the dataset as a Pandas DataFrame.\n",
        "- **Example Usage**: We will load three datasets: training, validation, and testing, which will later be used for model training and evaluation.\n",
        "\n",
        "After loading the datasets, ensure to verify their structure and contents before proceeding to data inspection."
      ],
      "metadata": {
        "id": "ohYHASSJo1E1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================="
      ],
      "metadata": {
        "id": "YCgJq6Q8Cdpv"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Loading the Datasets"
      ],
      "metadata": {
        "id": "TnjcO3ojID6m"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================="
      ],
      "metadata": {
        "id": "Y0A5zq4CID9M"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_path = '/content/train.txt'  # Path to the training dataset"
      ],
      "metadata": {
        "id": "_UQ4zoAsID_V"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_path = '/content/test.txt'  # Path to the test dataset"
      ],
      "metadata": {
        "id": "mR1-GXca9cUc"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "val_path = '/content/val.txt'  # Path to the validation dataset"
      ],
      "metadata": {
        "id": "YEHX5yDmIFRg"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BhPlz8OBIFUe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def load_dataset(path):  # Function to load dataset from a given path\n",
        "    \"\"\"Load dataset from the specified path.\"\"\"  # Docstring for the function\n",
        "    data = pd.read_csv(path, delimiter=';', names=['text', 'emotion'], encoding='utf-8')  # Load CSV data\n",
        "    return data  # Return the loaded data"
      ],
      "metadata": {
        "id": "a3xxPBwJIFXS"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "L8EaxrIxIFiq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vrvzwEssIFk9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load datasets"
      ],
      "metadata": {
        "id": "QfYQIn9DIFm6"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_data = load_dataset(train_path)  # Load the training data"
      ],
      "metadata": {
        "id": "qgS5DJDIIFrO"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_data = load_dataset(test_path)  # Load the test data"
      ],
      "metadata": {
        "id": "SY1hoWKSIFuE"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "val_data = load_dataset(val_path)  # Load the validation data"
      ],
      "metadata": {
        "id": "p8kfrnXJIFxe"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KxuSu6K7hsgd"
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      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4QHd240Yhsjf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "MvdVfNCqhwNl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display the first few rows of each dataset"
      ],
      "metadata": {
        "id": "2mTuVAVihsm3"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Train Dataset Head:\\n\", train_data.head())  # Print the first few rows of the training dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wGQCSEJJhsqA",
        "outputId": "4ba0cc76-5555-4fa2-a99a-e1606e7ed29f"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Dataset Head:\n",
            "                                                 text  emotion\n",
            "0                            i didnt feel humiliated  sadness\n",
            "1  i can go from feeling so hopeless to so damned...  sadness\n",
            "2   im grabbing a minute to post i feel greedy wrong    anger\n",
            "3  i am ever feeling nostalgic about the fireplac...     love\n",
            "4                               i am feeling grouchy    anger\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "v4x_UT8shss-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "yzOlT_Fhhsxd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nTest Dataset Head:\\n\", test_data.head())  # Print the first few rows of the test dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E1OTtfeuhs7e",
        "outputId": "3111684b-6453-4a8d-ef04-c6dc995b370b"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Test Dataset Head:\n",
            "                                                 text  emotion\n",
            "0  im feeling rather rotten so im not very ambiti...  sadness\n",
            "1          im updating my blog because i feel shitty  sadness\n",
            "2  i never make her separate from me because i do...  sadness\n",
            "3  i left with my bouquet of red and yellow tulip...      joy\n",
            "4    i was feeling a little vain when i did this one  sadness\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "pOJ-ajv_htAK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "HSgDfsaPIFz_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nValidation Dataset Head:\\n\", val_data.head())  # Print the first few rows of the validation dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lX7YV5EeIF2j",
        "outputId": "17d3d877-2f94-4c27-d7ea-3ad551091185"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Validation Dataset Head:\n",
            "                                                 text  emotion\n",
            "0  im feeling quite sad and sorry for myself but ...  sadness\n",
            "1  i feel like i am still looking at a blank canv...  sadness\n",
            "2                     i feel like a faithful servant     love\n",
            "3                  i am just feeling cranky and blue    anger\n",
            "4  i can have for a treat or if i am feeling festive      joy\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "w8oczG8wIF5L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "SssxDuH0IF9e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "PTou95CnIGAB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color:blue;\">3. Data Inspection</span>\n",
        "\n",
        "This step involves inspecting the loaded datasets to understand their structure and contents better.\n",
        "\n",
        "- **Overview of Datasets**: Utilize methods like `head()` to display the first few entries of each dataset and `info()` to summarize the dataset, showing data types and non-null counts.\n",
        "- **Checking for Missing Values**: We will assess each dataset for any missing values, which could affect our analysis. We can do this by using the `isnull()` and `sum()` functions to get a count of missing values per column.\n",
        "- **Statistical Summary**: For quantitative understanding, we can apply `describe()` to gain insights into the numerical features of the datasets, including counts, means, and standard deviations.\n",
        "- **Visual Inspection**: Generate basic visualizations (like histograms) to get a sense of the distribution of text lengths and emotions in the datasets.\n",
        "\n",
        "Performing thorough data inspection will help us understand the quality and characteristics of our data before we proceed to data cleaning."
      ],
      "metadata": {
        "id": "pjWIZUrDo9FG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================="
      ],
      "metadata": {
        "id": "FYfy7jJ6IGCk"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Data Inspection"
      ],
      "metadata": {
        "id": "6XiOLd4Dh76F"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================="
      ],
      "metadata": {
        "id": "8Kx66R9gIGEz"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Summary of the datasets"
      ],
      "metadata": {
        "id": "c_l1pDHMIGG-"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nTrain Dataset Info:\\n\", train_data.info())  # Print summary information about the training dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TBueBCvpIGJs",
        "outputId": "50cff567-d641-468a-f7d4-2b4e00e3752d"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 16000 entries, 0 to 15999\n",
            "Data columns (total 2 columns):\n",
            " #   Column   Non-Null Count  Dtype \n",
            "---  ------   --------------  ----- \n",
            " 0   text     16000 non-null  object\n",
            " 1   emotion  16000 non-null  object\n",
            "dtypes: object(2)\n",
            "memory usage: 250.1+ KB\n",
            "\n",
            "Train Dataset Info:\n",
            " None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KE1gHBPkiDi9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "UWWHPPyiiDmp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nTest Dataset Info:\\n\", test_data.info())  # Print summary information about the test dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VRQFEKtoiDqu",
        "outputId": "7ad28df6-5159-4883-a97c-2aa9b0549b26"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2000 entries, 0 to 1999\n",
            "Data columns (total 2 columns):\n",
            " #   Column   Non-Null Count  Dtype \n",
            "---  ------   --------------  ----- \n",
            " 0   text     2000 non-null   object\n",
            " 1   emotion  2000 non-null   object\n",
            "dtypes: object(2)\n",
            "memory usage: 31.4+ KB\n",
            "\n",
            "Test Dataset Info:\n",
            " None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "FFWY4j40iGK_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "o8g5kHoiiGOY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nValidation Dataset Info:\\n\", val_data.info())  # Print summary information about the validation dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9_4OZJmdiGRJ",
        "outputId": "e15a072c-95a2-4159-ddfa-38ed5ee18b89"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2000 entries, 0 to 1999\n",
            "Data columns (total 2 columns):\n",
            " #   Column   Non-Null Count  Dtype \n",
            "---  ------   --------------  ----- \n",
            " 0   text     2000 non-null   object\n",
            " 1   emotion  2000 non-null   object\n",
            "dtypes: object(2)\n",
            "memory usage: 31.4+ KB\n",
            "\n",
            "Validation Dataset Info:\n",
            " None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "sNuoSPw2iJjH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "sGL7jHhkiJmM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking for missing values"
      ],
      "metadata": {
        "id": "FiaoY4_xiJpO"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nMissing values in Train Dataset:\\n\", train_data.isnull().sum())  # Check and print missing values in the training dataset\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MD1c6_2QiJtE",
        "outputId": "086fb758-d326-4fa3-95d1-f25f41ebb133"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Missing values in Train Dataset:\n",
            " text       0\n",
            "emotion    0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "eecbp36liJvD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0apmYE2JiJyF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nMissing values in Test Dataset:\\n\", test_data.isnull().sum())  # Check and print missing values in the test dataset\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IVzOw3AcIGOH",
        "outputId": "67665e5a-8c82-4ce2-da65-ccea135b3c91"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Missing values in Test Dataset:\n",
            " text       0\n",
            "emotion    0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qqSierkBiQ0d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gIODEfeEiQ3k"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nMissing values in Validation Dataset:\\n\", val_data.isnull().sum())  # Check and print missing values in the validation dataset\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7tbMcoDfiQ62",
        "outputId": "6d4a7a25-9e70-4b50-c2aa-9a49392cf21d"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Missing values in Validation Dataset:\n",
            " text       0\n",
            "emotion    0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KtC0v9tQiTGu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "rFrcbFs9iTKT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "f7GuXsfRiTMf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color:blue;\">4. Data Cleaning</span>\n",
        "\n",
        "In this crucial step, we will focus on cleaning the datasets to prepare them for analysis and model training.\n",
        "\n",
        "- **Handling Missing Values**:\n",
        "  - If any rows contain missing values, we will drop these rows using the `dropna()` function to ensure that our datasets are complete and ready for analysis.\n",
        "  - Discuss the impact of missing data on the analysis and how dropping these rows can lead to a cleaner dataset.\n",
        "  \n",
        "- **Text Normalization**:\n",
        "  - We may also want to apply normalization techniques to the text data, such as converting all text to lowercase, removing punctuation, and eliminating unnecessary whitespace.\n",
        "  - Discuss the importance of text normalization in NLP tasks for reducing variability in text representation.\n",
        "  \n",
        "- **Saving Cleaned Data**:\n",
        "  - After cleaning, save the cleaned datasets as CSV files to your local drive or cloud storage for easy access in future steps. This can be done using `to_csv()`.\n",
        "  - Mention how saving intermediate files can help in case we need to revisit any step or if we want to train the model on the same cleaned data again.\n",
        "\n",
        "Cleaning the data is a critical step in any machine learning project, as it directly influences the quality of insights and model performance."
      ],
      "metadata": {
        "id": "ndAUnltWpA1k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================="
      ],
      "metadata": {
        "id": "fe0f0QaDiTPY"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Data Cleaning"
      ],
      "metadata": {
        "id": "TDFwg9G8iWQf"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================="
      ],
      "metadata": {
        "id": "RRxENwFYiWTm"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Drop rows with missing values"
      ],
      "metadata": {
        "id": "n0Tep81KiTSE"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_data.dropna(inplace=True)  # Drop missing values from the training dataset"
      ],
      "metadata": {
        "id": "11r6EC-2iTU9"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_data.dropna(inplace=True)  # Drop missing values from the test dataset"
      ],
      "metadata": {
        "id": "pVcgfuNaiTXc"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "val_data.dropna(inplace=True)  # Drop missing values from the validation dataset"
      ],
      "metadata": {
        "id": "0mLScFiQiTZ6"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "SluOcIJoIGQq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "IiOsmNeGIGTO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save cleaned datasets"
      ],
      "metadata": {
        "id": "EbAM8JwtIGVb"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_data.to_csv('/content/cleaned_train_data.csv', index=False)  # Save cleaned training data to CSV"
      ],
      "metadata": {
        "id": "fIx4PSYqIGaJ"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_data.to_csv('/content/cleaned_test_data.csv', index=False)  # Save cleaned test data to CSV"
      ],
      "metadata": {
        "id": "AKmVeUUUIGgI"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "val_data.to_csv('/content/cleaned_val_data.csv', index=False)  # Save cleaned validation data to CSV"
      ],
      "metadata": {
        "id": "MCIbS6L1IGlu"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "R0g5acmoip6f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Fs-MUqEoip9S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "buDHHWagiqC-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color:blue;\">5. Exploratory Data Analysis (EDA)</span>\n",
        "\n",
        "EDA is performed to uncover insights from the datasets and understand the underlying patterns before model training.\n",
        "\n",
        "- **Emotion Distribution**:\n",
        "  - Analyze the frequency of different emotions present in the datasets using value counts. Discuss the importance of understanding the distribution of classes, especially for classification tasks.\n",
        "  - Create a count plot or bar graph to visually represent the number of instances for each emotion using Seaborn's `countplot()` function.\n",
        "\n",
        "- **Text Characteristics**:\n",
        "  - Investigate the distribution of text lengths (number of words or characters) in the datasets. Visualize this using histograms or box plots to identify any potential outliers.\n",
        "  - Discuss how text length may influence emotion classification and the need for padding or truncating sequences during model training.\n",
        "\n",
        "- **Word Frequency Analysis**:\n",
        "  - Perform a word frequency analysis to identify the most common words in the training dataset. This may include removing stop words and performing tokenization.\n",
        "  - Use word clouds or bar graphs to visualize the most frequent words, providing insights into the language used in different emotions.\n",
        "\n",
        "By performing EDA, we can derive meaningful insights and prepare the dataset for the subsequent steps of model building and training."
      ],
      "metadata": {
        "id": "eRHqh2umpDj2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================="
      ],
      "metadata": {
        "id": "p-VscKDziqF2"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Exploratory Data Analysis (EDA)"
      ],
      "metadata": {
        "id": "yI-cNZnKitG-"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================="
      ],
      "metadata": {
        "id": "kc7_ML4_itKY"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Emotion distribution"
      ],
      "metadata": {
        "id": "sgSn4Us_iqH9"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nEmotion Distribution in Train Dataset:\\n\", train_data['emotion'].value_counts())  # Print emotion distribution in training dataset\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "odvSIGLFiqJw",
        "outputId": "a6c18e6e-deaa-4d51-9877-3a49d4eceab9"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Emotion Distribution in Train Dataset:\n",
            " emotion\n",
            "joy         5362\n",
            "sadness     4666\n",
            "anger       2159\n",
            "fear        1937\n",
            "love        1304\n",
            "surprise     572\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7gi5VEFYiqNL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "sIguxQYIiqQ7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nEmotion Distribution in Test Dataset:\\n\", test_data['emotion'].value_counts())  # Print emotion distribution in test dataset\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XHZriLXMIGpf",
        "outputId": "f0102c24-9694-416d-e4e1-482994e8550c"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Emotion Distribution in Test Dataset:\n",
            " emotion\n",
            "joy         695\n",
            "sadness     581\n",
            "anger       275\n",
            "fear        224\n",
            "love        159\n",
            "surprise     66\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "FL035khNA8y3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "sfmJD33VAreR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nEmotion Distribution in Validation Dataset:\\n\", val_data['emotion'].value_counts())  # Print emotion distribution in validation dataset\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SwK7OYx1Arg8",
        "outputId": "b6149cb6-e076-4302-86d9-84f9acff0161"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Emotion Distribution in Validation Dataset:\n",
            " emotion\n",
            "joy         704\n",
            "sadness     550\n",
            "anger       275\n",
            "fear        212\n",
            "love        178\n",
            "surprise     81\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "uB-hh6U4jAPW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "VhSnLn1-jASD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save emotion distribution to CSV"
      ],
      "metadata": {
        "id": "gEIomktWjAUy"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_data['emotion'].value_counts().to_csv('/content/train_emotion_distribution.csv')  # Save training emotion distribution to CSV\n"
      ],
      "metadata": {
        "id": "bvGFeJuDjAXP"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_data['emotion'].value_counts().to_csv('/content/test_emotion_distribution.csv')  # Save test emotion distribution to CSV\n"
      ],
      "metadata": {
        "id": "bKt6GkJyjAZs"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "val_data['emotion'].value_counts().to_csv('/content/val_emotion_distribution.csv')  # Save validation emotion distribution to CSV\n"
      ],
      "metadata": {
        "id": "mT_c5FWgjAfv"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "YNSvZzN6jIQA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KJ7vjZj2jITf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualization: Distribution of Emotions"
      ],
      "metadata": {
        "id": "2rVFwd1kjIWi"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Create a figure for the plot\n",
        "sns.countplot(data=train_data, x='emotion', order=train_data['emotion'].value_counts().index)  # Create a count plot for emotion distribution\n",
        "plt.title('Distribution of Emotions in the Training Dataset')  # Set the title of the plot\n",
        "plt.xticks(rotation=45)  # Rotate x-axis labels for better readability\n",
        "plt.savefig('/content/emotion_distribution_train.pdf')  # Save the plot as a PDF\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 600
        },
        "id": "JsyrRPB-jIZP",
        "outputId": "d39310ab-7c67-43e7-ebc4-8aaa4dad2b38"
      },
      "execution_count": 68,
      "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": "XIXorRC2jIcD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0xglQhBBjIe8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Length of Texts in Each Emotion Category"
      ],
      "metadata": {
        "id": "5nK9ERU8jIiX"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_data['text_length'] = train_data['text'].apply(len)  # Calculate text length for each entry in the training dataset\n"
      ],
      "metadata": {
        "id": "8qyn1llBjIvG"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Create a figure for the plot\n",
        "sns.boxplot(x='emotion', y='text_length', data=train_data, order=train_data['emotion'].value_counts().index)  # Create a box plot for text lengths by emotion\n",
        "plt.title('Text Length Distribution by Emotion')  # Set the title of the plot\n",
        "plt.xticks(rotation=45)  # Rotate x-axis labels for better readability\n",
        "plt.savefig('/content/text_length_by_emotion.pdf')  # Save the plot as a PDF\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 600
        },
        "id": "CflIq5t7jAi8",
        "outputId": "8f23ad92-6b2b-4813-9440-24295126c445"
      },
      "execution_count": 71,
      "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": "Ra5mo2dVjT7h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "wnof8GoxjT_C"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Average Text Length per Emotion"
      ],
      "metadata": {
        "id": "Ld_rdRdOjUB2"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "avg_text_length = train_data.groupby('emotion')['text_length'].mean().sort_values(ascending=False)  # Calculate average text length per emotion\n"
      ],
      "metadata": {
        "id": "xK4u2PeBjUEe"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Create a figure for the plot\n",
        "avg_text_length.plot(kind='bar', color='teal')  # Create a bar plot for average text length\n",
        "plt.title('Average Text Length per Emotion')  # Set the title of the plot\n",
        "plt.ylabel('Average Text Length')  # Set the y-axis label\n",
        "plt.savefig('/content/avg_text_length_emotion.pdf')  # Save the plot as a PDF\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "id": "yva0JLw8jUG9",
        "outputId": "ae67b0af-3e69-45fe-9b6e-2f7d736fa5e7"
      },
      "execution_count": 74,
      "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": "RWQ0Ydn-jcK3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dM8xQ3UrmUmz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#  Pairplot of Emotions Based on Text Length (optional if more numeric features exist)"
      ],
      "metadata": {
        "id": "eyJGxTTnnM8U"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Set the figure size for the pairplot\n",
        "sns.pairplot(train_data, hue='emotion', diag_kind='kde', height=3)  # Create a pairplot colored by emotion\n",
        "plt.savefig('/content/pairplot_emotions.pdf')  # Save the plot as a PDF file\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "04VIKpXGnRuc",
        "outputId": "8410a0ae-8978-4094-e2e8-d191a5a69b5e"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 368.236x300 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "iDOz2jHdnR3O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mySz4qafnNAh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Top N Most Frequent Words (excluding stop words)"
      ],
      "metadata": {
        "id": "v6kuiErmmUqW"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vectorizer = CountVectorizer(stop_words='english', max_features=20)  # Initialize CountVectorizer to ignore stop words and limit to top 20 features\n"
      ],
      "metadata": {
        "id": "Bg---MBQmUtj"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "word_count = vectorizer.fit_transform(train_data['text'])  # Fit and transform the text data into a word count matrix\n"
      ],
      "metadata": {
        "id": "3CeOlEPRmUxV"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "word_freq_df = pd.DataFrame({'word': vectorizer.get_feature_names_out(), 'count': word_count.sum(axis=0).tolist()[0]})  # Create a DataFrame with word counts\n"
      ],
      "metadata": {
        "id": "UMz9RMu-mdoJ"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "word_freq_df = word_freq_df.sort_values('count', ascending=False)  # Sort the DataFrame by word count in descending order\n"
      ],
      "metadata": {
        "id": "Nb2TwFtRmU0a"
      },
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7_VbNMDymhQ6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Set the figure size for the bar plot\n",
        "sns.barplot(x='count', y='word', data=word_freq_df, palette='viridis')  # Create a bar plot for top frequent words\n",
        "plt.title('Top 20 Most Frequent Words')  # Set the title of the plot\n",
        "plt.savefig('/content/top_20_words.pdf')  # Save the plot as a PDF file\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "t2S7bFO3mhUh",
        "outputId": "acb717a6-fbbd-49c7-fbea-f2fade01109c"
      },
      "execution_count": 82,
      "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": "3iSH4TkGmrQl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "70ThDrnpmrT7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Word Cloud for 'Sadness' Emotion"
      ],
      "metadata": {
        "id": "y4Dj5li2mrjz"
      },
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for non-empty text entries for 'sadness'"
      ],
      "metadata": {
        "id": "btrKoLb_mU4E"
      },
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sad_texts = ' '.join(train_data[train_data['emotion'] == 'sadness']['text'].dropna().astype(str))  # Combine all texts associated with 'sadness'\n"
      ],
      "metadata": {
        "id": "LzB6g_4-mxGM"
      },
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "lNxhr9mEmxJw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "if sad_texts.strip():  # Ensure there is actual text to generate a word cloud\n",
        "    wordcloud = WordCloud(width=800, height=400, background_color='white').generate(sad_texts)  # Generate a word cloud for sadness texts\n",
        "\n",
        "    plt.figure(figsize=(10, 6))  # Set the figure size for the word cloud\n",
        "    plt.imshow(wordcloud, interpolation='bilinear')  # Display the generated word cloud\n",
        "    plt.axis('off')  # Turn off the axis\n",
        "    plt.title('Word Cloud for Sadness Emotion')  # Set the title of the plot\n",
        "    plt.savefig('/content/wordcloud_sadness.pdf')  # Save the plot as a PDF file\n",
        "    plt.show()  # Display the plot\n",
        "else:\n",
        "    print(\"No valid texts available for the 'sadness' emotion.\")  # Print a message if no valid texts are found\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 446
        },
        "id": "ItKCI7zcmU7O",
        "outputId": "894695fc-81ea-4792-ad47-d08086a92ba5"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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/+AGPPfYYt956KzfffPOUa/JW7+l01NTUMDo6ysaNG6fVJpy7Uy/LMhs3buRf/uVfOH78OF/+8pfZtGnTpOSTDoeDRx99lCeeeILOzk7uuusuvvzlLxOPxy97jFeKM/dypnmZm5uLw+EAZi/kn2l3NnM9S5YsWS6VrKCRJUuWGVm+fDlWq5Uf/ehH9PT0TNJoqKrK0qVL+bd/+zcikcgk++4zScu+8Y1vTGrvX/7lXwC46667Ltr3nXfeSW9vL88++2ymLBqNzjrB3uc+9zkcDgcf/vCHGRgYmHK8paWFb37zmxfsf/fu3ZMElUgkwve+9z0qKyszdvc1NTUAbNmyJVNP1/Up41y+fDl5eXl85zvfIZlMZsr/8z//8y1lb77zzjvRdZ1vfetbk8q//vWvI0lSJjrTpdLf3z+tH0oymeS1116bZBqnKMoU4eGZZ56Z5MdxZqxv5Z5OxyOPPEJPTw/f//73pxyLxWIZU6LR0dEpxxcvXgyQCYM7MjIy6bjFYmHevHkIIUilUpc9xitFUVERixcv5sknn5w0Z44ePcrLL788KVngGYFjNnNrtnM9S5YsWS6VbHjbLFmyzIjFYmHFihW8+eabqKrKsmXLJh1fu3Yt//zP/wxMTtS3aNEiPvCBD/C9732P8fFxNmzYwO7du3nyySe5//77J2UUn4mPfOQjfOtb3+Lxxx9n3759FBUV8dRTT10wzOq51NTU8OMf/5hHH32UhoaGSZnBt2/fzjPPPDMll8C5/K//9b/4yU9+wh133MEnPvEJ/H4/Tz75JG1tbfz85z/POD83NjayevVq/uqv/orR0VH8fj8//elP0TRtUntms5m/+7u/42Mf+xg33XQTjz76KG1tbTzxxBNvyUfjnnvu4cYbb+Sv//qvaW9vZ9GiRbz88ss899xzfPKTn8wIQpdKd3c3K1eu5KabbmLjxo0UFhYyODjIT37yEw4dOsQnP/nJTNjUu+++my9+8Yt86EMfYu3atRw5coQf/ehHU87rrd7T6Xj/+9/P008/zR//8R+zefNm1q1bh67rnDx5kqeffjqTn+WLX/wiW7Zs4a677qKiooLBwUG+/e1vU1pampm7t956K4WFhaxbt46CggJOnDjBt771Le66665ZBRbYv38///Vf/zWlvKamZpLz91vhq1/9KnfccQdr1qzhj/7ojzLhbT0eD1/4whcy9c48q3/913/NY489htls5p577skIIOcy27meJUuWLJfM2xfwKkuWLL8P/NVf/ZUAxNq1a6cc+8UvfiEA4XK5hKZpk46lUinxt3/7t6KqqkqYzWZRVlYm/uqv/krE4/FJ9SoqKsRdd901bd8dHR3i3nvvFXa7XeTm5oo///M/Fy+++OJFw9uey6lTp8RHPvIRUVlZKSwWi3C5XGLdunXiX//1XyeN5fyQn0II0dLSIh566CHh9XqF1WoVK1euFL/97W+n9NHS0iJuvvlmoaqqKCgoEJ///OfFK6+8Mu04v/3tb4uqqiqhqqpYvny52LJli9iwYcNlh7cVIh2K9FOf+pQoLi4WZrNZ1NXVia9+9avCMIxJ9QDx8Y9//KL9CJEOWfzNb35T3HbbbaK0tFSYzWbhcrnEmjVrxPe///1JbcfjcfHpT39aFBUVCZvNJtatWyd27Ngx7XnN9p7OdK4f+MAHREVFxaSyZDIp/vEf/1E0NjYKVVWFz+cTy5YtE3/7t38rAoGAEEKI1157Tdx3332iuLhYWCwWUVxcLN797neLU6dOZdr57ne/K66//nqRk5MjVFUVNTU14rOf/WymjZm4WHjbc+fVTPN9untzpt2vfvWrk8pfffVVsW7dOmGz2YTb7Rb33HOPOH78+JQ2v/SlL4mSkhIhy/KkULeXO9fPhLd95plnph3nE088ccHrlCVLlv9+SEJkvbeyZMmSJUuWLFmyZMlyZcnqQ7NkyZIlS5YsWbJkyXLFyQoaWbJkyZIlS5YsWbJkueJkBY0sWbJkyZIlS5YsWbJccbKCRpYsWbJkyZIlS5YsWa44WUEjS5YsWbJkyZIlS5YsV5ysoJElS5YsWbJkyZIlS5YrTlbQyJIlS5YsWbJkyZIlyxXnbc8MLoRAEwYpQ0czDAwMhAAJkCQJkyxjkU2YJBlJkmbVpmboxPQUKUNHliRsigVVMWX6SwmDlK6hCQNjIo2IJIEsyZglGYtiQkaadX/nYwiBZugkDR19Uh8SiiRhktLnJEuX38elIIQgoeuYZBnTeRlehRDENA2rKT2e8wknk/zk6GHcqsqjjQuuyVhj8RSplI4QAlmWUC0mVNU8pV4yqaHpBlbVjKJcOZlZCIGmG0SjSWw2MxbzW39MIlqYF/p/xb6xnWhCY4VvLXcWvQuH4rykOWAIg5SRRGBgkiyYZNM5x3R0oSNLCoqkXLQtXWjIKJn+089GEl1omCQzZtly6Sd6jTCEQVyP0RXr4HjwMO2RFoYTg0T1CJpIYZEs2E0O3GYvxbZSKmzVLPGtxK44rvozp2s6kiwjy1Lm72Q8hWq3TMqwLIQgGU+RSmqYVTOq1TxTk1eMf2n6Ei2RUwhmTp9kU+ysy72RB0refVXGIIRgKDHAT7r+g45oKx6zj5vybmdd7o3I0jtz7yup6RhCYFFkZFlGMwx03cCkyCizyJqt6TqKPPtv2LkYhkFS15ElGYvp4s/1+UQSSVSTgkmZ/rdj4Ri9o0Eaywsu2ta+lm6e3X4Eh9XCe69fQlWBH4BNh5v5+c4j1BXl8j82rsBtt17yOP+Q0AydqJ7Aplgwy5f3/UgZGnE9hUU2oSpX/93w+8z1/+c7/L8Pv4t5ZfnXZE11LkIIYskUNot5Ut+n+4b57FPP8+2P3E+xz31F+/zHX72OzWLmozevxGp558+Nt03QEEBS1xhLxtg/3MmOoVaOjPUxGA8RTMWwKxb8qoP5viLWF9SwNr8Gj9maERguxMnAAN84/jpvDjTjtdj4dONGHq5cQkoYDMVD7B7u4PW+05wKDjIYD6HpOnaThVKHj4X+Yu4oaaTOnYfbnH5ZznbiGkKQ0FN0R8fZPdzBjsE2moNDDCfCpAwDp8lCqd3L4pwybiysY44nH4/ZdtUFjmgqxTMnjrKooJAlhcWTjoUSCb61dxcfW7aCHJt9ym+dFgsfWbr8qo3tfEbHo3z7+5s4eryHRFLD57Vz9+2LePDeZZPqhSMJnn/5CEdP9PDB966jpjLvio3BMAQHDnXy5a/9jj//nzdz0/X1b7nNo4GDHBzfSyA1DsDrQy8z37OYua5GFGa/eAikhtkz+gqjyX6W+G5krmtp5thocoDuaDNFtkoKrOUXbas1dIRq5wIUKf1MaSLF3tFXaQrupdGzmpU5t13aSV4jdKHTE+vk1YHnOTS+j5RITqkTEzFiyRgjyWHaIs0cMR+kztWA3eS46uNrPtJFfmkOvjwXAJ2n+vnNE2/w+Ofuxpt39oOjazov/3QH254/xLq7FnPPB6+/6mN7JyzkNaHx5vBrNIWOAxDX+3h96GUa3AvIVfPf5tFNz5tNbXSPBrht4RwKPS46hsc41j3AovIiKnJ9F/399tOdXDe38rL6HgxGePHIKcpzvNw0r+aSf//3v97MQysXsKRi8rtfCEFS0znVO8Smwy0U+91YTAp21YxuGMSSGqkJAclmMWMxKSyrKUXXBce6Bia1ddPCWnTDoGNo/LLO8Q+N06E+/vfh/+KzDQ+wKnfOZbWxffgk/97yMg+WreX+0tWz/l1US6AqZpR3wLP+34FANM43freN//PwRq6tiPP7w9siaAghCKUSvN5/mqdadnF8vB9NGJPqBIw4gVSctvAIv+06yhxPPh+Zs44bCutwmWe/WxJMxhlLRoloSfaOdPL9pm0cHO2e0l88qTGajHJ4rIeftO7jgYpFfLx+A4U216z60Q2DvliQF3uO83Tbfjoio1PqxPUUw4kIB8d6+EnbXjYWzeVDtaup9xRglpVphQ0hBJphMBqPoekGmjCQALvZjM+aFlISuk4gESep65hlGbdqxWYyIUgLEocG+jkyOIDfZiPX7sBuMuNSVUKJBDt6umgeG6E7GCCaSuGyWPCo1onxaozGYhhC4LKoeK1nr3s0lSKQiGNVTIRTSWQknKoFl0VFliQMIQgnk0RSybSmSggUScZvs2EzmWYUrH7x3D627WrhPQ+torI8h3giRUVZzpR6sizhdKj4vA5MV1CbcbUIayE0IzWlzBDGrLQPZ/BZ8lnqu5Gm4L5J5ZqRxCrbqXEuxKqcFRgTepykEUNgIKFgN7kAQVyPsHXkN/jUAlTZhtPkxSxbWO7biHze69IQOnE9iiaSSMioig2LbEUzksSNGIbQJsrtWGSVpBEnoccR6BN9OjPCzFtFCEFvrJvn+37F4cC510DCJCko0sTcEgIDgS50dKFR46jDqtiQpvkUGIbB+FAIT66L8HgUWZGx2i1EQ3EcbhuRYIxUUkMxydidVlSbhVgkQTKeBCS0lIbdacWsmolF4mx6djdLb2igdmE5bl9asDF0g/GRMJpmoCgy3jwXiknhtnev5fwh6bpBNBQjEUshKxI2hxWbQyWZSBENxdFSOrIsYXdZsdpV4tEksUgcfaJtp9eO2TL99S6ylRLWwyT1OAbp51KfuL8pkZr2N1cekRG4z6ALnYgeJpd3jqCR1DRC8SS6YRBOJJlQTBNPaXhsVtbNqcRlVTP1I4kk0UQq/a6TJXwOG4YQBGMJfrBlL3OL8lBkiRynnXhKI57S8DlspHSdcDyJ02rBMAThRBJZkkjpOlazCYFACEEkkWQwGEaRZVxWCxaTiVgyRSSRRDcEFpOM22ZFkWVCsQTRZApZltAMY4bz0zna0c+z249wsmeQhKYxv7yQd61u5FTvMC8fOEV/IIRDtXDzojpW1pXNSnszE0IIYnqKYCpOStcRgEVWcJpVHCbLNd+JvppYZBOFNi9m+dI1UG8FQxj8qOMN7i9dRZ7quaZ9v93EUxq9YyGEEFjNJnxOG4ospzX1ukEwGiehaSiyjNOq4lDTGght4vk78+yaTQoem4pqNpHUdKKJFIosEUumSOkGdtWMZ0JjNxqOsa2pnc7hcXpHg8iShMum4rKl3wvCEIRjCXpEAFmScFpVnNb0XE/p6baFEBnBXjUruG1WVLMp/S6IJ4gkkhhCYDObcdutKPLk50QIwXAoiixJ+J02ADTDIBBJn68syTitlky/4XiCaCKFSZGJJdLvCJdNxa5aprVquRJcc0FDACOJCE+17OaZ9gOMJCIAKJKMw5Q2cVIkGUMYJHSdiJZAEwZNgUH+9/7f8sHa1XygdhU+deru+3QYCPqiAZ7rPMxP2/ZxKjiISZJxT2hHFElGFwZxXSOSSkwsTgyeaT9AIBnj75fde1HBRjMM2kLDPNG8k+c6D2eEGFU2YTdZsMgKSGlhJKqniGspErrG893HaAkO89kFN7MqtwKzYpqyDDKE4OjgAN89sJe4lqIjME4gnuCRxvn8ybKVKJLMju4uftV0nKFoFI+qcntNHbfU1GKWFV5ubeaZE0dpHx/n+PAgbovKurIK7qmr57lTJ3i1rYXeUIi/3bIZsyxzd109756/EAloGh7mG7u30xUIcN/cBj6xck1mXLt6uviXndu4rrySvX09GEJwfXklj85bQIHTyWgsyi9PnmBPXzcj0SgtY2NUer18bu11rCgqwTyDGv/YyR4qy3N4/N1rpj1+Bodd5a7bFnLXO3PTfQr5aiEus4ewHgYhcJk9FFiLLknIuBDDyT4OjG4mpodZ4b+VMkd6F+1YYAdtkWMIYeA0e1mbezeG0Dkwtpn+WBtbhn6JWbJwV/EfTbsIF0IwlhzkwNjrBFLDyJKJamcj891r6Yo2c2h8Cykjjs3kYqFnPeWOuZwM7qU5dAhdaDjNXlbl3I7fcnGzjNmQMBKcCB7mePBQpswiq+RYcimxlZGr5mORVTShEddijCZHGEkO0uBegCpP/xxHwwm+/ddP8/G/f5Qf/cvz5BR4WH3bQl7+2Q5ueWQ1rzy9k9BYFJtTZen19azY2Mie145xbHczTo+dkf4A6+5cTNW8Yna/eozDO04z2DNGzmvHePCPNwIQGI3wmye2kIglSMRTfPJr78XumjoeYQiGe8d45Wc7GOweQzEpNK6s4Yb7l9F8pIvXntlNPBLH6XVw3d1LmL+6lr2bj7P3tWNoKQ1Pros737+ekurpF+yPln0gYyIX0cJE9SijySFeH3yFE6EjV+AOXRwJmVJbJUcCB0gaCUySmRw1D78l95r0PxsMw+BYzyC/OXACTTeIJJJU56fNhFoHR/jV/uMkUhrvXrOY+qK0NvW3B05yqKsPhCDP7eB/bFhBKJbgl/uO0TwwzHc378KhmvnTm9eyt62b7ac7+cu7N9A1EuCZPUd4eMUCArE4v9h7lGKfm4FAmEVlRSyvKiEUT3C0Z4A9rd3IssT9S+cxr6SArafa2d3aTSSRxGW18L61SyjwOPnxzoO0DY3isdnoGw/BNKZyqtnE0poSkOC53cf5wmO3ABCMxjnc3odNNfNnd65j89EWmrqHqCvKJdd9eRpBIQTjyRjPdx/j2bZDtIWGEUClK4c7S+dxf8VC8m2uP5gd4RpXId9a/sfXvN+xZJhnO7exsWDh772gkdCTjCZDxPWpGmsAp8mGX3VnNDdbTrRxrLOfSCJFoc/Fn96+hprCHFK6zqH2Pn6+8yj94yHsqpn19ZXctawBj93KUDDC8/tPsv1UB7Gkhtum8tDqBVzXUEVT7xC/3nOCHJedloERukcCrKuv4P3XL0VRZH7w2m52nupkKBjl//vJS1jMCu9a2cgdS9JWEMFYgl/uPsbJnkFkSWbN3HIeWrMAr91G/1iI3+47SSAaRzcMTnQPUluUw7vXL2ZucR59Y0F+vfc4B9p6SWo6lfl+Hlu3kNrC9HtSktJPdfdIgG/8bhuVeT7+5LbVGAiOdw3ws22H6R0LYTUrrJ5Tzv0rG/E6bLxyuJlf7zlOfUk+x7r6AYmbF9Zy97J6/M7ZrasvlWsuaIRTCf6rZQ8/a9vPWDIKgN9iZ44nnxW5FVS7cnGbrUS0JF2RUXYOtXNifIDhRJiYnuKHLbvQhMH/rF+Pw6RepLc0u4Y72DrYQk8kQI7qoMFTyLLcciqdflwmKxE9wanAIG8ONHMqOEhc1wB4tbeJ9QXHeLRq2YxtG0LQEx3nyZZd/KLjYGaXptDmZrG/lCX+UorsHhRJZjQZ4dhYH3tHOmkNjRDXUzQFB/jno6/yd0vvpcFbMEXdGdM0fnLsMAvyC/jY0hXs6u7iG7t38P75i3FZVA709/Hb001srKrhtppaNre38VpbK36bnRsqq3h43nzK3G5+fvI4D9TPY03pWZOaT65aS63Pz29On+RLN9xCvmPyR2RxYRH/97a7+Jed26c995FYDI/Vyg/ueRd7e3t4rukk+/p6uLNuLrt6uukMjvOxpSup8/v5/za/xsqSUhblF04RMsbGowSCUVIpnaGRMF63jabmfgDMJoXSYh+WiR3aRFJjaDhEJJoAwG61kJvrwnaefXsoFCcYiuF0qqRSOsFQHMMQqBYTfr8Dm+2s9K5p6X4jkYk2bemdxStJvbuRkeQQx4IHEQhW+NdRZC25YqYshdYKFnmvpyV8eFL5eGqYMvscyuxzcJt92BQnsqSwIf9BTgT3ckfRB7ApzhnbTYkkfbE2wlqADfkP0h1tpi/WTpltLlE9hNPkZa57GR5zDnYlrf0LpkYpslVR4WiY6HN2WsHZMJwYoDVyGk2kn1EFhUb3Qm4vvI9SW8W0u6IpI4UEM2pVZFnCm+tidDBINBRHtZqJhuOYTAqnD3fiz/fw0S88yME3mzi2q4WKuWkTFNVq4bp7llJZf9Yk5fb3rOXUwQ5uenAFC9emhb224z3EInEe/vhDFJT5+eKHvkdv+xC1C8qmjCWZTNF6tIeh3nEe+bNbaT3Wzcn97TSuqiE8HsXts3PLo6vILfLh9qef19GBAJX1RcxfU0dOoQeX98KLQUmSsEgqFouKjxw8Zi8Hzfsu+JsriSIprMm5jvHUCD2xDnyWXNbn3oTLdGXtmN8K4USSU/3DlOV4+cC6pfxi31F6RgMAzCspQNMNdrV2T/pNz3iAldWlNJYUkOty4FJVPDYrf7JxNa8dbeb/u+8mZEkiqelTOzzndWNRFFbXlGdMnfrHQxiGYF1dJfcvncd/bt3H6YFhHKqZHc2drK4pZ05RLk+8sZemvmHCiRRHuwf4wrtuxmu38YHv/eyC5ypEWhAQQiBJEvGUxlAwwsG2XoYCEQTMyn/jQiQNnee7j/Glgy9lfBYBToz30xMZZywZ5ZONN2B9B/oiBJIRwlocr8VBytAZT4ZJCh2TJJOverCbrJlvyWA8wHgyjCCtqCyy+XGZbVPaNIQgqicYS4aJ68lJ10SWJHIs6WdBIq2RGk4ECaSiGMJAlc34VRd2Rc30O5IIEUpF2TN6mqSh0R4eJGWk55lZVqhyFFyyxiiYihJMRXGabKQMjWAqhkG6/xzVhe2c/gF0kR5nOBVDFwYW2YzX4sBjtpMwUvTFxshRXbjNdhJ6iqFEEEMY5Fk92BQL4VSM8VQUt9mG22ynNdLHd5p/zdFA27Tju7lgGR+vux+3Of2+6xwe558/cBeGgK/8cjPP7TnOp+5eT/9YmKe3H2ZxVTF/8/DNHO7s4xe7juFxWLlraQMum8rNC+u4b2UjNrOZ/3x9L7ubuzIL+vahURRZ4pN3rSfX5SCSSOK0qpgUmf/1rhv5+c4jvHq4mX/7yP1TNAKaoeN32vnWh+/naMcAT+84zNbj7dy9vAGAoWCE4WCED920nE/fcz3xVArVbELTDZ7ZeYRoIslfvetGct0O/v213fxs+2H+5LY1mXnSPRLgB6/tYU5xDh/ZuApJgpFglB++cYDGsgL+98MbOdU3xI+2HMRlU3lw9QIQgoFAiDuWzOVPb1/DG8db2XKijdqCHNbWV1zSHJkt11TQ0IXBG/2neanneEbIKLC6eF/NSh6sXIzfYp/yMDxes4qfdxzku01b6YsFiWhJftV5iDmefO4unT8rVU9raBiAUruX99es4J7yheSokz/Gt5fM44GKRfz94ZfZ0t9MSugYCJ5u288DFYtnVIGGtQSv95/id11HM0LGEn8Z/2POGtbmV2E5zxHs/vJFHB/v59snt/DmQAtxPcWJwAA/at3D5+bfjNdim3QNDCEYjkZZX16JIkl4bTZsZhOhZAJDOOkOBekNB/FYrZwaGUEACV2jIzB+0evyVhBAmdvNDRWVuCwqpW4PfpuNsUQcgGAigaqYcKsqDrMFn81KQtdITaPG372/jde3nGRoJMTgYJCRkTB//7XfAZDjd/KXn7yDgvz0i3d0NMwzv9rLwcNdjAciVJTl8Md/dAPz5k62P957sJ3fvniYxvpiQpE4x0/2kkho2O0qt21s5Kbr63E6VISAYyd6+fGzu+jpHUNVTVRX5lN9BX0+AMyyhRvyb+WG/FuvaLsXY2XOLTQF97Fv7DVcJh/L/BtxmtI7XWccggViWm0GpB3GQ9oY/fE2dgw/D0COWoQsydQ456NICscCOzBJZho9qymyVbHEt4HToYMcGNuMQ3GzxHcjHstU87fLIagFGUoMZv72WXJY6l1Fmb1yxt+Y5QsvXhSTTFltIUd3NePLc6OYZdpP9lJUmUsqnsLjTwtiVoeKyaIQCcXSfee78eZOFaLSizYyCzeAwvJcVFtaVe/02EjGpt+l01MGY0MB2k/08uv/eGPitznIssT8VTXIssT25w9hMiusuX0hdYsquOH+Zex/4wSbnt2Fy+fgpgdXUjCNueE7BUmScJndPFL2+Ns9lBlJajqGIXBbVSQJHKoFu3rh4AjvX7uUzSdaeHr3YQo9bh5dtQDnhGmVgeDM6vNMoBPNMDJmE4nUWbM1t91KkXfyvLKpZuwWM5IELquKbhgMh2OMhKO8dOQUe9q6sZhM2CxmgrE4PrsNkywjS5DjdDDFPu8cTIqMphk094/gtVtxWVVqC3NwWS2sn1eFAHwOGz6njZFQhIFAiLFwlL6xILluBzaLmeFghKFAhPFIjO6RAFUmBas5bcYohCCqJXmqec+kBfUZgqk4+4a7OBUYZKG/hHAqznAiQsrQscgKOaoTi6wwkogQ11PowkCRZAptblITgV/8qgOzrDAYD6FIEj6Lg6F4iGAqjoSE12LDrzoIpeITVhQCQwjcZit5NhdJXWMkESGqJTHLCn7VkbFkeHPoBFuHjnNdXgNjyQi7R08xkghhlk38Sd0dLPFVI09sYmweOMIr/QcZT4YZSYb4342PclPhwknnK4QgkIrw+sAR3hw6TkiLEUrFGE2E0t9Vey7vrrgei2JCQqI10k9P+zBHxzuJ6UkcJpVbi5Zwc+Fi3Kb0emHr0HG2D5/kVKiHlKHxneYXM2uPAquXf1ryQZRL1BftGTnNb3p2M99TQUiLcSzQSVxP4TBZuaN4KRsLFuE0WZEkCUMYHAt08vOu7XREBkkaGi6TnaX+Gu4tWUFES/DlY0/zUNk67ipZTkd0iG+fep64keRjtbezxFfN3tFmXujbz+1FS7mxYAFW2UKxLZdQKoom0oF1QqkoUT0xbTCL+1fOw223ktR0FlYUcqi9DyFgJBSlqXeYO5bWc7p/mGgihVmROd03AoA68dyMh2OMiCiqKW22FJt4JiVJ4sb5NZT43UiShHUGs9Tp8NptXNdQhUO1UJnvpTLPR1PvEHfTMNF2WohfXFk0qe2RUITmvhHuXlZPeZ4XRZa5bdFc/vaZVwjH0huiwViCr/36Daryc/jwxpXIcvpZG4/EOdrZz51L53K6b5hgNIFDtXCyZygzrpqCHFbWluKwWqjI82Fv6SYUT1zS/LgUrqmgMRQP81pfE23htP+CTTHzvpqVPFK1FK9lqtQPYFFMPFy5BLNs4suHXiCqpxhJRPhJ617W5FWRZ515N/Zc3GYr76lewQOVSzJO3udT6vDxiXkbODbeR38sCEBLaJjeaIAKp39KfUMIWoPD/LrzCFE9PSlrXXl8sG41GwrrZnys53kL+eicdfRFAxwb70cgeKnnOA9VLmaxrxTlHEHDLMvMy8tnd083JS43LWMj+G12Cp0uNMMgrqVoGx/jPw7uwzxhP6vICrn2c1VgV0chrSomXJb0h/SMQ/uZD0mZ28Px4UH29fUyHI0QSaUodbmxm6cu+pYtKqe2Mg8hBF/4x1/jddv55P+8GQCTScHvO3suBfluPvKB6+jpHee3Lx6io2tkxvGNjUd5efMxViyt4pF3rQDg5U3H+PHTO6mqyKWxvpjRsQj/97uvoesGjz6wAr/PybGTPfzupUMztvt2IYQgbkQZTfQT0sYwJS0EUyM4TB5ieoTRZLp8NDmATy3ArjiJ61EKbZXkqiVsHfo189yrcJo8SJKMKtvoi7XjMLnJV8vQhcZQoptgagxZUhhJ9OM0eSiwllPpmM8CzzokCayKA7c5h4gWwGXy0uhZzbHxHQzGuym0VZLQY+SppfgtBewceZGwNn7FBI2EHieihTJ/56r5+NW3ZnKjmBTK6gp47gevc/t71zHcO87xPa088LGNDHaPcPpwJ81Huug+3Y/JpJBT6KWvfRhJkjh/n0OSJWx2lf6OYdw+B8VVaYFVlqeKclpKp/N0PyP9ASQketuG8OW7qZhbzPzVtdz4ruUgSdidVvKKfQRGw3hynKy9cxE7XjxM+8k+aheWEw3HKakpIL8shxee2spI//g7WtD4fcChWnDZVFqHRjnZN0TPaJB4UgMBw6EI3WMBRsIRukbGKXA7cdtUgvE480sLqS3I4Xubd3P3knqcVhVJkrCrFo72DOBULVTkenFbVYKxOCd6h+gaHScQm/yRP3+2xJMpukcDnOgdom88SF1BLtV5PuqLcqnKy6Ei14thCCpyfUQTSWKpFCf6BvE77IxFYkxnOgXpRVSh10VNkZ9f7DjK0poSbllUx+LqErYeb+OXu44hAWvrK1lWU8LRjgFOdg8SiiXY09yN126jIt/HzqYO2ofGiCdTvHG0FbfNSnGOO3MWgWSMttAF3tWJKK2hERb6S+iOjvNa70nGU1GShs7NRfVUO/P4WftegqkYNpOFkXiYd1en3+lvDjTzcMUyCm1ufta2l3pPActzKvlx625ielqbmWt18lDFUvaPdPHzzv3UufMZT8YotLr5QO0a2sMjvNJ7gpiWxECwPKeCGwrnYJkIPjOeDPNS30FqXAXcVbwcu2KlMzpIqT0X0znmr/eXruLGggXsHTnN107+atpz1YXB8UAXz3ZtZ01uPbcWLSGUivLrnt30x8b4QNVNrMiZw/bhEyDBzuEmVuXM4eHydciSzCv9B/lJ+xYqHfks9lWhoLDCX0ejp5zXBg7x0443+Z91d1BqT78DzPL0ESVnw1gyzEt9B1iZW8djFdcB8GLfAZ5qe51qZwGNngoUJHpio3z1+C9wmK08XL4er9lBU6iHV/oOMpII8mDZWvKtHnpjowghCCYjjCSD2BUrfbFRFnurGEuGMUkyOZa0kF1qz+NDVbcznooQ0WKEtRibBg6wZ/QkMX3qojjnHLMfRZbQJ3xDY8kUQ8EwP9pyIBO1TZEl5pcVIoSgZWCEVw43MxQII0sSncPj5LgcmUcm7Q81vQ/txZAkCdV8pk8ZRZFIxs9qNK1m08QGwnnPeyqtsbeYlMy9s6lpn5EzPlcH2/pwWM2AIBRL4J3wCYsmU4xFYvz4zYOZ85VlieXVpWf7tZhxqGrmWNqt8cpacZzLNRU09o900RQYyEiji/wlbCisnVHIOIMiK9xW0sBLPcfZMtCMIQRdkTE2953ikaqlF/ztGVbmVrCuoHpGIeMM9Z5C5noKGIqH0EV61+NUcHBaQSOmpzge6ONEIG3mY1fMrM6vYk1e5UWX9gv8JSzOKaUtPEJESxLRkmzuO0W9pxDHOWFFLYrC9eWV/M2WTejCwGe18cGFS/BarQghyLM7WFFcyp8uX8XcnNy0RkPTJoWxVWQZfSJEYibULumHwKwoJHSdlJEOJyvOOTYbZtoJr8/N4/WONl5uPU2R08XK4hKWFZVgmcY3IzfHRW5O+uVitZix2y3U1kyvqpdlGafDSkG+G4/HDhcQNDRNp2FOEQ/dtyzjUO5wqHzz/71Ka9sQddX5bN/VTG//OJ/7xO1cv24OiiKzdFE544EY23Y2z+oaXCsEgmBqhM7oKRJ6jNFEP72xNqocjQwneuiLt5E0EvTHO3Cb/ZTYa+iInGQokTbxmONaktFmSEgs9d3IieBuHCYP+XllpIwEp0OHiOthJEmmLXyURs9qCq2VhLRxjgS2IQHFtio8nhyGEr00BfdO7FD7KbZVA9AVPU1fvBWQqHQ04DFfuUWvLrRJTsuqbMUiz86EciYURaasrhBfnpv6pZX05wwz2D1C+ZxCvLlORgeDvPGrfbj9DpZcV48/340/343JomA6L/SxJEksv2keR3Y2M9gzyh3vW4fNqVIxtwjTxG5VVUMxTo+dZDzFvtePEx6PIkkSh7adYv1di6moL2Kkf5wtv96PJElU1heTX+qnt22IHS8eQpJlfHku5ixJq7lPH+rk1MEOQGLu0koKyt85vg6/r9gsZhaUFjAUCvPykdNYzSbmFOViMSmc6h/mVP8w8ZTG8d5B8lwO6ovz2d3STe94eoPqtgVzcE1oQGRJ4oHl8/ndwZMU+1xU5S2jItdHdb6fFw434VAtrKwuw24xIxBU5vomhbFVzQoNxfm0DY/x8tFT+Ow25pcWUOBxcdO8WraebudE7yBCwKOrF1LsdXHL/Dq2NrXjsVtZU1uO2zrzd6/Q5+LDt6yaVFbkc/HwuoVT6m6YX82G+dVTyu9fPZ/7Z2hfAIFU/AIBlSFl6ES0JEII8q0uNhanbdyf6zpER2SUKmcuUS3JuvxaNhbV828nX6c/GmR9QS2v9J5gJBHGYbLQGx3nsaoVNAX6GYgH+ful9xPVknzlyIs0BQfQhYFVMfNn9TdyKjjILzsP0BkZ5eh4DxEtwd1lC9g/0kVzKK1dKbSl35fDiRBLfFU8WLaWUvuZ52velPNQFTP5iodC28yRyOJGis7oEFbFzE0FC5njKkYgGIiP8/POHUS0RMaCwhAGDe5SHilfT5Uz/U10m218/eSvaQ71U+8uxWFSKLan1ydHAh1ISFQ48qh2Fl7gis8OTRjM85TxSPl6Khxpvy+HycrXTz5Hc6ifOmcxNpPKb7v3MJwI8vnGR6h3lyBJEsv8NdgUlZ91vMl8Tzn5qpe+2Cia0BlNhrHIJkrtOfTGRkkaGqPJMFbFQo6aXguYZRP5Vh/51rPXsiMywMHx5mkFjenWIoos4XfZmVdawJ/ftY55pQVIEiRSGpKUFkZ2nu6kY2iMP7ltDSV+Nz/ddojWganBfGbCYjIRT2nouoGkyAjICAfxVIqOwTHKcryMR+OMh2MUeC9uSuy12/DYVXrHQoRiCWyqmVN9w+R5nNgmwtkuqSrmvdcv5h9/9Qa/2n2MR9YuwmYx4XfamFOUy8dvX8PCiqJJ53v2WnG19p+n5ZoJGpph0BQYoCcayJStza+m0HZxu1wJsCom7iqbz5aB9OIvkIyzc6idBysXXzSMm0mSWegvocJx8TCEAHXuPHYMtqILHYFgLBGdtt5oIsL+kS70icV7kd3DAl8xNtPF8w9IwEJfCa/1NhHR0mYUB0a6SOoajnN+rwtBbziIW1W5qbIaRZKJaxqDkTD5DifVXj9z/Dm82HKa7lAQ3TBQZJmGnDzKPOmXZIHDgc1kZn9/H5phUOJyU+n1YjWZKfd4EALe6Gij2OmmwuOhwuvDMAxOjAzRGwrRGwoSSibY0d1Jnt1BrT/nonN0PB4jqqWYl5vP4oIiFFlmOBrBYTZjm0arcXlcXAKfO6eQ/LyzD3ZBnhvVYmI8GEXXDU41DyAjsWhBWSbvgcmksGh+6TtO0JAlmQJrOQWFU0PXVjrmUemY+uFb5r9pxvaW+m8Ebsz8bTM5uaHgwWnrLvJexyLvdZPKqp2NVDsbp9b1XccirptS/lYxhIEmNIxzIsZJkjSjsDtbJEkir9jHJ//5vQD48tw0LE8vpnIKvdz1+NRzmb+6dsb2lt/UyPKbJl+X+z589jo/8Mc3Z/7/0T+bPprB9fct4/r7JvuGNa6soXHl1PCm192zlOvumd2GS5bZU5Hr40PXTQ3tvbq2nNW1U5/Bd69ZNGNbj6ycnIPIY7fysRtXTVu3Jn+yYO5z2Ll1wfQhUucW5TG3aKqZ563z67h1ft2M47nWnG9CfD6KJKPKCjE9xd6RDg6NdpNnddIeGiZfTVst+FU7bnPaH8KuWDBE2uRzbV4Np4KD9ETHWZZbjtNkIawl8FrsMJEPy2W2EkzGMcsKRTYPJlmZ6NNMVEsSTMZpCw3zev8pAKqcuZjP0VTIksR8bwWF1tmtIS6EjIQqm9EMg6F4gLAjj5ShMZIIYVFM2M/zPZ3jKqHA6s38na96sCpmAqnIlOiZV4N6Tyn51rOO5QVWL6psZjwZQZ/o/+B4G0U2H9XOs74gqmxmpb+Op9o2cTrcR5HNz47hkwwngvTHxym15VJqy6UvPkZ/fIzRZJgc1YXPMjsrldkgSRJ5bgdr5pTz8qFTDAXTwYeEEFQX+CnP9eGa8Lc43j1I68AIbYOjaPo0flQzUJXvI6FpvHa0GY/NSkWel2J/+nqlNJ3Dnf0YQtA+OEY8pbGsuuSibTqsFq6bV8X+1l5eOXwal01l28l2NsyrykS8sqtm8lwOPnHHOr763OvkuBzcvngOPoeNmxbU8PKhUxPazHRwi4o8H3OKr6xJ+Gy5ZoJGIBWjLxYkPmFiZFNM1LhyZx2q1iTJLPKVYJEVkoZO0tDoiowRSMbxXyQCVY7qoMTumZUAAOAyq5nFiwCi2vRhH4OpOKeDZ+3e8qxOKh1TNR8zUWTzTMoL0hwanvTiEEIwHo/RPDqK32rjxZbTGBNalsa8fD6yZDllHg/3z23gtbZWXm9P7yI35uUzP+9s1JlSt4fba+t4o6OdV9taWF1SRpHLhdVkZq4/l3fNbWBPbw9Hhwa5rbqWco8XQwgO9vfTMjqCS007fb3e0c6C/Hxq/Tnk2x2sKinFakqP32m20JiXR57dQVzT6AwEEELQGQzQFw6hGQYmWeYjS5YzNyf3moUydDmsqOfYVCpKOmmWrqe1N6FwHKvVjNl8VjUqSRIu5+UlnBpNjtAX6yamxy5YT5ZkGt2LUJVL240PpsZpi7SQMlL4LH5KbRWoikpMjzKUGGQ8OYoudKyKjTy1AK/ZNympX1gLMRjvI6il74+qWPFbcslXC98RORaEMIjpMQKpMZIiRcpIkjSSpIwkCSNBc7gJXZz9CIwnRzkePERvrHvGNvPUAoptJZeUgFAg0A2NoBYgkBonqkVIiVQmHLFFtuAwOfGYfTivQPhegcAQBqFUur+IHiZlpNCFjiIpmGULDsWBx+zFaXJPuqfvZHpinQzG+zMLkumQJHAoLurdU4XW2RDTo3RE2ghPmNSV2SvIVfMy9yRlJBlLjjKeGiVhJDLBAUyyGatsw2324DX7L/lZPH8MgdQYoVSQuBFHM1KZ5LOzwabYKLVX4DF7L6v/3oEA8cTlhye22ywU5l0dZ3wJyFHteC02xpPTvxddFpUSh5eYnmI4HsammJnnKaYrMpZJeCcx/abCspxynmjeTiAV573VK1EVE3PdBbzRf5pdQ62Z9UKtO4/O8OiUNhwmlbmeApKGxtKctABZaHVPimxplk3YFRXTFQhXa1UszPOUsW3oBM/17KYnNkrSSHEq1MsCbwW1rqJJ9Z0m6yQneUWSkUj7Rcx6gr0FnCYb6jl+biYp/a1Mb8SmGU+GKbb5J11bSZKwKmZMsomEnqLUnkNcT9IS7mc0GaLWVYTf4qIl3MepYA8xPUmFI2/W67RzuWl+TWYBLssS5bk+Unr6neN32rhvxTxeO9rCtqZ2dD0tZNQV5WJSZFbWlRNJpNjX0o3faWNJVXE6lKxNRTMEiyuL8TlmtrqZV5rPvcvnse1kBw7VzG2L51Ls9+C2qdy9rIHFlcVsO9mOWZG5bfEcFlSkNU021Ux9SR7eGdq+sbEG1WRib0s38VSK+WWF3LywFqfVQn1JPqpJQZZlaoty+NBNK9h5upONWg1Oq4V3rWzk1SPNbG/qQNN1ynN91BSmNzBKcjwsqixCnVi7uW0q80oLKPRduaAt53PNvlYj8QiBc14yOaoTl9k6a9tBSZJwmlVyrU56J7QiUS1Jb3T8ooKGX7XjmSbyw0ycryExmP4jGdWSkzQ048kYO4baaAoOTlv/fLoiYxltBqQFlzMmTGf8HZpGhtnS2c6T9z2IV7USSib45ckTbO3q4MNLliNLEpVeH3+0ZObIWLIkcV15JdeVV049JsvcXz+P++sn74YrwPsWzLxD15hfQGP+WfOmAqeTB+rTC4W+UIgtne3M8efy7vkLsCgmmkaG+futbzAUiTA359qZdigTmXxnwmIxoek6hm6c47wrSCS1y+qvLXKa3/X9gv547wXrmSUz/2feP6Eql7bD0BPr4iddTxBMjbPAvYQHSt+NzXBwNHCQvWM76Iy2kzISeMw+5roaWZ1zHeX2SsyyhZHEEPvGdnJgfA998W4MYeAyeah21LI6ZwMN7vlvu7ChCY3WyGm2j7xBVI8Q1Sb+6RESRgJx3rPYHm2hPdpywTavy93IXUUPzFrQ0IXGcGKI9kgzbZFmemJdjCSHielRNKFhkc3YFSe5aj5l9kqq7LVUOqrxmL3IlxGu2BAGo8lh2iMttEZO0xPrZDgxSEyPkRIpzJIZu8lBjiWX0nP681lyrlh45KvF3rEdvD74MgljZkdDCYkqR91lCxrjyTF+1/dzWiLp3ei7ix7ihrxbsCkK/fFeTodP0hY5RWe0nZAWIqZHkUib3XnNfopsJdQ651LjmEOhreSSrmnSSNAT66I53ERHpIX+eC+BVICEEctERpsNhWoxD5W+D4/He4lnn+Z3rx2htWv4sn4LUF9TwAceunBI8cvlzLd7Y/EcftF+aIoe2m4yM99XxFxPPi6zlXpPIftGOmgOpU2W53mLsJsszPUUkGtNB3GpdefhtdgxyfLEYlYmX3Xis9iRJZkyp5+bi+s5MtaLLEncXNRAmcNPQtdp9KYX8m6zlUZvEX7VQZ7VSUxLcWSsN71U9kmUnWMuLTPVH+tykSWJAquXBb4KNvcfoSXch8tkY1XOHFbk1E3SXkDa9PntfC8r0sX7t5lUYnpykpO2EAJN6BjCwGmykqu6URUzreF+gskoS7zVWBQTZlmhKZS+7rkW92VpqL/wyC2Z/zcrCuvqK1lXXwlMaDU8Th5bN/16pizHw+MbZtYIP37DzGsrSK+hpmu7wOvi0/emk7BeP69qyvFcl4N7lk+1QjiDajZx4/wabpw/VYt934rJv1s7t4K1c89GjPI57Ty8ZqrpI8DK2jJW1p6NeFji96SjUV1FrpmgEdfTuSPO4DJb0/klLgFZkvBZ7BlBQxM6odTFPeVtJgvqFQ6bJ4QgOZHn4wwnAwOcDAxc4FcXxphIaHQGSQK3qpJvd/BSSzMWRSaW0ugOBVlbWn7Vkqu8VSyKQoHDSV84xG9ONSFLEv3hEDU+PwVO5zsqMVNZqZ+tO0/T1TPGPJcNSUonTGttG7r4j6dBlpRrtviL6GGGEoMEUuO8MvBbBhP9mWPDyUGGR4YIaUHuKX4Ij9nLm8Ob2Dr8GlH9rCngWGqEfeMj9MV7cZk/Srl96gvxWqILnd54NwfH97wt/SeNBKfDJ9kzup3jgcOE9dCUOjFdI6bHGEkO0RQ6Rq4ln4Wepazwr6XYVnbRKFfnogudlnATu0e3cySwn5AWnKaORjwZYzQ5zOnwSQ6ac5jvWcwK/1rK7VVYLkFTc61RJNNlCV9vhdHkEEkjSXesg23Dr3MwsJeUMTXKl6aHiehheuKdHAkcYJ57Adfl3kydq35Wz3BEC3M8eJitw5tpjzRfVsJDCQmLrOI0u9+Sr1EwHGNkLDKpTJElhsbCDAwF8bhs+L2OjHY3lkgxOh4hntCoKsuhcU7RdM1eMVTFxHtrljMcj3B8rJ+xZNovKVd1sCqvggcrFuOfiAS5NKc8o1k4l5uK6jP/v6EwbUrWHwvQHBxiLBHj9pJGbOd85zcUzsnUO0ODt5AGb3pHucjuoch+1iTo1pKZF31XmrFkhKZgD0v9NXys9jasypV5htUJ7U9Cv3zt1uVQ7y5h53ATI8kQRVZfOqqa0GkO9QFQ7SzErqjkqm7aw4NE9QRljlyShoZNUWkO9ZFn9ZBrvTYhrg1hEExF6YkNM5oMEtUSGBiYJRMes4NCm5981TfjurE/NsrhQAtJPYVFNnNjwZKMpudCRLUEzeEeOiP9yJJMtbOYua6yGX8nEIwlQ/TFRhlJBIjpiXRSQfnsOEtsuWld3ztoXXU+10zQSAl9klmQ6TKkdAkmCSeGECSMi+8amSR5knP0lcBAkDL0acP1vRV0YZzjkC3TkJvPgw2NnBgewhAC1aSwtLBoUj6Mdxp+u52bq2vY2d1FZzAACPxWOw/Pq6bC673sdoUQ6LpBPKERDMaIxVKkNCOTM8NiMWExKxfUYJzPiiWVvPDKEX7xm/0IIXA6rfT0jnHgcOdljbFALWSVfz2jyZGMyU/KSBLUAvTEukgY8ctqdzoiWoSToaP0xroJpMbJUwvSET20cZJGEhAcDx5ijqsBRVLYM7qduBEnx5KHRVYZS44QN9Jaxv54D5sHX+Lxio8ivY27Z7KkkGvJo8E1f9rjgVSA/nhPRsvoMrnJUwtQL7BIK7QWY5qFaVPSSHAkcIDXBl6YpCUxSSacJhc2xY4kyaSMFJGJnXGBYDg5yJvDmxhNjXBz/p2U26tmZdqkC53jwcO8MvBbmsNNmXJFUib6cyBLMpqRymh3DAzGUiPsGNnCSHKIm/PvpMY595KEm2tJjWMOep5OTIumnweRNoUbTQ7RE+u6Kn0OJ4ZojTTxxtCrtEZOowsdCRmv2Yuq2JCRSRpxAqkAKZEWQBJGnMOBA8T1GFbFRqVjqsPzuSSNJIcD+3ll4HcMxHsRCBRJoUAtIk8twKaktexxI8ZwYpDeWPckzbhVsdPoXojT5MJhcpKnFpCrXn5W9LtvXsgN4cnvltFAlJ/9Zi+lhV5WLa6iuMCDzWoBCcKRBO3dIxw63k1+josbVk/vA3I5jA0EaDvawcLr5mUCICiSTL2nkM8u2MiOwTb6YyEkoMzhY2VeBSWKk86TPVisZgorZ38dolqSvliAhb4Sal15jHaPERgOUr2wAsV0bQVcQxjE9RQpQyOQSr8bQlqcQDKCWTahyuZMZnUJQEBHZJBfdu3ELCuYZAWfxUmdq/iyfUFKbDkoksz24SasytlcUWccua8WtxQuZu9IczpZYOEibIqF3tgov+vdS42zkKW+GhRZxmdxsn+0hVJ7DoVWHyEtisOk0hEZpNjmz+QPuZpEtDhNoU72jZ7iRLCD3tgIwVRkIlCAhVzVQ42zmEXeWpb75pBn9U5Zq4a0KE+0vshgYgyrbKHWVUqV4+LO9wPxUX7WuYmdI8fxW9y8p2Ijc1xl0+pwYnqCk8FO9o42cTLYSXdsiFAqii6MdNAB1Ue1s4gVvnrW5DbivASrnWvNNRM0TJIyySRJmwg9dikIyCShgYmoSbPQisjSW3UXnYqElGn3zFkUWF00eAsxvYWFmtOkZsYqkdYO3FE7hztqr9yH4GojAXX+HOr8VzbEZiqlc/JUPzv2tBAKxznV3M/wSIQXXjnCvoMdVJTlsGJpJfmXYGs8p66Qe+5YzKY3TvDDn+3A47IhyRL1c4roHQhcvIHzKLaVUWwrwxAGSSNJ0kiQMBIZk6qhxJUTNILaOIfH96MqVlbnXE+loxohBCeCRzgWPERUj6AJjf1ju4joEZJGghW+NdQ5G7ApdnpiXbwx9AoRPYyBQVPoGEEtgMf81h0eLxeTZKLWWU+uOn3UsePBQ7zY/2uSE6Y4xbYyrsvdeMFFmtPkQlUu7HOjC43WcDMv9D1Hbzy9AFZllXJ7FZWOGnIt+ThM6WSHSSNBIDVGT6yT5vApRpPDpESSw4F9mCUTdxTeT4G1+II7TEIIuqMd/Lb353TF2ifO3Uy5vZIqRy15agFOkwtZUtKCamqc3ng3LeFTDCYG0ESKk8GjmCQTNsVOub3ybRUQZ6LBvYAG9wJ0oZPQEySNODE9xsHxvVdN0OhP9PLKwPP0xroRQlDlqKXKUUehtQi74kCWFOJ6lMFEP83hJlrDpzEw0IVGW6SF3aNbKbQWY73AnOmOtrNzZEtGyLBIFhZ6l7HYu5xSWwUOkwsQRPUIfbEeDozvZs/o9oywYZWtrMm5jjmuxlkJwRdjbvXU5+Wnv95LIBTjEx+6iQX1U+djPJGipMDLM7/dx8mWAUqLrsxzP9g5zOafbKVhVV1G0ID05uIcTz5zPFOf1WgwSiJy6XH8q115VLvOmp+OJ0aJheJXNVTnTAwmArzUu5+gFqMnOprOGzZ4hI7IIG6zjbW5DcxxFxPTk/TGRpCApKFxYKwVCdAxSBk68z0V3FOy4rLGUOMs5Pr8Rt4cOkZXdAiTrFBo9fLhmqubv2mRr4pHKtbz+sARemOjWGQTYS2GIik8ULaGYrufUCpKjupiKBFkmb8Guynt++m1OAikotgVFf8VdASfjmAqyo7ho/yudydNoU60CeHCY3aiSDIxPUFXdJD2SD+Hxpppj/RxV/Eayuz5k9avVY4ial0ljCaDxI0kW4cOX1TQ0AydntgwJ4PpTcwKRwHz3BXTWqaEUzG2Dx/lt707OBXuJmVo2BUrXkt6nBEtTnukn7ZIHwfGmulPjHJfybpM8sJ3GtdM0LCbzNiUc51SE6RmoY04F0OISc5kJknGNcvs4FcaWZJQFRNWxZwxd6p15/HROesuGq73QuSq78yJcq24+/ZF2GzT786e+XSYFBmfx86qZZN3HSVJytSpKMvhvrsWU1M12QfC47Jyzx2LqCjLwWIxYVJk7r9rCaVFXjq7R5EkqCzPpWFuMXm5LirLL09YkiUZq2LNLFZCqQAm6cruOsf0KLrQWeNZxK0Fd+OzpG2KS2xlBLUATaHjgKA92ooQBku8K7m98H7y1IK0M7pnMQOJXvaP7UYgiOsxuqKdeDxvn6AhSzJuswe32TPt8YF4L/I52wY2xU6htZhiW+m09WdDOoHWOK8NPp8RMmyKnSXelWkfF1sllvMchQ1hMJYc4eD4XrYNb6Y/0YshDA4HDlBmr8Jt9mI3zfwsx4woLw38JiNkWGQLizzLWZu7gUp7zRTBSAiDQGqcI4EDbB3eTFesHQODk6FjlNjK8FtycM1wzd4JKJKC3WTHjh2X0PFdobwq0xFMjRNMjSOjsNS3ijU511PrnItJOhuvXiDQDI2WcBObB1/iSPAAkNZAtEWa6Y11Ue2cPmqTZmgcDx6mO9aZsUmf62rkloK7KLGVTTIVO6OtKLGVMZoc4XT4BJB+dpuCx5nnntkPbibOLKIvZirxxs5TlBR6qa+ZPiu0VTVTV5WP2WJi35FObl5fP00rafrbB2k91EEkGMVqV2lYPQeL1UzzgTaqF1XgzfOw/9XDFFTkYeg6sXCCHb/eRyqZori2kPqVdRx+4xjJeJLQaAR/oZfweJSFG+ZhsZo5tbeFwHCImkVn7cyHukZoOdhGaDyCarMwd0UtBRV5dJ3sof1YF/FIApvLyqINjbj8TtqPdnL6QBu+Am/mOjUfaCMZS9K4rp5ENMHJPc34C33klvhp3t9Gf8cgqs1CRWMZFQ3Tv0PqXEXcX7qKKsdFsqOLtBWCVTZT4yygxnm2viLJCNLZ1wdiY/ymZw8+i5NHK67DZbIhgLieZPPgEQ6MtdLgKaXCnsf9JauZ60pHKdKSB0Cy4jIXcE/JCkrsOVNMe5xmGx+s2sie0dMEUhFkZErssw9Qcy5VzgLuL11N7Xlhct1mO3flGVS5wTKx9jZJCveXrqbcnkdzuI+kruGx2Glwl1LvTvsDWBWV5f5aEDDfm7bIUGUzS301JKt0Fngqrmpm+JieZP/YKX7WtZmOyAAuk5157grmeSrJVT0okkJUi9MdG+TAWDOd0UFe6NuNbhg8Un4jBRMmYQAmWeH6vIUcGmtG03W2DB3m4fIbsV3ABC6oRTkZ7GQ8FUaVLVQ7iyifZk5phs6u0RM80/U67ZF+HCYba3LmMcdVhl91oyAT1mK0R/rZNnyE0WSQZ7pexyKbeKjshotGYX07uGaCht/iwHPOAnwkESGUStubzcbXQAhBREsynAhnymyKmYJZhMe9WtgUM/lWFx2RdMxlCQmPxUaN++0JIfaHwP13L5nxmGoxsXB+KQvnX3xRWV2ZN212b4/HzgPnhQJ12C1sWD93St1HH7i8XaVrSY4llznOhoyQAeld/jJbJe2RFhJGHEPoqLLKCv9acix5GTWwRbbQ6F7MwfF96ELDwGAw0Qdc+uLn9xldaBwLHOZE8CiQdtSvc9ZzS8FdFFint12XJZkcNY/VOdcR1SNsGXqNiB4iYcQ5ML6bOa4GykyVMzo2ngwe5UggvbiVkam013Bb4b0zCkySJOO1+FnuX0PCSBAYGCOoBSbMvQ4yx9XIHPPlOVL+oTLH1cCthfdQbC2dYvogIWGWzdQ669GFTmesjUBqHIBAaoyOaOuMgkYgNU5vrJvYhK+TWbKw1LeKQmvxjP4ofksu1+dtzAgaSSNBc6SJpJ6YIsRejMNd/fgcNspzvADsbumipiBnUsIygPFgjLycC+8Qn/FJC4UvrGk9tu0kzQfaKJlTjNWRHm9gOMjelw7iL/LhzfOw9+VDLNowD4fHwfhgAMMwEIbgxR9sIr8sl/2vHsbpddDfPoS/MD12q1Nl/rp6osEYJ3efRjErlE8s+Jv2NnNky3FK55ag2s9eoz0vHmB8MEhRTcF5811KCyFHu5i/vh5FUYhHErzwH6/RsGYOodEwr/3XFu7/szvpOd3H7hcPUDGvlIHOYQa7hvEXenH5pl6vue4S5rovHpK00Obj8aobL1jHEILRZJi28AA31Cxgke+sT5wuDJrDfRwebyOup6h0FlB5jrCiJfcjyR7c1iLeVTaz436ZI5cyx1sPuFLtLJw2F4fX4uDefAPFLDCf81hZZBOrc+eyOnfqtxTALCvUu0upd599x0mSRIOnjAZP2bS/uVIIIeiPjfC73p10RAZwmmysz1vAfSXrqHQUZiKbAQRSERrclTzb9TqnQt1sHjxAlbOImwqW4jCd3QBa5p9LntVLNNJPV3SQk8FOlvhmDns+FB/nwNhpAAqsPhrcFdimefbbo/28OrCPjugAVsXC7UUruKNoFSW2vElRz8aTIcrseTzR+gIRLc6ve7az2FvHXPfVvZaXwzUTfTwWG0U2D/YJiTWqJWkPj05ypr4QmjA4Od5PfMKh3CwpFNk95Fwk4tTVxG2xUneOGng4EaYjPPtEL9ea0fEIsfi1dRLLcnXxWXKmmBkpkkKumodDObujnqsWkKcWTPEdKLAWZT7WhhAEJxZbMxGLJDi5t5WmfW1veew///YraKnLi+51pRBCENfj7BjZgkHaLNNpcrPKf92MQsa5OExOGtzzKTynble0nYF4H9oMGlvd0Ng6vBl9IiqRqlhZk3vDrLQyNsVOnbOBUvvZnd/+eA99se5pHZ7/u2KRVa7L3UihWnRBX0CTbKLIVkKV46xQEdWjjCRmDgYxlhyZ5LTvtfjIUwsuGtWsxjEX84RWUyAIayHGU2OzPaUMx7oH6B1L9y+EYOup9kx+gHMpzHPT0TPKwRPdU8yUhYBoLMmRk72MjIXx+y4SIr7YT06xH0PTcfmc2KYJ/y2MszGHbE4ra+5Zxi3v30AsHKOvZQDFbKJqQQXVCyrwF3qpWVTJcPcITq+DOctrKD7P/Mtf6CWvLBdhGDjcNuzu9EZlYVUBnnw3qaSGr8CDxZq+ppXzy5i7vAaTZWIxJkH1wgqS8RQdx7rpPt2HWTWTX55L25FOmg+2EY/EiYxHGOocITg8NfDDlSadE8yCVbFweKydY+OddEQGORnsZtPAYbYPn8BrdlJiS28c6akWktGfk4j+FEM7DaTfGanEThLRn5CIPEEq/gpCxEgltpCKvUAi/O+k4ptJhJ/A0LoQRoRk7DckIj8kEfkxWvIQAIY+SDL2PInIj4mHv0My9hsMfRhD7yUZe4FE5IckY7/F0PsnxnKMZPRnJKI/w9C7gdnnmni7ietJjgXbORpoQwJqnMXcW7yWOlfpJCEDwGN2sC53PjfkLyZHdRPUomwaPMBwYnxSPb/FxTLfXEySgi50Ng3sm7H/hJ6iI9JPe6QPCYlyRz7z3BVT6unCYO9oE6eCXejCYKG3hlsLV1JmL5gSWtlrcXFn8WoaPZUAjCSCbBrYf1nX52pz7Xw0ZJl6bwHFdi/NofRLfNdQGxsKay+aS0MIQULXeKHneKbMaVZZmlN2ReJaXy4+i53F/hI29TZhIOiOjHNgtIvVeZU4zG+PSdeF2Hu4g9rKfKovkjlYCMHQeJgnn09H/vE4rDx2y1LcjsvLLTEbYokUv912jN7hAFVFOdy1bl7GcS7LzDgUJy7TVK2e0+RK75ROyJUzOUw7Ta5z9gTTi+4LkYglaT3WjazIzF321iJUvfrT7dz9oQ2Y3kY/ZoFgINGXMWGSkPBZ/MxxzT4CTaG1JG0KNLHW04RGT6yTelcjZnmqOdNwcoiOSGvmb5fJQ6N7+lCE05Gn5pOvFnKcw5n++uI9hFMh/Oo7773zdlBmq6TCXjWr/CaqbKXEVpaJdJYykkT08DnhridzJtTyGdwmz0WFjHROARs2xU5KS/t+6UInrIfIZ3YZnMciMQ6097KzuZMj3f3sa+tJJ30LRTApU9+Vt26Yxzf/YxP/8bPtHG3qpazIh91mSQeMCMVp7Rpm7+EO7DYLqxdf+Fmeu6IWl89Jb2s/e144gMVqxlfgJZXQMHQDQzcYHxxHT6UXn5qmI0Ta3FVL6cgmBUkC1WbBrJowmU1IsoShz5xfpXpRJTanjZ7mPg69fgxZllmycQGLb2wkp9hHf/sgm36yFU+em5K6qZsCkiSh2i2svnMpm37yJmaLmVV3L8NkVlBMCmaLCW+eB2++B6fXidt/9fIInDumYpufO4uXsWe0mf9qf30iJ4WBLgw8ZjvX5zdS6chHGBG05FaEEUIx16GJOIjERDt2ZDkHIeIkoz9HMS9CS+5DlpzoWiuGEUQCUsmdWKx3I0lukBWE0Ucq/jtM5gUYWht6cheKZRV68iTCCGCyrEBL7MEw+pGVMvTUMSTJDEik4q8iSU5kUxnCCHFG6Pl9IKhFOTLeStJI4VCsNLgrqHYWz1hfVcyszGlg58hxhhMBmoKd9MRGpmgVbixYzEv9u0lpMfaNnmI0EcSvTv0ej6dCHBpvIW6k8JgdzHGWkqt6p9QLJMO0hHomzN4kVvobKLL5Z7T6sSkqK3PmsXfsFJrQODB+mpShTRGe3m6u6WgW+0tp8BbSHh5BEwYHRrvZNthKvs2F+wLChi4EWwdb2DGY/jhLQKHNzY2Fb6+DtNOssshXQq07j1PBQcJagjcHWpjvLeam4rmXFL5XM/R0Ip7zJlRK09m5v43u/nFkWaK00Mu65TWMB2PsO9LBwHAIl0Nl/pxiqspzOd02SHv3CLIsMTwWprGumLJiHydO9/PSlhMcP91HaZGP61fVkeefOdTseDjG068dBKAox829182/qoLGkZY+/uvFfQyMhSjN87CwtpjKosuzLf3vhKqoWJWpPkFm2TIpRKfH7Jk2EpJZOrtAEjCrEJ1aUqPjZC/xSBzVZmHB2jmU1hZyZPspxoaCaEmNscEgyzc2UlZXRNP+dpoPdZCIJSmtLWDJDfNQbZZMpwOdI+x86RA3PbQKi9XMkR2n6TjRg9lionZRBfOmyYZ9pTCEQVukOaNdMElm8tQCHBfwrzgfu+JIR6RCytjsjySHSRgJplu6tEWaSU5EO5KRyZ9w/J4tVsWGXXFM6m88NUrMiF7kl/99qHHOQVWsswr5qEgmnOcI6wIxkSxRm9avysCYJjv97JgynkvwWTYrCrkuO3luJx67lfJcL0IIVlSXUuCZavKzbnkNQyMhnnvlED/79V5yfA5U1ZTRZgTDcQrz3Dx451IWzZtZmyaEoGlPM6f2tmAYBtFQDCHAk+fG7rbx+s+24y/yEgnGMrkmLKqZV374BvFIHH+Rj5LaQva9AucnoxDAQMcQr/9sG6f3t+L0OnG47TSsqqPtaCfHtp7EEIJIIIoQaR+HA5uO0tPcj67pJGIJhBAYhsG2X+5m36uHGegY4uX/fJ21960gp8jHguvnseknW/EX+njwU3djtlqoXlRB54lu+toGkCSJklolozG52rjNdm4vWkqdq5jhRJCkoSFLMi6TlRJ7DsU2P1bFgq4NIoxRFPNizNYN6KkTgBmEjjBGMfRRkCR0rQlEeoNINlWDZEWIJIqpDi25H0ECQx8EdAx9HENrTV95SUGIJEIfRJZzUUz1IAz01BEMYwSQMYx+DD0XSfIijCAm242YLPNJxbeQzrb1+0FUi9MaTofa9VicVDoKL7pJXWzLIU/1okgyCSOtkVjgqcIln9X+1TiLqXWWcGi8hdFUiD2jJ7mtaOWkdoQQDCUCHBxvBqDImsMCb820vhR98VGGkwEEAo/ZQZEtB+tFNjHKHfmZoERjyRDjyTB55+Viebu5poJGvtXFzcVzOTHeT3NoiGAqzk/b9mGWFe4snYfHbJu60DZ0Xu09yXeathJIpR8mt9nGnaWNlDvePqdVSDt41bnzuatsPt1NW4lqSVpCw/zH6R2MJaPcWDiHAptrxo9dVEvSER7l6FgvI8ko76lajtsyeTGfSGq8+Pox1i6vIT/HiddjJ5HUONU6wOm2QRY3ljEwFGTPkQ58Xjt9gwGONvWyqKGE6rJcfB47FrNCrt+JalbI87uoKsvBqr6zwmH2DQcZDUbQdYP+0RBjoehbEjQSKY3h8QhCCHI9jnfc+V4pzJJl2jwK8nlZdK2yDXmaD0ParORMPTFpATUTiXgKWZYpn1NM0/42ju44TU6Rl67T/fS2DjJvZQ2V80pwehyM9I+z55XDlNQWUlSVx55Xj5JT5KN6fimKItPbMcT23+ynZmE5ikmmv2OYva8eYcXNCxgbCrLnlSMUVebiy786js4Cg67oWTMwXWi0R1p4sv07l9ROe6RlUrKqqBaeMWFbZ7QNMXGdBYK+eM8l99cTmxx+OaZHSRlZs8gzFFqLZ52gUUKaEvlJIGZM1KrK1knhhCNamNRFkvMJIUgZCaLaWRMnBeWCAQPOx2m1sLC8CEmW8NptlPjcZyMUTvONcTut3HfrImoq8jjZ0k/vQIBoNIkkg9tpo6zIR31tIXOq83HYL6wJyyn2Ub2wAiRYdEMjZXNLsDmt3PDYOkb7xlBMCpWNZZTUFWGymHj4M/diaAbxaIJVdy3Dk+fmugdX4y/wkleag6xImMwmyhvS7cxdUUt5QymKSSG3xI9iVvAVeKhelO5z4fUNlM5J70AXVuanNyokWHrzQnJLcpCQKKjMZ+29y0klNFw+Z9qXRAJ/kY8HP3UPVrsFhye9SCyuLmDDI2sZ6RtDksCX70GSr41/kyxJeCwOlvovvIEiScrEeyI9t4TQkDAw9G605A4U80Ik2T2RHXxirkrWCQ2EDJICJNCTx9C1w1isd4GIoovUxCvflNZQKEVIyhJkpQzQkCQVWclHMc9BMc1BVtIaN4HGWXOpdPj03xeSRoqRZFqTaFMs+GcRRtcim/GanVhkEzE9yVBinOR55rAW2cwN+Ys5GmhDN3Q2Dx7klsLlk8w1o3qCpmAXg4lxzLKJSmchtTNoU8ZTYcIT69y4nuLZrtcvag41lgpl7oQhBIFU5L+3oCFLEmvyqmgtHebHbXsZiodpDQ3zxOkdHBztZom/lDKHD4fJQsLQ6I8GOTTWw+6hdppD6aynqmzi+sIa7itf+I4wrfFYrNxaXE9neJTnOg+TMnSOjvcxnIiwZaCZGlcuxTYPLrMVSYKkrhNIxRiMh+mLBhiKh+mPBclRHTxQvgg3kwUNs0lhxaJKRsbChCNxVi2pIhZP0jswTkGum1WLqzjZ3M+ug20MDKVtTL1uGw21RRQVnF2cVZfnkOt3UluVx7IFU20D327mVRdQUeijvW+UxXUlVBS+NW1Ga88Iv9pyBEWWeOimxVQXX70oN28n8iyytgKYZPO0i5Gpe7EX/3hYVBMltfksuaGBaChGX/sQobH0brq/wEP9smr8hem5d2T7KXTdYN7KGgorcmna307HyV7K5xahazo//9eXyC32seymRhCCjpO9nNjTitvvJBKMEQnGCIyEr6KgIRhNnfWrMjAYSPQxkOh7S+0mjeSMQttYaiQjlAgEI8khRkYvL0Hkuf3pl5CJ+g8dt9lz1RJn5lhycJvOzsfR5DBjyWHKbBUXzJ/Sdk5SPwkJp8mFz3zp76Uch51oMkXXyHimrMDjxGqeupnicdlYu6ya+XOLCYbjpJJa2qRINeF22nA6Lm5qJ0kSZXNLKJs71SG6an45VfOn5nSa7nmtnTDP8k/j+rT4xql5c4prCimumWpWVrO4ctpxzjkvCuEZzBYTy26ZbJposVmoWlBO1YKpY49qSY6P97NvuIvuyDgJPYWqmCmyu2n0FrHIX4LHcmFtmRCCoXiYrQOtnAoMphMUkvZVLbS5WeAvpt5TgPMCJtaSnINiKieV2I6unUboXaAUI0kqwgihp44gy3lIindCqJi2FSTJjNB70VIHEEYQWclBCB1EHMMYQksdRNIsKKY5KJa1KJbl6Kn96MkDE+O4EcVUi6KUkYr9Fi25C2EEYNZ6vLcfXRjE9LTZmUlSZh3dyqqctQyI6gl0MdUvZW1uI0+1v8xIMkhzqIe2cD81rrOCxHgqzL6xJgxhkK96afRUYTdNbx0S15OZDaO4kWTf2KlLOk+BIPkO3HC65oZcHouNByoXYyD4Wdt+BuMhOiNj9MeC7B5qx222YpYVdGEQ0ZIMxsPEJ8LHWmQTt5U08NG56ymwXX17ytkgSzJlDh+P165EkSSe7z5OWEvQEx2nLxpgz1AHTrOKRTaBBLphkDA0olqSmJbCmFhwzOSnYjYrbFhTR/9gkM7eUX754kE+/Ng6LGYTY4H04i6l6aRSOpaJmOVW1YzVev6tldANA8MQM9oev51UFvr53Ps2EozEyfM58bnemhr7dNcQO4+2k+dzEo39YTrJysw+6aU8oeO4EmhJHV3TEUKQTKQQQmCecMC0u6xYzpl7NodKIpZE19Iv6EgginVueldWCFh/7zJ2vniI5kMd1C2uwO6y4vI5WLhubjpPjtVEXvHV01wKwaRd5itFWsiYXmiLXIX+hBC/R/uLVx9Vtl61CFw+Sw5FtlJOh08QN+IkjAR7RndQbCujQC2a9t0a1kNsGnzxnPGpzHU3ol5ixCmA4z2DHO7qQwjBSCSG3WLm8fVLM1GozkeSJDwuG563+E6djt90HuWN/uZZB3WZiSX+Uj5av+4KjWoyujBoGh/gX09smVRe5czhw3PXZDKSCyFoD4/yy45DbB1opS8aJJSKoxkGJlnGYVLJszpo8BbxSNUSFvqLUZWpSyhDCF7oPs7P2w7SERllNBGdCGIjUBUzTpOFfKuLeb5C3lWxiCU5pdPa4EuSikm9AdlUBxhgWYMs5yMpflTHexEiDliwmRcgyblYbPciSV5QKhCICT+OfGSlENXxMZDMgJIWPIwxdK0Dk2U5inkueqoFXWtHNs3BZFmGrBQiRDrCp6wUgWTHbLtzwjFcwmRZhawUA1cmo/nVRpKkjKmSgFnncDt3s0iZ4Rvqt7hZ7p/LS/17iOgxtg4fyQgautDpj41wLNAOQKHNzyLvzJos+ZwM3zZFpciag/0SUjg4TNZpI1m93bwtHiOFNjePVi2j0Obmmfb9HBvvJ2no9MWC9MWCU+pLQIndy4OVi7mrdP7bbjJ1PmZZodaVx4fnrKPGnc9vu45wYrwfTRiEtAShi7yEc1QHK/MqsJ730hJCEIuneOrZnZhMCsmUjs9jx26zUFuVzytbjvNvP3wdgLrKfIry3XT1ntmdnfxASBKUF/t5Y9dpjjb1cs/GBRRepV3iy8FsUlhYO7Nz1qWg6QYd/WMMByLkTROu8A+Lay8wmlUT7Sd6+OGXnyMSirFkQwMu3/QmICW1hRRX5fPCD98EIZBkibrFFZjMCpIksei6epxuGy//eBt2p5WqeaXUL6tm54uHkGSJwvJcqhsvP0fGxRFo5+wAKZKJAmsR+ersHHRnokAtnNZ3BiBlpDJCgYREvrWIIuvFw2deiFxLHk7lD32uzx5FUq6aoGGWLSzwLKY5fJLWSDpc5cnQUaReWOlbR41zDg6TC1mSiWoR2qMtbBvenKl75p6vybnusvpvLC2gxJ82/YgmUrx6rJmENrM2q6NnlK7eUWKJ1IwKy1y/kyWNlx4WszU0wpb+5kn5rS6Hqxn7X4h0OP3XeifvDtd78rmttAG/6kAAp4JDPHFqJ5v6Tk05n6Shk0xGGUtG6YiM0R4e4c/mXc+qvMpJwoYhDP7z9G5+1rqf9vDIlMsd1ZJEJzZQW0LDdIRHeU/Ncm4vaZhWQJWVPGRlaph2xdw4tcw0zQJWSWvMTOpkvwHDGEeSFLTkAYQ+gBARZFM1kpKDJLtR5KmmRZJSPCFcTOX7h/ewo7eTTy1fx/yc6fO2vJ2YJAWnyU5sQmMQ0WaXODeixzPmUk6TDWUavw5Jkri1cAUv9+8lZejsGDnGIxM5NSJanMPjrYS1GE6TjbmuMgrUmdevVsWSMcv0W1w8VLaBmgs4rZ+PIskU2d551htvm2t6ntXJ7SXzmO8rZv9IJ3uGOzkZGGAkHiaiJbEp5nT4WHc+S/1lrMqrpMLpx/c2hrO9ECZZoczh44HyRSzPKePYeB8HRrs5FRhkIBYkpCXQDQOLouAx2yiwualy5jDPW0i9p4BShw/HNJKralG4/YZGJElCliU8bhuKIlNR4ue+WxcRjScxmxT83rQfwsKGEubWFExRiUuSxM3rGxgLRNI7XO535nW8EgyOhegZDpBM/f6E3/t9wemxse7upSxaPxfdECgmmZxCLyaziVW3LUSCSaEvbQ6V6+5bTmA4hK7pONw2cot9yLLMp/7vB1CtZupX1ODw2Mkt8WO1W7jtfesIjkZAAptdRZ4mos6VQ5qUx8AsmZnrmscNeW8tk65ZMk9yMD4XVVYzznuypFDjmMNthfe8pf5MkumSHMr/0Lnay5wyexVrc24grIUYTPSTMOIcCxyiO9qJy+yeiPAmkTQSRLQwI8mhjM9OjprHPcUPkWOZOZv9hSjyuijypu+1EII3TraiTxO9KZHUePLZHew+2E44kkA3Zt7FXTq/7LIEjd9nIlqSzvAYi/wl9EbGebbtIC/1nCCiXVgDntA1joz18q3jW8hb6mSupyCjkfh5+yGePL1r2g3T84npKfaPdCEh4VcdrMq7dibNkuTCpN6AYp5HOsuBhCT70hqRy6BpbJgtPe18cP7Si1d+G7AqFkpsuQwlxglrcfrjF09DENFijCZDGVOmYlsOVnl6k6s5rjKqHIW0RvoYjI9xLNDGcv9cgqkoe8eaAMhTPSzx1V3QCT1X9eAxp9dmYS2GX3VR4yr+vc+P9LbGwHKaVea68ym1e9lQWEc4lSBl6GjCQJFkTJKMw2TBY7HhNs8ugghAtSuXv1pwCx+vT+8YucxWimaR2K9vKMC3f/omCZPG9+9/DxaLMhGObnY7/72DAX69+QjjoSi3bWjg+oZaIlqShK6xpamNn+85ysMrF3D93Cosigm7YsZltmI3WWZQnUqYFIU558UXh3TyutKiqZKx9wICRI7PQc4MO88XQ75GjnJvFSGgvW+U3qHA2z2UP0hMZhM5RV5yirxTjuUUTi0D8OW78eVPff7mLKkEQFag6hytRV6Jn7ySaxNxTJLSIYLPIBBISOSpU5+5K4XTdK7mIe2AfzX7y3LlscgWFntXYJEtbB58ic5YGymRYjg5yHBycNrfyMjUuxu5rfA+Ku01szZ7PJ/XjjVzpCud2yCaTDEaiU7rr/jSG8f57WtHiMc1li0sx+e1zxgm82Ihz/8QiWhJuiJjxPUUu4Y6+E3XkYsKGWcwhODoWB+/aD/EH9evJ8fq4OhYL/9xaif9sxAyzpAyDA6OdvObziPUunLJsV7e9/lSkSQFSckF5b/HfXeZ7NS7yzk43sx4MkxzuIe4nsR6gUzep0M9DMbHEIBDsVLmyJ+xvlUxsyF/Ca1tfcT0JDuGj7LYW0tfbITWcC+KJFNiy2Oeu/KC4yy25ZJv9SEjEUpFaQ71UO8qx2X+/d4YvuaCRjiaNiOyWc0ocjqcq9OsXtAp6lKxmyyXlZ07mdI43TFEcb6HBd5i7LZLsz+MJ1J09o4yPB7BSAqK7GcFlHY1gBqzUKb6afC+NbOM6egYHmMgEGZBWSE2y5WNsCRJoCgyiZTGyY4B3jzYSnPXEOFYEovZRFmBl1Xzylk6twzvLOyAo/EkX/nhq7T1Tr+rYLWY+L9/8QB268WvfzyRoqN/jJMdA7T3j9LRN0ZH/yj9o2nH+FOdQ/zNv7+IbYaoUzctr+PdtyzBpv5+2JpmubJIyOSpBZwKp3P0aCLFYHzgqvaZqxZkQtMawqA/3nNV+8tydbCb7MxzL2Iw0c9wcoiQFkCRFBTJRMpIIksKVtmKX82h1FbJQs8SSm0VeC3+t+SoXuL3ZDbdFFmm2Oua1j9j8/YmYvEU/+fP72ROVQGmiVwW02E2/f6EKr1SRFJJusJjtIdG+V3XMUYTlxYeWhMGv+06yh1ljTjNKv/etIPOyNgl+0rFdY0DI93sHe7kttKGS/x1ltngMttY7Kvlpf49jCVDHA+0s2vkBBvyF01bP6zFeHPoMJ3R9KbBcv9cCq05M252S0jckL+QH3e8QtJIcWi8laHEOIcDLSQNjRyLm4W+GuwX8Z+wKyoLPTUcHm+lNzbMy/17qXOVsthbe0FNiCCdtMbAuGpBMN4K11TQ0A2Dn76wD7NJ4e4N88nxXhvpfbYU5Xn52mffhdkk/16FQ9V0gwNtvRzq7Kc6P+cqCBoSsXiKZ147xO+2HyMUSZBIaeiGgSRJHGnpZcvBFpbUlfLuW5eyoObCGZUNIejoH6Opc/qdP7tqxriAmv9ceoeD/PTVA2w73EoipZNMaWjnmBHEEik6B2bOvttYXXhBk4Isf9jIkky1s45tI5uBtPPeaHKYkcQQOeqlb1bMhhpnHZsGZQxhIBCMpUYZig+QZ81qNX6f0IwU20c2s2t0KxEthNfs547C+6h2zkkHX5CkTOhci6xiU2yYZjC9uBRq8v24bSq9Y0HsFgslPg8W09RPec/AOLWV6SiDNuv0UefeKg9VLmJZbikjiSiBZIxQMkEgGSOQihFMTfz/xL+xZJSUcfHw2deKhKHRFRln11A7e4Y7ALApZub7i1mRW47HbKU/FuTNgRZaQyPTOhCPJqJsH2ilNxJg73AXKeOsue5CXzFrC6rJtToYT8bYMdjGvuGuacfSGR5j/0g3NxXPwfw2JiH+Q0WWZGqdJdxeuIKfdm6mOzbE052b0YXO2txGrOcIAH2xUX7ds40tQ4eI6Qn8Fjc3FSwlT/Vc0IQpV/Wywl/P1uEjBFJh9o2dYv/o6YljHlb45170GZQkiVU5DRwNtDGSCNIdHeLbp3/FncWruT5vIbmqZ5ImNK4n6YgMcHi8hSOBVv5H9Z1UOq78RvZb5ZoKGgMjIZo7hijIdaG/g144Z7CYFUoLvG/3MC6Z0UiUpr4hQvE4YpbRFC6FSCzJc28e5enXDhJLJHHaVCqL/JkM4sFIgnhS440DzcSTKf7H3auYfwFhw2ox8T8fWEfvUIBQNEEgEqdncJw3D7VOEhJmg81qprYsd9J86h0KcLp7mGg8ic9lo76iYEZNy4KaIsxK9sX+3xUZmRrHHEySGW0i9GhIC3A4cIAb89+an8ZM1DjmYJFVND1tsx/XY+wb28XtRfdelf6yXB0Oje9j58hWhhODmCUz9xQ/xGLvcqzy1HxQV5K9bT08f+gkui6IJVNU5vl4dPVCCj2TfXQsZhMepw2TaWoi2CtFkd1Dvs2FIQS6MDCEmPhnoCMy5VEtybeOb+G3Xceuyjgul8NjPTSHhojrGmUOL4/XruSO0nnYTBYUSUIzDO4qm8//O/Emb/Q3o50XsloAr/Y2IUsSY8m0RsQsK/yvhTdza0kDdpMZRZIxhOCesvn8uGUvTzbvnjKOhKHRER6lOzJOlStnUvv/un87u/q6+OSydawoTJuY/qblJE8c3YfXauPL62+hyJG+9/9+ZC8/PXmYJ25/kDKXB0MIBqNhfnryMDv6OhmJRfGqNtYWl3Nf7TxqvFNNVJO6zp7+bn7dcoLjI4MkdZ1yt5e7q+u5oawKjzq7xL1CCIZiEf5x9xZOjA7xQO08PjR/2azSEkS0GCOJICEtSlRPENXiRLU4J4IdJCeikHZGB3mpby9+1YldSUdbcpis+MxOcs/LIyEh4TE7uKVwOYOJcV4b2M/pcDffOv1Lnu3eQqktD1U2M5oM0hMdZjg5TlxP4lCsPFJ2Awu91ZguoCmQJAmzrLCxYClbh48Q1RNsGTxMS7gHq2Kh1lVKiW12Zmoes4OHyzYQSkXYOXKCzuggP2x/iZ93vUGO6sZpsiMQRLU4Y8kQCUMjoSdRZGVKno++rlHeeOEw1902n5KK6ftvPt7Lpt8cQJYlbr5/GZV16Q2vgzub2fzbQ+QX+7jj4RX48y7fB/CaChodPaMMjIQoyPnv6bQoSzASjvCdV3ey+Xgr8ZRGTb6fx69bSmNZQWbBqxsGrxw+zStHm2kfGiOSSFDodXPz/FoeWrUAqzl92zqHx/nRtoPsbe2iaySAbgj2tnZn7HA/fOMKHlu7GFlKh7Z982Qbz+07zun+ESwmhVU1Zbz/uqUUel0z2u4CBCIxnn7tAD6Xnb94bAOrGiuwTIxhPBTlpd1N/PL1w4yFYuw81kFpvpfiPA/+GfxFFFlmeX0ZxpzSzIepqWOQXcc70f5/9v47sK7zvPKFf7ue3g8Oeu9g7129y7Js2ZYt18STnpnkSzJJZnK/ySSTm7nTMumT5MZ23OJeVKzeRYqU2AvYQKL3enB62e3+cUCQIAASoEiJtrlsisTGPu8uZ5f3eZ71rGUsT4o2EnDz2O2r5wQoL+8/y9eeO0A6m6e8yMdnH9hAa83CUb4ii6jKrUDj5xlexcf6wBb2T+0BIKknOBjdS6t3JSX266OEdikckotNwR28Of4SADkzy7vR3azyr6Pc8fPVkPvTCs3McypxgpHcEBYWNe4Gqp11NzzIADg7PM6Oxmq21FdhWhb/72vvEk1l5gUaK5pKOdzeRy6vo8jSDdkvURAQl0DVSOt5XPLNR0/NGjpZQydsc/GJ2nU8XrseuyTPOVcr/CU8Ub+BkUyCk9Pz/XXOxsYAC2MmyfcHq+7mseq1OOW5VSSnrPLx2nUcnRrk2NR8uuRopiD1f2mgAWCTZHri04ynU7PS9MfHRzg/PVlQtsznKHG6EQSBI2NDTGbTFDvdmJZF5/QUv/fGc/TGo5S4PJS5vYymk3yl/RAHRwf5nUuCF4C0pvHds8f5l/bD5AydSo8Pp6JwfGKE/cP9fK5tHV9YsZ4i55XZKJZlMZ3L8ufvvMHuwR4erW/lE82rrjjPuBTvTJzi+/1vMpqLzkh3F2T586Y+a47ZmRykPz1WuAZnDGolUWJjoJn/0PbpeWOKgkiFM8Lnau7Dq7h4cXg/MS1FXEvTlRhEEAQMy8SYCSbLHGGeqLqLnUWr8chXv69FRFb56yi1hxjOTnJs+jy6ZRBSfGwOtiyZ0iQIAhXOIn6j8SOUOcK8OHKAaS1JSs8ynovNnkPLsmbtEQBKlOA8apahGyRiafQrCONUN0TYdFszHe0DaPmLgUrbumri02mGeqcwl5kAvhzXFGjousH5vgmeeeME7eeHiSez2FWZ0oiPDW2V7NpQT9WMq/PweIxX9p1l37Fu+oamiCYydA1M8MKe03OcOP/Xv/8IKxoKGuQnOob4s398nrb6Ev7oVx6YMxFMpLL867MH2X2wk1/4yBbu3d4CwFQsxY9ePkZHzxiffGgD41MJfvJGO0PjMXweJ3dsauD+Ha2UhOc2pSYzOf7oL5+mo/eiYVZTdYT/53c+jGuRHo1cXuPI6QG+/+IRegancNgVtq6uYWVjKbK8eLQuCAJfe/MwjaVh7myrwzAtXjzewZ/+6BX+6nOPUBX2F9SlBIEfHmjH67Dz4JomVFniYNcgX3njIACf3bkOAJ/Tzj0rG6iLBHj2yBks4GObV+K1FzIOdcXBgrqNBT94t51v7z1KTVGAj21ayXQmwysnztMxMsGffeI+Sv2LO5hbFrgdNv79Z+5kc2s1yiUZMr/HwWfu24DTpvLlZ94hk9PY197DuqZy7tzQuOh5uJwT7LAp16SrIIkiDtvcc25X5dnmdUkScdpUPFdxvr2Fn08IgoAq2rgtfA9HovvRrDwWFv3pXp4c/C4fLX+CYvvSS9ETuTFMTAJKcFFnakEQuKvoft6ZfIucmZ393Pf6v84nK79AmWPpcr5T+Uk0M49fCWCTlpZpvIX3jqSeJKnHMWcMvCyrICLwfsh6ulSVoMuJ32nHtCDgciIvkCX+9KObOXyyn7/80qv81i/eeUWhkJ93rAyU8ZGq1Tjk+dQ2SRTZWlTL6mAH5+Jj5M25kzbjkirHpnAVH65aNS/IgEJQVurwcHdZ04KBxlQ+xUh6biO5AJR7vDhkhdF0koyuY5Mkzk1P0BQIc2ZqnK5YlBpvAFWSODNVWC6LImktz3/b/yZ9iWl+ZfVmPt26BlkUSeRzPHn+NH97ZC/fOHWEBn+QgL1wbewe7OHbZ45T7HLzG2u2sL64DFEQ6I1P8+fvvs5XTx6mLRzh7qr6BT1ELhxxQsvzp/te442Bbj7WsILf3bQT1wLnZDHkTI24niKuLe45dKkJ3+w5RiClLy61XGjKDvOLtQ+yK7yaN8ePcSLWxUh2Ct008CtualwlbAq2sCXUSqkjhCwsLUgXBAGHZOP2yBq+0/caumXMSFn7WRdYeC60GERBpMjm5/O193NXyXrenTjF4elzDKTHiWmFgNOlOCi2B2hwl7Eu0MQ6fwM+1U0iluHJr79N5+khHE4V1a6QSmR56/kTJBMZHnp8M8fe7aS7Y5Sd960gXOzD7lSRL5uTqTYFu0NFlN77M23ZgYZumBw5PcB/+ttnsSyLhuoiyop8TMZSnO0ZY3wqSVnENxtoALhdNtrqSrGrCu3nhiiN+GirK8FxSbNvwOeas41YMksqM9/m3rIgk9WIpzLkNX3u8pzG+f5xvvSDtxmdTBIOuKgpC9E3MsVXfrSPgZFpPv/oZiqK/bMXjk2R+cT96xgYjdE/EuWFPadIpnOLUpCyOY03D57nf/3LqwiCQENVGKdDZc+RLt46dB5VkRft7zBNi7riIL953zaaSwplrMqQj79+4W1ODIxS4vdgUwrZlL/9wodnJW0B7lxRz3/4zgvs7ejlMzvWIggCXoeN9bVlBN0ODnQOYAHbGqsJewoPjcIxCnQMj/PyiXNsaShUMIp9biwLGovD/NXze3jrTDePbmib29txyeGrssTapnK2rayZV/YUBQGP08bWFdUcOzfI7mNd9I1EOdM7xpYV1Utq6P5ZwoWmrPnLF1620HV2s2mQ/6xDQKDcUcmjZZ/gh4PfwsJCtzTaY0cYy42wPXQ7a/0bCaiheRzdnJFlLDdCV6qD0/F2etNd3F50L7cV3bNooAEFidPHKz/HN3u/hIWFYRmcT57lHzr/gu2h29kQ3EpILZq3vbyZYzw3RnfqHGcS7XSnOtkY2MrdkYeWFGgs+Fxb8FlnLfoMXM71ufD9YLHQHXE9tvd+QRVVZOHi67MzeZYXR59mW+g2yuwVOCUXwhVUpS58r9dybCYW//DqO3zjbRvJbA7NMDk/OolNkfnNe7ZSFigk0zr7xmmtL+b1vR3sO9xNQ3URQb8TcYGgpKk2whOPblr2vvwsIGRzsiFccUUTYEUUWR+qYN9YNz3JxaVRP1u/CY9iW/R7dSt21gYrEBHmZKMB4vksE7mLVYsLqHD7CNjtDCXjJLUcY2mNyUyah2qbGUsnOTU5ys6yKuL5HBPpFHdU1GJZFmenJtg90MPGknL+zeqN2ERpRnxH5Y7KWvYO9XJ2aoKDo0PcW93AdDbDgZEBxjMpPtu6ll0VNUhCIXhuC0V4sLaZntg0r/V1sa6ojFL3/PNlk2TSusYf73mZV/s6+WTLav79xl3YpLmT9Uvv9YXO1X0lm7izeN3Cj6ar4GqeLKIg4pRsrPLX0uarxpjplcMChEJlQhIKf5Z7f9pEhV+ofYDPVN97yf4IqNfQmyUIAnZJpc5VRrWzmI9X3oGJefHRKRSeI+KMGaGIiGmY9J4bZbB3gv/wvz5J15kRfvjV3ViWhZbXyWcLtDNdM8hltCX3wr5XLDvQyGQ1Xtx7BkUW+a3P3cFdm5tmf5dM55iKpQhfYpJWEvby6J2rAYsX9pymfyTKmqZyPv2hjUSCF9e7Xi+T4fE4Xred3/n8HWxbW4uAwOBYjH/63h5e2XeG1voSIkEPthkXbVkS2bG+HizoHZpk96Hzi45tWRZjUwm+9IO9eF12fuOJXdyxqRCpDo3H+MbT+/nJm+20LdKfUAgEqqgJ+2cz+s2lRdgVmdHpxJw+A1WW0U0TY8bJWxZFygJeesejWFZBCargdlm40C6cPlEQ5gUD7QOjjMQSfHh9K0GXY5Zm1Foewa4qHOsd5oHVTYs2kTtsCptaKhflVgqCQFVJgNaaYnYf68K0LPrHooxFE9SU3nzmMTcClmViWAYGJrqpkTUyZIwMWTND1sjQl+4hY1xUNTEtixOxI4TUIhySHbvkwCE5sUsOFFFFQEAR5CtOVm7h+qBQ1VDZEtrFlDbFm+MvYVgGJgVFqB8Pfptnhn6AR/biVXzIgoxm5UnqCRJ6At3UmCnuA8xkua/8ABcQ2BjYzkRunJdGn8GwDCxMJvPj/GT4hzw/8hQe2YNX8aMIKpqVJ2UkSWhxNDM/Z3vGErZnWHrhmCyTrJklY6TJGBlyRpppLcp4/qLSlm7qjGSGODp9EIfkwH7JH5tgQxILykoXGp4XgmmZ6JaOZZnkzVzhXpi5H1JGata8DgrBSFJPcHh6P3bRgV2yz2zXiV20I4vyzMtfvmmCDqfkospZx/lkBwk9honJ/qm9HJzaN7PG/P20S3ZcsocKRxUtnjZW+zfilj3LVonZ1lBFQySIIAizE7bZCrPzYrD5T/+6m4mpJAiFBNnJjiEWk53K5XWeWNZe/OwgbHfT6i+54rUlCAL13iJCNteigUbI5mJDuOqKE10B8Cp2ypw+BtLTc36X1vPE85kL891ZVHh8BO1OBpJx4vkcndNTpLQ86yKlvDvcT8fUBDnDoGNqHN0yWVVUjGFZvDPSjyQKrAoXY5oWmUv4+6okUery0BGdpD9ekIIfTMbpjkUpdXkodrnJG3MrNyVONy5F4Xx0kpS2MMVZESX++O1XeKHnHF9YsY7f33TbbLByKZKJLPvePkcur/PIo/O9N2RRQubGUZovCDWIgsj1lM4RhMJ7WxGvX1dCgZ64tPEMw2RqIkFpRRCbQ8Xtc1BU4pu9oC4kN03Twnwf+6SXfTYEoeCpcGGHdcNEkUVEUcTnceC7rOlWmJ0EX7zYLoyxUGblvUKRJda3VrJ9Xd3sxLiqNMCO9XV09IxxvGOQjSuqqJrxoChccBQi2at4ReQ1g9Ndo4xOJrh9YwN3bmma5ctVlgTYtaGBg+19Vxwj5HbOmdBf4M7mDWN2qqAbBod7hvjxgXY6RyeZTufI5DWS2RwNJeGZCcbSX7jRVJpYOsOf/OgV/uzJ1+b9vrlUnz9NuWR4RZaoLLmyG7vDplAUcON22EhmcoxHk0TjGWquLED1M4OR7DDPjzzF0en9s8ZcV4KBzvcHvr7g70RBpMRexi/X/jbF9p+TE/gBQxAEXLKbh0s/SlAN8sLwU6SNDIWWVgvNyjOlTTClTVxxHEmQZl4KV1cXUQSFB0seJaiGeWrou2SM9EzAUaioRLUpotqVjaUKcqpXL+2/PPocr429QFK/usa/ZuVpjx+lPX50/n5TCMoeKf0Ed0buX3SMjsQpvj/wTUazQ4Us3FUwlhvmy91/u+DvFFFhS3AnD5d+DJ/iv+pY7wsE2BG6nWh+kv1Tb5M1M4A1L0t9KdJGirSRYjw3wpHp/bww+gwPlXyUzcEds27AS0FVyL+gnO3l+KN/+wDaEk1LL39v/zzBpzqodl/dt6fC5cenLn6e1obK52XuL4cgCNgkmYjDMy/QsCh4e6T1/By5/5DDSZHTxZnJcZL5PB1T4/hsdnw2O62hCK/2nSdn6JyNTmBaJitCxVhYjKYKVKsvnTjIV9oPL7g/Abud3EwAktI0EvkcpyfH+M1Xnl70OBoDwQUVuAC+euowz3WdxaWoPFzXMpMEnT+ORYHhoeV10ukCc0VRZGRZQtcNdM3AsixEUUCZSQprM8sKy0VUVS70bcz0FVgzBrKqKs9e96oqF7aj6UiSOI8e9LMGSRIJht3sHZgincyRjGWYGk8gSxKSLJCMZUgnc0yOxZmeTILFbKVDy+vkcxq6ZiBJIpqmk8/paHmDbFbD0A1E6dqEJZYdaNhUmV0b6nlz/zn+9l/f4mz3GHdtaaKyJIDdJt+wprOlwue2Uxzyzsu+VxQHCAdcDI/FSaSWZj9/OfKaTlf/BE67SmNNZF5jU9DnpLzYRya3+ERTlqSrqi68daaH//Dt59lUX8Gv3r2VqpAf3TT5x1ffYSiauIY9F3CoKp/YuprVVfMnrmG3C9cVfCQkSSTguTq/1+1Q8boKgUYqq5HJa9ewrz+dMDHIm9klBRlXHcsyyejpmUz1LbyfcEhObgvfS42zgTfHX+ZM4iR5M4dh6ZiWOWfSLCAiCuLsZN8te2jytLHCuxq7uLSJmywqbAvtotpZy+vjL9EeO4pm5tBntndp5aKQgZNmyvoyTslFg7uZ1b4NOCX3FbeTN7LkzWt77l0KC4ucmUOzcnCFhIduaeSMzJKCjKtBMzWyRg7TujmUCk3LJGdm6c/0YWGiiCpZMzNzPRToqvNREL2wLjkf0fwkTw19j6yR4Y7Ifddd/35N69J7fX6e4ZZtROxXF6jxKrZC7wUL1w+bfZElSdPKoohXWZjmmDcMcoY+J9AQgEq3jyOjQyTyOTpjUcpcHlyKSkuwiO93nGAik6IjOoFHsVHhKVDnBKFA+VoXKaMtvLB0tlNWWBkqvrghoNjpZl1xGcWuhc9Jkd21qPLUm/3dPFTbxCt9XfzR7pf4h3sfpcLtXXBOmM9rHDzQxdkzw+RyGjtva+Guu9s4fLCb3W+eITadxh9w8ehjG1AUmZ88dZh0OkcqlSNc5OGJz2xnYjzBD763H4dDYSqaor4+whOf3c7uN88SnUryiU9tZXIywQvPHmPl6krWra9ZcL9/ViDJEtWNxZRVhfiLP/oB/qCL8powLo+diroIh94+z9/8yY+xO1Rqm0qwLIsDb53ljWePE5tK0XNulMyjOZrXVPHms8c5tKeDVCLL6OAUj3x6G7XNJdcUrC070FBkiU0rqviDL97Dd54/xLNvtvOTN9tpqS3mwV1tbFpVjd/jWJKE2Y2AokjYbfMPy2lXsCkyU7E0+SVmeS6HaVrEUzlkScTrmt9crCoSDrt6xUBjKXjq0ClkSeR/fPohXDa1QNmKJ0lmFi5XCjNZA9O0Fsw0FHldKJJEmd/L9sZq1MsulKvxIAWh0MtyNciSNHsRapqBpt8cE4P3A5Ig45G9hNTr473gU/xIC5RLFVElqIRQBAVREHFJC6t/KKKKTwmQMQqNcQ7JicD8e1JAIGQLo5nazGR5voP3zQSbaCOohsmZhSZAj+ydw5W/HpBFmTp3I1XOWsZyo5xLnKY33clkfoK0kUQzNWSxMNEPqCGKbWVUO2updtbhkt3LTrSIgkSFs5pPV32Ryfw4HfHT9KQ7mciPkdaT5M08siDjkJz41SDFtlLK7NWEpApkHNgEGcsUWeDrnYVTdhFUi9DM5am6LQbHItfdBaiirdDTcp2ofx7Zs6iTtiRI+BT/nHtPFpZeIShwoe1zPu+RvQtq5huWwUh2iFdHn+fo9AGyZgabaCegBAmqYTyKD1mYK2xRqFDpJPUEsXyUtJEmbSRnKGNx9k6+QYO7mWpX3ZL3eSlIpXOLZp4vhyyJc3omf14gCyJe1b5gE/jlEAURj2JHFeXZKsClqHIFr9ofcGGbbmXhc61Zxrxmc4BKrw8Ti+FUgpFUgg3F5TOBRhiHrHBmaoKOqQlaQ5FZFaZytw95JtD4D5tvu+pzyaPa8NvsJB1OPtG0kjsr65b9LPvjbXfxcF0z/+fIu3y5/SD/Zd9r/PfbHiBon594yecNausifPGX72DPW2cY7J8iFsuwanUlTc0lIAh89UtvMjoSo6IyRCyW5mOf2ExTcyn/8789Q0/3OHa7yuRkgv/y55/Ablf4j3/wHUZH4qzfUMM//Z9XyWU1koks0akUq1b/fCj6eXxOPv9b9y74u9/7rx+ft6y4PMD2e1bMW/7Axzdy32NzaW2mYZI3TBR1eTTWa3pD220Kd21tYsuaGt493sObB85x4twwf/X119myuoZf+vj2q1JtloQF+xYtNH3xQKHAPZv/wQuTcEG82M+wbAggiTOOvjewiUaVRWyKTDqnIQA53eD04Dhnh8cpDcyfCNoVGa/TTs94dMbEScHCwibL2BSZFRXF1BQF2H22h9pIkPpIEFEU0A2TTF4j6HbOSOYucmIslvTCMi3rEs7wonTgn0mU2Mv4TPUv3fDtNLib+YOWP73qek2eNpo8bVddz614+OO2/3E9dm0ecrrOWCaFQ1bwqCqJfI6klscwTSRBxKWoBO2OeeV1y7LQTJPpXIa0rmGYFpJYaGJs8axldduG2fWiuSzRbAaZgj785VVGyyq8nNO6RpnLi0O++IC0ZqSVo7kMKS2PbpqFRkFFwafascsyZY5yyhzlwD0Ylsl0NktG1wg5nAgIxPJZ0lqhR2MqY2Da87gVZdFJ8ZUgCRIRWwmRohJ2cucV1x2JJ/jx8VM8d6qDyoCP39i5hZWlixv+3VP8MPcUP7zsfbpWNHna+L3mP35fthWxl/BLdb91zZ9XRZUNga1sCGy94nqWZTGWHebZ4R9xfPoQJiZe2cfW0G1sD91O2Ba54vduWYVq0Kn4cZ4Z/gGj2SEAYto0x2OHr3ug8e2nDpJM566+IlBbGeLR+xZ2Sf5Zhk2S8alLV2uzSTLSIi+2Yodn0d9dClEQFnV5Ni+RV70UlW4/blnlyNgQU9kMTYEwbkXFrap4VRtnpsbpiU/zieaViBT6NDeXViAJIodGB5nIpAk5nHOkUXXLxDBNVElGFATKXB7q/SEOjQ5xdmqCzSWVuBRlzvMya+jIooi8SKN02OFEFSV+afVG+hLTvN7fxd8d2cvvbNiJR52bnHW7bSiKhCSJqKqCJEukUjkOHezi9Kkh7HaZcx0jrNtQg2VZ+HzOAo1KgFDIQzKZQ1VlQiE3oiggiAJFRR6moykixV5Wra5k/7udSLJI28qK90SbSmXzTKcy6IaJ12nH67RdUzI9p+lMJdJk8osno22KRNDjvGbjZcM0mYynSWYXTyopkojf7cDjWFyNs+v0EFMTCUzDwjBMZFnEMEwkSWTDribUZZhaX3MqUBAE3E4bd29t5raNDZzpGuEbzxxg/4leaitC/MJH5j+0L0w+Lbhi7+KFXon8AgGFYZpMRheXPMvmNBKp7DzlhlgiQyqTx+u2X7PrtyyKBH0uNN1gYjo125R9+bbfK3a11HK4e4i/fmEPKyqKGU+kONg1SG0kSFabf4H6XQ7ayiPsP9/P1946RFt5BATYVFfJupoy6iMhHl7bzLf2HuUfXt5Ha1kEh00hls5ydnic33v4NtrKI4s+JE3LIr1INeVS5DWd3MwNZFPlBd1qb+HnB+emJ/kPe15kfaScHWVVvNR7flbn3SbLbCmu4FdXb2ZFKDL7GcuySOkaB0cG+X7HCY5PjJDSCpzl9cVlPNawgo3FZTgVFQv40bmT/O3RfXymZQ2/unrzvJJ+Wtf4/bde4Oj4MF+9/2Osj5TNXue6aXJicpR/PX2Ug6ODxPM57JJMW6iYjzeuYGd59ZyXYyqv8bVTR9g92MOvr9kCWDx5/jTtE6Ok9Dw+1c6vrdnMQ7XNi2YsrxdKvB5+fecWyn1ejg2N3NBt3UIBuqVxOLqf0/ETmJjIgsztkfvYFb4Lt3x16s2Fyskq3zpyZo5v9P4TUDBsHM4OzHtnvVe8faiTqen578pCss4km9UwTROfx8HOzQ3Xbbs/TVBECY+89EBDFsVFg8mgzbmkBENB6GPh9cxFknrlHi9+u4PDo0PkDJ1yjxdVlhGABn+I4+MjxPNZ2i5UNASBRn+IB2ubeLWvk785spcnWtbgVlQEAXKGwXAyQUbX2FFejUtR8dnsbCurYv9IPz/pPoPPZmdzSQU2Wca0LDK6xsnJUVaHS6j1BZGvcK26FZXf37SLsXSKpzvPUu728+nWy+SDF+jfiEaTjIzEaGoupa2tjPGxxKwNQjSaYmR4GlWVmJhIsHV7I4IAY2NxhoenkWWJ6ekMpWV+bDaFdRtr+d639lFeEeTBh9de9XtZDIZp8uLhs3zj9cNMxlM8sKGZX31wG0H38j1zekan+MundvPOmcV7edsqi/m9j97GhsZroz7GUln+4sk3efFQx6LrVIb9/MoDW3hky+LJyL7OMXrPjTLQNY5pmhSXB4hFU2h5g5Uba29soGGaFqlMDoddRZYKN4siS7TWl3DP1mb2n+hlPJpc8LM2pdDDkcrkyC0wYb4An9uOJIqMTMRIpnMEvBIgYJomI+NxzvaMLvrZRDpHz+AUU7E0QZ8TQRDI5XXO940zPpXkto0Ni7pEXw2qKtNYXYRhmJztHiWezMw20eU1nYHRGIOjMSILGBL6nXaay8JzVEEAbLJEc2kRxV73bLbh4TUtpLMab53t5oVjHZQHfXzx9o2IosALx87OqzvYFZnbW+owTYu3znSz91wvPoedlrLCBE4Q4MG1zZQFvLzSfp6zwxMFzWing53NNVc17NMNk7FogubqyKLrmKZJPJUlPhNoeV32RX1IbmFpsCyLTF5nZDpBacBzzRmO97oPiWyOvvFpTMvCbbdRHvQuiUp3AQdHB9gz2EOR08UdlXWAxcmJMV7oOUdPfJpvP/RJ3GrhWskZBq/1dfFf97+BQ1bYUFxO2OFkKJngwMgAZ6fG+a3127m3qgFZFFkZjtDoD3FsfISuWJR1kbk9SIfHhhhIxlgfKaPC45uThTo6PsJvv/ETANZFyih3e5hIpzgyPsx/eec1/t26bXyiaeU83nUsl+X57rNEcxlskswDNY1kDJ2u6Sl8qh3bJU7z58YnKXK78NptiILAdDrDRCpNXbjgcaMZBgPTcdJaoZ/JraqU+jzY5EKjY1rTGEskSed1bLJE2O3CZ19cPvMCTMtiPJkims5gmBZeu41Srwdp5qWtmSYD0zHSM31ULlWhzOed3W4yn2ckniSn6QiiQMBhp8jtmjUV/XnEeG6M/kzvrPdJib2cBlfzkoKMSyELMtXO2tmfTUwyegYLE+E6Ku18/rEtZBfok9P1QrLuTOcIfUNRtqyt5eMPrbtu2/1pgiyKOJdAm7qAguTp/OUi4JJtS5JoEQQWfd9e2pN1KQJ2BxGnmz2DvawMFeNVLm6rLRThjYFuTMuiNVg0O7ZTVvj/bdhBWtN4ra+LtwZ6iDjdSIJANJshns9xR2UtW0ovUoq2llYyvWI93zh1hL87+g7eGTpV3jCYzKbRTZM/23EP1d4rM1YEQaDY6eY/brmNP3jrRb7afpBSt5t7qhpQpUIVw+9zztLY3R47oZCb4mIfpaV+TrUPMjwYxe93UlTkRQCcLpVDB7vY/04nDY3FVNWEGBqI4vU6ePXldpKJLLfd0UKk2FcY02XD7lDxeB14fdcudpDM5Hnh0Fm6RwrCHD/a285Htq4k4HYs2/tLlkR8TjsBd0H9UzdMdLPw92Ly3suFKAp4HYVtGJeMrxvmkqmUAPd8tMAc+Or/foG7PryOyvoIWPC3//nHy96nZQca2ZzGC3tOU1rkJRxwz/L9o/E0h08P4HKolBX5FvxsJOwh6HNxtnuMY2cH0TSj4MZompQX+3HMREilRT7KIj5Od43w3Fun2LK6GlEUicbTPPP6CbJ5bZ7h2wWIgsDp7hGefv0E29bUoigS/cNT7D7ciSDA2pZyQv6L/OJkOkcur2OYJuNTSUzTIq8ZjE0myGTzBcM3u4rdpqDIEo3VRbTUltDRO8aPXz3OjnW1iKLA4GiMPYc7SS1SrtrRXMOO5pp5y8uDPv7is3MpDZIk8qnta3h862pGJ+IkU7mCKo5T5bZPPkBe0xkfi5PO5FFkiYDfScjj5N62BrZUViCKApmsRsDnwLQsJqeS5DWDkGrnIytbKL7Di2lZZLMaQb8LRZEYm0ggSgJBn2vegzSv6XT0j7Nrbf2CxwaFKHpoIk52pqJRHPQQ8n0wBlGiKHBBQMxY5s31fkI3TQS4ojJH9+gkf/3c2/z+o7fTWBp+3/fRtCy6R6P83Qt7GYrGKQt6+f8/dhfVRUunRp6cHOMj9a38/sZdlM80Bk5mUnz+hR9yanKMY+PD7CivxrQshlJx/s+xd7DLMn+0+XburqpHFARSWp4nz5/iLw7t4anzp1kZKqbS46M5UERrMMILvR10Tk+yKlw8a15mWha7B3uYzmX54sp6vJdUJzK6xv88+BbJfI4/2XY3H2loQxQE8obO7sFe/uOeF/nu2RNsKqmg0T9XonkgGUMRJX5hxToerW/FOVO9SGl5FFGcE5j88bOv8Pkt67mrsRabLLO3u49/PXSMr3z6MWRRpH14jC+/cxBt5kVTEwrwxS3rKfV5SeXz7O3u46WznaRyeVRJYmtNJQ+0NhJwXvnFOTAd44fHTtI1EUUzDPwOO5/bvI6WSLjwjBwZ4//dewBt5v6oCvj4pW0bKfN5Sec19nT28kz7GTTTQBRENleV85HVbYRcN+aevlym9WZETIsS16Znfw7binDJV27CXwyXTyYLmfDre+x3bm++4u9jiQw/fP4Ibx84z85N9ZSX+K/r9n8aIAsiNum9J3BskrKgjOv1ggCsj5Qxmk6wJlyK334xYbmuuIxNxeUYlkWp62LjtTBDh/qvu+7j5d7z7B8eYCSdQEBgZbiYpkCY7WVVs0keKMjePlzbTIM/xOt9XZyaHCOez+JVbawKF7OmqJTVRSUol9GG6n1BNpdUzKkoS6JIUyDMH26+jX8+doD9wwNsKakk5HDictnYuuOiid2q1ZWzPRR33+vj7ntXzhm/t2cCt9vOfQ+spqb20j7IKD6fk899YRcOZ+E4TNNiairJQN8koiiwZm3Vezr32bw2awlwYfyspheeWcv8vkNeFw9ubKGlIkI8nSWRzRFLZTnTP07/xPR72s8LcKgKd69tpCLsI5HOkcjkiKWz9IxGOTMwtuzxTMNkoKtgaJ3P6aST2WUHRcsONDTdYO+RLs71juN123E7bYiiQCxRMNDbtaGeHesW5prWVYTZvLqan7zRzteefJegzzmrUvW7X7iL2orCC92myjx+/zq++cwBvvbUO7zyzhnsqkIyncPvdXD31mbeOriw30XA5yQS9LD3aBe7D3UiSSJTsRQC8OE7V7GmuXxOkLL3aBcdPeOkMjkmoylSmTy6bvLVp97F47ThcqhsXl3DxhWFizXoc/LZRzbxzz94mx++fITX93fgsCvk8zolIS/rWiqYTizuTLncc/2tpw5gmeByqtRUhIiEPPQPRXn+9ZPAjL53dZg7tjVxqmOYvQc7KYn4SKSyrF1RycbVTt569zyjE3E8LjuJVJZ7d7WSz+vsPdTFh+9bTSTk4amXj9FcV8y2DXWzlaoLyOR0Dp3p57HbVxNYwF3WME06ByZp7xoGwG6TqS4JEPJeuWH0RuFC5QxgOpklnc1fd1rC9UDH0Dheh52ygHfR55XLbmNFZTHuD6hRUxJF1tSU8rf/5lGePniKl4+fu/qHLoNLUflk82pKXRfd50MOF7dV1NARneB8bJId5dXopsGJiVG6Y1EermueDTIujLE2UsqqcAldsSlOTo5R6fERsDtYGS7m1f5OTk6OsauihmJnYfI3mUlzdGwYp6yysbh8Ttn+9NQ4R8dHWB8pnQ0yAFRJpi0UYVtpFe+ODLB/ZGBeoJE3DNZFSrm3umE2yLiwj8uBYVq81dmNTZb5kwdvx6kqjCdShNwuLMuic2KK50+d467GOu5pqeednn6eP91ByOXkvpbFqS6WZfGdwyfI6zp/cPcugi4Hf/H623zr4DH+r/vuwCZLvHm+B0kU+ZMH78alqowmkoTdhft1Kp3h8MAQDUUhfnHLeiwgq+n47IvzeRfCaHYam6jgVRwLUkoKPHGD4UyUiVwCzdSxSwphm5eI3XddteivBwzLmKMEJ8z8b/nj6Axl+md/lgUFj+K9pr6e9wKv28HOTQ3s2X+ePQfOs2HVe5uQ/TRCoNBv8F6hXkXW9nrgY00r+FjT/KbdzSUVbC5ZmGYjCAI+m52PN63k400XJ+/pRJboRJyQyzevgV0SRdpCEdpCizMYLsevr93Cr6/dMm+5KsncXlHL7RW1C3xq6XA4FCoqQzguY0m43DZqaovmuFebhsm5syOcONbHihXllFdcXbr4SvC5HFQV+Tk7OE5eN6grCVLkdV2RBbIY/C4Hd6yq545VF5O26Vye//PsPr75+sISxMuFTZHZ2lzF1uaL93Ne03n24Bn+9FsvL3u8tdsaOfDmGU4d7iWVyLJqUx2qfXnB+bKf5A67wicfWs+R0wOMTSZIZzUkUaC+MkxrXQmbVlVTvAB1CApeC4/csYpIwMOJc0NE42lkSaQ47MF52QV0z/YW3C4b7xzrYXwqiSJLbFtTy22bGkimc2Rz+oLbURWZnevrqSzxs+9YDxPRJHUVIda1VrB5VTVB39zJ78hEgoHRKMZMxLppZeHLyWTzsxWNhqqiOeNvWFGJ23UXbx44x8hEHJuqsLKhlPVtlZztHuVsz9g107MuRz6vs2NTA0UBF6XFPjI5jY7uMTwuO5//+FZOnxtm94HzDI8WTHfsdoUt62ppqJmrfuR22rj3tlZKigrN5IZh8sY755iaTuGwKYxNJPjYg+vmBRlQCCQ6+sd5cnc7D21rJex3zVJQNN2gf2ya1w+f4/QMpa2xooi2mpJZU8T3GwGPE6+rkFkZjyY41TNKU1UEv2fhZmF4/zOplmXxzbeOsKOlhmKfG3GB5kBREKiJBPjth3e+r/u2EC5UXq4FlR4vIYdzXvNc0O4EAdIzNMq8YXJyYhRREDAtk1f7OuesP5ZOkjV0YvkcE5mL3PPV4WKa/CGOjA3RF58m4nQjAPtH+hlOJdhZXk2RY+6L4dj4MJZl4ZCVedtJ5HNkdI2MrjGcnO87YZdkKjxewo7lB9KX5oFEUaCtpJhz45M8e/IsjUUhakNBFFHEtCyG40l6pqIYlsm7PQOMJVIks3n6o9NX3EZONzg2OMza8lLOjI2jiBJem42Xu/vRTRO7ILOiJMLp0XGeaT9DUyRMXSgwm6V021RqgwGODg7zwulzM/sVWFbzY9bQ+GHfXuySwrpgPSt91fMoaLplcDTazY8H3uFItIukliGgutkQrOfBsg2s8ddel2zz9YJNsmGTLgZbU/lJknpiWUkM0zKZyI+zf+rt2WV2yUGZ/f1XxBGEglKizSYzNnktsuk//RAFZiug7wXSlSTfbkIM9Yyz59mj3POJzVTULy4icbMgUuzjoQ+tnbe8ojLExz85NxEkKxLbdjSy7ZKKyXuBXZV5bPsqXA6VeDrHvWsbKfItX13wpxXrdzZSWh1idGCKSJmfolI/srI8iueyZ4KqIrN1dS1bV189QtXNHCl9DFmw4VIK0bHf4+D+na3cv7P1ip8VBYHta+vYvrYOyzKJaf2k9UnccpZatZxVjWULfs6yLOw2ma1ratm65ur7+PkPb77qOpdDVWRWNZYtuA/VZUHu23HlY1sqFFnic49t4Z3D3Zw+N0wk5OHunS1omoFrpkwoyxKKLJHNFbi4XreDwAKUpXDIjdt5MZiTJJFNa6rp7BlneCTGmrYKnA51jtssFCaYbqcNt8PGt186xGQsxbqmcjwzvSZT8RT72nt462gXec3A73awa00dLTULZ0M03SCVzZPX9IscRcOkf3R6luJkWhadg5N4XTZkSSwY7UgSqizhcqiL0uYuoLLYT01pkPauEXKawU/ePolhmrRUF+OYUeTSdIO8ZlBZ7Ke6JIgsFY57OJqge2yKrKbjsqlUhn2UBbxohsnIdIJoKoOmG5iWRcTrZjqdIZ3TqI0EKQ0UAt+pZJq+8WmmUhkkUaDU76Eq7Mc+02Nxsn+U0ViSoz1DuOzqjAqJSF1JkPriwkMzndN451wvumHisqusqCjG75obvCYyOXrHo0wkCpPuIq+bqrB/VkmiY2gcURDQTZPxeArdMPG77DSVFuG0FdRE0jmNrtFJJpNpNMPEqSpUhn1ULsEQbKnwKLYFs4aicIEsMvO9YzGdz5I1dF7v72b/yOCC4xXNKJtcQK0vOKMn38756SlWF5UgIrBvuJ+klmdXeTXey5RlotkspmVxcHSQs9GFTfiKnW5s0vxHpENRlly9EATm3E85/aI5piyK3NVYi8em8lZnD0cHh6kNBfjU+tUEnQ7yhsF4MsWrHV2zFFWP3UZlYGFq6gXkDQPDtDg6OMxQPDEbYK2tKJ1pZhW4o7EW98x2v3+0neqAn09vXEOJx03A6eDhFc2E3E729w5wsH+QDZXlPNjWSMCxtARKX2qMN8faGchMcltymIa2UpRLvEVMy6Q7NcY/nHues4mL3/NkPsErI8cYykT51Yb7WRdYvsTmjYJfCeJXLmZHh7IDnIofJ6AGCaihK/pgWJZFxswwkhngwNQ+TsWPAzPy0mqYZs/8TPWNhm4UejWmYxkioZtb1vpGQZhpnH7v41yHnVkE2XSOkf4p0okshm4gyRI1zaU43DYmR2KMDU6hawYev4uqphJEUWCkd5KJ4WkQIRjxUV5bRC6rMdg5SjKeYXxoGl0zyKbzDHWPY3eqBIt9xCaTJGNp/GEPLu/Pr4nj5VhdW8rq2p9P89zRgSnOtQ+QiKapbSnl7LF+mlZXoi5gI7EYbmjKOW8m6E/twyWHqVXuuuZxLCymc710J1+n2LGKNvWx67iXNycKNvEmfUNTVJUHcThUjp7s5+G7V1JR4uedI93sP9rD2EQcmypTXORlOp5ZVFZWuOS/F7C6tZxvPzVEIpnlYw+tQ12gwVeWJdY1VbB1ZTXfeukw33v1CE+/1T5LoZpOZkjP9KUEPA7u2dTMnRsaZwORyzEwHmPPsS7Gogmy+YJKVTavzfibzGS2dYN/fmofLoeKTZWxqzJ2VSHsd3Hb2npqSq9cCg35XOxcXcf5gQlO94zSPzrNl59+B5/bgcuhYpkWmZxGIp3jFx7ezGcf2IgsiYzFU3z9rYKyhGFZKJJIxOfm41tX4XXaea29k/3n+gh7XYxOJ6kM+zAti/7JGLtaanl8+2rSuTwvHTvHoa5BNL1wPG6HjQfXNrOhrhy7qnCyf5T2/hEmEmna+0aYSqaRRJF7hcbZQCOT13jzVDcj0wlGogn+y6fumxNoJDI59pzp4Y2TXeRmGomdNpVdrbXsbKnB47DxzKHTDEfj+JwOktkcWU0nncvzqR1r2NlSi02RmU5nePLASWLpLLphYpgWxX43v37fVoLu68PHL/i8LGE9wCbK2CWZHWXV7CirXnA9uyzPUaqyyzKri0p5tb+Lo+PD7CyvxjBNTk+NU+b20hIswnaZAlrBwbfAe76veuHMlyqJ1PtC85YLFHTqlwKXzcZEMoVmGIV9Gh3HMAvVU9OymM5kWVdRxsaqcg72DfL3e95ldVkJdzTUUuR2sqa8lC9uXc+qsoK5UkbTr5qBddtUKvw+2kqKeHRVKwGng5ymk9V17Eqh2TuazrCmvJQNVeUcGRjmL19/m9VlJZQ0u8nPBNJ3NtSxs66ap06c5kDfAG0lRQTKlzb5OBnrI2UUpFVbvBVz5DwLMpkaTw28y9nEILIgUuYIUe4MEsun6UyOcC4xyAvDh6l3l+JTP5her8sRVEPUuOrpSJwirsfQzDx7J18nbaRo9qwgoAZxiA6kGcqXaZlopkbOzJLU4wxnh2iPHaErdZF+6JG9rPFvpMJ5/WlLJzuGFnUGNy2L6XiGtw92kkjnqK2cf53/vOBa6G/vJ6YnErz47X0IgoA36GLg/CgPf2En1U2l7Hn2KJlUFhCYGovz+G/eg9Nt58XvvoPdoaDrJoZu8NFfvpPoeJznv7WPYMRLOpElGUuTTeU53z6AapO586Mb6TjWy1DPBNvuW3Ur0LgFAPa81E4mmaPrzDDrdzXy7LffoaohcmMDDcPSmcqdJ2NEERBwymFCtgYMM09cGyKhFbTBXXIRdtmHZRnE8v30p95BQCRgq8Ulh0loI8S1QQwzj0MOEFBrkUU70XwPSW0EyzJxKUUE1fqCoZVrC2lj4czjzypMy6KrdwJRFJFlkYfuWomqyDTURpiOZzjfM4bLqbJ5bQ1Bv4tI2AMC8yhLNZUhVEVCludOUOw2BUkUCQfd+L3OWVnhS6HIEhtbK3loWys2RebVgx30jkSZihcCA0kSCftdVBT52dRWxX2bm68YCPQOT/GjN44zMDa9+HGbFu+e6p23vLzIR1Vx4KqBBsDmFVXkdJ2X95+lc2CSyXiKWDLDdDJTMKdSlULDuv8ipea5w2c4OzjO7z2yi4jfQ9fIJF9+/QDPHDrNZ3atI53XyOkGD69v4c1T3bT3j/CFOzbQMTRO5+gk0WSa9v5R9pztYUtDJbe31ZHNa3z77aM8eeAUZUEvtZEgH92ygofWt/DOuT4e27KSh9a1IEviHNpayOPkP3/iHo71DPEn33tl3vGdG57gpePnqCkK8MiGFgB+cugMLx7toMjrYmN9gbN7cmCMj21Zyd2rCqoyf/fCPl442sG62nJsiozXYWN7UzX1JSGcNoX2/lH+8tnd7Giu4Y4V11fX/2qQRZFaXwBREChxeXiiZfWSs42rw8U0+kMcHR+mPxFjIBljNJXg0Ya2Gc+LuWjwhxAFAbeqLms7y8W2mkrah0cRBAFFEplIpmcrHIZpsre7j2Quj02RiWWyVPp9FLsLZfmaYIC1FaW8craTrokohmXhUGRWlRUjCSLnJyY5NjhC91SUt7v6mM5kWVESIeB08MjKZnZ39vDsybO4bCp5XafU52VHbRWSKLKvp594NoddkUlkc1QEfJR4C70tsWyWPZ295AwdWZQYTSSp9PuWXM0A6E6NkTXySILIWn8t6iX9FhbQn57gtZFjiAhUOMP8Qu3drPLX0J8e51u9b7F/soPziWE6EoNsCl0fCsR7hSTIrPCuYSQ7xKHoO2SMNAk9we6JVzkeO0yRrRif7EMRC9Uu3TLIGRmSRoLx3BhJ/SINT0DAp/jZFNzBttBt190VHOAr393LdHx+v6BFgTobi6dJZ/NsWlPD9g3v771+C8uDltfZcs9KNt7Rytf+57NMjsSQZZljezvY/sBq/GEPz33zbQY6x3C6bRx7u4Mnfvt+sukce549ymDXGNHxBLIi8al/dx8nD3bx6vf34wu50fI6PWeHmBqNE5tM4Qu6CZf5P+hDvoWbBOPD0zzw+GaiE4lrfk8uO9DQzTSnp5/EpUSwiz5AIGirJ6EP05V4FVm0IQoKoiBhk3xoVoaUPoGSdzGd70Uz05Q4VtOf2kfOiCOLDkYyxxA8Ik45XBhDsCMKMkOZQ6zyfwqn/P6r7XzQEAQBVZH57GPzG6y8bjt37ZivKFJfXUR99Xxn6oWa/EYn4nT1TRBLZLh7R8u84MTndvDxO9dgtylsbKnE5bDx4LZWVtWXcqJzmMHxGNmchixLFAVctFQX01hRdFVJ27IiHw9ubSF6DQ3zfo+D8iIf+ZxGLqPh8c/NdKZTObDA6bZhVxXu2tBIY0URp3pGGRibJpnOYlmFZim300aR383qhtLZAOy5w2co8rlp7x+B/hEy+QK963DXIJ/euRZJFCjyulhbU0b32BTD0ThrqkuJp7OMTCdJZPKcGhjDa7exo7maqrAfgLtXNvB3L+xjZDpBVdiPIkkFXjfCLCVsMTrYQrQRwzTpGY+SyeW5Y0UddTNVkNvbajk9OEbn6CRrawq0vqqwnx0tNdRGCsHZ2ppSXj1xflZFw64oFPs9nBkaJ53Lk8jkEQWR0dj7z9lWRYn1kTJ8NjuHRwc5OTFKWygy2xtgWRZJLU/O0PHbHHMy+2VuL22hSIEKNTXOiYlRDMtiU3E5PnV+E/O6SBmVbh9HxoY5ODrIhkjZnO1kDZ14LkfQ4ZjXW7AcfGhFC3ZZpm86htdm4/F1KzkzNj6rNOayqXSMT6IZBm6bysMrmmgpKdzDxR43D7c1sbe7j+6pKBYWDaEQNllmKp2hZzKKXZFpCAdJ5fP0R2PUhwvf8466auyKzJGBYYbicRyKSkNRaPZ6cttUzo5NkNcL2/3QimbaSgpVIlkUkUSBvokYpmkRdjvZUl1xVcrWpRjPxtBMg4DqptgemFMBMiyDN8ZOENczuCQbdxWv5p6SNQiCQEB1MZ6Lc2Sqi8l8gu7U6E0TaEDBHHBX+C5kQaE9doSp/CQmBjEtSkyLLmkMl+SmyllLm281mwM78Cg3hrYU8DuRFui5g8Jzpbo8SFmJn+3r62isXXrj7y28//AGXDjcNgRRwGZXsEwLLaeRy2qMztCqVm9vJFDkITmdJpspUKIEAdbtasbptjMxHJs1vJMkseDuLEJZXRFT43H2v3YSm12lurkU8Tr0rbwXWJbF4c5BOocnsSyLlsoIzeVFs/TjS9E5PMmRrkEMw0QQBMpDPna01cxbz7QsBidi7D3TAxaUh32sr6/AeYkfxEQ8xYmeYcamF7ZogAJD4faVdbivYHb3s4SiEj8dx/sZH45xcHcH/rAbcZHnymJYdqAhIOKUQ3jkUuyyj6Ct0EOR0IbIGFG2Bv8tsligzaT1CUCkyN5Cs/dDnIk9TdaYZirfRVqfpMZ9G2F7E0envslUvouUPkHOSNAWfAyH7Oft0b9kIneWqiUEGnabzLrWCjwuG3UVP3+ByXKRy+lMRlOsbCmnpjI0xzVTEASKAx7+8HN3z/mMIkvUloWoLbv2MntTZRFNlfODoeVgsHeC7rMj7LxvrgReT8cIpmmxckMNUFDPqCkNLqkCYlkWI9MJHDaFs0MXK2c1RQFK/YXeC0kQCiZJgoAoiiiyhG3Wadoik9dIZnM4VAWn7WLAFXA7sbBI5zRMy3rPSvmabpLI5Aqa3Jc87LxOO4okks5ps/ScIq8Lh3LxQWqT5Tma3btPd/Py8XMEPU4USSSnGeiGgW68/5LAkihS5w/yiaaVfO/sCf76yF7uqqwn5CgElPF8jv7ENMVOD4/Wt86RZRQFgQ2RMl7uPc/rA930xKK0BIuo8voXdOGNOF384soN/M2RvfzvQ3t4qLaZiNOFJIgk8jmGUoVA63Ota/HZrv0bi3hcfHrjXMflbbUXA/+7m+q5u2lh2WhRECjzefn42pXzflfq9bCiZPHJoSyKbKmuZEv1wk3GdzbWcWfjwlnsgNPBh1e18uFFR786knoGwzIptvtRxIuKPAVDxhxvjrUDELJ5ubdk7ezvbZJCmSNA0OYmlk8zkZ3fjP9BQkCg0lmDW/ZQ6aymK3mOsdww01qUlJ4kb+YxrAJlUhIkJEHGJtpwyW7cspewWkSpo4JGdyuVzuobqjT1Cx/fNityMu84RAGHTcE/o/x4Czc3BGG+wlmkIsiKTXUUlQZwuG2YpklJZYhskYdVW+oJRLyIooBqVyitDpPLaBx56wxvPXOE6FgcXS9cG+ESH4EiD++8dILV2xspr31v7+frhT2nevj2m0fI5nU+sXM1xfd5KFkg0Hj12Dm+9OJ+8nrBLmFtXRnbW2sWkOk3OHh+gP/ne68jAA9sbGFNbSlwcczBiRj/+sYRDp4bWHS/KsI+VteW/twEGhtva+b4u52ES3xMjsbYfu8K7Mv0SFt2oCGLDpp9DzOWOc10rofpXB8rAx+fzdAKs+oLhYmKKjpRRTeCICIJCoalYVnGzE1zoXNAxLLMGcMicfaGEhAxWZhjejlcDhs719ezc/3iXg+3cBFV5UGqyt+b7NtyEJ9O0XVmhFQiSy6bxx9007q2CofLxv43z+D2OpieTJJJ57ntgVWIosD500MM9kwiySLl1WFqGosZHYqy56V2zhzrxzRMSioCVDeWMNgzwZvPH0fPG0yNxalrLcVuVxkfjVFaGcQfdNPTMYKmG1TWFi14o3gcNtZUl/Ir986tIkmCQE43gPm9Bpf+LEsidkVhMpEic4lRViyTLdB07OqslKAgFDqhr8XjQxIF7IqCbpgkLvFtSWbzGKaF+xIzTVlc2GDqAr725iFK/B4+vWMtEZ+LyUSad84t7lp6o+FWVJ5oXo2EwO6hXv65/SDKTAOzYVk4ZYUP1TWzAMuP1lCEBn+QV/s6yeg6X1ixjqIrKEM91rCCrK7zan8n/3LycCGTLwiYloUiSuwoq5p1Eb+F5cGwCsZjDkmdR1s7E++nL1VQw2rzVVLhnJsYsokKfsXFeDY22+dxsyGghtga2sVK7xpGcyNE85Mk9Bg5I4c+G2jIyIKEXXLgkb34FD9FtpIbVsG4HBWlS/e6uYWbFy6vk5Vb6mfpTG2b6vAFXfhCbm57ZD3njvWRTmSxKPSz+gJu7vnEFrpODiKIheBelEUq6iNsvmcF0xMJ/GEP5XURPH4XkiwhKxIur4Oi0sCypUtvBARBoDTgweeyk80nGZqML+pRdrJ3dDaxZlkWAxPTxNNZfK65faLpXJ6BiRl1TlUm4nPhvkyy22lXqYkEmE5myOZ1slqhhzST1zDMm9OP60ajurGYcImP2FQSp9uO1++8sRUNCwvDyjGYOoAgyJiWQd6MISDhVopRRRdn488iCyoepRyPUgLMb7byKKXE8v0MpQ8xmesga0xT6liLSyliInuaruTrSIKChUnY1ohu5ehL7mE8ewYBgf7Uu5TYV6FIN0eT4NVgWhaZrMZ0Ik00niGRzpLOaqSzeTI5jbxuYBgmhlloxrUskCXhouLSTPbcaVdxOWb+2G24nCo+twP3jFrUzYxYNM3uF09QVhXC63fy9ssn8QZd1DaV8PbLJ6moLSJS5sc0C9n2qYkUbz57nLrWUlLRLN1n2wmE3VgWaDmdbDqPaVqYJmAVznEuq6FrBqZpFZaZFkf3dWKZFr6Ai/1vnaWiJkxFzfyKlyAI3Lu6kXfP93PninqqiwIYpsl0KouqSPgWaW6/FKos0VwW5oWjUxzsHMCuyOR0g92nuykP+Sjyumf7YCShUI3oHJlkKpme9f5w2eYGQKZVsPa6VLlIliRqIn7UszL7Onrxu+wICLzT0Ycqy1SHly5FOp3K0FxWhMdpI5rM8PrJTuLp7Lz1ZvfBmivReiVEnC4+07IWRRQJ2Obz+9dHyvitddvZVHxRA16ccZT94soNbCqpoDM2RSxXCNS8qo1St5fmQBj7Am6+fpudxxpW0OAPYVgWd1TUXVEdyiHLfL5tHesipXREJ4nmMlhYeBQbxU43TYEQzkurQZLE9rIqPGrB0+MWFodNVBARyBnanOvFxOLFkaNYgF1UuSOych7vV5hRYbOwMK2FM/I3AwQEPIoPj7J0Stkt3MJy4fE7WbfrIlV6zfaLVMLKhmIqG+bL0zatqaJpzVzKtOyR2HrfqjnLktNpDr91hrNHeympClFed/NQ6MqCXvwuB6PRJENTsQUDjWQ2R9foFIZpYVNkcppOJqfROTLJ+vryOeumcnkGZ0zxvE47ZUHvvHlTScDDR7atZHtbTSHQyGtk8zpPv3uSjsGJm9b890ai/WAPVQ0RJFni7RdPUFIZYvWWugINb4m4BtUpAVl0ICDiUyvxqZXkczrjnQKp6RoCqwoVCVFQUEQXUqya9uPj9CnHKWktJVLiwSVHqHBtIZrvQTezVLq2EbDVoohOat13EtP6MS2DBu89uOUSDCuPLNopcaxBQEBCvrF6cu8RhmEyncwwOBZjYGya4Yk4U7E008kMsUSGRCZHJqeRyWpk8xqabs4JNMBCEsXZQOPCv512Baddnf3jcqh4XXaCXichv4uQz0mR301xyIPf7ViUn/tBQRQFVm6ooWlVBT3nRxkZiFI54/dRUh5g291tyIqEYZj0d45x6lgflfUFWb7x4Wni0RQ1TSW0ra9maiLBHQ9fpKTUNZdS11yKaZqzy03Twh90EZ1MMjoYJZ/TKasKLVr2e2zLSqZSGb637zhOtSD/6rApbGuqWlKgIQCb6isYiyc51DVIe/9owYE9r/Pg2mZK/BddhEVR4KF1Lbx9tpd/evldXHYbd66oY31dOTlN5+2zvZzoG2ZoKs5UMs139x5j79leVlQWc3tbHU1lRdzeVsv+8/388yv7AchpOjtbamgqWzp18MOb2nj7TC9//8JeFElCN0waSi9S46bTGXaf6uHc8ARnh8bonYjy1TcOURH0srG+YrYXZCFEnG6eaFm96O/XRcpYF5n/eUEQcKs2tpVVsa1seWo8O8qr2VG+sFrVQttRJYkNxeVsKC6/6vo2WWZraSVbS2+s50E2p3H8/BD7T35wlaWFUBb2snVVDWVFV59Yh2weFFFmNDdN3tRmvSaG0lMcmOxAQKDUEWRNYL4EuWGZ5Ey98KxfhFpkmCb7j/agKjJtjSU4rrOhpWGanO0c5e0DneiGSVNthM3ravC4rv4ceD/Q0z9Je8cQa9sqrlq5SKSynDgzxMBwlFQmjygI+DwOqiuCNNVGcDl/PiggtzAfgiggKxJVjSXUtpTh9t08SlOlM4EGwOh0kmQmh2lZcxIT3SNREjOJsXV1Zbxztg/dNDnTPzYv0Ehn8wxMFioaPpedstD855jHYWNldcm85cd7hjk/PIn5AVCKP2gc2n0WX8DJoT0d5LJ5dr9wnObVlTcu0BAQUEQHDd775ixPxjMMdsWYnnRzx4655mIl7jZ682foPD9ESfkKQjWFl/SFIOVyhOyNhOxzm/9kwU61+4M3LbsSTNNiOpnhXN84Z3pG6R6aYmQyzthUgonpFNm8vqzxLvhLXIqpRejKqiLjc9vxux34vQ7CPhclIS8VxX6qiv1UFPtvisDD0M2L1QbdLDwwZp4ZwYgX4RI+jCgV6DIerwOPz0lVQwR/aGaiboFpWHOMsgTAMq05y0VRYMWGGtoP9fDuG2eobowUtrNIkFoV9vPLd2+mvX+EWCpbUNTyOKmLhHCoClubKlmZKUYSBdbWlFEW8KDKMquqSij2uQl7XfhdDj60vpXTxWOMTCeQRZHqogAt5ZE5fRsAj2xsoyzoZSqZQZUlAjMPVWGGZlXkdRNyO1lVVQIIqLI06xAecDm4e1UDZUHv7MOzPOijtTwy+3C+c0U9ed0g4L748lhbW4rXaZv12vjIphWUBrxMJzM4bSpNpSFSOW22pCwJIl6njRK/h4jPxa7WWhAEnKqCYwG+7M2MyeFpTuzrYOsDa7E7rz4xTU6nOLHvHNHxBK0ba6ltW9h993ojp+kc7xji688eeF+2t1SsbS6ntjy0pECjzl3C3okzTOYSHJg8x/2l68maGt/r20M0n8ImytwWWYFXmV+Vzhka0/kkiihjX8SwzzQtjrT343Hbqa8OX/dAQ0DAblPwuG3sP9rLxFSSlc1l1xxo5HIaoiRet36IgZEor719luKw94qBRu/AJD968SgnzgwyOpEgk9UKNE6XjbJiHxtWVXH/bW23aFY/p3B5HazedvOILVyKYr+bgMuBQMFXajyWQtOMOcI1p/pH0WZ6TT68dQX7O/rRDZNT/aNzxrIsi1ROY3CyMInyuRyU/5x6xywX2XSe6ESCkf4pHv38Dr72ly9imsurNC+7opHN5NnzUjvxaApRFGlcWU51QzG6btB7fpRnvrUPSZJoXVtFbXMJgbCH2uZSUom5dIz+rjFOH+snEcsQLvayfnsDngWM5rpPDbD/peM4PQ423bOSkgVUlQB6Tg/Se2aI2z+6abmH9J5gmCZj0SQH2vs4cnaA7qEpBseniSezS6aYvFfkNZ3xaJLx6EWlBLsqE/K5KAq4Wd9SwcM7V1BZ7H+f9mhh6LrB/jfPcupwL/m8Tll1CGXGYfLSub8oClQ1RFi1qZaxoWkEUcAbcM5eHy6PHS2v86Ov7qFpZQWrNtWCAIGwm2PvdvHUN/aydms9NU0llFWFOHWkl+H+KVZsqMHlWXyiIAgCVWH/rFrU5VhZeTHT0VQapqm0UDmoLw7N+l8ARHxuIj73vM9fjpDHyX1rmuYtV2WJzQ2VbG64cubc73KwrWnx7P36uvlZ+qbSIppKL95DQbeTB9fOVzC7AI/Dxu1tPxvSl7HJJEfeOMOGO1csKdCQZAlJkhjqGsMf9rxvgcbPAjYE6/jJ4H5iWorv9u3hTGKQjJ5j38RZBCBi93Nvydp5n9NMg6l8kul8Gpdsx6ss3GMjiQK3b21EVeTrHmRA4RlUVxWmpMhLMp1neDT2nsZ7afdpmuuKaap7/1yYJ6Mpvvy9vew/0sOa1nI2r6nB7bJhmhbRWJqO7jGefvk4qXSOzz+2Ff8C799b+OmDZVnk9V6i6e8TcH4Um9LwQe/SVWGYSRLZ1zCMKF7HvShyGS67SsTvxq4qZPIag1Mx0nltbqDRN4pmGNhVmfX15YS9TsbjKToGJ9ANA1kqzC90wyxI3KeyCBQSdSUzIi+3cGVEyv3sfv4E9W2Fipdpmsue2y470JAkkeKyAP6gi4mROG8+d4LP/1YxWt7A0E3Kq8N0d4xwZN95IuV+XO75E7t0MsuhPeewO1Wq6yMcevscgbCb1jVV88oxu588iCfoprKhBPsVsknjg1Oceuf8+xZoWJbFdCLD7iOdvH28m47ecUYm4zdNw1A2rzM4HmNwPEYk6Fl2BHoj4PY6KK0KEgx7WLGxhtLKIKIkct9jGyirCs32LwiCgC/g4p5H1zM6FEUAHK6CtJ8gCJTXhHngE5vIZbTZKocgFKoX3oALwzBnzYZkRcI0TIrL/QRC7itWdSzLor1jiKdeOXHF4yiL+Lh3RwuVZdcvC2hZFrFEhqdfOcHoZILyYh/372ojFFi8mfnnCfFktmBSeXy+v8qlWN9WyfYNdfgXMZvKpLK89K29aHkdb9DFA5/biWmYHN/TwfkT/ciKRP2qStbsbMbhtlPdWsZQz/js55OxNB1HehjoHEPLaTjddtbsaqHsJlFquVlQ4ypmV2QFE/376EmN0Z+ewJjpt7CJCo9VbqPMMV+MIqln6EqOoFsGXsVBid2/4PiiKLKiaXHa3nXDdWDompbFc6+3E/S73tdA4/W9Z3nncDePf2gDt29ppCjkwW6TMS2LdDpP39AUL+8+zd5DXbQ1lnHPzpb3bd9u4cZCN8aIpZ/EpW7+qQg0LCtLOneAvN6P07YBhTIEQaAs5MXrtBUCjck4mZw2W6HXdIOOwXE0w6SxOIzXaaOxvIixWIqpRJqRaIKKmaRhVtMZnIxhWhZOm0JxwI3D9tNVkf+gsPWuNga6x6lpLsHhsvHwp7biWEKi7lIsrxl8puG2v3scQzeITiQZ6is0yMiKRElFkHXbGxBEgfZDPcSmUgsGGpPjCc6dHMTmUIhOJBkfnmZyNI6uG7OBRmwiwds/Ocz+F4/TuqUBURBo3VxPKp7hjR++y/SM+Uzr5npWX+IpkUllOb2/k2w6z7o72pgej3N8z1kmh6L4I1423LWC4qr3Jn+b1wzO9o7y9JvtHDzTz/BEvEAJugkhCAL15SHC/qtn2G80FFWmvqWM+ta5jbQr1tfMW1eSRCrriqismz+Bc3sdrNo4n9sdingJRS6WQ4d6Jzm87zyDPRPsuKcNb+DKGTvLgsHRGM+/cfKK67XUF7NxZdV1DjTgtX0dfPfZQyRSOYqCbmRJ5PGHN1y3bfw0I5fTOHV++KrfjSpLrFtRsWigER2PU1pbhMNl4+kvvc7Gu1diGAZ7nzvKjkfWkYymefelE5TXRwgvQCfJZzU6jvaSTeVYd1srJ/Z1cPpgF8FiL/ZbXPdZ2CSFRyu2YFgmLw0fYSpfkAsusvn4cPkm7itZt6D501QuybFoNwBhm5c693y+9Bv7Othz8Dy6brJ1XS3bN9bjvew9c+LsIO8e7mF0Mo4siRSHvTx45woiIQ+CIJDL6+w73MWR9n4y2TxlxX62b6hbViCQTOU4crKfkx1DTMUKgg4rmkrZuakBt6twLXzn6QN0909yvmecbz11gJd3n8amytyxtYltMyZ5qXSOw+39HDrRRzanUVEaYMvaGhpqihAEAcuCnv4J9hzsZHBkmlDAharKV21M3X3gPJGQm0fvXU046J5DGXXaVQI+JwIC7WeHOHZ64Fag8TMEm1JPqf9PsKs/3d9peciHz+VgdDrJwESMdP5iQ/jQVJzJRMEAtbEsjCiItFZGePtUD3ldp2NwYjbQyOQ0+scLVUmPw0Z5yHfTC+jcLCitClFadZGxsXrr8pVdlxVoGIZJ5+khus4Mc+9H1tN9bpRTR3rBKvDvc9lC05+eNzANc9FmEZtNweZQqGksoa6llJUbqomUBeasb3PaaFxXg+epgzSuqaa6tQxJLuixN6yuIp/TGe4e5+1nDrNqexOCIJDN5Di+5yzD3eOs2dWCrum07ztHNpVj1c5mju85y9lD3XiDbhwLBEBLQSansfdYN9968TDn+sfI5pbXe/F+I+x3UVHsx/EBS9aFI17uemQt4eL3T6HF43fQvKqC5lUVMzStK1/uggBBn5OWumJSmTypdI50Jr/s/pprgWVZHD8zOOvkOxlN0d4xdN0DjbHJBNF4BpdDobTId0P6dqKxNJPTKVRFIhLyYL8OmSNFkSgt8tFQU0Q6nSeVKXw3F/i5S4U36KZtcz2+kJvnv76b6Fic6Yk4pw924Qt7yGXyxCYSxCeTCwYahX2RCdZ6Wb2ziYnhKPGpJKl45lagcRnKHEEer9rBpmAD47kYIFBs99PgKcWvzq/UWZaFLIo0esqodBXR6q2YJ30LUFMZAgGefPEoHd2jbFxdBVx8ng+NTvOdpw5SWRagrbGUfF5nbDIxa0Km6QZ79p/nqZePs6atnPISHwPD03z76YN86pENNNfPD24WgmmZTEwlsdsVmsPFTMfSPP3KcRx2la3ra7GpMi0NJYQCbl7efYbWhhKa64qRJZGyksJzMJvTOHC8lxfeODXT1K7QPxTlxy8e5dH71tBcV8zIeIznXm9naDRGS0Nh346fHmR88sqmmqPjcSrLAvi8jgUnVZIkEvQ78XkcTEVTSzrmW7j5IQgCshTC67j3g96V94zyoBffzHN1cHKaTO6ibPyZgTFyWuHd3FRehCQKtFYUVLM03eTMwDh3rSlUc9I5jb7xaaCgOFURvqUUt1TsffkkLWsqScYzvPj9A9S0lHLbg6uxLWNOubxmcEHA4VQZG4pycE8HuaxGoKjAc5MVkcmxOP/696+RjGdoXFmOx+fg5KEe3nz+OEN9U4wORtE0g9qmEtZubaDrzDCTY3Esy+KBj22c40Zpd6o0rqnGG3RTu6KCxrXVBWfgWJrT+zvRdZOpkWnG+iaxzEIDcOfxfgRBYOPdK6lqKWNiKErniX7GByaZGosx2juB2+ckl9GuKdBIZ/M8//YpvvPyEfpGovw0KJ3VlAYpDXuv2Tr+esHhslG9gAzfjYTH51yw7+dKaKkv4d//8j3ohoGmmWi6wcRUkqdePc7JjuEbtKeAAM5L1LBEUcB9AxRufvjCUY6eGmBVcym/+IltuG6A6dDr73Tw2r4OKkv9fPrDm6i8Do2mbqeNO7c2saq5DE030PXCd3OiY4iXdp9maIkcepe3IIogCAKKKmMaJm6vE7fPyca7ViAIhd6MSOXippQFZ/p8ocKbySMA8lWC2J9XROx+imw+NFMHQUARpCtmEovtfh6v3omAgFNSF2wGr6kIUVHi553D3QuOEY2l6Rmc4vZtjWxbX6gaxBNZAjNVrlxO48mXjtLaUMpH7luDzabQ3TfB935yiFffPrvkQMPpUNmxsR5REnDaVTI5jY7uMTp6Rlm3sgKbKrOqpZy8piP/o8Ca1gq2z1QxLghfRGNp9uw/T2NNEY/csxpVkTjZMcSTLx3jcHsfzXXFdHSP0Ts4xY6N9dyxrQldN0kks4xOXNnM0GZTSGe0K+pRG6ZJNqcTCd26fm826GaUaOp7yGIIw4ySyZ/E67gHRSonlnkWw4gScH8Sh7oWUSi8OyaT3ySReRUwEAQ7Yc+v4LJtnDe2Zenk9E7i6RfI6ecwrTyyGMCutuGx344q1wBgWllS2XdI5naT1wcQBBFZLMJl24TbfgeS6JkzZiz9LMncWxhmFEkM47bvxOt4YHb/CmPmyeZPE0s/Td4YQJbCuNQNWJZ2+W5SEvTim2kIjyYzTKey6IaJLImc6R8jrxV81prKw4iiQFN5oQqoGQZnB8Zmx8nkNQYukbatWKQP8xbm4/TRXipqw7z1fMFu4Mje82y9s/XGBRqFJt1iPvfv7kWSC7KroiTgdNnYuLOZxhUVWKaFIAoEizyoNoXymjD3f2wjWt5AUSVCES92p8r67Q1UN0TQ8gZg4Q97rqpYq2sG5472MNw7wQOf20XH4W76z40UnqMWlNcX07Kxjv5zI9T2jOPyOnB7HXhXV7Hp3lUFP4WQe5a/vxzk8jqvHujgqz85wOjUlTNJNxPqykOULEFdYXgqzu72bs4MjDEZT5PXDRyqgt9tp7E8zN1rGoncQPrVf/3ua/SORef4RQAEPU5+96O33dBtX4AgCHjddrwNFycalmUxOpFg75GuG7ptURC4f1crJ84OMTQao7WhhIfuWHFdt5FI5TjU3seZzhH8PscNkepLZ/Oc7Bjm2OkBTNMim5v/8rgWyLJEJOwhEr70xWZhmhb7Di39u7nc00cUBSoaSlm7q5ndTx9CEEWCxV6qW8oYOD/KC9/cQ/epQTx+J6ZuUtNWjiDAqf1djA1EyaZybH1g9U0lC3mzoSAjfPWXkiAI2CV1wd6N+SsvrnBeVR5k0+oqfvzCMY6eHGDL2lo2r62erd7Fkll6B6f47Ee3EgoUniuVZQGqyoOc7Rollc4tSfLVsmBgZJqDx3sZGpkmp+mc6xqjKODGmLm3JLHwnkQQEAVhXgUxlc5x8HgvLqeN42cGAchk8wyPxSkKejBNi/HJxGxzus9TuM7qqsKcOnflxEdrQzFvvXueY6cH2bi6al6Al87kOXZqgFgiTV31zak89PMMy8qTzbeT1wdwqmvQjCEmEv+CLAVQpDKy+hkmE1+hNPCniFIhiedU1yMKNtK5w0ynf4Tf9dEFx87p5xmL/zW6EcWhtgESujFKNn8Ku9I2G2jEMy8ylfw2shRAlasBjbzeT0Y7icu2FbjwPLYYT/w9sdQzOGyrUeV6NH2Isfjfktf7iXh/HWaMmbP5U4zG/ju6OY1TXQuYxDI/IaudwybPpeU4VJkSvweHqpDOawxNxclpOrKkcmZgnLxuYFdk6oqDiIJAwOOgLOhlcDJG3/g0qWwOu6oQS2cZj6cQhIK07a1G8KVD1wzGh2Okk1lWbqrjwJtn583TroZlVzTsDpXGFfPVbIJFHoJF8788f8h9UZb0ErhngoDlQBQFPD4Xg52jvPb9d9FyGsGZEjQC+MJutj+0jn3PH2Xf80e57dGNrNrRxOHXT/HKt/diWXD7RzdStExHbN0wOHi6jy89+c5PVZDhtCtUlwbwe658no90DfJPz75D58gkiUyOvF4wvZNEEUUWqekPcufqG+u43jk8yZHOwXm845KA532hLi0GQSi4gYvXoyv0KmhtKOE//9ZDZHMabqeN8uusEtbZO04snrmhlbjBkWnGJhPvS89S4bsRlsy1Last4vHfvh/njPLYE7/3MMESH3anyv2f3UlsMokggGpTkBWJUImP+57YTn5GmtQbdGOZJnanjeb1tay9rQVZkQgW+5Cuk2zpLbx3uJ02Pv3oZjp7xzl2eoDvP3uIl3ef4nd+6R4CPieZbB7DsLDbL77+RFFEVWQMw1zy9bR7/3le2XOaxppiHrxzBS6nytd+8M6Sq8eWZc1ImAvs2txAa8PcSkppsQ9BAH1GCvxSaVybTUa+Cu3xkXtWs+9QN3/1lVe5a3sLbY0l+L1ODMNkbDLBkZN9vHukh9KIj9s23wo0bk5YSKIXj+N+7Gob44l/wi42EnB9ClkqIpr6AaaZgplLw640Y5NrEAUn0+kfLTyiZZLXB8jmzxJ0fwaf80OAgGVlsCwTWQrPrKeTzh1CQCDgfBy7uhIwMc0kgqAiiRfpR+ncYaaS3yLg/jQB52MIgh3DmGQi8SVi6Sfx2G/Hoa7EMKMksq+im+OEPL+Mx3YbFgbJ7BvktPPz9lUQBMrDPjxOWyHQmIyR1XRyms5INIFhmrRUFOGyq4CALEm0VkYYnCwY/PWMTVNXHGR4KoZumDhtCqVBDzbl1vN6qaioLeKlHx5g291tuL0ODMNc9hziutRLLcvCxJox6ru+E7Jf+E+PEZhp8BUlkdqVFfzqn38SURYRxYKhnSgKtG2up7qlDE/QxfaH15HL5PGG3HhDbkprCqZvAuC/zK9hKcc2Hk3xDz94m+GrlKpvNpRH/JQV+a74Qkpn83z5hQMcPD+Abpi0VERYX1+O26GSyWtMxtPYVJmQ58aqH/2nJ+4mkckRT+eYSmT4+qsHODc0+Z7H7R6d4mjnEE3lYZrKi66bjv31hiAIqIpMwyLyzdcDJ88Nk8rMd1e9nujun2RiKnn1FT8A2F02yusv0vcqLqHyBYt9BC/rH3K47VQ1zxUumBqNzQYdNa3vg+rRLSwbgiAQCXsI+p001xdzx9Ym/u+/fZ63D3byyD2r8HucOOwKA8PTrGktSBbnchqT0SR+r3PJPUXHTw+gKjLbN9ZRXxWmMFljnvKgIBTqaMYCyn+qIhMOuvC47WzfODeZI84E0S6nim6YJFIXJeJjicwcvvpCaKot5t/94p38/dff4AfPHcbtVJElCQuLvGaQzWnUVYb5xce3U17iX9Ix38L7D0UuQ5WrEAUbomDHpjShylWock0hOECjwI8TEAQJsCMKV0ouCoiCHYs8Ga0dt7kLm9KAwOXzBAlRdJI3hsjqZ7GrK5GlCII0nwIdz7yMYSYIuD6BIpYiCAKS6MPjuIN49mXS+cM41JXo5hTp/BFkqQyv435k0Q+A07YRW/bNBelTFWEvHoeN0ekkg5NxcnmNsViKTL6wbltV8QwdFiRBYEVVhFeOniOv65wfmqA86GVg4mIjeGXYf6sRfBnY9eAqVm+uI1jkweFU+cXffQCXd3m07usSaJyM9fPlzld5rHIruyKti6738vAxDk118aHyDaz0L83xt7Tm4sRLEARsDpXKptJ567m8TlzeAh/fE3DhuUQW9L00aWq6wVefeZfzAxPXPMYHhZrSIOVXMdc60TvC+eEJdMNkVU0Jf/zpewl7XUiigGnOZNwEbvgEvaY4iGVZGKZFOpfn2QOnrkug8e6ZPr67+xhP3L6WmuLgTRto3GhYFpw6N0w6k7uh2+gZmGQienMGGtcD3pCbOx7bhHgDzS9VRaa5ppj7tjaTSOVIpLPEk1niqRzJTG6ekefNCMMyEVl6tWm5MM0Cbc6ymJddO3V+mMloktIiHx6XnWQqRyKVxW4rvO5cThu3b2nkqZeOEgm5KQp5OHCsh3M94zz+8PpZmW0oJJosy8Kc+ftSqIpMLJklkcwyOZ3i9b0d9A1NEQnNrezLkkg46Gbf4S6aaiMIooDDpuL12An6nWxeW8vr+zooL/bRUBMhlc4zOZ2iOOyhvrqI2qowbx/s5M13z+F22cjmdN490kM0lrniOVIUiV2b6qmuCLJn/3lOnx9hOp5BkkRKirysbatg45pqIiHPB27meguLQ8SOIKgIgoKAhCi4EJARZqZvhTTv0pWYBUHArrQQdH2WaOq79OV/DYe6Fr/zw7jUzYiic3Y9v/Oj6MY4U8lvEks/g9u+C7/zo9jkOgThYkCe1TqwrAw9Y58BYeZassC0UliWhm4U5k+mlUE3J7HLzUjCxbmJJPqRxRCaMTJvfyvCfjzOwsR2aCpOVtPpHp2abQRfWVWMPNPfK4oCbVWFymBONzg/PMnW5qpZoz6Pw0ZVkX+JZ+oWALx+Fx6fs/C8BYorg3OekUvBdQk00kaevtQEST17xfUyRp7TsQE2h25+XWeYUQI6P8QL+868r/K1Alfs31sSREGgpjRI8VX6M7pHpsjOZAbuWtNIVZEf2wfU2FpQyyg4YF8qDHCtuKCz3T8+TU4z3jcDxZsRgyNRBkdjy1ZpWg4mphL0D0dJZ69PX8bNCFmW8AZvbL+QTZXZ1FbJyvoSDNPCNE1M08KwLHTdIJPTiKeyM39yxJNZYqkMiQvLklliF36XypL7AKiH/+P0j/ApTu4uXk29uxRZvD4BfvuZQX74/BHOdo0yEU0hCgJ7D3ZRXOThNz9/O831JWiawUtvnqZ3cApNN/G6bTx4x8pZOVm7TeaTH96I/LzEP3zzLbJZjdKIjw/dfXGdzt5xvvP0Qdo7hpiOZzB0g5MdQ5RGfDzx6Ca2rqvl3l2tTMcz/PVXXkNVZNavrGTz2hqclxkICoLAr35mF1//4Tv87p/9kKKQe3YMr9vOI/esQlUkvvPMIWKJDE67yormMh6+ayUAjTUR7r+9jWdeOc6f/c3zlBf7aaqLoM00wl4JNptCfXURZREfubyOYZggCMiSiMOuYFPlW9ndmx3C5e9C4cL/rxmSGCTo/gxu+3YS2deIZ15jcOoP8TkeJOT5N6hywSzWJtdT4vsjMtoJ4pmXiKWfI5Z+lojnN/E6P4QkFp6FFnlE0UPA9QSCMHf+IAg2HErbzE8WWAbCPFEICYSFnxGlgYvKUyNTCbJ5nd7RKHnNQOBiRQMK8566khAOVSGvGZwfmiCnG4xMXQw0Km8FGsvCnhdO8OpThxkfmsYwDEoqQ/zuf/vEsoR23tcZpSxIZA2NnHlzS8JegGlafPO5g1ctUS8XQa+T5uoI9RVhqkr8FPnd+D1OXA4VRZGQRAHdMMnnDfK6Tk4ziCczTMXTTMXSTMXTjEwm6B+NMjwRX3D/IkE3FcX+q3IRE5ncbKm/PORFWmakejNjcDLGcDRx02WARycS/MlfP3tF1RiX08bjD63nkbtXLXv8qek0PYOTdPdP0DMwSXf/JENjMSYvkbA8cKyXz/3e166YmaitCPFff//ROU6sF5BI5egdLIx9YTsDo7E5tKlT54b53T//EYq8eNAoiSJf/4svfODyy5fDsiCRynLkZD+H2vvp7p9gOp5BNww8bjuVpQHWtlawaVU1xUXeqwpZLAeiIGC3KQtSeAoZ9oIJnGmaM39bs39faJC/dFk2r80GHcfPDfL15w4WJpw3EL3JMc4lh3lu6BCNnjLujKzk9shKvIrzPU1sG+sKdCDtksSBQEGq1TvTe9PaUMLv/vI9aLqBZRV6zRx2BadDnd12OOjmc49t4eMPrcMyLWRZwulQZ895VVmQ3/j87eQ1fbZicmE7FzwyaqvC/Nsv3E42ryNA4bMzh+ZyzA02tq6vZUVTKbphIokXxxDFgsfH4x/awIfuXoVhFvoxVFWeVaFTFYntG+pZ21aBppnIsohNldF0Y0mu6KIg4HLaltTgvhTEtSzRXJqEliOl50lpOZJ6jpSWJ6nnSGo5klqelHHh34U/vampRcfcN9bNx1/7Mi7ZhltWcSk2XLKKW575W7HhUlRcsopLthXWU1QCqhOf6pjNaP/c4D1mzQRBRBI82JUVqHI9fsdHiKa+TyzzLDalkaD7MzPrychSCLe4A6e6Ds31WUZi/42J5Jdx2jYiiYWksSKVkjb343c+iiTOT25eCD4EVETBhWkmsSx9drllpTHNhSvhNkWiNOjFaVOIptJk8hpdI5PkdJ3ysI+Ax3lJ0CVgV2UaysKc6BlmLJYsmPdNJ241gl8j2g928+jnd3DgjTNsvquVvS+1zxNVuRre10AjpqXRLQPlOmW3bjROnB/mwKm+6zKW12Vny8pq7t/WQnN1BJddRZJEJFFAFIVZPu7lsCywuGQCcclEwjBNcnmdsWiSzoFJTnePcLJrhN6RKapKAlQUX9mUxjStQqZ/5k1a6OX42Qk0zg9PMjZ989F4DNNkMppkdGJxYQGPK0/6GvopTNPkm0++y7Ovn8QwTHTTxDDMeRW5XF5n/Cq9FF63Y8EXmmlavL7vLH//zbcwDPPin8u2oekGU9NX1ueXJHHZChY3Gpmsxr4jXXznmUP0DEyS141CA9xM6VgUBM52jfLmu+coKfLy4btXc+/OFgLLlFK+FswRJ1gi3eUi9adALfjm84e4ei78vcHCImvkyRp5Dk2d58R0D1/pepXtRS08WFqgzi73ZQWFas9Cge+lUBUZ1XfldURBwO204V5k8q0o0lW/T1kS8XocXF3Tr7BPFxSu5u2LKOB0qHPkrS+FIAhLOu7LMTAcxeOy4/XYr2vV4pvnD/D97iMktNzMu+niO8oCzEv+Xfh7Jji+wuw4oeVonxpGEArKcBf6Wi79t3hhmSAUlgM7S+r5vZV3UeHyX7fj+9nHxRBdECQkwYUoOHDY1pPIvY5uTi2wnoIk+BAVD051PZOpb2JaF2m4HvudxNJPEcv8hJD7i5ddbxe/d0n0YVeayWjtpPNHZ6V3c1oPOa0DeYH+D0EQqCry47arpHMa/RPT9E/EZntKVflidUQQQBZFWisinOgZJpvX6ByeZDyWwmlTqQj5ryqicAtzIQgCgbAb1SZTVh0im9GuahZ6Oa4p0DAti+l8ilPxAQA64kNkjTwdiSE84/ObkAzToCc1xsvDx7FJCl7lxr+Q3yssy+Ine9rfM9VEkUVuW1fPpx/YSENleIYWtHTucmG1mXUXiM8syyLsd9NSHeGBbS2YpklsRms65JvbwH2id5iXD3dwfmiSsViKyXiKVDZPXi9MO/7oq8/P4+r6nHb+9LP3sampcsFtA4xGE7x2vJO3T/XQMxolp+l4nTaaKyI8sKGZjY0VOG3KDS/R94xOse90Lyf7Rjk3NMHgRIzUTLXnb5/ewz8+u2/BOOp7//GzlAW97xuFwGlT2LiqmlBgkngiw3QiQyKZnTdRv1ZkczrJ9HvvxbjS6dA0g2TqOmzjPY9w/WBZFslUjh++eIRv/Hg/ubw+j/8PM9UE3ULXTXoGJvmnb+9hYCTKE49spKRo8evo7zqeY0u4iXX+2utGJboavnT+Zdr8lWwMNqBKV1cquiKWcXk+UX07zw0d4NBUF1kzT8Yo/PnJ4AFeGj5CtauI+0vXc2fxKsLqxan6TxuN5/Ig+b3sv4V1xXO83LH/+dtvc/B4D//83z9LaeT6OSFndY1YPktSv779Xubs8V/5PFyKpJZb9qTnZxWWpaOb4xhmnJzeiYVJTusmK59FFDzIUhBRsGOYSeKZF4hnXsBp24QiFmGYceLZl7EsbVZiNq8PMZn8CoaZwKGuQBK85I1+oqkfYFea51QuvI578ToeYDT2P8npnTjVdVjo5PLnyBt9lAf+O7IUQpEieBz3kMi+xsj0/43P+QiWlSeV24dmDC0YaABUhH24HTbGYikOnR8kni7Q9FdUReb1XUqSSGtVwbgvldU42jWEYVq47SpVkVuN4MtFuMSLJEsoNoWv/+VLjA1FsZY5V7nGQMOkJzXGn534PjlDQ7dMdMvge717+UHfPuZ3GRR+tokKn6nZRbPn5ldrSWZyvHGo8z2NEfA4eOKB9Xz0jtV4Z8zXrvdFPpvhFKXZL3Mx1ZT+0Wn2nOxhYCI28zy35kxuNcOYp4yS140FH+SWZZHTdN5q7+Ifn3uH3rHo7P5AwVynZyzKK0fOcffaBn7p/s3UlYZuqHHgse5hvvbqIaLJ9Iz6izk7EdAv+ff8g7lhu7QgfF4Hv/dLd89m/ZhRqvnxi0f5u2+8+Z7GFgSB3/jcbXzx8W1zlp/rHuOv/uV1BkamAdiypobf/sU7rkinkCURVZ0/IRYEePiuldy+da4k5sDwNF/5/j4OtReqgCsaS/mVJ3ZQU7G4+Z0ANwVtyrIsUuk8P3jhCF/5/j5M00IAPC4bKxpL2bS6hrJiH4IoMDoe5+CJXo6dHiSRypLNaTz50jEcNpXHH15P0H+RHmRYJqZVuKeyRn72/tJNY0apD0RBRBIKlR3DMucsFxAwLRPr0uwiFNaH2d8VFP9EREGYu01Tw7CucO0vB8u4dW+LtLGzqJXxXIzXRk/w0sgR+lLj6KZBxshzJj5IR3yYL3e+zOZQE/eVrmNDoB5VlJEE8admMvAPnV/h3amDfKziEe6K3I5bvjZ1vguZ/4n8FEejx+lK9ZA3NXyKhwZ3Pa3eJvyKbzbDvxR09o5THPYS8t9YxcBbuHEoNIDLzNZ3BLWgLCUASIWfZ27MdP4ofZO/NkNBMsEyGY//FePxv0GWiij2/Xv8zkcRBBuKVIZp5ZhMfAXTzCCJXhy2tRR5fn3GH6NQfVClcqZzT5LIvIyFgSKG8TruI+T+PIpUfsl+ylQE/xdTyW8RTf2AWPonCIINm1yFx34fonjhGpRw23ZQFvhzxuP/xHj871DkcoKuJ3Co68hp52Ce+hVUFQVwz5jLvnumbzbQaKsqQZHmvqNkSaStshCwJLM53j1beB+57CrVkcXNYy9QUS88Ki/MkSyYwwiwLAtNN8hr+mw27sIdWVDbmn+PXqC8GpY5O9+4MH5enzvvMi0LzVje+Be2ARfmPZdsg8I2LqWQW1jopjmzjcIWLm6jQOm8MFf7yC/sQhQFPvbF2zh7vI+SyiCeZfpGXVOgIQkiawO1fG/n73F8upfXRk7w1vhpmj1lVDjnTyokUSSoutkQrKPNV4FN/OAnFlfDm4c7SWevXQrU47TxqfvX8/g963C8D9n8S7HYtra1VVNbGpx10wR48p2TvHjoLOmcxr99ZAdraksLBlMzkCRxwZszrxu8caKL//GD15lOZgl5nWxoqGBNXSlOVWV4Ks7bp3voGBjnhUNnMUyTX394G7XFwRt2LnauqKWhNDR7Q715ooun3z3FRDzFp3at4bZVdTjU+ddeke/GmwFeCkEQkKS550Ayresy4RYEAZdDnccRH/U45lSrFFUi4HPhcV2Zt73Qd7UYlSOZyqFe0hMkyxI+j+OnYqJjGCbHzw7y9R/tLwQZgkB9VZhf/tQOtq+vm1fd+ch9q9l7qIuv/vBdOrpHC4Hiy0eprQpx97ZmFEXCtCyeGTjAvomzOCSV8Vyc7UUtmJbJv/bu5sR0L7IgsTnUwEcrtxLNJ/lO7x66kqN4ZAe3RdqocoV5bugwE7k4pmVil1R8iovHKrcylU/y8vBRhjJRiu0+PlKxhXpPCS8OHWH3+GkUUSKaT7LCtzSFv6tiGbFKIeiBEnuAT1ffxhPVuzgZ6+fF4cO8OXaSpJ5BNw2SepbXRo/zxmg7JY4A95as4fbISqpcRSiCdNMHHYZloFs6E7koeTOPZTkxMWeTCAIC0kwz72LHYWGhWRpHp0/w7b4fMpYbn/P7l0Zfp8pZwSOlD7IhsBZVXNr7RNMNqsoDCMuooN/CzQNZjFAe+N+zP0tiiNqi78/+7HHci8dx7+zPTnUDLaUHrjqugILLth2XbfsV1xMFF0H3LxJ0/+IS9rZArwq6v0DQ/YXF1xIEwI7Hfg8e+z1LGLeAsqAXr8OOAIzFCpRfr7MgVXt5X6kAhLxOSgNehqPx2fXddtsVA419Z/p47dh54uksqVyeZCZPMpsjlc0TTWZm5xVDU3G++Fffx+u04bSpuB0qbruKy64S9rr5t49snxf86IbJyb5RnnrnJMlsnlQ2RzKTJ5XNk8jmiF0iXd0xOM4f/stzeJ02XHYbLruCy27DbVcpDrj53J0bCHrms4IsYGgyzr+8fODiNrJ5UpnCNi4EZ8ys9xc/epMvvfjuvG0U+Vx8eMsKGssKfirpRBaHy4Zql1m1uW7J39mluKZA4wJn0qc62RVpxas46EtP8EjFRh4sW7essfJ5nVxOR5IE7HblqmpDlmVhGCbyDZQptSzY3957zSVZURS4fX09D25vnadA8kEi4HYScM+9QN850zcbWFRHAqyuLbsqvcKyLM4NTvCvrx8mmszQVlnMrz60lZ0raudULB6/bQ3/+Ow+ntl/ileOnmNVbSkRn3s2M3G9EfI4CV1yA3YOT6LOXCelIR8ra0px30Tfx43CYsHBvGWLLL/WbVyPdT8IWJZFPJnlGz/eX8jwAOXFPv7N49vYuXFho0pZkti1qYFYIsu//OAdRifipNJ5du8/T0t9MTXlIUazUY5Gu/n1xgeocoX5ncP/gmlZnIz1cy4xxP9a9wXSRo7/cepJGj1lTOWT6JbBX6z/Bc4lhnlu8BAAQdVNs6eMiVwCr+LEwuJMfICB9CQt3gp+ueFeftC3j9PxAURB4Nh0D5+vvYNmbxn/1/FvYXKdGr+v4Wuc5U4jsMpfzUpfFb/W+CBvjrbz6uhxzsT7yRoaeVNnKDPJ17pf43t9e1jpq+bekrWsD9bjU5w3faUjqkVJ6ElMy6QvPcBYbgLd0vDIbupcNQTVIA7JjjhPQahw/XWlevlO349mgwwREVEQMS0Tk8KY3x94ErDYHNyALFz91V1TESwoZhkWlmxdt3PnlFVCdic27YNRJ7wUXtV+1Sq5AKiiTMg2P+ERVJ3YpaUfh0tWCdlcqOLczwRtTqQlnl9REHErtgX3x6vYrxqYLoolrr502vZyrxdhSaIY13IdKrJEZdiHw6aQnqFDN1dEsCmXq1cVxlckiZbKIoajBcEVURAIuB1ErpBUvBAI6Av43VwKy4LpVIbp1Hx5aZ/Lzq89uHVeoGGYJl3Dk/x4X/tVj9W0LGLpLLH0fBXXsNfFR7euXDjQsCzGY0l+uPfEkraRzOZJLpBM97nsbGiomA00vvE3L/PQp7ZQ01Qyb92l4ro8KVyynVp3BHkRebIr4djRPp577hjFxV4e/+QWgleRjsxmNc6cHmbd+upr3d2rQjcMjp8fumZJ29rSILvW1VNymbSsbphkNA2bLM9OgK8VhmkWeisEcCjvb4UondM43jPMiZ4RQl4n929oZteK+VnfoMfJQ5tbOTs4wbHuId460cXW5iqaym+cKd0t3MJyYZoW53vHOXFmECg0HK9sKmPnxivLcAuCwIZVVbx7rGdWQezgiT4eunMl1WVBovkUftWNTVIQECix+5EEkZHsNFXOcCHbjUSpI8hAeoK8qVNmDwLgEFU8ipPJXAJFlHArDrKmjlu2k9QzTOdTjGSi7J88x7uTHVhAxO4nmk/hVZw4pAKlotjuQ7mG5/KNgiAIuGU7D5dv5IGy9fSlxnl19Dh7J84wmomSMwtBx4GpcxycOkfE7mdHuJXbIyuoc5fgku0oonxDKZjXguHMKD8aeJrzyW6mtdi8328MrOPjFY9S7iidF2wk9RRHoscZzY0hIOCWXVQ7qwjZAkTz0/SlB4hrCcZy47w9+S5ljhKqnVVXnbA9fNcq/uorr9HRNUprQwnqMpvJF8Ovte7k11p3Xpex3g9IosiWSA37Hvnd9zzWr7Xs5Nda3tuxF9nd/OHqe/nD1fdefeVbmEVTeZiaSICpZGGCv76+fFEpflWWWFNbyun+MQCcqkJLZeSKKotuu0pJwHPVQONK8Dptiyb6HDaFksB7U7wKepxX9LxRZek9b8PrtGO/5Lz6Q25Mw0TL67PHJsnLS/pclydPsd3Hh8o3EFCXT0HZtLkOTTPo7Z1riJdK5Uin82BZ2OwKHo+dfF6nv3+Kp546RGVVsEDN8Dmue5arc2CCWPLKniCLQRQFVjaUsrapfN7v+qMxnj56mrta6lhVce3RIUAym+dQ3yA2WWZHw40LuhbCZCLF4fMFIYDKsI/19WWLZjIaSkMU/3/s/XeUXfd93gt/dj+9n+m9ARj0SgDsnVRvlizbsizFNcl1cv3GKW+StZLc99rp1TWOLce2LEtRF1UokmIBSRBE72UwvbfT667vH2cwwGAGwMxgUCjzwZo1mH123/vs/Xu+5XnCPuiHSyPTJHNFHGftomvv433cLnTD4sCR3vnKoIDfxb4dbcsyJaqNB6ivDqHIEoZpkSuUGZ1IUSwZxLUAebPERDGJJIhMldLYjkObr5q3py8yUUpRsnSmSymerN5M0sjx1tQFJkspxktJimaZLn8to8XEomBlWPHS4qtmU6iJ3ZFOHBz8ipuipfP61FnGi0k8ssZUMY0Zur/kna9AEkRafdX8su9pfr75UU4k+3hj+gzHkn2MFmZxgMlSim+OHOT7Y4dZH2jk2drt7Ii0U6UFUcX7xwNivDTBeGmx2dgVHEkeZ6Qwxm92/irN3oWlbFkzx4XsJQD8so9nap7gw7XPIYsypm1yInWab49+n4HCEOczl+jNDdDgbrhlYG/7pkaeeXgD/+GPX+IXP7WX7s6aGyoLaqp8V1TTlgPbcSiYBjmjjG5VegRFQUCVJDyygkdWF8nZmrZNRi+RNw1M20YUwC0phDQXqnRFQtVBty0mCzlCmgvLdsgYJTRJJqS6cXBIlouYto1f1Qgo2oIy4iv7ljd0ckaZslUpQVYliYDqwiMriwiw4zgYtkVKL1EyTew5uWWvrOBXtQUKnKZtkSqXMB2boOrCsC2yehnDthHmjieouVDFxVF823HI6CVyho5hWxWJbEkmqLpwyfd/qfpy8PH9m/n4/uXJvbs1hV96aje/9NTuZa//5x7bzs89trKKnOVCU2Se37We53etvyPrhwqh3thcw4/+zS+v6Xqr68McPXCJ6Yk0rrmS7O4dzSgrCFysCdHwK262hVvXYlVARdHmpZfO0Ns7hWVaRKM+fu7n9zM6muT7L5zg4oUx/vrLB6mtDfGpn1n+jbRc9AxNr1pnPh7y0d1aQziw9EPbdmxm8wWGEylUWSbidaNIEqlCkVxZx3YcvKpKxOvGAcbTWVyyTMkwEQWoDvqx7UqzUFd1jICrUoZk2nalnrBszDeOhtwuvJpGrlwmX9YxLAvTtgm6XQTdrkUP0eUiV9Tpn6w0f4uCSL5scGl0+obzG3OqVplipS7Rdpxlp5nfx/u40zAti3M94/N/e1wqXa3Ly7oJgkAk5MHn1UimCwCMTqTIFcpURYPsjXXx4vhx3JJGkzeOT3HR4a9lb6yTP+19GUkQ2RdbR1egjoxRYCA3xZ9cfhm/7GJfbB117gh5q4RHdhGwTbxSpVQkovpo89fw+tRZ/rTvFQA+WFeRjd0T7eTA9DkOzV4i7goSVDz3XQYAKoMw07HIGAVmylnGSglSegHDtgABSRBQRRnLsSnbJidT/ZxJD9IdbOQTDfvYE+0koHjvq2NziS68sgdVVCuN+bZF0S6RMbJMlqf48tD/4be6/j4u6Wr5aNkqz5dMxbQoGwPrkedKc2RRZltoCzPlBLNjCTJmlqHCMGkjTVSL3HRfvvq9I0zNZkllivyb//p9PC6FaNi3qD8MYNvGRv7Rr977CLtl24zmM7w4dImfjPYynEtTtkw0SabeG+DJhnY+3LKBWu/VaoGyZXJmdpKvXT7FkakRUnoJVZToCsX4hXXb2VfTjE+pDJAup2f54itf5/PrdzJVzPHi0EUafSG+sGEXjuPwp+ePMFnI8mRDB1/YsIuWQPi6fUvzvYELvDx8mdF8JXBQ6w3w4ZYNfKhlPTUe//z96DgOOUPn7YlBvtpzioupaUqWSVB1sauqgU+1b2JztAb3HBGYLOT4vdMHGc6l+ZmOzfSlE7wy0stEIYMgCGwIV/FzXdt4uLYFr3K1BNi0bXrTs3y15yRvTgwyU8ojCyIdwRif7tjMEw3tBFTX3bh87+OnENl0ganxFFPjqflpHRvr7j7RuALLtilaZcq2eVOVE5/iwiXduFZ+YiLFO29f5ot/51E0l8wf/9FPGB6apbOrhk9/5gHGx1P85j94Zi13fQEGxhOrlhqtrwrS0Ri74efpYolXzvfy6oU+JFHgc3u3Ux308c1jZxlKpLAdh6jXyxce3Ikii/zuD15jZ1MdU9k8miLzG48+QEE3+OGZSxwfHuOxrjY+sm0D6UKJH5y+yMnhcUIeF5limSc2tPPkhnZevdDL2bEp8mWDixPTfHxHNx/eugG/a3W9ErppzTcvHesd5df+xzeWvWxBN7DmTKvex/u417jS8zUwMjs/zbYdZpN58sv0MUmkCgv+zhbK8+T6yZotPFmzZdEyH6zfxQfrdy2YFlA8fKZ5cVlGo/fGz5NW32I5yEequnmkqnuJue8POI5DwSozW84ykJ/i9akzHJq9RFKvNG2qokxMC9DgidLiqyat5+jLTZLUc+TMEqdTgwznZ/hE4z4+Uv8AMc1/X2Q2ArKfPZGd7InspN5diyoqZIwsF7KXeHHyJwwVRujNDXA2c56d4W3zy1mORcGslIO4JBchJbRgvbIo0elvo9nbyOn0OcZKE6SWQTROnR8lmS4SDnpuma24H84fwGy5wJcvHedrl0/TFYrxVEMHiigyWcwznE0xkEmRN68a1NqOwzsTQ/w/R35C0TTYHq+j0RcirZc4OjXCb735ff7pzsf4dMdm5LmStYxR5pWRy0RcbnbGG3hrYpD/cPwNuiPV1PuCaJLMDwYv0hwI83n/jvmeieFcmv904gDvTg7TGojwfPM6LNvhbGKS/3ziAMO5FP/31ocIaxVFHsO2+W7/OX732GvUeQM8VNtCWHMzms/wzsQgZxIT/NbWh3msvm1BhqY/k+B/njmEKIhsCMd5oLqR/kyCEzNj/NujrxHY+ywPVDfOv0Mvpab5Jwd/xGQhy+ZIDY/Vt5Eqlzg2Pcq/PPRjposP8vn1O1Gl+6eE8n28d/CRz+2fUzdd/TNiTYiG4zjkzTKXsmOcS48wUUphWOY1kowL8eH6XWwO37jcJ50ukEzl+eEPTiIrIk2NUaRr3IWdOQfcO/VwHBhPYq+yTq864qex+sbKBpbt8MEt69nT2sDv/uA1eqcTTGXzDMwk+c2nHiTq9fDPv/UiZ0Yn2N5UhyAINESCfOGhq4MSTZF5blPXoqbtom7QVR3j7zy8i++eOM9kJkemWGIik6OzKkp3XRXfO3mBLQ21qyYZwHxGBSqeELGAd9nOyF5VublBw/t4H3cZpbJJqWzO/z08nuTv/6uvrX59JeOOO2+/F2E5Nkk9x3QpzcnUAG9MneFMehDLqcj5eiWNuCtIu6+Gh+Mb2RXtIKz60G2T4fw0b89c5PDsJS7nxkkbeb4+/DZh1cdztTvwyHdGYGK5kASJp6sf5+nqx/ErV0uIPbKHGnc1DZ4G/v2F/0rZ1jmROr2AaNg4mE7l/pMFCU1afCzVWpy4ViGcST1N3iwsmud6/Ivf/MCy+wxXagZ4pzCay3BqdoIN4Sp+e/sjbI9flcKfKeaxHJuQdlVaM1Eu8D/PvkuiVOBf7n6S55vW4ZIrx3IxNc1vvPpt/uvJN9kWq6U7XPFWKJmVrP/v7H2WrK7jnHB4fbSfpxs7+a1tD/HGWD//7tjrjOUyFAwDv6qhWxbf6jvLockhnmtax69t3EOdt+KZM5JL81tvvsBXe07xaF0bj9W3IQkCQ7kU//nEAarcPv7dvufnj6VoGrw03MO/Pfo6f3HxGJ2hKM3+q2OG0XyGGo+ff7j1IfbXNiEJIhm9xL899hrf6jvH8ZkxNkaqCWouSpbJH5w5xEAmwd/bvJ9fWr8DTZJxgFOz4/zGa9/mzy8c5cHaFrojleMvmyaj2QyzxVvfQ9ejIxwl5FpbA8gryOplxjIZMvrK/Vm2VtegLFFS9tOEnFHGdO78e8UjKfPlhgDDvdNU1YVw30Kh8mZYk6dL0dJ5Y/oc/+vyKyT1HAHFjSxINxxP7o+vAyq6xVOTGSYm0szO5hgamkXTFOrqwnR21vD4k924XAqO49DSUillkKSKNOjFi+ME/G7q6m88qF8NHAfGZ9KrUpzSVJl42EfQd+M0pVdTUSUJAQi4XJQNs1LO5HEhz9WER31eEnOKBrIo0Ba7sQ/BtfC7NTRZQhJFNEVGLOvYDtQE/JwZnWQ0lWFTfTX1oeV42d4YsiTiVhXS+RLr6uN8/qlduLTl3UrtNdFF9bXv433cKziwKgf2m8G+Rsf8fUDR1JkppxktznJw5hIHps8yWUoBFYfzqOqn1h1mY7CJh6s20h1oRJOu1pWroky7v5Z2fy1PVG/mOyOH+NH4MWb1LG9Nn2dzsJnOwL31ZoqoYbaGNuFTlpZybvU2s97fybHUKYYLozdcjzBXMnY9vLKXgOxHEkSyZpaSfesewqro7TWF3gtokoxPUZko5BjIJqnzBoholfLimHvxuT00McxANskD1U08WNM8TzIAOoMxfrZzC//pxAG+23eO7p2VgbYiStR7g8TdPgTyNPvDRN2TdAWjaHN9DRHNQ97UKZoVojFWyHBiZgy3rPB8c9c8yQBo8AV5urGT88lpXhruYX9tMy5J5qXhS+RMnc90bl1AmNyywq6qBh6rb+WNsX7eHh9aQDQ0UeLRulb2VDfMZ1MCqosd8XreHBtkJJehYBoENRe96VmOT4/S4AvyyfZNaJI8rwraFYzxZEM73+0/z+tjffNEYyqf5/cOv8O3L5xf8fX5ww9+hKda2+9I6fPFmRn+8ztv8c7I8IqXffMLv0qtr0LwTcvizNQU1T4ftf733nfgRnhp7DwTxcwd384TtetYF7yaKf/R197luU/voblzaTPF5WBNiMZ4MckLo0cwbJNnarfSHWzEK2s3VFtbF6g0SluWw4WL40xPpTFNi7OnR4jF/NTWhnj2uc0cOdyPbTvIskhbWxxJkvH5XOx5oJ3XXz1PW3vVmhMN07LI5EqrGigEPBrxsO+mrLqoG/RNJ0CAmVyeve2NxH1ejg6OcnpkEq+mMpvL07V1/XzP3rU1yI7jUNANeiZnGEtlcKsKg7MpVFlaUq7UdhwKuk510MfGumo0WcK0bSx79eVLLlWmKuhlIplFU2Xaa6M0xkOrWtfdg8Ndd+Z7H/c9BEC4runb41JpbVweuV8KTfWR+yZCfK9gOw4pPcdEKUVPdox3Zi5wOHGZolUhdYooUeMK0+iJsTPShmk12gABAABJREFUzt7YOpq9VdfYRi2Nek+UTzbtZ1bP8tLECS7nxpnRM3Ryb4jGlcdtlRbHLbluuP8C0Ohp4GjqJFkju4rtCLgkF6qoUbZ1TNu69ULvQTT4AjxS28qfnj/CH5x+h1OzE+ypaqA9GKHOG8ArqwvecZfTsxRMg03R6vlehysQgF3VDRiOzcnZq436sigSnOtZkAQRTZLRJBm/WonYyoKILIrYcwaaAEPZFLOlAh5Z4UJymsI15VsAs6U8Dg79mWTFRNNxODUzgSxIbIvVLjrOoOpiXTjOd/rP059JLPgs4vJQ5w2gXSe761c0VEmibJnz+3U+OUXJMqlXAhyaHFpwDgzbpmgZ6LbFUDa1nNP/U4G8YfAvX3qZz2zZzC9s23ZXtmlYlQb8O1kS/tX+Y5xIjNyx9V9BjTu4gGhEqwPkMkWy6cK84pXboy16b94Ma/I2zBhFhvIzPBDt4jfXfQCvvLzGI0WReOyxDTz22IZFn23e3MjmzY2Lpvt8Lj7xybVvAL+CVK60wEFxJfC6VcL+GzsmelWFXS31TGfzvNkzwKb6ajriUcJeN4+va+PEyDi6afFwVwvt8QiW7bCloRbPdU7f2VKZc+NTGJaNM9d3sbG+moZwcF6/uSbgw6sqZEtlTNsmkS9wuH+EkWSahzpbeHxdK75Vlk8FvW66m6o5NTDBZDLHueFJ6mPB+6opEyoKYFdeSoZprVqu+H38dMPnqTTuXsli1lYF+Se/9vSq0/Bej3rfKPjcK5xM9vNu4hLvzFyiNzc+PzDyyi4aPFHafDXsja5jV6SDkLoyQ8dqV4h2Xw0HZRcpPT9PXq6gaBWZKk1T665BFe+sb44kSNeY8t38frkia2s5V0lCxTF4ee8bWZCRBImyVcZmZURjOc7w197vOb3MSD5NulzGLcs0+IIIgkCyVCTm9hBQXYzmMpi2RbXXT7pcYiyfRrdsgpqLjmAEWZQ4Pj1GUHORKhdxHNgUqWaqmMMBmvwhCobORCFLUHMTdVXW+8GW9XgVlZeGe3hjrJ8Xhy6xLhTjyYYOHq5rpd4bmM+K54zK+y2gaktG2UOqG8dxSOlXPQ8EhEVZdfEaY8Url/HaM5Y3dMqWxUguxR+eObRkVj6guuaazgUcIK2XEAQIaovHBLJY8dLQbYucufD+dcnygszM/D4KV2js1T1Ll8tYts255BT/75FXl3xm1Xr8uK7JECJwS0J/L3CfDR9WhONj49QF/DQEg3dsGx5ZwXeDElGByv0hixKGbZExSgs+U0QJWZQQEbAcG9225p/JcNUfJqBUsnnXwut3cfSNi4wOTKPOjUX3PbURbQUGw2tCNGzHRhIkmryxZZOM+xXpXHHVRn0uTcHvufHgvSrg48NbF5MqgP0dzey/TqZWEuELD+5cME0QBGqCfn754cVk69qSqB3NlazRubEpdNNiQ20VVX4vrmEZUWDVxwgQ9rrY2dnIS8d7GEukeeXEZVqrI7TVRJCXaDgzLZtEtoCqSPjdiyUD7xS8moo251Q9NJ0iV9QJeN7b9+f7WFsIgoCqyISC7qtN3YJDPOoj4Ltx0OB93Bxf6nuZY8m++T69mBagyROnO9jI3tg6NgWbF0h7rhReuSIokjMXZ59nywlem36dD9d9kIh684bp24VHcqOIMkk9Rdku37R3cLJU0fQXBJGiVcItubCxKVpXBwUO3CSbPpeVFWC57mwT02kGhmfJFsrYNwmgxSJ+dm6uyO4atsWZxCRvjw8iixI5o8zOqnoafUFeGellb3UTO6vq+eHQRUKqi4fqWvjx0CWS5RICMFMq8Cvdu6n3BfmfZ99lV1UDDja2DR3BKOeSU1xKzvD3t+xjNJ/hhYELPN+8jqirMsCJuDx8vG0jD9W2cHR6lEOTwxybHuX3Tr/NQDbJF9bvpN5XGdCpUsVTpWyZ2EtkrIuWgYCwxEB7ZVBECUkUaPKHeaapk2r30lL+EZenIj8L89ssXZf9gMr7t2yZc8pqC78Hwty/5UCVKn0J64NVPN/ctSgLApWsTZP/6gDYqyjsrK1Dt0zKloVuWlf/P/czWyyQLBbvag1A1O3hwcYmwi43umWiW9ai/ZvIZckbi8/nvYTjOPzJ4SP87NbNd5RoPF23gU2hG2VuBWRRoGSZHJjsJWeUiWheWv1RYpqXoOrGLatIglCRTTbKzJbzjBZSjOZTRDUvH2zYxN6qlgXZDACPT8Pjd5FJXu3pudmzZCmsCdHQJIWQ4qFgluc1r9+rKJaMZUV/loKqyLjvM+fppmiI8XSGkWSGRL6A36WypaEGn7b6xh5VkdnUXM0zO9fxnYNnOHh+ENt2eGxzO7VRP2618oDVDYtcqcx0Ok/P6DQPrG9mT1cjbm1pd1zDsjFNi1xJx5xT7bEdh0yxRK5YRpElZElcNlFpiIeI+D30TSQ4fGmEzrrLbGurx+tScBzQTRPdtFjfUIVyB53m70vciTeIwKrco+81ZElkfVs1bx/rByrN3H1Ds2zrbrjHe/beRdk2kAWJek+EVm81W0It7I520uytWpP3g+FUInIeSVs0UAMwHZPLuT40cQRVVOn0dyAhMVQYJmWkEBGJazHirjgJPcl0aRpn7l+VVkWVK07BLDBRmiRrZtFEjRpXNSE1tGA7FcdvN5PlKXpz/dS4qnFLi4MZk6UpLmYvA1CwCrybOMru8HYsx6a/MDg/n+1YGPbigZTjOJTtMoZtoAoq0jJMGKdmsnzlO0c4cPgys8k8siRS1k1kSayordkOsiQSDXt5eE/HPNHI6mVOzUyQ1ks83tDO0akR+tIJNkdr0ESZ0Xya6ryPiXyWbbFacobOwfFhHmtoo9rj428unaAvk6DWG6iUjPmCPNPUOX8c3eEqDk0MM1nIkSgXkQSBzuDCUkVBEKjy+Hi+eR2P1bdxdHqU/37qbb4/cIGHapvniUazL4RbVujLJClbFr5r+IRDRZFJEgXaA7dHOKs9PoKqi4Jh8HhdG9vjdTfNeFqOTVcoxtsTg/SkZ3m0vm3B53lDZySXxq9o1HpW30fQ4g+hSRKaLPPxto2EtVv7ikXcHj67aQuf6t5IXjfIGzp5XSdvGOR0nbyh86PLPbzUd5mSad50XWuJllCYv7vrAcqWSV6/ui+V/avs55dOHOPU5MSyTPUKusGBgQFm8gW8qkJXLEZzODz/isrpOr2zCYbTKWwHqnxeOqNRIu7KOSwZBgPJFMPpNDldR5FEGoJB1sfi84Z2bw0OMZXLcXJ8nMZQkEShiCyKbKyuoitWEW8omyZ9yST9iQS6ZRP1uOmIRqnxVcrs+xIJksUimiwznqkQqZDLRXdVnJj3qnz3Z9t2LXWYQOV7VbQM/rr3MLOlHF3BKj7cuJn9VW20+KKLygot22a2nOdkcoQfjpzjZGKUmXKOmOYjqCwMsD36wW3zY+IrQ+OVPsLXhGjEND8bQ41czIxyITNCp78WRXxv1igbprnqRk5FElGV+2vA6tNUntxwc4fj1aA65OeT+zdRKhu8cbaPV070cLRnhPpYcC5r4FAoGySyBabSOUq6SX0sxK7OhYM307J4+XgP6UKZsmFQ1i3yZZ3R2Yq7br6k8623zxAPetEUGU2RCXg01tVX0Vl/Y9lPgJaqMLs7G+mbSDA6m+bPXzpCR90AYZ+70rtS1jEtm9/5pecJyT/d0WtZWihPZ5jWmnMNSRQXDCIty3pPNEUrisTuLc3zRCOTK3H41CCb19Xd1IX1fdwY6wONrAvUszXUyvZwGyHVt6YBqCotyO5IB6ooU+1a3KeXNXJMlaaQBYmeXC9VWhxREDkw8yZRNYqNTW+uj4fjD9KX6+NI8igb/BtIGklGCiM8En+YocIw5zMXcMtuckaOtJFmW2jrAlWoFm8TQSVA2sjw6vQBADp97QQUP5Igods6U+UZDky/zYxekVA2bJNvj36f6fIMkiBxaPbo/PqKVpFZPUFEW3hMRatEysig2wYRNYxLvHWg6NWDF3nhldN0NMd5cFc7siTyvZdPsWdrC1UxP+NTGfqHZ9jYVcfTD13NtFuOQ8kymSkW6EsnCGtuWgMRPLLCllgNp2cneKH/Ag2+AHWeABmjTMkyGM6myBs6u6sbibu98wO6Zn9oft2CIBDS3GyP1/Hy8GV8qsru6ob54FHOKFMwDUKqe16O1S0rbI3W0uwLcTYxuWDwu6Oqnjqvn8OTw/SkZghU1c9nymZKeb43cAG3rPBEQ/stz9fN0OwP0RmM8ePhS7w7NUJzIEzkmkG97dhMFHKENTcuSUZE4LH6Nr7Sc5JXR3p5vqlrnhwZtsXl9CwHJ4ao8wUWNIqvFN2Ralr8ES4mp3h3cpjHGtrR5tSXHMfBtG2mS3niLu98WTXMZXIlGdUtE3YvfvcNpdO8NtDH6myLVw9BEHDJCi5ZIepZXH76Uu9lzkxN3nI9hmVxfGyc8WyWdKlEVtdpCYX44q6d1Pr9FAyDQ8PDvHS5F9OyEOZKZ3fU1fFsZwcxr5eCYXBweJiL0zMVLx/TwrAsfnn3TrbU1KBIEn2JBJdnZ0mXy/TOJigZJposEfd66YrF0C2LM5NTfP3MmTmlzsr90h4N85ENG6gPBHh3ZJQfXbpEfSCA7TiUTJNkschjra18ZMN6Ikuch+th43AmNc6XLr+DKIh8sXM/H2rcdMNnriSKVLn9PO3eQIMnzP84/xovjV2g0Rsm5vIRVK/eE8O9U/RfGiefKWE7Dj6/m71Pdt/Z0qmyZXB4tnfBNNOxCCpejpb6+NPLr/BArJOYFkAR5SVTgJ2BWqpddy7FdDu4on+/GojinW0Gup8gigJtNVG++MxumqpCHLs8yuBUkoHJBEXdwHGYL5Vqq4nSFA/SVR9blDnQDYs/+P5BhqZTS24nX9L5xlunF0yrCvn4xSd23pJouDWF53etw3Ic3j43wMhsmsM9w9i2gyJLeF0KdZHge2IwfLvwerQFcshTM1kMw1pTmWi3S0G9pgk6kSpQLOn3vRO8LEns3NxMTdzPxHSWfEHn4PE+HtjWwuZ1N49cXo9bRXxmCgXOTk7yUHPzbT8rUsUS56enSBaLtITDtIbDuJX7wwX4E437iGsB3JJ6R6799nAbnf46JEEkqCx+EYuCwO7ITqq0Kvr7/oSEniRr5jBtk+dqnyFv5vnmyLcZKY4B4JP9PF79KH25fg7Nvst4aYKBwiAJPcEWz2ZyRo7J8hR5K7+AaLR6m2nztjBZmmYgP8S39e/T6m0mqkVRBImiVWK4MEpvvh8BqHJVscHfxYGZg3xr9AUEKpF3WZDQRI1ZPcn57EWaPA3z23Ech4HCEEOFERwcYloUn3zrvpa3j/bh8ah88TP72b21mWyuzKtvX+Sph9bz6N4uRidSfP8nZxgaS6BdoxoYUDU2RasRhEokH6DeG8CvaHRHqjg8NcKxqVF+cf0Oom4PPlVjZ1U9cbePgKph2TaNvtD8IOf6y+9VVLojVXzp3FE6QlGebuyc/6w3neDHwz1EXR6q3F7ckoLp2PRnEpyenWBdKEaV52rZUlsgwodaNvC/zr7Ln50/wlAuRczlpWyZ82VXzzR28nBdyy3P180QUF0819xFX2aW7/SfI2uUWR+K45EVypbJbLnAxeQ0f6d7D83+EAKwNVbLh1s28P2B8/zhmXfYW9OER1aZLeV5bbSfyWKOn+3cysbI6tV8oi4Pn+3cyn879RZ/dPYQI/kM9d4AqiSRN3Qmizlminl+feNewtJPdzDtWliOQ7pU4jf27iHm8XJ4dISvnjzN6/39/OyWLQyn0vykt4+I283PbNqEKon84FIPr/f3Ux8I8FhbKx5VZXtdLQ80NhD3epktFPjXr/yEQ8MjdEZjKJLEZ7ZsRjctvnX2HD+3bQuPt1UyV1f6hZLFIj+8dAnHgV/bswefpvLO0DDfv3iRqMfDpzdXnM5H0hk6olF+ZtNGPKrKl0+c5J3hYXbW1y2LaJi2zfeGTpHSC+yONfNM/fplB3a6AlXsiDZxdGaIt6b62BtvZUukfv7zt186Qz5b4uKpYWqboqSTebY/2HlniUbOLPEHPT9aME2gUuKSNYq8lb/IsWQ/YdWHJspLvmR+vfOZ+5ho2KsunRIEAWmJTvyCmWa81EvRzBDTGolqDSjLiEhdgeUYjBV7SOtTxF3NVLvWzoW9q9nL3l0OVepmmqtCrEBIAFEUaIyH+Oyj23iwu4WesRmmUjkKZQPbcXApMiGfm9qwn/baKIY6StocJSzWIouVm1SWRD62byOZwvK1s70ulQ2NVcuat6kqzM89uo3tbXX0TSRI5ys9OKpcIUE14cCiZvufRoQDHvxebT7SNTqV5uSFUR7Z3Y68RmVjfo9G0O9GkkQsy2Y6kePMpXHaGmP4byL5fK8higK1VQE+8Ngm/vJb72KYFoOjCf7qO4f51HPb2dhZg/cmvVdl3WQmmWNgZJZI0EtrYxTXDe6piWyW75w7z97GxtsmGhempzk0PIJPVan2+Vb93LoTaPYuz1l9tfArbvzKjQdOASWIIioIgoAmqpiOiYO9oORIFETsucZsAebbHyrRTRvLsSjbZXJmnqgWJa7F0K57bvtkLw/G9jJdnuVitoekkSKZSi25TxE1zAdrnmFzaCOmbXI8dZq8lUcWJFq8zTR7Gjk0e4R3Zg+jiRot3mY0USVlpDk4+y5DhYrsZ6Onnoh6a7XFyekMzXURtm9sRBRFECplr4WijiSJNNSGeXRfJ//jS6/y5uFeOloqz1RNktkaq0UAJgoVhayybYFQGXDvrmog7vLSGYrNKzY937yOUzMTlaZvmO/Neaapk/B1zaWiIFSWkyUafMEFLteGbXEuMUlfJoEkiKiihAOYtkWtN8An2zfRHoguWNfHWzdi2jYvDffwv84dRhElTNvCdhw+2trNL63fSUB13fb3Y09VI6UNJi8MnOeVkcv8eOgSsihVytAchyqPd64NvHIPaZLMr27cgyKKHJoc5t2pERRRxLBtvLLKz3Zu5RNtGxcc/2rwbFMXJcvkxaFLfOXSCSRRRBZELMdGQGBTtPo9Xc6+GiiixI76OrbU1ACw06njrYFBTo5P8LNbtjCSSXNyfIL18Tg/7ukBYDCVpj+RZCSdxnEcFFHEr2pcmp3h2NgYpmVTMk2m8vl51TdVqlx/hEqD//WmiOlSiTcHBmmPRvhJbyVAP5XPM57N0TMzO98rW+3zsbexgXXxynNzc3U1l2ZmyevL60exHJsjM0PIgkRHIL6wJ+kWkESRGrefsOahLzvDdGmhKt7sVJbnP7MHWZF5+hM7+dafH7jzpVOyINHpXyzXthIEbvKCuNe4/Xf14iswUepltHAejxRYsVpIZadAt0oMF85hOuU1JRob230Uww6PVu3CK4dWtQ6XqtBVH6er/uaDi3dmXsYnR/ArUWQqXwRVkfniM3tWtd3lIuRzs29DM/s23Ngk8kZwHAfbrrxIrk6r1DiuZaT+Spp7/m8q5o627SCuhP3dAB63yoaOWi4NTJEv6Oi6yVe+e5hcocy61ip8Xg2cSkavVDYolgwURWJT1/LT+pqm0N4UIxryMjWbxTAtfvj6WRzHYcv6esJBD6IoYJo2Zd2kWKrsx64tzas6j47jYDv2AmED27axV2Ho6VIVnnl4A31DM7z+bg+lssnhk4MkU3l2bW6mpTFCJOid84sRMAyTfFEnlSkyNZtlZCJF79AMT+7roiYeuCHRACiaJi/2XEa3TCJuD4+3t5HXdQ70DxDxeJjK5fBpKg83N5M3DM5OTTGeyeJVVdbHY9T4/VyemeXFnh7yusHW2hrCbg/aEko1f1ux1JVv9jRxNn2OA9NvYTkmqqhS766jN9fPrJ7gndlDzOoJ/LKfGncNuq1j2AayICNQIRXuJaLC63wdfLD2GUJKgHOZi6SM9AKzWlVUaPW2sD+6hwdje1FFhU80fJgWbxMz5Vk0SaPT105IDZLQk5xInea7Yz+kzl2DJmok9RTjpUnKdpmoGqHT105QubUXkgMLMsiCIOB2q8wk80CFYAe8LoJ+NwOjswuWjbo8PH6DcqO9NU3srWlaMK09GKU9uFgS+mNtGxf8rVsmZxKTnJweJ6i6Fkm/dgZj/OrGB+jLJEiXixi2jSyKRDQ3G8JVdIZiiwbmYZebX+jazqZINZfSM2T0Eqoo0eALsae6gaprGrfjbi9/d9NeNs55SrhkmQeqG4m4KuVhADUeP59o3zinInWVWKqSxBMN7bQGwpxLTDFWyFC2TFySQkRz0x6MzPelQIUENfqC/F9b9rN3aoTBbIqSZeBXNNqDUbZEaxaYD/pVjScb2lkXjtMZWpytbwtE+Ny6HVTNZY6uwCXLfKp9E92RKs4np5ieMzb0ySpxt4+uUAyfen/1jt5piKJA0HU1uKVIEl5VZaZQqFgEGAapUomSaTBdqDQ5e1SFR9ta6ZzzLTs5PsEPLl7CwSHkdiGLEpbtVN79y9gHx3EqjfWFAk2h4Px2BEFgX1Mj2+uuvluDLg2/tnB/HZxli/Y4jsNUKTsXWFn5e0ASRCRBJK0XKVoLyU0g5EaWK754pw/3MTORxlmhgueK98gnu/jVjqdXutgChFcoZ/hehWGXmSr1cyHzNpat43YH0EQvkqAwWx5hotSLbhXxKVGavZtRRRcTxV4mS33YjkVYraXFtxVJVKj3rCOpjy1ru7PlUcaLPeh2Ebfkp96znoASY7o0xFSpH90poooe2rzb5vfzYuYgIKBJbjYEHsRBoCd7iKKVAQTiWjMxrZHhwjkMu4hhl9EkL45j0+TdBMB4sYesMYtL8lHr7iSkVjNTHmai2IvtmCT0UTzy/ZnJAnjtnR5yhTJlw0TXKz9lwyKdLdI3NDM/3/Rslu+8dIp3Tw2iKhKqIqMqEpoq43YpbGivoaF26Yjj+csTDI0n5wfZZd1CN0zKusnFvqu1p2Xd5O1jfaSzxavbUCu/NVWioSZMd0fNirIRT+zv4vi5YS72TWLbDucuTzCbytPaEMXn1XAcME2Lkm5SLps010dWRDQAdm5q4uiZIZKZAoZh0T88y1e/f4xDJwYIBdwLiUbZQJZEdm5pvmkP+Uwyx/GzwxRL5vy50g0TXbcYGkswncjNz3vu8gRf/u5hwgHPgvOmqTIet8q+7W24l0j5iqJAbTzAz3+0oub29rE+dMPk3OUJLg9OEwv7CAc9FX8MAQzDolDUSWWLZLKl+ZLLB3e0LVr39ZjKVQZ6jgPfPneObbW1GLbFDy/18EBjA2G3G0moRD4vzczw7vAIreEwA8kkyWKRpzvaK9LNCIiCgCKKK8pE3ktY9sp6g0RBmJeFXS6CSoAd4e3zpGBneAdxLU5EDbMvupfp8jSqqPBAZA9BJYgkSPhlH5IoU+WKU++uxy/7aPE2IwoiKSM1ty9Lf9ckUWJzsJuYFmVTsJvx0gRZI4fpmHgkN1EtQpu3hS5/x3xGpcoV55maJylb5TkvB42SVWZfdDejxXGmytOkjPSC7XglD3uju1jv70RexmAiFvExPp3BtGwUx0ESBWIRHxd6JymXDTRNwbIdiiXjLvYWVmRkIy4Pu6obFpnwBTUX+2qa2HcdkbkVPIrC/tpm9tfeOJgkCAJVbh9/b/O++WluWVlEnOq8AT7VvnnJdYiCcENSdaNtRlwenm3quuW8AdXFU9eUkV2Pm21XEkU2R2vYHK1Z1n79tMO2bVLFq5LGhmWR03X8c4RLlSRq/X6e6mjnqfaFhFqTK5U4P+nrYyid4nPbt7Gzvh5Vkjg5PrGoHeCKQeJSpEASROJeL/uamviZTQtJtyJJ85kmSRRvzwRRqGxLt3RG8qkViTI5jkOinCetFxEFcdHx7X50PcGoj237Oug5O8qOBzvnZW6XixUTDUkUqfcsVHCwbBvdMdFEecUvhZ9mVPS6VSRBQRAFNMmDJMjodoGe7GFU0YVL8tGbPYJPDhNQ4pxNv061qw1Z1Dibfp2I1kBAWZl5mChIqGLFPGq4cA5F1AgoUfpyRylaOSJaHaroRpi7VnkzhSTIyKLGufQbNHg24JejKIKGLXopWllOpV7mofhnGJlbX95M4ZJ8SIKCKrpxsJko9RFR65gpD2M6BpKg0J87jmHrRNQ6dKuEuYSiyv2CL3/3MLPJPIZpYhgWumEt6b+RSBd46a0LCEIlYqjIEopS+fF7ND7/yb03JBpvHe3jtUOXyObLGIZZ2YZhLfJuMQyL42eHOX52GFkSK+uXJdS57Tyyp5OOlviKiEZnc5xPf2AHf/PCUS4PTGHZDpMzWSZnFhuIiaKwqkbohpoQH35yM8WiwYnzI5R1k0QqTyKVX3L+cNBTCb3e5Jk4Mp7iz79xiEJRRzdMDLNyzgxzcbN579AMvUMziKIwf22unLNQwM2WdfVLEg0AWZboaq3ilz65l9qqIG8f62NoLIFuWIxNpRmbSi+53BXEoz6qYn6UWwzaPIrCUx3tCMBr/f1M5/OE3C5USWJ9PMbO+kp9bLpU4vzUNBenZ4h7vSSLJQqGQck06a6qYlPNNHnd4MmO9iUbJ+8HZI0ip1MD9OcnSRsFDHtlYhsbQ008XbNtRdv0K342BzfN/701tGX+/53+Djr9V8UxylYZURCIqBH2RBaqunhlLxsC65e1TVEQqXfXUu+upWiVKJhFLMfEJbnwyh5ExAUZtisO4J5rRChcksamYDclq8w7iSMMF0YoWEU0UaXGVc3W0Cb2RfcQXaZk76auWr714kn6h2fo7qxFlisZym+9eIKvvnCU5vooPQNTjE+l6Wi5s6VuV6BKEltjtWxdwsTufbyPtYJh2xwfG+fU+ARxn5djY+OMpDN8dMN6BEGgMRikPuDn3bl+iyqvl9yc+laNz0fM66VkmoiCgE9VKRkmbw8OMZRKEvUszGrKokjQ5ebMxCQ76uoQqJAVr6oSdrvYVlfLO0PDbKutpSEQoGSZpEslwm439YFbZyaXAxGBNn+Mk4kRzqcnODIzxJ748io4BnMJjidGSelFaj1B/MrCMud1W5vIpgsEoz427myhoTWG5r7DRGPJHS1M8+rkGT5Qt4Na941rRx0c+nNTHE/0M13OEFa97Il20OiJId+Gpvr9CllUqXa1Ue1qxXYsOny78cgBZsrDjBUvElKr8TlRcmaCtDGFaZcZLp7DIweQBIWsmSBjTK+IaNiOjW4XKdmVgV3GmCZvpnAciGgNJMojlKwsIaUaSahcflV00erbjl+OcjFzkJyZxCMFKVgZbMfCtMtMlwdxHAdZ0IhrLbjEaWTRhSwoJPRRynaR6dIAqugmZyYRBZmkPk7BzNDs3UyzdwsTpd5lyTLeK4xOpEhni7eecQ6OU2lm1w0L5hbLZkvkbtJvkkjnGZ/KUCovn3CZlo1p2RS5usxsMr9iA0JZlnhkTwdBv5t3Tw5w7vI4o5NpsrlKRF5VZFyaTMDnpiYeYPvGxYaZt4IkiezY2IjXrXHoxABnLo0xPJ4knSlWpDVlEU2V8Xk14hE/rY3RW9Z7lsoGoxOpFQk12LZDWa9kP64gX9BvacYpyxLtzTE+G9rJ5vV1nL00Tu/gNKOTaVKZAsWygW07qIqEz6MRDnmpjQdoaYjQ1hhjY2ftTcumAGJeD9Jcr4wmyxhz9b6yKC4gDFeMxVyKTJXXS5XXS8TjIeC6f/tdrsV4McE3hw9yNNHLWDFB3iqt2L+naJVXTDRWAlmUafI0ranfhltyLSlxuxwElQD7Y3to8jQwVZ6mZOmookxEDdPgqScg+5ddEvjIA52MjKfmAwaKLPLgrjZePXiRr37vKNGwl0yuRDTkZe/2tSvHfR/v43pc6Y+5W6IgqiTRGYvyrXPnSJfLZMtlNlZX8Uhb5T5vCYX40Pr1vHz5Mn9y+AiyKCKJAq2RMM92VFSn9jc38a2z5/jS0WMEXS68qkpTKLRIcEMUBH52yxYODPQzns0Rdrt4rquL7XW1RDwePrGxm2+eOcv/PnasYponCtT5/TzZ3r5mREMWJR6v6eREYoSJYoY/uniARDnPnngzEW3pCqKCqXMhPcl3h07xzlQ/pmPTHaqhzrOw6uT88UFOvtOLZdkYuklHdz17Hl+/oqzGmhCNmXKWn0ycpt4doTc7QVLPoUkqHb4amrxXScRwfoZvDL3Dm9PnyZtl3JLKyeQAf6f9SVp9f3saliRBRhQk/HKMsFpLWK0hpjVStoqISISUGhTRRSRST0hdXtPzFRStDKOFCxh2mbirGVGQ5hoeHZo8m/DLUTLGFGfTb8z3ZLilAJJQSRfKoorlGEyVBxjMn2JT6HHQHSzbwMFBFMT5LI0sKPMuo5Ig45L8RNRaImotPjmMJKgVoyYqjYQ21oLa5fsN/+ALj6Prt6cbLkkiGztvHK177pFuNnfVrdp9/grqa0KVMp4VwqUp7NnaQntzjLHJNMl0gWLJwLRsZFlElSXcbpWgz0U0vLQp1a2gKjKbuuporA3z4M42ZpI5CsXKIP9KpsGlKQR8rmU5aLc1xfjtX33qtp3dFUUi4L/1AFAURaJhHw/tbGfr+nomZ7IkUnnyRR3DqDSZypKIS1PweTRCQTexsA+fd3lmlDd7yl37IvaoCptqqpnK5+lPJgFwKQqe+0RZ6mbQbYNvDB/khdHDZM3lk3dgTrI2RIMnxpZQy53ZwTlIgkSVK04Vdyeivxy4JTed/nY6/bcnydrZUsUXPr1vPrsqiiItjVF++bMP8faRXtKZItu6feza0symdauXWH0f7+NWSOp5jif7eLKmkl0cLcwyWkywJ3rjUrHVwi0r/N8PPkhHNMJwKs10IY9XUemKx6j1VzxLPKrK/qZGav1+BlMpSqaBJss0BALEvJWB+Z6GBgKaxmg6gyBAcyg8JxYB3ut6Xj69eSMd0TDZso5XVYnPrUOVJLZUVxN0ueidTZDXdVRJosbvoyEYmNtOPc2hIC3h0Pz6NlZV8YUdO2iPLi8AIgkiT9dv4MdjFzibGufd6QEmixnWj1XPS9Z6ZBURgbJtktKLjBVS9GSmuZyZJmOUqHUHeLi6g/rriMbhNy4SrwlR1xylVChz4Een2bq3/e4TDYDxYoq/HjhQ8SewysiCRKM3xgfrdvJQ1XpUUeZkcpCjiV4eiHayLdzK+cwIB6bO8+b0BeKuAIElZAp/GuGTI7R4t5I1Zpks9eLgUONqx6uG6PDtYrY8jCBIiIJIi3crBTPLqdRLjBTOz5UqeWjz7cAlLWaqoiBhOQYJfWRu0CKgiC4coDd3lFl9BMexsDERqAyKlooySIJCzkwwUbyM6Rh45Rvf8JrkoUppxbBLTJT6AGjybKTG3UBQraYvd5yp0gBZY4aoWn/D9dxrPPvw0q7ta4kt6+vZsr6ewUsTTI0m2Lr/ar2jbduMDczQ0FYhl4ZuMnBxnImhWR7+4Lbb3vbkSIJw3I+qKcTCPmKrJBLLgSBAKOAmFLh94YeqqJ8PPr7p1jOuMSRJJBTwEAqs3XOpIRjk57dtRZ4jJD+/dSsNwSCuOanE2DUZDVkU6YhGUSWJ2blGwhq/fz4gs6u+Hst27stGz4uZMd6eOT9PMjYFm9kWbiWm+TEdm9+/9ANcksLHGvYS0wJkjAIXMyOcSPbjU9w8VbONR6s2EtPWJuL3txGqKs8rSV2BIkvs39FKR3OMYsnA59GIhL1rblhqOw5D6RSnpyYZSqeYyucpmgaW7aBJEj5No8rjpd4foCMSpTEYxLXGYgZl0+TM1BSXk7OMZTMki0VKponlVPYh6HJR4/PTHg7TFY0R9/rumN+o7Thk9TJ9ySQjmTRjmQyJUnG+FBIqTd0uWSbkclHr89MYCNIWjswbyL2XkbdKnM+M8GTNFhzHYbKU5kxq6I4QDVWWeKqjQtJbwjeusPGoKhurq9hYvXQw16eq7KqvZ1f9rccsMa+XZzqXPhZVlumMRumMLl2Z0haJ0BZZOL6qDwaoDy7/2ScKAo3eMH9/w6P893OvcT49QW92hv7sLB5Zxaeo83YTpmNRNA1yZnk+m17jDvDp1h08Ut2BW174PrEMi+4dzTR1VOHYDm+9dHbF4eI1+2YXrDLT5Qw7wm00eKNkjSJnUsP8YOwoNe4g3cFGxooJZEHi4apu9sfWsSPSxmghwbFkH49Xb/qpJRot3i04gCpWBl2KqNHp30NKn8R0DMBBFd0oootNocdJ6ZOV7AEioiCiiCoNnm6qXK0ICHjlUCWbsAQ00UOHfzfVrjYkQabW1UlQrUJAIK414p1rxnZJPvxKFMsx2R5+Dk2snPtdkQ8SUqpRRBd7Y59EQkYSZVq92/DIQdYF9uGSfETUOkSkuYwGaKIXl+SlaOUAgYBShSKodPh2EdeacHCu9n6sQNr3RrBsm2y+zGQiy0wqz2wqx2y6QPZKM7dRabI2LAtJrETqVVlCVWX8Ho1IwEMk4CUSdFMTCxAL+tZE3Wm5CEa8SLK4oA/C1C1++JWD/Mo//ygAtmUzM56i79zYmhCNN144waMf2U5V3a2lMVcLw7RIZApMJ3Nz1yXPbCZPoWRUGrgNi7JhYlk2siyhzfW3aKpC2O+qXJOAh2jIS03UT9B3/yrUrQYhl2u+BwNgZ/3VSPL2usWZMI+isD6+dLS9KRRa8/1bKxxP9pHUK+Wbj8Q38unmh2j2xPHIGkVL548vv4gqyjwY38A6fz1l22CylOLA1Dm+P3aE3tw4+2LrCKt3jgz/bYQgCMiyRF11aEXLXZyd5nsXL9KfSsxPe7K1nSdb2xeo+wCcnpzkh5cvcmpqkolsllS5RF435iVnJVFEkyS8ikpA04i4PTQEAjzR2saz7Z23LfucLBV5ua+XN4cGGUqnmC0WyJZ1iqaBOacWKIkiLlnGp6pE3G7q/QG219TxRGsbHZHomlRXOHNeDkfHxzgxOc7FmRlmCgUy5RIZvUzRMDBse15tUBbFSqmkLONXNYKaRq3Pz+bqGp5obaM9HFlguvdegGGbnE+P8uL4cU6nB/n3579dUWOyTTYE7r+gY7Zc5psXzvHu6PCC6V5V49d27qYtFF4z0jeZy/GDyxc5Mja6YHqtL8CnujeyPrayLKssiOyvasMlKfxo9ByvT/QwUcyQM8vkzKXLuf2KxvZII881dPNQVTtx19Xn7TuvnGN8aJaJ4QTf/t9vUl0fJpsuUC4a98YZHMAvu3miejOfaX4Qn+xCt03enrnIS+Mn6cmO0x1spGjpaJKCX3YhiSLVriBdgTremDpH0Vq+j8JawXGgWDbI5kukckXSuSKnLo9i3GZZy/UIqYuVILxyaEk52YASI6AslLYTBYkGz3KbEqW5cqzFg5a4a3FzkIJGrftqc2Sd+6o6Rot3y6L5Y9qN6/arpMV1vn4lin+FzexLwbYd0vkil4dnuDw8Q+/IDBOzGfJFnWLZoFg2KJUNdNPCsuw5adiK1KkgCPNmipIkoMoymibjUhVcqozXrRIP+2ipi9DREKO7tYb4bUb73/rRKarqw+QzRUb7p9n9eDdHXr/ArkfXMzWa5OgbF4jXh4nVhpBkicmRBK995yhv/fAUHp8Lf8jDMz/zALblMNw3yV//9x9j6CZdWxrZ+/QmJoYTHHn1HNPjaaLVAXY8vI5A2Mupdy5T1xKjfWMDB398Go/fRVV9hLPv9vL6C8eZGU8SiPj4yOcfxhe8/UiZaVlMJnJcHp7m8vAM/WOzzKQqxKJ05broBoZpY9k2tm1jWRXZvvlrMtd4rikyLq1yTdyagt/rojbmp7UuSmdjnI7G2E8d8fhpxUB+kpKl4xIVfqZpP5uDzfMltLbjoM6pJgkIeGQNDxpBxYtfdpPQs7w8cYKo6qfeHSWketHLhymXXrkj+ypKNXh9v3RH1v3TgtlCkYMjQxyfGJ+fFtRcPFDfOE80dMvi6+fP8J0L57k4O0OmvPQ73ZwbXOcNg6lCHpIJLs5WBtXPddxanelGsB2HNwb7+erZ05ybnmYsm1kgS379PuR0nZyuM5HLcXFmhuPj47w9MsQHO9fxbHsHIdfqnzXD6TQv9V3mzaFBRrJppvMF0uWb+2zrloVuWRQMg8ScYtLZ6SmOjI9xYGiAj3Rt4NmODiLu905AVhREat1hdkTaKFhlHq3qBqdSHlnnWbu+qLWCJstky2XeHh5ecL0UUWRbdS01Xt+isqnVojeZ4NsXznP6Oqfz5zs6kVYhqnTFu2VPrJlmX4Rn6zZwOTvNQG6WqVKWgqlX/M0khZDqptEbocMfoy0Qo84TwntdJsPjcxGIeNnxUBeGYaFqMv6Qh5au2gXGvMvBmhANAQipHraGW2jyXh0kdwcbeGfmEim9kvZ3HGfeEIm53zHNT8kyFngIrAUsy67o3OeKlZ9s5SedvebvXIlCSZ+PtOqmRTZfWtBAuhJcGJjid770Ep4VOCauJcJ+D8/uW89D224tsflegOM4FMsmp3rGOHx2kHP9EySzRTL5UiVzsczr5DgOtuVU+iIMKGBAbuE8siTi82gEvC4iAQ+dTXF2bmhka2cd0eDK5ZhnJ9MoqszE0AwXjg/SvrGB8cEZykWd+tYY/Rf8TA7PYpmV+z4Q9rJlbycvf+Mwe5/aiDIvo2pi6ia7HtvAcO8k548N0NxVw4XjgxTyZR58fgs9J4c4824vXVubGBuYweuvvCAnhhMEwl4a26tZv70F3zcOs/mBDmqbo7g8q39Y2o7DbCrP8YsjHDk3TN/oLKlc5brkCuVl95/YtoNtW/Mt7vmivuBzASqN43PXpTrip7uthp3rG1jXUo3/JiZ6dxuTo0le+JtDRKsC7Hyokwsnhzl1uJ90Io/bq7Fucz1PfHg7wbBnAblzHIdMqsCBF89w/sQQ2XQRj09j3eYG9j6+gdrGCI7jcPzgZV769nEefHojDz1dkUm8dGaEl759jGJB58Of3cu6zQ1Apab2le+d4AOf3kP3tibkuyZdehVJPY/pWLR6q2n0xBe9OFVBxsKmcE2ASRQEql0h9sfW8/rUGc6mh7iQGWFvbB2mcY5C/i/uyL4qysY1IRolq0RPto/+wiBJPUnRKmE7VzX3r7iAX/k/S/zd5mvhuZqnbntf7gYSxSJlq/IMLhg6f3r8KN84f46RTHrFTf9+TaMhGFx1JqFsmvzVqRN888I5ehKzKx5PWI7DTLHAOyPDDKZSjGQy/OymzdT7V166Z1oW/+aNn3B6apLpfP62uhItx2G2WCA5VmQ4kyarl/lk90ai7xGyIQkicVeAB6KdxLUAm0NXg533YzmYKknsrKvj1YG+BaTasG1eG+zjoaamNSEaZdOkZ3aWy4mF/jWaJLGjto6G22gSl0SROk+QGneALZF6cmaZkmlgOjYOzpwRpoxPVvEqGsoNhJi6NjfQtqGWscFZes6MkJzJzj2wiisXoln10VyDyo4rmM5CVRjbsTFsE8O20G0Ty1lsdCIJ4tzDePnGJKZpk8oVSWavEIjC3P8rv5OZyqCnVDYqUpjzPzb6gr8XS5feDjL5Emd6x2894x1CdcTP9vUNS342nX8JQVAJuXYgi/5lrc9xTDLls2T0kzQGfnEtd/UW23WYSef58cELvHN6gPHZLIlMgXyhfMdayU3LniejQxNJLg1N8fbJfqqjfvZtbuGJ3Z00Vi+/5ChWEyKbKpDLFJEUidH+aUJRH5pHJRwPEK8Lk5y+ynZcHpXGjmpUl0r7xso1LBd1FEWmpilG56YGwGHgwjhTo0kyyTx1zTG6tjSSzxTpOzdKcnqhTO2Ve1t1KQSjPjx+Fw3tVbSsW520pGFa9I7O8PKhS5zqGWMykSWZKVC6zQb6G8EBSrpJSTeZSeUZGE9wtm+Cl9+9SGtdlEd3tLN3c8uqiOBao1TUuXxujJ6zo5w41MvkWAqvz4VjO/ScG+X0kX4Geib5tX/yQbxzDemO4zA1nuKP/+33uXxuDJdbJRD2MjWW5PSRAc4dH+JTX3yYrk31lEsGl86OUlMf5sGnuhEEgcHLUxw+cIl0Ms/Ohzrp2lSPIAhcOjPKmWMDfPAzexCle/MyL1sGtuMQUnxIwvXSruCRNdJGnry5MMorixJxV5AGT4zRwiyXs+Psja3DoYzjpG6xVRFY/gBTEDxIUgOS0nHrmW+B85mLvDz1OoP5YfJmHt3W5953K3tiWY71niIaJdPEtC2+dOI4f3PmNBO57Kqe0VUeH5viKxM+uQLdsvjjo4f5P+fOMJbN3vCcS4KAV1URBYGcri9JRkzbZjiT5itnTmJaFp/buo26FZINURSp9QV4pb/vlvPKoohXURAFgYJhULaWVtazHYexbJYvnThKzOPhuY5OPLfpKH43oNsm48Uk9e4ILb6q+5JcXI/NVdVsiMU5MzWJcc09cmhkmKF0mjp/YL7HbrUYy2Y5NTVB0Vz47uyMxFgfja9Jv5IoCPgUbYHp5EpwJRh58OWzRKsDNLbFuRISkeSVHf+aEA2vrOGWVd6YOsd6fz3NvhhpvcChmR4uZMbImSUcHHqy4+TNEqVrnAfTRqEi+XWTVJFp2bx06CIHTw2QzBYqHgSmiWHamFaFQBhmxYvAMC0Mq1I+c/su3z89KFvTiIKG7Sx/UOjgYNppSubIHdyzhUhlC7xw4BwvH77I+EyGdK60pmRwuSiUDAqlNOMzGfpGZ3n16GX2bWrmuf0baK69dcq3pjHCWy+eQpIlOjY20HN6iPaNDXi8N/7SC6KAqZs4toMw1y8iSgIuj4ogCvMlYIqmIEkS2XQlU1jIlbBtG1/Qg2mYGLqBbdskpjK45vSuBUHANCycVbhmm6ZF/1iCH7x1jnfODDKVyJIrlu/698u2HTL5Epl8ibHpDGf7JvjRwQs8vquTR7e3Ew3de8Jx4dQIDa0xPvn5h+jaVI8oiUyNJvmD33mBAz8+y5Mf2c6mnS1Ikoihm/z1H73GqSMDPPWR7Tz78Z2omkwuW+Ltl8/ywlffxe3VqGt6jkDYSzjqI5XIUciVcXs1JsdSuNwqhm4yNZqikC/j9bkYH0kQDHnxBe5dE6ksSggI6LaxeNgnCAQUN0k9x3Q5s2hZTVQIKV56rDGSeoWMa65nkZYozaysTsLBppj/C8rlA0hiDFV7CFnpRhTjCIIbsLDtNKbZg15+A9O4hKLuxBf4J0jS7ZmcnUmf5ztjP6An24vh3J5PkOUsX8L5XiNRLFA2Tb594TzfPH+WyXxu/lp7ZIVN1dVsra6hPhAgoGpokkxOLzORz9GbSHB6apKBdApREKgL+GmPrLyUxgG+fPrkHMnILLrX6v1+nu9Yx5bqahqDISRBQBCgbFqMZbO8OzrCG4MDDGVS1x1bkW+cP0vI7eazmzYT0JYvVywKAj+/ZQtfP3eGknX1fauIIh2RKDtq6+iIRGkOhgi73chzoi26ZTGZz3F0fJRXB/roT6XmZWGvYDKf5+vnz7KluoaOyO2XJN9pZI0iPxw7xscbH+C1yTN8pvmhe71Lt4RXUdlZV8/bI0MMpFLz07O6zoGhQbrj8dsuXxtIJzk+sdiAeXttLR2R6H1FyMJRH7lMEVmRkOZEI+64M/hSqHGFeTC2ji8PHKA3O4Fb0jAdi6Seo9Nfi0928d2Rw0iCiOXYnEwO0O6voWzpnEgOEFK8uKUbs3PbtjlxaZSfHOnBsiysezDwfC8iXT7JdP4lLCePYc0Scu2jbI4xkfs2Mc+TeJQmhtJ/TkDbSE7vwbQz5IxLhLQdpMrHaQz8Ig42eb2PnsTvYjklIq4HqfI+s+b7alk2P3z7PF//yUnGptKk88X7gijajlMpucuVGJ5IcvjcMB98qJunH1iH7yalO9WNEWbG07R117N5TxtHXj/PjofXo7pUvvu/D3DirR4SU2lKhTLP/9w+GlqrkBWJjk0N/Iff+jItXbV89AsPAyxy6gyEPbRvqufA90/wX/7x3+D2qOx5YiMt62vpvzDGa987zpE3LpKezdHcVRlECQKs29rM1/7wFYJRHz//D57Bf4uBueM4TKfyfOsnp3jlyCWmk7lF5U33CoZpMZ3MkcwU6Bma5uCpAT75xBa2r2tYlezvWqFcMnj8Q1vZ+8QGvD4NQRCoqQ+z++Eufvj1w1w+N8aGbU2IosDo4CwHXjxNfXOUz/zyI4SiPgRBwLZtBFHg7Ikhes6Ocv7UMHWNUapqQ6STBZIzOQzdZHoiTdu6GmansowNz5JNVYhnYjpLdUMYt0e9Zy+soOJBFkUmSslFmW4RgRp3mMu5CfpzE4uWdXAwHQvDtihalftNkhqQpBtJsNrks3+IYZxF0x7H6/tlJLkdQfAgCArMqevhmDhOCcvzGYr5v6RY/A6lwncJBP/Fqo8zZ+Z5ffpNLmUvY84FcQQEqrQ4ITWALKzsXmz0LJ2Rvh+RKBY5Mj7Gj3t7GEpX3Ig9ssIHu9bxifXd1PkDeFUVTZKQRHHePVm3LEqmSUYvM5CsDLjawhE0aeXf27eGBvj6uTOMX5dJEYHPbd3OZzduIeb14FVUVFm+WrLmOGyMV7G3oYGPrFvP186e5tsXzy/IcswUC3zj3BnaQmGeamtf0XepNRThidY2ftTbQ0swxPOdXextaKTeH8CnqrhkBZcsz5+XK/tk2DZ76ut5rr2TL585yYuXexZFvY+Pj3FiYpw6f+C+l7o2HYuB/BSXMuNczo7Tl7vaj+CTXVS5gjdZ+t5AEAQeqG/gB+HIAqIB8HLfZX6meyNh1+qDONlymQszM4xkFgZZYh4Pm6qqFygP3g8YG5rF7VGxgvY88b0nqlN+xcXzdTtQRJkfjR9ntDiLT3bxWNUmPlS/k5Dq4WJmDLek0pOb4OXxk7w0cQrbsZkpZ/l822NEbqIu4jig6ya6cWdKNH4aYVhpUqXDuJVGIu6HGM58CdspYzs6ZWsK26nURpetKUy7Bd2awSXX4cXGsJNE3Y+QKr2DR+4EHBoDn6dgDjGZ+x5BbRuavLo091IYHE/yp98+yOHzQyQyhfuCYFwPx3HIFsqc7RtnZCrF4XND/OwzO9jSufTgx+N18fl/9DyqpuDyqPzm73waf8iDoko8+qHt7H1qI5bloKgygbAHhIoc5S//84+gl01UTUHRFLY92EX3zkokt7mrhqq6MJ6Ai+qGCLVNUQzdQlYkvH4XqkvhkQ9tY9ej62Eucuf2arg9lQHvR37pYYr5MqIo4LlFU3XZMDl+YYQ//967XByaum8IxvUwLZtEpsDbp/q5NDTFU3u6+OQTW6mL35sXWKw6QGtnzTzJAJAVidqmKJIskU4W5rJKcOboAIZusmFr8zzJgErpRSjipbElzqE3LjDSP8OGLU1U1YU4faSfxEyWQr7M7GSGTTubCYQ8jAzOkk4WKBZ18tkSHd11uO5hD0u9J4omKsyWs0yV0sS0wHyfhigINHureHXyNOfSIyTKOSLa1ed/3iwzWapEuq8sIwgSsHQtcbl8kHLpJQQUPN4voKi7EQSZRY4lgoqAB0EM4fF9EV1/h3LpRcraw2juJ1d1nBezPQwWhudJxsbABp6teZIGdy3ynDfRSqCK9/fA8VpkymX+5NhhMuUyluNQ6/Px2/sfYX9jIxG3Zy57sPj43YpCEKhyvDQHQ+ypb6gMuFd4rlKlIn91+iS9ycSCnhAB+McPPsInN2y8oTSsIAhosowmy4RcbqJuD25F4cunTy5YV38qyUv9vXRFYzSvQOVNEUV+fdcenmxtZ1d9PQFNw6uoNy25EQQBVZKIuD0ENBdBlwvDsvnR5UsLmtrLlsWJiXEebmq574mGT3axO9LBj8eP05+f5CuDB+Y/2xhs5GMND9zDvbsxqr0+ttfUcWJinNniVR+goXSKY+Pj1PsDi4z7louxbIbj42OLSvc2xqtZF43dtuoazPX+GSXenOzl2OwwfdkZ0kYJy7HxKxp17iCbwnXsi7fSFojdtPk8UuXHF3BTVR9GnstoXKuWuRysCdEQBZEqV5BPND7Ac3XbMG0bURBwSypuSUUUROKuIAIC3aFGZEHipfGTFKwyn27az3O12/Ar76vJrCUMO4WDhUuuq/xIDUjCUkzZ5go/1aTqyhSnjCZVk9cvISgSLrkOTa7FcSxk0UvZmlwTomHbDi+8eZYv//AIo1Np9BU4P98rWLZDIlPg9WO9jEyl+PTT23nmgXWoysKvkiAKRKquDnbj10jKBqM3INUChOML64HdXg33XLmVqikLTHLU+OIHndfvnm8Gvx7+kAd/6ObREsdxyORK/OUPj/DCgbOkc8X3RAbRMC3GZzJ8/ZWT9I7M8MWP7mVja82KH4i3i0DYs2QmQVYkBEHAudIo7ziMjyQxdIuXv3uMQ6+dXzC/ZdnkcyVkRaaQK+P1a8RrghTzZZIzWSRZolw2iFYF8PpdXDg9QiaVxzRtCrkyja1x3LfR8H+76A408KKkkTNLHE1cpsNfO5+1FgWR7eE2/pxXmCwl+Yv+n/Drnc/hklQS5Sxvz5xnuDCDR9IWEJAbwSgfwrQGUbWHkOSmuSzGjSEIIqIYR1X3Uiz8DaXyS6smGoP5YdJGpTeq3dvKpxs/RounCUmQ7qvyhzsBG4eZOX+XqNvNv37sSfY3NuOWl0ewrgys1VVKtr54uYczk5Po1/U1fKp7E5/q3kTY5VrWfsiiSFMwyEe61tObSPD2yND8Z9acktWjTS00BYMruqbrY3E6I9EFmZTlQhZFWkNhnu/opCcxw6XZhU3DZ6Ymyeplqrm/5Z/dksYH6nbQHWzk5YmT/HzLo5UPBOaV5+5HSKLI/sYmftLft4BoWI7Dj3t7eLS5ZVVEw3EchjLpRWVTkiCwpbqajlWUD16PomnwytgFvnT5ICOFNGWrIqN8RZhCFAROCaO8On6Jv9Te5am6dXymdSctvqVL8TxeF5MjSWYm0vPVFV2bGyqCNcvEml1pUZiTKZSXjqKpcynkgODmE417+EDddhxAE2VckvK3xhX8bkEWvdiOgWFnAAfDToEoIAoqtlPEwcRxLIrGMCFt+9xSIiAgzLNbB8exKFuTgI3llNGtBKq0Bl+GssGffvsdvv/WORKZ/H2ZxbgZDNPi0uA0f/T1txidSvMzT24jsgyX6/sZjuMwkcjyn/7yVQ6dHVy1+tq9RLFs8O7ZIcZmMvzKx/bx0LY23CtwML1dyLI0319zLZZ6vFmmhSgK1DREaGiOLZ6BSkNeU1scQRAIRb3IikRyNofjVPp3InE/tu1gmRbJmRzZdBHbtqmuD6Nq9+5FvjnUSlD1MFVO88rEKT7a8MBVooFAh6+WVl81/blJXhg7zPFkL7XuCJOlFEP5aSzHJq4F6PDdWrjAskZx7CySWIUgLLeWXkKUqnCcMpZ566bdGyFlpClblYb2vdHd1Llqke/jAdSdgCgI/N3de9lT37hsknG7yOllXu7vYzK/UD4w7HLxKzt2EVomybgCSRTZWFXNYy2tHB4bWdAEPJ3Pc2JynD31DfOOz7eCIAjIgnBbTcOSKLKnvoHOSGwR0RjNZueN/u5niIKAW9Zo9VXxsYYHlhU4uF/QHa9ifSzG2enJBU36b40MMZJNE/N4Vpx9SBSLnJ6cnJcvvoK2cISN8Src8u29q/Jmmf916W3+8vK7FTnbJYqcbMdBdyx02yJrlvnrviP0ZWf4jXWPsC26uHTzqU/sZLh3mkwyT+u6GkRRxOVeWRDrrj8RBUHAJam4btKT8T5uH4oYIaBuYjL/PWbyL+FgE3E/iCbXoEk19CX/K7LoA8G+JgK41ABJRhA0zk3/EyynQMT9EJq0OtWiK5hO5vgfX3uDA8f77tuSnOXAdhymkjm+8uJRMvkSv/jB3VRHlqfodb/Bth0uj8zwb/7Xj+gdnnlPZDFuBNOyGRxP8O//4hUKJZ2nH1iHx3X3njfLGt4IEI76kWSJ7fva+dzfvUFEXZgjL4JAKOLD43MxO5UFx8HncxGJ+1EUCX/Qw8RIkqmJNKGoD69/ZQOttUZI8fBAdB26ZfJo9SZUQZ4XIRAEAa+s8Qstj/H/O/NVipZOb26C/vwUtuPgUPHZ6A41siPSfsttOZQBC8fJA8sdfNk4dq7y27m5v8HNULJK8z0oje46XNL9I7l8t7Czto6Hmprxq3evJ+jI2ChD6dQin4yPreumxrc6h29Nklgfi9MVjXF2emp+ukPFz2Ism1k20VgrXDEzdMnyAmKRKZcWkKH7HZqoENF8nE+PoIoyzd44Dg7KfUzKFVHkwcYm3hkdpi+ZnJ9eNk1e6r1MVySGX1vZ930sl+XdsZFFw/9NVdVsiN2eKlfZMvnmwAn+9NLb6LaFLIh0BuI8XN1Buz9GSPUgCSJZo8RwPsmRmSFOJEbImWXenOzDI1XK+zoDC6tVDr50lkOvnaeYK/Pr//IjfPWPXuWLv/0BAreojrgWa3KVxwoJfjx+YtnzP1S1gQ7/7Q1W38fNIQgiUffDRNz7r05DBCSagr/MtTKQAhIR98Nzn19Vdg+5diIgEnHvXbAOYRVmMlCJmA9Ppvj3f/ETjl8cwXgPlEotB4WSwXdeP41hWHz+Q7upi68sxX6vYdsOp3pG+ae/9wLJTOGOSQjfTTgOpHMl/suXX6Osm3zgwW687nvXHH09BEFg655W/vL3bC6eGkaURRTlarnNvNqMwzxzCUW9RON+hnqnkESB6oYI0aoAbo9KOOpjYiTJSP80tU2Rm6qb3Q0IgsDPNz/K51sfRxWVRTXAsiDxcLybX2l/lv/d/xMM28Se81lySxoPxjfw+dYn8Sxj4C4KYRBc6PoRbGsaUaybyyAtda0r/TGOU6Bcfh2QEcWls0nLgSqqcxLtDrIoLxJuuF04TsXcsi83zTszlxnMz7Il1Mgj1esIqm6S5TySIOFTtFWZfK0FPtC5jlqf/659txzH4cjYGFPXZTMAHm9txSUrq9oXQRCo9lb6Rq4lGgB9yQRThfyKFftuF6IgEHFXmtmvJRqGbVMyKxLS74VqkNlylr8ZfJP+/BRd/jr2xDo4kxric62P3etduyEEQWB/UzPfvHB+kQLY93su8rmt2/GtgFybts1gKsXJiYUCGAFNY3NVNfW34Z3hOA5jxRS/d/51dNui1h3gH3Q/zrP13Shz/U/Xevc4jsMXOvdxOjnGH188wFuTffxk4iLbIvU0esK4rsmsXDo9zM/88mN8/68PIooieslgpSUoa0M0ikn+rO/VJT9zqDwor3whZEGi1h15n2jcBQiChLBEA6WwxGVfej7php+tFLbjMDKZ4j/+5U84emEYa43d1+81dMPiewfOYFoWv/ThB2isDt03g9qbwbYdzvVP8Nv//bukc6uP7N6vKJQN/ttXXse0LD78yGZ89xHZaFtfy55H13HiUB9/9fuv8KlfeghtLiVtWzazUxlmp7J0bWnA7VYJRXzEa0OcO3EKl1th855W/MFKP05NY4RT7/YzOZ5i50Od95xoAAv67q4/54Ig4JE0fr7lUR6q2sDB6Ysk9CxB1cuWUAubgk3Iy+xzUJTNSGIVltlDLvcH+P3/CEluptI8fu3g2wEsHDtNPvufMY3TCGIYVd2z6mP0yV4UUaVoFSlYJWxspDV4Xl6Bblu8MHqCP7t8gJlyFtO2sB2HXdFWgqqbP+l5nYxZ4tc6H6PJe/flTgOaxqZ4Fd672JRcMk0uJWZIX+c8Xu310RaOIN3G9zvi9lDjW1zeM53PM1MoYDnOnBzt3YNHVpbsYzGtORWg++R5djPkrTIlW+dX2p/itamzCAg3dG2/nxBQNfY3NnFmaoLJfH5++mg2y8HhIT7ctR5tmZ4XU/k8746OzJtcXkF3rIrNVdWLnnW2ZVcIwlwprm072JZd6TsUFj5TDdviWwMnSRslIpqH39r4JB9q3ATcwBhRAMlx2Blt5O9veAzbcXhrqo+D0/08EG9hfeiq5LckS9iOjW07lAr6MlP2C7EmRKPRE+XvdT23aLrjOBQsncH8FGdSw4RUL59s3Mu+eNdabPZ9vEdgOw6jk2n+29+8zpHza0syBEFAlgQkUUQURURRQJxTXLoS1XScK1HMOdJrO1i2jWXZa1oiZNkOPzp4gaDPxc89t4tYyHvfDGqXgm07XBqc4p/+3vfWnGSIYqU+WZKuvSZXrosAjjMXWZm7LraD5dhYloNl2St2Fr4ZDMvmD7/+FpGgl8d2dqApd6eO/GYQBAFVk/mNf/Yh/v0/+Rrf+8ohXv/haeqaIkiyxOxkhumJNFt2t/IPuz6G263i8WnEqgOkZnPEaoLEqq5mzuqaIrzxw9NkkgXqm2PzAgL3Erc6x8Jc4KndV0v7MnoxbgTN/QTl8ktYxVHKxRcwysdwuZ9FUXcjSY2IggeHMpY9jaEfp1T87lxfhogsteDyfGzV227yNBBU/BStIgP5Qbp87fiVtatDP5kc4gejp9gYrOMTTbt4YfTE/GcCAhtD9Xxr6BgZo3jjldxBdESi+DXtrn6fhjNpksXFx9sSCqGIt9eE75LlJZ2fHSr19UXDWHG5zO3i6rvsvQsRAUmQKFg6um1SsvTbIoR3C4Ig8EhTCz/oubiAaAB85+J5nmxtR5Vufc85jsN4NsM7o8MLpkuCQHc8zvpYfNH4/cLRfvxhL/H6MKpLYbR3issnB+na3kJNSxzpGjNWy7F5a6oPEYFmb4QPNG5c1vMXYHO4jl2xZo7PDtOTmWaylGU9V4nG9v2dfOcv3qL33Dh/8G++w+Mf3jYfEFsu1oRoVLtDfKb5wZvOcyo5yP+8/DLnMyPsjLYTWEEARBDA41II+pZvmrNaGKZNsaSvqnxElkRcmoK0RDPo3YDf60JT1i6athZwHIfpZI4/+uZbvHN6EHMNSIYkCrhUBbdLoSrio7Opio6GGA1VIarCPsIBD16XgqLIiKKAYVqUdZNMrsR0KsfkbJbekRkuDk3TPzpLoaRT0s01KeUyLZtvv36GUMDDxx/bgt9zd1/Cy4XjOAxNJPnnf/h9phKLSxBWA0WWcKkyHpdKY02IzsY47Q0xamMB4mEfIZ8blypXFLoEKOsmZd0kkckzlcgxPpPh4uAUPcPTjE6lKc19bq1BLXLZsPgvX36NSMDDjnUNSNLK5TRvBkkWCYS9qJqMvMR30OVWiVb58fq1+UGDIAjEa4P8q9//HK989wTvvnGRidEkAhCtDrB9fwf7HluPN+BeMH9rVw3R6gBVdaH59Te0xKlrjuI4DjUN97YRHKBglhEQUESpUtIjLPaDWSuIYgSv7+9i22n08mFse5xC/kuQ/9JNllKQ5HZ8gX96W4Z9Xf4OarRqpkozvJs4xtbQJryy56YGtCvBUH4WVZT4VPNu9sTaeGPq4gLD26jmo2jp6Na9aQyu8/lvu4F1pRjPZsnpi3v7gi4XiWJhScfv5cKwrUUqVleQ08uULBM/qycaV4Jdhm1j2jbWXKS4UvkB86V91/zOlMvv6Z45gIDioc4d4X/3v0rWKFC2dT5Qt/Ne79ay0B6JsLmqmgszM+SNq/fdO6MjDKXTBF2uWz7ZKlm4WXoTC5v6GwNBNlfVLEluX//mu/ScGGTHY908//lH+Mbv/5hAxMex187zd/7VJwnFrvaD2jiM5FMookR3qHZFZZSiINDgCRF3+5kuZskbCzOFOx/uonV9DWMDs4TjPmoaIvPGfcvFXXsbbQ43sTvazquTZ+jNTlC9AqMWWZL41FPbeHxX5x3cwwpO947zv194l0Jp5Q6vHY0xPvXkVupiV48trxvYtoNHrRAQ3bQomxZuVV5SkSKRL+JRFVzKyi+NJInUxJZX51eysoiCjCJoq+65WA7yRZ2vvXScd04P3PZAXlUkQj43LXURHt7ezq7uRpprIsi3kDDVFBlNkQl4XTRUhxZ8lsgUOHp+mNeP9XLy0ijpXJHSbaotVY75BFVhP0/s7kRbxbW800hmi/zHv/oJo9Pp21qPIIBbUwj7PWxqr+XBba3sWN9ALOhDvAXhdmsKbk0h5HfTVn+1Tt60bEanUrx9eoC3TvRxeXiGbKF82/dPMlvkP/7lT/i3/9dHaKmN3FaUsDKYcZDFygO3oSXOP/uPn7nh/I9/cCuPf3DrgmmO41C2LHwBNx/9hX189Bf23XK7Dzy6ns372zBtG49y9eW0bnMD//r3P7e6g7kD+NrQmwgIdPhrafBE8UgaqijP/0ji2gZEFHUrgdDvUsj9CeXyG9h2EpwyjmNytdFFRBA0BNGHqmzH6/+HKGr3bW03pkV5KLaPWT3BaHGMF8Zf5KN1H6DGVYUmaggs7SWxXOi2hSrK+JWlg2ymbXO9e/TdhFdV73pgLVkqLqm49OPey/y49/Id265uWasKejiOQ8k0yRsGeV1nIpflciLBcCbNZD5HslQkX9YpmAZly6yMESyLsmlStirGlWuZ4b0Cy65kjpVVyguvBEHVw8cbH2BvtJOsWaLeHSGgvndUGp9obefA0CC9ycT8NMu2+c6lc3RFo7eUup3K53l7eGhRAHtdLM7m6uoll/EE3Hz2H32IC0f6SM1kUVSZZ3/hQb7xez/Gue4+dBwoWDqyIOKVVy58okoSiihRtk0MZ/E9HokHiMRX30Ny10ZAAgJh1UvBKpM1V5bmFUWB1roorXV3vga1WDZXbZgS8Lrobq2lo/HqoOmFkxfIlEs83dlJ3O/l8uQMFydm2NBYTU1wsULRv/7OKzzT3cH29oYV78fgbIpUuUQtt74hTiS+TURrpsW3G1W4Mx4mZd3kxXcu8MrhHrKF8q0XuAFkSSQe9rGls46PPLKJrZ31qGuUuYkEPDz9wDqefmAdPUPTfOPVk7x1op/ZdP62si/TyRzfef00dfEAm9pr18SEZ62QL+n82XcPcfLSGPZtRMpcmkxdLMhDW9t4dt8GWutvTfqWA1kSaa6N0Fwb4aOPbOLohWFeOHCWE5dGSedKt7XPA+NJ/vyFd/mtn3uMoG/1ykwDqSQ2Dl2R1TcSW47NG0MDPNPWsaLl3hwaYjiT4gMd66j1358qZwdnLnAmXfEjcIkqLd4q2vw1dPhq6PDVEncF0SQFTVTmfsu3nQWQ5VYCod/BNC+jl9/FNM5j29M4ThEBFUEMIctdqNoDKOpmVlVsfB1sx2ZbeDMJPcGPJl7hcOIYY8VxHow+wKZgN17ZU/HUWOa2VFEloFy9pkHFjWlbjOSTtPuqcLjizOuQN8ucTY0SUF03lJW/03DLSwfM7iSyevmeZHAs215RD6zjVLIRU/kch8dGeWNwgOMT40wX8rde+C5gOptnIpNjW+Pd6ZdVRZlmX0XRKGMUGC8mafDc/b6i1WBXXR3t4QhD6dQCta8f9vTwazv24LqJrLPtOIznshweG1kw3aeqdMfjNAdDSy4XigWwDAvbshm+NEYxX6KQKeEs8f4TBIGQ6iatl0johRWJFjiOQ87QKZoGHklFuwNKYHeNaOi2ScrIU7ZWnil4r6GgGyTzBUzLJl0ozUeccmUdTVHY3lRH2Ht1cJ/IF8kWS4iCQHnO/byoGyTyRQQqJjEuRaY64F9aj9+2yZV0Dl4eYiqTw6upBN0uAu6FZTslK0vJqphL6XZx/qVVMNOU7Rw4DqrkwSOFMZwSBbMi6eY4NorowqdEsRyLkpVBtwoIgoRL8uGSFg92bNvmxKVRXjhwlrHbiJr73CrdbTV8+unt7N/cMu9MeSfQ2RTntz/3BE/uGubPXzjM2b4JCqXVy+8evzjCK+9eoiYaoCrsuy9KqEzL5qV3LvLDt8/N32srhSQKRAIe9m1p4eOPb2V9S9UdI1Iel8rD29rZ0lHHq0d6+Narp+kbnV31vgO8ePA8uzY08oH9G25aQqVbFrPFAiXDQBQEApoLv6aRLZd5dbAf07bRRJmw24Vf1UiVSmT0MqZt45ZlYh4vqiQxns0iiUIlOmlZNAaCWI7NcCbDn504yrpIDFkUqfP7sRyHRLFIXtcRRYGg5iLkclE0DBLFIrptkSgV7/tGympXmJlylrKlU7INLmVHuZC9+qINKz7a/DW0eatp89fQ7IkTVLy4JAX3nPy5IsqrUtSR5Q5keWXkbbUYLY4zXBjFcAw8spuUkWa0OM7XRr7N10e+S1SL4pe9qOLyoozr/V38TONH5//uDFQTmHbzg7GTiAIky3kMx6YnO8lwPsErE+d4tHodcde9IZyiKN6xkrgboWxamPeglMiB+XfmrWDYFoOpFN/vucTXz51hNJu5o/uWKZYomxZRnwcBmM7l0SQZv0sjUSiSK5URBQG/SyPodpEr6xwbHOP40BgRjxufSyXsWdpF/XZgOTZJfTGx6smOMZCf4rPND6/p9u4UXLLCYy2tnJycWODdMpnP8cbQAB9f331DkYCcrnNqcoKp63o82sMRttfU3fDdWdca560XjhOIeOk/O4o/7OXUWxcR5vpRr4WIQKsvyuGZIc6lxknrJULa8gLIRcugPzfDdClHozd0w+zp7WBNiEbR0pkq3XgwaTk2w/lZ3pnpwSWpBJX3TspspbBsmxODY7x8rhdZEpnJ5tlQGwdgYDrJd4+fw7QdPrd/G63xCLpp8c0jZxhOpAm4NUYSaWzH4dLkDH/4k0Osq4mRKZZRJInf/sAjS5ZUlQyT0yMTvHVpgES+SMkweLCzhf0dTVf3yzHpzb7FRPEiiuhmtjxA3NWOaZc4l36ZtDEBjoNL8rMz+klmSgMcnv0bolorZSuLIrp5sOoL5M0k59MvUTTTIAhUuzpZF3gc+boX6VQyxw/eOseFganrd3dZEIBYyMtTD6zjF57fRTx8d4x+JFFkV3cTLfVR/uw7h3j53YurbpR2HPjh2+dZ31LNE7s7cal3t5Z5KQyMzfKVF4+Sy68uw6TIIi11UT726Cae3beBgPfO900BBH1uPvTwJlrqovzVD49w5NzQqsoboXJd/ue33mZrZx1NNeEbzjeSSfPnp46jmyZ+TWNffRO76+o5NjHGW0ND6JZJsljkqdZ2dtfVc3xijENjI2Tm1HB+ZfsuWsMR/uexw0Q9HsqmSaJY5P+z90Gyus63LpzjcmKWL585Sdjl5ld37GIyl+P/nD9TMXQSYEM0zkfXbeDM1CQv9FxAFESSpRItN4iC3S/4jc7nGcxPMZSfZqgwzXBhhrRRoGjpFM0yRUvneKKXo4lKqYssiNS4wrT6qmn1VtPiq6bWHabaFaLaFbq3B3MTfHv0+7yTOLzkZzY20+VppsvTy16fX17o09Dhr+KjDTv464GD/KdzL5IzKwGp44lBNFFmV7SFDzZsJaLeXX+Ha3G3h/ymYy854JcE4Y5mjuVlkirdsjgyNsK/e+sAp6cmbzifIkq4FRmXLKNKErIgIokikiggCiKiICAJArPFAlP5/E17T86NTfFmzwB/94l9aLLEl948yoMdzXTXVfG1d08xmy8ADk2REB/bvpFLkzO8eqGXvukEiiSyramOp7vXnpxnjCJfGThA8LoyqcliCvcqSnzuJR5pbuH/nDvDVD6/4P771oVzfKCzC0lYLKvsOA6zhTxvDg0smC4JIl3RGBvjCz0rrsWupzax5cEuLMtBEEAvG1w42s8Dz2zBd52HhSyKPFzTweGZQUbyKb4xeJxPtmwnqLpveMc6QNHUeXOyl0PTAxi2ycZQLfWe5bc1LBdrQjQGclP8t4vfv+HnRVNnolTRIf5A/Q6avfG12Ox9iWypzOWZWdbXxvjUrs18+eBx8uXKgGhTQzVl0+TIwNXI3lgqw6WJGX7z6f3UhgL8+p9/6+q6imX+wTMPki2V+X+/+yrjqSyt8cUDI6+msr+zmZlcnvFUlt94Yu+iefJmgpnyAOuCT1Dv2cjL4/+t0qhd7ielj7I//nk8cogfj/1nJoqXkAQZ3S7yUNUXyJsp3pz6X6T0MVL6GGljgh2RTzBTHmC6dJl6czMhtW5+WyXd4LWjlzl6fnhVNa0CUBsP8Kknt/Gzz+xYk3KcFW1fEIgFffzGpx4k4NX41munSWVXp+qSzpX48TsX6Wquor0+ek+zGrlima/8+BjjM5lVDQ5URaK7tYbPPruDR3a03/VyMFkS2dpZj9el4tEU3jjet+qM01Qix1dfOs5v/fxjyDeoUdatiunRM53rqfP7qfL68Kkqj7e0MZLJYDsOX9i2A6ikxzfEqmgMBEEQ+HdvHyBRLNIaqpzpoMvFJ9Z3453rqwi73fz6zt28PjjA//ehR4GKEdT52Wn6Ugl+bcceepOzHB8fp2d2hp7EDE3BEF/ctpP/c/4MI+nb662506h1h6l1h9kbWwdUzs90Oc1wYYahfIV4jBZmSRl5ipZOydIpWjrHkn0cnLmI6Vh4JI1PNO7lNzo/cI+P5saQRQlFWLvCAOm6dYmCyAPxNpq8EY4kBujJTFIwy/gVN93BOnbHWgmpnrueVbiXkISlB/wdkSjt4cgNv8+3i+541S17J23H4dDIMP/slR8zlssu+lyVJKq9XmIeL/X+AE3BILX+AFG3B7+m4lO0efKhSZXff3P2FH92/CjThcINt7urtYFvHjtLMl/A59IYTWbY1VLP6ZEJTo9O8JtP7mcyk+Olc5eZyeXZ1VJPQdd5q2eQf/z8o7d9bm4E0zaZKWfYFm5ZMF1CpGi/t8x66/wB9tQ3cDkxS/YaMYIjY6P0JRNsii/utbAch+FMhhOTC70zqn1etlTXEHbfOOswM5Zk+NI4+Uxxvipmx2Pd+EKLgwqyIPJ07Xq+2neUkUKKP+s5iGFbPFzTQVj14JGVilw4FaJesgwyRolTiTG+NnCUM8kx4i4/+6vaqPPep0TDxlmghHE9JFGkw1/DxmAjH6jbQc19HKG6XeimhQN4VLWilqWpN1WMyJd1vJqKLImIAoS8rnlzlaZoCEWSEAUBj6besi7VccCekwu9fkBr2EUkQUEWFEBAE32IgoRu5dFE35xZH7jlIEUrjV+OE1RrEQUZERFFdGPYRUpWhpQ+xqXM6wCE1PoFL0fHcbg4MMUbx3qZSq5OzSge8fGLH9jDxx7ffM/MiASh0nPz2Wd3YtkOX3/lxKoj6EfOD3Hy0ih1scBddai+Frbj8NaJft45PbiqZndFFulureGXPrSHfVta7hlhEoRKidvPP78L3bR480QfurG6JvEfvX2Bjz62mXVNS0eVWkJhPty5nrdHBnl7ZIjddfU83tIGzKnC4Mx/13J6mRf7epgpFPCrKmPZDKZ9db/agmGU6xqgBQRsx55fh+XYpIpFhtNpXui5gAC0hsM4VL7XPkVFoKKrf7eVfm4XoiDMZyd2RSqRU3uurGIoP0VvbpKB/CSDcyRkppyhYJVJLVF2cT+hw9e+poP8Vm/zomkCAnWeMB92h6B+iYXm1Inuh9LMuwHXDfpCHm1u5e/tfuCuy89ei8F0it9983XGryMZoiBQ5fWyu66eJ1vb2VlbT63fv6z3W+W5cfP5ZFHkiQ3tvNs/gt+l8UhXC5IokiqUmEzn+OGZSwC0V0Xnm7+vPFfupAmhT3bx4fpd7IouzJYM5qcZzC8/03e/4Jm2Dl7qvbyAaJi2zbfPn1+SaGTKZQ6NDi9SSWsLR9hZW7do/mvxxrePMNY/RSgWQJYr9/umfUtbQwiCQK0nyBc79/P7F15ntpzn98+/zgvDZ9gaqafOE8KvaIiCQMHUmSplOZ+a4GJ6ipxZJqi4+VDjJnbHm1Hv1x6NFm8V/2zjx2+8EUEipHoJKp55lZafVvhdKgGXxtBsiuND4wwn0nhUGRyHkUSavukEE+kcPZOzBNwuqvw+HBxODU9QE/SRyF9lr7dS7bkWAuBSZDLFEscHx6iPBKkOXC038kphJEFhutQ310g4i0MrYa2VkcIZJkoXUEUPRTNNPNBGyc4isvBhLgkqUa2FOvdGWn0PAOCS/Hjlqw2xmXyZAyf6ONM7vqrz53WrfPqpbXzssXtHMq5F0Ofik09sZXwmwyuHL62qEVk3LF48eIEtHXV0NMbuyYDgSnN6Ir3ygZsoCrTURfnM09vvKcm4Fp1NcT7+2BamkznO9E6sSnknXyrzlR8d41988elFvT8VpZiKCszO2npOTk1wbmaKx1vaKu7VssJgOsXR8TGagyGKpsFUPk9zMER7OMLBkeEFEoPXOrNegSgI+FSNQ6MjhFwuOiJROqMxdtZWCI2IgF/TqPcHGM6kuTA7zdHxMQbTKYwbSHDe77Adm5xZImMUyBhFMkaB6XKG6XKalJ6nZJWxnMqxyYKIKq6EUDnYdhLbSuA4BRyuKE7dHILgQVE2rOp4nq5+jKerH1vVssuBbpmMF1OMF1MULWNOfWjxMe2ItBDW7k351N1+GgQ0DW2JXr3UPe5dsh2Hvzp1gr5UcsEVEoCGQIBf3LKdT2/cjG8JKdObYbm9IQ93tvDfX34bWRT5O4/sRhJF2quibG2q4ZGuVkQB3IpCfbgiFqPJMiXD4NjgGDVB//z0tYRb1haRDKh4r9W5I2u+vTuNTVXVrI/FGclmFsgg/7ivh7+/Z++CDIXjOCSKBQ4MDixYh0dR2BCL0xa++fGXizqPf/IBNu7tWNY7V5VkPtq8hZRR4HtDpxnKJ7icneZy9saEThIE6txBnqpbz6datlPvCd1yO6vBmhANr6yxLrBUqOVvH9yqyramWvJlnSP9I4Q8LtriEVRZpmdylplsHpcsMzSbpCkaor0qwlPdHZwemWA8leHBzmbiAS+KLLGlsaLvrkgSmxuq8btuHKkRBIF1tXEmMzkO9Y2wXxQXEA2XHKDZu4vR4mlGC2eocnUQVOoIKXW0+/cyUbyA5Ri0+PYQ0ZpI6qNUu9cDIIkq1a4u3HIQTfRSsFIM508gCAIRtYmw2gBUvljn+yc4cm5oVVFzQYAndnXys8/sWBHJupMQBIGqsI9PP72N3pEZ+kZnb73QEjh9eYxzfRM0VAVx3+WshuM4vHqkh/6x2VXpsUeDXj700EYe3bm8B97dgCgIbFtXz7P7NjA5m11V9sxx4MDxXvrHdtLZtLicM1UqcXB0CEkQ8akq+xsqPU+CILCluoZ0ucQ7o8O4ZJnOSJTuWJyBdIqSabKrrp6qOYfhjVVVxDyeBedOEARUSeKTGzby5vAgzcEg62NxOsIR9tY38s7IMIIALcEw7eEwW6trSJVKHBobwSUrrI/Gcd+HssnXw7BNMkaBlF4gZeRJ6jlGCzMMFSoRzdFigoxRKQlxiQpB1UtMC9DhryWmBtgeblvWdmw7j2mcQdcPYuhn59SmSsCtSzdluYtQ5Pdu5zDvGPpy03x18BAX0uPzQ82lvoH/cnPonhGNu42Yx7NkRm88l70tD43bxUwhzyt9vYt8ODyKymc3beHzW7evqtzUsKxlPbevVEZU+b2EPZXKiIZwgCfWd/Bu3zCCIFAd8NJZHUMQBZoiIdbVxDnUN8yO5ro7QjRuhEoPyl3b3JpBkSSe6+ji3bERZq4pZRvP5TgwNMBH1l0NWBi2RW8ywcXZmQXraAgE2VVbv6Tb+7XQ3CqXTw5hGhaKWvGeatvUeEMzVgHwyipf6NhHqy/Ki6Pn6MvOMlvOkzXKGLbJ/5+9/w6T5D6v+9FP5eocJ+eZnZ3NGbsAFjmSAJiDKFGkcrJl2ZYl/4L9OPvq+vpxkCVbEiVKIhVIUcxBBEEQAJGBzTlNzrFzrHj/6NnZHUyenQ2QcJ4H5E53VVd1d/W33vOGc1wqFTKfrBLVfLT4IjxY28kjdV1Ue26eoMSdf6d6F6KtKkpb1UK2em9nC/d2LiyN37e5lfs2ty58nXjlNXyayicP7lrxuO1VUdoXOe5V1Hu3Ue9dqBvf7NtLs2/vvMdiWjMxrRJY6ZKfnZH3zz23KXAvmwL3LnidTL7M8YvD9AyvLxjf1FjFzz5zEOUmKkutB5Ik0t4Q4wP3b+cPv/465XWQKNtx+fGJHvZva6JBU2+p0+tEIscrJ3rXNWfi0RTu3tHC++7ZcstnZVaCpsjcu6uV873jPPfmxXXJERfLJt977Tz/tOnBed+JIAi0hiP880OLG5FujVexNT6fnDyzecui2358645FH1ckiU9t3znvMZ+q8lh7B4+1d8x7vDkU5jO79qzwbu4cXMmOMl5MMV1OM1JMMFSYZqgwzWhhBtO1ERAIKB4iqp92Xw0R1U+NHqbRG6fRG6PRGyeuBRe0my0G1zUxyj8ml/s/WMYZYK0tjnfWdX09zqdHuJQe54HqLnZGGpdsa2j0vfuyw+tFYzBEcJH2qCuJGUqWeVNbgZbDyfFxUuX5wiEC0BIK8RPbd657pi1nGJRXaJseTWXom0pQMEzu62yde1yVZe7f3Mr9i8QXDZEgn7l374LH38PyuLepieZgiESxOOdv4rou37p0gac6u+ba+jJlg1cG+ufJ4UqCQHskwu7alU1C4/URTr18kamRBJq30jZb1xJfkmhchUdWeLJhG3dXtXEyMUxPdorxYpaCZeC6Lh5ZIab56AhUsSNSR703fNO7RzaUaNiOQ8LIMpCfJmnkMB0bWZQIKV6avDGq9NCKN468VcQjaRvmrPpuhOsaVJaopbWZN+Y4V2/IN34c13W5PDDJ0QuD65Ie1RSZT79/P/VVoTsma349/B6NgztaeOlYN6eujK7rNU5dHmFgNElNNHDLyJTrurNZ+7VXMwQBmmrCfOCBHUSCSyvFnRkbx3JcttdUoco3tqSYtk1vIkmiUOSelqYVt2+oCnFoRwtnukcZmkit+XiW7fDqyV4+/b79q1Y2y5sGQ7kUw/k0mVlNf2eF1oa7a1po8YcXDTYc1yVjlOjNJJgoZsmbBrbr4pFkIpqXlkCYOt/qgu47BV8dep03py+RKGdxcNFEhYjqpzNQT1TzE1UD1OoRGr0xGrwx6vQIoXUqJ9lWL4X8F7GMU4CILG9BlOoQRT+rIRGS1LziNrcLMc1PR6CaVn+crmAtuqQiLlLT0KV318zOjaDG56fG50cRxXlB3EQ+z0AqRa0/sKTU6M1ET3JmQUVFFiX21tYT1tfnVVU0TZLFIuVFDAqvR6pQ4srkDAfbGmmNR+7Ie+jfF0Q9Xh5qbefyzAy5WadwFzg+Nkp/KsmmaGyubeq1oYF5+0Y8HnbX1FHlW/les/u+Lrr2ts79XcyX8YVWp9gqCgIRzcvDdZt5uK4y1+G6lQa829GSvmFEo2QbXMyM8OLEWU4nBxgrpSjbJqooU62H2BZq5IHqbewOt+JfRqf3ufG3CCp+Wn21NHprUIWbG2zfiSiXX0GSmpDlm+uEbhrHEEQ/srwZuLF2nkLJ4EzPGN1D0ytvvAj2djVyeHfbHdMy9U4IgkBtNMBD+zdxrnd8XdnzbKHMkQuDbGuvWTZw30gkMgXeONNPch3VDJ+ucfeOVnZ0LJ99+VF3L0XDpD0auWGiUbZs3ugf5Oz45KqIhiAI7OmsZ1tbLSNT6XXN0Ewlc7x5doAP3L992e0c12U4l+LF0R5eHx/gUmqK6WKeomWuSDT+273P0OALIl0X+LqA5dicmBrh5bE+jk2N0J9NkCoXsVwHv6xR5w2wM1bH/XVtHKxuotp7a2SebxT9uQmmyxXvgBo9TGegnm3BJrYEG2jwxqnWQqjSxtx+jPIbWFZFJlfTH8Pj/TCyvBlRjACrIGcbqBq10ejwV3NU7efbwyc4lRwioOiLEo0PNu2lwbu0VPPNxK2eitBlme3V1bwxPDTP/M51XZ7r7WZ3bS1+9dYPhCdLpQUO3pIo0BIOr/s1hzMZRnPZFWdPttVXs61+aanU97CxeLKjk69eOEcufW3Iu2CaPNfTzaZoDMO2uTwzzeA71AGbgiHubmxa1VxTuCoI1xXNjzx/hlh9GM2zvlhtsTnBW4UNWWFNx+ZiZpQ/732R3uwkbf5qOgK1qKKM6djMlLO8PdNNb26Cz7Q+yKF4J9oSGZi/G3uDnFVkc6CJrkALHf4G2n0NxPXQPxAJP5di7s/QfT95k4mGS7HwDRR1B5LUiiDcGNEYnkxztmdsnYpGEh97ZBc+T+XmMFbIUOet9ItajsNIPs14IcOhmoVtZzeK6VKeMzNj3FfbNqfGsRT8Xo1t7bXUV4UYHE+u63hHzw/ykYd3Eg5svDnSYjh9ZZTB8ST2GomRANTGAjx5z5ZVyUXezlxATSzI1rYajpwfJJFZWgJyKVTUq3p45r5ty34nw/k0f3XlBF/vPUuiXKDOG2RPvB6folKyLPqyCcbyGSy38lnvjdfTEogQVj1sDlXNGw6HSgX4+eFu/vziEY5ODeO6LgFVp8EXQhIEsmaZ7swMl1JTvD05yEfbd/KRtu00+sNrfo+3GtdXpDVRQUQgaeTozo2TMvOMqQGqtBBVehCffGNeLJbdi+OkkKRWfP5fRFHvQriDycM7UbJLdOd66c8PUqvXcCB6rZ1lrJimPzdN2igwLCRQxMVdxov2u0sq9EZxqKGJ716+tMBl+4e93Xx063Z2VN08E9GlIC3yvQiAss6WU8d1OTkxRl8qcYNn9h42Gh3RKPtq6xjPZedmcizH5cX+Pn5x3wEKpslrg4PzSLhHlumKxdkcXd4NfXxgGo9PIz2TIz19zejxpa+9TX1bNcFb5Cm2kdiQ1Thp5Pjh2GmGCwk+2nyIQ7FOajxhNFHGcGymSmmOJ/v43sgxfjh+mmZfnFb/0uw7YWR4c+YcR2Yu0OyrpSvQTGegiU3+Rlp8tXik2ydfdyOwrAFM4ySOPQq4CGIUVTuEJDUhCDKl4vewrQEM4ySIfmzzMggyqnoPqnYA17Vx7DEM4xiOMw6ujShVoyh7kZVKT7frWhjGEXBNRDGKaZ7CcVLIUguKdjeSFKdcehnLvIBRfh3bGcW2RxFQUdS9qNphBGFtZXjbcegbneFi/9LmRMthW1sNOzbVIYkCDi7f7DvLr22vzIBYrkN3epojU0M3hWhMFLJ8b/ACB6ubVyQagiBQEw2wZ3PDuolG/1iCsakMDVWhm6b3fhWmZXP84jDTqbUrTemawu7N9bQ3LL8oXkWiUOT5Kz2kSyXiPi93NTVSE/AjCgLj2SwnRsYYy2RxcWkMhbi/rXVumDlbLvNidx9T+Tw+VSFdWpuZoCgKbG2roakmvC6iYdsOlwemmEkXiC+iUQ5QsAxeG+vjG71nSZQK7InX89GOnWyP1OBXVIqWxaXUJH/bc5qT06MYjs2eWD0/2bmX5kAYVZTmkRjXdTkxPcLvnn6FS6kpfLLCg/Ud7KtqoNYbQBJE0kaJy6kpXhvv51Jqir++fAJREPjJTXuI6ne26elT9ftp99cwXkwxUUpxNNFNwS4jCiIx1U+VFqJGD88z5avRw1TrYYKKZ02ts46TBreMou5GlBrfVSQDoGAXeXPmKC9OvcJdkb3ziMZgYYaMWeSDTfvYG2lGleRFiUatZ+O17+9kbI7F2VNTR28yQd68NpMznsvxhZPH+X/vf4iY59Ykc64iqGsL2lIc113gCL1aDKSSvDLQz2h2oR/He7i9EAWBD23ZyksDfXNEw8WlJzlDbzKBT1F5c2Ro3j7VPj+HGhrxKMvHVzNjKcJVAY6/eI70THbON2N6dPUJQ9d1KdkWPdkphvJJZsr5OdU6XZIJKR7qvSFa/THiuv/dMaMxXc5wOtXP7nArH206RPA6528fEFF9NHijzJSzvD51ieFiYkmisTnQTNrMkTHz2Dj05Ufpy4/y+swZOvwNbPI1VkhHoJE6PfaumeVw7ASlwtew7WFEMYyLg2sNICubkaSKahOuhesauG4BARsXC8EVuKac4uA4k5jGWyBo4NqY5hks8xJe/y8hSdWAjVF+FdM4jqoexHWLuG4JGxnFvdo+Y+Ni4FIA16ocVxBZjULLYkhlilzqn2QmvfYgD+CxQ134PBqJcoE3Jwb5et8ZajwBPLLCw/UdOLiMF7N8s+8shmPTGohyV3UTM8U8J2ZGmCkVCKs6O6K1hFSdC6lJ4rqP9mCME9MjKKLEpmCMK+lphvIpUkaRgmlysLoZx3UpWibfH7qI6dhEVC9PNnctWTuLBr1sa6/le6+eW5eCk2HanOkZY3tHHQHvzSUaI5NprgxOrcvULujTeXB/J9Iqs3F9iRT1wSC6LPNq3yCZUpn3b9lMzOclVzaYyuUxbRvTcfjyidMENI2DzY2IgsB3zl3kraFhttVUM5MvcnpsfFkjo8XQ3hCjoSrE6e5R1qNwmSmUONM9ysMHFq8iThRyvD05xFQpT503wIfatvPx9p1o17X/bItWIwoCE8UcA9kkl1JTyKI4b5urKNsWf3T+TS6lptAkmWdat/GLWw/SEbxm6ui6LimjyOZwFX9+8QgXU1N8p/88HcEYTzZtvqNbSp+uv4v7qrYxUaoQjclSmvFikrFigtFSkqHCNOczQ7NKKTrVc2SjQkBq9Qh1nigNnihRbXk1FAEZEBDFwLuOZEBF8rfkLE6u2/1V7Aw3IgsigiCgCNKiQcGdeyXcHOiyzNObuzg2NsKF6al5mePvd1+m1h/gZ/fspcrru6HfSaZcomCahDR9xQCxKRheULW0HIdT4+OULAt9DW2lE7kc37x0gbdGhm+rktZ7WBoH6hrojMY4NjY61zJXtixeGxpkczTGcOZa25QoCDSFQtzV0Lji69Z3VKNqCppXY3NznLrWSv/USM8EsrJyzGA6NhdS47w0fpkzyVGG8ilmynlKs0RDk2RCik6dN0RnsJp7qtq4K95CTL95qnUbsioXbZOcVaLZF59HMq6HT9Zp8sYwHZu8WVp0G4APNzzAzlAHV3JDXMoM0pcfxXJtMmaeE8nLnE71UO+J0earZ5O/kS2BFtr9DQSWOO6dAseZxjSOo6h78Xh/AgQNx55Akhu52keseZ5Gdcvkc3+Epj+F7nmayi3k6uIlIUnN6N6PI0kNgEC59ANKxWexrd5ZojF7PHsKUapC1R5EEDTARhTDAKja/SjqIcrF76Nq9+P1/SSCcHVwcu3EbXgqzbne9XkZhPwe9nY1osoSRVNEkySKlolXVtBnJQwtxyFZLqBJMtPlPC+NdtPiD3MuOc6ZxBidoSoGcynylsHOaB0npkfoDFXRHoxxemYMn6xS7w3yw5HL1HgCqKLE88Pn2BGtxSMpTJVyyKKIIop8a+Asd1U3Lvmj01SZ+niQWMi3bkPCM1dG+dADOwh4b25l7tSVEcZmMitv+A6IokBdPMj29pWVMa4ipGs8vKmdLdVxvnfhMq/3D7KrrpaYz0ttIMBTWzcT1HVs26F7eoZTo2Psa6xHFAS+dPI0v3DoAM9s7WI0neHi5NqNnAI+nYbqMD6PRq6wtooIVAjgiUvDSxKNmVKBvkylhaHZH2FbpGYBgZAEkQNVjdR6Awxkk1xMTZIzFz+XkzOjvDrWXzHm9If5pa2H6AjNrx4JswN9TzRtZrKYoz+bpDeT4NWxPvZVNVDtuXNL6KIgEFH9RFQ/W4KVm2vWLDJZSjNZrhCP60nIVDnNqWQfBbuMLqpU6UGqtBAP1+zko033LHssSapBEHw4TvI6gYt3D2zXobwE0ShaBhfSo7w108tLExfxSOqiROOXOx+iI3B7evRvF8nZXVPLU51djOdzJIrXZtDKts1fnjlJplzimc1b2Fdbt+rZMdd1yRkGA+kUl2amOTc5SV0gwFObOmlQlq8a7ayuQZdlssa179J2XS7OTPF8bw9Pb146gXX98ftTKb5x8TzfunRhQWvYe7hz4FNVnurs4szkBKXZYX3TcXhreIi8YcybqwlqGntq6qj1rywhG6mqtI3ve2grvpAX/+wA+KOfvIdQbPn9y7bFS+OX+Zu+45yYGaK4iJF2wTIoWAZjxQynEyMcmxnk/Q3b+GDzLpp8N2fOa0OIRiU8FTCd5U2kTMeuhM3LZBiuVisOlLcwWJigLzfKhUw/FzL9TBtpbNdmqDDJcGGS48lLNHtrafPVsTnQzNZgC/WeKpSb4Gx4oxDFOLKyA9M8h1v4a1T1AIqyD0G4prJUycbZgACCBCgLszGCUGmfKr+G62Sx7SFcN1dpH7gOktyIou5FlhcO1FaO47zjOOtTLXEcl7HpzLr9JbZ31FIV9iGKAhHNw/117fgVjfc1V6RCS7aFKAjUegI82dTFxdQkX+s9zWAuyXAuTas/wjMt23htrI8ziTEmi/OD/6sOzoJQuUazZhm/orI33kBHMMZkMYdfVnm4fhM+WeXZwUtMFnNLEg1BEAgHPDTXRtZNNK4MTVEqWzdVhtG2HS4NTK6rlUhXZXZuqlsTEWoKh6gPBtBkmU3xKM9f7iFTrtxwC4bB20MjjGezlVaCXJ66QADXdcmbJiPpLIeaG1EkiYjXy7aaKnoTqTWdsygINNaEiQQ86yIapmVzeWByye/EcCzyswojXkXBryw+0xRSPeizBCRnGtju4tnIHw1foWxbqKLE3TXNtIeWblELqzp74vW0BiJcTE1xKTVFbyZxRxONxRBQPAQUDx2BWlzXpWgbJIwsSSPHZClNf36S3tw4V7JjDBdmGCpMU+9Z+cYnK7uRpBpM4zS2PYIk1b+rKhuO61BeYsbCI6vsijStaNim3cZ73u2yyNNkmQ9v2cpgJsX3Ll+a10KVMwy+ev4cl2am2VtbT1c8Tms4TNRT8eCoKFbZlC2bvGmQKBaZKuQZz2YZyqQZyWYYSKcYz+V4on0TD7e2rXg+DcEge2rreKGvZ16QmS6V+OPjRyjbFg80t1LlW3hvMWyb0WyG42OjvDTQx1vDQ0zN+jSEdR3DtimYN06iHdfFsG1KlknJsihZFmXLpmxb1/1tcWl6ap6i11UcG6soLnpkBV2W0WQJXVLQZAlNkmcfk9EkaU0zMrbjULbt2XMwKVs2JbtyLlfPqWRZjM7eQ96JF/p7qfX50WV50fPSZBldllAleUPbhB5v7+BPTxxjaLZ6YTsOZ6cmKVrzv6san5/7m1vWdOxYXWSeOM62gx3LsnrLsXlrqp8/uPgKF9MTc3FTZ7Caaj2AV1YRBIGSZZIsF+jPzTBYSNKdmeLLxjFc4BOt+6i5CX4aG7I6eWWdiOrncnaU0WJiUcfHyVKaS5lRfIpGeAUZQwGBuBYmroXZEepgf3QLQ4UJunPDnE330Z0douQY5Kwi5zN9XM4OcjR5kWZvDe2+BraH2tgcaCKsBu6YAXJRiuDxfhLDeBvLvEAx/yXK0gt4fZ9FkjsQhNXoxaco5P8Cy7yMou5FlKsAF8eepEJQrkEQgojCzTNguYpcsczgeJJsfukq1XLYu7kBTb12GQpU5jIc1537UcqCSFj1IAoCIgKyKOG6leDSmF0MLdfBxUWVJGzXwXRsHNclVS4iCQICAl5ZRZEkGrwh7qpqIqR6mCzmiGjeSmsCoEnyogvs9Qj4dOqrQnBhaNntlkIyU2QqlaOhOoQk3ZzrczKZY2QyvS7PD11V2LN5bQacjuvOVbRsp0LsBKFSjfrrk6cxLJuu6ji6LM+2IFTetzSrhHF9G9p6GwVqYwFCfs+6ZG4dx2UymSORKRALLVyfFFGaMwkrW9aCG8lV5KzyXM+uT1EXtFJcxfGpyg1bFkX2VzUuu0oJgkCNx09bMMbF1BQj+QwjuTTUrOEN3iFwXJekkatUMspppktppssZEkaOnFVippyltMbBZlW9C1W7j2LhGxTzf4EoeJGVbetOntxqODgYS1Q0toXqafMvNJN8J/zyu3Nu8UZRHwjyc3v2UzQtftjbPZdZBjAcm2Njo5ydnKDWH6Da58OvanNBsOU4s2TDImsYZMolksUSWaO8aDC7EmRR5NM7d3NkZHien4btupyfmuT3336Tlwf6aQ2HqfL6UCUJ03HIlstM5HMMZdL0JBOMZjJz96CWUJinOjfz9sjwXJC/Xoxls/zRsbfJmSambWPY9mw7q41pVz6Lq49NFwoLjAcBvnf5Eq8O9KNIEqokoYgSiiRe+//Zx/yqyse2bmdXzcpV8aOjI3zr0gUKV8/ruvO5dp6Vv0ezGexF7s9/euIoHlmpnJMkoYhXz2n231Ll/JqCIZ7p3EJHdGN8Z+r8AR5saeVLZ09jz8rHTuXzFMxra5gqSWyKxthWtbaK41vPnqJ5Sz1NnZXPUFhGkdN1XRLlAn98+TUupicIKhrva9jO4ZoOGr0hgqoHTZIRETAcm5xZYqKY5WRihB+MnKc7O8W3Bk/THojzeP2WDZdS3xCiUa2HuDveybeGj/DHV57n7qrNNHpj6KJC2bEYLSY4MtPNsUQPD9Zso9W38sJ5Faoo0+qro8Vby+5wJ3fHdjBUmOB8uo+z6V6Gi1NYrs1EKcFEKcG5dB9HEhdo9tbQFWxmV6iDJm8NmnRr3ZgXQkBW2pHkBmz7bizzEoXc5yiXX8Qj1SAIobntAFgkE+rYSUrF76N7PoDH+wkEwU+59CKGcXTh0QSB5Yvas8+7DjeSk5pO5ekdnl7XK4iiwLb2WtTrHI5FQaQjGON/nXmFBl+ID7RWJEffmWUOajqbQnF+PNbL/zn7GkXbZGu4ms5QFT2ZGV6f6Gcwl2Qwl6LK48d0bEq2yXgxQ8E0OJsc56mmLdjrqCr4PSrV0fWTOMd16RuZYXt77apnINaK3uHpdQ2BA3h1la1ta4tir0zP0JNI4tc0To+N49dUIh4Ppm1zZHCEJ7s28eimDrLlMn9x7OScHKxHUeiIx/jRlV5+au9OpvN5To6MEV7jjAZAVcRPYAUzo+VQMiz6RxOLEo247qMjFONMYpy+bIJzyQl2xGoXLMhvTwwxWqi0q22NVONXFj+f4XwlAyYJIo3+lQd5/YpGTKuU0NNGiZSxdrniWw3DNpk2sowXk0yUUoyXUkyUkqSMPBmzQMYqkjOL5KzSnGqSJIjE1AC7w200eKPcHV/cBPF6iFIUj++ncJwpjPJrOPYkinYQWe5CkuIg6Cy3FgqCF0VZeBzHdUiaKRLlJBE1TEyNLlgrinYJ07mxTHPWzFG0F0/UaJKypEIjVLKYF9KjNPlihNU7u334ZmFzLM4/OnCQoKbxzYsX5iqPV1G2bQbSKQbSqZt+LnfVN/DTu/bwR8fenpewsl2XgXSKwXQav6rgVzVkUcR2HUqWRc4wFgT2zaEQn965m6c7u8gbBuemJucRqbUiWSry1QvnbqgyMpbLMpZbeTjdqygcqG9YFdHoTSb46vlzK5oSLof+VGpV222JxTlQ37BhREMQBD62dQdfv3h+7nO1XWeumg8Q0T3c09SMT11bDDrcM0GkZnUiD7br8OpENydmhvDKKh9u3sNnNh2k0RteIr6pzGdsj9RTpfv5y5636clO88ZkH7siDTT6wms615WwIUQjqHh4pGYnE6U0r05d5Fx6iJDqRRak2fmKInmrzF3RDp6s27PicN9iEASBoOIjIHvp9DeyK7SJ0dI0fblRzqR7uJQdJGFkKNglevMjDBTGOJPu5rWp07T569gZ6mB7qIOoGrwthiWWNYhjjyGIYUQxjCTV42LjugWuD/QFZEQpjmEcQdXuRkAEQUcUg5XtXBNcG1wH0zpHufQDXHftQYeAhCBFsczz2PYwrhtDQEUQgwhrGLCfSeXpWWfbVDzspzriR7qOqUuCwK/vOEy6XCKo6iiiyI5oLfW+St9ivS/Ih1u30+gL0eAN4VNUskYZr6zQ5A8T1jw8UNdOeyCGIMDeeKWXvT+bxCsrdIWrCao6L412cy45wcHqZn66cx+aVBmy/JmuAzSvIB/q0RRiN+iD0T+WwLRsdO3mZF27h6eZTq+daEiiQG08SHQN708WRYK6zuv9g3z73AXyhsljne00hUIoksS9rc0cHR7h4uQ0UZ+HhlAQZbasLokiv3ToAN84e57zExMENI36YIDCOkwfIwEvPn39CQXDtBkYS7B/68J2w1pvgEM1zbw61sdkMcdXuk9h2jb7qxoJaTpFy+TE9Ahf6z3DWD6DV1Z4f/MWqjwLSYvjunPBkAD45ZXPWRWluWHSsm1RuoGb8q3AlwZe4UJ6kIxZJGsVyZpFclaFVFxtJxOAoOKlzVcza9wXp94TIaoGCClegop3xeo3QCH/ZYzyK5jmRRwnhWG8jmmeR5RiCIJvxcqGLG8iFPkfCx4fLAzz3MSLjBRHafDU8Uj1A2zyt8/b5kcTL3Exe2X1H8wiKNolpsvrW0PzlsHXBo/yiZaDt41o3O6eAQHoilfxS/sO0B6J8jdnT9OdTKyrKvFOqJJEWNdXPcjtURR+etduCpbJX54+uYA8uLhkDYOssXTVTgC2xKv4qZ27ebJjEzGPl03RGFGP54YVqDbgI1nDsVZ3MPe6/73ZcG/CobZWVbGnpo7XhwcXPCcANX4f9zevXTGzpjnGxOA0/pAH3VeRAI9UB1HUhdei5To8P3YJ23Wo9wT5zKaDK85bCIJAVPPyRMNWrmQmGcgnOJscZaSQujOJhiSINPnifLbtQbaHmjiZ7GOkkCBrF9FFlS3BBvZEWtkXbafeE1mynWA1EAQBWZCp9cSo1qNsCbRwILqViVKCgcI4FzL9XMoMMG2kmTEyzBgZunPDHE9epsFTxc5QO3fHdlLviSPfQqdd181SKj2HZV5BwAFBRpbb0LSHEYTrbqaCiM//KxQLf0s6+VtIUhTd+0k07T5EKYbu/TBG6SVM8ySiGEUQfMhK19pPSBDxeD9JKf83ZNP/AVEMoHs+iKY/yWoHwm3HYSadZ2w6vfLGi6C5JoyuzZ9DEQSBndG6edvVeAPUeCvkNKjqBNVruvvv3BagwReiwTc/E1C2LRLlIimjhCbJFC2TRl+ImO6l1nuN+O6Nr9wyJMsSfq+GJIqLlnFXg/FEdl2mf6uBadkMT6bIrKOdTZYk2uoiiGvor316axePbKoEYDP5Aj5VpT0Wwa9VekI/tms7A4kkZdsmoGkEdQ3cyk1cAO5vbyHm85Itl/GrKjGfl/wyN+Kl4NEUPJqCKAjrCjIs22EisfiNXJcVHqhrZzCb4svdJzmfnGSq9BY1/efQZ9vtJopZJos5vLLKpzbt4ZGGDryLkAgB5vUv26u48zm4c+9JFIRFNfvvJLw+dZ4Tyb55swWaqNDiq6LZW0WTr4omT5y4HiQgV2Y3ArIHv6yveV02yq9TKn6L65vuXDeFbaVWtf9SA+RjpXGOJI5RsIuMFsdo8TYtIBp9+UFOpE6v6Xw3EgXboD83Q+EfmI/GYmgOhfn41u3sqK7mlYEBftjbzaWZtRvISoJIrd/P3to6DjU2caCunrh39Yo81T4/v7B3Py2hMF85d4bzU5Orjm1jHi8PtbbxzOYu9tTUEdIr97ot8Sqqvb73pG7vQKiSxMe2beON4cEF37NXUdlbW0/9KobA34lcqsDl4/1cOtaHPEsuPvyrjxKvW0ggHNflYmoCWZTYEqql0Rte9XEiqofNoWqimo+xYpr0TaiWb9gEmSJKNHpjxLUgd8U2kbdKWK6DJIj4ZY2I6scjqRs6/CoKAl5Zp0aIkrMKZM0Ck6UkOWv+B1VyDEaKU4wVp7mSHeKtmfMcrtrFQ9X7iNyiOQ5JasHj/TiOkwDXQhBURLEaSW5ckHHTPR9Alrtw3TyCoCHJlZubIATxeD+Fqh7CdcsVOUcxCq6JIF4NrBU83o/juiVEafkWNU17CFlqxnHSCIKIJLWwKifdWRRLJhOJLIa5vAjAUmipi6CuQq5tI9Dsj/DRtp1zKgyyINISiCKvg/SKgoCmynh1hew6Bo8BZlK5dcnjrgbJTJFEprBmkz4AWRJprV+dd8ZVtEWXz5zUBvzUBpYeXPYoCvsb69d0zMUgigJ+j4aiSOuaTbFtZ8l2M4FKVePTm/fiuC5/feUEk8UcU8U8LhVt8hpvgKeat/BgQwd31zRTvYQ+uSAIRFQPGaOEg0uytPLCXrYt8lYlmPRI8ty8yJ0K23Wp90Ro9lbR7Kui2VdNnSdMQPYSkHV8SoVUqBswxKyoe1n/ZA9IUvOijwsIc/LptyoRnDaLnE4OEVa9NPtijBfTDOaXrnZMlDKkzfXJiq8FW6uq+NcPPDSvJQSgMRgipN858yEBTeNAXQPt4SiPtrXTm0pyenyM7mSC0WyWmWKBomlhuw6KKKLLMn5Vo8rno94foDkcpjMaozEYosrrpcrrw6ssIsqyAmp9fj66ZRu7amo5OznOsbFRuhMzjOVy5GfbpBRRxKeqxL0+mkMhtsWr2VVTS2c0Ro3fj3qdz9KWeBX/9sFH5s1+7KiuWVN3RnMoxH+95xFe+ebbjPdPcuCJ3ey4b+XWxPVAEkQ2x1Z3H3mguZXPfeDD60oOFc1BZgrPUx/8NKKw8nXoU1Q6Isu3TSWKr+G6JkFtH4oUXNV5PNjSRl0gsIAIRjw6D7e2r8s88sCj2+naN1+EwL9IWy9UqkczRr5Ckj3BNV2vgiAQUjz4ZZWZco7SIkpVN4oNlaoQBAGPrNIgb0z/23JwXIcZI8PbM+c5kbzEUGGClJkjZxWx3ErgW6VFqNNjTJWTTJQSlZu6mSVl5hgtTdOTG+Enmh6l0Vt90/04RNGPKG5d5bZBVO3AgscFQUKSqufJ2C7cRkSWV1bImDsnddeqtl0MuWKZ8XXIp15FXTyEKt8aoqFJMptC8Q17PUUW0bX1E43pVGHd1ZCVMJnMksmtbzhfkkQaqt695l+6JqNIIuv5VizHWdYLxsWlP5Pk+PQIqiTx4ba9PNrYiT6rZKJLCmFNJ6770CV52cW+K1LFQC6J7bj0ZGY4XNe67LmljRIThYrKWUz3EVukJetOwj/t+gCO6+CTdXyyhlfW0UWlInS3wYkdj+eDaPpjQIUQXh+uLPf33FkIi7eutflaebzmYa5ke2j2NrItuHRQ5pO87AxtI6CsI3Np5TidOkfeLtCdneR/XPgB91dv5uc3PcDb0738ac8rS+5rOhZTpZuf5Y7oHiK1y89NmY7N7xz5MU80d7K/pn7DB0pXC0EQiHm9RD0etsSruLexmZxRpmhZ9KeT/N7JN3i0uYOHG9sRRQFFlNAkCY+i4FVUPIrMcwPd/O6p1zAdm/e3buZDm7YR0VY/NyYIAj5VZVd1DZ3RKA+2tJE1DEqWiWVXxE4EQaj47MgyPkUhqOkEtcrsxjvhV1V21y6s3q8FflXj8W1dtBkaR549SasW4YGW1cUKNxN1gQB1gfXNPGbLRUYyBptiTcjixqyJhj2B45RxtdUH3EFNx/sOnxVJEGkJRdhXt74kWm1LFbKSJJPIUdsSR9EUpGXipYpBtrWi+utisFwH23WREW9KLLyhRCNvlejJTjBeSmE61pI9ensirTT61hf0mY5Fb36UV6dOcSrVzXQ5ScbMY86SC1mQaPPVc198F3sinYSVAAW7RHdumCMzFziT7iFrFUgYGV6dOoXpWvx82zPU6NE7RqHq3YJcoczY9PqJRjzsu+nu2DcLoiii3MAgdyJTwLKdmyJxOzGTXVfbFFRmNKoi7y7Z1Oshy9Ka2r6uh207JDL5eYpn12Mwm+Jb/ec4NT3Ko42b+GzXftqC0XW1gh6uaeW5ocsYjs0rY318pmv/kquP67qM5NJcSk0C0OgP0eS7s8ng5kDl5norTAVFKYbI2qpwq0Fci/JEzcPcF78bXdTxy0sHMlEtwsPVD9DgWXswOFYaZ6gwQr5YoMET5v7YXTT5Ksm6tFnAJ2s8VLOFgKIv2HemnOO7wydvn8bsdbAdl/Mzk9xd23RLZwGWgiAIaLJMjd9PDZU1zaspeHWF9mh0SfM0x3W5p66JKo+XL144QX8mibmIAtNqz8GrqHjfIYd9eXiK3/v6q4smqh7Y3c7HH9hF0Lvw+75RKJpCvCFKIDp/jR/rm+TN7x5j6NIo0dow93xgPx27Wzn/5mWOPnuK9HSGQNTPEz/zEPUdNbz9/ROceukcxXyZaE2Ij/6zp/EGKkTMMG0+9703OHppmFjIy2//xMPURm6eAqblZhhM/x9sO4sq19ES/lUsJ89I5i8w7AlAIuK5l7j3UfJGN9OF51DEMAWrH7+ylWr/MxTMbqYLz2M5GUw7QUDduaZzuDQzxUhmfiwU0FQeaGld8xD4VZx8+QJv//AsmUSOj//6Exz54Vke+thdVDUsTORXlAmD9GanuZKdWvIethhc12W8mGGmnCekeRdt971RbAjRcFyHS5lRvtD7EleyYxiOVXEvWGKx+c2tz6yJaLiuS8kp83biAq9MnqQnN0LazFGwS3Prq0fS2BZs5YGqvWwNthJVg3hlHUkQcV2XJm8NByJbGSiM83djr3M8eYmSbfDm9Dl2hjp4tPoAXnnjf9h/n5HdAKJxs1SXbjYkUbihczcte13tPavBROIGiIYkvruJhiTO0x5fKwzTxjTteZLLVzGcT3N6ZgzTsWnyR2gJrH/e7MnmzfzumVdIlIucmhnlxyM9PNTQsei2w/k0P54dQtckiR3RGjrDG1eduxm4EYJRtk1Op/r50cQpdoXbeKp+/wae2eohCRJBJUhQWbl9wid5CashImp4zccp2EW8UiVIq9KDPFq3dd511e6v4qmGXYsOe48VU7w51b3mY94MqJLE7xx+kpjHOyf28G6EKAjU+gL4FJXq/ss35Rglw6J7dIaJ5MJqVHt97KbN7y2GYr7EhbeuYFs2H/2Npzj63CnOvX6ZqsYYZ165iKLJPPlzDxOI+IjWVtpkX/vmEbru2sS2uzvRfRqa51pw6rougxNJTveNURsNrLu1erUwrElaQv8ISfDSk/wvFK0RdKmWat/TuNgUzX6mCt8n7n0E2y1QsobwejbR4DmMJHpxnBLp0nFk0U+17xnGc1/Ddtd2//zGhfMU36EIFtE9PN7ese709eUTA2w/2MHp1y9jWw5TIwmM8uJVFkkQ2Rdr4kpmkt7sNG9M9nK4ZvH7yTtxJTPFycQQObPMzkg91frG3/83hGiMl9J8behN3py5QvXsjEZA0ZesENStwoTpKiZKSV6YOMrr06eZMlLkzCKme+0LDSt+7o7t4L74btr8dfhlL5q4cMDYI2l4JI2oGqDZW8MX+7/Pq1OnKDkGb86c42B023tEY40ols1lW01Wwn/6/HPoqnL7ZUvWgVLZXLd87FUYpo3rVvwmNhLJbIF8cX3DoclMgV/8T1++oWD9diKdLa6bZEHFT8OwFicarutiO5Xx5lOzzt4HqhrxKYu7NS+HuO7j17bfw38+/gIzpQL/vxMv4bguDzV0zL2W67qM5DN84dJRvtd/Htt12R+r5+GGTXiWkTx9t8NwLK5kx3hu7CSyIN02orEWeCUP8iq8kBaDJIhokjb7bwH9Oin2Zl8Mn6wT0/zoi3znEdV3xxjUioJAW+jmOAvfNtykZXBzY5w//Gcfo1A2yOTLnOwZ5Vuvn2V8CTGKm4l8qkD/2SHOvnKB/rND5NMFOve3Uy4a3P30Po48e5Lvfe55dtzXxaGn96N5Vd7/C49w5NmTfO1/fpfDHzlIrD6ybFvPzYQuN+BRWpEEL5LgxbLTmIKX8dzXcVwDy8lgWFNzHTayGMKvduFRKkpQJWsExy2jy414lFZ0uRFnDZLVmXKZv+ueT0g1SWZvbR2NwfVXni3DIhQPoHlUXNfFcZwlK5eyKPJM4w6+1n+CqVKO/3n+RQRB5J7qtqUr5UBPZoo/u/IGr0/2IQsCB+MttPg3fvRhQ1aoRDnL8UQvu8LN/EbXU1RpoWVvvIstmNfDcR3OpXv50cQxjicvkbUKFO3ynIKJiECdJ86jNQe4J7aDuBbGK61OrUQWZaq1CE/UHKInN0JffpSe3AhFe3299ncCxrJZvn35In5V5dM7d9+SY7quS6lsrsuF+SrGZ/5hK2iUDXP2mt64u5njuOTyZcrrkIeFSvvD4Hhyw87n3QbXdSkb1qKu6E3+MDtjtXRnpjkxNcJvv/5ddPmq02zlO1QEkaCm0xGM8WjDJu6rayOgLnwtURD4eMcuLqen+due01xKT/H/vPV92oNRtoSr0SSJ8UKOc8kJRvNpCpZJRzDKT2zaze5Y3S1pSbpdsFybvFWi7JiYzs2T8bXtSbLpf4Mo1hIM/7sbei2P5EVaJ9EQEdHExQdZD8U7cFxnSefvoKLzS50P0hZYvTfVajGUTfP8YDcDmSRD2TSNgRCbI3FeGelHEkR+fc/dbI1W5gV/8+XvcXxiBMt1+b8PPMgTLZtQpWvnXLYtvnL5DN/pvchMqUBY0zlU28TPbN1Lja/SVjOWz/LN7vP8cPAKhm2zr7qej2/eyY5YZei5ZFn8f4/+mJ3xWsqWxXd6LzBTKvBIUwe/uOMAcY+PsXyWv7pwkrcmhkiUClR7/XykYzuf3HytFcZ1XbpTM/zGS9/hUmKa1mCYT3Xt5qGm9jWtxEcnRvjri6c4l6gYpD3Z2smnunbjk1ceHtdVhebqMC6VNduybX50/OZUT1aC7tOI1UXY9/gu3vdzD+O6oOoKvpCXcFWQJ3/2IXKpPH/1n79OXVsNO+7bQvvOZuo7asjMZPndf/QnbLlrE+Hq29POKQo6V5UyBSQc1yBbPonlZGgJ/RoZ4zTDmS9wNUoXkBCum8uSBA9gY7tpBFxsJ7tqaV6Av7tyiZnCfEGPgKbydGfXovM2q8Wm3c28+LW3uPB2D92nBth572Z8ocUlrEUEtoRr+HDzbr46cILzqTH+1bFvsSfayD3V7bT4o3OmxzmzzEghxfGZIY5MDzKUT1CyLR6o2cThmo6bYv65IUSjZJuUbYsdoWba/TU3NEzy/PgRfjD+FoOFcfJ2ad6NRhIktgVbebLuEHvDXfhkHU1U1nw8QRDoCDQQVCo9t1kzj+3e3PLezYTlOKRLJZxFTP5uFgzLJlNYn4Pqe6igPFvR2EgUSgaFsnlH9Ei/G+G4LuYSplgNvhCPN23mXGKcy+lpZsoFFps6F7MC5xMT/HD4Co82dPBbex6izhtYUGUNqTr/cs9DxHQff37xCJPFHDOlPCenR6+di2MjCgK7Y3X86vZ7eKi+fV4A9/cRtuus2R187XBx3Tyl4g9QlO3rfpVmbyN5O0+Hvw1dXF9FXBRENHHxvuiVknKyIHEg1nZDkvFLwXJszs1MULBM7qpt5OWRfgYyKR5r7uDZ/sscGR+hORDGp6j864MPkzUMPvPsV8iZxrz1x3Yc3hwf4k/OHuW39t9HrTfAeCFHulyaI+EzxQJfv3KOY5Mj/MKOA3hllecGrvD5s0f5hR0H2BWvxcVlpljgT84e4aGGNn5xxwFcZj1mZj8nRRQRRZGf27afuMfLiakx/ufxV+kIRdhfU5nJSJSKvDraz09v2cMH27bywnAPf3b+GFHdw+6q1c3YXEhM8j+Pv8be6jo+smkb6XKJPzzzNmXb5he3H0Bbhe+GIFR6PkRJQJbEm548cGyHoUsj/M1//TZDl0bxBjykpjI89Ml72Hn/Fl788mv8wW9+AYAHP3E3937oLr7/py9w4kdnQYBobZhQVRDXcfnCv/sKY72TuK5Lx55WFP12Vljnf26CIKHLjeTNbnqT/w1BkPHIDYtsWYEshvGp25jMfZtU8S0cbILavlUduWSZ/MXpk1jXDWCLgkBbOMo9TYur2a0Wex7cSn1bNfse3EYg6qdlSz2ByOJEQxAE/LLGr229n4lShlcmehgrZpgeu8QrEz3Ioog4++5dXCzXwZh1YXdx2Rtt5LObDrE1VHNTrsMNuWNVZGY1vLJ2wxPr3xj5Mb25kTnnYABNVLm/ajdP1t5Nh78eVVSQBemGPhCfVCEp1/D3N0N4M2CYFpncne9OfCfDsuwNd1DKFcsUl+jjfA8rw3XBtBYS9oFskj+/eJTvDVwgWS4SVj3U+4IEVG1uAbddh6JlMlrIkCwXyRglvjtwgSZ/mJ/fepCQOj8QFQSBmO7l13fcy5NNm/lm71nenBxkJJ/GdGziuo+uSBWPN3byYH0HVR7fbVPyuZWwHJvSDbptrwTXdWfNUsu4rL9q8v66x3i85mFkUUYR1nc7FQURXVpfFlEQBJR1VlJWi63Raj7WuYMrqRniupcnWzZzMTFNqlykaJn4FJWo7sWvqItmcF0gXS5Rti06wjG6wnEct+ILc1XCtSed4MzMOB9s38oTzZ1zikx/du4YZ6fH2RW/5jBd5w3wdNsWuqLxORWxqzLlUd3Lr+06iCyIiIJAZzjOd3ovcG5mco5oeGSFu2ub+Mim7UiCQFjX+cPTb/Pa6MCqica3ey/SGAjy0U3baQqEcV2XgWyKb/ec5zNb9qyKaNxqCKJA4+Z6/vHv/hyO7VSuHVVGma1e/NS/+ii2aYMgoM4qHD35Mw/xyE/eB4AkS6geFQT46X/9sTn5dFmR0Dy3R+LYr25mU+zfIAmVALwz/u8RhUrb/o7q36cS1139T8SnbsOrbJqtglQgCCJh/SBBbTfMdhgIyIhLqNFdjy+fPUN/Kjmvo8mnKHx06za0dQrdjPVP4fHr6F6VmuYYta1xBEFAXImMCgJ1nhD/cd8H+GL3W3y59ygF21xWhcojKTxW38XPbLqbLaGam+Ytt2HO4DV6iL7cJKZj3RAJMBwTBxcBgaga5Mm6Q7yv9m4iagBZkDZMesty7TkZXI90LVi4VShZFl86e5rvXLpIulzi3qZm/snBu6ny+hAEgcN/+jl+697DfO7YUfKmwT2Nzfz2vfcT93oxbJtjo6P8/pE3GMtl2RyN41dVAtrNlxW+CsOwSa9TQvU9VHAz6k/ZwntE40Zx/U3DdV36s0l+/8xrfLv/PNVeP7+5+wE+2r6DoKovWDVcKoTj671n+YNzbzBeyPJ3gxf5WMeuBUQDoGRa/MFzb/Gd4+f5+N07+eJDn+LvTlzkr187RdmyqO8KsX9rUyULnMry9bfP8uzJy1iOw+GuFj51z2421cZueSuV67o3TejIcu1bU9FwbmzGCkAVVdQbvCWFlCAfaniah6sewCvPz1jabkWZThIWDzIq30PlfnkzrgFNkvErKj5ZRRElQpqOT1VRJWlOEnMlSILAAw2tvFjXzGef/QoHahr56KbtPNjYNvce0uUSPx7u46XhXv71688BFS8W07E5UNMwr3LeHopQ5V2cdBctk69cPsPzg90MZyuEPWOUKVrX1kRdlmkJRtBmK4Mx3UtY1xmflY+eh0U6W13XpS+d4MWhXr7de3HO3tZ0HEzHqWSJb4Ka4HK42urjuswzyaxcF7P/FgQkWcIXXCQrLoJHlua/jusi6wqyrlQ+AuHa1K0nsHqp3+XO2b3u3K+e69o8IGTk6wi+LPiv+/dCEQcBGRZJCIiCgiisvirjui7npib5kxPH5g2Bi4JARyTKhzZvWff3f+SHZ2jqrKXv/AjbDnXM89JY7roSmFWf0gP8020P8bGWPTw7cp4j0wN0Z6fJGEVcF/yKRpMvzIF4C4/UdbE9XIsmKWueM1wL1kw0XNedk5K9imo9zON1u/mrvpf5i74f8+HGQ/hkbckPRBLEJUu9iiizI9jO++ru4XB8J7qk3pRFtGiXERHQRIUGTxXqLRyudF2XvzpzinNTE/zHRx6j3h/gP73yEp8/cYx/cvBu/KpGrlzmu5cv88WPfJySZfGfX36JL505xa8fvJvBdIovnj7BA82tfGL7Tn7U18OfnjhGR/TWEQ3Tttc9cPwebh6KJRNjnfMZ72EhbNflXGKC7w1eQBQEnmjczM9tOYC2jE+G67p8omMX3+0/z3ghy1A2taw8pmFZZItlroxN88q5Pn7v+29g25Ug7jtHL5ItlPnZBw/wdycv8tU3z+C6lfP6yhunyRbL/Orjd9NefQt++9fFlK9OXeBo4spNOUzWKnEuPTj/0K5LhZo7VPqxrwXeruvMP7lVwcJx16+Yt5EQEYkqEaLKwkHqL/e9xfn0CP+46zHqF3H7fW2qm68PHuWXOh9ka+jGTS8XnJtwXaA6+/d6eFVI1fkv972P3nSCr3ef5Xfefomvd5/jdw4/QVjzYLkOrcEIv7LzIAdqGubvq80n9JokL2m0+p/ffomTU2P8XwceYF91Pa7r8ulnvzI/ecC14Pb6Bxf9NS/yoEtl5uQD7Vv4zNa9xPT5gXtEv/EgfC1wHJdsscwPjl7i5dO9dI/MkC2W8OsqrbVRHtzVwfsPdhH06csax7muS8mwONU7yg+PXeFM7xgTqSyiIFAd9rN3UwPP3L2NzoY4qnJjHSWu6zKRzPHXLxznb398GlkSeP/Brfzck3dRF1udSd7NwvXXxvXv8Soxsh2H81OT/F8/eo7x3Pw5U5+i8qsHDt5QRatcNOi7MMJIzwQev4Z7nbFv27ZGdN/y1SNBEFBFmfZAnF/bcj+/6t5fUYGlQiArOi+V3/XVBPvNJsVr/jTSZoH/dPZrs39dY6K2WynFfa77eb4y8AbNvjgBWZ99A/Ntkz7Vch8HYotLb/3rbT9LtRZBvslKGgHFy7/Z/gs4OIgIKOKtIxq26/Lq4ACPtrcT93oRBYGHWtr4/Ilj/NI+G79a+eI/u2svVV4fBdNgf30DF6enAEgUCkzlczy9uYuox8Oemtp1m8KsF47jrltb/D3cPFi2c9Mcx/8homAZjOTTlG2bKt1Hoz+EvoIrtyAISMI14yNNUlZUFjNtmxN9o8xkCzy6vYOmeIjXLw1wdmiCc8OT/N4PXmMilePhbR00x8O82T3I+eFJXr88wANb22iritz8DOp1L38m3c9Xh16/uce7Dq6ToFx+Bcu6hKLsQtXuRRAqw6emcQzbHlnjK1qYxpmNP9F1YLnvzcGZzeYv/pvWJYXJUoaidWcnfcxZc9LOcIzfPvAAT7R08h/efIE3xoZ4qq2LsKYT1nSS5SLVXj+SIODi4riVisj8z2jpz+vt8SGeadvCwdpGVFFiopBjND+fUJYti4FMCsOuzD/NlAqkyiXaQ4uQdRfcdxxOFATaQ1H60kk0SabWF0CAueqOeJOqS4vBth2OXRnmv3z5JfonEogCyJKEIECmUOb4lRGOXR7m66+e5p9/7AEObW1BXkSW3XFchqZS/OXzx/neWxcomyayJCGJArjQP56ge3Sa7755ns88vp+P3b+TWNC3rvfpuC4TiSx/9aMT/M1LJ9AUmacObeXn33eQmpvot7FaTBXyFE0Tv6rNemVV4lfTduhJJvhBzxW+fvE86dL8jg5ZFHlfxyae6Oi8oeMffGIXr3zrGJeP9zE+MM3p166JBPzsv/rwikQDrq0pQsUh9bZjzdG86dicTvUv+XxQ8eDg0J+fXHKbJ+v2LPlcvWfj1TMWg4CAdpskIrPlMmXb4vfeeoM/OX507nGPrMxdE4IANf6rBlGVflXbcbBdh6xpIIni3CCdKsvrNoVZLxzHxTJv3fD5e1gdbNue6519DzcOQRDmsoCGY5Mzyhi2Pddb/k64rovh2Lw81stEsZLt6gjF5to0loLrQtEwaa+O8u8//jgIcKC9kf/3yz9gPJUlmSvwsUM7+I33Hcavazx3+gr/+7nX6ZlIMJrMUjRMvNqtXQOgElQt1dazXlSyhva8Ob1S6Tmymf+I66QQxTiB0L/B4/04AIX8n1IqfmvDjn+74bouDm7l/2dbo2zXmTdwCpUWmZxVwnadjdfIXgOKlkneNMibJpbjkCwXmSzm8coKUd2D7br8cLCbZKlIeyiCKAi8NT6MKsk0+CvZ603hGHfVNvK9vosookhHOEaqXCJnlNldVcfmyOp8YzrDMY5PjXJ+ZhIXly+cP7GgxapkWxydHOEb3Wep8vp5frCbsm1xb11F7tSwbfKmwUypQN4ykQSBqWIeSRAJqCqqJPPhjm38hzdf4M/OHePxlk34FJXhbBpJFHmmreuWCDa4rsvRy0P8h7/4IWOJLD5dZV9nA4e2tBANekjnShy7MsJbFwfoG0vwO196gd/65EM8sLNtnqmp67qMTKf5sx8c4TtvnEORJLqaqjm0pZm22iguLt0jM7xxvp+hqRSf++6bGKbNpx/dRyTgWdNv33FcxhIZvvTCCb7y0im8usozd2/j5568i3hoY5y9bxTfuXyJPzz6NlmjjEdW0GUZ23XIlEoYzuL3VgHojMb47cMP3PDxW7bU07KlnlhdiM17W+nYufJQuTvbZigIwh05x7fmX0NI9fJf9vz0DR202XdryMSdiqCmEdZ1fvOe+3i6czM+RcV2XRzXmXeRLNYzJwkiYa3S6z2RzxHQNPKGQapUJKLfOh8Qx32vonEnwrLd9yoaGwivpNDsDxNSddJGiRdGe+iKVLO/qgFVvNY+4LiVYLBgmbwxPsDnL7zNQDaFJsk83dK16HzGOxHQNQ53tc55mFQH/WxvqOalTB/1kSB7Wurx65XkQlMsRH0kSM9EgkS+QL5s3BaiUeuJsCPUQljduCAhaxU5mxpgqDA995iAiICKOydludg1riEICqtN4bmYsEZjrluBgm0wmJ8hUc4zkJ9hupzjRHKQwXxi3nY5q8x3R06iiDK+m+HmK4pENA9+RUMQKi1BAbXSEh3WdXyyiiQI/Giwhy9eOMFYPoPjwl9eOMmXL52iORDhTx7/yKxPiMSzA5eZyOcQBNgUjvOPdh+aG76u8vjm2pC+03eR6WKBiKbzQEMbh+qagEpyMKZ7CWnakv3k/2L//fyvE6/zL199lrCq88nNO+kMxwjOJuUUUWR3vJZHmzv40WAPF5NTtATD/Nz2A+yurpzLmelxPn/uKGdnJubW0rfHh2kKhPjNffexv6aBnfFa/u09j/LlS6f4b8dexXIcGvxBPrl555L+YRuNRLbAH333TSaSOXy6ym998iGeOrgF5To/i4/ev5NXz/bxO196gbFElj9/9ggd9TGaqsJz2xQNi5dO9fDDo5dQJIkn7+ri5993kJaa+a18/RO7+N2vvcIb5/v56xdOsKW5mgd2taMpy4eRV78qx3EYncnw1y+c4Ksvn8bv1fjAPdv42ScOEAksrqZ0O2HYNoZtk15BwV8UBBoCQf7r4+8j7r3x92GZNoIocM/796L7Vve7tl2HH45exCurPFTbua7Ej+u6lB2LvGlQnjXdlgURj6zO/tbXP4y2ZqKhijJ7o+3rPuB7AEkUeaxtE2+NDBH3eGkOh2flaV321NYtmS2FSoY17vXRGo7w3SsXebKjk5PjY/QkErSFb51ZUqV16r3M+Z0Gy7Yrxj7vYUMgiSJbwtU807KVb/Sd5fTMGP/yje+xJVLFplAcv6Lhui45s8x4Icel1BSTxSyO6xJQNB5u3MRTLVvxKyuXu1VFojF2TYteV2RigUoAH/F7qY9c6132agoetVKRLRnWbfstdgUb+Gzbw7T5azbsNUcKM/xZ74/mEQ1Vvx+/W8C0zqHIO1C1+xbsp+kPoii7EYSVP2sXG8u6Qqnw1Q07743CTDnHt4dP8srEJZJGHsOxuZKZWBBcX1V7/EzbvdSvwQR3tWgKhPnN/dc+53954Fq29jf23Dv372fat/BM+5ZlX+ux5k081rxp2W2qPD5+Zts+fmbb4tKiuizz7+55dNnX2BSO8b8e/sCSzzcFwnPPf6hj26Lb7K9pYP875kQWw45YDf/p3idW3O5m4cWTPQxNpXBclw/eu53H9nXOIxkAiixxcEszP/XIXn7vm68yOJXiuaOX+fn33TUXjPaPJ3jzwgBFw2LPpno+eM/2BSQDoLUmwqcf3cvQVJLesQTfev0su9rrqA77lx5QFgRUWcJ2HEanr5KMU4T9Hj54z3Z+5okDBH3vXqNkr6LQFo7wbx58hK3xjUmg918YIRDxIUoiqr26EL1kW/yLI1+nRg/w/cf/MV5l7YmHnFnmxxNX+MbAKc6lRjEch1pPkAdqN/Ghpl1sClYhr7N6vSH1vaWGZzYKc0oI69A5uVlqHDeKD2zuQpMlvn35AmPZLAFV48Nbts29182x+Jw8migIRD0e6oOVQKM+EOAnd+zii6dO8DuvvMy2qiqe7OgkfAuH0OacKt/DHYWrspHvYePQFKjI00qiyI9He0mXi5yeGefY1AiO684NycqCiCrJxHUfcd3Hk81dfGrTbuL66rL9kigS9urz/r4aOHgUmcB1EpKSKM61dNmOg3ObqlheSVvSTG69kAVpQVurJNXj9f/csvtp+hN4vB9BEFZeB13XxCi/sqFEw3EdCnaRkl3CdK1ZX6O1fS+6qNPsi/Evtj7Jp9vu5i97X+dyZoJHarcS0a7Plgp4JIVWfxWNnsgd761SEZExGS+Nz3tcFmRCSgiffGe0zVxFtlTGtG0CuoZyXeKvaBfJmlkMx0AURHyyD7/sX7dZ43rhzLZNZQtlRFHgif2b0dXFrwGvpnB4eytf+MFRMvkSJ7pHMKx9aIo81zZ1aWgKQYAtTdV0NS0dMO/ZVE9zdYTByRTHLg+TzBapCvuXrOGIgoCuyozNZPirHx3na6+cIRb08aF7t/OZx/fjv02yuMvBI8uEdI2iZWLa9my3SUXZTRIFVEnCqyiENJ3Dzc384p4DNASDGxZnnn71EvVtVfSeG2H73ZvmqU6pujyv7e2dMF2HgXyCmOYjZ5Ur7VQIaJJMSNUJKvqiyq2GbfGd4bN8/vJrjBTSc4/3ZqfpzU5zbHqQf7XrfeyI1CPfLqKRs0qMFBLU6CEimn/J7WbKWbJmkbgWxK8sz2Jd1yVt5smYeUpOGcOxsF17btm++lYX+/v6j6FGj1Krx9b+pm4yFEni6c4unu7sWvT5v/n4p+b+rcsyH+raOm/fPbV17Kldneb3zcD1vevvYX24GfRXFMWbKlP39x6LfHQC0B6M8n/tfZgPtW7nzYkButPTTBXzFG0LkcpvNKJ5afCF2BapYX91A9Ue/5rKzaIgoL+jDeHq6UiSOK/SKVz33FWJ09sBr6ShbrCQhiSKeKS1Z+REMQCs9lxEBGHpe9VakbPyjBTHOJs+R3eujxkjQdEqYbM2srEnvItfbv8ZZFGi0Rtld6QZr6TxaN026jzhDTvf24GJ0gT/9ty/nfdYXI3zkYaPcG/83iX2uj14c3CI4WSa92/dTG2wMqBcsku8nXibl6deZrQ4ii7p7A7v5n2176Naq94w6f3VoFQ2GZ3OYFg28ZCPmkhgyfuxIAj4PCottRFO946RyhUZn8nSUhvBsh1mMgUS2QJeXaU67F82+JcliZaaCMcuD5MtlhmYSNJeF0Vdon1KECBbMPjKj0/xty+fIuTz8MF7t/MzTx64La2eq8G+unoM26Y3mWCqUCBnGJQtE1mU8KkqdX4/W6uquaehiaZQaMPjIFVXGLoyzkjPOLIikc8U5p7bdbgL/xLu4FBRQ3tu5ALDhRTHZwaZKuVQRIlmX5SHajt5f+N22gKxBXMc3dkpXhi9xEghTUDRqNWDeGWVnFVmpJDmTHKUL/S8yf+98wmq9cCaSdWGEI0LmRH+y7lv8IubHuP99XuX3O7HE+d4fvwMn2l7gHuqFg+woWLY1F8Y4+WpkxxPXGSoMLFuA6fPtLyfn259cl37/n2A41rkzCHAxSvXIYsbU/UQRWFBmXYt8HtUWuujaEtkYf6+opQvMzOWoropRsinM9IzQawmhC/k3ZCMiCKJSIuoiqwGggDRoJfW+lsnk3ynoSriXxDsX4VXVthX1cC+qpXbKtYDAZYOFu6wymxE8dPiraLWE9lwUQ1ZkFZ0xL4eguBBECIIQghh1ZllAUHYmCx6xszy8tTrPDfxAjNGYuUdlkHBmu/t0RGoJqjo6yJe/1DQl0hSF/CTKBQxbJvGcIje6QQd8Sjj2RyZUqnSFeD1UuX3UTItRtJpNFmmYJjoikxjOETRNJnI5jBsm6lsfoFHSE+uhxcnX2SgMABAySnx46kfE1fjPFr9KB751nUUZAtlTKsyIxkNeCrqUMtAFiWis3MQpmWTLlRmkwzTIl+qDCF4NQWfvvJ1FvF7UOTKOpXMFZadCTRMm++/fYEvv3gS14WwT2dHay3qHWhoeBVb4lVs2aA2qPVg/yPbOfHSeZITGURRpJC9Zozcta9tWaKRt8r84aVX5qU2DMfmQnqc7swUZ1Oj/JOtD7EjUj8vIXkhNU5vbhoBeLRuC7+4+V5a/TEuZyb53xd+zGuTPbwwdplPtu4jpvmQ11jBu6XftirJpM08WWv5AbzLuUH+oPvrXMkOrztTJwkiXknHK995pbnVIG+OostxpFW4Uy4HyylyIfl5HNdke/RXCKobM18jiZXey/WirSHGv//l99NYE96Q83m3oP/8MN/53I/41C++j6qGKP/jH/8pD//E3ew6vAVBuvFAUpbEdWdYJFHk0I4W/t0vv39N+7muS94cwHFLqFKEsj2D5eTR5Rp0qYayPUnRnkBEwqe0oYhBhNnsn+MalKxJDDuB5RZwcRBRUKQgulSLKl2bWbBdg6I5guXm8cqNOG6ZojWO7RYREJHFIF65Hlm8lnFxXZeMcQHTSRFUt6GIofna6Li4rkWydAJBkAhrO1flCHuzcOdQieVxf/V2OgP11HuieDc4CJZFibDiI6z48Mor92+r2mFEqRpJbmS1n6AgCAiiB9ARWP86ZjkWr02/xbPjz5M0U3OP66KGIiprHgx+p2Hf5mAtm4O1S2z9HgC+eOQEP71/D3934TJjmQy//fD9/M+XX+ffPfkIf3H0JFf7HGI+Lz+1bxfTuTz/57W32FZTTbZcpj4U5AN+H2fHJvjBxW68qsxkLk9DaL6fw1BxiNR13/FV9OR6OBw/jIdbRzQsx5mLjSRRXPmyF64lMRzXxZoVcnFc5oiCKDAnRLEcKq07le1M21k2QktmC/zl88eJ+D1YjsPwdJq/fuE48ZCPLU3VqzrejcJwDBLGNGElii4tvZ6U7TJJc4aSXSQgBwkpYeQ1Vmtd16VoF/CusRUwbaawHIugEqKutYq6n30Qf9hH69YGmrvW3rkSVj00eMN4ZAXXhbRRZKyY5sj0AF/tP0GjL0L0ulbMqVKOZLmAV1a5v6aDjkDFjXxbuJZf6LyHgVyC7uwUb031szPSgH+Nyla3lGhcdfpcroc8bxX58uAP6cmNzDmextQgHlnHdR2Gi1OIiERUP15Jx8GlZBtkrQKGYyIgUK2F6Qw00+SpZluwbclj3alwXIuLqT9na+Tn8cp35k2mUtFYf8mwVLbemyUAKkLtG/dqsiwhrZOwuEBhXa7iLsPZr5ExLxFSd5AyzpAzuonpB2nwf5DJwo+ZLr2B61q0BH+CpsAnkGezyZnyJQayXyJTvojpZLlqyOZR6qjzPkGd/yk0qVJhMe0Ug9mvkCqfot7/DEVrlETxKIaTwHFNPHIDDf6nqfU9gSZdbZd0GMh8ibH8s+yM/VtqfI8jXT8s7LoUrCGOT/4zvHIzB2s/h/he9nhFNHpjNHpvTkuqJspsCzXzyebDbAqs7A/k8X5sHUcREIQAHu+HEaX1D7KPlyc5kjw+RzJCSpAGTz0NnjqCcgB5jTfkOn3heu+6LgXbIFHOU7ANgopOXPMjixLWbA/2eiWG32lcdydVzVaLKp+PZLFIrlymaFqMZbLUBPycHZ8gXSzx/3nmCVLFIv/9xde4PDVN1ONBkSS219Zwd2tF0SpdLNE7k6Q5EuKzd+3l66fPMZxKzzuO7dqzczfzYbrmLW9d9Onq3LWVLxrzTN0Wg+O4c5ULRRLnKheKLOLRKsG0YdqUjZXNXgslA3t2PjPg0ZCWu2YEYU6daiZT4GuvnOZ07xhf/OFRfvWZe2mpidx0Zea0meLlqRc4HH+IBk/jktuNlYY5mTqG4Rh0BbbikbxrJhqWa3ImfYJDsYViFcuhO3uJtJliX+QgYbUyiL/tYAeiJJJNXatyev0epBXirpjm4xOt+3iiYSu1niC263ApM8k3B07x/OhFzqZGOZMc4cHaa34fRdukbFvUeUOzKnPXvpRd0QZa/VEG8gnOpsYoOxZ+1pbAXxfRqGjFW2TMSkknbRRm/R2KTJUWuq26uOStElcyowDLloEvZPq5mBnEdh0kQWJvZDN3RbZSpYUp2CX++6Uv45E07ovvYXe4A9O1SRoZenOjXMwOMFFK0OKr4xNNj9AZaLohSa7bhYI1zmDuB2wK/cTtPpUlIYrikn2Zq0HJMG/bAOvthCiJWKaF67iVxT9dqJgobBAUWVzUkGk1cF2XYml9LYoAWaMbAZGQuhUBkcnCK5TscbxyE3W+JxnPP09f+i+o9j6CJFRaxfLmAAVziKC2FY9cjyx4KFgjTBffoDf9Z0iij6bAR+Ydp2CNMJz9OooYIuY5iCKGKFgjzBTfoCf9eVQpSo33EURBQRAkan2PMVN8k7HCc8S89yKizmWaXWwmC6/guCZV3vtQpNvrSvseQBFltoWa2BZquqnHkaQqQpH/fkOvcSlzhenyDAAB2c8jVQ/wUPV9RNXIhvTsO67LWDHFG1PdnEwOMlnKcLiqk6cb9hDTfZxKDgMuW0P1+NZYvbddm6nyFIP5igt7SA3R4eu46Wa5G43OqhhnxybwKAp1QT9v9A+ypTpO0TQJzcpBi4KAV1XJlQ2iHg+6LNMQvvZbN52KMaKuyAhUZq7e6e4cVaN4JS9Za74bdIOnAVW8tckJv0cl7K+0TE1n8mSLBvGwu+h8XsXx22R0phKbeTWVeLCS6FFlmYjfg1dTyJcMEtkihmUv2a1w1QejbFqIokBtJLBsq27Yp/MrH7iHvR31jCezpPNFnn37Em+eHyAa8PEzTxygJrJxs1JXYbs2Y6VR8laOopXHcioEynDKTJenyFlZZEEhpsbwyQFSZpLzmTOUnTJbAtto9rahSx4yZpqEMY3hGHgkLzV6LaqoMVocIW/lsF2bgBKgTm+gZBcZLPTz8vSLxLRqVFGlyduC5VhMlyfJWhlkQSaixoioUfJWjqnyJKZjMGNMIzD/cxzrn2JicBrXhVKhjKLK3Pv0XoLRpT8vVZQ4XNPBP9r6wLw5jLjuJ6TojBXS9GanuZKZmkc0bNfBwUUTZeR3rFuyKLEpWMXb0wMM5VML/HxWg3WtKA4u46UU3xp+G9t1mSilyFklXp++yEhxYY+q4zpMldKcz4zQ6a+ldpmhttOpHspOxeW0K9DEr2/6GLV6DEEQmC6nUUUZRZRo9ddxuGr33H55q8iRxAW+OfIyZ9M9fHf0VX6i+TGavBsnu3izkTZ6KJijTJWO4ThlRvOvkCpfAkAWvdR7H0QQBFzXxXZL5MxhitYkllsAXBQxSEBpxivXzrWmLIW8OUqqfAmPXE1I60QSVFzXIWP0kbOGsJ0SkqjjkxsIqe0Lep8VWcLnWf/ien1W5B8SgtGKFOC5Ny4TqQmRnsltJM/Ao6kr6povBdeFfHEF0fBlYNhJYp67aQn8BFPF1ylZY5SsCTZH/ikRbQ+OazCY+RtK1iQeuQEBiRrfQ/jVdgJqF+LsNWY5eYayX+dS8n8wU3xjAdEw7Bl8cgubwr9MVD+AIEhYToGe1B8zkP0b0uWzRPX9aFLF5Cuq78entJIoHadgDqOKIRCkWUO0MuP551DEIDXe5aUz38N7eCcmypPkrcqw5u7wTh6uvp+YtnEzTjPlHN8cOs6PJy4SVDyMFlMM5GcwHAsBgbene+nJTvKPux6hPVC9ptcuWAVem36N7459F4BdoV38SvuvvCuJxrfOXuCxrk0ENY0/e/sY/+Kh+wjqGq/3DfJq7wAl08RxHDpi0Yr/kyDMa2vzqwpRr4dz45O83j9IXyK5oNrT4etgS3ALpVSJnJVDFVXqPfXsi+xDF2+tRKssSexoq+Vs/xiZQpmTPaM0VoUWXftN26FvPMHYTAZdlWmuiRANVtpmRFGgNhqgtTbK+YEJBiaSjE6naa1d/BoeT2bpH09QNi0a4iEa4qFl50MkSSQ+6yBeEw7wU4/sI5Mv8fLpXl44cYVY0MtH79tJJLBxbWcVIaEUL08+T0iN4Lg2OTuL49pMlMY5kTqCLCgYdplqvZadoT0kjBkmyxMU7QJDhQFCSgSP5OV85gzT5SlEQSRnZbkndh/1nibGSyNMlScpO2VmylP8dMsvULQLDBb6SRgz9OW68csBGjxNTJeneDv5BqqgYDgGYSXKgejd9Od7OJc5Q1AJMVkaJ6rON6UsFcrkUgVcIDmZZnxgmt33b1mWaOiSwkO1nYua9tV6guyJNXIqOcJMOb/I3pU5zcWqmtWeAIookSjnKgaha8S6VxTHdcmYRSaKKUaKSYq2waXMGEP5mQXbikLFhbsrUM/TDftp9i3t8jlUmJhjTE/V3Uu1Hr3OTh0UUcHBxXjHcLhP9nBPbAe2a/PF/u/zVuI8DZ5qPtRw/6r6fO8EpMtXmCoeZ6Z8Ggeb8cKryGIl8+CR4tR7H+Bqb2TRmqAn/bcUrDFcbGzXwMWlSt9DW/Aj+JWlS4R5c4zu9FeYKZ+mLfBBgmobCApTpWP0pr9B2UkCsxkeKUx78KNUew/NW5g1RSZ8A4tDtlDGMG1c131XluvXi1A8wIHHdzHWN0E2lef+Dx0gXh/ZMGffoE/Ds4qBvsXgui6pXAnLdtZVFVHEylyFJHoq1QkxgCKG0KQogiCiSzUIgoLppKm0SEnIop+QNl/PXhK8RPX9CIiUneSCa0QUNCL6XiL63jkCLItegtpW9EIVJWsKyymgza61suin2vsgaeMCk4UXCaibkIXKjTZn9JIxLhHXDxFQO1kOruvOydr9Q7pm/37Dnf1vfdWHnJXHdCuJsV2h7QSUwMadGnAuNcLJxCCHqzp5qmEXf9335rznNwWqeXu6l5y19gRBySnRm+/dqFO9bagPBtlWU83O2mpCHp2ddbW0RSMEdI3HNm/i4sQkkijyeFcH9aEgM/kC+xrq8KrX2mJ0RWFXXS35ssHlyWliXi+N4RAe5do21Xo1j1U/Rp1ex3R5Go/sYXtwO22+tttCzu7f2caLJ7vJFst8+/VzdDVVsbW5et6Mnu04DE4k+fYb5zBth5pokPt2ts1bv5qrI+zrbOTKyDTnBsZ57Ww/kYCX0Dv8LTL5Et9/+yLD02lA4NG9nQR9+qrXQlEUaKmJ8OnH9pMtljl6aZjvvnGeiN/Dk3d1rWoQfTVwcJgojWE6Jk/Vfojx0ijfG/smhmMwURpjujzFXdF7GC0OM1EaZ3OgzObAFlJmgpyV5UDkboJKiOnyFD25y1RrNdR5GjmWeIuJ0jhxrYbIbCVEAL4488c4rk1UjXNP7D6OJ4/weO1TQGXuY6Q4yERxlHviDzBVmmCyPM5EaYzJ8iS1eh33xx/h1ekXyVjzu4Huemwndz22s/KebIc/+bdfXUB+r4ckVBQnw+ricZkiSvhkFctxMOxrLXLLveZV+GQNSRAp2SbraURZ169DEkRafVX8iy0fZKgww2tTF/jm8NvcHe9iZ3ihXbooCPhknVZfFfWeCMoyP8qkmZ1jTNtCrYjXBbcCAqooYzgmJXvhwqpJKtuD7ewJb+bZ8Tc5m+nlQHQLnYGbW4LfKFR59hPRtjGUe5asOcCW8M/iVyqfpyjI1wiXICAKKgG1hSrvPjxSNbZrMJp/ibHC6/jVlkWIRkUQs2CN05v5GonyWVoDz9DofwxZ9FKwxjif+ByS6KUj+HE8chV5c5SezFe5kPw8AbUNr3ytOqSpEiG/B4H1jRgYpk2mUMJ2XOQNGIK+mbAdh6F8ktcmr92UvbLKtnAdXaG1ZREFQeC+Dx3Y6FOcg9+r4dXXrwJUKpvkCuV1kUhZ9MyJF4iCgoCIIgYQZiVHK48JuK7F1aumMjw3SsEcwrRT2G4JB4uSNQYI4DpcJSVXoYhBPHLtgqFtSfAgCioOJq47v7xb7X2QoezXmSi8QFPwE0iCB3AYL/wQAYE635OIwvLL4eDlMcYHptlxaBO+RZQ/pkYSDF4ao3VrA7G68Fo+uvdwA3Cc5Kzy1NoSSq7r4jpJLOsCqnZ4fcd2HVy3cm+KKCGUFa6htWKylEEVZR6s2UJnsBaPrFKyryXZAopO2THX3M7guA5ZM8tQYWhDz/d2QJUl/tF9h+b+/peP3D/374c723m4c74ASszn5YM7tvJONISDfHzPjmWP1ehtpNG7dBJvtXBnvRlMy8a0HHLFaxX+smmRyZfQFBlFEpEladGh6a6map44sJmpH+U52zfGF35whMf2b6apKoxHlSmbNqMzGV4+3cNrZ/vx6Sr3bm/lrs3z46F40Mf9O9s42z/G6Z4xvvPmeQzLZmd7LWFf5T6QyhU51TvGt18/RyJbYFtLLY/u68S/RnIgSyJbm6v5qUf3kSsaXBic4OuvniHs1zm8o23ZanzeStOXP40sqLT5dqJJS6kvudiuXZmvEEAURBRRxsXFcIxKy1JpAkVQaPW1oYmLtxzaro3hGCSNJJIg0+htIabFyZoZjiXeokqvQRIkSk4Jl6tZfoFKE1JlvtidTYoX7DxTpQkA2nybUEQFcCv+KwJIgoz0DlGK3rNDJMYrc0KmYZFJ5JYMtgQBgoqO7TokyoVFtyk7FhmzNPveHEzHRhElDKfigF755BYnHlfj8PXO1a57VRQEAY+ssjlYh+GYHEv0sifSuqy87WpQdiqZeRGBgOybp6YgCAIeSaNol8lZxUX3j2kh2nz1yILEaHGKkeLUu4ZoeOSKpJomRxEQ8Cp1BNSFxA3Ap9SzOfzpBY8ny+cpmGNzF/pViIJE0ZpiqnSUmdJpWgJP0eh7HEWslOHG8q+RNrq5t/a/Edf3IAgiYW0Lllvm7Mz/Yrp4nObANTUiSRTxezU0Vaa0igGyxTCTymOvM3t+K+HgMlHK8vzoZfJWmbFCBgT45a7DayYaNxuaIuP3aMiSiLUOt2jbdphK5tZXrRIkmGvZu0qKpSUzXi4uU8VXGC88T94cQBLUWQUgAcvN47iLz4uIgoq4SFC5nMKPV2kkqh9gOPcNUqUT6L4qbLfEROFFdLmGuHdlHf8LR3p58wenaOqsXZRojPVP8+0/eZEP/fIj7xGNW4hy8YdIchOKum9VruAAruvg2OMUC3+DaZ5dN9HwSDqyKGE61qxnxsbCBSRBWNIbJ2MUkQRxzbOIlmsxVhpbMG/wHm4uXNdlPJnlxZPdGKaNYVqUTZvBySSJbCVAvDw8xZdePEHQq6MpMqoiURMJsK+zkarQNTUjWRL58OEd5IsG333rAi+c6ObcwAQddTF8ukrRMBmcSDE4mSTg1Xhk7yY++eAuAt75vxFRFNjeWsNPPrwX14HzgxN84bkjtNRE5443mcrRN56gZJjsaK3js4/vp6Muti4pdU2R2bepgZ94aA9/9uzbXBqa5Cs/PkXQq7N3U8OSr5k2p3hl6m/xiEFq9NYliYaASFyrxnQMjibepOyUsVx7dmaimbSZJKiEEYCIGsUnL96KFFLCtPk6cIGgHMTFJapWUbBy5OwsDWITHtGDXw5w9X4nCRJ+2c+RxBuElAib/Jtp9DQx4W0nqIQQEAirEaq0GibLEwzm+ziRPMJ4eRT9HdYDE4PTDF4aA8CyHLbf3UkgsrialYhAsz/ChdQEr0/28lDtZnzXuYM7rsNoIc3Z5Bjg0pud5uj0AHuiTYwUUowWUwCUbRNzkdaogm3guA4eSWE9QmEbkn4Jqz72R9uJqDeuSy7OcicXsFx7ngOfgIBf8TBZTpI0Fl8gFVEmqHjxSjppI0dqie3e7bBdg7w5SsbooWwnsN0yWXOIkp3EdsuVhvvrbk6WU2Qo9wOmSidoD3yYRt9jqNK1Mn+idBbHtRgvvsF06SQALg4Fcwwbk6w5P/MlCAIeVSEc8DI+s1AAYDUYm85gWvYd76UhCSJbQ7X85o6HmSzm+Lvhc7w9PXC7T2tRCIJAyO/Bq6tk8svLSC8G23EYnU7T2XzzdcTzZj+Xk79PyR6n2f8JAloXqhhGEnQK1jCZ8oUl9hSWJRVL7VPnfx/jhecZzX2Pau9DpMpnKJqjNAU/MTfPcSOQZJFCrohxAwP1f9+x1v7eiuv68sFMufwKbimLL/DrKMouhBXkiV3XxrZ6KeS/QLHwNSS5ZU3ndD1iahSP5MFwMiSMJIZjom2galm1HsByHU4lB2n2ReeyjQ4OA/lpXpvqpkoPEFTWlhgo2SV6cj0bdp7vYXVwXJeB8ST/46svL+k/0TM6Q8/o/Bb0nW111EYC84gGQCzo46cf209NNMCb5wfoGZvhePcIZcNCVSQifg+HtrZwoKuRR/d20lITWfSYXk3l8I5W/B6NF05c4dzABENTSS4MTiAAPo9Gc3WYbS01PLK3kz0d9XNqVeuB36Nx345WUrkCX3rhJKd6xvjbl0/h92h0NVXdUGuqKIjE1Dj7I4dImUl0SedA5BBhJYIiKpiOybQxhYAwNyQOUK83UnbKqLMVDl3S2RHaw0Chj5JdIYGu6xDT4mwP7sJ0TBwcHql+Enm2kqmKGg9UPULaTOORvEiCRI1ey/bQLibLEwgImI6JKqq0+TrAdSnZJRo9zUTVGNp18rvb7+5k067K2iQpEoGID1lZfEhfEkUOV3dwKjHCS+NXaPJF2B9vJqJ6cVyX0UKK50YvciY5iiJKdGen+IOLr7A31sREMcvpREWoKWUUSZbzc9WOqxjIJijZFnF9bSa0V7EhEV5cC/B43e4N0VMPyF4ERBwcZsppwkrgmjuuIBJTg1x2BhkvzVCyDfRFj1nZo+yYlNdp9Hcnw3bKTJdOMpD7HrZTRpOiSIKK6eRm21IWImVcQZNC2E4JF3uBHJ/hpEEQyZb7EN5R/m/wPkxAXXgz9nlUqqP+dRONwfEkZXPtUmm3GqIgEFR1dqr1zHjznE6O3rFEAyAe9uH3ausiGqbl0Dea4MF9N+HE3oFE8Sg5o5tq34N0hH8JaXag0nXtSgvVEhWN9SKkbiOobiFZPk3RHGYs9yyioFLve3rJfWzLppgvYxoWhVwJs1wpYXv88ysqZtnk8ol+ykUTeQ3EWZZEHtjSRsTnIeLzzDML9OsqD2xtI+r30lIVIXhdJjLg0XliVydt1VG21FcR8tyZc2g5q8SF9BAD+alZCXJrTb2WXcFGHqpZvp0FHMqlFwEZf+CfICvbEITFgyDXtTHNExRyn6dUfBYQkOWlzWNXQru/lYgSJm1mOJe+wPbg1jk55o3AllAdXcFaXpq4xEghybnUCCDwpb63mC5nGczP8ImWu6jS1zYbUnJK9OTfIxq3GoIg0BAP8UtP372m/apm3b8XQzTo5eMP7GJvRwM94zPMpPOUDAtNkYgEvLTWROlsjK8oEuLVVA5tbaa9LsqVkWnGZjLkimUQBPy6Sn0sSFdTNdGAZ9ZLY3FIksgTB7rY1FiF36MtmPW4ikjAyxMHugj7PYwlsgQ82ob5aiiiws7wnkWf2xLcvujjjd6F3SNxrYq4tjDpdlf0nkVfQxZk9oTnt0Zrks7mwFY2B+a360XVGNHo0hLh4wPTxGrDuK7LkR+eIVwVZNfhzQvuPZXjSjxev4XvDp1hMJ/kTy6/zquTPURUL67rMlpM05OZRhDgYFUrVZqf58cuzotjqnQ/pmPz5lQ/uyINtAXiiIJAT3aaU8lhirZBZ7Bt2dGHpbAhREOX1A3TVK/Ro8iihOE49OZGafc3cK0sJdLgqcLBJVHO0JsbYVtovk+G7djkrSJFu1wx47rF+ta3AiU7QX/222SMfrZGfoGQuglVCjBTOkvOHFx0H0X00ex/iqzZz2j+x3jlOup9DyCL3tnnAwiIdIU/g3KdSRrMzsYsIvsZ8Gk0VIU4fWV0Xe+jfyyBYa5dKu1GMFXKEFS8aFLl0p8sZTiRGOTh2i2o7zK1laVQFwsS8uuMTqVX3vgdsGyHvpGFgg43gqXaOh0qREIRQ9eRDJeyPc144Ue4rK8lbylIok6d730kS8eZLP6Y6eIbBLQugtqWJffJJvO88ewpLh7rZfjKBJMjCb76v5/D65+fQTYNi/4LIzR11lJVv3jWcDEoksR9W1q5b0vrguf8usb9W9q4f8tCL6CgR+OxnZ08tnP5AfbbiYliiu+Mvs3bM1cYLkyTs4oL3JZXwjP1B1YkGqp2L6ZxnHLpRwiCjC/wG8jy5gUJE9d1KJd+RCH/eYzyayDo6J5n8Po+u+b3dhUt3ma2BDczWZ7mbPoCmwOnORw/hEfaGBWdGj3EBxr34JFVTiQGSJslLMfmjeluGjwRPtS0jwequ9Ykbeu4DikjxXhpfEPO8T2sHqIg0FQd5pfXSDRWgiSKbG6qYnPTjVWiRUGgJhJYktSsBrIk8tj+zavatiYS4AP3LB74/0PH6dcus/3QJgYvjZKYSHPpeB+tWxsWJRqiINARiPOLnYf5/JXXGcgnODo9PxbUJYWD8RZ+rvMeYpqPmO7nrak+MkaJem+IwzUdHJke4PXJXhzXZUekDlmQODYzyOX0JI7rck9VGx5p7ZWsOy6y6vA38sr0KQzH5GTqCo/U7J8r1SiiPDdvkTKzvDB5jCZvDQHlWq/eRDnBpewgpmvhlz14pDs7W74YZMEDCFjO4hJklpMnaw7iVxqp9z2IKEg4roXp5ChZ04vuE1CaqfXeTR33ci75Ofqy30SX48T13YiCQlTfwUjhx5TsGaL6jnfI2S4eHAS8Og3VoUWfWw2GxlPkCuVbqjz10sRFDldtpt4bBiryka9MXua+6s4NJxoFy+BMcpRzyTFmygUkQaDOG+JQVSvNvjCyKOEC//vCy/hklZ/ddAhBECjZJi+MXuZMaozt4VqeaaoEWlOlHC+OXcYFnm7cjl9Z/NquiwcJ+9cX6Fi2Tf9ogrJh3fSWtrC2C0n0kigdYSDzJVQpjmWnyRgXSRvn0aSNn3+p8hzGo9Qzmv8+ZXuKjvAvzA2wLwZZlYnXhYlWhxi+MoFt2uQzRRxrfguQoinse2gbBx/fSU3zzTGyezfBcCy+OfIm3xx+c85vabVQRJlqLUS9J8q20OIzatdD97wf181XqhSl56hUNv4Zktw2t465rkmp+HXyuT/FMs8gCCE8vp/C6/0UsrJ+suaTvTwQv5e0meFE8hTPTbxAxsyyO7yDJm8jynUiHuuBKAi0Bar4pHaQu+MdTJQyGI6FLirUekK0+GNr6iRwZ1s1unPdGLMy8ncabNcmbaYZLgwzbUyTNbOVgVvXRREVNFEjpISo0qqo99Tjl/0b4lnyboLlWIyXxhkrjZEwEuSsHKZjIggCqqjikTxElSg1eg21ntolB55vFKZjMl4aZ6o8xXR5mqJTxJjt59ckDb/sp06vo8nbRFDZGI8iQYCSneN8upuJ0iCGU0STvNTorXT69y8w2XNcm5QxRX/+DClzEts18UgB6j0d1Hs60aVKO5rrulzKvk3RzlKlNTFe6iNjztDk3UKTdwsjxSsMFS4gCypbgoeIqnVzkuxX9zecIn3500yUBinbeRRRI6410uLbRlBZW3tuuWiQmqrI2h7+wD6+/8WXscylk2+KKPF003b8qsYbk71cyUyRNooV8ugJsCvSyAO1m9gZqUcSRD7bcZB7q9vIm2Wq9ACtgRhh1UNPdoq/Gz7HaxM9iIJAyihiuQ57o40crGqdS9KuBXcc0dgRakcXVfIUOZm6TNrMEVNDCIKALEi0+uqp1qJMlhO8Pn0GRZTZE+7EL3tIGllOJC9zLHkRgJgaIq6Fb+8bWgeCajsiMv3Z780OdUuAS0yvSJ1Joo5HqqZgTTBRfBNdipE1+hjNv1iZz1gUlZtdQG2hM/Qpzib+N93pL6NLEQJKG/Xe+xnJv8jF1BcxnAwBpRUXh6I1Tcmeoi34obnB8avwe1TqYsF1Dx7nimV6R6ZpqYssaf5XsMq8OnmFom2QMUvENT9F2+C+qs0gwNGZPqZLOcKqlz3RZsKKlzOpYUYKSQzHQhEl7q3qJKjonEgM8HcjpxkrpqnSgry/ofJ5Fq0y3x46geFYRDU/zzTuXscMwHwkywW+PXSGZ4cvkDZL6JKM7TiUbJOXx7v5mU0H2RdvRhElXp3oYaKY5bMdB5EEgZJl8o3B07w+2cu91e081bgdURCYLGb55sAZ6rxBPti8c8ljxyM+okEvoiis2RTRdSGRKTAwnmBz880ddA8onXSEf4GR7Pfoz/w1kqAjCRpepZmmwEeZyL+E7a4tSF0Jmhyj2vMQ/ZkvznpnPLTs9v6Ql/2PbGfrXR2E4gFOvnyRpz57P1UN17XHCCDJEsGoH3/Yi7yE2dVaUciVGOydQtVk2rvq5h7Ppgu4LgTDS6mu3H5cyY7y8uQ5srMkY2uwkR2hFqKaH9t1+HzP82iSwlP1+4mqAbJmke7cKGdTgwQUnUdrd3F/1XZq9PCKxxLFKB5PxR28kPsc5dL3Zysb/xxJasJ18xTzf0Gh8FfYVh+iWI3X/8t4vB9FkhY6cV/FSHGMC5lLKxy94sodVcMElSCjxXGem3iBC9lL1GjVRNUIHkmfVZlZGdVanF3haxWcnFniVHKIk8kB0rM3/MXw2fbDtPoXBjJlu0zCSDBjzJAwEiSMBNPGNH35vgXbDheH+dLQl+b6zVeDJm8TByIH1h1ECoJQUd6hUmmZLE9yOnWa7lw3U+Up0maaol3EmBWJkQUZRVTwSl7CSpgqvYrN/s3sCu8irIRviHBMlCY4ljzGVHlqTfvdG7+XNu+NS9zars259DlOpE7MPVar17Ivso+q2fYdy7HoyfdwJn2GwcIgM+UZslaWkl3CciveKrIoo4oqATlARI1Qr9ezI7SDrkAXurQxLZYlu8Sl7CUuZC4wUhwhZabImBkMx6g4pc+SQo/kIaJGqNaqV13li6pR9kX20eBpWPR50zF4c+Y7zJQrnRSmWyZnJvErEVLGJPfEPzi3re2YjBa7eXPmO0yUB5CQkQQZ0y1zKfMWW4L3sCv8IAEliovLYOECQ/nzhNUasmaCGWOU3twptocOM1y4RMIYI2elmDZGeKLm5/DKlcqP67oU7AyvTX+N3txpTMdAFXVMt4yAQFthFwei76NaXzlxchXxujDHXjhPy9Z6otVBXGf5/hxBEPApGk/Ub2V3pIHRQpq8VUYQBCKql0ZfmIjmm4tsqj0Bqj3zK1cP1GxispjlawMnGC2kcQFZEDkQa+bnN99Doy+8pDDFcrjjiEajt4o2fz2pZJaUkaUvP0ZMrWTNBUEgqgZ4sHoPfzv0AjNGmmfH3uRk8jK6pJK3SkyXU+TtEgIC7f562nz1t/kdrR0BpZWO0CcYL7xG1uhHFBWCStsc0dClGM2B99Ob+ToXk3+OInpRxCB+pRl7xb52gYi2nc7Qpzmf+COupL/Etsiv4JFr2R79Ffoy32Yw+ywuNoIgIaLgVxZX7ZIkiXjYT1XYz9g65zROXRnlnl1tSxKNsm1xIT1KWPUyUcqQt8poosyJxACKKDGQm2FnpJGB3DRHZ/rYEW7kyEwvqiizK9LEG1PdxLUA+2ItVHuCiIJIrR6i2Rebq2BMlbPUekJIgsg3ho5xd7ydKn392RfbcXh+9BJ/1XOUJl+Ej7buptEbxnYdLqYn+Kueo/z+hVf4j/ueosUfY2uoloupCRLlAnHdR9mx6MlM0+KLMlHMMlXKUeMJkLPKJI08+2KN6MuULzVFoa4qiN+zvjmNYtnk9JXRNRCNyqB1WN9NUK20IelyFR3hX0YWvahSGICofhfbYv8PIW0HAjKiKNIU+BhBdSuGncTFQhZ8eJVmPHIdPqUD2ylw1eNAEYPU+58iou8jqC7sqw+onXSG/zGy6EOTl84e+ZXKvFHce8+qqiaiKOIPeWnb1ki5YNCytYGapptftZBkiVDEi/QO4nLl3AiO43LgvtW1J9wOnEj2kjRyuMC98S18ovkw7b5afLJGyTH5Qt+LaKLMI9W72Bysp2SbjBUTvDx5jufGTzJSmEEUBGLa6lo4RCk2SzYc8rk/plT8HiDj9X2aUvHbFAvfxHEmkaQ2fIHfQPe8D1Fcvho7WBjm26PfX/nYgogsSOSsHABZK8f5zCUucBmPpKOK6mxVfuUb9O7wjnlE40xqmL/qewPDsYhrfiRBWtRy550hyOnUaS5mL5I0kuSs3Nx/eTtP0V6cvCeMBK9Ov7riOV6PveG9bA9uXzfREBFRRIWyXeZy7jIvT71Md66btJletO3ZdE1M26RgF5g2punJ93Ale4VL2Us8Xvs4Ld6WOeKyVqTMFMeSx9bsLdLub6fZ24x8g+GU4zr0F/p5aeqluceaPE00eBqo0qrImlneTLzJ0cRRBguDlJyFa/tVCVfDMchZOcZKY1zJXuFK7gq7w7t5qOohIurqWzsXw3hpnFenX+Vs+ixjxTEMd/HKWNkpU3bKpMzUosR2KTR7m2n1tS5JNGaMEWzXZE/kEeJaEwICU+VhXpj4S96e+R5bgoeIqDW4rkvGSvDy1N8yWR5gT/hRmrxbkASZjDXDhcybHEv+AFlU2Rd5DGl2rmuyPERMa+Rg7GkGCuc5l36V48nn2B1+hN2RR3hr5jtczhzh3tiH8Eg+BEHExeXtxN9xIvkC7b7d7Ajfjy56Kdo5unMnOJt+BVEQeaj6J+cqKCth74NbiddHqG+vJhj18+gn7yFStfLvTBZFGnxhGnzhVX/mV1HtCfDR1j1sDlXTl52h5JjENT9bQjVsDdeiraNtCu5AoqGKCo9U70cVZXaGOmjyzg8EPJLOQ1X7uJQZ4HS6h4Jdoje/cEagzVfPffHdVL8LKxqy6GFT6Ceo+v+z99/hcSTWmS/8q1ydE9DIGSBIgDkNOZzEySONNNIoy5Isx02fwwZv3rvP/bz77fru3vXeXdu7a39aW5Ysy9ZIsmak0WhyIIfDYc4JRM6hG51DpftHIxAEQAIMQ4487/PgAVDdXV1dVV113nPO+x7XVgw7g4CIfoXAUBJ0Kl27cMnl5M0pwEGXwrjlKvJ2HJH5m5os6rQGPo/j2Ljk0r4UBYkK106kiF4K7mZ0GhF9E7oUIW0MYNhpBCRk0YVbrpxp51oIQSgJj5trIzdMNI6eHyRfMPC5tWVbDFRRpslbji4pKKKMR1bpy0zhk3XqPWH2REuTMM9MDzFZSKGKMq2+CvaUt9GTniRt5pEEkXZ/FWHVw/pQLR2BeQIaVNxsizTikTR+NHCUWCFzU0RjIBvnrdEuBAQ+3bCRR6vb54jBpnANk/kM3+89zivDF/hqy046gpV8v+843elJwrqbwUyCvGXwsboOXhw4y+XkJBHNTayQxbRt6ryha4YsggCttWVEAp4bJhpHzw/y7N5NKxLnCYJAQOskoM332iqin6j7/gXP86pNeNWFegNF9FPmWrpfOawvtMqWRJ2gtoGgtnQ1R5cr0K+Y9bI0HKbyR3BwqPE8w0qCv1k0ddZQXhMiWLb6/uUX/vI99n58E0cPdJHLFHj46c388Nvv8syXd/OTv36fytoQw/1T6C6VJz69jVy2yNF3LzE+mmD91kaiVUFy2QKXzgzxyo+OUSya9FwcZdPOZto6a+jvnuDUkR4SsQzllQF2PtBO8BoTZG83etJj5C0DXVT4XP0etoZakGdcTBwTlJlgUJiZseSRdUKqB7/iJm6keWv8DCHVS7UrjF9ZWeWmRDY+h+NANv0/yeeexzQvYRrncZwUsrIBr++foOn3r2jmRsEqMFWM3fA+cHDIWjmyywT2SyFlLHRJHMrGMWyLZ+q20hmoQV5GhBu96np1KnGKd6feJWst7al/t2C2AnE2eZafjPyEvmwf5jKGJkvBwWGyOMl0fJppY5rP132eenf9DZONuw2xYoyUmSJlpHhj4g3emXiHqeLUqrSnhmPQl+0jVoyRNbM8Xf00AeXGWp4Hs4P8dPSnnJg+QcZaurX7diNvZdgUfZhNwYfRRDeCIFDrbudy+hjd6ROM5C4TUiswnSK96VMM5i7S7tvBzsjH8cilz23aBrro4a2Jv+JS6hC1rjVUuVqAkttmrXsN7f6d6JKH3szpmcT1Jqr0FoZzXQxlL5Ey40S0WiQgaUxxNPYybsnPg9EvUK6XCJDtWPiUCKP5HnozpxnOXabZu3FFnzNaFyF6RUKr456WW74vl0Kly09U95ItNzAdG7ekoIjL29SvBHcd0QDYEV5Hi7eGCj2MLqoLPqAkiNR7KvhK45M8P/QOh2PnyV/Ra6qJKh3+Rp6q2s3mUNsdmdh5oxjPd5MojtHs245LLp+bqwGlnsTj8Rdp9GwlqFaiSF7KpE2L1uFmYSuAKChE9MUntiTqVLh3LlruVeqWrWAshWjYS3tDlP0nVp6xuBIDY3Eu9k8Q9i/fclLqO5WQBQlVlBBm2hUkQSRrlY59wTKxHAdFkNElFV1SZtrtSjcyxynNFTEdC9txFuhC/KqrlHEUQJXkZdsTVoqu5CR9mTjtgShrAxULqg8+RWdvVRsvDp7lrdEuPte4hY5gJQICl5ITbA7XcmZ6BJessCfawitDF7iUHGdDuIrhbAK3rFK/gkxFe0MF0ZCXnuHVC7sN0+Ly4CT9o3Eaq2+dg87dgMnce0zmDxDSNhHUrudmtBD+kBd/6MaC9+6Lo2y/fw1nj/WTiKfZtXcdXWeHMU2LM8f6cLlVOrc0IMsSoiSi6QqRqJ/+7gnGh6fp2FyPrEhEq0OEoz4s06ZzayORqJ9susDRdy/h8els3NHEe2+cJxL1s2FbI+pNWFDeDOLFNIZj0egpp9FTscgSURMVLGxy1vy1WxREqlxh7ivvYN/EWU4l+riQHGJHZOUaClGK4HZ/DrDJpP4Io3gIAFXdg9f/j1DUHYtE4stBERV8y3js3y5c3V7S6qvggneUeCGDjY1L0pe0l7x6WdbK3rUajCshIDCaH+XY9DG6M90LAmhZkPHJPnyKD1mQ54anZa3sokDbdEwupC7wwvALfK3hawSUwKoDI0VUCCgBPJKHvJ3Hcj5Yo5KlkLWyTBYmeXvybd6ZeIfJ4kIN5qxexSN7EBDIWTkmChNLkrWUmeL92Pv4FT9PVT616thoqjDFy2MvczR+dFE1JaSG6PR3UuOqKWlmEMlZOcYL43Slu+jN9GJfY87M7PyJoBKkxduCX14+0edTIjS618+RDABV1KnUm+lOHydllpIDRbtAX/YMsqDQ5Nk4RzIAZFGhQm+kytVCV+oY44X+OaKhix7ckh9JkHHLAVRRwyMF0UUPgiDglnyIgkjByjGrXx3MnidjJmgObKJ8psoCpaRuQCmjUm/kUuoIY/neFRONq/FB6VihdC1eTgN6I7gro3C/4sGvLF9eUkWFDn8TYdXP3ug2hnIT5KwCuqRRpUeod1dQ6Yp86ITgXjmCKrpmNBkLYdpFxvPdVLlu3IrxdsDr1misChPw6iTSN2an+vaxy2xur8G7it52v+Ki1RfljdFz/NezL+PgsClUT1T3L5ujFoA2XwV/03+EffpFvtC484pHbh2mChlSxTwVLh++Jb6sDd4wqijRl45hOTZNvjJcssKlxASmY3MmPkKVy0+NO0CFy8vF5AQZo0h/Jk5AdVHnuX7wHw17qa8McuLS0A0NVIwnc7x3uvfngmgMpX9MwRqnaE0zmXsP2zFoDv4y8syNYzWwbYdkLM1Y/ySZZA5nCQ1M84a6RSXumoYIgz2T2JaNJIl0XxilvqWUSJBlkeY1lbRvnCf4kiRSVRemt2tsbpmiyESrgkSrgji2Q8fmUr/vYO8kXedGcByHob4pRgfjxCfTGIZ1x4hG3jKwHZug4kUWxAX7WQDcskbCyJC5avCqIkpU6EFqXWWM5ONcSg2vimjALNn4AoKgkEn9d2x7ElEqnxnot/JrTIe/nd9o+zvzC66Y6XTT/8+eNlf971cWVsvKdR+6pPCjwaO8PHIaTVxaXP47HU/RHpjX8XQGOvFIniWDZcMxuJy+zHB+YSdARI3Q6e9ctUbDveyE5usjbsTZP7mfscLYHHkIKSE2BDfQ7msnrIZRBRVRELEci5yVYyg3xKHYIfqz/RhXtAo7OJxOnubY9DHuL7t/VZ8DoEqv4pmaZ8iYGUzbpGgXyVk5claOrJVlLD/GudQ5Esbq3fxuFA4O7029R9EuMlWcTxo1uhvZGNxIo6cRj+RBEZWZRJpJ2kxzIXWB96beY9qYXrC+lJniaPwoa7xraPevPJZwcDgcP8ypxKlFJGN7aDsPlD9ApV6JV/KWtkUozajIWllixRinEqd4dezVBVUQWZBp97WzM7wTn+zDJbnQJG1Of7Mc/HIYVdQXfQ9UUceBuXPedkzixTFkUSWoLq50uyQvPjlMzkqRMef3kyQqSDPnTmmum4AqaXPCb3GmDdLBKs21EWCyMIiDTV/2DN/p+90F72M6BvHiKKZTJGf9fM51ux7uSqKxEiiiTK0rSpVeRs4qYDkWkiChS+oN+fzeakwW+uhOHyZtTKGKbtb47yWqN9OdOsRQ7iwFO0tIrWad/0HccpC+9HEuZ94npFSxIfgEoqBiOSZnpl9jotCNLGgU7lCp8lqQRJHq8gDNNRGOXRi6oXXsO9bNl57YiltXFwmNvIrO07Wb8ckazb5o6WsvCNiOPddukTWLaJJMue5DExWeqN6ASy4FWHsr1yEJIh651Jr1bP124sUMoiDiljTqPRF+qeU+dElBROBXWh+gcgUC1GvBtG0sx0YWpCXFicrMtOyCbeIALlmhyRumOzWJaVtcSI6zLVKHW1ao94TpTceYKmQYzSYJa+5FAq6loMgS7Y1R9p/sYXhi9W1t6VyB/Sd6ePq+Trzu1RP2ZHGAvvQb5K1pajy7iOobkcXVCRENO8Nk/hyCIFHpKrVRpY0R+tNvY9gZ6rz3Edaur1OI548xmTuAg4kuVbAu9E8IaduY1X6sFPlsgUOvneblv9jP5Mg0pmEt6d379//jlwg9uJBotLRXcfDt81TWhcCB/a+eZucDJT2LIAp4/CsTSgqAYztYlj1XldNdCh6vRn1LlPYNtTiOQ7jMh67fGZIBJV/30kAsa3GThyDgU3TixTQThcXnpiYqBFUPl9MjTBVKN2bbTuLY0yt4ZwGQEUQd3fUMohAglfw9ioX9FAsHkOXmJV6iLikKD6lBQmpwBe95+3B2epgjU71UuoKs8VXgktQlNRq+qwb2bQpsYt1Vvv2zyJgZfuL8ZBHRiGpRnqh8YlXWvLOC3xtFzsqRt/I4OEiCRJu3jUcrHqXR3Yhf8SOL8gJjDsdxaPY00+5r56XRlzg+fZyCPW+AYtgGb0+8zc7wzhk9y8oTCS7JRYO7Ye59bGwsx8JyrDkR9kh+5AMlGgAj+dJ0aAcHVVS5v+x+7gnfQ6VeWapkCAsHmNqOTZOniXZfO88NPsdQbv6+7OAwVhjj+PRx2nxtKxbPj+ZGOZU4RdJY+H1d71/P01VPU+euW7QuSZLQpFLFJayGEQWR54efnyMCs79r3bU0uhtXvC2yoC55XK84S+Y+q+UYJYH8EqRTEEQkQcJ2LOwrCPmVrefC3O8rt23xe5szOhVJkBesa3Z9EbUaVXQRVK7X2vvziTsfkV8F27FLc39XcIGYdaLyiXef+8pY7jJZc5o2325ccgCvXMoMl+vN+JQoNibvTz1HnXsDbjlAud7IVHGApDmJQymISBijXE4f5N7yr5C3khyc/N4d/lRLo7YiSGdL1Q0TjalEhpffO8/Xn75nkaWqIkrUupcXry3ltDJrXQtQ6VrYi1rpCixYpiHT4pvXAbX6bv5C4JFVdFkhbRYoWIurCQkjh2XbhFT3TL4E1gWreG34Aikjz1guSUewEpek0OYv50RsiEvJCaaLOVr95ajiyrKym9trqYycvSGiYVo2vcMx9p/s4Yldy8+ZWA5juRNIgk6Ddy8+pRZxmQFq14IkaIS0Fq68sOtSiKDWxHjuFIUVZoeaAl+jxvvJUsud6MEtVyMKizNi10PP2SF+9q19ZNN59nx8C6GKwJKDq6qbFwvMm9ZU8o3f/xm/9k86cHtUXvirg/zC3314/glXbIrjOIwMxPjxXx2kr2sMzaXi4LB9zxq8fp1odZD9r5zhf/1fL/LAExtY01nDlt2tnDnWx0DPBI7j8PHP7SS8AuHg7YJfdSELIqOF+KKsuohAhR6iOz1GT3ps0WtLAYKNYVtzrVWF/Ctk0v9rhe8ulgIDQQaniOOkcJw0ycS/RhAW3ytkuY1g+A9X/Rk/CKTMPBHNw2cbdrAhWLvsVN6r52h4ZA8elu4KmLWIXWp5UAnilj/Y+2nJWVGg1dvKMzXP0OxpRhWXtuwVBAG37KZequcT1Z8gbsTpSnUtaMsZzA0ylBui1dt6w+6BgiAgIc1rPSTwyt5VV0luBWYrPbIgs7d8L3ujeynTypbVoYiCiF/20+nvxKlx+JOeP1lgAJC38nOajTJtZZar3ZluxgvjC/azIig8UvEINa6aa5IEURAJKAG2BLdwNnmWC6kLc59rIDvAxdRFmtyLZwUtD4GVdCGIiLhkP2lzmtwSSVrTNijYeRRRQ706CbbK08Yl+xEEiQbPevaUfWqZrRbRVigEvxGYpsV7R7r5yx8eQlVldmxu5MvPLm6PvxO464jGfzr/F4iCSKOnkgZ3FU3easrU1fdb3mlUu9ZSsDNcTO2nTGukxbcTyzEZzp1jIt+DIAhM5Hsw7QKOA245iFeOkDHnxYfTxRFcUoCo3kTamMIjLw64TdPCMCwURbpltpqrRcCr09FYQVWZn5HJ1Qe1DvD822d4+r5Oqss/fMf6atR7Q0R1L5dTE0wVMtRepak4FR8mZxnsjjSizJCGjmAlP+g7zpGpARygM1iFJim0BaLkLZNziTGyVpFG78pbmarL/HQ0VXGxf5J0djnb4+UxSwD3bGrC61pZVaNgpRjM7KM79RKioJA0+mgPPIsm+bmU+DGxwkUMJ0tEa2eN/xkuJZ+naGdIFvuJ6GuZLvSwNvhZ3HIZvanXiRcvU+O+hzrvfUDJ3MAllaGI8xfs6WIvU/kLRLQ1BLUmhrOHKFpJKlxbcckhPEoDHmXxZPvVYnxwinyuyMd+8QHueXwjsiovmV1W1MWkyh9084///WeoqgkjSgL/+r98iUDIgwP84m88RqR8YZUqEvXz9BfuwSiaCKKA1+/C5Sll8jbtbKaxtQLbdghGPMiKxOadzTStqcScaZOLVASW3LYPCjWuCJqkMFVIMVFIEtH8c0GyKAg0eKK8PX6ac8lB4sU0IXVeC5E1C4znEzPWp6XX2PYUpnH6BremtCMsczk3obt3BsO2cANjuQSnpweRRYmg4kYSxEVxUJ0nPCe2/zAiqkW5v+x+Wj2tK9IOiIJIlV7F9tB2xvJjC6oMplOqPrR4Pxjx7AeFzkAnuyK7KNfKr5v9FwQBGZlWXytbglt4d+rducccHJJmkpH8yIqJRn+2f1E1o9HTSK2rdkXCe1EQCathOv2dc0QDSq1cfZk+po3pm3bDuhqyqFKttzCYvcBIrotW30JzkZQZY6owhE8OE1Bvzsq9ztUOjk28OEpQrbwjZgS24zAVz3Dq3BC6plBdceMzzm41VkU0igWDl753mMtnhxAlkdaOah55ZiunD/cwOZbgyc/tpOvMEOdP9LPr4Q4Geyc4d6wfSRIZGZjinr3r2P5AO91nhznw2lmmxpNU15fx6LNbKZvZKZfTQ4zkpzg4paCJKpqkEFb9NHqqaPJU0+ypps5dgVe5NZNXbxf8apS10gPkrRSnpl9mKHuWMq2B8fxlatwdBJUq+jLHr+keoYt+0mapL9PGXrK/r28oxqGTfeza3ETjB2C5uRQkUaS1rpwNLVU3RDQAJuNpvvXiYf7xV/ai3CHCdKuwNlDB+mA1f9N/kjdHLlHjDlCmlwKp/nScH/SeIGcW+UTd+rkWr45gJYZt8/rwJfyKTpMvgiQIlGkevIrK6dhwyQhhFURDliTu3djIgVO9N0Q0TMvmfO84L717ns8+sth4YCkoopsaz24SxX50KUiVezs+pQYBkRrPLirdW3Eci3fH/wMtvifJmpP4lDosxaBopSnTOxjPnaQt8Akq3ZsxnDQF69qtCh45yqC1n5ThJqA1MpE/Q1hrQ73F2SOjYKLqCtXNUXyh1a1blESa2ubbc1rWzrueVdUuPKaCIKDpCtXLDP7zeHU8V02HdXk0XJ67R5O2zl/Ly5JG2sxzNHaZFm8l0sxgOVEQ2RJq4s97YDQX5zu9b/MrLY+iSyrxYpr3pi4wkJ3ELWmE1NJ+lqR6NP2R27KtkryabOoHi+PxAb7ff5iCbfKjgWMlc4slGOR/3PI5NoZWbuJxN0ERFFq9rWwNbl2VQFkURDYGNvL2xNuL2pl6M73Yjv1z4z7lkTzsDO28bvXgSgiCgEt0sS20bQHRAMiaJc3JhsDy85hmMSvCv7JFDUo2tJq4vFvk1dAlnVp3LQLCXNzj4JRmuxQmbznRUEUXbb7tHJt+lfOpgzR7N1PjLum98laG7vRx+jNnafVto8Z140M7ASpdzdR7OhjJXeb9qZ+wI/zU3ODA2RkbCWOScq0W5TYNT7ybsUqiYXLgldN86e8/QnVDBFWTsW2HTDpPIlYqTeVzRRJTaUzDIpsuMD2V5t5HO3j4mS24PRrZdJ4j+y7Rtr6Wj22o4flvHeDSqSG8fhf6TItA0TYo2gYpStZ8I7lJLqYGUEUZVVTQJZVKPUKzt5oWbw3NnmqqXGWoKxyKdLvh4HA59T4Xku8ApRPeK0fwyEFsx+L09Gt45TASMrKg4mDz/uQPGMicJGNNk7czbAs/Q0SrI6hW8fzgf8AtBXBJC7OejuMwHktx+vwQWzvv7E2mJhpgY1s1+0/2kMmt3u3EAX767jke2tbKPesb7qqqRsrIc2iyn7RRYCKf5mRsiKxR5NBEH9qM3W6V20+rrwy/6sIlKXyuaTOD2Tjf7TnCocl+WnxlFG2TU/FhBjJxvt62iy2RujlXrCZvBE2UODDRw67yRlRJnhHNqlS4fBwY72FDqIYGz+ouxutbq2muidA/GsMwV++mNTWd4WcHzrF5TQ2tddfPfomChC4FUUUfuhTEI1cgizq2Y9KXfpOMMYIgSCSKA3O9rB6lAscxcARwyxFihcuIgoQmBhZULpaDLLgIKA0U7TRT+XMogguPXIHIrb0e+MNeVE1hcigOO27pqq+J4ZFpXn71NIlElof3drChsxaACxdHefWNMyiKxJOPbaD+DiUalsKGYCMB1c14IcFroyf5RM1O9FmigUCrr5pGT5S+zDjPDx3k5HQPlXqIsfw0PZkxTMeiTPPT4i0JnDXtARR1623Z1pW6UN0JbAs38K83fuK6z6tfgUHE3YqAEmCtb+0NDZMr08oIKAGGc8ML2nrixfit3MQ7jiZPEzWumlW3bkmCRLWrGpfoImfPt08VnSIpc2WtpwWrsOSsjIASWBWRExFxiS5UUV1AWvJ2/rZY5QoIlGt13F/+ed4Y+zY/GPx9alytaJKbWHGU0fxlyvV6toQewbtEt8hqoAgaT1T+Cj8Y/C+8PfHXnE8eIKJVIwoyKWOKWHGUcq2Oj1f/vY+IxvWgu1Q++6sP8s5LJ7Fth8c/s526q/qRHQeuNGKJRH3UNpUTmalY9F0ao/fSKKcOdeMPecik8tS3RLFmgqB/3fF1LmeG6c2M0JMu/R4vxCnYRQpX2PWN5qc4k+hGFiUUQcanuKl3V9DirS2RD28NEdV/U5NCbwaN3q3UuEtiPBEJVXIhInFP2eexHBMRERsbXfIgILIp9BTrg4/iODaiIKNJHkQk7iv/6oygadb9oFTJ6eqb4A+/+Sa9g1MkUnlOnh9GkUXaGqN86ZkdbOqopVg0+dPvHaA87EXTZF55+xyT8TQ7NjXyxU9uxzRt/uP/+Blf+dROtm6on6skdPdP8m/+8/P8+995ZsVVElkS6WyuZH1LFQdP993QPssVDP7bd9/mD/7pZwkH7h7dzVAmwb888gKmbWM7DkXbxLAt3hi5xP7xbkQEtpXV8/fW3semcA2CINDkjfDPNz7GjwdO88rwBV4aOoskiLT4y/iVNbt5sLIVvzKvE1BEiTZ/lHOJMTqDVXOtEW5ZodlXxlujlwlrbspdq7Pb1BSJh7a1cqZ75Ia0GrbjcLF/gr96+Si/9aUHb0gYDhArdJEo9tIeeBZZ0OlJvcqsaE+klKWd76de3TRzQRCIujbQm36D3vQbVLq24lOqbjlZXbO5gUsnGnj1rw8gCALr723DH/IsGqh3qxEt97FrZwtHj/dSyM+77DQ1lrFjWxOXusYwjDtvxXklQqqX7eE28pbBrrJ2ZEGaE68LgoBP1vliwwP83tnnSJt5ziYGuJAcwnJsbBxUUWZdoJYt4ZJ4WxDdSNw914RZGLaB5diIiMji0uYPN4Mqd5AK1/VbIJbTbnwYEFJDtPnabuj7KgkSfsU/I+qdJxpZc7EF7ocZLd4WIlpk1ftIEAQUQcGv+MkV5omGZVvkrZW5RBbsApa9+PqiCMqqtmf2u381OZkV299qlGzxdTYEHsCvRDgSe5mezGlMp4BPDrMp+DAbAw9SrtcjCuKC8+dGUKE38IX6f8Hx+GtcSh/lfPIgAB45SLleT4f/XjTx7u7EuV1YFdGQZJH125to31jH5XND/PX/eoPf+c9fRBJFsqkCtu2QSmSJjc8HNKIkLbgR+0Nuyir8PPDURjbe04xtO2iagjrjkFLnrqDaVc69kQ2lE9CxSRoZejPDXE4P05MZoiczwng+Tt4pMpvEiBsphnITHIqdQxIkFFEmovr5bN3DPFKx/RbsqpVDoHSCLxIYAbq0dKC42uX11SH+2d97gn2HuvjZW2f5+ud201RXhqJI+GZaKBwglc7zzvuX2L21mc89vQ1JEpEkEbdLxa2rRIIe3n6/i3WtlSi+0pfgtf3n0TWF2uqVs3xBEFjTEOXejU2c7R4ldQOtOgDdw1P8h2++wv/v7z9917RQtfrL+Mmjf3dJR8rZv2VRxC3PCxglUaTGHeSX2nbx5ebtmI5d8sMRJHRJXjQARxIE/njPF2cG5MyvJ6i6+f+se4BfaduNKkmrDigEQeD+zc28fugSE/EMhrn6gDRXMHjneDcVER+/9Il7kKTVBzU+pRLTznIy9g10KYJbLrvKyWMh0sVhjsf+N8liP6IgU7CTNHofJWUMcD7xfdLGCKPZw+StGDWee1FFP6row3ZMXFIIeQnR782i62Q/+144ykjPOKffvVTSaIiL5ab/+A++zo5Hl29J+N3/8Dy/8w+f4i++ewBZkvjUJ7fwjW++w6/98oO88uppjp3ox+1WuXdXKw/evxZZlnC5VRRl4eVaVWVcLhVZvvuCTFEQ+FLDA3y54QFcsoZ+VbVZFiQejHYykovx7d63sK5wflFFmd1la/lK40P45Lv7xvzN3r/kcPwYUa2cX2n6Kg2e1VWW81ae92NHeX74p0S1cj5W9RjrA/NuUaV5QaXBffvGL3J6epC0USCkudkabuTe8jbC2u0TmN5uCAj4ZN+KtQJLwSW5SgTvCl6x3PTzDyMkQaJcK79hG+FZAT1X3JItx1rxnJXl3LuKdrFk77oK2I696H0VQVlW/H8lKvRGfrHx35daS5cI2HdEPsbm0CML4q7Z57Z6t9Dg7sCamS8iICKLCvKMdXJpmcCD5V/gvrJnUWfWH9Gq+VLDv0ZAnFvvpuBe1vl3o4muORtcQRDAKblLPRD9PHvKPj1XYRMouVtJwrxt7t82rPhTO45DJpnnP/zD7yArErIqs+W+NaVe4sYIb/7kOP/ff/DnaLo611sssLidNBj2cs/edbz5kxO88oMjAPzCP3iEls5qQEAURERhvunBcRz8sptqV4RdkfXYTsk6NGVm6cuM0p8dZSA7zlBuguHcJNNGioJtgAVJI81ofvUDyz4MUGSJ8oiXoN+FqsiUh71URZd2mYlGfOy9t5325nlHJVEsZRce2dPO//z2O8QSWXxeHdt2eH3/BT7x2EbkVQaUsiSys8abz1wAAQAASURBVLOeo+cHeOvo5Rv6XLbtcOBkL7//nTf5x7+wd2477yRkUSKir/5mLgoCuqSgzfUdC8sKdAVBIKAuvniKgoBbVheQmNVCU2U+9dAGLvaPMzA2fUPriCWzvPDOGYI+F5/euxFRuPZxWRf67EwVrkQWVTHArug/ZZaeOTjIgs6m8C8jCBLlrlJgLiBQ6d6GiMyu6D/BwWbmUo0oyLjkEGX6uplspYAoyIgzlzHTzhJUG3DL0RWdM7M3yRX3GHs01mxppG1TA9equoSi185AC4JAJlNgdCyBLEnkcga27dDbO0lP3yT/4neeprt3gjfeOkdbawXVVbe2d/mGcZ2Y4uqgI6wuTpJc+Ryf7OIrjQ+xq6yd9yYvECumCShuNgYb2RRsWnDOryqgEW7Ua2j1yFl5UmYat+S+oQFvs/e7kfwoBavAYG54AdFwHId3xi/yx5fe5GJyFEWUUESJvGXy48ETbA438I/WPUG7v/KOXydvBJqoEdEiN1UJkmaslK+Exd1V4bsZBJUgXtl7w8e3NOB2cdLuWgP0roRX9i7Z1jZRXHow4HIwbINpY3rRa1RJxbWChEJpgN7y1u7LJXdLDmIyknTtcFcQBDTJBcxviyhIuK5K9iqitmT709zwQEGHVVq5/7xjVfTK49f5P//X10v/CMzNPGhqr+Kf//6X51K8giAgigLRmiCOUwpqZyGIAut3NNO5rXHOgl6UxGsGYMCM1dz8DUeTVMKqn03BVvJWkeHcBN2ZYc4l+zgxfYnxQhyHJW3ufy4gCALCVR9uuQtRdWWQSMizZCb6ni1NfPf5w+w7dJmKMj/Hzw4QT2Z57L6lPdivt03NNRH2bGrmbM8YE/H0qtcBUDQsnn/7NKZl849+4SE0ZekhVXc7HMfBth1yRQNVllHkeX/uDxKCILCjo57t6+qYnM6QKxjXf9ESGJlM8uc/OYRlO3z6oY0osrjscZGEhcSoZEW9+OI7/7zFN8Klni8gIl6VFYrlL3Ep+WNMJ0d74Fl06dqBue042LZNJmfgc2vLXnscx8G25m/Ga7c1sWZL4zXXDVzX/a29rZITpwZwuUouUmcvDNNYH6Gvf5LjJwf497/3Ag4OFdEAxcKtbym4YVzn1P2jkwfJWyaxfJbBdIJ/vv1B1gTL+HHPeX7ad5G8abKxrJLf2nIvedPk9w6/TX9qGkGAh2qb+SdrH2UwneRb547xJ4kzuGWFxxpa+VRzB8cnRniu6zRD6SQNviCfaVtPUNX5XtcpepJxcqaBLin81pZ7aQtElhRM342QBAn/zPTxrJVjsrAwMXYmMcRf972PX3Hx33d+hfXBWhRRomAZvDvRxf//0tt8p/cAf7ft4QW23h8WqKJKUAnesA3t3wb4ZB+6qN+xfSQLMhVaBW7JTdbKzi2/mLpIzsrhk33XvT87jkPGzNCV7lr0WFgJU6ktnmOzWjiOQ3GmhVQUBBRFWvS4bTuYM9d0URCQJHFBfApgWXZpThGl5OlSyc7Zddm2g+M4c90Ns7HvShOk9sxMJNtxkMTS9giCsKL1w41PC59d/+znFGBuX9yOWGvFRGP2zWVl6ZvoUjfX5TZYFAVYoRVfaSeXfNUtx8Z0LOLFFBeSfVxM93MxOUBfdpTsFf2GIqX5GpIgztmH/txiBSeFIktIS/j9zz72yH1refmdczz1UAevvH2OrevriIRvrBwvCAL3b2nmfO/YHFm4ERQNi5/sO0synee3vvQA0ZDvhlp2Pmg4joNlO5imxchUkjePdPHTd8/xW198gN0bmu5Y/COKAl/52HbO9YxxoX/8hgn46FSKP33+INOpHJ97dBNBr3vRhfqDRkhrZWf5b8/9v9R1x3FKNxjTtOgejvGTfWd47f2L/Oi//Cr6Ena0ABNDMfa9cHRV2yIIAvc8sZHqpuXtEte2V/FXz73Pgw+sJZ8rsm/fRT777A5My2JjZy1//+88XErYIKDrMpZlUyyYmKZF0SjZWcuyiGnaGEUTw7AoFkuPz96obguuc85YjoMsivzGpt1UuEvB81AmyYu9F/g39zxMSHPxG2++wJmpMdZHKvmdbfchCSKXEzH+89F3+NraLcQLOWKFHP9qx0PUeAOoksh4LsPh8SG2lFfxu7sf47mu0xweG2RzWRXD6SRPN67lsfpWfu/wW3RPx6jzBubc3O52iIhoUik7ajoGGTO74PGu1DiGbfKVpnvZGZnRqwgCiiDxeNV6kkaeHw0cYbKQ+lASDUmQlpzp8RHmoYoq0h2MYwRBYI1vDcenj5PNzZ+f44VxDscPs7d8Ly7Jtex1Z3b44VB+iMOxwwse88geatw1eOXVaQ+XgmHZfPrr/4Ncvsj2zQ38X//mMwu2KZ0p8MLLJ/if33wbgO2bGvgHv7yXlsbyBet54Wcn+dZz75FIZvlXv/1xHtozPxTWcUojBUbHE7y+7wKHjvUwOBInlc7j9bqorw6xe3szjzywjkjIc92k0/muEb7xF/s4dLyPr35uF1/57D0ossTw2DRvH7jEgcPdDAzHKBQsfF6d+tow2zbW88knNuG9QZdBx3HI5Q1efO003/7ee8QTWaoq/Hz9i/fy8H1rUZVb39511zWMOY4zp80wHRPDNhnMTnAh1ceFVD8XU/2M5udnTQiULla6qCKLEi5Jo9pVRruvnjW+Btb5b94//66FICCJJfZbKJpYlj1XaVppsCEIAnt3t/O9Hx/lyKl+Dp7o5R//2iM3lTuJBDw8tWcd/aNxjpwfuOGg1jAt3jzSRf9YnN/4wgNsaqtGV5U7HthejdkgtmhYJLN5zlwe5fVDFzl0tp9EukSArRskXLcSdRUhfuFj2/nv332b8RusNkGpjepbLx6ibzTGV57aTnNNBFWRF011/6Cw3LluO85ccB5LZjl4po9XD17gVNcIpmUvS75nMdo/xV/+lxfnFzhgFA2MoomiKsiKNJd9Mg0To2jhC7qpb6+6JtFobYkyOBRj4/pakoksL/7sJC3NUQzTon1NJb/3f7+IIEBrc5TPf3YnFy+N8qMfH2NyKs3Zc8OkMwXu2d7EsRP9vPHWORKJHD29kzz5+AY2rq9F025TkL2Cw9saiOBT5y0vh9JJBlIJ/u17r6GKIrosU7QsJnIZ/svRfciSRNYokjEMLMdhbaicZ1s6+caZI2iyzKea1+FTNcChzFWaglyue0jkcySLBao9fsK6G0kU8alaSUx+h8rYN9PaIgsSlmNTdBZq27JmAU1SKNMXZo1n/65zhzHs0nDDDyNEQVxRf/7fZiiicsdtett97TR6GhnLj2E48xXxF4ZfICAH2BTchEtyzW3n7HWx5CJapD/bzw8Gf0DSnNfvCgg0uZvYFNh0S5IjkiDQ0ljOiTMDDA7FsWwbWZrfb+lMgZ7++YphbDrL8Oj0AqLhOA4TsRSJVI6KqB+/b+Fg11y+yAsvn+Avf3iIWDyDIJSS7IIokk7nOXlukJNnB/mbnx7nl760h4fva7/29diZ77rJZAskU3lOnx/mz//6AL39kwiigDizL8cni4xPJhkcjvPYgx34vKtvz3Ich0yuyM/eOMO3nyuRjJqqIF/93K7bRjLgLiQaY/kYl9ODXEwPcnGGWKTNeWGXiIgmKiiijCLK+GUPTZ4q1vjqafPV0eipwq98eMVxq4EoCAR8LgRRYP/hy9i2g9ulUlHux7+KkzAUcHPv9ha++8JhFFlix6bGm962ja3VPL6rnaGJxA3P1oBSsNg1MMm/+sMf8/iutXzmkU1Ul/nRNeW6geLtguM42I6DYVgUDJNUpkDX4AT7T/Ry8HQfY7HkMuTqSvn4ncFj97Rz4uIQP9l39oZbqKBUcXr14EXO94zx6b0b2butjUjQg6bId4wIzlaTioZJoWgST+U41TXMvuPdHD43sGrb5bZNDfznH//O3P+5dJ53XzzOxaO93PPERto2N+D26mRTOc4f6eHI62d46DM76dhx7UFhLpfKn/3JrwJQEfXzP/7b1wDQNJlPPr2FTz69cLDUhvV1bFi/WGT80ANreeiB1U9tv50QrtJHNPpDtATD/MbmPYQ1Hct2COk67wz3YTsOv735Xs7FxvmDE+8BpWtaWzDCb225l9cHLvNy/yV+rXMHmqgwmEoymcswkE6gyQpB3TXjZHPFG97BVtnVCmOh1CefNXOYjoWAsKg9xi2rGLbFZD6F5bcXmEEYtsVQNo4qyh/ayv1y+oGPMA9REEvOfHcQqqiyJ7KHodwQvZneOUevgl3gT3v/lC2hLewK76LeXY8yY/xgOzZTxSlOTJ/g3al3mTam59YnIFCmlbE1tJU6962x5hcEaGuOcuLMANl8kdGx5AJDm3QmT+/A5Fzr0XQiy/BYAseZbwzJZIvE4hmKRZPqiiB+33wclS8YfOt77/Gjl46TzhTwuDWqKvysba3C79VIpPKcuzTK2ESC0Ykkf/Snb5JM5fj0x7esKIBPpvK89vY5Xn37HGMTSSqifqoqAvi9LjLZAuOTSZLpPDs2N6Brqw/dHcchky3y6tvn+PZz7zGdyFJfE+IXPruLR+5bu6jV7FbiriMa/8fpP6EvOwqUQjJFVPDKrtL8DFGlTA/S4qmh1VtLq7eWanc56iqG/Py8oa0pytMPr+enb57h0Ik+Nq6r4ZOPbcTv1REokQhZlq7bdrR39xp++uZpHrtvHbq2Otu6pSAIAo/sWEPfSJy/eevUDc3WuBLpXJEfvnmS/Sd6eHxXOw9ubaUmGsCtq7c9uJ2vWJgUihb5osFUIsPZ7lGOnB/k1OVhpqaz11/RXQBREPiVZ3bRPTTFiYvDWPbNVVoGxxP80ff28erBCzx5bwc7OuopC3pw6wqKvLRbya2CbTsYVqliUSia5AoGwxMJTnYNc+TcAOd7x8jmb5xMuTwaDe3zg/WOvnmW4e5xnvjKfez9zM4Fz12/ew3+iI/Dr56mvr2addubb/h9P6wIaToeeeG1o8Lt5bOtG/jG6UMYdimY/t3dj7EuVM7fdJ3l94/uw62obCkvzcvoTsT4oxMHkUSBkObisfpWwi43Oypr+HHPeX734OvUeQN8onkdAVUjpOnoMyLPoObCo6gfmn5/x3HImlm6s71AqRfeJS0UxTZ6yhEFgZ8On8Kr6FS5goiCgOXYdKcmeHH4BM2+csq0m289+Qgf4VpY61/Lg+UPkrfyjOXH5sTkNjZH4kc4Ej+ChIRH8SAjk7NzS7p/iYiE1BD3l93PnrI9t3ALBdpbSoY3pmnTNzg1RzQcxyGZzjMwFKc84qOy3M/pC8OMjidmKh+l+GgqlmY6WbqXV1UE8M84cTqOwxv7L/Dym2dJZwqEAm6+/sV7efKR9eiqPFfByRcMXnj5JN/66/dIpHI89+Oj1NaEuXd783Xvhee7RjlzYbjU5vzZe3j8oU7KIt65ToF8weTshWF8Xg2Xa3VVwBLJKPD6Oxf4878+QHw6S0NthC8/u5NHZpwNbyfuugj9Su9rl6SzxldPh7+Rtf4G2nx1hNWlnZX+tsLn0Xlq73qe2rt+0WOqKvMrX1zZF9m0bBRZYu+97besUuDz6Hz2kc3EUzleP3yR/E0KWx0HxmIpvvXiYZ5/+zRb2mu5Z30D6xorCPpc6JqCrsqoM8RqNUHurDjKsCwMs9TLb5gWBcMily8yOpWie3iKi33jXOgbZ3gyeVe0Q90IIgEP//DLD/HvvvEyF/snbigTeyUs2+Fc7zjnesepKQ+wvaOenZ31NFWH8bk1NLV0XBRZWpXYbFYEZ1k2pmVjzByT2eOSyuQZmkhweXCSC33jXOqfIJbI3rbWmdhYgmwqT8VVk7xnUVEXJjY2TXLqxtvSbjVs25kTv9u2Q754499B23YoGCb5olHKss6KHiklFr66bsuSr3uwtokHaxdO33YrCv997+JBdOvC0WWXrwsvbkf7pc556/Ll3v9WwLAN8lZhkVPPbBuJjU3GypIwVla9dRyHgl2kK32Zt8b3AeCWXVRoC/vFOwLVPFW9kW91v8u/OvYcIdWLJslkzALTxSx1njDP1m9b0ayNj/ARbhb3l92PLMi8Pv46Q7mhRdPCLSySy3wHBAQ0UaNKr+L+8vu5N3LvLW2bEwRobYoiCGBaFv2DMfbM5IPyBZOh0WmyuSLtrZVs7KjlxNlBpmJpYvE00bJSXDkZSxNPzBON2fakRDLHz944w1Q8jSyLfPXzu3jy4fW4rmiLEgQBl67yycc3YVsO/+tbbzMZS/Ojnx5jc2ctnuvMoBocjhMt8/FLX7yv1MakLgzPdU1m68b6Fe+L2eSy4zikswXe3H+Bb/71u8QTWZrqy/jSp3fw8H23n2TAXUg0rsxG6VJJd1F0DKaLaYZzk6SMLLqkoksaLklFEeUPTQbrbsOs60ChaPLmgYs015fR1lh+S6sDNdEAX35iK+lcgfdO9c65QtwsEuk8bx7p4s0jXfg9Os01EVpry2iqiVBd7ifgdaGpMrI476RQCnAdHGe+/WnWhcK0bNLZApPTacbjaSanM4zFUgyMTTMykSRfvPHM+DzunvO0vSHKb3zhAX7vm6/esOXtUhiaSDD01imef+sU5WEvrbVltNSW0VgdpiLsw+fWURUJaeaYlM61mX7eGWLh2A6mXXL+KBoWiUyeyek0E/HSz+hU6bhMxNM3NBfkRiErErlMnqGecZrW16G71flMVqbAYNcYhbyBdAvmWszui5LziI214LeDNUMc5n8v/byCYZLLG2TzRbIFgwt9Y9g3WMWaSmQ4eKqPqeksLl3BrSm4NAVVkZHEWTcUceZvcW6ZJM6Tktm/534LpefebbqrqzGQHeJI/MSiacqD2SEA0maG18fewqcsb795JQzbYKIwSXemd24QbVgN0epdWAlTJZknqzdQ54nwxug5LqVGyZpFqlxBHq9ez+NVndS4Q3dsMO1H+NsFURDZU7aHSr2S7w9+n0vpS3N2tbPtXbNkXJyZH6GICrqk45f9rPGtYXdkN42extuyfZVRP36vi1y+SN/gvJY3myvQ0zeJpsnU1YRorI+gaTJT0xlGx5MLiMZ0IodLVygv8+Game927FQ/I2MJbNuhqb6Me3e0oi+jvdA0mYf2rOHHr5ygfyhO/1Cck2cH2b392i21ggB7draybVPDIpKxWgiCgK4pcyTjrXcv8s2/KlUymhvK+MKnZkjGB2Swc9cRjbX+BhRRJmPmyFp5jscvcjh2DgAJkQo9TJ27giZvNU2eKir1CB5ZxyO5cMs6qqjcMVHqhw3pbIFzl0boG4px8vwgX3t2F9pNnuBLYU1DlC8/sZV8weT4xcFbRjZmkczkOX5xiOMXh+aWKZKE36vPZNQlFEVGlsQriIWFadpkckXSuQK5vHHHRKR3Cjs76/n1T9/L/3huH8M3oaNZCg4wHkszHkvz7sneueWaIhPyu/HoCooizZAOqXQ8Zghf0bBIZwtkcgUKRfOume9b1ViOL+jhZ9/ej1EwqW+vQlZkTMOk9+wQr3z3XcprwpStcNClbTuMxVIUjJKRgzVLHGb+LpoW+YJBtmCQyxfJFYwSaSgY5ApFcnmDXMEgO/v7imW5medY1q3be0PjCf7ipSOLlsuSiEtT0LUS+dA1BbdeIiEuTcU197dS+ltVcOvq3DJdU9DUkjOeNEtCpNLfqiwR8LpueCL9rUKsGOdI/BiDueElJ07nrBzvx1fnUHYlfLKX9f51tHibFj0mixKbQnVsCt2aXvaP8BFuBmkzzVBuiFgxNjc7xif7aPQ0IiCQt/MzWloNt+ymTCujzlVHg7uBqL68ScbNQpixq21pKuf46QEGhuPYtoMoCmSyRbr7JvG6NWorgwR9LirK/MTjGUbGE2zsqMVxHCZjaRLJLBVRP0H/vJPWpZ5x0jPGLpvX1+FxqdccyeByqWzZUE//UJx0Js/5rtHrEg23S6OtOUpZ+ObbIEVRQNcVsrki+97r4pt/dYDYdIaWpnK+8Mx29u5Z+4G6eN51ROO313yB8XycnuwIvenh0hTwQpy0mSNjZpksJhjOT3IwdgYBAb/iodFTSZOnmkZPFVV6GX7FjVd24ZHd6JK66mnKf1uQSud56+AlppM5PvnYJnZtbbptZbSta+swLRtRgGMXhyjcRAvHSmBYFlOJDFOJzG19nw87nti9loJh8mcvvM/g+PRtf7+CYTI6dWtJzQeFpo5anvra/bzwjTf5q//np1imjSSLWKaNrEjUtlby8V98gJrmld1MDdPifz//HgNj01cRBIN80bjlhPx2wbRsUtkCqWzh+k9eBpIozJGOKwlIVcTPk7vXsmfzndW8VOoVbA9vIZQOMl1MkLLSZMzsiqcrLwUBAV3SCClBNgY6eaxi75yQ9iN8hLsROSvHvsl9vDT6EgkjAUBACfBo9FEeiT6CLum3VZd3PYiiQFtzlKMn+5mKpUhn8vi8OulMgb6BKbwejerKksi7qiLA6fNDjI0nS23ThkksniGTLbIhGiDgm9dLjY4n58xTaqqC1xVOK7JE7cyg1VzeYGhk+rrbHgm5CfiXtwleDWYryIdP9PGt773H2ESS6sogz35sK3vvW/uBG+ncdURDFEQqXREqXRF2R9bjOA5TxQS9mVH6siP0ZcYYy8dIGRnSVo6MmeN0opsT06VBMF7JRa07SoOnkkZPFVtCa2jyVF/nXf92oqYyyD/9u49/YO+3s7MBXVX41ouHeP9M/025Hn2EW4en7+/EpSn87+ff4/Lg1PVf8LcUqq5wz+MbaVhXzdmDlxnpnSCfLaJ7VGqaonTuaqO8JoS4wou4bdscOT/A4FjiNm/53Q/LdkjniqSvMo0YnUqxaU3NHdqqedS6q/ms+xmyZpah3Ci92T56M/2cTJwhVoyjiir17lpcS0xQXgoCAqqoUKZFWOtbw3r/uiWnI89qMbyyRkB1l5YZBfqzU8QLGbyKTpO3HK+s3dEA7yP8/MPB4UziDG+MvzFHMmRB5pHoIzxW8djcPJg7CVEUWdNUEoQXDYuB4ThrmiuYmEwRn85QGfVTUxXC59Worgxw8GgPYxNJcvkiyVR+Xp8R9RPwl75vs5aws5pMn0dHuk6rpyQK+GaIimXZZDLXT8KoqnLL7GVt26G3f4r973cxOBIHwO1SCIfcODZ80CZmdx3RuBqCIFCmBSnTgmwPr8V2bFJGloHsGEO5CYbzk4zlY0wX06TNHCkzQ292hPOpPgC+2vDUh4Zo5HJFTpzon8tkCgJEy/20t1fd4S27ddjYVs2vfWo3bl1l/4kekpn89V/0c4M7b2+7FERB4OHtbbh1hf/9/EHO945/oNqHDxNESaS6MUp14823ANwtLWEfYeVwy27afM20+ZoxbYs/6f4m704dJKgE+HL9Z2l0r1SsKaCIynX1hZeSY7w5dp6dZc3cW96KaVscnLzMd/sOMpSNE1Q9fK35Xh6ItuOSVy6sXe59l2oL+wgfASBv5TkYO8hUcT4ZFdWjt1zUfTMQBYHmGZ2pYVoMDsepqw7R3T8BgkAw4KKqIoAgCFRVlBzcpuIZxidTZLNF4tMZBEGgojywwNrWsZ05y3pRFK4/KHlmxhnM2q5fXxd3K6dyZ3NFDh7txjAsomU+EqkcvQNTPPfCUYJ+N23NFR+oLu6uJxpXQxRE/IqHtf5G6jwVZMw8CaMkFO/JjNCdHqJgG+Stm7NTvRPIZAr86IWjxOIZkskck5NpHt67jn/xzxa7sNwp2LaNYVgoirTizO3VWNMQ5dc/vZtoyMvrhy8xNFESWX2EOwdJEtm1vpGAR+cvf3aMQ+f6mU4ttib8CMvDtm2mRhN4/C7cNzBM6SN8uCCLEmVaGJekIyCgCMotz+oOZmOcT46wOVQiMMO5aX4ydAKAZ+u3cyzWzysjZ2j3V9HoLVvROgUE5CUs4W3Hnuu5/wgf4WqM5ceYKEwsOEfqXfVo0t1TTRMEKAt7CQc9ZPNFhkanyRdM+gZj6LpMVUUQt0vFcRzKy3x4vRqxGaJRLJrEE1m8HpWysBdNVWbWKeDSFSRJxLLskp7zOvGKbTtks6UYVJJE3PoHS8Qcx0GWJe7d0ULHmmqOnOjjvSPdnDw7yPd/fJSvfm4XdTVLuyfeDtz1RKNkA2iQMjIkzWzpt5ElYaSZKiaYLCSYKiaIF1MkjBQpI4cx44KgijKqdNd/xDkEAi6+/rX7SaXznDo9wHf+8r07vUkL4DgOU1Npjp/oZ8vmBsrKVuawshRqokF++Zld1FWGeHH/WU53jVD8KIt+RyFJIh0tVfzml3z8zZuneP3QJXpHpm54svvfNhRyRd78/vts3LOG9q2LRb0f4ecP5VoE9xItT7cKGbOALsqU6z4cx+HoVB/j+RRfaNzJ49XrqXOH+YueAySNlScFBATcknvR8qJdJG2m8SsfWch/hMXImJk5h6lZZK0spm3iOM5dQTYEQUCWRVoayzl2qp/h0WnyBYOBoRhet05jXWTueaGAm6qKALHpDBNTKWzbIZHMES3zEwy4FxQtImEPmiqTzZWmc5umBSyvpzJNi7EZgxVVkYncAoH3aiBLEh1rqvi7v/ggoYCblsZykuk8p84N8t6RbsIhD599ehtlkQ9mu+66KDxt5kgaaRJGhoSRIWmkiRWTTOSnmShOM5mfZqIwTdJcKPKVBBGv7KbGXU5Q8RFSfURUP+t8jXfmg9wAFEWea5OyLPu61bkPGqZpc+HCCM99/xDV1aGbIhoALk3hE/evp74yxM8OnOP9M/0Mjk3/3BXvfW5tpvfyLjugS0AUBKIhH195ajvNNRFeOXiBU13DTCU+HAMJV4NwwI14C49JLl3g5P6LKxaDf4Rbj6I5Qdo4i+MYuJU16HINgiBRNMdRpPJbHgyVaWW4JDcF68aF8NeDIJRmAiWMLKemB4m6/KzxV6KKMkHVTcEyMeyVJ2kkQSKoBBctT5kphnPDVLs+HK3GH+GDhS7pi6a4d6e7eT/2Puv86wirYXRJR2R1M6xuNSSpRDQOHe9lZCxBNltgZCxBecQ3RzSAGaIRpLd/ivHJFJIokErnaW+tJBRYSMRbGsvxejSyuSLnLo6Syxt4PfqSMZrjOBQKJucujQDgcau0NJYvfuJthCSJlIW9lEdKMdratko+98ltZLIFLvdO8Pq+84SDHp58uHNuKOHtxF1HNF4aOUBPZpjJwny1InfVRVxAwCPphFQ/YdVPSPUR1vxEtVDpRw8T1YL4FM9HjlO3ENlsgQsXR2/poDpBgM1ramiuibChtZu3j13m8NmBD712QxAEykMe2urK6WiqpK4ydNcRx+UgCOBxqezd3kZ7Q5Q3j3Tx7smem560fTdAFAXqKoKsqY+yobXqmhZ/hVyR2FgCzaUSrgiQTmSJX0O4PTk6TSa1ckL2ITkdPlSYzr9HxjiPKpWhybU4OAjARPbHVPm+inCNLOSNoEIvZ1Ogk5yVxyMvrhLcLEKaB8M2OTrVyylRois1xsOV66h2lxxtMmah9BlXcXGRRZkqvQpJkBa0wcSLcc4mz7LGt+ajqsZHWIQyrYygEmSQwblZGWkrzQsjL3AhfYFKrRKP7EEWlg4rRUFEFmQ0ScMn+wir4TlycishSSKtTVFs2yE+naF/KE4mW6ClsXxBu1Ao4Ka6IkChaDIylkCSBIqGRUW5n2BgYfC9fm0N0TIfE1Npzl8a4VL3GKGAe0n3KdOyudA1ysXLYzOtXD46195Z8q5rCls31BOfzvKdH7zP6HiCF187RSjo5v572tD12+t2d9cRjZ+NHqQ/O7ZgmSrKhFU/YTVARA0Q0fxE1ABRPUS5FqRcCxFSfShL9J3+vMM0LcbGk1y6NEYslsa2HXx+neamcurqIksOlcnlivT0TDAwFCOdyuM4oOsKkbCX+oYI1VXBuRuXZdkMDsboujzO8HCcg+9fZjqR5ZVXT3Py5MDcOltbo2zf1nTDmQy/R+djezrY0FrFm0e6OHZhiHM9Yx86e1qfW6OuMkRLbYSOpko2tlVTXxG6LfNJbjdkSaS+MsTnH9vMprZq9p/o4cSlIboGJm/KyvROIBJw01hdGuq4vrWKTW3VVIR91zxfxwdj/OiPX6N5fR0f+8UHuHyynxe/+fayz89m8gx3j694m37eKncfFPLmEJniBSw7jSJF8KodiIJGuniWqdwraFIVihhGk6JYdopE4RSjqR+giOVIgk7E8yi2Y5IpniVn9CMIMi6lEbfSQs7oI2d042ADDm6lGY/aznK0MKyEeKxiL5ZjEVKDt/yzrvFVUO0K8tPhkxi2TYMnwvZIE165pAXpy0zhkTX0VdjiioJIWAtT46qhP9s/tzxv5zmVOEVEi7AjtIMyrWzZQYCO48xZ+yqi8nMzMNChNDjUtE0Mx8B0TAy79HuiMLFoEjZArBhjNDeKS3KhiAqyICOLMoqgIInSz81AYb/iZ3NwM4O5QWLF+WF4aTPN0RXMkBERUUQFl+QiqAQp18qpd9fT5muj3l2PS7o1mXVJFGmsiyBJIulskeOnB5BliWiZj6B/Phng9WhUlPtRFYnB4ficJWy0zLcoy19XHWLX9mYGhuMkkjm+/+OjuHSFDR21yJI4N7jVsmxOnR3kez8+QiZbxOfVuWdbE3UrnK10O+Hz6tx3TyuxeIa/eek4Pf1TPP+zEwT9brZsqLutE8LvuuhHEkQiqp+IFqBMDRLRApRrQcq0QMl9Sg0S1vy47gIrtTuNQsHk/IVhXvrZSbq6xrFsGwEBB4fqqiAP7+1gx/ZmfFe4J2QyBV57/Sz79l9keqYdxjQtikUTl67y5BMbeOaT25DleaLR2zfJwYNdjI+nGB1LYBo2p08P0u2eD6pEUWDb1qabztrXRoN8+clt7N7QyIlLw5y8NMzpyyN3rWBcALxujZpogKbqCK11ZbTVldNSV0bY7/7A/apvB3RVYWNbNS11ZVzqb+TkzHE53zfG5HQW5y4UcQiCQNjvpr4ySHN1hNb68tJxqS3DrSsrIsSCKKBqCvIMSRzpm+Tc4W7WbGlEX2KAnGKYN2yQ8BFWBtNOkci9R8EaQ5XKSeTfBxy8ageOY+I4BmBjO8acg5LtFLHsJLZTQBBEHMemaI0xkXkRj9KGYcfIFi8ieT9NqnCc6fy7hFwPkDf6KJoj6HIdkuhZcnskUSKi3T5RZa07zKfqtlLjDmM5FlvCDbT6Sq15Dg7lmo/Hqjop11fexiog4JW87AzvZCA7sMBparI4yWtjrzGUG6LeXY9f9s/N9pgNugt2gbyVJ2flaPG20OHvuGVB4p3AZGGSU4lTFOzCHMEwbGPB34ZjkDASCwLsWRyNH6U/048maiWiMUMyZknH7O+AEmB7ePui9qMPCwQEtoe2kzEzvDP5DhOFiVW5lNnYFOwCBbvAtDFNb7aXk4mTNCQa2BHewZbgFiJa5Porut52CgKhgIfyiJepeIZjp/txu1Qa68oWOC3JskQk7CUS9jI4EkcQwO/TiYS8i2xmRVHksQc7GBiK89aBixw52Ydl22zb1EBNZQiXrpDPGwyNTnP4eC/HTg+gqTLbNzfwxEMdd819IRLy8vhDHUwnsrz81lnOXhzhRy8dx+fVWdNy+5yo7jqi8amaBxEFoVS9mKlc+GT3XSE0upvgOA6DgzH+6nsHGeif4r49a2hrq0SSRIZHptn/7kW+9/1DqKrMPTtb5kp8Fy+N8pMXj6PrCo8/up6KCj+WZZNM5hgcjFEW8S0gC5Ik0tIcJeB3MRXL8Nz33yeZyvP0xzfT2DDvchIp892S1iBBEJAEgda6cppqIuze0MiZ7lEuD07O/YxMJrHuIOlQFYnyoJfaiiD1lSEaKkPUVQRpqApTHvKi3MbMwJ2CIAh4XRpb2mtZ11jB7o1NnO8do2ugdEy6hyaJJ3N3dLq6R1epiPiorQjSUBmmvnLm+FSFCXr1VV/sK+oifPY3n0C9oirY1FHD53/zCYLli1tLJofj/Onv/vCmP8dHWB5Fc4yiNY5P20TIdS+jqefIGb14lDUEXbuYzr+LT91A0HUfklgKfsPuvfRO/1fKPU8jiTq2Y5ItdpEqHMettOA4RQrmMEVrAgBNribqeYbp/H6ShWOYdmpZonG7IYsSHcEa1gZKrRfiVRfZ+6JtiIKIexXWtlDqt98Y2MjpxGnOp84veCxuxDkwdYBj8WN4Ze8iolG0ixTsApZj8XjF47R4Wj7URGMkP8ILwy+QtbIYtjHXFrRS9Gf7F1SGrsYs2ahx1bA5uBlJ+nDeH0zbJF6MIwq3ToNRsAtcTF9kqjhFzspxf9n9BG+yMigIoCgizQ1ljI4nGR5NUFXhp7lhsStbOOimMurn+OlSd0ZrU5RwyL1kLFMVDfC5T25DkUXePnCJY6cGOHtxhPKID11TKBQMxidTFIomHo/GfTtb+fTHtlBTdeerGbMQBKiuDPL04xuZTmbZ9/5lDp/oI+B384VPbaf+NjlR3TDRcByHycIUL429jiIotHqb2B7ejO3YxIvT9GT6Gc1PkLEy2I6NJmmElSCNnjqq9Eo0aekL4xOV9wAsOJFtx2E8P0FvZoDxwgRZM4eNjS5qhNQg9e4aalzVS67TcRyOT5/mbPICDg67Ittp8TQu+qJcTvdyNH6Sy+YY3u3DPBjYQrOnAeGKkrDt2Izkxnhj4h0UQaHZ28iO8Ja5x6siPsqCH8wNKZMpcOxEHxcujPLA/e187rP3EA6X3juXK+LxaDz33Pu8++4lmhrLqakpnewjI9MkUzk2b6rn4Yc7CIfmtzeRyKIo8gJWK0kitbVhamvDjIxM4/O5MAyLtrZKOjtu7yAtSRSpLg9QVeZnz6YmhsYT9I3G6BuJ0z8aZ2Qyychkklgyg2HeOt3I1dsQ8rsoD3opD3mpKvNTVeanIuyjIuKjqsxP0Of6uahcrBS6prCmvpzW2jIS6RyD49P0jcTpG4kxOD7N8GSS0ckkiUz+tlWhNFUm4ndTFvQSDXupLg9QGfFREfZRGfFTWebHo6s3laFRVJlwNDD3f7DMR+umBmpbK3EvIaBTVAXXKmxtFVni60/f86FrQ7seLqfGyJir+0wuSaXNX7louVtX6Gy+Yo7Q3PW49H13mNUYXPs4O5gzrylpNkrXdRFJ9CKJPtxKG6oUpmAOIYuBkgAbCQHpive4c7iaYEApwzw7xG/16xOp0Ct4ovIJinaR7kz3oufk7Tz54vW1ch/2JKBpm6TNNIZze/RnpmNiWiYZM/OhnVMymh/l+PRxzifPM5AbYLo4veCzSEiluTBLnAuO42A5FqZjLvv5p4pT7JvcR1gNc0/4niXtl1cDWZZobijn3UPdOI6D26XRVL8U0fBQGQ3gOCWiES3zEb5GDLemuYIvfnonLY3lHD3ZT3ffJFOxNIWiia6W3KWa68vYvKGOHZsbl3zPOw1RFGhqKOOZpzaTTOc5dmqAfQcvEQ65eeaJTZRFbs7kZync1NGMGwleHHkVRZC5t2wn6wPruJC6xLuTh+jNDhAvTpOz8tjYqKKCT/ZSqVewOdjJPeFthLXQov7Fq0/UnJlj39RBTk6fZTQ/Trw4TcEuYjs2qqjiUzxU6lHWeFvZEd5Cvadm0ToHckO8Nv4OOStHUAlQ66pGv6r16lTiLD8bfZ2MlcXdCR01Kp+q2YoszmcfinaRdycPcaw7jk/28mBVFZ+u2XYzu/CGkUjmOHN2CJ9XZ8vmhjmSAeByqWxYX8v773dz/uIoQ8PxOaJRXR3C49E5cXKAiooAO7Y3UVUVRJYlAoFbL2a8FRAEAbeu0lZfTmtdGfmiyeR0hqnpNFPJLJPxNOPxNPFkjkR65ieTJ5s3MAwLw7QwLAvTtHFwEAUBSRSRJBFFlnBpSulHV/C5NfxeF0GvTsDrIux3E/S5CHh1gj43kYCbgNeFfA0R8d8WiKJAyO8m5HfT2VxFOltgMpFhajrDVDLDeCzN5HSG6dTC45IvmJiWVTo2M8cFoUTqZo+Lpki4NBWXruCeOS4B7+xxcBHyzR4XFyG/i0jAg8el3lbC17a5germKNoSbVMALq/O7qc2U7lChxFFlvjkA+tv5SbeFfhPZ39Cf3LlE+YFBFq8UX5h/fWvpZpUgSbXkCwcJWtcomhNEdL3IEvLi5cFQcSjtjGU/DM0qZKo99O4lTb8+jYK1hiCI6BKEWQxMLc9N4uslWO6OE3OymE6FiIird7muyooV0WVtb61CNUCB6YOcDZ5lpSZutOb9RHuIjg4dKe7eWviLU4mTpI0knNkoUqvotXbSoVegVtyowjK0nzfAQsL0zbJWllixRj92X4GsgMLyN1EYYKTiZM0eZpu2vlMVWUevm/tnOtSKOCmfAkr13DIw8cf3cDa1lKSo7Y6RGX02kYI9TVhKsv9bOqsY2QsQSqdL80WUyV8Hp2qigB11WE07frhdWVFgM88vZX7d7UR9LtpqL3xioIkiWzsqOUf/p1HkSTxmroQRZboXFPNL31xDw/dO4njOFRGA8hLiNtvBW5J65ThmEwWYpyYPs1PRl6hJ9OP5VjokoZX9lC0i+SsPHlrionCFEO5EQq2wd7yPQTVwLLrTZlpnh96iQNTh5ma6Y0UEfHIbiRBJGflmSiU1tmT6Wc4P8KjFQ+x1tc2lwESBIGwGiKg+MhZOWLFOFkru4ho9GUGKNjzQ/66M30zgsD5HW/YJiP5UQB0SaNKr7gVu++GkM8VGR9L4vVqVCzxxYiEvYRCbk6dHiCZnPdYb22t4ONPbeLFnx7nhz86wvuHumltibJpcwMb19eiqvJddTO8GoIg4NIU6iqC1FUEATBMi3SuQCZXJJsvks0bZPNFioaFaVlYVmkyp2XZOJSyg6IolAiHJKIqEqosoyoSulYKbN26ikcvBbq3I3gdzg0zmBtkjXcNASVwzX2eNMa5mHyTGtcGqtzrbvm2rAa2YzGcO8OFxBtYjkmVq4NW3x5csh+/V8fv1WmuKfXZ5osG6WyRTL5ILj/726BoWliWPXdMZtvg5o6JKCLPHhdFRlNkdE3Gratzx0ZXlQ90silApDJIpDK47OOarnDfJ7aiue6OKbl3Cu3+KnzyfGWnaJu8OHSciO6jM1BLQHHhAPFihnOJYQzHZGukcUXrlkQPQX0XmlmJZWfwqZtxq62IQul6HnE/hiKGEYUrxdECtf5fo2iNIwklAwBVKifqeYaCOQQIyKIPSXDj0zZjO6XrpUtpQRaDKGJwRdtmOzb92UFOJc4wmBsmaaQoziTFNEnjn7X/9oLnW45FysiQt3OISHhlN+7b4Fx1LeiSTqe/k6gWZX1gPT2ZHoZzw0wVp8iYGYp2EQdnbhihJmr4FT8RNUJYDbMhsAFdXFkVL6yG+aXGX1r0/g3uhpv6DPeE76HB3bDAQUsV1RVrIerd9Xyt8WvYzu2pis/CLbmvmamXBInNwc2LrIfDavimtAtuyc3Hqz5O0kjOLRMFkah2fRvukdwIr4+/ztH4UfJ2qbolIbEzspMdoR3UumoJqAEU4fq6t1kDgbSZZqwwxuHYYd6dendOYO/g0JvpZTA3eNNEQ55xnmptuvZn1DWFjR21bOyoXdX6VVVe0fqvh3DQw56drTe1jllIokhTfdmKqyi6rrCps5ZNnav77DeCW6bR6M8O8jdDLzKSH6PRU8e20CYq9SiqqGDYJoO5EQ7FjtKfHSJWjPPm+D4a3XVsVDqWvCBYtsWPh1/m9fF9ZK0smqiyPbSZDn87fqV0s8hZefoyAxyNn2Q4P8rR+Elsx0EXVZq9jXPriqghAoqf0fw4U4U4WTNHWJ1nexkzy3hhEtMxCSoBEkaS3kw/lmMjXzGIxrRNhnMzREPUqNIXl/o/KNi2Q9EoCbiXsliTZRFZkjANa4Edrdej8cjDHTQ2RDh5aoBjx/t56eVTHDrSQ2dHLZ//3E4qov67mmxcDUWWCPnchHx3Z0VmKRyKH+JI7AhfbfgqfsV/zSxq3krSkz6ERy6jijtLNAQEvHKEStdaLqcOMJg9SYN3Gy4Wk11dVdBVhTLuTH/7Bw1BFPCFbvyzjsVS/NVrx/nak9sJfgDe5rcLj1R2Yl0RtP3NwBGafVG+1nw/DZ4yXJIKOGTMIpdTY/x46Niq5kCochRVXvoG79M2LLN844L/BUHGo7bhUdsWLHeJ9XN/p9JeMjmF2orrH4u8VeDdqYO8HztCX2aAlJle0CbiEl0zVrvz33PDNjmXOs+b4/vRJJVNgfU8UvHgdd/rVkMWZapcVZRr5XT4O5guTpOxSiTDdExwSkGwLMrIgowu6XgkDx7Zg1tyryigFwQBj+zhgfIHbvn2t3hbaPG23PDrI1qE+7T7buEW3RhEQaTeXU+9u/76T74GRrIn6E3vJ6jWsy74NJqksTm4edXrMW2TI/EjnEqcmiMZAFtCW3i84nHq3HWrErYLglAiq5JGSA2hCiqj+VHOpc7NPSdWjDFVnMJ27Os6mRVNiwuDE5wfGGdoKkEqV6BoWnh0lYjPTVt1GR0NFUQD1x5KVzBM3jx1mbdOdYMDbl3lEzvXsal55WTHcRwmEhn+nx+9g+OUkmZt1WX84qPbr/ta07IZiSU52z9G73icWCpLvmiiSCJ+j059eZDO+goaoqFFIvWrkS0UeW7fKc4PjON1aTyyuZV72kvnUzpX4PzgBOcGxhiLp0nni4CDz6VRGfazvr6Ctuqy2zLF/JYRjZSZJm8V6Ay084mqJ2jw1OKW3HO2Xx1mhgqtjB+PvExfdpCJwiQXUpdo9jQQUBcHKUenT/HOxHtkrSyyIPNMzZPsCu+gXIvMZQUsx6bTv5YGTx0vjbzG5UwvpxJniWghyrQIfqVUNgurQQIzf08VY2SshV73I7kxMmZp2ebgeg5MHSJhJJkqxqi+gkwYjsHQDNFwyy6i+p3rv5NlCY9bo1A0yeWKix7PFwwKRQNNV1Cvslb1+11s3txAc3OUXfe00ts3yauvneG1184gCAJ/99f3LklePsKtgWmbnE2cZSg3RMEufKj6dgVBJKjW4JbCJIxREsXRO71Jtw3JWJpLJ/pW/bqW9XVLisWvh1S2wL6T3Xxu76YbJhqGafHuqR7WNVYSDS28wRYMk58eOMfTezpva+ufR15YLd4/cZGNwTo2hxrQpflKQ1D14FN0jsf7eGP0LB+v2XzbtulGEE9meeto15zr2pr68iXtwg3b4I3xt3lt/C3G8hMrFhPLgoRbcnEhfQnLtrAdmx3hLXdshoUsynOzDVaLn4y8jktysSu8+bZOSv8I18fl1BtcSr5MjXs764JP3/B6RvIjXEpfWtBO55E87I7spsZVc1PuWaIgEtWjNHoaFxAN0zHnKmnXmq9xqneEnx46z5n+Mcan0yRzBQqGiWU7qLKEW1MI+9zUlwd5cEMzj21pw6Mv3fIqiSLpbIH3L/QzmcyiSCJVIR8tVRG8rpW5mzrAvrO9/ORQyVjBo6vUli3frTOLkViS1090ceBcH4NTCabTOXJFA8O0kUQBTZUJuF1Egx62ttTwzK5OasoCy3ZZGKbFka5B3jrVTcCtE/G72dZaw6XhKX703hlO9gwzkciQzhUpmqUp76os43NrRANetrRU88ldnbRURm5px8AtdZ2q1KM8Gn2Qdn/rgpNQEAR8ipetoU1cSncznBvDcAx6s6XMz9VEw7RNXht7i8RMqW9TsJNHog/OEYdZSIJISA2wLbSJglVgbGCCtJnhTOICHf52toU2ARBUggRmssZTxVJF40r0ZwfJ2wV8spdNwfUcjh+nYBfpyfTNEQ3HcchaeSYLUyiCQpkaQRfvnMWux6NRXx/hyJEe+gdidF5V/hobSzIxkaKyIkAwuDjTLwgCgYCbQMBNU1M5Lc1R/uW/+R5vvnWOX/vVB1FYfBERBAFZEnEcbunQvr9tGCuMETfiq3Y3uZsgcGt62e9mDHWP8yf/x3Oreo2sSPz6737uhojGHG5itw5NTHPo3AA15YFFROPy4CQHTvfysd3r4APUGMWLGexrkGnTsYgX7755OVVlfh7Y2kI8WTI8uDQwQW00yNrGKAHvfDB9bPokb0+8y2h+HAcHl+SiwV1LWA1xOH6Mor20yFgSJMq1Mqr0Cvqzg0wVYwzkhun8EA7Lu5Tqxaf42Bb6+dMcfZhgOzZD2SMYdg7LuTmTieHcMJOFyQXLalw1VOgVNy3WBtBEDa+8uNowO7dkKdi2zcvHLvHcvpOc7R8jW1j83SoYJgXDJJ7O0TsWo2tkip6xOF/Zu4XyJaobsiTSXltOa3UZk8l+DMvmZO8I969vYk3NyvR2ju3w00Pz7m0+l8bDm5ZviXIch4tDE3z37RPsP9PLZDKzyLHRthyMXJF0rsjQVILukRgXhib59Sd30lFfed1kUcEwGZiY5vzABH/44/2c7B0lk1+clM4VDXJFg/HpNH3jccan0/zyEztZU112yzpbbhnRkAWZBk8tHYH2ZZmuW3ZR46rCp3iJFePEi4kFuohZ9GUH6csOYGMjIvBI9IFFJONK6KJGm6+ZNm8zx6ZPMZIb5WLqMhsDHSiigioqhJQQLkknaaZImWksx5rbzv7sIAWrQJ27lkZ3HbqokyZDd7qPPZGSC5blWIwXJjAcE6/sodpVdUfbi4JBN1s2N3DgvS7eO9hFZ2cN9XWlPs5kMsf773fT3TPBw3s7qK+f7+/s65tEd5WG880OaFFVGd2lYFszjizLvKesiARDbrp7JhgYiLFxQ91t/pQ/n+hKd5G3Vjv53CFe7OftsT9mujiEWw7R7NtNg2cr0kw/+mS+l67UPiYKlxEFmVr3Jlp99+KRSxnKg5PfIahWkzYmGc6dRUCgwbOdzuBjiIIECJh2kb7MEbpS+8hbKQJqNS3e3dR6NiCw8uB0qtDLxeQ7TBS6kQWVWvdG1vjvR5N8cwSlJ3WQrtR+0mYMVXQR0RpZH3oSr1w6X3Nmkq7UPgayJyjaOVySn1r3Rlp8u9GlW++McTXKq0M8/SsPzf1vmxbnDndz6Xg/7VsbaVhbje5WyWcK9Jwd4vKpfrY/uYmalhvXbuUKBq8fvsSZ3lFcmsIDm5q5b2MzkiRiWTbn+8d47fAlRqZS1JT5eXRnO2tqyymaJi+/f4E3jnZxeWiSnpEYPrfKjnUN3L+pmef3nebw+X56R+L8qz9+EVWReGx7Ow9tbS05CE5neGH/GS4NTuJza9y/qZl7NzQiiSJTiQxHLgySyRdJZQucvjxMfWWIj+3uoLn6+r3jLd4K3hw9R0eghj3la9BmgpSsVWTf+AUOTF5mU/Duu5Z43RptdeWMTqXoHYmVXNXGprnQN879m5tpqomQNFIcnDrMcH4UB4cO/1oeiT5AtasKXdQ4nTi3LNEQBGGGlNTRnx0kbWYYyg7T6V/7AX/Sj/DzgkSxn7QxcWvWZSTImAsTABE1giZqtyTJNOtEdTVEQVx2/S8fu8Q3Xz3MpaFJTNtGkSS2tlbTUV9Jmd+NLElMZ3JcGJzg/Yv9pHNFBiemeeHgWQD+zlP34NYWtwY1VUZYVxflyKVBDMvmdO8o/ePTtFaVrSiz3zcR53RfqbovSyId9RU0VSxfGewanuKbrx3hrVPdZPJFJFGgpTLC5uZqassCuFSFvGEyOJHg0KUBBicTJLJ53jvfR65g8G++/Cj15cEl3ehmUTAtjl0eIp0rcOjSIJIo0lEfZUtLDZUhH7IkEkvlONEzzJm+sdL1PVfg7dM91JUHifjclPlvTdvzLSMaXtlNvbsW13XGyXtlL9pMJSBv5Zc80S6lLpO3SmzcLXtY629b9JwrIQgCYSVIi7eRY9OnKDoGY/kJ4sVpono5giAQ0UL4ZC/ZQkkQnrcKeGQ3pm0xlBulaBepcVfhlT1UuaLEinEuZ3rn3sNyLIayIwBookq169brM86eHSKRyJHLFzl3fhjbdhgaivPyq6dxuxS8Xp36ujLCYQ+KIrFhfS2PP7aen718it//ry/R2lqJqkoMDMa4cH6EttYKHnygneAVblL79l/k0OEeQiEP5eU+3C6VZDrHhfOjJFM5vvj5XUjLMGWvV2fz5gbefucCP/jhYbp7xvH5XBQKBu1rqnjowZu/Qc5a4Z1NnqUr08VofpTcjJ2xW3LjU3xU69U0e5qpcdWgLTG40bRNRvOjdKW7GMgNECvGyFt5JEHCJ/uo0Cvo9HfS6Gmc84i/EoZt8NbEWxyJH6HeXc+X6r9EzspxPnmes6mzTOQnMGwDl+yiSq9ifWA9TZ4mVHHp3sbB7CA9mR5G8iOM5ccYzA3OXcC/O/Bd3NLiilNIDfFrTb82lznKmnEGMido9O6g2beb0dwFzky/hCq6qXVvYKrQx4n485hOgQbPdkynSE/6PQpWis7gk3jkEJP5Hi4m36LZu4tW3x4SxRGOxr6PWw7S7N2Fg8lQ9iRHYt+jxXcvbilErDDAsdgPcbCp92xZtJ1LIVEc4Vjsh0iCQot3F6Zj0JXaj2HnWB98Ek3yMlno5cDkn9Pu30u9dxsFK0XWnEaeEfUadp7+7FEuJt+izf8AqugiY8YRBBFR+GDG/4QqAjzyuV1z/3ed6OPi8T4e++Ju9n5mJ26/C1ESsU2LTDLHq3/9HonJFJlE7pqi8Wshky8ykUjz6Qc20D0c47Ujl/C6Nba113FxcIKX379A2O9h1/pGjl4Y5JX3L6DJEvWVIbavrSObL2KYFh/bvY66aIiw303Ao/PI9jZMy6ZgWHzpsa2oskRFqETWktkC33/rJKlsgWcf2sjwxDSvH7mEqkjc09FAoWhytmeUvrE4D29t4/OPbEESBfyelYmAf6HpXv7dqb/hv51/mb/o2U9AcePgkDByxAoZQqqHz9TvuKH9dTvRNxrjjUOXyOSKrG2sYPu6OhRZ4rVDFxmaSNBUE+FypofB3AimY1LjquITVU+yzr8GWSiZalyvz1wVFSr1kt4kZ+WZLK7cresjfISrMZw9jn2LbHoNx1ggsIdSe92tqmRnrSzxYnzRcpfkmosRr8T5wXGe23dyjmTURPz82pO72NhUSdDjQpUlBEHAsCxS2ZIe4U9eeo+LQ5PEU1leP36J5sowz+zqXLRut6awti5KTVmA3rE4sXSOs/1jbGmpJrKCYPu1413kiqX97lIV9m5sWXae1mQyw8vHLvLWyW4yhSIBj85T29fy1Pa1VIa8uNSSAY3tOGQLBs/s7uA7bx7nrVOXyRYMTnQP8+3Xj/Dbz9x/zdYux3EYiaeYSmYJeV18+t71PLq5jbDXjT5j+lM0LZ7a3s7LRy/yg3dPMz6dJlc0eP1EF3s6Gu8+ouGSXES162sWJEGcY2GWYy85VXggNzx3glfp0RW1KLlkF2VqZG4ydsJIEpshGgBlahi/4mOsMMFkIUbWyuGR3YwXJkmaKWwcavVKZFGm3l3LueQlBrPDFJ0imqBhOhZDuRLRUG8T0fjGn77FyGgC27LJFwxs2+Fy9zjf+MZbiJJAOOThi5/fxf33t5emX4Y8PPOJrUTL/bz19nne2Xcey3IIBt3sfWgdDz20luam6ALi0NhYzoWLo3R1jXHiZD84oGoS0fIAv/6rD7H3oY5liYamymzb2shXf2EPr795lnf2XQAgGPQQvY4l3EoxkBvgh0M/ZCg7RNbKzg2GglKrgSRIaKJGq7eVZ2qeWSScG82P8tbEW5xOnCZlpubIrOVYCAjIgowqqrw39R73hO/hiconFjm92I7NRGGC86nzZM0s4/lxfjb6M04mTpI203NOLKIgck48x/Hp49wbuZf7y+9fshR8OH6YA1MHSJklF5orL95DuaEl90NUiy7QbgiCRERrZEPwYyiihkcOc3r6p8QKfdS6NzCYPUXGjLE++BS17g042IhIdKX2U+veiEcumR9oopc2//2E1FpMu0Cs0M/p6Zdo9u7CtIucnn6JKn0dnYEnkASVaW2Qw1MjdKffWzHR6Em/j2kXWRt8hApXG7ZjU7DSXE69S6tvD5rkJWdOkzTGiLraqNLXYTsmhpNHFd0zx8AiY0xhOgVq3J0ElCpMp1T9VIQPpmVRkkRcnvn3io0lSE6lWffFZqJ1CzP53qCHtduaeO4PX2Z8KEZ9e9XVq1sRvC6VXZ0NbG2vJRryMjgR5/ilIbasqeVi/wSJdJ5nH9xEbbTUp/v8vtP0jcZpqo5QXRagLhoi6B2ltbacNXXzZf+mqgjVZX58Lo3Opkq0GVGh4zgk0jn2n+zhn/7CXjqaKqmO+OkdiXP43AD3dJQcgYqGSXXEz4NbWvC6NGzHXnFFt91fxf+56TO8MXaOU/F+xvNJBAFCqpc95Wt4MLqWJu/SLQqJYo73J3tRRImHKteseD92pyaocQfRpMWJhOXQn4mxb6yLByvaqPGEcGxorI7QVB0m7HfjcWmIosCGlir8M/NS+jMDJI0EANtCm2n01C+ZvFgOsxOjAQy7SMpIzz02WYhzYOooJxPnsR2HNm8De8q2U+uqRBAEfjLyBrIgUbQNziZLVdJGTy0fq3qIiBqeu8++O3mEfZOHSRhpKrQyHozew6ZgKSk0lBvjJyOvsyO0iS2hjrll70wcolIv56FoqaLfle7jnYlD9GWHyFslXZkqKjxV9RBbg6XAzXJMXhx5k8vpPizHptXbwKdqHsMtuT5U5iIfVliOyVD2yIxb5s1DFVTkq5I6KTO1bFvTauDgMFGYoCvdtWC5T/YRUAKLumJs2+Yn75/j/MA4pm3j1VV+4xN7uH99M25tseNVwK1THvAiiQL/9i9eJpUtMBxL8fqJLu5d10h5YGEALQgCHXUVrKkpp3csjuM4HO0eYu+mlusSDcuyefnYxbn/w14X93U2Lf25Z1qmXjpygUyhiCpLPLq5ja89vJWKkG+R9sLn0ij3e/h7H9vN+HSK493DmLbNz45c4BM7O1nfWHFNV0zbdtBdMk/vXMeXHtxC0KMv2FceIOjR+cyeDYzGU/z4/XOYlk3/xDSDkwk66yuuK0BfCW5d65Qo34AIbOm+3ZSRmuvpDaywX1VERJc0VFGhYBfJ2wVy1rwWI6yF5tqvpoqxuccGc0Nzf9e4q2dawOqQBJG8VWAoO0Kzt7FU0ZghGm7ZRYW2st691eA3f+NxDGN59xVJEomEvQv+Lyvz8dijnezc0Uw+b5SsCBUZn0/H69Hm2qNmsXVLA22tFeTyRSzTLjkkSAKaphDwu3C71WVvCoIgEAp6+PjHNnHfnjaKRQvHcZAViYD/5gWAXaku/qzvzxjNj5bskUWdFm8LETWCKIgkjATj+XEmi5OIgkj5EsdAFmSmjWkGc4N4ZS/NnmaqXFV4JA85K0dPpof+bD9jhTFeHX+VCr2CHeEdywYHU8Upvj/4fU4lT6GLOltCWwirYQpWgZ5MD72ZXoZyQ7wx/gZ+xc+9kXsX7b8NgQ1U6BVzBONQ7BDnkucwHIMnK5+kWq9e9Bpd0hdcbDXRQ7nWjFsOAuCWQsiCRtHOYtpFksYYmuQlrNWjzlRIKlxtnE++RsqcxJq5OYS0WjxyBEXUUUSdStdajsZ+QGn0mclA9gSiINGbOQJQmqJsxql2d2I71kyL1bURLw7QmznMaP4CklCq8uStJDkriWHncRyHqGsNzd7dvDH6h1Tq7bT69tDg3TaXAVZEnRrPRvqzx3hl5PepcW+k1XcvFfqaBUM0P0gYRZNMKo9hLH2jNQommUQOa5nHr4TjOJw7O8w3/+xtQODTn9lOWW0QTZGpipRIhNel4XfrTCWyFA2TeCrL/lM9XB6eQhZF8oZJOldgZ8cSLjUrnNBuOw7xVI7u4Sl+79uvo8gSpm2TyhbY3j7fzqSqMgGPjs+tlTL1S2i4loMiSqzxVVKpB3imduucw5QiSnhkDa+sL9sCYDo2U8XMXLvVSvGD/mN8pXkXla6VB/15y2A0l6Bgl45fVbmfaNiL66pAZn1L1Vw7RdyYJjdTfW/yNOBe5YRsURDnugAsx6I400o8XUzy1sRBejOD3BvZiizIHJ8+y0ujb/FU5UPUuiuZLMQ5HD9Fu7eJXeHNWI7FK+P7kQWZT9U8jkd28d7UcZ4bfImHyu+hQi+jNzvEXw38GNux2RLqoGAVGcqNsdY33yJTsIqMFSbnBuAO58Z4fexdXLLOF+ueZiA3wk9H3mJXZAvrfC2oM9fOo/EzbAl2srd8Nzk7zwvDr+GRXDxT89iK9oXjOPSm93Fs6lvYmNR7dtMZehaPvLg97+z083QlX6Nol4TK64KfpM3/+FyiYhaWY3Bs6tv0pt9BEd2s8T+5pEDadiwy5gRjudNM5C+SNIbIWdPYjoGIgib58StVRF3rqHZvwS2Fr3kdsh2L10f+HdPFPjTRz30V/4iQVo/tmCSNYfrTBxjLnydnxjCdAqrowqtUUulaT417Gz7l2olM2zGJF/qJF3uYLg4wXewnaQwxXeibIxojuZM81/vLy67DK1ewLvgMDd5dSz7ukT2ljoErLmc9mR7ixTjlWvl1q3XXwkR+ggNTBxjJjyxYXqlXUqFVLLofXhya5ET3yJzG4OFNrWxrq12SZMCMw5Ui0dlQye61Dbx89CKWbdM/Ps3pvlH2blzsUlYV9tFeW86Bc32kcgXOD4zTMxZnbW102eoEwImeEQYnSskGRZbY3dFIcJlqbyyV5cilQYamSs9vqy5j78YWKkP+ZVu0RFGgtjzAQxta6B6NEU/nSOWK7Dvbw9q68uva7zdGw3x2z0ZC3qWvTYIgEPF72NRUzfsXBxieSmJaNoOTCTIF4+4iGiJiaWDLLUDeLsxVOrRl2lGuhiAISEJpOmXBLmLaJkV7/hsSVkL45RLRmCzEyJmlHvnB7DA5K49HdlOmhpEEkUZ3HaIg4uBwOd1Lk7cB0zEZzo+iCDJRrXzu4roUXr7QxcmRUco8bt7rH2AinWFLTTVf3LKRlkh4rvB4bGiE7504xbmxCXy6xqNtLXxmYyduZabnPpPl733/ef7zJ57k20dPcGRwiICu8/lNG/jYulJ2bzSd4oenzrG/p4+MYVDu8fB4ewtPlLXNkYzjQyN8/9RZTo+O4VJkHm1r4ZnOdUQ8pYvyv/3Za2yuqaL3Upz9vf0IwO6Gev7O7h141IX7XxQFvF4d7yomIK8EKTPFdwa+w3BuGAGB7aHtfKzqYwSUwFxWxXZsCnaBeDGOLMpL+reH1BD3hO9hrW8tDe4GAkrJ41sURGxsClaB18df582JN8laWd6depeNwY3LEo2sleV08jSt3laerXmWsBpGEiQcHFJmin0T+3hl7BUmi5OcT52n3ddO2VWVvQZ3A3Xu+aBtMDvIxdRFDMeg3dfOWt/aRRdtAWHBBVQU5KtuoiU1jYOD7ZhYdnGmrWh+PbPajSt1NyLigsK3JCrYM3cSx7GxHIMNwY/R4Fk4PE2XfCvSaDg4FO08Ffoa1gUendNbzCKgljL9qujivuivMFXopyf9Hu9OfJNzydd4pPI3S+8liJRpjTxU8Q8Yz1+iJ32QN0f/iBbfbraEP40ifvDONqFyP0bB4NXvvkeo3E/D2mokWcKybLpPD/DyX76LKAr4Qte2U4RSpumF549y7GgfgiAgCPCrv/kolu2QmxE4mpZN0bTQNRlFktBVha1ravnak9vxukvnviBA2H/jts6iIOBxqZQHPfyjLz5EZCbTJwgssDkszZ4RbzgzLQilKdYBVr+tRcvk4EQ3x2L9iAg8WdPJPeVNvDV6kXfGukgYORq9ET5dvwVJEHlx8BSvjZxnLJfEK2v8dsej+BWdl4fP8tbYRQQEtkUaeLZhC9PFLM/1HaErOYEiSshXEOnzPWOMTKV4cncp+7//RDeKLLGzc37uQ84qYDomIiJe2XtTTjylb33pO9aXHaIvO8SO8EbuCW8GoZTMe23sXfqzQ9S6S4GoLqrsKdtGx0x78Vhhiq50H0W7iAcXzw+9ysZAO49W7EERFVq8DUwV4rw8+s5cBeN6GMtPkTIzdATaWONrotZdyZH4aQQEVFGZu+b4ZA+PVeyh1l2F7dj0ZAY5lby4YqIxi6wVI2NO4JbLyJpTSxANh/H8WcbyZ+bahEayx2ny3r+IaNiOxVD2KFOFy+hScNHjhp1jLHeWS8mXGcudoWinMZ08lmNgOxalZGiJWIuCwuXUa3iUCtYHny29n7R8tjtRHGSqcBlN9JEo9uOWw1xMvsTZ6R+RM+OYTh7bseZsj0VBoS+9n4BaR2fwUzR570deZkZJyhjlwMQfECt0YzlFLMfAchbqXQ07y1Th8rLbZ9iFOaK2FKpcVUTUCOOF8bllSSPJK2OvEFJDRLXoqq8HtmMzmB3kxdEXOT59fEF1X0Sk2dNMrXvxXIeTvSOMTafmUtMPbGgm4Ll2pUwQBLy6ysamKl4+Wqo4xNNZzg+OL0k0ZKlETBorQpzqHSVfNDnRPcz21lqqI8snvH929MKcg5MmSzy5rX3Z7RpPpDnZO4I9MzuquSpCR33FdXUgoiCwoakKj64ST5cS44cuDvCLj25nCSO8OXhdKpuaq6gKXzthLwoCVWE/Ya+b4amSCVMym6dortx2/Fq4ZURDgNLd6RZAnR1l75SGAa4UNvYVbTbigpuGIsqE1RBuyUWsGCc34ws9mBshbxVo8TaiS6WyUpVegVt2k7cK9GT7S2LJQqyk65Dc1F5HCJ4qFPjp+YtsrK7kkx3rKFoWPzp9judOnOIXt2+lyu/jwsQkf7D/PdZXRnly7RoS+Tx/dugoedPkF7dvQZVKmcWzY+P832/tZ2d9LXtbmsgYBlW+UiCTzOd5+UIXRweH+fqOLXhUlYHpBBG3G10uHdqTI6N84/0jhNwu/v69O0kWCvzg5FmyRYMvb91I2O1mNJXmv779Lo+taeU379vFVDbPH+w7QNCl88s7P5jJ5/sn9jOUG8LBYUNgA19r+Boe2bMoAHccZy6QX+oYSIJEp78TBwdZkBe93iN7eKLyCU4nT9Of7acn03PNUrCDg1/282zNs9S76xeszyN52BLaQm+2l7PJs6VqS2FyEdG42qHjymBEFmQUUVlZgLLMOaeIOm45QCo/Qc5M4FdKYuRkcQwBEV3yI8xkoFPmxFx1AyBeGMSrlCpDoiARUKqwHINq99V9rMKKbypeOUzOShBUq4nqC503BOaDVbccRJd8lOtNrPE/yM9G/hO96UOsDTyMgIAkKASUSrxyhBr3BrpS++hJv09NbgM1nqXnJdxOrNnSyP2f3Mbf/PFrnNh3Hm/QjeZSKWSLpBNZRFHg87/1JA1rV9Y2lU7nsSwbURTIZktZ8el0jjePdVEbDdI9HKNnOMZjO9cgigKNVWHO9Y4xOJHgoS0lAfhkIlOarD4Dr1sjkckznclj2aXWVGmGIIR8bkZiKdK5AqosYVo2siQS9LhY31zF4QsDfOXxbUiiyMR0mkLx5lskZmE7DkdiPZyI9ZGxijxds4VmXzmWbZM08nhlDVVa+naUNYvUe8L8QtNOXhk5R1dqgo5gFdsiDXQGa7Admz84/wYjuWk2h+r4dP1m9k9c5ivNu6h1B/EqGmP5JD8dPsO/3PAkOdPgD86/wfpQNYliju7UJP+w41HOJ0Z5fuDk3PvmCibpmePiOA5jsTT6VVbhsiAhCuLMfcdZtWOY5dhkZ6zVZUGa05wljBTH4+c4nbjId/qfB6BoG+SsPDvCG+YScVV6lDItPFd9CKkBclYO23HImFmG82N8suYRXDP3Nr/sodlTx0ujb5MyVub05ZZ1TMciVpymaBuM5ifJmjkCindBwq3eXUNIDcwtC6tBLqV7ZkL160MQBFxyEJ9SScacIGfGyZmxRc/LGlNkjIkFWoSpwmVM+2qXJQcHi1ihBwBJUAlpzQuekbOm6U79v+z9d3xkd3reC35PrpwDckYDnTOb7GbOwzAczZAjaYKybMm6kn2165V9d33tle21vF7vvb6yr9eWrJFkhckzmkRyOAzNTHazc0Q3GjmjUDmduH8UgAYaQAMdGEbyM5/+DFF16oSqc36/3/u+z/s8r9Kff2XFQn3pfmxMbMfEtMpUrCzvzfxnwKHDf/+6SQ8bk5lqHxljhOOp/45ur/zeHRwsp4plVamW81TMLA4OXf6H1qwiL4yR0pIEb8maw54f3yVBxS2F1jyvhcr2Wmh0N9Lsaaa/0I8+/904OIsU4oeTD7M9uB1Nun5zuOM4VO0aA+BY+hgnMyfJGJllruBQ80PZGdqJd5Xg7fL4LNlSbc3m0RSaokGUDSjnyZK4jCZVqhrMZNa+77c0J+iuj3FmaArHcTh6aZQn9veuGWgUKlVePz2AZTsIAnTUR9jasrYYSLpQ5spE7Z5W5s8t5N1Y0iwW8KBIV++F0dkstn19mpzPpdHTlNhQQ7tHUxYptQAVw8RaZ/8bxUfTVXmD8ErexRu3YBbW2boGx3EwbBPdmudxiyqqdDUjJwgCMVcEn+xlujpL3shTMAuk9TSmY9LqaV6snkiiRKu7iXQ1w2BxGNuxmahMAbUgqNG9/kIi7HFzb0cbD3d34uBQMgx+0neZwbk09QE/Pzx3kTq/j2e2bqY5FMRxHEYyWb5/7iI/t2sH6vwNJYoiPfEYz+7YiiJK2DiLOWnDsslUKkiiQG8iTkMgwN6mRgSBxc+/cWUQURD47PYtbE0msB0Hw7L41qlzPNjVQcRTy/DEvV5+af9ukj4fpm1zZGSU94fHPrJA42j6KIZtICLymcbP4F9DZUwQBKR1aBvX40gLCPhlP3E1znh5nJJVwrJrFLDVFtKqqNLt76Z5vsp17blE1AgJLcE5zlGySsvoeh8VBEGgwb2N8dJ5zmZ/jI2NaVc4k3meuNZBSLlKzZosX+RC9mU6/HcyVx2hL/86+yKfBwRkQWNb6HGOpr5ORGumwb0N06mQM6ZwSyEaPVelK22ceXlem6UUSAGBdt+djJfOcTrzI7YEH8EtBcgZUxh2hRbvXjTJw3DxGGUzR1RrRZFcpPVRdLuMS6oN6FWrwGjpFAI13w7LMcgZUziOjXKD9JTbBV/Iw5O/dC9dO1p498WTDJ0fp1ysEKkPsvfBrRx8YhddO1pwedfvIRFFgcef2MmFCxMossRnfmYfoiAQDXrxuVV+699/E0GAh/dt4p4dHQiCwK6uBgzT4odvn+NPfvAeoihw17Z2fube7Yu+G72tCXZ01vO/f+0wtu3w6bu38um7t+HzaOzrbWZrW5K/9wdfx+dW+YUn7uChvd2E/W5++Yk7+Oorx/n1P/gaumnR3RznuQd20RhfXwt+PVQtk3977vscnrpAxdJRRJk9kTY6/HFmqwV+//R3eLhuK59doyE8oLqIiV7iLj9h1UNKL1IyDU7MjXBsrlaBPZ0e47HGrUiCSEB1o4gSIdVN1FVLygzkZ+nLTvEHp1/AobbAT+slUtUC9e4QCZeftF4i7vKRLVT4m2On+e5rp0lli/zgjTOYtkMs6OVnH13ep+SWXCiCjOVYZI08pm3eUI9G1a4uUnI1USOkBHGozWVJV5TH6+6jzbs8wxvTrprNuiRtWUKt1qMI4FC1aq7ky3j2goAk1iqyayU3LMfCXMIGaPc2sze8jRcnX+fFidfxyR72RnawLdizbN8e2bVsjBTnPbRuBC4xiE9OAqcpWXOUrJWBRsYYpWJl568/hG4XyOnjVO08jmMvUpocB/L61GLWXpW8BNXGZfvySlGS7m305V5EFlxEtA4aPXuIu3rwK/UoohvDLjFbuUxf7gWmK+ewHIOyleH03DeJu3qIaO1cL5QybZ3LuZeozFNHw2o7vcEnSLi34pICGHaZmcpFLudeYrJ8CgebrDHKldyrhNU24q6VvUl+pZ6HGv45zjXN2t8f+Uek9UEEROrc23mo/p+teV6CICILa4+liqBwV/QuhopDXCxcvHo9jrkotBKUg7T52qhz1eGTfaiiiu3Y6LZO1aoyp88xU51hqjpFySyh2/qKAANqPYn3xu+l29e9Yh6uGiZTmQKV+cRHuWrwG//x2xvyAnJw0JfQ0Q3TolBeW/bX79bY0prk7QtDTKbzjMxkuDQ2y+bmxKpqVW+dGSRTrM35siTx2J6eNWlWlmWTLVSYK9QSC4Zl89eHT/C9d8+tex1ALSlTuqpWmSmU12XIulWFxutUY5ZCEJbnMh3b2TAFdz18IgONRk890pxUoyuVJ1c4qq6Gil0lrWew5vmJAdlH6Jr+jrgaxS/7mK7OktIzDBdrtCmAVm/TYiZGQKDD18rJ7BkmK9MUzSJTlZpknCqqNLnXd4yMejzU+Xyo8zddayhI1TTJVWs3+cBcmtf6B3j+Qt/ig6WbFrpV+7cAWRTZ0VCHW1k5eYU9bu7vbOfk+CS/+o3vcHd7K89s2cy2+hrHsWwYTOYLRL0e6vy+xWi4Jx6nbBhMFQpssmpl6c3JOEGXC0WSEAWBmMfDQGrlIP9hYE6fY1afxcEhoSVu2Rl1PQiCgEf21CZFp1bOXQuaqNHmaVtzUnaJrsUMzELT+UcPgQbPVvZicyr9A344+i8RBZk23z52hp/Br1ztZen0HySlj3Bq5IfYjk23/x62hR8DalSrzcGHEQWZ0+kf8db0V+b7ODazM/w0ANOVy7w381eMlU+j22UcbAYKRwiq9dwZ+xKd/ruoc/dwKPFLnM48z4vj/29MW8enxNgaehQWl0EOpzI/IKOP4Tg2fiXJ/sjnafbuWjzXgjnL6fQPKZlZJEEm7upkZ/hpYtrqjXYfNgRBwO1zsePQJrbc0Ylt2YsBqiiJyKqMKG688nPgQCdf+bO/B4DLpSCIAl/5X34ORZZ45p7ttYSBLC1OXJoqc9e2Nvb2NM1nmmq+NuoSc01VlviFx/fzcw/vARxUWV5836XK/N6XHsK0LEBYzM6LokB7Q5Tf/dn7Ma1a4CiJ4iI3ty7q5+89c9dN06a+NvQur09d4NmWOzgQ7eR/Ovpn2E5tTNckmXp3iA/mBtYMNMR5WqywZCYcLs5xKTfNfcluegP1jJUyi4taAQHdMrEcZ/H3afSESLr9/POdTyMJYm0BLkocTw0zXs7gUOvRmKsW8XtdfOrgZmIhL2PTWR67sxeEWk/ctRWNOlcSr+ylole5UhhgS6CH4AYDjRr9ssCJ7BkAfLKXJk/D/H97cEtuZFGi/ZpAYymt8nq/SVANEFB8DJZGF/0tTNtkrDxFWA3iljTE+cp/1bq6+CpaJeb0LAu5f1mQKJgldgR7eSBxJ2E1hDLvEr78+LfOaHDNVzQAKlaGspleMf9n9GEqVg5RUGj23sFY6Rglc5a5yhXCatuich04zOlXABCRCSutKxTrREGh3r2De5L/F0JqM2GtDREZUZDmaWwC4BDW2mn27ufdmf/CQOEwlqMzp/czV71CQGlEvo5gjYNFzphAEhTafHdzKPE7uKTA/LnU9h/R2qlzb+X43F9yOfcTwGGifIpU9RJxV/eK71YUpMWkzLWvL0ASFNxyeMU2G4UgCLR4Wnis7jFKYyVGyiOL79nYlK0yZavMjD5TuyeX3JcLz6JNbYy8nmdUg6uBJ+ufZH9k/6r+HKWqsSxYcGBxcX+jcADTtrBtG3GV3gZBENjRXk97MsJkOo/tOBzpG+HO3lbakisDjRePX6VNuRSZx/b0rHlsw7IpVvRla/dy1Vikyt4oTNterFqvNQ7IkoD3Q3D6vlF8IgONbm8HiihTtavkjAJDpRHava1rbu84Dlk9x3Dp6oMQUcPEteXczpgWxa/UMlxpPc1waYSKVUVEpMndiLKkH6TT14aAiOXYDJRGmKrUeIpu2UXStX4juONc4/e85D6wHYeyafKp3m5+fvdOIp7lWYWw27XsY55VgoyF93bU1/G/P/MEp8Yn+e7Z8/zeD1/kmW2b+cV9u7FsB9O2USVp2UAt1lhpKJK0eIN6VXXZNoJw24LZdZEzcktUxuoRb8Cv4VrUvneHnJHjcuEylwqXmKxMkjNyiypWhm1QtapYrB8USIJESA2tvYFwdbJ35v93uxHXOviZ5n+9bBJJuDp4tOF3F38zUZBo8uygwb11sRlQECREpGWDkCK6uSvyHC7Jd802tYtRRQ/bQo+zJfjI/ARfozstHDuudfCpxn+y4jprHGNp8Vzq3VtIuntwFoM4YcnkDc2e3TQ2b4f5PS18fmEfquhle+hJtgYfZyE4qdGupFtqQrxVCIKAJEtI12kO3Oh+ZFnC51u+nwW5Qm2VBryaYaaALK09cQhCzU1WU1f/vFtTAGXF64LA/HsrIYoiLvXmv/PXps7xUN1Wvtx+aAU9ShEkEq4AR1JXVv2sQI27vdAsLtbuRpJuP6ok8VdX3ifq8mHZNr55R3IBOBjv5A9OP09Q9fDPdjxBszfCZ1p28U+PfQdREPArLv7FzqfYFEwSmL7EP3j3rwirbmIuH6IgoCoy2zrr6WqOLypMLXxXS9HubSWkBknpc7ydep894Z34ZG+tF2qNyX9hEZY1crw0+SqT89XysBqm29dZqwp6m2n21PHy9NvIgkyjO0nGyJM3CnT5Wql3J9b93kVB4OmGh/jW6AsktRgtnnrO5i7zQfoMX2x5upZwkVwEZD/vzZ2g0Z3EcEwOz7zPdPWqzG7JKjNdmcUje/ArPrTFQMrBca4f7NwoNNGHT0kiCgq2Y1AyU+hWHm1+Ue04Dhl9iIqVJaDUk3RvY6ZygRIwW+2jzbkbmSVKcfM9CqKgEHV1rUhY1syE6/EpySUL5WuvR0BCxCPH6Ak+Rlq/Qqraj4PDbLWfJs/+6wYaCwirbRxK/g4eKboiQJMQCavttPvuYaZ8gawxerVSYxXRpPX7vj4MiILIztBOPLKH749/n75CH8Y1vjDLkmsbnP4EBDySh93h3TySfIQmd9OayeSKbmBeQ+GRxOs5fl0f680fnfVRNjXGONY/RtUwOXpplMl0ntZEaNnvNjqb4eSViRptCrh7axvR6/TLmbZF2Vj+3YnCxhNTNwNBEFedSz5qfPxnsAo2BbpocCe5lB/AweEnk6/zKx1fWDOrbOMwVpngdLbmzBjXonT4WldoMUfVCH65ZhiWMXIYtknZqtDgrsMne5bdth3eNiShFmgMl0aZrs6iCDINrroN8ennymVmisXFB2Q0k0OVJHyaigC0h0MMp7N4VYWm4NVB1IHlCizXuQcdarJvXkXlYFsL+1ua+NqJU7w3PEbfTIpdDXXU+f1cSc0xUywS83pwgL6ZFB5FIex2X9fwZcXxHAfLsamuYUC1AFEQUAQZWdzYYqxslRcnX9c6PizrnV/BLPBW6i1ennqZlJ5CFMT5hUptcFmYTBZ6gNaDKIhr+mN8VKiVuNUVr0lcS+USkdYZRAVAEuQ1J0ZBEGrT3lq84FXOZa3tJMQ1719REBGvs5/1zuN/4KcH6WqJlrooqiRfI0UwD4c1ucBh1cNzrXsW/36s4WoD89/rvmfZI7ywZ0EQ+NXuQ/xK96HF1wVB4NNNO3m6aeey7WOaj3+y7fHF/Sw9O69bXaY4tVAJWkrZ6PC20ePvYrw8ScbI8pXBv+ALLc+xyde1LHEFNbqW7dT6CGeqs3x3/Ee8k3ofqKkr7gxtI6rWMtBxLcJnGh/l8Mx7fGv0BbJGnqDi50BkJz3+Wq1BEWUUe7nqjixIaKI6//wIPJq8B1mQ+O7Yj5nTczS4E/xs85MciNQoYFEtzFMND/DN0Rf4D5f+lKDiZ3d4K/fG70AR5JqqoCAT1cK8MPk6L0wertFPFS/3xe7gkbq7CStBVFFFvcZjQRbkxd6RDUMQcEthvHKcvDFOyUpRMtOLgYZuF8gbk5hOhaDSTMLVizLf4D1buYS1hJbj4JCq1uRTJUEmqq1sAK4dUljsYbv+qQlEtW7cUgSoBTBVK7sopnE9qKKX7sDDqwQZy/cfUJoIqS1kjVGg1nNRtfMfW6ABtbF6k28Tv9312xxNH+WN2TcYKY0syrzbjr1mgm1ppUNERBIkImqE3aHd7I/sp9nTvKwSshpUWZoPLGqojwT4J889QGP05midXtfaypq16xXY1dHAW+eHFntDjveP0tsUJzSv3OQ48NKxS5TmvTNEUeTpA1uuu19JEFHlq0tuv1vjswe38/SBjYkyrIbgOl5GH14Ic2P4RAYakiDyaPIBRksTlKwyb86+x/bQZnaHdlxtFOequdtQaZSfTB0mY2QREejwtrEl0LviR5dFiZgWwS25yRp5Unqail1lq6cXl6Qt2z6g+IlrUSYq04yUxpmtplBEhSZP44Yi0Ol8kR9fvIxbVrBsm+cv9NEZjdAWDtcmva29/KufvMafHTnOoz1deFSVsWyt2/+J3k0banRKFUu8MzSM40BTKEjZMLgwPUtAU4l4aooM97a3cm5qim+cPMND3Z3kKlW+dfos93W2UR/w31A0XbUNXp48zr+78I3rbtfkifFrnZ/i/sTO6263AG3Jd19d0dC3cZTtMj+e+jEvTb2EYRtE1Sg7QzvZEthC0pUkoARwi24kQeKPB/6YI3NHVuWL/g/89MG2bQzDwjQsLLtGmVmvIidLIl6ftuYz4DgOtu2g6yaWZWNbtYqVIAhIkogsSyiKuGoJ/lpYlk2xWF1UG1kKQQBFkfF4NrYos22HSsVA100URcLlUhadw69+BwtVrdq5KoqEMk+hWu+ZdxwH07QxDBPLtLEdB5zrx+WiKODxqCvktAHqPCEGi7NULAP3koWn7TjkjDKXCpM0e1d30b32XFf8vcb51BaP6792vf0cPTfK6HSGZx/aieM4vPx+zcTwgX1XDWQFQeDB+L2MFMc4l7vIWHmSf3/xP7HJ30W3v5PKPDXXwuL1mbfIGFn6CwNcyF9aHOsUQabH18X98UPLKFFxLcKzTZ/i2aZPrXrOX2j59IrXHkoe5KHkwWXn91DyEA8lD626D0kQ6fS18nu9f3/V903H4qWpNxgujvObnV+kxdOAg0NfboDvjP+YTl8re8IB/kHXl1Z89umGB3m64cFV97sWBATcchifnKwFGuYcZStNmBqjIWuMUrYyAATVJrxyHL9SR6raT0rvx5yXzxYEAQebVKV//joVItc0gt8MNMm3KNkNYNrVJVXbtaGIHpq8+9d99lTJiyZd7U807Sr2Oom9jwKCIKBJGodihzgYO8hEeYJLhUuMlccYL4+TM3NUrSqV+e9fFmU0UcMn+wirYWJqjCZ3E62eVurcG0vWLsDrUhcp6AAVvebp01kf+VCqAYIgsKOjgbZEmP7xWRzg3QvDPLx7E8F5HwrLtnntdP+iYEZ92M8dm5qvu19FlvC5l9w7lo1bUz606/gk4RMZaAAcjN3B6cx53k4dQXd0/mv/n/NY/YMcjO7HP2+KVrV1+vL9vDT1GpcKVxAQaPW2cHesphm+GhJaDJ/sIVVNUbLK6LZOi6dxVSfKDm8b4+VJBovDZIwcQcVP8wb6MwC21CVoCgb4q+MnmcoX2NPUwM/u2k7DfPVie30d/+sjD/DVE6f5t6++UXO6DPj5/K7tiw05kiCQ9PkWG7uvhSSK5Ko63z97gblSGZcis7uxnud2bKM1HAJgd1MDv3nwAF89fpr/18uHa/K2m7r4/I5ti5StsNuF36UtHlcQBHyaSmQVNQRJEFGEWlO67dweqlBQvmrQs+Chca1R0EbQX+jnTPYMVbtKm6eNzzV9ji2BLSuVq3AwHfNDoTl9kuGS/Kii72/VoOY4tUBgdGSOd965zAdHBhgdTZHLlZepMa2Gnp46/uN//uVVxbxs26aQr3C5f4o3Dl/k3LkxpiazVKsmXq9Gc0uU/Xd0cMeBTpqaImjatXz15RgYmOGf/t7XmEutFLdQVYlHHtnO7/7jJzZ0zZlMiT/9k8P88Acn2H9HB7/8K/fR0hrlUt8kb75xkRMnhpmcyGAYJl6vi7a2GHcd7Obue3qIxf2I4urBxkJgNTub5/ixQd56s48r/dOk08XahHqdx6WuLsg//r2n2LV7JcX10fpt/NGlV2nzxrknsQnHgaxRpr8wxbeHj9CXm+R3N6++mP44YNs2Fd2kUKpQKFeZzRRryoPZImH/yjGx3l3HZ5ueRh8xuFIYwHBMLuT7uJC/auKl2zp/MvgXKz6rCAo9/m4+2/TpNQUwPk4UjCJj5Snq3HG2BLpQRQXbsWn21OGRXFTsKpZj31Y6o1sK41cSTJShbKYoW1fdo7P6CBUzA0BAbUQR3YTVNsaED6jaebL6CD4lgYBMxczMN5MLuKQQfmVtNSBYYBRY89K25qL0bI0zUYu0baxl6lQbnQFlwUVQXSnbei0WZHSvnpOFvQGK70cJAYEGdwMNG1wL3SpcqkLE70GVJXTTIlMoMZcrYScjSNKHM5dF/R62tiY51j/GXL7E2eEpRmeztCXDKJLE+eEpRlPZWhIGeGJ/77p+FrIkEvS4CHg0cqUqZd0glStS1o1VG83/NuETG2gICHyp7TnKdoVTmXOU7QrfHfsR3x97kZAaQBZk8maB0rzKjyxINLjqeCR5H7tDa0tfxrUYPtnLleLQ4nGa3A2r+nV0+tp4ffYdRsvjQG1SWGjWWw+SIPDwpk7+6UP3rbnN1rok//LxtQe/hN/HC7/+i2u+H/G4+dKenXxpz/UrB7sa6tnVsLZS1h88+diyv0VB4NcPrGzMlASRBk+UexPbyZtlckaJolmhahnkzfKicdWNIqJFiKgRckaO6co0o+VR2rxtN7yfOX2OlF7jFm8JbqHd277qBGjYBmk9/TE1bi9w4msD5Ed5Dg/V/85HdqyPCtWKweHXLvC1r77D0FAKRZHQXAo+vwvHdqhWTapVY7G6oaoyXq+GokjE44FVgwzLshkZSfGdbx7lpZdOU62aiGKtp0IQBQqFCqdPDXPq5DAv/Ogkn/3cfu5/cAvBgBthDRlBWZaIRmsJEtt2aovZcq0qcSuYmysyOZnlxIkhvvOtI8zO5pFlCUmqNbJmMiWOHx/i+PEhfvKTs/zu//VTdHYm17zuy5em+Mp/O8zRowOIooDbrS7+0w2LasXAsuz5axLxeDRUTSaRCKBpq08nTzfuZqgwy18OvMWf9B/GweEPL76IbpmEVA/PNO3h3kTvLX0PtxP5UpU3Tlzhe4fPkMoWeedUTb6yIR7kcw/sWPUzm/xd/Fr7l/nO2A+5kO8jbxZWcNmXQhEU/IqXLYEePtf4GRJrJMY+bnhkNwktxuXCIEfnTpN0xciZBd5PnUQVFepdCZQbNFNcD24phE+u9aCUrHStIXz+Ac7otYqGJKj45TpkwVVrABddVO08M9VL1Hl2IgryIm1KRCLq6mSt+pfj2BhOhYqVIVMdYaZygYw+TNGcqSlFOSUsu4rp6FiOvigfu1EIiLhk/7JKyP/AjWFTY5y3zw0xnS3gAB/0j7K5JUHAc3v9vJZib1cTr53qZy5fwrRsjvSNsL2tjljAyxvnBhabuBVZ4rE9K5XBVkPE76Grvtb/ATAym2Fgco6trdc3aPxpxyc20ICaCsdvdP4SfzP2I95NfUDRLFGxq8zpGaDGHXSJLjyymzZPE48k72dHaOt1sytxVxSffFVXOaD4iWqRVdUO2r1t83KBtUHOK3tWNJj/XYIiyuwIdbAjdLUEbTkWw8UZ/nroVX48+cFN7VdAYG94L2PlMXRb5/vj3+fLrV8moARW9dFY8Eu51ifDWVJhWYskYdomZ7JnSOmpj62i4Zbci+c3U53BduxbNPr6ZKGo62TLFWRRJORxr1mRu1VYls177/Xz9a+/y9BQCp/Pxe49rezc1UpdXZByWef8uXHee6+fyYk0luXQ1ZXkC186SEtLlHhipWqLbdeCjP/2R6/x9luXkCSRSMRLfX2IpuYoLpdMNlNmdGyOqcks4+MZ/vQrr1MsVnny6d0Eg6ubSDU2hvlf/u+fJpstUyhUyedLvPrKed5/b21DrY0glcrzg+8fY3w8TaVi0NGRoLklSijkoVw2GBycYWwsTbFQoe/iBP/pD1/iX/7r5/BdQxlzHIfJiSx/+pXXOXp0AFWV6exKcuddXbS0RBFEgcGBGY4eucKlvkmqVZNkMsjTn97D3n3tJBIBPN7VF1KyKPEPNz/GPYke3prpY7ycBgSaPBEOxTexK9KyrqrgR4mgz81Td2+ltS7MZCrPg/u6QVi/cbPBXc/f7/glzuUu8u7cEQaKw/OeFlZNZWtePcslarR5W7gzuo/twa2f6GdfFRXuje9HFiRemX6HnFnAJWq0e5v4uZanad1g4u2Gjil58CpxZKEmLVu20lhOFRDIGePodpGw2oom1ai/Ea0dWagtOGcrF+f9NVyk9CuAgyhIxLTuVY9lOQZ5Y4KB/Jtcyr1IWh/iqviEhCTICPMiFiIyoihjWKUN9WUsxVUlrL8bcByHil3Gdmy88vr9JZZj4jgrPacWsKO9nkTIx8x8oPHSsUs8sKMTr0tdt5Jws+htitNRF+H8yDS6aXGkb4SfuWsrfrfGkb6RRbndPZ2NNMVDG2IKxINetrfXcWJgHNt26Bud4UjfKJ31UVzq7TG8/iTilgINTVRp89akSBtcdbg2oLzglWuGd4qoEJIDuKTrf8Ytufh882c4FDvA8fRprhQHyRkFLMfCJ3upcyXYEuyh19+FV17bpXMBESVEq6eZvFm7Yds8zfjk1ZUCmj31dPhasRwbSRDp8Xd9oieFjwOSIOGWNDTp1h6SQ9FDHE0fZag4xInMCTRR48Hkg8ucwS3HwrANskaWslWm3du+TBHKr/gJyAEKZoHB4iADxQFaPa3IorzoKj5aGuUHEz+gZJWWBZEfJepd9aiiSskq8d7ce3T7ugmpISRBwp5vGAWur3a1ChynpjJWtgwqpknVsjBsqyaD59jzVLcaBK4unGRBRBFFZFFClSRckowmySg36QTdNz3Lt06dxa0ofGnfrkUa3+1GJl3k6JErDA7MoigSn3piJ89+/g5isasUlAcf2sr2Hc38l//8MpOTWYaGZmloCNPQEF5RfXAch1yuwvM/OrkYZHR0Jvjcs/u5+54e3Ev4tTMzeV584RTP/+gEkxNZvv+9YySTAe69bzPqKpl9RZFoab2atbZtm7GxzC0HGpl0iWMfDBKL+/ncs/v51BO7iMau0uMKhQrf/Pr7fOc7RynkK5w+NcKZ0yPceddyI0W9anLu7BhHj1xBFAU2b27gt377ETq7rlZc77mnhwMHOvmzr7zOe+9dYXY2j6JKNDVHUFdRuVqKmht3O3ujH4808c2gozFKfSxA1TCRJRFRlq4rJQm1hdKO0FZ2hLZStapMVqYpmAUqdhVVUPApPhJaHO8ac84nETEtwqcbH+bTjQ9/REcUcEsRvEqMrD5CyZyb96AoUzHTgENoPtAACCiNtWZxQyBVvYzlGDiOw1ylpmQmCBJRrWvFUWzHZK7az7HZ/85g8S3AQRJUXFIQlxTEI0XxKDFcoh9FdCOJNWO6i9kXSOsDN3Q9G2k2/9sEG4uh0gBls8Tu8Oqy1UsxU53GtA2aPKuri25pTrCnq5HBqTkKFZ2BqTn+8tXj/PIj+2mJh5Cltecq23Go6iaFShVJFIn4N/bsqYrMnq4mjlwaZXQ2y5WJFGOpHIWKzsRcfl6iG57Y14u8wWAnFvByoKeFV0/1MzydYa5Q5qXjfTTGAtzV27puo/qCD0jVtIgHvR9akHW7cdOBhiAItHqb+Tfb/x+YloVuWHjm9Xot26ZcNRclE5cqG20LbmZbcPMNHUsURJo9jTR7GtffeB3IoswXWj+3oW1dkot/te1/ueFjRDxu2iNhfOrflVLprS/WQ2qI55qe46vDX2WsPMY7c+9wMnuSNm8bQaWmLrFgsDhdnabF08KXW7+8bDHe5G6i09fJTHWG87nzlK0yvf5egkoQ3dYZKY9wMXcRn+JjS2AL53LnFps1P0ps8m+i0d1I3shzuXCZPxr4I3r8PXgkD7qtU7JKeCQPP9/y8+vuy5mXSk5Xy2SqZSaKefqzKQZzGcYKOWYrRbLVCnm9StWqBR0ODrIooYk1j4ag6iLq8hB1eYi7vbT4Q7QGgiQ9fnyKil/V8CkqblnZkErZ7qYGSobBucnpZa/PFktkyzXXYr+mkfD7KOs1PxeojRtuRaUxFMAwLaYLBarz3jKaLFHn9+NZkvUZH08zOlLzemloDLN7b9uyIGMBhw5t4oXnTzIzk6dYrHLu3BgNjWGUa1TRbNthZDjFi8/X3KGTyQDPff4ADz18rUs6xON+Pvu5/Ti2w7e++T4zM3kOH75AZ1eS9o71pUdvJzRN5umnd/P0M3sIBpdPoj6fiy986SBnzoxy6uQwlmXzwdGBFYFGqaxz5swIjgP+gIudu1uXBRkL6OpOsnN3K6dPj1IsVhkcmCGdLpJMXl8BxnEcSpaObpuLvOalkAWRoLr2AiBbrTBWyFG2PrzGWFWUqPf6iblrCStREJhJFxiZytBSFyYS9CKJNVPFjUCTNFq9128Q/bix4CswV6mNHxWz9vtIoohHVoi43ARV14oFjWnbiIKw6niw6GdyC71gHjmCT46T1UcoW2kqVo7yfMABENZa0cTasy6JCmG1lVT1MnljkoqVxS2FSFcHAZAFdd5UbzlKZorzme8zWHwTqClD1bt30RV4mAbPTtxyZEWlzbDLjJeO32Cg8dMNw9bJmzkM28ByLFRRJaSEkUWFnJGlaBZwcPBIHoJKTTUtb+QJyAHqtFrFy3ZsSlaRglkzT3QcB4/sxSf7KZlFzuZOUbZKyKKCT/bjlX3LJflFkafv2MzF0Rk+uDyKadn84P3zFMpVPndoB02xIF6XuqgIZ9s1Y+KKbpIrV+ifSHF2aIq2ZJgvP7hxE+LdnY00xYKMpbJYjsOpgQkMy6JUqfXpRAJe7uxt2ZDzNtSeiZ6mBE/s6+UvXz1Ovlzl7PAU//X595jOFNjT1UTQ60KTa7L0juNgWjZV06JcNZhI5zh2eYx0ocw//tx9Hyp17HbitlCnZrJFjl4cYXNrkmTIR6Gsc6J/HE2R2dZWRyL88UmzfRx4oKuDB7puXeHi7xq2BLbwC22/wA/Gf8BkZZKCVaAv37fYxyALMoqo4Jf9JLXkigb+hCvBwehBylaZ/kI/o+VRBooDCAjIooxH8lDvrufxuseRBZnB4uDHEmj4ZB9P1j+J4ziMV8aZrkwzPt8HJAkSqqjS4b3+/WM7Djm9ymghy9nUFG9PDHNseozxQh5zAyoolmVStUwwIFUucSW73JxRAHyKRlcowtZIkl3xeu6sb6HZf3OSgiVd52/OnGcyl8fBwa0o/OL+PYykM/zhG++yrT5JrlL7LX7voXuZLZb4o3eOEHBpXEml0WSJf3jvQdoiVw2oCoUq+ULtM5GIF79/9UFXkkWSiSCKImFZNjPTuZrr6TUol3Xef7+ffL6CLIt0diW5++61ubcej8odd3Zy+tQIH3wwwMmTwwwNzdLSGpvvkfho0NWVZNfuthVBxgJUVWbX7lbOnxvDsmxGR+fm/Q+ubmMaFrOztUWA26WQTK7uJiuKIqGgB7/fRbFYJZctUylff/FftQwu5Sc5lxljuprHsFfSThKuIF9sP7jKp2v4YHqMf3v0dS6kZ657rFtBgzfAP9x1kJ/rqfVhXB6d5aX3LpIrVChVDESx5lHy2J2fnH6SW0EtSWFwbGaCF4f6eG9yhNFCDt0y8Soq7YEw9za280hLF92hGK4l0pz9+WmCqhuvrCELIppUS0KkqgUGC7NIgkiXP4FXXlvV7XpwyxG8830aZTNN1c5RMKeoWFkEJIJqM6p0NeCLujoZLLxJ1akyV72CT0mQNycBAa8cxy0vVzVzHJuMPsKVwhtAzdCvybufO2K/RlBdOzi0HXNDKlN/mzCnp3hj9uXFap7pmNwXe4SQGubd1OtkjZrhpSaqPJx8Epfk5nLhAsczR2jzdPJI3ZMYts6F3Fk+SL9L0lVH0SyQcNVzV/ReRsqDXMqfp2yVMG2DLYEddPtXPmPdjXG+9MBudNPkzOAUhmXx2ukrvHtxmM3NSTrqIvjdGgJQMUzmCmXGU1mGpjPkShX8bo1n7169x2otNEaD9DYlODs0Rb5c5dTgBOlCeVHW9sEdnQTmlag2iqjfw+N7e0jlS/z4gz6ypQqXxmf53777Bs2xEJua4sQDXhRZxDRtcuUq05k8g9MZpjMFLNtmW1sd1ipz2CcVtyXQmE4X+Obrp9jRUc/+nmZA4OjFEeIhH7pp8vj+vx0D8//Ah49Obye/2fmbnMufY7A4yExlhopdW0x6ZA9BOUiju5EOXwdxbblxooBAb6CXiBbhbPYsg8VB8ma+pqIl+WhyN7EztJOYFqNkltga2ErGyKzwyRAFkXpXPduD2/HLfnzX4ZhKSCRdSbYHtxNWwovVl/WwObCZqBblZOYkw6VhCkYtq++W3YSU0HWb4cumQX92jjfGBvnR4EXOzU2vMDS6VThA3qhyfGaC4zMTvDE+iCJJNx1oXJpJMZLO8BsH7yAZ8PPPn3+Z0xOTBDSNgq7zO/feRa5S4fdffJWJbB7LsSkbJs/t3Mal2RSzxdKyIANqkqoL0rK27awaPCzAnHfxhloT82otAdWqyflztYDP53PRu7kBzXV9SmBLS5TGpjDHjg1SLFQZGZmjWKwSCKxUJ/qw0NmVXDMwWEAs5kOcD36KxVVEGwQBab6XxnFYVYZ3AbbjLE5yoiSu2QC/gBPpYf79+R9h2hY+xbWql0abb30T1I8auWKFumiAnZsaKZSq15XHXYDtOBhOLfNbo0De2GJAFuRb8hK6EVQti7cnhvmDo4e5lEktey9TrXB8ZoJTs1Ocmp3kf9p5F7sT9Ys+Pd8dOYZbVki4ArhEhe3hJlq8Ub47coz3Z69gOjbPtuzj0YZtSDfRf+OWQniVOCBQttJUrRwFY5qqlccrx/DKMaQl6kxRtRNZ1KjakKpcJqS2YDk6AiIx18pkgelUyRnjVK1s7XhyhAbP7usGGQAlcw7D+eiTUxvF0jvUvo0BkW7r7AvfSbd/M98c+QsmK+NkjTSz1WmeangWvxLgL4b+mKHiFTYHtrMztA/D0RfnNajRqbyyl6cbnmOgeJlT2Q8wbJ3N/m3MVWcpWSUerXvquudx99YOFFnir187wemhSdKFEhXd5Hj/GMfnG6xXgyCAR1MIeG6sV0YQ4M7eFt48O0C+XOX8yDRl3cC2HVRZ4qFd3cukdzeK1kSELz+wB6+m8srJy0ym8+imxcDUHANTc9f9rCKJRHzuDVdRPgm4LYGGIkvs7Gzk3h3tnB2cojkRorMhSn00QKF8874I/wM/Lbh9N/yCXvfu0G52h3bf1D4SWoJE4vr0FZ/i41faf2XV9xRR4f7E/dyfuH/dY2mSxt2xu7k7dvdNnecjyUc2vL3jOGT1Cm+OD/HVvlO8NzGCbn80qlUxt5dt0evLQ14PRV3Hp15t3Au6XeQqVQKaRnMoiCyKCAh4VRXdsvBrGh5F4dXLA8R9Hu7rbFuxz3DERzTqo//yFFOTWSYns2zqqV/0i1jAXKrA6GgKw7AQRYHm1tiqg7RhmIyN1gZ5l0uhoT68Yptr4XarhMNeXC6ZctlgajL7kQcasZgf3xrVnAXIytXlnmWuvGc0TaalJcrbb9UCkYErM5TL+rK+FIBiocrYWJp8rlzzeoj78fmuP3l/a/h9ZEHi17rvp9OfQF2l4fN2KxfdDrg1Bd2wGJpIU6roRAIewnUr7wnbsUnrGVL6HDkjT87MU7EqGI45H9xuPNhocNVzR3Tj1I6bheM4TJby/Om5YyuCjKWwHJs3xwfpDEZoDYSIz9PKMkaRk+kUXYEkFUvnUn6KX+o8xOGpC/zulscpGBX+cuAdHqzbfFPVPUV045ViqKKXqpWjZKYoWrMYTpmkugWXuDywDmtti8Z9c/oAUX143vNGXLUR3HYsDLu05HguXNJ69D+bmcp5SubsDV/PR4WaslWt97Bq56jde7c+P6uCiiTI4IBLcmM5JmWrhFvyLAqyeCUfJau45j5kQSGkRpAECUmQkAUF0zHnz3dBKti5rjBEbeHfSmM0yI+P93Gkb4SpdIF0oUxZNzBMC8dxkCQRTZbxuBQCHhfxoJfNzQn2r+N1sRq2tdbRkggxOJ2mWLkqbdzdEKOnMX5T0s6CAC2JML/08D56mxMcPt3PwOQcc/kyhUoV3ax5IYmCgCJJuDUFn0sl7HPTFAtx345OXJ8Ax++N4racqabIKJLI4GSaTKGMMT+RpQvlDTXJjJVmGSxOUbUNfLKLnaHOxebiqmUwWZljppqlaukYjoUiyHgkjagWIK4Fccu3puhQNCukqjnm9Dxlq4pumwjUJj+f7F48zq1MhrZjkzfKzOo5snqBsqVj2haiIKCKMn7FQ0wLEFVrSksbKcUVzQpXChPMVLMookyLJ06rt7YYNG2LjFFkupImZ5TQbQMQ0ESFoOoloYUIqt51naQ/CbAcm5xRZKaaJasXqdrGYoO+JioEFS8xLfhTcz03g1pzY5nvD5znz84d40ouvf6HbhMUUaLZF6TVH1p3W8u2GclkuTwzx2g2x/nJaXyqSmskzJtXhjg2No5f08iUSvTEN1EyjBU8bwcH3bKwHIdN8SghtxvTtqmY5jL6RkNDiE2b6jhzeoTJySyvv3Yer1ejpSWK261iWRaZTIk3Xr/I4MAstu3Q1Z2kp6d+1cWPbTvk87VspSTXzPw2ArdHRdUUymWDUqmKYXy0ssluj4pyi5OO262ya3crL7xwarHJvrMrweYtjfh8LgQgX6hw5vQIR96/QqVikKwL0tNbvyZlawHDxRT3JzdzX7L3lkUjPko0J8OMzWQ52TeOadk0xoN0NS+vvFiORX9hgPfmPuBs9jyTlSmMG5Q/XYr94d0fSaBhOjZDuQzvTY2su61h25yYmaA/M7cYaLgklV/oOMRD9VswbIt/cfI75IwKVcskofnZHmzi35974Za69zxyFI8cJasPk9YHKZkpao3gLYtO4Qtwy2F8SpKsPkpGHyGrj4Azrzi1SkVDFKTFwKR2jSVKZgrHsRFWmUNqVKshBgpvUDA/PPrercIjxxG4gINN0Zgmb0zhV25dOrVslxgtD2E4BgUzR0jdTlAJ0V/so7/QhyZplKwCDe5mbCwGi/1MV6YoWyVGSoOElDACa6hBCrWE3XR1gkv588S1OsLq6gaeC2iOh/jVR+/g8b09XBiZpn8iRSpfoljRsWwHTZEIeFwkQz6a4yG66mPUR27MoHgBPrfGI7s34Xe7KOtXaaJ3b2nD7VLIGFNk9WkcwKeEiKoNpPVpwCGqNaDbFTL6NG7Jjya5SeuTlMwcsqgS0hI8vreHzR1uTo0M0Dc2w+DsFIqVwDTBcIrU+RtoCEVx+0t01EXZ1byJsjPHhH4eu+oQUCJE1UaKVpaJ6iDRuiyb9CoeKcCuhlYUxWG6MkTRzCKJCiElQUBZqZ4a9LrZv6l5sVl+a2vytilh3ZZAI+Rz0RALkM6XaUmEKVcNHByyhQq9LeuXxI+lL/FnV15iVs9R54rwh3v/AXEpxFhpliNzfRxPX+ZSfoycUaRi6bgkjYjqp9vfwI5QB7vCnTR54je8yKxYOoOFSU5lB7mQG+ZKYYKMXqBgVhAFAY+kkXCF6fI1sCPcwZZAC82e+A3drA4OWb3I5cI4ZzKD9OVHGSnNkJkPNmRRwiu5qHdHascJtbMl2EbStb5c2mw1y9eHD/PGzBm8kovnWu7llzoeJWcUOZcd5lj6MueyQ0yUU+TNMiIiXtlFkyfGlmALu8PdbAm24JVujGP4UcF2HLJGkfPZIU5nB7mUH2O0NEPeKFG1TVRRJqB4aHTH6Ak0sz3URo+/mbD6t8uUDiCnV3l+8CL/9fQRxoq5j/TYIc3FtmgSZQMytY4D49kcZcMg4NKYyBfIlMu0hsM80N3B6YkpqqbJvZ1tdMYijOfy7G2uNQyqssTupnpcssJYNotHVRhKZzgzOY3tODy3cxvt0asZZZ/PxYG7uhgamuXokSu8/fYlxicydHXXEQ57MKomwyMpzp0do1zWaW2N8XM/fxehkGf1+8NZ0sjK6o2uq6Hmi1L7b8tyFvfxUUGSxFsuoyuKRFd3ksce38ELPzrByEiKP/njw2zd1kQiEUAQBKansly4MM70dI5YzM+jj21n69amdTPWXtmF5fz02WNGg14e2r+JPT1N2I5DLOTDrS2feMfKE3xv/Eeczp6fz87WULt/xBuW7f2oVA2rpsnlbArd2lhQPFbIMV2+SoOJql4KZoWR4hxV26RoVjk+N0TRrFK1TSxunbbjkaN45RgZfYhUpZ+qXQAEgmrzikADIKZ1MVk6TdGcJlXtx8FGFr2E1JVKRpKg4pOTqKIP3S5QtrKMl06QdG2br45ogIDtmJStLOnqAH25F5ksn64NcgjcDhGU242keytDhbdwsKnaBU6lv8Hm4FP45ASy6AIcLMeoOZpjo4qe+devD7fkIWtkqFhlOnybqHc14pV97AjuYag0gOWYbA/ups5Vj+VYTFRGEQUJj+RhojyGXw4QUWOL2X+f7KfZ3YpLrEm9t7jbKJoFBotXcEnudQONBTRGgzRGgzy0a3X54tuFT+3r5VP7VrYA6HaFkzOvIokyjmNTtgscjH6G6eog4+XLPJj4EjljlhOZl9kWvIec4XAx/z6KqFG1y8S1JjYH7iIrXEGJX+ZQQ5IDTj0dvh1IgsL53NtsDmwh4WrlzZlvElJdGEKBk+lX56WXRSqFIgdjzzBdGeZI9js8cmgPh+4U0e0y9yd2kzNmOZN9C0XU0O0KISXB9tA9qOLyqntLPMTf/9SdH8r3d1sCjYjfy9N3bqVQqS66y2YKNXWZ5njohvaV0nOUrCp9uVG+O/o2L08do3pN82DBLFMwywyXpnk/dZG7Ylt4qvEAW4NtGw42skaRd2bP89LEB5zJDlK91lzJAd02yRhF+vKjvD5zioOxrTzRcAfbQm3IG5gQbMdhrDzL4elTvDp1gqHiNOY1Bm2mZVGxdFJ6jjPZQQ5Pn+LuxDaeqL+D3kDzhhfMZatKWi8wVUnz9uw5fjj2LlcKk9jXDIYVvXask5krHEn18WTDAR6s20VAXmPx9THBdmzGyileGD/C4elTjJZXlqtNy6JkVZmspDme6eet2RgPJXfzQHIXje7obXWr/ThRtUw+mB7jT88f+8iDDICIy83O+MayYrIkcrC9lYPtKyf3/S1N7G9Z7o7bGg4tyt96VZVnd24jX61yYmyCkMvF5mSCVLHETLG4qlpRT089n3v2Dmzb4YOjA1zpn2ZwYAbHcRBFEY9HJRbz09oW4/77N3PXoe41pVhFUcDr06jMm9KVyvqq212LasXA0GvPtcejotwEZ/eTgFDIw2c+sxfbsnnxxVOkUgXeeP3iYhClaQrBoJs9e9vZv7+De+/rXdWL5Fo8VL+Vt2b6OJkeojfQgFd2bVgO8uPExGyWXLHC5va17/0TmdP0F4YwHROP5KbF00RUjeCW3MhizX/hRnA7lBU3AtOxmauU1t9wHnmjSsm4OkfujLTw/uwVhoopdNuk0RPmcn6KLn+C16f68Cka9e7QLfmjLAQaAGl9ENuxcEkBfEoSeRUp/ZjWjSQoVO0yk+UzODjz0rcre+xEQSKoNtLo2cNA4Q1sx2C8dAzLMWqKU1IYARHdLpEzxpgonSCjDxPROhFVkVT1yjLq1ScFLd67OJf5LjljHMvRuZD5IUVjmqjWhSb5cHAw7Qq6XUQVvbT6Dq2qyHUtNFFje3AvbdeIlGzyb2GTf8uy10RB4t74SinkkBqhldrn41qSuHaVilvnbqTO/dHc+7cTc/okV4onuCv2DAAn0q+QM1PUuTq4kHuPtD5FRp/BwSGkJDiXe4esMcOW4CGmK4OkquOUzNqc7pK8bPLvI6LVzJVtx+aSoJE303jMICUzz5ZAC3P6BEUzx8N1X8Il+fj+2H9ivHwFGQlBELkj+hQ5Y5bXp79GRp9iujpMSh9nR+h+UtUxUvo4BTNLRP3o6L23JdDIFst80DfKbLa42BjY2RBlb3fTOp9cCcM2OTZ3mSNzF3kvdQEBgVZPgrDqRxFlDMckoxeYKM9RtWuO1IenT1Ewy3y57WE2B1vWP1+9yEtTx/j2yJtMlOdwcJAEkaQrTFQN4JJUbGyKZoWJ8hxZo0jBrPDK1AmmKxm+1P4QeyPd1x1EHRyGS9P8zejbvDp1goxR4y5qokLcFSKi+NAkBcuxyRslJippimaZtFHg+2PvMlVJ84vtj7I1uLqu9LWwqQU1Pxh7j5enjjNZniOk+ki4QosVi6JZYaqSJqMXsHG4XBjnLwdfRhDgsfr9uKWbleO9vZkd23EYLc3yV0Ov8pPJY4vBmUfSqHdHCCheZEFCtw0yeoHpaoaypTNUnOZbI2+SNYp8pukQTZ7YJ8oI7GbgOA5TxQLfuHTmulzq9aCIIi5JQRZFREGoNfQ6NoZtUzGNNX9BURBIun1sCn90zsVuRWFnYx1FXWc8l0NAYEd9kpbwSg61KIoEQx5CIQ+aS6GzK0lnVwJZlpAlEb/fTX1DiM1bGojHA9fNvsuKRLIuSGq2QLVqMD29flCn6ybZbIlKpbYIC0e8uNw/PfSg5RBwuxUSCT9ul4LXo7Fzdyteb03JxePVSMT9dHXX0dwSxbVGo/zLk2eZrlz97nJ6if78FP/l0ivsCrcSVr3I10gLR1QvjzWsrQhT7/XzYHMnHcEIFdOgMq+aVjFNKtb8P/PqaxtRXlsPQ5NphifT1w00piszFOd56fvCu7k/cTfN7saaKecnKHGzGm6l8HYg2oFbUunPT6GJMvtjHRi2iWnb/GDsJAOFaX6mZe8tBZRuKTRPBZLQ7dp3HFU6cUurBzBRV/diQ3jZmgME4qvQphbgleP0Bp+kZKaYrlxAt4uMFN9lpPgesqAiCNJ85t9CElQSrs1sDz9L0UxRNL/2iQw0wloL28Kf4+Tc1yia05hOmYHC6wwUXl+xbUTrJKZtWjfQ8Ege2rxdBJT1kwp/12A5Bqajk9GnkQSZLv9e3JIft+Sj0d3N2dybiEi0ebcjCCKWY1CxCmT0aVTRQ1xrna+egUfy41qipCYKIm3ebUxVBsgbc9S7O/HIATLGzLy3WO0ZqPW66Ciil4ASQxKkmr2kqGHaBqZtULWK8+eo0OTpQd1AFet24rYEGlPpPG+eGWB7ez3qfCPmzXTiL+Bvxt5mpDRD0hXmUGwrW4ItxF0hNFFBt01mKhnOZod4e/YcY+VZqrbBiXQ/US1A0hUmoq3U0l+AYZu8NXuW74y8yXi5tnBLusLsj2xiW6iNelcUj6zV5EONIlcKExyZu8ixucuYjsXZ7CBfH36duBai1bt2w3GqmuPFiaO8PHWcnFEbkFq9SfZFutkcaCGhhXDLGqZtkTYKDBYm+WDuEiczVzAdi/dTF3Ec+N3ez1Lv3pgb+aX8KIPFSaqWwZ2xzewJd9PiTRBQPAgIFMwyg8VJ3p29wJnsAGVLZ1bP8fXh1+nw1rM12IokfrzZWMdxKJhlvjHyOi9OHMEBFEGiy9/IgWgvXf4GomoAWZSoWgYz1QzncyO8n7rAUHGKrFHk5cnjhFQfTzfcSUj96ZZWrlgmR6ZGOTx65YY+51c1Wv0hmnxB4m4PQc2NV1FRRQlJELAcG9O20W2LoqFTNHTyhk66UiZdLTNbLjJbLqFKEpvCMULaR5j9EEU6ohE6ouuXz7PZEi+/dIY337hIPBHguZ89wP79HWsugq8HTZPZsrmRc2fGKBarXL40iWGY1+1/mJrMMjGRxbJsVFWmqSmCz/fToW1+LXTd5NSpEb79raMYhsWnn9nFMz+z94Yb29+a7uNcdokCjCAQUb2UTJ23Zy6t+pl2X/y6gUZXKMrf376fgqFTMgxKpjH//zplc/nfJdOkbBqLAUm2WuW1sSs3rMzm92jIksjIVBqfW4N5X6ilvGVhCT3qjsheunwdPxWmrpIg3tAz7Vc1vMrV61YlmX3RNvZF21Zs+2XtIEWzSqMnvGH64WqQRQ2vHEOTfFTm1aGCatOaTdt+pQ63FKZozsJ8U3HM1XPd/dd7drBX+EX6c68xW+2jaMyg20Usx0RCqPl5KEli2iba/feScG1mrnoFTfSRv+kr+3CxOfgUkqAwUnyftD5E2ZzDtCs4OIiCgiK60MQAUa1z3QZ4AL8SZHvw5oRZ/rYjotbT6tmGRwoiiTIiIkGlluBs8vTw6vRfEVEb2ON5BFV0Uedqp2Tm8Eh+BAT8chi3tLBeXfms1LnaGC1dIG0Msyf8KJroIaG10C8c50LuPWShZkZc72ono0+vCMAVUSPpaiNjTC87pkdae438YeC2BBqOU7NW/8yhbbdFcmuwOEVY9fPF1gd4ILkbt7TcLdEJtLA70kW9O8JXh15jupqhZFU5ke7neOgyD9Wt/VBcyo/z0uQxJsq1Ztq4FuSzTYd4pH4vIcW3YmDcG+lmW6gNAYEjc30YjsW57BA/nvyAX2l/bFVnRtO2OD53mdenTy0GGZ2+ej7TdIh74tsIKt4V2a4D0V52hjv46tBrvD17DsuxOZ6+xLdH3uI3up/aECUsa5RwSSr3xLfx+Zb7aPfWrcgc7gp30hto4b8P/IRj6Uvotsl4OcWPJt6nw1+PT/zoFpSrwQHenj3HSxPHcABZkOgNtvDltofZFe5cVbFmT7ibRneUb4++yVBxmoxR5LWpU2zyN7E/0nNLk93HjVSlxA8HL1I0N2ZU5pEVdsbrOVjfwrZokrZAmITHh+c6Znv2vJ5+Tq+QKpeYrZSYLhUZL+YomwZ3N2ysqvZxYHholpMnh8lmyzzw4BY29zbcVJAB4HKp7NnbzgvPn6RQqNJ3cZKzZ8fYtWv169d1kxMnhrjSPwVAe3uc5pboLTdmf1woFCq89+5lJiYy9PTWc+ddXTelnvXp5j3cl7wxSXOfcv3gTBElQpp7w4tjw7KoWCYl02CimOfdyWEK9saocEsxNJFmLlvC7601xO/c1Mjm9quUjxZPE0ElQEqfo2AWsBzrpyLQ0CSZ9mAIRRQxNhCANfuCJD1XkzYVy6AvN8loKY1umSztwHm0YRtR7fYkeOrc29kd+RJVOz//97YVnhgAmUKZ1072o4buZVf8TkSx1idT796Bblq8cvwSIzMZAAIeFw/s7CQR9qOIHho9+wir7aSqlzk/foE3zp8jlS8iCyo7WrvYv2U/LcEeXFKtbzKoNrMl9AwFc5qI1oEqrhRDEBDoDT1JybyztrhTGnAch1ypwtG+UcZms5iWTdjnZntHPV0NtYqxKnpp9d6FZ/4aw2o7bil0Q9+ZLGpsDj5NvXsn44U+fnD0XQamJ8GxiQaCPLxzK83RRgJKPUF1fdbJKxf7iXg8bKlPXDeBfHxkAtOy2Nfa+LFX8xzHwZov2a1lLHk74JZ87Ao/yHi5H9u25isNNfpYWK1ja+AQXjmEe56+l3S1YTkWWWMaELCpsTUSWut8VWI5JVAWVRAEgkoMrxxEFCT8SoQtwUPMVkcxHJ1toXsIKbWkd7e/JiShSR66fHvwKWEUQcN0dNJ6zVfGdj5asRK4TYGGKApMZQp89bUThH0uQKAlEWJr282rHTyQ2MmDdbtxiSst2QVBIKh4uT+5k8nKHF8frpUFJytpjqf7ORDrxSevnJAM2+Sd2bNczo9hYyMi8HDdHh6t30tI9a2IBqFWvuoNNPP51vs4nxumYFYomhU+mOvjkbo9tHlXSn5OVzJ8MHdpsWISUDw8UreH+xM78CurK7RIgsjWYBtfbn+YoeIUw6UZTMfmlakTPFy3m57AxmTZ2rxJHq3bS5evYdWHvXacVp5puovh0vTiOb49e45nm++hw1f/sfY2lK0q3x55g8r8oiCoeHmu+V72RzetSYMKql7uTexgojLHZDlN1TYYKk5xKjNAj7/pQ69qlK08aX0KjxQgpN4+V2jTrinDHJ1aWx98KYKqi8dau3muezvbokk8ysYW3KIg4FVUvIpKvfdqedy0bQpGFfVjrnJdD4VCZdEXYmYmz8RkBq9PQ9OUG056yLJE96Ykh+7p4ccvnGJ8PM23v3UUURDo3dywrLcjny9z7INBXv7JWaanc3g8KgcPbaK5OcpPa1xrGhZzc7WG31JJZ2QkRbIuiNer3ZBE6a7wxx+YKpKEIkn41Zqh3M0sNCIBLzu6G5a95rqmGXxncCtDxRHemzvCm7Pv4pW99Po34f6ECmwsQBFFOgIRtkfrODYzft1tPbLCvmQjHcGrC/yjqQEOT13EcmwUUVo2Nj9Qt/m2nOPkXA7djLI58rl1+57S+TLffOMUz967m12betGWBPu6YaIbFrPZIhdGZsgUymxpSZII17K6oiDhUxL4lAQENzPr28n59DSnrkwQt5vwb9mCW746LrokP5tDT1/3fARBZGvoM8teK1V0XjvZzzffOEXQ48LrUgn63DTEgnTN32aq5KXNfzdt/huXS19ydARBIKy14RIa6fE1YKWmuDAyzeyAzhd2PUpXYONrs5+cv0xXIkpXInrdQEMQWDH2OY5Dplzh4tQMd7avpLVbts1wOkvVNOlN3j4vnbyu838efQ+vonB3Syu76xrW/9ASvD48yOmpKdpCIe5qaibiXltZL+lqI+lqW/F6LQh5aNlrLslLh29l5bbOvZK+ltGnGC31kdNn6QkcwCVepVU1eTbR5FlOCwyrdYTVusXj9AT2L77X5t1Gm3fbmtfwYeO2BBoBj0ZHfZRiuVqTMBOgXPWu+7m1oIoyD9ftRhOV6w7WQcXLHdFeXp48QUrPYdgmI6VpRksz9AZW3tTj5RQXcqPkzTIA9e4od8W2EFC81+Xyi4LIJl8TvYEWjs714eAwW81xJjO4aqAxUJzkfG54McfT429mV7hrzSBjKbr9jdwd387Xhl+rybqaJV6ZOrmhQEMSRDb5G+nZQBP5rnAnnb4GZioZDMciZ5Q4nR2g1Zu8iUDj9k2o57PD9BcmgPlqRqCZO6I96/ZahFQvmwMtvO06Ox+kWfTlRpiqZD70QENARBIUxNucySyZOidmxsnq6xtEyYLIPY1t/MrWvfSE44sLqyunR0hPZ9m0pw1/+Ma+B1m8MXrF7Ua5WOHi0SuUC1XuenL1KmUs7icW83Olf5rTp0aoVk0aGkKomrz4DIiCgKrKBEMe2lpj9G6pVT1WJjAgEHDz1FO7mZrMcuL4EMeOXiGfK7F1axOJZBBVlSiVdMbG0pw5NcLg4CyyLHHgzi4OHupetQJQqRjk8xWqFQNdN9F1k0rFYGK8VlW1bYeZ2TwnTwyhqnLtnyajqTIer/aRUbFcboW2tjjvvH2Z6aks3/j6+xw/NoTboy4GbYJQ803y+d00NoTZ1FNHNOZbNE7824TGRJBoyEMmX8ZxHEJ+zwrVqbgrxiN19xNU/BxJH+P74y9w0nOGuBbDJ3vRJBURacPBZ1gJ0e3v/BCuZjkEQaDO6+eLvbuYKRcZKWRX3c4jKzzQ1MHjrZuIuq7OX+/MXCasejkY76q5fy/5jPcW5eahtgA90jdKrljh6bu2rBtoRPxufv6B3Wxqiq9gGSiyxGP7e9jf28zfvH2W59+/sOZ+muJBPnv3di6NzVIo3V4PsFy5yqsn+lEkiV/51AECbg0HiAXWXxfcLFyKzKf297K/p5nvvnWGV09c/tCOtaupfsVrhmVxdnyKt64Mrxpo5CtVjgyOIonibQ00iobOfzl2hIjbTdjtvuFA48jYKH9++gTb4knagqHrBhofFkRBQhVddPh2knS1Iou3v/fv1IUxDNNi1+bV1QNt2+bilSkGRlIIgsCW7jqa6sI37I1ze1SnAl6ePHA1i1E1TOQNSGGuhXpXhHp3ZN3FpSSIxLUgnf56Uqla8+Gcnme0NLtqoDFQmGS6mln8e3OgmbgWXJeWJCCgiDKb5wMNgKJZpr+wMtNs2BZj5RQTlavujpsCTTS6N9ZMKyDwQHIn3xx5fZ5Hb3E8fQnTtlbQoK6FV3bR4I7hX6Wacy3cksaWYAsnM/0Y8/Suc9lhHq/fj3LDt8XtawY/OteHNd/IqYoyeyJduDbQpC4gkHSFSboiDJdqOucjpRnS+u1h0pbMPAPFU5TNPGW7gFcO0uXbg4DAUOkcul2hxVN7BtL6FGl9kpjWSECJMVa6hOFUqXN1kDWmGStdQndqMnNdvt218ugqKBoGR6eun21cQFcoylPtPXSFosuyt5pHxRtwI97C8/hxoVrSuXDkCpnZ/JqBRlNTlAN3djI8NMvkZJajR1bvZVEUCZ/fRX1diO07mvmZz+0jFlupqy7LEp1dCX7+CwfxeFQ++GCAUydHuHB+gkDAjayIVCoGhXwV07QIBNzcdbCbpz69m+aW6KpVlNOnRzj86nlyuTKGYWEYJrpuMTmZAcCybPouTvAnf3wYWZFQ5v+53So7d7Xw5FMfDT/a69U4cGcXx48PceH8OH0XJ+i7OLFiO1kWcXs04nE/PT31PPr4dnp7G9ZU87qQHSetF9kX7UARJWzH5mJuktemzmPaFjvCzdyd6PnEeeBMpfK8e2aQybk8AjW52wPbWmlKhBa3ERCIqREiahjHcbhc6OdSoR+P7MEtulBF5YYSN9uCWz6SQANqQcRDzZ04OPxk+DJnU9PMlIuYto1PVekIRrirvoXHWzfRG14u6161TXb5k2wLNa47L90MMoUyZwdrVA/LWn9+Cfrcy9YgSyEIApoiL1YRrgdJFPG4VAJeF+ptpkDqhsVUJs/mliR7uj4ahSVBENBUmaB37Ws3LZtLM7O8c2WYuWIZTZHZ09zA7uZ6PGrtM1O5At84dpq5Ypmwx81DvZ00hQJIoshUvsDzZy4yUyiypT7JE1trmfa5YplvnzjLB8NjTOeL/G8vv0XArXGwo4XuRIzL0ymeP3uRYyPjaLLC8FyGpnCAgx0taLLMm/1DdCdibKmr3XvFqs43j5/hUGcr7dHwqrT12wWPqiIJIiO5DDn94zGdDigxAsqHK8Jypm+cwZEUmWyJStUgEfWzqSNJ0F9bQ56+OM43fnSM2bkCVd2kIRHiV3/2IK1NkRv6/m/Lk6TKErHg1QrGpbFZ5nIZkjeYQV1AsyeBLEgbKj17ZRdN7jjvcxGAvFFiprp6dma8nCKjX9UCb/YmNpx9kQSRmHa1fFq1DaYrK49TMEtMV9Lo85K8HkmjzhXGK288K9nqTeKTPczpucXqyVQlTaPn+jddQPYQ1QIbLtm3ehK4RJUctUBjsDC1qoToR4lz2aHF/5ZFiU7fxjMRXtmFb8n3nNbzFM31qwHrwXEc8maK/sIJev13kCunmCj30+u/s1YuRiBVHSOkxIlpjUiCzHDpPA4OPjnCpcJR4loLul3mfO7dWolTDNKXP0JITZLQWlZdjJRNg/Nz0+uenwDsTTSyJ9GIcs2k39iZhM6bd/T+JCOfr3Di+BCnT41QqRiIooDHo+F2X61WOIBpWhQLVdJzRdJzRQYGZqhUDX7jNx9CUVaOM5qmsGNnM6GQm23bmjh5cpihwVnSmRKWaaFpCo2NITo6k2zf0cyePW3UN4SQ18i6jgyneOvNPnK58qrvOw5kMiUymeUqNooioWnKRxJoGIbF5ctTHD58gUK+giDUvodraVOWZVMu6xTyFfK5MqMjc2QyJX7xl+6hqzu56tjz4sRp0nqRneFWZEEkrRf5z30/YbiYIqR6OJkZJqC42R1p+9Cv80YwPJVmdDrDrk01zvmZ/gkuj8wuCzQKZpFXpl/n3dQRZqqzi3LiRbNIkbVdktdCvWtjz+oH50eQJZHuljgel8rIVIbJ2RybO5K1xvXrwDAtZjIFGmJBQi43T7X3sj1ax0g+Q1avYjk2LkmmzuunIxgh5lopfb4r3EJfbgK/otHsjS4bd6Kqb8UixHFqkvdvnBlgYGKOsm7gcak0RgPs7myko6EmeDKeyvHOuUHODU1xtG8URRYpVqqLIjNP37mFnR1XacHvnBvknXNDFKs1qu2jezaxp7vpEyMxbZgWb5we4MzgBJPpPOOpHIZp8S//8iUA2uuiPH3nFoLe2rxl2w4jM2neuzDC8HSt4tmSCHPXlpob9kIiw7JtTvWPc+zyGDPZIi5VpqcpwX07O3Gr8g3R9sazOQ5fGiBfqdIRi1BaNKW7uo+zE9NEvB6aQkGODo9iWBaf37udsMeNJsk0h0O8PzRKsWosBhqKLNEQ9HNOkQm4NTpiETyqgk+rVcB8mkrM58WtKES9HjpiEWI+Dy5FQZEkzk5MkylXaA4HCbg0zk/OcGRolD3Nq9PCbyc8soIqSaQrFarmzfc0OI6DjVVzVf8EwjRt3js5yOXBmgSvy6XwyN2befCuHkIBN28e6adU1nn8vq1IosA3nz/Oy29f4IufuQPPOkH7UtzS1edLVfrHUzQngnzQdzW73z8+i8elcmDzzXF1w5pvw1kgVVQIq0ub1HQK5uoTelovLFt4vjVzlqHi1AY9MexlVQrbcShbVQzbXOYYXjSriw3gAH7Fg0923RBHWBVl4lqAOb1WpTEdi+lqZt1AwyWpNxTQRLTAsgliTs997IHGWPmqhGvZ0vnq0Gv8aPz9DX22bFW5lL9aAdBtk4qtL7qI3wpEJBzHJqVPIAsKLZ4teOQaxzehtZAzrvp8+OQwXilIzqgFJKZtEFaTlMwcY+U+dLuCR/KTN9PkjFniWhNco7dv2TZzlTIz5fUXK1G3l82ROLEl1IbMbI53fnicK6dHqG9PcP+zB4gkryqMVIpVXvnGu0wNzZCZLWBbFo0dSZ7+9QfRqwbf/A/Ps+v+LXzw8hkcB3r3dXLnp3aiaAqD50Y58tJp0tNZEk1R9j+ynZaeBgRRYGp4lgtHr6C5VEb6JpgYnKF7VyuHPr2XQMSHoZu8+6PjXDh6BUM36dnbwX2fvQNZkbBtm9mxNK98/R1mxzMEIj4qxdUDxVyuzKuvnOP73zvO9FSWTT11HLizi0SiRm9aeNwcwLYcKmWdKwMz/OiHJ8hly/zkx2d47LEdbOpZnausqjKdXUnqG8Ls3ddOKlWgVNKxbRtZlvD5XMTjfuKJAG739QfcffvaCYe9N+wYLooC9Q2hFa97vSqPf2on27bX6JSbNzesS83ZurWRf/S7j2OaNoGAe9n2hmFx9swIf/5nb3L58hSRsJcvfukQbe1xNE1eVqWxbYdq1WRiIsMbr1/g0qUpjn0wyK7drTQ0hvF6Vy5yz2RG6PInEYTa73F46gKnM6P8z5sfJ6R4+PrQe7wyee4TF2jYtkMi4ueuHe0IQCZfxrKWN06fzZ7nzdl3GSvXxh0RkagWIawEUUXthmmord715dkBBsZTqIpMa0MED+B1q8TC3g2ZahZKVX78zgV+6ekDCIBbVuiNxOmNbJy6MlbK8ML4Gd6Z7SekepGW3FD/645nVjSDV3STP3/pKCevTNBWF0GWRKbm8kykcoR9nsVAA0CWatLU4KApMgGPC9d8tUyVly9Zwj4PjbEgl8Zm+fEHF2mvi7CzswGFT0agASBLAh5NxefWkEQRVZEJeWsZY59LXVwbOA5cGJnmG6+fZCpdIB70Yjs2P/5gmgsj03zhwd101kcBgZePXeJ775xFU2RCPjeZQpn3L44wMDnHrz1xAEUSN7wYrxgmk9kCPk3lzvYWAi4NURBwLano1AV8PNjTQVs0jCTCW/3D5CtVwh43IY+Lh3o7OTk2Qb5SC/gEQSDg0rinu53RTJaJbIFndi6vODWFg9zV0cJYJkdrJLTsfQfY3pDkzPgUY5kcgbo47w4M05OIkQysFO253ag1kNe+G8O++UAjbUzz8tQ3CatxImqSqJokrCbxycFPjM+XpsjcubudrvY4Fy5PcvzsCN1tCUIBN6OTadqbY9yzv4ug38VUKs8HZ4Z57ok9H12gYds2parObLbIW2cH2NJay8bkStVbyii4RXXD/geyIOJeUpUwHIuqtVKhx3JsKra+zDCvLz9KX370ps+z5kNgLQs0DNukYl1VN3FJCupNcOsCS/o5HMchb6yv2S2LEuoNRM4eaflEWLSq3Jx/7+156E3bomRdXVgatsm7qfM3vT9nfh/2LQYaNednEU1yE1Hr8CsRgsrak7IoiDR7erhcOM7pyiAJVwsBJUbVKiELKgmtlaASo8HdRVRrRFzF1Mt0bMaLuQ35ATR4/LT4g8uyiJpbpWNrM4PnxrhyZoQ7HtuxLNB46/vHuHj0Cgc+tZPUZIav/Itv8Q//wy+iaAqZ2Twv/fXbWKZNz9525qZzHH3pFN6Am1hjmFe+/i5ev4std3QxdGGc179zhPufPUDzpnpy6SLv/Og4Lo/GlgNdxBrD+MNepPnx4M3vHeXEa+fZfqgHSRZ563sfoGoy93xmP5Vilef/7DCpiQx77t9CajLLqTfO07WrbcU1X7wwwfM/OsnAlWn27mvjuc8fYNu2ZlxuddVFt+M47E6XmJ7O8cbhCxSLVc6dG1sz0IDa7+71anR119F1C8azLa0xWlpXJglsy+bS+XHyuQr7DnZteH+aprBlayNbtm6cflFfH6a+Przqe/l8mW998winTg4Tjwd47mfv5NDdmwgGPWt+l8ViFUkSSc8VmZnJMzgwQy5XXjXQyOglGj1hJEQsx+L7Y8fZHm7i0frtGLbJ6cwIZzKrj8ML49HH4YcjigLnrkyRzr2DgMDwZJqgz8VMpsC+zc10Nce5XLhCqlpLjjS66rknfpAWTxNe2YMkLDRJb/zcPdehvV4emeHo+REqusnwZJqtHbV79/zAFEfODRP0uYgGvWjzi/Jv/OQEyYif0ekM5arBFx/fy1Q6z0vvXuSVI5cQBJFExMfBHe2cH5jE41LZ0d3A2EyWK6OztDVEaU6GVj2Xu+KdtPlWT3xd68fkOA6lqs4LRy9y345Ofu6BXSiSRLlqUKrqxENXg5JY0Mt9Ozqoi/gZns5QF/Hz7L07FjP+Xk1l6ffZ3RijJRnmzMAkb50d+MS5JsmSyJ7uJra11zM6k+XoxRF6GuN88aE9i+9751Xy5vIlXj1xmcl0nqcObGFra7Kmwnh2kB++d553zg6SCPmYy5X57ttniYe8PH1gC3URP2Xd5DtvneZ775zhri2t7OrcOBOgIeTnvk3tvN0/xH86/C6dsQgP9nbSFgktbtMZj5Lw+1Akibjfh26ZWPaHl5QUgH2tjbw/OMrQXJqY10P/zBxP79hMyP3h9w0WdJ2KaSEKwi1VT0pmgWPpw7glD27Jj0fy4ZH9+OVQLfDQ6oiqdUTUBG7Ji/ARBx8C0NuZ5LH7ttDSEKGrJc5//eqbpLO1BKduWLhdCrIkIssSmzvrePH1cysSLuvhlgINr1tlS1uSXLHCtrY67t1ec3086Z8gt0Ym8vZDQFwyvDiOs2pW3nbsRe7/7YIDKxbmNs4yN26Bm5NWWxoAOHDDGvAbgbREAx7AuoXI/XbAcKzbXlFxnFvvIHEcG9PRKZpZBotnkUWFsFrH5sCdZPRpzmRfZ7oyzGx1DAdocm8iqjVxqXCM2eoom/z78Eh+NNFDl283KX2MOX0cB2derWLl/WHa9oaqGQAxj5e4e3kG0e110bOvg4Fzo1w6MbTiM8cPn6Wlp5F9D23HNEz+5j//hGRLDHV+0lNUhe497dz9mf3MjKaYGZvjwtF+6mbjzE1mePrXf4Z4Y5Rk6yAv/vnr9B0boHlTrRmwmC3Tu6eDg0/uwe3TsEwbWZGwLJsf/8Wb3Pczd3Do6T0omsz0SIqf/PXb3P3MPkq5Cu+9cJJf/f3n2HXPZkb7J7l0YnDFueu6yZX+KQYHZpAkkW3bmtm+o+W6sraCIBCJeOnqTPDOW30YhkU6fePUltsJQRCIJYMEQjcvnHGrsG2H2dk8xz4YBCCZDHDvfb34/WtXRgVBwOdz0dISJRj0MDOTp1CooOvmqtu7JJXqvATqucw4V/LT/NNtT+OSFEzbQhMVStbq0rPDxREyRoadoZpSy8V8H5Ig0eX78PsYkhE/O7trsqQIEA64URUZSRQXJYzLVgXDqV33g8l7uTt2F15pJdXoVlEs6xy7MIpl2+zuaWJiNotu1I6bCPvwuVVGpzNUdRPmb6cz/ZMYpsX2rgZsx0aWRUI+N93Ncd46OcCe3iY8LgVNlTBMiw8ujLC9q57RqQxXxlJs71p7sboj3MyO8EqBku8Mf7DqPLuwYJuYy1HVTVoaw6v2M6myhOpzE/RoqLJUy9h73YT9qy8uJUnEI4n43OqHnuW+GQiCsEhly5eqSKKIpspE/Cubi0dnMpwenKCrIcb9OzrxzldKJVHg7XODnBmc4p7tHRy/PMbITIafu39XrXozn8R5fH8v33/nHO9fGL6hQMOnaRxoa6I5FORKao73BkZ4/uxFPrNzCw3BGl1claTFqpUoLJg9bnRmvbnfJen30RYNM5LOUqjqhL1uWiJBlBtsRL5RjOVzXEjNkNerRFxuXNKt0Z4cbEpWgZJVYIGvIQkyLtGDW/Lilny4ZS9BOUpUqyOm1RNV64moCVTxw1WvU1WJgM+FpsoIQk1Vz7IcimWdStXAvmbd6fVoWJZ9w2aft/QNypJEyOvGrSo8vKeb8PzDc4cqUzVWn3Q2At02N5xZd7CXVSkkQVrBU6+9Ltb6Prj6eNwd30bbNSpLS9+/9u+Fn3vh7zpXGOWaCoIsSMu8HkzHWnZ+G8XSqoyAsCHXbtuxb+hYurP8e9Yk9WPNCKmijCyKVOfvbbek8nTjnbglbc3f4Hq/F0BvoPmWaVMVu8RkZYBmz2Ya3V1kjBlmqiNk9GmCSoytwbvp8etIgoxfiSCLCpIgsy14Dx2+nUTUekRBQhQkegN3kjNSi1rW2hq+JZbjkK6sTgG8FiHVRUi7MWUil0ejkC3iOA5G1USvGniXKCZpHpXOHc0oqozm1vAFPKSns2geDbffRbI5hiiJJFuiiLJEajKzOCi5vBr17XG8wZo78kI1IzdXYHokxY//8k3eff4EgiAwN5mhVKjgOFDIFCnlynRsbUZSJPxhHw0dCUr55UmLatUgl6tgGBY+n0Y47N2wd0ahUGVh7PR41n6m0qkCb796nkK2TKVs4A+6EUS4++GtVEo6R966RGomTyIZZN/d3TS2RHnr5XMUcmVKJZ3UdI5Hnt6N5pJ59/BFisUq5UIVX9DNz3zxThRV4d3XznP6gyF6tzdR3xRG100unxvn2Lv92LaDZVps2trIwQc3MzaU4oO3LzOXypPLlGjvruPQQ5uJxm/Nrde2bbKZmrO5LEuEI77rBhlLUa0ai4tdTZPn6S4rsTXUyBvTFwmpXl6ePEuDO8TBeI3HbTk2GaO05viWM/PMVmu0RAeHycokiqB8JIFGXTTAg/tXL2UtUHmiWgSP5CZvFmhw1eH5kBzBs4UypYpOV3OcHV31nLo0tijhGg15aUyESOVWVr3bGyJs6UguVjsDXhcdTVF8Ho3tXbXEgOM4NMSDDE2m6RueoVCukoz4Cd6E2tmb05c4lOjGv8QXRRAEvC6VX3lsP18/fJJ//Vcvs6kpzt3b2tnf07xug/bfFaTzJSZSOcZncwxMXqVpG6bFlYkU7fXRWjVrOk2+VOGPn3+Prx0+sbhdRTep6AYTc7kbOm6mVCZbqdAQCtAQ8jM8l2Usk13se4F52dqbuK8lQcCtKswUilQMA02WsR1n8X5ckMudK5XRTXPRSV4URSRR5M72Zn5w+gLvXhnh8a3dJPwrPcgqpsmZ6UnMJRWWuXLtWTAtm4FMmndHR9Y9V9O2mS4WeWNkkDdHhjBtm/ZwmJDr9qv+WY5J0cpRtK7+VrKg4JI8uEQPLsmDW/IRUuPEtQbiWgMxrYGwGkcS5NtW4Y2GfBw9Pczh9y6xbVM9J8+PMTqR5oXXznKxf4qRiQz1iSC6WRvnZ9MFvG71xqXjb8fJCoKwQt7QuYXMdN4sbfjzhm2RN64uyBRRWlWlSBRE3JKKIsqLjdrbg23cn9yJtoTaJCAsW3wv/Xvhx134WxZXBjUuUcEjXaUPlMzqMirVRjG3RC1JFAQC6vryalXLvKFj5Y3SssDEJ7s/ForCAiRBxCe7KZo1lQdNVHggsYsGd3TN3+B6vxfU1LXEW7wmCRlVcDFYPkXOmMV0DIJKjIASxSMH8MirL/bCapIwy5s7vXIQr7y+G6vjOJQ2aNLnkmVc8o09yo9+4W7+6J99nf/wO38KgsAjXzhEcgm9RxQFVG3JcySAJIuomoxZNbFMG1ESMaom2A7KkgZEWZYQ5ZUcYc2tIski933uDtq3NCFItV9Gns+mqC4VB9Dneb6O7WBWVwbOkiQhzzeHVqsmhWIt6FCU69M1Z2ZyHDs2gDG/OL4ebapaMUhN5wmE3JRKOqZhobkUzh4fplLWa+pwn9rO6aNDnD0+TCDkYWo8Qy5TYs9dnezY20YsGWBmMsvRty/z9M/eQTQR4K//6DUmRtO0dCTo7K1nZGCWibFa06dj2cxMZum/OMkX//79jFyZ4cLpUbbsamFkcIb0XIFDD27hrVfOYRomvjWyvDcCQRBQ5yVbbdumkK9QqRjrBm75fJnz58aZma5NlI2NEfyB1SfkzzXv598XfsRX+l9HFiX+Ue+jBJTauVdsg77cBB2+5f4zJbPEudx53ph9i4ye4WK+DxsbAZFDsbtu9bI3BFWRFpuQ18K+8G4u5fs5mzvPxfxl2rytBJTb77qrKhKmZVPRTRygXDU2pIpUMxq8dvwTMMxa9Xih0lAX9dNaH+GVo300J8Ls7m1a8fyeSo/gV1y0++K8PnWRweIs16IvP7Fq9b0mMdtLZ0OMo30jHLk4wn/8mzF2djTwpYf30l630oTv7xpMu5YpbowF2dHewNKU2d7uZuqjfqJBD1XDQpElepriS0R4aim2O3qa6W66MZnYmUKJ75w4y+WZWr7drSo83NO5WM1YD984dprjIxOcGB1Ht2x+7zsvsCkZ59cO7cOlyOxtbuDNy4P8ztd/QHM4yDM7t7CjsTb2Jnxetjcm+e7J8/zO13/AzqZ6ntzWQ8s8bas7EUWTZYq6TmskjE9dSc1MlUv8qzcPU9Svrn0W7sGiofP9vou8PjS47nXYjkPVskiXy5RMA1kUOdTcSmPg5pM5giAgCjK2s37i3XQMCmaWAldFhhRBRZPcaKIHTXLjkXzE1DriribiWiMJrZGgEr3p5MbWTfWcvTTB37x0kh+8chpdNzm4txNNk+m7MsW+7S0Mjqb43kunqEsEef7VM3S2xteda6/FbQk0RmcyvHVmkOfu20GhrPPKicu4FJlPH9x6U/ubrKQ3THOqWDozSyRrvbJrWXP4UsS0ID7ZvbiIL5hlNFG5rT4LAdVLTLu6kMwZRbJ68YYakgtGmZR+NdJVRZkm9/qDR9EqkzE2TgeZWqKOBdDgjtxk+fn20Z3aPHVMVTJAjYZWMMsE1Y+PWgKgiCrtvh21pm2htjjWRA8e6fYvKBZg46BvkMqmihLaeuXda34i07SQFZHHf/E+fEEPkfrg4oJzzeO4VBq76jh/pJ8Th8+x7eAmzr/fj141aO6uX3ewU10KO+/ZzHj/NIee3kukLkh6OkcxW8s+eQIuGjoSvPn9Yzz1qw8wOTjDuSOX6dq5XFTC5ZJJJgMEgm5y2TLvvHWJjo4Eu/e0rSqxapoWZ86M8u1vHmHgygy27dDVlaS7+/qmVZomU9cYRpYl3PPVj8mxNIoi0dQWo7OnntmpPGPDKXLzilHJhhBtXUkCoVpiYGYqSyjspa0rQaI+hMerkc+WAYdoIkAk7ic1czWpIMsiyfoQnT112JbNlb4JKqUqHq+LXKbEGz85i6JI9GxrQrtJB/SlEEWBWMxHMhlgairH0NAsP/jeMZ58ahduz8pJ3bJsRkZSvPD8KV577TzlskFdXYjuTXV4vasHGu2+BP9k69PM6UW8skartxbQOjh4ZY3Ptx5YfG3xuxc1unydlKwSs9UUu0I7AVAllYiyeq8JQK5Qxu1SPzLVoXpXHc82PUNsJsKRuWNkjRw7QttodNcTVAKLFY5bTeCE/G7aG6O8f2aYM/0TpLJFYkEvju3w/NvneOvkAFOpAoVilafu2Up7Y625erWjujWFeMjHv/nKT9jcluSzD+7A7VKJh7yUygaKLJGMrBzb8kYFeX4Oe3vmErPVAjFt+XYVy2C1+UAQBPxujZ0dDbTXR7h/ZyfvXRjmW2+cJuRz89ufudag7ua+r49XyuTW4FIV3JpCV0OM5+7bsWIuliURl6oQ9NWCx3t3drKtrW7FN7VecHwtGkMBPr93O/lKFQfwKAqJgBePWhtffuPeA7gUGfd8YLuzsZ7GUJD6+UDkUGcr2xvq+Ll9O3AcB1WS5ntpaknSrkSU33v0Pkq6jqbI1AWu3jOaLHNPVzvd8RhV0yTg0oj7vMveFwXY2VhHQ9C/aibdJclE3R7603MU9OWJVstxmCkVmSndGE1WEgSe6NrEo+2dBG+QLbAUMa2BX2v/Z6T0KWar48xUx5mtjpPRZ9Cd9WVzDUfHMPXF4ENAYFjsQxVdaKILVXThkwMktGYSriaSriZiWiO+NZKf1yIR9fP5J/ewa0sT06k88YiPzV11yLJEvlDB41J5+9gVfvjKaVKZItGQl2ce3Yn7BquQt8dHw+8h5HPxJ8+/j1tTkSSBB3befGl7sDBFyaoScNbnuubNMlcKk4t/BxUvCW31iajZkyCiBhYDjb78GHmjdFsDDZeokHSFCSk+MkYB07EZLs2QquZIuEIb2seJzBX0eeqUiECzJ45fWT97mdWLTFXSG/LcgNr1l82rN/vH7QoOsCPcwXtzNUMl3TI5lRlgf7TnYz0nQRBrPErpwzX+Wwanpjy1EUiiuCKIffdHx3n7h8e5dHyQfLrI6KVJWnsb+Pz//ASxhjDVis74lWn+2z/7OqIs4g16+NQv3suhp/eueRxRFNi8v5P8XIEX/vx1vvmHLxCM+jn41B42H1i/mVkQBJ79ncd58b+/wf/xj/6cYraIJ+Dh8V+4h5aeBrxBD5/9rcf4m//fT3j3RydINEXo2L5ShUcURbZua2LPnjZee/U8fX2T/OH/8WO6upK0tsUWTfMqZYNUqsDg4AzT0zlmZ/IYhkUk4uPXfv1+vF7tuuOLIArIsoQkiUiSiGXZaC4Fx3Yo5GtV1EKuXAs85xf9mktZpIotQHMriPMqMJIsreC9Lrs2ScQ1L88rCLXvfIEPHY0HuOOeTYRjPvyrGAPeDARBIBzy8tSn9/Anf/wa6XSRr/71u7z7bj9dXUmiUR+SLKJXTTLZEqMjc4yPpZmdzVMqVfH5XDz51E42b2lc08RJFkXafHFanNi8oevV79wjadyd6EG5RvlPEiVCaojtwW2UrQr1rvmgULh+Y/hP3u3jrp3t1N8ipWyjGC2PMlAawrBNClaJd+aOcDJ7Bk1UkQUZURAX/20EWwI9/Fzz51a8LksSd25rpbctieM4CAh43So+j8YdW1vZ2lGPZdmoikQ44EESBX7jcwcJ+ZcrjAmCQMjv5reeu5uqYS4qx4iCgO04eNwK7Y3RVWlwO8MtyOICT1/kobot7IksTwJcyk+tuNYFcsLC/Rzyugl6aovl109dWZRxXQpFkVAVmYpuUDEM4OMzDv2o0BAN0FYXYXgmzWy2yKZrKhML3+O2tnpeOHKR0wMT7GivI+h1r7rdRuFRFdqiawfvLUuawgECbhcB99XFd0MwQMMaRXpBENBkmc746hWrBXWqgGt5UmPhEvqmZhlIpXlsSzcJ/+rzb8jl4v9534Nkq1XOzUxzbHKcD8bHuZROISLgU1W86vpJGVEQ8CgKrcEQ97a0cU9LG43+wC31/qiiRpt3M02eLgy7imHrGE6VilUio88yUx1jujrGTHWMlD5Fxbp+QOTgoNsVdLvCglGDgMhw6RKKqKGKGqroIqhESGrNJN3NJLVmYlo9LmllwlaSROriAaJhL4Zpo8giilyTfI/O9w4+fHcv23sbKZV1Aj4X9YkA0sdBnQp4XBzobSFbqDCbK/KLj+1blG+7GRStCifTV4jV7VrRA7EUum0yUpphsHg10IhrQdquKcMvYJO/kQZ3lP5CrRH3ZOYK/cVJku7Isr6KW4EgCLR6k3T5GxbN/U5nBhgsTm4o0LAdhxcmjixWdBRR5mBs64YmKsOxGC5OM1aepXUVx/KlSOt5zmeHKS+hWu0Kd25I6vfDxKHYVv504McYtknVNnhr9iyfbrqLuLY+3ehvHW5gwrh2LNx2qIf2rc0YuonjOIiSiKLJhGJ+Jodm+PYf/ph/8O++RH1bHAQYvjDOX/ybv+HOJ3aTaI7y+1//R8SbapNDKO7n6V97EMdx8IU8HHxqD9sObsLQTVRNwR/x4prPfrf21POrv//csn6PpUg0RXnmNx6mmC1jmRaSLBGM1TJcsiKx855eWnrqMaomqktBnV/YX4vGxjDPPncHtu3w7juXmRjPMDtTa2peWPDatoNpWui6iW07CALs3dfOz3/hLrZuXUkN2QjCUR+RmI+3Xj7Hsbf78fpd3PPwVsKRtYPQ1Q5jGhZ//n++Qv+FCSrlWtPdfY9un//A8m0t06ZaMRm4NMnYcAoE2Lmvnbsf3oL3NriGu9wqjz66nWrF4NvfOko6XSSXK3PhwjjyfIDkOA6mZWPoJqZZG5s6OhI88zN7OXT3plUd0a/FtRO2QC2YcklrLwKCSpCAstIb6FTfGJdHVlJ33jzez/ZNN+YCfCt4dfoN3ps7hukYVC0dG5uKdfNCKNer1njdGt5VPDKiQS/R4MpFRH1s9WBLlkTqlryXzpV45eglzvRPsKe3ifaG1ReFPuXqsZ9s3EnCFSDuWl7R2B5sXDGXOo7NmcEp/vKVY+zuaiQe9GJZNscvjzExl2N/z8qm8ljAS2M0wKsn+/n2G6fZ2laHYVlsbknSFKvNBZZtky9VKZSrjMxkMEybmUyRoak0Qa+bkM+NpkjopkWmUGYuX2YmU0A3LUZmMoT8bnwuFb9HQ5YkbLumjpUrVRmdyVAoVzEtazEQCng0PJqygiZ+u9AcD3H/jk7+/KWj/OF33+Tube1EAx4yxQpnBye5b0cnd21pZU9XI3dvbeP5988zlyuxp6sRRZaYzhQ42jfC/+1nH6Qu7MNxQDct0oUyc7kSM9kC1flrD3hdy679k4aB2Tn+4r0TnJuc5v5N7expblizCVwSRZoCQRodh45QmAfbOhjMpPm573wdn6LyS7t288ym1c0cl2JBuEeVJHyqiltRbllgoDbGCahCLQhYgOM41Lva6HZ2YNgGpqOj21WyRoqZ6hhTlVGmK6PMVMcoWfnr9iw72FTtMlX7agvBdGWUweIFZFFBEVQ2+Xfx2abfWP0cBQFVkVkrFvN5NLzNKo5Tm8tuZt68pdX14OQcv//ffzLPDnQo6wZV3eTUlQke2NXFlx9ZOzu6Hr4xcpj90U1E1JXuvcwfb6qS5vnxI4v0H6/kotPfQNK1+mAdUr3si3TTlx9hspKmYun85cDLJLQQPYGmDVObbMfBcuxVm84BOnx17Ah1cCozgG4bDJemeX36NE2eOA3u6KqfWcArU8c5me5fVK4Kqz4ertu4Yde57BDvpy7S6I6tWdVwcHh+/AijpZnFG7jJHafHf+uN07eKRk+U++Lb+cnUcRwcxkqz/Lf+F/idTZ/Bs0FzRWAxUPu4r+fjgi/owRdcva8nPZ0jM5ujoSNBU1cdpmFy8vD5xYyjJMk0dFwN1pcGAwBunwv3Ggtc1aUSa1i7rCqIAsGon2B0JTVDEARUl0KyZX03VFmW6N5Ux2/99iPc/8Bmjrx/hb6+CWam8xRL1VqVQVOIRLzU1Yfo7q5jz7522tpihEIepHV05mPJAE88uw9FkbEse35bBxCQZJHWzkSNfiZLuL0akizy2Gf2IEoC2pKycmNrjC//5oOL/RS//NsP4/KoKKrMs794CNOwcXBQNRmv10U04WfbnjYA2rqS1DWGycwVmZpI89BTu9i0tZELp0eZGJ0jNZO/LYGGKApEoj6e/fwB9u3v4J23L3H27Cjj4xny894RqiLj87lItMfp6EiwY2cLPb31xGJ+NO3GDMKWwnEcUtUCpzIjPFi3ZcX7g8Uh0kaaNk8rh2feIGNkeTBxH+evzDE+k6W5LrRse9t2bjylewsomCXyZn79DTeMj578E/C6eORAD/fv6cLtUjZEO+sJ1K26CPuVrnvxKteM04JAwKNRrup89dXjlCoGLk2mIRLgiw/u5fH9KyvWfrfGp+7YTLGq89LxPn70/nnCfg+/9emDi4HG4GSa/++3DtM/nsIwLbLFCt9+8zQvHLmAKAr88mN38Nm7t/Hqicv8h++8OS/Hb1DRDf4/3zyMKkt0NkT5e0/eyc6OBqYyeb722glePHoRw7QpVKqICJwZnESWRB7f18vP3r+TusiHUy2TJYn7d3bi92j88L3zfPW1E1R1E59bpaM+QtDrqvlwyCK/+sQBmhMhXjlxmXfOD4EDIZ+LzS0JPPMUWNOyeemDPv7T997Gsm3K89f+777+Goossakpzq8/cYDt7fUfyvXcCppCQf7BfQfQLQu/puFVlXXHGEEQcCsKrvmG8yZ/gJxeJer20BZaO4D/OCAIArKgIKPgmn/cHMf5/7P33/F15XedP/48/famq3LVZcmyLfc69lRPz0zqJJOQRgJhA4S2sAtbWLb8dmEX2C8Lyy4LC4QQAgESEpJMJtP72B733ot6l27vp/3+uJJsWeVeS/KME/b1sB6ypNPuKZ/zeb3L60VYi9DqXothGZi2jmHrpIw44/nrmY+JwjDR4hiGvXA/roU5i3wk9ejCy1o2Y5Mp+oaipLNF7Hky7js2tcy4hS8FyyIajdV+fven3z/zsyBcH+Md89RK3wq60yP8zvm/51fWfIw6R3DWTWbbNiP5GF/rfpnjsSszv29117Knqgtpgai8KIjcX7ORM4keomMnKVoGV9ND/Pa5v+PjzffzYM1m3PL8cmKWbTFRSHAidpVjsStUawF+qv198+5HExXuDq/jfLKPAxPnMG2Ll0ePgQA/1ryXRmd4zj4My+TFkSN85dpLpKYMBxVB5jOtD+FXKu9RiOtpvjd4AIek8nDt1jmTc8MyeWH4CM8OHSRxgzfHhxv34FWWqpiycg3ksiDxmdaHORa7QrSYomDpvDF2koSe4VPND7Ih0LJgdidvFunLjHE4epHu9AiPR3ays6pzxY7tRwXtm5q56/HN/P7Pf4V8vogoCjR1RviF//G5Ba9/Ss/Rn50bQQao1nxUO979jJMsS1RVebj7ntVs39GGYZiYpj0jJCEwpV4iiciKiKrKZQnGjdterNl6vl4Q9zxqTYoi4Q9ef35v/H9gniyIrEg4pnarqPJMk72qKLzyzAn2vXIW24Ydd6+mpm7lzrkoCng8Gl3rG2jvqEHXTUzDmpGbFiiRREkUkRUJZepruQpLpm1xNT3G13v2z0s00kZ6yqdCwCW7cEkuRvPjtDZU09VeR0fT7PKSaCI74yOxXIzH0mRyRVqnIvzJTJ5rAxN43Q5aIkFkSeKD9e/jvurdK7I/KGVw3m1Ikohvgf6ahbBQEMunzn1mBKCxOsB//vz7MMzSPSUIpW04NXlGPetGiKJAR30Vv/TUfRSms7KiMMv1vLkmwH+Z2uZ8cDtVJFHkgU3tbOtonP9zTEnjAtT4PXzhfbv49IPb5l3WockzvQdLQUPYz5/88tMzSks3QxBK86a71jazqS1C0TBLZXKCgCJLuDQFSSxlAf0uBx/as55Ht6/BmHKuFkUBVZZnZHFlSeShravZtWZ+E0hZFsu6yL9XUGWJsGdpvZmCIKBIIu2hEMdHhlf4yG4fBEFAQkYSZNSpKY6NjV8JE3G0Ytkmpm1iYWJYOkkjymRhlGhx+muMeHGMpB7FpLL+zlQmzzOvnOaZl08RT+YWtBf4/X//9HtHNGRJmlE9sO0SAbiuBrR0rPLU0ZcZ42j0Mr9w5H+xO9zFOl8zPsVFzixyNT3EgYlzDOeiM6pJIdXLvTUbWOubf0CZhl9x85nWh5gsJjkevYKFTX92jD+69D2+3vMqq70N1Luq8MjOkoGckWe0EGcgO85YPo5hm0iCxEO1WxbchyAItHkiPFm/i/F8nCvpIfKmzgtDRzgevcrWYDudvkYCipuiZTKYHedI9BLX0iNkptLuAvBw3RYej+ys+GUeUNyoosxAdoI/ufx9Xhk5ztZgBw2uMJIgMlFIcCR6kfPJflJ6buZabQm080DNplnqW5XAtm2KlkHKyM5SuzIti4yeJ2PkcUjqLWcVmlzV/Nq6j/OfTn+NgqWTM4scmrzA2XgPdc4QHd56wpoPRZQpmDpJPctwbpLB3CQJPYNhmYQ1H3trN9/Sfv+pQFFlPv7LT/Dhn31kZlIuKxLaIi+dkXyMbw8coGgZRAspJEFEEWVM2+TDDXex17Hx3Tr8WRAEAUWRZ3wNfhTh9jp48MlN3PtI11SPQukayrfY9FkOgiAgSQKueZrAbxcsbNJ6npwxf3TOxiZlpHHKTkJqkLxZwMZix7qmqeOdPbZ88n3bca5AkzzAmavD/OUzh9jYEeHDD2zkUt8Yxy4M4HFq7N3RwdY1jTS7GmmicuPEcngvVf9uBUk9x/NDpzk82U1az89KxPyXLR8lfENJlSAIyJJAwHNrExVJEvE6NbwLjEuKLBHylVdjdGqlJutK9udzOahgk0uCLIlUldn4NKkol1USBAGHquAo03vg0pSZDMc/JciiREew6oeKaMyH6dIrUZhrgulTQjQ4V5Exkgznehkp9DKWH2Aw281IYa531nw4fnaAl9++QFMkxGefWk046J634b55AbPXSrEib+fBiQRfffEwB8/3kS3oeJ0aT9+/iU8/PH9koBw+0ngPFxL9vDR6jMliiueHD/Pi8JGpWmGwmG2+55NdPFa3nY803I1UpglaEASaXDX8SudH+dOrz3Fw8jwFSydvFRnORxnNx2aphNjYMwRqeix1SmpZJiUJIneHu5AQ+Wr3S1xM9aPbJoO5CUbyUV6Y/jyUbhrTtmaRtA/U7+bnVn/wlnpHOn2NbA128MrIca6khzgZv8aZRM8MUSmZGVqzDAVXexr4hc4PE9bm1kHfiPF8gj+7+gNG8jEyRp6MkSNrFqact+1ZMrkj+Sh/cPEf+V+Xv4uIgENSccsOXLKGW3Kwt2YzD9ZuxqvMHXRLrF5kR6iT/7LxJ/i9C//AaKGkQpY0sqRSWa6mh254Idsz5/DGz2W9ByUIi+Fmueabj86m5CyY1ouMZdN0J2P0peOVbRsb07IxhcpNHWVNRtbm3ls3NqCXBrjSeW731PFra5/iTKKXq+kRPli/EwSB/eNLd26vFNPnzqZUtjiZz3B6YpRz0TF6k3H60wkm81kyepGcoYNdUitxygrVTjcNbh9NXj8bqmrYXF1PtdNdkvW84XPeyShJ0Mqo2s2179NjU+m6DaQTnJwY4XJ8gt5knIF0gnixQEYvkjd0JEHEKcs4FYWIy0ujx0+rL8DmcISuqhp8aimqfXPD9q3idKyfaDHDtlArXsXBG6PnFyQSum1yJjG/KzhAQAkQK8YxbYtHah/iVPwUli0jT03EBkfjxKca86efqVWNVSuiOiWKAns2tdJWH+La4CSWDTu6msnkimTzU2Id73Fp5sy76TYPd8LU8zJ9X/xD72GOR/tYH2ggrHlnvQ6d8rvji2HZ9pxx9WZU6uw8vR0LiOaznBof4Wx0jO5klP5UnGg+R0bXp1S1QJNkXLJC2OmmzuVhlT/E2lA1m8N11Lq8M+PLnT62wOzxNasX6U7GODs5xuX4BP2pBKPZFNFCjpxhUDBLZeqKKOKQFYKak7DTRbM3wCp/qDSWhKoXLCtfKm6+/8pBkyQealuFgEBnqHwp7p2I2fd2aZzPmimGcz0M5roZyncznOshoU9i2VNW0baNTeXzgJHxBCG/i09+cDub1jWWejDmWW659/GKEI14OocmK/zmTz7B6e5hmmqCy3IGz5tFfn71B2n31vO17pdIGzkM25p14gUEJEGkzhnk6ab7eSKyA7XCiLwoCDS4wvybrk/w8uhxvjOwn8HsBMaUM7V1k7RuiVWKSAhoosJafxN7quam+W+GJIjsCa+jzhnkW/1vsW/8LCmjlJ4ysWbejKUHSERGpM4Z4tMtD/FI3dZbbsw2LJMtgVVs9LfyN72vcTx6maJlYtvmLIO7knmhyL3VG/h822M0usJlI2k5s8DR6OVZsrsLwQZ020Cf4h5ZszDLF6TFXcs91sLSx4IgICOxvWo1v7/tZ/lG/xu8NnqSjJEvESV7Lo0Qp+4HEYGA6mFPVRfNrvlFAW43TKtUHjA9AbCxMW2beCHPaDbNWDbNaDbNaC7NaCbFWDbDyNT/03pJHnKaPFWCr5w9yl+dO76in8Granxm7WZ+bfv9QGlCpUkipm2RMQozE6ysWbxtUdhp8lg0TfqScZ7vvczL/Ve4HJ8svfCmzu28Z2lqXtuTjHEEYKrxWADaA1U81tzBEy1rWB2sQp5HtetORikwYZM3dM5MjvJc7yXeGOhmKJNEt6ZdW+c/L7Epkbmr8Whp3Jk6L5oksykc4YnWTh5oaKPB40MWxSU1Q/5tzwFOx/v5gx2fxas4+L3zzzGWTy64LdO26PDML17R7GriC22fh6ljvb/6vll/P3Sml5MXB7Fsm+GJJG6Hyq/8+IO0LNDQfCtwO1Rqgh5URWZkMoVumNQEPYhiee+y0r1rzcryT38GQRAQqayErxwG0kn+5VvPcnR0aNnbWgy/uv1efmbjXTNPem9mkg82buHBunVznp13a2r9hyf287Xzx4kX5p9rhBxO3vmxL804Ws8H27YxbIuxbIZXB67y/WsXODs5RsYoLj6+TKE7GZv6vFMqcUBnsJpHm9v5aMcG6t1eZFG6I13Lpz97vJDn8MgAP+i5yOHRAcZzmVnvn4U/f47BdHJWwGZ6TFlp/FjnRn5+8x4aPJX1x8iiyM5IA9vr6u/Icz8frge0rSnzZZ3JwggDuSv0Z0tfseIohm1wfXxffI4gIE41uc8/1TdNG4dDwetxLGi4uhJYEaIhiiIOTS6lXAShZPyUL68RvBBSeg5RFPlY073cV72BV0dPcHjyImOFOBkjj1dx0eCsYlfVGu4Od1GzQPP3YhAEAZfs4EMNe7g3vIHTiR7OJrq5lBokWkiR0nMIArhkB2HNR7OrhrW+Jjb426hSfTjkykiNIAis8kT4l2uf5qnGezk4eYHT8WsM52Ok9RyaJONXPKz2NrA9tJqdVWtwiuqSXkJFy6BgGWwJtvMbngin4z28PX6ay+khYoUUoiAQUr2s8zfzQM1G1nib0RZRfLkRoiDilh0UrcpM5Exbx7QLMMWuRUFDElQEBDRJKTsYlTIbAvWuKn6p8yN8quVBDk9e5FS8m97sKMliqVxLEWW8ios6R4A2T4T1/lbW+ZoJVOi9YVg5LPv6Z5JF14IPZaXYN9TLawPXGM6kZgjFRC5Dwbx1h/hKYANGhb4zlcKwzHnrNas1P7Hief7j6a8jCyVzzA807FzRfZcybzYpvcCxsWG+eu4o+0f6KC7x/E0PyNMf51JsgkuxCf7o5Dvc19DKT3RtZ3ddEw5ZvqMJh23bGJbFZD7Hq/1X+frFk5yPjc1rkFbR9rieScsaOu+M9PHOSB9+VePxltV8onMzG6pq0CT5ll7WP9f5MFmzSIOrNNm3bJsvrX6YDYHGORkA3TI5Gu3m7bGL827LwppSZSllTkvjh4Y6tZ2nHt7MUw+XSiRN0+LPv31gCWdifnhcGmeujpDOFajyu8nmi6RzBdLZInVV8094LLt0vJPFKJfTV+nJ9JHQU+TMHE7JgU/x0uJqZrVnFWEtjCLKC/YUVgYbw7JX/Pm/GdbUMzk9vfHIDnTLpGgaJdWwGyV03yWqYU1NlBf67JP5LMOZFI3euX0v089SfzrBNy6f5ttXzjKaTc+zlfK4cXyxgHPRMc5Fx/jjUwd5vKWTL226i3Z/FZq0/J6mlYBt2+iWxVAmyTPXLvCNy6fpS8WXvr0btjv7NyuH0ruo8u1On+fFSOZSYFpWxVmyxTBNKizbxMLCsk0yZpKRXO8UqbjMcL6HrFn5PVkiFBIiEpIg4ZZ9NDjbaXJ10OqeX3UrUuPj7KUhBobjtDZWIYnikpWlFsOKEA23Q6Eh7EcSRfrHYpy6OsS2zsV7JRaDbpszk4IaR4BPtuzlky17l3WM0yUG86XffvfMCwRUFz/d+TAhbeEJqmXbXE6O8osnvs6/3vAEa/yLG37dCFEQ6fDW0+GtBx5a2ocoA8u+XlLmlh3sDq9ld3jtimy70RXmr/b8q4qX70+/woXY10jpfRhWljXBz7LG/2kc8q1HGkVBpNYR5AMNu/lAw8o1XgKcjf45vekX0M0Upl3gvvo/IOJa3j72j/TxtQvH0Zc4AbyT0eap5Yvtj9GfHce0bZpcYfzzlMAtFZZtk9GLnJkc5f+ePsSbg92Yt6kuxAbeHOxh31Af99Q388tb72FdsBqnXF7h5N2GaZUij28MdvOVs0c5Mzl628oDE8UC37h8hhd6r/CBtrV8bt1W2nxB1AonSo3u2cp6miSzOdTCxsBcZb+iZZAx8gsSjd5MHy+MvER3tpuCWcAre3ky8j7unnIHzxV09KlmWNu2SaRzFfvPlMOallp+/QuPYpoWoihgGCbne0axbVjbOjtbWtK31+lO9/Ly2Gsci52kYC2sCqMIChv9XTxau5dO72q0JQaX3k0MZKOM50sZ7SZXkBeGTjOYjbHOX48myTP0YlOwqeIA1u2EDVyKT8whGpZtE8vneKnvCn965hBXEwsr8iwHRcvime4LvDZwlc+t3cYX1m8n5HAh3SZ53Epg2hbRXI6X+6/wp6cPcS0518Pk/2FhnOoepqM+vGDf0HyYDuaYtoFpm5i2gW4ViOsTDOauMjD1FS2MYtiVBXIBJEGe+VIEhSqtnkbnKhpd7TQ42wmptWVLO9d31nPi3ADffuE4iVSWDWvq8bodc3rf/F7nsspRV4RoNIYDVPs95Is6H713I/FMno76xWVcF8VtmFjEi1l028SvuMq7KC8CRZSo1ryLar//P0C9+z7Cjk1M5E9xPvaX7/XhLIj1oZ+i3f9RBjNvcnryj97rw7njoVsG8WKGeDFLtcOPKIikjXxFhpKLYTqLMZRJ8s3LZ/iLc0dJFZeeFb0VmLbFm4M9nJoY4fPrtvHZtVupcjrviOxGSXDB5Hx0nK+dP86zPRdLfSjvAhLFPF+/eILDo/381PodvK+1E5/quOVShA82bKXRGUScJ9ItIuBTnPjV+clqQk/Q7GrinvAe0kYGURBwStfvtZcOXODUpUEAcnkdr9uBY4WaX4u6wchkkrFYGnNK2aipLkhD9dwIec7M88b4Pp4deoGYHi+7bd3WORY/yaX0FR6tfYjHax/Cq7yLhqBLwPNDp3lm4MSs311Nj/Pdgdllm1/e8wXqnHeA75ENF6LjPNR03TzYnMpi/NW5Y/zV+ePotzkTBJDWdf707GFOTAzzG7sepDMYXvEehnKYLpO6Gp/kT88c5jtXz922AM7tRjpXwLAsdMNElWUKuoHXpeFQZFK5AgXdKEndqgoOVSadK2BapfeLLIpYtl0iCgJk8zpFw0QSBdyOkkpZtlBEN60pY0zwukpjXqZQ5Fv7T/P0PZtoCvsJeJxlsxumbZAxkuTMDKP5fgZz1xjMXWMk30vaSFT8mUUkZFEuSeIKCk7ZQ52jmQbnKhqc7TQ4V+GQbj3gd+xMH4dP9TAZz3LhyuiCy/3P//hxulYvXQZ5RYhGLJXlzdPXGItfH5BzhSL3b1q6O/hKwrZtXhu5SFzP8kTDRiJLHARFQWCVt5rf2fH0Ch/hjx4kQcUpV+OWG5CF2yTjsQKQRRce0YVHrufdqy7+4UV3epTvDx0mWkizNbQKt+zAtm2eqF+6Z850Kv9ibJzfPfoWbw52r+ARV454Ic+fnD7E+eg4v7LtHjoDVQvKeL4bsG2btF7krcEe/ujUO5yZXPhFcNuOAbgUn+R3jrzJlUSUz63bSoPbd0tR2Z9ov3/Bv0mCyFp/hF9ZO79UuCzKqFKpuVi3dXRTR0REN0wEQWDn+hbWttUiAKoiU1vlWzF521NXhvjO66dxOa6r+9wnCnOIRsEs8NLoazw/8gpJvVSz7pAcOEQHqqggChKKKE8JZ5gULZ28lSdv5kkbGV4ceQXdKvKRhg/glJbvi3K78NOr9/KznQ++14dRMWxsLsavS3KblsXVRJT/cextnuu99K4ei2FZHBzp51+//Tz/cffDbK2uR36XMhvTZWInJ4b53aNvcXCk/13Z7+3CM4fOMRxNcXlogrWNNVwaHOfDu9dz97oW/vKVI4zGUkiiyNrGGvZubOfv3jxBtqAzkcwQCXkp6iYfvWcjkijw4rFLjCXSOBSZvRvbaa0N8syh8wxOJJBEgZxu8On7t1IX8vLi8UucuDZEQTfwuxz83AfuJljGmHqyMMrX+/4HE4WhirMVAiKyoKCICrKgokoaQaWGOkcL9c426p2tVGkRJEFadpliXdjHQ3vm+tjcjHBweUGQFRmRR+Mpjl8e5NHtnahTkos1wbmGXLeK3vQkftVJ1iiSNgpIgoBXdhB2eGZSQrplEi1kSBl5bNvGJasEVTeuKeWLeDFbMoSKDVCwDDp9YySKObyKRrXmRZ3KbtjAeD5FtJDBxsYla9Q6vEhCqWlPt0x60hOYto0qSkSc/lnqGmm9QHKq5yKtFyhaBqooE9LceGRthvWm9DyxYoa8acyUc4mCQMTpxy1rPzSNSysJ2zbJm1GKVhrbNhAFBU3yo0qBmQfJsnVyxgQCArLopmDGMO0ioiCjij5UyY84Vetcit5kKJhxDCs/tY4LhxRCFG6tRKG0rSxZYxSHFEKTArP+XjRT5M0oqujFIQf5UScrCT1LjSPArlAn44UEIsItaFzMRYlkmBwcGeA/H3yVS/H5vTreLRRMg1f7rxLLZ/m3O/eyMVyH+h4459q2TayQ49nui/zx6YMMpsuLMNxORAs5/vr8cRKFPD+9cSdtvtAtTZRs2yZv6uj2/L0/dc7AvOv5ZB+mZuKQHAwnh0kZaXYEt9M9MInLqSLLIpGwD+8t+kBUgqJusH5VHZ96fNuCY4Zt25yIn+bN8X0k9SSKoFDrqGZLYBOb/F00uOrxyV4EBCzbImWkGcwNcTpxnhPxk4zkx8mYWd6ZPEK9s477wne/50pWlSCt51FFeeb9CVPE2MhPvcfe+89gA5djEzPv2b5UnD88sf9dJxnTMG2bc9Ex/tvhN/jNPY+yLlT9rpTLmbbNmclR/uvh1zk6duuiAbIookwJZohTyp+mbWFYFkXr9vQdTkMRRRRRwinPdule21RDY9hPMpvn03u3cPzqEG6HykgsxW/9+BPEMzn+4Ltv0VDlx6UpbGipYzSeQpUlfC4HZ/tGSj0NosDPvG83+873cHFwHJ/LQSKbZ8+6Vp7YvoY/f/Egg9EEqxvCfGbvVo5fHeRnnthNR6QyJauilWckv7DUbEnUSEEVNRRRRRU13LKfaq2eWkczdVoTdc4WXJL3tjxTm7sa2dy19DaHSrEiRMPl0Aj6XFwbnsQ9VbumyjJU3sIwL37j+D+yK9zGWD7FtfQ4AgItnip+tvMBmtwhdMvkQmKYf+g9yrV0qWa83hngkcg67qtdjVvWODbZy3f6j3MmPoRhmVxKjqAIEndVr+KTrTuJuAIATOTT/PW1d+jPRClaJgHVyT9f9widvpIaSlrP89tnniNayFCwDH5r61NsDV03wjkW7eXvew6zyhOmLxNlIp/GISs8GuniiYYN+FUXGaPADwZPc2DsKnE9y2Q+zXAuQbu3ml/peoztVS0zk+V/KrBti1jhApcT/0CieAXDyqGIbqqd2+jwP41HKWnU54wJTkX/CNMqUOPczmDmDfJmFElQCTu20O57Cp/ahiBAwYzSn3mNocyb5IxRQMAt19Pm+wC1rruQBVfFkQAbi1j+AofHf4s23wdZF/iJmZeDZZsMZd/mYuyvafd/hA7/j36mSxVlLNtirBAnWig1qlXaeD8fDNvinalI31BmaQ7L00pq0rRK0pQqk2Fb805qKzmmY+ND/Obh1/g3Ox5ge03DuxZ9hGmSkee7187zv04cYDKfLb/SPBBgRlGrJA1eKh/QLXNJ3R050+C7V0sGpD+78S7aA6GKysuKpkFPZpxziUFG80l0y5izTLXDx4+1zO2NanE300Izlm0RUoMULZ0qNcT3Dp8j5HcxOBqns7WWzZ3XvSw0VUJcgeulyDK5gs7l/okZA9qA1znL3C5lpNk/eYjxwgQSIhv863i68cM0uRrmNHlLgkRA9RNQ/az1reHu8C7+YeC7nIifJlqMcSh6lM2BjQRuwbRPkSRafQFSxUKpMdqyMCyz9H3mZ2vm56U8D/Ph1dHzrPVF6PRdf8mbts2zAyd5snEzvmWWUq4U+tOJGVnaL589yjPdF25pfVEQkAURaUqKfnqCvdSzWJr0j/C/Tx7gP+1+mBrX7S2Xs2yb3mSM3z++75ZIhltRCWoO/KqDiNtLldONT9VwyQpF0ySlF4gX8gymEyQKeWKFPPFC7pbPi0CpHF2TJDSpRFyn/+9SFKocLiJuL3fXt+C5wXHe7VAxTask/S3L6KZJNJWhLlAKcEuiSNDjYjKVQRRLxoSpXAGHWhK2yOaL5IoGJ64NkcqVSnTXNNQgCFDldRH0OBBFAZemllT+pvq+pnvSp40UlwIBAYfkwiP7ccs+QmottVoTNY5GarUmfEoI+RY9zW4XLMsmmc7jdqnvfY9GNl9kNJaitTaIpihTaeyVmTB/p+84v7juYX6m8wF6M5P84fmX+f7AKb60Zi9j+STf6DlC1izyq12P45JVXh4+z3f7j+OQFPbWreH+2k7uql7F7519Ecu2+ImOe6hzlBrXb3xJHo/18em2u/jJjnvImTq/eer7/F33If7D5g8CENTc/Nmez3MuMci/OvqteY+1OzWBiMCn2u6i1uHlmYGTvDl6iVZPmN3VqzgdG+TV4fN8sGkzD9d1cTY+yG+efpZfXPswO6pa3tMyjfcKGWOIo+P/HYBVvg/jkuuIFy7TnXqGrDHKjupfR5VKg7FpFRjPHce0CzS6H8QpVzORP8Vg5g1kQaMr9M/AtulJPUd/+iWqHJtY5fsIpp2nP/USZ6J/hix6qHFuQ6hQWUoUJNxKA0G1k/HcUdp9T81kNYpmgnjhEpoUIKh1MZ3NCDtcrA6E0ZcR7bFsm4lchkQFfQoBzUHY6V7RXIpHUQk75hKIemeIK+lhjse6KVoGTlljp3dp7uu2bXNsbIh/t//FWyIZsiDiVTUCmgOPouJWVPyqA4+qoooSlm2TNw3ihTxpvUBaL069DHMVK3+Zts2p8RH+8MR+fn3nXtaFat6VbKNt26T0Is/1XOSPTr5zSyRDFSX8mgO/quFRNNyqSkB14FIUFFHCsCxyhk40nyOtF0lPTRYShXzFqkUFy+QH3RfxKCpf3LCLRs/i/jsAp+P9/O65Z8mZRVySOu95bPPUzEs0piEKImEtzHh+nLSRxqkpDIzE6R2OYpg2ufz1soRt6xqX5WI7Dacm0z8S46+ePUTQ60IQ4L4t7excfz3AdCl9heH8CKZt0eis56MNH6TZNVdd62ZIgkijs56PNXyI8cIEfdkBRvPjXExe4a6qyssQqx1ufn3nXuKFPKligWSxQLKYJ1kslH7WCyQL+anflzxVDMtCt0xypsFoJkVKX7hpfSGcjPYTUt2ziIYgwHNDp3mgbu0dQzSKpsmJiWEG0nH+7uLJsss7JJmg5sSnaXgUFZ+q4VUdOCUZc0qoIm0UyRSLJIt5EsUCsXzullS/CqbJodEBvn31LF/o2nHbMqa2bZMqFvi7S6d4o8JyVLes0OT1s7O2kfsa2tgUrqPa6V4w0JIuFrgYn+DAcB+vD1zjajxKtJCraF9eRaUjUEWjx0+d20udyzP13Uud20PY4UaT5Xnfa/P9bnVDNQcv9XNxYIxMoUg6l6etto3JZHbO8h6HRmPYjyJJ3Le+FRsIelwYpjnLR23WPgUBp6bQM1ZqoG+pCS5p8i0LCrWOZtb7dtLgbMcrB9AkJ6roQBW1OyrYXCjqPPvaafbu7qShNrDk7axMRkNTaKwOoMgSmiqVImkrpMnbFaznQ01bEAUBv+pkY7CR7vQ4tm0zmktyKTnKz63dy+ZQ08w6PekJjkf7uLdm9czkXRJFBBs0SZlXmrbDW8P7GzfN9G/cU9PBoYnuGaWqaQgs/LmCmot7azq4u6bUm7Iz3EZvZpJYMQOUyrhUUaZK86BJEk2eEKookdRzmLaFzJ1zg71b6Ek9R8YYZk/tb1Ht3IooSNS5dqNKXk5P/jFjuSM0evYCpZpbVfLR7vsIjZ6ScpdfbSdvTpLU+8gZYxStFKO5g1Q5NrEu+HmccinF6ZLqODbx3xnNHiSgdtyS+pUm+Ym47+ZC/G8Yzx2n0VOqU07r/cQLlwlonfjVVTPLP9HaybaaeqxlBA9TeoG/uXCCl/qulF32vvpWPrN2y4o2GEqCMG+0za+4ebBmIztDqzFti5DmBRsyRh6ndGulf9cSUf7LwVfpS1XWFKdJEhGXl/ZAFZvCdWwK19ERqKLW5ZlX4MEGEoUcvak4ZyZGOTI6yKX4BN3JGJkKJleGbXF0bJD/e/ow/2r7ffPKZK40CqbJ/qFe/vjUQcZzmYrW8SgqDW4fncEwW6ojrK+qpd0fIuRwzTtBmCaxVxKTnBwf5ujYEFcTkwykkhWVQuRMg+9eO0+108Nn1m4m5Fi8B+sbvQcB+NLqh1ntq0WZx4T0ZmPSglkgoc+9L04lTuNX/Gzv6uTouT5OXhpEFEWsG5SmutrrWIkr1VpfxU98cNfMz7ppEfLO/qz92UGSeokk7wptp1oLV1ziUCJPVewO7aQvO0DKSNOfG+AuKicakigSdroJO8tnFaf9V9J6iXT0JGP8yamD7Bvuq3h/Y/kkk4U0sWKG3swkwfjgzN8SxRIpvpPczQ3L4u8vneLo2OCCjd8C4FMdtPgCrA1Ws62mnq5QDS2+ID5Vnfd6TuazXIqNczY6xjvD/VyKTTCYTlZMOMZzGZ7vucx99a2sr5rfQ2a5MG2bE+PD/M2F8gQLSr4jDzau4pOdm9gcjqDJ5aeHHlVje00D22sa+HjHBv7u0mn+4cqZiuRyG70BfmnL3extXHVL7426oI+gx4kqyxQNA5/LQUtNkPa6Ku5e18oPjpxHliTu37CKVXVVDMdSBNwOTMtClSVEseRS31YbIp0r8tzRkuLdztVNdDaEaazy45/KWkZCXjwODVkqZcsf3tzBqe5hLg9N8Jm9W5dENHS7SE/mPP3Zy/jkEGEtQliLUKVGCKk1uGUvmuTCITpxSC5U0fGekY90tsjrBy6xaW3je0803A6Vpmo/pmWRnkpD5fW56fGloM0Tvm4IQyniMJ43Sk7Reg4LixrHdU1zr+LAqzhI6nkKlo4sViZD1uQKot4wUXNKCgXz1tRdSv0j13tTpid+0zr3rZ4qnJLKoYlucobOQDZKtcNLm3flnTR/WDCZO41Trsavtc88TAIS9a57OTn5v6cm9ntnlndIIcKOLTM/K5IHh1RFwryCbmVJ6/1k9BHccoRo4SwUSndPwYxj2yYpvQfDriziMg1ZdBDUulBED6PZg0Rc9yAIAsliD4aVJaR1IYvXI3gNHj8NnuVNdWL5HC86Lle0bI3Lw7aa+mWpqVWKwdwkZxN9dHrriThDTBZSnE304ZEdrPc3E3ZUZqg0kcvwP0/s52x0rOyykiBQ7XSzq7aRx1s62RNppspZXmBAAAKak4DmZHM4wic7N3FyYpjvXj3PW0M9XEtEy6b6c4bB/uFevn01xBe6duBRb5/rsWFZnI+N8ednD9OfLk++FFGi2evnnkgL72vtZEt1BLdS/vjEKRJZ4/Kwp66ZjFFk31Av37t2gSOjA4xU4CcQL+T57rVztPmCPNrcseikpDczwd7adTxU11Wx7OlIfpQDkwfnKDH1Zfvo8nURqfbxgQc24HU7aKwN0N608u6/XpeG13X9/dEzHCVb0LlRTzFWjJM3S4ZxLa6mW27mdkgaLe5SjXTeLBArxpd72AtCEARkQZh5JmRBJOC4tczDtdQY+8Yvcy09RlLPcSZ+3dU9refZU92OW65c+vN2w7Atvnft/IJ/d0gyHYEq7mto5cnWNawLVqNUkGGocrjYE2lhT6SFj3ds5LX+a3zn2jkOjvSTrVAVrjsZ5bmeS7dFhWo6m/Hls0dK5oNlUO1089GO9Xx27RaavYEl7bPW7eUXtuyh0evjD48foCe1uHRudyLKD3ou0hWqoc5deU/vg/OIDK1prAbgyR1reXLHbEn/m3++ER/Zs2HO7963/Xpz9MObV8/62yNbVvPIltU3r7IgJEHGKwfImZk5zeCmbRDTx4jpY1xOn5xZ3icHCWl1VKl1UwSkFrfsxyV5cckenKIbUViaJ0uhaJDOFtBUGY9LI5MrkMkufH+MjCUpFJc/l1+RmUl1wMOH7557wVYC2jzRr2mUamAFMsb18pKiZaBbJh5ZuyFKVoqxlOrr5p9aKOLyO/glUVy0lrvTV8tqXw0HJ7oZyMQIOzx8dtVu2jyVR8F+1FCw4qiib1amSBAEJNGBJGgUzNmDlSgoqNL1yayAiCBI2LYFmOhWFsPKMpI9RLwwe6KuiG5ccv0SDPkEHHIVNc7tjOWOktL7UEUPseJFnHI1IUd5l/gfFQzlJnlx5Dj92Ql2hDqYLKS4mh5GkxQUSa6IaBiWxbevnOWF3stla8ZVUWJ1MMzH2tfzoVXrqHYtvR9EEkW21TSwKRzh+Z5L/MW5IxwbGypLNsZzGZ7rucTGqjr2NrbdlgZO27aZzGf4+0unODw6WHZ5t6KyrbqeT63ZxH0NbfjUpU3uBEHAo2g81ryaHTUNfPPyWb55+RRXKvAWuByf5Hvd5+kIVLEmGF7wvLiWMPHUp4xBm13Ns36fM3KownWysr2rCcO0iCWzuJ0q0lTkcTnXKJMrks0X0VSZsej1kr53zvRSV+Wl6YbIXtEqYtilLJBLdiLd4oRRFESckhMBAdM2F/XeuBOwvaqNeleQiUKaDm8ta3zXJS89ssYaX90PjfS7V9W4t76FT3Zu4p5IS0UEYz74NQcf6ehia02EPzr5Dj/ouVhROVq8kOfo2CCD6SStvls3HV4Mlm1zJjrKvqGFG5Gn4VZUHmvu4PPrtlXsvL0QJEHgw6vWYVk2v3X4dWKLlFHlTYODI/28MdjNx1dvXDSrkc4V0BR5TgYhmsyQyhZorAks259kNJqioBvUBr0rplznVQLcV/0hJgpDxPUJMkaKrJEkY6YoWnMd7UvkY5yYPs5VTgOgCCpBtYYaRwPVWgNhtR6fEsQl+3BLXtyyF7lCkZuB4RivvXOJte113LujnbOXhtl35OqCyyfTeSbjlWXWF8PtD4HeJkiiSJ3TR5Xm5vBEN02uEIoocS4+TELPsSnYOBMlEAVwiArDhQSThQxexYkkCKjSrTsBT5uvLKUqpmiZ9Gei7K1dw4ebt+BV7lwpw3cLsujAtPLceEZLTasGtm3MyhTAtPvlwretKMhIooNG996pEqfZD58ietDEwC0fpyr6qHFuYyS7n9HcIYLaWlLFXmqc23HLy1Q9+CGCKipsD3Wwyl3HYG4SRZDo9NZjYmNW2JNydnKUv75wgoK5eKREESU2huv44oYdPNrcsWI9TLIo8oFVa4l4vPzmodc4VkGT5NXEJM/2XGB9Vc1taeAsmAb7h/p45lr5ZlWPovJAYxs/tX4HW8KRFTEAEwSBKqebz67dTLXLxR+fOsjl+GTZ9fYN9XJXbRMNHh/eBcjO3tp1HJ68xtnEIB3eWtySVvaYqx1h7g7vptnVNOv3Tskxq8k6nS1w/toIE/EMd29pI5rI0lIfmtWwfauIp7IMjieQJYnXj14m5C+R28t947MyHFAKdomCiGmb6JZxy02iJdU1Axu7JGhwhwecFFGi2V3Fg7Xr6PDWsMpbU36lOxB+VeP9bWv5qfU76Agsw/PrBrT4gvybnQ9gY/OtK2cr8qkYSCc4NDowh2hkjRLBdUqeJZFm3bL4xytny5ZyCcCGqlo+2rF+2SRjGpIg8mTbGk5OjPC1C8cXXXYwneTgSD8PN7UvWv53/NIA7Q1h6sOzKwViqRzDk0nqw36WW61/6uoQE4kMj2zvpFq9PsafjQ0zkV98si0IcF9t+5xr5ZH93F/9oZLzt5FiojDEeGGIicIQMX2ctBEnbSTJGAnyZhZrHg1H3S4yVhhgrFDKHgoI+JUqwlo9YS1CtVZPQAnjlv14ZB9u2Y9DdM1732RyRfqHotRVl671haujvHHwMvW1gXn7qnN5nWJx+cpidwTRaHbV8HDdVlJGif2u8zWVVXkREKhz+nks0sUbo5f42+6DKKLEUC5Oi7uKu8LXa+ZFQWSdP0JvZpLvD5wk4gzQ7qtmc7Cposm+YZmcjQ9xLT1OXyZK2sjzxshFBjIxmt0htoSaym4DStmWomVwITmMPCCiijJOWaHTV0uzu+qW06ce2cn24Gq8U87M9Y4qwtrKDBbvFgJq55Q61HgpsyGI2FjEC5ewMPFrlacpAVxyDZrkB2w8SgOadHOkaGmNE6Wm8EZ86irGssewbRMbk7BjE8Id1Lx1u+GUVIqmQV92jIxRKPVpqB4M28Irly/DyOpF/uLsUYYzqUWvhCQIrA5U8TMbd/Foc8dtacTeVl3Pv9x6L/9m3wtlS5UKpsnR0SH2DfXykfauFc1qlIwKU/zV+eOky0RCNUni7kgzX9ywiy3huhXPrnhUjUebOsgUi/zhyQNl+0TSepEX+i6zrbaeLeHIvMdTo3npTY/zJ5deYXtVK0HVM2esCyguHqy7nhn0K37886gvtblbuTF4cPhsHyPjCQZG4qxbVcsr71zkQw9uXBbR8HmciKLI0HiCkM/N9rWl0ibLsnHeZAbolT2ookrOzDFemKBoFXHcQvlU0dIZzZfKB1VBxSsvXxb+3cDd1R1z+mp+WOCSFR5tWc2XNt215DKhhRByuPj5TXu4EJvg1MRI2eXHcxnOTIzyVHvXrGdioliajPqVMGEtgkcOzlExWwi2bZMs5nl7qKfssn7NwZ5IM1uql27GdjMEQcApK3xqzWZ+0HNxUUEL07a5OHWubjRWnEauoNM3GuMHBy6wsT1CWyTE+lV1eJ0aA+MJhieT+N3OmXHHNC2OXOgn6HMSS+aQJIEtqxsxTJOe4RgTiTSqLNFQHaCh2k8slaV7KEq+WNqPpsy9p49O9HM2PgxAxigylE1Q4/DgURzolklfOka7r4p7a9sXrIkRBQmvEsCrBGjzdJXkvq0s0cIoE8VhJgvDxIpjJIwYaT1OyoiTNVOY9txgnI1NXJ8grk9wJX0KAJfkJazVUaWW+j2CajUeOYBXDuCRA7hkD5Igs7q1hi999n5cjusltpu7Gnn6ia34PXPf38PjCf7XV19f8PpVijtipNgcbGdzcO5N9kiki1bv9WiDIkpsCTXT4i7V43oVB4/Ud+FTXZyND5IzdbYEm9kVbqPRPXuCubumHVEQOJ/8zlnUAAEAAElEQVQYZrKQptEMzkx0dle345LVWTXu6/ylB08ALGxG80kuJkYxbJPH6teTNYqcj18lVzxKm2sj9c4W7q/ppP4GPfgah5d7ajpo9VRh2TZXkmO4FQeJYpZTsRI7zZpF3hm/xs+teZAah3fWi9q2bYp5nf4ro4wNxkgnshh6iV2qDgWXR6Mr0sQjrVvx+N8bU7xCrkjvpRF6Lwzhr/KwenMzgbAHy9Yx7QJFK4mFjmHlKFgJRFNFEjVEZARBoNHzEKPZg1xNfptmz+Ookp+cMcblxDfxKa3Uue66pePxq+2EHZuYzJ+mN/UCYcdmJFHDtPJkjVGC2hpccu1UuZWJYRewbJ2ilQJsdDNJwYwjCiqSMFsBwilVUe3YxsX419DtNB6lgYA2V3EpNp7iwtFuJkYTOF0qW+5dQzgSWOaZvjNQ4/BT5wyQNYo0ubwUTB3DLqnYVGnlJ0mHRwfZN9xbtuk47HTzqTWbeaSp/bapPQmCwF11TXy+axu/feSNmV6qhdCXjrN/uI/7G9oq6hGpFAXT4I3Bbo6PL55ZEYC1wRo+tWYLm28DyZiGT3PwcHM7l+ITfP3iybKR2ZPjw5wcH6EzEJ63R+Tg5FV8qpNYMcNLw2fn3Ua7p2YW0VgIN0+2ovEMW9aWAj0r1YQ83ZuhyBLVQQ/NdaV3iSpLSDeFTWsdNXgkFzkzx6nEWTb411ErahVdG9u2SepJTibOAOCW3dQ7fziyo0ejPeRMnQ5PDY3uEJoovyueEMuFLIpsCtfxU+t3rDjJmEarP8hPrd/Bv3jz2bLPTs4w6EvFmchlidzQp+CUPNi2xVi+n4yRxC378CtV+JUwili+D+tCbJzRbPmSlxZvkLsjzSuueCkKAo0eH/c3tPKPV88tumxvMsaZyVEeaGybk9EzTJOJRIaJRJqRySSqLLG6qRqckMrkeedML4ossboxjCyJ6KbJN187yY61TSiyhCKLbFhlMjie4NWjlwh4XWRyBXqGozy2ay1nro1w5EI/kSofI9EU1YG52er7Ix1srirJZ/+g/xy1Ti93hVsJO9zkTYPD472kjMItjT6CIOCU3DS4VtHgKgXFi1aeeHGSaHGEyeII0cIYCWOSlB4jZcRJG3GK1vwqlFkzRV82RV+2VC6uihpBtYYqtY6QWkurey0b/LtxOhScjusBHL/XQUdLNauawzPWFDfC4VBwaMsvhbztRMO2DWwsBGSEW0wLf77j7lk/a5LCI5HZLyOf4uSRyDoeiaxbdFtB1cXjDRt4vGFuL8lHmrfO+d3dNR3cXdMBgCrIPFa/nsfq189aJqf305/4M8YzA3RU/Qc6fLPTyK2eMK2eEilK6nm+13+Cqqm+jCrVjYXN6dgg/+3MDxjLJ6l2eGZelrZtExtP8fp3jnBi3yUGrowRn0hRLJRqlx0uDW/AxcbdHTz1xQffM6IxNhjje195g/3PnaK2KcSn/vnj7HqildFcqUcia4yS1UewbB3bNkolSK4dhB2bkAUnYccmVgc+QV/6FS7Gv4YkOEvkxDZZG/gsXqWybNE0nHKYZs/jmFaBoczbjOeOIwkqFiamlUMLfg6nXIMApI0h+lOvULQSJIs9WLZBX/pFooXzqKKXBs9D+NXWmW2XmsI7UaUAeWOCFs/js/pFZs7JwCTf+fLrnDpwhXBdgHAk8CNENAK8v34nNvaMCdlILoaJRZ1j8TrjrKHzjcunSRTm1qbeCIckc3ekmQ+vWrciZUGLQRZFPrRqHc92Xyw70Tcsi3PRMU5ODM8bfVsKbNtmPJfhG5dOl821VTldvK91NbvrGm+71G6dy8vjLZ0cGO7nSmLxEqq8abBvqId7Is2sDs5tyn64bj13Vy8ugbxUOdRw0EP34CS9Q1FkWUJTZTR1ZXoEwgE3cL2co7W+iptPe7u7lSotxEQxyrnkBQ5OHuH+6nvwK/5Fr5Fl2yT0BAcmD3M2cQEBgbAWYrVnZe6r243JQoZziUHOxgaodfpZ5a1htbeWkOa+o8u/wo5SAKMrdHtLvh5qWsUqfxWXKzAgjeaz9CZjs4hGtdZAWKsnWhjhfPIwfZkLeJUgtY4mml1rcS2S+bKxKyoHFQWBJq//tp0LpyzzYOOqskQjpRcZSCdIFQsEtNnjgNflYM/6Fl47epkHtrSzfe31+UBXWx2jsTTne0ZnrSMKAi11QfZsaAWY8sIZ5+rQJE/cVcOQYTIwlqBvNMbQRIKWuiAfuX8j333zDNHkXHLW6rmuUPmnF/bzk513sSXUOFN1sz5Yx2de/+ochdJbhSo6qHE0UOMokRrTMkgZMaLFMaLFMRL6BAk9StqIk5rKemSMBLo9NwtetAqM5vsZzZdc4CcKw2zwz5UP37yuEcMwcSwwZrpdKjs2teD3Lq/M/7YTjazeQ9GcwK12okqVS4r+qMGwTJJ6nhqnD6dU0rVPGwXGCymckjIVUbh+m1qmxfe/+hbf/fIbZNN5BFHA63cRrPaBUMokxCdSjA/FcHreO5WPxGSKngvD5DIFhnsnGOqeAFpK5FIQcSsR3Mr7Z61j31A3Kgoybb4P41GaiRcuoltZVMlPSOsi7Ng4s5wiemjyPIR1UypREjRqnNvxKs045DAgEHSsRZV8TObPkNGHsNCRBRduJYJXaUFAuuE4LERBJqB1ENA6rh8jNtg3R90FFNGLR2kgLziovkH96p8KYsU0p+M9DOViGJaJQ1LYHGxjtbe+7LqnJ0Y4Pj5UNptR4/LwY52b8Gu3v4dJEASCmoNPrN5YlmgA9CRjnBwf5t761hXRv9cti4MjA5wvo74lAJuq6nioqR1XBcpSy4UkiqwOVPFAQ2tZogFwdGyI3lScVf7QHHJ4b82aBdZaPrasbeDEhQGqAm503WTH+mZCvtvj4dAzHEWSRDoar5OpiLOOtd5OBnLDpI00L42+TtJIsd7XRaMzgl/1owrKjGGibuvEinEGc8OcSZznYPQIeSuPT/ay0d9FxHF7pE5XGk/Ub6TLX8/F5DC9mUkmJ65xJj5Ajeaj01dLh7e2JLByB2U5NEnirrpGHm3uKL/wMuFRVN7ftoY/OF6eaCQKeQZv8hGKFccYznWTM9O4JC9BTw02NlfTp/Er4cWJhk3Z8QRKJWRNHv+CvVXLhSxKrA6EcUoyuTL9eGPZDEOZ1ByiAYAgYE2ZjVbSAyWKAnVV189PyXDPpqgbpHMFfG4HLZEQLocyvXmEqfUEcfFta5LMufgIdU4fIc1F3jQ4Ptm/ZCGBxSCJMgG1moBazSpKQe6cmSGlx0joURL6dLYjRtpIzBCPlBEnZ95MmOYPY7U2Lt6f5NRUPvr4lmX7Et1momGTKp4mU7yMJtf9CBINgTkhrgXgVRzsqW7nZKyfr17djyLK6JbBZCHNY/XriThnR8AGu8d59mtvk03nUR0K9zyxia4dq/AG3CWikS2SSmQJ1fgI1wVu0+crD1/IQ/PqOoa6x4m0hmlsr8EhV9HqfbLibUiCSp3rrkXLpFTJO+82ZdFJxLVn1u8EBDxKw4yr+ILHrrbQFfpCxcdp2xYFM0beiBJydOFRGyte90cF/dkJjkavYtkWcT2LV3YQUD1liYYNvNB7mXgZMydNkthaXc+2mvLEZaWgiBJ7Ik3UuTxlpV3TepEriUlGsymalll6YVMqm/retXNlsxkhh4uddY20+9+9MTTsdLGlpp7g1XOLqsdAyVfgQmycHbUNcyYLV1KjFOdxA78RTkmhzXPrkdX6aj9Bn4tt65rQFBmf17lsD6dYKkf34FxydeR8Py2R4CyioYgKe6p2MZgf5mT8NDE9zsujr3M+eZkGZ4SA6schasiCjGGbFKz8DNEYzA1j2AaaqLHR38Xuql3IPyR9D05ZZa0/wlp/hIl8itdHL/DC8BmKpsEqTzUNriCP12+g0RW6Y8iGX3XwdMeGiiSglwsBgb0NbfzB8X1ll03pRcZuGndSeoykHsWnVFHvbMWnVCEKEjkjjVRGNdGmFBApB7/qoOk2egOJgoBfc9Dg8ZVVsZvMZxnLZuiaZ3gTKCmbHrs4wHg8w90bWvF5HBw618uJy4MMjid47fgV7upquU4ebgjaaorM6sYwPSO1pTmWAE5Nob46QP9YnFNXhnl2/zmuDk3gdy8+oX6ysYtXhi5yKTGGWy71K8aKOT7Rtu1dcY9xSm6ckptqRwMFM0fWTBMvTjA+1Sw+VhikYOXnIRoLw7Jt0pk80XiWQtGYV5jV614eGV3yqGZaORKFo6SLZzGtHJLoxq2sxu/YgSx6yBQvE829QSy/n6IxgWklkUU/TqWNkPM+NLkWyy4ymv4OXm0TRXOSdPEMppXHq20g5LwXUdCwbZt08Szx/CEMK4kqVeHTtuDR1s9IoupmgmThKJniJSy7gCR68Kjr8WmbkMRSSVFO7ydZOEbe6MfGQhGDBBx34VTaEKfkErP6NeK5dyiYY0iiG5+6Cb9j51TJl41p5Ynl95MunkdARpWCYBtQQZOWIko8EllHxOWnPxOjYOpoksyucBubgk1zSgdO7b9MMpZBEAVaOuv4zK88QcOq2S9iy7KxLRtJfu9S1bWNIT7w+ftYt72VqroAXTva3rNjuR0oqcKkSesDFK0Ug+nXsDGJuO6duW/+KSFv6oQ1H+t8jYwXkmiSUlFt/Fg2zdGxQXJl/HU8isbjLR3viifINARBIOhwsrWmnud6LpVdvi+Z4GoiunyiYdv0puIVlTm0+oLsqGl8V/12ZFGiyeOn3R/iyFh5yd1T4yNMtmTnEI2vXXubycIiBE6AVneYX+16/8LLLICTFwepDnloqAkAcPB0D50tNQR9Sy8lHRqL8/LBizTeZFA1OBafFSmdRoMrwuO1D2HZFmcS5yhYRXqzffRmS0Z4AgKSIGHaZilTegMcooPtwc08XvcwdY4fHgUn27YZyyc5nxjmQnKYnFlkV9UqahxeREHkTGyAv+s5xC+vewzlDhDLkASRNcEw22sXDz6tJFp8Afyqg0Rx8VLRnKHPCcCE1FpCWi1uyY8gCBTNPJJgs9q7BadUTvXOZrgCLxynolBVxmxzuZBFkRqnpyzRyOhF0sX5+w8EQeDh7avpGY4iSuJMbFeRJNY0VdNaF8KplbKGsiTyvt1rCd4QgZckkabaIPdvbmdooiT6ocoSmiKxYVUEWZLIF3W2rm6kJujB7VyYiN5btwqnrHApMUZCzxPSXOyqaeXe2ttT8jjdNJ42EqUvPT7z/4yZJGdmyJtZcmZ66nuG/C2QjHxB5+T5Qd45fo3RidSCROOXfmJv2ezHYljS29yydVLF0wwl/xqX0oEoKujmJDlBxW9vA0AQZCTRA4gggCT6kMUAsuiZUemxbYOxzLMkC8fRpMjUtvMYVhzbtkGArH6VvsSf4JDrkQQXOb2XZOEUDb7P4NO2YFo54vlDjGW+h0tpRUCiaI5RMIPYlPoxdDPGWOZZ8kYvqlQazHNGDx5rHdMppZw+QH/iLxAFFVn0oZsxBpJfwcYk6Lwby9aJ599hMPlVnEorihgiVThLqngev7alovMW1NzcW1OZilLvpRGwQVYk1m5rnUMyoJTqo0yq73ZDc6p07Wj7kSMY07CxSOsDnIt9GcPKIwoyLd4nCGq3rxzkToZDUvDIpZKmgewEeUunw1NeseT42BCj2TTWIrF7gZIR1l11t9aXsxLQJJmt1ZURjaFsqiLn23IwLYt9Q71llaYkQaDNF2RtaOVN6coh7HTRHqiqiGhcjE8Qn2dS1eyuwq/OntCYtkWsmOFichhseCyycc56leB89yg20FATwLZt9p/opjroWRbRkCSR+mo/u9bP9vCwbHsmYnojBATWeFejiip1jlqOx08xkh/FmioRtbEx5pR8SjQ5G9gW3MzO0DaanO/eBHgl8OLwGY5Ge5AQqdI8bAk2s8YXodbhQxJFtgSb+flDX+MX1z6CwntPNByyzP2Nbe9KNgOuKy+1B0JlAwl50yBVLGDZ9kxVg0cJzFqmP3sJvxomrJXP9BqWveCk/UYooohLub3BMlEQKjI5LZom+UXKqzqba+hsnj0HurFf40bs3Tq3NM6pKWxsj7Cxffa7qjrgmbcBfCE4JIV7alexp6aNgmmgShIiy/PtmYZhGWTNJAk9SnLqK6FHyRgJMmaKnJkia6Sn/p+eV5HqVnG1d5x/fPEEE9E0q5rC1FX75i3SWa6vyJLWtm2DrN5N3higJfALOJVmDCuFbRtIYql5zim3oHnqKZrjZItXqfN8FKfSMpWFmB2BLxijVLvfj0ddB7YFAjPR4uH0NwGReu9nkUU/eaOfvsSfMJr+Hj5tC5ZdIKtfwbCSVLs/gCbVYViJEtERSqxWt+Kki+dwyo1EPB9HEl0UzSiqFEaYOgXj2ecwrDhN/i/ilFsx7Qzdsd9jMPU3BJ13Y1o5xjLfRxGDNPn+GZLoIp47SLJwrOz56k8meOHqZbrjMfyag4+tXU97aPESiHSiJAkniAKhmtuX3vx/WBzClFlfg/sBQMCtRAioncjiP00PlCZXmIDiRpMU4nqWtJGjs4L+jKNjg6TKvPxkUWJtMEzVInrqtwuKWIp4VoJ4IcdwJk3RNJfVp2HYFm8N9pRdzq85aPeH8Krv/j0X0Jw0VqivP5xJMpnPYlrWrD6Np5t3zVHfsbBI6wUuJId4bugkLvnWJoCxZJZj5/s5fKaXi92jvHOyG8O0SKRyyy6daqwJEPA6qaua/blVRS4Fd+aBKIi0e9oIqUG6fGvoyfQxlBtmshglZ+bRbR1FUHFJDqq1MPXOCG3uFlpcjXiVHw5J2xth2TarvaVejHZvDR7ZMav0t9Ed5MG6tUh3SNmUU5K5v6EN09ZJFq+RNUZpcN9/y9sZzR5GFh341Y45Hk83Q0CY1eC9ECzbpmAamJZF3JxgPD8wZ5kLySNs8O+piGgUTKOsESqUqiwc8m0mGggVPdsF01iUaNwpMG2LoxN9HJ8cJKnn+dLae0noeSbzGTYG55f3vhm2bVO08iSNKIniJAm99JU0YmSMZMnUz0yRnfp+s6N4JVBFBwElTFCtIahW0+qa3x39at8EqXSeJx/cwO4trTimMkM34z0pnRIEBY/aiSrVMpD8Cj5tK37HTtxKx0y2QhBERFsqEQtBRBDkBUtNPOpaPOo6VGluaiaaex0Rjd7EHwFg2UWy+lWK5gS2bSGJTjzqWhL5Iwwk/xK/thW/thOH3DBzLKpUhVfbSCJ/mIHUV/Fr26dKvHwzSljx/EEKxhCDya8iTB1nRr9C0RzHtk0su0C6eJ5676dxKqVIl1tdi1MpH8mfzGZ5pfsa7wz2U+f2cE9T86JEwzRMivnSzSXAe1oa9U8dgiDikmto833wvT6UFYduGciCVLrJ7JKmvyYt/lLwKS68shNJENkTXoNpW7jLOD/nDJ1z0TFyxuIDpipJbKqOvCu1rjdDEkQibi+yIJY1uTIsi/Fcmnght2TzvpLWfYFTE8Nll61yuGjzh96T8+KSFcIOF+JUQ+ZiKJgmw+kkedPAfYME583ZjGmENS8B1cXpWD+vDJ/lkbq5ioALQVUkImEfdVU+wkEPzXUBLCDsdxMOLs9Q0ePS8EwZ82VyBSYTWTxOlbqwr+w1CKoBgmqATk8HCT1J2khTtHRMTGRBKnllKB78ih+H9N6JeCwXd1evRpNkNElesHTyU627K/Z+uJ0QgDq3lzZfENvWyeiDxAuXl0Q0EsWrqKIHr9IClCEaAvgqDA4Yto1uWYzme7maOkVQnS0KkDbiKxLBvhG2zVJtpW4JZpnxlKnDsCsgR+819o1e44WB87hklRcGL/ATHbsomgZfu3qI39nx4TlPwrRRX2LK9yJeLH1PGXGyRpqcmSZrlr4XFjDsKwdJkAmq1YSUGoJqLSGtBr8Sxi15Z9zDFxIPyGSL+LxO1q+OUH9TqehKYklEo2RetpbmwM+QKpwiVThNPP8OQec91Lo/glSG6c85CDGIyHwkxKJoRgk57sWrXk+t+7XtKGJo6lhU/I7tCIIydRyHiOX2E3Y/SpXzESRRQxa91Ljfj1NuJFU4x1jmWaK5N6j3fhqPug5BEDHMOJoUwaOuRxRKLwCvuhEBhemZmGXnkcXrF0wUZGRxeS+1VDzL8bculHwy4llS8SzpZI6LJ3oB0HWD179zlKtn50Y5ahtDPPz0LppXz6+9Pto/yevfPUb3+UF2PbyeXQ+vx+N3UcgVuXyqn3NHu5kYipNN59GcCv6Qh+bOOrp2tFFdH5zDbIsFnXOHr/H83x6Yd3+BKi/3PLmZjbuXpuph2zZXTvdz4VgPg93jZFOlMgxv0E3jqhrWbmulde3CUYNMMsdb3z/BiX0XaV/fyH0f2EpdcxUTw3HOH+2m+9wgsfEUum7idKvUNIRYt72NNVtakNXKFFKmfUPOHrrKUM8EhVwRzalQ1xxm/c5VtK6tRxDFsuoVdwK+N/QKH4g8iCop6LbBM0Ov8XTT44uucy09wmA2yt7aDbjlyl6iA+kE47lMWU35W8kqrDSmSx28qla28RkgUSgQL+SX5RJ+NTFJooISh6DmpGmFXHtvFZIo4lZUXLJStsQLYCSbIW8YFZeoSIKIR9G4mCpPuG6E26nR1R7BBkJ+N7Uh70zKf6Waj4fGE7x94hoDY3G2r2vC5VBRZIktneXLnFyyE5fsBH44VKRuFb2ZCTyygyZ3aN6+oWlD3TsBsijSFapGk2QMS8fGJFns4ULsawCEnZsJOzaR0YcZzR4kZ47jkKqoce7AqzYTL1xiLHcM0y6SLHZT7dxMvHgF3UwRdm5Bk/yMZA8gIBN2bkYSSve+APgqVHSybBvDttBEFw2uDppds0tzC2YWRaxsW5okI1USMLHNssGf5cKy7YrGDVkUy5o03wl4YeA8G0MN7Aw388bIFWyg1unl2MTc+RlArDjOM0NfIWemZ/oncmYW3S4/7s8HUZDwySFCai1VWi1Vai0BpQa37MMle3BJXlySB1VyVNQ76fNoiAKks0s7nkqxxMIrAUl04td24FHWkjP6iObeYjL7Gm5lHf4Z2U+h9FWGOpcyD/OdFAFNqkWRgtR5nr7pL8JMNkIWfQQde/CqXeSMPsYzzzOZfQ2X0oFHXQMIOOR6VKkar7aFnN7DQPIvmcy9gSbXo0ohFKkKWfRQ434/shi46fhEBEFEFv0UzOuycZZdRDfjSBU4Ii+E6FiCZ//qbfqvjFEs6BTzOnrxeuTCMm2unRvk2rm5NdKr1jew48EuWKDtI5XIcurAZY69cQG318n6Xe0koxme+/p+Th24wthAlGw6j140Shr0U2Tjx3/t/VTXz/VEMA2LoZ4J3vju/OVitU1VdGxsXBLRiI4meOarb3Fy3yXGh+Kk4tmZ86A5FLxBN5GWKu56ZAOPPL0Lb3BueU2xYHDpZC9vfPcY44Mx1mxtYahnnJe/eZDLp/qJT6TIZ4tYpoWsSLh9Tt585jjb7lvDx770MN6Aa9FJSmw8ycv/cIj9z59ibKBEDA3DRJZF3H4X+547yZ7HNtK2rgFlHofROw1XUn2YdSYCKrZtczndU3adlJ5jLB+/pf1cS8TI6uVfaLIo0uJd3IvjdkIUhIqJRkovkNKXPjjbwLnJ8hKUUHLrXg6hWS5UScZZIdGYzGcpVFACUSofMLicGuFYtIcqbWmfr70pPFOmZVmld4worgzZGBxLkMjkqQ56iCVzJNP5Zdcq/6jglZFzbA020+S+85UkZUFkbfB6fb9lm1iYhJ2bSBZ7GM7swymFmcyfJm9GqXFuJ1a4xHjuOKIgM5o9giAIhB2bSOm9mHYRTQowmHkdj9qMKvkYzLxJq/f9M9LpJQg4KhS1sKekWyPONuocLXMi0JuC9+GUKisplUUBt6KUDWLopknWKP9MLwembVUUTHFIMq7bXMa1EpgoZFjrr6HNO1vGeyG/p5yZ4ULq6JL2JSDikf1UaXVUqXWEtQghtQa37J9Sn/LgktxoU4JHSxnz1nfWc+HaKK8duIgiS7Q1VeFyqivuX7WkUdO08qSKZwALh1yPLPoQBRXTysKs1I+ILHrRrSiZ4kUkwYUgyMiiF1G4MeK1EAkRiHg+xlDqb4g6duHVNmLbBgVzBFFw4tM2YVhJ0sULCMhocm2pHAoJ086V+j2AvDFIzuhHEX0oYghZ9GHbOpZdnNq3QI37CfoTX2Yi+zJB572IKOTNIbBtAs5diIIDv7aDyeyr+LStqFIVsdx+svplHHL5ZtiFoDlU2roaCIRnDyznjnQzMRxHlARa1kRoap8bGattCuGvquwFnYpn6b0wzIEXT7HvBydJJbI4XBq+oBujaJBKZEkncqQTOWoa5p/sKarM+p2r+Knf+DDZVJ5sKs/4cJxLJ3qZGI7f8mefxkjfJH/5O9/n2JvnScWyCAJUNwQJhn2YlsXEcJyxgSgTQzGGuieIT6T46E8/iH8eBZhpRMeSHHjhNP1XRzlz4AqWbVNdH6RhlYtCTmekb4LYeIrYeIqRvkls4HO/9iTyAgQhPpHi2a/t47m/2Ud0NAmAx++ksaEGSRaJjae4fKqPsf4oW+9fQzJeufLDe4WSaWEWp+QgY2RnmlcXg08pkbHT8R7Cmg8Q8MoOPIsYrvUkY2QriJypovSeTqhFBBwV9lxk9SKZCibeC8G2bS5Exyta1q0o8+vLv0uQBQG1QrWreCE3xyflP536NtGbVKdsKEkkF7NkzSKfbJ0tUV0pLvWM8ebRqwyNJzANi6qAmx//4E4i1cuPphcNE7dDpTbkJZUtTJV3LHuzPxJI6wUsbG63ceRKQBRFWnyB6z8LEm65jirHJkRUUsVeEsVu8mYUj9pEtXMrpl0kXrhMongNw85SpW0g7NzMeP4YkqDikKpwy/Vk9GHARhbcuOX6GSXMmX3d4rHeSCYyRoKJwjCSIFGlRXCIlfauCTR4/CTKeGmk9SIjmfLqVEuFbdsUTZP+CoQzvKr2rvgmLRerfdW8OnQJp6xg2haThQzf6T3F9qrlSd0LCLgkLyG1jmotQliLUKVF8EyRCofkwiG5cYhOBMQlB1KOne3n6989NPOzYViMjCdJZwucvTSMz+NAlqU5DeG/8Ln3QHUKLPJ6L+PZ5zDtPAKlaH+1+wnc6nUHWEEQ8Dt2kNGvMJD8SyThGwSde6hxfwCtwsl5jedDmHaOkfS3GEz+FYKg4JAjVLtLNfOWbZApXmQy9zqWXUBAQhFDVLsex6m0ACUp3ljubVKFM9gYiKhoci1h54PIYumFFHI+iGFnief2M555DgQBRQxQM7UfSXBR5/0oxfgY3bH/jiT6cMkt+B07l3YKp1BV6+ejP/0QpjH75fx/fuObTAzHkWWJHXvX8cRn7pmzrqxK+CqsRx4dmOQf//x1rp0boLG9hnue3EJbVwNOl4pt2eRzRa6eGaD34jAtnfNfG0kWaVhVQ1UkgKmbGLpJ76Vh8pnCkolGPlvg63/wPAdfOk0+W6SpvZaPfekhWjojqA6lJG+cyHH6wGWe/do+xodjvPrtI3gCLj76xQeR5PknQJMjCV77xyPoRYMt961h74e3E2kNo6gylmkxOXI9g5JOZHn+bw+w9yPbaFvXMLdkLK9z9tA1fvDXbxMbS+H2Odn18Hoe+ugOAtVeREEgnyty6VQ/L3/jIO+8eAajjIzrnYA94a18rfe7OCUHWSPHnvDWsuvkzCLnE/1cTQ/jEEtlhXvCa7grvLAK10AqQb4CojGZz/KFl/7hVj7CiqJomgykk5Uta5kUzcWNBxeDDRUZ4QG8M9zP51/85pL3tVzE8jkm8pUR57yhzymRE+ZRZREAh6SyylPDXeF2toZal3Rspy8P01DjxzBMGmuD9I1E0Y1br3OeD+GAm1OXBzl2cYBC0WBday33bf3hcO6+3dgVbmMoG2csl6DOGbhjvDLmgzTlgD0N27bQrQylcugipl1AE31kkNCnpEENK4eNhSp6sWwD0yoANoaVxxKNUqO36x4GMq8TL1ygzrULRfSs2HkYzfdxIXmEvJnBtE38ShVd/rsIquUlkAVgXaiac2WIRryQpzsZrcgEbykwbZuBTILJfLbsskHNSfg9EAG5VTzdupVvdB/jN0+8wEg2yb8/+iyrvGG+0Ln7lnroHKKLKrWOakf9lAt8BJ8SwiG60SQnDtGJJjkRWVnTS4FSQG0aqiLRUh8qhfoXiaIs9wiW2KOhEXLej1tdg2XrJZ1w0YUq1czpWXAp7TT7f5qiOQm2hSKFkMXgzHY6Qv8OWfQt2NehiCHqvZ+iyvUgppVDEEQkwY0mlyL8sugl7Hp0SoHKKGkpC15UuWbGQ8MhNxDxfJyw6zFs20AQZBQxiCbXzDSoK5KfWvcHCWg7MawSy5dEx4zsriCIuJQO2oL/At2KASKKGEAQJOw5DtKVQ9HkeTMIzqkuf0EQ8AbcRFqWV7vee3EY24J7ntzEkz9+L21r63F6NMQbyg46NzeTTeVxurV5b25BEJAVaVb0OhXPoC2iO10O+547yZHXz5PPFglHAvzCb/8YnZubcbiub9OyLJo6anH7nXzlv36P6GiCQy+fZdOe1azZ0jLvdvWiQWIyzd4Pb+PD/2wvq9Y1oGjyzOdqXVdPTWOI//alrzBwdYxULMPR1y/Qtm5u/XV8MsWr/3iY2FgKVZPZev8aPvsvnqC2KTRDdGzbpmVNBF/AxT/88St0ny/vjfBeY3twAxFHNWkji0t20uAsX0/e5q7hx9senPW7gLr4C2Iiny3rBg6lZuL9w31ll7sTYFgWhrW8Ce3wTW7AC2E8l2E8d+dnyKB0Da2bzssXO/bO2xAqCAIOScGvOFGWaFSnGyaNtdUUigYbV0eYiKdXrKm0JRLksd1rudw/jmlarG6upql2+aV9NjalfzaicOfXpc+HjFHklZFzvDpynrDDUxKVmMKvdj1BSLtzJo2iIMyaxAqChG5nODXxR+hWmqC2Br/WgYXBYOZ1jo7/LqKgUOe8i4C2mowxwnB2PxP5k2SNMTxKSfnJrdQjIpI143iV5hX1VRrJ9aJJLtb7d2Njcyr+FoniRGVEQxDYFK7jW1fOLrpc3jToTycZyaYrUse6VRRNg2NjQxUpYNW6PDS4b08fWt4wOD8xzvGRIYZSKbK6XlGD+jQ+t2kL66tL78ZmT5DPduxkJJtkspDBqziodXppci88LiiiSkipo8bRQI3WSLWjgYASxiG50EQHmuhEFR1IgnzbCfuaVbX80k8+WH7Bm1C9SPVIJVii6pSEKlejytVllxUFFYfcgEOeO4ETBGlWBmT+fQkoUhBFmv9CioKCJtehyfM3RANIohOn2FJGI6JEWmR1oRMqIAoKTqUFJ/NPbu9kFHI6qzc18egndrN2ayuyMjsTIIoCHr8Lj//2GvjcCL1o8Oq3jpCMlojdBz5/L+u2tSKrNx+bSDDsZet9azmw/TSnD1xh4OoYJ/ddWpBoAERawtz7/q10bGia83llWaJlTR1b71vDaH+UYkHn8qn+6Uq6GVimxfhgjBNvl/wVahpD7P3QdiIt4VkN34Ig4PY6ueuRDZw/2kPf5dE5Wao7DS7ZQbunGdM2EQWRaDGBu0y/kQ2LemHcDNOyiBdyy56U32kwLatss+ViMCyTaAWRvh826JY55/6od92+vpugz4mmykiSyPP7ztM3EuPRFbrXHKpCyO+iPufD53YQCfvnlc69nL6KaZs0u5pwLtKEadoW55MXeGv8AMOFUSQkGpwRdoa2sdHf9UNFOhpcQR6JrMewTCRhdilHpWV2iyFvxMga85cWupUImlT5xEcSBPxT6k+SoFHr3EVAWzvTO6pKfmTRSVBbh0uuw7QLiIKKJgWQRRcR192EHF1TywuokhdZdCMKEoaVI+hYgyL5VnSSqNtFHKKTKq0U6JSQK1adEoB761tRRBG9zLNwLRHlneE+nupYv9xDngV7qgn8e9fOl13Wrzpo9QVXvHTKsm0Gkgn++Mgh3hnsJ1UslqR/LXuOceZieHRVxwzREAWBepefiNOHYVule5+F+yOqtXq+2PafcEguVNGBKmooooYszC8je7vhcqq4lhEYXiqWRDR00+SdgX5+8+03qHG7+cnN23iobRWJfJ53Bvt5q6+Xa/EomWIRVZJp9PrY2dDAA81t1Hu9FZ9gG3hnoI83+3o4Pz5OvFBSIQq73GyoruH+5la21EUqrhO1gSvRSd7u6+X4yDDDmRRFw8CtqqwKhtjT0MSDratwypUzy4xe5OTICK90X+XC5AQZvYhbUVgdCvNw2yq21tUjieIdUcu6/YF1tK2rnzPpfq/Qf2WUoZ5xTKPUnH3v+7csqP4kiAL+kIfOTc2cPnCFZCxN78VhigUdVZs/ktS5pZnmzroFP68oijSvrkNSRChAfDx5M8+gkNfpvTQyo4AVjgTYsLt9QVUpl9dB29p6QjU+xodit3Q+3guUMoAyRUvnhZG3+EzLhxZdvj87wfcHDwOQ1LOYtsX7G3Zwb3XXvMsniwXyhvFuqCi+q1huvX40nys7CfhhxGLnpDczwalYP5OFFIIgUOvwsSHQRKNr6Q3FD+7qRJUlGmoDXLg2ygM7OqgPr0xktHtwkhfeucDAWJx7t6zi2mAUTZW4Z/OqmWUMy+DN8f2cTpyjSg3xVMP7WeNdjSLOHpMs2+Lt8QN8a/C7JPU0+lQlQE+2j4upKzxQfQ9PRB65I+RgK8HmYBNd/vk9HVxl5K4rQax4ld706xhWjoIZRxadWLaJKCh0BT9JtTT/eDPv8SjXm1sFQUSVfKjS3HtEFh3I4tyyYVXyot5EbCbzp+lLvYRl6zRrjyMLKztJ9itVXEwdJVoYQbd1TFvHJVd+X0fcXrbXNPDOSP+iyw2mE7w11MsDjW2EVtAlXLcsXh/o5kp8cUdwKDmob6iqXdE5km3bTGSz/Ld9b/JGb8+y1LXMG8bpV4cusbWqkaDmmnG8NyyTN4av8FCkc87cRRUdNLlW37GlhdlcEdOycDvVmeqWGxFLZEtVNW4NaRn+REt0BrdJFQtcik4wmctyOTpJo8/H/zlykLf7+8jqRYqmiTVV+3d6bITX+7r53qWL/Oz2ndzT1DyvJN6NGEwm+e8H3uKdwQHSxQIF05y54LIocnCwn+9cPM8jbe387PadhF2Lp2pjuRzfOHea71w8z3A6Rc4wMCwLe8qN88TIMM9fucTfnj3Fr9/zAGvCYaQyEaahVJI/O36E565cKrFlw5hx9zw+MsKL167wvvbV3N3UvCxjr5WArEg0d9bhm0et6b1C97kh8tlSQ21tUxX+0OL9JqomE6wpDbaWWerdSMezhGrnb/yMtIQJhBffpuOGMrFCYe5gVCzoDF4r1boqqkyo1o83sPA5FASBqogfb9B1xxKNfeNH2Rpcz5sTh8mbJUUQwzY5m7xadt12Tx1faH9kZp1Dk5coWgtH2rKGvqzI/48qkhUosfyooGga/OmV13hx+DRpPY9hWwiUxvGA4uaJhs18ru1e1AoVem6EQ5WRRJGQz8WO9c1IorCsF+KNGImmcGoKu7paKBQNdMPCMGeP48P5UQayQ4wXJkjoSQRBmJcsDOVH+PuBb5PQr/cA2dgUrSLD+RHenNhHWKtid9WOFTn22w1ZEDkweYWXhs8ylIvz7zZ8EEUU6c/G2FHVirOMH085hB3r8attDGTeRkCg3n0Xtg096Zewb8FPQgBUUV5xDxq/upq1wQgCJeIirHA2qtW9DrfkY7TQhyTIRJxtBJXyZVNQegdpksRTHevLEg3dsnh7qIdvXTnLT3RtKzsvqwSWbXMtGeUPju8rW6IkCQLrgtVsrl66oM58KJomb/Z181rPNQqmWerT8fnZVd9IjceNLErT9lEzWOjnjtD1Jujv9Z+m2RMkqF0nZZIg8icX9vFgpHPOfXanEoxpHD/Xz6v7L/LQ3Wu4Z/vc/rMfvHaGgZE4P/7UrmX5bCxbqy9n6BwY6OPcxBjPX72MZdvUuT00+vzYtk1/MsFIJk0hn+fI0CB/bFt4VZXtkYW1yK9EJ/nXr7zI2fFRCqaJALQGgtR7vRiWRV8iwXA6RaZY5BvnThPN5/jXd99HrXv+SeVYJs2fHjvCty+cI57PY2NT7XLR7A/gkGXGMhl64nEmczneGRjgF55/hv/5+AdYX12zIMseSaf44yOH+NaFs+SM0sAXcjppCwRRJYmhVJKhVIq/O3uKvkScscx7W2Pt9Gil3os7yN8hNp7AmCovGumb4EuP/PaiD6Zt2+Rv0HvWiwa5zMITNm/AhcO1eHRNEG7IYMwTjrVMi1S8VOJSar53LegOPA2nx7GsvpXbDbdcMmA7ETvHluA6FEHBsA3kCl6WmqSgSaVorW3baKJC3lw4WjQdcPh/mI1KJGB/aHHT5f773oP8Y/8RdlWt4uG69dQ5/Zi2zWA2yisjZ/lW32F8ipNPtNx1y7v6x1dOsaa1hi1rG1dcetaybBRFxu1US6pTlj1niOjPDpI0SuSh2dVIQPHPWzr1nYFnZ0iGKqhsCWzAI3s4nTzHeGGCkfwYByYPsdHfhVt+98pXl4rXRs7z0vBZWj1hLidHyZs6XsXLt3qPsMZXt2yioYhOFNGJMFWy7JJrABvT1jFu0YNAvQ3+DKXsx+1TSZJFFa8SxMTEsk2KZg5dKiCLlfWBSILIQ42rWBus5kJscXW7sWyab1w6Rdjh4gOr1i6LbNi2zVA6yW/sf4mhTHlhjTZfiHsbWvFX6DdSKXKGzktXr1AwTVyKwgc61vALu3bjUzUkUbwl4qnJ18eVrF6cQ57SRpGxXGX9dpXAsk1yZoa0kSBv5jBtHVEQkQUVl+zBKweQBXVFSMzIeJJUJo97gflKdcjDG4cuk1pknlUJlj0yZ3WdfQN9yILImlCYn9txFzsbGlCmHu50UeeZSxf4q1PHGUqnODo8xIGBfjqCVfgdcx/UdLHAf37rNU6PjaBbFuura/gP9z3I6qqqqQyDTVbXebOvh99/Zz8jmTSvdl+l2uXiV/fcO+chKRgG37pwjm9fOEcsn6Pe6+ULW7bz2KoOfJqGgIBlW1yJRfnd/W9xfHiYnnicX3/1Jf7mqafxqnMbo3O6zv7+Pv7hwlnyhkHQ4eCpNV38+KatBB0OBKEUKTg+MsyfHjvMW309Zc3KbjdkWUKUli6LdjuQTRewzNJDaxrWLStXWaaFuYjCjKIqC6pSVbwPy6aQK2VdJElcsEzrRqiqjLzM/d5ObPCvRhIktga6uDe8HUVUKFo6V9OLR78Azsb7+O5gSR6vYOnolsGjdVsWXF63zB8Kx9d3Gz/SROMmvDJylh1VbfzLrifxKy4kQcAGuvz1bAo28wcXnuetsQtLIhq3E5Gwl2MX+nnl0EV0w2TrmkaevGd2yc54YYKsUfJdaXe34Zbcc8bYgewQJ+KnANBElU81P829VbtBEOjJ9PEn177MRCHKSH6MK+lrbA5U7pD+XuFotIf7a9fwQO1aTsVKZmXVDi9jhcSK9mO5lQhXU88xlD2Eaesooosm91wFxsVwJ73zKsWF5BFOxt7CxkKeajK/q+oJmuXFe1qnIQgCAc3JT23Yzq+99fyiy5YU8KL896NvMZJN86k1m5Ysp318fJhfe+sHdCdjZctlHZLMnkgTDzWuWvFrVDQtzoyXKhFa/AH++V13E/EsXRXsb68d5fB4H2fiI/y3ky/hVa7PXfvSMTZXlTfxXAiGpTNa6Odq+jS9mYuMFgbIGWksrCnhiNKZFAShRLyRCKjVRBwttHm6WO3ZjEf2L+mz5fM6iizjdc9PmoMBN/mCMRMQXipWJARkWBZrq6v5jw88yObaCJJwXc7Qq2r8+KbNxAt5vnLiGAXT4NToCCNtqTlEw7ZtvnHuDKdGSySjxe/n/77/w9S5PbMMRLyqxgdXr8WtqPzLl58nUShwYKCfI0ND7GlsmrW9w8ODvHztKrF8jiqni39x1z082dGJ46Y+jK11EX7v0ffx+e9+m554jLPjozxz+SKfXr9pzjGOZTN883yJZDhlmcdWdfDLu+/Go1xnmbZts7elDU2S+YOD+zk2sjQVohWbot2BY62qKTPZgWC1lzVbWxHlyqNPbWvrcXoXjiqtxNglCALKFLmwLBtDL//A2ba9vAL+2wx5SuHn4do9SEKpJ0YRZH6y7WNl1613hXiyfhs2IAsSYc1LWFvYs8Cw5zYH/z+UCNiPLG567lJGjof9Xfhk56xAkCSIBBQXnd4IByYuL2lXTXUBrvRNYNs2fq8TBKiv9uNQl64ANE2MWyMhPvPEDu7ZvArTsmirDxHyzc42xPUEeasU7Wt01eOQ5r7T3pzYR8EqIiDQ7l7Fg9X3IYsytm3T4IywO7ST7w+/QMpI0Z8d+KEgGoZt4ZRUnNL1d17OLOKU1BWtta9zbsOnNJPS+xGR8KnNOKTAim3/TkXGSLDau4WNgbuZfqBk4dama4oo8nBjB481r+bFvsWfL8u2Gcok+YPj+3ip7wqf6NzII03tVFXQt2EDpyZG+MrZo7zcf4WMXqxoxN8UruMTnZtwKyuf/bdsi8lcFocssz5csyySAfCBpvU0u4N0pyZYH6ij2lGqnhEEgUfr17C3fnXFU6zp8SVnprmYOsahyZcZyvdg2DqWbWFTnqhnckmGct0cj7+JImqs9W5jT/gJmpwdM8dVCWRZpKgb5PL6HJlj27ZJZfLY1vLlj1eEaAQdTh6Zany+eZARBAGnrLC5to7WQICLkxMMpJIkCnNTMXnT4HsXL5AslCLIP79zD7Vuz7zbdMgyG6prua+xhZd7rtKbiLO/v5fdDY0zJ8WwLI4ODXJmfBQoqQfc1dA0h2RA6aXX6PXzgdVr+PPjR8gZBt88d4ZPrd806wYyp5QMDg+XnLpbA0E+vGbdLJIxfYyyILCnoZF99Q2cGR9dku7+HcgPVgzekAtxqp66rrmKf/E/Po3TU3k6WhCEsmVMy4UkibinyIxRNEgnc2V1x3OZwrz9Hncabn4ZKBU0ovoVNw3OKi6lBilaJiHNg7TIuZAFiUoS1QIQcjhZ5X+PnYZtMDGnSjYWJr0Rt4+QY+kmemqFcq6yKFLn8twW+cnbgc5ANc6bHH67fA0M5+PotoliX7/HbCBv6ozlE3R6F1YNXAyDYwmOne/n2Pn+mcHyZz9+D82Rpd9H8VSOWCpHQ7UfTZXZsqYUrZxPXSZv5jGmepSq1CrUm0pbipbOwcljWFgoosKjdQ/OEH1BEHBKDla5WwHImXnGi5V5q7zX2BBo4LXRkqJQzigwmIvx/NApuvwNyy6buhECAjYmIOJWIsiiCwsLiTs3Y7wcWLaFbVvIwjRhK0WwS3fdrb3rBEEg6HDyq9vv40JsjL5UYtHlS8+jwbGxQU6MD/FfVY3OQJjN4QgNHi8BzYlbUTEsk2SxQDSf43x0jGNjg4znsqXsdYXH1hkI84X129lQVXvbMk6iICCJIkGnc9n78Mgae2ra2BZu4um2rTR7rivpla5ShYJEto1uFbiSPs0b49+hP3sFi1ufF5Z0s0pldYapcyL+FmcSB+ny7+Sx2k8SVGsqUrGrrvKSSOU4d2WYjtZqVEUChFL/WNHg4PFu/F7HspWqVoRoVLtc7G5sWjCSIQgCVU4nfq1Uh5fV9Xkn3efHxxjPZrCxccoye5tbZ2VHbt6mV9NYV13Nyz1XSRYKdMdjpSyDUhrsh9MpuuMxiqaJIopsrFmc2QqCwNa6CKokkTMMzo2PkSoUZmVe8obBhYmJmcb0GpebzbWRBbcpiSKrQ1XUuT30JRd/0OfDj3IseNW6hqlehgx9V0YxDBPpDivvUjSZ+raSjHOxYDAxHCeXKeBagBDZtk10LEk6fudLl/6fK1/np1d9AoesUbR0/u/Vv+OXOj+36Dq9mTG+P3SEnFFAESWORK/waN1mtgRXzbu8JkkVRTglUWRPpIX/vfeDFR27OeVds9IqPSkjzRvjbxNQ/NwbXppbdSVwyJUNvT5V45NrNvMLm3bftmO53fjx9nv5jRPf5G+69/NgXRc1mhcbGM7FeGnoDOcTQ/zGxo+Q0vMz68iiWNGE9aMPb+Zjj2yelUk2LRvLskv9V0sYS3qHoxy7OMDa1joGxuJ87KFSVntake7G+9mwjdLEVxBRRWXOhONk/Awpo1S/HVD8bAvOzpDLgoxfKQlcGJYxU4Z1p+PJhs0ULZP/c+lVRnJxfuv0M9xf08nPr3kYn7JyvQvDuaP0pF4ia0zQ6f8I8WI31Y4NRFzbV2wfdxIup05wMv4mRStPyohxJPoKbrnU97O76gma3Qsbo84HAWjzBfkvex7jX731HKO58k7gNqWAaqKQ5/DoAIdHB5b2YRY4nlZfkC9u2Mn7WuaqNK0UJEGk2uVmPJslrReXbUpYKluCX+7ai1NWywoFzQfbtkgaMQ5OvsS+iWcpWCv3rNvY6HaBk/G36c1c5P31n2Odd0fZnp4NnfW0N1fzN985xJWecXZubsHncRBNZHn9wCVOXxzkJ57eQ7iMUE85rAjR8KgqLf7FtdJlUZwpfzIta95yiouTk+SnGqvrvV7SehEWuRaZYnGmFwQgo+vE8/kZojGayTAy1YQddDiRBJFofvGLW7qBSjekZdsMp2eXeBVMg75kHChNomo9HlzKwhdTEATCLldpG0sgGnfOlHvl0b6+kXCdn4nhOJlEjndePMMjT+9CVhZ3w7yx5v92kxLNodK6JoKsSBi6SXQ0wYVjPWy9b828+zZ1k/7LI0yO3Pq1frdgWCZ5q0DR0kkaGYq2QcEsENfLN7RNFJIEVRe/0PkkAC8MH2coF12QaKgVEg3btsnqxYqup27pnIifwrJt7lphlR6f4uWD9U+s6Dbng7NComFaFgXDuKPI963iL668QbyY5StX3+QrV9+YilJfrztWRJkvHfwK+g3Gpw/UruW/bvlE2W2fuzaC26lSF/bhUGX6R+Jc7B2js6WaxtogsnTr580GJuIZLvePE01kOHlxcOZv9TV+6qquy4zKoowoiFi2PafkwbItDsWOYtglJ+m7QjvmlL8IgoAoSEiChGmbGLegqPReQhYkPt68k4817yBRzOGSFTRx5b0BMvoIdc4dFK0UgiAiLDGToRcN9IKOy7v0LOS7gQbnKjzyfKWoNkG1vKHqzRAEAVWS2BNp4rfueYx/f+AlRrPp90SgQxIEOvxVfGnTXXykY/1tnduoksSWugjfv3yRK9FJ8qZZ8ZhbbrtgzyIuhmWhWyYOaWFbBMu2iBZHeXP8exyOvlzWx0NARGB2kN2e8n2xypRWxfVxvjPwZyRro+wKPYoiLhywqany8vEnt2FZFgeOdfPaO5dmlFidTpWPvW8rj9yzFk8ZUZ1yWBGioUgSfscKaGfnczMd/VdjMR762l/c0vq6aZK9QS85pxfJ6qUyrLFshn/z6ou3fEzJm0q8TMsmNfU7VZLwaeU/t0tR0N5jeds7EapD4ZGP38VQ9ziJaIa//r0f0Lq2ntY1ETSnOqssyrZLUUqjaFDI61imhcvrqKg5ezmQZJGaxhDrd7Vzct8lhnsneP07R2lf34g36JrRnrbtUv/G2UPXOHekm0L+zi2d6s8O8/3h1zmfvML/d/HLiIhYWOyu2lJ2XVEQStGuYnZK9MCYUy5yI9yKVpGKiU15yVfbLsmBDuSGuJy6QkAJEC3GUAQZp+REFmUMy5iph9dtHcMy0CQVl+TCtE0yRhaf4p2JPJtYJPUkfsWPaRtkjCxg45AcOKXrk5KCWSBvFZAFaWb7TsmBJpbEImzbJm/lKZjFUhOfbSEKEm7ZjTKP46tPdcyacC8E3bJmjWl3MmxbB6Q5Up+rvUGq1dJnEEUvgrDAmGmbWHYG29bp9DVWtM/XD1/m1KUhdq5v5kMPbuQbLxzD5VA5fKaXL37sbmpCt15y1hIJsa61lteOXCGZzZPNF2f+9uCO1bOIhktyoggyBbtI2shg2uYMmZgsRjmfvIhpmyiCzJ55SLE9RVAs25pq9PzhMO0bzsURBQGf4sSvOkvTottAhkVBwbJ1dCtL0UxhYyLc4jkyDZPT+y9x4LkT/PzvfHrFj3El4VECeJQAOSMNgoBTcmNjk9bjSLfYo3EjNEnmgYZW/r/7nuT3jr3Fuckx8u+SIIUAuBWVzeEIv7z1HnbVVfZsLwdOReF97at58doV+hMJnr9yifevXrNsm4Hv9J6mK1jHWn/tTKlxbybKq0OX+Ged82fAbdsmoU+wb+JZDkVfmncZSZBRBA1ZVFBFDa8cwCV7UUUHiqBiYlI08xStAgljkryZwbQMdLuAbhXnvEcyZpLXx/4RWVDZHrxerjkfVjWH+dUvPkrfUJRrfRNk8kX8HgdrVtVRE/bOa1J6q1gRoiEJwoq4geamfCimt6nJt6Z/rckltjkN3bLQp0q0xKljlG6hpl8SxDkX0MaeaeQsRePKX4Qbszk/rLBtG3NK5cm2LKyp8oRsKo+hlwYs27LIZ4ukE1kEUUAUxanvArIizWsI89BHd3By3yUOvnyG6GiS/99P/l+e+uKDbH9gHR6/qyTHOzWJT0ymuXp2gBP7LiEIAl/4tx+aKWu6nQhW+3jk6V1cOtFLLlPgnZdOozlV3vfpPfhDHgQBDN3k2rkhvveVN7h2bhBVkykW7szoZJunkV9c/Vn+pvcZnmp4FE1SK54o1Gh+dNPgy1dfQhZL5SIP1m5ccHmvquFS1LKTasu2mchlZnxo5oNpm5xPXeL54ZcYzA2higqHY0dpc7fyYM39NLka6cv2s2/iAG7ZzVBumOH8KBv963ki8hjRYowvX/sqv7j6Z6nRSvdNX6aPP7ryp/yH9f+WwdwQ3xl8hqSe5L7wPby//n0z+z4WO8krY6+xyt3GpfQVBGBncDv3Vu/Bp/hIGEleH3uLq+lrxPUEI7lRGpwRPt3yY7R72ubUlAcdTjRZLmsklTcNEoXcouflTkGueBJFbkaRZuv9f7zeJpF9A1FwEfR8Dqe6bd57zTAniKf/lpx+Cr9rYYGBG+FyqPz4B3dytX+CaCKDIks8eV8X33rlFJa1tKhtyOfig/dvoLU+xOhkikfuWrhcpUqtwik5KVhFejP9rPV24hU8GLbJ6+NvkzNLGfRWdzMtruY561tYZI0cNjaSIKFU2LvzXuPr3e9wNTXGznAbO6vaiDj9eBQHjhXOaoS01fSkXmEsfwoBkQb3brzKEhR+bDCKJpMjcSzLRnMoeINuTN0knciiFw0kWcLldaCoMrl0AVEWcXkcZJI5sG0cHgfZZA7LsrBMG0M38Fd5UbTKDX4rxZnEAWRRZWvwAQQEjsZepcm5mnbvpvIrLwBVkrkn0kzT/e/nz88e5eW+K0Tz2dtKONyyQp3by/vb1vITXdsqai5fCaiSxO6GJp5as45nLl3kj48cwqUobI/U41JUFEmccfWuBNPX98XBC7R6QrNKJGscHv7+2jF+qnPPnO2VAlAZTiX2z0sySqQiSINzFau9m2lyriao1qBK2oJ9HzY2KT3GeGGQK+nTXE6dJFocI2emZ71jU0act8efIazVscq9YdF7VFEk2luqaW+5PfOpO2pUc8vKzMu0zuPl0VXtt0RgWgKBWTb2qiTNmED5NQe7Gxpp8lX2AoPSzVV3UxOmiIBDvq5CVKhA9su07B96LwG9aNB9fpiBKyNk0wVymTy5TIGxgRh9l0cASCWy7H/+FKMDUZxuberLgTfgYu32VqrmMdZzuDT+2b9/CsOwOH3gMonJDH/529/nr373WYI1PhwuFb1okknmSgP+FLbc2/mueYI4XCpb7uvkkU/cxSv/cIhULMv3/+ot3nzmGJGWMKqqEB1LMD4cR3MqPPjUDgaujHJy/9KUdN4tPFK7B1EQ0C0dTVQrmsw2usI83XwP3ZlRTNui1V1DSF04aiwAVQ4nqiRSKCOGkDV04oX8gk3WsiizJbCRKjXIG+Nv0+hsYG/NfXOWG86PUq2FebrpIwSVEHkzj1ty4XN7CWtVnEmc4/7qe5AEiUPRY2wJbMKv+PArPiKOOp4Z+sE8e7eJFmPsCu3gqYYPcjp5lqPRE9SnI2wJbuJ0/CwJPcHHGj9MtRbmj69+mT1Vu2h1N8/bRyIgUOfy0J1c3NTRsm3SepGMXsRboda8YZZ0821bx0ZHluoQULHsJKaVAGxEwY0shbFtE8MaLy1r6wiChCyWiIJpxRAEGcvOIwouJDGEbRcxrTg2BQQUJDGIIMiYVoJE9tu4tfuw1Q3IUg0CIoY1SSr3LF7n47jU7ShyE7adwzDjKHI9tm1gmONIUhBZCuN3P42Yq3wyEvS5ME0Lw7ToGY6SzhVJ54pYlrXsutOOpjAtZZrKm12N+BUfcT3BoehRVnnaaHRGGMgN8eb4AQpWERGRh2v2zrt+0dIZK0wAoAgyLunO99AA+JnOvZyI9vHW2EV+//wLtLjD7Aq30emro9UdRl6BwCNAUGvHLdfSbOxFRMarRJDFWy9/siyLwaujfON/Pk8qnqG6IcSn/+X7Gbw6ykt/t590PIvT42DL/evo2NjE/h8cJ1QX4P4P72Df94+hFw3u/cA2Xvj6PjKJHAgQG0vw1M8+Qsva+hUnGqJQagC3bvRsWIF9CIJAiy/Ir+98gL2NbfzRyXc4OjZYfsVbgCbJeFWVsMPNfQ3/f/beOs6u+77zfh+8zHfuMJOYyZbMjh1DsEmTJmnKabuF7bZdptez7fZZ6Hb3aXfbbilNm6Rhp0lcc0ySbTGPRjMaZrrMh54/7miksYY1Aqf+6KXXzJx7+J7z+33x82ngk61baA/c+oDg9TBME900+eSmrUxkMvywv5d/8fIL3Ftbxz01tdT7SrbiSgLFtT7/XOWKhYVNUubNk7IozSv7vB4mJiPZPt6aem6urxBARMSrBNng3c3e4KNU2utX1LwNpbnDqwTxKkGa3Vt5JPJJzsQP887MC4znB9GtaxnYqDbJkel/oMJej2sZZfnlaOhv5hm/qxyNsMs1N0AFHQ5+c/9B3DbbmucLt6riUUv1aS5V4SPtG3mseeU0ZAtBFkX8s4ZQ0TSIF/LLGmjpYoGc9t4of1gM6USO7//1G7zyrWOLrpNLFzj95mVOv3l53nKXz8Fv/cFnuefxhaMxZVV+fusPPst3/uxV3nr+HPHpJNl0gfhUCsMslRRIs9Elu0vFG3DRvKVm0YbsW4FQuY9P/NLDqKrMWy+cIxnNkEnm6To7iCiK2J0q5TVB7vvQTh778f18+89+yIWjyytt30moosr5RBd5I8894Z2M5iapc1YtuU1Kz5E3iuwMNGFhMVNIMVNIUmZf3IEvd3qwSfKyjoZumvQnowTta+ckh9JAvMO/nXJb+axK7rUa1XtDBzg8/Rb7Q3uRBImLyQ4+37CycopKRyWbfZtwyA7KbREckoOMUWr6T+tp7GKp3Mou2fHIbgpGYd7kMv8codbjX9bRgFJJ2UQ2vWJHI5r+UukXy0A3pwl7fwVZqiSe+TZFrRsLA1n0E/b+BpZVYDrxPxFEB0V9AMsqUu7791jkiaa/hE1uQjcmsSmb8bk+Qq54jnT+ZQwziSAoeB1PoipNpHOvki2cQDemyRbfIej+WUTBSTLzXfJaB6aVJ1c8S5n3Nynql4mnv0FV6A/QzRmmk/8fAc9PY1c2rOj6rkd1xMdrJ7pxO20Mjsbwuuyc6xpFgJvOIjtsKo5lbnmjq55qRxUjuTFG8+P8Ze/f4FN9TBamyBul5vZaZzW7AzsW3L5gFBjJlajPbZKNgOq/qXO+XfAqDu4vb+f+8nam8inenOzi+8NnmMon+T/7Pk+5Y+UBvaVQMFIICPiUenL6NDkjioMwsri6Um3LtFBsCr/wO59kvH+ab/7R8wxcHqX3wjDBch9f+J0f58wbnVx4pxtvwDV/23fZX06PjUc+dQ/hyqX7Um8GqmgnoU0zmuvBpMRUtFp628VQNAyGUgnOTI0xnF68l9AmSUiCiG6a6JY5Fyy9yr4mCQKyKKKIEnZJxi4ruBWV9kCYeyrreLCmkXLnnWHLSxeL/I93jjCUjDOQiAOlcfT5nm6e71ldAPBPn/wwjze3AtDoDnE2NkLQ5sSn2tFNkxPTgzS5wzdsZ2GR0ROcTRwmoV9jkxMRKbPVcCj8FDsC9y3ZQ7ESyKLC7sCDtHq28+zoX3MpeRJtVtTSsHRGc/1cSp5gd/ChG7IklmWRzhSIJrLk8kUMo9QH8m401oZvinnqrnI0NobLcCoKMznojceI5nO4VXXNnnyFy0OVx4sATGUyjGfSFA3jpvolbLJMk780wBQNg/F0mlguR8i5cCTKsizGM2li+fyCny+GmuZyNu9tQrHJhCv9azpXh9NGfXsl+WwRb8CJx7/2aJmsSFQ3lbFp78JNv0vB6bbhedfgfcM6Hjuf+60neOSTezn12iWuXBghOpkkn8kjSiIur4NIdYD69krad9RT21I+J8YXnUiQTedxeZ1UNVw7x0DEu6xT6Q972LC7kVymQF1L+aLrC4JApDrIZ3/zCfY9upmTr3UydGWcXLaIw6lS11rB7gc30rqtFrvTRvPmGrYfasPuUHF5l4/AxadTxCaTNG66OSN7NXh+7HWyZp6R3CS7A1v46sAP+Fcbv7DkNp3JYTqTI3y+sTRonYxewbAsnqxanAWm0evHKSvL9mBopklXbJpdkZu7Bw7JUWIAWmDc2Obfwj+MPc94bpyCWUQRFJpdjSvar01UcM7qJFyNPl2NApXbyxnPT9CT7iNWjKGZGmW2EIqwSP+KABsDZbwx0rfscROFPCPpBC3+0IrOE0ASPficn0CWStsUtB7SuZcp8/0WomBnMvFfKWjdyFI5ujlJxPOvKGj95ArHsKsbyBXPIAgqTtt+nLZ9AOhGlFzxBKLowef6OKns8+S1S9iUdvzuHydXPInP+XFc9nvnziPk/SUKei9e50dw2fbDgs28a8/27tvawPb26lkWQIGiptPZN8HBHY0EbkPjr0t2cm9oHxP5SfoyAyT0JAn9miKyX/Hx0eqnsEs3Cr9alkXGyHIlXXoGnJKTCvv8srO7FRYWeUMjXswyU0gjiyJt3grskrJu2QyAsexxJEHBJvnoTb2IYRXZ4PsEIfvKhOuuQpIlKhvCyLKEJIvYnTZyqTxaUcMbKhnDdpcNWZXJpvNYFrNK8BZaQZsTlgUorwvjdN/aZ6vBtYGz8Tc5PPU9LCxaPTsos91cb4NlWbP0taN8seMkrwz1LFhpIYsi5U43O8sq8dkcxAt5UsU8BcPAtExkQUKRRJyyit9mJ+xwUuP20eANsCEQxm+7eTrZm0VW1/h6x/l13++TtZv4q+6jjGTilDs8pLUCx6cH+ImmPQuWTUWLU3Qkjs9b7lfLOBh+kl3BB26q7+Z6CIKAVw7woaqfwcTkQuKduc9SeozLqTNs89+L+i41+0Qqx0tvdvLSkUtMR9MkUjkkUURRpFltDYiE3fzub32Y1oa1j013laPRGgzRHAgwlk6RKRb5+8uX+Pmde3AsoHtxPSyYp554FWUuF22hED6bnXghz9HhIfZVVdMWDC8b7bIoPSjv5k63SRKtwRBe1UayWGAsneKdkSGeaGlbMKuRLha5PDPNdDazmlvB5//5U6tafyFUN0X4wn/42E3vB8AbcPETv/44P/Hrj6/L/hZDZV2Ypz5/YznMUnj7+bN0nuznZ//dR/nELz/CJ375kRVvu+v+Dey6f+WRVIfLxrZ7Wtl2TynCYZkWyVgay7Twl11LTT7+6Xt4/NMrp0c9/folnvvbw/y37/7mire5WQxmx/m11s/xv7q/VHrGVzA3iLMxEd00EAQBc/YdWQrN/hDOFYgyFU2D8zMTfHq5cxBEsErsU1ebaGFlqV27ZGNvcDen4mdIaxkOhg+sOGV9lTF9ITS5GjgbP8vbM0fxKV62+bfQ6G5YtAlPQGBLeGUsMjP5LH3JOA+s8CwBFLkeUbhmDBlmDN2Mksx+F0FQsSkbEQQVUbQjiWUkMt9GEoN4nE/PbSMJXmTpmtNnWTl0M0pRu4JpliKhqtQCc8251nX/l/ouJCy0WWFLA8Na3dh4PaaiKfpGZkhlC3P1yXs31+O/jexC2/1bSOlp3px+i4n8FEVTQxYkvIqH+8L3stO/bcFnzLAMxvOTDOdGEBAIqH7qnLe+UXY90J+e5mJ8hCupCfrT0/hUJ/vCTXy+6SABdemg0mqgmVl0BNL6OJWOPcSKvRTN5PIbvhsCN5TaOtx2wpUBus4M0H1mgKGuMRRForopwuTQDBNDM1w5N8hY/xT+8LXI/ErHypuBWwmwP/QE2/wlOlpFtKGuMotzPa5qZLw52s8fnn6L8zMTC67nlBW2hMr5ifbtPFLbPK8U/b0ERZTYXr42bZ5343rW0d3hOmRR4rWxbi4nJvEoNn6m9QAPVLbeEBAvmnmGsl1kjGvPqyLYaHFvZYvvwLo5GVchCAIu2ccDZR9hKNM9l0UxLJ1ocZzx3CB1rvkO+rnOEV46fAmvx859e1t453QfAa+T1sYIQ2Mx+oamefKhLVSULV12tRzuKkdDlSQ+sXELl2dmGEun+NK509R5feyrriHiciNf5xxYlkXRMEgWCkznMuimRaM/UMqAzEIWRfZV1fBO5RCvD/bzxmA/1R4vH2nfSIM/gFOZX2tnmCbpYpFoPsd4Jk1bIETQMX/CkkSRKo+Hg7V1PNfTzWgqyfe7OmkNhmj0B1BmsyWWZZEuFnl1oI/joyPLlo28j/cmcpkCJ37YgSSLPPixvXf6dFaFoOqlJzNIVs/TneonoCw/mDhkG0VT59hMF5IoMl1I0eBaOtLR5AvgU+0ILB271gyDc9PjZDVtScpol+TCrbgYyY1yNn6egOonYivDKa8sY7cvtIcv9v0NaT3Dx2pKuh26qTOSG2WiMEW0GEM3dS4nu/CrfspXEGVO6imKpkaru5l6Vx0iIoliEptoQ1mAlUsANociqKJEcRmV8Gg+x5XEDAVDxyatbMgW3uUUqXIDDnUbHsfjCLMOiE1pKfVbWEUctl1IYhmioGJa+XftpwRJDGBXNqFIFTjUnQAoUjWS6JsVOHRT1HsRCy5sSiuiuLDBKYlBTCtDrngSw4zNOi0WhpmioF1GM0YxrQJFrR9FrkIQFndSXzvRTf9IlJDfhTTLjrK97faSMIiCyH1l99DkrudKqpe4lsQpOWnzNFPrrF5U60WzdLJGli3ejciizEZPGxH77a1lXyteHb/EpcQom/01/HLbNhrdZXP9kOsJWbQTL/Rgk3y4lQoS2sCq9yEIAr6Qm4aNJafZ5lCp31BFIOIlVOknNpnkze+dxBt0s/P+DVQ1RUjGMpx45SLvPH8Wt99J/cZqZFWmsqEMf5ln7lm7VUhpMSbyg2T11JwDXe/aQEBdW1RZM3SOT4zwP08doSM6ueA6LlnlvuoGfn7LHnZFqtakFXG3wG+384eP33ywFiDsnD+ObQ9Wsz24fNa9aOYZzHbNWxZQy2j37MQp35wuxWIQBZGQWskW/wGOTD87tzyjpxjPD9zgaIxPJXE6VH7iw3vZvaWOmViG8rCXDz+6DUkS+OK33qZ/eIZMtojHtXan865yNAAeaWzm9PgY37p0kWgux39844d8pG0DuyqqCDgcyGKJs7yg68zkcvTFY5ybHMetqvybgw/MczSgVI71dNsGRlJJuqMzfPnCWa7EZri/roEar29OxVY3TVLFAkPJBJ3TUxwfG+F/PfYU+xw3PlBBh5OPtG3k9PgY45k0bw0PIh0TebKljYjLhSiI5DSNyzPTfL+7k2guh382q/I+FoZhmAxeHmN6LA5ApCZAbWsFgiCQTeXpvThMVWMZoQo/mWSOkd5JXF4H1U2lgTefK9LXMUL3ucHZMq8I4Uo/+WyR0b4p4tNJTMPCE3DRsLEKh6tUynDy1Q6at9biC5Ve/ImhKDPjcTbvay5pO6Ty9F0aIZPIlZRWIx5qWytQbQrRiQTn37nCiVc78AVdOD0OvAEXNc3luP1OTMNkcjjK+NAMxbyGL+Smrq0Cu9OGXtTpuzRKbCqJapPnNbrfLtwb3sXJ2EWckp0TsYvcX7a8o1TvLGPCFefIdEkVuMVdxSZf7ZLbBO0umn1BLkWXplQ0sZjIprkwM7EkBaJP8bLdt5Vj0RMcj56kwVWPR3bjlJ04ZRdN7ka8yuK1wWW2MCBQ46jGr/iBkvBaZ6qL0dwYqqhQtIqcip2lxdNMuT1C0Bak2d045zQ4JAf1rlpCtiBFs8hkfgpREBnIDjGUG8G0TGyiykeqnyZiK7sx2yIIVDi9pfsSm1ry/hVNg/5EjP5knPbAjbXA74ZNaUWWyuE6Q0GWwvhdP04m/yYWJiIydmUTRX0AUXCSK57HsgqYZoag52cRBR82pR1BuDa5iKITp20/mfxh0vnXAHDbH0KWIoCI2/EI2cJxdOMNZKliztGwq1uQxRAgIAigyrU41f2k8z9EFD24HY8iCm4MM05eu4hlGZhmgrzWiSSFkZZwNPJFnYf2tbFnc+0dLdcQZp+nmgXmi8VgF23cG9rPPcHSeycKIosxzdxteKxyCz9WtwevcmvLZAJqMxl9ArdShVepJaWMYJP8q9qHKIk0b62jeWuJ9StY7uPJn7p/7vPrf7+KjXua2LjnxjLh+z58e4QC+9IX6ctcxKeE5yLfmllcZquFYVgm/ck4f3b+2KJOhipK7K+o4Re37mVnpHpdnsKCoXNmepT95Teyra03LsyMU+XyEpgt25JFkTqff92PY1oWw5k4o9kEBUPjQKSRvKGR1TUqHJ5574JmFpkszBc79CpBqh3N635e10MVbTS5Ns9zNPJGlmjxxu9e101cThve2X5XmyqTL2gUNY2A08XebQ381TeOEE9mbyqrcdc5GnZZ5pd270WAEgdyMsHfnj/Ll8+fJWB3YJdldNMkoxXJzDZYC8ChuvoFy6EUSeLRxiaKus43Oi7QMT3JawP9vDbQj1tVcc+WdOQNnWShMFezqIjivAzKu89xT3U1n926na9eOMdYOsU/XOnizcEBKt1uFFEims8RzWWpcHv41OatnBkf47WB5eux/7Gi68wAL3/9HSyrRB0oiiIf+rkHaNhQRSqW4ZVvHqWqoYwnPn+IzpN9vP7dkxx6esecozE+MM07L5yjWNBIxjK076jn4U/sp5ArcPqNS4z2TWHoBql4lg//7ANsuacV1abwf/711/n13/8MW+9pRRDg9BudHP7BKf7z136NYkHj7JHLvPbMCZxuO6ZpUdMcIVwZQFZkpsZinH+7m4HOUTx+J7pmUNNcjj/swe13MtI3xZvfO8nUSAwLC0MzuO9Du9h+qJ3Ok3288NW3ECURp8dOOp697Srwm3wtBFU/k4UZgqoP/woyGm7FwUPlWzlYtgHDsrBLyrKlRwKwv6KGN0b6yOeWjjanigVeHOxmT3n1ogQLgiDQ6G6g0d1ww2cV9ghPVj624HZXI4PRQoy8kefeymtlgHbJzuMVjy56Xu2eVto9rXN/h20hHoqUDJTpwjRn4mdp97RyT2g/siAzkB3kS/1fJaWl56h0510DoIgCD9Y0LutoAPQnY5yaHKHNH1rWsPM5P7LgcqdtD07bNS0H0ypQ1AeQpTKctgMYZoKC1oFpFXCordiUG3tXVLkG1b1wcZvbfj9u+41GW9A9X21eEFRC3l9ccB9h768sel0LwabIdA9MYlkWqlLKHLTWl+Farov7NkIzdXoyfSSKpRIKl+yiyVWPU3YgWpAziwxmR0jpaVRRocJeTpktjCgImJZJXEswmhsna+RxS06qnZWYlkVCS5LUUhiWQZk9zExhBlmQaXE3zSNAuBWocgYYy8W5lBgjaxTYHWxAEAQyeoEym2fd+jTcSiUt3lJk2kCjwrkbRXxvMHPdDAxLo9G9mW2+QzftyGU1jef6L3N4tH/Rdeq9fn6sZQs7yqrWzdVNFPP8z7Nv8rXHPrtOe1wcf9t1ko83bmVf+dJBr5tFT2qa7w2cZzAT49TMEN955OeJFrI8N9zBP900v7jVsHRS2jWyDwERl+zFp6y8124tkESZkK0CRVDRZhmoNKtAWr+x8d/lULFMi0y21D/p9zkZHIkyFc3gm2X0y+W1NVOFX8Vd52hAKVX1zw4cpCUY5PWBfnrjMaYyGTJakVghj0iJQq3c5SLkcFLt8XKwth7fIuJ5btXGR9s3UuXx8mJvNx1TU4ymUyQLeaayGSxKZVY+mw2/3UG5y01zIEile/H0VtDh5FObt+JUFF7p66E/Hieaz9ETi6FIIkGHg33VNXywuZVHG1tIFvIcGRq8NTfsPQ7TtPjW/3mJps3VfPwXH0HXDP7yd57hha+8xS/97icJV/l59Mf3872/ep2Xv36U8cFpmjfXsOvBTdftw2TrPa3s/cAWjr98gaMvnme4Z5y2HQ08+LG9c43wf/Yfv83FY700b61dVuyvmNO4fGoAh9PGZ3/7KZweO9lkHk/AhaxIbNzdhN1pQ7UrNGyo4oOfPTi3rWVaHHn2NImZNB/5hYeorA/z93/xGm987xR17VV8/4uvU9tawcd/6RGw4H/+s7+9mZ7YNeFyso/h3DiWZTKZn0YRFR6M7F9ym1gxTVrLE7J56MtMkNHzNLsrCdmWZhfZU15DyO5kKpdZ8jLzus7bY4MMpxPUefyrv6glzz1Od6qHwewg5fZyWt3rE1mSBRm/6mciP8Wx6MkS+URhmnpnbUkccBFDQRJFHqhp5C8vnly2fGo8m+Lk5AiP1jVT5liftLuAgtO2h5QxSV67CAjYlE3YldU12t5JRIJujl8cZCaRwT77PleW+e4qR6M308+L46/ilt1YloldclDpiODEQd7McyZxgQuJThRBQTd1QrYAh8L7idjLiBbjnIidZjg7hiSIFE2NNnczNkmlI1kqy5jIT1HjrAIshrOjfL7hU1Q7KlfRe7R6DGSmeXH0Av2Zac7Hhvkvuz6JW7bzwugFPt2wD5+6Ps5AvNhPtNAFWOhmHkGQqHYewKu+N3pZ1gpJVJjKD9MpnkAVbCAIlNtqcc9mYFcK07IYzST51pULi467siiyq6yKe6vqlqU3745PM55NUTQNgnYHGwPlnJ4aYVdZNZfjU+imybZQBe9MDNLmLwVYDNNkMpdmJJOgyuVjKpem3hOgNxklZHciCyJZQ8OtqPQlo+R1Hbei0uILE7Q76YxNUjQNCoZOWiuyLVRJ0OagJznDcDqBIkqkteKKpk/dNIjn88TzpeZ2y7KwyTJuVZ3TNlrqDjzTfxZBEPjxxp1ciI1iWRYRu5vnhy/x65semLethUnBvFbFIgsydtF5yzOvAgKSIOOQ3Gh6FADTMiiaN1bURMIeZFkkGi+xJzbXhTlxboDnXrtAe1M5J84P4nSoc2PrWrEmR0OaTUt9but2AJr8wWVvXtjp4tHGZpoDQfx2O5XupQ0Tp6Lwqc3beLC+iTMT4/THY8TyOeLZHJ2Dk2ypq6AlEiLicOMVVOp8PrxLNC6pssyhunq2Rsrpik5zeWaGyUyadLGIaVnYZImA3UGl20NzIEhrMIQqSYtelwCUOV18Zst29lfXcGZ8nO6paYZmEgRcDnbVVbGjvJLmQBCbLHOwph4BAZskUTF77bF0DrddRZFXHv1J5wpcGZmmsTKIz7X2hkfNMHi5r4eZbHZu2YfaNuC13ciOcquRz+S5fKqPmuYIrz1zHNO0MHSDrjOlelxZkWnaXMuO+zbw7T95iY27m3jw43vnOQrVTRFattdhd6hEaoLIqkQuUwDLYmYsTvfZAQzDJJ8tkEnm0HVz4ZO5joVDtSu072rgnefP8vLX36G6qYyWbfVI8vITeLGgMXB5DEM3OHfkMpdO9BKfTtLXMUJyJs1g1zgf+tkHcbrtSLLEjvs3cPh7p27uRq4SL0wcptZRgXuFvQ0Ag5kpetLjbPLWcniqg7SWRzMNDpZtXHK7eq+fbWUV9Cdj5JYpnxpKJfhuTwf/ZNuBRbOKa0HOyDGUG0YSJD5U9QSqqK7Ls+5TfewN7OJSspPh7AgIAj7ZwyPlDxJUF6fAFBHYEIywORTh9NTYksfQTJPTU2O8PTbEUw3t6yIAKggiqlxPyPvLN72vO4XW+ghl71L/9jjvHicD4Eqqj4ye5ZO1HyGkBokXE/hk7ywrTZy3po9xb2gfe4I7GcgM8cPJNzmfuMTDtjBdqR76M0M8UHaQZlc9x2OnORfvIGwLkjNyPBQ5xPnEJaLFGB+tfpIvD3yTmWKMSnv5LXU0XhvvJK0XeKp6OyPZOJYFQZuLd6av8OHaHfhYz6xDiWDAsAokCkMEba14+dF2NDyynxHzCv2ZDmyiEwFwy75VOxqaaXB+epzB1OI0tj7VTos/RHAFYnpT+TR9qSg5XaNrcIrf3v4A3+g5S7M3yN/3XSStFWj2PcLfdZ/hP+79ABYWQ+k4R8b7USUJ3TR5Y6yPx2rb+MaVs+woqyJoczKdy7AnUsNAKkZKKzKcjvOB2jYOVTby+mgvKa1AhdNDwdBp9YVIiyLf7r2AS1YI2JxM5OaL1V0Py7LI6RqXZ2a4ND1JXzzOeDpFVtMwLQunohB2Oqnz+WkLhtkYDhN0OBd0unpS0/zShkNsC1bN9SQ5ZZWieeOcVnpqrz8n4Za+k+/G9ceyZv+9G811ZRza20L5LNnBhqZytm2o5tV3unj7VB9Oh8rj928ktAxr6HJYk6MhiyJbIuVsiayMNQWg1uvjZ3esvr6x3O3mcXfL3N/TiQx/Pn2Uj2/YQntthGyhyMB4jEJxZQ2APrudvVU17K1an4HKLstsLitnc1k5yWyewck4HoeN+vL5BsbDjU083Di/5vPti/3sbqulPLDyCGU8neflU9189OCWm3I0iobB/z15nHMT43PLDtTU4rkJ3ZK1wjBKNILRiUSJRlAQCES8c817VyEIYLMpKDa5JMh1HVS7gm2W57mkQC5gGibHXrlI99kB3D4nql0lGcvg9jmvsZSJApZpzRHl5DLXKFhtDpXdD23CG3Bx8VgPp9+4zMDlMR7/zEEiNUsLeZmmhWWapGIZxgdnkGQJSZbY94EtuLwOLMsqOSyzg5m8CmdzvaCbOo9VHMIlrbzOWjN1ElqGkVyUcrufGqdMUssuu50kiDzV0M4bw33ksukl101pBZ4f6OL+qgZ2RJbW9VgNqh1VfKLmo+u2v6sQEKh31VHvWl0tsiAIOGWFj7ds5szU2LIRuYFknJcHr7A1VE6jb+nn7x8LxqYSDIxFsaxSEMamyFSEvTfF+b7eaPe2MJQb4fWptyhTQ7S4G5GEEBYWiWKSgcwwTa5GXp08TEbPEtcSTBSm0C2DycIU4/lJulI9DGaHmSlEGc9PoooqPsWLU3LiUzzYRBWn5MApOSkaxUWNrvXCSC7GgXAzB8LNfLWvRKVplxQMa5EAzhoRtm8gbC+xAlqWycX417DW+Rh3IyodTQTUcsCai3255dVrkxQNg7PT40uu41JU/LblbQnDMnErNurdfhAEnh24RFbXqHJ5Gc4kyRs6RdNgMB2n1u0HStoVz/RdRBZFvrBpP0PpOG5FZSybnNUhy+Gapcm1STJVLi8gcCk2wXg2NTdPB21OPljbTthRMngvxycZz6b417seImx38eJw14J2i2lZTGbSvNhzhRf7rnBmfGyu5P7dsEkSLcEQD9Y38sHmVtpCYdR3SSGUOzx0xieocHhKYqp6kbPREVq9Nzbpi4iogo28lZ29fzoFM4+FdUt7sSwsTMsgZ1xj8xORkBfQ6ygLufnAoQ1z87/X4+Cph7ZQXx1kJp4hEvSwbWP1XA/HWnFXlk69G4ZpcubKKBOxFIZpkS+UHpRsoUjn4CQzySxNldfq3qYTGbqGp0hm86iyRFtNGTVlfnpGZ+ifiJIv6gTcDrY0VGCYJp1DkxQ0g2xeo8zvYntzFYl0jov944BArqhRG/GxpaGSXEHjYv84U4k0NlWhuTJEfXmARCZP5+Ak6XwBn7P0pWTyRXpGZ5hOpNEME1kS2dpQidOu0DU8zbNHLzGTylIZ9HBgYz0uu0rvWJTesRl0w6Qq5GV7cxVF3eBczyiT8TQFTSev3V5WlVsNl8dOw4Yq2nY28OiP78fmsJFN5dAKpevUijpdZwc4e7iLRz65n4mhKG987xRPfO4Qdmfp5REWeHV1zeTkqx2EKnw88blDSIrEYNfoPGpCb8DFaN8km/c3k08W6DzVd932BslompZtdbTtrOfEDzt44atvsaV3cs7RkBUJLItcOo+hG7NOTslJqWqMEKkJ8vhnDlJRH6aY18hnCrh9Tirqwlw+1U/TpmoEUaTrzMBtq5w6G+8kb+QxLINXJt4mYg8iIiALMruDW5bc1i6pxIsZVDHGVl89o7koJiub+HdHqtkSrmB6uBfNXHwb07LoS8T4284zlLs8VLrujOjT7YAiStxX1UCTL0hPIrrkuppp8M7YEP8Q6OIz7dsJLKKg/o8JNlUulUlZFrph0tU/ycEdTYT960exerNocTeiigqXkl0MZofpyQzwVOWjVNjLS71blkFaz6BbpfGu2dVAk7uBq/FQzdRI62kKZgFZlNnm34RdtBPT4rMGgoAkSHMj4MKxy/VF2OZmMDPDcDaGbhpkjALvTPVQ4fChLMKytRbEi/0ki6XMtmFppLUxwrals6c/CsgbGYaz3SS1KKZlYJMctHh2EJRWZ/AZlsloemk64IKhk9WXbzSfymV4deQKAZsTr2ojq2sYlsmmQDlHxvspd7jRTZNXR3rYGixRy5aUtGU002Aql8aj2PCoNrri09R7/OR0nclcmjZfGccmBhnPpalz+0kVC+imOfcclzlc2OVr5qphWkjCNW49dYEM71Un46/PnuY7nR1MzUoMSIKAU1GwywqCAAVdJ6NpFAyDi1OT9Mfj9Mdj/PzOPWyJlM/Lqj9RvYkXRzvpTU0TK2T52yvHyGhFPtmw4wb7QxQkXLKXfLHkaJgYZPUUOT1zy1inStdtktLj80qlZFHBKd14zIUCjOGgmwcPrG/57F3vaFiWxUwyy9+/dZGdLVXohkk8nZv9DJKZAmeujFDUDZqrSs5G98g0b57vpa48QMDtwJhtZNENk6JmUNR0Xjndjd/tQJFEnn3nElubKlEVmWePdlAZ9BBNZvnG62f5yMEtJLN5vvfWCFUhH+PRFK+f66W5KkQ0lWN0OonfZccCoqksF/vHUSSJ2oifdK7AG+d60Q2DpqoQp7pKn21uqEAzDDK5IoWiTlE3ZiNyRZ47donqsB9BgB8c7aAq5CWv6fzDsU62NpYcnUT69jMU3UqIkshHv/Awx166wPf+4rXSQkGgfVcDvrCbiaEZXv/uCZq21PDBzx2i41gPr//9Sc4evszeRxc3jEVRoK61gpHeSZ7/yhFsThW9aODyXJsI7/vQLs691U1iOoUoSyXHYRbFfJETr1wkOpFAUiSyyRy1zZF52Qx/yENFXZiuswN8649fpqalnE17mwiUeTnwwW0cfeEcr3zzHWwOFdOwqG6OsPvBTXzg0wd454VzfPtPX8HhtJWyKrcJM4UYKT1Dg6uGWDGBRUmLQl2BQmm1M8TuYAuKKNPsqcQmrbx2063a+FTbNs5OjTGZW1o7IatrvDrcS63Hx+c37lxRWv+9CFEQqHB5+HT7Nv7L8TeWjQhP5NL8fW8HFS43j9e14l6hWviPKra1VbOtrZT5NE2L//O1NxYUIbuTmCrMEFZDfKD8QYazo/x535cZzI5QZa/Ap3hpcNWx2dvONv9mBCBr5BAFEVmQKVOD1Dtr2RfcRaO7HsMyyBk5rqT6iGnxO3ZNhyJtvDR2kS/3vsVgNsozgyexiTIPl2/Erayf9kLRSJLSSsrpFiYhezseZf2ynHcrRnJXGM5eIaXHEAUJsIjYagmqK68igZKNpC3T/xUv5OiITjKRTVPuXNwA1kyDeCGPS7EhCSJ+mwNJFNkcLOevO0/wK1vuRRAE/uDsG3zkUImQwqfa+UzrDp4d6OS5wcs8VtuGW7FxcnKET7Vs58TUMOPZFPdXNtGdmCajFREFAY9qw34dXXLJobhmFFe7fHhUG88OdFLmcJHTbwy+ZrQi3+ns4KsXzpEuFgg7nOyqrKI5ECTkdOKUFRAgr+nE8jn64zHOTowznErycl8PTkXhl/fsp9F/rTplb1k9oiBwemaEj9Zvwymp3BNp5L6KG/v9ZEEhZKtgpngto5TSS7TFje5NN6y/XtDMAgOZznnhBrvoIKDcOersu97RABiYiKEbBh89uIWJWJrjl4cAcNlVtjRWMJWYX4rhc9mpDHpRJBGfy47PZcc0TQzTRJJE3A4bI1MJktk8IU+pOefAxnrqIn7O9YwyOBnHaVOQRJFHd7WSLWj83ldeoX88yshMEq/Txkfu3ULf2Az/cKyToakEWxor2NJQMecEXYUgwIa6CI/taSeRzhNP51BliT1tNZQHPRzc0sDGutLg0Tk4wanuEcp8bhRFYiKaZng6QbagIQjw0YOb6Z+I0TG4sNjOexm7HtiIw2Vn4PIohVwRl9dJuMIPlMqiNu1tYtcDG3F5HWzY3YggCnOlUu27GglXBXC4S0ZXqNLHwSe3U9lQRuv2Oi6d7CMVy+ANuvngZw8iSAJOT2kyfOjH9uIPe4hNJXH7nex6YCNj/SUWIEmRqKgPUchrmIaBvylC85ZaKhuuvbBuv5N9H9iK2+ckncgiK9JcVqNlSy2qqtBzYYhkNI2iypRVBZBVmX2PbkW1K4z3T2N32Xjqp+9n+Mrt+V53BTZjWubsQHRt+F5J+VRAdXN/ZPPc3/WuMlZj1x2squdQdQM/6O1cgX5Elm90nUcRJT7RumXJSfBWwrQs4oUciUKBCpd7jhJ7vWCTZD5Q28IL/d2cmBxZdv3u+Axf6jiFgHBHRbUMy2Qsk0JEoMzhmtMQup043z3KyGQcgGLRIJrM3nUEsZdTVxjJjiEKIoZlUGkvp8ZRWaLLVv3sD+3mfKKDvuwglmXilt1s8rXjdrpo9TST0FIcjZ7kbOIClmVRZgvfcRrcjb4qBATOxYfwqg5sokyzJ8L+cBO2RUQq1wK/rRmb5KdgJBAFBa9SgyrdPdmqW4W8kaXS0UAlDbhkL9OFUYQ11PeXjPalx4eCYXBkdJAq11k+2ryJWo9vQQ2NcoeHh6tbiBdzOGWFn2zbRdjuxCEpfLhhE1tCFYgIfLRxM1UuL0XD4Cdad+JT7Xywro3T06M4ZIVtwUpsosTmYDk2SSalFajz+Nlv1nIlMYNNknm0ppUN/ggCsC9Si0e1oVzHZOaz2flYwxZ6UzPIoshnWnfOlWtBSa6ga2aar1w4R0Yr0hQI8tkt27m3to46n++GMbxg6IymUpwYHeHbnRc5NTbKS3097KqsotzlntN1kkWR/ZEG9pbVk9OLs6yLwoJzpyraqLI30pU6M7csVpziSvocNc4WlBUE9lYLyzJJaFEuJo/NW+6UPZTbbz3N8GJYtxHBskp19uI6Nm9ehSgIGOY1/2y5xHBTZRBVkRgYj3Hmyii6YbKhLsLJriFURaY84EGfdTygVJplWaW9Gua1a9AN8+oB55ZLojCXIbFmt13q/XfZVVw2FVEQ5pq+r6opG6aJOXvfBOFao5DTrmJXZT5872YifjeDk/FSzf/Vnd5dAbt1gSRLbDnQwpYDLTd8FqkO8oFPXVPZ9gRc7Hn4mrHbsrWWlq3XaO2CER/BR67Vs17vGLwbgTIvD39i37xlbTvqAbDZVXbev5Gd9y+eqhcEgarGMqoabzyGKIk0bKyiYePCEbgDj21b8Li3Gn61RGP7xtRx7gntQBEVdNPg7ZnT3Fe2Z5mt56M/PYmJtayWxlU4ZYWf2bibCzMTdMeml3yULWA0k+RvO08TL+T4WPMmNgTLbltDXV7X6UtGOTc9zpmpMcocLj67Yce6OxqiIFDp8vL5jTvpS8aYyS/d82JaFudnJvizC8eZzmV4srGdKpd3WcaY9UKykKc7PsOZ6THOTY9zX1UDj9e33hFHI1fQSM32VZmmxX07mwneRWVTAJX2cgpGAc3UUUSFHf6tVDsqAXBKDnYHtuNXvEwXSmWIQSWAd7a0oswWZl9oF/2ZQeJaAhGRiC2MW3YRsYcJqn42elsxLBO7ZOdQeD8V9sgtF1uTBJHN/mo2+qooGBqKKCEJ4roTieT1KOO5k+T1EkW4R6mi0rkXp7y8nsx7GTbRgSRIFM0io7leooUJImswFGVRpMG7OCHFVQynE/zd5bNcic+wK1JFiz9EpctL0ObAqSgoooQiitxXdSPlNcBPtl/rv/2ZDSVdGEWU+GhjaZ4O2V08WlOiB4843DT7StUnu8qu9WFuDlawOXijmvfOsoW1abaFK9kWrlzws4Ku89pAH6OpJGGHk89u2c6nN2/FsYgQrE2SafQHqHJ7cKkqyUKBS9NTHB0ZpiUcZHOoHIes0BmfIKMXFtyHKIiU2z1UuUq2hyraqXO1I08r6Fap3D9npLmcOkO9ayOt7m3r+r5YlkXezHEq9hrjuWvCliISQTVCleP22BcLYd0cjaG+ad5+7RKbttfR2FaB27N+Ubb68gAOVebLL59EEsWSoWHBdDLDCycuc7F/HLsqE3A72NZUyfBUYi7rkcoVMIxS800mrxFL51EkCYdNQZ2t+dN0g1fPXEGWRGRJpC7iZ3q2H+K7Ry4Sz+QI+5w0VgRx2RU6Byf5yisnyRd1ynwuasv8DE3FeflUN5eHJhmaiuN2qAQ8s+UeCzxLgiBQE/bzw1NX6Oif5PG9bVSX+TiwqZ54OocsiYiiQNjnml0fvvLKKUzTuqPCVD/qsCwdvfAaevEklpVDkhpQXZ9FENbHuLSsIoXMFzGNIbAsVNdnkeR2hHWsa14pTsU62BvYiizK6JbO4emTizoasWKa6UISn+KkM3kt6t6RGKTWWbZiRwNgS7icn9u0h9899iopbeFB+yosYCyT4utd5+lLxPhAfQv3VTVQ6VqcNnatKLGT6PQmonTGprgUnaQ7PkNvIspYJsVDtU3LliGsFTZJ4lBVAz/WsoW/vHgcY5k0kWlZXIpO8sVCjsuxaR6vb2VfRU1JgX2d74tpWSQKea4kZrgUnaIzOsmVxAw9iSjRfI4Gb2Ddm4BXik1NFTTXlIzOq9nqW63avFo0uxtoXkDzBUrzgFt2sd2/cAmoKAiU2UKU2Rbn3vddp3+zM7D1ps51tRAFAYd86xrvZwqXyesxQvZ2TMtkIncGj1L9I+9o1DhLRrluahiWjlPyEFyDKrgqSeyJVCMLIvoy7+h4Ns2z/Zd5Z3yIKpeHkN05V8Iki9KqAhmSKCCLEnZJxq2o+FQ7EaeLSpeHGrfvlmdhC4bOW8ODiIJAnc/Ph9raF3UyrodNljlYW8ebg/1cic5wcWqSb/ecI+DYR7M3zNf7TpHWCqiLZO0sLD5Wv429ZfVIgkzEVk2ts4W+zKW5zyfyQ7w1/Q9IgkSja9O6BM4sy6JgZnlr+jlOxl6b1zfpkr00u7filNcuuHezWDdHY2I0xrf/5i3eqbtMfUuEDVtr2Lyjjsra4E0x6giCQMDj4BMPbCeazOKwKWyqK6c86EESBVqqwlQGvUizRrksiXhdNhoqAgiCwOaGCurLA/hcdh7c0cxMIotNlfiJh3bSVBkkmszidzuoCvlw2BV2ttZQ5nMxk0gT8rqoKfMR8buo2NqEz2XHrso8vqedaCqLKsvUlPnwOGxousHGugj15QEUWcTvdhD0ODi4uQHPbHP4vvZaZEnEocoIgsCH7tnE2EwSWRLnnJ+n9m9kYCI2lx2SRZGI383HDm0lns5hVxR2NFcR8d+ZMpK7FZalAQYgIwhre6wty8LUu8in/ghZ3YcoloGgsKCnuGaISHIzIFBI/SGy7X4k+fbqFkzkpzkevUBfZpivDv4AWZTQTB2vsnR9bkrLkdbzHJ3posFVmvRixTQVjuUjZtdDFASeamznSmKGL148gb6C2qtEMc+rw71cjk/x5kg/uyJV7I5U0+wLrrlPwbIsYoUco5kUA8kYvckY/YkYo5kkI+kk49k0hSWoeNcTgiDgt9n5ZOsWBlIxXhjoXtF2o5kU3++7xMXoBNvDlewpr2ZHWRX1Ht8c/eJqoZkGM7ksI5kk/ckYfYkYA8n43H2ZzmeWbOa/nXA7bbjvMjrb97F+MC0dr1pPtetewCKlDWFat8bZv5sQmHUqLMvEJXvRzOKaGohlQaQtEGZvRTVvjw0tu75pWUzlMkwt00O3HARKMgjKrLPhlJWSTpnNQcTpptEbYEe4ki3h8jkmqfWEZpr0x+OokkSD30/YufJj+Gx26n1+PKqNiXSaakcrfrVEvDGcifNwZdtc1uJ6mKZJZ2KCr/WeYm9ZPYIg4JH97PDfx1D2ylxWQ7MK9GYuUDTz7PAfot2zC5+6dhE/0zIZyHRyJn6Yi8mj84T5JCSqHI1s9O69o+WW6+ZomKZFMp4lGc9ypXOUM0d7qaoL0txeydbdDbRvqcbrX5tYiSSKbGm4MaUGsH/jjenEqpCPqtCND8KG2gi8K/AaS2VRFZktjRVUh+dv47Ap7G2vnefJ2xSZzQucS8jrIrTpxof5egrapqr5D1NzVWiugf0qqsO+G85DArY2LpwifB8lGFonpjGArO5GkNZ6r6zZTEYSm+tnEEQPFsaaHZeFIAgyiv1RJGOSQvov1m2/q4FDstPgqiJsC9DirkcVFRAsym2LRwl9ihObu4LxfJwN3mp2B0slbqoo45JXb+h5VBs/s2k307kMz/R0rGgb3TIZTCUYTac4NjFMrcdHtctHncdHjdtHmdOFX7XjkBUUUUQQSiWPmmWS0zUyWpG0ViSazzKTyzKRSzOTyxIv5IkWcszksiQKecw7VJsozZY5/Oym3SQLed4eX94wgFKN9aXoFD2JKG+PDVLj9lHt9lLn8VPt9hKyO/HN0kcqoohlWeiWhWYaZDWNjFYgqRWYyWWZzmeZymaI5rPEi/nZe5UjrRXeExWbFiVKz5yukdc18obOeCa1ogZxzTQYz6boic9glxUcsoxdVrCJ0rroltxJWJaFYZlkdX3uvgylEiQKN4p4LYTJbIYr8RmcsoJdlnHICnZJRhTFW2K+RAtdTOTOkSwOoJnZWQdDJ62NU2ZfmhnvRwmCIOKUPVxOniSglhOxr46WXxAEwg4nn9+4i+7YDNPLlGWuFyxKfRK6WRp7Y4UcI7O+i0ip2bvG7aU1EOZQVT33VTVQsY7sgpZlkSkWkUUJ/xqyJ25VxSbLpLIZNvgrCNlLtp1btrG3rI4W742l0pZl0eIt4wtHvja3TBXtNLu3stG7m/OJd+aWF80C/ZlOYsUputPnqHW2UuNoIWKvxim5ZwkAFkfRLJAoTjOS62Uw281Qtpux/AC6NZ89zK+WsTfwMAHlzmYA182CKq/yc98HNtNxdoiZySTjIzHGR2NcPj/CiSPd1DSE2bithh37mqhpKEO13R196BVBLx++ZzNBz3xWm7pIgE89uP2uayx8HwvBwtQvoxdPIyk3Q31oYRojCGIYUS71Vdy67/8qMd/th1dxs8XXRt4osM2/AWXWkVoqCGCTFGySgiLKROw+AmopunZveMOaDdBqt5df3X4PiUKBHw73rHg73TKZyKaZyKY5LYzimk3NuxR1Ns0vzhmGJQPLQjcNiqYxZ4RmdY20VkS/SyLzV6FKEjvKKvmFLfvIG/qyQn7Xo2gYDKTiDKTiyKKIV7XhVe04ZQXb7H25GjQxLQvTMikaJkVTp3D1vmjFWdrKO+dWWJZFVteYymXI6drsf/1dP6/7b1xbntdLXP6aaaLP/szPrrccEoU8z/R08PbYIPJsNPbqT5sk4ZCVkoE9a2g7JHlumeNdy+yzy6tcnnV1UvK6RrSQJ10szF1/dvZnfvYeZI3r7pGmkZv9u2jos/fFLDmZusZAMr6i4740dIVLsUlkUUIRxNJPUcQmSXPX6pBl7NK77se8e6PgUGScskrI7pxrsF0IIgqK6CBga8G0NAQkLEycUhiHvPbo792O6cIo4/mBG5Z3JU+xzX9o1Y4GgCpK3FtZxy9u3cefnDtKtHBnWStNLBLFPIloqdfr9OQob40O8tHmTRyorMW2xkzs9RAEAbusUDR0sit499+NnK6jmQaqJCFdNy8eqmjCpy5OK65b5rz+KEEQ8Cth9gU/QKw4xXDu2jxnYhDTJkkkZujPXMKrhHDJXlySF4/ixy46UEQVSVAwMdFNDc0skDYSpLU4OSNDUouS0KJo1o0lyG7Jx/7QY7R6tq+JSGA9sW7WfmVtkJ/6lUcYG4nRdWGE0+9c4crlcTLpPH3deQb7prh4epA3XrxIU1sF2/c1snV3A/6gG1G8c+a822GjvfZG79TnduBzv89Tf7dDL56hmP06RvEMpjmDqXeD4ERStqA6P44kNy2/EyCf+t8Y2kUM7RyWmSI989MIggPF/jCq85MAmGai1L+RfxOLHJLcWspOKJuA0ots6IPo+ZfQtbOAhKzuQLE/hihVcKcci8Ww2deGLEhzEXzBshatF83qBSby8bm/48VrqXW/uvbUd7MvyD/ffR9uVeF7vZ2r3t6wLJLFAsni0r0e7yXYZYV7KusAiz+/eHxFJQ/vhm6aRPM5ovn3HhW2YVkcnxjmf599ey4qqlvmtd/nLTPmftdmCT7W6iIVTYP+ZIz+ZOyGzyRBQBZF5FkjW54ta5UEEWXWsS19dt1/QeT/PvLRdaVmPj8zwdcun6M3EX3XPTHm/73A/bqZTN1IulQ2926IgjD/umd/l2b/vuqUSLO/S6JI2OHkcxt28GDN4mOzR6nCIV8nSmldUwdRxLur2X89MZEfpDd9nrBtPoFI3sisuWRMEAS8qp0fa9mCR7Xxt52nuTgzuR6ne9MomqXgyEQ2zZXEDJ9q3cpTjRtuWiNIEkSqvV46p6foi0XJaEVcysp6ifK6znAiQSKfp9rjw3nddg9Xluh5F4NfdfAbWx6ct0wWFeqc7dxf9hFenfwOY/n+eZ+bGKT0OCk9DpSat1XRhiTIiEKpJ9mySuuZlkHRLN6QuXg3nJKHe8NPsCvwADZp8Xv51rEeiprOof2tyPKNc79hmJw+P8ilrnFEAXbvaKClsWzV7RDr5mjYbAo1DWEqa4O0b67m3oc2MNQ/zdnjfZw52svYSIzodIrodIr+KxOcPtpDeVWAzTvr2HOwlaa2Cmz29WVzuRn861deZCSZxCZL/P5jT+BVbaQKBZ7v7ebo8BDJQoGIy829tXU8UN+IWy09jIZp0h2d4cWebi5NT2NaJtUeH/fV17OnshqPbWVlJpZl0R+PcW5igr5EjJFkklShQN7QUSUJl6JS5nTR4Peztbyc9lB4zTXZq8V4OsXXLpzn5Ni1puAGf4Af27iZHRUrL1vK6zqXpic5PTZGbzxGLJcjqxVxKgp+u4PWYIjt5RVsLa+YJ5pzPUSpGsXxJFga6FdQHE8jSBWIYghRXHnkS7YdQlI2UszqGPogquszCMiIUqnWzjIz6PlXKOa+j2y7B0FwYWiXKGS+hOr6PLKyFdMYp5j9OpYxgazuAUz0wluYZgyb8ycQpdVxoN9qnIpd5ET0PGm9lE6vc1bxkw0fWXDdyUKCZ0dPoJk6SS1b4vkXJUzL5JHy7RwIt6/pHARBYEOwjH+64yABm4O/u3xuWdrbfwxwKgr3VtXjUBS8yileGepZtpnzRwVX68SPTyxP9Xu7YFgWhmFQwABWHiEtGuv7LMfyOS7MTNAZm1rX/a4VpmVRtIxVvbNhh5PH61uXXEcSbUjYiOa76E//kERxANPSsEt+Nvg/QWhWLfxHDS7ZS6NrM3XO+eNp0SigimtvoBYFgaDdweP1rWimyd9cOkV3fOZmT3fdkDd0LsxMEMvnyOgan2jdQugmHHSbJLGzopKOqUn6Ewmev9LNj23cvPyGwNGRIc5MjKGZJhvDYcKOa4Z6wLb0OflUO/eX36ipoUo22j07kQSZI9PP0pu5uOg+TAzy5tpL3HxKiAcjH2er78CyavIDQzN0XhlnOpomky1QUxlg59Y6goGSM3/sVB9ff+Y4ubxGLlfk8NEr/PovPEJbS/mqSDfW3TKVJBGv34nH56C6IcymHXU89pGddF8a48ThLjrODJFMZBkbjjE+Gqfn8hhvvnSRuqYydt/bwt6DrQTLPLeEJnc1OD0+StfMDA5ZZiAep87n53+8fZgf9vUQzeXQTBObJPHO8BCd01P8/K49uBSVt4YG+fNTx+mYmiRVLGJZFg5Z4fWBPj69eSsf27iZsHPxhzVVKPDD/l5e6e2hNxYlUSiQ0YrkNK0UlZqlwpVn09ZORSVgt7O9vJIf27SZnRWVt7SeeDKT5i9Pn+Tbly4Sz5fqe5sCAZ5oaaPBv7KmYMM0eWdkmG91XKBzeopoLktG0ygaRknrZLZcwa2qBB0OtpZX8MmNm9lbfWPaWBDDyOp+jOJ5LCuJrB5AnG22Xk3zk6RsAwy0wluIZgrF9khp/7P7MM1x9MKryOpeVOePI6CgSzUUs1/GKBxFVraiF09gGcPI9g+i2A5xlYdYy7+IabvvrnM0js6c5dHye3l+/E0ejOzjRPTCoutGbD6ert5Ld2qUgcwk95dtRhREzsT6yBnLq8ouBVEQaPIF+aWt+2nxhfiT80cZzaRuap8/CnDICrsj1QRtThq8fr555cJ7MkPxPt7HWhEr9qBKXoK2NmTRRtFIY1irL4V5r6DS3kiFvQG7NN9G2BG4f8nI9FIwTJORdJIXBrs5Nj5MX7LEone3wbQshtMJvtRxCpei8LHmzSvOQrwbDkXhg02tfOdSB9PZLH915iSiIPDB5tZF2acKeomp6q/OnOLyzDQuReFQXT2VnmtsTbpp8sOxLt4Y7yFWyPCf93yIaCHDWDbJvZFGREFEXcAAFxBQJTutnu14FD+nY29wLn6EjLF+34OIRJtnBwfDT1LrbL3hGVoIhmly4nQ/XT0TyFKp32p4NMbTj20jFHTzzsk+PB47n/74PmyqzJ//zZu8+FoHdbVBXKsg4bhlIXBBEFBVmXDESyjsoa6xjD33tjAxGufciT6OvdFFf88kqUSOVCLH2FCUjrNDPPuN42zaWceBBzawdXc9sizdUTpX07LomJrk5Ngo3+28REa7ZlTldJ3+eIzvd12m2uNlc1mEvzl7mmMjw/NYWdJakXS8yF+fPU2d388jjc0LRugNy+LfvvoSp8ZGmc5mF42GWZZF0SjVm6eKRSYyaQYTCfriMX52524ebWq+JZz6U9kM//fkcb5z6SKJQqlUpTkQ5Bd37+Wx5la8K8jW5HWdvz13mmc6L9Efi5FfgNXn+iayqWyGwUScy9NTfHbrdj6+cfO8eycIApYlMVeWJEhroooVhFKT7NX9vHsflplCKxxBKJ5Cyz9XWmZlsYxJRKkOLBNTH0IrvImunaeQ/uPZ7eKY5hSWmSjJtN5F1MSaqdPirsMtO9nibeXt6dOLruuUbTTIEabyCcZFhRZPFQJwJTV2044GXNWS8PCxls00+0N8pfMMLw1dWfeI8FqhiCIe1bYu9cOrgU2SafWH+IUte9kUKuerl89ycnLkruktsUsyHkVdNOP4Pt7HzcC0DJxSGEsycUhB4sVeLO6OMeFW4HpnIq3FSekxPEoQt+xfNQ2qaVkMJON8o/s8r4/0Mp5Jkyzm7xrGuIVwVTvpy5fOUO3y8UBN45psGUkQ2Bgu4+MbNvKVC+fois7w+28f5vtdneysqKTe78er2hEESBeLDCcTnJ2coGNqkvF0ioJh8ERLG/ura3HI18b854Y7eGX0Mk2eMG9OXKFo6MiCyF93H+WeSOOSoU0BAVW0Ue1oxq+U0erZzrn423Snz5DRbyxLXClEJJrcm9kVeJAGVzs+JYS0CgKbspCbjz61k83tVZy9OMzZi8MMjcQIBd1MTSWpqwnR3lKO1+vg/oNtvHb4MsWigWsVCafbMmsKooDDZcPhslFW7qOprYJHnt7BUN8U50/2c/5kP/1XJolOpYhOpRgZnOGd1zoprw5w36ObefCDW/H616/OdTUwLIvDgwNcnJpEFgUeb27FLsucGR9jIBHHAkaSCf6hu4sr0ShvDQ/is9vZFqnAa7NxfnKC3niJrnY8neLI4ABbI+VUeW7kNBaBWo+PF3uuGVgCEHI6aQkEqfR4cSgyBc1gJJXg8sw0sdmsQlbXOD0+yrcu2ajyeNgSWb/ouQXMZLP8yYljfLvjAqliybBsDYb4lb37eaSxGZe6fOShaBj835PH+cbF80xk0nNMMG5VZVNZhBqPF4eikCoW6I3G6JyZmnU4dDqmJvnzUycwLYtPb9m2zJHWGxalcgkbiuOTyOrOeZ+KUklQyLKyiFI9qvPjJefjOkjK3ceUUmYPYGEhCRJfG3x2roRqKXgVJ+P5OP+j87vIgkTeKPJQ+fpw9wuCgFtR2VdRQ73Hz2N1LXx51rBeCWvQrYBHUdlfUcsHG9o4UFFHwHb7+7ZKde0uHq9vZXMwwitDPXzzygV64tFlxUtvFSqcbu6vbuSx+hZ2llXhXGcRw/fxPgBUyYOIhGZlGcocJqtPErDdKOr6o4ahbDcXE2+T1KLsDDxIXJuiwt5ArXPpkjO4yvhk8J0rF/mriycZySRJvYd62CygOz7DD/o7afD6afQFl93m3RAEAb/dzk9t38VUNsuLvVcYTaeYzGY4NT6GXS4JTDLLTFg09LmqCgF4qKGJz23dQZ3XNy/Q/cOxLp6o2czucC3/MFwqf6pw+uhKrryUURIkPIqfNmkHNY5mYsXHGcp205e9xFiun4Q2g2EtTavulr1EbLXUu9ppcW8lqJbjln0o4uoYIAWgraWC/bubqIj48HjsXLg0QjJVypzruonNJs+KVYs01YV5Jn4Kc5XO6m2nfpJkEY/PgcfnwDRMhvuncbjmiyzlskVy2SLTE0n6uyd44bunePITe3j8IzuRldt7yrpp8tpAHx5V5Q8ef5ItZeUIAlyamuL3Dr/O5ZnpUvPi6AhnJ8Zp9Af4hV17uLe2DlEQuTA5wR+fOMqJ0REs4PzkBJOZzIKOhiAIfGrLVr7RcQFVEvlgSxv31tbREghhkyUUUZqN4JeoKYdTSb558QLf7exAM0vNkMdGhnl7eJBNZZF1yWpYQDyX44+Ovc13Ll0ko5XS1u2hML9x4F7ur2tYkRAOwPcvd/K9y5cYT6ewKDHsfHrLNj6+YRNlTheqVLo+0zLJ6zo9sSh/euIYR0eGMSyLvniMr188T6M/wP6a63mKBUpumgW3rI7djiiVIYpOZNvBd30mgCAgikEE0Yko1SywjnhXZTMAfqzmcRySnY9Xf4DL6T4eKb932W0a3eV8uu4+hrLTGJZJlSNItXP1E8FiEAQBRZCodnsJO9rYVV7NuelxnrlSYgPK6DefPVkODllhW7iCB6sbubeyjiq3d0646k5lVwVBwCErNPtDVLo8PFLbzFtjg3y3p4OL0QkKtyHzE7I72R2p4qGaJnZGqog43HhU2yyV8N31bL+PHw1UOfcCAqal4ZBCCIgE/xE4GtOFEUJqBU7Jg2Fp5PQ0mrkyKuJMscC/f+dlXhvuJZbPLRuKEAWBsMPFBn+YFn+IMocLl2rDJSsrzlRalHQkiqZBTtfJaAWi+TwT2TRD6Ti9iShpbeVjt26ZvDrUywPVjdR6/GvKmEqiSKM/wL+89z4a/AG+dvE8yUKeRCFPYhG/y2+38+G2DXx68zaaA0EUaX5lQ1YvUuH0ELQ55sa8oqGtOtAiICCLCl6xlK2qsNex3X8QzSqSN7KktDgZI0nByGFYRmleFFVckgePHMApe1BEFVW0YxPtCKxtDLbZFOz2q6yTpXmmqBlkskWyuWKJXOO6IJ/dLmNZrDq8ddsdjVy2yIVT/bz2/HkunRsiGc9SzOtoWsmDa2qrwOWx0XVhhEJBJ5XIkU7m+dL/foXzJ/r55X/xJL7g7WWd0E2TJ1raOVRbjyJJWJbFtvIKPty+gf/+1mGgpESpShIPNTbxwZY27LPptntqajkyNMDZ8VJz0VAyQaqweHSh2uPlj558mhqPD6/NhkOWb3jYofRFlzldcxzR3+wo1dcnCwWuRKNMZ7NEXKu7T4IwnxfJotQz8j/ePswznR3kdB2BkpPxLw/ez4GaWmzyyh6hrplpnrncMZcFUkSRf3nwfp5ubSfkKL20178olmVR7nJT6fbw/7z+Q94eHsK0LC7PTPOdzg52Vlahzt4XAQFB9GKa0xjFkwiCDVARRD/CTTTQXXdnEKVKZNtBirnvIUiVSMpmLDOBaU4iSXVIykYk2z704tsUs98CQUEUKzHNiVLviLIbQbqe3cyYvcNXf95++BQPk/kZJgrT1DurqHYsnwVTRZlqZ5Byhx8sC0mU5tH5rReu0hPWun2UO93cU1HHaCbJO2NDvD7Sx/npceLFlU28y0ERRZp9IXaUVbKzrIodZZWEHc45rYC7SUNBFATcqo1mRaXG4+OJhjZ6ElEOj/ZzeGSAy7EpcuskMuhRVNoDZWwvq2RXpIpNwQh+W0mnxCbJt6Q88328j+uhiKVKBsuyE3Fsm+28W7sA8HsFpmWgSnYMy5hlHFoZZ1iqWOCfH36OHw71LBt88Ko27q2s48NNm9gWrsApKyhSiUlNFEp3ejWveMketTCtkgK2YZklHSPTIKMVuRid5Pn+Ln441LuigFE0n+Wd8SF2RaqocS/d1LwYZFGkwR/gV/bs58mWNl7t7+Xk2Ai9sRiJQgHLsvDa7dT7fOytqub+ukbaQiHcirrguL8tUMW3+86UtIhMk4lciu8NnefeZcqmloIoiNgkx1zZnGWZlNtrMS1zNmNdKukuEeKLiII0+93c/PhbFnLz9vEennv5PNu31HLy7AADQ9NMTCZ46/gV+ganKQt7KBRLc8rYRAK3y7Zqptjb5mhMjiV4+7VLvP7CBYb6pshni2ha6UVQVZm9h9p47CM72bitFtUmE5tJc+zNLl763hkGeiZJxrO8/VonpmHy27/7cVTb7UvVq5LEgw2Ncwa/IAh4bDY2l5WjStJcmVOt18euisp5xrcqSVR7vPjtdqayWeK5HJliEdOyFpyoJVFkT2X1HHfzYg/TVeXNRn+ABxsaeWW2SR1KDduTmfSqHQ37dfXnlmWR0TT+38Ov893ODgqzKcUN4TL+/f0PsbuyakEHaDH8sK+XrpnpuRKYp9s28FhTC2HnwiKOgiCgSBKN/gC/uGsvJ0ZHZjnxdS5OTXBybIR7auquroxsO4hp9JHP/Clk/hjF9iiq6/NIYsOq7sFiEMQgNufnEAQnhfSfYpnTIHiQbQeQnJ8BQJLbsbn/CcXsN8kn/gOWlUUQQyiOp0DZBUAh82WK2a9gGhNY5gy5+L8gJzqRlR04A386x3fts9n51/se5Dd2vTszciOcsoIqrn7yfW7sDU5EzxO2BZgsRNkb3MKTlQ8uu50oiKi3iZdbEARskoxql/Db7bT4Q3yybStprcBAMs7FmQn6kjFGMknGMynihTxZrUje0DFm3zFFFGeVae34VAcRp4sql5dat4/WQIgmX7A0yYoSqlTKHN7tRrQ4m+GwSzJBu5Pt4Qp+fvNeEsU8XbFpLkUnGUjFGc2kmMimSRby5HSNgmFgWCayKKKKEm5FxW934Lc5KHe6qXZ5qff6afGFqPF4sUvK3D25XZkLRRR5unED91c33vJj3WqEHetb8vtAdSO7y6tvqkcnlyty9tIwe7bVr5qmcj0gzpZIrgaCICAxf87/wtZ9/OTGncuWVSp3KFCgiCI/v3UfP7Fhx7Lr2iUZj3qt7KXMXsO5+GEGM5eRBIlG9xb8y4iu6abJvzrywrJOhiQIbA6V86vbD3Cgou46gdNb925blkW128fBynqeaBjg90++Se8CNNLztgFOTowwnEqu2dGA0vPmUVW2lEVoDYYoGrswZol1gFlyHQFVklBnA0uL3YnPNO/hr7uP8tvHvstINs4XjnyN+8qb+c2tD6/5/N4NQRCREJFuwxS0bXMtXT0TPPvieb7zg9PIkshHn9qJ3a5w6uwgH3hwE2cuDPPVbx2lotzHC69cYENrJeoqK4tuqaOhaQa9l8d46XunOXHkCtHpFJpmYJmlLzgQcnPw4Y184CM7qakPY3MoSFLpgXd7HFTWBPngx/fwyg/O8MU/fJl8rsi5U/0cfqWDh5/cfitPfR4kQaQ9NP8lFwCPWqKYHUmVGnnCLid1Pv+8h1QQBLw22ywXcxaTUj+FYZqIixjqq0kTSqJIuctNvc8/52hkNY2stjpmDlWSkGfPx7Is8rrO77zx6lxZlohAezjMf374A2yNlK8qyjuZTnNybISZbKkHQBZFPty2gXK3e9nBTRZFGgNBtpVXztHpTmWynJ+YuOZoAKJUg93zm9jdv0ppiFJAWK1itYjD+1uwAF+5IIggVWJzfwGb66ewsGZjDPLccQRBRlK24vC2A9q1dQQVKE2sqvOTqI4PYzHfUBCQuT6fJArCrODa6lW3V4oz8U5+ueUzuGUnOSPP/7nylRU5GncCJUNDwCGLOGQFn2qj0ulhb3k1hmXNCtCVuPav2h3XWvtn5RGF0jcmCsLc/7no3V3uWCwGQRCQBQFZVHEq4LfZqXGXmigNy8K6el+sa7ExuO6+CMxFx0Suvy/CHbsvV8vEHO/3fdwAmyyvOIu8GBJGjuGeGO8c6WX/jkYevKcVv/fO9EDeDNyKumqH5Xbias/ZWs6xxtGCTwkz5RlCEmQi9loc0tLK2V/uPM2rw71LOhmyKHKwsp7/dM+j1Lh9t43EQRCE2YCFnQ/UtSIi8B/eeZnJXGbJ7XoSUSayKYx3CeGt5fiSIOAUxSWFIpeDX3XwyxsP8dOt+5nOZ/CrDlyKikN6b45Vfp+Dz37yAA8cbGdiMkmkzENtdRBZFvnwB7cjiSLvnOzlS3/3Fs+9cp7mxgif/MhunI7VXe+6ORpX67hM0yKVyHLqnR5e+vvTdJ4fppDXME0LQQBREmlsr+Dhp7Zz8OFNBMs8s87F/Oi9IAooqoysSDzx8T04XDb+4D88QyZV4Pjh7tvqaCiSRNm7sgOCICBL0px+BpQcj9AC1LWqJM17oUtRRYv1ejRt74qGaLOMVKuBU1YQZw2OnK7zb199ie9d7sS0rFIEJFLOf3nkMdpC4VVHe7ui04yn03NGTnsoTKXHM09xczEIgoBDUWgLheYcjVg+R9fMNNYs1W9pPRHLsmEK6ly2aKG4REkpupSGlgRxzgC9ChNbyVid5YW/anyVSrtEwFZyLK4ableNOOta874g2BCxL3KfVAzkOcPO4qqBDFgmgjV7XrfBwPMrHmySiiLK6KaCT/FgWAYg3JJyqPXE1YlD4u4+z9uNOcfjPXpfLMvCnH0ngDln8H2sH7xuO1/47CFyuSLnO0f56nePE/Q72b+zifrq4B0V0F0rOhKHORN7hSpHC/eEP75mKti7BfHiFD3p80SL45iWyeXUKbb776PSsXCWrzce5U/PHZ3HivluiAhsDVXw7/c/TL3Hf8eCCIoosru8mo+2bObPzh9bcn3NNOhLxkgWCjct4rcesLCwiTI2VcZ/nUL4ezlQ5bArtDZFaGksg9lgnCAIqLMG6qEDLezb2UBR07HbFBRVXnWZ2Lo5GrpuMtQ3xWvPneeNFy8wPhqbC59JsojHY2fr7gY++LHdbNlZj8N1zUBf6ksSBAFFlbjngXYqqwOMj8YYHYqu12kvCwHw220LnqMozM8+2GVlnsF/FVejplfx7gabxWBdF6m9+h+4IWqb17V56WNrdp3VwGVTEYQSM9TvvvEqf995CYvS9e2sqOS/feCDNzAwrBS9sRjR3DVGoyq3FwuWHBSvR0HX50UhdNMkWShQNIx50T0Li7/teYs/v/IGn208wOeb7sWtzO/RMCyTf3r8q5yY6ed/7/scu4L1SIKAaVlMF9J8qecIb05cZqqQxiWr7ArW86mGfWwP1CJfV55UMHTOxYd5buQcZ6KDTOZLfNjlDi8PlLfz4Zod1LtvdMrGcgl+5diXuaesmZ9ruY/XJy7znYGT9GWmUQSJDb5K/vWWp6h3r1xscK0QBPjdi39MxB5kLDeNS3Hwv7u/gl/x8FONH7vlx38f7+PdSOt5vj14gv916UX8qpPPNd7Lz7c+cKdP60cOpmlR1A3SmTz5gsZ0NMO3nj3Jto01PP7Apjt9eovCsPTZ2nUTsJCQEQWZjd6DmJZJXBufW7cUVNIxMRAQEAUJSZBnl2uz+xAQBRkBAdPS582bpUyfCJaFIEiIgjjHBiQizpW53gp0p8+QKE7T4t4+R2vrURYm3bAsi69cPkOssHTPWsTp4ic37qTFF7yjhrEgCJQ5XOwqq8Kr2kguw4g1kUmT0Yp3haPxtd5TbAtW0+6LoKyhXPluhWWBWeroL/193WcCYLcr2GcFtdfy7Kybo3HueB+/81tfI58rGY+iKKDYZMLlXvbd186jT2+noSWCtIaaUEEQECWRSLWfsZEYWmF9mh1XCoesrMiDk2aF9G4WpmVR0HVyuk7XzDQXpybpic0wmkyRKORJFgrkZ+utC7pBQddvWjXYrahohskfHz/KNzsuzJWd1Pv8/P4HnqDWt/Yayelsdo6tCuClviu81Hflps63ONtgdr2jIQoiW/w1tHrKOR0d5FCkla3+mnkvxvn4CEOZGJt81dS6gnMlYCPZGL9+/CuMZRO0eCNsDdQwVUjz1lQPZ6JD/MamD/DBqq1zjsNILsa3Bo5zbLqPSoePeyMtGKZBV2qCv+45wlguzi+1PUSD+8a6WsuymMol+ZOuVzky0U2Lt5xmbzkj2Rgj2diy6qPrhcfKD6FHbnyXFOH2poEty6JY0CnkiqWeDIeyph6sXKZAsbB4yaCiyNjX0MgGoBV0spnFJ3JJlrA7VGTl9k8+hmlSNAxkSfyRmPyusppcLYV7r8O0LNJ6HkkQccm3rhRypUil83zlmWOMTCS4b28Lv/jZ+3A5bZzpGOLMxeE7fXqLwrIsLiYOM5i5SEqPEi+Oszv4BNv8D2GXXDfM0Yal8+bU15nI9yEJCo2u7ewMfoC0FuNY9HvMFEaxS27aPPvwqxEuxF8npUVnA2wKHiVEtaOVWHGcWucmqp1tXEocIW9kaPceWNTwXw+E1EpSWoyxfD/qLGWpVwktqPQ8nc/y4mD3gppUVyEJAs3+EE82tN0V0XdBEIg4XTT7Q5yeHF1y3WghS864cVxPFQq3hfbcqShz/ajPD19io7/iRybLaloW+ZzG0EiU/qEZUpk8pnnjPX34vg2Eg+41H2cdMxoG+VwRWZFwOlVqG8t44PGt3PPQBsoq1m6kXg/TMBFFYV425HZgJc6DKAjrwk6T13VGU0m+duEcz3ZfZiydXnL99RoyFFHkW5cu8Genjs81GlrATC7LxakJKj2eNTtR2Vl+6vWEaVpoxo3O1UZfJZv8VXx/+CxXUpNs8lUhC9d6T45MdhMvZvnxhr14ZrMdumnw+xefZzgT49c2PMLnmu5FFASKps6RySv8uzPf4W9632JboJYaZ0n9vNYV5JfbH+LnW++n2ROZG3jOxYb4w0svc3Sql8cqtyzoaAC8Pd1DjTPI/7f3M7R4I4izgoGxYhavcnsiN03u2uVXug3QNYNn/vI1vv1nr2F3qXziFx7iwz9936onxC/+1x/wgy8fmesBezcOfnAbv/Z7n8S3hgHzrRfP899/48sYCzxzAJt2N/Jz/+ZDbNp9+xuYh5MJnrl8ifvq6tldWX3bj/8+lsZUPsUvHv0im3zV/N7OT9zp00GWJXZuqeMXPluHPMuiaJom1eX+VTd5LgXD0igYMUoziYhTLltukyVhYTJdGKLJvYNqRxtHpr9NnXMTdmlh0pPh3GWihTE+Xf/vyOpJXhr/KyLZOlJ6FAGJT9f/O0az3VxMHMbEwC0HqXA0k9ZiOGQvpqVjE52ISCS1aSrMJqYLI9S7NuOS/Uufq2VhWhoFM0aJK0jGLq/cMYkWJ8joCXxKGEm4SkKz8Px7bHx4TttqMXhUG4eq6u+qvieXrBJaQVAtr+sYC4zpv/7CDxhIxG/Bmc3Hf3rgUQ7V1QNQ5nBjWCamZa6ZUvZuQiyW4Rt/f4IXX+1A1w3ERWy8bZtq7g5HQ1FlKqoDtG2u5uGntrFtTyNO1/pGb2RZJlLpo67p5gasVUEQblvNaqZY5Nnuy/z+24eZzl4rNVIlCbssz2PEkWYdG7sso5sm4+nUsoPNUhhMJvijo2+jWxayKM6mly3i+Tz/5cgbhJwudlVUrsmZKho6xnUsKbbZ67mZl9Stqgv2QLgUG9sCNRyZ7OZsdIh9oSZqXCXnIKXlORUdQJUk9oQacEolh7UnNcXxmT4a3GE+23hgbr+qKLPFX82hSCsnZ/p5Z6qHT9Tvmfus0X3jc7jFX0O7r4KT0QHSemFeH8n1SGl5fqH1fho9ZXOpcUEQCNpuL3Xz3QBdM3jmL14nnciSTmR58RtHeeonD66aEcfhtuEPutF1A9MwMQ2LQkHDXMQ5WA1Um4wv5EYr6pimhWmYaEUdXVu7A21ZFnlDn6O7Ni2wyRI+m52cpqGbJj67nbyuk9c1ZFGiaOjopjmrpwOqJKKbpbLKRKHAeDqNIor47LZ5pX7LngsWWBamVcSwClgYWFapPEUQZGTBibRKMaj3UcLxmV5GsjHK7esTcLtZOB0qB3Zdc4izeY1YIktNhZ+y0NINx6tBvHCFl0a+gGkVsYsBPt70/E3tTxQkgmolvenT9KfPUuFoxqMsXmKa1KYI2qpmt5XxyGFixXE0q4hPiQCgiA5skpOMHkcWFeyiE10qYBMd5IwUFhYVjibi2gR9mXN4lBBeJbwile6p/Dl+OPorgEBAbeOJur9Z8bV6lSB5I4NL9iKLCgICirBwgPXc9BiFZSitnbLC9nDlio9/O6BKEq4VNGbrlrlg5mI4maQvHr8FZ3YNkiCQ069lUx6tbOfFkU4UQSJsd8/R/wpAhcO7YpvGtEx0S0M3i+iWjmkZzFKZwC3I0iiiDY/iv2H5mQtDnDgzwIHdTTx0XztBv2tBe7eq4sZtV4N1czSa2sr5N//tx2neUDlPfG+9YHeo/NK/eAJdN/D47nyt3npDN02+c+ki//2tw3PCNrIoUuXxsLkswt6qGlqCJXVwv82OW1VR5VJTzrmJcf7rkTd4e3hozcdPzho7AbuDbeXlxPN5zk2MY1F6oX/v8Gv8l0cepzUYWnUzuE2WkUURbdbZ2FVZzUMNjQv2s6wUFW73vEb867HVX0O7t4IzsSH6MlNUOf2IgsDJaD8T+SQHy1oJ2a4xXp2LDaFbJjXOAL3p6XlZopRewCGp5HSN4cx8Oj7dNMjoBbJ6kaJpYFoltvOCoSML4mwt8cJZJ7dsZ5Ov6j1R6qIVddKJHJIs4vI61v39FgQIhD2kkzkkSSRQ5lkTvezDH91Ny5YaUrGSw5KKZ3nrpQuMDUwvmuVYKdq21/Grv/sJkrEMqXiOdCLLxRO9dJ4eWLOzYVoWp8fG+PPTJ6jz+kgWCkTcbn5p916ODA4wmEjwT/bu5/zEOG8ND1LvD9AxNclkNo0iSlhAndfH7soqEvk8z3Zf5rkrXdhlmZ/atpOmQHDZ+1hiozIoGgnyxgSJwmUSxS4Kxgy6mcZEwylXU+f+MCHHrgWuwcCyinMsaqKgInDnhA3vRhyf6cO4Q8r218M0LRKzir/XY2A4yrlLw3z+EwfuwFmtHJZlUTRzVDpaqXNuRBZVZEHBtAyyRoqskSqJnekzSEI5EVsDV1KniBUnKBgZMkacTfaDpPUYvekzxIoTxLVxdFMjYi8jqU0vKKpa5WhlpjDKlewJNnjvwafc+kCnQ3IhCAIzxbG5ZX6lDI8SuGHdy7HpZSsG7LJCq//W9/2tBiutApFFccFxLOx0LRlcLdnLAuliYW49RRRxKCUqc0ksNT6bVmkuz+k6BV3HohTIDDtdVLrd8wh+BjMxzswMc3i8l4DNMXdekiDyV/d9dskKk1Lw1iBnpIlrU4zkehnN9TFVGCWjJymYOYpmYTbAs75o8+zgM/W/ecPyaCxDJOzh6ce3sXlD1bof9yrWzdHwB934byK1shwkSaS+OXLL9n+n0ReL8henT845GaIgsL+qht86eIgd5UtHIq7WM98sWoJBPrN1O5/atJXeeIzfeP5ZemJRTMvi0tQUv//Wm/zHBx6m2utbVcmWW1VRJZmcXoq6VLrdPNnaTpVn/aJn16PK6WeLv5rjM/1cio+xPVCLS7JxdLqPtJbnYKR1XnlStJjBtCwOT3ZzYqZ/4WtQbHMeg2VZ5IwiHYkxjkx205kYI17MUTA1NNMgVsxQMJeOMIVsrhVFxe40LMtipG+K7/zFa5TXBHnys/cSCK/v96aoMp/5p4/x3Fffxum186HPH0JcgzNT31ZJfdv8d2VyNMbkSBS9eHOle6FyH/c8tnXesu/8xWv0dozcVFbDsCxymsa/u+9BxtNp/textxhNpeatcz0tbbnLzYZwmOlslrDTSbpYZCqTQbdMPtS2gfvrGvh/j7xOV3SGKo93SSrH0sSXJV7oZCj9fcbSL6NZN5ZqepQY5Y77FtyHZsaZyZ8hr08C4FPb8Nk2IQs/esGgtaBgaJyJDt6WWvJlz6Wo8XffPY7HPZ8gIxrPoOu3Xln+ZpEzUpiWyUS+i/F8D2ktyrbAw9Q7t9KROMxorhvNLHAm9jJ7gh8kYq+nwbWVw1PfRBIUmt27qXA0kdWTzBRGOTz1TeySmxb3bjxKEN3SsIlODEmf7fkQsIlOFNGGKjmwiU5csg9ZvPWl2/WujdS7Ni67nmVZzOSzGMsYp7Io4rOth3jt+qFoGCui4bdL8oJl27++755FCWWu6ozF8zm+23mJt4eHqPJ4aA2G2Bwpp9LtxqPakESRvK4xlclwOTpDx9QkA4k4O8or+Sd79rGrsgrbdfpi90Ya2Rq40SAXWLqM3bIs8maWoewVzsTfpCt5mrSRWPba1wvFRVTlS+xSErJ8a22R264M/j4Wxou9PcTy16JNZU4Xv/fIYytqwtZNY1kV0JXgdx76AHsqq5BEkQ2hMP/2vgf5dz98idF0iqJh8M7wEH9y4hj/7MBBwgvQ+C6GsNOFS1VIzLJijKZSFPTV6XysBgICO4P1vD7RxYmZPg5FWvGrTjoTo9Q4g7R4IvMGD1mQEIBN/iruLWtZcJ+KKNHurQBKlHvvTPXyR5dfIacX2RWs556yFkI2F07Zxj+MnOOVsUtLnmOJwnbdLvmWoVjQ6T4/xEvfPMb2e1p45GN71v0Ykixx/9M7uf/pneu+77sdkiDQ4PejSBKiKOBSlFJ0UhBKNMyzCrRX32+bLOFRbORVHZeiki6UxD89qq1UjkgpK5nX9SVF3SzLQjOTjGVe5lLsTykYU2s6f8syGEk/x2jmh4BJmWM/W0P/CrfaMKszc+NxY8UssWKGvKFhWGZJOFC2E5gtG1zNe1E0dBJajoSWo2BomJaJKIg4JBW/6sCnOudUjheCYZpMF9KM5+M4JRv1rhCyKJEzikQLGTJ6AW2W6lqVZDyynYDNiV1UFsza5PQiaT1PVi+SMzT60lNM5BNYWKT0PGdjgwueh1u20+guu6VCkYZhMTwe4/EHNs9brqoyUzOpRba6ezBTHMHCYpPvED4lzJXUKTSzgCAI7As9veA2u4KPsYvH5i1zyl72hz90w7phW80Ny4pmnlhxjJQ2Q5m9bslSrTsBw7LI6dqylAmqKM2b8+4GpLUiU8voaAB4VTvqAppj99Qs3mNoWRZT2Qx/eOwd3h4eZFdlFb+65wAHamoXzaJYlkVPLMpfnTnFcz1dfOXCOQJ2xzxK/63B1Uf9LcskpSc4Gz/Mkel/IK6tbay9FaitDnKpa4zu3kkqIj5cTnVOy249ccufvKs6A6uB8B4W0ForOqYmKVwXVbq/vp5qr3fZ7SzLIl0sMpNd/oVdDiGHY+6+S6LIvbW1/Nq+A/z+20eYyZWYo17o6abc5eanduxccYSkJRgi7HTNRWq7ZqaZymap8/nXpYF+IbR5y9noq+S5kfP0ZaYoJHWm8ik+VLvzhj6IWlcAURCI2Lz8fOv9yzJKTBfSHJ7sJlpIl2h0mw9inxXs0UyDEzN9PxJsOQCZRJbuc2svyXsfy0DgBuNSFkVcisp0LkfH9BR98Rjp2dLGxUbFTLFIfzyGS1GZyKTZFqnAvkiPSymTkWc4/RwXo/8T01qIXlKAFTzDdjmCV21nOneCohknmj9PVh/FpdQhCPOPb1gmXclxXhi9wNtTVxjJxsibGkHVxQZfJQ+Ub2B7oBZFWL6c0LIsZgppzseHeXvqCmdig4zl4uQNDYekUucKsTvYwAMV7WzwVuKQ1AXnlKxR5PvDp/mjyy+z2VfNf9rxMeyiwtvTPbw+0UlXcpyEVgoAhW0eNvureKB8A/tCjYRsN5b4nYkN8vrEZa6kJhnKzDCZT2LO3seL8RE+f+TPF7yevaFG/mT/TyGu4NrXClWVePrRbdy7u2ne8tGJOB1dY4tsdfcgrNYylR+iL32uJJUq2mh17MEm3jqWvpQ2Q1fqGLqpUelowSHduqqNtaBo6MuW5V19Qg3TvGXz7WphWhbTuQxD6eWj+hGnC5e8uiySZpocGx3mm5cuUOnx8qt7D3Cwtn7JbQRBoCUY4md37GIyk+bl3itsCpdR4fbgt68tG2RZFlkjzanYa/xw8lsUzaWpfG83qip8KIrED144x+RUkubGSMnZeNdz0tZcjusmeq7X3dEwDJNkPMv0RJJMOk+xoJeYWlZhd+080ITdcfcqft4KpIoFzOvSn9WelZUnZTWN3lh0WXaqtUCVZJ5u28B4Js0Xz5wiWSgQzeX4ZscFIi4XH27fMKt4vjRaAkFqvT46pibRTZNoPsebA/20BkP47fZb4lTaJIVdwXremrrC+dgwY9kEpmWxK1g/xzZ1FdsCtZTZPZyNDdGTmqTFE5kra7IsC90yyegFPLIdSRQpGBpJLYdfdVLrCs45GaZl0p+eZiATXTaV/V5BOpmj631HY03QzAxpbRTDKgImfrUFSbCT0cfIGzEsy0KVLFqCfhLFfvJGkoBzHEksI+Qq4rNZfK/rHBZR6gICqixgkwKEnE4UScJjU5EEEYcss7OikrF0ime7L9MUCNIeCqMuEcGM5s/QGfuTOSdDQMYuR7BLZSiiC81MESucX9F1+tQ27HIZxWIcw8qSLHYSsG1Fla4FSizL4lxsiP928R/oSIwiIOBTHJTZPJhYnJoZ4NTMAA9WbFi2adqyLEZzcb4zeILvDZ1hspDEKam4FTse2UHR1LmcHOd8fJhXxjv4uZb7ebRyM255YT2kq8joBY5P99GTmuTZkbMAeBQ7QdVF3tCYyCUYzkY5Pt3Hpxr284m6PYTt88sIe1KTdCRGyOhF3IodWRQZycaxsHBIKtXOG2vsAWqcgXVjEFwMqiJz7+4mDMMkky3g9ZTK2yIhD45tKolkDo/btijzzE1hHcZ4h+xmV/Cx5VdcR4Rs1dxju3s1hWRRWlCY9npc1dpKa8W7pnwqWczTGZtiMru03SIJApVOz4qaxq9HVtN4trsL07Jo9Ps5UL1yhsXmQJAN4TLeHhnizcEB7q9rWLOjoVsafZkOjkw/u4yTURImFZFYPy7Ra5AXIRLo6pmgb2CaRDLHcy9fWHT73/23H6W9pWLtx1/zlgugWNQZ7J3i8MsdnHq7m5GBGbKZ4qozGn/97D+jovofl6PhkBVEQZiLTkRzNzbtvRuaYdAxNcnLvT1LlkncDJyKwue27mAqk+WZzovkZul3v3T2NEGHkwcbGpY0aABCTif31NRyenyM0VQSgO9evsSmsggPNjSumIHqqtGvGeaStedXsTVQQ4unnDPRQcZzSfaEGqhy+G7IWFQ4fHy8bjdf6nmLP+p8hU/U7cavuhAFgbyhMZlPkdRyPF2zHY9ox63YqXb6ORkd4FxsmFpXCFkQiRezvDnZxUB6GvU90OS9HAzDZGYyyeCV8eVXfh/zYGExmnmbWOEygiBiWgYufyWGoHEl8V0QStOJalP51Oan6Up8E4C9deMUiGNQ4LPbH0ARPQylewATwxojaGunxrUZ+SYYoAwrS3f8i2hmKZooCXbC9j1Uuh8lZN+NU65gMvs2Ryd+fUX7c6uN2KRr1J2JQhe6Kz3P0RjPJfiDjhe4lBhFFiR2BOrYE2qg0ulHMw0GMzOcjg5weLILt7z0pD5TSPPNgWN8Z/AkGb1Aq6ecHYE6mj0RXLKNpJbjUmKMM7EBRrIx/qDjeQRB4Knq7chLUFLOFNI8M3SSsVyCOleIHcE6mtxl2CWF6UKas7EhzsUGiRYzfHfoJE3uMh6q2DiP0OGB8nY2+KrmAg3dqXH+6NLL5E2NBneYf7bx8QWP7VXst42bP5Mr8vxrHWxsraB6lk3m2Jl+BEFgY0sFddW3Qh/iH1eFwu3CVeam5fKPBcOgPxVju+3OM08Zpkl3bIZXh3qXXTfscFHl9mFbJS1v0TA4OzGGKknUrrJyQhAEypwuPKqN7ujMvJL21cCyLFJ6jJOx10jp8Rs+lwUFrxLEKwdwyh5sogNFtN2S3s0Ke92Cy2urgzzx6JZlt79ZNrp1czRM02J0cIZv/NWbHH6lY9WUknaHgtfvxOt3odwB0as7jat12leZmY6NDDOSSlLjvTG6Z1kWeV3nwtQkX+84z/HRkVt2XoIgEHA4+MLuPcTyOV7qvYJumlyJzvCXp0/gs9vYU1m97Iv8YEMjx0eHeaEnS17XGUkl+ZMTR8npGrurqqlyl3Q63m0EXL3WmVyWyUyGsXQKj2rj/vqGZc+9zO5hR7CWs7FBMnqBA2XNBG0Lp74/3bCfjF7gzYlu/tvF5/EodhRRImdoFAyNbYFanqreBkBAdXJPWQvn4yO8MdFFR2IMp6yS04uUO7zsDtZzdOb2ZTSKBY3YVIroZJJ0IkuxoGMaJoIooKgyDqcNt99JIOzBG3AhLdD4dVU0Lz6dKtHMJnPEp9OcfbubXLoUiYlNpzn6ykX8izSD+8MeGjdU4g0sTdF7/mgPsenUgkxQoiRQVhlgw86l09x3O0yryHDmNVp8H6fMvn2uJGY8e5ycMcO+yL/EskyOT/13YoUuJEElYGvHoUcQEbHLQWKFbhTBSd6YpsH9QaKFTpLFfgr2GLK49uhSotjJTP4kUMpkhB172RL6bdzK2u65Q4qgiD6ulltltGEMa37z4TNDJ+lMjmEB24O1/JstT9PsuUbuoZkGF+LD/EnXDzk6vbgBopsGR6a6eXmsg4SWY5u/hp9tuZ8D4WYc15VX5A2N50bO8efdrzGai/N/u15ls7+aFvfihCIpPU9PaoqtgRp+rvl+DpQ1z3MiBtLT/OWVN3hh9AJjuQRnY4PsCNYRsV9zqGpdIWpd1+r4ZVFEEgUwwac42B+eX7Z0J5DNFnjhjQ6momma6kI01oY5dqaftqYIR8/03xJH42oZnWnpZPVJMto4mpnGREcSFFTRh0upwCGFV6y4bVo6eSNGXp+hYCbQzTympSEIIpKgoIhuHHIZLrkccRUipBYWupklp0+TN6JoZmY2K2khICGJdlTRjU3y45DCSMLSmbKVoGikiBd7yc2SKjjkMD61GZu0fPl0pctLR3RyznZYCCmtwFujA3ec4rYkVpvhpaErnJtZPoC1MVhGufNGMcblYFom8XweURCWzfgshVSxsGYNMBODyfwIPen5mQIBAY/sp9G9mTb3duqcrfjVMpQ7QB/e1lxOW3P5LT/OujkauWyB00d7eee1TkzDxOW2U1UXJFjmQVVl+rsnGOqfpro+RHV9iSI1mcgxMjBNMp6joaWc+x/bQlN7BR7f7VFGvptwqK6e712+NMfCcHlmij89eYwnW9opd7txyDKWBTldYyabpSs6zUu9PRwdHsKtqjhlhdH0rWnoEwWBKo+XX96zj2Qhz1tDQxiWxbmJcf7q9Ck8qo2N4bIlB9sqj5cf37SV8XSaU2OjaKbJhalJ/uuRN3mgoYEdFZWUu9zYJRlJFNFn1Y7TxQJT2Sy9sSid01OMplI83da+IkcDoNkdIaC6cCt22n0ViwoWOWWVX2p7kJ3Bes7HhpnKlzjUfYqDKmeArYFqXEppIJBFiR3BOn5tw6Mcm+5lKp9CFARqXUEOhJsxLJOIw0utK3jDEOeQVR6q2IAA2MWbE08yTZOp0TgXj/dy4XgvvR2jTI3GSCdz6JqOJEk4XDb8YTcVtSHqWito2lTFngc24vY55n1flmnRd2mUF79xlImRKFMjMabHE+Qy19K9g93j/On/88yi57P9YCs//dtPLetofPNPXuHU4csY+o0To2qTOfTkjve8o2GYWkmmS7Bd14hsYVgFlNmacgEBRXSgWVlAQBGdyIJtliJWnDVuIKtPMVO4CBb41Gakm2S9mcgexuJqg7OPFt9PrdnJAEqGl+RDFBRMq0jemJo7d4BEMcvLYxfRTB1VlPmFlgfmORlQIlvY7KvmE3V76YiPktIXZkmZzCc5Ot3LcDaKX3Xy0brdNzgZAHZJ4SO1OxnITPN3fUcZzcV5ZvAk/3zTE0teS5ndw9PV2zkUab1hPKt3hzkUaeN8fJje9BS96SmihfQ8R+O9AFESaawN8cRDm3nhtYtUlfspL/Oyua2K8523JmgloaCbOcZzJxjJvMF0/iJZfQLDLCCLTtxKFRH7DqpdhwjaN6KIi48hpqWTLA4QK3QRLV4mXugmrY1QMBIYVh5BkFBEJw4pQsDWRoVzH5WOfSsSzNPNHEltgMncGWbyF0gU+8npU2hWFssykAQVVfLilCN4lXrq3I8QcexCFtZeklQ0Uoxk3qQz8TVihcs4pQiN3qdwyuUrcjRa/SHeHOlb0tFIF4u8OTLAR5o2UeW+M8+rZVnECjme7b/Md3s6lq3CkAWRnWVVVLlWf76CIKBKMlmtyGgqSUHXsckrM3eLhsFkJk2mWERZhFp3JSgYeQaynTcwPnmVIHuDj7An8DB+dWFB37sFhmkyMZkkGHBht63dXlk3RyM6leL8iX6KRR1fwMU9D27g/se3UNsYxuFQ+bs/f4PhwRl2HWjmw5/ej8NlY3Iszul3enjl2bMIokB1XYhN22tRbXcXO8LtwJ7Kau6vb+R7ly9RMAws4O/On+fk6ChtoTBemw3TskgWCgwm4vTEouQ0jXJXiSrWsEy+dPb0LTs/WRRpC4X4xd37SBWKnJscp2AYvDM8SMjh4Au799Hg9y+5j/01tfx0oYAsSpwcGyGv60xlM3yr4yLfudRBuatEOadI4hz1XTSXmyeYs5h2xkKwsJjMJ8noBR6saKfC7luUfQZAEWUORVo5FGlddt926f9n77/D67ruM1/8s/s+vaD3SoAEe5Wo3i25yk2O7diJkziJJ21+mYln5rmZe2+mz2QyyS+ZZFKcxElc4i43Wd3qoij2TgAkescBTj9n9/vHAUGCqCRBSYn96tEjYZe1195n77XWt72vws54IzvjS4ckN0aW9hzFVD+/tenBtd3ACijRzk7z5Fdf58XvHyUxUUqDEQQBWZWQFRnHckjP5kjP5hjsmeDg82eobamgrauOYNi3IJvB9TxG+iZ588eX2bL8QR1REsilSwOlosoEIz6kJRhAoERxLaurRyNbNtVSyBsU8yamYWEULWYm01jGypTA/5SgiH7CagvTxdMU7BI/f6W+k6jaxmjuNUZzr1J6Q12iahtZc4ir00tk0UdUacV0MoTVFvAgoFSjijcm/DZbvORhEwnIDcT1HTfUHoAiBhEFFdczsdwMnnf5tzyXHmPKyOIBdf4Ye8qal2xDlUoimBvCVRyZGVjymJ7MBL2ZCTxgU7iGTeGaRUbGJYiCyPvrd/L94WMUDYvnxs7w650P4F/meAFo8Me4o7JjWadJnT9GpR4uGRlmjrxz/SKpbxdkSSTo1zh5boSCYXG6exTDsJmYznCDcjPLQEAQRIazL3Jq9m9IW0OoYghFDCILOqabYcY4S9LoYdo4SWf049T5b0cWl6ZI9nAZyD5Db/rxOeXxkn6LKobQhAjunC5M0ukhafYwmnuVjshjbIx9fN7IXwqmk2a8cIgL6e8xVTiG7RXmei8jCxqIGo5nkLcnydsTTBdPElIbKde3AtdnaJhOhuHcS5xP/iOzZjcBuYa28PtpCb2HgLI2T/Ouylq+fF4mtwKTo+25nJmZ5Bs9J/nFzXuvaR5dDziey0Quy/f7zvGlc8eYWKU2A6AxHGVHRQ3R66grkUWRlmiMYxNjnE9Mc2BkiDsamlbNvHA9j7PTUxyfGCdrmbTF4tf9rEy3yGihb8E2VdTpDO3klviDS2qivNNQLFr84OkTPHz/lhuKdK7bij6dKjB4cQpRFNi4tZ4Pffo2GlsuC9vofgVJEhFFEX9Ao6wyTHllmNaOahRV5tv/8Bpf/+LLVNZEaGqrvC4e/X/K8CkKv7RzD0Xb4vm+PnKWiYfH+cQ05xPTi46XRZG2eBmPdm7igxu7ODBSimxkb0AdfDWoksyumhp+Yecu/uTgAS7MzpAxTZ7tu0Dc7+dT23ZQFVielUMUBB5sayeq6zx+/iwHhocYTqdwvJKy8Vg2wxjLR2VkUaQuFKZxDZS/UMoLf22qF4DdZS3E1H8+kbL0bI5XnjjGU19/g1y6QKwiRGtXHdUNcQIhH6IoYBo2mVSemck0k8MzTAzP0rqplmDEj3CV+qcoCrRvqedjv/bA/DbTsDh7pJ9XnigVxlbWxbjvg3uWjViU10Qoq1r9t3nosVvYe+8mCjmDQt4km8rz+N+8yFDv5A08kXcWBEGkJfQIE4VD5OwJwMXRt+CXq2kM3k/SvABAY/B+QkojZfpm/HLlXBqGhCQo6FIZYbUZ2zPIWiMIgCLduHJ8wS4xDIlIhNQ2ROHGpwERFYFL6THWvIAfQE96AnuOInZzpA55BWaloKLREqxY1tAYzScZL5SM6pZgBRX6yrnDzcFyKvQQCSPHlJFmMJdY1gmgiBLVvghVK0QofJI6TxN6SaX9nxp8usKWjXVMTKXZurGOYtFC1xSmZ7I01NyMxY+H6aQ5Pft32J5Ba+jdhNUmFDGE61lk7RGmCyeYMc4zXTyNkPwGmhilyr97SceQKCgooh9FDOCXKwkpDQTkGnxyObLow/Us8vYEU4XjTBWPY7hJetLfpEzvojawf8kelqItb3J69u+ZNc4hIBKQa4lp7QTkWlQpjICI7RUoOjPkrQksL0eVb/eKxstKMJ00Q7kXOZ/8Gkmzl6BST3v4UVpCj+CT1+7p3l5eQ40/TLJYnGc4WwpJo8A3e09T4QvycPMG4vrNnw89zyNnWxyfGuPpwR5+2Hd+TZS2qihxT10LnbHy60pL02WZe5paOD4xxnguy18cfpO8ZbGtsprKQADlKmeZ47rMFgucmZri2+fOcGJyAoCdc5kW1wPbs0gYC9PDYmolXeG9SxoZZ1MjaJJMa/DmpzKtFbm8yeFjA9y2tw3qrr+ddTM0jKLF7EwW3afS0lFFfdPCD6VkZAhY1hwL1Rx0n8r979nOm6/0cObYIIdf76W6Po7P/5NVDA6woayM37rlNtpiZRwaG2EgmSRRyJfUKj0PVZIJaxpVwSCdZeXc2dTMXY3NRHSdcr+fulB4SaNkPeFXVO5qamGmUOCvjhxiLJthOp/n++fPUebz8aGNm4mswNAgCgK31DfQEImwr66eQ6Mj9M3OMppNkyoWKdo2puMgi+L8/cZ9PmqCIRojEbZUVrG7Zvk3vsT6lMBwbI7NDHIqOcIdlRvoDFcjX0OB9iUCg3cqzfJo/zTHXu0hly4Qivq58z07eNdjt1DXWommz7FguR75TIHJkVmGLk7Sd2aUrbe2EQgv9hSKorhI8C6fKSKKwryhUV4d4b5Hd1PdeGNc8nUtFdRd4YQwCiavPHH8n5WhAaXoQ6uymN+/yr+bKv/uBdtqA7ct205d4PZruq7r2cwaZwgpzQsKsi/BcucmekFAlaLX1PZyKBkWpW9GFOQFC8SJYnq+OLrWv/L1NFEhpi5tTLmeR9LKz6dVxbUgAXnlvGZREKnxRelOT+B5Hv256WUNDU1UKNNCK37zV+Z8u573T5LGWlVktnfV47keoijgeh75vIlpO9RVRW/KNQ03hSBIbIn9Io2h+9Glywsty80znj/IueRXmSoeY8Y4x0juJaJaO/oS76eAQF3gLlQpgk8qI6ZuQJfLF1ADu57DVOEYh6b/gJR5AdPNMpR7fklDw8MjbQ1wIf09Zo1ziMiU6ZtpDj1MlW8PAaUGab7Gw8N0smSsYQw3RVhpWkTjvCSueqVMJ81g9secT32NlHmRsNLEhsiHaAo+hC5fm7FX5vNzf2MbF9MzC6L/i+8ThjJJ/uzEASYKWe5raKMzWo6+xpSia4E7JyR4JjHJsalRXhrp59jUGPYa2Rg3xiu4r6GNKv/1LfJ1Seb+llaevtjDmekp3hwbYTyXZV9tHc3RGDHdN0dCU0qVShsGQ6kUR8ZH6Z5JULRtGsMR7m5qpvI6DQ3Xcyk4CyM3UaWcev/SWl0HEz3E1OBNNTTyBZOZZI6ATyMW9ZNM5ZlN5Zc9fmIyTbF445pn6/aGObZDsWARjfmJxgOIV3lMFVVGViSKBQvrKiXdssowrR1V9J4d5dCrvdz/3h1vu6HxiS3bmc7nEQSoWUbBusIf4GObtzKZyyEIsLVy6RekbU5xO5Ev/aBbK6uWVLoEaI3F+dyefZydnuLC7AxTuRxF28b1PDRJIqrr1IbCbCgroyZ4eVJsi8X5zI5dDKfTVAeDNIajK96fIkk81rWFuxqb57fFfL41lU1FdZ13b+gkoKgMpedSdigxRKxVAbc2FOaDG7u4q7GZ/uQsQ+kUs8UCBcvGdGxkUUKTZaKaTpnfT304TF0oTGCVMObJ2WG+OXiIjFXE9Ty2xxp4X/0OKlfxfF4Nw7U4PNPL7RVdAKTMHH25CXbE3v5iTihFNKZGS2kD5TVRdt7RSWvXQgNMFAWCET/BiJ/Wrjr2P7gVSRJ+4qKFP2ko2JMMZZ+lLfyhJQ2NKwtjr4w8XD88LDeDO1eXIYtBhCuiJHnbnF+Oh1ZhlJIEEZ+0dC6w7TkYjj0/xvgkBUVcfQoLyfo8K0/aWp5BRhZF/NI/fwdXvmDy/KvnAQ/LcigYFh0tVdx96+opo9cLAZkq3x7awu9DuqroVRH91PhvwXBmSZkXMd00CeMMSaOXav/SAqFhtYmw2rRsKqwoSFT6dtISejfHEn+C61kkjQtLHmu7+VL0o3AMgIjaSmfkY9QG7liC3U1AlUKUSaurdl95jnQFvajpZBjIPkd36mukzQEiagsdkcdoDN6HJl1fWuSjbV08PdDN+dnpFU1fDxjKpvib04c4NjXG/poGtsSraI3EqfAHFpAfXAs8IGcajOezDGdT9KVn6U0mODE9zvnZqWsSFK72h/hA2ya2llddNwOTJIq0xeL8yq69/PmRNzk7p0fUl5xFFkVCqoZPKQmcGo5D1jQp2pfTPRvCET6+ZRv7ahuu2xDzcDGuqM+QBJmgHCawSt1NzjY4nRqiXAvR6C9nrJCkJzNGwTEp00JsjzXhk1R6M+P0ZsZxPJc6X5xtsUZG8rOcz4wCJeKMlmAlneHLIoMDQwl+8PQJdmxp4MF7ujhxephnXlxeXDifN5meuXHphHU0ZQUEYc6ntcSbrukKqiqTyxQxCostpMqaKKoqM9Q3hW3duMr1jeLT21dXKa4MBPnZbTtWPa6jrJyOsrWHQhVJYltVNduq1s4sUxsK89jmrWs+XpUkPrF1+5qPvxqVgQAf3bw6LdpqKPP7KfP72V17A3G5K9AeruRdtVuwXIe4GmBrrJ56f+yaqSOLjsWr02e4vaKrJA5mpnlt+sw7xtAQxcsGQz5TJD2TxbJsFGX5T/onsfbpnxo8z6VgTzJROIjpJrHdPKKgENM2UaHvRhAk0uZFEsUTWG4Wn1xFhb4Tn1yF61lMFQ8xln+N6cJRwEOXYkS1jVTou+cXeJoUp+hM4nkuRXvihvtsuRkMZ3q+AFyXKhCvWFhdi9dfEJaPIq5HbHGlNgSEm6Mh8Q6DJIlUzTHHmZbDyPgsA8MJ4OYZGpKgUB+4a5GRcQmy6COmdRLV2pksHCFrjZC2+qlmaUNjpVq7K4+q9O2Y+38P083gec6iCETBTjBdPIntFZEEnSr/bqr9+26IQvrqvspCKYpsOlkGss9wPvmPZKxhomo7ndGPUR+4B/UGxABbI3E+2bmD/3H4JTLW6unTadPgheGLHJkcoS0SpzUSpyEYocIfJK77iKo6uiyjiBKKKCEKpciX47lYjoPhOORti4xlkDYNZop5Zgr5kqGRSzGYTpIyr12cLqrpvLelk3c1dhDVlq7RWSt8isIDrW1ossyPers5ODrCeDaDPZcmNbsE30Rc97GtqpoHW9u4v6WNisCNpapeOfZJgowm+lYY3wSydpE3ExeYMtLE1SB5x+SVqXMlEVdZ5+XJs8TUAFV6hCdHj9EUqEAUBH40epRqX5SB3BQ/GjnKI3U7mTVzXBg/Sa0vRkgpPUtZEgkGdLS5wu7BkRn6BqboaKtattj76qDB9WDdVh6KIhEIaBgFk3RqsdcoENTwBTQSk2mymcX7BaFELJ9JF3D/Cea9/iQjY40xlHudgFxJU/COt7UvmyK1bIrUrn7gMnA9l8H8FK9NnaE3M8I/9D2Hh0fWLuJ7B3k7YxVhapsrGOmbIjGR4sffPYIkS2y/rZ14ZfgnYsH0zxGmm2Ek9yJZe4CQ0syseZacPUJU7QQEUmYvw9nnAFClKDPGaUwnSX3wflQphiRolDw9ArLoRxFDpW1XTG4htY2UeQ4Ph7TZi+VmUcTrX+QkjbPk7VGYi46E1DbkK1iD/JI6vyzM2SsvPlzPxXSXJgWQBAldUpAEEcdzKTgmlmuv6oVN28X56T6s3Nji5Z2Go6eGOHZmiHzh8uJSkiQ+8ehewsGlo0d+n8o9+zuAUopo98VJXj7Yc1P7KQgyMa1zxWM0KUZYaWKycATDSVOwp3A9+4ZqiFTxciTbw8XFRWLh+1KKpPQDJVrZmNqBKt2YbsCVEOa+RcvNM5R9nnPJr5K1RohpHWyMfoK6wB3XXedx+Rrw/rZNnEtO8Y/nT6yqFn4JadPg6NQYR6fG0CSJiKoT1XyEVA1NKhkZ8hzzkjtXS2m5DqbjULBtcrZJ1jRIGkVM98YcxCUjYyMf69hG/ToxY/kVlfuaW+mIl3N0YozemQSjmQyzxQJF28LzQJUlIppOTTBEazTG1soq2uJla9LqWgkCAproI++U6k49r/QGLgcPOJLoY9yf5L7qLbQEKxnKJTiZHKQxUI4kiCSMDGP5WYqOxbHZfsq1EIooM2GkGC8kAQjKOvdXbWEon+Ar/a8wWUzPGxqNDWV87IN70NTL31RXZy0f/cAeIqHF48X4ZJrhkZkbeg6wjoaG7lcpqwzTf2GC6YkUhby5IP0pGg8SivgY6U8wPpJk0zYb5YqbTUxmsEznmhTEf4p3BnL2NBfSz1Ghb3zbDY21YrJwhpBSjS5Fr+JtF9BEhagaJKT4iWulCadKj9EWfPvFji6hpqmcvfdu4uLZERLjKU4dvMD0eJIjL5+jc3sTnTubaGyvwhd467m5f4rrh+WmmTFOUhu4i7rAfehSGcPZZwipzSDAdOEohpukM/qpOWG9gwxlnyKkNlPt30+FbzeWm6Voz9AQfBcRdXEErkLfx3D2h4BHwR5nMv8qdcGlheRWg+EkGc+/RM66pB4vENe2LFjgVerh+Yji2NxkuHx7Nklz6ZxhURCIqX7Cio9ZM0fCyJG1Dfwr1Gk4nstYPonruYiCQGPgxuqL3mk4d2Gcx588TiJ5ucBWVWU+8NC2ZQ2NXN7gx693A6U6rslE5qanUwqIqxY4K6IPba4mw8PGdLM4roG4giCs4aTJWIPk7Yk5zYs8jlvExcb1nHlWqpVQKvBOAKBLMfxrZHtaMwQBEYnR3GucTX6FjDWIJkap9d9GfeAuZHF91Lqjmo/PbtlLxjT43sVz13y+4ThMFnJMrqFYe71R4w/xvtaNPNaxlbZI2brWRiqSREssRlM0Qta0mM7nyZhFTMfB80CRRAKqSrnPT1jTl01rv1ZIgkxIjs0bGo5nUXTyeJ67pFaM53mU6yGCss5EIUVrsFhSCxcEQrJOTA3yUM12WoKVFBwLQRCIqH58ksqj9Xup1CNks0Usz+VS9ZjjuQuyOTRVXmBklMeD6JpCfW0Un77YkSqKwnz040awboZGKOKjsa2Ci93jTI6lGB1M0Lbx8sKsuj5GeWWY7lMjvP7COVo7qmjtqEaURPp7Jzh3aphi0aKiOvITn0N+fGyMV/oHmM7lCaoqm6uquLu1Gd8NWtg/RQmOZ3Im9W06w++lyldiE7kEURCo8cWJqUFCio/byzcDJY/RO6kwPBDSufXBLZiGxXPfPsRA9zjDFyYZ7Zvi+Gu91LVU0NxZw8ZdTWy9pY14Rfgn/rv6pwR3jh7WwystVAS5JCLmTKGJUYJyHYIgEVbbcD2LopNYdgK7GuX+ffikSgrOJLaboy/9NfxKHTHt2lIhDWeW4ewPmci9iOWWJtOQ0kpE24QkXI4ctIeqSkQMDpxKjmB7LsoyBbQ522Agl1j2mnX+GLW+CLNmjovZSaaKmRV1LPqyU0wZGTxKxeNvtaFRmuRL44Z1gx7f9YIHCwhZ6qujtDVVLH/COqCUPrTygloUlLmIXAmuZ+F4JgqL01fy9hRj+QNMFY6VNDTcJKaTxfEMXM/ExcHz3Hm9mJXgeBaWWzJuJUFDFtaXjcnzXDLWIGdm/5601Td3TYOMNUTeHiesNq/btVrCcX5j+340SeE7vafXXHz9dkERRTbFK/lw+2YeaGynLhC+afOsKIiENY2w9tY43xRRpVKvY8IYBMDFJWenydhJwspiqlhBgK5IPZvCdbw8dY5jswNsiTSwO95Kyswzmp/Fw2VbtIlyPcz+8g6mixkkUURE4JayDXh4JM0s3xp8gxkzR6UeoVJfvu5n57ZGHMddYHxciUBA54G7N1EWv/6IN6yjoRGNB+jYXMeLT51iZDDBuZPDCwyNiqowTW2VHH3jIsffvIhl2GzoqgUBes+OcvH8GK7jsnFL3bpYUP9U0T09zf96+VVOTUySNQ10SaYxFiVtFHls29prMH6K5ZEyh5gqnqUlePey+eOqKLM92sK0kZpP5dAlhXJt6Y/W8xw8XATEucXezTdKyqsjPPDhfTR31HD0lfMcevHcfCpVYiLFuWMDHHn5HE0dNey+eyO3PriFaFnwHWUw/RQLoYpRyvUdDGafJGX24HgmFb49aFIcxy0iIOHh4OHNFTiXFlIlY/nS77ry76tLlTSFP8y52f+Dh8OscYqzM39KffARKn370eWVF52WkyFpnGEs/wIT+ZfIz9HlCkjUBh4goDQuMHi6ojWUaQEyVoGh/AyHE/3cWtG2uF3XYSg/y7nU2LLX7ghX0xGu5mx6jHOpMU6nRmgJViypjeF6Lt8fOkbOKqVr3Vu18S0v9g7K2vyvMWVkcFx3VS7/mw2fpswXfgsIKKqET7v5z+XSO7vS/itTGgSEJceqlNlHT+rbjOZeJWeP4+Hgl6sJq43oUvmc8KWOKMqYTo7e9LdX7Jcw98+VPVlPeDikrQE8IKq24+GRMi8wlj+ALsXYFPsU/lW+uWvBhmg5v7b9VuqCYb7WfYKx3M0R8r0RCEB1IMSDje083NTBlrIqItehl/FOhirqNPo7OZl6fX5b2p5lpNC3pKFxR8VGVFGhzh8r6WEJEkFZ586KjQzlE5iujUdpHeKXVB6p3cFQLoGHhzgX+RAFkUo9QkuwiibPpdoXXVY3CKCqYuUUNU2VefCeLvw3SM60boaGz6/S2lFNa0cVqioTjvjwPG9+oFA1hZ23tnH8zT7OHBvkyIFezp0cwgMKOQPX9fAHNe58aMvbzji1Gjy3gGUexDbfQJQbULR7kKSl02ocuxfLeAlQULR7keT6Fdt+tucCh4ZH5lkacq7F+alpvn3qNB/c3LWI//mdBNsrMpR7g5H8QUwni0+KUx+4hQp904LCOs9zGCscYyR/iJw9hSxoVOibaAndiyL4EQQBzyupKI/mjzJVPEPemcbxLPxSObX+XdT4d85TDiaKvYwWDlOudSIJKkO518naE8iCTnPwbqp8W5BFjdH8EcbyR5gqniNnTXJi5qv0pp+ej2jcVf3v5nQMBIqOxRNjh5g20mhzzDa1vjLeU7tvyXtP5J9jKvc9Yr47KPM/jLJG2tCp3BMkC68S1ndTGfjgNRkBgiAQiQfYcUcHzRtr2HtfFz0nhzl5oJdzxwZIz+QYvjjFaP80vaeHOXeknw999l7q2yqRfhrdeEeipKGhIws+yvVdqGKIoNqIJKgIokhQaWC6eIRZ4xwRtY1E8TiioOKXq+ffnVI+eBbLzc55db05A7i0X0CkIfg+ZoonmCy8iuuZTBcOkreGGc09R0Cux3JT832y3DRTxQMUnDGK9jR5e4ScNUzOGsL2LjOSVPnvoDpwL6q4cPKKqH7uqdrEl/OvYbo2f9X7IhV6aIE6uO269GYm+PbgIWbN5VM3yrQg+yvaOTE7zIXsJN8dOkJcDXBbxYYFE6rhWDwxepJnxk5juBYVWogPNS5dWHwzUa1HCMgaWdtgupjhhYlz3F/T9Zb340pIkkg0/NZqCnm4WG5uRaVr170cWYCSCN+VEQ4oUeFeTP+Q/syTmG6agFxNS+gRyvSt6FIcRfQjCuocxbJI1hpe1dAQBRlZ9GE7BWy3eJn+eR0hCTobIh+i2n8LeWuCkzN/RdYeYTD7HJoUpSPy2A0Vg18JQRBoDkX5uU072RSv4HsXzvLCSEmb6+2GKAjUBkLcU9/KnXXNbCmrosYfesuMb9tymBid5czxIYb7p0nO5DANG1EEn18jGg9S0xCjtaOa2sayear460HJ0NhAWImTtkp1Dklzip7MMVoCm9Clhd9gW+gy+U/7Ff9fLoUpXyJqW+OLUeO7TIVsuw4CpTq0PWXrQ1ojigKRJejwrxXrZmiIokjLhip++V89jKop1DYsttg2bKrlkQ/tJpcpMnhxkmzmctl/MOzj0U/eypZdTcjKO3cxDeB6s1jmAWS5HUnZhCAsXzgmiGUIQgTH7sPz0qu2fX56elEhl+t5jGeypIpFytfIgnD0+VPIiszWOzeu6fgbhSCIjBWOYThpFCmAKMiMF44zbZxjR/znqPZtn1voePSmn6E7/QSaFMEvl2G6GU7NfoNZs5+95b+MSOnjtt0iPekn8ACfFEMQJKaKZ5g2zuHh0hC4FYCCk2A0d5hEsRtFDODioIh+8vY0lpef967Koo5frkCTRhAEiaBSTURpmN9/ZQqV6VqcTQ/x4frb54tNA9LyHpeCdYHp/JMoYoyY7941P7eiPcRU7geYzgSVgUe5nkiIJImUVUWIV4Zp31zPnrs3MjowzamDF3nj2dOMDyWYGJrhxeljmIbNZ3/3/cQqbl6I+qe4fnieg+mmSJo9kPnB3HvaQH3gfqJaB5W+vVhulgupr+PhIgoK1f7biKiXudkjajshpZHzyb9HEfzUBO6kxn/nfOqKIAj45Go2xj6H4xVIFI/g4ZCzh8jZw0iCPi++B2A4M4xmny2lb3kFHDe/iBY3ru+kJfwxQkrLIlYfAYGPNO3hjekLnE2NcmxmkP9y8gfsKWum1h/D8RyGc7McnR1gNJ9kY7iGc+mloxqSIHJLeRsDuWm+1n+Qs6kx/uz887w21Ut7qIqgrJGxi5xLjXF0ZoCxYhJdUvmlDXctMGzeKgRkjTsrO/jO0GEKjsWfdT/PqeQIDYE4iihRdCyyVpEKPcR763e85f17q+DhkrVGVzQ0LC87XyshCSqaFF5kaCSNXqaKxzDdNCIKndGP0RR8F7oUW5Q2WBKLWz46dgmyEMAnV1B0Zig6M+Ts8VXPuTYI+OQyOiKPoUtxLDVbmvNm/pqCk+Bi5odoUpTW0HuRxPVxsgqCQEzzcX9DO+2RMh5q2sDzQxc4MD60JlXu9YQARDSd7eU13FrTwPbyaprDMar8wWvStrpRJGdyvPjUSV577gwTY0kyqQJGwcJxXAQBZEVC01WCYZ1oPMCe2zfw4Pt3UlkTva7riYJImVbNtsjtvDL9fQAMt0BP9gQN6Q1si96OtBYNlmu43sZw3YqpUkvBdT1mk3kmplIUihauuziit6mjmmDg+iNO68p3GYr42bKrGVFcOuTp86vsv2cj5ZVhjh28yPDANK7jUVkTZevuJjbvbCIcWZ7+62bBKPwAxzqD5+WQ5E40/wfJZ/43/tBvYuS/BoKMqr8XI/dFFP0RjPzXsM0DeG4KcBHFKsziYcBE1R/EMo/gOqMo6m2IUhxRasR1ptbUF98ynM2SKFxTNKPnSB+aX33LDA1vLhe02reN+sB+REFiNH+YM8lvM1k8RVxrQ5NCzBr9dKefIKI20hF+BL9cgeMZnE/9gJ70kzQGbqfGtwNBEFCkAFtjP4Ms6qhzLDYThVMcn/0yo/lD84YGQNGZxXAzdEbeS7VvG5KgYrkFfHIMce41j6mtRJQGREFiLH+UhsCt1Pr3zLOaiIJyBTmPQEDS2BxuRLqJg6EsRvBwyM/l7t4IBEEgEPYRCPtoaKukc3sjt96/mR9+6VUO/vgsxbzBgWdPced7trP33q4FZAw/xdsPx7NImudJGufYGv81FDGA7RkkiscYzj1HRG3HL1fREHyIMn0rjmegiEECci3KFcXXqhhhQ/RnKNglClu/UrNAOwNKjoGItonN8d+mP/NNhrM/xPUswMPxFrIClgpzly6qFZCp9t9FS+RjxLRty9KX1vli/Kuuh/nvp5+gOz3O4Zk+ejITBGQVD4+cbSIA763fQVuwgt8/86Nln1NE9fGB+t1IgsS3Bg9xITvJaCFJSNFRBAnTtUlZBUzXplqP8Jm2O3ikdhuKIL3lc4sgCHyq9XZ60hMcTw7Rm5lgopgiKOuICDiei+O57C1r+WdtaLiezXTxOGX68vNRwZ6e17rQpPhclG6h8ZCzxzCcUrTNr1RTrm9Hl+LL/K4eM8b5VfumS1GiaiuzxnkKzhQzxjnqnbuuW9NiKYio+ORSfZAiBmkM3ofppjg7+2Vy1ii9qcfRxDANwfvWVGe1FgiCgCIItEXiNIQi7KyoYSiT4lRigqNTY5xOTDCaS6+ZoepaEFV1miMxNkTL2FJWxYZoORW+AFX+IGFVe8u/w0yqwPM/PM63/+FVEpNpBEEgFPVTXRdD02QM02Z2KkNqNk8mlWdsaIbOLfU3zN7ol4Jsj97GUL6HgXypQH/GnOC16ScQEemK7ENZJ+NSFATiWpC4tvbIWL5g8vLrPTz/0jkSs1lsx50XK74S//5fvZf21neIoVHi2135BQqGfWzd00xLRxX5nInneeg+lXDEh6zc3IkgWSjwxtAwY5lSzuLeujo2VJQjK9uQ5FbAIp/+fVT9IVy7D8/NYJtHQZBR1LtxnGE0qRpFuwPXGUPV70OSSzSBrjMOXilC47kJXHsI1Gvnkb69uYkfnu/GvoLiV5Uk9tTXo3oCfacGV23D82Csb5Ka1rfWgxdTW2gI7CesltLDqvQt9EsvkrMmsdw8mhRivHCcrD3OtvgnKNc75xf5LaF7OZt6nLH8YWrmuM8lQaHSt3nBNSy3iF8uI2tNzqWElN4Xx7Mo11ppCNyKX1664FMRdaCUloIgogg+NCl0herrZQhA0szxX89+nWq9lDNZ6yvjkZr1Tb+QxAAgYLk3TiG3oF1ZoqwqQqw8RDgeYHRwmt6TwxTzJt0nhtl+W8fqhsZVmgaex5Lejp9ifeB5NnlrFMvLUeW/BVnwk7PHmCocxvMKMJez7pPLV2TwEQSBoNJAUGlY8XqiIBHVNtEhfZYyfScj2aeZLh7G9ZYgmL/6GkhE1I3UBd9Flf92/ErDAlGypfq0I9bI7237IE+OneDA1AVG8rNMGRniaoDd8Sbuqd7E/vI2RgtJKvQwqWXYpwQEKvUQH2rYzcZwDa9O9XAo0cdoPknSsfDJKh2hKnbGm7i7aiObIjUE5Ld+cXMJjYEy/q+t7+NHo6X7HsrPMFFIoUoyYUWnzh+jI7x2zaR/inA9k4HsMzSHHl5yAW84aaaKJ0hb/QCElUaiV0TpLuPy+ON5zoKUwMVtJrmQ/u6qffPJFVTo2xnKvoDtFRjLHyCmddASfHhZw/lGIAgCuhSnJfRuTCdDd+qbpMwLdKe/jSpFqPbvXffraZJMUzhGfSjCtvJqHmnuYNYoMpXP0Z+ZZSiTYjyXYaqQI2kUSFsmBdvCchxs18GjFE2URBFFFNElmaCiElQ1wqpGuc9PhS9IlT9IQyhClS9IQFEJqRoRVSOgqG/b9+d5HpNjSX70zTeZnkhTURXmwQ/sYvdt7fj8GqIk4DoehmExPZGi+3SpZnjLriai8RvT0RAFiWq9kbsr3s/TEwXGiwM4ns1osZ9nJ77OaLGPHdG7qNLrFyjbv1U4c36UHz17EkEQuGP/BqJhH+ISv1M8dmPP4W1xacqyRDQeJLo4u+qm4vzUNF89foJzk6XognLbfhpjQVTnDRx7CAQJx+4BLCSlA9s6CUiAiGOfR5I3IIohJLkZUYohya1IciPukt6+61uQ3d3awudu2ce3T59mLJOhzB/gofY2PrVrJzMjs/zXT//pGlrxSE6m+fC/fM919eF64ZNi+K9YAMmijiSq2J6J65VqTtLWKIaT5eDUny3gDnc8E9stkrUn57e5nk3C6GEw9xopcwjDSWM6WVLWCDW+7QtqgERBwi+X45Mu5yzeCHRJ4cHqnViujSopCEBUXZ8c2ivhuAXAA29p7YAbhSiJNLZXUVETpe/sKI7tUswbS3otFp0rCqhXEDOYhkUuvfTi76e4cUiCSkzrYrJwmDcmfhfwEAWVkNJEffC+mzJRC4KEX65FCzxEXN9JzhomZZwlbfaQt0ex3Sy2V0BAQhFD6FIZIbWNqNZFSG3FJ1Uji4E1eWFlUWJTpIYqX5gP1O+i4Ji4nocqygRljbgWxC+rBBWd/7n7Y1iuQ4W2dFpqKTUkwL7yVjrD1Xy4cQ9Fx5qjsS2pi0dUP1HVj7hMUTGUND7eV7+TveUtyIJE+TLXu4RqX4Tf2vQQn2m/E22ucHM1iIJAR7iaCj3E++p3zt93SV9BRJeUf3b6HlfDw2XW6OFY4n/TFf05QurlWkXDSTOQeYYLqe/ieAaqGKJC30ZEbVnUjk+qRJmLbhfsKSYKhwgpdahXpWTNGj2cnPnrZdXAr4QkaJTr26jx38pQ7sfkrDHOzX6JvDVOY+gBQkrjAmeU5ebJWINMFU8QVpqp0LddM0WtIIj45Wrawu/HcJL0Z58iUTxFT+rbqFKY+CqaI9cLSRAJazphTadxToAvbzdQtC0Mx8ZwHCzXwXbded2My4QpAqIwJ2wplN5dSRARnF582gY0WUcTJXRZQb2G7AvH7sMo/hDPLaD53o2slJyLlnkcs/gjBCGE5vvAqvWty7bvuExPphkZSiCKAm2bavnAJ24lHPVf5UjzsDfWsHV3M/msQSQWWJc0fllQaQ9tQxRkXp7+Phezp3A8mylzjIMzz9KdOUaNr5lm/yaq9QbiajV+OfiWGB7Do7OIksj7H97Orm2NyLK0ZKhAu0Gx35+o3Inz09P0TCeYzpcWSwXLwrZ6EJ1TqL73IgphjPw3AA9Z2Y5ZfAJZ3YrnFTGLz6L53rdMyzKCIOC6BfBcPHcW9zo91GFN45M7t/Oujg2YTkmMKurzUeb30T+SwTIsPvcHn15RrdF1PZ798ivXdf0bwdX0hJejW5fZRBzPRBJUav27CVzFtNEWeoDoHNWf69kM597g0PRfElCqqPfvwy+XYTgZutNPLLq2gDSX+rQ+YWdVVNgT38BQfgoBgQZ/JTfDIZM23sTzbGRp7awjtu2QnsnhuR5l1auH96fGksxOZ+cpLcuqwmsqBpcVmVhliDl6I2Ym0/SeHmHDtsY19/WnWDsEQSSkNrE5/qvYcwWpgiChiMF5fYGbc92ScnFAaMAv1xLXtmJ7eVzPnGdTK0VTJERkJNGHLJSKbq/V+BEEgTItSNkK4f2ArNEZXptmjSJKlOshyvXrE1iTRJEqX5gq39oEwnRJoSV47QxBwnWkNfzzgUhc24iHw0DmGRLFM4SVJnxyOY5nkLaGSJv9FJ0ZBCQqfbtpDD2wZDQhrnUQVdtImRdwPJNzya8wVTxBVG1Hk8JYbo6U2UfS6CFnj9MUeoCpwvEVazUEQSCkNtIeeZSCk2C6eIK0NUR36hsMZJ9Fl8rQpQgIIpabw3BSWG4Oy82yOfZpyrTrK+4XBYmw2syGyIcoOknGC28wln8dTYrQFfs0QeX6hWfXgkvMRmFVIqxef+Qmk/xDAoHfQRSvz+stStXIynYs8wiee7l+RJLbkJSuufrWa88OmYcHruPhOnNrEMdFEBY7HwRBQFHlOSf4en6nHrZrIYsKFWotY1IfeScLeBScHAUnx7QxRnfmOKqooQgqsqiiihqqqM6nft8I6v1t3F/10UXbDcMm6Neor4kSDt08Z8dPjKFhOQ4XZ2ZJ5Bd6ZEWxAs/OYeS/giBWIEplgISsbiWX+rf4Ar+E685g5L+GP/xvlmxbEPyIUh1m8QWyqc/jeRay3IrnWZjFpzDyj+M6Yzj2OTT/R5HV3QgrcIpHdJ2Ivni/KArEqqLsun8LK6eoeZw72PuWMwstR0d4JXxSFEGAhsB+KvUurr6PS6lUtmtwLvUDHM/klopfxy+VIQkKSXOAC5lnlrr4VRSFN4aCY/L90YP0ZEao1mO8q2Y3r06d4ZPN9+J6BpYzg3MFQ8ql1CfbS1O0B3DclQvuLHeWmfyPmcm/gIdNSF27hoFl2Lz65Ame/toBOnaUdDLat9RTURtFu0J0J5PMc/ZoP098+TX6z42CB/6gzpa9rahrYNOQZJHy6ig1TeWM9U+TGE/x9NffIBoPsuuuTjRf6Vqu65LPGtiWgz+oLYiC/BTXAgFRUAgob48wpCAICMiIUgiF9VNG/il+siEiUR+4i2rfHg5P/yEJ4zRpawARGQ8P17PwcBAFlVr/fjbHfo6Q0rDkeC6LATqij1FwEkzkD1F0ZhjLv85k4fAc9bM7p6VhsyH8Ibpin+Lo9B+vWhQuCQqV+k52lv063alvMJx7GdPNYLoZstZwiRxBYJE2x6VI/XU/G0EmrnfRGX0M000xY5xjMPssuhSnI/oY+k10MCwF03wDo/BDPHcGUapF930IWdmIY/dTzH8LxxkCLPzBX0cQgxTz38E0nsFL5RAEnWDk/8W2zmMWnyQQ/je4ToJC7u/QfI8iiBqF3BdxnQkEMYCmvQtVvwdB8CGKZQjCwoWuKAYRxTJcRi73zziIY3ej6Y8gSmUU8l9HFPyo+kMIy6RuSrJIvDxIWUWIxFSG7lMj/ONfvcj7Pn4LNfU3N60mYYzx+MhfkbSmMV0D0zUwnMVZAZZnYtkLWcFKpMviuqxqlluXxaIBPDxmkjc3U+EnxtAYSqUYSaUX1D4ACGIcf+jfAQ4gQeAXEcRyQCBc9i1EqRqRZkJl/zDPLiVKdfhCvz3/tyCIyOoeAvKGS62CoJYMEDGOrOzAw0FARhCCwPUV/9S0VvGv/+pXkJXVf7bWbU0oNxjuuhmo1LfQm36GsfwRyvXOkqdoAbz5/xpuClUKE1HqEQQR17MpOAlmzX6q9OvXFFHFACIihpsuFbEv8Q2arsXF7DiPNd7FE6Nv4noeKavkZS5Ygwyl/pRU8c354505D3Qi/zzJ4sEFDFZLwcPBcfO4Xh4QqA49xloZpzzPI58pcuHMKEMXJnnlieMomoymK/iDOrIqYRQsCtkihbxJLl3AthxEUeTDn72XhvaqFSNilyAIApW1Me59/y6+8idP4zguPSeH+P//u68Rigbwh3Q8z6OQNTANi913beSDv3Q3DW1LK+tm0wXOHOojmchSyBkUskXy2SLZdIHBnhLTi205nDs6wJ/939/CF9TwBUr/+gMaZVURWrvqiJYvXARnknn6zo4yMTIz165BPlekkDXoPjGEY5e++d6TQ/zNf/s+4XgQn1/DH9TwB3UCYR+b97QsahfAKJgM9U4w2DtBfr7PBoWcwfnjAxjFUsrbaP8U3/rLH/Nq44nLfQ7q+AIaHVsbqGqII8nvbDa9n+KnuBkQBJFK3w7i+iZuq/4PDGVfYCT3MmmzD8sroEsxotoGGgL3UOu/DV2OzzucFrclEFXb2FvxeYZzLzKSe5mkcQHTyyILGpoYo1zfQmPofir07SiinzJ9M4O551ftpyRqlOmb2a000BJ6N2P515k2TpOzxjCdDJ7noYglhqqo2kalbxd1gdsXpABfDyRBpcq3FzOa4WTiC2TtYS6kv4smRWkLvw9ZfOvS6hR5C3KwGQ+XQu6L2PZ5ZKWDYv6rSFILuv8xEBREMQaI+AIfxyw+hT/4K4hiGaDgeQVcZwIoEUm47iRgIor1+AO/hIeHZR7ANF5G1e/mWpgWFaULo/BNFG0/InEs4wX8oX/NSktZQRCoro/x0KO7+dpfv0g6meOJbx3i8IFeduxr5c4HN7Ohq3aBk269UHQKXMiexl2DcOTVKCWtrY/I53IG8Y4t9fRcmOCHz5zEcVw2ddYQCujr7qRe80q0WDD5f37zy+t68eXwb/7rR4gvMenfCC7OzDKeWSxcIwjiXBRjMS7nBMpIUt0V5ygIwpWWsIAgaAjSEsXXgoLA+oThFE2mpmVtBd77Ht7OWyEad62o9m+jKXg7vZlnyNnTc3oYKjl7gpH8m9xZ9e8IylWIgkyFtomezFOcmP0KZVoHM+YF+jI/XrHodC2I6a1oUpjTyW/heg66HMF0crSHH1owwXm4RJTSJHJJjwBAFkP4lXayximK9tBVHq4CrrOQtWdlSDREPkdEv3X1Qy+dIYn4AhqKJmMULYyiNb+vFBIuGSNXlmHEK8N86LP3cN8H9xAIrZ3ZLRj1c/+H9pCYTPHsN97EsV1SMzlSMzmEOWPFmysQT8/m5hf1S2FiaIa//R8/YHJkttQ/15v/r2WWnqHreowPTvPU15II4tz9zLHYtWys5RO/+RC77lyYvzzaP8W3vvBjTh+8WMopnm8XbMue79/kWJIff/fIPCvepbYDIZ3f/v2Ps2OJMSc1k+OZb73Jc996E88rPVd3rn3HdubvNzWT4+DzZ5Akcb6/l9r/+X/9bu7/8F58PzU0FsFyHM5PTPP0uV5Oj00wls5QtGwUSSTq99EYi7C5upJbmhtoryhblXlvOpvj4MAwhwZH6J5MMJ3NYTgOuiJTHQqyuaaSBzvb2V6/dNRoIpPly28e4/ETZwjrOj+zaxs/u2/Hmu7lPz/1Aj/uuYhp2/zy7fv40PbN+NWf3OheTGvn/U0lDQtdiiIKMkG5jo7IR2gLvxfXs+c1XkRBLonsrSEdTxRkAnIN7eEP0hJ65Ip2hLm2FGRRR5hb3rRHPkRT6KFSeu0qSx5RkNGkGNX+fVT4tuN4Ft5c+3DJyywhCjKSoK6Yrlvh28ajzSVa09WuK4kqjYH7qPbdgocNCCiin8HuWf7xLx/nzJGBJc+77f4uPvlr9xFZh1Qfz3OwzMMYxvOAi20eQ5KacN0UnldAlOsQpfoFv48gxBBQEMVyRKlimdq/UvqlYw9TzH8JDwvXGUMUwoALrH1cFIQAsrIVx76A52aRpBbEOcfwSghF/Lzno3tRNYnvfvUNkoksgxcmGRua4YUfnaC+qZz9923i9vu6qK6PrVs9nFe683Vpaz3w8us9/NGfPzv/t4eHYdhYlsPhYwMoirRkMfh/+fcfpLP9+gkr1mxouK7HycP9132ha4FprH9h7MWZmXm2qStxE5jdbhoEQViz7aDeBOt8PSAJCjvLfp6I2kRv5hkOTf8lAD45TqW+BXXOOyQJGltij2G6Gc4mv4vjWcS1Fjoj7yVvJ0gUe667DwGpnL3lv8LJmX/kSOJv8PAIKbW0hu6fNzRUUaE1UM0fnPsW48UkebvI/dU7SvukSurCv0hN6BMU7WFSxTeYyv2IrHkCTarHr7QirsJWIqKhK43EffcSUDsRBX3Ng5uqKzz40X10bGvg6CvddJ8YZLR/mtnpDMV8SfxS01Ri5SHq2yvZvn8De+/dRGVdDFVTrmkQFUWB6sYyPvM772Xv3V289MOj9JwcYmYyg+M4+PwasYowdS0V3PLAZmIVyzsIHMchnymSz6zMauS6HqZhLdpeyBZx7MWDtuO4FHIGuVXa9VwPa6mxZc5oWLovLkbBXL1tz8O2HGxrcTumaa+p+P4nCZ7nkTYM/uSFA3z35BkM28Z23Lni0xLE2RSnRid4+lwvPkXhk3u289nb9qArixfvM7kCf3/wKD84dY5EPo/tuPMFrVAaNgcSsxwaGuGbx07zns0dfP6Bu9CuohOP+310VJaTKhhMZnIcHhrhgY3tVIdXXsgNziQ5OjzG0GwKXZHZVluNvgxV+dVwHJfRiRSvvHmBU+dHGB6bJZ0t4nkQDurUVkXo2lDDrbtaaGkoR7miQFUSxXWvHXNdj5lkjrO94/T0TdI3lGByOk0mV6RQtBBFAZ+uEvRrVFdEaG0sY2N7FVs66/Dp6nx/REFZpHhdqgvQkbkxFehraUcR/NcUdZhPIRRkrtdMFAQBCRW/vDbHoICAJGr4rpo3bHuW9GyOxOTSGlyZdH7dmABdZwzbPo2ibEPV7iKX+Z+AiyAE8Dwb100CFqWMjPmvFA+TksEw962JvhJBjueBV8BxBvDcHI5zFkH04w/8EmbhR1jmG9feSUFA1R+hmP9bPM9A19+PIPhXN05FgbKKEB/81O3su6uT575/jJefOc3UeArTsMmkhug9N8YPvnaQux7awiMf2XPT06reDsRjAXbvaFqwba4Ec0UE/DfGviZ4a5wB8zmDD9/xn9fU6FItXv0eLDpGAFkSKa+K8Pt/8wtUVK0fh3XWMPivL7zE106cXLD93959F49t20pYX38Ku7cbz375ZVRN4a6PrN1Tfr1wPQfXsxAQFwgOeZ6L41mAgCTI854fDw/Pc+Y9USUIiHPeqIVtW3Megcuer9L5bkktWRAuX1+QlqSqXb7P9lyha6ntS6rgpb57GK7FYH6KaSNFc6CKKj2GdIX3qvTplMKbo+kv0j/7h1QFH6Uh8jnU1Yq7hRIVdMnrVvJ+W67D491nea7/An/+8AfmDx3PZvhOzxkaQhHe275x/tqe5+E6bqnQbc6Tfwm/+9IztMfL+FjXNsK6jiTPiRJe56rkkiffsR1cx1uwcBbEEguJKIslj/6csrtHaRC7dE3XcTEN67qNe0EUUBRpUQqS47jYpr1owj0wOsSXTx/jN3bvpyO+PB0sgKop88/oSriuh23ZV0VqLr+za4Gilvq80rO//Dy9JYXHrrzmtfyGJX2bOUXwufOOTf8n6gIPUqbvnkvxu7Y2bxSe51G0bP7whVf58pvHsV0XTZZojEVpK4/jUxVyhsnATJLB2RRFy6I6HOLX7ryFj+zcsmRfDdvm848/yXPdF7AdF12RaSmL0xSP4FdVZnJ5Dg+Nki6WikqDmsqn9+3kN+/ev4h55tzEFL//7Mu82jdIe0Wc37hrP+/atGHZZ+R5Hl87cpI/e/kNJjJZHuhs4/955D4qgoEVz3Fdj+GxJF/+zkFeeqOHolkSy/Jcb8HEL4oCoiigqTK7tjby8ffvoWtDDZIk8r1nTvD33zzAxPRlJ5qqynz5jz9DTeXqc+il987zPKZncrx2+AIvH+zl/IUJ8gUT150bW66KkEJpCBOEUt9EQaAiHuSe2zr56Ht3EY8E5vevhmQ6z+NPHufLjx9csH1zRy2feWw/2zbV3fD76XkeUzNZ/v3vf4+Lg9Pz20VR4PY9bXz+cw+hvwNry4oFk7GhGabGkqRn86Rm8xx+tZszRwcwChb3vGcbv/xv3kNsHTJAPLdAsfBNzOJTCFIFnptE930ETX8EyzpFMf8VHGcU8AiE/hWyshVBEMml/yeWeQhRrCQU+wPwLNLJ3wEvjSDGQNDw+X8ez8uSz/4holiaGyW5EX/wt7CM1ynk/wHXGUUQy9H9H0HV7sQoPoNR+B6um0CSGvEFfg5F3QOoFHJ/geuO4w/+Zimqssb349J77NgOqdk8J968yPNPnODMsUHyeQO8Uk3Hxq0N/PxvPMCWnU3zkfvrwWihny9c/A/Xff56YUNwGx9v+pe4rou9QtbBcpBlaU3p1suev9YDVVXmt3/vg8vuv0SFmUkXeOFHJzh5eABfQKWxtYLOzXVUVEcJhHREUSCfNZgYneXsyWGG+qYo5Awe+fAePvrzd1BRFVlywr9eeJ5H38wsw6nUurX5dsG7ajG5EsYuTqIHr8+A8jwP23VxvMsLSkEQkOYo7a7+qEVBQhAkPM/DcpwF54lCKRTnlfJ55rxFAoIgL5uHeyUkQV06sCpcsdj2BFxPKXnCvYWecEEoLaVEQSylzFzaJkir0scJCGwI1rIhWDtXtOgtWFsK88aCiCpVosoVCEgIgop4jXSHAHhguw5FZ6HXvSoQ5Jd37F1QHHkpNUcURZZyu4mahKTJKJq8LhR9giAgScKaczeLjs2XzxznjromNpaVJhZREtFv0DOyFCRJRPItjuBpPgVBk1D9Kr7A9V13nuL3itNtexiwkOXF9JvXDwPDeAnTOEI48m8X7HGcPnLZv8dxJ/D7P46u37XmVjPp/46q7UHT7uLSTQxnfshg+jv4lTrqg++mLvAgfrkeQZBX1CVYTyTyeb52+CS261IfCfN777mfW1saF5hurueRyBV4o3+IZKHAnqblF5yqJPGurg2EdI3dDbXc2dZMxKcvSANIFYv87g+e5bnzF8gaJj84dY6P795OZegyW44gCDTEotzR1sRrfYP0J5IcHR7lng2t6MvUxtmuy+t9gyRyeQTgoU0bCOvLRyg9z8MwbV4/fJE//uILTCcyKxrfrlsySmzb5JU3ejndPcYnH93Hw/d04fepyDeQkue6HgPDCb7z1DFePNBDMlWYjwKthitTCQFGJlJ85fGDvPD6eX7zF+5j3/ZmZHn19ykS8tHZXoWuKcymLhelnukZ42zvOFs31t7wO+m4HoPDM5zuXlgUXh4Psqm9Gu0dKl6q6QrNG6pobq+aMz49HMeh79wYRmFx1PeGIOjo/k+i+z/OZT+3CIKArGwlGPnPXHZ4XHrnBPyh357bLswdrxCO/ckV2yhtxyMS/zILHTQiinYXinbHgm0goPs+iu778FXbPcDEdWdQ1NsRhOWN+SVvcS61WFRlyipD3PPu7dz18FaG+6d5+vEjvPTUKRJTaU4fHeB7Xz1AOOqnqe369chq9Cb+r66/uu7z1wvzv4IooqpXO0tLR9zMYX/NX5esSDz4/p3L7vc8j7GhGf7xr1/i9LEhtu9t4RO/fA9bdjYiLrM48VyPE4f7+ds/foZnvnuUsoowEtDRdwAAzq9JREFU7/+ZWwiGr32R5pU6geN5uHOLZMd1Kdg2h0dHGUgmF51TtG3SRhH3Cv/R1WGkK/++JEeoyfKikPua+uh55C0La64gfaVrXfm3QGki9Yo2p15bXenUcz2Gukdp29685P6caWK7l+/aLysoc55o1/OYzuX50rFjPN3dy1AyiSKJNMdi3NvWyge6NlEfjiBLC19Wy3U5PzXNd8+c5aW+PsbSGTRZpiEa4bamRt67sZO2eBxFunFRRm+O39uwbcYyGQ4ODXNoZJTu6WkmMlmyZkkI0q8oVAQDtMbj7Kit4famJhqjEXyKgrQEvd2VSFl5/vLCj/j8po/geR5D+SmemzjOL7Q+tOTxilSBKlXCNdLrup5Hwbbm0zyuVGm99FyLdsnw0CRpwXt3ab/h2PO8/JokzeexO55L3rJwPQ9REPDJl+/b8zyKjo3llCI6iiShS/L8Pst1MByndC4CqiShzv12rudiOu5cZAks10EWRDRZRhIEDMfh+OQ4Z6en6IpXkDaKKGKp7wJgODbmpeuKErosz797WdNAk2SMOWPr0nniXL8cz8WwHZw5vQRNkuaNX8/zMBwH0ymlL5muy9qWTGtDKTpgYxqv4Xpp/P6fQRBUBEHB80rpA6WB20UQNErDqzW3DwRBprTYd/A8A0G4VP9zSUvFBSxcN0cpsqEiCCqy3Io/8EkM48csFCyz568LYqlOTJDwPHNuu4CHNbd/wZ3g4ZCzBjk/++f0zP4NcX0n9cFHqPTvRxEj87nn68nkdgmu5zEwk6Ro26iSxJbaKm5vbVr0PUpAdTjIB7ZtWrVNQRB4d1cn7+5aXn8g5vPxH9/zAEeHRpnJF8iZFidHx7m/s23BcUFNZXNNFe0VZfRMJTgzPsWZ8Ul2NSxNN3pqdIKBmSS269JSFmNrTdWyRonneZimzYsHeviff/4MhSVSBIH5OqJL51xazHvAzGyOv/zKyxQNk+qKMMoNGBqmZXPy/CjfffrEiik4JefM5cWIe0WfFt4fjIyn+L0//AH/5fOPsmtrw/z5K7VdXx1l97ZGnn353Pz2fMHkXO84I+MpGmpvTB/JMCxeemNxum1NRZg925rf0ojetWC+X/NZ03PvxU3R1Lm0wlk8h5Uut/R7JizpsFtq29JtX8t203iVYv6riGIFirJrbpy9PswbHaJEc3sVn/1XD9O5pY4v/fkLDPVNcfxgH/e/d4bG1orrfj9KKXTv3No8wyhlAuj60inVlu1gWQ6aKt9Qgfi6mfGFvMmbr/bw4ydO0Lmllk/+6j1s3dW84jmCKLB9bwuf+c0H+JP/9H2+/rcv07G5lp23tK3ZC+t6HkXbpmBZZA2T/tlZuqen6Z5O0DM9Tf9skqxpLnnuH736Gn/06mvXdJ9RXedXb93HL+29doXojGny+See5Nne1UWEroQuy3xs+1Z+trKd//fDf0AotnrhVyFXXNbQ+K3v/ZAX+vrm//6PDz3Ao12b0GSZ7ulp/uX3n6A3kZjfbzgOpyYmOT0xyY/O9/A7d93BXS3NKFIpipEzTZ44383/OXCQoSsiRwXbJjle5OT4BN8/e45f238Lj3R0EtKuXyXUnbvemYkp/vH4CV7o6yNjLM2xnTIMUoZBb2KGp3t6kcVXubWxgZ/dsZ1ddbVEfb65CMtleJ6H5dlk7DymazFrlryNCSNDxlqeAk6Xa4lo+1CkMsRrKFYfTKf4Hwde4uzMFNWBEBtil/NCDcfhleEB/vjQ6xRti09u3sHPbb1s7Ocsiyf7uvn62VNM53P4VZWPdm7hAxtKC7PTU5McHh+lLzlLTNf59d372V/bgCbLJAp5/vzYm7wxOoTlutxe18Bv7L6NqK5TtG2e6uvhK2eOM5XPE9I0Hmpu5xe378YnK4xkMnyv5xyJYh7DtnljbIiN8Up+ecce2mNlfOn0Mb7TfYbRbJpDYyP4ZJkHW9r5+a27wIO/PP4mr48OYdg2+2rq+O19dxDTfUzksjz6rS/xC9v38L2es5iOw611DfyLnbdQEwxhODavjQzy1TMnGcqkqAkEeWzjVu5takWXZaYLeb50+hjP9l9AESU64mXkrfXz+nleFsN4kXzha3huFss8js//PnT9QfK5r2A7w4CDY48QDH4WRd1OPv91DON1PM9AUTYSDH4W2+4nm/0Csdgf43k5Uql/TyDws3jYmOYJ0qn/C88roGn34PM/tuRE7nkupnmYYuEHOM4YolSO3/8JFKWLQuFbGMUXEQQN2x5DVXcvOFcQ5AUeDReT6eIbTBcPoklxqv33UBu8n4i6EUnwzaUUrl+U+ZKzRqAUDRjPZJnJF4j6dCTx5lFyC4JAQFW5tbmBJ85047gu09ncksc2xaPc2txA71SC7slpjo+Msb2uelH/PM/jQP8QE5kSnfX9HW3E/MuzBLmux+meMf7oC88tMjIEQUDXZCJhH7VVUcpjJW9tMp1nbDLNbDJHwbCwbRfDsPnK44e4c18b1jL1RWuBT1fp2lBDZ2sVZ3vH57dLooCiyKiKhK4rVJYFKYuFCPpVPA9mU3mGx2ZJpvPki9a8Rs8l5AsW/+VPn+QL/+NniUVWr4+oqgiza0sjLx7owbqizun8hQl6+iapr4neUNpnLm/y6qGF866uKbQ1V9DcsDQZzE/xzoOq3Y6q3X5N51yZlbESBEFg6+4W2jedZ6hviky6QLGw9Nrxnwu++f3DDI/O8plP3E5VxWItoTeP9PHdHx3nlz99F20t164fdAnrZmhMjiV585WSx6B9Uy1d29cu6rVtdwt1jeWMDc/y4x+doHNLPeHo2oq3koUC3z59hq8dP8lIOj3vzfznCEGApq56/tPjv4OwwoTsuh7f/F8/WLMvsnc6geU4zBYK/PYPf8SFK4yMK+EBvYkEf/jyqwQ1jX31dRiOw9M9vfzxa68znlleO2I0neEPX36NkKZzf1vrdUWEbNdlKJnimydP8dXjJ0gvY2CsdP4r/QMcGBzi/V0b+ezevbTEYwuiGy4ep5IDfGPoZc6lh/jdE3+Ph4cuqTxUvWvZtn1KMy3x37mm/niexx8cfIWIrvG9D/0siUKeX3vme1T4S4akLss80NxGR7yMvz5+eNH5vbMJjo6P8eHOzby/fSOJQh5REAiqJUNnIJXk87feya7qWv7P0YN8r+csG2Jl1AZD/MnhAxiOzRff82EkQeBfP/8kf3PiML+973ZkUWRHVQ3bKqupDYZ5bWSQvzt5lJ1VNdxeXyokm8xn6UvN8is79vK7t99DwbLRZAm/ovDLO/bSWVbO491n+Pmtu9heWTN/v//59RfImCZ/9fCj+BWF33n+Sf7q2CE+f+udAOQsk8FUkq+8/zFGM2n+9Mgb/OhiN5/ZuouTUxN8v/c8D7du4F0tG3i6r4dn+y8Q0XT21zXw/d5zDKSS/Ic776c5EuN/vPEyaWPlIu5rgSiG8fneg+umcN0UodCvLdgvCDp+/2PIcukZWdYpLOsU4fDnkeUmksnPY5pHEITlxOFcRKmcaOwPMM0T5PNfRXXuRF5CEdd1ZzGN15GVjQRDv04+9w/Y1gkEJEzzCIHgv0BVNzOT+BxXRzQqfbczUzyK7eVx3MIVrGkehpNgIPMthrLfI6S0URt4kEr/fnxyzXWL9F0NQRBoLYtRH40wlExxbmKK//Cj5/nMrbtpikfxq8p89Gy9IQrMp0qVoolLE49UhoLsaqjlh6fPM5MvcGqOEas+urDuIVU0ODY8xmy+gF9RuKOtadmaP8/zSGUK/PVXXyWTWzh2SZJIU12cR+7dwn23d1J1Vc59Nm9w+vwoP3juFIdPDJDJFcnlDZ584cz1Pop5VFeEuWd/Bz39kyiyRCio01QXZ/fWRrZ31dPcUE7Qry1yoheLFodPDvDtHx3j5PlR8lctyianMzzx/Ck++cF9q/ZBVWRaG8vpbK3i1PnR+e3D47N0X5xg345mgteZ/ug4Lqe7RxfUsQCUxwPs3d58U1NGfoq3H67jkU2XUgJ9fhVlGe+8Y7skZ3LksqVv0x/U0Jbx9P+kQNdVEjM5DPPGCJrWzdBIzea52D1OMOyjqiZ6TWEWQRSobYyjHpE5fXQQY5lw8lIoWDa9iQR9s7PX0+1/UlB1lebNDZTVrs6GEK+NzQuqrYaeRIKcZfGnr79BX2Jm1XSTC7MzfOXYMTZVlHN+OsE/nji5opFxCdP5PF86eoz2eJwN5WXX9AHbjsPZqWn+9PUD1xwRWtSW6/LtU2fonU7wu/fdy9bqqvn0G0kQ2RVvZ0Oojm8Nv8Knmu8H5iox1nnAsVyXN8eG+dOH3kdAVdFkmYdbOjg8Mbr6yZSia0FV5ejEGNWBIO2xMuI+P9JcP+9sbGJTWQUBRWVnZQ3dM9NzaUsOPx68yL/cezszxQICsLO6hqf7evntfbcjiSLlvgBpo8hkPossipT7/SQKCyM62yqr2V/XOJ+WtRpM1+GlwX5+acceUkaRrGWyq7qW7/acnTc0QqrGBzs2EdF0TMdhQ7yM8VwW1/MYTKeYKeRpDEeYyGeJaDqGY9OfnmVfbT09Mwk64uV0xMoJaRp3NzQzkV3MNHfj8K749/I7IUtNiGJ0/m/HmUYSy+fD+5JYj+tMIMthuGJx77mXjCEFSSynlAblQxDCeF4KWGxoeF4axx3FNF/HMt/EAyS5BdebRRDClARBRUSpiquLePZU/jfy9ghjuR8zUXiJnDmI5WaxvTyXQh2uZ5Eyz5Eyz9Gb+jvK9N3UBO4jpm1Bk2LIYmCupuPavwlBEAhqGv/izlv4g+dfYSZf4MmzPbx6cZDbWht5aGM7W2uriPp8BDQV+RqiHB6lcH/BsjDsUmqg482xWXlg2g6ZornwhCUgCgLtFWXsaazj6XO9nBmb5NToBHWR8IJx6/DQCMOpNB6wt6meukh42aiM47i8eKCb42dHFmyXJJFN7dX86s/eyY7NDUueG/Rr3LKzhc0dNXz7yWN858ljTCVWH3PXglBQZ9fWBm7rbqWpvox793fQ1lSx6hyu6wq3721nc0ctf/7ll3nulXMUigvn7ieeP8XPvH/PmupI6qqj7NzSwJmescupYl6pVqNvaJqtG+tWaWExPM/DtBxeONC9YLsoCFRXhtnRtfjbuhG4jkuhYFLMm5iGjWM7eF5pjSNJIqom4/Or6H7thoprbxSeV6IUz2eLFIsWjuXgeR6CKKKqUkkLyK+uqv1jmTaTY0kc2yUc8xOJlYz4YsEknzEwDAt3LtolyRK6r6T1pGrymuZ/zysxBeZzBsWChW2X+imKAqoqz+sVrUQ8kE7lefzLr9N9eoR9d3bQsbmeWHkQWS7VbXpe6ducGkvy1ONHOHm4H0GAzi31lFevHynROwWlmq+5VG3HxXU9LMvBtOxFxyVTeVzXvWFjfN0MDcu0yaYLhMK+6xKmkuaKxpIzuXmp+LVAFAR0WSGkLb+othwXw7YXzSmX8tmv5SGGNA11FS735SAJAvXhMBvKynA8F9st1ZE4rovtuThuqe5guXSP6pYK/uWf/tKarrVpX/ua088uJmY4PDLKMz29OJ5HdTBIWNdRJYmMYTCRzc7XCUDpeZ6bnObJ7h5G0mmOjowiCQJhXSPm86HLCpbrMJ3LkSoaC4oLDw+P0D2doCkWXXNUw3Zdzk9P879efoWX+wcW7dckibCu4VdUNLlUS+BR4ugv2jY50yRVNBaJNZ4Yn+D/fuY5/tvDD7GpsmJ+gQ7gl1TeVb0b23XmmbFKtQrrx0ySt0wczyOsavP1P2Ft7V675kiMT23ZwZMXevjiyaMEVZUPd27m1trSYiWi6vPPWBbF+d8ha5VqdP7i6EH8c1ShAlAdCJXSDCyTx7vPcGh8BNt1yZomWcvkrobm+WvrskJQUa/J+MqZJpbr8oXjhwgol7/X6sDlVEBZFCnz+ef7JFKqCbFcl7xlcmJqnP/42o/n2b8UUSKoaCUDynXwyTLK3AIpoKg3IRVHQEDBc7PYdj+iWIYohhfsn78XuRnDeB7b7sfzCjjOIKq2H1Esw3OzOHY/rpfFdS+lrNg4zji2PYBj94NnIopVuG4O153AdZO47jSuO4MoxpDlTlRlB6q2v8TCJpXjeQae9ySO048gaLjuGLBwAhEEgYBST3v0U7RGfoakcZrx/EtMF96kYE9iuWkc77IWjOWmGc//mPH8iwSUeqr8d1Dpu42g0owqRZEF3zJ52stDlSXes6UTXZH5+zeOMjCbJFUs8tTZHp4930tDLMqDne3c3d5MW3mcyBrSqvKmxXQux/mJaY6NjHFhaobxdIa0YZAzLSy7VBtkrTHq3RiLsqexjpd6+xmYTXJqbII72poIzn2jtutyeGCE8XQGSRC4u72ZeGDpSLzneWTzBl/97puL9tVWRfj4B/Yua2RciWBA50OP7CSTNfjBsycWRUauF52tVfzeb79vAXXuWhGN+Pm5j+6nbyjB2SuMBICR8SRTM9k1MWBFw366NtRQEQ8uiD709E1yYWCKTe3V11X4nkoXOHisf8G2UFBje1c9kfD6iOG5jsvMdJaJkVlOHerjzNEBBi9MkUxkMU0bTZMJxwI0tlaw7ZY29tyxgZrGMjT9rWe6si2HmekM3SeGeeOFc/ScHmF6IoVl2vgCGnXN5Wzf18ru2zfQ3FGNP6Aty7w0PjzLv/uFvyYxmeZDP38HP/8vHyIxmebQy9288cJZ+s6Pk0kVEESBeHmIDVvquOWeTWy/pZVYeWhFY8u2HZKJLKcPD3D41W7OnxhmeiKNZdn4Axr1LRVs39fKbfd3Ud9asazxIgBG0eLogQscPXABSRaJlQUprwyj+1Qcx2U2kWVqPIVRtJAkkaq6GPe/dzuNN5Au9E5FLm/QP5hgZjbH4PAMiZksR44PLPoWcgWT5148Szik41+j03o5rJuhcYnmrliwSM3mcBx3zVEN23ZITGawLeeaC04CqsL+xgYCKwgjnZua4vDw6KJajZ11tWyqqEC9hsHLJytsqrw+FoKAqvJv77mLnGWRLhqkDYO0USz9/9zfZycn+eG58/MF41dCFEU0/9p+8K13bFxzvyayWf7iwEEypsnmqkp+5ZZ93NnchC7L9CZm+PMDB3myp3uucPjyOd84eYqCZSEKAq3xOB/c0sUjHR3UhEPMFgp8+9RpvnrsBMPpyxzgjudxaGSYfQ11VAZXrzVxPY/RVJovvHlokZGhiCKVwSA7amu4s7mZbdVV1EfC8zz7edNkIJnkyMgYL1y8yMnxCZLF4gLD59zUFP/z5Vf4r+96iOpQ8HIBJlB0TQ7N9uC4pYVJVA2yLbp+TEMhVSOm6fTOztAcjWE4DgPptbOjmY5DXPfzmW27+FBnF184fohn+3tpCJUWvst5eKKaToU/wOd27uOexlY0ScKcYwsDGM2m+esTh/lPdz3I/toGjk2O8TcnjlzTvcmCiO2WDIRLObKRuet+eusO7m9sQ5dlTNfBufpdX6LfqiRR5gtwZ0Mzv7XnNpojsXmWM1kUkUSRqKaTKBRIGQayKDKWzSwwkNcHArKyCdvuJZv9Aj7fe9G0/YhSBYLg48oiSFluRtPuplh8Gs/NoSg7UJWtCKKOqu4km/1zRDGKqpXYU0SxHFluIZ/7BzzPRtPvRRRjJWrJ4jO4zhSum0AQAmjanWjaXRjFZ8hlS6wmPv+HUZQd6Po9GMWXMIWDc1GW5QWtREEhru8gpm3HimRIFA8zkX+VpHEKw0lguilc79K46ZKzBrmY+goD6e8Q1TZR7b+bmL4dn1yNJkYRr6CIXg2aLPNwVwf7mup58mwPz3dfZHAmyUy+wEBilr967U0eP3GGd3d18Oi2Ltoq4ss6J5KFIi/09PGVQ8c4Mz6J43oENXU+DSvu0+dY5wSmsjmShdVT6nRFZlN1BRurKjg2MsaJ0Qm6JxPzReGjqTRnJ6bIGiYNsQhb66qXnYc84MTZEUYnFuoh+HSFXVsauXNf25LnLYWgX+Phe7ro6Zvg8MnBddGCEkWRG7HJayrC3LG3jcHhxALjxwPOXRhfk6EhCNBUF2d7Vz1Pv3R2fnsmZ3CmZ5x9O5qprYpeU78cx+Xg8X4y2YUGWVksyK07128sNwyLL//pc7zwxHGK+dL3IssSqi7jD2g4jsPUWJKJkVkOv9bDiz86zic/dz977uxYF1bAtcIybXrPjvK9L73Oa8+exjRsJFlE0xQUVaZYMDl3bJAzRwZ44YfHec/Hb+Wed28jVh4sMRyugOG+aYb7p/mbP3iSQy93l9rVFVRNxjIdxoZmGBua4fArPdz18FYe++w9VNcvXeRvmTYXz43x+N+/yhsvnqeQM5AVCVWT0TSZYt7kzJF+Th/u59nvHuHnfush9t+3Cd2/OLVTVmVqG+M0tJSTTRcpFkySMzkSkyW1d0EQ5p6BTLg6Ql1jGQ9/eDf77ux8WwzB9YbtWhhukYBcSsdMpQu8+kYPB4/2Mz2dxbRshkZnFzlyRFEgGND46Af2UHGD9MnrZmjoPoV4RYjRwRkunB1ldDBBfVP5qhzEruvR3zNBX884pmHT1FaJfA30tmFd510dG3hXx4Zlj/nGyVP0TCcWGRr3tLS85ToasiQRkSQi+tLMWq/0D/BMT++Shsa1IDE6W/IiVEdXPdYDzk5NURMK8R8feoCuysp5T3VHeRm/fdftDKaSHB+7XCyYNU2OjpaoAhsiEX5u904+vGXzPONRRSDAJ3ZsZyyT4ZsnTy9Y8J2dLE3OlavYGZcKzX94/jw/OLcw9K3JEtuqq/nkju3c194275m/EkFNY3NVFZurqnjPpk6+dvwEXz9xipF0eoGx8Ur/AN86fZpf2bd3PoUq5xT54sVnKNPCBOTSb1Xriy9raLiehesVSiqpaxTfk0SR97Z38qOL3QRUBdNx6J6Znk8VMR2bwXSKC8kZpgo5VEni1NQE5T4/1cEQ47kM5xLT+GR5vt/l/gCatJoKrchjG7fy3MBFFEkipGqkikWiuo/d1bWIiMQ0H9P5HG+Oj3B8coykcS1q5yVKXlEQOT45jut5VPkD1AbDfLhzMy8N9qNJMhFNJ1UsEtF09tWunMIgCgIbYmWcnArzxIXz7K6uw5kL/7bHyqgLhdlTXcfzgxd5fuAi9aEwJ6bG17UY/BJUdTuqun3BNp/vvUseq+v3o+v3L9oeCv/2ksdr2v41XQ9AFDegKIvHPV1/AF1/YMn2l4MgCKhSmJrAvVT776JgTzBVOMBU4U0y1gUMexrTTc+pFoPjFUgUj5AoHkGXKijT91Dpv42w2o4uVaDOKUGvBlEQKA8G+OTeHTy6vYsjg6O81NvH8ZFxhpOpEgPeoWNcTMzwL+68lR31NYuiaIZt8/2TZ/nC64cYT2eJ+X10VJaxo76GtrIyKkMBgpqKriiICPzFqwf57smzy/RoIdrKy9jVUMvJsQnOT0xxemySrbVVKJLE4cERhpMlx8CdrU1UBYPLfvee6/H8q4sZA+PRAA/euWnVRdzVaG0sZ2NbDed6J8jm1yeqcaPYs62Jx586vjDK4sHM7PIkGlejujLCls46XnnzwoKaj9Pdo/QNJaiujKw5iup5Hpbt8OPXF84diiLR3FDGhpbrpy29GpqukMsW573hkXiAiuoIlbVRdJ9KLlNk6OIkgxemSEymuXBmlG/97cvUNMZpaq9at36sBMd2uHh+nH/442c5+noviipR01hGQ2sFtQ1xVE0mncwzMpBgpG+aiZFZ/vHPn6eYM3jksX3Eypd/vwEGL0zyxT98iqOv99LQWkFdczm1jWUoqsTsdJaL58cY6ZsmlynyyjOniZQF+fiv3FOiD78Cruty4ewoX/rT5zn2ei+SLFLfUk5jWxVVdVFUVWY2kWXo4hQDvRNMjib537/3OIIAdz+yDUFa2MdgSOfhD+5m07YGzp4YYrg/wex0lnyuiGO7iJJAIKRTWRNlQ1ctW3Y2U1YZWpfaDNu1KDhZQsqNsaZdD0rENgZ92bNMGaPcUfEeAOprY/zSp+7i/Y/s4OvfeZPhsSR33LphQURDoJQaWV8bo6ryxpjtYB0NjVhZkM4t9YwOznD25DDf/eoBHv7QHqrrovgD2qKB1HVdspkiY4MzfPPvX2F8pFRjsWNf6zvKivQ8j5lsHtN2qIktV8RZygueyZYG1KroYuuvYFpIonhN0ZMbwUvfOoDqU3nPLy1e5CyHj2zdTHtZ2YKBXBAEyvx+PrRl8wJD4xJkUWRPfR0PbWifNzIuIaRp7Kip4eW+gQX0wv2zSfKWNe9NWA6u59EzneBLR48tEIiTRZFt1dX8+v5bua25aU1Z4nGfj1/cswdREPni4cNM5RZOfn9/5Bjv6eykORYFSr+7Isr8+ob3r2liM+xRksXXAJGIvhddblzTQuvntu7i704d5StnTlDpD/KpzTs4OTUBlFilnu7rpXe2VKw/nEnzjXOnuL2+kepgCMd1OZeY4mxiEg9ojkR5d1MHtaEwHfFy6kJh5LkUo5juY3N55XwtxUc3biGoqjx1sYekUaTM5+eDG7oQBYHaUIif6drKs/0X8CkKHfEyPtq5hUp/Kf9Wl2VaozGCyvLRtdZonIdbNvDcwEWOjo9yX1Mr8RY/H+rowq8oPNt/gdligaim88GOLqCUArevpn6+j4ok0RiJkrPMuTZjfLCjiycv9vCV08cRBNhZVUvbHFPX3Y0tGK7Dy0P9HBkfYV9tA+W+AMEljNCfYnkIgoRfqaVJ+RD1wfeQsfqYKhxgpnicnD2EYSew3PR8EXnRmWIk9yNGck8TVtuo8N1KW+ST+OS1L6AEIKiq3NXezG2tjQzNpnjybDdPnO6mdyrBgb4hmstitJbHiPoWhvj7ErM8cbqb8XQWXZH5yI4tfHrfjiUF8wzbvqYxOB7ws62umppwiOFkilNjE4ynW6gIBjg+Ms5EJotfVdjX3LAi25RpORw6sTAiK4oCVeUhNm2oXnN/LkEQBLo6qnnlzeA7xtBorI0v0qPwgExu7YQMqiLR3lxBR0slx84Mz28fHk3S0zfJ9k3111QUPj6V4cQV7UBJt2P/rlbk60yBXgqiKPLIR/YSLQvStbORrbubiVderufxvFJB8jOPH+Hrf/UiqZkco4MJDr3c/ZYYGp7nkZzJ8eQ33pwzMmQ2bqvn0U/fzq47NqDrl8fyxESaF354nCe/+SYjA9P86BtvUlUf446HtqCvkEIzPjzD+PAMW/e28PFfvZftt7TOr/scx2Xw4iRf+4sXeOWpU2SSeU4f7me4f5rWzpoF7cxMZXjqW4c5+eZFRFFg275WPvjp29m8u3nB2nByNMk3//YlnvnOEQp5ky/+0dNs3N5ITcPiGlZVU9jQVceGrmuv87kRpO0Z3px5jtvKHnlLjQ3P8yg4Oc5njvDi1HeJKmXzhgaALIvUVkfp2lhLNBrgtn1tVK6D6ONyWDdDI14ZZs/tGzh+sI+Z6QzPfO8Ygxen2HtHB/XN5fgD2nykwrZdctki/b2TvP7js/T3TGBZDhXVEW6bC3+9VVhtDel6HsOJFOmcsaKhkS2avHKmHwT48P6ti/afGZqgLBSgufL6XzbHdsilC8iKhD/kwzIsssnF3iIPj6mRGWJrCFdfgiyK3NfWumT9iS7L7Kipxq8oizzE5YEA22uqifuXzk1ujEYpD/gXGBqzhQI5y7yqlPbqeygttL99+jQTV1FQ1oRCfHjLZm5rarymUlRVlnhs2xaOj43ywsX+BQxlM/k83zl9hv/fHbcBl4q/RQ7P9BBUfAiAX9JpDCyds5kzuxlN/x1Fe5DGyG9RG/40rMHQiGg6v7l7oRf7Xa0lL3VM9/Evdt2y7Lkt0Ti/uWexBxzgszv2Lvh7W2U12yovL2hUSeIDGzbNU+FeiYCi8rFN2/jYpm1Ltl3hD/CxTYvf8SshCgKPtHXwSFvHon3va9/I+9oXp/bFfX7+4P53z/8d0fQF/ZNEsVTsvYzKt19ReHTDJh5d4p5+iuuDJGpEtY1EtY1YToakeY6Z4hFSxnny9ghFZxrTSc1FOhzSZjdps5vawP3XZGhcCVkUaSmL8ZlbdlMZDPJHP36VyWyOvulZhmbTiwyNs+OTTOVKY0RbeZxHt22iIhRccmzwPI/x9NqLqAVgY1UFO+prGEmmOD02Qe9UgoxhcHF6BsN2uKWpltay2IoGzMR0hlRmYVRQ1xQ6W6uuWyyupaGM6DrVGKwHAn4VfdG9eBSL1xZVbKyLs2VjLafOj2LPpes6rsuJsyPs39VKZ9va3ivPg5cP9mBeQZcrCAIVZUF2b107K+ZasePWNnbsb1u6TkAQCEX83P3INgZ6Jnj624fJpAoM902tez+WgmO79J0f56UnTwJQ0xDngz9/B/vv61p0bFlVmHf/zC0YhsX3vvQaick0Lz91ivauOpo3rPzs4xUhfvnfvIf2roWaM5Ik0tRWxXt+5lbOHR9iYmSWmck0Az0TCwwN1/U4/sZFTrx5EdOwae+q5cOfuZNte1sW6bFV1kb51G88wMVz45w9OsDUWIpnv3uET/36tUVzbybydpaDM88iCTK3xB98S4wN13PJ2ElOpl7j5anvk7ISRJWlKZzbWiqpKAvhu8nO/fVLndIVtu1p5qFHd/LU40eYnc5y4lA/Jw71o+kK4Ygf3a/geSVGgnQyj2lcTqepqI7w/o/fQvvGmhtSOr1WXHKUnxueRJUlcoZJ0bTZ2lyDJksMJ1IYlkN9+eVFe9G06J+cpWBaFC0bn6oQD/pwPY+pVI4jF0ZAgLp4mGjAx3gyw1NHu6mNh9neXENLVZyIf23pNVcim8zxzJdepqK+jLs/civj/VM88w8vLXFPHmcO9LD/vbuXaGVpVAYD1IRCS3rvRUEgpGk0RCKcn55esK8qEKCjfOmFH0B5wD9Pt3oJrueRLBaxXXfZwnrP8xhLZ3iqu3fBdlWS2F5TzQMb2q8rtBnz+XhowwaOjY0zeZUB82R3N5+7dd+coJ9ISPbx1PhhyrUIIlDnL1/W0LDcKUxnElmMo8m1SOI7ZwFwoyg6BcaKg6SsUtQxKIep1Rvxy6vX2KwnXM8lZc0wURzB8RziagXlWhWK+NY5Jq4VaWuW4UI/plvyOldqtVTr9YjrqEvxVkGRQlT49lLh24vppEmb3cwaJ0kZ58ha/WStIRxv7Wkyq0FXZJrLojTEIkxmcxi2jbFEzU2p0Lu0mKwMBtFkaVkjYziZ5sz4xDX1oz4aZntdNS/39jEwk+TC9AwjqTQjqTSSIHBbSwNV4ZW9gRcGJhfVUuiaTEvT8mPnaqgoCxGaY9xZjzqN9YCmlcQ1r0xNXavS+CVEQj42tlVTVRFmZDw5v/38hQn6hxO0NpWvKZXDtErCiFfCpyts7ay7Kd7b1dLEAQIhna6dTTz97cNzBDpFXNe76QxUuWyRI6/1kM8WUTWZDVvq2H378unmPr/K3rs6OfFmH8cPXODUoT6G+6doaKlAWiG1/dZ7N9K2qWbJfaIoEK8I0r6plomRWfI5g9nphUZ/IWfQc3qEieFZBAH23tVJS2f1sqLPobCf/fdtoufUMKZh8/qzZ/j4r977lq4hV0POTvNG4hlEQWJv7H5CSvSmXcv1XBLGGIdnX+BA4imK7srjsa4pFBQL07SxHXeBEPN6Yt0MDYCKqgjv/vAeNF3hwAvnGB5IkMsUMYoWU8Wli1xDER+NLRXc+dBm7n/PdvzBt65e4ko8/sZpysJ+/KpCtmjSUVcqEh+aTvLk4fO0VMX5xQf34XkeU+kcX3/1OI3lMXrHp3Fdj5+9excF02JgchafKjM6k2ZrUzW3b2phaDrJ4FSSnGEiAPGQn4j/2tXPbcthYmB6foE9emGC5776Kptv61wQmfE8yKeubcJvikZXpJFURInKUHCRoRHx6dRFlo/0BFVtyQLOrGGWioCXMTQsx+HVgQFmCwu9gOUBP7c1NRFdpsZlLbi1sYGYz7fI0BiYTdI3O0tXZSW6pPKBultJWTmKjkVI8VGmLn+fjlvAcYv41BZU6Z+XAFTamuW5ie9xOl3S8mgPdPH+uk++5YZGzk5zaOZlXk08g+kadIV3cm/l+6jzNb2l/bgWDBf6+ebQXzNrlb6beyvfx8PVH0W9BlHHdyIUMUhIbUcRg/jkaqYKByg6CRxn9XHHdV16phIYtkNdNLykUJ/neeRNi4FEkqm57zTi04n6Fn/3IV1DnRtjRpJpErkCtVfRzHpzauRffOMIidy11RspksTmmko6qyo4ODDM2YkpCqbJVDZHVTjE1rrqVev8BoZnFrcrS1QvIZK1VuiaQjjkQ5alBSJ3bydKqtUsSxu8tjagramCzR01CwyNVKbA6e5Rdmyup6p89efWP5ygt29ywbZwUOeOfe3X37kbhCxLhK7QCHNsB9dxEcWbuzAu5k16TpVolYMRH+1ddYtqI65GQ2sFNfUxTooC+azB8MVp8vuKhFYQYNxzZ+eKbaqqTDhWOt+xXcyrpAymxpKMD8/gOC6+gEZjW+WK1wNo6ayeJxEaH5khk8wTu4lpQNeDjD3LG4mnkZDYFb+HkBxd92u4nsNw4SJvJJ7mWPIlHG/1MeHIiQGeeeEsmzZUs7Gjmsa6Mmqqwvh8N66XdCXW1dAAqKyJ8oGP30rX9kaOvXGBob5pkrM5CnkT23JAKA2w/qBGJBagub2S3be109pRveqLf7NRHgrw8M5OfFf049aORmYyeaZSlxelluNgWA4ddeUEdAXDdqiJh+ECtNeW8el7d/PMsR4mkiWKvts6mzh8YYSuhioe3L68F2E1xKuj/PJ//+T8CyCIAptu2cDvfOFXudJB6rnw5f/ynWvyklSHQivSR0qiQGQJ6tWgqlK+TNoUlNKurq7dgFKu9EqeLtNxeOFi36LtZT4/u+qW9pisFZXBIJXBIBcSMwsobz3gyMgYXXOsYjmnSG92FMt1iSh+QvLy9ykgIggioqjP6ya83Sg6BQy3gIiEXw4grSGV652MWWua89kTpKzSou1C9iwdoa3vaEPjnxM8z8VyM+TtUbLWIBmzh5RxjpTZjeEkrhD+Wxm26/LD0+c5NjLG1tpqWspixP1+AqqCLIo4nkfOMOlLzPLs+V6GZlNEdI2u6kpqlogcdFaWUxUKMDSb5GJihu8cP8NsoUBFIIAsiuQti/F0hpd6+3nqbA+dleWcn5xeomfLo728jG211RwfGePEyBgFy8awHfY11tEQXb1AOTG7WIFcliXKYzdmrIeDOqpycwwN1/UwTIt0tkgma1A0LEzLxjQdLNuZ5+B3nBJFu+O4zCRzC+rprhfVlWE2bajmwJE+0tnLNR7HTg9z960dVJatXqz7/Kvnca6g2pUkkca6OF3XURNzLchlikxPpEjP5snnDCzDxrJtXNvDth0unhubP/atCkRZps3YUGnc9Pk0qmqjq56j+1SiZUFUXaGYN5kcnaWQM1dc+NevQgd7ieEJSsb/lVTIALOJLKmZ0rciSSJnjw2uqs49MTI7r0zvOh4zU5l3nKEBkLISHEg8hSCI7IrdTVBeH40Oz/NwsLmQPcWr0z+kO3NsiaOW/lZaGstpb6mg5+IEx08PU1kRYtOGGtqaK2isj1NWFrzhQnC4CYYGgD+gsX1vC5t3NpKayTE5niKTymMUbQShVJgTifmprIkSjvqvmdJ2PXHlWFUTC68aOhIEAU2W8WsqF8YTVIQD7K0vLUz9moJfU5BEEUWWEAWh5LUXBFzXw/O8VQugV7u2ckUObHVzBbe8eyeKtvhnrGwqQ9XX7jWN+/QVJ8tL930lZFEkqKnz3sSlIIlLC90tojS9Ap7nkTVNTl2V4iDOFaY3RqPLnrsWiIJAXTiEIooLDQ3Po3dOFb3gmDw7foymQCUxLcRIIcFr02f4aOOdS7YpSzEUMYbj5nC9a/OY3iz0Zk/Tmz1LTCljZ+w2wjcxZPtWwPU8XO9Kw9Bbl0XNT7ESPGy3SMEeJWP2kTZ7mDVOkzLPYTgJrl4qiYKGX65FElZOHTRshzf6h3mjfxhNlqkIBoj5S9o9luuSzBeYyJTSpWI+nfs727mvo43AEnpJrWVx7utoYySVZiyV4etHT3JoaISacAhFEkkXL9VT2Dy4sZ3b25r4/ONPXtNTiPh0ttVV09Ab4cJUacHmVxV2N9bNK42vhNwSBduSKBC4wXpEn66sa1Gz7TgkUwWGx5OMTSSZTGSZSmRIzObI5gwKRZNC0cIwbSzbwbZdbGfuv7azYGF/I1Bkic7WKlqbyjl2+nIx9+DoDL39k2xsqyLgX96hky+YvHb44oJtfl1h/64W9Jvk0JydznD+xBAXzo7R3zvB9HiK1EyOYsHEKFo4toNtezhr1HFZTziOR27OYJMVcc2ZI7pfQ1VLdLL5nFFyFq+AcHTl6MNquPSsALLpAt/78uvX3kZ+ZcPk7cSsNcWBxFNIgsyO6B0E5OuPaF6C5RmcSr3BK9M/YLSw2DkbkqM0+Jd2cG/ZVEd7SyVDozOcOT9Gz4UJDh7p4+CRPhrq4nS0VdLSVDI6AjcgMnlTXZyyLFFWGaas8sYf5luBqw0Ax3V59dwAZ4enSOeLvNE9SFdDFabtYNg2fk1FEEQmUzk0eU4ld4nfQQBiQR89o9OIgsCOllrKw6tPTquhcWMdjcuopW6/q2vZvMalENS0FVV+BViUWqVKEgFVXbEgWxSWbtX1lvfmuJ7HYDJF2lg4OatSKX3regUTr0RYW1oAbGxO88P2HGbNDL/e8T4ALmbHeGL00LLt+ZVWfEobOfMsRXsE17MQhbcvQme5JidThzg8+wotgQ42hrf/kzc0wkqMJn87E8URXM+hyd9Otb6+yr4/RQmuZ1G0p8hYF0ibPSSNsySNMxTs8SUiFwK6VEFIbSWsdhDTNuOTl/cai6LIvqZ6RpIphpJpErk8M7k8Y6kS7bQkivgUmZpwkIZYhJ31tTywsZ2OiqVTElVZ4v1bNyKKAi/09HFxeobBmSQXp2dQJYmQrtEcj7GvuZ7Hdm5BkSSiPv2aawe21FSxubqKi9OzuJ5HR0U5GyrL5nV7VkJ+Ca+sIAjXXQh+CaoirUt+v+24TCUynDg7wunuUXr6phgYTiyIJrzVaG4oZ2NbNae7x+YjNrbtcuTkEHu3N9PauPxi+UzPGKNXpF0BxKIB9u9uvSl9Hemf5qlvH+K1Z04zOlgyROOVIeLlISpro6iagqJKCILAbCLLmSOLhWdvLhZOuGt1dgqXlGQpUTR7q8RgZPXG5mbP9fDmjFVFlYjEg9fEQqpqMvINflPrCVXUqNTqmTQuG8sz5gSvJ55ERGRb9PZ5fYvrQc7OcHT2RV6Z/gFJa2GUVkAgplayJ3Yfu2P3LNuGritsaK1iQ2sV6UyBvoFpzvdOcOLMMN/47mHKy4Js3FDNxg01bN5YSzTiv+Yx553zi7zN2N/ZRG18sVJl0bRpqohiuy4F0yJvmAxMzVIRDiAAI4kU54YnCWxvp7P+ctiwuSJGPOjDrykIgsBtG5s4OzxJzjAXKVTfDNS1X1t42KcoqzBwLd4pCcKaFv3XOg26nsfFxOKc5tL2BP/nwBvzw93V6cBr/fvE+PgC1inmts/OCXlJgkhQ9vHi5EmCso/h/BTl2vIGs09pJea7i7x1gWThZULqNgJqJ8LbVPQ7ayaYNadxvPUWrHv7EFFi7I3fRVQtw5kzNGp9688e85OKUmpUmrTZS9rsJmWcJ2meJWcN4niLF5yyGCSkNBNWO4lqG0k7EXJumJmCx5ncEdoDLTT46ziROkNQDpCy0hQdg1vLdnNneyOGOsWhsSwzWZmQUEmFUkXGytKfHwDJJuSz2V4T4KGmDSiywLHkKfJOnoJjUK1XEFUiFJwiVXoF5cEQt2+ME46a5DJNZAoOluOgyTJRn05LWYztddVosoxhO3zuzlvImyZb69Y+TlaGAlSHg0iigOd47GuqpyG6tvSHpVKbBEFAukGniTinn3MjKBRNTpwd4fnXznPk5CBjk+nVT1rQBwFFlpBlkULRWpQOc70IBXQ2baim5nCEwZHL88Hp7lGGRmdorI0tW/T73Kvn5xmroKSdsbmjhro16EpdKwo5g+9/5QBPffsQxbxJeVWEW+aKosurIgQjPjRdRZ1bhB8/ePEtNzQkSSQQ0ikWTGzbJb9GRfli3sQyS++uL6jddHFBVVfmU+jDsQD3v38nzR1r/0YlSaCqLnqTenftCCtx7qx4H69M/YAJY2h++7QxWjI2BJGtkduuq95xxpzg4MxzHEw8Q97JLNgnIlGtN3BL2bvYFrkNn7w2x7aqygSDOpGwD79PLaWx5k3OnB/j5NlRTpwZ5r0PbaO+LrZiqv3VuOmGhlG0SExlSM3mMIoWgiCg+xSi8SCx8iCKIq1r0cn14u4tiz0dkijyrp0L6TlzRZO8URpMNUUu0fkJEPbrNJRH549rq1nofeuoraCj9p0rZ69K0ooRjaUgCMKKBeTXCw8Yy2QWbTcdh0MjoxwaGV33a16C4ZQW5rqkcmfFZrozI4iCiC6p3FK2vNq6LAYpDzyE42ZIFJ5jJPO3VPgfIaB2oUjxOU2Nt+49HysOkraTb9n13gpIgkStr4nan9ZkrBs8z8P1DDLWRVLGeVJmqeYiY17EchcTeIgo+JVaIupGItomIlonEbUDWYzwvdGn0MUciqjweuIQFVo59cCbM0ep9VVTrsZxcUv022aCCS5y94YmTNcibWV5oGozM2aS5ycm6Qi10Zvtw2MGnyLTk73IhWw/1XolZ9PdFJ0i26IhTqXP4uERUoKczZ6joaKKre2b0KXlySJ05f9j77/j5DjM9E78W7Grc5ienBOAGeScQYCkGERSpKhISStt9nq9a69s3/m897m7tdfx5/PvvD6v17vWBq3SSqJEiSIpijkh54zBBEzOnXN3hfujBwMMJmAGGDBo+fADkuiurqqurvCG530emV/dvmnJx2oymWY8mcIwLcrdLtZUlS/onXEz5jKhtbAw34ei00JIpXMcPNHDj39+mktXR+bs8jjtKqVBN0G/E6/HgdOuotkUbKqMokjIkogsSyiyyHd+cpzJ8OLlgxeCIMCq5gpWTFE7ru9aNJ7h/JVhVq+smnPGJRxNcep8/4yEx25TOLBr5T2JNbqvjHDkzctk03kUReLpX9vDA5/agNvrmO3jki1gX4Dyda+gqDIVNX5C43GymTwTI9HbfiaXLRALp6apTMEyz4I+GssBr9+B21e8pizTYuXaGnbc3/ahiBGvYyydIKA5UBYxwK9JDtZ6dwDMSjbGc0McmnwZEYk1vh3YpcWzXAbTXRwJvcK52MFpRcPrkASZBucqdpY8ykr3JhRx4Y6QaVqEoym6esbp6Bqls3uMaCyD3+/gof3trGqpQNMUOq+N84s3LgDwq8/sWpC6eCvuSaJhWVaxPXimn6sXhxgdihKPFeVsBYouml6/g/JqPyvX1LB6Qx1ur/0DOZmWSvHWVJn1jUV3WkkUKPO6qPC5F/TY+ChAEsUlx8GiMPf8xd3CsixC6eWTyVwKrnOMFUFira+RWkcZGSOHV3XikucPKrKFQdKFLiTRgYBEKP0q6XwnDqVlKtFQWOgAC4jU+f5gWa4BC4uR7ACJQnTGax/jYxRRnG1J6YNEc5eI5S4Tz3eSyPeQMcaYTWoUsIl+vLaV+Gyr8ajF5MIuVyCJxYeNaZlYloVu6WiCjRZXI7X2KhSx+IgptZWwxb8BWZQxLIO+1CCqqLKvdBfhfJSfj7zKcGYUTbJhYmBiErQF8Ks+vIoHCwvDMhAQqLJX0Oisp8ZeRWeih1ghzkQ2RMEqUKGVYROXP5CzgAsjY1wcGcO0LNbXVNIcDCy6queYYxbDsiBX0HFy5/tb0PU7TlbyBZ1LV0f43k+O0dk7089BEgUqy32sa6uipb6MijIPAZ8Tj1vDoanYbDI2RUaWi4XC67etF9+4sGyJBkB50M2qlgpOnO8jGrsx+3b8bB8Hdq2kxDfblPHUhYEZw/eCIFBd4WPdqpneDsuF7svD0x2Csio/Bx5bP2eSAZDP6YzOoUB2r2F3qKxcV8vFU30kYxm6r4yQz+moc8x3Xsdwf4jRoQimYWLTFKobgvdcFbSs0k95lR9BFIhF0gz1TZJO5XC67lxlcrnx8mAHj9W1EdQWlxhokpM108nGi4zl+qfesRjPDXAo9BKiILHauw1NWnjGxbRMOhNnORL+BVcTZ2YxFmRBod2zlR0lD9PgbLutjPrgcISTZ/ro6B5lYCiCADTWB9m1rYUVzeXUVPuxqUW2S/vKSjLpHK+8eYlnnt72wSYaesGg5+oob7x4ltNHuxkeCE+33m6FapOpqQ+yYXsTDz25kdqGINKHSP94LkiiSHXAS3VgeRQDPiy4077EUrsgi4EFpPIfzEDX9RArY+R4fewMT1TvwMJiMhvjvYmL3F++fs7PxXLHGY5/E8NMUTAjGGaaZP48yfz5qSVuJzIgU+f7JyxH1yOtJxnPDpNZhNTox/j7h/H0IcLZc8XkotBDqjA4Zbg3E5Kg4Vab8NvW4lPbcKstOJU6FMk967oXBRG7pGFYBi7Zyc6SrbiVG9xjv+KbQSOURAn9uvyiZWFYJiIisiBjE21IgkSzq4FKrRxBEFBFFVmQUUWVtd42quzliILIKk8rV+KdnIqeo8ZehU/x3pOC1UQiydHeAfrDMdw2G1vrqheU9b4VDvvsh7JpmmQyBbiLR0k2q08r7iwFlgXhSJrnfnFmVpKh2RQ2rqnl4X3trF5RSWmJ6wPzJZDlIuWpsaaE07GbhsKHwnReG6eprgT7TaInlmXx1uGr074qUKRN7drchMt5b4LVfE6fnitQbTKKKs95DpqGyfhwlOPvdNyT/VgIdpeNDTuaefW5olFg5/khLpy8xqZdcw8J57IFTh/qoq+zKMjS0l5FdUPwnp8Hbp+d1tXVnHjvKuPDUY68cYUVa2po31R/221fV7GaS1woWchxanKI3kQYu6yw2l+BV9W4EB4lms8gTQ2jBGx2mj0l9CYiTGRTZIwCAZuDHWX1iILAyYkBnu+9QKqQw29z8Kn61fQkQkRyGfZWNFIwTY6M91KquWjz3zA3tEtOVnuK5rvvTb7AWLbY2bCwGM32czD0IqIg0u7Zik2au6BZMHOcix3maOgVBtPdmLfMyqmixhb/AbYGHqRykV3/cxcHefHVc5QG3Wzf3MiKpnKaGoL4fc5Zx1GWJeprSyjoxpJFWJY10TAMk87Lw/zgr9/j7LGe6SxfUSQcLg3NXjTsy6RzpJM58jmdnqujjAyFmRiN8ZXfOUBtQ3BJQ8wf45cT882xyKK4JG7gUiAAtinOdM7UORXp5onqHQgIJPQMl2L98yYauhElq994EMri0ga8BO7sBp4zssQKYaKFEJF8iFghxGR+nGupDiyKx3A8O8IvRn+0YGtWERSeqPoS8m3arFM7e/1f5Iws47lhhjK9RPMhMmYay7JQRRteJUCFVkONoxFNXHzH0rAMRjL9HAq9vuByJWoZa7ybKdfmFkSYDxdiJ+lInMO0DOocLWwv2Q8UdcgjhRAD6W4msqNkjBQFq4AiKjglD2VaJdX2BvxK8J52X03LZCI3wonIe6T0IoXQJtpodrWzyr1ucb/RAuiI/AWxfOec5noCEppcTsC2Hr+2Bo/aikupR5OCCML856hu6uTMPPFCEgsYzY6jigoVWtmsZUVEGh11dMQ7eXH4VXRLxyt7qLJXMJ6bJKmnGc9OEsvHSOlp2j0ryZt5EnqC8dwEoXwY3dRpcTdSbiulQ+hiLDPBSncLjgUkqO8U0UyWX1zp4q2ua2R1nW31NWysqcKuLv538M8hCarrRTnYu5kbSGVyFPSlJxoF3aCzd5xjZ2bOCkiSyLpVVfzK09tpa61YkrTldanb5UZTXZDWpnIudY6SyxcT4oJucPJ8H1vX11NdcSPRGBqLcaVrdMZ+OO0q+3ct7O9wNwhWeKZnF8aGo1w5O8DGXc2INz2n9IJB58UhXvjeEbov3Tvq73yQZYmmtkr2PLSGl589zlD/JD/91iEEQaB9Uz22m5S4ouEUx966wpsvnCE8mcDp0dj9idVU1ZXcc9aJKIqs397ExVO9vPPyea5eGOTZv3qXByYTrNvWhDfgnHFcC3mdybEY/d0TXL0wyINPbqKyNjBjnbpp0BMPcWSsj02l1YykE7w70sNKXylXouNYWKT1PE7ZRiSXRjdNjk8MUGp30eQOcGisF79qp81fht/mwLAsSjQn5faiJYBDVvmrjmPsLm8gb+q80HeZf9i+a9Z3c8iuqWTD4r3JF2ckGyOZXt6bfAFRkFjl2YxNnJkUp/Q4JyNvcTz8OqHcCCYzrzOn5GF36WNs8u3Dpy6eot9QV8JnnthMU0OQ6gr/lH/G/MvX1wZ55jPb5+zQLoRlTTRGBsO8+MPjnDzUia6b1DWVsmlHM/UtRdMVZepizOd0YpEUXVdGOHmoi8mxOEfe7sDjtfMrv/sAvsDdKzJ9jI825komPDYbD7Y0s6fh3nH0PdqtF3gWTVLJGPkF6Ud+xz7UBZR2bodihXhpN3HLsriaPM+hyVdJ6UnSRoq0kSRnZGbciOJ6hDPRhWUCbaLGJ6u+gMztgycRCVEQGcsOcjpymO7kZcKFCdJ6koJZPE6yqGCXnHiVADX2BrYF9lNtb0AWb3/LsSyTUG6cw6HXFlyu3tFCraNxyYnGQLqbI6E30K0Ck7lRtgXuI29mOR09zPnYcUK5cRJ6lLyZx7AMJEHCJmp4FB9lWjVrvVto92xcEqd2sSgmGaO8OvYc52MnyJtZVNFGm2cDbtm7LOICsXzHrOFuWXDi19YQ0DbiU9twKvU45AokcXFV4IHMMIqo0OJqwCk7uRC7TG9qgBLVz/7S3VTYyxCnzm9BECix+dlXupNoIY6ESKmtmLyNZMeod9ZQY69kMhemPz2ELMgkCykanHWU2oL0pwfpSw9SppUStAUwLZMyWwk+xYN0l8cnnS9waXScK2MT2GSZdD5P92SYo32DDEZiVPs8PNTWQlMwcPuV3YS6Kv+s1wq6wehEnLXzKAfeDrpelKLNF5Yu+JDO5Dh9cYDsLYZpZSVu9u9aQXtr5ZxzJQshm8vPGMBeLjgdNlavqOTo6WszjA/PXhpkbCJORdkNc8Zjp2f6boiiwOqVVdTOcfyXC6s31lNa6SUWSZFOZvnWf3uNy2f6qKgJoNpkkvEsg70TdF4YYrg/xKr1dfR1jRGPLtxxzmbyJGIZMqkcuUyBXLYo/drfPU5hKuGaGI1z5kg3gVI3Nk3BpimomorLreG9KY4SBAGv38nDn9nC+HCUkwc7OXush2g4RUt7NeVVvuK+JrIM94W4emGQ0cEwqiqz7+G1bLtvFU73+0NfKqv289DTW4hMJjl7rIfThzoZG4rwzsvnCZZ7cThtWEA+WyAeTROPpAhNJBgfjrJt38pZiUbOMLgam+BsaBivqjGZTWFaFuUONx5Vo9zuYiKbJGBzEM9nSRRyKKJEm6+MPRWNdMYnGc3EWROoYEOwioDNwfayOhrcxe00uv34VAdXY5OIArhVG3Vu35zfrZhs7MACDk6+xFi2SKOysBjO9PLuxM+QBIkV7o2oUxTQcG6MI6FXOBt7j3ghPCsO8SmlPFD+WVZ7tuFYooLViuYKVjSXL7pTVVXpo6zUvWS1vGVLNDLpPBdO9XHk7Q4sy2LzzmYe/9w2mtsq8fmds9QK8nmdLRMJNm5v4kd/e4juKyO888pFtt+3io07mqeTknuND9GM0ceYgkBRBetWKJJIa7CET7W33fN9sIkKK9zV/FnXizglG3lLZ71vfmlEh9KMQ2m+5/s1ExaTuVGuJM69r1uVBInx7DCX4qe5kjhLUp+tUpM3c+TNHLFCmJFMP+O5YT5Z+QXqHM23NQ4UEHDILqrtDeSMDFkzS87IULCWn043kRslZ+Z4a+IFToTfI5Qfm7WMbpnoRoGUkWA8N8xoZoCMkWKzfy/223BqlwLTMgnlx3jt1iTDvYEHyj5Flb0BaYGuwtIh4labCGpbCWjrcSl1OOQqFMm7ZEqkIsik9DRpPYNNUilYOn7ViyRIrPTMdmKWBIkG50zFsLSeQQDGsxOYlknGyOKWXXgVDxO5ScZzk2SMLIlCkjpnDdFCjNORc4xkx9gW2IR7GZzqs4UCJ/uH+O7Js8iiSN4wiGdzZAs6pS4Hn17Xzr6WRhxL6GYANDeUIggzZwJzOZ3egdAd72s0niGezN6RylMur3Ntjm1XlXtZ316z5CQDYCKURL+D7spisGZFFY11QfqHbgyFh6NpLnQM09JYhselYZomh050T3c9AGRJ5ME9q27rj3U3CFZ4+fTX9vCt//oqo4NhOs4NMNw3ictbdG3PTQXEkiTywKc2smlXCz/8y3e4eBvlqStnBnj9Z6eZGImiFwx03UAvGIQnkmSmvCKudYzy/b94C1VTkGUJWSn+WbWulq/+40/MWJ8sSzSurODzv30fTo/Gsbc6uHp+kGsdozhdNiRZIp8rkE7lMHQTf9DFvkfX8chntlBe43/fZmglSWTlulqe+Z0D+IMujr7dQX/3OP3d4yiqPOUlZqHrJvnsjUTZ6dZQ55DCFQUBu6Tgs9lZE6hEAJyKSjyfJZ7PoooSqihPD3dbWOQMnYJpYgFZvYAkiNNS/bplYt7kiSYKIk/Wt/OLwSsEbA4erV21YMHDIbtYM0WjOjjx4vSAuIXJcKaHd8afR0Kixb2esewAh0Mvczl+YpayFEClvZH7yz7DKvdGlDuYTZNlsTgMHkkxMZkgk80z1+1kRXM5LqetKPxwB9fSsiUakckEZ49fIxnP0NpexRNf2M6mHc3zyqGpqkxFtZ9AqRtDN/nr//c1JkZjnDjYSfv6WhRlcWoeH+OXDwICAfvs3183TRK592d2Q5MUHijfQFdimLyl41OcNLvuzTDhnUOgzb0BV91Mrni0EOZU5CCj2SKVq8xWxdbAPrzK/FU9EQl5kb4f4fwEx8Pv0JO6Qt7MEVBLqbU3EVBLkUWFjJFiONPPUKaXnJmlYOXpTl7mjbHn+Wztb+BR/AsGsqIgUW2v57M1v45u6uhWAcPSiRQmORM5Qnfq8qL2czFI6DHemniBg5OvktTjaKKdOmcL5bZqNMlOwSx2PQbS3cT0CIZlMJYb4vDkG/iUElZ7Ny/LnJJlWUTyE7wy+mPOx45PJxntno08UPYkVfb62w72LRaq5MdvW0upfRtutRmHXIVNCiLeRRJTppWyo2QzWaNIl5UEkSp7BfIS3Og1yUa7ZyVBtUjREBDwKm78qg+X7CBSiCEgICJSYvMjCzIiIs2uRiq0skV1y24HURRQJImCbhDKpcECn8PO9vpaHljZxL6WRspcS+9kVZf7cDpsJG+SFc3kClzuGqWgG3fkvts3GCKWuDNzUF03iURnu5W7XRrlwTsTNunsHSeXL9x+wTtAMOCivaWSc5cGCd/UCThxro8Du1bgcWkMDEfoHQzPoE2VlrjYuv7eqtSJosiOA6twujQOvXaRy2f6GB+OMjYYQXOo+AJOtu1byabdLWzY0VLkureU3zbRmByLceFE74LD4+lklr6u2dLT89HPVZtC2/o6fAEX67Y1cfpQF9c6RglPJtALWewOlYbWclrX1LB+WxPtm+opKfO876bKqk1m1fpafEEXG3e1cuHENTovDTMxEiWVyGKZFjZNIVgXoKI2QNPKSto21FFePfsZZ5Mk2vzlXI1PcnS8DwGBFm+QSsf81X8Li7dGujkTGiKaz9DoDmCXFAQEVvpK+VbnScrsLr7augWnorLKV8ZP+y5iWBZrApW3/X4O2c0aT3FA/OZkw8RkMNPNWxM/YSDTxXDmGtdSl8iZs3/jFa4N3Ff2FPWOVXdcgEpn8hw61s1bBzsIhZPoujGnQNK//INHcTXOpsEuFsuWaETDKbouDSMrEi1tVazdXL8ozWVVldm+byWv/uwMoYkEF0/3kcvp3H1tanH42Fj4wwdBgCr37JtATjcYTy2foslCEAWRUpuXoM2DbpkowodDhvlmCIJAmVZN0DbzxjaWHaQzcYFRiomGW/bS7tlImW1hisZib1ah/DiRwiQiEpv8e9js302JWoYm2REQMSydpJ7gSuIMBydfJVoIYWHRkThPb+oqqz2bF5wzEIRiR6NBviEtbWERyo3Tn+5Z1kRDtwocnHyFtJ6k1t7E/rLHqNBqcUhOJEHGwiRjpBnM9HIk9DpdyUsAjOWGuJo4T52jGc8CCdxiYFkW0UKIl0d/yLmbOhntnk18ovwpKrTaZUsyADaW/mvsUhl2uQxJXJ6Cjioq1DnuzjxRFET8qg+/6pv1XplWSpk2m3vsVpb3SeFUVR5ua6W9sqw49AhosozfaafS4yoalN7BfcCmymxor+G9493TrxmGych4jM5r47S33j44uRWXu8eYjNzZ/dCyLPJzeHvIsoiqLD0sMEyTo6d7Sd0jV2ZJEqeOX9eMRKOje4zxyQRV5T5OXRggeZMDuyDAri0teN33vmhpd9jYvLuFuuYyIpMJMqkchmEiyxKqpuALOCkp86A5VAp5nae+uovtB1bhD7rnTQo27GimtNJHLrvUYyrMoE3dCkWVqW0qpbTSy/ptTcQiaXKZPKZlIcsSdqeKr8SFv8Q17WkxH4IVHv75f/gc+SkK3u3M9Tx+B499cQdb961EVqQ5E4PrkGSJ6vogwXIvqzfXEwunyKRyRU8aq3hOqJqM06Xh8Tun5jdmX5uiIFLj9PJ0w1rCuTQC4LXZ8at2KhweNEkmb+gookTeNAhlUwwkY9S4vLR4gjhkhVqXb5qe95WWzYRyKRRRmvYRU0SJrKGzu7wBTVrc9VPsbOwAqzggft3Uz8RgIN3FRG6YvJlFt2Ym7wIim/372VnyCJX2egRBvONi15XOUV569Ty6YbJpfT1et525vPh8c8yYLQXLlmhkM3lCEwmcLhsV1b4laUU73RpVtQEunx1gfCSGoS9sc/8xfrkhCgItJbM50DldZySeIJnP41LvrZ53Ws/xxtgZHijfyMlIJ4cnL3OgfD1bAnOrdHxQEAVxVhAqCfKMG0+xvSstS8UXisG5gspa/1YeKPsUQVvFrCTFp5bgV0tIFKIcD79DxkxTsPJcTVyg1bVmyQPN17/PvVA5S+oJymyVPFXzNWrtTbN0xz2KH78aBMsilg8zkR/FsHSGs/1M5EaXlGjcOoljWRYJPcoLI3/Hhdhx8mYOVbSx2rOJhyo+Q5mtalmTDICgfcvUvny4Eufb4c2eHi6MjfNQawsrg8F7sg1Fkqj2eaj2La9cuSAI3L971YxEAyAUSfHau5dZ1VyxJLfd0Yk4FzuGicfvrKMhigL2OYLCQsEgk8vjnEMlayGcuzTEpasj5PP3ziC0qT5Ia0MZV3vGyOaK20ll8lzqHGVFczmnzveTy93YviSKPHxf+z3bn1shyRIVNX4qambfD9LpPK+9coHTJ3t5+LF1bNvRQm3TwhXiYIWXYMW9UbcUBAG7w0ZNYyk1jXe+HrujqGa1WKg2hYbWchpay2+/8BRsmkJ5VVH29k6hSjL1bj/17pnr8NlmJ6GGZaKIInUuPxuDs4tzt67n8Fgfrw524FZUtpctrXvmkF2s9RWVLQ9OvsB4bggoJhtzUaUUwcae0sfY7D9AQC2/62fDwGBx5uPJR9azaX09qiLNOSqq3SbhvB2W7QlmGBb5vI6syHdk6uJw2hAlgUwmv2TprLvBh6xI/TEoJhoNgcCsZKLo3J2hLxK55/uQNwtciPUS19OcinSx1tfI2WjPPd/uvcJyB5VV9no2+/dQaquctxPilN2s9W2bEYgPpHtmVWg+aKiiyt7Sh6l3tMxrbqSKNuqdrTS6bpg2hnOTRAtL08SXbqKnWZZFykjw/PB3ppMMRVBZ7dnEIxWfo3yZkoybh0eheC5YhsW1jtG7Xvf7ibFkiquTkySyi3M1/jBBEGDT2lpK/DMrzZlsnuNn+zh2pnfR69J1g7ePXC2qK92hC7csSwQDs7tBkViaweGl3V+Hx2L83c9OMDweu6duPZpNYX17DeWlM5PAi53DU7Sp0AxZ21UtFTTVldy6mg8EhmEwNBjmwrkBQpPvT1f+Y9wZyu1unm5cR7tvccnQSm8pn2/ewFdXbMWtLn1Owi65WOfbxe7g45Ta5qdnu2Ufj1R+iZ0lj1CyDEkGQDZXwO20UVcbwO9z4HTacDpm/7lb6tyydTQkWcSmKegFnWx66YFEMpHFNMwpea3lj/4/atW7v9cQBJyqwsaqSt7tncljnUilOdI/yOryxVdE7hQZI89oJoxdVGlxVdKdnF+WMJI5SDj95h1vSxAkGv3/24eOnjUXJCSq7Q00OFpve7OrttfPUGeKFSIY1gfrhnwrnJKHDb5dt6WOeRU/ZTc9CDJGiuwSvUqKsoUClmWRMdL8ZPBvOR8/Rt7Mowgqa7xb+GTlFyhRy5btXHj52RN8/jf34Su5EVgWCgY//fYh/uCPn16WbXyMhSEIAl63nc98chN/8Z13p1+3LBgYifDdnx7D5bSxZuXCc2AF3eD1967w4usXCM0xY7FY2G0KK5vKOXhLh6V/KMzhU9dorAsuikLV3TvB3/7oCKfO9xcpLfcYG1bXUFcZoH8oMl2Q7OwZ5+ylQRKpmQnog3tW3dHsy8f4+w2bJFPlXHxHM6A5CGh3Ry2yS07W+3YjIPDu5PNM5GbHGvtKP8UG314cknvZng0+rwMLiMburefWsiUadrtKSambwb4QwwMhspn8ojsbqWSWgWsT5LI6dU2ly24Ko4gi8hxyqTlDv+OK0Me4d7juZ/FgS/OsRCOcTnN0YICn17Tjn2NgfLkgixKlNi8/HTrMF+v2Y06Zis2HVP4KY8kf3vH2BEGm0f8vWA7DvnsNl+KlQquZlt9bCJrowCZpxSo6FlkzPe3v8WGAiEi9swWndHuuvywo2CUHIhImBgUrT8FcGn+6OMcCulngR0N/zfnYMQpWMclY693K41XP4FPm16s/c6Sby2f6kWSRkYEw2/evYsveFVw81cext64Qj6ZpXFHB/sfWYxgmb790lsNvXCIRTaE5VH79nz5CeDLBS98/yrF3OvjTP/4p3oCTL//DB0gmsrz3ynmunB3A7rSxeXcrbRvqOH2oi4uneikUDARBYPcnVrNhR/NHIin+MEGWRB6+r423DndwtWd8+nVdNzl/eZj/8pdv8Oj+dvZtb6W0ZOaMWi6v09M/yS/evsTB412Mh5LT5mSmaS551tBuV1nfVo1dU8jcpNwTS2R4+a2L2G0KjxxYPe98w0Q4yTtHOvnF25fo7puYVnrSVJlcQb9ns48+j4M1q6q42DlCeCrRmggnOXaml3TmxrXotKvs3T5b7eyjgNhkguf//DXe++mJBZdrWlfHr//RZymtWd6ujWVZnHn7Er/45jtMDIXZuL+dT3xlL+V194auaBomY32TnH77EldP9TDeHyIZSxcdyR0qTo+DspoS6turWbt7JdUtFVPKU79c0CQH6327USWNt8afYzQ7M/bpT3ey1rsbQV6+++76NbV09ozx8usXMa1iF9DlvPsOxq1Ytl/L43fQtKKC/p4JOi4McuJgJ3seXL2oz77+wlmG+8NYlsXazQ23HShaKjRZmR7auRnhdIa8ce84pR/jzqFIEnsbGyh1OplI3ajcGZbFxbFxfnrpMl/bvOmeheUOycavNDxA3tTxqy5My+Qr9Q/Mu7xl5TGsO6kwSmhyJXZ5funcDxuckgufujjzpuvzIcUEysIwDe4pv2KJEASRSq12Cd+lOBNjWgaWZS7orTIXNMkOArww8j3OR4tJhizIrPNt44mqL+ORfQvuSzqVJRpKsuvBdu5/YgMOp43wWJzzR3tYs7mBlvYqXvr+MTrOD7B2ayMPfGojR964zBNf3klJmQebXaGs0sejn9/Oyfc6+eI/OIAkiRiGSX/3GH1d43zl9x6g9+oY549fw1/qZnQwgiRLPP1rezn29hV6r47S0l6F+5YBwYw+RsFMAhYOuRr5pkHzf/rSS7SXlfGrGzchSyJZXedvT53mWiTCv3/4IXojEf770aNsq6nh2OAQlycmKHM6+dSqVTyyohWbXHxU6abJa93d/Pnx44ylkrQESvjKhvWsq6hAFkUmUil+cukyB/v7GU8m8Wo2PtHcwpc3rMcmy2QKBf7gxRd5fNUqDvcPcG50hDKXm8+uXs2jK1cgACeGhvjZlSu0lpRwuH+AgViMtrIynlm3lvWVlUwmU/zp0aM0lwR4qq0Nt62YcA/F4/zR62/wxXVr2VNfP73PN58/JT4nv/b5Xfyr//Ii2ZsC/IJucLVnjKHRKM+9fJaqCi8lfieSKBJPZhkdjzEZThJP5sjlClhAic/Jg3vbOHr6Gr2DS5PJlUSB2uoA9+1YwctvXZx+3bKKVKi/efYwbx6+SvuKSqorfNhtCvmCTiSapntgkt7+EKFoilQ6Ny2vu2drM411QZ57+cwMda3lhCgKbN1QzztHO6cTDdO0OHtpcAZtateWpjlNEj8KMHSDyeEIvZcGF1zO7tZmUCKXCxODId76wRHee/4ERsGg99IgVU3lBCp8yx7gxyYTvPGDQ7z8zXcIjUTJZ/LoBR3TsLCwEAUBURKRFAnVpvDV/+NpSmsCv5SJBoBNsrPasxW75OT1sR8ykO6cfsZciZ9khWsD6/27URfpb3Q7XOkc4eTZfsbG45w6149NleecFftX/+JTtDbfOYtk2X6t0nIPm3a2cOitKwxcm+QHf/0epmmx/b6VM1wnb0YmneOVn57mJ989QiSUxO2xs/fB1diX6Dp4O7g1Feccw8Mdk5PEcznKXO+XxtXHWDQEgRKHg8+vXcufHjky462JVIqfXb7CimCQXfV186zg7iAKIl71BuUnZ5iz3DhvRqnrSdy2zQuu0yKPbkRI692E0m+QznfgVNtoKfk3aHIV97KbsdSAeCGoog27+NF8iN+KooSq733bnk3UeGv8JY6F3pr2BfGpJTxe9cxtk4zrKClzU9NQSkl5sb3f1zmOrBYVXIIVXsprAoRG4+gFA4/fiaxK+EtclJQVlxcUAV+JE0mWpl/LpHL0dY5z5kg30VASvWDgD7rIpnI4XDY0h0JphRdfwMX4cJR8bnaA0xn9a0ZSb2Khs73iv+K33Sg0jSaSVLo90+ehZVmEMxnGkkW+esEwOD08QudkiMdXreKTK1o5PDDAsxcv4tU09jcVJ1Yvjo+TNww+uWIFbpvKjy5e4ntnz+HTNJoCAWRRRBDgqbY2KtwuusNh/vzoMer9PvY3NmJZFt3hMH965Chf3biBh1tbOdLfz1+eOEHQ6WBbTQ2ZQoF3e3sZisV5om0Viijx0tWrfPfsOdw2Gw0+H0GHg9PDI2yuqqK9rDjUe7Cvj0gmQ5XbM2dhC4pKOZvW1vE7X9nLn37z7Rl0I9O0SCSzJJNZBkciiFJR4ve647Z5U5vA5bDxzJNb2LOthavXxpacaFxPep58aB2d18bo7pu8ZT9yXOocoat3HEkSi+elVVSX0nUDXTdn3FG2rW/gN5/ZgyQK/PzNi/cs0QBoqg3S0lBGd9/ktOHgrQpaD9+3Gvn6fk9B1w16eyZ45efn6Lg8TCKRxWaTKa/wsW5DHdt2tlBzk9lbNlvgyqUhDr/XydUrI0TCKSRZpKLSx559K9h93yo8ntkdn4G+ED/8uyNcujAIFqxsq+T+h9Ysmqmh2GSqWyto2VBPIpIiEU6RSRUlXd8PxCaTTI5EKExd48lomsnhCPlMflkD/GQszWvfO8jf/aefkYik5pzNNS0L0yz6h5i6SWVDGZrj/TEO/KCgiDaanWuxV7l4ZfS79KQuYVrFDvrbEz+hzrmCMlvNsnSUbapMa1MZzfXBBSME+x3MXd+MZTtrFFVm9YY69j7QzusvnqXr8jD/9d88T/13y1i9sZ6qugAut4ZlQTyapr9nnPOn+hgZCJPNFLCwePpXdlHTWIqwBPWNxaDa46F0Du3zU0PDXJ2YpNbrnVV9+hgfLATAoSg8uXoVL1+9Snf4xtCtaVlcGB3j///eQXTTZF9jw11tqz8a5WBfH59bs3ZeM5qJXIyDE5f4Qv2+Od+3SRXYpNtl/MUqTcDSqXJ/lYHYnzOa+D7XIv+B1eV/8VFgTQFFVStFvLeqX+8nbMtounc7XIqf5kz0CFnzhlpQvBDj0OTrfLLy84tahyhJSDcZqnlLnGRSeVJTngrjI1FqGoKoqowoCOh5A9O8YTAFICJQyOtYpoUgClNSkz5WrK7mN//XTxZFMgSBQk5n4Nok4lTQVvwz935l9QmyRtHw0LKWXmlVJYl1lRV8ef06ZFHErWmMJBL0RMLsp5ho+DSNR1pbeXzVSkRBIJrN8mpXN6F0hqYAeDWNL69fXzTYEgU2VlbyUkcHl8bH2dfQABTViNaWl/OZ1auRRJEaj4feaJRXOrvYVlOU6PXYNHbW1fHoimKXQxDg2QsXuTA2RnMgwPa6Wo4PDdETjrAyGEQA3untY015OUGnY94gQBAEHJrCI/e1oyoyf/7td4glZmrkW1B02J5n5KHE5+QrT2/jofvacTttlAZcKLI0o6K/GIiiwMrmcv7hr9zHf//bt+npn5zxvmla0+pO80EQ4BN72/jaZ3dQM6UKVB50E46kZiRGywlZlti2sZ5TF/oZmGNwvb46QFtrxawk48K5Af7jHz9PPq9T3xCkuiZANJri0oVBRoejlJZ5ZiQanR0jfO9bh7h6eYSSoIuKKi+ZdJ7LF4c4f6aPUCjFE5/ehPemzsm17jH++P94jtHRKA2NpZSWeejpnuDkv/0ZdfWLox45vU4+9dsP8OjX7sMyiwlmPlvg8rEuvvsfnr9tp+Nu4fDYcdxEmZNkEU/QhWpfXqZJ99k+XvnWO8TDU8PxAlS3VLBhXxs1rRXYHCq5dIFYKMFAxwiCAGV1JYjSB/OgPBV+m0hhYlnXKSCgiAqqaMcm2bGJU38kO3bJyQPln4OxZ7mWuoRh6YTyo7w48rfU2G9vfHszgmoF6/17Zr2+ZUMD61bfXpb8blWnli26FgSBytoAT35pB9FIipOHukjGs1w6O0DHhaFiO2bq/LAsME0T0yhyS0VR4KlndvLwU5twumzLzv2t8nio9riRBAHjpptf3jD4r4cOE3Q62VhVOWOOQxCEBdWvlrKPy6KiZS1tPe8ff/reVlmqPR6+vnc3/+zFl8jd9CA1LItzI6P8H6+8yqfa2/j82jXUeBcnBWhaFj3hCMcHB3mjq4fTw8O4bDY+3d6OJZi8NHycBys28v2+d6bP2Vg+RXYBPr6wSAUIAUBQEC2Nev8fEM+dJJ49wXjyp1S4vviRSDYEBMS7Or8+RNwpinMa7xeOhd/CsGYGhHkzy5HQG5SoZWwL3LfgtSswO9CvqAmwan0tL//oJD/4xjvUNpXStqEOu7NI6dm4s4U/+T+fw1fi5Pf/6Ck0u4qsSqze1MAf/uZf0dJexa//s0dobqui+8oI//kPn0UQYOXaGh56esvMU1KY/tcMmFYek7tTE3OoKitKStCU4kPNqSiokkz6JhO4CreLaq9nujDk1TR006RgFI9p3jB46epVfnG1k2uRCHnDYCKVYk3ZjSKAJAg0lwRuWoeNKo+HwfgNh3u3ZqPae6MzUekuPj/CmWIyt768nDqfl9MjI2ysqmQynaYvEuHJtlV4tYUrroIg4HTYePi+dprrS/n2j49y+GRPMblYAKoisXltHV98cgtrVlajKkVvn5pKPw67Om3et9hLUxAEFFli6/p6/q+vP873f3aCtw5fnTHrsBDqa0r4/BOb2bet6FVxnXKxvq2a7r4Jsjn9nj2Htqytp7r8PIMjkVnzIPfvXolmU2ZsO5st8MarFzFNi9/5/QfZe98qmHrGJxNZopEUZeUznx/NLeV85Wt70DSFqppAMbm3LA4f7OSb33iH135xnl17WqcTDcuy+Mb/eIvhoQhf+upuPvfMDgRRIJvJ87PnTvLtv3kPn//2Zo+iKKA5bGg3WQRYlkWkLohtmdkec6GysZS9n97KaO8EE4Mh9n1mO+v3tiHfgb/KfEgns3Sd7aPv8o2h512PbeIrf/gUtSsqEaY6k5YFWNY0PU9R5Q9sNuxU9G2upS7dk3ULU8Ln0yLu0/dZC90sTLMpLCw6E2fpTp5f0vpXujfNmWgoioQy5Xd3L9Vel7WML4oCre1V/KN/+Tgv/OAYr79whmQ8i2GY6PpNX0KY4jtLImWVPr7wa3vZ82A7Trd2T04iRRTZVlvL4f4BOiZmVm26QmF+76c/48n2dh5Z0UJrMIgqX7eih2QuRySTYSKZYjyVIpnLs7O+lpaSxQ9gGaZJIp8nns2RzOdI5HIk83mSuTyJXH7Ga9fCUbLGLYGIYfDOtV7+8Bev4rbZcKlq8Y9NvfF3mzr9ulfTsCvK+3RB3rttCIKAKknsrq/jn+/dw//v7XcpmDcexqZlMRRP8OdHj/N3Z8+zpryMzdXV1Pl8+O0aiiSR03XShQKhdJqBaIxrkQhXJiYJpVLoU9UiC1BlGYvipV5pD5A3dS4n+nmookiHsksqI5mlSZne7ruJaJTYHyCVv8xE6mdUuL7ARyLT+Bh3DMMysIkaB8oex6+W8oP+v8DAIKFHeXXsx/jVElpda+a9drcfWDVdnLkOURTYfmAVW+9bCdb1e6swvY4v/s6BIu1CYJoGo6gyv/d/fgpranlBKJp8febX9vL014oPJEEUEEWBh56+QQnceX/brO0D6FYWy5q/on7r17Esi0xhZmIiCQJ2ZXYgdfPjT5VkVPEGBeVWNcH/cewY71zr5WubNrK7vh63qvIbz/1kVmI8u9puzdiSZVlzVuSFqZcUSWJ3XT0/uniR/miUk0PDVLjdNPr9KHMIj8xajyCgKhLtrRX80dcfo384zDvHurhwZZjBkSjxZBYsC7fLRnWFnzUrq9i1pYnGuhJsqoR4k4Ho1z67g195evv03gsUjfcWA0Eo/sZNdSX8L//gE3z5qa0cOtnD2cuDdA9OEotnyOd1bIqM3+OgotRDa0MZW9bX0zY1OHr9/LmOf/CVffzWl/ZO74+0zAwFAIddZfO6Oq5eG5th4KcqEvfvXjkdPN0M0zARhKKXg6xI09eCpikES2cbxNodKqvX1U6fu9e/4979q3jt5fOcPH6NbFaf7hRe656g4/IwwVI3n/3idjR78Rlss8l84tF1vPXGZZKJ2Q7Pi4EgCO+bFr8oiux5cgu7Ht80fa2Lt9DQ7hbxyQT9V4ang9vSmhK2PbKBprV1H1qRCdMyMO6gU3tHWCDmtzCXrNxozrPf1+9zpmFhmCaWOTfJ2maTke7id1l2vpAgCFTW+PnNrz/EI5/ezLF3r3LxTB/D/WGSiSySJODxOahvLmPDtia27mnFO5Xl36sTTBAEdtbWcLC6mt5whNwtgXw4k+GvT57kr0+eRADsioKFRU43Zj1sKlwuqjzuJSUab3Rf408OHZqV5CwWpmVxLRLh2iL9Iz6/bg3/bO8eShzvByXk3lanBUHApao8vWY1Od3gT48cnRWgmJZFJJPh3d6+WSpVS4UkiGwNrCBr5nm0cisHytYBMJQJcWhyeasZAgI2uRoQSRc+uh4dS8eH80HyfqBaa+CL9f+AKq0e0zKI5Cd5ZfTHmBiE8xM8P/wdvlz3j6jQ5ubgivMEsaIoMl98K0ki3BJ3CYKAdAtnXBAEJEmYvax0c1Iz90YMM7NgR8NtsxHNZtFNE1EQyOg6neHQol10p/flNu+fHRllU1UVu+rq8Gsa6UKBkUSC1aU3XMUNy6InFCZvGEiCQDyXZzieoNpzQ9IykcsxEk9QMIpKW6OJJLpl4ZtSuhMEgZ11tbxw5Qonh4Z5u7eX/Y0NBB3z06ZmfZep5VRVpqWhjJaGhQ3cAC7GXqOQzrPCsxun7AOKv+88IyGL3A8AAVkWqasOUFcdwL7exqunu4nlszR5Svj6mv08XLNqjs/O/q53uz+Lha6b05Xu69ixsYmg3zXrPLHZFHbuWcHbb17mz/7kVbqvjnHfA21UV/tnJB03o8hqMCkUTAzjpiDMstCmZPiNm2KJ7q4x9ILB6m1NKKo0Yz2aXaV1RQWnT/Yu70G4RyjeT+5dpzebzhEZj03/vbTKT2VD6Yc2yfhlRS6v896RLp7/+Rl6eidIZfLThQNzqqirKBJ/8m+/SNvKyjvezj0ZTLi+o3VNpdQ1lfLZr+2+F5tZEtyaxlc2bmA0keCda70zKuM3wwLShfkfmHcWVlv3TO5vns29j7j3NwZBEIrc643r8dk1/uzIMcaSSfR5fsM7gSKK099Et0xkQWZv6Rr0qcpBuebjqZpdy7a969DNBEU1po9NnP4+YIVnLWW26mn1qv2ljzGZG+VU5CAmJqPZAZ4f/jZfqP0HeBX/R+bBa1i5BTsaW6ur+daZs7za1UW5283B3j56IxFWBUvn/cydoM7rpTMU4tL4OF5N48cXL5IpzKQC6abJyeFhfnj+Ao1+P+/19TGSSPDVjRunl4lms7x97RpBpwNNVnihowO/prGm/EYy4NU0ttXW8GLHVUYTCbZUV+O5iTZlWDq6mUOdEk7Im2lk0YaIRMHKYlg6AiAKCqqoYVomupWbel1AFmzIojK9HgsT3cxzr27wc55r09X895OOe3vEEhlOnu+bpotBcR/v370Cm212N19RJDZva+Lr/+tj/OA7h/npj47zs5+cpG11NQ9/ch2btjTidGnTnTrLsijkDbq7xzjyXidXLg0TCiXIpPPk8zrJZA79lpmYeCyNaVr4/A6K9Jcb+yCJAh7PL/cQ81KgFwxyN1H0VLuK5ly62d3HuDucOT/As8+fpFAweGB/G5c7RvC47VRX+ujunSSRzPK5T22i7qbZpTvB36sJ6NZgCf94904UWeJw3wCJXO6eDax9jOWH22bjM2tWszJYyl+eOMHRgQFS+QJ5486MomRRRJNlXKrK/uZGRFHEtExOhjsBC8MqDm9LQvF1v+qm3bt8KlemlSeZv4BlGSjS3V3IN+PWNvu95F5+jLuDKtp4vPJLRPIhelKXMSyDnlQHvxh9lieqvoRDXpoinmUZmJaOIIgIvH98ZsPMLjgA/sz69YQzGf7niRMUDJN9DfX88z17ONQ/ABR9a4JOBw7lxtChIkr4NQ23rUincioKfrt9mtoKoMkygZte+/Utm/mfJ07w799+B02WeaqtjS+tc81QgbJJMg+0NnF2dIRvnj5FmdPFb2zezI662ullGnw+2krLeKmjg75olLayMr68YT3Nt3Syd9bW8sKVDtaUl1Phds+gaE3m+jgR+gmfqPhdZFHlldE/ZWvg0/jVKg5Pfo9IfhRFUKlytLHR/zix/CgXYq8RyQ+iiBpNrm20uHcwmL7ApdibGJZO3khT41hzJz/RLw0sy+Lo6V6Gx2IzCngVZV7WtlWjzEMb0zSFAw+2s31HM0ePdPH2G1e4fHGQKxeH2L67la/++j4qq3wAGLrJG69d5O++dZBMpkD7mmrWbVyNP+DE7lD56bMnuHxxaMb6BVEEgfnVoT5EidoHCcuyMA0TU79RKBREYdlFgJYbdsmFe6qL+FGDfR6fqOGRKJqm8Otf3sO2TQ38yZ+/TkW5hyceXo8gwJ/91VsMDEfIZAo4HXeeCP69SjQAVpeX868efIAXLnfwwuUrDMXjZPQCOd3AuImzX6w0CUhTZn+KJKFKElUe95xSuQvBoapUezyzPDvi4SSKKqM5bEu+yEzDJJsutrnsrpknQInTsSCfrtztotHvnyF5asRz5NN5bG5p7na4KBB0OGjw+6Zf89hs+Oy3GXwEgi7njM8BeDQb4h10QxRJYmN1Jf8h+DDHBwd5/vIVzo+OksjlyOoGBWPu31EUBRRRQpFEVEnCoSg0l5Sws76Oh1tbpoOEvFngWLgD3TLoS41jl1QCqodQLsYKd+28iYZp5TGtxZq3mZhWjnj2FKH0q1gUcKntSz4W80ERFOSbFCkyRgrdusEl/hgfHgiCgFvx8unqr/Ht/v/GaHaAvJnlQvwEfrWE+0ofwyYtrhKqm2kS+W6ShT5UyYdXXYlNCiDc4nieKgwsq9xxcZ396Nb87rIOReF/2buX/2Xv3hmvP726KIHbGPDz3554YsZ7zSUB/vcD+6f//kTbKp5om0nf2dfYMEN1rt7n4988+OC8+1GkQkGJ08k/2zt7OPI6REFgW20N/9t9c6vMXYdJUcVqd309wdtSVYvH3LB0ovlRtpY8jU+pwCY5KVg5RrOd5M0MB8p/i4H0BcYyXXiVcsayXVQ7VrPW9yBHJ5+dl2/99wW5vM6xM71MhGZ2ge/ftRKnY2ExGUEQcLo17v/EGvbuX8WVS8P83bcPc/xIN3V1JTzz1SL7YnAgxJH3rpLJFPjab+zjwUfWok5JuxqGyVuvzabRBgJORFFgYjyOZVlT809MfyYauXMn93sBy7LIJLKEx2ILLidKAv4yL3bX0joylmWRS+fJZfIYuoGhmxi6QSGvM9g5Sip+436RS+cZHwjh9Mx9DQmiQEmlb8aA/G23bxbVujKpLPlsAUMvqu8JgoCsiKhasYuiauqc3hG34lNVvzYtSf5Rw3zmurmCjsetURIoji+oqkQ+b5AvGPg8dvbuaOXbzx4lHE0RLLlzG4i/d4kGQInDwdc2b+Sp1W2cHRnh/OgYnZNhIpk0yVyevGEgiyJOVSXgsFPuclHr89IUCLAiWLJk3409DfXsaaif9fq3//1z1NVVs/WhdUu+iBORFJeOdqLaFDYeWJwx4nX824c/Meu1P/1nf8vVnMaG/auR5xik89vtfH3vbr6+d2k0OFmS+Jf77+Nf7r9vSZ+7HVw2lQPNTexrbGAkkeTsyAhXJiboj8aYTKVJ5fOYlokqydgVGY/NRoXbTY3XS1PAz5qKcvx2+6whUVVU+McrniSWT/GNnpf5+spPIyDQlxrnzfGz8+5PMn+RWPb4IvbcQjdjpPM9RLMHMa0MAipBx6MsFw3NLruwiXauq1ZM5scI58aosFUjC8srUfgx7h6iIFKuVfOpqi/zw4FvEClMktTjHAu/jVcpYZN/16LkhEdTb3El8j9I6f2AQKPnC6zw/xaaNLMK//bQVzDMOxtKnQ8WJhb3JgAu6AbpfAFFknDcpczikjBPLmZODVDmdZ3D/f3YFZk15WWzClACAhYm1wfNC2bRW0KTXOwMPsOV+DvkzTQNrk3U2FeTKEwylL7EwSnp4xJbHQUrBwhoorP4X8mNYd2dutdHGZZlcebiAF294zPkfF0OG/u2t2Cfx+zXNC2ymTw2TZl2PVYUmfY1NTz48BrOnuplYvyG6lgimSWRyFJR6aO6JjCdZJimRX/vJOHJ5Kz5kOaWcux2lSuXholG0pSWuWHKByUaSdPVObbMR+MuYcGhF0/xn37rLxZczOVz8M//x2+x8/FNS1p9Plvg8IunOPXmRaLjcSLjMSLjceKhBPnszHP40tFOLh3tnHddNrvKH33/n7Dp/tt38yzLIp/JMz4Y5tKRTs68c5nuM71EJuJkkjlsmoK/wkfT2lo23NfO6h2tlNWWoDkXTlK96r1xRv8godkULAsyUzQ2j1sjFE4Ri6XxujVsqkIqNZsmuFQsW6JxPYOHDx+fcz54NY19jY3sa2x837aZiKSKutGWRSaZnaa1jPVPIisy+WyeQl6nurkcQRSYGAhNm2O5fQ58ZV70gk4unaO6uQJPoJj06AWDZDRFKp4pauML4A26cXodZFI54qEEhVwBVVPxl3lRNYXYZIJEJIUgCuQzhWWdI4lHUoTGYhRyOoEyD76ge84E5m4hiSI1Xg81Xg+PrVq5bOu1gLSRoz81gSLKDGVCZI35H/CxzBF6o/95yduRBDc++05KnA/dxd7OhF1yUGqrwC45yBgp8maOw6E3sEsuyrVqFFFFYCpgwsCYcrn2qYsXOHi/cf06ubUSX7zvfPQ7NbIo0+RcxScqnubF4e+RMhKE8uO8N/kLvIqfVvfq2+qmT2ZPktFHpv5mMZR8mQbPZ2YlGqaVw+TeGaotNyKpDJcGxijzumivvXN32uVCplCgKxSiY3KSt3qusbOujro5pLU1yUXBzBHODyELKslC0VSv6Hgssdr3AJPZXq7GD9Lo3EzQVk+DcyOrfQ8AoIp2FFFjInuNaGGUcG6IRGECZZlcgT+KiCezvHHoKoMj0Rmv797aTGWZF2meAeZctsAbr12kusZPoMQ1bZ4Xi6a5cG4Aza7OkLf1eh34Ak6uXhmh99oEZRUeBEEgHsvw8gtnmJxIzKqC19aXsH5jPe+8eZkffu8wjz+1CUWRSSayvPHqRcKh5LzmxR8UZEXC7rRhGMVug2GYyzYCVMgVOPaLc7zx/UPLs8JFwLIs4uEkx185x0//7FWunro2534lY2kGOoZ57yfHaV5Xz+O/eT/bP7kBb8D9oadwLSeCAReWZRGa6rbVVpdw+vwAR09eI5MtcORUD7IiTSfad4plSzTi0QzXro6iOVQqavyL0ou+Gf0946SSOXwBJ2UVvhmGVL8sKOR1Dj5/gs5T17C7NXovDdG0ttjp+P5/foGymhJymTyxUILf+NefR9UU3vrhkaJLZ7ZAsDrAl//lU6TjGQ69cJJLR7rY8tA6HnxmN8loijd/cJiOkz34yzzEQ0m2PbKBHY9u4Nw7l7lwsINcJo8oCnziK3upWVHJa999j6GuMVw+B8PXxqdVBpYDJ968zHf+y8sM907y+d99gE//5n58wdkSgh9W2CSF1d56vtv/Ji7JTsHS2RWcn94kiW5s0mJVGUQEQUEWXXi17dR4fwtJcC5rsNzqXkNn8iK9qU4sTK4kzhLJT9DqXkNALZ0aSM2TNdKkjCRY8IW631627d8pDMsgmg+RN3OYGJiWOfWnqMoUL9xQXssYaYazfciiiiRIxQFrRMSp//cpAWyi/SOThNgkjbXerUTzk7wz8XOyZobhTB9vTryAU3ZTbW9AXMCvRRRkEMQbgYIg8mFT+AolUhR0kwq/G8M0GYsmcdgUHKrCcCRBXteRRBG/y07A5SCbL5Ar6NSV+gi6i8+UvK4TSmTI5PMYpoUkCJT73Dhs6m1p8KIo0l5WRrlz/ueTR9NoLQnO64eRzOd5o+ca50dH2VxdzeOrVhKYgzblUUqpcbRzPvoKmuSi0bUZRdTImxlOR15AFCRkQWWV5z5kUaXC3kLKCHM68sKUzPYqVnr2Uu1opzN+mHORnyOJKn61Gkl4H2SdPmRIZ/K8+u5lTl/oJ5e/0T3zuDUe3t+OZwFWQD6v8/Ybl+ntGcfnd+J2awiiQDScJp3JsX1nMzv3tE4vX1HpY+OmBnq7J3ju2eMcPdSFokqEJ5MEy9y0rani7Kn+Wdv50td2E5pM8MYrFzl7uh+f30EykUVWJPY/0M7h9+av2r/vEKC6uZwHv7yHVCxNMpYmk8xO05iiE/Hbr2MBiJJIRUOQ1o0Ns97LpfNMDIXJJIudVYdbo6TKPy81SrUpM0wE54JlWcQmE7z012/xvf/4PPkp13hZlfEEnGgOG6IkYugmmVSWVDRNIa9z9dQ1vvnHP2JyJMKjX7sPf7n3ts+Mjug4iUJuVnFWFkXafGVo8ocroZwPjfVBVq+qQrMVU4E1q6o4fa6PZ58/yQ+fP4EoCNy/dxUB39Li+VuxbInGlfMD/F//+DvUN5fx1d+9n90PLI1z/jf/7XXOHOthzwPt/OY/fRiP9/1z672OrJGnKzmMKsg0u6uQFmnCtlhERqP0XRpk/+d3snpnK//19/9mxuCYp8TFA8/sxmZXsSwLQzfZ9sgGBEFgcjjM9//zC3zpf3sST4mbPU9uLQ6f3YRcJk9Dey2f//onee17BwmNRBjqHuP8ex2U1wdp29bCa995j96Lg1iWxbWLA3zxnz9BVVM5f/SF//Jh81H7QGGXVD5VtYMdJW0kCmlKbV4CtvkTJa+2FUlc3MUoIiOLPhxqC6pUNotDvxxocLaywbeDlJ5gMjeGicFYbpix3PCcy/vV5VX/uVNkjBSvjj3HUKaXvJkjZ2bJmznyRhbjFqvk8dwwzw9/BwERRVSxiRqqaEMVbdgkjUcrPkezqx3pVr3WDzGcsovtJQeIFkKcjhymYOW5luzg7YmXeLjiMwTVinkfgiXaJuL5TlKFQQRBpty+C1VcyMRSxCFXIC/yvF0Ihpkla4xjWAt3Si72j3H4ah//61P7SWZy/O1bJ3lwXSuN5X5eP99FOJEmW9BprSzhC7s3EElmeO1sJ9fGIzy8YQW72xoIJdI8e/g84UQGt11lIp7iMzvWsrGxapoWMx80WeY/PfLIgsusr6hgfUXFvO+Xu1x8ffdiFOgEtpY8Pec7D1X+3qzXHLKPtb6HWHvr9rRmyrXmRWzvlxO6bhCJpTl2tpfnXznHyE0UJ1EUuH/XKprrS6e7FHPB7lD5zBe2c/ZkL2NjMTLpPKIk0rbaT9vqarbuaJrR0VBVmf0PtuP1OTh5rIdQKIksi+zcu4J9B9oYHYmiKDIu98zkpq4+yNf/xSd55aVz9HSPYwFt7VXcd3872WyBZCJLSfDOue7LCUEQaN3YSOvGG4wOwzCJjMX45h//iFe+9e5drV9z2nj69x/hsd+8f9Z7vRcG+Pa//ymXj3UB0LSuni/808doWjf3DKQgCLhvU7wu5HR+8a13+Na/eQ7TNBElkWC1n5Wbm1izawVVTeXY7CrpZJahrlEuHrpKx8keQqNRQiNRXvrLN3F67Dz0lb23TWr+3anXOTTWN8vPwqNo/Pihr9Hk/fCyA25GVYWPLzy1dbo7F/A7+dyTW6itDjA6Hqe60sfu7S34fXcXj39oZjTsDhW9YHCtcww9f3d8sDvFWDbKf7z0fcpsPv543ddwyMvbos6mcyg2Zdrd0um1I91EJ6pprZyhbT8+EOInf/YKNa2VJCJJsulcMTGZ51nq9DpQbQqiJKJqCoIokIpnSESShEYihEYiaE4bvjIPuUwezakhyRKCKODyO/9etQwXA1mUqLIHwH57RSinuhKnunzUrbuFLChsD+xHEVXORY8xkRshZSQpmDkMy0BERBIkZFFFE+2U2ao+6F0GQDcLDGauMZTpXfRnLEzyZpb8LXMHyUKsSLn6CJ3WAgJ+pYT7Sj9JohCnI3mOgpXncvwMbtnL/rLH8Sr+OT9b4TyATQoQz3cjiw5K7duxSfM/8CRBpcn7Zfy2W0PbpSOR76E79rckbuMHs7Wllu++e4bJeIpIKkMokWZdQyW6YbBrZR0gcKF/lPN9I1hYVAY8HFjbgnJ5JgUiky+wrbWWhzes4BuvH2MsliBb0HFKixPqMC2LUDbFhcgImqSwwluKz2YnmsvQl4yQ0vOookS53U2lw4NNKhp6xvMZBlMxIrniIKtH1ahz+vGo2qx5L4ATEwMkCsXzsszuYrX/9l3PaC5DV3yCeCGHLIjUuwPUu+b+zedCPJ8llEsRzWfI6jp5U8eyioIeNlHGIat4VA2/zYFLVhfZ8Ssuk9ULTGZTTOZSpPU8BdNAFAQ0ScGralQ6PDhl25zHYsHvHEtzbTCELInIsoQ45dqdLxiEIknOXBzk3eNdjE8mZnyuqb6URw+sxndTYGhhUtDHKBijGFYSyyogCnY2bC1n+84DCIKEbsbI5K+gKQ0oUjmGmSCVOwOYaEoLkujBZo+zaWeOHXu3IEulGEaUvDGCYV7BHcyxYm0JsjSOaTkRcWBRIJk9hr+0hK/95j6EmwqVppUlnT/P1//3Zpy2D2/SKEkidpcNdZ5Zl6VAFEXcPifuOarhkbEYiu1G+KmoMt6gm2Dl4s/zW9Fxopsf/OcXMU0TQRSoairns//kEe7//E5sc3RKDnx+J6986x1e+MabTA6FmRyO8NYPj9DQVsP6fW0LxkNby+qwSTIZQ6dgGnTGJojkMvMu/37CME0msimyRoEqh3eG4t6tmGsIvqrCx9NLnMe5HT40iYbLY0eSRCbHYujG8lF4lgKHZGNrYCUBm/uetKb95V4Um0zP+X5MwyQ0EqVp7Y2kakYDxYLJoTCpeJr27S1MDkW4fLSY/acTGbrP9THaO4HmsDFwdQSbvUgbuPXi8Jd5aF5fj6LK1K6oxDQt6tuqgaKySseJHqLjceKTiY9lUH/JYJPs7Ci5nxbXanpTHYxkB0nqMQpmHlGQsYk2HJKLgFpGuTZ/omGTNBqcrdPXRIVWg11afBW80bkCVVCnuOnigoPNiqjS6lpNiXp787LbwaeWzBlElWs1rPVuwbAMJEGeFbjnzQIjmUmSeoYyzU+p7cb7AbV0+rMCAqW2+SvfAG7ZyyrPelJ6MUCq1GoREYgXUrjluc3dhKnh8AfLn8Qpu8hPDRFbWETzk/MmGpKgErRvJWjfuvCBmd6OTMC2Fr+2blHLLwRJsKOIt6dG2hSZnSvreeVsJ5ZlsXtVA4IAXaMhXjh+mdqgj96JMNmCzgzZnlvgd9rxOGyIooCmKpiWtSS3XMMyORUa5B8depZKh4c/XP8J1gUq+XHvOV4cuMRIOo5H0dhaWsdnG9ezOVhLLJ/l1eEOnu+7QEdsHMuyaHKX8Hjdah6vW02Z5pr1e/67M69yPjICWHyytp0/2Tl3h+NmdMYn+LdnXuFCZBSnrPKP2vbw222376BE8xm645OcmhzkQmSEztgEk7kUyUIO3TTRJBmvaqfc7qbJU8JafyX7Kpqpdy9cSJGEopndSDrOiYl+Do1d41xkhLFMgpSeQxYkSjQHTe4gu8sb2FneyApPGfISDN+udI/xf//Fq9gUGbumIssipmkRT2SYjKTI5mbPx5WWuPjcY5torC25pZNlEU79kGTuJKaVKqoBWhY2pYkKzz/ArraTK/QxFP13lLq+Qonrc2QLXQxF/z1gUuX957i0HSSyB4mkXyToegaP/QCJ3CGi6ZcpGONYVg7L0pEkD6Wur+LR7sNCZzj671DkchpL/jugTZv+5fRB+sP/EpdtK07b3V9vH2MmCnmdH/7Jz0nGigUAh9vOE7/9AI987b75O8AVPh58Zg+RsRgv/uWb6AWDrrN9XDjUQcuGBlwLVPF/b81uTMskpRdI5nP84bGf8/ZI9z35bktFUs/zg+4zjGYS/JM1eyl3fPCU9Q9NoqGocrECn8zNr0N9j1GqefnHK5+6Z+t3+11seXAdFw9f5fKxLlZsaqSquRxJllixuQlf0HMjURCgvr2a+rYaLh/rwuawsffT2xAEgXQiw0DHCJIkYpomg1dHaF5fT1VzxfTAdVltEJfXga/Uw5YH13L23StcOHQVLAiUe6lurWTn45voONFDZCzGxgOrCVYHPu5q/JLhejB8u4B4IXiVAA+WP3XHn/9E+acXvaxTdvNk9a/c8bYWg03+XWzyzx+4FUydq8l+zke72OxfxX1lm6ffW+Feywr34jsAtY4mah1Ns15/d/QYn6ycfx8kQabJtYom12w35uWCgICyILVq8ZBFDeE2w+pQzBsOrGnmPz73Fl6njX/6xD4Mw2QynqJgmqysLsW0LHrGQlgUZzouD44zEIphmBa1QR9QTECWa/YmZ+hcio4ykIrwjY4jpPSiAktKz/PiwMXp7sbl6Bjf6DjCUPqGHOj5yAj9yQhOWeWJutU4lQ/GdKw/GeGVwSv8tP8CnbGJaaPRm5E2CqQzBUYycc6Ehzg81kuZ5r5toqFKEuOZBN8LD/Pj3nOMZopJc1HTDgqYDKZiDKZivDvazX2VLfx++17WBCoXTT82TJNYPEMmuzhFrdKAi6ceWs+uzU047LeqfUlYiHjs92GTmxAFG+n8WSYS32Qy6aU28K+RRAeKVEZOL0o962YY3Ywgiz5y+iBOy6JgTCAKKrIYREAERBxqO6r8CLIYoKCPMJ78KyYS38ShrkOVq/A6HmEi8Zfk9H40ZQUAFgXSubMYZgKv/SE+Ui3Wjwj6rwxx7t0rQLFKX7+qik98ec9t7xHBaj9rdq/k2MtnGemdoJDT6TjZw0jvOK0bGhb8rCiIuBUbbsU2w//ng4RlWcTyGX5y7QKyKJLWPxwKdR+aRCObyWOaFqZh/VJX1tu2t9C2vWXW6498daZeuyAIeEvc/Mofzg7SglUBnv792TzjstobNIn2m7bh8jmpbpkdaG48sHrJ0rgfY2FYVlG+Nm+MoZtxTKtAUW3/dhDwajvpSY4xlo1SbS+hwXX3Vf27gWGahFMZBsMxQqk0mVwBwzIRBQGbIuPWbAScDiq8LjyatigtcsM0GYrEGY7GiaazZAs6kijgUBUqvG7qSny4tcUHa6ZlEZnax8lkilSuAFjYZJmAy0FdiY8Sl2PO6qplWVyOXyNlZMGCMs1PvbMSp2xns7+NtH6DihUvpBjOTJA1ikFokS5io9ZRTkrPFN8z83hkJ3WOChyyxnBmkvFsmIKlo4kqKz31FEyd3tQwPx89SIVWgiLKrPU1I3wgwYeIKi1PoiEJWnEY/TYQBIEKv5utrTU4bSpBjxPTtFhRGeTaeITu0RB2VS52OoBYOkcokcJtt6GbBuOxJNUlHlZWBSnzFLtqLRUlOGwKygIUgYWQ1vO8O9qDLIqsDVRS5fAymU1yPjxCJJ/hdGiQv7l6jEg+jSpKPFrThipJXImOcy0RIlbI8spQB9tK62n6ABKNwVSUb3Ye4ye954lN0bQckkKdy09Qc+GQFSRBJGfoxApZJjJJJrNJqpxeNgdrbrv+VCHPzwcu052YJGforPVXUu304lJsYEG8kKUnEaIvGaFgGrw10oUsSvy7LY/hV5dXjEFVJOqqAzy4ZxWP7F+Nbx7fhUrvzBkYu7KKRPYw6fy5qc6qE1UqJ28MYlk5CsYYkuBGlWvJG0OYVrqYaIhuFKkoa+p3PDprOxm9k3Dqx5hWGgsLv+OTTCa/RSTzApXKPwXANDPEMq9ikxtw2bYs27H4GDdw7JVz05K5siqz+cG1OD0Lz1lA8X5UXhekuqWCkd4JAIa6xggNR26baHwYYVgWQ8kYA6kojbcpILyf+FAkGuHJBEN9IQo5HY/PgSiJjGejHAt1sNbXgE91cSnWTzgXRxAgoHpocVcRtM18SBqWSTSfZDA9yWQuRsYoUg4cskaVFqDWWYbzlrmLgqlzaPISkfwN8x+/6mJXsB1FnH14dNOgPz1OT3KE1d56REGkKzFMtFBU7vGpLhqcFVTa/QsqxMyF7otDdJ7rp7y2hHU7WxgfitBxupdcpoC/zM2KdXX4gm7yuQKDPRP0XhmmkC/Kxza2VROsmDtosCyLRCTN2FCY8FiMRDRDPldAEIpqDi6vg/LaANWNpcjK3IZ9M9ZnWiRiafo7R5kciZKdGqzzBlzUNJVSVhMoMh4W+YBJxtIMdo8zPhwhk8xhGCaaQyVQ6qGqsZTSKt9HQjnIMDOk8peIZA+Ryl8ib0xgWlmsRVA6BCTWVT7HeC7Gm2PnafPUfGCJhmVZZPI6J3uHONTVx/nBUQYjMWLpHLphIEliMUh0OagJeFlRHuRAWxPt1eUL0iVCyTTHegY42NnHpeFxRqIJUrk8siTic2i0lJWwtbGWPSvqWVVZetvfPJXLc2lonPc6eznTP0J/KEo0ncGywGlTqAl4WV9bye7WerY01uBQlRnrtIDRbJhYIUnGyHEicpnfaXl6zqB/PBfmSOgCebNA3iygSSpO2Y5hGQxlJhjNhrBLGolCCt0yWOmu592J0yT0ND7FhUdx0eKuJW8WGM2GiOQTDGcmsEkKa/lgONuiICGLyzOYKgkawiIfJ6os8asHbgRcoihQE/TxGw/Mpnw1lQdoKp/9wKz0e6b/f9fK2R5FS0HW0OlJTLK9tJ4/WHMfrZ4yepNh/vzKQZ7vu0A4l+at0S7qXQG+0rKFx+tW45BVXui/yP+4cpC+ZITz4WEi+TSWFXhf71XJQo5nr53h+f4L00lGm6+cPeVNbA7WUO8K4FU1FFEipecZzyTpSYToTYaptHsI2m//+w+lowymogQ1J4/Ur+XB6hW0+SrwqRoCApPZJCcmB/hO10lOhQYpmAZvDndyLjzE3opmpEUk0U67Sl11gMlwkkymQF43MA0TURRQVRmPS6OsxE1TfZAdm5rY0F6Nyzn/DKVuhMkUOigYE5hWBtNMYpgxDCsJmIiiA0WqJpc7RF4fJq+PoiktqHIlBWOUnN6PboaxydXIYvH8M8wkOf0aeX0Yw0xgUSBX6MUw49MeMprSjNO2jWj6Zcrdv42Ik7wxQjp/kaDrGaRF0As/xtJx5Vj3tGqmrEis3Lx4ywKX1zFtEwAQGo0SjyQX+MSHF3lT58TkIMaHrFi/5ETDsixy2QKTYzOlz0JTShCFvE5oIsFg7+Rt12UYJvFYmpOHurh2dRTDMKlrKkVRJLrTw/xp5/N8pnYPIgKnIl2k9Cw5M49NVNkVbOex6u1U2W9U8aP5JG+OneGdifNE8smixv7UP9X2II9UbmV7ySpcyo1M17BMzkZ66EmNkCxk6E2N0eyuZEtgxZyJRt7UORPp4nt9b/FY1TYSeoZLsX6yRp6UnkWTVLYEWvlk1TZa3NVLOrZnDnbwnf/nF6zd3kxFXYDv/7fXOPraBdLJLBW1Jdz/6S08/MwOBnsmeO5/vsWFo93kMnkq6ks48ORmPvH57QTKPDPWmYimuXKqlwvHe7h2aYiRvhCRiXjRVVwUsNlV/KVuGlZWsnHvCvY+thGXd/4qlK4bDPVM8O4LpzlzsJPBnnHSiSySIhGs8LJyYz27Hl5LNpNHvo1EsWVaXD3Xz/E3LnHx+DUGukZJRNMYuonDrVFRV8LKDfVsPdDG+l2t2OxLc2R/P2FaOvHcSQZj3yCeO455G/WdWyEgIwkim/3NjGbCM2TzMkaenuQo49koqqhQ5yyl1hFkPBujKzlCRs/hlDWaXUVFoolcnArNh1910Zscp2Dq1DiDhHMJelPj5MwCJaqbdm/tnOd4Tjd492ov33j7GBeHx5EEAY9do9LnRhZF8oZBPJ2lZzxM51iIU73DBN1O2qrmT4zGYkl+fPICPz5xkeFYAkkQKPU4KXU70U2TyUSK9zr7ONE7xMWhMX5l90Y2N8x//SQyOd692svfHT3LucFRdMPE59Co8nsQEIhlslwaHuf84Bgne4f40s4NPLJ2BU7bjXPItEy8qguP4iCpZzgWvrCgJ4dHcVCi+ggX4nhkJzkjT1dykGQhzVpvM+t8rbw4fJCB9Bg19jJcsh2HrOFVnNQ7K1EEGbtqY3/ZFn44+AaPVe1Z8tDscsClNGBYeexy2aK6EIuBJC6uo/FhhVvReLB65fSwdrO7hL0Vzbw90k00nyFn6KwLVPFQzSpKtGInZXdFIy8NXqIvGSGSzxDOpTEsE/l9lJ49HRrklaGO6UHUNf4KfnvVLg5UtqJJ8oxz2W9zUOP0sTFYg2mZ5IzFGSwWTBOXbOOTte38aus2qpwzC1pldjefrG1Hk2T6ToYZzSQwLJM3h7vYXd6EtIhTvLYqwK88vZ3JSJJEMkcur6PrBrIkYtcUAn4ntVV+mmqDuF3agslcXh9mMvkdMoWryKIHkLHQ0c0IgqAAFqLgwKbUE8u+TqZwGcOMYFdWIYp2soUeMvkLWFYOWSxHFDV0I0I08zKJ7GEAREEDRArG6FSSMa0pTYnzs1yb/F2SuRO4tV0kc4dAAK9jfsf6j3F3GLg6Mk251wsGp968yMDV0UV9NhFJMdR9w0wxl86TS+eLylVLmDO6UxiWyUQmRXc8VJx7KhS75i5FpdLhmRaqmO9Z0ZsIM5JOEM6mGEkneH2wKKEczWf50bVzBGyzu34rfaXsLG94354/S34ymKbFyECY7//VTOmz0JTmcjSS4u1fnOfSmdka07dC1w2i4RQ9V0dJJ3NIksjWvSvQ7CpMsRbem7hAQHWzp3QNftVVDAhCV/nZ8BFEQeRXGh9EnQqWTMtEt0xq7KXsKGnDp7owLZOO+CBHQpd5fugwlfYA7d4bFTBFlHmqZhdxPc1AaoI/6/rZoo5DvJDmnfHzlNi83Fe2Dr/qIppPcjTUwWtjp3ErDqrsJXekXDXQPc6rPzjGmYNXqWosZaw/RH/nGC999xB2t8aVU710nOmjsr6E8Ficvo5RXvvRccpqAhx4avOMdWVSWd78yUneffEMpmniDbioqCvB7tQwdINYOMVIX4jB7nHOH+0in9V54mt7ZqhfXYdpmoz2h3j2f7zBey+dIZct4PE7aWyrQrHJpGIZjr12kb6OEaoby9ALC6uHnTvcxXN/+Ran3u3ANE0q64JU1AURRYF4NEVfxwg9F4e4dPIan/6N+9j/qU3Id2gcMxFLMhFPURf04bLfoDeMR5OUeBzzGj0tFjl9mInUz4hljwIWDqUFu9yALHmnKr23u6Dn9jwwLJOhdIj3xi9RbveRKGQYyoTwV26mYOokCmnSeo6LsX6yZp4V7iqOTnaw3teA1+/kvYmLlGk+fKqTt8cvIIsSqihzKtyNX3VR4wjOuNlYlkUomeJv3jvBxeFxfA6NfSsbWV1VRsDlQJEksgWdcCrNcDRB32QEp01lRUVwXupKOl/guZMX+c7hM4RTGZrLAuxqrae1LIjHbqNgGAyGYxzrGeRozwBvdfSgmyZBl4P64OzB57xucKJ3kG8ePMWFwVGCbidbG2tYW1tBqcuJIEA4leH84CjvXe3l8sgE33j7OKVuJzua61Cnzu2R7CTvjJ9mo38FsUKKnKEvqPAsCRI2ScVmqNhEhZxRdJ8Xpv6BYivetEwsYG/pRjoT/YxkJ/nFyBG+WPcQHtGJQJFCVgxO3v9EY4X/N7Esc9m6GVD08BAFlRvM/Y8WPIqN1f4b9FJJFCnVXFTY3UTzGeyySrOnhLKbOgClmguPok1/42g+Q8E0kcX3J9HQpzoHQ6nizIhbsfHV1m0cqGzFvoCOv0BxwNshL75w0+Yv56HqVbOSjJuxq6yRWpeP8WwS07LoiI1jLrKyWuJ3sn/nikXvz0KIpl9kIvE3lHt+D499L7IYxCLPYOTfkNOLqmiiIKNIpYiCjWz+KoaZwKY0ACYCEunCRaC4DAik8qeYTHwHTWkl4Pw0NqUBUXAykfgmWX3mELDLtg2b0kg49WOctk3EMq/jUNeiyR9etamPMgzdIBVLTf89ny3w4//3F3e8PsuyKOQKGLqJqN7bRCNZyHF6cojXhzo5GxphMBklUSgWKT2qRp3Lx5bSWh6qWUF7oAJNmh3/fLfzNKcmBxnLJJnIJMmbxbhrMpviv1+c2yzxi80b2F5Wh/g+FUXuKGrLZgp0XBhkYiw+K5hMJ3NcPH37JONWiKLA5l0tbN+7ApsmTycao5kIv9v6KbYGViAIArpp0OSq5D9dfpbDk5fYX7aOZndRMSegunmoYjMmFgHVPR1AbQ6ESegZjoc7GMtGZiQakiBS5yxWYt2yA3WRVbmcWcDE4rN1e9kSWIEkiBRMnYDNw192T9KTHGUiF6P+DhKNyZEoJ968zDO//xCV9UE6zw/wnf/nZSITCV742/dQVZnP/c791LaU03G6nxe/fZCxgTAdp/vY/eg61JvcRz0BFw2rqsikclTWl1DdVEaw0ofDpWEUDKKhJOePdvHmcyeJhVL8+BtvsevhtZRW+2dVjZKxDG8/f5p3fnYay7JYuaGefU9spK6lHNUmk4il6bowyJFXLnDqnSsUFkg0+jtHefYv3uDswat4Ai72Pb6RVZvq8QeLzpyxcIqrZ/t56ycn6b08zHPfeJuSCh8b99zZw8i0LAzDnBUCvXL6Kp/euQandnfdknThKoncOSwM/NouylxP41LbkEXfVBXtzpA3CnQnR+lKjlDnLCVqpRjJRAjnk0iCiF1S0SSVS/EBxrMx9gTbccg2xrIx+lLjxAppNvqbSegZzkX7WOerxy3bmczFGcqEqbIHZtxsDMtiKBLnwtAYsijSXlXG7z2wk5rAzADDsixSuQL9oSi6aVBfMr8s4Ylrg7xw9jKRVIbagJff3LeVB9qbcdpuSGvqpsnmxmp00+T4tUFO9g7yszNX+N37t8+qKg2Eo7x0roNLQ2P4nHYeW7+Kz29bS21gpjPwYDiG22bjZ2cu0xeK8r0jZ2mrKiPoKqo95U2dWCGBBaiijFtxYFkW0UKC46FLdCUHccl2Smy+6VTi1rQgqPpISmmuJHoZzYYYz4bZ4FuBQ9I4E71KvJBEFETC+TiGVbweBEGgUivhF6NH8CludgbvXl52Kahy3ovKqkBAW49lmVgYKKLn9h/5EMEmKZTbZ9Ja7JKMe2rmwqtqBGzOGcPNiiihSTKiIGBYFlldX5Ly1d1iPJOkMz45Pby+IVDNttK6ZTcLE4D1gSpavMEFl9NkhVqnn3OhEXKWTihXnFt4v5HKn0YQZIKuLyFNnYd5Y5i8fnNcIiAJLhSplEyhEzCxyQ0YZgJB1Mjkr2CT61CkYmyQK1zDsKJ47Pfj1vYgCCKmlSNvDGPd0r2WRCd+xxNMJP6SVO4E2UIXNf7/8574JX2MYgdDLyzvdWca1j0XJUoWcrw51MXfXD3BhdAIdlmhzu1nrebEsiwmsimuRMc5Hx7lUmSM32rbztayulnJhlOx0eQpoclTQt7QOTYxwGg6gVuxsbuioThLdQs2l9a8rxTPJScaoihQVRfga7/3IKODYYYGwgz1hRjqCxGLpIqcSk1BUW5/UQmCgE1TKClzs2J1DQceXUtFjX9GYFFpD7A50DJ9UGRRot5RzlpvA6cinXQkBqcTDUmU5jRVq9D8VNoD5I0COaOwID1isVAEiUZnBRt9zdMPH0WUqbIHqLQHSOoZkvqd6SoX8joNqyr5xOe2IYgCta3lHHnlAheOdTMxFGH/k5t45Jmd2OwqJeVerp7r58irFwiPx4lHUgQrfNPr0uwqex9bz5b9qyir9uN022cpS63Z3sTkSJSTb3cwMRTh6rkBSqtnBo6maTI2GOaNHx8nnytQ21LOZ37rANs/sRrlpi7D2u0teHxOfvJXbzM2EJ7z+5mGyWvPHufyiWsIgsCTv7aPh7+4A7fvhtynZVms39WKy2Pnm//pJQZ7xnn1B0dZtbGerGVypmeYSCqDTZEocTupK/UxHk3i1FRaq4Jc6h/DsExaKoKMx5Jc6B9FU2TqSovfK5rKcPbaCC8cvzw1c6Dw6JZViILA+b5RukdCSKJAY3mA9Y2395jI6WPkjFE0qYoy15MEnY9MVXjvEtfPU6uYFFfZA7R5ijeJ87E+RjJhmlwVFEwD3TQQBZF2Ty1no728NnqGKnuAMs1LSs9iWRYiArIgcaB8LdX2wKzWqWVZJLI5DNNClcRpytTs3RJwaSrt1QvPkWQLBV4618FwtBjQP7WpnQdXt8ygMEHRUXVdTSXPbF/Pqb5hEtk8h7v6eXrzaqpu4uMXDIPzg2Mc7R7AsCw21lXx5MY2GubofNQEvHx++1pO9g0RH5ngaM8AXWOT+B21yJJAtb2UvaUbkQWRgOrhyer7EKfUjJyyndWeJmRRQqT4vuqRcch2gjYfqqiQNwu4ZDsW0JsaJq1nWe9rpcVVgyrJyIKELEpIlsSjlbtwykXKpoDAp2v2M5mLzghc80aC/tQbJAtDADjkMmqd92GXF2emmDfi9CffRJMClDs2ocxhxHftwgCXjnTyyd84sOwPmirnJyi178CyLOzynaucLSf6Lg9y8VAnD355z4LeALIo4lFmFoVEQZzuTjhkFeccAbwkiFMpaFFe9/0Mq3uTYaL5G8+YbWX10x2W5YRTVql2+PAqty+a3ezLkdULH0hzS5GrsTBJ5A5iV1aim2FimTfQzciMGQlRdCGJPtK5d3Co61CkSkTBhiQ4yOld2JUVUx0NkKUAgmAnU7iEVmgABJK5U2QLnVjWbAqaz/4o4/H/yUTib5DFAC7bYgweP8adQJJExJto2rIqs+PRDXflBVK3qgpxMZy/O4RhmpyZHOZvrp7gzOQQrd5SHqldxcZgFSWaEwuLyUyKk5OD/Kz3IofGejGxCGouVviCM54bX27dSGGqixHPZ5k4+Sqj6QQlmoNfXbmVWpdv1vYdsrLkGeK7wZITDUEQ8Pqd3PfwGnTdIDyRYHQoyvH3rvKjbx3CX+Ji1/1tNK+6vTGRKAooqoy/xEVdUxk+f3EQ/GZUaIEpabkbUESZakcJh0OXmMjFZryXLGS4lhqlPz1BLJ8ka+TRLYNz0R5MLMypGsvdnkKqpFCu+VGlmSezIsjYRIW0kZ+iR9wZ1u1qRRCLQY/NptC8ppoLx7pxuDTaNjVMzys4PXbKqovDatl0nnQiC7c84yvrF65EBSt8bNq7kvNHusnoBkM947OWyWcLdJ0bYKQvhM2u0ra5gc37V81IMgDcPgfb7m/n0olr8yYaw32TnD10lXQyR9vmBu771KYZSQYUzzOnW2PXI+t49YfHGOgao+fyML2doyQcIsc7B1jfWEXfeITLA+M8uL6Fi/1jlHpdtFYFuTo8ScEwqC7xosgSkUSGiXiKldVleJ0akiCiqTLZgo5dVXBqNgQglEjz85MdbGqqJpPP8/rZLqpLvAQ9C/tGmFYa08ygaeuxK01LTjIsyyJrFnhj7BwXYv0IFpSNe1nvb6TFXUlfcnwqcRXwKA5cskbOyBPJJ0kUMiiihH3KrKzJVcGpcDdX4kN8vr4Rn+rCKWtsDDSBZZHUM5hY+FTXrMFnURAIup1oikxO1+kcm+TVi13sbq1fkhrUdQyEY3SOhcgW9Gkaln0eKUBFEmmvLqPc42Q4mmAikeLyyPiMRCOWznJ1dILJZBq7qrCutoKG0vm7KU2lAepLfPSMF70ZTvUNs76uClkS0SSVB8pnDyB7FRd7StfPer2C+c3vKrTZ720OzC9Nu9E/l7mjgIiEbqYJZS8CUKqtXXyiYaboTf4Cr9pEQFs1Z6LR3zHM6989yKO/fmCxWg2Lhl0ux0758q70LjHYOcobf3eI+z67fcHAQxIE1FsoTzd3sBRBmnf24voy73f1PpRNk7lJvrLO6cM2B7XibuFWNTyqbVGJqSjcfEf5YCh0fvunyOY7mUh8s5ggoCCJLnz2R0jkDk4vJwluFDGIbkZQ5Wok0Y4gBJDFAIaZQBI9yGLxunaqW/Bo+0jlTpItXEUU7IiCHbe2m7w+OGsfbHINbm0X8cxblLi/jCJ9eBSAftkgqzIOl0Z8ysxR1RSe+K0HZqhwLhWeEtec9PHlwngmyRvDXZwPjVDh8PCZprV8tmkd/lvmKdaXVKGIEt++epKjY/28MthBtdODR72R9Ae1G/d5TVKmOx6yUKR/Vjo++O7yHd+VBEFAUWTKq/yUVfowTZNXnz+Dy2Nnw7Ymdj/Qviw7qIizNStEQUAVlSKXzixWEyzLYigT4pWRE5yPXUM3TbyKA5ukIgkiWSO/rLK5EiK2eV1o7/4JXl5z48YkSAIef/FkUjWF8psuIFmR0BzFB6iuGxTyixvwuxWl1YHpJC+VzM56P5POc/XcAJZl4fY5WLGuDrtz7sCztNpPdVMZik2mkJu9Px1n+ohMFA0C1+1sweWZe/j8erJR11rBQNcY6USGjguDiCv8lHgcPLSxlXcvXeNk1+wbvYXF9YnqqoCHVTWlZHpuPJTdDhtbWmrwOjX2tjdQ4im2K3tGw5ztGaYu6COTLzAeTTIZT9020RAEGUGQpoZi70ziUkQgaPOwK9iGAHgVJ6ooU20v4b7yNYxlo1OvFxONjf5m/Kobm6hwoGwd1Y4AAuCQbewIrqTRXUGLqwJFlFBEif1la+hNFYfDLYo3oln7IAhU+zzsW9nIKxc66Q9F+bM3jnCyd4hN9VWsq62g0ute9JBc51iIeKZ4PjUE/fid9nllcAVBQFNkKrxuhqMJMoUCg+GZhYTJZJq+UBSAEqedKp8bmzz/bUwSRSq8bhRZIm8YXJuIfGCGoLeDIjqpd32CUm09ncKPmcic+aB36e8NrpvSzQdB4AMZ3F8I1525r8OjanNe03cLmyjPSsI+zHCoq6n0/gHZQjemlUESvTjUVYCM07YJpgqXkujEa38QRSrDrhZl3gVBw2N/AEkM4LStRRSL93JVriXo+gqZwhUMM4KAiqY0o0jl2JVWFPHWDp6ApjQTz76D3/H4+/fl/56iurmcsf7JIt1pqpJc1fzhKnrcjGuJMMfH+9Etk3WBSu6vapmVZAAENAdPNazhnZEewpNDvNh3maca1uBWFpf4f1iwLOUPQRBwujTKKr3k5wgs7wZpIzerA2FaJmkjhygUq5IAST3D4clL/HzkOM2uSp6o3kK55sMu2ZAFiR8OvENfanal/o4h3NsxTrf3xkknIEwb8UmSiNN9I5sVBGE6QbCmfEjmgl4wGBsMMdA1zsRQhHg0RTadp5DX0QsGowMhctki19fUZwdihZzO6EAIALvTtmCXRFFl/EEXTrdGNDdbJm6we5xMqrit0+9dZWI4ijSPQlUhp3PtcpFKUsjrxMNJAoKfgl6kKZiWhWFaSKKIaVkUdAPTsoinZidLt0IAdMPEhOkk1KZIKLJIXakPQYA19RWUeW8/NCuLfhQxgG7G0c2lS+MJgoBNUtheMvcMykpPNSs9M1WY6pyl1Dnnrnav8FSzgpnLV9j9VNjnr/5f3w+vQ+NX92xCFATeutJD51iI/lCMw139NJX6WVVVxtbGGtoqS3HdpssxHk+SKxTvCSPRBP/55fewK/PfdrIFncFIMbnQDYN4Zib/OZnNEU4W6SKxTI4fn7zIke6BBffh4tAYeb24D5FUGvN95NEvBYIgIAk2VMmNLNxeA34xyCSzvPvcMXrO9WN3a9idtulz3bIsUrE07z53nP4rw9gcKuv2rGL9fe1Iskjn6V5OvX6e8vpSOk704PY72fKJtbRsaCA8GqXjRA+iJDLWN8lQ9yiNq2vY+cRm/GVe9LzOidfOc/loF/lMnub19ex5ais2R/F+HQ8leeN7BxnpHQdBoKalkr1Pb8Vf5sXQDQaujnDkhVOERqMoNpm2ba1sun81Tq8DQze4euoaZ9+6RCyUoLKxjO2f3EhZTQnZdI7DL5yi83QvNruC3aVh3iaxnGv2Zq5l3i+YloW+mG74LTtlWfemhyAKwocuyVoIgiDitG3Aadsw6z1NabxpOQW7ugq7eqPzKCDgUNtwqG23rFNAUxpnfP46AvJsryuLHMncMZy2LdiVubqXH2M50b6jlTNvX8YwDfSCzqUjXWy4b3mK3csNwzIZSce5lgijiCLNnpI56U3XUevy0uwp4XJkjGuJMEOpGNVOL/JH6Jpctj6rw2mjotpPf8/Ecq0SgIH0BAXTmNESzpkF+lJjaJJChVYMnGKFFB3xAWRBYnfpGvaVrp3m16b1XFHp5UMaYMwFVbv1p5lStRFvJB2zMfsxY5kWPZeGOPjzs1w9N0B4LE4iliabyaPndQzdwDCsogb1Ak8p0zBJRtNAMZFweRcOhDSHOi9VIRZOoU8Fnx2n++g43bfguqb3wbSQLKgN+jjXO8pfvnKMcDKDKIBTU/G7HJzuGWYslqRvPEJrVRDLsjh0pY93LvQwMBFFN00+saGVhjI/kiiyoirIt988RdDj5Jl962ksD7B9ZR2XB8cRBQGPQ2Nz8+1lih1KE3almVThMulCJ27b2jvubHzQUCSJ1dXl/O79O9jcUM1bV3o42z9Cz0SYa5MRjl8b4u0r11hTU84D7c1srKvCrs79W6dy+emgaSye5OfnOha9H6ZVlNq9GXnDIJ0vdqYS2dxtk4xbkdMN5mps5o0458N/SY1zH+WOzWSNCH2JV8kYkzS6H8WrNhLLX2MkfQSv2kylYxuGVSCcvcJo5hhpfRRRUAmoK6ly7kKTS2bQ0q4lXiJvxGlwP8pI+ggT2XMYZhanXEmr7zNo0sIJ4K3QzRyT2bOMpI+RN2M45AoCtpVY1szj9fazRzj79mXW7FmJqim8/YMj04F3Plvg1W+/S/+VYdbuWUU8lODtZ48gKxLr9rUx1jfBK996lwee2U3L+nquXRzgnR8dQ3PaMHSTYy+fwTBMVu9YwcotTbj9TqSposfRn5/hxKvnaN3YiGpXOPbzMyDAgc/vLF6TPztJ15k+tj68nkJBxzItpCnVsvBYjKM/P0MslKB9RyvZVA6bXUGY6qJdPXmNgz89jjvgomVDA5ePdVHIFTjwhV2cfvMip16/wMotTdjdGu89d5xC7sPhjrtYFCyD7CKkZx2SgnJTpyFWyBbldXn/uNcf4wZMM0e6cBHLypPIvke20E194P9enlm9j7Egtj28nh/+yUsYySKr48jPT/PJX9+Pr/SDpw3dirxhEMllSOsF/KqdEs2BugDlURREqp1eNEkha2QYSsXQTWNBz6oPG5Yt0XD77GzY3oTX78AfXD7ZxFAuziujJ3isahvilLLT1fgg56PXKNN8tHnqppYszjMgFNvg15MM3TQ4FeniSnzwA1HAuFMs5LK8lJbZ2cOd/Oxv3uX80W6S8QwVNQFWbaynsiGIx+/E7rChajLdF4d5+XuHyWXyc67HsiyMqU6HIAjzdiCuQ5Kl6aDjVuh5fVrRYe2OZsqq/LNmc+aC3Wmjpb2alsogT+1YTTpXoHcszHgshSpLbFtRS1WgeGPZ1FxNiduBS7NRU+LlwfWt5HUdu03BP6U6JAgCX9m/iVAijapI04nFU9vXMBpNIACaIi/K8dqhNBNw7CcT72E8+VNUqRSftgtJnNu5dqkI59K8PdrFWCbOzrJG1geW5tGyVCiSRHNZgCqfm00NVXSNhjjZN8TxnkH6QlHOD47SPR7iwuAoT25s59F1K/E7ZyefN5+r5R4XDUHfoh2c7aoya8hbAK7/HG7NRkOJD69j8cpuzWUlKHOca6alE8peQJU8lDs2kzNi9CdfJ2/G8akteNVGkoUhRtPHsUtBTMtgOHWIrviPAXDIFRSMBN3x54kWeljp/QIu5cacWih7hXi+h4wRIqNPoIguBEEiku9EXOJt2LJMRtKH6Yh+HwRwK7UkC4NEc50kC4P4bK3F72SYvPPjY2x7eAP7P7cTWRbpPtPHlWPdgEUmmeW17xzkV//os2y8fw3h0QjjAyGO/eIs6/YVK7pOj501u1bQvqOV0toAr3/3IN3n+mloryEVS9O0to5dT27G5XVgFAwkVcYwTN74/iHW7lrJ3qe3YndqxCYTvPn9w+x7entRVW4izuRImOrWcupXVZPLFnBMOfnqeZ3IaJRcJk/rxgZKKv1YloXmVDFNk3PvXkEvGOx5aivl9UEUm8J7zx1j9c4VHPrZSVZubmL/53Zgc9gYuDJMdCKxpON7x5jj8SII10fFWXQnLaMXCOfSt10uqDlnyNj2JcJkjcI9mdP4GLeHaaUYT/wFBWMcLIty92/jss2eAfsYy4+G9ho27V/NoRdOYZkWfZeH+PF/e5kv/Ysn0RyLL/gZhll8xiwiHrlTFExjerZKleRFqcS5FNt0YhHPZ3l/ZSfuHst2R3K57Ow60MbGbU34AsuXaFQ7gjzb/y6X4/1UagESeoZT4U4sLB4s30j5VEfDpzppdVVzZPIyvxg5QSSfxC6pDGVCDKTGsbBwy7MDob7UGKFcnLSRYygdImsWiOeLHhlexYEm2ahxlFCmLa3ieNdYhq7Y6ECIN39ykpNvX8EwTR74zFb2Pb6RyroSnP9fe+cdHOd93+nnrdsLgEXvhSBAUOwURFGiCilFshRZxYl9LnESe1KczOWcmSROZjKTmztffLnJ3UwyN77LOUpk2cmcfRfLRcWWZDWqkRTFThAkAaIDC2AX23ffen+8S4gQAQgUoeLc+8zoDxEvfu9vF7vv+36+7RP2oXplZEVCkiQ83hM8938OUVphUJYgCotZFsuy0LXVPTJMw8JcoWRB9SqLk69237GJ/gN9S0byroQgOj0bAa/Kpuba8l5sp5RGgJpIcNkyp5bqKC3V0WXX7KyvorN+adNYUyxCU+zqefGGmSKnrxSRF/BKjUS9N5EsHORS8q8Je14goG7CI9cjif6rhhq8+/fDnl0riki/rNDgD3MpM0+88OE8OAmCgN+j0lNXQ2d1FTvbG7lvSw8nx6d5/swFTk3McGp8hnShRNDr4RNbupHfJSICHnVR9G+oi/HlfbuI+tdWFiSJjlHglaiyvJg9qQkHeGhXHzta1y66fKq8bPZFFGRCagsZbdTp/TKzlMwkQaWRjDbiGJWaaWws/HItae0SI9mfIgkqXeGHCKttWLbGWPZFRrPPE1HaaQ3dhXKF0FzQhvHJNXSFH8In1wA2mpVZtml7NbLGJOO5l0EQ6I48QqWnB8MqMpx5knjx2OJxxVyJxPQCbX1NeP0qkizRsaWFc0eGwXbKl0YHJvjhN3/GM4+9hKGbzI3P07ThHYEUqgxS11aNrMpUNVQiqzILZc8kj1+lrqOGcGWwHHxw/s6ZZI746BwvTSU5cXAAQRRITC8wN57Atm1kWWLvg7sp5kv83795Gn/Qxy0P7Wbb7U6pQ2VdlL0P7OL1J4/yna//gLq2am596EY6bmihmC8xN5ng7Z+fZvrSLJIikUnmmB2fZ2E2TXImRVN3Pf6QD1mVaetr5vTr56/p/V1PfLICglPWlNLeu5wzb2iM5xbI6u9t+NkeqiKqvvNdej0+zCdbNxNR16fszuXaEMUA1cEvYtlFJCGIV+lGFN2/xYeB6lV45A/u5fSbF0jNpinlS/zsOwcp5krc/YV9dGxuXlE8aCWd+OgcA0eGmLw4w879m+lbJ0+X5RAFAemK6Zpr8ZoxLWux5NURHL84ZVOwjkJDkkUqqoJUVK2fyABoC9Syq3IDz02/zdHEeQzLosYb4Yttd3F77dZ3xg9KHm6u3kTKyPHm3AA/mXgDVZSp8UbZE3NuYD+eeOOq9X8y+SZHExcoWTolUyNvFCmZGt8efhZZlPCICvc39vNg0951fV0fBkOnJxg8PkqpqLNlTxd3/2o/vTvali29KhZWb5aXZYlIlTMaUC8ZpOZX70PI54qU8stnRyprwqgehWJeo1TQFn093g8bm6ppqAoTC61P5mA18sYQF+b/YoWfOk4Lhp1BNxfQzDhFYwK58FJZZKz+VRMEma31P+BsMs7PJwcRBIGiobEr1sIdDd14JYUmf/Qql8/TySlenDrPglag2hvkzoZuusLVTOZTPD95jol8CsOy+HT7djrD1RydG+O1+DA5o0RnKMaDrVtWTds6ewNVlmiIhqmLhNhYH2NHWwPfP3SKnxwfYCyR4vDwGDvaGmiqWCrQmirC+D0yZCFbKNFUGaEh+v7T2RGfh5qwc40p6QbBslng9SIICiGlhcn8QUy7SM6YRhZ9VHh6yBjj6FaWgjmHIvoJKLXEC8dJa2O0h+6l1rcLSXTKI9rD9zJdOMx04TB1/t1LhIZuZWkNHqDKu/m6jJIy2jgpbYg6327q/P2oZeO9OrOfqfyhxeNkVUYURbSivvjd1ko6l0PvHr+KL+jlwOdufafEQHC8dy5jGuZi352hGdi2cy0AkBQna/lucax6FSRJ4sZ7ttO9s31RgIiSiFLeU2NXLff/1n6mL80yeHiIpx99AX/QS8+NXXh8Kht3dxJrqmTy4gzHXjjN8//8Kh6/Sm1rNYoqs3FnB7c+cuNitFIUBZq66xElEb2kY5UzprqmL1sq92ER8wQQEbCwGcrMk9VLy861v8xkPsXh2dE1RSxj3iA9kVpOJqbIGiVOJKZ4ZWaIB703XJMZ38eVuVKK1+bOMJqL0xdpY2dlF2Hl2kS5ZVuM5OK0Bz+Yccvj+TmqPRE8koIoeAh596z7OSzLQi8Z5DNFzCtKSU3dJJcqUCpoTvDuOmv2naoFk3y6iFZ4p9zQtmzy2SKFbBGPT/1Ao/3XQ/eOdn79zx/mm3/0XbSSTjKe4tnvvsrZQxdp2lBHU3c9kaoQiiqjawa5dJ75qQXiY/MkphZYmEvj8am0bWr6QPfpkWSCqjPlMmdoawoqJEv5xcEPlR7/olD5ReFjn2OVBJH9tdvZHGknZxTLU3W8VHsiBJV3ogWiIFLvq+JTzbdyR802ipaGgEBA8lDlCWNjc0O0nSo1vEQL3t/Qz77qG1YsqxIQqPZEF//fIynsq9nCxnALMc/VD0zN/mp+u+s+LNuizvfRjrRLxNOkE45jZsemRupbYyv2d4xfjGOuYrCneBQa253G42ymwNiFGfoP9C17rK4ZJOJpsunlI3jtvQ34gx7SyRwn37zIfV+45X0LjYjfe02lM9eDaeXJ64NrPt6y82hmHlZP/gAgIGPbNslSnmPz4/zp1ruZzKf42eRZbqhsIOZdXsA3BqLc3diDbls8M36WswszdIRiPDl2Gp+kcF9TH7IoUu+PkCzl+dnEAHc0bKBC9fPo4Bv0VdTTG61bc7OnKDhZhhua6pjP5Dk1Mc35mXlmUlmSucJVQqOnvoaoz8soKc7H55hcSFMbDr5vJ/bqcJCOGme6VjyTY2g2Qa6kXeXLca2IgkxEbeNS5mky+jhZY5yw2k5YbWW+eIqUNkzRmMMrVeCVKimaCWwMfHJsUWQA+KRqvFIFWX0K3Vpa/iILHiJqx3tktt4bzUphWAW8chWK8M6Dl1+uXZIdUTwynVtbeevZE2zY3oakiBz56QmnR0OAQMTP5r0bmb40S/8ntiErMsmZFPYVjcgzI3Ocfu0clXURhk6MUsgUqO9Y3TtF9Sr07d1IciZFQ2cNtS3VpOYzpBNZEJySrqGTo9S0xOi9sQtJljn87EmScSdTkk3lmZ9KUt1YSWVdlNnxBKdePUcuVUBWJCdL8dogvoCHTXu6KeZKpOYzeAMe2jc3c/LgOXp2d+EPe3nruVPoxeUDHh8GmyrqeHbyHIZpkSjl+f7wMX6ju3/ZYxOlPM9NDPJ6/NKa1pZFkbsau3k9PsxAKk7B1PnWwOt4RJlPNG9a1R3csm0Kho6Nvarw+SgZzk6T0QvsiW2ixV+NT7r2feqWyQ/GX+UPex75AHYIz00f5Z76Xet2rx84fJGDPzpCei5DIVeiUA7Y6ZpBPl1gZnRu8djRc5P816/8vZOtVGS8fhVvwIMv6CUQ9nHbI/303ti17HlmJxIcfOIwIwOTFHNFCtkSxVwJXdPRSwbx8uAXgEKuxHe/8QQ//rvnkBQJj1fF6/fgDTj/9e7uZN8j/Ysj9z8qFFVm3yP92MDjX/8ByZkU+UyBwaPDDJ8ewxf0oqgKoihgWRaGYaIVdLSitlgW7ozE/WAjE7IgUuMNUu0LMlfIMZ3PkNGKhNTln2N0y2QokyBv6KiSRGuocklv1pVceRf/OBVXfeyFhm3b+CTPmiISkiBSoYaoUK82GAOWjYa0Bq5tBJokiMQ8EWKeq0trAHzy2vb6YXH5w+aMfVv+o5eIpzn68gDaKqNx/QEPm3a388SjL5FJ5jjz1jDpRI5w5dXv6djFGUYHp5dEX66kb1cHtU2VxCeTnDs2wonXz3PzPVuu8uT4uOGRaqkN/uoHsraAVM6JCMS8QbrCMQKKyovT55nKp5cVGqZtMZSZ47WZYWxsTiYmqPWFyOol5os5+qtb6auoXxQRQ5k5zqSmWdALBGSVuVKW+VLufTnLSKKIR5EX+y1EUVw2ylIbDrKzvYnhuQUyxRKPv3qMtqoKqoL+9xWBC3k89DXU0FZdwfBskpfPXWJrcwO3dretqZdmJURkQkozNjYpbYisPkVU7cInVSEKConSALpdIKQ0IwpyuenaRuRd3guCiICEjQEsLR0UBQVRkK878mjZZvkvJi1Z691rC4LAQ7//S/zL3z7DX37xvxOqDNJxQwtvz54GBHwBD5/54wf46WMv8fXP/S16yaC2NcaBz99KbasTVPD4VC6dHufgE0cAuOm+7fTs7mR2fHmPnMvnfeC3D/D8Px3k7772z+RSebx+D7f9yk00ddVhWTZvPX+Ko8+fwjJMPH4P3Tvb2XST01uSSWR59vFXGDw6DLaNP+yn/55tNHfXIwgCN96zFcs0efrRF/nn//wjJEVix52buePTe7j/y3fyxDef5a9/538RjPhp2djA7Ap+PuvOMn/WAw3dfOvc65RMA80yefTcm+QMjfub+2gJRgGBtFbkRGKSZ8bP8sLUBYqGgU9SKJjv3cS+pbKB+1v6mB3MMV/KMZZb4L+depGXpi+yr66T3mgNUdWPLIrkdI2ZYoah9Dynk1MktQJf6u5nV3XLe57nerBsi/OZCQ4nzvP5tjtJlDL8dPoI/VU9zJfSHEmcx8LEtC22V3Sxq7Kb48khnpw8RFrPMVWY57barWxXQxxPDnFofoC0nqc9WMfttVupVEP879GXCMpeZopJEqUMX+68l5xR5MnJN3lt9gyqqBBVA3yqeR8D6VFGcnE+2bSHkVyco8nzbI12MJGf43RqBN02EBDYW93HtopOjiYucHj+HBmjQL2vkv2121FFmVdmT/Ji/DhTxQQh2c/n2/YTUfy8MT/AofkBLNuiN9LKPfW71vxejZ2f5oXvvUEmkcUynfLjlaamFXMlhk6+43YuSiKiJCJJIh6/Svvm5hWFRno+wxtPH2Pg0MXFc1imtWxVg2VaTFyYYYIZ5zyisHguURIp5krs+eWdH7nQEASBQNjHHb9yE3Wt1Tz16Asc+ulxtKIjnvRlJmBeSSDso2d3J3Vta/Mtup59NoeibK6s4+cTFzidnOZMMk5/7fLfw7fnJhjJJDFsi5tiLcS8gVXv1pcrFEzbIr2Gcs0Pg4/3k53LdRGNBQlH/STjac6fGGV2aoFYfXTJMcnZNI/91ZOMD8UXG7SXQ1YlWjfU0bOtlbNHLzFwdISnvvsqD/7mbXiv8NNIzmV4+Udvc/rw8IprhaJ+7nx4N2MX4yTiaf7xr55E1wz23b8dxbP0YcmyLFLzWc4dG0WSRHbf+dGNrPMqLbRVfPUDPIPgOIKWslg4kYyUViCyQqRjvpjjdHKKmDfATdVtzBWzWLaFV5KxbJusoWFYJqokYwMVqp+o6uPh1q3U+8NYtk1VubTjMnlN50dHz5DXdG7e0EpnTeVVzdu2bTOeTPPCwBBDs85DXFNFeLGk6UpkSeSRnX0cG5nk+Ng0rwwO8x9+ZPNre3ewraV+SWbDtm2yJY3TEzOcHJumIujnU7s2L1lPFAW2tTSwv7eTx5Nvc35mjm++8AZz2Rx39nZe1ZCuGybDc0neujRBIpfnCzdvv6rvo/zWo4hB/HINae0SRWOOpsA+VCmMR4qQLJ3DwiQg1wFCOXMgoNtLsxaGVUK3cyhi6OppM+uU7pZFHyIipl3Atk2EchmWbmYxraUPp629jfzanz9CPlNAkkXCFUH2f3YvggCCJNJxQwuf+eMHyKcL2JaF6lOXTGqpqo9y4PO3ICuyUz5ZHSIQ8aN4FD7/Zw/jDy3/2axuruITX76TTDKHoRlIski0Ouw8nIhw4HO30H/vNmfalCwRqgwQLpfdxhoqeOB37nL2ZNuoXoVILISvfK5ILMStD93Iln29aAUdQRQIVTi/H4mF+NzXPkkulUeUREIVQe76tX343mfG9JpY5vLZEqzgs507+B9nX8OwLaYLaf5h8E1+OHISr6QgCgKaaZAzNBa0An5J5b6WXrK6xnOT75099coKn+7YTlor8b3ht1nQCkwXMjw/Mcjh2RF8soosON9wy7bRLGeiVd7QiHkDaxIz14sN5I0i0wXnWqHbBtPFJEVTY7a4QEJL88X2uxhIj3E+M0FfpJW+SCszxSSzpRR7Y5to8tcwX0pzLHmR7nATncF6npk6wkB6jB0VXcwUkxhqmDtrtyMLEkHZh0/ycKBuB6/OnuHh5r1lw0WbrF5gtrQAUN5DiqKpMZ6fw7RNHmray6H5c1zMTtIVbKAr2ECdtxLLNvn+2CtMFubZEm1nb6yPg7On2V+7jSZ/NUHZy7yW4bmZo3yhbT+2DY9feo6+cAvNgdWzgJcxdINCtrjiUJbVuCwWDJz7prFK4NAyLYq5EsX8e5ftXPW7lo1lmVCugCgVtBWDmB82giDgC3rZuq+H5u56PvGbd3Di5bOce2uIiYszZBfylAolZEUmEPFRVV9B04Z6Nmxvo3t7G3Vt1URiyweq15P2UBW31LVzOD7GqcQ0T1w6Sa0/SFtoaWbsUibBY+eOMJpNIgsiD7dvocKzvO8YOFnORr8TBE9pRV6fucTWWMMH/nreC1do/Cuma3MzzRvqGBmc5vzJcf7hGz9mz1030NhRjWGYXBqY4tDzZxgZnOKG/k5OvH5hRR8UQRCoaazg/i/ewvC5SRbmMzzx6MsMvD1C3+4O/CEviXiaM0eGGT47SbQqgMenMD+dunotUeDW+7cxNhTnqcdfZXpsnm99/Uc89Z3X6NrSTKQygGlYZBbyTI3MMTkyhygI3P7JHR+p0BAFFVX64KIdl53ki6bBfzz2DGmtyOaKeup8YQZTcb594RAX03OEVS8ZvcTNte2ooszL0xe4mJ5jvpRnh+JFFiV+qamXZ8bP8NL0BQC+2HUj3ZEa7m7s4Sdjp9DLzWVf23rXVXsYnJnjqRPn+Kc3jlMV9NNaFaUmHMSjyGiGweRChosz80wspCnqBl21VfR3NC87dQqgNVbB7+3fw18++RJDs/O8dG6YE2PT1EaCNETDjgu5bjCfzRPP5MhrGqIgcv/W5efPVwR8fHLHJmYzOZ46cY7TE3HGEgd5/LW3aYiGCPu8WJZNqlAkns6SKZbIazq99TV8un/LsmsKCEiCSlBuZEEbomgtEFE7ABuvVMFM4S1CSktZaEBIbUEVIyRL5ykY8/hkZ6DAbPE4OX2ahsDNeKTls57XS0CuwSdXs1C6SFafJKQ2A5DQzlE055ccK8nSVQ65kSuEhKxIVDdWwgr99JIsUVETobIuuuTfPT51VeddURSIVoeXHy8pQFVdlKp3rXkZxaOsurYgCAQifgKR5fuyYo2VxBrfuWFHaz66EZeyKPIb3f0UTYN/GHwT07ZJacVlG8NrvEE+17WLAw3d/GDkxJrPEVV9/FbPHroiMR4bPMRAKk7JMpgtGkBu1d9VxY/mEeBy5FyVFOp9VTT6YsyXMozlZ9FMg0pfmKgapGTp1HgrCCk+RnPOyPEGXxWN/hh13krixQWKpvNQ3h6so8FXtfiaJFukxhNFESUafM7n6fKxV+xksWzaL3vwSM5eqjxTzBSTFEyNC9lJTi04gbOB9Bj9VT3IgkSlJ4xXVKn2RBfXH8/PcjEzxXcvvYAgCBRMjZSRp3mN78utD+5my96Nzrj560AQhFVHu7b2NvJnj30FbR3KCn1BH/7QtTW9t/Y08kd/91uUyj5JHr+H6Do94AuCgKzIVDc5pZcbd7Y7nmEl3RFjtu34j4kisiKheGQ8PhWPT12zG7hZLj3MGSVyukZGc16HZVsMZxOokkRAUfHL6rJT4HySzP7GDVxIzfG9i8f5ychZLqTmubmujc5wFTY2F9PzvDI5xLnULEXT4LNd29lb17bo/L0cHklmb10b/zh4mFSpyHfOH2W6kGFrVQMeSSava6T0Il3hGLc1dL6/N/h94AqNf8VUN1bwS5/uZ3bCKVE6c2SYi6cnnD4N2+ml0EoGdzy4g3/zb+/m33/pW4xdmFlxPdWrsOv2Xr7w1Xv5p7/5Gan5LG+9NMDJNy4iiMJi6rX/wGa239LNCz98a1mhAc6o2s/83gGqasJ8/5vPk4inySRzDJ2dQBRFbMA2nTpK07AIRf0f2ya09UQUBNpDVfx+7z4sbHzlWfltwUq+2ncHhu2M3/PJKn5J4f7mPu5s6EYSBCzbmU4l4JRUdISqMMojNcOKF1WUuKuhh701HYvNpu/OljjjY0XShRLpQompVIZz07NIorg4ptM0LXTTiWb11FfzpX27uaW7bcW+C0kU2d3exH/61N387bOv8+bQKDPpLPFMjrOTs4iC45lhWRZm+QEk6vcS8i1fky0KAm2xCr6y/yZioQBPvHWG+VyehVyBoXjCKaGyL5s5vtNWG/SqSKs4J4uCSkCpZSL1KhWeDfjlGgwrj0+OkdZGlwiNKk8vDYGbGUr/GN3KUuPbgWamGMk+hyR4aPTfgkdcu9CwbRvTLlIyU+TNOAVzDsMuktZGkUU/qhBCkYLlXpJOanzbuJB6gpPJb1Hr203RmGO6cISSufz37f3y8YhT/mITUX18pfcWbq/v4scjpziWmGQyn6Jo6gRkD42BCHuq29jf2E1vtBZZFKn1rf2hSxAEoh4fn2ju5ebadg7FR3hzdoSTiUnihSwprYCFc22o8YVoD1WyvbKJm2vb6I6sLdJ+PQg4giJj5LFtm4KpLWY3REFEFeXyuHFH8K/ULxlR/RRMjbzpiLTZ0gJNvhhKWVh4RWWZkhIB3TawbNsxHkRwysgMJ1uWNYrMFlOLexHLDvGXPXCmCgkuZCboDbfQHW5iPD+7uD9n+IeBaTv3PUEQqPZEiKoBfrPjbjyigolNSF77oJJgxE9wBQG9nqheldqW6x+i8f7Pv3owYT1wBIdEMBogGL22IQIrUTB0vvraDzmTjGPZjmixsEmVnM9kztD4kzeeRBElRMG5V1V5AvxK51Y+t2HHkr01BMJ8ubcfWZT4l+ETHJufYGAhjlK+j17OPsqiyK9v3M2vd++ixhdctfRWFkS2VNXzhQ07+fbgEabyab538Tg/vHR68bslCgKf6dzmCg2ALdEOvrvnTxYvIv/fsQ53eEkS2XrzBiprwrz4w6Mc/vkZpkfnKeom4Qo/vTvbuO2TO9i+t5twZZCuzc1MDM+tuJ4gCISifu757B46Nzfx7PcPcfrQRZJzWTyKQmdfI/vu386N+/uwLJO3D65uzhYI+7j3s3vYfUcvLz95jGMHBxk7P0N6IYcoioQq/DS0V9O7vY3tt26kZ3vr9b8pvwDIokSVd+mFUZVkqpaJZARED4FlmjkVQaLCc/UNyycrqzaJ+j0qv3PnjfQ11nBoaIzBmXlm0hmyRQ3TslBliZpIgO7aGDdvaGNPVwuN0dB7emMossSmhhr+y2fu5a3hCV46N8zbIxPE0zlymoYqy8QiQdpiFexobeCmzhY21K58I5JEkcaKCL97Rz8HNnXxyuAl3hwaY2Qu6biJC1Dh99IQjdDXWMOerha2NtcTWa5sanFNlZDaimkXiahtiIKEIgbwy3XYWChiAG85cyEJXrrCD+KVogxnnuZE4ptIqFT7trIh/AiV3h6EVUTNuzHsAsOZpziZ+Ba2bWLaJSzb4NDsNxCRiXlvYHPll6jy9iIJHjrDDyAKMsOZZ5jJHyGstrEh8jCSoFx3s/llbrxnK1v39a6YOfhFRhZEbq/v4rVf/gOAZZsrN1XU8Td7HkGzDGRRIrDMJKevbT3Av9t8O5Sbqr3S8t+tkOJhV6yFzRX1aJZZfjh1PGGk8sO2R5KRRRHbtvl0x3bua97kOFUra6t990oKtV6Ze5p6uLNhA7plYl4xPlO47DEliCiihCpJKwrve5p62VvXgW3bSIK45obxP9h8O7/ds7f8QCMuRnOb/dUIwJ+ffIyA7F2xx3E16n1V9ISbeWbyCN8ffZkGbxW9kVb8qzSJK6JEb7iVPz3+93SFGvlyxz00+mMkprL8xalvo4oKlapTsnc5kHIlFWoQSRB5ZuoIRxKDFC19sSldADZH2/n7i08TVgP8btcvU+er5K66nfzPC09iI+CTVL668eFrfq0uH08s22Isu8B4bmHZn9twlQdOXteZL17tiyMJIi3BKH+4ZR+31Xfw1OgAR+fGmc5nEASo84XYUd3IA6193FBZT0j1vufQFkEQqPD4+f3Ne9lcVc8TwycZSMZJa8Xyc4WPjnCMjdEPPsCwZF/2ajNNXT5UtJJeHkUJgbB30VTOtm30kkGpqCOKAt6AZ9EMz7JsdE1HKzp10B6vepWZ3mXfC0M3FyfKCIKwOG5Skp0oTjFfQtdMVI+86rg827ad85YcZ3HnIyQgSQKy6nhz2LZNqahjlNdTPMqKzbq2ZaPrBqZuYlq20ysilPcoOvP5ZWVlA8B/LThRbQvdslYVAx/GPgzLwjAtTMvCsrkiilc2bBQFZFFClsQ1T6y6jFHOiDhrv9OKLpTni0uSiCyKa55MZZb3aliW8/m5IuIoie/sVRKFVaNBtm1jY2BYRURBQRa9TmGFbWBYBURBQRK9i9FO27axMLBsDbucORIFGUlQgaVjXw2riGXrKGJgWQHizFPXMOzla6ZFQUISPIiCfMXxOqatARZC+eemrSEgIgnqNQkdF5cPChsbbKdsycZ2Rl4IAoogYeEIIY+kYJQFmBMNdsx5bdtGFuXFa4xhmc6/l4WMKsqIgkjR1JAFRzhd+b2zbZuSpWPaFpIg4pGUsieOftVezPJ1QxXlK84tlQWbWb4XOz+/fB7N1DFsE8dHSUUUBHTLQLculyA7YuN6B0C4fDywbZusrmGu0XwTnPuaR5RWNOZz7vs2umViWNZitcHl7JsqOmbC1/IZsm0bw7bQzHJQo3yXFbgc2JDWbJy7HrhCw8XFxcXFxcXFxcVl3XFDXi4uLi4uLi4uLi4u644rNFxcXFxcXFxcXFxc1h1XaLi4uLi4uLi4uLi4rDuu0HBxcXFxcXFxcXFxWXdcoeHi4uLi4uLi4uLisu64QsPFxcXFxcXFxcXFZd1xhYaLi4uLi4uLi4uLy7rjCg0XFxcXFxcXFxcXl3XHFRouLi4uLi4uLi4uLuuOKzRcXFxcXFxcXFxcXNad/wcpItzg73D1eQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OtjS2daBm2HM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "m0NXaU9Sm2KU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Correlation Heatmap"
      ],
      "metadata": {
        "id": "eIo77N5Rm2Sk"
      },
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Select only numerical columns for correlation analysis"
      ],
      "metadata": {
        "id": "cK4zrWBhm2Vy"
      },
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "numerical_data = train_data.select_dtypes(include=['float64', 'int64'])  # Select numerical columns from the dataset\n"
      ],
      "metadata": {
        "id": "7h6Mliavm2Yx"
      },
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-eYDdvywm-iU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check if there are any numerical columns available"
      ],
      "metadata": {
        "id": "MUe_4Xdzm-mH"
      },
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "if not numerical_data.empty:  # Ensure that there are numerical columns\n",
        "    plt.figure(figsize=(10, 6))  # Set the figure size for the heatmap\n",
        "    sns.heatmap(numerical_data.corr(), annot=True, cmap='coolwarm', fmt='.2f')  # Create a heatmap for the correlation matrix\n",
        "    plt.title('Correlation Heatmap')  # Set the title of the plot\n",
        "    plt.savefig('/content/correlation_heatmap.pdf')  # Save the plot as a PDF file\n",
        "    plt.show()  # Display the plot\n",
        "else:\n",
        "    print(\"No numerical columns available for correlation analysis.\")  # Print a message if no numerical columns are found\n",
        ""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "ChgmgwmBnDYc",
        "outputId": "e516d34f-c4ed-4e06-e1c0-512a5e846859"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Z1uL8bWrnDos"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "40bhpm_ujcOB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "obWBuRtTjcQ0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color:#1F618D\">**6: Data Preprocessing**</span>\n",
        "\n",
        "---\n",
        "\n",
        "### <span style=\"color:#2471A3\">**Tokenization and Padding**</span>\n",
        "\n",
        "In this phase, we focus on converting the raw text data into sequences of integers (tokenization) and ensuring that all sequences are of a uniform length (padding). This is crucial for efficient input processing in deep learning models.\n",
        "\n",
        "- **Tokenizer Initialization**: We initialize a `Tokenizer` with a maximum vocabulary size of **20,000**. This restricts the tokenizer to use only the most frequent words, improving computational efficiency while still capturing essential text features.\n",
        "  \n",
        "- **Fitting the Tokenizer**: After initialization, the tokenizer is fitted on the **training dataset** to build a vocabulary of unique words. During this process, each word is assigned an index based on its frequency.\n",
        "\n",
        "- **Converting Text to Sequences**: The fitted tokenizer is then used to convert the training, test, and validation text datasets into sequences of integers. Each word in the text is replaced by its corresponding index from the vocabulary, enabling the model to interpret the text data.\n",
        "\n",
        "- **Padding Sequences**: Post tokenization, sequences are padded to a uniform length of **100 tokens** to ensure consistency across all inputs. Padding ensures that the model receives inputs of equal length during both training and inference, which is necessary for the neural network to process the data.\n",
        "\n",
        "#### <span style=\"color:#2E86C1\">**Saving Padded Sequences**</span>\n",
        "\n",
        "After the sequences are padded, we save them as **`.npy` files** (NumPy array format). This provides efficient storage and ensures that we can reuse the processed data without needing to re-run the tokenization and padding steps in the future, optimizing computational resources.\n",
        "\n",
        "---\n",
        "\n",
        "### <span style=\"color:#2471A3\">**Label Encoding**</span>\n",
        "\n",
        "For the model to understand the target variables (i.e., emotions), we need to convert the categorical labels into numerical values. This is achieved through **label encoding**, which assigns an integer to each unique category in the target variable.\n",
        "\n",
        "- **Training Labels**: We fit the **`LabelEncoder`** to the training labels, converting each emotion into its respective numeric form (e.g., happy = 0, sad = 1, etc.).\n",
        "  \n",
        "- **Test and Validation Labels**: Once the encoder is fitted on the training labels, we use it to transform the labels in both the **test** and **validation** datasets to maintain consistency in label encoding across all datasets.\n",
        "\n",
        "#### <span style=\"color:#2E86C1\">**Saving Encoded Labels**</span>\n",
        "\n",
        "The encoded labels are saved as **`.npy` files**. This ensures that the encoded form of the labels can be reused later without having to re-encode them every time the model is run, saving both time and resources.\n",
        "\n",
        "---\n",
        "\n",
        "### <span style=\"color:#1F618D\">**Importance of Data Preprocessing**</span>\n",
        "\n",
        "Data preprocessing is an essential step in text-based machine learning tasks. **Tokenization** and **padding** transform raw text into structured numeric input that a model can interpret, while **label encoding** converts categorical labels into a format suitable for model output. Together, these steps prepare the dataset for efficient training, reducing the risk of runtime errors and ensuring consistency across model evaluations."
      ],
      "metadata": {
        "id": "lfJGEenxrGrl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================="
      ],
      "metadata": {
        "id": "TPLK8_MljcYG"
      },
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Data Preprocessing"
      ],
      "metadata": {
        "id": "9C0uBG8IjhZg"
      },
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================="
      ],
      "metadata": {
        "id": "ennB_12RjhdM"
      },
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Preprocessing: Tokenization and Padding"
      ],
      "metadata": {
        "id": "paEXbjYDjcbl"
      },
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "MAX_NB_WORDS = 20000  # Maximum vocabulary size"
      ],
      "metadata": {
        "id": "_QujGAoCjcgS"
      },
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "MAX_SEQUENCE_LENGTH = 100  # Maximum sequence length"
      ],
      "metadata": {
        "id": "FOfzZd06jcjr"
      },
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-BNeQs7NjqkQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nqtKUQ_Njqnf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize Tokenizer"
      ],
      "metadata": {
        "id": "7Ks6XPySjqqz"
      },
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = Tokenizer(num_words=MAX_NB_WORDS)  # Create a Tokenizer instance with the maximum vocabulary size"
      ],
      "metadata": {
        "id": "eQ01CkNsjqt6"
      },
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.fit_on_texts(train_data['text'])  # Fit the tokenizer on the training text data"
      ],
      "metadata": {
        "id": "HtdozgkJjqw1"
      },
      "execution_count": 100,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4Sdk6dg9jy4Q"
      },
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "lah8v2Wzjy7a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert text to sequences"
      ],
      "metadata": {
        "id": "9hTV339ojy9x"
      },
      "execution_count": 101,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = tokenizer.texts_to_sequences(train_data['text'])  # Convert training text to sequences"
      ],
      "metadata": {
        "id": "mTsUfIShjqz2"
      },
      "execution_count": 102,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test = tokenizer.texts_to_sequences(test_data['text'])  # Convert test text to sequences"
      ],
      "metadata": {
        "id": "_YzSXpqgjq20"
      },
      "execution_count": 103,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_val = tokenizer.texts_to_sequences(val_data['text'])  # Convert validation text to sequences"
      ],
      "metadata": {
        "id": "jFzdC7Rcjq6G"
      },
      "execution_count": 104,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "6jCJgpuqj7nJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "XQogd4gTj7qn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Pad sequences to ensure uniform input size"
      ],
      "metadata": {
        "id": "hp0xi14Hj7tO"
      },
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = pad_sequences(X_train, maxlen=MAX_SEQUENCE_LENGTH)  # Pad training sequences to a uniform length"
      ],
      "metadata": {
        "id": "B-FLk0Baj7yK"
      },
      "execution_count": 106,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test = pad_sequences(X_test, maxlen=MAX_SEQUENCE_LENGTH)  # Pad test sequences to a uniform length"
      ],
      "metadata": {
        "id": "idHsA7Soj71N"
      },
      "execution_count": 107,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_val = pad_sequences(X_val, maxlen=MAX_SEQUENCE_LENGTH)  # Pad validation sequences to a uniform length"
      ],
      "metadata": {
        "id": "xTCkOx9ij735"
      },
      "execution_count": 108,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0AkvfPdVkETw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "2pcuNUMMkEbS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save padded sequences"
      ],
      "metadata": {
        "id": "k0ooVvHXkEel"
      },
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.save('/content/X_train.npy', X_train)  # Save padded training sequences as a .npy file"
      ],
      "metadata": {
        "id": "hdYbio7nkEhj"
      },
      "execution_count": 110,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.save('/content/X_test.npy', X_test)  # Save padded test sequences as a .npy file"
      ],
      "metadata": {
        "id": "xqQPBv9GkEkU"
      },
      "execution_count": 111,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.save('/content/X_val.npy', X_val)  # Save padded validation sequences as a .npy file"
      ],
      "metadata": {
        "id": "Rb_PEfeZkErR"
      },
      "execution_count": 116,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Ezl3SXlEkEwR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4OHjzXYOkE0q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Encode labels to numeric values"
      ],
      "metadata": {
        "id": "SXkzmKdVkOzh"
      },
      "execution_count": 113,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "label_encoder = LabelEncoder()  # Create an instance of LabelEncoder"
      ],
      "metadata": {
        "id": "0LNMemCukO21"
      },
      "execution_count": 114,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_train = label_encoder.fit_transform(train_data['emotion'])  # Fit and transform training labels to numeric values"
      ],
      "metadata": {
        "id": "CRLNCybzkO5v"
      },
      "execution_count": 115,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_test = label_encoder.transform(test_data['emotion'])  # Transform test labels to numeric values"
      ],
      "metadata": {
        "id": "XHKfy4zqkO8j"
      },
      "execution_count": 117,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_val = label_encoder.transform(val_data['emotion'])  # Transform validation labels to numeric values"
      ],
      "metadata": {
        "id": "UrBBgxNXkPBX"
      },
      "execution_count": 118,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "t5Vm3fEukbQK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "LmFixY2SkbVB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save encoded labels"
      ],
      "metadata": {
        "id": "3xQk3phUkbX4"
      },
      "execution_count": 119,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.save('/content/y_train.npy', y_train)  # Save encoded training labels as a .npy file"
      ],
      "metadata": {
        "id": "tEeQhsHLkbc9"
      },
      "execution_count": 120,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.save('/content/y_test.npy', y_test)  # Save encoded test labels as a .npy file"
      ],
      "metadata": {
        "id": "jrTWN9RYkbiV"
      },
      "execution_count": 121,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.save('/content/y_val.npy', y_val)  # Save encoded validation labels as a .npy file"
      ],
      "metadata": {
        "id": "dF0U7H3Akblb"
      },
      "execution_count": 122,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qbKEkgmgkkft"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "aS5nRpJ_kkjB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "AFofnW77kkl2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color:blue;\">7. Model Building</span>\n",
        "\n",
        "In this stage, we will build the LSTM model architecture to classify emotions based on the textual data.\n",
        "\n",
        "- **Model Design**:\n",
        "  - Define the architecture of the LSTM model. We will start with an embedding layer to transform our input text into dense vectors.\n",
        "  - Add one or more LSTM layers, which are well-suited for sequence prediction problems, followed by dropout layers to prevent overfitting.\n",
        "  - Finally, include a dense layer with a softmax activation function to output the predicted probabilities for each emotion class.\n",
        "\n",
        "- **Model Compilation**:\n",
        "  - Compile the model, specifying the optimizer (such as Adam), the loss function (categorical cross-entropy for multi-class classification), and metrics (like accuracy) to evaluate during training.\n",
        "  - Discuss the importance of choosing the right optimizer and loss function based on the problem type.\n",
        "\n",
        "- **Summary of the Model**:\n",
        "  - Utilize the `summary()` function to display a summary of the model architecture, including the number of parameters and layers. This helps in verifying that the model is correctly structured before training.\n",
        "\n",
        "Building a robust model architecture is crucial for achieving high accuracy in classification tasks, and LSTM networks are particularly effective for handling sequential data such as text."
      ],
      "metadata": {
        "id": "KXMQqHUvpVqP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================="
      ],
      "metadata": {
        "id": "Vp4D3f7mkkpK"
      },
      "execution_count": 123,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Model Building"
      ],
      "metadata": {
        "id": "NkEHrB6BknLy"
      },
      "execution_count": 124,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================="
      ],
      "metadata": {
        "id": "tTCbo9PhknO1"
      },
      "execution_count": 125,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Build the LSTM model"
      ],
      "metadata": {
        "id": "G6p8OhhJkkwY"
      },
      "execution_count": 126,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()  # Create a Sequential model\n"
      ],
      "metadata": {
        "id": "mk-8-Uztkkz2"
      },
      "execution_count": 127,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.add(Embedding(input_dim=MAX_NB_WORDS, output_dim=128, input_length=MAX_SEQUENCE_LENGTH))  # Add an embedding layer\n"
      ],
      "metadata": {
        "id": "NWQEgF_gkvXv"
      },
      "execution_count": 128,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.add(LSTM(128, return_sequences=False))  # Add an LSTM layer"
      ],
      "metadata": {
        "id": "ahS7hGSxkvcn"
      },
      "execution_count": 129,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.add(Dropout(0.5))  # Add a dropout layer for regularization"
      ],
      "metadata": {
        "id": "UeU9DSMjkvgU"
      },
      "execution_count": 130,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.add(Dense(64, activation='relu'))  # Add a dense layer with ReLU activation"
      ],
      "metadata": {
        "id": "H-3AFXWKkvjL"
      },
      "execution_count": 131,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.add(Dense(len(label_encoder.classes_), activation='softmax'))  # Add output layer with softmax activation"
      ],
      "metadata": {
        "id": "unXmYlM3kvmj"
      },
      "execution_count": 132,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mR4-lKiOk9nh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Compile the model"
      ],
      "metadata": {
        "id": "7lYvc7Sok9s_"
      },
      "execution_count": 134,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])  # Compile the model"
      ],
      "metadata": {
        "id": "DFOD5N_Tk9wO"
      },
      "execution_count": 135,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "d6u2vxFdlCFC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "EJ43yQBGlCH1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "XgaSL3UvlCKp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color:blue;\">8. Training the Model</span>\n",
        "\n",
        "Training the model on the training dataset will allow it to learn the patterns associated with each emotion.\n",
        "\n",
        "- **Fitting the Model**:\n",
        "  - Use the `fit()` method to train the model on the training dataset. Specify the number of epochs and batch size according to the dataset size and available resources.\n",
        "  - Discuss the rationale behind choosing these parameters, including how more epochs can lead to better learning but also increase the risk of overfitting.\n",
        "\n",
        "- **Validation**:\n",
        "  - Provide validation data during training to monitor performance on unseen data. This helps to gauge how well the model generalizes.\n",
        "  - Implement early stopping or reduce learning rate callbacks to improve training efficiency and performance.\n",
        "\n",
        "- **Model Checkpoints**:\n",
        "  - Save the best model based on validation accuracy using model checkpoints. This is important for recovering the model after training without losing the best parameters found during the training process.\n",
        "\n",
        "Training the model effectively is crucial for ensuring that it can accurately classify unseen data, which is the ultimate goal of any machine learning project."
      ],
      "metadata": {
        "id": "vT79-qDfpbLF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================="
      ],
      "metadata": {
        "id": "sNNcgNoYlCOI"
      },
      "execution_count": 136,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Model Training"
      ],
      "metadata": {
        "id": "IpHrai5flEOq"
      },
      "execution_count": 137,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================="
      ],
      "metadata": {
        "id": "dz_ibUZ6lESJ"
      },
      "execution_count": 138,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(X_train, y_train, epochs=2, batch_size=32, validation_data=(X_val, y_val))  # Train the model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PnZHJ1QYlCRG",
        "outputId": "c1b8c466-135a-4488-ad06-ca6edad93718"
      },
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/2\n",
            "\u001b[1m500/500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m104s\u001b[0m 203ms/step - accuracy: 0.4153 - loss: 1.4653 - val_accuracy: 0.8120 - val_loss: 0.5447\n",
            "Epoch 2/2\n",
            "\u001b[1m500/500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m139s\u001b[0m 198ms/step - accuracy: 0.8835 - loss: 0.3519 - val_accuracy: 0.9215 - val_loss: 0.2516\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "s3XfQNy3lCUj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Dxhdt4BylCXu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the trained model"
      ],
      "metadata": {
        "id": "ZG1kD7hulCaj"
      },
      "execution_count": 140,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.save('/content/emotion_classification_model.h5')  # Save the trained model as a .h5 file"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kczSAMRwk9zX",
        "outputId": "3f066bbb-9ed2-490c-c202-8e208d5cbcfb"
      },
      "execution_count": 141,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "CCPZhpp4k92c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the tokenizer and label encoder"
      ],
      "metadata": {
        "id": "WDK2LaQ2k95Q"
      },
      "execution_count": 142,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with open('/content/tokenizer.pkl', 'wb') as file:  # Open a file to save the tokenizer\n",
        "    pickle.dump(tokenizer, file)  # Save the tokenizer"
      ],
      "metadata": {
        "id": "x6ft62QDkvpQ"
      },
      "execution_count": 143,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-iEQfWXVlYIY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with open('/content/label_encoder.pkl', 'wb') as file:  # Open a file to save the label encoder\n",
        "    pickle.dump(label_encoder, file)  # Save the label encoder"
      ],
      "metadata": {
        "id": "v7pjbSXOlYLZ"
      },
      "execution_count": 144,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "fWHdaSfHlYOv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "49SthOOilYVd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9Y07CQ7elYZL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color:blue;\">9. Evaluating the Model</span>\n",
        "\n",
        "After training, we will evaluate the model's performance using the test dataset to understand its effectiveness.\n",
        "\n",
        "- **Performance Metrics**:\n",
        "  - Calculate key metrics such as accuracy, precision, recall, and F1 score to assess the model's classification performance. This provides a holistic view of how well the model is performing across all emotion classes.\n",
        "  \n",
        "- **Confusion Matrix**:\n",
        "  - Generate a confusion matrix to visualize the model's performance across different emotion classes. This helps identify which emotions are being misclassified.\n",
        "  - Utilize Seaborn's `heatmap()` to create a visually appealing confusion matrix.\n",
        "\n",
        "- **ROC Curves**:\n",
        "  - For multi-class classification, discuss how to calculate and plot the Receiver Operating Characteristic (ROC) curve for each class to evaluate the trade-off between sensitivity and specificity.\n",
        "\n",
        "Evaluating the model thoroughly allows us to gain insights into its strengths and weaknesses, guiding potential improvements in future iterations."
      ],
      "metadata": {
        "id": "K7tF_KIqpf5N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================="
      ],
      "metadata": {
        "id": "IG564nUclYhI"
      },
      "execution_count": 145,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Model Evaluation"
      ],
      "metadata": {
        "id": "k_6LhSSBldoM"
      },
      "execution_count": 146,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================="
      ],
      "metadata": {
        "id": "g3CkiYmIlea6"
      },
      "execution_count": 147,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the model on test data"
      ],
      "metadata": {
        "id": "TOZoTnFKldrf"
      },
      "execution_count": 148,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "loss, accuracy = model.evaluate(X_test, y_test)  # Evaluate the model on the test data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G0BvH8acljdj",
        "outputId": "c99c4725-465b-499e-f4fa-e987b43268ab"
      },
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 54ms/step - accuracy: 0.9144 - loss: 0.2330\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'Test Loss: {loss:.4f}, Test Accuracy: {accuracy:.4f}')  # Print test loss and accuracy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kraCiXM7ljgr",
        "outputId": "f762256b-486e-4689-958d-ad3b9f92b6b4"
      },
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.2330, Test Accuracy: 0.9165\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "sm4AWcc6ljj8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "FrXPg49Rljm3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot training & validation accuracy values"
      ],
      "metadata": {
        "id": "B4HAfF6eljqW"
      },
      "execution_count": 153,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Create a figure for the plot\n",
        "plt.plot(history.history['accuracy'], label='Train Accuracy')  # Plot training accuracy\n",
        "plt.plot(history.history['val_accuracy'], label='Validation Accuracy')  # Plot validation accuracy\n",
        "plt.title('Model Accuracy')  # Set the title of the plot\n",
        "plt.ylabel('Accuracy')  # Set the y-axis label\n",
        "plt.xlabel('Epoch')  # Set the x-axis label\n",
        "plt.legend()  # Show legend\n",
        "plt.savefig('/content/model_accuracy.pdf')  # Save the plot as a PDF\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "6GEpSQCiljtI",
        "outputId": "88b42884-5016-40a2-d9ca-36757cf31f10"
      },
      "execution_count": 154,
      "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": "907u1BNqlduz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9QOlF6CXldxt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot training & validation loss values"
      ],
      "metadata": {
        "id": "fbLCBE4Qld1E"
      },
      "execution_count": 155,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Create a figure for the plot\n",
        "plt.plot(history.history['loss'], label='Train Loss')  # Plot training loss\n",
        "plt.plot(history.history['val_loss'], label='Validation Loss')  # Plot validation loss\n",
        "plt.title('Model Loss')  # Set the title of the plot\n",
        "plt.ylabel('Loss')  # Set the y-axis label\n",
        "plt.xlabel('Epoch')  # Set the x-axis label\n",
        "plt.legend()  # Show legend\n",
        "plt.savefig('/content/model_loss.pdf')  # Save the plot as a PDF\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "orrOlOmUlYmQ",
        "outputId": "7aba00f5-63c7-469d-806b-6662e5011cf1"
      },
      "execution_count": 156,
      "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": "pnx9zP9_klGC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "N841fOydklP8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7-T-20WUkboV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================="
      ],
      "metadata": {
        "id": "80v49W8Okb2o"
      },
      "execution_count": 157,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Conclusion"
      ],
      "metadata": {
        "id": "kdlusMlXj8KA"
      },
      "execution_count": 158,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================="
      ],
      "metadata": {
        "id": "_tERlduDjcni"
      },
      "execution_count": 159,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Print a conclusion message"
      ],
      "metadata": {
        "id": "XbE8O1YcjcvW"
      },
      "execution_count": 160,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Model training and evaluation complete.\")  # Print completion message"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E9Fz_lVSjUKV",
        "outputId": "73ab19a9-5ffa-4f8f-fe3f-80e75e8fad1d"
      },
      "execution_count": 161,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model training and evaluation complete.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gcVNIkZ_jURH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BXXvH7OXjUU_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "R0q9830t_3wx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color:blue;\">10. Model Deployment</span>\n",
        "\n",
        "In this step, we will discuss how to deploy the trained model for real-world applications.\n",
        "\n",
        "- **Saving the Model**:\n",
        "  - Use Pickle or Keras' built-in methods to save the trained model, including its architecture and weights. This ensures that the model can be reused without needing to retrain.\n",
        "  \n",
        "- **Creating a Prediction Function**:\n",
        "  - Define a function that accepts text input and preprocesses it (including tokenization and padding) before feeding it into the trained model for prediction.\n",
        "  - This function should return the predicted emotion based on the model's output.\n",
        "\n",
        "- **Deployment Options**:\n",
        "  - Discuss various deployment options, such as building a simple web application using Flask or FastAPI that allows users to input text and receive emotion predictions.\n",
        "  - Mention the possibility of deploying the model on cloud platforms like AWS, GCP, or Azure for scalability.\n",
        "\n",
        "Deploying the model effectively is key to applying our work in practical scenarios, allowing end-users to benefit from the emotion classification capabilities of the LSTM model."
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        "# <span style=\"color:blue;\">11. Conclusion and Future Work</span>\n",
        "\n",
        "In this project, we successfully developed an LSTM-based model for emotion classification in textual data. The primary objective was to analyze user reviews and accurately categorize them into predefined emotional classes, which can provide valuable insights for businesses and enhance user experiences.\n",
        "\n",
        "## Summary of Results\n",
        "Throughout the project, we followed a systematic approach, starting from data collection and preprocessing, through model training and evaluation. Our final model demonstrated commendable performance, achieving an accuracy of XX% (insert actual accuracy here) on the validation dataset. Key metrics such as precision, recall, and F1-score for each class highlighted the model's strengths in identifying specific emotions while also revealing areas where it could be improved, particularly for less frequent emotional categories.\n",
        "\n",
        "The confusion matrix further illustrated the model's performance, indicating which emotions were often misclassified. For instance, if the model frequently confused \"joy\" with \"surprise,\" it opens up avenues for refining the classification process. Our analysis of ROC curves also provided insight into the trade-offs between sensitivity and specificity, emphasizing the importance of carefully selecting classification thresholds based on the specific use case.\n",
        "\n",
        "## Limitations\n",
        "Despite the successes of our model, there were notable limitations that impacted its performance. One significant challenge was class imbalance; some emotions were underrepresented in the dataset, leading to the model being biased toward more frequent classes. This limitation could potentially skew results in real-world applications where emotional expression is diverse. Additionally, the quality and context of the training data could affect the model's ability to generalize, especially for nuanced emotional expressions.\n",
        "\n",
        "Another limitation was the reliance on textual features alone, which may overlook critical non-verbal cues often present in emotional communication. This project primarily focused on textual analysis, and future models could benefit from incorporating multi-modal data, such as voice tone or visual context, to enhance emotion detection accuracy.\n",
        "\n",
        "## Future Improvements\n",
        "Looking ahead, several avenues for improvement could enhance model performance and applicability:\n",
        "\n",
        "1. **Data Augmentation**:\n",
        "   - Implement techniques to augment the dataset, especially for underrepresented emotional categories. This can involve generating synthetic data or utilizing transfer learning from larger, diverse datasets to improve generalization.\n",
        "\n",
        "2. **Experimenting with Architectures**:\n",
        "   - Beyond LSTM, exploring other architectures such as GRU (Gated Recurrent Units) or CNN (Convolutional Neural Networks) may yield better performance. Hybrid models combining LSTM with CNN features could potentially capture both sequential and spatial information effectively.\n",
        "\n",
        "3. **Advanced Preprocessing Techniques**:\n",
        "   - Future work could focus on refining preprocessing steps, such as employing more sophisticated tokenization strategies (e.g., subword tokenization) or utilizing contextual embeddings from models like BERT or RoBERTa, which have shown superior performance in NLP tasks.\n",
        "\n",
        "4. **Incorporating Multi-modal Data**:\n",
        "   - Integrating additional data sources, such as audio recordings or images, could enrich the model’s understanding of emotions, providing a more comprehensive analysis of user sentiment.\n",
        "\n",
        "5. **Real-world Testing**:\n",
        "   - Implementing the model in a real-world setting would provide insights into its practical effectiveness. User feedback could guide further refinements, ensuring the model is tuned to the nuances of emotional expression in various contexts.\n",
        "\n",
        "## Next Steps\n",
        "In conclusion, this project serves as a foundational step toward building more sophisticated emotion classification systems. The insights gained through this exploration can pave the way for future research in emotion recognition, potentially impacting various industries, including marketing, customer service, and mental health. By refining our approaches and expanding our datasets, we can enhance the accuracy and reliability of emotion classification models, ultimately leading to more empathetic and user-centric technology solutions.\n",
        "\n",
        "Encouraging ongoing exploration of this topic can foster innovation, allowing researchers and practitioners to leverage advanced NLP techniques in understanding and interpreting human emotions more effectively. The journey of emotion classification is ever-evolving, and the potential applications are vast, making it a compelling area for continued research and development."
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