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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "**Setup and Installations**"
      ],
      "metadata": {
        "id": "KZwOLg16thN7"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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        },
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      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: kagglehub in /usr/local/lib/python3.12/dist-packages (0.3.13)\n",
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            "Requirement already satisfied: datasets in /usr/local/lib/python3.12/dist-packages (4.0.0)\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.12/dist-packages (2.10.0+cu128)\n",
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            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.12/dist-packages (3.10.0)\n",
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            "Requirement already satisfied: requests in /usr/local/lib/python3.12/dist-packages (from kagglehub) (2.32.4)\n",
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            "Requirement already satisfied: cloudpickle in /usr/local/lib/python3.12/dist-packages (from shap) (3.1.2)\n",
            "Requirement already satisfied: aiohttp!=4.0.0a0,!=4.0.0a1 in /usr/local/lib/python3.12/dist-packages (from fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (3.13.3)\n",
            "Requirement already satisfied: hf-xet<2.0.0,>=1.3.2 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<2.0,>=1.3.0->transformers) (1.3.2)\n",
            "Requirement already satisfied: httpx<1,>=0.23.0 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<2.0,>=1.3.0->transformers) (0.28.1)\n",
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            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.12/dist-packages (from jinja2->torch) (3.0.3)\n",
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            "Requirement already satisfied: aiosignal>=1.4.0 in /usr/local/lib/python3.12/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (1.4.0)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.12/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (25.4.0)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.12/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (1.8.0)\n",
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            "Requirement already satisfied: propcache>=0.2.0 in /usr/local/lib/python3.12/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (0.4.1)\n",
            "Requirement already satisfied: yarl<2.0,>=1.17.0 in /usr/local/lib/python3.12/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (1.23.0)\n",
            "Requirement already satisfied: anyio in /usr/local/lib/python3.12/dist-packages (from httpx<1,>=0.23.0->huggingface-hub<2.0,>=1.3.0->transformers) (4.12.1)\n",
            "Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.12/dist-packages (from httpx<1,>=0.23.0->huggingface-hub<2.0,>=1.3.0->transformers) (1.0.9)\n",
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            "Requirement already satisfied: psutil in /usr/local/lib/python3.12/dist-packages (from accelerate>=1.1.0->transformers[torch]) (5.9.5)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<2.0,>=1.3.0->transformers[torch]) (2025.3.0)\n",
            "Requirement already satisfied: hf-xet<2.0.0,>=1.3.2 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<2.0,>=1.3.0->transformers[torch]) (1.3.2)\n",
            "Requirement already satisfied: httpx<1,>=0.23.0 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<2.0,>=1.3.0->transformers[torch]) (0.28.1)\n",
            "Requirement already satisfied: typer in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<2.0,>=1.3.0->transformers[torch]) (0.24.1)\n",
            "Requirement already satisfied: typing-extensions>=4.1.0 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<2.0,>=1.3.0->transformers[torch]) (4.15.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.12/dist-packages (from torch>=2.2->transformers[torch]) (75.2.0)\n",
            "Requirement already satisfied: sympy>=1.13.3 in /usr/local/lib/python3.12/dist-packages (from torch>=2.2->transformers[torch]) (1.14.0)\n",
            "Requirement already satisfied: networkx>=2.5.1 in /usr/local/lib/python3.12/dist-packages (from torch>=2.2->transformers[torch]) (3.6.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.12/dist-packages (from torch>=2.2->transformers[torch]) (3.1.6)\n",
            "Requirement already satisfied: cuda-bindings==12.9.4 in /usr/local/lib/python3.12/dist-packages (from torch>=2.2->transformers[torch]) (12.9.4)\n",
            "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.8.93 in /usr/local/lib/python3.12/dist-packages (from torch>=2.2->transformers[torch]) (12.8.93)\n",
            "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.8.90 in /usr/local/lib/python3.12/dist-packages (from torch>=2.2->transformers[torch]) (12.8.90)\n",
            "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.8.90 in /usr/local/lib/python3.12/dist-packages (from torch>=2.2->transformers[torch]) (12.8.90)\n",
            "Requirement already satisfied: nvidia-cudnn-cu12==9.10.2.21 in /usr/local/lib/python3.12/dist-packages (from torch>=2.2->transformers[torch]) (9.10.2.21)\n",
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          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package punkt_tab to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt_tab.zip.\n"
          ]
        }
      ],
      "source": [
        "# Install required packages\n",
        "!pip install kagglehub transformers datasets torch scikit-learn matplotlib seaborn pandas numpy wordcloud shap\n",
        "!pip install transformers[torch]\n",
        "!pip install accelerate -U\n",
        "\n",
        "# Import libraries\n",
        "import kagglehub\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import re\n",
        "import nltk\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "# Download NLTK data\n",
        "nltk.download('stopwords')\n",
        "nltk.download('wordnet')\n",
        "nltk.download('punkt')\n",
        "nltk.download('punkt_tab')\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "from nltk.tokenize import word_tokenize\n",
        "\n",
        "# Set style for plots\n",
        "plt.style.use('seaborn-v0_8-darkgrid')\n",
        "sns.set_palette(\"husl\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Loading the Dataset**"
      ],
      "metadata": {
        "id": "0WcAl4Uqtpgy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Downloading the dataset - as the dataset was in large (Size), so I decided to use Kaggle Link. Second method we can download it, then upload it in the Google Drive, and call the path from Google Colab. But, due to large size of data, I decided to call it directly from the Kaggle link.\n",
        "import kagglehub\n",
        "path = kagglehub.dataset_download(\"pankrzysiu/keras-imdb\")\n",
        "print(\"Path to dataset files:\", path)\n",
        "\n",
        "# List files in the directory\n",
        "import os\n",
        "print(\"\\nFiles in directory:\")\n",
        "for file in os.listdir(path):\n",
        "    print(f\"- {file}\")\n",
        "\n",
        "# Load the dataset\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.datasets import imdb\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "\n",
        "# Method 1: Try to load using Keras built-in IMDB dataset\n",
        "print(\"\\n\" + \"=\"*50)\n",
        "print(\"Loading IMDB dataset using Keras...\")\n",
        "print(\"=\"*50)\n",
        "\n",
        "# Load the IMDB dataset directly from Keras\n",
        "# This is the easiest way since it's a built-in dataset\n",
        "(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=10000)\n",
        "\n",
        "# Load the word index to decode reviews back to text\n",
        "word_index = imdb.get_word_index()\n",
        "reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])\n",
        "\n",
        "# Decode function to convert sequences back to text\n",
        "def decode_review(encoded_review):\n",
        "    return ' '.join([reverse_word_index.get(i - 3, '?') for i in encoded_review])\n",
        "\n",
        "# Create a DataFrame\n",
        "print(\"\\nCreating DataFrame from Keras IMDB dataset...\")\n",
        "\n",
        "# Decode first 1000 reviews from training set to create a sample DataFrame\n",
        "# (Decoding all 25000 might take time, but I can do it for a sample)\n",
        "sample_size = min(1000, len(x_train))\n",
        "decoded_reviews = []\n",
        "sentiments = []\n",
        "\n",
        "for i in range(sample_size):\n",
        "    if i % 200 == 0:\n",
        "        print(f\"Decoding review {i}/{sample_size}...\")\n",
        "    review_text = decode_review(x_train[i])\n",
        "    sentiment = 'positive' if y_train[i] == 1 else 'negative'\n",
        "    decoded_reviews.append(review_text)\n",
        "    sentiments.append(sentiment)\n",
        "\n",
        "# Also add some from test set\n",
        "for i in range(min(200, len(x_test))):\n",
        "    review_text = decode_review(x_test[i])\n",
        "    sentiment = 'positive' if y_test[i] == 1 else 'negative'\n",
        "    decoded_reviews.append(review_text)\n",
        "    sentiments.append(sentiment)\n",
        "\n",
        "# Create DataFrame\n",
        "df = pd.DataFrame({\n",
        "    'review': decoded_reviews,\n",
        "    'sentiment': sentiments\n",
        "})\n",
        "\n",
        "print(f\"\\nCreated DataFrame with {len(df)} reviews\")\n",
        "print(f\"Shape: {df.shape}\")\n",
        "print(f\"Columns: {df.columns.tolist()}\")\n",
        "\n",
        "# Display sample\n",
        "print(\"\\nSample reviews:\")\n",
        "print(df.head(10))\n",
        "\n",
        "# Check class distribution\n",
        "print(\"\\nClass Distribution:\")\n",
        "print(df['sentiment'].value_counts())\n",
        "\n",
        "# Method 2: If you want to load the entire dataset (all 50000 reviews)\n",
        "print(\"\\n\" + \"=\"*50)\n",
        "print(\"Do you want to load the full dataset? (This will take 2-3 minutes)\")\n",
        "print(\"=\"*50)\n",
        "\n",
        "load_full = input(\"Load full dataset? (yes/no): \").lower().strip()\n",
        "\n",
        "if load_full == 'yes' or load_full == 'y':\n",
        "    print(\"\\nLoading full dataset (this may take a few minutes)...\")\n",
        "\n",
        "    # Load all my training data\n",
        "    all_train_reviews = []\n",
        "    all_train_sentiments = []\n",
        "\n",
        "    print(\"Decoding training set (25000 reviews)...\")\n",
        "    for i in range(len(x_train)):\n",
        "        if i % 5000 == 0:\n",
        "            print(f\"Progress: {i}/25000 reviews...\")\n",
        "        review_text = decode_review(x_train[i])\n",
        "        sentiment = 'positive' if y_train[i] == 1 else 'negative'\n",
        "        all_train_reviews.append(review_text)\n",
        "        all_train_sentiments.append(sentiment)\n",
        "\n",
        "    # Load all my test data\n",
        "    print(\"Decoding test set (25000 reviews)...\")\n",
        "    for i in range(len(x_test)):\n",
        "        if i % 5000 == 0:\n",
        "            print(f\"Progress: {i}/25000 reviews...\")\n",
        "        review_text = decode_review(x_test[i])\n",
        "        sentiment = 'positive' if y_test[i] == 1 else 'negative'\n",
        "        all_train_reviews.append(review_text)\n",
        "        all_train_sentiments.append(sentiment)\n",
        "\n",
        "    # As, It gives me alot error, so I have to use this method, I am not creating full DataFrame\n",
        "    df_full = pd.DataFrame({\n",
        "        'review': all_train_reviews,\n",
        "        'sentiment': all_train_sentiments\n",
        "    })\n",
        "\n",
        "    print(f\"\\nFull dataset loaded!\")\n",
        "    print(f\"Shape: {df_full.shape}\")\n",
        "    print(f\"Class distribution:\")\n",
        "    print(df_full['sentiment'].value_counts())\n",
        "\n",
        "    # Use the full dataset\n",
        "    df = df_full\n",
        "else:\n",
        "    print(\"\\nContinuing with sample dataset (1200 reviews)\")\n",
        "\n",
        "print(\"\\n\" + \"=\"*50)\n",
        "print(\"DATASET READY FOR ANALYSIS\")\n",
        "print(\"=\"*50)\n",
        "print(f\"Final dataset shape: {df.shape}\")\n",
        "print(f\"Columns: {df.columns.tolist()}\")\n",
        "print(f\"Memory usage: {df.memory_usage().sum() / 1024:.2f} KB\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VkwPQAF8tnhq",
        "outputId": "4180fa40-806a-462d-b6af-9858e351e4cf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using Colab cache for faster access to the 'keras-imdb' dataset.\n",
            "Path to dataset files: /kaggle/input/keras-imdb\n",
            "\n",
            "Files in directory:\n",
            "- imdb_word_index.json\n",
            "- imdb.npz\n",
            "- aclImdb_v1\n",
            "\n",
            "==================================================\n",
            "Loading IMDB dataset using Keras...\n",
            "==================================================\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb.npz\n",
            "\u001b[1m17464789/17464789\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb_word_index.json\n",
            "\u001b[1m1641221/1641221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 0us/step\n",
            "\n",
            "Creating DataFrame from Keras IMDB dataset...\n",
            "Decoding review 0/1000...\n",
            "Decoding review 200/1000...\n",
            "Decoding review 400/1000...\n",
            "Decoding review 600/1000...\n",
            "Decoding review 800/1000...\n",
            "\n",
            "Created DataFrame with 1200 reviews\n",
            "Shape: (1200, 2)\n",
            "Columns: ['review', 'sentiment']\n",
            "\n",
            "Sample reviews:\n",
            "                                              review sentiment\n",
            "0  ? this film was just brilliant casting locatio...  positive\n",
            "1  ? big hair big boobs bad music and a giant saf...  negative\n",
            "2  ? this has to be one of the worst films of the...  negative\n",
            "3  ? the ? ? at storytelling the traditional sort...  positive\n",
            "4  ? worst mistake of my life br br i picked this...  negative\n",
            "5  ? begins better than it ends funny that the ru...  negative\n",
            "6  ? lavish production values and solid performan...  positive\n",
            "7  ? the ? tells the story of the four hamilton s...  negative\n",
            "8  ? just got out and cannot believe what a brill...  positive\n",
            "9  ? this movie has many problem associated with ...  negative\n",
            "\n",
            "Class Distribution:\n",
            "sentiment\n",
            "negative    616\n",
            "positive    584\n",
            "Name: count, dtype: int64\n",
            "\n",
            "==================================================\n",
            "Do you want to load the full dataset? (This will take 2-3 minutes)\n",
            "==================================================\n",
            "Load full dataset? (yes/no): yes\n",
            "\n",
            "Loading full dataset (this may take a few minutes)...\n",
            "Decoding training set (25000 reviews)...\n",
            "Progress: 0/25000 reviews...\n",
            "Progress: 5000/25000 reviews...\n",
            "Progress: 10000/25000 reviews...\n",
            "Progress: 15000/25000 reviews...\n",
            "Progress: 20000/25000 reviews...\n",
            "Decoding test set (25000 reviews)...\n",
            "Progress: 0/25000 reviews...\n",
            "Progress: 5000/25000 reviews...\n",
            "Progress: 10000/25000 reviews...\n",
            "Progress: 15000/25000 reviews...\n",
            "Progress: 20000/25000 reviews...\n",
            "\n",
            "Full dataset loaded!\n",
            "Shape: (50000, 2)\n",
            "Class distribution:\n",
            "sentiment\n",
            "positive    25000\n",
            "negative    25000\n",
            "Name: count, dtype: int64\n",
            "\n",
            "==================================================\n",
            "DATASET READY FOR ANALYSIS\n",
            "==================================================\n",
            "Final dataset shape: (50000, 2)\n",
            "Columns: ['review', 'sentiment']\n",
            "Memory usage: 781.38 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Exploratory Data Analysis**"
      ],
      "metadata": {
        "id": "W1nYCojmt2Gc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Dataset Columns:\")\n",
        "print(df.columns.tolist())\n",
        "print(\"\\nFirst few rows of the dataset:\")\n",
        "print(df.head())\n",
        "print(\"\\nDataset Info:\")\n",
        "print(df.info())\n",
        "\n",
        "# Check class distribution\n",
        "print(\"\\nClass Distribution:\")\n",
        "sentiment_counts = df['sentiment'].value_counts()\n",
        "print(sentiment_counts)\n",
        "\n",
        "# Visualize class distribution\n",
        "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
        "\n",
        "# Bar plot\n",
        "axes[0].bar(sentiment_counts.index, sentiment_counts.values, color=['skyblue', 'lightcoral'])\n",
        "axes[0].set_title('Class Distribution')\n",
        "axes[0].set_xlabel('Sentiment')\n",
        "axes[0].set_ylabel('Count')\n",
        "for i, v in enumerate(sentiment_counts.values):\n",
        "    axes[0].text(i, v + 10, str(v), ha='center')\n",
        "\n",
        "# Pie chart\n",
        "axes[1].pie(sentiment_counts.values, labels=sentiment_counts.index, autopct='%1.1f%%',\n",
        "            colors=['skyblue', 'lightcoral'])\n",
        "axes[1].set_title('Class Distribution (%)')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "# Basic text statistics\n",
        "df['review_length'] = df['review'].str.len()\n",
        "df['word_count'] = df['review'].str.split().str.len()\n",
        "\n",
        "# Plot text length distribution by sentiment\n",
        "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
        "\n",
        "# Review length distribution\n",
        "for sentiment in df['sentiment'].unique():\n",
        "    subset = df[df['sentiment'] == sentiment]\n",
        "    axes[0].hist(subset['review_length'], alpha=0.5, label=sentiment, bins=30)\n",
        "axes[0].set_xlabel('Review Length (characters)')\n",
        "axes[0].set_ylabel('Frequency')\n",
        "axes[0].set_title('Review Length Distribution by Sentiment')\n",
        "axes[0].legend()\n",
        "\n",
        "# Word count distribution\n",
        "for sentiment in df['sentiment'].unique():\n",
        "    subset = df[df['sentiment'] == sentiment]\n",
        "    axes[1].hist(subset['word_count'], alpha=0.5, label=sentiment, bins=30)\n",
        "axes[1].set_xlabel('Word Count')\n",
        "axes[1].set_ylabel('Frequency')\n",
        "axes[1].set_title('Word Count Distribution by Sentiment')\n",
        "axes[1].legend()\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "print(f\"\\nAverage review length: {df['review_length'].mean():.2f} characters\")\n",
        "print(f\"Average word count: {df['word_count'].mean():.2f} words\")\n",
        "print(f\"Min word count: {df['word_count'].min()}\")\n",
        "print(f\"Max word count: {df['word_count'].max()}\")\n",
        "\n",
        "# Additional statistics\n",
        "print(f\"\\nReview length statistics:\")\n",
        "print(df.groupby('sentiment')['review_length'].describe())\n",
        "\n",
        "print(f\"\\nWord count statistics:\")\n",
        "print(df.groupby('sentiment')['word_count'].describe())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "DLg6uxj4tnk8",
        "outputId": "90ee1c6e-0efa-4477-b66e-4118e67830d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset Columns:\n",
            "['review', 'sentiment']\n",
            "\n",
            "First few rows of the dataset:\n",
            "                                              review sentiment\n",
            "0  ? this film was just brilliant casting locatio...  positive\n",
            "1  ? big hair big boobs bad music and a giant saf...  negative\n",
            "2  ? this has to be one of the worst films of the...  negative\n",
            "3  ? the ? ? at storytelling the traditional sort...  positive\n",
            "4  ? worst mistake of my life br br i picked this...  negative\n",
            "\n",
            "Dataset Info:\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 50000 entries, 0 to 49999\n",
            "Data columns (total 2 columns):\n",
            " #   Column     Non-Null Count  Dtype \n",
            "---  ------     --------------  ----- \n",
            " 0   review     50000 non-null  object\n",
            " 1   sentiment  50000 non-null  object\n",
            "dtypes: object(2)\n",
            "memory usage: 781.4+ KB\n",
            "None\n",
            "\n",
            "Class Distribution:\n",
            "sentiment\n",
            "positive    25000\n",
            "negative    25000\n",
            "Name: count, dtype: int64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Average review length: 1176.14 characters\n",
            "Average word count: 234.76 words\n",
            "Min word count: 7\n",
            "Max word count: 2494\n",
            "\n",
            "Review length statistics:\n",
            "             count        mean         std   min    25%    50%     75%  \\\n",
            "sentiment                                                                \n",
            "negative   25000.0  1166.68732  837.998402  32.0  649.0  885.0  1413.0   \n",
            "positive   25000.0  1185.58940  901.941950  54.0  634.0  877.0  1442.0   \n",
            "\n",
            "               max  \n",
            "sentiment           \n",
            "negative    7803.0  \n",
            "positive   11652.0  \n",
            "\n",
            "Word count statistics:\n",
            "             count       mean         std   min    25%    50%    75%     max\n",
            "sentiment                                                                   \n",
            "negative   25000.0  233.33168  166.721735   7.0  130.0  177.0  282.0  1571.0\n",
            "positive   25000.0  236.17912  178.871378  10.0  127.0  175.0  287.0  2494.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Text Preprocessing**"
      ],
      "metadata": {
        "id": "eIwBf7jdyApK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize lemmatizer and stopwords\n",
        "lemmatizer = WordNetLemmatizer()\n",
        "stop_words = set(stopwords.words('english'))\n",
        "\n",
        "def preprocess_text(text):\n",
        "    \"\"\"\n",
        "    Clean and preprocess text data\n",
        "    \"\"\"\n",
        "    if not isinstance(text, str):\n",
        "        text = str(text)\n",
        "\n",
        "    # Convert to lowercase\n",
        "    text = text.lower()\n",
        "\n",
        "    # Remove HTML tags\n",
        "    text = re.sub(r'<.*?>', '', text)\n",
        "\n",
        "    # Remove special characters and digits (keep only letters and spaces)\n",
        "    text = re.sub(r'[^a-zA-Z\\s]', '', text)\n",
        "\n",
        "    # Remove extra spaces\n",
        "    text = re.sub(r'\\s+', ' ', text).strip()\n",
        "\n",
        "    # Tokenize\n",
        "    tokens = word_tokenize(text)\n",
        "\n",
        "    # Remove stopwords and lemmatize\n",
        "    tokens = [lemmatizer.lemmatize(token) for token in tokens\n",
        "              if token not in stop_words and len(token) > 2]\n",
        "\n",
        "    return ' '.join(tokens)\n",
        "\n",
        "# Test the preprocessing function on a few samples\n",
        "print(\"Testing preprocessing function:\")\n",
        "for i in range(3):\n",
        "    original = df['review'].iloc[i]\n",
        "    processed = preprocess_text(original)\n",
        "    print(f\"\\nOriginal {i+1} ({df['sentiment'].iloc[i]}):\")\n",
        "    print(original[:150] + \"...\" if len(original) > 150 else original)\n",
        "    print(f\"\\nProcessed {i+1}:\")\n",
        "    print(processed[:150] + \"...\" if len(processed) > 150 else processed)\n",
        "    print(\"-\" * 50)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R27gqqIktntf",
        "outputId": "a84ad2c8-549d-47fd-9f7f-eaa6e0c901a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Testing preprocessing function:\n",
            "\n",
            "Original 1 (positive):\n",
            "? this film was just brilliant casting location scenery story direction everyone's really suited the part they played and you could just imagine being...\n",
            "\n",
            "Processed 1:\n",
            "film brilliant casting location scenery story direction everyones really suited part played could imagine robert amazing actor director father came sc...\n",
            "--------------------------------------------------\n",
            "\n",
            "Original 2 (negative):\n",
            "? big hair big boobs bad music and a giant safety pin these are the words to best describe this terrible movie i love cheesy horror movies and i've se...\n",
            "\n",
            "Processed 2:\n",
            "big hair big boob bad music giant safety pin word best describe terrible movie love cheesy horror movie ive seen hundred got worst ever made plot pape...\n",
            "--------------------------------------------------\n",
            "\n",
            "Original 3 (negative):\n",
            "? this has to be one of the worst films of the 1990s when my friends i were watching this film being the target audience it was aimed at we just sat w...\n",
            "\n",
            "Processed 3:\n",
            "one worst film friend watching film target audience aimed sat watched first half hour jaw touching floor bad really rest time everyone else theatre st...\n",
            "--------------------------------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Apply Preprocessing**"
      ],
      "metadata": {
        "id": "BxvXiwY5yB_S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from wordcloud import WordCloud\n",
        "import time\n",
        "\n",
        "# Apply preprocessing to all reviews (this might take a few minutes for 50,000 reviews)\n",
        "print(\"Preprocessing all reviews (this may take 5-10 minutes for 50,000 reviews)...\")\n",
        "start_time = time.time()\n",
        "\n",
        "# Process in batches to show progress\n",
        "batch_size = 5000\n",
        "processed_reviews = []\n",
        "\n",
        "for i in range(0, len(df), batch_size):\n",
        "    batch = df['review'].iloc[i:i+batch_size]\n",
        "    batch_processed = batch.apply(preprocess_text)\n",
        "    processed_reviews.extend(batch_processed)\n",
        "    progress = min(i + batch_size, len(df))\n",
        "    print(f\"Progress: {progress}/{len(df)} reviews processed ({progress/len(df)*100:.1f}%)\")\n",
        "\n",
        "df['processed_review'] = processed_reviews\n",
        "processing_time = time.time() - start_time\n",
        "print(f\"\\nPreprocessing completed in {processing_time:.2f} seconds ({processing_time/60:.2f} minutes)!\")\n",
        "\n",
        "# Show sample of processed reviews\n",
        "print(\"\\nSample of processed reviews:\")\n",
        "for i in range(3):\n",
        "    print(f\"\\nOriginal {i+1}: {df['review'].iloc[i][:100]}...\")\n",
        "    print(f\"Processed {i+1}: {df['processed_review'].iloc[i][:100]}...\")\n",
        "\n",
        "# Create word clouds for positive and negative reviews\n",
        "print(\"\\nGenerating word clouds...\")\n",
        "\n",
        "# Combine all processed reviews by sentiment\n",
        "positive_reviews = ' '.join(df[df['sentiment'] == 'positive']['processed_review'].tolist())\n",
        "negative_reviews = ' '.join(df[df['sentiment'] == 'negative']['processed_review'].tolist())\n",
        "\n",
        "fig, axes = plt.subplots(1, 2, figsize=(15, 6))\n",
        "\n",
        "# Positive reviews word cloud\n",
        "wordcloud_pos = WordCloud(width=800, height=400, background_color='white',\n",
        "                         colormap='Greens', max_words=100).generate(positive_reviews)\n",
        "axes[0].imshow(wordcloud_pos, interpolation='bilinear')\n",
        "axes[0].axis('off')\n",
        "axes[0].set_title('Positive Reviews - Word Cloud', fontsize=14, fontweight='bold')\n",
        "\n",
        "# Negative reviews word cloud\n",
        "wordcloud_neg = WordCloud(width=800, height=400, background_color='white',\n",
        "                         colormap='Reds', max_words=100).generate(negative_reviews)\n",
        "axes[1].imshow(wordcloud_neg, interpolation='bilinear')\n",
        "axes[1].axis('off')\n",
        "axes[1].set_title('Negative Reviews - Word Cloud', fontsize=14, fontweight='bold')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "# Most common words in positive and negative reviews\n",
        "from collections import Counter\n",
        "\n",
        "def get_most_common_words(text_series, n=20):\n",
        "    all_words = ' '.join(text_series).split()\n",
        "    return Counter(all_words).most_common(n)\n",
        "\n",
        "print(\"\\nMost common words in positive reviews:\")\n",
        "pos_common = get_most_common_words(df[df['sentiment'] == 'positive']['processed_review'])\n",
        "for word, count in pos_common[:10]:\n",
        "    print(f\"  {word}: {count}\")\n",
        "\n",
        "print(\"\\nMost common words in negative reviews:\")\n",
        "neg_common = get_most_common_words(df[df['sentiment'] == 'negative']['processed_review'])\n",
        "for word, count in neg_common[:10]:\n",
        "    print(f\"  {word}: {count}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "dDT-TBkDtnxO",
        "outputId": "152802fb-8ed0-40fe-e57a-23ff0b7960a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Preprocessing all reviews (this may take 5-10 minutes for 50,000 reviews)...\n",
            "Progress: 5000/50000 reviews processed (10.0%)\n",
            "Progress: 10000/50000 reviews processed (20.0%)\n",
            "Progress: 15000/50000 reviews processed (30.0%)\n",
            "Progress: 20000/50000 reviews processed (40.0%)\n",
            "Progress: 25000/50000 reviews processed (50.0%)\n",
            "Progress: 30000/50000 reviews processed (60.0%)\n",
            "Progress: 35000/50000 reviews processed (70.0%)\n",
            "Progress: 40000/50000 reviews processed (80.0%)\n",
            "Progress: 45000/50000 reviews processed (90.0%)\n",
            "Progress: 50000/50000 reviews processed (100.0%)\n",
            "\n",
            "Preprocessing completed in 96.19 seconds (1.60 minutes)!\n",
            "\n",
            "Sample of processed reviews:\n",
            "\n",
            "Original 1: ? this film was just brilliant casting location scenery story direction everyone's really suited the...\n",
            "Processed 1: film brilliant casting location scenery story direction everyones really suited part played could im...\n",
            "\n",
            "Original 2: ? big hair big boobs bad music and a giant safety pin these are the words to best describe this terr...\n",
            "Processed 2: big hair big boob bad music giant safety pin word best describe terrible movie love cheesy horror mo...\n",
            "\n",
            "Original 3: ? this has to be one of the worst films of the 1990s when my friends i were watching this film being...\n",
            "Processed 3: one worst film friend watching film target audience aimed sat watched first half hour jaw touching f...\n",
            "\n",
            "Generating word clouds...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 2 Axes>"
            ],
            "image/png": 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xMZH333+fBQsWsGzZMgYOHEhNTQ1//etfe3w8HfH73/+e6upqzGYzL7/8Mm+99RYrVqxg8uTJ+Hw+fve73wEtjezdu3fHfLijovkll1wS9xnaNs7nz5+PEIIBAwbwySef8Oqrr7J48WK+853vMGLEiFhUUW/ROkIlWpdoQ7ygoICzzz471rCNDkC01zh/6aWXWLlyJQDf/OY3+fTTT3nnnXeYP38+drsdv9/PAw88gKZpberQ0NDAsmXLeP3117nwwguprq7mlVdeAWDw4MEsXryY//73vyxYsKBNVHdHlJaWxqJwWg8gHStLlixhwYIFAFx66aV8/vnnvPXWW3zwwQdkZ2ejKAq//OUv8fl8x1T+888/j9frBfT7+s033+TDDz+M8780MDAwMDAw6BxN0/j2t79Nbm4ukUgk1kbtiO627wD++Mc/AiDLMv/617949913Wb58OeFwuN2y//GPf2C1WsnLy2Pp0qW89dZb3HnnnYAekBAVQ1966aVYRHH0c2fWgldffTWgt2M/++yz2PKNGzdSW1sLwDXXXAPoAvE777wD6NG3y5YtY+HChTz44IMAPPPMM3GzN3tKOBzmueeei32OHkdP+gc9OZ72UFWVX/3qV0Qikdi5XrBgAR9++CGpqans3buXl156CUmSYsEfrduya9asAdq20+vq6jh8+DDQ0k5/8803AbjuuutiHvAff/wxs2bNYtiwYXFid28QbWfX1tbGZrJG75uxY8cyduzY2PdRkTh6bOnp6bF8Rg8++GAs4Oqvf/0rH374IYsXL+bhhx8G9Hbz73//+zb7r6uro6ioiBUrVrBgwQJyc3NZsmQJu3btAuArX/kKH330EW+88Qa//vWvu91OjwbmAAwYMKDb56Mj/vznP8fq9MADD7B8+XIWLlzIc889hyRJ1NfX85vf/OaYy49aiVosFt544w1efvllFi9ebNhAGhicxhgiuoHBl4Tbb7+defPmMW/ePK644gquu+46/H4/FouFhx56CJPJxJ49e2JedZdcckmcUPa9730v5pn+0UcfAXr0utmsp0b48MMPWbRoEdXV1SQlJfH666/z2muvcemll/baMRQUFDBhwgRAn4Yaja755JNPYmJ2tDG7atWqmHB47bXX4nA4AD3aPdqwix5Hd2h9/m655RbOP/98Hn/8cUBP+BkdZKirq4s1VGfNmkVBQQGgR21Ez0U0eqUnx9MeqqqyfPlyQJ8mHI0OstlssaRVq1evxuPxxEVmb926NfYdwHe+8x3sdjvr1q0D9Mj4aLRLtJEcPX8VFRU8//zz7N69G1VVuffee3n77bf57W9/2+1z2R0mTpwYmx0RFc+jjfOJEydisVhix3u0iJ6VlcWwYcMAPdoKwG63c/fdd8cGLYYPHx67HocOHYo1cFszd+7cuMilzZs3x8T26667LpZIaMiQIZx//vndOq7WYnZqamq3tumM6PGBPiskamGTn58fi9b3eDysWrXqmMqP3svJycmxe1GW5bjBLwMDAwMDA4OucTgc/PSnPwXg008/5eOPP253vZ607xoaGmJttilTpsSipxMTEzucNbZo0SK2bdvGihUrYm371kkej1W8vuiii2LtxdbBIVFR2mKxxOxHom1hgK9+9auxv6+77jpMJhORSIQVK1Z0a78lJSWxNvq8efO46aabmDVrVmyW4Ny5c5k1axbQs/5BT46nPbZv3x4TiC+77LLYzMLc3FzOPffcuPMQbW/v2LGDcDiM1+tl27ZtZGVlxbzcoyJ6tL1rtVqZOHEi0NJOX7VqFQsWLKCsrAyr1cqzzz7LG2+80WXEdE+J9iugpZ0e/e/EiRMZNWpU7N6Ktt+jeYuiuY+amppiQUOTJk2Ka0tff/315OfnA3rASHvBLrfffnusXd96/wC33npr7LurrrqK3Nzcbh1Xb7bThRAxO8i8vLw4O84ZM2bEntV169bFrDN7QmNjY2yAYsaMGQwdOhTQZ0efSP9/AwOD48NILGpg8CWhdcPCZDKRnp7OxIkT+c53vhPzn249nWzQoEFx29vtdnJzczl06FAs4iAhIYGf/OQnPPbYY+zdu5d77rkH0BsKU6dO5eabb+7QruJYueqqq/jiiy+oqqpi27ZtjB49OtbYzcjIiDVKW0cc/PrXv+bXv/51m7KiAwbdofX5a80DDzzA3LlzY4Jva/uaDz74gA8++KDNNo2NjdTU1JCdnd3t42mPxsbGmNi+efPm2HTG1qiqyv79+xk3bhz9+vXj8OHDFBcXc9ZZZ7Fjxw5yc3MZPnw448ePZ/Xq1ezdu5fy8nKEEDgcjlin6s4772TDhg3U1tbyxz/+kT/+8Y84nU7GjRvHFVdcwRVXXBEbUGmPe+65h7q6urhlf/zjHzucdWC32xk3bhzr1q1rE4ke7TBMnDiRNWvWxDoT0fVaN+yjHcvc3NxYByNK6wiT0tLSNvfq0REorTuV0cGRjtbtiNai/NHn41iIHp/Vau20Tl1NG++I6PTXPn36xF3f/v37I8tyu50aAwMDAwMDg/a57LLLePnll9m4cSO//e1vY5HErelJ+661BeKQIUPi1unIr/nAgQM888wzsXadoihx3x/rb3tCQgJz5szhvffei9n/mUymmJh97rnnkp6eDsS3l6OR1kfT3Xa63++P896OkpSUxJNPPhk3u7En/YOeHE97tD7GZ599lmeffbbNOtGcVNH7IJrfx+VyoSgKU6dOZfTo0TgcDtavX48QItbenTBhQkyo/sUvfsFPfvITKisr+fnPfw7oQSWTJk3ixhtvjNk6tkdtbS333ntv3LLMzMxOZx1E9x0MBtm0aRNTp06NtZMnTZoUC3ZZt24dxcXFTJo0KWbLEm2nl5aWxu61o/udkiQxYMAAjhw5Ehssam0tBHpbtDWtLVuObhP379+/W/l8erOd3tDQELOGGThwYJzgH10WvaaHDx/u0r7xaFr3S6IDDlF6I4rewMDgxGCI6AYGXxI2bdoUS4LZHdprQEeXRZPJAHz9619n5syZvPfee6xfv56dO3dSXl7OggULePfdd/nzn//MnDlzjv8Amrnkkkt45JFHCAaDLF26lKFDh8aiGL7yla+0K+QOGzas3WiC1g2lrmh9/j744INYY3PXrl0dZpfPzc2lX79+7X4XtfQ4luNpj7S0tFgEwtFEr9e0adNiIvrgwYNRVTXWkJ0yZQqrV69m7dq1sSmqraPBo/YlCxcuZOXKlWzevJna2lpWrVrFqlWr+Pjjj3nyySc7rN+WLVva+ON3lfRm+vTprFu3jtLSUnbv3h3r+ERF9GgER3l5OcXFxTGv+dadpSjteYK29so8umELxDom7ZURvX5RutvhHDBgAA6Hg0Ag0G5isCiKolBWVtZty5eoP3/r42h9fK2f2dbbtOboY2q9TnvHe7w5BQwMDAwMDP4X+cUvfsG1115LaWkp//rXv9ptg0Tpqn3XWdukPaqqqpg3bx719fWALl5mZGTg8Xh6JQHlVVddxXvvvYfb7Wbt2rWxZKrQ8ezKCRMmtGs/kZ2d3a19jhw5krfeeiv2+Ve/+hWvv/46Ho8n1jZsj+70D47leNqjf//+5OTktPtdOBwmNzeXgQMHUlJSQnFxcUwgnT59OlarlfHjx7Nq1Sr27NkTE9Fbt3cvueQSxo4dy9tvv826devYvn07tbW1LFq0iEWLFvGLX/wiLhK6NaFQqM0gRF5eXqfHY7VamTBhAqtWrWLz5s2xgJacnJyYgD1p0qRYXVrnfmqvnd5eO7qrduzRwTG90U6P2sxAywBHe9TX1yPLcreF766O7+h3wNFt7KMHuo5e5+jjbV22gYHB6YUhohsYnEG0jgLYv39/3HderzfWoDs6WmDw4MGxKHRFUdiwYQM//elPqamp4e9//3uviuiJiYmcf/75fPDBByxdupSRI0fGonWuvfba2HqtIxC+9rWvcf311/daHS677DLeeustVq5cyVtvvcWll14a81psHQkwefLkLhO7dPd42iM9PZ2EhAR8Ph+DBg3ipZde6nT96dOn8/rrr1NcXEzfvn2BlmiQaITK2rVrcbvdgD41sDUJCQnMnTuXuXPnAnrSySeeeILFixezePFi9u7dG7NROZruTsk9ur7RKJho4tKsrKxYdMXYsWOxWCxEIhFee+21uO2iDBo0iJ07d1JZWYnX641ZsEC87+HR93R7tO78RGdjROlutJTFYuH888/n/fff58CBA3z22WdxfvpR3nzzTR588EEmTJjAr3/963aj0KL1PnDgAJFIhEOHDsWJ7u0dX+uINZfLFXtOvF5vuxE3OTk5HDx4kKqqKsLhcGxQZe/evYaIbmBgYGBgcAyMGDGC66+/ntdff52///3vbX7je9K+a/3bHfXJjtK6HRBlyZIlMQH9zjvv5K677ootj/59PEybNo2cnByqq6tZunRpLBgkKysrrr3Tup3+2GOPdRh0ciz8+Mc/Zvny5dTV1fHoo49y9tlnx2Y+9rR/0N3jaY/WfYJLL72UH/7wh52uP3369JiIfrSt4tSpU1m1ahWfffYZO3bsANq20/v06cMdd9zBHXfcgaZpbN++nZ///Ofs27ePp59+ukMRPT8/v0ezc6NMmzaNVatWceDAgZhFZDTQpfXfBw4ciFlGDhgwICbQt57VeHS/U9O02Azp1NTUbonVR7fTo5Hr0Vkb3WHMmDHk5eVRXl7Oe++9xw9/+MN29/3II4+wdOlSLrjgAv7v//6v3UC19PR0UlNTcblcHDx4EE3T4gYDos+nLMuxvk20ne5yueKCY1onlW3veI9+9o/lehoYGJwcDE90A4MziMLCwpi1y5IlS2KZ3IUQPPXUU7FR7iuvvBLQ/RivueYaZsyYEfvxNpvNTJ48ucsIhiitI0+6m/Ql6gdZUlLCM888A8CoUaPiInWmT58eSzz5+uuvx5Ijer1e7r77bn7wgx/ERa30lF//+texhs4vf/nLWPkZGRkxC5QVK1bEGj1CCJ544gnuvPPONsJ6d46nPWRZZvbs2YDuj9g6adA///lP7rjjjlhCI9Ab4LIs43K5Yh59URG9qKgIp9PJhg0bYtc92nAPh8PcdtttzJo1K24qakFBAVOmTIl97m1RtaioKBYN9N577wHxjXObzcaoUaOAFv/KwYMHxzUqo/dqOByOJXQFPTI+6kM5YsSIDkXq1kS92AEWLFgQm5a6adOm2OyB7nD33XfHGts///nP46JzABYuXBjzmK+srGwzJbU10XsH4Mknn4xFqpSUlMSSTGVlZcWmCbfu0EXvAYCnn3663SiZ6D3g9/tjSVUjkQhPPfVU9w7WwMDAwMDAoA0//OEPSU5Oxu/3xyWThJ617zIzM2Nt7qh1Buj5UNqzD2kd8RpNXO92u3n++edjy6MWFBDfTi8rK+vyuGRZjvmEr1ixImZ9cvTsytYBNi+88EKsDblnzx6+9a1v8aMf/Sjmod1TkpOTuf/++wE9kWhry5ae9g+6ezztMXLkyFjQynvvvReLYI9EIjzwwAPcfffd/POf/4yrG+gJRfft28eAAQNiXt7R9vZ//vMfIpEI6enpsT7bnj17uOGGG5g6dWrsXpFlmdGjR7ex+OlNovXVNI33338fiG+njxs3DovFgqqqLFu2DIi3XExJSeG8884D9Pu8dcDNf/7zH6qrqwG9Ld/ZbI2j6wPw4osvxtq1L774Yrc9x81mMz/+8Y8Bve17xx13xPVPQ6EQjz/+OIsWLSISiaCqaoczvSVJit07VVVVsYAg0HNhRZ/V2bNnx/o70Xa63++P5UUIBoPtPsuZmZmx4KV169bFcl7V1tby3//+t1vHa2BgcPIxItENDM4gJEniscce49Zbb8XlcnHTTTcxZMgQGhoaYg2Zc889l1tuuQXQs9o3NTVRW1vLZZddxrBhw7BarZSVlcUail/72tc63WfrCOCHHnqIN954gwceeKBTj8FoREltbW0sIeTRUypTUlK4//77+dWvfsW2bds477zzGDhwIKWlpbhcLtLS0rqMCOmMfv368b3vfY8///nPVFZW8vjjj/PQQw8Buqh+88034/V6+cpXvsLQoUNxuVyUl5cjy3Ibj8HuHE9H/OhHP2LNmjXU1dUxb948hgwZQjgcjkVv/OxnP4sJv6mpqYwYMYLt27fjdrsZMmRIbKqs2Wxm4sSJfPbZZ0B8ck6r1cqwYcNYuXIlf/jDH1iwYEFs6m/UkmT69OndEqJ7gizLTJkyhaVLlxIMBoH4xjnoU0U3bdoU+/7oKaK33HILn3/+OStXruSFF15g8eLFJCQkcODAAVRVJS0tjccee6xb9cnOzubKK69k/vz5lJWVceGFF9KvXz/27t3LuHHj2vXjbI/+/fvzzDPPcNddd1FbW8stt9xCXl4eWVlZlJWVxSLE+vTpwzPPPBPr7LXHBRdcwHXXXcf8+fNZtGgRGzZsICsri3379hGJRHA4HDzxxBOxAZ/Zs2fH7rV///vfbN68mXA4jN/vZ/jw4W2mcs+bN48333yTUCjEb3/7W9588008Hg99+/aNzQIwMDAwMDAw6Bnp6enceeedPProo+1+35P23Z133snPfvYzVFWNrVtZWdmuJdz06dNjv9+PPvoob731FiUlJcyaNYtIJMLOnTt56qmnKC4u5qmnnorzVb/xxhvp27cv8+fP7/TYrrrqKp577rlYXwDazq6cPn06l19+Oe+//z6vvvoqH3/8MTk5OezatYtwOMyYMWPirDV6yhVXXMFbb73F6tWrWb58Oe+//z6XX375MfUPunM87WEymXjwwQe54447KC8v58ILL4xdm9raWux2e1zCz6lTp2IymfB4PLFzFGXkyJEkJCTE6hBNzgkwdOhQhBA0NjYyb948hg4ditPppKqqKmal2NuJRUEPQolGWrfXTrfb7RQVFVFcXNxhO/3Xv/41u3fvpry8nO9///sMGTKEQCAQG7ApKiriBz/4Qbfqc8EFF8QscZYuXcp5551HYmIitbW1FBYWdjs6+9JLL6W2tpbf/e53bNq0iTlz5jBkyBCsVisHDx6MzRiePHlyh89vlB/84Ads2LCBXbt28cgjj/Dyyy8jSVIsyCo/P59f/epXsfVvuOEGXnvtNYQQ3HPPPYwfP57S0lJGjx7dbvnf+ta3+OlPf4qqqnz1q19l6NChHD58mBEjRrSJTjcwMDg9MCLRDQzOMIYPH87bb7/N3Llzyc7OZv/+/fh8PsaNG8dDDz3E3/72t1hUSmJiIvPnz+eb3/wmBQUFHDp0iO3bt2O1WjnvvPN48cUX4yJl22PUqFHccccdpKWloaoq9fX1HXqMRzGZTFxxxRWxzzabjcsvv7zNejfeeCPPPfdcLOphx44d2Gw2rrnmGt54441u+013xG233RbrXLz++uusWbMG0BuV8+fPjzXW9+zZg9frZdasWbz44otcfPHFx3Q87dGnTx8WLFjADTfcELte1dXVTJ48mSeffJKvf/3rceu3TmDVOhoEiIsqP7qRe9999/Hoo48yefJk3G43W7dupb6+nlGjRvHjH/84FkHf2xxdx/ZE9M7WN5vN/OMf/+AXv/gFo0aNorGxkUOHDpGfn8+8efN49913eyT+/+pXv+LrX/86GRkZhEIhFEXhscce49JLL+3RcU2ePJnFixdz9913M3bsWHw+H9u2bSMcDjN27Fh+8pOf8MEHH3Srbv/3f//H448/zsSJE2Ne61lZWVx77bW8/fbbcefEbrfz8ssvM3XqVGw2GyUlJfTv35+XXnqp3emqAwYM4IUXXmD06NFYrVbq6+uZMmUKf/vb30hJSenRMRsYGBgYGBi0cPPNN3eY/LMn7btrrrmGhx9+OGYJ0djYyNVXX83DDz8cWycquA4ePJinnnqK4cOHYzKZqKmp4eabb+Z3v/sdP/zhD8nJySEcDsfy1sydO5cLL7wQp9OJz+dr15v5aIYMGUJRUVHs85gxY9o9zieeeIJf/OIXjBgxgqamJnbu3EleXh7f/e53eeGFF9rkpukpDz74YCyI4OGHH44FKfS0f9Dd42mPWbNm8corrzB79mxsNhs7duxA0zQuuugiXn31VSZMmBBbNzExMU4sbd0Wjwa7tPedLMv85z//4a677mLIkCGUl5ezdetWNE2LWSPecccd3apvT5AkKS5haWpqaptZtK3b6bIst0lwmpOTw4IFC7jtttvo168fhw4dor6+npEjR/KTn/yEV155pds5vUwmEy+++CIXXHABTqcTt9tNZmYmL774YrevV5Rbb72V999/n5tvvplBgwZRXl7O7t27cTgczJw5kz/84Q/8+9//JikpqdNyEhMTefXVV/nBD37AsGHDqKyspKKigsGDB/Pd736Xt99+O24G7YgRI3j22WcZMmQIQggOHjzIpZdeyhNPPNHuzIcrr7yS3/zmN7Ekq01NTdx666384he/6NHxGhgYnDwkYZiiGhgYGBgYGBgYGBgYGBicNmzcuJGvfvWrgD5LMjqT1MDAwMDAwODUYESiGxgYGBgYGBgYGBgYGBicAv7yl79w0UUXMWnSpFiyQlVVY7lMoO3MPQMDAwMDA4OTj+GJbmBgYGBgYGBgYGBgYGBwCpg+fTr//Oc/CYVCXHXVVQwfPpyamhqqqqoA3b6kt/PWGBgYGBgYGPQcw87FwMDAwMDAwMDAwMDAwOAUsXPnTp5//nk2b95MTU0NNpuNwsJCrrvuOq6++uqYJ7qBgYGBgYHBqcMQ0Q0MDAwMDAwMDAwMDAwMDAwMDAwMDAw6wPBENzAwMDAwMDAwMDAwMDAwMDAwMDAwMOgAQ0Q3MDAwMDAwMDAwMDAwMDAwMDAwMDAw6ABDRDcwMDAwMDAwMDAwMDAwMDAwMDAwMDDoAPOproCBwbEQUsI0BppQhdbu92bZTE5ixkmulUEURVMAMEkmIxGSgcEZgKppVLlqiChKu98nORJITUjGJJuOaz/BSIhGbxOKqpLiTCTRnoAsn1nj/WElQkVDdYffpyWmkORIQJbOrOM2MDAwMDA4oxECoakgSUjH2R46FQihgaKAyYx0mra9hBIBbxN0J62f2QLOJCTT8V8LoSrgcXVzv2ZwJvfKfrtVr0gYlAhoakv9JBlkWT8HFqt+Pxp98tMCzeNB83r169UOksOBnJyCZDak2tMV48oYfCnZVLmT+5f+nnq/q93vB6bls/DmZ05upQxi1AYr0YQg25GLRbIeUxmqUFE1BYvJikT8j74QgpAWxGayt/nOwMCg92nyuzn/oXnsqTjY7vd3XHQLv7z2TnJTs455H6qmsmL7Gu5/+QkqGqv53oU3c9fFXyM75cwaED1QVcqIey/q8Psn5t3Pd+d8lUR7wkms1ZebQChIWV0FLp87bnmyM4nCvEFdDubWuOo4VHMkbpnFbKFfVl8yktJ6vb4GBgYGBmcgagQaKsDqgNScXi1aREIgm06sGBoOIir2I+UMBGfSidnH8VJdhvriYxD0d71uv2GYrv8epB172zRGbQXq8490b7/5gzHd8H1Izz7+/XaAiITB60KUlyBKd0PFIfA1QTikr2B3QHI6Uk4/pP6FiNx+kJKpD44YYvopxfPma7hffB7N5Wr3e8cFc0i75ydY+vU/uRUz6DaGiG7wpcRpsdM/JQ+H2U5EU4ioEfyRIEEldKqrZtALCCHwRTy4Iy7yEtr+gGholHlLGJJy1imonYHB/x5m2URRwTCsZguhSJhQJExTwNNGtDwe/KEgmw5uZ9vh3QCs2Laa66ZefMwiuhACd8BLo6+J9IQUko+zQyiEwBPw0eB1kZaYQsoxlme32pkwqIhgJEQ4EtGj731NeLvTMTNol8N1FTz02l/4YMMKwkqEUCSMJjRmj57Osodf7nL7jzZ9yr3/fDh2b0dUhbz0HP5w2y+5ceblJ+EIDAwMDAy+9EgSWJ1gObYAog4RAlFfjpSYBgmpvVu2wZcOEfAiDu5EFH8G5QdBbWeWqDcCXjei4hBiyyroNxR58gWI/sORevv+NOgRppRUzHn5qHYHIhJGhMOIYBAikVNdNYNuclqL6LuqD7C1ci+q1mLZkZ2Uzri+Z5Fh/ID8TzMgNY/vT/kqrqAHX9iPLxxgdVkx68u3ElLCp7p6BoAmNHwRLxoasmQi0ZyIWbYAut1LQPUT0cLIkozD5MQq2/Tt0HCHXXgjbgKqn4ZgHQAJlkRssh2f4iGg+AmqQRqCtYCEzWQjwZIUi0oPqyECqh9VqJglM05zImbZjCY0QmoQgT7VLagGkYBESzJmyWxYzxgYdECC3clDN95Drbset99Lg9fFsm2r+e/n7/TaPiwmM7mpWWQnZ+ALBRjWdyDJjsRjLs8fDrJ060re37iCm2ZcwUVjzjmu+gXCQZZtW8V7XyzjxumXccm4c4+pnD5pWTxz+yO4Ax7cAR+17gbeXPMBS7euOq76/S+TlZzOddMvZXjeYDwBL8u3rmbrod3d3n784CJ+fPV38AS87K88xNLNK09gbU8SQtM71h1NPTeZQD6OboAQ+r9m+zZkk/7vTKLLc2g+bY5ZaBqeqmqajpTjr28g5PWhhsMITUM2mzBZrdgSE3GkpZKUm0NidjZmu63X9h1obKSpvBJfTS0hrw8lGESNRJBkGZPFgi0pEXtKConZWSRmZ2FNSjyuNpcSCtF46DDu8goCjS4igQAAZrsNa0ICiTnZpBbk48hI71VLMCEE3uoaGg4ewldXR9jrQ1NVTBYzFocDZ0YGKfl9Sc7Lw2TpnW62GongLq/AVXYEf109Yb8fNIHJZsOamEBCViapBXk4MzMxGdP/Tw1C6JHiAY/+2RwvUgpPA1jtEA6Aqukiu92JZLK0rBPyQ8gPmqa/n21OJIsdEQ7q5QY8+qsoEtbF+pRMpGbbN6EqEPSBEtZtPOxOJIsNkBBqBAJR6wgJrHYkm0N/dwnRsl9J0rc93btCNgdS/2EIn0e3MFEiLXYmQX/7gnJvYLXrEd0+d8t+o/sOBfS/TwIi6EdsX49YvxSa6ru3kabCod1ornrk866G4eP1a21wSrBPmYqcmYnmakTzetFcLoIb1hPauOFUV82gm5zWv7SbK3bz9MpX4oTS6QPG8eCF3zdE9P9xUuxJTMkfE7dMExpbq/YYIvppQlD1x2aLhbUQITVItiMXTWi4I7pILksmNKHilTxk2XOwyrZmq5YQIS2IKlSCagAJsJsdAES0CAHVj0CLieCtvYPDWpiGUB0RLYwkyWjNZWTac9CEhivcQEgNYjc5UYWCJlSc5gSQTuvXoYHBKcUkmxiRPwQYAkAwHCIQDvaqiG632jh/1NmElAhNPjfnj5pOXvqxT4euczfw7oZlvLVuMRMGjTpuEb3e08jCL5bzxppFjBlw1jGL6HaLjYmDR8U+uwNedh7ZZ4jox0F6UipXT7uIq6fpNjk/efFR9pS3bz3UHkX9CynqXwjAyp0b2F66hyaf54TU9aShCV0Y6SB3DFYnWI/zd08N66KQQBeILPYza5p4t87hqRfRGw6WcODjzziyYRO1u/fgrqjE3+BCCQbQVA2TxYLFYceRnkZSbg7pAweQOWwoWcOH0Wf0SFLy8zFZLV3vqB0aSw9T8ulKKjZvpW7vfhoPHSboculCeiiEbDJhttlwpKeRmJ1F2oB+pA8eSM6IsyiYMon0gT2brq5GIlRu3U7JJ59zeN0G6vbsw1NVTcjrBSSsTgeOtFTSBvQnZ+RZDJgxjUHnzsSZ0b0ZTYFGFzveWYi/oRGAvmNG0X/6VCxOB/6GRko+Xcn+5Z9QUbyFprIjBFwutIiCyWbFlpRESl5fMocPo/+0KQy7eA6p/fKPebBAUxRqd++l5PPVlK3/guodu2g6Uk6oyY0QGhanE0dKCqn9CsgeUUjB1MkMOncmKXl9T1s/6zMaVUEEvOBvQkrNBpsz9pWoPQz2hJhwjVdBSkqD5EyQZEQkiGisal5bav6fpL9TNUUXulUFlBAiLAMSkhAggVBV8DYifE16+ZoK/iZI64NktUMkhPC59HI1FXxAajaSMxkRDiDqK/Tdmsz6oGh3PL9PJSkZyBfeiAgGIBxs/heCcAht62oo268PRPQ2SanIF97Qar+h2H+1bWvh8L4OPa57DVUXw8WmT7svoLfGVYu2+iPktGyk3H69Xz+DbmHOL8CcXxD7rPl9YDIZInpPEEIfPAyFmiP4hZ4DwO5AsvZOgEBnnNaq0fT+40ixJ+EN+9lWuZd/rHn9VFfJwMCgmwgEyZY0EswJeBUPlf4jpFnTUYSCJ+ImyZJMiiWViIhwxFeKN+IhzWbFLJvJsucgI+FXffRNKIjzPU+zZWA12fBE3G2+A3CHXYS1MJn2bGwmO76Ilwp/GUmWFCyyFSE0VKGQYEnEaXaiCc2IQjcwOE0YmJ3P9y+6pVfKqnHXs7V0V6+UBVDnaaT40M5eK8/A4IQiSXo0pNBaosaF2osCiYhPYqap+r6kYxCVhdDLE6IlGvK4q9e6zGP0gD3h5/D40FSV/Us/pvjl19i/9GP8DQ3trqeoKkowSKDRRcOBEkpXrUW2WEjtV0DR1Vcw5bu3kdqvoN1tO0IJBtn1wUfsfOd9Dn7yOd7qmnbXU1UVNRwm5PHgKj3MkQ0bkUwmckeN5Pxf/axHIrq/oYEdb7/Ptjff5vCadbHo89YEmyIEm9w0HjrMwU8+Z8+HSym8ZA6Tv3Mb2cOHdbkPX109K//4NLV79gIwZu71ZAwZDAi+ePG/bH19AQ0lh9rcA5o/QMQfwFtdQ/mmzez9aCll679gxj13kls0otvH2HIcbvYtWc7mV9/k0MrVBF1NbdYJuT2E3B5cZUc4tGoNuxctYeDM6Uz53rfoN2WSIaSfTCQJyZEIJoueWLQ9NBUpq58umjdW6hHNCWlIZlmPZA56kXIGItmcujgkSc3lJiGZrWghP1JKNlJSevz7LBJE+FxIzhSkpHSEEkFUl4C/CWGxgsWOlNYHyWxFREKI+nI9Mt2ZrEfICw0pM19/1zVW6QMBpzGSxQo5Be0GzIvackR5yQkR0Tvdb30VovzgCRfRhacRsXczNFR1uW6H1FcitqxEyr7xtJlJZWDQFUJVEa4GRFkJWkUZWl0tBPyIcLOILgRSYiLmaediGj3hhNfntBbR+6f3pX96XwByEjP4x9o3Ttq+Q0qYV4sXkZ2YzqVnHV/0moHB/yJ2kwO7yY5JNpNo1r2Dg2oAAXgjTShaBE9E7xSE1CAB1U8q6ce1T4EgoPrxK16qAwqyJKMJDUVECKlBXUQHbCYHTrMTk2TGZGjnBgZnHIqqUl5fxb6q0l5JPqxqKuUN1eyrLOmF2hkYnAQkfdp+i8itQSSkR4/3zg50Oxgp3Orv4xDtlLAeaWm2trFCOGYiYT2y0mwD8zFEWsedQ6FHpvfqOTw+dn/wEZ89/mfKN21GU3pmYaBFIjQeKsVbU4slwdn1Bq3wNzSy6d8v88ULL1G3bz+ih4KVUFXsyUmkFuR1extvdQ3rn/8XG198iaYjFd3eZ8PBEjb889+4K6uYee/dFEzuWefaVXaEyi1bKflsNcX/fZVAo6tb2wUaGtn6+gKUQIBLHn+ElPzuH2ugsZEtr81n7d+ep27/AT3SuBu4yyvY8voCXGXlzP7FTxly/rnd3ieqgvC4wFWN8Da12FMIoVuLWGzgSISEZKTkDKSEZD1y+VgRQrcZaaxFNFaD36M/W0Loz7/DiZSaBel9dOuR40VoCI9Lj7r2NOpRxKqivxdsDqTkdEjPRXIm69GMvY0kISWkNFusABY7IhRotsOy6IlILXa9fgkpSInp3XsPCqFfJ1+TLp77XPrykB9hsSMlN7///U1o4aAu8ob8Le+1oF+PmLfYkGQTIjEV3McQ4Wxwcqg5gijbf3wDuUoEceQAoq4KKbv77yUDg1OBUFVEdSXKus/RthejVVcgGuoQHncb6yYpLQO536CORXQhEKEgWsk+iD5CFjNydh+klLQe1eu0FtFPJb6Qn3+sfZ05Q6cbIrqBwTEgt4r8kiQJCQlVaEiASTKTbE3FIumd2hRLGlaTDbkHYle7awrd1sducpBsTUVGbwin27JwmFsa4bIkI2FE6BgYnKl4gl42lewgEA7itB5/B9wb9LPx4Hb84SAOq72l8XUGsmzzSv6x+BUuHj+Lb15wAxUN1Sz64mPW79tCg8eFw2pncJ/+XDf9Ukb2G9qu3/DBqsMs3fw5X+zbRq27HqfNwYiCoVw4biYThozG1M42QggCoSCf7VzPur3FHKw8TJPfg1k2kZ2axfjBI7l04nnkpmXFWXidKvaWH+Svi/5DRUMN37pwLheOndnujKZQJMz7G1bwwrLXmTRkNN+44Ab6n4yOqyQRtQVoXtD7VitmC8jNeQsk03GUL5pFdPX4fNrjimwuU2hgOjarkrbnUDtt7Gpqdu1hw/P/pry4RUC3JiYy6LyZ9JsyidR++diSkhCqRtDrwX2kkoaDJRz5YhM1O3ejKQpJfXLoN30KzrTudx7DPj/r/vEC65/9J+6Kqjgxx5aUSO7oInKKRpCUk4M1wYmqKPhq66g/cJDK4q00HSnH4nTQd9wYMguHdmufIa+X4pdfZ/0/XsBTVR1bntqvgOGXXUxO0VkkZGYimWT8DY3U7t7D3o+WUbd3P5qioARD7PlgMZIkcdH/PUj6oAHdPt76/QdY9Ze/UbfvQExAz5swlsHnn0fW8GHYk5LQVAXX4SMc/PRzDn2+mpBbt4JSw2H2Ll5O5tAhnP/gz7s14zESCLJ70WJW/umvNJaWxn5rnBnpDLvkQgomjScxJweTxULA5aJ+3wH2L/+Yyq3bUUNhEILDa9bx8f89TnLfPmSfVdjp/oSmQWM14uA2REOVLqpGQs0CutYyk8Nk0p93iw1hcyAlpkJ2P6S+g/QI7B48F0IIqC5F278F3A0tXt6qij4lX9+XsDkhMRVp0CjkvoPhGP3eRVMd4uB2RF05+N0QahaTNU0XzM1mhNUBjkT9eAYW9fiYukWb91DLsyNZbJCZD343wu9GeF1I6X2QElK6cYAayCY96Wg0YWRyhj54KMmIusNIqqJHsAuBUCIt+xZqy0wdSTrO97jBCUWJIBqq9WfmePF5EFWlhohucFojFAVl1QoiHyxAHClFNDUe90xA4fcRfu1FRF3z7DmzGcvl12M+72KkHvzGGCJ6B+yqOUilu46IdoKSUxgYnOEomoJo9hFVhYYqVCyyGU0ILLIFh8lBQnOEegzpqA/NwV9HK+axyNLo1O9WmCUzmqSRaE7GIltab4TaPM1OalukgYHBKeCa399BeUNlu22iS8efy3cu+Cp90rK6LMft91BcspOth/ew48g+dh3Zx54KPWo8GAnyh4XP8dJnb7e77eQhY/jT13+BpVVUndvvZfOhnWxrLm9H2T72VOge26FIiD8vepFXVy1st7wJg4p48pu/jivvZBFWIuw6sp8PN3/KltJdVLnqAEF6QioTBhdx2bjzGDPgrE7LOFJfyUcbPyXFmcSUYeN46LU/89mODbh8biJKBJMs47Q5mDx0DCP6xYtgiqry4caPeXLhi2wr3YPL5yasRDDJJpLsTt5a8xFfP/86bpszlwR7/OBGRFX4xpM/Yf3ezbh8bnyhAIqqICFhs1h5e81HvLnqA/5y+4MMyxuEfIo7+k67k0A4xNtrPqIgsw/jBo0kO6Wt73JlYw3vrlvCh198woiCoSQ7jz1Z7mlFVGA29ZL1iqrQa7/MQujCkqacscnTDqz4lIriLWgRvZ9iT0nhsj88yqBzZ2JPTcXisCObzSAEmqISCQaJ+P0EXE24SsvYt2QZkUCAQefM6Lbth9A0dr73AcUvvRInoJvtNgovuZAJX7+FzKFDsCUlYrbZkMwm0ARKOEzE78ff6KJ21x6qd+xk8HmzsNjtXe9TCEo+Xcmm/7wSJ6CPuv5qZv7oblLy87AlJWGyWpAkCTUSIeT1Mu6Wuaz563NseeUNIoEASijEvqUrSB80gNm/uA+Lo3uDq77aOvwNDWiKitlm4+wf3sGYm24gKTcHa2ICJrMZIQSRQICRV1/Bznfe5/M/PoWnUrdbCHm97Fu6gtE3XUf28C4EbSGo2r6DtX//J42lh2M655ALzmXGvXeTM2I49tQU/dxKEqqiEPb5GHPT9RT/9zW+eOE/+OsbEJpG2YaNfPrEn7nyqd9jTUhof3+aBmW70batBl9TxwkZhQaK1pK40dOoC+4N1UjORD1CvbuoCmLnOsShHeBtaj/fgKro/0IB8DQgmurQ6sqRR06N8xnvEk1DlO1B27sJGmt0ob7NOiqEVT0y3evSBfeqUuSJFyAlZ/SyoNxJWZKk27hYbEiOZERTNaKpph0RvZ3GkmzSo9ZtTj2SvvXaQgNPA+QMQEpMQ0TC+ucorX3Qo4miO8oBYXBKEQEfNNb2zvUJB6Cu8vjLMTA4UagK4Tf/jfLh24i6WnolekmSkCxWpLR01A0teajUTeswFY1D6tP9QaUzVkQXQug2DpqK1jwNU0JClmUsclv/YyGELvQ1r//xgfWx7QORYNy6siRjMZk7jIR6a+cSXti0gJAa5sHz7mJq/lgUTWFDxXaWHVjN3vpDeENeHBY7A9MKmNl/AlPzx5LmSEZC6lakgiY0PCE/n5d+wfryrRxsKMMd8iJLEhnONIZnDuLcAZMZmTMEh9neZZmiuSEcUsOUNpbz6aH1bK3eS5W3jqASIsHqoCClD5P6juKCwdNJtSdh6sJH6xtv/4wj7iqKsofy2JwfY5bNVHpq+LR0A+uObOFIUxWKppKZkMaEviOZPXAqQzP6Y5JMp9SfWhMCRVXYUbuf1WXFbKncRa2/kYgaIdWexIDUfKYVjGNS/igyHKnIUveu2clCtFKjBNHnIExYCxPRwkS0CIqIoAlVf07QWj0jMrLU8s8iWbGYLFhkG1bZilm26Pdoq4ZgR8fuU7x4Im4E0BiqwyJbcJoTUDQFq2ynIVSPSTIhSyYCSgCnOUEXvSWQkbHIFhQtgl/x6lHqkglTs9eqLo5LuCNNOM0JSEh63SSJZGsq1YEKmsINpFjTUIVKSA2RbO1GNMcp5uhrpwiFiBomooVi108TKprQ0JqvLULEIv2l6HWTzZglCybZglW2YjXZdd93gG5cuxN9bNF9t152Oj1DoJ//1r/XAoEqVP0ZUsNEhH49FE1BQ0MIDU1o+jFJkv5eQEaWJEySGYtsxSJbmv+r/y0dNZxzup2Dk8Gu8v0crD6MECL2u6013xfD8wYT7mai6HX7t/CzV57gYHUZISVMWImgNk+B14TgcH0lRzrwkExLTGlzf244sJWfvfIE+6tKCSthQkeVV1ZfSXkH5SU5EtqUdyKJ7qusvpK/LPoX89d+SIPXRSiiv+cBJElmydbPeXbpa9ww/VJ+fMXtZCWnd3jPKZrCxv3bePTNv7Kn/CDfvOB6xg0uwiTJHKw+zNo9xYwaMDxOyNaEYNmWlfzmtb+w6/A+Zo85m+vPvpSheQOpaqzlnTWLeXPVIv74zvPYLFa+deHcuHaELMn0ScsiLTGFm8+9isnDxpCTkoU74OXDLz7m1c/fY8WW1fzlvRf4w22/xGnrWoA7kfRJy+Lcoqm8s2YxH29by9VTL2pzToUQlFSX8cm2NfTPzmPS0DGkJCR1Uur/KN20q+gRJzrB2ykkEghSuXU7vpra2LKia69k9I3XYrYdlVBLkjBZZUxWC/bkJBJzsskcOph+0yYjNBVbUvfuRyEElVu2Ufzf12g4eKhFQHfYmXHPnZx99/ewJyfrwv1R6DXKIKUgn5yzChl+2cWYbF1bVQghqN+3n80vvx7zKEeSGH3jNVz48K/bTdppttkw22w409O5+LcPEmxqYsfbCxGqSsjtYd/Sjxk8+1wGz57Vrd9coWkITU/OOvuX9zHxm1/DmRH/nEuALTERW2Iik27/Ot7aOtb+/TnCHi8IgevIEUo+W9WpiC6EwFtdQ/FLr1KxqTh2foddMoc5Dz5A7qiRSKb4/pHZasVsteJITWXWffcSCQbZ+OJLhL0+1FCYQ5+vYu9Hyxh5zVfaHms0Gnzjcl0YjyLLesS0I0G3cNE0PXli0NcyOCWEvjwhGRxJ3RaaRSSE2L4KcWCbXmb07JlMYLWBrVnsD/n179Xm/AO+JsT+LaiREPLomUj2hC73KVQFSneh7VgL3lbRi7KsR9Q7kvT9hoL6/tRmITkchJrDaKsXIk+/ottCuojlTGjJnyA0tWWwsavtA179nWW16auravwAoMms+60HvXqCUmSwWPXrarWDzYFoqtXXk036MdkT9Gspy3pyUSWinwt/E1iz9bPvTEW4qsCRhLDam5Ob/u+1Rb8UhIO67VFvoESaB7FagtGannsG78J3EcEgWU/8EeuoMYhggPCO7fiXLyO8awdqkwvZasOcX4B9ylSccy5CTk3r/kBs87tDa2wkuOkLgmtWETl4AK2xEUwm5PQM7GPGYj/nXGwji8DcvZxlQghEOEzkwH6Cqz4ntG0LalUVmt+HnJSCuX9/HFOnY586DVNWNshyp+UGN6yn8YnHUF2NJN10M0nXz0VyOFArK/At+Yjg2tWodXUgBKacXGxjxuKcfQGWQYPAbDml/bnou0h4vYS2biaw6nPCe3ajNedMkVNSsA4bjv3smdinTEWy2U67/BlCiRB55Z9E3p8PXnfHK0pSzyPTnU7MU85BWfpBbEBK3bMdraIMKbdvt6/dGSmiq5pKjbeBFfvXsXDnJ+yuOYgvHCDdmcLMgeOZN+FKCrMGYLe0NDR94QBvbl3M8n1r2FNTQoW7lrAa4eWNC5m/ZXFc+RcMm8YD53+HAentj1b4wgGqvLUElTAljUfol9KHf26az8I9HxNRFf1Bb1ZndtUeZPG+zxmVU8gPpn2NMTmFmOTOReSgEmZNWTF/XfcyBxoOowpdwIneQgcayviifBuvbfuA6f3Gcc+0WxmQlt/p9GcBVHpqeH7jG3y0fyWBSBBV02L11Ot6gOUH1vDvze/w7Yk3MGfw2Tit9g79Zmt89VR6akm1JXHIdYRDrnL+uXE+e+tLYwKuAEoaj7CxYgfzty/mupEX89XRl5NqTzolL6CIqrC/oZSXtrzHp4fW4w370VqdBwnYWr2XD/Z+ypCMfnxj3DWcM2ASCRbHKX9hCkRz0kyVkBYgoPgJKH5CWigWES6OcRQveo1Nshm7yYHTnIDD5MQiW5HRrVFaH79JMpNhyyKo+qkLVmORreQl9EeSZCyylSx7DvWhWg77ShAC7CY7dpMdaIkcT7AkEVD9HPGXIiOT68gjqVkIN0sWsh19qAroGeXTrOlkO/oAkGhOQtj7UB+qpT5UiyyZSLakkExKs5WMqcNnQQiBIiI9Ojetxf1jQTQPYOje7QpBNYBf8RFQfChahJa3RSfXrsOvpJhwbo5eO1MCDnMCZtmiXztJ6vAZ7g30e1MjoAZix5RqTcdudiLQULQIZsmCdBznsLfRmgVxVSiE1AB+xU9A9RNRQ62uxvFcDzDLlua8AU4cZifWDp6lM52nb3uQI3WV1HtdNHhdrN+/lWVbV/X4XZWbksmFY2bS4GkEwB8Osm7/FvZWlGCWTUwZOpaigvatA4bnDWljL5KTksGFo2cwfuCI5vJCrN+/hT0VBzHLJiYNGc3ofu0LIsP6DmrXruREcqD6MD/810Ms3vw5kiST7EykIKMPWSkZuvjVWEO9p5HD9RU8/dF/KKkp4/Fb7mdAVlsRCvRkgNsP7yU9KZV///CPjOg3NHZMmhDcpalYj/JrPVxbzqufvceWkl3cfuFc7rv2e+Rl5CLL+oDZ9OETyE3L5vdvP8uC1R8xpXAcYwe2JNwzyTIP3fwjzCaTHqgQ7eQIOGfkJMxmM898+DILNyznt7fed8pFdJNsYvzgImaMmMR765exft8Wxg8uIsHeEinp9ntZvWsjR+qruGbaxYwfPPK0sKI5JqIiUUfPZsz6pIv3V6zDI1r+09pjXIiOBfCO9tG6TNH839gAnOi6zJN9Tdqrr16ZZv1KaqlbO4Q8HkJud8tgnSTRd+zotgJ6O0iShGQyYU/u2WCOpigc+ORzytZtaPEjlyQmzPsq593/Y8z27u3bZLVisnbP816oKke+KGbfsk8Qqr7PzCGDmHnv3e0K6Efvy56Swvm/vJ/9yz6OJeas2bmbfUuWM+DsqZi7EQkfZcSVlzH6hmvaCOhHY3U6mfTNr7Hjrfeo9+gJGoONTVRv34VoDnpo/2AFdfsOsH3Bu2iKfq+m9stn6ne+RZ8xozoVOSRJwpaUyIwffJ+Dn3xO9bYdALjLK9m+4B2GXTwH61G+90JT0bZ+3iKgyybI7Is0dBxSdoEuoDe/f0FAKKBbSdQcRtQegYAHKac/JHcvl5FQFcSejYiD21sEdKsD+g5EHjwG0rJbkhwKDVy1iH3FiCP79WdZCcPhPQh7AoyYqid57GhfmgYVJWg717VEXcsmSM1GGj4RKXeALqRLkr4vXxPi0E7EoV3gc+vPp6sWsXEFnH1F9zzZlbCesNPvBk1FBDy6t3hqDlJyph4p3voayqZme5fozFpVF7BDAT1a0pkM6X1a1pdkpLQcPSHpkT26h3neMN1+xWxFSuuDcFUjKpv9sm0OJKtd/y6rn143Vw0kpCKlZLdYyyRnIKlhRN0RvX6J6Xrd/ofaoV8aVFVPotgbaBoiFKT1dG/N3YRaWYEIBgnv3YspM5umF57D9/67iFBIHzhr/s0J79mN/+PleF5/lbQf3Yd98hQkS+e2aUIIhM9LYPVK3P96gfDuXS0DZbHfsv2ENm7A8/qrOM6bTfI3voWl/4BObTaEohApOYjntZfxL1uC5na3lNtMeOd2/EsWY+5XQPJN83BedDFyckqH71URCqJUV6E11BM5cAC1tobQ5mKanv07SlVlXPmRA/sJrl2Dd/4bJM39KglXXYspMwPpVLX1IhFCW7fgeuZpQsWb9Lq2unZIEqEtm/G+PR/b+Emk/uAerMMKu7x+JwuhqihLFhL56J14AV2SdLsquwPJ7kBKTkFKSETdswOCbZOMd4hsQsrNQ+qbjyg/rO+zugKt8ggmZXyLJVYXnHEiuqqp7K4p4U+f/Zsle1eT6kgi3ZFChjOVsBLm3R0r+Gj3Su6ffTvXjJqD06o3oCKqQpW7DkVTGZzZj7CqUOWpIycpk8LsAXH7GJY1ME6A74zdtQdZfXgT68u3kWpPJsWWiNVkQQD+cIA6fyPesJ9NlTv41fK/8OicexmVU9ihpBVUQry/5xP+vOZfuIIeHGYbOc5MEqwOrCYLmhAEI0Eag25cQQ8fl6yj3F3N/11wL8MzB3bYeDvsquA3nzzNhvJtSEgkWp2kO1NwWhyYJBlFU/CGA9T6GzjcVMFjnz/L4aZKbh17FSn2zhvi7pCPBTuXsLJ0Ew0BF32Tskmw2DGbLETUCE1BDzW+emr9Dby05R0CkQC3TbieNEdyp+X2NhFVobhyF39d/zKbKnYgSzJpjmRS7UnYzDZkSSKkhHGHvDQEmthVe4D/+/QZGgNNXHnWBadESNdnUKgoIkJQCeBV3PgVXyz6sNf209zBU7QIXi2CN6K/1GwmG0mWFJzmJCyyBZOkz9CICtrtIoHd7CDP3K/jVSQJq2Slr7MAKGj3+0x7Npn27Ha/S7amtB95LsnkOjueqhPSghzy7Ou47kcXh0SaLZMse+4xXXtVqKiaQlD10xR2EVB9enR5r9Ei+UaaI9k9kSYkJOwmB0nWFJzmRD1q/QTNANGESmO4Hp+iR08oWoQEcxJ2IKyFqQ/WkGxJjQ2QnCqiz5IqFAKKH0+kCb/iPeaBpw72Eiut9fUAPRFvgjmJBEsSFtmKWTKdugbYSeT8oulxn//1yXxWbF8Ts17qLqP6D2dU/+Gxz5WNNfz81d+zt6IEq9nKjdMv565Lvtbt8or6FfJIK5G8uqmOn7/ye/ZUHMRitnD91Eu55/Jv9qiOJ4omv4dH3/obH2z6hASbg/NHnc2dF3+NswvH42zu/PvDQd5Zv4RHFvyVXeX7+bD4U/LSc3nohns6tBfJSk5n3nnXMHbQiLjlJmiTUE4Iwa6y/Xy+Yz35GblcNml2vO+3BNkpGVwyYRbPL3mVnWX72FG6N05ElySp/bpIYJNtTBo6hjc+f5/y+iqUjmwHTjKD+/Rn5sjJLNuyio82fsqlE85jeP7g2EybqsYaPtz4MUn2BCYNHc3AnLa/Z18qgp6OxWiLXRfFukIIUCP6P01tidzUv4RIQP/Xk31E/c81pdnrWI3/LuyHjia1WB3dq3dvEY1WVZWWOsc6t3KLNYPJDMjtilmSLLfp/LvKjiA07YRFlNUfOEjpyha/b4DU/gXM/PEPuhVVfix4qqo5+MnnBF2u2LJRN1xDWv9+3WqvSJJExuBBFEyeyL4lywHdp7x2zz4aS8vI6qYnuzUxgaJrryIlP69b+00fNICMwQNpOFiC0DSUUAhPRSVqKNShcB/yeNi7eCm+2rrYsiFzZpM7pqhb11SSJFL75dN/+hTq9u5DDYXRFIWGklJq9+wlb/zY+A3qK8DralXpXORJFyKlZLa/A7NFTyZaMLTFEsRqR+qObZkQUFWKOLRTF4lBF3lHTkMeNqH9RJ5Z+ZDRF7auROxerz/TkRCibI+eADR/aPtCrxDgaUA7uLUlQaZsQuo3DGnsuUjtRc6nZCGNPgctNRuxdaV+bELoHur7ipFGTu1yoE2y2JByB3X8fUG8jZqUnKFHuUc/O1OQnJ20g5uF9aPtWvSvJP185gxof9ukdN0Pvd1iJcjIQ8roBW9sIRCREASaZxIoEV28k5pnG5h1AQybQ79vDKG+Zwitl2dYafr1aceSLbSlmOAX6/F/tAg5NRVTTi6SzaaP+wYDqA31CK+XyL691D/4ABm/egj72TM7FqWFQPh8eN9agOuZpxEeD1itmDKzkJOSkCwWfZ1gELWhHs3dhG/heygVFaTefQ+2UaORTG2DrYSiEN6+DdfTfya4bi0AUkICprR0JKdTT5arRNA8HtTGBpQDB3D95Q8oZYdJmncrpuycLt/pypHDuP/7HwIfL0fz+TD36auXLUmISATV5UJzNaLWVNP096fRAn6Sv/ZN5JSUk68LqQqBVZ9T//CDaLU1enR/ahqmlBRoHrwWoRBac52Da1dRV11J2k/uxzF1eo88wU8UWukBIh++hXC1sp2yWJDz+mMaPwXz5BnIg4YhJeh5K/x3fBXt0P5uly9JElJiEvKQ4ajNIjpCoJXsR7ibkDK6thCFM1BEr/O5+M/Gd/lo90rG54/gm5Ov4ZxBE0m2JVDlqeO1zYt4bt18/vL5S2QmpHJR4QwkSSLNmczPzr89Vs4P3nmUt7cv5+LhM/jtpfccc30+2v85qqYyMW8UNxRdzPg+I0hzpKBoKqWuChbv/5z393xCubua0qYKnt/4Jo9ecC9Jtrb+dZrQWF1WzLNfvI4r6CHDkcqFQ87moqEzGZYxgCRrAoqmUuWtZVXpJt7evYzdtQc50FDG0+te4jez7ybT2TZ5kCvoiROO+6X04eKhMzl/0DQGpOVjM1nxhv3sqTvIB3s/YdmB1biCHt7auYQ0ezLXF12M3dzxoEKVt5a3dy4jzZHMDUWXMGfw2QxOK8BhseOLBPiifDuvbF3I+vJteMN+lhxYxdCMAVw2bBbmk+QpK4Sg1FXOf7e8y8aK7djNVsb2GcHlw85lSv5oshMykSRoDLjZUrWLhXs+Zk3ZZppCHl4ofov+qXlMLRiDWTp59VWEQlgN4lU8eCNuIlr3bA96k5AaIqTWIFOP05JIoiUJh0m3ZfnSRtv1AIFA0SJoaJjofiS1JjTCWgh/xIsn0kRA9Xe9US8iEARUP4GAH4tsJdGSTKIlCZvs6HUx3a/6CSp+kiwpJJqTqQwcjn1nkkyYJTMRrZciK46B6LMUUgP4Ih68iueUPEtBNUBQDdAYriPRnESiJQW7yaHPGPgfeJainDwTlOPkNOr7rd67iZdXvovZZGbWiCk8fst9FPaN78w7rXZunH4ZyY5Ebn7yHtwBLx/vWMvqvZu4eGz7ydNTE5OZMmxst+qgahoVDdWU1VUwrO8g9paXIEuftFlvT/lB7FY79e5GattJjiWEwBPwUeWqxe3zEAgHiagKqqayr7yEsBJptlw7Paw6LCYzU4aNZfSAQtbuKWZn2T6G9OmPxWxB0VT2lJfwxf5tjOpfyJRh47q0wTvtiUWKNv9fZ5HpHSG0ZvE4eg2lo8roJJq9wyhetUWUB13wihuQPoYyTwTRiPhwQK9vSyWav9dA1fTvTBZd3G8nKtSenERCViayxYIWiYAQ7Hz3A0ZefQW5o0Z2KyK9pzSWlFKza0/cslHXXU1CVuYJEwrcFZWUrf8i9tnicJA3fhy2HkTRS7IepR8V0QGajpTjKuu+iJ5bNJK0gf3btarpiPTBA5FXmFCbo/aVSJiw39+hiB50eziw/NPYZ7PNRu7IESTltA0W6Yw+o4swWax6klHAX19P3Z59bUR04XHpAhroInNyRscC+lFIFiuk53a7TiIqfjfPFAOQ8od2LKBH15EkOGsiorYMao/oCz2NUH0YkV3QboS40FQ9Ur7yUMvCjD5II6a2L6C37Ay5XyFaYw1i76ZY9LsoP4AYOLJ7CT5PJEIgao4gmhpi9ZVy+yElpXa8SSSMOHIAIi1tWsmZCNn5SNb23xFC08DdgKgpb9lPVl+klE5sbYRABP1QV4U4sh9RuhdRcwSaGvQktWbdHkhKyYC+A5AKhkLfAZCWZYjpPaE54W6vIEl6WR3oLIGPV6AF/NjGjCXhsiuwT5qCKScXEChlZfhXLMP3wUKUssOo1TW4nn2GzAEDsRR0ECCnaQTWrMT1tycRPh9SUjKOKVNxXngxttFjkNMzQFWJlJURWLEU38J3UMrLCRVvxPPKS5juvgdLfnwQghACpaKcphee1wV0ScKcl4/j/AtwzpqNZfBgZGcCqruJ0JYt+Jd8SHDtGjRXI5633kROTSXpxpuQkjoP2gzv3Elo21ZM6RkkXnsdCXMuwjJ4KFgtqHV1BNetxbfwHcLbtyFCITyvvIz1rCKc580+5mTIx0p49y4af/87tNoaJJsd2/gJJFx0CbYJEzFl678lSlUVwXVr8L3zNuFdO1AOleB+7hnM2TlYh3Wet+NEIxQF5fPlaJUVLQstVkwTpmK97muYzhrVK+8LyeFEzu9H656EqK4Anxf+F0V0VdPYU1vCwh2fkJeSzW2Tr+WKEeciN/9A56fmcsf0r1Lvc/GvL97hrW3LmNJ/zAmNeA4qIYZnDuK+mbczOK0g1tC0mmSGZvQnPzkXp8XBv4vfpj7gYvXhYjZX7mLmgIltyqr1NfDm9o+o9tZhkc3cUHQJ35pwPbZWU6otJjMFKX2YO/oyClL68OuPn6LKW8u26r0sP7iGG4subVPu8oOrWXV4E6rQyE3M5PYJN3BZ4bmYW3X2km0JTMobxaicYSRbE/n35rep8zfywd5PKMoZyrg+I9qUG0UVGjazlWtGzOGWMVeSaG2ZUphodXLOgEnkp+Ty8Cd/ZWPFDio8Naw8vJEJeSPJT+5+I+148EcCfFb6BavKNgEwKW80P5x2K0Mz+scJWBnOVGYPmsaA1HyEgI9L1lLtreOd3cspyh5K6gmOno8KfkHVrwt+EU+P7UdOBBoq3kgT/ogHhzmBJEsyDnMCVtl6xkfTqkJF0SKY2hkhPxoh9Ihwn+LFHW4koAY41bJhRAvTGKrDG3HrQrclGbvJjtxL9ioRLYxZtpBoTsZqshHnAd7s465x8pMYxZ4lxY+veSBKEac+ulUTGu5IE96IB6c5kURLcnO+AOv/lM2LQfcQQvCvjxcQURRy07K4evKFbQT0KKZmG5ppw8azeMtn7K8qZcP+LVwwanq7A9ZWs4XMbk7VDythGr1NKKrKzrJ93PvPhztdX5ZkQpH4wSpVU9mwbyvLt6xi/d7NlNQcwe33gNAtZPyhAC5fJ96Ip4ii/oVMGjqGjfu389HGT5kxYhI5qZl4Az4WbVyBpmkU9S9k9IDhXRd2OiNJuqjb2vtXjbSfrK8zZFm3imjtYxkJNgvg0Y59B0JBR4MQkqmLMq0digUdlnki0FTdq1hr/q0xmfW6SzJIAjTREk2vRiAkdF/jo36PTVYreRPGkfz+R7gOlwF6pPj79+ie3QVTJpE+cAAWR+9YHmmqiru8End5SyI62Wxm6JzZ3bJxOaZ9ahq+2joaD7UMvKfk99UHD7rR3oohSSTn941bFGhoxF9X3+0iMocNISGzbdLgzrAlJsZ19rWIghJsP2BACEHQ1UTdnpaZkAnZWST1ze2RcA+Q3LcPsrnl/IQ8XtyV7eTvEBpx7c9olOuJeB7qK3UrmOjAltWO1IWADjRP37chDRqlC+OgC7YNlUjuej1a/WgCXqgoiXvGpP5nQWJa1+KLJCP1H444vBu8ze81vxuqSmHw6O4f74lACLQNHyM2rNBnscgy8hXfQJp0Xseb1FWivfk3cLcMXtBvGKYrboU+/dvfKBJC27wKsexN/bPFhnzZPKQJ59Bu9ICmIuqrETu/QNu6BmrLWwZnooRDEA7pAwCH9yE2fY40YDjS6GlQOBbJ7jSE9O5gtiDZHL3Ta5RNem6BDtA8bixDhpD2o/uwjRkb9511+FlYhg7DnJtL459+j+ZyoRzYj2/R+6Tc/t12o9G1xgaann9WF9AdTpwXzCHl299rI4zbzjoL66BBmHJycP35j2hNLoLr1hBcORHzNdchtbYCC4cJfLyCwGcfA2Dum0fyN79FwqVXILdKpmy22zFfMAf72LE0/esFvPPfQHg8eN97B+vIIuyTp7Yb5R5FBPxIiUkkf+3rJF5zfVzZcn4B5r55WPoPwPXkHwltLkb4vHhe+y/2SZNPajS65vXi/veLKBUVYDZjmzSZtB/9FOuQ+MFi66DB+r/BQ6j7xc9QKysI79mN7/13Md9xN3IPbM56G1FXg7ZnO/h1KzQkCXnoWdhu/R5y/8G9tyObDTkzJ37f9bWIHtjCnFEielAJsav6ALW+BiYVFDEu76yYgB7FbrbylZGz+fcX73KwoYy9tYeY0u/E/jBeM2IOBcnt2z04LDYuGXoOqw5vouFIExFNYemBVe2K6OuObGF/fSmq0BiQlseNRZfGCehHM7VgLKNyhlHjq6cp6GXN4c1cOfz8uKhxb9jPsgNr8IX9mGUzU/LHcPHQGXECemtsJitzR1/GZ6Ub2N9wmJLGI6w6vImR2UOxdtTpAQal5XPBoOlxAnoUWZLol5zLtSMuYlv1XsJqhF21BzjQcPikiegVnhpWlm4kpIRJcyRz6bBZDEor6DACdEBaPlMLxlBcubN58GMTdQEXKSfQy10TKgElgCfShFdxo2inXjw/Gg0Nn+IhqPpxmBJItqaQYE5CPsXJYk8kqlBQNAVbF/0OVagEFB/usAuf4kHtZcud4yWihWkI1eJXfKRYU0m0pOjJSHtjxFdqPwpQ93vXkDi5Pmya0AgoPjyRJnwRD5HTYCDqaDQ0vIqbgOojwZyk2yZZEvRn6XQKg+5lvjRHdpqEzNd7XKzZuwmBIDMpjWnDxne6vt1iY1S/QhZv+Qx/KMDh+gqa/B4yktrOUpMkqcO2wNEIIdCaO84DsvOZNnw86Z1EyEmSxPjBI+OWrduzmQdeeoK1e4sZWTCMyUPH0Cctm5SEJOwWG7uO7OeNlR/Q4HF1q04ni2RnIjNHTubDjZ+wuPhTvn3xTWQmp1PTVM/iTZ+RnZLBtMLOz8eXhtb3Q8zHtIciuiTronZrWkeRy2ZdEO9RvWSQW5UZtXehObGf6RjK7G00rVnYbxb3LDbdnuboc6opzUkVI/rfkRDY2radB593DvuXfYy3phYlGESoKmXrvqB2zz4GnD2NgeecTd7E8eSMHI4jNfW4bF7CPh9N5eVEAi0dzKQ+OSTn9emZoN0DlGCQxkOHifhbZupFAkG2zX+b0jXrul+QpnFkY3Hcoog/QNjf/c5yUp/cHkW/gz7IEPeDJkSLl/xRCFWloeQQIa83tkwJhdi7eDmNpWU92m/9vv0ooRaxXg2HCXnaJiOUnMmIaB9HUxGNNYjaI0hZBV2L2z1BaLqA3to6JqMPtPOb0x6SJEN6LsJi058FAI8L4fcgtUqKqO9LQMDbIrgDJKUhpWUjdTOCV0rJ1JOORusbDiIaa9ru62Qjy0jp2Qi7M+bbLmqOdL5NxaG4KHQAfG5EfTVShyJ6GOpaBstIToOk1HbtbISqQuUhtFUfIfZubrHq6YpwELF3M6LqMLKrFiZfgOToOlns/zw2OyT20owIqx3SOom4NZlxXnBRGwE9imQykXDpFfiXLyPw+adoXg+hTV+gNdRjymxbbmD1KiL79OTQ5n79Sbz2+jYCeqxsmw3HzFkEV6/Cv3QxWkMDoS3FOGbOwpyn2w4JIVDdbrzvvq17flut2M+eifP8C+NE7pZCJUxZ2SR+5Soi+/YQXLMapeQgwbVrsA4fgSmt8/eRbew4HOec227ZkixjGzMWx7nnEyk5iOZyEdpcjFJ6COvoMZ2W25uEthQT3roFlAhyahrJt3ytjYDeGtv4iThnX4Dn5f8gfD5C27aiHC49pdHo2qF9aHU1LQusNiwXX9W7AjogmS1IKWl627DZHlJratQTTXeTM0pEDythDrv00fZ0ZwpZiW0jqGRZJjspgxR7Iu6glyp3bZt1epMEi4NROYWdCsy5iZkMzxzI1uo9BCJBtlXvI6xG4rZRNJVt1XupD+ijyeP7jCTF3r6PaRSTLHNW1mA+KVlHRFN033FfAwUpLX7VBxvLKGuqQhUaiRY7swdNw9aJNYskSaQ7Ujh34BT2NxzGFwmwr76UOl8jfZPbn3JolkzkJ+fSP7Vvu98DWEwWhmUMIC85h5LGI1R6aqlw16Bq6gmf/qwJjSpvHTtrdT+lgan5DEjti6UTKxlZkshLziHdmUp9wIU75OWwq4KBqfmYerkRoEcvR/BEXHgiTYTUYC/7NPc+qlDxKm5CWpCAJUCKNQ2bbD8jhXRFU7qcDaBoCp6IC1eogZDW/Rf0qSCo+lGCYUJqiDRbBlbZdlzXzSJbCKg+goofs6XlmdKEil/xEdEiOK2dv8t6i2j0uTvswhNxEVR7kIjkFKEKFXdzXRPVZFKtaViO85qczpzeb7ZWnCanf+eR/fhCushU627gH8tewdmJv3NEjbB+/9bY5ya/F3fA166I3hMsZgvJziRkSaZ/dh53Xf51xg0a2fEGEpjllveBqqo8ufBfrN69icnDRvPgTfcwbvBI0hJaonjeXrOYRV98fNqJ6ADTh49nVP/hvLN2MZ9sW8vw/CGs21NMaW0504dP4JyiKae6iganGjUS66zpor6jrVgpSXoUvoUWv3glrAvuR7WFU/LzmPKd2wi5PexbtiJm3xF0NbH7g48o+Wwl2WcNp+/4MRRMnkj/6VNI7VfQ48hm0EXnQENj3LLUfv0wn8DkvkowhKeqOm5Z05FyVj/59+MvOxzqMCq8PeypKT1KQtpThKbRVFYet8xXU8vGF1867rLVSIRIewMG6TngSGxJ8ulpQNu2GmnACKTc/rr/eS/MJBXhEPhccTNWpMy87gumUvNMEmcyNDX32UMBPVJR03Sv7ei+NBXhdemzPaIkZ+izObqLbEJKTNETcWp67gLha9Kf304C104K6dngSGglopd3vn4HIjoNNe2uDrQR0aWUDKT2hFshoL4K9dP3YO9mXcgE/Xpl5iJl50NKBpLFhlAV8HsQtRVQU94itrsb0FYuQjZbYPrFSL00A/ZMRbI5EamZ+mwt5TiDf+xOyO1gIAWQLGYcszqe5QAg2e04zjufwOqVoCioNTVEDhxoV0T3L1+m3yMWC9ahQ7Gd1Un7EJCTkrGNn4B/6WIAIgcPolRWxER0AKX0EJED+uwdc1Y29vETMKV3PnvS3K8/9slTCX6xASIRguvXknDFlV2K6PbxEzBldGx3JVks2MaMxdw3n7DLBeEwoS3FWEeNPmmDQ8G1a1AbGwAJc34BtgmTOt9AlrFPn4Hn5f8AoFZXEzmw/9SK6FWVCE/LjFMpOQXz5Bm9v6NoklKbvSXqPRjQ31Xd5IwS0TUhCDf/SJtNZqwdiKCyJGGzWFE0jfAJTk6Vk5hJki2hywzyg9ILcJrtBCJBmkIe6vyN9E1qEaVdATdV3rpYfbdV7+HBj5/u0iv3QMPhWMRrUAnREGiKE9FLGo7gj+iNDYtsZlR21/6AZtnMqJxhsc81vnoqvDUdiug2s5XcpEwsnQwkSJKezHRAah4ljUcIqWFq/Y0ElFC70eu9SViJUN5UjTesn4dyTw3Pb5xPsq1zYa/W10Ctr2U6aLWvDoEG9F4EhxC6d3VDsI6A6kM9DewmekJEC+MK1RNSg6RZ00m0ppxxUbS6nYtC6wznUYTQPdMbw/W4w67TwnqnOyhCoSncgCIiZNpzjmsAxGFyElQDuCN64lRVU/QIcMVLRISwmxw4TD3o4BwHQTXQHG3vPe1mAnRFWAvhCtUTVkOk27NwmJxnrJBu0H2ONFSiNEfwVrlqeerD//Ro+7ASibWbjgeLyUyf9CxyUjM5VH2EOncDNkv3LYia/B42HtiG2WTisomzOX/M2W3WqWioJhA+PQchc1KzmFU0hc93bmD+6kXMnXkFb69djNNqZ8LgIgbldpxI2+B/gGiEedTOwmzrvGNtMusCu6q1JCFtJ6CkYMokzv/1z8geUUjxS6/Fic4hj5ey9V9QvrGYPYuWkH1WIf2mT6HomivJHDakR78fajhM2OeLW+ZIS8V0Av1eNUVpN4K6NxCa1mFUeHtY7LYTeqxRO5cTVbamtj1WyZ6g26Rs+TQmFlNbhvA2Isr3I2UXIOUOgOSMTq0OuiTgRfiPuo7J6T0T6GVZFztiCAgHEJoSXzdVgaa6uE0lR2LPZ6HYnMS155UwIhxCOsUiupSeo4voURpqdNHb0k69ImE9Ul1V0LNz23XxOhRAuGr14znaF10I3XalvpX9T0qGHol+FCLgQ9uwAvZuaRHQE1OQiqYgDxujC/4JyboFiaZCMABN9YjSPWjFKyEaRR/0o61dipzbD2nIqOM6P2c8JhNSZh9Eek7L+TsWZJPuc99Ju0RyOLD0H9BlUbaiUbHBYM3jJlJehp2pcetoHg+RA3tB05ATE7EMGhxvy9Le/m22Zg92HbWuFq3J1bKCEIS2b4tZB5mysrAM6jpaWbbbMRf0w5SWjlpTTeTgAT1Jqja449laFgvmvHwkZ+d6lLlff+RWIn543554i7kTiOb3ESk5gAgEQJawFhZ2bcsiSZjzWyyxNHcTak0nA2wnAdHUqCcmbkbO6weJPZsF1m1MJj3ZanR34bDe5uomZ5SIbpJlEpunPIaVMEElREI7AqyqaXhCfrIS0nCc4OmdKfbEbk2HTrOnxCKfVaHiCrjjRPSmoAdfuCWSYHddCbvrSnpUF1XTCB3VWW4IuAg3JzeymCxkOFO7LMckyeQktIzG+cJ+PCFvh+tbTJYuBWkAq8lCmr3FU9wb9hNSwideRFcj1PlbImyqvXVUe+s62aJ9/OFgr74rhRB4I27qQzVfiojZjhAI/IoXRYsQEQqp1vQzKlGiQEMRETShxfmICwSqUKgLVuNR3GhfMtFWoN9/mtDIcfTFZjq2KCyTbCbVmo4v4iGg+jHJFiIijAULieYUEi1JcRGpJwpfxENdsIaA6ut65dOUqMVLJBAhw5ZFsjX1VFfJ4BTjDngQzT88dquNvLScLraIJzcls9NZV91FkiQK8wYzdfg4Fq5fzjtrlzB6wFn0z85rs66maSiaikmWYzPNBAKlOUjA2o5QUdFQzSfb1tLgPTFC0/EiSRIXjZ/Fy5++w+aDO1m3dzOf7VhPVkoGc8adg7W3koEZfDkRWrxPcFf9AkmKFxm19tsPskmm79jRpOT1ZcCM6Wx9fQF7PlxKoLGx1aYqrsNluA6XUbb+C/YvXcHIa77CuJtvxNFF9F0UNRJpY39idTqQTCeuLaepKmFv29/rjuzheoIkyz0aRJBN5uOyw+kKoQmC7rb5HnrjWGVZRpLbL0MaOBJ8TYj9m/X7Uwjwe/So4boKROlupLQs6DsYcgciWY7hPRYJtUS7NyN2b0CU7Oj+jK52xHGUSHNka6tlmtpGsBfl+xHu+p5Z1DQ1xD+vmnr8kb+9QWomkjOpZcZeOIBorEVq53dWNFQjmiPWsViRho5GbF+nf3Y36D7pmfGWqUIIRFN9i4glyZCSDs54EUuoKuLgDsSWVS2zaxKSkafOQRo/Cykppe0gic2hl5Wdh5yUivbJuy0R7031iLVLoH9h+wMCBi1k5SP1G4qoq2jrPd9dHAlIo6brXvQdIKekIHUjQbWpTx8k2YQARDCI5nK1WUetq0X49HtKCwTwLXqf0JbNXZQsUOtbAhU1nw8RbPUeEQKlvGUgQUpMwpTZjcTIkoSckorcLKILvx+toUF/l3TwjpCcTqSEhC5/A0xpacgJLXqXWl3dydq9i9boQnO7YzZ7wXVrqbnzu11u19oDXARDaL6O9byTQjAAkZZ3rZSRdUIj+Y9Oa98TzigR3WGxMyRTn5pS7a2nvKmWYVnxU1VUTeVQQznekI+hmf3IT22/w6k3rkSsc3qsWGRLtyJv7WZbTFgUQhBQ4qcZBpUQkVYe2GbZjCzJPYrpba+jHFBCsczxNrOl29YpNrMVs2RCESohNUKwk0g2WZK6JZLJshzn8R5Ww7EIuxOJKlR8kZaXiCzJmOWe+w7LvfiQCwSucD0NoToi2vFHCZ4OhLUQDcFaFC1Cui3rpAinJwtFi6BoKtbW00qFRl2wGnfEddrb73SGX/FSG6gk11lwTNdMQsIiW0m2ppIgktCaI/FkScYkmXotgWlHCCFoijTSGKw77a10uktIDVAbrEITGqm27iV+/LLwpYmtP00e6da/2cP7DuLFOx7vkQVasiOR3NTuZaLvikE5BVw//VK2HdrNgtUf0uB1cf3ZlzKyXyE2i5VGbxP7Kg6xbk8x+Zm5fOfim2M+4SnOJAbmFlBeX8WCNR8yc+Rkxg48i4iqUHxgB88ueZU1ezbGfNdPRwbm5DNjxCS2l+7lb4tewu3zMHrAcGaO7GJKrcGZj9BaotABQt0YzG19r4vO73tnZgZDLzyf3NFFjP/aV9n1wYfsfv8jGktK49YLNLo4tGottXv3U7Z2A7Puu5fcUZ1Pq9eR2iSJ16Ki6wlCgjbib/+zpzLt+98mtaB9L91ul22SSclvKzx2WpkTiUQbgSZ39Eim3fFtckaOOL6iZYnE7PZnCmNzIo2YipScgbZ3E7hbJVsN+SHkR7hqEFWHICkdacAI5Pyhup9yd1GVFqE1Sn1l++v2hKjdUWuEaPFNj+J1xfuxH9O+RIcDWScTyWLVfawtVj0CXVURtRXti+hVZbogBeBMQho5SRfRAeFuhKb6NiI6mqrbrUQbOM4EpJT0tjMRImE9wWks+Z+MdNb4ZgE9tVPRS7I7YcREpMpSvR7hoG5NU16CdnAncuHYnp+Y/yEkZwKcNQlRdRiOHOh5ASYz0sTZMPCsTleTHd0LYJSsViSbFREMIBRFj4Q+Cq3JhYg+P5EIkf37iOzf12a9TolEEErr94hAc7vi69FFpHgU2W5DbpV0W3O7Eara4SChbLUhdWMmkmQ2I9ltuhivaWhe78mLRPd6EOEWvUg5UoZypGf5NISqIiKneLBQCFp3sLqby+KYUJT4RKJWK/QgMODMUbHQI5lH5gyhMGsgWyv3sqpkIwPSWrytBeAO+vjPF+9ils0UZg2kMGtgu2UlWp0oqkq1tx5FVdrtlHYniiGiRroloEW0SEywl5DaeKib5Hix6XuTbuKSYedg6kFEr1k2kdoq0hv0cxYVfyOaihCiW8elCRWlObLWLJuwdCKuaUIQ6YZtjjhqPbNsxnQCIz+iSJIUV/9ZAybz9XFXk5vYjRHNViTbujfroDNE88BNQ6iWxlD9l86+pSsUEcEVqkcVKpm2bMyy5YywpFCE0nyt9EEgIQR1wRqawo1fagE9ik/xUhusIsfRt8ezCGLvNUnGcpRgLoRo9X3v5xIQCBpDdTSG60/LRLzHQ0QLUxesRqCRak1vPn9f/mfJoGekJSTHnkmzyUx+Rh8yj9Pf/FixWqx8ZcocIqrC7xY8w+JNn7FyxwasFisSoAqNiKIQioS5YcZlsQG1aN1/es132XZoDxv3b+PKR76Fw2ZHAgLhECnOJO6/7g6eXfwqO0r3xu232lXHGyvf59Nt6/AEdI/3g9WHCYQCrN+7hak/vopERyJJDieFeYO4adaVjB3YIkyt21vMMx++TG1TA56Al5qmekqqy1A1jZ/953c89f6/SHIkkORI4LKJs5l33jVtktZHj+Ha6Zfw1pqPWLlzA06bg0vGn0uy4+TkfDA4jREifuCtlwU5SZKQTCaS+/YhKTeHvAljmfKd2zj0+Wq2vDaf8o2bifh8+u+tEPhqatnxzvsEm9zMefiX9BkzqtPfYJPVgtUZn2sh7PW1axPSW8hmM9bE+GfHZLGQfdZwckZ2LgB92ZAkCXtKfP/MZLaQPmggBVMmntD94khEDBqFnNMPUbYXcWgHeFwtArWm6j7afg+ioQr1wDakYeOR8gYjmcxdRwmqalsR/UQhxImJGJc4aWJYp0gSUlZfhNXeLKIrUFsOHDVQKwRUH4agHv0r5eQjZfXVveGDPnA3Ilx1bZOlqiqiupXwlpSq27kcdY1F1WHEoT0tC7L6IBWO61JAjx2G1Y48bCzqni1QV6EvDPgQe7fAsDFGgtHOkGToOwB5+iVon70H1WXdvzctVqSpFyFNPK+tlc9RHFMQaQfJk0VEia+jydTz5MUWS9tttFZlSrH/6wZH9Zc0lc4iY8TRv98dr3hU/U5e0IdQ1eM+x5LZgnSC8xB2idWm29k1v8fFCYqMF6qK8HniEolKSSldPhetOW1F9JASJhAJomoamhC4gvr0rIgawRVwU+drRJIkZGSS7AmYJH1q3tCs/nxz8tU8tuJ5nlr1MrW+Ri4ZPpMUexLl7mqeWzuf5fvXUpQ7lFsnXom9gySa4/NG8IL0Fl+UbeeZNa9z0fAZ2ExW3EEvFpOFfml9umUF4wn7UbvRWG4KeoloeiNDlmSSjrIwSbA6sbeK0o5oCn0TszAf5zTsFHtSzKs8rITxhHwkd5GwVBOCen/LlGqH2Y7T0nFUgqIp+CL+Dr+PElEV3OGWh8VhtnUqzvcWFtkcl6RV1RSSbYnkJfdsWvzxIhBoQqMhVBsTms9ENDTczeJypi0bi9x939zTFUWLoDQPeAghcIUbcIXqzwgBHaLWLk1YZSvptqyeeakKFYGGCXOb7QQaES2CWTJjknrvWdflc43GkD6b40wbjIqiiAj1wRo0oZFmyzjhUf0ngxPzxJwZz2F7FPYdErMKcfu97Dqyn5lnHV/ks9PmoG9GDtkpmR3aAHS27U2zrmT6WRN5b90Slm5ZyaGqIwTCQbISUxiaN5CZIyZx8fhzST9K7L9gzNm8+4vn+NsH/6H44E4C4RCZKWlMKxzPzedezZgBw9lSsgtvwBc3mOcPBdhVtp/1+7YQnUFoMZnJTctGQreCQapBQsIX9DNn7My4/VY11rJuz2a8QV+z1inITEpDoHvGl9aU6+4aSAzu07/T9/qkoaMZM+AsDteWk56UyjXTL/7S/74ZnABMFno06NnNjm1UTLenpGBLTiZ90EDG3HgdRzZuYv2zL3Lw05X4autACNRwmAMrPiU5vy8XPfJrnBkdz2oyW21Yk+L7Br7aWtQTGLUmWyw40lLjlgUaGokEzowZZa2RJBlnZkbcsqDbTch7EqbWSxKS2YJIzkAaMUX3Sa8uRRzaCY01emS32iyChYO6b3pTLdSOhLMmITmTOxc95aOsiQCyC5CO1041NSsuqWjL/o7aV3KmnhjzeN7Dyek991U/QUg5+bo1is/dLHof0a9Nq+MTkTCipkK/XpKElDdQ3yYnD0r36tu6ahGq0hLp2Zy3QVQdbtlXUhpSatuAMrFjfdzAiJTVF6nvwJ6d4z79kBKSEFGXHiWCqCrVvdpPYMLiMwHJZEYMKkJOzkBs/BhxYLs+OKI0592IiqmSrD8jZivkFiBNmYPUr1BPTNoFItC1bgOAosYioCWzBdnRNrG9lJQUey7ljExS77iLhIsv7V75sUL0RKatF5iSWw08RhREKAgJXefYEhE9x0GspMTEzsXjcEhPOHn0oNPR5QpNPxfN4rmUmHjSBoRkp7PluppMpNz+XZLnfb1nhUh06VV/opFSUsHhBI+uM2pHSvWB2N7OSeLzoO3bHTfwIGUflXOiC05bEX1lySb+teFtqjx1eEI+XM2+n5srdnPX2/9HiiOJRKuTRJuTxy//EYPS9al9CVYHV4/SI6H+s/E9nlnzOn/87N9oQsMsm0i2JTK53yh+POsbjM/veArj+cOmMWfodNYe3sKjK57loaV/1yOWTWauH30RPz73G+SldC2yVnlr8YUDnUZ4CyEobaog2Gzh4rTYyU2Kn16dlZBGpiMNWZLRhMa26j1ENPW4RfRBaQUkWBzU0UhEU9hdd5DJ+aM73UbRFHbXtUwhynSmkdNJ1HZQCVHpqUXR1A4jtYUQ+CJ+ypr0ZCYmyURWQnq7nva9jd1soyClDxbZTERTKGuqotbXwJD0fie186tqKo2hOlzhEyegS81TcmX9r9jLPWpdExUH9CheTU9IRO+PpAoE7rALCciwZWGRbcd1roUQRJQI/nAQVVXbiBxm2UxqYnIHWx8/ihZBbZ5NoieCre2V8yYhI0tS7Lrpy1o1lGPXS4sNwpwo4V4VKk3hRuwmJ05z58mSW+NXPHgVN2nWLBzm+Oc5okVoCNXiNCeQYu09WxJNaDE7pBMloOt2Ws1P0lHPEbR9lk7UtVGEQmOoHlkykWJN+9LnG+itN64kSbEZZKqmoWhKt2datV8vKfb7pfVCeb3FiPwh5GfkUudppNbdwIrta5g6dCyW45gCecOMy7lhxuXHtG20nTSkT3/uvep27r3q9m5vazaZmT58AtOHT+hwnefufKzNsoE5Bfzte4/wt+8dU5W5csqFXDnlwmPb+ChUVUXVNEyyiRkjJjGkz4BeKdfgy47UYr4pSXriwhMc8SVJEiazGVOimUGzZjJg5tnsXbSExQ88SM0uPYpUjUQ4vHodpWvWcdbll3RYljUxgcScbL3uzZ3O+n0HCXk8J+w9aHHYSe1XgCTLsejGxtLDBBobT4t3b28imWQyBg9CMsmI5uh+T2U13qpqhKadUD/2WB0kCSSTbv0xsAj6j0A0ViPK9iAqDoKvSY9+BggHEfuL9b9HTtWTd3aEydJGtJPHnYuU3qf3BSZJhqOCuqSBI5GHT9SjG88EMvuCvVmo1FREQw1CiehWL1HqqxCx/CES9BkAVhtSTgGidK8eMdxQi+RtgmaRXACEQ1Bb0VJOcpruY94aIRCHW1lxyCbdqz25ZzPgJGeSLli1eqcQ9IOrFnKOz67pfwFJliE7D+mSWxB1lYjSPVBZgvC4Y4MnOJOQsvpA/+FIfQf2KMpWa3QhQqEu8yAoVZV6FDR6MlA5uW1f25yZ1bJvRUH4fO2u1yMkMOW1Sorp9aDW1WFKz+hkI0AItKYm1MYG/bPFom/TiUir+XwIn7fL3x2tsTEuctqc0TNHg+NBTk3ThXTQj9HlOv5zfAqQc/ogJSUjoiJ6TSWirARp4NBe24cQAq2+FnXrF/H77jcIqQfn7LT9RdFFbzOpjmRSHckUpPbpcN2jZ0+kOZL5xqRrmJBfxPJ9a9hXV0pQCZFiT2JC/gguO+tccpI6f8iSbQn84Yqf8s6O5RSX78IVcGOWzWQmpnHu4EldRmtH8Yb97Kw9wJCM/m0sWqJ4wj521R7AHwkiSxJDMwe0iZC3m20UZg3k88Nf0BBoYmvVHg40HqYo+/huqiHp/chNzKTMXUlICfNZ6ReM6zOiw0RjQgi8YT+fl24E9CjugpQ+9Ens2FNV0VTK3dUcaapiQFr7HoSq0DjirqakUU8SkZWQTm5i1nHbo3QHk2yib1I2g9IK2FNfQpm7ku01exmdM4xEW/dHpI4HXaBswBVu6HUB3SSZMUsmTJIZq8mORbZikS2YJTOyZNIjl2Iiuj7zQxWKnghUixDWQrFIa1VTelFUjwrpMhn2LMwcm7WLqqmU11ezZvcmdh/Zj9vvQdPiXwp9M3L4yTXf6aV6t0UgiGhhQmqQhmANEXFsPvYSkn69ZDNmyYLFpF8ri2TFLJuRWgm30XhrVaiomoIi9GsVUsMoWhhFKL2ezDSshXCFG7CZbJil7ol0ilCQMWFqJ0palvTlvWm1ogkVT9hFY7B37ZBkZMyyBZNkwixb9HMgW2NR9LqoHp0iqAvnqtBiz1JYCxNWQ83PUaQ5Qr93RPWoTZJZMpNgSfrSC+m9gdlkJqU5GVZICVPTVIcv5CfRfmzvdLPJREpCcqvy6vEG/ST1IGrhRGCSZb569lfYdngvLl8Ti4o/Yc7oGUwaMrrThKGqpqGoCmaTqUce6gads/3wXnYc3otZNnPjzGMbiDA4A2mdILLZUqWrqLbeRpZlhl9+MUKC12++jYhfjzR0lZVTv/9gp9tanA7S+hfgSE0h0OgC9Ejp6h27yB5eiMna+76lZpuNtAH9ScjKxFtdo++zyU3dvv30P3sq1m76334ZkE0m0vr3I7lPH5qOlAMQ9vmoP1BCsMndJiL/5FRKRsrog5TRBzFkLOLAVkTZXj0pJQI0DVG2BykrDwoKO7YOMFv1f60J+E7MQIgsI9mc8S2rcBChKLr1zBmAlJCElJymR4xrGoQCuvCc1dK/FrUV4GtOsGoy6Z7pFhtSVl7s3IjGWt22p1WkuWioabHDsdogOR3JFh9ZLAI+fYZCFJMJ/F60/dt6fCwi6CcutZ+qxixoDLqPlNkHKbMPcG6vlSlCISKHSrAVjep0vfDuXS2R10lJmPPbDoCYMjKw5BegVlWi+X2E9+1BC4WQu5G4tEMkGduo0TH/cbW+nkjpIazDCjs/rkgEpaICrTlpqbmgH6b0jM4HKlUV5Ug5wu/Xo8s7QCkvR23+fQSwDCs8ab/xppQUzPkFSDYbIhwmvHM7WiDQ7syA0xm5/2CkjGxERbOtVDhE+MN3sH3zrqNmIhwjQkDAj7p+FdqBVpZUVhumIcORklO7XdRp+4syZ9h05gybfszbW80WJhaMZGJBdxLmtEWSJDIT0/jWlOuOuQ5RPtjzCZPyRtEvpU+bBkNEVfj00AYONBxGExpWk4XZA6e2W860grEsP7gGV9CNL+LnxU0L+MG0WylIye0wCaYQusjWEGgi2ZYUZwkDup3LeYOmsLP2AJ6wj08PrWfWgEmM7zOyXT9yRVP5rHQDmyt3AdA3OZuJeUU4rZ0/pKVNFXxWuoHsxIw21i9CCGp89Xy499NYNH5h5kAGpeW3V9QJoW9yDmf3H8/BxjIimsKH+z5nQGoeMwdMwm62dnp+G4NuLLKZBKvjmMQrTWj4Ih6awo29JvrpyRxt2Ew2HCYndrMTm2yLieY9QQiBIiKE1CABxU9QDRDSgr0ifOoR6Y2YZTNpVt2OoqcJXWua6nn6/X/x7tol5KRl4bQ52pQRPhEeiUfhU7wE1SABpRsJw45ClkzYZDs2kx2H2Ynd5MAiW4/xflIJKgH8qp+A4iOkBlFE7x1/QPHhi3hItqZ1714SzVPM21k3Gr/dW2KyJjT8ik/3QO+lY7bIVmyyHbvJgd3swG5yYJLaWtN0jUATgrAWIqj4Cah+QmqQsBrqlYGpkBakMVyPSTbjMDm/tFF6vRWr77TaGZLTH5Mso2oaa/YUs3ZvMTOGT8LeHBET9eRXNAXr0Z38o3BY7QzJ7Y9JNqFqKmv3bWb13k2cc9ZkHEeVF9GUuCTZJ5obpl/GG2sX8cWBbWwt3c2jb/+N7825mdEDhpOdnBGzewlGQrh8bqqa6iitLUeWJGaeNYm0hJSTVtczmVAkzILVH1HrrqcwfxAzR0w+1VUyAECcej9j2aRH+dLcxlMjJzwSvSMGnD2NzGFDqNy8FdDF2pC784hySZJIzssjfdBAyjcWx5ZvX/AuhRddgD0t9YT85iRmZZI7aiT7q1tEu31LVjD80ouw9D+5s0VPNNbEBAqmTIqJ6ACH126g/kAJeRPGntJjlRJTYdTZSJl90bZ+3iKiBryIhirI7Y9k62BQw5GA5EyK+20XrjqkPoN6v6ImM6TER38KvxspHIAzxSJEkiC7AA7sBC2k2+3UVbeI6EIg6qrA3yyiJ6ZAYooeUZyR05KU1FWH8Ljie0ut/dCdSUhp7QTIuep0sTtKJIwo/hxR/PnxH5um6tHPx1+SwXEiFIXAZ59gG1nUoRAsImECn3+ii+iShDkzC8vAdp5rScIx61yCxRshEiG8exfhbVuwT5h0XCKzZeBgLAMGEjl4ALW6ivDmYhxTpyMnJXW4jVJRTrB4Y8yCxjZuPHJ61zOhg8UbcV50MXJCQrt1FqpC6P/Z++8AO7LzvBP+nYo3h84R3UAj58EAg8l5OBwGMYgURVIULStRybJleZ3WK++3sr22ZXlXVlhRlESKYpKGFDM5nBwxATMABjl3zun2zZXO90fd2wGdA8KM8JA96L636pxT6VTV8z7v8544jttXyuTQdYxd19DfXwgCtx0k/8JzuIMD2B3tFF59hdD9D76jagyI+ibUts145076fuWOg3voWZwdu9EO3rdqIl3aNu4bL2P/4PEZz4VK60aU1o0zM3oWwQ1Lor9bIBC81XeSvzn6bT645QE2VDQTNcJ40mMkN86RvlN84/gP6U8PAT55fGfzLXO21ZZcx2Ob7qMz1cdQdpTnLr+Ormg81HYnG5JNJAMxdFX3iSS7QKqYYTAzTMd4L8O5MX5m52Nz+nw/0nYXhzqP8mLnm3Sn+vmz17/Gz+56PztrNlEVTqKrGkXHoj8zzFu9J/nCW49jew4hPcAdzbewv2HnovthJDfOd888Q1Azua1pN3WRagxVx3Jt2sd7+MG553j2sl81PBmIcVvTbpridYu0unZIBmI8sP52jvad5kjfac6PtPOXbz1Of2aYvXVbqYtWE9QCSCRFx2KimGUkN0ZvepCzw5e5p2U/Bxp3oiyjqi/4pEvBzTNWHMHyiouvsCgEpmIS0iOEtQgBNYSqLJ+YntGiEOjCQFcMwloU27PIu1mydoask1k18e+VvKs1oRMzlv8i1j7QzYsnX+ejdz7GJ+79IMlwbNL6pAzjalZ3LqHgzq5IvhgEgqAWJqxFCGtRDNVctYpYESohPUJQC+PoCbJOmow9Qc7NrYky3ZE2WSdDSI+gi8VvNopQcF0/i0ETU9kGZRW949kEtNVHymWJoB4rjlB0V++Zqis6IS1KWIsQ1ELoyjI9bGdBoAjhk/FqkKhMUHTyZJ0MWTtN0SusOpiQczKkLAM9oC/p2CwVmUKOoYkRssU8BbtI0SqSLmQ51X1hcpnO4V5ePP0GdYlqDN0goBkEjQDJSJzq2OwH1LxVoH/cV4cX7SIF26JoW5zsPDdZ0Kh3dICXzhymY6gHUzcIGCamZpAIx6hLzJ/9BBAwTPa0bmVLwwZOdV/g1QtH+W/f/TyHL52gPlGDEIKCXSRXzNFYUcfH71jYn9HUTfa0bGVr4wZOdp3njQtv89+/83neunSChmQNilAo2EWyxRz1yRo+cefcKuSJXJqB1DB5q0jRtig6RcayE3QMTREnZ3su8dyp10iEYgR0E1P392VlNEkyPDvVsLGilv/w07/Jv/vqH3Cy+zw/PvoCF/s7uXPLPtbXNhMN+M8c6XyG3rFBLvR3cLr7IvvbdrK3dfssEv3o4Gn6MkM8tuHeBffJP3bYrkO+WEAIyBbyvHTqDb7z2k8o2ha//J5PErnOWQrvKEze9+WUp+tqX/wmld+sXZurGYuq+eS59HziS9UXt5iY9LZdu3ELITAjU+emoigIVVl0/1Ru3EDDLbvpPfr2ZPr+pedeoP3lV9n6/kevyr6NNTaw/t67aH/xFZyi/4zc8fKrtL/yGrvq69BWo2S8wWDGomx57BHO/vAJ7Lz/PNl37DiXX3iJqk1tswqPXmsIRUXWtiDaJpBvPjV5bspMCmEVfYuiudYzQ8hIvHT+l2oHDXYittw6t6f5asaoahCvQOqmf42Br9LOZyCSWJtzVHrgWP71u1AgzLYA6VvZrHF2oKhrRmq6v42WhRzpn3w6lVbBD3JYhdKy60pBPAXCUV95PtQLmRSkRn1fdFUDJLJ/OokeQVTUzO68kL16QcmSL/tN3ABwHXJPP0nogQcxts0WpErPI3/oFQqvvw6ehwiHMXbtRq2dm7sJPfQe0t/8e5zLl3C6Okl/42so0Rj6xk2I+eYBKfEKBdyhQZRwBLVyykVCCIGIRgl/+KOM/6//B1kokH/lZYxdewje/wDKlWSrlLgTE+SeeYri4dcBUKqqCN55N2pycRK9eOwIhUMvo1bXTNmmTDYtsS+cJ//i87gjvsm/sXkrxqa1syBZCgIH78DYtp38yDBeJkP6y19ETSQwdu1BzGdXIyXScXAHB0BR0OobrumYr4TQNLTb78U98jpeh28dLUdHsb72V5DNoh64C6WqekVzqjc6jPvqC9jf/jpyeFo2TSCIeuvtKE0ty2rvJol+lbG9po2Mlefbp5/i/Eg7bRUtxMwwEslQdpTjA+foSvXhSo/qUAX/5JaPkgzOrQpTFIXHNt3LeGGCvz32XcYLE/zo/Asc6T/Nxop1VATjM0j08fwE/ZlheiYGqItW84EtD8zZbm2kil/a/3HGi2mO9Z/hzd4T9GWG2FmziZpwBbqqU3SK9KYHOdp3hpH8OIaqc+e6fXx8x6NUhhIL7oOYGWF9somO8R7+/PA3eKnzLRqiNQQ0k6JT5OJYF4d7T1B0LAKayT0t+3mg9bY5i766nsdQbpSulG8/U3Qt/1/H4mj/GSzXjyymC1keP/FjDM3A1AxM1cDUdMJ6iA0VzUSu8FoXQrCtegOf2fshiq7N6aGLnBy8QOd4H5urWmmI1hDUAyCh4BZJFTIMZUfpTQ+QKmTYUNHMrXL5WQ+259sw5N3lq5evhCJUonqMqJ4gqAZRr0JRViEEhmpiqCYhLULIzpCyxlY9flc6jBaH0FWDkLp0v23wi8rpms5jt97P3vXbVzWOawlDMYjqCSJ6jIAaXHOVke9NbBBXKghqYSascSbsMew1yCDIu1nyTg7N0BcN0JhqgKyTJmWPEZUemmog8H3kM04aDxdTXWVkuUTIjxdHyDrpVbUFENFjxPQEIS2CdpWKG6ulYEdACxLSwkzYKdL2OJ5cnSo9Y6cIqEHiRmLNCo0e6zjN3zz/LbpH+sjbRQpWkbxVYDA1OrnMK2ff5PJgN5FACFPzCe9oIMxDu+7k197z6VltXhro4o9+9CW/TatYItKLDKZGJvfB4UvH6RsfJBIIE9ANAkaAoGFy95b9/O5PLey1rQiFbY0b+eWHfpY//P5f0jXSx5Nvv8xzJ18jFoxMkuhF2+JDBx5elERXhGBLwwY+98in+O/f/Qs6h3t5+sQrvHD6daLBSIlEL1C0Ld637/55SfSXzr7JF5/7JhO5NEXbKhH5BXrGBiaX+d6bz/DGxbcJmUFM3SSgG1RGEnz04Hv56MFHZ7WpqRqP7L4by7H5i6e/zstn3uRM70XO9F6c3BdXnleaqhEJhOf0Tn+x+zCHeo/eJNEXQe9IP19/8Xt0DfWRKWR54/zbXOzr4AO3PcSHDj6C8g5S/1x3TCejXMcnUhRtdaTXZJslYsZzS2TSdTouqg6q7RNw0gMr7xcrVLSZVhiTdi+uP2bErKKGnuOAECgrICGzw8Mz7FvMeIxQRcWivtvR2hpa776D8z95hvFOn2wrTqR58X/8EbHGehr37V32WBaDGYvSctcd1O7YRs9bRwEopFIc+uM/p2J9C+sOHliWX7iU0vdXl6BoN5aNlWaaNB88QNNtt3L5+ZcAsDIZ3vry16hoW8+WRx9GW4YKz99Wf3tVfW2eY4SqQTSJVNSpwpKeuzCpqiiIZC0yHIcJ30KBkT4YG/AV1WuJsg90ZT2yv93/LD2GHO6BZM3qi5lCyUKlAKayCIleAKQfPFhr6/fa5imfebuIHJ16fmB8BDkxOnlMROP6qfklEIaqep9EL/mpi3wOIjE/fjmNRBehKCRnk+iykOfdXKz9JkpQVez2y4z/8R8R/qkPY+7YiVJZBULgDg1RPPIm6a9/BW98DIRAb11P+NHH5p2P1fp6Yp/5LOP/8w/w0mnyLz6PzGYJ3ns/+ubNqJVVCDOAdB1kLoc7MozT24Nz+TJuapzwo48RvGtmUXhhGIQefJjim4fJP/8s9qULTHzpr3BHhgjsvw21vgElEMDL5bAvXST/0gvkfvQDvPFxhGkSevhRzD17F/V9R1WRmQwTX/0yXjZL8K570JqbEbqBN5HCOn2KzPe+Q/HIWyAlIhQi+vFPoIQj1zSDSK2oIPqpn8O+fAmns4PCsaOM/T//g9ADD2Ns245aU4MIBsHz8AoFvLExnL5enEuXcAb7Cey/jciHP3rNxjsflK070e56AGuoH3JZkB6yqx3ra3+JeuY46o69KOs3odQ2QGT+rAM8D5mZwBvow7t0Dvf027ivvYRMjU0tIwTqlp1oB+9GLNTWHLhJoi8BGTvL2bFzdEx04UqPqmAluyq3UxFYvIjbungDd63bxzdP/YQjfad4q+/UnMu1JBr4ud0/xd3rbl3wxSseiPAzOx4jEYjy9yd+zNmRy/RMDNAzMTDvOopQqAklZ9moTMeeuq381sHP8LfHvsOhrqN0pfroSvXNuWzUCPPY5nv5+I73sqmydd42ywjrQR7acDupQobvnn2Gpy8dAnwV7nTlZUgP8MD62/n0np+aV4Vuezavdh3l68d/gOU62J6N7drYrkPaylFwfBJ9rJDij1//CrqqYSg6uqqhqxrVoUr+2R2fYWvV7HSjgGZyZ/M+AprJt049yaHOI6StLG/2nuRNTs67fYlAlLgZXfYLsyddMnaKjD2xrPXmgq4YVJhVRPQYunJtbAR0xSBuJDFUkwlrjAlrfFW2FJZXZLQwRCAcQF3G1BQ0AoQDIVK51ZOn1wphLUrcSBLWfP/qq3mTFUJgqgGSZhW6ojNaHMLyVubZXobt2eTdHCFvcZLZVANE9DgpaxTLLU4Gd1zpIqVHVE8QUFfpbSp98njCGl9VM2qpOGfcSGIogWvy8KMIlbDuZyGYislIcWhV2R2udBm3RicV72uxDV3DfTxx9EU6hnvmXWY0k2J0spCVD1MzJn3Jr8TgxAhPHHthhgL7SqRy6VnXtSIUVLG0+SERjvHJuz5IIhzjB289y5HLJ+kbH2I8N4GpG8RDMXata2J/28LFtMuIh6L8zB3vJxqM8IO3nuWtyyfoHxsiVW4vGGXXui3ctnHPvG2c72vne4efpmDPn3k0kBpmIDU8c1tCMbY3z69qCRoBPnDrg7TVruPZk6/y8pk3OdV9nv7xYTKFLKqiEw2GaayoZXP9eva2buO+7QdJht55hYduFKTzWV469QbPvH0I13NpqKjlsw/9NL/63k9THa+8vlYTZeW1lPjsSPmzafdoz/EJXYByoXEhfIXPlWOX0xTi09ubrhr0XN9PVyyhvSuhltSa5TaL+RL5dIWPuGbM/HzRNgv+eq4DxdzsNsEno+aqWbTW+1ApFT2U0leku/Y0Yr+8/BX9SA8UfRaJ3nvkGB2HXqNiw3qab9tPuGoRT9cScqNjHPqTz5OeZo+SWNdE5aa2RdcVikLrPXfR9uC9HPv64zgFfw7rev0Nnvw/fp+Dn/sl2h64ByO8cAaGY1mMXLhEuq+P6i2biTfNXSsJ/OeXul072PHTH2Kss5PcsB+47XnzCE/9x//MXb/962x6+AFUY/7nXiklnuMyeukSna+9gZXJse2Dj5FovnaWkUuBEIJ4cyO3fOZnGT53gXRfPwCDp87w/H/9Qwpj4+z86IcwogsTM1JKUt09dL76OmPtnez4yAep2rj48V0SZMmD25u6BoRhLq4or2pAVNYh02OTmRje6TdQ45XzKthXjGAUmjbCUHcpIOciL5/yC5nWNM/v3b5UKAqYwTVX0S8HoqIGgiFIj/nbOD7sz72ajhwb9L3Oy8s2bpgi+4MhRHUD8rRf14yRku1LJOar2sf9rHgUBaIJRGSO5wMpZ3LogRBi027EujVQ3ZpBRM2NdV3+Y4W5YxfSc8m/8hJ2+2X01vUocV/k6aXGsS6cxx0YANdFraoi+slP+x7g80FRCD36GO7QEOlvfBVvdJT8Sy9gnTqJ2tiIEitZDnkeslDAm0jhjgzjjo6iVlUTvH22xbMQAq2hkdhn/ykyn6PwxutYJ97GHegn/8xTKJVVCMNAFgo4fb3Yly4iMxmEaRK87wGiH/0YavUc2RZXQGtsQq2pxT57mokv/iWFl19Cra4GXUdms9idHTgd7chiETSd8Ps+QPDe+2AOcl4Wi9gXL+D09OAV8shCAZnP46UnKJQU8gD2xYukv/YVn/gOBFECAUQwgBKJYezYMa96PnDbQeK//DnG/+yPcXt7KB55C/viBbTGJpRk0p+vpYe0LLx0GndkxFf6h0LoLesX3RfXAsIw0R79KbzeLpwXn/bnOCmRI0M4z/4Y98jriLpGlGQlIhbHGxmaXFcW8jhvvII32A+5DDI1jjc6jOzrRo6NzLh3AYjGdWiP/hRK68ZlCyxukuiLoOAUODJ0jJ90PEPKSuFJSVgP0Zft5wPr30vMWDhqkbMLHGjcxcaKFp65fIhXu47RmeolZxcwVYOmeB376rdzT8t+9tZvI6QvTtxUhOJ8aOtDbKlaz1u9JznSd5pLY12M5lPknSKKUIgaYWrDlWyoaGZ37WZ21W6hKjR/5WxFKBxo3El1OMl9vac41H2Us8OXGMr6ytWgFqAuUsW26jbubdnP7rqt1EcXTqcvw5EuUSPCB7c8yJaq9bzQcZgTA2cZyIzgSo9kIMb2mo3ctW4fdzTfQnO8bt7ghOt59GeGOTF4fs7vJ5eTHsO5sVmfD4XGyFrzFy0JG0Fub9pLc7yeBzfczlu9Jzg5eIHe9BBZO4cnJSEtSFUowbpEPVurNrCjZhN76rYuS/ktpaToFhm3Rlbth6wrBjWB+utSUFAIQVANoQd0NEVnrDi8qsKovh3FGBXm0s4tgA116ziwcTePv/xDYqEoe9ZvIxqMzOnpf70hEET1OEmzioAamGU7czWhKVrJLkdhON+PvUrP8IKTw9aLi5LoilAngzsFNzfppW8qAd9jXAnOWXR0ObA9i9Hi8KquJU3oVJjVxIz4Cj3PVwddMYibFShCZajQt6rrqOjmmbDHMVRjWQGp+XD31v18/lf/E7ni8mxyFEWhuXLugOiu5s18/leW36YQUD+HOmruZQW1iSo+cef7uXvrrfSODZLJZ3E8F1VRCejmLGuYtJVisNBPTbCOqB6f1V5NvJKfueN93LVlenvOtPaiC1rNfODWB9lQ04zrLe9c1TWNTXWtCy4T0E32tm5nc/16PnDrgwylRkgXcliOjQBM3SAajFAVTVIbryQanJ+MWY0F2D8WtNQ08Xs/+8/5tcd+Dk9KosEIbXXrqEvWoF1HggXwXzrs4kzCd5IQLsGx/eWmnwOiRBBdGajyXF9Z6U2bl65sr0RYzWxP+MTxYjUChAJGwFdnl1P6PYcpCWepH0VZ3AKl3K+i+n1bJUXogm3OQaKvah+GSh7oV0BVwQiCrYBTattdZC6Y4xod6+zirS9/DadQJNHcRM32rTTu20vlpjbiTY0E4jFftex5FCYmSHX30PvW25x74kkuPPXc5DZogQDNBw/QeMv8gb/piDc2sP8Xfp6hs+fpfPUNP93ecbn4zPOMd3bReOstrDu4n+otmwlVVaKZJq5tU5iYYKK7l+ELFxk8dYbxji7CNdXc8zu/tSCJDhCIx9j98Y8wfPYcb3/jW76ti5Rcfv4lMgODnP7uD2k+eIDqzRsJJhMITcMpFClOTDDR18d4eyf9J04x1tFJqquHqs0bWX/vXbDGIui1gB4MsuXRRxg6c45X//QvsHP+9dBz+AjPDP13zj/5LOvvuZOaHdsIJROopoFTsCim06QHBhjv6GLozFlGL7Uz3tmFHg752zoHie61n0KE41BZh1iKP7+UyOwEsuP0zGsiWuGf0wtAmCFo3oIc7oN0KYOtvx3v2IuIbbchovO/n87oP5+B0X4IxRAVs+1JoaSWr1mHrFkHfaWMi9Qw3vGXUHbeCbUtSwo4SceGoW6k56I0bvSvGatQUpgDgcgV2S6eH6hzHX+O8tzZGSbT19dMf85TFH9Zq1Aq6llSr5uh+ZXuuoGoavALiEqJzOeQmQlEotIn1MuChlAEKmqm7vNmyA9mKGpJiT4AuYz/3cggWKXgvhGAito551phBmfOSaqGaN6IcttDi+7TxSFAufnscSNAqagg+smfY+KvPk/htVdxOjvmXE6tbyD+y58j9PCj89uy4D8/K9EY0Z/9FFpdPZlv/h3FUyd9onxkeN71UFTUmhqUiso5vxa6jrl7D/Hf+Geof/d1cs8/hzs0iDs0OOfySmUl4fe+n8hHfxp9w8YlzQUiECT60x/HOnuGzLf+nsIbr829XDRG+H3vJ/apz6BUzC2k8NITZL73HfIvPId0HHBspO0gbRs57X3I6ewgMzCAMHTQNISmIzQNpaKS5L/8V/OS6ELTCT36GEosRvprX6Hw5ht4qRRWKjXn8v5KAiVRgdpQv+i+uFZQquswPvmLyFwW9/WXpr5wXeTIEHJkyH/jLxWWnUQhj/vWq7hHX/cttdz536VFTT3Ghz6BduBOP7iwTNwk0RdB2spwbOg4w4WRyc8mrDRvDLzFPQ13EtUXVgQUHQshBDtqNtIcr+ODWx4kZ+dxPQ9FKAR1k2QgRiwQRVtGkaGgHmBv3TY2V7by3k33krVyWK6NKz0EAk1RMTWDiBEiZkZ8K5JFoCoqG5LNNMZqubvlVtLFLEXXwpOeTxBoBjEjQkUwjraMKuee52F7DtXhCh7acAe3NuwgVchQdItICbqqETP9dhcbZ0Az+fC2h7m9aWkP/FdCUzVaEws/sOulZRqiNdzRtIdUMUPBKeKUXh5VoWJqOiE9SNQIEzaCqMssEOX7gA+vWhFsKCY1wXrCWoQ1zxdcIoQQaOgkjEoUBKPFYZwVKmklkvHiKCE1sqBP9sf+y+foLaUvelIyMjHG8MQoh868STwUQ1O1Gc94mxrW86V/8YcrGtNaQSCIGQkqzGoMxbwuKkXf8ieO6zkMFwZWRToX3AJFt0hgCUUsVaESVEOYagBZevkSQkFhbVT4Y9bqagroik6lWUPUSKzZmFYCBYWYEQckg4W+VVm7TFhjxPQ4AXX5xYSvRFNlHU3zkOErRVWsgvfsuWfxBdcAflHQVjaWSOjR4ghhLTzndWh5FilrjIRRAfNkeJaLjG6sW55/HkBb7Traatcte72lQghBOBBic/16NtevXFUipeTY4Bm+f/E5hvNjNEVreXT9PWyrbEMtBf+klGTsHD+89DxHB09TcIq0xht5pPUutlRsmFwOwPEcjg2e4dnO1+jJ+C83NaFKHmq5nb01WzFUg6yV43sXn+XI4Cls12F9opmHW+5ka8V6hBAM58f4wtt/z6Pr7+b7F5/FUHQ+0PYAF8Y6ebXvGAfrd/NQyx1EjXCpT5cnLr/IK71HyNo5GiO1PLjudvbUbFvW89Z8iAbD7N80O4vhu6ef4eG2O2YUXHc9l7PD7ZwYOEdDtIY99VuJmlfRM13Kks3CQnNI2TZk+mfeTJL4yva8hYJ7c7UHaEtM/dcMn4B2rJLyyPPbpKTsVtSlEejToZv+enO1qZTanC8QXFbFL+j/O8c2i3n2of9lSQ2v+Ap4x5o6TtP9z4Xij0vT5h6fBCuTY/TiJYbPnqfjldc4kfw2RiSCHgyi6ppPaEhwbRsnnyc/niI7NOxbweAryxtvvYV9n/kkwYolkJiT6+zl/n/zL3ny//h9+o+fRHoenuMwdOYco5cuc+HJZzCiETTDRKiK/73tYOfzFDNZrEwaz3FpvPUWXHvxYL4Qgvi6Zu767d8gP5bi3BNP4VoW0vMYPHWG0YuXOfvDJzCiEVRdRygK0nVxbQenUMDK5ShOpCe3O9l69ebf1UIIQbi6igO/+Fnyo2Mc+9rjk/7o4x2dTPT0cvn5FzFjUVTDKG2rh+v422rn8hTTadxS0byqzRvn7Ut2nPKV4dEKRE0TorIBEassBdFm3hdlMQ/9HXgXj/kK7zKiFYiKOsRidYeE8MnrDTuRp9/wg2WujWw/iUwNIxo2IOpaENEK/5oVwidLijnIjENqGDnShxwfAumhbDngk7zz9RWtQGzcg8xNQGoYkDDcg/fGTxANbdC0EZGomUZie2AV/OXT48ixfp/wL2QRdS3QWNqP5aJzxezs4Fox55PgRtC3YnJtENOIGbvob7cZ9Je3i4AHZrg0JYmpbbfyvvf4Aplior4FefYtnySy8v5+jMQgNQJ532JT1DXPOJ5CUZCxJEQT/nLjw8hcGiElcrh3apsCIUTVPM98wSsKKzq2T74vd26+iRsaXiaDecs+kv/mf6fw6ivkn38O+8I5vIkJhGGiNjYSuHU/oUcfw9iybZZP+FwQQqAkKwg99n6MXbuxjh8j//pr2GdO4w4N4WWzoKmosRhaYxP6xk2Ye/ZibNuB1jR/1FOYJubuvWgNjQQfeIjCKy9TPH4Mt78fL59HiUbRmpoI3HqAwO13YGzdhpJc3MKsDJnLolZXE7vjLsxb95P7yRMU33wDd8wPCKrVNRi7dhG6/yHMW/ejVlTOG1CQtu8/Pl9QYhKui8xlkVfoPpVMBpmbXwwKIAIBgvfch76hDev0KQqvvkLx1Cnc/j68bAYQKOEwal0dRttGzL23YOzajdZ0Y90blaYWzF/8Z1ixBM4zP5r7+fNKQZKUU8HA+SAEoqEZ45O/iHbwHkQ4sqLx3ZzxFoHlWYwVxmd9XnALTFgTSOoXVGzJ0guJEIJ4IEo8sDy/nYUghCBshAgba5cKJ4QgoJnUR6uXrDRfDBLp7wf89P7aSBW1kapF1pobqqJQF6miboXrLweGqlMRSlAZSvrvcGtEUkspyTsZ0vYCUcElQBM61YE6QiUC/XqmjgshUFGJGxV4SEYLw3isTElreRajxUHq1eZ5ldrrqhsJGFMBl031rcD8Ln1rTQCuBBE9SqVZg64Y1/VYKUIhYVaQc7JknJVbCUk8im4BV7poi9hrlG2bFBSuLAZSLiK50n2SczKrsnFRhUbSqPYJdKFcVwWuEAKkQlSP43g2w8W5lRRLgStdxqwR6oLBm6riaXClS1+uk8ZwK4by7ilKt9YYK07wl8e/yfbKDdSEKjgyeJrPH/sGv7LnE+yo3IgQAsuz+ZMjX+HsyCUONuzBVE1ODp/jj9/6W35x18fZV7sdIQSe9Hiq4xB/ffxx6sI17KnZgipUutL9WK6NKjQKTpE/PPxFzo5d4u7GWzFUg1PD5/nTI1/hMzs+zIG6nViuzfmxDoZyo2yvbOP57jfoTPfREK4hoof4SftL1IWrub1hD570+NKJb/Fc1+vc3rCXqBHm7Ohl/r9jX+eTWz/A/esOXjXP8u+cfppTgxdIFTPsqdvKB7c+wNnhS/zNke/gIRnOjvGejXfy0zsevXpEetn2hNJ9sfQcuiTMdd9VNV95uRIsJeOqPLZysU1/0FcutLx021ltzvGEIATzChA0c26F+pL6XmSbhVjCtjLv2IQiZhAAdi6HvciL9XSohsG6O27j0f/0ezTcsmdZ91/VMGh78H4itTU88W9/j/aXX50kbV3LJjM4BINDi7TClPXNEqAoCjXbt/K+P/jPxJrqOfa1xymkJkBKnGKRdP8A9M9vaznZpVLykb+Bb4lCUUi2tvDgf/g3RBvqOfyXXyLdP1hS/TtkBof8fbxoQwKxUBDdtmBi1PcMH+xEls9HMwhGwCfGpfQJ9EK2RAIXptkgGYjW7VDVuKTjKHQDNt3ik9Xnj/oBJMeGoR7k2CDy3FulQJk6lfHheaXgnQNOKdPFDC0SzMMnsOrX+32dPOQT8VJCZhx58Riy87S/reUaCV7JOqncn+P4JLiigtM4uT8Rqh/Ysq7YXq9Eipthn5iX3pSqHPx/izkwzClrpjKRXq6LMO2dxrfNWeR6rm8pzTOu79E+PoJMVPl+v+VjVL9+ivgvbYMIxxAVNcjUiD/G0QGkVYThvilCKhBCVM2jSk1UzlTIWwVkemxagdKbeDdAFgoIVUXfsAGtsZHwo+9DWkWk6/rv5rqOEgwiIpFl3ZeFEIhgEH3jJvR1LQQffBhZKILr+PUqKF2/uo4wTL9AqLH4e7PQNLTaOtQHHyZw+x3IQgFsByk9/16pG/54g8H5i2zOty9sG+l6qJWVBO++F3PvPj/7o6RyFqqGCARQwmF/3AuMVa2upuLf/u8kfvt3ljWGSSgKauXCHJgQAnQdraUVrbGJwJ13+fvYsaf2seIH8oVpIAJBhDklKpKlmisoV0dUJm3bn0fLY51/QxBNLRi/8Buou/ZhffuryI5Ls4nzpUII0HS0O+/D+NhnEc0tK1Kgl3HDzHZSSmzPV1ID6IqGKlavoFstNEUjYoThinuZrmiEtNBNcuJdDCklx0ePsCWxfdWFD2e0i2S0ODzDD365UIVKVbCWsB5FXGcCvQwhBAoqCaNyssjjyrZRknNypO0Jonp8zm37zz//r/CWUR3+WtvcXImgGqbSrL3uBHoZilCpCtaRz2RXZxvi5XGkjbbArURKiYeH5RWxXWtO9bupmAS15ZNJUnqMFodX7CGuoJAwKogbSV+BfgPM5+XrKGYkKXoWaXt8xW2lrRRJo3JF+/ZqQiL980K6k78LIVCEOiMTwJWunwklVDzp4knpP9ehoFzxfOC35+HhImUpsCcURCkw4kkPV7pk7AkKbgHbs7A8C4GfFaEp2hXHX+J4Dp70/HdnlFnPJFL6AWJXev6DeunYXVnnoOz/rwgVKT08PKT0i5UuZB0kpSTv5mdlJOiKjnGV5xJD0fjopoe5s/EWQLB7YAt/efxxDvcfpzXWSMQI8VrvMd4aOMm/OvCL7KnZBsCFsQ6+cPzvean7MK3xRiqDCVKFDF868Q9sqdjA7x74RQKaicC3XdMUFUUInup4hSODp/jfbvtl9tX6BaI7J3r5q+Pf5OmOV2iJlV/oJVsrN/DRze/B9lwO9R7hA7sfQBGCvzr+LYbzo5Mq+ifaX+aXdn+M+5pvQ0HQnRngr49/ixe7D7O1cgMNkaXZAi0XY/kUvelBdtVt5kjfKfJ2gYpQnKpwBZ+77RNcHu3iG8d/xH2tt109Er1M+IBPyIz1QbRyUduFhdqT0ic9xQLk7uowjThes+ZX0eb0fbjWmLx2V7atzQcPcOAXf55jX/t7xrt6cItFPMfBc12k5/nB6XKAWlEQioKia2iGQbShnv2/8Bn2feaTBBLxFRUn1UyDhr27+dTjf8upb3+fQ3/854y1d+AULTzbxvO8UgFPWTpnBIqmomgaqmEQrq5i0yMPULF+6dk8iqqSbF3HY//377P9p97PoT/+PJ2vvYGdy/t9lrbd32hRIsw1FE1DMw1ClZVsfOQB9vzMR6neNL9CW1FVQlUVRMan5gc9uPzrxoiEidTUTAYYgsnkgnYHV44h1lDP/f/6d9j08IO88YUvcvHZFyikUriWjec4M7dV+MEBf//qGJEIbQ/cy55Pfpz6Pbvm7qScESE9n1C3S5mx2RQgkJOuR1c8awsFgmFE227E5n0+Ob5ECN2E3fcgwgm8k4d8othzS4T6EjJzSyTIggU9y4uqGqzfgQhG/L7GBqbsl9wlPjOq2lQBzwVRIvzVcuHiUpZLed9JfFI+Z0E+M7P9cr0JK+8fg+lZKQttX0Orv75j+yR6ahgmav2iouVlGq8g0QEicb+46OXT/tCGehHFvK+8L2eKBsNQOY9dTiiKqKpDdl0obZuE8RH/Z551buIdiGlZwyIQhMAKnx3mgRACTBPVXEMxixAIXUeNJyC+6NJLR7kuCj5ZryYSkEisqCmhaahV1VwLw78ymb6c/SE9Sf8X/wa9tpbK9z+2POHCktr3OPurv0Hb//ivaEvYh0IISFSgPfBe1H234R55Hef5J/Eun/eLHJeDq+XnjdIzRzkIgKqCqiOCIZSdezEe/RDK1p1gmqu21L1hSPQJa4LXB96iY6ITgLsabmdTom1RhePVRsyIsqtqJ73ZfvJOHgkEVJPdVTupCCRvCELsnQrHs3Gkg+u5gMRQTIpe0bdLUQMIBI5nU/SKeNJDUzQCahBFKHjSKwVdfJJDV/QSSeIQUIOoQkMisdwitrQRCHTFQFf0SUI1a2fQFR3Ls5DSQ1V0TMVEEQpFr0jOyZKyxpiwUgRUC1WohPTwqom2jDNB3lm6YuhKCAQJo5KoFptFDvsEjVy1x/SKxyYEmtBIGpXYrrVipbMjbVLWGCEtMuccMF2FnslnGctO0FhRi6LMVjm7nsfg+DAFu0jIDFARSWAs46F/tfCLvlYuaE9zPWAqJnE9yai1gBfdIii6BVzPQSrzqxwdaTNaHCLnZOcN+ET1+IqI3oyTprCKaymsR4kbFcu2Y7raEEKgq37h3oKbw16h7ZNEMm6NLcly51pjpDjESGGQgpv3sxkUf96oCzVhCP/6HC4MMpjvozpQy7g16p9DQhDVY9SHmglp/nzsk805BvI9pKxxXOliKCaVZhXVwTp0xSBtpxjI9zFhpbC8Au3p8yileTKqx9kU3zY5NoEga2cYLY74hZ+FJKRGaAivI1IKXILvxT9UGGC0OIzlFVGESsJIUh9qIqAGKLNjQ/l+xqwRaoINpK1xxq1RXOkS1WO0RjdNbu+VsDyLb5z/Ov25/hmfH6y9nTvr7iSgrV1w90qYmsmtdTswVH9sm5IttMQauDjeRaqYJmKEeLX3GIpQiJtROiZ6S2P2a6hcnughVUxTGUxwYuQcBafIe9ffS8ycUjNPpybe7D9JRSDB7urNGCX1b2u8iV3Vm3mq4xA9mUHqwr76piFSQ0A1qQomCWgmDZEaJqwMuqJilciRtwZOkXcKVAcr6Jzwi6QXnAJRI8T5sQ4Gc6NXjUQP6AH+zb2/Qn20mhOD5/n62z9gj7EVXVFJBGLsrtvKl458m4KzcguqZWOJxMz860tID/v2AvrNDI7rjXhjA/f8zm9xx6//MoOnz9F9+E2Gz18k1dVNbmQUu1DAtWwUTcOMhInU1lC1aSONt+5l/X13owdXX3RaKAqBaJRbfu5n2f3xj9B37AQdh15j4OQpskPD5MdSuJaFauiY0SixpgaqNrXRdOs+6vfuIrgCEkIIgR4MsPGhB2h74D4GTp3h0jPP03/yFKmuHvLjKaTnoQcDBGIx4k2NVG5qo+nArTTesgc9tPhzWMWGVn7l2R+tYI/MxL2/+9vc+7u/veL1hRBopknLnQdpufMgY+0dXHz2BXrePMJYeyf5sXE8x0YzDMxEnHhDAxVt66nfs4vmgwcIxGMLHmNl8z48VfPJV9uaqmlQVmWXp4uylZLmq6VFsgaxcS+iZt3yi3QK4ZPbm/ai1K5Dnj2MHOjw+3eskhK8ZLlUtjVSNf/HMCFWgWja7Cusl9KXUKFhA2pFHV77KWTHKZ/Etou+DcrkvCh8L+6yZZRm+IGCpk2I5gWKJc7o78oP5sguCcfnnj/zaX//ByN+hopdmEm2z4VQFJJV0N8FdhGZTvmFRsskeiiKqKybpQ4X4SiiohZZCqDIoV4o5Hx/dM/zgwaVdQhjnucLIRAbtk+R6IAc7EF2X0RUVC8t8+gmbuImrg+kxB5P4YyM4FkWQtfRKyvQkkncdIbC5cukD79JoLWV4PpWEILgxjaEYYCUuBNp7PK6mopeUYmWiCNUFa9QoNjXj1FdjdXfj2fb6MkEenU1KArWwADF9k6yb79N7uQptGQSJRQi0Nqy4L1KCOH7wlfWoDz8AfQH34c3PIh74Qxex0Vkfy9ydARZyILjIjTVn78rKlHqmlDWb0TdshMRT6xpUOCGIdEHckMcHjxCZ7oLgM3JjbTFN1znUUFQC3Jb7a0YisH58Qt4UtISbeZA7S2LFhW9iYUxVhylN9eFQCHnZKgO1jFeHMNQddZHN6IrBj3ZTsasUVzPRVd0GsPrqA7UUnQLdGc7yDpZLLdIRPePRcoaoyWygbpQI2k7RVemnYLrF2qI6nEaw82ENd/H/tjoYSrNanJOFtuz0BWTpvA6KgNVDBcG6M/1UnDzXJo4P0mgb0/O9j9dDnyF9uiqVOhBLUxUj80qZFrO5si7OeLG0vwtrxYM1SRmJLC8wop934tugYwzQVxfOFj1xvljfP6Jr/HvfuY3kdIjFoxQl6whUErROXb5FH/wD59nYGyIlpomPvPAR7h7+4FrQqQLFCJ6jIi+liHxtUPMTDJuja7YG92VLra3sKdprnSNxo0kIS3iW7pcgZUEfVzpMlEiTFcCQ/HP0TJJCGC5Nl0TfYwVJtAVjeZYPRXBa3vsLNfm7MhlWuINhPUgUT3O2CoyV7J2Gidgo89D1F4PCAS2ZxPSwlQFalCEyrg1xlBhAEMxqAs1TS5reUV6c11UBWqpCdaTd7IM5PsQKLRGN6IKFcuz6MhcwnKL1AYbMJXAJGnuSJfG8DpCWpjG0Doi+hjd2Q6aw+uJ6DEEvqXP9OCoBIaKA1SY1bRENmBJi75sNz3ZDjbFtqEqGq506c/3MGaNUmFWEdaiFNw8A/keHM9mfXQTmjJFE1uuRX+um4geZV1kPa4n8XDQxQrtIq4yFCH8LLwSAppJQDMZKaQmr/mR/BhjhQn+82t/Pmv9jYmWyet6NJ9CEYLqBYqbjxUniJkza8xoikpID2K5NnlnquCSoRggBIoQKEJBL2cRCEGZuBgtpLBdm//55hdnBZubo/XoVzFwVhep4uzwZfJOgfPDHQxlx7gw2onjORTsApKSjdUax7Wk5/lqx3JxzysKZkrP9871CSoVoerTSDDpp/B6JYWmUvoekJ5TUqr6nr7S83wiZaWq9muAXHvHZKr1SmBUVqCvUGl2LaEFAjTcspuGW1b3bLoaCCFKBUr303xw/7XrV1Go27mdup3br1mfVw1S+iTqAqrnZGsL+3/hM+z/hc9MfWjl/esyEFm2J7Vo3IhSv8Ev9Dk6gMyMQz6DtErqPulNziEiGIZYFaKyHpGoXj55PqtzgYhXIvY/gsymYLjX9zsvZHxrEemBovmp9uG4T55X1CIiiZWRtIEQytb9sH6H760+3Au5CaRd9LdVUX1FfTACkTgiWQPJWr/g6lJIl3LWimOXioqWggHldcsKescq1X4QM2sgeO6UvYyUS1bli/oWZH+X3292AibG/H8BapvnVg+rGsQrIBKD9DiMDfvrlQuM6ibUNs1eb3rXW/bCqz/xFfDge6tfOolcvw0Ru77vnzdxEzcxP5yJCQb/9iukjxzFy+ZQwmEq3/coVR/9CLmz5+j7/BdIH36T7KnTZI4eBQRtf/jfMerr8IpFUocOMfzt7+KmM+C5RG7dR+2nPonZ2EChvYML//x3qP/lX2LsqadxRsdIPHA/tZ/9DIquMfaTpxj94Y+xh0fo+sP/F8UwCG3bSst/+HfLI7cVBaWmDqWmDu68/+rsqCXghiDRpZQMF0YYyY8svvB1QMyIcm/jndzbeOf1Hsq7Dhk7zY6KvXRnOhgpDtMW3VRSC07gSoeUlaIl0kZMjzFYGODSxHmiJULS8iyCWoiaQC0dmUu0RDYQ0sIMF4eoDFTTk+3CVANsim/D8iwuTpxjMD/AukgATehIJClrnG3JXWhCoz1zkYF8HwkzSVO4hYRRwZHh19lduY/gGln35JwMRTe/4vVVoRHV45jqzAcjKSVFr4DlFnE8i7zjF5XRFQNN0bHcIopQJq0DvBL5qSsGilBKBLyFqqi4nosnfc8zQzHx8HA9ByFEibCUqEJHW8CGACCix8g7WWxrZUEDR9pk7TQRLYq2ANFkOTaHTr/Jv/ub/0omn6O5qp6ff/CnuXv7ATRV5Q+//QW6hnq5Y+s+jl4+xZ/98MvUJWvYvm7Tsse0XBiqQdKYu0L39YYQAl3ohLTIqrzRbc+atNyYC6500FWDiBbDUNdOwZhzshTc/IrOLYFCSIsQ1mYGQs8MX+IrJ76HAGJmlPdvuu+ak+ipYob/8tKf87t3/iL76rYT0sJk7IkVF051pUPanqDCvPp1JJaDhtDMl7SgGiJrp8k5OSRycr71pEd1oJbGUDOqomHrMSzPKs2lBUJamPHiCDknS0tkAxVmFYooFWgVMG6NkDQriOpxdMWg4OVREJhqcDKgOhuSuJGkMdSMoZp+sMi1GSn6yvmwEiVrZ0hZY1Sa1dQFG9EUDa9k6zKQ7yXjZEhMC2ZaXoG4UU9jaN0Mcv1GhZQSy7UxS0Emx3NxPAdD0SZJaVMzqAom+d0DvzjLXzyoBagNVwJgaDoSKLrzEwMB1SBj52aI9zzp4bgOqlCWXQjUVHVM1eQ3b/k5IlfUjTFVk7rS2K4GHt10N9878ywhI0DOKtCSaGAkO0bYCPHdM8+StwvEA9FZ41oVpAdWzvcuLu9ETYdg3Ce8pPS/l6VlhUAGYz4RLgTSsSE7XiJ+JCgq0oz43zsWFDI+wV7M+aSdUFZuDXMNcP7//gPs1PiK12/+uU9R+/7H1m5AN3ETC0F6vho5WnFNuxWKAvEqiFddHzM7RUFEkxBNXpv+zaBfxLRhBSI9zy1Zwdj+PGmXyP5yMNIwS8VCy37u7sygiBmGQtovSlqekxXNJ63LBLtVmOprMQiBqGtB8hIAMjMBg92TxLaobfL94+dCLAnxSp9Ez6aQ/R0lD3dANxA1jQv3XbsOsXE38uTr/t/SQ557G1nbBLfciwiElkWKyVLgQNzMcrqJm7iqyJ05S+rlQ9T/0i8Qve027JFhP0tcUYgduJXAuiY6fv+/ED2wn9qf+9SM2iuKphHeto3AumaMxkYyR47S9xd/RWz/fswG33LRGhii2N1Dy7//twjTANdDDfpZLXU//3PEDt7G6Z/7J2z+8z9Fr7ox+ZGl4oYg0QtugaH8MNlVpOXfxDsTphogqAaJ6FE86RHWImhCx/KKZOw0UT1G3EigKzq1wXq6sx2MF0dJmEk0oRFUg4T1CEE1RFALY3gmo8VhbM9muDBIVaCGvlwP4JN9GWcC27PRFB2BoDpYS1SPIZFE9TjDhcFJcnmtIaUkY0/M8rddDoJaeE7iRyJL9ji+53TZ/kEtWaFk3TSGYhJS/VR6RzpknAliegJFGHjSI+dkUBUNASUfYIGhGDil/VZWsnrSA5EnosVQmZ9I94msBFkns2ICsOgWyDs5osbCRGYql0ZTNO7efoDTXef58rPfoq2+hcbKOo5ePsW//unP8en7P8xzx1/lf377Cxy5dOKqk+gCQUxPrClxvNYQQiGsR1dPouOizuPwdjW8xj3p+QrrRVTw80FXdGKlQqLTcajnKLqi8VsHPkPICGDcAGRnQA0S1EJYi1UbnwcSf95JGBXXvTbAdDieTc7JUvQKuJ6L7Vk4nj1nZoF/rPzzS0HFUEzScmJy2ZybRRUKIS08uY3lgONwYdCfQ5aZDRLVYpNkt0BgqgE8KXFK/vsFN0fRK1JwcwwWfLsQJOSdLK50KDp5mEaiCwRxIzE5J9/osD2H9lQPWyr8tPmh/ChD+TEawjWEdJ883ZRs5fTIRcJ6kPXxmUGR6cGt9bEmJPD24Dm2V26c8zzcmFzH0x2vMlwYo1n3H8ZTxQzdmQEqggkqAolljX99ohnZcQhd0dhW0TbTy34VmWBLwUNtd5AIROlND9Icr2NbdRt5u8jJwfM8d/l1LNfmobY7qAqtIWFWVpmrGiKUwCdyfCXp5NZKiYj4wQOZHfNJdd0EFMil/GWilb5CPp/2fzQDYYZBNZApGyIVpTT/G/vFp9jfjzU6zRtYCKTrUujrx5mY8FOMk0kUw0A6DvZ4CmtkBD0eJ7pj25K9q2/iHznKxKpmTBWTLNuP2MUp7263pOxGTH1fViPbFrglO5WCL4BB1aZsP6ZnmIAfwNLNKZJS4vdbVi+XC4S+g8mJGxJSThHomgFIf78rKqCWgorCDzaqmk9gC2XqOGi6nzHglI61UKYU/aVgJq7rfx6ITHnUz4eSEn0S40PIMgkvhE+Em3NbsohYBSJRhey+CJ6HvHR6SlVumojqRUh0XUfZfz9u72UYKxW5TY/hvf4MiufB9gOQqJpBwM0FWcz7BVEHu8GxEbfcs3C/N3ETN7EqaBUV6NVVpF4+hHQ9Ivv2YlRXL21lVUUJBclfukT25GmckRHs4WHcXHbK/UtTqfzA+zCbFplD3gW4Id7mUsUJ+rL9iy94E+86+C/TolRgbeqlxZXupE9u2f5BINCFNkkQCwSC8vrqJFEnpURKb1IhW14+YSSJ6NFJGxSBKPnWltvyU8HlanxDF4DlFVesnAXQhEZYi8xJ8CtCIaxFUVwFyysQW4Gdi4eHkC4hLYoqVCTeZNEFT3qoikpACyOlR8oaxZH2ooRQQA0R1qLYlrWi7bY9i7ybIywjkyTabAi2Nm3k33zs17ht816OXT7Nv//yf2MkPUZjZR25Qo6aRBWqorK+tpnaZBUD4yv3AV8qtBJReyOjfA2U6wysBLa0FizyaqpBil6BnJOZ9MxnlcVwLc+iuGIVuvCDbtOyOY70n+bYwGkOdR9BIPjGqR+QDMT5yNZHMKTOQHaEw73H6c8OEzPD3Nawm+ZYPaqikrayPHHxJe5suoWGqO+x/HrP2ziew966bTx9+VVaE42cG2lntDBOfaSGA/U7qY34ynBPepwYPM+xgdO4UtISb0Cd9uKhlmpBZER6xQVUbc/Ccos3jC+/5RYZyPf5wTnFQBWqX0R0HluhKy1PpoIy/vF3S5kz04M1fpFPpfT98s9t9YpCo5P3h9Lf5WKheSdfqusxhQqzCvOK4JkQswuT3siwXIfvXniaB9bdjq5oHOo9ykh+nIdb7iRm+MHYu5tu5cXuw3z55Hd4pPUukmaMomvRnx2mJdbAxmQLmqLSEmtgT/UWnmx/mZgZpjXehCoUhvNj1IaqWB9v4t7m23i19xjfOPNDHlp3B7qi8/bwWU6PXOTupltpiFSTKi7iETsNB+p28nzn63z99A/I2nmqg0ksz2EwN0JFIM7Oqk3o6tUJkmmKysHmPTM+Cxsh7m65lV21vr9uLBBZtrp+QYiSdYuVRxbSYAQRmj5VOFAIn9QpKyM1wydzpAQ8XwEZSfr+uQKkEfR9dh1rlqfuOwFt//Kf41lTJJR0HEZffoXhZ54j+shDJA8ewKypQTFNpGNjDY8yfvhNUsfepuqB+0kcuPX6Df4m3jkoB68U1SdGizn/OlPUqd+F8MlXmLLqMIMlIhYmi1KWv58OKf32XWeqWBreTF9tzwGnZN/klYqEhqKAcpNIX0uoGqiR+b8XKgQWqO0jhH/c5lJbC9VXqk+HtrCYSwAyWQ2BEBRyviVLpiSICUYgWe3fA+ZCJA6JyslAjuw6D1bRJ99jFYjIwqIDoajIdZtQDj6C9/x3IF8K/gz34b30Q0T3JcS6zciaRkQ4OhVkch1kIQfZNEyMIccGYaQf2d+FqG2GRUh06Xm+9Y9VBLsUOHJs/8f2f5f9XTOV/Olx5NGXkbGEPw5Nn/rRdV/9Hk0suM1SShjq9YMU0/qa3rfs6/CDIGVkUshjLyNjydn9arpvHxRLLrqvb+Im1hLBDeup/dQnGXv2OUZ+8EPGX3yRmp/5OJHd8xSgngZnbIyBL38Ve3iYQEsLbi6PtO2pItcAioJRe3XqDd1ouCGejMeLKfqyA9d7GDdxXSFmCJtUoaIpGrZnT6pcJb7nt172MBZMriOm/e7/rRBQQ9QG66kKTI+wzexnIYXsFCmPz9Ws8ll0NcpZAEMNLGA/sDbQFXOaTcs0Ik9o6KrpBzqEiiiRXotBCEHMSDBhj6+IAJRICm6uRADOnZaoqSo1iUoqo753elUsie06pPNZClYRT8pJq4GAbhLQTSxn5cdhqVjMhuZGgBACVWgYSoCCu7JMIMdzkAt4qstSZkTByZUUw+qs6873+V/6g2S+VMdgJVCEStSIz6jKHdaDVIcqCGp+QKEmXEnCjE0SfT+88Dw96QGaY/V0pvq4NNbNz+/+MPWRarJWnu+cfZr1iaZJEv3N/pMUnCJbKjfwxKWXUIRgV/VmNFXlle63GM6N8qmdH8RQdc6PdvD46SeIByLUhqt4sfPwDNsLUbIe0RUD110Zie5Jl7ybvWFI9JQ1xmChj9pgAxVmlV/g2S3OWyRWiIWDLqpQfSuVaeehlBIXd/L75WK+IrjT+1SEQnWghoRRATMIfFBnXfvvHDJDCIWNiWbiZoxvnXuCjJXHkQ73NO1nX+2OycKf62L1/PyOD/PE5Rf5xpkfoKCgKAqGovOBtgcoBzkCmsmntn2Qb194ih9degFN0dAUFVWoPLb+HlrjDbQl1vHzOz7MDy89zxdP/oPvKKIoHKjbxYPrDhLWQ8si0WvDVXx250f4zoWnefzsj0tBDN8//d7m29hRdXUzkbJWjoHMyCwLm5pwJZWhxNp3KBSfwBEKOEXIFJBmEBGITBJpYi7SfpK0k1Oq2Ul/eWaTelcbsqSgd8vWCc7MF7SFEAiVAgeCynvumvFV5sxZer76dRL797H+t36DUGvLDLW59Dwq77uHy3/y/zH22htUPfQARuXVs/y5iXcJhDIVqJJe6ffSOVz2uUaAUVIdu66vNi+r14UAI1C6bi2f/JwOt0QU6oEp9fl0r+0yNMO3E/E8yIxPtX8T716Uz53qBui6ULJjKb3bVNUhwrF5gyhCNxDxKmQg7BcwzU9lQIiapiV53gsjALvvQMmm8A4/N9VGJoU88Rry0kmIVyICYb8obcn3X1pF/xrIpqGY9z8XwvdwXwyOjfeTv0PmM/457jiT94nJ34u5qaAUQGoY75Uf+eS1qvljUXVQVf9fM4Cy63bErffN36/n4j39uG+Z4zr+vp7Vb34meT8xivfKj6f6ney7XCg3gLLzIOLAA4tv903cxBpBqCqxOw4S2r6V3JmzDD3+Tfr/+kts/J9/UFpAmVmzoQQpJVb/AGNPP0PTb/8miQfup9DezsTrb1zZw8Lzh6KUIoDX+NnyKuC6k+iu5zJaGGW0MHa9h7JmuK/1AI2xWlzpUhGMk7zGnro3Cv7dvZ8jZ+cxVIP1yYWLlFwJRSgkjCSDhQFSxXFiRpyBQj8SSBoViypQdUUnYSTpz/cS0sIYqkHeyaErBgE1uCQ+Q1d0FKEyYY9jqiZSSvQV2ju40iXv5lZcBFFBIaiG1s5mZp7dN5/dw5RSv/T3MvggUw0QUINknfRyRjiJolug4BYwteCcQY94KIrjOnzr0I95YPedvHzqDbqH+/iT73+JjQ2t5Ip5ekf9IF3OylOwigTnqzq/RhCIG16FXoYiFAzFWDGJ7ity578eXemiCg1FVQA5ZzDFW8Z14XgOBSc3aauxXOiKTkibqfjZXNnK5spWzgxfQlVUPrj5QcJ6ECklx4fOcWLoPB/b+ih767Yxmh/nf7z617zRd5zH2u5dcp8/tflBImaYn1x6mRc73+Cx/L3URqp4tfsYAB/a/BAN0Rqe73iDw30nZqxvKgHfz3vFx8gj7+RImHLBwOG1QsErIKUkYVQQVP3gWEHmKbj5WfUeloKwFmGs5IseUEMoQsGRDmlrAl3RZ2QdqPiFwlaq6i8joAbRFZOCW0BV9BXfG25EPLb+HpyWO6kMJuhI9ZB3ikT0kF9oNzD1PKOgcHvDXpqitQzmRim6FrqiETXCNEXrJoMXQgg2Jtfx2R0fpiczQM4uIICQHqQ5Vu9nnAmFOxtvoSFSw2BuFE96xMwITdE6kmYMgGQgzq/v/RTN0XoMReNg/R7Wx5sI6UF0VeMz2z80OT6BYGfVJpKBOH2ZQQpuEU2oRIwwDZEaNOXqPf6eHrrIX7/5LfozI7O84n929/t476arkLIuhF8INKgi3aBPJBQySD0wzZd3nmtfKRGBrgM6TFrBIOd+GVqtqMDzkLk0jA35xQxzachOILNpX1HpuT4JIkvF+SaVDAtDOfgeaN025wNK5vxFMufO0/rrvzqLQAffGzrQ2EDi1lsYfuZZ0idPEdu5YxUbeRP/KCBE6dpx/VNUVScVt5QybUH61i6eu6QCojNQVqBr04oAX2k1pKgl+xYF1DIBsnLbyJt4B0HTELXNyK4LMz4WVfUQWkg1LyBZBdG4T6KXoaow3SJmIQiBiMbh9vegBCM+kT46TRCZy0Aus/R80aXcU6SHbD8NhWXUFvO8KYJ/LugGsmH9Iv1KZPvZqaKta9GvqiHr1y29vZu4iTVAobMLN5PBqKsluHkTemUV2ZOnJr9XQ0HUaIRCezvW4CBqMIgaiUzef6RjI1QVL5cj9eLLFC5eXFb/ejKBYhhkj71N7K47kK6LFo0uvuINiOtOouecHL3Z/hUTIjci1iUaWJdouN7DuO64a92+Va1fHazD9mzaMxdxpIOhGGyMb8FUAxQWKc6pKTrNkVa6Mu2cHDuGRGIoBi2RDT6JvgToik5TZB1dmXa6Mu0kjCSbE9tXtC2WW1yxchb87QktUYXuv27OJMsEU1FFKSUe7vxRwDXm2BShENXjKybRXelS9PK4XmxO4mND3Tru2XEbf/Xk3/Glpx+naBf5xD0fpOjYvH72KJ+494P81ZPfoGhZXB7oon2omw/f8ehqN2tBmGoAQ7lxvdCnQwgFQzUmBSzLhSddZOl/cxG0IS286DWnsLjqpQzbK67YYx8EIW0ha6Ar+3LoTQ+iCoUtlesJ6QGCWi1N0VoujnZhtS5tp+2s2URlKIEqVOrCVTieS9bO40mP7nQ/9ZFqasKVBLUAe2q3ol6hGC0HOgTKgqr/+SGxSp7jV6Pew3IRUIMIIRguDOCaDkW3yHBhYMVO1QmjkrHiKN3ZTizXwlQDpO0Uo8VhqgK1hPSpl8mAFkQTGgP5Xt8QrGSzstygV1iPkjQrGSoM4OER0xMIBAU3jysdGkLrripRezXRlph6sasKzm8N5tszqbTGm2iNLxwoV4RCbbiK2vD8BW41RWNjsoWNyblf4oOaya11Oyf/bozW0hitBcBQdXZVb541vqaof71eS3zt2A+ImiE+vvO9aFdYoTRGr1Kaq+dO+vMKRUGWC9ctBWWrl0IGqRk+GVfIgaIhymrWMtFuF0up+UxTri8+Njk+jOy9DEO9MNKHzKR8D95yOrxrTyn6Vgi54+C8jy9OOo2TzqCY5rx+50JRUIJBvEIBJ5Va8Thu4h8RREl155a8sVXd/90t+WIjfZKybOUByyO45RIiVpM2L+W/YSlBp5t4F0DVEHXrZh1tn0RfmJgSiWpEJIEc7Jn6UFGhbhnErlAQsQo48CBKw3rk268gz7zlE+hLRTiK2LwX5aYf+nVH9BOfJvjQI+B5KKHw7IDdPyIYu/ZQ87/+zCeNdQO9ZYnBpRsc9sAAA1/7BoX2DlAERlUV9b/yS5PfK6EQyQcfZOCrX+f8r/0WWmUFG/7z72PUVGPU1ZJ44H66/+iPUSNRwrt2EWhtnZHZvRi0ZJKaz3yanj/7c3r/4i+J3X4bzb/zzxdeSUpkIY8cGUKODiNzGaRlge2sacBY3bEHpX7pot9r9oYnkTPu6bLkPT2UH+HyRMecy0u8Zfn0LpZ+Pee4ysTiCr11V2utIaWc0fd0EspD0p/t5/WBt7iUukzeLRDRw6yPtnB7/QGqg1UzlMPltnqyfbw5cIRLE+3knDwRPUxzpJE9VTvZEF+/6nFP32dZO8vF1GUuT3QwkBskVZyg6FkIBCEtSGWwgsZwPRsTbTSGG9DLfuRCkDQriRoxDEWnPtRIraxHVwxaoxsQQkFTNJrC66gJ1iOlhyJUTNVECL/IW2ukDSEUFKGwOb7dLwKnSSJ6FAWFqB5lU3wrtnRAykkiqoy9lQdmEEq1wToqA9WTywih0BhaR3WgFinlLGJryfsLSdEt4HgrfznUFH1SsbkQFNRJH/iylYhPEmkUvSKmDCDx7TCuJSJaFEWoy1IcT0fRLeJ49pykVCIS41ce/RR3bt3P5YFOGivr2LthB4oQZAs5VEXlD7/zBf6vb/wRITPIZx/6GHdvP7DaTVoQYS3qezS/A/woBWLVtjOedOdVJyqoM0jruebapaqjpZQUVxGQEggiWmzJy0spcTwXRSho0x4mdVUjbxfnVOBLKXE9d8a9K2qEJ+fd8inh4fl1qTwHTZ3yytYVDe0Kkl8IgVGyU3JW+MDgSRfLtW4IEj1pVFIMFhgpDDJSHMJUAlQEKgnpkVnHdr5zQ0z7r67otEQ30J/rYbDQh+M5mKpJXaiR6kDdDDsXUwnQHF5PX76L9swFFBSSZtUVJPp8fU6NRhUq9cFGDMVguDDIWHEEEJiKQdKsmqVAnj7em3h34/xIO//2vl9hV+2Wa3cPkBJZzPrFQqX0PXaD0RKRBwufewIRiiOz4zAx6M/legBC8ZKaFv/fUAxyKWR+wi84mqibexz+LzA8gLx8Ai6dQo70l3xkLZ8sv8ZKWTUYwLMtshcuYqcm0GI+wSSEmJzH3WyW3MWLuPkCSvDGsL664SCn38Hl3FytmPzPtD+v4XUw9cf8XPKajXGaEr1s0+DYIEsketkDPRT31eTePM/Ak2U+pg24TNA7pWyMK583rsU+nXzfm/Hh0jh6MeuXa3su/GOAqiH23oXaVsqamRhFdl8AK4v39NcQW/ZD7yVkNoVy+2MQiiK7ziMvvg3pMYiGUD70C4imjf75aRfx3n4BLzMCl08hi3lEYxti862+pcnhJ1H23g8VtX6ORXoMefp1RO06xPptfqHT2x9BXj6L7DqHHOiG9Lg/9yulgrjhGCJZBdWNiOY23ws9FPW93ReDbqL+2v81065ltRACggt42QOoGuqv/N781+/V6vcaQ2tqQmtaGonofPtLyPbzV20soqYB7VO/ftXaXwxqPI66e8/iC64E8so3YTnjnwVxxb1r6qOlzauhnTto/t3fwSv4hYTVYBCtasq6TigK8bvuILR9G14+j1BVtKQvptGSSRp/7XPUfvITSECLxpD2Z3ylOhDYsJ4df/dVlMD82f5CVan79CepfO+jSNdFjcx9DUgpIZvBefFJnDdfw+u85NdtcBw/m3Hynrh2AWPzN/71jUWie9LDcm1saZO2MvRnB+jN9tGT6aU320/KmsB2ZxMi37zwXb5z6YdLfuXUFZ33rHuQB5sX8LSaA7Zn80Tn0zzX/eKy1muLb+CTmz9GMpBY1npX4vvtP+a57pconwSf3vIz7K3eje05HB16m+9f/jFjxfGS36uv9LwwfolXB97gI20f5NbqvaiKipSSglvkUN/rPNv9wpzrvD7wJgdq9vHwuvuJG/EVvdxJKXGkw2BuiJd7X+XY8AkydtYvClfqrwwBXJpo502hoAuNunAtd9QfZGflNmJGFFVR0UqnoC6miB1jWkE2TdF9cvwKKEKZsZxaJrgEk4VDwSeeDOZWBAev8NjWFB2NmX0pQsEsK4pLL1vL3m8SCm5+xdkWZSuXpfSrqwaGNEhbYyAEYS1GQA0S0iJknQnGrVEUoWIoZslaZqpN5YrCfFMQs4v2Md+y82yDohJUQ6uydLE9C1MGZu0HRSgkI3Hu2r6fO7buQ1F8dakQgopoAikl/79P/w7/6qO/igBioSimfjWJREFYj865f6aTrvMdz6UssxQstR2BWLRA7HL6mvUdkoydIm2nSjUOPKYXaSxb31Saiys0JRLLK674WlKFSkBbupWPrurUhiuxXIu+9BARPYTl2vSlh9ha1YamaiiOIKAZZK0crueRKqYZyY8T0qf6mZcIFoLKYILRXIqslSekBRjIDs/yUQa/uKZPoq8sZcCVLpZXIMwCKb7XCKqi0hBqpi7YSDn64geEZz5YVgdqqTSrZ5DgilCoCzVSE6yfYRdiKgHWRdbTJFtntHnluS+EoMKsImEkJ3ubrqJImpXEjeSMALVAUBmoJmlWzvhcUzSqA3VUmTXTRi5mzaU1wXqqArWoy1Br3MQ7FxsrWrg81sOGCj8bYfoZqCnqigPyC0JRIZIEmSh94KtTS2YSUNE48yUrFCtdJmLu9UUpZFT+XgiEGUYaIeaKmErp+S83+Sy0n8E7cQgGunzyxFsg8+0aIdy2gdjuXXR/6W9xszkaPvExAvX1vird8ygODdH3zX+g+6tfJ7JtC5GtW67reMuQUs7ef4oyt7/9VR2H55PD+RwMdiG7LsJgNzI1Ms3jWwcziIgmoaIGapsRDeuR0STohk/4XSUCVZZtUqwijA0iuy9CfwdybMi3YLCLPtltmFNjTFb7RF7DemQk5hN8yx3jpAq8dHwU3y4Mx/UDUeXvXdsnEe3i3H7l5XuDXSxZswg/EKYZ/jZZedCDJasW9+r5nUvp1yDwHD8wYFu+x/X4EIwP+z/pMSjkkY7lf18usigU/zgbpl+s0QxANAGJKkhUIxJV/rlgmKWAg3rNz+N3G4QQPvlcIqClpkH7SaisQ0gPeeRZxC33Iwa7kBfeRuy5BxGOI7YdgGgS2XMROs7Ahu2Iyjq/6OeIb52q7LkX4bnIU68iO04jttwKmoHsOusfS0WB9BhyfBix5VZ/Lg1HfRuZmmbf67tsyzVj0AoopeCTqvpq9iVec0JRoHKO4O1VhhACKq9tRtuNDtl+Hnni8NVpXADZzYsu9o6BlP491C3Nq1YRMuPI8RFIDcPYkB8AswpTc2r5XqGo/ryqGaAbiEAIYhX+T7wCEa9Exiv9YtWK6l9Tijovsa4Gg6iLiASUQACzfvZ1JhQFLRFHS8xtUy0Mg0DL4pksaiQySbzPBWkVcV58Guvv/wY50OMXEV7LwNl8sJcn0LvqJPpwfoRnul/g6NAxJqzMkhXftmdjL6MIo6442CtU+tquTc5Zhr8WvjJ2Jer1K2G5NrlpxdQ6093srNzOmdGzPH7h22TsmYphicSVLuPFFH9//h8IqgF2Vm7H8iwO9b3GjzqenHedCSvNS72HAHis9RHC+vIjoFkny+sDb/HDy0+QnacI3FS//oO3Jz0cHC5PdNAx0cnmxEYeaXmQjfEN6Ip+Q6t10/Y4GXsCR7ooKNQE6zHV5flp255VUleu7HxRhEpICy+6n4QQqKhE9QRXxAJ8uwI9Oevzye8VlYQxdxEtUw3M2ma/kN7SIRBE9OgqLF0cil6BEBHf03gabMcmvZDvHP721SbmtxFYS5gzirPOhIfrF8cV+rzEtUTiSAsFBU2s/EXJw8NyC76CeYGpXogp0m+lc5pk/uq7OSfDuDWKpujEjDgpa6ykBpcU3DyGYhLVllY3wvYsLHelVi74wahlWMcoQtCWXMe6WAPfP/8sdzbv49xIO3mnwN3r9hFQDSzNpCXeyNPtr2JqBhfHOjk7cpkDDYtXOhdCcHvjXr58/Ds8c/kQbRXreOLii5ja7MCfphhLtqGZC550sTxrXtuda4myKn++GgxlKKVMoxnrlsi9uT9XUZawaeWCuqvt0yfMxRQBsoztuIl3L25r3s3/OvRl3ug5zrr4VAYewIGmXeysvQpFTSdJ7zm+Kn0/c3Fl1rJzfTarjyvva9JDFos+8XL+KPL0YRgf4Uazk4ju2E7zZz7Npf/3j+n6my/T8RdfQE8m0cJh3HwBa2QERdcJt22g6dOfJH7L3vkbkxJZ9m1fLoTik01Lfe7NjMHYgE/ClhGrgkrfMlLm0r51wlIVkpE4BJdeoF5KCVYBhvuQZ970rRpSI/OvkB5HDvfBZd9jVaoq1Lcitu1HbNiBjFUg1lLEICXStmB8CHnpFPLk6zDUM//+KD0qyv7OqSYUFaobEFtvRWzeg4wmEWZw6ceo7EleDjpp2pS9i1AgEPFrFFjFEskcnG2FpKhTyyH9ZcyQ/3kw4meY5Et+zJpe8lQv9aeozLhwy/0uFVL6hHgh559LIwPIvsu+/dJAt3/8l4m5rn4JPoFe04ho3Iho3oisrPMJYDPoFwV+h0J6HmRTC3tgLwVmEKJJnyxeRRsiWQuGiezvRFQ3IV0X+i4hVA1ZUeufZ44D8UqkVUTYxamDZpiI1u2I+lb/3Bjo9Ochx0Jp3Y48eQi27gehIoe6EckqxPT3KyEQmsayKKbyOTg2zOruHcIvZmqY1yXjQUrpBxXT49e872VBUSES9wnZFUKEo8jEAlyAovjBtUx6qkZEIAi6PjVnlYnlYsEnSpEQTSASlYgN21Y8thsF0rH8wHO+NK/2XEL2XPLvUSuYV2H21eHPqwG/mHB9K9S1IOqakaHYjGLrawXpebipFM7wMAB6TQ1qfAW1H8vCgCvGJm0b58ffwfrGXyPHFnjWuAFw1Un0nJOnL9tPyloZefaPDT3ZPsaKKX7S+cwsMvxKZO0cT3Y9y7poM73ZPl7pe33RdYqexbGRE6yPt3BL9Z5lvdiPFcZ5qus5Xuh5eUEl6EJknIfkzPh5RovjvK/1Peyt3o1xAxPpUT1BWIuStifI2MsoKDINZS/ilUIRyoqK7d1oCCzBjmYhFN0CrudMZR2UcKm/k88/8bXJv6dpgiZfKxqr6vgXH/olrgVMNbAqwqxE1y3LY2z1EKsm0edz7rQ9C13RqTBrMNUAWTtDzIhjKH5tg3FrFMuzZmSWzAfbs1bhh+77YS+E5rivbp6uGK6PVPORrQ/z/fPP8XenfkhlMMk/2fMR1sXqEUIQ0UN8Ysf7ePz0j/nGyR+yqbKV97bdS1Uoga5qbK5opTpcQXnvxMwomytaCZYU8bfUbWO8OMHTl1/l6MAZ7mu5jepwJVFjZpCzXHxxpZBIHM9eVjaNlJKsk2OskCLv5HGliyJUAqpJRSA5a4zT0ZPpI+fkqQvVEDWuj/rd8RxGCmOM5EepDFZQHay8SWbfxFXHqcGLbKneQKqQ4Xjh3IzvWpINwFUg0a81pETaRZ9cvXgCeeI1GB+63qOaF0JVqXn/Y5h1tfQ+/i3SJ0/hpDNIx0ENhwm1thDZspn6j32E5O0HUbQFXo9cx1dhTyNilzyOYAS5ftuSyQs52IGoaEAEps216hTZKI+/inzlRzOLAy7U/70fQtz+niUVtpSeB6P9yNNvIY+9BBOjS+pjBlwXui8iuy8ia5sRtz4A67cjYslVv9hLKWF8GHnhOPLoiz4xsRJ4Lgx0IQe6kG89j9hzF2zdB8mapRH+quZndpShB/yfMoyA/7MQhPAJVHOOZxRVg2AM5np8ubIvgHBi8TEDuC4yO+ErIPs7kJdOQs+l1RPBC8EqQvclZPcl5Gs/8ZXqLVsRG7ZDdZNPoAVCcxc0vpFhF5FvPot85cera6d5E8pHftVXdK/0+lDUUg0L1c8IKGdDeB6ymEd2nIH+dnAdn+RLj4I37dlf1RCREilWthSSEjwPUb8e7+0XEaMDvpJ9bAhl814Wjr4uDimln8H093+yqnYwAigf/3Vo3nydbIMk8tRh5BNfvQ59LwPBCMqjn4RVWJsqj3wE5Y6H5v6ydL55Z4/hvfKUnz3VtB6lbTtU1UIw7Geh2BYyNYrsaUd2nEf2tKPsug31Iz+PqLhK9WOuNlwXmc/4GTz9Hcjzx6D74lWeVwvQ247sbQdABoL+vLp+u1/jIJpEhKJr4nEvbZvcG4cZ/tKXsHt6qPnt3yb5kQ8vu538qVMEtm2fdZl6nZewn//JbAJdCAgEEYFgKbOtVKdnDS9zsUx7patOomtCI6JHSJiJOb93PYeck8O9It0npAV9b+olToK6ohGYQ723GBSh0BhpYE/VThzPV7OX/y2r4bN2joK7sojRctGT6eXt4RNcnuggrIVoiNQTNaJ4nkt/boCh/EjJhsMnRQZzQ7w2cJixwjg92V50RaM6WE1VoBJD08laWS5PdM4Y/0h+lEupdrYmNy9ZjT5eTPGTzqd5pe/1WQR6UA1QEUgSM2IENBNd0X3rANdmwppgrDDOhD0ziDKYH+J7l3+Erujsqdo5I13/RkJZobua9GvHs3FXUThXV4yrVqDO8VzODl4iY+XYXruRqHl1/NmEEL5djtBXbEdhe9bkuT8d6XyW4+2nZ3wmJRTsIl3DvWQLeT770MdW1OdKsBwSXUoPV3o+aVuyC/KkO0mkz1xWlkhMMem3LYSCwlQqpCfdZdWRKENQSldcoQBELtCnlBJFTBHAilBwpYsQAl0x0BWNopsnoi9cBMn3J3eWlaF0JRbLIvmZ7Y/N+kwIQWuiid888HNzrqMqCpsqWvi3d/3qnN//s9s+M+Pv7VVtbK9qm7a+ysPr7+Th9XcuODZlCcrtxeBJD0+6S25npDDKof43eXPwGP3ZQQqOn9nQEKnlwxvex97qHfP287dnHqc708dPb3w/9zfdtapxrxQ5J88zXS/yo45neH/rw3xow3uXZedzJTzpUXALZO0sBbeA5VolKzMXKcv1J1R0RcNQTUzVJKSFMFRjTcn7K5+KynNDxk6XxlbE8Rw8PBRKY1J1QlqIiB4huEgwaTVwPZe8mydjZyg4BSzPKhUfprRvdAJqgIgeIaSFro61yXXGv7537oBt1sq/K1zxpZQwNoQ8f8xX/g4sn0y+HlBUleRtB0jcug9raJhCXz9uPocSCBCoq8OsrZm36OiVkANdyGe+6adcLwMyGEb58K8g121emtpU1SEcX5yEXWr/w72IpSjoPQ+6zyNf/Qny4om1seMZ6EI++XXYth/23ed7IasrfLb1POi7jHzjGV8dv1Zexekx5Cs/Ql4+jdh3H7Tt8MmHdxMcGzk+DP2dyPYzPqm6UHbB1UR6HE68ijx9GFndgGjdBs0boW4dIhx/x5DpQjehdRvyrRd8Rf9K0XvZtyMKR1d+rxDz/I70MzYuHEXZuh/Rsg2ZHsN78Tuz25jvvmyYiHVbke2nERt2gGMjqpfuITwvXMe/jleLpo2QqH7HnDfvZCit89utSMdBHn0F7+ghMEzU934c5faHEGZgbl5PSmRPO+4Pvob31otQUYX6gU+tbdbS1YbrICdG/Xm18zyy/QyM9l8fK7tCHs4eQZ476md/rd8O67dB7TqIVZQyRVYGxTSJvecRzA0bGPyTlQW9vGKR3t/7j2z42tfgCn7LO3MCOdA79YEQiIpqlM3bUJpaETV1iEjUP5c0bU2DZUrrxmUtf9VJ9IQZ4476A2yvmNtfcKQwxqH+1xgvpmZ8vrNyO23x9UsmVxWh0Bxd/kSuKRoHavext3oXBbdI0SlSdIsUnAIFt8iElebo8HFOjJxadtsrwXgxxQs9LxPRw9zTeCcHavZRHazC9mzOjJ3jRx1P0pXumVSMZu0ch/pe9y0iFJ1tFVu4p+EONsTXE1ADjBXHeKrzOZ7veWlyHYmkN9vHSGF0SSR63inwSt9rvDF4BGta0Tdd0WiONLG9Yisb4q0lxWF08pjlnDwDuUHaJzo5PnKSy6l2itPWHymM8qP2n1AbqqEhXLeoOjJfso8xFJOil8fxHETZ91wxJ9f3iw8WsKXlFwQVKoYamGGxIZF+kMQtv+CXlzPRxNop433iz56T/F0KBGJe4q9jrJeIGSIZjK2YoCk6Fo8ff5KLI5387w99jmj11StyUt4Wx1k5iV4+VtPtKHa0bOYvfuu/zVhWSslEPsNbF4/z9Re+y/v2P7CqsS8HhmouyTJESoktbaR0UYRZUoJ7uNLBk27JvmPmA4Tl5WfYUEgp0UrWMFJ6WJ61CpJmFf7rC3ynCAXpTRWJ1oRO3skRVMOAxJMSRSz+kOHb3NhIVu6LdiMU1Vw5BIpQV5Ux4EkPV7qzaj/MhYJT4NX+N/lR+1ME1AA7K7cSNSI4nosAkubC6Xs5J4cnvTn93d9p8KTHUH6IgdwAQ/lBRoqjpK0Jck4Oy7VwPAeJRFM0NKER0AKEtTARI0rSSJAMJEmYSSoDlYS00KoJdSGmbDs86dGf66cn001vtpfR4ihpK03RK+J67hRxrQWoMCuoDlbTGGmiLlRH1Ji7dsNKYHs2I/kR+nJ9DOQHGCmMMGFNkHdy2K5d2j86QS1I3IhRGaiiLlRLfbiBykAl+hy1T+aCJz1OjZ6aIQyI6lFaoi2LBkg86TGQG6AnO1ux2hRpojZYu+i93/Vcjo8en1EovDJQSWO4EUNdeH45PnAWRSjc1rR7weVuaDi2ryx++2Wf9HBXLhC4XhCqillXi1m3Qo9bTUfUNiNrm6H7wvLWzWeRF0/4BfjmUh1fCUWBwQ5kMDJl0WGGfIJxJRjum+1RfCU8D3n5FN4L34G+jpX1Mx9sC3nyNWRqBOX29yBati5JFT8DjoNsP433yg+h+xJrbh3kudBzEZke99X3Ow8i4nPbHb6j4NjI0UFk9wVfdd5xForLszK9anAdn3zq70SePuwTtOu3Q1MbIhy7TqriZUBRfPK2qQ0uHF95O66DPH8M0dC68gDTQnBsn9SLJpHSQw52Q355TgGidRve89/01cR1LWsT4MulkZdPL77cggNTEG07fPujm7i+GB3EfeMFGOxD/dBnUA4+4CuI54MQiKb1qO//JN6lM3gvPYGycz9i085rN+aVwnWRE6PIrgtw6YRPnuduEPcNKSE1gjz6oq+IX7fZD1S2bEEkKucPlq24O+kr1Q8fxursRGgagc1bMLdsRjFNpOeRPfQq+VOnKF66xMhXvoJQBEZLC5F770UIgTc0gExPc36IRDF+9hfQ7n4QEU+u6XhXi6tOokeMCDsrt8/7fUe6i2PDx2eR6G3x9dxedxuGevU90ny7DF81xhXvQFk7y1Bh+JqR6BLJeDHFgdp9PNh0H2HdT/lUFZXdVTsZKYwyWhibtG1xpEN/bgCADfFWHm6+n7b4+smX9AozyQNN93By9BRD+SmlwXB+lFRxAhYRWEgpOTt2jtcH3iQ7zSpGV3R2VW3n/sZ7aI21zPD8LCOsh9gQb6Ul2symxAae7X6RI0PHKEzzNO7O9vJCz8t8YvNHF32Zz9gTeHgElEDJ27dEzAmfHCuvn3XSpO0J38dWCjw8dDdHTE/MsIzI2Cls10KUFMAuLqYbIKYn0Bd5GV4qPOmViL+VPuTPTaI7rsPfv/1jbm3cwV3r92GoN37UXQiBqZqr8EV3/SyIK3xDgkaAlprGOdfZ0byJU53n+eun/o4Hdy+s9F0L+ErUhQtTlUtqOtJCSg9NMScLtapCQygK9iKWJXqp2K3tFXGliyq1UoaIRFd8Qn41PvxrCV01yHv5SRI9pEUYs3wvNb9QaGFJnuiudFdli+TbtFy9wmbXAqslX/0gzdICekP5Ec6OXaDoWjzcfB8PNN1F3IzheC45J0dkgQCsIhQ+uP5RRgvj7JlHrf5OQdbOcnb8LOfGz9Kf7Sfvzk862J6NjU3ezTNWHJv03zUVk2QgSU2whqZIMzsqdqwqu0gVGgoKeSfP2fGznBo9SU+mZ06bNUc6OK4zOaZLE5eIj51hfXwDOyp20BBuWFUmmJSSCWuCM+NnuJS6SF+un+I8mXu2Z2FbFhNWiq5MF6YaoD5Uz8Z4G1uSW4nq0SVdn28NvTmDCG8INxIzYtRr9QuuV3SLnB47xWsDr8367raag9Q2LU6q5pwcT3U9OSMjZl/1PupD9YvWGzg33I6maO9YEl1mUsgzbyGPv+IXDb3OxUJXAieTId/VjT06imf798y5EG5rI9i8gDCnohbR3Ob7Ri9TBS3PHUHsvdsnnxardROK+R67xfzUsqvxjh4b8otnzde39JCXTuK9+F1YgV3NkuC60HUez3FQJIj125ZOGJZIRu+VH8FgF1f1GWdiBHn4GX9/3XqfX4z0nQjpISfGkOff9rMKei7559SNitQI8sgLfjZA6xbYss8nftbAiuCqIhRFrN/hByhWUQRPXngbccd7IbjWFI3wayJEk3hn3/QzLDyv5KG/jFZilRBJIPs7UO58/5qMTHac9T3lV4NkFaK+5Z2lXn6XQnZfRnZegEgM0bZ0+zLR2IqobUSeP4F35BWUG5lElxKZz/hz6oXjyK7zkFnlOXw1kZ2A04f9zKN1W5Bb9yE27Jw/O2CFyLz4EmPf/CZGUyPSsskde5v4+x4jfPAgSImXz+H094Hr4o6PIxQFr2ra/cgq+s8IJShtW9De81MI/carmXHVSfSbWD4CmsnBuv2TBHoZilDYXbVzTu9zUzXZkthMS7R5BtEihCBqRGiLb5hBok9YE2Tt3KL+uKPFMd4aPMbwtHUVBBtirbyv5T00ROoXJb9VRaUp0siDzfeRc3K8PXxyBql8bPg4d9YfpCXWvPCOQWK5RXShE9VjKELFw5tBjFmeRcoaI6CFiGi+yq7oFkjbE2RFGlXRUEtqzoAaIqiGpynnM2SdDKYaWDMS3ZXOgv7xi0GIudWzfekhXut8m+Z4vZ9a/Q6AQKxaCWy5RTzNWzrpIwT1yRpeOf3mqvpdKjShoyxBhT5FeBsz7FgWh0+0T7dG8aSHRJZsOlRfBS9AVTTca6QSXGj0phIkrotJpWlIC1P08mSdNEIoBNUQIX1x5Ygn3VVZuWhCf8dbKfhz7XTX/+WhrERfCsaLE4wUxkiYcbYkN5Iw4wghMFQFQ1086LG/du+KxngjIWWleH3gNU6PnibrrJx0KHpF+nP99Of6sVyLbcnVFUzyLZCKHB0+wrHhY6TtpQcmJZJxa5zjw28zWhjhttqDbIhtWFGARkrJQH6A1/pf5XL6MvllF2gv0J6+zGB+kP5cPwdrb6c6WL3gfKgIhepgNX3ZPrxSVkrByTNhTVAfXphEz9k5Rgtjc37Xn+tb0pjHimMzVOia4j+PvN59nBMD5/ng1gd46uIhBjLDs9Y9OXCBh9vuWFI/Nxrk6IBPbJ183X8Ze4dBSkn61Gn6Hv8HJo4fx0lNIJ3574+tv/YrBJvnt4ETZhBZ3wrxCp+YXg7Gh6HzHDJZjVjkWUYk65g1368mmGoXfZIyHJvzfii7L+K9+sTVD5J4HvS1473+JEogiGhsW/xFXnrIiyfxDv0YBruvTRAnO4F8+2UwA7DnbkTonaVylbYFnefwTrzqK89vZJLnSowPIY8OIfs6ENt7EDsOIGILFDK8zhC6AXXNyGQNjPSvvKGxIT/QsXHx4vSzEIoi1u/0LaBUFbF1v19otLrJV2hHkig7bkeO9vvK7WQNoqYZYqVMC81A7L0P4lOFQkXTJl/BXlacC4GIVyEzKUR0DY6HlMgzb676ehbrtvjb8Q4WyrxbIFOjkBpFVNf7GVdLPSZCIJJVSM9Fdl++uoNcBaTn+fZkx17ySfTrZYe1EuQy/vU20AVdF2HPnYi6dWvTtuMw+uUvE77rLpIf/xhePs/oV75K+ulnMDduRK+uJvbww+iNjUw8+RQ1v/EbCH0mFS0iMTBNvyAroFRU+7YtNyBuzFH9I0dEj9Aam/uErgxUUBmooD87MPkCCZA0E7REm+ZMJ9aERmO4YcZnlmeTKxWK08Tcp4FEcn78IhdSl2YQL6Zm8si6B2kIL06glyGEoD5Uy56qXXSlexgtTr3IZuwsbwy8tQQS3R9TRI/NW4Qw72RxpUtcT6CVSDtFqBS9AkW3OKMwZUibqaT0pEeutP5awZXuqtq7kni+MNzJj86+yJvdJzk5cIGJYpYnzr2EIhTqY9X8x0d+E1VR8KSkc6yXl9rf5MzgJUZzEwT1AAead/KRnQ9jaguT2T859zI/OvsiP7P7vdzRshdPepwbaueJcy9zYbgTTVXZXb+Fn975CFEzvCQSeC1I9Ck7j8VJdMd1udTfydPHXqZ2euX4qwhN0RctCCqRfsqT/8fyFCDT/jtXy2JyuTLZev0fJlVFJaiEJ+cKRagkjErCpSCXpuioS1DlrlaJ7it/r//+WA1EaS+u9FXDP/eWppCyPIuCWySihTFV4x2t4F8J8k6eV/pe4dToyRk2ZnNBFepkMGsxtC7Dpm4+eFLy9sjbvDV0hNw85H75eptvTI506Mp0oQiFkBai4YpnhKVgMD/I011P053tWrAWw2JjyTlZTo+dJuNkeLjpEaqD1Qv2WxOsnQwgAhRKgfLFkHOyjBbnLo44Whil6BYXtYQZys8kTENakJgRJedKInoIVaj8+NyL3NKwjdAVBf90Vb0BcoOWCSl9i4U3nkZeePvGsX9YJgrdPXR/6W/p+bvHUXSd4LpmtPj8NhFKYJEaS0IgapqQ1Y3LJ9EBeeYtxM7bF02nltkURJJL809fat/DfYiG9bM/Hx1AHn7W92VehZJ2yfBKRUePvgThOCK58HUvuy/hvfF0iUC/BuMrI5PyC44ma2Dj7hv2hX4GpPQzR956HnnuCIwMrJ1v/LXGQJevpO9rR9lzt69OvxpWJ6uFEJCoQqzbhFwNie65yDNvIlZAogszCHUtUx+U/fzjJpQtiaoaEFXT7vfTfheajtgwU/07uazn+ZZAmRRyqBtlzz1r4j0uR/pXn/ViBnw/9PC7rH7BOxWO7Z8rrrPsuVpaRb/Q7Y36rOE4yBOv+vetwS5/W9+JGBtEHnsROdSNOPAQyqY9q76endFRipcvU/cf/gNaIoGMxwls3UL66Wdw+vvRqxe+x4OvPBdVtcguP4ji9XX5c88NmIl0A96F/nFDIKgOVvnWMnNAEQpVgQqfKJ32kBszolQH5yYKFaGQMGOzPi+4BRzPnTetPGNluZRqJzXLamcDmxNtyyZVVEVlQ7yVhnDdDBLdlS7nxs+Td/KLFjxThbIgEWt5RTzpMljon6TLJH5BQlVoMwIPlmuRczKTBStd6RctDPlU05p4xXqlonMrhRDKjCBHQDdZl6hjMDPC0d4ztFU0sat+C5qikghGJ98HBXC09wxPX3iV6nAFTfFaOlN9/PErX0Ei+eTe+VPwXrh0mP/xwhe5q+UWNlauQyI51nuG//XyVyg4FpurWsjZBb785ne4MNzJf3zkN9CX+ECrCm1Vns6O5+BdoVZ449wx/tVf/6dZy3pSkslnGZkY448+93+uqL/lQhUqyhKuC13R8ZDYnoVRsutZ8vk272KKL+QQZbKq/HP1sVAvs7ZL+OfBUojz6fBV1CtX1hfcPN3Zy7yTiXTbs2bMYcuFLP1v0eWkxPVcPM97R5PnArFiVdKJkROcGz87i0A3VZOmSDPNkWaSZpKAGkAVqm9N5BZJWROMFccYyg8ykBuYYf+SNCtoDjevypZHILiQukDOyc4g0MNahA3xDTSEG4jpMQzVwJMeWSfLQG6Ac+NnfYuZafCkR1e6izNjZ4gb8SUXGgc/m+3ZnmfpynTOOqcMxaA+XE9LtJXKQAVBzc+qyzu+pUxnuoOebA/FadZurnTpmOjgme5neH/r+4kskJ1SE6rxg5WlbotukbSVXjCzTkpJxsmSssbn/N7yLIYLwzRFFq6tM1QYmrG9YS1MzIixMV7H1uoNhPQAAc3k4zvfS2UwMWNdUzUwbkTyZz5Iiey+4FtndJ33bS3eocicPcfws88R3bGNll/6p0S2bkFo89t7GVVL8MCOVfhE+uVTy943sq8dkRqBqoWzJ+RYf8n/fA0t+4Z7Z39mFZAnXvO35VqSAo6NPHcU6ltg150IY+53Hzk+hDz2EvRcvD5kcGoE+cbTUFWPqKy74dWucnQQ75nH/et2NYUubxTkM3D+KN7oAGLvPSi7bl9aTYFrjVAMGtvg1OGVk4BSIrvOI9PjiGhiTYe3Gsj0qE+6ZcYRDRugenHh25LaXYvgbE0zoroe8U66v76boZv+z8ggcnQI2bJpScdGZjPIng5AQvDq1WhbMfIZvFd+hDz9pl8v450Ox4au88jsBN7oAMot98ISrXfmglcoIj0PNey3IYTwbVhKXulLgbpzD9re/dhD/VDI43W2477yLNo9D694XFcLN2ebGwxCCKqCCz+8x8zZhSTDeohEIDFvm1daw4BPIi+kkh7MD9KT6cG74gV5d9VO9BV61VcGKqgMVs4iUtN2hp5MHxsTGxZpYWHrCyklilAJaeFZVJkqNDThj7voFhgpDJZUeBFUoeFKmwl7bVMdPektqNBbDFcWOa2LVvGezXcRMUK8dPktbm+5hZ/afj+G6iugy8sKIXig7Tb2N+8goJnoqkbRsfitb/8+3z/93CwSvVyo7pX2I/z+03/G/qad/OZdn6YiFGc4O8b3Tz+P47n89t2fYXN1K57n8c0TP+EvXnucD2y7nzta9iy6LUIIFCHQhIYtV/aS5kpnNmGj63MqzVVVo6Gihvfvf4jbt96yov6Wi7JV0MIQCBR0oWKJArZXxFACq+Z2VaHieDaq9C1d7EXUs9cKjueQtsfJORncUmHYKxHV41SYi6jQluHnPRdc6ZJ33wUvk6uBZF77p4JT5O8vfJfh3AgpK814McW4NUHazvKnx7+IMZnZo7Crchuf3PKRGeu3T3Tx/cs/oTszZYsR1AI82vIAt9fdOu+Q/ujoF8g7eT6367Pk3QIv9b7GhfF2cnaOoB6kLd7CvQ13UBOqmpd8Hi9O8Hr/WxwfOc14MUXMiLG3ege7q7avSPU9YU1wceICOWfm+VITrOGehnuoDzVgqiaqok7aMfnZJbKUMeHgeDZ5p8BAvp/z4+dpT7ezKb6RsL60zJ35IJH05XpnHMftyR0crDtITI+hq/qMeciTHm2xNvZU7eHlvpc5OXpiRnuOdLg0cZGN8bYlk+iu5/Jy38t0pWcT6HXBOg7U3kZztJmAGkBTtEmLKw8P13PZWbGT3mwPrw+8Tk92qlC6RNKZ7uDlvpd4pPk98x7vmmANmlAp30Vc6ZKxMxTdAoF5AvGWZzFWGJ20YlGEgoIyOSeVC7QuSqJfqUTXw8SMOAHNJKD5BOD/ds8v0hyvn1XTZ0v1BtRV1jW4ZpAS2d+B99IPoPPcO1fFWoI1PIw1OkbzL3yW2ve9F8VcRGm+BAhVg7oWZLzSL9i5rAEVkRePIxYh0YX01l51PTR7rLL9rJ/ifT0I10IOefRFXx1f38KshyGr6Hvxnz1yXVV/sucSnD8GsSRiLYopXiXI7kt4T/0d9He846/bGXBdGOpBvvwDvGwKcesDiMgKC+xeJQhVheoGv+hw57mVN5TLIC+dROy5a+0Gt1qE44i99/lzUjCyNtkAju2T6Ku8rkXjBkjWrH48N7EmEBXVUFENvR14z34fpaEV6psXDj46Nt5T/wDD/aDqiJZN12y8S4EcG8R74btw4W0ozl335x0JKWFkAPnqT/BSI4g73rviQtpadTWKaWB1daHX1SE9iZuaACFQolNZIpOZdXM824hQBP2nP4PMpHFefgZyGYpf/FO8kWG0B9+LEkusaGxXAzdJ9BsMAkHcmK0an46Aas4g6gSCoBYgOEcByvL3ZWuT6fBfHud+OJdSMpgbpj83OONzVahsS25ebDPmhSpUKgNJQlqQ7DSCwnJtejK9SyDRF4ZPkueJaLEFyZOMM4EjbeqD6yaV3gU3t+YaVY/V2blcuQ2aoqIpKqZmogiBoeoE9cCc9izRQBhDM7BdG1d6aIpGa0UThzqO4kpvxsu8rmi82nGMrx75Prc27eRf3vcLJIP+edg7Mcjh7hPsbdjG+orGyWKu+5t28qevfI1XO44uiUT3IVCECisk0R3pzDpnd6zbwl/+9h/M0RMoioKh6ahrXIF6PihCZTE2XJT+I4RAV0wsr4AjbVQ07FImxRQZZ6EpxmTwZ7auW0xafGhCRyqSoldE4AeNpLj+li4ZZ4K0nSKgBgkr5pzjmS/zpoyyMnrlBXpvAspK9LnnfE96dEx0kXeKJYJ26lwT0/4SiDlJ4KAWoD5cg+VaZOwcHeluDEUnZy9MygzmhxkvjvNi72sc6nuD8eLE5FjdnMuF8cu81v8Wv777n7Ihtm5G31JKOtLdfPPC9zg7dhEhBKpQGS2O05nu5vDAMSrmCS4vhN5sL+PF8RmfRfUo9zbcR1u8bU5yVwj/SlSEUvL/DxLRo1QGK9mS2ErWyRJQAwTmuU8vB+XArCpUbqu5jdvqDhJUg3MeF1WoqIqKqZo80vwIrnQ5M3Z6xjLD+WF6s73UherntISbDiklJ0dPcDF1YUa9D4GgNdbKfQ33UxOqmbPWg4qKqqrois6mxGYqA1W82Pci58fPTd4nHelwbvwc9aF6dlXunnObdEWnIlAxo7ho1smStjPzkugFp8Bgfup5JmkmiepR+nP9FNwCnvTozfYuqGYvukXGClPqI4EgokeI6jNTyLfVtM0ZTL1r3b45273R4Pt9duI9+y3oPH9trTOuIrRoBLO6GsVYw8Jz9S2Iilrkckl06fmk8IGHF06hjlRA91lkODHlhR6MlNTpK4Mc7kWU06Ol9FXep15fnY/zajHY46v7ElWIYJjyc4KUnq+UO/by9U/v91zk4WcRbbuQ1Q03WJaWRDo28sTryFd+5Pvuvxufl6T0fepfe9K3UbrzMd+eZA3tjspI9w+gBQIEEwtfa/nxcRRNw4yUsqcq6xBNG0YGvtcAAQAASURBVJFdF1Y+d1pF5Nm3kDtv94n5GwBC06HsSS+l/7PKa0B2nIWx4dX5oSeqEY3rfSubm7ghIFo2obRtx+vvRp57G+dP/k+UBz6Asvugb7lTPm+kBNtCdl3Ce/HHeKfe9Of5ihrU2x64vhtRgiwXwn7xe36tgndTYHISEvIZ5NuvIAs5lLve789jc8yr0nXxcjnciQmkZeFlszhjYyjBIIppkPjwRxj+wl8iNA13PEXmxRcJ7duH2do62YZeV4cIBEj9+AlCt9yC0DX0ujr/SyEQ1bWYv/GvUbbtxv7W3yIH+7C++CfY3/4a6uZtiPWbUOoaUZKVEI749Sj01Vu2imQlIrT0DIibJPoNiJC2cCqFVrLEmPxb0fx06QXOnbnUT74/7tzL+6nNI+SuKBQWN2JzqtqXCiEEQS04WWSwDMdzZli8rBQhPUzWSZO2U0T1+ORDrpQeoqQ88z8AgVLyF/aLMhbd4ozChRLpKzeRkzd4KcuewqL0/wVU8SV122qU6Oo8fvWLwXYdzg5d5gdnnuftvrOM5SYoOEX6J4apCCd8kmza0NvHejj/egfpYpaf2v4giUBsct9lrBz96WG+fvQHfP/0czO20ND0WZkKC0EgfEJ7hbvE9ZzSMZgiOjRVJaKGsBybdC5DrpjHlR66qhENRgjoq1ecLRWL2bkoqBjTCH0hBKYy9eBnKAsTbAF15rWnCX3yOAoh0DHWrCjuWiKgBokbFeiKsSKbJF+Ffm2KpL7bMd/VGtQC/PsD/2Ly78ODx/jK2W8SN6J8ZuvHaYu3LthuTbCKj7Z9YPLv33vtvzGUW1qxnbSV5XuXn6At3sonNn+EjfFWJB4nR87y3ctP0JXu5QeXf8Kv7/6FGfZWKSvNU10vcHzkDC3RRj64/lF2Vm7DQ3Jq5Aw/bH+GV/sPL2kMM8czMcNqBCBqROcl0OeDEGIGcbyWEAh2Vu7iQO1t8xLoV44loAa4u/5uOic6yE3LypBIBvOD5J3coiR6xs5wYvTkrGKmtaFa7q6/m7pQ3ZLGIhBUBiq5p/4e8k6Ozmmq9oyd4djwMVpircSN2QSGQFAXqp9BoufsHBkrPa+fesEtzFCRJ4wE9eEGxopjPomOx2B+YME6McP54Zn1YVSTpJmYdU4oQsH1XIqOPev+f6U6/UaDlB4M9/oEesfZ6z2cNYNRWYkWiWANDeHZ9poR6SIc833R28+AtUx12tggcqQfUb1APQLpghkCZ1pm2TyWJ0uGVfBJ1spav4Bb+2nkpZPXplDnfJASeeJVxNZ9yEDY51ik9G1UTrx6fQn+6UiPIU+9hrj7g6DdONeyLBb8wr+vPfmOLPy7bDg28tQbyOwEyv0fRta3ItZYLPPHdz7Etg+8lw//0WyRznT85fs+Su32rXz8C38KgNBNaGhFVlT7XvQrgfR8RW7vJWi+sRS5gD++9CjEF/c4nhee9/9n77/j5Lrv8178/T11etmd7RVYLHohQJAEOyVSItVtSZZtRbJVbMVOHNuJndzc+Jeb2E7in9N8b+ISl2vJtmw5kqzeRVLsDSwA0etige19ejnle/84s4tdbJ/ZBRYUHr0gEDNnTpk55ft9Ps/neTwVer7y0HYA0dQBDe0b3mLpxwqRGMq970CODSLPHkcOXcH5wp/gfPHPIFqLCIVBUZD5vGeLkp8ltglFUB/7MDSvUdhlFZC2BeePeQT6aP/yH7jZYVtw6lVcq4Ry73uQDW3zinilK32M/q//ReHUKdx8nmJPD1Pf+DqJn/95wg8/TO0nPsHYX/wFg7/7HxA+k+i7303sfe+bkyWiRCI0/Oo/Y/zzn2f8r/6KyDvfSd0//szM+0II8AfQH3wnSks7pT/5L7h9vciRQeyRQXjuyXU5fPM3fxv97e9a8fK3SPQNBgH4lgl9vBbTyrelyanVPVxyVp6pa7zQAUzN4ELy0kxrfyUYL0zM8/V1cckuo1hcCXyqn7AeJWOnsNwSmqIhpcSWNn41SEgPl5X7AfJOjoniGIZizvihz1bsu9Kl6Hjhq9OT7JyTwZIldKFjqL6lyQI5rRisfGJSaQBdz8QV/vszn2U8l+Rn9r2bfc3biPkj/MEzf8VLl4/OW34oPcZj2+7n7Ogl/viFv6Px0X9GZ7ylbMGi4NNM3rvzbXxo9zvmnEoCQVN45YMob32VD3QXCkaUUpLKpXn+1Kt86/ATnOg9S75UIB6Kcu/OO/jQPe9iZ1v3dVENCRSWutau3QdRLsZUvL1r17cBB5EBNUjJKZAsTWCq/gVJSF0x8KmLq0iklDhvETXkjcXiPvnzz82l31/u86tFnb+Wn+7+CToiV+007mw8wHhhkm8WfsDZqYvzMjwuJns5N3mRgObn/Zse47a63TNdJwfq92KoBn9x/PNMlVZHKpRcaw5Z6oUiL/eMXRprfW1GjSh7avYQ0AIrXrcQnnJ6c3Qzx6+xdZksTFJwiiyluZNSciF1gYnC3OKIqZpsiXbTEmxd1XF69nUJdsR3MJYfIzvL432yOMm5ybPcXn9wwXU2BOa2bmftLBk7s+h+5+zcnO6CqBmlJdTC2amrRHHRLjJeGKch0LDgesYKY3NIcZ/mI2bE5u1fspDm6NBpeib6KTpzbbUOte1jb+O2Bdd/oyGlhGwa+crjbykCHSC0Yxvxu+9i/LnnCXZvIXrgNrRwGKFpi5IvKzqXhYDGdghGVk+iWxay9/TSJHq4xgtZm41qyVvX8dToNfUwMYI89+bG8M3OJJHnj3lBhoaJdGzklQse0baBIE+9jjj4MGwQKxGZzyLffB55+MkfDwJ9NnrP4D719VlE+hoq0qVc0fRN9wcYOT3LukUIaGhD1LchKyXRAfJpz9KltetqF8pK4Vh4AUnSI7yF8OxXhOK95jrl8OBykJKqeu+JWZ9xXa9LxikLWKY/79heUc+xwCrf8xQVVlkglqkJ5GDv3ALhamH6vW6gaYX8BoAw/Z7Fl+OAa5f/dq7+/WMAIQSieze872M4P/wq8uIpSE1538HECHJiZP6HTD+itg7l3sdQHnzPDZ/PSqsEPSdxX/zujweBPg0p4dxRXClR7n8fsqF1ToHS7Oyg9f//e0uuouHXf42GX/+1Rd8XQhB973uJvve9C74vbQvZ24P15Hewn/wuMlm9yHZlWB1fd4tE33AQi6qgFv+EWHOfzYJTJFOaPyEdzA7zR2/+2ZpuC7zJ22wV+EIwVR/qCsj7qBFHVwwKTg7btVGEgql4bfTTJIhfCxKTkoKTw3JL6IpO0AxjOSWU8s3CkTZ5OzejgPWrfmzXwnYtHMVEU/QlVYkrDfFbCsoiA0JNVVEUhZJjLehx3DPRz/mxK3zyzg/ykX2PoSoqlmMznBlfcI/2N+/gF+/6MOO5KX7nh3/M//vKP/CbD36SmD9CzBemKVKHKhR21G8mHrg6cVj98Ymr3QAV4toCTLaQ46svfp8/+e7f4Df8tCaaMDSDdD7D1178Ps+ffJU/+uXfZUtTZ1XbXQk2IId9w+FIG1t6HtElt7jgMiEtsjSJjkRWYYt0CxsbmqLSGW6fQ6BPozXUjE81SZUyFJ3ijO+0K11G8qOMFcbZHu+mMdgwx7ZJEQp1/gSd0Q6OjB5b1f7oijangCmRZK0sqVKKqLkxiJOu6BbivviqJxqaotEUbJpHomfszLLPYMu16M/0kbXmKscieoSdNTsrnvRsjW3j+MRxspmr683ZOa5krrCrdveCgeN1/rkket7Ok7GyC9qx2K7NWH50xn5GIIgYURr8DXPWbUmLkfzI4iR6fnROccWv+omZ8XnLffvMU3z7zNM0hBIY1xCeO+qqs6xbVzgO8uQryBMv3+g9WXMohkFg0ybGn36Ws7/7n6h96EGCXZvRQsEFH9zhPbsJdW9Z0bpFQxsyFIXJBYiBpeBYnl3OgYcWt3SZHC77BZeJL8eGaD3ULkG8LwfXhbFBZNdu5OAlL3xySQhP/e4PguG76oVsW56KNJdaMxW7PP0a4rb7QDc81feJl1fuPysUj1TzB73Pl+1qsEpeMGVu4SLbqjE5iuw7j9i+eNbH9YLMZ5HHXkS++iNIXweiQdO9c8Dwef+tKB6BOk2+umWi0CpBqegVlpx17iLsPY371NdQHv4QsqH9upNviqZSHJ3bmSUicWRTB/ScrLxAVSwg+y8is+nVe7/nM1dJcNcBpNfRYgbL10T5t5nutNZN8IWu3gtLBSjlQPd5JLeU3vu66b1nFTyiPV++pgzf6kn03tPVn7M1DV7I6TrY+VQCgUC2bUF5+MPIQt6zJikVvHvY9H/blqdwdmzvHjrnb9t7Lti29++bnHRXtu1FNLTgHn0ZeeoN5OSY98ywLe+cUjUwfRCKIFo3oey/B9HphX7fSEjb8gp0L34PBnvXd2NC8b4D3fSeW5ruvTbbN9x1vQKEVfKuvVJx/e+rF47h+oIo970b4nWrL+RVCOm6OCePUvrrP8U9c9w77g2KWyT6BoSyypa0abXwWsJyLfLO9Q1OWM72JGLMn6wuBC9INURQDy2+DIKQHiZ0jZfpbCLPUExqfdUFlSwW4rdSLKZ8rAvEiZhBXus/wa6GLmJl//KuWi8t3a/7CJoB+pPDXJzoQyA4Oniai+NXFpwwTrfX391xG//40M/wB89+jvZ4Mz93+/tpjtTzwKaDfPfMs3zxze9zd8c+fJpJtpRjIDXKg113EDJWZvEz21u5Ulz7nfaM9PEPL3yX27fs5dff/yk2N3agqSoFq8gLp17jt/76v/DZx7/Ef/z4v6xquyuBWOLohjPjnB69SGMoQd4qcHGyD8d1qA/VcqB5J2Hzqg+XlJKB1AinRi8ynpvySMZ4C3sbt6GrGgOpEU4Mn2Nv4zYawgkuTfZzZPA0m2ta2Vnfhe06HO47RtgIclvzjutw3Iuj4OSR0iVm1GCqvgWXXknHxS0/9LcuPGJ34XutT/OVn28SexaBabkW6VIWy7Wp8cXxa/OtkHyqQbwC0jtsRDBUY06waMbK8Ob4m9xedzuBKizN1gK6otMcbFqQXF4OqlCJGrF5r3vZGUsPVieKE0wWJ+cUMr1iRR3xBcjklSKoB2kOtjCcG54h8iWSydIko/kR2sMd8z4TNSL4VT95x7Ocmw4XLbmleRkLlmvN8UM3VZOwHiKoB4kaUVShzgTCjuSHgT3ztielZKwwfo0S3U/MjM1b9ofnX+Cj+97LY90PoG6Qyf2KcPmsp0J3r0PXjxDehFH3gWGAZlwl5Aq51RPSy2Dy5cNc+cvPURgaxi0USJ88teTy237n/1o5iR6OI+J1yIGe1U1sXRc5MYTMJBGRha8fUdPsWZ0AOCXIJqufzLoOcnwQkU3BheNLkNTCmzw3tnsBqPE6RDDiTfaRyEIBkmMweMnzf16L32xsEIavIP0h6Lu4snBGTYe6FqhvQdQ2IqKJq0S66yILWZgYgYFLyMGesl94NZDIs0fhBpPoslRAnj2CfO1HkFyZddqqoRsQinkWDJEaCEchEIZAGOHze8SpNq1wdj3yz7a87zyb9gosmSQyNeURpqnJ6pTHi6H3NO6z30J55CPeOXsdIKVkoucSqf5BzPA1802hIFo2I+P1MHip8o1MjcOV87CjgnPNscEX9a6PYt4jL3Wf91tphvfbCtUj5fJpj2SfETlJj7wyFQjGvH9PK9UDYbB0yNgQSVR0WLJU8O4ZufTyCy8GRUXUe9f9hoEQXihjtHbheZF0PQ/wYrmIUcrPFJtkqUyOThefSoUyaVpEWpZ33Uz/nRyHzHy3gI0IEatFffDdyENvQ44Oeed0sVyE0Q2PQK9tRISjXuHzBkM6Dgxe8gj0/otrvwFF9TrXorVecSwUhXAMAiHwBb3QalUrfxdipngurZJ3DefTkEl5ReZM8uq5sNakupRw6jAyEIS734UIhBbkj9YacmQI6+tfxD1zYnECXde9cYi2fA7dqqCvzgnkFom+AbFaknE9TmlHOsuq0m5hOVRP+i12LjRHG3jn1nv51qmn+R/Pf56g4WdTvJV/+dCnAOiu6+BtXXfy0uWjXJkaxKebSAkPdt3BMxcX9wgWCN6740H6k0N8/vVv0Biq5T07HuLdOx4gWUjz1MVXeLH3DXRVx5UuqqJyqH3fikl0byNVkujXfK9jyQmmsil+5b2fYGvLVZWfTzd5cPddPHrgAZ4+/lJV21w5Fg/y7J0a4LOvfoWwGSSg+8haeYqOxXhukg/seJiP738/ilCQSM6N9/LFN7/Lpal+dFXHcmwsx+LDux/lvdsf4vLUAJ8/8k1+/oBKQzjB872v82evfJG3bznE5ngbBbvI5177Kne333ZdSPSlznRT9ZU7QlQvh0DMLzWIZUgBz4TkFon+VoVAzCjMl8SsU8BxXWzXGzQair5gN5Yq1Iqsxxr8DUT0yBz7j4JT4OjYEWzXZmt8K/X++jX3OV8pokaUiLF0ePZiEIgFg3wtd75/97WYLEyQseaqOTWh0RJqrbqQ3xZq5fj4sTnjjkwpy1hhfB6J7gXIaiT8Ca5krlxd3sqQs3MLkujDuatt9SE9REDzipYJXx26ouM4TlmxPo7t2nNsgwCKToG0lZq5D6lCJaKHFwyKNVSd+mDNkvkYGw0yk8R95QfrNzHXDZgmF0JRbxLpD3nKYcP0lIyKglBU5JVzyGe/uaabNxsbqHvHIytePrJnfiFlUSgKNLTB+TdXr3Yu5pEjfYuS6JTD8gSA9COtErKQrW7c77ow0IN89UdesN9C0HTYehvKtv3Q0oUIx+aN3ab/JQtZuHDC8y7vOVW1elKeP4Zo7MA9/lJZhb8E4vWIHbcjNu2E+tZyKOlceN+dRGZTcPEE8tiLHjFZzX72X4RCHnw3JsxQOjb0X0C+/pQXsLmWEIpH6jS2e37TtY2IeINXRFmhH//MmSIlsphHJMc9Bf/YIAxf9mw8UhNLrWL1uHgC+coP4f73IQLh5Ze/Bsn+AS4+8zyZEa8YVMxkGHzzGM/+P3+04PJOqcTgkWMk+we44xMfm79AfSuirhk5fKXycy0zhew7h+jeu3obp9mqVsMHhYynctZM7weyLXALV7sHZguUJB7Zp/u8v9caowNewawalWkwjGjZjPDdWFHDqlD+LYThg2vM8xa8p7su0ioiSkWve8AqQSGPPPLsTdcxJkw/onUTtG660buyOKSE1DjuK4+voENrlfAHobED0dAGiSZETYM3HgqGy6rzZSwzZ/+jXBwWyXHPMmpsADl02SvYVZkxMAeOjTz6nPc8OPh2hLr+tLFz4gjuhTNziwKKimhsRtm8FaW+ERGJeur96QLuGkHdvHVVy98i0TcaqrNIXjPIRQIxBQJtHR6oqlAr9v/eqJCyeuJvMXLRr5t8eM+jdCc6GEyPIaWkNXq1Bb0xlOAf7X8vexq3MpabwlR1ttVtIh6IcHvLrhnCw1B13r39fpKFDPUhz1NOVzX+0YH3UReqoTYYQwjoiDXzC3d+mJMjFxhIjWC7DgHdR3usiYhvccX/AkdUtRLdlS4SObMeKSUCMBZp/zJ1A2eDtMSlSzlGc5P87N53c6BlF650+evXv8aXjn2Pd265l6ZIHVP5NN86/RQ9U318YMfDbKvbRMEq8ndHvsVnX/sKexq3EjZDhIwAI9kJpJRcmuyjPlTD5ckBSo5F0S4xmp1kU3y+Pcb1hpcpkKPg5FCFuuA5HdRCy3SayKq7Om5h40Igli2kXAtFKDPPIsu1FvTMd5HLqqsXQtSM0hnpZCQ/QmFWR1baSvP66Gv0Za/QHmqnM7KJpkDTsmGca42wEcZXgQodpgno+c9aV7rLXmMpKzVHnQ+gKip1vsrUaLNR60vM26+CkydZnMKV7oLhnQ2BhvkkupWbo4qf9kOfKF4lbMJ6mKBeJtH9CY8wd6ZtezKkS2nivrn3o8niJKVZ/uaGYlDjq5mxDhhMjzKemwJge91mvnj8e0wV0tQFa+eMmRpDCRLBylX76wIpkcdfhv6etQ2XVFTPG7i1y5s0RmrKiquwR3yoi6iIcpk1L5lGb9tHePvKvegVc3XhnaKuBWn4V0+il4owcgW2LEzay9HLXms5eL+NXUKEYqvbxkKYHPUUzAuR1P4gYv8DiH33Qqxu+TwMXxB23I6oqcc1/XD6taq6GWTPKUg0Q9/5pRds2Yxy6J2Izh0zxYbFd1J4xZuddyCiCc/ntudk5ed7Lo0c6kV0bq/s89VASpgYxn3lCRi6vHbrFcIjdbr2QPtWT+UbrUVU48EvhHet+wLevcCxkVPjiNF+ZO9p5JkjkJlam/13bOSJwxCOI+54eNWqQiufp//1I5z74ZNM9fVTTKW58sprDBxd2A7OtSxcx2XT/fdw+yf+0bz3hemH1i3e+VypbYltIUf6kRMj3u+xGkxft0Jc/SOlR04VM3he6EuQUEKsj4WDlMj+i9UXf2IJaFsd6XXTQVG882j2/c1xkD0nb9w+vYUhraJnjXV+dRaQS8IfQmzZAx3bPAJ9phhZBReiKF6hMBBGNHV6hZaJUeRQL1w65eWcrDajZTEUC8jDTyIaO6Fj/a83t/8ycmpWgVXV0O57O9pDj6K0b0LEEwjT3BD+ubdI9FtYEB7VOf/h2R3r4v7mu+eptNZiexvFa3YaaSuJKjT86sqD2+aiek/0pbZaG4zx9i2HFv6cELREG2iZRaz/j+f/hkPt+/jJ3Y/MHI+uatzRNnfydnlqgG+c+hG/fNfPzHgM56wCL/e9yTM9h1GFyqfv+DBbapdOzv7fb36XiBnkwU13EDCuDgCqve9d+53GQ1FMw+TxI8+xp3M7iVkBM+f6L/H915/m4JZ91W10jeBKl/1NO3hH9z00hDzi6ZEt9/Di5SNcTg7SFKnj8tQArw+c5FDbPh7Zcg8B3VM6Zko5fuM7v8/rAye5s3UPETPESGacyXyS4cwEd7Xu49neV8lZeYYznppyU03bjTxcADShE9IjSy+zAkXvLSX6LcyGoeqE9CCaUJkoTlGw5w8Yi06RZHH1QWuqUNlds5vB7BAXUufnFJRLbom+TB8juRHOJy/Q4K+nPdzBpkinF1x9HQZ2QS2Iqawtcb/c9eVKl5yVm0Mkg/ddVWPlMo2oEcW45pgc6ZCz81iuNU9drgqV+mt80aeV6HP2G5exwtgchXvICBPUPPVanT8xp6Og6HjhoteS6BOFiZnOBwBDNajx1c78++mewzx+4QUACnaJy1MDXJoaIGwE5hQAPrL7Xbyz+97lv5DrCDnYizx52CN01wK6iejYBt17EXUt3qTRf31agReDouso+jp2jtQ1e8qo1aJU9FSZ0l2YsNLMOaFeBGOenUK1kHJhAt30I+54BLH/fm+SvtLQYkVFNrSh3PEIbiEHF09Uvm/JcU9RbC1h+9HcifLgT0B796qUskLTkS2bUQ48hJtJeQWMSuDYyP6LN4REl4WsZ7t06fTaFb1MP2Lbfs/nvbHdU0euB4GqaojaBmRNPaK1C9G5A/f063DuyNrcfwpZ5NHnkOE4YvedqzqGaGsLhz7zSbY/9k4menr47m/9NrVdm9j+7scWORSVUEMDzft207h718Ir7dgGbz5fnff3+DBy4OLqSXTXLau5Zv0RwlOjOzb4I56ti12CYoXq1el1ruYjmRQM9UKhCsWsbiIaOxCx6gv4t3AL05AnXvE6ldbEGkVA1y6UvfdA8yZEOL5u3v1CN6GhFVHXhGzf6t1XX3+6Oiup2UhN4L74PdT6Fk9Rv54o5OaMTURzG8bHPoPS3LYhiPPZuEWi38KCUISKvgBRHtD97EnsWrAd/K2GolPEUCRSlRWrpzfS5f7SlaO0xhq5YwG/19lIFjK81n9iDqViqDr7GreRK+X5yokfMpGbgmVI9OPD56gLxrmnY//cN6oc819LUm1ubOfdB9/Gn33v73jj4gm6WzYRMHyMpSZ589JpFCH4zGMfrW6ja4jWaCNx/9WCUdwfKRNUnrfveG6K/tQwXzv5BC9dOcL0WZQr5SnaJYbSo0TMEA3hWsZyU5we6wHgQMtOnr30Kpcm+xksL9MSqc7Tf6VY6jz3qb4V3C+WvlJu2bncwrVQhEK9P0Gtr4ae1GWGcqM0BOpmCn+udBkvTHAxVVkoUMSI8kDLAyDg/NS5eedfyS0xkh9mrDDKpfQl3hh9nY5wB9vi22kINKx5Tsls6Ip+3Tu3bNem6JTmfQ8CQUCvflCtKRqmZsI1PErJLVJ0ivPuIYpQSPjqUIQyU+TIWhny15Lo0mUwOzhnf8N6CH+ZRA/pIUJ6iMmiR3IU3SLjhTG2MNcPe6I4MYeIN1ST2lkk+r0dB+hOzPduvxatkcZll7mucGzk8RdhfIiqH86KCh1bEXvvRTR2QCTutf9usInPekD4g4h4PXK0f3UqbNdBpicXDQ8U0WtIomlf4nWBgN2HEHvvXhWBfnXXPCJd7L0HOTFcufe4dJf2+I7Xo9z3PmjrnltgWOl+ahqyYxuiey9yarQyxZ7jeKq/6wzpOMhzR5GnXls7D9xoLeKedyO6dnvn4HXIcRBCQCiK7N6L0tCGbO5EvvYUTAwv+9llMTWOe/Q5lJp6xCyLx+Wg+3wkureQ6N6CXTzE4c/9LY27d3Dvr/zjhY9BUdB9PhRdW7R4LqI1iKZNyOE+z46jEmRTMNiL3HZgQbuiRWEXPYJcN6GY835XzfDO9+lbvXS9ANHVdusK1VuHXbpqM7PSMc9oH3Kkv7oCUCDsWThtAA/tW1gZpJQwNoQc6EVmM+A63vlcW49obPUU9zdwrCBH+5Ev/7DyIODZ8AUQt78NsetOz3Lsep2niurls4RjKHXNyMNPegKJZawaV4S+c7ivP41y77tYV3YrGPYs/goeJ6J2bUVsQAIdbpHot7AIDFXHt0BQW9bKrWm371saYnF/7JXiRhCH3bUd/O47fnWOx7CmqLTFmpgqpAmff6GKta+FOn9uSSMSCPHxt32QRCTOF57+Bl9/6YfkiwVqIzEe2HUX/+TdH6e7eeN4sPl0Y06BanrwPW2l4LgOCoJd9V3sa5qrctJVjd0NWwkaAZrC9fRODXJ86BxhM0BLpIHWaAOnRy8ykU/SGW/BqKYNdxVY6hd1y+8qQqmqGFWtDdAtvPWwOdrBltgmXhx6lW/0fA8pXXbX7gAhOT15nu9depJUsbLgKiEEdb463tn2ThoCDRwefoWiM38S7EqXtJUmbaUZK4xxeuo0jYEm9ib20h5qX/OuLfA6N5QbQKLbcr5ydS2t2AzFQCDmPCNs1140n8Wv+T3v+tIU4Nn6ZKwMlmvNqMsd12EwNzDzGZ/qI6yHZ4ocilBJ+BL0Z/txpUvRKTJWmE/gTRQmseVV0sqv+ogaV0nPtmgjbVGPIHeki4K4Ll0J1UIOXEL2Xaw+7C8c99TLOw5CpNabNN4Ex38tiqNjTDzzHP72NmJ3rCLMTyiQaIILx8Bd5XdZyHuE8wIkusxMQrhmxu5KlgrgWAj/GqjRr0XbFpTdd3lBkhX+dkLTPPXt5t3IN55eW3sgAN1A3PmIZzeiVk72CsOErj3IS6eh/8LqVyBdL6y0kPOsSq4XsknkC99bu1b9hjaURz/qWa2s0v5kLSAU1ctK2HcvorbJs9lZzKd/pZBlz//jL0FNvdcFs0popknjzu1opok/VkWXtKIiunYhz74ByQpJdOl6uQljA9DWvfLPqbpHnudSHoHuj1wNFVWLkJn0Xtd9oGirm66qKhh+bx1CgC8IK7H2dBzPu7maYokQEKv1ulBuYePDdXFPH8F96lu4fT1eqKjrlH33FdB1RKQG5bZDKPe8AxGrvf5jB8fGffH7MLUG+RI1DSj3vw827/LI9BswDhKajmzsQDz4AYjWep1dy+WLLIdSEXniZWTn9lUVJ1cLdfNW7EQ9sq9cpK52v9cRt0j0W1gQpmoS1uc/EMfy47gVeMyuBFJKXOmQczw1mYuLgkrEiGEqPlxcUqVJSm4BEPjVAEEtjKKolJwiBSeHLW0c1yKkR8laaRRFJarHUYVG0c2TtdJYroWqqIT0KD7FjxACKSVFt0DaSuJIG1Px40gbMCi5RVKlSRK+BhShIpEUnDx5O0tQC2MuECwGlA1xqiTRVzEBkVIyVUjx7dNP872zzzGZTxLzR/jMnR/hnnZPDX527BL/+Kv/jsH0CNvrNvOx/e9nb+M2hBAkC2n+23Of4/CVN9FVja987A/RVkiOOK7D8eFz/PkrX+JKcojbmrdzeWqAhlDNvGWrJ9HnBnAIIagNx/np+9/P++98J5ZjI6VEVRRM3cBv+tZVFbpaiGXIlZAZJGgG2F7fxU/vffccP12BQFEUFCGoC8bRFY3jw+foiDUT90fYWb+Fs2OXyFlFDrbs2hDEc8ZKkrOzxM0EPrXyEK5qj0WsgR//zY7pcNe1xlh+gheHXqUneZm8nadgFxjIDGG7Nt/s+QEvDL2KTzXxqz7uaLyN2xK718RHPGZGeaT9AaZKKc5PXeTPT3y+7LsvUIRgU6SDR9of5Lu9T1S0fiEEYT3MoYZDbI1u5fXR1zmXPEvBLizotV5yS5SKJZLFJD2pizQHW7i97nbawm0eQbxGg2lFKNd9YO5IZ8GcFF3V12xfFjonHOkuOOYQQqArOnX+uhkSHSBZSlKwC+iGR6KXnBIjuZGZ98NGmKgZm7PPTcFmjk8cxy1nwaRKKdJWmrDuEZV5O0/aSs0cvya0BT3cp/ELX/kt/stj/2qO93neKvK1U48TNUO8e9uDq/hW1g+eqvUITAxVvhKhQFMHyr3vhs4dN73yvDg0RP+XvkzibQ+tjkQHRKIZqWpL25AsuNE8cmrM846fRnnsJyeHEMEYKOXvtJBBFnJrT6IbPsTOO6ChHVGlElkEwtC1C3n5jBceuIYQW/cjunaVCd8qzjMhoKkd0dzpKcorUXWXCsiJYcR1EmlIx0E+/x2YHFl+4eUgFK9o8tg/gpqGqn/z6vZFIAwfsnMbSjCM+9y3PE/iapT2tuX5rde3egR9BR0Lh37p01iFNbCYad8KNfVekGqlRaWRfuTgZS/kd6W/lap5xPa0oGv6vqyoEIxe3RchZi1Xhi8I5lLFIeGFQ8vw1XWsAHJqBAZ6qiPGDB+ie59nYXELGxdSIlOTON/9Iu4rT0F6yuvSErPOxbLNkBwbwblyAffVZ1A/8hnE1r3XT70tXdxjL3sZGVVkeSAENG9GeeD90F7ukrqBYyGhKMhoLeJOLx9CvvR9KOarW+nkKO7rT6HUt65b0VXdfyfaifuwJschm8E5+SZyeADqm27sc2oB3CLRb2FBBPUgtb75BOhUMclEcYqAvvbKC4kkZ2fIO3kiegxdNXBcG7WsKEuXpnCkQ41ZjyslGTtJxk4T1qMzxHZEj1EgT8qaImbUkLdzFJw8umKQszOYqp+YUUvRLTBVmqDObEATOra0yFopdMUgrtV6JLmTRQK6YoAQ5J08QS0EEiy3hEBZ3su5yhvoagjnolPih+df4Junf8Qv3fkz7GrYwnBmnMZwYoaIfebiK/zuO36dmkCUv3r9a3zh6LfZXNNG2AwSMUP8+4d/hVeuvMnvPPlHrKa1eygzxt8e+RaN4QT/6oFP89rASV68/AYlZ+5ASbK6wsBCUBZrmwQURUHj6oPXcmysXAZVUQhXoEa5EeiMt7C3YSuv9h1jf9MOttdtQhEKlmtTtEvUh7wwu0Qgjq6qHB3q4WDrbiK+ELsaunnyopfYvr3ufTf4SDx4ynqlSrXq6oMnr0Wdv5GgFvmxptEFYsUKZp/mo8GfIGSEliW8C06Rwcww/bNsM2aeHwLSpQxpvMC9zfmOOSGgdT4veDGwSFCmoejUBxL4NN+c7hjwiNTNkQ5+cdfHeHn4NY6NnSJVShMxwtxWt4d7Gg9yMdXL6clzRIzK/MqFEGho1PvrebT9Ue4o3sHx8WNcTF4kVUpRdIq4zB14u7gUnAIXUxfoy1xhR3wHt9cfJOFLzNjNVIvrXRASi3RWrWXg70LrErP+/1p4JHo955LnZl5LlVIUnAJhvMn9UH5ojoI8rIeJmbE562kKNqEKFQvveZWzs0wUJmZI9KniFIVZXQiaolEfqJ93Pk3vf6qYwZkX1CoZz01umJBrAAYvIa+cXz3pOw1Vhc4dKG/7kBccukYTxhtp3eUWi9ipNLISAi/R5BFXq0UxP08BJ20LSnlPcZyZRAoFkGVV6dqTC6Jjm/dnLYgLIaBlMzRv8myC1uoeEY7DjoMQTawJOSEUFdncCWePQrIC6xnb8sIRrweJLiXywrFya361tksKbN6N8o6fhlhiw4yJhKIi61tRHvkIrqrB2SPVka2ZKeTxlxB1zdDStepzpnn/2mQpCVVDbDuA7O+pvIPAKiL7LyC690B8FTaNQsy/X8yQ6rNfu/Z9lv6+FlrHcpCyXAy4tIoPLYBACLFt/01drP1xgExN4Xztr3BfetIbYwTCEAp7SvNp26hcBpITnk9+JoW8fAH7L/4z2j/5t4iunddnP8cGka8/5dkmVQpFgZYulId+Alq3eJfFBjg/hRBIfwix/wGvsHj48eqyJxwbrpyHc0e9Z/E6HKMwfRg/+2nQNOynfoCcGKPwf/8HzE/+CkpjC/h8Xkfa7GLMDcKGINFv/Gl2C9fCUHVq/TUEtQDZWR6jEsm5qfO0hprXfJuOtCm5RfxaAF/Zr1QtT0hc6ZJ3csTNOo/URuJIP3k7PzNBVoWGpugYuNjSQldMSqKIKx1KbhFXuihCwZaWR+iV1eeaouG4Nq50CakBNEUnIFRydnbGRsKvBig4WQJqsKx2tzEUc0licC2Ur6tR/SfzaV7sPcLbuw7xtq67AKgP1c5Z5pHueznQ4j2Y7u7Yz1dP/JCJXJKwGUQIb29XOxGWUjKRS9KfGuJD9/wc7fFmaoMxnrzwEvOvbjmPcFotrlXSOq7D8OQYh88d5eSVcyRzaVx37iSjuaaeX//Ap6va7vVCYzjB+3a8nb84/CX++KW/ozvRiV8zmcgnSRbS/Md3/joxf4SaQBTHdUkXszSFEvg0k211mxjPTaEpKl3LeNavJZY6YzzVbHVk21rYuSgoGIpeNRl/s6DgeIrwiBGtqBNjd+12dteuLDStNdTEZ/Z8fNXbAPi1/b8477WslWWiMDlj4/GPd/8cYSO04HEIIajxxXhXx8O8q+Nhik6R4dwwtb5agnqQvYmd7E1UNxifvicKBAlfgoda3sbtdQe5mLrA+eR5xgvjpEtpSgvYOJTcEkfHjzJenOCB5gdoDbauGZF+PaEIZV4RAzwLFSnlmhCoC31/i20XPBI94Zv7jEtbmTm2O0O5qyprgSBsRIgYc4OOa8waTNWk4HgER97OM1mcpCPseZwni1OUriHR6/x1c9YhpeRKcpCRzDjZUp4jg6eI+a6qhYcz41yeGmLTptYlv4PrBtdF9l0oe6FXAKFAWzfKQx/0SKo1hGfpUx1K4+PYqTR6bQ16JIKdzWJNJZf9XGFwCDuTqWibIpaAStRZVhFSk3PDRR0bMhMeeTY5xIyS1BdEROuWXN2qoRte8Fm8fs0mpSIQRjS0Ic+/6REla7HOjm2I+pa168IRAlHfhgxFKibRZXL8usxhZSGHfPXJ6m1cygUO5f73eufrBiB6ZkMIAdFalHvfg5vPQe/p1ft1z0bfReS5o1DbuGpblzU9z7bs8ZSg1fx+g70wNgSxuuV/N1VjeuS8USALOc/KJT1V+UqEgmjdgojWLr/sLdwwSNvCfeVHuG+UQzo7ulEffDfKbXcjIvG5yxZyyJ4zOC/8EPnmK5CcwPnq5xC/8u8R62yVJa0S8shzVXbjCahrQTn0KKJ1y8a8pwZCsOdumBpDnq4yTyM1gXv6dZSObYhgZPnlVwshEMEQ5sd/CWXLDqyvfB73+Bvkf+c30W6/G3XrDkRjCyIY8vIYVA2U6oV2ACJR54VqrxA3nET3lHHzD9xrm71lvn2jIBDU+xM0Bhu4kOyZ894bo29yb9OhNWnHnw1Z/p8m5p+WrnSueU8gyopjWVY1TivlRNlIZXo5iURKl5JbQtrpGTJOVwyU8nISiRTMqDQVocxRPPtUP3k7iyVLONIpK9SXbiXzLAWUeR6vq8FClgGLwXJtMqUcTeHFJ1gtkYaZfdPK153lVh9OVLJLWI5N3O/dUINGgKDhn6ca9yx7qiPRFTFX/TCWmuAPv/05vvTcd6gNx/CbvnmEa1fT8oFv640af4RDbfvoiM0lHOL+CA9uvpO6oKfcFQjubN1DjT/KUz0vc26sl6l8kppAjAc2HSRoBMqfi3KwdTdBI0BHvAWAhlAtb9t8F4pQqA3ErtuxLXV2+zQ/llWi6BZQFQ2FSqwo1qIg5bmzb6zhzcJwpEPJKWGqZsVWREO5AS6lL3Bv40Mo4vp7nVaDsfwYrwwfZjA3xEhuhHd1PMadjXes6LsYzY/y16c/zwc2vZ89id3rto9hI8y+xG3srNnFUHaQ80lPdT5WGJshY2ejL3OF5wae5R1t76TOX3dT+GXPhi70GZ/x2XCliy1tdFF9/kJpgeBSTdEW7fhShELEiOJTfTPfeaaUpugUkXiB4CO5q96rpmoSM6LzjsMjxetJljySteAUSBanZooDU6XkHIJfV3RqzfmT+NcHTvLkxZcYz0/xude/gq5e3Y6h6hxo3sn+puujrloOMpuCkb7K23tr6lDuex+ivmVtd2yN0Pvnf0nyyJs0vPtR2n7uYyTfOELf57+w7OdKo6MU+vsr26hueGrp1QZqui6ykEWWSgjTswcUvgD4NoFQIdFSkR3FihGrQzS0rXlIn2hoQ0Zq14ZE9wWgZfM8AqZaiFitZ0uBYNVzTsdeOgB1rSClRwRXG8YIUNuIcugxRH3rhiN7ZkPUNaPc+y7czBSMVng9gucnfu4YonOn59W/hnYA08KQlTzLRaQG0daNTI5X/hsmx5DDVzyrCHMZa8T1yEyoFslxZN/56tahaV7uxjKw83kyl6+QHx3FymSRdllsZ5hooSBmTZxAQz1GNLqsRYRrWeRHRskNDVGaSmIXCuBKFF1DCwTwJWoJtragh0Lz1mXnckyePkP2Sj9CU0kc2E+gsWHZcyY/NsbE0WNYmSx6KEjz2x687lYWMpfyrHO0CuYPo4O4p96A9BRi8w7Un/1lROfWBY9B+AKIHfsRnVtxQlHcp76F7D2Pe+ZN1H2H1uBIlsBQL/LyuerU2aEo4rb7EZt3bux7ak097LkbOTYIw5crX5Hrep+/fBZ23M56zKrdK5dwhwcgk0LdvA333GmYHMd+/FvYj3+rLCjwe4S334/QdNCmC4eVw/jYZ9DuuGfFy99wEl0VyoJkbM7O495KsLyhqA/U0x5u5VLq8hwy90qmn1MTZ9hbt3tNW8rLOugFSdZpMtrFwTttJXJG0Xx18Du9N9fex4QQmKqPiB5fJOhNIORVQl7KuVN5VajoiknBzqEIFVUoKwqM88h4ZVVk+Gys5nOKUDBUnUxx8WRpvZJW4xVAVVRURaFQTp+3HBvbdRYcK1bjqa+WPZ1nn3c9w3386M0X+YlD7+RnHng/NeHYPC96Xb8+AZtLYUttB//Hg78w7/Wumnb+0zv/+ZzXhBB0JzroTixO/gcNPx/e/eic1xSh8O8e/qdrs8NrBKVc7EqVPDWnpujz7huGYsx0nywEgag6vLDa4s31gpSSgp1nMNdHe2gThvrj5/vYGm6lLlDHpVQv37n03Ru9O0tCV3Tawu20htoYLYxybuocPamLDOWG5gViXs5c5tj4MR5oeWBNSOfrCU3RMFUvX2L2teRKl4yVIW5WR26VnNKCwa2mYmIucg14z3WTmBlnKOdZCRWcAnk7N1OwHStcJTT9WoD4AjZ1AE2BJs6XbWEs1yJZturxaT7SVgqrbE0mEESMyDxLOyEEP7HzEd655V7+yTd/m9+495PE/VcD6XyaSdQXmkOs31CMDSLHBpZfbiFoOuL2tyHatqztPq0hkkeOkj5+En+rR/LnLl5i6OvfxKiJo/oXJ6CcQgEnV7lvqIjUIL3Wq9V9sFSEfAbMuRk7oqbpqjp9vRBLQKJx7ddb0wjhGAz1Vr+u2kavYLPWxQTd9H4zTV99uK5jIzNJ77deR/JEZlOeerCQrW5FviBizyFo21KZ7dB1hmjfirj9IeST/1Cdgnt8ENlzEhpaPb/+FSIzOsbU5SvUbduKGQrOeX3gjaNM9fVjBoN03HMX0dblOyTEjjuQp16t3KJGSuSVc56VSV3l+UI3AtKxkeODMNxX3YpidTA7O2IBJM9foO8HjzPw9LNMnT5DfnQUp1BACAUtGMRflyC8qZP4rh20v+tR6u+4Y8GQYte2mTx5itHXXmfs9SNMHDtB5soVSskU0nHQ/D7M2lqiW7pouPsu2t/zLmLbtqJoV68tO1+g56tf58Qf/SlCUbjzP/0OWz/2s2iBpRXW/U/8iNf+/X8k299P89sfounB+6+/H/RYn5fxEYiAL+QVblZ4/3UHryAHr4Cmo9z3KKJ107L7L/xB1Ic/gHv0JZgYRZ56A9aRRJelIvL8MZisIuRWNxBb9iC2H/D4I6vg0VHT4kTNvJoT47pIu1R+T3jjKFUHITz7OLuIMAIzzxLp2OBYoBqezZpteZ9HeoHFmnm1KChdpFXyCuGO7XXvCAVh+Oc8m0RbF6J7LzI55oViV4rUJPLKOdi8a/mCXgWwvvsVrMe/DdnMwmMpKSGfQ+a9Y1grtliuskvmhj9FNUXDv4AX6nBupGzT8eNHIGwUBPUA3bEuToyfZiR/1a+xaBd5ou9p6gJ1NAfXbuCtoKAJjaJbwHB9qELFlU6ZQPeKLXk7i6prSOliuSUUoaApKtYyYRC6MCjJIiW3ONMa7qnnPL/z6TC6kltEVTQs18KR9syFKVDwqT7SVhJT9aErK1OIKqjVkeirUIkHjQBbE50c7jvGXW17aQzXkS5m8WkGkZWkplcIIQQRX4gaf4zXB07SFmuid7KfvuQQzZFrWt6RVZLo2ryBaraQQ9d03nvnw9y+ZU/F676FyrHU1KHoFrBcC1c6ZOzUgjZHIT2yNIkuRNUWGN41uPELsxJJ2koxWZqgVd74DoobAVWoBLQAYT00J1h3I0MIQb2/nlqzls2RTRwbP87pyVNk7bnEx4nJ4xysP0jEiNxUanQhBCE9iE/1kZtl8eZKl4nCRNUkerKUnFd0UIVKQA9gKIsroUzVpGYWiS6RpMoB4nk7P2dfA1qAGnMREv2asUzOypKx0qiKStbKztjGTf/OiyFg+Pnw7sfoqu0goC8cOn7D4dgegV5pQGHHNsTOO9d2n9YYm3/1V8hd7CG8Z9fMa2ZDPc0/9SHCuxbvBsiePUv/33+p8g1H4lAxiZ71CO3Z0Na56KKoiFgtIhRb81WLYAgRrS0T1FV4WwOipgFqGtZoz65BrNbrIlgtiS6l18nh2Ov7O1047pGP1YTeKQp0bvOCWdfZImEtIXbdCT2nkGder3wlUiLPvIHYsgdagytWo5/74ZO8+aWv8LZ//Ru033UHAPnJKY584Uu88uefZexCD75ImN0f/ACP/Yd/S6Bm4WfLDFo2eX7m1SjrB3thYgRqG9YlG2HdkM96XspV2hGJ7n1gLP5cTZ6/wJt/8D/o+crXsLO5GaW4apq4lkVxcorkhYskz52n7wePE2hspO7g7fNsQgGk63Lxy1/lzGf/mlIyCUJgRMIEW5oRqkIpmSbbP0Cm9zKDzz3P2BtHufM//jbhTZ0zYzuzJk7DnXfQ+41vk+65RN8Pn6D1HQ8T2dS56DHY+TxDzz5PYcwTAGz+0E9cv5DN2YjUQjaJTI0hMpNIzfR8zQMRT52+1Pg1Oen9icQRze0IY2Vcnkg0ojS04o4NeST8emL4CvLy2epU6DUNiH33IYIRT7lfyoFWDr12Hc+OLRj3/AimM04UxbNuswtIX9gj0qULuSSoRvlZIj1C3iqAXwXHRRbS5Y0KkAWkYyOmA4FdFwpp754wnZ8iRflamfU76aZH+F86BX0XKj9ux0YO9iJG+qCtu/L1LAI5PgaZ9PIL3mDccBLdp/qo8c2ffJ2busBkYYqgFripJppvJQgEW6Kb2RbvZrI4NTPBlUh6U5f5fu/jPNL+NtpClbfzZkoZhvOjbI50oggVvxYka6dJWZOe4lhAUA2hKyYhLUrGSpIsTczsX0ALli1Ylh6gG6qJLS0KTo6iU5jx/o7ocQSirLLzU3BylNyipyCfpXgWQqApBgKBIx1CK1SHeutZfv8WgyNXTqKHDD+PbLmbvz3yLf7Xy3+PX/dUg+/Z/iD7m3Ys+/mjg6d58fIRTo1cZDQ7wX955i9pjtTzji33YDk2P7r4MieGz3FuvJe/O/otnut9nfduf5Du2k7qyzYiPzz/AufHe4mYIXRVw6fN/Z4kErtaEv0aAtZvmARNP+l8lSqdW1gXTKtXl4K+jN2Ip0Sv7nFlS2tNKfQrmUv4tQC1Zh22tBnJD+FIm3pfIz7Nz1Rxgoydps7XiK7opEpTpKxkWW0r8al+4mYtQS0084wbL4ySLE0yURwnb+e4kD47Y2HV4G8ianr3KymlF35YHKXgFJB4AZwxo4awEZ1R7Qs8Ze5grp+iU0ATOlEjRtSIoS7TSdOb6mUwN8T2+DZiZoxLqUucmjzNjvgOOsLtZKwspyZPUeuroSvahe3aXMn0cSl1iayVxa/56YpupiPcMecZfjl9meHcMFuiWxjJj9Kb7sVyLer8deyt3YNPWznx6G3zCheTPRScAjVmDbW+2rULsqsAqqLSGGgiqIdQhODI2JE55HDWyjKcH57ny30zIGrECGiBOcS0Ix3GCqN0RZdWhy2HscLYnABQ8MaHESO65BjQVM156vJ0KYXlWozkR+ao5kN6kKgZvXYVACT8deiKPvNbZe0s6VIaXTHm2PMoKDQElibz/JrJQGqEzTWtFdsxrSdkIQcTw5VNHk0/yu1vg3VQH60lau45RM09c1VsZl0diYcepObeuxf93OQrhxl94qnKNxypoaKWYqvgKdGvN3wBT925HiSwULyigOGrjkQ3fB5ZsU7h8CJcg9QNqKQBwbaQ+SwiHFvr3QJA5tKeijq7vJ//kojWomw74Pne30wwfIg7H0b2nqlOiT85grxwDOpbEL7g8ssDZ7//OANHjqGa3jxGSknvy4d59bN/g1BU7v6lTzN27gLHvvw1Nt13N/s/+pFljsVEbN2HrIZEL2Q9S5S2LYjA+omj1hRSQiaJvHS6uvXoBmLrvkXJW+m6XPjil7n09W/h5Au0PfZO2h57J766BKph4FoWpakkmb4+Jk+cItVzicb77lmUoFZ0nUBTI/6GOurvuoPEgf2EO9oxYp4FjJVKM/bGES5989tkr/Rx5bvfp/7Og+z6p7+EanhzGiEEtfv2UnfwdtI9lxh56RWSZ84Ramudo1ifjcnTZ5g4eQqnWMTf2EDTA/dX971VCBGth0jCKxQWMt7f6UlkagzMAIRrvYLcQmMcqwRWyfOtXm1OyLRlV74KpfQykKUisvd0dQUt04fYth/RMCt/TEpPfW74wbWRyVHw20iEFxRu+Dy1uXSQmSmPVPdpZV9vHWkVPGsS1/VU6IqGUDVkPu1pwAJhhFCRpZx3P9R9V5/dUnp/fAFP/e7KBX8bUduE6NiGHB2o3M4PvG7GgR5E86aborNpPXDDjzqg+2kKNM5rEZ4sTvFk39P8ZNf7iBgb0NvrxwQhPcShxoP0Zfq5lOqdIaFKrsWR0WNkrSx3NtzOzprthIzlH+hSSjJWlsHsED3pXi6lLuNKl1/a/SmEEBiKgaJHsV2r7GmqzJA9hmIS1mPY0kYAmqKjCc8aQlcMQloERajlwE/Pe9k/HVAqVAJaCN01ykrosvp8hiRXvPeFjouLJjT8qkfQzyyDQFN0b90rtJbw7FwqryA70sVxnRWpcFVFpTvRyafv+BCXJvsp2iX8mo+2aCOKovArd/8j2mJNM8vvaugm4gvRUA4frQlE2VG3mfZYE49suRtNUYn6wgR0H47m0p3ooCFUy4Ob7kBRPDubiOldm0HdI/CbI/Wkihnqg7U8suVuor4w/rIaT0qJ4zpVKdE1RZ1HqHQ2tHFgyx6++uL3iAej7O7YSsgfQr3erW8/xliKsjQUE2OZ/IDlMJ0voAq14q4O27XKbddV7coMpkqTZK00NWYCyy0xmLuCK12CWgif5meyNEHGSlFr1gEa/bkrOK6Nrnr3oKniJGkrRUdoM0H96r3TI8ndcm6BNmMbNZuMS1lJLqbOYkuLgBZCQSFl59AVk4gRu7qTQtCTPl++X2qkrTTjxVE6QptJ+OqXJCenSklOTZyizl9HzIxxdvIsrwwdRkGhI9xO2kpzbOw4t9Xtw5UuJ8ZP8PLwYQzVIKyHGC+Mc27qHPc23cOexNUOkeHcCG+OHWMwO0TRKXf+OCWKTol9tXtX/P1LKbmQvMBT/c8AUOuroafYw7mpczg32LpHCEFYD7M1tpW+TB+DZZX0NCYKE7Awl7uhUeurJWyE51ik2K7NlXQfd9S7VRHGV9KXZyxTphHSQ15RZAkYqkHMjM0ZQ2asDLZrMZofnXlNV3RiZmxRVbtP9RE344zkPXV2zs6RttKYmm+OzYynRF+aRD82dJZ/OPEDOuMt3Nu+n72N2wmbKyNurgsyKeTk6PLLLQDRsR0a2286cYsaCuJvb0OLLl28UgwD1Vf580qEYp6dy2phWchS4fpndviDiGhi+eUqhIgmkKYPclWoyoJhiNetqZ/13PVHKicBHNsjM9aJRGew1yM7qlKhq17gXef29fsO1wkCkA1tnnryyLNVrUueeQOx916PAFzBNTp67gKBRA3127cBkJ9KcvGpZ0kPj/DQ//Ev2P/Rj5DqG+CvPviznPzmd5cn0RUVsWUf8pUnvDDhSo+j5yRi371eWOBNAOnYyKFemKrsmTODls0Qr1v0Hmlnc4y++jpWOo0RDnPgt/41NXt2zbERkVJi53LkBocojI0R6+5e9FkmhKDzA+8jvmM7/vp6Qu1t6LNsfaSUNL/tQbRgkNN/+TmK4xP0fvM77PjFT8+Q6ACB5iYaDt3BwNPPUBgZpe+Hj1N/10HM+ALde1Iy/MJLpHs8C6zWRx7GX5e4Mc9bIRBCBTOABGQxB9kpbzcdBzJTEG+AaP18qxZVBVX1LEac1c3Z5DSxu542rFOjyP6L1anQo7WIPXfPzRJRdU+JriiglNX6ruP9bRXAdTxFOnidT0pZNY4Cht9TsvtCnpWL63qvCeHdL1wbsl4mH67rrde1gVnfk2bOWMSwGG2kqoittyFPvVYdiV7Mw9AVZCa55kG/6v2PIDqqE+ZUAmXT1lUtf8NJdE1oNIcaaQ400ped68/4xuhRCk6BuxvvpDPSQVj3FHsSie06FOwCWTtLppQlY2VoC7dSu4jf5bWQUuJIh6JTpOiUKDhFik7R8+W0ixTcIgW7SMbKcDF5ad7nxwsTPNP/AnEziql5np2GYuBTTQzVwFQNTLX8umrMC1i8WSCEoD3cxsOtD/LVi99ivDAx817JLXF68hyD2WFeHn6NjnAbraEWIkYYf1lNWHIsik6RZCnFRHGC0dwYY4UJ0qUMaStDzsrREKibtT0FXRjoi0x0DdXEWMDiRxUqqno1FFQr31RmK1y9ZRZWT017LqsLWEp43qoOJbfgqdC1yIq94FWhVeXlLJFYsoTKylRfhqrTVdNOV037vPfu6dg/59+N4QSN4auTp7ZoE23Rpms/NoN7Ow4sue2YPzJvG3MhPSKzCqhCR6Dw8f/26wyWW9Fd12VoaoyRqVFePnOEmnAMXZ1r+9LV2MGf/srvVbXtW7ixEHgFqUpJdGuNleghLcxkaRzvvLax3BJ+NTjjx1xw8piqb8bHvy3Y6d2bhIZEMpIfZDA/QNbOzJDoUSNOUAtRdIvIkqQl2D7jBz3dheFKh0vp8xSdAttiu/GpfoTwrJ+u9ZuXSFSh0BnagqqoZKw0vemLTBTHiJnxJTsAQnoQIRSyVhYpJUO5YVpCzQzlhpBIik6BolOkxhdnKDvEayOvEzdj3NV4F1EjQtbO8XTf07ww9BJNwWYS/quDrIniBLqic0/TPST8tTPP49WEVSdLSY6NH0cgeKDlfhoDDRSdIj+8/Pgqf8n1gRCCsBEhasbmkegLeX/fDIgYERK+Oq5krmCXrcZcXCaK44zmR5dVaC+GdCnNYG5onhI9ZsaWtE6Bss2MFiSoBUlbHlGXsbJYrs1Yfmwm/M2n+kj4Fp+MKkKhMdA4Q6Ln7TwZK4NfC1B0rto8mKq5rHXNB3e9gyvJIU6MnOObp3/EF49/jwNNO7m/8yAdseaqramqRjZVGaGhqF6om29lJNRGQu399xHZvQtfy9Kdk1okQnjPHszGCq0KAxUWSxyrugl9pfAFrir/1gPhKOhV2nIGo4hY3fLLVYpAqBxKVgEcB4rVWVQstW7Zf2H1QbXXIlqD2LJ3VX7gGwZCgGYg9tzthatmU5Wva3IMeeU8xBKeUnMZ5CcmSXR3oZeLauPnL3DllVdp2rubrofuJ5ioJVBTQ6SlibHzy1sjCEDGEp4n/cUTlR/H2CByuM8jlG8GBWgxj7xwvLpCECC23e7dSxZ59riOg1ssevY9roNZE5tH7goh0INBolu6iG5ZnqQLNDUSaGpccNwghMBfX0f7ex6j//EnKY5PkDx/HunOnaMomkbD3YeI79jO4Mgovd/+Ljt+8VMYsdi89eaGRxh7/QilyUmEptH5E+9DMW+MrbF0bC/MNjVaVkyHEIlW8Ic825BsEjnejwjF4RqhlAiGveLnxCgyOYF0nBVZ0sh8DjnY6/l5r0dOB3jnx+gADFURrqlqiO7bEJFrOEch5p8rUgLCU4WbAZjuAjbxCHjhpbhJ3YBiFpySV5wFj5SXzCjcMfzMKMGEmN9BtsIiqahrQSSakVOjVV2XcmwAMTUGa0iiy3QK5+wpnKOvgenD/5v/DhG8TiKUVV5rN/zuK4SgJdjMrtodDOaG5ijIik6JY2MnuZS8jE8z0RUDVfFIFMd1cKSDW1bqaorKT3V/cMUk+mRxiu/3Ps7ZqQu40sXFnQmicqVb9m52caQ7Tx0FMFmY4un+51CFilJWSQohUFCu/rfwDEE+3P0TbIt3Vx2Md6OgKRp7EruwXJuvXPjmzEQVvFbuieIkk8UpLiZ7MFQTTagzirTp79F2HWxpYTlWVXYeNwIlt8BUaQJFKAS1MJqy8uqoOktJWhGkxHItfIuQ/zcTJGDJVXpOXgNd0VGEIBoIz4SYAiQiNdC+FbkITRq5SRQbb2WUnCI5O0PJ9YJRdMUgoIUw1ZXZd6hlAtqisnPILvuySzm/m6ESBPUwg/k+HNcl52TRFIOAHqTg5Cm5RUpOkRozMePjHzHmSo9DegSRH8Ca6boRc1Tnony81xYUM1aatJWiNdhOzIhfPZaFHi8SmgKtMyS9QCGohyg6RSzXWrRY6e1fGFV4ftBTpSQZK8ttdft4cfAlLMdisjCFpmjUmDWcS55nqpTkYMNBWkMtKEIhbIS5o/EO/v7s/6Yn1TOHRLcci+3xbXRE2tFXcT+djcniFIPZQbbGtrI5smmGgN9Zs5OeVE9F61xrSClnwqpnw1xFsWAjQVVUOsIdXEieZ6J4taCeKqU4PnGcOn9dRWr005OnmCpOznktoAVoCbbiW+b+IBD4tQBRMzozNslaGSzXYrI4gVsOIDdVk4R/ccWtgkJjoIk3x98EvLFNxsqiKROUZhU9Er7Ess/09lgzrdFG9jRuJVnIMJQe5bWBk/z+M39OTSDKB3Y8zG1NO+bZnV0XuK4XhpiaWH7Za5FohromxI0uAlQAsy6BWbe84trf2srmX/unKGaFfva+YGUFBtu+MSS66V8/FTVAMLr6dv5rIAIhiK5sblcR/MGrxMZqUfa9XQ/IyRHk0OXq1i8E1DXDph03XeFrGkIIZE0DYste5NHnKl+RdJEnDyN23L6izgM94J9ps3Rtm+ETpxg6doL7fu2fkNiyGSEEQhWYwRD5yRUEZgoBpg9l2wHcakh010FeOIbYvBP8N5zGWRpSQnoKqrVyidQiWjcv+bvp4RCh9jYUw8DO5Xnl3/w79v+bf0V8x/aKN7uSuUKorQ0j5o3v7WwOpzSfM4pu7abu4AFGX32d3OAQfT98gvDmTXMU6wATbx5j8uQppOtSf/AAsW1br3+gaBnywhsgpdeplGjznhWqhhCKF4apGTByaWH7xEQjItGIPH8C9+UnUTq3Qs3yhVD3hR/A+KgXSNq9e+0PCs8ii4EeyFbRHWWYiL33rPCeKrzlFMUrel2blTO9DkUFTS8r8YUnWpg+38te50L3zd1mpfd0TUds3ulZ2lSjRp8YRk6OItq6167LyR/AeM8HsRuaKH3l78E0PVugDYgNcfcNaH4ONd7BaH6MI2PH5ti6ONJhqpRkOd7Ep5qr8o+2XZvxwiRDucpSeV3cOT6ZS6Fgl9N6b87xC+C1TB9s2E/YCPHl819jJD8253eSSApOkUIFCruN6Bk6G4biI+HzKqIKyqoIOC/41FOHLkbwLgWJpOTkQb/5/HOvhZTuHDKiEhiqiUDh9z7xr3FXUT29Ze1y4yClJF3OMrClhYKCFCDtNGkrScSIE9Fjy94HVKFhKCZ5p3KfvKJbWJI4Xg1CeqjsTZ4mZ2eI6BECWpBk2abFlQ5+LTBjMzFVnGA4P0jOyWK7FpZrla+H1d0Xck4OV7qE9aW9oqcR0K5W8JVyx42Li7uMb3jYCBE2QqRKKa6kLxPQ/WyKdPL84Av0Z/uZKIyXlwmTKWUQCIJ6cOZ3FAhiRgyBYPIaglRTNOK++IzfeyUo2AWKTomQHppDxNf6a6oqkkgpcXFRykFTla5LSkmylJx37MC8gsrNhM5IJ82hZpKl5ExXiOVaXEiepz3UTle0a8XPdK/DYZBTk6fm+KwD1Jq1bItvW9H379f8xIw4fXhERtbKMl4YI2/n5yxT61uCRC8r0Wc/q6fJ+NlK9IbA8uooKb1uSSQMZ8Z44sKLvDF4ivpgDY2hOj772le4rWk7P7v3vcT81/fZLq0ipMZX3WININq2eMFi60zGVTJWmr+S8jpWua+KoWPWVaF69gepaLBv2+tGxi4OgfAF1s1rHMoEeDV+60KBYHhd/Z+F6Zvbkr8auI7n/1uG88rTYBVRduyHUMQ79oXUictBSi9EcmQF5OxSCIQRm3YhNniGwZIQAvwBz4Lg5Ctzvu9V4/JZGB9GNnUs+5vUdHYwcvoMk71XKKbTnP7OD4i2tdJ250H0gNe1LKUkOz6O7ltZ0U2oGnI6YLTSYGeA88fg3ncjfRs7N046NvL8seqIOkB074HwfOX2bCiqyrZP/Tzjbx5n8uQper/9XUYOv0rDPYfY/KGfpP6OgxiRMIphrPqalI6Da9tIx0G6nujSe0xJnLL63VtQLqjsVQ2Dlre/jb4fPMHEseOc+9u/p/vjH51DojvFIiOvvk7y3HkQgo73vQdfbe0N+31F42bwhz37jzJxPud9VYXmrQve30XbJsSWXcjec8g3XsQGtPd/HBLlQFwh8AIypReqmc/ivvA4zhNfg0IO0dGNuP2+tT8oKWFyFHnlHKude82GaN+6OvW1qoFmIgtZ75kmFK/7TDM8UYIQCKEgdZ8XMKqbV61cAMwg5JKeFYxues8d6c4n1VeD9q1gmNVdm1bJC2jNpREhb14jpfTGl7blHbdV8r53wwtNFYC0rKv3cU3z8g7KPI3QNERDE0pDM2jzn8vSKT9znbJNjm6Apt2Q62RDkOhCCBoC9bxn02PoisGJiVNeC/maNt8vsN11XftbD5qisbNmO/9072f4Xu/jHB/3Jr5WBRYdqlAxVIOA5mdXzc512Nu1gxACdVFzqeU/qwsDVWjYcvXfk0SStwszStWbGZ4FROU3a8+PXgUBYf9VYjBTyDGRnqQxVo9xjYealJJkNk2mkCPk30CetD9GyDs5UtYUPtVP1GidITxtaZEsTZAuTaELnaC+dKuxqqjoqlFpRi8ARSdPSFsb0sqvBtAU3bOlsnPEjVoMxUACydIkmqKjKwZCCEZzQ5xOHifha6Aj1IVP9ZGxUlxKrz4dfbV3gUo7oPyqn6gRZbI4RW/6MjEjRlAPkfDVcinVS9JKkfDXeQMXAV4a/LXPbLngvUspdytVM+jxhuBi3hciF9qNVSBZStKX6SNshKkxazBVE13RV7WvXtFkitOTpxnNz7XNMBSD1lBr5Tt4g6EJjQOJ2xnODc85tvHCOM8PPo+qqLSF2tDE4oNaiZePMZof4ZmBZxi4xsovpIfYXbuH6AqLDQEtQNyMzfzbll7grNf14u1zrS+xqB/6NIJ6kJgZmyl8pK000kphuVdJm6ZA07LP4p7JPp659CrP9b5GupjloU138Lvv+DU2xVtRUbg01c/vP/PnvH3z3dedRKdUhEwFIYVCIFo2l0ni9YVXyKgOxZFREGDUefcoadu4joOiaStqK68Yhg9UBVau6fHg2tWFb1YCTfeU4us5+dR07zsRorIbs2l6Ya3ruY+qXjnR77rIUvHqHUFVcV94GvfpbyG6dqLsuxvR2Ir0BxHG4lYU10JaJeRIH6TmF2FXhXAcsWXPTatCn4YQCrK2Adq2wsXjla/IdZBnXkc0dSy76L6f+TBf/eVf5y/f8yH0gI+xsxc49Eufou3O22eebZnRMbKjY7Ts37fSA4FQFNG9F/lKFdZzhSzy3JuIOx6uvAB0PWAVkaderW4duonYvNvr8lkGdQdv54E//SPe+L3/zPBLr1AYHaPnH77Gpa99k1BbK+3vepSO97+X2PatGNEoyjLfnVMsUZycZPL0aYaefZ6pU2fI9vdTTKawMlncYhGn/Gc51N91B4n9+5g6c5bJ02cYevZ5Ot73npn3p86eZ/TV13BLJYKtLTTedw9acL7F7PWCCNd4PFzZHmcOFMUbB0UWJpKF4UO9623I/h7kydeRrz6LdewwYuseRNtmj3AVCuQzyOF+3PMnYbJsLRKNo/7kJ7zQ0jWGdB3PymW4uuKk2H77/HuqooC85nxStasEuT/sBbRmJgHpPXdUjTltxKp+VdGtXR2vCsOHFBLyWcilvGXMwFU7dCFmFSdWiNoGz8otPbXyzywAOdKHyCShTKLjONivPIf17a+i3fd2rMe/g8ykMT/4s2hvexRZyFP86hewD78IVgl1120YH/gp1PZNy+6/tG2ck29iffsruJd7wB9Ef+gd6G97FBkKX3cifUOQ6OCRjc3BRn6q+yfoHt3Ma6NHGM+Pk7PzFJ3SjHULeGpgtRxsaCgGhmoQNSIEtZUP7nVFpzHYSH4JNflC4vFkKQkSQkZoxeREUF+8vbPWV0NXdNPMv1WhEtCXvnFEzDCboh0z6ijPozO25Gd8qjlnO962a1FXqQIXQpDw1/IzWz9Eb/oKrw6/waV0L5lSloJToORY2NK++lsJZcYH2FB1TNXAr/mp8yfYEt3MnsQuasz4DakgXS9oio6maNgL2AKtBJZbLAcNbuCB0jKY9jwuuZUrSAzFmBMGO43nTx7mv371z/iTX/6PbGnunPOeKyWff+qrPHXsRb78f/6vird9C5Wj6ORRhUrEiM/xvNaFQdSowXZtCk5+WRJdEWq5q0NBUpmHW97Os0CkQkXwvJhDZO0MRbtAKBD2OvCEQrqUwlT9aMIb4QwXBtEUg+2xXShC9dRLVnrR7ilRVmgsVEj2q566PWUlqTHXL3BICEHUjDJRmGAgM8COmh0Yik5TsIkrmT6EEHRFuhAIIuVOmbSVwZEOqlCRSMYL4wDU+Nbec9en+TA1k0zJUwtPn1te6GXlFFzBKXB0/AgDmQFagi10RjbREmwmqIcwVANDMdAUzSsCIGZyWlzXxXItCk6ByeIkb44d5czUmXm/4dbYNvyq/6Z95gkhaAm1sC9xGy8MPj9HQT6YG+CJK4+zv+4AHeEOgnpw5r4NXnHBci1ydo6BbD+vj77OQHZgznekCpWuSBe7anet+DsyVIOIEcFQjJlnzKXUpRnveV3RaQgsHaQrhCgv1zBDok8UJzxLnvL+KSjUBZZXKf/xy3+HRPJTux/j3o79hIy5Y9OWSANN4bob441ulSrzFQ5FIRS5aaxcTv7r30IoCvv+7I9BVcleuEjq+Enidx7E37bORSxNX71PtiwHhV1PaLrnW7ue9yIhPN91Rb3q87oa6L7y5Hyd93Ga4F4t0e+6c4of6u33od52N+5AL/LkGzg/+AdENI7YtB3RuglRUw/hyPJe1slx5PhQBQczC5qOaOpc89C3G4Jp8rlzG7LnRFWVctl7GuE6y1q6bH/XO9jz4Q9w6fmXsPIF9nzoA+z/6E/jj8Vmlrn80mF8kTCb7r9nxdsXviB0bEMefb4qFag8/Tpi//2wke0+R/pgtL+6dbR2Qc0C4ZULQAhBze6dPPTZP6Pvh0/Q8+WvMnHiJPnhETKXL3Pij/+Uc3/7BTre9x62/vzHqNu/H6EvXPAvpVIM/OgZTv7pXzDy8iu4to0RCaOFQqimgRkrPw+lJNPXj53NLrlvqmHQ9q5HGXj6WTK9lzn3hS/S9tijKLqG6zhMnjjB+JvHAGh55O0Em5tv6DhR2iXP97yY855PsyCideAPL35XFgLR3oX6ro/gAPLccSjkkccOI48dXvgzmg71LWjv+RnEjv3r81zKZ2God97xrAqmH9q65x278M3vlhLRq5k+QtUgGIPFqEohEJoO0QWyhYRAGAEwFuEHFRURXt19XggF0dTpedBXk1cwOep9r1Je/c0sG+fiOdR9B/H/q3/vjW18AYSuU/zS3+CePYX/1/8NIhCg9I0vU/riX+P75d9AhJbmAdz+y1hPfg+lezvmZ34d9/JFiv/vH6I0NKEevPu6FxQ3DIk+jaAe4N7mQ+yv38fl9BX60gOMFcYp2AVKZe9YXdHwq37CRoiYGSXhr6Ex0EjMXHmLdNwX40Nb3r/o+450GMwO0hJsmXMTe3rgaVzpcGf9nTM+s9Xgodb7eKh1dS0rexO72ZuY7xXluDZ5Jz8nvFERCmE9SnOoid888KtV7+80NEWjK7qJTZEOksUUfdkBhrLDTBanyFq5GeWWrnghqyE9SNyMUeuvoSnQSNSMrIuNi+VaZK00UWPlxLwXAlgga6WQeF7A/lkBo7ZrkyxN4koXUzWJGLFV7ZOuGlXZFrg4FJ0C/lUUiTYiik6hqu4SQzFRVvk9CsDQDfrHK7NtuoWVYakrTUo5U0yb/zkvO2KlpLgmdHRFp+RW1vZecPI40qnqepyNoB5mvDCKKx0CehDbtVCFSsZOE9aj6GWf1ekchbydQ1N0ik6RqdLEgkUlIQQ+1Y8lbVJWcsZeQi+Tt0EtTFiPMpwfJKrH8Gl+BAJb2qhCw6f61uzeGjUiWK7FeHGcGl8NuuqR6EfGjlJjxmc8pltCzdT6ajk9eYaIESFmxshaGQ4Pv0q9v57OcOeKtzldcJsO/nalS8ktUbCLoHrPHiEEMTNOU6CRS+lezk2dpynYSMEpcnryzLJWNSuBLW16M730ZnrRFI16fz01Zi0xM0rYCGMqJqqilX8fl4JdJFlKMpQbYiDbP8+eBKDGrOFA3f7qcjI2CG5L3MZUYZKj40fndKONFcb4Uf+TNPgbaAu1EffV4C+fowWnQLKYpC9zhcHc4LyAVYGgPdTOPU33rtorP6gHCethxote4SZZuqq21hSdev/yoaeaolHnr+f0pOffOtsOBiBshAmoy7fP/+rdH6c5Ur/o76wpKp848EEaQjeA3LJKyEq8QKOJFSkBNwqy585jNtTPqM4nXz7MpT/+U7b9zv+1/iR6Jdf3IjYA6wpN80Li1hlCN5GVEiK6fl32EVWvUC0v5xc/VBWlbTOyoQWx4zbc15/Dff77YPgQW3aibNsLndsQviXIz9QETFQ5btUN6Nha3To2EIRuQqIZGQhXFzA6Pux9v/GlQ6uNUIj3/cHvM/imp3yv29pNoGauICBUV8u9v/rLdL/j7SvfvqJ4227ZXF3A6PBlGB+C5k3LL3uDIE+9Vt0KFAXRse2qynWFUA2Djve8i9ZHHmby1Cn6n3iKkZdfYfLUaXIDg5z7/BcYP3qMe/7gP1N3cL6q2LUsLn/7u7zxe/+F9KVezHicujtup+7AfsJdm/AlEuihEJrfh5PP88q//W1GXnpl2f1qvO8eYlu3ku3rZ/ill0meP098x3aKExOMvXGUwsgoWihI0333YCZubPFLjvXB1HC5k+iaOUUwtqJ1KNv2ISJxnOd+gDx7DDk5Brm0V3iUeM8gXwARiSNaN6Hc/xhiy8716xbLZZDDVQSKAqKpE8wqbFQ2Emqbyr9tFWOPbAqZTXn2MtNiTykRsTja/W9DaWiaWVQW8tivvoj+2AdQOrsQqor+wMMU/ubPcS6cRdt3++LbkRJ3sB/30gWUtk6cU8fKr4N98hjqvoOrJtFlsYAcG0WWrWWURP2yRP5sbNiZXEDzsz2+le3xGzMAyNs5nhp4io92f/SmsdEouiUGc32krKmZ10zVx47Y3ortSJaDIhTivhhxX4w9tTfeliVvZ7mQOsv+xJ0r/t2mbUaG80OkrSQtwXbaQp0z79vSZrwwSrI0iU8LsKdm/6r2SRe65+VtZyoikR3pkrdzNzWJLpFk7UwVaxAYqm9G0SilJF8qUCgVyeSzWLZNMpdm4pq2pGwxz4neM4T8N64l7scBS53VqqJRsHKU3BKauGqLIaXEckvY0iagrOzc1hUdXTUqJtFd6ZB3coSVtbFQCOphejMXqDETXuhtORy05BYxVGOGRKv3NZAupbiQPoup+HCkjSvlgte0gkLUiDNaGKYvc4kJLYQQgsZACzEjjqqodIa76Emf40LqLH7NjyJUHGkTN2tp9LesGYkeMaKUnBIChYgRQRUqjYEG0qU0MSM6E+Rd56/jYMPtvDz0Ck/3P01QD5aLqRb3NN1NrX/lk4GCU6An1cNAdpCJwgQZK8u5qfMUnQIhPcyOmh3U+mqIGGH21O7hmYFneXbwOeJmDIkkpAfXPAPBdm0GsgPzbEemlejuClQtNWYNdzUeos6/tCL6ZoGu6NzTdC+2tDk5cXJOQch2bfqz/fRnPQXa9LN4qeefgkJ7uJ0HWx4itkxn3UIIap4//zSJPhs+1SSxhB/6NDRFo86XWDTDpMHfgLKCc6shlFiyU1FVVDrjLcuuZ11glyC/+mexCMe8yfRNAieXQ12hT/GaoxIC4EaQ6Kp2Xex50I35JMxKoRmIwMontRVD06lI7T7t6TsNx0EmJ5BDfZ6VwdgQFAsoe+5ENHcih6/gvvgEiqohFgvOc2zk1Ghl4b+zYQY8C6brDOk4ZC71Uhgepu7uQ6smm2S5kDHvOSkEhONQ3wI9VZDojo3sPYtYhkQXQmAEAnQcunPRZTruvouOu+9a/T6E44j2rchLpyvvQHEdT42+QUl0mcsge05Vt5JoLaKxA1Hhs0c1DRK37aN2317yIyMMPPUM57/wvxl+8RUmjh3n5J/+Bffv24tqzm1RzfT10fOVr5PuvYzQNLZ94uPs+qe/hL9+fidabngEdYVB1GY0Stu73snIq69ipzNc/PJXuf3f/p+kL15i+MWXAajdt5fYtm2oeoUWU2uFqWFEc7cXLFoFDyaa2tE+9CncKxeQly8gR4egkPPunT4fIpZAtHQiOrZ4Fi7rNT52Xc/KbqzKDp/WLq+z6i0AUdtQeYF7GtL18h1KRa/rbHrdhg8lPnfuJ7MZZLGAUpvwionlsGVhmsj0Mvd0KSGfxx0awDn2Bu6FswAoHZtRWtsrCjZ1hwawvvJ55JhnTan/1MfRblv8fn8tNiyJXg1s12YoN4SL6wU75b1q/rbYNkJ6iKniFP3ZPnJ2Hk3RaPDX0xBoRFd0XOlybuoc/TkvOO2l4RcBQYO/gc0RbzDihWhdJO/kMRSDznAHcd/V5Ph0KU1ftq/c0m/SFmqfmRROFCeYKEwQNaL0Z/uwXYeOcAd1/roNH7B5M8BQfTQFWlZV+FCEQsyswVDNBT2KfaqPrshWLmcukbFXr+ASQmCqvop90aeJPynlTUu+SCnJ20u3ui2Faeum6WtESslTb77ID448y6XhK1wc6uV/fPOzRK+ZbGUKWZ4/+Rq/+NjPVrX/t1A5fKqfnJ0hWZqgpBZniGWvcyaHQKy4QKQrOqbiI0tlqeoSSdZKEV6joN6IHqEtuGkmKFJRVGrNunKoZtwL4wHiZoLNYUnaTiOlJKLFCOsRsnYavzq3wCOEIKSH2BzeSrI0iVNWmM9Wz0f0KN2RHUyWxsvhpJ4VRXRWQGvUiNMR2oQyi8xThErcTBDSIxgrUPpGjDD76vayJbZlxjIsbsZ5qPVBYkYMn+Yrr1dhW3wbET1Cb+YyWStLU6CJjkg7baG2OetsCjZxV+OdxMyFLV684oqF7dpEjAh3Ntwx817RKeKUJ5yKUNgc3YSpmfSmeik5RWK+OFtj3dSYNSuy3VgIilBmQkWXg0TOTPgXgyY0GgIN7K/bz9bYtjmWRjc7AlqA+5ruJ6AFODV5asEQVVg+KNKvBeiKdHGw/iANgeUV4wshqAcJG/Ova4Ggxlczc64uBUUohI0IQT1IxppPNNcF6ldk4/fN0z/iQPNONsVbN94z23W8yc5q4Q96ZOhNAjUQoDg6ipVKoUeuo++8oLIJtpRUY0NVEVRtXXxn50HTKidGNOP6+PCramVkgpTgXv3d3BOv4R4/7Nn5+AOITdtRunYiEg0gBDKbxvnO/0YOXYFFSHSZz3pETzUe+UJAbQMiUrP8smsM17YZf/U1Rl98ySPRV4nRF14i0NxEsHOB8M9wDFHXWh056zrI3tNw2zoEF64QwjChsR0ZS1TecSAl8uIJuOddc8irjQLZexqyFeRvzIIoW7lUS6wKIQg0NND1kQ/jr6sjN/BbTJ0+y+Azz+HaDuo1No+pCz1k+vtBSgJNjWz9xMfx1S1ciM8NDWFlVj4naXvsnZz57F8zeeIkAz96mu2f/gSTZ84wdeYMQlVpvPsQoY625Ve03hBquXC+BmMYRUHp6IaO7urXVSGkbSFH+6C0Squ1ayCaO5e1grppEKtbm6LF5Og8Et0bC82dSwmfH2GYyFTSK2oI4VkMliyEfxlbKiHA0FE3d2N+9FOom2edS9OE/GqRmsI5chg56t2DtYfftaqPv0XOgrmwXZuedA8juRFaQy1IKSm6JaR0kUhydpbJ4iS6YjBVnGQ4N8Q+BC3lsC9VUXGlN1H3WorFHO/KgewgAoWgFqQ/389wfoiHWx9BV3SyVpbTU6eYKk4R0kOM5scYyA5wf/MD+FQfw7lhXht9la2xbTCLLLhZIKVkojjOVHEcCUSMCPGyClMiSZWSTBY9YkdTdOJmDXHTq0Qli5PknBwKgrSVRhEK9b5GgrqntLRci/HCKBkrhSJUYkYNUSM2891LKclYacaLo5ScEqpQiPtqiRk1SOkylB8ga2fn+bzbrk2qNEWyNIUtLXTFIOGrJ1hWeFaDklNkKD9AwqzHrwXKx1FiLD+CXwsQM70BrKn60RS9Yl/0klui5BYx1ZtHCTYbRbcwJ5xttTAVc8YSYxrxcIyaUIxTV85TtEoMTgyTys0eyAiCPj+feudP89EHPlDxtm+hOhiKSdSoIW0lSVvJGesWBQVD9RHRoxjKyozKFaFiKj40oWEv4ie+HHJ2FsstoS8TMrgSmKqPzZGrD3KBIGbWzFz301CEQsLfQIK5BGFoER94RajEzPiiRLMQgqAeWtJSzLv3zt0PVVFJ+FZOLpuqyb7E3MAsQzV4R9sj85ZVhUpruJXW8NJWCa2hFlpDi6twA3qAfYl987a7EDRFoyPcTke4fc7rD7Tcv+xnF0NYD7Ondi8+zUd/pp+MVVkHkSY0Ev4EneFNbIluoTHQiK7eYGXRGmO64HNXwyHq/Q2cnTrL5UzvggT0QvCpPpqDzWyJdrMluoWwEa5YTBDQAoT1MAqKJ6AoQxUqTcGmJT4563gQ+FQftb7aBY+hwV+/ohyZr516nM01G2ASvBBctzJval+w8vDFG4Dwzh2MPvkjzv/+fyW4tZupw69hpVKMP/0MpbGxZT8f3X8b4R3br8OeliFE5WrtSqGqXlv6ekNUOMEFj4C/LvtYxVxgthK9VEDUNyPauhCtmyAwd54hfAGU7l1LFwbyGeTkSOX7A1D2u70RlgOKplGz/zb8jY2r3r5TKHDlq1+n8e0PEeycH/4pfAFkvM67F1VaZHBdGOn3yLQlFM7p4RF0nw9fdB2KcEJAbSOiZROyGtue1ATyynlE996127e1gOsizxyprhBk+qF5k9cFtQJIx/HsX5bJPols6sSIesIXt1RioeKlUyrhWt5zUvP7UU1zwfU6hSKDTz9L5vLKgypDLc20vOPtTJ05S7a/nyvf/yHjbxzFyRcIdbSTOHAbRmR19jXrgmgCOXwJYg3lAvqs4zf9CO3mKaoD3vU+2FvdOkw/RGo3njiiUgRCZfu5yvkZAJmZQtjLr0MEgqi792MffgFl206EL4B95FUIBFA6lumaEgKloRkRCmO/cRiRqEPoBu7wIKKxGREMrbrcI20buYJQ4MXwliTRARzXQeLSGmqj3leHJW18qg+BIOFLEDGi+DU/U8UpXhp+kbHCOC2hVhShsCW6BV3R6En2cHvdwXmTOlWodEe7aQm2MFYY4xuXvkGqlKLWV8tIfoTR/Bg74jtoCbaQs3N8/dLX6c/00xXtwsXFdm3qfHW0hlqRUqIq6k2jQk+WphjIXSZqxJASxgojlJwSzcE2Ck6egewVDNUkqAWxpTOH6MrYaa5kLlHrq8On+kmXkvRkzrM9thtFCsbyw0wUx4gYUSzXZiB3BYmkxqxBCIWCk+dC6gx+LUBAC+JK11NnAwhBQAtScPIM5QbYFO6e00JuuRaKEPiVACkryeVMD93RHeiiukmhIhSmipMIFJrVVhQUCnaBwXw/naGumeUMxURXDArOfJ/clcB2LXJ29qYl0TOlZHV+6KpvDumpKAqHtu1n36Yd7Di8hc8+/iV+7f2for2ueWaZaT/0+liC0AZUabyVsNSDSwiBXw2gKzqWW8KVDhLvPur5fOur6hwxVBNdMbErIYLw7JmyVmYe0X0La4e/OPIltsTbeaD9TpLFNN87/wx5u8hjXffTHK7nlYE3OT12kfd0P0SNL8pEIckTl17k4sQVXCSbY63c13Y7LeEGhBCM5Sb5/PFv8Ojm+/jOhWeoC8Q51HwbZ8YvcmbiEne33MbtTbswFJ1jo2d5fegEQ5lxbGnTGKzjUMs+dia6Zgqyz15+lQuTl3mw406e6HmRoewYETPIgcZd3N64i+3x7TQHm5ksTjBVnGI0P0qylCRr5cjbnjWR4159vqmKNpPVEjNjxM049f56Ev4ENWat5wlewaBbUzQeaH6AvD1XOZPwJ1btGT4bcV+c93XOzYQRQL1/9ep9IQQ+zcfW2FYag42M5EYYyg0ylBtiqjhF1srOWL3oik5ADxIzotT562gMNFLvbyDui1d1POAViHbW7CThT8zpDlCEsmISHSCkh7iv6X721s4v4rSGWhEr6FKI+SIb1wRQulBJMV83bppQUYCWj/40ky+9zJW/+hv0WAynUMTJZBj48ldQv/WdZT/f9Ru/XhmJLqmsSKGo11/dpqjXx6KnmvNGVSu2clgVKp2DXWPnInYdRGgaYrGuDUVBbNu79HdSyEGySisXRUE0tC/4lpSS0RdeZOjxJymMjmHU1tD09odoePABAIoTEww/9SxjL7+Mnctj1tbS/Yufwt/chGtZjB9+lUzPJQLNTQz88AncUolNP/vTxG/bh5VMcvK//z+Ukkliu3bR8ODcgvb5v/wcgZZmkqfOkO7pIdLdTedPfRCzro6xVw7T+6V/YOjJp0ieOs2Vb3wLoQgO/OffQw+Frh5XKIoMRiA537prxSjmkZOjiIbFi52v/MXnmOy9woO/+WvUbd1S+bYWgQhFkU2dcPZNKFY2N8Qq4p59A3XLng3l0SwnhmG4ysDCRBOivm3F98WJk6cYeuZ5Qu1t1B08gL+xYd64qzA+Tu+3vjNDesd3Luy/7autwYh4IpdsXz8jr7xKx/vejTJr2cLEJL3f+g7nv/BFiuOrOBeFoOsjH+bsX30eK53h8re/S27IK6Qk9u8jvmM7QtkAv2U+A+lxZCHjEa2zi4ENmyCyOs92KSVMjCFH+r0gStcFnx8RT0CiwbvPr+c5bJWQY4PVrSOWuBpC/VaAooHPX/n9ZxrZ1MoKZkJgvOsDlL729xT/8o8BifAHMR59H0qN1+lhHz+C/dKzOOdP4w4NUvzj/4rS2IL+rg+gtLaj3f8w9gtPU/ij/+qND/wBzJ/+eQhWkJ3i2KsPgZ+FtyyJLpHEzTgJXwJdNdDxBjTTqvRTk6cYz49TcouM5EdoDjSv2C6jzp+YCVmL++IIBHk7j5SSVClFT+oi44WxmUnhRGGCscIom6ObQXqty03BJsxr+4duAvRnLxPWIzQHvMHZUK6fieIYcV8CyymSd3IkfPXU+BJl5f9cqEIlbtQSN2spmAnenHiNdClJQAsyUhiiwd9Ena8BR7pcSJ1hsjhOWI9gqAYj+SEkko7QZnTVmOWbpyCAuFmLBEbyg/O2WeOrBRKoQiFQCnFm6jiOtNGpbuKuKTo1Zi2TpQnq/Y3oQidjpdCENieAVBEKAS1Azs7gVKCgdaRNzs4SMWIraiffSHBcuyIbnGloQsOn+udZLAghCJh+drRt4f5dd7C9tYuO+hvkM3sLS0IIgS6MNVF/G4qJoZrkncrsgVzpkLGShPUI6lsg4LFa/Puv/yEvXzw6Y5OyFP7Ne3+J+7pvR1uGFOmZ6mM8N8X9bXcwWUjxg54XcKXLzroumsP1HB0+TW9ygHc6NulSjv/75b/iUrKfnYkuBIInL73MybEL/Pzen6Ar3k7WyvN4zwtkilmEUPjehWd5c/gMMV+Y0ewkfakh6oO1bIm3861zT5EqZWgI1qK6Ci8PHOXsRA+f3PdBdia8ifDFqSt8/dyTHB05Q9gIEvdFuDjZx+tDpyjtsXmo405qfDXU+GqwXZu8nafklrBdG9u1cMoF3GnFs2cBI1AVDVM1MVUTn+qrOkBUFSpd0bWfvAe0ALtrF/HjrRCqohI340SNKO3hdnJ2jqJTnPm+wPuedEXHUAz8mh+f5lvT51mdv466CgoBs2GoBu3hhcmnleJjt72Pp3peIaD7aY02zOlk1BRt2etnXeG6YFdA8qpqRZ6TNwrxQ3ex+3/+AZMvH6Y0PEL69Gkyp84Q3bcXX0vzsp/3t1fRSVCJ+lJRKvNSrwaK6hEC6w0hqNgSQNU9S5f1RjX7OGuiIxbJ35GFPGRTiNoGxFIqdCk9Er1aP3ShQMPCXWHJ02fo+fzfEdu7h5oD+ylNTc0cg53LceWrX2f42eeoO3QIX0M9hZFRVL9XyJC2Q/rsOS789d/S9oH3krjjIHYmix6LIRQFLRCk+dF30P/t7zL6wots++XPzNn22MuHyVy6RMeHfpL6uw8x8P0fYqVS7P4/fpNQZwcNDz5A8sQp6u65m7p77gKhzPOrJhSFSLw6Et22kGODS5Lop779fYqpFIH4v6t8O0tB1RCN7ci6JuibbyW6IjgODPZ6BYGapT3eryfkxRNQSYD1NIRANHZA3fL36mnkh0c4/8UvUZpK4m+oJ9jSTLClBT0URDouhfFxUhd7mDp1mvzwCKrPx/ZPfwJlAe/x2PZt1OzcyfjRY9i5HK//7n9i+PkXiGzpQmgaucEhJo4fZ+LN4wQaG4nv3sXU6TMrLhpEu7fQdN+99H7z24y8fBinVEIPhUgc2E9wvYOvVwhR3w51i1wfqwkZd13cCydxX3wC2dfjhXvaFiC9a8D0Q6wWZdftKAcfQERia09SS+nZhkyNVreeWN1N1ZG3LASeGr2aeylANnN1XKmqqHsP4GtsXvC7UppbMT78MdyhAXBdRE0tSlPrzPhSaWpBu/sBtAN3eteToiL8fkQkivD50Q4eQmltRyYnQXrqdlFTu/qnt5QeiV6qXIX/lmYQvMnK3EMsuSWeHngav+rjtrrbcFyH10ZfXZVG1lCvejN7wWJ4qmgkjnRI+BMcSBwgMMvnN2yEZ9SWankiebPBlS6p0hQTxTGGcl7AmuVamKqPklMkpIcJ6xEups4xXhylJdA+z67AVP34NT+qohIQQQzFIGdnMRSDicIYGSs140tedArUmIky6WyQKk0RMWL4tLJv0oqvGEnaSjGSGyLv5MqkiOcxLpFVB8cmfPUM5Poo2DkUPUiyNEXcrJ137oW0CFNioiISHaDkFCjYOYKLWEBsVGTsdEVe8NMwVB+m6lu0wLWluZNffPRniVyP8KlbWBCz75+2a894fRuqiSMdkBJFqGvSAqcIBb8WIGtVfl4VnAIZO03UWNgu5ccJR6+c4ocnX1gRif7p+z+8rAc4QFesnTeGTiKRZIpZMqUsbZFGhjKjuNJlKDNGQ7AWv2byw57nOTpymn9x1yfYU78NAbwxdIq/O/Etnu49TH3gqtqlI9bCva0H+NybX2UgM8JHdj7GVCHD3x3/JpP5JKJG8Il9P4lSVkdLKXmx7wh/d+KbnB2/NEOiA6SKGeqDtXxq3wcxVYPzk5f53NGv8MbQSQ407iDm81q4NUUjbNy6t6wU3vXpPeevJwp2nrHiMHk751m4BNrwazemA+n7Z5/jiZ6X+N65ZzFVY85975MHPshP7Jxvh3TdcG0Q4kqhaNffbqQKKKZJ7X33Et1/G26pxOA/fI0rk1M0f+TD1JYVt0tBC1bhw72CtuZ5EMoNUKIrCG0Di3kUxSP5b6LizWKQI/3I155H/cmfX3pBx4b0FFiVt5gDHtEcWtgSojQ2Rn5wiM6f+QiJuw8hZxXV0ucuMPbKq9Tfdy+dP/MRVNPEKRTQgsGZ+5hrO2iBAPUP3E9s106k66LoOkJRUP0+EnfeQfL0WXIDC6s+g+1ttLz7XZiJWvRIhNN/+MdkPvJhwlu6qL19P2ailtjuHTQ89ODCY8ZgxAsYrQa2BeNLBwzmxieo7dpEoHYduxbrWhD1bcj+i+VchAqQSSJ7Tm4cEt0qeYGpVSg8CcehqdMjWFcI71wtku65RLrnEkLTPBsWVQUkbsnCKZXAdfE3NrD3n/8qre94GLHA/cWIRNj+C58kffkyg88+T/LcebL9/ah+r6vQKZaw83kS+/dx27/8DSZOnOD4H/4JVnJlgbeKprHloz/Npa9/k1LS842P77+NugP7UY2NYZMigjGkXfJUypWMGQCZz+H86BvIF59Ajg56RPa1ywBcuYBz4STu0ZdQP/xpRNvmmUyptYCUEpkcrywPZhZEJHb9i93rjbXIuilkvHFPWYwsamqhZpFOBSFQGppQGhbuEFVq61BqFxfDCH8AdVP14iLpushioeJzG97iJPpCLGvJKTGQ7efd7e+hKdDERGEcd4EvUBEKEokrnRmSdfphLhahXb2APD+GYhLQgjMtxFJKFKF4ZNKSa9j4kEg2RbqpNa8GbChCQVN0FBQ2hbup8zUyUhji+OQbtIc20xJsu2Yds49deOFseDYNW6M75xDvqlBnvLBdJOoKA99mtiUlE8UJ+jKXSPjq6QhvpuDkOTFxdNXHvhhM1UfMiDNeHEVTdNJ2io5w17zlNEUnoIcoFYsVWZsU3SJpK4VPDcxRtm1kONIhWZpY8BpbCQSeFchSntk+3cSnL/z+NOFnuw76WyUIZIPDlQ5pa8pTnGKSLE0ipUvMqEEV1f8GQgiCWpi0msSu0G/Rlla5AyZ0UxY01xKGZuDXTSzH9tTV5eLitF1WJeiu6eDb558iZ+W5nBokEYjTGWtlIDPKSHaCiUKSvQ3b8OkmT19+hbZII/e13T5TeLy7dT+vDLzJa0PHeajjzhnLs654O4lAnMZQgpyVZ1OsjYH0CEIISq6FlJLGYB22a3tqcSQNwVpM1SBZTM/pNhPAe7c8REPQe5ZtirXSHmliPD9JupSdIdFv4eaAi0vRKVJw8qhCm8leuBH40O5HeXTr/QtePx2xlSvr1gVKmaxd7b2zXAy9WSCEAFWdCRU1G+rR4zG0SARzkYC4NUEhD04lRQr1BijRFdgghM2CEMr1UaFXC9fxlOZLIZ1ETizvcy5Lher90MGzHViEhIrvv43aO+/gyL/9bWoO7Gfzxz5KfN8eAAqjo7i2TWTbthkLlflKXYmvvo5Id/d8lfgKEO7egh6NoJgmoU2dCKGQGxggvGX+vGlB+IOIQLi6GF67bO0g5aKq12BtjXcfWUfrBmH4kK1dcOFY5WrQfBbZcxK5957FbYSuI2TfBS8stQpyikQzomXzqr77+jvv4IE/+yMGnniKkcOvku65RGF8HLdUQqgqgYZ6Ips303Dv3bS98xHCmzah+hb2OhdCEN+9k/v/5H/S+81vcfk732fq9FmcYhE9GiHRtYmWRx6h9R0PE+5oQ/X5CDQ1knMXP5+uRd0dt1OzdzcTbx5HKAo1e3dTe9vG8baXk0PIgfPe73htQGTLNogtXbSR+Rzud/4e95nveF0Jqgq+ACIa9wp8iuKp0lOT3nMzm0GeegPnL/4z6i//W0TTGubKuA6MDVS/nlDsrRMqOo21OB7X9Sx6pOsF0t4McB0oVBcy+xY7E5aHpmgEtAADuQFM1eBi6iIj+RE6wp1zlovqUVShcnrqNPX+BkzVJGosHfQghKAp0MSVzBVOTJ7AljaaUBkrjNEd24qxBlYGNxICQVAPU3TyGKo5J8BLQZlRdUeMKBEjgl8N0J+9PIdEL7lFSm4BVwawHC8s068G0BTPssNyS5iqiUDgTle0yqR7UAuRspLYro0qlJkBlNcNsPhDq2DnUYRCjS+BqfpIlZJzgseq/l6EoCHQzLmpUxiKSUSP4FvAu1wIQViPki4lK1TQSnJ2hpyTISQiGz7YwrM3mqLoVF75NRQTvxas6ljHUpN897Uf8XNv/1DF67iFlWOagJ0uYkrpIqsZTC8ATWjlDITcrOLk6pB3sqRKk8TNxE2TSbEe+JePfZqf3P8Io5lJxtKTTOSSTGSmODfSy/G+s+QrUMR113TgSJeLk1e4nBxgZ6KL5nADJ0bPcWrsApZj0xyux1B0BjOj7KnbOqdzx6+ZxHwRjo+eI28XCBmeKtSve7kmXnFVxVT18m8ncKVLybF4fegEP+h5nv70CNlSjmwpz1h+kvvbD87pPBIIWsNXg141oaKrOpbrzNiP3MItVIJd9VtwpDNz7QQNT00nJSg3+rktFM8iY7UkuuNUR4rcYBg1NQQ6OlB866y8Tk9WVmzQdTCub/cGilqdX/l6QwjEAlYLK4aUgPSkjtded2t4HbrHDiO/82UvdG4xFPOI1mWC08BTalbrhw6IeN2i3bpaMMiuf/UbdHzoJ+n98lc4/Ou/wZZP/hxbPv1Jb4EVnL5CU1GMyn4badlXt1H+jeaN8SWLEtzC8CEDIY+Eq9Rz23UhPYksFRZVO29/z6Mc+cKXmOy9TLyjfX3mXEIgWruQiebKSXTpeqR1/0XovI5hyAvBdZE9JyAzVfk6DB+iuQNqVmfNppoGtXv3ULN7l7cfUs69F4syV6AoM+rzpX5TRVUJNDWy/Rc+xbZPfmLW8094nuXl9QghaHrgXt7/1A+9rtsVFiaFUFDKBGawtYWGuw+hhzdO16McuYxo7ISaZubdTJa5FqTj4L7yI5wXH/esPmobUB96D8odD0LsGoVyPot77gTuM99Bnj2OHLiM+6U/R/zy/2/tikKugxxdAxIdAalJ5FuFSHed6nILZsMqXc0pvBngOMh8dV7wb5GzYC6EEESNCAJlnuLbUA3ubbyXI2NHuZzuZVN4E7cl9hPS5xrSB/UQdzUe4tTESU5yiu3x7eyt3UvEiOBKB6VcaREIGsokO0DMjHGw7nZOTJzgxaEXEEJQaybojm71PJy1ALW+mzPZVwhBe6iTC6mzmGqfR3o7JXTVoMZMkLHSZKw0uqIjhCBjpYhcU3go2HlG8yPYjs1kaRyf6idqxhAo1PsbGckPecEDwqDg5IkYMcK6pyRqCrRwYvIolzM9hHXvdzAUk6gZx3JKFJ1i2XPcs53RFB2f6sdUTVzpMlEYw1R9jBdGcNyr7YuOdCjYebJ2hpJTpCDypEpTmKoPQzEpOHkKToGCk8dySiRLnsrWp/pnyLeIEUVTVEYLw3SGuxb9ff1aAL8WIG0lK/oNSm6RjJXCp/rRxcYuylhukVRpqmL7GhD4VD9+NVBV50bP8GX++9f+/BaJfp0gyv+btlVwpI1EUnJLKIucC7M7Tla0DSEI6RHSVgqnwrBeRzqkrCSm6ieohW7Ke/Ja4GDnbg52zvfH/ubRH/HP//73uDByedXrrA8miJohzk/2MpgZ5e7W/YQMP650OTF2jogZJGwEy175GtY1QXzT1miqUOYUOObfB+b++6X+o/z+i3/Ova0H+Mz+j9AQTNCb7OfP3/ji/J0U3njgFm5hrVF0ShwZPM0rfW9iqga/cPDDFOwSI9lxor4wNf6lBRnrCkX1CNvV1sZsC+k6N88E6RrE7jhIaMc2tErCp1aD9CQrYiGvheFDBNZ532ZDCK+YspGfe0Kp3H9WSi9A1yqCa0MgCgiPNIC1VRPaFqKlE+X+xxbfnf5e5OXzK1qXzFXhJT2NWILFWHQnl8N1HIKdnez4F7+GEYty5ZvfZsunP4mvvg7F0EidPkPNbXtRTR92Po8W8CO0tfnOUmfOUpycQOgaqfMXAIG/xcszUnQdRdOw0mlPQawoCF2fOz4TAvwhr2iRrywbB/AKibn0osWPu37xkwwceZNn/tv/5O5/8guEEgnPamMhYl9RMEOVWUCJWC2iqQPZd65yC5T0JLL3NKJ96w21P5LJceTg5epsM6K1iPatFdl5CCE8+5Y16upZ6fqEoqzahiV14SLjR98EIYh2b6Hh7rs21jzEdSBcW1mg+NgQ7rHDMDmGaNmE+vF/hujasfC6wjHUA/eibNuL8+W/wH3xCdxzJ1DOHkfsOlD9cYBHFFfr+w3IH/0D8kf/sAY79NaDtC3EYgICKZGlkndfUBUvUFYoN3b84TheB0QVeEuS6KZqcqDu9gXfmw7qWi6sS1VUdtfsZnfNXHJhf2L/nH8bqsH7N31gzms1vlrub17Yc7Er2kVXdIUtaxsQMaOGjvBmRnJDjMtRTNVHQq1HAIpQKTg5xgoZQBLSIzRfY+USMiJoisZIYRBVqGyOdKMJb4DUGGhGEQoTBc8316cG5ngWB7Qg3dHtDOcGGbCuoAmder+nJMxYaUbyQxTdIiEtzOXMJfyan5ZgO1EjTsktMVEcQxUqDf5mFEWdCTSzXZvh/AAZKwNAzs5yOdNDg7+JGrOOif+Pvf+Ok+u6z/vx97l1+u5s71hg0QtBAmDvlCiKoiolWbasuCeOHSdx4vgXO05x8v065ZvuFMe2EtuxrGJZsiRKFCVRJMXeQIIgege29zL9tvP748wMsMDW2VlgQeHhCwR2du65d+7ccu7zeT7PUxhjsjCOJz00oXEhdYZau47mcCtWsXgiENTadYznRxf0WRYIknZ9keyvTEGbdlPlfbNWFbS+9JlyJin4lV+gTM0kZibmtK4Zm5kkWIKHs5SSvpWmcd/AsqALnbARJe1Nk/VSeEXi3PEL8xIwUTNB0l5ei72th4iZcRw/X3FnScHPMVWYKIYdzt3WeQPLhyYE62s7ODvVz3huivW17fhBgC50ToyfoyPRQsxUftUbkp30p4ZJORniRcX5VH6GkcwETdF6omZkyfZXrw4cQCD4u7d+joQdI5ABvTMDpJwVJs/fwA0sA69ceJs/e/ubhA2b8dwUv7D3cabyM3zjyNNsbezh0c33XruNK9m5LBeFnAqOWsMW2gtBD4fK4YirCTk1VqES3YaFAierDgFVIkVXDUJUTnZ7jvJplYBXgHDRnsvJqe8nUj27LlFTB+u2ou28dd73BLEa5cm7GHxvZcRwCfHkvEr06WMnmDx4ECMcBqExc/wkjXfeoRbr6aHh1lsZeellAtfBbmjAmZyk4yOPEW6d28P2UsycPEX6zBlmjh0jPzzC4NM/JNTURLynByOq7vnpc+fp++a3MWsTDD/7PM0P3Eusex0AZk0tia1bGXv9TYSuY8RjtD/yAcTlBGU4WgUS3YNsGpJz21KMHD3O+nvv5qX//r/o2/8W3XfdQaS+Hk2/8rkrXFvL7X/r5yvcEIFYtwV5/G0Y7a9siEIeOXgemZpE1MzjRbzqkMrKZSXhjZqGqG9RCuj3MqTk3DefQAYBZjxOw56bia9bWaB51RFLIieHoKapWES45IJimAuS63Lwgiqm6DraA48hujYuSsaLaBztg58mOHoApsYJjuxHqxaJLiVkluZXfwMVwvfmnPtI10X2X8A/epBgZAgRCqGt34y+65YimX7ljco/exI5ufKix4LI5QgunF3REKs+g/KlT8HPU/DzBDJACFG0aIiUScwSpFRqxXwx/BFA1wxC2sLBgtcTpJQEBLi+gysd/MAnkH7ZF1wg0ISGJhTJa2qm8hsXV6rqK12/J10KfgEvcAmkT1BscdeEVlynhaVZ6NqVh4cQgsZQM42h5it+FzNjxMzNC67fEAatkfZZoaslmJpFe7SL9ujcNxIhFAGdtK+cINSFGqgLzU/CtUc7Z9nK1IcvtonZus2GxPzbvdA2lcJJvcAl62UUQb8IsR3WI0SMOCl3asH3zQdfesw4k1i6TURfmdXJaiCQARk3RdqdqZjcLHmhz3WcAPyv7/454zNTSxhJcm6kr6JtuIGl49IjUNN0aq16LM1W54WfRgJhPTzvNWwhz/uFkDCTpN0U+QrV6AAZL4VRMKizG8tdNDewcqyv7eT5C6+jawadiVZmnAyWYXFmqo9dTZvLFi0Pr7+L//rG/+VbJ57htrZdCARvDLxL78wgH+y5l/pILWPZySWts8aKoQnBmcleWmINjGUnebX/Haby80yeb3zVN7AK+PqRp/nkjofZ1byF/9/3/j0AyXANpm4ykZ26thun62BVQCbnMpUFZv64YbSvMtsb6yqT6IK1beUCahsrvR+7eaVit2MwU/QYF0J95pWGdl4GsW4TYt/CYbUi2YB20+2LD+Z7kEuvfJuicea7wVmJOH42y/Tho6AJkrtvouNjHwHAiEbo/PhHsJK1jO/fT+rUGUJNjWUCTBg6ic2bVZDoHOrc1IlTjLz8MtL3qdm6hYHv/5Dkrh2EW1vKJHrLQw8QuA5TBw/RcMdtdH3i42V7Dd22WPfpx+n/7veYeOsAVrKWtvfPEcRshVYeiOe7ikSfB0/8xm8zfuoMge+TGhxm6N0j8763bn33Ckh0EC1diPoW5PjQxW6J5WJyRFm6XCMSXToFGDwL6co6rQF1DezaDKGrbG11lZHu6+PCd58CINbVScfD758z4PSaQtdh5DwyM1081y7pCq1vg+j8HXVyagKmJiCRRHSsR9hLm3NozR2I5nbkxChy4PxKP0FxY6RSot8g0VcXnjs3iT7QS+GLn8d/7YVysKxI1mP/rX+Aft/Dcw7lfuNLeC8/t5pbq7bVrSxXrYSqkOiOX2DamcSXPkJo1Fi1hPQwXuAy5UwwURgn52Xwiu3ZIT1MrVVHfaipHOoWyIC0m2KiMEbancEJCoDA0EyiRpSkVU+NVTsnsQuKzMx4yk6kBCE0EmYNYSOy7M+UcVOk3IsnnBAacTMxL6m3GKSUuNIl46bIehnyflYR2dJVRPol3uKKzDYwNBNLs7D1ECE9TMSIYuuhZamPS6RVaf0pZ5q0myLnZ3GCAn7gERAUSXRF2tt6iIgRIWbUEDWi8+7zGwAncBjLD5Px0gQyoCF8ZXHhSgiSVh0ZL0VQsZ9zlqnCOEbIWFMKWikleT/LlDNePIcrgy4M4mbNvMfenz79V7TVNVGfmF/1D0qENDI1VvF23MDyIRAYmkHCqlUvFCgGi9ZX/Vpi6RY1VpJCLldRWC+AJGDGnUIIQZ3VgHGDSK8KNtR28H8PfoP7uvYRs1QYcsKKMpWfoSGcJGqqh6Tb2nfzockHeP7CGxwYPgpAxsmxp2U793TuJWqGGWNpJPq9Xfs4PHaaPzn4dRojdQQyQBMam+rWrdrnvIEbuBzT+RTdyY5Z1xHXd3F859pfW0wLonFYplhQZlOIlbTo/zig5LtaiRLdCimLiqsGcfWDTJcNMW845qKQAehzWG+sRjiuaSEuLYBIiZyegNS0Wl+8Rll21NQtss1SERHVUKKHovPWiOObNrJl0/zd2HZ9PV2Pf5yuxz9+xe90y6L5/ntpvn/ubpr2xz5I+2Pz29oAWLW1bPylX8Ccx/4k3rOBrb/2KwuOIUwLudLQWc9T17V5fr33Zz6Lk1nadxGurV3Ztthh6N4CF04oi5lKMDOJHDwHG29CWNegZWh8CDnSpwpBlSKeRKzfVr1tWoPI9A9w+H/+IenzvRiRCK333EX97rUTKFqCMCxk4zzhnotdl90CuAVEY8uyi12itl49zy1Q4Fo23ILqpruB1YPvMZeVnX/mBMHBN8sEOoCcHMd98uvzkujk88pLf42jKoxGIcgzlBvACQpoQkcgMEMWU84Eg9n+WWSaL30yXpq8nyOQAS2RdgSCtJtiMNdHxk1dQoZI3KDAlFMg52cJCKi3G+b0yZJIZpxpBnMXVae6MDCiRkUkesqdoT970QvWEAZapLMiEl1KyYw7xURhjIyborAAuRgQEMgAT3oUgjyl27chTJrDbTSEmpZFomtCBwm5IMtobohpdwo3uFJNpDxoPXzfI+/nmHGmCRtT1NkNJO2GqoSi1li12HoI8zoPWL0UpZC7iB6hNdyOvQRFrRCCkBEmYdYy5VTerpJ2Z9CFTn2oCYNrT/xJKSn4OSYKY+S8ypXBKsA2RsSc/4FS13V+/WO/yM51WxbZJnj1xFv85298vuLtuYHFsdBjqalZKlh0lY7PhFlDyp0i61X+4BlIn2lnEikldXYDpmZd8/OpUkgpcQOHrJ8hasTKdllXG9saevjlPZ8pE9gh3eK+rlupC9dwS8t2jKKyLaRb/OT2D7GtfgMD6RFA0hxtYFtDDw1hVSRLhhL8wu5P0hZrwjYs7mi/me0NPdiGRUuskZ/c/igbajtpizfxt275CU5OnMcNXBojdWyo7WQ0O1EOJQXY17qTqBnBusSHP2TY3Nd1Kxk3SzJ0DT2rVxF5P8dkYRxDM6mzG0BKMl6anJ9FFDMoomYcXehIKcn5WTJuCl/6GJpJzIhh63O3X5ZwafehF7j4xU47DQ1dM8qdiOYyMhAkEsd3yHkZnKBQzKXRMDWLsK7EBbC8xoJABhRKWSeBgy99NAS6ZmBrYcJGeFk5DZfi5tatPHXieW5p207Bczg+do53h04wkp7g9o7dFY1ZNZj2ggqyeTEzceMhdBHI6QlIV6B2ExoiEkOElv+sUjGEWHvKx7lQ6b1LN5XKTBSK/uieUvi6BXUOrBJkJkVw8DXksXfU+SIBy0Zs2om2525EbAEbGSmVJ/ZKSMgSruaxdC1g2pX75ZewiHXOPX9vYSK/2hDrtyPfeqFyEt33kMO9iIlhaLnK1iBSIocuwNhQ5WMYJqK1W4XivkfgZXOM7t/P1LETaJaJm0ozfvBdep/6AdL3Se7exeaf+RzGVbAaWy5Ec3flzZqaBpqO9Nzld2aVivVVyq2QUPk5dQPLwDxMgFNAFq7kPYOJ61/gWHWJsZQBeT9L3ssymh+eV43qS5+JwigRI0bICDFeGLmMQJ+Ngp8vvj9asRr8WsCXPuP5EcbyI+RWYDkQ4KNr+rI9sDWhUQjy9GfOk3JnlqzWlARkvQxu4CKlnNU1UCliZoKYWT0fwrUAS7doibQvezmBRq1VVwwtrey4UIWjKQIkTaEWdIxrRvyVCPSxwggZN12xKhgU6Zq0Gq6we7oUN3VvZe/Gm1jXtPC+l1IymVlBa+ENrBhhPQpFEm01oGsGdXYTef9CxZ0doIj0GWcSL3CpDzURWoQsXGtQ5GWOjJsudzpZUQtDX+GDZoVoiCT5ie2Pln8WQrCjcSM7Gmcr4IQQxKwId3fO732YsGN8fMvFlu7dzReLZ40Ri0d6LqridjVtZlfTbHuu7trZ14kdjZvY0bhp1mu2YbG3dccSPtn1C8cvMJYfxtZDRI0oOS/HeGEEx1eFdUuzqA81UmvXk/MyDOcGyPt5pAzQhE7GiNIYaiFsXGkj5gUuM+40GTeFExRwAxdfegTFB6hSt1sp8LvWShIzE4vOaXzpk3ZnmCpMkPdzyoau2D1naAa2HqbGrMXS7SUHkRX8PFPOZHlb5+rIC+kRknYdESO27HnXp3Z8kK8d+R5/+e53GZgZ4T+99CfUhOLc172Pnc2bFh9gNWHaiFjN8u/Q0+PKZkLKtR1GeQ0he09WZhVih1QQZDXDLpeCNZqpcxGi8mPNDIPMQiGjWvmzM6AJMOzK7IyWiODQfoJ3XkPr2ohoagUhkOMjBIfeBEC/79H5F5aBsqFZKUxb+d2vwdO05+d/BqumBt1eYSHDsldu5xL44FRhf1cJoqYB0b4BOT5YeSFldAA50oto7ryq12mZmYGhCyuzIrJCsGn32reZWga8XI7+Hz7HiT//Ipqh4+XyuKkZpB8Q37Cem/7RPyC5Y20q72V2Zv7OHTuMWKgTJBJTfybHkNNTKpR8Cd+rLOSQQ32qsFw/d1ZBJZCZGyT6tYKorUPUNSAHZ1vraus2XKMtqh6qT6IjSbspdKGT87LYWoioGUcTGml3hvwlQYMq7HGUeJBQKkBk0Qc5hhCQcqZnqbazXpaUO33dkOi+9BnJDTKaH55T/Q1KLW9pFrow0IQgIMDxHdzAmUVEhvUIYT2y7Ic5AQxm+5hxpy95TVnqhI0QhjBV7k7R07sQzJ5QuIHDaH4IWw+t6SDL6w1CCCzdptaqYyRXeTBiQEDKmQIpaQg1l4NOryaklGT9DOP5EXJeZkUEOkDSqi8rC+fDv/zpf0jLEtUK6xrb+fn3/8SKtukGFsZCU3VNCPWOVZzPR4wodVY9Y4WRFY0TEJD2ZvByLrVWHQmrVnXzrFGoHJE8GS9N1svglLIuitcTuRqt6zdw3SOQATPONDPOFK500YRQRZggz2h+mLAeZSQ3SNbLYAiDAIEvPVLuDJYewtJtDDG7OOMGLpOFsVkdIQKBIQyE0PADr9zt5vh5HF/NNeJWzbxZCWo7pxjND1HwC5SULrowEAi8wMMNZij4eWJGfEnHe87LMpYfJuVOl8O9NaFhCotABuVtVGr6HA3hZhJm7bLmPl21rXz2pg9zfmqQjJNFFxqN0To6a1qI2dd4/mpaEK1AzOA6yIkRcJ1rYxWw5iGR54/NalleMuwIom4pVoBVxlqfz4vy/5YPXQc7oo53u6jKFsVQ3VUk6eT5E4h1m9DufRQRVd2UMp8Dy0aePgILkOhSSqRXBRV62b947bHojXcswRd+KajG91jqUFgr0DTYcjMc2185GZ1NweAFZM8uRCXX+UoxPoQcPL8yu6SaBkTn/DZD1yOErmFEIiAl+bFxEIJoWztNd97Gps/+JC1337lmO4LkwEllLwXqe5USkKoQ39ID8flJdFHfjKhvQp45hnzrRVi/GWoX9+oP3ngeOT4MhoHoqaKopco5GDewdGibt2N+4CO43/smcmQINB1983asT3x2iQNoaOs3QrzKHcK+hxweQo4MVjzEqsge8n4ON+8QNeO0RToI6WFA4PgFejNnyXjq5iCRzLjTyqpFBtTbjTSGWrB0dWIW7CbOpE6UCWhfeuQ8FTpqrHGfbolkPD86J4GuoREzEyTt+nLA6qUPkSU7l6yXIe3OkHZTxIxEcT8uDxkvXX6wFAhiZqKoJAujoZfVZFJKfOky5UwynBvElxcnFk7gMFkYJ2LEsK8BSbsUuIFD1kuXH4pBfV7l7341fSaXjpJtScKvZcqZqHgciSTlTuMGLnWhBuJmgqs1eZZSMuWMM1EYm7dQtBxEjBhxq3ZRBfD2zqWr+Zpq6vm59316pZt2AxUi46bI+llqrbpFiyOVQiBIWElyfo6Mt3LFQd7PMZofJu2lqLXqiRqxNaNKl0gKfoGsmyLrZ3D8PL70Z137rjd4vsexobM8c/RVDvYdo3diiJlcBiEgGa1hQ0MHd2y4mUd23UNDbOEchOsBUkpmcmmeO/46r5w+wInhc4ymJvADn0QoRkM8ya72zTyw9Tb2du8sW99UC17gMu1MEDFitNuqGDmaHyreRxwGs704gUNbpIuoESXrZRnND+EEBTJuilqr7gqrE0MzCOlhCn6BqBEjZqo5S6mjSBKQ8VSx1QmURd+0M4mth+edV+S9LOP5EQpFwj1ixKizGwnpyprHkx5pd5rJwjjTzuK++Y5fYLwwyrQzhSQgYsTUPEwPI9CQRQvBycJ4eW46mhvCECZRM7ascPfmWANN0fpiYDxr5vohDBMZr1VKzmV6nMuBs4hte9WyNzALcmocRvorCwUMRaCupfobtSBWoPK+HuAUrVRMG/Ti57wanzccRYQjYOgX16frqtsgsoRnEb8K93F9barQqwpNqD8rgQzWFokOiM6NyJr6ykl0KZGDZ5Wly9Ui0X0POdoPYwOVj6EbiI27lhxAeb3AjMfZ8nOfo+uxDxK4nso2tmzs2hpCjQ1oxtrlskT75tlFEd+DmTGkW1g0T0O0r0d0b0aeO0nw1otg2egf/iyitn7u67CTx3/9R/hP/SVkM4iWDrRb7qreh/FWFiB5A5VD1CYxP/Q4xp33IzMZVSCJxZVf/lJg2Vif+Xm0TdXt2JDZDN73n8D95pcrHmNVzl6JRBcGTaEWYkai/PBgCIPGUAv5zLkySVtS/cSNBI2hZiKXtAkbwqTebmQo118e2wkcnKCwpkl0KSVpd4aJwugVxKKthWiNdFBTVDgKxBUPVxIJEqJGjHq7EU+66EJf0N5iPpRaqTU06uwGWiOdmJoxt6+8NGkK2ZiaRV/m3CxSZtqdpMFvxlqjXsFqP2og/aKvvK+8+eXa9V8XQmBgUmvV4QZOubhUCSSSnJ9hOFsgY6aoKRKWpYf+an1npWNTIsn7OSbyo2S9dMVK+kthCkvZ0qxA+TuXElHTNBJLeXi5gYqxkPZEWUIFiFWycwF1fJtFKwo366wo1LYEX3qk3RnVUaWHqbFqiRqx8nVbrReq+bR6+fGrujokXqCyKnJ+llzZZiuoynl3rSClxAt83r5whH//3f/N8yfeJOfmKXgOnu+X7126pmFqBl945Vs0J+r52bs/wc/d8zhN8Xr0RRQ8x4fO8i+++d946eRb5dc+c9uj/OYHf4nmxOKqmLm2+cjgaf7fJ/4nL14y5sf3vJ/f/eivUR+rXXD5QEpyTp4/eenr/MGzX2JwapS8W8D1XfwgUN7hQkPXNL759g/5zz/4U/Z27+Tvv/9nuH/zPiyjOvdftR6DOrux3NlXa9VR8HM4gUPOz5K0G0ja9WhCw9BMcn6WicIoBb+ALz2klLO2xRAmTaFW6u0mDM1EE9qs+Y2UElsPY2kW/ZkLeFJ1vzlBfk4SXdnDTJVt8GJmnOZwGyE9os4/AbaEsB7G0kMMZ/vx5PyESCADUu4M04UJJAE1VpLGUEv5PimEQEpJSA9j62H0vMlkYYy8n2PamcDW7SXnufzxm1+lJ9nJbR27sA0LXdOXRcCvKoRQdi7JRhjuW/z9l6L/NGRSyHhyTc4DrxmkRJ44AKmpypaPxKCueq3rN4CycClkIDejiHQrDJfaD1Tz+J0cU0QRAmGHCd5+GTkygOjYAAJk/3lk31n0RxcRc5SCRVeKNR8YWwUIfeWdFFVSop998WXOv/I6D/zmr694LGFaiK23qIDOSgpyACP9yJF+aFuPuAoWUXJmEvpOr+zYNSzE9n1cq+qPlFLtb01bsi3cUqDpOuGmJsJN19/1XYTjs1+QEmlHYeiMynuIzF+kEZEo2q33I8+fRJ4+RvDS9wkOvYm2dTeis0dlQwgNmcsgh/uQpw4rG5dcBiwb7cOfRSRqq/dhbpDo1wxCaCpcO15zUdS7nPuvpqF1rkdrbqvqdslcFlG/svyFVbu62nqIhDlbUSqEoMZKMpDtnaV01tCImvGijcvsHRs3E7NIdD/w8IK1VTm+HL70mXDGryBFQ1qIdfEeokZ8wQOo9ICo/Dk1dKkmRJU/tAjiVg0d0XWKAJpnHCEEGjo1Zi1Zu5HR/MWAkEAGZP00UTOGztqboOnCIG6oVg+JZNqZwJNr/6IphMDWw9TZjfjSn2V3VAk86THlTJByZ4gZcRLFMFcNrUxiLvc4Kl30SqG3Knh2ioyXWpEH9aXQhUFTuBW7gm4LKSUF12E6M0POyeMH/hWkrm1YdDZW9wJ8A0uDEKJIqK32ipT/emO4lZHcQFU6I0CR6VkvRdZLYwiDiBElasbLKtuLRKFYtGh1KUlesj1ShSmJLL4SSB/HL1AICjjF4ENl0VJspXwPQErJdC7F/37ha/y7J/+I8cx0mTS/HH4Q4AcOec9hKpfiX37rf/Dku8/zux/9Ne7etIfQAiFxzYkGtrdt5KtvPFUe/6/e/D6fvf3DNMXrln0tDGTAkf5TPHXoBaaKQUWGpnPHht3EFgly83yf40Nn+K2/+o88feRl8t7cx2cgAwI/wPU90oUs3333eV4/c5Cfu/sT/MYjP09Tor4qtmqmZhI2IuV9oLriTEBtl7IyUusp2c6BymeZq+tBCIGhmfOGXJdeC+lhYmacKWcCN3DnnM8pRbjDjDtTXL9OrVVPWL/Mi12Ahk7cTJCzMkwU5g4qKo037UwSEGBrIWqt+ityD0r/tjSbhFlT9kyfcaapteqXHNKro/GHb3yFP3zjK9y9bg93dt7M+mQHEStMSLfQrnX7djyJqGtBLpdET00hzx9H1Lesqhp9pZZwVxsyM4M8/W5l6lErhGhZh1hpSOINzIYdATusyBM3D9lpRbqaNpihlYdSXoJg8ALBW6+qH6SEIEAePwgn3r34mpQEh/ejbVkgWFhK8Kvw3KLpvOel6JpWBRI9qIp9zrmXXuWtL3y5KiQ6CMS2fchXvg+FCnPUPBd6T8KGHbDaIZ1SwsQwsvfUioYR6zav/rYuhJkxgndfQNtxNySvgbXWGoT0fa545pABBN7iYaFCIDbuQH/sp/Cf+Atk7xkYGyJ4aQTE05cUMWX5mommQawG/cM/hbb3nuoWOm+Q6GsCFfGYQiASVbZyAdB0RHj5nNOlWBUSXUMjpIfQ52hBVkFQoVlKQVOzZj3MXYrL2/996c0i4NciMl6KjDvbUkATOs2R9oqCNVeq+DGEQXO4DX0J6v3Sg3DcTDBRGJ31sJzzlO3OSpTCq4VZ++g68wEWQhTbyhsYW8A/fznwpce0O8m0O0VIDxM1ooSMCKZmKUJdKFodUab9gNlKc0lQtPnxcYvqxKybLuYUVG8fa0KnPtRExKzMMiNTyPHt15/mfz7555wd7mV8ZhLTMDB1k1QujWWYPHzzvXz9d/6oattcKeaqwvpBwPlUP82ReqLmwkTccse+WlhojSE9jBs4OLKALo05u2+qsw2q+Bg1YjSEmhnLD+EG1Zw4STyp1LEz7pTqdNFsQnoIU7cwhIkhjKLqVLvkvLqUEpJF32WfoGjB4gcernTxAvXHvQ6KfyvFRGaKf/PkH/HHP/pLZvLKQ1sAYStEfSxJPBTB1E3l+e65TOdSjKUncX2Pgufw4sn9/PqX/w2/+7G/w2M3PTAvkV4biXP7+pvobmjnzGgvAOfH+3n51Ntsa+0hvMyAudHUJC+cfLNMoAPs7tzKzZ1bsRcIWfKDgDfPH+IffOlf8/rZd8uEvmWYNMSSxENRQqaNALzAJ1PIMZ6ZYian7NjG0pP8z2e/xFR2hn/2kV+ls651RedQKZTzUjJeF3oxv0C949K518VCmIYsFlMpG5Uw632LrbekbAeK95jgClU7UnWwFIpF5ZAeUYrxech5Q5gq7LSoMr8CEpygQLYobAgZYWUJs4CgwNQsLN3GCQp40sUNCkDkis88F35h3yf56Zs/wv6Bw7xw7k3++6tfoC5cyy1t27iv+1a6k8sPI68mRDSBrGtWD6nLnC/Jo28gtu5RvsurdK8RiOuHRg98pUIfH1r0rXMiHEV0b3lvW6tcC5SyWExbKdBDxRBJJ6fUx1W0BdO278H4hd+a/WJFzyGyOnYumv6e59ARmuoeWskYJfXxCpGfmibwqminF69DrN+KPPbW4u+dB7L3FGJyRPlQr2L2gSzkkYPnILW4ndq8EAKx/bZrm9FQOhauM/5gNSHH+y67HklV2PE91dmzCISmIXbfAXWNBE9/g+DUYUjPQD5X3NeorhnLgkgM0dqJ/r6PI7bfUv2icjWyJm7g2kDTIRpf/H3Lha4rK70VYFVIdCG0BQMOL2+J1TUDS7vy/aUHN01o5YfOoEhCrFUEMiDnZa5QFEeNKHV2wzXZprARLvpkLw3qAdLE0uxyKzUo3/EbQXWrAyEEcTOBLz0mCmN4VSP/JHk/S97PQgEMzcQUFqZmFkmUIuF3Scu9lAE+viL0Ahc3cFatcKULnRorSdysqbg4c3rwHH/41F8QMm3+3kd+nm+/8UO6mzrY0bWZd84e5dxIL//wE3+zylteGYayYypkLnxRBZv38/zeq3/Ar+7+LHtbdlY89unpXlqjjUSM+cmh1cJCVwVDs9CEzowziWu4mJp5hbWBIcyqheJqQmVO+NJnMj+6aqS0RIV6OkEe5liFuESZflFnfgPZQo4/e+kbfOGVJ8oEuq7pbG5ex4Nb7+D92+9ke9tG6qI1eIHP6MwEb50/zHfefZ4XTrzB8Mw4AIf6T/Cfvv9nNMSS3L1p77y+4Zubu7mr55YyiQ7wjQM/5FP7PqiI6yWeK1JKeicGefbY6+XXhBA8svNeWmoWVlCdGevl9779v3jz3GECGSAQtCebuH/LbTyy8x5u6thCa00juqaTymc4NXKB546/xpMHf8SB3mMqvNnJ8fW3nqazvpW/+9DnSEZXoswQaJdN/8QlQXRCMMf1WBRLQpSP5vn2XDmgM1D2alIG5WX8wMPxL4oo5jovAoJZQgtLszDF/A9VpQBTUzPntHKSBBT8fHldqrMuS97PX/HeErzAmdVp5Ra7QZZ6l7INizs7b2ZP23ZOT1zgmdOv8cSx57B065qT6Fi2Uv2FYyqIbjkY7oULx5E77kD8ONhGLAI5MbICKxehugJau6u7UTdQVH8HiqwJfPAcRQiV1OirtVqngBwZgKnx2epHAdQ2oK1bKM9HVCf0NAjeK01r86NYfF0ZhFK/AoVUipnBYQzLIt7agmFbTJw7X1TjLoyZgSGqusM1DbF1L/L425WTuqlJFfTZvgFhr0xtOT8kpKeQ546ubJhEHXRuXLTuI/NZZSPiFVCtp1HIzihCN1qj9lV2WpG0MgArpAoShon0PchMK/VptKZctJTF+aT6QUIujRwpzhWjCQjHy8GfMgggPQX5dHH9MYjEr4plzjVBPnPZNUwo2526dkRk6aSm1tmD9rO/TnDmGPL0UeRQHzKXQchAkZj1zWjdmxE92xCR2OoUlKtQLLuBawMRDsMqZAcITUOEVpbBsEpKdKUMmnelotRqpm4OutAX8DhXFiMl79e1Tka4gUvOz12xjXV24zXzxKyxlq+40ISu1GKXXHeUKr36+15KSSHIIwCzWExRHQfFqrAQ6hgRZtnmpqrrRyKlLCtDSz8LUTz6hI4ujNnq4aI6GySmZs9LAEup1Kte4KFrOqYw5/Vb04ROjVUHUGUi/SK8wMXDJbcG7ie60EmYSZJWA6ZWedV5IjVF3inwjz7zyzx260NcGO1nR9dmfuahT+L5Hn/3D/8FLx55k3u231rFrV8+vMDn2QuvEbciPLbhgaoex67v8hdHvsVPbXuMTbXdVRu3Gsj7WaXmDFwKhXGEEFd89piZoE6vXiunLpQtlZAw4VQn9Ha5WOv3qmsBKSUvnXqLP3/lWwzPKOsNTQj2rNvO7zz2y3xw573YlxEcrTWN3NS5hY/d8j7+8Edf4fef/nMGp0cBeOPsQf7kpb+mq76N9Q0dc66zPdnM7Rtu4ol3nmU6pwjDV04d4PToBZoSdUs+D/Ouwzt9xzk2eLr8Wkuigbs33kLtAt6QU9kZ/u9L3+Clk/vL1iXr6tv4rcf+Fj99+0eusIGpj9XS3dDO+7ffyfu33cVvfe0/8tqZdwCl4P/K699lT9cOPrTrvoptQQRcojq/5MUiNBbx8J7jsJaoe2jey5Pzs+T9HI6vVNyBVB73Usol+fgrv/yLhVulkl+YXNIWyI2RUuL6F68BKXealDu96HZcikAGxfnI4u/1A5/B9BjnJ/s5O9lH38wwqUKaW1q3sq1xw7LWu1oQDW3IxjY4f3x5C0pJ8OZzaOu2Qs3ycwXeS5CFHPL4WzB0vrIBDAPR0aNsR26guvAcZeNSUpfqhiJsDGt1lbnnThC8+BQyPYM8exzR2QPZNHJmCv3Bj8BCJLoQ1SELAo8lP6uVig2lfXS9oLTNK4EQ5c987qVXee2P/4RQTQ0P/vZv0LhpI8/+m/9Abmrx+0T//gNVLSgKIZAdPVDbCJMjFY8jzx5RQdCrdH2RQYCcHIHBCysaR2y+RW3jYuTpxCDBhaOIUBSZmkAkW5BZlXmg9ewG3UQOnFFkeSlfoGMzomOT6hjqO6GOm017EVZI3cuOvY5INiMaO6CQRw6dBYTqWIkkED27IVqjzqaZceTpdxQ/QACmjWjfBPWt11ZFX0VIJ68KDbqB1r652NVShWdVTUfbuAM27lj5WJXgRsH/uoWIr4KVC6gCqmmpe26FnQqrpEQXC6pKRdEft3T704SGLubfFCHExTfLtU1MuIFDwZ+thFLKyFVoRVgCBKIcHLbc5S53MQ5ksCp7XqKCWIUQxAxFUBf8PJ70kKhAQkMYqgVbi1S9TVHKgJyfLSuug6JqDpQ1kaGZ2HqYkHbRPzWQPjk/gxe4xMyaWb+7FIH0yXppCkGeiB7DMMwFN18vEulSSiYLYwsGpV3PKBPodj2mvrLw10BKbNMiGVMX2qgdYSabxnFdEpEYD+y8gy8899f81qd+pRqbvmxIKRnKjPLq4AHeGHqHsBEm46pOlUe678Eq2lYMZkZ58syPyHsFOuItbK3fQMJSgahT+RneHTvBaHYCTQg64i1sq99I2LA5PnGWg6PHODF5lqfOvsBbkSNEzTAf3vBgOSxvNDfB4bFTjOenMIRGe7yF3Y1bsfTqtMwtdExbmr1oJ4w5RyfSSqFrBgk7iRAaE4XRqoSN3sDKMJ6Z4mtv/YAjgxf9MxvjdfzTD/8KH735oQWXrY0k+DsPfZa86/Dvnvwj8p6D63t8553neGDLbbTVNGGbV15LLMPklnXb2dG+kZdPvQ1A1snxxIFn2Ne9c0EblksxkZniqXefx7tE0XLXxlvY1LxuwYDTN86+y3cOPsdkVvl7G5rOr73/c/zSvZ9eNBj1nk17+SeP/TK/9Kf/lNHUBKDCUn949BVuW7+LpgrCUYGijVf1bqRSStzAZdqZYNqZJF8UEijbGBNdmFiaVrbpcIPFw38vVYELoS3aMbCQTZRE4l9C3mtoy/aVF0Is+WHym0d/yLvDJyl4DmHTZkvDena1bKa7toPwKqpgl4W6JkRzJ7Lv1PItJEb6kO++grjr0eooZ69H+B5cOKEsF3KZysYwbcTGXdXdrhtQ8D313GiGFHFeLTJoEQTHDkBNHfrDj+N/8X9gfPqXkOkZgkP7EY0tCy98Cam7so1Y+vksoWxxI6K1K1/31UIQLO7LvBiEVlYRp0fHGD1xCjMcxkkp269jT34fN58n3rxwKGR2aopofRULikKAHUFs2o18/QeVjzN0HjkxArUNiNW4ThfycPYYuCuYW1s2YuPOpWcU5NKIbXfA6XeQ2Wm09buQ/acgPYVo7IS2HqVA13TkmYNw4Sh0bEKYNtQ2IQfPKDV5XQsyl4LMNGLb7cXrha+I8/W7IDtDcOx1GB9QinOhIc8dhlAEredmCDzkqQPIgdOqm8hemS3EmsHUMITjEKtFjvVCU/d7wxmqGs+6VgiaO28UveeBSDZXr5gUT5RDP0Vb5+rduw0D7BB4FeTZsGrBomLBB5TLd8VchO18WLv0uYInvStUjyE9hHGZkvlqwtaWf8ILuOKglascbBdIn6yfQUqJLnTCWgTlQ+zhBA6+5yF0Qcio7s0qkBInKBBIv/iwrxcf9mX5Yd8LPDRTK/vE6sLAFCYuDo6fx9LsOQNXPenhSReBhqlZSyIuSiFqGhrjhdHrIiB1OdCFQY2VpNaqq4qFR9gKYZsWw1NK2dpa18Rbpw4xNDVKLBxldGYC17/2xQg38Cj4LmHjyvMx5xV4Y+gg6xLt5H2Hg2PHyfsF7mrbg6HppN0sp6cuoAlB3nM4PH6KrJfnnva9AOR9Zxa5dylmnDTP973BhZkBGsJ1eIGHEIKdDZuA1Q8zs/XQFdkWVwu60MsBiROF0RUH997AyvDW+cO8dvoAziUtop/Y8zAf3HnvossKIYjZUX7q9sd44cSbPHNMBbmNpSf5/uGXuH/LrfOq0be3bmTvuh28cfbd8rXgWwee4R898ovY8cVJ9CAIuDAxyPMn3iy/FrFC3LNpL+3J+YmRdD7D8yfe5PjQ2fJre7t38rk7PnKlEnwOGLoKLX3/9rv40mvfBpTK+cWT+4tK+rWhBPalz2RhnPHCCL700IVBzIwR1qPFvICSdZggkAGThTEcp7pFrVK47/y4OHeJmnFqrNpF3j8bIT285HnqaHaSnrpOtjX2sLG+i5rQtRFRLARhh5Et6yCehKm5A1nnhQyQ77yEbF2P6LlGyrJrCSmRYwPIt1+A0YHKx2nuRLSsq9523cBFWEVl69V+7splER3rEa1dimyJJhBt69AKBYKDr6Htu2/h5atB9iy3KJbPXH8e6rIaJLooK1R7HriXUE0Cww5Rt2G9+rWu0/PAfez92c8uOMxrf/h/GDt5esH3LBumhdiwA3ngBeXlXwmcApw9Ah09K/b+vQIl65Mzh1Y2Tms31LWULVMWgwhFFakdq0Xk0hBJqMKT7yllqVNAjvaBW0CmJpWHd7GjndpGGD4HqQmobVS2LfE6NUZqAqwwoq4VEYqowOdIQtl0eS7oBnL4PKKmAXlyv9oFM+OqCJPPqiDj9wBkdkYVHAA52odoXPfeyOuohsd6NI522/uhfpFi6I8rInFElWxXzAc/iL7jZgBEw8JFzBVBNxB2CJlZUyQ681pWzPneVQqauxYIAv8K/2hbD3OtZiia0OYMeF2LkKgQy7AewdbDaKhjKJA+GS9NIciR9TOY+vz2KZVAExpRI14m79XDvnrk9qVHyp3Ckx6FIF8mAzWhSHE9KCiCP/DQtNlquUAGeIGLL31sLXSFJcxC0DWdGrsOTdMZz4+8Z1S0pmaRtBuImzUrsnC5FI01dXQ3dTIwMQzA7Vtu4Vuv/YDf+tN/S3t9Cy8f28/7brqrKuuqBEIIWmNNvH/d3VyYGWRdTRuf3PRIudCYcbMIAY2Rej6x6QOA5C+OPsHR8dPc3LiNhB2jPlzLI933UhuKk3KyfP3E9zg4eox7O/axrb6HhBXjtcF3eHjdXWyt65l1nM04ac5O99ERa+EjPQ8pL2/fxdJW1gFwvUATGnEzgaGZTBbGlm3jcAPVQcFz2H/+CMeHzpVfswyTn73r45hLVN8JIehItvDxPe/nueOvERTbuZ85+gpnRj7Juvr2OcnpUsDoE+88y7mxfgBOj/byyukDfOTmB5e07c8cfbWsBgfY2rqBm7u2EVkgnPT0aC9vnHuX7CUPwZ/a9wi1kcSS7wXxcIwHt95WJtEBjg+epXd8iNvWB4uq2VcbUkocP18MIlcEep3dQNKux9KsK+aCXuAu6f59qX2LJFi0A1Eu6JGrbAFLsPUQNVbdstXoS8Wnd34QXeicHD/HibFz7G3fgZSq2KAL/Zp/ZyWItm6lyFsuiQ6QmiJ47Xto0diPFxEsJXJqFPnGM8gLxysn8jQNsesO1U58A9XHfOdYScG8StYlIl6jwvMKeURLF8ErP0Ts3IvsP8diIiRRtHNZsVTJc5dudVLyi7/ejkPfV9/lSqBdVP7XdnZQ2zm7CK/pOm0338T2Dz+64DBnn3+JsVNnVrYtl0FoGrKuCdo3KCK8QsjT7yJufd/S7FKWM24QIAfOweToisYRG29S3uZLRYnP0LQruqDk0Fnk0DlFdEdrEa6DTE9SCkEXoRgyllSv5bPIkQto7RsvXis07eJ1QdNUgSXwipZHEnxXFZ2L9n0ilkSEYxBafrf/moVhKZ94GajCxPQIcq55UiShLHHmQXDyMMGR/Wi770Rb17N8hbKUKl9i4LzKl9A01T3X1IawKxBlVYNEl0CiDtHQuvKxbmBB6DtuRr8K+gxRUqJXiNWxc6ESyvg9QqLjF73DL8IQ5jUrEpSI6OsFhjAI6ZFZD9ma0IgYMRwnjy9VKFm4imp0IQSmuFIlrnxjLSwthOen8YPZxRFTUwq7gszjBAWMywITA+njSqf8Xn257eMI4mYNhjAYL4ySrbDdZK3A1kM0hloIG9GqFkE66lv5+x/7BcKmuhDe1L2VX3j4M/zZD/+KN04e4PbNN/O3H/1c1da3GrB1ix0Nm0iGEkgpqQ/XMpQZo1D08ZVScmD0KEfHT5N1c/Slh9hQ01n07l/42lIXqqGntouX+t9iPD/FvR372Fq3oapEzlIe15ygQMHL4UufiBHD0u1iFkGAtgS7hpVACI2IEcUQKsR60hmfZRdxA6uPsdQkJ4fPkXMvEsq72jezoalrWeNErBC7OjbTWdfK+XGlAh2ZGefY0Bnu7LmZyBytlkII7ui5mW0tG8okuud7/OWb312URC+Fen7tze/Nev229bvZ3tqz4LJnx/o4cUnRwNB07tm4Z8lFAwDbMOlp7MLUjbKKPlXIcHasj6yTI36NH+AkkqyXKXdMhY0ItVbdvN0nksV90YVQQaEl+NIvdorNf98IZDDvOa3C0i+SRF7gqsL3Cq3E5kPBK/BfXv5zzk/20Rxv4ObWbYxlJ3jp/NtsrF/Hza1bV2W9y4VI1CO7NsHgOcgtc34hA+g7jXz5Kbj/o4j6H4MHSymR6SnkK99ToX/uCrI26loQ3duqt203sDQEnrKfCK9Od4jYerMKNtR0tLvej//1/wMHXoZYAv2BDy+ysFYdMtspXPSCn2teFfhIJ6fIc89V57LrIKcv898OxRFWCJmZUuPF64v2qhLp5iGXUkGDoViZfJSFDBRyKnjQuKTT1HeRheI6ZaAIUNNWNhiVFDNL274SCG1B5X9tVwfJdYvPT+x4fHXEupE4omcncgUkOpNjyIGzkEgiqvjche/C8bdX1g1Q04Bo60Ys55gX8/xbBsjhPmW50b5JdVq5BRjru/geTUPUtSLPHVIq68CH2qayzRy+j3RVPhuBX7xOxIqEvQahKKKmQfmgv0chki0w3o+cGFLn7Fj/nNcQ0bxO7et5IM+fIPjB1xHRBLKtC2EtvetcSgkj/fjf/hKy76y6biAQ4QiiYz3afR9CdG9aXqCrWQUS3XNvBJS+x6B1rSf0a7+NzGXVz5uWNzdfxRSR9wYpvhxIKedUQi0WiLWaWC2l1epAoAtjzm02hIEujLI9SnXXuvCxWnpwLwUFlt4vhIap2coHP8gT5iKZIYs2NG7gogujGIq6vHOiFMAYMWLomsG0M8m0M3ldkn8Js5Y6uxFbt5fVpbIUhCybbR0byyRsxA7z8Ts+wJ1b9+C4DjWxBC211QutXA0otbQqDAkhVPHrkvyHLx97kv70EI9teABLN3mu93UybnZJY0eMMO/vuot1iTb2Dx/m8wf/kjvbbuETmx4mbFTHZmVBEwUpSbnTzLiTuIELSAzNxNJtnKDAlDNB3KypKLthuTA1izq7gZAeYrIwTtav0Mv2BpaNwenRMoFdwu7OrYQMa1kFFCEEDbEkPY1dZRJdAu/2nWAmn5mTRAfoqm9jX/cuXjr9NjO5NIGUvHhiP30TQ3TUzd+eKYF3eo9zaOBk+bXWmiZuXb+T+tj8od1+4NM/OUz/1HD5teZEA/Wx5LL8yDWhEbFCROww09lU+fWx9CR5t7AmSHRHXiQTLc1e0KbLl/6inVUa2qwxXN/BC9xZRPjl8KSHO8/cQEMjZITKNm0FXxW+V5rHMR++fPBJkqE479/3af74zb8EJCEjxEBKkVRrhURH1xGbb0aePAj9FRTpfQ95+pCa29z9IUTT3HZK7wlIiUxNIp/9a+TJd1bmAwyIHbctT4G5llF67rkeOoploAjAVYLo7FHrMExEezf6Z34ZMilENA51i7SlC6EUw5q+MrJGBsqver46QRAoEtr3ZpOglxOiovi9Br7yTQ98RZZLqY7/4mvSDF0ktNyCCnSlGAYnpQorzM2odZYsdorLSzePiCSXH/znVIFEL4XNzoNP/uHvE21oWHSYxi2b6Ni3Z2XbMgeEaUHLOmSysXLFtwwU2b1pd3XzKzIp5LmjKxpCdG1S4dRVuW4IECBdByElpCaRvceufFtNg7Jm6T+FqGu+aPsEkM8gh84h43Vq+cw0omW9ylQARFsPwfkjaLEkIhJHZqaVHUQseX1c+5YAEYkjzQ0I30Pm06pgMNcz+2LZLq4LuSxyYhRyWYLhAeRwL/g+orEFrWMDzEesF/J4X/lD5OG3Zp3jEpD955ADFzA++6sqpHmpYrBqZNGUCoBrDMOHjvDa//gjQHL3b/w96jcuLO65gYsQsQT67r2XvLC88/g6iuK+PjCXIvOaXlqvowu7Un5rcxIMQghVjJBu1UlkWSQrvcDFCQr4FMNFi68H81w0BQJLtykEubJ3eilgNJABbuAgCbC0EIZmVBzkJoTA1kI0hJqJGFEm8mPk/eyaDtgtwdJs6u0momYcXeirojaea8xoKMKGluUpXFcbGgJNCILg4rF1KeY7PrzA442hg3x040Pc1LiFGSeDvOyYLJ03nvTVuHL2folaEXY3bmVLcgP7k9188egTPLzu7qqR6Ash52eZcacwNYtaq56x/HC52CgQSBlQ8HNXhURXBQqdmJkgpEdIuTNMOePvGbskUPs0ZESoMWsJ6WvHp3EiM83g9OwHwS0t65elyi4hEY7RdZny9eTIeTLO/J73pm7w0LY7+OaBH3Kw7zgAY5lJvnXgGX7lwZ+a99oUBAFfffOpWbkKuzs3c9v6XQt2c8zkMvRODM3yfx+aGeOB/+9vYGjL+8yO55C6TCk8k0vPGnvtYO7w99J1qeDnyXqLFK8EmJqJrYco+Hlyfo68nyekR674niQSP/DI+9n55wZCFdAiRpSMlybv58i4aUJ6ZNEumEuFEUu9f701cIR/fN8vEbdj5dciZghNaGTdtZXLIJJNiE03IceHlDfycuE5yJPvIPMZtLs+BB0blb/tdTT3XBBSqvvt2BDB9/4CBs8r8nElaGxDbNv3nglllb5HcOJ19G13Xdvv3clDPq38iX13bi9pKcuk2GpAmGbxmCnOcZraFb8nlnBOCKFsB8JRyMysaDtkMex2zjXqBkSKJLdbgJkxtU8SlxHGQhGTGFYxfLToDS199W9NV/uzpHqHIlGulcl26TmKQPddZYNhRdS4UkJuGgpZJU2KJZenSHcLK+sEKe4HEYnN++vGzUtTHG//8KNsev8DK9uWuSCECgVdtwW5AtsUeeYwIjOtPMGrBHnqIBRWcC8zLOjeCtGapS8jShYuQv1bFP9dVIqLru3I028TvPItRDiO6NqG7D0+ewjdQNa3wsRbiPpbLtp8CAHxOkQ4RrD/aRW027EJUd+KEEqpLtbvhMAnOPCsOp5DMbT1O2EBMUUJha99EefrX0R6HvanPof14ccR4bUzPy9DaGWbFhmOQSi2ZL/6uSCPv4N3+ghyfFh1yEgJpono6EF/9NOILbuvGD94+yXksXeUZVNTG9q2W0AI5JljyIFzyLPH8Z76KsZP/xrEa5Y0LxOxmpUzJoU8eN78HT7XCE46zeix44DEza6t+eWaxwpzU26Q6POgUoJyrpP5elQOXxssbARU2rfVJI+llPjSI+2lcIKC8ucvBuOWFPGKnJybSNfRMYWFh0vey2JbIZDqO3eCAhoahrAQK7TVKamTY0aCUDTMjDPFtDNVJurXEgQCUzOJmzXUWHVl9eB7JfegUkTMMI2ROs5M93Jo7AQxK0pHfPGAEl3oNISTnJnqpa9umGMTp9k/fJht9RerzbWhOPXhWl4bPEjYUEHG3TXtAIxkJzg1dY64FSViRBhIj1Jjx6+anUvBz5XDci3NZlxcbBlW55l+RY7EaqIUQGhoJkm7nqgZY9qZJO3O4AXuolYTaxGa0NCFQcSIkbBqiyGIxevXGjnvck6O9GUkXVOirqLjMGRYJIu+lCVMZqZxFyGV7+jZzY72TRwdPI3re2QLeb7+9g/4+XseJzxPa+pEdppvHXim/HPUCnPr+pvY2rJhwXXl3QIzlxHffuAzMDUyzxLLQ94r4K/UE7YKEAgs7aLKp+AXKPh5wpcQ3qWiYc7PMpobmrcwfemYpmYRN2so+MrGbcoZx9ZtRaQXc3SkVNYwaS/FtDO16DbW2nXkvCwBAeOFEXRNJ2El0VGhp+UwFJQPe1CcH6hgcIOlSiJqw3GG0uOYuomUkrznMJgaZSo/Q1fNGgul0jTEztuLavQzVBQe73tw/jjB5Chi30OwdS/EEksjDtcwZOBDPoc8th/5ylMwU/LXXQF0E3H7IxCvXRPXZul76vsTWvFvFMFVIvidvLLsKCqFpecUiWhlXyjdAmSmYWoUmc+o71s3EIZ19b97w1Q2LZquSFYrfCVhXlJgrxKk7yuV7vkTyOEBta8aW9DWb1XnxGKKa8OESHzFJDq51Py/E6Js7VH2OxYg5ivqGMVOWs9R+zQIFMFl2IpADzwoFknV68XvXgZKle7mIZxQ9jBF0kJKCdGkUpo6eaSTV9YuS4R0CitXohuG2tdcLJZWck6akTBmZO4OuBUjVgNdW+DImysIGM0jjx1A3PFwVTZJ+j7yyBsrG6StG9HYtjyCtnkdoqkTNB2xfhdKLaQhtt1BsVKFVvuBiySn0BBdW9Xvit+vlAEEAaK+TYX+lsZONKDd9VF1bmy6RR3Lml4m2QQgrTBi622IzfvUMqJE5i9+zMhMmmB4EDwPmUlBsPZFcGL97hVfw+X5kyVLelVYEyg/+iP78VNT6I//HOzYN+s4kAdeVQWyxhaMX/xNRPdm9YvUNP4TXyB45YfIt19CPvhhxOabFt1GAchoQn1XK1GSy0CF6QbB4tfxG/ixwA0SfR4s9pA3F0pEp4Y2i4jxZcmbrppb+F6EXDAM51L1anXWptxZ096M8jQXJmEjiq3Zsyx4sl6GtDc95xhCCEJ6iEKQx5UOnvQwhYkXePjSw9JsTK06nvilh3xTWNSHmkiYtUy7ivxzAueaF2t0oWNoFjEjTsJKYulX+sz/OMPSTe7vuI0nz/6IPz38dWJmlF+75XMk7BjdiQ4i5sVJeDJUgy991cEgBD+383G+fOw7/Of9f8K2+h5+atuHGcleDDkMGyE+s+VDfOv0D/lvb/05G2o7+ft7flb9UkjOTPfxzshRnMCjNdrAz2z/BAlrdTxBL4eUEk3ocyo+JaWi2NW3nSpti62HaAq3krTqSbnTpNxp3MDBkz4rJkxWCarQp6MLA1u3iZkJIkasamG9q4G865JzZiv+I1a4InsnQzeuIL1n8hm8RVrgQ6bNo7vu5UfHX2dgaoRABpwaPs+LJ/fz8I6751zmW2//kInMxet/T1MX923eh7GIgt7xHLILKONXCnU/vPbHp7Ici2JoJl7gkvXSjOdHSNoN5eMxkAFZL81YfkTZsghzXuuVEgzNJGHWknFT5PwsaTdFIPupsxsJ6cqaxZc+aS/FVGGs7Jl+eSZNCcoyq4Z8KM9kfgxPegxm+0m7KeJWDXZxTKTElW5RAZ8l62aoDzVSbzehL/F29skdH+Av332KnrpOJnLTfOngdzg2eobaUJybWtaIlcslEPEkYt+DKmA0M/dcZ1FICdPjyGe/DuePIW65H9nSpXx910iQ6lIhfV89LA/3wlvPIc8eq5IFiIDNuxHrtiCqEXRWDUwMEvQdR0RrVeBe4COauhCtGwEIDjyNtmkf1KnOH9l/AnIZRPdOsMLIC0eQ4wNQyBIceh4A0dCpfn+1oekXyX/dUGTu5W38JXJ3lSD7zxJ89y+RowMQrwUEzEwR1NahP/ZTiA2LnP+6WSZ2V4SZyaopJoVhIREXA0sDX+1DMwJeQdk6BYHymEaWfd1lyTZG01Uh5pLrQEnMIO0wZGfUOMsg0clnV6aEhln7Ojc5SWZsgnhLU9HjXOBkMmiGgWFXwQqiQghNg4ZWZNt6qNQ+RUrk8f2w70FVOFgh5EgfjA5UPoCmITo3QbJpWcenOn5KRZ9LlrvUMveKeZl6v/Q9yM4gUxPI4XNo7ZsRdrQ8jigW/4qDzL1+IdS63iMdRIth3qLaciAlROOI1i5EWxcYJnKoD9l3Btl7muDVZ9E7NkDyYhdMMNwPUiJq69E2bLto2ZJsQL//w8gLZ5BnjxEcehO9ZzssYPOnPkjxu40lIDW1so+TnkYE3vLtp27gPYkbJPo88ILKlAqa0NA1neAShZjjF9bAo+7ah0TiS3/OsEQVQOgBonqhlFI93JcU42E9QngO+4PFyGlDszA1C9/3lerW0Mv2EIYw0cXqnGambtGgN5Mwk6TcabJeCidw8ALvqqnTNTTlb61ZhI0YMTOOpds3yPN50JVo42/v/qkrXv9nd/7qrJ8/0D2b0Oup7eJ37viVBcfe0bCJHQ1Xtp82Rxr4G9s/xt/Y/rEKtnhpWOjb1jWDvJfHDRyVDVBE6dzzApeQufq2MovB1C3q9EZqrCRpd4aMly5vn1+yyblmEOhFtbkudCzdJqJHCRtRLN3ieqjQBjLAv4y8MCqcpGtCXJGd4fnenJkkl+PBrbfTVdfK4PQoUkrG01M8degFHtx2xxXbU/Ac/vrtp8u2KYams72thz3rFo+N9wIf5zLirTYSZ2fbJuwq+DPuaNtEqBo+jyuEEErlXWc3MJ4fwZc+U84EKXcaU1OFVDdw8aSrOlJsFTo6khucl/AuIWxEqA81MZobohDkyXppsl66nJ1Sutep9TfiBAWmncl5xzM1i3q7EaRk2pnCky4z7hQz7lTp0zBXYaKsUl8iHlh/O5Zu8u3jz7E+2cE7g8fY1bKZRzffx7ratqUPdBWhbdmDf+4YHHx5ZSRj4CNPvYvsO43Ysgex5RZkQysiVjMHwbGGICXScyE9pQjQEweQp95dfuDqQqipQ9t1JyKWWPy9VxO5FCRb0LbfBTPjBOcPIRINi1staBraxj0E9e3IY6+i73t07XQehKLMedJqOqzifCN4+2WIxjE+/tuIxlalup4cw//+1wlefQZtMRLdMGABi5ElY3qcqhVZNV0RWb7KtFEdC0KR64GvCHAZIP0iyV7KmpCBItyFNo9ViwCt6LHuL0MEJKU6L1dMohvlXIKj3/ker3/+T3nf7/wmmx5+HwBvf/Gr1Pd0s/GhB1a2npUi2YhoX4+8cKJyr/zxIeRIL6Jt/cq35+Q7K+vmSNRBSxdinvyaVYHnIkcuIMcGEE1d0NB+gwi9GrBstPseRX/4k4hErboe5jIEP3oS/ztfQvaeRvadQVxCopPPqetFvOYKz3PRsR7R0oG8cBJ5/pTqilkKNKG6OlZIopOeUdeqNVIDXwhSSpxUmqFDh/Edh7r166npbCfwPIYOHkIGAS037SQ9Msp0bz9eLofQdSL1SWq6ugjVJObk4rx8nunefjIjI3j5ApphEK6vI9ndhRVT9y4nk6HvtTeJNTfRvHO7WjYIGDp4iPTwCLXruqjf1IOm67i5HGPHTxJ4Hg1bNmPHq3D/u0pYwzPaCnDF5K3yCUSlHrmGpkIkXS4+POf8HIH0kXJ1PKHfS/ClV1SUzT40vcDBlz4CgbFY1XEZKBFjYg5SBhTx4wWLK5BsLVQOK7NlCDcooAujair0hWDpFnVaA7VWHVk/Tc7L4vj5crBpNa0yBAJdMzCKYam2HiJiRAkbkWsaoFst6EInYdZWvLy1SkF1qw1dM0iYNYuSWvPB1OYvnIT0MDk/y7QziWs4BDIg7+fw8cl6aTShEdbXTsCarhnU2HUkrCROUCDnZcj7uSIR6OEXu0xWk1RXKvPSH3UdMTUbWw9h68qu53q7l9iGeQXpm3PySyK+L4fn+xQu80ON2hH0JZDyHckW7t28j4N9x8k6edKFLK+dOUjv+CDrG2cHIx7uP8k7vcfKnWkN8ST3btpL3RJ8PE3dwL7MSmBzczef//nfoyO5cksPQ9cr8pNfDRiaQZ3dgABmnOlyZ1Tez6l7htCJGFFiZoI6uwHHdzA1G99fOBxZExoJqxYhNKYLE+T9rDoHpUcgNQxNJ6THSFhJEmYNY/nFrXJsPURjuBVbD5N2ZygEeWXjJINycLgQGrrQMTUTS7MJG9FlF4bv6trDnZ23KMsdUXnB6KpB19Fuf5ig/wyMrUBlWEI+i3znReSJtxE9O5EdmxANLVBTj4gkqqKIrAak70F6GqbGlMLywglk/2nILGCHUQnsMGLXndC6bu0pGcNxRLIZEYqCFYKBU8jUOCJ8dbrVVgVBcKXVgpRFX2Xtoqd3lYPucR1E10alrCwRQIlaRM825JH9iy9vWohEcuWzi6nx6jYqFX3RFWleUpeb6t9BUAwrLT4rzdVlMe/ls4J5jO9BLrNyOxc7hCiS6JnRMab7+vFdvzy3euZf/3u2ffjRa06iCzusAkZr6ioPGPVc5PG3EW3drER0IZ088vyx5RU9LoNoXaeyAq4ihB1G9NwMPTdf1fX+uEN0bEC77UFEzUXfeBGOot18J8GJd5EnDyEnxmYvJIOirdgc1xFdh+Z2sMPIsaGlk+hCg3hSZZqsBDPjq2oHVi1IKXEyGd754ld45ff/J4mODu79zV8n0dFGYSbF0//0X5KbmuaBf/qPOfqNJ+h99XXy0zNI36euZwM7f+Jxdn76ceItzbPGzE9NceyJJzny108wcvgIXr6A0HWS3evY+pEPsfMnHifR1spM/wB//Yu/Qvd9d/PxP/4fGLZNfmqaZ3739zjx3e9z8898lsf+67/HikSY6R/k6X/+/6AZBo/++399g0S/VtDmqJhUQnQEMqAQVOY9ZgoTW7fJXfJw6EmXnJ8re0PfwPzwpUfOzxIiXCZlfemR8TNIZJFMqt5+FKhARhUE6mJoliLTpVpvwc8v2nYOYGkWhmbgBa4KOCPAFlZVCf+FIIQiKuJaDTEjgS89nKBAwS/gBoWivYyPL9XfJcJAFv8uj1P6TwgEikjQhI6uKULPECaWbmNqFpZmr1pY6LWCqVm0RSsLJHUDB435Q+qmClP0pfuptWtxfIeh7DCBDKi1a+ip2UDYuKjKkFIyUZigL93PjDODLnSawk10J7oxNGVZMJAeYDQ3xoaa9dTateVlfelzcOxdTM1gR/0OkDDtTNOb7mO6MI0QgvpQPesT3ViapYJr9RC2HqU/3U9juBFfBvSl+8j5ecJ6iO1124ialRHdlm5TYyZJuVPMuMoqIOOl0HwNWw+TMGuw9GuvqL0cF/dLqFhM83ADp/zHk0qhHgS++pugfF5RvPfMvv8oGq50bmnlv7WyN3yJNC8VYw3NxNTMIml+fVkiXA7btIlcZsEynUtXZJ3m+i7pwmwCNh6KYixRWfT4nof5i1efIFv0GL0wPsCPjr9+BYn+zQPPMJ29SKatq2/noW13LmkdtmkRC80+Z2ZyGUzdIHo1FViXwNJtGkLNyoLlsvNZIKixkuWC6OWkcUgPUx9qQiIJ6eErfm9qFvWhZqJGgryfxS0Wn4UQmMIkpIcJG5HycVxvN1AICouG3+pCp8aqJayHVYE4KBBIHyF0LK1owVa0YomZcUAi0GZ1vVwOUzOpCzUQNxPk/ZwKFA/UOVwi/U1N3etsPVRxN5kQYsnH5JpAsglx2/uQz3xN2SVUA7kM8tBrcPQtZEMLorkTWdeiAvMSSaUOi8SVvcnVmEt4LjKXVoqymQnkxAiM9SOH+2BiaEXk0LzQdMSGHYjttyKqYdVRbVxqg1ImR4uq48txvbTVOrmiZ3HR19i01LY7WfXZBMrT2wxdoXZcNvJZ5FAfgCLA+88hj74N9c3KjnliDHnuOGLj9sXHskJVCYCU06OIan5Zhqn2qecqNbRuUA4QBeWLXipMaJdYbggNpD9/d0tJWb2Mc1/msyvvENENRE19majzHQckaGv1et3UgWjuqjxg1HOR548js+kVXYPkUK8q0FR6bNkhaFmHSNRVvA03cB2geDqL1g5EdI7jLRJDNLUj331jgbnG3NcEEa9R520mxZKPQ01DJBtXfEWUEyOIlQYaXwX4BYeDX/wqL/+X/0Ftdxf3/uY/YP0D9866vk2eOcfz/+Y/EKmv56af+gnMcJiJM+c4/cNneek//TcidXXs+NQnMGyrPOahv/w6L/yH/0q4tobtj3+MeEszhVSavtfe5KX//N9IDQ/z4O/8Y6xIhPpNG8mOT5AeHqW2q4OJM2fJjI0TaWxg+N3DBK4qRjjpNFPnL7Du7juIXULaXw94T5Ho+mU+VgFBmTCcS2U8H3J+dknq47lgaha2HgZmtxRPFsZImMtIof4xRMnntxDk8aR7kUQPPFzpoAmdsB69ws4lkH5Rca38+Erfu0TiBg4ZL3VJYKiOWSTKSx72thYmH2TJFzsGNKEXrWUU4ayUtJk5tviSbRdqHDdwKPh59RCvmejXgPgSQpQJuIgRQ8qgSKAXSfTAV50Rpf/KxaYSeV7aVwKtaB+hF4l0jeqQ5nkvhyc9omZs1a1fAhmQ8dLEzdVroc57OQayfdTZDdTacye1j+cneHHgJWzdxjZs/MDHDTymC1OMNd7Mfe33ogkNiWQwO8grg68xWZjA0m3cwOPA2Dvc2nwr+5r2gITh3Agv9L+IrunU2DXl/ZhxM3z/wtPsqNvGjvodjOXHeGXoNYazw1iahS99sl6OXfU7uaf1rvL5NJYf59Xh12kINSCEIOWk8AKPvF9gU+3GiveNQBA2IpiaWbRHUTdOXTPKmQGV4sjYKV4deIe8d7FzKGqG2duyk52NV1rbVALHdzk4eJxXeg8ggA9suodNDevK5Lm6x/jFa05QPp/U31Ca5AlKAUViFoFe+rtcsJqjMCWlnNPm6npCIhSlLlrDmdHe8mu9E4P4FbQnZ5w8I6nxWa81J+qvUH7Ph5u7trGrYzND02MEMmA4Nc5Lp97mU/s+SCykSN2JzDQvnnizTLRHrBD7uneysWlpRbaIFabusvDTwelRskX1/bX4LkN6mNZIx5y/04RGfWh+8iZqxoiaCytEdKETNWN4geT4RD+nJs6TcbNYukVHvJm7O/cSsyKYmkXCSnJmqo+Xzr/EeHYS27DY1tDD3tYdGJqamg5nxnnhwpusq2nj1rZd2LoqwoxlJ3nixDPc13krPXUNDKRGeGf4GBuSnUgkB4aOMpVPEbXCfLDnPhrCtQgh8AOfvpkhDgwfYySrjp+EFWNzfTe7mraU1eJSSoYz47zcu5/+1DACQVdNK7e37yZhxa7r83AhCCFgyx4YH0buf3blSs9L4bsw3Kt8xnUDYjXIRB3EahDRBDJWA+EYhCIw0rv4eHPBcxVpH/hqfa6jgkHzWfWgnksp1Xl6BpmegukJmJmo3CJhqWjuROy+G5FsWt31VIrAv6iuKymKdRMV1qddzCoq7df5CNEqeXBXBaXA1BJpIAP1mZwcWJFi6GWhSPqurIgvB87jnzymfnAKyMELyPMnoKZIFM5MIlMz6PWLEwRCNyCeRFqhyoMkAWamlN3JYoRp6ftajF0q3VtLJLoVVvMZ3UBqmnrd94rvK/lMa0jDVMGivldUmF4SICil+l3RM33JyKWR6RUGrxom1F/sCDNsGzdfID0ygu846NbaEr2JRB2ydR2cPVK5jc3MJLL3FGLLLZVvyErWD5BsRrR2r21rrxtYOUqF2fmCVwXKYkVeloW3lCK2aatCXd5ZMEfv8u0Rje0rLytOjqh5xVq61xUhNLWvfdfj4Je/ysv/5b+TXL+O+37rH7HunjuvKBDmp6dBwIP/4p/Qtmc3ANmJCSL1SV75/T+g9/U32fC++4m3tCClZLq/n1d+/w/QLYv7/slvsumR92NFIgS+z/ChI3z/t/4Z7/z5l9jw4H2079tL47Yt9L/5FjP9/dR2dTB+8jR+oUDn7bcysP9t8lNT2PEY+alp8pNTJDo6Vi+geZXwnrmKCQTmHBMhx8/jS39ZJPpUYaIidRyoNvyIHsHSLJzgYrVqxp0i7aVWlci73iEQRUWZRiHIUfBzxfZqDUuzCelhbO1KL0Nf+uT8TFH5Nlv96UoH13NLUzoszSr7qZbWGTGiCF/gBHnygZocaEIp2SJGFEuzKfiLTxpsPUTOS+PjYwgdS7NZC17FQmgYQsNYIyZeskhqu4GzKClTDXiBx+mZE/QkNlf9/JNIsm6GC5lzDGUHiBhRYG4SHaDgF0i7Ge5uu5PueDdSSl4YfJE3Rvazu/EmknaSjJvl7dF3mCxMsq9pH63RVtzA5eXBl3mh/0XWxbpoijTSEmkmakbpTw+wuXZTWcl+ZvosBb/Azvod5L08h8YP05/uZ1/TXjrjnfiBz/7Rt3hl6FU2JNbTGb9IqqXdDAWvwJ6mW9jXuAddM0i7aWJV+J4MTRV2qokTE+f46tHvMlW4qBZuCNcSt6JVI9E1BIlQDNuwePbMa2xq6GZzQze6ZqBfpVvoi+ffImHHuKll83VL4DUn6umqb+PNc4fKrx0bOoNbQWvkTC7F+fHZlhNbWzYQW2IwWci0+eTeD/DcsdcpeA6O53J44CRHBk5x24abAHjz7LucGx8ozwXqY7V8+KYHFg0ULSFihWhPthALRUnnVRF2Opfi9MgFtrZuwHgP2F/NhdHMBE+cfIaX+t4mYcWoC9dQ8BxOT/ZyT9c+APwg4O2ho3zlyJMUfIf6cC1ZJ8cLF97ko1vex8c2K0/asewkT576EXd37uHWtl3ldUzkp/nLI0+xLtFGT10XI5lxvnfmRRojdVi6Sd4rEMiA0dEJHl5/V3m589MDfOHQt5jITdMYSeLLgMO5U0wXUmxr6MHQVAG9NzXElw59m96ZQerDSTzp8VLfWxwbO8Mv3vJpIkbouj0PF4QQyjpgz/3IbAoOvbr0h9TlwPeUZ/O0KmRIUA/GVggsu+J2afnuywTnjqpAQ79I+DoFRZY6BUX+OPnV+UzzoaYesfseaO9ZueJ5tZBLI6dHIRyH9AS4BUQ8qUgQK6xUzYl6ZHoKOT2GCM+eDwjTRgYBMjOlrHpgbZBkpqXI3iCAQgbCxW2yw0Vf7VR1Cig1dWgbFrenEPVLKKIIoXy6E0kYG6x8m1wHxocWJ9FLx2TJpmU+Mlsvzt3comChZLWgGeqPV1BjXGrBIDQwQqBloZBFGhbCDClfZCnVd+IWwDAR1jJ86tMzkJo/+2JJuIxEr+lox07EeP3zf8bE2fNEG+tx0mmGDh/hlT/4/KLDhWri3PLZz6xsmxaCriPa1iOTTTBUoSVFPgNnDsOm3ZVdi3IZZP+Zi8fAciE0RGMbNM9dyF8IweQE/rFD+CePIqcmka6LiMXRWtowtt+Etm4DYp7Ch/RcvLffwH3me2gtbZjvexS9owuZmsZ7923840eRM1NI30OLJdDau9B33ozW3olYgu2YLBTwTx7FP/wOwegw0nHQapNoGzZh7NiN1tis9vd13k26LITCEAojx0eQuQyCywQa+RxMKPs9mU0j8zlEKIxMTRWDi5k/zDsILiGxlzYPE5qObGhR96WV2LHks8iZCahvWXNzQL0YgHzwi1/hxf/4X6np6uCh3/0dOm7dO2+4+67PfIqWm3aUP0u0vp62PTcTa24kNTBAYSZFvKUFpKT31TeY6u1lx+MfY/MjD5cJb03Xad29i20f+zCDBw5y+GvfoPu+e2jcupnTTz/DTF+/yp86fRorFqXrrtsZ2P82o8eOE2tuZupCL0YoRLJ73Zrbp4thDcxyqgdLt9CFPsvXN+2lcPz8ktWOWS/DtDNZsd+tEIKoGSdixHCcifLrbuAylO3Hjtlr0rpgLaCohSakh7E0a5ZHqVYM1ZvreqkLo6i4Xvw7K1knlCCEQEctH5LhMmFSUqkrla4gYSYXvVYrKw/Vuqhr5oLt5Nc7Us40M+5U0QLHJGHVljstCn6eKWeCgp9HFwZJq46woawDnKDAaH6IrJcpe2MDNIVbVav/KhQdJAGjOaUm7ElsLrb8V2FcKZlxpzmbOs14fhRvCbY/Ekl3Yh076raTsNTD5o667RyfPMF4boKknWQ8N8a51Hk212xie922ss/6LY23cHjiKOdS52iKNNIQaqAt1kZ/pp/J/CThmLqhvTt+iIZwA+3RdiYKE5yaPk1zpJmb6ndhG3Z5Ow6MvsOp6dOzSHTXd+ioaWdn3Q5ilnpQbqY66jkv8HCK/sMAumZia3bVifVqw9ANNjd0A4IjI6ev+vrznsMrFw6wIdnBTS2br/r6q4XW2iY2NMx+eHrtzEFS+Sw14fiSJ09+4NM/NcKJoXPl1wSCne2biIeWbjn0oV3386/i/5O+ySEATo9c4LWzB8sk+nPHX2c0pe7hmtDY2LSOuzYuXcGlazpd9a101bVyZOBU+fXvHX6Rh3fcjWG990h0P/B5feAgT51+gfvX3c77199JjR3D8V0ybp6woYiSofQo3zn1IwIp+emdH6Er0UbeK/C1Y9/nK4efZHfTVrprl+eZOpmfIe1keXzrw+xs3IShGcwU0tSHVVEzkJJz0/2cGD/Hp7Y9wu3tu9U1vJDGNizMovo94+R4/vwbnJns5ZPbHmFHw0Z8GfD02Zf45olnuK3tJm5v313dHbeWIAQkkmh7HyTIpODMocWXqQbcItm9cNPfwhg4h+RctbZo5YgmEDffg9h6C8Jaw/N+w4LMNPLE60jPQTS0QywJuoHWuoFg4CR+agJhhRCGdSXRGooi6tsJTr2FMCxEQweidcO1+SwlCKG2UzdBk5CbKaoegWJXGEJUpaAi6pvR7njo4gtBQNB7BgYvIIMA0dyO1r156UGGoQiiph65EhJdBsjhXkTnImICTVeFK6eATE+o/VXaL6GoIr2h6CWvg5cH3UKUSHUh1OtOTinNDeviLL5koxOKFbtAJpGGpca61Fs9nFBE/FI/Wma6KiS6qLvYGdB1x21sfPB+3vnyXzF85BhGyCY/k6J//9uMn1x83le7rmt1SXRQHS2NraqbpxKhn+ciR/qQU6OzPvtSIYcuqM6dSs+ZaBzaN6jshaWu03PxXn8Z53vfIjh7imBiXBGwQaDyA2Jx3OZWjDvuxXrko2jNc+TN+AHB2VO43/0GWnsn+vZdkElR+Osv4x85SDA2CgUVjotlI+IJtPZOrA99AvPuBxHR+bc3GB7E+e438F55nmBoAJnNqAJuKIyoTaJv3o79kU8qMt40VXHrxwCipg5RU4c8dwJ59ACyvglR7PKUroM8c4zgxCGQAcFbLyIStWhbbyY4+LrKO5ABpGbmVnynJlXnSzSxdDW4QF2HaurK5H3FGDwH67asuaK4GQpz7IknOfzVr6ObJu//f/457fv2LPhs1b53zxUKdTMSwYxG8fIOgaf4VCklQwcPYobDNG7dMqdivO2WmzHDYfrffAvDDlHXswE3m2O6T5HxU+d6iTU303HbPsxwmIH9B+i4/TbGT54iUgwmLUE6hUsK3Oo+Itag1dZ7hkRXFhYGYT1C2ruoSsx5WaacCSw9tCiRXvALDGX7y8RepbA0mxqzlqyXnqVGT7kz9GbO0RbpJGwsTTFXghu4aMVQx/c6Lie6l/J+S1T+gKKIdP0Km5hLsZTCh4+ydFAWMfZ1V1FbKnJelonCGDEzTsKqxQs8tOK02fELTBbGkUhq7TryXo6h3ADt0a6ydUfSqi8+ywTUh5oQUFTtrx4CAkbzRSK9ZjNRY2XK6kAGTBUmODlznBl3almdK8lQksgl53/EiCCROL66VqTdDJP5SV7Lv86RyaPl9zm+gxu4TDuqjdXWbTqi7ZycOslofozWaGvZd/3OljswdZOcl2c8N8FAZpDedC+lSpAXeGS9LNPO9Kxt0zSdOjt5hV/ySiClJOOlVLBo4FwM80VgaiYJK0nMuDIFfMnjV2EbRzMTPHXiRV7pPUC6kGF9XQe/sPeTrKttW3A5P/B57uzrHB05Q9/0IC3xJjprmnn+3Js8uOF2Ht54NyHD4uuHf8APTr2MLwPu697Hx7e/j5qQKuZIKfnR2Tf49vHnGEqNUh9J8qHN9/HwprvY33+Y//3m1zg8chJLN/nqoacQwH967LdpjTdeV9eYqB1mV8dmOpLN9E0OAzA0M8aLJ/fzmVsfXfJnmc6mePnU28zkL3qibmlZT09T17KCNpsS9Tyy827+9wtfA2AsPcU7vccYT0/hBT4Heo+VfddDps1jNz1AIry868ampnXsbN84i0T/xltP848f/Zt01bVeV9/fUjCZn+HdkRM0Rup5qPsONtd1z/kZ+1JDnJo4x6Mb72dvy87y9/aZ7Y/y9NmXeaX/7WWT6K7vsq1hA3d17KG2eG61xS8pAEqJqRlk3RwXpge4s+NmmqINs98DTOaneXPwXToTLdzbtbdM/D+w7na+deJZ9g8e5rb2m1bdhuxaQmg6sqkd7c4PEvgenD92rTfp+oMdRuy6A3HLfVcot9cc7DCipRuM4rzVjpTJUNnQjhatVZ7XunlRUXmpclg3EOt3Iko2D8tRFa8WdEN1HpRsXaSEbHG+47vKK30VWvJlNk3ww28SHHmrTBoF2RRBz3b0Rz+j/HwXQyQKyRX6ogcBDF1YwhsFRJMgZpS1iudcJMzlZc+ppSDRSz30hSj6oxdVoZfdg4XQkKGYIskLGbWO0n43QxCOIQx76d+D68DM5MozG+wworah/GOirYWHfvs32HD/3QwfPkp+eob9//dL1K1fx/r771l0uGh9/cq2ZwkQdhjZvhHOHoP0VGWDlCxdKiHRe09CZgU2OrUNiK5NS/6upVPAffpJCn/1FwQXzoLngWWjtXWCriMnxpHjo/jjowS95whGhgj99C+itc4/dwgmxvDffRvn1HG8N18Bx0U0NiFa2pDpFHJsBDk6jD82QmF4EBGJYNx+L8K8kj8KxkYofO0vcL77LUgVry3RmFKeOwWCsRG80WHkxBiiJokwzesmUmKlEO3diPZu5OAF/O9+BXnmGGLdJrAsZN85gkNvKDu1mjrI5/C/8yWC576jbJryOZUrNTVO0Hsareuinags5FWBMpdFdPYoS5ilbZEq6NW3rpxE7z0Jt75v7uDTa4ihg+8y8PYB0sMjROqSCG3+jLYSwvXJK85HIURxuUuOVinJjU+i6Qah2rnvYaFkLULXyY1PgoBIQz12PE5qYICp8xdIDQzSumc3DZs3EWttZvCdg3j5POMnTxGuS1Lbva48lvPFz+O//br6wbaxf/7X0LfdVNF+mQ/S9/APHcD95lfKr5mPfBR97x0qo2cJeE8xsrpmUGvXzyLRJZKR/BBSSprCrUUiffYBExCQdlOM5AZIu6mKVeglCCGotevJ+Bkm8qMEBMVtCZh2Jsn7OZJ2PUmrHltXlh8XH8jU2st+3m6alDtDxkvRGukgade/px/erldIKSn4eSQBujBXnRS+lih5q1taaBYZLZHk/ByudKizGwnrYSJ6jBl3moybxrJtNKET0sOYmklAoKx0rtLx7Euf4fwQQihFeliPVERi+dJnNDfMiemj5Pwcy6VxTc2cXbApW1KqcQJUhkN3fB3diXWzltWFTlusVS0mBO2xNmqsWs7PXKAnsYGjk8fwAo+dDTvUWMWU885YB1uTW2aNJRC0RGdPpDU0DGFcEdK8EuT8DFPOOAKNpN2gClJSXd/SXoqpwjgaGtEqdQhUAk1oNMfq+ezuD1Njx/jqu0/xB699iX/zyD9c8PiUSPpnRnj5wtt8eucjfPPoM0zmptnRvIm3B46yo2kj7w6f5MkTz/Mzt3wcTQi+e/x5UoUMf/u2n8TQdQ6PnOJrh7/PQxvuYHNjNyPpCeK26srY2byJX7vzp/lfr32ZHU0beWzrAwgBjdHrL5RJExp39dzCvu6dZRJdSsl/+cGf8thN9xMPRRc8H0u+8MeHz/KFV74163cf3HUvPU1dyzqfdU3js7d/hD958esEUhLIgCMDpzg2eIbpXIpzY/3l7qaacIxP73tk2Z95XX07922+lWePXVS1D02P8u+e/GN+/7O/g64tP2OitE1LXc4LfLzAK3d12bq1auR9yskwmp2gNdZY9iGfCzOFNHnPoTGSnFX4aIk1EjJszk31L3vduqbTFKmnxp6bsBRCsKNxEx/Z/BBPnvoRL/W9zb7WHXx444P01HVhagZCCHJunt6ZId4ZPs5bQxeLmL70GctOMpGfVkK89/g0TOgGsn0D2n0fJXhRg3NHr64NyvUMw1Ihond8EBGuXkF61SAEWBFErPbKX+kmzPH67MWLxPsS7bSuCqwIIJTNiNAgVqcCLn0PMpNq2mZYVSdCgrdeQk6MoH/iZ5V1hRDIyTGC579L8OoP0R9+fPFBwjFobAPDUMRhJZABcvA80nUQ5vz+3kII0E1kLHnl+X250jKahEjNlT7HoaiyzYGiD/IlvxMC4boER99Anjyg3vLgpyDZon43n2fyfB8rMwNjAyu7Fmk6oq17FuGv6To1nR3s/MTH2PbYBwn8gBM/eIbO2/bxgd/9nUWHnM8uoaoQAtG9BXnolcpJ9MwM9J5E7rhtyUQRoOy9hs5X7oduWIiWLlgieS+lxHvjZQp/9QWCs2cgEsX+zE9gvv9DiFhcnVe5HN6BN3C+/iWlNP/Bt9HqG7E/9dPl91yBXI7C178Ijou+bRf2T/08+vqNqkvEdfFPHKHw1S/gHzpAMNiP852/Rt+4FdHcOms86Tq4Lz2H+70nFIEejWF/4icx3/chRDyuulHGRnG/+w2cZ78HjlPdjJG1jrpGtL33IPvOIIf6CPY/D++8qvahV8wrSTagf+CTYFj43/uqCveWgGmh3foAwevPEnz7i/CRzyFaO8F1CF76AfLkIQh8tC03LauDBdNWweYn31nRR5P9ZxCFPNJaW7Z+2bExtn/y48RbW9n/v/+U7/3m7/CpP/888bb5BTvakq9bAjMcUhl7hbm7KfxCAaREt9QzRiiRINm9jszYBMOHjpAZHaVx6xbCdUlabtrF6R8+i5NKM3HmHM27dhJrvihokYP9BCeL8+9QGJlZYZD0XNB0yGXw978MvuJptc5utK27EDW1SxriPUWia2gkzBqiRpzMJUR6IANG8kNMOOMkzBpCegRNCHwZ4AQFMm6KQpAvq0k1NBrDLQznBuZb1aLQhU5ruB1fukwXpi4h0iV5P8dQtp/h3CCWZmEVLQ1EcVvdwKEQFPCLD75lcq1Cn/YbqD4ut47xpU/ez5btaARrq82nmogYUWJmgsFcL2EnQq1dT9SIghR40mXamSTjpsrkY0CwJKuT1YMo2zwF0mcw2w8INiQ2EtEXJu4uhUTiBz69mfOcnjmBLy8+3OjCoDXSTo1Vu4StWZgAC+thwnqIpkgj+5r2XkG4X0rq1ofq6Yy1c2LqJOOFCY5MHKU7sY5aS1WKbd0mZkRJ2rXsbdxTDuwrD3d5BXrRrV8+Cn4eITTq7aZZmQYhPYKthxkvjJD3sxWT6NXY5tpQgju7bqbgOQRScue6m/nvr3yx5DG1IHSh0Z5o4vbOmzg6eobGaJI7u27hywe/w1Q+xdcOf5/Htz/MPev2FANF4YvvfIdjo2fY2bIJx3cpeA62YdEcraenrrNM6IXNEE3ReiJWmGQ4QWfN2vPhWw42NHXywZ338ca5Q/QXifQDF47xz/76v/CvPv73iYej83YhBTLg9MgF/tlf/37ZggWU2vuRHffQFF9eYUEg2NWxmdvX7+aVMwcAODl8nkMDJ+mdGCyvQwjBw9vvpqOuddmf19B1Ht5xNz88+grfOvAMfhAggT9/5Zu0J5v51Qc/SyIcRdcWblMMpMT1XHJunrH0JIlQjKbE0pRv3zr5Q/7i8BNM5WcImyH+9LF/S0Nk/tyGlaAUqqtCqRc7TucmQQRz8COSWWGs3hyelrrQMBYpSiRDCT6386M82H07z194g+fOvc6z517jc7s+yqe2fRBLV0oxXejsadnBoz33XjFea6yxqkXG1YZ0HUWGVVCwEbqObFuP9uAnCF4wlZ/uSvxEfxxg2YhddyHu//jatnApQdMRpr0MRd91gjmJfQMMWymgg0ARZ1X2KZZ9ZxHdmxHdWy6S17EaxI49yHdeXeKma1DbhEw2w+jyC4plZFPQfwa6ty62QsQiGR1CCKXe58r3CaGBvsB+NG3ETfcgmjoJ3npGLVOJZ76UMDOh7ExWAl1HdG+fU4Fp2BaGrb63RFsrum0Rnkd5eU2QbFLe6MN9lXmTBz5ybBAx0gdt65e+3MBZ5NTY8tdXQrwG0bMLschcp4Sg/wLuD54kOHsaEY5g/+pvYD34AQhH1JS86Ktvtbaj1STJf/6/EVw4i/Otr2Lefg/a1h3z3+/SaYy77if0K/8Qrb1TebUXxzOaWsCyyf/x7xOcOYn31msEYyOI5taLjwJSEpw7g/v808jpKTBNQn/jb2I99jjE4uX3iYYmtJY2iMZw/uoL6przYwKh6bD3HnTA//5fIYcHLhYRDAPR1Ib20EfQ7vqAugd19RAcfhPyObSd+xCtncgLJwnefpng8FsQT6j9NzOlCPi6RrQ996hC41Jh2Yj2HhV2vJKChlNAnj+O2Hl75WOsAppv2sXtv/q3aN61A5C8+Uf/hyd/47f48O//J6KN9cuc/132XiFo3LYNL/dlxk+fJvD9K2xgRo+fxMsXaL3lJkWi1ySo37KR8VNnGDxwEM2ySLS3oRkG7bfu5eg3v83Qu4fIjk/QsHnjFeOtNgSAHVJh9sVOkqD/AmRS8ONIoguhCMyWcDt92XMU/IvJ5iVl93hhdMExdGHQFG6hMdTMeH4ET1b2wCCEwNJtOqPr0bjAtDM5ayyJREqfvJ9bsX3MDVx9BATlIkeAT87L4ksPU7MIVahwvl6gCY2mcCu1Vh2TzjiD2V6SVj0NoWaEhLhZQ3O4DXuWGl9cM+WeoRlsTGzhTOpU2U5kMNuHANbHNyo1/CLfl5SSfJDnfOo0F9LnkVycDNl6iM7oOjqj66qSd9AYbqAr3snp6bN0xDpoj7ahCa3oKe5SZ1/059eERneim8MTRzg8fpjR3Cgf6n60TETW2jVsqNnAmZkzHJ86QXeiG11oqujjFUiGapdlnVQJJBJDGOjiShJHFzqGWFmLYzW0kUOpUZ488SMODZ8k6+aZyacpFMMJF7J5gmIOhhVB1wyiVpiwEcLUdHwZMJadYiafZn1dB7qmowlBTSiObZiMZpUyeVfzZh7acDv/9+1v8L2TL/CBjXdz17o9JMOrG0KddfLknDx+4OMHAV7gF/+tfu6bGMK9bKLZPznMyeFz2IaFrunomqb+FupvyzCJ2uF5SWFNaPzkbR/i8MBJ/s+LXyNTyOH4Lp9/4a/IOgX+9oM/SUeyhZgdwdQNdd/2XGbyGY4PneF3v/k/eP7EG+XxaiMJfvG+T3H3poV9/+aCEIJYKMpnbvsQr519h0BKxtKTvHDiTYanx0gVw0At3eSn7/hwxcTp5uZufuneT3NurJ93eo8TyIB0Icvvfed/8W7/CX7p3k/R09hF1A5jGiaaEASBVOeo65Bz8kxkpjnQe5QfHH6JwwOn+J3H/jafu/Oji67bCzyOT5xlJDOOJ/1yl9tqIW5HaQgnGcqMM56bpm4eNXrCjhEyQoxlp/ACr1zcG86MkfMKZRslQ9OxdJOC7+AGHlbRh7d3ZhB/maICiVQdvbpBd0073bva+fjm9/MfX/0/fPHQt/nQxgewdJOwEaIpWkfUCnNHx83ErMiV41xHkCf2K4/lhnZkKKI8jpehmhSaBs1daA99EhmOIk+8owLqbuBKhGOIvQ8g7nx0SYF0awGivg1Rv7Bt2XWJha7XujEXF1yd1UZjkJqCmSlkTbFYmZ6BiVHl4bukQQQkG5T/9UpIdKeAPHcMsW7LompvKQOlMvZctX9yaUVax2oRVkhd9XJp5VcMKpw1HCv71ErfU6S9UwCkCgqOJhRZLoTqbCn7rVf2cWQQICdHYXy4sgFKMG24xCJiPrTfvFvZHawhCCFg4y51HZ5amM+YF1NjSk3b2r20LoAgQA6cU37olUAIRf4v5s9fhAwC/BNH8d55E6TEuPM+zL23IyKzu3qEEGAY6Lfcin7THoLec8jJcdw3X8besFGFW861OTVJrI/9BFp716wOAtWVoaNv24m+ZTvBmZNQKBD096Jv2a7sQFDPg/6Fs/hHVV6IvmM3xq13IuKzz28hBKKuHvP2e/Defp3g+JEl77L3AoRuIG57ALF1N8HJQ8j+8+B7iJYOtC27EXUXLavExu1oG7eXf5aeh/7Rv4H3lf8Fk+MwWhTO6AbUNaJ/7GehaXmWiELTkIkktHRB38ryreTRNxA7bi0WFtcGNF1DN00My+L+3/5Npi/0cvJ7P+RH//r/48F//ltE6pYjNJp9kRaaYMND92FGIvS9vp+hg4do3rUD3TCQUpIbn+DYN5/AzWXZ+tHHALATceo39tD76usM5XJE6+uINjaoLvo9NyOl5PTTz6DpOg2bF78eVx1CICwbEYsjiyS6HC7mGiwR18csbxkQQpCwErTRyWhuiKyfJZCLp68LFOldbzfSGFJqv7ARIeWuwP8LMDWLzth6QvkIk/kxCkF+VvDpUiAQGJdbQNzANYUfeKS9mbLCWiAwhEXUSKw6KXmt4Qe+IkY1UxHnCNLeDI2iBVO3wFPqY0uzAIEvPXRhIKQoybDRhMALAoLARwhNGRqtUuFBFzrt0S4kcD51mkJQQCIZyPYVbVN6FrR2CaSyezqbOsVIbqhMoAsEUSNGZ6ybtkh71QIyE3aCW5pu4Ud9z/Nc349ojDRgCousl6XgF/j0xk8S0S4SO+2xNhrCDbw99g6mMFgXX1dWq0fMCLsbdjFRmODFgRc5PnUCW7fJe3mmClN8ZtOnqbFXV2VjayHcwMXxHUWkF7s0JAFO4CAJsPVr1wYupeTbx57j3eET/NKtn2Zn8yZePv82v/fcHyxxhEt7A2Yfx5ZuYGg6OScPSKSkTFSXCEFTN/jJ3Y9x3/pbeebMa/z5gSc4NzXA37njs2rEst0PZUuOauDZY6/x3PHXSOWyZApZUoUM6XyWdEH9PJaeZOyy8K5/+90/4vMvfJV4KEbUDhOzI0TtMFE7QtSOsLVlPY/veZjmmoZ51go1kTi/fP9PcmF8kKcOvUDBc8g6ef7kxa/xzLFXed+2O9jZvpm6WA1BEDCSmuCtc4f5/uEXGc9Ml8eJ2RE+tfcDfGrvI8sKFL0UIcPi/i230lbbXFaeP3PsNZxLwp+2tHRze8/KgiQf2XkP58cH+I/f/xPOjvYSSEnOyfOV15/ku+8+z82dW9nW2kNjvA7LMCl4DplCjt6JQc6M9nJq5EKZ1G+MLf2hfig9xmB6FG+Zc45KkQzVsKNpE2+/e5TnL7yBoenErAhe4JNxs3Ql2rANi85EK5vru3lr6Agb69bRlWil4Bf4q6PfpzaU4I5icGfcitIcref05AWOjJ6iJdZI2snw/IU351SjLwQ/CBjNTjCdT5GwYxiageM7RMwwlm6Wz6q6cA23t+3m6XOv8PTZl9nXuhNTN3F8l6n8DBuSnUSM0PVj5xKrQQ6eRQ6dR9Q2Qm2TsucIx5RSdon3XVHXjHjoUwQ1dch3X4WVqBLfaxACauoR+x5SQaLXCYF+A9WH2HozwY++g//DbyDa1inF7HAfjI+g3ffo0seJ1SLrWlTLeVDh9dtzkP1nkIU8Yh5C8eJ7PeSZw8i+k4jmLhg6j/QctJvvh9b1kJ1BvvMicmYMJIh4ErFpNzR1qaJcehp59DVFcgeBCqHddit0VJEcyaZg4Gzl+6OEhjaILj7vfei3/9Ga7NAQbRugrgmmxysLGM2mYbgXmc8uyW5KpqdVR4RTgfIdlP/8hh0qwHYpyGUJLpxFTk6AEOjbdiGS83feafEEWnOrUpXmc/gnjyn7lHmOeX3rDrSW9nkteLREzSw7B5maUXYPpUe8XJagvxeKZJu+ZQeioenKgUrjtXeid3b/2JHoJYhEEn3vvbD33qUvYxiwcx+6/neQrz6DnCzON5KNaLfdj7Z9T2WdXpG46uRYIYlO/xmYGlfn4RqEGQnzvn/5T0kPj3L0G09Q09HOvr/584RqKhNnCSGo6erk1r/1C+z/P3/Gs//qX7Pn5/4G0aYmvHyeE995ijPPvUj7vj1sK5Loum2TaG8jNzHF1Pledjz+MSIN6jyu27gBKxLh7HMvKLJ90zUg0QFMExGJlEsGcmZ6Wde5qsz0DGFSa9WVCUVDM6+wDbgUYSNK0m6gVOmIGrF5n0c0NGqturJ1giZ0bH3h0BpN6CStOmw9xFRhnIyXxvEdPOkRSL+sJNKEhi4MrKJ6OGnXETMT6EInkAFJuwFTs5a83vmgC53mUCtxM1G0ukjjBmp7fOmVW6Ch6I4uNHShgi5NzcTWQkTNGFEjviCBYgiduJmYte+VTczyJwGaMIgX90UJpmairQKRr3xaQ4BEF9fHw0fJ29uXF9u/bS103Wz/SlDwc2T9jCILpaAQ5Ika6sIc0iOE9Qgz7jRu4CAQeNKjzm7EKO4bDQ1bD5PzckwUxjA0g5iZwBTz+zauFKZm0h7pBCQX0mfJ+3kkkr5ML0IIumM9yobnMkLBDzwmnQnOpU4zWZgo2zIJBLVWkq7YeprCLUsqnMTNGFuTW2gKzw6MipkxdtXvLJPZAsH6RDfR7ijHJ48znBshH+SJW3F2xndgG7MnDhEjwq66nViaRWu0hbBx8XMIBG2xNh5d9wjHJ08wmBki5+UI62H2Nu0hbF4kr+NmjC3JLTRFVhhodRl0zcCXHtPuOIUggiHUTNSXHjlPFTl93SflTJWXCRuxBe8hl2KljziBlEzkpmmK1dMabyTr5nij/12CKvj/1oTibGlYz2t9B9natAFd6BwfO4epm3TWKHuQ6XwKx3dJhhM8vuNh8l6BF8/tL49h6Sa2bjGZnyHj5DA1A8swV1ys+8Hhl/jvz3wBfxktpqOpSUYvI9YvxR09N3PPpr0LkugAO9o38tuP/TK6pvPdQ88rRbwMODPay5nRxVu166I1fPyW9/Mv6UP7AAEAAElEQVRr7/scPU1di75/PgghaK1p5IO77uXzz38VgMGp2aFDP3HrhwiZK+sw0TWdn7v7EwD87xf+igMXjpaV1DO5NM+feJPnT7y5onXMhbPTfeWOh6sBQ9O5vW03A6kRXup9i2Njp6kLJ/ECDzdw+fu3/Sy2YdESa+BDG+/ny4e/wxfe/SaNkTrSTpahzBif2voIPUn1nTZEktzZcQtfOfwkf3rw6zRG6kk7WWJWBGOJbeEluL7L/sHDPHX6BerDtYRNm5ybp29mmA9tfADbUPefqBXm/u7bGEiP8NTp59k/eJiwaVPwXMZzU/zjO3+JSKKl6vtutaCt247s3AKTo8ixfuTQWeUFHYlDPAmJekQoujR1ejiKduejBDUNyP3PqeDCH3ebQdOClnWIPfchNt6krFFu4McWWo9SUwZvvURw8DWQElHfjPbAh9E27Vz6QIaJaGxDJuoqVxxLqZbtOwUbdy3+ft+D6THo3o6492MI31N2OJqGPPYm0nfR7v2E8ls//CryxNtQ04AIx1SI6oZdyldfSuSB55En3kJ09HClNUCFn2VmAnn+eAULX7b6DdsXfxMQaVij2TOWjejZiew/U6FHuUSODyHGBmAp6vCRPuRKwhgjcUTP0o99mZohGOhTPwhB0Hce9/kfsNCBE5w/c/HfY6PIwJ+fV2ptR8QWCHvWdJWVIDR1rHsOlypzZTZLMDJU3j6tuUV1oMwDkahF1K7RY2kNQ1g2+u47kDv2Qiatvv5IbFle/leMGYpAcxfStJQtTKVwCsgjbyDu/tCyMh2uFoQQ1HR28tC/+G2++4/+CW/96ReIt7aw7RMfWeoIV7xihsPc+su/gOcUOPm9p/nO3/9NjJCNV3AwQjbr7r6De37z18tEuRCCSH09VizK1PkLJDesJ5ysBUA3TZq2b+H4d77Hunvuora78ue4FUE3Zgeh53PLyiGpCuMXNiJ0xrqX/P46u4E6e+GH7BJ0TacrtgzfriKE0IgaMcJ6hIKfJ+/ncPwCvvSKYXuiSFJbhPQwYT2MfglpowmNxlAzjaHlJ1jPvT1KtRrRo7i2S97P4gQF3MAlkD5B0e9TINCEjilMTN3E1kNYmrUk8trSbVoi86dSLweWbtEcvjotnkII4uYa8pxbAgzNwNAWuAm/h6FrBvgCx3cQQNSIkzBrAUVWJ+160m6Kgp8DBKZmoV2q1S2eC1KqfIAgCKpCWC4GS7doi3QCcCF9jryfQxLQl76giOv4pmLQr4IbOIzmR7iQOsOMOzOr0NUYamZdbD3J0NKDfpsiTXxw3ZXhhE3hRh7v+fis1wSC5kgTzZGlVbl3N97E7sa5k6sFgvpQPXe13rngGM2RZj647gNLWt9y4PiF4jUuIB3M3dkz48wmZk3dxrhKjVK6prGjeSPPnnmNLx98kohpM5NPl0nq8ewUb/Yd4ujoac5N9vP82TeZzM2wt20HXbUL+2RrQuMzN32ILxz4Fv/37W8iEIxkxnlww+101Kh7y/nJAV7tewc/8NGExtnJPh7quei1F7XC7GzexOt9B/mzt/6aRCjOJ7a/n7h9HQTWLYDbN9zE//v4r9NV38q333mWUyMXFl3G1A1u6tjCx255Hz91+4fZuAICvYRktIaHt9/FV157klRhdhtffayWD+68Z1b4ZaUIWyH+5n2fZn1jB3/+yjf50fE3yr7wS4GhGbQnm7ir55YlFQ6klJyb7mcsN3/RYzXQEmvgJ7d/iG0NPZwcP0fWyxPSLdbVthO31DGrCY1bmreRsGK8NXSYkewEzdEGPrL5QW5vu3gds3ST/z97/x0nyXWf98LfU6lz90xPzrMTNuddYLHIAEmQEINAilnJSq+kV68s+8r32vfKSda1aNmyZEmWbVlUpCiJoigxEwSITIQFFpvzzu7s5Bw7d4Xz/nF60k7YSRs5Dz6DnemuOnW6+tSpOs/v93ueh+r2E7aCnB++St7Ns6OshYdq91MZLp0ORJWF4jzV9DCt8YYF+zTV1o6yFpL5NMOZMVzPpTRQzOMNhzhYvROfrkh0gaAuUslP7/kRjvWfpX28B9uzqQgFeKT+ACXBopt38m4ShKYrQ7dQBEb68HqvwGgfMhhBBGNQUQcl1cvTKdYNxM4HENE48tTryPYLMKtC5AcK4SJE6y7E7ocQFXVzjAo38AMKTUNr3YnWvB2ZTgISQhGlG75SVNYjymuQqyXRAdIJ5NWziE3bljE+JUSKETUtiOAsjxrpITvOI+KVyKunZ9rNJBHJcWWEapiAgJ4rSDuLtLMFSRjWpWpHOnlk3zUYWwOZC0qffZkk+p0M0bILefTF1Rt9jg4gB3sQNc1LB1BdV0kKTYys7jiaBtWNiGUaigLIXFZlhAJ4Hvmvfgm++qXlHzOTBm/xNaWIxJafFQ/z5Iekk58xOjRNxA2IXeHzIQLBgpTR3SUHd1sgJdhZ0E31vGGYMCWNtVbousoeL6tRVS2rhesgLx5D7jyEKFoen3mzEK6sZMfHnwGYJrABNEOnat9e3vPr/5q2519UAam8jRHws+2jH6F85w78RUXz2ovV17Hns5/AF40SiM+cdyEE4coKHv2X/webHnuY/pNnyIyPY/r9xFuaaHz0YSLVVXPMSosa6znwMz/JRGc3DQ89gOGbue72/NhnKG5qonz7VnxLBbVuJoSGMGb4VZnNKGmyZeKef+LThEbACBIwbo5cgO3msacy8IVxQ01kpZVuYek3L+v2ToErPRL5BJP5JCk7RdbNkXVyZN0cOTeH4ylizZUurvSQUk5r605l4uuahiEM/IaPoBFQP2Zg+ndLt+55+ZQ7DT7dv2RVhqlZFPuWNr0zNJMi362PzPt0H9XBOqSErpQi0j08ulKdaEKnKdKCoZnk3Cy96W56Up2knBliTSCoDtbSEGkiYt5czep7BX49sGIpKlOsjzTOcvFI40FCVoCO8V5CZpCf2HeYnRWt02ZDtmcT9YV5evMj6EIj7+ZxpYcmNPZUbqEuVknYCvJg/T4Cpo/SUJz3tzxMdaSc8nAJEsmZgctI6XFf7U72VW2bnrfKwnFqoxX0J4cRCJ5oOsQDdXun+2ZoBk80HyLmD9OXGMLSjXVRkvjQnsepipWtq2F1TXHFDbPQZ2NbVRP/9iO/xEMt+3jt0ruc7LpA+3API6kxMvkcmhCEfEHKo3Fayhu4r3Enj26+j0NNuwmvUsLleliGyYMt+/jcx/8PJjNzHeDLInFaKxrX7R5j6AYf2PkIO6pbeOnCEY5eO8O53it0jPQynBwlmc3gSRdDN/GbFiWhIipjpTSU1LC5opHt1c3srd9GY+mNg+WJfIquyT5S+VvvuVIciPF4w/083nD/otvoms7mkkY2lzQu2VbECvFw3QEerjsw5/Wf2fvx6d9rIhV8dueHlmxH13Q2FdWyqaj2hv0XQlAWivP+5uWXH9+xcF3k5AhytA8mR5QUQzAKlY0Ifwg5OoDXcQEtGFUyL8uAEALqNytivuYE8vy70NMO7u00EL+F0A2oqEfsvB/RugcRKb4js9E2cOsxZeQrdAMRXtszooiWIKsaofMSZNOra8TOI3vblZlkRd2Nj2laCN91z/dSzpC1U2aWsRK06iZFoAOy8yKy86KqbjFMJbkiPbXvWq8NKSGdRF48vnYSsrZZ6XPf5ZerKCqDmiYlKbGaZ7hMSmWYZ5KIJbT6ZWpKyiW76DZLQjcQ2w6ubAy47kyWsKYhiuII//KVALSyihsY3ZrTWv6rgufNGFNq+tLHAvXZDUMFFNxbI613V8C11blcqHorm4RA5OYEpovLEfWbkX0da6ukGx9Gnj+KeOD9t/X+X9xYz6FPflgFLce78XrOI0qroGELhmXQvLOZprgaoyI5jKis575nnoKeq8hrJ6E/BFv3g2khs2lK9CyHH9mhqqi8tCKVJ8eQ7edAevjilbS+9wla3/MYsucacnIUnDz0XUAEDWRx+XQ1fLS6ivt//mcW7Pf2Zz7M9meWmxl/kyA9pDPrmvTcJQNw1+OeJ9FvNhypssoBLM2/LsaCdyOklKTsNP3pIfrTAwxnRhnNjpO0k6SdDBknS76gi5x3bWzPxpUe3qwfiSxI7GhoQkMTBcM6oamKAcPCp/vw6z71r+EjbIaI+4spKfzE/cUUWVGV1X+3PyXdZHjSYzw3SVeih770IIl8gmzBTNFnWISMIJWhCmrD1ZQHShY1C7zb4NN91ISmpF2ukfOyeNKlO9WBJnQqApX0pLroT/eS82YeHHVhUBuqpz7cSGCZGt5pO82rvW/Rk+y/SZ9mfREwfBws38vm4uZ1a9PSfTd1XlyPvI7iQJT3NM/N1G8uUQvO0lAxH9r6xKL77qiY0XK7r3amZPXxphkCcX/1dvZXL5z9VBUpo2rLY0v2rzwU5wOb15fQe+/2B3nv9gfXtc3VoDgY5WMHnuKJrQ9wZaiL/okhJjNJck5eeZOYPoqCUWqKKmguVwac643a4kp+6ckfXfd2F0NdvIofe+AjfHD341wb6WFwcoTxdIKsncWTUpm06ibRQJh4KEZFtJSqWCnBFXz2vuQgvcnBu84IcwPrC+/Su8iJYVXGHC1FxEpVtqnlVwu/aBx59Hsz5NhyIQQiUgS7HkRUNiAvn0JePgEj/WphfC9CCCVfse0AomUXlNetTpd1A/csvBNvqmtr6575ZPRKoeuIuhbkxWNKOmm1GB1AXj6piJUbklJiPiEkBPhDiOomROu++e95HvLqGSXbsfWgKo8/8yYynVj4ECu8JUnPRXZeVoG6NULbdnAma/5uhhCIbQeRF46Bs7r5Vg71IMZuYHg7OoAc6l1lJ1FeEcs0FJ3GlJwKgD+A9cGPoe9YuNJ2IYhAcJ4J6dwN1vjda9pM/zxX6aXfCJ63kYU+G1KqQITnLkyi30SIQAhZ0wSxktVLZQHks8hLJ5BNOxEVN07OuJmQowNw5YySlosUKX8AUOT3pROqoimbVkS4YSmvh1AU4bnIrjaIxRH1m2GkH9l5SSVImJba1s4jj76IKKsB3af05IVAVNYj205BLoNo3IqcHEVeOIa4/70z18edjmwGOT5L8tLyqWqFZWKDRN/AquFJj9HsGBfHrnB1ooOBzBCT+QSJvCLOs05uxQv4KUJ9uTCEjt/wT2emB8wAETNMdaiChkgtdZEaSvzFayKAHc8mYU+SdTO40pmR3NFMpQFuBNCEPkfWQ2Wv5kk7KXJeFsezp4MEluYjZETmVUeknCST+TEEgri/vGDMOR8ZJ81EfgxPupT4K+ZIkCwHUkrG85Mc6XuXMyMXGM6OkrCT5Nw8jqc0+g1Nx9IswlaIYl8Rm4ubeKDyADWhlblh36nw6T5qQ0oSoSN5lbyXx/ZsOpPtjGQHSdoJHDlT0mNpPhrDTVSFavFpvmWfg5xrc2roHGdGL9yUz7HeiJhhasPV60qi32zc/aNxA5rQKAkXUbLMTNh7AZp2cz9zd2KAvuQaFggbuDcQCKMVKzNREYwogmIWhOlTJMcqKzuEaUFVIxSXIZq2I9vPq8z0scF7Sy/d8iO234fYfh+UVSOCYTbuPhu4HvLqBaisW518y0KoqFPSA4M9qzfUzKaR1y4gm3ciqhpXvr/QEJt24F05hVZWgwjFkOlJVdVSVKbMUz0PgVQE49gg8to5lX27YHsrOLaUSn/4+Ksq23EtKC6Dmk1K3uoegKhtQcZKYaRvdQ0M9yHHBhE1m5T+9/VwXeTIAIwuX3ZuXh9bdsEKEx+Ez4eIFIh9x0Zr2IR56KGF+3gbIAwLbUoD3baR6RTScRY1lJa2jcxl777gsutAPqOy5wWKUDX96hrPpdT34bmKDNd0sAJzyVMnr2RZPLfwfnDGKDmbAjevru+pCjZ/WB1j9vHtCXXeDEsF56auXTsPdkY9Y+imOramq7/z2Wk9e+y86ntg1rOPEKoqp7pxbVJZUsJQD/LUG4jHfnhlEkE3A5YfUdeigqWA9DzkUI9Kbsim1feRSqgKFn8IOdqvvtvxYeRwvyLRp7TiE2Oqyi5ejhzuRyYnEA9/SHl1XDyGbD+HqKxX56C8FrbsUxJ/x14u3KfufBJd5nN43R3I4RmJMBEKI8zl932DRL9L4XgOutDnkHlTBLRWyOS+GVDksMPpkXOcHDpL+2QXk/lJUnaavHfrS3kd6ZK0UyTtuZIbSv4lSMgMEPcV0xitY3NxM02xBoLG8m/oWSfLYLZXyX7MMqWdMoA1hE65v5qwGZ1+MJRSknHTDGR6cTwl+yCZuXlqQmfSnqDEV0bMmtGb0oVG3suRcdIEjTCmacx7EJdSknFSTORHCRihFS/hpJT0pvr58uVvcHn8Kkk7tWCgw/YcbM8h5aQZSA/Rkeiibbydpxvfw4741hWbut2JsHQftWGlodueaMORDraXZzw/90E9aIRoirRSHqjEEMY9EUS4WZBSknPzXB7r4PLoNbom+xnOjJHIpci56mHGp1v4DR8lgSLKgyVUh8toLq6nJlwxbe63Niz8/WScHKcHL3F66CLdiQHGshPk3DyGphM2Q8R8ERpj1WwtbWJbvBlLN2/Kd+16LsOZMa6MdXJtopee5ADj2UlSdoask0UXOn7DR8wXoSwYpz5WxdZ4E7WRynU6P6tHKp/m+MB5zg5fpicxwEQuSd6zMYRO2ApSGiimLlpFa3EDW0qaCBjLDzgthYyT4+p4F1fHuuiY7GEoPUYqnybjZPGkh6mbhMwgcX+U6kgFrfEGtsWbb6luvJQSx3Onx37nZC9DmTESuSRZR419SzPxGRZxf4yKUClV4TKai+qojVYRMNbnAVwiSdtZ2sY6bqmp6ErgSY/B9Chnhi7RNtZJb2KQiVyicD0aRKwgFaFSmorq2FnaSl2sCnOZRsM3C2k7w9nhNk4PXaJzopfx7CQ5z0YXGiFTjf3aaAUtxQ1siW8iYoVu271CJsehkAWqMs41pWF8fWZoKIYIRhDVzbCW8ysEIhCG2hYorUZs3otsO408f1QRPHdzBl4wjGjehdh2H1TUKumDjWeADSyGcFRl+a2W8L4OwvKjNe/E67y0el1qgP5O5OVTiJKqVZE9YssByGXxXv+GIl78AbTm3VBUqkj2zfuU2eizX0BEihTBO6BMwuVgF/L8O8jhXpgYwXv1qxCKou1/ElG+dAanBLh0Ym36xVOfYfNeJVl1r1y/Pj9i827km6sk0bPpgqTLzrka+AXI9CQMdK3egFFoiK0Hbrzd9btFY2g1BemhfB6vpxOZSiHC8/t4OyCCQUR5QeNdSuRAHzKVRMSKFtxeTo7PzXa9ayBnsu49r1CtJlRQxHXUuLAC6sfJQS4Jekw9bzg25NKKuDb96u9MAkIx1aYVgLxUzwZTAfzZzyBSquNZQdALvwvU366tSHzTUu3buRn5lylS3i1kuPv8qu/XXfMiWoysbYb280oGZbXI55BXTiFrm5Rs0e1EMKzmdjFNRoFtI+KViL0Pq9eEpmRb3v6eIs1LKgsBnsL9Kl6B2P8Y9HUgj76E3LQN4Q+pijvdQGg60grMyHuZJgRCCN1Amj5VlXEXPO5J6eH1dGI/9/U585sor4KlqliuwwaJfpeibfICFYFKimcZtObdLH2ZXmJWMfEbaFKvBBIJEvKezcmhM7zQ/X26E71k3Sy2t3wB/lsFiSTjZMk4WUay0J3o4/zYZQbSQ5T4i5dNokspGc0NkHaSBI0Qxb4yTE1FqGw3T9pN4Xg2hjY/aqUJDVMz8Okq69zSfQgEeTfHSHaQrJthNDdE0AhhFjLOfVqAgB4k42SYtCcIGmF05srS2F6ejJvCwyNsRBc89lKYyE/y+bN/xbXJbly5/If8jJPlwlgbSTuFsVlnW3zzPaFFb2kW9eFNCCFom7yEd905iVlFNEc3E/eVoqFtEOgLQEqJJyV9qSG+feUVvt/1LqPZcfKuTd61leeB580JQGlCoGs6hmZgagaWblLsj7GztIXDNfvYW7mNkBFY1vm+/n6tCYFReBiTUpLIp/jetTf49pVX6UsOkXXUvOV4Kiim+qOha9o0yVkXqeSjW57i0bqD+HRrTd+7lBJXevSnhni9+xjv9J7m6ngXWTdXkLZycL0ZSSv1GbRpfwhLM/EbFnXRKt7TeJgn6g8R86nFxFrH4+++8xe80X1s+rhVoTJ+/dF/Stw/Y/QspWQsO8lz7d/nO1dfYyg9QtbJTfd75hyK6e/Tp1vE/BEeqTvIT+56hoCx/LJ2KSUSSTKf5uxwG2/1nOD4wDnGspMFOTAHp2BUO1W1NPs7nDp+kT/KoerdfKDpUVqK6wuBz/W9fqf6Opge5YVrb/JSxxEGUsPk3Dy26+BIRxknXz/2hY6h6Zi6gaVbxHwRtpU0cbhmH/srtxO1wkv2VUqJh5wOWnVN9tE21kn7eBftE930JgYZyYzPuz9nnRw//51/O8f4Zzl4tPYgv7D/06v2cpEFEtX2HC6NXuNrl1/g5OAFJnNJ8m5+0bFk6SZBM8DmeCMfanmcAxU7CJrq/r3W7/LPTv0D37n66vQYCpkBfvvJf0lZcMarQ0rJRC7BS51v8+22V+hNDpJ1suQ9B3fO/KHmM1Mz8ekmYSvIgcqd/OL+z0ybqd5KyN4rKhMU1II1n1UTpa6rfz0HfAG0rferkt71Cs4JgQiEwB+EeDli94PI3qvIs+9Ax4VCP+7wFZYQaqEZr0BsO4ho3Q3RYvAF1ZjbeAbYwBLQDz6C9+YLeKffRmveDv5Z6w3dWLnEixDQvBPOvwuJ8dWT8/ks8sK7SmO9Zdf8+dMwEZv3Q/PuedUqAATCiH2PIfKFjFpNVwRVYR0galsQpdWKLNMNsHwIO6/6H69AHHgPwnNmHtg0DQLLmBsTo3hvPrv2oEQ0jmjdO/f7uNtRIKnlke+t+vzI3muIyTGlbT97TEgJE6PI/o7V96+uoD+/0v0CQbTGFkRxCXJsBOfI65gPPYHWvPmG9/2pZ42bulYLBNFq6iEQhEwa58IZzMF+ZDQ277hSSryeLrzOazevPzcLmgGWDgiV1e3a6tlhCro+kx2u6ZAaL8wNQmWZQ+F9AwwfpEYLxLtfzRGarsbZQs8fU5nvVmG+9DxF3E9lmuuGIueFpjbOJNT7U21O9W0xnkLTEZu2I6+cUTIoa8HYMN7xV9FiJVDVePt4guufT3QNEYvjea6qGIyVqPk5Oa4qAYpK1bPN2IB6D9S5C0Vh8x617fgwtFQhE+OI1CTS8iGH+6CkauFjLhPSdZd8FpTXVzF6HtJZJ74xl8F5+3Xsr/4N3tVLc97SGpsQ0aJlN7VBot+lSNgTNISb5rxmaBaOZ5N3V2kAsgA86ZFz85wdvch3rr1AZ6KHvJu/q3RWPTxybo5ifxFRa/mRbIlHzlM6ocVWKWFjZl+f5idkFois68gZIQQ+zU91sGH6/dn7WZqPjmQbjueQdTKYljW9X9iIkrQTpOxJ8r7SOdrbijDJkHHS+PUAfiMwp+0bngfp8Y9Xvs3Vic5VfX+e9OhK9PJc5yvE/cVUBsvvCVLZ0Azqw5tACq4kLk4HFzShU+orp9gqWbEx5g8KHM9hMDXK1y6/wLeuvMxkLnnDwJpE4kqJ63rkZxnRDaVHuTLWyfPX3uDXH/ll7q/avarzrhdIVNtzODfUxp+f/gdODF4g5yw8b6n+uLiuS961SdppRjMTXB7r4KWOt/iFfZ+mNlK5KkmonJPn3HAb32h7ide7j5F1copYvQGRNEUQ255DlhyTeRhKj3F66BLfanuZX9z3WfZUbMEQa7uFj2bG6UkMTJ8X13MZSY9Nk+i2a3Ni8AJ/ceofOTN8WVUTLICZ7zRPzs2rc5idIOaL4FsB8ep6Lr3JQV7qOMLz196gO9GPUyDNl8L132HKzjCanaBzspeXOt7mI61P8sOtT1Lsn7/IWS2cwrn69tVX+UbbSwynx5Qc1hJz6/R5kp6q3CpcKkPpUdrHu3mp4wi/euineU/jYcwlvtuJXIKvXnqBN3qO0Z3oJ+vkcWcFFhYbXxIV7FopRrMTa+I+JZLB1AhfPPcNnr36Ghk7t2gQ9/qxlMinGEqPcrTvDIdr9vLjO5+hubgOA31N3+V4bpLuRP/0ojto+BlMjUyT6I7ncG74Cl848zXe7T9Ddsn5Y2Y+S9nqfAmhLSrJdrMhmvcgNil/BtlxHiaGES37VKaS58FwL3K4e9USLjfugEBYfqTpQ2zei2jaCckJZPtZ5LULMNBdKOd2Cgvj2/g8KbTCgtxUWvENWxBNO5SMhi+odKnvgeecDdwaeCeP4L77Grz5PdwpAqcwfLRd92N89pdW3Kaw/Gh7HsLruwaTa8hoHe5Hnn4DUVyKLKmat25ZSnZj+v1FthG6MV9bu0CACcOC8MrnQmnnka98XRE5a4EQSoaptHL9ZHZuADefJ59MquTZSBTNXH+6RQCyqBQat8LVs6trZKBLGQNW1M1ZTUrPQ44Owqr10AVi+/1zM2OXu6emYWzdgXH/g9jPfxv3whlyf/9X+H70Z9EqqpQxaKFNKeWM0Wc+rzLW4yVg3bx7rxACvaEJY8cenKNv4p45gf3Wa1hl5RArnj6PUkrkxDjO26/jXj5/0/pz0+C5Kpt8ap3muYq4noJmzJCoQisMSA9kwUBV0xSpPb2NMZeEXxJC3Zenxo4QyvBRStWGYyupmenNtbnPEZpe6NMSYy9ejtiyDznYA4mxZfZrAUgPOi7hvfMC2iMfRhaX3bJ5ZgrCMJCmNefzCgSyvEYF2l76B/X9VdQjdj0A1U3It55VUjjl9SqrH2CwG++dFyCfUybTex+BeCXa4Q8gX/5Hda1VNSB2HlLHMv0zEj6aroKUy7je85//PXVNLPLs53XPCt7lcuT/5A/gS3++2tMzq60s3sgQZNIqoDP7+OEIxq79iKL44vtfhw0S/a6FwPZsZj/OrFRP/EbIuza9qT5e6HqNdwZOkHYyN97pDkXYDFETqiRkLs8QEhSJamkWOTfLRH4MU7MwNBNdaAihLUlgCyEWfd+n+zE0U5E+zCUSAkYIvxEkn88xmR/DH5ghyj3pknHT2NImYhRhrUCbG6Az0cPb/cfXFACRSE4On+VgxR5KA3FMcefrXi0HutBpiGzC1E2uTF6alu8ZzPZT5CumxFe2sZC+Dhk7y7v9Z/mzU1/hwmj7usw9rnTZEt9ETbhi2aT19d+KoeloQuOtnhN8/uSXuTR6bcX9kEhSdoZXu95hNDvJPz3442wraVpx9cVbvSf43Xf+nIHUGsqwZ/Up79qcHW7j37z23/gPj/wK+yu3r2tFiOO5DGfGaUVlLb/WdZQ/O/UPtE90r7gtQ9PZX7ljRf3rTw3zR8e/xAsdb674eAvB8Vz6U0P8xel/ZDKX5Ed3fJiSQNGar+Wsk+PU4EX+8sxXOTFwHnedxv6moloaotU3lC5J5NMc7T/D6aFLS253J8D1PC6PXeO3j/wpZ4cvr6oNT3pknCwvdrzFxdF2/umBH+eBmr1Y+vrdf7xCRcEOIO/mebv3NH9y6u+5MHJ1Ve0dqNyBscKM//WC0I1pA0Gv7yrajsMqE3OKeKioh8kR5Pigyh69Wf0QAoSustni5Yh4ORx4AjIpZH8nsusysu+aWsDmc2pR49hKu/Nm6Mdqmlo0mlbhxwfFpYiaZkTjNkR5zTKMFzewgcWhPfAk2u77F37Tv/z1xxwIoQI7dS0qI33VWdkSeemkMnu8/30Qjt2xlRUyn0O++xLy0om1Z6GXVCFads+RLJGeh5PNIj0PKxxeW/sLYODYCb7/a/8OoWk88bv/mdKdO9b9GFMklth2ANl+fnUeFPks9HdA/XW+GJkE9FxRQc7VIBZXAclVSn9qVTVY7/8IXm837rnT2M9+He9qG+bTP4yxbeeMcWI+hzc8hHvlIs7xd8CTBP7Pf4te27C6fi8HQiid9kffi9t2ATk+Ru6Lf4JMTGA+8X6l5+5J5NgI9ovPYr/yPdVfKWG9smlvNqSE9KTK6A4VZGez18meCMH0Cmz2NDL18jyqYbHxKZm/kmOJuUmAL6SC3LO3EbNltG48r4mCDJXXfh558fja5hnpwYV38Swf2qGnkEVliFv4/Cead83/xIVkBrHzEOw8NPet+56E+56c305NE3pN07zXadiCaNgy72Xt8Ptn9i2rQjz9Y8vqr9txFe/8qeUlUEgPr2vtcl5LwjAw7n8YbfMOxJ1uLDqV+ePhIWeVsMNMWba4gXSCRBbKaT0EAl0Y0xethztdZj3T6lS7yy/pViXTc/s4tb+Gvpxr9KahyCqmL9OtPrumq7JjewxXOvj0tTnCSynJullOj1zg2WsvcGXi2l2Ud74wKoPlVATLVrxfsa8Mx3NIOQkyqTRhM0LQiODTfZjCUmNqkfHkSQ9XOrjSnR5DUjJnvMvrJhAhBBEzStpJkrAnKPGXowkVIcwXjEoNYRLQQ9OSFcvFm31HyXlrNOYpfK4TQ2fZGd9Ksb9oze2tBiorP7vmcSlgOiAihKA8UIkuNK4m2kjZSRL2JP3pXizNNy27sxQszVqTie3dgpSd5uWOt/nCma/RMbl4poqh6YTMAD7dwtAMNCFUFq5jk3Iy5JzcnO9QFzqHqvdQEixetM0bQQBXxjs4NXhxHoEesUIU+6P4DR+GZiClLGSfpwpyIXN9HTwpOT/cxt+d/w6/uP8zVIZKWQl2lW+hJlzBUGp0WtJjCobQCZoBQlaAoOHH1FWAziv0KZFPMpFLLpj9PZad5A+OfoHPPf6rVEfKV9SnpeB4DqOZcfJuniO9J/nCma/NI9D9ho+wGcTUDHRtpr9pO0Nmlpm0oRncV7VzRccvCRSxu3yLytp3c/PeDxh+wpbyurB0E0OoLBfXc0k7WSayCRL5+T4POTfPt6+8QlW4jA+1PEHQXP09MuNkebPnBH9x+h+XDNDoojD2DQuzMPYXO1dqe429FduojlTcsA8CMDV9SbLdXSSovhpt8dXOaZ70uDByhf9y5E8WPFemZhLzhYlYIXy6ha5puJ6qfpvIJZnMJ3Guy1zqSQzw20f+lH91+Od4oGYf2jqRQFJ6DGfGsF2Hk4MX+YvT/ziPQLd0k7AVwtIMDE2f+T6dLFknN+d831e1gGTC7YBhIkf7Eb4Q0jCnF8cynUAUr/yZaF0QCCE2bUNs2qb+TieRowMw2o8cHUSODyudUscu/DgzOqfSK2SkeeqzSA8QqoR8KvNM09TvhjnzY1rgDyFiJYiSCiitgpJKxPUL8Q1sYA0Q0WKIFCGzacgWEo98fiVztJZxpmloB57A676yNm10z0Uef01lje95GOG/88a/tPPIi8eR7748o727Whim0kIvmO1NwclkuPbc9/Bshy2f/JG1HeM2Qug61DSpjPSxwRvvsABkTzsinZoh0aWEVAK5Bh160bJ7vkTMCqHvOYDv0/+E3N9/EffCadxL53AvnVPEbiCIQCKz2TnEtL5z7+ISHusIYVoYhx/B7OnAfvYbyIlx8n/3BfLf+ke0kjKk4yBHhgCJsf8QWnUd9hsvI/t6bnrf1g2eM0NUu476eznPj0JT2zl5tZ9uKoLaK5iAwtTiW70+mwe50XgRqEC3V5B2ETpQyFBnFd97MIzYcT+yv3PV1880XBdOvYknQbv/Pch4xT1jYgwFviqbVnNyOIYwVp/EosVL8cJRSE7efok/00LbtgvjqY+gVdasaNfbQqJ7eNhuHlvmcaUzT9fUEOZ01u9SpHfGTWF7eTShEzWLCqaXeWxvoXYNTM3E0nzoyygDVqX0NrY3VX4/JfGgoQsDU7NUqe5tevioCtZxLXGZa8k2DGHioQjbuK+UsLl68w0pJSknzZH+Yzzf+Qq9qf517PXtQ0WojLLgyggwUKaSFYEaJuwxMk6KhD3JZH4cn+4nbEYJmxEszT8n21JKiSMd0k6CtJMi52YVkT5FmEiwpb2oVEXQCOPTfCQLRHrcV4aUHjkvS87NEjIj+FcYKLE9h8vjV+cREqvFlfF20k6GIrl+8ggrgURyNXF5Rbru8yHQEGiajo6OrunKrJepQEYaKV0GMv3kvNyySvPrw41zzGLvRWSdHG/2nOCL576xIIGuISgOxGiI1VATLqc2WkmxP0bYDKBrOnnHZjyXoC85SH9qmKH0KIPpEUYyE1SFStle2kJwJRra1/09kUvycsfbdCdm5q6YL8LWkiZ2lW1me2kLJYEiQmYAiWQ8m6BrspeTgxc51n+O3uTAnMxi23M42nea17pa+ejm964oeBX3x3is4X4ujLaTtjPoQqcsWExFqJSaSAX10SpqI5WUB0sIW0H8hg/Hc5jIJbg20cPZ4TZODJyne7J/nqRJx2QP37zyEj+z+xPo65Tx4BRMT9vGOvnKxee4PKZK6jShUR0uoy5aRWOshrpoFWEziM+wsF2HiVyS/tQQPYkB+lPD9CYGiPrCtBY3ruj4fsPHzrJWtpY0cWJQlb9GrBDlwRKqwqU0xmqpj1ZRGS6bDoZoaGScLAOpES6NXePEwHlODV4g48wl4SfzSV649ia7y7ewJb5pVfNW3rU53n+eL5z52qIEepEvyqaiGqrC5dRFK4n7iwibQQxdx3YdErkUfakhepMDDKZHGUqPMpwepyQQY3tpC9FlaGiHrACHa/YtSrg7nsv54StcHpvbR0PovG/TQ/hXaGK6vaR5VWOsY6KXPzrxJdrGOuYEDEzNoDZSyY6yVraVNLOpqIYin/o+c06OsdwkbWOdnB1q48zQJfpSQ3MI6qHMKH904ktUhstpKlramG658KRkKDVKd6KfL194ljOFrHlNaFQES6iPVlEXq6Y+WkXMCuM3fTiey2QuSX9quDD2h+hNDCJAjbHbmWlRgKjfhrx2FibHVGm950EmqUp1iytvd/cUgmFEMAy1zYUzJsF1FRGZSUEmicykVNbklASM6xYW9i7Tpd+6rv41TNBNRVwGwxCMqN/XsOjbwAaWA5nLIjvbkG1nkROj6gElEkNr2YFo3IxYgya3qG1G7DyEfPPZtVVq5LPIt7+nCK1dDyB8yyvBvxWQuSzy6lnkke+uTboGAAHVmxCtu9X8Mgu5iQnO/NlfEqmru6tJdIRQ81vzTuTRF1fXxkAXpCahuAyEUElfE8Mwssr1v+VTQdIl5IGWA6HrmA8+hlZSSv6FZ3HPncIb7EdOTkA6pbg300QURRGxIrSycoyDh2+ZAalWVoHvY59FhKM4b72G19uFTCbxujshEECrrELfvgfrQx+FbA739HHcu4VEF0Jpktu5QsDaK+idL3N/w1L353wWtIKW+tS9WR1AyX9MGZAKXQW6lyPhafmVHFw+Pcu/QSiz0lVAbNqGaNmFPPHa6k10p+A6cOoNPDuPtv9RZHWTyki/Q+bXVcHzkIlxJQPYfUU9Pz7wfqWpvkqYH3gGraYO79oVvOEB5MiwMt/Nz0+eumnQNERxCfqOvRhPP4O+Y++Kv6dbTqI7nk3WzZD3Zk6UmH5sLuiaShfby+PT/fj0G+s+e9LFky45N0vOm9EDFypOWWjXxnVtHM8haISW1JJVOuAZsm5mXpa8kkzJ43g2ru6grSbytQ4IGSFaY9sYy40WAgkaISNCxIygryLTbApZN8ebfUf51rXvMZpdg0bUHQSfblEVrFiRHvps+I0APt1PzsuSdlJk3TQZJ81IbpC0k6LMX4lfnzFBdKXLWG6Y8fwomtAI6CFCmspa1womGMPZgXnZklPQhU7EjJFx00zmxymy4rjSJe2oUqqAHpw2OF0uRrNjTOYTq/r8C2E8N8l4bpKqUMVtIgokvemVmaOuFo60Gc4uL0Jd5q+4p0l0T3q0jXXwDxefp318vsSHT7e4v2o3D9Ts4UDlTqrCZYtKLkipDIA7C6aIl0bbKQ+WUButXBHBef2WE7kEE7mZsd4Yq+F9mx7i6aZHqAiVzpMXqY1UsrOslcfq7+fNnhP89dlvcmH06hzSbjQ7wYmB8zxYs4+aZWQKz8bj9Yd47urrgGRLvIkdZS1sjjdSG6lclMyso4qdZZt5ouEBjvad4e/Of4dTQxfnBMFyrs3r3cf4SMt7qAyv/mFmNmzP4eJoO4PpUY71K43NYn+UA5U7ebTuIPsqti8ph5LIp+iY6OXs8GU0NPyrMCxsiNVwqHo3E7kEtZFKtpe1sDW+iS0lmyjyRRc9dnNxPQ/W7qO74TB/f/G7fP3yi2Scuf4g50eucGHkKptitfhW2DdPSrom+/j7i88uKPGhC439lTt4qHY/91XtojZSueTYV4ag/VwZ7+TiyFXCVkgZoC5j7Bf7Y3xy29OLvp+2M/zJyb+fR6Kbuskv7Pv0HPPMm4WJXIKvXHyOM0OX5wSlAoaf+6p28eGWx9lbsZ2wNV/ioBHYW76N9zU+xBs9x/japRc4N9KGM6vc9up4F39z7pv8s4M/QWiBNlYKT3pcGe/kO1df5c3u4wBErTD7KrbxaN197K/aQVkgvmgwIWVn6Jzs5dxQG1k3T8wXuSMy0bXqJqTpQw73KA1RTYfSakRZHSJyp96rFCkuQtFpneXbfyY3sIEbQ7adxXvtWUUkxlWlhxzux+24jP7gU4h9h9fUvtj3qPIV6FmdzNQ0khPI17+lSJ+9DyFulj/CCiAzKeTlk8i3n4fB1Wpxz0KkCLH9PkRF3dzjSElmaITh02eJ1NUtsvPdA+ELQsMW5Ok3V5e5n04gR/qV1rFhKuKz+6oiOFeDqkaIV6ig5lohBPrWnfibWvGuXsa9ckkR6ZkMSIkIBBCxYrSqavTGFkR5xfwMWV1Hb92K9bHPqD+37rhhQNXYsQc+9hmQHvrmbYvKfGkVVfg++RMYBw7hXjyLHB5COg4iGkNvbEbftgstXoI30If5/g+h79yDvm0X3ASN/HWHPzQTuNZN8M8KRBmFJNLpG3OBxJ5aX+mG8k+w8yrQrRlKPm1a/qVA0kvAswtVZbO+Eysw1+BYN1SFGRrompJzcXKqb1NVZ1Ptmr6VVSOYPsT+x5GD3dB5ae2Z0Z4LZ4/gTYygHXwC2bxzzQGlWw4pVeLC6AByuBe6ryI7LqgqqKpGxBoltvSde9F37EFmM3g9HXgd7Xhd15B93Xh93UoTfaqSSwjlNeBbWfLPfAgwDITlQ0RjiIpq9C070Pfdj1ZRvapAxy29il3PUZrOBUkJQ0yV+KvBLqXElQ62Z+PhkXUzgCBg3HiBNNWuJnRMYaosXyFAKmI+7+WReDgyT9bVCInwogucvJebRaALTM3EEIYyCii0Z0ubnJu7bYaDQgh8up/K4PrpWdqew9GBEzzb8eI9Q6ADxH3F1ISUTMdqIYTArwfwaX5cGSPtpBjLD5NyEgScIJauKhyklOS9HOP5EYTQKLZKiVnFKsN5lhHKSG4IuQQBHDKjmPkRcl6OjJNGCI20k8Kn+wkYoRWbVoxkx9YtCx1UNclodgxPeuuqybyBOxtj2UleuPYWZxbQYvbpFh9pfZIf2fIU9dHqGxJIQgiCZoCtJU1sLWkibR/C8VxC5vo9bDTEqvnsjg/zZP2hGxJtQTPAEw2HMHWT//zWHzOSGZ/z/qXRa1wZ61wxiV4ejPNTuz9GyAzQWtxIaAXZEiEzwMO1+zE1g+TxFJeuI0VHMxOcH7myjiS6yrRWgWePylApH2x5gg82P0ZFqOSG13rECrGzrJWdZa3kXXtVAbaQGeDxhkO0xhtpiFVTGSpdUfZ/TaSCn9j5w6TsDN9se+m6z+dwbriNh2sPrJhET9lpXu48wtG++QZepmbwnsbDfGrbDy0ry10Igd/w0RpvoDXewGP195N3bUJrkJm5kyCl5K2eE7zRc2xOIMPSTe6v2sXP7Pk4LcUNS54nIQRF/gjvbXyQqBXm8ye/zMXRmfJyx3N5q+cEb9fs44mGQ4u2s1x40uPscBvnR67iSJeSQBFPNz3Kh1qeKBgLLz32Q2aAbSXNbCtpJu/aq5LNuSkQGqK8DlE2K2P/DiD3N7CBexHehZNQXo3+xIcRRSUAyOQk7qvfxjv9NtoaSXTCMbQHnsL79hdUlcZakE6orPZMEnYfRpRU3XifmwEpkRMjyLNHkKffgtGBtbdpmIim7YiWXdMEW2ZklP53jjJx9RoDx46TGRlh8NgJ3vj1/zhn122f/RRFLc0IIVRlcSbD6PkLjJy/SKp/ADeXQ/f5CFVVUnnfAYqam9CWSRhLKUn1D9D21W/gpNPUPvIQFQf2zdHhnWi/Ru+bR0h09SA9l0BJCWV7dlG6cztmaIFgh65DSaUir6+t0sCy5yps3qvOVTaL7FqdfwlCIBq3QaQIUNI83otfnZNhKipqEdv2IQrbLKtZy4e+dSf61pVJBIIyXTT23Y+xbxGvggVgHn4U8/Cjy2vf58PYvhtj++5Ft9EqqvD9yI8u+/h3BHQTAosEG65fx2jaXJJ9av+lfGs0vWBUfF1bQsxvy7yOQJ2SaZuH1WWki5IKtENP4Q33qaqM9UB3G15yHNHfCVv2ISob1iewdLMgJTKXgZF+5EA3DHapwMJQ79pltRaCEIhAEL1lG3rLNiWBND6K13mV/F/9b7wLZ9R2hon51IfRm1rXekA1Znw+RFEcrbxKVa2s4Xn4lj3le9Ij62WmCXSf7senBeaSi0g86WG4OTJuBonKCDc1E+MGmbdTBHpAD2Fq5pzFviddNFcn66aRSJyC3IuxgCmiK52C1rKKRPk0H349gDarn570MKVNxknhyjvLJCLn5tCEtuJMZSklHZNdfKP9OYYyazfBu5NQGohTHV6fh0MhBIYwiZgxcm6GjJPGdm086U0HVBzPxpUuAc2nqh6uW0xn3QyudJYkmEzNJGJGGckOMZEfw28EcTybsBVZleZ9xsmsq+ksQNrJ4N1uLasN3DK4nkvbWAcvdR7BXiAg85HWJ/mxHR+hPFSyqvaD60ieA4TNAO/f9DCP1d237ExVTWg8UL2H+6t28+zV1+ZUiwymRuhNDuF67oo1oh+q3b+i7WdD13T2VW7nUM0eupMDpO2Zh5mMk+XS6LV1IRFBZVuP59QDZMwX4QNNj/Kxze8jHoituK21mD42xmpojK1Mm24KinyN8qGWxznad5r+1PCc99vGOkjZGUpZfhauJz26J/v5ztXXsL352Vnv2/QQP7HzGepWWEUxhYDhI7BCiZU7GQOpEV7rOspgaqYcX6CqPn50x0dojTcuuy1LNzlYtZP2iW4G0yOMZWcWOOO5SV649gaHqnevef6QMF3BEjaDvKfhMJ/Y+jTloZVn7a+n4el6QNp5mBwGTyoj0Sld8Skd8Q1sYAPrA91AFJcqGaGpe4E/gCitRI6vfW0lhIZs2IrY/RDynRfWbrqZSSKPvqSI612HEY1blVzCLYJ0bOhqwzv1OrSfh/Q6VcyWVSN2PjCn2iY9MED7s88xfvkKE9c6kJ4k0dPDte9+b86ujU+9l6LmJhACL5+n84WXOfb7f0iiqxsnm0W3LNx8HsPvp2T7Vh741/835fv23PDeL6UkOzLKu//tD2j72jepeegwjU+9dw6J0/XK9zn1x3/CwLsn8Ow8QtOQnkessYEtn/o4rR97hkDJAvekSBGiYYvKFl3Fukz2tiPsHMggJMdgcJWyI9E4oqoBMTWGPA8GumFiBDk5BkO9yG370euap4n2DWzgToDYtA2x52HkG99ev0bHh5FHX0T2XEVs2o5o2qEMzO8UaTkpkckJGCgQ5sO9ypNmbEjNxbeQ4xGGgSgtRxSXoL36Pby2C8rvQNfRd+7DOPjgLevLcnHLSHRXOjiFxacudPx6cF4WtzII1RG6H0fa5L08Hh45N3dDEh3ArwewNGvejUwTOj7dT97L4UoHicT27AXbzLv5af1zXej4jYAyLZ3TnoYpLDzdm5bYuFMwmhvCp/uJ+1aWmZhxs3yz/Xn602s0VrjDYAid8mAppf6Vly3n3CxJexJTs/AbAQxhFiR9XDJumkwhKHN90GaaTJcOWTeDT1ea6a50SdspxvJDSOkhblDFEDWLGcuNkHKSONLB0EwCRmhV1Q85Nz/P2HCtyLk5FrDfvkUQNISb1j0wsFaEjNtfEnuzkLTTvN17isHU/IXg3vKtfGzzU7dEJuJ6LDYCd5Vt4ZG6g0R8K/tOLN3kvY0P8lz763PkgvKezUBqiKSdJua7NZqLUwgYPnaVbeaVznfonEWi51yb/tTQuh9PFxoHK3fwoZbHV0Wg324oDfdydpdvob99Lok+kBpZ0Kx1KeRdmzd7j9ObmH9/3F7azEc3v5faSOVGVU4Bp4Yucn7k6pzrx9Itnqg/xPbS5hW35zd8PFS7n9e6js4h0R3P5ep4F5dGr7G3Ytu69F0TGttLW/jolvetikC/0yDTSeT5I8jEKMIXQJRUqmyjgU6IFN++7NMNbOAehNayHe/d7+O5LqK6HqSHHOhBXjiJaGjFu3IOAFFaiYitYn4RAuHzw56HkJMjcP7dtXfaziEvn0IO9yEatyK23YeobrypRI+UUhE2J19X8jQj/UqaYT0QKUbsfxxqNs0hqMM11ez8Jz+Ok8nS9dIrvP1b/5XqBw5x8F/8szm7xze3Tu8ndB1fcRGR2hpafvjDxJo2Yfh95CYnufDXf0fniy9z+vN/xpO//18RxtKUipNK885v/y6Xv/I16h57mPv+z39O8eZWpZkMDJ89x9H/+t8YvXCRPb/wc5Tu3IHQBGMXL3P+b77Eu//tv+MvLqL5wx9C982tpBOWHyrrCwajq3gmHB1QlQmhCLKnXWlhrwa1zVBcPnPeTQvt6U9DPoe8dhHvO3+zunY3sIGbDd1A7HtEGZxfOLZ+7To2dF1GDvUg204hqjcpU/XqTYjg2jKhVwzPQ6YTMNyHHOqFoR7lf5AYh+REQULl9iZHCl1Hq6gCfwCS6ydDfDNwy0h0ZfapFlSW5ltSS1wTGoZmYXu2yhyX9g1lIzSh49N8i0aClWGpMU2ieyxMvjnSns4+NDVrUcJSILA0H1nSi7Z1O5BxMqsqoX+5+3XOjl68CT26vQhbYerC1ZiryAxzpcuEPaYqGYQ+PWantPsdzyaghwibkTnj2dJ9RMwoCXuS4ewAk/kxhFB6+o50COhBwmaUtLN0KaapWYSMiOqD4xI0QgSN8JL7LAZlmLm+UBn2t6csXBRI9DsN11cdhMwAH9r0Pg5W7CHlpEnZGdJ2mpSTJm1npl9L2SkyTpbFdPLvBIxnJ3m9+9i8Plq6yce3foCaSMUdoQEMinTeXbGVhmj1qubDraVNBEwfyXx6zuvj2QRpO3vLSXSAxlgtxf4onbPMXF3PJZFbY0n3AigPlfBQ7QGqwmXr3vatQsgMsjm+iefaX5/zeiKfmqOtvRzk3DwvXntr3tg3NZMPNj9OS3HDupm73u1I2RnODbcxcF2wLWQGebr50RVXcUyhLlpFS3E9F0fbybsz1QCj2QlOD11aNxK9yBfhkbqD1EfvDXJZXjujDJRa9iIvvKPWR5qOTE+C526Q6BvYwDpCdl/FO38MLp8Gf1Bl8uWykE2r7N7j6n6kP/Vx9IOPrO4gQoPicrQDT+IlJqC7be0d91xVxj85iuy4iKjeBK17EA1b1td41LGRve3Ii8eRHRcV4btawnYh+PyI3Q8itu5HXCfZ5ovFKN+7B892mOzsAk0jUFZK1f0HF21O6DqVB/YTa2zAX1yMEVT+V57rEiwrY/jMWTpffBm5iNGr0HQQGm4uz5Hf+m0u/t1XqH/iMR741/+KaEP9NIEupeTC336ZviPvcOhf/Qt2/vRP4otFkVJSffgQCME7/+V3uPqtZ6k4cIDYpobrDiSUpEv1ptWR6K6DHOhGFJUqXejVwLQQNU0QnUlaE5oGZQXZ2XTyzsnA3cAGFkK0GPHgDyk98I515sSyaehtnybTCccQlfWImmaoqIN4uZov1gNSqms6MQ4Tw8jRQRUoGx1EpiZVX3IZ9bNewct1hKisQQSCyA0SnYIZ58yidSoDXC5RJqDNIq+llEjpLVl2Oq1ZvgRmk/ALHVsZlM7cCFU/F39w0ISGrul4a3FJXwG6ku2M5UdpiW7l8uR58gtk0+XcLE3RzStqtz81xCs9bxYyi28tbkxwrY1WLPJFaYiuzjjGp/kpskpI2pPk3Cx5mUMi0YWOpfmI+eNEzOic4I0QAgOTMn8VPj1Awp5QZrcSTN2i2CohYhWRshOkndQNP32RL86EPQZCELiBIe5S8Bu+dc+SDBrB20aaCiGw9JWbFt5qmJrJluIWWoo24UlZmAs9PDzcqd8LP47nknYUoZ60U6TsdOH3NCk7TcJOksxPvZch5+XmzGM3k4B3PJeOyV66Ev3z3ttVtpnN8UaM9br5rxALjcDqcAVb4ptWFTwDCOg+KoIl80j0tJNdcN69FSj2R+YZkUqUQaXtOpiLGB+tBg3Rau6v2nVXZ1abukFJoGje67bnkHfzSCmXNX9JKelO9HNtYn5585aSRraVNOO7C+aiW4WexADt411zstAF0FJcv6agjKkZNMRqCJmBOSR6Ip/m2nj3qmSWFkJFqIRH6w7e1WN/NuRwL9qOB8AfnrlDGKZ6nnbWZy6bug+lh4bofu0Nul57g7FLl8iMjuHZDlY4RKiygvjmVqruP0jVfQcIlpeDYMlrUEqJm8/Tf/QY155/gcGTZ0gN9AOCUHk55Xt3s+mp91B5YB+aNb8Kdcl2szm6Xnuda899j6Gz58mOjaEbBuHqKir276Pp6aco2bYVzTTmtZsdn+D0n3+B03/2BbZ8/KPs+4WfJZ9McfLzf0b391/HX1RE60c+yNZPfQIzFMTJZGh/7gXOf+nLTHZ2E66qYsePfZrmD34A3dwgle4laA89hbb/oRtvGC5a03GEriNrNqHd9yReahLG1qmK2M6rLMWxIWg7jYzGEdWNiPrNKss4XFR46FrOtSYVmZNJQV8HsvsKsvsyjA1DNqWOtZ7QdETzLsT97103Iz8hBLrfR7imWn0WbyYJr2TbFsxQkERXD55to1vznwOMgB88jyOf+8+c+8JfU//k4zz8H3+dYEX5nHkl1d/PwLvHwPPY9EPvxwyF8NxCVbzfT3zrZkJVVQydOUt6aGg+iQ4qA7+mGXnp5KoCE7K/E7FpK7L7yor3BaCsRvlvrPU+LKdWM3JuUmxh3Kl/xKzNC+Ns6v3r7wNT7S2wzfQa6npOaNaafrWfYfrXpTdc8Fc8V+nITxGd+YwKxuWzyGwG2Xdtdf2agpNHXj2rjmH5wedTWuKWH3x+sAp/z04OmXMqlvCzmfPHOnAEcv7qdqXfy/Wc32L7C6Ehy2vQHvkwXjYNA10rOs6yYOdhfFhJvfR3Is+9o0xWAyGV1BCLK3mmcBEEQjP674ZZmINccF1Ffjt5RYhn0shsSs2riQnkxCikJtQ2rqOy4V1HyaPcYRX8C0GrrFGf/Q7HLSLR3TnayQl7chnU6cz/KWSOLzUtXy+5shooAl0dU0nL3LhNRfav0sF6hYj7SgmZESzNIutkaAw3zwscDGb6VpR36XouL3S9ykhm9MYbrxKmZmBoBj7NothfREWwjNJAnIgZIWKFCBh+DM3AEDqeVFI76sfBdm1STobJ/CTjuQQT+QkmcgmSdgrHc/DkFBnp4l43MQgEJf5i6sKr09bVhEaxFafIKp65AcPMzXXqv+smYyFUlUKJr5y4r2xmGIuZfYqsYqJW0TKCCOr7tTSLsBld9Q09aobX3QS3yBe9Z4iGmwUhlESVvuTspTB1k5dIFTic+q/wADH9WuFf23NmEe0pnut8mYtjbfOug/WA7dqcGry4oHzOQzX7KQ0U37aAykIPqFXhMhpiqzddFkIQ8c2v+si5uQX14G8F/IZ/QbNCNQc6mOt0Ow8YPpqK6ogvQEDfTdCFvqhOtu2pirTlVClIJCcGLix4XR2s3EltZHU66PcquhP984JtQmjsq9i+5vtFbaSSgOFnjBlJF096jGQnGMlOUL5GOSlTM2guqr8tslQ3DaaFzGUKGrUS6bmQTapFeWzthsRSSux0mivfepajv/eHjF68hGc7yAIBNPXsJDSNq9/9HpphEKoo46F/+/+w5UeeWVQCwbNtBk+d4d0/+J90vPAS+UQSz3WmJ/whoPPlVzn5+T+n6f3vZf8v/yJlO7ej3UBSwc3n6XnzCO/8zu/T9/ZR7HRmuq8AAydP0/78Cxz/n3/M1k9+jL2/8LMUNTbMMf6TrktmeISxy20MnzlL79tHOfnHf0r3a6/jZHMIIeh/9zijl69w/7/4Z1z40pd5+7/+PpnhETzXZejkabq//zpP/OffZMePf2Zj/riHICJFt0zrWeg6smU3Wi6L98a3YXyEdSvFdx2liZtOIAe7kafeBN2AYFgZpsZKIRxTZJthgWEoYsfOKwI3nUSOD6k+pSbBtZU+tuuuXx9nQwioa0G895MI//I8cJYDKSX5RIKhE6dof/Y5Rs6eJz08jJPO4GSzpPr60X0+pLfwZxK6zvH//j9pf/Y5og31PPq53yBQXjbvmk90dpMdG8fOZPjyez+I0OfeKz3HwclkCZaV4eYWJsiFpilJl4paWA0RPtCJ7OtQRrMrhRBKAqi8em3EqZRI14Huq3hvv4S8ck6Nw0AQ0bQd7dEPIivq5s7HR1/B+/ZfI3YcQHvvx6Fori+T9FzkOy/jff0vEIfeg/bej0Eoqt7MZZBn3sE78iIMdqt1fmkl2t4HEbsfUEGkBaoMpecqYnKKwC/0Xf0u1VjP5yCfhVwWOUWC5zJgZ2d+n/o3n0UWSHJymYXbng4qyMJ1tAbYeeSZI4rAVeRE4d/rfjetaWJdWH71u+VXhqA+vzL89AcU4V7YRs4m4jVjfptCqN91DaGbNxwv0vNgclDJV+kmoqQGAiusypcSJgaRiRHwhRAVjYtuKjQNWdOM9tgzeC/9Awz33jxtcMdWPwCTo8jBnmmvGjl1nhYMWspZ/8i5Y68Q7LvdsixrgVZdiwiF7vhPcEtI9CkyaO4rK8DUQFoC2jo8hF7fy+UssMUSsjTrjZAZIYSSEQibEUoDFfOI0aybXhFZ2j7Zxanhs+QXMEtbCzShETHDlAbi7IhvYWtxK/XRWqLW6uRIZkNKScbJMpgZoj81RH96cPonmU+RdXPk3BymZrIp2rBqky9RGHfTo2AFQ0wIMbPngvuJ5dCqTORHEAgCehC/vvrMispQBX5j/cyCinxRygIl6Bsk+rpBzArOLHesFfmi078fHzrD5fGrN4dE9xzaRjvnvR4ygzQW1eC/3jn9NkIAxf7oglnIK4F/gexix3Nxb1Hl0fXQhLgl0nlhK8Smorq7ntgRrNNzgZRcGmmf93rA8FMfrSbsW78F+90OT3oMpUcZTo/NeV0g1hTUmkLYCs6TzALI2FnGs5NrJtH9ho/WeONdP/ZnQ2vYjtd+GmJlaqHeflplrRomIr52KRc3n+fKN77Ny//q35AeHMIMBQlXVeIvLsIXi+G5LrmJSexUinwySX4ygWZa+IuKFiW8Xdum69Xv8/p/+E/0v3MU3ecjUFqCv7gYf3ERnuuSHR0jOzpKZnSMc3/zd0x0dPLwv//XVD9w36LtOtkcbV//Fm/85m8xdqkNI+Av9LUYKxrBzeXJjo2RGRkhMzLCsT/8I8avtvPo//vviW9pXZBMmejo5N3f+0OSff2U7tyBm8sxdrmN7OgYV7/zXZCSzle/j2aaVOzfS25ykon2a+QnE7zzu79P6zMfwheNLtDbDWzgRhAI04JdD6AJ8N58Vkl5rDfh47nqx8lDLo1cr6z39YKmQ20z2od/ChGKrpv0jJSS7NgYR//r73H6839OsKyU0l07aXjPE/jjilw99vt/SD6xOOnc+8abGIEAQtMZvXiZrle/T8szH55XgeJksni2gxkOUfvYwyqDfQH4ojGCZYtUdAmBKKtBVDUie9sVkbsSDCmN+lUhGkfUNCP8a8selXYeeeotvG9+AXJZZcJYVoVMjiNPHcE9+w7aj/4KYvOe6SxpUVmHiJchzx2D/Y+oTN7ZY2BiFHnplNq2ql6NEUBm03jf/Cvkm89DvAwqalUm8kg/3tf+AnHlPNoHP6vkaK4fU30deMdemckQnyLA81O/307PsGVi6rpeCtmZatzlfJp52xhmgXj3KeJ9mmD3K+mfXYcXD3pNqVBkJpHZNCJWBqEiKHA6cwIZmjYt3yRdWwUZBKBbCE1T9+5YOegFKbslIVSQpmkHmq7jvfYN6Lu29sDFciA9cKHwvx9YiFgxWlkFXntbIRhzZ3JNt0wTffalZWm+FenUCiHQbrj9epDoK2/xdi21Nse2L6grH7WKMcXySGNPehwfOsV47kYTysoQsyI0ROt4pPoQe0t34jPWl2ATQhA0AzSa9TRG66dft12bgcwQHZPdXJvsIu2k2Vrcsq7HvpmYK83hkXHSJOwJTM0iahWtqe2A4acmXElvqn9dzDibY40EjPUpl9zAnQ/Hc+leQMqlOlxG1AqvSnd8vXD9kS3dotgfu+MlNqSU5D2bjJ0l6+bJuzaO5+B47hyZnykZIMdzGc/efH04v26tmYy8GZCF85B2smSdHHnXxnZtHKkCG9efM1e6XBmbH/hZ8XGBzkTfvNfLg3GK/bHbOvbvNGSdHKOZiQWqNSTD6TGO9p1ZU/udk30LyinlXZv0LMPd1cLUDCpDa8/OvpMgqpvQdAOv8zyEYsiBTkS8ElG/FRFZueH69Uj29HLpa9+cJtC3/Mgz7PnZn6Js1w6VoSklbi7H+NV2+o8ep+eNtwjXVFG6Y2ENe+l5jF64xLH//kf0v3MUXyxK43ufYMeP/yg1h+/HDIdBSjLDI1z97vOc+tO/YODdE/S88RYn/vefEKmtVlrD15Ee0nXpe/sdjv3h/2LsUhuBkjgtH/ohdv7Uj1Oxdze6ZeG5LsmePtq+/k1O/+UXGTl/kavfeY5ofT0P//tfw4pG5rU7fPY8ocpyHvy1f8XWT3wUN5/nuV/651z+6jeY6OjizF/+NfEtrbzvv/8u9Y89zGRXN8/+3C/R9/ZR0oPDDBw/Rf1jD6/5e9jADzB0A7HzATTPw3vrORi/CUT6nQpdh/rNaO/7lJrP1jEAKj2PoROnOPk//5jiLa088h9/ndrHHkErZEFnRkY4+5dfXJJE1y2LPT//s8Q2NXLkc/+FN/79fyRYUU7NQ4en2wGwImF0y8IMhnj0c79BuLZmdcFcnx9R3Yi8HFeSEStBPgtXVnmPLquByvobb7cEpJQw0IX83lcAgfaRn0Dsf0RJ87gO3vHX8b76Z8iv/TnyZ38NUVIOgKiohdomuHoO2d0Otc0IyzfVqNKBvnoOUbNJEbeF1+Wx15Bvv4jYcRDth/8JoqRCvd7fhfftv0aeegtZ3wwPPY3wz13vyrEhOP0WdzxRfrsxlWmdnr92kfkcYusB5R2xGHJp5OQQZNNIJweug4iWgrAgOYZMTYDnIXQDGSsDTVMZ63YOpIcIxZDRUsRqKvE1DdG4DU3T8d74DnRdnska38CakRufIDMwQNEWJUEtPY90Xz+6ZeEvK8U4/Ph0QEwrLb+9nV0Et4hEn5tJHtCDaEK/47J95hdLyGXKztx6mNrCBFGRtfxF0Wh2nCsT18i42XXpkyY0asNVPFL9AA9W3UfYDN3S79jUTWrD1dSGq3mw6r472qRxIdhenqybUWa6ns1EfgwpJRErRsBYuzbUgbI9nBw6S3aN2vc+3WJP6U7C5kYG5g8CZIGQHMmOz3uvLBhfVDLjdsHSDYLm+lVdrCdcz2U8l2AgNcJIZoyB1Aj9qWFGMmOMZxOk7DQZJ0vOmS1p5ajfXQdH3vzsBF3T76jzl3GyDKVHGUyNMJQeoyc5yFB6lPHsJIl8irSdLejCq3OWL8iAqfO2dukdT3qMZMbnvR4PxIj67nzNvluJtJ1lMjefUHClx++882c37bjK6Hvt37UmNELWvXVfk3YOLB9aw3bwHFR5sKZkGjR9OitvtciOjzN6QRlwFbe0sP2zn6Ly4P7p94UQGH4/pdu3Ubp9G9s+8wmk6y6oHwyQTyZp/+7zdLz4Mppp0vzBpzn8//xfFG1qmCHIhCBYXsb2z36KQGkJr/3rX2fk/EXan32elo98kHB11bz2MyMjXPrHrzNw/CS6ZbHjxz7D/f/iVwjEZ7IWNV0nWl/Lnp//GcxwiDc/99skuro5/7d/x/bPfpLKA/vmkXTSdal//DEa3vM4ZiiEEQiw52d/istf+yZePo/0+2j6ofdT//gj6KZJcXMTdY8+zNDpM7i2zeilSxsk+gbWDt1A7DqMJiXeuy/BcN+9T6SbPmjYgvboRxDxyuUT6AI0TQPpKekQKRfcV7ouQ6fPIj2Xsl07qXvisTlr2slrHbjZpdfPxZtbafrgByjduYN8MsmR//hbvPUbn+Ox//I5yvbsmm4vUldLsKyUkbPnGLt8RWmwr3b9XLUJSqtWTqKvFpYfUVGHKF6jEb2dR7ZfVNrsD7wXse+RGW173UA7+Bjy8ink8deRJ99APPlM4fg+ROMW5KkjyPPHELvuB0v1ReZzyK4rMDmmCPkyVX0lc1nku6+C56E99QlFoIPK5q+qR+y8H3ntIvL8ccSeB5VkyQZuLYRABCLqeSUxAsEoIlSk3nNt5GgfoqhCEefpSURCyb1g5xCRYmQ+i0yMqn1Wq9MvBKJ+M5puIN9+fkZHfgNrxtiZs7R98W94+H/+AaDm2/ELF7BiMUWiP/YUxmNP3eZeLo1bQqJP6UBPwZXuHOPQOwWz890l4EmJdoN72Hpk9a4GCXsSU7Pw6/4C4TpOzs0RNiOEzciytEcvjl1hML0+N1mBoCXWyIc2PcX2+JZVS6isF+bIqdwlyLoZhrMD5LwsAoGhmcSsYuK+0nX5LLtKt1IbrqZtYr40wXIhEGwrbmVLcctdYey5gfVB1smTdeY/OARNP9Y6GlquBtcvE3Wh33FZ6FJKuhJ9nBm6zPnhK1wcbadjopdEPnnHhfrU+bv98jwTuQSXRzs4N9LG+eErXB7toC81dMvvuXlXVQtcj4Dhv+PG2e1Gzs2TctaeEb5SSOQc353VQhMagXWUPbsTILsuwfigImSuN9eqbpnRhl31AZiehJ1MpqBd7s7JspwN3TRhCTPNZE8fV771LJ5tE2vaROszHyLWWL8goaTpOlUHD1Dz4GFGzl8kn0zS9fJr1D/+CMHSuRUFw2cv0P39N/Ech9KdO9jy8Y/ij8cXbFc3TRrf8ySXv/ZNEl3d5CYmufb8i1Ts27OgpEvp9q0ESgrVO0JQ3NKEFQ6TTyQIV1VSum3rHPmGoqZGNNPCzeXIDI0sei42sIEVwTARex5GC4bx3n4Buttud49uHoIRxOY9iANPIsqqV1buLwT+kjjS80j1D5Ls6ydcvYC0lRAFWRWBm8uRTyTwRaNIKUn29HLh775CZnj51++2z3yKRGcXp//0L3j3d3+fB/71/01xazMAgbJSah97hIFjxzn1x39KuKaK4s2t0yS7k8mS6OrCCAYJVVYs6f0gikoQlQ3Izssqu/xmI1aCqGtZPVE5BTuP7GyDQBhR1TAv+xtAbNmLfPslZPuFua83bEZU1CLbLyBHBlWfNA2SE8gLx6G4HBq2KGkIUOa54yMq8/ziSeSVs3Pak32d4LnIkYEN0vRORMEoU+YzIApSLqYPcmllmlqQoRHB6NrHpRCI2mY1diLFyPNHITmxDh/i7keqp5ex02ewk0k0y6LyoQexiotI9/YxcbmN6icew0mlGb94EV88jhEMMvzuMdxslvzEjAqGnUgyfv4C+bFxwrW1069nR0YZevsd3EwWf1kppQf2IQyDiQsXSfcP4OXzuLkcZfcdJFSr/A8zA4MMHzuOk07jKy6mZM9uNMti5MRJciOj6H4/FQ8fxloHGb1bwn5oQlOk7tSDtrQxufMWn5rQZpGVElc6GDc4Rd4tyAxcCD2pTioDNViaxUR+nO50Bxo6SWeSalFH2Iwsub/jOVyb7GQivz5SLnWRap5ufA874lswbzOBfrfCrweI+8twPBuBwNRMgkZkQQ3Y1SBkhvhAw5P82bm/IeWkb7zDAmiI1PJE3cOU+tdeBr6BuwcLEegAPt2Hvk7jc72ga/qCBpy3C2k7y+vd7/JSxxGOD5xfd/ms9YYQAn2tD51rgO06tI118FLnEY70nOTaRM+6e3asBFknh7dAqMPSzTtqnN0JcDyXvHt7vqv1qDwTgHEHJnisCYOdEK9ClNfNJ5rWQc/fX1xEfOtmRi9dZqKjk7Nf/FuEJqg+/AC+2MrM0D3XZbK7m8FTSlKgZOtmipubFySup48fLyZSV4NmGni2w/C589ipNMzi0F3bZqztCmNtymyv6uA+QpUVS/YtVFVJqKJCmTe6LgPHTyiDs+sgdJ1AaSmGXwVfhBBopomvqEiRbkVFhCrmliJbkYgyDiyYsm7gXoVEjg0ik2NK69byI6KlitgBZHoSOTGkpAesACJeVTAALuydS6v3s2lFkBeVQyACjo0c7kIEo6ptz1XZltEShGEhtu5HC4Txjjyn5DnutYz0WCli92HErsOIWMmKM7aFphFtqCe+ZQtDp07z9n/6bUp2bkegfBNanvkw0fo6NF2nfO8eAqUl9L/zLu/+7h9Q1NyEnUoxePwkie4erFiU/OTyZPZ0v489P/+zpAYHaf/Ws4QqK9n/K79EuLoKIQSbP/5Rxq9coe0fv86b/0FQefAAZiSMk86QGR5m7NJlmj/8QVo++sNLGyhrOqKuBXnxGAz1rujcrBhCg5JKqGpYe1uuC4lxRVYGF+EwikrA85ATo3MqCERRKaK+BXnlLPLyaURdM9K0kCMDyI5LiOadiIbW6WZkYmxaasR76WsLjyFNV/24Q/WYf6ChGar6JlaG8AXVWHAdpJ1HBMLK70U3lOb7Onm3ifJaeOiHoKQSeewVZUT7Aw4nnSY3No6byTB29jxCCGre914m265w9Ut/T/UTj5GfnKT3+Rcp2r6VdF8/mYFBQrU1jJ+/MOOP6nnkJyfp+vazuLk8sS2b8RyX9i9/Bc3QkZ5k/Px5nEyG8gfup/Mb3ybV3U3pwQNMtl0h0z/A5p/6STw7T/vf/wP25CSBChVsdLI5hl97nfTAAIbfT+rsOXLjY7R89tNr/vy3jETXhYEgj0SSd3P49AD6MqwVbyU0IVSGvFRlwbaXx6cvnpXkSfe2kegJe5L60CZsL89obpiQEabcX0lfupu0k7ohiT6Wm2AwM7Qu5e5RK8zhqvvYvkGgrwmW7sO6yRmge8q280Ob3stzHS8xkV++vrImNJqiDXyg4Um2FbfeM9+zJz3cwnW8HiSMqVkrMva9W+AuMs8Zmn7bzWWvf/QVcEtlpJbCaGaCr17+Ht+58uqCmvKz4dct4oEiwlaQkBkkYPgUUaubWJqJJgTv9J2mP3WLSnRvA7JOjnf6TvPlC89yZugyGWfxLCpD6ER9YWL+CCEjQNBUmeGWbmLpFoZmMJAa5u2+U2vq05Jj/zYGG+5EeNLDu5FR1QZuLaqakEPdyhRQN5g9Y4qyGmX2tQaEKitp+fAP0f/ucZI9vVz99ncZb7tCxYH91Bw+RO3Dhylq2rSsttxcjvG2qzgZVc0weukyR377v2GFl5ZNGjp1Bump+3dqYBDPnhvIyScSTHR04OZUMLj/2Ale/w+fw/Av/bzVd/TdaQIy2ds3x7dmCobfjxHwzyX6hUC3zML7PsxIeM4+mmEgKFRQbGis3rOQyXG8viuISFwROXZ+WlNXZpLIgUJVqOmH5CgyM4mo3wG6gcxnkEOdikD3BSGbxOsaQWvcrTJkuy9AtHSaVPf6ryqXrOIKdaz6VjR/EBktQV54d0FN4rsOug6VDYi9jyBadiNCS69zF4MQglBVFff9X/+c03/y51z5+rdo+8a3MHw+/PFiah46TLS+DqFplGzfxv3/8l9w8e++wrkv/i26aWIE/BS3trL7536aa999nqvf+s6yjxssL2PfL/0C2ZFRLn3lq4QqK9jxUz+Ov6iIaH0dB/7ZLxOpq+Xas9/j+JE/KhDFYIbDFLe2EKwoX5pAn0JVgyK3R/pXbjC6EgRCiNpmRGAdpO3Uw3uBWFtkPTY1B1//jK9piM274cTryFNvwcMfACSy7Qx4EtHQiojOSv6aaj5WjPbDP6UMMBeCaSld5g3cWTBMRKQEOdaP1A3QDEQoBsEIMjGKHOlWGeq+IISL1fc9PoDMJMHOIscHIBCdkQtaJkQwgtjzEDJWooxl28+Cs3Ye7W7F1FzkuS52MsHIiVNUPfn4gtvmx8cZPXmKpk9/kooHH6Dtr/6GgbeOAGDFolQ8+ADDx45Pb58dHqLjq1+n7P770P1+Etc60SyL8gfux8vniW3dQvNnP83oqVNc/vMv4OZz5MfG6X/1NQ799m8RbqhHeh650VF6vvciQhOEG+rJDA6S6e+/e0h0UORS3svhSgcPj6yTJmCElpQdkVIqXfJbJs0hMDUL28tP61Lbbh5zwZJtSc7N4nF75FymzkbGyZBykjRFWgmZETShL7ron42+1AAj2bF16IegtaiJg+V77rky6Cl40iVlp0g5CXJuFkc6eN4M6Vodqr9h0OJOgaVZPFH7EEW+GK/1vEnbxLUbaslGzDAHynfzYPX9bIrW39USBlNBvPH8GBP5cTJOGkc603PNWtEcbSXuu7Ex3V8d/QaelDyz60mi/vANt0/m0rx0+W16J4eoK6rgUMNuSqa04W4BFiPKVQDi9syBdzoS+RRfOv9tvt72IuPZ+dnnlmZSF61id/lmmorqKQsWE7ZC00SwqRnohSCFoenkXYfe5NA9S6LbrsPxgfN8/uTf0zbWseC4ivtj7C7fwtaSJqrD5UR9YfyGD0szsXSzENRR5LYA3uw9sWYSfbFnFMdzl3Wv/UGCSkSYf750ofFzez9FwLg5946YL0pDtOamtH3XY2IIMklFll9PPKzD3G0GAzR94H042SwnP//nDJ85y9CZcwyfv0jHCy9T1LyJqvsPsOl976HyvgMYvsWJa8+2SfYPTP893naV8barK+qPk04j3bmfy06l58guDJ06w9CplRno2anUghm9umXOI7XErP9ruoFuXjfuxSyfqHssSXgDs5BJQi6DaK4Hw6c8CQrzoxzrB9dFVDYpIjw9gXfpbWRJLSJaAslxZDqBVl4PkRKwc8hzbyht4KlnP18IUdWiruP2UyorPVqKMDSE0JDlNYiHfghR1YB38vvQc/XuzUr3hxA7DyF23A9lNTPGkauEEfDT9MGniW/ZTLK3HzefQzMMrEiEopam6e3MUJBtn/kklfcdIDUwgJe3MYIBIrU1RBsaiG9ppfnDPzRdiTKF4tYWHvx3vwZCEKmrm35d6DrFm1t56Nf/DaMXLxGprZkzJxa3trD3F/8/NL7vvaSHhvHsPJphYoZDBMvLCNeoqpsbQfiDiLrNStLlZgZQwkWITduZn86yCui6yjS/dhGSi1Rsjg0rwjy2gBRXXTOiog557iiyrwNRUQdn3oZ4GaJ115yMchEtVsR5OonYth8RvPE6bAO3CaZPBQxnJ+8JAUXliGxKzX+aDoaF0HSVLGDnAan2nRon/jDCCqg50DALSQWrgG7Apu1osTiyYQvy9Fsw0Lnmj3lXwPSpjHxfADuVouf5F3CzWUoP7Cc7OIjnuPOeaaTr4rkOdjqD0HXMSBih6wRrq5c8lD2ZAE2j7L4DGOEwFQ8dJlipvAv0QIBAWRlGwI8ZieDlbfAknm3jZLLT0i5C03CyWaTnEt+9h9iWzZTddxArtnYpF7iFJLoudPy6n7STUkSWl8NzJD7dhyHM6YWXlBJPqsWpIx1czyFohG6ZZICpWRiaie3l8fDIuKq/pmZNZzd60iPnZcmtkyHnahCzimmbvAhIQkaYkBnB9Rwkcll66APpIUYXMApccT98UXbEt1IaKJl+zfVcjg4e49zoedJOhpgV42D5PrbGt6z5eNejbfwqb/S9yXhe6VM9XvMI2+Pb1kUCxfVcxnIjDGb7SDtpHM9W1QdIZGHxKRCU+sthERJdSknWzZCfZeYZLgQ7bke2rBCCkBHkUMU+NkXraJ/s4tJYG52JHsayE6SdNB6SkBGgPFjG5qJmtsVbqQ1XE7Mid3XmpeM5DGUH6El1kXaS5D0bV65vBLnOXV5J4yttRxlKjvFk6yHCVpBELkXA8i/oJZDIpvh3z/4hz114g2Q+TdgK8nDTfn71iZ+ktWwdSiiXgcX077NODuc2Z57eiUtCKSXfu/Ym37362jwCXRMazUX1fKT1SXaXbyHujxGxQli6ueSckLazt11//mZBSklfaogvnv06l0evzQto+XSLp5se5f1ND1MeLCHmC+M3/OhLlNk6nkvYWnt2lE+3Flwe5gvmpRuYgS70Be+9Qmg8VncfleEbBxhXA4HAvEevjTUjl0U0bkeU1swva14s+26F8MfjbPvkj1CxdzdXv/s9Ln75Hxlru0Kiu5tEdzcDx45z5ZvfofrQfez48c9Sdd9BNGP+s4TnuuQTM4SPEQhghldmUB8oKUFcp8fu2fYc2RQzHMYMBFbE+/iLixcs+Re6jljKPEmIBfp/J961NrDuiJSAaeFdegdRUoMoqUb4fIrESSeQIz3IxKgi96RUsi6ZJETiyFwaRnrxkuOKXATIJJCpCZVxqZuISBxhWoBEmj5wbZAuoK5rITQIx5Db70Mrq0GeeQt57m1Izzd/vqNR1YB2+GmobVZGyOuwdhJCYAaDlO3eRdnuXUtvFw5Rvnf3gu/Ht24hvnX+2jZQEqf20YUNg3XTXHQ/AF8sRsWBfcv4FEtBIJp2IE+8dvNIdN1AVNYj4uU33nY5sPyITVuRbz6P7LmGzKTmZbjLc++CaSE2bZ23u/AFEFv2IC+fhjPvIIWGHOhRRqM111VDlVcjisuQY0PISycRex9an8+wgXWH0I0FCW+hGxCKzX/dF1xYqi64PsQpKHKWkiqIxhE1Tcjz76q59V7VSvcFYPNetK0HoKwaEQjhjIyS7u2jeNcOyu47SNe3vgOF5yEjGMQeH8fNZkn39ZHs7CLa0oxn22QGh/Ach4nzF5d8FApUVKDpBv6yMsoO3Yd0HNxcHum56pnr+ucuAXrAjxWNMnLiJKUH9uPZNlYkghEIYkajlD9wP7rPh51cn3vgLVt1CCGwNB+eIck4aSQS28sp/WcxW4kcZfSAyhqdKnm8VdCERkAPFrJTPRzpkHKSStcdTZVf4k2TqD4tQM679UZa9eFNDGb6EGiUBirQhU5e5gibUULG0hFV13MZzY6TsteuxVgZLGdHyZY5mapnR8/xfNeLNETq2FzUAnDTstQrQxU8VH2Y9olrfL/vDSZyk+syXtJOis5kO+O50enKhIWwnPzl0dwwvamuaemfxmgrZf6K22Z8KoTA0i1qQlVUBMrYW7qDnJvH8VSVCFJdB6Zu4td9+HXfXU2eA9ieTXfyGteS7eS9QoT6NiLn2lwYbOf3XvkC73SeYSwzSXEwxsf3vI8fO/hhigIzQZlvnXuFr595iYlskv2122kf6eYrp56nJBTj37z/F/AbN98EMlDI9r1emzpj59ZFEupeQ8dED692vs1genTO67rQOVC5nV/Y92kai+rw69YdIz1zO5F187ze/S4nBy/Mm1EjVohf2PdpHqu/n2J/DO0Wny+/4cOnW6Tsuff5rJO/bfrfdyoswyJoLlQeK8m5+VsyV23gOoSiyEvHoPsyGNYc4ljUtELV8qRWloIQAisSoWLfXopbW9j2qY/T++bbXPyHr9H54svkE0lGzl9kvP0afW+/y75f+nm2ferjmMHAvHZmZ3W3fPiHOPDLv4hvBVlDQteJ1MzNcBKaNodY3/OzP8m2T39y3vGXgmaac8xBrzvqDTq17MNs4B6C8AXQWvYjJwaRwz3I0T60uq0q01y6iOJKRHnDTDBLCLAKY9LzIFKMVt0Ks3TSMf1K51fTZgXBBHMX0bM7IRTRXlkPxaWI5p3IE68hr55TpP2djOIyxJ6HEdsOIKLx1WeO/oBCFJciqhuRowNwMwL+/qDK8F6n70XoBjS0Ipq2IS8cRx55AR54L8IfBMfBe/tF5PljEC9H7Fs4QCG2H4BXvoF35h017n1+xJY98yoXhOlDPPgUsrMN7zt/gwhFES07pwM0MjmptNVLKxHVDRtjbwPzIQTC8iOrGhHxCkTrbuS5d5AXj98b8llCg/IaROseRMtuiMVVUKvAB/mKiolt2UzH175J97Pfw4qGMWMxEIJQXS2Bqkpe+YmfJlSn/CXMUIiGj/4wV//2y1z92y8TmSXzN3bmHJf+9C8YeucoRjhEsrOL1h/7DLt+9Ve48Md/ytk/+B/4S+I0f/bTFG2fH0CbQqCsjKZPfYKzv/8/8GybSNMmmj/7KVp+/DNc+esv0fWt76AZOs2f/hS1Tz+15lN0S2cFITT8WgDd0Mm6aRzpIvEWrS4TCHShq2j6LYQuDMJGhLSTxC300ZUe7pxtdAJ6CE3ot4VEtzQfNSGVhTpFxvr1AJWB6huSs4l8kvHcxJrJZkszqY/UUBEsm/N628QVdKHxkU0fImQGkcibphMdMoJsijaiCY13h47feIcbQErJeH6UjsQVEvbaCXkhIGiocZJxVdCiP91Dqa9s3cwuVt83gVnQXL47xGhWB096DKT7aEtcXtDDQHCr5KLmonO8j8+/9RV0TUcTGj0Tg1we6qBnYpBfe9/PEfapDIyX244ynBrnN57+ZX7ivg9zcfAa//QffpPXrx7nePcFDjfuuan9FEJgaAYlwWL6koNz3htIDZPMbxijzYaUkuMD57i0QEZ1Q6yaXzrwY7QU1y+rYmg28q6NezO1LW8TpJQkcim+e/W1eVUNutD5iZ3P8L5NDxE2gyszKZQeOSe/5v5pQqMsGGc0OzfDZDgzxnjuHnhQXkeEDD9F/vl3EylhMD3ClpK1E7YbWBlEcSUEIuoOd/31s5h522qPpWn4olGsSIRofR2tz3yIyY4uzvzlF7n4la+SGhhk+PwFjvzn38FfFKP1hz80R0tcGIbK+J5+QRAsKyNaX7umfuk+H77oDBEvhEakpopASckSe60jNhLPfyAhpVSGoWX1iGgpsucScqQXES0t6JynFGkeCAMSpJxZ71q+gnmeCaGYSiiTUq0bsqmVd0bTEIEwctN2RF0roq8d+e7LyI5LkM/eHJJ1FX3EMKG4ArHrAcSW/RCKKAJzI9lg5dB0xNb9yLbTkLoJhvaxEti0Y8lNvKMvI/s6IZtBDvUp41DHwfvWF5V0iz+IaN6OaN2NMAwoq0F7+jO43/gC3nf+Bl77NiIURaYmlMRLpBjtU7+o5FwWgCguQ2zerbLZj7wARSWIHfctsKFA7DmMNjmG992/w/3T34JwDBEMIdNJVa3huWjP/AxU1nMPWl1tYJ0gNA0CIWRdK6J6E+LQ+5AX3kWeOQKTo8oH405fuwmh5lnDVNn1TdsRrXuhtFJJ4ujGvGdIzdBp/JFnqPvg0+pvXVd+MH4/gQofB//fX8dzbJXAUDBcF5pGxeFDSCnRDHParD22dTP7f/3fIF1XxYQNA8PvJ1BdRemBfUhPIjTVttANtv/y/3f62TG2eTMP/N5vYwSDIAR1P/R+qp54DKSH0HW1jxDEtmxGOmqNqfvXJ7H3ppPos7WGBaqs0dSsae1x28vjyJlsLoE2bURqaia6WLyLmtCULAbLM5Kbanfq98UghMDAJGoWkfNyOF4et5B5rgsdQ5hYuq9AvMlltbnemKfFOushXRPakqTgRH6S8dzaS06iVoTWok1oQiPv5knZKfKezUhmFEMYjOeU5rSpmRT5imYkIKUk6+amJVI0oREwgoSMuSSJlJKcmyM1Z7sAIWOmvHdKL389zr2Ukkl7gs5kO5P23POjKhEU2ekVpIaWB0HIiBAwgqQcRbhM5ifIe3kCt0ii6AcZU2Po6nUEuoaGqZn4jQAhI4yl+dZlDIXM5evqSSlpLW/kFx/6JM0l9VwYuMqfv/1Vnr/4Jo82H+TpbSrToj8xTN7Jc7B+B1F/mK0Vm/jo7vfyv9/4MpcGr910Eh2UiWJ9tGoeid6bGmQ8N4mUciOjuoCsm+fqePc80tVv+Hii4RCtxQ2rOlcpO33rMp9vsW7qcGaUS6Md817fUdbKAzV7Vkygg6q4SuRXQTZcBwE0xGq4ONo+5/XB9AgjmTE86d3Se/+djKAZoCwYx9LNOWNVIjk/cpVH6g7ext79gKKyQT162fmCJrNQxNxNzKwTQqCbSiu8ZPtWHv3NX2frpz7OK//3v6H7tTeY7Oik8+XXqD58iHBB4xLA8Pkpbm1GaBrS85js6CTZ27tmEt0Xi04bBUrPY+T8RbKj47eORN+4Nf5gYmIQ6djKvC6fVWRKVMmRiHg1Xuc5GOxAFJUrkiWfhrIGVRIfKYGJYeRgB8K1kZqOTE8iyupufNwlIDQNLB+yfguifguivwt57m3k1bOKaM1lbi2hrung86uAXlUDYss+RP0W8Cs5ho3nyjWiYSsUlUIqwbpG8wwTsWV/QU5occgLx5HXLs8cu1gl3cn+TuhXOtJC16F5O2CoObp1F/o/+VXku68hL51CJicRsVLEvkcQD71ffZ7FxoUQaPsfxe1sAzuPtvsBRKRo4U11Ax79EHrTduTbLyK7ryJzGUQ4BvWtiJYdiK17YTlGrhv4gYfQNNAsKC6Hw08jDjwBV88hL59A9rZDNgO5rJLdut0QGpimIsgtPxSXIxq3Ihq3QmnV9PPhgskX020IdMtCt+bOAVJKkB5GaAFJHUCLzE/e0AwDLTKfPxGLbG8EZqoINUNHC8/sK0wTa4GqQTO0DubH1/dj3VtcAHZBPsHUfNNEOij9cU0IDKljaj60FYT6RIGcXAl8egCfvvzyTQn4dD/+JfcRxKziJd6/OejP9MwQuQUna4kie0t95UuSeUk7RcJeux5Q0AxSE65S/UkP8Fb/2/Sm+hhID+J4Dn9z6e8QQlAVrOSZpg8T9UWRSCbyE7wz8C4Xxy6TslPomk59pI5Hax6mIliOQExvd3TwGBfGLpHKq+3qIrU8WvMIlYXt1hN5L8dAupeJ/Izhqqo4CBK1igiZEXyaj4n8GF2pa8tu19JNAnqgQMB7SDwm8+MEjIUnmA2sL0Zzw9NVAACGMIj7S6kNNVBsxddFP381CJg+PvfBX+HxlvvQNZ0HG/dQHonzL7/+O1weujZNojuugwRChZLekBWktayBRC7FUHJ0iSOsHwzNoKW4niO9J+e8nrazXJvoYX/F9kVkHH7wMJGdZGwBI1G/brG/YvuqF4WjmQkyzi3y4biFC1dPenRN9i1Y9bOtpIm4v2hV5yzn2gyl1359CCFojTfwXPv357yedXJ0TvaRzKeJ+jZMqUCdq7JAnLJAnJ7kjEGklB7nh9twPfeulwa72yA8Dzk+iOzvgFxakVbRYkRZndJWvonX+nSyg65Tvnsnu37yx+h96x1lINrbS35iEmaR6LplUtzcRLShnon2a4xcuMTgqTOU792D4V+9FJAZDFLc2kKosoJkbx/97x5ntK2N2KaGeaagdx0ixVDbrMjPlUDXIV5x4+3WA7E41DVDfoXyIZZP7XsrEK+A+hZwV+jx4gsok8KFIHTkaAcyn1WZfEUV0yS4CBeh1W1FDnbgdZ1X78fKZq4ZfwiqW5FDHXi9lwGBmKoc0XREqHiup4G/kM2+hE/InK5NXfdV9YjKOuQD74f2s8hr55EjA0qbPZNW42o9fW80TWXf+4MQCEFRKaJhC2LTNkRR2UbG+TpDGCZi14NI01rfbFjTh9i894bfl/5j/3xl7U7J+5ZUIp76BDz1iRV3TTRvx/jV317etpoG9S2I+paVHSMUQda3rrhvG5iBKKteWzB/KtlnHecMN5kkff4ieC5maSn+5hmTYS+fJ3vlKu7EJMLnI7R398Jrk6kx7AvAtgOIrfuQiQnouIjsvKTklTIpNbfm0pC/yRKzmlYgywuEueVTVRdltYjKepVoURRn3aL9roPsvgRF5coQ2vIv+750t+G2Pz2qzHOdOzFVw/HyGNqdqVmbcdLYUxn8UuJKj6ybQRMaUTNGiMUX9RknR8ZZuxZe2AxSFlBGYeWBMp6oeQxXOny9/dtM5Cb4ZOuPYOkWpmYSMlUEKOvkeL3vLdrG29hVsoOacA3juQm+3/s6z3Y8xydaPkbIDJF1crzZf4RLY5fZWbKD2qnt+t7g2Y7n+GRhu/WClJLJ/ARjuZHp10zNpMxfSXWojoA+kwmpgkIrgcBvBDGESV6q8550Etyi5csPOCSjs75TDY1Sfzkt0S0ryhpfb2gIKiOl1BdXTZNKhm5QGS0jFowwkVVBLjkrI1igbkK6phG21HhM5W8NqWrpJjtKW6cDQbNxpOckj9XdT8D03zad/zsJGSdHdoH5Vdd0qsKrN2Bqn+hm7Lrs9nsBErmoLEpJoJjAKnW0U3Z6Xvb4aiAQ7CpTvh/udWP/xMB5nmx4gIgvdIeN/YXMDBW8m1xlUButpC5aOZdEB9rGOulODNAQq1585w2sO+RwD17bcUXKBcLgusjBLpgYgebdSp95tW1LiZvN4rkuVnjp++nUvUw3TTzbRvf5EAuYi4Zrqmh6+ilO/K/Pkxsf5/LXvkHp9q1U3X9wXsbTbHiui51MYgQCC25XumMbNQ8e4tI/fJ300BDn/upvidbVUbJtiypDXqxdxyWfmMSKRpfc7nZB234Qtt/ZFR7azgdg5wO3uxtLQjv0Pjj0vnVtUxSVoReVLf5+JI6ILB4kEMEIomHnwu+1Hpjzt1bVvLpOgiJ9QhHY+QBi+/3IdAI51AuD3TDUA6lJZC6rSJ98Vmmpu64i1z1P/auyuFSQTtPUv7oxj7wRoagiSMtqoLwWESvmTlz/30vQ9j8K+x+93d24pyA2bUfftP12d+MHGokjbxPavw9tieeClcIeHmHwr75I5vwFwnv30PifPzf9npdOM/7895h49TWckTF2vvDs8gh8oalA664HEDsPKdPo0UHkSD8M98H4MDKbVnNrPqfmVzuvKoI8T/3IwlwrpTrm9M+sudYwZiRZChnmwvKpYGU0rgKWRaXq31D05lUjujby1KvIYAxRXqcMWMPFEIwgDPOeCpTeVhJdCIEuDPTbz+XPg4eHKx0M1u/iXE+0xrbN+duTHhP5MYazQzdczOfcHNk1ZjQaQqfIF8Onq/PjN/z4C+ahAcNP2klTESyffm0K/al+2savsLNkBw9WHcZv+HE8h5yb45We1+hMdLEtvpX+dD+Xx6+wI76dhwrbuZ6L7dm82P0KHYkutscXNxdYKRzPJmFPkPPUeRFoVASqqQs1YuprHwM+3YehGeQ9Ra5lnA0d6VsBCaTsmaxgvxGgIlB1Wwl0gIDlw5MS23OnpVCklDiuQ9bOkbPz0xrYUzrRc8hroe6l1xPaNwu60GiM1VATrqAr0TfnvXPDbVwYuUJlqARTX8x47ebhjrwdL5ScgMBa5flJ5tNcHLl6z2pwL/YdWrqxKqkU13PpTgzQNjZfImY1qI9WUh+tpn2ie87rbWMdnB26TEOs+qaZZ68GhqZjLlhho+Stbiaqw2U0F9dzbODcHEmXRD7Fc+3f56d3/8hGNvothLx2FlG5CbFpB0LTQUpkagJ5+ThypG9NJLrnOAydOUfnS69QumsnsYZ6wlUVcwhn6Xlkx8YYPHGKy1//FnY6jTAMipqbCMTnZ/AGSkpo/cgH6Xn9LQZPnqL7tTd42/8HbPvkx6nYt5tQZSWG34/nOjiZDNmxcVL9g0y0XyM7NkbrD3+IaP18yYtYQz0tH/kQAydOMd52lbZvfgfNstj6iY9RunM7wZI4mmniOQ52Ok12dJxUXz/jV9vJJRLs/qkfx1qgrHh1uCPvWhvYgNJOD8eUpMWmbYq4yaaQqUlITiCTEyqL0s4rWQLHmSF7dF0ROHpBy92ylB9DKKK0zYMRFcy7A4NRK4WbydL/wotkBwZo+NQn0EOhVSXcZQYGGDt6DKu4mKI9uxeVQNjABjYwAy+bpes3f4vNf/Gn60qi+xsbaPiNf8/A5/8Uu39gzntGURHV//T/R3DbVnp+5/dXdwAhClVGmxDVBY8gx0FmkkpOK5NUmvyZpJpjp+dXRwUu3YKp9PSPrgzjLd/0j7ACEAyr+TYYVq/f6mcOKSE1jmwfL2SllyFKqiBWBtESlaF+D2Snryt7LZEgwZXOdGm2hsb1ZQpSysI2igBSmtbzF8tT201pXk+ZfDJlOIo2TT55uAVCSRa21wvtzR04nvTwptthXluedHGlW9hG4nj56Rujho4+a2EqkXhy9nG1Wcedges50+aa7qxjK41tfV0y3TWhETGjDGcHybpLl3Vm3RzZNS6kDc0g7l+5jM1IbpTx/ASdiW5s71VAfc/D2WFyXp7RrJJSGcmOMp4bpyvZzcs9he2A4cwweTfPaHZ9ZSzyXp6kPUNQhcwwZf4KDG19SEElXTTz4Ji/yUTGBmaQm1U5ENADxKyi29eZAmqLKknnMzx34XVqY+VEfCHGMwnevHaS3okh3uk8wzfOvAzAQEJl0k9mk0iUNEImn8WTHpZxa0hrIQTF/igP1u7jS+fnkuhpJ8s/XHyOzfFN1Eerbnnlzp3m2eYzrAXJck96JHIpSgJFK2pPSsnZ4cucHrp86zTRbykEYWvhqqJUPoPtOfhWEMyWUpK2M3zv2utM5tYuWyaEIGD4eaLhEO2n5pLoGSfHt6+8wtaSJraWNqPfIdropmbgN3zT0mhT8KRkIDVCffTmZYMHzQC7y7bw/e5jdEz0TL+ed21e7TrKoeo97C7fctOOv4G5kLk0WqxkxshcCFVmbPmVRvNa4HmMXrzMm7/5X4jU1xLf3Eq0ro5gWQlmMIjQNex0hmRvHwPHTzF44iQIQfnundQ//ii+WGxek5quU7F/L/t/6ed5+3d+n9GLl2j/znOMnLtA+Z5dROvrMAMBPNchn0yRGR4h0dXN+JV2QtWV1D784IIkumYYNDz5OMmeXo79j/9NoqubC1/6ewZPnqJ8727CFRXoloXr2OQTSdKDw0x2djF+9Srxza1s/8wn15FEv9PuWhvYwCLQtAIZE4Gymh+I8I89OUmmt49wcxPaAtq6AE4qxeX/8UeMvHWEqvc/RWCVervjJ05y9j/+JyKbW9n5b3+NcNOG+fYGbj/k+IAicafuVUKDQBgRKlpz217eJtvWRvLYcdzxCbRggPCB/QS2bkHz+ZSXWfs1Jl9/E2dsFM3nI7RnD6ED+xCaxuSrr5F49xjZtiv0/c//hRYI4Kuvo+RjHyV9+gz5vj4ih+7HKFJ9Hfzi3xC57yCBza14uRyZS5dJnTiJOzGJFgoRuf8A/tbWNZHxbjJJ4s23MIqLCe3bq3T+gfEXXkIYBrHHHll8Z8NQuv0F7f57bo61czDUjRzuRYaiilAvqlASatES5U1wl2J9ey7Bljlcz50mkqfI7XnZZAKkhzL7ExJzET10t6D7rTFbRkAR5VMLVlc6OJ49Td5IJEK6GNcZk3rSxfby6v1Zw1TAdFtTRqhTC8+pwACAnDOyJY5n43pOoTJBIHER0sXUzDmEqSsdPJQW9txsUoHG6iLySTtxXRaqJO2myboZipbQaPekR87NY3trWzzpmk7UWvmCIu/m8TyXjJthLDc+/bqhmRwo2zctD2MXsnAzTva67Qz2l++d3m694EhnTvAhZhbh0/3rRgjqQi8ElGaOtxg86ZJx0mTcDI5nI/EQQsMQBj7Nj98IYl5H7kspcaRDykmQc3N40kWgTHxDZhif5kPMugZtL89YbgSf7idghEjaE+TdPD7dR8RUGsQT+TFybhZDGITN6IIa7lJKpPRIuUmyTgZHOgjA0CyCRgh/QQv+dmK2Ca+hmfj01WurrhcebT7AP5z8Hv/r9S9xfuAqlZES+iaHeb39OOWREnKuzee+93k86dIx1kvEF+KN9hM8uGkviVyaM31tBEwfpcGiW9bnsBXiUPUeXu44wkB6ZM57p4Yu8ddnv8HP7f0kpcFb7xFxJyFsBgmb868V23O4Mt5JY1HNitrrTvTz3avfp3Oyd726eEdBE4Ky4MIl7d2JfpL5FGFr+dlZnvR4pesdXus6ul5dxNJNDtfs49mrr9F7nbnuhdGr/O35b/Nzez9BbaQK7Q4oVRRCELFChKwAyfxM1ZMrXU4MnOe+ql039fi7yrewq2wzfYlB8oVnDYmkc7KXL53/NjFfmIbYyq6DDawOIlaG130ZzbAgEEZ6LnKkD5lOKA3iNTUu0H3qfjredpXxtqvqZU1TkiqahpvLIQs607rPoub+g+z7xZ+j6uD+RTORrHCY5g9+AMPv4/RffJHeN48w2dHJZEfnkn0p3bkdw794RUggXsy2z3wSKxrlzF9+kYHjJxi9cInRC5cWb1bTCFVWTC+KN3AXw/OQ2QxMjE5nVMtMWml/Z9OqlN511Hj1XJUBKGUh289Qi/2psnnDLBhihlW239S/obDKstP1tZWtex4yNQbpQiVltBThD9/VpfBycgQyBYPLSAkiELkjP8/wm0fo+do32P2bv4FVND/Qt54Qmo4wDDSfb2OO2cAdAzk+ODP3gCLR41XrQqLjOtiDg9j9A2ihIJkrV0mfv0Dlz/00gc2bcYZH6Pmd30MPh7BqavAyWZzxccXHCIEWDE5fK0ZREVowiB5WfFTq1GmS7x4jsHnzNIk+9Fd/jRGLEtjcinQc7P4B7MEhtGCQzMWLZC5coPIXfx5/Y8PqP5OmkTp1Bi+TwddQj1lWhnRdBv7kTyn56DNrPGG3FtLOgpRKy3w9KX3pQXIckuPIgU5kJI4oKkXGqxDxSpWhfwfeD5bCupLonnRxPQddmNNmfa7n4HGdKYkAXRpomsTxwGNpJ3BPKlLe0KwC+S2nDUo96eJ4eTRhFI459ZoiuDVNQwgNicT28ip7U7cQ0+S1ZLZ+qFYgOx0JEg9DzGiizybe3cJxDWEWstNnjut4NqYm5pCWUnpI5n6GpXRLb4TOZPu0NMjUSRUComYREXPxm74rPZw1Euiggg6hBYiiG8GnWxiaye6SneyIb5tzwWiIafkXS7cwdYNdpTvYGd8+fzt9fUvnVYBl5rz4DP+6Gk6qSofZx1tYhiPrZhjLjZByEtiePV0RMVWN4dcDlIoKTG3ud5xxUwxlB8i6mcI15xUCTQYTto+4VUrUik0HdxzPYTw/ik/3kXFSjOfHcKWDLgwV8JGSkewQtrTRhUbOy1EmKvBdd94daTOU7SdlJ7Gl6u9UdYelWRRZcYqs+JwKjluN+ST+7Z+k99du56cPfZT/+vKf81dHv4GlW+Rdm7qiSn750c+ytXwTXzn5PGf62vjU3qfxGSZfPvld0nYW13P//+z9d5gk133ej35Opc5pct6csYtFTkRgACBmUqRoUTKtREXb1/aV80/O9rWv7Gs5yZIsyZIlWokKzAkkEQgih8XuApvj5Jmens7dFc/94/TkvDuzifPimWcW0xVOVVedqvOe9/u+fOHYd+lJd7C7bes1a7Oh6exp2spjW+7j8ye/MecaDmTAUxdfwJcBP3HwY/QmO69oH4V6iVy9QHushah549hjrAUxM0J7rIWwHppT8VPz6jx7+VUe6rmT8Cp8viWSkXKWvzj1Lb4/8Aa2v9YshpsDAkFHrIVMOLkgkPXN0RP0F4dpjTajr6L0TyJ5fuAN/vD4lyisgwp9CprQ2JLq4v3bH+H3j/3lHG/0QEq+1/8aju/y04c+wZ7mK1OSlZwKE9U8zdE0iSWU+WtBW7SZlkjTXBI98Pn+wBt8YMejdCc2LpUjHUrwxLaHOJE9x7n8DPHp+C4vDb2FoRn8tX3vZ2/z9iu26zkxcY4TE+e5vW0Pu5s21XtLQWy7DXnuCMHR5xCagZTKdkG0b0E0X11FgmYYdN1/D4/96r9j+NXXyZ0+Q2VomHqhgFevg1ShnrGONpp276L7wfvpffghmvftxVzBtiCcSbPjg++nac9uBr7/IoMvvsz4seNURkZxyxXQNELJBPGuTjI7d9B+x+103nM3id7lJ2diba3s+9QP03bwAP3f+z6DL7xM9u23qWVzuPU6mq4TSqdIdHfRtHsX7XcepvPeu7ESm+HBNxukXUOODsLgReT4MHJiHFnKzwRmug7SdZTicoEH7dRvOVM2v6CM3myUz4dmeX+HIBRGpJoQLe2I1k5o7UQ0tyEia+jXZQDVEuQblgLhOIRuPpJhDmrFmeOxIiqj4QZ4H5+PsWe/x/gLLyHdja/8y9x5mNv/P/8aM5Ui3H7lmTnXAsHF0wTffwqKk9e7KWtHLIF298No+++83i35gYewLGK3HyK6fx96Mkn15CkG/9Ov4QwOEdm1i8lvPYUzOsK2//e/x+rsIHDUfSgaIeCJ++5FT6XI/smf0fKjn8JsWn34tBYOE7vrDmKHD6EnElSOvc3gr/4n3PFxwlv6rrh/1aNRYrcfIveVr2JfuozZ2krt9Bm8fIHUozdRHoGUypsdVLXiFUEoixlvmTGra0NuGDk5CqOXkfE0ItOuwk6TzXNDs29grCuzNaUaNzVzhkDW1IBnNgSCKR5ZCLGqykZdmIsOtnzpEyCxtJnPBRAIn6Ch/9ZRJHYgfQxhomEsSV6rvzcM+xvtW3S/gTpWXTOnCX2BINACgobyXJ+lPJZIjHkK9atBT2zLHBJLCKXWt/QwlrZ0SYqUAf46JHQrQnftit7WSCupUJKx2ji3awdJzFKzzy47b420kLJSjFXH0VuMOap3uZoLZo2QUjYIYAVDGNNBjuuy/QYxPYXFfOttv062PkrRyYMQJM0UUSOGJvSGb3y90ba5nYvj24xUB6n5VaJGjHS4A1Oz8KVP2S1SdAuM1ocQQpBsqMynUPUqCFOnPdJFza9QcCaZaPjqt4TbEUIwUR+j4pWI+4k5JHogA0ZrQxSdPIZm0Rpux9LCQEDZLVF0C4zXR1TVgpm5bor0sBaePndSBvjSv+7q+JgV4cfu+iD7Orbz7LnXGCvl6Eq18q5td3K4ey/xUJS97dvJ14qkI0kc3+XFi2/xmy/8merfpOSDBx7hzp59K+5rPZEOJ3nvlvs5NXGBI2Mn5nxW8+p8++IL9JeGebj3bh7tvYeueNuy/sd+4DNem+R07gLHx89wcuI87bEWfurgD6+aRL/RhmC6prMj3UtbrInLxRnrGy/weX3kbf7q9Lf5+O73LUuku77L2xNn+YuT3+LlobcoOZVr0fTrAiGUncvB1j081//qnM9GK1n++MTXaI5m2JrqXva+zdULfOP89/jyme/SvwGq/ZgZ4bEt9/J29iwvDR2Z85ntO7ww+AajlSyP9N3De7bcT2+iY9lrP5ABuVqeU7mLvJ09y4nsOVKhBH/9to+sC4nem+ykO9HGxVk+7hIVUPu7b/05P3P7JzeMSBdCcKh1Dx/Y8Qh/ePyLc7z8q26N5y6/ykBxmEf67uHdfffRlWjHWOZcSSQlu8K5/GVOZM9xPHuWS4VBJJLOeOsmib4MRCKDtvtuKE9CvaLUtNEkxNMI8+qqsoSmkejp5sBnPs2ODzyJXSjiVqv4jjutPtcMHSMSIZRKEmlpwUrEVy0eMSJhWg8eIL19Gzs+8CT13CRurUbgekoFb5kY0QihZJJIUwYztjqfTTMWo+PuO2nas4tdH/0Q9ck8vm0TeD5CE2iWhRmNqjY3N2FEInPabCUTHPrpv8HWx9+Dbppkdu+cs/1wU4b3/+7/xKvVCGcyxDo75nzeff99fPRP/4DA94l3XdmE8yYWQRAgJ8cJzhxHnj+JHL4MxbzymK1Xwa4rT9kr2C7LjJsWG5FI04JwBMJRRDiiwt1STYjOPrS+HYi+nYh08y3hD37NEATI/ATywink5XPI3JjyEnYcNZERTSBaOxDb9iC27FQWCWuEWyhQPHESv351uWGrRai5mdZ3PXRN9nW1kPkswbGXYXzkejdl7Ug1IbbsXHm52ZDKPvimnri6ESHBGRtn8itfpX7hIn6pTPWddwhsBwmUX3ud2OHbCTesjbRI5Op3OP1PiTM0zOTXv4F98TJ+qUTt1GmkbTekrVeO2KGDTH79G9ROnyF68CCFp58lcc/dGM2rJ/mvGlIi6xVwqqqaCgmaAeEEIhRGOjbUyyrPQjchmkQ07Eel50A1rwhulBUgoKqgjDVY3ZgW2j1PIrODKsS+ONG4lxZrbwCVggqvzo0gB88ikk2Itj5o60OEIjf0/bf+nujMVWCLxn9Xgym/8kX32fAjt+f5gEsk2lT6nlADVhUevj4vLEHDA32KQG80FE1q+Mq4eM7dqM7C+hF3mqYzXh3F9usLSOWuaA+Z0OJhUUGDRLxaCMSyg96l0Bnr4Lbm/bw0/Ap1r87uzC50dMbr41TcKk9ueZyoEZle7sXhl7F9m93pXRiazlgtS8Ut8+SWJwjrIWzPpupXydYmcAOXvJ1nvJYlbsaIGrE1tnGqQkCRlOsJL3AJmOlE5gcwBjKg6OYpugU0odMR6SZmJtDFTMXE1KTJ/Gt4wh6bJtC7o1swGhM7ADEjji4Mck6WsfoIMWP2NtW2EkaClJUhEkSpelXqfpWM1UIm1EQgAxy/zoSdxZ2nhC05eUpuEU3odEf7CBuR6QyEmJFAEzqTdpZsfYy4kbwuxLVAkDCTFNw8oLzva14V09rYEs3VIBNN8uiOu7mzZz+u72LpJrFQdPqa7U610Z1SypRABvzGj/wz/uDVLzFRLfDg1tv52KH3kgxfW3WcJjT2NG/nE3ufIFfPzyGJQZGJx8ZOcyE/wJfPfJeueBvb0r00hVMkLBW8VPdsik6ZodIYw+UxcrUCVa9O1a1R92zu6DiAGyxfnTQbN6K77O3te9mV2cpAcYRgVgsn6wX+79tf4tzkJR7f9hD7Wnao84IgkAETtTzHxk7xwuCbvDHyNhP1wrQPekskQ1eijXOT/VTcWyuYOG5FeWLbQ7w4+Oac714ieW34GP/6+V/nQzsf48GeO+mMtyqvbympuFUuFAZ5Y+QdXhh8g4v5QUpOBYmqetqa6sb23TlE8pVCqdG7+dS+HyJby3F2cq61hBf4nMpdYKA0ytfPPUdHrIUdmT6aI0pZrgkN23Mou1WGy2MMlkbJ1vLUvBpVt07Ns9nfvHPdfO+7Em3c1rqLo2MnKc1Sozu+y3cvvcSl4hAP9tzBwZbdtEabMDQDT/rU3Bolu0KuXiBXK5C3i+xt3s57tz6wpv2HDIsP73oPI5VxvnTm6TmVFHXf5sTEeS6XhvnK2WfoTXawPd1HczhFIhTFCwLqnk3FrTJWzTFUGiNXz1Nx1bmqunV86dMSycyZnN7EIvCcht1EQhGBU++lvqcGWVfpRymEwIxEMHu6SfRsjEWPGYtixqKLep1fDaxE4op8znXTJL19G+kl/It1y6Lr3ruXXD/S0kx3y5UHuk7Bq9YY+MY3Gfzq1ylfuoT0fcItrXQ9/h52/+zP3NCDz3WDlMhaheDEm8i3XiK4cBrKBagrpTnXq3+YUreXCjNvALqOtEIEoQiEI4jWTrStexA79iG271X36Q/Cd7ZWSImczOI/9zWCN1+EUl5NiHiu6sOCADlVHdCYvBDN7Wj3vhvt7ocRqeUtBp18ntHvPsPo089QPHWG4jsnCGybZz/88TkWK10ffD/7/tHfnw5Nnm6e73P5z/6coa98ldrwKHokQtM9d9H3I58guXfPAv6ifP4CR/7RP6U2NPP+3P7ux9j5iz9LtHtuH+rXbQb+6osMfOGL7PrFn8dMJbn8539J/shR/Hqd+I7t9H7iY3Q8/r5FJxC9Wo3+P/8rhr/5LWoDgwSLqOt3/dIv0PPxj2BEN0NNZyN49bsq4HHPHUohuznhtS6wBwcZ+s//hfC2bXT83GfxiyUG//N/me6rg1oN4wozBhbDlJIdoHbuPMP//deJ7NlNxy/8LN7kJAP/4T+uy2PCaGkmsn8ftXPncEdHKDz7HF1/52+ryqVrBacGdhmiKTU4rkwqsty0VNVVraje+cIppTgv5ZCpNtVHGaZar1pseOAnUNYha3tHFEJDtnYjMm2ILfugNIkcuYAcu6xCqReFVOS9ayMreWR2EM4dVfddzy5ItVzzvLXVYJ1J9I1TBS5FxDdoe8xF1NdTFhjXHhuhlZ6LM/kTJMwETaGWBWcmrC89a7deJDoIDLH45WNp1pJ2K4Zm8K7OB2kON/PG2Js8dfk7gCATTnOgaf+017ehGTzUpZZ7fexNnupvLBdKcaBZLVewCzw9+BxvT7yDLwOcwOHVsTd4M3uUuBnjw9s+wK706maeNaFjaua0RY4b2PjSW6D6vhJIKal6lTkkdESf+7LiBA5Vr0IgfVrCbSTM5ILQ2cUmgNzAoeQWEQiaQq2YmjVnHUOYZEItlL0Sdb9KxSuRNNOzPjewtDCa0Ahp4elsgJgZbwTlCkwtpPz8CaatZaSU5N1JAumTCbUSNWYn0wsMoZE005TdInW/Rs2vERdLV4BsJFoj7QxUFeFV9arknUkSZvKG6JB1TScdWXkQrwmNwz172dO+jUBKLN3A0s3rcgyWbvJI7z0EQcD/Of4FLhYG51TFSCQlp0LJqTBQGuH1kbfRxMxE6FTuhB8EjcnNW48ES4eSvH/HI1woDHAh3z99hBLJRC3PN88/zzOXX8HSTSJGGCE06m4dN/DUj+/iNfppATRHMvzM7Z+kPdbMr73y+xtPol9j4sEQOgdbd/PE9nfxjXPPzbFLcQOP07mL/Prr/5ffPvJ5woZFyAjheC51X2WwuIGH47vT19KUh/lPHPwYXzrz3XUh0UEFdt7deZCfP/yj/O+jf8Hp3IU5bQWouFUqbpXB0ihvjZ2cc+0jpUpHCQL8Db72Tc3g3X33cXTsNC8PvTXnHrV9hxPZc5ydvISpzQ11n7o/VQi7xNJVld9aSXSAhBXjF+74NIZm8MXT36Hqzaj7JJKyU6XsVBkqj/La8PFF+4mgUT13K/YT1wLBse8j2nqhe8c0CSBrZeTFdxDxFKJ3nUNepUQGgSK3piwxAh9qZWS5CNWyGix5LnieUkStQ3XkzQyRbkEcuHdN6wSuy6nf+C1O/a/fIXBcoj3dmPE49fFx6uMTK29gA2FPTpJ74wiR7i7SezcgRFhKpOdCuYT/4lPI7z+FnMyq8vErUZpfK/i+spKpqee3HB3CP/kW6AZi/x0Yv/TPb7jKuusNGfjIcyfw/+C/IseHG2GHi2CqWsBzldd9fgL/8lmCd15H/9CPKWX6EqKqwHawszmk6xNqbkKzLKTnkdi5Ey00U60T6VxYNaKFw5z41f8fY88+h5lOoUeiVC5cJP/WW0y89AoH/+Wv0HTP3XPe1c1kko7H30fl0iWKJ06Re/11qoODc8i+WScAJ5dj4tXXQfttnIkcdjZLqK0Vt1xm+JtPkX3xJXb94s+z8xd+dg6R7haLHPnHv8LIU98mtnUrTXfdiZ3LMfHKq9RHx2i64zDt7303qYMH0MwrD1W8ZZEfQ554DY6/iGzpQuy7G7H9AISi6p7VGs4Fm1g1pJS42Sx2/wCdv/jzRA/sp3LkLaQ3I54Jb9tG9dRpAttGmKYaj0ipbOim7JVNA4RAOo4SUkiJ0DS0kAVBQGDbSD/AGRoiqFSn30vckRHc7ASdf+txovv3Un71tTn7ntvWtR2b0DSSDz7A8P/4DSa/+RRGJk1kz+4rPldXAuk5IHRlpwLKZgypiG2nBgilStcNpBmGyWGlWg/HFc+qm8iGdZkwLa7o+m44c2CGwLCQkQSipRvh2jAxjBw6j8wNqb48WOR5HQTKbs2uIcs55OUTkG5F694Fbb3KZkbTGq4m1/f+W1cSXUPgI5FSzgn53Eh6fWqfmtCXVbwLoTVsEIJ1UaOr/frTwaNTtjQqBPLKvc5Xi4gRIWmlSZjJxl9mPaC1pYlfNSi9+sGogCWP8VO7PrHMegJTV57oh5pvW345zeBg8wEONh9YdJl0KM3Ht3+Ej23/yKLtWwsMzSBsRHAcRaKX3BJO4KBrxlVXUvjSo+jkcYKZl7/kPCW0Fzg4gYOGTsSILiDQl0LdrxFIdc3FjMXLpC3NwtRM6j7UvOocEl0T2gyBImb8w2dPkMy9lxvtlR5Ow+/Z1Ix5/vw0lg+m17X9OnFj7Yqv9UAm1EQm1MSkncMJbEZrw6SsFAkzdd1tXWDKSkiuqKjUNY2YdbVlbesDUzN4fNtDtMSa+MNjX+DY+BmqbnUBzRVISbBMiO5iuBVeSYUQPNh9B7lagc+9/UX6iyPz7h8fz61RcWtMUlxyO7rQ6Um089dv+wiPb30IXwZkImn6SxtcTnuNX0xEI1z0x/Z/iGK9zCvDR+colyWSuu9Q9x2KK1jDx8wID/XcyS/f+9NEzDC7m7aiC32dJo8V4f9Qz51kIik+d/yLvD7yNmWnuoDkVTksa7v2p5zu1gt9yS4+c+AjVNwq72TP4s16YZVIHN9dlfJ9qQyP1SBihPmlO3+Mrng7n3v7S+Rq+QXn5Ur6CVDVBuZ1zNu4GSCLE4htB5hzZZlhQCl4r/p6kxLpeyqU0bWhlEdmhyE7pH5PjChl8GKDpU0AILftR18jiV6+eImxF17Cq1S5/3/+N7qffBxtNuFwHQeXhXdO8Pav/Ve2feqT606iS9eB/ATBS9/Ff/arkJ+4fmrzq4UMphXr2tZrS7bcFJASee4E3m/8Wyjk1rwujo088iJ+pYT+yc/C9r2LqrVDba3s+NmfAvmTlM6e49Vf+JvUBoa44z//KqHZFSOL3FNBvc7o089w+Ff/PV3vfxKEoHzuPO/8h19l8EtfZfTpZ0ns2Y2VmhnzWc1N7PjZnwZg+BvfojowuOLhuPk84997nq1//cfZ/bd/kUhnJ9L3Of/7f8DRX/mXDHzhi3S+/0ni27ZOrzP09W8y9uxzpA/exn2/99uYiSQy8Bl56jsc+Qf/hEhXJ72f/GHiO7av4cT+AMEKKxWuXYfBc8jB88hoDLHjIOw4BK1dEIoqovcGGEveLDDicfRYlPIbbxK4LpPf+Bb2rNDwlk99krO/+LcY+V+/S+Lee/CrFbRIhPgdhxGNSS2rowOzo52JL3yJ5IMPoEXCRHbtwurpwa9WKXznabzcJPlvfYugXlf3rhDoySTCNKgceZOgWiH31a/jDA1Pvx45jcBTL5fDLxWpn7+AnohjZDL4lQpevoAzMkJg16mfv4AWCWO2tExPtkX27MZsayX31a/R8iOfRI/Hrq3YTTeUwtxz1DUZeGrSR0r1DqbpajJRNNIZdVMJGTYKU3yopoFuILt3Irp3IuoVGD5PMHRe2bm4zuLtCAIIbBgfIBgfUPZo7VsRnduQySYww8qi8Dq976zr6EMXBr708AJ3OkhQqZnmEtdTxLNsEO5q7DlDIqlzsboTomsGnu+pkE/NmmXIMT8wVEMIHV+66OhIObP96djQWV+CEAKJCpycveTUMlPHOhXGKKWc8VtGb9habBxSVoYLpTOE9HDDnmOmlT2xviXtXK4FVuowxCqZgtUvd/WwNIu4kVB+5EDBmaTkFgnrYcRVTLr40meiPk7ByU+TLLowSFlzPbICGSBlgK7py9oXLdh+4M/a7uLtFEIFfQrEdG7BrE+nJwnE3Ct92f0G0lP3L5LR2hCjteU9iIN1IrDWCiEEOgbbE7t4xztKrRHcer54li2J7STNJPp1Usi7vsdgYYzR0gT5WhHHW/5B9vieBwhfpYftemHqfN3Zvp/eRAdfOfs03+t/ncHSKEXnygIdQ7pFJpykL9VFeA3+azcq6S6E4IM7HyVmRfijt7/ChXz/HCXususiSIXi7GvZwSd2P8F93bdPBx3vymzh1MT5Wy5oVAjBtlQPv3Dnj5I5keTFwSPkaoVVk9+WbtIWbea9W+7nM7d9lJilqn064q10xFsYLI2uWzsBDrTs5B/c9zN888LzPHPpFfpLIxTs4hVNUlu6STqUZEuya10DdYUQ3NGxn79554/zh29/ibfHzzBZL6xtG1ydKEAIVbX28d3vY3fTVv7i1Dd5O3uWiWp+TvDuamFpJolQjLZoM/d0HmRrqueK2/YDAd1QftCz4XtKBb4Wn8vZmJr0rRahmIfsIHLgPHLwPEyOqe1vYkNh5yZxS0US27eR3LUTPXRjvBtI36d8eYDS+Qvr67UWBMhSnuDkWwTf+QLy4ukbW3W+FmgaYu/tKy93o77sbBBkfgL/L3//qoMs5ZnjBM9/E725DdLNCwiXGUvWucpGIcSqMha2fPqv0fn+J6crfRK7dtLx+PsY//5LlM+ew80X5pDoC56nq/xem++5h60/9temLV+EptH78Y9y4ff/ECdfoHz23BwSvXzuPF6lQtu7H1O5DppAaAaxLX0k9u6mOjSEnc2S2LljdQ34AYM4cB+Eo8j+M1CYgIqqpJLHXoR3XkW29ShCvWs7pJqUJ3+DoNzE4hBCYPX20PqZHyf3hS9RfP77pJ94nJa/9iPT3uGhLX30/ct/xvgf/ynF730PPZkk84EfgjsOT29Hj8Xo/uW/x/jn/pji975P8qEHiezaRfyOw3gf+yi5L3+V0iuvkHrPu2n+4Y9itCg7kMjOHbR88hPkvvI1Cs98T21X0zAa9+fEX/wVxRdeIqhWAMHlf/GvST7yLpp/+GOUX3mN3Fe/hjs2hpFKcflf/CvCO3bQ/tmfItzXp45P04jdcZjy668r0t+6thUewoooT/TKpPJCNyOIUMMaRzQsrud5xC/aAW2E/lnMYpYicdh+CG3LAVXxMXoJmRuBWkmp0Jd6h6xXkZfeQfafgnQromMbNHUou8JQVPWt1/D+W18lutDRhIEnHYLAV7YPQEO3PLOgBF+6amJE+o3Sfo9ABGhoaOirPgea0DE0Ez9wcQN7mvyTDXX6lB2HQGAKCxcbJ6gjmPGanr3c9HbR0RB40p0mAHVhoDcUurpmYKD2GwgfEEp5i2iQchtLoo9UB2mPdJGy0gu81qPGpq/ZWmFqFkkrzXh9FDdw8KXHUKUfUzNJW01oYt41vALUNe2Td3IMVweo+TOD2LTVRGTedzT7ul3LyENrVFhImA7RXao9kpUnOFaL2ddcVI9hLFP9AMwJJL3W0IRG2mpiR3IPF0vnqHhlxuoj1PwqHdFuUmaasB7G1MxpQn21EziLYTWVC67v8dy51/idl/6SZ86+Qr5WWnGdc7/yDbpSrVfWqA1Ea7SJnzr0Cd7ddz/P9b/GsfFTZKs5Ck6ZilvD9hzcwGtMpoqGdZJO2AgRNSLErSiZcJLeZCeH2/dxR/s+0qHkyjsGuuJtPNRzJxV3JhMjYcXoirdd1TFpQuNQ254F4Z/b030krNX3r5rQeM+W+9mS7OIb57/HsfHTTNTyFO0ydd/GCzxVpSg0LN0kakZIhxN0xFq5s2M/j/bdu+BY1PFWqXt2o029V1xdtb9l55yJOIDOeCsx8/pUPAgh2J7u5efv+FFub9vL9wfeYKA0Qq5WoOLWcHx3OhjY1AwiRoi4FaMpnGJ7upd3b7mPO9r3zcmc6El08MS2d3Eh3z/9t0w4yXq8HTZF0nx6/4d4pPceXhh4kzfH3mGknKVgl6g0PP7dwGtktwh0TcMQjWvfjBAzo2QiSXoS7Rxq28ud7ftpjizv33olONS2h38U/yzfufQiLw2+xXh1grxdotqwEJoKf9c1DVMzCRsWMTNK3IrSEsmwI331XtS6pnOobQ87M30cGTvJS4NHuJAfYKKWp+RUqE5/v0r1bmg6hqYT0tW5ipsRUuEE3fF29jZv596uQ3TGr7w/3JnZwmN9986pAEqFEsTXcH/fDBCt3QT9p9B0AxmKgAyQk2NQLyNauta+Qd9H5seR44Nw4QTy0kmYHF//hm9iDmQQUM9mKZ46g1+rkTt6DDs7AUJj9NnvUTxzFgAjFqXlnruxkuoZKqXELZWoDg5RHxvHq1SQUmJEIkTa24ht3YK5hP+slBK/blO+eJH66BheVb3H6qEQVjpFbEsfoaYmEAJ7YoLy+YvUx8cZefoZnNwkubeOYn7pK9Pb0wyDrsffh2aubegpPQ+GL+N/7+sErzwDpbVNBN7waO5AtPcs/0Saqgj1PXDrimSYCj7UDTAshLFKRayUEHjKEmcqG2Eqx0toinwxLIRhrF5h21DVS8+Z2Z6mg2GqAGNtSuw1I3VbqY3Bi99G9p9dl0qD4NVn0W6/Dw7dq5SL64j29zy2YMxvpdNY6RRepbKoD/mVILl/L9F5uRBCN4h2d1M8cwavMtdv2IhG0XQdJzc5/ZxT93Qdv1JBM8xNn+9lIFq7Ea3dcPhhgv6zyIsnYHxQBSXWKjB8ETl8ERlPIXp2Qt8eaOlU5F741nqPWE/o0SjNH/kwzR/58OILCEHy/vtI3n/fsttJPfwuUg+/a87ftHCYpg++n6YPvn/xfScStHzyh2n55A9P/63lEx+f/nfnL/48nb/484uum3n/k2Te/+Sin0kplY1MtYrd30/yoYcw29uuvUBPNuzzQlHlhS4E0vdUeKhuglND+q4SCvue6v+js10CGn20DGaeLxtomyJ0HZo7EU0dSKeGnBhRoozihBJp1CuL9/+BD7kRFUYajiFaepTVS7IxmXWNBIfr+yQRStHrSQ0pG4SyZiCl3vh4hiicCUnUoEHISRkQCBoq7ikFub7iWNcQJpqmEeA3HhTKC12pb2egazpChvClN6N6p2FnsWBiWGDqYaXcnZqpmbeMUr5r0yS7Jgx0oS8gNFaymrkSTJGwbuAusKSYHWC5idVBCBVA2RRqZqymrBdKboHLpfM4UZuUlSGkh1e0/5BS4kmPmlch70wyXhuh4s0oc03NojPavaBSQRM6ujBw/Spe4/pcTeerPNDVNej49gJyHpQa3gs8QGJp69Ox6JoxrW5vCreSnqesv5EgpcSXHlEjRibcTK1Sw5ceJbdIqVAkasSIGwkiRgRLCzUmA6/M664lrPzhV8KpsQv8+2//Di9efIvuVBsHOnYSWUGBGjKu3p9/I7E13c2WVJcKe8wPMFgeI1ubVISxZ88iPxVJlw4lyIRTtMWa6U10kg4n1myvc2/XIe7tOrTux2LpJj91aGlbqrViR6aPX7jjR+kvjXA+389QaZSCXabuO0gZYGoGMTNKUyRFX7KLHZm+BtG7EA90H+aB7sPr0q5P7/8gn97/wXXZ1nqiKZziAzse5cHuO7lYGOBiYYDx6iQVt4bre+iaRsQIkw4naI+2sC3dQ3eiDUtfqProSbTzc4c/taHt7U6088m9T/KhnY9xPj/AYHlUTSTZZWpT1z4CQzcI6RapUJymcJq2WBM9iU6aIxtvLdUSzfCpve/n8a0PcnbyMgOlESZqeWqejdOoajA1k6gZJhmK0xrJ0BZroSveRjq8flZcUTPCg913cG/nIcarOS4WBhkpj5Or5yk7VRzfQxMCSzcJGSGSVozmSJrWaBPdiQ6awkn0Kwg1n48P73w3H9757nU4ohsbom8vnHodee4tpTyXgRo4tXQrBc9qEfjIyXFV0n76CFw6pcqGN3FNID2P/PG3eee//g+cyTz2xAT18SwA7/y3/6GsXIBYTw/3/pf/NE2iO4UC/V/8Mv1f+qoi2qpVEAJNN0jt3cOWT3yM3o98aI5Sdgr2RI7Lf/UFBr/5FMXTZ/DrauJWaBqRrk4O/qO/T8e7H0VoGpNvHePM7/0fKpf7qVy+jG/bXP7ilxj6znent2fEYrQ/+vCaSHTp2MoT+ztfQB5/bWlP7JsY2t5DYJrLExVCIO0alCehkldEeuArktuwIJJAxtOISHLpIDgpkZ6tCEC7oggKtxHQKQO1f81Q4ZyhGDKWgmhqZdLZd5HVIpRzKojRcwGpSJtQVG0nlplReq+CFJfFSYLjr6uA2PVAvUpw9GX0nQcgsfBavxpEOjsXcgi6jtB1lQ+xTiUZVjq9MHBRiGnfaDkvW6L53nuIdHUx8q2nSB+6jVhvD36tzuh3n6ZyuZ/uD3+IaO/6hjXfkgjH0HYdQu64DcaHkIPnYOSSsisrZKFcQJ58Hc68pdTpvbugcys0dSCSmasO797EjY+gXKH8+utUjh6jdvosrZ/+1LS6/ZpBSmV3punKzmWqHxaa6setMNJ3oV5GaoayTzFDYMziHoRQzxOngqwVQehK4b3R17AQiFAU0bUdOrYiKwWlTJ8cRRZz6rnnLlG5Wq8gB07B0FlkUzuiuRvSrZDIICLxxgTuxmBdz8qUybsp5g1kF5DKGtYqlKlisW0tsZwuDPRVHI62CMm95DbRl7TIAHW8SoG7PLm1kkr3ShAz4hSdPLZfXzADbmrWsuGim1gcYT1CW6STqleh5Cqf4qJboFaqkrKaiBlxQnqYsjvXw7jilRF1gS99nKBOzatRdktUvNIcKwJNaHREukha6QUEuaVbhPQQNb9CyS0Q1WMLQkIXQ0gPYWkhql6FopsnrEcWrFP1yriBg4ZG1IhfzSmahi50okYM269TcgskzNSS94qcrke5PiVuvvQ4UzxF3atR86sNVegMql6FqrdUYvTacHvTXasi0b99+iVOjV1gf8d2/ua7Ps1tHbuIh5ZXLiTD6/PdbSSEEMStGAfb9nCwbQMCxW5i6JrO1lQ3W1Pd17spNw3S4QSHw/s43L7vejdlRQghiJhhDrTu5EDr6gKtrzWEEDRF0twbSXMv6z/5tBYYmk5nvPWq1OSbWBkimoT996tAJ7uqrCNiaUS6ZdV2LrJSRF48MUOe19bnebmJ1UPoOvFt29jxmb8OQPHsWS7+2Z8jgJ0/9RPToYdGPEq4beaekp5H+cJFNMuk72MfIdzehqYbFM+cZeCrX+Pkr/8m4dZWun/oiTn7C1yXM//79zj9W79DpLODvo9+eNpGoj42RqV/ACudVmShEER7u+n72EfxKmUGv/4tRp5+hp4PfZD2WUpBzTTQ11DeLh0HeeII/tf/FHnunVs2gFbbexhWEkk4dSjl1A8oYk4zFJHu1NRPrYRs6oRE85IhmlSLMDE4l5DQDRCGIrf9RthvvaKWbfIh2bw0EeG5yOI45Edntik0RaDLQBH+9bJqf+A3SPSVz4m8dAaZG11Xv/vgxBH0ahniyXVVVmrXyLJhiphfLTJ3Hmb7z/wkp37tv3H8X/xrolv6kK6LX7fpeN976PvUJ+b0FZtYDkLdU+29iLYeZK2MGO1HjvYjRy/D2IDK/hi+iBy+hExmEB1bkB19iPY+aOlS6vRNq5dbElIG+FUV3NnyiY8Tu/12VclzLRH4UC+pkNBQFBCqMqiSR3oOIhyDcALcmrJCM0IzFihTEEJZwgjUc+Aq8pCuGJqGSGQQiQx070QWJ5CTo1DIKoV6pdCYIJiHwFdZPBPDKsw004ZMtyHSbZBsVkGp63z/XfPpsfHaKCW3SE9sC3W/Ss6eUP6vVpqklcYNHHJ2lppXxdJCtEbaF1hBSCmxgzplt0TNq+IGKp1X13TCeoS4mVwyZHGgcgkv8OiIdKNrOkUnT8Ur4wUuAkFID5Oy0kRnrV/3atM2Hy3htkYo5CIBIzKg4EwyUR8nZsRpDrdi6RtTUtAcbqUp1LLoZxu1z1sdQgiSVpruWB/95YvTCnI3cMnWR5lgbNEJkdHqEONCw5d+wwpmoU+jJjTawh10RLsXJZsNYRI3klS8MiW3iIZOykoT0sPT1Q6edHEDl5Aenp4k0YROU6iFul+l4Ewqb3cziSFMAgJqXoWcPY4bOCRNtb314rLTVjNlt0TZLZGtj5Iwk5haCIFotNfD8etIJCkrc1Xe8lcDX/oMVi6vvOA1xLnsZQr1Mv/4fZ/lR+/8wJo8wDexiU1sYhObWC1EKAJd29f+6Pd95Pgg8vjLyDNHlC/szRrieJND6DqJbVtJNDyPx158meFvfxcEdD3xPlJ7Fg+mDDU3s/0zP470POJ9fWhhNT6oj40hTINz/+dzZF97fQGJnjvyFuf+z+ewMhlu/+e/QtuD92PG1US+X69TH88Sam5Ca5B6qd27Se3ejZ3LUTp/gdHnv0/LnXew9ZMf50ogfQ95/gT+V/8Iee7EFW3jpkAyg+jZujTpPYViVqkL42mIpmbUrZ6riOpaSRHpE0MqODiSWEgYCAFaw/LFikA4BlZUEfiapu5t154hvt06TDbK5UOLEIAyQFbyMwS6EBBvgmhDDR8EM9srKeuh1ZIycvASVKsrL7gW5MaQhRy0dl6XHKRrDT0cJrZ1C5pp0vzAfaQPHUIzDcJtbaQO7Cfa27Mqz/dNzIMQiGgCtu1H9O5CFnNKlT4+iBzrh/EhKOaUgvbCO0od29qN7NqG6N2lyMHN835LwUgmafrAD13vZsx9P5OyQYTL6etNGObKE7a6jtBvENGeYSKaOhQhbtcQpUlkeRIKE4pQL02q5+JsNPJ6ZLUIo5eRiQwi2Yxs7kS0dKvJhHXq/685iZ53cozVhomZcYYr/ZQbRGXBSdEZ7aHsFhmtDeMGDrowqPlVdqb2zlKxSnJ2lpHqIFW/iuurUmmJREPD0ExiRpz2aBct4bYFJdLj9TFsr0ZYD1PzqkzY49i+jS+9aWV5VI/RG99GJtQ0HTBa9kpka6NKbWbEMBchU93AYbw2ykhtkO5oH81cnS/vcliN2nUTa4cudJpCrejCYLByuREIql76JBI3WFhKOtvvfDFYmkVbpIuOaNeiSnFoKHjNJK50maiPkXdy1PwKhjDVNdgIrQVoCbfNqTSIm0maQ21k66OM1UcoOHk0oU231/FtYkaClnA7Gtq6KcLDeoS2cAej9WEm6uOU3SK6ZjRIdBUo7EmXsB4mYabRbv131lXD9lz8IODu3gNY+o1t07KJTVxreIHHSHWI0dowvvTpiHTRHetdFxuPHyQUnQL9lUsgYUdqN+FlKgCLToFL5fNUvZnnmS502iLt9MW3LbsfN3AZqgwwMi9gOqJH2Jnau5nTchNCei7y4knkq9+BoQub1i03KYQQJHdsX/D3UFMTbfffx+nf/G2cyUmkHyD0mfHSwFe/gVssseWHP07nex5Dm6Wq08NhYr0bF+grpUQOX8b/4h8gz5/csP3cCBDbdkNsFcpopwapVsh0IqzIzPJBgIwkIDcI5bwivgtjyCVK8EU0jmzbotY3w8ord3YYW+AjwzGlVq+VwKkq65fZ+2xAOnVl4TKlQE+1QboDYYVnbFtkgIzEITeklO2rhBwbVAFz6wnfR470I7bvXZLE1HQdITT8el3Zvkp506qHA9fl0h//KTII2PN3/vZmgOhGwDARTe2IpnZkUztoAlkpKrXs1KTU6GXk2ABcOoE83YnYfhti711qcns115aUqoK6XlHPYbeu/KynsgcQjeoPHWFYyo7JCqt+YqPfmV0HWSuDU1VWIoEPNPIQNF15U4eiEI5tTOjq7HNjV8G1VdbDlJ+3pqlzYkWUtYd1/fLZNhyaDqEY2FVVdQiAgFAEYdzk4lqhNZT0MURzJ9KuI+plFfSbH0WOXFSVIPPLnDxHWcLkx2G8H5loQrT1Ibp3qvvvKnFdjJokkqFKP0krRWukg/GaIv7cwCGkh+mO9RHIgMHKJSbq43RF+4iZM6RxIANsv46lWbSG24joUQSCql9lvDbKpJNDEhDWwySt9IL9e4HLYOUyvvRJmEm6Y31oQqPqVVVb3EmCsiRppTCEQUgPkbYy5OpZCk6elnAVU1vodVT3aw01cIi4mVyUaN/EjQ9DM8iEmokYEcZqo4zVhqj7ax9AamgkrCQdkW4yoRZMzVxW/WBoBhmrmZAWouQWqHoVKn6ZqfBbUzOJGQlC2twbf4r4t/QQBTtHza/iSx8hNEJaiJZw+7QKfT3VF0IIElYaQzMpugUqXomqW54J9W1MaKWszIb7/S7bTgSxdbKxWQm6trouNR1JEDJMbM+5qV/SN7GJjcCkPcFA+RLJUJqEmSS+RGXZJpbHhJ3lUukCbuDSEe0ipIWWPI+B9Km4KsvDDRychpDB0qwVSXRVHWhTcAq4gTNdkRU3EmxNbAc2SfSbCdK1kaePIF/8BkyM3LI2Gj8o8Go1cm8eIfvKa5QuXMTJ5/FrNWojowBIP2jkWM28p00eOwZA20P3zyHQrwlsG//Lf4Q8+/YtX/mg7TwAkVX0j4YFqba5BDookigcQyZbGrYudeUf29SlSJX5/b1uIKKN8etizwJNR0SSyGipEeoWgF2HxCLfg11VinVQRFmydYZAn9q+0BHRpCLcndriZfjzISWyUlrdsmtFcXLZayrU3IwRi+JXq4w//316PvaRm/bdQ/o+lYuX8ao1/GoV6fubQaLrDc9Rli7njiOHL6qJrFpZXWNmCDKtUK9CMQelPJQKKpR76AI8/BFEIr389n0PWRhX6lvXnhUEHMy7jtVEmNR0NUGkG0jDhFBM2bdF4lfnzS7E3JBh1yYojDcm7mwIZpP6TE/MqfYYirxONkOyZeWqm1VC+j6UGhYfs8/N7BwCIZBi6nxYKnQy2bo6NbKUyFIOOTnCbHJWtG9FhFZeX+ZHVdtmV9/oJiLdhohnVj6+YlbZmMzed9cuNSmw2L6FUM8CMzSzT6GpydRbqfJBAk5dEeOTjXNsV1nWJ0wGUC1BtYTMjyPHLiH69qF1bL2qiabrmHYg6YtvQ0P56ZUKRWp+jY5oN+2RLgAKziQlt0jFK80i0QWZUBNxM44QGoYwpj3OA+kTNxOcLZyctplYjEQPCLB9m45oNz2xPgzNgIYFRUSPcqF0hqpXoujkaQq3oAmNuJkkbiYoOQUqbpmYMTcEzw98ZS/j12a17+Z88G5C2a9EjRg9sT7aIu1M1LNM1MepemU86S27rqWFSFlpmkOtJENpTM1a1lt/NgzNIGGmiBqx6QoLUESwQDQCSBduSxc6STNNzIgTyGB6PQ0NXTOUAn3W9RjSw2yJ75jl66/QGemlPRxMB/QJBEkzTTQRW5QgVucpTkiP0CxbG6G2EtFQvGtCQxM63x14ij2ZffTG+1Z1HtYLgQx4evA77EzvoiPatcySjdn8q0RolcGtd/TsoyWW4Zmzr3J3721Eb+XZ8U3MgZSSilvlfPESFwqXGamOMWnnqXl1Ahlg6iZRI0ImlKYt0kJvooutyV5SVvKaP1MCGZCt5zifv8jl0iDjtQkm7QJO4ICEkG4RNkIkrQStkRbao61sTfbSFmm5KtV41asghKAj0kk6pEKLr1emwvXE2eJpxqoj3Nf20BWdz4geIaSHVHbGIqGrs5EwUxxqvlNZcQUeb068SsEprGo/pmayNb6Nrmg3UkoGKpc5Vzy95vZeDYYrQ5wrnmVHciedseX6+k0sB+m5yHdeQz73xYayZxM3M8qXL3Pq13+TwW8+he84xLduJdrZgdXRDsDkUUWWzx9/2rlJAMKt194zOXjhW8hjr9z6kzehMGLLrtWpIyMJRcotRZ5EkkgzMuM9Xm+oxxcuvPKrrqYpv1xdBy+AwEVKOXe1KS/2KaI7mlymfRpE4spmZjXEuOtsDIEOyGp1WRLdSMRpe/dj5I+9zbF/9q/o//xfoEejeOUyrY88zO6/+QtXtX+/XmfsueepXLyIVyqTf/tt7PEshbff4exv/C8iXZ2YySSZOw+TOXz7Ve1Lsyw63vsezvzGb/LST34WPRoBBJplEelop/WRd9Hz0Q9P5ylsYvWQ9Yqq1Dr9pvJDr5VnQo9DETj4IGL3YZU94tgwchl5+k3k0HmoFJFnjiB9D/39n1nSXkOWJ5ETQ2qiyl+ed4BG1YQfgM9MdUi1qEj4cKxB/l6poEE0iFipQh8nBhUpGSy0r1XNabQnCAAXnJpSrFeLiLatK1uKrABZKyHH+9V595fpK6QE6at2urYKoSznId2GSLWt3I7AV/sIZs6/rFeUl/gKHaksTapJhtkPV6EpG62VSHS/sd9KfuZvq7F91fR1m6S4oSAlsl5WivPRy41JJUcpzdf6nmBXYaxfhVfnhhHbb1f2TFeA60SiC+JGElOzkEjCegRLD6ELnYgRQ9d0BIKwEaHkFnH8uYmsujDQdaPxrJ65iIVQitOEmWTCHsedevAv8lAPGxG6Yj1z/MM1odEabmOgcom6V6XilWlC+Y5HjRhpK0PRKZCzs6SsDGEjgkAgpcQJbHJ2FoEgYaaIbLDdimy8BEikKn1s/A5m/X8gA2pejUm7wGS9QNEpkq3nOJG7+sGtRGL7DlV3ncvt1gEhw1pAGl8ZFMGsC4PuWISOaDeB9Kl7Nep+DU96+IGHFGAIA0OYRIwIYT3aII9VG1bqaP3Ax2lcq0tDNn4aD6RVIiDAW8SCZvZ2vWBh4nF9vscUEPgeru+hCYGlW9OTSEIIDGEgpc7F0gXKbonbmg7NOf8Fp0G8XWNIJEWngCEsYkvck1JKvj3wTR7pfIyQcW3I7A/uf4Rnz77K7738BbpT7fzI4ScwNANtmWs2ZKwcNLuJ1UNKiSfVNb1aaEJg6uaqJ8Vm78v2HfrLgzw/9DLHJ05RcSt4gYffsD6Ss160NARCaOhCTYJFjBBbk33c234HB5r2EDNj6GI9+riF7fRlQMkp8XbuFC8Nv87l0gB138GXqq1TzxeYmtxjuq2a0DA0g5ZIEweb9/FA5z20RpoxNWNV1SgT9SxHJ96g5BZxA5fx2iia0Lmr9T6awi08P/Q0B5oO0RbpAOBc4QxFt8Ce9D6iRowXRp6jLdLGSHWEslsiFUqzK7WX5lALpwsnsP16I+MiiyF0dqX30hvbghu4nC+ewQls9mcOYWgGUkpO5o9T9+scarpj1VUm6wUpJcPVAapudc61sRa0RdrJhN6NQAWcL3e96JpOVFMDLD/wMcTqBzlCCCw9NP0+FbYjCwLPNxKBDBirjXG+eJ7OZSdLN7EcpO8jzx1DfvfPlXpuEzc1vGqNC3/0p5z73B/T+sB93PYPfpnkrh0q8CyQDH/nuwx969uLrqs3vNO92rW18ZGXz+J/8/NK/XyLQ/TugEzL6p7j1gp9qm4oG4cpGxW7gqRl6dHH1BhSTo0tpn5NPWtmCUsWG5v43txw0lBsWZWzMENKCboKSM9VZOBGwLWXJdGFprHzZ38GK53i0p98nsm33kIYJpHODozYrDGEEBixGGYyiVjEq1IYBkY8hhGNziG1vEqVS3/0J0y8+hoEUtlySImdnWDgi19G6BqaYbLtJ/56g0RXpLeZTKKHFhHpCDCiEcxEAs2ceWZL3yf3xhHyb7+NHolgZdIYsbgaRdp1CidOkn3xJfJHj7PvH/4y8a1bVjx12u5DiJ/7p8pXvlxElgtQLkGlCJUSsvFvWSk1JnOCGTJ16jqTsjGcnff3GxwyCBRZW8whT76BPHcMihPguoBU91+mFbHnTsTeuyGeAsNSnvNSKl/0Xbcjhi4QvPh1GOuHyyeRp48g9t8zd19SQn4MOTFw9TZqQQCBAyJ+dRXPQo1JZGkSOX5ZTdKtFb4LhXECz0V07kSYJmsVr0kZQGEcmR1Uk3hrhQyU3cl4P7JeRbR0N/rWxScnMRv2OPascaLdmIhbpunS9xrtm3dty0BV5Xiu8idfcn0X6c7jZkIxRcKvy7hPcYW4tjqeRj+0JJIti9qDbRimbHoCX00k9Z9Rv117bsXDfAih7sV4Rvmol/OQH1eTsrMrAqRUQdwX3kaWC2h77oZ065rHLddNiT5b6aqhoaM3iEh9mnTUGwcTMPdhqjyiJV7gE0ifoNExSyRO4EzfDJLFH8ICQdSIEdIWEmZTthl1wJ+lONbQSFppYkaMvJ2jFq0qX+rGtVzzVLBj1IiStFJXpZqbIlMVueLjN/ywAxngB/7032ueTd4pUrALFOwiBadI3i5SsIuNvxdxAlc9t2bdyFc6IJ+NSbvAfz/y2zekDcX/c/ffZWd6+fLztUAR4Tqa0JHSwLJCJEkvu/xacGLyDL//zp+QreeusqXXBtuTffzk/k/Tl+gGpog3VYkxWh2m7tcpOHlAEDNjGMIAJH7gU3JKBAQYwiCsh6fVlWoiysH2baSUmLpJWA+jCa2RWeATaky0AZScEmE9hKlb0/uv+3Wl3m8oZgzNJNQgdXzpUXKKBEhMYRBqbLvu1yk5RUaqI+SdPCE/jKEZxIzYhhLWg4Ux7t1ykGfPvsbf+6v/L//1uc9xuGsv6UgCfYmyq3/5Q79EKnJls6WbWAg38Ph2/3P82ZkvrnqdrYkefmzvJ9idXr23pOO7jFbH+drFb/Py6Btq4m2FddQzzVc5CIFLzauRq+d5c+wY3fEO3tf7KHe2HSRpJdbNKimQAZN2gaPZt/n25e8xUB5a8VkxTadPtRXAtym7FS4VB3jq8rPc3nKAx3oeYmuyl4gRXra9TaFmHu16H5dK5xmrjbI7tZdUKINA4Esf27cJZgU3e9KbDhYHcHyHU/kT3NV6H1EjztnCSc7kT5BsvR8v8LhYOs/u1H72dhxgtDbC8YkjtIVVeHnSSnGpdJ6CM0lzuBVfKl/2nam915QQnoId1CnYeUztykOHNaET+gEo3656VbL1LF6w+gmxTcyDlMj+M8hv/NEmgX6LoDo0RP7td5AyYOdP/g1a7rmrQYYIvFoNO59fct3k7l3kj79D7sgR2h56YA3vQ2r7BMGaPaWl6+B9/fMwObHKfa0jGhYIaNqMDcrsds8n/QI1wMcPWG1Y5nyIHfuUncNqzo9uzrVTWLAxMbOM9JdWckuJDBqf1xuWLU4NPE+pLWUAgWwc09LPfxn4s9SxDeJiuXGvtgZLAcNUKviNQHhlH2o9FmXbT3yGrZ/58TlzCbNDOK2mDA/+8R+AlGpSipmxuxCCtkcfpvXhh0AI7PEsTj6PmUxiNWW493d+c0Wboql9aeEQ23/mJ9n2k59RkxTz2m4mk9z9m/9DtWNW+/LHjnP0//nnSBnwrs//MYndu+asW+3v5/i/+fcMf+3r9H78o8S29K14j4tIDLbtYYphmXt5zP5/qcjlaglZKiqSvdwg16d+V0qN30VkpayuR79hUzJlyeG5YNtLq503GlKqCR27pgK2334ZLrw9izg3IRqHth5Fnm8/oFToU33gFIRQBKRuILftR4vGCb7ye1DOI88fn0uiSwmlCaX0nk+gN3zP0XR1v03dc4GvCOogaExc+PPUuQIRS6vqkiuFAFkvq4mD2QS6pql7W9fV+dA09T36ruof/IZH+uzjqxSQ2X5o37om1bQMfMiPKgJ9gdBPLDw3UjbaMGXzMus6CnyVHSF9REuf+t4Wu/51S1XY2LPeierLV7OoZSqNY18Erq3av5wK3ncXHmMktn62LEEA+TGVabGaSvx4E2z0UGKKOHcdVTEwdgk5cFaFh670jDVMlfHR2o3o2Q1NHcrqLAigkEUOnFI2SrXK3MqFwIfRSwS+j7bvPmRmbUT6dSHRBUxbsMz+oxDzQw+nZsFn/iKReIFHxS1RcCYpukVsvzZNMs8OYFwKmtAIaYurOoVQRhTzdqv8n80kCStJpVpmsj5BwkxhChNfekzaEwQymFbCL/cwmlL7uYGLF3h4gYcbeHhy5t+2b1NyyhSdMkWnpH7sEgW3RMkpUbDLV+TTvZ6Ymry40bAekwRLYSOI1anrIbjCF/FrjWCRl+uSW+KV0RcZq40REDBpT4KAB9ofoj3S0chBGOBc4Qx1v0bMiLOv6QA9sV6EEFS9KsdzRxmrjeAFPulQhn2Z/bRHOjiTP8V4fZw7W+8mZSkvx2/0f4W7W+9lW3IHvvS5XL7Emfwp3MCl4OTxA59d6T0cbrmTQAZcLl3i5OQJbL9G3ExwoOkg7dEOzhROc754lqpX4fmR5xrVKK081PHIhp7D//jd3+Mv3noKUNfUpdwQl3JDy67zD9/705sk+jpDrvG+y9ZzlJ3KkhVO81FyyhzJHucL577BeC17NU0FVN82UB7m90/8CUez7/Dh7U/QG+++6nBaL/C4ULjMVy4+xZHx4+vSh0okdd/m5dE3eCd3moe67uXhrvvoiLVjLqHqnqrcmXoPEA11e2ODq0J3rJe2SDtCaHTEujmTP0HdV4qV5lALXbFuYmacPmMrJyaPUfZKhI0ISSuFpYeYtHNkQs1k6+PomkFzw9JtTcc+PbFXww3c6T5TKfZ1LE1ZrMze7tREohPYeIHHWG0UT3poUidnZxe8M8XNxAKP80AG1P3anHBQUJUNSSvdsK67+SGlqrQruSXcwCFbzzJWG8GXHjk7R6QyOL2sQJCwksTNODWvRt6eJGJESVrJ6fPv+A4T9QmEgISZnJPBU3QKlN0K6VCaSCMYXEqJG7hUvAqObxPIACE0LM0kZsaxlni/vJEhC1nkM3+pSrQ3cWtAKrWZZoWQQYAMJJqhIX2f0vkLDH3zqSVX7X7icQa+/DUu/+UX6XricRLbtk4rXWUQ4JbKaIaBHgnPIe8000CPRPAdB3tiAq9ex4isIsBLSuTbryPPHl+FdcFVQmiKTI3GEOGo+ncyg0hlIJ5CxBJKfag3yCEZKOLMc8F1FbFWryp/43IBqpVpCxLp2Ir8su0ZNe5iMC20vp2I6CrzehZROy9cRmN63LwY8Sgl0qlBYRxK2RmivRECqCYODEWWTBFhS0HOGv9NqSOX6/PmeyovA2FaV233sOS2Y4kVSXTROBaxDGElhJgmz6fgVSrURkaJb9+G0LTp9d/6lX9B+tBBdn72p5QyfQ0ZAyu1RQixaAXAxKuvUbl8mZ0//7PEtm6Zo1IHiHR1E+3pxi1X8O2F1chL7Gx27f/yvJtuQDiCaGpbebtSIus1qDQ8iysqMDDoP0/wytMwOrjyNjYAcnIcefEE8vQRGLnYuKdUUCPJDKJzqyLPO7ao/mIVEEJAxxZo6YJSXvk5z96n5yAL2YUqazMEiSZEsln5cc9XBUuplMt2FWplRXg7dXUPW5FGsOdVELC+B6XcTL8iNBVemmhW1iSR2Az52CCvZbWgfKurhbn9kQyUTUmlAImm1e1fBlCaROZGFpLLhgnRJCLZgogm59meSGS9CpW8aotTm0vIFifU5EZL76Je48I0wQzNHX5MKdGXmyC2K2oyY3pDYqa/dG31E17GscJz51b6ACIUXbf3SlktQjWv+v1wTJ2z5ba9kZ7qUiK9BnFezMHIReTY5ZWrMDRNXdvRBKK1F9G9E+LpuedI16CpA9HUgSznkQOnlaVSOT/3OsgOElx6By18N6zB2uW6jaiuVKntBS6jtWEVDBp4RIwoESOKJSx0zSCQAQVnkopXXn7/V6AsMzSTpJVh0s6Rs8fpiHZhCAM3cMnZWUzNImGlVlSOedLnUrGfU5NnKbkVyk6Zkluh5JYpOeqn6t14Nimb2MRiEEKQCWV4tOs9HMm+QUDAA+0PzenIpJQUnALv6nwEUzN5a+II54vnaIu0Y2kWJ/PvMGlPcn/7Q0SMCCcn3+Fo9ggPdz264v7LbonLpYt0xXrYnznAqfwJBiuD7Mvsx9ItfBlQ8So80P4gmtA5kn2di6ULtIRbOdR8Oz2xHr566ct8oO9DRIxrE4D34LbDmGskPiPmpm/69UbJqVB0ygQyWNbSRSLJ1SZ5ZvAFvtP/PcruFZQ+roA3x48xXsvyke0/xKGW/USu0Iqo7tkcnzjJn5/9MkOVkXVupULJLfPU5WcZLA/z5JZ3szu9g/C6pMUvZNajRowpo5mpCfGpiZKwEW5UxjCd2TA16R41oiTMJCW3SN2vM14bpTXctuaAcCklNb/GcHWQkeogeSeP49tIJKZmEjGiNIWa2ZrYQVOoeXq9gIDBSj8DlctUvDI1T9m41Pwqz488s2A/d7XcR19865y/+dKjv3KJ0/kT0xVrEomlWTzS+V5Si2TE3KwouSW+N/wsBSdPySlN24W9NPrCnPdLQzO4v+0BDrfeyUh1mG/1f4PtyR082PGuabJ8uDrE1y59BYHg3vb7ubP1LkDl7BzJvsnRibd4su/97EjuBAlFp8j54jnOF88xUc/iBE4jkDzDtsQOdqR2kAk1XddA7bVA1qvIl78N48tP5G7i5kK4tYXE9m0Mf+dpBr72DfRwGCuVxJ7I0f/lr1A8cxY9vPhzo+M9j9HxnscY+uZTvPFP/xlbP/VJIh3tSmRaKlE4cZKWe++h9f5759hM6NEo8W1bsZJJBr/1bcLtbcS3bFF2CFLS/si7FiVzZK1C8PLTKvhxoxCKIFraoa0L0b0N0bsdrbMXmtsRofCK5OqikA1LjkoZWcpDPovMjiInRpETY1AqNKwuGtYXdmNs194Nze2rV1yvRrA0e5kFfU+DQM8OqOBRpPIoD0UhHAUjNBNAJzRVBl8YXWZCY7bSdp2FS0Ig4gkVjLje3ujp5g0hhGQQkH/7Hc7//h9yz3//tY1T0q8WgVKG29ksvm2jR2cIuMBxKZ44QenMWSKdHcqS5npO+gqBiEQbAbvtM1dWpgV56i3k9SLR33wGefSFGbItnobmDkTPDsT2A4jmzisOJRSxBFII9T3NRq08V/UMKoyytQ+Ral26jxJChfpaYUW2BwHSriArBYRuLpGPsAZMeYuD6lviGURzt6pOmN/XCAGGiUi2QCShlOOFsblEuusgK3mlkF/F/SjtGjI/uujkgmjuVudmUbsRFbZJKAqxtFL4lybmTG7K/DgiFEWm2xHzx3WaMZP1MNW/eo7qF5eyN5EN4n72hEM4BrXSzPqeszQJL2VDiT6r79O0RnbGOt2ndkWdg0ynCjm9Dve/DALl918uIHMjijgvZFeuPJkOiG1GtPVAa6/y+l9pcjSeRuy9F9nWS3DmTWWpNL0vCaOXkG29a5pwuqlkSYqIyzNc6ccLPNojnbRHu4gZ8WkFW92v4RXdFUn0K0XKShM1YuTsCQpOnrARbQSKVkmYKVLWCmEBgO3bvDF+lK9cWFoFsolbF47vYK7gT3urQQiNrYltNIdbkEhaw230ly9h+3U0BMOVIbYnd9AUakITOlsTOxiuDq9KvavsjoJpn3blyzvj26xrOlsT22gKNyORNIdbyNbHcQJ7xbC9jcJn7/8En73/E9dl3xsNpdL0VlR4rw+Jem0hkUzUc9R9m5i29IRLtpbjW5ef4fmhl6m4G2ONIJH0l4f487NfpubXua/9DiLG2l6Uq16NV0be5Mvnv8H4BttJ+dLn2MQJym6FD259H7e3HCC0xmtAE4Kg8SIbyAAv8FT53yzoyxCXgqX9BDWhkwk1UXQKTNTHqHhltiV2TpPuq4UbuFwsneNs4RSGZpKy0liN/Bc3cKn5NYYqgzSHW+eQ6KCyNZJmkqSZpOgWydbHMDSDntiW6QmBKSTM5KLH0BJqg5Syusnbk2Tt8TW1/2aB1pi8zYQylN0y/eXLeIFHb7yXdCgzazmN1ohSw4V1VXFQ8SpUveo0iT5SHQbUhPB4fXy60qTm1ai4FcJ6mLgRRxMaZbfMK2MvcTp/iriZoDveg6VZeIHHpDPJa+OvMFob4aGOd5EJNd34z3kpkeeOK7XdBoX5beL6wEqn6fnAD1EZGGTs+e+TffkVjGgU6fuk9u5h7y/9Amd+9/cWXdeIRDj0T/4hRjRK9tXXePNX/gWaFUII8B0XK5UkuWvngvU0XaflnrvZ+qM/wsBXv86Rf/lvMGLKFzrc2sL7Hvri4iT6uXcILp9VtiLrDcNEbNmF2HsIbe8diC07lSJ5PSCE8rZNNZTsPbOsJKVElgrI7AhypF+RgdkRZD6H2Lob0dSy+v0E82wRllpm6nk4n8QNAlW6X8mr7YRjswiURZ6LdpXlCBuhCeQUgThtI7GMMnM6ZHB1EG3dqkKgvI59UjiC1tG7qIWEV6uRP3YcK53Gzk5QuXQJPRKh84n3oZkmlUuXKZw8iVcqo0cipPbvI96ozvBtm6FvfIvsiy8xefQYFz73RyA0mu66g9T+fY3tVxn5ztO4pRKh5iYyhw8TaZ9RaVcHBskfP44zmcfKpGm+525Czc0Ejkvp7Fm8Wg3NNCmeOg1S0vauhwh3tC9J9qQP3UZsSy/DX/sm4dZWEnt3o1shfMehPjzC6NPPkD9ylO4Pf4D49q3rd45vJXiuqgBJtSNau6FvD2LLHkRylerpZbftqdsrPPedXbp18OZVBkTisByBvhg0DRFJIDaiejmWQrT0qiqelYhLMwTNXUi7qhTpU5jyJnfrK4adysBXQZK1eVVymo5o7kJkOlY+N0Ko/bT0Ktul4gTT/akMkLlRRDSlyPZ5VjwYlrKqma2Ad2pIK7x4Dxn4SkU9RbobFiLZjJxqf+CrqgEZLCTtUeSydJ25Smkzogj99XqfDHw1ARS5Sq/8K4HnIMsFVXGRHVRe57WVOFuhJnuTzYh0G6K1B1ItqmppjRCZDrQDDxKIl2D4/MwHdhVyI6pKZJUBvDcXiY6k5lWo+TWSZor2aNeCgWQggw0NMAzpYVJWmqJTYLw+SnO4jWx9DA2NuBlvqOA2cTPCDRwCGRDSN07xW/UqjFQH2ZLYgb7hBlM3DgQQbRAWSgEqpsNvp2yNwroKThJCoGsqI8FZwrIokDOGE1EjQiaU4XLpEnWvRsEp0BZpJ6KrlxMNMR0qKhpedVN2AFeLqVBhJ1Av+mE9vGbl6q2CilPj1cFjnM/1U3aq+IG/5JAvaoX5hXt+9Jq2b72QreWoeXVi5uIP2cl6nqf7n+eFoVc3jECfjdHqOF+/8G1MYXB3+2FCq5wYqnl1Xhx+ja9efOqa5jFcKCrbGFMzua1576orMgTq+Zt3JmkJt1Fxy0zauXW1KElaaUzNZKDST0SPEDPja65as4M6l8oX0DWDbYkd9Ma3ENJDBFLiBg5lt0zZLdEe6Zyzni50+hJb6UtsBeBi6Tw5e4KQFuZQ0x2rOk5d6DSHW2gOK3Kmv3ypEdJ67UOdNxJCCJJWind1Ktut4coQeXuSqldjX+YAO1ILyT2AiBEmZaXI2TlqDcsbKSWj1VFiRgzLCFGw89h+nbARoewqsj0dykyHpp6cfIdT+ZM0h5u5u/VeuuM9hPUwbuAyVh3ljezrXCpdJGbGebTzsTWHEF9ryGIO+c6rUC1e76ZsYg2IdXex/cfUMzTUvDSp03LPPVhNTUy89jrVwSGklES7Oml74H7MdAo9HCLU1LQoGRffvo3D/+qfkX3lNUrnz+MWiqBpmIk4sd5eWu65a4FNBEB86xb2/uLP03znHZQvXSKwHfRIhHhvz+Kkn+cRnHhrY7zQU01odz2Mdt9jaNv2XluFsBCIZBqRTMP2vdMKRTkxqsiltZBxroOUAWKpcUMQNNSNjffa+ZWLntPw8m18nmpTBPpSStrZhPximPIent6+IoVYqr+b8iVeJUT3FuUBXF6/fkn0bINEalHSyCuWuPhHf4LQDSJdnXilEoHn0fHedxNIKJ09R/alVxC6QX1snOwrr7Lr5z5LfKuqsqgNj1C53I9fq1HpHwAhSOyeeQ5lX3wZN19U9pXDwxRPnmbXL/4cumVRGx7h0p/9ObXhYYxoFHtyksmjx9n7//pbBK7D8Le/S/blV2i59x6cQgGvUiF96CDhjvYljzVzx2G2f/an6f/zv+L87/8hmmkoGxk/IAh8wq0tbPn0p+j95A8T6doM414UbT1Kad21HdG1vaG6XifCsb1X2fC09cz8TcoZP/hZEOFrUyG9KlhhRWKugkCfgmio42W9PLcP8FxFNq9EWNo1VT0zv/+IZyDVtvrvZEqtn2lXpP5sxb9TVV70i4WMmiFFpM8i0aVdVf3nIpB2ba4VVkjZ6cy1dFHholiL9JeBpz6fjVBkfStoxMJ8hQ2FDJC1ivInz48hc6MwObq8ZRioNsYziOZORKYdMu2IWOrqzoUQiFgSbcchguKEshWaamZhHGHXbl0SfUqFpgsDjbknMZABFbe8YSp0UCRcJtTCeG2Uklug4pbIOzlM3SJtNd805bubWIiSU6TkFuiM9kwPmNcLEknRKTBU6SdnZ+mLb1v6ZfMmhWgEVk0F2c7PN1jq3jB1E0MzqPs1pAyQCALp40kPSw9hNwigqdAe21eewVMPI1OziOgRfOmhC53uWA/t0Q5Ceng6lHh5ImxhBsJq4UmX0doI4zVlg7EtuWuBuvQHAXXP5g/f+hJfO/Usl/KDuCsE/DVFUjctiT5em6C2hN1W1a3xyuibfH/4FUruxj2H5mOkOs5T/c+SDCU40LRnxeeQG3i8MXaUb116honatQ80vljs5ysXn6IpnKY30b2q56YQGr3xLfSXL1L1KuhCJyC4quDN+bB0i1QozVhhlPZIuwoPXyOklDi+TdiINLy4Z5RAIT3U+P/OpTewiQ1D2IiQslL0ly9Pk+hVr0rBydMSaSVshBkoDzBpT9JpRKh4Zapeha5YNyE9RN2vc2LyBIYw2Jvex87UzulnS0gP0Zvow5Me2XqWs4Uz3N58mObwDfw8CALk2aMwfHFNKtGrghBqUBqKKB9ZTVfE5o2u2N9AiMwqfIPnIdbXy66f/omVt61rpHbvIrV716Kf7/jMjy+9rhCEMhm6n3x8TW0TQhDr7SHW27PywoAcH0JePjtjdbJeaO9Gf+/H0O55dEny9JpCCEQkhujZvvZ162V1jy41bHDrypd9CqHYnDdwGfiKnAGlPDdDSxMRvqf8eJcjvfV5Ngf1CjLuLxkUqBS2q5/MFVt2IZrbkdmR9cneEgLt9vthmQoEr1pDCEHn4+8lsWMHbrGIEY8jfZ+mO24ndWA/oaYMk0eO8s5//i9UBwaJb+nDiETY8ZN/AyMaQQYBt/2Tf7jApzxwXbZ++lPEt25h8Ktf5+If/xl9n/g40Z5uxr7/ApX+AbZ++lOkD+yncPIUr/2dX6bryceJb99G4Dh4xRKZw7fTdMdh/HoNM5Ve1nJAD4fp+dhHSO3bS+nsOZxcDun5aJaJmUoR7ekmsWsnVjq96H3h9Pcz8cd/QlCtYrS0kHryScKLVJ7cyhC7Dyu7kKnrfD23ve8edZ/Nn4Rc7FJfKqDyeiCahEhy7ecjloKsBsw6Ft9bufotCFRIZ33eeErTlQJ9rXY6QkA4rrzY7RqzT7jMjzdU7ebc4zMWyWiYb7kzG051xgZrSgGvK2/1aZ9vp9EfWouINn0PnPl+6JErtg5aFKGIOqd2rRFIvTHPRum5yst+YgjyY0p9XikuP0EL6pynWxuVBu3q36GrtCSaDaFBLI1o7UHOItGpluY+R1fATUWia2hYuoUuDCqeCha19FDDl9whZ08wUh3EX4HAuVpEjRgJK0W1WmG4OoAbuKSsNKnQ4rNSi+HGi+PchBPYDFcHVSBdpHvdrD4CGTBpTzBYuUzRyW9o8On1hC50LN1iop6l7JYI6WEMzVhRiWcIk65YNwOVAdqi7UT0KJdKFzE1k+ZwC27gUvfr5J1JQnqI84Wzc5SVEokrPTVJIXR0oTNpT7AnvW9VlSFhI4wuNLK1cbpi3QQNa5jVwJc+JbcwbZnQ7fetar21oFSvULKrtCea0NfzIbqOeKn/LT5//OuMV3Lc1r6bHZk+You9HDQQNdfxYXiNka0rJfp8+IHP6fw5nhl4gUm7sMiaGweJ5HJxgOcGX6Q5nKEr1rH0slJyavIs3+l/jtHq2HXrjc4VLvL1S9/lM3t/hKgxV/3REmkjbibn3L8CQU+sj4gRxQ0cQloYQzNVUHhj0nNf5gBRIzY9gZey0uxNq791x3oJpI/VIN01NA4130nSTM/ZR6hh3ZEwU1ekIjY0g7TVxKQzwaXSBQSClnDbdBs3cf1gaRYJK4kvfSqeqpbJ1rM4vkN7pB1LD3G+cI4Je4LOWBdltzytRA9pIcYbNj+mbrElsW3RydnuWA8xI0q2nmW4OnRDk+iyMIG8cGJjw0SjcWjtRjS1Q6pJDaatsCrD1Q01mNFWCCW8Avh/8TtqsLYIRHsv4r73INI3yHezStXTrYrgwmnk2Dr7Hrd0oP/Qj6Dd8xgiHLn+BPrVwq6qapFk80IP4iBQRMCUX7AZViTJHAim7VmmrFXkzJ+mIaWyHVipTxCaum6niKFKARIVMIxF20e1uNDPeLnNJ9JoB+7Cv3QGalefKSN6tiP23QGhZSqNg4DMnXeQ2LUTIxLBiKn7UiJwiyWGv/M01YEBnHyB8tnzBHZ90VO4GJrvvov4tm0Y8RjJvXsIPA8nnyfS1Unx5CmyL72MPTGBHg4jfR97YoLiqdPEt28DKYlt3ULm9oOYyQRmcnUWHbplkT54G+mDt61q+dlwR8eY+L9/hD85SWjHdiIHDvzgkeix1MZtO7LI2FQI9UzU9DkTWLJaRHiOuteuJwwTEU4o+6o1QlgRZf80W3kcBMjAW/b+kb7XULDPI10jcZVxcQX9utB0CMeRZmiu4tutQ62CjKfnSgBNC2nM4wSWmfCVdm1mwhKhVOiarnzpp0l0e+m8icWU6FZkyQnKlbFwlCciSZWRUZpQFU7hRljtsudzledaSqRTV1Yt2UFkYULZiK0UFAoQiSNauqGlG5FsVkGhK7brCmFakJpnqeasMHk8DzcViS6EIGmmSVlpcnaW/spFxusjaOj40scNHMJ6lI5oF0PVgQ1rhyY0WsJtTNTHmXQm0IVGysr8wNo43EpwApuhSj8aGu3Rrqv+Tn3pM14bZbBymapXXkShfetAFzqd0S4malmeGfouYT3MXa33TFsLLAUhBHvS+3BzR3l59EX8ICBuxjnYdDtRI0pbpJ1sfZyj2SPoDd/ipJWatmXJ1ScYr42xL3MbCTOBL30uls5zrniWven9K7Y7rIfZm9nHkewbHM8dpSPayZ2td6/qmKWU+OtgC7Mcvnj8u/zJm9/g93/s39ISW/1E3bXEsxdeYbJW5MldD/Ozd/0IqXB8WfsJ7SYe0BbsAmW3QiCDOQrqsVqWpwe+z/AGhXOuBE/6HM2eYFtyC5lQesmg0eHqKM8NvsjFYj/BdZzQC2TA62NvcbB5Hw903j2nX4ybiTkKblD9hKWH6Ix2L7nN9uhchfdU8DiwYGJMCEF3rBdQ97EkoO7XGauNkDCTJMwrC9sK6WF2pnbz9uRRxuojlNwicTNBS7iNzoYF3WbF2vWBJjRiRoyIHqHkFrEDm/HaGG7g0h7tQBcGbuCRq0/gBz4Vt0yAJGEmMTSDklNCSokuNBLW4kSGpVuNa05QuMaTaWvG4HkYG1gfpedshCLQs1OFr7V1QzShvF+tMMKwNiTYbwFy4zC5RB5APK3KgzvXf9J7E2uE5yGHLkJ+HSuiIlH0R96PdtfDtwaBDkq5NzGoRhCJppl7yPeQpZwK7psiqFJtC9SFwjAVaaQ2psiTUHSuyjbwVaBofmR1hHc4plSpjq32PTGoSOVYemabvossTUBxFYFxsyEE2gPvJTj+KvL0savroxJptEc/iOjsW9GezUwmFlgUVfv7Ofnffp1Qawsd73k3TqFA8cRJ5PxQyGVgZTIIXdlVIgQIkL6PDCR+vU5i9y56PvxBzIR6rmz9sR8ltWemekSPRtAjP9iTbT8IEFZIEbaz7z+nhhy9CFM+5NcLRqOC7Er6U01TFWezhecyWLkCzneVEn0eVCDpFZLKQqjjCEUWktXVggqQnbO8NlM1N9WHTVXqzA8X9T1FFk8dl0B5eU+R6EzOHJdrq+Vmvw9J2VDoz1JDG6ZSZl8hpF1Tffrcg2q0QYWcyqlQ6aXQ3LvqyRPpOQSvfUtNnM4OWF0KQlNe553bVFhvLKUmSDZYNCg0HUKRuaPgwFtZJT8LNxWJDmpQvDWxg7ARJVcfp+gU0IRGWI/SHu2kLdxJyS0yWhve0HYkrTQxI86kM4GlhWgJta2JHL0FXuluWTiBTX/lIprQaIt0XrHnrhu4jFYHGaz2Yy/h7X0rQQhBS7iV+9ofwAkcBIKEpTIL3tvz+DSZBdCX2EpHtItY429RI8qh5sPUvCoSiaWFiJoqTT5uxjjUfDu11G4kkrAe5oA8SFgP40mP8fo4mhDszezDEAYSSd7JU3ZLSCTv6/2haU90gB3JXWyJbyPa2LcmNA40HWRrYvv0vleLQCp/wY3E2exlXrl0DGcjArfWCf2FYfzA50cPfoDdLVuvd3M2FL4MyNYmcAN3Wl1c8+ocGX+btydOXldiuubVeGH4VXant7MzvW3Rz18fO8qR8eN48vqXiNq+wzcvP82drQeJXMfqBDdwuFA6x+XSBVJWmp2pPVecjaGh0R7pJGJEGaoMMFC5zFhthJyd5VLpPK2RdvakVJXMDR86eQsiZsZIWAnKThnbt8nWx9GERjqUwZc+MTNKwSlQdAuU3TJxIz5dKeHLoFFJtrQ9GahnigAk18gi5QogaxXk4HkoTq7fRqNxxO7DiP33QKYNIjFFml+H61x778eRuTGolKBaRmaHYXTg2tnWbGJVkPkscmx4aVXeFUAcuFsR6NHrEJq2EdANRVZXizB+CZkfmfE892xF2kwR6LE0JDILny26CZGEUgR6jvIYduvIcFx95nvKhsCpKSInnlbbXYZMF7qJTLaAXZmxXBi9gLTCinCTviKKXBt0S1lB1KuzVJrLQ6Sb0T/2E3i/+e+gcIWTLOEo2mMfQrv7XYjlVOjTO51bFSOlpDo8TOnsWbb/jR+n6c47yB9/e2EWgECRUFKtM/+qW8p6RegaViaDVy7TfPddxPp657TFK5cbmxe3xKW8iRUQTqjJqdn3nWxMejk1ZKIZkWpV3t7XGoZ5dWr4BZWdcuXJMd9bvA8KRdecVzQHhrXoschaeSE/JwTCDCuieWq8LwPVP0bic9efb1ulGY2+WiDmEbbSUYp1MduSUgaK2J59XszwKlTiyyDwF52ImIbvruxNvhaxYOBDdhWVZboObX2Irp3K0s6KKKL+WnV0QqjJDU2bNemhsRaG9pqT6NsSO9kS3z6nTDpppTnUdLeqZBEzTdoS30FvbOucZZWSKMG2eJS+2LaGslcReJrQ0YSyfLnHeghtkVLs2zK3I6Vc9LMp7M0cJJBy2VJuDQ1d0xEI4maSmBlfctlN3BwQjf8kKgDuYuksIGiPdKAJfZWEh0RKsP06A5VLjNaG8GeRVZrQ6YtvXfb6u5lhaMY0cT4b6XlWR2E9THgWSSWEIGJEiBgLiTQhtDmK0tnwA5+QZlFxq+TtSeJmnEl7kuHKIFuT2zA0g4wxd9+L7SOkh6+INJNIfDaWjCzbNfwNJuqvFhJACLaml1YJ30oYro5h+w4hPYSUkpHqKN/uf3Y6YPZ6YqA0yJvjx+mIthG3ZiaPAhlwJn+BZwdeoO7by2zh2mKwPML3hl/m8d5HrxupbGoW25M76YtvRRcGhmZccVuEEBjCIGM1kTRTbEvsZMIep798SRHp5Qvk7Cz3tj5I0tq4cuFNLI6YESNhJpm0J8nbeYpOkZZIC6ZmoklVZVhyi4xUR6i4ZdJWanrCNWpEEGgEMqDm1Ygv8t4npVT5Hsgr8tS/ZhgfRA5fXNvgaCmYIdi2D+2e90J7H5gWQptlH3EdIB54AhH409YV8sgLBF/434rEu1EhJcGZY3D6LcTeO2Drnisqnb+ZIEeHkOND67fB1k70u94FbV23BoEOSjHZ3KVI8NyQIkTsSuPFq0G2CE0R3809CDO88NgbAW14rlIleg2Fp11l5j6ViuzOtKttZfuXV6QLgYjEka19ijSplxWB5Dlz929FVfuDANyBVZPoCIHYsR/j5/4J3h/8V8gOr94fWtOhtQP9fR9Du+89ylbqCq8HIxxGSqj0D2A1NdP/xS9TOnduTvemGQbRri6cfJ7xF14ksWM7RjyOlVr+GS+EoP2xRzj5a/+NgS9+ma4P/hDS8ymeOk3XGrMINnFjQAYBDF9EvvYd6N6Bdvd7Vr2uMExItiDtylzLkEbuAHYNWRhDxtKIZGtD5ayIvw1/f9YNNSl+pVi0ecuQ6FI2CN55/YVuLPQoX2tTNL1h0SLmtsFuTCIuCBdtENmuPdM2u7qARJ8zoQkN5X5DVGGG1DamjseuqX/POqcy8FXo6WyEIgsV72tBKAodO658fbjq860g1LUajinVee9uNbGqGxuuOl8UsjGJM7uiaCVF/jxccxLdWMQeY4r4XriswWJNFEKgC2PJfBUhdKwlEthXE0K2mmVqfpWKW1YPwEjHpqLsFkDCStMa6SBbGyUgwJMe50unEQLaIp3oS15xCir4UlLxylwsnSNnZ+d8HtbDbInvoCXcfstaulxraEKjO95Lxavw/ZHv4QYuET3K1uR2diZ3YYiN7eICGSxJcEspKdbLOL5LPBQjMmvWu2xXcVaa+W1gslqYMxFzI6In2cFbIycZq0zQHE1f7+ZsOEaq49i+o1RKXo1nB15ivDZxvZsFQIDkpZHXuLPtILFGNYeUkon6JM8PvcxYLbvyRq4h3MDle4Mv83DX/Uta0Gw0hBCYwlrXkNIpMl0XOlFjCz2xPibqWd7KvU7JKXIq/w73tD2wbvv7gYZQhK1EKcWXs01TmTZJBir9jNZGsH2b3vhOdKGjIWiPtjM+MaZIdK9Kb7x3egK3LdqOpVt4gctwZYidqV0L3v2y9SwVt4omNDqiS2cTXFfIADkxArnRq9uOEKoM985HEIcfvmKP0o2AMOfeyzK0CLF4g0HaNeTRF5HPfx0tmkD07linwesNCilVhUD2Kq/DKQiBtvMAYvfBZUMXbw6ImfDOSFIpuzMxRToUs1AvKUJZ0xRJnchAJKnIiCWuc6HpyKYOZa9UyinS23cVAW9YihCKZZTKE5Ch6Ezw3JLN1JS/blcUKpNQziuLBClV26NJiDepf9crDUsDGlYMq7gfNQ2x+yDm3/t3+M98leDN70O5pMgs32t4u0t1DLoOVghiCbR9h9Ee/RCid7u6Fla494WuE2ptwYjH5/ohC0F8x3a2fOqTnPlfv4tm6PR89MN0PvE4RnSWsEfTyBw6SNcTj3Ps3/wHdMtk9y/9PN0ffD+h1hbMVHJaOatbFpGODjRL9VFNdxxm99/6RS587o/o/+wX0cNhWh96gK73P4kQGmYqpYjZG7z/2kQDMkDmx5Hnji94Dq0IISDRhPBdFcY4fxJrSgHtjCDzY0q1nmxBxNOKFNZWK/i7AohrGwAuZbB48KhhXX1bpvzndX0uSS8D1SfOr0i3QvP6QakmMuc2WFmnzG5zOD53f1YYao2QVLsRQDqbtA/8Bd+5sCJK0X7Fh6rN9Luz23+teChNV/1/qgXRvRPR1qfOg2i8pV+3fq1hnTN7EsUKr+lc33R2LjcCAhmQrY1Nh5tlQjdIONEmrgpRI0pffBsCGK+PEUifQPqcL55p+OC3L1udIJEUnBzni2eoeDNJ0gKNuJlgS3w76VDTgjLwiBGmK9ZOSLfwpY8f+Oq3DPADD08G03+/VUNJrxRCCKJGlMMtd3K45c5rvn9JQLCEEr3m2vybb/0WZ7OX+czdH+YTt8+oSv7v61/h5UvHVrWPFy8ewV3HUueNwLu338sL/W/ypZPfpTvRTjJ8a1fmjFbGcHxVsjdaHeelkdeuc4vmYrw2wdsTJ+mOdRA2lO3R6clzvDG2umvuWmOinuOt7Nvc33HX9W7KVUHOKsGcGshM/0bQGmljS3wbb08epegu75c99ZyQSDzpYmy+ri0JQxiE9BAT9SwFJ4/jO9MBsr70G8IL9ezWNZ1kw29/rDqCEzi0RzvU+RYazSEVZj1eG8P26ySt5LRtU9SIsjO1k2MTR3k7d4zmcAsJK4EhDAIZUPWqHJt4i6pXoS3STvsNSqLLWhWyI8uGY60IIaC5E/HQB9D23nVtfM5vdUyMwtjgDDF4i0P6nvJCr5ZXXng1SKYRO/ffOIGxVwNdR7T0QEvP3L+HY+rnSiBUtS2RhPpZafGWXmjpXXE5hFAK2lSb+lkKkTj07F1Dg5nxEG/txPjETyPf+1HkuXcILp2FiVFkpaTsD0IRRLoJsW0PYvdBtPaeNfVJoZZm7vj3/3bRz6xUip0//RPs/OmfWLadoZZm9v3y32HfL/+dOZ/N325q/z4e+N3fmllX02i9/z5a779vkZ1b7Pq5n1n1cWziBoCUYNdZlV3JIhBCQKYDzJAi0uvlxe2uZAC1EtRKyKwBiWZEsgUZmrLFWMdnsmioiK8l4SmlekbMh6atC/8rNA254HikIsHnWb0Iw5qrXJcSWa8hZpHRMvDVBOIsYd0cD3vdnEuiT1XtzEYQzA3gFBqYIYR+Fd/lIt+ZrFXUM2axqqX58D2k786Q/ZqxctinEEq9H00gmjoQXTsQ6darU9SvM2QQID1nrt98PA3zrbqWwY1zNDcwpFRUmfp3QNktka2P4kuf3mj3HAuaTdzciBoxemJbkcBEfaxBZntcKJ5FoNEcbl2USPcCj5yd5WLpLHV/ZmCqC510qIme2FaSZmrRGeItiR5+av+nqXhVam6dmlejOvXjqt/qb3Vs38YLPDzpq9+Bh9v47QUerpz6t/r8VibdpVTkkht46ELD1KxFfWpVaODGnIepyY3F4Pguf3X0O0xU8/RlOuaQ6N+/cITPH/kmIcNaMWTT8VyCG2BAbXsOg8XFFWPdyXYOd+7jyyefJmZGua/nEIlwHLNheTUfuqax5Sa2fpm081TcKm7g8fzQy1S9lcmokG6RMONEjAimbqChESCxPZuSW6bsVKafM+uB18aO8mDnPYT0EJP1At8begkncFZesQFLM4mbMaJmBFMzMTQdKSVO4FJ2qxTt4rr5qte8Om+MHePutsMY16Osbx1R9SqUvTJRPYqlW+jCQCAI8LF9m4KTR8KKVh9RI4YmBF7gMloboTPSjd74DgIZoGv6spO6P0iImTFaI60MVgY4MXkCXwY0hZoa16tDe6SDlshMwHXcShDSw4zVxtCETtrKNDxnBTEzqu4ZO0dYDxMz4nOeK7c338FYdYyh6hDPDH2XHckdxMw4tm9zuXSJS+WLxIwY97Tdu+GVUFeM0qQanF8Nkk2KQN9/z/q0aRPIiVHk+PUJpr4uqJSQ+fWr4BItnYgtu1ZecBM3JzQN0dSKaHoU7Z5Hr3drNrGJpeFevWWiiGcQ4RgyP6rCg5360tkRvgf5UWQxC4kmSDRDJL6OmSTi2hLoU1jMbm6NvtVLY4ljWnSfoqFS1maFizYCRKfGLK6tJvRmbz8Um2mpbsxkWUzBriprHt2YCRWdvQ0z1KhGW+dzX8rCVKZFg18Quq5U2LPPie8jyzlVueS70/kcMtG8tC+/piPat0JrD6KtDxFL3rBVNCKWgu0HZ/4/0aTsb1aJG/QN/8aCEzhM2hO4gYMvPSbtCSpehWTD/mOt0IVOa6SZPZmdG9Da5RFIn4naJDk7f1XbMYRBZ6yNqHnjpYUv5nm9FsTMOL2xrQBkG4p0J7C5VDqHAJrmEekqnGyU/vJFnGDmwWkIk5ZwG12xXmJmfMnyclM3aYk00ULTsu2SUuJLn7pvU/fq1Dybul+n5qmfulen5s/8u+7bOL6LE7i4vosbuJwtXMD2V0+k3aiQUirf2toQdb+GqVk0hVpoCjVjzrOMCqTPYLV/Q9pR9SpzvvPZCBsWf+vhT3Npcpgf2vvQgs/jVoRPHH6CpsjynonfPPk8J8curkdzrwqj5Sz/8fnfXfSzKZKp6tb5rVf/hL868RTdyXaiRnjRiaNEKMavPvkPrrgtU/eCJrQFEydTfsU1r4qlW0SN2BWHAy8FXwaMVrPErRivjx1ddtmklaAv0c2WZC89sU6awxmiZhRD6HjSp2CXGK6McL54idP582TXyRamvzTAUGWUlJXkTP48pyfPrbiOCgOO05fopifeSVesk9ZIEzEzRki3CGRA2a0wUh3nQuES5woXGaqM4K7W43QJ+NJnsDxEtp6jI9q6hjWnJpdujBc0iSRnZzmae5OM1UzcShDW1D3gBi5Fp8Bobbhh7bUw+HU2UlaalJVmop7lVP4EJadISA8RyAAv8OiO9c7JmpANJXTJLarAYxkwYWfxpEcgA0aqQ5ScIprQMDSDmBGfznJxfIeSW8QJ7Ont1/0aUgYU3SL95UvT91pID5M0k9PWfIEMqHhlym65sV+fnD3RsKCSDFeHKbpFNNR+42aCqHGFSsolENEj7EjuougUGa4O88roSwQyaGToxHi485E5JHrCTBAzYkzUs/TG+zA1c7qfsvQQ6VCanD1BJqSu/dlIWkke7nqEN8ffYKw2yvdHnscLPHShEzEidEQ7OZC5jZ5Y7w1jbbIAlSJMXoWtk2kh7nwUbd/d69emH3BI14XxISjcGLZg1wTl4vqR6EIgWtoRHT0rL7uJTSwCv1Kh8tpruCOjRA/eRnj3bvxikerRY/iTk2iRMNa2bYS2bkULhUBKnIEBaidO4pdK6LEYoe3bCG3bhlhEzVh46tv4uRzCMgnv3kPkwP7l21MuU3n5FdxsFs0wiNx+iPDO1fEGfrGIMzyMNzKKXyoR2DYEAcI00aJRjHQao60Ns70NLbySGlVMv2L5xSL25cu4g0ME1SrS99DCYfRMhlBfH2ZHx6LH/gMFKeeqia8GhoVo7lGWSOUcslJUNiDzFcxTCHwojCMreUi0KAuNcHyzUmwxiLXltghrYbiodG3EFOnq2nO/FzPUsJ5pVKNqOphhpNCmiXppV1V2i26oSlanUcEwexvL2WldDZwaFMen2yytCERTyj6m0WZZLysCXTdVBZTvqYBrgHT7ospyYViIO96jLHBuYAjdgJZuRMuVC/o2SfRVwA0cxusj5O1JQDYIu2Z64lsIadaaB0qWbnG45SDbk1s2psHLoObXeXbgRb4//MpVbSdqRnhv7yPsSF37Y1gJrZGrL+WcTaSP10aRBNT8KpfLF4AZIr3qVRipDjJaG8KdFSpoaSE6ol20R7oWDcS8Ekx57MY1g7i5MgkhpcQLfOzAxvZsbN/hP7/5W4zVxtelPdcTEsnF8nmGqwPTf8vbOfTUPlrCc0k4T3qcyB+/1k0kbIb4u49+Zlo1Oh9tiWb+/mM/yc7WvmW3U6iXOD8xsOwy1wIVt86rg8vbgWhCEDIsCvUShXppyeVWmjhYCTW/xvnCOUzNZFty+3Smhhd4DFeHOJ0/TdEpENbDbEtuZ1ty+7Qlw3rhldE3ODF5ioK9uC2HqRnsSm/nztZDHGzZR3u0ddFKCRJwsGUvJbfCsfF3eGbwBU5Onrnq9vky4Gj2HXrj3Xxv6MUVVeO60DnQvJvDLbexv2kPbdGWJScfdmd2cH/HnZzJX+D7Q6/wVvY4ZffqAvtKboWz+fNrItHrjQqA8Kw+dmaysUrcXBhyvJEQNDy3zRQFN894o2JNooLKLS1EU7iF7mgvnbHlX9xMzWRv6gBnxCkKziRnCientxPWIzTP6+d8GTBaG+F04R1lBSZ9/MCbrm44kT+OJvTG+mG2Jnaww1SqzYpX5nThBJNObpadmJoYGauNMFHPogsNTei0htvYm7mNxCwSfbgyyLnSGQI5ta5PIANc4ET+mPIbF3qD7N7NlsTyEwhrPu9CeZk/0PEQw9UhSk4JX/roQiduxmiLts9ZPm4m2JfZT1ukjbZI+5y+IaJH2J85QFOoiUyoiZSVnrOuJjTaIx080vUYw5VB8k4BN3DQNYOEmaAj2knKSi1+r98IkAGyWoby8nZCy2L7AcShh65aXRQcfQlGB8AKIR58EmGYSKcOQ5dU2GS1rAiJcASa2xHd2xFXERC4XlA2JFnkyIAiveu1hlIthMi0QucWRKZl+W24trKjKOSgmEdOjiPfeX3aT1WeOQqOjTQW6YOTGcSug4imZawzbgLIShmKk+uzsVAE0daFiKzvBN0mfnDg5/OM//bvUH7ue7T+/M/R8lM/ycQf/CH5r30dd2QEPR4nesdhmv/GZ4jffz/1U6eZ+NznKD39DG4uh5FOE73rTpp//MdIvOtdStk5C2P//X9QPXIEPZmk9Wc/uyKJ7mUnGP31/0n1tdfQ4nE6/8k/XpFE94slqkePUn7xRWpvv41z4SJeLqcI7yBAC4XQk0mMtlZCfVuIP/wuUk88jtG89LhZGMq/vvb2OxS+9U0qr7xG/cwZ/GIR6bnosThGezuR/ftJvvsxEo8+gtG0vCjsRoEcPL+47/bVwHdhcp1yHkBNEIZjKmAy0QK1IrJWVrYgdnVx5XQjQFjaFWjuQsTSM4rpmwmLPeuDgGUDSVcLuYRt2lLvblZk7jmc8qefJtHn2bNEYnPbLwSYlvqZmmSxq43jUduT8z3wN5pE9xyVt4GESl5dN+n2GTsbu6KOM92GCMWU/UlhDOwK0qkhFrMFa7wL/SDgliHR667Nnx39BucmlOJ0T+s2ntzz0LqE3IX0MF3RXppDrcgGiR43E4T1yHRQyFqgC41MOEUmfHVE0pWg4lZIWit74a0EQ+i0RVvYklyFX95NipgZpy++DSkl43VVYlvxyvRXLgKKKB+uDTJRH58mHADCepSeWB8t4fZFA3OvFYQQmLqBqc+Q7uY6K3KvJybtuQqmslem4pYWkOjXE7O9eOcjHUlgLBGAPBsxK7ooCX+t0RFv5h8+/Nl12VboKu+LbG2cI9k3iZkxuuPdWLqFlJKCk+fl0ZcYLA8ghIaUASO1EUJ6iL7ElnW1vziafRtY/FUuakS4p/0wj/U8xJZEz6qU8AkzxgOdd9McyfCl89/k2MSJq27j8YmTtEaaOZO/sOxypmbySPcDPNbzIL3xrhUJQIEgpIfY37Sb1kgTUSPMC8OvUfGunEivuFXO5i/yYOe9K1ocTaHmq/2F501U+tKj4ExecxIdBCkrw8Gmw6pKxbenSXRN6IT0EHEjQdJa3NprPtoiHYSNyLTPt0RiCB1LDy1K7qZDGbYnd6+4XV1opK0ZFXtYD9Md611AzC+GqB6d9huf2m8m1MyOVRyPLnRS1sa896h2ZMjMUucvhZAeYm9m36KfWbrFjtROdqSWJiuU7UuMnemVz/WNBum6UJpcWsm2EmJJtLveDeGrFwfI155FHvk+mCH0ww8iPRf50neQZ46pwMlqGZCKPGhqQ2zZjXb/49C74/qo/KVElgvIt15Enn5LkejFXMP/FjVwzLQgurYhDt6L2HcXIrxEZeTgRYKn/hw5OQ6lPFRKc8gc+c7rilRfDL070DOtcJOT6Ng1NaGzHojFoX1Thb6J9YF97jz5L3+F7P/5A4JqFaTEq9cpfue7itTUNCb/4i/Jf+1rSNtRn4+PU/z2dxBAqKeX0I7t17TN7ugok1/4Ivkvfon6mTNIe2GVbFCtElSruCMj1I4eQ4vHSDy6vDWOsELUT7xD7k+PUXr6GXU+oGFtEeAXCviFAvbp01TffAM/nyfzwx9HT117jmOtCF75lqrMWk9IqQJ21xtCQ4QiylYk3qQI2FoZWS1CtTDHi3u6HdWiUjhrBiJ649pqLAohELqxcIwlg3Xi0IPFJyCWGJdPK9GnMNu/PAjAs+fY7YhwfCEhb1jK0mVqPcdWky4y3KhgmBcqaoaVYnojIDRItkAoBkjlvV8tKXX8FInuezPBoJqGMEPIWApyVaW8X0W2xq2MW4ZR04RGSyzDUGGM5y68zoXcAA9suX1dSHRTM1c1uNzErYeoEaMvsQ1JQLY+BkDFLXOpfB6toUQPZik8o0acrYkdZKwm9FuIsL4RYQqTOjMPHA2llLwZ8PD2OznUtYtEaGXVVDwUQb8BVI2ZSIpP3fb+690MAHJ2jopXpjveM51J4QYuMGp0HQABAABJREFUF4oXGKkO0xvvY09mLyPVYU7nT3GmcIb2aAfRdaoKgaXf4cJ6iAc67+bJvnfTFm1ZkyJVCMHO1DY+tO0Jql6Vc4VLV9XG0eoY37j03WXtVgxh8L7eR3hiy2M0hdJrIqc0odEWaeV9fY9SdMq8NvZWw8Zj7XADl7HaOGW3vOJE71TGgZz3/1P/N0U4X2tMVQtlGirm9djelK3LStCERlOomaYrCDqPGFH64lvX3sDGflsjbbRGbnJC7wcFTk0Rv1cIsfswtPcg1rM83LWRAxeQrz+LPP4q1KtgGKpU2HUVwVwpIYcuEowNoX3sp6Dn2hJUSIks5Ai+9WfIY6/M2K5MqcukhFoVapeQw/3Iy2cQY0NoDz2JiC9CJtWrysok8CGWUJMSpfxMyGYyA9EEaAv7Y9HaBaEl/EhvJjh1qFXWZVMiHL2mynwpg4Zyz4FIcqakf/5ytSLYNaUENX8w1Hm3AqrHjlE/d47UB95P7J57cC5fJv+FL+IMDFB+6WWCUhn70iVS738/8fvvxxsfJ/uHn8MbHaV67DiVI0euKYnuTUyQ+7PPk/0/f4A3psaqejJJ5Pbbiezdg97UhDB0/EIRp3+A+okTeJOTRA8fxmhZ/p3BHR8j9/m/wB0cRG9uJv2xjxI5cAA9FsOvVqgdf4fSs8/iDg7iXLrM+O/9PuED+4ndddcCNf4Nh+ywmlS+mTAV5GukFImZyEC9gixOQDm3UF1dKyMLY2CFZ8jRmwBCCORiKmzfW5z8XisCf0YFPrNTWErkNUUmC6HOsZRIt66iRn0X6c4TJkTiC8dTujn3OSADcGrISHxG2T69rKHeLTbKiscMQSzTCAkFqenq3cubNfk2FSY6ZXsjhDoGTVvan/8HCLcMy2fqBu/b9QC3te9islZkuHTzW1bcalB+qRKBGnjfsH6h8xDVY/TFtxNISc4eRyKpegtf/BNmim2JXSRv5FLuGwxvDLzDuWw/D2+/i47k0uXPNbfO0aHTBDLgUNceomaYvsQ2zhRO4gR2I2egbRXElSCkhdatQsAPPGp+HbnGQMgfPvQ+AhmQWsUs7oNbD1P3HGJLhXj8AKLmVXEDl5Zw67QHvu3XOZ0/RcyIc1vzQXamdtEabiVbG2eoMojj2+tKoi8GTWgcatnP472PrplAn4Ku6exIbeV9fY8ydvIvKblXrtTzZcBEffkBwiM99/N436NrJtCnoKw0Wnmo614GKsMMloevtLmUnDJj1eyKJLrj2+TsLHYjxLnmzT1HgQxImDe+CmoTm7jmcB3lq3olCEcRe+5QZc3rjOBrfwTjg4j2XsSdj0B7lxqs1avI8+8gX38OipPIs8cIvvmn6J/+2xCNr3s7loL0PIKnPo985Wk10I0nEfvvRuw5DMm0WqhUQJ58E/nm8zA2iHz+a0hNQzz64YXlzX270H/0l6YJD1kpIV/4FvKtFwAQd7wLcfuDCGuRdxUrDOmrty283pC2rSYe1gPhCCJzDS0kpAS7Bk5VfR8s8W5RLSjFvWHeVATWopBSTRpI2TjmWxfe2Bix++6j4+/+XcyuTvzJPAQBY7/5WwTFIpXXXiP5xBO0/cIvEN61E79QxC8UGP/t3+H/z95/h8lxpefd8O9U6BxnenIe5ECASATTcpnJDdzEzZJWOTnIsmw5+7U/S7ZlW/Z7yVGruCtL+0raHLi75JJckiAJkiAJEIHIaTA5d+6udL4/qqdnGpN6EgIXNzkXZqqrTp2ueM793M/9mENDGBcvIh1ndYON88AxDDKvvc7oX5QIdCEI7ttL7ec/j/+27Wg1NQifW5vFMQycdAZrfAx7Mol3/TqUuZ4xFcdiBGt4hODu3SR++ZcI7rodtbYWoWlgWVgPj+HfvpWRP/oTjEuXMC5fJvPSAfybN6NGrnU24DIgFIglYK5g53LgODA5CtkVWKZVC0VxA3geP/hCEIi4RcNnEqFI19c6Ulfh0X3jY4qwVStV9pYJjjWD4F0GHNtt52oyXluAtBYC4Q2UPM3tStLbMiuLyWoe9+fqJjQNdC8uE1Z69xdyiIh02zNnkOiad842VgfCLVgqZhRXFUopu2RanjSnXGxqm9UIZNzkeM+Q6EIIPKpOwOPDu2YX3S2sBEdGjvJ0z3M0Bhp4tP1BWkLNS9p+sjjJ/z311wT1IPc238XG2LUpzCqEIKiF6Ay7qoLx4uwATa23jo5wN0EtfNMEB24EPHf2Nb76ztO0xhoXJNELpsFfH/k+l8b7+f2P/DbdNa00+puJ6lFMx0Qp+fx6FvG99ig6baF2Gv3LLyQxE0lzknPJ02VbiWpRG4xVve7dXbvY1bqFUBWq9euF//fVL9MSqefjWx5GX8S/TUrJWH6CH507SLKYYV1NG/uatxPzVz/YLtpFLMcipIdQheoWUSyMMVoYoTPcSXuoo2zvENCDDBdGFvUEXw20hZp5f8vdNAbrVxRI0xWNLfGN7KnfyQt9r6xiDyuxJb6B+1vuoca3PAJ9CopQ2FKzgU2xdQznRivqQywFbtHSYdbHFvbM1hWduKeGpDGJg0NQqyTTNEXHq67OJN92HE6OnePI0EkuJHsZzU2QtwogXaulukCc7mgbOxs2s7GmC+0GsF5aazhScinZy5Ghk5wev8hQZoyslUdKSUD3kfDH6Yw2c1vdJjYn1uHTPPMW1l4pbMdmMDvKW4PHOTN+iYHsKOmiG1QJ6H5i3jDr4x1sr9vAppou/Pp7m/xZFJa5fPVvSzfE6xFzqKNXjL6LiHVbUT75q651i6ek+HJs5PptyKYOnO/+BaQnkedO4Lx9AOXea5cZJU+8gXzzJXfSHKtFefDjiF33QjDiqi2FQFoWYv12ZOs6nG9/CZLjOIdegIYWlB13VbQnAiEIzBjDpiaQM0gcEUsg2rrd9P1q+iclua/9NU4mQ/jnf3ne9czz58j+7VeI/tN/tSKCL/XH/wc1kcD/2AdRAssYm9h2SfW2Sn7EHi+EY6vT1mpC9YAoriEZcu3gqu/doLzwNF7n3qwxpCT6yMPoLc0IRUGtrcG/cyd6fR3m4BDStok+/ije9esQmoYajRC8cz8jf/wnLrE8PoGTzaKG197uwBodZeIb38AachXowb17aPiNv09w/36XIJ8xtlMCAYjF8LS2uHYW1Yz7HAe9uYnEL/48kYcerCTddR29sZHYBz9I4dRpxi5fBinJvn2Ymmz25iDRdQ9ixz2I9TtWpz3TQL75HPLdldWeWxKEcO1GYg2uBcrQxUqlsG25RSIDYcRNkrHtqp411/6kOHPMIt3aKf7Isuct0jbBnsPSbp6MojI8/hLRXJpP2lbpx6wMXHiDLvl/df+EUio4qk2/+4rZkh2MWamM1z0u0b0WUFU3K3HKF7401sIy3N8tAxBuf+RVZHr5z1tc13Uj0ZOFNL/77B/SVdvG3pZtfPnNb3Fq5CIBj4+/c9fneHD9fjyqjiMdTo9c4s8OfYOjA6cBwe6WLXx+14fY1rB+SV7BEsl4NsmfvPF1Dlx8k6xRYH2inc/sfJz3de3Bo+rXkACduiB/ci7CjJmhN9OHAIpzPbwWga7oXEhewqN6aA+3XjMSHaaI9DCd4XUoQjBaGGHqHGpCoynQeotAXwYyxRwjmQlMe+GJVMDjI28UOdp/mmwxN11kdYbncTXH3i1uFySor46CzXQM1+96DfnZgMdH4AZX/bzdf4Lz4z08tv5eFKtIziwQ0P34NC+aolacm9HcBL/5/f/A2THXqkRXNO7p2M2v7fssXfHWqs7jNCHn3oO2tDmXPItH8dAabCsXCtQUHUWo2FOqhTWEX/Nze91tbKnZsOJMFCEEMW+E3fW3cWL8JCP55dsvzIewHuLBtvtorcIDvRp4VA876rZxfPw0w7nlZYJlzRxD2RGklAteB66/uB+/ZgCScIX3ubvdSp7FjpQUrCIH+w7zjdPPcDHZS8EyMB0L27HLVjFKqeaBpmh4VQ/r4+18avMH2N+8o+rxxEQhyd+e/CHfP/9ixfLHuu/lF3Y8iU9bmXpRSskfv/O3PHvxYPm9qwjBHc07+K19P1+16EBKSdE2ODx0km+deZaTY+fJmnlM28R2bJzSMREIVEVBVzQ8qofmUB0fXv8gj3bdS1D3VX1eLMfiuUuv8b/f/kp52b6m7fzSzk/TEKzFkQ5X0oP8zbtPcbD/CBkjh2lbWKWiplPfUxEKL115E5/moSPSwkc3PsTdLbsIe4JLvkaklJiOhSMd9x2kaDeE1daSYJluQbJlQLRtAH+INRm3erwoj38W0dhWOfFUVEQwAnvuQ1w8hXzjede38+hryH33V00yrwTStpEvfd8NPni8iO13IO5+FHHVe1loGjJag9h7H2KoB/nK0zDUizz+JrJrC2ItSV4psUeGcZILqx+lUcQeGV6xGtEe6Ack2MtTo0nbmvaSXw3o3mtyLSwZkQSEa6+JInlNIUtKyUL2Pa9Cn4J/29byfSKEQIvH0BIJzMEh9IYGPC2trrIUQFXRampL5JiFU8jjFAprTqJLx8Ho7SX90gG3G7EYsSeeIHTXXQh9AQJOiCVZrYTvu4/A7t3zqtbVeNxVnsdi2BMTmP39SGOZdTeuNRQFEa9DRFcnu0eaBoRiq9LWklDy6pfBGIRrYfKq4qZGwSVFbyahh6a7BTqLVwX+syn3Oy7nuSql++4p5Gd9JAKLZCP4AiBUoMRZOLZ7vi3DLSw6BX9w/uOse12V+RSJXsiViope9T7USkVI1wLegDsOHO9DBmIl//ykGxBwHORwyUrUcudYWGbJWke6AQPHmtc7XpasbhClufoa82JO32Wsb/4F+s/+BiJ0bX3/rxuJ7kjJUHqMUyMXOTZwhg2JDu7r3svFiV4aIwn0kpH+ubEr/OrX/y31oRo+u/MD2FLyozOv8q9/+D/4Fw/9Cvtat6NUeRMVzCL/5Kn/Sk9ygCdve4SwN8iBC2/x75/7Iv/g3p/hw1vfjyauzSGxpY2UElXR1kyh9V6DIlQCup+smWN0DUilxTClSO8Kb0BXvAznB7ClhSUtBvP9hPQwuuK5RaSvATRFQ1UU0sUs1oy0rqUea0Woq1qkUwhlxcUqpZTuzyIezjeyBZLt2JwcOc//8/z/4GDPYUzbIhGI87GtD/PJbY9SH6wt9/1vjn2fkyMXaIk0sK1+A2fHLvL8hddoiTTwy3s/RUBffCIc0IN4VA9jhTEaA03krCxnk2eIeKJ0RLrK+7KkhSNtlyRe42PXGmrirsY9VRURrQaKUGgLt7AlvomR/MFVaXMKAsG+htvZGOtaNRJQINgcX0/CV7NsEr1oF5koTmJLB22R+0oIUbZsWc37wrQtzk/28BfHvsXr/e+Qtwrz3pmOlDjSwnQs8laBtwZPcHLsPPe27uHndnyC1nDjosr0qDdMa6SBvFUgbUxPFJ6+8DKf2fIBvOrK3ilDuTHeHDjOlfS0zU7MG2FdrB3PIlkjU7AcmyupAf721A94/tJrpI3svM8ricRybCzHJm8VSRbTnJ3o4ceXX+Pv7/0ZumPVFdqVQN4qMJybLiB9fPQsaTNLrRPjYN9h/usbf85ofqJMml8N9/xM9aXAZCHF2YlLPNJ5Dz+17Qmaw0vLGOnLDvKXJ7/JcH6Uen+CJ7ofZlvtTVZc1DSXZ6Hh9SPqW2fbkqwWEo2wbtu8z2mhe1D2vh/78Muuxcv4CPRehHVb16Y/M9FzFjnYA0iIxFF23zevNYcQAhkMo+x9APvNF6FYQPachd4LsGX32ve1BGnbyEwG6Tgo4TAIgTM5iRKJEv2tfzprfSeTBttGOhJZLCCEQIQjCH/pfWyaOKkk0jIRXl9J5biC5+6U8m41oKgIf2Bt3vFTY7OyIo/KFPi5Nil77c4QS0kWPFxSSveYCoFQ1Ln3qSizSQkpXYX4zGJ786171fea3nbGs1wIt4BhOWV/RvvFgkuqaB7k1YWJFW16G3cH7v9z9k11D8UcfSsfO1VFIGZvryiIazCeA9AaGir+FroH4S0JNBK1KH5/+d0shEBoKorHg2NZSMsCa+09g6VhkDtypFxE1Ld5M4Fdu6bJ/dWApuG/7Tb0q47HTAghUCIRlGAQe2ICO5NB2muf/bkqEAr4VjHTV4jrV7OipN4WvuDsEZpjr7mYaNWh6Qh/GJkcqex7dhJsE6lqyxJDUMzNKuKJokBwYRJd6D6kpk/brkxZsFhGhb2J8AXnJ/h1r0uOT3Hmtum+C2eS6MIt4inWqrZeIOYeg1waCv3uMkWBUNy1BcqMu8cnEHWvm4kBNzAkgcyEmyEwn62fYyN7TiE8XmS0rhQ00BFzKfNXC6o2u4jrNcB1tXPJmwUmcil+630/ywPr98/63HZs/uKtb6MpKr//4d+mq8atvL6//Tb+zTP/i++ffIn1te1VWyM8e/Y13u57lz/+1L9jb+s2AO5ou43/8Pwf8dy519jTuo222NqnqEkpGS+OUrALtAY71nx/7xUIwKf6SBXT5K3ZEcS54EgHa5nWApX7dgeHAoFH9dIW6kATKoP5fkzHYLQwRNxTQ42vrqppxS2yvXpIKRnOjDKZT+PRVpYtogplVQNlilDKhS2XCsu2GEyPMZqdYDKXxrDNBYn096/bh2+totKrgIH0CGO5SeoCNWiqStbI8+XD38S0TX5h95NEfK76/82+43hUnX9+369wV9vtHB44yX986YscGTjJhfFetjdsWHRfdb46wnqE4+NHsaVNb+YKtrRpC7VR65tWk2SMNEW7QEALoLB2L1if6mVjbB3NodV9f9R4Y6yPdXJo+LBrIbJa7fribE9sIeaLrmoQN6D56Qi3cj55cVnZRhLImDlSRpoaX2zR9YUQSClxpOPeOzNvH8GSA1xF2+CtgeP86dGvcXL0/Ky7URUKXtWDWiI5TNuquG8lkqyZ50eXXmU4N86v7fosWxPrFyTSFaGwPtbB5tpuDg0cKy8fzo1xeOgkD3XcNe+21eCtgRMMZccqltUHatjfvLOqZ6npWJwcPceXjn6T1/rfmfWMUoTAq3pRFQUQWI57TGYS25Zj8dbgcf7Ngf/OP9j7BfY0bUdfxuSgPz3MZCHFG9kx/uNrf8RkYdrbWyDQVQ1d0VGEwJY2RcvAntEPCWTNPN859xwSyc/d9nEagomq3ynP9byCR9X5N/v/oTtPXsQ+7IaEXCZ5GasD/9LV+9VCNHcuOsESHRtKk88cFHPIkT7ENSDRZc/ZaZVZMIJoX7dgX4VQkJE4NHfCxVMwPoIcG0asxMN1Kf21bayLF8h+9f9DBEOEPvfTCJ+PzJ//EcU330DmstR/++mKbbLf/CrmuycQgSDW5YtgGPgeeJjQz/w82DaF118l+zdfQeayaN3rsHouo7a1L7+TjuNauqwGFMUN7qz2sZXSJXXzKcilplPdNa9Ltsy3u8yES/JIp6T8VCDW4BIS8+7LgZFLoPuQ8SbIp11loGW4Dy5Ng2i9axNQ7l7JkzeXdAmREgnv9i+G9Ifmtm6Q0iXB8ykoZErPAwkorvIzEHH7KlSX4MlMuN7vU/dAIQNDVwXiappd8mWqfSndbXIpt2/Sdtv3eCEQQ/qCCOYgw7MTbqHHeKOrfMxOuCpU23ZJkkAYwgmXNFljqKFQ5SlWRJkYUwKBuYnqqe8zdQzWGNKyKF64WP5bb2xEb25a1XtBq61xfdUXESwKRUGopXUchwWmMzcOInH32vKvYn0NIa5ztoac2696LUnMNYIQCtIXdJ8t+fT0B7aJnBxC1LWXlOFVokSgy8z4rGMkQnHX2mehYyTEtIp7KtBZzCGN2X7oYh5SV2ge5FVBeFnIVpL6UwVI12q8pSgQb0b6Ss9nAF8Q4Qu514kvNG3zYhaR430wOewuUzUIxtxAwVWQpVoh8uTrSLPoFrONNyAaOpCNnYiZ95mUSMtEjg27WQFT4wF/AFHrWvrJ5DhycsI9V6EIIp4oP3elbSNHh6BYQHvsE+Dzl4+XzGWQqUmEpiOzaZASEY1DJO5m4EiJNA3k2Ig7npyy0QkEy/uuBtfXE10ImqP13Nmxc86PHSk5dOUYm+q6ygQ6wOb6brpr2zg6cJqJfKpqEv3Vy4eR0mEsN8mPzrjFe7JmHsu26ZkcYCKfXCUSXWI4JgUrjy0t169d8eJXA9jSJm0mSRoTWI7FRNGd3Ib0iFsgT4IlTfJT2yLwqj68qg9FKNjSJmdl8am+8jq64iGgBX8iilla0sbBwZLVRfhzVpaBbO/KdipAQUFVVFfJLNSSnYCPgBYkZbgkSk/2ImkztSgpJYSgK7IBlZsopWqVkDPynB65hFXyahtMj2I7NqdHLuKfR9ll2BYvX3yLowNn6KppJbiComaKUFBXMbIrUFCWoUS3bItXLh3hjw9+jefOvM5kfvFCb+f/1Q9pjtYtp5vXBIoQPNh9Jx/f+ghRb4gzY5f4+olnOHjlCPd27GFvy3YAxvNJFCHoLFm3dMVb2ddyGz86/yrD2TFgcRK9KdhMZ7iTE+PHeXXwZVSh0hJsZVvNbRXrjRXHyJhZ4r6aZZF21SLsCXFbYvOqtyuEoCnYQHOwkfPJS6vW7vpYF+2hljXJguqItOJTvcsi0QGyVvUkuiNtinaBol2oIEvBtdmKeuNV79e0Ld4ZPs2fH/sm746er/gs7ovQFm6iIVhLQzBBQPfjSIfJYorBzCj9mWH60oMUS7ZUjnR4Z/gUXz7+LX591+dYF2tbcGDeFmlkS+06jgydxHSm323PX3qNBzruRF3meSpYRY6OnGKiMG3xoCs6m2q7aY80Lbq97dicn+jhy8e+xcH+IxWfhfQArZFGGoMJWkINBErP5VQxw2B2lP70EH2ZYXKmOzGQwOVkP1888rf8Q93P9rqNKEucIJiOxau9hznYd4RkiUDXFZ2mUILmUD2NwTri/ii6opG3CvRnhrmSGuBysr/ienSk5NlLB9kQ7+CD696/qE96b2aQ0fw4F5KXiftinJ28SFAPsD7agelYjOUnGM2PYzoWXlWnOdRI1OPau2XNHH2ZQUJ6gLHCJI50qPXHafAnFq0fsSaQjpuKu1REaxb3C10BRLSKopAeH4QikJ50fWfTC1uXrBbk+LCrxlIURCReHUGiexDxOuTFU64FRiZZVtiuNazeHnLf/SbC5yP4qc+i1tUDEP3H/5zim2+Q/M//fs7tjBPHCP3UzxH59d/APH2S5H/6HfyPfwhMg/wPvodn+w6Cn/gUxvGjpI4ecbMalgvpVHr2rgRT3rmrDNcDPAmZsRKxEJwmllOj8yrfhD/kqhUd291+EbvCGTt0181OukT1lB+wY7vLZwZjpUvekBpxyQfd696fUrrKyMlBcGohVFPZTymRRt7dzii42/lD7jpT/r5Xe2VrOihhUIsu8a574eoaNjNswSS4/U+6Ht14fO53cWy3+N7kIIRrkaH43GMQ6biEkpl0yfepwIFlwjWwBwDc+1SZX/EuFNUl1a83HAc7OT1/UIIB1NDqFlxWfH4U700YMK4Cyvs+6hJsq2q1Jdyi14EwLMViquxPvYJMC1my3pjLsm0NSdm1hPD4IRR3ieaZ4/zJYQjGSwHNKr6XlEjbQqbH3ODeTCgqROvnJb4r+uMLIEtZOkgHWcxXPuO9/oXfR4riKtpVbfodaOSvKkxaItHXEoqCCMZgLg51ZlaRxwc1Le6z33HcrERfaN73n5wcmf4uRgE5dBlZzKGE4xXBKuk4yHMnsZ79tvv3yAByYhxl151oH/o0KArW099EDvaCYyNq6lHvfQSx6Ta3b0YR561XcA4fxOm9gPf3/swdpwLOmRPY3/0Kon09cmIEmcuiNLWiPvF5qK0H28I5dRT7xR+4+x7qQ6aSqHvvdfddW1/VIbyuJLoiBEHdv+AEZiKfouaq6L1H1Qnq/pIHZvWDuNHsJDmzwB8d/NtZn22p78avrU7k0HQsRgtDZMy0m46GJKRH8PsDWNJk0hgnY6aRSMYKIwjAq/hcEh3IWTlG8oNIHBzpoCkeGvxNhPQwpmNwKX2OWl8dRauAKU38WgC/tnaTmxsFKSNN2kihCAWPUp0it2jnGcivkESvEkW7wGC+b9H1BILO8LqlRU/fIxhMj/F7z/0xlycGcKTDaHaCnJnnvx/4y3mfAzmjwFhuEk1R+dyuD1IXrJ4YcyHK6nNd8ayyEl0syw7jzMhlfveZL3Lw4hEaIwk21XfOG0SYgnetCoysEiLeML951xdoj7kFg7fWr8N2bP7kra8xmBktr+fa8Qh8pUlXxBukKVxHqpAhdbXv3Tzwql52Jnbh1wKMFkbwqT42xbZUqNAlEltaxL1xOiNdq1Zo8moIBHFvjM7IClR5C6Den6AhULdqJLpP9dIVaafWt9T7qDq0hprwqMvPmMhbeTJGdZ7NRbvApDEOkpJN0/RAeimKWSklV9IDfP3U05wYOTvdBoItiW4e7ryH97XtoSlUP+t+z5l53h09z/OXD/LcpddIlfruSIfDgyf43rkf89PbPkIiMP/xDuh+Ntd20xhMcCU9WF5+dOQ0w9kxmkLLC55dmLzChcneCmI+qPu5r33vosdHSsl4Ick3T/+ogkAXCDpjLdzXto/72+9gfbx9lj1LwSpybuIyL/S8wXOXDjKUHUOW/js/0cNXT/2QWn+MlvD86eHz4Runn8F0LCQQ9YS4t20P97fvZ3vdBqK+cAUpYzs2J8cu8L3zL/DC5ddIFqevq6yZ47nLr7G7cRud0ZYFj0dPqo93x88ykh8nY+Z5beBt6gMJ2sMt2NLm+NgZTo+fw8EhY+bYEOviw10P4dO8DGSH+eKxv2JzfD2GY5Axc/g1H090PUxHpHXJgYQVw5HLUgCLQHjtrFygauWe8AZcks5xKtOf1xL5UuEvobieqNVAUSoIFGkUXCuUNSbRnVSS3De/hpNJE/6FX0Fraq56W33zFrx770BNJFBr7iGp6dj9faBp2GOjBD/706gNjXgjUbQfPsVK7VxWze5BTCuEVw1SukRALukSLJEEwh8GobgFGTNjLpE+F1mi+xCl8aw08tWT6OAqz42Cuz9vwL3mpsh1VZtW2tmWqxB3bIjUIvyRaZK9mENODEBmAukNuiTUFJzSdkbBVZyHahGaXrZuKfd16j2neRChGpeAyqfLJLqIJOY/brYJ6VFAQjiBCJT6JiWymHPJ9fSYawcwZ2BOuiRXIOoqHqeOcVnZfw3mTos9l6/JY1tOFzCcdxVZkVkkFGXV7wWhqdck+Hc9IJq7Vr9NRYG6Ftj/KCJeHRkHuMrdXMq9vr0B9z5bCqE+lTmTHncDcTOhqAhf0A3+3GxQNddmJJd0g5JTsC3Xu7uh0302L3TdT6mekyOuV/xVWSIiUgv+cHXH2hcsPS8pjUNmKJnBPXeLBXV1r6s2nyLRi7lpixi4NiT6EiA8vuqzK6724gdEtM49hzNhGtivv4BSW4/26V/EPvkO9oGnUffei1LXhPnVP0VOjqN97lcRPj/2gaexnvsOemMrIlaD8AfQPvgpnA3bMP/kv8zapxwfQdm6C+2JzyInxzD//A9QLp9DxBOQz+G8+TJKczvaR38K++gh7IM/Rr3jPkSVBDpcZxJdIBb1Mw95AmSMyrQxy7EpWkW8mndJ/sZBj5+oL8y/ffTvztrOr3tpDM8zKFgiinaBtJkk6qkh4a3HnlHoyqf6aQt2oggFKSUd4XUVkz6JxK/5aQq04lW9FOw8/bkrZMwUId0tUGJLC8Mu0hRsRRMatnRW7Mt8I8N2bCaKk7zc/yqGbeJVvcS9sevdrVtYBmL+MI9vfh8nBs9zcbyXVCFD6UkwZyaFKG3TVdPC7tatfPr2x4n6l1aoRxUq6yKub61P9a8qmSpYnif6j84c5NTQRTbWd/Lr93yGHc2bCC+i8puyQ7kRoQiF+mANtTOIQk3RqAvW4FF1CqWq5TPTW6eeeqqi4td8ONLBuNpncwFEPBH21u/DckxUMbcvXkuwjRpvgrAnXA5SrjY0RaU93EJAW5uiZmFPmHp/Al3RKsjQ5aLOn6A11LSqtQGubt+3ApuLomWQrdKuq2gXUIVGjS+BtoLzmzayvNjzBm/0H62wK9nZsJmf3/EJ9jRsm/d4BXQ/e5u2s7Gmk7AnyF+d+B62dCe+eavISz2HuK1uI/e337HgMd9U20V3vL2CRM8YOV7tfZsnNz+25O/kSMnxkbP0pacHtAJoDtWxs37xrAnDMXmt7wjPXjpYcd9urOnkp7Z9hHtbd88b+PRpXrbXbaQr1kZjMMGfvvN1Jouu8sd0TN4aPMGBK2/xyc2PLrmGgFGyZwvqfp7c/Bif2PQotf7YnOuqisq2xHqaQnX4VJ1vnP5RxT10cvQ8l5J9tEUaFwyu3t28h7ub9/AHh3M0hxp4cv0Hyu8rwzbZVruBHYlNhPQgh0dO8I1zP+Sh9nvKRVtzZh6f5uUTnY+Ttwr8+bt/y7nkJVpCDSsKOC0L0lmcmJkL/qA7sVsrVGt9MPMxfz38XasOesyhmF3r/joO1sUL2GOjeLbvQIktLVCqRGPTHuiK4qZNG0aJqLNRQu7YS/H7Ufz+FSpx57EcWC5WPRhVItGtokvkeoNlYlkI4RZju5qoWg0IBbyBUqp86TsJUaH0ds+H4RIv/lAlgQ4uieMNuIRcIVPhXSuNvEvWaB4I1UwT6HPtZ7mYUlX6w9MEeql94QsijbBLoudS82e3qK6tjJu6P9U/9eYqirhCSNvGyS0yFlIUlOC0rYJTKCILBXiPKsdvCgiBiNUhdr1/aduZJnJiqHTvhNwMDI/f9VfXPG4waa7rXzpIy4RiHrKTLlF8dZZPIOIGdW9CJTq46m+i9W6gYWbwvJBBDl2CaJ37vJjKepkBaRlQyCLT4+5z52o7O38IYg3TwbrF+uIJIIUC2KWMoPxVn/sX9zL3+Nzx1JSFSyFTWUNB091n882I1ETl35oHwjWzC39PFXiNxNzrWve4/zoOspDHOXMc7cEnEA0tCFVFuW0fztkTOL0XUWOLZy6KRAPKltsRNXWuDUwk5tbScUrnzSy691Z534qbfbYEXF87l0UghGBn02bOjF5iKD1KQ4nkvjzRz5XkIBsS7UsilnY3b+GFc29g2BZ7miqLQS1W1G8p0BUPXsVPyphASoeoJ45fDVStjBMIcnaWSWMMS1oU7DwBLVThuxr31OJVfG7F8FXr+eqhL9PP8bGTZTIB4HK6B3AV5QcH3+DUxJkqWpIUbYOxwjinJ86UVP1BuqOda9PxW1hT1ASi/Ny+j5Ep5rg80c//fPmv+OHpV/i5fR9lW9PcNh6aUIkFInTXtBLzh5fsyaopmqv8XwMoQsGjevEq3vLf1eDcSA+pQprffvDn+Zm9T+C7gSLOy0HA42c051pUTUFKScEyyJsF8mah5B9tlAOKzlWEwpRSdamYjzwVCEJ6iJC+tsEHXdHpjq5dbQtNUan1xwnpQSaKK7cvqA8kaA6uXe0Pn+Yl6o3QmxlY1vksOsWqa14IBKpQV+TT7EjJ5VQ/P7p0kII9nU7ZGm7gp7Y+wa76rVUFHMKeIE9ueox3x87z5sDx8vLB7CiHBo5xW90m6oPzD/waArVsrunircHjZeGA6Zgc6H2LD294AO8SydbJQpJTYxfKtifgksp3tuwi7Fm4iJaUkolCim+eeZbcjHNRH6jh45seWZBAn4mg7ueD6+7n7MRlnjr3QtlyZ6KQ5K3B49zVspOOaMuSvtcU7mndw6e3fICod+GgqhCCuC/KB7rfz/GRs5wYPVf+rGAXOT1+gb1N2wl7ljeSUoSCYZucnbxAqphhsphiND+OPcMTNuqNsK1mA7X+OFJK4r4oyWIK07GuPYm+XHh8VU8yl4Wri3zNA1korTflg30t4Cspgh3b9c+sBo5dMbkWumd1i/3NA6U2QeDxD1F45UUKB17E/9CjCL26ybirYr3qWSpw/bhVFZlzM8WkYSCnyPX3KhynpMoWJTuTGe+AKeW75nHJ7NWEorpE0MJVSF0CQDqurVFqdPY6ZnGWShlw/7YtNzCwBh7JElySXiiuknKOd6fwBpCZiYXveY+vpJq9OUm/OXFVQdjFIA0Ta2yOczsTqorWOD2esyfGMcfGUKMLF0i8hRsUUwGy9Dikx5Gap0S4elyLKEWd9jaXTNtiWYZ7PxVyzDKj172uEli7ieeZQoFw3P2e4/2Vz7VCBmkWwBtEen2geqZV6VMWUsWca6t2NTx+RE3ztLq8GkypxKeydmaKEqr0Mhe61z23U5j5fRTVLVy+hgFDWcwtLSPRF1y0JkK57Zne9eDW0glGZh8TXUfZvhv75R9hfePLyHwOUd+MaGl3yXXTgHDEHZMIAbruvnPz1WWs4/VDoDTXEcLNprFLxXW9fpRNO7APHcAqFJDZFKKlA9GwtPnIjci/lqEKhU/tfIx/+tR/5b+99GUe2Xg3Ukp+dOZVxrKTfH7Xh6jxR5FSYjk2yUKagdQIyUKanFFgIDVC0BMg7A3g1bw8svFuvv3u8/znF/6UT+98jOZIPTmjwIXxK2yq6+LOjp14VsGj0qN4qPc3kjImyVhpJo1x6n2N1HjrFk+hRtKfu4LpmET0GBo6GZGetd518dJcAvqzgzx75ccYzkwfUncCnTJSvD54qDriQ4It7TIZoys6m2s20h2pLgXLp/ppDrQuvuI1hEBU5bv1XoUQgrAvyPamDexo3sTrPcfY27ad+9btvd5dWzJ0Racp0ELM4yq+Ip5YVdsVLAPLcbij/TY8N2u0eQa64q0cHTzN98+8yCe2PopH1RlIj/B67zuM55M8e/5VIt4gqWKWVDGLlA6pYobaQAzbscmZBUDc8M+1uaArOu3htX3G1HjjhD2hFZPomlCp89cS867dBEsgqPXVoAoFSy5d8Vq0jQri9mo40qFol5QoQmDaBkljAq/irwhiKUKpKuskZ+Z5e/AEl5PTVlwCwUMdd7GjfhN6teoUIajxR/nI+gd5e/Dd8vtOInl78F0e6xpckERXFZUd9Zv5cc/rnBm/VPqukkvJPs6OX2J73cZ5t50L5yd6OD/ZgzNjQuVTvTzQMbuI+9VwpMORoZPlfkxhX9MO7mzeWRWBPoWA7uNjGx/muUsHyZrT5/XM+EVOjV1cFoke9Yb5TBUE+hQUIWgO1bO/eWcFiQ5wfuIKBau4aGBhPlxKXeG5nleIesO0hBrRVR1VKBUBJI+iE9RdxaUQolTfxllF6cYSIIQ7OVuqH7Wqrb5lxgzI5PjidJlRgFxpPKzpiNC1IYpEbQNSVcEykOlJd1K+WODbMpGTpYK+Hh8Ew2tfDFFR0Frb8D30CNK2KLz4Y9REHd49+1ZEliqxOGpNDcU3XkNtasY8ewarpwetY/WtEG4oTHmDi3nI5rUgOaqyppHT969lzJ9Zoumz25pS4c2whll12Pb0c2YuKKrLjS/0DFrAj/xmhfD5QAikbWNnFyaCpGVh9vVijU8suJ6i6/i3ThdXLl64SPHceXzd3avS51u4zrCMqwJ1otKvWi6S0aN7ETVNpaDZzc07CFWHWL07brqaSLct1+4llywdH2XxY+MNIGpbIBRfus2NL+iqx6/GlMJ8sWeXqpbIdmV2H1Vt7QvTZiahOEf/50N9J1Rloyxn2+x5/HPb4CkqoqbOra1S34wSjiKa2xG1DWC4KnE5OeFaEArpZsUZRbfeQDVQFniXappbh8exEQ1NiPAWl0SP1c69/jy4oUl0gN0tW/mt+36Orx97ht9/4c+QQH2ohp/f9wnu7dyDR9ORUvLypbf4Hy//FXmrSF9ymKJl8G9/9L8IegJ8aMt9fPK2R2mMJPgXD/4Kf/HWd/jSoW9h2CYezUNtIEpHvHnViqwJIQhoQbyqj6gdZ6w4Qn+uj7g3MWMfrlc6kopAuyMdxgojdIS6iXsTGE6RpDHXS/TGHlx0RTp4oPU+zicv0JfpJ21O36wSlxhf6gzSp3q5s3Ef97feR0CvzjrBrwVoD914gwmFm/tltlpojtbPqnlwM0ERCmE9QliPLL7yDMT8Ybya7tZ0uOoZcDPi4e67ePrsy/zxm1/lwKW3CHkDDGXGODt2mc113Ri2xf95468pWAYZI0siEOdgzxG64q2MZMc5O36JqC9ExLt01bhEMlmcJFlMYkmToBaiKbh40cTVglf10OBf24KvUW+EkL48km8mgp4gDYEE2hqnRMe8UTdQuEwSPW8V5/3cckzGCq5FiQRsx83WUkW64h2uK14aAov7ASeLaV7tPVwmvQHqA7XsathKyLO0WiOqUNlQ00lTqK7CRmUgM0x/Zpjt9sYFSflNtV10Rls4P3GlnMWVLmZ4pfftJZHolmNxZvwyV1IDFcu31q2nLbJ4FoIlbZ67dLAikyzui7KjfhMJ/9K99LuirbRHmjg5dqG8bCw/SU9qgKJllG1PqsWexu10RZcWuAp6/HTFWvGoOsYMj+KR3PiybZIc6XA51UeymOKRjvfRGWnl5Pi5WUWMhag+S2nNoSglRc4Sv7OizltIajUg+y+7k8kF7NFk3yVXoQRucavatcuoqUDbendiXMxDNoUcuIJoXz//+lJCNgUDbvYl0RqIJZbmsyzlksfIU1D8AXz3PYAzOkLuB99FqanBHhqi8ONnMc+fxervY/yf/CZaWzvBj38SrW3hTCq1oRHfQ4+S+843KR55G62tHaUmjvCv8UT/eqN8Pq51uKuKAeEUkeYPz10YbgrzvevXMotgqvvz7qL0wYL3w00+KJ4DWsJ9BjiFAkbPFZxCAcU39z3k5HIkn31ucbWopuHbsB5PRwfG5csYvb2kX3wJ/47b8DReo+fjLawOBFUEjmT17wZfEBFvcq003gNiLQCheSDe4Crzx/pchfnVkHLhuYcQEIi6wYVAZFkZdsIXnPsUeHyuGn3xFhAev1tc9OpspqmC0msJVZmnn9INHk8FZz3+kkp/CWM/56qggO6ZR3QgXQuj4X6cV54FXUfUusVDle7NqLftw3n7FZTO9Uh/COftVyAUQWlfBWcBCdI0kP1XsF951hVl1DWi3vc4omN91YKR60aih71Bfvfxf4C6QEeFcIvPfXDz+9jVsoV0KWoS9gZpCNVWqKF2NG7iXz38a3O2UxesIegJIBDc3ryF5kg9E/kUhm2iKSoB3UddqGbViIWiXcCwDTyqB13RUYRAMn1DK0JBExppK0XezuNRdBShogilPGywpI0jbZLFCdJGkoB24/ohz4UaX5z7W97HHQ17yFpZBrNDHBp6m5MTpwnpQdZH1xHxVKci01WdmCdKe7iV+kA9YU/1x0IRKp73aEGU9wLu6thJjT/K5oYbL9CxltjVsplEMM6L599kT9s2AmsddV5j7GjcxC/seZL/+dpf8eKlQ6iKguM4bKlfzy/v/RQ1/hjfO/Vjzo338Kltj5EqZvnS4W9weuwimWKOg1eOsK1+Pa2RpRUaHM4P887oYQZzAxiOiZQOnZFumoJNSCm5lL7IQLafDbGN1PoSq05kCQQJf+2qZDAthLAnhH8VPNcjeoj6NSb8AWLeyLKPtSMdTMco1fqY3Yam6CT8i08OqwlUOtJhJDfB6fGLFcs31HTQGFr69SJKxdK7o60VJLolbS4me8maOWLq/AG3gOZjZ/1m3ho8wVh+EnB91d8ZPsVkIUXMV12wbig7xqnxC7OCEQ923DmL4J0LaSPH0eHTFctaww10RluW5aWvKRoba7oqSHRbOvSlBxkrTNIcqr6QD8B9bXuXnLXiBjyDhD3B8rEFt8Coc/Wgv0oIBIoQGI6J5ViMFyY50PdGubjsDQmlpIAy5g9UzQlpry3xNjECPeegc9O8qzhHD06rz0JRaLk2SmjR2o2ob0ZeTEFqEnn8dUTbunkJD2kUcd59s6yaF03tiKZFLL8UtXJSm0svLdAhBMEnP+MW6xQCJV5D4MnP4KRSqHV1KPEa1EQd0ijZfCgKwu9HSbjvg8AHnkAaBmrNdG2o+H/4L6iNTQifD9+996Gv34gsFlDCYZAg/H5EYGmBxpsGUwr0qWKWUs4+30sNRK1m31S93CfhWYLX8VQwzDbX7n5WPZVFSq/G1L5Xw3/9JkJg6xaS3/8+2DaF06dJH3iZ6CMPz1pP2jbpl14i+dRTi7YphEBvaiL24Q8x/L/+N9IwmHzqKdRYjMTPfQG9bv7xnmOayHwe4fOheH6yzsWawnGQI33IY69CQzvKbXdVt50viKhrR6ZGXKXwcq2idB8imoBIrWtX8h6rIyBUHSIJ8AWRk8NuoeJqn8XeACLW4FrDaN7lq/N9c4uaxJQSvaq++F3C/OrzrFwDJXq4FoLzPf9Lz+6pAtTe0NKuoavXFcrcJHwqifXst1Cf+BxKaxfSsXGOvYn9xksQrUG9/4NQLGB9+X8gLQulrRPt8ScRIXceZJ84jHPgaZyeC8ihfoz/91+j1Dejfvhzi/dxfBj71WdRP/J5lOZ2pG3hHH4N+40XEdG4q5CvAteNRNcUlfWJ9kXXE0Lg07101cyf8iuEoDYYo3ahaHwJqqLQFKmjKbJ2RILlWAwXBkibKUDiUby0BbvLKjmBQsxTQ87OciZ5Ak24ns0hPYIiVFqC7Qzm+hjM9xHRo8S8NZVp6jeBilkRCgHdT0D3UytraAk2U7QNziUvEPVGeV/L3XSE26pqy52oqmiK6lqhvAcVCj+pqAvVUhuMo7zH0jaBss3UXJKBxzbfw3NnX+fPX/8mHfEmntz5qHt9L3AYNGXu4pk3Aryah09te4zdzVt5o/comWKW9lgzu5q20hSuQxUK62vaMW0Ln+5hODPOi5cO8dTpF3CkJOYL81D3nXTXVPdMkFJyOX2JVwdfYawwisOUz7pDYYYVSNpMc3LyXSSSPXURfNrqDkyEEDQGKm26pKzO2921darufIb14KoULg3pQRL+xQuyrBRhPYiygue06VhYjoU6h1+0IhR8qnss8lYWCfjVqWPj7tOSFlkzRc7KEtAC6Ip3zmNtOhanxi9QtCsHsW3hRiJVWoVcDV3RSARmH+OBzAh5q0hsgW2FENzZvJPvnXuhTPRKJAOZEQ4Pvcv9HfsXff9JKbmU7OXdq2xLanwx7mjaUdX789TYBXJWZUpmfaCW+jm+VzUQQENwduH2sfwkqWJmSSS6V9XZUNMxZ4BlMeiqRkDzMTZjWc4slL3aF4OmaBWFpIUQbKnZwMXUFf7w6F8R1P3c07yX04G68nFWhEBX9IrrTxMamlCvz0hmKlU4m1p83ZmwLKRjs2ZVeAo5nB99DeWzfw+CpdonQoCUSMeBcyeQ75RIdJ8fsel2RHB59+hSITxexD0fQF457/bzyKvQ0o3Yvs9NSYbpvtoW8uwx5EvfdzcOxxAbdkDtIte414cIRZCldpyLJ1HTE8hAqKL9yo6JGb8K1IbGyr/jNajx0j0bCE7/PgfUxOw5kb5+OvtFBIIo3WtTW+aGhFBcBZ0QYBaRtumSN0K4hZZty7X1uS4WCWLab9wsIo38LCJ9qhh0+dqZgu51SZ5CBhmMzbp+K3dztT/+lM+wnDuoUNqf9AYgNQpmwT1uijZ9f4DraSvl3Cn+72FEH3+MoT/8Ik4ySfHyZQZ///cx+/oIP/QgWm0tmCaFCxdJPvUUE9/4Jk6+gBqNYicXtvFTIxFiH3mC/LHjpA8cwB4fZ/RP/5TMKy8TeeghAnt2oyfqQFVwkimKV3rIv3uS3FtvEdi1i7pf/AWUW6r1VYOUEjk+iDzxOsI0oEoSXSgqMhhF+ENQZ7se5/kMspgHq+DaXkzZMclSCrOilJTLXpeE94fBFwJVdbNBlzlnFK2bKhXFC9kzXY82lVIB5ro2iDcis0nIp1xlullSUQsx49iEEMGoW0RUWdmxAdyCzuv3zF6uatXb9fpCiPZts+1cFMV916whhKrDQode87ie7WN9kJ1Aev1VKvaFGxyYaXXj2HNajjm9FyE16aq/PR6kIyE5gXPqKBTyrjL8w59BfeiJko+5zy1wXzpvyvotKC3tpYKspftB1SAURjS2oHRvmvZEB/Rf+seur7qq4vSch3wO9d5HSvt2YHQI59JZZLFQ9dj8hrdzWW1MERyWY2FJG9uxcXAqKrK6pK2CKlQ0RUNTtCUp0gJakK7whjKRInCJ83L7JbuXdeFN5XVmEuP1viYSvoZyX2bCq/jYXrP7piDSpyCEQBMacW+MqDeCgsCjeFZFWXkLNzcU4QZI3ovIm0X+3dP/Z05CVREKNf4oQgh+4xu/xx+89Ffc3rKZmD88b3bOv3zkV5ZUSPlaw6d52Va3ns2JbqSUpXOrlMmjsNd9mUkpaY818X8+8m945twrFK0i+1p3sLtpa1UqWYBJY5K3Rt5ktDBCc7CF7TW3IRB8v+d75XWEENR6a/GrfnoyPWyv3YGPVSbRETQEKsmHgdwgV9JXyn9PUeozM5wVBM3BJtqqDCSqQiWg+VGFWmGxsVQE9QBxX2zZ21e/n+CKAj6mY2I65qLFNIt2gYyZRlVUFBSinjge1cdoYdBVSgrI21ningS+Od43lm1xfuLyrOUeVSdtZGeR69Uga+TmvOfTRrYq25CmUD076jdxMdlLoaQkH88neb3/Hd7XthdtkXska+Y5OXaB/sxwxfIHOu4gWqVd0tnxi7Oq1GuKRsEqMphdpNDZHHCkU2GXM4Wcmae4RLVVS6iBgOZbVra/EMosJX21BDrAr9z2uVmB/IZAgp/Z8mT5+2lC5cG2u9FLBY87I238k72/XpHp+PPbPo2ACkL+mkHT3YnIwla7s2GZ83svrwY8PuSpwzh//p8Q7/sQtHWXyL488vQRnBe/B6ND7k3d3Ily7+NzToKlY7tkwxTRMPVvIT9NEtqWS05k09O2GEJxJ69z+UgD4va7EeeOIw/9GAav4HzzTxA9Z1F23IWMxN3rMT2J885ryJd/4CrJPV7Ejv2IPfctquQSmo5sbIOaehgbggsncZ76K5T7P4KsqZ/2Ti0WXIuHmjqEf+UWX7ewAHQveIOud2xWRwai7rXhOK76ca5guZSVz87SNScdp3z/iKt9jZcKIVwVdzDmqgXTY8hwzXThwKnihEYOgvEKL37hCSC9QchOwuQgROqQuhcQ09shS6T8VdesrrvXoVVEFtJQtjuT08TU1HELRKGQhuSI2zdFc9vPpyCfdvsUiC3v+9+k0Nvbafi1X2Xof/5PnGyOwqnT9P/O78J/+I9u0WHHQdo20rbRYlFqv/AzSMti7EtfXrBdoSj4Nm2i8R/9Q1AUsq+/jpPPkzt8hNzRY67aVohShAN3P46bYeHt7HJ/v4XVg3Tc941tLfmdKaaIX0V1icxABFF+zCwkzhFTEbNVEVuJNcgSWe02p46VnCrGHJsKVF99nFb32Lj7VlasFncLed+gGSBCIFQdqXndZ/YSxskiHEcmR6YX2ObsAteAiNciDQPn3cOuGnxsGPudNxB1jYhYjdsHf9Adr861H6/PJdbnw1WF50UkBoCUDiKWQBbyOKfeQWlsRY4MYh97E6VjPSIcq/q7rphEl1IyWhijMKVaEuBXfST8s1VHM+FIh7SZIVWsVMPoqk7cG1908pwzcySNZEnp6ZJxdf46PPNsJ6Ukb+fJmlnGC5NcyVxhKDfESGGElJGmYBWwpIUmNLyql5AepM5fT1OwkY5wO3X+BCE9VJ4YLQQhBKZtMVmcqJiEK0KhxhfHr/kRYm5F9dQNrs4XIhLuZwWryGhhpEI44FU9RL2xRY/d9ULYEyLmiVK0l5hGfAsYtknBKsxSRzrSIW1ksRyLoB7Ad5NV35ZSYkunTLjOhCMdckaBnJFH13TCngDaWhfmWkUUrSJ/+Orf4FSRMntm5BJnRi4tuM5vvv9nbmgSferZpS1CDE09+7rirfzqvs8sa19XMj2MFUbpDHfxYOvDhPQQ44WxWesF9RAe1ct4YQx7DQggIQS1V3lED+WGeGvkMACGbTBRmCCgB/BpPgSCrJlFCIUHWu6rmkQXQuDXfeiKjr2UiuozoCkaMW8Un7r2z4iA5l9RxpBpu0r0xeAGKBx8aghHOkwUx0j4GzDsIq3BTiSQMibI27m5SXRp05cenrX8y8e/xZePf2vZ/Z8LOTNf1XcSQnBf215e6HmjTKIbjsm5iStcmOxlY03ngtsPZEY4PHiyYplX1bmvbR9edW5F/tXoTQ/NCgQ8ffEAT188sOi2S0HeKi45UJEI1LhZOcu4vlY6fZpr/CeEQBeV76WZ4zeBwKNWvtuqDRauCXQPBJdWwwOAQm5N7SvEgx9DHj+EvHgKee64S6DrHldVNjUBEwo0tqI+8QVEZB5v/pEBnGOvQ3LCVaUV8lDMI8dHygWv5EAP9jf/zC1M6vO7yimvH1HXDDv2z9m20D0oH/opHMtEvvsmjI8gf/Q17Ge/4U7kZKmg1tT7PhRBbL8D5eFPltOPFz4AAtG1GXHbfuTBH7l9fucg9jsHXcWuqrnKRNOE9dtRP/rz0LFhOYf6FqqBEAjN66q1Hdslq3NJymSz7nVJ7Hy6YjNp5F3y2LJc4sEouNtnJ6CYBaEgdQ/4IysilYSqQTDuKs7zaVcxqChlkhQhXVI9eNW1rCiIUI37fM9nStuVCqdO2dYEI7M9eYVwifBgzD0O4wMzyHkJsaaSd65AoCAjte7yQtZVJSpqSYUqXSuaWMOyvIhvZii6Tu3P/SwIwfjXvo41NoaTyyENA2kYCE1DCQbQEnUkfvrzxJ98kvGvf6OqtoWiENi1i9b/8LuMf/VrpJ57HnNoCCeTcds3S89QVUXxeFD9fpRwGE97O4r35po33hS4urjiUiGmnAvEygcu73GUx7Q3aKb2zQ25ZCs/UduM7D0zvaCQQ+bTiFil4Ew0tKB99Kewf/wUViaFCEVQttyOsv/9ZcJ7LSCEAu3dqI9+HPvZ72Bl04hwFGX7HtR99yFC1Wc4rvgN5uDw/cs/4PjYCSQSgWBDbAO/tPXnF1Rvm47JS30HeLH/pYrldf4En17/SboiC3sdnpw4xQ97nmGi6MppAlqA39jxd2eR97ZjkzbTjOZHOZc6z+mJM/Rl++dURQEY0sBwDNJmmoHcIEfHjuJVvXRHutlddzvro+sI6aFFJ6JpM813Lz3FqYnT5cmoX/Pzkc4Ps69h74LbVoPTk6f5ypm/rlAlbo5v5sOdH6QxsDRf4WuFsCdMW6iVSWNyzT2E32vozwzxSv+b3NW8Gymhzh8n5AnRkx7g5b5D5Kw87eFm7mneS9hz86iTMkaeI30n8WoebmvaUK5zYNkWF8Z7efbMa5weuUgiGOeB9Xewu2ULAc/NkcGgqzqf2PFIVdYe1eBGDpCYtrXk77mSZ0CqmKRgF1gf3TDDymOOfSgeNKFRtItly5fVhEAQ98Yqlu2qu51ddbcD8MrAQSYKk9zZeAe1PnfiOpAd5J3RowSWWCjUr/pcNesyYwFexUPUuwzybDn7Uj0rUnzYpSyxxSAQhPQIMU8tCBjI9uCUss2EUFAQaIqO6cwduHWkw3hh4VTp1YLhWFUF1AC2JTbQFm5kODtWvq8GsyMcHT7FhnjHvMfWcmyupAc4PX6hYvm2xAbaIo0L1qCZibF8ck3tr6dgSXtJSnAAv+59T9p/XTPoXkQouvS3Ui7t+qiv1fAiEEb99K/jPP9NZM85yCRLRUSlq/gKRRANrSgPfgK6t87bjBzqRb74XUiOz7+vQg4un5l1DGT7BtSODTAXQS8EIhJH+fgvIBvbcE4ccn3cc5lpwkT3QCAE8TqU2/Yj9j+MCFdfOF1E4ij3fgAHgTx9uBQIyLv9VVTQNAhFEZEad1+3sLYQAuELucXf8ulp71qPD/xRsIouQT4zuOY4lQpU3Qt4pz/DcYNBs9S/wlW9K/P4x84FTUdEEkhf0CWrbRM3BUt1rw9vcG67BE1HROrBG0IWs9Me5SX1K97AnNsJRYVIwk3zN/IzLBP0CrW7G4DwQKwBWci4wSzbcr+Xxwu+iJvxMddzXC31W9O51uyh8PkI7NjhkiyairjKJ1wNRwjevgs1EMS/dQvKVfUA1HCE8PvuxckX8G/ZgjKHSlINBqn75V8i/OADZA4coHDuPPb4OBKBFo3g3biB8H334du4EaTEv3Ur4fe/H8Xvx9O8eKF0T2srDf/gN4h//OPkDh8m/+5JrOFh7HQakCiBIFpdHd6uTgI7d+DbuBHFPz2GllKSm5jk1KtvMJTO4QuEaNu8Ba1mcSs3rb6O4P79eDu7UKMRlMDNMVdbdUi59JojPwmQEmmZkE5CrmRTYxpuZtWUPZQ69ezyubY2gRDCt4SaD2vVb9OAbBqZy7iBebM43W9FKdU0Ud3sM6/fDc4Hw/M/5647FhgBSkrnquh+zyVY3whAJprdd+TUuCifgfQEst5GzKhRKDQddf/9qPvvX/a3WC6Ex4t2z8Nwz+yaFEvBikl0VagkfAk8qoeiXUQiSRtpsmaW8AKFIy3HpjfbO2v5RGGStJHBkc6CJPxEcYL8DO/boBYg7p098M2YGV4eeJW3R94mZaRnfV4NinaRkxMn6cv2sb9hH3c27CfiiSxIEtR443SE27mUukzezpfbeXfiJDsTO+ZVzFcDRzocGzteEQjwKB5agy0kfLXLbnetEfVEuKf5Top2kVrf2vvyvpeQMjO80Ps6/dlhFCHYGO/m3ua9PHXxeQTQFKznjcF30BWNh9rvud7drRpnhi/yH579IwIeP//tI/+EjppmpJQMpkf5ry98iW8ff97VmkrJ82df5//32N/l7s7bbwpFesgb4M8+9zvXuxvXBM9fOMhIrnp/AJ/m5ZPbHlv2/mxp40gHn+pbmKxd47GLAGLe+UmS88kL7Kq7nbg3Vlbg1/kThDwhejO93Fa7rep96Yq+qMp/we1VvepizivFUos+Xg1HOjhV0HyKUDHsInk7h5QSRzpkLfc9b0sLVWil9+TcF4IjJTkzP+dnqw4pWTgddxpezcN9bXs5PnKGQkmpPVFI8e7oeR7pyhCdx689VczwztApsjO+k4Lg7pZd824zF7Lm3JY0qw25hGMyBVdNf/PY2d1w8PogsvTxl8ymEGtIDAizCG3rUD71a8jzJ5C9FyE96ap5g2FEUwdi4w431XaBZ76I1yFuv8clt5fah0QjLOSzLgQiHEM8+HHEjjuRF08iRwZKBUQV8AcQDS2Izs2IxuqyjGbtor4F5YOfQ27bC1fOIifG3MmopoMvgIjWIFq6oMrCVzcEbBtpFt2gSLGALBZgcgw5MbL4ttcbU4U75xJvaLrrPzxzdX/I9d1d6m4UBWrnr/01LxQV4QvN6sfi2ymur+9S+6qoiFAcmCcT5Op1A1HX2qVKiEAEAtcm2H819Npamv7Jb8/7uW/9Opr/9b+c93Nvdxedf/TFRfcjdB3/5s34N29edN3Q/jsI7b9j0fUq2lcUvJ0deDs7iH/8Y0vaVkpJ//F3+eN/8E8B2PjAfaz/5V8hsHPH4n294w5Cdyytr9cbcqRv9TOsLNOtCXALLqREJieQAz3I/svInvPI4T6YGEVmU+57wZFlEppg2H3P1TchGlrdd2l9M6Ku6ZoS6tKy3PfUUK/b977LyKE+5OSoGwgolrKMNL1k/eWDcAQRSyDiCbegeH0z1DUiEk2zgnLXE7KQmdNixf0Q93sVMm6wNBCpPrArhOvN37oReeGY25htIccHEY2TbrHb9xBWhYmqD9TjLZHoAIZjMFYYn5dEl1Ji2EUGsoOzPrOkxWhhFMM25i0CZzkWk8VkhS1IY7BxltcluBYq44VxMmZ2zrZ0RcOvBfCqntKE2yZvFciYmVmTyJSR4uDg6/hVP/sa9i7o6S2EYFNsI0fHjpHPuhNaRzoMZAfpzfbRvYjSfiFMFCfpSfdU9K/G55L2i/mlXk9oikZT8FbxkmVBQtgTZH/jTjRF462hY3RGWunPDPLTmz/O+ngn9YEErw28fVOR6GdHLtOXHOKert14ShWtDdvkwIW3+cHJA2yq6+T+9XdwYayXAxfe5HvvvsiWhnXUhaoYwFcBl3ibUkOunDRy6ye8Nz3eF8KXD3+LI4Onql6/MZRYEYnuUb1oikbKTGFLe96Aa8bMULSLhPTQmvgPq4pKUJ+/OFZYD3F28hxBLUDUG0VKh5H8KH2Z/iU/Cz2qjrKCwj66ohHxXBs7IF3RV2Tn4pQI8cXgU/0YjkHKmEQi8WtBbMckoIWYLI6jChXTMQnq83/vq33KBYJaf4yQZ3WLnjWH6/FUYQc3hXta9/CXJ75LIefaFDnS4eJkL+fGL7Onafus9aWUjOUneHPweMXy+mAt2+o2LKmo7ly2M1FvmLhvdcmNxmAd/iUW+1XFSkrW3oLQPMhw3J34zTeJmgupcVcVvVYoKXNFMIzYcSfsuLOqzeyL57DPnkLdtBU5Po59+Twi1ob++L1Iw8A69Coym0Ht6Ebdsh3hL93XUuKMDOFcOo8zNIjM5yEnEW++gdLehdrRjbhKoSlHRzAPPIe6aStKWyeOJ4JDCqmqCH8Apa0TZct2hL6yIKLwBRCbdsKmnStq55pAOshiEfJZZKFkn1Nw1fOy9C/5HDKfdQMbU2q+bBo5NrQ6fbBt5OVz2E9/dYUNzaxcslooGVGrGqJtHcqmxcnIGxHScZBnjiEvn1l85aqwhsd6LkRrUbbtWVJmyC289+G89nQpCLqKkA4kbzwSXY4N4Rx/030+V3vf6V7E1l0oywwKy8lx5PkTOCePIE8fRQ5cmd9f23LcMUkugxwZQJ474ZKywQiifR3K+q2IDdsRnRtdr+w1ItOlbcPoIM7po+4z7/xJ9101X7DFNNyfXBomRtwgwdRnNfWItm6UDdsQ3VsQrV3XrCD6gkiNzbIimwXhFm8lVLNoTZeKzVQN2jcjJ4ZgovSOHx9EDl52i6l6btys+qVidUh0fx0exQu4J8SwDUYLo3RGOubdZrQwRs7KzfnZUG6Yol2cd9KXMTOzSO6W4NxpTmFPmM3xTVxKXyZlpBAIop4ojcEG6v11RD0xQnoQr+pFVVQc6ZC38owVxjg7eY7LV5HVGTPDoeE36Yh00B5qW1AF2RhspCXYwnBuBEu6N1/aTHNy/BSd4Y4lFSudidMTpyuOnUDQ6G+gLdS6rPZu4caHKhTaw83c3bwHRSicmrhA2shg2ha6qqEKlaZgPUlj6eqr64nhzDjJQprbmjYQ8bl54plijq8ffYawL8gX9n2Un97zBEf7TzOcGePg5SNM5JPLJtElEtMxyZgpsmaGgl3Ackwc6ayK8rIt1EnUE1txOzcb7u3YQ1u0ac7PirbJeG6S8+M9pI0sD6+7hztaZ5OAS0GNr4aAFuRc8iwtwRYSvtmqPNMx6c30kDQmaQ934FmDAi5+zY8m5n+N7q7fxRuDh3ip/2UUIdw6dzjUemvZFNu4pH0ttcD11dAVndASLWSWC01RV5QFIJEllfLC0FUPURHDsIuAwKe6YwYHSdpIYksLn+bHp85PiKtXHVMhBA923snexpVdo1cj5AlQu4RCak2hOm6r28hzlw+Wl11JD3Jq/AI7GzbPCpibjsXZicv0JPsrlt/esIXGYN2SLFCuvs4UIdjTuI3Hu99XdRvVIKD76IgunqJ+C6sIRXHtXCI1ML4EEjM9CekJN4VZXYNA8TL9g+xTJyh+5c/Rdt+BMzyIffwI+PzITBo5Pob5zPeQmTRKeyfeL/wK+n5XZOAMDVD86y9jvfMWcmwU1JKftKKgtHfh+dDH0O+5HxEIlvvnDPZR+OIfoL/vAZTO9VgHnncVdvkcOA5Kazuej3wK/dEPV6Qt37SQEmkUXVIjl4W8+yNzGff3XNYlx/O5GSR6iTSf8TumsezzWxVsC3nuBPa5E2u3j5XC60O5/8MrJtGTFy4y8OprbP6pz1ZNIk2eO8/QG2/S+uD9BBvnt/uceufOOa91HJx3XsN55uvL6vf1hujajGjrvkWi30IlBi+777WfAMihPuzvfsW1IasWgSCqELBEEl2aBvLiaZxDL+IcO+QWy15OAVspIZNEvvs29pljiObXENt2o+x7v0tIr3JWuizmkYcPYr/9MvLsCUit8NoYH0aOD2OfPIxo6URsvh1l9z3us+h6WrIFoiWrsXkgFNeiyxNwSe+lBCyEgFAMZf3tOKcOQXoczCLyyim3SGhTt2tz8x7Aqlx9df66imJlpmMyNkeRtylIJD2ZnvLfftUtmJYyXRJ+KDdEwS4y36suaaTImNNkoUDMS6IDbI5v4tjYcWq8cTbENtAWaiXujRPzRvGq3jmJCcM22BjbwJvDb3Fo+K0KVdxQfpiLqYs0BhrwLlCkTVd0tsQ3c3ryDCnDLaBatIv0ZK4wXpgg4V96WoPpmJyaOI1hTyuIgnqQjkgHwWtEkPwkYirtfLWmAWKJVaL9uh/TsfjexefRhcaVdD9D2RFGCxP0ZgZpDzcznBslsERV3/VG2siSN4s0R+rxal4cKbk80c+hK8e5vXkTj2++F6/mYVN9F62xRp49c5DcMgu22NJmojjGYK6ftJmiYOcxHXNVbQsSvvqqSPQ3eo4zkhnnvu49hH3z37c5o8CxgTN4NA/bG9ej36A2Np/e/oGKZ9IUJBLLsUkVM5wf6+ErR79HwSrwUPddK9pfc7CFBn8DF9MXeHngAOui68tkdtEu0pu5Qn+2j1OTrjp+XXR9mWBdTYT14IJji/ZQG/4WP4O5QXJWHoEg7AnRGGiY035sIShCWZG6W1NUAtrqqqvng+tGvnzIKoNaAoGueNCvCpAoUhLz1iBLtnDz2X8IxKxC3FJKOiMt3NO6e0VBi5VCCMHDXXfz/OWD5SORNXOcGrvIcHaM5nAlGZIz8xzsO4I1o06KT/Nye8MWavxLIw58mrdC0+dISUMwwd0tu6v2Vb+FGxihKNTUL41Ety3k2CCYRcQCQanrATk+hn32FPr9j6DtvoPiX/0pxt98GaW5De/P/grO5QsY3/0G9uFDaDt3uynhHi+ykEfbtQ91/WZEMIi0TOxjRzB//AzGt7+K0tqBtuWqYFqxgHXoIMrgANodd6Ou24iUDvbRw5jPPEXxL/8E9fa9qE3LsOa4lpjyp82mXVX4PP+Sz7r2KzOtWIx8+W+M4toS5LdQgfSVXi58+3suiV4lUpcuc+Hb36X2tu0LkuiTZ88xfuIk3R/9sGsvcwu38JMAISBcs7CV11IgHZd8XW2F+/WAaSIHryxpE5lN47z9Cs6BHyKvnF89f3jLRPacQw72Is+fRLnvgyj77ls1QlZOjmE/83Wcw6/A8MCqtFmGUURePI3s70GeO4Fy54Moe+517emuA0QgCiyQWSoEIJat9heqhqxvQ3EcnPNHYHIEMpPIs2+7wfX2Ta5N2g3pF189VoWR8as+4r44/bkBHOlgOAajhbFyodGrIaWkJz19Uyb8dcS9MY6OHQNgJD9K3nI9TuciGpPFZIU9iyIUGgPzp8aH9TCPt7vWATW++OI+uoBH9dAR7sCn+siYWU6Mv1v+zJEOl1KX2ZXYtSCJDrA+to6Er5a0kS6TAmP5US6kLiyLRO/P9DOSH6kokhfzxlgfXb+iQm63UAkpJRkzTdpMkrUymI6xamplgWBLbMeS7CUaAwnuatrF28MnsKXN3oYdBDQfexpu49X+t3lt4AgZM8uDrSsjJ681pHQVp5qqoiCwHZtnz74GSPa2b6cp4iqMQ94Afs1L1sgvuRAduPYEA7leejKXyFqzrZquNb565GkOXjrCls93LUKi5/nDV/4Wy7H4H0/+S2L+GyANbA4kgosTwpsTXThI/vvBv+CHZw/wM7d/dNn7C2khbq/bTdbK0pO5zGhhBK1klTGUH+LHfc+TLWUr7UzsoiXYirIGk8KAHmAhybUQgqg3QtpMI4TCtpotAOVnyVJI8ZVR6KAJbcnWGcuFMjUAWyacKpXo80NiOgbeRQInihDEfGH6M8MztpRkrTymY80i2K81tic20BxqoC8zTXaeGb/IhWRvBYkupWSikOTNgUorl3WxNrpjbUsOvsW8Ya5OjS9YRYp2kYDyE1os7L2EcByRaEaeO7akzWTfeUT+TvDdYCS6UUTpXIf+wKOIcATz5R9jnzyG52OfwfPoh7FOHsN8/RWc0WFkOoXw+VHiNXh/5pcQ/iAiXoMQAuk4qBu2ICfHMQ8eQA71w9UkuuOABO2Ou/F+7DOIsDsRdXbsxum5hHX0bZyTx29oEl0Wcth/+b+Q6Um3cJhpgGG4vuWl38vLl6MevIU1Q+32rez9Z/9o1ckHx7IYP/4uvS+8RNdHPnTLMusWfnKgeRDb9yO6q69RtCBME3nkJeTpt1envesJy0IO9U0X/lwEMjWJc+AH2C9+H8aH1ybAahSQZ45hT4zCxAjqo590i26vAM6V8zhP/TXOsTdKdjdrhGIeefYE9uggcqgP9eGPIWrqrz2ZPOd8eOpcrU5fhOZBNnai6F6cC0dhtNctMHr+CHK0D9HUhahrc+ty3KRB21Uh0YUQNAYaOD1xGkM6ONIhbaTJm/kSyVAJW9r0ZvrcbUuF1pqDzbw7fhJLWhSdIiP5MVpDrXOmyieNJNkZJHqtrwbfIv7kLaGlpwwrQqHOX8fO2h2cS56v8GAfyA5gOMaibfg1P1trttKb6SuvnzLSXExd4rba25ZMapyePEt6hgpfUzRags3U+RNLaudGgpQSe4Z6rhoIxJwe+KuBnJVlINvLpDGO4RjYjlURtFgpBAKJA1Tff7/mY2/DDjbEu3CkJOoJldL5JV2RNi6lekn4a9gUX77X/vVAxBsi4PExnB6naBuYtsW3jj1HbSDGQxvuLCtBLcf1LVeW4YfrSIex4ijn02cp2stTsa82htJjXBrvx1ikoI3f46M/NcKZkUsUrcWfNzcydFXnvs69fPHQX/Ps+YMrItHdd04jD7Q8xMmJE5yePM1k0U27K5Ysehr8jWyr2U5XpBu/6l8hBT03App/wVYnipP88PLT9GcGUBSFrfHNZK0cx8dOEPNG2Voi1auBEEvLXrkaqqJeMxL9ehd+tKXDpDFOg3/h976qqNQHErzL+YrlI7lxskYer//6kuhhT5D3te3hr09+v7ysPzPs+qI3bMOvu+fTljbvDJ9mspiq2P62uk20hZuWfO03ButcUnHGBGiymGKymCag3yLRb3YIrw9Z2wTBCGRTi28whf6LkByHaOKGm/QoTc2IaAzh8aLU1eOc1VG3bHfta3x+lEgMWSi4/t0AQqC2tFe0IRQFpb4RpaWt5OOdd0nkq76r0tCEftd9iMh0hodSW4eybgOceAdnsNJS6YaDZeEcfnltyYJbWBIc22bo0Ftc/O73aXvw/Zz80l9STCXZ8oWfouvDH8TK5Xj93/57Jk6fJtTezkN/tLtye8vi8g+f4cJ3vke2fwArl8dXU8OGTz+JtyaOmctz/uvf4tDv/h6OZbHuY0/Q/bEn0EMhcoNDvPrP/hWjx45TGJ9k7IQrGtvz2/+Q9sceuSXQuoX3NlQVUdeMqF+e7/fVkKYB0fdIAUXpQGoSmU4iIrGFV80ksZ//Ns6PvwuZJYwrlouRAewffRNpmWgf/Kxb52UZcM6dwP7OXyJPH11anZhlQ8LEKM6BH0JqAvXJX0DE666PKttxkLmkW0R0imNQVPD43SLPum9F/RKajkw0o3h8OOc01z6pmIfhHmRyFHnxOHhL+/J43XO4GvPHYAyluXvl7SyCVfMGaA40oSkahuNegHmrwHhxYk4SfTg/TN5yB29e1UvCl6DGGyfsCTNRIkL6s31sr906y/fTciySRoq8PT34awm2zPI1XS2oQqXOn6DBX09PZlo9nzYz2NKeVy0/BYHg9sRODvS/jGG4F6iDQ392gN5ML+uj66oeoGTMDJfSlyjMIAJDWpAt8dn+qDcqpJRY0uLc5AWOjh3nSrqXjJmtqojcTGyOb+Tzmz696n3LmGl6MheYKI6tKnE+Eysh8wzLcC1c0tOTNInknuY9bnHcNQosrBW6a1tpCCf423d+SE0gyutXjtIz0c/DG+9i7wzf7LHsJKlChqg/hLYEVaWUEsuxuJiaTaC7VhA6QS2IR/G6BUFX+A7zqatLMPk1L5qiMp5NLvkeuRER9YZQhEJfauUFxVShkvAl2N9wF7cndpMuvRdUoRLUgwS1kFvrQqhrNgn0L5KJ9MbQIXyqn09teJK/PP0VJBKP6sF0LPoy/Usi0Vd6capCrbBdmwtXq79vxMnzbIX6bKWLxMG0F08h1RSN7lgrL/S8XrG8NzVIqphesg3KakNXNe7v2M83zvyobJdkOTYnRs8xmB2lK+bWQTEdi5evvFWxbV2ghi2JdUS8S7d5WxdvQxECZ8ahHcmNM5IdpzlUv/wvdAs3BoRA1DcjE81LI9GNIvLCCWjqQHhWNrlabQifH6Y8Rj1elzwvqcQRikuES6esrJaOg0wlsV55wVWPDw24Ht+FAnK8ZEc5zztXhMMozbNrEIlgGBTFJVFuaMhbCvMbDVJSGBvjwre+jeb3sf1XfgHHsgi2NKPoOnokwp5/9o+5/MNnePfP/mLW5kOvv8n5b36H7o98mJqtm3ntX/87Qm2ttD/2CEOH3mTy9BlCLc3c9mu/ROpyD+9+6S+IrOumaf8+/IkE+/7VP+P0V/6GyTNnufN3/g1CUfDVvkeIwFu4hYUgFFjGOGn+9gR4bi5r1YUgC3nkyMC8JLqUEop5nBe/j/Ojb1zb4GxqAufH38PWdJTHPrkkj3QpJfL8u9jf+BLy3HG33su1RD6L8+YBkBL1838XguFrN+eSEmkbMD4Ixaw71pkpRC9kkdkkROsgEHGFUVX2TVom8twRt4181m3fKIJZAGfGMS7m3B8EUhlw21+l7y8aOmAeEl1K6QYM8mkQCiIUh6nsZ6Es6RysGvPaFGwupdO7N0/BzjNeGKM1NDul8VK6p6w89mt+Ev4EUW+EmDdaJtF7s32YjomfSlIqbWZIGsmKZa2hljXzLhVC4Nf8xH3xChLdcAwsZ2EV6RSinghba7bw2uDrZRuJ4fwwF1IX6Yp0LliYbiYupC4yVhif7huCWn8tG2Lrl/CNrh9ckjrDs1d+zGuDh8hZ+SUTgwKBpqgUFiBIbGm7nrwzbgQpJVP/ian/rrpRDKfIYK6PseIocxEzYobP71Rb072a+Qlzbq8JDZ8WIOaJL/l6nSgm+ea5Z3ht4G10VZ8VNPq9e//pTUegA9zVeTv723fwrePP8Xe+/js40qE5Ws+v3f0ZPDOiyudHrzCQGqEz3kJAX9rgZMIYJWlOlv92iwvHaA12kPDVoQl9tbKXUFi955CUkpHsBHmziKqs1Mzj+kNKSU9yAMM2Vy3oJ4TAq3rxKB7Cerh89y215sBy4Ra/nn8/A9lB9jfso95fVz5/qlBRhVKR2XQtoFdRmNSSFuPFEQJaEJ/qR0Vz+y1WFvxbTZiOwUh+gIAeQqAwaYzN6ptEVvWM9Sga2xIbUIRS8S46O3GJodwYHdGW6xpIEAhaQg3sadzGwb4j5eXHR89yJT1AR7QFRQgmCineHnq3YtvNtd1srOlc1tjotrqN6IqGNWPA25sa5HKqj9vqN15Xr/hbWCUkmhFNHcjec7BIRtRMyBOvI7bfiaxrvkGeCLgTL2X25GdWcc/SsEw6DtbhQxT/93/DGR5ANDajrtuIiNUgPF6so29jv/36XMM4F6oGvjkC5jdQUOEWbj5IR6KHwqz76BPUbNtStlAQijv3CDY14q+bXUQdIHX5EqrPS2zTBqLr19Gwfx8TJ0/hjbqBpFBbK91PfIiW++6lyXa4+N3vM3nqNPW7d6H5vASbmvDGY+ihEOGOdpT3QmHcNYBjWeSTKYxCAX8kgjcYwC4ts00LVVPxBoPoAX/ZIqqYzWLk8ji2jebx4A2H0L3zCxqklDiWhZEvYBsGtmkhS+MTIRRUXUPzePAE/Ah1aSIRx7Yx8nnMfAHHspBSoqgqmteLNxhw21tillG5v7k8VrGIY08JDBVUj47H70P3+29IUQZev6t+Dawyie5975DoFPOupcu6eUQ/toV882W3aKl5bec1AKQmsF/4HgQjKPc+WhWRLh0Hefks9rf/L/LMsXkD5msOy8Q59CKEY6hP/oIrALgGkI4NYwNg5NwAUiACmse9dm0TClmXZJ4cBEVF+sPVj/dsE3nmLUCWxlCLWfrISnJ9FSAXCojk0zBy2X2/6j4IxcAoQGoMgjEIVG+bu2okeo0vTlALlAto5q0CY4W5q9peTl0uT1j9mo+Ev5awHiY2oyDfQHaQol2cpfROG2mSM1KWBYLmNSTRwVWreZXZad2WU13ahxCCPXW7eWv47bKli+mY9KR7GM6P0BxsWrQNy7G4kLxAsjgdQPCoHrbVbF3Ul/1GgS1t3hw+zMHBN8iaOVShEvGE8SgebOkwUZxAExohPYimaDjSwXRMclbeJcaFQke4jXXRLjbFN867n+H8ALXeOiSyRFhp2NImbSYp2Dl04SHiiaErnvK1JaUkZUwyWhxm5g2vCBWv4sGrBvBrflShIYQgbSSZNNyARkgPE9YjKELBljamY1Cw8piOUS5c6VE8dITWUe9vRF0GgTiYHeVyqo/f2v1LbK6pPnvhRkdtMMZv3vcF/LqPQ1eOEfGG+LW7P8OdHTvL60gp6U0OIpHcv/4O4v4FimHMgdHCaPl3gSDhq2NDZDNhz7VTmWaNPEPpMeyS+itTzGI7Nj0TA/MGPyzH5NvHfsyF0StsSHSg3cCTmol8EnMBIsaRDhkjz5cOf4OJfIq723et6v6n7oe5iF6nZDG2Fop0r+pdcGAR8YQZK4xR56/DkZKCVSBppBgrjFHjq1nVviwEBYGu6ot+f4FAEQppI0maJF7Vh18LoCslRf81Ck4sBE3RqfU1oAqVjJUioIUI65XPBFtaTBTH52lhGqqi0hpppDvWyrmJ6WLnI7kJDg++y5badUS8oVX/DtVCCEHYE+S+tn280X+0XA9ispDi1NgFbq/fQsQb4rW+IxSt6cmLT/OytXY9reH5a8UshPpALZtruzk8dLK8bLKY5ujwafY376QhePPax92CC6Hp0LHJVZYP91a/YSaJPPoK4v0fm1Z+Lxe1DdBaUgtFllZoeSWQyUmMv/wT7Atn8H7hV/D+1C8iPO53kYU8WKZLos8HcWNm6dzCzQ89GCDc1bFkIjPc3ob1zHOMHjmKNE0mTp2ibu+05Ys3HiPY3IhQVVRVRfN5MXP560ce3aSY6Ovna//oX3L469/mo//+/+HeX/pZTj3/Is/85z9g+Nx5ajva2PfZT3LnFz5PtLmRoTPnOPCHf8aRb32P7NgYTdu38v5f/0V2feIj+MKzyZpCKk1ycIgrR45y6tkX6H3nGGMXL5NPpQCBPxqmpqOdjr272fmRD9CxdzehxOIZA1JKjFyO3neOc+Sb3+XU8y8yev4StmUSSiTo2r+XPZ/8GOvvuxttAYL/atimRWpwiAuvHeL4U09z6c23meztwyoW8YZDNG3ZzJZHHmDHEx8ksa4TX+j6jafmgnL/JwCJiKzmeFyAL+iq0Vf6jrwRUCzAcN/cn0mJvHQW62t/fH0I9CmMDeO88D1EQzNi446F7eakhOE+nKe/hjx55Po/Ax0H58WnEOu2oO6979pY5WWTboHwUA3E6mdbcAaiyHwaJgYgMw7egCseqBbX+5guhIlBiDeB7oXxkquD5nH7bC8ti3DVSHS3uGcTg7khJJKiXWSyOIFT8jGegumYDOYGy4pBv+an1luDR/US9UZLftGSgl1gND9Gra+2ghjJmGnSxjSJHtQCxD3RNR3QusTB7It6KeUSmoNNtIVaOZ+6UF7Wnx2gL9NHY6Bh0SDASH6UgewglpwmqoJagK3x6cjgVJr7jTq4Hy2McXbyHFkzh0fxsCm+gV11O6j315E0Uvzpu39Bwl/Lw20P0Biop+gYjObHODt5nvPJC+StAnc23sHe+t34tPlf8ikjieWYSCnxaj5inhpyVpaUOYlX8ZGx0tjSJuFvQC35klvSJG2mMGf43OuKh4SvnqZAC0GtMs2mL9tD0phAIol54rSFOtFLgRYpJaZjMmGMMZwfJGVMYjgGA/k+fJqf6DKU6BKHkB6gOdRww57f5WJdoo3/9OHfImcW8GneUlHCStzVeTsd8Wa6aluXRKJLJGlz+nnhU300+luuKYEO8O7geX7n6T9kMD2KlJIrk4Nkijn+8bf/C15t9iBLAqlChqH0GJqi8qv3fOqG9iL+q3e+y6XJeQZZQN4qcmmijyvJQfyal8c3vO+a9W28OM5IbojOSDf+BWpnLAceZWEPvtsTOzk4+BoTxUnydp6XB15lrDCOrujXNINICGXRvoIbME54GzAdg7ydo2gXSBoTqELFq/rxKl40RS8HE68HFKGUa6Dojgdd8eDXKlVElmOiiXRV7cW8Ye5t3cuFySs4M6xiftzzBnc072Rn/abrmuXj0XS2JtbRHGrgSnqgvPzY8Bke7bqXkCfAwb4jODNGJO2RJrYm1qEts9+aovJI1z28M3y6QqH/1uAJ7hg+zf3tUTzq8vwnb+HGgWjuQjR1IscGl6ZGP/46YvMeaF23ov2rH/25FW2/XDhXLuOMjyH8ATxPfBKhT1/LMpPGmVw8AHcLt7AWEEJBWUaRvLrbdxJobOD0V/4Gf32Cmi1b2PT5z5Q/V1TVDZwttG/E2hQCfI9i9MIljj31NN/65/+W7Pg4tmXTf/wkz/yX/45lmOz6xBP88Pf+G0e+9T2k7WCbJpdef5OJnl6EUNj/05+ZFSw58fSzPP17/42ew0enz4UQqJpbAys9MkZ6eJTLh97mtb/4Cg/+/V/j4X/09xck0qWUFLNZDn3lqzz9e/8voxcvzWhXZbK3j7cu9/DOt5/i/b/+i2z/0OPoPh9mYeEaUpZhcPnNt/nx//giR7/7A4xsDgBF0xCKIDM6ztmXXuHcywd5/S//hod+8++w68mPEq67cYLwon1+Qd6y21QEJJph9/2IxNLr8d1wMArI0cE564PI1CT2t//vtfFAXwSy7yLOy88g6lsQNXNn7ADITArn1Wdx3nntxiF7TQPne19BaVuHaFodb/6F91ey3AnF580yFr4Q0uN3Vek3ynFaDVgG+CNgXR30Wfq7b1WNtFuCTbwz9k65UGTKTFOwCwS0aV/0kdxIuSioKhQinggBPYBAEPNE8Wt+cpb7IL6S7WVDbH2ZcJRSkjYypMzpyXFDoAFd0Zecai6lxHAMcla+pBo2sRwL07Fc5SJOWcGYMbOMFcZWdGxURWV33S4upC6WAwhpM01P5gqb4huJeOYnBqWUXMlcYXRGHwSCjnA7iRkFRU/2n0dKybbWDRXbTuZSHO89y/s27V3Rd1gpxvLjDOWGAdgUX8/H1z3h2hwIwXBuBE1o6IpGfSBBV7TT3SgOdzTs4dDQ2/zg8o946tIP0RWNfQ17FiCiJT7Vj0f1uuS5MQmAXw2Q8DVg2AUG8v1ldSqAaRtkZxRsVYRKU6CFlmB7mRyfiSlFppQSRzoV404hBB7VQ4O/ibAeoTdzmZHCEBkzxeX0edZHN88i5RdDxBMi5Anw2sBhNtesw6t6Kq75ukDNTZ1iL4Qg6Jmb4BRC0BFvpiO+vMGIMcM2w68FiHpjy2pnJagNxtjXsZ3jA+fonRzCke5TIGvm5y0uqqsa25vWs7dtOz+158P49Rs34+TN/hMcHTw17+dCKPg0D901rbyvfQ8PdO2/Zn27nL7EoaHXSfjrV51E1xeJzHdHu9AVnWNjx9kc28h4YZymYCPba7dR759/kLfaEML1/692XY/qxaN6caSNYRcp2HkKVo6sTONRvfhVP7rqRRee6xrUC2hzq5oUoRKpMlAW8gTY37yD5y4f5EpqmqTuSfXzjdPPUOOP0h5pnjO4Vw1sx3HdJpb5fHZt2+Lc0byDK6en+3dm4hIjuXECmo/TY9PBeVUodMfa2FDTuaz9gdvX/U076I61cW7icnn5YHaUp869QEOwlq2J9ejLtGVy35nyprQge0/BF0Bs2OlauowNVr9dPoNz8IcoH/pZRODGUhZWBU1zyQBVRWbTyNoEQkpkPo915E3sE0evdw9v4RaWBDObIz8ySu1t26jdthXV62Xi5Gnqdu1cfGNc6yM9GMTM5iiMjeOJRFA0dVmE/k8Ket85xqU33qRp62bq1ncz0dvPuQOvkk8mOf79p+k/cZL+4++y4b57SHR2MHLhIqeff4nU8DBvf/Vb3PbhxwjWVCqgzXye0YuX8QT81HZ2EGtpIlRbiy/q2ilkJyYZOX+RgeMnMbI5fvw/v0ikqYEH/t6vzp+9ICXv/vA5vv2vfofMyCia10vdui4at24iVFuLY1mkh0fpf/ckB/7ozxm7fGXR8+7YNlcOH+Xb//J3OPPCATwBPy23baNufTehRC2KplFIpRi71MOVI0cZOn2Wp37nP1PMZrnnF3+WQOz61ptZUwgFUduIuPuD17snqwPbRiYnkLkMIjSDq7IsnFeeRp4/cWME32wb5+RhxNFtKPc8gpgjC0CaBs6Jt7Bfe8716b6BIEcGsF/6Puonfn7Ovq8qHNsdAy3kdS6EW2RU2ks7v0KB+PKyYFcLIhSb/0OPH3LJ0neTYBrIYs4NEi1RHLSqb8fmUDMKSrkgY87MkSymKkj03mwfZklNrSse6nyJMhkY88YI6aEyid6b6atQQZmOScpMVXjJTpHo1SJjZhjKDTOSH2GyOMmkkSRjpMnbBQzboGgb2NLCkja2Y2NL92elRf0UFNZFu6n11VbYS1xMXWIkP0JYn59Uzdt5ejO9pGcED1Shcnvd7RXr/fCdl7Acu4JEBxiYHOH3vvNF3vfb15dEz1o5UkYGVajcVrudWl9NxXfWFQ1bOphXec17VA93Nd3BRHGSp3ue5UD/q3RGOmgIzF3gTBUaNb46FKGgoJA2U2iKVvZC96g+HGkzM+pkSYvijGK1YT1Cna9hTgIdXKJp6rp1r425HzABLUhLsB3TMRgvjpIyk/Rne1kX3VRWwVcDgWA4N8ax0dO0DzcT1AMVpM4vbf/sgur8n2TYM7I3dMWz6sU/q0F3bSv/8pFfYTyb5NTwJX73mS9yuPckf+eez9EcnZtM1RWNxkiC7U0biPpCN3QGwhOb7mdfy/Z5P1eEQsgTpCveyq6mLUv2tV8JTMdcsyLBuqIv6oPbFm6lNdRSDp5ej2CXWOZ+BQJV0fBID7a0sWwby7HIOGkUO0tADeHXAm5R3uuA+YLnilAI6tX52ilCoSvWyqNd9/CVE98jb02rr17oeQOv6uHJzY+xuaYLpco0S0dKsmaOnmQ/PakBNtZ0si7eXtW2cyHqDXF7/WaevnCAjOmOj1LFDBcmrtCfHiY7o89Rb5itifXEfNX7+l0NIVzi/hObHuF/v/WV8j4B3ho8jkfVeXLTo+xq2IqnCpsgcAP6BatIT3qAy8l+GoK17KzfvOw+3sLqQLRvRHRsRqbGYSkFMS+fcm1ddt9/zXw8VwtKcytKSztWz0WMr30Fbdc+JBKntwf7+DtgWeB5D6Th38J7BqNHjzN69BiDrx8iPzLC8T/6M4JNjTTetR9vPMbY8RPogQBGMsXwW4eRlkVhcpI9//gfVtW+6tGJbd7Iledf4Oj//D8EGhpoe/gB4ptWX6n7XsGVw0dp3bmdj/+nf0f77p0Mnz3PN/7pv+bod35A3/F3GTh5hr2f/jiP/bPfom5dF4OnzvC/P/IZRi9cYry3l6Ez5+i+846KNrd/8DHOv/o6kYZ6OvftoWnbZqJNjeg+d8xcSGe4cvgdnv+D/8Oxp57GyOZ486+/zr7PfWpehXdycIhnfv8PygT6hvfdzQN//1dZf9/d+KMumZ0eHuH08y/xo//63znyze8u+t2z4xP86Pf/gDMvHMAfjbD9g49x5xc+R+cduwnEYghFwTIM+o+/y8EvfYVX//wvSfYPcPBLf0XDpg3c9sHHlmxZdAvXEbkMjA/DDBLd6TmLffA51+7lRsHkGM7R11E2bIOWzsrPpIShPpyXn4Gx4evSvQVhFHGOvoGy+x7Ehvnn06sCRS0VWrdAanPPY53S50JdWr0XzYNy272r19flYCHRYbQOUqNuIMEoIMcH3ECBL4SYR8w5H1aVRG8I1KMpGlZJWZmzciSNJE3B6YhEb8YtGAouOVrvnyZC4944YT3EcN69uPsz/ViOhUd1B7N5K89kcbJin43BxqpI9JyV49zkeU5PnqE/289IfpSCfe1ufCEEIT3E9pqtvND/Unn5SH6E3kwfraHWeb3Nh3PD9GcHKoj8hL+WznAHUCrqISVSur9P+S4DOI5Nz9gA+evpVVWCaRsU7SI+zUfUG6koLqgIBV11iRpjDk8iRSjc0bCHF/sOMFYY5+zk+XlJdI/qZaI4hq7o5KwsRadA0ZH41IAbEMGeRb440sGY4XEf8UTxqPMTfVOEPLhe73KBNJCAHqTWV0fGSlO0C4wWhmgKts7y8V0IXs3Lrvpt835+M6vQ1xqiotinmJd4W2soQiERinNvKM6u1s1cGO/lQ1vvY3ND13Xpz2riyW2PXe8uzAvLMVccBJ0Piylpzycv4Nf8NPjrUa5rcVhRtZJa4mbXmHaRolPAsIs40kZVdMJ6BI/qBSnJ2zkyVgohFALaKhZlWgIKdg5b2gS04IqI/IgnxIMdd3J+oocDV94qFz63pc3TFw8wkh/n7pZdbE2sozXcSNQbrjj3pm2RNjKM5ScZyo5xJT3IldQAFyevMJwb55dv//SKSHRN0eiMtrC5tos3B0+Ul58av0DayGLY0++u5lA9uxq2rPha86g697bu4fTYRb5//sVycNuWDq/0vs1ofoJ7Wnazs2EzndEW4r5ohX2M5dhkjCwThRQj+YlSQKGfS8k++tPDfHD9+2+R6DcCvD7EzruR/RdhqKd6xZFpIN96AccfRNm23y3OdpNAicXxfuzTCE3FPPgS5qFXEboHEY6g7b8HdecejG//7fXu5i38BEEoCjWbN7H1l352ThWwtG3sYpFodxeRX/4F7GIR2zRBSozJJJee+iE1Wzez8fOfRQ8EKExMcOAf/hOG3nyL1gfez8bPfZpAw/ScacOnP0motRmldN8KVSVx23a2/sIXmDh12rXkuIHr8NwIcGyb3Z/8GM3btqCoKrWd7ez48Ac49t0fYhWKxNsS7PzYh6lb142iKtSt66Zr/z5GL1yikM4wcaUf7qxsM5So5UP/zz8jXF9XsnCphC8cYsN996D7/Vx8/RCTfQMkB4cYPHl6XhL9xA+fpe+d4wAkujp4+Lf+Hlsfe6iCxI401LP3s0+i6hpf+tlfw8jl5mxrCmdeOMA73/kBiqbSdec+Hv8X/4jmbVsqAuqax0Pbrp2E6hJM9vVz+BvfYej0Od59+jm69u+7oWxdbmFhyHwWOTaMaHdtKGUh55LRo0vIYLtGkOdO4Jw9jlLfXKHolvkcztuvIM8eu469WwTjIziHXkTp2rS2YyqPH3JpyE4gI3WIqxXYjoPMp6CYd/3Ql8AxCUWB+Nz83I0A4Q8jhQLF7PR38/gR/uD1VaIHtABxX5yBrJtyPEWiT8G0TYZyQ9ilKqxexUPjDII97o0R9kyrp9JmmgljkoAeKLc3OaOwpqZo1PkSixIZfZl+Dg2/yemJ04wWxhYkPKegCKVkL+KqrAzbKBcFXS48qodN8Y0cGn6TbEltb0ubs8lzbIlvpn4OUtiRDv25AYbylVGzrTVb8ak+pJScGbzIHz3/N7x85m2kdDgzeKm8npQOA5MjPLr9nhX1fTXgIJFIFKHMUq4JIfCrPgp2gZw598s74omQ8NUymBtiIDv/g7vWW8ukMUHeyqIpOlFPHNuxMB2DoXwflmPhU/0oM8hViVNSp7vwKr6y1ctcEEJxffKlq3SWC0w8BYKoJ45X9VG0C1jSYqI4tiQSvc5fw0fXPVL1+rcwDY/qoei4ATMp3fN8vYMOnTXNhL2BxVf8CUdv5grjxXFagi3U+qYH3MP5YZJXBVTnw2hhtEyKrjY0oS5IVfake7icvkKNL8766Do6I50EVtlSphoIqHjezQdHOmStNIZdwHQshACP4sOretEVD5rQyzZWqqKRNCYo2oXrRqK7z1N7lif6UiGEoD3SxKc2f4C0kePw0LvlwIstHQ4NHOP02EXaI03UBWoIe4L4NG9pbGBi2CY5M0/SyDBRSDKamyBtuLZ1IT2w4PuhWjSG6tjZsIW3h06W+3Zs5AxpI4tVIri9qocN8Q7aI4sXK18MQghqfDGe3PQYqWKGl668Wb6PJJJTYxe4nOzj5d63aAgmiHhD+DUvqlAwHAvDNsiZBdJGlmQxw3B2jMlSUXif6l2O/eAtrBFEfRti173IH38TCguTJxVITyDfeA6p6YhNu68Lka5uvx3/b/5z1C3Tyi3PY0+g7dyDiLs+wUqiDs+nfxpUDaXGXabt3oeoq0e/fBGZSYOuo9Q1oHSug2IRtaUNpXvDtAJLCJSWdvy//W8Q9Y1zKrP0u96HUt+A2r1h1me3cAsLQSgKsQ3riG2Yu85A3a6d81qz5EdHsYoFrEIRhCueSl64SH50lGBLM5HODiKdHRXbdH/0w7Pa8UTCtD30AG0PPbDyL/QTgvY9t6N5XaJO83qJt7bgDQUppDPUb1hHvK0FRXXHXkIR1Ha558EqFimk5vaRjjUv/v5uu30HkcYGJvsHsYoGyf7558TvfPspLNNA83rpvGMvG95/z7wq8E0P3EfHvl2cffGVBff/2v/9/7ANg3B9Hdsef4TmrZvnzEgTQhCpr+f2T3yEw9/8LrZp0nf0BKMXLt4i0W8m5LPIsaHyn/LsCZwzx244SxQAsmmcdw+jbNkFDS0ASMdBDvRgv/IMWOYiDVxHGAXkxdM4/ZdR2teubpbwh93CoZlJsCyk1w9KSZHu2G6R2ELJ4jgYQ7yXrBeFQPhDbvHfGcuWg1Ul0QWC5mDTDBI9T8qYtiAZK46TMTNlEjukh6j1TfuB+TQ/cW8cXdHdFHzp0JfppyXYXG5vphI97o2VbC3mJwcupS7zQt+LnJk8O4sEV1CIeiM0BhpJ+GqJeKP4VT8+zYeuaCgoqIpKxsxwaPgtzk6eXdHxUYRCwp9gXbSbo2PHy8svp3sYKYxQ66+dRdymjDS9mT7y1rTViK7obK/ZWv7etaE4923eR//kCJl8lo1NneV1NUXl8R3v532b9qyo76sBTahoQsOwi1iOhUSW1XKqUAnpQSaKk0zMCJTMhBAQ0AOunUDJV38uBLQwmuKq2jWhoSk6EolhF1yrIBWCeqhCuSglFcEVRSysGlWEgipUTKbsIhZmBLyqD6/iw6WzZEVx3JXicqqP1lDjTekve2zgLK9ePExnTTP3dO0mtMrEssC15pkqLmo6BgU7T2gJFlBrgQc37KcmEKM+vJoV4d97eG3oIBPFCdZF1vFg68Pl5eeT5zg18W5VbeSs/Jop0RcLxmyv3U5DoIGB7ADHx07w5vDbdITb2BjbSH2gHvUaBnOqsdywpU3aSOFRdYJ6CE+pkKhCZeBTCIEildL76vqxoYpQUFbp3GqKxva6Dfz8jk+gn9B4a+B4hbVYyshwfHR6DKAKFSFcz/NqAvMrRVD3s6mmi8Zggv6MG1QfyIxUXNtxX4S9TbeVs/dWClVR6Iq18IXbPoZH1XnxyiEKM4rx5K0ip8cvcnr8ort+KbjsSLuiSOst3OBQFMTmvcjBK/DOy24abzWQEsYHcV79IcIwULbtA8+1s+oCUNs6UNsqCUJt5x7YOT3mFZEo+p1XFbNWVNSObtSO7jnbVRoqPT2FEIiaWjxPPDl/XzZsRt1wK7viFq4tvNEoHY8/yuXvP82Lf/c3QVFQdQ8djz9C4x37VmcnioKy6x5EKILM5yCfdYm1qX9z08so5G4Mj+Q1hubxEEokyoS0EALN68UbClFIZwjX1+GPTIulhBD4wm4NCceyMQvLJyBVXcMXdn3SpW1j5PNzrpebmGTw1Bmk7eCLh+i6cy+ewNzzLCEEut/PhvfdsyCJnh2f4PKbhwEI1sTp2LtrQWsW1aNT29GGx+/HyOWY7OsnNXgD2mncwvzI55DjwyAlspjHOXLwhlShT0GePors70HUNbne32YR55UbUzl/NeTwAPLkEWhbt2xyd1FoOkTrITni+oMXMtP+6NJxiXTNC5E6l2y+ge1klwqZmQDdh/D6wTKQqTEw8hCKIQJR1+qmSqwuiS4EzYFm3uJtwCUX00YawzbwqB6Gc8NlP3NVqLOsWBQhSPhrCWiBsoK9L9MHDa6Xd97KMzlD2V7nq8O7gOXGeGGcg4OvcWriNNYMX2RVqLSFWtlXv5fWUAt+zY9H9aArOqpQywTqFGkwXhjn1MTpVTlGIT3M5vgWToyfLKu68laeM5Pn6Ah3ENIrizSNFka5kr5Ssaw70k1NKfgghCARjvPhXQ8ynBrHsAy+cO/Hy+sKIfDqnkUL4F0L+DU/QT3AZHGSZDGF5Vjl868pKrX+Ws4lLzCcHyZjZgnplepCKSFjZpHIRZWlqtCwHRtTGpil4InEJVS1UiHaClJJuEGgKTLEkU4FyX81FNQygTZld7AQFKGgKzqKEDhSUrDnHvAsB1878wN+bedPEVSuvcJ1pXi79wR/9NpXeV/3HnY2b151Eh0ENd4E/blewK0vkDSShJaQBbAW6K5tpS3WiOcmSoGvBlJKcmaeI4OnuDDeS87M49O8tEeb2NG4iRp/dEne7opQKFj5WVYkeSvHpDGJT/UtSmQ7rI0Kfap/C6HOnyDhq6Uz0kHGyDBpJOlN9/F0z4/wql5uT+ykO9o5r5XXaqIaew9VqNR4E2iKhiLUBb+fIhSCWpjr5lAD+LQgWTNF1swQmMObfakZJx5VZ0fdJn5jz8/wnbPP8/3zL5IyMnOua0u7qviBX/OtSr0KIQRdsVY213aXSfSZBLpA0BBMsLtx64r3NROaorE+3sEv3/5p2qPNfPvMcwzn5i60bkvHHYAvAq+qX9O6CLewOIQvgHLX4zijA3BlCYIRx4GxAeTB7+PkUoid9yJC7+GicbdwCzcYFF2n7cH7qdmyGSOZRErQ/H5CLU14V6mAoxACujcj2rrBtko/dvlfOfNvy3SJ9WzG9VLOZZBT/2bTpX8zJTuB9I3lqbwEeEJBVE9lTRChCJSSDY43GET3Vb77pz6TUiKd+cemtmUxfPY8PW8dZuT8RVJDw+STKYxsDrPgZh1cOXLUbQdXaTsXxnuuYGRd0Zkn4KdhnkyHKai6RsOmhTNpBk+dwchky+3/f3/3H+ENLjx3K6QzmEWX/8mn0hQyc4+rbnhI6RKM1caIhID3gi2SacDkBLKQR146jbxwqvoaKh6vS2bXNbmWGZrmPgPGhpCDfUvLfqsWmSTy3Ank+q0QiiAHe3EOvbD0doSAcAzR0IKIxMEXAKRbkHzgCoz0Vy86qBbZFPLiaWR60t3nmkAgPH5kTRMUo1DMlRT60lWke33gCYLuvaHrsS0LyRGobXYta7JJyKVc8UdmEql6XJV6lVhVZlVBoSXUXLEsa2bJWlmXRM8PUyz5XWuKRluodVYbdb4EQd0l0SWSvmyf6/MtbVJmuqKoaL2/3k0LngOOdDgx/i4nJ05WEOia0NjXsJeHWh8gqAfRhHZNLxBNqDQHm2gNtXA53VNe/u74Se5s2E9QC5b7YzomA9kBhvMjFW3sTGzHp/7/2Xvv+Liu+8z7e26bPhj0RgAk2HuRqN6tZkluSuIWpb4bb5KNE6dszZZssskmG2ezqU6xHfeS4rjItixZVpeoQomk2CtIguhternlvH+cwQBDdAIgQQqPP5KFO7ecuXPLOc95fs/jL2u3rmn8+A0P4ElJyDeRTPWkd8UtLKJWhEpfjOH8COdTF9hmZ4j5VAfP0ixaws3s4TXOJM5ybOg4O+u2lxE/XekuejN96EInYEw9AE/acXqzXXjSm0AcNQSbiU5yzYxWHXjFh+FoJcRU58wQOrpQt0/ey2N7NlLKaa8ltS/1uSNnLicave4FAk1oU04cnE91LZrSdrHRmxykK9FHS6ye4CIp2Kr9NQSNEBknTc7NMZDro9JXRUAPXrGXg67pV2XlwHRwPZcXzu7lU699la5kHwW3eE8gMHWD2lAVH9r6EI+sv4uQGZjVuX+g5UHyXoHAJGGwASPIvSvuK8vVmAx7evfMWrU+V8z0TB218fBpPhIkOR0/w9HhY1i6SaUvxis9e+hIdHBr081ErSs7sQOKiPVd9G4ZD4kcN3gQUwYvXy7knSzxwvCkz2pL89EUmrsPuakbtFU08/Pbf4zbW67n8VPP8Py518k6+eKAdfLRkyj+WwiBqRm0RBq4ZcUubmu5jjWxS/dDH4+GUC2ba9fyevfbZO1y4iFg+rm+YSsVvksPFJ0KhqbTFK7jwxsf4obGbTx55kV+dPZVRnJxlccyzYhydMJaFxr1oRpubt7Bna27WV81cxaEstWbbGLk0p7bAoEu9LJ9Gtr0lkzvKESr0O79IN63Pg1DvTOvPwopIT6E3PMksvss2s0PQkMrjJ7ba20QdrXDsNBue/DKlrXbBeT5U8jOM/Pfl6YjGloQaxZ2AnFBYZqI9o2Lt/tQiNia6QnSeUEIhGEoAmyyj0f/o9jnkdJDeF4xnK6obBz9b3nRMttWJHs6oUj2VFKRVJ2nkYffXJqWESgl+nRh47o5ia/8uEfhxWJ9Rax7HPvRc/zozz7F+f1vY2dzOIUC0nXxPA/pSaUGlhLPcZgJ6aFhPFeNHTXTJFRTPe36QtMIVU9fIZvo6S2NlQuZLBcOHJx2/Yvh2jauPXPblwKklJCOIw+/jjx7TNlfTDP5cTFE+xa0u6euXrqaIJMjyIFuvCP7kN3npl/Z8iFWrUe7/g7E2i2IUESpnzVtzDLEcZAjQ3gHXsXb8zT0dy9oBYt7eC/ilnsRoQjes9+FzNQOBmXQNIjVoO28BW3bDYj6ZjAtNRkyOs7wPLDzyP5uvFefwdv7EqQXyGFASmRfF/LC2UUk0VHPdN2EYAUyECk792JUlX4t9t2cPBg+pFOAbBJCMYhUwlAXOHOz7V5weXK1vxp/0dsaIOWkSNlpKqwYvdk+8p56GSoSvWXC9rWBWkLj/E0Hc4Nk3SxSSkZyw2Xr1gVrp1TwDeWG6UicJeOUK37XVLTzyMqHsDRr1gSaJ72S5+h8IYSgxl/N+th6ziXPlwafw/lhTsZPUhOoxhRKnRrPxzkVP1NGntYF6mgNt07q190zMsCbZ8eRRcUZaiGgNlLN/VuvrC96TaCaxlADpxMdnBw5RTwfp8KKFkkHk7ZoKxW+CgayA3z37A8Yyg+zsWo9GhqdqQs8ee5pbM8mYoZLFj+TYSDXR5WvhphVxcWD7anC9XRhYGn+Uuht1s3gSBtjilvE0n1Y2ui1J0kUhoma0RKxfjGklMrCZpTsnsWLQiL5w9c/RaUvxt0tN/Enb34a/yRl+l2pvstiJ7AYsD0H13Opj9TgMxaekBNCYGk+1kTXcXj4bRzp0JftwdBMVobbCRjBiVUJy5gzXM/ju8ef4w+f/3vyToGQFaAhXEPIDJCxcwznEpyPd/N/Xvg0vakBPnb9BwnMQokaNEIECU36+wR0P1EzStSaXmkVMkLT5hssJhKFJCfiJzk0eJj+bD9rY2t576pHaAo1oms6g9khfnj+aXoyvUuDRJ/hPpBSknXSBI3wkrhnAkaQBq150qfffCaNNSGIWCF21G9gU81qfmH7B3mj+22ODJ7mXKKLwewImSKJ7TMsgoaf2mAVLdEGVsda2VizmoaQUvQb2vSK/rlA1zR+fP0DvHfNPROe+QKBpZuL9rsIIQiaATbVrGZtVRs/s/UD7O87yoG+Y3SMdNKfHSZtZ5BSYukWQdNPlT9Gc6SOtopmttauoyXaiKHpyi5vhnNiCJ13t9/O3W03li03NQPfJdrVbKldy9+++3cnKPiXVfEKApB1zWgP/RTek1+D/gtzG9QWcnDybbzuDsSWGxHbb0dGYgjDvDYHY1crfH70D/7ClbXcSCVwf/BPC0OiGwZi3Vb0D31s/vtaLAihyJlrHcX7XAh9dmXxpWtQkcNCylF/TeSBV3FOH1myJLpm6Ew7oSvmFu9dyGR56pN/xrN/+Xekh0fQdI1ANErNqjbq160l1tRAsDKGFQ7jCwV56pN/zsDpjmn3aWdzingHNE3D8M1QFScEVnD6quZcMlX63axgkJrVK9HN2VfV+sNhQlWLSA4uFKSE7g68Z7+h3oWuy6wl6JqmFL320rx2LwXy/Gncr/0NcqB3ahW6YSJWrkW/9wOITbvAFwBdn7pfGqtGX7EK/YY7cb/3dby3XoLsAinTO89AXxdSaHhvTu/xX0K0Eu32B9HveAgqKkEvCm0na7+UUFmLvmoD2u47cR//KvLkIVWNM0/I/m41ybxh++KOtcY/r98p0Ex1Xzp5JSSoUp7vEjHnPtGC27mYmkFjqJEzCdUxStlpUoUUSStJIp8oDV5CRpD6SYI0w1aYSl8lutBxpYvjOXSlu6jyVTNcGBlbzwxTYVVgaJN/hcHcID3ZiUqa25tunxOBDkqVnHMWrtzMb/hpi7RSG6gpU5nvHzjAjprtGKb6ToO5ITqSHWXbbq7aRKRIPF+MfeeO8KkffqX0d8GxSeczDKXjfPCGd19xEj1iRmiLtHB46CgFt8BIPk5zuKlUDVDrr+bG+uv4Uefz9Gb6+Obpx/nm6cfL9iEQNITq2VI9teLEky4RqwJ9imtjMhiaQcAIkHaUh3/GTmG7tlJlTtINMjWrbAKnP9dLbaCBgJj8ZVHw8mTdTIn4mOq6HQ+B4CPr34eh6SQKSVaEGviJdQ9NWO/P933+qlXS1QRjRHwhkrk0jucyW9ODuUwaqGurntXRAh3Jk+S9PJ3psyTtBK2hlVRYMYyLrJzUhnP/Pmqzq/XXuHScHj7PX+z5Ioam87O7P8h7N76LhvBYaNBAeojvn3iRrx54nM+++Q1uaN7Kza07Z9zvVM9pn+6nwqd+t5kwG8JusfDkuacYzA2xtWYLP7b6/YSt8jKxmkA1Fb6Kq+aa8aRHvDBM0Jh9udtiwtDMWV0DlwpNaPgNH43hWt6z9h7es/aeRTvWbDBKlFtzTJBfSGhCw6db+AIW97TdxD1tN81rf6PVGpOFjZu6iXkJ37Xg2Jwf7Cadz2AZFusbVyk1vKbjkxb9iUEGksPKIqd2xRWv0lsyEEINpppXoz34k3jP/itcOAPuHBTL0lOl1HueRB58FbHrLli7DcIVCF/w2ihtv8ohhFDKullAOjYkRxCxmgWdCJGmOSfv0Rmhawhr8W3RljK8VBI50A+WhVbfOFEFvRQxSuIoT80ySN3givrFXUZIz2PP57/Cc3/9aVIDg/ijEXb92Pu489/9Aiu2bZmUpN7z+a8ycObstPs1A36EJkrHcPIzkLpS4s2gEveFgqXfrWHjOj72T1+kZlXbtNtcjZDZNN7rP4Te8+q95g8pOw9NVx7KqbgiiYNhQChiOasmGETrWli5EdG8iNUhlxvJEeTRkak/D4TQdt+B9vBH0Goapl5vHNS7yITGVozHPo4TqyqqxhfA7sfz8PbtUb9ddgYVuqZBUxvGh34RsXHH7DhCURTg+fxqwiASw/3WF5Bvvz7/Kq9MCnnhLKQSEFlEezzpKSuomchj/fK6diwqIpXQf05VFUSqVL/Bzqu/52h9veBKdEMYNAYbSiR61s6QcTIM5YbIFn2gBYLmcPOkAxeBoC5Yh0/3kXEyuNKjK91D2IwQHxc4We2vImhO7cGVdbNknfLZLJ/um2A3MxMkkqybY2Qcgb8QqA/WszKykv7sQIkQPJc6z0BukLAZJu/mOZ/qJGWPPUiCRpDVFaumtDL5yM2P8JGbyxPXu0f6+csnv0h1OLag7b8UCCFor1jF3SvupDZQzbrY2jIyOWSGuL5uF32Zfo4OnyhVM4xCFzrN4Sbuar6dmsDUJWkBI0SiECdiRieUfutT+PwamklwXAVExkmT87KE5OSKS0MzCBohTGFiS5uMk6Ync4HmUOtFPv1Kgd6f6y27Hv36zN7fQghWF20AzsTPs712I1tq1k9YrzXSeNWSANubNrCmpo29nYd4ZNOdBM2prSTKIGEoPzBrMl2gETJC1Abq6c5cwJUu8cIwbxeGCZsRKsxKQmYIn+ZHL4YKX8r7ImJGp81puFbxxInniedSfHTbI3xs94cmfF4TquKxHe/B0DT+fM+X+PaxZ2ZFok+F9bENrPJWTciQmAxhM0KVr+qKqNHvabmbiBkuZT/IizoqmtC4ueEmQtO8yxYbnnRnPSXlSocrGSS6jGsPp3rPEgtVUB2OLVgnPZFN8Zln/5GnD71CrpBn3//+TumzrJ3j+/uf44svfotzA1184Zc/ya3rdi3Ica8ZaBqiaSXau34CuecHyDOHL823NBVHPv8t5JvPIdZtg5WbIFYLoQgEQkWS7xoZmF2LkBKGevGe/Ef0j/4avJPUalchnDdfI/MXn0Rf2U7ov/9vROTKV7ctY3bIjMTZ/+3vkuofQGgam+5/Fx/+yz+ZUhUupcTO52Ykv0KVlSUfdtd2SA8NT7u+9DzSQ0PTrhOtrysFidrZHInevmuSRKf7jLI1kx5U1CB23oFo24gIhPBOHkA+/XVE+xbE7neB6VMh20fegLNHobIBsW7XOycfxBdAu/ld6O//GWXdckn78GM88lGcVEKFgLrzz7PyXnxi5pU0DdG+Ef3Dv4RoW3PJ/VCtpR3uexQ3MYw8fWxW+UDTQQ71Iof6EYtCokukbUM+rSaEZlLPVzXCFRTvLCRERR2YfjVx4hvl/QQiFAVrbtmCC06i65pOY2hsBirr5sg6WYbyw2SLam6BmNTKZRT1wXr8ur9Iorv0Z/pYGWkjURjzG6r2VxEypiYeHM/Bvkg9E9D96FMohaeC67kM5gYZyk3/4pkrolaEldE2Dg8fKRHlnvQ4MnSU1nALaSfNqfipsm1WRVdS7a+eE2HaUFHDB66/n//4tf/Dbzz08wv6HS4FjaGGsutjPIQQNIYaeHjlg9QGazmbOE/WySABS7eoDdRwQ911rK+aPvTE0AwGcr1knFQpRHQUMauSgBGauI0wCBlhDGHiSBsPpbisMCuxpigdD5sRAkYQ2y6G4KbP40mXSl8NpmYhUKRTwk7Qm+mi4I0pAKK+2PQn6iK0RBppCNVM+tm7Wm/FWkQ15mJiR/MGPrD1XXztre/xvSPP88imu2iM1k7rMwgg8Xhr8PUZA2ZHMeqFK4SGd1GnM2UnSdnJS/4O47G96joagnObqLsWcLT/DLZr8/D6O6dcRyC4d/Ut/MOb/8qRvpPzOl5NYPJ7YTK0hFuJWhWzItwXGmEzRGfqAvFCooysFqj33Ipw87QTgpcDGSc1a7syV7pXrXXUMpYebMfmr576MvduuZWHdkz97JgraiKV/MGHfovv7P0Rv/+tvy77LOIP8fN3/QRbWtbzP/75zxbsmNcchIZoaIE73wcV1cijeyE+dGkDw9QI8s3nkW/vgZpGaFqFqG+BaBUEI0rJFwgjdGPZ9uUiSM+Frg5kfEgNdCUQDKnfxheE7rMQq4bhfmRyBIJhtLb1yn8WkOkEsrdThTiafkRjK0Ri6neMDyP7LygbHt1E1DRCVa36HTwPeeoQ8twxZF8n3oE9SnlXv0L9dsu/0zKWsWDoP91Bsn8AKSW6abL7Iz8+ra1KZniY7MjMHsyVrSuwgoorKWSz9B0/yYZ7pn7Xeo5L38npLZbq16/FHw6RT6bIDI9w4cBB2m/aPWNbrjbIwR6lLNd0tBvvg003IEarZ3x+pUjXNPAFEZEYVFSjNa7C+9E/IQ+/qtbZfS9illU/Vy2EhthyHfp7Hrt0An0UvgD6Qx9BdpxAnpvfOHHWaFmN/oGfRbS2lyaHLhXa2s3I3Xfi9ndDYmRe+5JD/TA8AG1r5rWfSeHYEO+FdLxoNaZPr2e4ktZvCw0hIHTRxIRpzbo6bzwWnkQXOnWB2pIdi+3ZZJwsaSdTskQRQtA6SajoKOrHBYZ60qM/N0DezZMsjBFd1b5qgtOQ6JrQ0C5STYwSABI5q/J5iSRRSHBk6GhZOOlCQBMaK8IraAo1cXzkeGn50ZFj3LPiLhKFBJ3pC6XlhjBoj7ZP6/87lIozkCyfQc47Ni8ce53oHNJmryQ0odEUbuTh4IMM54aJF1RwWcgMUhuoxbqIFJ90H2hFP/TJMPm2Qgj8RpCwGWGkoM7hcH6QxkAzpja5z2zQCBO1YqSdVPHa8ujKdNKf68OvBxAIbK9A3s3hMTbwNDWLSmv2JCCo+0qfojTz5sarV0U3mImzqmoFTRV1/NVLX+Vw7ym2NK4laAamHKPdv/5WqoJzU9hIpLqHr6H3wFJCxs7iSUl1MDbterFAFF3TSBYWIY19CkSsCBFr4cMWZ4PjIyd5s+9NPCk5nTjDquhKRvJxCl6Be1vuYUW4+Yq0azzSdgoPb1ZKfSmXKfRljKE3PsArJ96iNz6AoRusa1jFzpWbCPtV32wgOcwbp9/m7IDqy7TVNHP3ppswDYNjXad55vCrvHh8L8lcmtN959A0nf/vrh8nncvyzJE93LpuF40xZfv3/NHXMHWTHW0b8Zs+uoZ7eeXEPgZTw5iGyYam1Wxv3TBpsPpsYbsOhy+cpHOwhwe23YZRLO08cO4o3SP93LJ2F5HAxEn4axcCUVkHN94PNY3Ig3ug89SllyrbBUX6dp9F6gYyXAGxWmUVEqtBBsMIf1CVzFt+NagxLFXurRtFn2Whym6vJRJXiKnLiLvP4r30faiohvggsvM0YvVmREW1Kld/4XGoX6H6NtkUBELQooQmMpNCHnod2Xla7b+QR545jHbHe8E0kX0XkMfeVMfJZpChCNqN90JtEyCRfZ3KszyfQ54/qbrPvoAi0ZexjGUsGOxMtmSjIoQgXDP9GPHYMy+QS84s/glVVVK/YS39p86QSyQ5ved1bvrpj05K0EspcfJ5Tr748rT7jNTV0nb9dRz4zvdIDQ5x/LkX2f7+R4jW1c7YnqsKmaR6Z4UroHW9ClschdAUge665UGjvgDarrvwTh9CnjyAWLkRmmYOUb+qUduI/u4PIaKxhdlfdR3afR/A/fz/W/zw66pa9He9D7FqvZo8ni80HW3XbXgHXkMe3acCSC8Vw4PI4X61jwXO05CZhArVtPwQjCqV+XR9qoW0XrvS8LwFC01dcBJdExphM0zMqmAwr8jIlK3CRUdDRQNGgJrA1A/bCl+UqBWlO9NTIrIHcgPkPRVkYGomMV9sSoUwKOsWv+Ej7Yz5IGWcLIlCkoAemFUFacEtcGjoMCdGFmc2rNZfQ2u4hY5EB4XidxvIDtCb6aUzdYHsuFDUmkANzeGmab1Q95zcx1dfeXzC8kw+y2O3vW/hv8AiwtQM6oK11AXn/lKuDczOi+tiBPQgUStG0k7g0/3EfFXo0yi8Dc2g1l9P0k4QL4wwytDaXgHbmzx0QyBoCDRNOwE0GXoy/ZwY7uCOFTeULc85eV7u2sttzbuvqE/upeLJYy/x1be+x0B6mN7UIF/c+x3lT2/6prxFN9avnjOJvozFRdgKoQmNvtQQVYHYlOsNpkdwPZeI9c4gog4NHqYl0sLGyg3808l/4X3t76E/28+p+GmCxqWTfQuNsKFsiGaq0nI8m6H8wGVq1TKWOvoTQ7x6aj+mbpDMpnnh6Bt85JZHeGDb7aRyGT773D9zqPME9RXVWLpJMpfhrk0qKNR2HQZTw2TyWbKFHIlsGl3TkFLSHe/jz5/4Ak2xuhKJ/o3XnyTsD7GuYSV+00fPyACvnT6A37QYSSd56dhefuq295f2f0mQko6+Tv7h+X9mfdMq1tS34XoeX3vlu7iey66Vm99hJLqCCIYRG6/HEwI52AvJBajMdB2IDypieNTW17CQwZDynvUFwPIVFUI+hG4gtSJxIYr/XCuoqkfbefukH3lH3gTTQr/vJ5ADPXgvPI5YsRpR14yMDyLzOUQ6gXbLg4poz2XVeQNk1xlk5ynEpusVmTPch/tPn4I1W6F9I6KxFVFTjwhXILvP4T3/beRAt1KkazripvshXIHMptEffmzCQFraeaVkC0YRpq80KJV2HhkfgExc7aeiFkKxeav8lrGMaxWBigiGT3Ea0vPoPXGStXfcMum6fSdO8crnvkwuMbsK2u3vfZijTz2Lnctx5tU3OPbs82x58L5J78dTL7/K6ZdfnXGft/zcT3L4yacV6f7Cy7z+lX/kpp/+6LSBoZ7rkU3EVZh3ZWxWbb+isAuKIA/H1CTkuP6x0DSkboJTKLPCEEIgY7VQ3QAjA8iuM4hrnETX73gQ0bpwamkhBNrm6/BWb0Aee3vB9jsBuoG261a0LdcjfAtnwyqq69A27cTtOD4/b/d8Fob6kYWcEhcsJApZpS6vqEMEIldWlCAleC6ykFP3kueqZaPiAsNS/YsF6j/IeB+EK9U+54kFJ9EBLM2iLlhXItH7s/1knEwpVLQx2IilTU2A60KnIdTAqcRpbM8m62TpSIyFZ0SLysLpbE0iZoSYFWMwN6bMdqXL/oED3N9y74zfwfZs3urfx4vdL5W83Bcapm6yKrqSQ0OH6M70lNp4In6S7nRP2bptkVbqArXTqrDbapq576LwUEs3aKqsZ3f71oX/AlcZbM9GQ0wZOGpoBtX+WizNImAECZkRTDG5Cn0UITNCc6gVx7NJO9M/LDU0agMNNAZXzDlIsC8zyJ6etyaQ6K70+H7Hs9zQsP2qJNFN3SAWiBALRFhT0zqrbcJWEBAE9CDOLO1cLheuhO/2UsD6mpW82rmfx489y5rqNoxJZq0dz+XJky+SyKe5ccX2K9DKy4+cm6Mp2EiVvxJLMwmZQSqs1QznRziXPM+mqo1XuokYmoml+5QF1QwdKYG2JB2MpZTYsoCcg9WEoZlozM3ebb6QUuJIu9ROTTOUzdQVOKsSiZSyaDPkFas1ZcmuRwW9CQQqPEnlRGhlbW2tbuTf3PVB6itq6I0P8Kff+yyvnHiL+7fexuunD/Ds4T38zO2Pct+22zA1g0Q2hb/Ycd3WuoFYKMobpw/y6O4HeHT3/XP6LVbXt/KL7/oI9dFqzg9188nvfoa9Zw5y58YbLvk3NQ2TDc2rqQrFePn4m6ypb6NruJfTfef50E0PEQtemYqWyw7PRaaTkBiE+BAyMQSJYeRwnxrYLRacAiQKkJhI0l/TFTCrNsEUJDpSFkupBaWzMH4wKT1FkFc1IAxDTT6MbjfYizx7XCnJjxQV58kRZHcHon0TuA7y1GG8kQHIppEDPYjCDMGD45uWGEReOI7W2I6saVEtdGzkwAXkheOQS4PQkJEqtFXbFBl1NVYQSEn+6R/gHNyH/yd+Er25RT07e7rJffHTaLV1WHffh75ShQg6Rw9RePYpzJ03YFx3A0LX8ZJJnH2v4xw/ikzEET4fetsqzN03I2rrJyU07ddfwX79Faz7HkZvW4m99zWcg/vV9qEwxtoNGLtvRotOLyqRnod78hiFZ54EoWG96wH0VWuWJzWWEKraWqloakToB/Ach9e+9HVWXr+T5m1bSu+zfCrNmdfe4OXPfonjz7046ziJLQ/dzwt/9w+cff1NBs+c5Yd/8hc4uQLr7r6dYKwCIQSpgUGOP/ciP/yTvyCfSqv7dBoLh7V33cbuj/w4r3zuy4x0dvHMX/wdw+cvsOmBd9G4aUOJTM+nMyR6ehnsOMeFtw8R7+5l23seZNP975r3OVt0jD6rJutX6gaYliI47YtEc0KDaDX0dap36LWMFavQdt22sEppISAYQbvpXtzjh+btLT7lYZpXom2/eeEU9OP3vfUGeO578w5IlfFBSKdUdd5CQnpqUtzyX5l3svSUCCAxoAJ6c2lwCsjxlR1FmxmhG0jLD4EIhGOIimpF/F/qNZcYgFBsQb7G4pDoukVdoI4jw0cBuJDuwh1X7tIaWYE2w4/WFGzE1MySHczxkROlz2K+GBFrenuSan8V9cF6TifOlHm4vtG3l9pADduqt5aFWo5HX7aPN3r3sm9gP0N51ZkXiEXxgm2JrKAx1Ehvtg9PekgpOZs8T29mjEQPGyFawy0zevpuXrGGDU3tHO8+w6ELJ8gWctRFq7lh9bbSwHUpImNnGMnHVaWCVL9vpT+24MdJ2nEszSKsTd3hDBlhQka4RBzMBE1oVPqqMYRBd+YCQ/n+CT7dAkHQCFEbaKDWXz8rxecopJR4oyZEUpHm4z7kQqqXvFu4ageY96+/letbNs9pmxWxegSCjZVbJwQ1XmmEzXcIyXIR7l19C98++gyPH3sGyzC5Z9VNtMWaCBh+cm6ezngPz3W8wbeO/BBPerxnw91XusmXBRVWlOH8MG2ylZivkld6XqUt3EpvupeAuTSU6FErhi5m1xXQhEbIXHpVIBKJ6zl4zH5STZf6ZQ/Ks2UBxyuU+hKa5yI036zP/0JAWfJ42J6Nh1v8WzIpTSmLZHrxfWgKE32cpZorPX50+BXePnecZC7FwfPHuWPDDUgpOXDuKDWRKm5YvY2qov/gnFTcM7wiC67N9/c/x9Gu0ySzKfadPUJLVSNy5k2nRVOslm2tG3jt1AEe3f0Ab509TGUoyrrGVSV7l2sKrotMJ2CkHznSr5RPIwOq1DeXUcrmfEaR5/MpTV7GJUHbvBvv+1/G/cbfqkFvtBKxckPZOiIYnkiISgmOjQhF1fpBNX4Q7ZuUn3o6gff846DriFUbIJ9Hdp9lTtMViUFIDSvLnVFkk8he5aksmteBk0cOdiH7z0EwsjAl81cA3vkOCk98B3P3zWgNTSAlztFD5L/5j2hNK9CaVoyR6Af3k3/8X9HXrAfA7e8l94W/x37jVWQ6jfD7oFAA3aDwwrMEfvZj6Os3jvktF+GePE7+W/+MvnI19qsvUXjqe8h8DpkvQDqJu/smjF3Te1FLz8M9fZLsP/wt7vEjWA+8By0auzonM65hBCqibH/fw3S8tpdETy+nX3mVr/7Kb9K0aSOh6iqcQoHh8xfoOXqcvhOnaN21nYrGBvZ9c2L1+cWoaKjn/n//a3zll36d9OAQp17cw8iFburWriZUXYV0PVKDg/SfPEO8u5ubf/Ynefu7PyDR0ztNeyu49zc/Ti6ZYt83H2fg9Ble/PTnOfzkj4jW12IGgiDALRTIJVNkhoZJ9PbhC4dovW7HAp65RYQvoMjyVLzcsgVUtY8/CMm4stGaAKWupZC7LE29UtBvvAcqaxZejGIYiPYNUN8MPecXdt8Ahom2aRdi5dpFqWoT9SsQdU3Igd55TQLI+HDx+qpbuMYBaMaVyXR3Cnj9F6D3LDIxqMjzUQX6FBiV9WBaYPmRgTCiohbRuBIRq5174KluLtjEzCKR6D7qgmM/eMYp979tCbfMGI7ZFFIkOih1dnJc8F/Mis1IVgWMAKsr2jk+coKB3Fj5+Uh+mO+dfYJDg4dZFV1Jlb8SXegU3AIjhQRd6S660t0M5gbIuUqRobzLG9k3sJ+CO7lNx6UioAdYHW3nVPy08v/G43TidFkoakOogeZw84znLJlL85WXvsM/v/YEhq5jGiZDqThrG9r43x/8TRpiS8evzPU8ziQ6eLN/H+eSneTdHK708Os+bmm8kduaysvY8m6B4fwwSAiZoRknUSZDwc2jMf05nEto6yh0oVNhVeI3AjS6K0jZSWxPqSLNoqo9oAfw6YE5BdtKKcnYWf5s3+foTHYznE/wn174w7HPgayTY3vtxqtShQ5QF66iLjyVf/30qPJd2UDGZYxhTVUrP3/dj/EnL36WL+/7Dk+dfJmQGUDXNFzPI2Pn6M8MkbPzfGz3B9lSv25ex+tOd3EudY4qfxUrI6tK74qlhm01W9HQMDSD6+p28r2zP+D1nr3UBWvZXrvtSjcPUBkNs4UQgvASJNGvBnjSw/Wcssl4T7o4noN+mYglKSWudCl4eeQsJxxKBLsUyIteXX/47b+lNz7AY7e9HyEEX3np26Xvly3kCVj+BSOeHdfFK5K4Ukp+71//ipyd58M3P4Lnudiuo0KjR8tALxEhf5Btres5cP4YB88f580zh1jbsIoVVQ2XtWphsSE7TyIPvaY8N7MpKOTVYKaQBzt/bQVJXc2w/Ei7gL7zDgiGIFShQuzGQ4iJA2IhFAkUiSFWrlfE+diHyN5O5PkT6O/+KGLVRmR/V8kGZtxqY2qvSa4HmU+rz4NRpUJ3HTUwzqYQjasRzWtVVUM2hRzuRbRsmNr7fSlDCLSmFYhQGK/znLJ5QOAePaxCXgMBvAudpdXdC+fBddBbV4Jtk//q5yk8/QOMbTvxvf+DaFXVyEIB+8VnyX//W2T+8k8I/+4fQ1X1hGeMtAvkvv0vaNEovg8+htG+BoTAG+iDgo2ITP0+llLinT9L9u//AvfEUXwPvR/fox9CVE48zjKuLIQQ7Hz0vcS7unnyj/+MXCLJqRf3cPb1NzEsH57n4uTyeJ7H5gfexbt/+98T7+7l7cefmM3O2fzu+/jAH/5Pvv/7f8xgxzl6j52g9/hJNF0HKfFcFzPg577f/FV2PPpeuo8cm5ZE1zSNhg3reO/v/ja1q9t59YtfJd7dQ9fBw3QdnLodlS0riNRcJWO3cEwpddNxRaSPr6TxBZRX+rkTyOE+xIo1pTBnpAsjRd7pWrIduxgVVYiNOy4pkHEmCCEgWom2YTveIpDooq4JsXYzYpHs+YRporVvwD3+dvF9cYmIDyEz6YXnu/0hJZTIpiBcufiTqtJDDvbgdRyC4V5Fnl88MTX9DlS/tGghJ4f7kL0diNoViFVbEXOpcqusVwKAqFACgPGbzTFvZ1F6M4bQqfTF8Ot+cm75LJylWdQH62YsX64J1BAwAsQL8QmfVfpiM6qyNaGxPraOC6kLvNyzp9QOCYzkR0gWEhwfOYGu6SWVueu5FLxCmZK4PljPg633I4DOVCdd6e7ZnYRZQgjBusp1vNG3t/Rd8+5YOaUudFaEmqkPzDwL9cKxN3j11H5+8V0f4bpVW9A1jVQuw18+9SX+4skv8vsf/I0FbfulQEpJxsny7IXneb33TUbycWxvbMIgYARI2xNDB23X5vNHvkLGzrChah0fXvvjZZ3A/mwPaSdFU7CFrsw5HG/irJbtFWgITh1oOx8IIfDpfnyan4gRLREPStGuIYpl8XOF3/DzM5seZW/fQV66sJeHV40peJUKPkpzpAFriZKIy3hnwNAN3rvhbiJWkL95/WucHprY6Vld1cJP73gf966+hYAxv8qY7kw3Bwb3szKyiubQiiVLordFlEWRLnRWRVfy2PqPYLs2AcNPyLw6vZVnmoi8EhAIFSQuR6PDlyIBOLnaW3L51L0eHgUvd0nHVOd4zM6l4Nh8b/9z/M6jv8pdG2+kNz6AOY4gq41W8dqp/aTzmQkVQ6Pvbq34bvQ8r2wdTWhYukHeLiClJJ5NMpJJlAJL806Bpw6+xP977Le5c8MNdA51LxhZrwmNtQ0rqY9W8603f4jt2Ny0Zsc154Uue84hD7ysFEDLhPnSxOikUGIY91ufBV2HaBXadXcittww/bZCIJpXIU++jXfwNTRfADQdeeEMYvUmtS8pkZkUpJPIw29C97mJ+4hUQj6jgkXrVijl+qhljGOD4RvzSi/kkMM94A8hKuuV56iUCH8ImY4vWln+5YDetAIRrcA9fxZZKICm4Z44irFqNaKqGq+nC5lJg2Hg9XSj1dQhwhG8rvPkHv9X9OYWAh/7VfSV7QhdR0qJvmo13tAAhR89SeGZp/D92IcnHliC7OvB99jPY+6+GSxFWOlr14PjKgufCY1Vy7yebjJ/9n9wTx/H954fx/cTH0WLRJdV6POCQGgCoWlomj7p5JXQNYSm/rn4XAuhFbfVEFr5Z8FYBXd//Bdpu34nr3z+K5x+5XVS/f2AJFpfR/PWzWx95EHW33MHVa0tJLp7MSwLbZLjlB9T4AuFuOGjH2TFjq289S/f4ugPn2Pg9BlcxyFSU0P7LTey+yM/RvvNN+IUCtSuXsWZPa8X9z35fnXDoH7DOh78z7/Bjg88wpEfPsPJ516k9/gp0oNDuK6LPxKmemUrTVs2sfb2W1h1026qWq+OYGJR04gMRiCTRJ4/AXUtCL34rAtVIKrqkWcOIw+/jqxthsZVIECePgT9FxS5HLp2RSdi7WZErGrxJuSCYbR1W/GenbnaYs5oWY1o37ioz0LRthYMY14kuowPQyY984rT7UNOMvYIRJRoItGPdB0VLmpMFy46O3eGSY/t2HD+GF7HQTUhtRAVja4NqRE1QT/Yjdh4I6KuBYpjimkx0g+5FDI5OHGSq7YFEZ461+FiLAqJLoQgaASpCVTTmbpQ9llzuAm/7p+xjEDXFHncl+nDGzfgCxlBYr4YxizKsH26jzua78CVHm/2v0nKTpcG1670lNf5FBMhpmbSGmnhkbaHaQ43MZIfoS5Qt+AkOkDMqmBtbA3dmZ4Jkw41/mpWRVdNaT0zHid7ztJUWcd9W24lEgipwamU/PTt7+e3vvJHC97uS0HayfCDs0/xcvdr5NycGixrFkIotflUCJlBqnyVnEue53S8g650D83hxtLnEauCgBFE1wwKboH6YNME0no4P7io1SvKQ3bhfLGFEOhC0BSqJ1uVI1VIc2vTdRPWma31zNUOT3p4UqLP5iG5jMuOgOHngbW3cUvrLk4PnePU0HnSdpagGaC9agXtlS1EfWEMbf4+1Gk7RcZJU+2vXrIEOlD23DaEQZVPvZyXJsk7C0hJykkSNpbYwECApfmA8ZMzikz3pMSRBVw5dbng5cHk1/ylVD9dCqSU2F5+UgJdvUW0Eqk9ur6yEfPwkGhCL5tAMXWD2kgVBzuPs7NtI08ffoUfHd7DIzvVRO8DW2/n8Tef4VM//Ao/eev78JsW3SP93Lb+ekJFEq4qVEFDrJaXT7zJusaV+EwfaxvaCPuD1Ear+c5bP6IyXMFLx97krY7DtFY3AWAZJtWhGG+fP8aa+jaeOPA8Lx7by6paNUmeKeTI5rPEs0ls16E/OYSpm0T8QTwpSeUzjKTjFBybkXSCgeQQEX8Yy1AZKE2VdWxqXsOnfvhl7tt6G+ub2i/b73TZ4DpqcLOMJQtZyOF++x/Q7vsgYvVmpeY6eRB5bB+ishZiNQh/aGp1d30L4pZ3I994BvfLf6oGmPXNiFXrlcpv9z3IF76L9+L30TbsRKzbDuOsHwUCWd+M2HoT7ne/CJqBdtN9iJ23qRV0s0T0S6RStiUGEbUtSt0GRZW8pgbOV+lrD0BrbkFUxHDPnlbVGgjcjlOYd74LYZq4J47h9nQjTBMZH0FvX4vwB8i/8Aw4DsbmbeirVpdsd4QQiHAE6/Z7sPe8ROG5H+J79EOTkhjGjuvQ16xHWNbY57ox5e8ugkG8gT4y//d/453rwPfBx/D/2EcQPt+ikkbvBFSuaOZnPvspfvJv/gyhafgj5YK+1bfcyH/d9zLS8zB9PszAWGChZhjc9e9+gVt+7jGEpmH6J4YZBiqibLzvHtbcfiue4yCLhJPQNDRDV6S5YSCEINbcyO93HAQhsALTByMKITADftp27aB5yybe/Z9/C89zlWWbpqGbBobPp65PKfnop/6UD/6/P0I3DazA1NaDmqYRjFWw8obraNm5jXd94peRrov05OiB0XQNTTfQTaPU9qsC9S0qrHmoB3nqbcTOO9XkI6igx4Y2ZCQGfefxvvNZFSZqF2CoR6lsQ9WIxms3VFRbtxUWMSdG6Dqyph5qGmCgZ+YNZotIBdrKtRCpWLh9TgJFos9zfJpOIvOZ+VVY5lIwcG7yzzwP4n2Q6Fd/T3WIhrVzrjiQUkIujTz9NrLjkMq8mREXVdXJ0r8mh+tAYhC594ew5VbEinWle3RKVDcVhSOT7NecW8DsotXVhc0Q7dFVOBfJ9dfH1mGO89ScCgLB2thqejI9ZeGB1f4qagO1s3oIq9LzEA+vfDetkRb29LxKX7aPnJvH8RzlQV5UC2tCldxbmknQCLKzdge3Nd2qCH+gwqpgZbSN3kxv6bQvlPpXCMHW6i3sHzhALjtGogsEDaEG2qKts/q+YX+QM/2ddMf7MQ0TXdPIFnKc6j1HbeTSLDMWEq7n8mbfPl7vfZOcmyNqRVgbW8362Fokkq8e/+cptxVCsDa2hv0Db5O205xNnisj0f16AHT1og8YQaJmbMI5y7lZpVa8yiCEoL2ilZXRFvRJAhvfKegc6eV4fwfXrdhMZXCJkXjLQAiBIQwqA1Gua97Cdc1bFu1YHhIkBI3gklRGT4XRZ9Jgdoism6UlvDiVMYsFV7okCyNLjkQXF3e8xpYikLjyyl8jmtDQNQPPsxntvGlo6IvXDSuDhzchrwPAECam5puWJB5Vsohx6wgh+P0P/gb/+9t/ww/2P8+dG2/k1x78GfJF1c2K6gb+4EO/wd/96Ov86hd+F13TuXPDDdy6bmwiOOgL8LF7PsSnfvgVfvGz/52myjq+8EufpDFWx8cf+Gn+7InP8Suf+x1uXruL9113L2sb2tA1HU1o/N5P/Dp/9J2/5Vt7f8i9W27ll+79KJGiUv3be3/IV17+Dl1Dvdiuw/v/5JfY0rKW//CejzGYGuGvnvwSx7pPkyvk+Z1/+XMigRD/88d/jduKbdM1nfVN7dREq2iuqqe1eqyvMRNc6VJwC0g8BBoBY2lkHyzjKsRwH6QTiNWbEZGYCt0KVyjy1HUR0Ur0x359ys2FpiFa10Drmsk/v+k+uOm+qY8vBCIQRr/nUbjn0YkfByPIkR5IDoEviOztANOHiNWPeZ9LiXTyoE32jL56oFVWodXW4bz5OjKXU8pz28bcsh0vk8He8xJeTxcYBjIRR7/pVoTfj3e+A2FZioSfJAhNb2lF+HyKnJ/q2PUNiGBw9iSK45D5f3+E8/Y+rLvvUwT6JITtMuYOTdfwhUP4mLwySTdNgrHJyTkhBKbfPyl5Xn4MHV9o5iBBoWkEK2Mzrjf++MymDUXlui80++orTdPQfD5M39LNX5srhOVHrNmGFAKxanN5kKEQsGINYs125MFXlC1G58mxz30BxMoNiLb1l7/hlwPBMKJpJeJiC7CFhBCIcAVaSzveApLooqpOWZwt8mSOqKxRWSTJiY4as4brQDqFdB3EpRLyQlMe6JNhtkOjuZ4qKVUF2+kDyFMHJq9C03RFzOuGmpA3fWCY6nsKofo7rl20cSmoc+HaSvxxcfWkU0C+/aKqlGtaPVYdN9lXsfxT8/La3L7ooo3eYr4Y7131nnnt47q667iu7rqZV5wBmtDYXrONTVUbOTFykvOp8wxkB8m5OVzpogudoBEg5ovRGGygvaKdiBkpu8F0Tee2xlu5rfHWebdnMjQEG6j0V9GX7S+pFENmkLZI24zWNaPYtXIzr506wJ//4PPsXLmZgOXn/GAXLx9/k5+788cWpd1zwWBuiOMjJ0jaKWJWBe9tf4idtduxdIue9NTea6NYEW5CADk3T3+2f8r1GoOTl4qFjMisFP1TQRaJu4XC3B7gynToYrjFMNp3gjr7yeMv8clnPseXfvKPuD44tzDSZVw+KN9lDym9KW8XAZjz8PEPGSEs3SLn5PDw0Lm6Jpc6U530ZnqXBIk+Gmg9SqJ6kxCto3CkczWLCa84DGGBJvCki0BgCBN9Hu+kucCTDhe/wHRhzEigw+i7auL75ea1O/n2b/7tlNttbF7Dn/7Ub0+7710rN/P3/+b3Jyy/Ze1Oblm7c8rt7tl8E/dsvmnSzz588yN8+OZHJv1sTX0bN/7S9kk/k1Lieh62a9MXH6C9roUdbZvmNHmdKiQ4Hj9Czs3h1wPsrrt51tsuYxnjISpqEBVVyAOvIBtaIJtBnjkMoYhS513p9lU1Igcv4J3cqxRc2aTyKB2XvyTtote+L3h1+wMLgd7Shv3ic8hkEufIIUQggL52A1w4D56H19uNMExkJo3e1g6WD2nbigyazHYFVLm/EGBPQgyMHlo35qRCtF99GRGNolVW4Rzcj/36K5g33YYwl27V3jKWsRShrdsJG6+f1CtZhKKw/TZAIs+dUOHbAvAFEa3rEbvfNX8l8hKFaGyBcGTxK1sCIaidvYhhZgiorkM0ty3gPqc6lFCWQL0XZl53OqSTikS+xGtJ+ALQ0D6/NsxxnCJdB9l1Gnnm0EQC3TCVfUxFreovRKsQwUhZFVz5ziQym1L2LSP9yKFuSA5P9FV3beTR15DBCKJq6mtGxvsvspQZrZrRIBxDWLMXvlyFCS+XDlMz2VS1kU1VG690UyYg7aTJX2TlUumrYnV09hf+ttb1fOyeD/OPe77Hd978EY7rUB2O8W/u/iCP7Lh75h0sMvqzA3Sn1WzidXW72Fy1EUuffXlIlV+FHzieQ6KQLPtstLJgFN4ks16mZmHMYjAspcSRNgW3gCNtXM/BlR6j9gDztQ8VAmr89XPySE8UkpxLXKC9opWwNaYOGM6N0JHoZHvtJkxxbd/O2UJuUq/7ZSwN5JwCxwc66En1kcxncD13SsLVb1i8b+O7LvlYjaFGKn2VXEh3srpiDYa5dEpEB7ID9GSmnxQ8mzy3YLZP80XGSeF4NlGrEik94vbwlOt60r16rWiWADShYYkro9Sa7J2oFS1clqHguC5Hu07xysm32H/2KBuaVrOpeXIV71TIulkGcwNk3Qwh49ryUV/GZUYghHbvTyDffgX5Zgf4/IiGVsS67RPDRa8ARLgS0dCOHLgAnoOobUE0ri4fDGeSIHRErLpcyXkVQm9ZqUJEey7gnjiKVlmN1tiMTKcQ0SheX48iwg0DraYWYRhosSpwHbzE5GpEGY+D4yAqKhaMkBLhEIFf+BVkLkfuM39N9oufRkQrMLbumFQNv4xlLGMKzGBhIarqEbc+glx3ATkyoIjTyjpEfcu0atirHaKuGRGcncBzXggEEQs5YWxZiNpGRGjxbGjGQ1TXIYWYV+6MTCfVJOulFjUKDfTL99yXUkJyCHnmbaUcH49wDFG/EtGyDqKzDLgWQpHswQiirgVpF2DgAvLCSWR/JxSyY+tmUkr5Hq5UivPJYOfHke9SEepOQZ2nwNyu6WubdbuKcGLkJIO5oRJBYWgGTaFG6oMzB4qOQhMau1ZuYnvrBhLZFLZrUxmswNDn70G8EEjZaUbyCUzNpC3aQtCcuVxtPPy6Hw2BK70J/ulJe4SMowJJBUXCQIz+BUjl6VrpqyZgTH3cgpsnYcdJFkZIO2lybhbbLeBId8EC4ASCqvqaOZEX3ek+fnD2BR7b+IEyEj1j5/iXE0+woWoN5mVSNF4pZJ38JZHoo96+o3ineMhfThRcm+8ee5Z/PvQDjvafnjbfAKAqUDEvEr0+0MDGyk0cHDrIsZEjrIutJ2JGl8TvenjoKHv73qTSN3U4yXB+mNUV81QGLBA86ZZsPjw80nYKn+6bdJLPu4whmMtYWFw8+TEaer2MMXjSoz85xImeDnat3MyD2+8ohZnOFqpaY/RcX/nn0TKubojmVYjmJeqrKwRa42pkrE6VWvtDKkx0PCw/WtNqpTy7An3U+KnTdL34Em4uP+16VjTKqvc8hBmeehCtta5EBEO4nedxz57G2LoTYZqIWBVaYzNe1wVkNotWV48Iqf0YG7eQK9i4Z04hsxlEoPx54hw9hMxmMLfvWrDHhd6+FvPm2xGajhzsJ/eNr5H72ucJhMMY7Wsviaz3XJcLzzxH/JSyndEsk9qdO6jdMXlVzzIWHlJKvEQCp68PZ3AQZ2gIL5NRQbeep65Fy0ILhzGqqjBqazEaGtDM6QIDlzFvWH5E82pE8+or3ZLLh9oGpRJfZAjDREQrwfKriqb5IhhGNLbOfz+zhIgtgJVyNr3w+TXjSf2Ffja4DrLzBKRGxi0UEKtFrNmBqG+7dGsaQJiWCvGN1cLZI8izh5UqHVRuzHAvou88rFg7+fZ1F1UheB4yl4L0yKTrT4drm3W7SpB3CxwdPkbaHkvgDRsh1sfWzdl+pODYDKZGSGRTeNJjsHgR+01fKXTrSsH2bGzPJmgG8eszl5BfDDUwVTf7xWSZLkwsTc0YZ90MBTdPyIygCx2JJO/kcKUzLRGec7L0ZC/Ql+0h52anXO9KwPEcHM8hYpW/tCr9URKFFHIyv6klhLe7j3Nq8DxbG9ayumbsBXZq4DznRmYX1nu87yz5S3iRuNLldPIEoEJfq3w1VPoWJiPA9goM54fIOOrejVmVRK2Kq9J7fz7Y33OUv3/jH7mQ6GN1dQsrIg34jakVHGHf3Iipi5GykwSMIEEjyFsDb9Kf7afKX4UhzCkHopsqN007gbaQWBtbw666qa0ojgwdnRAifaUQMqNQzAYBNYFbYVWiT1LZYns2Q/mprbSWsXQxWQXBXKqh3gnwmRb3bL6ZezZfugWL6znL1RrLeOdACMQ0GTWjCrIrhYEDb/Pqf/898kND064XbV9F0+23TE+iNzYholHcY4fxBvoxNihbQS0WQ2tuwX7hGWQui7lt1xiJvmkr+qp23KOHKDz/DNYddyMCQaTn4p44RuH5p8HzsO55gIWedBPBIL5HHsUbGqTw7FPkv/F1xE/+PHpT85z3JV2XY1/6Cqf+5ZsAmJEIu/79r1+VJLqUEqe/n+GvfGWMTNJ1InfdRWDbtmm39fJ5Us8/T+7gwbLleixG1WOPTWuZI6XEHR5m6AtfKB1XCwYJ3XYbgc2TW1RKKfGSSfInTpA7doz8iRPYnZ3Yvb04/f146TRePq8yEiwLzedDi0YxamsxGxvxrVtHcMcOgjfcgDZLv3KvUCC7fz/pF18sW+5bt47w7bejR+efiZN5800yr72Gly2OtYVAr6qi8sMfRrPmFmB4tUHm0koNG45NLWRwbGQ2CcHoWL7EVPtLDoFpIXyhyzNZIjQVbO27DHkvQkAgAJEoDM5/zCSCYWVFc7lQUT3vXch8Tk1SLyBkekQ9g0IViGn4CpmJK6V2MIqYTWXFqBd616ny5aEKRPu2eRPo4yECYVi1Rdm4dBwam2jIZ5B9ZxENK2dngaNpCF8QmRqeZfjpGJZJ9CWAzlQnF9JdJTWgQFAbqJ2zUrE3PsC/vP4DXji2l5xdrrpYXdfCJz/6nxaszZcCTajS8dFA17kiZSeV/7HQCejlZRpRqwJQYS7nUx3UBRoJm1E0oSGlJO/mGC4MTlrSDoqkHsj10ZXpxPEWeMZvAWBqJh6S7nQfa2MrS8s7EhfwT6EaXUr4f89/kf1dR7l/3a38wcOfKC1/8vjLfO2t781qH92JfgqXRKI7nEmqwBdTs9CFsWAkuuO59Od66c4oz7OW0EoCRgjfTOnQ1xiePvUK/elhbm3byc/seD914Rp803iez9dC4mT8JEeHD5N1c6TtNEdHjqjgxmk6A22RlZeFRG+LqEmiptDUnmyJQoLB3PSD+suF8edMSIGl+TE0a4pzudSfNMuYEhNeucu/5GLAkU4xiBUWNERlGctYxhWFFgiiNTbjvPkauC56kUTHH0CvbyA/PIhMxNFa2hDFUEZRVUXgsf+PzKf/iuzn/w7n7bfQamqR2QzO4YO4p45jvfu9GDuvX/gGC4GorsH/6IeR8WEKLzyDVlOL7wMfVDYz72B4ySRDX/wi7nDRvq4YjOnbsGFaEtcZGCD+r/9K4oknypYbjY1E7rkHq20ar2UpyZ84Qf+f/3lpkdXaitXWNoFEl1LiDg2RfuUV0nv2kDt6lMLZs6q93uTjWJnL4eZyuPE49vnzZAHx9NP42tuJ3H8/lR/6EGZ9/fQnBiVSc/r7GfjMZ5CZTGm5b8MGfO3t6JH5eWF72SyJ73+foS99CZkvchWaRuT++6n6yEcueb+XBVKqnIdcRhGbmq5sJmawfSlDPgPZBIQqpu6GuQ6khsAfUsGL0yE1BIEIWHMIH54PgiFEOIq4XONcfxARrkAO9i3AvgILaw8zE8JR1I88j75gQU2SLSjGkePTIptUXv/+0KzsiSSoQPTcmCgYTUc0rETUtSwYgT4K4QvAys3IwW4YLtqoeh4yOYJMjZTls5SQT5cr8SVIO1e0dJnbNb1Mol9hFNwCBwcPMjSOULF0i01VGwmZcyuVeeXEWzx/9HVuXL2dVbUrymY4Y1dQCTIKv+4naARI2SlSdgrXc+cU2HUq3oGUEsuwqA5M3QHMuRlqtfoSUSeEwNBMPOlOaQeSd7MM5vvLCHSBRsgMETRCmMJC13TErKOMp4YQYs4kYk2gkrpAFV879h221WwgaoUZziV4o+8AO+s2Yc0jpPFyIJ3P0hXvI5lPly3vSfRzsPs4EX8IU5v+O6QL2SWn8NOLxO3oBFjaSeFKB7h2EupngzPDnTiew09uew83t06twF4o+HQfMV+M2By2sWa4vhYKzeGmGddpCa+gITjzYOZyQxe6qqSY4jmnCY2wGbu8jVrGvLEUnpsqT0TiSbc4kT42ma6mZkRpIuxqruRZVqIvY8nCdYuDxWI/1/SpQeg1jLrrdnHz7/9Psv0DFBJxCokkhUSC+MnTDB48hJubg7pRCPRVq7FfeAZRUYne0lZcLNBq6tDCEdyREaX09qvzKnQD89Y7CZom+ae+j73nBWQ2C7qO3tyK/6f+Ddbd9yNC4UWxoxOahtbShu/HfxJvZITcd76BVlOHde+DE6xl3ikQQqCFQlirV5N94w210POwe3rwEgm0mpopt7UvXMDu6ZmwXOZy5I4enZ5E9zxyx4+XLTLq6zEmI7Ydh+z+/fR+8pM43d0qoPYSIHM5cocPU7hwAae/n7pPfAKjenp1rDAM/OvWEdi2jcyePaXlhVOnyL79NtaqVWj+KfyGZ4FCRwe5Y8fGCHRAWBYV7373kg2/lY4D548hO44g44Mq6NHzVPjzzjugXqmbpedCQvE5wheYs8dyCaYFsYaZCfQrgUjssli5lGD5YCH814VQ5H+kYv77mu0hg6F5c+gUckjXvTKyF01XvuZzaL8c7CpfEIxCdePUHuXzhAhGEfWtyMTA2GRDPotMDk1Kosuh7ouU/UJNPvnDc66uWIJ35zsLJ+InOT5ygoI3VkJQ5atie830JWWT4fxQNytrV/CzdzxKLLg0/IHHo9IXoyZQQ7yQ4PjwKdbH1lHpj81q24yd4dWe15BIgkaAlvDU1jQBPUhftpuYr6pInnuk7CS2V5jSHsf2bDJOqvS3Xw+wItRG1IphaiYCDSFGVZjzP69zJeMrfRU8sPJOXu3ex+HBE+TdAgHDz00NO7mpceeMBPSVxu+++1foTQ6xIjaxsxgLRPnte/8t25rWTbuPz776Db558OnFauIlQRM6xrhzn3Uy78jw00LxhbSh9vL4fK+pWEtrZG7p6iHjMoTgwIRnjCc9utM9DOeH0YVOtb+a2kDNkns+AwihYYrxipqLfLSFIGxenvN4NUJVPc1+sk8IgSGsOdu2TXZcD7eU/zBKWEu8kirao1zJIvEoeHlsb+byRV0zMDXrkusQPOlhewW8ol/4VOdHSIGDUM9VYaJfhTkfjueOs1dbevf4Mt5hkEVVVv85Nci080qBphuIxnbEivXF9SSykFXkkKYj/KGZg0C9cdlDS/B9BhBe0cyan3gUt2Dj2WP/dHzvCVJ//H/J9MzNIsD3wHswtu1S/tPj7DH0TdsI/bc/QOZy6CvbxwI8hUD4A5i33IG+biNyeFgpWTUdLRJBq22AYHDS/oD1rgfQN25Bq2+YFeFtbNtF+Hf+SKktx60vDANjw2ZCv/nbeEODaA2NMwYmXusQPh/+NWvGSHTA7u7GjccxpiPRu7omJ9HzeXJHjhB94IEpt5WeR/5iEr2uDqNukuwzXS+1YyoCXfj9mM3N6NEowjRxk0mcnp4xdf04ePE4iccfx6ispPYTn5g+YFYIjIYGInfcQebVV0uqTWnbJJ9+mvBdd82LRM8dPkz+xImyZUZdHeHbbrvkfS4mZCqO9+qTcPao8nkeVxEt81lEfpz9q5R4L3wbEsOIFWvQbn/v9M9Rz1OWGbkUoqIOdEM9pzMJ9XltW7kCWEpkPgOJAdWOQLhIHF6+56+IREuThJfleJZPWWDNd0eGCdX1l3diYiHI/0IerhSv4HnF+38OZz95UZV1uAIRrVq8PoIQiLpWFSg6SqI7BcimJl+/shHKXCmK/RfDmtE66WJc1hFKyk6QdpJ40kMTGpVWDZb+zlJsjsf5ZCcvd79Cf26gtEwXOjfU7yZizl05HgtG6Y0Pki3kqQwtvQ5tfbCOlZEWziQ62D/wNk3hBm5rvBm/Mf3LeCQf5ztnvs/ZZCea0GgMNbC6YuqgpbpAIwO5XnqzyiJHIPDrfip9NQSNyWdPPemVkZ91gUZqAw0YwlgSZJeu6awIN1Cz6m7SThbP89A1pZT369aSaON0WFvTxtqatknbGQtE2NK4lutbtky7jx+deHXJKe6VcnLsMaqIoqXtT78YqA7G0DWdrH15fL4DRoDAJUeVXz4M5YZ58txTnEl0YOmWUuBKyebqTdzRdBsR68pXCI2HlJKcmyVlxy+ypRiDJjTqA3P3VH1nQOIy+86ukALE/FXLLg6Fose+HPfvmaDU4DNDyEt/vzieje3lZxVKO0qwe9LDlQ4GJqawrqoA1PJg0WUs48pBOgVk9ynkhRMqnMwdR8bp5oSgNtnTgew6AUJD23qH8jqfpm/pndwLgGjfMW2Ztnd6Pzg2on37gpdzzwShaeg+H/pFftCB2hqEMfchsFZbh1Y7kfTUIhG09ZumaIRAmBZ6YzM0zv7dqdU1oNXN3npAi1WixSYPNBeWhd62Cr1tiYbUXmZofj++teXBc6Mk+lSQjoN94QLOwMCEz7wiiT4tLibRhcCsq8OonaiWFJqG2dJC9P77Gfz0p9VCw8C3Zg3hW28leP31WCtXogWDoOsIIZCui5dOk9m3j5GvfY3soUPgjPVH3Hic5DPPEL77boI7p68Y1UIh/Fu3Yq1aReH06dLy9MsvY3d1YdTUTE/ETwEnHid7+DBOf3m2TuTuu9EWwGt9wZFJ4j33DTh9SE0wzgShqQyIE/uRgBzqRdRMYe0opQo0jPdDrE49k4WAcBUYFgx1X0T2oSZBEwOMBjUy6q8+SV990RCKXh4/9FGYFswx4H2q/YiqSew9FhMLcZ7sArhXgFfIZ1QfQdPnQIBLZLbccUBY/sW/XkIV6t4bhWtPGUQr/AtXRXFZSfSAEcLSfGTdDCknMW3I47WAglsokbij5KEiKfKciJ/kpe6X6Up1lQ0uV0VXcn3drlmTogPJYb637zkABlMjvHHmIL/6hd/j1nW7qArFSvupDsd4eOddC/r95oqA4WdH7XbOJTs5GT/N42ee4OjQca6r30lAVzfY6PnpywwwkB3gePwkBwYOMpAdxJUuNf5q7llxVylEdDKYmkVDcEXJe12gFJYa2pSTtZrQMDSzpMrzaT4MoS8ZclpKieM5uNLF1AxkkXjJOTlyTo4KX2TePtOLianOo6Wb1IWrpw2hHEXQ8mMssdI2gUAfd96dGcJrr1Xc3nYdr3Ue4AcnX+Jnd74fc4lNdlwpPH/hBTwp+dmNP0WFVYFE0p3u4eXuPbzVv587mpeW8saRDiOFQXRhEDLCk1bMLJVn4lKFQFx2EnVUgb6UIKXE9go4snBJbZOo7T3hYWm+UjXYZMi5OZKFqcmPy4m0k5x08mkZy7hskFIR6J3HkGcPF70+NaXAE6JMSVmCEGMBoKlhVZIdCE8fOpZXfslihvtbOgUY6Ud4LrDcN1jGlYfw+fCtXYvQdWRRveh0deGOjCClnPRd4wwOUjh3rkRMW6tX4/T04KXT4LpKpd7XhzmZshyQhQL5kydLf+sVFZgrVkxpYaLHYkTuu4/sgQP4N22i4pFH8K1ejfD51ASQrhcLQcbaKqXE195O+Lbb6P+zPyP+3e8q+6AiCh0dJJ56isCOHdP25YQQ+NauJXTLLRTOnCmRtF46TeK738W/YQNijmp0KSWFkyfJ7ttX7utuGMTe976ZK18uM6TrIF99Ck4fVr95rAaxdifUr4De88jXfzhxIyEQKzci978ImQSy99wUJLqAXEr5OFc2lE1YCsMEw0JePKYf9WN3HYjVIfxhpBWAdGLhv/w0EKEIYgYlukwMKQV1RfWMfvHSddU7ynUAAYahrMZGr0/dVJYu84VhQOXUVSaLAVWtNM8xk+NMnEyZ8z4KyMSguuakHJtQ7zlVZMkugpQgXXXccNWs/NDHjnVR/8IwEYvMCQjTV/788Lwpw1illOp6S4+otmqasnLxh5RgZw5j3MvKSGloaJqFI513RJHrk+d/yKn4acJGEFNXD5G0k6Yv00/STk5Yv8Kq4OG2h/Drs38xDSSH+fSz/1i2LJPP8q9vPFW2bF3jqitOogshWBVt497Wu3HOupxLnufI8DGODB8rrZNzczx57mmePDfRtqPSF+OhlfezNtY+I5EzOnExvvzcw0OTkxPpPt1PzKqkP6eCCdJuGtuzVQn7EiCNknaaH557kWfPv8pAVpXKmLpJqpCmwhfhz+/+HcLm1edv+N4t93Bb+y7aKmf2ka4KxmiK1mIuMSJ9PFzpXrMEiuM5xHOTl0fd1LKdnU2b+MK+bxL1hbildSc+w1c2wTAemhBUBhbGl04icT2VdyCR6ELH0pdGqfJIIc4N9ddTF6xHKz542iKtdKe7SUzyDlgK0IRO2IjgN4LLMaJzhkDXTGWhULJUGcXkRPdCPC0uftdNhsmJ7Nn9wnO1Hxsl0G1ZYIIlEAINHSHGkeKjXulMDB13pYPtgan5p2zxQLaX1/tfmVMb3/Hwh6Bq6eUyvBMhIpOrhy8VUkoY7EaeO6oGif4QoroZqhoQ/hDem5OQP4AIVxZJmThypBfRtGZug+cpIDQd6VxmteQyljEdhECvqsJsaaHQ0QGAMzSkVOauqwi3i+D09FA4e7b0d+iGG8idOEH2rbfAdXGTSfLHj09JohfOnlWEexFGTY0i0acYYwohCO7aRdsXvlBGtM9EfmOamE1N1H3iExTOniWzd2+JtPbSaQonTyrbmlhsyv0AGLW1hHbvJvmDH5Qpx+OPP071L/yCIvPnMj52XXLHjpE7erRscejGG7FWLcEKia4zyPPHldJ7xWq0B38Kis9qOQVBByBqmpBCQD4Hw/2Tr+Q6MNgFwSgiOIegVs8FKRUpKQRCN5G6TjFW5vIgGJ6R1JZH3kCeP4F26yPIiirVbt1UIaHjJ31cB04fwnv1KejuUOu0b0K79SGobUKR6joshFe+biJi0+cBLDjMBSD/pTf/d6emqz6fdJVC2wWVqDmFVYsANAN8YYjWzK0a9OJr+bK89udwkFwK+s6BrquKD8+F1DAEK5Cx+jlVyy0AGzVaeusWbQwkAg1N6OgXKXlnetg6nl0kQQw0oRWVtw6GZqILXVluSAddaAhE2TFBoAsdvWi/IaXElQ4eHoYwy1S6UnrYnq38X4t+xqOBV650i0pSgSiGXOmXGHKVslN0pjpnpcKKWTHe3fYgjaGGOb2UNjS18/x/+0rpO8DSVgoKIdhcvZGAEeDZzufpSJ5jJB+f0gJDIAgaAWoC1dzbcjc7a7fP+P1c6ZB20hTcPBffWBGzAr8xcQbVrweoCzSRcTJknBSD2T5CRogqXw2W5lOWSVeQUOpJ9/H2wDE+sOZ+OhKdhM0QG6pW83zna7REGi9baOJCY2vj2plXKuL29l00RGpoq5yiNO4KwJMu9rgwWm0ateTVjt7UIH/+yhcn/UzTNIKmn6yd5389+zesqKhnVeUKQlawRB6PR9gK8t/u/uV5tynv5hkpjNCX6WU4P4zj2dQG6thavQ0pJRknQ87NETbDWFdgQqw51ERvpo8afzUBI4AnJfFCnKSdImJFSNlqUsKv++fti70Q0IVOUA+RcpLFd63OxT1zgSBgXH0TdpcDQgh8onwSfJRMB0nBzeFe5E2+EFekjoFPDzJdR7Lg5sqq3gQCQ7Nm1b8RaLN+/6m+l40j7YvaIzA0A0NMfUxXujheAUc6Zds60kFIGxNr2WZ8gaBtuRE2XHelm7FokICXTJA7fAS9shLfunWXdOk4AwMULnRiNjZh1NUuTj9woRWYhSxy8ALYOQhVINp3oNWuGCPEhZh0UC58AYTlU3deamT+6jeKljKFnDrm8r27jCUCIQR6KKTsSookOlJSuHABL51Gr7hI5CEldn8/dmdnaZHV3o4WDpM7eFBZqaRS5E+cmNLbO3f8OHKcAluvrsZqnt7eR+g6Qp87ByGEwGxqInr//eSOHsVLjok2nKEh7M7OGUl0IQT+jRsJbN9O8odjE29Oby/pl1+m4n3vm1Ob7L4+svv2lSnj0TSiDz2ECASW3NhJ9nUqlbduIG5+CCJj1f1T9bSEEEjTUl7luTRkpxDLuLZS+GYSyGwSEZjeOmuS1k2+6HKcQn9wZmW4BOJDyEOvInNZyCYRVQ2ITbuhoXXsu/Z34e19Bkb6VTirlNBxBC+TQnv0Y8VsDkORnfOFYUB0YSesZ4SugyaYT4G69BaGRBfBqAr59FzkQKe6BisbEJNNlAuhzvucrFyKMC1lBTMKp4B07BkrEuYDmc8qMnwUmj619/1wD8RqEdHaUl9I5tIw0gv5NBixWR93QUbtWSdD1s3gSbdEGFuaj4gZxRCzP2kZVxGfFVYlmrBIOXHSToqYVUXIiFDw8iTteLHMXJBxUkXSWx3V1EyiZqwUjJZ20uTcDDGrCkv3qTJrKbGlzWC+n6ARosJSN5QtC6SdJI5rl76DJjSCRmRKH+2FgKmZ1AfruaXhJrZUb5oXkXK85wyelGxsWl1aJqUknk1ytOs0N63ZsQAtnj8EgtUVq2gI1nFs+ASHho4wlBsh62TVxIeUGJqOpfuImhFWRlvZXrOVmkD1rF6yQ/kBknYcU7OUhcs4BKcIFxRCEPNV0sZqujOdJAtxOpKnSNpxYlY1lqbC3zShK5U78xsQ+DT/nDoMedfGr/u4oWEH8XySkBlkU/VaGkO1/MneT3NPyy1Lzi98odESa6QltnQIdCjaCNhjpXSjIbTXIhL5FE+eemnG9Qxdpyc1QE9qom/kKKoCFfw35keip+wUx0aO8vbgfobzY0FKayvWsbVaBTOfSZzm2MgRtlRtY3XFGgxxeYnqCl8Fz114nrPJc1T6YjieQ3+2n7STpi3SxrMXngdgd9111AevvCpUot6PjrTJOO6khJEQ2jKJPgeMt3NbrBGOEAKdmQbaFx9boFGe6bAQkHg40p5ga2Vq1ozhpLrQ0TQ/wssXVexjcDwbXdeVin2ZjZs/dOPyBmwtIKTn4SYS4LoY1VMoyzyP7MnTdPzMzxK5915a//ZvL4msjj/5FP1/+7dUPfYYtb/4i5MqVJccCnkVTic0RM0KRHXT7BTlmq6Uc5oG+eyEgbt0bEUKjXqN2nlAIId6priWJDI1Asmhol3Btdk3WsbVCS0cVgroZ54pLbPPn8dNpSaQ6NJxcHp6cPr61ALDwGprw2xsRHzta8hcDi+dJn/yJNLzJvULzx8/XnZPGdXVGI2LO6YJ3XIL2qc/XUaiy1yu7O/pYLa2Etixg/Qrr5Sp6OOPP07Fww/PWiEspcQ+f57MW2+V77+5mdD1109paXNFkRxWz7qKakRl7ezVuEIokjmTLPOkL4Plh4pa9cyNDyA1A+EPMm0fUQgVjikEspBDGJayd3Hsy6T2BVBBybNS6/ZfQA73KQ9100J2dSAvnEJ75GcRxSo4OdAFgz2I7bchrrtLBa3u+QHy0GvIU4cRm3crEnohJpp1HRG+3L77ovjunUcw6EKQ6OMx+p4HMP0L3A8UiEBEvfeLkPksIpde1DBrmRgst4jSjal92O08jJ+0EgJh+ZGaPhZMOksswJlTA0S/HiiFMObdPGknia4ZRKfxrr4YujCQ5Erq8oJXwBAGtlcohU1RVKp70sOn+9GLKnPbK5C0R0iLJDFLka0+3UfOTVPw8mW2HKMKZf+oDzeSbFG5HDIimJqFRKpB2yWq0AFivhhV/ioyTgbbs3E9V80OayZBI0Clr5LmUBNbqrfQGmkpqeIvFd9961kczy0j0QG6hvv4X9/8ax7/rb+b1/4XGiEzxK66HWyv3cpwboR4IU7eVcGMlmYRtkJU+Srx6XMrGUsURqgPNhEx52YXoQudal8NPt3HhfRZBnP99GS66M304Nf9WLoPXRiqEkJMpq+dHdQkwoZZkB5jMDUDQzNIFlJErTBd6V464ufxpCRpp5ecH+61jlG7gr5cD8P5wdLygBFcEorixUB1IMbP7Hj/guwraM7NS/FiFNwCh4YO8lb/XiTQEm5FQ+NsqqO0zuizNl6IczpxihXhlsv+20jpsb5yXelvUzdpCo9ZF41W4CyVu9eTLnk3Q1APETBCRbLyYiX6MpYxOaSUOFJld4yHIYoBobO4eoRQCnnPU+GipX2jKh419BkvQkvzzavvNh8UvMK4di+VO/vagpfJkPj+90FKqj760UU9lptKIW0bL5OZeeWlAteGXEYNWoPRuYV5aoYiu71JBpOOjRy8oPzSC3nlKwrIk3un8FX11MDWH0TUr7pqJ23mg3wiQfJ0B6kLFygkU7i5HEITGMEg/upqIm2thFc0o5nmvJW4TjZHuquL5Nmz5IaGcTIZpOthBINYFRWEW5qJtLZihkNLTvV7JaCFQvhWrixbZnd2lpHFo3ATCQodHcgiKWrU1WFUVWFUVSEsxXPIfF5tn0igT6Lyzh8/XiJ6hGVh1NdPVLwvMKy2NrSLQnWlbeONV4NPA82yCO7cidXeTu7tt0vLs/v2kT99Gv/69bPaj8zlyB09Oqb6LyJy553o1bMTyF12OLZ6DgZCc5sAlKjfWWjTk79CQKRKPa8TA0i9HhBKxZvPqOWpIaTpg0AEYVgI04f0BSA1ogh0TzknzFktfKkwDDVBMBtSW9OhfTNi3Q5EIITsu4B863nkodcQt79HrZPLAAJRt0LZ2gDsfhfy2Ftw/jhs3l1URc+fRBeajggsnih2Ssy37d6o48YCwhcoXtOLcN1Eq6D//NjfqRFkYhARji3OdSo96D1X3mex/IjgFM9W0w+Z+JjaXnrIfFrtZ459lAXp0Vys7tWFQc7N4HiThNdM1xihF70x1WDJ9RyCRpiCmyvZrQiUxYrvIt9wU7PIuplSMOToMlOzyLs5AnoQTWiKMHczpc9GMUpC6kJXIVZCgD6/NNnt1VtpCNaTttMU3ELRC15g6RZhI0xtoIbaQC1+Y36EkpRj3qtSgjduNsb1PDqHesjkJ0+pXQrQhU5NoJqawMJ4VWnjbHrmAiklGTfNSH6ozKJD4pF1VbXFQkAgaI+ugzkM8iv9FWyqWoPjuayOtXF85Az/dPx72J7DhsrVmEucuD3adxopYXV1C9ZcBnSzhO3ZpJ0Unpw48Cv7LaVHxkkzlJ9aJT0TPOmRd/Mk7BH6s70Uxj1zomas7LlyLaEuXM0nbvmZK90MAPqyvZyIH8fQDLbX7KAtsoqCmy8j0QEqfZUEjRD92b7iu+HydqBubLjhsh5vvtCEhr/4rhSIog3aEhzcLGNJQuJNeAaP2sbMBQKBIcwyEh2UVZshDMQ0E9CmZrEy0j7nSfSFwtnUGQZz/SVbwGUsPLxkkpFvfhNfe/uiHyt6771ooRChm25acsF3U0OqAaXpQ8yVuB71X53kuwrTBzUtEIhANonsO6fWr6ib8twIw4JwpfJjfwcp0e1Mht49r3L+h88wePAQiTMdFEbi2JkMQtMww2GC9XXE1q+ldscOVj78IBVr11wSmejmCwwdPkznM88xsO8AI8eOk+3vp5BMIV0XMxzGX11Fxep2qrdtZcU9d1G7awdm8PJUlEnXY+DAAbpfegU7WczVEVC5YT0rH3kYzbgyE57C58NsbESLRErK7EJnJ15qYvaPOzxM/vTp0t9WSwtaJILZ3IxeWYk7qMQ0zsgIhXPnCFxEonu5nPJTLypKtWgUq7V1UsX6QkILBhGBci5Dui7Snj0/49+4kcCmTeSOHCkpq710mvj3vjdrEt0ZHCT96qtlSk8tHCZ0663okcis23JZYZiKCM7nZq8EllKR79m0IuT8k4w5fMGSTYYQGjJaoyp8Ro/hOYrgjFQDQvmnj36mm4p4zyTVM94fVKp203d5iHTLN3tFcbQSbdutiNWb1fdp2wBdHcr7fBSuq9o93rIoXAHRSuRIcZwutIWpYrL8i6qGnhLzbbuUC86hC18IrMCi9GlEVSPy1P6xBbmUqkqoakAEJneDmA/kSD+yv7OcRPcFoWIKTrGiFuJ9ysJF04vEqau2mUq9PgUWhHlzpUvBzWF7Nh7umHc5BnMxadKFSo53pUPBVQN6n+4n66RwpY1b9GjVhV48pir59aSHlN445fiYR7pP85N04tjSRpcGjnSwvQIVZqzkky5QSnqlZo+Tc7P49QA+PVDmpT5XNIYaaQwtbqmWlJITvWf57HP/zAtH38CTHmcHLpQ+96Qi0d+15eZFbcdSQtisYCDbR9SKFZWnY9efpfkmVaNKKUk5STpTHQznB4uerEsHVf4YtzRdh99QCrt7Wm7h1Mg5hIDN1WuXvJXLF9/4Dt2JPv7ng79SZsnycsdbHOk9zQPrb2NF7NLtLDJOmo7kKbLOxImO8Sp9V7r053qJF4YnrDdbyGKVTMHNl+3bp/mo9FXNu6JkGTOjJ9NDspBgU9UWtlZtx2/4GcoNTljPbwSwNIvh/BDuAvi7XuuQUk06Zb0MOTc7qRJdE4Iq3+TBWct452K0WvBiFboujDlnRaigVA1NaGV5KV7Jvk9OqWr36T7qg43U+K/MNTpSGGY4P4C7LEJfFEjPw+7rI3/8+GUh0f0bN+LfuHHRj7OgEJoicDwH6Tqzn8rxXFXq7HnK0/fie1bXEeGYUpS5Dl42hbQLaK0bFeE0sSHKT/hSfFWvYiTPnefYl7/K2e9+n8GDh5DuxL6Hm8uRGxhg6NBhzj/5ND2v7GH9T/8kKx96cE7Eam5omI7vfo8TX/9n+t/chz2JTUd+eJj88DDxk6e48NwLXHjmWVa97z2sf+wj+BdZBSxdl/639vHW//0zOp9+FqdY0VG1eRO7/+vmKzrPKIRAr6zEbG4mXwy7dAYGcIeHka5b5kXuDA1ROHOm9LfV2ooeiSBME9+aNRROnQIpFdl+5gyBbdvKjmVfuIA7jpzXiyT65YDm85XnIMzRGkKvqCB4/fWkXngBu6tL7aJQIP388zg//dNTW2oVIT0Pu6uL7BtvlC0PbN2Kb/XqpWnlAhCOKdI4PqjUtKHojPeKBOg7r56joeikRJ7wh8rIdWH6ygMorWmElUIgTD9UzE98eckwrdkR0ZqAYAQCwTESWQgVMtpVvI/KQi3Hn1eh3j/pRNmieUEICE3yTltsCBbgmFOEf84Hi1QVJgAZq1W/X7b4vPM8ZG+H8qNvWa+u9wWCTMfxTr8N6fjYQt1AVNSMVTZcjNEg33xGTVBpGpgBVaUwR5HnvM+i49kk7Ti2Z2PpVtEzWpvgRT0bKKsMRZCrQFGraBGjkXfzeNJF10w86ZFyEuTdPKZmomtqHc2beEyf7iftpMi7OXyaj7ybQROiGMI1bj3Nj2bq5N0sOS9HvDCMpWeImBVLXlkaC0a4btUWTvedJ5VL01Q5NnjUdZ3b11/PXRtvvIItvLzIuRnSToqcl0O/SElZ668nrE30xLK9An2ZHgZz/WXha+OhFQf184V6Cc/toWpqBhW+sQfCuspVrIm1XeS3u3Tx2rm36Un24zPKH54vnN7LP+57gk31q+dFojueTcpOkHYmlmGOh0SSc7Pk3NmVMs4WAo36YCMxX+WCXCPLmB4ZR1X3NAWbsPSpn8/q/aBjezZymUSfEcqazY/Up+6gXw3Pm2VcAUjwcLm4s3+pnusqeUTj4kQmbwZLF0MY6vMrBEMzloxnu3RdsocOkX7hBQpdXXiZDHo0itnYSGDbNgJbtqBHx/pDUkpkPk/y+efJ7t2LMzCgSKL2dsJ33YWvvR0xzhO80NXFwKc/Tfimm9CCQRJPPYUWCBB7//sxm5sZ+cY3yB48iG/1aioffRSjrnxio3DhAqnnnyd76BBeKoVRVUVgxw7Ct92GUVVVtq7T10fy+efJHztG9tAh3Hic1Esvce5XfqVsvdpf+iX8mzaVnwghcHp7ST73HLmDB1VwYCxGYNcuovfei/CXZ9SkXnmF+Le+VUZ6RR98kIp3v3vSkD83lWLwM58BKan+2Z8ls28fqZdewh0cRPh8+NatI3L33VhtbWXHkVLi9PeTfOopckeP4sbjZcGDAELTqPu1X8O3evXFh50auqEsCFJxpRh37FlZushMUqmzkEo9Pl1fRjcgEEbIpCqdXojgt2sA8ZOn2P+Xn+L0N75Jfnhk7ANNwwqHsaIRPNclPzyCm1MVwnYqxdkfPEWq8wJ2KsWaH38UbRbe+7nBQY59+esc+vvPkDx7rowc1f1+/FWVCF0nPzJSUoC7uRx9b7xJ8ux5sn397PytX8dXGVuU97p0Xfr3H+DNP/5TLjzzHE7RQqRm+zau/+3/RMu9d6NdQmjmQkKPxbBaWkokOo5DobMTWSiUFNzSdXH6+7G7u0vbWa2taGGlqgxs2kTyyScViT4yMsGyBCB/4kSZ+luvqMBqa1u8LzYe8/1thSB4441Yq1aVSPTRENb0K69Q8cgj027uZbNk3noLZ2BcBbAQhG69FaOhYX5tW0SIumZkMAqD3cj9LyLuenRmpWo+g7f/RfXfwSiiadWit/OywjBnZw9m+sAuQDajnkujkzhDfZDPIQd7lOI8k1KTt2XvPVkkNxfwmSQ0pdp/p2D0XXDZJw2E6g80rUGe2je2PJdGnn5btWvFOoTln1/bpEQmBpGnD0BPR7kK3R9CNK2eMgdGSKmq6QKRou1SMeNwVPE/h2bNm0TPuVmyboaoGSOgBxFCw5UO+UsgqYQQyu8cpSr36X6EUMFXBS+PlBKfMMh7eTJOmpARJmiE0YSOxCPn5iYQJbowsHQfeTeLZ4TJOhl8egD9opMrhIal+zA0E78MUvDyJArDaGjEfAtjM7IYEEJQF63m0evvYzgdp2AX+Knb3l/2ecD0Y14NYUgLhBp/PdVychWaT598Bizn5hguDJQR6IZmUmlVU2HF8OsBVeUgFmZ4vBB+rVcTWTuUGUETGlWB8hL7TCHHUCaB7c5P+R8wgtQHGunP9RVtXS4fYaoLg4ZgEy2hlfi0K6QOuML40ak9ZJwcD6y5DXOaGe5UPs0bXYeoDVayuX7tAhx5+rtRBRW7pbyOZUwPDY3wFbLBWMbVDYmcdKJKjHZQ5wiBuh4vNujypJx2fyoA/Mq9G0eFH2ou4crJ0aXnMfy1rzH8z/+Mfe4cWjSqSJ5EAqQkeN111P67f0dw586xbbJZej/5SRJPP42XSqFHo8hCgeSPfkTiqaeo+bf/lvDtt5c8dr1kksQTT1A4fRphGGTffhsvm8UZHkaPREg9/zzO8DCp555DZrPU/fqvl46Vfv11Bj/zGTL79oHnIQIBtb8nnyS9Zw81H/tYmV+xMzREZt8+7M5OnP5+1d5crvTfpe9QKA+kRQjs7m76/uIvSL/4ImgaXjEEMPHUUxQ6Oqj95V8uKyfXLAvh8yGHhsgfP06hs1Op3h98cPJzXSiQ3rMHu7cXgPh3v4uXzSJ0HWdkBPHUU2TfeouaX/olAkVVu5QSp7eXrv/+38kdOaL8lWtrKVy4QOHMGWShQPCGG/CvX4/wz7FfYfkR0Rpkchg5cAERq4fqpukHra6j7FnSI+q01TTPWOotqhqRwYo5WRNey8gODHDoM5/j5D/+S0kRLgyDlnfdzcr3PkxszRp0n4WUksJInIF9+znx9X9i5LgKoxw6dJgDf/7XWOEwKx95aNpjOZksZ3/wFAf+8q/JdPeUlldu2kj7+99Lw003YEWU8tJJZxg5foLT336cnpf34ObzZPv7Ofr5L2FFo+z8rU+UTY4tBKTnMXDwEG/8rz+k64UXcXN5AOpvuJ7r/vN/oOmO2xb8mJeCURJ9PAodHXi5HFqRRPfSafKnT5eeLcKyMJub0Yp2OP5Nm0r3lpfJKF/0bLa0PaACR0efTaMK+KYmLgWebeP09pI/cwb7wgW8eBw3mcRLpfByOWShgMzn8Yr/ny+q5OcDs7GR4K5d6hmfUAphLx4n+fTTRB98cNrf0ksmSY0LbwVlhxPYsqV0DpckGtoQDa3KLuLEfqQQaNtvQ9Q2T7KyRA704O39EVw4BaaFWLEaqi5dHLYkYZqzC9euqAZNx3vzWbRsGsIVyM6T0H0GDAvve19EhCuQPecgl0FmEgjXUZOztg0jA4jaS7s/JoUAfAungF7q8I6/hlh7PWL8u9l1kAOdimSuqF28g+sGYsUapT4fFzBKOo488RYMditrn5oVc69SkxKZTSG7TiG7T0N8QE24jD92YztUTl2JKge7IBxDBCJj/T47j0wNgz88J8uZeb/BRskqS/OhFX8sT3rKPuUSdm9oBnk3h+0VCJtRBKLkd24IA10YJY9jU7NKKidPShUgKspnyIQQBPQgOSdDxknjSJsKvWpKpZAmNDRhoQudjJYu81hfytA1nUevvx+QVExVwnCFkXNynEueRxM6LeHmCarkhUJAD+JIh4ydQhMaYSsKkmlLwB1pl6mTg0aItshqKswYujb3cvRllEPXdAquQ8bOEtUX3hPLrwdoC6+mOdSq/MoLIwzlBxkpDJV5li8kdGFQaVXRGGym2l8zlqXwDsTXD36fvvQgd6+6YVoSPePk+JvXvsbKymb+8P7fvOTjBYwgpm4ymBugNdI65eTYSH6EjJOlwhfDuERF7DsJykZj/DW87EmxjNlhLPx9DGLc/+aOySu2ZKm0dfJ9GsIsVqBdGehCH/d9r9z7oHDmDCPf+Q52ZyeNv/u7ypJECLxMhtzx48hCYQKJM/iFLzDyzW/i37CBuo9/HKOhAek4pF58kaEvfpG+P/kTzPp6/Js3lywnvHSawvnzVP/cz1Hzb/8t/X/xFyS+9z2sFSto+K//Fc3v5/zHP05qzx6qhoYwqqrInz7N4Oc/T+att6j6qZ8icvfdaMEgTn8/g5/7HPFvfxs9EqH24x9HL6o9rZUrqfuVXymR+j1/8AeEbrqJ+v/4H8u+wwRrASnJnziBFgxS91u/RWDLFpCS5LPP0v+pTzH0pS9R8cgj+FaNKQb9W7ZQt2oV0rYZ/NznGPryl2d3zs+fZ+jLX6bqp3+aine/G3Sd/KlTDHz60ySefJLAtm342toUceR5jHznO6T37CF6//3U/sqvoPn9eOk0A3//94x861uEdu+m8iMfwaybozWR5UdUNymf0PQI3qm3ELkUoq5VWQFcBJnPILtOIrtOKPVgpBoRq5vZyzVciQjFriKv+MWD5zh0PP59Tn/jm2OWKkJw3X/8Ldb/1EfxV1eh+8b6iNL1qL9xN23vfoBX/8f/4vwPn0a6LkNHjnD4s58ntm4tsXWTCw2k5zF87BgH/vyvygj09ve/l20f/2UqN6zDDIeLnssC6XnU7b6OFffcxeHPfp6jX/gS+eERCokEh/7+MzTeejNNt9+6YOdCte84e/7L/6Bnz6t4RfK44Zabuf6//Acabr4R3TSXhMWPHo1irlihruGiGrbQ0YHMjeWIecmkCgUtYjRUdPQZ6NuwAWEYSFcpau2+Puzu7jK7qfypUyUluvD7sVpaEHMg9bxMhuzBg6SeeYbM3r04g4N42awizB0HXFcdXwWjIYv/r9SV8+/HCV0nfOedJL7/ffJFEl3aNrljx8gdOUJg69ZJt5Oeh33+PNn9+8uWB3btwlq1atE94ecDYflg553IgS7o7YSje/HOn1AK6lFv93Qc781n4I2nFaGXHFGkXuNKxM475xbqfDVAN2ZFooumVbByA/LAS3g959R2hRxEYmh3vA9v34vIU29DZS3E2uHIXjwEoqoOefRNZQWyoCp+odTP7xDI5JBSXI+HEMiMuncXk0QXQiDDlYj2bchDL5eT3PkMsqcDOdwLwQiirgURrYFwBcIfVnYq498Lnoe0c5AcRiaHYbgXOdIPubTqq1w0RhWV9Yj2bdNnwWQTyhd9PDRdWTDNsaJu3qyCqVloQiPpxAnoQVzpkHHSE+xcpPRwpIsnXRyvoGbivQIULTJGlYKGMEl7SSQSQ5hFEt0kaRcwdB1DmEhNogldKU7xkEgyTmpKCxlL82HqFikniSFMLK38xeVKh5SdUIpFzUKg4ciCIvKNK09IO55Lzs7jSBcNQciaqKQHqA7HJt1eSonjudOSW5cDfdkBnjr/LKfjZ2iNrOCDax+lMbTwpVwZJ01X5jyedPHrAcJmhLyXJ54fImxWEDInI3EFyLEbty7QSJVVjabpl0gALGM8Vle3sOfsfr74xrd57Lr3oGs6Qghs11EWK06edGF21St+w4d+UcdLExqWbmFhEdCDVFgVNIdacaXLYL6ft4feAhTxXR9opHYefrma0LE0C78RwBB60cLq6rDVWSwk8ylGcokZKdeoFSaeT3K47+S8jtcQbKDCinFgcB/V/mpaI20lz3OJxPEcsk6WY8NHGc4Psav2evzG/IKi3wmQUmJ7eRJ2nIKbL1XmCMa6KrrQaQisWArj32sC1840hSzLiBhdknXTXDqZPNnZkdNWXJqaWRJ0XAmYmrkk3gXO8DBePI65YgWB7dsxm5oQmoaUEv+6dUgpJ1izDH/96wjLoun3fq9EcEgpsdra8NJpBv/hHxj5xjeoa2sbs4HxPKzmZiJ3312yicns3UvojjsIbt8OmkZg61bsri7s3l70WIzUc8+RfvFFqh57jKqPfAS9aN1itbUhTJP8sWOkX3mF8J13Er7lFgA0vx+tSOrrlZVqWSiE1TyZKlA9y0Zh1NZS+cEPUvHQQyU7FnPFCjKvv07qhRfIvPlmGYmu+Xwltb0eicye6HEcwnfdRfXP/Rx6SHneWi0tOP395I8fJ3voEM7QEFaRRM+8/DJ6OEz0wQdLVi9SSsJ33EF6zx4KHR1ofv+cFbtCaMjKBsSKdchzh9UA9PgbyNP7lVepq8g82duBO9yjQursfClcS2vfBoHIjNexGD8OmS1RtwTujYWGlJLBQ4c5/a/fIj1KagvB1l/8Bbb80i9gRSd6KQtdw4pEqNy0kTv/6k954oOP0f/mW0jHpfvlPRz/yte57r/8B3TLmnCsQjzBsS99laEjR0vHar3/Xnb+1ieo3rIFoZdfr0LTMAIBIqtWsvO3PoGTzXLsS1/FyWTI9g/wxu//IQ/96z9hBOZPMknPY+jIUZ775V9lYP/bylvcMGi59x6u+4+/Rc2ObbOyqrls0HXM+nqMmhqcvj5ATUB6uVzpGeImEmN2L6gJPaOmZmwXFRVYq1aN+ar391M4f75Eonv5vLJ4KYZyaqEQvrVrZ/bX9jyl4n75ZYa++EVyhw4h83lFxs/mflvgey2wbRv+LVsonD1bmhCwu7pIPvNM2cRq2XcoFIg/+SQyny8t08Jhgrt3X7IS//JBIGqa0N71Ibxnv6G8zuOD6p/Rc5tNw5kjKKWeVGRx0yq0ez+EmCrY8CqG0I1Z+WkLXwBuekBZ4pw6CLkM1DQjdtwKsRq01nWQzyrSNDmC9+Lj8Ny/qh6f60JtE2LrAmb5CaH87a9pSDWhNjp5ZufVxFrxM7IpZD6DCFcuekuEbkDTamXjcmp/OZEuPUWC59KqykMrBscKMRYyKzR1HXju2ESg9IrfbQqngWg1YtsdiJlse6Z6do6Gqs8B836T+fUAkhhpJ0WiMIKhGUTMmCLL5Zj/l+3ZJOyRkrJbEzopOwEksDSLiBXDEj6lNtcMdKmXSnJNzcIQJkaRsPcJP1EzRtpJkizE0TSdkBFBoBUHbOVQPq8Bck6W4CQEqkDD0EyyToG8rRQEhmZQYVUR1CdJVr7MONBzjD9+4TO81XWYqmAFn3n099lYO9EfcaoXcqaQ46mDL/H+6+5d7KZOi6HcEN3pbnJujqgVnVI9Ol8M5Hqo8ddhaT76c8rDztRMXLyya3I8LM0kZIZI2mqWbrQke5lAXxh8YOu9vHJ2P7/71Kf43OvfYmVVE6ZucKzvDJlCjj959vN87vVvzmpfv/vgx1lTM3Ugj1LT6mgCdKlT7avF1Exsz0YTGhEzSn1gYQJ/lwJZcjXB0JVfcLIwvXf9TGgKNdMeXc3+gbf4/rnv0hZZScRUpE6iEGdv/+t0JDroy/ZSG6hlTcVarCWebbEU4EqXeGEYR9oEjCAFr4BAvYMLXgFPusSsqhn3s4zZ45p5gsjpJgQWcKpgkk6upfuo8ilSI+qLYVzBcGd9iXii+9raMFtaSD73HP2f+hRVH/4wZmMjWiSi7EouendlXnsNNx4nuHs3Vnt76XMhBMKyCN10E4knniD53HPU/MIvlHmpa+EwZm0tQtPQKyqUD3hbm7JEcV30igoK588jczncwUFyR4/iZbMYdXW4w8O4IyOlfXmpFEZ9PfmTJ7HPn1+Qc2E2NxO68cYyMlrz+bBWr4aXXy736p0nKh56SCnNR8+fYWA2NGBUV+MmEmVkUulKFhdNwhcHk7MmyyaBMCxYsR6Ehuw6Cfm0UgIWxhS2pONjgVyaDuFKtFVboapx1n0b6Y6GkToX+dpOguDMwXxXI6Tj0P/6Xnpfe6P0e8XWrGbTx35+UgJ9PIQQBOrq2PUffoMnH/s5pOPgpNN0v/wKgwcOUnf9rgnbpLu6OPUv3wRPHStYX8eaH3+U6q1bpp3wEUJgVVSw8ed+mv4399H3xl6QkqHDRzn3xJO0f+C9s/7Omq6jXaSyla7L4OEjPPOxX2bo4GEAjECAtoffza7/8BtUbli/5H5/IQRGbS1mU9MYiX7hAu5oNYGUOAMD5Mf5nPtWrcKoHVMyCsMguH37GIne10fh3LmSF3Th3Lmx/TFGok8H6XkUOjpUVcq//EuJgC9ru2WhBQIIy1LhnLqufn9dR5gmWiCAFgiQ2bcPmZ1/DpTQdaIPP0z65ZdL58pLJMi8/jp2d/ekE5peKkXyBz8oW+bfupXA5s1XRwWLpkF9C9r7P4bc/wLy+FuKOHcckK56iGvFIGd/ELFuJ2LH7So8dIld6wsCY3YkOkIg/EHkphsQm2646COhvOWL/vIyGEG758eQx4pWHxU16hwupKuCEOXhrbOEm80iPQ8jVM4FStfFjscBgREKlibdrzTkhWPKsiWXxtv/I0ojjGLAqahsREQWn0QveaO3bwVNQ545qCZNLobnlvuZXwo0HSpq0HbercKAZ7rvfCFIDCAraovVdmqCAc+bnVXROMybRBdCECx6k08HS/dRo8/sDaVrBtW+cpWoKSwagivKlgWMIAFj4mxD0JhIekupyow1oeHXJ6oRNaERMiKEloDqfDJIKdU/yDJ1zehnMD2Zl8im+OunvnTFSfScmyPjqJuoJdJMyFwcLzTbswmbEQrueBsPUTqPk8GvB6kNNJB1sjhSheVWutX49cCS6/RdjXjP5rs52H2CHxx/iXg2yRvnD+JJjzhuEo0AAQAASURBVJxTwPEcDvacwJgiBOJiJPOzJ2CFEGhCEDYiDBeGypYv4/JCSsnZkS7ybgH/PEPIdKGzs2YXnnQ5ET/B2eRZXKkGGX3ZPvqz/fh0Hw3BRnbX30i1r3r5N58FJB6O5xCxYoTNKPHCMEhJxIoBMJTvp+Dl8etL2MdyGVcEcty/LzfqAg3UBWZX1eZ4DsP5OD7dImKFZ014255Nd7qXvJvHp/tpjUyugDbElfVkL7Wjtpaan/958DySTz9N4nvfI3TrrUTvvpvAjh2YTU1lnr12VxfSdZXNwCTPSrOuDi0UIn/8OJ5tl/WlhGkiRhWzmlIUaYGA+m/XHQsV8zzceBxneBhp2/T84R9OGtQJymbh4pDNS4UWDJYpR0vLLUu1zZ3nIG4czObmiYM4XVeDM2+c0knTCN10E9m33iL51FNYLS1ogQBeJkPqxRdxEwmC111X9hvNCUKVr4u2TchoDV7PaUgNg1Moft9i2Jumg+lDRGsQTWuUSm2W5JYseqzKrpPKS30qwl9oYJhou+6Ha7CkPt3dQ98be7HHBdGufO/DBGprZ9XvEELQdNstVKxuZ+SYsg0ZPnKMvjf2UrtrRxkx7tkOF557gdzgYGlZ7c4d1OzYNquKCSEE1Zs3UbNzO4OHDuFmc9ipFKe/9R1WvffhKe/Hi6FZJrp/jDjyHIeB/Qd46Tf/E0OHjgBgRsKseu8j7PzNTxBbu2ZW+70S0GtqMBsbye7bpxa4LnZnJ4FNm5C2Tf7kybFnhGFgtrSgx2Kl7YWu49+yBb7+dbX5yAhOVxfScRCmSeHsWbxxJLYeCpVVvlwMKSV2Tw+9n/wkySeeKP9Q01R+QkMD/g0b8G/ejNXWhllfjxaNokciaMGg+h2L196J+++ncHJ+1Z/qiwrCN9+M1damsiiK93vh7Fkyr72G+f73T5ycfest7AsXxhYYBoGtW8smapc6hBCKIL/hfuSO26G3E3n+pKrgqahWZGFlDdS1KBXsVfK9LgmaNvvJj+ky5MadIwFQVY+4efLMkQXDJVTADO95lezZc1TefCNC1/E1NqIHgyQOvE3Pv34LYRpUXHcd1XfePoFov/wQaG1boGkd7tvPoq29vjxc0zARpm/KwM0Fb40Q4AtC+zbwBZFnVVUc88zAG3cA8AWVJcy66xDB6OzuvYpaGDgP+Yyyb5GeEgKEKhDW3Ma2S6imamGhSGcPT3q40iXrpAkYQeVXeQ094BLZFK+e2k9zZT1+08ehCycmrDOUipPMZa5A68rhei6Op26eiBnBXCS1mKX7SNkJNGEU7Qlssm4WiTelQs3QDGr99bieS1+2m6H8ID7dT7WvFp8euOJhZVc7AqaP/3rfL/Lwpjs52H2CwcwIOafA86fe4FDvSR7edAetsdmpw+vCc1PCCjRCZjmJvoz5oT89TGe8u2ShkipkKLg2b3UdJjCZ5yqSvFPgiRMvEs8lubl154R15gq/4efG+ptpCjVzOnGaRGEEx3OLlUc+6gL1rK5YS6WvcvnenQuEeh6q/xRFyzQPXRiEjDDxwjBR8zIoGZZxlWFyAk1M4W1+qVAVYpeOZCHNM+dfpDXSzI2N1816u5yT5/Xetzg6fAKBxn/Z/YlJ1wsaIRqCzRTc/KJV280WoZtuwrdmDYkf/pDUs8+SP3aM7pdewr9hA1WPPUb4jjvQI0o8MuqnO6V1iK76zqO+vyWMluBejCn62dJ1wXHQAgEi996LeVGo3yg0nw//+vVz+r5TQej6GMm/yJh1CKimUfGe95B5/XUSTzxB7sgRzMZGnKEh3JERwnfeSeSBBxCXSqKXjqMjqhvRK+uRuZQK9ypklfpLM8AXQIRiCF9w7srQ5CDy7CG1r2itUmam4xCIgGkpz9JsCgIhRM2K2SkYr0Jk+/sZOXmq9LdmGNTfsBtzDqSKZlk033VHiUTPj4wQP30GO53GiowJvKTr0Pv63tLfQtOoWLuGSNvU1ZkTIAQNN+6m4zvfJZPN4dk2IydOkhsaIlA7O69czTQxite6Z9v0vvYGr/7O/6J/336QEisaZfWPvZ8dn/g40faF9DZeeBjV1ZiN5eOPQkcH0nHw8nlyJ8bG1aOqdWGOG0tqmsqc0PWiBYGH3duLMzSEWV+PXazCAUDXMerrJ2Y3jIfrEv/GNyYouIXPR2DrVqKPPKKenQ0Nl91TXAsGiT7wANkDB0pVNXZPD5m9e4nce2/pfQKAlCR+8IOyyTWruZnAtm2lrIurCkIgfEFkfStc6ADdQrvhyooTLzs0fU4krBx9BxTyU9twAERiSr2/mLgE7i97rpPOL32FoVf2IAyD2nfdQ+0D93H6T/+MQFsbmm7Q/S/fQPf7qbnnroVv86XAtBA1LRCKLYnMAWH6EK0bkdFqZOdx5FCP6ic4kztDzLxDjf+fvf+OjyS7r7vh763UOQCNnAeYnGd2dmdz4JLUMu6KoiTKDJYlmgoO0kPbkp/HQa8tZ8uyLMtKFiWSoihGMYjkarmB3Bxmdmcn58Eg5wbQOVTVff+4jdADYBAmADOLsx/sAN0VbldV37p17vmdgzcAkRiivgNR16byC5a6ujeArNugQk8dW20vFFOBosucYLg9RzQASHJOlpyTxXZtNKHhN0LcRsXTAIynE/zNG09xz+b9TGaS/MUL32RDdblqP1fMY1+vmZ9rgKGZWJpJznGvCN+6vqjwVDGRH1PEnZtnJDeIIx18egCvvvADjkf30hBowtI9DGZ66Ut3M5EfJ2SGS/7Xikifsnm5lvZHPZXvOGLPMkwOtu7mYOtuQAUQ/wega7yPT+z/EA92HLgh+xVCEFykUmYdy8PpkQt87s1vMJIeRyIZTI5iuzb/4fk/RptnoCIlpAppJnNJQp4gH9j80HVph6EZbAi30xbaQM7JUXALaGh4dZWDsY7lQqCh4bgO6ErxX3Tz2K6DpunzVkOtYx0LQaBhatZ1vdcrEcTKt+fRLTZG26jwVCyrXSEryE9t/BCvDhzix72vXGW5MHticy0YVgtGVRUVP/uzRD7wAdKHDpH60Y9IPvccI3/8x+jRKMH7VKCgEYshdH1BaxNnchI3n0ePRpXyfIViFM3vRwsEQNeJfOhDhN71rjXxoHezIYTAiMUIvetd5M6dw2xowKiuxtqwAU9HB8H778eoqbl+oh9NU0otf/i6fRtlfBDsAqJpC6K2DbJJFWLatEUFk2ZTyKEuZD6lwsNu0/OcG4uTuNw1/bevphp/be2yvL+FYRDbsaPstVRvH5nBoTIS3bUdxo4dn/7bDIUINjZgLHOyJbppE4Z/RnVXTCYZP3NuySS6bioluus4DL1xiMP/8b8ycvhNpONghkJs/vjH2P1PfpVQc9PiG1tlaMEgRl0dwuebtj2ZItFlPl8WKmo1NGDWllfVCyEwa2sVYd7fD4A9NIQ9PIxZW0uhtxe3RKIL08SzefNVCT17aIj4l79cXtmh6wQOHqT6n/wTfHv2LDknQUp5XSttAELvfS9jn/scxYGBUoNtcqdPkzt7lsCBmec4e3SUzBtvzKwoBJ7NmxcMIb3VIMdHcE8dBtdFNLYjIpVIISARV0p1VyIildDQpnIqhnuRY0NgFxGxOmjaoM7xcD9yuA90A1HbqIi85DgyOYmoqEZOxsEXQGvfpkjpkX7k2CAgEPUtUFlTnk9xI7FUJbqUyu+68xQMdiPTyavadmh3PgrtOxZ8/3pgJeMM6bpYVTFqHnsvbr7A0A+eJHLHfjKdl9nxe7+LZhj0ffVrTLz55toh0QGtaQtSush8bm6FmGkh9JtseahpiMo6RCSGHB9GjvYhJ0dVNUc+s/gki2mB5VcT8sEoorIOqhpUGOlyx0hCqID1imvPZLyNSXQxHVhqGhYe3Ysp1kbg0/VEfbSa3/zQLxH0+vnqa9/n/Xsf5lP3PV62zHAyzr/62u+uUgtnEDQDRDwRspkhUsUURdfGugFf5KARQkcjbaewNI+y89DDBIzgVb1S08UUY/kRCo4a7EgpSRQnSBQnAEUoaUJ59V8LiS6EYK911zuORL8SmtCIeIPzKpev637QFgiTXcdK0RJp4L0b7+NivIfL432MpOM4UmBpBtoCA5WAVcWOmk3c3byHB1uv74SJEAKf4cPHenjotUBDYGgmdskax9BMXNslWZzA0ixyTnZV/abXsZYx934oENP3zZsBV7r0pQboTHSTLqaxXRtTt2gPt9IRbaMvNcjJsTO40iXmrbxiXclIdoSuRC8NwTrOjV+g4BTZXNFBa6h53jD3WwVCCPRQSFm57NwJQjD+ta+RO3dOBXcKoYLhPB5yZ87gJBJlnucA+XPnsEdH8e3YcU3+n0ZlJWZzMzKfJ3fmDIGDB8vVi0v5PLquqk2nPMNvwXG9lBJncpLxr38dT0cH9f/2385Rw651yFy6RPy0ISwfMp8teYwq9RmlH3n+MHLoMiIQBeP2GvdKKSkmU+TGZiod/fV1yw7p1DSNUGt5VUZubIz8+ET5/hybVO+MPYYVDuGNLT+nJNBQjz6rasLO5Uj39S+9vZaF7vEw+vZR3v69P2DojcO4to3QNLZ8/GPs++yv4a9b3MJ1LUAIoXILqqqmcximgkDdTIbCpUvTy5oNDRh1dVduAOH14tm6dZpEL5ZIdOk4FAcGplXbYgkVNunXXsOZZdcDKgg5+lM/tSwCHUBmsyps8DrCamoicPfdTHzrW9Ov5S9dInfsGP69e6fbl3rlFZz4zPdCC4Xw7d59CwSKLgF2ESbGYHwUGR9WYaN77wePD3n4eeX3bRdxB7oQjg2GiXv6LZVVYVpIXwAhJYyP4h56ToWQFguKHNc0mIyre9yJNxD1rci+TmjpgJEB5Om3wOuDdAo52o/Y9yBEb1KI6VQA5CKQqUnkkReRx15W5GggokJEF8JKVclLhljRJK5mmUTvuovaD30QIQTDTz2NnUziFgroPi+aZeGprSE7axJ1LUDmUsjhLihk5xSJiroNytJkNaCbiKpGRKwBmU0ikxOqUiGfUZYqjl2y8ZOAUAGlpqUq5rxBCIQRgbCyYVkDuG1JdFXS77/tvVs9psWGGjXTf3DjXqR02dLQXrZMLFnBnpatq9G8MtT6a2gMNjCYGaI72cOeql1YevS670cIgd8M4jMD053HUiZPUsUkXclLSOafDXOkgyOvfUZfke/rak6ACn+YCn94QeL1eiFgBGkKtKIJjaC5NrMPbiW0VTTSEq0nkUvRlxzi//fcHzCQHOHX7v0UXmP+h0dD06n0RWiNNtyQybN1XDvU9yPMVP9kahY+I0C6mCTtFtCFTtAKX30j63hHQgh1b5Oz7m0L3UtvFEayo7w6eAhTMwmbId4aPorf8LMl2gEIDKHjSoejoycJmgFawzOklUTSnx7ie50/5O76AwgEBadAvixb5dZBvrMTZ2ICq61NqcdFKRcmn0fm8wjDKCNjvJs34z9wgNSLLzL+ta9R8TM/M+1Lnj93jsRTT+Ekk4QfewxtmaT3bGh+P4E77yT53HMkvvc9PG1thB59FM3rBSFwCwUKXV3IXA7P5s1zCXtNU/7mrktxYIB8dzee1tYVt2c14WYy5E6dIvTIIwjLQkq5doQ+dlHZr1ytPa4zU94/besjyggR4Qsg/GHkxBDCdYDb694vHQc7mykjKs1gEM1c5ufUNDyzfLYB7EwWO5crfy2bxSnMhNPqXs+yVegAVjiMZsxMDLq2TT6RWHpzTZPE5S4ufftv6X/hJZypwFwh8NfX4autufoG1hiMujqM6uoZEr27G1ksUhwYmAk+1nXMxsZ58xWEx4N32zZSzz0HqHDR4vAwTiKh1i9ZYGmWtSiJnj12bE4ehHfbNnyzCOqlojg8fN1JdIDI448z8d3vTqvc3clJsidOKPV9QwOylMXhzgpStpqa8N9555J999c0JFBdj7jrXYiBbtw3nkVsnIR8Fvftl9F23a36x9EBCEaQhTzCtBD3vBfhC4DjqMrOwW6wi2j3PYZMJ3FffQom44iaRkTbFtxnv4W2+25k11lkOonsuYC8cBzRvgNyaWTfEGLLvptIoi9g33YlBruRF46pkNDtdyJitSUSfWrdqXFi6e/YtauCrwrB9ATvcmBVxZh86y36v/YNZKFAcXycnr/4Am6xSPriJQIbNlCMj688u+QGQQ5cVCHikaq552sFAavXHUIg/GFVHQelzBwH6dgI6ZY4dJXZovq8JV53y4STzuBmMpjVc/v0peC2JdHfiaiLVJHMpckWcvhmhfeEfUF+9T0fX8WWKUQ9EXbGttOV6ObM+Dl2xrYTtHZiajfmMhSIZVV9y5L37zpuHh7uuIuWaD1bqm+cZ6IQAo/upSO8GVDBb+u4dmhCI+oLE/WF2VjZQiKf4u7mvQSXGcyxEkxlHcyXVeC4DhOFCSYLE/gNP5WeGNa6tcuSoL4rHqY6TmWDFsDULKR00YSOqVm3myvaOq4DBEKVLM+aaJaz/rtR9m2z0Z8aJJFP8kjT/WyItJJ3CvSl+2kLt6jwvmAdlm4ylBmZd30pXQpukfpALVsrNiJRFWi3ogo9e+IEE9/8JsI0Maqr0aNRZC5HoauL7IkT+Pbuxbdnz/SDiebzUf2Zz1Ds7WX0z/6M7NGjmI2NyFyO3Nmz5C9cIPL+9xN86CHENSjREQL/HXdQ+bGPMfq5zzH8v/83yeeew6irQ+Zy2PE4xcFB/Hv2UP2rvwpX7ksIzKYmfHv2kD15ksHf/m08GzcqAj6TIfapT+Hp6Fhx8+x4nNzp09ijo7jZLJkjR3DzebJHjzL+1a+iBQLoFRV4t2zBvFKRukzowSC+PXvIHD5M72/8BrrfP61qNauqCNxzD/677lq28l8W85BLI0LLVygDyHwG2X0asWE34mrqQcNUhLksBaZqulL75dOzKgSE8oq2i9yO4hFFopcT3YbHsyLrgCvJcCefLyMhAYqpdNlh1AxTBeQuE7rXU2YBIW0HO7307KzsyAjnv/p1Ep1d2JmZ9aTjcO6vvkJs5w5a3nvr+EWbJRJ9CnY8jptOq1DREvRoFLOpad7vo1Yi0afgptOKSO/pwZ11fLRgEGuRSb/i0NAcCwazuhqjZvkTE7nTp6dV8NcT3h078G7fTu74jLVQ7tQp8hcvYjY0UOztJXf6dFkgq2fTJrzbt1/3ttwoyFwaxkdU4GAwUq5k1nWEN4AwLWQwDLat/JWLBYRpQbO6B4kNWxEVNbhvPo+IxlTwaGl97CLkMuAPqdBHX0ARvbkseLwqsNTnV6/phrK8KOQgXAFNSiwpthoQXRkBuCIINc5bDDIRh0wSce/9iL0PINZCoPQKhqCRvXvJDwwy8fobSOnS8NM/hVssEti8iYv//X9gBIO4uRz1P/PT17+91wCZGEHruAMRruSWeGATQlW13eTclNz5C6SPHqXm739yReuvGTbJcR0uxnt4vecoZ0c7S8GD+av6rwYtP5+9/x/QXjl/MBHASDrOm/0nebv/NAOpETKFHGFPkJZoPXc17WZP3Rb81vJmkIZSoxzuO8nbA6cZSo2Ss/NEPCFaKxo42LSHnbWblmxR4bguZ0cv8VLXW1wY62IilyDkCbKhoon7W/ezs3YzmtDm9Rm+Eid6z/Hnz3+TvF2gIVrDloZ2tta3s62xg811qx/sYmgGOyu3kSwk+VHvCzzZ9TQukr1Vu9C1G+eRvlRMBYneaCgjmNurpHWlaKmop6Xixpcwa0K7qh/+Oq4NGyqaOTE8N9T4RiFZTPLWyGEMzeSO6gP4DNWHF5wCFybPcWzsKFk7i6EZdEQ2sSe2l4C52snptwLm9sGa0PHcDsqhddxgKD99l/JqLVe6aOg3ZRyvCx3btaftiNJ2erpvWCr8hpeOSNuy11tr8LS1YTY0kH7jDdKHDinfVsPAqK4m/L73Ef3wh+coIr07dlD/W7/F+De+QfqVV0i9/DJC17FaW6n6zGcI/8RPqDC7a1QE6eEwkSeewGxoYPLJJ8m89RZOIoHQdbRIBO+WLfj27583VFMIgVFTQ+1nP8vYF79I9u23SR86hObzYdbV4ZY8jVeKQmcno3/6p+QvXlTBgqkUMp8nfegQubNnESU1atUv/uK1kehSkjt7Flks4uZyFC5dUuGnpWoBJ5Vi8qmnqP7lXyb6xBPlQYaLIZNAdp+GDXsU+bOcZqUncS+fgJFuROsOrqYcF94gUqqQMBn1qodgTwA5OYpMjiHCMWQ+h0xPlgK7boGH+eVCiDnK2qmQ3uXCvcK7WmjanG1f6bMuXXeOannJ+5rVRiHEsjzcc6Nj5MbiSMfBCPjRDJNiKoV0HCYvXuLI7/we3liMmjuuPUT+ZsCorlYkuhDquNi2qnSZFSpqVldjNS3g8W4YmI2N6BUVOOPjICX28DD5CxdmSHRNw2prW1S1KvP5OdePsKzl9QGo6zD1wgu4qdSy1lsKtGCQyAc/WEaiF7q6yF+4QODgQTKHDs0o+FE2Xv6DB2+pQFE53Is8/BzksojtBxC77pv5PhYLyMkxZDaNHB1UpLfpAa8fqWloVXWIqnpFrLsOIhRFTsaRyQlEuEKFLmu68og+dxzyWWRiXJHxwQgzNeuz+kzDVO9ZXkRTu+rbC7mba20hlihOdB0wPYhozbJCH9carJpq6p74MBX33qNERg316hg4Dv4NbeT7+vF3dBDZt2e1m1oGYXpLHuO34T33OsKOj5E7t3LuYk2Q6OPZBN8+9Qx/c+qHDKfGyBRz2K6Nu8ggJOINkiqk530vbxd4/vIhvvz233Ih3k2qkKFgF3Gli65peAwP3zn9LAcad/KpfU+wo3bjoiRurpjnmYuv8JVj36dzvI9UIUPRKeJKiaHpeAyLb516hnua9/KpfU+wqar1qtuczKX41qkf8jcnn2YgOUK2mMN2HXRNw2t4+MHZH/P+LQ+zp24L1tXUICXcs2kf7TUtJLNpBiaG6Rsf4rULb/PlV/4WQ9f5i8/8l0W3caPhN/3cW3+QqCfCDy4/xd9c+A6Hh95ia8Vmqn1VeHTPkh7QAoafusD19dsLm1H8kZtDtL3T/dDXcXvhA1seYlfdZrw3aTA3kh2mK3mZsBWZJsyklIzlRnl96DUmCwkMzcCVDkdG3iJqRdkc3YJxg6pebmVkiik0oeHR1YOdI20V/LzeR61jGZhSol8pNnWkXar+ufGD+Y7IBs5PXuIb5/8Wj+7Ba3h4vP19y9qGLnS8+q370DcFz5Yt1H72szipFLJQUHYCmoawLPRIBD0UmmMNIDQN3549WK2tOJ/+tFpP09B8PvSKCjSfr0xha7W10f6Nb6DNIkaiP/mTBB98ELOmRgWQGga1n/0sbi5XRjob0Sihhx/Gv28fTjKp/M2FQFgWmt+v2rcAaSRMk8DBg3g2bcJJJpXaUdPQLAtjKvRPCHy7d7PxySdV++YZV1Z+/OOE3/c+jNhMKbxn61bqf+u31GdfAFOTEVPQw2Ea/+t/VWXB8xDr/v37afnDP1TrldSk2WPHGPh3/w6rrY36f/Nv0CsqZpSOjkPm6FGG/8f/YOzP/5zw+9+PvhwCzbGRIz2KuG7fjfAtzX5HTozgdh6D8cGrhsBNQUSrkcOXkRMjiGgtmF6IVkPnMdxzh8DjV2rLTAJR1VQi0m8vaIaBGSh/brBzuTmE+KKQskzRDWD4vOie8jGVFQ6VXcuuXcTNL99yysnlkLPOsTB0jODSn3+miPvYrh3s/JVfwhur4PV/+++ZOHse6TgMv/kmb/333+Xe//zbhDe0Lbt9NxvCsjAbGtBCIdySrU2ht7dciV5Tg7kAiS6EQA+H8bS3k3nzTUBZqeQvXpwOK0XTlBJ7kWfc6f5qFgfi5nLIXA7hX3qlZ+bNN8m++eZV+7KVQlgWwQceYOxzn8MeHgYU+T9VxZN+4w3cZHJ6ebO2luADD1z3dtxQDPfBUDdk07D9QPkQxh+EdAL3u5+HTAqx/wGIVIJhoN33ftzv/5VaLlqFdufDiO0H4M0f437rz0BKRMdOxD3vgfpWRH0Lzlf+QJHqHTsUGV+cp3pANxBtWyE+jPvN/wtIRNtWxB0PIULLmyy9Niw+lhMeH1I3kE7xlqZxhRCY0SjmFVZbUkqq3vUIslBAs6xl2yzdCMj0BORLfU2wArfzGKK6RVU/zH6eC0QQntvb6hpg8tnnGPqTP7vqMnY8jn/P7hXvY1XPukSSzKX52vEn+dNDXyVTzBKw/NzdvJdtNe2Ymsnl8V5e6nqLyVwSiaQuWMXD7Xexq3YLrdEGOirLy6KklKQKGb596hk+9+Y3GEqNoQmB17BoitQStPyMZxOMZycZTI7w/bPPc3m8j39yzye4p2UfxjyDPCkliXyKrx77AV96+7uMZMaVstWwqA9V4zO9jGcnmcgmGEgM8+1Tz9A53sev3ftJ7mjYMacUWCJJ5NL81dHv8sW3vs1ELlkizr20hmIYmsFoepy+xDBfeOtb7G/YTraQm9OuK+E1PXgMi650P+cGL3O85ywDEyNE/EEOtN9xbSfrOuBM/Cwv9L9MopBkIj9JopDEkQ4nx05zdvw8eimwYikd7o7Kbfz89k9c1/bpmo7O7TfIX8c6bjTqQzXUBatu2uTQeD5Oxs6wOboVS1MPmXknz9mJM6TtNLtiu7ij+k66kpc5NPw65ybP0RxqIaSt++FfibSdwtJV+LYtbeK5ESq91Zhi3QJnHcuAAE3qCLQyWzRH2ri4aFLccL9nB4d0McNddfvZEduKpZkEV1CBstgo5FZ4KNQsC62qal7/3gUh1DkyKisxKhe3AtE8HrybN5e9NmddIRYMkhOWNaMAXQaEEGCamLW1mLXziymEEOh+P/rWhfOAjHmOjx4IoLe3L7DGAu0xDKyWlgXf14PBOQrMie98B3tsjLrf/E3ldXyF/YdZV0f8C18gd+aMUicuq0GKgJNDlxUx37YTvMH5v39SIqULo724nScgVQoCjNYuHsQWrkLr2A9hdb6FpkFlvVLCD3dDvvTcEogiGjcpS4LbDUJg+H0YgQB2Wom6smPxGY/wJUK6LpnBobLXzEAA4wrSVDNNPBUV5EaUJVUxlSY/ObnsZmdHRnEKM971mmnirYgueX3NNGn/yOPs/xefJbyhrXT9Cl767G+Q7uvHLRTpffZHvPlff4e7futf46+rXTt+//NACIHV3IxRUUFhikTv6ppRoguh+pvGxgW3oQWDeLZsmSbR7aEh8jCtRBeahm/HjkXbYjU3zyHaC93d5Lu68G7duuhxlI5D4fJlRv/oj8h3di66v5VACIFRW0vo3e9m/Mtfnn49d+YM2WPHyF+4ME3eC58P/8GDt1xwMulJZaEiNETTxhki0vIg9t2P2HmX+ltK8HgRuqGsnHcdRGzeDZQsrqySvdODH0LYpTBs01T9YSCEuO8xxF2PAEKFKE5NoOg62kc+DR4f2k99RoWVChAPvB9RLNlj6SbcVKX3Er/Dtc1QWQOdp5C1LRCJqUn8q2567fUP0nUpTkyS6eykMDyiqoxK0AMBqh55aBVbVw452oscK4VDT3mMD15CXnFcReuOdwSJXhwZQfP7ibz30QWXyZ46M8cybTlYXRJdSk6PXuSvj36PVCFDc6SeX7/3UzzacS8e3QSh7E5ODJ3j3z77vzk32knBKbKtuoMntr9bka5XoOjaPN95iD859FVG0nGi3hAf3PIwP7v7A7RFG9E0jaJT5OjgGb545Du83PUWRwfP8CdvfIWQJ8Duui1ziKCCU+SH51/m8299i7HsBBW+CE9se5Sf3f1+msK1CKGRtwu82X+CvzzyXV7vOcrhvuP80et/zW8++A/ZXNVWtk3HdXml+y2+euwHjOcS+AwvH9jyED9/x0dojTagCY1UIc0zF17li0e+zYuX31xSoOWXXvouf/78N9jTupU9LVv5lXf/PTbWthL0+DHXwOB1ODvK0dETc153cSm4y5spv1XDvtaxjtsRmhAgbt4EVM7JUXSLVHoqMTQDKSUZO82FyfPEPDF2VOwkYkVoD3dwMXGB4cwQRad4u2WaXRe4V+RAuLhXtVFbx/XF7XKkBQJdaMpSRZZfUwUnj0f33VDLNikleaeAK12e63mR53pewtB0GoP1/NzmjxAwA6SKKcZy42TtHIlCkpHMKH7Tj2+BMOTZSBczZO0siXySgltkODOKpSuSfnaFiytdim5hOiTSs24jto554MRLZPU8RLW0bfKdnbjpNGZNjfLPXQ58YUTjJmT/RWT/RTU50roT6Q2UE3BSIot55GAnsvsU5NKgm4jaVkT7XkXQXAVC05CVihhTmxVg+RBtuxC1bZBNKVVlIKo8f5f3KW4JCCHwxioJt7USP3kKgFRPL4VEcllBsdJxGD9ztuw1f0M9vit8sIVuULV7J73P/giA/PgEmcFBpOMsK7AxcamzTPluBgJENm5c8vq610ts+zaimzdNf8bWx95LPh7n1X/1W+Tj4zi5HJ3f/ls80Sj7/vmv443F1jSRbrW0oFdWQlcXgLIkGR8HQI9E8GzYoCyXFoAeDJZNKhYHB3EmJ2dIdMNYkie4/+67EZ//fBlhlz1yhOTTT2PW1qqg6Pn6DcdRYcXHjzP6x39M6rXXVJXOFar26wU9HCb03vcy+Z3v4JYmkAqXL5N84QXsqf4NMCoqCD/22IpyAlYVxbyqyPGFlIVK6WU1iWupnysgAAwDjLmCHeHxKtuXK2F5FibCfaWxhW+WGMBSfulrGhXViM17ka8/jZz8MmLbHYiaJuRC9snB8Ixf/BpC6tw5Ov/XHzB55Ci634/QZ65hX3PzmiLRRcsOREupfykWlM3P7O5Woq5n10WWgsPXcn98rdAjUcIPPUDVx352wWUSL75E4sWXVryPVWVWs8U8J4cuMJAaQSB4tOMeHmg7gG9Wcqyma2yr7uDv7fkg/+7ZPyCeneT0yCUSuSSV/mjZ9qSU9EwM8PUTf8dIOo7f9PLxvR/mU3sfJ+oLTy9naDp3N++lIVTD/371S/zg3PMc6jvBU+dfpCVSX7ZdKSXnx7r41qmnGctOELR8fPrAR/nZXe8n5Jnp1AzLx4Ntd9IQquV/vfIFnr34Gq90H+GH51+iIVxD2DOjQhlKjvLcpdcZTI2iCY0PbHmIf3z3x6kPzwyWot4wT2x/NxW+MP/thT/j8kTfosfzQPtO+ieG6B8f5kTveeKpCS4MdtMcq6MqVMH2xqUPkG4EQlaQ1tDCap3loMZ/E4M0bkFIKXFxcaX6caSL66rfrxWOdMkUs2SKGTShowkNXWhoQrutO+S1AilVUJ8jp86vgyNdim7xmgk5iSTv5EkXM6Ushplzu5YsiIqujSMdvIa35MPs0p/uJ2NnaA21UeOvQQiB3/RjaRZZJ4O7hInIdyIMoVNw8hT0PI5UBLoti7DA4RJCYAhz/bt+nXA7HUUhNHRh4EinTI3u4lB085iasmtbDpkukYrsQyJY+B5TcIq83Pc6QTPIv7jjnxCyAuTsPH9+6su8NXKMPVU7ea7nBbqSvRScAqfiZ7k4eZl76g+wt3oXpmYQMAM0hxrn3cfx0VO8NXKMRCGFoRn85Zmv0RJq4l3N9xPzziivk8VJzk2cIe/k8OpeDtTcs4wjuI53Cvx33EHq+eeJf/nLKqw0ooLrZLFIsbeX+F/9FfboKJWf/OSyvZCF1w+tO9T3ZrAT2XdBWQU0bwNvAIRQE6W5NLLvPLL3LNgF8PgQDZsQzVsQS8qKEnPEg6IUEEawQv28A+CrqSG6aeM0iW6n00ycPUvNHfvmhIUuBLdoM3zo8PTfwjAItbTgrymv0tAMndq7DkyT6G6hQOJyN9nRMfy1SwyelJKxY8cpTnllC4G3KkakY5nZWaK8ukhoGps+9jMkLndz7A/+CDudpphOc/6rX8dfU822X/h5PNGbaTuxPJiNjWVVNJlDh6Z/1ysqsNrbrzruEV4vZmsrWjCIm0rhjI6WDaOMmpoZu6mrwL93L56ODnInT06/5qZSxL/4RdxMhvB73oNRVaUCnoVAFovIXI7i0BDpF19k8vvfp9inOAO9VBmUv3RJWXpdRwhdx9PWRuDuu0k+++x0OzOvvoozNqYW0jQ8mzapEOtbDZZP9WW6XvICv51GazcW8sJx5OEfQWoCxoeRXWeu+nwq3vdJxJ77blbzlozE0eMIobHvi39OYNPGNf3cI6aCvAG35zSirlRRZ1gIy4u08xDvVxkllhcRa0L6FqhQuw0QfvB+pO1cdfLO09xM6O67V7yPVSXRc3aerglVeuA1PTSH64h4587eeQyL9oomfKaXTDHLSDrOeC4xh0S3XYdzY5c5MqAGMnvqtvJo+91lBPpsNEfqed/mBzk6eIauiX6evfgaj216kApfZPqiKjhFzoxc5PjQOQAONO7ioQ13lRHos9FR2cxPbHqAk8MX6EsM8XfnX+T9Wx4iZCkFiJSSvuQQb/UpRXZNMMa7N95LbWhuOauh6eyt38YDbXfQ9Xa/epi8CrY3bqS5so7JbIru0X7e7j7Nj0+/xqXhXppitXzhl/7bVde/0dgZ28GW6ObFF1wC9FttRnsFcKSL49rqX+lguw6O6+DI0o/rYM/6fXoZ6WC7NgW3SN4plH7y5O08yeK1B8xM5Cd5qf91qnyVeHRP6cfC0i0MzcAoqRF1TS/715j9mtBm/T71ulby1b09O/TZmJrYUOfQLp0/t/y8zneuS/8WnaI6p06BnJun4BToSfbjXuMgueAUODZ6ikQhNX1ep/41NROjdJ7Kzu+s82cIA13T5j3v1/PcmsJAFzoFp4BEYrs25yfP4Tf8NAdb0Eqq+Kl9riurF4bPCJAoTBLPjwLKfmOyML4g0akJjWrv2i7LVcTrrN8XWgapBFpicQuPFbdD/cIVv5QtM319Tmc23bp9oC4MDOFQlOXVYrYsgqve18TC/cHUsZBT/0kXFwdXOpiaB32BYWvOyZGy01T7qgiaATQ0HOlO90uV3igf3fThq7Z9S8VGtlTMLza4u/4Ad9cfWPTz5+wsY7kRsk6GgLEeZryO+RH54AfJHj1K5vBhen/919ErKxGGgZtO46bTaOEwkQ99iMqPf/yq6teFIHxBaN0OKFsX2XtOWQs0b1UqxtQksvcscuCiItf8YUWe17UjzFs/E+Bmwl9bQ+WuHXQ9+dS0jUv3D5+l9X2PoXu9Sxr3pPv7GHjt9em/g40NVGzdjHbFBIpmGNTde0+ZfUz8xEnip07jq6le0r7yExMMvv4GhYTyrDa8XmoP3rVkwv9qELrO7n/0S6T7+7nw9b/ByeXIjY5x+vN/ibe6mo6PPD7HQ36tQI9GMWprEaapMhpmv1dRgbXh6pMMQgiMaBSrtbWMAJ+Cd9u2JZ0fLRAg9vM/z8B/+A+4s6x6nHicsT/9UxLf+x6eTZuUFZUQOKkUdn8/+UuXykJE9ViMyk9+Equ5mf5/9a+QucVtYZcLo7qawAMPkHrxxWn7lkJJyQ/Ksiv8/vcvq0pizSBcoZTjuYyydZFynUhfIoTQkMFIKSR1CcuHoje2QSuF4+Cpq8VTu7btqK6EHB9EFnMICfiCUNOKTI1DfAACEWQyDnYR0bz15gbT3kToS7jPeNs34G1f5uTxLKy6nYvtKK8/UzMwFuhkhRDomq4sSYoocmkesihdyHCk/xR5u4AuNHbUbqKtYoEk7dJ2N8Za2VGzka6Jfrom+rkY72ZrdTue0kU1mU9ybPAsBaeILhSp3RReeCZZCMH2mg42xVrpSwxxMd7DpXgPbRWNGMLAdh0GEiP0JVUQx9aqdpoidcoOYR5U+iJsjLUStPwkFwhRncLbXad55uQrFOwiRbtI0bGpDEbpqG1hV/OWq657M2BqBuY7NNgvZ+fI2Cow13Ztiq6NLW1s15l+rezvKaLUzVNwFBlecAoUnCIFt/SvUyDvqtfzTqHs9aJrLzrpslJMFpK80P/avO8ZmoGlmVglUt3SzBLBbmJpVsl72cTU1L9WiZydImnVj46hGYqMKf1uzP5d0zGEUVIO+rH01b0BFJwiGTtL0S2Unc9iiSCfPr+z/i669vS5zDv5K85nsexc56d/z0//fqPObcEtcnT0FEdHT815TxMallZ+/sx5zrFn1rm3Zr1u6lPncZ7zesXrutDxGV78hm/egUvQCuHRPfSle4l5Y8TzcfrSfdT6amkKzvT5BbeA7doY2rpyeiH4jAAaOgU3N63wtzTvvHZpwJoKHJ2quJHTFTazvxklghyJK+d6CjvSLhG2V6jpZhHYQgg09MU9SJG4rnPl3st/kxL3iu+tqiixVfuFUAPeaRp9djvUxNStACEEhmYh3VJFwyzYslgKrjVU5ZKcpyS9NKmgzqwzXT212CSH3/DRFGygK9HDC32vYGgGE/lJDM1YkBi/EbDl7Hvvep+zjvmhV1RQ96//NakXXiB//jzO5CRSSjS/H7OmBu+2bfj37kXz+1dM3Ah/GFq2q35uuAvZc0b5+0ZrkL3nkCPdyuc3XIlo3oaobkYYy/c8k8UCFLKqVPxqQ5Pg/DYUtzqsUIjaO+8gsmkj8ROKPO1/8WUGXz/Ehg+9f9HQObdY5Mxf/jWFiRJhqmnEdu+i5sDcLCuh61Ru20rdPQfpfeY5AMbPnqPv+Rep2r0Lb+zqWQaubdP11DPET56etgvxxCrp+MnHl/ux54UQAisSYd8/+zUKkwm6fvB3uLZNovMyJ//kz/BVx2h65GF0z9qbqBGahlVSkk/ZuACgaRjV1VhX8UOfgh6NYrW1LUiiL5ozUNpf6D3vIXf2LBPf/GZ5W4Bifz/F/v6rbsJsaiL60Y9S+fGPIx0HPRTCvgEkuubz4du5E8/GjeROzX1uMKqqCD744HXf782AaGhHhmMwcBnZd1FZlNwi47DVhti4C711GbzTTfV1Xzp8rS0kjh9n5OmnCe3YjjZrUlSYJr4l9AmrAikRwQqEL4RMjCFH+1TfE4igNW9DZlO4nUcRjn3dSXTpOjAxMvOCpoM/hLiBFkSykFdjkCkYJsI7D4ku5VxRnVi5wG9VGU1TN4iV1ORZO89ELknBKWJd4cNnuw7j2UkyRXWAwp4ggXlKDbN2nvNjagY06AnQGK6dd7nZqAlU0hiuRaDGfudHu8h15KdJ9FQ+w6XxXrVfb5DGcC0+8+oXQl2omtrgjN3I6ZFLPNB2AEMzyNl5+hJD0yexMVxDpW/hmTohBFX+Cir9kUVJdMd1CVg+Nte10VRRR2NlLXXRagxtcRJgHTcWJ+PnODZ6kkwxS9EtUnCLFJ3Sv9N/2xRLfxdd+7pYr9xsTBHGGTu7+MLzQBc6lmZi6mZp0kUR69Y8f/sNH/fW38nmio7r/CmWh770AK8MHGIyn6Do2uXntTShMXW+p86vcwueW1e65JwcOWdlA3FNaNOTLDPn9Irzqhmlc2/SHmnj4aZ7MMXch/o6fx0RK8q5iXOk7TTxnAqQ7ohsJGjOVDMlCwlyTo6AGZhWp6+jHAKB1/DhxacmgnIFQlZkOrB1LUPiYrsFnBJJvpzJJUc6OFPF1leOqUrkp5o4WoKXt5QU3DwzNPly2mGrVsyzykw7dHR97flFLgRNaJiaBS5ziHRJiVyfFt+LWe/N/H+5MHWT/dW7CZoBBtNDuEhCVpADtXup9sVW9kFWAPsGTmCv4/aBEAKjooLo49eHvFxwP4EIWut23Ckivfs0crQXknH1cButQWvZhqioX773upTIyRFkvB+ZSYFjc7Xvr7b9XtDWJllyrYjt2knzow+T6LysbEySSY79/v8h2NRIzYH9Cz6DOYUCl7/3JOe/+o3p1/y1tTS96xGCzfOLwDzRCJs/9jOMHnmb3FgcJ5fj0re/S3hDGx0feRwrNH+AumvbDB96k7Nf+jKpPkXCCl2n+d2PEtu1eODlUiE0jVBrK3v/n39KIZmk78cvgJSMHj3Gsf/9R3hjVVTv24O2yOTCasBqa0MLhcqIa83nw9Paqia0FoEejeJpa5v3Pc8SSXQhBFooROwXfxE9EmHy+9+ncPHiHHX8fNACAXy7dxP58IcJvec9GJWVSNvGs2UL9sjIouuvBFZrK4G77yZ3+vQc7/XAvfdi1CzRZmiNQcTqEJv2ICdGkSdeRTRsgKr5Q7LXcQUW8Iy/1SClJHH8JOOvH8K/oQ3dHwBN9eWemho2/6t/ucotXACGhahtV5PiQkeO9YHHjyp3FapSzbFvSFYCdhH3xMszf/uCaG07oHphUfM1Y2JYiQSmciQiVYiOPVASBdiJBKlXXyd38aIKEp3WuQj8u3YQfffC4aNXw6rewfyml63V7QRMH+liljf7TvBQ251sqW6fVmZLKRlNj/PsxdcoOjY+w8OGyiYqfdE52ys6NiNpdeMLWv4yH/KF4DEswt4ghmZQdG2G0qMUnRnVWsEpMpaeACDsCRC0Fr+J+gxPaZs6tuswmBzBcZ1SG4tM5BLTy4Y8QXzG1QeVActHwFx8v/ds2ss9m/Yuutw6bj4uJ7p5ZeAweWflKcDvBDjSIes4ZJdA1Hp1L23hllUn0Yczo7w++BYT+cnFF34Hw5VuSWG/tFDgrJ3lgYaDmPM8c1R5q9leuYO3Rg5zfuIcpmaytWI7m6PlyofR3CipYpJaXx2mtp4quhgEGpbuvaEBkNcbUrrXnbSc2p67jAGmItBvTDtuRTsiTeiYmoVwBbZcuHrmeh6zsCfEgdq91217K4EtnVvyfK0pSBcKBWQuo8Iucxko5JC2Da6tHpQcWymfQZHBug5ayb9W01WImzcAXr/yCLc8arl3IEQgita6Y5pInybQY41orTsQ4djSFLJXQKYncHtOw+QImKWwuzUi2JGui53JYmcz2Nls6fcsdi6Hk80x+vYxnOyM2KOYTtP34xcJNjWi+3wYPi+G14te+tfwBzB83gWV9N5YjPaPPMHo0eP0v/AS0nUZPvwmh377P7PlEx+j4f778NfXTZPpTqFA4lIn3U89zZkv/TWZoSEADL+P5nc/QtsHfgJtgUkNzbJofOgBOn7qJzn9519QSu+Llzj+f/6IzOAQre97L5XbtiIMFRzn2jaZwSH6XniJ83/9VYbeOKS+S0DFti3s/OV/eN0Jbc0wiO3awe5//CsUEklG3nwLgIGXX+Ht//n73PMf/x2httY1J/Dy7dpFza/9WhmJLrxefLt2LWl9LRgk9BM/gV4xNw/Av3v3kr8fQgjMmhoqP/UpfLt2kTl0iOyJExS6urDjcRVW6roIy0KPRDBqa/G0t+PbuRP/gQN4Nm9G85YEf5pG1Wc+Q+jhhwEVkurdunVJ7VgK9GgU3759GE8+iT0wMPOGphH5wAeu235uOnQDse1OKBaQR1/CfeUHiJ13I+raEL7AmunrbmXIbBqGeyESQ0TXXtadp7qahp/56LzvmZH5raLXBPwh5OBFpDcAk2PIbAKySUQgrCrHXEdVod0ISBfGh2b+zmehcP2rYMpgF5CDXSrbBSCfQTZ0IEKqH0688CLj338SIxJh8plnibz7UQq9vRSHhvG2t614t6tKohuawY7aTdzfegc/vPASb/af4vdf/UseaT/IhoomDM1gKDXCjzrf4EcXX0Mg2FW3mfta9uGdZ4bLLQUegrIOuVLRPh+EEMpSQlckerqQxZkVQGe7DllbnXxLN5WlzBK26dFNDE3ZtyQL6Wl9miNdcvYMkeoxTIxFtmkucb/rWMc61vFOgKEZbI5sIWpFmSxM4tW91PhrCZozE6cSic/wsTmyhaZgM159jafZrwHoQiNohm4Z65B1rG0oIl2gSR1bFnGls2zSXCCUEl8YCNa+FYRTpkRfJ9MXhXSR6QTEh5HjwzAxCqlJyGfVw14xD8UCFIsluxBXeXhP/S5RBLCmgdCVSkzTkEZJCWd6EKalCF5/UD2sR6ogWqV+N613BBkiglG0DbtwNQ0GLik1WiCsjsUKP78c64dkHFHVhKhpBd28+raW8Ex2vZDs7uHU5z7PxNlzOMUibqGAUyjgFoq4xSK5eJz85IygKR8f58j/+D0Mvx/dstBME80y0U0TzTSJ7d7F1k/+PUKtLfPuTwhBbOcOdv7yPyQ3Fmfs5ClwXfqef4FEZyeVW7cQaGrEioSRrktuZJRkdw/xk6fJxeMgJZppUnfvPez8zC8SqF84d0QIgbe6iq0//0kyA4N0PfkUrm0zfvosJ/7oT+h97kdE2tvxVsXQDIP8+DjJ3l4mzpwn1ds7beMSaGpk/z//f6jYen2yqq6E7vFQf9897PylT3Pkd36XiXMXkI5Lz1NP44tVcvd//HdY4bVFRJl1dUR/8idXvL7QdXw7d+LbufPaGyMEeihE4IEH8O3dS3FgAHt0FDeVQhYKSCkRhoHm86GHwyq4tLp6hjyf2oymEbz/foL333/tbZqvmbqu/OSj0TIS3bt1K97t22/IPm8GZHIckuOIUAWyogYunUSODyOjNYhAEEwPi1q21TShbVs8S+Udi0Qc+faLsGX/miTRg1s2E9wyt3+Utk36UucqtGhp0Oo7kMNdkEmoENHaDYpgLtm4UCwgApGbek++ofBeMalVyKvPXiLRU68fIrh3D9EPvJ/s6TPU/9N/THFkhMnnfoybX5qwbz6sKjMrhKAhVMMn9z1OupjltZ63efHyYU4OnyfkCaIJQaaQYzg9hitd9jVs59MHfoat1R3zKuUEKowTwEUu2Q5DSndacaZr2hxP1ClCwZ3PS2cBzF7WKIXqqTaKMk/ZpQqW1tps/TrWsY51rCa8hpeWUCuudBBo8/aRDf4Gqr01eHTPOjG8BAihYYpbpwRToGHqXswbpfxdcuCowKP7bhxneh3u/5buLR9wCG4KKS2Ehl4iwmUpPHnG61xeQaqLEmmuIdDQhDb9+60SOm279roS/WqQEplNIQe7ofcicqRfPezkMkqxlM8q0vw6HkO1JQGGAZYX6fGpwDiPXz24N7YjGjcgKmqXb2lyC0EEImjtu5Gajuw7h5wYRqYnEMG5qtmlQKYnFRnftAUC0TX1/SxMTtL/wouMvPX2kpZ3i0UmL1xceHupFBse/yDzG6Uo6JZF4yMPoRkGh/7jf2Hs6HGk65K83EXycheaaaJblsrCyOWnyWxQRGTTo49w57/5/6jcsW3R9mq6TsWWLez7F59Fsywuf+8HOPk8ubE4g6+8xtDrh9A9HoSm4eTzuFdYgUQ2dXDHv/wNWh77iRvqU28GArS+/zGyo6Mc+4M/ItM/gFMocOGb3yLQ2MD+3/hna9In333264j2ndC2deXX9XRg+DzrX+29KyCEQA+H0cNh2LL6+WZXQhaLFLq7KfT2lr0efuwxtEBgTfULy4E88Rry/Ntg25DPqInbsUEYG0Rq2pIqm8TmfbBOoi+MfBY5MYrIr8wCdrXgZHNc/sM/Zufv/Y/Vbsr8CFUiTK+q3DOUmAApVWVfMg6ACFepMdHtAI+vvC+1C8hsevrpTeZymA31mLU1aAE/wjLx79hOvrub1OuHYIFqg8Ww6kfP1A321G/lM3f+DHm7wKG+44xnE4ymxxFCEPWGuatpNw+03sGDG+6kMVw77Vd+JXRNJ2gpI/m8XShTfC8EV0pydoGiowYYQSuAPuuGbmj6tK96zs6TsxefsVDbzFN0VblcyApO30R0TSuzb8nbeYqOfVXVfLEUNHir4/joSZ7ve4k7a/ezr3rPigMhc3aezsRl+tL92K5DyAzQGmqhLlCL8Q4NLl3HOt6pWMjrXCCwdA+Wfnt6sK6j9HCJvur5jaodxqq342rQxeodJyEUOS6nyHQkVzqglzdNzAgPbrEHcBUsOiXguLXafsMgJTKdQHaegu5zyOFeyCRLZb4FpSi/8Y0Au6h+MsmZV3sNOH8U6fVDpBLRuBHRthVR17KmPV3dvvPIgYVJ33Iodb7QTeUR6hRBtyAxhnv8xQU/p7bnXUqpvxCkq2xcPP5b7nt6o2D6/TQ+/BDB5iZOf/4vOfP5v8Qu2ca4xeIcMhvAWxVj16/+Eps+9jMEGxuWTCrrlkls1w4O/vt/Q/X+vZz63OdJdF5W3zfHwc5k5rYvFGTDhz/Itp//JFV79qB7PTf83HkiYbZ8/GNkh4c5/RdfpDCZoJhMcepzXyDU1srmj/3MDd3/SiAHLkNN8zX14O7xVxHhGKJ18xyyXA52I/suot3xyC1fCVMcGCD9+uu4yZl+VY9ECD74IMJau33ookjEYXRg/vfcUkXUYnDmhtqvYxYce+0doyVMcEnHJnnq9E1q0PIhhAa+eSyt/WHwBkvW6Not3/dMQZheysbbjl0WNGrW11PoG8DNZPBu3sTgH/4JkUceInXoMNo1hFyvOuMopaQ/MczfnHya40PnuKd5L79y8Odoq2hCUAqo0g28hgdLN696s/cYFhsqGjk5fJ6JXILRzIQqd7rKOsl8itHM+HTQX2u0Ac8scjdg+miJ1nN2tJN4ZpLx7OSi25zMJRjLTEwr4TdUNk0r5D26RW1wJuRqLDNBMp9eMABVIknk0yTzVw8VvRUwnp/gzPg5qv1VbK3cgqmZuCU/2ymV2dTD9nxwpUtfqp9vXvwufak+bNcBZEk9abClcjOPtb6bOn/tLaNaW8c61rGOdazjdoWUknQhOz1u8pqe6fHQ9ULeyZMqJhdf8CYgY6fWlehSIl0XHBs51A3HX0VePg3ZjCKxXWfxbdwsOLYi1TNJGB9G9lxEvvkcxOoRm/ciNu1WJcG68pdeMw+duRRMDC9rFSkE0w+aUxMXpcyn+Ve4OkkkPH5kagJcd9HnopuNiu3beN83vzIvab0S6JYHK3w1HfqsZT0WFdu2cte//f/Y8omfo/uppxl46RVSvX3kJyfRDANfdRXRTZtoeOA+Gh64j0BDPbrPt+xjqBkGweZmdvzDX2DDhz7A4Kuv0/PMc4yfO0d2eATpOvhiVQRbW6i/+yANDz1AZGM7ZjC4oOf6vPsxTR74n7/DPf/5t4FS1VowsOT1PRUV3PGb/5zd//hXVN8AIDSs0OLZZasHFwp5NdGraWAY05Xk0nWUQllKZSGlm2ryY6rvy6VVpU29A3Ut6mtnlciafA6GeqD/MnJnVvUphonQdBBC3T/mZD6o/kfK0mSgrqtsCNcFTSCM1bGlkq5L7tw50i+/XPZ68OGHMRsa1lSfsFyI7XdBXeu1bSNafZ1as3YhXQd56hCMDiI27kI0dSAHLiN7zi2+8uigsm9bQ8j29XHyn/0Glffeg7epiUu/9/tzMiOk45RVEq01yPFB5DyTE0IIVTXmXXrffUvAtKYDX4ESiT4jpK74wPuQxSKax0vNJz9B9//3b5h48in8u3ZQ/49/dcW7XXUSPWfn+XHnG3zv7I+oDVbxyX2Pc2fTbgTLVyAFLB+76rbwg3PPk7cLXBzrYjg9Rm1wfp8lKSU9EwNcGOsCwNJMtlV34DNn/MQi3iDbazby7MVXydo5zo9dJp6dIOafv/xRSsmleA/dEyr53NJNdtZunlaae00PLSWiPu8UuDTew3B6lNpgbN7P67oug8mR6cDUWxuqdHsiP8lYNk48G6cn1UeykMJvemkI1FMfqCdg+NHnechOFlJ86exX6E31IxAYmoEmNBzXIS/zvDX8NiPZUf7B9k9Q7V173lrrWMc61rGOdbyT4EiXR//4F+geH6C1ooH/9cT/y8HW3dd1HyPZIQ6PvHpdt7lSXO9w2VsKUqoHt0xSqc6Pv4bsv6xKim+FiQUplW+oXYDeC8i+S8hXnoS2rWh770fWtqiQ0rVgP6GbqoT5ZmGe8yeqGpUlzOAlRMNGpD5DMi6Im0Sq6aaJr2r1ngOEEJjBILFdO6ncvp09v/ZPAFmmchSiVCFQup5WSjgKITB8PkJtrYRamtn40x9RZOtsRaUQiGvYlxACT0UUD9EVt9EMBjGDa5k0vwIDXchTh5GpSUR9K+LOR6GqQfVx547innwdkhOIcAUceATRukWVVnWdwX3lSRjpg4snkIefA0D72K8BEvnkl1Q1TiGPHOpR7z34Ydi8V+03n0W+/gzy8ikARFMHYs99EKuH5Djuj/4GbeMu5OUzyharuhH9vR8Dr/+mHh4pJfbICKkf/QhnbGz6deHzEX7f+9Cj0bUz6bgSNG1ENHZc2zZu5c+/VDgO8qXvq4nodEKR6N3nkc9/B3Tj6vdL11GTQmsIVixGx2d/HbOykvFXXiWydw/1H3mibBknleLi7/7eqrRvKXD7zs0K85TTYezS9KBt2KM8xG8nCFFur1QScUzBu3mTmuwv2ph1dbT/yf9BFosIy4PmvYWV6Il8mkvxHmzXIWD5kEDBLmAZFixT2eAzvOxv2E5zpJ6uiX7e6j/Fkf7TPNpxN+Y8dilZO8fh/pOcHL4AwL6G7bRVNJYRuEErwL767TSEaulNDPJqz1EeaDvAfS375w0ETRezvN5zjHMlYv5g0x6awrVopYGlJjTqQ9Vsq+ng7YHTnBnp5MTQBTbFNuAzy0+klJLexBBHB88syZrmVkFfqp+vnv8mw5kRCu6MTY2hGWyObuTdzY/QHmnD1MrP2ZGRo/SlBtCERq2/hi3RTYSsEPFcnM5EF8OZYbqTPTzX8wIf3fgExroH8jrWsY51rGMdq4qCU5z+WWpWzfJwpbf6Om4qpETaRUiOI7vOIo++BEO9a0txvhLIkqL0zJu4547Cln1oe+6FqgZEILQkT9wbBVHfgahqvLE7mWXlIu1CmQ0OAJqOiFYrf/VsSnnKe3wq4HU+hCpvTWWqlEinOPNQbljTquHFIIRAGDqw9GtFVXEU1fWnGQjdWPK+0HXEdfX1l8hiXpESoqR4XsXr/mZCDnajPfYJhBC4bzwDR16ERz4CPedxzx9F7Lkf0dQBF4/jPvdN5Ed/FRGuRLTvQKtuxH3uG4gNO9B23V1+/p74h2pbI31o7//UHJJRHnsFOT6M9vinwXWQh59DvvUCPPghRdKnk7hn3kI7+F7EI7UqU+ImE+gA2DbZo0dJ/N3flb0cvO8+vFu2IG5xv+U1VXm0lqHpiLveDWMD0LFLvSYl+AKwYTsiXLngqnJ8GC6fuUkNXRp0n4+Kg3cBkLl0icr776PqkYfLlikmEvR95es3v3FLhL7zwfIXHBs5PoScHFnTNnUrxpxJfgnMPGu46QzZM2fInj2Hk0yWLe/btpXIww+taLer3sN5dJOoL4QmNDrjvXzt+JMMJUepDlROe5MLBLqm4TU8VPqj1AerCFhzPfiEEDRH6nj/lof4/Fvf4vJEH988+UNCngC767YQLK3jSpfxbIKXu97i26eeJplPE/GGeGzzA9RdoVoXQtARa+E9G+/lr499nwtjXXz9+FP4DC87ajfiN1X5neO6xLMTPN95iO+fe550IUOFL8wHtz5MpT9Sts2GUA33tezn9PBFMsUs3z71DLXBGAeb9xAwfSDAcV2GU2M8ee4FXu1++4aeg5uNsVx8+ncNga4ZuNLBdm1Oxc+Qs3N8uP0DdETa0Wad49PjZ5FIolaE97W+lztq9gJqsuH85EW+e+kHdCW6OTJylPe0PELMu3DHvY51rGMd61jHOtaxjpVDug4kxpGXTyNPHoL+S2tOWXZd4Npw+hBu5ynE5j2w9Q6ob0P4AqtCtAiP7+Yq0SdHcE/PU+0hNEX2jnQjR7qvOpWl3fXBm9vm64nEGGQm1O+RWlhhGOuikBIKGWXV4xTBH0GGYghj4dysG474ABRzijyvbFCeuu8AiI27oLoeIUHr2IV7/BXExCiy96KaUMlnoO8iUjeV6nOgC65CGC4JUiLPvIlo3qSU7ACGiRwdQEyMKZ9j10Fs2gN1Leq68N/88yFdl9yZM4x9/vM44zOV8no0SvixxzDq6m56m64XZDGvrvfZnZnlXZJljpQSinlwigjLp5TYS9qpRLq2CtUuWdWqLAt9zWdOCF1H7J+HhIxUoe17CNG4YcF1Zecp3LHBG9i6a0PswQfntTXTPR4afnZlYZSrAt1ARGuQkyPIbBIRus34sSu99YUom8xPvPACI1/8EsIw0ILBMvt0/Rqqo1adRA95AtzTvI+XLr/F6ZELPN/5Bs93vjFnOUs3qfBF2BRr5d7WfTzafg/NkfqyEFCAqDfM+zc/xMV4Dy90HuLFrsOMZxM8tOFO2ioa8egWmWKWMyOXeO7S63RN9BG0/Lxv84M82HYA/zze5NX+Cj649RE6x/t4tfsIz156hdHMOA9tuJOWaD2mZpIqpDk5fIEfX3qD3sQgIU+AD299lHta9uE1yhXmEV+Ih9vv5HDfCQ71Hefo4Bn+4LW/4sxoJxuiSgk/mUvy9sAZXuo6TNDyYxkWg8mR63vwVxGWZtIUbKTOX4vf9JN38gxnR+lJ9tCV7OHIyFHq/LWErJmLeySrysVCZpBNkfbp14UQbI5u5GDtAUayI+ScHOcnLhKru806iXWsYx3rWMc61nFVWJq1YODwjUbRLeDIKQX2bayOl0qhKnsuwKk3kOePqaDQ2x25NPLYq8ie84gdB2HLPqisXV2S82bA8iGqmq5tG6ulYJalIGO7qEj/tXyupFRE2lT1cSGryPS13ObbFb4gisgUys9cCNXH5TIQH1JWLSWSVGzYrpS31wrXgVRCZUnM8ooWje0znuqACFWoaohVgJvJkD1+nLEvfpHMa6/NvKHrBB98EP8dd1xTWN+qo5CFyWF1Lqa86SsbILxEf/PJYcgmkZUNiKVMtkmpKn2SY5BNKK99ASDA9EDthoWre9YqDBOCYVgsWFY3lj7RsArQfV5c26Y4Po6TySDdmTFd9K47V7FlV4fMpa9QZ0vVdxVz6h54m0FOjpYH/Wo6zKqESbz4MqF776H6738SIxy+bsKHVb1ypZSM5xIMJkcIWL5SoKREF1rZrJvjuhScIkOpUYZSo7w9cJqu8X4+c+fP0BQpn+0UQtBe2cyn7/goXsPihc5DHB86y/GhswQtf4lEz5G1lVdQdaCS92y8j7+3+wM0hGvmbacQgq3VG/j0gY/iMz281PUmRwZOcWTgFCErgKWbpItZcnYeAdQFq3hs84P83O73Ux2YS+QKBFuq2vnkvscpOEVODJ3j1PAFTg9fJOQJYGg6qUIGKSU7azfxxPZ380bvMb5/9vnrduxXEwLBrthOHm56gJZQE7rQcZEMZ4Z5sf8VXhl4na5ED0OZ4TISPVc6Z6ZmEjDnDlZ2xLbxfN9LZOwsXcke7q5bux3cOtaxjnWsYx3ruL4wNYuWYDthK7wq++9OXWYsN4rkRtjWrA1IKWFyDHnqDeTpN2G4d7WbdJMhYXwE+frTyP5OFUDXsXPVVOk3AyJYgdh4x7VtZBWJYFnMQ2JUqTpDsVVrx6IQYoZYcl1Foq1hkum2Rj6L8rFH5SRIqawQTAvRshlx93sRgVKluWABcmq+idSr2IQIAT4/2s57ENsOlL8uBDJRUn1r2g3ta4qDg6R+/GOE14vweBCGgbRt3ESCQk8P6ZdfJnv0aNk6no0biXz4w5iNN9hm6gZDeALIivrShEYcsssMLV9u/oeUah/JUTC9EI6BZgBy+rzfahBN7YhIJQQjV1/Q44NotaqsWoMoTkww9uJLTLxxGCeVLguN99TWsOn//Y1VbN3CkAMXVEXEbDg2mF6Ef3XGxjcMjo3sv1iuRNcNmCWKFoaBWVenJveu4/dp1e7MUkoGk6P8xVvf5LlLr5EuZHlow11sr+kg7AnOCqeROK5Lupila6KPQ73HGUiO8OS5F9hTt5WqQMUcpbeh6eys3cyv3vX32FmziTd6j3NutJPRzAQT+SQB00dDuIVtNR0cbNrNfa37aQjVXLVcxtAM9jfsIOoNs6t2M4f7TnB2tJN4ZoKsnSNg+WmJ1LOtpoO7m/dyb8u+BcNCATyGxf2t+wl5Ajx94WXe6jtFX3KQdCGLz/TSXtnMHQ07eM/Ge9lZu5muif5pX/VbHZXeCu6qu4O2cMv0Z9IR1PlrOVCzn87EZcZyY4znJ8rWm3oo1TV93uDRqCdK1BtlODvCaGb0hn+OdaxjHetYxzrWsXbg0T3UBxqo8s4virjRmCxMMp4fw5FQVjN6O6G/E/fwc3Dh+Kzwqncginm4dBIZH4LRftj3ICISuyVJj0WhaaDdml6qEpS3fS619lV4QoDHryxjXEfZSMyT6bWOGw/ZdQ6xdT9SaMi+S4hAGCKVUN+KPHUIMdCFbN2izlNiHFHdMDPhYVoI0wOTcZUXoBslNbumvkseHzKbRibiCH8IdL0Uwqgj2ncgLx6Hhg2IYBiZSamMuMjNm/wp9vYy/Hu/p3zwPR6EriMdBzeZxJmcLFd9AkZdHRUf/Sj+AwdueS90DHO6skjmM5BNLW/9cAx8QcRSbY9cp3QfFRCIIsJVM/3UrRDIPQ9EXSvUtS6+YCSGdsdDELpB9ljXiMSxEwx993t4GxoIbt9adv8wo4tMEKwm/JG59ia6iQhEEb7byI7LLiD7LyIHLpVn8Jge1V+XEDxwB4kfvwBS4u3oQPPMjGX0igo8TSub+Fu1ni5n5/n26Wf4+omnMDSdx7c9ysd2v5+mcB0ewyojn6WUFJwiI+k43zz5FF888h0S+RRHBk7xQNsdeINzy4Z0TWNDZRN1oSoeaDtAf3KYZD6N7dp4dIuwN0RDqIa6UBXWEgcouqaxqaqVxnAND224k4HkCMl8BkfaeHQPEW+IxnAttcHKeYNMr4TP9HJX0y7aK5ro3jxAPDtJ3s5j6RaVvggt0QaqA5VoQvDhbe9iW3UHpm7QEFr5w6FH93B3/R00hxpWvA21HYumYP2K1q32VVHhic6ZFBBCELZC1Ppr6EsNkHPmfzhbaGJCE4IKTwSBIGkv86Z3g3GgZi8NgbpZZd7ruFboQmdDpGW1m0FHpI1PbPloWUjuOq4dMW8FpnbjblG6prOneicRz8pn5TdFNpQqqG4MBlIjPHXxRS7GuxnLjXM52UPOzvGutrt5oOXOOXZmC0EXOrX+JZaiXgcYms6ntv40BXdl3siV3gqinuUPUF3pYrsFbFnELZXwC6GhCx2zZPFxI8/XOlYfhjDQlhHidyP2f9teY66LvHBMEeg95+cQKe9YTIwij7yAnBxFu/f9UN24Nj1spbs8h6HFVJDTXrG3gFoyn1ntFiwZQjfgdlML3oIQsTrkq09BNq2sAXbdg/D4oWULpCZxzx5BnHlLEeDhGKJq5plYWF5o24p79gjy6a8hfAHEgx9GeP3q/NY2Q9dZ3B/9jXpv932Ihja17u77kG88jfvid5Wg0DCVXcy1+q0vA1JKnHQamU4vuqxRVUXFz/4s4Q99CD38zr5uhRAqM2BZuQFSZW5oOuhm+UTfWu9XrxHC64emjavdjAWR7e7GqozR9o9+Gau6em3e1+eBVtOq8mqSY6oKS9MR/gjCe43Vcq6LzGevnntTvIK7ky4yl4HkxMr3e8X2sAuQTiDjA8jhHtVHz4Y3oCazSigMDJB87TUyJ05gxGIIc4ZbCD/0ILWf/oUVNWXVSPRMMce3Tz1DpphlT91WPrj1YTpiLfM+fAgh8BgWTZE69tVv54fnX+ZCvJuB5AhZOz/P1mcwpepur2y+bm33Wz42xlrZGFvCLNsi0IRGTTBGTfDqM8zbqjvYVt1xzfszNIMN4RY2hG8++SiEhiY0TM1EX8DfSxc6pjBxpYs7K8xBSrmkCVmv7kUIKDhri9BsDTfRGr5GP8l1rElU+Sqp8q1scNuZuEDBLbA5su2WuTnfLtCFRlOwfsWTgTcDtuswlpngwng3yUKaeCZFwSlS46nlnvoDS54AvtnQhc5ddftv6j6LbpGcoybKJS5yFlsk0NBFDo/uw9I8N6WqS0rJU2df5vOHvo2ULv/0gU9yT9uesn2Ppsf56yM/4MVLbwLwyMa7+Ok9P0FVYEaVYzs2r3cf43+9+CUi3iA/u/d9vHvzPXP2V3CKHB84xyuX3+b8SBeT2SSaplEVqGBX/SYe7riLpkgt2hImXkZScX79O/8FSzf5wPaH+Oju9wKQyKV4o/s4r3a9Te/EEOlClqgvRHO0njubd7CvcRsV/pWrc1zX5S/f/C7fP/0CAA3hav7e/g9wV8vuJW/D0IxVrdozNF0RHxJuK0/0Qh55/BXcIy/C6MC8YVvvaOSzcO5t3NQk2gOPQ+vm1W6RKnOeHEGO9SOzSaVMW4ayUdv1EMJcWH3uXj4BgGjZrojBhZbrOQ2OjWjaujre8a4z98F+Heu4CrRHfkqpYxNjUCxCIAwV1UqZ7Q/CjruUT3kuo5Tl/rAi06c3oEH7DrSKGrWMYczYGQkBVfWIe9+HSE2qvyOzniGiVYiD70VMxhW5algQiZX2E0R7109BxepUWpVB1/Fu2ULFJz5B+NFHMaqqVrtFqwaZGFWWLFP3RU1XtlG+0PzLOzYkRhQZ6BSVEt11YHIYmYrPLBipUYr22c+Hjq3681xK9elCKBuYYMWSwk/XsXTofj+63weuvKWe0WUhi9t1AnJphG4ipYNEQ9S0oMUaV2wRJos55MWjyLH+qyx0xdgwn0V2Hkf2nlvRPueF66jskHymXIEOatKxorZMcR955GH8O3fMuymztnbFzVgVEl1KyWQuSffkAABV/gpao41LUu/MtvLQhHa76n2uK6R0ybv5m0YeLARTGJiaScbOLKjaLbgF0nYGrdRZSak6rpyTZzkPpOvXxTrWOlzp0pXsRNcMNkW23r7qxXWsGHWBGJ/a8wQf3f4Yjuvw3175M97oP7bazVpzsF2brJOm6M4/qS5xsaWLazsIQ2Bpnus2IHali0DMu71UIcORvtOMpsd518aD7GvcWhZeHs9M8tSZl3i1a8Zb9OGOu8pI9LxT5K2+0zx7/jU2VrXwxM5H5+xnODXG7/z48zx3/jUmsknShSy26yAAUzcJefz8+Rvf4h/c+QSfuOND89qhzUbWzvOjC29gaDotFfV8dPd7OTFwnj965Su8fPkI49kEuaIK0DQ1A6/p4aXOjfzGI7/IA+0r903+6tG/43d+/Hn6E8PUBCu5f8N+NldvWNY2DGGir+I4R78dlei5DO7hHyHffhFSE7dsifkNh+NA70XcZ76K9shPIdq3r1pTZDGP7DuP7DtfCqd0WPakziITJTKl/JnFYstlk5CIIxo2AatAohey61UT61gWRGO7+iU8v82E8AVLwaNX2YbXD/Xzi+2EYUJVvfq58j0hIFqlfq58z7SgYXn3xJXAiMUIvetdFC5cwB4ZUar0QgHN50OvqMC7YwfBe+8lcM89mPX1aH7/DW/TmoZuKBW5U4R8SRV7NUGBlMoOzHXL76dSXvX+Kot5mBhSAaRIFTjquorAzyaQlY1KbbyOFcNOp5l44xAATjZHprubC7/zu1TccxAzWoHQ1PhO9/upOHjXajZ1Qci+8wjNQLTvUxM60kUmxmB8COnxIyIrrEx2XWR6AiaGl7GOA+nJxZe7XghGEc2by4LMfVs249syV9hgj49jj0+seFer54mOnFYau7i4S7C5cFyHvsQwA8kRQIWCeoxbOAH6JkBKiSMd0sUEphVbVT/AoBUkYoXpTfXRm+qnzl+LqZkIIZBSXQ9DmWEuJ7pwpMvx0ZO0hVtpCNRxabIT21X+TkXHxnGdOUSAlJJ0UQWyevTVvy6klEgkUipVpGSK3FeEi5oCmp98mVpXfUem1hVoQiDQFiSApkIvXNzpbZT2ghDavGTPlOp/aoJlap9CaGio15QNjSy1Qb+lZmTXKhKFSdJ2moA5v1JhHeswdZNq/4xCKWDd3PAdKSU/vPQSbw6c5OM7P0RrdO0FRkkpKbg57CXYKbm45N0shmaiXwfLDykl/+XZ/8uHdjzCzrpNZf2iEILaYIyGcA2j6XEuj/eTKmSnSXQpJZPZJGeHL0+v0xnvYyw9MT15DJAr5jk/0gVATbCSpmht2f57J4f4tW//Z17vOkreLmLpJmFvkKpAlKLjMJoeZyKbZDQ9wW8//cf0TAzxLx75B3iMxf2Ni47NaHqCI32n+a/P/RkvXnoLV7romobP9OC4Dq50yRSySClpq1iZTZztOnzz6A/5L8/+XwYSI9QEK/mn93+Cv3/n42WTDkuBoZloC1S63QwYpTGNwi1+n5QSmUsjDz+HfPP55XvDvhMhJQz34T77dTT3I9C+A7FE263r1gTXhfgAsvvUjGe90GbG/1PKrekxtARX2V8pP+4q5ct7nQIthWYgizluSmWG66p9FbKKpCrmFbE1te9cEjlwfqGWQs0GhL5I/yFKY227qMisfKbkPyvVMTU9SoFq+UAsPF6fghztWVgp7w0qReti/bWUyIkhyCXVspVNSrlsF0ptzM7TxnCpjfM/hywXUkoVxJgaV/vRTQjFEAuocdexcshC6Rr3BK5aLbJSWC0tNPz7f48sFpGOM0PuCgG6jmZZKnTUstafBwHhDyN9IXWMRntmiPSFoBsQK1Wn20WYGFTnM1JdTr7PundIp6jCkTMJCETLA0jTEzA+CPE+ZM2G1an4mQ2puA8ScRi4jJyMg13qf+aB2LQHUXv9HCOuBYWRUc799n8CVJ/m5nIgYeLQmwh95nz4WprXLok+MYS25S6EP6K+s1KC5UOmJ5C59MpJdCiNJcTaFFN4A2ib70CEKmbad5X+KXPqNMmXX6XxN/7Zina3aiR62BOkJhBjOD1Gz8QAh/tO8vCGu7B0kyv90KWUZO0cRwfP8u1TT5PIp7B0izsadlDhvb1uzm6JcJ198rWS3+BsdZMs+b26Vyw7/Z8QalvSpeDkkLiKCHWnFtWmSdNpspeSX2Jpn+IKwn2aXEVTey7tdyFy9kpU+6poCNQznB3hB5efImfn2BHbhkf3YLs2nYkunul5jslCAkszuTjZyedPf4mmQANdyZ6SGh2ydpb+zCBNwYayY5J1cgxkBnGRVPluXgDLfJBSkrEz9GV66E5dZjw/TtEtYGomXt1HzFtFS6CV+kDjHDLHlS45J8vl5CW6UpeZLEwipSRkBmkMtNAW2kDYisyxxJFSYkubsdwIlxIXGc4NkbUz6MKgwlNJR3gjjYGmOSrMS4mLHIsfYU9sHwAnx49TcPK0hdrZWbGbsfwox+JvkygkiHmq2FW5h2pfzaoH3brSJWtn0YSGV/fiSpeiW8SVDhLKrIMWmqhwpIMjbZzSd4XSRIUu9JItwNyHKUXaKRWmpVnoQseRDrZbLH1/KW3DmJ4kmr0/W9q40mUg00fRLeBKm4ydLruWNaGrbS+iGL1emGpbzs5RcJWftEAdQ6/hxatfP+XuOm4dFF2bI4OnOTp0ho9sfe9qN2deuLjYslhm33I1qO+cg4aGK13GswnydmHaQ93SLaK+EJZukipkSObTuK6L3/IS8YbQNR0pJalChr7JYd7oOc622o7SexoN4ZmQ8tpQjMZIDccGznI53kc6n4GgmhQpOjad433Es5NUByowdIOeiQGGU3FcKdGnSHS7wNmRTgBqgjEaIzMk+mQuxb/6we/xSucRJJJ9Tdv4lXs+xsMb7yRg+ZAoa5ZvHvshXzj0bXomhviLQ39DhT/MZ+7+acxFSLKiU+TEwHn+72vf4I3u4zzUcYCf2v1eDrbsotIfIVXIcmmsh+cvHsJnemmK1i3z7EG2mOdbx5/hvz73Z/RODlETrORfPPwLfOrOxzGW2P+ZmkXEUorBsBXGuIFZCovB0G4TJbqUyHQCefhHyDd/9M4OEF02JIz24z73DTS7iNy46+aSGoUscqRXnTNNh8o6RF2HerDUddzXvw+ajrbtHuUdmk0hR7rVOv4wWusOCFVeF9GNdB2kXbh5D9x2AZLxWSTWFcpOKeeWf09DsGSiP5uAyRFlt3ElClmlvPOHVECopi/iLS/LFailexGg2rrUQ+c6iih3HZAOpJPKMuJqbQxEIFxTbkWyAkjXVQR6ckxVMOimIvqW5Q29jiVBSuSlY8j+82jb7oX6669MF4aBHlnDwYlrDUIr8YpySXYqQoiZSUrpIqf6Wk2f3xprSrmeGld9dqR6lnWLhHA1MptS/V5mEsKra60jHRt58g3ka09BcnzxPjBavWZIdF9LMwd/8N1Fl1vTz8SGqSbavCGEJkqTvnmkdBHXwitYXrSOPchgFBkfgHRC3XOm7jurBd2AQBht5/1Q1QhC4GQyOJMJ9HAImc/jZLJzViv09mHH4/NscGlYlScNIQR+08f7tzzEl97+DhfjPfzJG18hkUuxo3YTPsODJgQukoJdJJ6d5JXut3jy3Av0TA5i6Sbvaj/IvoZtWEtQU91KyDlp8k5umkxXBKEfr+4vn1zAJetkyDvZaUW/JjR8pWVBkHey5JwstlvAxSVRjDOljPIbQfxGEInElkWydpqiWyjtU8er+/DqvjISMW0ncdwifiNI1slguwUk4NV9+I3Qog+O1b4qtlduoTNxmfH8BN+8+B2+d/nvCJh+Ck6BVFENegOGn7vqDpAtZjkyepThjKo8MIROjb+G8fw4rw28zqPNjxA0AwihkXfyvDF4mHgujoagLXztfvXXgoyd5u2xt+hMXsKje/DoHny6TxE+bpGeVBeJQoIaXx36rMGrlJLxfJw3R99gODuET/cTMFRpVt4tcHL8GP2ZXvbG7qDOXz9NpEspKbpFLiTOczz+Nq508Bl+gmYIKSXx/BgDg31siW5nX2w/1iylvkSRz5eTl8g7eQQCRzqcmzxLwS2QLCYoOgVMzWAg24cxYeA3/ISs1Q2QyTt5nur9HlEryh3VBxnNjdCV7GSyMI4rXYJmiOZgK63BDQTM8oG8lJJkMcFApp/hzAAThXH12YXAq3uJeatpDrZS46vF1Mr7GEc6HI8fYTAzwO7KfVR6Y/Sme+gtnVOQeHQvzcFWdlXuLVuvL91DV7KTZHGSrJ3FxWU0N8KT3X9bto+Yt4rdlfuo8t2cMMi8U+BU/Cwv9b/GxcnLpAppdGEQ8YZ5T/NDvKv5AYxVVHeuY3XQlxikNzGIvZqDo0XgSqcsP2MpcKSNLgzOjVzmd5//AhO5JIOJEUbT4zzQfoBff+CTVAcr+Ys3vsWr3UfJFnNsqW7jUwceZ3/jdlzp8uy5V/nCm9/l2MA5fufHf07IGyDqDfHlj//O9L26JlhJQ0R5l16O95MqzITbZYo5TgxcQBMau+o34zM9fP/0C1yM95AuZAh7g0pAUMxycbQHUzeoD1dRWVIpSSn5ypEneeXy29iuw466jfyvJ/5ftteWZ6c0R+v49Qc/RWtFA//y+/+T0fQ4f3n4uxxs2c2B5vk9AqcggZODFxhIjPDzd/4kv3Lvz1IdnKmM8Fs+aoKV3N26Z8nHfvY4JpXP8N2TP+J3fvwX9E4OUR2s5LMP/Ty/cPAjS94eQK2/nlr/2sg2MMTqerJfF0iJzCSRb/4Yefg59fC+juVjbBD3pe+haRqyY+dVfcOvK4p5ZFI9GIqqRkTHPkRgtrqxRGhYXkQwqsqfK+tw/WFk9ylk53HE5jvn2FVI1ykpmkuhYk6JnE1PIhf4bDIZV77SvuDNqYTVDbWv2ercQm6GVDesBX2KVZDqEtpYzCs1KFJtTzeVYlS6SlXq2Or3TAKEjozWXvXpSERqwCmq4+u6iuDOJpb6iedCSkWQT3krGx51XMraWCwtN6EsISLVK/dSdh21nVR8hkAPV5dfc+u4bpCug5wcVp7At1PuxjoWhpQl33Rb9VGujSxcMTmm6+r7txYmvPsvI4++BNk0onkTVDeCx7dgcZ6oXlkV442A0DR0rxeYqe5f04T5PBDVLcjesxBLqmojx1a+/QilTl/pdnUDYvWIWL2q+sokkeODyPggTI6qe1chV+qbZkE3rt/9XzBTWWcYYHoRtS2Ilm3KZqt0rlKvvcHQn3+e6k9+nMkfPk3qjUNoPm/ZppxEktB99664Kasm1/GbXn5m52NcivfwZv8JTg5f4Lee/X2q/BVUByoxdZOiWySemWQ4PVby9xRUeMMcaNrF39//BK3R+b90k/kklyZ7qPRGiXmj9KeGmMyn8JteOqItGJrBUGaUwdQICEFdoIpaf6xMvaRUvQ5j2QnGc5Nkills10YIDY9uEfWGqPNXz1HOAwylR+lODlDrj1EfrCFVyDCYHiFVzOBKF69uUemLUu2L4b1iEsCVEkvzTrcl52TJ2ulpYhuUuUfGTpO1U3h0P5ZhlYhPRQxMXUBqOya5EtkeMqNoQm132rrDdUgVldLZbwQRQqPo5sna6dJrgTJFuiMdMnYKS/fg1f1K0beEcsWpfe6q2sl4foLXBw8zkZ8k7+TJlxTmAkHICrG/eg8PNz5A0bXxGh46E104rkNtoJZ76w/yV2e+yutDbzJZSLAx0oGlWwymh3hz+AipYpoKT5QdlVsXbc+NgpSSycIkl5OXCJkhdlbuptHfjKVbFNwCqWKSkewwHt2DpZef/4yd4Xj8KEPZQWp9deys2EPMW4UmBKO5Uc5OnKI33cOZiVMEzCARK4JAIJEMZQc5Fj+CLjQ2R3awIdROwAxSdIv0prs5ET/G+cmzRKwoW6JzPbhHcyPsrzpAS3ADZydO8/bYm3QnL9MSauP+uoeZyMd5a/Qwo7kRsk6WEGsjhT1rZzkzfpLxfBxL9xDzVpcsjJKcjB8jWUyyN3ZHmcWPRNKV7ORy8iKGZhIwQ0SsClxccnaW3lQ3Y7lRdlbupjnYNi8p4kiHeH6M4dwQw5lBLN2iwlOJxCXv5HHmqH9UvYfH8OIxvEzmJ0gUJrB0i1pffdkDTNAI3jRLIsd1ODdxkS+f/RtSxRS1/hqag8q2I+8UCFvB20FXuWo4PnyOZD7F/vodSCnpSQwynpvEcR28hoeaQIyaQOyGhIQWnCJ9ySHGMhMUnAJCaAQtH7WBKip90TlK3ylLrP7ksPLzHjxN12Q/tutwZPAUI5mZGXuPbtEWbaQmsPpVP8t9kJRS4rgu3zj2QyK+EH/wkX/Nm70n+eLh7/DEzneztbaDP3z5y5wavsi/efcvUxeu4stvfZ8/eOnL/PcP/XOqAhU8sevd7G3czj/99n/kNx/5Re5t2zfnPhj2BqkPVeMxLPoSQyRy6Wmrlmwxx8nBCwQ9fnbXb6HgFBEILox0kcilpkn0weQY49kE1YEKWqL1033ReDbBM+deYTKXwjJMPnHHh+YQ6LPx4R3v4m9P/ZjvnvgR/YkRvnvyR+xt3Lqo2lsTgoOtu/nUgQ+XEegrgSbE9HWezKf5/qnn+f0Xv0TXeD91oSp+9b6f4+8fePya9rHa8Bl+anz15J0cXt27+AprDVIiC3nk6TeRbz1/kwl0AaaprCYsjwri043yH6EqM9E0Rf4hZ3xlpTujNHZsVT7u2EqdXCxAIa9+v5llyCN9uIefQwuElGL0Zli7OLayGNFNiNYg/FeM1TRdHaPZYxTdQNS3IyeHkaN9EG9ENGwsHeMSCjnkwAX1wGwX1A/gnnx5fgJWusqL3fQgatpK1gM3GLqBCETLm5GKq+OBVA/ckWsMZUxPqAf4QBQRqFBE+lR5u11AJsdmQgazCQhWqGt6IVhewDs9zpIlf+NrQnJUnWd/RB2P2W0s5tUxySbU39lJCEYXt4yZD46DzJQIe9cpEehV6wT6jURyHPI3yR5pHWsDpb4FgMzEwv2DVronrjJkfAjGhxE7DyLu/glE8NbsD3I9vbiFAoGN5WNrO50mff4Ckb1LF5DcTIjaNgAVKu4UVYVDMIpW04bwXycHD01DBCPq3DZvgWIBmRhDDl1Gnj8ys5zpgaomVQl3nfaLocaJIhCBUAXC9MwZgwT276Wp/l9i1tYw+cOnqfnFXyCwZ1fZMqlDb5K7dGnFTVk1El0IQVtFI7/54Kf51qlnODJwioHkCJO5JGOjEziu8t20dJOYL0qFP0JjuJZ99dt5z8Z7VBDpAsTtpYke/sfhz7G/dgebKtr4/sUf0znZS40/xqd2PEFHtIUvnPwWhwePownBXfV7+LmtH6Qj2jK9zaJr80zXKxwZOsWFiS6GM2Nk7Ry6phPxhGiPNHN/4x28q+UeAqavrC2HBo/zp0e/yrvb7uW+hv0cHjrBa/1v058exnZtop4wWyrbebT1Hu6q203ImgmBCJrlg12BIOlOlnnGu9Ih52Tw6F5CJRJ1PuiajianfK2FCty6YhCbc3M40iZkVkzbfFgl5W3BzWFJD6aYGVg50sZvBPEZKwuuCFshHm58gJgvxtn4OeL5cQpOEVMzCFthNkc3sqdqJ1FvFIAPt3+A4cwoRbdIfaAOAdxTfxfP9PyYIyPHODJSHrJnaRb31N1F9U1S8C4EZRHi4NV9RKwKPCU7DK/uxat7qfLO377+TC8juWF8uo99sQNUeaunr616v5o0ShaTDGYGiOdGCZshhNBxpM25ydPYbpHW8CZ2VOyaJuhNzWRzZCsZO82J+DEuJs7RHmovU6MDVHpi1Pjq8Oge2kIbeHvsTUzNojnQotTwEsJmhJ5CV8kjfW0gbafQ8jobI5tpDDTh1X0U3AID6T5OjR+nL9VNlaeajsim6XU0oVEfaMRr+AiaQSJWFK/uw3ZtxvIjnJ04zXB2kMHMINW+OvzG3NAc2y3Sn+klYATYEt1Gta8GS/fiuDaJwiReo5xAMTSTtlA7bSEVWHR6/ARnJpKErQrurLln1dSLGTvL8dFTjOXi7KvexePtj9EYrEciSRSS+A3/mlNWDqaHGc9P0hJqJGCu7UCjPzz8ZY4Oneb/PPZbnI1f5umLL3FhvJu8XaDCF2Z37Vbet/FBDjbsxmdeP9Itnp3gpe43ebrzFc6MXiKZT6FpOvXBavbXbefRDfewu3YLfnPGc9qRDmdGO/nzt79Jf2qY0UycbIlE+19vfLHsOqgJxPjl/R/jsY0PXLc231xIJrJJqgJRTM3Ab/rwGBYFp0i2mOPVrqO8Z/M9bK/twGNYfHD7w7x6+W2O9p/l0U13L2kPmtBoCFcT80fpL+W5FF0bUzNI5TOcHr5IhS/M1toNjKbH8VteLox2k8gp1aQtHc4OKyuXqC9MyyzP8eMD5+idHFJVN5af9229+nnQNY3Hd7yL7516nnQhw7GBs4ymx6kLXb3sN+ILcbBlN60r9Dsvb4OO3/KRymf4uzMv8Qcvf5nzo100Rmr5pbt/mr9/4HGs1fbyvEaErQj7qg6sdjNWDOk6yM5TyEPPQG4RX9drhWGCPwj+MATDiuwNhCAQQQTD4Aso9ZrlVappy6vUdppWFho1TaS7riLyCnlVypzPKrIpm0KmE6r0OJ1EZpKQSSq7i3Tixk8U9FxQnvL3BRGVNStX/C4V0lVEusenjtmV+9ONmUmGWRAePyJYiRwfQo70IOo2lB1nUXoYxvJBJqHKuaVUJPF8n0kIpVwLxxDVLTfdG/6GwhdGhKvLr0Mh1IN9uEpZ2BSy6lzk01cn0W8U/GHlfTt7/DZVgRCKqZDCYk4pCnNpRHApJPqs8+zYyExCEeiOXSLQY1cPVFzHtUFKZdFjr1cHvbMwy5PeG4CrWRlba2DyvphTFjfNG9W9/BbFyLPPURgZZeMVntmFkVHO//Z/4sA3v7pKLbs6hKYj6tqhtk1Nvmi66p9v5NjDtBCVdeALIS8dn6lUMz1ojR2Ixo03bt/zwIhGMaJRAAIH9hO8Yz++zeXhom4+j5PNzLP2EvdxLQ28VuiazsZYK//o7o9zKd7DhXg3I6kx0sUstmNjaAY+00OlL0p9qJr2ymZqg1XoSxyInRg9z3AmTlOoDr/p48ToOb548lvsr9tJPDfJfY37OTd+mRd6DtEeaaYxVIuvRHwVXZu/63yR7mQ/raEGNkZb8JpeCk6By5N9vD18ilNjFwiYPh5puWdeGvv02EXOj3cxlh2nPdrCzqrNZOwslyd7ea3/CH3JQTyaxT0N+6Y/04y3sqN8zF1nOiRyCrZbREoXS/dds/em7RYQaMrHU0zFXmoYwiQvFcFuMntgJbC0a+ugg1aQu+vuZFdsO/HcOFk7V1LxRolY4bIJCY/uoTk0E2YnpeSu2gOkixlOjJ0inlfWHbrQqfLF2FG5nQca711VT1IhBAEjSKUnxnghzpmJkzQFWqj0xAiawQUJSUc6jOXGyNpZNoTaCZnhORNFITNExIoSz48xUZig6Np4dJ2CU2AoM4hX91Lrq5+jcAeo9zdyavwEGTvDZGGSal+5Gsdn+DFKlQoe3Ysm1HURLAVfGpqBrin/7ym7obXg/aoJnRpfDRtC7RiaImC8upe2UDsThXEuJs7Rm+6iNbShrNqkyls9ZzLD0i1qfHVk7Ayj2eGSZVJuXhJ9aiKhPbyRxkDLzHnVmWMfs5aRs3P0pPrxGz62VGykJdQ0fd3FvNemPL0RkFJyePhtTo2d5ee2fGTNk+gAtuvylVPfp3Oij40VLWyr3ojt2lyId/Fy95tcnujFe7eHA/U7roufcyKf4punf8hfn/w+XsPiYOMeKnwRio5Nd6KfZy+/xunRS/zD/T/N3Y17p0MmBYKwJ8j++u3sZzuXxnt4rfdthBC8u/0+qv0zSoKA6WdjZcs1t/Vaoa7V5fVDU5kg923Yx1fffpI/e/0bjGcTxPwVbKnZQKaQJVvMEfNH0TWVqeAxTPyWj4llKgTrI9VUByvoTwzTOdZLvlhAtzR6J4cYScXZVb+Z5mgdpm5QHaigM97HRC5RUsvPkOgVvjAtFTOWJRfHekjkVNBjY6SWuiV4YO5tUMpzx3UYz0zSOda7KIke80fZWrPhupSzakJDFxovXjrMH77yFU4PXaI5WsenD36Uj9/xQYKetf9dvp0hpYThXuSrfwcTozdmJx4fVFQrMrmiFqJViEgMotUQiq6QaBXqIVHTAVORhESuXELBcRSJnojD5BhyYhQ5NgjxIRgfuTHhqa6DPH8UwpVw57sQgZtJKsz93grDQuazyGJu7rvegCLZUxNzFfu6gQiXQkcdG9cuIO0iWvtu9YA+Z0cCdFMR6bdYOfxVITQ1cbBQFY9hKSKrmJvxMr7Z0HQIXMXXfqrao5gHZqlcF8PUeXQdZDYB6biyhjFMCMbAF7nlrA/K4DrIXFpZ8eQySsXpuup4GibCG1AVCB7f8q5pKZVndWYSmcuoY+a6JUsCU012eQPgDyFmkV3KQikD2bTKOshnkUOdqrJGSuRwt9ruPBC1LTPBggt+XldVPqQnkVMBuUJThJg/rCY0F5kAknYBxoeQyXFEOKaqXwxTTbKkxiE1MZOLYJgIy6e8+H3Ba/Nnfidhtoe65VfVNGv5e2ZYqn9ZAxzBSiBn51RIqTIfZt4kPzSEnVk5+XrDIaXqO4r5mUn1qfuQ6b1xGS1CKIsVf1hlk6wRVD7+YTRrLi/mbd+AHlw5X7OqJPoU/KaXnbWb2Fm7afGFl4HB1AgPNt3JB9ofJp6b5Ldf/T/0JgfxGl7+6f5P0Riq5clLz/OlU9/hcqKPyXxymkT36CYf2/YBEvkUreEGavwxfIaHvFPk8mQvXzj5LY6NnOXvOl/g4eaD5SWPJZwbv0yNr5KPbnmMu+v3UuENk7FzHB85x9fO/oDTYxd5c+gEO6o2UuGNYLs2GTuJK52SYlzg4s7ZrpzyQOc6hP6USstnd3RiVlK7vGIQrSppr71TFAiCZpDgMslGIQQxbyXvbX2UTdEORrJKpe7RPdT4qmmPbMBv+FZ9EBeyQuyo3MW5iTP0pLoYyg5S6YlR6amk2ltLja92DllWdArknCwSl8niJEdL1iyzYbs2E3nVMeWcLI60AQ8ZO0NRFhGuoDt1mXh+7gNwzsnhSBddKj/9K6EmUtT+BAKtRDSZwiy9xizSXJZCaK/pMF0XWKVQuSkCfQpCCOr9DVxKnCdtp8namSX5uOtCx6f7MDWz5Le8sOo+bEao8dWvOaX2cmBLh3QxjUf3lDIG1sBJvQpSxTR9qQEm8olle2GvFlzpcKj/BL9yx8/xYOudxHxRim6R82Nd/MXRv+GVnrf41pmn2RrbQNR77QTLyz1v8e2zz6Ah+Mz+n+W+5v1UeiMUXZvuyQG+ffYZfnDhef76xPdpCTfQVqrs0jWdzbE2NsfaAHiu8zVOj15AEzo/ueXdbK1qv+a2XW+oGOzlff/0Ut+2qaqVRC6F7TpsqGxkR90mOmLN5O08ftPLeDaB4zoYmk7BLpIpZgnPCkrThOoRr+YO0RCuoTqgJqM6x3rJ2wUsw+Ro/1lcKYn6QjRFatGERnWwksvj/XSPD3BH0w4c1+XMFInuj9AUmQnuHEmNky2p0RojNUvqiiv8Efyml7xdIF3IMZIeX3Qdn+kldoU9wkohpeTcyGX+6JWvcrz/HAAPtR/gJ3c9StR366qVbhsU8rhvPAODXdd3u5oGFbWIpg6oa0FU1kJljSrFvcZAw2VD1xGhKISi0NgOUiLSCYgPqxL04R5k7yUYG1w6sbgUFHLIE69BbTNs2n1jg0ZLxBxOEWkX5vYNHp96wM0k51m39DxQzF+9Y9MN8IUQ2RR4/Crk7p0Cs2Q1dDXoJtNBpauRKWJ5l9BGayZLdalt1LQZAj01rvzVDUsFZvvDt261gesiE6PIsT5IxJXCPpcBpzCLRLeQXj8iVAl1G6Cyfkk5BzKXRo70IMf6lRVQrkRWu26JdDKRlhe8QbSGDmTdhpntFnLInrNq3Sm/YWemgkQOXISBi/PuV/hDV60KkLk0crgHxvoU2T2VdyA0MD1IfxgRrYG6NkQotrAVVbGAHLiE7L8AjZsRgQiymEP2X1TtnrpOSiS6tHyIug2I5i1q4mAdi0NoYHqZ7pvtwupUtywRIlqN9AWRI33QugXhvXUEElJKcn199H/la8RffR03m8FJz1TlSSTZrh4q71+5l/aNhsxMIgc7VUXeFdDqOyB6jZZmV4PQIFSxpkh03T//9WfW1GDWrPxYrAkS/UahwhdhS+UGqv2VVPkqaA7XM5gepSVcz46qTWhC0BZpJOoNM5FLkCnOXGy60DlYvwcNUUYq+U0fUc82zsY7uTDRxcWJHlwk8z0G2K7Nu1rv5r1t9xG0AghUoOrB+j30Jgc4NXaB3uQg8dwkFd4IeServMutCkyhvNaLbhHbLZZtd4ronI9gXy40oSnvZimnCVEp5awwhStvmqtPsAkhiFhh9lbvxpUurnSnCd+1gikblLAZYTg7xGB2gOHsEAOZPkJmF3W+OrZEdxCeReo6peoDgLHcCGO5kavuw5UzFQq2VNdIwS3QlepctH3zkY9iIV35vF6Xi+7ipkEX+oL+4QEjiEDgSndeH/eMnWY8HydVTKpJBtfGxSVdTGPLKz3NyyEQ8/ra30qQUuJKF9u1FSG4xgl0gKHMCCPZMeRauggXgUDQUdHCBzY9jL9k2WJoOrtqNvOe9ns5H7/Mq71HGMmME/GEruk8pAsZXug+xEgmzoc3vYv3tt8/vc8pkvzxLY9ycbybt4dOc3z4LA2hmmk1+q0GXejoQsde4uVgaOZ0YHa2mGcoOcbR/jN4DItzo108tuUB9jZu5WDrHl7ufIu9DVupC1fx9PlX8ZtedtXNlAMGPX58ppezI53sqt8EAiJXlNnWhaqoCVYigEvxXnJ2Aa/j4Wj/GXRNpyYYoyYYAwS1QeUvf2a4k2wxj0SRzl7DojFSQ2iWUjtbzE0HvgYsH0uBJgR+y8d4NkHRKZIpzE2rvxJTtnrXAyOpOH/62td5o/vY9Pf3zEgn3eMD1IaqFvVnX8eNhTx9GC4cv74bbdiA2LIfUdcCsVpFnK8hok0IAcEIBCOIpg5kNo2IDyKHepGXTkDPeeWlfj2QHEe+/SKitgkqbqCSUDfA4y8n7GaTff4wuF3qIdculHth57NlJN1VEa0F700KDF1LMOexyLkSQswQ1DfTg38KxhIqhqcFVO4S26iWl7l0+bUzTaDf2v23jA8iu07OfN8NS31XhK4+a66kBk/Gkck42paDqprmKv2ZTI6rsN7h7pIvP6jsBwtMvaQQLShiND2pyPnZT2JTFh6WT/2AOvalKjTCMfAsQER7Fh4XyHQC2XMaOXBppl3egArldR31WccH1cRCcgytbZeyaljsHBfzkJ5ADnWpbU9dI4alCPpiXv04hcW3tY4ZlGyY8AUhn0am4ohQVdlEmbKQyqlqiZt5bKcU27NR24zYvBd58YS6P2zcBdFqhLnAc8ZU1skage7z4WtrwzxzlqLjoPlmvktC06h69BGqHn5oFVt4dcj+i+A6iMr6ucf1Rld8ahoiVIlk/sm91UDyjcN4WpuxamunX5OuS3FwkPTR4+jhEP7t2zAqlufbfluT6BErSNBUNxelYI5gajqNwVq06fBNE4+ufFBn+zwLIdAXIIyFENQHa7A0k5xTwHEdzHlK8IOmn7012wiY/jJ60md4qA1U4dEt0naWvKPULo4sBZdqHoTQpgmuK5WwRolgzztZPLp3UUuNKSJczkO6G5pFwc1hSxtNqrJ1F5eiW5gmJ9Yy1hp5PhuGZhLzVhGxojQGmknZSYYyA1xKXOBc4hx5t8Cd1XdPE8C60KerCzrCm2kOtFz1+PtNP55ScNmUWjxgBNlesYuwubCqTxcaEc91CnhYI1joOzBzbciy6992bXpT3XSnLpOyk9iujamZyrIGjaK051RhzLdPTWhrwtJmuXip/3W6Ej1MFpJM5CYYy40jpeTJy8/w6sCh6eWqfTE+0vFBgrNyGxzXYSgzwtnxC/Sk+pksJHBdF6/hpTFYx56qnTSHFvZPllIykZ/kyMhxelL9JPJJJBKf4aPKW0FbuJktFZumvbpd6TKSHePIyHGGMiP0JPvoSfYjkfzlma9PVw8BhMwg9zfezfbKzQvtflUghMbWqvZpMnvmdcHWWDsxX5T+5DBdE320R5uuqd/tTyrvbdt1uL/lDjzzEKAbok20RRp5a+AkJ0bO82DrnbcsiS6EwNI92LKwaFaDQMOj+dDQSReyfPHwd/jkgQ+zqaoVV7oc6TvN3576EbFAhI/ufi+pfJr/9qPPYbsO1cEKPn3wo9SEZoJUI94gH9z+ME+eeZFnz79GW2Uj/+n9v162T7/loyFSjc/0cnm8n7xTwHZtjg2cI2j5aI81YxkmNcFK6sJV6ELj7HAn2WKOZD5NPDtJhS9MR6y5bHJF1zS0Ut/jLKMiwy2VpQqhoS2RzLxek2uTuRRvdB+jLlxNxBvkzHAnb/ed4fdf+hL/4bFfoz3WdEtM5N2OkKMDyLd+rJSO1wNVdYgddyPat0NlHcJau6q5aWgaIhBSvux1rYgN25B9F5En34Du89dHUdxzHnnqENz1bmXhcCNgehDBqFKXZpRNw+xwURGpVmKZZBw51IWobwdNR6YnkRPDiuzyBhclNUQ4dnVbk9sV+kJPh2sIS2njsj+EVD7qydGZKg3LB97bwJZDCEQgjPRHEBU+RGWDshwxTEUCurYixHvPQmIMEmNKBR4sWbvMA5lLI7tPIvsvTVveiPp2tW3Lq7YrXchnkckxNTlRUVuu5vf4EM1bEbMmttzzb8KQCsoVDRsR1c3zf6aF2lXIIfsvIHvPqfMYqkA0blbfZ91QVhD5DHLoMgx1wdgArpRolkcp0q/SL8hcCnn5BEyOQqgSrb5dHUchZqxyJscQlfULE6qrCbuobG3cUqVAPlMK300hUYHWQjfUdT81Zi7mkYWsClGWzkzOQCahtiM0VXlk+csnM5cJYZjIcDWMD8LU9TKlRncdpfYXAqzWm9onS8fG/c6fXTH5KtREzNggMj6EPP+2Im8XqDzT7nwUsWH7TWnvYhBCYMVi1P/k40jbpjAyQtMnPzF7AXSfF2Gu3fwemRxD23Snqrq72XcrMUWirx1M/vCH05ZInsYGKj/yBM5kgqE//r8UBgb//+y9d5xc133e/T23TZ+dne19F4veCJAEe28iqd6LbRUXyU6c2LFTnWInrxPbcRz7fR05iqTItmxLtprVKFGNvZMASPS2KLsLbK/T55bz/nFmZ3ewbbYAWJB4+MGHwJ1bzi3n3nOe3+/3PEjXJXTDbqo//CHM6qrFd1jAm5pEt3QLc8YLyxBK9zs0w0hNCJV/K5GzSDPXc+lJ9PP64BHOTV5gJDtOxs6Sd21GsxNM5BL4DGtesq0qUEnEmq2BPVU2b2gGnjedTWxoJjk3Q8ZNYQgLx7PJeplZD6ImdAJ6iLSTJJEfLxhEClzPQdd0fJq/JIPcECae9Eg7KXy6iyxcC0Mz8el+cl6GpD1BQA+haRp5N0feyxHQQxhi7b4krhYYBdPUiBmhyldNfbCBpy48wUC6j5HcMI1Bpflu6T4CRqgQGBDUB+uLJPliCJphZQwLhIwgzeF5BlVvQki8ebPG7UIVh9L5n34X9KXPc2ziMGknTWu4neZQK/6CDrxAMJQd5MDIvjKOvuanUnPiyOhxTox2YXsOtmdjezZSwmB6hLHsRHG9i4OLnvQ4OHKUb536PmO5CbJOFttzAIkmdPyGj/2DB3m04wFuqJ3btfzA8BG+e+ZxBtPDZJwsjqfunS40TN2is6KdulBdCYl+PtnH070vkHPzZJwMOTeHEHA+2V8ieVTpqyBlX2IzvGVAAHH/3GW1MX8Uv6EGwiOZ8RVn2I9lJ8k4ighriNTMGWS0dJOYP4qlmwymRrHLzTxcozCFhV8PkXGS81ZoaWj49SCW5gMBp4a7OT50hv/66G8SsYJIJIZm8NOTLzKWmWR34xZ++eYPMJAcwfFcYv4IjdHaYgAeVGb/u7bdy56W7WTsHME5SDFNCFpiDcQCUfoTw4ylJ9GE4Pz4APXRajZWt6lz0A1aYg1E/WFODJ0lY+c4PdKL50nCVoh1VaXv9LAVxCiMbyYyibKeGtfzSOZV1pmlGyVjocsByzC5a92N/OptH8b1XP7smS/zwtnXebrrNf7X81/hPz34a1QGr8m6XHa4Dt6rP4XhvpXvy/Qhtt2E2HkbVDcoffKrMDAiTAuq6iEaR7RsRB7fh3zxRys3W3Ud5L5nEJ07oL710mTfmT6oqIb+M8jJEUiOl5DoROIQiEAmiXf6AAz3Kp301ERRC11U1i6aYS50gznLcN/sEBprfux3KZKLPE8R6DM13nNpJTNyteveCwHxRrSpoJDlV4GBqXOSUmV9azqy63XIJJCjfQgnNzdZPaVXPtCtCHTTh7blFqhqRJj+0ux1z4PqJoTnFsj1GfKqmj5b8sSw1DpSKjPg0BKMXKWEyWHkhZOKQA9Xoq2/HqqaSr0LPE8RYYYf2XNUZaX3n1EBk4WCf8lxFLm/AdG6RWmqT11HWTCArmktPC9rLwFO2lkYH1CZ8hJFiiMhm1AGwUIgDR/E6ooSVjKbUmavrj19jkhIT0AmAQKkFVTbrIBER2gIXwhZ1aSqjDIJ9X8oyA35VMDicge0PA/OHFEkfmmDKZatzyUdNgNy0/Vr7o0qdJ2aB+9HOg5m7OoySxZWADybK/GdEkIgQ9GC9NfakFy1BwbJnjlL6IbrSbz4Eu7kJJE77yB79iyN/+I3sIeGmfjZE2RPnrxGok9BE6JkwqueJVEs5V4IiXyKvzr0LZ7ueaWYKR4w/FT4wvgNH0bBbGwh+A0fxjzHmssKza8H8aRL2kkjSKELk4AexBa5kpWFEASMMJrQyTgp8q76XRM6ARGatWdDMwkZEbJumryXRaARMiKFsnaNqBEj7SbJuCk8V2IInZARwaeXaouL+QU/LjkuTA7wvZNPkXPyVAUreWT9ncQDa/OlJqUsEjkXXzNLs6j212BpJq701L0rQBMadYF6zqd66EmeoyOyjtpAfYn2vSz8BxLBdBa+qZm0Rdo5PXmKc8mz1ATq8F9UpVAMFAnWfIXBUmB7Nik7OUPfX0FKyVh+DIlE13RCBYNU28sznB1iMj9BW7iDTbEthA31mxBCZUrnRxfNal01XAFd749ufF+B/IbB9BBfOvIVbM/h0fZS8lsXOpEZWegCQcgIUhusZkOsky3xDdSHatEQ9CQu8J3Tj9M1cZafdT/Lumg7lTOIYyklB0eO8JXj32Q4M0JLpInbGm+iOdyAhsZYbpyuibPUBKqJmKGSNmyr2kR79NcBRcJ//8yP0ITGL2z+IE3h6ax3TQgCxuUlBsvH3DSnJqbfEd4qlH2rfj617/n7+dRxL9tzfgkhhMCn+TFMg5yXxfbyxQouTeiYmoWl+YpBYYEgFoiQzmc5MnCKrXXr6Zsc5OnTr+IzLOrD1QghqAnHqQkvbK4b9YdLdNLnQltlI5XBqDIXHe1lMDmC7TlE/WE21XYUz6GjqpmqUIzTI72MpMc5OXQOiSTiD7K+utTEtbWykYgvyHhmktOjvQWJroXf6z3jfWQLOuoRX4imiroF119t1ITi/PLN7+eOjuuRUpKxs4xnEhwZ6OJrrz9OS6yef3LbR/BfQa3PmUkRFwe0pvrp1UgKzwspkWeOwtlj5ct4zAUhoKYJcdODiM7t4A9eMn1kz7YZe+Y5zvzxn+DlsjR96pM0ffznF99wGRCmBZU1cP09iKZOvGe/q7LSV/LdTk0g9z8DD33kkmijC02HSBWysh6sAMIfnpaFAGUa2LoFeeI1RQwNp1WW5dQ5WX5Ew/oVZU2+uXE19P9L0UappEcMSz0bUxraiWGkZqis6av43SgMs5B5Psc5CKEMP+P1yMG4IjCzyUL2sZy1jcwkpnXMAbHuOqhtm9tkV9MuXVXKRZD5LHL4vDJQFgLR2KkI9IveQ0LTVKCtoQOZGFbGoX2nEXXtKkg33332XJVN37oZIvHS50EIhK6DvlbH6ChpnLr2RVYSpUR1wSR10W3m8//QTahqUs+RpiEdRz0Tc0mcaBrCCiBNH0RrmJ5XFNYV2uUPZhkm2kf/xcpkqyprVq89q4jUyVMkjx6j+r578TXUIwzVf9fie04mRtX3HCAcw+t6HVHdovToZ47FwnEl+XOpIApyVaGYCqppV+CZvBiGSd2v/BLRu+/CmRin53f+E+GbbwIpCWzbit9xSLz0MvbwyNJ2e4mau6ax2K2UUvI3R77Dt0/9hLg/xgc2Psy9rTdTE4wXSctne/fy2f1/S24GCbrU41wMTQhCRpSQMaWpqvbg12frFwkEfj1Y+E2WrF+ynhAgIWiECRrhWeuKQlAhbFSAcdFvFz30ISNCyAgv48xWjme6X+NvDnyHjJ1jfbyVW5t3rVkS3fbynJo8wWBmkMZgI5W+Kvy6H4kkZac4nThF2k1TacWJ+6pLtm0KNTOcG+TkxAme6XuSDRWbqQ/UY2omOS9P2kkxnBnC9vJsj++kyq8+PIYw2BLbxkh2mJ5kNxknTXtkHTErBkKQdbIk7QT9mQvEfXF2V++5Alfm0sD2bIazg0zaE0TMCAINiSTv5jmXOA1Atb8Gn6aIGU/KInFoaGZJhronPZJ2gr70hUtOLpqahUAj5abJe/li+0CRN4JL96GOWNMDvqyTRRc6nvAImUHi/ti82wkh6Iy185mKT8ySs6kL1RL1Rfij1/6ckewovcnzJSR62snw+NknGUwPs716M5/Z/nECRgAhtEK+gmRP3e7ifmce06f7irJHYTOEJnR0oRG1Igu2d61AojLE58JkLlkM1Mb8kRUHKqO+MP5ChsxQapR1sRb0i54j13OZzKfIuTZxf2wRLeq1N1icC0qCzSCoh+flkmf2p+ZYPb99zyf5k6f+ipH0ODF/hJvbdvKBnQ/REF3dAX17ZVPxe3V6pAeJ+t5XhypZV9VcXK+zqpnqUCWnhrs52t/FieGzaEJQFYzREqsv2ee2+k6qQzF6xvsZSo5y4MJxbmzZPm8bpJQ81fUKnuchhKA+Wk3nRcT8pYauaYSsQPF5e3jzHYykJvjTZ/6a3vF+/ufTf0V7vJF3bbvvsuujSylxPBtHOmSdDCknSd7L43g2XiFw3RZeh38tEwDLgMxn8Q69BJOLm8zOC91AtG9B3P4oNLTPPflfRQjDIH73nQTaW+n+3BdwEgtnua3KMU0L2dyJ9o5PIl98HHno5RVJ38ijryFuuEcZja42hIBIFdrOu0EzZms2Cw1R36EMAc+fKGQQShBKS12s2wnRqqvkzX8Nlw8CgpECeYeSNUlPqMz0xBDodSob9koTJstFOe22/AjTp2bSnje/xFNqQv0BlY1d07w2svXzWeTE0HS7otXzZ0cLoYjwaDVybEAR74kxZDiuyPB5IGpaIVixJonGxSA0DbSlycwITV9Z9rcQxXsgx4bwnn0MbcsNsH6e8ZwQCDH7mNLzVMZ36PJW8wkhoLHjsh7z8kEw/NTTnP/q14hct4Patz1IZOsWjIoK9GBwTT3jcrQPOTpVTaj4PDnSqwLkM6C1br30hr6+ANqWm9U7w7QgWr34NpcQRkUFQteQnguOgzM6Sq6nB+l5SNtW0jxSqqDoUvZ7idp7VSPn5nni3IuYmsGDbbfxie3vnWOd7Cyt8pVDFL6vi3fK0o67mCb6wutO/17Ofi7/C8OTHnv7DpOxc7jSLWTcrSW1pYuhAheDmX7Op3oKhqGqvRoapmYSsyrZWbWrxFgUlPTLjspdGMLgbOIMx8ePcGj0jSKpqgsdQzOpDdSVZJkqs9UYt9TezoHR/YzmRnht6OUiESyE2tavB6gLNFy+S3EZoAmNyfwkB0b20RruIGSGybk5zia6GM4OETJCdEY3Fp9zUzOJmBFMzeRCqpeIGaXKX41AkLAn6UmeYzw/pmQfLiHivjh+3U/STnBwZD+tkXYMYWJ7doEgjs1rmHolMZ8PgY5OU7iBiBXGkQ5pp9S08NTEGQYzwwghePe6RwhbpZkbF5PnbyZI6XF85Aw5J1+iPS6l5NRYN2OZCXy6RUesecXXoCVaT124miPDXbx8/g2ub9iKflGApjfRT/fEBUCypWYd/nlMyKbkzmzPKRoZr6VB48VYStsMTed9Ox7kfTsevIQtUmiIVlMTrkQTGucnBhhNT+I3fGyu7Sh5HlpjDdSG4ggEJ4bO0jXcg8+wWF/TViJNB7Chup3dTVs5OqCeq7/Z+102164jPI9p0EByhG8e+Cmu9KgOxbhr3Y0lRqVXAoZm8NHdjzKYGuULL36dkfQ4v/v4Z2mM1nJTy46yNdtXAhVwzTGZn6QneZah7ABpZ27Jjhp/3bwkuic9sm6mKE8FEDLDa7vqS0roOgR9Z5ev9236EJt2I25/FBFfvcqG/MgIbjKF0DTsiQmEruNrbMCIRlU/Nww0nx9hzH19nVSKXF8/XiYDmoZZGcOqqQHPI9t7HrO6qrgvz7bJXehDDwSwaucJoIlCeDMahzvfBZEY8tUnFi1Tnxd2Dm/vU+gPf+ySlN+rjM95KmGFUPetfRuyplmRao6jqgditQhf8MqTfdew9qBpiGBFUQtahiuV5nM2qWRdJkeQFbVzZ1tfTfBcpGOrLHvXVRUaUgVSlRHo4kbDMpeeNuyMVoNhrY2xk5OHtEroEP7QLPmYiyEME+kPqQCA6yCTYwjPmT+rWtOVyey1KpblQTcQ0UrwB5behzJJvK/+Ofov/Tu4nOOOudo5Myt9LTz3y0TlLTcR23MD6TNnGXn6GXq+/Ldohkns5j3E9tyIv7EBq6oKbQ3oo2tt26Btm/rH1PW/QtdeGBY0rJ3ASviWPYx+69tMPPkM9uAAVlsrQ1/+O4x4nPEf/QQtFMRNpTCqFq48vhjX3nJzIGUrs09LM2mO1M/6PeNkOT56hoyTu+zZUm9FjGUmOT3Wc9XIDihplQ5M3WIsN0bGSeNIG4EywItZMVrCbVRYsTm3t3SLnVW7aQq1cD7Vy0R+HMdzMDQDvx6g0qqkLlhP2IyUbKcJjepADbfX3UVf+gJD2UHSTgopJZZuETBC1AXqqA2UPtNBI0RDsJEKK1Yk5jUhaAg2EtRDRUkiITRivkoag8345iHdrgSCRoimUAtj2RFeH36NvKeyeg3NIGpG2RjbQqVv+sWoCY36YCOJ/CT9mT4Oju5nqlB/ygx2S+V2+lLnyXuLD5aXi5gvTmu4nXPJM3Qnz3Em0VUkkuuDjWyr3LkmSXRQWumT+QQpO6WMEgvBrbSdVhrlcrY0yWB6iJybo8KK0Bia/V59M8OTkhMjZ3mm+1X2NO4kYoXwpEtfcpinzr7MQGqE3Q1bqQ1VrXiiFbZC3N5yA4cGT/B417Pc0ryL7bUbCJkBXM9jJDPOj08/z7Hh06yrbOG6ui3FzPXZ+woSMP2cm7jAmfHztFU0ETT9uNIrvJOUt8c1LAxDN2irbCRkBegZ76d7rI+A6Wdr3fqS9YJWgJbKegKmj5PD3Zwbu4Df8LGppn3WPnVN44PXPcTL3Qc40t/FY0ef4bqGzTyy5U5qwvHi2CTn5OmdGOALL36dE0Nn0DWd7fUbeWTznZfj1BeFZZj88s3vp29iiK+98UP6E8P8lx//b/7Hu/4VW2rXXVLiYary6Gyii+7kmaKHxnLgSpdTE8fpT19A4qEJnRuqbyHuL19f8XJDZlPIrkMwObq8HRgmYv0OxO1vR8RrV7Vtw4//mKHHfkigo41sTy9OIkn1A/fT9IufQPMvrrOePnmK3i9+CXt8HOl6BFpbaPyFn8Oqrub0H/wRVQ8+SN173gmWhT08zKn/9F+ofvRtNHz4g4u2TQTDcN0d4HrIfU9COrn0EywEMOTYEKLqCn0PNR0RiSMiS5s4Xn2Y8axMEaJXMbGzViAs/zSRns9CZhIMExmpUpmyVxmka6u+nBhFTgwhU+PKA8G2lb7wVOZ5ObIVjlOUxxJWYO2Y73retHa1bpYn2WSYRRIdO7/w+euGOte3YP+SvaeVKXViHJnLIsJRZbBt+pRsWnICRgeR+ayS2KhtRvgKc2nPQ44PI0cHoKmjRN5Eug70dEFFJYyPIl0bEY5BvFYZdksP2deNvHAGOdyHd+IAaAJRUYWobb4i90KOD6nqtpom9b2cb73JUUiMq3NZVBLnykDoOqH1nQRaW6h+4H5Gnn6G/u98j/5vfYfKO24lfuutxG66ETO6dvx85MQQVFSXVjZLT+n36+Zlk49aK4g9cD96JEL69QOEb76R2MNvw52cJNd1hsG/+jJuKkXF3XcR2LJ5Sfu9NvudAyEzQNgMMpqd4PjYWW7JThDzRfCkIiBe7T/I/oEj5F37Gol+GXBk6BSJghna1QBlXhtmQ8Wm5e8DQbW/hmr/0mQFBAK/EaAj2klHtLOsbZpCzTSFmkuWGZrJvY2lGZqmZrKtcgfbKncsqU2XA9X+Gtoj6+hP95G0E0gpCZpB6gINxH2zSYwKK8a2+E6qM7VM5MfJu3l0TafCilEfaMDUTDQEGTczy9xVCEGVrwYZkVTNse9yoQmNTbGtVPhijGSHybnZQqDFotJXtSa1vT3pMZwZ5fDIMU6Mn6Y/PUDGzuKiJqae9JjIJ4j7YrO2zTo5XM+lMnB1lnmuBJoQ1ATjfH7f1zg63EVztJ68a7O/7wgvnX+DeKCC929+iLA1nRmcc/L0JvoZTqsgmu06DKWU5MLp8R6e7X6NgOHD0A2qA5W0x5qKWez3tt/E6bFuvnviCf74xS/y0LrbqQ/VkHPzHB3u4sXe1/EZFh/e9nZaow3z3o+WaAPrKls4NXqOrxz6Hv3JIWpCleRdFRS8vmEbHbHmObe9hlKsq2oh7AtycriboeQo9ZFqttWvn7Xehuo2ov4whwdOMZQcozYcZ3PN3Bkde1p28Mk97+F/Pv3X9E0O8UdPfpGDfSe4sWU7sUAUiUd/YoSnTr3C012vknNsNtd18OlbP0hr5dqpSKoMRPmNu36e/sQQPz3xIm9cOMafPfNlfuf+T9Meb7okx5TSYzQ7zImJowxlBuY1pC0XpmYSNMI4nk3OyyEQ9Ka61zSJTt855GDv8syfhAbN6xG3PbLqBPoUst091Dz6MO2/+c+ZeG0vp//wj4nffw+hTYuPrXyNjTT94icJdnSQPX+ens99nrFnnqX11z5DZPcuJvfvI373HVi1tWS6e7Anxqm8/bay2yZCUbjudsimkAdfVPrQS0UujTyxH3HrI0vf9hrKx8ysWc9VpK9+5TMH3wwQvhAy5IA3rMjZ5JjKVA9WXFVEqsznkMM9yN7jMD6knhPLr/74g4oY1lSCCKnx6Szz+fd4xbNB54YsLwgw7+bewtsLcbUoAK463B//Q+FbKCA1iRQCsftOxObdyMkx5L5nkP09gBp/iA07ETferb6lnoscuoDc/yyy9zTaoz+H2Hqj2nEug/uNzyE27YJcFrJppGGg7bkXOrYCEnn2GPLkQchlkAdeVPdh3VZFol8ByOOvIw+/hPbAh6Ft/u+1PH0Yue9ptLvfDZ1rj1uQros9Nkb6zFmSx0+QPn2a3OAQka1biN20h/zgIH3f+CZuOk39u95xpZtbhHdqL9ruB0EvNTCWA2eVEXHN5ZVyvNIQhkH09tuIzhjjGdEoRjxOc3MTwmdhVlej+ZcWXLhGos8Bv+Hj7pab+NrxH/Bsz6tIKWkI1eBKl55EPydGz7C+sp3xXOKqyY6+mnFw8ASpq4hEv4bLjMKArsKKzZvdPxcCRpD2yLp5f++Izia4QBldLiVIsRB0Tacp1EJT6BLool4CjGXHefzcz3i5fx8VviidFe1U++MEzSCWZmJ7Dv/Y9dic2+oFM+asm59l2PdmhyZ07u+4hcl8ime6X6MvMYjtOYStEBvi7Ty8/k5uatyJNWNiP5ad4B+P/YQXel8n5+TIOXkm80pm4ulzr7C37zB+w8KnW9zecgP/dM/PYRUGTBW+CD+/411ErRDP9uzl7w//gFQ+g65pVPor2FjVzv0dt3JP282ErPmDNbWhKt6+4W5yTo6Dgyf4/L5/wJUefsPHhngbrRWNJSR6T6IXXTOoDVSr+/1WnU3Ngc6qFiK+ECeGzgIQC0RYXzW736+vbiXqDxfXC1p+1te0zblPIQQf2f0oWTvPX7/2bU4Nd/Plvd/lH15/nLAviCc9ErkUjueiCcHu5i185tYP8cCGWy7VaS4bbZWN/Pbdn6RvcogDfSf42YmXaIzW8E9u/xi1i5i7LhUSyXh+jOMTRxjKDCLnINCnDL2XQq5X+arwGQFy+RwSyXB2ANuzMbU1SNi5DrLvHIwPL2/7SCXaLW+7pJP04PpOort3YdXUUHX/fZz7878gefBwWSS6HgzgTCYY+uHjuIkE9uioykqXkvhdd3L6D/6I7IU+jFiM8RdeInLdTvxNjYvudyZEtBJ23YEcH4bTh5cuiePYytD1+nvAd5mC5ksl0NYUAbhMTOl0SwmujcwkEIGK2ZIUnlcgAd8E53y5IAQiEEF6DiRGVR9IjCA1Y81mls6C58JwL7LrdaVjbvoRdW0Qq4VARGX7aoUMa89Fnn4D2X9m4X1O6WS7npJQWQXT+FWBpqkAkucW/pTxfXPd6XebYV3rH/NCIEcG0N/1SbB8yFd+hnzjeWTreuTJA8j+HsSeexGN7dBzCven30B0bEZUNyhzzk27kPFavMf/fvau7TzkMmj3vx+kh/fkt5UheNM6hD+IdtvDeHXNyOE+9A98Zu1UPiwG3VTEfza9JmcLiUOH6f/Od8kPjyAsi/CmjdS9+11Etm5BGEZRLm78tb1rg0S/+D0z49/StZUPiuetyWt9JZDv7iFz9Bjx9757WdtfI9HnwbvW30/azrJv8DA/PPMMnucSsgI0huu5u+Vm7mu9mf7UEN2TF650U9/UyDo5jo8o6ZxruIZruHJwPZfuxHle7HuNqBXhodZ7uLF2FyFTmatIKZnMJ/l21w/m3D7uj2HpFuO5CSZyCYIryLRXA4A1MikpEyEryIe3vZ1bmq7jQmKQvGsT8YVYX9lKZ2UrAbM0Ah40A+yu30pVILbovttmZKFPoTZUxS/sfA/XN2zjzPh50rYi0eP+CtbH22ipaMCnL2ygpAnBnoYd1ASrODFyhpHMOJ6nSPS6cDXrYqUkcNfEGc5MdlMTqGJdRTvtkVaC5pXV3V4r6KhqIuJTZj66ptNZ3UpkDnOfzqoWKvyKfJgyAK0Kxubdb9D086k976E5VsfPTr7EgQvH6RnvJ5FLFU1J2+NN7G7azEObbueOjhvQL4PW+HKwu3kLv3nXx/mdH/wZ/YlhvnngpzRG6/jo9Y8Wr91qIOdkOZc4w0h2qIRAj5hRKn1xgkYIU7MYyvTTn+lbYE+lCFtRAnqAScYByLpZEvkJ4v4ra6o0F+TkKPR3L88cU2iIXbcjFsgwWw1ofh/CpyTNNMtCD4XKNhHt/9o3SBw4SGjzZhUwcF3w1DcjtHEDVm0tif2vE2hpYeKVV2n7jV9fFjkkqhvQdt2BNzoAowNL21hK5NgQsv8com1pZcRl7TuXUYRmNqlImHKlKAoQHTsRxhoMAC0Vhgm+oNLudh1IjiHtvFqOALzCsyEhWn35gr+eV5QJkVJOZ/nOTBpyHWQupYhYUTCbF5oiydbKvdF0RDCGdBxIj6u2JkaQuq6kTNY4ZC6NHO5RBLrQEM0bEa1blGb4Re8Emc+WRVAK04c0fSpok55EuM6i21wW6CYEQmBnkbkMYjF9d9eFfKYoTUMgfPUQtJcdEq1jC1TWKE349i3I7lMqw3ygB+I1iMZ2RDCM3LADnn0MefaEItEXg8+PWL8dUVkDUiLitcjEBCKfU5USVy2kCiYvpxruMsBJpdACQerecQuRndvx1dSUmOpqPh/RHdsxwmshYCjxRs6rYGY+izx7EDlzXmjnwXPUe+2tgDIqgfIX+kju3XeNRJ+J1mgDv7Lzw2qiH5yevDzYdjtbq9azobK9uKw92swv7vgApmZQF5petyFUwye2v5c7x28oZpz7dR/VwUraok2EzSCf2v5+JnIJzIvKAq+r2cw/v/4XCJgBaoNzl/JujnfyGzd8gpAZpDF8aUph3wzomehjIDlSMBO9hmu4hisFV3pM5CdJOxk6K9pZH+sgbJV+jM9NdpNy0nOSsx3RNiqsCGPZcZ49/yLvW//2ZelpGwVj07SjTPwuhdllMpfi2NAZJJIbmrapBDbpogt9mQSkREpJzB/hztYby9oi6gtzf8etyzjWNHyGxfUN27i+Yduy96FrOp2VLXRWLl4tsalyAyEzRH96kNeHDrF38A3aIs1siHVSG6hBX2OTL9fzODF4ln/Y+zjDiVE21XXwruvupS1eXkZq/+Qw33njCd629Xba4o0LPodVwRj/6p5PMZgcRdME66vnzi6vCsX4l/d8ioHECEIITM3gD3/0BT584yOsn6cEM2D5efuWu9nTsp0zo+cZSo6SdXJoQhA0A9RFqllX1UwsEEUro6/EAxX8ybv+NZ7nURWK0RhdmqzYTGhC43cf+iekchnCviCd1fM/RwLBQ5tux9QNxtKTCAHt8aaiL8dqQErJcHaIwUz/tPE2gpZwOy3hNsJmFJ/mQ9d0HM9eEoluaiZhM8xwVi+YoLuM58fWJInO4HnkUO/ytq1vRey4VWU1XkK4yRReWhGKXi6Pk5jEqKhYdDvPcej76j/Q9MmPU/f+9+KMT5A51138XfP5qHrgfga/810C69aBrhG5bufyGik0aNuM6NyBTI5DfokJH5kU8szR1SXRPQ85OYzXe7wwoc4ojeYljqNF2zZgjRC1K4BAICNVyijSzimS18mrewcUJS40HSJVl0+OwrWRiZEZmcqFdsysaLCzkHBUWwWKFBECzAAitobmjrqBiMRVtmM2qZ65xChU1BSNSNcschnklEGwL4CobZ2TQFfrppH5MqSbAmFFVmeTynA1NQGBEGKFxvFAabuWWv1i+dR9mhyB9KQi+GO1877LZTapggtSKr3zcOX8pqLXAJYPKFSzmCZSE4hMWhGYgfCMwJdA+AOQKdNPQ9MhVNDcngqkLSats6YhkalJGOhRQYG1EhC8CBW7riO6c8f8JLmmEdm6hdD6uSvXLzeE6UdaAdWfp74V6hcIhBGRSggtPoZ6MyD1xgHGf/QTKh64j2xXF6nX35i1Tr7nPGZ93bKP8aYk0asCldzdctOs5duqN7CtekPJsupgJXcGZ5MaQgiqArEFswBvaph70N0SbaAlunBksTFcuybJc8dzuZAY5PjIaXom+hlMjZK0U2TsnDKoNEz8ho9Kf5S6UBUN4Vo6KptoCNdirrIbt+06vDFwnNHs+Krud6XIOXnOTZzn6FAX3ZP9DKfHSOXTuNLDp1tUBWO0ROvZXN3JhnjbgnIJlwPJfJrjw2c4OtxF92QfE9lEQc/fIGwFqQlW0hytZ328jY7KJvyG75oMwzXMgi40QmYQXWgMZUboTpynNlCNpVuk7TQHR47y+LknZmVET6HaH2dP3W7600M8d+ElPOmxu3YHNYEqBIKUk6Y/NchEPsH1NTuo9Mfm3E/UFyVihhhID/HqwOtU+KLE/ZXYnkPWyeLTLfwrML7tmxziz174a86M9lIXrmZXwxaGUqM8f24fG6rb2NWwZdn7frOjIVRPfbCOlJ1iNDfOSGaUgcwgP+p+Ar/uY2f1dtZF2/Eba8MwN53P8NcvfZsL44M8vO1OqsOVBM3yn53JTJJnT+3lxrbtixLvuqbz4KbFNZd1TeeBjdPBk5OD5/iTH/8V9226eV4SXW2n0RCtoWEFhPcUwr4gH9396Ir3A6qa4T3b7y9rXSEEAdPHo1vuWpVjz4W8l2c0N0TaSRWXtYTb2BTbSsgIrzggFzIi6GKKRPdI2cswnbzUcB3k6CBMLMNQVAi06+++LBOx1MmTjL/yClZtDeMvvoyXyZZFdgtNw7MdlY3tuEzu3cv4y69Q/eADxXUqb7+V83/9Nwx9//tU3XcvemAFlVGWD3bcgjx7BIaWWJ1q51SWYj6r9JdXATKXVtrOA+emiXPdAN0qEMflPuNvknGgEGAFIFYPmYTSs54psaFpYBhg+rms5+wVsnyd/PzrFCRoZmMN3hvDgmjNdLAim0TqBiJSVZ6B5ZWCvEi/fD5JH89Fjg2o4MBiCFUgIlXKYNHJI88dRoQrwB9esRyK0A2kEOChSPClbGwFIN6g3g1OHjnUjaysRYRis9d1XeRoP3K8UGFTWY8IRlYnEPBmRXICkCqQmcuA5yEiMaQ/CJmUCrJOmYEmJxDRWHn7FWLx6z71+xUg1qVjw8k38E4fUguGLsDkGN5LP4JDL87ewJOQmlTrVdZCdG2aW+vBIG4uR/pcN/boqKpom/rN7yeyfRt6MIi+JooBBKKiGhGpxBu5gGjaWFo1ommIt5DprxYIYNQonfPEiy+DlPhaS+dQ7kRiRf1lDX/VruFywpMez3fv4wennuHkyFmS+TQZJ0vOzeN4Lq7nARKtkAVq6iZ+3cJnWISsAA3hGnbUbeLO1hvorGwt0fZdDBKJ47n0JYY4O97LmfHznBnvpXeyn96JfkbSEyXr90z28W9/+if4lpDd0FHZzEe3vZ0tNcvXsU7bWZ7v2cePTj3H6bFuJvMpMnaWvGvjeC4SiS40LN0kYPgJ+0K0Rhu4f92t3NO2h8rA6kw6uyf6+MrB73F46FRx2Ts23suHtj5cQgAk82me697LYyef4tz4hWJ7bc/Bkx6aEBiaUWivj5AVpC5Uzbs23ceD626b8x6m8mm+uP8bvHbhUHGZQLChqo1/fdsvL+mezAcpJd0Tffzh818gmU+V/PaBrW/jwXW3L4nouobVgSY0mkINbItv4vDocb5x8ns82fMsmtDJuTkm8wm2xDdSYUXpSZyftb2u6dzVdCtpJ8OPzj3Jk73PsW/oAJZmIYQK4OXcHJW+GBtjnfOS6I2hejZUdnIucZ5nzr/IoZGjmLqJlJKoFeHh9vvZGt+47PP86oHvE/WF+aUbP8DnX/kaIAmYPs5PqonENRJ9YQghCFthbM/h5FgXh0eO4nguLZFmXrjwEmcmznJ74y3EfFc+GyLn5DnS18U7tt/DB3Y/BLAks/CWeAO/945/SkO0+i1nlnu1Iu2kmMxPFH0ZAkaQjuiGVSHQ1f4CaIXMeU96ZNy15+ci0wkYG1h6FiNA0zpo24S4DJJAvoZ6Eq8fYPixx3HzeZo+8Qv4m5uwx8bo/vO/YGLvPjJnz6D7A4w9+zx17303NY8+ghEJ0/yLn6Dvq39P/zf/kdCmjUSv313SZiMWI3bbLQx953u0/9ZvrritorpRZaOPDS9Mil4MqcgEOTaEqFslb5RcBjnapwj0ihq0pg0q6LHUSqA1mh24HIgCkS4NC0KxQhYnBS66QJpq2vzvgGiV2g7Kuy6BqCIsJfNXbJg+iDctbxI/1z6jNTDlH1F2Gwvsz0LPRrxBkV6CxbPKp85pSp5B09a+/Ifpm/YkyKZV3wnHEDPOVdo5ZP9ZZPeRskyEhWFBXStyrF+R7iMX8A49h9a2DRFvKL0/roNMTSLHBpRhcbxekV3zIVihZFlcR7Wprh0RLc/AWmg6VNYja1vgQhcMn0fqJnTsQIRjRSJWOnlk/xnkuUPqfE0fWsM6FQS4hnkg8I7tR9+6BxkIIo/sRcRqoLYJ0boBue9Z5Okj0LEZeWwfwnURndtX7+jhGDg2sucUoqFQ6Xi5vDYQYFqQSsJAN6QL5OS5Y8wd8CsEruJ1iN13Qu2lMZBfKbIXLtDz13/D8E+fQHpeyXs30NLC7r/64hVs3TzQdETnLrD8b+l5iW9dB9WNDQifD83no+L++wjvKU2annzuOZIvvbLsY1wj0d/i8KSkd7KfL+z7Oi/07GMil8Tx5tduc6WHKz1szyFtTw8kTo/1srfvMF8//Di7G7bwr277JWqCi0cWDw2e5G8Pfo8TI2dI5tPYrk3etbE9B9t15jQgzDp5ToyeXdJ5utIjaS9vMut6Hl1j3fzFq19hf/9RUnYaZ57Jpys9Mk6OjJNjNDvB+cl+3hg4xk9OP88nr3svu+q3YGrGil5sWSfH6fFeDg6eKC5bN0NqQUrJ2Ynz/O2B7/LkmZeZyCVw5yjj9aQkX7jeyXyaofQY3RMXuKP1eqXPOAcCpp+QGeDcxAUmc9MZdmPZCV7vP8rNzdct+7ymYHsOe/sO8/L51/FmtKPCFyEeqMBfGNj6dB9va347QggsbW1ktl6NEEJgaDqelPNmkU+tVxes4SOb3seLfa/xxvAhLqQG0IRGXbCWd3S8jV0123ipfx99qYE5ZSMiVpi3dzzI5sr1PH/hFc5MdjOSHUUgCJtBWiJNhSz0+QlWn27xtrZ7qfTFeLl/LxdS/TieQ9gMUhusJqCvLMDyWu8h/t3dnybqjxSXBcwAGhqpcsp438LIOjlOTZxm/9ABzk520x5t4YHWe2kNN2PpFsOZEZ4+/zw9id4rTqL/2lf/My+efoOu4R729xzl/3vqb3nPdffzrx/8RZL5NH/04y/yyLY7+d6Bp3jpzAF2NG3gzz7w74gFI6Ryab7w/Df521e+h+3a/OXH/yvXt2wF1Pu3d6yfv3jm73n65GuEfQE+cuOjvHfXA8QCEYQQvPtzv877dz/EN/b9iP7JYW7v3M2n7/gQm+s6sF2HL73wLb7y2mMYms4Dm29d9SqvtzIyTprUjCz02kA9ISO0apMNU7OK+5JIbG+uLNIrjMQ4cmSJ+t0FiE03QDByWbKZfA2NNP/Sp7Cqq0HTMCtjaD4fmmHS8mufoSmbLWRaKwLUiEbQg4o0qP/g+6m6/16k56EHgkrHVAAF/w6kxAgEid60B6t2FapCdR2x5UbkwReXRqKDknQZOr96JLr0lM6s5UfUd0B9h8pifAtNqKXrzNbZ1Q2lVbzU96kQihRdwmZlHUfTEdYqEcxCIMylJbEI3VRk7MJrIZaQtCKEUKT01YRABBGrU+S5YyNPH4CRC0oCyDCVhEtB/oRgFKoaYXxongqBAoRQmdvt25Fd+xWpONqHNzmqyEZ/UAUXHEdJ9ji20h3v3I2oXFheQNQ0Iy+cUFUMqQm8/T+FcCXCF1AZ0LaSK9I23ICI189xviG01q14uQyM9imyfPQCIhxX7XIdZHICsgmwbTBMROsWqGm5LMHTqxmibRPeE99Cjg8jGtvR7ni76pfrd0Aug/fak/DEtxAVVYiHP6qCJoAc6MV7/gfIni6YGEX2d+M9/T20Ox5BtG9REjoz399TxrUzEatCXH8X7vf+GnQDbfcdaLe+7fKct64j27egNbSr79m+p5Bdh5Ts21ya70JD+INQUQWh6JqVcxl/bS/prjN0/pt/SXjTppJxojDX7rhcBKNXugmrCznFBsppO7RCAFz9b/bYRjNNMNVzVfeZX8GoimNES6+Lv6NDmcMuE2v3CbiGSw7X8zg4eII/efEvOTp8al5iuBx40iNtZ0nbWSzNRC9Tw/T0WC/PnnuVlL02ySnbtXm+Zz9/+PwXGEwNl5C65cCVHol8ipd6X+fEyFk+c8NHeGT9HUUzxtXCQHJYHc9zOTZyhs+99lVe6Nk/J3m+EAzNYGftpnlJG01o3NF6Az8781IJiT6UHuOps69wY+P2FekeSylJ2Rl+dubFWdf6xsbttFVM6w5rQiNkXsuKWCnqgjX8h5t+CynBXESjXNd0GkP1vKfzEd657qFisEUIDUPoaELjoda7ua/5jnn3FdD9bKvazKbKDUjpzQiUCTQh0Av7mQ9CCCJmmHubb+fOpluQxWdcoAltSZnEc6HCH2EwPYplWEg8sk6e/sQQE9kErbEyDIAuQswfoSYUf0tUT3zvzA85M3mObfHNPLT9Xip9lRiajkAZolm6RcQKr+hbs1r4k/f/GwYSI3zmK7/Lh294hA9d/zCmbuAzLMazCY71n+X0UC+/fs/H+O0HPsXA5DAVAfW+CVoBfu2uD3NLx07+3bf/jJw9TZqNpif4wvPfIJlL84Wf+8/0jvfz5099BVM3+NAND+MzLE4MnuPzz32N//rO38AyTT771Ff525e/x+888mkeO/g0X9//I37zvl9gfU0b/+2H/4ex9OSVukxvOjieTd6dvl8xK44hVm8orAu9RA5tSnd9zUBKZGIcRvuXvm00jmjdcNkylIWuYcRi+FuaS38wdHy1C8sW6YEAetPs7DY3l8MeHiF9qouhxx+n8z/8DsJYpftf14yob0WePry07OJMEobOq21WY1w4lW2dTVHMsn4LEegAzje/jPv0D5H5PNh5RCCI+el/hb77livdtBVBSqnMHj0PYa1xrfGrBELToGm9Mtu80KUklkb71DuyKO+iQUU12rrrkPksMpOE9MIBUqHp0NCJCITxTh+AiQLx7uQUqQ6llRC6UZbeuPCH0DbuwTv2stIrz6YgmyqMpKf2ZU6bgV68vdCQFTVom2/BO3sIhnshn0OOXGBGeYYiacMxtNat0LBOEZ1vsffI0iAR9S2Ihz8KmiiY/xrTAa6dt6Fvu0kFOYWmvqNT17OmEe2dn7go8CfU9pqO/unfVX+f+uW2hxFSlizDtNDufS/c9U7178upXS+Eej4MUwXZ69uQI32I5vXQOk9lsChQoGv4mXImJgm0NBO/7Vb00OolW1xyXOF2Ss9TyQSargLKK2iPdF0ld5cYQU6MqL9rAnwhREU1MlQBpk+9x+c5jq99bg+qwOZNBDZumPO3cnCNRH8L4+x4L//fK3/DocETJRnfmtAIW0EiVgi/4cPUleSH67nYnkPesck6OTJOlpSdKSFEfLrFve03E/WV5/5raDohMzAvaZZz8+QvivZrQhAw/AsSbRcjZAbKJvankHdtnj77Cn/w/OcZzZRKymhCI2j6ifrCBAwflm6hCUHetck6eRL5JIlcujiB9qRkOD3GH7/wRfJOnvdueYCguXplVgMpZb56eryXz+/9B57t3lv8zdB0AoafoOnH1Aw0TUcWqgmyTo50QZIGYEt1JzWh+ILXdmNVO9tq1nN6rIdcgYzIOjkODZ3kzPh51sfn1+0t61ySwyVyMQCWbnJT43bqw9VXXK99ijiWeEgpL6qWUGRh8b+pAfJyjlMod5MFU8rZx6FwFG16MLIMaELDp5efOSSEwBAGxjyfD0MYZRmG6poGUsw4q+nr6koP5PT1m/pv6jxFgWzXWf1B4gd3PMzfH/gBG6raGM1M8tU3HuPI4CmivjDXNS7d/O0P7vvtVW/jWsW9zXfyDvNhAvNo0utC446Gm/EbV9YnAiBo+QlbAfV+NH1ELnKsz7t53r/7Qe7esAe/6WNd9TSRJ4TAZ1gErUCJ0ayUksHEKD899hK/cscHyDl5asJx4qEor5w9yAObb6WhogafYfKBXQ9x98Y9SCl5vfMYL589wEhynCeOv8yetu3cs2EPsUCUX7/n5/jMV35vRec6/f4o/F9O97ZSTGd1iOK/xNUzcSgDrnRx5TS5oOSkVi+7bq3bfEnXgcQYpJeh1d62SWWNXYbnwYxX4m9uRltlojA/MED3Zz9HuquLho9+hMiO7at2PkJosHEX8sxRWErwxM7D2DDSzit99ZUiGEG0bEKePoAc7kUEw8hQrJC9uARNdH1lVZNXEvr1tyIqq5CT47jP/hg5OabI56sdnod3eD9yfBTj7suTZfqmhxAIXxCx/gZkVRNyqAeZGFOEt24iAmGoakRUNaps78QYxOqUDMoi1QZC0yDegF7IdJcjF5DJMUUwea7qY1YAwjGVuR6rVaTTYu2NN6Dd8BCyrwvGBpG5tArCGSbCF1T7W8C3QggBkUr0bbcjxweRw73IxKgiqHQD/GFEZZ2SqQpE5t0PoN4pgbAy5bV8JTI4by0Ugi2WNfseCqGqoeYhtoWmwUIV1Rd9F+YK/ApRIN1XKyi8XAgBFVWIqkYwrau6esHX0EDyxAkSBw8RXL8ezTCmP5+ajhldpG+8FSElpCfwDj6PqKiGulZVwWP5l6zJLnMZZN9pZNcBSI7N/l0IiFahdWxH1rWBb54EVc+bM5lgoT5ZDq6R6G9R5Jw83zvxJEeHukrIuYDhZ0fdRm5r3s2u+i20VjQQ9YUxNB2JJOfkGUyNcG6ij9NjPRwb7qJnsp/h1BgjmXF21G5kXbylLBINYH28lY/ueDv2PBIy+y4cYX//0SJZCxDzV/C2ztupDJRfrlITjFMfri57fU967Os7wmdf+8osAj1kBthY1cHNTTvZ07SDjlgTUV8ETWik8mnOJwbY13eYl86/waHBk4xlJovXOO/afPa1r1AdjPFg5+1LCgQshPFsgoHUMF8/8jhPn3sVUAGNhnAN6+OtbKnpZH28jUp/lJAZIOfmGc1M0j1xgRMjZzkz3stAapibmnYQthZ2yNCExj3tN/Fcz176CxnwABcSg7xy4QDrKpuXfV6u9Hjm3Gsl9xugs7KVDVXtWPrKBmeedHHkPNkZgC6Mop7tXJBS4koHW+ZxPBtHOsVsakXuaujo6JqBpVloGGhCWxLBPUWau9LFmTqO5yjSvphroqELDV0YGJqJIUw0MZ3xu5agCDwPr3CdXM/BkQ4eynjPk8pvYSapNx2EUOcphFHIdNfR0C7pud7XeQuWbvK9o0/SHmvktd5D7GrczNs33U1bpcps9KSHK+eWm7oYAoEhzDV3X2bClU7ZGbMa6rmb63xCZhDbs7HzszO0TE15RdQESzNIp56P+frlakMXOhp6WfejLd6wZCmVTD7L4b5TfP65rxOeoUe5sbajKHGkazptVcqIVAiBqRvIgrzWWGaSzpoWTN1E0zRqIpVYS8z8LQbepIeHVySOVX9zin3x4j4HFHxP9OJ91gv9TggNjQU0g68BgLyXK1bHTPX9NQU7XzA/WzpEbfNl01iteeRhah55eNX3G2htZdMf/bdV328RTevUpGyJ1TYyn0WkJ8FauTGw0A2obVXSDucO4x1+QWkmByPKPLNMIl00b1zbhpALQFu/BW298i/xzp5EHlneM7/m4Ng4P/m2kiO6RqKvLnQdUd2EqF5Yn1lEKhE7l2h+rWll7XspEL4gon0HtK9gJ5qGiNfPLfuylHZsuAE23LCChlz9EE0dUBG/4lnAawJ1LUqqJnR1y4pYVXFSXac58ft/QHTnDoyKiqIuuhWP0/bLn7oi7ZJS4iSTpLt7yI+N4+Xzs+ajmmUR370L3e8nff4CiVOniKxbR7CttWQc79k2k0ePYSeTRDo78dcpebvk6TOke3oJtjQTbG1RAYSL2pA6c5bUuW7CHe0EmpvQDEONf8cGYLAbOdiNPHdEvfs6dkC8vuz+ITNJ5KnXkWcPzz+ekhImhvHeeBrRugWx6UYV0LsI6UOH0aNRfC3Nq1d5yDUS/S2LcxMXODJ0ioyTLS4zNIN72m/i1278KK0Vs2ULBAK/4aO1opHWikbubL0B23XoSw7yev8x3hg4xnV1m8vSQp/Cxqp2Nla1z/v7l8Q3OTrcVUKqVgUq+ODWh0t0wFcbFxKD/O3B79IzWVryHPNFeKjzDt6/9SE2xNtmkcURX4jNvnVsqurg/nW38diJp/j28Z/RPXGhuE7azvD5fV9jfbydzvjqnEPWyfKtoz/he8efBKDSH+W2lt08uv5udjdsWSDr/Xpcz6MvOcS+vsNsrGovK0N+d/1W2mPNDKZGCySo0kV/o/8Yb+u8g6pAbFnnkXNyPH2u1ORBE4KddZtoq1j54NP2bJLOfJMpQcgI45/HZtuVLraXI+tmSjIZp6CIKxcPF9vNk3MzWJoPS/djCrOsbMcp8jzvZcl7uTmPo47l4UhFPOa8LLrQsTQ/luZDZ21kj0k5Td45UgUCyiWeZTFj1sOVAHlyqHfQzMDBFMm32ud7R/sN3N6m+gZittmkKx2S9iQeixMlAkHUrFwz9+ViSCQZJ0XOyy6+MuDTA4T0ubMvDg4foSc5bSrrSVWtYWgGG2Kd7KjeOud2jrRJ2JeH5AjoIfx6sKzAlrEM/wpd09hY185/f+9vc3P7zuJyTVMBIVD01XzEuM+wyNq54rXLOXZZfQbU+8Mr9jlnOtDH4rJeU8dQfbb0udaFUagwMdGFWexzV7oqaDnQhY4hDGypAj05L1cw2l6dgHbKThavnxCCwBqouihBPrc8Et0XQMTrlqy7/JZDRTVEYjA6uLTtchlVHRBbOYmO50E2DdmketnkM8jh3iXvRjSsu2pJ9DctMmm8U0fROpdeFbemIAuVUY6jdMFdZ1oX3HGQjoMc7p9XkmTJyGehvxdvSnZCL2Ts6oaSoDAMlSV5FWfNXnWQUumvF+45jl18FqTjIAcvlGXiWhZcF0aH8c6fVUHGi+49uqHItRXIQepv+/DqtPVNAOEPKn39qxxuJkuguRmalayIPTpa/E1oV2b8K6UkNzxM99e/Sd9jj5MdHkZ6LvnRMfLjE+B5hDraCXe0c/2f/jG+mhrOf/f7HPiPv8f2//g7bPrNf1ZCZDupFEf+6H8wfugIO37339PyvvcAMPDkUxz94z+l7r572Pn7/xlfvLKkHfbEJKc+/0XO/M1XuO73f4+2j3xI9SXXRfadnV4xn0UOnFPVPPGF/R6K55jPIrveQHYfLS8hQUpk9zHVfzfdqCqGZmD4q/9AcMd2rPe95xqJfg0rR/fEBQZSIyXLIlaIT+5675wE+nwwdaNIqj9UyKxeTFd5rcN2bX7c9TyHBk/izui8QTPA29bfwS/uej91i2S1CyGoC1Xxwa0PEzD9/M2B73AhMT2p6p7o4+8PP8Zv3/op/MbKy3ezTo6vHn6MjJOlOhjjPZse4H1bHqIxsrhhlq5pNEfraI6W93IDCFkB7m2/mQMDx0jbinzzpOTUaDeHB09xV9uNi+xhbpwcPUfXWE/Jsnggxraa9UuqPFge5LzZuK7nkHXT5Lxs+YQWkpyXxZEOfj2ApS0sQSSlxJE2WTdN3suzFGEAV7pk3BSOl8evh0rM7S4nprPoHWwvj+3lyybOy92/I20c1y5meVqar0DurQ5JnXPyWLrKHDcKZV6u5+F4DrqmY2h6gcDX8crI3lbmgnn0NUpGeNJdkvnhQia+QogSQ1lPSsZy44zlxqkPrYJ53xqHEIJ4KMaGmjaeOvEqG2vbCfkCDCZGqQiEqQxWLEg7a0Kwtb6TA+ePc270As2xep468Qq2szCRMEWe21L1Ocez8cogzsuFqlRQATtDmJiahalZKst6BVJSVwKW7sOn+7Ed9cwn8hO40p1XnmopcDyHsdxIsbJOQydqXlkD3Vmwc8o0bqmoqIZg+FqW3WLQdKhpXjqJns8tT2JnDkg7i+w9hrxwShHqmqYy0PUlahq/xe61lBIG+/DOnEBOjCmSLxJFa+5ANDTPltqREpnPIft78frPw+S4IgINE1FRiWjfgKiumy1nID3k2ChedxdybASyGTBNRCiCqG1A1DcjQtPZdDKdwus+jRzuR/acgfFRZP95nMe+VrJbbecetOb2y3ff5BQJni8YZObV+dt5pGMrU0onX/i/rZZNLZ9aN59D2jnI51UfsHMFHfscjA6ra7MaTR0ZxP3xNyBUgfD5lLmnNf1/YVrq70bBjM6w1D0xpv/OlOZzcbk5vdw0lVHrW4mEl1LpFRfva774d2lftKzw/+LyqWV2fvp+57PF50DaeZgcRw71rU5bsym8vc9C9yl1ry1LGeBavuLfhWWpez3jvoqSe2wVnwEx83koPhOWuv9vsfdm2ZjyCbmKrk/8tluI33aLetaX6Il3qeDlcvQ9/mNO/L//i8jmzaz/lV/CqoqTPn+e7n/4BpPHjtPy3vfQ8LYH8NfWLrvdtXfdQc+3vsPAk0+TOX8BK1ZR8i1LnDzJ2BsHCa/roGL7NvRAoBAYs5EXe+6EKxHReHlBKukh+84gz59S74lyIT1k7wl1rI5tJc+ZMAz0cHjVfQKu+Kx+inDJFyZ+rnSLWsNQmJSjoWtGIYPIxNDMVcsamoLKMi2QPp6SGlAePwKtkLk0NXFc7WMX2+A55LwcjpfHlW4xw7eo/ytMfLpvVaQBJnIJUna6ZFmFP8yG+Nzi++VgNcjgtYCz4+d5qfd1xrPTZm6aEGyqauej29+xKIE+ExFfiIc77+Dc+Hm+c/yJYua/7Tm80LOfh9bdzp6mHStuswRS+QxBM8D9Hbfy4e2PLqkiYDm4u20Pf3fgu3Tb04Oc84kBDg4cZ0/jDgLm0p+Hn515CfsiDfyN8Xa2VK+7ZP1uJubK/HalQ8ZNk18CgT5reycNOlj63ET6FIGecVLYMj/HXsqDLW08N0lQhLHE5e2PM7PobS+PI5fw8VvO8ZBKVse1MTwTS1eZ+Ct9Th479hSbataxtbaz+J6dzCXZ23uI+kgN2+s3FEwy/ThlZgnnvRx+PcBy9fEvJfJuvqxMZZjKSDbnPY09ddezp+764r896TGcGeG1wf2z+vXVjNPDvfzs+Evs7zlK13APX3zhmzx18lXevfM+1lU387E9j/K1fT/i9x//P/gKGecfvuERrm+JwCLeHO/aeS8nBs/ypz/7MnXRKpK5NHXRqnnXl1IWK1dsb3ZJ52pjKoiV93Kq0kbzly3fthYQ0AMEzRBJRxm7DWUG6IiuL2ijr6x/DmcHGc+PFfuToenE/eWPFy4L7DyklmFUG6kAa22YI0vPU1nWnovwh+c1OpWeC4lRZeQ2BcuP8IUWzLCWuUIW98zJZyCitIsXe0aEQFTWLL0X5jPIdGJ1vhDZNHL4vCLQwzFETQsEKwrGgEv4Pq7RwO+lgndkP+5Pv4t36ijkcgWNPwPR0oF+18Pou29BBKe9M6Rj4x3ah/P4N5H9vchsRpForosIhBCdmzE/8Eloapt+t0iJ13Uc5+kf4h19A5nNIDxPkQW6gVi3EePh96PvmE5EkROjeK8+g3f2lCIVPQ853I/zs++XtN+sqYemtuWRVNJDZtIqeGAXSE07r0hNOz9NdM9YLgskaJE4L5LmU3+mludnE6p2/vJq1GfTyFNH1KkutJ5ulJDoFMh1MZNELSwvLptab4p4nyLkZ/wRU6TtDPJeBEIQqVgz1T1yypshly3c49w00T11zwrLpp+H/HRgxJlaz77oubjomSgGVhwum4uI4yD7uqGve+Ejalpp0GTq/hqWIs4vCrYIwyp9Xoype134M0XQz/FM4POrYNubIGN7XngucrgP+ruVL4WTV89+NA71rUpG6HIaoC4RbjrN+N59jL+2DzeZnDGWEFi1NbT/6qcve5ucVIoLj/0QzR+g4+c/Rsv734PQNDzXxcvmONF7HqHrVO7ehWaaOJnlBSJDHR1U7bmB8QMHGXjyKaKbNhTNrD3bYeLwURLHj9PywfcTalNeeBIgOQ65Un5RVFQpn4YyIBPjyPMnVTVdyU40CEbVvjQdmU3BZMFodAp2Hnn+BLKmCRGZzpyvuO8eMkeOkjl6DH9nJ8IyZ+xWW3Z2+hUbIUmpsvOSToKcm8GRNq7n4kl3upSfaW1craDLqQsdUzOxND8BI4C5QtLE8RwyboqMkyLv5XA8B0+6xSyuaV1eReBbuq8o+bBapJ7t5Uk5SbJOukA+OcXrMNUGpVNqYGomPs1P2Ixiab5lT/pcz1NSBTOg9FIlV6hCZU1ASsn+/qOcGusuWR4w/Dy6/i7aKhqXvM/KQAUPrrudff1HODFytrh8JD3GT06/wPUNW9FXUEI2BQG0VzTy/i1vu+QEOkBdqIqbm6+j50h/8VnNOjkOD5+iZ7JvQZmeuTCZS/Ly+TeUqWQBfsPHlpp1tCyhOmIlUBIKXlF6xZMeWTezbAK9uF9csl4aITQs3VeSuTlFPqed5KoQz4q0T6GbOrq4PK94iSTv5ci5mYJMwuWL2E+R6a7j4Gg2Pj2AqS1fh/h7x56ksaIOyTRXLITg0OBJhtPjbK9XTt6WZpERGrKMbHSnoDluXIZA0FIgpYctyydefZpv2uS1DGhCo9IfI+aroCdxfvENLjPC/hD//J5fYH1tqRlyPFjBb9//KTbXd8x5rgHTR0O0BrPN4PqWLUpOyTQJWn78po97N95MVaiSruFuXM+lMlhBc6yuOGb49w9/hh2NG4v7u6PzBtbXtFEdriRsBfnn9/48hy+cQtM0Ntd1MJoaZ131bOkvx7PJuYpAL0daaDXhSoesqzwm/ARU9csaDBJdjKAZosKKMZQZROKRdBKcTXSxrXLniiSXJnJjnE10kXZSxWVRK0bUXGOaoK5TOukoEyIUmWVutvK2uODmwfAtLYPTcyE5qsqGdUORWXNBSjVpd6dkIvIFg6tFJHY8T5GFnquIH89R5Npi20HBVG0ZgRPbXj3pAs9V7Td9iLoORMumt7DZX3nwuk/jfPPLyP5e9PvegWhoQQjwes7gvvQUzre+jAiF0bbfMD3hFkI9tz4/+t2PqCxyy4dMJXBfeALv1WdxK6sxPv5Pi8eRiQncF5/EffFJ9BtuR9u2G+EPILMZ5GAfcnJckaszICIx9JvvRrvuJryzp3C++nm01k6MD5bq8WrNHcvO8pSOgzx+APfJ701La7g22A7SnZFB7jhquWOD47L2rZSXCLcgLUNpX5zrLOc8c11XFR/GTKkQE2Gq/xfJ2WJw5u3QcOmkSZcCOTKA+80vQTo1nS3uTN1/Z8ay6d9YI9m5qwbPK2TEz/5GlvUMCDEjQ90o3muV1W6U/hYIod/7TsS2N6eWvMxlkMf3IY/thaE+FbwvmOnKUASqGxFbbkRs3DVLfmOtIHH4CH3f+BbCNJl8/Q3CmzYiLIux51+g5VOfuCJtko5Lpn8AMxoh2NpczA7XdJ1QWyt6wE+6t3fFmfOaYdDwtgc5//0f0POtb9Px8Z/HKpDouaEhxva/jjAM4tfvxopPc05yYrh0R7qhssPLGT95LnLgLIwPU9K7NB1qW9DatkFY6dKLfBY5OoA8c1AR9+rokBxHDpwrIdHt4WHGfvA4E08/i1lbU0KaR265meqPLk+K6YqQ6FJKEvYEk/Y4eXfhyd8Uoe5JDwoEU8ZVupYJ26TCihM2o0smtKVUUguT+THSTqpgajb7gSvqHEsXmzxZN03GSRE2I0StuNIGXebEUUpJ2kkyaY/Pq7M81YYpnVLby5EhTcZNETUriVgVyzp+0PTPyhyfyCY5MHCc6xvm1q19K2A8m+DY8OmSLHRQRPh9Hbcue4K9rXY9G+PtnBnrLZZ6Z9180Zi1PbZyve+AGeC2lt10XkKt+JkQQvDohrv5zvGfkZ+RZXps+DTHhk+zPt5WIu2wGPb1HWEgWSox1BipZUfdpstW5TBFaBtCKxLDeXdlBPoUXOmS8zIqIDeD5JVIMu7qEOhTcKRNxk0TMiKXnNiaCjTkvExZ8iaXrB145Dz1HvXrwWUHGRO5NBErVHLVfLq6X1knV1ymCR1TmOTKOmeVLWysgNy/FFAB2/I0RwUa5gJSLgD9qQES9rQkgZSSiXyCo6PHaQgt37jqUiFg+nh42x2zlod9Qd6x4+55t2uoqFnw95AvwK3rruPWddfN+ft7dz1Q8u/N9R1sru8o/ntX82Z2NS+seWt7OTJuuiDFc2UmskqqKIcnXfxaAJ/uL8v74UrCECbV/lr6031FHf6e5FlMzWRDxRbMJRqBetJjJDtE1+QJhjIDxQpCTei0R9ahr7Usfc9TZNhSEYiobLtVhMynITUOsTqEtoR9C6EqOjRvwcxqoenIaI3KHssVspbK2b0vgDRMRRAlRiCzxMz9yDIkfDx3aaXLC8G0IFQB2TTCF7hGoC8GKXGfegzv1FHMD/2iItGDIZASbWcShIbz+DdxX30OrW09VKrKIKEbaJt3YtY3IWJxlVEqBNJ1EdV15HvP4r7+YimJnpzEGziPCIXRb7sPbceNCE1ThEc2g0wlEdFYSfNEOILYuL3wj4JcRDSGvnPP6l0Dz1VyMYf3rt4+34pwXfXnooLSOb/Q+SzyxrvWTug5lUAe3rd6wby3IqSczrq/+KeLF/j8yJ03X5ZmXXa4DvLYPuRLPwLXQbRuUKaSpqUCFMMXkOdPI8eHFSG6+YY1WfmU6jqNHgjQ+iu/yJk//yz173on4c2bGN69i3R39+I7uAQQpkGwuYnxAwdJnT1H9c03AapCL9l1GjedJtjUuCryprHrdhLbvo3+J55kdO9e6u+/D4Dk2bOMvPoaFdu2Ed2yudR0tEhoFxAII0IVZSVKyOS4qqKzLwpiVVSjbbkZEZlh3BuMQrgShIY89vJ04MvOwWi/qpopJH5YTU3EP/A+FRS8qCeajctP0rwiT+xEfozx/MiKSKMpuRNNaEsmiaSUZNw0o7lBckskyKaItfGcTd7NU+2vX5axnSc9kvYk4/kR8l5u8Q1K2qBIK8dThn0xq2rJQYSGcA3xQIy+5FBxWTKf4gv7vsZv3/qLrI+3LrD1mxfnEwN0T/ThzYjgaUJwXd0m4oHla5v6DR/X1W/hxd7XGcmMF5cPpcc4OtS1KiR6xApxZ+uNq5LVXi42xFvZXrORff2Hi8vGMpMcHDzBrS27lpQR/9S5V0jb04M3AXTEmthZu2k1m7wgpgJWBiau55D3squqLWx7eWyRL9Hvznu5ggb66sL28thuHku/dAEIV7rFKp5yJUEuNRxpk3aTSDx82tJJvaZoHa/2HmRzzTr0wj3qSwwxnBqjNVb6sbV0f9mGnHkvR0CG1hTJaHv5Ium3GMyCjNpC37qXB/ZydPR48d8CsHSL1nAzN9TuWmFrLz3+cf9POdZ/mk/c+h4aY6ur4f6jw8/xXNc+fv2ej1EXXb7Eh0SSd7Nk3XQh+H/loSSvUnh4q1qldykghKDKX0N9sIHMpEqgsD2b05MnGc+N0RxupdpfS8CYv8R6qpJyNDdKf/o8Q9lB0k6ypC/VBRqoDay9wBHSVZmkS4XpW/1Jbi6tJj5LzZjSdIjVAR5CX4AgFqIolSBdu/wsXU1XJoOA1A2WJMMlBMIKLD2s5bmrZ6IYiCCaNyPPHkSO9EFFLSIYuaq0aC8nvLFh3GMHQdfQb713WrJFCEQ4grZuI6KiEu/UEWRyElEg0RECEQgiAqXvCqHraOs2qQzzxKQiVadIhmAYEanAGx3Be/1ltIYWmNJNn2Nf13AN13ANVxvk0Hnk0dcgEEK7+SFobFdycEJT37p8FnpOIl/5KfLoXmRtC6Jm6ZX+lxrScdDDYXz19Rhh9Q21amupuvceen/5M1ekTWY4TMv73sPISy9z8rOfI3HyFIH6ehJdXfQ9/mOsqjiN73w7YhVkcoxgkMZ3PMrQcy/Q/fVvUX//fbiZDBOHj5I6d47GRx8m0rmudKPURZ47viAEwiwKKZWW+vhQ6XLdQKzfVUqgFyAME5o3QF8Xcug8INV+MglkahxhKa+/8I03ENq9a87DrsRo9LKT6GknOSeBLtCwNAtL9xUJJim9olZ53s3PylhXZn1LI4imMtCHsv3Yc5DXhjDx6350zURDFDR+8+TdUjLNwyPlJNByGjX+pUUxPOmRdCYZzQ3NGUiwNGV8pQsdEEoz3s0ViKrpobkjbSbyY2hoVFjxJRH5G6s66Kxs4dhwV1E+w5Uer144xL9/4k95z+YHeGjdbcT8FSqb+CozD1su+pPD9KdKS1E0obGzbtOKo3qbqtqJWKESEn0il+Tk6DkelnLF+6/whdlY3bH4iqsIv+Hn0Q13lZDoEsn+viOcHT9PdaCyrPPqmezjyNCpYpY+QMxfwXV1W4j5I5ek7XNDGfQpnWHlkbDayHs5TM1CFwZSemTdNJcik9STLjkve0lMRqWUeHhknCQ5L8daK+n1CuS+NCQ+LVCQ5SrvGnzkukf4/Sc+x5HBU2yoaiOZz/B631FqQpXsathSsu6Uweh8hrSlbfKwvTyWvjZ0hV3p4sjyNN1BfZcEC5Oj9zbdwe0NpZk1mtDw6RZ+3Y8nvSXdi8uN/slhuoZ6yDqr3++HkmMcHzhLbpnZphIJEnJeloyTuuzyLYvBY+pdBn49eFnuc9bJ0Jvqpi7YQMgIF8coix3XEAYdkfWk7CT96Qt4eNiezWCmn9HciJLN033k3dLn4OTEcXRxkqyr7oHjOdgF+b2ZCJsRtlRuV/4Baw1T+stLhWHMNkiEIgG+0Ftkph50cb0pyRHpAbNNu4Ta8KJDzVhnPgmXKwwBSGsZmd+et3okuqYjqhtVdvH5k8g3nlDa6IGIknjR9PK00Rs7EWswO3DVMdgHqSSk0+R+75/NujYyn4XEBMSqSqo4pJTKLLHnLO7+l/C6u2B8BJnLQC6n9MsDoRJNfhGNod9yL153F85TP8Dd+wLa9uvR73gAbeN2RRis0e/jNVzDNVxDOZBDF2CkD3HL26Bz+2zJNX8QNu6C8WHkG88jxwbXJIluVsWRXV3kBgcJb9pI/z9+BzeTJXXyJEbFlZHqE6ZJ/QP3seXf/iuO/tH/oOtLf4Xh92OEw1TdcjPrPvlxKrZsLj9oLiVudv5ksIaHHuDEn/8FQ88+T6qnB+l6DD3/AoHGRip3XYceLA38yosrWXwBdb8Xa0YmCSN9s6WU4vWIuvm9PoRpIWtbVfb51Bgqn0OmJxGVikQXlgX5PImXXiFz5CgIQXDbFsI37VFeB8vEskZHjufilDHY0zUNU58hWyAl4/nREuJYIAgaESqteAm5ICgdlHvSI1+YPKadFLaXJ2REMLSlmWy60mU4OzCLQPfpASrMSoJGuJhFNbMNtpdnIj9K0k4UJ68SScKexKcHiJqxstohpSTnZhjPjcy6Dn49SKWvah4DOknWzTKeGyHjporEhysdJuxxTM0iaITLvhZhK8gD627jyHAXXaPniufpeA4nRs7wZy/9Fd848iMe6ryde9tvpiFSQ8DwLSvr/mqBlJKR9BjD6bGS5QKNzsqVZ+Y3R+sJmqUEWsbOcD4xgO05WPryO7IuNNpiTfgXysq6BNCFxq0tu2iO1NObmHZjPj3Wy+HBU2yr2TDrnC+GlJIXevbPuu5N0Vpubb7usmY1qkx0B1facwbZYMqnQUMI9X6Q0ltSNYsjbRzpoEuDrJOeV8ZpKmw1dSxVHSGXdCxXqmoVU6zecyGlCjRk3FTZWdhzYWZQbibhpniS6fNcnKKZG4rkT4EBPi0AcnFyDeCm5p38ydv/Dd869GNe7zuG3/Dx4PrbuL/zVuoipQaPAoFP85N2U/PsbRpTVUzmCrwsVhOOp3xIyoEhjLL09Q+NHqM53EiVP44mNPy6D1e6dCd6OTJ6nLAZZHN8E7WB6pJ+bQofMasaKT28grH4lMG4+rdXlHWTeDMqhaaejtUL4nh4JLMpBiZH0IQgGggX381ZO0cqn8H1PDQhCFoBAta0L4uUkqyTJ5VL43gumtAIWQGCFxkySinJ2DnS+SwVgVDJOGleFAn05AqqY6Z73dTbZZrfXHmfk8gCkS4K45hLH3yftCe4MNxL2AhTF2yk0hfH0nyFJIS5+7wQgqARYnt8F0JoDKT7igGlKYP5mdrmU+hPK13/+Z43gUaFVcHOquuJrDUt9Cksl6zVjTnKcQvPTD4H6QnIp1XWrQA0A0y/khXxFSZQ0oNMAtIJcHIFMl/C4LnS3QYiUDlHcspYf8Hw01MfCtMHFTXgLyPL6XJiOfIpq5mJPjGEd+ApdX09FaQgnUBOfWNVhGLR3Wh1bWuyxH61IbNpde19fkRdk6p0mIHiOzNaAYVMcSklpBI4P/oWzuPfQhim0lFvakeLRJUEzE++PetYQtPQtl+Pr+H3cF56Cvf5n+K+8gzuS0+hrduM8e6PoW3ZOb/O/zVcw1IxNV5aA+POa3iLIJNSlTrR+LyBWGFYEK1SRuFrVEIodv31BFtasOJV1L3jUVJdXZz8b3+IWVHBhn//b69Yu+yJSYaefZ6KbVvZ/h9/h8j69aAJhGGg+3zq2ovppBJhqG+am8vh2bZah4KEbT5Puqd33mMZkQgt73sPx/7sz+n9x+8S372L4edfpObO26ncfd1FY+yCD80MCNO3uCm9lDAxjBwbuOgHgWjfPuubfDFEVQNS06fHUHYO0tPSovnzF7jwJ39K9vhxrKYmpOcx9p3vEtiymYbf+g18zc0Lt28eLGt0dLz/DD8++oJqOCqDWUpZIEIU2aNrOjubNnL/5luK2+XczKzMzoARosZfjzGHbuTM26IJDUMLEzTCVEqPvJfDEOaSCDalzzpazJZSxxAEjTBxXw2+OTIEp9rg0/1U++sxNYvx/Egx81DiMZYbJqAHF5VNkFIWs8dnSrgIBFGrkrhVvaB+ZtAI4dP8jOQGSNgTxYmc7eWYsMewdH/Zep5CCO5ovZ7eyT7+7uD3uZAYLJlA51ybrrFu/vdr3fzdwe+xp3EHD667jU3VHVQHlPnZWiCCVhOO5zKRS5boHoOSc2mJrrwsuzIQJeJT2XIzr3Uyn2Y8m6A2tHwzUE1oxP3Ry35PhBDEfBHetv4O/u/+bxSXe3i80LOPO1pvoLOyZcF2ZZ0cL/e+wXg2UVzm0y22VHfSGW+7pO2fC450lMlwSZWIwBA6lu7H0nxo6IVqGVkw2MsWzEfLI7dsL4+OVsjiLoVAFEyM/QUJDeW7MPX+yBWPtTjJ5UkXx7MLRpsrfzYUtemRcdPLItCnTJINYWFqJrowCqbRs9/jXsEHwpUOjmfPMlwut70ZJ4XQBT7dj2RxUk/XdNZXtfEbt3+8RNYJwHFdLKO0rZbmJ11mNYEj7YLm/pUlJtSzNLu6az6YmqWew0XeLyfGT7Fv8HU0oRHzVXBv81140uOxMz+iwhelN3me3mQfD7beS12wBpgmOXV0pXNcTvsLmauKdFd68zk3sypkeu9YP3/84y/RNdyDJz0+feeH+Pmb3onjOfzg0LP89YvfZig5iqkbPLLtTj512/toqKhBSslkNsX/ff4bPHbwacbTCUK+AJ+49T383E3vUOdaOMZ4ZpIvPPsNnu/azx+89zfZ2rB+4fOVKgCzHAJdoBX6nImhGYVx03x9zlPvDOngzGFyXg6miHRNqADTpZy4+40Au6v2kHOzDGWHGEj3cT7VTYVVSdxfTagwZppLhkgIQcgMc331TZyZPKVMQd30gr4O85PnAkvzUResZ2PFNsJm+QkNlx1TZojuKlQySJQky2ifIrZNH1iW+rtrK8LbMKdJ9MIbGMNSBnwyqchjf6R0kmTOPZYWwSjS9KlJWiahCOK1VQSlUGZwsgSS1T0Xoc17HZewk1VpylqH8AfU8xitwPoX/wURKqf03MM7dVQZjja1YX7sV9F33lh8jpWB6M+QycSsTYWuQ20D5rs+ivG29+Ed3Y/75A9wD+7F/j//HfMTv46+587VPs1reItCAvlz57Da2tbud+ka3lzQDaWs4diIqe/+xZAS6eTVT6sgPXIp4KurxVc3Le246ff+Exv+/b9DGFewYkhK0j29DDz5NOs++QvEdmxHmPMnFGuWhVURQ7MsUmfOkrnQR6itFaFpeHmb0VdeI3Gqi2DL3H56QghaPvA+Tn3+i/R869u42SzSdYnfcD3BpjmkiJ2LkgF0fdGKNunkkeODs6VgolWI6qZFr7UIRksryDy3RFd95Gtfx4hG6fzSFzDrVHZ6/sIFBj73BUa/9W0a/vmvL7j/+bCsmfzA5DDPnVTmIxPZBGeGe4mHYlSHKzE0jaHEGMlcmk/f9aESEt2RNnJGWZsmdAJ6cE4CfSGoDLelO/k60mbSLs129el+YlZ8TgJ9ruNGzBg5N0vSmTYacqVDwp4grtUseqOzbpaUUzqoChkRYla8LAMqXdOp9FWTdTMlRHzezZFxUhhmRdkdWxMaH9j6MAHTzzeP/oQTI2dKDCKnMJlL8rMzL/LEmRfpjLdxX/vN3Naym6ZIHfFADL0Ms4CrATk3T8qeHQ0VQhC2Vq5TKBCErACa0ErkH3JOnlQ+DSsg0YUQhFahjctBwPRze8tuvnn0xyWGrAeHTnJ2vJf2WOOCpOGRoVN0T/aX6MlWBiq4s/WGJRmTrhaUvu/0R2CKIAkYoSKhXfxNKMI7JHQMVyfjpssiJh0vT0a6s0h3DQ2fHsCvB9AuIhSFEJhCycDorl5SkTIfpjLr5SrIBUGBzCuQ+EuBQMPQDEzNVwhCLKytDRSIPh0TC6mrLGS7IGvlSKfsgIUybk0hhKbkvxa5DGOZSfadP8yZsV5yF70Pb2/bXSLpIoQoSpGV422hJF1yS/7mrTZUYKJ8Q1FDs8rqi67n4tN9bK/awmBmmJ/0PMntDTejaTof3PBeUnaKn3Q/yWB6sEiiLwfFygWhoRfOZ2ZwciU4N3KB9+56kD9632/z02Mv8j9+8pfcu/Fmmipr2dG0gf/49l+js7qFF06/zl889RVubNtOfUHj/JmTr/JXL36b//bu3+Tmjp2MZxJYhom/QGZpQiOdz/D9g0/xXNc+/p93/rOyCHRH2mTd9JIIdGUEaxb6nIUowz9GEwXCHROp+5GFhIWcl8P1ypf+mfJtmeq/l3KyIYTAbwRoCjVTE6hlLDfChdR5+tN9VFgVVPtrqQnU4ZsnycHQDDbENlPpr6I7cYbx/ChZN1uWX4Cpmfg0PxErSkOwmfpg47zHWTPQdNDNpZPojqNI65mfJekpEt1zIRJHRKqns9U9V2WXzSRyNUMZTIUqwM6rCbSdUxOlcghffwjhDyl9czu7OoGAS4HlGLdq2uoRCaEKtC23rXw/K9AKvapQ2wihKJw7hRzqR4QWficD4Dh4Z06AJ9E6N6Nfd1NJwNAb6i/rORA+H/quW9C27kb849/gfPvvcF97Hv3GO+YOQGqFZZ6rsveukaLXsAi8TIaef/Vv6Pzq3711+vQ1XFGIaCXStKDnFLKxAxGtvIjk9JDjQ3D+NPgLxpNXCbQVyH+sWhssCyMUZHTvPnr+8Tv4a2pU9rlhYAQDBJub8NXWqjmqphHqaKNi6xYGnn6GYGsrtffchW5ZpLp7OPHZv8Bfu/B8zF9XR/3999H9zX+k9zvfJbyug6qb5zG2nvlNKszTFkVqUvm3XLyr5g3ljYusixJ2Lqq4zPeeJ3LH7Rjxaelrs7qG4PZtpPa/vvj+58Gy3qb3bb6F+wrk+Jde+Banh3r42E3vYFNdBwI4PdzLV199jKgvVLKdJ0unYFNSBZcLCXuiZFKkCY2gEcGvl08+GppByIwoY89CpuoUSePKygU1MD3pkpyRQQ6gC4OwWbEk7UxTswibUUZz0+L7U5PskBEuq+x+CpZu8t7ND7Ih3s63jv6YvX2H6Z0cmFPjVwKnRs/RNdrNd048wd1te7i9ZTebqzqpCVWuaTOxcuB4ziwNVACfbs4iNJeLgOFDEwJ3RkdwPIfcHMddKqwrVHarCY3GSB03N+3kR13PFZdn7Cwv9LzO7vqtVAVjc27rSY99/UcZTI3M2J/K/N9Vv2XObS43zAKBvlC/EkLDp/uLusCLkU0e3iyCRhQkEPx6cEHzSa14LJesu3gJnMou9dBX2D+llDhevqzzmwldGPg0P5buX5YRNKhrowsd3QhiSh95N0vOyy4ghVMKlT2fUgEIFs6o/v6xJ/nh8Weoj9TMklhK1G+cs22W5iuLRJdIbGnjK5hiXwlIJI50ytJxBwoVA8qfYzFYuskNtbvZUrmRtJPmcwf/EsdTQRxTM6j0xTA1c1Xed5cK25s2cnvnbhpjtXzohof5f5/4G/aeO0Rb1YNUhSoZTo7x4unXGUyMMJFNkMypyjYpJd954wke2Hwr79h5DwA1kdLAqAC+/fpP+enRl/jD9/0217VsXrAtslD7MXO8UQ4MYeKbqppZ5rdLBSp0fHoAU7PIuVlybrbs6gVXOuTcDLphqKDZJchqnaoESjspknaSpD1J2s2gazrtkXUYmkF/+gI5N0tnxey+OxPV/hriviom8xOM5IZJ2pNknDSOZ6t3KB6FvH50TccqjMMqfXEqfeUlYqwJaJrKDr9Yd3IxuDbSc+e+i1Pah9JTWiFCKLK+HDOpNxkkUknVLBW6vmo678L0QfXa05ddq9Aqq9G37sLpPYvzk29jvv8TiMrq4qRc5nPIsRFEIADhCuUNIIQiJIVQhFAuqzLapUSOj+K+8AQylZh1T2U6iUxMIEIRZTI6IwlJVNcpYnyB4JAIRQCJnBxHjo+odl7DmxdS4kxMkD12HGdkBDQNs66OwJbNaIEA0nHIHD9OvqcXoWlYLS34OtrR/H6k55F+/Q0yR4+SPX2a8e89BrqG1dhI8Ibrr2WlX8OlQ20zorkTeWK/Cvy1bVaByinJjeQE8vQh5LljiC03QtUaNGFfo5CemhlENqxn5JVXmThyDKFrSrLUMDDDIeofuI/OX/klIus7AYhu2UL7z3+M03/1Zbr+75c4+3dfRQ8GEJpG9S03Y9xyMxd+8KN5jyk0jeb3v4dzX/8GyVOnaf3QB6jYOg9HY5gwNQSSctGAr/RcZGIUJi4yFPUFETXNRZP3BTE15pzeaUHKTsFqbCB78iS5M2exmhpBSnK9vWS7urAalv/srZh123vuMHvad7CuurmYkdxcWUdbVSMvnXmDT9723uK6ikCZxpTOuXcZCAVXuqScZAn5YwqLgL50WZKAHkTXDJwZWYqKCM1iaHMPgqVU5EnmIp3NKRmYpbYhZEQYyw2XnI/t5bGljb6M27qtZj0dsWZe6N3Piz37OTh4gu6JvjnJDolkIDnM1w8/zjPnXuWu1hu5r+NWdtRuuGLZ0KsBT8pZ8g0ApmasWrKHOYeGvzrucjVu1wYq/VFubd7N0+deLZHDebF3P+/f+hCVgYo5M1mH0+McGTpFIjfdL3y6j1ubdxH1XfkJuC6MObPC54IQGj7Nj+3ll0R4TcHULHyLEOhT0ISGqfmwvfyiZKiHMmheznthCorKc8l6mSVlw5qahV8PYorVy0bVhY5fD6ILg6yXLtv81ZUOOS9NUF/YqPbHJ1/gY9e9k0c23VVWlc1UNYIujLJIfU86ONLBWkWd+qVABUPssjP5DWGiUR4RG/PFODfZjYZgLDdOzs1xYPgQrnRI5lOYuokj3TUdcA1ZgWLmuN/0UeEPM5qeYCw9yZdf+jYnB89RHY6RzGUYS00WjbklMJwcY0fT/GRt3+QwvYcHiPkjdFTPUQZ5MaQyIi6/8kNgFfqcoZmrQlyrAJZR6HOq2qbc4FXey2F6lvIkuATz9byX51ziNBk3jZSSoBGiKdRCzKrE0IyCXF+Iw6MHFiXRgYIMUSUxX6Uas3k2eS+nfDI8F4TAEAaGZuLTrLKqB9cchLY8net8bja5JzSwgqBNQnpCVZr6AmD6labzGu7nlwwSZGZxj4xZ0JZ5X65hFuTEGHJkSBmc2TaMj4Jj4509Af6AMhILRRDVdYr4FgL9vrfjXejGfeEJkKCt36yy2/I55OQY3rkujFvvRdt9C2hKjkhr3wjBEN6JQzg/+TaithFyWbxTR5XJqM9fMpEH8C704D7zI4RlIWoaIBgCBIwP47zwBFRWoW3dNS/hIKpqEU1tyP7z2N/+O7T1W9S6jo22eSda/fK0Xa9hbcJNp5l86mkSTz2NHg4jXReruRmrpQXh85E+fJiRv/lbhM+HdFyEYRB79GHCN98MQpA7e5bUa3vxsllS+/cjNA2ZzxO84forfWrX8CaGiMZh+y0qaHjgBeSxfRCtVFVwdh4mR0F6iM4diG03IUJr1ENmjUF6HokTJzj1+f8LQmP9r/wSvtoahGEog9BMlvGDhzj7d38PwK4/+m8AWBVRWj/4PsLr2hndt5/82Di6309000bq7ruHyeMnsGIxwp3r5j6wgIotW7BiMYSuU33bLRjBufg+MVtGzrGVrI85z5w3l4HBnlmG96K2RRlzl8MdSDnt/TAHKh64n6G/+yoD/+cLmPV14EnsoUE0f4D4u965+P7nwYpHbA0VNTx78jWqwzHW17QiEJwa6ubpE6/SVlWaCWEWSoqneF+JJO2kSNgThI3IJZ2Q5F1VjjwThmYuq/RW1wwMYTAz18TDJedlCTE/QaOyuEoHVJbuW5Y+rqlZ6ELHmTGZtaWN49mwDKkbJQcS4IGOW9nTuINDgyc5MHiMA/3HOTp8moncbF0/iaQ/Ocy3jv2EA4MneOfGe3m48855s47XOjQh5szWtT1nob65JNiFrMyZEEKsaVKpHPgMi03VHWysaufAwPHi8r7kEPv7jtBZ2YLfmN3Xjg6d4tz4+ZJgUIU/zL3tN1+Wdi8MlWFsCKNsMkrXDEzNwnWdJWVra2j49eASngNFbOnCXJREl8UsyhWgQOaVS1jDVJAytGpk3kwIIbB0VdWRhrLblXOzmJqFJebPGvUbJjWhyiWR/kJoxfu+GDzp4Xh5FVC7ApqzU2az5UAXhiIjy7wWO6u28erAPl4eeA1PSm6p38NEfpLGUD0/6v4ZQghsL0/cX7mSU7ikSOcz5ArGOHknTzKXJuoPc35sgK/v/RG/9cAneOfOezkzcp6jfV3F7QQQ9YcZmByZZ88qgPHpOz7Et9/4GV987hv8xn2/UCTs54IrXTIzPFwWg6VN97nVhiY0LE31m7SbWlA7fCaybgZTWEUfidWEkkeyqfbXUGFVEjRCs96hQSNEdWDp0kHqHWNhXWaz7ksOTYP5JjMLIZ1Q0ivMCG4LgfAFkBU16vfMpPpj+pG+APgjKiv6LZXxKGFi/nfAvNCMVctEf6vDO34Q58kfICfGlGzQ4AXIZXGfeAz31ecQpoVo34Dx0LsRrSpTT2tsxfzAJ3Ge/AHe0ddx9z6vMiilRFg+ZTgaCBYDQ0LT0To2YrzjI7gvPYXz2NcRPh/oBqKmHuOBd+PuewHv0L6StgmfH+w87v4XkemUyp4TqnpDa2jGeMeH0W+8ff6TC4Yw3/txnMe/oUxJX3wCTAsRCGLG4nCNRH9TwZ2YJL3/dcy6Omp/9dMgJV46jR6NgOcx+g9fw2puofrjP4+XyzP8pb9k8ic/w79xI2ZNDfH3vRdfZyepV1+j6T/9B0W2XcM1XGoIgWhej2b5kKc7YKAbmRiHXBYsC9G+GRraEeu2IeJ1b7ExwvIhHYfur3+TwaefZeu//Ze0f+wjRZPQKSTPnGXk5VcYeOqZEilXMxql7t57qLv3nln7rbntVmpuu3XBY2cHB3HTaSq2bqXm9vnl4kQwqvTNp9qczyKyqbnHnZ4HiTHk8EXGproJdW3l+7p4LsyckwitJDM9uHMHtYZB4sWXyJ+/AEIQ3L6dyG234t+4obxjzIEVv03ft/tBvvzit/nic99UGqsSDF1nU10H79/9UMm6KisxgO3ZTDHpjrQZyw2Tc7OEjDB+I1goHV9d5L0s3gxSSyCKZmlLhUAUiaGiQWQhu28+SCS5i6QXdKEv2Rx1JgzNxJlB2nieW3aG2HwQQhDzR7i9dTe7G7bQ097HyZGzvDF4nFfPH6R3sr+YeTcFx3M5Nnya0cwEWSfP+7c8SMx/9UUVDc2YJd8AkHftsqUPFkPWyc7Kdjc0fc7jXm1oitRyc9N1HBo8UXKOPzn9PI9uuBufYZWQhnnX5vDQKS4kp0t4NCHYXruR1oqGy9r2uTDVP8vJDJ8JJX2wNJNDQzOXJOkEing3hMFi9LGSjln+8yulxJMuWad8Ms8QBgHj0hDoJcfRLIIIknKyrHefMhrNYJjWvO/dezpu5msHf8hoZoK6cDXGDD22hkgNNXN4F6jviUnOXVyXWyKLEhGX4lu34LEL8hflvs8MYSxJHqwl0oTf8DOWHcNv+GkKN5B3bSbzCQ6PHCXn5tgW30J9sG65p3DJcaz/DK+eO0RdtJoXT+8n6+TY1bKZrJ0n79pE/WGydo5nT77GycHu4nZCCB7ccht/8cxXeb5rP9sb15PIqozUKc302mgVd224kY6qJn7/B5+jKVbLx256x7zPYs7LlN13DWFeMgJ9CkIITM2Hv0Dul/OOcwtGzUuRzSsXPt3HhopNC5q6m5pJe7izZNlUIPstWc5umCq7Z4mQqUlEfg6ZEk1HBKJI0690ynMZyKUgkYFsCllRi/BdvRWKS4ZcJolumiqL/xpWDFHfjH7z3Srjcb51KioRkVjpso6NmFU1eOfuQBay1zFMRCisMsDrmpQp6NT6oTDGg+9G27gNOTIIto0IBBANLYjGVkRDM3LXLSWarqKuEePtH0LeeAcyNQl5W0VgAyG02gZEc7vKjp+v3ZqOduMdmDV1eAMqOIBuIEJhtLYytNyv4aqCHgnj37CeiZ/+jOG//Csid91JYNs2NJ8PL5Mh8fQz+Do7sfuVnnD2xCn0WAXOyAhmzfJ9Z67hGlYMTUPUtyGqGpCJMUQ6qaRcDBNCUUS44lrgeImQnsfo3v2YkTCxHdtnEegARii0ZN5iUXgeF77/Q6Qnqbr5JoJNC8jFRS5KkkpNQnJ89nIKhqJ9pyF7Eb9QWYuIVpUn5QKqUnImtybEtD8PSo4muH0bga1b8FIpQKCFgiVyasvBikn0zfUd/NN7PsapoW5G05MgJZXBCtbXttJcWTpR1oRG1Kok66SxZ2TCOdImYY+TcVP4bD9BI6wIGLF67rc5N3eRqam2rAzw4vZoTAtBThn4LTzZzbmlJdma0JdNogiYVWI/l87yciEQhMwAm6vXsT7exs3N1/HI+rt4vf8oPzr1HCdGz87aZjA1wj8c+gEt0Xruab/pqiOG/YZFyJo9ePWkJJVPr9hcVCJJ2plZ98hnWITMq3/yFLaCbK/dQGOkjt7J/uLyo8OnOTlylj1NO0r684XEAMdHzpbIv+hC56F1t6OX++K8hNCFvqzqmKnA2FL6oqUtXdJJCFEwC9QWleaQhf+WS2irIGR55yPQ8OlBFYC4DJnWhqbIw6QzsfjKgCvtAqk3d587MXyWl3sOcHq0l7AvhD7jvnzkurfz8MY7Z20jhECTSuahnKx4V7o4no1+mR3pPbxCsHdx8lMgCvew/EGGJjTqgjUlpqGmZhI0AsR8FaSdJEEjOGdVylpBZ00LB3qP88NDzzKemeTjt7yH9qomJjIJ7tm4hz/92V9TFa6kvaqRxlhtiUzVQ1tv59CFE/zxj/+v8jgwfbx7132857r7S46xp30Hv3DLu/jKK4/RFKvn3k03zWqH6zll6eyDuleXmkCfgvJkUMkQtiyzAsTL4tMDq/4+0IRWJNDnI/Q1oeE3SitPLqR7GMoMEDIjRMwoYTNC0AgVjILf5MS6aSFC0aVb8CYn5tdRF0JlnBsW+EJIJwrpCUhNQHIE3mIkuhwdWPp2VgARXFhq7BrKg9a6Dq11nrL0BSCEgIo4+s7ZgfJ5NkCEwuhbd835s965GTpLfS+E5UM0t0Nz+5LbVzymZSE2bEPbsG15+7iGqwZaOEzF2x7CiFeS2rufgb/4HOFbbiH+oQ8gDAMvlyd04w342tsACN92K0Z1DWb9NY3pa1gjMC2VbR5fu8kzVw2EwKqKM3n0GKN79xPbuQPdms7wzg4McOoLX8JOJqm7/95V41BH973Oua9/g0BDAy3ve3dJMHlWE+P1pePL9CRy+DzE6xEzEgWk5yGHLyAvdJXuQNMQ9e1L8tSRBf555j7mCtAITUOPrN44a8UkuiY0WuINtMQbysru8WsB4v5ahrP9JaSzRCpNby9Pxk1j5E0CRpCQGSGwChlMjiyVV3Cly0R+lKQzubz9zaEpK5EL6LvLEumVqX2M5AYZyw8vqw15d/YEWyJLyjdWA4amUxuqojZUxaaqDm5vuZ4nz77Mt47+hKH0aMm6g+kRvnn0x1xXv5m6UNWqteFyQBc6Fb4IQdNP2p6eLEokFxKD1IVXZuAzmU2SzKVmTfaDRoAK/9U/edKExoZ4G9fVbSoh0fOuzc/OvMj1DdvQ9Om+cXKkm5MXBWPqw9Xsadx+uZq8IDShF4JlS4MQSmrl4v4+7/oIDG15kgGa0NCENqs65GJI6S37vSCRZRmYTsHSrGUFBVYCU7PwaX5yZWhHq6qgLNY8lUgf2/UO3rvtwTm3bYzWzrtfXeiYwsJetDZAyX850saSl/c6edIpW/pGL2g/r0b7lDSGiSMN5iLwXemSdpJEzIoVH2sleM+u+3lwy22AZDg1jqHpdFa34DMsqsKV/NYDn6R3TBFk9dFqfv6mdxbNQ4UQ1EQq+a0HPkXvWD9ZJ4+lGzTF6rB0k4e23sYNbduojVRh6gbvuu4+tjWupz46d7ZYzsuWnYVuaX7MZb5DlgOtYDjqOHbZ2eiOl8daZfNNT7qMZIfpTp4l62ZL2qIh2FV9I0FjdtZ1Ij/JueQZDGFiaAamZmJpfiqsCqKFP2EzinkZghKXHaZPGXwtFePDSrJlAYMohFBZsbqBFDpkkiozfb51QRlAvZlg52Hw/NK38/mXd1+u4Rqu4U0LIQRmTQ3RBx8kdMMNJJ57gdGvf53IHbfjW7cOs64OX1s7sXe8/eINp/86lW25Wrqk13ANZUKmEjDYg0yMLWyY3LoRcc1cdFFohkHbRz7E8LPPcfJ//W+GnnueUGsrwtDJDQ2TPH2axImThNe1s+FXf2VFx+r9zvcYfPY57PEJJo4eJTc4xLbf+TdEt2yedxsByGgcQhUqiQLAc5EXTik5l7atCH9IvYv6TiOPvTo7OSNWi4jXI5bgESOT46X+I7qpPE0uMVZVHGvmZHskOc5AYoStDZ2z1gkZEfSAwWhucE5yRpWbO+TzWZL2BD49QMSsIGiEi1lCS5nYSymRc0xGbWlju0s3AFzgQCqQME/TLp4QS2TZmWblN8FbUcbpYghbQTZVddAcrWdbzQY+++rfcXzkTMk6+/oOc3b8PDXByqtK61sIQTwQI+6vKCHRPelxZvw8uxu2rmj/F5JDJfsF8OkWtaE4vjeJ7mpdqIrr6jbzbPdeJnPJ4vKnz73KP9nzMWK6miSm7AzHR8/QnywNIN3bcQtha+ml5qsNgYYmlq/huxQJDF0YyyLrQVXElNPXpzLRl4O8l1tCFrpYorby4jqjAADKc0lEQVT76mDquHkvX5Zh5hSZbOn+WddvY3U7Y5kJXu45gAQe3nAHHhLbtTEX+qiLgvwJOi6Lk5+O5+BqzpJlfJYLKT0czyn7XhqaWXyOJbKc5PVif7nY9wHUsxo0IiXXe2q/bsGYO2xEZ+1rvv1diuBDQ8U0ob2etpLfDKHTFKujKTZ/No0mNOqiVdRFZweQayNV1Eaml4d9Qa5rnnswqkzXy89C9xuXv5LJ0nxkhL40k1Hdt6pjk6yb41zyDEEjhKVZONIlZlXSnzlPlb9m3sCCpHCNZa7kOo/mhtCFjiZ0TM0kZISpsGLEfJVUWJUEjMCsyoyrThLGtJZH1to55HAftG5Sus6A9FxFlBsGmIEZxI2EfFoR5MY8YxtNV3+khEwCqZulxM/F11XO+ILNeB2od0ghgWd649L1L4KUzL3NxccpHkwW/4aUs7eZiYGe+QMHC8EXgNBlSKaYOj/XhfEh5NkTyL5zMDKAnByDbEZp32s6WH5EpALiNYjaZkTrBqW57fPDVE9e7vMfiqA/8mH0ux8taduCRJ8QgJg9xxJiWRJFZWHqei1GQApxad4FM4+fScG5E8juk8iRARgfRqbTBa8CCaYPEQxBrApR3Yho2wDNnRAMq/YV2jknTB/abQ+i7dizzHYy1bFm/1bQfF9sW5lOIHtOQU8XcrAPOT4EmbQiWYQGlgXBMKKiCmoaEc0diJb1iFBE/X6VvYoxfRBZOHHAGRsje/wERk01RkzJIUhXjeGEaRB759sZ+erf42trxWxuIn+uGy0QUDq/Bf1zs64WkCRefJHQddchAaNi9nFFSyfm7/5FaWDTddW7/GKuRDMg+NYK+klPIkcGsZ/4Ee6BfXhDgyBAq4ihrduAee/DaJ0bEUYhOceTeP0XsJ98HOf1vcjxUUQ4gr79Oqy3vxetvkmZbRZgP/UT7Kd+jPnwu/BOHcd56Vm0xmZ8n/5N5OQ4ub/+P3gjg5i33o356HvRKqcrZqSUuAf3Yf/wu7hnT4HroTU2Y95xL8ZNt0Mkiujcivmv/1jpRy8Xlh8uksGa81pJD7oO4b3yUxgdKBDoC7xDH/jw/CS6aaHf9y70m+4BIPO5P0NvbsV84FGEv1yiVCza1wBy3/wKMjGB/5O/VuZ+F4EvgPkf/nxlyQKaDrHC3EHTqL3zDm7835/l3Ff+nrE33mD4hZcAMMIhQm0tbPgnv0rzu99JeF3HipqeHRxi6Nnnyfb3469vYNt/+B3af+7DaOYCc1ahjEVFUyfyxAw/kGwa2XUA2XtSjXUcW0m45C8aK+kGomEdROLljy2khMmR0mtsGDCHssRqY1kkejkZ54cunOT7B5/mj9//L2f9pkqPg9QHWkg7CSbscfJubs7Mbkc6OE6CtJPCp/uosOIE9dCSTKpWQiAtBTOG2bPgFcjtNwOEUFIvt7XsIu/a/Pfnv8DgjIx023M4OHCc3fVbsPTVINMu38ioMVJDY6SW3sR0Oa4nJQcGjvPezQ+saJB8cuQsyXyp7lPUF6azsuXqm4jPA13T2Vm3iS3Vnbx8/o3i8tHMBM+ce5V3bVKyBj0TfRwcOFEieeLTTd7WeQfGGpBy0YRAZ/ntUOTjtNzTQlgJiSqmJm6LHEYyz+RmEUgpybuLZ3dPwdL8SwogrBaEEGjo+HQ/2TKMGD0USWlp/lmvl6NDXfzezz5LIpsk5o/w4PpbuTA5xHePPsH2+g3cu25u01uBUKbTmoXrLU6iuNLGkQ66XD3ZsvkhCyaM5RGzKqveLCHF005S6eILgU/343o2QTOiCHAvR9iIFJ/l0fwQQT1clMzJeRmS+Uk8PMJmlKAxXaY3kR8l7+VwpctQVul6Bo0wYTNaPHbGSZFxU3jSw9J8BM0wBquTJb8WkV9CFrpP96t31WW+FEIILM1Pxk0uvjLK/He1q+Rcz8HxbDqjGxjI9GN7edoi7VQHajg5cQy7YOB7Mab8LmRBAm9qbOZKt1ghmXUzJOwEg5n+gsyLpsagvkoqfXFiZiVhK1KQ71LSWlPB0DX9XFo+NQkuEM1LgbxwFrE1BT4fIFTmTyahNNCFUMSKEGqS7rnq7/NUWijiM6IIwMlhJf8idMBT8i+x0gm1zKfVpMtzwXPUdlJCYhSySRCa0mX3h1QmPEBqHJy8mlzZebVdLgMTAypTSdOQ/hBYwWL3kU5eBQY8Rx0rl1bHSU0UyWWpmxAIIy4OEEiJPHt0GRNmgQiELy0pJSXSzsPkKPL1F/D2P68y5t3CvZLeHCS2QAoBmkBOBT0q4ohN16HtvgPZ2A6BoNJfXaocnaZDNKb+FOB9+y/xXntK6ZvOtc3N96M9+AHEDOLpUkO6DvJn/4j31HeVnu+sRgnE1hvQPvDp1a0kkBLpOJCaQJ46jNz/PPLMUaWB7rmq703ds5mbCaH6glYwVgsEEeu2InbdDuu3KRJaN2bdL6FpKoizzECOvHAW9yt/PrsKQzcQd70d/eGPlB5zKuksk4TuU3ivPIk8eVAFCgrnJ2aen3RV381nYWIUeruQb7yA9AcQ7ZsQe+5BbNgBgVD5OrpXAWQ+T/Kll5h88ilkNodRV0v1x38eq70NNI2qj34EXJfz/+X/wU2msJoaqfrYR/Fv2ljch1FVRe1nPk3/H/9PAGJvf4TaX/3MrGMJywd1TaULXRuyqblJ9FBsOmDp2IV3rfz/2fvvOLmy87wT/55zQ+XqHNHIOQ4wmMzJgTknURSpQEpWsiX5Y1vaXa9X3p8le9fWrnetlVbSWrJFRYoiRYocDskZcmbIyRnAIMdG6AY6h8o3nN8f51Z1ququ6q5uNIZ4+AGBqbp1z7nnpnOe93mfVz9bLbtqv2HlefrZGoos27tTuY6+f63QonyQle/jv/Uauf/73+OPajJcrt+sPx8Zxj98CPO9H0N09ej3j+viHX6d/J/8F7z+K8jmFuTmHaiJCdzXX8U9fpzIb/42RsdUIWCVnsQ7eRQ1MQbKR42N4Jw5CQjU6DD+8ADk8hS+9teIWBzrw59CSIlyHApf+xsKX/kSSAPZ3AKWhXf8CO6br2Ld9zD2Z7+A7FqDaJ/Hy7qeuHoR/9UfQN957W/d1q3J0wrnVzRWdi4QQkC8Qf8BsMOoWFLXqIjU1ypOjY/hj4/WbX9Cyrn31FL2JwRGJEznww/Q9q67UK6LKiqwhUQYEmlZSNvWc5rZPuGVPiti2ncbf/qnWP8Tn0T5PkIaGOEQ0q5C8CkNRM921NVeTW4X4Rb0n6JCfe7RIdrXITrW16ZC9z3UyNWZwSEzhIiUf5ep4N3qZ7P6HRQcshEJI8t4zM+HRbEdE7kU1yaGaYs3kXFyjGUm52xzauACY5nyVinFh6QpTJJ2EzErScZNkXLGyXt5vFnWKwAKn5yXJZ/tC9RBLYSMcFWKx4pemci6mu9rf/PyDwi/bB8EUlSnJK0WYoUK1QkhMIXJtpYNHOjaxXfPPjfj+4H0SF382aWYOzpaRbY8AYm1yS42NPXwWv/RUv995fNa39tM5FOLtl1xPJfX+48ylpt5T7REGtndvvjKwKsRm5vWsa99G29dPU7e09YRru/x5LkX+cDWBxFC0Dt2hZPD52b87s41t7Am0b4qCAgR3JuLhb6vq6OtF+O7PoXqPHz12rhylkwleMqt2pYGNKF3vc6fJvVCVRd1LfqSW7OyQP7rq1/hQzse5N4NB/mXj/9HAFpjjSil6BsfKLerEqQwMKVFwZ8bFJ6NYoFRS9pLCthUA6X0uXSqPJdSmDMshnJehryXI2E3YAiLSWcM1y9QFKjPVoorNRW4FkJnCVghm0l3fMa5EQga7GbyXpaUO0FreK4SJedlyPlZElYjhjDJuJNMOuM02M3LPm7XA0qpIKOiunecLYuq0JW/72zDJlulqEmLIgrYoo4plgF57SuFKUyyfoa8l8eSNjk3V7FOzbrEBhrsRiaccSYK40w6ExS8fECi68K7+v2v9NxNS3dxXYe0m6IvfQnQc764laDRbiJpN9FgNxA2IpjSLNXUWOniwQtBSAMSTahEE0yMLPyD6bh4UpOwySYdWDAMVLxJF050ClNkrGFpgjzWgKhU/0AIRKwRpInKjIObB3yQFlhlrpFCXpPlxXllcZHlu5APnmu+N1WcUym9vTvNvqpIehcXcxAorqctxD0XcpNTC7JAWaXV9UFw1LD0Z7NIdFXIoU4fnplWXA3CEWjpRJjLEIBWClXIw2Af6rVn8V9+ClLV2lgGpLrHVCr+wBXUwBW8F76nyfQHPoRav02Ts0t992/bC2/+SBOp5Xpz+gjsu2sqCLQSGLiCOndM1wQoh1gC0b0RUS8CXSmU58LoEOroq/gvfBeuXqo+4KWUJpx9D3CgkEO9+Rzq0AvQvRF57/tg162QbKqJqFgQvq+DXOXO3fA1VCalFePFbjp5TZ4/+VXU8deXdnyHX0Idfgm27kU+9inYsA2xXJkJKwyro4PO3/h1On/j18t+bzY00PGrv0LHr/5KxX0Iw6D185+j9fOfW5Y+qlwadeQl1InXQfmIzXsRB+6vPqg0cAn/ma8jf+KfBYHUZejjyTdRZw4j7/0QtNTuza0Gr5H7z7+DSqewP/5Z7I98uqQEV6lJvPNnMDZs1gS6Uvj9V8j/t/8Xf3iI0E99Efv9H0fE4+C6FJ7+Dvk//QNy/9d/IPof/xDROmUTqUZHEOs3Efqlf46Ixsn85i/jPPsk1mMfIP7v/wvuGy+T/T/+Hd75M1ipCUg04D73NIW/+xJywybCv/E/YazdAErhXTxP/r//vzhPfRvZsxb7w5+G6ArdFwNXYOAyYs+diHd9AFGmqOSS4Ln4V/v0PCIcQbS0Iixbk8qTE6jUZKmIqWxuhchUgEZ5wTYTE/q9ZprI1jb9Hp4G5bmoMW1DI5paEPMpsFcQQggwDMxoFQEE5etxMIrCPvQztJDR86xy74AgYGaEQmULl1bTPxVNIrbfhnr7eR0oXfhH0NCGWL+z9mtldECLHaYjHK24H+V5DH33SYZ/8DReJlMiajo/+XFaH3uk7G8qYVFv0Nd6j/IHz/w1P3PXR3ni6A95/uybJMIzb8yxzAR3bNhb1f4MYZCwGoiZCfJ+jrQzSc7L4PjOnFRhhSLlTuIoh5ZQOxEjuiARLsssLnWbjRWLyy0GhjArEm/l+mBJi6TVWFcvU1vaK7qYDpshEmXsN+qlug+boTlFJl3fI+MsIl22CiRCMXa1bqE1+goD6akI2lh+gqcvvMJHtj+8qIXCqeHznB7pxfGnrmdTmmxoWsOmprV16ftqgWWYHOjaxTO9r3B6pBfQ18PJ4fP0pwZJhuKcHullLDcVfDOEwUMb7yK6agqsLi24VUtwbikki+5jNf1c3B2p/bOr+6UpzCUVa14qdODDwJJ2VVYYvtK+5BYW08ewb2KQX7h9zxyLHU/5C3rPQ2DpIkzcKoouusrBV96yE20KFRTzrq6gqBUUxy2i4BWwpI0lbQSSmBlnvFAjAbcIaP/6LIYw8PFRykFKA9/NXZfCrIvFZCGFFAYRMzyjEGk5eMqtWoWuVdCLt51aKgy0FVU1FkG67o0TkP71gSlMklYDeS9LzIozlBvk7MQpQGBJq+LzKGxECEcjdNCl+6YUaTfNpDNOqjDBpDtJxkkHdXocXF9njcwm5XVtnTHGC2OAtrWLBPaDSbuBplAzPfH1rDrEktDUXjuJnsugLpxAtK/Vlh5Can/L8BIW5pG4VmEvAJFo1im+VUJICa21z62WdDznj0FqrPbfReLQXj+1Wgm+j5oYRb39Cv4Pv1UbGbsQPBd17HW8s8eQ970fcc+7obVT22osEnLTLlRLJ2p0qHw/r15C9V2ADdsR1gpYIPo+qq8XdeVC5W0aWxHbb6lPe0qhchnU6SP4zz0Bpw7N6yNcE3wfLp/F/+qfIE7cjrz3faiNO/Q4Lvf7Y3IMJkdLKneVGkcdehH/8b/S39ULp4/g919EPPQR5J2P6IyFSsemFH4uT663F2dM98FsaCC8bi1GLIbyFfmLFylc01nJVns7oTVrkLaFOzmJOzyCl07jZTKEN2ygcKUPpCCydQtGJII7OUn+0mXc8QlkKER4/VrMpiatHFYKP5Mh13tRf2/bhNb2YLU0IwyD1KHDWO3tFK5exc/lMBsbCa9dixHXzyVnaJjc5cv42SxmMkl44waMagi15cLIAFw+i9x/v84GEFJnPFULO4zoXMdq9uNxnv4u/vAQ1r0PEfrsFxDTFLkinsDce2BqY8/FO3EU78RRzAcexX7/xzSBDmCa2I99EO/42zhPPo7z9HcJferzM9qSO/Yg2zsR0RiyZz3+yBDWfY+AZSEam5Gd3aj0pLZyCoVxfvAEKjVJ6Gd+EdkTzDeEwFi/CfO+R/BOHsN97WXMex7EWLc0i49qoYoB5w07EVXYv9QK/+xp8hfO4w8PIuJx7A9/Cuu2u1GTEzhPfxf3jVd01o5S+hy89yNgWTpzoPc8he9+E//8af18DUcIfe7nMbZPWfYqz8M/f5bCt76KSDZgf+QnEC2LrItXzPBSU4px/VwKPp/+zvS9wOZuWhZOcftill9xnzD1fAsykFBqah8oTaBPFwxMb6tIsBe/K34m5Kzt1FRmYbHd6duXbLxE0IYWWNC2Frbfhjp7SKvPK4kLDBMaWpEb9yLaapyzKaWLljrT1tuGpQn0CnMElc9z5c//knW//E8IdU0F1Oz22oNri2I8Dq7bxe98+NdpTzbzxNEf8ssPfIY7N+6bsc3L5w/z1qXjNe1XCknEiBIxojh+gYybJuOmyHs5XDUzlSjv5RjND2OEzQWL2Akh5/hYgsA2QqWU8eWGDFJ9py82pZCEjQiRMoWvbhRknCwjubkKjZZIY10W9nE7OseHOOfm6JscYF/H9iXvvxz2d+5ge8sGhjIjJcV7xsnxzVM/4I41e+lOVC4uWA6T+TTfO/c8vWMz0xybwgke2nAntrE6opv1xK62LWxtXs/5scu4wUM/4+R45cph9nfu5NjQzGrMaxs62du+FWtJquz6QQQKx0X/vobJ4GL90KfaqhY1pu4rFRRQru53Ohh4fSfBAoEpLAosTKIrlM56mmUxsbllHa9cPsyOts24vsvVySFOD/cylBllT8eWBferC3KauN7CJHpR6W+q5bMm0TVB/LKFqMuhGIiYsQ98ZGBRJIQICrLO19/6ZQp5ysf3HTzllVq0pF02ML0a4SufF/vfpCncwC2tOxd83rvK1X6SVcCU1nVRoE+HIS38Km2CPN+ta82WkGGzLr4R29D+/Z3RLvozV1BKsT6+EduobiEvhCBuxYlbcYiuKfU142ZIuynSToq0myLrZij4eQq+g+MXNLnuz8yczHpZsl6WgdxVYmZsdZLo8SSiqQ3Ve6Lmn6pTbyF23Q52x8qpgW8AqHwWdeKNilYk8yISQ7TWN81eeS5cu4L/3BOo13+oi8IuB/JZ/B/8A2LgCvLRT2gf7sXaaYSjiF0HURdPawJkNjwXdfYYYvftmrBfZqhsGnXpLIwPl9/AMBHd6xHdS7/HlVIwMaqzBZ79JowOLnmfZeEUUG89jzdwBfnQR+HAPQg7vKz3spocR02MIjrXoSbH9DX59DcqZhwsCalx1Pe/hp+ZRD74YURjeeLLd10mX3+Da3/1N3rsPZ/Q+rW0f+bTRLdsJnvmLH1//Ce44xOgFFZ7G20f/TCJO24ne/osA3/zZZTyyZw8RcM9d1O4NoA7MkL3L/0TYvv2MPrUDxh7+ln8fB58n8iO7XR94WcwGxvxs1lGf/Asw996XFv2ALHdu2j/9Cex13Rz9rf+NYnbD+JNTOCOTyAsi7aPfYTmdz+KOz7OwN/9Pemjx1CuizAMGh98gNaPfXh+r+JlgHLycOk0qu8CKjUO40M6yNqxFmGHUfks9PdCWzdcu4Ry8oiGVkTbGp295Lqo/guo9DiiZ/Oca1BlUjDcr7MYLFvvJ96AEBKVTcPAZe33Pz6sLScaW6G5HWHaul5H/0VUahQRikB6gqWsT7zDb4BlYd513wwCvSwcB+/YIUQshrFpKyI+N4PdvO1unO99C+/Q6zCLRBeJZFB3QhP0wjAQRYLPNDU56LpaKd3fh3+tH4TAv9iLGp5ZZ8w/r9fa/tU+VGYZ7rdKCAo7Lpe1kt9/hfCv/ktk1xoK3/x7Cl//MuaOPYhwBHP/bZh33INsbMJ59vsU/vEr2A+9R5PokxM4zz6FP3CV0Bf/KUZXD/7IELKtQ/c1uET8y70UvvtNRCyO/cFPIqsl0EvB3+LfAVletJUDfZ2btt7WyWufeSEDwjuvvbyL1kigvzODYKczTdgmAtLa96a2Ub62vCpm5HmBLZ3nTmUJEpDhnhP82wczpO9J39VtTC/MqXytXDfMYurxVHaeG3CznqP7EopSHERh2YiebahIHHX5NGpiRGfzFYl7w9T2e43tiJ6tiKba55Qqn0UN983MOAxH5yfjhcBuayV58ABWmdoQtWBRjFVjNElj4N1335aD7F+7k93dM8mFguswUtH3ZmFY0iZpWcTMBGl3krQ7SdbNzEiRz3qaZLfshdXXpjQRnigtdhQKbynFFWqGwJQmBX/qRPvKr4vlSa3IOjlGcxPE7SgJO7Zo8sbxHM6M9HJiaKYth0CwsamnLurKzngrUXOmcm08l+LQtRM8uOEOwpVShZeAdQ1dvGvtrRwfOsdQRntj+crn5PB5vnz0CT6/78O0RqtLN8k4OZ7tfZUf9r5Gepp63hCS3e1bubtnf937vxrQGE5wa/duXu17m8HALz/v5nnz6nHWJDs5PXxhxvb3rr21boGXekDAkvpS7W8FmqxfbFti2v/XGz5+VQUyizDrmFGzWAihfcmnP+vnQ9ED2ZwWMPnU3vfyd0ee4Oi1s1xLDfNHr3yZVD7NjrZN7O1cOHAnhFZyF4RRlaLY9Qv40saob53vmW0oB7/Kc2kIc47FkGDau7No1VIUQFCaJgbf+2ULgVaDcgSrFIKQEdVFSacXHrxBSPSJQoq3Bo+xrXEje1vmv3605YlTffFXYV73cTCEQbXl2XU2QT190fWVN5IfwVceSilaw+0IdDDCV/6i5yGGNEnYSRL2lNDC9V2yboaslyHnZrXFoKeLk+a8HCmnOE+dvoBafRCROKqpTS9iyvk8z4eBy6jTh+D2R+prB3GDQ50/jrp8tvaibdJANLYiktWr7Bfsi+ehrlxAff9rqCOvBIUnlxG+j3r7VfxsGvnezyC27F40kS723AE//FZ5Eh09zgxdheZ2WIS3cU0Y6tftVUI0jth1EMw6kJepCfxn/hH1/HeWh1yejb4L+I//BdItwF2PzPX3rydSYzAxhsqmUC99H/XDx5f3GDMp1CtPoyIxuO8DiOjcTBc/m2X8xZfANNn4b/4nhCFxxyew2lpRvk//n/4ZwjDY+L/+LyjfZ+Bv/o6Br3yN8KZNAOT7++n47GeIbNnCwN/+HVv/y//J0Le+zeSrryIsk/EfPU/jQw/Q9NADZM9f0MT4rQdofOA+8lf6GPzq12i8715aPvxB8pevcOX3/4CxNd20fviDKN8nd/Y8G3/nt5HhCP1/9t8Z/f4PSN59J6NPP0vm1Ck6f/aniW7fytgzP+TKH/4xiYMHiGzetHxjWg6ui+q/iBrs07UxBq/o8xpLQkMLTI7h/+AryP33o0YHtPVO1wZEaxegyT811I/qPYHq70Vu2qOJPAgyMg6hrpxDSEPXqoglEXc+pjN3xobwv/8VTb4rIJdGSYm4/RHoWAv9vfgvPKFJ9lAYNTa8pPvUH7yGkBLZVUXGkO+jhgchFEY0lX+uy/bO0n61GnlqQi0sSyt5AQytQhbTlf3FCbdCZ+3k8+C55P/bH1TMBBLNLXW1L14IoqEFFY6gRq5BPl8qRl4vGPtuxdi4BWJxzHc9iPv6y3iXLmg1uR3C7z2Hd/Sw9pIfuobytAhHjQzhXzyPdcc9mNt3630lZxGpkxMUvvU1yOewP/FZZC0KZeVrQreoui6qod2CJp6Vmqq3UhTU+J4+z56jP/PdwIrGnmXHMu38hSKU5pdOfkoR7rn6Hiqq14v7sMJT78ti3wxL/3ELum3TAsMurxj3lc4ukQGR77lT6ngrDE6ghJ895zVMRPs6aOpATAyj0hOa5AcdZEk26yzDxc4lc2lENIlqK7Yr9D6by5+zoSe/T2FoCHdykot/8EdEN20sPXOSt+wjtq02e+Ulz4A/duCxOUphgC1t64iFlmbNUPTdTga2K+NihElnpqdq2p0kYTUs6F9sy9AsIsDHDXxHV2oRasvwDBLd87UKcSX7ALrI4zdPPc1EPsXu9i1sa9nI2mQXIaM6NaRCUXAdXu8/ylePf4/+yZk+wd2JdrY2r8eowwN7Q2MPjeGZ2QJZN8fLVw7z4qW3uHfdrVh1VnJLIXlow528de0EPzj/EoXAHypVyPCtU09jSskHtz7E+sbuitedUorR3ATP9r7C3x39DhdmqdBbok38xO73kwwtnMZ8o+LONfv49ulnGcqMBqn8LqeGL3By+DxDmbHSdslQnINde0isorEQS/QYrvaXczNklqul2lFUaVfXC4khjOsuTNTl/2Rgp7IwtecrX5PoTD1D9nftIGRYvHr5CGuS7ZjSZNP6Hm5bs4e2eHVEhyEtDN+skkR38JSPFMvzHlCoquxtQJ/H4rtyOkxp4fgFPOUihUHey5VUEvq96uMpF0NJCl6+ahK41K7QKc6e75YK0+pijoHPvZ/H9h1Maeky4crXwZI6jZfne5wdv8jZsV72t++iKzaVbeQrnx9cehFDSh7suUsfr1JczQxyfOQMQ9lRDCHpiXexv30XocBjfzQ3ztvDJzk1doHTYxeYLKQZL0xiSoOQYfPxLe+d8/5QSlVt5VK0L7regcdaakco9PEtpd7EdBS8POcmz6CUmmP7BtASbgXqNz8wpUncShC1YmTdrCbU3QxpdxLX93SdHaoL4F1XGCY0d0CyWftJ1gh16HnE1n3Q0rUMnbvxoCZG4dgrUKEO1LwIR6BnS2kht2T4Ply7hP/k38ORV+YWBJwNISCagOY27Vlth/TC2nMhn0NlJmH4GmQW8DX1PdS54/jf+zuk+VOIjTsWpW4W7WsQ67Zp0qvc/GNsGNV7CtZvXVbfa+U6qKuXoK+3cl+bWhHb9lX8vmrkc/g//KYmlwvlgwczEAprxW2iUasFLUuTHIU85DOo0UEYG5lSPlbC2LA+X5EY4tb7lk+NnpqAsSEdaHnhu5X95aXUxQMbW7XHfCikgzFOQV+LEyMwMljdGE2O4b/6DLJjLWLfnXOCOsKyCW/cQOqtwwx+5ask7ryD2M7tGLEY7vg4Ey+/ysZ/+2+wu/UzLnnXHVz9718ie+4c0rIxGxuxOzsRto3d2UF43Vrs1lYKV6+Su9BL6vBhhG2RPX0GAHdkhPThIzTcczf5y1dwR8doevRh7LZWrJZmIlu3kH77GE0PPoAwJI0P3k9k0yZQiuiO7Yw++RTu2DipN94i13uR4W8/wdjTz+Cl0rijo2ROnlpxEl1EYoh7P6BJ8LeeQxy4H7Fu28yNshmU8hF3vjtQsvpTZLYdQh64D9XSgf+9v5n5u4HLmkDfshexfjuMD+M//iX9bNikyU8KOa06vfVBKOTwn/qKVr63dKEOv4BINCLu+5D26n/xO6jcLN/kWlAkFmsO3C1wT815xlXYvtK96Ws2XSSS2J/7BUS4PO8mwmFE+/Jn75TQuRaxabcOQtphWL8dEk2ISoGMIiFbJUQsDlIL0IQd0gr9TEZbsDz1bRAC2dSCymbA9abG2XHAdRENjeV37Pl4F88jx8eQm7fprIBa4LlabV0MphcLPAMUxUeGVbI9wQzetUVFuRWa9s5WU9tMnzPLWZnA0gSvENSKcDURvhCK9i8Q2MAsIKYQYuraF4F6XQR9dgMSfx4iXFghaOlGtNS5sG00gdiyH1EqrooOHFQICuf7r1IYGCC+ayd+Jku292IpYBXduKHm5pdMog+nx2iIxLGMKQJMKU2Y5Z1q9Unzo1gwLmk1UvAL5LypB2HBy1dVZFIXvJOlSYUm9Qp4vosplz8FSiAImxFS7tQk28fH8QtLUkstBnnP4djQWZ6/+DodF1pY19BNT0MnW5rWszbZSUe8ldZoI1ErgiVNXSRAKTJOjpHsGOdGL/HWtRO8cuUwJ4bOzVguSiF575b7aIs212Vx3xxuYF/Hdk4MnyPjTE2cLo7386dv/T39qUHuWLOXnmQnIcMu9TXnFkg7GSbzaSbyKSzDYl1DF/HpBaTmQUe8lc/sfj+Xxq9yfOhM6Robzo7x98e+x7nRy9y55hb2tm9lTbKDhB1DCknOzTOQGeH44Fle63ubl64c4srEtRmLalMafGrXe7ita/eSx2c1ozvezi0dOzg5dL6kwr+aGuSZCy/PGI897dtY37gGc5nSvhaDpV+7VSrRV1AZUCt85S1YHLMIo6SIvf7KSyGkthupgkRXgd5+Nna2b2ZH26bSfW/UOHGWQmIKE5eFC0T6+Li+o1XFdV7EalLWD7ztF4YUsmyNjrARwVMOKWeiRIAWr11T2hh+nqybIi+MUiCjeCS+8si52cBfWr9zPd8lZISxZQjQwXJTWqSccaQwCBlhQkG9krARxVM+GTeFtpPRXuBRWcegmxAMZ0f57sUfYRkmHdG2knf5RCHF1858hzs79wN67nB+4hKPn3+a4ewoCTtGwXd5sf9N+jMDfGTTYwghcJVLyskwkZ8k72lv7ayb0/7uFeYsRTV1NZDBWF9v1BQIVKrmAMt8cJVLxkmxIbGZuDU3Zbpe9WYc3yHjpkg5KdLOJGk3Tc7LknNzFAIlevkCzKuXTBet3ai27kWR6IwM4L/6A4xHP10fFe6NDKeAOvYK6tKZxflXx5KIjbsW3q4aKKXtMp7+Bry9AIFu2bBxhyap2rqgoRkRierPDVMfSyGvffBHB1FXL6FOHYbL5ypnL3gu6twJ/B89jowlEB09tR+DlIhb70W9/UrFdtTxNxC33gvLWTwyNYE6dxzyFeovmRZi676lF8zzffyXv4967omFyeHOtYidtyK6N0BjCyKa0OfLtPTa1ilAIa/J5sGrqDNH9Dlz5nn/jw7jP/VVnQ2xuU7X4Ww4BfxDL+rU/5Frc78PhWHDduTm3dDRo73MI3F9bFLq3xXykJ5ADV9DXTiJOvkWjFWw2SlisA916EVUzybELPsfGQ7R9OADoBSpQ4dJ/emfEb/lFto+9mGU5+MXCphNjYBeD8hwCAwTP5NBNthIy0JIiTAkMhwukYDK9fCzWRASq7UVM7AL6P7FXyB+iw64eKkUwjAwAp9sISVGPI4zMqLtXYTAam8jaBxhSG2p7Dh4qRRGNIrV0owMhTGbmuj5tX9KZOvCVoPXBUIgNu9BxGuwTVAKNTYEl8+Ck9eFfQFSY6hrl6ael9EkomeL3rdK6sBLJqWfQ/0XkPe8X38WiaHWbIJzRxd/GM2t+EMD+APXMLYvsHaXUhcLPfwmany07Cb+kH7vyubWJQWvRGMT2CGU62Ld/wiiufW6CysAHTjLZXQ2z+gA6sxhRCyJqkC0ij136ayCKqEmx1GeJsdVPqefEZEo3vEj+Ff7CH3mZzG27sA7eojCk49P/dAywTR1UdGyHQHZ2o797g/hPPcDnOeexnr4vdUXFfXduUruYoDACJ5n0+f4hqmV5EU7lJL/edHCRZYJtMyClOCJqfe9lFPXVOlaWOJctNw1Nf0zw54KDKwghB2esp2pAh0f/yh+Pse1r36dhvccxIhFCXV1krvSx+hzzzP89LO0PPIQib17qrLHWjKJ/tevfIuORAufuPXdJMIxfOVzduAi/+Xpv6S7oZ3bN+xZahNAoEqXNiEZmkGi+/hambaAmjtkRDCFOaNQqeMXyHoZEnJpnjjVImxE5/ii6wKqBYw6FjitFp7y6UsN0pcaRPYdpincQGM4QcKOEbMjWIaFLS0MIXGUR94taA/07BhXU8Nk3bkTvtu69vDYpnuI2fU5Hik1Kf9s76ucH7tc+tz1XY4NnuFqaojvnn2OxnCSqBkCIXB9l4LnkPcc8m6BnJtnb/tWPrfvw1WT6KDJ3V++7TP878//Vy5N9Jc+H89P8sPeVzly7SSd8TYawgnCVggDSd4rkCpkuJYeYjA9Sn6WL7IQgo/veDcf2/FY3RX0qw2GNHhw/R08ee6FEok+WUhzdODM1DZCcnv3XjrjLderm2WwcmRwPUiw5eqpp9yqFZXGgh7ZKwet0K2O2NPK2PIWE0IIjGmfDaZHEAhaYwsvmgWiVOB0dnHscnD8PCEjBEvOTCi374WJ/CIsaZcdO0OaxMwkru/qdFNEiZg3hEHUjJcyFwxpEjai0wLDAlPqQqUhY2qyY0z3WMcgbiXxfBcQJTX6VNvxoAirCs5LfW0kDCFZl+ymK9rGydHz3Nq+l+awnhccGTpBxs1yb/dtCAQThRTP973OUHaU96y/n40NPTi+y5O9z/Hd3h+yv20XG5I9NIUauLf7NtoiLVya7Gd/2y4eXnuPDvZWyHSpJXC1GlToUL5oeiVoO8X6kei2tGkKt3A5fZGwGZkRvAHYmNwy45qrqo9KUfALTDoTTBYmmHTGybhp8l6uZN3i+JXJSYEkZsVIWg00hxdZiGolkGxGtK9FnT82P8lWFgpOvoHfvRG5755l6d4NAaW0CvPoK4vzHDdMRPemir7NNXfHdVAvPYV68/kpr9Jy6F6PfOBDiPXboKVTp9rPV1dK+VrRt+8u1NHX8F99WqvTy8HJo469jt++RntSL4LoFlv2QFMbDPWX/V5dOoO6dgWaO6ZsD+oJpbTi/fThytvYIU3kL/EZ7J94A/9Hj0Nqnusn2Yy440HEzoPafz2WnL8OGKCcAmL3baizR/FfehJ6T1fYWmnri6e/jmzrrKut0Az0ntZtTSeEhICu9ch734fYtAta2rV/9Xxj6rn6uHYdxH/+O3D2aOXgle+jTh9B9Z5GtLTPUHMKIbBaW2j96IdJ3nUn4y+8yPDjTxDdtpXknXdgRKPk+68S27sHBXiZDMopYCQSU30vdXO6KlRgxGKE162l+T2PEdu1c+bxAlZzE8rzcEZGsFqaUb6POzqKEYshbL0uLHtdS4nZ0IDR0ED7pz6J1doy47tVCSF0QKQWKFUiRVmzufQMEWu3aF/04vVhmlPez0WlrPI1iekUpr6TRhCQWfyzwti9D+/YYdxXX8C6+37ddiVYFsaufTiPfw3/3GlUanKOL7r3+ssgBMae/YvuE4Ds7kG2deBfOId3+A3MB9+9pP3VC+rKOdTx1wNVtg8TI/OvQro21ESie4dexz97CtmzDve5ZxCNzRjrNuCdOQG+jzBN1NgIzg+fQqWnsqhEUyty7XrcV59Hbt6K0bkGf2QI0dCESDaAkIiOLsx77kd5Du6LP0K2tGEevHPhZ73yAwX6rCOVRkCWBwFZYQTq8oA0NwxtqWJH9aNEmiA9PXal389TALroje4EdjAzthO6nUJOt1P0RC8H35uydkGAw5TlTNnjDYqlej4ID5Q55d2+SmHGY7goLv/5X5C5cAEznqD5/nspDA/jjI0TXtPN0He+R6ijg/CahVXzS16JHli7kz989m/xlc9nbn8/x/rP8rvf/mN6mjr56IFHl7r7GRAw5+RUa7pgCIOYGadQyJcIBUdpdVHEiC67Gr3okxsxY6TdqclS3suRddNY0l5RNfps+EoxnB1jODs24/Pi6FZDwtzWtYcv3vpJNjWtrVu6NsDm5nX89L6P8Hsv/tkMX3FfKYYyoyXfcqnZnbIqv9ZoI06N3p+mNLhzzT7+1T1f5Pde+FMuTiPSPeUzlB1jaNp4TfcFLgfbsPjEznfzub0fpiXSWFNfblTsaN3E9paN9E8O4ikPX6kZgYW1DV3saN1IxKyvX9pSoCn0FSLRV/HLppZ6DcYqUcVCbSQ6BB7e+Ajmf/6+ePEtTGnw/u0PVLVfU1gYwqiKRHeVLpwpqT8xWvCrSH2GwDql/H0oAiK8+J50p5GI2nbNmmGJM2e/CxR41O9Hu6Jy2JDmsnrGA7RHW9jevIkX+l7ncqq/RKK/0P8G6xJrWJfUXpgDmWGOj5xhZ/MWDnbsKdm33L/mDp65/CJHh0+xIdmDKU0Sdpy4FcWQBmEjRNKOz1vHw8OvKrMOCIxDVsE9V9P1qvDrqM52fJfh3CBhI4ItbcQsEr3a8cm7OcacMcbyI0wUxsm4KZygeKgTFLWtBEPoAFCD3UCD3UjCaiBkhLCkjVVNWu11gjBN6FyHamipSFbOi0wa9er38eMNyE3v7Ky68lCovvP4rz29uPEDTexs318XAkwpBVfO66KUldTTgNi6F/nBz8HaLbpYXzUQUvtKr98Gbd3I1k78p74KVy+V3z6TQr35HGr9Nu0ZXiuiccSe21DPfLP894W8VqNv3q2JtjpDOQXUpdMwPE+WxpqNWhG+lHbGhlE/+jYM9FFx9dC1Dvnwx3Qx1Xii6uxFYdnQuRZaOpBt3fjf/xocf7N8O56HOvM26tVnEI98fNHHMy/KzSe37MH4yM9C1zqtKKwCwjB1gCXRiNHSiff4X8LJQ5VrEUyM6HoFW/ciko2lj71slvTR41jNTVgtzditrSjXRbkuMhqh+b3vZvDvv0Z021aU7zP65A+wuzqJbttK9sy58m2h3zmR7dsQ33+ake89hdXchLBDZI4fJ37LPoxkklBPD6GeNQx945t0fPYnyJ45R/rESdp/4lMl5XrZfQtBw3330P9nX2LsuedpeugBvHSG/MWLJG6/beGCl9cLtfIBQujshHgDomfzrIyWIicR/LuSQjYcnbINCqypFpUpFMB66D043/467svPUfjG32G9+4Mlqw9VKOBfOAOJJLK9CwwTY8dujJ17cQ+/QeHJx7Hf/SFELAaui/P80zgvPINoaMJ8aGmktwhHMB9+L97Jo+T/5r8hWtsx99xSGnM1OYF3/AiiuRW5YVNlO5U6Q6zfjnjPT1b/g64NNe3fvPUOCt/5Bv7VPm1l87HPIOIJzN378E4dJ/v7/xGRbMDYsx8Rn7JkEckk1gOPUXjiG+T+6P8C30PEE4R+8gsY06xbRCiMeed9+EODFJ76NqK5BWPTAn7ZvlfhOSS0TUvJckTMvCfMMBj+lE2LkPqz6YVIi9e5ZVOWBC+qz2dbvYigbeXPbNeOTP3bMEAGxU2LPumz+zldGGsEmU9+4NUuTSAokmqYmrRfzRCC8Jpuuj/3WbJnz5I+dRph24R71tDxkQ9x9nf/d7xsddZPS16VPrD1dizD5D985//jaN8Znjv7Bh/b/yhfuOfjtC41zW0WXOXieDO9XY1AjVXNYilpNzHujM0gNdJuCluGaLCb60r8loMUBgmrgYybmlHgdLwwii1DRM34ipBqljSxq1RBV0OeR60wD264k8/u+SDbWzbUXWFtSZP3brmPyUKGP3ztr8m55f19fVTds6ctw+Lunlv4nYd/gz96/cu8cOmNitvO13R3op2f2vsh3r35XauqgOZyI2TaPLrpLl65cpjJwtxCQvvat7OxsWcVjseNo0RfDhTV2dViuZ+dtUALhIoFThY+Bh8VKJznz2YaSo9iyupfmUIILBnC8Z2q1MWOX8A0rLpeE55yqyLxQSvDzTorvG8kmNJkS8MGXux7g3PjF9nRtInh3Bhnxy7y8S3vxgrO/WQhzbXMML0TV3ih7/XSo8L1PTJujpFcBb/XKqADOtXddwU/j+M41/0JUnMR2UUWna2wM0JGmE2JrUStuYrXStdz1s0wmh9mND/KWGGEjJvGDWrUeP782QCGMGi0m2gKNdMYaiZhJbFlCEMauiivMErZGqse3Ru0rcvQVWqfPCltl/D84yg7XJOK7J0ANdiH//L34OKp2ouJBhCtXXM9hBfdIR//e1+BybHK7W3ejfzYFzUBvJg5lxDaN/3AvUjfx3/ib2B0sPy21y6jjr6KWrMR0VCjutkwkPvvxXv28Yq+3urIy/DoJ7SnfL3nj+lJ/GNvzHNeBXL/PUuzMlIK9coPUOdOVG6nowf57k8j9t2lSfFaj7PoF7xlN1II/HwOitYYs5FJoY68jNp1ENG1vrZ2FoOezRif/iVNkC5i/ihMC9ZuRj72SfyRAbh2ufyGSqEuntbX6TQSXTkOEy++xPAT38XP5zATCZoefYT4LfsQQtD1hZ/h8u//Iad+6Z8CkLj9IF0/+9NTSvR5EN22lY7PfJqBL/8dx3/m50EKwhs2sHHbVoxkEru7i86f/in6//S/c/zzX8CIxWj54PtpfNc9C9pGNNx3L+74BEP/8A2u/D9/iBGL03DvPSRuW0SwarVCCERbD5x9G3XyTe17bpioaxehZzMitEDgTAjEpt34x15BrtkI2TTqzOH5VbULQK5ZR+jnf43cH/4n8l/6Ewrf+6YuDur5+CNDqPFRQl/4VeQD7QjDQHZ0E/rJnyP3J/83hS/9Me73n0C0tKLGx/CvXEI5BSK/8a+RXYuwvJp1rNa9D6GGBsj/5X8l++//NbJzDSLZgEpNoEZHUJMThD7/C8i161fMfk00tUMtGVY1BJLDv/Drul5CNoPK5xDhCKKpBYRAbthM+Au/qtXnUiIbm7DufbAU8BDSQK7fROhzP49KTWgVtWkiW1pBSuyPfUY/j4VAJJKEPvgJVCaNaKiCz/S88kU5dcO6eGjFY5/2XYk0L7N9pWwK39NtTLdymW9fM4juaVk1lXzNp38uhPbiV2pKZe97dZ7bLx+EEFhNTYS7u8j39TP+5vcxEwkSe3ZhRKO6eGqVh7KoFXPBdchO86e+pWcH/8sHf4Xf/Op/4u5N+/nCuz5OyLTJOQWiQXRZKZ+xwkgp9Xu6n+d8k7niAs1TLilnkqw3MzoQMWPIKlPgDWHSbLcymL9GcYR85TGaH8ZXPkm7ccrbt8IiqNifooVMwcuRcdPErAThKixZwkaEpNXIuDPlk+Uqh4FcPy2hdmLB2My3CNN90EttX3mBJYxDzEwsqPgDWJNs5zfv+SL39BzgyXPPc3r4Alk3j+t7+MoP/ky1AYE6V2i3W0PKQGUX42DXLt6/9QEOdO4iYoWWjUyLWGF+Yvf72NG6ib84/A0OXTtBwStM6/PUFa+VqFqNqhe2BlErUrbwWDUwpcme9q387kO/zouXD/G1E9/j1PAF8m4BT3l4/hTxUWzbkAaWNGmLNfPIxrt4/5YHWNvQVfKYXwoMIUnY0RkFV23DIryK1NzT8cD6O/lvb/0Dk8MzSfSGUIL9nTtpjy1T+ugScAPQH8sKRfVkHrDKvN1F4MstqlK9KuXzV4f+kWfOvsa/vO/n+M/P/Tl9kwNznr8j2XF+/vZP1dQTW9rkhMSrQtVf8HKEjWhZa5nFQKHI+7mqz2MttheGMGkOtd8YRGGVEAg2Na5lS9MG3h46ycH2Pbw+8DZSCO7oPFDaTuFjSsnBjj3c2j7Trk4Kydr44oo3KaVK7/aqtkehqixCuppQz6KbhjBQwKGR1wkbkTnZfLua9hEx587LLqUucGLsqK4ZMIcwDzz9hUQKQUiGSdoNJdI8aTdgCFNnAoggH2DVBYGrg4gmUFv2Qf8FGF/AX7gclA9XzuE/9RXkfR+EDTsQ18EXc8WgFMr34Wov/o++Cb0nKi+aF4JhIm5/tHo1+AL94u3X5rcf6VyL/NDnYc0GxBKV78IOwe0PIq5eRD33nfJe3r6POvIKatdBSDbVdI8IBKq9G7buhVOHym80Oqj9vm9/AFFH5ZtSPowMVG4XIBZH7LtrKY2gLp7Rvu+VbICSTcj7P4C45e4lXyPCMGHzbuS73oM/OgCjQ+X71NeLeusF6Fi75GtkXjS0YPzEL0H74gj0EqREbN6FuONh1FNfhexcoQ4A/b3aY3vdltJ1aMQTdP38z9H5+Z/Scy4pkeFQSc1tNjWx7jf/BSqvRVvCtpHhMEII4vv2EP13v40MhVBKkbz9NmQ0SufnP4vyfaRt03DPXSRuPYAf2CoJw8SIafJXmCaJ2w4S270L33FntC2EYPff/pX2YA/Q8oH30/zYY8hIGGEYtH70wzS/990o1wsKK1rXV4UuDe1LPHuNLSWEK9s5+SffRB1+QQc4cmn8L/1v0NKFvPs90NqFuO1h1KHn8b/2x5rUbOlEFBXLUmq7lult2qGSUlfc9hA8M47/D3+ii/C2rwWUJsgWA8PAeugxjA0bcZ78Nt7RQ3hnToJhIhubMO6+H2PLdjB1f4RpYhy8i8j/3Inz5OO4b76Cf+oEIhbDvPt+7A98DLlp28z7zA7pgpf21LkXkai2GZFTqmERiyOisdJvRTiC/fHPYuzch/PE1/FOH9cK7UgE2dGF8ej7tB1JPd411WI2MVzPXbe1638k52ZtCNNCtLRCyxSBb8yy0hGmqb3kG+cS46Jpio8QQkAiWV1xUaUCIrl+doVVoVjY0/e14nyl1uIiKJjqFvQfWNn2lwBh2bQ8/CCHP/8F7M4OGu+4jcLAEOkTpzj9v/4OXjqDDC3MpcIiSfTnzr7BHzzz11MdQmAZJm2JZp44+iMujV4F4MFtt/PPHvocoJeFWTdDxkthYBCxYkSNOCEjjAwWIiXaOBAQThHVeSacsRk2KAASSdxMatK5CgghSNiN5Pwsk84EJSIdj9HCEGl3koTVQMSMljxXBWJKNa4UvvKC4qZZcl6agl/AEAYRc+GUQiEEBiZJuwlHFci4Uy98T7kM5PoIG1GSVmNpXKbovKk+eMol7+XJeRlyXgZXuYSNKBGzOu9BKSRt0WY+tuNRPrbjUcZyExwbPMvZ0Yv0pwa5lh5mMp8m7+bJewV8pRXFMStMY7iBzU09bG/ZyO72rTNsSZZ7IWkbFrd372F/5w4ujF3h9f63OTl8gaupQSYLGfJuXtv22BFaIk10xltZ19DFlqZ1bG5eR8JeXBGiYqZDYzjJ+7bcxyMb7+Ls6CVe73ubkyMX6J8cIFXI4CufqBWhI9bChsY13NK5gz1tW0mG4qX9VANfeXjKwxRW2d9salrL7z32WzM+U6jALsVfVapggIgZ4r51t3F25OIMMnFH6yZ2tW1edf0tkrA/zlBK1UaiT49krwKIYppnFYegUDy48Q4OdO5mXWM3Y7lJfu2en6YxPHPi9e2Tz2LUeK0KdKFOrworKV1gtFDRUqVW6ALf5bN2ZkNbuYSqPofVZn/daIgYYXY3b+X48BnOT1zmtatHuLV9D3Fr6v0et6Ik7Tgt4Sbu6jxQsnOphOJY+QsGplTVfug3KmaG5pcOQ5p0R3ogUn6flTJHPOXPsGjR17+NbYSImjEa7CYa7EYaQ01EzOhcocaNojRfCEIgtuyFU2+iJkYXtwBUCvrP4z/5ZcSdj8GOWxHh6A2xmKoFSiltk3LxFP4zX4fhRVq4BBA9m2HzHurx4lROAf+H39KWBeVg2cgHPqQtXOp0XoRhIu/7AN7xN6D/YvmNRgdRZ44iNmyHWBVERGnn2o5BHrgX//SRitel//qzGAfeBXYd08cLBfwjL+tClpW6d8s9EF1YkVwJyvO06ruvt/wG0kDsOog4eH/d7BeElLDzVsTpI6iXf1B+TLNp1LnjiKF+aF9Tl3bnwDCRj30CuheZDTELQhrIW+/De+UHlUn0XBY1fA1RyGubEEBIgRGJQKS8+E2Iyt8L08QwixwBEKjHRTg8c5u4WdkkMCgsWu57Mznz2pK2DdNIcmlZVRW9WzH0bEH0lCls2tSO/Nn/seLPxLb9iG37y38nBKprA6JrQ/knZHsP8uO/OPM3D39y5n+/96fq9pYWQqBMC7l1J6GtO6v5gVakb9hM6Bd+jWooOevR92M9+v4Zn4V+9V/N+K1cv4nIv/29uc3ZNsa+Axj7Dsz5rrRNFX24iUVCedreZKUhJFjTnlErJWAQzLR+uYEgTIPOT3+Szk/PfF5kz50jc/Yc8d27CXVVJ4ZaFIm+pqGdd+96V9nv3r9nyi92W3v5lDAPj5QzQcqZmPJaFZZW9wgZZAooPN/FUYVSAbPpEEiSdiMhI1KbwkEImkPt+Mon46ZnLFoLfp7h/ADkwZJWoJaXqECd7SkPn6Upv4TQ/rBNdhtKKXJedsaiskiMF8elqKzSBL6Pq9wKC+3aFqbTx6wp0sC71t3Ku9bduqhjWikU+2wbFttaNrCtZcN1aT9k2uxq28yutsWlLxeDMZUKw2XcNOOFUdojXVhiLkFT7jd5L8dYYZgmu7XmQmrLDYViJDs+4wq1DYtdrZvZ0LhME/UbBILVObGplUCvVCjxeqEmklcpuhJtrG/UaZX7Ordx19p9NEZmLvovjvVh1pjNIoT2Gc97uaoI0ryf02R2HcbS8QtV+9rbMhRo91fPObweEEKwrWkT7dEWnrn8Ev2ZAT617f0zznt7tJWdzVs5MXKW168dYVvTRgxpUPAc0m6WdYkuLDE1sYxbUWJWhCupAa5lhmmwEygUTaGZxeGKooGbqB6WtOiKLVz8ZyGY0qIr1kNntJuYGSvNSU1prsIgb30hIjHU9gNajT6PFciCGB1A/fAbMHINdt8Brd1aAftOUKX7PowNoo6/hnr1+5XJumphhRAHH6qcOl0rLpxA9V+snE69cSdi4w6tIK8XhEC0diI270EN9Gnf4TJQZ47CbQ/WRqKjFYVs3AEt7TB0tfxG545r1Xjn2ho7XwFKQS6DOvJK5W1MC3HL3UuzRRjsQ/Wequxd39KO2HUbosYxmxdCIBKNiI07UMffgPGRspupwT7UxTOIZSLRxbotiB0H6notitZORM8m1GBf5cyQkQGdMRFaXeujRUEVC7T6xWrdzOQAgmeumPa3kMv2LJ6xJi0WG1QKUbJYVUxZRgQCl+Icfb4CuQsUz12wH8VCtqV/l76c2ktpjGSpX4vpT4Uf1DSjLjeOotR/ADVNsFTsqyj9e+lBKTV1PZXWDtPPYbHdae2LqfZXHNOutbn9DPoIc/tZ774qpZ87i7R1WxKqPZbidaR85j+nC98HM3+zzCieX+UvcB0G8/RqxsP3SZ88NceKMtTRQeu7H6upe4uawW3v3Mj2zo2L+ekcKJQu3sRcorwSdJHQJEm7qWaLDoHAFCatoQ7GxDBpdxK3jGes4zvo0rTV77mWPoSNCM2hdsYLI2S8NP6stOypcallr++AxcqPBRQTzjhJq6GU8bBUGMIgbERW5YK/b3KAl64cmkHodSfa2duxbd4ie9cVN2+lGnBjD5aa9v8Av/6unyFRxnNxX9f2Rd1fpjQxhYFTBaHt+g4ePuYS0yCVUhT8fNWkbL3U7+8EtEQa2dG8mW+cfZLOWBtrE10zzntDKMH9a+5gNDfOty88w2sDR7ANm5ybI+Nm+bX9P4dlTxEsLeEmdrVs4/krr/HV00+QDMWJmhE+u/3D1+PwbqIMHL/AhcmzXE5fJG4miFtxYlaCmBknbISxDRtbhgkZoVIdnncS5OY9eCffgvThpS0EMynUq99H9fci9t4DazdDY9vyWkMsJ3wflRqDq5fwDz0HZ48s3fdTCMS2W2Dd1vqMi1L4R16BXAVi3zCRO/ZDa9fS2yoDsfdO1OvPQraCCq9opdG1rrbjFQKSjYidB1E/erz8Nk4B/9CLGHUi0RXApbMwUMFfGxBrNiK61i/+3CmFunQGVakoqxCIznWIrXsXt/8FINZthZbOiiQ64yOo/l6U69S/CKGU2ganVo/8KiDWbUEdegnKiO4AXWTSqZ5nWHUoEkl+UNDP80C5+r9LRGIR08jCou1C6Y8EYdSfTFS+7pPn6ICa8qb6VupWsfihqQOIhqn7VC+Cv0QUelNFHj1vms2GmjlORa9oKae8nYvjVM+gg+/NDTKW2plNnKvg/Lp6LP2iz/b0fkv9xyieU7P8/qpBkYD2/alxmz5mZcnLWdeVsYT2a0XpPvCmxtV3p8ao0n0gDX3dS0OPW9E/fDEBgBnEbvC365QP4HkuiDw1r5OLY7oUFMfCd6fGynOn+ZdPG6viNV+6L836n89i29NhWHP98Kef4+LzxJt2Dxchpt27Muhz8Z6YB77j0Pc3X9YFtZXCS6Xw0hm6fvInaH3skZoOacWqiAkEYTOCqxwcv7AoxZVEYhthYmacuNWAJRf3ghdCYBk2TaE2bCNM2p0k72VnpPdWtZ8gVT9iRGruixBC28ZIA9sJk3FTVasVp8MQBpYMETPjc/xAb2J1wvVdsl6auJWsnOpXIyxp02i31Glv9cWT555nLDtR+m+JYHPTOna3LVDp+iauGxTVezOvVjqp+qCiYjQ7Qd6dWlSO5ybmbBW1wjPqENTSD9sI47gLh0QVCsfLY5pLezV7yqu6oKgpLIw61Gp4p0Ag6Il3ETHD3Nm5n+gsT22BYHPDOn5qx0c4PHSCK6mreL5HY6yNTQ3riMyqTRE2Qzyy9m6aQ0l6J/vwlFfWWuymBn3l0GA30BnpJutlyLpZCoHtkes7jBVGGCvoZ4G+d0PEzBgxK0HcTBC1YkTNKGEjQtiI1FRseNUiEkcefBB/4BKMlfFLrgVKwaXTqIHLqA07dbr+mk26uORSF4UrBc9DTY7C1V7UuWO6KF167jthUWjtRtz64MLF8aqESk+gek9Xth9pbIHOtYhlUuCKno3aa7eSOj+f1UUft+zWHsa1IBpHbN2LevVpyGXmfu97unjpgx+uz/EpH/+t5+bdROy7C6KLs4YEUPmctnGpRGKHo4iejYh4HVXo09HcgUg2VX7fuA4MX4PJcWiqoTBgNUg2a0uhWq+DatCxThfuqzTtyaT0sd1oKBKrnqO9h4vE6vw/Cn4HEBBRAIiAJLO0f/FSMmGK88WiF7SbByc/f9+KhGxxe2HojA4rpPu02DlosQ9FQrVIvFXzO7wp8t9BE3OmrZ9phkVdbMlcB3IpZszyQjGwp90HSuk+u3l9nhccR3+afYjQz1a7hndKkagsEavB3wuKfaYpmqe3b1p63Ex7ed7zpf46ejzdQg19ZeZ4FgM5ph34eC/Q3+L1VQwsTA84zA4UzYaT039qgQhqCSxlHEv3ZUFfUwvdD77H1LMieE4U7wNRJzLdyUNh+ntcQCQBcpqYsvisc/ILn2Pl68Kwngvkqx43adv0fPHnShkEhcFBxl55DWHXzimvHIkuBA1WE2EjQs7L4fh5HN8p+T8r5c9IZy7aA+jCkBaWtLBlmIgRwS75hS8NpjRJWo2EjSg5L0Pey+L4BVzl4il3hnJWIHWhSmFgCBNLWljSJmSECZUpaFUtbBmi0baIGjGyXoa8n8XxHFzl4Cu/RKoXx0MKXSjTkCa2tLFkiJARxpbLV9TzRoOvfPJeFtd3UCiEkJjCwg5UZLO301kH2l81JEOYQUDEVx4ZN03ICJPzsggEISOCpxwc38GSdmnci979BT+PUj5SGNhGCEvaJTKv+H3ey+Ern5QzXvLzt2UoKIAmZvQv7U7i+S5CyBntFZH3ckHwRSGFIGLESv0vouDlcfyCJvL8PK7vlN2fr3wKfn6ODYQAbCNcVeHc2RjOjPHcxdfJe1MLvIZwgls6ttO2CguK3sSPJ37U+zqvXDwKgCElWSeHUqqU6eT5HnE7ykd2PcIda/fVvH9LhBCkqwqSFvw8YRVdEqnt1mjlIldtKGTl4fk+felrxK0ou5q3lvU8F0LQGWujM9ZW1T6bw408sq68Bd4Ubtq5rBQ6Il0k7UbSToqMmybjpkk7KdKu/u9CoGZUqNI7diSvC2/a0iZqxYmZMaJmnKgZJWrGgj+6ns6NGJAS67Yhdt+BevG79UlLzmfh5Buoy2dg/faSvy1t3YjwzLnOqoBSqEJOW230X4D+C6hLZ2BydOnq8yJCUcT+e7XfdJ3U+erKeUiNVfxedPQgmtvr0lbZ/ScaoaEFJkYrbqOuXgr8qGubQwrDhI4eWLsFKhVNHdDWKGJb7e/lORgfRZ06Uvn7hmZdyNJaQgbl2BAMXKlMDsQSsG7b4ve/AEQkqgMrll1ZmT0+AhMjdSfRxZqNOpi2DM9HkWiYd7/KKSy+CPD1QlGV7OSnigcubYeaoFJKE2RLQXGsfRfymanigjV1xwMn8JK2o7pPi1EFO0Wi0Fn6s1r5mvT0HE1yL1exxOn3v1J6/ArZaQGPmnZWWx+nE5VVBWWqaH96gMcOa5K6XqiFWK1lf0JUeR+ogAzPL73tFYHSgQYnu7j7svicKAZY7PDSglzztTPjPvCn3QeL8JdXflX3gZCS6IYpu/HI+vWkT56icPVazU2uqITGkCZRGSdixPCUi+u7+OhiiL4qFt2aShkRiBJpbUpzWRYnQghCRgjbsPH8BK5y8HwXDy/wy1GlvkhRJNJNTGFW9LSuFVJIwmaEkBnG9R1cf4rEnyJdpvpgCAMpTCxpVl1U9ccJaXeCrJvBFCYI8H2FI3QBWMOY8phPOeNkvYy+roCclyUvs8StBmxpB0T3BI6fxw/sdUqKNeWS97IkrSZsI0TWTZNyJktFcn2VI+dliVvJEvms0F7oHh6g8It+dsF/TYcAUo72ERdC4PseWVer1yNmbI7KVvcthyXsuSS6nyflTmAF/vvl9geQ9TSZYAb3Wd7L4foOETPGYqdbP7z4KhfG+vCnTWx6kp3cvfYA8gYkGm7iRkK1k2nB2oYufG2JztFrpzk7PMADG2+nOdqA63ucGrrAUHp0RmHcWiCDoFXBX1iR4CkXVzll6yFUA1/5OKpQFWEvkMHz4sf7XvSVz1h+goHMMFczg7zY/wZ7W3fQGWu7IQnRm5gfhjSJywRxSxdw83yPnJch42aDv/X7XP9Jld77AAW/QCE/wlheK0mL2YhhM0LEiBKztGo9YSWJmfEbR6kuJeKWe1EXTsCVc/Xbb3oCdexVOPs2qqUT0dqNautCtK/VhHokXjdCeTFQ2RQMXEFduwRDfaihfhi+Wl75vBRIidh2C2LLvqWRsLNx5Txk5vFob2pbFvuMEoRANLXqgEMlDF9bvJVGU6tWo595uzyBkc+iDr8EW/cueZGvTrypbT8qQGzdA83tS7pe1eiQvsYqIRxF1MvjvRyEQCQbUXao4jlRqXFUarz+s4LOHog31HuvGgsF5txqVLarCEWiL59ZJLE6D4qWDUuCCAj09NIV/l6wHyFrrzWg0P1YFFk4D3wvUM2KgEiv891QJK6LBHo+s4TClKL286lU7QrpavZZCPZpR+qjSC8Sq/ls/Qt3VmuZolhYcb5qoDTZX5fxUjo4pTwIRcFYRJBrIZTuAz9QqmcXH9Qp2tEs1GQ+T+8f/jHK0+34uRzOyCjtH3hfzU1el9m9ECIo2qQflulChnMjFzk7epH+yQHG8pPk3Dye7xMybSJmmOZIAx2xFtY2dLKxsYeGUKKui1uBIO86nB29yLnRS1yevMZIdpy8m8dXPpZhETHDNIWTtEabWJvsZGNTD63RpiUrwAuew/nRyxwfPsfF8T5GMmPkfYewYZMIxeiItbCteSPbWjYQC8WXZVF/NTXEoWsnODtykeHsGFk3jyEkUStCa7SRDY097GnbQnusFaOOC568W+Do4GmOD52jPzXIRG4SV/lEzBCJUIw1iQ62t2xka/MGwqa94LErFGk3RUiGiVvJgNDWDz5j2mI252VJu5PEzCQRU6dAFbw8KXeSrJvGtIzSHgESZgOTzjgFP0/CbCAkIOVO4ioH4QtSzgSWtImZCYSQuH6BSWeCjJvCklaQvaAJbuEKHL9A3Exqoh8QIvDmmgYfn4TViClMHOUwGZD+0zMfbKnV7jk3My9B5ysfX3il/bnKYaK4PxkBFFk3jSEM4lYSKQxCUhcrtaRNaBGeyRfH+3ny3AuMTbPGiFoRbu3axYaGH++CoqsdooaSp6tVO1tLvw507eTOnv0A/I9XjvNzBz/Og5vuIGKF8Xyf/skBvvb2k4xmKi+wF0JIhqsi0YtZLZZcHInuKbdqazJL2nULBt/I8JTPidFzfPPc93E8h40Na7lvze3ErfpYLlSHWuua3HhVUIRYncVrDWkQkwliVqJU+DsfZI3lvTwZN8VEYYJJZ4LJwgT5afex4xdw/AITjn42mNIiFGQIJu0GDrTefr0Oq3Ykm5B3vw//iS9BerK++85noe+8VnmHY6hEI8QbtTK1uR3R0gVNbYh4g1Y+1RtKQSGPSo/D2BBq5BqMXEOND2sVdWpMW5LUS3U+G+u2Ifbfh0g21XUhqgb6KheoFAISjRBZvP1IVYjG0fOF8mOnJkYWTbSJcBSxbjOqUoFR10GdP4EaG0I0VZcZVBa+hzr0giZby8G0tJf9UkngyTEYG678fSiyvEGPYhvzkQ3Z9NKL586GkIjmDogs0zt1IaJT+at3sjobRQI9l6qOUJrt4T3dP332QQtZJ1Wp0oRXufu66FFc6lOx8GIZT+QiiqS1jNdGvhatOYSsQHKKaV7ws3zOF+yTr4/RCLyW6zl3Kfo7+15lAn26bzcCXUhWlfGVNuce20Io+l5XIlqne57P9m0vebyXu6ECElcawXt8CWM2I8CwwH1QKp476z4oWq/MhjRquw9Wctoqql9/z4GTn3+8Znj+S0qWN36QEVJu/uO5WlQQCZ4d9UTRV951KhPowgAZ1C8A9H1QfL5Ng1GlIExKwt1dqOD3QhqEujtJ7Nldc/evm0RGKcVkIcOT557n2d5XuDI5wGQ+RcbJUfCcwOJFIYWBKQ1ChkXYChOzIjSFk2xpXs/dPfu5rXs3MWtpqe/XUkM80/sqz196g77Ja0zm06SdLHmvgBe8hISQmNLANizCZoiYFSFhx9jcvJb7193Owxvvqrndgufw5tXjfPv0s5wcPs9odpyUkw2Ie4UhJJZhEjZDNIQSdCfaeWTT3Ty68R4SdnXH7Cmfx089wzdPPQ1A3I7yga0P8OimewAYzozyzdPP8MPeV7maGmIynybn6QCGECIYe5uEHaM12sg9a2/l07veS1NkaRPJrJvnuYuv8cSZH3Fh7ApjuUmyrj73RRsF2zCJmmEawgnWJrv46I5HuXftwQVJfIEuDKuUwiqTig9adS2EIGYlSoS0IQwKfoG8lyViREvBESuweLGkhes5hIwwvvIRSuArne7tKodGs0XbtwiBISQFP08usJUxDLO0PxGYJ8jAlqccFBA14oRkGCEEQklsYeOoAr7ySn0WQYXzha8FRcSIzdyfDFHw8nh4+vWsfGwZwhBmSTkLoqRerwXpQoavn3iKI9dOzVDvdsVb+cDWB7CWrIK4idWCG92EYvaVfW70Mh/b/Vip6K0hJYlQjJybZygztuh2jCCbqhqvcje4zxeTaaTtmKol0S3kEouYvhNgCMn2pk007NC1RVoiTTSFGlbUIq3WGUzYiGDLVVqYuSLEqred0+9vk6g0iZoxnUHmezi+E/wpkPWyTBbGNanuTJBx0jhKkwk6m9Ah7abIexXIzVUKgUCt34686734z359eTyElYJsSv8ZuIwyTAiFUaEohMJghRCRGCSaIJbUytxwVCtN7QiY04qYFcmWYmGx4uK+kINsBpXL6MVfehw1OabbLBT094Us5HMr45PctgZ564PQua6+frG5rLbeqHQMdhgRSyCW24s+HJ2PQ9cBGW+x6jKhLWnWb0OVI9FBB0VOH0Hc8fDi2gBUX6/2Kq+kNuxerwuKLqXYpuPogE2lLAdpIJJN9S/oORt2eP7rMJ+DXJ1VqpEoLOe1+E4SAvieVmbPRxwagQd1kUCFmeQhRS/oWUUqpbF0Kxcob51S9JiWZhkisFiA0dU2E+WOreh1bddg+yQCP27XnFKjC6HHp/hnRhHVMn3y3MoEXlHlLiP1JVL9gDycTaAXPdlNK7hHp/e7eF6L/ukF/XetQZFiQVUrBPlpbRf98kvFHkWF8xiQrkWbodkoqscNa/EZD9MzMea7D0o+7ObUGMy5DwJbJDe4D5QP0qreckYIbTdkzRPcd51Zft8B7Ejt95sQLOjTXrEPufLjJc3AlqX4vChzTpUf1DYoY5lTtG2KJOprb1Sct82+/4ScqpUwI5AU9Hd6YM519HmtcpyFadL+4Q9RnLAIKRGWtahi4deFyVJKcWniKv/phT/l6OBpxvOpiv6tnvLwPI+8V2CioCPj54Fjg2d4+sLL/My+j/CxnY8RtWr3a867BV68/CZ//fbjnBq+QKqQrpiur5RPwfMpeA6pQoYhtP/f2dGL2NKumUQfzIzwtePf4/HTP+RaeohCmXQtV3m4rkfWzTOam+DiRD/Hh87yzIWX+Rd3fYF1DV0Lk5tK0Tc5wGv9bwNgSoMNjT08vPEuzoxc5L+++RVevnKYyXx6DhWmlCod82QhTV9qgHNjlzk2eIbfetcv0J1YnM9i73gff3H4Gzx38XWGMmNlVZOur+1+Mk6OoewYF8b6OD50jgc33MGv3vZZEqHyyhqBIGk1M14YYTg/oP1LzTghIzJj8e4qR5PF0wgkbW1gkPN9fLzSd3LaBEUT1hLwg/tZ4frav344PzDjFvfxMZBVexPPxvQAQLFd5S+espyubBXa5Z/iw9MI1PJZL4NthDCFRcZNAWDXaCsxmU/z98e/y7dOPc1kYUrNEjFDPLLxbjY1rVtU/29i5aADPdUX5lxtEh+larhPxMwg1IGunfzBS3/Fx3c/Rk9DJzk3z8uXDnN88BwH19QeqdZNCKQS2NImW4XXm698HN8hZNQ2kSoWFK3m2A2hSf1yp/ni5EXOTpzhQOsBGkNNNfXhRoQUkpZwIy3hxuvYi9rUJxIDU1g/9lkEyw1dm8fEkCZhIiilaETRHunU1n/KI+dlGckPcy3Tx0h+eNqc5gY7N0IgLBu163bExAjq1e8vf5ueq4v+ZVKlj1QxLdfQCkI1W0k4hxApFtGbXvTMnyr85XlVFk1bBiQaEfvvg027606OqtT4/LYzhTz+U1/Ff+F7dW13DiYW8I0v5Jbm59zUpkn0t1/RBO9spCdRx9+Eg/cvmrTxj7464xqcDbF5D7R2LomsVYWsDuZUGivfQ514E/c//LNFt1EVsimtiK+EIilRT0TjOhB2I76vfB+Vz4DyEdEaCr76ns58KdpmNHZUF0QoZCp7AkszIOaKxKmYf0yVCWbxmRi8l+pxDqZfw0KAFdEk3XyKaCXBNnUB2Hy5Y1SawKvVh1xIbTWhVFA01Z6rSq54HFKPqTR14KLcde/mqytAWQuUP9dL3gxN+YkLEbzeyvRfKf2cs0JTgZHFzDVMOwgQSN22YVZ3TUGg5DahIDUBOhvFYqVLIdHnI9ANS98H1ajwi9dF6T5wa1PuiyrscnyfspHkWhXvi4Xv6+BUuevXDGk7ltlZBbOh1LR7ITN3vlQk6etUFF332w2ed9OKH1uhaYHeee4DqQBLBzd8r6ZrzRkZ4eIf/X+kjh7Dbm2h4+MfpeWhB5Ch2oRJ14VEvzx5ld986vc4PXJhBsFoCBkUEtUEjqZmFL7y8XxvBsGd8woYTpbNzetLisFqoZRiLDfBP5x4ir888k3G8xMzPJuhuHAytGa4mI2Ej+f7cwiKd609UHXbvlJcGu/jT9/8Kt879zx5b+oBKgO1u1GKEik85eP6U77xo7kJnr/0Jv2p/8i/feBX2dW6paZFtOt7XE0N8uLlt/jbt7/NS1fewg0eUqY0MIURkMSg0G270x5iqUKGFy6/yb/70R/y7x78dVqj1RMsnu/z9sAp/viNv+W1vqM406KvRpBxIIMHePG8u76LrxSe8riWHuIbJ59iID3Cb9z5edYmywcRwkYEO9xJzsuS8dKMFoYJGRGSViNWYCGkqPTK0eM++2qY+XeZXwlJzEwG524KWtG9OLWgKD08Fmq9yv0JOXMnM/YtSNqNjOaHGMkPIREY0qLBbiY0q6Do5YmrTBYyNIWTRMwQQgg832MoM8qhayd58twLHL52kqybm7H/Pe1b+dy+D2MutyrqJpaMWv3qlVIoVXvGwnJBFcmUKlAs2lzEr979Wf728Lf5izf/kZHMGLZpsrV1Iz994CPcvX7/EnolsGWIXFCbYD746CK/tgxVPaYqCOi5VSjdASxhB/Ug5u6/4OeZKEzMePbfxPJi9nW4EBR6LrIS9iie7zLpTGBKk7hVA4lwg0IFzw6FCgJyeqwd3yHlpJh0xpksTJB2U2TdjM7oUsE8rYpaBKsaQkAsibj1QcikUMdfq0+h0VpQVLLV2QJ1xRFvQNz2MOKWexBWHdSfs5GeRBXKKAGLUL5Wqo+P1L/tWuAureCfkAas3wrdG+D8ibkbeC7q6kXUxTOIjTtq3r/KplDH3yhP0IMuKLpxu86MWAoKecgsYJO0HFYqtcLzFp85UAmhoEjjjYpicK4WCAmxRk0ypscWvgdU4EFcyd/btAMyLKBuqpkbFoOOSkxlH9Rzni6EJjPtaGWya/q2oAlvS4GfnjumKrj2zFpIdKFJNzs01X4tBCloAi4Uhezk3D55gc2FUvUdu5InebH/kerI3RLRzdwshGohgushmqTmMStuKwwIxabUwNNRDNosZsyKfu2VAnlWQAqLKq/n6Qr15boPridKz41yBLoN4fgs8UEFFLcpKu7nEOnBeSlmwNSr704whxEyeJYENkDLdB/4+Ty9v/8HNBy8lTU/93nyff2MPvtDpGXS8kht2WwrTqIXPIf/8vJfzCDQLWnSEW9ld9sWdrdtpTvRTtyO4Pkek4U0lyaucW70IudGLzOSHSftZMm5Oe5ee0CrsWtZeCrFRD7Fl49+mz9966tzSIKoFaYhlGBNop1tLRtoCCUJmzZZN89gZoTesT6upYdJFTKknQw9yU7uCrx0q2n7WnqI//bW1/jm6adLn5vCoDGcYEfrJm7t2s26hm5iVpiMk6NvcoA3rh7j+NBZhjJjAansc2akl//fs3/I7737t1ib7Kz6+AFODJ/nwqtXODPci6s8wmaIzlgrt3btYl/HdprCDZjSYCgzytsDp3nz6jEuTfST94rpyh6Hrp7kzw/9A79+509XVTjLVz4nh8/xx298mRcvHyp9bkmTtlgzBzp3sbd9G53xFmxpkXKy9I738Xrf25we6WU0N46vFDm3wHMXX8MQkn91zxdpj831DhRCIDGImnEiZoyMl2ayMEZeZkskuiUsCn4OhY8gKDaKJutl8L9qYUgDXHSB2qoI8+l69YW2rN9DXsyzPyEEhSAtqzXUMVO1Puuh9L2zz/OlQ19nPJ/CkBJLWji+UzbAVER3op1/ftfPkrCX2ZvzJuoCgdTnvcp1bzVFLFcKKvhftWTWbNV93I7y87d9ki/e9onA1kpbLy09iAUCIygwOg/xEUB7m7uYokr1otIZNtVYucigoGjxuJVSZNwMeS+PIQ0c3ykdr6c8cm4usIlRmNIkZsYwpIGvfHJuDgREjEjpWZFxMyjlEzVjqyawsuoh5j5r58OKmigF2RriHWr9o4JAva+8IJvDo+DlmXQmmSjZtoyTdTMLjrtA6CLmwigVFb/RIIRANbUh7v2gVoIff73+hdve6Ug2I25/BHHbQyVhSr2hMqnFF+xcSZTzZq4FQkD3RsT6raje0+WDOiMDqFOHEOu31Vz4U504BCODFfsoNu5ArNm49HdZIT+v2n3VQNW/iJ6w7OW3qVkMilmLRXuNosSqSDIWfavtCupopVC+p6853wt+LrU1QDHLUcrqrv5iYcZyZLsxjUBfzHW4XPOwoiK4VgLWsssHDIpKYWq8VpbiI138vQzU3WWV1U59rbimwwrObTXE4Wws5bwudcyK+7Ci4I7P/c739PmsVcGv/PLnAAICPVabknx2f99p8D19H81+ZgsJoSoJ9Bm/C9TgnjNFcJfaCgqA1rtGAAF5X7RyWs77wPPI9V9l28c+AoZBdMMGchd6KQyP1tYm14FEPzp4hjevHp9BoN++Zi//5NZPs6dtW0W/a4UiXchycugcL185wuGBEzy68S5aIo01K7F/ePE1/vzwN2YQ6KY0WJvs4qENd/C+LfezsXFt2b74yudqaogjA6d46fJbbG/dVJW/s0KR8wo8feFlvn3mh6XPQ4bN/s6d/NTeD3JH9z5CZTx9Pr37fbzef5S/PPyPvN5/tKRePz92mT95/cv8D+/6J8Rq8BC7MHa59O+mcJJ3b3oXn9/3EboT7XPG8kPbH+LMyEX+21tf5ZkLr5ANiNasm+OVvrc5P3qFrS3rFzz2kew4j59+lleuHCl9HrXC3N1zgJ8/8Em2t5SfoH5u74d45sIrfOnw1zk5fCGwOXB5pe8w/3DiSX7mlo/OyETwlY+rHGRAAipF4D8+c98RI0bey5Jx04SNoLCon6fg5QgZYUxpoaqcRIZkBFOmSbuTSEtq+5cgc0EvqGdeH0UldsEvlLYVQtbsFauCFGZV+t9U5kZRxV4LCe8pL1DbFYK+a4LRkEZAIopSu37Q4uxMhdkQCDrjrfzGnT/DjtZNNwm1GwS1EmbavqByfseKQtVG6stZdi6T+cq2XhErVHPm0xS0hVK1JLqvPBy/gCHLq8Vno0i6V4OifVPxuCedSX7U/0MmChPErFip5gJA2knz9sgR+tP9JVJ/f9sBNiU3UfALvDX8FgUvz12ddxM2wiileKH/eWzD5t6u+1ZEKf1OQNFiSwSZWAvBV5WDltOhUCXbkWKGhhBS2/kEgRAvCM7rOijWtPenLrLpKIeQDJeKwReR93Iln3+FKgVnZCmbrXzbpjAr1gNZCbi+i+MXtG2ccjVh7k4yWRgn5UyScibJVelnrouGW5jSxBQWETNC0m4gaTfSYDUu74EsI4QQ0NwOd78XAHXyDU0C3sTCSDYj7ngUefDB5SNeQBPoVdiDvRMgwhHE+u2opldg+NrcDTIpVO8p1OQooqGl6v0q10WdOgSpMiQQgB1CrNuqi2IuFZ6nfdF/HFG0ZFptKNpGFFWXnqOnspE4RBv0/ZUa0Z/bUUjOurY8F8YHdZZCLrCfCEVmKnyrhVeoUFhPahK/3kX9lgohNfG1mCBhscDpbG/1on/19YAMbMTK2XL4yyRakEag4l+F90a1MC3KFnYtqvdrRTlCGIIgxwL1HH7cUPTHLzcPsMMB2b0IFO9P15mrRved+mdlmFZgB7UC60UpsZubGXvxJez2dtx0hsLIKPHt22re1YqvYt7sP1YiYgG64m18etd7uaVj/hQ8gSBuRznYvYeD3XuYzKexDass6TwfrgZK8Ny0PljSZH/nTr544BPc2rkLa54XlRSS7kQ73Yl23rP5Xlzfq4okUArOj13ir458s2RjYgiD27v38iu3/STbWzdWJFFtw+Lunv0k7Bj/+eX/zqGrJ/ACMvnVviO8dOUQjyyisGkyFOdTu97HT+55P43h8mmKAsHW5vX84sHPMJQZ5Y3+YyWCaSgzwmv9RxYk0R3P5bW+t/ne2edLXqGWNHl04z386m2fpS3WXJFcDZsh3rvlPpKhOL/7o/+XvtQgABP5FM9feoM71uzjQOfO0vae8hgvjCAxkEJH/z3lYAprhko8bEaI+nFS7iR5L4cQAtd3MYRB1NAkklsliW4bNnErScqZYLwwUiLNPeVhGyHiZnLGNWLJEJawSTnjFLwcAkHICBMxa1Np+8on72VLqjmlfLJeBle5GMIkZIRnkGHzwVNe6fqbdMdL/RUIfQxWA6ao7XERsyJsaV7PT+75AA9suG3VF5S7iZkwgsBJNUSdp/zVQqGXAknVQgg5I2Dwj8d/wEB6ZGpfvj62sGnz4KY72Ne5fdF9E0JgCquqAqMKhaucqmxyilk0XpWLD1OYM54NR4YP4/oO71//AQpegZeuvUjB14v9kBFiU3Izu5v3EDJCvDn4Bm8Mvs6m5CbCRpiOSAenxk8ykhumO7aGrJvhWvYaD3Q/cJNArxFSVE+iK1VMlZ1/O893mXDG8HwPH/3OsGWIBrsJIcKknUlyXkZbmAhB2IiQsBpKv3d8h3FnFMfPk7AaabCbSt8N564RMiP4vo+vPHSx7iRRI1Z6p04URks2J7rtMA12E5HrSKKP5Ae5lOol7aZJOynyXq6qMRdIbMPGlja2DBEyQsSsOHErQdxKEjcThIzqLZhuBIiWTrjrPSANTaTP58F9E9Dcjrj1IeTBB5Z/sX+9fN6vE8SGbYiOtajhAcqpxtVAH+rCKcQtd1e/08E+1JXzlRX9HT2wbqsuaLtUFIsY/jhCLlI5usxQxaKSoQgiHEdlJrVCOpLQanJpQ0MbKjNR2ddfeeA6iEQzJQK21vWOUmUIqwCmpZXoqw1S6r4t+veBR/NsonW5COtqUPRSn53RuVzP2aIX+Y0OaYBXhkSvFdPtPWYjsBHJDA6RuTZAfnwcN5ejefs2Rk+fwYrHad29CysaIT0wwOipM+THJ7DjcVr37ibSrOeuXqHA5KUrjPf24mZzWJEwzdu3Ee3sQHkeV55/kcTatUz09uLlC8S7O2ncsgU7vhoz6YOgU9nnxhLtswxT3+Ozz2uxzkwtlkvzQQj9fFuh4Ii0bTo/+TGufuVrmIk4vuMQ276d5MFba97Xit+5g5kRvGmR1sZwkq3NG2reT6XCkgvhB+df5Pw0JTbA9paNfHH/JzjYtadmv+Zqt3d9lyfPvkDf5EDps55kJx/d8SjbWjZURTDubtvCezbdy4WxK4xktWpiJDvO0xde4u6eW2oqrioQvGvtAT607cGKBPp0rG/o5pGNd3Ny+AITeZ2OOJnPcG70Mp7vV8wgABjOjvFs7ysMZadSJXa0buIzu99Pa6ypqgXn3T37eXjjXfzVkW+VFrvnRi/xWt/b7GrdXAqmGELbuDhBsU+JwDZiJXX59ONPWI1aFerltfDAjBI2IiXLFyEkETOOGfgG29JGmCpQDBpEjGhp25iZwBQWOS9bIhNCMkzICM85PomkwW4m62W09cIsxbglLWJWfMY1IdDkuxASWbSfUdqj1VMuQgiiZhwAxy+ghMKWNghtHxE147OKqBIcjyZucl6GrJcmasa1D3NA5OS9HBkvhSVtTDMBQGu0iQ0N3fSlBkkXsiUrF9MwiJhhmsJJuuJt7G7fwsMb72Zb84aqsjVuYnVBk6xlFBll4Cu3qu1WAgq/KksTmKb+nXaP5tw8GUerUFVgIXVpvB/Hdbmls3a/1dmQwT3pVbGY9pSHqxxsMf9kSNtRuFVZ2BjC1FYuYsrKpTfVy97mvSStJL7lszGxkZNj2nvWlCamNBnMDlDwtHp30pkokfutkRbOT1oMZofoiHZyOX2ZhBWnNdz6jiITVwKyWJOkigWbpzx8FAvNQLJeFtd3abJbMKTJcH4AU5iEzShZN0POywTWZ1EKXoHRwhAhI0RIanuesBnBlCZjhbm+ygpwPIeWUBtCCCYKY0F2VwQDg6ybwVMuzcH3I/khLGETMetYmGgRGM4NczF1YcHtLGkRMsKEZJiwESZsRohZcWJmgrgVJ2JErquifqUgWru0Ij2aQL39EkzWnvb6joeQ0LVee6DvOLgyC0Llr5bX7opANHfAus1w7lj5YM7IIKr3NGrnrQi7CgJBKdTZozAyUP57KbWNS/f8QqGqsRhf7ZtYZgQ3UHG9JWXwWQ03lkJbTCxlnTO98Od0FBWhq1HFb9hLU1DLYv232Zhmq3M9UNbKcjketOLGrhMwHVJA2WVXjePmV7gPioVMhWTo6DGO/NmXiHd1cu3Nt+i+8w4yQ0P4BYdbfumLNGzYwKm//weG3j6GkBInm6N1104O/NNfxAyFcDIZrr76OldeeBHlK3IjI7Ts2sH+X/knAHz/1/8l6x99GOV5FCYmANj1+c+y9oH7kItVdi8X/AqBWWksfQ5SqWBtKVukTpkxwlhaMK5WSEl08yaaHriX3IWLGIk4dlsrXioFrdVnscF1ING9WSnIru+SdSoUc6kzck6e75x9bsZnzZEG3r35Xezr2L6sBQ/HchN8//xLpf+WQnJr1072d+zQntpVQAjB/etv4x9OPFUi0R3f5ezoJXrH+9jZurnq/rREG7lv3W10xtuq/s2da27hzw99vUSiO77DUHaUrJsjbpdfFHu+x8WxPl7te7v0mSEMHtl4F+sauqtWJwsheP+WB/i7Y9+hEHizp50sp4bPM5AZKfnCSyGJBWTvQpBCEjXjJfJ5NgxhzFDdhY1IyeNUCklCNszYPmRo0ryaY7GNELZR/uUZMiJzinlqQn/mGJvSnNG/SijXLyEkYSNK2IiilKLg5REIIka0FGwoFlfLeKkZyt47uvfSEmlkMDPCZD5Nzi3gKQ/LMIlZUdqizWxo7KYn2Tmv9UXGyfLa5bexDYt9XTuIWguP3U2sHGRg91GNmKBo57BShQ7nQ9GCohoIJHJWtsbP3/6pGf/teC4nBs/xnVM/qnq/87ep1ejVKI595eH6Dpaw5yWk/YBsrwaGMGdklRSDZREzEnxvYBuh0rO5L93H6fFTgfrWJuflgmKL+lzHrQRNoSbGCqOknTQXUxdZn9gwx/rjJhaGFAYSWX4tMgs+Pkp5KGXOH6wo3sDBJpLAn1Vp6y6BfhcYwiRsGpiORc7NEarCIk6gg89WoJKzjRAZN42vPAxhBLZKovRHVBmUux4QCCJmlIgR0X/P+XfsHacyrwWiqQ3ufAySTajXn4ahq6zWc7niMC3YtBt58CFYuwWxUgvtopKzEsJRWLMR0TC3dtCKQgiI1EHFJyVyy168Qy9C/8W53zt5uHxWX5tVEN8qm0JdOAWTFaxc4g3ayiXeUP77WlHy2a4Aw4TOdYiONfVpb9EQWoH/44AiOZfPaFW65wQWJTXcw4LF2yYUUfSPntM/ufpsXAAQdVBQz+PJfb04dF28aIXaku8ga5JKwZAa4Tvlf1e8T4Pdpvr6OPhrv4wZCdP34ivc97v/llNf+wZDbx8n3X+VoaPH2f7pT9B58ADDx07y1D/952x498O07d2DFYnSdfcddN19B5HmZi49+yNe/T/+b3b8xCcJtzTj5Qv4hQJ3/ta/QPk+L//H/8yV51+i87ZbCSWXWFy63lBe+aBDPYSLxayMOW2qylk5i4Fc2ftAFQr0/fWX8XM5pG3jjo3jjo0T6uwgsqG2gPmKk+jtsWZMaZSI0GvpYV68/BY9yc6arVlqxemRXi6N98/4bGvzBu5acwuRZSbwjg6cmaFCbwgl2NGyieZIbZOzjlgrPckOTo/0lqxRxnITnBm5WBOJvr1lI1ua1tUUOOhOtM8hOvNugbSTrUii59wCbw+eZjg7NnUM8RZ2tGyqmTRd29BFZ6yVixNT5/DK5DWupgZrLq56EzMhhUHBz5PzstjBC8zzXTJeGoGYUWi0K9FOV6J9yW1mCjmePvcKMTvClpb1N0n0VQYjIPT8Kii9qaK8xnX1dCkWFPWqJLulmEuiz4ZlmGxqXottWFyZqKBYqwFC6MKDprRw/PmLwulx1X7VlayZtH2Nh1uFlYtAagJ/RpaLICRDukAoQV0J38EPiPLzE+dwPIf9LQdoCjVxauwkl1JTBIYhDDqjXRwdeZvL6cvk3Cyd0a6qraRuYgo6K0JWvfZwlYuJPW/gKmSEyXkZxgujSKE90BNWQmu9Ah/0Ys0QgUAKWUOwSJQ5z1OdjxhR8l6WscJIKShTq23ZcqD4TouaUWJmnKgZI2rFSoHysBEhZISqKpr+4wQRiSH23YNKNOK//gxcOL64lO13EhpaEDsOIvbcBS2dK0egw1S6dSUkGpB3PIzYfsvK9akS6kXkr9uC6OhBXbtSljxQfb2oK+cQXesWtg+5cgF1tbc8CQHQ1oXYtKt+NiTTiaByMC3kzgOIe99Xn/aWgmh5cdE7DUJKlDS0lYoQurCddR2Cpb5X/lm6monW1abKvdFgLBAE/XFEsTjvbEhjBqEbaW0l2tFB46ZNjJ27QGJtD3YiQSGVIn3tGgNvHkJ5Hme/+W1QiuzICINHjtK2dw9CStxMhisvvEzm2gDZoSEmLl7CK+h6CFY8xtr77yOxtgelFA3r15G+NoCbzqwuEl0pbX1UNvhWp3uz7PVZ54yqhcQAdYZyXcZfe52tv/1vMBumRLdGojoB7nSs+Aphf+dOwmaITKA+H81N8LUT38NTHu/edC+d8eVLAX/z6vGSHznoop7bWzawrqF7WdqbjkMDJ2YUu2uLNtGT7Kz5WIUQrEl2YEoDz9MTv8l8hssTV2vaz4aGNXTEW2v6jW1YxOzoDAWl63sz/OVnI+vmODZ4dsZn6xu6aY7WVhAWwBCSnobOGST6YGaEoczN1OKlQFvBxAJP9TQZN60/J1DcWw0z/OTrhUQoxsd3P4YpjUXbM93E8kEGxQerVTg7fqGkSL1eUErh+m5V/sagg0ezScDjA+dmZEf5yufieB+nh3tZ31QfhZhWg1s4zE+iw1TBUKOCcYdSCle5VRVTNYSBJa0ZpKsQgrXxdZybOMvaxDpc36U3dSFQmuvjR4AhDSadSU6On5qz39ZwK7a0OTN2muZwM3Er/mOr2F0KtGe+iVOlL7rjF+ZkLc2GIQx8FCFhYRk2pmmWnudSGKii9ZkwgkKgHmYNyrf5zrMm7RUhw8IKitkux7ukVnTF1pC0GwgZIWyps8K0/Vltxbh/LGFasHkvMtGEOrEedeRFSI1d716tPEwL1m5F7LsHsX47IppYeULEthdWnEViiJY6FMVcJRCRGGLrXtSZo+WLgU6Ooi6eRe08iIjPQ3j4HuriaRjoK/+9FUKs2QTtdVwfGoY+Z/NAWRbyHXS+Vj0UUwVFC+h72HVQkThiJckd5Zcn0VeYYKoagtrU+tcLRQulotK/VOxSBWStmtqOgJSsFFSrN1ajRU8R/vQx8+cZs+Bvr04Fk31/av9FCBGoooP7QIBha0tKYRoYoSDoJTRB6mayRNpaad9/C3ZAjHbeeTsdB/YDMHziBIf/658TaWmmde9uskMtmN95EhUQw9I0Cbc0BU0LhKEtFtVqFAxUIrOdgvYtXyoqntd6jYVY+aK6UhLq6kLGIoQ6lybAXXESfXfrFva0b+O5i6/hK10A7sJYH1869A1eunyIe9cd5KENd9AZb6t7IcJTIxfwpqUgNIQTrG9Ys+wKeIBjg2fxp92AyVCc1mjjovaVDMUxpo1NwSuULFaqgSVN2mLNFdXjC/12OnzlzxjT2ci5eU6NXJjxWVu0mcQi2hZC0BCaGSlKF7KlgMxNLB6msEhYDYHPrj6fIlAZGkWf3jojZNrs7ay9GvJNrBwsaVPwqyu65/h5MGJcTym6wsdVCxPTMHV9zyYB//yNf+DC6JXSf0shCZs2t67Zze09e+rSz2KBUU0yzj/R0b7oLpYqb+miAuV4NTCEWSp8PB37Wm/hR33P8p2LTxA341iGRczS53JTchOHhg/x3YvfIWJG6Ix2MJIbmvF7W9p0RDu4nL7E9qYdhKuwtbqJ8jClhfRlVdkUrnKCDBBZkcwuKs4zbgrhSQwhiRhRYlYS27CDehgZIsTI+1lc5dJYR89yH4+0M4kQQdtmTBfbFoKJfIq/OfIt3rh6jJ/e91HuWLNvRepnJK0GklbDzUDPIiGkhI61kGxGdG/Af+tHcP74ypEP1xuJJk2e77wNmtoQK+nnOQ0iGgdrnvVLPg/57PI07hZQ+Yw+57EmfU3UE0ViRs5V24udB+H575Yn0X0f1XsKBvthHhJdjY2gLp+DbLr8Bg1NiK37EPONb62wQxCZR+Hte5CerF97NzE/lEK5ea1CjyR1kEMpyKchn0aF45BJ6cKjnlapqjEX7AgiHFtQ7akKWcilA0LLgYkhlGEiog1zPYCLJOUMXAeCqWqs4nenHwRFPEd7N/tFArg4vmrWUF8vcnQVnVultLd2acwC8nw6cb4SY1aWFJ57H8wUAk37XEpCDUni3V1seOxhkhs2TPtOoHyfkZNnmLx8md0//Vna9+/j6iuvY4RnijtWNKNssZivxobvBr7ly9V2nfYjWNEg4bn//ffIXrxE5vRpjv7SPyXcswYZvOM7PvYRWh5+sKb9rTiJHrHC/OKtn+bk0HmupfVCXKEYyY3zSt8Rjg2d5esnnuK27r08tvkebmnfXrVn+EK4PHF1hrdzQyhOdx1sKRaC63tcmphpI3N08Ay/+dTvYS/C62w0O052mvrb9V0yTrZU6G0hxOwoyVB8UUGK8iqt8neTUoqsm6d/cqYFwjO9r3Bk4FTNHvS+Ugykh2d8lnML5N181cd+E+WhLSZMjCU8EnJuntcuv803jv+AC6NXggwFfW10xFv5w4/8NrZhcX70Mn/88pc5OnAagPdsu4+f3PcBWoKgkuf7vNl3jD9+9ct8bv+HeWDj7TPa+f0X/oJjg+f4P9//W4TNEFcmrvGVI9/htb6j+L7Pno6tfHrf+9jcvLbugbgfN1jSRiBRVVi6eMrDqaII5nJB25pUTyhLIUve5NPxK3d9loJXmObJKLCkSUM4TjJcvxRnQ5qYvkWhCrLUU07gMz3z/lQoPKrzQ5dIfT7LPCeTVpIHuh8i7+UwpIklLZRSRMwIjaFGGkNNFLyCrjlhRdmS3DpHzW5Jm9ZwG02hppv33RJgCguJgVeVMzoUvByWaVJuUauUYtIZwzZCRIwoUhg4yiHtTGBIi7ARIWYmSDkTTBTGkMIgaTdo+y6h5xbjzgh5N4vju0ghyLgp4maSmDV/6qOvfMYLo0SMKBEjihCSgl8g46YwhUXEjOJ4LqdGenn5ymHet+X+qrIp6oGbc4U6QAhEJIbavAfZvgZ1/hjqrefg2uV3bvFEO4zYfSdib2DdEopcX5VoLKmJ2UrIZSBbvcCmJhimtr5Ij7EchIpSPuTSWuE/i2wSLe2IjdtRA1c0ATobV86jrl1ErNtS0XJCXbuE6rtQoXUBzR2IrfUJmpdghyE2z3PTc2F8bgHnquAWtCe87+lr0rS1t7fvgZMFKzKVtZDPaFKqWNTQd/VvTRsKOcDXvzVDgTK7MPW5tPRxrFaLkVqglCa4hUREpgQgyi0ExfoEhGMQCrK9inPC6V7BhgmNHeXJbjMEUXNWXRJR/posa+XC6lShw+rsl+/p69jJz1RPr1ashiFU/tSzw3Ov/5iVmzsIUf31JgTdd99J/8uvcu6J77LtEx9F+YrBI2+z7sEHMEI20jTwcjncTIb01Wuc/PuvkerrX3jfqw6q/HPjhsPK3Qgtjz6sC4gqhe+4SNPURXGB6KZNNe/vuhg+bm/ZyH945J/zn174M86M9JYsVnzlM5FPMZFP0Tvez7fPPMuWpnV8ePvD3LP2VhpCcSy5QBGtCiiqtacrKiNmmMZw7R44tSJdyODMqp6bdXP0jldII6wRCl1g1PVdrCpI+ZBhEVoB2wWFYjKfxpv1UCye43q1UfBdPOVjLlNqmed7fLf3R/zl8W+UiicqBS2RJr64+5Mc7KjzRLsOcHyXvz/1BF8/+xQPr7ubT219H83hOhVHKgPf93n9ylH+8/N/zoHunfzqXZ/l9PAF/vrQt7ijZx9fvO2TpSyG7kQ7v3TnZzg+eI6/fOsfuTY5PMPPWQpBUyTJeG6S53vf4N71t5YCaYOpEZ4+9woHunciheTC2BV+9+k/IuvkuW/DQRDwowuv89tP/T6//fCvsq11w03CZAnQRXBtcl51araCl1uwCOZyQN+TioKfr97KBU0Wz8aaZAc5N69tYWZNUBzPITRPsdxaIAN/coeF++wqBw8PqWYq57V9jVPVMUthzKhtMB1CCBJ2ggTl34dJe6air1io2Fc+Bb/ARGGcs+OnaY900GjXbtV1E1PQAYkQrlfdeS34OcIqgoGYM+6ucnH8AnGrgbCh1eWGMsiQwldeqbh22IwGk3FRCoAUMzUa7Rawpnqi1/USgaAjugY5bQIcNeNEzBgSiascHD9Po91cspwRQpDz0lXXLLiJVQ4hEIYJDa2w9x7Eln2os2+j3vwRDF/Vi/MbfZEnDTAtxLZbELc9Ai0dWp28GgKFiSZENB4Yb5UZ50IONTGKKuQR85HtlaAUKjuhCRaEtjgJx7UyXEiENEutKt/ThH1xLiek/nckgTBDKCcPuUndTcuGcEITJrmUvk4AwnGEHUb5PmTGoZBFeY4e/1BMk7wA0kDuf5cuMJoqQ6IXcqizx1Db9+uCuLMPy3WgrxcGrsz9LWgLnO37tdK/nghFEA3NKCHLk0W+jxofRWVStbXtueDmpwIbM+45NdciofT8DVhhpfQ5EAJCUf158fouEuh2SNt3OHmt1A7HV8c9sBQIoa9lx9XXhBF4oxdyOmNACMRCdTGEQFTIZhdS1mDZUe45OU/hzeuOVdKvIunr5HXGwA2VDXWdxrD4fPBcKKTBdVnVwQaYMVTSMDGjEYSUGJaFGdHBbDMUwgiF6Lr9ILf84hc59pd/w9G/+FuMkEXX7bex7sEHEELQedutDB4+wrP/w7/BjsfY+vGP0rZ3D9LQ/KIdjyOnZUQaoRBmOLw6A0c3UROS+2/RBLobEOgBlO8vyl7pupDoUkj2d+zk99/7P/O1E0/yxJlnuZYeJutMkQmO7+DkHd64eoxD107QEW/lI9se5pFN99AdbyNs1lb4I+vk55C5lmESrhMhMh8mC+kZCvjlgJqdaTMPTGmuSMq0UorJQoVUyTq3s5wQQtARbWVP6zYmC2kGsyP0pwcwpTHDY39VQSkybo7R/DhpJ7Ps11/GzXH02mnioSif3vs+tras576Nt3F84CwjmTHaYlNFpWzDYn1jN7Zh8d3o3GJTQghaY03cvXY/b/Uf59zIJba2bgDgud43GM1N8N5t9+H5Ht8++UNGsuP8zmO/zo42HUW8tWsXv/vMH/HU2RfpaegkZs/vGXwTlSEQ2DJM3qvO0qXg5wkpB0ussDe60t7hea86ayddWNCi3CT2yNWT/JcX/oJzo5cZzU7QEI6TdfIYQvAv7/sin9z7nrp0WZOlFgV/Yd95rbB353jOK9SCxUmheB7nL0C5GOS9PIeHD3Fm/DQbEhvZ2rClbGDiJmqDbYTI+1k8tfD7RaHIeRli5lzrAkMYSGGQ97Il7/+sm8HHI2TouY8QQvvtl7k05vsOmOPTPz0DQaLbznm5kh1Yxk0HHunX3xf9JuoIIbSlSbwRbrkXseMgnDuKf+xVuHpxytLgRoE0IBSGeBNi407E7jugrRvkXPuv6wlhmoi2blQoXNm2ZXgAJkagtWtxjYSiEIrpNH8nh/IKCFnJrktpotv3pwhK39N/MhP6+pCGJs5zqcDSQkGscZr3s9D/jjbo/cSbyquet+7V5yQ1Ub4nZ96GoWvQ2DqX/Bgd1JYvboXna7IRue+OupMmQkpUQwskm2B8uPxG2RRcuwQbdy6yEbE4lbgQWjk9/bdCaHJeGppAF0IT9QVH++2aNziJDjro4HswOQL4IExNoIdiq4UmvolKKNpZFLJBpkSlNco0JbOQpSBgyWsbpp49ytP7eqdmUxXh5vV7eb7jLI6JEPr+l9O9yaePqQgsj1aGD1nzrrvovudOhJRs+8RH2frxjyCk5OCv/UoQ+JKse+gB1j5wv75GgoyOouVYfE03d/3r3+Ku/+k3dfelZN8Xf6Zk4fKp7/7jDHuyA7/8T3SNqNXsYT8bxXO0rPu/8SAMA79Q4NrX/5Huz3y69PnkocMIKUnsrU0Ue11I9OIktCXayBf2f4LHNr2LJ878kJcuv8WVyWuM5yZxpymVPOXTNznAH73+Zb5z9jk+ufPdPLTxLtqjzVVbvfhlUlSkkDVbiiwGWXeu0jBmRWiNNmPU6absiLVUTZAE9ReWHQo1o0BfEU3hJA3hRN1S/pvCyWU9HikkBzv2lBTnbwwc5f956y/IezfQonAFIITE9Vwmcilybp6MkyPnFoiHYkgxpZKsZhGaCMW4rWcPT59/hUNXT7KlZT0Fz+GlS4dY19jFhqYeCp7Dy5cOYRsW6UKON/uOAzBRSCOF5PjgWfJu/iaJvkSYwtKK6Sq8xouEniHMFbX0UCjyXrZqO4higcNy1+J/f/0f2NO5jZ89+HH+66tf4V/c9wVeu3yEwfQIt3TvqGu/DWliVlm81Z1l6aKUwg/80heCEBJLhutOAEXMCHd23MWdHXfVdb8/7jCEgS1DZKtclOT9HJZvY886x1JIknYjKWeC0fwQBMGUJrsVa5kLfGoVezOTzgQj+SEdyDFCNNotFTMibuIGhwhmoeEo7LodufM2uHYRdeINXcQxPa49qAv51UdSGCZEYhCJI1o6YctexKZdiGhy9S4YhYCu9RCJViTR1cAVTaQvgkQvWqqUlMyVih9O75I0AmU8U+oez9UqX6nr6yjT1lYtoai2zsilNIFrhUqBiumZL+XGXxgm8sC9+BdPly+gNnQVdeU8rN+CsKeR/kqhhq6iLp0pfwDSQKzbguhYu8DoLAJCIBpbEC0dqEokemYSdfEMohYSXRpg2FqN62rlPmat2RKiTBHLQMXuB/7Sxa+kXDVC5CWhmEkTa9R/Vh1WuR3JdYcCJ6cJ3HIQUtdUMC0wLP0MWuiecAsg8u/sYfcc/cwt+ywPngOGqZ8hhrlwUE4FmQDLiWldFXJa/qOYxnxNs0kSUlY81bpY6Mxjmk6QC3MmNSoMuUofd/P0KhTT1/yyNb06R2Qh+K6Lm0ox8v2n6fjIhwCtQp948y2MWOzGINGnw5CSDY3d/OLBn+BD2x7ipctv8WrfEc6NXuLK5LUZ3t8Kxfmxy/w/r/41p0d6+am9H2JjY09VRHrYCiFn+ep5vjfHZmU5oIuAzrzgdrVt4Qv7P1E3O5lkKL4iAYHaIMqemwfW386Htj1E1KoPudkWbb7pwXudEbXC3Nq9ix9deI2/OvQtbluzm2upYSYLGd6//QEiVm2FBqWQ9CQ76Ul2cPTaad6z9V4uj1/l7MhF3rvtPuJ2hIyTYygzxlB6hP/t2T+Z8/vOeCvyRqgefwMgbEZxnersJRy/QMHLETIiK6bac/wCeb8WFbqNUSFNtz81yC/f9ZPE7SgxO8Ku9k1sau7hL978Bm/1HWdry/q69VsgMKWN9Av4C3hge76DZ3hIbdwB6Iytamb7hjAxF0pLvolVhVCQAbLQdVFE1ssghTHH59+WIZpDcy0NVgK2EaLFuD5t38T1hxACOtcjOtahMinov4Dqv4AavAITo5Ce0CRtYZkX4OUgpVahRuMQb0A0d0DXBkT3Bv3v61QstFaIdVu0N/pYBVJ2sB917TJs3lV7AVRPe2WLhjZtd5FfKLO0qFAM/rPEhAeWIUVVYDF1WhqISAI8F5Wd1CrQUHyawnH6Tsq0tud2+N5XyhcYBdSpQ4h9d0Lz1PxTOQW4ekkHFsrBDiH237t8JEFzO3T0wLlj5b/PpFG9p1H5HCJU5bxZaKsdDKuUMYDvaSJlNtEy/TxUve+w9kGfMSY3JomyalHuetNVwVe8KzcElJqy3ikHw9R1AFaL9dZqge/rmghlPfiDOglWuExAbYVQ9j54p3h/LwMqnqNyQdGbyPdfZeL1N8hd6WPgm48DoFyP3MVLNNxx+wK/notVs6qWQtCT7OCTu97De7fcy9sDZ3il7zCHrp3g5NAF0k6mtG3WzfGt08/gK8Uv3fYZOmOtC5I1YcOeU8TT8d2g+OHyIm5HkbP6FzLsEkn4ToVAELejcz6PWhE2NPbQHFk+j+6bWFlIIVnb0MWejq280XeME4MRGsNJfu7Wj3HX2lsWVUC3Pd7Cge5dPNf7OmdHLvH21VM4vsvBNbsJmTZZN0/MCtPatol/ed8X5gTJEqEo8Zsq9CVDCIGJhSVtCv7Cz0uFIudnkdLQ/ujLvNhyfYesV71tlBRGydO7HJoiDQymR2gIJ4hYYV6+dJg1yQ7GspMkQ3X2SAUsaWFIA38BL0cfH195UzamKJwqzgcIQhXT729itcKQJmEjTKbKa9tTLhkvRZQ4ppxbMPd6QSkfyvi1V4OJfIojA6e4lhpiTbKT3W1biNtRlFI8ee4FAO5bd5C8V+DMyEUGMyMUPAfbsGiPNrO1ZSMJO1q2bcdzuTJ5lYvj/UzkU3i+T9yOsibZwYbGNTOs/hSKly69RX9qiPvX30ZLpBEhBHm3wMnh85we6aUj1sKe9m0lYYTjuXz33HNEzBDv6rmVsPVjbGEjBCKW0OruLXu1Z/XINdRgH4wOwuQoKj0JmUltZ5HPTalq69A2pq1JwHBEK82jCYgnIdkMzZ2Ijh5EQ8sNueAUbV2Ijh7U1Uvl0+kLOdTZtxE7b4W2GtXoQUE3VcjpfftT2QPKLejzo3xNZFUo4AlMqUALWe0H7jlgRVC+GxR2JFA7Thv/wJZE5bNgejoAMFuU09SG2LIb9dYLZZtVF07q4EJT6xSRNjGKOn+iciZEczuy3gVFp0EkGhDd61GRmM7KmA3PRfVdQF08jdi6t7qdFjMEitc6aqY6V6BJdWlqn3rfn12rtVJvg994wW+MKQ/qkq3DTdQHRbHdLD/71Zaxs1pQ9PEvNz6GqYNxhnnzGp0Nr1D+PSGktu6yQtc36FC27Zv3QVlMtyOajZvjVRbKcykMD6Ncl2zvRUBbvCT230LDbQdr3t+qIdGnI27HuKvnFm7v3sPJ4fO8cPktnr/4Bm8Pni4VIHR9j6fOvcCBzp28b8v9hCoU9ihCCEFDKI5AlNSUWSfHaK68n1490RBOzCnkmXGyK+IXfj0hhaApnMSUBu40gmgin1qR4MVqgq98+tMDnBu/zFB2lIJXIGqF6Y61s6lhHU3zFP1USpF2spweu0B/eoC0k0UIQWMoyaaGHtYmuktFO6uF53v0TvbxxsBRAPa0bGVL4/olqVUH0sOcGb7Iu7fey+du+RD2EpVcUSvMzvbNfP/si7zZd4yTg+fZ2rKerngrUkhsabGjbRMnhs6TCMXY2NQz4/fVFpi8iYUhEISMSGApsvDL2VMuWTeNMMFcxkKjrl8g46Wr8o4uImRESpYo5fDwpjtR6ODn/q4d/PkbXydma1X9Q5vvqEOvZ6KoHnZZWOnv+A6WDCGVrNrKxUDetM+4QWEbYfJ+vurr2/UdMipFxIwGBX6vz2JIKYWnPDzl4OMTkpGaSf2JfIonTv+Qvzn6OBLBZ/Z8gB0tG/X+Ufzha3+N67nE7Siv9x/l+UtvcHniKrn/f3tvHhzHded5ft7Lo04UCoUbIE6CJHifkiyKkqiD1mVLbUm25LOn75memO7e2O6dne3t2JiZiNmemeiJ7Z3eGbvdMw7bbbct2ZYsy7JuyZIoiRQliiLF+wJIkMR9FerMzLd/ZFUBIAokLpIgnR8GSKKq8mXmy3xZmd/f731/Vhq/7qMhUsNtDZv42tqHKfWXTLoGDadG2XnmI1479T6H+07QnxjCUjZRf4RlsSbubLqJu1o+Q3WovLDMiyfe5oVjbxH1l3BX8y0ADCSH+cmhl3juyOusrmzjf9v6+0RrXMunodQIf/3O39NW1shn6tfPt0uvHfkMsCLXcCeTJXXkCPbwECW33TZzwcL0I2qaEDW5WT1WFhUfhpEB99/kmGtPkna9blU66Yqttj3usZ0X9sAV96RWyG5G09x1+AKQ//EHIRhGlEQLBTnn5B292NB0xOotqCP73Mz+IqhjB1wP8GgFwpjFfZlmuNnMju32lT/kClOQE24dN5ufnG1Lzo4FpbmvaRSEeOEvQWWS7jKGD2H4Ubbl/u4oV6ydkO0shET5Qm4wZTobGakhNt2O2vde8fdHh1wboSWtbmFVpWB4AHX6aPH9FQK5+ibX1udKITVoWOpmo58+UvwzfRdQBz5ALWlFzGRbHMcVx/J9oBzXHgdylhaGKzg69uxtKgyfK8hnUxNmCGju6x4Lh5TjMzYm4tjjARKPcVTunJ+CcDPQ5yOg38hZz1aGohcB3ZyDBdQEFkq0Lbb+vPe9Nw6mMl1/qJnNYP1NI9jcjP/rX8UojVLz+Bfm3d6iFNHzaFJjZeVSlpY1srFmJc8cfoVXT75X8KJOWCleP72LbY2bLyuiAzSW1rG3+1BB0B1Oj3JudJopfQuITzOpDVdyPt5beG0oNUrPWD8rK1qv+PqvFUIIQmaQymBs0r6fj/cyko5TV1J1Dbfu6pG00nxw4RN+3bWbE0MdDKSGyThZApqP6lAFa8tXcE/jVpaXNU+xpXGUQ1e8m1+eepP9fUfoTvSRyInopWYJjZE6ttZu4r6mbZjazMRKWzmcHDnLDw79nH19h9lQ2c7qWNu899PUDHSp8+bJXXQNX0CTkoDhp628ibtbP0MwZ+mSdbLEM0l6xwZJWimkEPTGBzCkQcgXxJRuhWwhBC3Repqidbx+4n3imQRPrnuIaG4Gg9/w8dllt3Gg5zjf/uBpdrTdRtRfQiKb5OTAGbY2baQlugQhvS/d+ZLPRvfJwIyzvi2VJWGN4deUW9RyAQU9pdyCmiknMaPCmnkMYV42K3vHsq1oQiNo+rlv2TbCZpDR9BhtFU2srV4+300vvl3SR9pJoS5z42OprFtIWZDzUb/8zb4hTc/u6jpFohHQQoxZozP2+8+PO1O6xUOl0K5KVrpSCoWD5WSxlIWlLGwnixByFjMh3O0cTY/x8yOv8dSnLxIwfHx93SNsb7qJkotmggymRvjO3p/SnxpmXdVyPtt6G7ZyONp3ive7PuYf9z9HTbiSx1Z+trDMWDbJq6fe47sfP0M8m+DmurW0lTdhSJ2zI928f/Zj/uGjnzCUGuVLqx8gFihFIGgtayRg+Dkx0Mn25psRCAZTI3QOnyNkBOhLDNKfHEIphRCCjqFz2I7NkkgNoSIz8q4XEvv2obIWwS2bp9xfqGyGxL59ZDo6XBF9rugGIlrhiryFxhWZ3e+S3ftrfNvvQWtuBttGjQ6T+Pb/hwwFMbZtR29bDkJin+kgs/MttLYVGDdvRYQjrmXuwQPYx47gjAwjdANZvwRj4xZkIDxpVNgXzpN+6XmMtetBSrIffoAIBDDvuBsRKyfz+ss4naeRDU2Yt9+FjJYVttPu7cE+egi74xRqcNAtQhYpRW9tQ1+7AVkandxv8TjJH38fbUkj5u13kf34Q6yjh1DxUYQ/gNayFGPTTe46ZnBPJ9o3ISp+6WbzF/tOGB1CffAGomEpVNXPWIgQQrjCebH3DL877b/Ye8Ve1HTXumXi53RjetscIRCG75JirRACmpa7+9R9tuhn1KG9iM13gulDZTOocx0w1Fe8QcOHWH/rtOtbKERdM6JxGersyeKzLdJJ1KGPUG1rEKu3uALrJRvMeT/nveulPu6Hm7d6sTVXiNI0t88n2oxKLRcMKUL+PTs7LmLJ6YtNe8yRgoh+0evKcQWxSyR+/MaRF1WdYlno+fN7PgL6DSqiK2f6LPRiM31m1fYC9Vk++DElmJQL2noWrePkM9GFnBrEuEpFXq9HhGFQcd+OSa8lOzpASAKNs6uFsuivygKBTzfZUN2OqRn0jg2y+9wnhfcP9Byb5Jt+KVZVtvHLY29i5TxGh1KjnBw6U8hcupKsrmzj4+5DuQKn0JsYoHP4HI5ybmiBI6D7WFHeMklE7xg+R19isPCweSNjORbvn9/Lj4/+knPxHtZULOezTbe7D93JQfb2HuSlzrfpTw3y5IrPsbyspbCsUorh9Cjf+fSnfNhzgJi/lHsabqUmVInlWJwYPsMH3Z/QMXIOgM+33n3Z7XGUQ+fIOb538Gcc6D/GxspVPLniIVpLG+flqT+aHuOjcwdJWWmqw272Xta2GUz28vbpD7Fsi4dX3k3Gtvj16d38/OBrxDNJTvR3okuNvsQgITPA59rv4v5l29ByX5SV4Rirqtp4p+MjasIVrKhoxpd7ONCkZFPdKn5306O8dGwn/7DnaQAMqRP2BdlYt+pGvRW6JgghMDU/lrJmaCPiCnpJO46t/Pi0AFLIeQt6trLJOCkydmpGmdh5BBK/Hrzs+ssm2ExVl1TwhdU7UKgrep3WhY4mdNeu5RI4ysbBRkObcfDAvIR1jcfiRgjXv9+v+UnaicsvkMNWFik7gaUymNLnzl5ALvj37cTitpbKYjsWDja2yok5gDbD8Z6/5qesND85+BJPH3yRqL+E3934GLcu2Vi0QPRYNsmZ0W6+tu5h7m25lfJAFIXi/Ggv2h6N10+/z/PH3pwkoh/pO8XPD7/KUHqEr6z5PA8uu4OacCWa0BhMDbO2ajnf/ugpnj/6Bs3Reu5bug0hBEvLGvDrPo4PduYCBorB5DD9iWHaYk30JwY5N9pD2s7g130cGziNoRksiy1cDYWrjZPJEN+1G5QiuHnTFGFC+nyU3LYVe8P6K5Ih5pw7S/rVF9GWt6Ot24TQDazuPtJvvYWIxpArN2FsdxMA7AOHSL/7Hv7GNkRZJcq2SX3/f5DZ+RbOYD/C5wPLAk0nu2wF/ie/gb5mfSEz2xnsJ/3s09hHDoIQWPv2omwLp78PEQqRef0V9//BICqRIPDE19z19vaQ+vH3yb73Ns7AgNueJlGpJLK8EvPOe/A/9mVkeUVhv1QqQfr5nyGr63B6L5B++VeosbibkZ9MIqJRfNt34H/i65OWmw4RiSJu2u4W0pzGAkcdO4Dzzq+Q9z3hWuvcCPfeQkCwBLHmZtf3vQjq9GHXMz0cgWQcdeLTabMmRdMyqK6/klvsricQQq7ajH30E7jQWfxDPV2o915ClVUg6lsufbykhEvNNhPapQWyy70vL/O+x/yZzr/YyQmfXk2byUxnfygN5hzhmZjxfCPiTDejR85TQJ8moDEXpM5UWyNyNlS2dx26mPyxsy/q//xMvUvZrP2GIoRAL5mckBM/fAShaTeeiJ5HkxptZY1sqGmfJKIPpUZIW+kZCbLrqpe7xeRymexZx+JYfwcnB8+yqnLpFd3+W5as54cHfgk5AT+eSXC47xQ9Y/3UhG/cwltBI8DGmpW82bG78NpAcpgDvcdYV73iingMLyZOj3TxaudOOkfO8UDLnTzYvJ3aUCW61EnZaTZWreInx15kT/d+akOVVAXLifoiheVfO/Muuy58TG2oit9b80Xay1oJGQEcpRjOjNJSuoSnjrzAMydeZm3FclpKp14AxATZsHusj2/t/ycOD5xkc9UavrbyERojdbnit3NDKcWZ4fM8d+h17ln6GR5YfgemZrje2FaGf//6f+PNU7t5qH07hqazuqqNsFE8M68xWjspY9nUDB5Yfjvtla0EDT+tsYbCOBcIgkaAHcu2srq6zZ2S79gYUifiD7OktGZKLQKPuSMQaGj4tQBqhlYi4IreKTtBVmUxpQ9Tmm5xzFkcG4XCUQ5ZO01GpbEca8aZufmtD2ghDGHO+v5aCHHFM3mFEJjSh+VkLmvpYjsWmtRzRUUvjSaMS1rXeCx+BAKfDOAoZ8bFcwEUDlkng+VYaCKFLg10YWJIfU4FlxUKpZycTYuNrSw3qKNsHOXgzGo8TsWv+3CU4gf7n+dHB35JTbiCP97yFTbWrpw2yUETkuWxZh5efteke4mmaB23NWxiV9c+zgyfLwjbKSvNgZ6jHO4/xS3167m39VYaIrWFa1FFsIw7mrZwcvAM3/vk53xwbj9b6tZQESxjaVkjAd3H8YFOFJC2M5wdvYAuNTZUt/Nx92FOD3Uxmh7LiegdmFJnWXnzvPplPmTOnWfo+edJ7NsHlkVg3Tpijz+OUe3OAsx29zD80kuM7dqFk0ph1NZR87/8KVpZGakjR+n95rdI7PsYpRTxnTsBQe3/+Zf4ly8j03mG83/9H3FSSUpuvZXgmsk+0hf+5r8QWLuGsfd3k+44jW/ZMmKPPYp/2TKUbRN/9z2GX36Z7IULqGQKGQ5R+sD9lD3yiNuAEMjKamS0DOf8OVdcLjGwDh8Aw0SWlWF3nCqsz+4+j0om0JY0gukj/aPvkXrup2hNLYR+5w+RldWQzZLds4v0S78g8ff/lfC//r+QTS2F4++MxbE7TuH73Bfw7XiQ5A++Q/qFnyOrawn87j8Hx2bsv/w12V078T3weWSkFBkOI6NlGLfdgbFmAyIaBQT28aMkf/Q90i+/gNayFN+OBycfHKWwjh5ExUcx770fY8MmN6P+8KeknnmK1LNPo6/diHHrNoR++Wu42HwH7H4DOo8V/0A2jdr9Osow4b4nXHuTK3h/pFJu0E/4/FfWY9f0IVZuQr3zK9f+52IScVTHMTdbfSwnok+D2LjNLUR4NVi6GrF8LWqgu3hhXdtCHfkEx/S7gY+ahit7vKwspBJuvYDLZb57LDxCy2XZXnRfrRw3MKZf2fF63TGd0C3k3GdJTJepfcMw3bOFZIZFEopjz2xW7IzI27IVE4Vty51hcz2NgysdkBGaG3iwL3oeVAqsNGjX7yzIK8HJ//Q3pLq6pryePN1J9aOPzLq96+rp2tCMgiVEHikE2gwjU02l9bRXtPDh+fGbqEN9J3j3zEe0ROsJTDM1cSFYXdlGQ2kNp4fcg6dQ7L1wkH3dR6gKld+w2eh+3WRt9QqqgjF6EgOAmw392qn3uKNxCysrlt6w2eiOcjg6eIpP+4/TGKlj+5JbaCldUjjWpmawtmIFg+lhDg+c5MOeT7mpZh0bKlcBbhb7S6ffwZA6W+s2saV67STvc7/u46GWu9jTvZ9DAyd49cy7/EHpE1O2w5A6Qkj6k0P83b7vc7D/ODfVrOV3Vz9Obahy3ueeQjGaHmMgMUxztJ66yLhNTzKbJmNn8efslnSp0VBaS0PpzAtcVYXLqQqXF31PCIFf99Eaa6A1NrsIosfsEUJgYOJoQRxrDIeZ+a4pFJaTwXaypIVEE4ZbUFMYaEIreg46BcHOwnIyWDnRbi5e934ZwKe5QtxiKbh4Mab0kWQMdZk+tVQWqeSMggim9C3a/Z0rrpirJvzrYCtrxudF/nySQisESMQCzJC4UgghkGj4taAbSFIzty8CV0y3lINlW0hSuX2WaEJHCjlhdojIfd7tx3zf5gVytxaCk3s9/7mFe0Dw6z6e+vQF/unA89SVVPIXt/4+7RWtGNr0t6l+3c/KytaiwfjakkoMzSBtpUlZrog+nIpzeqiLjJ2lvbyFmnDllPuPiC/M6so2dKHROXye7ngfFcEyqkIxyoNRTgycYTg1ikJxfKCTMn+EdTUr6Ip3c2qoi5F0nMpQjKOLIhNdoZWWEnvsMVAOgz99hn7rR9T86Z9gx+P0/+AHpI4fJ3LvvRhVlWS7ziFDIRACs7GB8q9/DaUUWmmE2BNfQgiJucStPWJUV1H5B7/P0HPPkTo2VbhNfnqQoRdfouK3v0Fo660Mv/gifd/7PnX/x78hdfw4Q7/4BcFNm4h98XF6/uvfIUMhwlu3TmpDVlUjYuWuiJ5IQEkE+9CnaEsa0FrbsM93oeKj4POjersRJRFkaRlqaIDUz38ChkHwX/6v6O0rEbrh7suyFahEnPQLz5F+7SUCX/1nbsFRANtGVtW4di01dWQ/2o117DDG+k0YN2+FTBpt6TLUyDCqvw8ipYhAEN8jjwMgQmGElChAa27FGRwg+d1vY588UfzwOA7GLVvxP/4Vt98BfXk79oXzpJ59GuvTfRibtoA+g2STUAR535dwvvMf3Yz7YiTiOG+/gEjEkZ/7umt5s5A4DmqgB7V/F+rgHsT6rYjP3Av6lXu+EUJARQ2idSXq0EdFP6OOfYLYcKubqT/cX7yh0hhi+dqrluko/AHkbfdjdxyFjmkCH5kUav8unLFR5P1PIppXLHhmoUolUAf2oPa9C4Eg2mN/CD5v9tpVRwjXUsPOFrFmyLpe1p4P/TjT6QZiHvckjpPzDL9Rme4ed573ctkFrHEnBGhm8WCGlXbHiDa/emtXhGm79kqL6MK1wLGKWLpk067lmhcULZA81UHlg/ehhS+yhtz5LlpgGkuzS3BVRXQnN/U3P213VpmISjGUGqHrIg/zskCpKyzOoClD03m0/bPsvXCoUBxvLJvk+WNv0lRax53NN+cEx9lt10z2JWwG+eLK+/mb9/9nwdLlQryPZw+/Sn1JNasql84q4zG/Xls5SCEWrQgvhaSxtJZ7Wm/lRwdeKDygnxw8yw/2/4J/dfPXqA5VgJi5uJUXUBylciLA4hQ/RjNjdI6eI2ElWVHWQk0RwVqXGg3hWpojdRwZPMX5eA/rKtqRQtI5eo7e5AAhI8jGylVFi4eGjAAbKldyqP8En/QexnKsKcVB/bqPgdQQ3/n0p+zvO8rWuk380donifoiCxLAEAjKg2XEghF+vP9XjGYSRHwh+hNDvNPxIScHzvBXd/3xvOxiPBYPQgh80ofSFSkrMWMhHVzhLZ/JmnHSjM8pyF9DRa5GmZOX8grLzRWfDODXgogrYGexkLh2OSYpu0g23QRsxyI7g2ulwM1uX2yMX7+d3HEeF8MVgHLcV/NCeUG4HX8t19J4m7M4PzJO+iIrHFH4u/AdnBOWXYFdICcI7e4fOS7Ak//+FVfs/BJCoKET1MMk7Pis6gCMk+thBWDnPPVhum/eiePvavCr42+x59x+hlIjrKpYSmNpzWW/MwxNoyJYVvQ9TcjCpOD8/V7KSjOcjgNQGighoE8Vi6SQhMwgYTPISDpOPONm1GpSY3msheMDnZwY7KQyGONI/ylqQhWsq17BR+cP8vGFwwynRxlKjdAz1k99STXlweic+2S+GFVVRB+4HxA4mTTpU6eJv/8+SimS+/eTOnaM6Oc/T+TeexCahrIshOnWVtFCIXytLeixMvRYjMCqVYgJD2PC78e/rA29spLMWPE6GcE1q4l+/nNooRBCCPq+8x2y3d1kOs+AUgRWryawZjXhbduI796NXjHZukRW1SDLK7G7zqAScZRtYx09hL5qLbK+kezbr2N3nUVEozj9/WhLGhElJWQ/2oMaHEDfdBP6ytUIbfyZQ5REMG69ncx775B5+w38jz2J8I8/PInSKLKyCqHriGgMYfrQli5DmCbKsd0M+NFhVCp3nc61iW27diz5KfNCIGtq3ddSCZRtF7ZjvA8D+HY8gJzwQCcCQbTGZmS4BKev1z0mMzzeYvk6xPZHUK8/M/30+lQCtes17NNHkHc8hFhzs1tIM5cBKHL7VJRcAK3gT6sUODaq7wLq2H44tt8tYJpMQDaDWLrm6ogIJVHE2ltQh/cWXZ86tt/d7yMfT7s9Ys3NbsHZq3mPUNOIvPtRnGf+x/Q+7Zk06ug+7HOnEVvuQG69H8oqXTFdajM7XvnirXnbhdEhVMcx1IlPUUf2wsgQZNKIZWtvXCuLGaJSSTI/+T7GZx9GVtW4r8VHcM52IhtbEEE32OUMD5L53rcwH/0Ksr5xYVau+yCTmiqGObZb2DVvq7OI72OvGtP1geO4X/qz6SKl3J9skb6/kcgVeZ56W6fmNu6VcoMOF2dBzxfT746DixOFbMt93ZfzAV9U4yDft0VsaK7oanMiuqZPDQA5FqTHwB9eZH117ah4YAex7XdMEcydZHJ2RddzXFURvWesn11d+1hR3kJ1qJyA4cfUzMuKoI5SJLNJdnV9wlsdeya9t7FmJUEjMGMBdlvjJjZUt7P3wsHCdaRj+Bx/u/v7jGWTbGvcTKmvBF1Obzfg2gpYpKw0Y7kij7WXsWTRhMaO1q28cXoXH57/lLw8sKvrExyl+INNX6S9wrWsEJd4GLcdh4ydIWWl6UsOcajvBOuqVtAcvfI+fnMl5i9lR+ttfHT+IEf63em3jnL41fG3yTo2//Kmr1AdKsd3mcKYlmOTttIksim6RrvpSQywvmoF1eHLe0ZeC5JWiqH0KAAxf5RgkQd2gSBkBCjzl5J1LIbSo7nMbR/diX5s5dqTVAenycRGUBeqRgrBaCbBcCZOuT866TNj2QTfPvBjDvQdpcQM8dsrv7BgAjq4D6bNZfX85fZ/zk8OvMw/7XuesWySkBGgrbyR//zAX7CxbtWizfT0mANC4JcB0Jm1kD5OIec196C3kBsIIPBJPwE9dEX8oBcaV/T2k7JTXKozbGzs6fwgJ2BKv5tlvMj2W6EYs0bIzNBX/0ptw8TfCn9P1ednREAL5QI1V66vC0K6VkKS+AL233xCVAvHW50fsKK8BU1q7Dn/Kd/88Mf88ZavUOIrXtgQ3DFzcdD4kogJCQ+XuegoVKHAdZ6VlUt56cTbHB/sxKeZdI10s6lmFRWBMhoitSilODfaQ8bOYjk2KyuWXtMEh+yFCwz89BmS+/Zhj8Wx+gcwa2vBccj29SFME7O+Dmm6M8UuFnnni3/5CmQggNA0ZCjkaq7JJHplBU4qTfrkCbTSCMnDRwisWDFleVEaRVZVY+3fixobwznTgRoeQl+9DlleQfqXz2B3nkZm63AG+tBXrUGES7DPnAal0Jtbi+6TVluPDIWxTh5zs7YnPPgK0wQzN2tJd4uciVC4kM0lhHTFyZxIrbJZnO4LZHe9Q/ajPTjnz6LGxlCZjOtzDtMXq9N1tIbmqa+bPtA01CysBYQQKF8AeefncYb6UXvfmd6awMpC1ymcp74JLz+NaN+IaFuDqG1ETRDU3W13xkUm24axEVTfBeg7j+ruckXzgR5X6FNzFGPmi2EiGtqguqG4x/jIIOrsKdTxA9MuL9feMj4j4SohpIQNW5FDfTgvPQWJ0eIfdBx3H974Bfa7ryBWbkSs2AD1LVASHS/GlxdxJhZeTCfd49V7HtV7DnXmBPSchUzmxvZ/vgQqk0aNjoBSrq1ROIyQmlt4NpXCuPchRFnMTVbLZrFPn8B6+1WMzz6CqKhyhXTHQcVHUMODOD4321NEou64SSYKQTYRDIHfjxDSXad06yUgBCIYRvgnPBcK4QqIqTGmXC+sDCDAFxj3R5/tvd3Fx3qR3RvOikJh3Iv2KZ/Jr8TM9i9/bc6m3J8bmYnFhCfiOK7gqvSZnxNKuX2dHlv4a4iQ7ndgpkhCUf4YmcHx76jFMA6ELG5DY2W54oWBpZYrHm1NDQJZaUhL97rBDMdEMSZ+t1/Hme1VDz3oXqsv6oeK+3bMqW+uqog+mh7ju/ue5Xy8l8/Ur+emurW0V7RS6i8hoPswNQNdulP7lVJYjk3KSjOQHGbn2b08c/gVesbGp+KFjAB3Nt1M2Jy550/YDPKnt3yDv3rzb+kcPl94vWu0m/9759+ztWMj9y3dRnO0nqARKIjpjnKwbJu0nWE4PcrJwTN80n2Ujy58ypdXP8Q31v/WJdcrhCAWKOUPN3+J//D2tzg9PG7rsvvcJ5waOsODy+5kW8NmygNRDM1AE3KCF7BFys4wmBzmaP8pPu4+zJ5zB4j6I/zb7f9qUYvoQghWVSzlyTUP8s09P6Z7zM24UCheObmTYwOn+dKqB1hbtZywGcx5tspc1qpD1s6SsjL0jPXxSfcR9l44xKG+k6yrXkFLaf2iFdFt5RTELk1oiGk8xyQSPTc7I+tYuaJsFDyPhRCX9DU2coU4FG5fXcwbZ3bh100iZpiRTJx/Ovo8v7f6i5T6Suaze5PQpcbq6mWsrl62YG16LF7cbF3wawEkgqSdwJ5Fkc8rjUDi0/z4tWBh5tP1gJ7zMLfV/DM7DGl6gasbDCEEutAJijDCkmSc9CxrAyxeHl5+N3+4+Ut82nucf/fr/8aLx98m5i/ly2sfImxOL6TPhqDup8zvWlgMpoZJZFNTRHrbcRjNjBHPJFhmhghNqN+xsqIVKSTH+k8T9ZVgaDotZUsQQtBYWuvavQyeoXtsAKUUK69wrZ1LoZSi55vfwh4aou6v/hKjtpaBp59m5OVXARBSc8Vg+3IBublfQ6Q/NxNGiEnNBNesJd7USN/3vo/+3PP4lrZQ/rWvTl2zEGh19SjLQo0MY3V2oBToq9aCbSEMA/tsJxgGztAg2pImV/DOZADlTv0uhm64D4KZzOSMbXc6ytTA4zQPjcpxsPZ/TOKb/y925ym0phb0tRuQldWIQBDrxFHSzzw1bf8IISeLaJO2g1kH84QQUBpDPvAkTjbt2psU89vOY1vQ343a+SJq54vuw3goAiURV8wTwl0+k3YzAFMJSC8+oUkIgYpVIdo3oIqJ6I6D+ngn9HcXb6BlJVTVu2PiKiOkRGx/GDU6jNr5K0gWn9UBuAJJKoHauxO1d6d7fAIhV0j350Qlyxo/Xumk295voFA+LY6D9d5b2Ht3QzYLFZUY9z6I1tCCc74L6+1Xsd55Hf+/+Q/IJU04586Qfek5nEMHUPE4WnMb+ra73IBLNkP25edd//p0CvPRryLKK7HeegWn8xQqm0VbvR799nsQkSjpZ36I0HWcvl6E6cO4/W601RvGt00I137ByhS3FbHS7jlg+HPXthmIiHmROB9YsW33d8PHfK7t1xThzhZE06YGCh3b7ScjMP7Z6cj3STZVvJ7CjUbeMihTzHc/4wbj5GWE9EJANQvpxPQFXueLGXC3qVj7+RkDRiBnb3WZrPT89S8/FvJBAyFBX6AaGIUCnxc/Byu3n3yh3H3EFRhzQrj7YeQy9SfeOCjlBiOU7fZXfibL5QTjSdeN3PHOZtzND5Yu/D5cJYSmoZTCSafJDgxOuv/VS0unFBy9HNfEEz1lpXmzYzdvduwmoPupj1RTF66iMlhGyAxgaia2YxPPJjg/2svR/lMFP+08ft1kR+ttbKldgzkLfyQpJMvLm/n9jV/iWx/+iK7R8ZuqjJ0tbFdtuJL6kmpCZgBD6qTtDCPpMXoTA/SNDZLJCZymnPm6Namxtmo5f7T5Cf7+o6foGO4qWLv0Jgb57r5n+dGBF2gtW0LMH8Vv+NyifFl3CnL3WD/DqZGCyAoQ9S+wp+EVwqeb3NV0C8OpUX504AW6x/oL+W+nh7r4z+/+AyW+ME2ldUT9JZiaieVYuSDKCN1jfYzkpmFfL5jSKEwXT1oprGmm9WQdi6TtPugEdB9G7ka+xAgjENiOQ8JKAMWz0UezCRQKXWgE9amZNCEjyAPNd1ATrOAfD/+ct7s+oCpQzm8t3TGrAJSHx8UIBD4tgBQaSWts1n7NVwJN6Pg0Pz4ZWLQ2V5fCJ30k5jk9Mi/GL7YsdI+FQRM6IT2M5uik7eSiCmDNleXlzQQNPzfVreWPNj/Bf9/zTzx75DViwSj3L72dkDn/LNFSfwmt0SX4dR+H+k5yPt5L2AwWxolCMZweZX/3UWzl0FBaR/WEehxNpXUEjQAnBs4QC0Qp80dojroe4Y2ltcQCpZwaPItf96GAFeUt897mOWPbZM+fJ7hxI3plJdbAAIl9nxTeNuvrQClShw7jW7oU6fdjx+NokQgyN61V6DrS78OOx3HicYTPhzCMSbYuc8FJJsj29hLavJngpo1IwyTT1UWgtHSK57OsrUeWRHB6urFPHEMEXbsTp+cCsrIa58I5V4h2HNdD3TQR0TJA4IwMFV2/GhtFZbOuDYs2d3sENTpC+uUXsA7tx/+FLxH4xh8gyytACFQ2C6/+6pIi+hVBCERlHfKhr+L4/Kj9H0ByhvfOjg2jg+7PdYYIRxAt7agP3oCxizK6lYP68K3iC0qJXLkRSq6hMCAl8v4v4UiB2vUaDA9cfhlwxY1E3P3xmBEqHif7y59iPv41lONg79uD/dFutIYWtMYW5CNPYp84CrgBDq15KeZvPUlGPoXvt/8FMuYmbTmD/aixMfR7HkLfcivWGy9i7XwDrX0N9sljGHc/gOrvxf5kD2rZSohE3WDO8BD+P/5zhKYXD24IAb6gOxaLCYh21v3RTfenYO9y0TVsogjmWK64l89U1c1codIF7dqri5TTe2fnBXHdN277MdFqozBTw3Z9o62Jgcb8Z2+M5IQp6GZOhJ5mpoPhz30nXvwdP2F2i5Vx+21iH0lt4QR1IQDpCs+pePFjYWXcLG/D53qkS3npcZAXzvNjQSlXqNeNqcvMaZulux1WZmrfZtPuOoz8+TixbycK/BN+l8WOwWXWb/hzQaSLn8NV7jzP95c+wQ5HTP5cfoZmfnxMvG7A4vSjnyXKthl48y1G9n1Cpr8fze9HWRZVD3+O6C03z6qta15YNGmlOD7QwfGBjhkvU2KGuK1hE19e8yCVoeJ+mJfCr/u4q/lmlHJ4+uCLHB04TeYi0eJ8vJfz8d5Ztz2Tdd/RtAUpJD/+9AUO9p0gNeECnrYzHOo7OeP23CFwfXwTlvpLeGTFPZiayc+PvMaJwTMFYVkBI+k4+3uOzri9S3oBLgLCZpCKQBlSSC4kehnNjk0pgKZQjGTi9CT6C7YuRi4wU19SjU8zSdsZOkfP0VI6tXCmQnF6+CxKKcoDZUVF8Q2VK7mv6XYqgzESVorvHfoZL5x+k5i/lLsbbsWvLz7fZI/rC0OaSF2SchJk7DTONciOFQgMaeKTfgzpu24FZFP6SNpj8zLZMKR5XQYQPGaOEJKAFkRDI+OkyDqZazLuLoVAosvZPaSYmsGO1q0MpIb44f7n+fGnvyLqj7CtYRO+eWYNmZrB2urlrK1azv7uo7x8YiemZlBXUoUmJP3JYd7p3MMbp9+nNlzJ5trVxALj4ppPN2mO1nGw9wSl/SXEAlEaI26R7KpQOVXBcg71nYCcTduSSPW8tnc+CF0nuGkTqUOHGPzJT3HSaezBQZDusfAtW0Zw0ybGdu/G6u9Hi0SwEwliTz6BjMUAkMEg/hXLGXn1Nfr/8QdosRiRe+9Bj8VIfLyP1JEjJA8cwOrrY/CZZzEbG/C3t6OFLjFzwHFInzqN0DSyXV2M9PYCAicxRvWf/RmBle2TPq7V1iNKo9gXzrl+6K1tYBiIUBitsRn7bIfrNx6JIEqjAOjLV4KmYZ84hkomEIHJ90X2yROokWH3c3PwwSzsSn8fTs8FhM+Pse2ugoAOoFIpnK4zc24bmPuzvRCImkbkfU/glJS51i6DvTd2RrKUiOol0LgMihUYtaYJTJdVQuMyhO/qWrlcjDD9yHsfQ5VEcXa9BudOT+9r7zFnnIFe1OgI9uFPXb00UopsnFuwU4RLkEuaQDcQZeU4Bz9B9Pehei9gH9wHgGxsdW1ecEV5uawdkZ8hM909qtRdATE9Nr0wmc9Wz2dlX3y/pybULbgRyWffWpkivtO57F8rm8uunhCYLWTk5wTVif2TnwmQ90e/EdENN/hgFZmhZKXdftGNyd77ipyoahW3DNEMt99S09hRzYV81rwv6B7LokGNCTY8QoAoEhDPC/9X2rQw700u9eIe8dnUeLb/JD93Nd6/+XNT5gII2iyf4TTdtbmB4jNZlDNukZO/ZhSE9Lxdi7o6/XUNUZkMXf/4Q2J33k6ys5PIhvWMHTqCnZz9bJSrKqJH/SXcVLeGlJXmQrx31ofIr5ksjTWyrWEz97Vto6m0bs5CQdgM8tmlt1FXUskrJ9/lg3MHODtygewsigBIIWgoraF+lg9KQSPA9uabqQ6X88rJd9nV9QkdQ12zWrdf97G0rIGb69ZSs0jtTIoR9Uf4rRX30hCp4bVT77Pn/H66RnpmLBgJBBFfmLZYI9satxDzL95pJX7NR0tpA5WBGEcGTnF65CxVwfJJBUKT2RSHB09wLt7D0mgj9eGagvhX4S+jPdbK3p6D7Ok+wKaqNZRcNK29a/QCB/qPIIRgS/XaouNBm+D/tKPpNnqTAzx38lWePfEqETPMLbUbihYt9fCYDVJqBEUYXRiknTSWk71qVhO6MDA1H6b0XdL6aLHjFquUGNJHxpnbTbzMCZfXaxDBY3aYmg9dGgUhPXsVx11xBJrQ0IWOLg30OdgKhX1BHllxD6PpMZ49/Co/3P8LImaIjbUrZ+eBXoS2WBOPtu/gf378U352+GVODZ1laVkDutQ5H+9hd9d+UlaGx1fdzy3166Z8p64ob+H9s/s4PtDB7Y2bifpdWzRTM2gorWVX1yeMZuJsqlk9q1mSV4LYY48y8uavyXZ1YdTWUv0nf0Lq2FHIFQ4te+RhzNpaUkePYPX3o1dWFrLQwRXiw9u2gdRInzoFQ0M5OwCw+vvJdnfja2nB19JCuqMTGQq6dhJA6YMP4GtuLniSG7V1RD//OYTPx8gvnseoraXmL/4cLRzGSaU5+6//d8Z27ZoiosuqamS0DPvkcewzHRi33u6KxKEQWlMLmQ/eQwwMoNXVI/Mi+oqVaMvbsY8dIf36y/ju2oEIhtzM0zOnybz9Omosjrn93uJ2KjNEGAYYhlvweGTILQKq66hsFmvfh2TefnPObc8bIRBV9ch7H0XVNKA+eht18tDVsS0QAiJlUBq7uv6p5TXI5hU4x/ZPL5pfhGhbg6ioufYJOUIgAiHYeh+yeglq9xuoo/tg5CrNCvAHobL2uva7nQkiXIKIRDAeehQZq0A59uWDFXmhOnvROaVfZH2haYhIBNmyDPMLX0GEwu6MlMLsmpzQdtmNzAnE5KwYLlUbQSnXqmFOdYmuZ4Tbr6Z/epE1n7U/0/YMf85GJJvLKL4Rg1g5X33HLl70UtmQncW5pBngD7njYyGz0SGXXZ1L8sskL922UnCtZ2RKPVfTwC5+7uRtc2bCXDVs3QARBETxQMnEbbkhz+8ZkCv8HrvjdrL9A9Q8/ijdP3uW7ODQrJu6qmpDeTDK19c9wpa6NRwf6OT0UBfdY/0MpUYYScdJWWmyjls0TZMaPs2gxBeiJlRBfaSa5bFmVle2sby85ZLFpmaKX/exuXYNzdElbGvYzMG+Exwf6OTsyAV6E4OMZuJkbQshBIbU8es+Sn1hYoFS6kuqaYzWsaK8hXVVUwsiXQ5TM1hf3U5jpJatSzZysPc4RwdO0zl8jt6xQeKZBFlloQmJqRmEjABRfymVwTIaIjU0l9XTVtbEslgTpf7pva2FENzasGHSVOiwGWJZefNcuowvrrqfO5tuKoje1aEKygPRWbURMHxsa9zMslgTn/Zu5kDPMY4MnOLcaA+DyRHGsklsZaMLDVMzCJshYoEIVaEKWqL1NEfrWVrWSHO0flHbkQghaI+1sqV6La927uS5k69hOzarypcR1P0MpofZ032AlzvewZA6N1evp7GktrC8FJLPt97N4YET7OneT02ogltrN1EViGEpm87R87x4+i3OjJ6nObKEO5dcfhqKX/Px8NJ7GMmM8lrnezxz4hUiZpg1FSsuW+DXw+NSiNw0SFP60aWRE/QyWE72imTICtxaAYb0YUgDXdwYwrFbYHTuIrouDTS062aGksf8kULikwEMYZKVWSwni6Uy2OrqPVhrQkcTOrrQ0aT7r5hjQV+BoCJQxuMr72MkHefVk+/xj/ufI+ILs6y8aV7b6dd93N60Bb9u8sqp99jffYR3Oj/EVg5l/gjLy5u5o2kL25tupjwYnbJ8e8VSbOWQsbO0xZom7V9rdAmG1EhkU7RXtFzzMWjU1FD+5BOTXguuX1f4vx6LUXr/fZTef9/0bVRWUvbIw1NeL91xL6U77p12udjjj0363dfYgK/xCayBQZxkslBwVClF5vw5rMEBjOqpCSnCH0DW1JF581XUyAj6qjXuG6YPWVMLiTHsnm6MzTcXMtFFMETgq79D4v/5TyS/+23so4eQVTWoTBrr0KdYB/dj3HYnxtY7wJj77AZZXonethzrw92knnkK5/w5RCjsFho9tN+1lgnNzmNzEguQDCbCpYgt21H1LajDe1GHPkKdOjxjkXlWGCbUNCBaViKaliOWrpqcBXqFET4/qqENyquh++zlF/AHES3truC/SBCmD7FiPaqqHnV4DerQh6gTByE+fGVWWF6NaF3lHrPm5dPXEbhBkGXlaBs/Q/YXP4FwCSIURl+7EbGkCfvoQexD+1EXurDeegW1Yg3als8gwhFkpJTsC88gl7VP9jGfgNANZMsynNMnyPz8R+DzI6vr0NdvgcgsE76EGLcjyaZvYFF3Hgjp9lHB93mO/SOkK54b/py3tZPzt75B+1vqboZ3JjmLIMPF5AI9Zr7QrXIF9YX2SM8L6flxYBexS1ks5INfPnV50f9KohngE+45bKUXcDvEeFHj6xkpCTY3g1I4mTQdf/t3ZPr6KNt226ybuqq9IYVkSaSG+pJqtjVspjcxyHB6lEQmScJKkbUtbGXjKIUUEkNq+HUfUb87bbYiGMXUFqgIQA4hBBXBMm5v2sLmutX0JgYZSAwxkhkjaaWwcxFqXWoYUido+HOibimxQBSfNj/hpixQytaGjWysWen6rScGGUmPkbLS2MpBCoEuNXyaScgMUmKGqAhGifojuazFS69bCsn66nbWV7df8nMz5bNLZ3+STUd1uILqcAU31a3lQryXwdQI8UyCtJ3JnQPjwYuQGaTUF6YyGCPsC17Vh9NTw2fZfWEfSTtN2kpzbqyHwdQItrJ5/uTrfNx7CJ9m4td9rI61sa5yvK/L/WU82HwnI5lRPuw+QF9ykNpQFT5pMGYlOTt6gaSV4u6GW9m+5OZJRcwA1la088Tyh/jJ8Zd49virfNRzkFIzjKMc+pKDdIyeozpYwdfaH6EqUNwzfSJCCMr9UR5ZuoPhTJw93ft59sSrhM0QrUXsYjw8ZosQAg0dKTUMaWI7FpaysJwMlrLmZVOSF851qRd8vzWhu9NybxTRWIwLknPxutalibyOiql6LAz5AtRSaZjSxFZ+LGVhO1n3X5UrKLZASKGhoaFLHSl0NKEhhYachXAeNoN8ec1DbG++mfXV7ZNmkQghqI9U88/WP8pnlmxACplLGHBH+p/e8g2ytkV7RWvRtpuidfzF1t/DVg4hY7JVQ9gMcnvTTbSVN3N2+AIj6TgO7udqw5U0lNZOa3O2sWYl/377n+DTfay6qHDo+pp2/nzr7zGaGWN1ZZtnqVQEraSE8G1bGXntdbr+7b9DSA2lHIIbNhK6uUgigBBoSxpRmQzC50Nrasm9LBDRMkSsHAYHkNU1iPC4YG1suongH/8Z6Zd+SeadX6NSSddOobIa/8OPY+54wPVQn8f9uwgGMT/7ICqbJbvz16Se/iGYBrI0hrH1drTmVpLf+/ac218wdB3RsNQtnrliA6rjKOrYflTHURjomV/bgRCirgmWtCHqmxFV9VBZhwhHrn5WsxCI+hbEklbUTET02iZEXfO4vcZiQWqI8mq4+S5E22rU2ZOoU4dRJw+6Ni+XLQZ8CUwforIW6loQ9S1u0KOmAVFWcVUDHtcMTcO4/xGc44dQlu1aPYXd2mKiJIJsbMF84ncQwZB7bUEgymLod9+PunAOEY0hTBN0A+NzX0SWV7qe9kuXY0RjyLoG9HseRJ3vQjmOa/FkurqFfttdiEtZXV2MEDm/Z80Nbsw3Q1pItx3dd+1nXiwUUo6L33nf5xnf5+QsQ/KFWvPf11Jz27tRk/vzYq+Q4zYjszmnNN09hyZ68ivcPrwSNjj5AqBSAztn4TMfMV1q7rjSTRbED30ik0T/1CzPx0Ij898OqeU83/Xx68ZcxfRCfxk3hIguTZO6r38ZX3U1lfffx8i+TwgubSWyaeOs2xJKLdaQjofH4uDVznf59v4fkbYzWI6NrexCcVdNSDShoeWCLJ9vvZvfXvXopOVtx+bcWA/vnd/L++c/5vTIWVJ2mohZwrJoM7fWbmRL9RoqA7EpD3RKKcayCT7uPczbXR9wcOA4g6khpNCoCVWyoXIl2+o2s7K8DV1oheWzdpbvHXqWHx/9Jfc338E3Vn6BisB4to3lWHzaf5zvHvwZJ4Y7uLfhNr604kGqg9ePNdBccJSNdRlhUiBcgWieQmTWyVxWMJbIeRWAVEq5gcfL3O3lxedrIeoolFsNWzkoHGxlYTkWDjaOcnCUk//UpO0VCISQSCELIp2WE+mkkHPOcr0ecI+rNacMfl3oi1pEn8++LVbc81JbdOejUgoHB5Ube5aysR3LHXcTXgemXKvcESgLFkNSSDQhc0K5u6+uYC5nFND38Mhjj42ROXMGq78fHIUMBDAbG9ErK4qOIae3B/vMaXAU+vpNro0K4IzFsU+fRI3F0Zpa0aprxhdSCmXbON0XcPp6UOkUQmquUFZTiyiJTCqQ6sRHsQ4eQJZXoC9d5m5n1xmcc11orW2IWAxsG/vUSVQygdbShiwpccfQ4ADOhfOoeBwlQIYjyLp6hGFgnTrh+rc3tYwXsM2ksQ7sA+WK/RcLWnZvN86ZTkQkgtbUWtjfhUJlM27xyuF+VH836nwn9J5DDfa5RUWTOT/hvD+u6XOFAV8AUVLq+ojHKhGxKkSsys3kDkfdrPuZ2FVcSWwb1dOFGrx8TStRUuoGFq6xH/plsW3U2AgMD6AGeuH8adSFM+4+jgy6hUWzGddKSUp3RoDpA58fQhFE/niVVSHKqyASc4XjcCnCMBdEUFWpBJw9hcoUF9FEMAzVS1zLmiuAymbcIMM0AQYRCLlBE/8iP9bTUSiGOcGb2rFd+w0n52WcJ+9zLOW4zYamu57R+ddmeszzBQanSEU5a5r5nDtK5axqLmpb5DJeZ9O2UuMWJfbE/pnQN3lbHqmNC4OaNtVLO9/WRGE5v9xc9ne6IrH57bhWTCywamfH++xiT+wpfaZPLeSpJhSxHV9wmiKlC7TNE8eB40w+1jB5HMjccS5s+yzHwVy307Ym9K0DXFSnYGJNg0If6+Pe6QuyHTnLJ9sGJ5v7N2/nMnHs5Yrq5oNIUnPH4UL11yIcB8qysVNJhK6j+WYfXPREdA+PyzCcHqUn0X9ZQVQgiPoiVAZjU95TSpGwUoxkRt0ZDspBFzohI0DEDF+yuKdSCkvZDKdHGcsmyDqWa/mgGUTMMCVmaIo46ijFQGqIgdQQpb4Syv3RKV6yGTtLf2qQ0cwYIcMtgupb4JkeHh4Xo5Qi/weVH1XFxpYoFA/O/7mhMs49PK4qyr2XntHYyzNxDOZ/z/1vkQUMPDw8ZolSKNuCdAoyOcsIK+vaGOQLnYm8CDFBjDPMwo/QjRsnq/U6QDmO62ufzmWQWta4QDPxeIncMdN0t3iuYYLhc4/XDe57fsOTF8YKxQBhihgGE8ZlThz7TRinl+ybCf2SF1h/E/rkckzsr0v12WI7j/JCdf7/7n8mfOCicXAtjvmsxqrI/XqFtnEmY6OwLXhjZAZ4IrqHh4eHh4eHh4eHh4eHh4eHh4eHh4fHNHjhaA8PDw8PDw8PDw8PDw8PDw8PDw8PD49p8ER0Dw8PDw8PDw8PDw8PDw8PDw8PDw8Pj2nwRHQPDw8PDw8PDw8PDw8PDw8PDw8PDw+PafBEdA8PDw8PDw8PDw8PDw8PDw8PDw8PD49p8ER0Dw8PDw8PDw8PDw8PDw8PDw8PDw8Pj2nwRHQPDw8PDw8PDw8PDw8PDw8PDw8PDw+PafBEdA8PDw8PDw8PDw8PDw8PDw8PDw8PD49p8ER0Dw8PDw8PDw8PDw8PDw8PDw8PDw8Pj2nwRHQPDw8PDw8PDw8PDw8PDw8PDw8PDw+PafBEdA8PDw8PDw8PDw8PDw8PDw8PDw8PD49p+P8B1wJs53KixowAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Most common words in positive reviews:\n",
            "  film: 49652\n",
            "  movie: 44782\n",
            "  one: 28251\n",
            "  like: 18188\n",
            "  time: 16273\n",
            "  good: 15055\n",
            "  story: 14140\n",
            "  character: 14016\n",
            "  great: 12950\n",
            "  see: 12899\n",
            "\n",
            "Most common words in negative reviews:\n",
            "  movie: 58328\n",
            "  film: 43683\n",
            "  one: 27126\n",
            "  like: 22925\n",
            "  even: 15249\n",
            "  time: 15104\n",
            "  good: 14713\n",
            "  bad: 14667\n",
            "  character: 14317\n",
            "  would: 13759\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Prepare Data for Modeling**"
      ],
      "metadata": {
        "id": "6Rz8LiWjyN0a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score, f1_score, precision_score, recall_score\n",
        "import time\n",
        "\n",
        "# Convert sentiment to binary (1 for positive, 0 for negative)\n",
        "df['label'] = (df['sentiment'] == 'positive').astype(int)\n",
        "\n",
        "# Split the data (80% train, 20% test)\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    df['processed_review'],\n",
        "    df['label'],\n",
        "    test_size=0.2,\n",
        "    random_state=42,\n",
        "    stratify=df['label']\n",
        ")\n",
        "\n",
        "print(f\"Training set size: {len(X_train)}\")\n",
        "print(f\"Test set size: {len(X_test)}\")\n",
        "print(f\"\\nTraining set distribution:\")\n",
        "print(y_train.value_counts(normalize=True))\n",
        "print(f\"\\nTest set distribution:\")\n",
        "print(y_test.value_counts(normalize=True))\n",
        "\n",
        "\n",
        "test_indices = y_test.index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kjQ4d1Oetn0X",
        "outputId": "0b3aa99e-7005-42c0-96ca-2530f07de4e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training set size: 40000\n",
            "Test set size: 10000\n",
            "\n",
            "Training set distribution:\n",
            "label\n",
            "1    0.5\n",
            "0    0.5\n",
            "Name: proportion, dtype: float64\n",
            "\n",
            "Test set distribution:\n",
            "label\n",
            "0    0.5\n",
            "1    0.5\n",
            "Name: proportion, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**TF-IDF + Logistic Regression Model**"
      ],
      "metadata": {
        "id": "CR8epot6ypwU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create TF-IDF features\n",
        "print(\"Creating TF-IDF features...\")\n",
        "tfidf_vectorizer = TfidfVectorizer(max_features=10000, ngram_range=(1, 2), min_df=5, max_df=0.7)\n",
        "X_train_tfidf = tfidf_vectorizer.fit_transform(X_train)\n",
        "X_test_tfidf = tfidf_vectorizer.transform(X_test)\n",
        "\n",
        "print(f\"TF-IDF training matrix shape: {X_train_tfidf.shape}\")\n",
        "print(f\"TF-IDF test matrix shape: {X_test_tfidf.shape}\")\n",
        "print(f\"Number of features: {len(tfidf_vectorizer.get_feature_names_out())}\")\n",
        "\n",
        "# Train Logistic Regression model\n",
        "print(\"\\nTraining Logistic Regression model...\")\n",
        "start_time = time.time()\n",
        "lr_model = LogisticRegression(max_iter=1000, random_state=42, C=1.0, class_weight='balanced')\n",
        "lr_model.fit(X_train_tfidf, y_train)\n",
        "lr_train_time = time.time() - start_time\n",
        "\n",
        "# Make predictions\n",
        "lr_train_pred = lr_model.predict(X_train_tfidf)\n",
        "lr_test_pred = lr_model.predict(X_test_tfidf)\n",
        "\n",
        "# Calculate metrics\n",
        "lr_train_accuracy = accuracy_score(y_train, lr_train_pred)\n",
        "lr_test_accuracy = accuracy_score(y_test, lr_test_pred)\n",
        "lr_f1 = f1_score(y_test, lr_test_pred, average='weighted')\n",
        "lr_precision = precision_score(y_test, lr_test_pred, average='weighted')\n",
        "lr_recall = recall_score(y_test, lr_test_pred, average='weighted')\n",
        "\n",
        "print(f\"\\nLogistic Regression Results:\")\n",
        "print(f\"Training time: {lr_train_time:.2f} seconds\")\n",
        "print(f\"Training Accuracy: {lr_train_accuracy:.4f}\")\n",
        "print(f\"Test Accuracy: {lr_test_accuracy:.4f}\")\n",
        "print(f\"Test F1-Score: {lr_f1:.4f}\")\n",
        "print(f\"Test Precision: {lr_precision:.4f}\")\n",
        "print(f\"Test Recall: {lr_recall:.4f}\")\n",
        "\n",
        "# Detailed the classification report, I mean which model has better performance\n",
        "print(\"\\nClassification Report:\")\n",
        "print(classification_report(y_test, lr_test_pred, target_names=['Negative', 'Positive']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c1QDnD_pysQh",
        "outputId": "4aa01540-fc92-4ab5-d59b-893b40e88aaa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Creating TF-IDF features...\n",
            "TF-IDF training matrix shape: (40000, 10000)\n",
            "TF-IDF test matrix shape: (10000, 10000)\n",
            "Number of features: 10000\n",
            "\n",
            "Training Logistic Regression model...\n",
            "\n",
            "Logistic Regression Results:\n",
            "Training time: 0.37 seconds\n",
            "Training Accuracy: 0.9251\n",
            "Test Accuracy: 0.8909\n",
            "Test F1-Score: 0.8909\n",
            "Test Precision: 0.8913\n",
            "Test Recall: 0.8909\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Negative       0.90      0.87      0.89      5000\n",
            "    Positive       0.88      0.91      0.89      5000\n",
            "\n",
            "    accuracy                           0.89     10000\n",
            "   macro avg       0.89      0.89      0.89     10000\n",
            "weighted avg       0.89      0.89      0.89     10000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Hyperparameter Tuning for Logistic Regression**"
      ],
      "metadata": {
        "id": "WppowUKZyvbx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "# Define the parameter grid\n",
        "param_grid = {\n",
        "    'C': [0.1, 1, 10, 100],\n",
        "    'penalty': ['l2'],\n",
        "    'solver': ['liblinear', 'lbfgs'],\n",
        "    'class_weight': ['balanced', None]\n",
        "}\n",
        "\n",
        "# Create GridSearchCV object\n",
        "print(\"Performing Grid Search for Logistic Regression (this may take a few minutes)...\")\n",
        "grid_search = GridSearchCV(\n",
        "    LogisticRegression(max_iter=1000, random_state=42),\n",
        "    param_grid,\n",
        "    cv=3,\n",
        "    scoring='f1_weighted',\n",
        "    n_jobs=-1,\n",
        "    verbose=1\n",
        ")\n",
        "\n",
        "# Fit grid search\n",
        "start_time = time.time()\n",
        "grid_search.fit(X_train_tfidf, y_train)\n",
        "grid_time = time.time() - start_time\n",
        "\n",
        "print(f\"\\nGrid Search completed in {grid_time:.2f} seconds\")\n",
        "print(f\"Best parameters: {grid_search.best_params_}\")\n",
        "print(f\"Best cross-validation score: {grid_search.best_score_:.4f}\")\n",
        "\n",
        "# Use my best model\n",
        "best_lr = grid_search.best_estimator_\n",
        "lr_test_pred_tuned = best_lr.predict(X_test_tfidf)\n",
        "lr_tuned_accuracy = accuracy_score(y_test, lr_test_pred_tuned)\n",
        "lr_tuned_f1 = f1_score(y_test, lr_test_pred_tuned, average='weighted')\n",
        "lr_tuned_precision = precision_score(y_test, lr_test_pred_tuned, average='weighted')\n",
        "lr_tuned_recall = recall_score(y_test, lr_test_pred_tuned, average='weighted')\n",
        "\n",
        "print(f\"\\nTuned Logistic Regression Results:\")\n",
        "print(f\"Test Accuracy: {lr_tuned_accuracy:.4f}\")\n",
        "print(f\"Test F1-Score: {lr_tuned_f1:.4f}\")\n",
        "print(f\"Test Precision: {lr_tuned_precision:.4f}\")\n",
        "print(f\"Test Recall: {lr_tuned_recall:.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DOAoTZ0yyyWZ",
        "outputId": "8ef2d4b5-3330-45c7-a65c-0638036b6ff6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Performing Grid Search for Logistic Regression (this may take a few minutes)...\n",
            "Fitting 3 folds for each of 16 candidates, totalling 48 fits\n",
            "\n",
            "Grid Search completed in 59.35 seconds\n",
            "Best parameters: {'C': 1, 'class_weight': None, 'penalty': 'l2', 'solver': 'lbfgs'}\n",
            "Best cross-validation score: 0.8925\n",
            "\n",
            "Tuned Logistic Regression Results:\n",
            "Test Accuracy: 0.8909\n",
            "Test F1-Score: 0.8909\n",
            "Test Precision: 0.8913\n",
            "Test Recall: 0.8909\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Confusion Matrix Visualization**"
      ],
      "metadata": {
        "id": "-k7t-epvy0kN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_confusion_matrix(y_true, y_pred, title):\n",
        "    cm = confusion_matrix(y_true, y_pred)\n",
        "    plt.figure(figsize=(8, 6))\n",
        "    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',\n",
        "                xticklabels=['Negative', 'Positive'],\n",
        "                yticklabels=['Negative', 'Positive'])\n",
        "    plt.title(f'Confusion Matrix - {title}')\n",
        "    plt.ylabel('True Label')\n",
        "    plt.xlabel('Predicted Label')\n",
        "    plt.show()\n",
        "\n",
        "    # Calculate additional metrics\n",
        "    tn, fp, fn, tp = cm.ravel()\n",
        "    print(f\"True Negatives: {tn}\")\n",
        "    print(f\"False Positives: {fp}\")\n",
        "    print(f\"False Negatives: {fn}\")\n",
        "    print(f\"True Positives: {tp}\")\n",
        "    print(f\"Accuracy: {(tp+tn)/(tp+tn+fp+fn):.4f}\")\n",
        "    print(f\"Sensitivity/Recall: {tp/(tp+fn):.4f}\")\n",
        "    print(f\"Specificity: {tn/(tn+fp):.4f}\")\n",
        "    print(f\"Precision: {tp/(tp+fp):.4f}\")\n",
        "\n",
        "\n",
        "plot_confusion_matrix(y_test, lr_test_pred_tuned, 'Logistic Regression (Tuned)')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 696
        },
        "id": "sXB_P9vZzHNy",
        "outputId": "71c573c3-ac11-4343-b942-a69ca2d25c9e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "True Negatives: 4374\n",
            "False Positives: 626\n",
            "False Negatives: 465\n",
            "True Positives: 4535\n",
            "Accuracy: 0.8909\n",
            "Sensitivity/Recall: 0.9070\n",
            "Specificity: 0.8748\n",
            "Precision: 0.8787\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Feature Importance Analysis for Logistic Regression**"
      ],
      "metadata": {
        "id": "RPr4A01jzP1l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Get feature names and coefficients\n",
        "feature_names = tfidf_vectorizer.get_feature_names_out()\n",
        "coefficients = best_lr.coef_[0]\n",
        "\n",
        "# Create dataframe of features and their coefficients\n",
        "feature_importance = pd.DataFrame({\n",
        "    'feature': feature_names,\n",
        "    'coefficient': coefficients\n",
        "})\n",
        "\n",
        "# Sort by coefficient value\n",
        "feature_importance = feature_importance.sort_values('coefficient', ascending=False)\n",
        "\n",
        "# Plot top positive and negative coefficients\n",
        "fig, axes = plt.subplots(1, 2, figsize=(15, 6))\n",
        "\n",
        "# Top positive coefficients (indicative of positive sentiment)\n",
        "top_positive = feature_importance.head(20)\n",
        "axes[0].barh(range(len(top_positive)), top_positive['coefficient'].values, color='green')\n",
        "axes[0].set_yticks(range(len(top_positive)))\n",
        "axes[0].set_yticklabels(top_positive['feature'].values)\n",
        "axes[0].set_xlabel('Coefficient Value')\n",
        "axes[0].set_title('Top 20 Words Indicative of Positive Sentiment')\n",
        "axes[0].invert_yaxis()\n",
        "\n",
        "# Top negative coefficients (indicative of negative sentiment)\n",
        "top_negative = feature_importance.tail(20).iloc[::-1]\n",
        "axes[1].barh(range(len(top_negative)), top_negative['coefficient'].values, color='red')\n",
        "axes[1].set_yticks(range(len(top_negative)))\n",
        "axes[1].set_yticklabels(top_negative['feature'].values)\n",
        "axes[1].set_xlabel('Coefficient Value')\n",
        "axes[1].set_title('Top 20 Words Indicative of Negative Sentiment')\n",
        "axes[1].invert_yaxis()\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "print(\"\\nTop 10 words for positive sentiment:\")\n",
        "print(feature_importance.head(10)[['feature', 'coefficient']].to_string(index=False))\n",
        "\n",
        "print(\"\\nTop 10 words for negative sentiment:\")\n",
        "print(feature_importance.tail(10)[['feature', 'coefficient']].to_string(index=False))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 897
        },
        "id": "xQ5dog9mzRQc",
        "outputId": "4d3d46af-0830-4e88-8528-8fcb09830941"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Top 10 words for positive sentiment:\n",
            "  feature  coefficient\n",
            "    great     7.086727\n",
            "excellent     6.463412\n",
            "  amazing     5.069813\n",
            "  perfect     5.043484\n",
            "wonderful     4.901537\n",
            "brilliant     4.566894\n",
            "hilarious     4.444717\n",
            "     best     4.429810\n",
            " favorite     3.988644\n",
            "    loved     3.940657\n",
            "\n",
            "Top 10 words for negative sentiment:\n",
            " feature  coefficient\n",
            "horrible    -5.144005\n",
            "    dull    -5.146716\n",
            " nothing    -5.419093\n",
            "terrible    -5.835656\n",
            "    poor    -5.984426\n",
            "   waste    -6.205862\n",
            "  boring    -6.738797\n",
            "     bad    -7.690242\n",
            "   awful    -7.800619\n",
            "   worst    -9.325169\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Now, I will do the BERT Model Setup**\n"
      ],
      "metadata": {
        "id": "0tAuiQ2O0CaC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments\n",
        "from transformers import DataCollatorWithPadding\n",
        "from datasets import Dataset\n",
        "import torch\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "# Check if GPU is available\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "print(f\"Using device: {device}\")\n",
        "\n",
        "# Use DistilBERT (lighter version of BERT)\n",
        "model_name = \"distilbert-base-uncased\"\n",
        "\n",
        "# Load tokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "\n",
        "# Prepare data for BERT (using a subset for faster training if needed)\n",
        "use_subset = False  # Set to False to use all 40,000 training samples\n",
        "\n",
        "if use_subset:\n",
        "    # Use 10,000 samples for faster training\n",
        "    train_sample_size = 10000\n",
        "    test_sample_size = 2500\n",
        "\n",
        "    X_train_subset = X_train[:train_sample_size]\n",
        "    y_train_subset = y_train[:train_sample_size]\n",
        "    X_test_subset = X_test[:test_sample_size]\n",
        "    y_test_subset = y_test[:test_sample_size]\n",
        "\n",
        "    train_df = pd.DataFrame({'text': X_train_subset, 'label': y_train_subset})\n",
        "    test_df = pd.DataFrame({'text': X_test_subset, 'label': y_test_subset})\n",
        "\n",
        "    print(f\"Using subset: {train_sample_size} training samples, {test_sample_size} test samples\")\n",
        "else:\n",
        "    # Use all data\n",
        "    train_df = pd.DataFrame({'text': X_train, 'label': y_train})\n",
        "    test_df = pd.DataFrame({'text': X_test, 'label': y_test})\n",
        "\n",
        "    print(f\"Using full dataset: {len(train_df)} training samples, {len(test_df)} test samples\")\n",
        "\n",
        "# Convert to datasets format\n",
        "train_dataset = Dataset.from_pandas(train_df)\n",
        "test_dataset = Dataset.from_pandas(test_df)\n",
        "\n",
        "print(f\"Train dataset size: {len(train_dataset)}\")\n",
        "print(f\"Test dataset size: {len(test_dataset)}\")"
      ],
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        "outputId": "9c014490-dfcd-482e-e945-ba6fd30068ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using device: cuda\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/483 [00:00<?, ?B/s]"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n",
            "WARNING:huggingface_hub.utils._http:Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt: 0.00B [00:00, ?B/s]"
            ],
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          "output_type": "display_data",
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              "tokenizer.json: 0.00B [00:00, ?B/s]"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using full dataset: 40000 training samples, 10000 test samples\n",
            "Train dataset size: 40000\n",
            "Test dataset size: 10000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Tokenization Function for BERT**"
      ],
      "metadata": {
        "id": "2U4hjo3g0NCj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Tokenization function\n",
        "def tokenize_function(examples):\n",
        "    return tokenizer(\n",
        "        examples['text'],\n",
        "        padding='max_length',\n",
        "        truncation=True,\n",
        "        max_length=256  # Reduced from 512 for faster training\n",
        "    )\n",
        "\n",
        "# Tokenize datasets\n",
        "print(\"Tokenizing datasets (this may take a few minutes)...\")\n",
        "tokenized_train = train_dataset.map(tokenize_function, batched=True, batch_size=1000)\n",
        "tokenized_test = test_dataset.map(tokenize_function, batched=True, batch_size=1000)\n",
        "\n",
        "# Set format for PyTorch\n",
        "tokenized_train.set_format(type='torch', columns=['input_ids', 'attention_mask', 'label'])\n",
        "tokenized_test.set_format(type='torch', columns=['input_ids', 'attention_mask', 'label'])\n",
        "\n",
        "print(\"Tokenization completed!\")\n",
        "print(f\"Sample input_ids shape: {tokenized_train[0]['input_ids'].shape}\")\n",
        "print(f\"Sample attention_mask shape: {tokenized_train[0]['attention_mask'].shape}\")"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tokenizing datasets (this may take a few minutes)...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/40000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "70a04602a0d54ffa838120cb4bdf481c"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/10000 [00:00<?, ? examples/s]"
            ],
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              "version_major": 2,
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          },
          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tokenization completed!\n",
            "Sample input_ids shape: torch.Size([256])\n",
            "Sample attention_mask shape: torch.Size([256])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Loading and Configure BERT Model**"
      ],
      "metadata": {
        "id": "JA-gvEMc0PbD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load model\n",
        "model = AutoModelForSequenceClassification.from_pretrained(\n",
        "    model_name,\n",
        "    num_labels=2,\n",
        "    id2label={0: \"NEGATIVE\", 1: \"POSITIVE\"},\n",
        "    label2id={\"NEGATIVE\": 0, \"POSITIVE\": 1}\n",
        ")\n",
        "\n",
        "# Move model to GPU if available\n",
        "model.to(device)\n",
        "\n",
        "# Define training arguments\n",
        "training_args = TrainingArguments(\n",
        "    output_dir='./results',\n",
        "    num_train_epochs=1,\n",
        "    per_device_train_batch_size=32,  # Increased from 16 (T4 can handle this)\n",
        "    per_device_eval_batch_size=64,    # Increased from 32\n",
        "    warmup_steps=500,\n",
        "    weight_decay=0.01,\n",
        "    logging_dir='./logs',\n",
        "    logging_steps=100,\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    save_strategy=\"epoch\",\n",
        "    load_best_model_at_end=True,\n",
        "    metric_for_best_model=\"f1\",\n",
        "    greater_is_better=True,\n",
        "    report_to=\"none\",\n",
        "    fp16=True,  # Enable mixed precision (2x faster on T4)\n",
        "    dataloader_num_workers=2,  # Parallel data loading\n",
        "    gradient_accumulation_steps=1,  # Can increase if memory issues\n",
        "    optim=\"adamw_torch\",  # Use PyTorch's optimized AdamW\n",
        ")\n",
        "\n",
        "\n",
        "\n",
        "print(\"Model and training arguments configured successfully!\")\n",
        "print(f\"Model parameters: {sum(p.numel() for p in model.parameters()):,}\")\n",
        "print(f\"Training device: {model.device}\")"
      ],
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        "outputId": "93cb4e13-3d8f-4a45-d3bd-8bdb4880c89b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/268M [00:00<?, ?B/s]"
            ],
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading weights:   0%|          | 0/100 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "DistilBertForSequenceClassification LOAD REPORT from: distilbert-base-uncased\n",
            "Key                     | Status     | \n",
            "------------------------+------------+-\n",
            "vocab_transform.bias    | UNEXPECTED | \n",
            "vocab_layer_norm.bias   | UNEXPECTED | \n",
            "vocab_projector.bias    | UNEXPECTED | \n",
            "vocab_transform.weight  | UNEXPECTED | \n",
            "vocab_layer_norm.weight | UNEXPECTED | \n",
            "classifier.bias         | MISSING    | \n",
            "pre_classifier.bias     | MISSING    | \n",
            "classifier.weight       | MISSING    | \n",
            "pre_classifier.weight   | MISSING    | \n",
            "\n",
            "Notes:\n",
            "- UNEXPECTED\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\n",
            "- MISSING\t:those params were newly initialized because missing from the checkpoint. Consider training on your downstream task.\n",
            "`logging_dir` is deprecated and will be removed in v5.2. Please set `TENSORBOARD_LOGGING_DIR` instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model and training arguments configured successfully!\n",
            "Model parameters: 66,955,010\n",
            "Training device: cuda:0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Define Metrics for BERT Training**"
      ],
      "metadata": {
        "id": "uKhL29vV0VS2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score\n",
        "\n",
        "def compute_metrics(eval_pred):\n",
        "    predictions, labels = eval_pred\n",
        "    predictions = np.argmax(predictions, axis=1)\n",
        "\n",
        "    accuracy = accuracy_score(labels, predictions)\n",
        "    f1 = f1_score(labels, predictions, average='weighted')\n",
        "    precision = precision_score(labels, predictions, average='weighted')\n",
        "    recall = recall_score(labels, predictions, average='weighted')\n",
        "\n",
        "    return {\n",
        "        'accuracy': accuracy,\n",
        "        'f1': f1,\n",
        "        'precision': precision,\n",
        "        'recall': recall\n",
        "    }\n",
        "\n",
        "# Data collator\n",
        "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)\n",
        "\n",
        "# Create Trainer\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=tokenized_train,\n",
        "    eval_dataset=tokenized_test,\n",
        "    data_collator=data_collator, # Removed tokenizer=tokenizer\n",
        "    compute_metrics=compute_metrics\n",
        ")\n",
        "\n",
        "print(\"Trainer initialized successfully!\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "erJxSnHx0Wys",
        "outputId": "410a5c91-2998-4be9-e7bc-fef0819f42ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Trainer initialized successfully!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Now, I will train the BERT Model**"
      ],
      "metadata": {
        "id": "IgaKIGqi1Z1M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the model\n",
        "print(\"Starting BERT training...\")\n",
        "start_time = time.time()\n",
        "\n",
        "# Train\n",
        "trainer.train()\n",
        "\n",
        "bert_train_time = time.time() - start_time\n",
        "print(f\"\\nBERT Training completed in {bert_train_time:.2f} seconds ({bert_train_time/60:.2f} minutes)\")\n",
        "\n",
        "# Evaluate\n",
        "print(\"\\nEvaluating BERT model...\")\n",
        "eval_results = trainer.evaluate()\n",
        "print(f\"\\nBERT Evaluation Results:\")\n",
        "for key, value in eval_results.items():\n",
        "    print(f\"{key}: {value:.4f}\")\n",
        "\n",
        "# Save the model\n",
        "model.save_pretrained('./bert_sentiment_model')\n",
        "tokenizer.save_pretrained('./bert_sentiment_model')\n",
        "print(\"\\nModel saved to './bert_sentiment_model'\")"
      ],
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          "height": 621,
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            "f14abeb9075b4068b166f3d58c39bfea",
            "15a8afe1ddcf406cbab57a163788889e",
            "b3d6347891d04143adb49eda7ca6ecf2",
            "89a9f6f88e7e4e5f809e4bd1ea45b199",
            "f4c9c4322b644b1187619f2c854f3f3f",
            "40b68e6bdad2490eb65ba99a817c59f4",
            "cc549b861258401ab6696c8623e24324",
            "ee93a42446b641dd8f41fc318ec6562e",
            "074ab18cfdf44600b7dc63fa67d50ae9",
            "2ed2ef22eb44422dbe072a9899023b54",
            "9125db4e7b7c4d7a9de8b7d68048f4e3",
            "2a308a1b33de430e9f5daef1583d936e"
          ]
        },
        "id": "MiliMFNh1ddS",
        "outputId": "bc41ef7e-626d-445a-a1ce-53c88a3065b3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Starting BERT training...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='5000' max='5000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [5000/5000 10:01, Epoch 2/2]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>F1</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.270727</td>\n",
              "      <td>0.276238</td>\n",
              "      <td>0.894500</td>\n",
              "      <td>0.894300</td>\n",
              "      <td>0.897509</td>\n",
              "      <td>0.894500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.144775</td>\n",
              "      <td>0.294405</td>\n",
              "      <td>0.905200</td>\n",
              "      <td>0.905199</td>\n",
              "      <td>0.905215</td>\n",
              "      <td>0.905200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Writing model shards:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3a44970adf034ec69c4f08a08c5b0901"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Writing model shards:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "edfa801cfbb84894bdccf8a8aec74544"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "There were missing keys in the checkpoint model loaded: ['distilbert.embeddings.LayerNorm.weight', 'distilbert.embeddings.LayerNorm.bias'].\n",
            "There were unexpected keys in the checkpoint model loaded: ['distilbert.embeddings.LayerNorm.beta', 'distilbert.embeddings.LayerNorm.gamma'].\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "BERT Training completed in 605.85 seconds (10.10 minutes)\n",
            "\n",
            "Evaluating BERT model...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='313' max='313' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [313/313 00:19]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "BERT Evaluation Results:\n",
            "eval_loss: 0.2944\n",
            "eval_accuracy: 0.9052\n",
            "eval_f1: 0.9052\n",
            "eval_precision: 0.9052\n",
            "eval_recall: 0.9052\n",
            "eval_runtime: 19.2626\n",
            "eval_samples_per_second: 519.1410\n",
            "eval_steps_per_second: 16.2490\n",
            "epoch: 2.0000\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Writing model shards:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "15a8afe1ddcf406cbab57a163788889e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Model saved to './bert_sentiment_model'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**After training I will make Predictions with BERT**"
      ],
      "metadata": {
        "id": "GDOfDcxC1c_M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Get predictions\n",
        "print(\"Making predictions with BERT...\")\n",
        "predictions = trainer.predict(tokenized_test)\n",
        "y_pred_bert = np.argmax(predictions.predictions, axis=1)\n",
        "\n",
        "# Calculate metrics\n",
        "bert_accuracy = accuracy_score(y_test[:len(y_pred_bert)], y_pred_bert)\n",
        "bert_f1 = f1_score(y_test[:len(y_pred_bert)], y_pred_bert, average='weighted')\n",
        "bert_precision = precision_score(y_test[:len(y_pred_bert)], y_pred_bert, average='weighted')\n",
        "bert_recall = recall_score(y_test[:len(y_pred_bert)], y_pred_bert, average='weighted')\n",
        "\n",
        "print(f\"\\nBERT Test Results:\")\n",
        "print(f\"Accuracy: {bert_accuracy:.4f}\")\n",
        "print(f\"F1-Score: {bert_f1:.4f}\")\n",
        "print(f\"Precision: {bert_precision:.4f}\")\n",
        "print(f\"Recall: {bert_recall:.4f}\")\n",
        "\n",
        "# Classification report\n",
        "print(\"\\nBERT Classification Report:\")\n",
        "print(classification_report(y_test[:len(y_pred_bert)], y_pred_bert, target_names=['Negative', 'Positive']))\n",
        "\n",
        "# Confusion matrix for BERT\n",
        "plot_confusion_matrix(y_test[:len(y_pred_bert)], y_pred_bert, 'BERT Model')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "iSig0Z3H1kLM",
        "outputId": "e8be64ed-0e27-4176-ddb0-bd41d61869f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Making predictions with BERT...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "BERT Test Results:\n",
            "Accuracy: 0.9052\n",
            "F1-Score: 0.9052\n",
            "Precision: 0.9052\n",
            "Recall: 0.9052\n",
            "\n",
            "BERT Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Negative       0.91      0.90      0.90      5000\n",
            "    Positive       0.90      0.91      0.91      5000\n",
            "\n",
            "    accuracy                           0.91     10000\n",
            "   macro avg       0.91      0.91      0.91     10000\n",
            "weighted avg       0.91      0.91      0.91     10000\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "True Negatives: 4511\n",
            "False Positives: 489\n",
            "False Negatives: 459\n",
            "True Positives: 4541\n",
            "Accuracy: 0.9052\n",
            "Sensitivity/Recall: 0.9082\n",
            "Specificity: 0.9022\n",
            "Precision: 0.9028\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Compare Models Performance**"
      ],
      "metadata": {
        "id": "YuzEKbf57U6Q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create comparison dataframe\n",
        "comparison_data = {\n",
        "    'Model': ['Logistic Regression (TF-IDF)', 'BERT (DistilBERT)'],\n",
        "    'Accuracy': [lr_tuned_accuracy, bert_accuracy],\n",
        "    'F1-Score': [lr_tuned_f1, bert_f1],\n",
        "    'Precision': [lr_tuned_precision, bert_precision],\n",
        "    'Recall': [lr_tuned_recall, bert_recall],\n",
        "    'Training Time (s)': [lr_train_time, bert_train_time]\n",
        "}\n",
        "\n",
        "comparison_df = pd.DataFrame(comparison_data)\n",
        "print(\"=\"*80)\n",
        "print(\"MODEL PERFORMANCE COMPARISON\")\n",
        "print(\"=\"*80)\n",
        "print(comparison_df.to_string(index=False))\n",
        "\n",
        "# Visualize comparison\n",
        "fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n",
        "\n",
        "# Metrics comparison\n",
        "metrics = ['Accuracy', 'F1-Score', 'Precision', 'Recall']\n",
        "x = np.arange(len(metrics))\n",
        "width = 0.35\n",
        "\n",
        "axes[0].bar(x - width/2, comparison_df.iloc[0][1:5], width, label='Logistic Regression', color='skyblue')\n",
        "axes[0].bar(x + width/2, comparison_df.iloc[1][1:5], width, label='BERT', color='lightcoral')\n",
        "axes[0].set_xlabel('Metrics')\n",
        "axes[0].set_ylabel('Score')\n",
        "axes[0].set_title('Model Performance Comparison')\n",
        "axes[0].set_xticks(x)\n",
        "axes[0].set_xticklabels(metrics)\n",
        "axes[0].legend()\n",
        "axes[0].set_ylim(0, 1)\n",
        "\n",
        "# Add value labels on bars\n",
        "for i, v in enumerate(comparison_df.iloc[0][1:5]):\n",
        "    axes[0].text(i - width/2, v + 0.01, f'{v:.3f}', ha='center', va='bottom', fontsize=9)\n",
        "for i, v in enumerate(comparison_df.iloc[1][1:5]):\n",
        "    axes[0].text(i + width/2, v + 0.01, f'{v:.3f}', ha='center', va='bottom', fontsize=9)\n",
        "\n",
        "# Training time comparison\n",
        "axes[1].bar(['Logistic Regression', 'BERT'],\n",
        "           [lr_train_time, bert_train_time],\n",
        "           color=['skyblue', 'lightcoral'])\n",
        "axes[1].set_ylabel('Training Time (seconds)')\n",
        "axes[1].set_title('Training Time Comparison')\n",
        "\n",
        "# Add value labels\n",
        "axes[1].text(0, lr_train_time + 5, f'{lr_train_time:.1f}s', ha='center', va='bottom')\n",
        "axes[1].text(1, bert_train_time + 5, f'{bert_train_time:.1f}s', ha='center', va='bottom')\n",
        "\n",
        "# Sample size comparison\n",
        "sample_sizes = [len(X_train), len(X_test)]\n",
        "axes[2].bar(['Training Set', 'Test Set'], sample_sizes, color=['lightgreen', 'lightblue'])\n",
        "axes[2].set_ylabel('Number of Samples')\n",
        "axes[2].set_title('Dataset Split')\n",
        "for i, v in enumerate(sample_sizes):\n",
        "    axes[2].text(i, v + 100, str(v), ha='center', va='bottom')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "cwuNZF4O7XKv",
        "outputId": "19c52bf9-4af7-4e2e-b525-31c6d711c248"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "================================================================================\n",
            "MODEL PERFORMANCE COMPARISON\n",
            "================================================================================\n",
            "                       Model  Accuracy  F1-Score  Precision  Recall  Training Time (s)\n",
            "Logistic Regression (TF-IDF)    0.8909  0.890872   0.891306  0.8909           0.369622\n",
            "           BERT (DistilBERT)    0.9052  0.905199   0.905215  0.9052         605.848861\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x500 with 3 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Model Interpretability with SHAP (for Logistic Regression)**"
      ],
      "metadata": {
        "id": "xmE5suHS-smL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import shap\n",
        "\n",
        "# Create a smaller sample for SHAP analysis (to avoid long computation times)\n",
        "sample_size = min(100, len(X_test))\n",
        "X_test_sample = X_test[:sample_size]\n",
        "X_test_tfidf_sample = X_test_tfidf[:sample_size]\n",
        "\n",
        "# Create a function that predicts probabilities\n",
        "def predict_proba_lr(texts):\n",
        "    # Transform texts to TF-IDF\n",
        "    texts_tfidf = tfidf_vectorizer.transform(texts)\n",
        "    # Get probabilities\n",
        "    return best_lr.predict_proba(texts_tfidf)\n",
        "\n",
        "# Create SHAP explainer\n",
        "print(\"Creating SHAP explainer for Logistic Regression...\")\n",
        "# Use a subset of training data for the background\n",
        "X_train_sample = X_train[:100]\n",
        "X_train_tfidf_sample = X_train_tfidf[:100]\n",
        "\n",
        "# For linear models, we can use LinearExplainer\n",
        "explainer_lr = shap.LinearExplainer(best_lr, X_train_tfidf_sample, feature_perturbation=\"interventional\")\n",
        "\n",
        "# Calculate SHAP values for test samples\n",
        "shap_values_lr = explainer_lr.shap_values(X_test_tfidf_sample)\n",
        "\n",
        "# Get feature names\n",
        "feature_names = tfidf_vectorizer.get_feature_names_out()\n",
        "\n",
        "# Summary plot\n",
        "plt.figure(figsize=(12, 8))\n",
        "shap.summary_plot(shap_values_lr, X_test_tfidf_sample, feature_names=feature_names, show=False)\n",
        "plt.title('SHAP Summary Plot - Logistic Regression', fontsize=14, fontweight='bold')\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "# Bar plot of top features\n",
        "plt.figure(figsize=(10, 8))\n",
        "shap.summary_plot(shap_values_lr, X_test_tfidf_sample, feature_names=feature_names,\n",
        "                   plot_type=\"bar\", show=False)\n",
        "plt.title('Top 20 Most Important Features - Logistic Regression', fontsize=14, fontweight='bold')\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "# Force plot for a single prediction\n",
        "plt.figure(figsize=(20, 3))\n",
        "shap.force_plot(explainer_lr.expected_value, shap_values_lr[0,:],\n",
        "                X_test_tfidf_sample[0].toarray().flatten(),\n",
        "                feature_names=feature_names, matplotlib=True, show=False)\n",
        "plt.title(f'Force Plot for Sample Review (True: {\"Positive\" if y_test.iloc[0] else \"Negative\"})')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "INhfaFVy-sI9",
        "outputId": "f5686d17-ce91-425d-a50b-78923d120ff7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Creating SHAP explainer for Logistic Regression...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x950 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x950 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x300 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x300 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Error Analysis**"
      ],
      "metadata": {
        "id": "CE-OKJ7w-40g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Identify misclassified examples\n",
        "lr_misclassified = X_test[y_test != lr_test_pred_tuned].index\n",
        "bert_misclassified = X_test[:len(y_pred_bert)][y_test[:len(y_pred_bert)] != y_pred_bert].index\n",
        "\n",
        "print(\"=\"*80)\n",
        "print(\"ERROR ANALYSIS\")\n",
        "print(\"=\"*80)\n",
        "print(f\"Logistic Regression misclassified: {len(lr_misclassified)} samples ({len(lr_misclassified)/len(y_test)*100:.2f}%)\")\n",
        "print(f\"BERT misclassified: {len(bert_misclassified)} samples ({len(bert_misclassified)/len(y_test[:len(y_pred_bert)])*100:.2f}%)\")\n",
        "\n",
        "# Find samples misclassified by both models\n",
        "both_misclassified = set(lr_misclassified) & set(bert_misclassified)\n",
        "print(f\"Samples misclassified by both models: {len(both_misclassified)}\")\n",
        "\n",
        "# Analyze some misclassified examples\n",
        "print(\"\\n\" + \"=\"*50)\n",
        "print(\"ANALYSIS OF MISCLASSIFIED EXAMPLES\")\n",
        "print(\"=\"*50)\n",
        "\n",
        "test_set_original_indices = y_test.index.tolist() # Get the list of original indices in the test set\n",
        "\n",
        "for i, original_idx in enumerate(list(both_misclassified)[:5]):  # Show first 5\n",
        "    # Find the positional index of this original_idx within the current test set\n",
        "    try:\n",
        "        position_in_test_set = test_set_original_indices.index(original_idx)\n",
        "    except ValueError:\n",
        "        print(f\"Warning: Original index {original_idx} not found in the current y_test. This might happen if y_pred_bert length is smaller than y_test or due to some sampling.\")\n",
        "        continue # Skip if not found\n",
        "\n",
        "    print(f\"\\nExample {i+1} (Original Index: {original_idx}):\")\n",
        "    print(f\"True label: {'Positive' if y_test.loc[original_idx] == 1 else 'Negative'}\") # Use .loc for label-based indexing\n",
        "    print(f\"LR prediction: {'Positive' if lr_test_pred_tuned[position_in_test_set] == 1 else 'Negative'}\") # Use positional index\n",
        "    print(f\"BERT prediction: {'Positive' if y_pred_bert[position_in_test_set] == 1 else 'Negative'}\") # Use positional index\n",
        "    print(f\"Review text (first 300 chars):\")\n",
        "    print(df['review'].loc[original_idx][:300] + \"...\") # Use .loc for df['review']\n",
        "    print(\"-\" * 80)\n",
        "\n",
        "# Analyze confidence scores for misclassified examples\n",
        "print(\"\\n\" + \"=\"*50)\n",
        "print(\"CONFIDENCE SCORES FOR MISCLASSIFIED EXAMPLES\")\n",
        "print(\"=\"*50)\n",
        "\n",
        "# Get probability scores for Logistic Regression\n",
        "lr_proba = best_lr.predict_proba(X_test_tfidf)\n",
        "\n",
        "for original_idx in list(both_misclassified)[:5]:\n",
        "    try:\n",
        "        position_in_test_set = test_set_original_indices.index(original_idx)\n",
        "    except ValueError:\n",
        "        print(f\"Warning: Original index {original_idx} not found in the current y_test. Skipping confidence score display.\")\n",
        "        continue\n",
        "\n",
        "    print(f\"\\nIndex {original_idx}:\")\n",
        "    print(f\"LR Confidence: {np.max(lr_proba[position_in_test_set]):.4f}\") # Access lr_proba by positional index\n",
        "    print(f\"LR Probabilities - Negative: {lr_proba[position_in_test_set][0]:.4f}, Positive: {lr_proba[position_in_test_set][1]:.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pejM1xOj-6Wm",
        "outputId": "8c2a8935-7f44-471a-9453-17a1628ed678"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "================================================================================\n",
            "ERROR ANALYSIS\n",
            "================================================================================\n",
            "Logistic Regression misclassified: 1091 samples (10.91%)\n",
            "BERT misclassified: 948 samples (9.48%)\n",
            "Samples misclassified by both models: 593\n",
            "\n",
            "==================================================\n",
            "ANALYSIS OF MISCLASSIFIED EXAMPLES\n",
            "==================================================\n",
            "\n",
            "Example 1 (Original Index: 20483):\n",
            "True label: Negative\n",
            "LR prediction: Positive\n",
            "BERT prediction: Positive\n",
            "Review text (first 300 chars):\n",
            "? this is a simple tale but it feels very manipulative it lacks pathos for it does not leave a room for imagination or a personal thought or time for reflection br br the animation is well done but i feel like it is too ? i would have preferred more images from behind more space in the background an...\n",
            "--------------------------------------------------------------------------------\n",
            "\n",
            "Example 2 (Original Index: 16387):\n",
            "True label: Positive\n",
            "LR prediction: Negative\n",
            "BERT prediction: Negative\n",
            "Review text (first 300 chars):\n",
            "? it's hard for me to criticize anything that ? ? does she just ? beauty and grace on the screen and is a phenomenal actress that notwithstanding yes the plot was predictable i think perhaps if jill had slept with richard then it may have made him a little ? than he already was which would have adde...\n",
            "--------------------------------------------------------------------------------\n",
            "\n",
            "Example 3 (Original Index: 24580):\n",
            "True label: Positive\n",
            "LR prediction: Negative\n",
            "BERT prediction: Negative\n",
            "Review text (first 300 chars):\n",
            "? over the years we've seen a lot of preposterous things done by writers when the show just had to go on no matter what keeping 8 simple rules going after john ritter died comes to mind but this is probably the first time i cared the idea of having that 70's show without eric or to a lesser extent ?...\n",
            "--------------------------------------------------------------------------------\n",
            "\n",
            "Example 4 (Original Index: 49162):\n",
            "True label: Negative\n",
            "LR prediction: Positive\n",
            "BERT prediction: Positive\n",
            "Review text (first 300 chars):\n",
            "? this ? sweet and ? paced 5 ? is definitely not among harold ? better films gags are sparse and mostly uninspired ? melodrama is ? the setup takes forever as lloyd the unconventional but ? kindly country doctor makes his rounds bringing a little sunshine into the lives of children the elderly and ?...\n",
            "--------------------------------------------------------------------------------\n",
            "\n",
            "Example 5 (Original Index: 28691):\n",
            "True label: Positive\n",
            "LR prediction: Negative\n",
            "BERT prediction: Negative\n",
            "Review text (first 300 chars):\n",
            "? film follows a bunch of students in the nyc high school of the performing arts there's ? irene ? a black singer who will make it to the top despite everything she's helped by bruno lee a white musician then there's doris maureen who wants to be an actress but she's shy and scared she becomes frien...\n",
            "--------------------------------------------------------------------------------\n",
            "\n",
            "==================================================\n",
            "CONFIDENCE SCORES FOR MISCLASSIFIED EXAMPLES\n",
            "==================================================\n",
            "\n",
            "Index 20483:\n",
            "LR Confidence: 0.7360\n",
            "LR Probabilities - Negative: 0.2640, Positive: 0.7360\n",
            "\n",
            "Index 16387:\n",
            "LR Confidence: 0.6882\n",
            "LR Probabilities - Negative: 0.6882, Positive: 0.3118\n",
            "\n",
            "Index 24580:\n",
            "LR Confidence: 0.8057\n",
            "LR Probabilities - Negative: 0.8057, Positive: 0.1943\n",
            "\n",
            "Index 49162:\n",
            "LR Confidence: 0.7377\n",
            "LR Probabilities - Negative: 0.2623, Positive: 0.7377\n",
            "\n",
            "Index 28691:\n",
            "LR Confidence: 0.6612\n",
            "LR Probabilities - Negative: 0.6612, Positive: 0.3388\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Final Comparison and Insights**"
      ],
      "metadata": {
        "id": "GtfEC43D_EyO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create comprehensive comparison table\n",
        "comparison_summary = pd.DataFrame({\n",
        "    'Metric': ['Accuracy', 'F1-Score', 'Precision', 'Recall',\n",
        "               'Training Time (s)', 'Inference Speed', 'Interpretability',\n",
        "               'Model Size', 'Handles Context', 'Resource Requirements'],\n",
        "    'Logistic Regression': [\n",
        "        f\"{lr_tuned_accuracy:.4f}\",\n",
        "        f\"{lr_tuned_f1:.4f}\",\n",
        "        f\"{lr_tuned_precision:.4f}\",\n",
        "        f\"{lr_tuned_recall:.4f}\",\n",
        "        f\"{lr_train_time:.2f}\",\n",
        "        \"Fast (<1ms per sample)\",\n",
        "        \"High (Coefficients, SHAP)\",\n",
        "        \"Small (~10-50 MB)\",\n",
        "        \"No\",\n",
        "        \"Low (CPU only)\"\n",
        "    ],\n",
        "    'BERT (DistilBERT)': [\n",
        "        f\"{bert_accuracy:.4f}\",\n",
        "        f\"{bert_f1:.4f}\",\n",
        "        f\"{bert_precision:.4f}\",\n",
        "        f\"{bert_recall:.4f}\",\n",
        "        f\"{bert_train_time:.2f}\",\n",
        "        \"Slow (~10-100ms per sample)\",\n",
        "        \"Medium (Attention)\",\n",
        "        \"Large (~250-500 MB)\",\n",
        "        \"Yes\",\n",
        "        \"High (GPU recommended)\"\n",
        "    ]\n",
        "})\n",
        "\n",
        "print(\"\\n\" + \"=\"*80)\n",
        "print(\"COMPREHENSIVE MODEL COMPARISON\")\n",
        "print(\"=\"*80)\n",
        "print(comparison_summary.to_string(index=False))\n",
        "\n",
        "# Calculate improvement\n",
        "accuracy_improvement = ((bert_accuracy - lr_tuned_accuracy) / lr_tuned_accuracy) * 100\n",
        "f1_improvement = ((bert_f1 - lr_tuned_f1) / lr_tuned_f1) * 100\n",
        "\n",
        "print(f\"\\nBERT Improvement over Logistic Regression:\")\n",
        "print(f\"Accuracy: +{accuracy_improvement:.2f}%\")\n",
        "print(f\"F1-Score: +{f1_improvement:.2f}%\")\n",
        "\n",
        "# Generate insights\n",
        "print(\"\\n\" + \"=\"*80)\n",
        "print(\"KEY INSIGHTS AND RECOMMENDATIONS\")\n",
        "print(\"=\"*80)\n",
        "\n",
        "insights = \"\"\"\n",
        "1. PERFORMANCE COMPARISON:\n",
        "   - BERT shows higher accuracy and F1-score, especially for complex sentences with nuance\n",
        "   - Logistic Regression performs well on clear-cut sentiments and is more interpretable\n",
        "   - The performance gap reflects BERT's ability to understand context and word relationships\n",
        "\n",
        "2. COMPUTATIONAL EFFICIENCY:\n",
        "   - Logistic Regression trains much faster (seconds vs. hours) and requires minimal resources\n",
        "   - BERT requires significant computational power (GPU) and time for training\n",
        "   - For inference, Logistic Regression is ~100x faster than BERT\n",
        "\n",
        "3. INTERPRETABILITY:\n",
        "   - Logistic Regression offers clear feature importance through coefficients and SHAP values\n",
        "   - BERT provides attention visualization but is harder to interpret globally\n",
        "   - SHAP analysis reveals which words drive predictions in both models\n",
        "\n",
        "4. STRENGTHS AND WEAKNESSES:\n",
        "   Logistic Regression:\n",
        "   ✓ Fast training and inference\n",
        "   ✓ Highly interpretable\n",
        "   ✓ Works well with limited data\n",
        "   ✗ Cannot capture context or word order\n",
        "   ✗ Assumes linear relationships\n",
        "\n",
        "   BERT:\n",
        "   ✓ Captures context and nuance\n",
        "   ✓ Handles complex language patterns\n",
        "   ✓ State-of-the-art performance\n",
        "   ✗ Requires significant computational resources\n",
        "   ✗ Black-box nature makes debugging difficult\n",
        "\n",
        "5. RECOMMENDATIONS FOR DEPLOYMENT:\n",
        "\n",
        "   A. For Production with Limited Resources:\n",
        "      - Use Logistic Regression with TF-IDF\n",
        "      - Deploy on CPU-only infrastructure\n",
        "      - Suitable for real-time applications with high throughput\n",
        "\n",
        "   B. For Maximum Accuracy with Available GPU Resources:\n",
        "      - Use DistilBERT (lighter than BERT, 60% faster, 95% performance)\n",
        "      - Deploy with model quantization for faster inference\n",
        "      - Suitable for batch processing or when accuracy is critical\n",
        "\n",
        "   C. Hybrid Approach:\n",
        "      - Use Logistic Regression as a fast baseline\n",
        "      - Apply BERT only for ambiguous cases\n",
        "      - Ensemble both models for better robustness\n",
        "\n",
        "6. ETHICAL CONSIDERATIONS:\n",
        "   - Monitor for bias in predictions across different types of reviews\n",
        "   - Ensure transparency in automated decision-making\n",
        "   - Regular auditing of model performance on diverse datasets\n",
        "   - Consider the environmental impact of training large models\n",
        "\"\"\"\n",
        "\n",
        "print(insights)\n",
        "\n",
        "# Save results\n",
        "results_df = pd.DataFrame({\n",
        "    'True_Label': y_test,\n",
        "    'LR_Prediction': lr_test_pred_tuned,\n",
        "    'BERT_Prediction': y_pred_bert[:len(y_test)],\n",
        "    'Review': df['review'].iloc[y_test.index].values\n",
        "})\n",
        "results_df.to_csv('sentiment_analysis_results.csv', index=False)\n",
        "print(\"\\n✓ Results saved to 'sentiment_analysis_results.csv'\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B0rL7BAl_JbP",
        "outputId": "40dddf32-a0e2-40a5-caa1-022c7ebfdbc1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "================================================================================\n",
            "COMPREHENSIVE MODEL COMPARISON\n",
            "================================================================================\n",
            "               Metric       Logistic Regression           BERT (DistilBERT)\n",
            "             Accuracy                    0.8909                      0.9052\n",
            "             F1-Score                    0.8909                      0.9052\n",
            "            Precision                    0.8913                      0.9052\n",
            "               Recall                    0.8909                      0.9052\n",
            "    Training Time (s)                      0.37                      605.85\n",
            "      Inference Speed    Fast (<1ms per sample) Slow (~10-100ms per sample)\n",
            "     Interpretability High (Coefficients, SHAP)          Medium (Attention)\n",
            "           Model Size         Small (~10-50 MB)         Large (~250-500 MB)\n",
            "      Handles Context                        No                         Yes\n",
            "Resource Requirements            Low (CPU only)      High (GPU recommended)\n",
            "\n",
            "BERT Improvement over Logistic Regression:\n",
            "Accuracy: +1.61%\n",
            "F1-Score: +1.61%\n",
            "\n",
            "================================================================================\n",
            "KEY INSIGHTS AND RECOMMENDATIONS\n",
            "================================================================================\n",
            "\n",
            "1. PERFORMANCE COMPARISON:\n",
            "   - BERT shows higher accuracy and F1-score, especially for complex sentences with nuance\n",
            "   - Logistic Regression performs well on clear-cut sentiments and is more interpretable\n",
            "   - The performance gap reflects BERT's ability to understand context and word relationships\n",
            "\n",
            "2. COMPUTATIONAL EFFICIENCY:\n",
            "   - Logistic Regression trains much faster (seconds vs. hours) and requires minimal resources\n",
            "   - BERT requires significant computational power (GPU) and time for training\n",
            "   - For inference, Logistic Regression is ~100x faster than BERT\n",
            "\n",
            "3. INTERPRETABILITY:\n",
            "   - Logistic Regression offers clear feature importance through coefficients and SHAP values\n",
            "   - BERT provides attention visualization but is harder to interpret globally\n",
            "   - SHAP analysis reveals which words drive predictions in both models\n",
            "\n",
            "4. STRENGTHS AND WEAKNESSES:\n",
            "   Logistic Regression:\n",
            "   ✓ Fast training and inference\n",
            "   ✓ Highly interpretable\n",
            "   ✓ Works well with limited data\n",
            "   ✗ Cannot capture context or word order\n",
            "   ✗ Assumes linear relationships\n",
            "\n",
            "   BERT:\n",
            "   ✓ Captures context and nuance\n",
            "   ✓ Handles complex language patterns\n",
            "   ✓ State-of-the-art performance\n",
            "   ✗ Requires significant computational resources\n",
            "   ✗ Black-box nature makes debugging difficult\n",
            "\n",
            "5. RECOMMENDATIONS FOR DEPLOYMENT:\n",
            "\n",
            "   A. For Production with Limited Resources:\n",
            "      - Use Logistic Regression with TF-IDF\n",
            "      - Deploy on CPU-only infrastructure\n",
            "      - Suitable for real-time applications with high throughput\n",
            "\n",
            "   B. For Maximum Accuracy with Available GPU Resources:\n",
            "      - Use DistilBERT (lighter than BERT, 60% faster, 95% performance)\n",
            "      - Deploy with model quantization for faster inference\n",
            "      - Suitable for batch processing or when accuracy is critical\n",
            "\n",
            "   C. Hybrid Approach:\n",
            "      - Use Logistic Regression as a fast baseline\n",
            "      - Apply BERT only for ambiguous cases\n",
            "      - Ensemble both models for better robustness\n",
            "\n",
            "6. ETHICAL CONSIDERATIONS:\n",
            "   - Monitor for bias in predictions across different types of reviews\n",
            "   - Ensure transparency in automated decision-making\n",
            "   - Regular auditing of model performance on diverse datasets\n",
            "   - Consider the environmental impact of training large models\n",
            "\n",
            "\n",
            "✓ Results saved to 'sentiment_analysis_results.csv'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Saving my Models**"
      ],
      "metadata": {
        "id": "Emtdbi2b_f5J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import joblib\n",
        "import os\n",
        "\n",
        "# Create models directory\n",
        "os.makedirs('models', exist_ok=True)\n",
        "\n",
        "# Save Logistic Regression model and vectorizer\n",
        "print(\"Saving Logistic Regression model and vectorizer...\")\n",
        "joblib.dump(best_lr, 'models/logistic_regression_model.pkl')\n",
        "joblib.dump(tfidf_vectorizer, 'models/tfidf_vectorizer.pkl')\n",
        "print(\"✓ Logistic Regression model saved!\")\n",
        "\n",
        "# Save BERT model\n",
        "print(\"\\nSaving BERT model...\")\n",
        "model.save_pretrained('models/bert_model')\n",
        "tokenizer.save_pretrained('models/bert_tokenizer')\n",
        "print(\"✓ BERT model saved!\")\n",
        "\n",
        "# Save comparison results\n",
        "comparison_summary.to_csv('models/model_comparison.csv', index=False)\n",
        "print(\"\\n✓ Model comparison saved to 'models/model_comparison.csv'\")\n",
        "\n",
        "print(\"\\n\" + \"=\"*80)\n",
        "print(\"ALL MODELS SAVED SUCCESSFULLY IN 'models/' DIRECTORY\")\n",
        "print(\"=\"*80)\n",
        "print(\"\\nFiles saved:\")\n",
        "for file in os.listdir('models'):\n",
        "    print(f\"  - {file}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361,
          "referenced_widgets": [
            "5c10e6e686b242e581e251390b56b46a",
            "352c154647cb452689bb5513306cb413",
            "728b3a6baab44fedb9d2498969162ae2",
            "bd58ea55ef864ce79c1c1d818075cf43",
            "8532721bdb0342cc94217081cc2b2733",
            "c925b258bed24a9e80dbe081d8002a79",
            "30a95b894db54c06b4ad9d19c71226af",
            "5a6ea90aa1ff438fb4924fc9af031c53",
            "0e0cb85e1e294ffa9a4219b531cffca1",
            "d014820dc56a40da9198886fdfa06bf9",
            "d22e766c69b64dbb81f0cc93ee36e566"
          ]
        },
        "id": "Nphfv-Z2_fJr",
        "outputId": "4772949c-69c7-4919-a71d-0a5868815994"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving Logistic Regression model and vectorizer...\n",
            "✓ Logistic Regression model saved!\n",
            "\n",
            "Saving BERT model...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Writing model shards:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5c10e6e686b242e581e251390b56b46a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "✓ BERT model saved!\n",
            "\n",
            "✓ Model comparison saved to 'models/model_comparison.csv'\n",
            "\n",
            "================================================================================\n",
            "ALL MODELS SAVED SUCCESSFULLY IN 'models/' DIRECTORY\n",
            "================================================================================\n",
            "\n",
            "Files saved:\n",
            "  - logistic_regression_model.pkl\n",
            "  - tfidf_vectorizer.pkl\n",
            "  - bert_tokenizer\n",
            "  - bert_model\n",
            "  - model_comparison.csv\n"
          ]
        }
      ]
    }
  ]
}