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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Stock Portfolio Recommdendations"
      ],
      "metadata": {
        "id": "9ffJJeNSfHdC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Objective"
      ],
      "metadata": {
        "id": "6Wj_SfmcfQ8X"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Creating a portfolio out of Nifty50 Stocks\n",
        "The NIFTY 50 is a benchmark Indian stock market index that represents the weighted average of 50\n",
        "of the largest Indian companies listed on the National Stock Exchange.\n",
        "Objectives:\n",
        "1. Create an active stock selection strategy. (Main Objective)\n",
        "2. Compare the performance of the strategy with a benchmark.\n",
        "3. Summarize the performance of active strategy and compare it with benchmark.\n",
        "4. Create and host an app to present the above.\n",
        "Use your OOP (Object Oriented Programming) skills to complete the task. Each functionality\n",
        "explained above except hosting an app should be the part of your main class.\n",
        "Start by creating a class Stock and historical prices of each stock should be class properties. When\n",
        "you instantiate a class it should download the historical prices and compute necessary properties.\n",
        "Method of this class should be:\n",
        "1. CurPrice(curDate) – Which gives the closing price of the date curDate.\n",
        "2. NDayRet(N,curDate) – Which gives the N-day returns as on the curDate. (N=5 will give 5-day\n",
        "return)\n",
        "3. DailyRet(curDate) - Which gives the daily returns on curDate.\n",
        "4. Last30daysPrice(curDate) – Which gives the array of last 30 days prices.\n",
        "1. Benchmark Strategy:\n",
        "Our Benchmark is going to be Nifty50 index itself. Compare your active stock selection strategy.\n",
        "2. Active stock selection strategy:\n",
        "Your task involves creating an investment strategy where, at the end of each month, the\n",
        "performance of each stock in the previous month is assessed. The criterion for selection is\n",
        "positive returns. For instance, on March 31st of a given year, the 30-day returns of all fifty\n",
        "stocks will be examined, and only those with positive returns will be included in the portfolio.\n",
        "This portfolio will be maintained until April 30th, when a revaluation will occur based on the\n",
        "same rule for the upcoming month. This process will be repeated monthly.\n",
        "3. Summarize the performance:\n",
        "Get the following performance metrics for Nifty Index, Benchmark Allocation & Sample Strategy\n",
        "a. CAGR (%): ((𝑉𝑓𝑖𝑛𝑎𝑙\n",
        "𝑉𝑏𝑒𝑔𝑖𝑛\n",
        ")\n",
        "1\n",
        "𝑡\n",
        "− 1) ∗ 100 ;\n",
        "𝑉𝑓𝑖𝑛𝑎𝑙 : 𝑉𝑎𝑙𝑢𝑒 𝑜𝑛 𝑓𝑖𝑛𝑎𝑙 𝑑𝑎𝑦, 𝑉𝑏𝑒𝑔𝑖𝑛: 𝑉𝑎𝑙𝑢𝑒 𝑜𝑛 𝑏𝑒𝑔𝑖𝑛𝑖𝑛𝑔 𝑑𝑎𝑦, 𝑡: 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑦𝑒𝑎𝑟𝑠\n",
        "b. Volatility (%): (√252 ∗ (𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑 𝑑𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛(𝑑𝑎𝑖𝑙𝑦 𝑟𝑒𝑡𝑢𝑟𝑛𝑠)) ∗ 100\n",
        "c. Sharpe Ratio: (√252 ∗ 𝑚𝑒𝑎𝑛 (𝑑𝑎𝑖𝑙𝑦 𝑟𝑒𝑡𝑢𝑟𝑛𝑠)\n",
        "𝑆𝑡𝑎𝑛𝑑𝑎𝑟𝑑 𝑑𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛(𝑑𝑎𝑖𝑙𝑦 𝑟𝑒𝑡𝑢𝑟𝑛𝑠))\n",
        "𝑑𝑎𝑖𝑙𝑦 𝑟𝑒𝑡𝑢𝑟𝑛𝑠: ( 𝑉𝑡\n",
        "𝑉𝑡−1\n",
        "− 1) ; 𝑉𝑡: 𝑉𝑎𝑙𝑢𝑒 𝑜𝑛 𝑑𝑎𝑦 (𝑡), 𝑉𝑡−1: 𝑉𝑎𝑙𝑢𝑒 𝑜𝑛 𝑑𝑎𝑦 (𝑡 − 1)\n",
        "4. App to host the performance:\n",
        "Create & Host an app to which will have the following features.\n",
        "Need to take the following as inputs:\n",
        "1. Start date and end date of simulation\n",
        "2. Number of days to measure the performance for stock selection required for the sample\n",
        "strategy. ( We have described it to use 30 days returns but it can be generalized to have N\n",
        "days returns.)\n",
        "3. Initial Equity\n",
        "Need to display the following:\n",
        "1. Equity Curves of Nifty index, benchmark, and the Sample strategy for the given period in a\n",
        "single plot.\n",
        "2. Stocks that are selected for the sample strategy.\n",
        "3. Performance metrics for all the 3 stocks"
      ],
      "metadata": {
        "id": "9rxdV2OafTqV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1. Obtaining Nifty50 ticker list from wikipedia"
      ],
      "metadata": {
        "id": "ZBlMdCgqe9du"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Defining Imports\n",
        "import pandas as pd\n",
        "import yfinance as yf\n",
        "import sqlite3\n",
        "import yfinance as yf\n",
        "import matplotlib.pyplot as plt\n",
        "from datetime import datetime"
      ],
      "metadata": {
        "id": "ggMOJKumgDuC"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nifty_50_symbols = pd.read_csv(\"https://huggingface.co/spaces/pvanand/portfolio/raw/main/nifty50-stock-tickers.csv\").Symbol.to_list()\n",
        "nifty_50_symbols"
      ],
      "metadata": {
        "colab": {
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        },
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        "outputId": "96394854-9aa5-4383-a44b-98e71ad5bd54"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['ADANIENT',\n",
              " 'ADANIPORTS',\n",
              " 'APOLLOHOSP',\n",
              " 'ASIANPAINT',\n",
              " 'AXISBANK',\n",
              " 'BAJAJ-AUTO',\n",
              " 'BAJFINANCE',\n",
              " 'BAJAJFINSV',\n",
              " 'BPCL',\n",
              " 'BHARTIARTL',\n",
              " 'BRITANNIA',\n",
              " 'CIPLA',\n",
              " 'COALINDIA',\n",
              " 'DIVISLAB',\n",
              " 'DRREDDY',\n",
              " 'EICHERMOT',\n",
              " 'GRASIM',\n",
              " 'HCLTECH',\n",
              " 'HDFCBANK',\n",
              " 'HDFCLIFE',\n",
              " 'HEROMOTOCO',\n",
              " 'HINDALCO',\n",
              " 'HINDUNILVR',\n",
              " 'ICICIBANK',\n",
              " 'INDUSINDBK',\n",
              " 'INFY',\n",
              " 'ITC',\n",
              " 'JSWSTEEL',\n",
              " 'KOTAKBANK',\n",
              " 'LT',\n",
              " 'LTIM',\n",
              " 'M&M',\n",
              " 'MARUTI',\n",
              " 'NESTLEIND',\n",
              " 'NTPC',\n",
              " 'ONGC',\n",
              " 'POWERGRID',\n",
              " 'RELIANCE',\n",
              " 'SBILIFE',\n",
              " 'SBIN',\n",
              " 'SUNPHARMA',\n",
              " 'TATAMOTORS',\n",
              " 'TATASTEEL',\n",
              " 'TCS',\n",
              " 'TATACONSUM',\n",
              " 'TECHM',\n",
              " 'TITAN',\n",
              " 'ULTRACEMCO',\n",
              " 'UPL',\n",
              " 'WIPRO']"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2. Fetching data from Yahoo Finance and storing it in a SQLite database"
      ],
      "metadata": {
        "id": "8HjqVdFKg3vE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This step helps in faster retrieval of data when further analysis is required Also by using SQLite, only required data (specific date range) is loaded into memmory."
      ],
      "metadata": {
        "id": "z3_X7wI9hDR4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to fetch data from Yahoo Finance\n",
        "def fetch_data(symbols, start_date, end_date):\n",
        "    for symbol in symbols:\n",
        "        data = yf.download(symbol+'.NS', start=start_date, end=end_date)\n",
        "        data.to_sql(symbol, conn, if_exists='replace', index=True)\n",
        "\n",
        "# Create a SQLite database\n",
        "conn = sqlite3.connect('nifty50_stock_data.db')\n",
        "\n",
        "# Example: Fetching data for some symbols\n",
        "symbols = nifty_50_symbols  # Add more symbols as needed\n",
        "fetch_data(symbols, '2000-01-01', pd.to_datetime('today').strftime('%Y-%m-%d'))\n",
        "# last updated on 2024-01-07\n",
        "conn.close()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
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      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Storing nifty50 index data in the same database"
      ],
      "metadata": {
        "id": "LBreo5HmiqAG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import yfinance as yf\n",
        "import sqlite3\n",
        "import pandas as pd\n",
        "\n",
        "def fetch_data(start_date, end_date):\n",
        "  data = yf.download('^NSEI', start=start_date, end=end_date)\n",
        "  data.to_sql(\"NIFTY50\", conn, if_exists='replace', index=True)\n",
        "\n",
        "# Create a SQLite database\n",
        "conn = sqlite3.connect('/content/nifty50_stock_data.db')\n",
        "\n",
        "fetch_data('2000-01-01', pd.to_datetime('today').strftime('%Y-%m-%d'))\n",
        "# last updated on 2024-01-07\n",
        "conn.close()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I4wxRYq8KAB3",
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      },
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      "outputs": [
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          "name": "stdout",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3. Selecting stocks and building a strategy"
      ],
      "metadata": {
        "id": "xRlQXAWIjYYb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "class Stock:\n",
        "    def __init__(self, symbol, db_path, start_date, end_date):\n",
        "        self.symbol = symbol\n",
        "        self.db_path = db_path\n",
        "        self.start_date = start_date\n",
        "        self.end_date = end_date\n",
        "        self.prices = self._download_prices()\n",
        "\n",
        "    def _download_prices(self):\n",
        "        conn = sqlite3.connect(self.db_path)\n",
        "        query = f\"SELECT Date, Close FROM `{self.symbol}` WHERE Date BETWEEN '{self.start_date}' AND '{self.end_date}'\"\n",
        "        prices = pd.read_sql_query(query, conn, parse_dates=['Date'])\n",
        "        prices.set_index('Date', inplace=True)\n",
        "        return prices\n",
        "\n",
        "    def CurPrice(self, curDate):\n",
        "        return self.prices.loc[curDate, 'Close'] if curDate in self.prices.index else None\n",
        "\n",
        "    def NDayRet(self, N, curDate):\n",
        "        if curDate not in self.prices.index:\n",
        "            return None\n",
        "        start_date = self.prices.index[self.prices.index.get_loc(curDate) - N]\n",
        "        start_price = self.prices.loc[start_date, 'Close']\n",
        "        end_price = self.prices.loc[curDate, 'Close']\n",
        "        return (end_price - start_price) / start_price\n",
        "\n",
        "    def DailyRet(self, curDate):\n",
        "        if curDate not in self.prices.index:\n",
        "            return None\n",
        "        previous_date = self.prices.index[self.prices.index.get_loc(curDate) - 1]\n",
        "        previous_price = self.prices.loc[previous_date, 'Close']\n",
        "        current_price = self.prices.loc[curDate, 'Close']\n",
        "        return (current_price - previous_price) / previous_price\n",
        "\n",
        "    def Last30daysPrice(self, curDate):\n",
        "        end_loc = self.prices.index.get_loc(curDate) + 1\n",
        "        start_loc = max(0, end_loc - 30)\n",
        "        return self.prices.iloc[start_loc:end_loc]['Close'].values\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "  # Assuming the Stock class and other necessary imports are already defined\n",
        "\n",
        "def calculate_portfolio(start_date, end_date, investment_amount):\n",
        "    # Initialize variables\n",
        "    db_path = 'nifty50_stock_data.db'  # Replace with the correct path\n",
        "    nifty_50_symbols = pd.read_csv(\"https://huggingface.co/spaces/pvanand/portfolio/raw/main/nifty50-stock-tickers.csv\").Symbol.to_list()  # Replace with actual NIFTY 50 stock symbols\n",
        "\n",
        "    initial_investment = investment_amount\n",
        "    stocks = {symbol: Stock(symbol, db_path, start_date, end_date) for symbol in nifty_50_symbols}\n",
        "\n",
        "    # DataFrame to track the monthly percentage change for each stock\n",
        "    monthly_pct_change = pd.DataFrame()\n",
        "    daily_pct_change = pd.DataFrame()\n",
        "\n",
        "    # Calculate the monthly and daily percentage change for each stock\n",
        "    for symbol, stock_obj in stocks.items():\n",
        "        daily_pct_change[symbol] = stock_obj.prices['Close'].pct_change()\n",
        "        monthly_pct_change[symbol] = stock_obj.prices['Close'].resample('M').last().pct_change()\n",
        "\n",
        "    monthly_pct_change.fillna(0, inplace=True)\n",
        "\n",
        "    # Starting the portfolio with equal investment in each stock\n",
        "    num_stocks = len(nifty_50_symbols)\n",
        "    investment_per_stock = initial_investment / num_stocks\n",
        "    stock_investments = {stock: investment_per_stock for stock in nifty_50_symbols}\n",
        "\n",
        "    portfolio_value = [initial_investment]\n",
        "\n",
        "    for month in monthly_pct_change.index:\n",
        "        month_performance = monthly_pct_change.loc[month]\n",
        "        total_portfolio_value = sum(stock_investments.values())\n",
        "        positive_stocks = [stock for stock, pct_change in month_performance.items() if pct_change > 0]\n",
        "\n",
        "        if positive_stocks:\n",
        "            investment_per_positive_stock = total_portfolio_value / len(positive_stocks)\n",
        "            stock_investments = {stock: investment_per_positive_stock if stock in positive_stocks else 0 for stock in nifty_50_symbols}\n",
        "\n",
        "        month_gain = sum(investment_per_positive_stock * month_performance[stock] for stock in positive_stocks if pd.notna(month_performance[stock]))\n",
        "        current_portfolio_value = total_portfolio_value + month_gain\n",
        "        portfolio_value.append(current_portfolio_value)\n",
        "\n",
        "    adjusted_portfolio_value = portfolio_value[1:]\n",
        "\n",
        "    # Create Stock object for NIFTY50\n",
        "    nifty_50_stock = Stock('NIFTY50', db_path, start_date, end_date)\n",
        "\n",
        "    # Calculate monthly returns for NIFTY50\n",
        "    nifty_50_monthly_return = nifty_50_stock.prices['Close'].resample('M').last().pct_change()\n",
        "\n",
        "    # Initialize NIFTY50 benchmark portfolio value list\n",
        "    nifty_50_portfolio_value = [initial_investment]\n",
        "\n",
        "    # Calculate NIFTY50 benchmark portfolio value over time\n",
        "    for return_pct in nifty_50_monthly_return[1:]:\n",
        "        nifty_50_portfolio_value.append(nifty_50_portfolio_value[-1] * (1 + return_pct))\n",
        "\n",
        "    # Adjust lengths of the NIFTY50 portfolio value to match the dates\n",
        "    adjusted_nifty_50_portfolio_value = nifty_50_portfolio_value[1:]\n",
        "\n",
        "    # Calculate CAGR\n",
        "    final_value = portfolio_value[-1]\n",
        "    num_years = (datetime.strptime(end_date, '%Y-%m-%d') - datetime.strptime(start_date, '%Y-%m-%d')).days / 365.25\n",
        "    cagr = ((final_value / initial_investment) ** (1 / num_years)) - 1\n",
        "\n",
        "    return adjusted_portfolio_value, adjusted_nifty_50_portfolio_value, cagr, monthly_pct_change, nifty_50_monthly_return\n",
        "\n",
        "\n",
        "def plot_chart(start_date, end_date, investment_amount):\n",
        "  # Receive additional variables\n",
        "  adjusted_portfolio_value, adjusted_nifty_50_portfolio_value, cagr, monthly_pct_change, nifty_50_monthly_return = calculate_portfolio(start_date, end_date, investment_amount)\n",
        "\n",
        "  # Plotting the chart\n",
        "  plt.figure(figsize=(12, 6))\n",
        "  plt.plot(nifty_50_monthly_return.index, adjusted_portfolio_value, marker='o', label='Portfolio')\n",
        "  plt.plot(nifty_50_monthly_return[1:].index, adjusted_nifty_50_portfolio_value, marker='x', label='NIFTY50 Benchmark')\n",
        "  plt.title('Portfolio Value vs NIFTY50 Benchmark Over Time')\n",
        "  plt.xlabel('Month')\n",
        "  plt.ylabel('Portfolio Value (in Rupees)')\n",
        "  plt.legend()\n",
        "  plt.grid(True)\n",
        "  plt.savefig('portfolio_chart.png')\n",
        "  plt.show()\n",
        "  plt.close()\n",
        "\n",
        "  return 'portfolio_chart.png', f\"CAGR: {cagr*100:.2f}%\"\n",
        "\n",
        "plot_chart('2021-01-01', '2022-12-31', 10000)\n",
        "\n",
        "\n"
      ],
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      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA/8AAAIjCAYAAABViau2AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd3gUVRfH8e+mkwRCqAm99yogRSGAdAEjCgKioCA2UAQUsVClK4qCIoqAlFeKgIi00DtSpVfpEIqUAAFSdt8/hiwsCZCEJJNNfp/n2SezM7MzZ3c2kDP33HstNpvNhoiIiIiIiIikWS5mByAiIiIiIiIiyUvJv4iIiIiIiEgap+RfREREREREJI1T8i8iIiIiIiKSxin5FxEREREREUnjlPyLiIiIiIiIpHFK/kVERERERETSOCX/IiIiIiIiImmckn8RERERERGRNE7Jv4hIOrB582Zq1KiBj48PFouFHTt2xPu1EydOxGKxcOzYMfu62rVrU7t27SSPM6n069cPi8VidhgiD9ShQwd8fX3NDiPeChQoQNOmTc0OI9VJ7f8WiojcS8m/iEgyikmcYx5eXl4UK1aMLl26cO7cuSQ91+DBg5k7d26s9ZGRkbRs2ZJLly7x9ddfM3nyZPLnz5+k506s8+fP4+bmRrt27R64z7Vr18iQIQMtWrRIwchSr5jv0ldffRVrW8z3bcuWLfZ1MTdCLl68aF/XoUMHh+/lvY9FixY9cNv9j6FDh2KxWFi8eHGcsTZp0gQ/Pz/OnDkDGAlkXMd56623Yr32ypUrdO7cmezZs+Pj40OdOnXYtm1bvD6j2rVrOxzfw8ODggUL0rlzZ06ePBmvY0jSstlsTJ48mVq1apE5c2a8vb0pW7YsAwYM4MaNG2aHZ3fs2LF4f//vvSEqIuIM3MwOQEQkPRgwYAAFCxbk1q1brF27lh9++IEFCxawe/duvL29k+QcgwcP5sUXXyQ4ONhh/ZEjRzh+/Dg//fQTnTp1SpJzLVmyJEmOkyNHDurXr88ff/xBeHh4nJ/F7NmzuXXr1kNvEKRHI0aM4O23307098fT05Off/451vry5cszefJkh3W//vorISEhsdbXr1+fadOm8c4777B7924yZMhg3zZz5kwWLlzImDFjyJUrl319hQoV6NGjh8NxihUr5vDcarXy7LPP8s8///Dhhx+SLVs2vv/+e2rXrs3WrVspWrToI99fnjx5GDJkCAARERHs3buXsWPHsnjxYvbt25dkv3fyaNHR0bRt25YZM2ZQs2ZN+vXrh7e3N2vWrKF///7MnDmTpUuXkjNnTrNDJXv27LG+51999RWnTp3i66+/jrVvUv1bKCKSImwiIpJsJkyYYANsmzdvdljfvXt3G2CbNm3aYx3farXawsPDbTabzebj42Nr3759rH1WrVplA2wzZ85M1Dli3sPRo0cfI9IHmzx5sg2w/e9//4tze4MGDWx+fn62W7duxfuYffv2taXV/+IAW4UKFWyA7auvvnLYFtf3LeazuHDhgn1d+/btbT4+PvE+57vvvvvAz3PDhg02FxcXW+/eve3rwsLCbLly5bJVq1bNFh0dbV+fP39+27PPPvvI802fPj3Wd/b8+fO2zJkz29q0afPI1wcFBdlKly4da/3o0aNtgG3JkiWPPEZyS+g1MMuNGzdsNlv8r11cBg8ebANsPXv2jLVt3rx5NhcXF1ujRo0eK87EiHlvj/Lss8/a8ufPn7zBiIikAJX9i4iYoG7dugAcPXoUgKioKAYOHEjhwoXx9PSkQIECfPLJJ9y+fdvhdTH9bhcvXkzlypXJkCEDP/74IxaLhRs3bjBp0iR7SWqHDh3o0KEDQUFBALRs2RKLxeLQP3X58uXUrFkTHx8fMmfOzHPPPce+ffseGX9c/VzPnz9Px44dyZkzJ15eXpQvX55JkyY98ljPP/88Pj4+TJs2Lda28+fPs2zZMl588UU8PT1Zs2YNLVu2JF++fHh6epI3b14++OADbt68+dBzxJTyTpw4MdY2i8VCv379HNadPn2a119/nZw5c+Lp6Unp0qX55ZdfHvleypQpQ506dWKtt1qt5M6dmxdffNG+7rfffqNSpUpkzJiRTJkyUbZsWUaNGvXIcwA89dRT1K1bl+HDhz/yvSe3atWq8dZbb/Hll1+yd+9eAD777DPOnz/PuHHjcHGJ/adGRETEQ0u9Z82aRc6cOR26emTPnp1WrVrxxx9/xPq9iK+AgAAA3NwcCx/jc71XrlyJxWJhxowZDBo0iDx58uDl5cUzzzzD4cOHY51r06ZNNGnSBH9/f3x8fChXrlyc1/f06dMEBwfj6+tL9uzZ6dmzJ9HR0fbtMd/dL7/8kjFjxlCoUCG8vb1p0KABJ0+exGazMXDgQPLkyUOGDBl47rnnuHTpksM5/vjjD5599lly5cqFp6cnhQsXZuDAgQ7nAeP3ukyZMmzdupVatWrh7e3NJ5988sDPc9KkSbi5ufHhhx8+cJ+bN28yYsQIihUrZq/EuFezZs1o3749ixYtYuPGjQA0bdqUQoUKxXm86tWrU7lyZYd1U6ZMoVKlSmTIkIEsWbLQunXrWN07Evre4uv+fwvv/Z7079+f3LlzkzFjRl588UWuXr3K7du36datGzly5MDX15fXXnstzu9zfN6TiEhCqexfRMQER44cASBr1qwAdOrUiUmTJvHiiy/So0cPNm3axJAhQ9i3bx9z5sxxeO2BAwdo06YNb775Jm+88QbFixdn8uTJdOrUiSeffJLOnTsDULhwYQBy587N4MGDee+996hSpYq9tHbp0qU0btyYQoUK0a9fP27evMl3333HU089xbZt2yhQoEC838/NmzepXbs2hw8fpkuXLhQsWJCZM2fSoUMHrly5wvvvv//A1/r4+PDcc88xa9YsLl26RJYsWezbpk+fTnR0NC+//DJglJKHh4fz9ttvkzVrVv7++2++++47Tp06xcyZM+Md78OcO3eOatWqYbFY6NKlC9mzZ2fhwoV07NiRsLAwunXr9sDXvvTSS/Tr14/Q0FB7ogmwdu1azpw5Q+vWrQEICQmhTZs2PPPMMwwbNgyAffv2sW7duod+Vvfq168ftWrV4ocffqB79+6Jeq/3jgMA4O7ujp+fX4KPM2TIEObOncubb77JN998w5gxY/jwww8pW7ZsrH2XL1+Ot7c30dHR5M+fnw8++CDWe96+fTtPPPFErBsHTz75JOPGjePgwYNxHvte0dHR9vcXGRnJvn376Nu3L0WKFOGpp56y75fQ6z106FBcXFzo2bMnV69eZfjw4bz88sts2rTJvk9ISAhNmzYlMDCQ999/n4CAAPbt28f8+fMd3mt0dDQNGzakatWqfPnllyxdupSvvvqKwoUL8/bbbzucd+rUqURERNC1a1cuXbrE8OHDadWqFXXr1mXlypX06tWLw4cP891339GzZ0+HmxcTJ07E19eX7t274+vry/Lly+nTpw9hYWGMGDHC4Tz//fcfjRs3pnXr1rRr1+6Bpfjjxo3jrbfe4pNPPuGLL7544HVYu3Ytly9f5v3334910yXGq6++yoQJE5g/fz7VqlXjpZde4tVXX2Xz5s1UqVLFvt/x48fZuHGjQ8yDBg3i888/p1WrVnTq1IkLFy7w3XffUatWLbZv307mzJkT/N6SwpAhQ8iQIQMff/yx/bq4u7vj4uLC5cuX6devHxs3bmTixIkULFiQPn36JOo9iYgkiNmlByIiaVlMGfbSpUttFy5csJ08edL222+/2bJmzWrLkCGD7dSpU7YdO3bYAFunTp0cXtuzZ08bYFu+fLl9Xf78+W2AbdGiRbHO9aCy/xUrVsRZ9l+hQgVbjhw5bP/995993T///GNzcXGxvfrqq7Hew71l/0FBQbagoCD782+++cYG2KZMmWJfFxERYatevbrN19fXFhYW9tDP6a+//rIBth9//NFhfbVq1Wy5c+e2l47HdHG415AhQ2wWi8V2/Phx+7r7y/6PHj1qA2wTJkyI9XrA1rdvX/vzjh072gIDA20XL1502K9169Y2Pz+/OGOIceDAARtg++677xzWv/POOzZfX1/7a99//31bpkyZbFFRUQ881oMAtnfffddms9lsderUsQUEBNiPm5CyfyDW495req+Hlf3HmDVrlg2wZcmSxVaoUKE4P6dmzZrZhg0bZps7d65t/Pjxtpo1a9oA20cffeSwn4+Pj+3111+P9fqY70lc3/97BQUFxfn+SpYsafv3338d9o3v9Y75PSpZsqTt9u3b9v1GjRplA2y7du2y2Ww2W1RUlK1gwYK2/Pnz2y5fvuxwTKvVal+OuQYDBgxw2KdixYq2SpUq2Z/HfHezZ89uu3Llin197969bYCtfPnytsjISPv6Nm3a2Dw8PBy6ycR1Ld58802bt7e3w34xn9vYsWNj7X9v2f+oUaNsFovFNnDgwFj73S/m34Y5c+Y8cJ9Lly7ZAFuLFi1sNpvNdvXqVZunp6etR48eDvsNHz7c4Xf92LFjNldXV9ugQYMc9tu1a5fNzc3NYf3D3tujPKzs//5/C2O+J2XKlLFFRETY17dp08ZmsVhsjRs3dnh99erVHY6dkPckIpJQKvsXEUkB9erVI3v27OTNm5fWrVvj6+vLnDlzyJ07NwsWLACI1XobMyjaX3/95bC+YMGCNGzY8LHiOXv2LDt27KBDhw4OLe3lypWjfv369pjia8GCBQQEBNCmTRv7Ond3d9577z2uX7/OqlWrHvr6Bg0akD17dofS/6NHj7Jx40batGljbwG+d0C5GzducPHiRWrUqIHNZmP79u0JijkuNpuN33//nWbNmmGz2bh48aL90bBhQ65evfrQEeeLFStGhQoVmD59un1ddHQ0s2bNolmzZvb4M2fOzI0bNwgJCXmseGOqDMaOHZvg13p5eRESEuLwiGsGgfh64YUXaNKkCZcuXWLMmDEO1yrGvHnz+Oijj3juued4/fXXWbVqFQ0bNmTkyJGcOnXKvt/Nmzfx9PSMM+aY7Y9SoEAB+/tauHAh33zzDVevXqVx48ZcuHABSNz1fu211/Dw8LA/r1mzJgD//vsvYFQtHD16lG7dusVqoY1r+sn7ZzqoWbOm/Vj3atmypUNVRtWqVQFo166dQ4t61apViYiI4PTp0/Z1916La9eucfHiRWrWrEl4eDj79+93OI+npyevvfZarPPHGD58OO+//z7Dhg3js88+e+B+954PIGPGjA/cJ2ZbWFgYAJkyZaJx48bMmDEDm81m32/69OlUq1aNfPnyAcZgoFarlVatWjlcu4CAAIoWLcqKFSsS9N6S0quvvoq7u7v9edWqVbHZbLz++usO+1WtWpWTJ08SFRUFJPw9iYgkhMr+RURSwJgxYyhWrBhubm7kzJmT4sWL2xPa48eP4+LiQpEiRRxeExAQQObMmTl+/LjD+oIFCz52PDHHLF68eKxtJUuWZPHixdy4cQMfH594H69o0aKxyrRLlizpcL4HcXNz46WXXuL777/n9OnT5M6d234jIKbkH+DEiRP06dOHefPmcfnyZYdjXL16NV6xPsyFCxe4cuUK48aNY9y4cXHuc/78+Yce46WXXuKTTz6xv4+VK1dy/vx5XnrpJfs+77zzDjNmzKBx48bkzp2bBg0a0KpVKxo1apSgeGvVqkWdOnUYPnx4nNPlPYyrqyv16tVL0GsepUqVKixYsCBWn+wHsVgsfPDBByxevJiVK1faZ3TIkCFDnP2gb926Zd/+KD4+Pg7vr1GjRjz99NNUrlyZoUOH8tVXXyXqescknjH8/f0B7N/HmC49ZcqUeWSMXl5eZM+ePdbx7v9ux3XemBsBefPmjXP9vcfYs2cPn332GcuXL7cn2DHu/73JnTu3w82Ne61atYq//vqLXr16PbSf/71iEvuYmwBxiesGwUsvvcTcuXPZsGEDNWrU4MiRI2zdupVvvvnGvs+hQ4ew2WwPnP3h3uT7Ue8tqSXkelmtVq5evUrWrFkT/J5ERBJCyb+ISAp48sknH5kQxdUqGJf4JD7OqF27dowePZr//e9/9OzZk//973+UKlWKChUqAEYLev369bl06RK9evWiRIkS+Pj4cPr0aTp06IDVan3gsR/02d4/4FnMMdq1a0f79u3jfE25cuUe+j5eeuklevfuzcyZM+nWrRszZszAz8/PIbHPkSMHO3bsYPHixSxcuJCFCxcyYcIEXn311XgNknivvn37Urt2bX788Uen7AsckwzdO0hdYGAgZ8+ejbVvzLp7pw5MiEqVKuHn58fq1auBxF1vV1fXOPe7t4U6vh50rITs+6h4rly5QlBQEJkyZWLAgAEULlwYLy8vtm3bRq9evWL93jzs35fSpUtz5coVJk+ezJtvvhmvG5ExNwB37twZaxrSGDt37gSgVKlS9nXNmjXD29ubGTNmUKNGDWbMmIGLiwstW7a072O1WrFYLCxcuDDOz8HX1zfe7y2pJfZ6JfQ9iYgkhJJ/ERGT5c+fH6vVyqFDh+x/KIMxENmVK1fInz9/vI4T35sHMecEY/DA++3fv59s2bLFu9U/5ng7d+7EarU6tP7HlBTH5z1UrVqVwoULM23aNOrXr8+ePXsYNGiQffuuXbs4ePAgkyZN4tVXX7Wvj0/pfEzr7JUrVxzW31+RkD17djJmzEh0dHSiW8ULFizIk08+yfTp0+nSpQuzZ88mODg4Vhm7h4cHzZo1o1mzZlitVt555x1+/PFHPv/881hVIA8TFBRE7dq1GTZsmMOgYc4ipsT93hbwChUqsGbNmljfp02bNuHt7U2xYsUSfb7o6GiuX79uP+fjXu/7xQy0uXv37iSvrEiMlStX8t9//zF79mxq1aplXx8z00hCZMuWjVmzZvH000/zzDPPsHbt2kfeiHn66afJnDkz06ZN49NPP40zof31118BY5T/GD4+PjRt2pSZM2cycuRIpk+fTs2aNR3OV7hwYWw2GwULFnys70Rqkhbfk4ikHurzLyJisiZNmgA4lLMCjBw5EoBnn302Xsfx8fGJldw+SGBgIBUqVGDSpEkOr9m9ezdLliyxxxRfTZo0ITQ01KGve1RUFN999x2+vr726QYf5eWXX2b79u307dsXi8VC27Zt7dtikoZ7W1htNlu8psfLlCkT2bJls7f4xvj+++8dnru6uvLCCy/w+++/s3v37ljHiekr/igvvfQSGzdu5JdffuHixYsOJf9gjDp+LxcXF3sLc2KmsYvp+/+g0vXU4NKlS7EqLSIjIxk6dCgeHh4OUyS++OKLnDt3jtmzZ9vXXbx4kZkzZ9KsWbM4xwOIjxUrVnD9+nXKly8PJN31vtcTTzxBwYIF+eabb2L9PiamOuBxxfV7ExEREeu7H1958uRh6dKl3Lx5k/r168f6Lt/P29ubnj17cuDAAT799NNY2//66y8mTpxIw4YNqVatmsO2l156iTNnzvDzzz/zzz//xPo9atGiBa6urvTv3z/WZ2uz2R4ZW2qUFt+TiKQeavkXETFZ+fLlad++PePGjbOX6P79999MmjSJ4ODgOOeNj0ulSpVYunQpI0eOJFeuXBQsWNA+KFhcRowYQePGjalevTodO3a0T/Xn5+cXa977R+ncuTM//vgjHTp0YOvWrRQoUIBZs2axbt06vvnmm4cO9nWvdu3aMWDAAP744w+eeuoph+kGS5QoQeHChenZsyenT58mU6ZM/P7773H2j45Lp06dGDp0KJ06daJy5cqsXr2agwcPxtpv6NChrFixgqpVq/LGG29QqlQpLl26xLZt21i6dGmsOdTj0qpVK3r27EnPnj3JkiVLrBbgTp06cenSJerWrUuePHk4fvw43333HRUqVHCo/oivoKAggoKCHjmwopnmzZvHF198wYsvvkjBggW5dOkS06ZNY/fu3QwePNhhasQXX3yRatWq8dprr7F3716yZcvG999/T3R0NP3794/X+a5evcqUKVMA40bUgQMH+OGHH+zTr8VIiut9LxcXF3744QeaNWtGhQoVeO211wgMDGT//v3s2bOHxYsXJ+h4j6tGjRr4+/vTvn173nvvPSwWC5MnT36sGxFFihRhyZIl1K5dm4YNG7J8+XIyZcr0wP0//vhjtm/fzrBhw9iwYQMvvPACGTJkYO3atUyZMoWSJUvG2d2lSZMmZMyYkZ49e9pv1NyrcOHCfPHFF/Tu3Ztjx44RHBxMxowZOXr0KHPmzKFz58707Nkz0e/TDGnxPYlI6qHkX0QkFfj5558pVKgQEydOZM6cOQQEBNC7d2/69u0b72OMHDmSzp0789lnn3Hz5k3at2//0OS/Xr16LFq0iL59+9KnTx/c3d0JCgpi2LBhCR5UMEOGDKxcuZKPP/6YSZMmERYWRvHixZkwYQIdOnSI93GKFi1KlSpV2Lx5s8NAf2AMdPXnn3/y3nvvMWTIELy8vHj++efp0qWLvSX3Yfr06cOFCxeYNWuWfbC9hQsXkiNHDof9cubMyd9//82AAQOYPXs233//PVmzZqV06dIMGzYsXu8jT5481KhRg3Xr1tGpU6dYg3S1a9eOcePG8f3333PlyhUCAgJ46aWX6NevX6xBE+OrX79+8b5RZIayZctSqlQppkyZwoULF/Dw8KBChQrMmDHDoR83GK3VCxYs4MMPP+Tbb7/l5s2bVKlShYkTJ8Y5SGVcTp06xSuvvAIYXWL8/f0JCgqib9++9nEkIGmu9/0aNmzIihUr6N+/P1999RVWq5XChQvzxhtvJOp4jyNr1qzMnz+fHj168Nlnn+Hv70+7du145plnHmvWkLJly7Jw4ULq1atHs2bNWLRo0QP71Lu6ujJjxgx+/fVXfv75Zz7//HMiIiIoXLgwffv2pUePHnF2M/Ly8qJ58+ZMnTqVevXqxfpdBePGQrFixfj666/tN4by5s1LgwYNaN68eaLfn5nS4nsSkdTBYjOjBk1EREREREREUoz6/IuIiIiIiIikcUr+RURERERERNI4Jf8iIiIiIiIiaZySfxEREREREZE0Tsm/iIiIiIiISBqn5F9EREREREQkjXMzO4C0wmq1cubMGTJmzIjFYjE7HBEREREREUnjbDYb165dI1euXLi4PLxtX8l/Ejlz5gx58+Y1OwwRERERERFJZ06ePEmePHkeuo+S/ySSMWNGwPjQM2XKZF8fGRnJkiVLaNCgAe7u7maFJwmga+acdN2cl66dc9P1cz66Zs5J18156do5L2e4dmFhYeTNm9eejz6Mkv8kElPqnylTpljJv7e3N5kyZUq1XxhxpGvmnHTdnJeunXPT9XM+umbOSdfNeenaOS9nunbx6XquAf9ERERERERE0jgl/yIiIiIiIiJpnJJ/ERERERERkTROff5TkM1mIyoqiujoaLNDkYeIjIzEzc2NW7du6Vo9Bnd3d1xdXc0OQ0REREREUPKfYiIjIzlz5gzh4eFmhyKPYLPZCAgI4OTJk/EaOEPiZrFYyJMnD76+vmaHIiIiIiKS7in5TyEnTpzAzc2NXLly4eHhoaQyFbNarVy/fh1fX19cXNQzJjFsNhsXLlzg1KlTFC1aVBUAIiIiIiImU/KfAtzc3LBareTKlQtvb2+zw5FHsFqtRERE4OXlpeT/MWTPnp1jx44RGRmp5F9ERERExGTKbFKQEklJT1TdIiIiIiKSeigbFREREREREUnjlPyLiIiIiIiIpHFK/p1MtNXGhiP/8ceO02w48h/RVpvZISWZ/fv3U61aNby8vKhQoUK8XtOhQweCg4Ptz2vXrk23bt2SJT4RERERERFnpQH/nMii3Wfp/+dezl69ZV8X6OdF32alaFQmMFnO2aFDByZNmgQY87bny5ePV199lU8++QQ3t8R9fTp06MCVK1eYO3euw/q+ffvi4+PDgQMHEj093OzZs3F3d0/Ua0VERERERNIqtfw7iUW7z/L2lG0OiT9A6NVbvD1lG4t2n022czdq1IizZ89y6NAhevToQb9+/RgxYkSCjxMdHY3Van3g9iNHjvD000+TP39+smbNmqhYs2TJQsaMGRP1WhERERERkbRKyb9JbDYb4RFR8XpcuxVJ33l7iKvAP2Zdv3l7uXYrMl7Hs9kS1lXA09OTgIAA8ufPz9tvv029evWYN28ely9f5tVXX8Xf3x9vb28aN27MoUOH7K+bOHEimTNnZt68eZQqVQpPT09ef/11Jk2axB9//IHFYsFisbBy5UosFgtbt25lwIABWCwW+vXrB8CuXbuoW7cuGTJkIGvWrHTu3Jnr168/MNb7y/4fFaOIiIiIiEh6oLJ/k9yMjKZUn8VJciwbEBp2i7L9lsRr/70DGuLtkfhLnyFDBv777z86dOjAoUOHmDdvHpkyZaJXr140adKEvXv32kvvw8PDGTZsGD///DNZs2YlMDCQmzdvEhYWxoQJEwCjtf7s2bPUq1ePRo0a0bNnT3x9fblx4wYNGzakevXqbN68mfPnz9OpUye6dOnCxIkT4xVrfGIUEREREZEUtmIIuLhC0Eext60aDtZoqNM75eNKw9TyL/Fms9lYunQpixcvJl++fMybN4+ff/6ZmjVrUr58eaZOncrp06cd+vJHRkby/fffU6NGDYoXL06mTJnIkCGDvZogICAADw8PAgICcHNzw9fXl4CAAHx9fZk2bRq3bt3i119/pUyZMtStW5fRo0czefJkzp0798h4Y5L+R8UoIiIiIiIpzMUVVgwyEv17rRpurHdxNSeuNEwt/ybJ4O7K3gEN47Xv30cv0WHC5kfuN/G1KjxZMEu8zp0Q8+fPx9fXl8jISKxWK23btqVFixbMnz+fqlWr2vfLmjUrxYsXZ9++ffZ1Hh4elCtXLkHni7Fv3z7Kly+Pj4+Pfd1TTz2F1WrlwIED5MyZ85Gvd3Nze2SMIiIiIiKSwmJa/FcMgivHoekoWDvSeF7n07grAuSxKPk3icViiXfpfc2i2Qn08yL06q04+/1bgAA/L2oWzY6riyVJ4wSoU6cOP/zwAx4eHuTKlQs3NzfmzZsXr9dmyJABiyXpYxIREREREScX9BEcWgLbp8COaWCzKvFPRir7dwKuLhb6NisFGIn+vWKe921WKlkSfwAfHx+KFClCvnz57NP7lSxZkqioKDZt2mTf77///uPAgQOUKlXqocfz8PAgOjr6kectWbIk//zzDzdu3LCvW7duHS4uLhQvXjxer09sjCIiIiIiksxCd8PprcayzQoubkr8k5GSfyfRqEwgP7R7ggA/L4f1AX5e/NDuCRqVCUzReIoWLcpzzz3HG2+8wdq1a/nnn39o164duXPn5rnnnnvoawsUKMDOnTs5cOAAFy9eJDIyMs79Xn75Zby8vGjfvj27d+9mxYoVdO3alVdeeeWRJf+PG6OIiIiIiCQjmw0WfGgk/TGsUbDkc/NiSuNU9u9EGpUJpH6pAP4+eonz126RI6MXTxbMkmwt/o8yYcIE3n//fZo2bUpERAS1atViwYIFjxxF/4033mDlypVUrlyZ69evs2LFCmrXrh1rP29vbxYvXsz7779PlSpV8Pb25oUXXmDkyJHJHqOIiIiIiCSjXbPgxHpjudrbcHw9nP0H1n8LHr5Qu5e58aVBSv6djKuLheqFs6bY+R42pZ6/vz+//vrrA7d36NCBDh06xFqfPXt2liyJPS3hjh07Yq0rW7Ysy5cvj3d8K1euTFCMIiIiIiKSwm5fg/ndjOWCQdBoKFw4CD/WgqibsHIwWCzqApDEVPYvIiIiIiIiKWfVcIi4Dhn84eWZxrrsxaDBQGPZxQ2uPXpqb0kYJf8iIiIiIiKSMi4chI3fG8vPjwM3z7vbqnSCIvWMvv+nNkFUhDkxplFK/kVERERERCT52Wyw8CMjuS/WCIo1cNxuscBzYyBDFgjdZZT/S5JR8i8iIiIiIiLJb/98+HcFuHpAoyFx75MxAJp/ayyv/QaOrUux8NI6Jf8iIiIiIiKSvCLCYdEnxvJT70OWQg/et2QzqNAOsMGct+DW1RQJMa1T8i8iIiIiIiLJa903cPUE+OWFp7s/ev/GQyFzfuM1CzXtX1JQ8i8iIiIiIiLJ59JRo4QfoOEg8PB+9Gs8M0KLcWBxgX/+B3vmJGuI6YGSfxEREREREUk+iz+B6NtQqDaUbB7/1+WrdrdK4M9uEHYmOaJLN5T8i4iIiIiISPI4uAQOLAAXN2g83BjRPyFqfwyBFeDWFZj7DlityRFluqDkXySVmjhxIpkzZzY7DAe1a9emW7duZochIiIiIs4g6jYsutNfv9rbkL14wo/h6g4tfgK3DMZMAX//mLQxpiNK/p3FiiGwanjc21YNN7Yngw4dOmCxWBg6dKjD+rlz52K5567dypUrsVgsXLlyxeH5/Y/PPvuMAgUKxLkt5tG2bVu8vb2ZNm2awzmtVis1atTgxRdfBKBfv36xXluiRAmH19y6dYt3332XrFmz4uvrywsvvMC5c+ce+p7r1q2Lv78/rq6uWCwWcubMScuWLTl+/HhiP0YRERERkfRnw2i49C/45oRaHyX+ONmLQYOBxnJIXzi/L2niS2eU/DsLF1dYMSj2DYBVw431Lq7JdmovLy+GDRvG5cuXE/zaAwcOcPbsWfvj448/ZvPmzfbnv//+e6z9fvjhB4YOHUrXrl05e/as/VhfffUV//77L2PHjrWvK126tMPx165d63D+Dz74gD///JOZM2eyatUqzpw5Q4sWLR4Zd/v27Tl9+jRnzpzhjz/+4OTJk7Rr1y7B7z+tiIiIMDsEEREREXEmV0/D6i+N5foDwSvT4x2vSicoUt8YO2D2G0ZVgSSIkn+z2GwQcSP+j+rvQq0PjUR/+RfGuuVfGM9rfWhsj++xbLYEhVqvXj0CAgIYMiTh1QU5cuQgICDA/vD19SV79uz251myZIm1n5+fH127dqV8+fK88cYbAOzfv58+ffowbtw4smXLZj++m5ubw/Hv3Xb16lXGjx/PyJEjqVu3LpUqVWLChAmsX7+ejRs3PjTuDBkyEBAQQGBgINWqVaNLly5s27bNYZ/du3fTuHFjfH19yZkzJ6+88goXL160b69duzbvvfceH330EVmyZCEgIIB+/fo5HOPKlSu8+eab5MyZEy8vL8qUKcP8+fMd9lm8eDElS5bE19eXRo0aOdwQ6dChA8HBwQwePJicOXOSOXNmBgwYQFRUFB9++CFZsmQhT548TJgwweGYvXr1olixYnh7e1OoUCE+//xzIiMj7dv79etHhQoV+PnnnylYsCBeXl5xfk5//fUXfn5+TJ069aGfp4iIiIikM0s+g8hwyFcdyrV6/ONZLPDcGPDOCqG7jDxIEsTN7ADSrchwGJwrca9dPcJ4POj5o3xyBjx84r27q6srgwcPpm3btrz33nvkyZMnAcEmjsViYcKECZQrV46ffvqJ8ePH07p1a5o3dxwd9NChQ+TKlQsvLy+qV6/OkCFDyJcvHwBbt24lMjKSevXq2fcvUaIE+fLlY8OGDVSrVi1esVy6dIkZM2ZQtWpV+7orV65Qt25dOnXqxNdff83Nmzfp1asXrVq1Yvny5fb9Jk2aRPfu3dm0aRMbNmygQ4cOPPXUU9SvXx+r1Urjxo25du0aU6ZMoXDhwuzduxdX17tVHOHh4Xz55ZdMnjwZFxcX2rVrR8+ePR2S7eXLl5MnTx5Wr17NunXr6NixI+vXr6dWrVps2rSJ6dOn8+abb1K/fn37tcuYMSMTJ04kV65c7Nq1izfeeIOMGTPy0Ud3y7EOHz7M77//zuzZsx1iijFt2jTeeustpk2bRtOmTeP1WYqIiIhIOnB0NeyZbUzTl5hB/h4kY05o9i1MfxnWfQtFG0CBp5Pm2OmAkn+Jl+eff54KFSrQt29fxo8fH+/X3X+j4Pjx42TNmjVer82fPz/ffPMNnTp1Ik+ePCxZssRhe9WqVZk4cSLFixfn7Nmz9O/fn5o1a7J7924yZsxIaGgoHh4esQbNy5kzJ6GhoQ899/jx45k8eTI2m43w8HCKFSvG4sWL7dtHjx5NxYoVGTx4sH3dL7/8Qt68eTl48CDFihUDoFy5cvTt2xeAokWLMnr0aJYtW0b9+vVZunQpf//9N/v27bPvX6hQIYc4IiMjGTt2LIULFwagS5cuDBgwwGGfLFmy8O233+Li4kLx4sUZPnw44eHhfPLJJwD07t2boUOHsnbtWlq3bg3AZ599Zn99gQIF6NmzJ7/99ptD8h8REcGvv/5K9uzZY30+Y8aM4dNPP+XPP/8kKCjooZ+liIiIiKQj0ZGw4M7flJU7QmC5pD1+yaZQsR1snwJz3oK314GXX9KeI41S8m8Wd2+jBT6h1n5ttPK7ekB0hFHy//QHCT93IgwbNoy6devSs2fPeL9mzZo1ZMyY0f7c398/Qed87bXX+Pzzz+natSuZMjn2E2rcuLF9uVy5clStWpX8+fMzY8YMOnbsmKDz3K9ly5b07dsXFxcXzp07x+DBg2nQoAFbt24lY8aM/PPPP6xYsQJfX99Yrz1y5IhD8n+vwMBAzp8/D8COHTvIkyePfd+4eHt72xP/+18fo3Tp0ri43O3BkzNnTsqUKWN/7urqStasWR1eN336dL799luOHDnC9evXiYqKivX55s+fP87Ef9asWZw/f55169ZRpUqVB8YuIiIiIunQ3z/BhX1GeX6dT5LnHI2GwrG1cPkYLPgQWoxLnvOkMerzbxaLxSi9T8hjwxgj8a/zKXx+wfi5eoSxPiHHSWTZTa1atWjYsCG9e/eO92sKFixIkSJF7I97k9T4cnNzw83t0fepMmfOTLFixTh8+DAAAQEBRERE2GcgiHHu3DkCAgIeeqxMmTLZY37qqacYP348hw4dYvr06QBcv36dZs2asWPHDofHoUOHqFWrlv047u7uDse1WCxY78xNmiFDhke+p7heb7tvzIa49nnYeTds2MDLL79MkyZNmD9/Ptu3b+fTTz+NNaifj0/cXUMqVqxI9uzZ+eWXX2LFIiIiIiLp2LVzsPLOOGHP9AXvLMlzHs+MxvR/FhfYOR12z06e86QxSv6dRcyo/nU+haA7ZTRBHxnP45oFIJkMHTqUP//8kw0bNqTI+RLi+vXrHDlyhMDAQAAqVaqEu7s7y5Yts+9z4MABTpw4QfXq1RN07Jg+7zdv3gTgiSeeYM+ePRQoUMDh5kaRIkUemDTfr1y5cpw6dYqDBw8mKJbHtX79evLnz8+nn35K5cqVKVq0aIKmMSxcuDArVqzgjz/+oGvXrskYqYiIiIg4laX94HYY5KoIFV9J3nPlfRJq9jCW538AYYmoqk5nlPw7C2u0Y+IfI+YGgDU6RcIoW7YsL7/8Mt9++22KnO9hevbsyapVqzh27Bjr16/n+eefx9XVlTZt2gDg5+dHx44d6d69OytWrGDr1q289tprVK9e/ZGD/d28eZPQ0FBCQ0P5559/ePvtt/Hy8qJBgwYAvPvuu1y6dIk2bdqwefNmjhw5wuLFi3nttdeIjo7ftQgKCqJWrVq88MILhISEcPToURYuXMiiRYse74N5hKJFi3LixAl+++03jhw5wrfffsucOXMSdIxixYqxYsUKfv/9d7p165Y8gYqIiIiI8zixCf6ZZiw3+RISUfGbYEG9jBsNt67A3LfhTqWrxE3Jv7Oo0zt24h8j6CNjewoZMGCAvYTcTKdOnaJNmzYUL16cVq1akTVrVjZu3OjQT/3rr7+madOmvPDCC9SqVYuAgABmz350WdCkSZPInTs3gYGB1KlTh4sXL7JgwQKKFy8OQK5cuVi3bh3R0dE0aNCAsmXL0q1bNzJnzpygrg2///47VapUoU2bNpQqVYqPPvoo3jcPEqt58+Z88MEHdOnShQoVKrB+/Xo+//zzBB+nePHiLF++nP/973/06NEjGSIVEREREadgjYaFHxrLFdtBnsopc15Xd6P83y0D/LsSNo1NmfM6KYtNnXaTRFhYGH5+fly9etVh4LTIyEiWLFlCwYIFKVSo0APnS5fUw2q1EhYWRqZMmRI1RoEYbt26xdGjRylYsGCKfO8jIyNZsGABTZo0iTXmgaRuunbOTdfP+eiaOSddN+eVLq7dll+M0ntPP+i6FXxjDxqdrDb/DH/1AFdP6LwScpZKksM6w7V7UB4aF1Mzm9WrV9OsWTNy5cqFxWJh7ty5Dtv79etHiRIl8PHxwd/fn3r16rFp0yaHfQoUKIDFYnF4DB061GGfnTt3UrNmTby8vMibNy/Dh8fuHz9z5kxKlCiBl5cXZcuWZcGCBUn+fkVERERERNKU8Euw7M5U1HU/TfnEH4wpBYs2gOjbMLszRN1O+RicgKnJ/40bNyhfvjxjxoyJc3uxYsUYPXo0u3btYu3atRQoUIAGDRpw4cIFh/0GDBjA2bNn7Y97ByELCwujQYMG5M+fn61btzJixAj69evHuHF3p4NYv349bdq0oWPHjmzfvp3g4GCCg4PZvXt38rxxERERERGRtGD5QLh5GXKUNpJwM1gs0Hy0Mb3guV2w/Atz4kjlHj1/WjJq3Lixw1zt92vbtq3D85EjRzJ+/Hh27tzJM888Y1+fMWPGB07dNnXqVCIiIvjll1/w8PCgdOnS7Nixg5EjR9K5c2cARo0aRaNGjfjwQ6OfysCBAwkJCWH06NGMHat+IyIiIiIiIrGc2QFbJhjLTUaAq4npZcac0Pw7+K0trP8OijWEAk+bF08qZGrynxARERGMGzcOPz8/ypcv77Bt6NChDBw4kHz58tG2bVs++OAD+7zwGzZsoFatWnh4eNj3b9iwIcOGDePy5cv4+/uzYcMGunfv7nDMhg0bxuqGcK/bt29z+/bdcpKwsDDA6BcSGRlpXx+zbLPZsFqtqWKgPHm4mGEwYq6ZJI7VasVmsxEZGWmfKjE5xfyu3fv7J85B18656fo5H10z56Tr5rzS7LWzWXH9qycu2LCWfoHo3E+C2e+xcANcy7+Myz9Tsc1+k6g3VoGXX6IP5wzXLiGxpfrkf/78+bRu3Zrw8HACAwMJCQkhW7Zs9u3vvfceTzzxBFmyZGH9+vX07t2bs2fPMnLkSABCQ0MpWLCgwzFz5sxp3+bv709oaKh93b37hIaGPjCuIUOG0L9//1jrlyxZgre3t8M6Nzc3bt26xbVr14iIiEjYByCmuXbtmtkhOLWIiAhu3rzJ6tWriYqKSrHzhoSEpNi5JGnp2jk3XT/no2vmnHTdnFdau3Z5/1vDE6c3E+XiyTJqciuVjJnmaguitkcIvmGnCP3lFbYVeOuxj5mar114eHi89031yX+dOnXYsWMHFy9e5KeffqJVq1Zs2rSJHDlyADi02JcrVw4PDw/efPNNhgwZgqenZ7LF1bt3b4dzh4WFkTdvXho0aBBrtP+lS5fi5eWFi4vLI0dgFPPZbDauXbtGxowZsVgsZofjtMLCwsiQIQN169a1V+Ikp8jISEJCQqhfv36qHY1V4qZr59x0/ZyPrplz0nVzXmny2t0Kw22sMdWzpXYv6lZvZ24897FUzIvt12fJe3k9gbVfw1bq+UQdxxmuXUwFenyk+uTfx8eHIkWKUKRIEapVq0bRokUZP348vXvHPa991apViYqK4tixYxQvXpyAgADOnTvnsE/M85hxAh60z4PGEQDw9PSM8+aCu7t7rC+GzWbDz8+Pixcv4uLigre3t5LKVMxqtRIREcHt27c11V8iWa1WLl68iI+PD15eXin6fY/rd1Ccg66dc9P1cz66Zs5J1815palrt+wruHEBshbBtUZXXN1S2fsqWANq9oTVw3Fb2BMKPAV+uRN9uNR87RISV6pP/u9ntVod+trfb8eOHbi4uNgrA6pXr86nn35KZGSk/YMJCQmhePHi+Pv72/dZtmwZ3bp1sx8nJCSE6tWrJ1ncOXLkwNXVlfPnzyfZMSV52Gw2bt68SYYMGXST5jG4uLiQL18+fYYiIiIiacn5fbDpzqDojYeDm8fD9zdL0EdweCmc2QZz34JX/oB03rBnavJ//fp1Dh8+bH9+9OhRduzYQZYsWciaNSuDBg2iefPmBAYGcvHiRcaMGcPp06dp2bIlYAzmt2nTJurUqUPGjBnZsGEDH3zwAe3atbMn9m3btqV///507NiRXr16sXv3bkaNGsXXX39tP+/7779PUFAQX331Fc8++yy//fYbW7ZscZgO8HFZLBYCAwPJkSNHqh4wQozyntWrV1OrVq1Ue4fPGXh4eKhyQkRERCQtsdlgwYdgi4YSTaHIM49+jVlc3aHFT/BjTTi6Gjb9ANXfNTsqU5ma/G/ZsoU6derYn8f0oW/fvj1jx45l//79TJo0iYsXL5I1a1aqVKnCmjVrKF26NGCU3v/222/069eP27dvU7BgQT744AOHvvh+fn4sWbKEd999l0qVKpEtWzb69Oljn+YPoEaNGkybNo3PPvuMTz75hKJFizJ37lzKlCmT5O/Z1dU1RUY+l8RzdXUlKioKLy8vJf8iIiIiIjH2zIFja8DNCxoONjuaR8tWBBp8AX91h6X9oVBtyFna7KhMY2ryX7t2bfu0anGZPXv2Q1//xBNPsHHjxkeep1y5cqxZs+ah+7Rs2dJeUSAiIiIiIiL3uH0dlnxmLD/dHfzzmxtPfFV+HQ4uhkOL4fc3oPMKcEu+geFTM9XkioiIiIiIyMOt+QrCTkPm/PDUe2ZHE38WCzw3Gryzwfk9sHyg2RGZRsm/iIiIiIiIPNjFw7D+O2O50RBwz2BuPAnlmwOa34l//WhjDIB0SMm/iIiIiIiIxM1mg0UfgzUSitSD4k3MjihxSjSBJ9oDNpjzNty8YnZEKU7Jv4iIiIiIiMTt4CI4HAIu7tBomFFG76waDoYshSDsFCzoaXY0KU7Jv4iIiIiIiMQWeQsW9jKWa3QxRs93Zp6+8Pw4sLjCrpmwa5bZEaUoJf8iIiIiIiIS2/pv4cpxyJgLaqaRlvK8VaDWh8by/O5w9ZS58aQgJf8iIiIiIiLi6PJxY4R/gIZfGK3maUWtnpC7Ety+CnPeAqvV7IhShJJ/ERERERERcbTkU4i6BQVqQukWZkeTtFzdocVP4O4Nx9bAxu/NjihFKPkXERERERGRuw4vg31/Gn3jGw937kH+HiRrYWMAQIBl/SF0t7nxpAAl/yIiIiIiImKIirg7yN+TnSFnKXPjSU6VOkCxRhAdAbM7GwMcpmFK/kVERERERMSw6Qf47xD4ZIfaH5sdTfKyWKD5d+CdDc7vgeUDzY4oWSn5FxEREREREQg7C6uGG8v1+kOGzKaGkyJ8c8Bzo43lDaPh31XmxpOMlPyLiIiIiIgIhPSBiOuQpwqUb2N2NCmneGOjCwDA3Lfh5mVTw0kuSv5FRERERETSu2PrYNcMwAJNRoBLOksVGwwCr8wQdhr+6hl7+6rhsGJIioeVlNLZFRUREREREREH0VGw8CNjuVIHyFXR1HBM4ekLpYON5d2zYOdM+yaXNV/CikHg4mpObElEyb+IiIiIiEh6tuUXOLcbMvjDM33MjsY8zUZBgZrG8h/vwtVTFAudi+vqoVDnUwj6yNz4HpOSfxERERERkfTq+gVY8YWxXPcz8M5ibjxme2UuZMwF0bdxG12RkmdnE13rY6dP/EHJv4iIiIiISPq1rD/cugoBZaHSa2ZHYz5XN+gwHwALNqwWV6w14xgDwAkp+RcREREREUmPTm2F7ZON5SZfOn2f9iSz+3cAbC7uuNiijT7/aYCSfxERERERkfTGaoUFd1q0y7eBfNXMjSe1WDXcGNyvzqdE9T7LvsAWRp//VcPNjuyxuZkdgIiIiIiIiKSwHVPgzDbwyAj1+psdTepwT+JP0EcQGcnBgGCKFS2G64pBxj5O3PdfLf8iIiIiIiJp2Yohji3XNy/D0n7Gcr6qxmj/AtboOEf1t9bsaay3RpsUWNJQy7+IiIiIiEha5uJqtGiDkdiuGAzh/4F3Nji8FPJWNTe+1KJO7wdvc+IW/xhK/kVERERERNKymMR1xSC4fu5uS3/4xTQxf73Ej5J/ERERERGRtO7eGwAxlPinK+rzLyIiIiIikh7kq3532dVdiX86o+RfREREREQkPfjjXeOnxQWiI9PE9HUSfyr7FxERERERSevmvgNXjhuJf7ddsGOa4yCAkuYp+RcREREREUnLVg2HHVON5UqvgV+e2GMA6AZAmqfkX0REREREJC27dNT46eIONbvfXR+T8Dv5/PUSP0r+RURERERE0rKrJ42fT7xitPrfSy3+6YYG/BMREREREUmrjq2DY2uMVv+nuz96f0mzlPyLiIiIiIikVauGGj8rtoPMec2NRUyl5F9ERERERCQtOr4ejq6O3ddf0iUl/yIiIiIiImnRyntb/fOZG4uYTsm/iIiIiIhIWnN8AxxdpVZ/sVPyLyIiIiIiktbY+/q/rFZ/AZT8i4iIiIiIpC0nNsK/K8HFTSP8i52SfxERERERkbQkpq9/hZfBP7+5sUiqoeRfREREREQkrTixEf5dYbT61+xhdjSSiij5FxERERERSSvsrf5t1eovDpT8i4iIiIiIpAUnNqnVXx5Iyb+IiIiIiDinFUNg1fC4t60abmxPT2JG+C/fBvwLmBqKpD5K/kVERERExDm5uMKKQbFvAKwabqx3cTUnLjOc/BuOLFervzyQm9kBiIiIiIiIJErQR8bPFYMg6hZkLwn/HYJVw6DOp3e3pwcxff3Lt4YsBc2NRVIlU1v+V69eTbNmzciVKxcWi4W5c+c6bO/Xrx8lSpTAx8cHf39/6tWrx6ZNmxz2uXTpEi+//DKZMmUic+bMdOzYkevXrzvss3PnTmrWrImXlxd58+Zl+PDYpUEzZ86kRIkSeHl5UbZsWRYsWJDk71dERERERJJY0EcQ9DGs+Qpmd0qfif/JzXBkGVhcoWZPs6ORVMrU5P/GjRuUL1+eMWPGxLm9WLFijB49ml27drF27VoKFChAgwYNuHDhgn2fl19+mT179hASEsL8+fNZvXo1nTt3tm8PCwujQYMG5M+fn61btzJixAj69evHuHHj7PusX7+eNm3a0LFjR7Zv305wcDDBwcHs3r07+d68iIiIiIgkjcgbjs8DK5gShmli+vpXaKNWf3kgU8v+GzduTOPGjR+4vW3btg7PR44cyfjx49m5cyfPPPMM+/btY9GiRWzevJnKlSsD8N1339GkSRO+/PJLcuXKxdSpU4mIiOCXX37Bw8OD0qVLs2PHDkaOHGm/STBq1CgaNWrEhx9+CMDAgQMJCQlh9OjRjB07Ns7Ybt++ze3bt+3Pw8LCAIiMjCQyMtK+Pmb53nWSuumaOSddN+ela+fcdP2cj66Zc9J1ezDLgQW4rf8OABsWLNiwzXqNqK7/gJefydEl/7WznN6K2+Gl2CyuRFXvBvqOJBln+L1LSGxO0+c/IiKCcePG4efnR/ny5QHYsGEDmTNntif+APXq1cPFxYVNmzbx/PPPs2HDBmrVqoWHh4d9n4YNGzJs2DAuX76Mv78/GzZsoHv37g7na9iwYaxuCPcaMmQI/fv3j7V+yZIleHt7x1ofEhKS0LcsJtM1c066bs5L18656fo5H10z56Tr5sj79nnq7usNwCXvIqwv2ov6u7vhGXGdG9/XZVWJgSZHeFdyXbtqR74kJ3DCvwY7NuwF9ibLedKz1Px7Fx4eHu99U33yP3/+fFq3bk14eDiBgYGEhISQLVs2AEJDQ8mRI4fD/m5ubmTJkoXQ0FD7PgULOpa+5MyZ077N39+f0NBQ+7p794k5Rlx69+7tcMMgLCyMvHnz0qBBAzJlymRfHxkZSUhICPXr18fd3T0Rn4CkNF0z56Tr5rxSy7VzWT0MLK5Y4+gr6bLmS7BFY63Vy4TIUrfUcv0k/nTNnJOuWxyibuE2phIWWyTWjLnI+O5qGrp6YCkfiO3XpmS+eZymkX9ife4HU8NMzmtnOb0Nt+07sVlcydV6JLn8VfKflJzh9y6mAj0+Un3yX6dOHXbs2MHFixf56aefaNWqFZs2bYqV9Kc0T09PPD09Y613d3eP84vxoPWSeumaOSddN+dl+rVz84AVg3B1dXUcJGrVcFg9FOp8iqu+Ww9k+vWTBNM1c066bvdY3AuunwO3DLh0CsHFy8dYX6gmVHsHNn6P68GFuEaHp4ry/2S5duu+BMBSvjXuOYol7bHFLjX/3iUkLlMH/IsPHx8fihQpQrVq1Rg/fjxubm6MHz8egICAAM6fP++wf1RUFJcuXSIgIMC+z7lz5xz2iXn+qH1itouISDoQ9BHU7m1MF/VnN4i4cXee6PQ2arSISGq3cyZsGQ9Y4KUp4JfHcXvdzyFLIYi4Dos/MSXEZHdqKxxacmeE/x5mRyNOINW3/N/ParXaB9qrXr06V65cYevWrVSqVAmA5cuXY7VaqVq1qn2fTz/9lMjISPtdkZCQEIoXL46/v799n2XLltGtWzf7eUJCQqhevXoKvjMREUl2t6/B1VN3HifvWb7zPOyMsd/WCbB1ImBT4i8iktpcOAB/vm8s1+oJRevF3sfDG54bAxOawPYpUOr5uPdzZjEj/Jd7CbIWNjcWcQqmJv/Xr1/n8OHD9udHjx5lx44dZMmShaxZszJo0CCaN29OYGAgFy9eZMyYMZw+fZqWLVsCULJkSRo1asQbb7zB2LFjiYyMpEuXLrRu3ZpcuXIBxowB/fv3p2PHjvTq1Yvdu3czatQovv76a/t533//fYKCgvjqq6949tln+e2339iyZYvDdIAiIpJEVgwBF9e4E+pVw8EaDXV6J/y40VFw7ex9yfxpx+e3rj76OBZXsEUDNmNZib+ISOoRcQNmvGpM7VegplGx9SD5a0DVt2DTD/Dne/DOhlRR/p8kTt/T6l8r9lg1InExNfnfsmULderUsT+PGUCvffv2jB07lv379zNp0iQuXrxI1qxZqVKlCmvWrKF06dL210ydOpUuXbrwzDPP4OLiwgsvvMC3335r3+7n58eSJUt49913qVSpEtmyZaNPnz72af4AatSowbRp0/jss8/45JNPKFq0KHPnzqVMmTIp8CmIiKQzLq5GKT04JNYua760962PxWYzEvcHttqfgmtnwGZ99Pm9MoNfXqNE1OFxZ932ybByyJ3zRsOiT6DR4Md/3yIi8nhsNpjfHS7sB9+c8MJ44/+Uh3mmDxxcBJePwuJP4bnRKRNrcls5zPhZrpVa/SXeTE3+a9eujc1me+D22bNnP/IYWbJkYdq0aQ/dp1y5cqxZs+ah+7Rs2dJeUSAiIskoJuGPuQFQ4wOKn52N6/a5ULEdZM4Pq7+MndxHXHv0sV3cwS/33UQ+U27HxN4vN3hmfPDrVw03Ev/an8DR1XB8LWwcY7QU1dZI/yIiptr2K+z8DSwu8OIvkDHno1/j4Q3B398p/58MpYOhiJOX/5/eBocWG59DrQ/NjkaciNP1+RcRkTTgnhsAbisGUSJm/fYpxuNBvLPel8zf12rvkwNcEjmW7f2D+5V5AX6oAdG3YeVgsFjUBUBExCxnd8KCO4lu3c+hwNPxf23+GlD1Tdg0FualgfL/VXda/cuq1V8SRsm/iIiYw90bAMudpzZXTywxrfNxJfeZchstOMnFGu04uF+2IlD7Y1jWH9y84Fb859EVEZEkdOuq0c8/+jYUawRPdUv4MZ7pAwcXG+X/Sz6D5t8leZgp4sx2oxuDWv0lEZT8i4hIyvvnN1hi9O23WVyx2KKxPvUBrnUTMdBfUolrkMEaXWHPbAjdZYwpICIiKctmgz/eNZJ2v7wQ/EPiKrw8fIzR/yc2MboPlHrOOcv/V97T6p+tiLmxiNNJZG2kiIhIIh0KgblvG8t5niSqdyj7AlvgunqoUXqfmri6G61DFhfY/TscWGR2RCIi6cvGH2Dfn8aYLi0ngXeWxB+rwFPG6P9glP/HZwaY1OTMdji4UK3+kmhK/kVEJOWc/Bv+19oYlT9nGXh9MVgsHAwIJrrWx0af+9R2AyBXRajexVj+q7vK/0VEUsrJzRDyubHccBDkqfT4x3ymD/gXNKaCXfL54x8vJcX8/1i2pVr9JVGU/IuISMo4vx+mtgRrFGQpBJ1XOpRuWmv2NPrcW6PNi/FBave++8fisgFmRyMikvaFX4KZHYz/M0oFw5OdH/WK+Ikp/wfYNgkOL0ua4ya3MzvgwAK1+stjUfIvIiLJ7+opmNICbl2B3JXhrbVGSf39gj6Ku++92Ty8odkoY3nzz3Bio7nxiIikZVYrzO4MYacgS+E73a8sj35dfBV4Cp5801ie955zVHTFtPqXeRGyFTU3FnFaSv5FRCR5hV+Cyc8brebZisPLM42WF2dTKAgqvgLYYF5XiLxldkQiImnT2q/gcIgx00qrX8ErU9Kfo15f8C9g3GBY8lnSHz8pnf0HDvylVn95bEr+RUQk+UTcMEr9Lx40pup7ZfbjDdZktgYDwTen8X7WfGV2NCIiac/R1bBisLHc5EsIKJM853Gm8v+YEf7LvADZi5kbizg1Jf8iIpI8oiONeZlPb4EM/tBuNvjlMTuqx5PBH5qMMJbXjoRze8yNR0QkLbkWCrM6GoPCVngZnnglec9X4Om7Ywn8+X7qLP+PafXHArU+MjsacXJK/kVEJOlZrTD3HTi8FNwyQNsZkKOE2VEljZLNoURTYxCqeV1T5wCFIjFWDHngDBoua740toukBtFRRuJ/4zzkKG20+qeEev2M8v+rJ+/OLJCa2Pv6q9VfHp+SfxERSVo2m9F/ctcMsLga/TXzPml2VEnHYjH+KPX0g9NbYdOPZkck8mAurnFOoVksdC6uq4ca20VSgxWD4Pha8PCFVpOMgVZTgocPNB9tLG+dCEdWpMx54+PsTtg/H7AYA+KKPCYl/yIikrTWfQMb7/SjDP4eijUwNZxkkSkQGtyZ8m/5QLh8zNRwRB4o6CNjCs0Vg2DFUDi3B5c1X1Ly7Gyia32shEJSh4OLja5UYIzsn9Kj2Resebf8f17X1FP+v+revv7FzY1F0gQl/yIiknS2T4Gl/YzlBoOgfGtTw0lWFV+F/E9DZDjM/8CoeBBJjYI+gtqfwKoh8EMNXFcP5UDO5lhr9jQ7MhG4csKY1g+MBLxMC3PieKYvZM5/p/y/jzkx3Ct0l1r9Jckp+RcRkaSxf4ExXzLAU+9DjS7mxpPcXFyg+bfGVFRHlsM/v5kdkciDZcrl8DTg6nYj6RIxU1QEzOwAt65AriegwRfmxeLpe3f0/60TzC//t7f6t1CrvyQZJf8iIvL4jq+HWa+BLdoYoblef7MjShlZC0Ptj43lxb3h+gVz4xGJy5UT8Fd3Y9li/Onnd+skbhPqw7G1JgYm6V7I58bYKV6ZoeVEcPM0N56CNaHKG8byvPfg9jVz4gjdDfv+RCP8S1JT8i8iIo/n3B6Y1hqibkGxRtDsW2NQvPSiehcIKAs3L8OiXmZHI+LIaoWJTSE6AjLlgc8vEl31HQAs4f/Br8/B5vEmBynp0p45sGmssfz8j+Cf39x4YtTrd6f8/4R55f8xrf6ln087M+VIqqDkX0REEu/ycZjcAm5fhbzV4MUJ4OpmdlQpy9XdGCna4gq7f4cDi8yOSOSu/70EV46Dizu0nwcurljrDeBAwHPGdmuUURUwvztER5obq6QfFw/DH12N5ae6QfFGpobjwNMXnrsz+v+WX+DflSl7/tDdsG8e6usvyUHJv4iIJM6NizClBVwPhRyloO1vKTc1U2qTqwJUf9dY/qt76hkpWtK3i4fh8HJjudEQo5vKHfsDXzBG+y9YC7DAlvEw+Xm48Z85sUr6EXkTZraHiGuQrwbU/dzsiGIrWAuqdDKW/+iasuX/q+9My1k6GHKUTLnzSrqg5F9ERBLu9jWY+iL8dxj88kG73yGDv9lRmat2b/AvCGGnYVkaHvNgxZBYc8bbrRpubBfzRUfB3LfAFgWF6txNZO5hrdkT2v8Jbf4HHhnh2Br4qbbR8iiSXBb0hHO7wSc7vPhL6q0Wq9cfMudL2fL/c3tg7x+or78kFyX/IiKSMFG3YXo7OLMdvLPCK7NjjSSeLnl4G6P/A2z+GU5sNDee5OLiaswZf98NAJc1XxrrXVxTPibdkIht/Sg4tRk8/YwS5oeNw1G8MXRaaty8unICxjeAvfNSLlZJP7ZPNaaExQIv/AyZAs2O6ME8fY0uXZBy5f8xff1LPQc5SyX/+STdUfIvIiLxZ7XCnLeMP4LcfeDlmZCtqNlRpR4Fa0HFV4zleV0h8pa58SSHoI+gzqdGor+wF5zbQ5mTk3FdPdTou1v1LWP6Lpst5WJ6wA0JI/E36YaEmUJ33b3h0XgY+OV59GtylIA3lkPBIIi8ATNegZVDjd95kaRwbg/81cNYrvMJFKptajjxUigIKnc0lpO7/P/c3jut/kCQBo+V5JFK62xERCTVsdmM0ez3zDYGD2s9BXJXMjuq1KfBQDi0BC4ehDVfQt3PzI4o6VV7B3bNgk1jcd80FntP8nXfGA8ALODmZUzdZX943fPTK4518fjpGse6UsFw84qR6EfeNPoQx1Qi1Pk0fQ2aFRVh3KCzRkKJplC+dfxf650F2s2GJZ8ao7CvHGIkbME/GK2gIol1+xrMaA9RN6HwM1Czp9kRxV/9AXA4xKiKCekLTUcmz3nU6i8pQMm/iIjEz+ov4e9xgAWeHwuF65odUeqUwR+ajIAZr8Lar42pmnKWNjuqpHPpKPz2Mlw8YF9lA3DzwhJ1b6WDzfhDP+pmysa3dqTxuWNLf4k/wKqhRn9q76zQ9JuET7vp6mZUC+QsA/M/MEYdv/QvtJ6WeqZiE+dis8G89+C/Q5AxF7T4CVycqPg4pvz/1+bGwJilnjMqApKSWv0lhSj5FxGRR9syAVZ8YSw3HgZlXzQ3ntSu1HNGq+v++Ub5f8eQtFF6/u9KmNkBbl42un1E3sDm6oElOoLoGt1wrfOxMZ981C1jbIg4fz5s24Ne+7DX3PcTm/GwWJyrdTEpnNpy58YHRuLvmz3xx3riFaNLz/R2xs2En+pAq8lQ4KkkCVXSkc0/36kYc4OWE8Enq9kRJVxM+f+W8TCvC7y9IWmrYVYPB2x3Wv3T0M1iSXWU/IuIyMPtnWdMXwdGMlX1TXPjcRZNvoSja+D0VqOEOmYqQGdksxnvYfGnYIuGjIFw7SzU+ZSoGh9w+JfOlFw9FFxdjZZ2N09z4lw5DFYOvhvzlBbw6lxzYklpEeEw502wWaFsKyjV/PGPma8adF4Jv7WFs/8YLZ9NRkDl1x//2JI+nN4Ki3oby/X6Q76q5sbzOOr3h0N3yv+X9oVnv0qa457fB3vmGstq9Zdk5kQ1NyIikuKOroHfOxoJxRPt02b/9eSSKRAaDDCWl38Bl4+ZGk6iRd6Cue/Aoo+NxD+grD3xjympPxgQbMwZH9egeyll1XAj8a/zKTS7M+vCvyvgDye+6ZIQywYYU29mDIQmSXgN/PLAa4ugzAtgjTK6AszvDtGRSXcOSZtuXjYqhWLGn3DmG6AAnhmNmTPAqGY4ujppjrvqTqt/yeZq9Zdkp+RfRETidnan0eIXHWH84fbsyIT3H07vnmgPBWpCZDj82S1lR8BPCmFnYWIT+GcaWFyg4RAo3iTOvvTWmj2N9dbolI8zZlT/mLieeBXK3RnobvsUY5CutOzoatj0g7HcfLQx7kRS8vCGF8bDM30Ai1H6PPl5uPFf0p5H0g6rFea8bbSS+xeA58akjf8/CgXdrXz54124ff3xjnd+P+yZYyyr1V9SgJJ/ERGJ7dK/MOUFuB0G+Z82/vB3VU+xBLNYoNkoY1T6f1fAP7+ZHVH8ndwM42obZbtemaHd71D9HWOKrgcNohf0EdTpnZJRGqzRjjckLBZjRO7sJYzne+aYc1MiJdwKg7l3WlQrvQZF6yXPeSwWqNkD2vwPPDLCsTXwU20I3Z085xPntv5bOLjQmJ2j1a+QIbPZESWd+gPAL9+d8v9+j3esmL7+JZtBQJmkiE7koRKU/FutVlasWMGAAQPo2LEjbdq04b333mPChAmcPHkyuWIUEZGUdO0cTG4BN85DzrLQZhq4e5kdlfPKWhhqf2wsL+4N1y+YG098bJ9itPhfD4XsJaHzitQ9u0Od3rFvSHj4QMtJ4O4NV44bs1WkRYs/gasnIHN+Y5rJ5Fa8MXRaCv4FjeRnfANjXBCRGMfWGd1QwBggNrC8ufEkNc+M8Nx3xvLmnxJf/n9+P+yebSyr1V9SSLyS/5s3b/LFF1+QN29emjRpwsKFC7ly5Qqurq4cPnyYvn37UrBgQZo0acLGjRuTO2YREUkut67C1Bfg8lEjmWg3C7z8zI7K+VXvCgHljD6wC1Px1HPRkbDgI6OcNaa7R6cQyFLI7MgSJ0cJaHpn9PuVQ4zZCtKSg4th+2TAAsE/GElJSshRAt5YDgWDIPIGzHjFGGzRak2Z80vqdf0CzHrdGB+kbCuo1MHsiJJHodpGpQ3AH10SV/6/egRgM/6dDSiblNGJPFC8kv9ixYqxc+dOfvrpJ8LCwtiwYQO///47U6ZMYcGCBZw4cYIjR45Qs2ZNWrduzU8//ZTccYuISFKLvGXM3x66C3yywytzIGOA2VGlDa5u0Pw7sLgaU14dWGh2RLHd+M/ox/33j8bz2p8YU7ulVEKZXMq3NsYAwAa/d4JroWZHlDTCLxnTSIIxkFpKT8HnnQXazYaqbxnPVw6Gme0h4kbKxiGphzXaGCD2eihkK27ceEsL/fwfpMFA8MtrVBYt65+w1144ALt/N5bV6i8pKF7J/5IlS5gxYwZNmjTB3d09zn3y589P7969OXToEHXrpuLSQBERic0aDbM7Gf14PTLCy7OMcnVJOrkqQI0uxvJfPYy+2qlF6C6j//axNeDhCy9Nhdq9wCWNDA3UeDjkLAM3LsCsjhAdZXZEj++vHnD9nJFk1f3cnBhc3Yyy7uajwcUd9s2D8Q2N7gCS/qwaBkdXGV1tWv0Knr5mR5S8PDMaN3UB/h5nzI4TXzEj/JdoCoHlkiU8kbjE63/1kiVLxvuA7u7uFC6sPxhFRJyGzWYkEvv+BFcPo49/rgpmR5U2BX1s9JUOO53wlqLksmeO0W/7ygkjtk5LoWRTs6NKWu4ZjP7/Hhnh+FqjldqZ7f7dqCCxuMLzY80fk+OJV6DDfKNi6NwuY6DIY+vMjUlS1uFld6f5bDbK6BqSHhSuc0/5/7vxq3y5cPCeVv9U3A1M0qQE39JftGgRa9eutT8fM2YMFSpUoG3btly+fDlJgxMRkRSwcghsnQBYoMVPULCW2RGlXR7e0PzOHPSbf4bjG8yLxWqFZQONebgjw6FQHaMfd4743/B3KtmK3P3s13wFh0LMjSexroUaN+sAavWE3E+YG0+MfNWg80pjcLfw/+DX5rDlF7OjkpRw9TTMfgOwGYlwuVZmR5Sy6g+4W/4fn9H/Y0b4L/5s2hsMUVK9BCf/H374IWFhRqnirl276NGjB02aNOHo0aN07949yQMUEZFktGmcUaoJ8OxXUDrY1HDShYK17vRBx+izHXkr5WO4dRV+awNr7oyAX72L0dXDO0vKx5KSyrSAKm8Yy7PfgKunzI0noWw2+PN9Y+DIgHJQ60OzI3LklwdeWwRlXgBrFMz/AOZ3NwaSlLRhxZC7LfxgXNtZrxk3fHxzgndW82Izi1emuzcW/x4Hx9Y+eN+Lh+62+tdWX39JeQlO/o8ePUqpUqUA+P3332natCmDBw9mzJgxLFyYCgcwEhGRuO2efXfk+dq9oUpHc+NJT+oPMP5Q/u/Q3QQ8pVw8DD/Xg4OLjDm4n/8RGg4y+m+nBw0HQWAFI4Ge+ZpzJabbp9y5bh7GdXONexwmU3l4wwvj4Zk+gAW2jDcGkrzxn9mRSVJwcYUVg+7eAFjaD05uMv4tuX4O3DxNDc80hevendngIeX/ruu+AptVrf5imgQn/x4eHoSHhwOwdOlSGjRoAECWLFnsFQEiIpKK3N9SA3BkBczuDNggV0WNNpzSMvhDkztJ/9qvIXR3ypz3UAj8VBcuHoSMueD1hcZo+OmJmye0mgSefnDq7/iV6aYGV07Aot7Gct3PIGcpc+N5GIsFavaANv8zxlk4tsYYUDKlvueSfII+gjqfGjcAZnWEDaON9dG3jfXpuQ97/Tuj/18+Bktjj+nie+sslj2zjSfp+XMSUyU4+X/66afp3r07AwcO5O+//+bZZ58F4ODBg+TJkyfJAxQRkcd0f0vN6W0wvR1Y77R4FmuUtqdjSq1KNTdGerZGGeX/1ujkO5fNBmu/gakt4fZVyFvV6J+du1LynTM18y8Awd8byxtGw/6/TA3nkaxWmPsORFyDvNWMbhrOoHhjYwBJ/4LGzYvxDYyBRcW5BX1k3NzZPevuuvSe+MN95f8/xir/Lxb6BxabFYo30aC6YpoEJ/+jR4/Gzc2NWbNm8cMPP5A7d24AFi5cSKNGjZI8QBEReUz3ttQs7AVTX4SI63e29YLaH5sbX3rW5EujBfrMNtg0NnnOERFuzG+/tC9gM8YbaP8nZMyZPOdzFiWb3k2i575ttNalVn+PM1rP3b2NmxYurmZHFH85ShgDSRYMgsgbxo3HCc/CymFx779quFGtJKlb9ntG83f1UOIfo3BdeKK9sXxv+f9/h8lz+c4Ar/qsxEQJTv7z5cvH/Pnz+eeff+jY8W7/0K+//ppvv/02SYMTEZEkEvSRMc3cprHGwEwANXtCnU/MjSu9yxQIDQYay8u/SPoE9MpJ+KWh0ULn4mbcbGj2bfrtl3u/ev0gz5PGAIgzO0DUbbMjiu3ioTs3bjDGisjqhNMpe2eBdrOh6lvG85jpFpd/4bjfquHGTUpnurmRXq0aYfy0uEJ0ROyuZelZgy+M7i6Xj8GyAQC4rhuJBRvWog2N7le6wSUmSXDyD3DkyBE+++wz2rRpw/nz5wGj5X/Pnj1JGpyIiCShe6cEc3WHZz43Lxa564lXoUBNY7q9P7sZJfpJ4dg6Y7710J3GCNyv/gFPvqEuHvdydYeWE4wxGM5shyWfmR2Ro+gomPMWRN0ypmKs0snsiBLP1Q0aD4Pmo8HlzkCFq0fA4js3IGMSf5WPp37LBsB/B43lN1ffrSzTDQCDVyYoYXSLZtNY2DoJy50uEraMgbrBJaZKcPK/atUqypYty6ZNm5g9ezbXrxulo//88w99+/ZN8gBFRCSJLL/TwmxxMUY41x9qqYPFAs1GgZsX/LsC/vnf4x9z83hjnvXwixBQ1ujfX+Dpxz9uWuSXB54fZyz/Pc6YBSO1WD8KTm8xuoY8Nzpt3Lh54hXoMB98shvPN4yBAdmU+DuLVcNhzVfGctYikLO0Y9cy/b9iaPGjMasIwJ/vYbFZueYZgOu2ifqei6kSnPx//PHHfPHFF4SEhODh4WFfX7duXTZu3JikwYmISBJZMRhCdxnLHf7SH2qpTdbCd8deWNQbrp9P3HGiIox54P/qbgwkWLoFvL4EMudLuljTomIN4OnuxvK89+C/I+bGA8bva0xpcONhxk2KtCJfNeOGVMxUZ9ZIoxpACVHqZ42GbMWM5VLBd29IxdwASM6BS51N+z/BM5P9acbboUTX+ljfczFVgpP/Xbt28fzzz8danyNHDi5evJigY61evZpmzZqRK1cuLBYLc+fOtW+LjIykV69elC1bFh8fH3LlysWrr77KmTNnHI5RoEABLBaLw2Po0KEO++zcuZOaNWvi5eVF3rx5GT489h+7M2fOpESJEnh5eVG2bFkWLFiQoPciIpJqrRoOq+4MruUbYIwWrpaa1Kd6VwgoB7euGAMzJtT18zCpGWydCFjgmb7w4i/GvOvyaHU+hfxPGSPqz2gPkTfNiyUqwij3t0Ya84GnxekY/fJA0QZ3n1sjU1+3C4mtRhe4fNxYLh3suC3oI6jTO8VDSrW8MhnTit4RbXHDWrOniQGJJCL5z5w5M2fPno21fvv27faR/+Prxo0blC9fnjFjxsTaFh4ezrZt2/j888/Ztm0bs2fP5sCBAzRv3jzWvgMGDODs2bP2R9euXe3bwsLCaNCgAfnz52fr1q2MGDGCfv36MW7cOPs+69evp02bNnTs2JHt27cTHBxMcHAwu3drPloRSQOs0UbpN0Cp58Dlzj/9aqlJXVzdoPl3xgBae2bDgYXxf+3pbUb//pMbjRLxtjOgZve0USaeUlzd4IXx4J0Nzu1K3A2YpLJqKJzbbYzV0OybtHkdVw03+vw//cHdluT139kHSJNU6uBiiL4NWQpDzjJmR5P6ndoCgM3VA1dbFC5rvjQ5IEnv3BL6gtatW9OrVy9mzpyJxWLBarWybt06evbsyauvvpqgYzVu3JjGjRvHuc3Pz4+QkBCHdaNHj+bJJ5/kxIkT5Mt3t4QxY8aMBAQExHmcqVOnEhERwS+//IKHhwelS5dmx44djBw5ks6dOwMwatQoGjVqxIcffgjAwIEDCQkJYfTo0Ywdm0xTL4mIpJSa3WHjnTnNS99XuaXyw9QlVwWjZW3dKJjf3WiJ9sr08NfsnAHzuhqDwmUtCm3+B9mKpki4aU6mQHjhZ5j8PGybZHz+5V9K2RhObYG1XxvLTb8G3xwpe/6UcP/gfpVeg/H14fo5oz+5i7takFOrPXOMn6WD0+ZNqaR0z/c8qsYHHP6lMyVXDwVXV/3fK6ZJcPI/ePBg3n33XfLmzUt0dDSlSpUiOjqatm3b8tlnyVuudfXqVSwWC5kzZ3ZYP3ToUAYOHEi+fPlo27YtH3zwAW5uxlvbsGEDtWrVchifoGHDhgwbNozLly/j7+/Phg0b6N69u8MxGzZs6NAN4X63b9/m9u27UwKFhYUBRneFyMhI+/qY5XvXSeqma+acdN0ezHIoBLfbYdh8A4gKfAJS2Weka3efp3ritncelstHiV7SB2vjEXHvZ43GZcUAXDca1XPWIvWJfu5H42ZBCn6Wae765Xsal5of4rpmOLb53YjKXhqyF0+Zc0eG4za7MxabFWuZF4ku2iRZrqXZ18wlKgJqfYy1xgfG+/PNBS/9D7fJzbBE3MC2dx5RT/VQcnkfs68bEddxO7wUCxBZrGmq+78kNXFZ8yWuq4cSfed7HhkZycGAYAoXLozHikFER0erC4CTMP33Lh4SEpvFZkvcnEInTpxg9+7dXL9+nYoVK1K06OO1MlgsFubMmUNwcHCc22/dusVTTz1FiRIlmDp1qn39yJEjeeKJJ8iSJQvr16+nd+/evPbaa4wcORKABg0aULBgQX788Uf7a/bu3Uvp0qXZu3cvJUuWxMPDg0mTJtGmTRv7Pt9//z39+/fn3LlzccbTr18/+vfvH2v9tGnT8PZW/0oRST2eOPYjeS+v40j2BuzO087scCQesl3by1OHjfFr1hT9lEu+jsmne9QNKh8bQ45rRve0gzmbsS/wBWMmB3l8NivVj4wgx7U9hHnlZnWxfkS7eib7acucmkLhC0u46e7PihKDiXTzSfZzpibZw3ZS7chIXLAa3+lcLc0OSe6R+/JGKh/7nuueOVlWcrhuzjxE8bOzsVlcOBgQHGtbsdC5WGxWDgS2SPnAJE0KDw+nbdu2XL16lUyZHl4tmOCW/xgBAQHcvHmTwoUL21vZk0tkZCStWrXCZrPxww8/OGy7t8W+XLlyeHh48OabbzJkyBA8PZPvP+revXs7nDssLIy8efPSoEEDhw89MjKSkJAQ6tevj7u7e7LFI0lH18w56bo9QNRt3L55F4D8jd8jX95qJgcUm65dXJpg+/kvLOd28fSl6US1WGlMBQhw4QBuvz6L5dYVbO7eRDf9loKlgiloUqRp9vpdfxLb+Dpkun6aJrYQohuPSdZkx3JsDW7blwDg/sJY6hd+JtnOlXqvWROs/+TDZf57FDv3J4Ur1sJa6TWzg0o1zL5urrNmAJChUhua1Hk2xc/vXJoAUOTOM8drZ2wrbFJkkjBm/97FR0wFenwkOGsPDw+na9euTJpkjF558OBBChUqRNeuXcmdOzcff/xxQg/5UDGJ//Hjx1m+fPkj72ZUrVqVqKgojh07RvHixQkICIjVeh/zPGacgAft86BxBAA8PT3jvLng7u4e5xfjQesl9dI1c066bvf5dyncvgYZA3Er8NTdwf5SIV27+xStD+d2YfnvMO7rv4FnPof9f8HMDhAdAZ5+WDrMxy2wnNmRAmnw+vnnhhcnwKSmuOyagUvBmvBEwsY2irdbYTD/fWO50mu4lWiUPOe5T6q8ZpXbw/VQWDkY18W9cPXPC8XjHh8qvTLlut2+DkeWAuBatgWuqe174yRS5e+cxEtqvnYJiSvBfwX27t2bf/75h5UrV+Ll5WVfX69ePaZPn57Qwz1UTOJ/6NAhli5dStasWR/5mh07duDi4kKOHMYAOdWrV2f16tUOfSFCQkIoXrw4/v7+9n2WLVvmcJyQkBCqV6+ehO9GRMQEe+YaP0sFp+rEX+JQry+UfsFYXjsS/uoBv7U1Ev/M+eG9bZBKEv80q8BTUPdzY3nBhxCaTLMALf4Erp4wrmuDgclzDmcS9BFUfAVsVpj5GpzaanZEcmixMaiof0FjSlIRcUoJ/ktw7ty5jB49mqeffhrLPeVvpUuX5siRIwk61vXr19mxYwc7duwA4OjRo+zYsYMTJ04QGRnJiy++yJYtW5g6dSrR0dGEhoYSGhpKREQEYAzm98033/DPP//w77//MnXqVD744APatWtnT+zbtm2Lh4cHHTt2ZM+ePUyfPp1Ro0Y5lOy///77LFq0iK+++or9+/fTr18/tmzZQpcuXRL68YiIpB5Rt+HAAmP5/vmYxTm0/AWyFTeSoM0/G+tyV4auW8Enm7mxpRdPdTPmo4+6BTNeNVrpk9LBxbB9MmCB4B/AM2PSHt8ZWSzGTAdF6kPUTZjWCv5L2N+YksRibiRrlH8Rp5bg5P/ChQv2VvV73bhxw+FmQHxs2bKFihUrUrFiRcDov1+xYkX69OnD6dOnmTdvHqdOnaJChQoEBgbaH+vXrweM0vvffvuNoKAgSpcuzaBBg/jggw8YN26c/Rx+fn4sWbKEo0ePUqlSJXr06EGfPn3s0/wB1KhRg2nTpjFu3DjKly/PrFmzmDt3LmXKaP5SEXFiR5bD7TDImAvyPGl2NJJY7efdXXZxgzeWgWvqLD1Mk1xc4PkfIVMeuHQE/nwfEjdWcmzhl4xpGgGqv2tUGojB1R1aToTA8hB+Eaa+CDcumh1V+hRxAw7dmX67VLCpoYjI40lwn//KlSvz119/0bWr8Z9VTML/888/J7hMvnbt2jxssoFHTUTwxBNPsHHjxkeep1y5cqxZs+ah+7Rs2ZKWLTWqrIikITHzMZd6TiX/zmzbr8ZPV3eIjjTmjtYc0SnLOwu0nAATGsOe2ZC/Bjz5xuMf968extz22Yrf7V4gd3n6QtuZML4eXPoXpr0E7f8ED82qlKIOLjYqMPwLGDdjRMRpJTj5Hzx4MI0bN2bv3r1ERUUxatQo9u7dy/r161m1alVyxCgiIgkVeQsOLDSWSz9vbiySeKuGw4pBUOdTI+GPeQ66AZDS8j4J9QcY/fMXfwJ5KkOuiok/3u7fjRsJFld4fiy4ez36NelRxpzQbjaMrw+nt8DvHeGlKeDianZk6cfeucbPUsEq+RdxcgluCnr66afZsWMHUVFRlC1bliVLlpAjRw42bNhApUqVkiNGERFJKIeS/ypmRyOJcX/iD8bPOp8a61cNNze+9KjaO1CiqTHo4oz2cPNK4o5zLdRo9Qeo1RNyP5FkIaZJ2YpCm9/A1dMYx2TBh0nX9UIeLuIGHDSmoNTYMSLOL8Et/wCFCxfmp59+SupYREQkqcS01JQOVsm/s7JGOyb+MWKeW6NTPqb0zmKB50ZD6C64chz+eNdohU5Ia6jNBvPeg5uXjVHTa/ZMvnjTknzV4IWfjJsuW8ZD5rzw9AdmR5X2HVpilPxnzg+BFcyORkQeU6KS/+joaObMmcO+ffsAKFWqFM899xxubok6nIiIJKXIW7D/zij/GpzJedXp/eBtKvk3TwZ/YyC6XxrC/vmw8XtjsL742j7FmDbN1cMYSNDNI9lCTXNKPQeNhsKiXrC0H2TKDeVamR1V2qZR/kXSlAQ3B+3Zs4dixYrRvn175syZw5w5c2jfvj1FixZl9+5kmv9WRETi78hyiLhm/GGskn+RpJf7CWg42FgO6QMnN8fvdZePw6I7N3XqfAo5SyVPfGlZtbeg+p2pmOe+A/9qvKlkExFutPyDbiSLpBEJTv47depE6dKlOXXqFNu2bWPbtm2cPHmScuXKOUyfJyIiJtEo/yLJr0onKN0CrFEws4Mxbd/DWK1GN4GIa5C3GtTomiJhpkn1B9757CNhejsIVeNTsji0BCLDIXO+xxvcUkRSjQT/Vbhjxw6GDBmCv7+/fZ2/vz+DBg1i+/btSRqciIgkkEb5F0kZFgs0GwVZCkPYKZjzppHgP8jf4+DYGnD3huDvNVr943BxMWZIyP+0MbDp1JZw9ZTZUaU9GuVfJM1JcPJfrFgxzp07F2v9+fPnKVKkSJIEJSIiiXRk2d2S/9yVzY5GJG3zygStJoGbl9FKuu6buPe7eAiW9jWW6w+ArIVTLMQ0y80TWk+B7CXg2hmY8mLiZ1+Q2CLC4eBiY1mj/IukGQlO/ocMGcJ7773HrFmzOHXqFKdOnWLWrFl069aNYcOGERYWZn+IiEgKixmcqVSwSv5FUkJAWWh8Z9rF5V/AsXWO26OjYM5bEHULCtUxugtI0sjgDy/PAt8AuLDP6AIQddvsqNKGwyH3lPxrKkqRtCLBw/M3bdoUgFatWmG5UwJkuzPXarNmzezPLRYL0dGahkhEJMU4lPwHmxqKSLryxKtwfD3s/A1mvQ5vrQXf7Ma2dd/A6S3g6WdME6jy6aSVOS+8PBMmNDG6Vcx9B1r8pJufj8t+I/k5fWdF0pAEJ/8rVqxIjjhERORx2Uv+86jkXyQlWSzQdKTRWno9FGZ3gnaz4fxeWDnU2KdgLdg2+eFTOEriBJaDl341+v7vngV+eaB+f7Ojcl6RN++W/JfS2DEiaUmCk/+goKDkiENERB6XRvkXMY+HD5R5wRjY79+VsGKQkUBZIyFrMdj/p5GkSvIoXBeaj4a5bxnVFn554Mk3zI7KOR0Kgcgb4JfPmNZSRNKMBCf/q1evfuj2WrVqJToYERFJpMibGuVfxGxNRsC1UNg3D9Z8ZaxzzwD/HYQ6n0LQR+bGl9ZVaGPMvLD8C1jwIWQMhJJNzY7K+dhH+W+ukn+RNCbByX/t2rVjrbPc8w+D+vmLiJjg8DKIuG6U/OdRyb+IaV6aDD8GwdkdxvPIm0r8U1LNnsa0f1snwu8dof2fkPdJs6NyHpE34cAiY1k3kkXSnATXhV6+fNnhcf78eRYtWkSVKlVYsmRJcsQoIiKPElPyXzpYLTUiZnt9EVju/Inl6qHEPyVZLNDkKyja0JhhYdpLcPGw2VE5j8NL75T854XclcyORkSSWIKTfz8/P4dHtmzZqF+/PsOGDeOjj/Sfm4hIiou8CQfVUiOSaqz/DmxWI/GPjoBVw82OKH1xdYOWEyBXRbh5Caa+ANcvmB2Vc9Ao/yJpWpKNCJUzZ04OHDiQVIcTEZH4OrzUKPlXS42I+VYNNwb7q/MpfH7B+LlikG4ApDQPH2g7A/wLwOVjMK0VRNwwO6rU7d4byaWCTQ1FRJJHgvv879y50+G5zWbj7NmzDB06lAoVKiRVXCIiEl9qqRFJHe5N/GNK/WN+rhjk+FySn28OePl3GF8fzmyDWa/DS1ONygCJTWPHiKR5Cf7Xr0KFClgsFmw2m8P6atWq8csvvyRZYCIiEg8a5V8k9bBGxz24X8xzqwZFTnHZikDb6TCpmdGqvaAHNP1GN0rjYh/lXzeSRdKqBCf/R48edXju4uJC9uzZ8fLySrKgREQknjQ4k0jqUaf3g7epxd88eZ+EF8bD9HbGLAB+eaFWT7OjSl0ib90zyn+wqaGISPJJcJ///PnzOzzy5s2Ll5cX27Zto2lTzaUqIpKiYkb5V0uNiMiDlWwKTUYYy8sHwo7/mRtPanNkGURcg0y5IbdK/kXSqgQl/4sXL6Znz5588skn/PvvvwDs37+f4OBgqlSpgtVqTZYgRUQkDg7zMbcwNxYRkdTuyTfgqfeN5Xld4Mhyc+NJTe4dO8YlycYDF5FUJt6/3ePHj6dx48ZMnDiRYcOGUa1aNaZMmUL16tUJCAhg9+7dLFiwIDljFRGRex0KuVPynw9yP2F2NCIiqd8z/aDMi2CNgumvwtmdj3xJmhd56+7YMRrlXyRNi3fyP2rUKIYNG8bFixeZMWMGFy9e5Pvvv2fXrl2MHTuWkiVLJmecIiJyv5jBmUqr5F9EJF5cXCD4eyhQ0yhzn9oSrpw0OypzHVlufBYZc0GeKmZHIyLJKN7J/5EjR2jZsiUALVq0wM3NjREjRpAnT55kC05ERB7g3pL/UhrlX0Qk3tw84aUpkKMUXA+FqS/CzctmR2Wee0f5V8m/SJoW79/wmzdv4u3tDYDFYsHT05PAwMBkC0xERB5CJf8iIomXITO8PNNo7b6wH357GaJumx1Vyou6fc90scGmhiIiyS9BU/39/PPP+Pr6AhAVFcXEiRPJli2bwz7vvfde0kUnIiJxixnlXyX/IiKJ45cH2s2CXxrB8XUw5y1jSsD01Pp9ZDncDrtT8v+k2dGISDKLd/KfL18+fvrpJ/vzgIAAJk+e7LCPxWJR8i8iktwiwuHgYmO5tEr+RUQSLWdpowvAr8GwZzb45YYGXzjus2o4WKOhTm9TQkxW9lH+m6evmx4i6VS8k/9jx44lYxgiIhJvh++U/GfOB7lU8i8i8lgKBUHJZrDvD1j/HWTKA9XeMratGg4rBkGdT82NMTncW/Jf6jlzYxGRFJGgsn8REUkF7C01wSr5FxFJCi/9Cr8+B/+uhEW9INOdsQBiEv+gj8yOMOn9uxJuXwXfAMhbzexoRCQFKPkXEXEmEeFw8M4o/xqcSUQk6bwyF36qC2e2wYxXjHVpNfEHlfyLpEP6TRcRcSaHQyAyXCX/IiJJzWKBTkuBOxVVFpe0m/hHRcCBv4zlUsGmhiIiKUfJv4iIM4kZ5V8l/yIiSW/NV4DNWLZZ4Y8upoaTbP5dCbeugm9OyKeSf5H0Qsm/iIiz0Cj/IiLJ597B/Sp1MNZtnwwrh5kaVrLYO9f4WbI5uLiaGoqIpJxE9fm3Wq0cPnyY8+fPY7VaHbbVqlUrSQITEZH7HFpyp+Q/P+SqaHY0IiJpx72Jf9BHcO0c7JxpzKyycrBRaZVWugBERcD++cayxo4RSVcSnPxv3LiRtm3bcvz4cWw2m8M2i8VCdHR0kgUnIiL3iCn5Lx2skn8RkaRkjXYc3C9jTqjRFVYNBa/MRsKcVhxdZZT8++SAfNXNjkZEUlCCk/+33nqLypUr89dffxEYGIhFf4CKiCS/iBtGyz+o5F9EJKnV6R17XY2usHUCXD8HPllTPqbk4jDKv0r+RdKTBCf/hw4dYtasWRQpUiQ54hERkbjcW/IfWMHsaERE0j5PX6jdG+Z3g1XDoHwbyJDZ7KgeT3Tk3ZJ/jfIvku4keMC/qlWrcvjw4eSIRUREHiSmpab08yr5FxFJKRVfgWzF4eZlWDvS7Gge37+r4NYVo+Q/fw2zoxGRFJbglv+uXbvSo0cPQkNDKVu2LO7u7g7by5Url2TBiYgIRsm/fZT/YFNDERFJV1zdoP4A+N9LsHEsVOkEmfOZHVXi7b0zdkzJZir5F0mHEpz8v/DCCwC8/vrr9nUWiwWbzaYB/0REksOhJRB1E/wLqORfRCSlFWsIBWrCsTWwfBC0+NHsiBInOhL2/2Us60aySLqU4OT/6NGjyRGHiIg8SMwo/6WCVfIvIpLSLBZoMBDG1Yad06H6OxBY3uyoEu7oKqP7gk92yP+U2dGIiAkSnPznz58/OeIQEZG4RNyAgxrlX0TEVLkqQtmWsGsmLPkcXv3D+W7Gxowdo5J/kXQrXsn/vHnzaNy4Me7u7sybN++h+zZv3jxJAhMREYy+/vaSfydsaRIRSSvqfg57/zBa0A8vhaL1zY4o/jTKv4gQz+Q/ODiY0NBQcuTIQXBw8AP3U59/EZEktneu8VOj/IuImMs/PzzZGTaMhpA+ULiu87SgH11tlPx7Z1PJv0g6Fq+p/qxWKzly5LAvP+iR0MR/9erVNGvWjFy5cmGxWJg7d659W2RkJL169aJs2bL4+PiQK1cuXn31Vc6cOeNwjEuXLvHyyy+TKVMmMmfOTMeOHbl+/brDPjt37qRmzZp4eXmRN29ehg8fHiuWmTNnUqJECby8vChbtiwLFixI0HsREUly95b8q6VGRMR8tXqCV2Y4vxd2TDU7mviLuZFcspkxg4GIpEvxSv6Ty40bNyhfvjxjxoyJtS08PJxt27bx+eefs23bNmbPns2BAwdidSt4+eWX2bNnDyEhIcyfP5/Vq1fTuXNn+/awsDAaNGhA/vz52bp1KyNGjKBfv36MGzfOvs/69etp06YNHTt2ZPv27QQHBxMcHMzu3buT782LiDyKveS/oEr+RURSgwz+UOtDY3n5IOMmbWoXHQn77pT8a5R/kXQtXrf+fvvtN1q3bh2vA548eZITJ07w1FOPLilq3LgxjRs3jnObn58fISEhDutGjx7Nk08+yYkTJ8iXLx/79u1j0aJFbN68mcqVKwPw3Xff0aRJE7788kty5crF1KlTiYiI4JdffsHDw4PSpUuzY8cORo4cab9JMGrUKBo1asSHHxr/mA8cOJCQkBBGjx7N2LFj4/W+RUSSXMwo/6WDVfIvIpJaPPkG/D0OrhyHDWMg6COzI3q4Y2vg5iXwzgr5nzY7GhExUbyS/x9++IH+/fvz2muv0axZM0qWLOmw/erVq6xbt44pU6YQEhLC+PHjkyXYq1evYrFYyJw5MwAbNmwgc+bM9sQfoF69eri4uLBp0yaef/55NmzYQK1atfDw8LDv07BhQ4YNG8bly5fx9/dnw4YNdO/e3eFcDRs2dOiGcL/bt29z+/Zt+/OwsDDA6K4QGRlpXx+zfO86Sd10zZxTmrtuEddxOxSCBYgs3gzSyvuKQ5q7dumMrp/z0TV7XC5Yan+K29zO2NZ9Q1S5l8E3R7KfNbHXzXXXbFyA6OLPYrXawKrrntL0O+e8nOHaJSS2eCX/q1atYt68eXz33Xf07t0bHx8fcubMiZeXF5cvXyY0NJRs2bLRoUMHdu/eTc6cORMd/IPcunWLXr160aZNGzJlygRgH4TQ4Q25uZElSxZCQ0Pt+xQsWNBhn5j4QkND8ff3JzQ0NFbMOXPmtB8jLkOGDKF///6x1i9ZsgRvb+9Y6++vYpDUT9fMOaWV65br8kaqRN3kukcOlm09CZZTZoeU7NLKtUuvdP2cj67ZY7B5Usu7EP7h/3Jqahd25u2QYqdOyHWz2KJpuHsunsCmsAAuaEwrU+l3znml5msXHh4e733jPeJH8+bNad68ORcvXmTt2rUcP36cmzdvki1bNipWrEjFihVxcUmeIQQiIyNp1aoVNpuNH374IVnOkVC9e/d2qBYICwsjb968NGjQwH5zAozYQ0JCqF+/Pu7u7maEKgmka+ac0tp1c/19JgAZKrelSZ1nTY4meaW1a5fe6Po5H12zpGEpkxmmPEeB/1aR54VBkK1osp4vMdfNcnQVbjuuYfPOSpVW3cFFg/2ZQb9zzssZrl1MBXp8JPhfgGzZsj10ur+kFpP4Hz9+nOXLlzsk1gEBAZw/f95h/6ioKC5dukRAQIB9n3PnzjnsE/P8UfvEbI+Lp6cnnp6esda7u7vH+cV40HpJvXTNnFOauG63r8Nh4w6za9kWuDr7+4mnNHHt0jFdP+eja/aYitSGYo2xHFyI+8ovoM20FDltgq7bgT8BsJRoirtnhmSMSuJDv3POKzVfu4TEZepo/48Sk/gfOnSIpUuXkjVrVoft1atX58qVK2zdutW+bvny5VitVqpWrWrfZ/Xq1Q59IUJCQihevDj+/v72fZYtW+Zw7JCQEKpXr55cb01E5MEOLoKoW5ClEASUMzsaERF5kPr9weIKB/6C4+vNjsZRdBTsM5J/jfIvImBy8n/9+nV27NjBjh07ADh69Cg7duzgxIkTREZG8uKLL7JlyxamTp1KdHQ0oaGhhIaGEhERAUDJkiVp1KgRb7zxBn///Tfr1q2jS5cutG7dmly5cgHQtm1bPDw86NixI3v27GH69OmMGjXKoWT//fffZ9GiRXz11Vfs37+ffv36sWXLFrp06ZLin4mIiH0+5tLPa5R/EZHULHtxeOJVY3nJZ2CzmRvPvY6vhfD/IEMWKFDL7GhEJBUwNfnfsmWLfbwAgO7du1OxYkX69OnD6dOnmTdvHqdOnaJChQoEBgbaH+vX372zOnXqVEqUKMEzzzxDkyZNePrppxk3bpx9u5+fH0uWLOHo0aNUqlSJHj160KdPH/s0fwA1atRg2rRpjBs3jvLlyzNr1izmzp1LmTJlUu7DEBEBo+T/0J1BZUoFmxqKiIjEQ+3e4O4Dp7fCntlmR3PXnrnGz5JNwVV9/UUkEX3+k1Lt2rWxPeQO6cO2xciSJQvTpj28j1W5cuVYs2bNQ/dp2bIlLVu2fOT5RESSlb3kvzAElDU7GhEReZSMOeGp92HlYFjaH0o0BbfY40KlqHtL/nUjWUTuSHTLf0REBAcOHCAqKiop4xERSd/2zDF+lg5Wyb+IiLOo0QV8A+DKcdj8s9nRwPF1EH4RMvhDQZX8i4ghwcl/eHg4HTt2xNvbm9KlS3PixAkAunbtytChQ5M8QBGRdOP2NTi81Fgu/by5sYiISPx5+ECdT4zlVcPh5mVz44kZO6ZEU3BNnSOUi0jKS3Dy37t3b/755x9WrlyJl5eXfX29evWYPn16kgYnIpKuHFx8t+Q/p8YcERFxKhXbQfaScOsKrBlpXhzWaI3yLyJxSnDyP3fuXEaPHs3TTz+N5Z6S1NKlS3PkyJEkDU5EJF2xl/xrlH8REafj4gr1BxjLm36EKyfMieP4Orhx4U7Jf5A5MYhIqpTg5P/ChQvkyJEj1vobN2443AwQEZEEuH3t7ij/aqkREXFORetDgZoQfRuWDTQnhphR/ks8q5J/EXGQ4OS/cuXK/PXXX/bnMQn/zz//TPXq1ZMuMhGR9OTgYuOPxaxFVPIvIuKsLBZocCfp3zUDzmxP2fPfW/JfSmPHiIijBE/1N3jwYBo3bszevXuJiopi1KhR7N27l/Xr17Nq1arkiFFEJO2LKfkvFaySfxERZ5arIpRtZST/Sz6H9n+m3L/rx9fDjfPglRkKqeRfRBwluOX/6aefZseOHURFRVG2bFmWLFlCjhw52LBhA5UqVUqOGEVE0jaHkn+11IiIOL1nPgdXTzi2Bg4tSbnzapR/EXmIBLf8AxQuXJiffvopqWMREUmfDiy6p+S/tNnRiIjI48qcD6q+Ceu/hZA+UPgZcE3Un93xZ42GvfOMZY0dIyJxSPC/QidOPHzk0nz58iU6GBGRdCmmpUaj/IuIpB01e8D2yXBhP+yYApU6JO/5Tmy4U/Lvp1H+RSROCU7+CxQo8NBR/aOjox8rIBGRdOVW2N2S/1LBpoYiIiJJKENmqPURLO4NKwZD2Zbg4ZN857OP8t8U3DyS7zwi4rQSnPxv3+44amlkZCTbt29n5MiRDBo0KMkCExFJF+yj/BdVyb+ISFpTpRP8/SNcPgbrR0PtXslzHms07LtT8q8bySLyAAlO/suXLx9rXeXKlcmVKxcjRoygRYsWSRKYiEi6EDPKf+lglfyLiKQ1bh7wTB+Y9TqsG2WU/mfMmfTnObERrp8zSv4L1U7644tImpDg0f4fpHjx4mzevDmpDicikvbdCoPDS41ljfIvIpI2lW4BuStB5A1YOSR5zhEzdkzxZ1XyLyIPlODkPywszOFx9epV9u/fz2effUbRokWTI0YRkbTp4KK7Jf85SpkdjYiIJAeLBRp8YSxv+xUuHEja41utGuVfROIlwWX/mTNnjjXgn81mI2/evPz2229JFpiISJoXMziTRvkXEUnb8tcwWuUP/AUhfaFtEv7NfHIjXA8FTz8oVCfpjisiaU6Ck/8VK1Y4PHdxcSF79uwUKVIEN7dknr9URCStuBUGh++M8q+SfxGRtK9+f6Pi6+BCOLYWCjydNMe1j/LfRCX/IvJQCc7Wg4I0b6iIyGM7sBCiIyBbMchR0uxoREQkuWUragz4t2U8LPkMOi0Hl8ccfstq1Sj/IhJv8Ur+582bF+8DNm/ePNHBiIikGzGDM6nkX0Qk/aj9MeycDme2w57ZUPbFxzveyU1w7Sx4ZoLCKvkXkYeLV/IfHBwcr4NZLBaio6MfJx4RkbTv1tW7o/yrpUZEJP3wzQFPdYMVX8Cy/lCyGbh5Jv549lH+mzzecUQkXYhXrZHVao3XQ4m/iEg8HFh0p+S/uEr+RUTSm+rvQsZAuHIC/v4p8cexWmHvH8ayRvkXkXh4zI5GIiKSYHvmGD9LB6vkX0QkvfHwhjqfGMurR8DNy4k7zqm/7yn5r5t08YlImpWo4flv3LjBqlWrOHHiBBEREQ7b3nvvvSQJTEQkTbp1FY4sM5Y1yr+ISPpU4WXY+AOc3wurv4SGgxJ+jJhR/os3Vsm/iMRLgpP/7du306RJE8LDw7lx4wZZsmTh4sWLeHt7kyNHDiX/IiIPYx/lXyX/IiLplosr1B8AU1+Ev8fBk2+Af4H4v/7ekn+NHSMi8ZTgsv8PPviAZs2acfnyZTJkyMDGjRs5fvw4lSpV4ssvv0yOGEVE0o6Ylhq1+ouIpG9F6kHBIOOG8LKBCXvtqc1w7Qx4ZFTJv4jEW4KT/x07dtCjRw9cXFxwdXXl9u3b5M2bl+HDh/PJJ58kR4wiImmDQ8l/sKmhiIiIySwWaDAQsMDuWXB6W/xfG9PqX7wxuHslS3gikvYkOPl3d3fHxcV4WY4cOThx4gQAfn5+nDx5MmmjExFJS2JK/rOXUMm/iIhAYHko95KxvORzsNke/RqbRvkXkcRJcPJfsWJFNm/eDEBQUBB9+vRh6tSpdOvWjTJlyiR5gCIiaUbMKP/qnykiIjHqfgaunnB8LRxc/MjdLWe2Qdgp8PBVyb+IJEi8k//o6GgABg8eTGBgIACDBg3C39+ft99+mwsXLjBu3LjkiVJExNndvAJHlhvLaqkREZEYmfNCtbeN5ZA+EB310N0t++60+hdrBO4Zkjk4EUlL4p38586dm48//phMmTJRp04dwCj7X7RoEWFhYWzdupXy5csnW6AiIk5NJf8iIvIgT38AGbLAxQOwffKD97PZcNn3p7GsG8kikkDxTv7fffddZs2aRcmSJalZsyYTJ04kPDw8OWMTEUk79s41fmqUfxERuV+GzBD0kbG8YjDcvh7nbv7h/2KJKfkvUi/l4hORNCHeyf/nn3/O4cOHWbZsGYUKFaJLly4EBgbyxhtvsGnTpuSMUUTEud28AofvjPKv/v4iIhKXyh3BvyDcOA/rv4tzl1xX/jYWijVUyb+IJFiCB/yrXbs2kyZNIjQ0lK+++op9+/ZRvXp1SpcuzciRI5MjRhER53ZgIVgjIXtJyFHC7GhERCQ1cvOAen2N5fXfwrVQx+02G7muGINu60ayiCRGgpP/GL6+vnTq1Im1a9fy559/EhoayocffpiUsYmIpA0xo/yr5F9ERB6mVDDkqQKR4Ub5/z0sZ7bjHXERm7sPFK1vTnwi4tQSnfyHh4czceJEgoKCaN68OVmzZmXQoEFJGZuIiPPTKP8iIhJfFgs0+MJY3j4Zzu+7u2m/Mcq/rWh9lfyLSKIkOPlfv349nTp1IjAwkHfffZcCBQqwYsUKDh48yMcff5wcMYqIOK8DC4yS/xylIHtxs6MREZHULl81KNEUbFYIudMNwGbDZd88AKwlnzMxOBFxZm7x3XH48OFMmDCBgwcPUrlyZUaMGEGbNm3ImDFjcsYnIuLc9sw1fqp/poiIxFe9/nBwERxaDEdXg4cPlqsniXLxwFb4GbOjExEnFe/kf8SIEbRr146ZM2dSpkyZ5IxJRCRtuHlZJf8iIpJwu2ZCYHk4vRWWfA4FawJwLlMFAjZ+DxagTm9zYxQRpxPv5P/MmTO4u7snZywiIs5txRBwcb07V/P+e0r+9/4B1mj9sSYiIo/m4mok/q4ecHYHnN8LgA0LrquHQp1PzY1PRJxSvPv8K/GXpBJttbHhyH/8seM0G478R7TVZnZIIknDxRVWDIJVw43ne+caP32yGetdXE0LTUREnEjQR0aCHx1hPI+OwObiTp4rm4iu9fHdm8wiIgkQ75Z/kaSwaPdZ+v+5l7NXb9nXBfp50bdZKRqVCTQxMpEkEPPH2IpBEHkTjqwwnh9dbfwRpz/WREQkvoI+MpL/1SMAsFgj2RfYgiI1e/6/vfuOb6rcHzj+SdK9aaELCpRNC7JH2cgWcS8UBREQxKvCver1J1fEcVHcA8WBoFcQFRFFEUX2KLsFuphldgGle6XJ+f2RJrR0paVtRr/v16s0yTk559s8Kc33PM/zfZBLyUKI2qj1Un9C1NSGmGRmfXuoTOIPkJJZwKxvD7EhJtlCkQlRC3o95KUblmE6vQ2OrobIT6AoFwJvgp3vGob8gyT+QgghaufmeaA29NUpakeOB95h2XiEEDZNev5Fg9DpFRasi6OiAf4Khro1C9bFMSosEI1a1cDRCZt0/fz60rYtqt38er3eUKQvNw1ySr6Mt3MvQU7qtdu5l0BfXP0xVZXEKIQQQlRn2yLD3xqNEypdER1S1gK3WDoqIYSNqlXyr9PpWLt2LfHx8QCEh4dz2223odHIICRRsX2J6eV6/EtTgOTMAvYlphPR1q/hAhO2yzi/Hsok1+odb0PpYkhlEvpUyLlUeXJvbkJfmosPePiDu7/hu4e/YTRA4jZDb42+2PDhTS4ACCGEqIltiwx/50pGj+k2L6Tz9jfQ7egAN0vxWCFEzdU4+T958iTjx4/nwoULdOzYEYCFCxcSEhLC77//Ttu2bes8SGH70rIrT/xrs58QZebXXzmF2j+cQcf/hyb3OPi2gfh1sH8p5F2ufULvEQDuzUqS+2aG+6bbJd8dnMs+d9siQ+JvHOpv/PBWOmYhhBCiKtcl/gD6wf/i+InjdN7+BmhkVJkQouZqnPw/9dRTtGnThsjISHx9fQG4cuUKkyZN4qmnnuL33383+1jbt2/nrbfe4uDBgyQnJ/Pzzz9zxx13mLavWbOGJUuWcPDgQdLT04mKiqJ79+5ljjFs2DC2bdtW5rHHH3+cJUuWmO6fO3eOWbNmsWXLFjw8PJg8eTILFy7EweHaj79161bmzp1LbGwsISEhzJs3jylTppj/wogq+Xu61Ol+opEryIQTGw1LH2mc4MgqNIBpzEj66fLPcfEpn7yX7rE3JvfuzcDBqXZxVfBhrcxFitL3hRBCiMrodRXWizkeeAcd2ndAo9dZKDAhhC2rcfK/bds29uzZY0r8Afz8/HjjjTcYOHBgjY6Vm5tLt27dmDp1KnfddVeF2wcNGsR9993H9OnTKz3O9OnTeeWVV0z33dzcTLd1Oh3jx48nMDCQ3bt3k5yczCOPPIKjoyP//e9/AUhMTGT8+PHMnDmTFStWsGnTJqZNm0ZQUBBjxoyp0c8kKtY31JcgbxdSMgsqnPevAgK9Xegb6lvBViGArGQ4th4SfjdUzzcW0ytFQYV+2ItovALLJvc3ktDXRCUf1kz35cOaEEIIc1RRs0Y/+F9oZAluIUQt1Dj5d3Z2Jjs7u9zjOTk5ODnV7MP1uHHjGDduXKXbH374YQDOnDlT5XHc3NwIDAyscNtff/1FXFwcf//9NwEBAXTv3p1XX32V559/npdffhknJyeWLFlCaGgo77zzDgCdO3dm586dvPfee5L81xGNWsX8CWHM+vZQhdsVYP6EMCn2J8q6fAISfoP43+DigbLbmnaETuMN8/kPLkMpKYaEvhh6PmyZeKsqMCg9/kIIIYQQwoJqnPzfeuutzJgxg6VLl9K3b18A9u7dy8yZM7ntttvqPEBzrFixgm+//ZbAwEAmTJjAf/7zH1Pvf2RkJF27diUgIMC0/5gxY5g1axaxsbH06NGDyMhIRo4cWeaYY8aM4Zlnnqn0nIWFhRQWFpruZ2VlAaDVatFqr/VIGm+XfqyxGtGxKR890I1nfzpKvlZfZpuHswMD2zSxitdJ2syCFD2qpChUx9ejPrYe1ZUTZTbrm/dG6XgL+g7jwK896h1vozm4DN2Qf1PY/2nO/m+2oRgShp4RYRvkd862SfvZHmkz2yTtZruk7WyXLbRdTWKrcfL/4YcfMnnyZCIiInAsGXJUXFzMbbfdxgcffFDTw92wBx98kFatWhEcHMyRI0d4/vnnOXbsGGvWrAEgJSWlTOIPmO6npKRUuU9WVhb5+fm4urqWO+/ChQtZsGBBucf/+uuvMtMOjDZu3Fi7H9AO+ThoyNeqGB6kp7OPwnen1FwtLGbB//5icGBFkwIsQ9qsYaj0xTTNiSco8yCBmVG4aq+atulVGi55hJPs05MU754UOvrAVWDvCTqkvEXn5DXEB93F8eww2LgRStY/7rz9DY6fOC7rIdsY+Z2zbdJ+tkfazDZJu9kuaTvbZc1tl5eXZ/a+NU7+fXx8+OWXXzhx4gQJCQmAYZh8u3btanqoOjFjxgzT7a5duxIUFMSIESM4depUva488MILLzB37lzT/aysLEJCQhg9ejReXl6mx7VaLRs3bmTUqFGmiyWNWaFWx9y9mwGFBQ8NI8jbhWZ7z7HgtwR2p7vzyuRBOGrUFo1R2qwBFGajOrUJ9fH1qE5uRFV4bSqR4uSB0m4k+g63oLQdia+LF75A+HWHUG8/iq79v2k3+F+041q7tXp4Mbo9Heig6Gg3RNZCtgXyO2fbpP1sj7SZbZJ2s13SdrbLFtrOOALdHDVO/o3at29P+/bta/v0etOvXz/AsCRh27ZtCQwMZN++fWX2SU1NBTDVCQgMDDQ9VnofLy+vCnv9wVD7wNnZudzjjo6OFb4xKnu8sYlLyUWnV/BzdyLEzwOVSsXEfq1ZvPU0SZkF/BF7ibt7tbB0mIC0WZ3LSbtWsO/0VtAVXdvmEQAdb4FOt6IKHYzKwZlqLwGNmAeA5rqHHR0d0ZSsf3z9NmHd5HfOtkn72R5pM9sk7Wa7pO1slzW3XU3iMiv5nzt3Lq+++iru7u5lersr8u6775p98voQHR0NQFBQEAARERG8/vrrpKWl4e/vDxiGbXh5eREWFmbaZ/369WWOs3HjRiIiIhou8EYiJikTgPDm3qhUhuJ+Lo4aHhvUhjc3JPDJ1pPc2aM5ain8Zx+unDIk+wm/w/m9UHqtB9+20PlW6HQrNO8NasuO+BBCCCGEEMKemZX8R0VFmQoJREVFVbqfMZkzV05ODidPnjTdT0xMJDo6Gl9fX1q2bEl6ejrnzp0jKSkJgGPHjgGGnvrAwEBOnTrFypUrueWWW/Dz8+PIkSPMmTOHIUOGcNNNNwEwevRowsLCePjhh1m0aBEpKSnMmzeP2bNnm3ruZ86cyccff8xzzz3H1KlT2bx5Mz/88AO///57jX4eUb2Yi4ZhKV2Cvco8Pql/Sz7ZepJTl3L5Ky6FsV2CLBGeqMqWhaDWVFy1ftsiwzJ2w/4NSVHXEv5L8WX3C+55LeFv2gFq+H+GEEIIIYQQonbMSv63bNlS4e0bdeDAAYYPH266bxxVMHnyZJYvX86vv/7Ko48+atr+wAMPADB//nzTMn1///0377//Prm5uYSEhHD33Xczb94803M0Gg2//fYbs2bNIiIiAnd3dyZPnswrr7xi2ic0NJTff/+dOXPm8MEHH9CiRQu+/PJLWeavHsSW9Px3ae5d5nFPF0cmR7Tm4y0nWbzlFGPCA2t8MUnUM7UGtrxuuF36AsCWhbDtDUPvfdT/IOtiqec4QOvBhiX5Ot4C3s0bNmYhhBBCCCEEcANz/uvCsGHDUJTKq7tPmTKFKVOmVLo9JCSEbdu2VXueVq1alRvWX1EsVY1qEDdOq9OTkGwo7NYl2Lvc9kcHtubLnac5ejGTHScuM6RDs4YOUVTFmPBved0wXz/wJtj2JqTGGB6/eMDw3dEd2o809O63HwWuTSwTrxBCCCGEEMLErOT/rrvuMvuAxiX2hLjeidQcinR6PF0cCPEtX0jRz8OZiX1bsmzXGT7ZelKSf2s09DlD4b7tb5V93K0pdBwHnSdA6FBwdLFMfEIIIYQQQogKmZX8e3uX76UVoqaMxf66BHtXOqR/+uA2fLvnLHtOp3PwbDq9Wvk2ZIjCHJkXrt1WqWHKegjpa5gWIIQQQgghhLBKZiX/y5Ytq+84RCMQc9E439+r0n2CfVy5q0cLvj9wnk+2nGLpFEn+rUpKDBz/w3Bb7Qh6LZzZAa1kZQwhhBBCCCGsWa3X1rp06RI7d+5k586dXLp0qS5jEnbqWvJf9UiSmcPaolbBpoQ04pKyGiI0Ya6fphm+N+sML12G4S8aagBsW2TZuIQQQgghhBBVqnHyn5uby9SpUwkKCmLIkCEMGTKE4OBgHnvsMfLy8uojRmEHdHqFuGRDIh9eQbG/0kKbujOuq2Gpv0+3nar32ISZ/nj+2tJ9d39h+D70ObkAIIQQQgghhA2ocfI/d+5ctm3bxrp168jIyCAjI4NffvmFbdu28c9//rM+YhR24PSlHAq0etycNIQ2da92/yeGtQXg9yNJnLmcW9/hCXOc2WX43mEsBHa99rjxAoBeZ5m4hBBCCCGEENWqcfL/008/sXTpUsaNG4eXlxdeXl7ccsstfPHFF6xevbo+YhR2wFjsLzzYC4264mJ/pYUHezO8YzP0Cny2XXr/LS7j3LVe/8H/Kr996HMw/IWGjUkIIYQQQghhthon/3l5eQQEBJR73N/fX4b9i0rFXDRvyH9ps4e3A2D1wQukZBbUS1zCTLs+BH0xhA6BkD6WjkYIIYQQQghRQzVO/iMiIpg/fz4FBdeSsfz8fBYsWEBEhFT8FhUzt9hfab1b+9I31BetTuGLHafrKzRRnexUOPSN4XZFvf5CCCGEEEIIq2fWUn+lvf/++4wdO5YWLVrQrVs3AA4fPoyLiwt//vlnnQcobJ9er5iq9le1zF9FnhjWln2J6azce47Zw9vh6+5UHyGKqkR+DLpCaNHH0PMvhBBCiEZNp1fYl5hOWnYB/p4u9A31NWtapxDCsmqc/Hft2pUTJ06wYsUKEhISAJg4cSIPPfQQrq6udR6gsH3n0vPILizG2UFNu2YeNXru0A7NCA/2IjYpi+W7zzB3VId6ilJUKC8dDnxluD34X6CSP+xCCCFEY7YhJpkF6+JILjUlM8jbhfkTwhjbJciCkQkhqmPWsP+ePXty9epVAF555RUApk+fzjvvvMM777zDtGnTJPEXlTIW++sU5IWDpmYzTVQqlWnu//JdieQUFtd5fKIKez+DohwI6Aodxlg6GiGEEEJY0IaYZGZ9e6hM4g+QklnArG8PsSEm2UKRCSHMYVYmFh8fT26uYbm1BQsWkJOTU69BCftiLPbXJbhmQ/6NxoQH0qaZO1kFxazYc7YuQxNVKcyGvUsMtwfPlV5/IYQQohHT6RUWrItDqWCb8bEF6+LQ6SvaQwhhDcwa9t+9e3ceffRRBg0ahKIovP3223h4VDx8+6WXXqrTAIXti02qebG/0jRqFbOGtuXZ1Uf4Ykcikwe0xsVRU5chiorsXwoFGeDXHsJut3Q0QgghhLCgfYnp5Xr8S1OA5MwC9iWmE9HWr+ECE0KYzazkf/ny5cyfP5/ffvsNlUrFH3/8gYND+aeqVCpJ/kUZiqJcq/Rfg2X+rndHj+a8//cJLmbk8+PBCzzcv1VdhSgqos2HyMWG24PngloutgghhBCNWVq2ecsum7ufEKLhmZX8d+zYkVWrVgGgVqvZtGkT/v7+9RqYsA9JmQVczdPioFbRIbBmxf5Kc9SomT44lJfXxfHZtlNM7BNS4/oBogYO/Q9y08CnJXS919LRCCGEEMLC/D1d6nQ/IUTDq3H2tGXLFnx9fcs9XlxczPbt2+skKGE/jL3+HQI8cXa4sd7j+/u0xM/diQtX81l3JKkuwhMVKS6CXe8bbg98GjSOFg1HCCGEEJbXN9SXIG8XKqsApMJQ9b9vaPk8QQhhHWqc/N98882kp6eXezwzM5Phw4fXSVDCfsQah/w3r12xv9JcnTRMHRQKwCdbTqGXgjL148gqyLoIHoHQfZKloxFCCCGEFdCoVcyfEFZhwT+j+RPC0KilQLAQ1qrGyb+iKKgqqPp95coV3N3d6yQoYT9ikkoq/dey2N/1Ho5ohaezAyfSctgYn1onxxSl6Iph53uG2wOeBEcZuieEEEIIg7FdgujdqkmF26YPDmVsl6AGjkgIURNmzfkHuOuuuwBDUb8pU6bg7Oxs2qbT6Thy5AgDBgyo+wiFTTMO+w+/gWJ/pXm5OPLIgFYs3nKKT7acZHRYQIUXo0Qtxa2F9NPg2gR6PWrpaIQQQghhRbQ6PcdSswFYcFs4Pm6ObElIY210EgfPZVg2OCFEtcxO/r29Dcmboih4enri6upq2ubk5ET//v2ZPn163UcobFZaVgFp2YWoVdA5yLPOjvvowFC+3JHI4QuZ7Dp5hUHtm9bZsRs1vR52vGO43f8JcK59gUYhhBBC2J/9ielkFxTT1MOJSf1boVGriGjrx/qjKRw8e5VD567Ss2XFIwOEEJZndvK/bNkyFMUwy+ejjz7Cw0MSA1G1mCRDr3/bZh64OZn9VqtWUw9nJvZtyfLdZ/hk60lJ/uvK8T8gLQ6cPKGvXMgTQgghRFnGKZc3d/I3ze3393Th9u7B/HjwAkt3JNLzIUn+hbBWNZrzrygKK1asIDk5ub7iEXYk5mLdzvcvbfqQNjioVew+dYVD567W+fEbHUWB7W8bbvedZhj2L4QQQghRQlEU/i5J/kd0Diiz7bHBhoLMf8Qkcz49r8FjE0KYp0bJv1qtpn379ly5cqW+4hF25Np8/xuv9H+95j6u3NmjOWCo/C9u0OktkHQIHFyh/2xLRyOEEEIIK3MiLYfz6fk4OagZfN2oy06BXgxu3xS9Al/tSrRQhEKI6tS42v8bb7zBs88+S0xMTH3EI+xIbEml/6710PMPMHNYW1Qq+Ds+lYSUrHo5R6OxvWSuf6/J4NHMsrEIIYQQwuoYe/0HtvWrcDrn9MFtAPhh/3ky87UNGpsQwjw1Tv4feeQR9u3bR7du3XB1dcXX17fMlxAA6blFXMzIByCsHnr+wVBL4JaSJWU+3Sq9/7V2bg+c3QlqRxjwlKWjEUIIIYQV+jvOkPyPDAuocPvg9k3pGOBJbpGOVfvONWRoQggz1bgK2/vvv18PYQh7E1tS7C+0qTueLo71dp5Zw9ry+9Fk1h1O4p+jOtLSz63ezmW3jHP9u08E7+aWjUUIIYQQVudyTiFR5zMAGNGp4uRfpVLx2OBQnlt9hOW7zzB1UCiOmhr3Mwoh6lGNk//JkyfXRxzCzhiL/dXHfP/SujT3ZmiHZmw7fokl20/x3zu71uv57E5SNJzcCCo1DHzG0tEIIYQQwgptTkhDUQxTOQO9XSrd7/buwSzacIzkzALWH03m9u7SqSCENanV5TidTsdPP/3Ea6+9xmuvvcbPP/+MTqer69iEDTMu81cflf6vN3t4OwBWH7hAalZBvZ/Pruwomevf5W7wa2vZWIQQQghhlYxD/kd09q9yP2cHDZMjWgHwxY7TpmXChRDWocbJ/8mTJ+ncuTOPPPIIa9asYc2aNUyaNInw8HBOnZJ518IgtqTSf5fg+k/++4b60qd1E4p0er7ccbrez2c3Lh2D+HWG24PmWjYWIYQQQlilAq2OHScuAzCyc8VD/kt7qH8rXBzVxFzMYs/p9PoOTwhRAzVO/p966inatm3L+fPnOXToEIcOHeLcuXOEhoby1FNSLExAVoGWM1cMa7zW97B/oydKev9X7D3H1dyiBjmnzdvxLqBAp1shIMzS0QghhBDCCkWeukK+VkeQt4tZn+t83Z24u2cLAJbulE4ZIaxJjZP/bdu2sWjRojKV/f38/HjjjTfYtm1bnQYnbFNcyRJ/zX1caeLu1CDnHNahGWFBXuQV6fg68kyDnNOmpSfC0R8Ntwf/07KxCCGEEMJqGZf4G9HZH5VKZdZzHhsUWrIccxqnLuXUZ3hCiBqocfLv7OxMdnZ2ucdzcnJwcmqYRE9YtxjjkP/mDdPrD4YKs08MN8xZX7brDDmFxQ12bpu06wNQdND2Zmje09LRCCGEEMIKKYrCpvg0wLwh/0ZtmnmYVgVYujOxXmITQtRcjZP/W2+9lRkzZrB3714URUFRFPbs2cPMmTO57bbb6iNGYWNiS3r+G2K+f2njugQR2tSdzHwt3+2V9WUrlZUE0SsMtwf/y7KxCCGEEMJqxSZlkZJVgJuThv5t/Gr03GmDQwH46eAF0mVKphBWocbJ/4cffkjbtm2JiIjAxcUFFxcXBg4cSLt27fjggw/qI0ZhY671/Dds8q9Rq5g11ND7/8WO0xRobXsFCp1eIfLUFX6JvkjkqSvo9HVUMXf3x6ArgpYR0Hpg3RxTCCGEEHZnY0mV/yHtm+HiqKnRc/uF+tK1uTeFxXq+3XO2PsITQtSQQ02f4OPjwy+//MLJkyeJj48HoHPnzrRr167OgxO2J6+o2DS3K7wBh/0b3dGjOe/9fZzkzAJ+OnSBh/q1avAY6sKGmGQWrIsjOfPa0oVB3i7MnxDG2C5BtT9w7hU4uMxwW3r9hRBCCFGFTQnmLfFXEZVKxbTBoTy9KppvIs8wY0ibGl9AEELULbN7/vV6PW+++SYDBw6kT58+fPnll4wcOZIJEyZI4i9M4pOz0Svg7+mMv6dLg5/fyUHNjCFtAPhs22mKdfoGj+FGbYhJZta3h8ok/gApmQXM+vYQG2KSa3/wPZ+ANg+CukO7ETcWqBBCCCHsVnJmPjEXs1Cp4OZONU/+AW7pGkSQtwuXc4r4NTqpjiMUQtSU2cn/66+/zv/93//h4eFB8+bN+eCDD5g9e3Z9xiZsUGySZYb8l/ZAn5b4ujtxLj2P34/eQKJsATq9woJ1cVQ0wN/42IJ1cbWbAlCQCfu+MNwe8i8ws2KvEEIIIRqfv0sK/fVs2QQ/D+daHcNRo+bRga0B+HLnaRSljqYwCiFqxezk/5tvvuGTTz7hzz//ZO3ataxbt44VK1ag19tez6qoP0cvlCT/ZqwDW19cnTRMLflD88mWU+jraq58A9iXmF6ux780BUjOLGBfYnotDv4FFGZCs87QcXztgxRCCCGE3dtUssRfTar8V+SBvi1xd9JwPDWH7Scu10VoQohaMjv5P3fuHLfccovp/siRI1GpVCQlyRAecU1MSaX/cAv2/AM8HNEaD2cHjqVmsykhzaKx1ERaduWJf232MynKNQz5Bxg8F9Q1rvUphBBCiEYit7CY3SevADAqrHZD/o28XBy5v09LAL7ccfqGYxNC1J7ZGUBxcTEuLmXncDs6OqLVaus8KGGbCrQ6TqRmA5Yd9g/g7erIwxGGYn+Lt5y0mWFm/p7mDaurcT2Fg19D3hVo0hrC76p5YEIIIYRoNHacuEyRTk8rPzfaNvO44eM9OrA1apXhuPHJWXUQoRCiNsyu9q8oClOmTMHZ+VpyUlBQwMyZM3F3dzc9tmbNmrqNUNiM46nZFOsVmrg5Euzd8MX+rjd1YChf7Uwk+nwGkaevMKBtU0uHVKXcwmJ+2H++2v2auDnSN9TX/AMXF8LuDw23B80BTY0X+RBCCCFEI/J3qSH/qjqoERTi68a4LkH8fjSZpTsTefvebjd8TCFEzZnd8z958mT8/f3x9vY2fU2aNIng4OAyj4nGK+ai4Upul+bedfKH4kY183Tm/j4hgGHuvzVLSMnito938nN0EuqSl66yVzAzX8uvhy+af/DoFZCdDJ7B0G3iDccqhBBCCPul0ytsKZkyWZsl/iozbXAoAL9EXyQtq4bTF4UQdcLsLsBly5bVZxzCDsRYQaX/680Y0oaVe8+x8+Rlos9n0D3Ex9IhlaEoCj8cOM9Lv8RSWKwn0MuFjx7swZWcQhasiytT/C/I24WWvm7sTUxnzveHycjT8ujA0KpPoCuGne8bbg98ChxqV61XCCGEEI1D9PmrXMktwsvFgT6tazDSsBo9Wjahd6smHDh7la8jz/DsmE51dmwhhHlk/K+oM7EXjZX+rSf5b9HEjdu7N+enQxf4ZMtJPn+kt6VDMsktLObFn4+ytmTd22Edm/Hufd3xdXcCYFRYIPsS00nLLsDf04W+ob6ogFd/j2PZrjMsWBfH1Twtc0a2r3ykRcxqyDgLbk2h5+QG+smEEEIIYas2xhl6/Yd19MdRU7cFgqcNDuXA2aus2HuO2cPb4eYkqYgQDcmiJb+3b9/OhAkTCA4ORqVSsXbt2jLb16xZw+jRo/Hz80OlUhEdHV3uGAUFBcyePRs/Pz88PDy4++67SU1NLbPPuXPnGD9+PG5ubvj7+/Pss89SXFxcZp+tW7fSs2dPnJ2dadeuHcuXL6/jn9a+aXV64lOMxf4st8xfRWYNa4NKBX/FpXK8pCChpcUnZzHh452sjU5Co1bx/NhOfDW5jynxB9CoVUS09eP27s2JaOuHRq1CrVbx0q1hzB3VAYAPN53g5V9jK17OUK+HHe8abkc8AU5uDfGjCSGEEMKGmZb4C7uxJf4qMioskFZ+bmTkafnp4IU6P74QomoWTf5zc3Pp1q0bixcvrnT7oEGDePPNNys9xpw5c1i3bh0//vgj27ZtIykpibvuulbNXKfTMX78eIqKiti9ezdff/01y5cv56WXXjLtk5iYyPjx4xk+fDjR0dE888wzTJs2jT///LPuflg7dzIth6JiPZ4uDrT0ta4ks52/J2PDAwFYstWyc/8VReG7fee4Y/EuTl/KJdDLhe9n9GfWsLao1ebVSVCpVDw1oj2v3B6OSgVfR55lzg/RaHX6sjsmrIPLx8DZG/pMq4efRgghhBD25OyVXE6k5eCgVjG0Q7M6P75GrWJqyZTFpTsT0VXUeSGEqDcWHWszbtw4xo0bV+n2hx9+GIAzZ85UuD0zM5OlS5eycuVKbr75ZsBQm6Bz587s2bOH/v3789dffxEXF8fff/9NQEAA3bt359VXX+X555/n5ZdfxsnJiSVLlhAaGso777wDQOfOndm5cyfvvfceY8aMqdsf2k7FlAz5Dw/2sopif9d7Ylg7/ohJ4ZfDScwZ1YEQC1ygyCkZ5v9LyTD/4R2b8U6pYf419UhEa7xdHfnnD4f5JTqJ7IJiFj/YE1cnDSgKbH/bsGO/GeBiPVMxhBBCCGGd/o43DPnvG+qLt6tjvZzjnl4teOevY5y5ksem+FRGl3TQCCHqn01PtDl48CBarZaRI0eaHuvUqRMtW7YkMjKS/v37ExkZSdeuXQkIuDZ0acyYMcyaNYvY2Fh69OhBZGRkmWMY93nmmWcqPXdhYSGFhYWm+1lZhkr3Wq0WrVZretx4u/Rj9ujIhQwAwgI9rfJn7RTgxqB2fuw8eYVPt55gwYSwSvetjzaLT87m6e8Pk3glD41axdyR7Zg2sDVqteqGznNLuD9uD3Xnye8OszkhjYeX7uGzh3rgfXEbDilHUBzdKO41DaywTepaY/lds0fSdrZN2s/2SJvZpoZot42xyQAM79i03s7jpIaJfUL4bEcin28/xfAOfvVyHmsiv3O2yxbariax2XTyn5KSgpOTEz4+PmUeDwgIICUlxbRP6cTfuN24rap9srKyyM/Px9XVtdy5Fy5cyIIFC8o9/tdff+HmVr5XeePGjeb/YDZoZ6wGUKFNO8369da5rF4PJ9iJAz/sP08n3Rm8q+lwr4s2UxSITFPxU6KaYkWFj5PC5PbFtMiOZ8OG+Bs+vtHjHeGLBA0HzmZw23ub+Nl1Af7AKZ8hxG7dW2fnsQX2/rt2I/QKnMpSkaUFL0do66Vg5myTBiFtZ9uk/WyPtJltqq92yyuGfYmGz3Pq5FjWr4+tl/MANC8CjcrwueXTH9bTyqPeTmVV5HfOdllz2+Xl5Zm9r00n/5b0wgsvMHfuXNP9rKwsQkJCGD16NF5e1wreabVaNm7cyKhRo3B0rJ/hU5am0yu8cHAzoOPBcYNp52+d/4MrisKuL/dz6FwG51zb8fyYDhXuV1dtll1QzH9+jeP304aLTMM6NGXR3V1o4la7Yf7VGZmczdRvDtIi9zD+ygkUtROtJr5FK8+gejmftWkMv2s34s/YVBauTyAl69qIpUAvZ+bd0okx4XVf1KkmpO1sm7Sf7ZE2s0313W7rjiSj33+U9v7uPHLXwDo//vUOFR9l7eFkjtGCWbfcVO/nsyT5nbNdttB2xhHo5rDp5D8wMJCioiIyMjLK9P6npqYSGBho2mffvn1lnmdcDaD0PtevEJCamoqXl1eFvf4Azs7OODuXXzPd0dGxwjdGZY/bg7NpOeQV6XB11NAhyAeNNXUlXufJm9sxdfkBvtt3nn/c3AFvt8rb5EbaLDYpkydXRpF4Obekmn9Hpg1qY3ZRv9q4qaUvq2cOIO2TV0EPaxjGTdomtLfT911l7Pl3rbY2xCTzj1WHub6sUmpWIf9YdZhPJ/VkbBfLXySStrNt0n62R9rMNtVXu209fgWAkWGBDfK+mDG0HWsPJ7MhNpW03GKa+1T8mdueyO+c7bLmtqtJXBat9n+jevXqhaOjI5s2bTI9duzYMc6dO0dERAQAERERHD16lLS0NNM+GzduxMvLi7CwMNM+pY9h3Md4DFG12CRDsb+wYC+rTvwBhnf0p1OgJ7lFOr6OPFPnx1cUhW/3nOXOT3aTeDmXYG8Xfni8PzOGmF/N/0a0Lkygr/4wOtS8l38L934WSdS5q/V+XmG9dHqFBeviyiX+gOmxBevipOKyEEI0Ylqdni3HDJ+VR3ZumNFgYcFeDGznh06vsHxXYoOcU4jGzqLJf05ODtHR0URHRwOGJfeio6M5d+4cAOnp6URHRxMXFwcYEvvo6GjTXH1vb28ee+wx5s6dy5YtWzh48CCPPvooERER9O/fH4DRo0cTFhbGww8/zOHDh/nzzz+ZN28es2fPNvXcz5w5k9OnT/Pcc8+RkJDAJ598wg8//MCcOXMa+BWxTcZK/12CvarZ0/JUKhVPDG8HwFe7EsktLK6zY2cXaPnHd1HMWxtDUbGeEZ38+f2pwfRq5Vtn56jWdsOKFbrwe2jaogMZeVoe+nIvO09cbrgYhFXZl5hOcmZBpdsVIDmzgH2J6Q0XlBBCCKuyPzGd7IJi/Nyd6B7i02DnnTaoDQCr9p0nu8B6C6oJYS8smvwfOHCAHj160KNHDwDmzp1Ljx49eOmllwD49ddf6dGjB+PHjwfggQceoEePHixZssR0jPfee49bb72Vu+++myFDhhAYGMiaNWtM2zUaDb/99hsajYaIiAgmTZrEI488wiuvvGLaJzQ0lN9//52NGzfSrVs33nnnHb788ktZ5s9MMRcN80zCm9vGcnLjuwbR2s+NjDwt3+07VyfHjLmYyYSPdvLbkWQc1CpevKUzX07uTZNaLuNXK6mxcOx3QIXTsGdZMa0fg9o1Ja9Ix9Tl+/njaHLDxSKsQvT5DN76M8GsfdOyK79AIIQQwr4Zl/i7uZN/g47iHNqhGe38PcguLOb7/ecb7LxCNFYWnfM/bNgwFKXyoaZTpkxhypQpVR7DxcWFxYsXs3jx4kr3adWqFevXr682lqioqCr3EeUpikJMkrHn3zaSf41axcyhbfn3mqN8seM0D0e0wtlBU6tjKYrCt3vP8eq6OIp0epr7uPLRgz3o2bJJHUdthh3vGr6H3QbNOuAOLJ3SmznfR7P+aAqzVx7iv3d25YG+LRs+NtFgdHqFv2JT+HJnIgfPmj/lY/vxS4wOC8TVqXa/C0IIIWyToij8HW+ofTUyrGELwKrVKh4bFMoLa46ybNcZpgxojYPGpmclC2HV5LdL3JBz6XlkFxTjpFHTPsA6q/xX5M6ezQn0ciE1q5A1hy7W6hhZBVqeXBnFf9bGUKTTM7KzP78/Ncgyif+VUxBbMuJl8D9NDzs7aPhoYk8e6BOCXoF/rznKkm3WuRSjuDHZBVq+3HGaoW9tYdaKQxw8exVHjYo7ewTT1MOJ6vpxfjp0kZvf2cov0RervCgrhBDCvpxMy+Fceh5ODmoGt2/a4Oe/s0dz/NyduJiRzx8xKQ1+fiEaE0n+xQ0xDvnvFOSJow1dqXV20DB9iGGe2ZJtpyjW6Wv0/KMXDMP8fz9qGOY/b3xnvnikNz71tIxftXa+B4oe2o+GoG5lNmnUKhbe1ZWZQ9sC8MYfCbzxR4IkeHbifHoer/4WR8TCzbz2ezwXrubTxM2Rf9zcjl3P38x79/fgtTu6AJS7AKAq+Zo+OJTmPq4kZxbw9Kpo7lkSyZELGQ38kwghhLCEjSW9/gPb+uHm1PCDgl0cNTwc0QqAL3ecls8nQtQj28nWhFUyDvkPt5Eh/6VN7BtCEzdHzl7JY72ZV5oVReHr3We4+9PdnL2SR3MfV36cGcG0wW1QqSy00kHmBTi8ynB7yLMV7qJSqfj3uE78e1wnwHDB44U1R6XCu41SFIUDZ9KZ9e1Bhr61haU7E8kpLKadvwf/vbMrkS+M4J+jO+Lv5QLA2C5BfDqpJ4HeLmWOE+jtwqeTevLi+DA2/XMo/xrdATcnDQfPXuW2j3fxrx8Pk5YltQCEEMKebSqZ7z+igar8V2RS/1Y4Oag5fCGTAzWYsiaEqBmLzvkXts9U6b+59Vf6v56bkwOPDgzl3Y3H+WTLSSbcFFRlAp9VoOX51UdMQ9JGhQXw9j3d8Haz8Jqfuz4EvRZaD4aQvlXuOnNoW3xcHfm/n4+yav95sgq0vHd/91rXPBANS6vT80dMCkt3nObwhUzT44PbN+WxQaEMad+s0iUlx3YJYlRYIPsS00nLLsDf04W+ob6mwk4ujhqevLk99/QKYdGGBNZEXWT1wQv8cTSZ2Te3Y+rAUFwc5X0ihBD25HJOIYdKlgQe0dnfYnE09XDm7p7N+W7feb7Yfpo+rRtwpSQhGhFJ/kWtKYpCbJJh2H9XG6n0f73JEa35bNspElKy2ZyQVulV7yMXMnhyZRTn0vNw1Kj497jOTB3Y2nK9/UY5aXDoa8PtIf8y6ykP9G2Jt6sjT68yFALMLjjAkkm9cHeW/w6sVWaelu/2n+Pr3WdMy/Y5Oai5s3tzpg4KpWOgp1nH0ahVRLT1q3KfQG8X3r2/Ow9HtGLBujiiz2ewaMMxVu07z//d0pkx4QGWf98LIYSoE1sS0lAUQydOkLerRWN5bFAo3+07z8b4VBIv5xLa1N2i8Qhhj+TTvqi15MwC0nOLcFCr6BBgXvJhbbzdHJkU0YrPtp3m480ncHPSkJyRx+lMFTq9gkPJMP/X18ej1Sm0aOLKxw/2bNA1cKsUuRiKC6B5bwgdavbTxnUNwtPFkRn/O8COE5d56Mu9LJvSp2GXJhTVSrycy7Jdiaw+eIG8Ih0ATT2ceLh/ax7q35KmHs71du4eLZuwZtYA1kZf5M0NCZxLz2PmtwcZ0NaPlyaE0SnQ9kb7CCGEKMtU5d+CQ/6N2vl7MrxjM7Ycu8SyXYm8cnsXS4ckhN2R5F/UmnHIf/sAT5seDvzYoFCW7kgk6nwmE7/YW/Kohh/e3k6wjyvR5zMAGB0WwFvWMMzfKP8q7F9quD3kX1DD3thB7ZuyYlo/Hl2+n+jzGdz3WST/e6xfuXnhomEpisKe0+ks3ZnIpoRUjHWPOgV6MnVQKLd1C26w3ze1WsVdPVswJjyQT7ee4vMdp9l96gq3fLCDB/u1ZO6ojvjKBSMhhLBJBVod249fBqwj+QeYPrgNW45d4scDF5g7qoPlCikLYaek4J+otZiSIf9dgm27B/DQ2asUV1D4Li27kOjzGWjUMH9CGJ893Mt6En+AvZ9DUTYEdIEOY2t1iB4tm/DD4xEEeDlzIi2He5bs5szl3DoOVJijqFjPmkMXGP/hTiZ+sYe/4w2J//COzVgxrR9/PD2Y+3qHWORCm7uzA/8a05FNc4dyS9dA9Ap8u+ccw97awlc7E9HWcLUMIYQQlhd5+gr5Wh2BXi6EW8lnuYi2foQFeZGv1bFi7zlLhyOE3ZHkX9RarKnYn23O9wfQ6RUWrIurcp8mbk48EmEF8/tLK8yGvZ8abg+eW+Ne/9I6BHiyeuYAWvu5ceFqPvcsiSSu5MKOqH/puUV8vPkEg97czNwfDhOXnIWLo5qH+rXk77lDWfZoXwa2a2oV778QXzc+eagXq2b0p3OQF1kFxbzyWxxj39/O1mNplg5PCCFEDfwdVzLkP8zfKv7GgGF1ommDQwFYvvsMhcU6C0ckhH2R5F/UmnGZP1us9G+0LzHdVECtMpdzitiXmN5AEZnpwFeGYf9+7SDsjhs+XIivGz/OHEBYkBeXcwq5//NI9p+xsp/Zhuj0CpGnrvBL9EUiT12pcEnFk2nZvLDmKBELN/H2X8dJyy7E39OZZ8d0JPLfI3j9zq608/ewQPTV69/Gj9/+MYiFd3XFz92JU5dymbJsP48u28epSzmWDk8IIUQ1FEWxiiX+KnLrTcEEeDlzKbuQdYeTLR2OEHZF5vyLWknLLiA1qxCVCjoH2W7yn5Zt3hrm5u7XILT5sPtjw+1Bc0BdN8PAm3k6892M/kz7ej/7z1zl4aV7+fShXgzvZLmlf2zRhphkFqyLK3NRKcjbhfkTwhgTHsjOk5dZujORrccumbZ3ae7FY4NCGd81GCcH27gmq1GrmNi3JeNvCuKjTSdYtusMW45dYseJ7Uwe0JqnRrTH29WKpskIIYQwiU3KIiWrADcnDRFtql4FpqE5OaiZPKA1izYc48sdp7m7Z3OrGZkghK2zjU+ZwuoYl/hr28wDNyfbvYbk72lecTtz92sQUd9Cbhp4h8BN99fpob1dHflmaj9u7uRPgVbP9G8O8Ev0xTo9hz3bEJPMrG8PlRtNkpJZwMxvDzHwjc08vHQfW49dQqWCUWEBfD+jP+ueHMSdPVrYTOJfmpeLIy+OD+PPOUO4uZM/xXqFpTsTGf72VlbsPVvhqAchhBCWZazyP7h9U6ss2vxQ31a4OWlISMlm18krlg5HCLthe580hVUwzfe3kgIxtdU31Jcgbxcqu56swtBr2zfUtyHDqpxOC7s+MNwe+DRo6r5n1dVJw2cP9+L27sEU6xWe+T6abyLP1Pl57I2xfkRFqa7xsaTMAlwd1UwZ0Jot/xzGF4/0pl8bP7vo0WjbzIOvpvTh66l9aefvQXpuES/+HMOtH+0k8pR8cBNCCGtiTUv8VcTbzZH7eocA8MWO0xaORgj7Icm/qJWYiyWV/m242B8Yhi7PnxAGUO4CgPH+/AlhaNRWkpwd+R4yz4O7P/SYVG+ncdSoee++7kyOaIWiwEu/xPLhphMoimLWfPbGyJz6EQAfPdiTl28Lp3VT9waIquEN7dCMP54ezPwJYXi5OBCfnMXEL/Yw69uDnE/PK7Ov8b207kgyJzJV8l4SQogGkJyZT8zFLFQqrHpq36MDW6NSwbbjlziemm3pcISwC7Y7XltYlLHYX3iwbSf/AGMvLeevnnk8cmpYmeQt0NuZb9puo/2lI8ALlgvQSK+DHe8abg94Ehxd6/V0arWKl28Lx8fNiQ82neDdjceJOn+V+KRsUrLKz2cf2yWoXuOxdubWhcgtLK7nSCzPUaPm0YGh3N69Oe9tPM6KvWf5IyaFTQlpTB8cyhPD2rHjxKXraiNoWP3Odl6+LbzRv5eEEKI+GQv99WzZhKYezhaOpnKt/NwZExbIhtgUlu5I5M17brJ0SELYPOn5FzWWkVfEhav5AITZ+LB/ANQa2sd9yK6IA3w3vT/v3tuVJ8N0bO+7n/ZxH9ZZQb0bFrcW0k+Biw/0ntogp1SpVMwZ1cE0OmJLwqUyiT8Y5rPP+vYQG2Iad0Vem6wfUc983Z149Y4urH96MAPa+lFUrGfxllNELNzEzApqI6RmFcp7SQgh6plxyP+Iztbb6280fYhh2b+foy5yKbvQwtEIYfsk+Rc1Ziz218rPzT6qeQ99Doa/iHrrf4m4sJQJNwUxPv9nHHe8AcNfNGy3NEW51uvffxY4ezbo6R+JaF1pWxsHai9YF9eoh22HBXtVWbDP6upHNKBOgV6smNaPzx7uRUgTV7IKKh79IO8lIYSoX7mFxewuqcMyykrn+5fWs2UTuof4UKTT8789Zy0djhA2T5J/UWMxpmJ/tj/k36T7Q9D2ZtjyOg6vN6Nz8hr0LfqAaxM48TdcPgnFRZaL7/gGSI0BJw/oO6PBT78vMZ3MfG2l2xUgObOAfYnpDReUFbmUXchDX+6hqFhf4XarrB/RwFQqFWPCA3n9zi5V7tfY30tCCFGfdpy4TFGxnlZ+brTz97B0ONVSqVRMH9wGgG/3nKVAq7NwRELYNpnzL2rsaEnyH97cxof856VD/K9w5Ec4uwtjv6Oq5Lv6wn64sL/UE1Tg3QKatDZ8+YaW3C757lZPPbqKAtvfNtzu81j9nacK5s5nN3c/e5J4OZfJX+3jXHoefu5OTB/chq8jz1xXP0LqIhhdzav8IlJpr6yLZUjHZnQK9KRjgBdt/d1xdrCSKThCCGGjNhmH/HcKsJmVZsaEB9CiiSsXrubz06ELPNSvlaVDEsJmSfIvasw47N8me/6L8gy96Ed/hBMbQV8qEfFuCZnnUNQOqPTF6Fv0Qe3uD1fPwNVE0OYZKu1nnoczO8of28X72oUB4wUB4wUCrxagMfPXbctCQ50B43SDxG1w8QA4uFzbPrxhCxDKfPaKHT6fwaPL95OeW0SIryvfTO1HaFN3pg9pw77EdNKyC/D3NAz1b6w9/tcz9z0Sn5JNfMq16s4atYo2Td3pGOhpuCAQ6EWnQE+a+7iiltdWCCGqpdMrbE4wFPsbGWb98/2NHEqKyL76WxxLdyYysU9L+X9fiFqS5F/USHaBlsTLuQCE20qxP10xJG6Fo6shfh0U5VzbFtAVut4DuZcg8mMY/iLFA+Zw8qsZdL6wxjDnf+JKQ+977iVITyy5GHDGcEHg6hnDYzkpUJAJyYcNX9dTO4B3SPnRAsb7pefwqzWw5XXD7aHPXev1D+gCuz4wxNTA+ob6EuTtQkpmQYXr2AN4ODvQu1WTBo3LkrYcS+OJbw+Rr9XRpbkXy6b0pZmnoWqyRq0ioq2fhSO0TtW9l1QYCgU+PbI9J1JzOJaSTUJKFlkFxZxIy+FEWg6/HblWENDdSUPHUhcDjBcHfNycah2jTq/IxRshhN2JPp/BldwiPF0c6NPaturP3N8nhPf/Ps7pS7lsOZbGCBuoVyCENZLkX9RIXEmvf7C3C35WvDwMigIXDhh6+GPXGBJ3I5+W0PVew5d/Z9i2yJT4M/Q50Go5HngHHdp3QFM6CffwN3y17Ff+fEV5kHH22sWA0hcHrp4FXWHJ/cSK43XzK3tBoNOthgsAl48bRhmo1IbefwsVINSoVcyfEMasbw+hggqTtpzCYp787hDv3tcdd2f7/q9l9cELPP/TEXR6hcHtm/LppF542PnPXFeqei8Z0+vX7+xSZoqEoiikZBWQkJJNQnI2x1KySEjJ5tSlHHKLdBw6l8GhcxllzhPg5Wy6IGC8KNDO36PaqQMbYpKvW4JQlrMUQtgHY5X/4R39cdTYVtkvD2cHHuzbks+2n+aLHacl+ReiluTTqqiRGOOQ/+ZWOuT/0jFDwn/0R0PibeTmB+F3GRL+kL5Qep6bXldhUq0f/C80Go1he3Wc3AwXEvw7l9+m10N2cvnRAsb7eVeufV08UPa5R380fFf0Fl95YGyXID6d1LPCxGh0WADf7TvPn7Gp3P3pbr54pDchvm4Wi7W+KIrCJ1tP8dafxwC4o3swi+7pVmWVf1FeZe+lQG9n5k8IL5dkq1QqgrxdCfJ2ZXjHa0NVtTo9iZdzSUgxXBA4lpJNfHI2FzPySc0qJDXrEtuPX7vwp1GrCDVOHQgwjhLwokUTw9SBDTHJzPr2ULmLW8blLD+d1FMuAAghbNbfcbazxF9FJg9ozdKdiew5nU7MxUzr/SwqhBWT5F/USKyx0r81/YebeRFifjIkyilHrj3u6A6dxsNN90GbYaCpZFnCqubP10WyrVaDd3PDV+uB5bcXZFU8lcB4H0DtaBVLDo7tEsSosMAKh0Tf3qM5M745SEJKNrcv3sUnD/Wkfxv7Gfqu0yssWBfLN5GGpYYeH9KG58d2knmHtVT6vZSckcvp2GievH8ILs7mD9d31KjpEOBJhwBP6BZsejy7QMvx1OySiwLXvmfmazmZlsPJtBx+p+zUgXb+HhxPy6lwVIuCYVTCgnVxjAoLlCkAQgibc/ZKLifScnBQqxjWwTaT/2AfV8bfFMQv0Ul8ueM07z/Qw9IhCWFzJPkXNRKTZEz+LTzfP/8qxP1imMd/ZiemwcNqB2g30tDD33EcOLlbNEyzuHhB0E2Gr9K2LTIM/dc4ga7IcN8KLgBUNp+9Z8smrPvHQGZ8c5CjFzOZ9OVeFtwebhdVeQu0OuZ8H80fMSkA/OfWMB4bFGrhqGyf8b2k1Xqx/kJUnSXVni6O9GrlS69W1+a0KopCalYh8SUjBIwXBU6lGaYOHL6QWeUxjUsQbk5IZVRYYJ3EKYQQDeXveEOhvz6tffF2q6QzxAZMG9SGX6KT+O1IMs+P60SQt6ulQxLCpkjyL8yWX6TjZJqhWJ5FKv1r8w2V+o/8CCf+Klupv+UAQ+G+8DstshRenTMm/sah/sb7YBUXACoT5O3KD49H8NxPR1h3OIkXf47hWEo2/7k1zObmFxpl5muZ/s0B9iWm46RR88593ZhQqpdZ2AaVSkWgtwuB3i7lpg6cuZzLt3vO8nXJqI6qTP/mIE09nOkQ4EGHAEMdAcPoA48bKjIohBD1ybjE38gw254r37WFN/1CfdmbmM7y3Wd4YVwF0y2FEJWS5F+YLT4lC70CzTyd8fdqoCXddMWGpe6O/gjxv0HRtaW/COhiSPi73AM+IQ0TT0O4PvGHa99t4AKAq5OGDx/oTqdAT9768xjfRJ7lRGoOnzzUkybutpUcJWfmM+Wr/RxLzcbT2YHPHunFgLZNLR2WqEOOGjXtAzwZ2yXIrOQf4HJOIZdzCtl96kqZx+vrooCsPiDqQun3kZ+bA/rKlm4RdiczX8u+xHQARtrofP/Spg9uw97EdFbuPcc/bm4vBXeFqAH5bRFmM833r+0Sf9evX1/atkUlhfdeMFTqv3jQkPDHrIHctGv7ebc0JPxd74WAsNrFYe0qKUBoum9OAUILU6lUzB7ejvb+Hsz5PprI01e4ffEuvpzc2zA/2wacSM1m8lf7SMoswN/TmeWP9iXMVpa3FDVmzhKEgd4ubHhmCImXczmems3JtByOp2ZzIjWHixn51V4UaO/vQfuSGgXt/T3Muhgmqw+IulDR+8jHSYNj61Ru7d7CgpGJhrDt+CWK9Qrt/T1o5WcD0yGrcXMnf9o0def05Vx+PHCeRwfKNDwhzCXJvzBbzMUbrPR//fr1Rsae7r6Pw+bXSyr1l1oSz83PMJy/670Q0q9spX57VN8FCBvQ6PBA1jwxkGnf7Odceh53Lt7FBw/0sPphh/vPpDPt6wNk5mtp08ydrx/ta5erF4hrzFmCcP6EMLxdHeke4kP3EJ8yz88pLOZkWg4nUrM5UYOLAu39PQwXBkouCHQI8DRdFJDVB0RdqOx9lFEE/1h1GAcHjbyP7Jyxyr+1/+01l1qtYuqgUOatjeGrXYk8EtFaRkMJYSZJ/oXZjMX+wms737+ioet/zYPdH4FHIOz77Nq+xkr9Xe+FtsMrr9QvrF7HQE9+mT2IJ1YcZM/pdKb/7wDPjunIrKFtUVnhhZw/Y1N46rsoCov19Gjpw1eT+9jcdAVRO5UvQVh9T7uHs0OlFwVOGS8GlFwcOH7dRYHI0+UvCrRr5s6Ri5my+kANyRSJsgyrlMRV+D4yXtaS95F90+r0bDlmGEFpD0P+je7u2YJ3/jrG+fR8/oxN4ZaucgFLCHNI8i/MUlis43iqYb79DVX6L30BYMt/MfWv5aTYZqV+YRZfdyf+91g/FqyL5ds951i04RgJydksuucmXBw1lg7P5Ns9Z3nplxj0iuFD0kcTe+LqZD3xifpX1XKWteHh7EC3EB+6XXdRILdkpEDp6QPXXxSoinH1gT2nrzCwndShAJkiUZGNcSllXo/rGd9H+xLTK1zFRdi+/WfSyS4oxs/die4hTSwdTp1xddIwqX8rPtp8ki93nJbkXwgzSfIvzHIiNQetTsHHzZHmPje4rMrQ52DrQlD0hvstIwwJf9gd4C4fPuyVo0bNa3d0pWOgFwt+jeXXw0kkXs7li0d6E+jdQAUkK6EoCu9tPM6Hm08C8ECfEF67owsONrpCgbgxlS1nWZfcq7ko8MOB86zYe67a40xZto8uzb0JC/IiLNiLsCAvOgV6NbqLVjJFwuBqbhF7Tl8h8vQVIk9d4UTJCj3V+X7/Odr5e9DM07meIxQN7e84Q6//8E7+dje64+GIVny27TSHzmVw8OxVerWyn4sbQtQXSf6FWWJMxf68b3yo9rZFhsRfpQFFB21vhj6P1UGUwhY83L8V7Zp58MSKgxy9mMmEj3fy+cO96NHSMn+0i3V6Xvw5hu8PnAfg6RHteWZke6uckiDsn/GiQF6RzqzkX6tTiDqXQdS5DNNjahW0bupe5oJAWLAX/p6WvchWX7TFel76JbZRTpHIzNeyt1Syn5CSXf2TKrA2Ool1R5IZ3rEZ9/Rqwc2dAnBykIuftk5RFDYllMz372wf8/1L8/d04Y4ewfxw4AJf7jhNr1a9LB2STZDpUY2bJP/CLKb5/jcy5B9sdv16Ubci2vrx65ODmPb1AY6lZnP/53t4466u3NWzYatO5xUV8+TKKDYnpKFWwWt3dOXBfi0bNAYhKmLu6gNfT+3LsZRs4pKziEvKIi45i0vZhZy+lMvpS7n8diTZ9JymHs5lLgaEBXkR2tS91h/6jB8gkzNyOZ2pQqdXqI/qLFqdnpTMAi5czediRj4XruZxseS28b5OX/nzjUPb/7P2KLd2C6ZLc2+8XGyzjkxOYTH7E9PZfeoykaevEJuUhXLdG6RDgAcRbfyIaOtH71a+TPh4Z6XvIwBvV0dCm7oRfT6Tv+PT+Ds+jSZujtzevTn39GpBeLCXXAy1USfTcjh7JQ8njZrB7e1zetBjg9rww4EL/BmbwrkrebT0k+K8VZHpUUKSf2EWU6X/2hb7A5tfv17UrRBfN356YgBzvo9mY1wqc384zLGUbJ4b26lBrkCn5xYxdfl+os9n4Oyg5qOJPRgdHljv5xXCHOauPtChZOnACd2CTdvTsguIT842XQyIS8rk9OVcLucUsv34JbYfv2Ta18VRTafAaxcDOgd50SnQE/dq1s0u/wFSw+p3tvPybeE1/gBZoNUZEvmr+SUJ/rXk/sLVfFKzCupkTfqV+86zcp9hhE9rPze6NPema8lXeHNvvF2t74JAXlExB85cNfXsH72Yie66F6NNM3dTst+/jR9NPcoO3a/sfQQKKlS8eXdXxnYJ4mRaDj8dusCaQxdIzSpk+e4zLN99hk6BntzTqwW3d28u0wJszN/xhiH/A9r5Vfs7bas6BnoypEMzth+/xFe7Enn5tnBLh2S1rHl6lIxGaDj2+T+BqFPFOj3xyTe4zB/Yxfr1om55ODvw2aRevLvxOB9vOcln209zLDWbDyf2qNeeufPpeUz+ah+nL+fi4+bI0sm96dXKt97OJ0Rt1Hb1AX9PF/w9XRjaoZnpsbyiYtMIgfiSUQLxydnka3VEn88g+nyGaV+VCkL93OlcapRAeJAXzTydUalUlX6ATM0qrPADZHaB1pDIp1/rrb94NZ8LGflcvJrH5Zyial8LJwc1zX1cTV8tmrjSvInhdmp2AU99F13tMfq0bkJSRgEXM/I5cyWPM1fyyoyMaOXnZroYcKMXBGr7QbZAq+PQ2WvJ/uELGWh1ZV/plr5uRLTxY0A7Q7If4FX1dI7K3kc+TvDaXd1MbdXO34Pnx3biX6M7svPkZVYfNPSmJqRk89rv8Sz8I4FhHUqmBXT2x9mhcdWVsEV/x9vvkP/Spg8OZfvxS/xw4DxzRnbA2836LuRZWlUrfxgfe3FtDO38PWnq4YSXiyPqBkq+ZTRCw5LkX1Tr5KUcCov1eDg70OpG1jq3o/XrRd1Rq1X8a0xHOgZ68uzqw2w9dok7F+/iy8l9CG1a9ys+xCZlMmXZfi5lF9Lcx5Wvp/ahnb9nnZ9HiLpQV6sPuDk50KNlkzK1NXR6hbNXcstMGYhLyiItu5DTl3M5fTmX38tMG3CiU6AnUecyqvwAOfeHw6w+eIGLGQVcvJpHVkFxtfG5O2lMyXyLJm6m282buNLCx5WmHs6VfhDV6RUWrk+odorEqhkRaNQq0nOLiLmYydGLmabvF67mc/ZKHmcruCBQeoRAl2DvahOLmnyQLSzWEX0uw5TsR53PoKi47ByG5j6u9C/p2Y9o61erorvXv4/83By4FLeHMeHlk0KNWsXQDs0Y2qEZmflafjuSxOqDF4g6l8GmhDQ2JaTh4+bI7d2CuadXCF2ay7QAa3Qlp5BD564CMMKOlviryKB2TekU6ElCSjZvbIinfxs/6T2+zr7E9CpX/gC4klPEyHe3AYbaMd6ujjRxc8LbzfDdp+R7EzdHfErdv/a4U42LzVrzaAR7Jcm/qJZxyH9YsFeDXQUUjc+EbsG09nNnxv8OcOpSLrd/vJPFD/VkcPtm1T/ZTLtPXmbG/w6SU1hMp0BPlj/a1+IrDQhRnfpafUCjVtGmmQdtmnlw603Xpg1cyi40jA4odVHg9KUcLucUsfPklWqPm1ekMw03NvJxczT01vu40tzHrVSib/jydnWsdQJp7hQJYxLg6+7EkA7NGFJqZMTV3CKOVnFBoPRFkJa+bnRtUfEFgeo+yH70YA+CvF2IPGUo0nfw7FUKtGWT/QAvZ9Mw/og2TQnxda2T5Lr0+0ir1bI+vvrneLs68lC/VjzUr1W5aQFfR57l68izdAwomRbQI9hui0raos0JaSiKYXnmIO8bXKXJyqlUKvqG+pKQks13+87zXcn0Huk9NlAUha3H0qrfEXDWqCjUKegVuJqn5WqetkbncnZQmy4IXLs4YLhgcO1xw31PF8dGW6zVkiT5F9UqXelfiPrUtYU3vzw5kMf/d5CocxlM/mof88aH8ejA1jf84ffXw0n884dotDqFfqG+fP5Ib6uc4yuEpTXzdKaZZ9nkOL9Ix7HUbL7be860MkZV7u3dglu6BJmS/Pqeb1zbKRJGTSq5IBCTVPaCwPn0fM6l53EuvfwFgS7BXuw4cbnKURH/WBlVbntTD6drPftt/Aht6m6VPemVTQs4lprN6+vjeWODTAuwJsYh/yM62feQfzBcdPtf5Nlyjzf23mND0n+J9/8+zuELmWY9Z/nUfvRq1YSM/CIy8rRczS3iap6WjLzS3w3bMvK0XC31eLFeobBYT0pWASlZVY8yMCt+DMVa9yWm1/vyu42JJP+iWrEllf673GilfyHM4O/pwqoZ/Xnx5xhWH7zAK7/FcSwlm1fuCK/1h8kvd5zmtd8N3Vzjuwbxzn3dcHGUD6ZCmMvVSUP3EB/yi3RmJf939WjR4B/W6mqKhFETdycGt29WZvRRdRcEqqMA7s4aBrdrxoB2hmS/nb+HVSb7lanLaQFS5Kt+FGh17DhxGYBRYfad/Fc3l70x9h5XlPS7OKjRqFXkFlVcX8s4Pcr4O2isHVOTc+YUFpsuCFT23XihICNPS1p2QbmRTxVJy77xCwniGkn+RZX0eoXYJMOw/643UuxPiBpwdtDw1j030SnQk/+uj+f7A+c5dSmHTyf1qlG1ab1eYeEf8XyxIxGAKQNa89KtYTJ9RYhaMncJwr6hlimgWV9TJIwquiCQkVdEzMUsvt9/nnVHkqo9xut3dOWOHs3rLcaGVHpawKlLOfx08AJrDl0kJaug2mkBUuSr/kSevkJekY5ALxfCg+2746a6ueyNqfe4oqTf1VHDIxGtmD6kDQfOpDPr20OGfUs9r6LpUTWlUqnwdDEM5Q8xsz5Y5KkrTPxiT7X7yXSiuqW2dADCuiVeySWvSIeLo5o2zTwsHY5oRFQqFdMGt+GrKX3wdHHgwNmr3P7xTtNIlOoUFeuZ80O0KfH/97hOzJ8gib8QN8I4vx6ufWA0qosPkLbIx82JQe2b8mC/lmbtX111flvVtpkHz43txK5/38w3U/tyW7dgnB3UpmkBEQs3M3X5ftYfTWbd4YvM+vZQuaTNOEx7Q0xyJWcR5thkHPLf2d+mRpXUhrm9wsmZ+fUcieUoisKWhDTuWLyLR5fv5/CFTFwdNTw+pA07nh/OC7d0pqmHs2l61PW1jgK9XSwyNcJ4Mbmqd6glLybbK+n5F1UyzvcPC/JqVB/mhPUY1tGftbMHMv3rA5y+nMs9n0byzn3duKVr5X+kcgqLmfm/g+w8eRkHtYpF99zEXT1bNGDUQtivyufXOzN/Qnij7bW19lERDUWjVpnqJ2Tma/n9SDKrD57n0LkMNieksTkhrVxhRqPGOky7LimKwqaSgpv2vsQfmN8r/PKvhimE9/YOoZ2/fXRmVdfT39Sj/EjJup4edSOqKtZq5OHsQE5BsSzfWIck+RdVMg757yJD/oUFtW3mwc9PDOQfq6LYfvwST6w4xFMj2vPMiPYolAz7y8jldKaKlMwCZq6MJjYpCzcnDZ9O6lVmvXMhxI0r/QEyOSOX07HRPHn/EFycnSwdmsXUdNWBxsDb1ZEH+7XkwX4tTdMCvtt3rsoK4o1pmHZ9iE3KIjmzAFdHTaN4/aq76AaGZeuyCrR8tv00n20/Te9WTbivTwjjuwbVezHS+lCbpL+0+p4eVROVXUz2c3cit7CYk2k53L1kN8sf7UOLJjew3Lgwsb13vGhQUulfWAtvN0e+mtybN/5I4MudiXy46QTbj18iOTOf1KzCkr00fBq/HZ1i+MOx7NE+3NTCx5JhC2G3jB8gtVov1l+IalRJbWVudNUBe2acFtA+wJM530dXu/+7fx1jTJdAOgd50TnIC1/3xnthqSaMVf6HdGjaKArbmnPR7cOJPXDSqPnhwHm2HLvEgbNXOXD2Kgt+jeXWm4K5r08IPVv6WP0UiRtN+q1VZaMRTqRlM+Wr/ZxMy+GuT3az7NE+hEs+csMsOud/+/btTJgwgeDgYFQqFWvXri2zXVEUXnrpJYKCgnB1dWXkyJGcOHGizD6tWxuWACv99cYbb5TZ58iRIwwePBgXFxdCQkJYtGhRuVh+/PFHOnXqhIuLC127dmX9+vV1/vPaGkVRTMl/uFT6F1bAQaNm3q1hvHXPTTioVUSfzyiV+BvoSv7yPz2ivST+QogGN7ZLEDufv5nvpvfngwe68930/ux8/uZGnfiXFmhmzYP9Z6/y2u/xPPTlXnq+upH+/93Eo8v28dafCfx2JIlTl3LQ6Svr6228jEP+RzSCIf9G1c1lv/WmYEaHB/Ll5D5E/vtmnhvbkdCm7uSWrF5y96e7Gf3edr7YfprLOYWVnMVyzJ3Tb8uMF5Nv796ciLZ+aNQqOgV6seaJAXQI8CAtu5D7P9vDjhOXLB2qzbNoz39ubi7dunVj6tSp3HXXXeW2L1q0iA8//JCvv/6a0NBQ/vOf/zBmzBji4uJwcbn2C/7KK68wffp0031PT0/T7aysLEaPHs3IkSNZsmQJR48eZerUqfj4+DBjxgwAdu/ezcSJE1m4cCG33norK1eu5I477uDQoUN06dKlHl8B63bhaj5ZBcU4adS09/es/glCNJC7erbgjT8SuJJbVOk+n247xUP9W0lvpBCiwVnTsFprY84w7SZujkwe0JqE5GziU7I4eyXPtHb4lmPXPvy7OmroGOhJWLBhdEBYkCedAr1uaCi3LS8/mJyZz9GLmahUcHMnf0uH06DMncvu7+XCE8PaMWtoW/YlpvP9gfOsP5rMibQcXl8fz5sbEhjZOYD7+4QwpEMzi7a9vfb010Swjys/zhzA4/87wJ7T6Ty6bL/UcbpBFk3+x40bx7hx4yrcpigK77//PvPmzeP2228H4JtvviEgIIC1a9fywAMPmPb19PQkMDCwwuOsWLGCoqIivvrqK5ycnAgPDyc6Opp3333XlPx/8MEHjB07lmeffRaAV199lY0bN/Lxxx+zZMmSuvyRbYqx179joCdODrIwhLAe+xLTq0z8QeaMCiGENTJnmPbCu7qWGSmRU1hMQnIW8clZxCVnEZeczbGULPK1OqLPZxB9PuPaMVTQytfNcEEgsOSiQLCXoap4NcO6bX35QWOvf48Qn0aRGF6vJhfdVCoV/dr40a+NHwtuC2fd4WS+P3Cew+cz2BCbwobYFAK9XLinVwvu6x1CS7+Gm28uSX9Z3q6OfD21L//68QjrDicx94fDJGcW8MSwtlY/VcMaWe2c/8TERFJSUhg5cqTpMW9vb/r160dkZGSZ5P+NN97g1VdfpWXLljz44IPMmTMHBwfDjxYZGcmQIUNwcro2V2zMmDG8+eabXL16lSZNmhAZGcncuXPLnH/MmDHlpiGUVlhYSGHhtaFBWVmGwnharRat9lohG+Pt0o/ZiiPnrwIQFuRhk/HXli23WWORnJFr9n5arUxZsXbyO2fbpP1sj6XbbETHpnz0QDdeW59ASqmpW4Hezrw4rhMjOjYtE5uzGro196Rbc0+gOWDooT9zJY+ElGzik7NJSDF8pWYXcuZKHmeu5LH+aIrpGN6uDnQO9KRTyVfnIE/aNfMwdW78GZvKP1YdLjcawbj84EcPdGNMuGWH0lfXbhvjDD/vzR2bye9jDbho4N6eQdzbM4hjKdmsPnSRXw4nk5JVwMdbTvLxlpP0D23CPb1aMCbMv1a1FMz5nVMUhW0nLvPR5lMcuWjIK1wd1TzUryXTBrbCryTpb4xtqwbeviucAE8nvtx5hrf+PMaF9FxeGt8JB039dlBa+v9Lc9QkNqtN/lNSDP+BBQSU/Y82ICDAtA3gqaeeomfPnvj6+rJ7925eeOEFkpOTeffdd03HCQ0NLXcM47YmTZqQkpJS7Xmut3DhQhYsWFDu8b/++gs3t/JXBzdu3FjVj2uVtsarATX6K+dYv/6spcNpcLbYZo3F6UwVUP0f39Ox0ay/EFX/AYk6Ib9ztk3az/ZYus2eD4NTWSqytODlCG29ctGdPUhNPnKogDAgrCnQFHK0cDFXxcW8ku+5KlLzITO/mD2JV9mTeNX0XLVKIdAVgl0VYjJUJYl/2Z5EpeTfeWui0Z7RYQ0zACpqt0Id7DqhAVQ4pMWzfn18wwdmJ3oAXbvC0asq9qSqOJapMr13/qNR6NVUob+/npBarBhYUdspCsRlqNhwXs25XMMbzEmtMChQ4ebgYjx1J9m7/eQN/lT2oStwd2sVa86o+W7/BY6ePMfk9nqcGqC2paX/v6xKXl6e2ftabfJvrtI99jfddBNOTk48/vjjLFy4EGfn+hsW88ILL5Q5d1ZWFiEhIYwePRovr2s9jVqtlo0bNzJq1CgcHW1njUpFUVhwZCug5f7RA+jWovFU17TVNmtMdHqF1e9sJzWrsIr1tJ158v4hNjNXszGT3znbJu1nexpbmxUW6zmZlkN8yegA40iBrIJikvIgKa+6vxMqMoqgWVh/+oX6NkjMFamq3TbGpVG8L5qQJq5MvXuQDIeuA7eVfE/KyOenqCR+OnSRixkF7ExVsTNVTedAT+7p1ZzbbgrCp5J16HV6hQNnr5KckcfZhKM8ftcI05Ko5vb0i7JuAYbHpvLP1UeJuQrfJjXhs0k98KunFUFs4f9L4wh0c1ht8m+cw5+amkpQ0LV5VqmpqXTv3r3S5/Xr14/i4mLOnDlDx44dCQwMJDU1tcw+xvvGc1S2T2V1BACcnZ0rvLjg6OhY4RujssetVUpmAem5WjRqFV1aNMGxESwXcz1ba7PGxBF4+bbwatbTDm/Ua47bIvmds23SfransbSZoyN0b+VM91bX5oMrikJSZgFxSVn8fOgC62MqH+1pdCWv2Cper4rabcvxywCMCgssM9VV3LhWzRyZO9qLZ0Z2ZPepK3x/4Dx/xqQQn5LNq78n8OafxxkTHsj9vUMY0NYPdUmnQ/kaEhpWX4g0fD5x1Mic/htwa/cWBPq4Me2bAxy+kMkDX+zj66l9aeXnXm/ntOb/L2sSl9Um/6GhoQQGBrJp0yZTsp+VlcXevXuZNWtWpc+Ljo5GrVbj72+ochoREcGLL76IVqs1vTAbN26kY8eONGnSxLTPpk2beOaZZ0zH2bhxIxEREfXzw9kAY7G/9v4ejWKdWGF7Kl9P25n5E8JtojiTEEIIy1CpVDT3caW5jysezg5mJf/+nuYtU9jQdHqFzQmGYn8jOzeuKv8NSa1WMah9Uwa1b0pGXhFroy7y/YELxCdnse5wEusOJ9GiiSv39gqhmacTL/4cU76GRFYhs1YcMt2XpL/2erf2ZfXMAUxZto8zV/K465PdLJ3Sh+4hPpYOzapZNPnPycnh5Mlrc1gSExOJjo7G19eXli1b8swzz/Daa6/Rvn1701J/wcHB3HHHHYChmN/evXsZPnw4np6eREZGMmfOHCZNmmRK7B988EEWLFjAY489xvPPP09MTAwffPAB7733num8Tz/9NEOHDuWdd95h/PjxrFq1igMHDvD555836OthTWKSDMl/eHDjGe4vbE/ppX2SM3I5HRvNk/cPkR5/IYQQZjNn+UFPFwf6tG7SoHGZK/p8BldyiwwxWnBaQmPi4+bElIGhTB7QmpiLWXx/4By/RCdx4Wo+7/193KxjTB8cyuND20rSfwPa+Xuw5okBPLpsP7FJWUz8fA8fP9iDEZ0tW5zTmll0/bYDBw7Qo0cPevToARjm7/fo0YOXXnoJgOeee45//OMfzJgxgz59+pCTk8OGDRtwcTFceXV2dmbVqlUMHTqU8PBwXn/9debMmVMmaff29uavv/4iMTGRXr168c9//pOXXnrJtMwfwIABA1i5ciWff/453bp1Y/Xq1axdu5YuXbo04KthXYw9/12aS6V0Yd2MS/tMuCmI9t6KzPEXQghRI8blB+H6cn/XZBcUM/eHwxRodQ0XmJn+jjdMXR3W0R/Heq58LspSqVR0beHNa3d0Zf+LI3nv/m50DvI067k3dwqQxL8O+Hu68P3jEQzp0Ix8rY7p3xxg5d5zlg7Lalm053/YsGEoSmXXWA2/UK+88gqvvPJKhdt79uzJnj17qj3PTTfdxI4dO6rc59577+Xee++t9liNRUxJ4ZEuzaXnXwghhBD2rbKpZEHeLgzv2IwfDlzg18NJnLmSy+cP9ybQ23qmAGwqSf5lyL9luThquLNHC9QqFU+viq52/7Tsgmr3EebxcHZg6eTe/N+ao/x48AL/9/NRkjPzmTuqgxS/vI7VzvkXlnMpu5CUrAJUKggLkp5/IYQQQti/0lPJ0rIL8Pd0oW+oLxq1igndmjNrxUGOXMjkto938vkjva1ibvG5K3kcT81Bo1YxrIMk/9bA3NoQ1lpDwlY5atQsuucmgnxc+XDTCT7afJKkjALeuLurjIgpRV4JUU5syXz/Nk3dcXeW60NCCCGEaByMU8lu796ciLZ+pqlkEW39+HX2IDoEeJCWXch9n0XyS/RFC0d7bch/39a+eFey3JxoWMYaEpX1N6swjCjpK/UZ6pxKpWLuqA4svKsrGrWKnw5dYOry/eQUFls6NKshyb8oJzZJhvwLIYQQQpTW0s+Nn2YNYEQnf4qK9Ty9Kpq3/kxAr698Cmt9Myb/I2TIv9WoqobEteWIw6RGUT2a2LclXzzSC1dHDTtOXOa+JZGkZck0C5DkX1TAVOxPKv0LIYQQQph4ujjy+SO9mTm0LQCLt5zi8W8PWqRnMTNfy77EdABGhUl1c2tirCFxfW2IQG9nPp3UU5YjbgA3dwpg1Yz++Lk7EZecxZ2f7OZkWralw7I4Sf5FOaZl/qTSvxBCCCFEGRq1in+P68S793XDSaNmY1wq93y6m/PpeQ0ax7bjlyjWK7T396CVn3uDnltUb2yXIHY+fzPfTe/Pu/d25ckwHVvmDpHEvwF1C/FhzRMDCG3qzsWMfO7+NJL9Z9ItHZZFSfIvysjM03I+PR+AcOn5F0IIIYSo0F09W7Dq8f409XAmISWb2xfvYu/pKw12/k2mIf/S62+tZDliy2vl587qmRH0aOlDZr6Wh77cyx9Hky0dlsVI8i/KMBb7a+nrhrerFI4RQgghhKhMz5ZN+PXJgXRp7kV6bhGTlu5l1b76X2Ncq9OzJSENgFFhMt9fiKr4eTizclp/RoUFUFSs54mVh/hqZ6Klw7IISf5FGcYh/11kyL8QQgghRLWCfVz58fEBjL8pCK1O4d9rjrJgXSzFOn29nXP/mXSyCorxdXeie0iTejuPEPbC1UnDkkm9mNS/JYoCr/wWx+u/x1m0YKclSPIvyoi5aKj0L0P+hRBCCCHM4+qk4eOJPZg7qgMAy3ad4dHl+8nM09bL+TbFG3r9b+7kL0PJhTCTRq3i1du78NzYjgB8sSORp1ZFUViss3BkDUeSf1HGtZ5/Sf6FEEIIIcylUql4akR7Pn2op2mJsTs/2cXpSzl1eh5FUUxL/I2UJf6EqBGVSsUTw9rx3v3dcNSo+O1IMo8s3VdvF+qsjST/wiSnsJjEy7kAhAfLsH8hhBBCiJoa1zWI1bMiCPZ24fTlXO5YvIvtxy/V2fFPXcrl7JU8nDRqBrdvVmfHFaIxubNHC5Y/2hcPZwf2JqZz72e7uZiRb+mw6p0k/8IkPjkLRYEgbxeaejhbOhwhhBBCCJsUHuzNL08OomdLH7IKipmybB9f7UxEUW58fvGmBMOFhIi2frg7O9zw8YRorAa2a8oPj0cQ4OXM8dQc7vpkF3FJhinQOr1C5KkrrDuSzIlMFTo7qQ0gyb8wibloGPIv8/2FEEIIIW5MM09nvpvRn3t6tUBfUmDshTVHKSq+sUKAW44Zkv+RYbLEnxA3KizYizVPDKS9vwepWYXc91kk7288zqA3NzPxiz3M/fEoH8dpGPbOdjbE2P4SgZL8CxNjsT+p9C+EEEIIceOcHTS8dc9NvHhLZ1QqWLX/PJO+3MuVnMJaHS9HC4fOZwAy31+IutLcx5XVMwfQN9SXnMJi3t90guTMgjL7pGYVMuvbQzZ/AUCSf2ESayz2Jz3/QgghhBB1QqVSMX1IG76a3AdPZwf2nUnn9sW7SEjJqvGx4q6qUBRDbaYgb9d6iFaIxsnbzZHlj/bBxbHi9Ng46H/BujibngIgyb8AoECr40SaoRqtVPoXQgghhKhbwzv5s+aJAbTyc+PC1Xzu/mQ3f8Wm1OgYR68alvUb2VmG/AtR1w6fz6RAW/m0HAVIzixgX2J6wwVVxyT5F4Ch2J9Or9DUw4kALyn2J4QQQghR19oHeLL2iYEMaOtHbpGOx789yOItJ80qBFio1ZGQIcm/EPUlLbug+p1qsJ81kuRfABBTUtkyPNgblUpl4WiEEEIIIexTE3cnvp7al0ciWqEo8Nafx3jm+2gKtLoqn7f3zFWK9CoCPJ2lPpMQ9cDf06VO97NGkvwLAGJLKv3LHxMhhBBCiPrlqFHzyu1dePWOLmjUKn6JTuL+zyJJzaq8R3FTQhoAwzs1k44aIepB31BfgrxdqOy3S4VhSfS+ob4NGVadkuRfABBTUuyvq8z3F0IIIYRoEA/3b8X/HuuLj5sjhy9kctvHOzlcUs2/NEVR2JxgWOJvRKdmDRylEI2DRq1i/oQwgHIXAIz3508IQ6O23YtvkvwLior1HEvJBgzD/oUQQgghRMMY0LYpv8weSLtS64z/ejgJAJ1eIfLUFT7ZeoqUrEIcVQoRNtzrKIS1G9sliE8n9STQu+zQ/kBvZz6d1JOxXYIsFFndcLB0AMLyjqdmo9UpeLs60qKJLBsjhBBCCNGQWvm58/MTA3h6VTSbE9J46rso/jiaTNS5DFKumwqw9fhlbu3ewkKRCmH/xnYJYlRYIPsS00nOyOV0bDRP3j8EF2cnS4d2w6TnXxCbdG2+v8whE0IIIYRoeJ4ujnzxSG8eH9IGgD9iUsol/loF/rHqMBtiki0RohCNhkatIqKtHxNuCqK9t2LTQ/1Lk+RfEHPRUOm/iwz5F0IIIYSwGI1axXNjO+Ht6ljJHoYEZMG6OHT66pcHFEKI0iT5F6Zif+FS7E8IIYQQwqL2JaaTma+tdLsCJGcWsC8xveGCEkLYBUn+G7linZ74ZGPPvyzzJ4QQQghhSWnZlS/3V5v9hBDCSJL/Ru705VwKtHrcnTS09nO3dDhCCCGEEI2av6dL9TvVYD8hhDCS5L+Ri7lYMuQ/2Bu1nRSyEEIIIYSwVX1DfQnydim3zriRCgjydqGvLPknhKghSf4bOWOxv/DmMuRfCCGEEMLSNGoV8yeEAVRwAcBQ5G/+hDC7qT4uhGg4kvw3csZif1LpXwghhBDCOoztEsSnk3oS6F12aL+PE3z0QDfGdgmyUGRCCFvmYOkAhOXo9QpxSSXF/qTSvxBCCCGE1RjbJYhRYYHsS0wnLbsAPzcHLsXtYUx4gKVDE0LYKEn+G7Gz6XnkFBbj7KCmbTMp9ieEEEIIYU00ahURbf0A0Gq1rI+3cEBCCJsmw/4bMWOxv85BXjho5K0ghBBCCCGEEPZKMr5GzDTfX4r9CSGEEEIIIYRdk+S/ETP2/EuxPyGEEEIIIYSwb5L8N1KKopiW+ZNif0IIIYQQQghh3yT5b6QuXM0nM1+Lo0ZF+wAPS4cjhBBCCCGEEKIeSfLfSMWWzPfvGOiJs4PGwtEIIYQQQgghhKhPkvw3UqYh/zLfXwghhBBCCCHsniT/jZSx0n+4zPcXQgghhBBCCLsnyX8jZCj2Z6z0L8v8CSGEEEIIIYS9k+S/EUrLLuRyThEatYrOQZL8CyGEEEIIIYS9k+S/ETL2+rdr5oGLoxT7E0IIIYQQQgh7J8l/I2Qs9hfeXHr9hRBCCCGEEKIxkOS/kdHpFbafuASAh5MDOr1i4YiEEEIIIYQQQtQ3iyb/27dvZ8KECQQHB6NSqVi7dm2Z7Yqi8NJLLxEUFISrqysjR47kxIkTZfZJT0/noYcewsvLCx8fHx577DFycnLK7HPkyBEGDx6Mi4sLISEhLFq0qFwsP/74I506dcLFxYWuXbuyfv36Ov95LW1DTDKD3tzMwbNXAfhmz1kGvbmZDTHJFo5MCCGEEEIIIUR9smjyn5ubS7du3Vi8eHGF2xctWsSHH37IkiVL2Lt3L+7u7owZM4aCggLTPg899BCxsbFs3LiR3377je3btzNjxgzT9qysLEaPHk2rVq04ePAgb731Fi+//DKff/65aZ/du3czceJEHnvsMaKiorjjjju44447iImJqb8fvoFtiElm1reHSM4sKPN4SmYBs749JBcAhBBCCCGEEMKOOVjy5OPGjWPcuHEVblMUhffff5958+Zx++23A/DNN98QEBDA2rVreeCBB4iPj2fDhg3s37+f3r17A/DRRx9xyy238PbbbxMcHMyKFSsoKiriq6++wsnJifDwcKKjo3n33XdNFwk++OADxo4dy7PPPgvAq6++ysaNG/n4449ZsmRJA7wS9UunV1iwLo6KBvgrgApYsC6OUWGBaNSqBo5OCCGEEEIIIUR9s2jyX5XExERSUlIYOXKk6TFvb2/69etHZGQkDzzwAJGRkfj4+JgSf4CRI0eiVqvZu3cvd955J5GRkQwZMgQnJyfTPmPGjOHNN9/k6tWrNGnShMjISObOnVvm/GPGjCk3DaG0wsJCCgsLTfezsgxF9LRaLVqt1vS48Xbpxxra3sT0cj3+pSlAcmYBkSfT6Bfq23CBWSlraDNRc9JutkvazrZJ+9keaTPbJO1mu6TtbJcttF1NYrPa5D8lJQWAgICAMo8HBASYtqWkpODv719mu4ODA76+vmX2CQ0NLXcM47YmTZqQkpJS5XkqsnDhQhYsWFDu8b/++gs3N7dyj2/cuLHSY9W3g5dVQPVL+v21Yy9X4qUAoJEl20zUnrSb7ZK2s23SfrZH2sw2SbvZLmk722XNbZeXl2f2vlab/Fu7F154ocxogaysLEJCQhg9ejReXteW0NNqtWzcuJFRo0bh6OhoiVDxS0znmxMHqt1v9OB+0vOPdbSZqDlpN9slbWfbpP1sj7SZbZJ2s13SdrbLFtrOOALdHFab/AcGBgKQmppKUFCQ6fHU1FS6d+9u2ictLa3M84qLi0lPTzc9PzAwkNTU1DL7GO9Xt49xe0WcnZ1xdnYu97ijo2OFb4zKHm8IEe38CfJ2ISWzoMJ5/yog0NuFiHb+Mue/FEu2mag9aTfbJW1n26T9bI+0mW2SdrNd0na2y5rbriZxWbTaf1VCQ0MJDAxk06ZNpseysrLYu3cvERERAERERJCRkcHBgwdN+2zevBm9Xk+/fv1M+2zfvr3MXIiNGzfSsWNHmjRpYtqn9HmM+xjPY+s0ahXzJ4QBhkS/NOP9+RPCJPEXQgghhBBCCDtl0eQ/JyeH6OhooqOjAUORv+joaM6dO4dKpeKZZ57htdde49dff+Xo0aM88sgjBAcHc8cddwDQuXNnxo4dy/Tp09m3bx+7du3iySef5IEHHiA4OBiABx98ECcnJx577DFiY2P5/vvv+eCDD8oM2X/66afZsGED77zzDgkJCbz88sscOHCAJ598sqFfknoztksQn07qSaC3S5nHA71d+HRST8Z2CarkmUIIIYQQQgghbJ1Fh/0fOHCA4cOHm+4bE/LJkyezfPlynnvuOXJzc5kxYwYZGRkMGjSIDRs24OJyLYFdsWIFTz75JCNGjECtVnP33Xfz4YcfmrZ7e3vz119/MXv2bHr16kXTpk156aWXTMv8AQwYMICVK1cyb948/u///o/27duzdu1aunTp0gCvQsMZ2yWIUWGB7EtMJy27AH9PF/qG+kqPvxBCCCGEEELYOYsm/8OGDUNRKq8ur1KpeOWVV3jllVcq3cfX15eVK1dWeZ6bbrqJHTt2VLnPvffey7333lt1wHZAo1YR0dbP0mEIIYQQQgghhGhAVjvnXwghhBBCCCGEEHVDkn8hhBBCCCGEEMLOSfIvhBBCCCGEEELYOUn+hRBCCCGEEEIIOyfJvxBCCCGEEEIIYeck+RdCCCGEEEIIIeycJP9CCCGEEEIIIYSdk+RfCCGEEEIIIYSwc5L8CyGEEEIIIYQQdk6SfyGEEEIIIYQQws5J8i+EEEIIIYQQQtg5Sf6FEEIIIYQQQgg7J8m/EEIIIYQQQghh5xwsHYC9UBQFgKysrDKPa7Va8vLyyMrKwtHR0RKhiRqSNrNN0m62S9rOtkn72R5pM9sk7Wa7pO1sly20nTH/NOajVZHkv45kZ2cDEBISYuFIhBBCCCGEEEI0JtnZ2Xh7e1e5j0ox5xKBqJZerycpKQlPT09UKpXp8aysLEJCQjh//jxeXl4WjFCYS9rMNkm72S5pO9sm7Wd7pM1sk7Sb7ZK2s1220HaKopCdnU1wcDBqddWz+qXnv46o1WpatGhR6XYvLy+rfcOIikmb2SZpN9slbWfbpP1sj7SZbZJ2s13SdrbL2tuuuh5/Iyn4J4QQQgghhBBC2DlJ/oUQQgghhBBCCDsnyX89c3Z2Zv78+Tg7O1s6FGEmaTPbJO1mu6TtbJu0n+2RNrNN0m62S9rOdtlb20nBPyGEEEIIIYQQws5Jz78QQgghhBBCCGHnJPkXQgghhBBCCCHsnCT/QgghhBBCCCGEnZPkXwghhBBCCCGEsHONMvlfuHAhffr0wdPTE39/f+644w6OHTtWZp+CggJmz56Nn58fHh4e3H333aSmppq2Hz58mIkTJxISEoKrqyudO3fmgw8+KHOM5ORkHnzwQTp06IBareaZZ54xO8bFixfTunVrXFxc6NevH/v27Suz/fPPP2fYsGF4eXmhUqnIyMio8etgS+yhzYwURWHcuHGoVCrWrl1r9vFtka2325kzZ1CpVBV+/fjjj7V7UWyItbff9u3bmTBhAsHBwZX+PimKwksvvURQUBCurq6MHDmSEydO1Pi1sDUN1XZr1qxh1KhRNGvWDC8vLyIiIvjzzz+rjc+cdnn99dcZMGAAbm5u+Pj41P7FsBH20GZGhYWFdO/eHZVKRXR0dM1fDBti6+22devWSv/O7d+//wZfHetm7W23Zs0aRo8ejZ+fX6W/S9XFZ68aqu127tzJwIED8fPzw9XVlU6dOvHee+9VG581/41rlMn/tm3bmD17Nnv27GHjxo1otVpGjx5Nbm6uaZ85c+awbt06fvzxR7Zt20ZSUhJ33XWXafvBgwfx9/fn22+/JTY2lhdffJEXXniBjz/+2LRPYWEhzZo1Y968eXTr1s3s+L7//nvmzp3L/PnzOXToEN26dWPMmDGkpaWZ9snLy2Ps2LH83//93w2+GrbBHtrM6P3330elUtXylbAttt5uISEhJCcnl/lasGABHh4ejBs3rg5eIetm7e2Xm5tLt27dWLx4caX7LFq0iA8//JAlS5awd+9e3N3dGTNmDAUFBTV8NWxLQ7Xd9u3bGTVqFOvXr+fgwYMMHz6cCRMmEBUVVWV85rRLUVER9957L7NmzarDV8Z62UObGT333HMEBwfXwati/Wy93QYMGFDu79y0adMIDQ2ld+/edfxqWRdrb7vc3FwGDRrEm2++Wek+1cVnrxqq7dzd3XnyySfZvn078fHxzJs3j3nz5vH5559XGZ9V/41ThJKWlqYAyrZt2xRFUZSMjAzF0dFR+fHHH037xMfHK4ASGRlZ6XGeeOIJZfjw4RVuGzp0qPL000+bFU/fvn2V2bNnm+7rdDolODhYWbhwYbl9t2zZogDK1atXzTq2vbDVNouKilKaN2+uJCcnK4Dy888/m3V8e2Gr7VZa9+7dlalTp5p1fHtjbe1XWkW/T3q9XgkMDFTeeust02MZGRmKs7Oz8t1339X4HLasIdrOKCwsTFmwYEGl22vaLsuWLVO8vb2rPKc9stU2W79+vdKpUyclNjZWAZSoqKgqz21vbLXdjIqKipRmzZopr7zySpXntkfW1HalJSYmVvi7VNv47FFDtt2dd96pTJo0qdLt1v43rlH2/F8vMzMTAF9fX8BwJUir1TJy5EjTPp06daJly5ZERkZWeRzjMWqrqKiIgwcPljm3Wq1m5MiRVZ67sbHFNsvLy+PBBx9k8eLFBAYG3tA5bZUttltpBw8eJDo6mscee+yGzm2rrKn9zJGYmEhKSkqZ+Ly9venXr1+j+/+0odpOr9eTnZ1d5T7SLuaxxTZLTU1l+vTp/O9//8PNza36H9IO2WK7lfbrr79y5coVHn300UqPa6+sqe3MUdv47FFDtV1UVBS7d+9m6NChle5j7X/jHCwdgKXp9XqeeeYZBg4cSJcuXQBISUnBycmp3PyLgIAAUlJSKjzO7t27+f777/n9999vKJ7Lly+j0+kICAgod+6EhIQbOra9sNU2mzNnDgMGDOD222+/ofPZKlttt9KWLl1K586dGTBgwA2d2xZZW/uZwxhDRW1cWXz2qCHb7u233yYnJ4f77ruv0n2kXapni22mKApTpkxh5syZ9O7dmzNnzlT3Y9odW2y36y1dupQxY8bQokWLSo9rj6yt7cxRm/jsUUO0XYsWLbh06RLFxcW8/PLLTJs2rdJ4rP1vXKPv+Z89ezYxMTGsWrWq1seIiYnh9ttvZ/78+YwePdrs5+3YsQMPDw/T14oVK2odQ2Nii23266+/snnzZt5///1aRmz7bLHdSsvPz2flypWNttff1tuvMWuotlu5ciULFizghx9+wN/fH4AVK1aUabsdO3bUOobGxBbb7KOPPiI7O5sXXnih1jHbOltst9IuXLjAn3/+2Sj/ztl62zVmDdF2O3bs4MCBAyxZsoT333+f7777DrDNtmvUPf9PPvkkv/32G9u3by9zhTMwMJCioiIyMjLKXDFKTU0tN1w7Li6OESNGMGPGDObNm1ej8/fu3btM5c6AgACcnZ3RaDTlKnVWdO7GyFbbbPPmzZw6darcFci7776bwYMHs3Xr1hrFYWtstd1KW716NXl5eTzyyCM1Orc9sMb2M4cxhtTUVIKCgsrE17179xrFYKsaqu1WrVrFtGnT+PHHH8sMdbztttvo16+f6X7z5s1JTk42nauxtktVbLXNNm/eTGRkJM7OzmXO07t3bx566CG+/vrrmr0QNsZW2620ZcuW4efnx2233Vajn93WWWPbmaMm8dmrhmq70NBQALp27Upqaiovv/wyEydOtM2/cQ1WXcCK6PV6Zfbs2UpwcLBy/PjxctuNRSJWr15teiwhIaFckYiYmBjF399fefbZZ6s9Z02LkD355JOm+zqdTmnevHmjLvhn622WnJysHD16tMwXoHzwwQfK6dOnzTqHLbL1drv+uHfffbdZx7UX1t5+pVFFwb+3337b9FhmZmajKPjXkG23cuVKxcXFRVm7dq3ZsdWkXRpLwT9bb7OzZ8+W+Rv3559/KoCyevVq5fz582adxxbZeruV3jc0NFT55z//adax7YE1t11p1RX8qy4+e2SJzydGCxYsUFq1alVlbNb8N65RJv+zZs1SvL29la1btyrJycmmr7y8PNM+M2fOVFq2bKls3rxZOXDggBIREaFERESYth89elRp1qyZMmnSpDLHSEtLK3OuqKgoJSoqSunVq5fy4IMPKlFRUUpsbGyV8a1atUpxdnZWli9frsTFxSkzZsxQfHx8lJSUFNM+ycnJSlRUlPLFF18ogLJ9+3YlKipKuXLlSh29StbFHtrsehUlK/bGXtrtxIkTikqlUv744486eFVsh7W3X3Z2tul5gPLuu+8qUVFRytmzZ037vPHGG4qPj4/yyy+/KEeOHFFuv/12JTQ0VMnPz6+jV8k6NVTbrVixQnFwcFAWL15cZp+MjIwq4zOnXc6ePatERUUpCxYsUDw8PExtnZ2dXYevlPWwhzYrrbKExd7YS7v9/fffCqDEx8fX0Stj/ay97a5cuaJERUUpv//+uwIoq1atUqKiopTk5GSz47NXDdV2H3/8sfLrr78qx48fV44fP658+eWXiqenp/Liiy9WGZ81/41rlMk/UOHXsmXLTPvk5+crTzzxhNKkSRPFzc1NufPOO8v8ss2fP7/CY1x/JcicfSry0UcfKS1btlScnJyUvn37Knv27CmzvbLzl/4Z7Ik9tFlFP5O9J//20m4vvPCCEhISouh0utq+FDbJ2tvPOPLp+q/Jkyeb9tHr9cp//vMfJSAgQHF2dlZGjBihHDt2rA5eHevWUG03dOjQatugIua0y+TJkys89pYtW+rgFbI+9tBmpTWW5N9e2m3ixInKgAEDbvTlsCnW3nbLF2piNwAABJ9JREFUli2r8Hnz5883Oz571VBt9+GHHyrh4eGKm5ub4uXlpfTo0UP55JNPqv08aM1/41SKoigIIYQQQgghhBDCbjX6av9CCCGEEEIIIYS9k+RfCCGEEEIIIYSwc5L8CyGEEEIIIYQQdk6SfyGEEEIIIYQQws5J8i+EEEIIIYQQQtg5Sf6FEEIIIYQQQgg7J8m/EEIIIYQQQghh5yT5F0IIIYQQQggh7Jwk/0IIIYSwWSqVirVr11o6DCGEEMLqSfIvhBBCiBqbMmUKKpWKmTNnlts2e/ZsVCoVU6ZMqbPzvfzyy3Tv3r3OjieEEEI0NpL8CyGEEKJWQkJCWLVqFfn5+abHCgoKWLlyJS1btrRgZEIIIYS4niT/QgghhKiVnj17EhISwpo1a0yPrVmzhpYtW9KjRw/TY4WFhTz11FP4+/vj4uLCoEGD2L9/v2n71q1bUalUbNq0id69e+Pm5saAAQM4duwYAMuXL2fBggUcPnwYlUqFSqVi+fLlpudfvnyZO++8Ezc3N9q3b8+vv/5a/z+8EEIIYWMk+RdCCCFErU2dOpVly5aZ7n/11Vc8+uijZfZ57rnn+Omnn/j66685dOgQ7dq1Y8yYMaSnp5fZ78UXX+Sdd97hwIEDODg4MHXqVADuv/9+/vnPfxIeHk5ycjLJycncf//9puctWLCA++67jyNHjnDLLbfw0EMPlTu2EEII0dhJ8i+EEEKIWps0aRI7d+7k7NmznD17ll27djFp0iTT9tzcXD799FPeeustxo0bR1hYGF988QWurq4sXbq0zLFef/11hg4dSlhYGP/+97/ZvXs3BQUFuLq64uHhgYODA4GBgQQGBuLq6mp63pQpU5g4cSLt2rXjv//9Lzk5Oezbt6/BXgMhhBDCFjhYOgAhhBBC2K5mzZoxfvx4li9fjqIojB8/nqZNm5q2nzp1Cq1Wy8CBA02POTo60rdvX+Lj48sc66abbjLdDgoKAiAtLa3a+gGln+fu7o6XlxdpaWk39HMJIYQQ9kaSfyGEEELckKlTp/Lkk08CsHjx4lofx9HR0XRbpVIBoNfra/Q843PNeZ4QQgjRmMiwfyGEEELckLFjx1JUVIRWq2XMmDFltrVt2xYnJyd27dplekyr1bJ//37CwsLMPoeTkxM6na7OYhZCCCEaG+n5F0IIIcQN0Wg0piH8Go2mzDZ3d3dmzZrFs88+i6+vLy1btmTRokXk5eXx2GOPmX2O1q1bk5iYSHR0NC1atMDT0xNnZ+c6/TmEEEIIeybJvxBCCCFumJeXV6Xb3njjDfR6PQ8//DDZ2dn07t2bP//8kyZNmph9/Lvvvps1a9YwfPhwMjIyWLZsGVOmTKmDyIUQQojGQaUoimLpIIQQQgghhBBCCFF/ZM6/EEIIIYQQQghh5yT5F0IIIYQQQggh7Jwk/0IIIYQQQgghhJ2T5F8IIYQQQgghhLBzkvwLIYQQQgghhBB2TpJ/IYQQQgghhBDCzknyL4QQQgghhBBC2DlJ/oUQQgghhBBCCDsnyb8QQgghhBBCCGHnJPkXQgghhBBCCCHsnCT/QgghhBBCCCGEnft/fVob5BUBckgAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('portfolio_chart.png', 'CAGR: 1.44%')"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    }
  ]
}