{
 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "# Deep Otello AI\n",
    "\n",
    "The game reversi is a very good game to apply deep learning methods to.\n",
    "\n",
    "Othello also known as reversi is a board game first published in 1883 by eiter Lewis Waterman or John W. Mollet in England (each one was denouncing the other as fraud).\n",
    "It is a strickt turn based zero-sum game with a clear Markov chain and now hidden states like in card games with an unknown distribution of cards or unknown player allegiance.\n",
    "There is like for the game go only one set of stones with two colors which is much easier to abstract than chess with its 6 unique pieces.\n",
    "The game has a symmetrical game board wich allows to play with rotating the state around an axis to allow for a breaking of sequences or interesting ANN architectures, quadruple the data generation by simulation or interesting test cases where a symetry in turns should be observable if the AI reaches an \"objective\" policy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Content\n",
    "\n",
    "* [The game rules](#the-game-rules) A short overview over the rules of the game.\n",
    "* [Some common Otello strategies](#some-common-otello-strategies) introduces some easy approaches to a classic Otello AI and defines some behavioral expectations.\n",
    "* [Initial design decisions](#initial-design-decisions) an explanation about some initial design decision and assumptions\n",
    "* [Imports and dependencies](#imports-and-dependencies) explains what libraries where used"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## The game rules\n",
    "\n",
    "Othello is played on a board with 8 x 8 fields for two player.\n",
    "The board geometry is equal to a chess game.\n",
    "The game is played with game stones that are black on one siede and white on the other.\n",
    "![Othello game board example](reversi_example.png)\n",
    "The player take turns.\n",
    "A player places a stone with his or her color up on the game board.\n",
    "The player can only place stones when he surrounds a number of stones with the opponents color with the new stone and already placed stones of his color.\n",
    "Those surrounded stones can either be horizontally, vertically and/or diagonally be placed.\n",
    "All stones thus surrounded will be flipped to be of the players color.\n",
    "Turns are only possible if the player is also changing the color of the opponents stones. If a player can't act he is skipped.\n",
    "The game ends if both players can't act. The player with the most stones wins.\n",
    "If the score is counted in detail unclaimed fields go to the player with more stones of his or her color on the board.\n",
    "The game begins with four stones places in the center of the game. Each player gets two. They are placed diagonally to each other.\n",
    "\n",
    "\n",
    "<img alt=\"Startaufstellung.png\" src=\"Startaufstellung.png\"/>\n",
    "\n",
    "## Some common Othello strategies\n",
    "\n",
    "As can be easily understood the placement of stones and on the bord is always a careful balance of attack and defence.\n",
    "If the player occupies huge homogenous stretches on the board it can be attacked easier.\n",
    "The boards corners provide safety from wich occupied territory is impossible to loos but since it is only possible to reach the corners if the enemy is forced to allow this or calculates the cost of giving a stable base to the enemy it is difficult to obtain.\n",
    "There are some text on otello computer strategies which implement greedy algorithms for reversi based on a modified score to each field.\n",
    "Those different values are score modifiers for a traditional greedy algorithm.\n",
    "If a players stone has captured such a filed the score reached is multiplied by the modifier.\n",
    "The total score is the score reached by the player subtracted with the score of the enemy.\n",
    "The scores change in the course of the game and converges against one. This gives some indications of what to expect from an Othello AI.\n",
    "\n",
    "<img alt=\"ComputerPossitionScore\" src=\"computer-score.png\"/>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initial design decisions\n",
    "\n",
    "At the beginning of this project I made some design decisions.\n",
    "The first onw was that I do not want to use a gym library because it limits the data formats accessible.\n",
    "I choose to implement the hole game as entry in a stack in numpy arrays to be able to accommodate interfacing with a neural network easier and to use scipy pattern recognition tools to implement some game mechanics for a fast simulation cycle.\n",
    "I chose to ignore player colors as far as I could instead a player perspective was used. Which allowed to change the perspective with a flipping of the sign. (multiplying with -1).\n",
    "The array format should also allow for data multiplication or the breaking of strikt sequences by flipping the game along one the for axis, (horizontal, vertical, transpose along both diagonals).\n",
    "\n",
    "I wanted to implement different agents as classes that act on those game stacks.\n",
    "\n",
    "Since computation time is critical all computational have results are saved.\n",
    "The analysis of those is then repeated in real time. If a recalculation of such a section is required the save file can be deleted and the code should be executed again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext blackcellmagic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports and dependencies\n",
    "\n",
    "The following direct dependencies where used for this project:\n",
    "```toml\n",
    "jupyter = \"^1.0.0\"\n",
    "matplotlib = \"^3.6.3\"\n",
    "numpy = \"^1.24.1\"\n",
    "pytest = \"^7.2.1\"\n",
    "python = \"3.10.*\"\n",
    "scipy = \"^1.10.0\"\n",
    "tqdm = \"^4.64.1\"\n",
    "jupyterlab = \"^3.6.1\"\n",
    "torchvision = \"^0.14.1\"\n",
    "torchaudio = \"^0.13.1\"\n",
    "```\n",
    "* `Jupyter` and `jupyterlab` on pycharm was used as a IDE / Ipython was used to implement this code.\n",
    "* `matplotlib` was used for visualisation and statistics.\n",
    "* `numpy` was used for array support and mathematical functions\n",
    "* `tqdm` was used for progress bars\n",
    "* `scipy` contains fast pattern recognition tools for images. It was used to make an initial estimation about where possible turns should be.\n",
    "* `torch` supplied the ANN functionalities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import abc\n",
    "import itertools\n",
    "import os.path\n",
    "import warnings\n",
    "from abc import ABC\n",
    "from enum import Enum\n",
    "from typing import Final\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from ipywidgets import interact\n",
    "from scipy.ndimage import binary_dilation\n",
    "from tqdm.notebook import tqdm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Constants\n",
    "\n",
    "Some general constants needed to be defined. Such as board game size and Player and Enemy representations. Also, directional offsets and the initial placement of blocks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "BOARD_SIZE: Final[int] = 8  # defines the board side length as 8\n",
    "PLAYER: Final[int] = 1  # defines the number symbolising the player as 1\n",
    "ENEMY: Final[int] = -1  # defines the number symbolising the enemy as -1\n",
    "EXAMPLE_STACK_SIZE: Final[int] = 1000  # defines the game stack size for examples\n",
    "IMPOSSIBLE: Final[np.ndarray] = np.array([-1, -1], dtype=int)\n",
    "IMPOSSIBLE.setflags(write=False)\n",
    "SIMULATE_TURNS: Final[int] = 70\n",
    "VERIFY_POLICY: Final[bool] = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The directions array contains all the numerical offsets needed to move along one of the 8 directions in a 2 dimensional grid. This will allow an iteration over the game board.\n",
    "![8-directions.png](8-directions.png \"Offset in 8 directions\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1, -1],\n",
       "       [-1,  0],\n",
       "       [-1,  1],\n",
       "       [ 0, -1],\n",
       "       [ 0,  1],\n",
       "       [ 1, -1],\n",
       "       [ 1,  0],\n",
       "       [ 1,  1]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DIRECTIONS: Final[np.ndarray] = np.array(\n",
    "    [[i, j] for i in range(-1, 2) for j in range(-1, 2) if j != 0 or i != 0],\n",
    "    dtype=int,\n",
    ")\n",
    "DIRECTIONS.setflags(write=False)\n",
    "DIRECTIONS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another constant needed is the initial start square at the center of the board."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1,  1],\n",
       "       [ 1, -1]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "START_SQUARE: Final[np.ndarray] = np.array(\n",
    "    [[ENEMY, PLAYER], [PLAYER, ENEMY]], dtype=int\n",
    ")\n",
    "START_SQUARE.setflags(write=False)\n",
    "START_SQUARE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating new boards\n",
    "\n",
    "The first function implemented and tested is a function to generate the starting environment as a stack of games.\n",
    "As described above I simply placed a 2 by 2 square in the center of an empty stack of boards."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0, -1,  1,  0,  0,  0],\n",
       "       [ 0,  0,  0,  1, -1,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0,  0]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_new_games(number_of_games: int) -> np.ndarray:\n",
    "    \"\"\"Generates a stack of initialised game boards.\n",
    "\n",
    "    Args:\n",
    "        number_of_games: The size of the board stack.\n",
    "\n",
    "    Returns: The generates stack of games as a stack n x 8 x 8.\n",
    "\n",
    "    \"\"\"\n",
    "    empty = np.zeros([number_of_games, BOARD_SIZE, BOARD_SIZE], dtype=int)\n",
    "    empty[:, 3:5, 3:5] = START_SQUARE\n",
    "    return empty\n",
    "\n",
    "\n",
    "get_new_games(1)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_number_of_games = 3\n",
    "assert get_new_games(test_number_of_games).shape == (\n",
    "    test_number_of_games,\n",
    "    BOARD_SIZE,\n",
    "    BOARD_SIZE,\n",
    ")\n",
    "np.testing.assert_equal(\n",
    "    get_new_games(test_number_of_games).sum(axis=1),\n",
    "    np.zeros(\n",
    "        [\n",
    "            test_number_of_games,\n",
    "            8,\n",
    "        ]\n",
    "    ),\n",
    ")\n",
    "np.testing.assert_equal(\n",
    "    get_new_games(test_number_of_games).sum(axis=2),\n",
    "    np.zeros(\n",
    "        [\n",
    "            test_number_of_games,\n",
    "            8,\n",
    "        ]\n",
    "    ),\n",
    ")\n",
    "assert np.all(get_new_games(test_number_of_games)[:, 3:4, 3:4] != 0)\n",
    "del test_number_of_games"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualisation tools\n",
    "\n",
    "In this section a visualisation help was implemented for debugging of the game and a proper display of the results.\n",
    "For this visualisation ChatGPT was used as a prompted code generator that was later reviewed and refactored by hand to integrate seamlessly into the project as a whole.\n",
    "White stones represent the player, black stones the enemy. A single plot can be used as a subplot when the `ax` argument is used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_othello_board(\n",
    "    board: np.ndarray,\n",
    "    action: np.ndarray | None = None,\n",
    "    ax=None,\n",
    ") -> None:\n",
    "    \"\"\"Plots a single otello board.\n",
    "\n",
    "    If a matplot axis object is given the board will be plotted into that axis. If not an axis object will be generated.\n",
    "    The image generated will be shown directly.\n",
    "\n",
    "    Args:\n",
    "        board: The bord that should be plotted. Only a single games is allowed. A numpy array of the form 8x8 is expected.\n",
    "        ax: If needed a matplotlib axis object can be defined that is used to place the board as a sublot into a bigger context.\n",
    "    \"\"\"\n",
    "    assert board.shape == (8, 8)\n",
    "    plot_all = False\n",
    "    if ax is None:\n",
    "        fig_size = 3\n",
    "        plot_all = True\n",
    "        fig, ax = plt.subplots(figsize=(fig_size, fig_size))\n",
    "\n",
    "    ax.set_facecolor(\"#0f6b28\")\n",
    "    if action is not None:\n",
    "        ax.scatter(action[0], action[1], s=350 if plot_all else 200, c=\"red\")\n",
    "    for x_pos, y_pos in itertools.product(range(BOARD_SIZE), range(BOARD_SIZE)):\n",
    "        if board[x_pos, y_pos] == PLAYER:\n",
    "            color = \"white\"\n",
    "        elif board[x_pos, y_pos] == ENEMY:\n",
    "            color = \"black\"\n",
    "        else:\n",
    "            continue\n",
    "        ax.scatter(x_pos, y_pos, s=280 if plot_all else 140, c=color)\n",
    "    for x_pos in range(-1, 8):\n",
    "        ax.axhline(x_pos + 0.5, color=\"black\", lw=2)\n",
    "        ax.axvline(x_pos + 0.5, color=\"black\", lw=2)\n",
    "    ax.set_xlim(-0.5, 7.5)\n",
    "    ax.set_ylim(7.5, -0.5)\n",
    "    ax.set_xticks(np.arange(8))\n",
    "    ax.set_xticklabels(list(\"ABCDEFGH\"))\n",
    "    ax.set_yticks(np.arange(8))\n",
    "    ax.set_yticklabels(list(\"12345678\"))\n",
    "    ax.set_xlabel(\n",
    "        f\"W{np.sum(board == ENEMY)} / {np.sum(board == 0)} / B{np.sum(board == PLAYER)}\"\n",
    "    )\n",
    "    if plot_all:\n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "\n",
    "\n",
    "plot_othello_board(get_new_games(1)[0], action=np.array([3, 3]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_othello_boards(boards: np.ndarray, actions: np.ndarray | None = None) -> None:\n",
    "    \"\"\"Plots multiple boards into subplots.\n",
    "\n",
    "    The plots are shown directly.\n",
    "\n",
    "    Args:\n",
    "        boards: Plots the boards given into subplots. The maximum number of boards accepted is 70.\n",
    "    \"\"\"\n",
    "    assert len(boards.shape) == 3\n",
    "    assert boards.shape[1:] == (BOARD_SIZE, BOARD_SIZE)\n",
    "    assert boards.shape[0] < 70\n",
    "\n",
    "    if actions is not None:\n",
    "        assert len(actions.shape) == 2\n",
    "        assert actions.shape[1] == 2\n",
    "        assert boards.shape[0] == actions.shape[0]\n",
    "\n",
    "    plots_per_row = 4\n",
    "    rows = int(np.ceil(boards.shape[0] / plots_per_row))\n",
    "    fig, axs = plt.subplots(rows, plots_per_row, figsize=(12, 3 * rows))\n",
    "    for game_index, ax in enumerate(axs.flatten()):\n",
    "        if game_index >= boards.shape[0]:\n",
    "            fig.delaxes(ax)\n",
    "        else:\n",
    "            action = actions[game_index] if actions is not None else None\n",
    "            plot_othello_board(boards[game_index], action=action, ax=ax)\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def drop_duplicate_boards(\n",
    "    boards: np.ndarray, actions: np.ndarray | None\n",
    ") -> tuple[np.ndarray, np.ndarray | None]:\n",
    "    \"\"\"Drop boards that follow each other and are duplicates will be dropped.\n",
    "\n",
    "    Args:\n",
    "        boards: A set of boards to be reduced.\n",
    "\n",
    "    Returns:\n",
    "        A sequence of boards where boards that where equal are dropped.\n",
    "    \"\"\"\n",
    "    non_duplicates = ~np.all(boards == np.roll(boards, axis=0, shift=1), axis=(1, 2))\n",
    "    return (\n",
    "        boards[non_duplicates],\n",
    "        np.roll(actions, axis=0, shift=1)[non_duplicates]\n",
    "        if actions is not None\n",
    "        else None,\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find possible actions to take\n",
    "\n",
    "The frist step in the implementation of an AI like this is to get an overview over the possible actions that can be taken in a situation.\n",
    "Here was the design choice taken to first find fields that are empty and have at least one neighbouring enemy stone.\n",
    "This was implemented with element wise check for a stone and a binary dilation marking all fields neighboring an enemy stone.\n",
    "For that the `SURROUNDING` mask was used. Both aries are then element wise combined using and.\n",
    "The resulting array contains all filed where a turn could potentially be made. Those are then check in detail.\n",
    "The previous element wise operations on the numpy array increase the spead for this operation dramatically.\n",
    "\n",
    "The check for a possible turn is done in detail by following each direction step by step as long as there are enemy stones in that direction.\n",
    "If the board end is reached or en empty filed before reaching a field occupied by the player that direction does not surround enemy stones.\n",
    "If one direction surrounds enemy stone a turn is possible.\n",
    "This detailed step is implemented as a recursion and need to go at leas one step to return True."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1, 1, 1],\n",
       "        [1, 0, 1],\n",
       "        [1, 1, 1]]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SURROUNDING: Final = np.array(\n",
    "    [[[1, 1, 1], [1, 0, 1], [1, 1, 1]]]\n",
    ")  # defines the binary dilation mask to check if a field is next to an enemy stones\n",
    "SURROUNDING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.79 ms ± 225 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "983 ms ± 59.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[[False, False, False, False, False, False, False, False],\n",
       "        [False, False, False, False, False, False, False, False],\n",
       "        [False, False, False,  True, False, False, False, False],\n",
       "        [False, False,  True, False, False, False, False, False],\n",
       "        [False, False, False, False, False,  True, False, False],\n",
       "        [False, False, False, False,  True, False, False, False],\n",
       "        [False, False, False, False, False, False, False, False],\n",
       "        [False, False, False, False, False, False, False, False]]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def _recursive_steps(\n",
    "    board: np.ndarray,\n",
    "    rec_direction: np.ndarray,\n",
    "    rec_position: np.ndarray,\n",
    "    step_one: int = 0,\n",
    ") -> int:\n",
    "    \"\"\"Check if a player can place a stone on the board specified in the direction specified and direction specified.\n",
    "\n",
    "    Args:\n",
    "        board: The board that should be checked for a playable action.\n",
    "        rec_direction: The direction that should be checked.\n",
    "        rec_position: The position that should be checked.\n",
    "        step_one: Defines if the call of this function is the firs or not. Should be kept to the default value for proper functionality.\n",
    "\n",
    "    Returns:\n",
    "        True if a turn is possible for possition and direction on the board defined.\n",
    "    \"\"\"\n",
    "    rec_position = rec_position + rec_direction\n",
    "    if np.any((rec_position >= BOARD_SIZE) | (rec_position < 0)):\n",
    "        return 0\n",
    "    next_field = board[tuple(rec_position.tolist())]\n",
    "    if next_field == 0:\n",
    "        return 0\n",
    "    if next_field == -1:\n",
    "        return _recursive_steps(\n",
    "            board, rec_direction, rec_position, step_one=step_one + 1\n",
    "        )\n",
    "    if next_field == 1:\n",
    "        return step_one\n",
    "\n",
    "\n",
    "def get_possible_turns(boards: np.ndarray, tqdm_on: bool = False) -> np.ndarray:\n",
    "    \"\"\"Analyses a stack of boards.\n",
    "\n",
    "    Args:\n",
    "        boards: A stack of boards to check.\n",
    "\n",
    "    Returns:\n",
    "        A stack of game boards containing boolean values showing where turns are possible for the player.\n",
    "    \"\"\"\n",
    "    assert len(boards.shape) == 3, \"The number fo input dimensions does not fit.\"\n",
    "    assert boards.shape[1:] == (\n",
    "        BOARD_SIZE,\n",
    "        BOARD_SIZE,\n",
    "    ), \"The input dimensions do not fit.\"\n",
    "\n",
    "    poss_turns = boards == 0  # checks where fields are empty.\n",
    "    poss_turns &= binary_dilation(\n",
    "        boards == -1, SURROUNDING\n",
    "    )  # checks where fields are next to an enemy filed an empty\n",
    "    iterate_over = itertools.product(\n",
    "        range(boards.shape[0]), range(BOARD_SIZE), range(BOARD_SIZE)\n",
    "    )\n",
    "    if tqdm_on:\n",
    "        iterate_over = tqdm(iterate_over, total=np.prod(boards.shape))\n",
    "    for game, idx, idy in iterate_over:\n",
    "        if poss_turns[game, idx, idy]:\n",
    "            position = idx, idy\n",
    "            poss_turns[game, idx, idy] = any(\n",
    "                _recursive_steps(boards[game, :, :], direction, position) > 0\n",
    "                for direction in DIRECTIONS\n",
    "            )\n",
    "    return poss_turns\n",
    "\n",
    "\n",
    "# some simple testing to ensure the function works after simple changes\n",
    "# this testing is complete, its more of a smoke-test\n",
    "test_array = get_new_games(3)\n",
    "expected_result = np.zeros_like(test_array, dtype=bool)\n",
    "expected_result[:, 4, 5] = expected_result[:, 2, 3] = True\n",
    "expected_result[:, 5, 4] = expected_result[:, 3, 2] = True\n",
    "np.testing.assert_equal(get_possible_turns(test_array), expected_result)\n",
    "\n",
    "\n",
    "%timeit get_possible_turns(get_new_games(10))  # checks turn possibility evaluation time for 10 initial games\n",
    "%timeit get_possible_turns(get_new_games(EXAMPLE_STACK_SIZE))  # check turn possibility evaluation time for EXAMPLE_STACK_SIZE initial games\n",
    "\n",
    "# shows a singe game\n",
    "get_possible_turns(get_new_games(3))[:1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Besides the ability to generate an array of possible turns there needs to be a functions that check if a given turn is possible.\n",
    "On is needed for the action space validation. The other is for validating a players turn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def move_possible(board: np.ndarray, move: np.ndarray) -> bool:\n",
    "    \"\"\"Checks if a turn is possible.\n",
    "\n",
    "    Checks if a turn is possible. If no turn is possible to input array [-1, -1] is expected.\n",
    "\n",
    "    Args:\n",
    "        board: A board where it should be checkt if a turn is possible.\n",
    "        move: The move that should be taken. Expected is the index of the filed where a stone should be placed [x, y]. If no placement is possible [-1, -1] is expected as an input.\n",
    "\n",
    "    Returns:\n",
    "        True if the move is possible\n",
    "    \"\"\"\n",
    "    if np.all(move == -1):\n",
    "        return not np.any(get_possible_turns(np.reshape(board, (1, 8, 8))))\n",
    "    return any(\n",
    "        _recursive_steps(board[:, :], direction, move) > 0 for direction in DIRECTIONS\n",
    "    )\n",
    "\n",
    "\n",
    "# Some testing for this function and the underlying recursive functions that are called.\n",
    "assert move_possible(get_new_games(1)[0], np.array([2, 3])) is True\n",
    "assert move_possible(get_new_games(1)[0], np.array([3, 2])) is True\n",
    "assert move_possible(get_new_games(1)[0], np.array([2, 2])) is False\n",
    "assert move_possible(np.zeros((8, 8)), np.array([3, 2])) is False\n",
    "assert move_possible(np.ones((8, 8)) * 1, np.array([-1, -1])) is True\n",
    "assert move_possible(np.ones((8, 8)) * -1, np.array([-1, -1])) is True\n",
    "assert move_possible(np.ones((8, 8)) * 0, np.array([-1, -1])) is True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def moves_possible(boards: np.ndarray, moves: np.ndarray) -> np.ndarray:\n",
    "    \"\"\"Checks if a stack of moves can be executed on a stack of boards.\n",
    "\n",
    "    Args:\n",
    "        boards: A board where the next stone should be placed.\n",
    "        moves: A stack stones to be placed. Each move is formatted as an array in the form of [x, y] if no turn is possible the value [-1, -1] is expected.\n",
    "\n",
    "    Returns:\n",
    "        An array marking for each and every game and move in the stack if the move can be executed.\n",
    "    \"\"\"\n",
    "    arr_moves_possible = np.zeros(boards.shape[0], dtype=bool)\n",
    "    for game in range(boards.shape[0]):\n",
    "        if np.all(\n",
    "            moves[game] == -1\n",
    "        ):  # can be all or any. All should be faster since most times neither value will be -1.\n",
    "            arr_moves_possible[game] = not np.any(\n",
    "                get_possible_turns(np.reshape(boards[game], (1, 8, 8)))\n",
    "            )\n",
    "        else:\n",
    "            arr_moves_possible[game] = any(\n",
    "                _recursive_steps(boards[game, :, :], direction, moves[game]) > 0\n",
    "                for direction in DIRECTIONS\n",
    "            )\n",
    "    return arr_moves_possible\n",
    "\n",
    "\n",
    "np.testing.assert_array_equal(\n",
    "    moves_possible(np.ones((3, 8, 8)) * 1, np.array([[-1, -1]] * 3)),\n",
    "    np.array([True] * 3),\n",
    ")\n",
    "\n",
    "np.testing.assert_array_equal(\n",
    "    moves_possible(get_new_games(3), np.array([[2, 3], [3, 2], [3, 2]])),\n",
    "    np.array([True] * 3),\n",
    ")\n",
    "np.testing.assert_array_equal(\n",
    "    moves_possible(get_new_games(3), np.array([[2, 2], [1, 1], [0, 0]])),\n",
    "    np.array([False] * 3),\n",
    ")\n",
    "np.testing.assert_array_equal(\n",
    "    moves_possible(np.ones((3, 8, 8)) * -1, np.array([[-1, -1]] * 3)),\n",
    "    np.array([True] * 3),\n",
    ")\n",
    "np.testing.assert_array_equal(\n",
    "    moves_possible(np.zeros((3, 8, 8)), np.array([[-1, -1]] * 3)),\n",
    "    np.array([True] * 3),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reword functions\n",
    "\n",
    "For any kind of reinforcement learning is a reword function needed.\n",
    "For otello this would be the final score, the information who won or changes to the score.\n",
    "A combination of those three would also be possible.\n",
    "It is probably not be possible to weight the current score to high in a reword function since that would be to close to a classic greedy algorithm.\n",
    "But some direct influence would increase the learning speed.\n",
    "In the next section are all three reword functions implemented to be combined and weight later on as needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "190 µs ± 3.89 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n",
      "35.4 µs ± 237 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n",
      "36.9 µs ± 796 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "def final_boards_evaluation(boards: np.ndarray) -> np.ndarray:\n",
    "    \"\"\"Evaluates the board at the end of the game.\n",
    "\n",
    "    All unused fields are added to the score of the player that has more stones with his color up.\n",
    "    This score only applies to the end of the game.\n",
    "    Normally the score is represented by the number of stones each player has.\n",
    "    In this case the score was combined by building the difference.\n",
    "\n",
    "    Args:\n",
    "        boards: A stack of game bords ot the end of the game.\n",
    "\n",
    "    Returns:\n",
    "        the combined score for both player.\n",
    "    \"\"\"\n",
    "    score1, score2 = np.sum(boards == 1, axis=(1, 2)), np.sum(boards == -1, axis=(1, 2))\n",
    "    player_1_won = score1 > score2\n",
    "    player_2_won = score1 < score2\n",
    "    score1_final = 64 - score2[player_1_won]\n",
    "    score2_final = 64 - score1[player_2_won]\n",
    "    score1[player_1_won] = score1_final\n",
    "    score2[player_2_won] = score2_final\n",
    "    return score1 - score2\n",
    "\n",
    "\n",
    "def evaluate_boards(boards: np.ndarray) -> np.ndarray:\n",
    "    \"\"\"Counts the stones each player has on the board.\n",
    "\n",
    "    Args:\n",
    "        boards: A stack of boards for evaluation.\n",
    "\n",
    "    Returns:\n",
    "        the combined score for both player.\n",
    "    \"\"\"\n",
    "    return np.sum(boards, axis=(1, 2))\n",
    "\n",
    "\n",
    "def evaluate_who_won(boards: np.ndarray) -> np.ndarray:\n",
    "    \"\"\"Checks who won or is winning a game.\n",
    "\n",
    "    Args:\n",
    "        boards: A stack of boards for evaluation.\n",
    "\n",
    "    Returns:\n",
    "        The information who won for both player. 1 meaning the player won, -1 means the opponent lost. 0 represents a patt.\n",
    "    \"\"\"\n",
    "    return np.sign(np.sum(boards, axis=(1, 2)))\n",
    "\n",
    "\n",
    "_boards = get_new_games(EXAMPLE_STACK_SIZE)\n",
    "%timeit final_boards_evaluation(_boards)\n",
    "%timeit evaluate_boards(_boards)\n",
    "%timeit evaluate_who_won(_boards)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Execute a chosen action\n",
    "\n",
    "After an evaluation what turns are possible there needs to be a function that executes a turn.\n",
    "This next sections does that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "class InvalidTurn(ValueError):\n",
    "    \"\"\"\n",
    "    This error is thrown if a given turn is not valid.\n",
    "    \"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "108 ms ± 2.36 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def do_moves(boards: np.ndarray, moves: np.ndarray) -> np.ndarray:\n",
    "    \"\"\"Executes a single move on a stack o Othello boards.\n",
    "\n",
    "    Args:\n",
    "        boards: A stack of Othello boards where the next stone should be placed.\n",
    "        moves: A stack of stone placement orders for the game. Formatted as coordinates in an array [x, y] of the place where the stone should be placed. Should contain [-1,-1] if no new placement is possible.\n",
    "\n",
    "    Returns:\n",
    "        The new state of the board.\n",
    "    \"\"\"\n",
    "\n",
    "    def _do_directional_move(\n",
    "        board: np.ndarray, rec_move: np.ndarray, rev_direction, step_one=True\n",
    "    ) -> bool:\n",
    "        \"\"\"Changes the color of enemy stones in one direction.\n",
    "\n",
    "        This function works recursive. The argument step_one should always be used in its default value.\n",
    "\n",
    "        Args:\n",
    "            board: A bord on which a stone was placed.\n",
    "            rec_move: The position on the board in x and y where this function is called from. Will be moved by recursive called.\n",
    "            rev_direction: The position where the stone was placed. Inside this recursion it will also be the last step that was checked.\n",
    "            step_one: Set to true if this is the first step in the recursion. False later on.\n",
    "\n",
    "        Returns:\n",
    "            True if a stone could be flipped.\n",
    "            All changes are made on the view of the numpy array and therefore not included in the return value.\n",
    "        \"\"\"\n",
    "        rec_position = rec_move + rev_direction\n",
    "        if np.any((rec_position >= 8) | (rec_position < 0)):\n",
    "            return False\n",
    "        next_field = board[tuple(rec_position.tolist())]\n",
    "        if next_field == 0:\n",
    "            return False\n",
    "        if next_field == 1:\n",
    "            return not step_one\n",
    "        if next_field == -1:\n",
    "            if _do_directional_move(board, rec_position, rev_direction, step_one=False):\n",
    "                board[tuple(rec_position.tolist())] = 1\n",
    "                return True\n",
    "            return False\n",
    "\n",
    "    def _do_move(_board: np.ndarray, move: np.ndarray) -> None:\n",
    "        \"\"\"Executes a turn on a board.\n",
    "\n",
    "        Args:\n",
    "            _board: The game board on wich to place a stone.\n",
    "            move: The coordinates of a stone that should be placed. Should be formatted as an array of the form [x, y]. The value [-1, -1] is expected if no turn is possible.\n",
    "\n",
    "        Returns:\n",
    "            All changes are made on the view of the numpy array.\n",
    "        \"\"\"\n",
    "        if np.all(move == -1):\n",
    "            if not move_possible(_board, move):\n",
    "                raise InvalidTurn(\"An action should be taken. A turn is possible.\")\n",
    "            return\n",
    "\n",
    "        # noinspection PyTypeChecker\n",
    "        if _board[tuple(move.tolist())] != 0:\n",
    "            raise InvalidTurn(\"This turn is not possible.\")\n",
    "\n",
    "        action = False\n",
    "        for direction in DIRECTIONS:\n",
    "            if _do_directional_move(_board, move, direction):\n",
    "                action = True\n",
    "        if not action:\n",
    "            raise InvalidTurn(\"This turn is not possible.\")\n",
    "\n",
    "        # noinspection PyTypeChecker\n",
    "        _board[tuple(move.tolist())] = 1\n",
    "\n",
    "    boards = boards.copy()\n",
    "    for game in range(boards.shape[0]):\n",
    "        _do_move(boards[game], moves[game])\n",
    "    return boards\n",
    "\n",
    "\n",
    "%timeit do_moves(get_new_games(EXAMPLE_STACK_SIZE), np.array([[2, 3]] * EXAMPLE_STACK_SIZE))[0]\n",
    "\n",
    "plot_othello_board(\n",
    "    do_moves(\n",
    "        get_new_games(EXAMPLE_STACK_SIZE), np.array([[2, 3]] * EXAMPLE_STACK_SIZE)\n",
    "    )[0],\n",
    "    action=np.array([2, 3]),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## An abstract reversi game policy\n",
    "\n",
    "For an easy use of policies an abstract class containing the policy generation / requests an action in an inherited instance of this class.\n",
    "This class filters the policy to only propose valid actions. Inherited instance do not need to care about this. This super class also manges exploration and exploitation with the epsilon value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "class GamePolicy(ABC):\n",
    "    \"\"\"\n",
    "    A game policy. Proposes where to place a stone next.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, epsilon: float):\n",
    "        \"\"\"\n",
    "\n",
    "        Args:\n",
    "            epsilon: the epsilon / greedy value. Should be between zero and one. Set the mixture of policy and exploration. One means only the policy is used. Zero means only random policies are used. All mixtures inbetween between are possible.\n",
    "        \"\"\"\n",
    "        if 0 > epsilon > 1:\n",
    "            raise ValueError(\"Epsilon should be between zero and one.\")\n",
    "        self._epsilon: float = epsilon\n",
    "\n",
    "    @property\n",
    "    def epsilon(self):\n",
    "        return self._epsilon\n",
    "\n",
    "    @property\n",
    "    @abc.abstractmethod\n",
    "    def policy_name(self) -> str:\n",
    "        \"\"\"The name of this policy\"\"\"\n",
    "        raise NotImplementedError()\n",
    "\n",
    "    @abc.abstractmethod\n",
    "    def _internal_policy(self, boards: np.ndarray) -> np.ndarray:\n",
    "        \"\"\"The internal policy is an unfiltered policy. It should only be called from inside this function\n",
    "\n",
    "        Args:\n",
    "            boards: A board where a policy should be calculated for.\n",
    "\n",
    "        Returns:\n",
    "            The policy for this board. Should have the same size as the boards array.\n",
    "        \"\"\"\n",
    "        raise NotImplementedError()\n",
    "\n",
    "    def get_policy(self, boards: np.ndarray) -> np.ndarray:\n",
    "        \"\"\"Calculates the policy that should be followed.\n",
    "\n",
    "        Calculates the policy that should be followed.\n",
    "        This function does include the usage of epsilon to configure greediness and exploration.\n",
    "\n",
    "        Args:\n",
    "            boards: A set of boards that show the environment where the policy should be calculated for.\n",
    "\n",
    "        Returns:\n",
    "            A vector of indices. Should be formatted as an array of the form [x, y]. The value [-1, -1] is expected if no turn is possible.\n",
    "        \"\"\"\n",
    "        assert len(boards.shape) == 3\n",
    "        assert boards.shape[1:] == (BOARD_SIZE, BOARD_SIZE)\n",
    "\n",
    "        if self.epsilon <= 0:\n",
    "            policies = np.random.rand(*boards.shape)\n",
    "        else:\n",
    "            policies = self._internal_policy(boards)\n",
    "            if self.epsilon < 1:\n",
    "                policies = policies * self.epsilon + np.random.rand(*boards.shape) * (\n",
    "                    1 - self.epsilon\n",
    "                )\n",
    "\n",
    "        # todo talk to team about backpropagation of score and epsilon for greedy factor\n",
    "\n",
    "        # todo possibly change this function to only validate the purpose turn and not all turns\n",
    "        possible_turns = get_possible_turns(boards)\n",
    "        policies[possible_turns == False] = -1.0\n",
    "        max_indices = [\n",
    "            np.unravel_index(policy.argmax(), policy.shape) for policy in policies\n",
    "        ]\n",
    "        policy_vector = np.array(max_indices, dtype=int)\n",
    "        no_turn_possible = np.all(policy_vector == 0, 1) & (policies[:, 0, 0] == -1.0)\n",
    "\n",
    "        policy_vector[no_turn_possible, :] = IMPOSSIBLE\n",
    "        return policy_vector"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A first policy\n",
    "\n",
    "To quantify the quality of a game AI there needs to be some benchmarks.\n",
    "The easiest benchmark is to play against a random player.\n",
    "The easiest player to use as a benchmark is the random player.\n",
    "For this and testing purpose the random policy was implemented."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "class RandomPolicy(GamePolicy):\n",
    "    \"\"\"\n",
    "    A policy playing a random turn by setting epsilon to 0.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, epsilon: float = 0):\n",
    "        _ = epsilon\n",
    "        super().__init__(epsilon=0)\n",
    "\n",
    "    @property\n",
    "    def policy_name(self) -> str:\n",
    "        return \"random\"\n",
    "\n",
    "    def _internal_policy(self, boards: np.ndarray) -> np.ndarray:\n",
    "        pass\n",
    "\n",
    "\n",
    "rnd_policy = RandomPolicy(1)\n",
    "assert rnd_policy.policy_name == \"random\"\n",
    "assert rnd_policy.epsilon == 0\n",
    "\n",
    "rnd_policy_result = rnd_policy.get_policy(get_new_games(10))\n",
    "assert np.any((5 >= rnd_policy_result) & (rnd_policy_result >= 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "class GreedyPolicy(GamePolicy):\n",
    "    \"\"\"\n",
    "    A policy playing always one of the strongest turns.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, epsilon: float = 1):\n",
    "        _ = epsilon\n",
    "        super().__init__(1)\n",
    "\n",
    "    @property\n",
    "    def policy_name(self) -> str:\n",
    "        return \"greedy_policy\"\n",
    "\n",
    "    def _internal_policy(self, boards: np.ndarray) -> np.ndarray:\n",
    "        policies = np.random.rand(*boards.shape)\n",
    "        for game, idx, idy in itertools.product(\n",
    "            range(boards.shape[0]), range(BOARD_SIZE), range(BOARD_SIZE)\n",
    "        ):\n",
    "\n",
    "            if _poss_turns[game, idx, idy]:\n",
    "                position = idx, idy\n",
    "                policies[game, idx, idy] += np.sum(\n",
    "                    _recursive_steps(boards[game, :, :], direction, position)\n",
    "                    for direction in DIRECTIONS\n",
    "                )\n",
    "        return policies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Putting the game simulation together\n",
    "Now it's time to bring all together for a proper simulation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Playing a single turn\n",
    "\n",
    "The next function needed is used to request a policy, verify that the turn is legit and place a stone and turn enemy stones if possible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.2 s ± 41.3 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "1.08 s ± 60.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    },
    {
     "data": {
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nAKbEvZTKyspSSUmJzj//fM2fP18TJ07Uf/7nf3Z7/3nz5qm2trb9VlVV1aPAANAd+gmAzeLpKPop/biOoyafpPr65DxhXZ2afK3PC6Qa/QTAlISvKdUmGo12OH3zRKFQSKFQqKdjACBu9BMAm52so+in9LR9QEhnb9uWnCfbtk3bBvIzADPoJwBeiWspNW/ePM2YMUPDhw9XfX29nnnmGa1Zs0Z/+MMfUpUPAGJCPwGwGR2VGdYW9dG4ygoFN22SyspaL1oer0hEqqhQ8+ZKrTsrP+kZgRPRTwBMimspdfDgQX3ta19TdXW18vLyVFZWpj/84Q+aPn16qvIBQEzoJwA2o6Myw9Pj83XblhrpkUekxYsTexK/X3r0UQXd1ucDUo1+AmBSXEupRYsWpSoHAPQI/QTAZnRUZqgsyNZbhX100YrlCtx/vzR0qBQMxv4Ezc3Sxx+r5dkVWl/YR5UF2akLC/wd/QTApLgvdA4AAACga3dMK1SkKazo5Ze3XvS8uTm2BzY3S/X1ik6frkhTWHdMK0xtUAAALMBSCgAAAEiS3flZun1aobRjh6IXXSR9/HHrH0QiXT+g7fsff9x6/507dfu0Qu3Oz/ImMAAABrGUAgAAAJLo+bG5um16oZp371LLmeOlOXOkioqu71xRIc2Zo5Yzx6t59y59c3qRnh+b62FaAADMieuaUgAAAABO7bmxeXr39D5aUF6tScuWqfnXyxSceI5UWirl5kp1ddL27WqurFDQldYV9dGdU0dxhhQAIKOwlAIAAABSYHd+lq760ghNPHRMs7fW6OK9WzVuc4WyolKTT9o2MKR1Z+Xr6fH5XNQcAJCRWEoBAAAAKVRZkK3KgiHtXzuuK9dxDCYCAMAOXFMKAAAA8BALKQAAWjmu67peDqyrq1NeXl7r8H7en6jlHmmRXEmO5PQ1c6IYGchgUwbT8yXJbWyRJNXW1io319zFXU33k2TJ8eBnkgxk6JjBgo6in8hgy3wyWJaBfpJkybEgAxksmW9Nhhj7yejb99pCmhlueD4ZyGBbBtPzLWP8tbDheJjOYHo+GchgKeOvgw3Hggzm55PBrgyWMP462HAsyEAGW+bbkuEUjC6lOFOKDGQwn8H0fMnOouRf+sxlMD2fDGTolMGyjqKfMjuD6flksCwD/STJkmNBBjJYMt+aDDH2k7mlVF+/cueUeD62bulOuY0tcvoGjMwnAxlsy2B6viTVLtkhHYkYmd0lQ/0k2XE8TGcwPZ8MZDiRVR1FP2V8BtPzyWBXBvqplQ3HggxksGW+LRli7ScudA4AAAAAAADPsZRKY46316gHgJjRTwAAAABOxeg1pRCfiYeOafbWGl2y76hKD4eVFZWafNL2ASGtLeqjp8fnq7Ig23RMABmIfgIAAAAQL5ZSaWBUTZMWlFdrUvVRNTtScOI50vRxUk6Osurrdfa2bRpXWaHbttTorcI+umNaoXbnZ6U0U9mw8RozeJT6Z/dTw7FG7TiwW5v3bk3pTJvmA2hFP9mbAYB9bOgGGzIAsE/k0DFFDjdJzVEp6JN/QJb8Hv+DIv2UmVhKWe7692v1WPl++bNC0pw5Ct51l3TuuZ3uF9y0SXrkEV20YrnWLd+t26cV6vmxuUnNkh0M6brzv6DbJt+kicVndvrzyqr39PibT+n5d1/WseZwUmfbMB9AR/STfRkA2MeGbrAhAwD7uC1RNe+oU3jzYUU/6fy/fd9pIYXKBig4JldOIDVX/qGfwDWlLHb9+7V6/LVqBUeXKPDeVmnxYqmsrOs7l5VJixcr8N5WBUedoSde26fr369NWpYzTh+pDf/y/2nB7Ad01tDSLu9z1tBSLZj9gDb8y/+n0QUjkjbbhvkAOqKf7MsAwD42dIMNGQDYJ1LTpPpnPtDR8v1dLqQkKfpJWEfL96v+mQ8UqWlKegb6CRJLKWuNrmnSY+X7pTFj5Fu3Tho6VHIcye/v+gF+f+ufDx0q3/r1UkmJHivfr1FJKI8zTh+p1+9ZrqL8wXIcR35f1xn8Pr8cx1FR/mC98b0VSSsN0/MBdEQ/2ZcBgH1s6AYbMgCwT6SmSQ3P7ZHb0BLT/d2GFjU8tyepiyn6CW16tJT62c9+Jsdx9J3vfCdJcdDm0fJq+bJC8r36qpSTIwWDsT0wGJRycuR77TX5s0JaUF7doxzZwZBemPukckL9FPDH9m7PgD+gnFA/vXjHImUHQ2k9H+mLfkod+smuDEg/9FPvZ0M32JAB6YmO6t3clqgaV34kNUWlWD8s2ZXU1Po4tyXa4wz0E46X8FLqnXfe0X/913+prLu3ayBhEw8e06Tqowre+BVpxIjYf+FrEwxKI0cq8OUbNan6qCYeOpZwluvO/4KKBxbFXBZtAv6Ahg8aqi+dNyPh2TbMR3qin1KHfrIvA9IL/ZQZbOgGGzIg/dBRvV/zjjq59S2xL6TauJJb36LmHXU9zkA/4XgJLaUaGho0e/ZsPfHEExowYECyM2W82dtq1OxIuusuKZrgJjoSke68U82ONHtrTcJZbpt8k6JuYhki0Yhum3xTwrNtmI/0Qz+lFv1kXwakD/opc9jQDTZkQHqhozJDePNho4+X6Cd0lNBSau7cubrqqqv0+c9/Ptl5IOmSfUdbP1b93HO7v0bLqfj90vnnKzjxHF1cfTShpygbNl4Ti8/s9v29p4zg8+uc4RNUNmx8Ws5HeqKfUot+sisD0gv9lBls6AYbMiD90FG9X+TQsW4vah6r6CdhRXpwpjv9hBPFd76cpOXLl+svf/mL3nnnnZjuHw6HFQ5/+oNfV9fz0/16u9LDYWn6uCQ9WanGba5I6KFjBo9KSoSSwaO0ee/WtJuP9EM/pR79ZFcGpA/6KXPY0A02ZEB6iaej6Kf0FTmcnAuVR2qa5C/ITuix9BNOFNeZUlVVVbr77rv19NNPKzs7th/C+fPnKy8vr/1WXFycUNBM4biusqJqvXhwMuTmKiva+rzx6p/dLykRchJ8HtPzkV7op9Sjn+zLgPRAP2UWG7rBhgxIH/F2FP2Uxpp7fpFySa0XSU8Q/YQTxbWUevfdd3Xw4EGdd955CgQCCgQCevPNN/XII48oEAgoEol0esy8efNUW1vbfquqqkpa+N7IdRw1+STV1yfnCevq1ORrfd54NRxrTEqE+gSfx/R8pBf6KfXoJ/syID3QT5nFhm6wIQPSR7wdRT+lsWDCn3PWUVbiz0M/4URxvX3vsssu05YtWzp875ZbbtG4ceN07733yt/F9UVCoZBCIT6yMR7bB4R09rZtyXmybdu0bWBir/+OA7uTEmHH/g/Scj7SC/3kDfrJrgxID/RTZrGhG2zIgPQRb0fRT+nLPyArOc+Tn/jz0E84UVxLqZycHJ111lkdvtevXz8NGjSo0/eRuLVFfTSuskLBTZuksrLELiYciUgVFWreXKl1Z+UnlGPz3q2qrHpPZw0tTehCdJFoRFv2btOWjxP7Bdb0fKQX+skb9JNdGZAe6KfMYkM32JAB6YOOyhz+gmz5Tgv16GLnvtNCCV9PSqKf0FmSzt9DMj09Pl9BV9Ijj0i+BA+R3y89+qiCbuvzJerxN5+Sz0ksg9/n1+NvPpXwbBvmA+iIfrIvAwD72NANNmQAYJ9Q2QCjj5foJ3TU46XUmjVr9PDDDychCtpUFmTrrcI+almxXPrwQ6m5Ob4naG6W9uxRy7Mr9FZhH1X2YJP9/Lsvq+pv+9QSaYnrcS2RFn30fx/rd39ZlfBsG+YjvdFPyUc/2ZcB6Yl+6t1s6AYbMiB90VG9V3BMrpycgBTvJT0dyckJKDgmt8cZ6CccjzOlLHXHtEJFmsKKXn5560WFY/3Fr7lZqq9XdPp0RZrCumNaYY9yHGsO64uPfUP14caYS6Ml0qL6cKOuXfB1HWtO/NRQG+YD6Ix+sisDAPvY0A02ZABgHyfgU7+rh7derDzWxZQjKav1cU6g5ysE+gnHYyllqd35Wbp9WqG0Y4eiF10kffxx6x908Qk9Hb7/8cet99+5U7dPK9TuHlyErs2ug3t02UM3al/NgdZR0a4ztH1/X80BXfbQjfrg0Ic9nm3DfAAd0U/2ZQBgHxu6wYYMAOzjz89S/+tHyukf2yWmnf4B9b9+ZI8ucH4i+gltWEpZ7PmxubpteqGad+9Sy5njpTlzpIqKru9cUSHNmaOWM8erefcufXN6kZ4f2/NTK9t8cOhDfeanV+n2/56nLXu7vqjclr3bdPt/z9NnfnpV0svC9HwAHdFP9mUAYB8busGGDADs48/PUs4/jlafaUPkO63rT1P0nRZSn2lDlPOPo5O6kGpDP0GK89P34L3nxubp3dP7aEF5tSYtW6bmXy9TcOI5UmmplJsr1dVJ27erubJCQVdaV9RHd04dlZQzEE50rDmsZ9a/qGfWv6iyYeNVMniUcrL7qf5Yo3Ye2K3Ne7cmfaZN8wF0RD/ZlwGAfWzoBhsyALCPE/Apa3y+ssbnK3LomCI1TVJTVMryyZ+f1aNP2YsV/QSWUmlgd36WrvrSCE08dEyzt9bo4r1bNW5zhbKiUpNP2jYwpHVn5evp8fk9umhwPDbv3Wq0IEzPB9CKfrI3AwD72NANNmQAYB9/QbYnS6iToZ8yE0upNFJZkK3KgiHtXzuuK9eJ92MTACD56CcAAAAA8eKaUmmMX/gA2Ip+AgAAAHAqLKUAAAAAAADgOcd1XdfLgXV1dcrLy2sd3s/7dw+6R1okV5IjOX3NvHuRDGSwKYPp+ZLkNrZIkmpra5Wbm7xPZYuX6X6SLDke/EySgQwdM1jQUfQTGWyZTwbLMtBPkiw5FmQggyXzrckQYz8ZvaZUW0gzww3PJwMZbMtger5ljL8WNhwP0xlMzycDGSxl/HWw4ViQwfx8MtiVwRLGXwcbjgUZyGDLfFsynILRpRRnSpGBDOYzmJ4v2VmU/EufuQym55OBDJ0yWNZR9FNmZzA9nwyWZaCfJFlyLMhABkvmW5Mhxn4yt5Tq61funBLPx9Yt3Sm3sUVO34CR+WQgg20ZTM+XpNolO6QjESOzu2SonyQ7jofpDKbnk4EMJ7Kqo+injM9gej4Z7MpAP7Wy4ViQgQy2zLclQ6z9xIXOAQAAAAAA/s7x9tLbGc3o2/cAAAAAAABMmnjomGZvrdEl+46q9HBYWVGpySdtHxDS2qI+enp8vioLsk3H7JVYSiEhZcPGa8zgUeqf3U8Nxxq148Bubd671bP5kUPHFDncJDVHpaBP/gFZ8lMSAGS+nyQ6CkDX6CcAtrKhn0xkGFXTpAXl1ZpUfVTNjhSceI40fZyUk6Os+nqdvW2bxlVW6LYtNXqrsI/umFao3flZKc2UaVhKIWbZwZCuO/8Lum3yTZpYfGanP6+sek+Pv/mUnn/3ZR1rDid9vtsSVfOOOoU3H1b0k87P7zstpFDZAAXH5MoJ8M5UIJOY7ieJjgLQNfoJgK1s6CeTGa5/v1aPle+XPyskzZmj4F13Seee2+l+wU2bpEce0UUrlmvd8t26fVqhnh+bm9QsmYylFGJyxukj9cLcJ1U8sEhRN9rlfc4aWqoFsx/QvTPm6toFX9cHhz5M2vxITZMaV34kt777K/hHPwnraPl+HXvnE/W7erj8bLCBjGC6nyQ6CkDX6CcAtrKhn0xmuP79Wj3+WrU0Zox8r74qjRghRbvOoLIyafFiBe6/X77p0/XEazvlyNVzY/OSkiXT8U8hOKUzTh+p1+9ZrqL8wXIcR36fv8v7+X1+OY6jovzBeuN7KzS6YERS5kdqmtTw3B65DbF9pKTb0KKG5/YoUtOUlPkA7GW6nyQ6CkDX6CcAtrKhn0xmGF3TpMfK97cupNatk4YOlRxH8nedQX5/658PHSrf+vVSSYkeK9+vUXRlUrCUwkllB0N6Ye6Tygn1U8Af24l1AX9AOaF+evGORcoOhno0322JqnHlR1JTVIr1AxBcSU2tj3Nbutl2A0h7pvtJoqMAdI1+AmArG/rJdIZHy6vlywq1niGVkyMFg7E9MBiUcnLke+01+bNCWlBe3aMcaBXXUupHP/qRHMfpcBs3blyqssEC153/BRUPLIq5LNoE/AENHzRUXzpvRo/mN++oaz3dPN5P5HQlt75FzTvqejQf6YN+yjym+0mioxA7Oiqz0E9IJ/RTZrGhn0xmmHjwmCZVH1Xwxq+0vmUv1oVUm2BQGjlSgS/fqEnVRzXx0LGEs6BV3GdKTZgwQdXV1e23P//5z6nIBUvcNvmmbt/feyqRaES3Tb6pR/PDmw8bfTzSC/2UWUz3k0RHIT50VOagn5Bu6KfMYUM/mcwwe1uNmh1Jd93V/TWkThkiIt15p5odafbWmoSzoFXcFzoPBAIaMmRIKrLAMmXDxnf5CQix8vv8Omf4BJUNG5/QR3lGDh3r8hNi4hH9JKzIoWN81HGGoJ8yh+l+kugoxI+Oygz0E9IR/ZQZbOgn0xku2XdUwYnndPkpe7GH8Evnn6/gxHN0cYKvAz4V95lSO3bsUFFRkUaPHq3Zs2fro48+Oun9w+Gw6urqOtyQHsYMHpWU5ylJ8Hkih5Nz4Tgu1pk56KfMYbqfJDoK8Yuno+in9EU/IR3RT5nBhn4ynaH0cFhK1ttTS0s17m89+wcAxLmUuuiii7R06VK98sorWrhwoXbv3q1LL71U9fX13T5m/vz5ysvLa78VFxf3ODS80T+7X1KeJyfR52lO0gU2m7hQZyagnzKL8X6S6CjEJd6Oop/SF/2EdEM/ZQ4b+slkBsd1lRVV68XNkyE3V1nR1udF4uJaSs2YMUM33HCDysrKdMUVV+jll19WTU2Nnn322W4fM2/ePNXW1rbfqqqqehwa3mg41piU56lP9HmCSfpwyCw+ZDIT0E+ZxXg/SXQU4hJvR9FP6Yt+QrqhnzKHDf1kMoPrOGrySTrJP1rHpa5OTb7W50Xi4r6m1PHy8/M1duxY7dy5s9v7hEIhhUI9/9hIeG/Hgd3JeZ79HyT0OP+ArKTM9+cn53mQXuin3s10P0l0FHrmVB1FP6Uv+gnpjn7qvWzoJ9MZtg8I6ext25KSQdu2adtA/rfQUz3654+Ghgbt2rVLhYWFycoDi2zeu1WVVe8pEo0k9PhINKKKj/5XWz5O7H/0/oJs+U7r2f/IfaeFuEBnhqKfejfT/STRUegZOqr3op+Q7uin3suGfjKdYW1RHzVXVkibNrV+il5CISLSu++qeXOl1hX2Sew50C6updQ999yjN998U3v27NHbb7+tL37xi/L7/frqV7+aqnww7PE3n5LPSWx36ff59fibT/VofqhsgNHHI33QT5nHdD9JdBRiR0dlFvoJ6YR+yiw29JPJDE+Pz1fQlfTII5IvwXN0/H7p0UcVdFufDz0T11HYu3evvvrVr6q0tFRf/vKXNWjQIK1bt04FBQWpygfDnn/3ZVX9bZ9aIi1xPa4l0qKP/u9j/e4vq3o0PzgmV05OQIr3bbqO5OQEFByT26P5SB/0U+Yx3U8SHYXY0VGZhX5COqGfMosN/WQyQ2VBtt4q7KOWFculDz+Umpvje4LmZmnPHrU8u0JvFfZRJWeU9lhc15Ravnx5qnLAUseaw/riY9/Q6/csV06onwL+U//ItERaVB9u1LULvq5jzT37iEwn4FO/q4er4bk9rZ8AE8sHGziSslof5wS4QGemoJ8yj+l+kugoxI6Oyiz0E9IJ/ZRZbOgn0xnumFaodct3y3f55fKtW9f6aXzB4Kkf2Nws1dcrOn26Ik1h3TFtVI9yoBX/3wantOvgHl320I3aV3NAkrp9/2/b9/fVHNBlD92oDw59mJT5/vws9b9+pJz+se1Qnf4B9b9+JBfnBDKA6X6S6CgAXaOfANjKhn4ymWF3fpZun1Yo7dih6EUXSR9//Pdh3Vxjqu37H3/cev+dO3X7tELtpiuTokefvofM8cGhD/WZn16lL503Q7dNvknnDJ/Q6T5b9m7T428+pd/9ZVVSNujH8+dnKecfR6t5R53Cmw8r+knn5/edFlKobEDr6er86x6QMUz3k0RHAega/QTAVjb0k8kMz4/NlSNXj5Xvkv/M8Qp8+Ubpzjul88/vfOeKCunRR9Xy7ApFmsK6fXqRnh/LW5yThaUUYnasOaxn1r+oZ9a/qLJh41UyeJRysvup/lijdh7Yrc17t6Z0vhPwKWt8vrLG5yty6JgiNU2tp6Nn+eTPz+ITYoAMZrqfJDoKQNfoJwC2sqGfTGZ4bmye3j29jxaUV2vSsmVq/vUyBSeeI5WWSrm5Ul2dtH27misrFHSldUV9dOfUUZwhlWQspZCQzXu3elJS3fEXZPMfUAC6ZLqfJDoKQNfoJwC2sqGfTGTYnZ+lq740QhMPHdPsrTW6eO9Wjdtcoayo1OSTtg0Mad1Z+Xp6fD4XNU8RllIAAAAAACBjVRZkq7JgSPvXjuvKdeL9+FIkgjeNAwAAAAAA/B0LKe+wlAIAAAAAAIDnHNd1XS8H1tXVKS8vr3V4P+/fPegeaZFcSY7k9DXz7kUykMGmDKbnS5Lb2CJJqq2tVW6uuU+yMN1PkiXHg59JMpChYwYLOop+IoMt88lgWQb6SZIlx4IMZLBkvjUZYuwno9eUagtpZrjh+WQgg20ZTM+3jPHXwobjYTqD6flkIIOljL8ONhwLMpifTwa7MljC+Otgw7EgAxlsmW9LhlMwupTiTCkykMF8BtPzJTuLkn/pM5fB9HwykKFTBss6in7K7Aym55PBsgz0kyRLjgUZyGDJfGsyxNhP5pZSff3KnVPi+di6pTvlNrbI6RswMp8MZLAtg+n5klS7ZId0JGJkdpcM9ZNkx/EwncH0fDKQ4URWdRT9lPEZTM8ng10Z6KdWNhwLMpDBlvm2ZIi1n7jQOQAAAAAAADzHUgoAAAAAAACeYykFAAAAAAAAz7GUAgAAAAAAgOdYSgEAAAAAAMBzLKUAAAAAAADgOZZSAAAAAAAA8FzcS6mPP/5YN910kwYNGqQ+ffro7LPP1saNG1ORDQDiQj8BsBkdBcBW9BMAUwLx3Pnw4cOaNGmSpk6dqlWrVqmgoEA7duzQgAEDUpUPAGJCPwGwGR0FwFb0EwCT4lpK/fznP1dxcbGWLFnS/r1Ro0YlPRQAxIt+AmAzOgqAregnACbF9fa9lStX6oILLtANN9yg008/Xeeee66eeOKJVGUDgJjRTwBsRkcBsBX9BMCkuJZSH3zwgRYuXKgxY8boD3/4g7797W/rrrvu0rJly7p9TDgcVl1dXYcbACQb/QTAZvF2FP0EwCv0EwCT4nr7XjQa1QUXXKAHH3xQknTuuefqr3/9q371q1/p5ptv7vIx8+fP149//OOeJwWAk6CfANgs3o6inwB4hX4CYFJcZ0oVFhbqzDPP7PC98ePH66OPPur2MfPmzVNtbW37raqqKrGkAHAS9BMAm8XbUfQTAK/QTwBMiutMqUmTJmn79u0dvvf+++9rxIgR3T4mFAopFAollg4AYkQ/AbBZvB1FPwHwCv0EwKS4zpT67ne/q3Xr1unBBx/Uzp079cwzz+jxxx/X3LlzU5UPAGJCPwGwGR0FwFb0EwCT4lpKXXjhhXrhhRf0m9/8RmeddZZ+8pOf6OGHH9bs2bNTlQ8AYkI/AbAZHQXAVvQTAJPievueJM2cOVMzZ85MRRYA6BH6CYDN6CgAtqKfAJgS15lSAAAAAAAAQDKwlAIAAAAAAIDnWEoBAAAAAADAcyylAAAAAAAA4DmWUgAAAAAAAPAcSykAAAAAAAB4jqUUAAAAAAAAPOe4rut6ObCurk55eXmtw/sFvBwtSXKPtEiuJEdy+no/nwxksC2D6fmS5Da2SJJqa2uVm5trJINkvp8kS44HP5NkIEPHDBZ0FP1EBlvmk8GyDPSTJEuOBRnIYMl8azLE2E9m0v1dW0gzww3PJwMZbMtger5ljL8WNhwP0xlMzycDGSxl/HWw4ViQwfx8MtiVwRLGXwcbjgUZyGDLfFsynILRpRRnSpGBDOYzmJ4v2VmU/EufuQy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      "text/plain": [
       "<Figure size 1200x600 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def single_turn(\n",
    "    current_boards: np, policy: GamePolicy\n",
    ") -> tuple[np.ndarray, np.ndarray]:\n",
    "    \"\"\"Execute a single turn on a board.\n",
    "\n",
    "    Places a new stone on the board. Turns captured enemy stones.\n",
    "\n",
    "    Args:\n",
    "        current_boards: The current board before the game.\n",
    "        policy: The game policy to be used.\n",
    "\n",
    "    Returns:\n",
    "        The new game board and the policy vector containing the index of the action used.\n",
    "    \"\"\"\n",
    "    policy_results = policy.get_policy(current_boards)\n",
    "\n",
    "    # if the constant VERIFY_POLICY is set to true the policy is verified. Should be good though.\n",
    "    # todo deactivate the policy verification after some testing.\n",
    "    if VERIFY_POLICY:\n",
    "        assert np.all(moves_possible(current_boards, policy_results)), (\n",
    "            current_boards[(moves_possible(current_boards, policy_results) == False)],\n",
    "            policy_results[(moves_possible(current_boards, policy_results) == False)],\n",
    "            np.where(moves_possible(current_boards, policy_results) == False),\n",
    "        )\n",
    "    return do_moves(current_boards, policy_results), policy_results\n",
    "\n",
    "\n",
    "%timeit single_turn(get_new_games(EXAMPLE_STACK_SIZE), RandomPolicy(1))\n",
    "VERIFY_POLICY = False  # type: ignore\n",
    "%timeit single_turn(get_new_games(EXAMPLE_STACK_SIZE), RandomPolicy(1))\n",
    "VERIFY_POLICY = True  # type: ignore\n",
    "_turn_result = single_turn(get_new_games(EXAMPLE_STACK_SIZE), RandomPolicy(1))\n",
    "plot_othello_boards(_turn_result[0][:8], _turn_result[1][:8])\n",
    "del _turn_result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simulate a stack of games\n",
    "This function will simulate a stack of games and return an array of policies and histories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9tKnpfpIsOR88J8lAhvYZLOgo+okMtswng2UZ6CdJlpwLMpDBkvnWZIixn4ze6Lw1pJnhhueTgQy2ZTA93zLGj4UN58N0BtPzyUAGSxk/DjacCzKYn08GuzJYwvhxsOFckIEMtsy3JUM3jC5K8UopMpDBfAbT8yU7i5J/6TOXwfR8MpChQwbLOop+yuwMpueTwbIM9JMkS84FGchgyXxrMsTYT+YWpfr6VXDLWM/H1j6yRW5Di5y+ASPzyUAG2zKYni9JNYsqpENhI7M7ZaifJDvOh+kMpueTgQzHsqqj6KeMz2B6PhnsykA/RdlwLshABlvm25Ih1n7iRucAAAAAAADwHItSAAAAAAAA8ByLUgAAAAAAAPCc0RudAz0V3t+o8MEmqTkiZfnkL8qWvzjH0wxlQ0s1bvAo5eXkqr6xQRX7tqm8cpOnGQDYyXRH0U8AumJDP9iQAYB9bOgG07/DZSIWpZA23JaImitqFSo/qMiBUIef+wYGFSwrUta4AjmB1LwIMCcrqGvPuEyzp8zSpGEndfj5hl3va8HqxVq+7hk1NnfMCKD3Mt1R9BOArtjQDzZkAGAfG7rB9O9wmY5FKaSFcHWTGp7aKbeu64+VjBwI6fCqKjWuPaDcK4bLX5id1AxjBo3UijkPa1j/EkXcSKePOeXECZo/8x7dNX2Orpp/q7bu35HUDADsZLqj6CcAXbGhH2zIAMA+NnSD6d/hwD2lkAbC1U2qf3y73Pqui+Jobn2L6h/frnB1U9IyjBk0Ui98b6lKCgfLcRz5ff5OH+f3+eU4jkoKB+vFO5dpdPGIpGUAYCfTHUU/AeiKDf1gQwYA9rGhG0z/DoeouBalHnroIZWVlamgoEAFBQU699xztXLlylRlA+S2RNTw1E6pKSK5sW4kqSm6ndvS+Yp7PHKyglox52HlB3MV8Mf24sKAP6D8YK6evH2hcrKCCWdA9+gnmGC6o+in9EFHwWs29IMNGdA9+gles6EbTP8OhyPiWpQaOnSo7r33Xq1bt05vvfWWpk2bpiuvvFLvvfdeqvIhwzVX1EZfShlrUbRyJbeuRc0VtQlnuPaMyzSsf0nMhdkq4A9o+IATdc3p0xPOgO7RTzDBdEfRT+mDjoLXbOgHGzKge/QTvGZDN5j+HQ5HxLUodfnll+uyyy7TuHHjNH78eN1zzz3Ky8vTG2+8kap8yHCh8oNGt5ek2VNmdfke5+6EI2HNnjIr4QzoHv0EE0x3FP2UPugoeM2GfrAhA7pHP8FrNnSD6d/hcESP7ykVDoe1dOlSNTQ06Nxzz01mJkBS9OM4O/v0g3hEDoQU3t/Y4+3LhpZq0rCTunyPc3f8Pr9OG36yyoaW9jgD4kc/wQumO4p+Sl90FFLNhn6wIQPiRz8h1WzoBtO/w6G9uD99b+PGjTr33HPV2NiovLw8rVixQied1PGjG1uFQiGFQkdOeG0tL3NDbMIHk3MDuXB1k/zFOT3adtzgUUnJMHbwKJVXbkrKvtA1+gleMt1R9FP6iaej6CckwoZ+sCEDYkc/wSs2dIPp3+HQXtyvlJowYYLWr1+vNWvW6O/+7u9088036/333+/y8fPmzVO/fv3avoYNG5ZQYGSQ5iTdPK6p5/vJy8lNSoT8JO0Hx0c/wVOGO4p+Sj/xdBT9hETY0A82ZEDs6Cd4xYpusODvmTgi7kWp7OxsjR07VmeccYbmzZunSZMm6T/+4z+6fPzcuXNVU1PT9rVr166EAiODZPX43aXtZfd8P/WNDUmJUJek/eD46Cd4ynBH0U/pJ56Oop+QCBv6wYYMiB39BK9Y0Q0W/D0TR8T99r1jRSKRdi/fPFYwGFQwyMe5In7+ouzk7Kew5/up2LctKRkqqrYmZT+ID/2EVDLdUfRT+jteR9FPSIQN/WBDBvQc/YRUsaEbTP8Oh/biWpSaO3eupk+fruHDh6uurk5LlizRyy+/rOeeey5V+ZDB/MU58g0MJnQTOt/AYELv8y2v3KQNu97XKSdO6NHN+MKRsDZWbtbG3Zt7nAGxoZ/gNdMdRT+lFzoKXrKhH2zIgNjQT/CSDd1g+nc4tBfX680+/vhjffnLX9aECRN0wQUXaO3atXruued00UUXpSofMlywrMjo9pK0YPVi+ZyevTTT7/NrwerFCWdA9+gnmGC6o+in9EFHwWs29IMNGdA9+gles6EbTP8OhyPieqXUwoULU5UD6FTWuAI1rj0gt75FcuPY0JGcvICyxhUknGH5umd01/Q5KikcrIA/9kumJdyiPdX79MTbKxPOgO7RTzDBdEfRT+mDjoLXbOgHGzKge/QTvGZDN5j+HQ5HcGcuWM0J+JR7xfDoTeScWDeSlB3dzgkk/hRvbA7p6ge/prpQg1rCLTFt0xJuUV2oQVfNv1WNzT1/WSgAu5nuKPoJQFds6AcbMgCwjw3dYPp3OBzBkYT1/IXZyrtupJy82FbRnbyA8q4bmdQbz3308XZdcN8N2lO9T1L0vcydaf3+nup9uuC+G7R1/46kZQBgJ9MdRT8B6IoN/WBDBgD2saEbTP8Oh6iEP30P8IK/MFv5N41Wc0WtQuUHO70pnW9gUMGyImWNK0jJyvXW/Tt01k9n6JrTp2v2lFk6bfjJHR6zsXKzFqxerCfeXsm/7gEZxHRH0U8AumJDP9iQAYB9bOgG07/DgUUppBEn4FN2aaGySwsV3t+ocHWT1BSRsn3yF2Z78ukHjc0hLVnzpJaseVJlQ0s1dvAo5efkqq6xQVv2bVN55aaUZwBgJ9MdRT8B6IoN/WBDBgD2saEbTP8Ol+lYlEJa8hfnGC+H8spN/AIFoFOmO4p+AtAVG/rBhgwA7GNDN5j+HS4T8dozAAAAAAAAeI5FKQAAAAAAAHiORSkAAAAAAAB4znFd1/VyYG1trfr16xcdnuv9La3cQy2SK8mRnL5mbqlFBjLYlMH0fElyG1okSTU1NSooKDCSQTLfT5Il54PnJBnI0D6DBR1FP5HBlvlksCwD/STJknNBBjJYMt+aDDH2k9EbnbeGNDPc8HwykMG2DKbnW8b4sbDhfJjOYHo+GchgKePHwYZzQQbz88lgVwZLGD8ONpwLMpDBlvm2ZOiG0UUpXilFBjKYz2B6vmRnUfIvfeYymJ5PBjJ0yGBZR9FPmZ3B9HwyWJaBfpJkybkgAxksmW9Nhhj7ydyiVF+/Cm4Z6/nY2ke2yG1okdM3YGQ+GchgWwbT8yWpZlGFdChsZHanDPWTZMf5MJ3B9HwykOFYVnUU/ZTxGUzPJ4NdGeinKBvOBRnIYMt8WzLE2k/c6BwAAAAAAACeY1EKAAAAAAAAnmNRCgAAAAAAAJ4zeqPzdBTe36jwwSapOSJl+eQvypa/OIcMHmcwPZ8MdmVAlA3nggx2ZDA9nww4lg3nggxRZUNLNW7wKOXl5Kq+sUEV+7apvHKTpxlsOA6mM5iej/ZMnw/T88lABpMZWJSKgdsSUXNFrULlBxU5EOrwc9/AoIJlRcoaVyAnkJoXn5HB/Hwy2JUBUTacCzLYkcH0fDLgWDacCzJE5WQFde0Zl2n2lFmaNOykDj/fsOt9LVi9WMvXPaPG5o4Zk8GG42A6g+n5aM/0+TA9nwxksCUDi1LdCFc3qeGpnXLruv44w8iBkA6vqlLj2gPKvWK4/IXZZEhyBtPzyWBXBkTZcC7IYEcG0/PJgGPZcC7IEDVm0EitmPOwhvUvUcSNdPqYU06coPkz79Fd0+foqvm3auv+HUnNYMNxMJ3B9Hy0Z/p8mJ5PBjLYlIEl+OMIVzep/vHtcuu7PjFHc+tbVP/4doWrm8iQxAym55PBrgyIsuFckMGODKbnkwHHsuFckCFqzKCReuF7S1VSOFiO48jv83f6OL/PL8dxVFI4WC/euUyji0ckLYMNx8F0BtPz0Z7p82F6PhnIYFuGhBal7r33XjmOo29/+9tJCWMTtyWihqd2Sk0RyY11I0lN0e3cls7/JYoM6TWfDHZliAf9dOxG6nXPBzKYn0+GnqGfjt1Ive75YEOGnKygVsx5WPnBXAX8sb05IuAPKD+YqydvX6icrGDCGWw4DqYzmJ7fE3TUsRupVz0fyEAG2zL0eFFq7dq1+s///E+VlZUlHMJGzRW10ZeuxXpiWrmSW9ei5opaMiQhg+n5ZLArQ6zopy70sucDGczPJ0P86Kcu9LLngw0Zrj3jMg3rXxLzglSrgD+g4QNO1DWnT084gw3HwXQG0/PjRUd1oRc9H8hABtsy9GhRqr6+XjNnztRvf/tbFRUVJRzCRqHyg0a3J4Md25PBrgyxoJ9Svz0Z7MlgensyxId+Sv32ZDhi9pRZXd5DqjvhSFizp8xKOIMNx8F0BtPbx4OO6v3bk4EMNmbo0aLUnDlzNGPGDF144YUJB7BReH9jp3ebj0fkQEjh/Y1kSCCD6flksCtDrOin7vWG5wMZzM8nQ/zop+71hueDDRnKhpZq0rCTuryHVHf8Pr9OG36yyoaW9jiDDcfBdAbT8+NFR3Uv3Z8PZCCDbRmkHnz63tKlS/X2229r7dq1MT0+FAopFDryH1pb6+1LUHsifDB5N7HzF+eQoYcZTM8ng10ZYkE/xbGfNH8+kMH8fDLEh36KYz9p/nywIcO4waOSkmHs4FEqr9zUo21tOA6mM5ieH494Oiod+0kyfz5MzycDGWzMIMX5Sqldu3bpW9/6lh599FHl5MQ2dN68eerXr1/b17Bhw3oU1FPNSbqhYFMC+yGD+flksCtDN+inOKX784EM5ueTIWb0U5zS/flgQYa8nNykRMhPZD8WHAfjGUzPj1G8HZWW/SSZPx+m55OBDDZmUJyLUuvWrdPHH3+s008/XYFAQIFAQKtXr9YDDzygQCCgcDjcYZu5c+eqpqam7WvXrl0JBfZEVkIfSnhEdgL7IYP5+WSwK0M36Kc4pfvzgQzm55MhZvRTnNL9+WBBhvrGhqREqEtkPxYcB+MZTM+PUbwdlZb9JJk/H6bnk4EMNmZQnG/fu+CCC7Rx48Z23/vKV76iiRMn6q677pLf3/F968FgUMFg4h8p6yV/UXZy9lPY8/2Qwfx8MtiVoTv0U5z7SfPnAxnMzydD7OinOPeT5s8HGzJU7NuWlAwVVVt7vK0Nx8F0BtPzYxVvR6VjP0nmz4fp+WQgg40ZpDgXpfLz83XKKae0+15ubq4GDBjQ4fvpzF+cI9/AYEI3/fINDCb0vkoymJ9PBrsydId+il1veD6Qwfx8MsSOfopdb3g+2JChvHKTNux6X6ecOKFHNzsPR8LaWLlZG3dv7nEGG46D6Qym58eKjopduj8fyEAG2zJIPfz0vUwQLEvsY1AT3Z4MdmxPBrsyIMqGc0EGOzKY3p4MOJYN54IMUQtWL5bP6dmv+n6fXwtWL044gw3HwXQG09ujPdPnw/T2ZCCDjRkSXpR6+eWXdf/99yccxDZZ4wrk5AckJ84NHcnJDyhrXAEZkpDB9Hwy2JUhXvTTMXrZ84EM5ueToefop2P0sueDDRmWr3tGuz7Zo5ZwS1zbtYRbtPOvu/XE2ysTzmDDcTCdwfT8nqKjjtGLng9kIINtGXilVBecgE+5VwyP3rQr1hPkSMqObucEEj+0ZDA/nwx2ZUCUDeeCDHZkMD2fDDiWDeeCDFGNzSFd/eDXVBdqiHlhqiXcorpQg66af6sam3v+do5WNhwH0xlMz0d7ps+H6flkIIONGWi54/AXZivvupFy8mK79ZaTF1DedSOTejNCMpifTwa7MiDKhnNBBjsymJ5PBhzLhnNBhqiPPt6uC+67QXuq90mK3iuqM63f31O9Txfcd4O27t+RtAw2HAfTGUzPR3umz4fp+WQgg20Z4rrReSbyF2Yr/6bRaq6oVaj8YKc3AfMNDCpYVhR96VsK/jWDDObnk8GuDIiy4VyQwY4MpueTAcey4VyQIWrr/h0666czdM3p0zV7yiydNvzkDo/ZWLlZC1Yv1hNvr0zKK6SOZcNxMJ3B9Hy0Z/p8mJ5PBjLYlIFFqRg4AZ+ySwuVXVqo8P5GhaubpKaIlO2TvzA75Z+IQQY75pPBrgyIsuFckMGODKbnkwHHsuFckCGqsTmkJWue1JI1T6psaKnGDh6l/Jxc1TU2aMu+bSqv3JTyDDYcB9MZTM9He6bPh+n5ZCCDLRlYlIqTvzjH+P8wyGB+PhnsyoAoG84FGezIYHo+GXAsG84FGaLKKzd5sgh1PDYcB9MZTM9He6bPh+n5ZCCDyQy8LhQAAAAAAACeY1EKAAAAAAAAnmNRCgAAAAAAAJ5zXNd1vRxYW1urfv36RYfnen9LK/dQi+RKciSnr5lbapGBDDZlMD1fktyGFklSTU2NCgoKjGSQzPeTZMn54DlJBjK0z2BBR9FPZLBlPhksy0A/SbLkXJCBDJbMtyZDjP1k9EbnrSHNDDc8nwxksC2D6fmWMX4sbDgfpjOYnk8GMljK+HGw4VyQwfx8MtiVwRLGj4MN54IMZLBlvi0ZumF0UYpXSpGBDOYzmJ4v2VmU/EufuQym55OBDB0yWNZR9FNmZzA9nwyWZaCfJFlyLshABkvmW5Mhxn4ytyjV16+CW8Z6Prb2kS1yG1rk9A0YmU8GMtiWwfR8SapZVCEdChuZ3SlD/STZcT5MZzA9nwxkOJZVHUU/ZXwG0/PJYFcG+inKhnNBBjLYMt+WDLH2Ezc6BwAAAAAAgOdYlAIAAAAAAIDnWJQCAAAAAACA54ze6BzpK7y/UeGDTVJzRMryyV+ULX9xTsbMl6SyoaUaN3iU8nJyVd/YoIp921ReucnTDDYcB8A2NlwXpjPQT4CdbLgubOgHGzLYcC4Am9hwTdjQDTZksOFceIlFKcTMbYmouaJWofKDihwIdfi5b2BQwbIiZY0rkBNI/ovwTM+XpJysoK494zLNnjJLk4ad1OHnG3a9rwWrF2v5umfU2NwxYzLYcBwA29hwXZjOQD8BdrLhurChH2zIYMO5AGxiwzVhQzfYkMGGc2EKi1KISbi6SQ1P7ZRb1/XHOkYOhHR4VZUa1x5Q7hXD5S/M7jXzJWnMoJFaMedhDetfoogb6fQxp5w4QfNn3qO7ps/RVfNv1db9O5KawYbjANjGhuvCdAb6CbCTDdeFDf1gQwYbzgVgExuuCRu6wYYMNpwLk3rXEhtSIlzdpPrHt8ut7/oiOZpb36L6x7crXN3UK+ZL0bJ64XtLVVI4WI7jyO/zd/o4v88vx3FUUjhYL965TKOLRyQtgw3HAbCNDdeF6Qz0E2AnG64LG/rBhgw2nAvAJjZcEzZ0gw0ZbDgXpsW1KPXDH/5QjuO0+5o4cWKqssECbktEDU/tlJoikhvrRpKaotu5LZ2vNqfLfCn6cs4Vcx5WfjBXAX9sLy4M+APKD+bqydsXKicrmHAGG46D7einzGPDdWE6A/2UPuiozGLDdWFDP9iQwYZzYTv6KbPYcE3Y0A02ZLDhXNgg7ldKnXzyydq7d2/b1yuvvJKKXLBEc0Vt9GWEsV4krVzJrWtRc0VtWs+XpGvPuEzD+pfEXFatAv6Ahg84UdecPj3hDDYch3RAP2UWG64L0xnop/RCR2UOG64LG/rBhgw2nIt0QD9lDhuuCRu6wYYMNpwLG8S9KBUIBHTCCSe0fQ0cODAVuWCJUPnBjN5ekmZPmdXl+4u7E46ENXvKrIQz2HAc0gH9lFlsuC5MZ6Cf0gsdlTlsuC5s6AcbMthwLtIB/ZQ5bLgmbOgGGzLYcC5sEPeiVEVFhUpKSjR69GjNnDlTO3fuTEUuWCC8v7HTO//HI3IgpPD+xrScL0U/EnTSsJO6fH9xd/w+v04bfrLKhpb2OIMNxyFd0E+Zw4brwnQG+in90FGZwYbrwoZ+sCGDDeciXdBPmcGGa8KGbrAhgw3nwhZxLUqdffbZeuSRR/Tss8/qoYce0rZt2/Q3f/M3qqur63KbUCik2tradl9ID+GDybtReTrOl6Rxg0clJcPYBPZjw3FIB/RTZrHhujCdgX5KL/F2FP2Uvmy4LmzoBxsy2HAu0gH9lDlsuCZs6AYbMthwLmwR1xsop08/8r7JsrIynX322RoxYoQee+wx3XrrrZ1uM2/ePP3oRz9KLCXMaE7SjdOaergf0/Ml5eXkJiVCfiL7seA4pAP6KcPYcF0YzkA/pZd4O4p+SmMWXBc29IMNGWw4F+mAfsogFlwTNnSDDRlsOBe2iPvte0crLCzU+PHjtWXLli4fM3fuXNXU1LR97dq1K5GR8FJWQk+PI7J7uB/T8yXVNzYkJUJdIvux4DikI/qpl7PhujCcgX5Kb911FP2Uxiy4LmzoBxsy2HAu0hH91ItZcE3Y0A02ZLDhXNgiof+C+vp6ffTRRxoyZEiXjwkGgyooKGj3hfTgL8pOzn4Ke7Yf0/MlqWLftqRkqKja2uNtbTgO6Yh+6t1suC5MZ6Cf0lt3HUU/pS8brgsb+sGGDDaci3REP/VeNlwTNnSDDRlsOBe2iGtR6nvf+55Wr16t7du367XXXtPVV18tv9+vG2+8MVX5YJC/OEe+gcGE9uEbGJS/OCct50tSeeUmbdj1vsKRcI+2D0fCWr/zPW3cvbnHGWw4DumAfsosNlwXpjPQT+mFjsocNlwXNvSDDRlsOBfpgH7KHDZcEzZ0gw0ZbDgXtohrUaqyslI33nijJkyYoOuvv14DBgzQG2+8oeLi4lTlg2HBsqKM3l6SFqxeLJ/TsxcV+n1+LVi9OOEMNhwH29FPmceG68J0BvopfdBRmcWG68KGfrAhgw3nwnb0U2ax4ZqwoRtsyGDDubBBXGdh6dKl2rNnj0KhkCorK7V06VKNGTMmVdlggaxxBXLyA5IT54aO5OQHlDUusZfzmp4vScvXPaNdn+xRS7glru1awi3a+dfdeuLtlQlnsOE42I5+yjw2XBemM9BP6YOOyiw2XBc29IMNGWw4F7ajnzKLDdeEDd1gQwYbzoUN0v+uWEgpJ+BT7hXDozdQi/VicSRlR7dzAok9xUzPl6TG5pCufvBrqgs1xFxaLeEW1YUadNX8W9XYHEo4gw3HAbCNDdeF6Qz0E2AnG64LG/rBhgw2nAvAJjZcEzZ0gw0ZbDgXNugd/xVIKX9htvKuGyknLxDT4528gPKuG5m0m66Zni9JH328XRfcd4P2VO+TpC7ff9z6/T3V+3TBfTdo6/4dSctgw3EAbGPDdWE6A/0E2MmG68KGfrAhgw3nArCJDdeEDd1gQwYbzoVpsf2XI+P5C7OVf9NoNVfUKlR+UJEDHVeGfQODCpYVRV+GmORVW9PzJWnr/h0666czdM3p0zV7yiydNvzkDo/ZWLlZC1Yv1hNvr0zK6vmxbDgOgG1suC5MZ6CfADvZcF3Y0A82ZLDhXAA2seGasKEbbMhgw7kwiUUpxMwJ+JRdWqjs0kKF9zcqXN0kNUWkbJ/8hdkpv/O/6flS9GWeS9Y8qSVrnlTZ0FKNHTxK+Tm5qmts0JZ921ReuSnlGWw4DoBtbLguTGegnwA72XBd2NAPNmSw4VwANrHhmrChG2zIYMO5MIVFKfSIvzjH6IVher4U/ShRLwrqeGw4DoBtbLguTGegnwA72XBd2NAPNmSw4VwANrHhmrChG2zIYMO58FLvet0XAAAAAAAA0gKLUgAAAAAAAPAci1IAAAAAAADwnOO6ruvlwNraWvXr1y86PNf7W1q5h1okV5IjOX3N3FKLDGSwKYPp+ZLkNrRIkmpqalRQUGAkg2S+nyRLzgfPSTKQoX0GCzqKfiKDLfPJYFkG+kmSJeeCDGSwZL41GWLsJ6M3Om8NaWa44flkIINtGUzPt4zxY2HD+TCdwfR8MpDBUsaPgw3nggzm55PBrgyWMH4cbDgXZCCDLfNtydANo4tSvFKKDGQwn8H0fMnOouRf+sxlMD2fDGTokMGyjqKfMjuD6flksCwD/STJknNBBjJYMt+aDDH2k7lFqb5+Fdwy1vOxtY9skdvQIqdvwMh8MpDBtgym50tSzaIK6VDYyOxOGeonyY7zYTqD6flkIMOxrOoo+injM5ieTwa7MtBPUTacCzKQwZb5tmSItZ+40TkAAAAAAAA8x6IUAAAAAAAAPMeiFAAAAAAAADxn9EbnSF/h/Y0KH2ySmiNSlk/+omz5i3M8m182tFTjBo9SXk6u6hsbVLFvm8orN3k2XzJ/DCQ7jgNgG65NjgFgK67NKI4DYB+uyyiOg/dYlELM3JaImitqFSo/qMiBUIef+wYGFSwrUta4AjmB5L8ILycrqGvPuEyzp8zSpGEndfj5hl3va8HqxVq+7hk1NnfMlwymj4Fkx3EAbMO1yTEAbMW1GcVxAOzDdRnFcTCLRSnEJFzdpIandsqt6/pjHSMHQjq8qkqNaw8o94rh8hdmJ23+mEEjtWLOwxrWv0QRN9LpY045cYLmz7xHd02fo6vm36qt+3ckbb5k/hhIdhwHwDZcmxwDwFZcm1EcB8A+XJdRHAfzuKcUuhWublL949vl1nd9oR7NrW9R/ePbFa5uSsr8MYNG6oXvLVVJ4WA5jiO/z9/p4/w+vxzHUUnhYL145zKNLh6RlPmS+WMg2XEcANtwbXIMAFtxbUZxHAD7cF1GcRzsEPei1O7duzVr1iwNGDBAffr00amnnqq33norFdlgAbclooandkpNEcmNdSNJTdHt3JbOV3pjlZMV1Io5Dys/mKuAP7YX9gX8AeUHc/Xk7QuVkxVMaL5k/hhIdhyHdEA/ZRauTY5BuqGjMgfXZhTHIX3QT5mD6zKK42CPuBalDh48qMmTJysrK0srV67U+++/r5///OcqKipKVT4Y1lxRG30pY6wXaitXcuta1FxRm9D8a8+4TMP6l8R8kbYK+AMaPuBEXXP69ITmS+aPgWTHcbAd/ZR5uDY5BumEjsosXJtRHIf0QD9lFq7LKI6DPeJalPrXf/1XDRs2TIsWLdJZZ52lUaNG6eKLL9aYMWNSlQ+GhcoPGt1+9pRZXb6vtjvhSFizp8xKaL5k/hhIdhwH29FPmYdrk2OQTuiozMK1GcVxSA/0U2bhuoziONgjrkWpp556Sp/97Gf1xS9+UYMGDdJnPvMZ/fa3v01VNhgW3t/Y6acPxCNyIKTw/sYebVs2tFSThp3U5ftqu+P3+XXa8JNVNrS0R9tL5o+BZMdxSAf0U2bh2uQYpBs6KnNwbUZxHNIH/ZQ5uC6jOA52iWtRauvWrXrooYc0btw4Pffcc/q7v/s73XHHHfr973/f5TahUEi1tbXtvpAewgeTcwO3nt4IbtzgUUmZPzaB/Zg+BpIdxyEd0E+ZhWuTY5Bu4u0o+il9cW1GcRzSB/2UObguozgOdonrzYuRSESf/exn9bOf/UyS9JnPfEbvvvuufvOb3+jmm2/udJt58+bpRz/6UeJJ4b3mxG/eJil687geyMvJTcr4/ET2Y/gYSJYchzRAP2UYrk2OQZqJt6PopzTGtRnFcUgb9FMG4bqM4jhYJa5XSg0ZMkQnnXRSu++VlpZq586dXW4zd+5c1dTUtH3t2rWrZ0nhvay4P5yxc9k92099Y0NSxtclsh/Dx0Cy5DikAfopw3BtcgzSTLwdRT+lMa7NKI5D2qCfMgjXZRTHwSpxvVJq8uTJ+uCDD9p978MPP9SIESO63CYYDCoY7B0fVZhp/EXZydlPYc/2U7FvW1LmV1Rt7fG2po+BZMdxSAf0U2bh2uQYpJt4O4p+Sl9cm1Ech/RBP2UOrssojoNd4lra+853vqM33nhDP/vZz7RlyxYtWbJECxYs0Jw5c1KVDwb5i3PkG5jY/3B8A4PyF+f0aNvyyk3asOt9hSPhHm0fjoS1fud72rh7c4+2l8wfA8mO45AO6KfMwrXJMUg3dFTm4NqM4jikD/opc3BdRnEc7BLXotSZZ56pFStW6A9/+INOOeUU/eQnP9H999+vmTNnpiofDAuWFRndfsHqxfI5PXtZpN/n14LVixOaL5k/BpIdx8F29FPm4drkGKQTOiqzcG1GcRzSA/2UWbguozgO9oj7CHzhC1/Qxo0b1djYqE2bNunrX/96KnLBElnjCuTkByQnzg0dyckPKGtcQULzl697Rrs+2aOWcEtc27WEW7Tzr7v1xNsrE5ovmT8Gkh3HIR3QT5mFa5NjkG7oqMzBtRnFcUgf9FPm4LqM4jjYI0l3+EJv5QR8yr1iePQmbrFesI6k7Oh2TiCxp1hjc0hXP/g11YUaYr5YW8Itqgs16Kr5t6qxOZTQfMn8MZDsOA6Abbg2OQaArbg2ozgOgH24LqM4DvZgUQrd8hdmK++6kXLyYrsvvpMXUN51IxO68dvRPvp4uy647wbtqd4nSV2+77b1+3uq9+mC+27Q1v07kjJfMn8MJDuOA2Abrk2OAWArrs0ojgNgH67LKI6DHeL69D1kLn9htvJvGq3milqFyg8qcqDjqqxvYFDBsqLoSyGTsHJ8tK37d+isn87QNadP1+wps3Ta8JM7PGZj5WYtWL1YT7y9MiWrxqaPgWTHcQBsw7XJMQBsxbUZxXEA7MN1GcVxMI9FKcTMCfiUXVqo7NJChfc3KlzdJDVFpGyf/IXZCX36QCwam0NasuZJLVnzpMqGlmrs4FHKz8lVXWODtuzbpvLKTSmdL5k/BpIdxwGwDdcmxwCwFddmFMcBsA/XZRTHwSwWpdAj/uIcTy7OrpRXbjJ+YZo+BpIdxwGwDdcmxwCwFddmFMcBsA/XZRTHwXvcUwoAAAAAAACeY1EKAAAAAAAAnmNRCgAAAAAAAJ5zXNd1vRxYW1urfv36RYfnen9LK/dQi+RKciSnr5lbapGBDDZlMD1fktyGFklSTU2NCgoKjGSQzPeTZMn54DlJBjK0z2BBR9FPZLBlPhksy0A/SbLkXJCBDJbMtyZDjP1k9EbnrSHNDDc8nwxksC2D6fmWMX4sbDgfpjOYnk8GMljK+HGw4VyQwfx8MtiVwRLGj4MN54IMZLBlvi0ZumF0UYpXSpGBDOYzmJ4v2VmU/EufuQym55OBDB0yWNZR9FNmZzA9nwyWZaCfJFlyLshABkvmW5Mhxn4ytyjV16+CW8Z6Prb2kS1yG1rk9A0YmU8GMtiWwfR8SapZVCEdChuZ3SlD/STZcT5MZzA9nwxkOJZVHUU/ZXwG0/PJYFcG+inKhnNBBjLYMt+WDLH2Ezc6BwAAAAAAgOdYlAKQFI63n5kAAAAAAEhzRu8pBSB9TdrfqJmbqnXunsOacDCk7IjU5JM+KArq9ZI+erS0UBuKc0zHBAAAAABYikUp9EjZ0FKNGzxKeTm5qm9sUMW+bSqv3JQx8zM5w6jqJs1ftVeT9x5WsyNlTTpNumiilJ+v7Lo6nbp5syZuWK/ZG6v16pA+un3aEG0rzE5pJuBomXpt2jRfksL7GxU+2CQ1R6Qsn/xF2fJ7vFBtw3EAjmbDc5IMUaY7yoZjABzNhuckGaJM95Pk7XFgUQoxy8kK6tozLtPsKbM0adhJHX6+Ydf7WrB6sZave0aNzaFeN58M0nUf1ujBVVXyZwelW25R1h13SJ/5TIfHZb3zjvTAAzp72VK9sXSbbps2RMvHFyQ1C3C0TL82bZgvSW5LRM0VtQqVH1TkQMcZvoFBBcuKlDWuQE4gNXcQsOE4AEez4TlJhijTHWXDMQCOZsNzkgxRpvtJMnccuKcUYjJm0Ei9efefNH/mPTrlxAmdPuaUEydo/sx79Obdf9Lo4hG9aj4ZogtSC57fq6zRYxV4f5P0u99JZWWdP7isTPrd7xR4f5OyRo3Rb5/fo+s+rElaFuBomX5t2jBfksLVTapbslWHV1V1+suUJEUOhHR4VZXqlmxVuLop6RlsOA7A0Wx4TpIhynRH2XAMgKPZ8JwkQ5TpfpLMHgcWpdCtMYNG6oXvLVVJ4WA5jiO/z9/p4/w+vxzHUUnhYL1457KkPVFNzyeDNLq6SQ+uqpLGjZPvjTekE0+UHEfyd55Bfn/05yeeKN+aNdLYsXpwVZVGpaBAkdky/dq0Yb4U/WWq/vHtcutbYnq8W9+i+se3J/WXKhuOA3A0G56TZIgy3VE2HAPgaDY8J8kQZbqfJPPHIa5FqZEjR8pxnA5fc+bMSUoY2CcnK6gVcx5WfjBXAX9s7/YM+APKD+bqydsXKicrmNbzyRD1q1V75csOyvfnP0v5+VJWVmwbZmVJ+fnyPf+8/NlBzV+1N6Ec3aGjMovp68KGDKbnS9GXmzc8tVNqikixfginK6kpup3bEkk4gw3HoTv0U2ax4TlJhijTHWXDMegO/ZRZbHhOkiHKdD9JdhyHuBal1q5dq71797Z9Pf/885KkL37xiwkHgZ2uPeMyDetfEvMTtFXAH9DwASfqmtOnp/V8MkiTPm7U5L2HlXXD/5NGjIh9QapVVpY0cqQC19+gyXsPa9L+xh5n6Q4dlVky/dq0Yb4kNVfUyq1rif2XqVau5Na1qLmiNuEMNhyH7tBPmcWG5yQZokx3lA3HoDv0U2ax4TlJhijT/STZcRziWpQqLi7WCSec0Pb19NNPa8yYMZoyZUrCQWCn2VNmKeL2bAU2HAlr9pRZaT2fDNLMzdVqdiTdcYcU6eFqfDgsffObanakmZuqe5ylO3RUZsn0a9OG+ZIUKj9odHvJjuPQHfops9jwnCRDlOmOsuEYdId+yiw2PCfJEGW6nyQ7jkOP7ynV1NSkxYsX66tf/aocx0k4COxTNrRUk4ad1OV7Srvj9/l12vCTVTa0NC3nkyHq3D2HlTXptOin7HV1D6luQ/ilM85Q1qTTdM7ewz3bR5zoqN7N9HVhQwbT86XoRxZ3dUPOWEUOhBRO4BWUNhyHeNFPvZsNz0kyRJnuKBuOQbzop97NhuckGaJM95Nkx3GQEliUevLJJ1VdXa1bbrnluI8LhUKqra1t94X0MG7wqKTsZ2wP92N6PhmiJhwMSRMnJiWDJkzQxE+8+YjjWDqKfkpfpq8LGzKYni9J4YPJuclmIjfrtOE4xIt+6t1seE6SIcp0R9lwDOJFP/VuNjwnyRBlup8kO46DlMCi1MKFCzV9+nSVlJQc93Hz5s1Tv3792r6GDRvW05HwWF5OblL2k9/D/ZieTwbJcV1lRxS9uXkyFBQoOxLdb6rF0lH0U/rK9GvThvmSpObEb7ApKXqDzx6y4jjEiX7q3Wx4TpLhU4Y7yopjECf6qXez4TlJhk/xO1SbHi1K7dixQy+88IK+9rWvdfvYuXPnqqampu1r165dPRkJA+obG5Kyn7oe7sf0fDJIruOoySepri4pGVRbqyZfdL+pFGtH0U/pK9OvTRvmS5KyevxvW+1l93w/VhyHONBPvZ8Nz0kyfMpwR1lxDOJAP/V+NjwnyfApfodqE98t1j+1aNEiDRo0SDNmzOj2scFgUMFg6j/KFMlXsW9bcvZTtTUt55Mh6oOioE7dvDkpGbR5szb3T30fxNpR9FP6Mn1d2JDB9HxJ8hdlJyWDv7Dn+7HhOMSDfur9bHhOkiHKdEfZcAziQT/1fjY8J8kQZbqfJDuOg9SDV0pFIhEtWrRIN998swKBHq1pIU2UV27Shl3vKxwJ92j7cCSs9Tvf08bdPVvQMD2fDFGvl/RR84b10jvvRD9Fr0chwtK6dWou36A3hvTp2T5iREdlBtPXhQ0ZTM+XJH9xjnwDE/uLiW9gUP7inB5vb8NxiBX9lBlseE6SIcp0R9lwDGJFP2UGG56TZIgy3U+SHcdB6sGi1AsvvKCdO3fqq1/9akKDkR4WrF4sn9OzlwT6fX4tWL04reeTQXq0tFBZrqQHHpB8PXx5qN8v/epXynKj+0slOipzZPq1acN8SQqWFRndXrLjOMSCfsocNjwnyRBluqNsOAaxoJ8yhw3PSTJEme4nyY7jEPf0iy++WK7ravz48QkPh/2Wr3tGuz7Zo5ZwS1zbtYRbtPOvu/XE2yvTej4ZpA3FOXp1SB+1LFsq7dghNTfHt4PmZmn7drU8tkyvDumjDQms5seCjsocmX5t2jBfkrLGFcjJD0jx3irOkZz8gLLGFSScwYbjEAv6KXPY8JwkQ5TpjrLhGMSCfsocNjwnyRBlup8kO45Dku6uhd6qsTmkqx/8mupCDTE/UVvCLaoLNeiq+beqsTmU1vPJEHX7tCEKN4UUufji6E3PY12Yam6W6uoUuegihZtCun3akIRyAEczfV3YkMH0fElyAj7lXjE8eqPNWH+pciRlR7dzAon/KmLDcQCOZsNzkgxRpjvKhmMAHM2G5yQZokz3k2THcWBRCt366OPtuuC+G7Snep8kdfme09bv76nepwvuu0Fb9+/oFfPJIG0rzNZt04ZIFRWKnH22tHv3p8O6eP9x6/d3744+fssW3TZtiLYlcCM+oDOZfm3aMF+K3mQz77qRcvJiuw+JkxdQ3nUjE7o557FsOA7A0Wx4TpIhynRH2XAMgKPZ8JwkQ5TpfpLMHwfuYoeYbN2/Q2f9dIauOX26Zk+ZpdOGn9zhMRsrN2vB6sV64u2VSf9XHdPzySAtH18gR64eXPWR/CeVKnD9DdI3vymdcUbHB69fL/3qV2p5bJnCTSHddlGJlo9P/OWlQGcy/dq0Yb4U/aUq/6bRaq6oVaj8oCIHOs7wDQwqWFYUfbl6Ev5171g2HAfgaDY8J8kQZbqjbDgGwNFseE6SIcp0P0lmjwOLUohZY3NIS9Y8qSVrnlTZ0FKNHTxK+Tm5qmts0JZ921ReualXzyeD9Pj4flo3qI/mr9qryb//vZr/6/fKmnSaNGGCVFAg1dZKH3yg5g3rleVKb5T00TenjuIVUki5TL82bZgvRV+Gnl1aqOzSQoX3Nypc3SQ1RaRsn/yF2Ql9QkysbDgOwNFseE6SIcp0R9lwDICj2fCcJEOU6X6SzB0HFqXQI+WVm4z+j9P0/EzOsK0wWzOuGaFJ+xs1c1O1zqncpInl65UdkZp80ub+Qb1xSqEeLS1M+U3Ngc5k6rVp03wp+lHHXvwCdTw2HAfgaDY8J8kQZbqjbDgGwNFseE6SIcp0P0neHgcWpQD0yIbiHG0oPqHtz47rynXi/egIAAAAAECm4kbnAJKCBSkAAAAAQDxYlAIAAAAAAIDnHNd1XS8H1tbWql+/ftHhud6/e9A91CK5khzJ6Wvm3YtkIINNGUzPlyS3oUWSVFNTo4ICc5/SZ7qfJEvOB89JMpChfQYLOop+IoMt88lgWQb6SZIl54IMZLBkvjUZYuwno/eUag1pZrjh+WQgg20ZTM+3jPFjYcP5MJ3B9HwykMFSxo+DDeeCDObnk8GuDJYwfhxsOBdkIIMt823J0A2ji1K8UooMZDCfwfR8yc6i5F/6zGUwPZ8MZOiQwbKOop8yO4Pp+WSwLAP9JMmSc0EGMlgy35oMMfaTuUWpvn4V3DLW87G1j2yR29Aip2/AyHwykMG2DKbnS1LNogrpUNjI7E4Z6ifJjvNhOoPp+WQgw7Gs6ij6KeMzmJ5PBrsy0E9RNpwLMpDBlvm2ZIi1n7jROQAAAAAAADzHohQAAOjVHG8/0wUAAAAxMnpPKQAAgGSbtL9RMzdV69w9hzXhYEjZEanJJ31QFNTrJX30aGmhNhTnmI4JAACQ8ViUQloK729U+GCT1ByRsnzyF2XL7/FfMMqGlmrc4FHKy8lVfWODKvZtU3nlJk8zALCT6Y7K1H4aVd2k+av2avLew2p2pHddabmkOkn5EWniX0P6yichzd5YrVeH9NHt04ZoW2G26diAp0z3k5S5HXU0G84DYBsbrgv6KcrLc8GiFNKG2xJRc0WtQuUHFTkQ6vBz38CggmVFyhpXICeQmnem5mQFde0Zl2n2lFmaNOykDj/fsOt9LVi9WMvXPaPG5o4ZAfRepjsq0/vpug9r9OCqKjWFXS2S9IArre/kcae50h2Srtt7WG8s3abbpg3R8vEF3oYFPGa6nyQ6SrLjPAC2seG6oJ+iTJ0LFqWQFsLVTWp4aqfcuq4/VjJyIKTDq6rUuPaAcq8YLn+S//V7zKCRWjHnYQ3rX6KIG+n0MaecOEHzZ96ju6bP0VXzb9XW/TuSmgGAnUx3VKb303Uf1mjB83v1oaRLJe1Q1zfNLJf0VUk/kvRc2NVvn98jR64eH9/Po7SAt0z3k0RHSXacB8A2NlwX9FOUyXPBEjysF65uUv3j2+XWd32BHM2tb1H949sVrm5KWoYxg0bqhe8tVUnhYDmOI7/P3+nj/D6/HMdRSeFgvXjnMo0uHpG0DADsZLqjMr2fRlc36cFVVfpQ0nmSKj/9fue/Vh75fqWkcyVVSHpwVZVGJfH/GYAtTPeTREdJdpwHwDY2XBf0U5Tpc8GiFKzmtkTU8NROqSkixfrhSa6kpuh2bktXfy2JXU5WUCvmPKz8YK4C/theXBjwB5QfzNWTty9UTlYw4QwA7GS6o+gn6Ver9ioUdnWppFpJ4Ri3C3/6+EskNYVdzV+1N1URASNM95NER0l2nAfANjZcF/RTlA3nIq5FqXA4rH/+53/WqFGj1KdPH40ZM0Y/+clP5PJRy0iR5ora6EsI432KuZJb16LmitqEM1x7xmUa1r8k5rJqFfAHNHzAibrm9OkJZ0D36CeYYLqjMr2fJn3cqMl7D2u5om/Zi3VBqlX40+2WS5q897Am7W9MdsQjs+goeMx0P0l0lGTHeegO/QSv2XBd0E9RNpyLuBal/vVf/1UPPfSQ5s+fr02bNulf//Vf9W//9m/61a9+lXAQoDOh8oNGt5ek2VNmdfn+4u6EI2HNnjIr4QzoHv0EE0x3VKb308zN1Wp2pAfU85d++xTdvtmRZm6qTlq2Y9FR8JrpfpLoKMmO89Ad+gles+G6oJ+ibDgXcS0Lvvbaa7ryyis1Y8YMSdLIkSP1hz/8QW+++WbCQYBjhfc3dnrX/3hEDoQU3t/Y44+vLBta2uknMMTK7/PrtOEnq2xoaUZ+lKiX6Cd4zXRH0U/SuXsO690uPmUvVhFJ70h615XO2Xs4OcE6QUfBS6b7SaKjJDvOQyzoJ3jJhuuCfoqy4VxIcf7D4nnnnacXX3xRH374oSRpw4YNeuWVVzR9etcvXQuFQqqtrW33BcQifDA5N05L5AZs4waPSkqGsUnaD7pGP8FrpjuKfpImHAxpc5L29YGkiZ+k7mOe4+0o+gmJMN1PEh0l2XEeYkE/wUs2XBf0U5QN50KK85VS3//+91VbW6uJEyfK7/crHA7rnnvu0cyZM7vcZt68efrRj36UUEhkqOYk3dixqef7ycvJTUqE/CTtB12jn+A5wx2V6f3kuK6yI1JdkvZXKyk7Et2v6zhJ2usR8XYU/YSE8DuUHSw4D7Ggn+ApC64L+ulTFpwLKc5XSj322GN69NFHtWTJEr399tv6/e9/r3//93/X73//+y63mTt3rmpqatq+du3alVBgZJCsJH04ZHbP91Pf2JCUCHVJ2g+6Rj/Bc4Y7KtP7yXUcNfmk/CTtr0BSk08pWZCS4u8o+gkJ4XcoO1hwHmJBP8FTFlwX9NOnLDgXUpyvlLrzzjv1/e9/X//v//0/SdKpp56qHTt2aN68ebr55ps73SYYDCoY7B0flwhv+Yuyk7Ofwp7vp2LftqRkqKjampT9oGv0E7xmuqPoJ+mDoqAm/jU5b7mbKGlz/9T1QbwdRT8hEab7SaKjJDvOQyzoJ3jJhuuCfoqy4VxIcb5S6tChQ/L52m/i9/sViaT2JaXITP7iHPkGJvY/PN/AYEI3XSuv3KQNu95XOBLvB41HhSNhrd/5njbuTtZdT9AV+gleM91R9JP0ekkfneJIpymxT9/7jKSTHemNIX2Slu1YdBS8ZLqfJDpKsuM8xIJ+gpdsuC7opygbzoUU5+9wl19+ue655x796U9/0vbt27VixQr94he/0NVXX51QCKArwbIio9tL0oLVi+VzevbXHb/PrwWrFyecAd2jn2CC6Y7K9H56tLRQWa50h6KfotcTEUW3z3Kj+0sVOgpeM91PEh0l2XEeukM/wWs2XBf0U5QN5yKus/CrXwOic0QAAQAASURBVP1K1113nW677TaVlpbqe9/7nv72b/9WP/nJTxIOAnQma1yBnPyAFO8tPhzJyQ8oa1xBwhmWr3tGuz7Zo5ZwS1zbtYRbtPOvu/XE2ysTzoDu0U8wwXRHZXo/bSjO0atD+ug6SSMk+ePc3v/pdtdKenVIH21I4asR6Ch4zXQ/SXSUZMd56A79BK/ZcF3QT1E2nIu4FqXy8/N1//33a8eOHTp8+LA++ugj/fSnP1V2dmrf54zM5QR8yr1iePTmabFeKI6k7Oh2TiDxm7c1Nod09YNfU12oIebSagm3qC7UoKvm36rG5tR9xDiOoJ9ggumOop+k26cNUbbf0bOK3qw81oUp/6ePf05Stt/R7dOGpCqiJDoK3jPdTxIdJdlxHrpDP8FrNlwX9FOUDeci9S0HJMhfmK2860bKyYvtvvxOXkB5141M6k0hP/p4uy647wbtqd4nSV2+/7j1+3uq9+mC+27Q1v07kpYBgJ1Md1Sm99O2wmzdNm2Ixkt6XdLQT7/f1S84rd8f+unjx0m6bdoQbUvxjYQBE0z3k0RHSXacB8A2NlwX9FOU6XMR16fvAab4C7OVf9NoNVfUKlR+UJEDHVemfQODCpYVRV+CmIJ/Vdq6f4fO+ukMXXP6dM2eMkunDT+5w2M2Vm7WgtWL9cTbK3vN6jmA7pnuqEzvp+XjC+TI1YOrqrQx7Gq5pAckvdPJYycpeg+paxV9hdTXpw3R8vGpf3sMYIrpfpLoKMmO8wDYxobrgn6KMnkuWJRC2nACPmWXFiq7tFDh/Y0KVzdJTREp2yd/YXbKP5lEir7Mc8maJ7VkzZMqG1qqsYNHKT8nV3WNDdqyb5vKKzelPAMAO5nuqEzvp8fH99O6QX00f9Ve3bL3sGY60ruu9IGkWkXfqjdB0ilO9Kbmr5T00Ten8gopZAbT/STRUZId5wGwjQ3XBf0UZepcsCiFtOQvzjH+P+7yyk0ZU1AA4mO6ozK1n7YVZmvGNSM0aX+jZm6q1jl7D+uaT0LKjkhNPmlz/6AWDemjR0sLU3pTc8BmpvtJytyOOpoN5wGwjQ3XBf0U5eW5YFEKAAD0KhuKc7Sh+IS2PzuuK9eJ92NlAAAAkGq8WRkAAPRqLEgBAADYiUUpAAAAAAAAeM5xXdf1cmBNTY0KCwujf+jr93J01KGjPubRxHwykMG2DKbnH5Whurpa/fr1M5NBFvSTZNX5MJbB9HwykKGLDCY7in4igzXzyWBlBvrJnnNBBjIYn29Zhu76yfN7StXV1R35w9EHygTT88lABtsyGJ5fV1dndFHKqn4igx3zyUCGo5jsKPqJDFbOJ4M1Geino5CBDDZlMD3fggzd9ZPnr5SKRCLas2eP8vPz5fTgHg+1tbUaNmyYdu3apYKCghQkJEO6ZDA9nwzJy+C6rurq6lRSUiKfz9y7iuknMvSmDKbn96YMNnRUov0kmT8fpueTgQy2ZaCfjjB9LmzIYHo+GciQ7Ayx9pPnr5Ty+XwaOnRowvspKCgwdnLIYFcG0/PJkJwMJl8h1Yp+IkNvzGB6fm/JYLqjktVPkvnzYXo+GchgWwb66QjT58KGDKbnk4EMycwQSz9xo3MAAAAAAAB4jkUpAAAAAAAAeC7tFqWCwaB+8IMfKBgMkiHDM5ieTwa7MtjAhuNABjLYMp8M9jF9LEzPJwMZbMtger5NbDgWpjOYnk8GMpjK4PmNzgEAAAAAAIC0e6UUAAAAAAAA0h+LUgAAAAAAAPAci1IAAAAAAADwHItSAAAAAAAA8FxaLUq9/vrr8vv9mjFjhuezb7nlFjmO0/Y1YMAAXXrppSovL/c8S1VVlb75zW9q9OjRCgaDGjZsmC6//HK9+OKLKZ999HHIysrS4MGDddFFF+l3v/udIpFIyucfm+Hor0svvdST+d3l2LJliyfzq6qq9K1vfUtjx45VTk6OBg8erMmTJ+uhhx7SoUOHUj7/lltu0VVXXdXh+y+//LIcx1F1dXXKM9iGjqKfjs1hqqNM95NktqPop47oJ/rp2Bz0E79D2YJ+op+OzUE/ZVY/pdWi1MKFC/XNb35Tf/nLX7Rnzx7P51966aXau3ev9u7dqxdffFGBQEBf+MIXPM2wfft2nXHGGVq1apXuu+8+bdy4Uc8++6ymTp2qOXPmeJKh9Ths375dK1eu1NSpU/Wtb31LX/jCF9TS0uJphqO//vCHP3gyu7sco0aNSvncrVu36jOf+Yz+/Oc/62c/+5neeecdvf766/qHf/gHPf3003rhhRdSngEdZXpH0U8dc5jsKFP9JNFRNqKf6Kdjc9BP9JMt6Cf66dgc9FNm9VPAdIBY1dfXa9myZXrrrbdUVVWlRx55RP/4j//oaYZgMKgTTjhBknTCCSfo+9//vv7mb/5G+/fvV3FxsScZbrvtNjmOozfffFO5ublt3z/55JP11a9+1ZMMRx+HE088UaeffrrOOeccXXDBBXrkkUf0ta99zdMMJpnKcdtttykQCOitt95q9zwYPXq0rrzySrmu63mmTEdH0U9d5TDFZAY6yi70E/3UVQ5T6Ce0op/op65ymEI/eS9tXin12GOPaeLEiZowYYJmzZql3/3ud0ZPSn19vRYvXqyxY8dqwIABnsz85JNP9Oyzz2rOnDntnqStCgsLPcnRmWnTpmnSpEl64oknjGXIFH/961/15z//ucvngSQ5juNxKmR6R9FPaEVH2Yd+op8QRT/Zh36inxCVyf2UNotSCxcu1KxZsyRFX1JXU1Oj1atXe5rh6aefVl5envLy8pSfn6+nnnpKy5Ytk8/nzWHcsmWLXNfVxIkTPZkXr4kTJ2r79u2ezDr6XLR+/exnP/Nk9vFyfPGLX0z5zNbnwYQJE9p9f+DAgW057rrrrpTnkDo/D9OnT/dktm0yvaPop/Zs6CgT/STZ01H00xH0E/10NPrJfD9JdFQr+ol+Ohr9lJn9lBZv3/vggw/05ptvasWKFZKkQCCgG264QQsXLtT555/vWY6pU6fqoYcekiQdPHhQv/71rzV9+nS9+eabGjFiRMrn2/5yPdd1PVu9PfpctOrfv78ns4+Xo6tVbS+8+eabikQimjlzpkKhkCczOzsPa9asafvlIlPQUfTTsWzoKJv6SfK+o+inKPqJfjoW/dQRv0OZQT/RT8einzrKhH5Ki0WphQsXqqWlRSUlJW3fc11XwWBQ8+fPV79+/TzJkZubq7Fjx7b9+eGHH1a/fv3029/+Vj/96U9TPn/cuHFyHEebN29O+aye2LRpk2c3gTv2XJhiIsfYsWPlOI4++OCDdt8fPXq0JKlPnz6eZensv7+ystKz+bago+inY9nQUaYy2NJR9FMU/UQ/HYt+Mt9PEh0l0U8S/XQs+ikz+8n6t++1tLTov/7rv/Tzn/9c69evb/vasGGDSkpKjHziWivHceTz+XT48GFP5vXv31+XXHKJHnzwQTU0NHT4ucmPj121apU2btyoa6+91liGTDFgwABddNFFmj9/fqfPA3iLjoqin9CKjrIH/RRFP6EV/WQP+imKfkKrTO4n618p9fTTT+vgwYO69dZbO6yWX3vttVq4cKG+8Y1veJIlFAqpqqpKUvSlnfPnz1d9fb0uv/xyT+ZL0oMPPqjJkyfrrLPO0o9//GOVlZWppaVFzz//vB566CFt2rQp5Rlaj0M4HNa+ffv07LPPat68efrCF76gL3/5yymff3SGowUCAQ0cONCT+ab9+te/1uTJk/XZz35WP/zhD1VWViafz6e1a9dq8+bNOuOMM0xHzBh01BH0U8ccR6Oj6Civ0U9H0E8dcxyNfqKfvEY/HUE/dcxxNPopA/rJtdwXvvAF97LLLuv0Z2vWrHEluRs2bEh5jptvvtmV1PaVn5/vnnnmme7jjz+e8tnH2rNnjztnzhx3xIgRbnZ2tnviiSe6V1xxhfvSSy+lfPbRxyEQCLjFxcXuhRde6P7ud79zw+Fwyucfm+HorwkTJngy/+gcV155paczj7Znzx739ttvd0eNGuVmZWW5eXl57llnneXed999bkNDQ8rnd/Xf/9JLL7mS3IMHD6Y8gw3oqPYyvZ+OzWGqo0z3k+ua7Sj6KYp+ao9+op9a8TuUefRTe/QT/dQqE/vJcV3L764GAAAAAACAXsf6e0oBAAAAAACg92FRCgAAAAAAAJ5jUQoAAAAAAACeY1EKAAAAAAAAnmNRCgAAAAAAAJ5jUQoAAAAAAACeY1EKAAAAAAAAnmNRCgAAAAAAAJ5jUQoAAAAAAACeY1EqzfzmN79Rfn6+Wlpa2r5XX1+vrKwsnX/++e0e+/LLL8txHH300UeSpAULFuj8889XQUGBHMdRdXV1l3NCoZBOO+00OY6j9evXx5Rt1KhReuGFF477GNd1NX36dDmOoyeffLLdz3bu3KkZM2aob9++GjRokO688852/51dOXz4sHJzc7Vly5ZOf+44TttXIBDQ8OHD9d3vflehUKjtMXv37tVNN92k8ePHy+fz6dvf/nan+/qf//kfTZw4UTk5OTr11FP1zDPPdJsPyGRedNaf/vQnnX322erTp4+Kiop01VVXxZRt6tSpevjhhzv92Q9/+ENNnDhRubm5Kioq0oUXXqg1a9a0e8zbb7+tiy66SIWFhRowYIBmz56t+vr6hGeff/757Xpr8ODB+uIXv6gdO3a0e9yLL76o8847T/n5+TrhhBN01113xdSZQKZKdR9dccUVGj58uHJycjRkyBB96Utf0p49e2LKdrzfof72b/9WY8aMUZ8+fVRcXKwrr7xSmzdvbveYtWvX6oILLlBhYaGKiop0ySWXaMOGDQnPHjlyZFsX+f1+lZSU6NZbb9XBgwfbHtPY2KhbbrlFp556qgKBQKcd/Morr2jy5MkaMGCA+vTpo4kTJ+qXv/xlTPmATNBbf19qzdrZ19q1a7ud7UU/xfr3QKQOi1JpZurUqaqvr9dbb73V9r3/+7//0wknnKA1a9aosbGx7fsvvfSShg8frjFjxkiSDh06pEsvvVT/+I//2O2cf/iHf1BJSUnMucrLy3Xw4EFNmTLluI+7//775ThOh++Hw2HNmDFDTU1Neu211/T73/9ejzzyiP7lX/6l29nPP/+8RowYobFjx3b5mEWLFmnv3r3atm2bfv3rX+u///u/9dOf/rTt56FQSMXFxbr77rs1adKkTvfx2muv6cYbb9Stt96qd955R1dddZWuuuoqvfvuu91mBDJVqjtr+fLl+tKXvqSvfOUr2rBhg1599VXddNNN3eb65JNP9Oqrr+ryyy/v9Ofjx4/X/PnztXHjRr3yyisaOXKkLr74Yu3fv1+StGfPHl144YUaO3as1qxZo2effVbvvfeebrnlloRnS9LXv/517d27V3v27NEf//hH7dq1S7NmzWr7+YYNG3TZZZfp0ksv1TvvvKNly5bpqaee0ve///1u5wOZKtV9NHXqVD322GP64IMPtHz5cn300Ue67rrrus3V3e9QZ5xxhhYtWqRNmzbpueeek+u6uvjiixUOhyVF/+J66aWXavjw4VqzZo1eeeUV5efn65JLLlFzc3NCsyXpxz/+sfbu3audO3fq0Ucf1V/+8hfdcccdbT8Ph8Pq06eP7rjjDl144YWd7iM3N1e33367/vKXv2jTpk26++67dffdd2vBggXdHR4gI/TW35fOO+887d27t93X1772NY0aNUqf/exnjzvbq36K5e+BSDEXaWfIkCHuvHnz2v78D//wD+6cOXPc0tJS96WXXmr7/uc//3n35ptv7rD9Sy+95EpyDx482On+n3nmGXfixInue++950py33nnnW4z/fjHP3ZvuOGG4z7mnXfecU888UR37969riR3xYoV7Wb6fD63qqqq7XsPPfSQW1BQ4IZCoePu96tf/ap71113dfnzY2e5ruveeuut7mWXXdbp46dMmeJ+61vf6vD966+/3p0xY0a775199tnu3/7t3x43H5DpUtVZzc3N7oknnug+/PDDcWf6r//6L/fss8+O+fE1NTWuJPeFF15wXdd1//M//9MdNGiQGw6H2x5TXl7uSnIrKioSmt1ZB/33f/+327dv37Y/z5071/3sZz/b7jFPPfWUm5OT49bW1sb6nwVknFT/DnW0P/7xj67jOG5TU9NxHxfL71BH27BhgyvJ3bJli+u6rrt27VpXkrtz5862x8TaR93NHjFihPvLX/6y3fd+8pOfuCeddFKnj7/55pvdK6+8Mqb/jquvvtqdNWtWTI8FMkFv/H3pWE1NTW5xcbH74x//uNt9meinrv4eiNTilVJpaOrUqXrppZfa/vzSSy/p/PPP15QpU9q+f/jwYa1Zs0ZTp06Na9/79u3T17/+df33f/+3+vbtG/N2Tz31lK688souf37o0CHddNNNevDBB3XCCSd0+Pnrr7+uU089VYMHD2773iWXXKLa2lq99957Xe43Eono6aefPu7sY3344YdatWqVzj777Ji3ac147Ar7JZdcotdffz2u/QCZJlWd9fbbb2v37t3y+Xz6zGc+oyFDhmj69OkxvXqxu846WlNTkxYsWKB+/fq1/QtaKBRSdna2fL4j/xvt06ePpOjbVJI1W4r+K+Vjjz3WrrNCoZBycnLaPa5Pnz5qbGzUunXrYt43kGlS+TvU0T755BM9+uijOu+885SVlXXcx8bTCQ0NDVq0aJFGjRqlYcOGSZImTJigAQMGaOHChWpqatLhw4e1cOFClZaWauTIkUmbLUm7d+/W//7v/8b9O9Sx3nnnHb322mvdvsIeyCS98felzvb317/+VV/5yleSOltKXj/BeyxKpaGpU6fq1VdfVUtLi+rq6vTOO+9oypQp+vznP6+XX35ZUnQBJRQKxVVYruvqlltu0Te+8Y1uX055tN27d6u8vFzTp0/v8jHf+c53dN5553VZLFVVVe0WpCS1/bmqqqrL/b7xxhuS1G353HjjjcrLy1NOTo4mTJigk08+WXPnzj3uNrFmPF4+AKnrrK1bt0qK3s/g7rvv1tNPP62ioiKdf/75+uSTT7rcLhQK6dlnn9UVV1xx3P0//fTTbb3xy1/+Us8//7wGDhwoSZo2bZqqqqp03333qampSQcPHmx769zevXsTnv3rX/9aeXl5ys3N1YABA/TBBx/od7/7XdvPL7nkEr322mv6wx/+oHA4rN27d+vHP/5xt/OBTJeqPmp11113tV23O3fu1B//+MfjPj6W36GkI52Ql5enlStX6vnnn1d2drYkKT8/Xy+//LIWL16sPn36KC8vT88++6xWrlypQCCQ8Oy77rpLeXl56tOnj4YOHSrHcfSLX/ziuNt0ZejQoQoGg/rsZz+rOXPm6Gtf+1qP9gP0Rr3x96VjLVy4UJdccomGDh163H2a6CeYw6JUGjr//PPV0NCgtWvX6v/+7/80fvx4FRcXa8qUKW3vOX755Zc1evRoDR8+POb9/upXv1JdXV3cizVPPfWUPve5z6mwsLDLn69atUr3339/XPuNxR//+Ed94QtfaPdqhc788pe/1Pr167VhwwY9/fTT+vDDD/WlL30p6XkAdJSqzopEIpKkf/qnf9K1117bdt8Vx3H0P//zP11ut2rVKg0aNEgnn3zycfc/depUrV+/Xq+99pouvfRSXX/99fr4448lSSeffLJ+//vf6+c//7n69u2rE044QaNGjdLgwYOP20exzp45c2ZbZ73yyisaO3asLr74YtXV1UmSLr74Yt133336xje+oWAwqPHjx+uyyy6TpG77EMhkqeqjVnfeeafeeecd/fnPf5bf79eXv/xlua7b5eO7+x2q1cyZM/XOO+9o9erVGj9+vK6//vq2e8wcPnxYt956qyZPnqw33nhDr776qk455RTNmDFDhw8fTnj2nXfeqfXr16u8vFwvvviiJGnGjBlt97SKx//93//prbfe0m9+8xvdf//9+sMf/hD3PoDeqjf+vnS0yspKPffcc7r11lu7zWyin2AOv7mmobFjx2ro0KF66aWX9NJLL7W99LmkpETDhg3Ta6+9ppdeeknTpk2La7+rVq3S66+/rmAwqEAg0Hbj8M9+9rO6+eabu9zuqaeeOu4K+qpVq/TRRx+psLBQgUCg7V/trr322rZPkzjhhBO0b9++dtu1/rmzt/vFOrvVCSecoLFjx2rChAmaMWOGfvSjH2nZsmVdfmJfV/voLOPx8gFIXWcNGTJEknTSSSe1fS8YDGr06NHauXNnl9vF2hu5ubkaO3aszjnnHC1cuFCBQEALFy5s+/lNN92kqqoq7d69W3/961/1wx/+UPv379fo0aMTnt2vXz+NHTtWY8eO1eTJk7Vw4UJVVFRo2bJlbY/57ne/q+rqau3cuVMHDhxoeyXq8eYDmS5VfdRq4MCBGj9+vC666CItXbpUzzzzTNurujsTTyeMGzdOn//85/X4449r8+bNWrFihSRpyZIl2r59uxYtWqQzzzxT55xzjpYsWaJt27Yd95Vasc4eOHCgxo4dq3HjxmnatGm6//77245TvEaNGqVTTz1VX//61/Wd73xHP/zhD+PeB9Bb9dbfl1otWrRIAwYMiGmfJvoJ5rAolaamTp2ql19+WS+//HK7jwn9/Oc/r5UrV+rNN9+M+2XnDzzwgDZs2KD169dr/fr1euaZZyRJy5Yt0z333NPpNvX19XrppZeO+37f73//+yovL2/b7/r16yVFX720aNEiSdK5556rjRs3tltVf/7551VQUNCuQI9WUVGhHTt26KKLLorrv1OS/H6/JB33XxCPde6557atwB+d8dxzz417PpBpUtFZZ5xxhoLBoD744IO27zU3N2v79u0aMWJEp9u4rqv//d//jeseBa0ikYhCoVCH7w8ePFh5eXlatmyZcnJyuuykRGZ31VmO46ikpER9+vTRH/7wBw0bNkynn3563PsHMkkq+qgzra9O6Kw3pNh+h+qM67pyXbdtv4cOHZLP52v36catf27NkKzZUs9+h+pMV50KZLLe+vuS67patGiRvvzlL3d7nz0b+gne6vqN5rDa1KlTNWfOHDU3N7e7SeSUKVN0++23q6mpqUNhVVVVqaqqqu3VQRs3blR+fr6GDx+u/v37d3gZaF5eniRpzJgxXb7v99lnn9X48eOPeyPNE044odNXEw0fPlyjRo2SFH0rykknnaQvfelL+rd/+zdVVVXp7rvv1pw5cxQMBjvd7x//+EddeOGFMd2Qvbq6WlVVVYpEIqqoqNCPf/xjjR8/XqWlpW2PaV0sq6+v1/79+7V+/XplZ2e3LYp961vf0pQpU/Tzn/9cM2bM0NKlS/XWW2/xccZADFLRWQUFBfrGN76hH/zgBxo2bJhGjBih++67T5L0xS9+sdMc69at06FDh/S5z32uy6wNDQ265557dMUVV2jIkCE6cOCAHnzwQe3evbvdfufPn6/zzjtPeXl5ev7553XnnXfq3nvv7fKl5rHMbnXo0KG2+9Xt27dPP/nJT5STk6OLL7647TH33XefLr30Uvl8Pj3xxBO699579dhjj7X9Qgagc6noozVr1mjt2rX63Oc+p6KiIn300Uf653/+Z40ZM6bLf7yK5XeorVu3atmyZbr44otVXFysyspK3XvvverTp0/bW3Yvuugi3XnnnZozZ46++c1vKhKJ6N5771UgEOjyL6+xzG5VV1enqqoqua6rXbt26R/+4R9UXFys8847r+0x77//vpqamvTJJ5+orq6u7Xeq0047TZL04IMPavjw4Zo4caIk6S9/+Yv+/d//vd1HtwPonb8vSdF3zmzbti2m+8h53U9S938PRIqZ+tg/JGbbtm2uJHfixIntvr99+3ZXkjthwoQO2/zgBz9wJXX4WrRo0XFnvPPOO13mmDVrlvtP//RPceeX5K5YsaJD9unTp7t9+vRxBw4c6P793/+929zc3OU+Pve5z7m//e1vY5rV+uU4jjtkyBD3hhtucD/66KMuH9f6NWLEiHaPeeyxx9zx48e72dnZ7sknn+z+6U9/ivm/GchkqeqspqYm9+///u/dQYMGufn5+e6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YzmB6PhnIcCyrOop+yvgMpueTwa4M9FOUDeeCDGSwZb4tGWLtJ250DgAAAAAAAM+xKAUAAAAAAADPsSgFAAAAAAAAzxm90TnQU2VDSzVu8Cjl5eSqvrFBFfu2qbxyk6cZwvsbFT7YJDVHpCyf/EXZ8hfneJoBgJ1MdxT9BKArpvtJoqMAdI5+ykwsSiFt5GQFde0Zl2n2lFmaNOykDj/fsOt9LVi9WMvXPaPG5lBKMrgtETVX1CpUflCRAx1n+AYGFSwrUta4AjkBXogIZBLTHUU/AeiK6X6S6CgAnaOfwKIU0sKYQSO1Ys7DGta/RBE30uljTjlxgubPvEd3TZ+jq+bfqq37dyQ1Q7i6SQ1P7ZRb1/VHW0YOhHR4VZUa1x5Q7hXD5S/MTmoGAHYy3VH0E4CumO4niY4C0Dn6CRL3lEIaGDNopF743lKVFA6W4zjy+/ydPs7v88txHJUUDtaLdy7T6OIRScsQrm5S/ePb5dZ3XVZHc+tbVP/4doWrm5KWAYCdTHcU/QSgK6b7SaKjAHSOfkKruBalHnroIZWVlamgoEAFBQU699xztXLlylRlA5STFdSKOQ8rP5irgD+2F/YF/AHlB3P15O0LlZMVTDiD2xJRw1M7paaI5Ma6kaSm6HZuS+er/kgu+gkmmO4o+il90FHwmul+kuiodEE/wWv0E44W16LU0KFDde+992rdunV66623NG3aNF155ZV67733UpUPGe7aMy7TsP4lMZdVq4A/oOEDTtQ1p09POENzRW305ZyxllUrV3LrWtRcUZtwBnSPfoIJpjuKfkofdBS8ZrqfJDoqXdBP8Br9hKPFtSh1+eWX67LLLtO4ceM0fvx43XPPPcrLy9Mbb7yRqnzIcLOnzOry/cXdCUfCmj1lVsIZQuUHjW6P2NBPMMF0R9FP6YOOgtdM95NER6UL+gleo59wtB7fUyocDmvp0qVqaGjQueeem8xMgKToR4JOGnZSl+8v7o7f59dpw09W2dDSHmcI72/s9BMY4hE5EFJ4f2NC+0B86Cd4wXRH0U/pi45CqpnuJ4mOSlf0E1KNfsKx4v70vY0bN+rcc89VY2Oj8vLytGLFCp10UsePbmwVCoUUCh054bW1vMwNsRk3eFRS9jN28CiVV27q0bbhg8m5iV24ukn+4pyk7Atdo5/gJdMdRT+ln3g6in5CIkz3k0RHpRv6CV6hn3CsuF8pNWHCBK1fv15r1qzR3/3d3+nmm2/W+++/3+Xj582bp379+rV9DRs2LKHAyBx5OblJ2U9+IvtpTtIN7Jq4EZ4X6Cd4yXhH0U9pJ56Oop+QCOP9JNFRaYZ+glfoJxwr7kWp7OxsjR07VmeccYbmzZunSZMm6T/+4z+6fPzcuXNVU1PT9rVr166EAiNz1Dc2JGU/dYnsJ6vH73BtLztJ+8Fx0U/wkvGOop/STjwdRT8hEcb7SaKj0gz9BK/QTzhW3G/fO1YkEmn38s1jBYNBBYOJf2QjMk/Fvm3J2U/V1h5v6y/KTkoGf2Fy9oP40E9IJdMdRT+lv+N1FP2ERJjuJ4mOSnf0E1KFfsKx4lqUmjt3rqZPn67hw4errq5OS5Ys0csvv6znnnsuVfmQwcorN2nDrvd1yokTenQjvHAkrI2Vm7Vx9+YeZ/AX58g3MJjQjfB8A4O819gD9BO8Zrqj6Kf0QkfBS6b7SaKj0gn9BC/RTzhWXK83+/jjj/XlL39ZEyZM0AUXXKC1a9fqueee00UXXZSqfMhwC1Yvls/p2csi/T6/FqxenHCGYFmR0e0RG/oJJpjuKPopfdBR8JrpfpLoqHRBP8Fr9BOOFtcrpRYuXJiqHECnlq97RndNn6OSwsEK+GN/uraEW7Snep+eeHtlwhmyxhWoce0BufUtkhvHho7k5AWUNa4g4QzoHv0EE0x3FP2UPugoeM10P0l0VLqgn+A1+glH485csFpjc0hXP/g11YUa1BJuiWmblnCL6kINumr+rWps7vlLMls5AZ9yrxgevZGdE+tGkrKj2zkBLjOgtzLdUfQTgK6Y7ieJjgLQOfoJR+NIwnoffbxdF9x3g/ZU75MUfR9xZ1q/v6d6ny647wZt3b8jaRn8hdnKu26knLzYVvKdvIDyrhvJze+ADGC6o+gnAF0x3U8SHQWgc/QTWiX86XuAF7bu36GzfjpD15w+XbOnzNJpw0/u8JiNlZu1YPViPfH2yqSsnh/LX5it/JtGq7miVqHyg53eGM83MKhgWZGyxhWweg5kENMdRT8B6IrpfpLoKACdo58gsSiFNNLYHNKSNU9qyZonVTa0VGMHj1J+Tq7qGhu0Zd82lVduSnkGJ+BTdmmhsksLFd7fqHB1k9QUkbJ98hdm8wkMQAYz3VH0E4CumO4niY4C0Dn6CSxKIS2VV27ypKCOx1+cQ0EB6JTpjqKfAHTFdD9JdBSAztFPmYnXngEAAAAAAMBzLEoBAAAAAADAcyxKAQAAAAAAwHOO67qulwNra2vVr1+/6PBc729p5R5qkVxJjuT0NXNLLTKQwaYMpudLktvQIkmqqalRQUGBkQyS+X6SLDkfPCfJQIb2GSzoKPqJDLbMJ4NlGegnSZacCzKQwZL51mSIsZ+M3ui8NaSZ4Ybnk4EMtmUwPd8yxo+FDefDdAbT88lABksZPw42nAsymJ9PBrsyWML4cbDhXJCBDLbMtyVDN4wuSvFKKTKQwXwG0/MlO4uSf+kzl8H0fDKQoUMGyzqKfsrsDKbnk8GyDPSTJEvOBRnIYMl8azLE2E/mFqX6+lVwy1jPx9Y+skVuQ4ucvgEj88lABtsymJ4vSTWLKqRDYSOzO2WonyQ7zofpDKbnk4EMx7Kqo+injM9gej4Z7MpAP0XZcC7IQAZb5tuSIdZ+4kbnAAAAAAAA8ByLUgAAAAAAAPAci1IAAAAAAADwnNEbnaej8P5GhQ82Sc0RKcsnf1G2/MU5ZPA4g+n5ZLArA6JsOBdksCOD6flkwLFsOBdksCdD2dBSjRs8Snk5uapvbFDFvm0qr9zkaQbTx8H0fLRn+nyYnk+GI+gn7zOwKBUDtyWi5opahcoPKnIg1OHnvoFBBcuKlDWuQE4gNS8+I4P5+WSwKwOibDgXZLAjg+n5ZMCxbDgXZLAnQ05WUNeecZlmT5mlScNO6vDzDbve14LVi7V83TNqbO6YMRlMHwfT89Ge6fNhej4ZjqCfzGZgUaob4eomNTy1U25d1x9nGDkQ0uFVVWpce0C5VwyXvzCbDEnOYHo+GezKgCgbzgUZ7Mhgej4ZcCwbzgUZ7MkwZtBIrZjzsIb1L1HEjXT6mFNOnKD5M+/RXdPn6Kr5t2rr/h1JzWD6OJiej/ZMnw/T88lwBP1kPgNL8McRrm5S/ePb5dZ3fWKO5ta3qP7x7QpXN5EhiRlMzyeDXRkQZcO5IIMdGUzPJwOOZcO5IIM9GcYMGqkXvrdUJYWD5TiO/D5/p4/z+/xyHEclhYP14p3LNLp4RNIymD4OpuejPdPnw/R8MhxBP9mRIaFFqXvvvVeO4+jb3/52UsLYxG2JqOGpnVJTRHJj3UhSU3Q7t6XzVVYypNd8MtiVIR7007Ebqdc9H8hgfj4ZeoZ+OnYj9brnAxmicrKCWjHnYeUHcxXwx/YGjYA/oPxgrp68faFysoIJZzB9HEzP7wk66tiN1KueD2SIop/sydDjRam1a9fqP//zP1VWVpZwCBs1V9RGX7oW64lp5UpuXYuaK2rJkIQMpueTwa4MsaKfutDLng9kMD+fDPGjn7rQy54PZIi69ozLNKx/Scx/4WsV8Ac0fMCJuub06QlnMH0cTM+PFx3VhV70fCBDFP1kT4YeLUrV19dr5syZ+u1vf6uioqKEQ9goVH7Q6PZksGN7MtiVIRb0U+q3J4M9GUxvT4b40E+p354MdmWYPWVWl/do6U44EtbsKbMSzmD6OJjePh50VO/fngxH0E/2ZOjRotScOXM0Y8YMXXjhhQkHsFF4f2Ond5uPR+RASOH9jWRIIIPp+WSwK0Os6Kfu9YbnAxnMzydD/Oin7vWG5wMZosqGlmrSsJO6vEdLd/w+v04bfrLKhpb2OIPp42B6frzoqO6l+/OBDFH0kz0ZpB58+t7SpUv19ttva+3atTE9PhQKKRQ68h9aW+vtS1B7InwweTex8xfnkKGHGUzPJ4NdGWJBP8WxnzR/PpDB/HwyxId+imM/af58IEPUuMGjkpJh7OBRKq/c1KNtTR8H0/PjEU9HpWM/SebPh+n5ZDiCfrIngxTnK6V27dqlb33rW3r00UeVkxPb0Hnz5qlfv35tX8OGDetRUE81J+mGgk0J7IcM5ueTwa4M3aCf4pTuzwcymJ9PhpjRT3FK9+cDGSRJeTm5SYmQn8h+TB8H0/NjFG9HpWU/SebPh+n5ZGhDP1mUQXEuSq1bt04ff/yxTj/9dAUCAQUCAa1evVoPPPCAAoGAwuFwh23mzp2rmpqatq9du3YlFNgTWQl9KOER2Qnshwzm55PBrgzdoJ/ilO7PBzKYn0+GmNFPcUr35wMZJEn1jQ1JiVCXyH5MHwfT82MUb0elZT9J5s+H6flkaEM/WZRBcb5974ILLtDGjRvbfe8rX/mKJk6cqLvuukt+f8f3ZAaDQQWDiX9copf8RdnJ2U9hz/dDBvPzyWBXhu7QT3HuJ82fD2QwP58MsaOf4txPmj8fyBBVsW9bUjJUVG3t8bamj4Pp+bGKt6PSsZ8k8+fD9HwyHEE/2ZNBinNRKj8/X6ecckq77+Xm5mrAgAEdvp/O/MU58g0MJnTTL9/AYELvqySD+flksCtDd+in2PWG5wMZzM8nQ+zop9j1hucDGaLKKzdpw673dcqJE3p0M+FwJKyNlZu1cffmHmcwfRxMz48VHRW7dH8+kCGKfrIng9TDT9/LBMGyxD4GNdHtyWDH9mSwKwOibDgXZLAjg+ntyYBj2XAuyGBPhgWrF8vn9OyvG36fXwtWL044g+njYHp7tGf6fJjengxH0E/2ZEh4Uerll1/W/fffn3AQ22SNK5CTH5CcODd0JCc/oKxxBWRIQgbT88lgV4Z40U/H6GXPBzKYn0+GnqOfjtHLng9kiFq+7hnt+mSPWsItcW3XEm7Rzr/u1hNvr0w4g+njYHp+T9FRx+hFzwcyRNFP9mTglVJdcAI+5V4xPHrTrlhPkCMpO7qdE0j80JLB/Hwy2JUBUTacCzLYkcH0fDLgWDacCzLYk6GxOaSrH/ya6kINMf/FryXcorpQg66af6sam3v+lpJWpo+D6floz/T5MD2fDEfQT/ZkoOWOw1+YrbzrRsrJi+3WW05eQHnXjUzqzQjJYH4+GezKgCgbzgUZ7Mhgej4ZcCwbzgUZ7Mnw0cfbdcF9N2hP9T5J0XuxdKb1+3uq9+mC+27Q1v07kpbB9HEwPR/tmT4fpueT4Qj6yY4Mcd3oPBP5C7OVf9NoNVfUKlR+sNObgPkGBhUsK4q+9C0F/5pBBvPzyWBXBkTZcC7IYEcG0/PJgGPZcC7IYE+Grft36KyfztA1p0/X7CmzdNrwkzs8ZmPlZi1YvVhPvL0yKa9AOJbp42B6PtozfT5MzyfDEfST+QwsSsXACfiUXVqo7NJChfc3KlzdJDVFpGyf/IXZKf9EDDLYMZ8MdmVAlA3nggx2ZDA9nww4lg3nggz2ZGhsDmnJmie1ZM2TKhtaqrGDRyk/J1d1jQ3asm+byis3pTyD6eNgej7aM30+TM8nwxH0k9kMLErFyV+cY/x/GGQwP58MdmVAlA3nggx2ZDA9nww4lg3nggz2ZCiv3OTJX/KOx/RxMD0f7Zk+H6bnk+EI+sn7DLwuFAAAAAAAAJ5jUQoAAAAAAACeY1EKAAAAAAAAnnNc13W9HFhbW6t+/fpFh+d6f0sr91CL5EpyJKevmVtqkYEMNmUwPV+S3IYWSVJNTY0KCgqMZJDM95NkyfngOUkGMrTPYEFH0U9ksGU+GSzLQD9JsuRckIEMlsy3JkOM/WT0RuetIc0MNzyfDGSwLYPp+ZYxfixsOB+mM5ieTwYyWMr4cbDhXJDB/Hwy2JXBEsaPgw3nggxksGW+LRm6YXRRildKkYEM5jOYni/ZWZT8S5+5DKbnk4EMHTJY1lH0U2ZnMD2fDJZloJ8kWXIuyEAGS+ZbkyHGfjK3KNXXr4Jbxno+tvaRLXIbWuT0DRiZTwYy2JbB9HxJqllUIR0KG5ndKUP9JNlxPkxnMD2fDGQ4llUdRT9lfAbT88lgVwb6KcqGc0EGMtgy35YMsfYTNzoHAAAAAACA51iUAgAAAAAAQDuOB5+LZ/SeUgAAAAAAADBv0v5GzdxUrXP3HNaEgyFlR6Qmn/RBUVCvl/TRo6WF2lCck9SZLEqhR8L7GxU+2CQ1R6Qsn/xF2fIn+cl5PGVDSzVu8Cjl5eSqvrFBFfu2qbxyk2fzbclg+jwANrLhujCdgX4C7GTDdWFDBhs6yoYMgE1s6AYymMswqrpJ81ft1eS9h9XsSO+60nJJdZLyI9LEv4b0lU9Cmr2xWq8O6aPbpw3RtsLspMxmUQoxc1siaq6oVaj8oCIHQh1+7hsYVLCsSFnjCuQEkv/O0JysoK494zLNnjJLk4ad1OHnG3a9rwWrF2v5umfU2NwxX2/JYPo8ADay4bownYF+Auxkw3VhQwYbOsqGDIBNbOgGMpjPcN2HNXpwVZWawq4WSXrAldZ38rjTXOkOSdftPaw3lm7TbdOGaPn4goTnsyiFmISrm9Tw1E65dV1/rGPkQEiHV1Wpce0B5V4xXP4krZxK0phBI7VizsMa1r9EETfS6WNOOXGC5s+8R3dNn6Or5t+qrft3JG2+LRlMnwfARjZcF6Yz0E+AnWy4LmzIYENH2ZABsIkN3UAG8xmu+7BGC57fqw8lXSpph7q+8Xi5pK9K+pGk58Kufvv8Hjly9fj4fgll4J8p0a1wdZPqH98ut77ri+Robn2L6h/frnB1U1Lmjxk0Ui98b6lKCgfLcRz5ff5OH+f3+eU4jkoKB+vFO5dpdPGIpMy3JYPp8wDYyIbrwnQG+gmwkw3XhQ0ZbOgoGzIANrGhG8hgPsPo6iY9uKpKH0o6T1Llp9/vfNn+yPcrJZ0rqULSg6uqNCrBLHEtSv3whz+U4zjtviZOnJhQANjNbYmo4amdUlNEivXG+66kpuh2bktXT+nY5GQFtWLOw8oP5irgj+2FfQF/QPnBXD15+0LlZAUTmm9LBtPnIR3QT5nHhuvCdAb6KX3QUZnFhuvChgw2dJQNGWxHP2UWG7qBDHZk+NWqvQqFXV0qqVZSOMbtwp8+/hJJTWFX81ftTShH3K+UOvnkk7V37962r1deeSWhALBbc0Vt9GWE8X4SpCu5dS1qrqhNaP61Z1ymYf1LYv4lolXAH9DwASfqmtOnJzTflgymz0O6oJ8yiw3XhekM9FN6oaMyhw3XhQ0ZbOgoGzKkA/opc9jQDWQwn2HSx42avPewliv6lr1YF6RahT/dbrmkyXsPa9L+xh5niXtRKhAI6IQTTmj7GjhwYI+Hw36h8oNGt589ZVaX7/vvTjgS1uwpsxKab0sG0+chXdBPmcWG68J0BvopvdBRmcOG68KGDDZ0lA0Z0gH9lDls6AYymM8wc3O1mh3pAfX8nk4+RbdvdqSZm6p7nCXu+RUVFSopKdHo0aM1c+ZM7dy5s8fDYbfw/sZO7/wfj8iBkMI9XDUtG1qqScNO6vJ9/93x+/w6bfjJKhta2qPtbclg+jykE/opc9hwXZjOQD+lHzoqM9hwXdiQwYaOsiFDuqCfMoMN3UAGOzKcu+ew3v30U/Z6+ibAiKR3JL3rSufsPdzDvcS5KHX22WfrkUce0bPPPquHHnpI27Zt09/8zd+orq6uy21CoZBqa2vbfSE9hA8m5wZuPb0J27jBo5Iyf2wC+7Ehg+nzkC7op8xiw3VhOgP9lF7i7Sj6KX3ZcF3YkMGGjrIhQzqgnzKHDd1ABjsyTDgY0uakJJA+kDTxk54vsMX15urp04+8p7qsrExnn322RowYoccee0y33nprp9vMmzdPP/rRj3ocEAY1J+nms009209eTm5SxucnsB8bMpg+D+mCfsowNlwXdKTxY5BO4u0o+imN2XBdWJDBho6yIUM6oJ8yiAXdQAbzGRzXVXZE6vqf7uNTKyk7Et2v6zhxb9/Ttw9KkgoLCzV+/Hht2bKly8fMnTtXNTU1bV+7du1KZCS8lJXQ0+OI7J7tp76xISnj6xLYjw0ZTJ+HdEU/9XI2XBd0pPFjkM666yj6KY3ZcF1YkMGGjrIhQzqin3oxC7qBDOYzuI6jJp+Un5wEKpDU5FOPFqSkBBel6uvr9dFHH2nIkCFdPiYYDKqgoKDdF9KDvyg7Ofsp7Nl+KvZtS8r8iqqtPd/Wggymz0O6op96NxuuC9MZ6Kf01l1H0U/py4brwoYMNnSUDRnSEf3Ue9nQDWSwI8MHRUFNTEoCaaKkzf2DPd4+rkWp733ve1q9erW2b9+u1157TVdffbX8fr9uvPHGHgeAvfzFOfIN7PmTS5J8A4PyF+f0aNvyyk3asOt9hSPxfkBlVDgS1vqd72nj7p6/W9aGDKbPQ7qgnzKLDdeF6Qz0U3qhozKHDdeFDRls6CgbMqQD+ilz2NANZLAjw+slfXSKI52mxD597zOSTnakN4b06eFe4pxfWVmpG2+8URMmTND111+vAQMG6I033lBxcXGPA8BuwbIio9svWL1YPqdnl4nf59eC1YsTmm9LBtPnIR3QT5nHhuvCdAb6KX3QUZnFhuvChgw2dJQNGWxHP2UWG7qBDOYzPFpaqCxXukOJffreHZKy3Oj+eiquhl66dKn27NmjUCikyspKLV26VGPGjOnxcNgva1yBnPyAFO/bQx3JyQ8oa1xiL+ddvu4Z7fpkj1rCLXFt1xJu0c6/7tYTb69MaL4tGUyfh3RAP2UeG64L0xnop/RBR2UWG64LGzLY0FE2ZLAd/ZRZbOgGMpjPsKE4R68O6aPrJI2Q5I9ze/+n210r6dUhfbQhgVeNZd6dRREXJ+BT7hXDozdQi/VicSRlR7dzAok9xRqbQ7r6wa+pLtQQ8y8TLeEW1YUadNX8W9XY3POPprQpg+nzANjIhuvCdAb6CbCTDdeFDRls6CgbMgA2saEbyGBHhtunDVG239Gzit6sPNaFKf+nj39OUrbf0e3Tur6Hbyz4TRDd8hdmK++6kXLyAjE93skLKO+6kUm7ce1HH2/XBffdoD3V+ySpy/sCtH5/T/U+XXDfDdq6f0dS5tuSwfR5AGxkw3VhOgP9BNjJhuvChgw2dJQNGQCb2NANZDCfYVthtm6bNkTjJb0uaein3+9qkaj1+0M/ffw4SbdNG6JtCWaJ7b8cGc9fmK38m0aruaJWofKDihzo+K9GvoFBBcuKoi9DTPK/fG/dv0Nn/XSGrjl9umZPmaXThp/c4TEbKzdrwerFeuLtlSn5Vy0bMpg+D4CNbLguTGegnwA72XBd2JDBho6yIQNgExu6gQzmMywfXyBHrh5cVaWNYVfLJT0g6Z1OHjtJ0XtIXavoK6S+Pm2Ilo9P/G2MLEohZk7Ap+zSQmWXFiq8v1Hh6iapKSJl++QvzE75pyc1Noe0ZM2TWrLmSZUNLdXYwaOUn5OrusYGbdm3TeWVm1I635YMps8DYCMbrgvTGegnwE42XBc2ZLCho2zIANjEhm4gg/kMj4/vp3WD+mj+qr26Ze9hzXSkd13pA0m1ir5Vb4KkU5zoTc1fKemjb05N/BVSrViUQo/4i3OM/uWivHKT8V8cbMhg+jwANrLhujCdgX4C7GTDdWFDBhs6yoYMgE1s6AYymMuwrTBbM64ZoUn7GzVzU7XO2XtY13wSUnZEavJJm/sHtWhIHz1aWpjQTc07w6IUAAAAAABAhttQnKMNxSe0/dlxXblOvB8PGB9u6AAAAAAAAIB2Ur0gJbEoBQAAAAAAAAMc13VdLwfW1taqX79+0eG53r970D3UIrmSHMnpa+bdi2Qgg00ZTM+XJLehRZJUU1Pz/9m78/Co6nt/4O8za0J2IGAChH0JaoLiWnqLoFgRBRUsvYVbqFRuK2DrfWyrXX6tK7W2vVZBelFEW0DxinipV1ARxatVVJQENUCQLZAEg2QnmWRmvr8/jglZJmTW8/2Eeb+eJ88jycx83pxzvm/Gk5kzSE2N/BMcwqW7nwAh+4PHJDMwQ/sMAjqK/cQMUuYzg7AM7CcAQvYFMzCDkPliMgTZT1qvKdUSUs9wzfOZgRmkZdA9Xxjt20LC/tCdQfd8ZmAGobRvBwn7ghn0z2cGWRmE0L4dJOwLZmAGKfOlZOiG1pNSfKUUMzCD/gy65wMyi5K/6dOXQfd8ZmCGThmEdRT7Kb4z6J7PDMIysJ8ACNkXzMAMQuaLyRBkP+k7KdXLjtT5IywfW/P0fqh6L4xeDi3zmYEZpGXQPR8AqlcXA6d8WmYHpKmfABn7Q3cG3fOZgRk6EtVR7Ke4z6B7PjPIysB+MknYF8zADFLmS8kQbD/xQudERERERERERGQ5npTqwQxrr1FPRERERERRxOfzRBTvtF5TikKTX9GIOUVVuLy0AaMrPXD5gSYbsDfDjfeyE7E2Nx0FmQm6YxIRERERUQB8Pk9E1B5PSvUAQ6uasGxbGSaUNaDZAJz544ApY4CUFLhqa3H+nj0YU7ALC3dX4d2sRCyenIWD6a6YZsobmIuR/YciOSEJdY31KD5+EIVHi2I6U9J8APBVNMJX2QQ0+wGnDfYMF+wWP4mQsB2I2pJwTHJtchsQBSLhmOTaNMXjdpD4fJ7k4Lo0cTvEZwaelBJu1r5qLN9WDrvLDcyfD+fttwMXXNDpds5PPgEefRSXrn8O7z93ELdNzsKGUalRzZLgdGPm+GuxcOJc5A8a2+nnBSWfY+X2Ndiw8xU0NnuiOlvCfABQXj+ai2vgKayE/0TnGba+brjzMuAcmQrDEZt3x0rYDkRtSTgmuTa5DYgCkXBMcm2a4nk7SHo+T3JwXZq4HZiBJ6UEm7WvGitfLwNGjoTttdeAwYMBvz/wjfPygKeeguO3v4VtyhQ88fp+GFB4YVRaVLIM7zcEGxc9iUG9s+FXgTOcN2A0ls15AL+Yugg3LFuAAxWHozJbwnwA8FU1oX7TEajarj/a0n/Cg4Zt5Wj88ASSpufAHuXfcEnYDkRtSTgmuTa5DYgCkXBMcm2a4nk7SHo+T3JwXZq4HZgB4IXOxRpW1YTl28rNf8Defx8YMAAwDMBuD3wHu938+YABsO3YAYwYgeXbyjG0qiniLMP7DcHWO59Ddnp/GIYBuy1wBrvNDsMwkJ3eH2/8bD2GZQ6OeLaE+YC5SOteOARV1/UibUvVeVH3wiH4orD9W0jYDkRtSTgmuTa5DYgCkXBMcm2a4nk7SHo+T3JwXZq4HZihRcgnpY4dO4a5c+eiT58+SExMxPnnn4+PPvooKmHotMe2lcHmcpu/UUlJAZzO4O7odAIpKbC9/jrsLjeWbSuLKEeC042Ni55EijsJDntwL6xz2B1IcSfhpcWrkOB09+j5gPkyxvpNR4AmPxDsB6QoAE3m/ZS3i9+GhUDCdugJ2E/WkXBMcm1yG/Q07ChrSDgmuTZN8b4dpDyfDwb7yRpclyZuB2ZoK6STUpWVlZgwYQKcTic2b96Mzz//HH/605+QkZERcRA6Lf/LRkwoa4Bz9nfNl/gG+w9YC6cTGDIEju/MxoSyBuRXNIadZeb4azGod3bQZdHCYXcgp88A3HTh1LBnS5gPAM3FNebLGEP9xF4FqFovmotrIs4gYTtIx36yloRjkmuT26AnYUdZR8IxybVpiuftIOn5fHfYT9bhujRxOzBDWyGdlHrooYcwaNAgrF69GpdccgmGDh2Kq6++GsOHD484CJ02Z08Vmg0At9/e9XvOu+PzAUuWoNkA5hRVhZ1l4cS5Xb6/t9sIfh8WTpwb9mwJ8wHAU1ip9f6AjO0gHfvJWhKOSa5NboOehB1lHQnHJNemKZ63g6Tn891hP1mH69LE7cAMbYV0UmrTpk246KKLcPPNN6Nfv3644IIL8MQTT0Qcgtq7vLTB/JjYCy7o+j3n3bHbgfHj4cwfh8vKGsJ6iLyBucgfNLbL9/d2G8Fmx7icc5E3MLdHzgfMj8IM9MkDofCf8MAXwW+3JGyHnoD9ZB0JxyTXJrdBT8OOsoaEY5Jr0xTv20HK8/lgsJ+swXVp4nZgho5COil14MABrFixAiNHjsSrr76KH//4x7j99tvxzDPPdHkfj8eDmpqadl90ZqMrPcCYMVF6sNEYczK8A21k/6FRiTAizMfRPR8AfJXRuXhbJBeBk7AdegL2k3UkHJNcm9wGPU2oHcV+Co+EY5Jr0xTv20HK8/lgsJ+swXVp4nZgho5CehOn3+/HRRddhAcffBAAcMEFF+DTTz/FX//6V8ybNy/gfZYuXYp77rknopDxxFAKLj/MiyFGQ2oqXH7zcZVhhHTX5ISkqERICfNxdM8HADRHfuE2AOaF48IkYjv0AOwn64g4Jrk2uQ16mFA7iv0UHhHHJNemKY63g6Tn88FgP1mD69LE7cAMHYX0SqmsrCyMHTu23fdyc3Nx5MiRLu9z9913o7q6uvWrpKQkvKRxQhkGmmwAamuj84A1NWiyIax/wOoa66MSoTbMx9E9HwDgDPkDKgNzhf84IrZDD8B+so6IY5Jrk9ughwm1o9hP4RFxTHJtmuJ4O0h6Ph8M9pM1uC5N3A7M0FFIr5SaMGEC9u7d2+57+/btw+DBg7u8j9vthtsdPx/3HA17M9w4f8+e6DzYnj3Y0zu87V98/GBUIhSXH+iR8wHAnuGKSgZ7eviPI2E79ATsJ+tIOCa5NrkNeppQO4r9FB4JxyTXpinet4OU5/PBYD9Zg+vSxO3ADB2FdErrjjvuwPvvv48HH3wQ+/fvx7p167By5UosWrQoohDU3nvZiWgu2AV88on5qRvh8PmAnTvRXFiA97MSw3qIwqNFKCj5HD5/eBl8fh92HfkMu4+F9w+y7vkAYM9MgK1vZP/o2vq6Yc9MCPv+ErZDT8B+so6EY5Jrk9ugp2FHWUPCMcm1aYr37SDl+Xww2E/W4Lo0cTswQ6fHCOXGF198MTZu3Ihnn30W5513Hu677z488sgjmDNnTkQhqL21uelwKgCPPgrYwnwpnN0OPPYYnMp8vHCt3L4GNiO8DHabHSu3rwl7toT5AODOy9B6f0DGdpCO/WQtCcck1ya3QU/CjrKOhGOSa9MUz9tB0vP57rCfrMN1aeJ2YIa2Qj4SrrvuOuzevRuNjY0oKirCrbfeGnEIaq8gMwHvZiXCu/454PBhoLk5tAdobgYOHYL3+fV4NysRBRGcudyw8xWUnCyF1+cN6X5enxdHvjqGFz/eHPZsCfMBwDkyFUaKAwj1bfwGYKQ44ByZGnEGCduhJ2A/WUfCMcm1yW3Q07CjrCHhmOTaNMXzdpD0fD4Y7CdrcF2auB2Yoa0oXdmKom3x5Cz4mjzwX321eZHEYP8ha24GamvhnzIFviYPFk/OiihHY7MHNy7/IWo99UGXhtfnRa2nHjcsW4DG5sg+vlb3fAAwHDYkTc8xL+AW7GI1ALjM+xmOyJeZhO1A1JaEY5Jrk9uAKBAJxyTXpinet4OU5/MkB9eliduBGdriSSmhDqa7cNvkLKC4GP5LLwWOHTN/0NV70lu+f+yYefv9+3Hb5CwcjPCiYwDwxZeHcOXDs1Faddwc1cX7f1u+X1p1HFc+PBsHKg5HPFvCfMC8eFvyrCEwkoP7bAAj2YHkWUMivuhbWxK2A1FbEo5Jrk1uA6JAJByTXJumeN4Okp7PkxxclyZuB2ZoEdKn75G1NoxKhQGF5du+gH1sLhzfmQ0sWQKMH9/5xrt2AY89Bu/z6+Fr8uC2KdnYMCryl9K1OFBxGJfcPw03XTgVCyfOxbicczvdZvfRPVi5fQ1e/Hhz1H/zrXs+YC7WlO8NQ3NxDTyFlfCf6DzD1tcNd16G+TLIKJw17kjCdiBqS8IxybXJbUAUiIRjkmvTFM/bQdLzeZKD69LE7cAMAE9KiffCqDTs7JeIZdvKMOGZZ9D8t2fgzB8HjB4NpKYCNTXA3r1oLtgFpwLez07EkklDY/IblcZmD9bteAnrdryEvIG5GNF/KFISklDbWI/9xw+i8GhR1GdKmg+YL2905abDlZsOX0UjfFVNQJMfcNlgT3dF/MkDwZCwHYjaknBMcm1yGxAFIuGY5No0xfN2kPR8nuTgujRxOzADT0r1AAfTXZh202DkVzRiTlEVLjtahDGFu+DyA002YE9vN94/Lx1rc9NjfhHEFoVHi7T+z4Xu+YD5EZpWlMOZSNgORG1JOCa5NrkNiAKRcExybZricTtIfD5PcnBdmrgd4jMDT0r1IAWZCSjIPKf1z4ZSUEaol8knIiIiIiId+HyeiKg9Xui8B+M/YEREREREPRefzxNRvONJKSIiIiIiIiIispyhlFJWDqypqUFaWpo5PMn6dw+qU15AATAAo5eedy8yAzNIyqB7PgCoei8AoLq6Gqmp+j5lRnc/AUL2B49JZmCG9hkEdBT7iRmkzGcGYRnYTwCE7AtmYAYh88VkCLKftF5TqiWknuGa5zMDM0jLoHu+MNq3hYT9oTuD7vnMwAxCad8OEvYFM+ifzwyyMgihfTtI2BfMwAxS5kvJ0A2tJ6X4SilmYAb9GXTPB2QWJX/Tpy+D7vnMwAydMgjrKPZTfGfQPZ8ZhGVgPwEQsi+YgRmEzBeTIch+0ndSqpcdqfNHWD625un9UPVeGL0cWuYzAzNIy6B7PgBUry4GTvm0zA5IUz8BMvaH7gy65zMDM3QkqqPYT3GfQfd8ZpCVgf1kkrAvmIEZpMyXkiHYfuKFzomIiIiIiIiIyHI8KUVERFFnWPsZGkRERERE1ANpvaYUERGdHfIrGjGnqAqXlzZgdKUHLj/QZAP2ZrjxXnYi1uamoyAzQXdMIiIiIiIShCelKCx5A3Mxsv9QJCckoa6xHsXHD6LwaFHczJeSwVfRCF9lE9DsB5w22DNcsFv8P/4StgPpM7SqCcu2lWFCWQOaDeBTBWwAUAsgxQ+M+cqDH5z0YOHuKryblYjFk7NwMN0V00wS1oXuDLrnS8nAfqK2JByTzCAng+5+0D2fZJGwJphBTgYJ/WDlduBJKQpagtONmeOvxcKJc5E/aGynnxeUfI6V29dgw85X0NjsOevmS8mgvH40F9fAU1gJ/4nOM2x93XDnZcA5MhWGIzbv0JWwHUi/WfuqsXxbOZp8CqsBPKqAXQFuN04BtwOYVdaA9587iNsmZ2HDqNSoZpGwLnRn0D1fSgb2E7Ul4ZhkBjkZdPeD7vkki4Q1wQxyMkjoB13bgSelKCjD+w3BxkVPYlDvbPiVP+BtzhswGsvmPIBfTF2EG5YtwIGKw2fNfCkZfFVNqN90BKq264/X9J/woGFbORo/PIGk6TmwR/lVKRK2A+k3a181Vr5ehn0ArgFwGF1fpLAQwC0A7gHwqk/hiddLYUDhhVFpUckiYV3ozqB7vpQM7CdqS8IxyQxyMujuB93zSRYJa4IZ5GSQ0A86twMvdE7dGt5vCLbe+Ryy0/vDMAzYbfaAt7Pb7DAMA9np/fHGz9ZjWObgs2K+lAy+qibUvXAIqq7romhL1XlR98Ih+KqaopZBwnYg/YZVNWH5tnLsA/ANAEe//n7gf0JPf/8ogMsBFANYvq0cQ6NwbEpYF7oz6J4vJQP7idqScEwyg5wMuvtB93ySRcKaYAY5GST0g+7twJNSdEYJTjc2LnoSKe4kOOzBvbDOYXcgxZ2ElxavQoLT3aPnS8mgvH7UbzoCNPmBYD/UTAFoMu+nvF2dLgiehO1AMjy2rQwen8I1AGoA+IK8n+/r238bQJNPYdm2sohySFgXujPoni8lA/uJ2pJwTDKDnAy6+0H3fJJFwppgBjkZJPSDhO0Q0kmpIUOGwDCMTl+LFi2KOAjJNHP8tRjUOzvoRdLCYXcgp88A3HTh1B49X0qG5uIa86WUwRZFCwWoWi+ai2siziBhO3SHHRV7+V82YkJZAzbAfMtesCekWvi+vt8GABPKGpBf0Rh2FgnrQncG3fOlZGA/UVsSjklmkJNBdz/onh8M9pN1JKwJZpCTQUI/SNgOIZ2U+vDDD1FWVtb69frrrwMAbr755oiDkEwLJ87t8n2t3fH5fVg4cW6Pni8lg6ewUuv9ARnboTvsqNibs6cKzQbwKMJ/qa0N5v2bDWBOUVXYWSSsC90ZdN9fSgb2E7Ul4ZhkBjkZdPeD7vnBYD9ZR8KaYAY5GST0g4TtENL/U2RmZuKcc85p/Xr55ZcxfPhwTJw4MeIgJE/ewFzkDxrb5ftau2O32TEu51zkDcztkfOlZPBVNAb89INQ+E944IvgFSkStkMw2FGxd3lpAz79+lP2wn2xrh/AJwA+VcBlZQ1hPYaEdaE7g+75UjKwn6gtCcckM8jJoLsfdM8PFvvJGhLWBDPIySChHyRsByCCa0o1NTVhzZo1uOWWW2AYRpe383g8qKmpafdFPcPI/kOj8jgjwnwc3fOlZPBVRu9iyOGSsB1CFUxHsZ9CN7rSgz1Reqy9AMacDO8fQgnrQncG3fOlZGA/UVsSjklmkJNBdz/onh8O9lPsSFgTzCAng4R+kLAdgAhOSr300kuoqqrC/Pnzz3i7pUuXIi0trfVr0KBB4Y4kiyUnJEXlcVLCfBzd86VkQHPkF48DYF68LkwitkOIguko9lNoDKXg8gO1UXq8GgAuv/m4IROwLrRn0D1fSAb2E7Uj4JhkBjkZdPeD7vnhYD/FkIA1wQxyMojoBwHbAYjgpNSqVaswdepUZGdnn/F2d999N6qrq1u/SkpKwh1JFqtrrI/K49SG+Ti650vJAGeUPiTTFf7jiNgOIQqmo9hPoVGGgSYbkBKlx0sF0GQzHzdkAtaF9gy65wvJwH6idgQck8wgJ4PuftA9PxzspxgSsCaYQU4GEf0gYDsAQGiXef/a4cOHsXXrVrz44ovd3tbtdsPt5keZ9kTFxw9G53HKD/TI+VIy2DNcUclgTw//cSRsh1AE21Hsp9DtzXBjzFeRvfe8xRgAe3qHt/0lrAvdGXTPl5KB/URtSTgmmUFOBt39oHt+qNhPsSVhTTCDnAwS+kHCdgDCfKXU6tWr0a9fP0ybNi2i4SRb4dEiFJR8Dp8/1A99N/n8Puw68hl2HwvvCjS650vJYM9MgK1vZP/w2/q6Yc9MCPv+ErZDKNhRsfNediLOM4BxiOzT9y4AcK4BvJ+VGNZjSFgXujPoni8lA/uJ2pJwTDKDnAy6+0H3/FCxn2JLwppgBjkZJPSDhO0AhPH/FH6/H6tXr8a8efPgcIT1QivqQVZuXwObEd7/etptdqzcvqZHz5eSwZ2XofX+gIztEAx2VGytzU2HUwG3I7JP37sdgFOZjxcuCetCdwbd95eSgf1EbUk4JplBTgbd/aB7frDYT9aQsCaYQU4GCf0gYTuEvAW2bt2KI0eO4JZbbol4OMm3YecrKDlZCq/PG9L9vD4vjnx1DC9+vLlHz5eSwTkyFUaKAwj10jsGYKQ44ByZGnEGCdshGOyo2CrITMC7WYmYBWAwgFA/xNb+9f1mAng3KxEFEfxmRcK60J1B93wpGdhP1JaEY5IZ5GTQ3Q+65weL/WQNCWuCGeRkkNAPErZDyCelrr76aiilMGrUqIiHk3yNzR7cuPyHqPXUB71YvD4vaj31uGHZAjQ2R3btGd3zpWQwHDYkTc8xLyIXbGEYAFzm/QxH5Bexk7AdgsGOir3Fk7PgshvYAvNi5cGemLJ/fftXAbjsBhZPzoooh4R1oTuD7vlSMrCfqC0JxyQzyMmgux90zw8W+8kaEtYEM8jJIKEfJGyHKF1unc5mX3x5CFc+PBulVccBoMv3vbZ8v7TqOK58eDYOVBw+K+ZLyWBPdyF51hAYycG9pNpIdiB51pCILzzXloTtQPodTHfhtslZGAXgPQADv/5+V/+gtHx/4Ne3HwngtslZOBiFY1PCutCdQfd8KRnYT9SWhGOSGeRk0N0PuueTLBLWBDPIySChH3RvB75hmIJyoOIwLrl/Gm66cCoWTpyLcTnndrrN7qN7sHL7Grz48eao/1ZH93wpGezpLqR8bxiai2vgKayE/0TnGba+brjzMsyXYkbhzHVHErYD6bdhVCoMKCzfVo7dPoUNAB4F8EmA2+bDvIbUTJivkLp1chY2jIr8pb4tJKwL3Rl0z5eSgf1EbUk4JplBTgbd/aB7PskiYU0wg5wMEvpB53bgSSkKWmOzB+t2vIR1O15C3sBcjOg/FCkJSahtrMf+4wdReLTorJ4vJYPhsMGVmw5Xbjp8FY3wVTUBTX7AZYM93RXxpx8EQ8J2IP1eGJWGnf0SsWxbGeaXNWCOAXyqgL0AamC+VW80gPMM86Lm72QnYsmk6LxCqiMJ60J3Bt3zpWRgP1FbEo5JZpCTQXc/6J5PskhYE8wgJ4OEftC1HXhSisJSeLRI6z+cuudLyWDPTLCkJM9EwnYgfQ6muzDtpsHIr2jEnKIqXFbWgJtOeuDyA002YE9vN1ZnJWJtbnpEFzUPhYR1oTuD7vlSMrCfqC0JxyQzyMmgux90zydZJKwJZpCTQUI/WLkdeFKKiIgiVpCZgILMc1r/bCgFZYT6MR5ERERERBRPeKFzIiKKOp6QIiIiIiKi7vCkFBERERERERERWc5QSikrB9bU1CAtLc0cnmT9uwfVKS+gABiA0UvPuxeZgRkkZdA9HwBUvRcAUF1djdTU6H0qW6h09xMgZH/wmGQGZmifQUBHsZ+YQcp8ZhCWgf0EQMi+YAZmEDJfTIYg+0nrNaVaQuoZrnk+MzCDtAy65wujfVtI2B+6M+iezwzMIJT27SBhXzCD/vnMICuDENq3g4R9wQzMIGW+lAzd0HpSiq+UYgZm0J9B93xAZlHyN336MuiezwzM0CmDsI5iP8V3Bt3zmUFYBvYTACH7ghmYQch8MRmC7Cd9J6V62ZE6f4TlY2ue3g9V74XRy6FlPjMwg7QMuucDQPXqYuCUT8vsgDT1EyBjf+jOoHs+MzBDR6I6iv0U9xl0z2cGWRnYTyYJ+4IZmEHKfCkZgu0nXuiciIiIiIiIiIgsx5NSRERERERERERkOZ6UIiIiIiIiIiIiy2m90DlRuHwVjfBVNgHNfsBpgz3DBXtmgqUZ8gbmYmT/oUhOSEJdYz2Kjx9E4dEiSzNIIGFfEEmje12wn0y69wORRBLWBTtKxn4gkkbCumA/mazcFzwpRT2G8vrRXFwDT2El/Cc8nX5u6+uGOy8DzpGpMByxeRFggtONmeOvxcKJc5E/aGynnxeUfI6V29dgw85X0NjcOePZQsK+IJJG97pgP5l07wciiSSsC3aUjP1AJI2EdcF+MunaFzwpRT2Cr6oJ9ZuOQNV2/bGS/hMeNGwrR+OHJ5A0PQf2dFdUMwzvNwQbFz2JQb2z4Vf+gLc5b8BoLJvzAH4xdRFuWLYAByoORzWDBBL2BZE0utcF+8mkez8QSSRhXbCjZOwHImkkrAv2k0nnvuApeBLPV9WEuhcOQdV1vUDaUnVe1L1wCL6qpqhlGN5vCLbe+Ryy0/vDMAzYbfaAt7Pb7DAMA9np/fHGz9ZjWObgqGWQQMK+IJJG97pgP5l07wciiSSsC3aUjP1AJI2EdcF+MuneFzwpRaIprx/1m44ATX5ABXsnAE3m/ZQ38NnuUCQ43di46EmkuJPgsAf34kKH3YEUdxJeWrwKCU53xBkkkLAviKTRvS7YTybd+4FIIgnrgh0lYz8QSSNhXbCfTBL2RUgnpXw+H37zm99g6NChSExMxPDhw3HfffdBqWDTE4WmubjGfAlhqIeYAlStF83FNRFnmDn+WgzqnR10WbVw2B3I6TMAN104NeIMEkjYF2fCfiIddK8L9pNJ934IBjuKrCZhXbCjZOyH7rCfyGoS1gX7ySRhX4R0Uuqhhx7CihUrsGzZMhQVFeGhhx7CH/7wBzz22GMRByEKxFNYqfX+ALBw4twu31/cHZ/fh4UT50acQQIJ++JM2E+kg+51wX4y6d4PwWBHkdUkrAt2lIz90B32E1lNwrpgP5kk7IuQTgv+85//xIwZMzBt2jQAwJAhQ/Dss8/igw8+iDgIUUe+isaAV/0Phf+EB76KxrA/vjJvYG7AT2AIlt1mx7icc5E3MLdHf5SohH3RHfYTWU33umA/mXTvh2Cxo8hKEtYFO0rGfggG+4msJGFdsJ9MEvYFEOIrpb7xjW/gjTfewL59+wAABQUFeOeddzB1atcvXfN4PKipqWn3RRQMX2V0LpwWyQXYRvYfGpUMI6L0OLpI2BfdYT+R1XSvC/aTSfd+CFaoHcV+okhIWBfsKBn7IRjsJ7KShHXBfjJJ2BdAiK+Uuuuuu1BTU4MxY8bAbrfD5/PhgQcewJw5c7q8z9KlS3HPPfdEFJLiVHOULuzYFP7jJCckRSVCSpQeRxsB+6I77CeynOZ1wX76Wg/oJyD0jmI/UUQErAt2FETsh2Cwn8hSAtYF++lrAvYFEOIrpZ5//nmsXbsW69atw8cff4xnnnkGf/zjH/HMM890eZ+7774b1dXVrV8lJSURBaY44ozSh0O6wn+cusb6qESojdLjaCNgX3SH/USW07wu2E9f6wH9BITeUewnioiAdcGOgoj9EAz2E1lKwLpgP31NwL4AQnyl1M9+9jPcdddd+O53vwsAOP/883H48GEsXboU8+bNC3gft9sNt/vs+LhEspY9wxWdx0kP/3GKjx+MSobi8gNReRxdJOyL7rCfyGq61wX7yaR7PwQr1I5iP1EkJKwLdpSM/RAM9hNZScK6YD+ZJOwLIMRXSp06dQo2W/u72O12+P2xfUkpxSd7ZgJsfSP7B8/W1x3RRdcKjxahoORz+Py+sO7v8/uw68hn2H1sT9gZJJCwL7rDfiKr6V4X7CeT7v0QLHYUWUnCumBHydgPwWA/kZUkrAv2k0nCvgBCPCl1/fXX44EHHsD//u//4tChQ9i4cSP+/Oc/48Ybb4woBFFX3HkZWu8PACu3r4HNCO8liXabHSu3r4k4gwQS9sWZsJ9IB93rgv1k0r0fgsGOIqtJWBfsKBn7oTvsJ7KahHXBfjJJ2Bch7YXHHnsMs2bNwm233Ybc3Fzceeed+Pd//3fcd999EQchCsQ5MhVGigMwQryjARgpDjhHpkacYcPOV1ByshRenzek+3l9Xhz56hhe/HhzxBkkkLAvzoT9RDroXhfsJ5Pu/RAMdhRZTcK6YEfJ2A/dYT+R1SSsC/aTScK+COmkVEpKCh555BEcPnwYDQ0N+OKLL3D//ffD5Yrt+5wpfhkOG5Km55gXTwt2oRgAXOb9DEfkF29rbPbgxuU/RK2nPujS8vq8qPXU44ZlC9DY7Ik4gwQS9sWZsJ9IB93rgv1k0r0fgsGOIqtJWBfsKBn7oTvsJ7KahHXBfjJJ2BexbzmiCNnTXUieNQRGcnDX5TeSHUieNSSqF4X84stDuPLh2SitOg4AXb7/uOX7pVXHceXDs3Gg4nDUMkggYV8QSaN7XbCfTLr3A5FEEtYFO0rGfiCSRsK6YD+ZdO+LkD59j0gXe7oLKd8bhubiGngKK+E/0fnMtK2vG+68DPMliDH4rdKBisO45P5puOnCqVg4cS7G5Zzb6Ta7j+7Byu1r8OLHm8+as+cdSdgXRNLoXhfsJ5Pu/UAkkYR1wY6SsR+IpJGwLthPJp37gielqMcwHDa4ctPhyk2Hr6IRvqomoMkPuGywp7ti/skkgPkyz3U7XsK6HS8hb2AuRvQfipSEJNQ21mP/8YMoPFoU8wwSSNgXRNLoXhfsJ5Pu/UAkkYR1wY6SsR+IpJGwLthPJl37gielqEeyZyZo/4e78GhR3BTUmUjYF0TS6F4X7CeT7v1AJJGEdcGOkrEfiKSRsC7YTyYr9wVfF0pERERERERERJbjSSkiIiIiIiIiIrIcT0oREREREREREZHlDKWUsnJgdXU10tPTzT/0sls52nSqzcc86pjPDMwgLYPu+W0yVFVVIS0tTU8GCOgnQNT+0JZB93xmYIYuMujsKPYTM4iZzwwiM7Cf5OwLZmAG7fOFZeiunyy/0Hltbe3pP7TdUDrons8MzCAtg+b5tbW1Wk9KieonZpAxnxmYoQ2dHcV+YgaR85lBTAb2UxvMwAySMuieLyBDd/1k+Sul/H4/SktLkZKSAsMwQr5/TU0NBg0ahJKSEqSmpsYgITP0lAy65zND9DIopVBbW4vs7GzYbPreVcx+YoazKYPu+WdTBgkdFWk/Afr3h+75zMAM0jKwn07TvS8kZNA9nxmYIdoZgu0ny18pZbPZMHDgwIgfJzU1VdvOYQZZGXTPZ4boZND5CqkW7CdmOBsz6J5/tmTQ3VHR6idA//7QPZ8ZmEFaBvbTabr3hYQMuuczAzNEM0Mw/cQLnRMRERERERERkeV4UoqIiIiIiIiIiCzX405Kud1u/Pa3v4Xb7WaGOM+gez4zyMoggYTtwAzMIGU+M8ije1vons8MzCAtg+75kkjYFroz6J7PDMygK4PlFzonIiIiIiIiIiLqca+UIiIiIiIiIiKino8npYiIiIiIiIiIyHI8KUVERERERERERJbjSSkiIiIiIiIiIrJcjzop9d5778Fut2PatGmWz54/fz4Mw2j96tOnD6655hoUFhZanqW8vBxLlizBsGHD4Ha7MWjQIFx//fV44403Yj677XZwOp3o378/pkyZgqeeegp+vz/m8ztmaPt1zTXXWDK/uxz79++3ZH55eTl+8pOfYMSIEUhISED//v0xYcIErFixAqdOnYr5/Pnz5+OGG27o9P233noLhmGgqqoq5hmkYUexnzrm0NVRuvsJ0NtR7KfO2E/sp4452E98DiUF+4n91DEH+ym++qlHnZRatWoVlixZgrfffhulpaWWz7/mmmtQVlaGsrIyvPHGG3A4HLjuuusszXDo0CGMHz8e27Ztw8MPP4zdu3djy5YtmDRpEhYtWmRJhpbtcOjQIWzevBmTJk3CT37yE1x33XXwer2WZmj79eyzz1oyu7scQ4cOjfncAwcO4IILLsBrr72GBx98EJ988gnee+89/PznP8fLL7+MrVu3xjwDdRbvHcV+6pxDZ0fp6ieAHSUR+4n91DEH+4n9JAX7if3UMQf7Kb76yaE7QLDq6uqwfv16fPTRRygvL8fTTz+NX/7yl5ZmcLvdOOeccwAA55xzDu666y78y7/8CyoqKpCZmWlJhttuuw2GYeCDDz5AUlJS6/fPPfdc3HLLLZZkaLsdBgwYgAsvvBCXXXYZrrzySjz99NP44Q9/aGkGnXTluO222+BwOPDRRx+1Ow6GDRuGGTNmQClleaZ4x45iP3WVQxedGdhRsrCf2E9d5dCF/UQt2E/sp65y6MJ+sl6PeaXU888/jzFjxmD06NGYO3cunnrqKa07pa6uDmvWrMGIESPQp08fS2aePHkSW7ZswaJFi9odpC3S09MtyRHI5MmTkZ+fjxdffFFbhnjx1Vdf4bXXXuvyOAAAwzAsTkXx3lHsJ2rBjpKH/cR+IhP7SR72E/uJTPHcTz3mpNSqVaswd+5cAOZL6qqrq7F9+3ZLM7z88stITk5GcnIyUlJSsGnTJqxfvx42mzWbcf/+/VBKYcyYMZbMC9WYMWNw6NAhS2a13RctXw8++KAls8+U4+abb475zJbjYPTo0e2+37dv39Ycv/jFL2KeAwi8H6ZOnWrJbGnivaPYT+1J6Cgd/QTI6Sj202nsJ/ZTW+wn/f0EsKNasJ/YT22xn+Kzn3rE2/f27t2LDz74ABs3bgQAOBwOzJ49G6tWrcIVV1xhWY5JkyZhxYoVAIDKyko8/vjjmDp1Kj744AMMHjw45vOlv1xPKWXZ2du2+6JF7969LZl9phxdndW2wgcffAC/3485c+bA4/FYMjPQftixY0frk4t4wY5iP3UkoaMk9RNgfUexn0zsJ/ZTR+ynzvgcSg/2E/upI/ZTZ/HQTz3ipNSqVavg9XqRnZ3d+j2lFNxuN5YtW4a0tDRLciQlJWHEiBGtf37yySeRlpaGJ554Avfff3/M548cORKGYWDPnj0xnxWOoqIiyy4C13Ff6KIjx4gRI2AYBvbu3dvu+8OGDQMAJCYmWpYl0N//6NGjls2Xgh3FfupIQkfpyiClo9hPJvYT+6kj9pP+fgLYUQD7CWA/dcR+is9+Ev/2Pa/Xi7/97W/405/+hF27drV+FRQUIDs7W8snrrUwDAM2mw0NDQ2WzOvduze+/e1vY/ny5aivr+/0c50fH7tt2zbs3r0bM2fO1JYhXvTp0wdTpkzBsmXLAh4HZC12lIn9RC3YUXKwn0zsJ2rBfpKD/WRiP1GLeO4n8a+Uevnll1FZWYkFCxZ0Ols+c+ZMrFq1Cj/60Y8syeLxeFBeXg7AfGnnsmXLUFdXh+uvv96S+QCwfPlyTJgwAZdccgnuvfde5OXlwev14vXXX8eKFStQVFQU8wwt28Hn8+H48ePYsmULli5diuuuuw7f//73Yz6/bYa2HA4H+vbta8l83R5//HFMmDABF110EX73u98hLy8PNpsNH374Ifbs2YPx48frjhg32FGnsZ8652iLHcWOshr76TT2U+ccbbGf2E9WYz+dxn7qnKMt9lMc9JMS7rrrrlPXXnttwJ/t2LFDAVAFBQUxzzFv3jwFoPUrJSVFXXzxxeqFF16I+eyOSktL1aJFi9TgwYOVy+VSAwYMUNOnT1dvvvlmzGe33Q4Oh0NlZmaqq666Sj311FPK5/PFfH7HDG2/Ro8ebcn8tjlmzJhh6cy2SktL1eLFi9XQoUOV0+lUycnJ6pJLLlEPP/ywqq+vj/n8rv7+b775pgKgKisrY55BAnZUe/HeTx1z6Ooo3f2klN6OYj+Z2E/tsZ/YTy34HEo/9lN77Cf2U4t47CdDKeFXVyMiIiIiIiIiorOO+GtKERERERERERHR2YcnpYiIiIiIiIiIyHI8KUVERERERERERJbjSSkiIiIiIiIiIrIcT0oREREREREREZHleFKKiIiIiIiIiIgsx5NSRERERERERERkOZ6UIiIiIiIiIiIiy/GkFBERERERERERWY4npXqYv/71r0hJSYHX6239Xl1dHZxOJ6644op2t33rrbdgGAa++OILAMDKlStxxRVXIDU1FYZhoKqqqtPjDxkyBIZhtPv6/e9/H1S2oUOHYuvWrQF/9u///u8YPnw4EhMTkZmZiRkzZmDPnj2dbvf0008jLy8PCQkJ6NevHxYtWhTx7LZ/J7vdjuzsbCxYsACVlZWtt2lsbMT8+fNx/vnnw+Fw4IYbbuj0OO+88w4mTJiAPn36IDExEWPGjMF//ud/BpWPKF6F21knT57EkiVLMHr0aCQmJiInJwe33347qqur293n9ttvx/jx4+F2uzFu3LiQskXaWR270jAMPPfcc93ObWhoQFJSEvbv3x/w520fz+FwICcnB//xH/8Bj8fT7nZr165Ffn4+evXqhaysLNxyyy346quvgvzbE8WfWD+H2rdvH2bMmIG+ffsiNTUV3/zmN/Hmm28GlW3SpEl48sknA/7sd7/7HcaMGYOkpCRkZGTgqquuwo4dOwLe1uPxYNy4cTAMA7t27QpqthXPoV588UVMmTIFmZmZSE1NxeWXX45XX301qHxE8SrWz6FafPXVVxg4cGCX3dZRd89jpk+fjpycHCQkJCArKwv/9m//htLS0taf7927F5MmTUL//v2RkJCAYcOG4de//jWam5u7nX348GEkJiairq6u088OHTrU7jmUy+XCiBEjcP/990Mp1Xq7K664IuBzuGnTpnU7n2KDJ6V6mEmTJqGurg4fffRR6/f+7//+D+eccw527NiBxsbG1u+/+eabyMnJwfDhwwEAp06dwjXXXINf/vKXZ5xx7733oqysrPVryZIl3eYqLCxEZWUlJk6cGPDn48ePx+rVq1FUVIRXX30VSilcffXV8Pl8rbf585//jF/96le466678Nlnn2Hr1q349re/HfHstn+nI0eOYO3atXj77bdx++23t/7c5/MhMTERt99+O6666qqAj5GUlITFixfj7bffRlFREX7961/j17/+NVauXNltRqJ4FW5nlZaWorS0FH/84x/x6aef4umnn8aWLVuwYMGCTjNuueUWzJ49O6Rc0egsAFi9enW7vgz0P2Mdvf766xg8eDBGjBjR5W1aHvfgwYN4/PHH8fe//x33339/68/fffddfP/738eCBQvw2Wef4b//+7/xwQcf4NZbbw1uAxDFoVg/h7ruuuvg9Xqxbds27Ny5E/n5+bjuuutQXl5+xlwnT57Eu+++i+uvvz7gz0eNGoVly5Zh9+7deOeddzBkyBBcffXVqKio6HTbn//858jOzj7jvLaseg719ttvY8qUKXjllVewc+dOTJo0Cddffz0++eSToLMSxRsrnkMBwIIFC5CXlxd0ru6ex0yaNAnPP/889u7diw0bNuCLL77ArFmzWn/udDrx/e9/H6+99hr27t2LRx55BE888QR++9vfdjv7f/7nfzBp0iQkJyd3eZutW7eirKwMxcXFuOeee/DAAw/gqaeeav35iy++2O6526effgq73Y6bb7456G1AUaaox8nKylJLly5t/fPPf/5ztWjRIpWbm6vefPPN1u9/61vfUvPmzet0/zfffFMBUJWVlZ1+NnjwYPWf//mfIWe699571ezZs4O+fUFBgQKg9u/fr5RS6uTJkyoxMVFt3bo16rMD/Z3uu+8+NXbs2IC3nzdvnpoxY0ZQs2+88UY1d+7cYKMSxaVIO6vF888/r1wul2pubu70s9/+9rcqPz8/6EyRdpZSSgFQGzduDPoxWtxyyy3qF7/4RZc/D/S4CxYsUNdee23rnx9++GE1bNiwdrd59NFH1YABA0LOQxRPYvUcqqKiQgFQb7/9duv3ampqFAD1+uuvnzHT3/72N3XppZcG/Xeorq5WADo9Z3rllVfUmDFj1GeffaYAqE8++aTbx9L5HGrs2LHqnnvuCeq2RPEq1s+hHn/8cTVx4kT1xhtvdPn/hx119zymo//5n/9RhmGopqamLm9zxx13qG9+85vdPtbkyZPVihUrAv7s4MGDAbvvyiuvVLfddluXj/mf//mfKiUlRdXV1XU7n2KDr5TqgSZNmtTu5eBvvvkmrrjiCkycOLH1+w0NDdixYwcmTZoU8uP//ve/R58+fXDBBRfg4YcfbveS0a5s2rQJM2bMCOrx6+vrsXr1agwdOhSDBg0CYJ5x9/v9OHbsGHJzczFw4EB85zvfQUlJSVRnA8CxY8fwj3/8A5deemnQ9wnkk08+wT//+c8z/naRiKLXWdXV1UhNTYXD4Yg4U6Sd1WLRokXo27cvLrnkEjz11FPtXh4eiN/vx8svvxxSZ+3btw/btm1r11mXX345SkpK8Morr0AphePHj+OFF17AtddeG/TjEsWjWD2H6tOnD0aPHo2//e1vqK+vh9frxX/913+hX79+GD9+/BnvG0ofNTU1YeXKlUhLS0N+fn7r948fP45bb70Vf//739GrV6+gc+t6DuX3+1FbW4vevXtH9DhEZ7tYPof6/PPPce+99+Jvf/sbbLbgTguE+jzm5MmTWLt2Lb7xjW/A6XQGvM3+/fuxZcuWbv+fqqqqCu+88w6mT58e1GwA+Oijj7Bz584zdtaqVavw3e9+F0lJSUE/LkWZ7rNiFLonnnhCJSUlqebmZlVTU6McDof68ssv1bp169S3vvUtpZRqPdt9+PDhTvc/0yul/vSnP6k333xTFRQUqBUrVqj09HR1xx13nDHP0aNHlcvl6vbM+vLly1VSUpICoEaPHt3uFQdLly5VTqdTjR49Wm3ZskW999576sorr1SjR49WHo8notmDBw9WLpdLJSUlqYSEBAVAXXrppV3ep7vf8g0YMEC5XC5ls9nUvffee8a/MxFF3llKma9CyMnJUb/85S8D/jyUV0pFo7OUMl9h8M4776iPP/5Y/f73v1dut1v95S9/OeNjvvvuu6pfv37K5/N1eRsAKiEhQSUlJSm3260AqOuuu67Tbxiff/55lZycrBwOhwKgrr/++jP+FpKIYvscqqSkRI0fP14ZhqHsdrvKyspSH3/88RnzNDY2quTkZPXpp5+e8Xb/+Mc/VFJSkjIMQ2VnZ6sPPvig9Wd+v19dc8016r777lNKdf1qgY50PIdq8dBDD6mMjAx1/Pjxbm9LFM9i9RyqsbFR5eXlqb///e9KqTN3W1vBPI9RynxFV69evRQAddlll6kTJ050us3ll1/e+jxn4cKF3T7m2rVr1UUXXdTlz1u6LzExUSUlJSmn09n62F3ZsWOHAqB27NhxxtkUWzwp1QMVFxcrAOqf//yn+t///d/Wl1AfO3ZMud1u1dDQoH7zm990emtHi2BLRymlVq1apRwOh2psbOzyNo8//riaPHlyt49VVVWl9u3bp7Zv366uv/56deGFF6qGhgallFIPPPCAAqBeffXV1tt/+eWXymazqS1btkQ0e/DgwepXv/qVKi4uVvv27VNvvPGGuvTSS9U3vvEN5fV6O92+uydUBw4cUIWFhWrlypWqd+/eat26dd38zYniW6SdVV1drS655BJ1zTXXdHnSJZSTUtHorEB+85vfqIEDB57xMX/+85+rW2655Yy3AaBWrFihiouL1Z49e9TLL7+sRo0a1e4tNp999pnKyspSf/jDH1RBQYHasmWLOv/887t9bKJ4F6vnUH6/X02fPl1NnTpVvfPOO2rnzp3qxz/+sRowYIAqLS3tMs8rr7zS5ay26urqVHFxsXrvvffULbfcooYMGdJ6Qucvf/mLmjBhQutzmmBPSul4DqWU+T+WvXr16vZtjUQUu+dQd9xxR7vnFcH+/2Ewz2OUMk+E7d27V7322mtqwoQJ6tprr1V+v7/dbY4cOaI+++wztW7dOjVgwAD10EMPnfExv/Od75zxBQEt3bdp0yZVXFysPv/8c/X888+r/v37d/l2w4ULF6rzzz+/278PxRZPSvVQAwcOVA888IC688471Y9//OPW748YMUK98cYb6pvf/Kb64Q9/GPC+oZyU+vTTTxUAtWfPni5vc80116hHHnkkpPwej0f16tWr9YTOU089pQCokpKSdrfr16+fWrlyZUSzA10P4b333uvyOg+hXA/hvvvuU6NGjQrqtkTxLNzOqqmpUZdffrm68sorz3hCKJSTUtHorEBefvllBeCMJ/HHjBmjXnrppTPOQoBrSj377LMKgCouLlZKKTV37lw1a9asdrf5v//7PwXgjP8DTESxeQ61detWZbPZVHV1dbvvjxgxot31YDr60Y9+pH7605+G/HcYMWKEevDBB5VSSs2YMUPZbDZlt9tbvwAou92uvv/973f5GDqeQz377LMqMTFRvfzyy2ecS0SnxeI5VH5+frvesNlsrb3x//7f/+sySzDPYzoqKSlpPbHWlb///e8qMTEx4MlupcznYampqWrXrl1dPkZXJ+SXLl2qHA5Hp21QV1enUlNTQ35OSNHHa0r1UJMmTcJbb72Ft956q91Hgn7rW9/C5s2b8cEHH4R1PamOdu3aBZvNhn79+gX8eV1dHd58882QrkcAAMo8Idr6EecTJkwAYH5EaIuTJ0/ixIkTGDx4cFRnA4Ddbgdgvgc7En6/v9PHtBNRZ+F0Vk1NDa6++mq4XC5s2rQJCQkJEeeIVmcFsmvXLmRkZMDtdgf8eXFxMQ4fPowpU6aENBvo3FmnTp3qdP2Hltuobq5rRRTvYvEc6tSpUwDQaV3abDb4/f6A91FK4R//+EdYz2PaPv949NFHUVBQgF27dmHXrl145ZVXAADr16/HAw88EPD+Op5DPfvss/jBD36AZ599lh+9ThSCWDyH2rBhQ7veePLJJwGYn+63aNGigDnCfR7T0oFneg7l9/vR3NzcZV++9dZbyMjIaHctvWDZ7XZ4vV40NTW1+/5///d/w+PxYO7cuSE/JkVX5FeLJS0mTZqERYsWobm5ud1F4SZOnIjFixejqampUzmVl5ejvLwc+/fvBwDs3r0bKSkpyMnJQe/evfHee++1XiQvJSUF7733Hu644w7MnTsXGRkZAXNs2bIFo0aNwpAhQ7rMeuDAAaxfvx5XX301MjMzcfToUfz+979HYmJi60V5R40ahRkzZuAnP/kJVq5cidTUVNx9990YM2ZMl08Mg5ndora2FuXl5VBKoaSkBD//+c+RmZmJb3zjG623+fzzz9HU1ISTJ0+itrYWu3btAgCMGzcOALB8+XLk5ORgzJgxAMyPN/7jH//Y7mORiSiwUDur5cnUqVOnsGbNGtTU1KCmpgYAkJmZ2fo/Rfv370ddXR3Ky8vR0NDQum7Hjh0Ll8vVKUe0Ousf//gHjh8/jssuuwwJCQl4/fXX8eCDD+LOO+/s8nH/53/+B1dddVVQFyGuqqpCeXk5/H4/iouLce+992LUqFHIzc0FAFx//fW49dZbsWLFCnz7299GWVkZfvrTn+KSSy4J6ePgieJRLJ5DXX755cjIyMC8efPw//7f/0NiYiKeeOIJHDx4sMsTMDt37sSpU6fwzW9+s8us9fX1eOCBBzB9+nRkZWXhxIkTWL58OY4dO9b68eU5OTnt7tPyUenDhw/HwIEDAz6u1c+h1q1bh3nz5uEvf/kLLr30UpSXlwMAEhMTkZaW1m0GongWi+dQw4cPbzfjxIkTAIDc3Fykp6cHzBHM85gdO3bgww8/xDe/+U1kZGTgiy++wG9+8xsMHz4cl19+OQBg7dq1cDqdOP/88+F2u/HRRx/h7rvvxuzZs7u8GPqmTZuCvsD5V199hfLycni9XuzevRt/+ctfMGnSJKSmpra73apVq3DDDTegT58+QT0uxZC+F2lRJFpenjhmzJh23z906FDrRXk7+u1vf6sAdPpavXq1UkqpnTt3qksvvVSlpaWphIQElZubqx588MEzvhVl7ty56le/+tUZsx47dkxNnTpV9evXTzmdTjVw4ED1ve99r9NbAqurq9Utt9yi0tPTVe/evdWNN96ojhw5EtFspcyXnrf9+2ZmZqprr72200s7O96u5avFo48+qs4991zVq1cvlZqaqi644AL1+OOPd3tRPiIKvbNa3iIT6OvgwYOtt5s4cWK3t2krWp21efNmNW7cOJWcnKySkpJUfn6++utf/3rGPvjmN7+pnnjiiTPOVkq1+3sYhqGysrLU7Nmz1RdffNHudo8++qgaO3asSkxMVFlZWWrOnDnq6NGj3T4+UbyLxXMopZT68MMP1dVXX6169+6tUlJS1GWXXaZeeeWVLnP8+te/VnPmzDlj1oaGBnXjjTeq7Oxs5XK5VFZWlpo+fXq7C5139fc70zWlrH4O1VVXn+kj7InIFKvnUIHuc6bLuwTzPKawsFBNmjRJ9e7dW7ndbjVkyBD1ox/9qN3zk+eee05deOGFrc+hxo4dqx588MEzXqZh0KBB3V6HrmU7tXzZ7XY1cOBAdeutt6ovv/yy3W337NmjAKjXXnvtjI9J1jCU4uv8KTxerxf9+/fH5s2bcckll8TNbCLqmXT2xokTJ5CVlYWjR4+if//+ls4mIpny8vLw61//Gt/5zncsncvnUEQUKp3PYz7++GNMnjwZFRUVXb6Sino2XlOKwnby5EnccccduPjii+NqNhH1TLo7689//jNPSBERAKCpqQkzZ87E1KlTLZ/N51BEFCqdz2O8Xi8ee+wxnpA6i/GVUkREREREREREZDm+UoqIiIiIiIiIiCzHk1JERERERERERGQ5npQiIiIiIiIiIiLL8aQUERERERERERFZjieliIiIiIiIiIjIcjwpRUREREREREREluNJKSIiIiIiIiIishxPShERERERERERkeV4UoqIiIiIiIiIiCzHk1JERERERERERGQ5npQiIiIiIiIiIiLL8aQUERERERERERFZjieliIiIiIiIiIjIcg6rB/r9fpSWliIlJQWGYVg9nogEUkqhtrYW2dnZsNn0nStnPxFRIBI6iv1ERIGwn4hIqmD7yfKTUqWlpRg0aJDVY4moBygpKcHAgQO1zWc/EdGZ6Owo9hMRnQn7iYik6q6fLD8plZKScvoPvexWjwdO+fTOZwZmkJZB9/w2Gdr1gwba+wkQtT+0ZdA9nxmYoYsMOjuK/cQMYuYzg8gM7Cc5+4IZmEH7fGEZuusny09Ktb6ks5cdaT8YafV41Dy9H6reCyPJgdT5IyyfzwzMIC2D7vkAUL26GDjl0/6Sb939BMjYH7oz6J7PDMzQkYSOYj8xg5T5zCArA/vJJGFfMAMzSJkvJUOw/cQLnRMRERERERERkeV4UoqIiIiIiIiIiCzHk1JERERERERERGQ5npQiIiIiIiIiIiLL8aQUERERERERERFZjieliIiIiIiIiIjIcjwpRUREREREREREluNJKSIiIiIiIiIislzIJ6XefvttXH/99cjOzoZhGHjppZdiEIuIKHTsJyKSiv1ERFKxn4hIp5BPStXX1yM/Px/Lly+PRR4iorCxn4hIKvYTEUnFfiIinRyh3mHq1KmYOnVqLLIQEUWE/UREUrGfiEgq9hMR6RTySalQeTweeDye1j/X1NTEeiQRUVDYT0QkFfuJiKRiPxFRNMX8QudLly5FWlpa69egQYNiPZKIKCjsJyKSiv1ERFKxn4gommJ+Uuruu+9GdXV161dJSUmsRxIRBYX9RERSsZ+ISCr2ExFFU8zfvud2u+F2u2M9hogoZOwnIpKK/UREUrGfiCiaYv5KKSIiIiIiIiIioo5CfqVUXV0d9u/f3/rngwcPYteuXejduzdycnKiGo6IKBTsJyKSiv1ERFKxn4hIp5BPSn300UeYNGlS65//4z/+AwAwb948PP3001ELRkQUKvYTEUnFfiIiqdhPRKRTyCelrrjiCiilYpGFiCgi7Ccikor9RERSsZ+ISCdeU4qIiIiIiIiIiCzHk1JERERERERERGQ5npQiIiIiIiIiIiLL8aQUERERERERERFZjieliIiIiIiIiIjIcjwpRUREREREREREluNJKSIiIiIiIiIishxPShERERERERERkeUMpZSycmBNTQ3S0tLM4UkOK0cDANQpL6AAGIDRy/r5zMAM0jLong8Aqt4LAKiurkZqaqqWDID+fgKE7A8ek8zADO0zCOgo9hMzSJnPDMIysJ8ACNkXzMAMQuaLyRBkP+lJ97WWkHqGa57PDMwgLYPu+cJo3xYS9ofuDLrnMwMzCKV9O0jYF8ygfz4zyMoghPbtIGFfMAMzSJkvJUM3tJ6U4iulmIEZ9GfQPR+QWZT8TZ++DLrnMwMzdMogrKPYT/GdQfd8ZhCWgf0EQMi+YAZmEDJfTIYg+0nfSaledqTOH2H52Jqn90PVe2H0cmiZzwzMIC2D7vkAUL26GDjl0zI7IE39BMjYH7oz6J7PDMzQkaiOYj/FfQbd85lBVgb2k0nCvmAGZpAyX0qGYPuJFzonIiIiIiIiIiLL8aQUERERERERERFZjieliIiIiIiIiIjIclovdE4UrryBuRjZfyiSE5JQ11iP4uMHUXi0SHcsy/kqGuGrbAKa/YDTBnuGC/bMBN2xiOIeO4r9RCQV+8nEjiKSh/1kird+4kkp6jESnG7MHH8tFk6ci/xBYzv9vKDkc6zcvgYbdr6CxmaPhoTWUF4/motr4CmshP9E57+nra8b7rwMOEemwnDwxZBEVmFHsZ+IpGI/mdhRRPKwn0zx3E88KUU9wvB+Q7Bx0ZMY1DsbfuUPeJvzBozGsjkP4BdTF+GGZQtwoOKwxSljz1fVhPpNR6Bqu/54Tf8JDxq2laPxwxNImp4De7rLwoRE8YkdxX4ikor9ZGJHEcnDfjLFez+dXafY6Kw0vN8QbL3zOWSn94dhGLDb7AFvZ7fZYRgGstP7442frcewzMEWJ40tX1UT6l44BFXXdVm1peq8qHvhEHxVTTFORhTf2FHsJyKp2E8mdhSRPOwnE/spxJNSS5cuxcUXX4yUlBT069cPN9xwA/bu3RurbERIcLqxcdGTSHEnwWEP7oV9DrsDKe4kvLR4FRKc7hgntIby+lG/6QjQ5AdUsHcC0GTeT3kD/+bhbMJ+Ih3YUeynYLGjyGrsJxM7qnvsJ7Ia+8nEfjKFdFJq+/btWLRoEd5//328/vrraG5uxtVXX436+vpY5aM4N3P8tRjUOzvosmrhsDuQ02cAbrpwaoySWau5uMZ8OWewZdVCAarWi+bimpjkkoT9RDqwo9hPwWJHkdXYTyZ2VPfYT2Q19pOJ/WQK6SjYsmVLuz8//fTT6NevH3bu3IlvfetbUQ1GBAALJ86FX/lhNwK/nPNMfH4fFk6ci3U7Xop+MIt5Cisjvr8rNz06YYRiP5EO7Cj2U7DYUWQ19pOJHdU99hNZjf1kYj+ZIrqmVHV1NQCgd+/eUQlD1FbewFzkDxrb5fuLu2O32TEu51zkDcyNcjJr+SoaA34CQyj8JzzwVTRGKVHPwH6iWGNHsZ8iwY6iWGI/mdhR4WE/USyxn0zsp9PCPinl9/vx05/+FBMmTMB5553X5e08Hg9qamrafREFY2T/oVF5nBFRehxdfJXRuYjd2XQxvO6wn8gK7Cj2U7iC6Sj2E0WC/WRiR4WO/USxxn4ysZ9OC/uk1KJFi/Dpp5/iueeeO+Ptli5dirS0tNavQYMGhTuS4kxyQlJUHiclSo+jTXOULmDXdHZcCC8Y7CeyAjsK7KcwBdNR7CeKBPvpa+yokLGfKNbYT19jP7UK66TU4sWL8fLLL+PNN9/EwIEDz3jbu+++G9XV1a1fJSUlYQWl+FPXGJ2LK9ZG6XG0cUb0LtvTXFF6HOHYT2QVdhTYT2EItqPYTxQJ9tPX2FEhYT+RFdhPX2M/tQrpQudKKSxZsgQbN27EW2+9haFDu3/JnNvthtt9dnxkI1mr+PjB6DxO+YGoPI4u9gxXdB4nPTqPIxX7iazGjmI/hSLUjmI/USTYTyZ2VHDYT2Ql9pOJ/XRaSKfVFi1ahDVr1mDdunVISUlBeXk5ysvL0dDQEKt8FMcKjxahoORz+Py+sO7v8/uw68hn2H1sT5STWcuemQBb38j+4bf1dcOemRClRDKxn8hq7Cj2UyjYUWQl9pOJHRUc9hNZif1kYj+dFtJJqRUrVqC6uhpXXHEFsrKyWr/Wr18fq3wU51ZuXwObEd5LEu02O1ZuXxPlRHq48zK03r8nYD+RDuwo9lOw2FFkNfaTiR3VPfYTWY39ZGI/mUI6EpRSAb/mz58fo3gU7zbsfAUlJ0vh9XlDup/X58WRr47hxY83xyiZtZwjU2GkOAAjxDsagJHigHNkakxyScJ+Ih3YUeynYLGjyGrsJxM7qnvsJ7Ia+8nEfjL1/Kti0VmtsdmDG5f/ELWe+qBLy+vzotZTjxuWLUBjsyfGCa1hOGxImp5jXsgu2NIyALjM+xkOLnWiWGBHsZ+IpGI/mdhRRPKwn0zsJ9PZ8begs9oXXx7ClQ/PRmnVcQDo8v3HLd8vrTqOKx+ejQMVhy3LaAV7ugvJs4bASA7u8wmMZAeSZw05Ky5+RyQZO4r9RCQV+8nEjiKSh/1kYj+F+Ol7RLocqDiMS+6fhpsunIqFE+diXM65nW6z++gerNy+Bi9+vPmsOXvekT3dhZTvDUNzcQ08hZXwn+j897T1dcOdl2G+HPQsOXtOJB07iv1EJBX7ycSOIpKH/WSK937iSSnqMRqbPVi34yWs2/ES8gbmYkT/oUhJSEJtYz32Hz+IwqNFuiNawnDY4MpNhys3Hb6KRviqmoAmP+CywZ7uOis+gYGoJ2JHsZ+IpGI/mdhRRPKwn0zx3E88KUU9UuHRorgpqDOxZyac1QVF1FOxo9hPRFKxn0zsKCJ52E+meOuns+t1X0RERERERERE1CPwpBQREREREREREVmOJ6WIiIiIiIiIiMhyhlJKWTmwpqYGaWlp5vAk6y9ppU55AQXAAIxeei6pxQzMICmD7vkAoOq9AIDq6mqkpqZqyQDo7ydAyP7gMckMzNA+g4COYj8xg5T5zCAsA/sJgJB9wQzMIGS+mAxB9pPWC523hNQzXPN8ZmAGaRl0zxdG+7aQsD90Z9A9nxmYQSjt20HCvmAG/fOZQVYGIbRvBwn7ghmYQcp8KRm6ofWkFF8pxQzMoD+D7vmAzKLkb/r0ZdA9nxmYoVMGYR3FforvDLrnM4OwDOwnAEL2BTMwg5D5YjIE2U/6Tkr1siN1/gjLx9Y8vR+q3gujl0PLfGZgBmkZdM8HgOrVxcApn5bZAWnqJ0DG/tCdQfd8ZmCGjkR1FPsp7jPons8MsjKwn0wS9gUzMIOU+VIyBNtPvNA5ERERERERERFZjieliIjorGRY+zkeREREREQUIq3XlCIiIoqW/IpGzCmqwuWlDRhd6YHLDzTZgL0ZbryXnYi1uekoyEzQHZOIiIiIiL7Gk1LUI+UNzMXI/kORnJCEusZ6FB8/iMKjRZZm8FU0wlfZBDT7AacN9gwX7PwfXiLLDa1qwrJtZZhQ1oBmA/hUARsA1AJI8QNjvvLgByc9WLi7Cu9mJWLx5CwcTHfFNJPuftA9n4gCk7A2mYGIApGwLpkhPvGkFPUYCU43Zo6/FgsnzkX+oLGdfl5Q8jlWbl+DDTtfQWOzJyYZlNeP5uIaeAor4T/ReYatrxvuvAw4R6bCcPDdsUSxNmtfNZZvK0eTT2E1gEcVsCvA7cYp4HYAs8oa8P5zB3Hb5CxsGJUa1Sy6+0H3fCIKTMLaZAYiCkTCumQG4kkp6hGG9xuCjYuexKDe2fArf8DbnDdgNJbNeQC/mLoINyxbgAMVh6OawVfVhPpNR6Bqu/5oS/8JDxq2laPxwxNImp4De4xfjUEUz2btq8bK18uwD8A1AA6j6wslFgK4BcA9AF71KTzxeikMKLwwKi0qWXT3g+75RBSYhLXJDEQUiIR1yQwE8ELn1AMM7zcEW+98Dtnp/WEYBuw2e8Db2W12GIaB7PT+eONn6zEsc3DUMviqmlD3wiGouq7Lqi1V50XdC4fgq2qKWgYiOm1YVROWbyvHPgDfAHD06+8HPmV9+vtHAVwOoBjA8m3lGBqFNaq7H3TPJ6LAJKxNZiCiQCSsS2agFiGdlFqxYgXy8vKQmpqK1NRUXH755di8eXOsshEhwenGxkVPIsWdBIc9uBf2OewOpLiT8NLiVUhwuiPOoLx+1G86AjT5gWA/zEsBaDLvp7xd/W8yRRP7Kb48tq0MHp/CNQBqAPiCvJ/v69t/G0CTT2HZtrKIcujuB93zKXjsqPgiYW0yAwWL/RRfJKxLZqC2QjopNXDgQPz+97/Hzp078dFHH2Hy5MmYMWMGPvvss1jlozg3c/y1GNQ7O+gTUi0cdgdy+gzATRdOjThDc3GN+XLOUD9dXgGq1ovm4pqIM1D32E/xI//LRkwoa8AGmG/ZC/aEVAvf1/fbAGBCWQPyKxrDzqK7H3TPp+Cxo+KLhLXJDBQs9lN8kbAumYHaCumk1PXXX49rr70WI0eOxKhRo/DAAw8gOTkZ77//fqzyUZxbOHFul9eQ6o7P78PCiXMjzuAprNR6fwoO+yl+zNlThWYDeBThvwfdBvP+zQYwp6gq7Cy6+0H3/Sl47Kj4ImFtMgMFi/0UXySsS2agtsK+ppTP58Nzzz2H+vp6XH755dHMRAQAyBuYi/xBY7u8hlR37DY7xuWci7yBuWFn8FU0BvwEhlD4T3jgi+CVGBQ69tPZ7fLSBnz69afshfvCaT+ATwB8qoDLyhrCegzd/aB7PoWPHXV2k7A2mYHCxX46u0lYl8xAHYX86Xu7d+/G5ZdfjsbGRiQnJ2Pjxo0YO3Zsl7f3eDzweE7v8JoavsyNgjOy/9CoPM6I/kNReLQorPv6KqN3IWJ7ZkJUHou6xn6KD6MrPdgQpcfaC+Cmk+E9KdHdD7rnU+hC6Sj2U88lYW0yA4WK/RQfJKxLZqCOQn6l1OjRo7Fr1y7s2LEDP/7xjzFv3jx8/vnnXd5+6dKlSEtLa/0aNGhQRIEpfiQnJEXlcVIieZzmKF3ArokXwrMC++nsZygFlx+ojdLj1QBw+c3HDZnuftA9n0IWSkexn3owCWuTGShE7Kc4IWFdMgN1EPJJKZfLhREjRmD8+PFYunQp8vPz8Ze//KXL2999992orq5u/SopKYkoMMWPusb6qDxObSSP4wz7Ha7tuaL0OHRG7KeznzIMNNmAlCg9XiqAJpv5uCHT3Q+651PIQuko9lMPJmFtMgOFiP0UJySsS2agDkJ++15Hfr+/3cs3O3K73XC73ZGOoThUfPxgdB6n/EDY97VnuKKSwZ4enceh0LCfzk57M9wY81Vk1wFoMQbAnt7hHQO6+0H3fIrcmTqK/dRzSVibzECRYj+dnSSsS2agjkI6KXX33Xdj6tSpyMnJQW1tLdatW4e33noLr776aqzyURwrPFqEgpLPcd6A0WFd7Nzn92H30T3YfWxP2BnsmQmw9XVHdCE8W18332tsAfZT/HgvOxE/OOnBOAUUIryLndsA5AM41wBWZyWGlUN3P+ieT6FhR8UPCWuTGSgU7Kf4IWFdMgN1FNLrzb788kt8//vfx+jRo3HllVfiww8/xKuvvoopU6bEKh/FuZXb18BmhPeySLvNjpXb10ScwZ2XofX+FBz2U/xYm5sOpwJuR2Sfvnc7AKcyHy9cuvtB9/0peOyo+CJhbTIDBYv9FF8krEtmoLZCeqXUqlWrYpWDKKANO1/BL6YuQnZ6fzjswR+uXp8XpVXH8eLHmyPO4ByZisYPT0DVeYFQroVsAEayA86RqRFnoO6xn+JHQWYC3s1KxKyyBtwD4CgAXwj3twMYCGAmgHezElEQwW+5dPeD7vkUPHZUfJGwNpmBgsV+ii8S1iUzUFu8MheJ1tjswY3Lf4haTz28Pm9Q9/H6vKj11OOGZQvQ2Bz5dWcMhw1J03PMC9kFey1kA4DLvJ/h4DIjirbFk7PgshvYAvNi5cG+wdf+9e1fBeCyG1g8OSuiHLr7Qfd8IgpMwtpkBiIKRMK6ZAZqi1uSxPviy0O48uHZKK06DsC8VlQgLd8vrTqOKx+ejQMVh6OWwZ7uQvKsITCSg3u1lpHsQPKsIbz4HVGMHEx34bbJWRgF4D2Yr3wCuv5HreX7A7++/UgAt03OwsEorFHd/aB7PhEFJmFtMgMRBSJhXTIDtYj40/eIrHCg4jAuuX8abrpwKhZOnItxOed2us3uo3uwcvsavPjx5qi8Qqoje7oLKd8bhubiGngKKwNeGM/W1w13XgacI1N59pwoxjaMSoUBheXbyrHbp7ABwKMAPglw23yY15CaCfMVUrdOzsKGUdF72bXuftA9n4gCk7A2mYGIApGwLpmBAJ6Uoh6ksdmDdTtewrodLyFvYC5G9B+KlIQk1DbWY//xgyg8WhTzDIbDBlduOly56fBVNMJX1QQ0+QGXDfZ0Fz+BgchiL4xKw85+iVi2rQzzyxowxwA+VcBeADUw36o3GsB5hnlR83eyE7FkUnReIdWR7n7QPZ+IApOwNpmBiAKRsC6ZgXhSinqkwqNFlpyEOhN7ZgILikiAg+kuTLtpMPIrGjGnqAqXlTXgppMeuPxAkw3Y09uN1VmJWJubHtFFzUOhux90zyeiwCSsTWYgokAkrEtmiE88KUVERGeFgswEFGSe0/pnQykoI9grVxIRERERkdX4hkgiIjor8YQUEREREZFsPClFRERERERERESWM5RSysqBNTU1SEtLM4cnWf/uQXXKCygABmD00vPuRWZgBkkZdM8HAFXvBQBUV1cjNTV6n4gWKt39BAjZHzwmmYEZ2mcQ0FHsJ2aQMp8ZhGVgPwEQsi+YgRmEzBeTIch+0npNqZaQeoZrns8MzCAtg+75wmjfFhL2h+4MuuczAzMIpX07SNgXzKB/PjPIyiCE9u0gYV8wAzNImS8lQze0npTiK6WYgRn0Z9A9H5BZlPxNn74MuuczAzN0yiCso9hP8Z1B93xmEJaB/QRAyL5gBmYQMl9MhiD7Sd9JqV52pM4fYfnYmqf3Q9V7YfRyaJnPDMwgLYPu+QBQvboYOOXTMjsgTf0EyNgfujPons8MzNCRqI5iP8V9Bt3zmUFWBvaTScK+YAZmkDJfSoZg+4kXOiciIiIiIiIiIsvxpBQRERFRnDCs/XwbIiIiojPSek0pIiIiIoqd/IpGzCmqwuWlDRhd6YHLDzTZgL0ZbryXnYi1uekoyEzQHZOIiIjiFE9KhchX0QhfZRPQ7AecNtgzXLBb/GSOGfTPB4C8gbkY2X8okhOSUNdYj+LjB1F4tMjSDBK2g4QMZJKwL5hBRgb2k5wMugytasKybWWYUNaAZgNw5o8DpowBUlLgqq3F+Xv2YEzBLizcXYV3sxKxeHIWDqa7YpZHwjHJDMzQlu5+0D1fEgnHg+4Muuczg6wMEvrBygw8KRUE5fWjubgGnsJK+E94Ov3c1tcNd14GnCNTYThi845IZtA/HwASnG7MHH8tFk6ci/xBYzv9vKDkc6zcvgYbdr6CxubOGaNBwnaQkIFMEvYFM8jIwH6Sk0G3WfuqsXxbOewuNzB/Ppy33w5ccEGn2zk/+QR49FFcuv45vP/cQdw2OQsbRqVGLYeEY5IZmKEt3f2ge74kEo4H3Rl0z2cGWRkk9IOuDIZS1l5coKamBmlpaUAvO9J+MNLK0eb8lqvQJwV3FXpfVRPqNx2Bqu3+4wyNFAeSpufA3s1vGpkh9AyxmB9qhuH9hmDjoicxqHc2/MoPu83eOaffB5thQ8nJUtywbAEOVByOagbd+yFWGVo+maG6uhqpqdH7H6JQ6e4nQP/xICGDhGOyp2U4W/tJSgYJHRVsP83aV42Vr5cBI0fC9tprwODBgN8P2DsfE/D5AJsNOHwY/ilTgP37sXBKFl4YlRY4Qw87Js/WDKH2EzN8/fhn6XPZntRPLSQcD7oznK39xAyhzwf091OsMgTbT2f3KfgI+aqaUPfCIai67ncMAKg6L+peOARfVRMzRDGD7vmAWVZb73wO2en9YRhGwLICALvNDsMwkJ3eH2/8bD2GZQ6OWgYJ20FCBjJJ2BfMICMD+0lOBt2GVTVh+bZy84TU++8DAwYAhhH4hBRgft8wgAEDYNuxAxgxAsu3lWNohNtEwjHJDMzQlu5+0D1fEgnHg+4Muuczg6wMEvpBd4aITkr9/ve/h2EY+OlPfxqVMJIorx/1m44ATX4g2NeSKQBN5v2U188MUcigez5gvpxz46InkeJOgsMe3DteHXYHUtxJeGnxKiQ43RFnkLAdJGQIBfup451w1h0PzMB+kpQhFLHqp8e2lcHmcpuvkEpJAZzO4O7odAIpKbC9/jrsLjeWbSsLO4OEY5IZmKEt3f2ge344YtVREo4H3Rl0z2cGWRkk9IOEDGGflPrwww/xX//1X8jLy4s4hETNxTXmS9dCfXOjAlStF83FNcwQhQy65wPAzPHXYlDv7KDLqoXD7kBOnwG46cKpEWeQsB0kZAgW+6kLZ9nxwAzsJ0kZghWrfsr/shETyhrgnP1d8y17wZ6QauF0AkOGwPGd2ZhQ1oD8isawckg4JpmBGdrS3Q+654cqls+hJBwPujPons8MsjJI6AcJGcI6KVVXV4c5c+bgiSeeQEZGRsQhJPIUVmq9PzPIuD8ALJw4F34V3hlgn9+HhRPnRpxBwnaQkCEY7KfY358Z5GRgP8nJEIxY9tOcPVVoNgDcfrt5Dalw+HzAkiVoNoA5RVVhPYSEY5IZmKEt3f2g+/6hiPVzKAnHg+4Muuczg6wMEvpBQoawTkotWrQI06ZNw1VXXRVxAIl8FY0BrzYfCv8JD3xh/paRGWTMB8yPBM0fNLbL9xd3x26zY1zOucgbmBt2BgnbQUKGYLGfunc2HA/MwH6SlCFYseyny0sb4MwfZ37KXlfXkOqO3Q6MHw9n/jhcVtYQ8t0lHJPMwAxt6e4H3fNDFcuOknA86M6gez4zyMogoR8kZADCOCn13HPP4eOPP8bSpUuDur3H40FNTU27L+l8ldG7qCEzhJ9B93wAGNl/aFQyjIjgcSRsBwkZgsF+CuFxevjxwAzsJ0kZghHrfhpd6QHGjIlGVGD0aIw5GfqTVAnHJDMwQ1u6+0H3/FCE0lHhPH+ScDzozqB7PjPIyiChHyRkAEI8KVVSUoKf/OQnWLt2LRISEoK6z9KlS5GWltb6NWjQoLCCWqo5ShcUbIrgcZhB/3wAyQlJUYmQEsnjCNgOIjJ0g/0Uop5+PDAD+0lShm7Eup8MpeDyw7y4eTSkpsLlNx83FBKOSWZghnZ094Pu+UEKtaPCef4k4XjQnUH3fGaQlUFEP0jIgBBPSu3cuRNffvklLrzwQjgcDjgcDmzfvh2PPvooHA4HfD5fp/vcfffdqK6ubv0qKSmJKLAlnBF9KOFprggehxn0zwdQ11gflQi1kTyOgO0gIkM32E8h6unHAzOwnyRl6Eas+0kZBppsAGproxO4pgZNNvNxQyHhmGQGZmhHdz/onh+kUDsqnOdPEo4H3Rl0z2cGWRlE9IOEDABCutT8lVdeid27d7f73g9+8AOMGTMGv/jFL2APcA0Dt9sNtzvyj0u0kj3DFZ3HSQ//cZhB/3wAKD5+MCoZissPhH1fCdtBQobusJ9CfJwefjwwA/tJUobuWNFPezPcOH/PnoizAgD27MGe3qF3o4RjkhmYoS3d/aB7frBC7ahwnj9JOB50Z9A9nxlkZZDQDxIyACG+UiolJQXnnXdeu6+kpCT06dMH5513XkRBJLFnJsDWN7L/UbX1dcOeGdxL9JlB5nwAKDxahIKSz+Hzd/4tdjB8fh92HfkMu4+F/z8KEraDhAzdYT8F72w4HpiB/SQpQ3es6Kf3shPRXLAL+OQT81P0wuHzATt3ormwAO9nJYZ8dwnHJDMwQ1u6+0H3/GBZ0VESjgfdGXTPZwZZGST0g4QMQJifvhcP3HmRfQxqpPdnBhn3B4CV29fAZoS3VOw2O1ZuXxNxBgnbQUIGMknYF8wgIwP7SU4G3dbmpsOpADz6KGAL8+md3Q489hicyny8cEg4JpmBGdrS3Q+67y+JhONBdwbd85lBVgYJ/SAhQ8Qnpd566y088sgjEQeRxjkyFUaKAwjtcgqAARgpDjhHpjJDFDLong8AG3a+gpKTpfD6vCHdz+vz4shXx/Dix5sjziBhO0jIECr2Uwdn2fHADOwnSRlCFe1+KshMwLtZifCufw44fBhobg7tAZqbgUOH4H1+Pd7NSkRBmL/1lHBMMgMztKW7H3TPD1csnkNJOB50Z9A9nxlkZZDQDxIy8JVSXTAcNiRNzzEv2hXsDjIAuMz7GY7INy0z6J8PAI3NHty4/Ieo9dQHXVpenxe1nnrcsGwBGptD/1jtjiRsBwkZyCRhXzCDjAzsJzkZJFg8OQu+Jg/8V19tXvQ82BNTzc1AbS38U6bA1+TB4slZYWeQcEwyAzO0pbsfdM+XRMLxoDuD7vnMICuDhH6QkOHsabkYsKe7kDxrCIzk4K4HbyQ7kDxrSFQvRsgM+ucDwBdfHsKVD89GadVxAOjy/cct3y+tOo4rH56NAxWHo5ZBwnaQkIFMEvYFM8jIwH6Sk0G3g+ku3DY5Cyguhv/SS4Fjx8wfdHWNqZbvHztm3n7/ftw2OQsHI9wmEo5JZmCGtnT3g+75kkg4HnRn0D2fGWRlkNAPujOE9Ol78cie7kLK94ahubgGnsJK+E90PiNq6+uGOy/DfOlbDH6bwQz65wPAgYrDuOT+abjpwqlYOHEuxuWc2+k2u4/uwcrta/Dix5ujcva8IwnbQUIGMknYF8wgIwP7SU4G3TaMSoUBheXbvoB9bC4c35kNLFkCjB/f+ca7dgGPPQbv8+vha/LgtinZ2DAqOm8VknBMMgMztKW7H3TPl0TC8aA7g+75zCArg4R+0JmBJ6WCYDhscOWmw5WbDl9FI3xVTUCTH3DZYE93xfwTMZhBxnzAfJnnuh0vYd2Ol5A3MBcj+g9FSkISahvrsf/4QRQeLYp5BgnbQUIGMknYF8wgIwP7SU4G3V4YlYad/RKxbFsZJjzzDJr/9gyc+eOA0aOB1FSgpgbYuxfNBbvgVMD72YlYMmloxK+Q6kjCMckMzNCW7n7QPV8SCceD7gy65zODrAwS+kFXBp6UCpE9M0H7PxjMoH8+YH6UqBUFdSYStoOEDGSSsC+YQUYG9pOcDLocTHdh2k2DkV/RiDlFVbjsaBHGFO6Cyw802YA9vd14/7x0rM1ND/ui5qGQcEwyAzO0pbsfdM+XRMLxoDuD7vnMICuDhH6wMgNPShERERGdpQoyE1CQeU7rnw2loIxQP2KHiIiIKDbO3jcrExEREVE7PCFFREREkvCkFBERERERERERWc5QSikrB9bU1CAtLc0cnmT9uwfVKS+gABiA0UvPuxeZgRkkZdA9HwBUvRcAUF1djdTU6HzyUzh09xMgZH/wmGQGZmifQUBHsZ+YQcp8ZhCWgf0EQMi+YAZmEDJfTIYg+0nrNaVaQuoZrnk+MzCDtAy65wujfVtI2B+6M+iezwzMIJT27SBhXzCD/vnMICuDENq3g4R9wQzMIGW+lAzd0HpSiq+UYgZm0J9B93xAZlHyN336MuiezwzM0CmDsI5iP8V3Bt3zmUFYBvYTACH7ghmYQch8MRmC7Cd9J6V62ZE6f4TlY2ue3g9V74XRy6FlPjMwg7QMuucDQPXqYuCUT8vsgDT1EyBjf+jOoHs+MzBDR6I6iv0U9xl0z2cGWRnYTyYJ+4IZmEHKfCkZgu0nXuiciIiIiIiIiIgsx5NSRERERERERERkOZ6UIiIiIiIiIiIiy2m90Dn1XL6KRvgqm4BmP+C0wZ7hgj0zIW7mM4OsDERtSTgmdWfQPZ8ZiAKTcEwyg4wMuucTdSThmGSG+MzAk1IUNOX1o7m4Bp7CSvhPeDr93NbXDXdeBpwjU2E4ov8iPN3zmUFWBqK2JByTujPons8MRIFJOCaZQUYG3fOJOpJwTDIDM/CkFAXFV9WE+k1HoGq7/lhH/wkPGraVo/HDE0iangN7uuusmc8MsjIQtSXhmNSdQfd8ZiAKTMIxyQwyMuieT9SRhGOSGZgB4DWlKAi+qibUvXAIqq7rA7QtVedF3QuH4KtqOivmM4OsDERtSTgmdWfQPZ8ZiAKTcEwyg4wMuucTdSThmGQGZmgR0kmp3/3udzAMo93XmDFjohKEZFJeP+o3HQGa/IAK9k4Amsz7Ka+/R89nBlkZzoT9FH8kHJO6M+iezwzBY0fFFwnHJDPIyKB7fjDYT/FFwjHJDMzQVsivlDr33HNRVlbW+vXOO+9EHILkai6uMV/CF+wB2kIBqtaL5uKaHj2fGWRl6A77Kb5IOCZ1Z9A9nxlCw46KHxKOSWaQkUH3/GCxn+KHhGOSGZihrZBPSjkcDpxzzjmtX3379o04BMnlKayM6/szg6wM3WE/xRcJx6TuDLrvzwyhYUfFDwnHJDPIyKD7/sFiP8UPCcckMzBDWyGflCouLkZ2djaGDRuGOXPm4MiRIxGHIJl8FY0Br7ofCv8JD3wVjT1yPjPIyhAM9lP8kHBM6s6gez4zhI4dFR8kHJPMICOD7vmhYD/FBwnHJDMwQ0chnZS69NJL8fTTT2PLli1YsWIFDh48iH/5l39BbW1tl/fxeDyoqalp90U9g68yehd37InzmUFWhu6wn+KLhGNSdwbd85khNKF2FPup55JwTDKDjAy65weL/RQ/JByTzMAMHTlCufHUqVNb/zsvLw+XXnopBg8ejOeffx4LFiwIeJ+lS5finnvuiSgkadIcpQsrNoX5OLrnM4OsDN1gP8UZCcek7gy65zNDSELtKPZTDybhmGQGGRl0zw8S+ymOSDgmmYEZOgj57XttpaenY9SoUdi/f3+Xt7n77rtRXV3d+lVSUhLJSLKSM6LD4zRXmI+jez4zyMoQIvbTWU7CMak7g+75zBCR7jqK/dSDSTgmmUFGBt3zw8R+OotJOCaZgRk6iOjedXV1+OKLL5CVldXlbdxuN1JTU9t9Uc9gz3BF53HSw3sc3fOZQVaGULGfzm4SjkndGXTPZ4bIdNdR7KeeS8IxyQwyMuieHy7209lLwjHJDMzQUUgnpe68805s374dhw4dwj//+U/ceOONsNvt+Nd//deIQpBM9swE2Pq6I3oMW1837JkJPXI+M8jK0B32U3yRcEzqzqB7PjOEhh0VPyQck8wgI4Pu+cFiP8UPCcckMzBDp8cI5cZHjx7Fv/7rv2L06NH4zne+gz59+uD9999HZmZmRCFILndeRlzfnxlkZTgT9lP8kXBM6s6g+/7MEDx2VHyRcEwyg4wMuu8fDPZTfJFwTDIDM7QV0oXOn3vuuYgHUs/iHJmKxg9PQNV5ARXCHQ3ASHbAOTKyl/Pqns8MsjKcCfsp/kg4JnVn0D2fGYLHjoovEo5JZpCRQff8YLCf4ouEY5IZmKEta6+aRz2O4bAhaXqOefEyI9g7AXCZ9zMckR1iuuczg6wMRG1JOCZ1Z9A9nxmIApNwTDKDjAy65xN1JOGYZAZmaIstR92yp7uQPGsIjOTgXlhnJDuQPGtI1C7KqHs+M8jKQNSWhGNSdwbd85mBKDAJxyQzyMigez5RRxKOSWZghhYhvX2P4pc93YWU7w1Dc3ENPIWV8J/wdLqNra8b7rwMOEemRv23OrrnM4OsDERtSTgmdWfQPZ8ZiAKTcEwyg4wMuucTdSThmGQGZgB4UopCYDhscOWmw5WbDl9FI3xVTUCTH3DZYE93xfyTQXTPZwZZGYjaknBM6s6gez4zEAUm4ZhkBhkZdM8n6kjCMckMzMCTUhQWe2aC1n84dc9nBlkZiNqScEzqzqB7PjMQBSbhmGQGGRl0zyfqSMIxyQzxmYGvCyUiIiIiIiIiIsvxpBQREREREREREVmOJ6WIiIiIiIiIiMhyhlJKWTmwpqYGaWlp5vAk6y9ppU55AQXAAIxeei6pxQzMICmD7vkAoOq9AIDq6mqkpqZqyQDo7ydAyP7gMckMzNA+g4COYj8xg5T5zCAsA/sJgJB9wQzMIGS+mAxB9pPWC523hNQzXPN8ZmAGaRl0zxdG+7aQsD90Z9A9nxmYQSjt20HCvmAG/fOZQVYGIbRvBwn7ghmYQcp8KRm6ofWkFF8pxQzMoD+D7vmAzKLkb/r0ZdA9nxmYoVMGYR3FforvDLrnM4OwDOwnAEL2BTMwg5D5YjIE2U/6Tkr1siN1/gjLx9Y8vR+q3gujl0PLfGZgBmkZdM8HgOrVxcApn5bZAWnqJ0DG/tCdQfd8ZmCGjkR1FPsp7jPons8MsjKwn0wS9gUzMIOU+VIyBNtPvNA5ERERERERERFZjieliIiIiIiIiIjIcjwpRUREREREREREltN6oXMKT97AXIzsPxTJCUmoa6xH8fGDKDxaZGkGX0UjfJVNQLMfcNpgz3DBnplg2XxuA2Ygmbg2Tbq3g4RtwAwkje51Ccg4JrkdZGTQPZ9k4bpkhnjOwJNSPUSC042Z46/FwolzkT9obKefF5R8jpXb12DDzlfQ2OyJSQbl9aO5uAaewkr4T3SeYevrhjsvA86RqTAc0X8RHrcBM5BMXJsm3dtBwjZgBpJG97oEZByT3A4yMuieT7JwXTIDM5h4UqoHGN5vCDYuehKDemfDr/wBb3PegNFYNucB/GLqItywbAEOVByOagZfVRPqNx2Bqu36Yx39Jzxo2FaOxg9PIGl6DuzprqjN5zZgBpKJa9OkeztI2AbMQNLoXpeAjGOS20FGBt3zSRauS2ZghtN4Cl644f2GYOudzyE7vT8Mw4DdZg94O7vNDsMwkJ3eH2/8bD2GZQ6OWgZfVRPqXjgEVdf1AdqWqvOi7oVD8FU1RWU+twEzkExcmybd20HCNmAGkkb3ugRkHJPcDjIy6J5PsnBdMgMztBfySaljx45h7ty56NOnDxITE3H++efjo48+ikoYai/B6cbGRU8ixZ0Ehz24F7U57A6kuJPw0uJVSHC6I86gvH7UbzoCNPkBFeydADSZ91PewGf+g8VtwAyhYD9Zh2vTpHs7SNgGzBA8dpQ1dK9LQMYxye0gI4Pu+cFiP1mD65IZmKGzkE5KVVZWYsKECXA6ndi8eTM+//xz/OlPf0JGRkbEQaizmeOvxaDe2UEXVguH3YGcPgNw04VTI87QXFxjvoQv2AO0hQJUrRfNxTURzec2YIZgsZ+sxbVp0r0dJGwDZggOO8o6utclIOOY5HaQkUH3/GCwn6zDdckMzNBZSCelHnroIQwaNAirV6/GJZdcgqFDh+Lqq6/G8OHDIw5CnS2cOLfL9xh3x+f3YeHEuRFn8BRWar0/twEzBIv9ZC2uTZPu7SBhGzBDcNhR1tG9LgEZxyS3g4wMuu8fDPaTdbgumYEZOgvppNSmTZtw0UUX4eabb0a/fv1wwQUX4Iknnog4BHWWNzAX+YPGdvke4+7YbXaMyzkXeQNzw87gq2gMeNX9UPhPeOCraAzrvtwGzBAK9pN1uDZNureDhG3ADMFjR1lD97oEZByT3A4yMuieHyz2kzW4LpmBGQIL6aTUgQMHsGLFCowcORKvvvoqfvzjH+P222/HM8880+V9PB4Pampq2n1R90b2HxqVxxkRweP4KqN3ccdwcBswQyjYT9bh2jTp3g4StgEzBC/UjmI/hUf3ugRkHJPcDjIy6J4fLPaTNbgumYEZAgvpzax+vx8XXXQRHnzwQQDABRdcgE8//RR//etfMW/evID3Wbp0Ke65556IQsaj5ISkqDxOSiSP0xylCys2hfc43AbMEAr2k3W4Nk3at4OAbcAMwQu1o9hP4dG+LgERxyS3g5AMuucHif1kDa5LZmCGwEJ6pVRWVhbGjh3b7nu5ubk4cuRIl/e5++67UV1d3fpVUlISXtI4U9dYH5XHqY3kcZwhfzhjYK7wHofbgBlCwX6yDtemSft2ELANmCF4oXYU+yk82tclIOKY5HYQkkH3/CCxn6zBdckMzBBYSK+UmjBhAvbu3dvue/v27cPgwYO7vI/b7YbbHflHV8ab4uMHo/M45QfCvq89wxWVDPb08B6H24AZQsF+sg7Xpkn3dpCwDZgheKF2FPspPLrXJSDjmOR2kJFB9/xgsZ+swXXJDMwQWEintO644w68//77ePDBB7F//36sW7cOK1euxKJFiyIKQZ0VHi1CQcnn8Pl9Yd3f5/dh15HPsPvYnrAz2DMTYOsb2T84tr5u2DMTwrovtwEzhIL9ZB2uTZPu7SBhGzBD8NhR1tC9LgEZxyS3g4wMuucHi/1kDa5LZmCGLh4jlBtffPHF2LhxI5599lmcd955uO+++/DII49gzpw5EYWgwFZuXwObEd5L4ew2O1ZuXxNxBndehtb7cxswQ7DYT9bi2jTp3g4StgEzBIcdZR3d6xKQcUxyO8jIoPv+wWA/WYfrkhmYobOQV8R1112H3bt3o7GxEUVFRbj11lsjDkGBbdj5CkpOlsLr84Z0P6/PiyNfHcOLH2+OOINzZCqMFAdghHhHAzBSHHCOTI1oPrcBM4SC/WQdrk2T7u0gYRswQ/DYUdbQvS4BGcckt4OMDLrnB4v9ZA2uS2Zghs5ie9U8ikhjswc3Lv8haj31QReX1+dFraceNyxbgMZmT8QZDIcNSdNzzIuXBXugGgBc5v0MR2SHGLcBM5BMXJsm3dtBwjZgBpJG97oEZByT3A4yMuieT7JwXTIDM3TGlhPuiy8P4cqHZ6O06jgAdPke5Jbvl1Ydx5UPz8aBisNRy2BPdyF51hAYycFdF99IdiB51pCoXZSR24AZSCauTZPu7SBhGzADSaN7XQIyjkluBxkZdM8nWbgumYEZ2gvp0/dIjwMVh3HJ/dNw04VTsXDiXIzLObfTbXYf3YOV29fgxY83R+UMekf2dBdSvjcMzcU18BRWwn+i8wxbXzfceRnmSwCj/FsdbgNmIJm4Nk26t4OEbcAMJI3udQnIOCa5HWRk0D2fZOG6ZAZmOI0npXqIxmYP1u14Cet2vIS8gbkY0X8oUhKSUNtYj/3HD6LwaFHMMxgOG1y56XDlpsNX0QhfVRPQ5AdcNtjTXTH/ZBBuA2Ygmbg2Tbq3g4RtwAwkje51Ccg4JrkdZGTQPZ9k4bpkBmYw8aRUD1R4tMiSkjoTe2aC1n84uQ2YgWTi2jTp3g4StgEzkDS61yUg45jkdpCRQfd8koXrkhniOQNfF0pERERERERERJbjSSkiIiIiIiIiIrIcT0oREREREREREZHlDKWUsnJgTU0N0tLSzOFJ1l/SSp3yAgqAARi99FxSixmYQVIG3fMBQNV7AQDV1dVITU3VkgHQ30+AkP3BY5IZmKF9BgEdxX5iBinzmUFYBvYTACH7ghmYQch8MRmC7CetFzpvCalnuOb5zMAM0jLoni+M9m0hYX/ozqB7PjMwg1Dat4OEfcEM+uczg6wMQmjfDhL2BTMwg5T5UjJ0Q+tJKb5SihmYQX8G3fMBmUXJ3/Tpy6B7PjMwQ6cMwjqK/RTfGXTPZwZhGdhPAITsC2ZgBiHzxWQIsp/0nZTqZUfq/BGWj615ej9UvRdGL4eW+czADNIy6J4PANWri4FTPi2zA9LUT4CM/aE7g+75zMAMHYnqKPZT3GfQPZ8ZZGVgP5kk7AtmYAYp86VkCLafeKFzIiIiIiIiIiKyHE9Khcmw9vrwRERERERERERnFa3XlOpJ8isaMaeoCpeXNmB0pQcuP9BkA/ZmuPFediLW5qajIDNBd0wiIiIiIiIioh6BJ6W6MbSqCcu2lWFCWQOaDcCZPw6YMgZISYGrthbn79mDMQW7sHB3Fd7NSsTiyVk4mO6Kaaa8gbkY2X8okhOSUNdYj+LjB1F4tCimM6Vl0D2fGU7zVTTCV9kENPsBpw32DBfsPEGrhYTjgRlkZNA9X0oG9pMcEo4HZmAGSRnYT7LoPh50z2eG0ySszXjLwJNSZzBrXzWWbyuH3eUG5s+H8/bbgQsu6HQ75yefAI8+ikvXP4f3nzuI2yZnYcOo1KhmSXC6MXP8tVg4cS7yB43t9POCks+xcvsabNj5ChqbPVGdLSWD7vnMcJry+tFcXANPYSX8JzrPsPV1w52XAefIVBgOvks4liQcD8wgI4Pu+VIysJ/kkHA8MAMzSMrAfpJF9/Ggez4znCZhbcZzBkMpay+OVFNTg7S0NKCXHWk/GGnlaHN+y1Xok858FfpZ+6qx8vUyYORI2F57DRg8GPD7Abu98419PsBmAw4fhn/KFGD/fiyckoUXRqVFlKHF8H5DsHHRkxjUOxt+5Yfd1jmDz++DzbCh5GQpbli2AAcqDp/xMXtahljMZ4bQ5wOAr6oJ9ZuOQNV2/xGfRooDSdNzYO/m1YMtn8xQXV2N1NTontANhe5+AvQfDxIy9LR+kpDhbD0WQs0Qi34CZHQU+4kZwpnPDHIysJ9inKGHHQ9naz/1xAyxWJsS/v9KQoZg+4mn4AMYVtWE5dvKzRNS778PDBgAGEbgE1KA+X3DAAYMgG3HDmDECCzfVo6hVU0RZxnebwi23vkcstP7wzCMgAsVAOw2OwzDQHZ6f7zxs/UYljk44tlSMuiezwyn+aqaUPfCIai67ssKAFSdF3UvHIIvCmuB2pNwPDCDjAy650vJwH6SQ8LxwAzMICkD+0kW3ceD7vnMcJqEtckMPCkV0GPbymBzuc1XSKWkAE5ncHd0OoGUFNhefx12lxvLtpVFlCPB6cbGRU8ixZ0Ehz24d1o67A6kuJPw0uJVSHC6I5ovIYPu+cxwmvL6Ub/pCNDkB4J9faUC0GTeT3n9EWcgk4TjgRlkZNA9X0oG9pMcEo4HZmAGSRnYT7LoPh50z2eG0ySsTWYwhXRSasiQITAMo9PXokWLIg4iRf6XjZhQ1gDn7O+ab9kL9oRUC6cTGDIEju/MxoSyBuRXNIadZeb4azGod3bQC7WFw+5ATp8BuOnCqWHPlpJB93xmOK25uMZ8OWeob/hVgKr1orm4JuIM3YmHjgJkHA/MICOD7vlSMrCf5JBwPDADM0jKwH6SRffxoHs+M5wmYW0ygymkk1IffvghysrKWr9ef/11AMDNN98ccRAp5uypQrMB4PbbzWtIhcPnA5YsQbMBzCmqCjvLwolz4VfhZfD5fVg4cW7Ys6Vk0D2fGU7zFFZqvX8w4qGjABnHAzPIyKB7vpQM7Cc5JBwPzMAMkjKwn2TRfTzons8Mp0lYm8xgCumkVGZmJs4555zWr5dffhnDhw/HxIkTIw4ixeWlDXDmjzM/Za+ra0h1x24Hxo+HM38cLitrCOsh8gbmIn/Q2C7fW9ttBJsd43LORd7A3LDuLyGD7vnMcJqvojHgJzCEwn/CA18ErxwMRjx0lITjgRlkZNA9X0oG9pMcEo4HZmAGSRnYT7LoPh50z2eG0ySsTWY4LexrSjU1NWHNmjW45ZZbYBhGl7fzeDyoqalp9yXZ6EoPMGZMlB5sNMacDG8nj+w/NCoRRkTwOLoz6J7PDKf5KqNzETsrL9gZTEf1tH4CZBwPzCAjg+75UjKwn+SQcDwwAzNIysB+kkX38aB7PjOcJmFtMsNpYZ+Ueumll1BVVYX58+ef8XZLly5FWlpa69egQYPCHRlzhlJw+WFe3DwaUlPh8puPG6rkhKSoREiJ4HF0Z9A9nxnaaI7SRTabrLtYZzAd1ZP6qYWE44EZZGTQPV9KBvaTHBKOB2ZgBlEZ2E+i6D4edM9nhjYkrE1maBX2SalVq1Zh6tSpyM7OPuPt7r77blRXV7d+lZSUhDsy5pRhoMkGoLY2Og9YU4Mmm/m4oaprrI9KhNoIHkd3Bt3zmaENZ5Q+qNNl3Qd+BtNRPamfWkg4HphBRgbd86VkYD/JIeF4YAZmEJWB/SSK7uNB93xmaEPC2mSGVqFd7v5rhw8fxtatW/Hiiy92e1u32w23O/KPbLTK3gw3zt+zJzoPtmcP9vQO7+9efPxgVCIUlx8I/76aM+iezwyn2TNcUclgT4/O43Qn2I7qaf0EyDgemEFGBt3zpWRgP8kh4XhgBmaQlIH9JIvu40H3fGY4TcLaZIbTwjqltXr1avTr1w/Tpk2LaLhE72UnorlgF/DJJ+an6IXD5wN27kRzYQHez0oM6yEKjxahoORz+PzhZfD5fdh15DPsPhb+CTbdGXTPZ4bT7JkJsPWN7MmHra8b9syEiB4jWGdzR0k4HphBRgbd86VkYD/JIeF4YAZmkJSB/SSL7uNB93xmOE3C2mSGNo8R6h38fj9Wr16NefPmweEI64VWoq3NTYdTAXj0UcAW5svQ7HbgscfgVObjhWvl9jWwGeFlsNvsWLl9TdizpWTQPZ8ZTnPnZWi9f7DO9o4CZBwPzCAjg+75UjKwn+SQcDwwAzNIysB+kkX38aB7PjOcJmFtMoMp5CNh69atOHLkCG655ZaIh0tUkJmAd7MS4V3/HHD4MNDcHNoDNDcDhw7B+/x6vJuViIIIzhpu2PkKSk6WwuvzhnQ/r8+LI18dw4sfbw57tpQMuuczw2nOkakwUhxAqJdIMwAjxQHnyNSIMwTjbO8oQMbxwAwyMuieLyUD+0kOCccDMzCDpAzsJ1l0Hw+65zPDaRLWJjOYQj4pdfXVV0MphVGjRkU8XKrFk7Pga/LAf/XV5kXPgz0x1dwM1NbCP2UKfE0eLJ6cFVGOxmYPblz+Q9R66oNesF6fF7WeetywbAEamz0RzZeQQfd8ZjjNcNiQND3HvJBdsKVlAHCZ9zMc1lykMx46SsLxwAwyMuieLyUD+0kOCccDMzCDpAzsJ1l0Hw+65zPDaRLWJjOYrPsohx7kYLoLt03OAoqL4b/0UuDYMfMHXV1jquX7x46Zt9+/H7dNzsLBKFyU8IsvD+HKh2ejtOq4OaqL9962fL+06jiufHg2DlQcjni2lAy65zPDafZ0F5JnDYGRHNzLuo1kB5JnDbHsAp3xRMLxwAwyMuieLyUD+0kOCccDMzCDpAzsJ1l0Hw+65zPDaRLWJjOE+el78WDDqFQYUFi+7QvYx+bC8Z3ZwJIlwPjxnW+8axfw2GPwPr8eviYPbpuSjQ2jovdS2wMVh3HJ/dNw04VTsXDiXIzLObfTbXYf3YOV29fgxY83R+XMsbQMuuczw2n2dBdSvjcMzcU18BRWwn+i8wxbXzfceRnmy0Et+g1fPJJwPDCDjAy650vJwH6SQ8LxwAzMICkD+0kW3ceD7vnMcJqEtRnvGQyllIraowWhpqYGaWlpQC870n4w0srR5vyn90PVe2EkOZA6f0S3tx9a1YRl28owoawBzQbgzB8HjB4NpKYCNTXA3r1oLtgFpwLeyU7Ekkndv0Iq1Awd5Q3MxYj+Q5GSkITaxnrsP34QhUeLQnqMnp4hGvOZIfL5AOCraISvqglo8gMuG+zprpA/gaF6dTFwyofq6mqkplpz7YRAdPcToP94kJChp/eThAxny7EQaYZo9BMgo6PYT8wQjfnMICcD+ynKGXr48XC29NPZkCEaa1PC/19JyBBsP/GVUt04mO7CtJsGI7+iEXOKqnDZ0SKMKdwFlx9osgF7ervx/nnpWJubHtFFzUNReLQorMV5NmXQPZ8ZTrNnJlj2UcV0ZhKOB2aQkUH3fCkZ2E9ySDgemIEZJGVgP8mi+3jQPZ8ZTpOwNuMtA09KBakgMwEFmee0/tlQCsoI9RL1REREREREREQE8ELnYeMJKSIiIiIiIiKi8PGkFBERERERERERWU7fhc4BGEnWv3tQnfICCoABGL30vHuRGZhBUgbd8wFA1XsBQM6FzqGnnwAh+4PHJDMwQ/sMAjqK/cQMUuYzg7AM7CcAQvYFMzCDkPliMgTZT1qvKdUSUs9wzfOZgRmkZdA9Xxjt20LC/tCdQfd8ZmAGobRvBwn7ghn0z2cGWRmE0L4dJOwLZmAGKfOlZOiG1pNSfKUUMzCD/gy65wMyi5K/6dOXQfd8ZmCGThmEdRT7Kb4z6J7PDMIysJ8ACNkXzMAMQuaLyRBkP+k7KdXLjtT5IywfW/P0fqh6L4xeDi3zmYEZpGXQPR8AqlcXA6d8WmYHpKmfABn7Q3cG3fOZgRk6EtVR7Ke4z6B7PjPIysB+MknYF8zADFLmS8kQbD/xQudERERERERERGQ5npQiIiIiIiIiIhLEsPYz6bTRek0pIiIiIiIiIqJ4l1/RiDlFVbi8tAGjKz1w+YEmG7A3w433shOxNjcdBZkJumNGHU9KUY/kq2iEr7IJaPYDThvsGS7YLV6geQNzMbL/UCQnJKGusR7Fxw+i8GiRpRkkkLAviKTRvS7YTybd+4FIIgnrgh0lYz8QSSNhXejIMLSqCcu2lWFCWQOaDeBTBWwAUAsgxQ+M+cqDH5z0YOHuKryblYjFk7NwMN0V00xWbgeelKIeQ3n9aC6ugaewEv4Tnk4/t/V1w52XAefIVBiO2LwzNcHpxszx12LhxLnIHzS2088LSj7Hyu1rsGHnK2hs7pzxbCFhXxBJo3tdsJ9MuvcDkUQS1gU7SsZ+IJJGwrrQmWHWvmos31aOJp/CagCPKmBXgNuNU8DtAGaVNeD95w7itslZ2DAqNapZdG0HnpSiHsFX1YT6TUegarv+WEn/CQ8atpWj8cMTSJqeA3uUzx4P7zcEGxc9iUG9s+FX/oC3OW/AaCyb8wB+MXURbli2AAcqDkc1gwQS9gWRNLrXBfvJpHs/EEkkYV2wo2TsByJpJKwLnRlm7avGytfLsA/ANQAOo+uLfhcCuAXAPQBe9Sk88XopDCi8MCotKll0bgeegifxfFVNqHvhEFRd1wukLVXnRd0Lh+CraopahuH9hmDrnc8hO70/DMOA3WYPeDu7zQ7DMJCd3h9v/Gw9hmUOjloGCSTsCyJpdK8L9pNJ934gkkjCumBHydgPRNJIWBc6MwyrasLybeXYB+AbAI5+/f3Ap+1Pf/8ogMsBFANYvq0cQ6OQRfe+4EkpEk15/ajfdARo8gPBfviAAtBk3k95u1rWwUtwurFx0ZNIcSfBYQ/uxYUOuwMp7iS8tHgVEpzuiDNIIGFfEEmje12wn0y69wORRBLWBTtKxn4gkkbCutCd4bFtZfD4FK4BUAPAF+T9fF/f/tsAmnwKy7aVRZRD93YAQjwp5fP58Jvf/AZDhw5FYmIihg8fjvvuuw8qTj6qkKzXXFxjvoQw1ENMAarWi+bimogzzBx/LQb1zg76yVQLh92BnD4DcNOFUyPOIIGEfXEm7CfSQfe6YD+ZdO+HYLCjyGoS1gU7SsZ+6A77iawmYV3ozJD/ZSMmlDVgA8y37AV7QqqF7+v7bQAwoawB+RWNYWeRsC9COin10EMPYcWKFVi2bBmKiorw0EMP4Q9/+AMee+yxiIMQBeIprNR6fwBYOHFul9c/6I7P78PCiXMjziCBhH1xJuwn0kH3umA/mXTvh2Cwo8hqEtYFO0rGfugO+4msJmFd6MwwZ08Vmg3gUYT/1jUbzPs3G8Ccoqqws0jYFyH92uKf//wnZsyYgWnTpgEAhgwZgmeffRYffPBBxEGIOvJVNAa86n8o/Cc88FU0hv3xlXkDcwN+Qkyw7DY7xuWci7yBuT36o44l7IvusJ/IarrXBfvJpHs/BIsdRVaSsC7YUTL2QzDYT2QlCetCd4bLSxvwaRefshf0fACfAPhUAZeVNYT1GLq3Q4uQTsx94xvfwBtvvIF9+/YBAAoKCvDOO+9g6tSuX1rr8XhQU1PT7osoGL7K6Fw4LZILsI3sPzQqGUZE6XF0kbAvusN+IqvpXhfsJ5Pu/RCsUDuK/USRkLAu2FEy9kMw2E9kJQnrQneG0ZUe7IlKAmAvgDEnwzuxpHs7tAjplVJ33XUXampqMGbMGNjtdvh8PjzwwAOYM2dOl/dZunQp7rnnnohCUpxqjtKFHZvCf5zkhKSoREiJ0uNoI2BfdIf9RJbTvC7YT1/rAf0EhN5R7CeKiIB1wY6CiP0QDPYTWUrCutCYwVAKLj9QG50EqAHg8puPqwwjtDtL2BcI8ZVSzz//PNauXYt169bh448/xjPPPIM//vGPeOaZZ7q8z913343q6urWr5KSkogCUxxxRunDIV3hP05dY31UItRG6XG0EbAvusN+IstpXhfsp6/1gH4CQu8o9hNFRMC6YEdBxH4IBvuJLCVhXWjMoAwDTTYgJToJkAqgyYbQT0gBMvYFQnyl1M9+9jPcdddd+O53vwsAOP/883H48GEsXboU8+bNC3gft9sNt7vnf5wrWc+e4YrO46SH/zjFxw9GJUNx+YGoPI4uEvZFd9hPZDXd64L9ZNK9H4IVakexnygSEtYFO0rGfggG+4msJGFd6M6wN8ONMV9Fdi2nFmMA7Okd3nrUvR1ahHRK69SpU7DZ2t/FbrfD74/tS0opPtkzE2DrG9k/eLa+7oguulZ4tAgFJZ/D5w/1gzpNPr8Pu458ht3HovWuYT0k7IvusJ/IarrXBfvJpHs/BIsdRVaSsC7YUTL2QzDYT2QlCetCd4b3shNxngGMQ2SfvncBgHMN4P2sxLAeQ/d2aH2MUG58/fXX44EHHsD//u//4tChQ9i4cSP+/Oc/48Ybb4woBFFX3HkZWu8PACu3r4HNCK8u7DY7Vm5fE3EGCSTsizNhP5EOutcF+8mkez8Egx1FVpOwLthRMvZDd9hPZDUJ60JnhrW56XAq4HaYn6IXDj/M+zuV+XjhkrAvQvpX4rHHHsOsWbNw2223ITc3F3feeSf+/d//Hffdd1/EQYgCcY5MhZHiAEJ9i6wBGCkOOEemRpxhw85XUHKyFF6fN6T7eX1eHPnqGF78eHPEGSSQsC/OhP1EOuheF+wnk+79EAx2FFlNwrpgR8nYD91hP5HVJKwLnRkKMhPwblYiZgEYDMAe4v3tX99vJoB3sxJREMErlSTsi5BOSqWkpOCRRx7B4cOH0dDQgC+++AL3338/XK7Yvs+Z4pfhsCFpeo558bRgF4oBwGXez3BEfvG2xmYPblz+Q9R66oN+UuX1eVHrqccNyxagsTk67xfWTcK+OBP2E+mge12wn0y690Mw2FFkNQnrgh0lYz90h/1EVpOwLnRnWDw5Cy67gS0wL1Ye7Ikp+9e3fxWAy25g8eSsiHLo3g5A+G9hJLKMPd2F5FlDYCQHd11+I9mB5FlDonpRyC++PIQrH56N0qrjANDl9RFavl9adRxXPjwbByoORy2DBBL2BZE0utcF+8mkez8QSSRhXbCjZOwHImkkrAudGQ6mu3Db5CyMAvAegIFff7+rEzQt3x/49e1HArhtchYORiGL7n0R0qfvEeliT3ch5XvD0FxcA09hJfwnOv/mzNbXDXdehvkSxBj8VulAxWFccv803HThVCycOBfjcs7tdJvdR/dg5fY1ePHjzWfFb/cCkbAviKTRvS7YTybd+4FIIgnrgh0lYz8QSSNhXejMsGFUKgwoLN9Wjt0+hQ0AHgXwSYDb5sO8htRMmK+QunVyFjaMit7be3VuB56Uoh7DcNjgyk2HKzcdvopG+KqagCY/4LLBnu6K+SeTAObL0NfteAnrdryEvIG5GNF/KFISklDbWI/9xw+i8GhRzDNIIGFfEEmje12wn0y69wORRBLWBTtKxn4gkkbCutCZ4YVRadjZLxHLtpVhflkD5hjApwrYC6AG5lv1RgM4zzAvav5OdiKWTIrOK6Q60rUdeFKKeiR7ZoL2f7gLjxbFxROo7kjYF0TS6F4X7CeT7v1AJJGEdcGOkrEfiKSRsC50ZDiY7sK0mwYjv6IRc4qqcFlZA2466YHLDzTZgD293VidlYi1uekRXdQ8FFZuB56UIiIiIiIiIiLSqCAzAQWZ57T+2VAKygj1Y/F6Hr5ZmYiIiIiIiIhIkHg4IQXwpBQREREREREREWlgKKWUlQOrq6uRnp5u/qGX3crRplNtPoZWx3xmYAZpGXTPb5OhqqoKaWlpejJAQD8BovaHtgy65zMDM3SRQWdHsZ+YQcx8ZhCZgf0kZ18wAzNony8sQ3f9ZPk1pWpra0//oe2G0kH3fGZgBmkZNM+vra3VelJKVD8xg4z5zMAMbejsKPYTM4iczwxiMrCf2mAGZpCUQfd8ARm66yfLXynl9/tRWlqKlJQUGGG8R7KmpgaDBg1CSUkJUlNTY5CQGXpKBt3zmSF6GZRSqK2tRXZ2Nmw2fe8qZj8xw9mUQff8symDhI6KtJ8A/ftD93xmYAZpGdhPp+neFxIy6J7PDMwQ7QzB9pPlr5Sy2WwYOHBgxI+Tmpqqbecwg6wMuuczQ3Qy6HyFVAv2EzOcjRl0zz9bMujuqGj1E6B/f+iezwzMIC0D++k03ftCQgbd85mBGaKZIZh+4oXOiYiIiIiIiIjIcjwpRURERERERERElutxJ6Xcbjd++9vfwu12M0OcZ9A9nxlkZZBAwnZgBmaQMp8Z5NG9LXTPZwZmkJZB93xJJGwL3Rl0z2cGZtCVwfILnRMREREREREREfW4V0oREREREREREVHPx5NSRERERERERERkOZ6UIiIiIiIiIiIiy/GkFBERERERERERWa5HnZR67733YLfbMW3aNMtnz58/H4ZhtH716dMH11xzDQoLCy3PUl5ejiVLlmDYsGFwu90YNGgQrr/+erzxxhsxn912OzidTvTv3x9TpkzBU089Bb/fH/P5HTO0/brmmmssmd9djv3791syv7y8HD/5yU8wYsQIJCQkoH///pgwYQJWrFiBU6dOxXz+/PnzccMNN3T6/ltvvQXDMFBVVRXzDNKwo9hPHXPo6ijd/QTo7Sj2U2fsJ/ZTxxzsJz6HkoL9xH7qmIP9FF/91KNOSq1atQpLlizB22+/jdLSUsvnX3PNNSgrK0NZWRneeOMNOBwOXHfddZZmOHToEMaPH49t27bh4Ycfxu7du7FlyxZMmjQJixYtsiRDy3Y4dOgQNm/ejEmTJuEnP/kJrrvuOni9XksztP169tlnLZndXY6hQ4fGfO6BAwdwwQUX4LXXXsODDz6ITz75BO+99x5+/vOf4+WXX8bWrVtjnoE6i/eOYj91zqGzo3T1E8COkoj9xH7qmIP9xH6Sgv3EfuqYg/0UX/3k0B0gWHV1dVi/fj0++ugjlJeX4+mnn8Yvf/lLSzO43W6cc845AIBzzjkHd911F/7lX/4FFRUVyMzMtCTDbbfdBsMw8MEHHyApKan1++eeey5uueUWSzK03Q4DBgzAhRdeiMsuuwxXXnklnn76afzwhz+0NINOunLcdtttcDgc+Oijj9odB8OGDcOMGTOglLI8U7xjR7Gfusqhi84M7ChZ2E/sp65y6MJ+ohbsJ/ZTVzl0YT9Zr8e8Uur555/HmDFjMHr0aMydOxdPPfWU1p1SV1eHNWvWYMSIEejTp48lM0+ePIktW7Zg0aJF7Q7SFunp6ZbkCGTy5MnIz8/Hiy++qC1DvPjqq6/w2muvdXkcAIBhGBanonjvKPYTtWBHycN+Yj+Rif0kD/uJ/USmeO6nHnNSatWqVZg7dy4A8yV11dXV2L59u6UZXn75ZSQnJyM5ORkpKSnYtGkT1q9fD5vNms24f/9+KKUwZswYS+aFasyYMTh06JAls9rui5avBx980JLZZ8px8803x3xmy3EwevTodt/v27dva45f/OIXMc8BBN4PU6dOtWS2NPHeUeyn9iR0lI5+AuR0FPvpNPYT+6kt9pP+fgLYUS3YT+yntthP8dlPPeLte3v37sUHH3yAjRs3AgAcDgdmz56NVatW4YorrrAsx6RJk7BixQoAQGVlJR5//HFMnToVH3zwAQYPHhzz+dJfrqeUsuzsbdt90aJ3796WzD5Tjq7Oalvhgw8+gN/vx5w5c+DxeCyZGWg/7Nixo/XJRbxgR7GfOpLQUZL6CbC+o9hPJvYT+6kj9lNnfA6lB/uJ/dQR+6mzeOinHnFSatWqVfB6vcjOzm79nlIKbrcby5YtQ1pamiU5kpKSMGLEiNY/P/nkk0hLS8MTTzyB+++/P+bzR44cCcMwsGfPnpjPCkdRUZFlF4HruC900ZFjxIgRMAwDe/fubff9YcOGAQASExMtyxLo73/06FHL5kvBjmI/dSSho3RlkNJR7CcT+4n91BH7SX8/AewogP0EsJ86Yj/FZz+Jf/ue1+vF3/72N/zpT3/Crl27Wr8KCgqQnZ2t5RPXWhiGAZvNhoaGBkvm9e7dG9/+9rexfPly1NfXd/q5zo+P3bZtG3bv3o2ZM2dqyxAv+vTpgylTpmDZsmUBjwOyFjvKxH6iFuwoOdhPJvYTtWA/ycF+MrGfqEU895P4V0q9/PLLqKysxIIFCzqdLZ85cyZWrVqFH/3oR5Zk8Xg8KC8vB2C+tHPZsmWoq6vD9ddfb8l8AFi+fDkmTJiASy65BPfeey/y8vLg9Xrx+uuvY8WKFSgqKop5hpbt4PP5cPz4cWzZsgVLly7Fddddh+9///sxn982Q1sOhwN9+/a1ZL5ujz/+OCZMmICLLroIv/vd75CXlwebzYYPP/wQe/bswfjx43VHjBvsqNPYT51ztMWOYkdZjf10Gvupc4622E/sJ6uxn05jP3XO0Rb7KQ76SQl33XXXqWuvvTbgz3bs2KEAqIKCgpjnmDdvngLQ+pWSkqIuvvhi9cILL8R8dkelpaVq0aJFavDgwcrlcqkBAwao6dOnqzfffDPms9tuB4fDoTIzM9VVV12lnnrqKeXz+WI+v2OGtl+jR4+2ZH7bHDNmzLB0ZlulpaVq8eLFaujQocrpdKrk5GR1ySWXqIcffljV19fHfH5Xf/8333xTAVCVlZUxzyABO6q9eO+njjl0dZTuflJKb0exn0zsp/bYT+ynFnwOpR/7qT32E/upRTz2k6GU8KurERERERERERHRWUf8NaWIiIiIiIiIiOjsw5NSRERERERERERkOZ6UIiIiIiIiIiIiy/GkFBERERERERERWY4npYiIiIiIiIiIyHI8KUVERERERERERJbjSSkiIiIiIiIiIrIcT0oREREREREREZHleFKKiIiIiIiIiIgsx5NSPdBf//pXpKSkwOv1tn6vrq4OTqcTV1xxRbvbvvXWWzAMA1988QVOnjyJJUuWYPTo0UhMTEROTg5uv/12VFdXt7vPkSNHMG3aNPTq1Qv9+vXDz372s3azutLQ0ICkpCTs378/4M+nT5+OnJwcJCQkICsrC//2b/+G0tLSdllnzJiBrKwsJCUlYdy4cVi7dm1Q2+Tw4cNITExEXV1dp58dOnQIhmG0frlcLowYMQL3338/lFKtt/vss88wc+ZMDBkyBIZh4JFHHun0WEuXLsXFF1+MlJQU9OvXDzfccAP27t0bVEaieBDLfiooKMC//uu/YtCgQUhMTERubi7+8pe/BJUr0n5qbGzE/Pnzcf7558PhcOCGG24Iept0N7ttPzkcDuTk5OA//uM/4PF4Wm9TVlaG733vexg1ahRsNht++tOfdnqcYDqMKJ6F208AsHLlSlxxxRVITU2FYRioqqrq9Pj79u3DjBkz0LdvX6SmpuKb3/wm3nzzzaCyTZo0CU8++WTAn/3ud7/DmDFjkJSUhIyMDFx11VXYsWNHu9ucPHkSc+bMQWpqKtLT07FgwYKAz4kCGTp0KLZu3RrwZy19YhgG7HY7srOzsWDBAlRWVrbeJth+9Hg8+NWvfoXBg/8/e3ceHlV97w/8fWYmMwnZkQAJCWsCBDGg1AVpRUCpgFuFSi3cilK5vQa19WqV+7O1rVpstbVVKC0uYIsULYhyuUALslgXUBEIyhYkLCEJBskOmWRmvr8/xoSEbDOZmfP9TOb9ep48Tw0z5/P2e855dzzMnOkHh8OB/v3745VXXvEpI1FXF8rXT1999RVuuOEGpKWlweFwICMjA3PnzkVlZWWHudp7DXP06FHMnj0bAwYMQExMDAYNGoTHH38cdXV1zR73xhtvYOTIkejWrRv69euHZ555xqc1Mev104svvohvfetbSE5ObuzYjz76yKeMFBy8KBWGxo0bh+rqanzyySeNv/v3v/+N3r17Y8eOHaitrW38/ZYtW9C3b18MGjQIRUVFKCoqwrPPPovPPvsMS5cuxYYNGzB79uzGx7vdbkyZMgV1dXX44IMP8Oqrr2Lp0qX4+c9/3mGujRs3ol+/fsjMzGwz9xtvvIGDBw9i1apV+OKLLzBt2rTGP//ggw+Qk5ODVatWIS8vD3fddRd+8IMfYO3atR3OfvvttzFu3DjExcW1+ZhNmzahuLgY+fn5+OUvf4mnnnqq2Yuhs2fPYuDAgXj66afRu3fvVrexbds25ObmYvv27di4cSPq6+sxceJE1NTUdJiRKBKEsp927tyJnj17YtmyZfj888/x//7f/8O8efOwYMGCDnMF2k9utxsxMTG4//77cd111/m1Jh3NBoAlS5aguLgYBQUF+NOf/oS//e1vePLJJxv/3Ol0IiUlBY899hhGjBjR6jZ86TCiSNbZfgK859cNN9yA//mf/2lz+zfeeCNcLhc2b96MnTt3YsSIEbjxxhtRUlLSbq4zZ87g/fffx0033dTqnw8ePBgLFizA3r178d5776F///6YOHEiSktLGx8zY8YMfP7559i4cSPWrl2Ld999F3PmzOlwTfLy8lBWVoaxY8e2+Zhf/epXKC4uxvHjx/Haa6/h3Xffxf3339/457724+2334533nkHL7/8Mg4ePIi///3vGDJkSIcZiSJBKF8/WSwW3HLLLVizZg0OHTqEpUuXYtOmTfjRj37UYa72XsMcOHAAHo8Hf/nLX/D555/jueeew5///OdmPbl+/XrMmDEDP/rRj/DZZ5/hT3/6E5577rmgvHYDgvP6aevWrbjjjjuwZcsWfPjhh8jIyMDEiRNx8uTJDjNSkCgKS6mpqWr+/PmN//zTn/5U5ebmquzsbLVly5bG319zzTXqzjvvbHM7b7zxhrLb7aq+vl4ppdS6deuUxWJRJSUljY9ZtGiRSkhIUE6ns91Md999t3rkkUd8/nd4++23lWEYqq6urs3HTJ48Wd11110dbmv8+PFq0aJFrf5ZQUGBAqB27drV7PcTJkxQ9957b6vP6devn3ruuec6nPvll18qAGrbtm0dPpYoUoSqn1pz7733qnHjxnWYKZj9dOedd6pbbrnF5211NBuAWr16dbPfzZ49W02ePLnVx48dO1Y98MAD7c70tcOIIk2g/bRlyxYFQJWVlTX7fWlpqQKg3n333cbfVVZWKgBq48aN7Wb661//qq688kqf/x0qKioUALVp0yallFL79u1TANTHH3/c+Jj169crwzDUyZMn293Wr371KzV9+vQ2/7y1LnniiSfUsGHDWn18W/24fv16lZiYqL766qt28xBFMjNfP/3xj39U6enpHWby9/XTb3/7WzVgwIDGf77jjjvUtGnTmj3m+eefV+np6crj8QQ0OxSvn5RSyuVyqfj4ePXqq692+FgKDr5TKkyNGzeu2VvCt2zZgmuvvRZjx45t/P25c+ewY8cOjBs3rs3tVFRUICEhATabDQDw4Ycf4pJLLkGvXr0aH/Ptb38blZWV+Pzzz9vcjsfjwdq1a3HLLbf4lP/MmTN47bXXcPXVVyMqKqrdfN27d293W+Xl5Xjvvfdw8803+zQbAD755BPs3LkTV155pc/PaSsfgA4zEkWSUPVTW4/p6PwLVT/5wt/ZgPcjQJs3bw64n4iopWD104UuuugiDBkyBH/9619RU1MDl8uFv/zlL+jZsydGjRrV7nPXrFnjc0fU1dVh8eLFSExMbPxb/w8//BBJSUn4xje+0fi46667DhaLpcXH/AKZDQAnT57E//7v//rdT2vWrME3vvEN/Pa3v0WfPn0wePBgPPTQQzh37pxf2yHqysx6/VRUVIQ333yz3XdIAp17DXPh6zKn04no6Ohmj4mJiUFhYSGOHTsW1NnBev109uxZ1NfX87/vTMSLUmFq3LhxeP/99+FyuVBVVYVdu3Zh7NixuOaaa7B161YA3hcpTqezzdI6ffo0nnjiiWZv7y4pKWl2QQpA4z+39/bz7du3A0CHJfDII48gNjYWF110EY4fP4633367zce+8cYb+Pjjj3HXXXe1u81169YhJycHaWlp7T7u6quvRlxcHOx2Oy6//HLcfvvt+MEPftDuc9rj8Xjw4x//GGPGjMHw4cM7vR2iriZU/XShDz74AK+//nqHH1EJRT/5ytfZd9xxB+Li4hAdHY0hQ4bg4osvxrx58wKeT0TNBaOfWmMYBjZt2oRdu3YhPj4e0dHR+P3vf48NGzYgOTm5zec5nU5s2LChw79YW7t2bWNHPPfcc9i4cSN69OgBwPv6rGfPns0eb7PZ0L1793Zfu508eRJ5eXmYNGlSu7MfeeQRxMXFISYmBunp6TAMA7///e/bfc6Fjhw5gvfeew+fffYZVq9ejT/84Q9YuXIl7r33Xr+2Q9SVhfr10x133IFu3bqhT58+SEhIaPM+dg18fQ3T4PDhw3jhhRfwn//5n42/+/a3v40333wT77zzDjweDw4dOoTf/e53ALz3ewp0dihePz3yyCNIS0vz+3YN1Hm8KBWmrr32WtTU1ODjjz/Gv//9bwwePBgpKSkYO3Zs4+eOt27dioEDB6Jv374tnl9ZWYkpU6Zg2LBh+MUvfhFwnrfffhs33ngjLJb2D6mHH34Yu3btwr/+9S9YrVb84Ac/aHaz8QZbtmzBXXfdhRdffBEXX3xxh7N9eZfU66+/jt27d2PPnj1444038Pbbb+PRRx/t8Hltyc3NxWeffYYVK1Z0ehtEXZEZ/fTZZ5/hlltuweOPP46JEye2myfY/eQPX2c/99xzjf20du1aHDp0CP/xH/8R0GwiainQfmqLUgq5ubno2bMn/v3vf+Ojjz7Crbfeiptuuqnd//DavHkzevbs2eFrnXHjxmH37t344IMPcMMNN+D222/Hl19+6XO+1qxZswbf/OY3kZSU1O7jHn74YezevRt5eXl45513AABTpkyB2+32eZbH44FhGHjttddwxRVXYPLkyfj973+PV199le+WIvpaqF8/Pffcc/j000/x9ttv44svvsCDDz7Ybh5fX8MA3ovcN9xwA7773e/innvuafz9Pffcg7lz5+LGG2+E3W7HVVddhe9973sA0O52db1+evrpp7FixQqsXr26xTu8KHTa/kwEiZaZmYn09HRs2bKl2Q0q09LSkJGRgQ8++ABbtmzB+PHjWzy3qqoKN9xwA+Lj47F69epmH0/p3bt3i28bOHXqVOOftWXNmjV4+umnO8zdo0cP9OjRA4MHD0Z2djYyMjKwfft2jB49uvEx27Ztw0033YTnnnuuw3cy1dXVYcOGDe3eeLRBRkZG443ysrOz8cUXX+BnP/sZfvGLX/hdOnPnzm28kWh6erpfzyXq6kLVTw327duHCRMmYM6cOXjsscc6zBPMfvKXr7N79+7d2E9DhgxBVVUV7rjjDjz55JPt3uCTiPwTSD+1Z/PmzVi7di3KysqQkJAAAPjTn/6EjRs34tVXX23zL8HWrFnj01+sxcbGIjMzE5mZmbjqqquQlZWFl19+GfPmzUPv3r1bXKByuVw4c+ZMh6/dfJndo0ePxh7KysrCH/7wB4wePRpbtmzx+Z0Eqamp6NOnDxITExt/l52dDaUUCgsLkZWV5dN2iLqyUL9+6t27N3r37o2hQ4eie/fu+Na3voWf/exnSE1NbTWPr69hioqKMG7cOFx99dVYvHhxsz8zDAO/+c1v8Otf/xolJSVISUlpvLg9cODANrep4/XTs88+i6effhqbNm1CTk6OX8+lwPCdUmFs3Lhx2Lp1K7Zu3drsq0KvueYarF+/Hh999FGLt3ZWVlZi4sSJsNvtWLNmTYuLMaNHj8bevXubvbjZuHEjEhISMGzYsFZz5Ofn49ixY7j++uv9yu/xeACg2dd2bt26FVOmTMFvfvMbn741ZuvWrUhOTm7z2xTaY7Va4XK5WnxtaXuUUpg7dy5Wr16NzZs3Y8CAAX7PJYoEoegnAPj8888xbtw43HnnnXjqqac6zBHMfvJXZ2cD3n4CwHcQEIVAZ/qpI2fPngXQ8m/+LRZLY59cSCmF//3f//XrnikNPB5PYz+NHj0a5eXl2LlzZ+Ofb968GR6Pp82PvlRXV2PLli2dmt2ZfhozZgyKiopQXV3d+LtDhw7BYrHwL/eImgjV66cLdfQ6x9fXMCdPnsS1116LUaNGYcmSJW2+s8lqtaJPnz6w2+34+9//jtGjRyMlJSWg2W3NAfx//fTb3/4WTzzxBDZs2NDs/nxkDr5TKoyNGzcOubm5qK+vb3ajurFjx2Lu3Lmoq6trVloNhXX27FksW7YMlZWVqKysBACkpKTAarVi4sSJGDZsGP7jP/4Dv/3tb1FSUoLHHnsMubm5cDgcreZ4++23cd1116Fbt25tZt2xYwc+/vhjfPOb30RycnLju5QGDRrU+C6ELVu24MYbb8QDDzyAqVOnNt4HwW63t3mjOV//lg8AvvrqK5SUlMDlcmHv3r344x//iHHjxjX+jWZdXR327dvX+L9PnjyJ3bt3Iy4urvFKe25uLpYvX463334b8fHxjRkTExMRExPjUw6iSBCKfvrss88wfvx4fPvb38aDDz7YeP5ZrdY2X9gEq58A7zu06urqcObMGVRVVWH37t0AgJEjR3Z6doPy8nKUlJTA4/EgPz8fv/rVrxrfsdWgYV51dTVKS0uxe/du2O32xr8w8KXDiMj/fgK8920qKSnB4cOHAQB79+5FfHw8+vbti+7du2P06NFITk7GnXfeiZ///OeIiYnBiy++iIKCAkyZMqXVHDt37sTZs2fxzW9+s82sNTU1eOqpp3DzzTcjNTUVp0+fxsKFC3Hy5El897vfBeB9x9ENN9yAe+65B3/+859RX1+PuXPn4nvf+16b99vcsGEDBg8ejP79+3e4XlVVVSgpKYFSCidOnMBPf/pTpKSk4Oqrr258TEf9+P3vfx9PPPEE7rrrLvzyl7/E6dOn8fDDD+Puu+/m6yeiJkLx+mndunU4deoULr/8csTFxeHzzz/Hww8/jDFjxrTZAb68hmm4INWvXz88++yzKC0tbfyzhndpnj59GitXrsS1116L2tpaLFmyBP/4xz+wbdu2Nrdr9uun3/zmN/j5z3+O5cuXo3///o2vL+Pi4hAXF9dhBgoCjd/8RwEqKChQANTQoUOb/f7o0aMKgBoyZEiz3zd8jXFrPwUFBc2eP2nSJBUTE6N69Oih/vu//7vdrxT95je/qV588cV2s+bl5alx48ap7t27K4fDofr3769+9KMfqcLCwsbH3Hnnna1mGzt2bJvbzcjI6PCrlhvWqeHHarWq9PR0dc8996gvv/yyzce1Nr+t9VuyZEm7GYgiTSj66fHHH2/1z/v169dmjmD1k1Ler0ZvbX4gs5Vq3iuGYajU1FQ1ffp09cUXX7T5uNb+3X3pMCLyv5+Uart/mv7//8cff6wmTpyounfvruLj49VVV12l1q1b12aOxx57TM2YMaPdrOfOnVPf+c53VFpamrLb7So1NVXdfPPN6qOPPmr2uK+++krdcccdKi4uTiUkJKi77rpLVVVVtbndmTNnqv/3//5fu7OVatl7KSkpavLkyWrXrl3tPq61fty/f7+67rrrVExMjEpPT1cPPvigOnv2bIcZiCJJKF4/bd68WY0ePVolJiaq6OholZWVpR555BFVVlbWZg5fXsMsWbKkzdkNSktL1VVXXaViY2NVt27d1IQJE9T27dvb3a7Zr5/a6q/HH3+8wwwUHIZSAd7FlSLa6dOnkZqaisLCwhbf2hdqn376KcaPH4/S0tKAv7adiLoenf2kczYRhYecnBw89thjuP32202d63K50KtXL6xfvx5XXHGFqbOJSD6+fiKz8Z5SFJAzZ87g97//vZbScLlceOGFF3hBiohapbOfdM4mIvnq6uowdepUTJo0yfTZZ86cwU9+8hNcfvnlps8mIvn4+onMxndKERERERERERGR6fhOKSIiIiIiIiIiMh0vShERERERERERkel4UYqIiIiIiIiIiEzHi1JERERERERERGQ6XpQiIiIiIiIiIiLT8aIUERERERERERGZjheliIiIiIiIiIjIdLwoRUREREREREREpuNFKSIiIiIiIiIiMh0vShERERERERERkel4UYqIiIiIiIiIiEzHi1JERERERERERGQ6XpQiIiIiIiIiIiLT2cwe6PF4UFRUhPj4eBiGYfZ4IhJIKYWqqiqkpaXBYtF3rZz9REStkdBR7Cciag37iYik8rWfTL8oVVRUhIyMDLPHElEYOHHiBNLT07XNZz8RUXt0dhT7iYjaw34iIqk66ifTL0rFx8ef/4duVrPHA2fdeuczAzNIy6B7fpMMzfpBA+39BIjaH9oy6J7PDMzQRgadHcV+YgYx85lBZAb2k5x9wQzMoH2+sAwd9ZPpF6Ua39LZzYrEu7LMHo/KpYehalwwYm1ImJVp+nxmYAZpGXTPB4CKJfnAWbf2t3zr7idAxv7QnUH3fGZghgtJ6Cj2EzNImc8MsjKwn7wk7AtmYAYp86Vk8LWfeKNzIiIiIiIiIiIyHS9KERERERERERGR6XhRioiIiIiISANDKd0RiMTheRFZTL+nFBERERERUSQaUVqLGfvLMbroHIaUOWH3AHUW4GCyAx+mxeC17CTsSYnWHZPIVDwvIhsvShEREREREYXQgPI6LNhcjDHF51BvAFEjRgLXDwXi42GvqsIlBw5g6J7dmLO3HO+nxmDu+FQUJNl1xyYKqQvPi88UsApAFYB4DzD0KyfuOuPkeaFBTno2snoNQFx0LKpra5B/qgB5hftDMosXpYiIiIiIiEJk2qEKLNxcAqvdAcyahaj77wcuvbTF46J27QKefx5Xvr4C21cU4N7xqVg1OEFDYqLQazgv6twKSwA8r4DdrTxupALuBzCt+BzPixCLjnJg6qjJmDN2JkZkDGvx53tO7MPibcuwauc61NY7gzaX95QiIiIiIiIKgWmHKrB4YzGiBmbCtm8/8MorQE5O6w/OyQFeeQW2ffsRNWAQXtxYhGmHKswNTGSChvOiwK1wCYC7AeS18di8r//8EgBH3YrnRYgM6tkfHz32f1gw4ykM7zOk1ccM7zMEC2Y8hY8e+z8MTOkXtNm8KEVERERERBRkA8vrsHBzCZCVBcv27UCfPoBhAFZr60+wWr1/3qcPLDt2AJmZWLi5BAPK68wNThRCDefFIQBXAyj8+veeNh7f8PtCAKMB5AM8L4JsUM/+2PTQCqQl9YJhGLBaWu8oq8UKwzCQltQL7zz8etAuTPl9Uerdd9/FTTfdhLS0NBiGgbfeeisoQYiIAsV+IiKp2E9EkeeFzcWw2B2w/OtfQHw8EBXl2xOjooD4eFg2boTV7sCCzcUhzcl+IjO9sLkYTrfCDQAqAbh9fJ7768d/G0CdW4X8vIgU0VEOrM59CfGOWNisvt3dyWa1Id4Ri7fmvozoKEfAGfy+KFVTU4MRI0Zg4cKFAQ8nIgom9hMRScV+IoosI76sxZjic4ia/j2gXz/fL0g1iIoC+veH7fbpGFN8DiNKa0MTFOwnMk/DebEKwDH4fkGqgfvr560CQn5eRIqpoyYjo3uazxekGtisNvS9qA9uu2xSwBn8vtH5pEmTMGlS4IOJiIKN/UREUrGfiCLLjAPl3m/Zu/9+wONp+yN77XG7gfvuQ/1fX8WM/eXYk9I7+EHBfiLzNJwXzyvvu2Pa+sheeywAngcww0BIz4tIMWfsTHiUB1bD/45ye9yYM3Ymlu94K6AMvKcUERERERFREI0uOoeoESO937LXmQtSgPd5o0YhasRIXFV8Lqj5iHQYXXQOn339LXuduSCFr5+3C8BnCjwvApSTno0RGcPavIdUR6wWK0b2vRg56dkB5fD7nVL+cjqdcDrPf11gZWVlqEcSEfmE/UREUrGfiMLbkDIncP3QIG1sCIbm7Q7OtoKA/USdNaTMiVVB2tZBALedcXb4OGpbVq8BQdlOZq8ByCvc3+nnh/ydUvPnz0diYmLjT0ZGRqhHEhH5hP1ERFKxn4jCl6EU7B54b24eDAkJsHu825WA/USd0XBeVAVpe5WAqPMiHMVFxwZlO/EBbifkF6XmzZuHioqKxp8TJ06EeiQRkU/YT0QkFfuJKHwpw0CdBUBVkP7zu7ISdRbvdiVgP1FnNJwXQbpUiwRA1HkRjqpra4KynaoAtxPyj+85HA44HIF/TSARUbCxn4hIKvYTUXg7mOzAJQcOBGdjBw7gQHc5fcB+os46mOzA0K+C85G7oYCo8yIc5Z8qCM52So4E9Hy/L0pVV1fj8OHDjf9cUFCA3bt3o3v37ujbt29AYYiIAsF+IiKp2E9EkeXDtBgM3bMbUbt2ATk5nf/2vd27UZ+3B9uHJwU9YwP2E5nlw7QY3HXGiZEKyEPnv31vBICLDWBJakxwA0aYvML92HNiH4b3GdKpm527PW7sLTyAvScDuwDv98f3PvnkE1x66aW49NJLAQAPPvggLr30Uvz85z8PKAgRUaDYT0QkFfuJKLK8lp2EKAXg+ecBSyfvmGK1Ai+8gCjl3V6osJ/ILA3nxf0I7Nv37gdCfl5EisXblsFidK6jrBYrFm9bFnAGv98pde2110LxZmJEJBD7iYikYj8RRZY9KdF4PzUGV76+ArbHHwf69AGionzfQH09cPIkXG+8jh2pMdiTEh2yrOwnMkvDeTGt+Bx+CaAQgNuP51sBpAOYCuD9EJ8XkWLVznV4ZFIu0pJ6wWb1/fKQy+1CUfkpvPnp+oAzhPxG50RERERERJFm7vhUuOuc8Eyc6L3peX29b0+srweqquC5/nq465yYOz41tEGJTDR3fCrsVgMb4L1Zua8fGrN+/fh/ArBbDZ4XQVJb78R3Fv4QVc4auNwun57jcrtQ5azBrQtmo7Y+8HuE8aIUERERERFRkBUk2XHv+FQgPx+eK68ETp70/oG7jfeGNPz+5Env4w8fxr3jU1GQZDcnMJEJGs6LwQA+hPedT0DbFyYafp/+9eOzAJ4XQfbFl0cx4ZnpKCo/BcB7r6jWNPy+qPwUJjwzHUdKjwVlPi9KERERERERhcCqwQmYc30q6gu+gGtYNjBrFrB7d+sP3r0bmDULrmHZqC/4Avdcn4ZVgxNMTEtkjobzor/VwF4AS+C9eXlrRnz953sB9LcaPC9C5EjpMVzx5BTc+7d52FvY+o1/yWjNAAEAAElEQVTL9xYewL1/m4crnpwStAtSQCfuKUVERERERES+WTk4ETt7xmDB5mKMefVV1P/1VUSNGAkMGQIkJACVlcDBg6jfsxtRCtieFoP7xg3gO0GoS2t6XswqPocZBvCZAg4CqIT3o3pDAAw3vDc1fy8tBveN4zukQqm23onlO97C8h1vISc9G5m9BiA+OhZVtTU4fKoAeYX7QzKXF6WIiIiIiIhCqCDJjim39cOI0lrM2F+Oqwr3Y2jebtg9QJ0FONDdge3Dk/BadhJv3kwRo8V5UXwOt51xNjsvlqTG8LzQIK9wf8guQl2IF6WIiIiIiIhMsCclGntSejf+s6EUlGFoTESkH8+LyMZ7ShEREREREWnA//AmaonnRWThRSkiIiIiIiIiIjKdoZRSZg6srKxEYmKid3is+Z8eVGddgAJgAEY3PZ9eZAZmkJRB93wAUDUuAEBFRQUSEvR9m4bufgKE7A8ek8zADM0zCOgo9hMzSJnPDMIysJ8ACNkXzMAMQuaLyeBjP2m9p1RDSD3DNc9nBmaQlkH3fGG0r4WE/aE7g+75zMAMQmlfBwn7ghn0z2cGWRmE0L4OEvYFMzCDlPlSMnRA60UpvlOKGZhBfwbd8wGZRcm/6dOXQfd8ZmCGFhmEdRT7KbIz6J7PDMIysJ8ACNkXzMAMQuaLyeBjP+m7KNXNioRZmaaPrVx6GKrGBaObTct8ZmAGaRl0zweAiiX5wFm3ltmt0tRPgIz9oTuD7vnMwAwXEtVR7KeIz6B7PjPIysB+8pKwL5iBGaTMl5LB137ijc6JiIiIiIiIiMh0vChFRERERERERESm40UpIiIiIiIiIiIyndYbnRN1Vk56NrJ6DUBcdCyqa2uQf6oAeYX7dccynbu0Fu6yOqDeA0RZYE22w5oSrTsWUcRjR7GfiKRiP3lxHYjk4WsHr0jrJ16UorARHeXA1FGTMWfsTIzIGNbiz/ec2IfF25Zh1c51qK13akhoDuXyoD6/Es68MnhOt/z3tPRwwJGTjKisBBg2vhmSyCzsKPYTkVTsJy+uA5E8fO3gFcn9xItSFBYG9eyP1bkvIaN7GjzK0+pjhvcZggUznsIjk3Jx64LZOFJ6zOSUoecur0PNmuNQVW1/vabntBPnNpeg9uPTiL25L6xJdhMTEkUmdhT7iUgq9pMX14FIHr528Ir0fuq6lxqpyxjUsz82PbQCaUm9YBgGrBZrq4+zWqwwDANpSb3wzsOvY2BKP5OThpa7vA7VK49CVbdd2k2paheqVx6Fu7wuxMmIIhs7iv1EJBX7yYvrQCQPXzt4sZ/8vCg1f/58XH755YiPj0fPnj1x66234uDBg6HKRoToKAdW576EeEcsbFbf3thns9oQ74jFW3NfRnSUI8QJzaFcHtSsOQ7UeQDl65MA1Hmfp1ytX3HvSthPpAM7iv3kK3YUmY395MV16Bj7iczG1w5e7Ccvvy5Kbdu2Dbm5udi+fTs2btyI+vp6TJw4ETU1NaHKRxFu6qjJyOie5vNJ2sBmtaHvRX1w22WTQpTMXPX5ld63tfpa2g0UoKpcqM+vDEkuSdhPpAM7iv3kK3YUmY395MV16Bj7iczG1w5e7Ccvv/7tN2zY0Oyfly5dip49e2Lnzp245pprghqMCADmjJ0Jj/LAarT+Nsb2uD1uzBk7E8t3vBX8YCZz5pUF/Hx7dlJwwgjFfiId2FHsJ1+xo8hs7CcvrkPH2E9kNr528GI/eQV0T6mKigoAQPfu3YMShqipnPRsjMgY1ubnajtitVgxsu/FyEnPDnIyc7lLa1v9Jgp/eE474S6tDVKi8MB+olBjR7GfAsGOolBiP3lxHTqH/UShxNcOXuyn8zp9Ucrj8eDHP/4xxowZg+HDh7f5OKfTicrKymY/RL7I6jUgKNvJDNJ2dHGXBedmfl3tpoDtYT+RGdhR7KfO8qWj2E8UCPaTF9fBf+wnCjW+dvBiP53X6YtSubm5+Oyzz7BixYp2Hzd//nwkJiY2/mRkZHR2JEWYuOjYoGwnPkjb0aY+SDfyq+saNwT0BfuJzMCOAvupk3zpKPYTBYL95MV18B/7iUKOrx0AsJ+a6tRFqblz52Lt2rXYsmUL0tPT233svHnzUFFR0fhz4sSJTgWlyFNdG5ybK1YFaTvaRAX0Kdvz7EHajnDsJzILOwrsp07wtaPYTxQI9pMX18E/7CcyBV87AGA/NeXXjc6VUrjvvvuwevVqbN26FQMGdPxWMYfDAYeja3xVIZkr/1RBcLZTciQo29HFmmwPznaSgrMdqdhPZDZ2FPvJH/52FPuJAsF+8uI6+Ib9RGbiawcv9tN5fl1ezM3NxbJly7B8+XLEx8ejpKQEJSUlOHfuXKjyUQTLK9yPPSf2we1xd+r5bo8bu49/jr0nDwQ5mbmsKdGw9Ajs//gtPRywpkQHKZFM7CcyGzuK/eQPdhSZif3kxXXwDfuJzMTXDl7sp/P8uii1aNEiVFRU4Nprr0Vqamrjz+uvvx6qfBThFm9bBovRubdmWi1WLN62LMiJ9HDkJGt9fjhgP5EO7Cj2k6/YUWQ29pMX16Fj7CcyG187eLGfvPxaAaVUqz+zZs0KUTyKdKt2rsOJM0VwuV1+Pc/lduH4Vyfx5qfrQ5TMXFFZCTDibYDh5xMNwIi3ISorISS5JGE/kQ7sKPaTr9hRZDb2kxfXoWPsJzIbXzt4sZ+8wvvuYNTl1dY78Z2FP0SVs8bnk9XldqHKWYNbF8xGbb0zxAnNYdgsiL25r/eGfr6WtwHA7n2eYeOpThQK7Cj2E5FU7CcvrgORPHzt4MV+8uoae5O6tC++PIoJz0xHUfkpAGjzc7cNvy8qP4UJz0zHkdJjpmU0gzXJjrhp/WHE+fb9BEacDXHT+of9TQCJpGNHsZ+IpGI/eXEdiOThawcv9pOf375HpMuR0mO44skpuO2ySZgzdiZG9r24xWP2Fh7A4m3L8Oan67vMVeMLWZPsiP/+QNTnV8KZVwbP6Zb/npYeDjhykr1vi+0if4tAJB07iv1EJBX7yYvrQCQPXzt4RXo/8aIUhY3aeieW73gLy3e8hZz0bGT2GoD46FhU1dbg8KkC5BXu1x3RFIbNAnt2EuzZSXCX1sJdXgfUeQC7BdYke9h/EwVRuGJHsZ+IpGI/eXEdiOThawevSO4nXpSisJRXuL9Ln5i+sqZER0xRE4UTdhT7iUgq9pMX14FIHr528Iq0fuqa738jIiIiIiIiIiLReFGKiIiIiIiIiIhMx4tSRERERERERERkOkMppcwcWFlZicTERO/wWPNvaaXOugAFwACMbnpuqcUMzCApg+75AKBqXACAiooKJCQkaMkA6O8nQMj+4DHJDMzQPIOAjmI/MYOU+cwgLAP7CYCQfcEMzCBkvpgMPvaT1hudN4TUM1zzfGZgBmkZdM8XRvtaSNgfujPons8MzCCU9nWQsC+YQf98ZpCVQQjt6yBhXzADM0iZLyVDB7RelOI7pZiBGfRn0D0fkFmU/Js+fRl0z2cGZmiRQVhHsZ8iO4Pu+cwgLAP7CYCQfcEMzCBkvpgMPvaTvotS3axImJVp+tjKpYehalwwutm0zGcGZpCWQfd8AKhYkg+cdWuZ3SpN/QTI2B+6M+iezwzMcCFRHcV+ivgMuuczg6wM7CcvCfuCGZhBynwpGXztJ97onIiIiIiIiIiITMeLUkQUFIa535lAREREREREYU7rPaWIKHyNKK3FjP3lGF10DkPKnLB7gDoLcDDZgQ/TYvBadhL2pETrjklERERERERC8aIUdYq7tBbusjqg3gNEWWBNtsNq4gUI3fOlZNBhQHkdFmwuxpjic6g3gKgRI4HrhwLx8bBXVeGSAwcwdM9uzNlbjvdTYzB3fCoKkuy6Y1MEkXBu6s6ge74UOenZyOo1AHHRsaiurUH+qQLkFe7XHYsimIRzU0IGnptcA5JHQjdIyMBz08vMdeBFKfKZcnlQn18JZ14ZPKedLf7c0sMBR04yorISYNiC/8lQ3fOlZNBp2qEKLNxcAqvdAcyahaj77wcuvbTF46J27QKefx5Xvr4C21cU4N7xqVg1OEFDYooUEs5N3Rl0z5ciOsqBqaMmY87YmRiRMazFn+85sQ+Lty3Dqp3rUFvfcp2Igk3CuSkhA89NrgHJI6EbJGTguemlax14UYp84i6vQ82a41BVbX+to+e0E+c2l6D249OIvbkvrEF8d4zu+VIy6DTtUAUWbywGsrJg+de/gH79AI+n9Qfn5ACvvALb44/Dcv31eHHjYRhQWDk40dzQFBEknJu6M+ieL8Wgnv2xOvclZHRPg0e13k/D+wzBghlP4ZFJubh1wWwcKT1mckqKJBLOTQkZeG5yDUgeCd0gIQPPTS+d69B1/6qUgsZdXofqlUehqtsui6ZUtQvVK4/CXV7XJeZLyaDTwPI6LNxc4r0gtX070KcPYBiA1dr6E6xW75/36QPLjh1AZiYWbi7BgC6yHiSHhHNTdwbd86UY1LM/Nj20AmlJvWAYBqyW1vvJarHCMAykJfXCOw+/joEp/UxOSpFCwrkpIQPPTa4BySOhGyRk4LnppXsd/LootWjRIuTk5CAhIQEJCQkYPXo01q9fH5QgJJNyeVCz5jhQ5wF8/XI1BaDO+zzlauOdNGEyX0oG3V7YXAyL3eF9h1R8PBAV5dsTo6KA+HhYNm6E1e7Ags3FIcvIfoo8Es5N3Rl0z5ciOsqB1bkvId4RC5vVtzeB26w2xDti8dbclxEd5QhxQnZUpJFwbkrIEA7nZqiFwxqwnyKLhG6QkCEczk0zSFgHvy5Kpaen4+mnn8bOnTvxySefYPz48bjlllvw+eefBxyEZKrPr/S+ndLXsmigAFXlQn1+ZVjPl5JBpxFf1mJM8TlETf+e9yN7vl6QahAVBfTvD9vt0zGm+BxGlNaGJCf7KfJIODd1Z9A9X4qpoyYjo3uazy+mGtisNvS9qA9uu2xSiJKdx46KLBLOTQkZwuHcDLVwWAP2U2SR0A0SMoTDuWkGCevg10Wpm266CZMnT0ZWVhYGDx6Mp556CnFxcdi+fXvAQUgmZ15ZRD9fSgadZhwoR70B4P77276HVEfcbuC++1BvADP2lwczXiP2U+SRcG7qzqD7+VLMGTuzzfsfdMTtcWPO2JlBTtQSOyqySDg3JWQIh3Mz1MJhDdhPkUVCN0jIEA7nphkkrEOn7ynldruxYsUK1NTUYPTo0QEHIXncpbWtfgOCPzynnXB38p0xuudLyaDb6KJziBox0vste23dQ6ojViswahSiRozEVcXngpqvNeynrk/Cuak7g+75UuSkZ2NExrA273/QEavFipF9L0ZOenaQk7WNHdW1STg3JWQIx3Mz2MJxDdhPXZuEbpCQIRzPzVCQsg5+X5Tau3cv4uLi4HA48KMf/QirV6/GsGEtvy6wgdPpRGVlZbMfCg/usuDdhDcc50vJoNuQMicwdGiQNjYEQ8+E7mtU2U+RQ8K5qTuD7vlSZPUaEJTtZAZpO+3xp6PYT+FLwrkpIUM4nZuhEk5rwH6KDBK6QUKGcDo3Q0nKOvh9UWrIkCHYvXs3duzYgf/6r//CnXfeiX379rX5+Pnz5yMxMbHxJyMjI6DAZKL6IN0At66T29E9X0oGjQylYPfAe3PzYEhIgN3j3W4osJ8iiIRzU3cG3fOFiIuODcp24oO0nfb401HspzAm4dwUkCGczs1QCac1YD9FCAHdICFDOJ2boSRlHfy+KGW325GZmYlRo0Zh/vz5GDFiBP74xz+2+fh58+ahoqKi8efEiRMBBSYTRXX6053N2Tu5Hd3zpWTQSBkG6iwAqqqCs8HKStRZvNsNBfZTBJFwburOoHu+ENW1NUHZTlWQttMefzqK/RTGJJybAjKE07kZKuG0BuynCCGgGyRkCKdzM5SkrIN/t1hvhcfjgdPZ9sdxHA4HHI6u8XWJkcaabA/OdpI6tx3d86Vk0O1gsgOXHDgQnI0dOIAD3c3rA/ZT1yXh3NSdQfd8KfJPFQRnOyVHgrIdf7TXUeyn8CXh3JSQIZzPzWAJ5zVgP3VNErpBQoZwPjeDSco6+HV5cd68eXj33Xdx9OhR7N27F/PmzcPWrVsxY8aMgEKQTNaUaFh6BPZ/OJYeDlhTosNyvpQMun2YFoP6PbuBXbu836LXGW43sHMn6vP2YHtqTFDzNWA/RRYJ56buDLrnS5FXuB97TuyD29O5fnJ73Nh9/HPsPRmki+9tYEdFDgnnpoQM4XJuhlK4rAH7KXJI6AYJGcLl3Aw1Kevg10WpL7/8Ej/4wQ8wZMgQTJgwAR9//DH++c9/4vrrrw8oBMnlyEmO6OdLyaDTa9lJiFIAnn8esHTybbJWK/DCC4hS3u2FAvsp8kg4N3Vn0P18KRZvWwaL0bl+slqsWLxtWZATtcSOiiwSzk0JGcLh3Ay1cFgD9lNkkdANEjKEw7lpBgnr4Nf0l19+GUePHoXT6cSXX36JTZs2say6uKisBBjxNsDfWwAZgBFvQ1RWQljPl5JBpz0p0Xg/NQau11cAx44B9fX+baC+Hjh6FK43Xsf7qTHYE6J3ZbCfIo+Ec1N3Bt3zpVi1cx1OnCmCy+3y63kutwvHvzqJNz9dH6Jk57GjIouEc1NChnA4N0MtHNaA/RRZJHSDhAzhcG6aQcI6hPfdTSnkDJsFsTf39d5IztfSMADYvc8zbIEdYrrnS8mg29zxqXDXOeGZONF703NfL0zV1wNVVfBcfz3cdU7MHZ8a2qAUUSScm7oz6J4vRW29E99Z+ENUOWt8flHlcrtQ5azBrQtmo7a+7XvPEXWGhHNTQgaem1wDkkdCN0jIwHPTS8I6dI1XoxRS1iQ74qb1hxHn233xjTgb4qb1D9rNc3XPl5JBp4IkO+4dnwrk58Nz5ZXAyZPeP2jrHlMNvz950vv4w4dx7/hUFHSR9SA5JJybujPoni/FF18exYRnpqOo/BQAtHl/hIbfF5WfwoRnpuNI6THTMlJkkXBuSsjAc5NrQPJI6AYJGXhueuleh4C/fY8igzXJjvjvD0R9fiWceWXwnG55RdTSwwFHTrL37ZhB/tt33fOlZNBp1eAEGFBYuPkLWIdlw3b7dOC++4BRo1o+ePdu4IUX4HrjdbjrnLj3+jSsGtw1PiZE8kg4N3Vn0D1fiiOlx3DFk1Nw22WTMGfsTIzse3GLx+wtPIDF25bhzU/Xd5m/5SS5JJybEjLw3OQakDwSukFCBp6bXjrXgRelyGeGzQJ7dhLs2Ulwl9bCXV4H1HkAuwXWJHvIv8FJ93wpGXRaOTgRO3vGYMHmYox59VXU//VVRI0YCQwZAiQkAJWVwMGDqN+zG1EK2J4Wg/vGDeA7pCjkJJybujPoni9Fbb0Ty3e8heU73kJOejYyew1AfHQsqmprcPhUAfIK9+uOSBFGwrkpIQPPTa4BySOhGyRk4LnppWsdeFGKOsWaEq31P3B0z5eSQYeCJDum3NYPI0prMWN/Oa4q3I+hebth9wB1FuBAdwe2D0/Ca9lJIbupOVF7JJybujPoni9FXuH+iHkhSeFBwrkpIQPPTa4BySOhGyRk4LnpZeY68KIUEXXKnpRo7Enp3fjPhlJQhr9foUFERERERESRqmveVIKITMcLUkREREREROQPXpQiIiIiIiIiIiLTGUopZebAyspKJCYmeofHmv/pQXXWBSgABmB00/PpRWZgBkkZdM8HAFXjAgBUVFQgIUHft/Tp7idAyP7gMckMzNA8g4COYj8xg5T5zCAsA/sJgJB9wQzMIGS+mAw+9pPWe0o1hNQzXPN8ZmAGaRl0zxdG+1pI2B+6M+iezwzMIJT2dZCwL5hB/3xmkJVBCO3rIGFfMAMzSJkvJUMHtF6U4julmIEZ9GfQPR+QWZT8mz59GXTPZwZmaJFBWEexnyI7g+75zCAsA/sJgJB9wQzMIGS+mAw+9pO+i1LdrEiYlWn62Mqlh6FqXDC62bTMZwZmkJZB93wAqFiSD5x1a5ndKk39BMjYH7oz6J7PDMxwIVEdxX6K+Ay65zODrAzsJy8J+4IZmEHKfCkZfO0n3uiciIiIiIiIiIhMx4tSRERERERERERkOl6UIiIiIiIiIiIi02m90bm/ctKzkdVrAOKiY1FdW4P8UwXIK9zPDBGYQfd8ZjjPXVoLd1kdUO8BoiywJtthTYk2NYMEEvaFhAwSjgcJ66A7g+75UjJIOB6l0L0/dM8HZBwPEtaBGWRkkHA8SqF7X0jIIOF40L0GzCArg5nHpPiLUtFRDkwdNRlzxs7EiIxhLf58z4l9WLxtGVbtXIfaeiczdOEMuuczw3nK5UF9fiWceWXwnG45w9LDAUdOMqKyEmDYuu4bMiXsCwkZJBwPEtZBdwbd86VkkHA8SqF7f+ieD8g4HiSsAzPIyCDheJRC976QkEHC8aB7DZhBVgZdx6ShlFJB25oPKisrkZiYCHSzIvGurHYfO6hnf6zOfQkZ3dPgUR5YLdYWj3F73LAYFpw4U4RbF8zGkdJj7c9vuAt9rG93oWcGGRlCMZ8Z/J8PAO7yOtSsOQ5V1fFXfBrxNsTe3BfWJHu7j2v4ZoaKigokJCR0uN1Q0d1PgP7jwd8MoTgewq2fJGSQcCxIyBCK4xGQ0VH+9BPAYxII3fGgex3CrZ+Y4etts58a6d4XEjJ01X5iBv/nS8mg87/xxF6CH9SzPzY9tAJpSb1gGEarOwYArBYrDMNAWlIvvPPw6xiY0o8ZulgG3fOZ4Tx3eR2qVx6Fqu64rABAVbtQvfIo3OV1QcsggYR9ISGDhONBwjrozqB7vpQMEo5HKXTvD93zARnHg4R1YAYZGSQcj1Lo3hcSMkg4HnSvATPIyqD7mAzootTTTz8NwzDw4x//OChhGkRHObA69yXEO2Jhs/r2CUOb1YZ4RyzemvsyoqMczNBFMuiezwznKZcHNWuOA3UewNf3VyoAdd7nKZcn4Az+YD+FNoOE40HCOujOoHu+lAwSjkd/hKqfAP37Q/d8QMbxIGEdmEFGBgnHo7/4Gip0GSQcD7rXgBlkZZBwTHb6otTHH3+Mv/zlL8jJyQk4xIWmjpqMjO5pPu+YBjarDX0v6oPbLpvEDF0kg+75zHBefX6l9+2c/n7gVwGqyoX6/MqAM/iK/RT6DBKOBwnroDuD7vlSMkg4Hn0Vyn4C9O8P3fMBGceDhHVgBhkZJByP/uBrqNBmkHA86F4DZpCVQcIx2amLUtXV1ZgxYwZefPFFJCcnBxziQnPGzoRHde6Km9vjxpyxM5mhi2TQPZ8ZznPmlWl9vq/YT+ZkkHA8SFgH3Rl0z5eSQcLx6ItQ9xOgf3/ong/IOB4krAMzyMgg4Xj0FV9DhT6DhONB9xowg6wMEo7JTl2Uys3NxZQpU3DdddcFHOBCOenZGJExrM3PUnbEarFiZN+LkZOezQxhnkH3fGY4z11a2+o3MPjDc9oJd2ltQNvwBfsp9BkkHA8S1kF3Bt3zpWSQcDz6KpT9BOjfH7rnAzKOBwnrwAwyMkg4Hv3B11ChzSDheNC9BswgK4OEYxLoxEWpFStW4NNPP8X8+fN9erzT6URlZWWzn/Zk9Rrgb6RWZQawHWaQkUH3fGY4z10WnJvYhfqGnewnczJIOB4krIPuDLrnS8kg4Xj0Raj7CdC/P3TPB2QcDxLWgRlkZJBwPPrKn44Kx36SkEHC8aB7DZhBVgYJxyTg50WpEydO4IEHHsBrr72G6Ohon54zf/58JCYmNv5kZGS0+/i46Fh/IrUpPoDtMIOMDLrnM0MT9UG6yWZd6G7WyX4yL4OE40HCOujOoHu+lAwSjseOmNFPgP79oXs+ABHHg4R1YAYhGQQcj77wt6PCsZ9EZBBwPGhfA2YQlUHCMQn4eVFq586d+PLLL3HZZZfBZrPBZrNh27ZteP7552Gz2eB2u1s8Z968eaioqGj8OXHiRLszqmtr/Ps3aENVANthBhkZdM9nhiaiAvqizvPsQdpOK9hP5mWQcDxIWAfdGXTPl5JBwvHYETP6CdC/P3TPByDieJCwDswgJIOA49EX/nZUOPaTiAwCjgfta8AMojJIOCYBwK/bvE+YMAF79+5t9ru77roLQ4cOxSOPPAKrteXnIR0OBxwO37+qMP9UgT+R2t5OyZHOP5cZRGTQPZ8ZzrMm24OSwZoUnO20hv1kXgYJx4OEddCdQfd8KRkkHI8dMaOfAP37Q/d8QMbxIGEdmEFGBgnHoy/87ahw7CcJGSQcD7rXgBlkZZBwTAJ+vlMqPj4ew4cPb/YTGxuLiy66CMOHDw8oSIO8wv3Yc2If3J6Wf2voC7fHjd3HP8fekweYIcwz6J7PDOdZU6Jh6eHfi48LWXo4YE3x7WMrncF+Mi+DhONBwjrozqB7vpQMEo7HjpjRT4D+/aF7PiDjeJCwDswgI4OE49EXfA1lTgYJx4PuNWAGWRkkHJNAJ799L9QWb1sGi9G5aFaLFYu3LWOGLpJB93xmOM+RE9hXAwf6fCkk7AsJGSQcDxLWQXcG3fOlZJBwPEqhe3/ong/IOB4krAMzyMgg4XiUQve+kJBBwvGgew2YQVYGCcdkwBeltm7dij/84Q8BB2lq1c51OHGmCC63y6/nudwuHP/qJN78dD0zdJEMuuczw3lRWQkw4m2A4ecTDcCItyEqKyHgDP5iP4Uug4TjQcI66M6ge76UDBKOR3+Fop8A/ftD93xAxvEgYR2YQUYGCcdjZ/A1VGgySDgedK8BM8jKIOGYFPlOqdp6J76z8Ieoctb4vINcbheqnDW4dcFs1NY7maGLZNA9nxnOM2wWxN7c13sjO19LywBg9z7PsImsG79J2BcSMkg4HiSsg+4MuudLySDheJRC9/7QPR+QcTxIWAdmkJFBwvEohe59ISGDhONB9xowg6wMEo5JsS33xZdHMeGZ6SgqPwUAbX7WsuH3ReWnMOGZ6ThSeowZulgG3fOZ4Txrkh1x0/rDiPPtOxKMOBvipvUP+Q06zSZhX0jIIOF4kLAOujPoni8lg4TjUQrd+0P3fEDG8SBhHZhBRgYJx6MUuveFhAwSjgfda8AMsjLoPib9+vY9sx0pPYYrnpyC2y6bhDljZ2Jk34tbPGZv4QEs3rYMb366PihXCplBZgbd85nhPGuSHfHfH4j6/Eo488rgOd1yhqWHA46cZO/bQbvQ3/A1JWFfSMgg4XiQsA66M+ieLyWDhONRCt37Q/d8QMbxIGEdmEFGBgnHoxS694WEDBKOB91rwAyyMug8Jg2llAra1nxQWVmJxMREoJsViXdl+fXcnPRsZPYagPjoWFTV1uDwqQLkFe73b/7Sw1A1LhixNiTMyvTrucwgJ0Mw5jND4PMBwF1aC3d5HVDnAewWWJPsfn8DQ8WSfOCsGxUVFUhI0HPvBEB/PwH6j4dAMwTjeAj3fpKQQcKxICFDMI5HQEZHBdJPAI9JIHjHg+51CPd+YgYv9tN5uveFhAxdpZ+YIfD5UjKY+d94ot8pdaG8wv2dOiiZoetl0D2fGc6zpkSH/KuKw4GEfSEhg4TjQcI66M6ge76UDBKORyl07w/d8wEZx4OEdWAGGRkkHI9S6N4XEjJIOB50rwEzyMpg5jHZdd8XSkREREREREREYvGiFBERERERERERmY4XpYiIiIiIiIiIyHT6bnQOwIg1/5ZW6qwLUAAMwOim55ZazMAMkjLong8AqsYFAHJudA49/QQI2R88JpmBGZpnENBR7CdmkDKfGYRlYD8BELIvmIEZhMwXk8HHftJ6o/OGkHqGa57PDMwgLYPu+cJoXwsJ+0N3Bt3zmYEZhNK+DhL2BTPon88MsjIIoX0dJOwLZmAGKfOlZOiA1otSfKcUMzCD/gy65wMyi5J/06cvg+75zMAMLTII6yj2U2Rn0D2fGYRlYD8BELIvmIEZhMwXk8HHftJ3UaqbFQmzMk0fW7n0MFSNC0Y3m5b5zMAM0jLong8AFUvygbNuLbNbpamfABn7Q3cG3fOZgRkuJKqj2E8Rn0H3fGaQlYH95CVhXzADM0iZLyWDr/3EG50TEREREREREZHpeFGKiKiLMcz9/goiIiIiIqJO0XpPKSIiCtyI0lrM2F+O0UXnMKTMCbsHqLMAB5Md+DAtBq9lJ2FPSrTumERERERERM3wohR1iru0Fu6yOqDeA0RZYE22w2rif/Tqng8AOenZyOo1AHHRsaiurUH+qQLkFe43NYOEdZCQIVINKK/Dgs3FGFN8DvUG8JkCVgGoAhDvAYZ+5cRdZ5yYs7cc76fGYO74VBQk2XXHDjkJx6TuDOwnORnIi/vCS8I6SMjAjtI/n5rj/pCxBhIysJ/Mz8CLUuQz5fKgPr8SzrwyeE47W/y5pYcDjpxkRGUlwLAF/5OhuucDQHSUA1NHTcacsTMxImNYiz/fc2IfFm9bhlU716G2vmXGYJCwDhIyRLpphyqwcHMJ6twKSwA8r4DdrTxupALuBzCt+By2ryjAveNTsWpwgrlhTSDhmNSdgf0kJwN5cV94SVgHCRnYUfrnU3PcHzLWQEIG9pPeDLwoRT5xl9ehZs1xqKq2v9bRc9qJc5tLUPvxacTe3BfWIL4jQ/d8ABjUsz9W576EjO5p8ChPq48Z3mcIFsx4Co9MysWtC2bjSOmxoGaQsA4SMkS6aYcqsHhjMQ4BuAHAMbR9g8A8AHcD+CWAf7oVXtxYBAMKKwcnmpQ29CQck7ozsJ/kZCAv7gsvCesgIQM7Sv98ao77Q8YaSMjAftKfoWte8qWgcpfXoXrlUajqtg/QplS1C9Urj8JdXtcl5gPestr00AqkJfWCYRiwWqytPs5qscIwDKQl9cI7D7+OgSn9gpZBwjpIyBDpBpbXYeHmEhwCcDWAwq9/3/r/hZ7/fSGA0QDyASzcXIIBXWSfSDgmdWdgP8nJQF7cF14S1kFCBnaU/vnUHPeHjDWQkIH9JCODXxelfvGLX8AwjGY/Q4cODUoQkkm5PKhZcxyo8wC+fqGXAlDnfZ5ytfWfyuExH/C+nXN17kuId8TCZvXtzYU2qw3xjli8NfdlREc5As4gYR0kZGhPpPTTC5uL4XQr3ACgEoDbx+e5v378twHUuRUWbC4OVUTTSDgmdWdgP8nJ0JFI6ahw2BdmkLAOEjKwo/TP90Wk9BMQHvsj1CSsgYQM7Cc5Gfx+p9TFF1+M4uLixp/33nsv4BAkV31+pfctfP5+w7wCVJUL9fmVYT0fAKaOmoyM7mk+l1UDm9WGvhf1wW2XTQo4g4R1kJChI129n0Z8WYsxxeewCt6P7Pl6QaqB++vnrQIwpvgcRpTWBjuiqSQck7ozsJ/kZPBFV+8oIHz2RahJWAcJGdhR+uf7KhL6CQif/RFKEtZAQgb2k5wMfl+Ustls6N27d+NPjx49Ag5BcjnzyiL6+QAwZ+zMNj9f3BG3x405Y2cGnEHCOkjI0JGu3k8zDpSj3gCeR+c/e22B9/n1BjBjf3nQsukg4ZjUnYH9JCeDL7p6RwHhsy9CTcI6SMjAjtL/fF9FQj8B4bM/QknCGkjIwH6Sk8Hv/67Jz89HWloaBg4ciBkzZuD48eMBhyCZ3KW1rd513x+e0064O/luDN3zAe9Xgo7IGNbm54s7YrVYMbLvxchJz+50BgnrICGDL7p6P40uOofPvv6Wvc6+UdYDYBeAzxRwVfG5oGUzm4RjUncG9pOcDL7q6h0VTvsilCSsg4QM7Cj98/3R1fsJCK/9ESoS1kBCBvaTnAyAnxelrrzySixduhQbNmzAokWLUFBQgG9961uoqqpq8zlOpxOVlZXNfig8uMuCd3PHcJwPAFm9BgQlQ2YA25GwDhIydCQS+mlImRMHgrStgwCGngnNV9qaQcIxqTsD+0lOBl/421Hh1k9A+OyLUJOwDhIysKP0z/dVJPQTED77I5QkrIGEDOwnORkAwK8PUE6adP5zkzk5ObjyyivRr18/vPHGG5g9e3arz5k/fz5++ctfBhSSNKkP0o386jq5Hd3zAcRFxwYlQnwg2xGwDiIydKCr95OhFOweoO1LbP6pBGD3eLerDCNIWzWRhGNScwb2k6AMPvC3o8KpnxqFyb4IOQnrICADO0rAfB9FRD8BYbM/QkrCGgjIwH4SlAGdvy0JACApKQmDBw/G4cOH23zMvHnzUFFR0fhz4sSJQEaSmaICOjzOs3dyO7rnA6iurQlKhKpAtiNgHURk8FNX6ydlGKizAPFB2l4CgDoLwvOCFCDjmNScgf0kKEMndNRR4dRPjcJ0XwSdhHUQkIEdJWB+J3XJfgLCdn8ElYQ1EJCB/SQoAwK8KFVdXY0vvvgCqampbT7G4XAgISGh2Q+FB2uyPTjbSercdnTPB4D8UwVByZBfcqTTz5WwDhIy+Ksr9tPBZAeC9QXNQwEc6B74V9nqIuGY1J2B/SQnQ2d01FHh1k9A+O6LYJOwDhIysKP0z++srthPQPjuj2CSsAYSMrCf5GQA/Lwo9dBDD2Hbtm04evQoPvjgA3znO9+B1WrFHXfcEVAIksmaEg1Lj8D+o9XSwwFrSnRYzgeAvML92HNiH9wed6ee7/a4sfv459h7svN3ApKwDhIydCQS+unDtBgMN4CRCOzb9y4FcLEBbE+NCVo2s0k4JnVnYD/JyeCLSOiocNkXoSZhHSRkYEfpn++rSOgnIHz2RyhJWAMJGdhPcjIAfv53TWFhIe644w4MGTIEt99+Oy666CJs374dKSkpAYUguRw5yRH9fABYvG0ZLEbnLgFYLVYs3rYs4AwS1kFChvZEQj+9lp2EKAXcj8C+fe9+AFHKu71wJuGY1J2B/SQnQ0cioaOA8NgXZpCwDhIysKP0P98XkdJPQHjsj1CTsAYSMrCf5GTway+sWLECRUVFcDqdKCwsxIoVKzBo0KCAQ5BcUVkJMOJtgL+3nTEAI96GqKzA3s6rez4ArNq5DifOFMHldvn1PJfbheNfncSbn64POIOEdZCQoT2R0E97UqLxfmoMpgHoB8DfL7G1fv28qQDeT43BnjD+mz5AxjGpOwP7SU6GjkRCRwHhsS/MIGEdJGRgR+mf74tI6ScgPPZHqElYAwkZ2E9yMoTxXdrIDIbNgtib+3pvXubrgWoAsHufZ9gCO8R0zweA2nonvrPwh6hy1vhcWi63C1XOGty6YDZq650BZ5CwDhIyEDB3fCrsVgMb4L1Zua8XpqxfP/6fAOxWA3PHt32vrXAh4ZjUnYH9JCcDeXFfeElYBwkZ2FH651Nz3B8y1kBCBvaTnAzhf1ZRyFmT7Iib1h9GnM2nxxtxNsRN6x+0mwDqng8AX3x5FBOemY6i8lMA0Obnjxt+X1R+ChOemY4jpceClkHCOkjIEOkKkuy4d3wqBgP4EED6179vq8wbfp/+9eOzANw7PhUFXWSfSDgmdWdgP8nJQF7cF14S1kFCBnaU/vnUHPeHjDWQkIH9JCODb1Mp4lmT7Ij//kDU51fCmVcGz+mWV4YtPRxw5CR73wIY5L9F0D0fAI6UHsMVT07BbZdNwpyxMzGy78UtHrO38AAWb1uGNz9dH5Sr5xeSsA4SMkS6VYMTYEBh4eYS7HUrrALwPIBdrTx2BLz3kJoK7zuk7hmfilWDw/+t501JOCZ1Z2A/yclAXtwXXhLWQUIGdpT++dQc94eMNZCQgf2kPwMvSpHPDJsF9uwk2LOT4C6thbu8DqjzAHYLrEn2kH8The75gPdtnst3vIXlO95CTno2MnsNQHx0LKpqa3D4VAHyCveHPIOEdZCQIdKtHJyInT1jsGBzMWYVn8MMA/hMAQcBVML7Ub0hAIYb3puav5cWg/vGdZ13SF1IwjGpOwP7SU4G8uK+8JKwDhIysKP0z6fmuD9krIGEDOwnvRl4UYo6xZoSrbWodc8HvF8lakZBtUfCOkjIEKkKkuyYcls/jCitxYz95biq+BxuO+OE3QPUWYAD3R1YkhqD17KTwv6m5v6QcEzqzsB+kpOBvLgvvCSsg4QM7Cj986k57g8ZayAhA/vJ/Ay8KEVEFOb2pERjT0rvxn82lIIy/P0KDSIiIiIiInN1vQ/HEhFFOF6QIiIiIiKicMCLUkREREREREREZDpDKaXMHFhZWYnExETv8FjzPz2ozroABcAAjG56Pr3IDMwgKYPu+QCgalwAgIqKCiQk6PtmON39BAjZHzwmmYEZmmcQ0FHsJ2aQMp8ZhGVgPwEQsi+YgRmEzBeTwcd+0npPqYaQeoZrns8MzCAtg+75wmhfCwn7Q3cG3fOZgRmE0r4OEvYFM+ifzwyyMgihfR0k7AtmYAYp86Vk6IDWi1J8pxQzMIP+DLrnAzKLkn/Tpy+D7vnMwAwtMgjrKPZTZGfQPZ8ZhGVgPwEQsi+YgRmEzBeTwcd+0ndRqpsVCbMyTR9bufQwVI0LRjeblvnMwAzSMuieDwAVS/KBs24ts1ulqZ8AGftDdwbd85mBGS4kqqPYTxGfQfd8ZpCVgf3kJWFfMAMzSJkvJYOv/cQbnRMRERERERERkel4UYqIiIiIiIiIiEzHi1JERERERERERGQ6rTc6p87JSc9GVq8BiIuORXVtDfJPFSCvcL+pGdyltXCX1QH1HiDKAmuyHdaUaNPmcw2YgWSScDxIyKC7oySsATOQNBKOBwkZdPcTIGMddGfQPZ9kkXA8SMjAforMDLwoFSaioxyYOmoy5oydiREZw1r8+Z4T+7B42zKs2rkOtfXOkGRQLg/q8yvhzCuD53TLGZYeDjhykhGVlQDDFvw34XENmIFkknA8SMigu6MkrAEzkDQSjgcJGXT3EyBjHXRn0D2fZJFwPEjIwH5iBl6UCgODevbH6tyXkNE9DR7lafUxw/sMwYIZT+GRSbm4dcFsHCk9FtQM7vI61Kw5DlXV9tc6ek47cW5zCWo/Po3Ym/vCmmQP2nyuATOQTBKOBwkZdHeUhDVgBpJGwvEgIYPufgJkrIPuDLrnkywSjgcJGdhPzADwnlLiDerZH5seWoG0pF4wDANWi7XVx1ktVhiGgbSkXnjn4dcxMKVf0DK4y+tQvfIoVHXbB2hTqtqF6pVH4S6vC8p8rgEzkEwSjgcJGXR3lIQ1YAaSRsLxICGD7n4CZKyD7gy655MsEo4HCRnYT8zQgBelBIuOcmB17kuId8TCZvXtTW02qw3xjli8NfdlREc5As6gXB7UrDkO1HkA5euTANR5n6dcrV/x9hXXgBlIJgnHg4QMujtKwhowA0kj4XiQkEF3PwEy1kF3Bt3zSRYJx4OEDOwnZmjK74tSJ0+exMyZM3HRRRchJiYGl1xyCT755JOAg1BLU0dNRkb3NJ9P1AY2qw19L+qD2y6bFHCG+vxK71v4fD1AGyhAVblQn18Z0HyuATP4g/1kHgnHg4QMujtKwhowg+/YUeaQcDxIyKC7nwAZ66A7g+75vmI/mUPC8SAhA/uJGZry66JUWVkZxowZg6ioKKxfvx779u3D7373OyQnJwcchFqaM3Zmm5+t7Yjb48acsTMDzuDMK9P6fK4BM/iK/WQuCceDhAy6O0rCGjCDb9hR5pFwPEjIoLufABnroDuD7uf7gv1kHgnHg4QM7CdmaMqvS5O/+c1vkJGRgSVLljT+bsCAAQGHoJZy0rNb/fYBX1ktVozsezFy0rM7/TWa7tLaVu+67w/PaSfcpbWd+vpIrgEz+IP9ZB4Jx4OEDLo7SsIaMIPv2FHmkHA8SMigu58AGeugO4Pu+b5iP5lDwvEgIQP7iRku5Nc7pdasWYNvfOMb+O53v4uePXvi0ksvxYsvvtjuc5xOJyorK5v9UMeyegXn/wgyA9iOuyx4N3fsDK4BM/iD/WQeCceDhAy6O0rCGjCD7/ztKPZT50g4HiRk0N1PgIx10J1B93xfsZ/MIeF4kJCB/cQMF/LrotSRI0ewaNEiZGVl4Z///Cf+67/+C/fffz9effXVNp8zf/58JCYmNv5kZGQEFDhSxEXHBmU78YFspz5IN1as69x2uAbM4A/2k4kkHA8CMmjvKAFrwAy+87ej2E+dJOF4EJBBez8BItZBewbd833EfjKJhONBQAb2EzNcyK+LUh6PB5dddhl+/etf49JLL8WcOXNwzz334M9//nObz5k3bx4qKioaf06cOBFQ4EhRXVsTlO1UBbKdqCB9OaO9c9vhGjCDP9hPJpJwPAjIoL2jBKwBM/jO345iP3WShONBQAbt/QSIWAftGXTP9xH7ySQSjgcBGdhPzHAhv56dmpqKYcOaf/4zOzsbx48fb/M5DocDCQkJzX6oY/mnCoKznZIjnX6uNdkelAzWpM5th2vADP5gP5lHwvEgIYPujpKwBszgO387iv3UORKOBwkZdPcTIGMddGfQPd9X7CdzSDgeJGRgPzHDhfy6KDVmzBgcPHiw2e8OHTqEfv36BRSCWsor3I89J/bB7XF36vlujxu7j3+OvScPdDqDNSUalh6OTj8fACw9HJ2+6RnXgBn8wX4yj4TjQUIG3R0lYQ2YwXfsKHNIOB4kZNDdT4CMddCdQfd8X7GfzCHheJCQgf3EDC224c+Df/KTn2D79u349a9/jcOHD2P58uVYvHgxcnNzAwpBrVu8bRksRufeCme1WLF427KAMzhyAvsq2ECfzzVgBl+xn8wl4XiQkEF3R0lYA2bwDTvKPBKOBwkZdPcTIGMddGfQ/XxfsJ/MI+F4kJCB/cQMTfl1JFx++eVYvXo1/v73v2P48OF44okn8Ic//AEzZswIOAi1tGrnOpw4UwSX2+XX81xuF45/dRJvfro+4AxRWQkw4m2A4ecTDcCItyEqK7C383INmMFX7CdzSTgeJGTQ3VES1oAZfMOOMo+E40FCBt39BMhYB90ZdM/3BfvJPBKOBwkZ2E/M0JTflydvvPFG7N27F7W1tdi/fz/uueeegENQ62rrnfjOwh+iylnj8wnrcrtQ5azBrQtmo7beGXAGw2ZB7M19vTcv8/VANQDYvc8zbIHd9IxrwAz+YD+ZR8LxICGD7o6SsAbM4Dt2lDkkHA8SMujuJ0DGOujOoHu+r9hP5pBwPEjIwH5ihqbMaTnqtC++PIoJz0xHUfkpAGjzs7cNvy8qP4UJz0zHkdJjQctgTbIjblp/GHE2nx5vxNkQN61/0G7KyDVgBpJJwvEgIYPujpKwBsxA0kg4HiRk0N1PgIx10J1B93ySRcLxICED+4kZGvg2lbQ6UnoMVzw5BbddNglzxs7EyL4Xt3jM3sIDWLxtGd78dH1QrhxfyJpkR/z3B6I+vxLOvDJ4TrecYenhgCMn2fsWwCD/rQ7XgBlIJgnHg4QMujtKwhowA0kj4XiQkEF3PwEy1kF3Bt3zSRYJx4OEDOwnZgB4USps1NY7sXzHW1i+4y3kpGcjs9cAxEfHoqq2BodPFSCvcH/IMxg2C+zZSbBnJ8FdWgt3eR1Q5wHsFliT7CH/ZhCuATOQTBKOBwkZdHeUhDVgBpJGwvEgIYPufgJkrIPuDLrnkywSjgcJGdhPzMCLUmEor3C/KSdne6wp0Vr/j5NrwAwkk4TjQUIG3R0lYQ2YgaSRcDxIyKC7nwAZ66A7g+75JIuE40FCBvZTZGbg+0KJiIiIiIiIiMh0vChFRERERERERESm40UpIiIiIiIiIiIynaGUUmYOrKysRGJiond4rPm3tFJnXYACYABGNz231GIGZpCUQfd8AFA1LgBARUUFEhIStGQA9PcTIGR/8JhkBmZonkFAR7GfmEHKfGYQloH9BEDIvmAGZhAyX0wGH/tJ643OG0LqGa55PjMwg7QMuucLo30tJOwP3Rl0z2cGZhBK+zpI2BfMoH8+M8jKIIT2dZCwL5iBGaTMl5KhA1ovSvGdUszADPoz6J4PyCxK/k2fvgy65zMDM7TIIKyj2E+RnUH3fGYQloH9BEDIvmAGZhAyX0wGH/tJ30WpblYkzMo0fWzl0sNQNS4Y3Wxa5jMDM0jLoHs+AFQsyQfOurXMbpWmfgJk7A/dGXTPZwZmuJCojmI/RXwG3fOZQVYG9pOXhH3BDMwgZb6UDL72E290TkREREREREREpuNFKSIiIiIiIiIiMh0vShERERERERERkem03ug8HOWkZyOr1wDERceiurYG+acKkFe4nxlMzqB7PgC4S2vhLqsD6j1AlAXWZDusKdHMoCEDeUk4LyRkkHBM6l4HCWvADNSU7nNCSgYJxyTXQUYG3fOpOd3nhe75gIxjkusQmRl4UcoH0VEOTB01GXPGzsSIjGEt/nzPiX1YvG0ZVu1ch9p6JzOEKIPu+QCgXB7U51fCmVcGz+mWMyw9HHDkJCMqKwGGLTRvRGQGakrCeSEhg4RjUvc6SFgDZqCmdJ8TUjJIOCa5DjIy6J5Pzek+L3TPB2Qck1wHZjCUUipoW/NBZWUlEhMTgW5WJN6VZeZo7/yGu9DH+nYX+kE9+2N17kvI6J4Gj/LAarG2eIzb44bFsODEmSLcumA2jpQeY4YgZwjFfH8zuMvrULPmOFRVx19tacTbEHtzX1iT7OIz+HsshCJDwzczVFRUICEhocPthorufgLC77zQ3Q2AjPNC9zp01X6SkkFCR7GfwjMD++nr7QtYB90Z2E8hzhBm50VX7Sd/M3TVdQi3fgpVBl/7iZfg2zGoZ39semgF0pJ6wTCMVk8SALBarDAMA2lJvfDOw69jYEo/ZghiBt3zAe9JWr3yKFR1xycpAKhqF6pXHoW7vI4ZgpyBvCScFxIySDgmda+DhDVgBmpK9zkhJYOEY5LrICOD7vnUnO7zQvd8QMYxyXVghga8KNWG6CgHVue+hHhHLGxW3z7laLPaEO+IxVtzX0Z0lIMZgpBB93zA+zbGmjXHgToP4Ov7ChWAOu/zlMvDDEHKQF4SzgsJGSQck7rXQcIaMAM1pfuckJJBwjHJdZCRQfd8ak73eaF7PiDjmOQ6MENTfl2U6t+/PwzDaPGTm5sbcBBppo6ajIzuaT6fJA1sVhv6XtQHt102iRmCkEH3fACoz6/0vo3R3w+6KkBVuVCfX8kMQcrQkUjpKAnnhYQMEo5J3esgYQ2YwTfsp/axn77WhfoJkLEOujPonu+LSOknQP95oXs+IOOY5DowQ1N+XZT6+OOPUVxc3PizceNGAMB3v/vdgINIM2fsTHhU5676uT1uzBk7kxmCkEH3fABw5pVpfT4z+C5SOkrCeSEhg4RjUvc6SFgDZvAN+6lj7KfgPR/gOkjJoPv5voiUfgL0nxe65wMyjkmuAzM05ddFqZSUFPTu3bvxZ+3atRg0aBDGjh0bcBBJctKzMSJjWJufa+2I1WLFyL4XIyc9mxkCyKB7PuD9KszWvnnAH57TTrhLa5khwAy+iISOknBeSMgg4ZjUvQ4S1oAZfMd+6hj76bxw7ydAxjrozqB7vq8ioZ8A/eeF7vmAjGOS68AMF+r0PaXq6uqwbNky3H333TAMo83HOZ1OVFZWNvuRLqvXgKBsJzOA7TCD/vkA4C4L3g0umSGwDP7ypaPYT+GbQcIxqXsdJKwBM3QO+6l97KevtxPG/QTIWAfdGXTP74yu2k+A/vNC93xAxjHJdWCGC3X6otRbb72F8vJyzJo1q93HzZ8/H4mJiY0/GRkZnR1pmrjo2KBsJz6A7TCD/vkAgPog3VyyLoDtMEOn+NJR7KfwzSDhmNS+DgLWgBk6h/3UPvbT18K5nwAR66A9g+75ndBV+wnQf17ong9AxDHJdWCGC3X6otTLL7+MSZMmIS0trd3HzZs3DxUVFY0/J06c6OxI01TX1gRlO1UBbIcZ9M8HAEQF6Qsq7QFshxk6xZeOYj+FbwYJx6T2dRCwBszQOeyn9rGfvhbO/QSIWAftGXTP74Su2k+A/vNC93wAIo5JrgMzXMi/291/7dixY9i0aRPefPPNDh/rcDjgcAT+lY1myj9VEJztlBxhhgAy6J4PANZke1AyWJM6vx1m8J+vHcV+Ct8MEo5J3esgYQ2YwX/sJx+2w37ybieM+wmQsQ66M+ie76+u3E+A/vNC93xAxjHJdWCGC3XqktaSJUvQs2dPTJkyJaDhUuUV7seeE/vg9rg79Xy3x43dxz/H3pMHmCGADLrnA4A1JRqWHoH9n66lhwPWlGhmCDCDP7pyR0k4LyRkkHBM6l4HCWvADP5jP7WN/XReuPcTIGMddGfQPd9fXbmfAP3nhe75gIxjkuvADC224e8TPB4PlixZgjvvvBM2W6feaBUWFm9bBovRubehWS1WLN62jBmCkEH3fABw5CRrfT4z+CcSOkrCeSEhg4RjUvc6SFgDZvAd+6l97KfgPR/gOkjJoPv5voqEfgL0nxe65wMyjkmuAzM05feRsGnTJhw/fhx33313wMMlW7VzHU6cKYLL7fLreS63C8e/Ook3P13PDEHIoHs+AERlJcCItwFtf8lk6wzAiLchKiuBGYKUwReR0FESzgsJGSQck7rXQcIaMIPv2E9tYz99rQv1EyBjHXRn0D3fV5HQT4D+80L3fEDGMcl1YIam/L4oNXHiRCilMHjw4ICHS1Zb78R3Fv4QVc4an08Wl9uFKmcNbl0wG7X1TmYIQgbd8wHAsFkQe3Nf7w3cfD1ZDQB27/MMW+A3kGMG30VCR0k4LyRkkHBM6l4HCWvADL5jP7WO/dTwJHSpfgJkrIPuDLrn+yoS+gnQf17ong/IOCa5DszQlHlf5RCGvvjyKCY8Mx1F5acAoM3PvTb8vqj8FCY8Mx1HSo8xQxAz6J4PeG/eFjetP4w4397ObMTZEDetf1BvTMkM1JSE80JCBgnHpO51kLAGzEBN6T4npGSQcExyHWRk0D2fmtN9XuieD8g4JrkOzNCg635gOEiOlB7DFU9OwW2XTcKcsTMxsu/FLR6zt/AAFm9bhjc/XR+Uq7bMIG8+4D1Z478/EPX5lXDmlcFzuuUMSw8HHDnJ3rdBhuBvtpiBmpJwXkjIIOGY1L0OEtaAGagp3eeElAwSjkmug4wMuudTc7rPC93zARnHJNeBGQBelPJJbb0Ty3e8heU73kJOejYyew1AfHQsqmprcPhUAfIK9zODCRl0zwe8b2+0ZyfBnp0Ed2kt3OV1QJ0HsFtgTbKb8u0ozEBNSTgvJGSQcEzqXgcJa8AM1JTuc0JKBgnHJNdBRgbd86k53eeF7vmAjGOS68AMvCjlp7zC/aacGMwgez7g/QpN3S8emIGaknBeSMgg4ZjUvQ4S1oAZqCnd54SUDBKOSa6DjAy651Nzus8L3fMBGcck1yEyM/B9oUREREREREREZDpelCIiIiIiIiIiItPxohQREREREREREZnOUEopMwdWVlYiMTHROzzW/FtaqbMuQAEwAKObnltqMQMzSMqgez4AqBoXAKCiogIJCQlaMgD6+wkQsj94TDIDMzTPIKCj2E/MIGU+MwjLwH4CIGRfMAMzCJkvJoOP/aT1RucNIfUM1zyfGZhBWgbd84XRvhYS9ofuDLrnMwMzCKV9HSTsC2bQP58ZZGUQQvs6SNgXzMAMUuZLydABrRel+E4pZmAG/Rl0zwdkFiX/pk9fBt3zmYEZWmQQ1lHsp8jOoHs+MwjLwH4CIGRfMAMzCJkvJoOP/aTvolQ3KxJmZZo+tnLpYagaF4xuNi3zmYEZpGXQPR8AKpbkA2fdWma3SlM/ATL2h+4MuuczAzNcSFRHsZ8iPoPu+cwgKwP7yUvCvmAGZpAyX0oGX/uJNzonIiIiIiIiIiLT8aIUERERERERERGZjheliIiIiIiIiIjIdFpvdE7UWe7SWrjL6oB6DxBlgTXZDmtKNDNEaAYiaXSfF7rnMwORXBLOC2bQP59IIgnnBTOYn4EXpShsKJcH9fmVcOaVwXPa2eLPLT0ccOQkIyorAYYtNG8CZAY5GYik0X1e6J7PDERySTgvmEH/fCKJJJwXzKA3Ay9KUVhwl9ehZs1xqKq2v1bSc9qJc5tLUPvxacTe3BfWJDszdNEMRNLoPi90z2cGIrkknBfMoH8+kUQSzgtm0J+Bl+BJPHd5HapXHoWqbvsEaUpVu1C98ijc5XXM0AUzEEmj+7zQPZ8ZiOSScF4wg/75RBJJOC+YQUYGXpQi0ZTLg5o1x4E6D6B8fRKAOu/zlMvDDF0oA5E0us8L3fOZgUguCecFM+ifTySRhPOCGeRk8OuilNvtxs9+9jMMGDAAMTExGDRoEJ544gko5Wt6Iv/U51d630Lo7yGmAFXlQn1+JTN0oQztYT+RDrrPC93zmcF37Cgym4Tzghn0z/cF+4nMJuG8YAY5Gfy6KPWb3/wGixYtwoIFC7B//3785je/wW9/+1u88MILAQchao0zr0zr85lBVob2sJ9IB93nhe7nM4Pv2FFkNgnnBTPof74v2E9kNgnnBTPIyeDXjc4/+OAD3HLLLZgyZQoAoH///vj73/+Ojz76KOAgRBdyl9a2etd/f3hOO+Eure3011cyg5wMHWE/kdl0nxe65zODf9hRZCYJ5wUz6J/vK/YTmUnCecEMcjIAfr5T6uqrr8Y777yDQ4cOAQD27NmD9957D5MmTWrzOU6nE5WVlc1+iHzhLgvezSWZIfwzdIT9RGbTfV7ons8M/vG3o9hPFAgJ5wUz6J/vK/YTmUnCecEMcjIAfr5T6tFHH0VlZSWGDh0Kq9UKt9uNp556CjNmzGjzOfPnz8cvf/nLgEJShKoP0o0d6wLYDjPIydAB9hOZTvd5oXs+M/jF345iP1FAJJwXzKB/vo/YT2QqCecFM8jJAD/fKfXGG2/gtddew/Lly/Hpp5/i1VdfxbPPPotXX321zefMmzcPFRUVjT8nTpwIKDBFkKggfTmkPYDtMIOcDB1gP5HpdJ8Xuuczg1/87Sj2EwVEwnnBDPrn+4j9RKaScF4wg5wM8POdUg8//DAeffRRfO973wMAXHLJJTh27Bjmz5+PO++8s9XnOBwOOByOgEJSZLIm24OznaTOb4cZ5GToCPuJzKb7vNA9nxn8429HsZ8oEBLOC2bQP99X7Ccyk4TzghnkZAD8fKfU2bNnYbE0f4rVaoXHE9q3lFJksqZEw9IjsP/Ds/RwBHTTNWaQk6Ej7Ccym+7zQvd8ZvAPO4rMJOG8YAb9833FfiIzSTgvmEFOBsDPi1I33XQTnnrqKfzf//0fjh49itWrV+P3v/89vvOd7wQUgqgtjpxkrc9nBlkZ2sN+Ih10nxe6n88MvmNHkdkknBfMoP/5vmA/kdkknBfMICeDXxelXnjhBUybNg333nsvsrOz8dBDD+E///M/8cQTTwQchKg1UVkJMOJtgOHnEw3AiLchKiuBGbpQhvawn0gH3eeF7vnM4Dt2FJlNwnnBDPrn+4L9RGaTcF4wg5wMfl2Uio+Pxx/+8AccO3YM586dwxdffIEnn3wSdntoP+dMkcuwWRB7c1/vzdN8PVEMAHbv8wxb4DdvYwY5GdrDfiIddJ8Xuuczg+/YUWQ2CecFM+if7wv2E5lNwnnBDHIyhL7liAJkTbIjblp/GHG+3ZffiLMhblr/oN4UkhnkZCCSRvd5oXs+MxDJJeG8YAb984kkknBeMIOMDH59+x6RLtYkO+K/PxD1+ZVw5pXBc9rZ4jGWHg44cpK9b0EMwd8qMYOcDETS6D4vdM9nBiK5JJwXzKB/PpFEEs4LZtCfgRelKGwYNgvs2UmwZyfBXVoLd3kdUOcB7BZYk+wh/2YSZpCVgUga3eeF7vnMQCSXhPOCGfTPJ5JIwnnBDHoz8KIUhSVrSrT2/+NmBjkZiKTRfV7ons8MRHJJOC+YQf98IokknBfMYH4Gvi+UiIiIiIiIiIhMx4tSRERERERERERkOl6UIiIiIiIiIiIi0xlKKWXmwIqKCiQlJXn/oZvVzNFeZ93n/7eO+czADNIy6J7fJEN5eTkSExP1ZICAfgJE7Q9tGXTPZwZmaCODzo5iPzGDmPnMIDID+0nOvmAGZtA+X1iGjvrJ9BudV1VVnf+Hpgulg+75zMAM0jJonl9VVaX1opSofmIGGfOZgRma0NlR7CdmEDmfGcRkYD81wQzMICmD7vkCMnTUT6a/U8rj8aCoqAjx8fEwDMPv51dWViIjIwMnTpxAQkJCCBIyQ7hk0D2fGYKXQSmFqqoqpKWlwWLR96li9hMzdKUMuud3pQwSOirQfgL07w/d85mBGaRlYD+dp3tfSMigez4zMEOwM/jaT6a/U8pisSA9PT3g7SQkJGjbOcwgK4Pu+cwQnAw63yHVgP3EDF0xg+75XSWD7o4KVj8B+veH7vnMwAzSMrCfztO9LyRk0D2fGZghmBl86Sfe6JyIiIiIiIiIiEzHi1JERERERERERGS6sLso5XA48Pjjj8PhcDBDhGfQPZ8ZZGWQQMI6MAMzSJnPDPLoXgvd85mBGaRl0D1fEglroTuD7vnMwAy6Mph+o3MiIiIiIiIiIqKwe6cUERERERERERGFP16UIiIiIiIiIiIi0/GiFBERERERERERmY4XpYiIiIiIiIiIyHRhdVHqww8/hNVqxZQpU0yfPWvWLBiG0fhz0UUX4YYbbkBeXp7pWUpKSnDfffdh4MCBcDgcyMjIwE033YR33nkn5LObrkNUVBR69eqF66+/Hq+88go8Hk/I51+YoenPDTfcYMr8jnIcPnzYlPklJSV44IEHkJmZiejoaPTq1QtjxozBokWLcPbs2ZDPnzVrFm699dYWv9+6dSsMw0B5eXnIM0jDjmI/XZhDV0fp7idAb0exn1piP7GfLszBfuJrKCnYT+ynC3OwnyKrn8LqotTLL7+M++67D++++y6KiopMn3/DDTeguLgYxcXFeOedd2Cz2XDjjTeamuHo0aMYNWoUNm/ejGeeeQZ79+7Fhg0bMG7cOOTm5pqSoWEdjh49ivXr12PcuHF44IEHcOONN8LlcpmaoenP3//+d1Nmd5RjwIABIZ975MgRXHrppfjXv/6FX//619i1axc+/PBD/PSnP8XatWuxadOmkGegliK9o9hPLXPo7Chd/QSwoyRiP7GfLszBfmI/ScF+Yj9dmIP9FFn9ZNMdwFfV1dV4/fXX8cknn6CkpARLly7F//zP/5iaweFwoHfv3gCA3r1749FHH8W3vvUtlJaWIiUlxZQM9957LwzDwEcffYTY2NjG31988cW4++67TcnQdB369OmDyy67DFdddRUmTJiApUuX4oc//KGpGXTSlePee++FzWbDJ5980uw4GDhwIG655RYopUzPFOnYUeyntnLoojMDO0oW9hP7qa0curCfqAH7if3UVg5d2E/mC5t3Sr3xxhsYOnQohgwZgpkzZ+KVV17RulOqq6uxbNkyZGZm4qKLLjJl5pkzZ7Bhwwbk5uY2O0gbJCUlmZKjNePHj8eIESPw5ptvassQKb766iv861//avM4AADDMExORZHeUewnasCOkof9xH4iL/aTPOwn9hN5RXI/hc1FqZdffhkzZ84E4H1LXUVFBbZt22ZqhrVr1yIuLg5xcXGIj4/HmjVr8Prrr8NiMWcZDx8+DKUUhg4daso8fw0dOhRHjx41ZVbTfdHw8+tf/9qU2e3l+O53vxvymQ3HwZAhQ5r9vkePHo05HnnkkZDnAFrfD5MmTTJltjSR3lHsp+YkdJSOfgLkdBT76Tz2E/upKfaT/n4C2FEN2E/sp6bYT5HZT2Hx8b2DBw/io48+wurVqwEANpsN06dPx8svv4xrr73WtBzjxo3DokWLAABlZWX405/+hEmTJuGjjz5Cv379Qj5f+tv1lFKmXb1tui8adO/e3ZTZ7eVo66q2GT766CN4PB7MmDEDTqfTlJmt7YcdO3Y0vriIFOwo9tOFJHSUpH4CzO8o9pMX+4n9dCH2U0t8DaUH+4n9dCH2U0uR0E9hcVHq5ZdfhsvlQlpaWuPvlFJwOBxYsGABEhMTTckRGxuLzMzMxn9+6aWXkJiYiBdffBFPPvlkyOdnZWXBMAwcOHAg5LM6Y//+/abdBO7CfaGLjhyZmZkwDAMHDx5s9vuBAwcCAGJiYkzL0tq/f2FhoWnzpWBHsZ8uJKGjdGWQ0lHsJy/2E/vpQuwn/f0EsKMA9hPAfroQ+yky+0n8x/dcLhf++te/4ne/+x12797d+LNnzx6kpaVp+ca1BoZhwGKx4Ny5c6bM6969O7797W9j4cKFqKmpafHnOr8+dvPmzdi7dy+mTp2qLUOkuOiii3D99ddjwYIFrR4HZC52lBf7iRqwo+RgP3mxn6gB+0kO9pMX+4kaRHI/iX+n1Nq1a1FWVobZs2e3uFo+depUvPzyy/jRj35kShan04mSkhIA3rd2LliwANXV1bjppptMmQ8ACxcuxJgxY3DFFVfgV7/6FXJycuByubBx40YsWrQI+/fvD3mGhnVwu904deoUNmzYgPnz5+PGG2/ED37wg5DPb5qhKZvNhh49epgyX7c//elPGDNmDL7xjW/gF7/4BXJycmCxWPDxxx/jwIEDGDVqlO6IEYMddR77qWWOpthR7CizsZ/OYz+1zNEU+4n9ZDb203nsp5Y5mmI/RUA/KeFuvPFGNXny5Fb/bMeOHQqA2rNnT8hz3HnnnQpA4098fLy6/PLL1cqVK0M++0JFRUUqNzdX9evXT9ntdtWnTx918803qy1btoR8dtN1sNlsKiUlRV133XXqlVdeUW63O+TzL8zQ9GfIkCGmzG+a45ZbbjF1ZlNFRUVq7ty5asCAASoqKkrFxcWpK664Qj3zzDOqpqYm5PPb+vffsmWLAqDKyspCnkECdlRzkd5PF+bQ1VG6+0kpvR3FfvJiPzXHfmI/NeBrKP3YT82xn9hPDSKxnwylhN9djYiIiIiIiIiIuhzx95QiIiIiIiIiIqKuhxeliIiIiIiIiIjIdLwoRUREREREREREpuNFKSIiIiIiIiIiMh0vShERERERERERkel4UYqIiIiIiIiIiEzHi1JERERERERERGQ6XpQiIiIiIiIiIiLT8aIUERERERERERGZjhelwsyf//xnxMfHw+VyNf6uuroaUVFRuPbaa5s9duvWrTAMA1988QXOnDmD++67D0OGDEFMTAz69u2L+++/HxUVFc2ec/z4cUyZMgXdunVDz5498fDDDzeb1ZZz584hNjYWhw8fbvFnR48exezZszFgwADExMRg0KBBePzxx1FXV9fscf/85z9x1VVXIT4+HikpKZg6dSqOHj0a0GwAMAyj8cdms6Fv37548MEH4XQ6Gx9TXFyM73//+xg8eDAsFgt+/OMft7qt8vJy5ObmIjU1FQ6HA4MHD8a6des6zEgUKULdUQ2++uorpKenwzAMlJeXd5gr0I76xS9+0axLGn5iY2M7nH3s2DHExMSgurq61dlNt2e325GZmYknn3wSSqnGx33++eeYOnUq+vfvD8Mw8Ic//KHFthYtWoScnBwkJCQgISEBo0ePxvr16zvMRxQpQtlPe/bswR133IGMjAzExMQgOzsbf/zjH33KFYzXUEopPPvssxg8eDAcDgf69OmDp556KqDZAF9DEZmlq75+uvB1TsPP9u3bO5xt1uun1l7jDR06tMN8FDw23QHIP+PGjUN1dTU++eQTXHXVVQCAf//73+jduzd27NiB2tpaREdHAwC2bNmCvn37YtCgQfjss89QVFSEZ599FsOGDcOxY8fwox/9CEVFRVi5ciUAwO12Y8qUKejduzc++OADFBcX4wc/+AGioqLw61//ut1cGzduRL9+/ZCZmdnizw4cOACPx4O//OUvyMzMxGeffYZ77rkHNTU1ePbZZwEABQUFuOWWW/Dggw/itddeQ0VFBX7yk5/gtttuw6efftrp2Q2WLFmCG264AfX19dizZw/uuusuxMbG4oknngAAOJ1OpKSk4LHHHsNzzz3X6jbq6upw/fXXo2fPnli5ciX69OmDY8eOISkpqd18RJEklB3V1OzZs5GTk4OTJ0/6lCvQjnrooYfwox/9qNnzJkyYgMsvv7zD2W+//TbGjRuHuLi4Nh+zadMmXHzxxXA6nXjvvffwwx/+EKmpqZg9ezYA4OzZsxg4cCC++93v4ic/+Umr20hPT8fTTz+NrKwsKKXw6quv4pZbbsGuXbtw8cUXd5iTqKsLZT/t3LkTPXv2xLJly5CRkYEPPvgAc+bMgdVqxdy5c9vNFWg/AcADDzyAf/3rX3j22WdxySWX4MyZMzhz5kyHa8LXUEQydNXXTw0aXuc0uOiiizqcbdbrJwC4+OKLsWnTpsZ/ttl4mcRUisJOamqqmj9/fuM///SnP1W5ubkqOztbbdmypfH311xzjbrzzjvb3M4bb7yh7Ha7qq+vV0optW7dOmWxWFRJSUnjYxYtWqQSEhKU0+lsN9Pdd9+tHnnkEZ//HX7729+qAQMGNP7zP/7xD2Wz2ZTb7W783Zo1a5RhGKquri6g2QDU6tWrm/1u9uzZavLkya0+fuzYseqBBx5o8ftFixapgQMHdpiHKNKFqqMa/OlPf1Jjx45V77zzjgKgysrKOswUaEddaPfu3QqAevfddzvc1vjx49WiRYta/bOCggIFQO3atavZ7ydMmKDuvffeVp/Tr18/9dxzz3U4VymlkpOT1UsvveTTY4kiQaj7qal7771XjRs3rsNMgfbTvn37lM1mUwcOHPB5G77O5msoIvN0xddPbb3O8YVZr58ef/xxNWLECL/zUfDw43thaNy4cdiyZUvjP2/ZsgXXXnstxo4d2/j7c+fOYceOHRg3blyb26moqEBCQkLjleAPP/wQl1xyCXr16tX4mG9/+9uorKzE559/3uZ2PB4P1q5di1tuucXnf4eKigp079698Z9HjRoFi8WCJUuWwO12o6KiAn/7299w3XXXISoqKqizDx06hM2bN+PKK6/0+TkAsGbNGowePRq5ubno1asXhg8fjl//+tdwu91+bYeoqwtVRwHAvn378Ktf/Qp//etfYbH49n9hweioC7300ksYPHgwvvWtb7W7nfLycrz33nu4+eabfZ79ySefYOfOnX53VFNutxsrVqxATU0NRo8e3entEHU1oeyn1h7TXo8Awemn//3f/8XAgQOxdu1aDBgwAP3798cPf/jDDt8pxddQRLJ05ddPN998M3r27IlvfvObWLNmTYfbMfv1U35+PtLS0jBw4EDMmDEDx48f93sbFADdV8XIfy+++KKKjY1V9fX1qrKyUtlsNvXll1+q5cuXq2uuuUYppRqvgB87dqzVbZSWlqq+ffuq//mf/2n83T333KMmTpzY7HE1NTUKgFq3bl2bed5//33Vs2fPZu9yak9+fr5KSEhQixcvbvb7rVu3qp49eyqr1aoAqNGjR3d4Bd+X2QBUdHS0io2NVQ6HQwFQN954Y5t/W9fW3/INGTJEORwOdffdd6tPPvlErVixQnXv3l394he/6PDfmSiShKqjamtrVU5Ojvrb3/6mlFJqy5YtPv1NX7A6qsG5c+dUcnKy+s1vftPhtl577TX1jW98o80/b/ibvpiYGBUbG6uioqIUADVnzpw2n9PeO6Xy8vJUbGysslqtKjExUf3f//1fhxmJIkmo+ulC77//vrLZbOqf//xnu3mC0U//+Z//qRwOh7ryyivVu+++q7Zs2aJGjhzZ4bu0+BqKSJau+PqptLRU/e53v1Pbt29XH330kXrkkUeUYRjq7bffbndbZr5+WrdunXrjjTfUnj171IYNG9To0aNV3759VWVlZcf/0hQUvCgVhvLz8xUA9cEHH6j/+7//U8OGDVNKKXXy5EnlcDjUuXPn1M9+9jM1cODAVp9fUVGhrrjiCnXDDTc0e1HR2YtSP/3pT9Xdd9/tU/bCwkI1aNAgNXv27Ga/Ly4uVllZWerhhx9Wn376qdq2bZsaO3asmjBhgvJ4PAHNBqAWLVqk8vPz1YEDB9TatWvV4MGD1fTp01t9fFsvqLKyslRGRoZyuVyNv/vd736nevfu3e58okgTqo76yU9+0uy89fVFVTA6qqnly5crm83W7KPObbn99tvVr371qzb/vOFF1Zo1a1R+fr7at2+feuONN1SvXr3afLt8exelnE6nys/PV5988ol69NFHVY8ePdTnn3/eYU6iSBGqfmpq7969qkePHuqJJ57oME8w+umee+5RANTBgwcbf7dz504FoN2P9PE1FJEsXf31U4P/+I//UN/85jfbfYzZr5+aKisrUwkJCbz9gYn48b0wlJmZifT0dGzZsgVbtmzB2LFjAQBpaWmNN9fcsmULxo8f3+K5VVVVuOGGGxAfH4/Vq1c3+2hc7969cerUqWaPb/jn3r17t5lnzZo1Pr21sqioCOPGjcPVV1+NxYsXN/uzhQsXIjExEb/97W9x6aWX4pprrsGyZcvwzjvvYMeOHQHP7t27NzIzMzFkyBBMmTIFv/zlL/H666+3+W0zrUlNTcXgwYNhtVobf5ednY2SkpIW34JDFMlC1VGbN2/GP/7xD9hsNthsNkyYMAEA0KNHDzz++ONt5glGRzX10ksv4cYbb2z2UefW1NXVYcOGDT7NzsjIQGZmJrKzs/Hd734XP/7xj/G73/0OtbW1HT63qYZvnxk1ahTmz5+PESNG+PwNYESRIFT91GDfvn2YMGEC5syZg8cee6zDPMHop9TUVNhsNgwePLjxd9nZ2QDQ7kdQ+BqKSJau/vqpwZVXXtluf+h4/dRUUlISBg8e7FfHUWB4USpMjRs3Dlu3bsXWrVubfU3oNddcg/Xr1+Ojjz5q8VnjyspKTJw4EXa7HWvWrGn8BocGo0ePxt69e/Hll182/m7jxo1ISEjAsGHDWs2Rn5+PY8eO4frrr28378mTJ3Httddi1KhRWLJkSYvPMp89e7bF7xpeuHg8noBmt6Zh2+fOnfP5OWPGjMHhw4eb5Tl06BBSU1Nht9v9zkDUlYWio1atWoU9e/Zg9+7d2L17N1566SUA3m+nyc3NbTVHsDqqQUFBAbZs2dL4rS7t2bp1K5KTkzFixIgOH3shq9UKl8sV8H+seTyeZl/dTkSh6SfA+9Xj48aNw5133omnnnqqwxzB6qcxY8bA5XLhiy++aPzdoUOHAAD9+vULaHZr+BqKKHS66uunpnbv3o3U1NQ2/1z366fq6mp88cUX7WakINP9Vi3qnFdeeUXFxMS0+AjJq6++quLj4xUAVVRU1Pj7iooKdeWVV6pLLrlEHT58WBUXFzf+NLyV2uVyqeHDh6uJEyeq3bt3qw0bNqiUlBQ1b968NnM888wz6qabbmo3a2FhocrMzFQTJkxQhYWFzWY3eOedd5RhGOqXv/ylOnTokNq5c6f69re/rfr166fOnj3b6dlKed96vmTJElVcXKxOnjyptm7dqoYPH64GDx7c7Fspdu3apXbt2qVGjRqlvv/976tdu3Y1+9jL8ePHVXx8vJo7d646ePCgWrt2rerZs6d68sknO8xAFGlC0VEX8uXt58HqqAaPPfaYSktLazNTU7m5ueq+++5r9zENbz/ftGmTKi4uVidOnFDr1q1Tffr0aXY/GKfT2dhRqamp6qGHHlK7du1S+fn5jY959NFH1bZt21RBQYHKy8tTjz76qDIMQ/3rX//qMCtRJAlFP+3du1elpKSomTNnNvvzL7/8ss0cweont9utLrvsMnXNNdeoTz/9VH3yySfqyiuvVNdff31As5Xiaygis3W1109Lly5Vy5cvV/v371f79+9XTz31lLJYLOqVV15pc7tmv3767//+b7V161ZVUFCg3n//fXXdddepHj16tNvfFFy8KBWmGk7EoUOHNvv90aNHFQA1ZMiQZr9vKJ/WfgoKCpo9f9KkSSomJkb16NFD/fd//3e7X3f8zW9+U7344ovtZl2yZEmbs5v6+9//ri699FIVGxurUlJS1M0336z2798f0GylVLN5hmGo1NRUNX36dPXFF1+0+biGn379+jV7zAcffKCuvPJK5XA41MCBA9VTTz3l03+cEkWaUHVUa89p70VVMDvK7Xar9PT0dm9u3FRGRobauHFju49pWKeGH6vVqtLT09U999zT7MXQhY9r+Bk7dmzjY+6++27Vr18/ZbfbVUpKipowYQIvSBG1IhT99Pjjj/v0OqKpYPbTyZMn1W233abi4uJUr1691KxZs9RXX30V0Gyl+BqKyGxd7fXT0qVLVXZ2turWrZtKSEhQV1xxhfrHP/7R7nbNfv00ffp0lZqaqux2u+rTp4+aPn26Onz4cLvzKbgMpZRq821URO04ffo0UlNTUVhY2OG9VbrSbCIKDzp74tNPP8X48eNRWlra6n1niCiy8TUUEUnF109kNt5TijrtzJkz+P3vf6/lBY3O2UQUHnT2hMvlwgsvvMAXVETUKr6GIiKp+PqJzMZ3ShERERERERERken4TikiIiIiIiIiIjIdL0oREREREREREZHpeFGKiIiIiIiIiIhMx4tSRERERERERERkOl6UIiIiIiIiIiIi0/GiFBERERERERERmY4XpYiIiIiIiIiIyHS8KEVERERERERERKbjRSkiIiIiIiIiIjIdL0oREREREREREZHpeFGKiIiIiIiIiIhMx4tSRERERERERERkOl6UIiIiIiIiIiIi0/GiFBERERERERERmc5m9kCPx4OioiLEx8fDMAyzxxORQEopVFVVIS0tDRaLvmvl7Cciao2EjmI/EVFr2E9EJJWv/WT6RamioiJkZGSYPZaIwsCJEyeQnp6ubT77iYjao7Oj2E9E1B72ExFJ1VE/mX5RKj4+/vw/dLOaPR4469Y7nxmYQVoG3fObZGjWDxpo7ydA1P7QlkH3fGZghjYy6Owo9hMziJnPDCIzsJ/k7AtmYAbt84Vl6KifTL8o1fiWzm5WJN6VZfZ4VC49DFXjghFrQ8KsTNPnMwMzSMugez4AVCzJB866tb/lW3c/ATL2h+4MuuczAzNcSEJHsZ+YQcp8ZpCVgf3kJWFfMAMzSJkvJYOv/cQbnRMRERERERERkel4UYqIiIiIiIiIiEzHi1JERERERERERGQ60+8pRRQM7tJauMvqgHoPEGWBNdkOa0q07lgRKSc9G1m9BiAuOhbVtTXIP1WAvML9umMRacWOkoH9RNQS+0kG9hNRS+wnOczsKF6UorChXB7U51fCmVcGz2lniz+39HDAkZOMqKwEGDa+CTCUoqMcmDpqMuaMnYkRGcNa/PmeE/uweNsyrNq5DrX1LfcVUVfEjpKB/UTUEvtJBvYTUUvsJzl0dRQvSlFYcJfXoWbNcagqV5uP8Zx24tzmEtR+fBqxN/eFNcluYsLIMahnf6zOfQkZ3dPgUZ5WHzO8zxAsmPEUHpmUi1sXzMaR0mMmpyQyFztKBvYTUUvsJxnYT0QtsZ/k0NlRvNRI4rnL61C98ihUddtl1ZSqdqF65VG4y+tCnCzyDOrZH5seWoG0pF4wDANWi7XVx1ktVhiGgbSkXnjn4dcxMKWfyUmJzMOOkoH9RNQS+0kG9hNRS+wnOXR3lN8Xpd59913cdNNNSEtLg2EYeOutt4IShKg1yuVBzZrjQJ0HUL4+CUCd93nK1fpVXvJfdJQDq3NfQrwjFjarb2+ytFltiHfE4q25LyM6yhHihOwnMh87Sgb2E1FL7CcZ2E9ELbGf5JDQUX5flKqpqcGIESOwcOHCgIcTdaQ+v9L7dk5fy6qBAlSVC/X5lSHJFYmmjpqMjO5pPpdVA5vVhr4X9cFtl00KUbLz2E9kNnaUDOwnopbYTzKwn4haYj/JIaGj/L6n1KRJkzBpUujLkQgAnHllAT/fnp0UnDARbs7YmfAoD6xG62/nbI/b48acsTOxfMdbwQ/WBPuJzMaOkoH9RNQS+0kG9hNRS+wnOSR0FO8pRWK5S2tb/QYGf3hOO+EurQ1SosiVk56NERnD2vx8cUesFitG9r0YOenZQU5GpA87Sgb2E1FL7CcZ2E9ELbGf5JDSUSG/KOV0OlFZWdnsh8gX7rLg3MSON8MLXFavAUHZTmaQthMs7CcKBDtKBvYTUUvsJxnYT0QtsZ/kkNJRIb8oNX/+fCQmJjb+ZGRkhHokdRX1QbqBXR1vhBeouOjYoGwnPkjbCRb2EwWEHSUC+4moFewnEdhPRK1gP4khpaNCflFq3rx5qKioaPw5ceJEqEdSVxEVpMPTzk+pBqq6tiYo26kK0naChf1EAWFHicB+ImoF+0kE9hNRK9hPYkjpKL9vdO4vh8MBhyP0X2VKXY812R6c7SQFZzuRLP9UQXC2U3IkKNsJFvYTBYIdJQP7iagl9pMM7CeilthPckjpKL8vL1ZXV2P37t3YvXs3AKCgoAC7d+/G8ePHAwpCdCFrSjQsPQL7PzxLDwesKdFBShS58gr3Y8+JfXB73J16vtvjxu7jn2PvyQNBTtYc+4nMxI6Sgf1E1BL7SQb2E1FL7Cc5pHSU3xelPvnkE1x66aW49NJLAQAPPvggLr30Uvz85z8PKAhRaxw5yVqfT+ct3rYMFqNzb5O1WqxYvG1ZkBO1xH4is7GjZGA/EbXEfpKB/UTUEvtJDgkd5ff0a6+9FkqpFj9Lly4NOAzRhaKyEmDE2wDDzycagBFvQ1RWQkhyRaJVO9fhxJkiuNwuv57ncrtw/KuTePPT9SFKdh77iczGjpKB/UTUEvtJBvYTUUvsJzkkdBTvDkaiGTYLYm/u672Rna+lZQCwe59n2HiIB0ttvRPfWfhDVDlrfC4tl9uFKmcNbl0wG7X1zhAnJDIfO0oG9hNRS+wnGdhPRC2xn+SQ0FHcmySeNcmOuGn9YcT5dl9+I86GuGn9efO7EPjiy6OY8Mx0FJWfAoA2P3/c8Pui8lOY8Mx0HCk9ZlpGIrOxo2RgPxG1xH6Sgf1E1BL7SQ7dHRXyb98jCgZrkh3x3x+I+vxKOPPK4Dnd8oqspYcDjpxk79tBefU8ZI6UHsMVT07BbZdNwpyxMzGy78UtHrO38AAWb1uGNz9dz7/ho4jAjpKB/UTUEvtJBvYTUUvsJzl0dhQvSlHYMGwW2LOTYM9Ogru0Fu7yOqDOA9gtsCbZ+Q0MJqqtd2L5jrewfMdbyEnPRmavAYiPjkVVbQ0OnypAXuF+3RGJTMeOkoH9RNQS+0kG9hNRS+wnOXR1FC9KUViypkSzoITIK9zPF1FEF2BHycB+ImqJ/SQD+4moJfaTHGZ2FN//RkREREREREREpuNFKSIiIiIiIiIiMh0vShERERERERERkekMpZQyc2BlZSUSExO9w2PNv6WVOusCFAADMLrpuaUWMzCDpAy65wOAqnEBACoqKpCQkKAlA6C/nwAh+4PHJDMwQ/MMAjqK/cQMUuYzg7AM7CcAQvYFMzCDkPliMvjYT1pvdN4QUs9wzfOZgRmkZdA9XxjtayFhf+jOoHs+MzCDUNrXQcK+YAb985lBVgYhtK+DhH3BDMwgZb6UDB3QelGK75RiBmbQn0H3fEBmUfJv+vRl0D2fGZihRQZhHcV+iuwMuuczg7AM7CcAQvYFMzCDkPliMvjYT/ouSnWzImFWpuljK5cehqpxwehm0zKfGZhBWgbd8wGgYkk+cNatZXarNPUTIGN/6M6gez4zMMOFRHUU+yniM+iezwyyMrCfvCTsC2ZgBinzpWTwtZ94o3MiIiIiIiIiIjIdL0oRERFRl2aY+50uREREROQjrfeUIiIiIgq2EaW1mLG/HKOLzmFImRN2D1BnAQ4mO/BhWgxey07CnpRo3TGJiIiIIh4vSlFYcpfWwl1WB9R7gCgLrMl2WCPwPzBy0rOR1WsA4qJjUV1bg/xTBcgr3K87FlHEY0fp6acB5XVYsLkYY4rPod4APlPAKgBVAOI9wNCvnLjrjBNz9pbj/dQYzB2fioIke0gzEUnDfvLS/RqK+4GoJZ4XXrr7CTB3X/CiFIUN5fKgPr8SzrwyeE47W/y5pYcDjpxkRGUlwLB13U+mRkc5MHXUZMwZOxMjMoa1+PM9J/Zh8bZlWLVzHWrrW64TEYUGO0pvP007VIGFm0tQ51ZYAuB5Bexu5XEjFXA/gGnF57B9RQHuHZ+KVYMTgpqFSBr2k5fu11DcD0Qt8bzw0t1PgL59wYtSFBbc5XWoWXMcqqrtr5X0nHbi3OYS1H58GrE394W1C/7t96Ce/bE69yVkdE+DR3lafczwPkOwYMZTeGRSLm5dMBtHSo+ZnJIo8rCj9PbTtEMVWLyxGIcA3ADgGNq+aWYegLsB/BLAP90KL24sggGFlYMTg5KFSBr2k5fu11DcD0Qt8bzw0t1PgN590XUvNVKX4S6vQ/XKo1DVbZ8gTalqF6pXHoW7vC7Eycw1qGd/bHpoBdKSesEwDFgt1lYfZ7VYYRgG0pJ64Z2HX8fAlH4mJyWKLOwovf00sLwOCzeX4BCAqwEUfv371l/Snf99IYDRAPIBLNxcggFdaH8QNWA/eel+DcX9QNQSzwsv3f0E6N8Xfl2Umj9/Pi6//HLEx8ejZ8+euPXWW3Hw4MGgBCFqjXJ5ULPmOFDnAXz98iQFoM77POVq6z9Lwkt0lAOrc19CvCMWNqtvb3C0WW2Id8TirbkvIzrKEeKE+rGfSAd2lP5+emFzMZxuhRsAVAJw+/g899eP/zaAOrfCgs3FAeXoCDuKzMZ+8tLdUeGwH9hPZLZwOC/MoLufABn7wq+LUtu2bUNubi62b9+OjRs3or6+HhMnTkRNTU3AQYhaU59f6X0Lob/f5q0AVeVCfX5lSHKZbeqoycjonuZzWTWwWW3oe1Ef3HbZpBAlk4P9RDqwo/T204gvazGm+BxWwfuRPV8vSDVwf/28VQDGFJ/DiNLaTmfpCDuKzMZ+8tL9Gioc9gP7icwWDueFGXT3EyBjX/j1b79hw4Zm/7x06VL07NkTO3fuxDXXXBNwGKILOfPKAn6+PTspOGE0mjN2JjzKA6vR+ts52+P2uDFn7Ews3/FW8IMJwn4iHdhRevtpxoFy1Bvem5pb0PZH9tpjAfA8gBkGMGN/Ofak9O5Ulo6wo8hs7Ccv3a+hwmE/sJ/IbOFwXphBdz8BMvZFQPeUqqioAAB07949oBBErXGX1rZ6139/eE474Q7h33ybISc9GyMyhrX5+eKOWC1WjOx7MXLSs4OcTDb2E4UaO0p/P40uOofPvv6Wvc6+edwDYBeAzxRwVfG5Tm7Ff+woCiX2k5fujgrX/cB+olAK1/Mi2HT3EyBnX3T6opTH48GPf/xjjBkzBsOHD2/zcU6nE5WVlc1+iHzhLgvOjdPC/WZ4Wb0GBGU7mUHaTjhgP5EZ2FH6+2lImRMHgpIAOAhg6JnQfMXyhXzpKPYTBYL95KW7o8JxP7CfKNTC8bwIBd39BMjZF52+KJWbm4vPPvsMK1asaPdx8+fPR2JiYuNPRkZGZ0dSpKkP0g3s6sL7Rnhx0bFB2U58kLYTDthPZAp2lNZ+MpSC3QNUBSWB96bndo93u6HmS0exnygg7CcAAl5DheF+YD9RyIXheREK2vsJELMvOnVRau7cuVi7di22bNmC9PT0dh87b948VFRUNP6cOHGiU0EpAkUF9OnS8+xB2o4m1bXBuclkVZC2Ix37iUzDjtLaT8owUGcB4oOSAEgAUGfxbjeUfO0o9hMFhP0EQMBrqDDbD+wnMkWYnRehor2fADH7wq8bnSulcN9992H16tXYunUrBgzo+K1iDocDDkfX/zp6Cj5rsj0420kKznZ0yT9VEJztlBwJynakYj+R2dhR+vvpYLIDQ78KzkfuhgI40D10feBvR7GfKBDsJy/dHRUu+4H9RGYKl/Mi1HT3EyBnX/h1SSs3NxfLli3D8uXLER8fj5KSEpSUlODcOfNuDEqRw5oSDUuPwP4Pz9LDAWtKdJAS6ZFXuB97TuyD2+Pvl517uT1u7D7+OfaeDNadV2RiP5HZ2FH6++nDtBgMN4CR6Pz9CCwALgVwsQFsT43p5FY6xo4iM7GfvHR3VLjsB/YTmSlczotQ091PgJx94ddruEWLFqGiogLXXnstUlNTG39ef/31gEIQtcWRk6z1+VIs3rYMFqNz/8lltVixeNuyICeSh/1EOrCj9PbTa9lJiFLA/Qjs2/fuBxClvNsLFXYUmY395KX7NVQ47Af2E5ktHM4LM+juJ0DGvvBrBZRSrf7MmjUr4CBErYnKSoARbwP8vcWHARjxNkRlJYQkl9lW7VyHE2eK4HK7/Hqey+3C8a9O4s1P14comRzsJ9KBHaW3n/akROP91BhMA9APgL9fqmz9+nlTAbyfGoM9IfxbV3YUmY395KX7NVQ47Af2E5ktHM4LM+juJ0DGvgjvu4NRl2fYLIi9ua/35mm+nigGALv3eYataxzitfVOfGfhD1HlrPG5tFxuF6qcNbh1wWzU1pvzNedEkYYdpb+f5o5Phd1qYAO8Nyv39cKU9evH/xOA3Wpg7vjUgHIQScN+8tLdUdwPRC3xvPDS3U+AjH3RNfYmdWnWJDvipvWHEefbffmNOBvipvUP+5vfXeiLL49iwjPTUVR+CgDa/Pxxw++Lyk9hwjPTcaT0mGkZiSIRO0pvPxUk2XHv+FQMBvAhgIbvi2rrBU7D79O/fnwWgHvHp6KgC+0PogbsJy/dr6G4H4ha4nnhpbufAP37wq9v3yPSxZpkR/z3B6I+vxLOvDJ4Tre8Kmzp4YAjJ9n7FsQucvX8QkdKj+GKJ6fgtssmYc7YmRjZ9+IWj9lbeACLty3Dm5+u5zukiEzCjtLbT6sGJ8CAwsLNJdjrVlgF4HkAu1p57Ah47yE1Fd53SN0zPhWrBneNjwEQtYb95KX7NRT3A1FLPC+8dPcToHdf8KIUhQ3DZoE9Own27CS4S2vhLq8D6jyA3QJrkj3sv4HBV7X1Tizf8RaW73gLOenZyOw1APHRsaiqrcHhUwXIK9yvOyJRRGJH6e2nlYMTsbNnDBZsLsas4nOYYQCfKeAggEp4P6o3BMBww3tT8/fSYnDfOL5DiiID+8lL92so7geilnheeOnuJ0DfvuBFKQpL1pToiCmo9uQV7udFKCKB2FF6+qkgyY4pt/XDiNJazNhfjquKz+G2M07YPUCdBTjQ3YElqTF4LTsppDc1J5KM/eSl+zUU9wNRSzwvvHT3E2DuvuBFKSIiIupS9qREY09K78Z/NpSCMvz9WhkiIiIiCrWu+aFMIiIioq/xghQRERGRTLwoRUREREREREREpjOUUsrMgZWVlUhMTPQOjzX/04PqrAtQAAzA6Kbn04vMwAySMuieDwCqxgUAqKioQEKCvm/B0t1PgJD9wWOSGZiheQYBHcV+YgYp85lBWAb2EwAh+4IZmEHIfDEZfOwnrfeUagipZ7jm+czADNIy6J4vjPa1kLA/dGfQPZ8ZmEEo7esgYV8wg/75zCArgxDa10HCvmAGZpAyX0qGDmi9KMV3SjEDM+jPoHs+ILMo+Td9+jLons8MzNAig7COYj9Fdgbd85lBWAb2EwAh+4IZmEHIfDEZfOwnfRelulmRMCvT9LGVSw9D1bhgdLNpmc8MzCAtg+75AFCxJB8469Yyu1Wa+gmQsT90Z9A9nxmY4UKiOor9FPEZdM9nBlkZ2E9eEvYFMzCDlPlSMvjaT7zRORERERERERERmY4XpYiIiIgoYhjmfscPERERtUPrPaWIiIiIiEJpRGktZuwvx+iicxhS5oTdA9RZgIPJDnyYFoPXspOwJyVad0wiIqKIxItS1Cnu0lq4y+qAeg8QZYE12Q6riS/odM8HgJz0bGT1GoC46FhU19Yg/1QB8gr3m5pBAq4DSSOhH3Rn4HnppXs/kF4DyuuwYHMxxhSfQ70BRI0YCVw/FIiPh72qCpccOIChe3Zjzt5yvJ8ag7njU1GQZA9pJgnnJjPIwH6ipiScE8wgJ4MEZnYUL0qRz5TLg/r8SjjzyuA57Wzx55YeDjhykhGVlQDDFvxPhuqeDwDRUQ5MHTUZc8bOxIiMYS3+fM+JfVi8bRlW7VyH2vqWGbsKrgNJI6EfdGfgeemlez+QDNMOVWDh5hJY7Q5g1ixE3X8/cOmlLR4XtWsX8PzzuPL1Fdi+ogD3jk/FqsEJQc0i4dxkBhnYT9SUhHOCGeRkkEBXR/GiFPnEXV6HmjXHoara/lpHz2knzm0uQe3HpxF7c19Yg/i3jbrnA8Cgnv2xOvclZHRPg0d5Wn3M8D5DsGDGU3hkUi5uXTAbR0qPBTWDBFwHkkZCP+jOwPPSS/d+IBmmHarA4o3FQFYWLP/6F9CvH+Bp/bxATg7wyiuwPf44LNdfjxc3HoYBhZWDE4OSRcK5yQwysJ+oKQnnBDPIySCBzo7iJXjqkLu8DtUrj0JVt32ANqWqXaheeRTu8rouMR/wltWmh1YgLakXDMOA1WJt9XFWixWGYSAtqRfeefh1DEzpF7QMEnAdSBoJ/aA7A89LL937gWQYWF6HhZtLvBektm8H+vQBDAOwtn5ewGr1/nmfPrDs2AFkZmLh5hIMCMJxIeHcZAYZ2E/UlIRzghnkZJBAd0f5dVFq0aJFyMnJQUJCAhISEjB69GisX78+KEFIJuXyoGbNcaDOA/j6ZTUKQJ33ecrVxt9Mhsl8wPt2ztW5LyHeEQub1bc3F9qsNsQ7YvHW3JcRHeUIOIME0teB/RR5JPSD7gzSz0uz6N4PvmBHmeOFzcWw2B3ed0jFxwNRUb49MSoKiI+HZeNGWO0OLNhcHFAOCecmM8jAfqKmJJwTzCAngwQSOsqvi1Lp6el4+umnsXPnTnzyyScYP348brnlFnz++ecBByGZ6vMrvW/h8/fbkxWgqlyoz68M6/kAMHXUZGR0T/O5rBrYrDb0vagPbrtsUsAZJJC+DuynyCOhH3RnkH5emkX3fvAFOyr0RnxZizHF5xA1/Xvej+z5ekGqQVQU0L8/bLdPx5jicxhRWtvpLBLOTWaQgf1ETUk4J5hBTgYJJHSUXxelbrrpJkyePBlZWVkYPHgwnnrqKcTFxWH79u0BByGZnHllEf18AJgzdmabny/uiNvjxpyxMwPOIIH0dWA/RR4J/aA7g/Tz0iy694Mv2FGhN+NAOeoNAPff3/Y9pDridgP33Yd6A5ixv7zTWSScm8wgA/uJmpJwTjCDnAwSSOioTt9Tyu12Y8WKFaipqcHo0aMDDkLyuEtrW73rvj88p51wd/JvGnXPB7xfCToiY1ibny/uiNVixci+FyMnPbvTGSQIt3VgP3V9EvpBd4ZwOy9DRfd+6Ax2VGiMLjqHqBEjvd+y19Y9pDpitQKjRiFqxP9n793DoyrPvf/vmpnMJOSMBEwknCRAUAOC1VraIiAq4lmUbaFVa+VtRe3hp7XsHuxBpa3trlXYtqhFW0TwFbFsXw9VEbprBRWEoAIGORPAIEkmCWSO6/fHmJAwOcyaw7rvSb6f68p1aTJr3R+e536+DE/WrDUWXzx4PK5TaFibdNAB84m0RcOaoIMeBw1oySjLm1JbtmxBTk4OPB4Pvv3tb2PlypUYPTr6sYkt+Hw+eL3edl8kPQjVJu9G5elYHwDKBgxNisPwJJ1HinQZB+ZT70FDPkg7pMu6TDXS82AFKxnFfLLOyFofMGpUkk42EqOOxvdGXcPapIMOmE+kLRrWBB30OGhAS0ZZ3pQaOXIkNm3ahPXr1+M73/kObrzxRnz00Uedvn7+/PnIz89v/SotLU1ImNhIIEk3VvTHeR7p+gByMrOTopCbpPNIkS7jwHzqRSjIB2mHdFmXKUdDL8SIlYxiPlnDME24w4jc3DwZ5OXBHY6c1yoa1iYdlMB8Im3QsCbooMdBBUoyyvKmlNvtxvDhwzF+/HjMnz8fY8aMwR//+MdOXz9v3jzU19e3fu3bty8hYWIjGXF/urM97jjPI10fQGNzU1IUGpJ0HinSZRyYT70IBfkg7ZAu6zLlaOiFWEtYyCjmkzVMw4DfAaChITkn9Hrhd0TOaxUNa5MOSmA+kTZoWBN00OOgAiUZZe1W8x0QDofh83V+ebPH44HH0zMel9jbcBa6k3OegvjOI10fAKoO70qKQ9WhnUk5jxTpOg7Mp56LhnyQdkjXdZlspOchEbrKKOaTdbYXenDWtm3JOdm2bdjWN77x17A26aAD5hNpi4Y1QQc9DhrQklGWtrTmzZuHf/7zn9i9eze2bNmCefPmYc2aNZg1a1ZCEkQnzqJMOPol9heOo58HzqLMtKwPAJX7t2Lzvo8QCofiOj4UDmHT3g+x5UCS3iQLkQ7jwHzqXWjIB2mHdFiXdiA9D7HCjEo9b5dkIbB5E/D++5Gn6MVDKARs2IBA5WasK86K6xQa1iYddMB8Im3RsCbooMdBA1oyytKm1KeffopvfOMbGDlyJKZMmYJ3330Xr776KqZOnZqQBNGLp6KwVx8PAIvWLoHDiO+SRKfDiUVrlyTsoAHt48B86n1oyAdpB+3r0i6k5yEWmFGp5+nyAmSYAB5+GHDE+VECpxN45BFkmJHzxYuGtUkHHTCfSFs0rAk66HHQgIaMsjQLTzzxBHbv3g2fz4dPP/0Ur7/+OsOqh5NRlgcj1wVYvaWCARi5LmSU5aV1fQBYseEl7DtajWAoaOm4YCiIvZ8dwPMbX07YQQPax4H51PvQkA/SDtrXpV1Iz0MsMKNSz+aiTLxVnIXg8mXAnj1AIGDtBIEAsHs3gs8ux1vFWdicwG9+NaxNOuiA+UTaomFN0EGPgwY0ZFTq75pH0hrD5UD2FYMiNy+LtVENAO7IcYYrsRaTrg8AzQEfrl74LTT4mmIOrWAoiAZfE65acAuaA/E9UlobHAeiDQ35IO3AdRlBeh6IHm6fXIyQ34fwRRdFbnoe68ZUIAA0NCA8dSpCfh9un1yckIeGtUkHHTCfSFs0rAk66HHQgIaMYsqRbnEWuJEzYwiMnNjui2/kuJAzY0jSbsooXR8APvl0N6Y8OBPVdYcBoNPPH7d8v7ruMKY8OBM7a/YkzUEDHAeiDQ35IO3AdRlBeh6IDnYVuHHb5GKgqgrh884DDhyI/KCze0y1fP/Agcjrd+zAbZOLsSsJfaFhbdJBB8wn0hYNa4IOehw0IJ1RCT99j/QOnAVu5H5tGAJVXvgqaxE+Er0z7OjngaeiMHIJYJJ/qyNdHwB21uzBufdNxzXjpmHOxNkYO+iMqNds2b8Ni9YuwfMbX+4xu+cnw3Eg2tCQD9IOXJcRpOeB6GDFiDwYMLFw9Sdwji6H6/qZwB13AOPHR7940ybgkUcQfHY5Qn4fbptaghUjkvdxKQ1rkw46YD6RtmhYE3TQ46AByYziphSJGcPlgLu8AO7yAoRqmhGq8wP+MOB2wFngTvmTQaTrA5HLPJeufwFL17+AioHlGD5gKHIzs9HQ3IQdh3ehcv/WlDtogONAtKEhH6QduC4jSM8D0cFzI/KxoX8WFqw+iAlPPYXAX59CxpixwMiRQF4e4PUC27cjsHkTMkxgXUkW7pg0NClXSJ2MhrVJBx0wn0hbNKwJOuhx0IBURnFTisSFsyhT9C9O6fpA5FGivSWguoLjQLShIR+kHbguI0jPA5FlV4Eb068ZjDE1zZi1tQ5f3L8Voyo3wR0G/A5gW18P1p1ZgKfLCxK6qbkVNKxNOuiA+UTaomFN0EGPgwbszChuShFCCCGEkB7L5qJMbC46tfX/DdOEaVh9zBAhhBBCUgE/rEwIIYQQQnoN3JAihBBC9MBNKUIIIYQQQgghhBBiO4ZpmqadBb1eL/Lz8yPFs+3/9KB5LAiYAAzA6CPz6UU60EGTg3R9ADCbggCA+vp65OUl76lHVpHOJ0DJfLAn6UCH9g4KMor5RAct9emgzIH5BEDJXNCBDkrqq3GIMZ9E7ynVIilTXLg+HeigzUG6vjLEx0LDfEg7SNenAx2UIj4OGuaCDvL16aDLQQni46BhLuhABy31tTh0g+imFK+UogMd5B2k6wM6g5K/6ZNzkK5PBzpEOSjLKOZT73aQrk8HZQ7MJwBK5oIOdFBSX41DjPkktynVx4m8m4bbXtb75A6YTUEYfVwi9elAB20O0vUBoH5xFXAsJFK7Q4TyCdAxH9IO0vXpQIeTUZVRzKde7yBdnw66HJhPETTMBR3ooKW+FodY84k3OieEEEIIIYQQQgghtsNNKUIIIYQQQgghhBBiO9yUIoQQQgghhBBCCCG2I3qjc6tUDCxH2YChyMnMRmNzE6oO70Ll/q29ziFU04xQrR8IhIEMB5yFbjiLMm11kB4H6fp0OIGGftSAhrnQ4KChHzSMg7SDdH0tDhr6UQvS8yFdnw500ObAfDqB9FxocJCuTwc6nIydGaV+Uyozw4Nrx1+KORNnY0zp6Kifb973ERatXYIVG15Cc8DXYx3MYBiBKi98lbUIH4mu4ejngaeiEBlleTBcqbkATnocpOvT4QQa+lEDGuZCg4OGftAwDtIO0vW1OGjoRy1Iz4d0fTrQQZsD8+kE0nOhwUG6Ph3ocDJSGWWYpmkm7Wwx4PV6kZ+fD/RxIv/msi5fe3r/IVg593GU9i1B2AzD6XBGvSYUDsFhOLDvaDWuWnALdtbs6bp+y13os2O7C70Gh1CdH02r9sJs6P6RikauC9lXDIKzwJ1UB+lxSEV9OlivD6SmH1uezFBfX4+8vLxuz5sqpPMJkO8Hqw7MJx0OGnpBg0Mq+hHQkVFW8glgT/Zkh3TLJzp8fm7mUyvSc6HBoafmEx2s19fiIPlvPLVb8Kf3H4LX71qGkoIBMAyjw4kBAKfDCcMwUFIwAG/cvRzDigb3KIdQnR+Nz+2G2dh9cwCA2RhE43O7EarzJ81Behyk69PhBBr6UQMa5kKDg4Z+0DAO0g7S9bU4aOhHLUjPh3R9OtBBmwPz6QTSc6HBQbo+HehwMtIZldCm1K9//WsYhoHvfe97SZFpITPDg5VzH0euJxsuZ2yfMHQ5Xcj1ZOOF259AZoanRziYwTCaVu0F/GEg1uvZTAD+yHFmMJywg/Q4SNenwwk09KMVmE+pddDQDxrGQdpBur4WBw39aIVU5RMgPx/S9elAB20O6ZZPAN9DpdJBuj4d6HAyGjIq7k2pd999F3/+859RUVGRsMTJXDv+UpT2LYl5YlpwOV0YdMppuGbctB7hEKjyRi6fs/oBSxMwG4IIVHkTdpAeB+n6dDiBhn6MFeZT6h009IOGcZB2kK6vxUFDP8ZKKvMJkJ8P6fp0oIM2h3TKJ4DvoVLtIF2fDnQ4GQ0ZFdemVGNjI2bNmoXHHnsMhYWFCUuczJyJsxE249txC4VDmDNxdo9w8FXWih4PyI+DdH06nEBDP8YC88keBw39oGEcpB2k62tx0NCPsZDqfALk50O6Ph3ooM0hXfIJ4HsoOxyk69OBDiejIaPi2pSaO3cupk+fjgsvvDBhgZOpGFiOMaWjO/0sZXc4HU6MHXQGKgaWp7VDqKa5wzveWyF8xIdQTXPcx0uPg3R9OpxAQz/GCvMp9Q4a+kHDOEg7SNfX4qChH2MllfkEyM+HdH060EGbQzrlE8D3UKl2kK5PBzqcjJaMsrwptWzZMmzcuBHz58+P6fU+nw9er7fdV1eUDRhqValDhidwHg0Oodrk3DQskZuPSY+DdH06nEBDP8YC88keBw39oGEcpB2k62tx0NCPsZDqfALk50O6Ph3ooM0hXfIJsJZR6ZhPGhyk69OBDiejJaMsbUrt27cP3/3ud/H0008jMzMzpmPmz5+P/Pz81q/S0tIuX5+TmW1FqVNyEziPBgcEknRTQ3/855EeB+n6dGiDgn7sDuaTfQ4a+kHDOEg7SNfX4qChH7vDjnwC5OdDuj4d6KDOIQ3yCbCeUemYTxocpOvTgQ5RKMkoS5tSGzZswKeffopx48bB5XLB5XJh7dq1ePjhh+FyuRAKhaKOmTdvHurr61u/9u3b12WNxuYma3+CTmhI4DwaHJCR0IMRT+CO/zzS4yBdnw5tUNCP3cF8ss9BQz9oGAdpB+n6Whw09GN32JFPgPx8SNenAx3UOaRBPgHWMyod80mDg3R9OtAhCiUZZek271OmTMGWLVvafe/mm2/GqFGjcM8998DpjP48pMfjgccT+6MKqw7vsqLU+XkO7Yz/WAUOzkJ3UhycBfGfR3ocpOvT4QQa+rE7mE/2OWjoBw3jIO0gXV+Lg4Z+7A478gmQnw/p+nSggzaHdMgnwHpGpWM+aXCQrk8HOpyMloyytKWVm5uLM888s91XdnY2TjnlFJx55pkJibRQuX8rNu/7CKFw9G8NYyEUDmHT3g+x5cC2tHZwFmXC0c9a2J+Mo58HzqLYPibQEdLjIF2fDifQ0I/dwXyyz0FDP2gYB2kH6fpaHDT0Y3fYkU+A/HxI16cDHbQ5pEM+AXwPZZeDdH060OFktGRUaq8FjZNFa5fAYcSn5nQ4sWjtkh7h4KlI7FGsiR4PyI+DdH06nEBDP2pAw1xocNDQDxrGQdpBur4WBw39qAXp+ZCuTwc6aHNgPp1Aei40OEjXpwMdTkZDRiW8KbVmzRo89NBDCYu0ZcWGl7DvaDWCoaCl44KhIPZ+dgDPb3y5RzhklOXByHUBhsUDDcDIdSGjLC9hB+lxkK5PhxNo6EerMJ9S56ChHzSMg7SDdH0tDhr60SqpyCdAfj6k69OBDtoc0jGfAL6HSpWDdH060OFkNGSUyiulmgM+XL3wW2jwNcU8QcFQEA2+Jly14BY0B3w9wsFwOZB9xaDIjcNibRIDgDtynOFKfHqlx0G6Ph1OoKEfNaBhLjQ4aOgHDeMg7SBdX4uDhn7UgvR8SNenAx20OTCfTiA9FxocpOvTgQ4noyGj1KbcJ5/uxpQHZ6K67jAAdPpZy5bvV9cdxpQHZ2JnzZ4e5eAscCNnxhAYObHdk97IcSFnxpCk3hBRehyk69PhBBr6UQMa5kKDg4Z+0DAO0g7S9bU4aOhHLUjPh3R9OtBBmwPz6QTSc6HBQbo+HehwMtIZZenpe3azs2YPzr1vOq4ZNw1zJs7G2EFnRL1my/5tWLR2CZ7f+HJSdgo1OjgL3Mj92jAEqrzwVdYifCS6hqOfB56Kwsjldyn4jYr0OEjXp8MJNPSjBjTMhQYHDf2gYRykHaTra3HQ0I9akJ4P6fp0oIM2B+bTCaTnQoODdH060OFkJDPKME3TTNrZYsDr9SI/Px/o40T+zWWWjq0YWI7hA4YiNzMbDc1N2HF4Fyr3b7VW/8kdMJuCMLJdyLtpuKVjtTiEapoRqvMD/jDgdsBZ4LZ8x/t0H4dk1KdD4vWB5PRj/eIq4FgI9fX1yMuTuXcCIJ9PgHw/JOrAfNLhoKEXNDgkox8BHRmVSD4B7Mme5JDu+USHCMynE0jPhQaHnpJPdEi8vhYHO/+Np/pKqZOp3L81rqbsaQ7OosyUPxq2O6THQbo+HU6goR81oGEuNDho6AcN4yDtIF1fi4OGftSC9HxI16cDHbQ5MJ9OID0XGhyk69OBDidjZ0b13OtCCSGEEEIIIYQQQohauClFCCGEEEIIIYQQQmyHm1KEEEIIIYQQQgghxHbkbnQOwMi2/5ZW5rEgYAIwAKOPzC216EAHTQ7S9QHAbAoCgJ4bnUMmnwAl88GepAMd2jsoyCjmEx201KeDMgfmEwAlc0EHOiipr8YhxnwSvdF5i6RMceH6dKCDNgfp+soQHwsN8yHtIF2fDnRQivg4aJgLOsjXp4MuByWIj4OGuaADHbTU1+LQDaKbUrxSig50kHeQrg/oDEr+pk/OQbo+HegQ5aAso5hPvdtBuj4dlDkwnwAomQs60EFJfTUOMeaT3KZUHyfybhpue1nvkztgNgVh9HGJ1KcDHbQ5SNcHgPrFVcCxkEjtDhHKJ0DHfEg7SNenAx1ORlVGMZ96vYN0fTrocmA+RdAwF3Sgg5b6WhxizSfe6JwQQgghhBBCCCGE2A43pdIYw9571BNCCCGEEEIIIYQkDdF7ShFrjKlpxqytdTi/+jhG1vrgDgN+B7C90IO3S7LwdHkBNhdlSmsSQgghhBBCCCGEdAs3pSwSqmlGqNYPBMJAhgPOQjecKd4IGlrnx4LVBzHh4HEEDCBjzFhg6iggNxfuhgactW0bRm3ehDlb6vBWcRZun1yMXQXulDpVDCxH2YChyMnMRmNzE6oO70Ll/q0prampvhYHiX7U6EAiaJgLDeuCDvL1AR39qMGBRNAwFxrWBR30OEj3pHR90h7p+dCwJuigx0G6H+124KZUDJjBMAJVXvgqaxE+4ov6uaOfB56KQmSU5cFwJfcTkTM+rsfC1YfgdHuAm25Cxp13AmefHfW6jPffBx5+GOctX4Z1y3bhtsnFWDEiL6kumRkeXDv+UsyZOBtjSkdH/Xzzvo+waO0SrNjwEpoD0eOU7vW1OEj2oyYHEkHDXGhYF3SQrw/o6EcNDiSChrnQsC7ooMdBuiel65P2SM+HhjVBBz0O0v0o6WCYpr03JvJ6vcjPzwf6OJF/c5mdpSP1W+5Cnx3bXehDdX40rdoLs6H7xxkauS5kXzEIzm6uUorVYcbH9Vj02kGgrAyOf/wDGDwYCIcBp7MD0RDgcAB79iA8dSqwYwfmTC3GcyPyE3Jo4fT+Q7By7uMo7VuCsBmG0xHtEAqH4DAc2He0GlctuAU7a/Z0eU4rDqmon44Okv2YSoeWJzPU19cjLy+5m6lWkM4nQL4frDpIZwMdUlffqoOGfkyVg4aMYj5Zd9CwLqSzgQ5tzi/8Hor5lGKHNOuHnppPdLBeH5Dvx1Q5xJpP3ILvglCdH43P7YbZ2P3EAIDZGETjc7sRqvMnXHtYnR8LVx+KbEitWwecdhpgGB1vSAGR7xsGcNppcKxfDwwfjoWrD2FoElxO7z8Er9+1DCUFA2AYRocLFQCcDicMw0BJwQC8cfdyDCsanHBtDfW1OEj2oyYHEkHDXGhYF3SQrw/o6EcNDiSChrnQsC7ooMdBuiel65P2SM+HhjVBBz0O0v2owYGbUp1gBsNoWrUX8IeBWK8lMwH4I8eZwXBC9R9ZfRAOtydyhVRuLpCREduBGRlAbi4cr70Gp9uDBasPJuSRmeHByrmPI9eTDZcztk97upwu5Hqy8cLtTyAzw5PW9bU4SPejFgcSQcNcaFgXdJCvD+joRw0OJIKGudCwLuigx0G6J6Xrk/ZIz4eGNUEHPQ7S/ajFwdKm1M9//nMYhtHua9SoUQlLaCRQ5Y1cumb1w40mYDYEEajyxl17zKfNmHDwODJm/kfkI3uxbki1kJEBDBkC1/UzMeHgcYypaY7b5drxl6K0b0nMC7UFl9OFQaechmvGTYu7tob6Whwk+1GTQ1cwn2IgiXOhYV3QQb4+oKMfNTh0R2/JKA1zoWFd0EGPg3RPStePhd6ST4D8fGhYE3TQ4yDdj1ocLF8pdcYZZ+DgwYOtX//6178SltCIr7JW7PhZ2+oQMADceWfkHlLxEAoBd9yBgAHM2loXt8ucibMRNuNzCIVDmDNxdty1NdTX4iDZj5ocuoP5ZM/xgI51QQf5+oCOftTgEAu9IaM0zIWGdUEHPQ7SPSl9fKz0hnwC5OdDw5qggx4H6X7U4mB5U8rlcuHUU09t/erXr1/CEtoI1TR3eLd5K4SP+BCK8wql86uPI2PM2MhT9jq7h1R3OJ3A+PHIGDMWXzx4PK5TVAwsx5jS0Z1+trZbBYcTYwedgYqB5WlZX4uDdD9qcYgF5lNsJDoXGtYFHeTrAzr6UYNDrPT0jNIwFxrWBR30OEj3pHR9K/T0fALk50PDmqCDHgfpftTiAMSxKVVVVYWSkhIMGzYMs2bNwt69e7t8vc/ng9frbfelnVBt8m5qGA8ja31Asi6ZHTkSo47G12hlA4YmRWF4nOeRrq/FQboftTjEAvPJwnkSmAsN64IO8vUBHf2owSFWrGQU8yk+NKwLOuhxkO5J6fpW6On5BMjPh4Y1QQc9DtL9qMUBsLgpdd555+HJJ5/EK6+8gkcffRS7du3CV77yFTQ0NHR6zPz585Gfn9/6VVpampCwLQSSdENBv/XzGKYJdxiRm5sng7w8uMOR81olJzM7KQq5cZ5Hur4WB8l+VOXQDcwniyQwFxrWBR3k6wNQ0Y8qHGLAakYxn+JDw7qggx4H8Z6Urh8jvSKfAPH50LAm6KDHQbof1TjA4qbUtGnTcN1116GiogIXX3wxXnrpJdTV1eHZZ5/t9Jh58+ahvr6+9Wvfvn0JCdtCRpIeSui2fh7TMOB3AOjiH9KW8Hrhd0TOa5XG5qakKDTEeR7p+locJPtRlUM3MJ8sksBcaFgXdJCvD0BFP6pwiAGrGcV8ig8N64IOehzEe1K6foz0inwCxOdDw5qggx4H6X5U4wDA2q3mT6KgoAAjRozAjh07On2Nx+OBx5P44xLtxFnoTs55CuI7z/ZCD87ati0pDti2Ddv6xjf+VYd3JUWh6tDOtKyvxUG6H7U4WIX51M15EpgLDeuCDvL1AR39qMEhHrrLKOZTfGhYF3TQ4yDdk9L146Un5hMgPx8a1gQd9DhI96MWByCOe0q1pbGxEZ988gmKi4sTktCGsygTjn6JBa2jnwfOosy4jn27JAuBzZuA99+PPEUvHkIhYMMGBCo3Y11xVlynqNy/FZv3fYRQOD6HUDiETXs/xJYD8W2wSdfX4iDdj1ocrMJ86pxE50LDuqCDfH1ARz9qcIiHnphRGuZCw7qggx4H6Z6Urh8vPTGfAPn50LAm6KDHQboftTgAFjel7rrrLqxduxa7d+/Gv//9b1x99dVwOp244YYbEpLQiKeiUOz4p8sLkGECePhhwBHnvqHTCTzyCDLMyPniZdHaJXAY8Tk4HU4sWrsk7toa6mtxkOxHTQ5dwXyy73hAx7qgg3x9QEc/anDojt6SURrmQsO6oIMeB+melD4+FnpLPgHy86FhTdBBj4N0P2pxsDQL+/fvxw033ICRI0fi+uuvxymnnIJ169ahqKgoYRFtZJTlwch1AVZvxWQARq4LGWV5cdfeXJSJt4qzEFy+DNizBwgErJ0gEAB270bw2eV4qzgLmxPYuVyx4SXsO1qNYCho6bhgKIi9nx3A8xtfjru2hvpaHCT7UZNDVzCfYiCJc6FhXdBBvj6gox81OHRHb8koDXOhYV3QQY+DdE9K14+F3pJPgPx8aFgTdNDjIN2PWhwsbUotW7YM1dXV8Pl82L9/P5YtW4bTTz89YQmNGC4Hsq8YFLlpV6wTZABwR44zXInd7Ov2ycUI+X0IX3RR5KbnsW5MBQJAQwPCU6ci5Pfh9smJXXbbHPDh6oXfQoOvKeYFGwwF0eBrwlULbkFzwJfW9bU4SPejFoeuYD51dxCSOhca1gUd5OsDOvpRg0N39JaM0jAXGtYFHfQ4SPekdP1Y6C35BMjPh4Y1QQc9DtL9qMUh9SmXxjgL3MiZMQRGTmz3gzdyXMiZMSQpNyPcVeDGbZOLgaoqhM87DzhwIPKDzu4x1fL9Awcir9+xA7dNLsauJLh88uluTHlwJqrrDkdKdfLZ25bvV9cdxpQHZ2JnzZ6Ea2uor8VBsh81OZAIGuZCw7qgg3x9QEc/anAgETTMhYZ1QQc9DtI9KV2ftEd6PjSsCTrocZDuRw0OCT19rzfgLHAj92vDEKjywldZi/CR6B1RRz8PPBWFkUvfkvjbjBUj8mDAxMLVn8A5uhyu62cCd9wBjB8f/eJNm4BHHkHw2eUI+X24bWoJVoxI3uW+O2v24Nz7puOacdMwZ+JsjB10RtRrtuzfhkVrl+D5jS8nZedYU30tDpL9qMmBRNAwFxrWBR3k6wM6+lGDA4mgYS40rAs66HGQ7knp+qQ90vOhYU3QQY+DdD9KO3BTKgYMlwPu8gK4ywsQqmlGqM4P+MOA2wFngTulT8R4bkQ+NvTPwoLVBzHhqacQ+OtTyBgzFhg5EsjLA7xeYPt2BDZvQoYJrCvJwh2ThiblCqmTaQ74sHT9C1i6/gVUDCzH8AFDkZuZjYbmJuw4vAuV+7cmvaam+locJPtRkwOJoGEuNKwLOsjXB3T0owYHEkHDXGhYF3TQ4yDdk9L1SXuk50PDmqCDHgfpfpR04KaURZxFmbb/hbGrwI3p1wzGmJpmzNpahy/u34pRlZvgDgN+B7CtrwfrzizA0+UFCd3U3AqV+7fasji11tfiINGPGh1IBA1zoWFd0EG+PqCjHzU4kAga5kLDuqCDHgfpnpSuT9ojPR8a1gQd9DhI96PdDtyUSiM2F2Vic9Gprf9vmCZMw+pt8gkhhBBCCCGEEELk4YeV0xhuSBFCCCGEEEIIISRd4aYUIYQQQgghhBBCCLEdwzRN086CXq8X+fn5keLZ9n960DwWBEwABmD0kfn0Ih3ooMlBuj4AmE1BAEB9fT3y8pL31EirSOcToGQ+2JN0oEN7BwUZxXyig5b6dFDmwHwCoGQu6EAHJfXVOMSYT6L3lGqRlCkuXJ8OdNDmIF1fGeJjoWE+pB2k69OBDkoRHwcNc0EH+fp00OWgBPFx0DAXdKCDlvpaHLpBdFOKV0rRgQ7yDtL1AZ1Byd/0yTlI16cDHaIclGUU86l3O0jXp4MyB+YTACVzQQc6KKmvxiHGfJLblOrjRN5Nw20v631yB8ymIIw+LpH6dKCDNgfp+gBQv7gKOBYSqd0hQvkE6JgPaQfp+nSgw8moyijmU693kK5PB10OzKcIGuaCDnTQUl+LQ6z5xBudE0IIIYQQQgghhBDb4aYUIYQQQgghhJBej2HvM8AIIRC+pxQhhBBCCCGEECLBmJpmzNpah/Orj2NkrQ/uMOB3ANsLPXi7JAtPlxdgc1GmtCYhPRpuSqUhFQPLUTZgKHIys9HY3ISqw7tQuX9rr3KQrg8AoZpmhGr9QCAMZDjgLHTDafNfWnQg2tCwNumgY13SgWhDel3S4QQa1qa0g3T93s7QOj8WrD6ICQePI2AAH5jACgANAHLDwKjPfLj5qA9zttThreIs3D65GLsK3Cnz0dAPdKCDlAM3pdKEzAwPrh1/KeZMnI0xpaOjfr5530dYtHYJVmx4Cc0BX490kK4PAGYwjECVF77KWoSPRNdw9PPAU1GIjLI8GK7UfDqWDkQbGtYmHXSsSzoQbUivSzqcQMPalHaQrk8izPi4HgtXH4I/ZGIxgIdNYFMHrxtrAncCmHHwONYt24XbJhdjxYi8pHlo6Ac60EGDAzel0oDT+w/ByrmPo7RvCcJmuMPXnHnaSCyYdT/umTYXVy24BTtr9vQoB+n6ABCq86Np1V6YDZ0/2jJ8xIfjqw+h+d0jyL5iEJxJ/o0KHYg2NKxNOuhYl3Qg2pBel3Q4gYa1Ke0gXZ9EmPFxPRa9dhAfA7gEwB50fpPlSgDfBPALAK+GTDz2WjUMmHhuRH7CHhr6gQ500OLALXjlnN5/CF6/axlKCgbAMAw4Hc4OX+d0OGEYBkoKBuCNu5djWNHgHuMgXR+ILNLG53bDbOx8kbbFbAyi8bndCNX56ZBkB6IHDWuTDjrWJR2INqTXJR1OoGFtSjtI1ycRhtX5sXD1IXwM4EsA9n/+/Y63ak98fz+A8wFUAVi4+hCGJjgvGvqBDnTQ5MBNKcVkZniwcu7jyPVkw+WM7aI2l9OFXE82Xrj9CWRmeNLeQbo+ELmMsWnVXsAfBmJ9IIcJwB85zgx29lcdHUj6omFt0kHHuqQD0Yb0uqTDCTSsTWkH6frkBI+sPghfyMQlALwAQjEeF/r89RcD8IdMLFh9MG4HDf1ABzpoc7C8KXXgwAHMnj0bp5xyCrKysnDWWWfhvffeS1iERHPt+EtR2rck5jcSLbicLgw65TRcM25a2jtI1weAQJU3chmj1SfEmoDZEESgykuHJDl0B/PJPjSsTTroWJd0iB1mlD1Ir0s6nEDD2pR2kK4fKz09n8Z82owJB49jBSIf2Yt1Q6qF0OfHrQAw4eBxjKlpjstDQz/QgQ7aHCxtStXW1mLChAnIyMjAyy+/jI8++gi///3vUVhYmLAIiWbOxNmdfva/O0LhEOZMnJ32DtL1AcBXWSt6PB1ig/lkLxrWJh10rEs6xAYzyj6k1yUdTqBhbUo7SB8fC70hn2Ztq0PAAB5G/B8VciByfMAAZm2ti+scGvqBDnTQ5mDpVye/+c1vUFpaisWLF7d+b+jQoQlLkGgqBpZ3+HSUWHE6nBg76AxUDCyP+zG/0g7S9YHIozA7evKAFcJHfAjVNMf9CE06xAbzyT40rE066FiXdIgdZpQ9SK9LOpxAw9qUdpCuHyu9IZ/Orz6ODzp5yl6shAG8D+ADE/jiweOWj9fQD3SggzYHwOJG8apVq3DOOefguuuuQ//+/XH22Wfjscce6/IYn88Hr9fb7ot0T9mA5PxFMDyB80g7SNcHgFBt8m5wSYfEHLqD+WQfGtYmHXSsSzrEjtWMYj7Fh/S6pMMJNKxNaQfp+rHSG/JpZK0P25J0ru0ARh21/g95Df1ABzpocwAsbkrt3LkTjz76KMrKyvDqq6/iO9/5Du6880489dRTnR4zf/585Ofnt36VlpYmJNxbyMnMTsp5chM4j7SDdH0AQCBJN5f0J3AeOsQE88k+NKxNOkDHuqRDzFjNKOZTfIivSzqcQMPalHaQrh8jPT2fDNOEOww0JOl8XgDucOS8ltDQD3SggzYHWNyUCofDGDduHB544AGcffbZmDNnDm699Vb86U9/6vSYefPmob6+vvVr3759CQn3Fhqbm5JynoYEziPtIF0fAJCRpAdUuhM4Dx1igvlkHxrWJh2gY13SIWasZhTzKT7E1yUdTqBhbUo7SNePkZ6eT6ZhwO8AcpN0vjwAfkfkvJbQ0A90oIM2B1jclCouLsbo0e0/n15eXo69e/d2eozH40FeXl67L9I9VYd3Jec8h3amrYN0fQBwFrqT4uAsiP88dIgN5pN9aFibdNCxLukQO1YzivkUH9Lrkg4n0LA2pR2k68dKb8in7YUejErSuUYB2NbXY/k4Df1ABzpocwAsbkpNmDAB27dvb/e9jz/+GIMHD05IgkRTuX8rNu/7CKGw1QeWRgiFQ9i090NsORD/p6elHaTrA4CzKBOOftb/0mmLo58noRu/0SE2mE/2oWFt0kHHuqRD7DCj7EF6XdLhBBrWprSDdP1Y6Q359HZJFs40gLFI7Ol7ZwM4wwDWFWdZPl5DP9CBDtocAItr8vvf/z7WrVuHBx54ADt27MDSpUuxaNEizJ07NyEJ0jGL1i6Bw4gvNp0OJxatXZL2DtL1AcBTkdjjcBM9ng6xwXyyFw1rkw461iUdYoMZZR/S65IOJ9CwNqUdpI+Phd6QT0+XFyDDBO5E5Cl68RBG5PgMM3K+eNDQD3SggzYHS39TfeELX8DKlSvxzDPP4Mwzz8SvfvUrPPTQQ5g1a1bCIiSaFRtewr6j1QiGgpaOC4aC2PvZATy/8eW0d5CuDwAZZXkwcl2AxY+NwwCMXBcyyhK/pJkO3cN8shcNa5MOOtYlHWKDGWUf0uuSDifQsDalHaTrx0JvyKfNRZl4qzgLMwAMBuC0eLzz8+OuBfBWcRY2x3lliIZ+oAMdtDlY/vXJZZddhi1btqC5uRlbt27FrbfemrAE6ZjmgA9XL/wWGnxNMb+hCIaCaPA14aoFt6A5YP1RpdocpOsDgOFyIPuKQZEbuMW6WA0A7shxhivxG8jRITaYT/ahYW3SQce6pEPsMKPsQXpd0uEEGtamtIN0/VjpDfl0++RiuJ0GXkHkZuWxbkw5P3/9qwDcTgO3Ty6O20FDP9CBDtoc7Ek5EjeffLobUx6cieq6wwDQ6b0BWr5fXXcYUx6ciZ01e3qMg3R9IHLztpwZQ2DkuGJ6vZHjQs6MIUm9MSUdiDY0rE066FiXdCDakF6XdDiBhrUp7SBdn0TYVeDGbZOLMQLA2wAGfv79zv5B3PL9gZ+/vgzAbZOLsSvBedHQD3SggyaH2KoSUXbW7MG5903HNeOmYc7E2Rg76Iyo12zZvw2L1i7B8xtfTspvtrQ5SNcHIos192vDEKjywldZi/CR6BqOfh54Kgojl0Gm4DdbdCDa0LA26aBjXdKBaEN6XdLhBBrWprSDdH0SYcWIPBgwsXD1IWwJmVgB4GEA73fw2jGI3EPqWkSukLp1cjFWjEjOxyk19AMd6KDFgZtSaUJzwIel61/A0vUvoGJgOYYPGIrczGw0NDdhx+FdqNy/tcc7SNcHIpc3ussL4C4vQKimGaE6P+APA24HnAXulD8dhQ5EIxrWJh10rEs6EG1Ir0s6nEDD2pR2kK5PIjw3Ih8b+mdhweqDuOngccwygA9MYDsALyIf1RsJ4EwjclPzf5Vk4Y5JiV8hdTIa+oEOdNDgwE2pNKRy/1Zb3jxodpCuD0QeoSn95oEORBsa1iYddKxLOhBtSK9LOpxAw9qUdpCu39vZVeDG9GsGY0xNM2ZtrcMXDx7HNUd9cIcBvwPY1teDxcVZeLq8IO6bmltBQz/QgQ5SDtyUIoQQQgghhBDS69hclInNRae2/r9hmjANq48hI4QkAj+sTAghhBBCCCGk18MNKULsh5tShBBCCCGEEEIIIcR2DNM0TTsLer1e5OfnR4pn2//pQfNYEDABGIDRR+bTi3SggyYH6foAYDYFAQD19fXIy0vOU03iQTqfACXzwZ6kAx3aOyjIKOYTHbTUp4MyB+YTACVzQQc6KKmvxiHGfBK9p1SLpExx4fp0oIM2B+n6yhAfCw3zIe0gXZ8OdFCK+DhomAs6yNengy4HJYiPg4a5oAMdtNTX4tANoptSvFKKDnSQd5CuD+gMSv6mT85Buj4d6BDloCyjmE+920G6Ph2UOTCfACiZCzrQQUl9NQ4x5pPcplQfJ/JuGm57We+TO2A2BWH0cYnUpwMdtDlI1weA+sVVwLGQSO0OEconQMd8SDtI16cDHU5GVUYxn3q9g3R9OuhyYD5F0DAXdKCDlvpaHGLNJ97onBBCCCGEEEIIIYTYDjelCCGEEEIIIYQQQojtcFOKEEIIIYQQQgghhNiO6I3O05GKgeUoGzAUOZnZaGxuQtXhXajcv7XXOYRqmhGq9QOBMJDhgLPQDWdRpm31OQZ0INFoWBcaHDT0pPQ4aBgDOpC2SK8JLQ4aepLjoMNBuj5pj/S6kK4P6OhJjkPvdOCmVAxkZnhw7fhLMWfibIwpHR318837PsKitUuwYsNLaA74eqyDGQwjUOWFr7IW4SPRNRz9PPBUFCKjLA+GK/kX4XEM6ECi0bAuNDho6EnpcdAwBnQgbZFeE1ocNPQkx0GHg3R90h7pdSFdH9DRkxwHOnBTqhtO7z8EK+c+jtK+JQib4Q5fc+ZpI7Fg1v24Z9pcXLXgFuys2dPjHEJ1fjSt2guzofPHOoaP+HB89SE0v3sE2VcMgrPAnbT6HAM6kGg0rAsNDhp6UnocNIwBHUhbpNeEFgcNPclx0OEgXZ+0R3pdSNcHdPQkx4EOAO8p1SWn9x+C1+9ahpKCATAMA06Hs8PXOR1OGIaBkoIBeOPu5RhWNLhHOYTq/Gh8bjfMxs4btC1mYxCNz+1GqM6flPocAzqQaDSsCw0OGnpSehw0jAEdSFuk14QWBw09yXHQ4SBdn7RHel1I1wd09CTHgQ4tcFOqEzIzPFg593HkerLhcsZ2QZnL6UKuJxsv3P4EMjM8PcLBDIbRtGov4A8DZqwHAfBHjjODHe94xwrHgA4kGg3rQoODhp6UHgcNY0AH0hbpNaHFQUNPchx0OEjXJ+2RXhfS9QEdPclxoENbLG1KDRkyBIZhRH3NnTs3YRFtXDv+UpT2LYl5kbTgcrow6JTTcM24aT3CIVDljVzCF2uDtmACZkMQgSpvQvU5BnSwQm/JKA3rQoODhp6UHgcNY0CH2GA+dQ3z6XN6UD4BOsZB2kG6fiz0lnwC5NeFdH1AR09yHOjQFkubUu+++y4OHjzY+vXaa68BAK677rqERbQxZ+LsTj/X2h2hcAhzJs7uEQ6+ylrR4zkGdLBCb8koDetCg4OGnpQeBw1jQIfYYD51D/MpeccDHActDtLHx0JvySdAfl1I1wd09CTHgQ5tsbQpVVRUhFNPPbX168UXX8Tpp5+OiRMnJiyiiYqB5RhTOrrTz7V2h9PhxNhBZ6BiYHlaO4Rqmju8674Vwkd8CNU0x3Usx4AOVukNGaVhXWhw0NCT0uOgYQzoEDvMp+5hPp0g3fMJ0DEO0g7S9WOlN+QTIL8upOsDOnqS40CHk4n7nlJ+vx9LlizBN7/5TRiG0enrfD4fvF5vuy/tlA0YmpTzDE/gPBocQrXJu7ljPHAM6JAIsWQU8yl9HTT0pPQ4aBgDOsQH86lrmE+fnyeN8wnQMQ7SDtL146Gn5hMgvy6k6wM6epLjQIeTiXtT6oUXXkBdXR1uuummLl83f/585Ofnt36VlpbGW9I2cjKzk3Ke3ATOo8EBgSTdWNEf33k4BnRIhFgyivmUvg4aelJ8HBSMAR3ig/nUNcynz0nnfAJUjIO4g3T9OOip+QTIrwvp+gBU9CTHgQ4nE/em1BNPPIFp06ahpKSky9fNmzcP9fX1rV/79u2Lt6RtNDY3JeU8DQmcR4MDMpL0cEZ3fOfhGNAhEWLJKOZT+jpo6EnxcVAwBnSID+ZT1zCfPied8wlQMQ7iDtL146Cn5hMgvy6k6wNQ0ZMcBzqcjLXb3X/Onj178Prrr+P555/v9rUejwceT+KPbLSTqsO7knOeQzvT2sFZ6E6Kg7MgvvNwDOgQL7FmFPMpfR009KT0OGgYAzpYh/kUw3mYT5HzpHE+ATrGQdpBur5VenI+AfLrQro+oKMnOQ50OJm4trQWL16M/v37Y/r06QkV10rl/q3YvO8jhMKhuI4PhUPYtPdDbDmwLa0dnEWZcPRL7C8cRz8PnEWZcR3LMaBDvPTkjNKwLjQ4aOhJ6XHQMAZ0sA7zqXOYTydI93wCdIyDtIN0fav05HwC5NeFdH1AR09yHOgQdQ6rB4TDYSxevBg33ngjXK64LrRKCxatXQKHEd9laE6HE4vWLukRDp6KQtHjOQZ0sEpvyCgN60KDg4aelB4HDWNAh9hhPnUN8yl5xwMcBy0O0sfHSm/IJ0B+XUjXB3T0JMeBDm2x3Amvv/469u7di29+85sJF9fMig0vYd/RagRDQUvHBUNB7P3sAJ7f+HKPcMgoy4OR6wI6f8BixxiAketCRlleQvU5BnSwSm/IKA3rQoODhp6UHgcNY0CH2GE+dQ7z6XN6UD4BOsZB2kG6fqz0hnwC5NeFdH1AR09yHOjQFsubUhdddBFM08SIESMSLq6Z5oAPVy/8Fhp8TTEvlmAoiAZfE65acAuaA74e4WC4HMi+YlDk5mWxNqoBwB05znAldtMzjgEdrNIbMkrDutDgoKEnpcdBwxjQIXaYTx3DfGo5CD0qnwAd4yDtIF0/VnpDPgHy60K6PqCjJzkOdGiLfY9ySEM++XQ3pjw4E9V1hwGg08+9tny/uu4wpjw4Eztr9vQoB2eBGzkzhsDIie1SXiPHhZwZQ5J2U0aOAR1INBrWhQYHDT0pPQ4axoAOpC3Sa0KLg4ae5DjocJCuT9ojvS6k6wM6epLjQIcWeu4HhpPEzpo9OPe+6bhm3DTMmTgbYwedEfWaLfu3YdHaJXh+48tJ2bXV6OAscCP3a8MQqPLCV1mL8JHoGo5+HngqCiOXACb5tzocAzqQaDSsCw0OGnpSehw0jAEdSFuk14QWBw09yXHQ4SBdn7RHel1I1wd09CTHgQ4AN6Viojngw9L1L2Dp+hdQMbAcwwcMRW5mNhqam7Dj8C5U7t/aKxwMlwPu8gK4ywsQqmlGqM4P+MOA2wFngTvlTwbhGNCBRKNhXWhw0NCT0uOgYQzoQNoivSa0OGjoSY6DDgfp+qQ90utCuj6goyc5DnTgppRFKvdvtWVhaHdwFmWK/sXJMaADiUbDutDgoKEnpcdBwxjQgbRFek1ocdDQkxwHHQ7S9Ul7pNeFdH1AR09yHHqnA68LJYQQQgghhBBCCCG2w00pQgghhBBCCCGEEGI73JQihBBCCCGEEEIIIbZjmKZp2lnQ6/UiPz8/Ujzb/ltamceCgAnAAIw+MrfUogMdNDlI1wcAsykIAKivr0deXp6IAyCfT4CS+WBP0oEO7R0UZBTziQ5a6tNBmQPzCYCSuaADHZTUV+MQYz6J3ui8RVKmuHB9OtBBm4N0fWWIj4WG+ZB2kK5PBzooRXwcNMwFHeTr00GXgxLEx0HDXNCBDlrqa3HoBtFNKV4pRQc6yDtI1wd0BiV/0yfnIF2fDnSIclCWUcyn3u0gXZ8OyhyYTwCUzAUd6KCkvhqHGPNJblOqjxN5Nw23vaz3yR0wm4Iw+rhE6tOBDtocpOsDQP3iKuBYSKR2hwjlE6BjPqQdpOvTgQ4noyqjmE+93kG6Ph10OTCfImiYCzrQQUt9LQ6x5hNvdE4IIYQQQgghhBBCbIebUoQQQgghhBBCCCHEdrgpRQghhBBCCCGEEEJsR/RG54TES6imGaFaPxAIAxkOOAvdcBZl0qGXOhCiDel1IV2fDoToRcO6oIN8fUI0omFd0MF+B25KkbTBDIYRqPLCV1mL8BFf1M8d/TzwVBQioywPhis1FwHSQY8DIdqQXhfS9elAiF40rAs6yNcnRCMa1gUdZB24KUXSglCdH02r9sJs6PyxkuEjPhxffQjN7x5B9hWD4Cxw06GHOhCiDel1IV2fDoToRcO6oIN8fUI0omFd0EHegVvwRD2hOj8an9sNs7HzBdIWszGIxud2I1Tnp0MPdCBEG9LrQro+HQjRi4Z1QQf5+oRoRMO6oIMOB25KEdWYwTCaVu0F/GHAjPUgAP7IcWYwTIce5ECINqTXhXR9OhCiFw3rgg7y9QnRiIZ1QQc9DpY2pUKhEH76059i6NChyMrKwumnn45f/epXMM1Y7QmxRqDKG7mE0GqLmYDZEESgykuHHuTQFcwnIoH0upCuT4fYYUYRu9GwLuggXz8WmE/EbjSsCzrocbC0KfWb3/wGjz76KBYsWICtW7fiN7/5DX7729/ikUceSViEkI7wVdaKHk8HXQ5dwXwiEkivC+nj6RA7zChiNxrWBR3kj48F5hOxGw3rgg56HCzd6Pzf//43rrzySkyfPh0AMGTIEDzzzDN45513EhYh5GRCNc0d3vXfCuEjPoRqmuN+fCUd9Dh0B/OJ2I30upCuTwdrMKOInWhYF3SQrx8rzCdiJxrWBR30OAAWr5T60pe+hDfeeAMff/wxAGDz5s3417/+hWnTpnV6jM/ng9frbfdFSCyEapN3c0k6pL9DdzCfiN1Irwvp+nSwhtWMYj6RRNCwLuggXz9WmE/ETjSsCzrocQAsXin1ox/9CF6vF6NGjYLT6UQoFML999+PWbNmdXrM/Pnz8Ytf/CIhSdJLCSTpxo7+BM5DBz0O3cB8IrYjvS6k69PBElYzivlEEkLDuqCDfP0YYT4RW9GwLuigxwEWr5R69tln8fTTT2Pp0qXYuHEjnnrqKfzud7/DU0891ekx8+bNQ319fevXvn37EhImvYiMJD0c0p3Aeeigx6EbmE/EdqTXhXR9OljCakYxn0hCaFgXdJCvHyPMJ2IrGtYFHfQ4wOKVUnfffTd+9KMf4T/+4z8AAGeddRb27NmD+fPn48Ybb+zwGI/HA4/Hk5Ak6Z04C93JOU9B/Oehgx6H7mA+EbuRXhfS9elgDasZxXwiiaBhXdBBvn6sMJ+InWhYF3TQ4wBYvFLq2LFjcDjaH+J0OhEOp/aSUtI7cRZlwtEvsb/wHP08Cd10jQ56HLqD+UTsRnpdSNengzWYUcRONKwLOsjXjxXmE7ETDeuCDnocAIubUpdffjnuv/9+/L//9/+we/durFy5Ev/1X/+Fq6++OiEJQjrDU1EoejwddDl0BfOJSCC9LqSPp0PsMKOI3WhYF3SQPz4WmE/EbjSsCzrocbC0KfXII49gxowZuO2221BeXo677roL/+f//B/86le/SliEkI7IKMuDkesCDIsHGoCR60JGWR4depBDVzCfiATS60K6Ph1ihxlF7EbDuqCDfP1YYD4Ru9GwLuigx8HSplRubi4eeugh7NmzB8ePH8cnn3yC++67D253aj/nTHovhsuB7CsGRW6eFutCMQC4I8cZrsRv3kYHPQ5dwXwiEkivC+n6dIgdZhSxGw3rgg7y9WOB+UTsRsO6oIMeh9SnHCEJ4ixwI2fGEBg5sd2X38hxIWfGkKTeFJIOehwI0Yb0upCuTwdC9KJhXdBBvj4hGtGwLuigw8HS0/cIkcJZ4Ebu14YhUOWFr7IW4SO+qNc4+nngqSiMXIKYgt8q0UGPAyHakF4X0vXpQIheNKwLOsjXJ0QjGtYFHeQduClF0gbD5YC7vADu8gKEapoRqvMD/jDgdsBZ4E75k0nooMuBEG1Irwvp+nQgRC8a1gUd5OsTohEN64IOsg7clCJpibMoU/wvbjrocSBEG9LrQro+HQjRi4Z1QQf5+oRoRMO6oIP9DrwulBBCCCGEEEIIIYTYDjelCCGEEEIIIYQQQojtcFOKEEIIIYQQQgghhNiOYZqmaWfB+vp6FBQURP6nj9PO0hGOhU78t0R9OtBBm4N0/TYOdXV1yM/Pl3GAgnwCVM2HmIN0fTrQoRMHyYxiPtFBTX06qHRgPumZCzrQQby+Mofu8sn2G503NDSc+J+2AyWBdH060EGbg3D9hoYG0U0pVflEBx316UCHNkhmFPOJDirr00GNA/OpDXSggyYH6foKHLrLJ9uvlAqHw6iurkZubi4Mw7B8vNfrRWlpKfbt24e8vLwUGNIhXRyk69MheQ6maaKhoQElJSVwOOQ+Vcx8okNPcpCu35McNGRUovkEyM+HdH060EGbA/PpBNJzocFBuj4d6JBsh1jzyfYrpRwOBwYOHJjwefLy8sQmhw66HKTr0yE5DpJXSLXAfKJDT3SQrt9THKQzKln5BMjPh3R9OtBBmwPz6QTSc6HBQbo+HeiQTIdY8ok3OieEEEIIIYQQQgghtsNNKUIIIYQQQgghhBBiO2m3KeXxeHDvvffC4/HQoZc7SNengy4HDWgYBzrQQUt9OuhDeiyk69OBDtocpOtrQsNYSDtI16cDHaQcbL/ROSGEEEIIIYQQQgghaXelFCGEEEIIIYQQQghJf7gpRQghhBBCCCGEEEJsh5tShBBCCCGEEEIIIcR2uClFCCGEEEIIIYQQQmwnrTal3n77bTidTkyfPt322jfddBMMw2j9OuWUU3DJJZegsrLSdpdDhw7hjjvuwLBhw+DxeFBaWorLL78cb7zxRsprtx2HjIwMDBgwAFOnTsVf/vIXhMPhlNc/2aHt1yWXXGJL/e48duzYYUv9Q4cO4bvf/S6GDx+OzMxMDBgwABMmTMCjjz6KY8eOpbz+TTfdhKuuuirq+2vWrIFhGKirq0u5gzaYUcynkz2kMko6nwDZjGI+RcN8Yj6d7MF84nsoLTCfmE8nezCfelc+pdWm1BNPPIE77rgD//znP1FdXW17/UsuuQQHDx7EwYMH8cYbb8DlcuGyyy6z1WH37t0YP348Vq9ejQcffBBbtmzBK6+8gkmTJmHu3Lm2OLSMw+7du/Hyyy9j0qRJ+O53v4vLLrsMwWDQVoe2X88884wttbvzGDp0aMrr7ty5E2effTb+8Y9/4IEHHsD777+Pt99+Gz/84Q/x4osv4vXXX0+5A4mmt2cU8ynaQzKjpPIJYEZphPnEfDrZg/nEfNIC84n5dLIH86l35ZNLWiBWGhsbsXz5crz33ns4dOgQnnzySfznf/6nrQ4ejwennnoqAODUU0/Fj370I3zlK19BTU0NioqKbHG47bbbYBgG3nnnHWRnZ7d+/4wzzsA3v/lNWxzajsNpp52GcePG4Ytf/CKmTJmCJ598Et/61rdsdZBEyuO2226Dy+XCe++9164Phg0bhiuvvBKmadru1NthRjGfOvOQQtKBGaUL5hPzqTMPKZhPpAXmE/OpMw8pmE/2kzZXSj377LMYNWoURo4cidmzZ+Mvf/mL6KQ0NjZiyZIlGD58OE455RRbah49ehSvvPIK5s6d265JWygoKLDFoyMmT56MMWPG4Pnnnxdz6C189tln+Mc//tFpHwCAYRg2W5HenlHMJ9ICM0ofzCfmE4nAfNIH84n5RCL05nxKm02pJ554ArNnzwYQuaSuvr4ea9eutdXhxRdfRE5ODnJycpCbm4tVq1Zh+fLlcDjsGcYdO3bANE2MGjXKlnpWGTVqFHbv3m1LrbZz0fL1wAMP2FK7K4/rrrsu5TVb+mDkyJHtvt+vX79Wj3vuuSflHkDH8zBt2jRbamujt2cU86k9GjJKIp8APRnFfDoB84n51Bbmk3w+AcyoFphPzKe2MJ96Zz6lxcf3tm/fjnfeeQcrV64EALhcLsycORNPPPEELrjgAts8Jk2ahEcffRQAUFtbi//+7//GtGnT8M4772Dw4MEpr6/9cj3TNG3bvW07Fy307dvXltpdeXS2q20H77zzDsLhMGbNmgWfz2dLzY7mYf369a1vLnoLzCjm08loyChN+QTYn1HMpwjMJ+bTyTCfouF7KBmYT8ynk2E+RdMb8iktNqWeeOIJBINBlJSUtH7PNE14PB4sWLAA+fn5tnhkZ2dj+PDhrf//+OOPIz8/H4899hjuu+++lNcvKyuDYRjYtm1bymvFw9atW227CdzJcyGFhMfw4cNhGAa2b9/e7vvDhg0DAGRlZdnm0tGff//+/bbV1wIzivl0MhoySspBS0YxnyIwn5hPJ8N8ks8ngBkFMJ8A5tPJMJ96Zz6p//heMBjEX//6V/z+97/Hpk2bWr82b96MkpISkSeutWAYBhwOB44fP25Lvb59++Liiy/GwoUL0dTUFPVzycfHrl69Glu2bMG1114r5tBbOOWUUzB16lQsWLCgwz4g9sKMisB8Ii0wo/TAfIrAfCItMJ/0wHyKwHwiLfTmfFJ/pdSLL76I2tpa3HLLLVG75ddeey2eeOIJfPvb37bFxefz4dChQwAil3YuWLAAjY2NuPzyy22pDwALFy7EhAkTcO655+KXv/wlKioqEAwG8dprr+HRRx/F1q1bU+7QMg6hUAiHDx/GK6+8gvnz5+Oyyy7DN77xjZTXb+vQFpfLhX79+tlSX5r//u//xoQJE3DOOefg5z//OSoqKuBwOPDuu+9i27ZtGD9+vLRir4EZdQLmU7RHW5hRzCi7YT6dgPkU7dEW5hPzyW6YTydgPkV7tIX51AvyyVTOZZddZl566aUd/mz9+vUmAHPz5s0p97jxxhtNAK1fubm55he+8AXzueeeS3ntk6murjbnzp1rDh482HS73eZpp51mXnHFFeabb76Z8tptx8HlcplFRUXmhRdeaP7lL38xQ6FQyuuf7ND2a+TIkbbUb+tx5ZVX2lqzLdXV1ebtt99uDh061MzIyDBzcnLMc88913zwwQfNpqamlNfv7M//5ptvmgDM2tralDtogBnVnt6eTyd7SGWUdD6ZpmxGMZ8iMJ/aw3xiPrXA91DyMJ/aw3xiPrXQG/PJME3ld1cjhBBCCCGEEEIIIT0O9feUIoQQQgghhBBCCCE9D25KEUIIIYQQQgghhBDb4aYUIYQQQgghhBBCCLEdbkoRQgghhBBCCCGEENvhphQhhBBCCCGEEEIIsR1uShFCCCGEEEIIIYQQ2+GmFCGEEEIIIYQQQgixHW5KEUIIIYQQQgghhBDb4aYUIYQQQgghhBBCCLEdbkqlIX/605+Qm5uLYDDY+r3GxkZkZGTgggsuaPfaNWvWwDAMfPLJJzh69CjuuOMOjBw5EllZWRg0aBDuvPNO1NfXtztm7969mD59Ovr06YP+/fvj7rvvblerM44fP47s7Gzs2LEj6me7d+/GLbfcgqFDhyIrKwunn3467r33Xvj9/g7PtWPHDuTm5qKgoKD7AQGwZ88eZGVlobGxscPahmG0frndbgwfPhz33XcfTNNsfd1jjz2Gr3zlKygsLERhYSEuvPBCvPPOO+3OZZomfvazn6G4uBhZWVm48MILUVVVFZMjIb2BVObT5s2bccMNN6C0tBRZWVkoLy/HH//4x5i8Es2n7du3Y9KkSRgwYAAyMzMxbNgw/OQnP0EgEOi2djLy6cMPP8S1116LIUOGwDAMPPTQQ1HnamhowPe+9z0MHjwYWVlZ+NKXvoR33303pvEhpDcQbz4BwKJFi3DBBRcgLy8PhmGgrq4u6vwbN27E1KlTUVBQgFNOOQVz5szpcN13xKRJk/D4449HfT8QCOCee+7BWWedhezsbJSUlOAb3/gGqqurW19j9T3WyQwdOhSvv/56hz9ryRzDMOB0OlFSUoJbbrkFtbW1ra9Zs2YNrrzyShQXFyM7Oxtjx47F008/3e48zz//PM455xwUFBS0vuZvf/tbTH6E9AZS+f7ps88+wyWXXIKSkhJ4PB6Ulpbi9ttvh9fr7dYr0fdPzc3NuOmmm3DWWWfB5XLhqquuinlMuqoNoN37J5fLhUGDBuEHP/gBfD5f62uef/55TJ06FUVFRcjLy8P555+PV199NepcCxcuxJAhQ5CZmYnzzjsv6t+AJLVwUyoNmTRpEhobG/Hee++1fu9///d/ceqpp2L9+vVobm5u/f6bb76JQYMG4fTTT0d1dTWqq6vxu9/9Dh988AGefPJJvPLKK7jllltaXx8KhTB9+nT4/X78+9//xlNPPYUnn3wSP/vZz7r1eu211zB48GAMHz486mfbtm1DOBzGn//8Z3z44Yf4wx/+gD/96U/4z//8z6jXBgIB3HDDDfjKV74S85j8/e9/x6RJk5CTk9Ppa15//XUcPHgQVVVV+MUvfoH7778ff/nLX1p/vmbNGtxwww1488038fbbb6O0tBQXXXQRDhw40Pqa3/72t3j44Yfxpz/9CevXr0d2djYuvvjidmNOSG8mlfm0YcMG9O/fH0uWLMGHH36IH//4x5g3bx4WLFjQrVei+ZSRkYFvfOMb+Mc//oHt27fjoYcewmOPPYZ7772329rJyKdjx45h2LBh+PWvf41TTz21w3N861vfwmuvvYa//e1v2LJlCy666CJceOGF7TKMkN5MvPkERNbgJZdc0uH7FgCorq7GhRdeiOHDh2P9+vV45ZVX8OGHH+Kmm27q1uvo0aN46623cPnll0f97NixY9i4cSN++tOfYuPGjXj++eexfft2XHHFFa2vsfIe62QqKytRW1uLiRMndvqaX/7ylzh48CD27t2Lp59+Gv/85z9x5513tv783//+NyoqKrBixQpUVlbi5ptvxje+8Q28+OKLra/p27cvfvzjH+Ptt99ufc3NN9/c4T8OCemNpPL9k8PhwJVXXolVq1bh448/xpNPPonXX38d3/72t7v1SvT9UygUQlZWFu68805ceOGFlsakq9otLF68GAcPHsSuXbvw3//93/jb3/6G++67r/Xn//znPzF16lS89NJL2LBhAyZNmoTLL78c77//futrli9fjh/84Ae49957sXHjRowZMwYXX3wxPv30U0u+JAFMkpYUFxeb8+fPb/3/H/7wh+bcuXPN8vJy880332z9/le/+lXzxhtv7PQ8zz77rOl2u81AIGCapmm+9NJLpsPhMA8dOtT6mkcffdTMy8szfT5fl07f/OY3zXvuuSfmP8Nvf/tbc+jQoVHf/+EPf2jOnj3bXLx4sZmfnx/TuSZPnmw++uijHf5s165dJgDz/fffb/f9KVOmmLfddlun5wwGg2Zubq751FNPmaZpmuFw2Dz11FPNBx98sPU1dXV1psfjMZ955pmYPAnpDaQqnzritttuMydNmtStU7LyqS3f//73zS9/+cvdnivZ+TR48GDzD3/4Q7vvHTt2zHQ6neaLL77Y7vvjxo0zf/zjH3frSEhvIdF8evPNN00AZm1tbbvv//nPfzb79+9vhkKh1u9VVlaaAMyqqqounf7617+a5513Xsx/hnfeeccEYO7Zs6fT18SSYaZpmr/85S/NmTNndvrzjvLmV7/6lTl69Oguz3vppZeaN998c5evOfvss82f/OQn3ToS0luw8/3TH//4R3PgwIHdOiXz/dONN95oXnnllTGfq7vaAMyVK1e2+94tt9xiXnrppV2ed/To0eYvfvGL1v8/99xzzblz57b+fygUMktKStrNBUktvFIqTZk0aRLefPPN1v9/8803ccEFF2DixImt3z9+/DjWr1+PSZMmdXqe+vp65OXlweVyAQDefvttnHXWWRgwYEDray6++GJ4vV58+OGHnZ4nHA7jxRdfxJVXXhnzn6G+vh59+/Zt973Vq1fj//7f/4uFCxfGfJ66ujr861//avdbw+547733sGHDBpx33nmdvubYsWMIBAKtjrt27cKhQ4fa7fLn5+fjvPPOw9tvvx1zbUJ6OqnKp85ec3KOnEyy8qktO3bswCuvvNLl1QVA6vLpZILBIEKhEDIzM9t9PysrC//6179iPg8hPZ1k5dPJ+Hw+uN1uOBwn3lpnZWUBQLdrcNWqVZbzyTCMLm9xEEs2xlP7wIED+J//+Z9u86mr+qZp4o033sD27dvx1a9+NebahPR07Hr/VF1djeeff77b9zCpeP8UK/HU/vjjj7F69eou8ykcDqOhoaHV0e/3Y8OGDe3+fedwOHDhhRfy33d2Ir0rRuLjscceM7Ozs81AIGB6vV7T5XKZn376qbl06VLzq1/9qmmapvnGG290+Zu0mpoac9CgQeZ//ud/tn7v1ltvNS+66KJ2r2tqajIBmC+99FKnPm+99VbUbwi7oqqqyszLyzMXLVrU+r0jR46YpaWl5tq1a03TNGO+Uurpp582zznnnE5/3nIlQlZWlpmdnW1mZGSYAMw5c+Z0ed7vfOc75rBhw8zjx4+bphn5MwIwq6ur273uuuuuM6+//vpuPQnpLaQqn07mrbfeMl0ul/nqq6926ZOMfGrh/PPPNz0eT2uGdHfOVORTR1cutLhNnDjRPHDggBkMBs2//e1vpsPhMEeMGNGlIyG9iUTzqbMrpT744APT5XKZv/3tb02fz2cePXrUvPbaa00A5gMPPNCpT3Nzs5mTk2N+8MEHMfkfP37cHDdunPm1r32t09d0lWFt2b9/v+l2u6P+LG0ZPHiw6Xa7zezsbDMzM9MEYJ533nldHrN8+XLT7XZH/Znq6urM7Oxs0+VymR6Px3ziiSe69COkt5Hq90//8R//YWZlZZkAzMsvv7z13zidkcz3T6Zp7UqpWGoDMDMzM83s7OzW92aXXXaZ6ff7Oz3mN7/5jVlYWGgePnzYNE3TPHDggAnA/Pe//93udXfffbd57rnnxuRKEodXSqUpF1xwAZqamvDuu+/if//3fzFixAgUFRVh4sSJrZ87XrNmDYYNG4ZBgwZFHe/1ejF9+nSMHj0aP//5zxP2+fvf/47LLrus3W8IO+PAgQO45JJLcN111+HWW29t/f6tt96Kr33ta5Z/a/b3v/89pqsQli9fjk2bNmHz5s149tln8fe//x0/+tGPOnztr3/9ayxbtgwrV66MuvKAENI1duTTBx98gCuvvBL33nsvLrrooi59kpFPLSxfvhwbN27E0qVL8f/+3//D7373u25rJzufOuNvf/sbTNPEaaedBo/Hg4cffhg33HBDTH9uQnoLieZTZ5xxxhl46qmn8Pvf/x59+vTBqaeeiqFDh2LAgAFdrsHVq1ejf//+OOOMM7qtEQgEcP3118M0TTz66KMdvqa7DGvLqlWr8OUvf7nbh8rcfffd2LRpEyorK/HGG28AAKZPn45QKBT12jfffBM333wzHnvssag/U25uLjZt2oR3330X999/P37wgx9gzZo1XdYmpDeR6vdPf/jDH7Bx40b8/e9/xyeffIIf/OAHXfok8/2TVWKt/Yc//KH1/dOLL76Ijz/+GF//+tc7fO3SpUvxi1/8As8++yz69++fsCNJItK7YiR+Bg4caN5///3mXXfdZX7nO99p/f7w4cPNN954w/zyl79sfutb34o6zuv1mueff745ZcqUqB3yn/70p+aYMWPafW/nzp0mAHPjxo2duowaNcp84YUXunU+cOCAWVZWZn7961+P2vnOz883nU5n65fD4TABmE6ns9Pfpvl8PjMvL8/ctGlTpzU7u2fL/PnzTZfLFTUGDz74oJmfn2++++677b7/ySefdHier371q+add97ZzZ+ckN5FKvKphQ8//NDs379/l1dRtSUZ+dQRf/vb38ysrCwzGAx2+PNU5JNpdn6lVAuNjY2tV3Ref/313d5bgZDeRrz5ZJqdXynVlkOHDpkNDQ1mY2Oj6XA4zGeffbbT13772982v/e973Xr7Pf7zauuusqsqKgwjxw50uFrrGbYJZdcYj700ENdvqajvHn77bdNAOZrr73W7vtr1qwxs7OzzT//+c/d1jbNyL1fTr46n5DeTirfP7Xlf//3fzv8BEhbkv3+ycqVUrHURgf3lHrmmWc6vJffM888Y2ZlZUXde9Pn85lOpzPqPN/4xjfMK664IiZXkjj89WkaM2nSJKxZswZr1qxp96jQr371q3j55ZfxzjvvRH3e2Ov14qKLLoLb7caqVauirgI6//zzsWXLlnZPG3jttdeQl5eH0aNHd+hRVVWFPXv2YOrUqV36HjhwABdccAHGjx+PxYsXR+18v/3229i0aVPr1y9/+cvW36pdffXVHZ5zzZo1KCwsxJgxY7qs3RFOpxPBYLDdY0t/+9vf4le/+hVeeeUVnHPOOe1eP3ToUJx66qmtvyUEIuO5fv16nH/++ZbrE9KTSUU+AcCHH36ISZMm4cYbb8T999/frUey8qkjwuEwAoEAwuFwhz9Pdj7FSnZ2NoqLi1FbW4tXX33V0v0YCOkNxJNPVhgwYABycnKwfPlyZGZmdpo/pmnif/7nf7pdoy1XSFVVVeH111/HKaecEvUaqxnW2NiIN998M658cDqdACL3tmlhzZo1mD59On7zm99gzpw5MZ0nHA63e3Q7ISR1759OpuW9S2drMJXvn7oj1tod0VE+PfPMM7j55pvxzDPPYPr06e1e73a7MX78+Hb/vguHw3jjjTf47zsb6fzusUQ9kyZNwty5cxEIBNrdqG7ixIm4/fbb4ff724VWS2AdO3YMS5YsgdfrhdfrBQAUFRXB6XTioosuwujRo/H1r38dv/3tb3Ho0CH85Cc/wdy5c+HxeDr0+Pvf/44LL7wQffr06dS1JbAGDx6M3/3ud6ipqWn9WcvjzcvLy9sd895778HhcODMM8/s9LyrVq2K+QbCn332GQ4dOoRgMIgtW7bgj3/8IyZNmoS8vDwAwG9+8xv87Gc/w9KlSzFkyBAcOnQIAJCTk4OcnBwYhoHvfe97uO+++1BWVoahQ4fipz/9KUpKSnDVVVfF5EBIbyEV+fTBBx9g8uTJuPjii/GDH/ygdY06nU4UFRV16JGsfHr66aeRkZGBs846Cx6PB++99x7mzZuHmTNnIiMjo8PzJjOf/H4/Pvroo9b/PnDgADZt2oScnJzWRyW/+uqrME0TI0eOxI4dO3D33Xdj1KhRuPnmm2NyIKS3YDWfAODQoUM4dOgQduzYAQDYsmULcnNzMWjQoNYb5i5YsABf+tKXkJOTg9deew133303fv3rX3f68bgNGzbg2LFj+PKXv9ypayAQwIwZM7Bx40a8+OKLCIVCrdnXt29fuN3umDLsZF555RWMGDECQ4YM6Xa8GhoacOjQIZimiX379uGHP/whioqK8KUvfQlA5CN7l112Gb773e/i2muvbfVzu92tYzN//nycc845OP300+Hz+fDSSy/hb3/7W6cfQySkt5KK908vvfQSDh8+jC984QvIycnBhx9+iLvvvhsTJkzoNAOS9f4JAD766CP4/X4cPXoUDQ0N2LRpEwBg7Nixcdduoa6uDocOHUI4HEZVVRV++ctfYsSIEa3/rly6dCluvPFG/PGPf8R5553Xmk9ZWVnIz88HAPzgBz/AjTfeiHPOOQfnnnsuHnroITQ1NfH9k50IX6lFEqDlYx+jRo1q9/3du3ebAMyRI0e2+37LJecdfe3atavd8dOmTTOzsrLMfv36mf/f//f/dflI0S9/+cvmY4891qXr4sWLO63d1THd3ei8tLQ06vLxk2kZp5Yvp9NpDhw40Lz11lvNTz/9tPV1gwcP7tDv3nvvbX1NOBw2f/rTn5oDBgwwPR6POWXKFHP79u1d1iekN5KKfLr33ns7/PngwYM79UhWPi1btswcN26cmZOTY2ZnZ5ujR482H3jggS4vkU9mPp38upaviRMntr5m+fLl5rBhw0y3222eeuqp5ty5c826urou6xPSG7GaT6bZef4sXry49TVf//rXzb59+5put9usqKgw//rXv3bp8ZOf/MScNWtWTK4dfbU8Ij6e91izZ882f/zjH3dZ2zSj3xsVFRWZl156abuPHN94443d5tOPf/xjc/jw4WZmZqZZWFhonn/++eayZcu6rU9IbyMV759Wr15tnn/++WZ+fr6ZmZlplpWVmffcc0+XH0NO5r/vOvs3ViK1TdNsdy7DMMzi4mJz5syZ5ieffNL6mokTJ3ZY+8Ybb2x3rkceecQcNGiQ6Xa7zXPPPddct25dt/VJ8jBM0zS72LMipEuOHDmC4uJi7N+/HwMGDLC19saNGzF58mTU1NR0eqUCIaT3wnwihGimoqICP/nJT3D99dfbWjcYDGLAgAF4+eWXce6559pamxCiH8n3T5K1iRy8pxRJiKNHj+K//uu/REIjGAzikUce4T/4CCEdwnwihGjF7/fj2muvxbRp02yvffToUXz/+9/HF77wBdtrE0L0I/n+SbI2kYNXShFCCCGEEEIIIYQQ2+GVUoQQQgghhBBCCCHEdrgpRQghhBBCCCGEEEJsh5tShBBCCCGEEEIIIcR2uClFCCGEEEIIIYQQQmyHm1KEEEIIIYQQQgghxHa4KUUIIYQQQgghhBBCbIebUoQQQgghhBBCCCHEdrgpRQghhBBCCCGEEEJsh5tShBBCCCGEEEIIIcR2uClFCCGEEEIIIYQQQmyHm1KEEEIIIYQQQgghxHa4KUUIIYQQQgghhBBCbIebUoQQQgghhBBCCCHEdrgpRQghhBBCCCGEEEJsx2V3wXA4jOrqauTm5sIwDLvLE0IUYpomGhoaUFJSAodDbq+c+UQI6QgNGcV8IoR0BPOJEKKVWPPJ9k2p6upqlJaW2l2WEJIG7Nu3DwMHDhSrz3wihHSFZEYxnwghXcF8IoRopbt8sn1TKjc398T/9HHaXR44FpKtTwc6aHOQrt/GoV0+CCCeT4Cq+RBzkK5PBzp04iCZUcwnOqipTweVDswnPXNBBzqI11fm0F0+2b4p1XpJZx8n8m8us7s8vE/ugNkUhJHtQt5Nw22vTwc6aHOQrg8A9YurgGMh8Uu+pfMJ0DEf0g7S9elAh5PRkFHMJzpoqU8HXQ7Mpwga5oIOdNBSX4tDrPnEG50TQgghhBBCCCGEENvhphQhhBBCCCGEEEIIsR1uShFCCCGEEEIIIYQQ27H9nlKEJINQTTNCtX4gEAYyHHAWuuEsypTW6pVUDCxH2YChyMnMRmNzE6oO70Ll/q3SWoSIwozSAfOJkGiYTzpgPhESDfNJD3ZmFDelSNpgBsMIVHnhq6xF+Igv6ueOfh54KgqRUZYHw8WLAFNJZoYH146/FHMmzsaY0tFRP9+87yMsWrsEKza8hOZA9FwR0hNhRumA+URINMwnHTCfCImG+aQHqYziphRJC0J1fjSt2guzIdjpa8JHfDi++hCa3z2C7CsGwVngttGw93B6/yFYOfdxlPYtQdgMd/iaM08biQWz7sc90+biqgW3YGfNHpstCbEXZpQOmE+ERMN80gHziZBomE96kMwobjUS9YTq/Gh8bjfMxs7Dqi1mYxCNz+1GqM6fYrPex+n9h+D1u5ahpGAADMOA0+Hs8HVOhxOGYaCkYADeuHs5hhUNttmUEPtgRumA+URINMwnHTCfCImG+aQH6YyyvCn1z3/+E5dffjlKSkpgGAZeeOGFpIgQ0hFmMIymVXsBfxgwYz0IgD9ynBnseJeXWCczw4OVcx9HricbLmdsF1m6nC7kerLxwu1PIDPDk2JD5hOxH2aUDphPhETDfNIB84mQaJhPetCQUZY3pZqamjBmzBgsXLgw4eKEdEegyhu5nDPWsGrBBMyGIAJV3pR49UauHX8pSvuWxBxWLbicLgw65TRcM25aisxOwHwidsOM0gHziZBomE86YD4REg3zSQ8aMsryPaWmTZuGadNSH46EAICvsjbh493lBcmR6eXMmTgbYTMMp9Hx5ZxdEQqHMGfibCxd/0LyxdrAfCJ2w4zSAfOJkGiYTzpgPhESDfNJDxoyiveUImoJ1TR3+AQGK4SP+BCqaU6SUe+lYmA5xpSO7vTzxd3hdDgxdtAZqBhYnmQzQuRgRumA+URINMwnHTCfCImG+aQHLRmV8k0pn88Hr9fb7ouQWAjVJucmdrwZXuKUDRialPMMT9J5kgXziSQCM0oHzCdComE+6YD5REg0zCc9aMmolG9KzZ8/H/n5+a1fpaWlqS5JegqBJN3Azs8b4SVKTmZ2Us6Tm6TzJAvmE0kIZpQKmE+EdADzSQXMJ0I6gPmkBi0ZlfJNqXnz5qG+vr71a9++fakuSXoKGUlqTzc/pZoojc1NSTlPQ5LOkyyYTyQhmFEqYD4R0gHMJxUwnwjpAOaTGrRklOUbnVvF4/HA40n9o0xJz8NZ6E7OeQqSc57eTNXhXck5z6GdSTlPsmA+kURgRumA+URINMwnHTCfCImG+aQHLRlleXuxsbERmzZtwqZNmwAAu3btwqZNm7B3796ERAg5GWdRJhz9EvsLz9HPA2dRZpKMei+V+7di876PEAqH4jo+FA5h094PseXAtiSbtYf5ROyEGaUD5hMh0TCfdMB8IiQa5pMetGSU5U2p9957D2effTbOPvtsAMAPfvADnH322fjZz36WkAghHeGpKBQ9npxg0dolcBjxXSbrdDixaO2SJBtFw3widsOM0gHziZBomE86YD4REg3zSQ8aMspy9QsuuACmaUZ9PfnkkwnLEHIyGWV5MHJdgGHxQAMwcl3IKMtLiVdvZMWGl7DvaDWCoaCl44KhIPZ+dgDPb3w5RWYnYD4Ru2FG6YD5REg0zCcdMJ8IiYb5pAcNGcW7gxHVGC4Hsq8YFLmRXayhZQBwR44zXGzxZNEc8OHqhd9Cg68p5tAKhoJo8DXhqgW3oDngS7EhIfbDjNIB84mQaJhPOmA+ERIN80kPGjKKs0nU4yxwI2fGEBg5sd2X38hxIWfGEN78LgV88uluTHlwJqrrDgNAp58/bvl+dd1hTHlwJnbW7LHNkRC7YUbpgPlESDTMJx0wnwiJhvmkB+mMSvnT9whJBs4CN3K/NgyBKi98lbUIH4nekXX088BTURi5HJS75yljZ80enHvfdFwzbhrmTJyNsYPOiHrNlv3bsGjtEjy/8WX+ho/0CphROmA+ERIN80kHzCdComE+6UEyo7gpRdIGw+WAu7wA7vIChGqaEarzA/4w4HbAWeDmExhspDngw9L1L2Dp+hdQMbAcwwcMRW5mNhqam7Dj8C5U7t8qrUiI7TCjdMB8IiQa5pMOmE+ERMN80oNURnFTiqQlzqJMBpQSKvdv5ZsoQk6CGaUD5hMh0TCfdMB8IiQa5pMe7MwoXv9GCCGEEEIIIYQQQmyHm1KEEEIIIYQQQgghxHa4KUUIIYQQQgghhBBCbMcwTdO0s6DX60V+fn6keLb9t7QyjwUBE4ABGH1kbqlFBzpocpCuDwBmUxAAUF9fj7y8PBEHQD6fACXzwZ6kAx3aOyjIKOYTHbTUp4MyB+YTACVzQQc6KKmvxiHGfBK90XmLpExx4fp0oIM2B+n6yhAfCw3zIe0gXZ8OdFCK+DhomAs6yNengy4HJYiPg4a5oAMdtNTX4tANoptSvFKKDnSQd5CuD+gMSv6mT85Buj4d6BDloCyjmE+920G6Ph2UOTCfACiZCzrQQUl9NQ4x5pPcplQfJ/JuGm57We+TO2A2BWH0cYnUpwMdtDlI1weA+sVVwLGQSO0OEconQMd8SDtI16cDHU5GVUYxn3q9g3R9OuhyYD5F0DAXdKCDlvpaHGLNJ97onBBCCCGEEEIIIYTYDjelCCGEEEJIr8Gw9xk/hBBCCOkC0XtKEUIIIYQQkkrG1DRj1tY6nF99HCNrfXCHAb8D2F7owdslWXi6vACbizKlNQkhhJBeCTelSFoSqmlGqNYPBMJAhgPOQjecvfANZcXAcpQNGIqczGw0Njeh6vAuVO7faqsD54KQaLgumE9EnqF1fixYfRATDh5HwAAyxowFpo4CcnPhbmjAWdu2YdTmTZizpQ5vFWfh9snF2FXgltZOOVwXEaQzivNA2iLdj1rguoigoR/snAtuSpG0wQyGEajywldZi/ARX9TPHf088FQUIqMsD4ar534yNTPDg2vHX4o5E2djTOnoqJ9v3vcRFq1dghUbXkJzIHqckgHngpBouC6YT0QPMz6ux8LVh+B0e4CbbkLGnXcCZ58d9bqM998HHn4Y5y1fhnXLduG2ycVYMSJPwDi1cF1EkM4ozgNpi3Q/aoHrIoKGfpCaC25KkbQgVOdH06q9MBs6f6xk+IgPx1cfQvO7R5B9xSA4e+BvO0/vPwQr5z6O0r4lCJvhDl9z5mkjsWDW/bhn2lxcteAW7KzZk1QHzgUh0XBdMJ+IHmZ8XI9Frx0Eysrg+Mc/gMGDgXDHPYmKCuAvf4Hr3nvhmDoVj722AwZMPDci317pFMJ1EUE6ozgPpC3S/agFrosIGvpBci567lYj6TGE6vxofG43zMbOF0hbzMYgGp/bjVCdP8Vm9nJ6/yF4/a5lKCkYAMMw4HQ4O3yd0+GEYRgoKRiAN+5ejmFFg5PmwLkgJBquC+YT0cOwOj8Wrj4U2ZBatw447TTAMABnxz0JpzPy89NOg2P9emD4cCxcfQhDe0hfcF1EkM4ozgNpi3Q/aoHrIoKGfpCeC0ubUvPnz8cXvvAF5Obmon///rjqqquwffv2pIgQ0hFmMIymVXsBfxiI9WE5JgB/5Dgz2MlvRtOMzAwPVs59HLmebLicsV3g6HK6kOvJxgu3P4HMDE/CDtrngvlEJNC+LuyA+RQbzCh7eGT1QTjcnsgVUrm5QEZGbAdmZAC5uXC89hqcbg8WrD6YWlEbSId1YQfSGZUO88B8sg/pftRCOqwLO9DQDxrmwtKm1Nq1azF37lysW7cOr732GgKBAC666CI0NTUlLEJIRwSqvJFLCK0+vdkEzIYgAlXelHjZzbXjL0Vp35KYw6oFl9OFQaechmvGTUvYQftcMJ+IBNrXhR0wn2KDGZV6xnzajAkHjyNj5n9EPrIX64ZUCxkZwJAhcF0/ExMOHseYmubUiNpEOqwLO5DOqHSYB+aTfUj3oxbSYV3YgYZ+0DAXljalXnnlFdx0000444wzMGbMGDz55JPYu3cvNmzYkLAIIR3hq6wVPV4LcybO7vTzxd0RCocwZ+LshB20zwXziUigfV3YAfMpNphRqWfWtjoEDAB33tn5PaS6IxQC7rgDAQOYtbUumXq2kw7rwg6kMyod5oH5ZB/S/aiFdFgXdqChHzTMRUL3lKqvrwcA9O3bN2ERQk4mVNPc4V3/rRA+4kMozX/TWTGwHGNKR3f6+eLucDqcGDvoDFQMLI/bIR3ngvlEUk06rotkw3yKH2ZU8jm/+jgyxoyNPGWvs3tIdYfTCYwfj4wxY/HFg8eT6mcn6bouko10RqXrPDCfUoN0P2ohXddFstHQD1rmIu5NqXA4jO9973uYMGECzjzzzE5f5/P54PV6230REguh2uTcOC3db4ZXNmBoUs4zPIHzpNtcMJ+IHaTbukgFzKf4iCWjmE/WGVnrA0aNStLJRmLU0fR9BHs6rotUIJ1R6TgPzKfUId2PWkjHdZEKNPSDlrmIe1Nq7ty5+OCDD7Bs2bIuXzd//nzk5+e3fpWWlsZbkvQ2Akm6gZ0/vW+El5OZnZTz5CZynjSbC+YTsYU0WxepgPkUH7FkFPPJGoZpwh1G5ObmySAvD+5w5LxpSRqui1QgnlFpOA/Mp9Qh3o9aSMN1kQpU9IOSuYhrU+r222/Hiy++iDfffBMDBw7s8rXz5s1DfX1969e+ffviEiW9kIyEPl16AneSziNEY3NybjLZkMh50mgumE/ENtJoXaQK5pN1Ys0o5pM1TMOA3wGgoSE5J/R64XdEzpuWpNm6SBXiGZVm88B8Si3i/aiFNFsXqUJFPyiZC0u3eTdNE3fccQdWrlyJNWvWYOjQ7i8V83g88Hh6xqMrib04C93JOU9Bcs4jRdXhXck5z6GdcR+bDnPBfCJ2kw7rItUwn2LHakYxn6yzvdCDs7ZtS87Jtm3Dtr7pO/7psi5SjXRGpcs8MJ/sQboftZAu6yLVaOgHLXNhaUtr7ty5WLJkCZYuXYrc3FwcOnQIhw4dwvHj6XsjSKIXZ1EmHP0S+wvP0c8DZ1FmkoxkqNy/FZv3fYRQOBTX8aFwCJv2fogtB+J/o54Oc8F8InaTDusi1TCfYocZlXreLslCYPMm4P33I0/Ri4dQCNiwAYHKzVhXnJVUPztJl3WRaqQzKl3mgflkD9L9qIV0WRepRkM/aJkLS5tSjz76KOrr63HBBReguLi49Wv58uUJSRDSGZ6KQtHjtbBo7RI4jPgui3Q6nFi0dknCDtrngvlEJNC+LuyA+RQbzKjU83R5ATJMAA8/DDji/CiB0wk88ggyzMj50pl0WBd2IJ1R6TAPzCf7kO5HLaTDurADDf2gYS4sjYBpmh1+3XTTTQmLENIRGWV5MHJdgNVbOhiAketCRlleSrzsZsWGl7DvaDWCoaCl44KhIPZ+dgDPb3w5YQftc8F8IhJoXxd2wHyKDWZU6tlclIm3irMQXL4M2LMHCASsnSAQAHbvRvDZ5XirOAub0/y38OmwLuxAOqPSYR6YT/Yh3Y9aSId1YQca+kHDXKT33cFIj8dwOZB9xaDIzdNiXSgGAHfkOMPVM1q8OeDD1Qu/hQZfU8yhFQwF0eBrwlULbkFzIPHHWnMuCImG64L5RHRx++RihPw+hC+6KHLT81g3pgIBoKEB4alTEfL7cPvk4tSK2gDXRQTpjOI8kLZI96MWuC4iaOgHDXPRM2aT9GicBW7kzBgCIye2+/IbOS7kzBiS9je/O5lPPt2NKQ/ORHXdYQDo9PPHLd+vrjuMKQ/OxM6aPUlz4FwQEg3XBfOJ6GFXgRu3TS4GqqoQPu884MCByA86u8dUy/cPHIi8fscO3Da5GLt6SF9wXUSQzijOA2mLdD9qgesigoZ+kJ4LS0/fI0QKZ4EbuV8bhkCVF77KWoSPRO8KO/p54KkojFyC2EN2z09mZ80enHvfdFwzbhrmTJyNsYPOiHrNlv3bsGjtEjy/8eWU/DaFc0FINFwXzCeihxUj8mDAxMLVn8A5uhyu62cCd9wBjB8f/eJNm4BHHkHw2eUI+X24bWoJVozoGR8LaYHrIoJ0RnEeSFuk+1ELXBcRNPSD5FxwU4qkDYbLAXd5AdzlBQjVNCNU5wf8YcDtgLPAnfZPYIiV5oAPS9e/gKXrX0DFwHIMHzAUuZnZaGhuwo7Du1C5f2vKHTgXhETDdcF8Inp4bkQ+NvTPwoLVBzHhqacQ+OtTyBgzFhg5EsjLA7xeYPt2BDZvQoYJrCvJwh2ThvaYK6ROhusignRGcR5IW6T7UQtcFxE09IPUXHBTiqQlzqLMXhNQXVG5f6v4X1icC0Ki4bpgPhF5dhW4Mf2awRhT04xZW+vwxf1bMapyE9xhwO8AtvX1YN2ZBXi6vCDtb2puBa6LCNIZxXkgbZHuRy1wXUTQ0A92zgU3pQghhBBCSI9lc1EmNhed2vr/hmnCNKw+ZogQQgghqaBnfiiTEEIIIYSQDuCGFCGEEKIHbkoRQgghhBBCCCGEENsxTNM07Szo9XqRn58fKZ5t/6cHzWNBwARgAEYfmU8v0oEOmhyk6wOA2RQEANTX1yMvT+6pR9L5BCiZD/YkHejQ3kFBRjGf6KClPh2UOTCfACiZCzrQQUl9NQ4x5pPoPaVaJGWKC9enAx20OUjXV4b4WGiYD2kH6fp0oINSxMdBw1zQQb4+HXQ5KEF8HDTMBR3ooKW+FoduEN2U4pVSdKCDvIN0fUBnUPI3fXIO0vXpQIcoB2UZxXzq3Q7S9emgzIH5BEDJXNCBDkrqq3GIMZ/kNqX6OJF303Dby3qf3AGzKQijj0ukPh3ooM1Buj4A1C+uAo6FRGp3iFA+ATrmQ9pBuj4d6HAyqjKK+dTrHaTr00GXA/Mpgoa5oAMdtNTX4hBrPvFG54QQQgghhBBCCCHEdrgpRQghhBBCCCGEEEJsh5tShBBCCCGEEEIIIcR2RG90TuKjYmA5ygYMRU5mNhqbm1B1eBcq92/tVQ7S9bU4aCBU04xQrR8IhIEMB5yFbjiLMqW1iBAa1oWGnpQeB+n6WtDQC0QPGtYFHfQ4SMN8Im3RsCbooMdBA3ZmFDel0oTMDA+uHX8p5kycjTGlo6N+vnnfR1i0dglWbHgJzQFfj3SQrq/FQQNmMIxAlRe+ylqEj0T/OR39PPBUFCKjLA+Gixdk9nQ0rAsNPSk9DtL1taChF4geNKwLOuhxkIb5RNqiYU3QQY+DBqQyiptSacDp/Ydg5dzHUdq3BGEz3OFrzjxtJBbMuh/3TJuLqxbcgp01e3qUg3R9LQ4aCNX50bRqL8yGzh/xGT7iw/HVh9D87hFkXzEIzgK3jYbETjSsCw09KT0O0vW1oKEXiB40rAs66HGQhvlE2qJhTdBBj4MGJDOKW/DKOb3/ELx+1zKUFAyAYRhwOpwdvs7pcMIwDJQUDMAbdy/HsKLBPcZBur4WBw2E6vxofG43zMbOw6otZmMQjc/tRqjOn2IzIoGGdaGhJ6XHQbq+FjT0AtGDhnVBBz0O0jCfSFs0rAk66HHQgHRGWdqUevTRR1FRUYG8vDzk5eXh/PPPx8svv5wUERJNZoYHK+c+jlxPNlzO2C5qczldyPVk44Xbn0BmhiftHaTra3HQgBkMo2nVXsAfBsxYDwLgjxxnBjv+zUOyYD7Zi4Z1oaEnpcdBur4WNPRCdzCj7EPDuqCDHgdpmE+kLRrWBB30OGhAQ0ZZ2pQaOHAgfv3rX2PDhg147733MHnyZFx55ZX48MMPExYh0Vw7/lKU9i2JeZG04HK6MOiU03DNuGlp7yBdX4uDBgJV3sjlnLGGVQsmYDYEEajypsSrBeaTvWhYFxp6UnocpOtrQUMvdAczyj40rAs66HGQhvlE2qJhTdBBj4MGNGSUpU2pyy+/HJdeeinKysowYsQI3H///cjJycG6desSFiHRzJk4u9PPtXZHKBzCnImz095Bur4WBw34KmtFj+8O5pO9aFgXGnpSehyk62tBQy90BzPKPjSsCzrocZCG+UTaomFN0EGPgwY0ZFTc95QKhUJYtmwZmpqacP755ycsQtpTMbAcY0pHd/q51u5wOpwYO+gMVAwsT1sH6fpaHDQQqmnu8AkMVggf8SFU05wko65hPqUWDetCQ09Kj4N0fS1o6AWrMKNSh4Z1QQc9DtIwn0hbNKwJOuhx0ICWjLK8KbVlyxbk5OTA4/Hg29/+NlauXInRo6Mfm9iCz+eD1+tt90W6p2zA0KScZ3gC55F2kK6vxUEDodrk3MQu1TfsZD7Zg4Z1oaEnpcdBur4WNPRCrFjJKOZTfGhYF3TQ4yAN84m0RcOaoIMeBw1oySjLm1IjR47Epk2bsH79enznO9/BjTfeiI8++qjT18+fPx/5+fmtX6WlpQkJ9xZyMrOTcp7cBM4j7SBdX4uDCgJJusmmP7U362Q+2YOKdaGgJ6XHQbq+GhT0QqxYySjmU3xoWBd00OMgDvOJtEHDmqCDHgcVKMkoy5tSbrcbw4cPx/jx4zF//nyMGTMGf/zjHzt9/bx581BfX9/6tW/fvoSEewuNzU1JOU9DAueRdpCur8VBBRlxf9K3Pe4knaez0zOfbEHFulDQk9LjIF1fDQp6IeYSFjKK+RQfGtYFHfQ4iMN8Im3QsCbooMdBBUoyytqt5jsgHA7D5+v8c4gejwceT894XKKdVB3elZzzHNqZtg7S9bU4aMBZ6E7OeQqSc55YYT6lBg3rQkNPSo+DdH0taOiFeOkqo5hP8aFhXdBBj4M0zCfSFg1rgg56HDSgJaMsbWnNmzcP//znP7F7925s2bIF8+bNw5o1azBr1qyEJEg0lfu3YvO+jxAKh+I6PhQOYdPeD7HlwLa0dZCur8VBA86iTDj6Jfbmw9HPA2dRZpKMomE+2YeGdaGhJ6XHQbq+FjT0Qiwwo+xBw7qggx4HaZhPpC0a1gQd9DhoQEtGWdqU+vTTT/GNb3wDI0eOxJQpU/Duu+/i1VdfxdSpUxOSIB2zaO0SOIz4LoVzOpxYtHZJ2jtI19fioAFPRaHo8d3BfLIXDetCQ09Kj4N0fS1o6IXuYEbZh4Z1QQc9DtIwn0hbNKwJOuhx0ICGjLI0C0888QR2794Nn8+HTz/9FK+//jrDKoWs2PAS9h2tRjAUtHRcMBTE3s8O4PmNL6e9g3R9LQ4ayCjLg5HrAgyLBxqAketCRlleSrxaYD7Zi4Z1oaEnpcdBur4WNPRCdzCj7EPDuqCDHgdpmE+kLRrWBB30OGhAQ0al/q55JG6aAz5cvfBbaPA1xbxYgqEgGnxNuGrBLWgOdH4vnXRxkK6vxUEDhsuB7CsGRW5kF2toGQDckeMMF+OmJ6FhXWjoSelxkK6vBQ29QPSgYV3QQY+DNMwn0hYNa4IOehw0oCGjmHLK+eTT3Zjy4ExU1x0GgE4/99ry/eq6w5jy4EzsrNnTYxyk62tx0ICzwI2cGUNg5MT2jAQjx4WcGUNEbtBJUo+GdaGhJ6XHQbq+FjT0AtGDhnVBBz0O0jCfSFs0rAk66HHQgHRGJfz0PZJ6dtbswbn3Tcc146ZhzsTZGDvojKjXbNm/DYvWLsHzG19Oya6ttIN0fS0OGnAWuJH7tWEIVHnhq6xF+Ej0n9PRzwNPRWHkclD+hq9Ho2FdaOhJ6XGQrq8FDb1A9KBhXdBBj4M0zCfSFg1rgg56HDQgmVHclEoTmgM+LF3/ApaufwEVA8sxfMBQ5GZmo6G5CTsO70Ll/q093kG6vhYHDRguB9zlBXCXFyBU04xQnR/whwG3A84Cd8qfEkN0oWFdaOhJ6XGQrq8FDb1A9KBhXdBBj4M0zCfSFg1rgg56HDQglVHclEpDKvdvFV8Y0g7S9bU4aMBZlMk3UaQVDetCQ09Kj4N0fS1o6AWiBw3rgg56HKRhPpG2aFgTdNDjoAE7M4rXhRJCCCGEEEIIIYQQ2+GmFCGEEEIIIYQQQgixHW5KEUIIIYQQQgghhBDbMUzTNO0s6PV6kZ+fHymebf8trcxjQcAEYABGH5lbatGBDpocpOsDgNkUBADU19cjLy9PxAGQzydAyXywJ+lAh/YOCjKK+UQHLfXpoMyB+QRAyVzQgQ5K6qtxiDGfRG903iIpU1y4Ph3ooM1Bur4yxMdCw3xIO0jXpwMdlCI+Dhrmgg7y9emgy0EJ4uOgYS7oQAct9bU4dIPophSvlKIDHeQdpOsDOoOSv+mTc5CuTwc6RDkoyyjmU+92kK5PB2UOzCcASuaCDnRQUl+NQ4z5JLcp1ceJvJuG217W++QOmE1BGH1cIvXpQAdtDtL1AaB+cRVwLCRSu0OE8gnQMR/SDtL16UCHk1GVUcynXu8gXZ8OuhyYTxE0zAUd6KClvhaHWPOJNzonhBBCCCGEEEIIIbbDTSlCCCGEEEIIIYQQYjvclCKEEEIIIYQQQgghtiN6o3OrVAwsR9mAocjJzEZjcxOqDu9C5f6tdOiFDtL16XCCUE0zQrV+IBAGMhxwFrrhLMq01UEDGuZCg4OGftAwDtIO0vW1OGjoRy1Iz4d0fUBHP2gYBzrocNDQj1qQngsNDhr6QXoM6KDLwc6eVL8plZnhwbXjL8WcibMxpnR01M837/sIi9YuwYoNL6E54KNDD3aQrk+HE5jBMAJVXvgqaxE+El3D0c8DT0UhMsryYLh67gWZGuZCg4OGftAwDtIO0vW1OGjoRy1Iz4d0fUBHP2gYBzrocNDQj1qQngsNDhr6QXoM6KDLQaonDdM0zaSdLQa8Xi/y8/OBPk7k31zW5WtP7z8EK+c+jtK+JQibYTgdzqjXhMIhOAwH9h2txlULbsHOmj1d12+5C312bHehp4MOh1TUp4P1+gAQqvOjadVemA3dP+LTyHUh+4pBcBa4u3xdy5MZ6uvrkZeX1+15U4V0PgHy/WDVIRX9kG75pMFBQy9ocEhFPwI6MspKPgHsSSB1/SA9DumWT3T4/NzMp1ak50KDQ0/NJzpYr6/FQfLfeGq34E/vPwSv37UMJQUDYBhGhxMDAE6HE4ZhoKRgAN64ezmGFQ2mQw9zkK5PhxOE6vxofG43zMbuwwoAzMYgGp/bjVCdP2kOGtAwFxocNPSDhnGQdpCur8VBQz9qQXo+pOsDOvpBwzjQQYeDhn7UgvRcaHDQ0A/SY0AHXQ7SPZnQptSvf/1rGIaB733ve0mRaSEzw4OVcx9HricbLmdsnzB0OV3I9WTjhdufQGaGhw49xEG6Ph1OYAbDaFq1F/CHgVivrzQB+CPHmcFwwg5WYD6l1kFDP2gYB2kH6fpaHDT0oxVSlU+A/HxI1wd09IOGcaCDDgcN/WgVvodKnYOGfpAeAzroctDQk3FvSr377rv485//jIqKioQlTuba8ZeitG9JzBPTgsvpwqBTTsM146bRoYc4SNenwwkCVd7I5ZxWP/BrAmZDEIEqb8IOscJ8Sr2Dhn7QMA7SDtL1tTho6MdYSWU+AfLzIV0f0NEPGsaBDjocNPSjFfgeKrUOGvpBegzooMtBQ0/GtSnV2NiIWbNm4bHHHkNhYWHCEiczZ+JshM34dtxC4RDmTJxNhx7iIF2fDifwVdaKHh8rzCd7HDT0g4ZxkHaQrq/FQUM/xkKq8wmQnw/p+oCOftAwDnTQ4aChH2OF76FS76ChH6THgA66HDT0ZFybUnPnzsX06dNx4YUXJixwMhUDyzGmdHSnn6XsDqfDibGDzkDFwHI6pLmDdH06nCBU09zhExisED7iQ6imOaFzxALzKfUOGvpBwzhIO0jX1+KgoR9jJZX5BMjPh3R9QEc/aBgHOuhw0NCPVuB7qNQ6aOgH6TGggy4HDT0JxLEptWzZMmzcuBHz58+P6fU+nw9er7fdV1eUDRhqValDhidwHjrocJCuT4cThGqTcxO7VN+wk/lkj4OGftAwDtIO0vW1OGjox1hIdT4B8vMhXR/Q0Q8axoEOOhw09GOsWMmodMwnDQ4a+kF6DOigy0FDTwIWN6X27duH7373u3j66aeRmZkZ0zHz589Hfn5+61dpaWmXr8/JzLai1Cm5CZyHDjocpOvToQ2BJN1k05+6m3Uyn+xz2hxWwgABAABJREFU0NAPGsZB2kG6vhYHDf3YHXbkEyA/H9L1AajoBw3jQAclDgr6MRasZlQ65pMKBwX9ID4GdFDloKEnAYubUhs2bMCnn36KcePGweVyweVyYe3atXj44YfhcrkQCoWijpk3bx7q6+tbv/bt29dljcbmJmt/gk5oSOA8dNDhIF2fDm3ISOhBnSdwJ+k8HcB8ss9BQz9oGAdpB+n6Whw09GN32JFPgPx8SNcHoKIfNIwDHZQ4KOjHWLCaUemYTyocFPSD+BjQQZWDhp4EAEu3eZ8yZQq2bNnS7ns333wzRo0ahXvuuQdOZ/TnIT0eDzye2B9VWHV4lxWlzs9zaGf8x9JBhYN0fTqcwFnoToqDsyA55+kI5pN9Dhr6QcM4SDtI19fioKEfu8OOfALk50O6PqCjHzSMAx10OGjox1iwmlHpmE8aHDT0g/QY0EGXg4aeBCxeKZWbm4szzzyz3Vd2djZOOeUUnHnmmQmJtFC5fys27/sIoXD0bw1jIRQOYdPeD7HlwDY6pLmDdH06nMBZlAlHP2tvPk7G0c8DZ1FsH1uJB+aTfQ4a+kHDOEg7SNfX4qChH7vDjnwC5OdDuj6gox80jAMddDho6MdY4Hsoexw09IP0GNBBl4OGngTifPpeqlm0dgkcRnxqTocTi9YuoUMPcZCuT4cTeCoSezRwosdrQcNcaHDQ0A8axkHaQbq+FgcN/agF6fmQrg/o6AcN40AHHQ4a+lEL0nOhwUFDP0iPAR10OWjoyYQ3pdasWYOHHnooYZG2rNjwEvYdrUYwFLR0XDAUxN7PDuD5jS/ToYc4SNenwwkyyvJg5LoAw+KBBmDkupBRlpewg1WYT6lz0NAPGsZB2kG6vhYHDf1olVTkEyA/H9L1AR39oGEc6KDDQUM/xgPfQ6XGQUM/SI8BHXQ5aOhJlVdKNQd8uHrht9Dga4p5goKhIBp8TbhqwS1oDvjo0EMcpOvT4QSGy4HsKwZFbmQXa2gZANyR4wyXyrixjIa50OCgoR80jIO0g3R9LQ4a+lEL0vMhXR/Q0Q8axoEOOhw09KMWpOdCg4OGfpAeAzroctDQk2pT7pNPd2PKgzNRXXcYADr9rGXL96vrDmPKgzOxs2YPHXqYg3R9OpzAWeBGzowhMHJie0aCkeNCzowhKb9Bp91omAsNDhr6QcM4SDtI19fioKEftSA9H9L1AR39oGEc6KDDQUM/akF6LjQ4aOgH6TGggy4H6Z609PQ9u9lZswfn3jcd14ybhjkTZ2PsoDOiXrNl/zYsWrsEz298OSk7hXTQ6SBdnw4ncBa4kfu1YQhUeeGrrEX4SHQNRz8PPBWFkctBe9Bv+NqiYS40OGjoBw3jIO0gXV+Lg4Z+1IL0fEjXB3T0g4ZxoIMOBw39qAXpudDgoKEfpMeADrocJHvSME3TTNrZYsDr9SI/Px/o40T+zWWWjq0YWI7hA4YiNzMbDc1N2HF4Fyr3b7VW/8kdMJuCMLJdyLtpuKVj6aDHIRn16ZB4fQAI1TQjVOcH/GHA7YCzwG35CQz1i6uAYyHU19cjL0/m3gmAfD4B8v2QqEMy+iHd80mDg4Ze0OCQjH4EdGRUIvkEsCeB5PWD9Dikez7RIQLz6QTSc6HBoafkEx0Sr6/Fwc5/46m+UupkKvdvjasp6dDzHKTr0+EEzqLMlD+qOB3QMBcaHDT0g4ZxkHaQrq/FQUM/akF6PqTrAzr6QcM40EGHg4Z+1IL0XGhw0NAP0mNAB10OdvZkz70ulBBCCCGEEEIIIYSohZtShBBCCCGEEEIIIcR2uClFCCGEEEIIIYQQQmxH7kbnAIxs+29pZR4LAiYAAzD6yNxSiw500OQgXR8AzKYgAOi50Tlk8glQMh/sSTrQob2DgoxiPtFBS306KHNgPgFQMhd0oIOS+mocYswn0Rudt0jKFBeuTwc6aHOQrq8M8bHQMB/SDtL16UAHpYiPg4a5oIN8fTroclCC+DhomAs60EFLfS0O3SC6KcUrpehAB3kH6fqAzqDkb/rkHKTr04EOUQ7KMor51LsdpOvTQZkD8wmAkrmgAx2U1FfjEGM+yW1K9XEi76bhtpf1PrkDZlMQRh+XSH060EGbg3R9AKhfXAUcC4nU7hChfAJ0zIe0g3R9OtDhZFRlFPOp1ztI16eDLgfmUwQNc0EHOmipr8Uh1nzijc4JIYQQQgghhBBCiO2k5aaUYe+92QkhhBBCCCGEEEJIkhG9p1SsjKlpxqytdTi/+jhG1vrgDgN+B7C90IO3S7LwdHkBNhdlSmsSQgghhBBCCCGEkBhRvSk1tM6PBasPYsLB4wgYQMaYscDUUUBuLtwNDThr2zaM2rwJc7bU4a3iLNw+uRi7CtwpdaoYWI6yAUORk5mNxuYmVB3ehcr9W1Nakw766mtxCNU0I1TrBwJhIMMBZ6EbTps3aDU4aID9EEHDONBBvj6gox81OGhBuic0zIX0GNBBl4N0T0rX1wT7QccY0EGPg3Q/2u2gdlNqxsf1WLj6EJxuD3DTTci4807g7LOjXpfx/vvAww/jvOXLsG7ZLtw2uRgrRuQl1SUzw4Nrx1+KORNnY0zp6Kifb973ERatXYIVG15Cc8CX1Np00FNfi4MZDCNQ5YWvshbhI9E1HP088FQUIqMsD4YrNZ/Q1eCgAfZDBA3jQAf5+oCOftTgoAXpntAwF9JjQAddDtI9KV1fE+wHHWNABz0O0v0o6WCYpr03aPJ6vcjPzwf6OJF/c1mHr5nxcT0WvXYQKCuD4x//AAYPBsJhwOmMfnEoBDgcwJ49CE+dCuzYgTlTi/HciPyO67fchT47trvQn95/CFbOfRylfUsQNsNwOqIdQuEQHIYD+45W46oFt2BnzZ6ux4AOlh1SUT8dHUJ1fjSt2guzofvHaxq5LmRfMQjObq4etNoLqXBoeTJDfX098vKSu6lshVjyqYWe2g9WHaSzgQ6pq2/VQUM/pspBQ0ZZySdAvic19IOGdSE9D3Roc37h91DMpxOwH3puPtHBen1Avh9T5RBrPqnbgh9W58fC1YciG1Lr1gGnnQYYRscbUkDk+4YBnHYaHOvXA8OHY+HqQxha50/Y5fT+Q/D6XctQUjAAhmF02KAA4HQ4YRgGSgoG4I27l2NY0eCEa9NBT30tDqE6Pxqf2w2zsfugAACzMYjG53YjlIS1oMlBA+yHCBrGgQ7y9QEd/ajBQQvSPaFhLqTHgA66HKR7Urq+JtgPOsaADnocpPtRg4O6TalHVh+Ew+2JXCGVmwtkZMR2YEYGkJsLx2uvwen2YMHqgwl5ZGZ4sHLu48j1ZMPljO1Tji6nC7mebLxw+xPIzPAkVJ8OOuprcTCDYTSt2gv4w0Cs1zaaAPyR48xguEc4aID9EEHDONBBvj6gox81OGhBuic0zIX0GNBBl4N0T0rX1wT7QccY0EGPg3Q/anGwtCn185//HIZhtPsaNWpUwhItjPm0GRMOHkfGzP+IfGQv1g2pFjIygCFD4Lp+JiYcPI4xNc1xu1w7/lKU9i2JuUFbcDldGHTKabhm3LS4a9NBT30tDoEqb+RSSqsftjUBsyGIQJW3Rzh0RarzqQX2QwQN40AH+fqAjn7U4NAdvSWjNMyF9BjQQZeDdE9K14+F3pJPgPx8aBgDOuhxkO5HLQ6Wr5Q644wzcPDgwdavf/3rXwlLtDBrWx0CBoA774zcQyoeQiHgjjsQMIBZW+vidpkzcTbCZnwOoXAIcybOjrs2HfTU1+Lgq6wVPV6LQ3ekMp9aYD9E0DAOdJCvD+joRw0OsdAbMkrDXEiPAR10OUj3pPTxsdIb8gmQnw8NY0AHPQ7S/ajFwfKmlMvlwqmnntr61a9fv4QlWji/+jgyxoyNPGWvs3tIdYfTCYwfj4wxY/HFg8fjOkXFwHKMKR3d6WdKu1VwODF20BmoGFge1/F00FFfi0OoprnDpx9YIXzEh1ACVw5qcIiFVOYTwH5oQcM40EG+PqCjHzU4xEpPzygNcyE9BnTQ5SDdk9L1rdDT8wmQnw8NY0AHPQ7S/ajFAYhjU6qqqgolJSUYNmwYZs2ahb1793b5ep/PB6/X2+6rM0bW+oBkXSo6ciRGHY1vgMsGDE2KwvAEzkMH+fpaHEK1ybvJZjo7xEIq8wlgP7SgYRzoIF8f0NGPGhxixUpGWc0nQL4nNMyF9BjQQZeDdE9K17dCT88nQH4+NIwBHfQ4SPejFgfA4qbUeeedhyeffBKvvPIKHn30UezatQtf+cpX0NDQ0Okx8+fPR35+futXaWlph68zTBPuMCI3N08GeXlwhyPntUpOZnZSFHITOA8d5OtrcUAgSTe49CdwHg0O3ZDKfGqB/RBBwzjQQb4+ABX9qMIhBqxmlNV8AhT0hIK5EB8DOqhyEO9J6fox0ivyCRCfDw1jQAc9DtL9qMYBFjelpk2bhuuuuw4VFRW4+OKL8dJLL6Gurg7PPvtsp8fMmzcP9fX1rV/79u3r8HWmYcDvANDFPyAt4fXC74ic1yqNzU1JUWhI4Dx0kK+vxQEZSXpIpjuB82hw6IZU5lML7IcIGsaBDvL1AajoRxUOMWA1o6zmE6CgJxTMhfgY0EGVg3hPStePkV6RT4D4fGgYAzrocZDuRzUOAKzdav4kCgoKMGLECOzYsaPT13g8Hng8sT0ucXuhB2dt25aI0gm2bcO2vvE9prHq8K6kKFQd2hn/sXQQr6/FwVnoToqDsyD+82hwsEqy8wlgP7SgYRzoIF8f0NGPGhziobuMsppPgHxPaJgL6TGggy4H6Z6Urh8vPTGfAPn50DAGdNDjIN2PWhyAOO4p1ZbGxkZ88sknKC4uTkiihbdLshDYvAl4//3IU/TiIRQCNmxAoHIz1hVnxXWKyv1bsXnfRwiF43MIhUPYtPdDbDkQ/wYbHeTra3FwFmXC0S++DdYWHP08cBZlprWDVZKdTwD7oQUN40AH+fqAjn7U4BAPPTGjNMyF9BjQQZeDdE9K14+XnphPgPx8aBgDOuhxkO5HLQ6AxU2pu+66C2vXrsXu3bvx73//G1dffTWcTiduuOGGhCRaeLq8ABkmgIcfBhxx7pc5ncAjjyDDjJwvXhatXQKHEZ+D0+HEorVL4q5NBz31tTh4KgpFj9fi0BWpzqcW2A8RNIwDHeTrAzr6UYNDd/SWjNIwF9JjQAddDtI9KX18LPSWfALk50PDGNBBj4N0P2pxsDQL+/fvxw033ICRI0fi+uuvxymnnIJ169ahqKgoYREA2FyUibeKsxBcvgzYswcIBKydIBAAdu9G8NnleKs4C5sT2LFbseEl7DtajWAoaOm4YCiIvZ8dwPMbX467Nh301NfikFGWByPXBVi9RZoBGLkuZJTl9QiHrkh1PrXAfoigYRzoIF8f0NGPGhy6o7dklIa5kB4DOuhykO5J6fqx0FvyCZCfDw1jQAc9DtL9qMXB0qbUsmXLUF1dDZ/Ph/3792PZsmU4/fTTE5Zoy+2TixHy+xC+6KLITc9j3ZgKBICGBoSnTkXI78PtkxO73LQ54MPVC7+FBl9TzI0aDAXR4GvCVQtuQXPAl1B9Ouior8XBcDmQfcWgyE3kYg0MA4A7cpzhSvwmdhocusKOfALYDy1oGAc6yNcHdPSjBofu6C0ZpWEupMeADrocpHtSun4s9JZ8AuTnQ8MY0EGPg3Q/anFIfcpZZFeBG7dNLgaqqhA+7zzgwIHIDzq7x1TL9w8ciLx+xw7cNrkYu5JwQ8BPPt2NKQ/ORHXd4UipTj5z2vL96rrDmPLgTOys2ZNwbTroqa/FwVngRs6MITByYns+gZHjQs6MIUm9OaYGBw2wHyJoGAc6yNcHdPSjBgctSPeEhrmQHgM66HKQ7knp+ppgP+gYAzrocZDuRw0OCT19L1WsGJEHAyYWrv4EztHlcF0/E7jjDmD8+OgXb9oEPPIIgs8uR8jvw21TS7BiRPIuc91Zswfn3jcd14ybhjkTZ2PsoDOiXrNl/zYsWrsEz298OSk7pnTQV1+Lg7PAjdyvDUOgygtfZS3CR6JrOPp54KkojFyKmYLfrmlw0AD7IYKGcaCDfH1ARz9qcNCCdE9omAvpMaCDLgfpnpSurwn2g44xoIMeB+l+lHYwTNM0k3a2GPB6vcjPzwf6OJF/c1mXrx1a58eC1Qcx4eBxBAwgY8xYYORIIC8P8HqB7dsR2LwJGSbwr5Is3DGp+yukvE/ugNkUhJHtQt5Nwy37Vwwsx/ABQ5GbmY2G5ibsOLwLlfu3WjoHHRJ3SEb9nuAQqmlGqM4P+MOA2wFngdvy0w8S7YVkONQvrgKOhVBfX4+8vNTfO6EzrOTTyfSUfkjUQTob6JC8+ok6aOjHZDloyKhE8gmQ70kN/aBhXUjPAx1OIP0eivl0AvZDz8knOiReH5Dvx2Q5xJpPKq+UamFXgRvTrxmMMTXNmLW1Dl/cvxWjKjfBHQb8DmBbXw/WnVmAp8sLErqpuRUq92+Na2HQoWfV1+LgLMq0/THBGh00wH6IoGEc6CBfH9DRjxoctCDdExrmQnoM6KDLQbonpetrgv2gYwzooMdBuh/tdlC9KdXC5qJMbC46tfX/DdOEaVi9PTwhhBBCCCGEEEII0UJafliZG1KEEEIIIYQQQggh6U1abkoRQgghhBBCCCGEkPRG7kbnAIxs+z89aB4LAiYAAzD6yHx6kQ500OQgXR8AzKYgAOi50Tlk8glQMh/sSTrQob2DgoxiPtFBS306KHNgPgFQMhd0oIOS+mocYswn0XtKtUjKFBeuTwc6aHOQrq8M8bHQMB/SDtL16UAHpYiPg4a5oIN8fTroclCC+DhomAs60EFLfS0O3SC6KcUrpehAB3kH6fqAzqDkb/rkHKTr04EOUQ7KMor51LsdpOvTQZkD8wmAkrmgAx2U1FfjEGM+yW1K9XEi76bhtpf1PrkDZlMQRh+XSH060EGbg3R9AKhfXAUcC4nU7hChfAJ0zIe0g3R9OtDhZFRlFPOp1ztI16eDLgfmUwQNc0EHOmipr8Uh1nzijc4JIYQQQgghhBBCiO1wU4oQQpKIYe+zIwghhBBCCCEkbRG9pxQhhKQ7Y2qaMWtrHc6vPo6RtT64w4DfAWwv9ODtkiw8XV6AzUWZ0pqEEEIIIYQQog5uSlmkYmA5ygYMRU5mNhqbm1B1eBcq92+11SFU04xQrR8IhIEMB5yFbjht/kevtIN0fTrocpBgaJ0fC1YfxISDxxEwgA9MYAWABgC5YWDUZz7cfNSHOVvq8FZxFm6fXIxdBe6UOmmYCzrocJCuTwd9SI+FdH060EGbg3R9TWgYC2kH6fp0oIOkAzelYiAzw4Nrx1+KORNnY0zp6Kifb973ERatXYIVG15Cc8CXEgczGEagygtfZS3CR6JrOPp54KkoREZZHgxXaj6VKe0gXZ8OuhwkmfFxPRauPgR/yMRiAA+bwKYOXjfWBO4EMOPgcaxbtgu3TS7GihF5SXXRMBd00OEgXZ8O+pAeC+n6dKCDNgfp+prQMBbSDtL16UAHLQ7clOqG0/sPwcq5j6O0bwnCZrjD15x52kgsmHU/7pk2F1ctuAU7a/Yk1SFU50fTqr0wGzp/pGL4iA/HVx9C87tHkH3FIDiTfEWGtIN0fTrocpBkxsf1WPTaQXwM4BIAe9D5zfkqAXwTwC8AvBoy8dhr1TBg4rkR+Ulx0TAXdNDhIF2fDvqQHgvp+nSggzYH6fqa0DAW0g7S9elAB00OPXsLPkFO7z8Er9+1DCUFA2AYBpwOZ4evczqcMAwDJQUD8MbdyzGsaHDSHEJ1fjQ+txtmY+fN0RazMYjG53YjVOfvMQ7S9emgy0GSYXV+LFx9CB8D+BKA/Z9/v+Pt6hPf3w/gfABVABauPoShSRgPDXNBBx0O0vXpoA/psZCuTwc6aHOQrq8JDWMh7SBdnw500ObATalOyMzwYOXcx5HryYbLGdsFZS6nC7mebLxw+xPIzPAk7GAGw2hatRfwh4FYH+hlAvBHjjODnf1TOX0cpOvTQZeDNI+sPghfyMQlALwAQjEeF/r89RcD8IdMLFh9MCEPDXNBBx0O0vXpoA/psZCuTwc6aHOQrq8JDWMh7SBdnw500OhgeVPqwIEDmD17Nk455RRkZWXhrLPOwnvvvZewiDauHX8pSvuWxLwh1YLL6cKgU07DNeOmJewQqPJGLp+z+oR5EzAbgghUedPeQbo+HXQ5dEcq82nMp82YcPA4ViDykb1YN6RaCH1+3AoAEw4ex5ia5rhdNMwFHXQ4SNengzXseA8lPRbS9elAB20O0vVjpTfkkwYH6fp0oINGB0ubUrW1tZgwYQIyMjLw8ssv46OPPsLvf/97FBYWJiyijTkTZ3d6D6nuCIVDmDNxdsIOvspa0eM1OEgfTwddDl2R6nyata0OAQN4GPFfYupA5PiAAczaWhe3i4a5oIMOB+nj6RA7dr2Hkh4L6ePpQAdtDtLHx0JvyScNDtLH04EOGh0sXQb0m9/8BqWlpVi8eHHr94YOHZqwhDYqBpZ3+JS9WHE6nBg76AxUDCxH5f6tcZ0jVNPc4R3vrRA+4kOopjnuRzdKO0jXp4Muh+5IdT6dX30cH5gdP2UvVsIA3gfwgQl88eDxuM6hYS7ooMNBuj4drGHHeyjpsZCuTwc6aHOQrh8rvSGfNDhI16cDHTQ6ABZ/4b9q1Sqcc845uO6669C/f3+cffbZeOyxx7o8xufzwev1tvvSTtmA5ITw8ATOE6pN3o300tVBuj4ddDl0R6rzaWStD9uS5LodwKij8f0FoGEu6KDDQbo+HaxhNaPief8kPRbS9elAB20O0vVjpTfkkwYH6fp0oINGB8DiptTOnTvx6KOPoqysDK+++iq+853v4M4778RTTz3V6THz589Hfn5+61dpaWlCwnaQk5mdlPPkJnKeQJJuauhP4DzSDtL16aDLoRtSmU+GacIdBhqS5OoF4A5HzmsZDXNBBx0O0vXpYAmrGRXX+yfpsZCuTwc6aHOQrh8jvSKfNDhI16cDHTQ6wOKmVDgcxrhx4/DAAw/g7LPPxpw5c3DrrbfiT3/6U6fHzJs3D/X19a1f+/btS0jYDhqbm5JynoZEzpORpAcjuhM4j7SDdH066HLohlTmk2kY8DuA3CS55gHwOyLntYyGuaCDDgfp+nSwhNWMiuv9k/RYSNenAx20OUjXj5FekU8aHKTr04EOGh1gcVOquLgYo0e3v9dSeXk59u7d2+kxHo8HeXl57b60U3V4V3LOc2hn3Mc6C91JcXAWxH8eaQfp+nTQ5dAdqc6n7YUejEqS6ygA2/p64jpWw1zQQYeDdH06WMNqRsXz/kl6LKTr04EO2hyk68dKb8gnDQ7S9elAB40OgMVNqQkTJmD79u3tvvfxxx9j8ODBCUloo3L/Vmze9xFCYasPfY8QCoewae+H2HIg/jvQOIsy4egX3z9aW3D08yR0wzFpB+n6dNDl0B2pzqe3S7JwpgGMRWJP3zsbwBkGsK44K65zaJgLOuhwkK5PB2vY8R5Keiyk69OBDtocpOvHSm/IJw0O0vXpQAeNDoDFf1t9//vfx7p16/DAAw9gx44dWLp0KRYtWoS5c+cmJKGRRWuXwGHE909Pp8OJRWuXJOzgqUjsMayJHq/BQfp4Ouhy6IpU59PT5QXIMIE7EXmKXjyEETk+w4ycL140zAUddDhIH0+H2LHrPZT0WEgfTwc6aHOQPj4Weks+aXCQPp4OdNDoYGnX5Qtf+AJWrlyJZ555BmeeeSZ+9atf4aGHHsKsWbMSFtHGig0vYd/RagRDQUvHBUNB7P3sAJ7f+HLCDhlleTByXYDV284YgJHrQkZZ4h+VlHaQrk8HXQ5dkep82lyUibeKszADwGAATovHOz8/7loAbxVnYXMCv1HQMBd00OEgXZ8OsWPXeyjpsZCuTwc6aHOQrh8LvSWfNDhI16cDHTQ6WL4U6LLLLsOWLVvQ3NyMrVu34tZbb01YQiPNAR+uXvgtNPiaYt6YCoaCaPA14aoFt6A5EN/j3ttiuBzIvmJQ5MZhsTaJAcAdOc5wJX7jMmkH6fp00OXQHanOp9snF8PtNPAKIjcrj3Vjyvn5618F4HYauH1ycUIeGuaCDjocpOvTwRp2vIeSHgvp+nSggzYH6fqx0hvySYODdH060EGjgz0pl6Z88uluTHlwJqrrDgNAp/eYavl+dd1hTHlwJnbW7Emag7PAjZwZQ2DkuGJ6vZHjQs6MIUm9IaK0g3R9OuhykGRXgRu3TS7GCABvAxj4+fc7C9KW7w/8/PVlAG6bXIxdSRgPDXNBBx0O0vXpoA/psZCuTwc6aHOQrq8JDWMh7SBdnw500OYQW9VezM6aPTj3vum4Ztw0zJk4G2MHnRH1mi37t2HR2iV4fuPLSblC6mScBW7kfm0YAlVe+CprET4SXcPRzwNPRWHk8rsU/EZF2kG6Ph10OUiyYkQeDJhYuPoQtoRMrADwMID3O3jtGETuIXUtIldI3Tq5GCtGJO8yfA1zQQcdDtL16aAP6bGQrk8HOmhzkK6vCQ1jIe0gXZ8OdNDkwE2pGGgO+LB0/QtYuv4FVAwsx/ABQ5GbmY2G5ibsOLwLlfu3ptzBcDngLi+Au7wAoZpmhOr8gD8MuB1wFrhT/lQODQ7S9emgy0GS50bkY0P/LCxYfRA3HTyOWQbwgQlsB+BF5KN6IwGcaURuav6vkizcMSk5V0idjIa5oIMOB+n6dNCH9FhI16cDHbQ5SNfXhIaxkHaQrk8HOmhx4KaURSr3b7VlE6ornEWZ4n9pSTtI16eDLgcJdhW4Mf2awRhT04xZW+vwxYPHcc1RH9xhwO8AtvX1YHFxFp4uL0jopuZW0DAXdNDhIF2fDvqQHgvp+nSggzYH6fqa0DAW0g7S9elAB0kHbkoRQkgCbC7KxOaiU1v/3zBNmIbVx1cQQgghhBBCSO+j535Y+f9n78zDoyrP/v+dJTMJWVnCEtkNhKAGlFZF2rIoIgJii9a2UEFR3ta4tP60le6uaG2rVZS+KIIWUXxVLKWKgixWRUAUggoYlD0EQZJMEsgkmTm/P8bsy+znvifz/VzXXFednHPuD8+5n2+mT86cQwghAnBBihBCCCGEEEICg4tShBBCCCGEEEIIIcR0LIZhGGYWdLlcSE9P9xVPNv/bg8apWsAAYAEsnWS+vUgHOmhykK4PAEZlLQCgrKwMaWmRezpdsEjnE6DkfLAn6UCHpg4KMor5RAct9emgzIH5BEDJuaADHZTUV+MQYD6J3lOqTlKmuHB9OtBBm4N0fWWIj4WG8yHtIF2fDnRQivg4aDgXdJCvTwddDkoQHwcN54IOdNBSX4uDH0QXpXilFB3oIO8gXR/QGZT8S5+cg3R9OtChhYOyjGI+xbeDdH06KHNgPgFQci7oQAcl9dU4BJhPcotSnWxIm5VtelnXkr0wKmth6WQXqU8HOmhzkK4PAGWLC4FTHpHarSKUT4CO8yHtIF2fDnRojqqMYj7FvYN0fTrocmA++dBwLuhABy31tTgEmk+80TkhhBBCCCGEEEIIMR0uShFCCCGEEEIIIYQQ0+GiFCGEEEIIIYQQQggxHdEbnccieb1zMajHAKQkJqOiqhKFx/ah4PCuuHPwHK+Cp6QaqPECCVbYOjtgy0w0rT7HgA6kJRrmhQYHDT0pPQ4axoAOpDHSc0KLg4ae5DjocJCuT5oiPS809AMd6CDlwEWpAEhMcGLaiMsxZ/QMDOsztMXPdxz6DAs3LsUr215HVY27wzoYtV7UFLrgLiiB90TLGtZuTjjzOiNhUBos9shfhMcxoANpiYZ5ocFBQ09Kj4OGMaADaYz0nNDioKEnOQ46HKTrk6ZIzwsN/UAHOmhw4KKUH87s3h8r8p9Gny5Z8BreVrc5+4wczJ9+P349MR9Xzp+NL48f6HAOntJqVK48CKO87cc6ek+4cXpdMaq2nkDyFX1hy3BErD7HgA6kJRrmhQYHDT0pPQ4axoAOpDHSc0KLg4ae5DjocJCuT5oiPS809AMd6KDFgUvw7XBm9/5Ye8eLyMroAYvFApvV1up2NqsNFosFWRk98PadyzEws1+HcvCUVqPi5f0wKtpu0MYYFbWoeHk/PKXVEanPMaADaYmGeaHBQUNPSo+DhjGgA2mM9JzQ4qChJzkOOhyk65OmSM8LDf1ABzpocuCiVBskJjixIv9ppDqTYbcFdkGZ3WZHqjMZr928CIkJzg7hYNR6UbnyIFDtBYxAdwJQ7dvPqG39Lw+BwjGgA2mJhnmhwUFDT0qPg4YxoANpjPSc0OKgoSc5DjocpOuTpkjPCw39QAc6aHMIalGqf//+sFgsLV75+flhi2hj2ojL0adLVsBhVYfdZkffrmfgB+dN7BAONYUu3yV8gTZoHQZglNeiptAVVn2OAR2CIV4ySsO80OCgoSelx0HDGNAhMJhP7cN8+oYOlE+AjnGQdpCuHwjxkk+A/LzQ0A90oIM2h6AWpbZu3YqjR4/Wv9asWQMAuPrqq8MW0cac0TPa/H6xPzxeD+aMntEhHNwFJaL7cwzoEAzxklEa5oUGBw09KT0OGsaADoHBfPIP8yly+wMcBy0O0vsHQrzkEyA/LzT0Ax3ooM0hqEWpzMxM9OzZs/61atUqnHnmmRg9enTYIprI652LYX2Gtvn9Yn/YrDYM73sW8nrnxrSD53hVq3fdDwbvCTc8x6tC2pdjQIdgiYeM0jAvNDho6EnpcdAwBnQIHOaTf5hPDcR6PgE6xkHaQbp+oMRDPgHy80JDP9CBDtocgDDuKVVdXY2lS5fi+uuvh8ViaXM7t9sNl8vV5KWdQT0GROQ42WEcR4ODpyRyN3cMBY4BHcIhkIxiPsWug4aelB4HDWNAh9BgPrUP8+mb48RwPgE6xkHaQbp+KHTUfALk54WGfqADHbQ5AGEsSr322msoLS3FrFmz2t1u3rx5SE9Pr3/16dMn1JKmkZKYHJHjpIZxHA0OqInQjRWrQzsOx4AO4RBIRjGfYtdBQ0+Kj4OCMaBDaDCf2of59A2xnE+AinEQd5CuHwIdNZ8ABfNCQz/QgQ7aHBDGotSiRYswceJEZGVltbvd3LlzUVZWVv86dOhQqCVNo6KqMiLHKQ/jOBockBChhzM6QjsOx4AO4RBIRjGfYtdBQ0+Kj4OCMaBDaDCf2of59A2xnE+AinEQd5CuHwIdNZ8ABfNCQz/QgQ7aHAAE99iBbzhw4ADWrl2LV1991e+2TqcTTmf4j5Q1k8Jj+yJznOIvY9rB1tkREQdbRmjH4RjQIVQCzSjmU+w6aOhJ6XHQMAZ0CB7mUwDHYT75jhPD+QToGAdpB+n6wdKR8wmQnxca+oEOdNDmAIR4pdTixYvRvXt3TJo0KaziWik4vAs7Dn0Gj9cT0v4erwfbD36KnUd2x7SDLTMR1m7h/cKxdnPClpkY0r4cAzqESkfOKA3zQoODhp6UHgcNY0CH4GE+tQ3zqYFYzydAxzhIO0jXD5aOnE+A/LzQ0A90oIM2ByCERSmv14vFixdj5syZsNtDutAqJli4cSmsltAuQ7NZbVi4cWmHcHDmdRbdn2NAh2CJh4zSMC80OGjoSelx0DAGdAgc5lP7MJ8itz/AcdDiIL1/oMRDPgHy80JDP9CBDtocgp6Ra9euxcGDB3H99deHXVwzr2x7HYdOFqHWUxvUfrWeWhz8+ghe/eiNDuGQMCgNllQ70PYDFlvHAlhS7UgYlBZWfY4BHYIlHjJKw7zQ4KChJ6XHQcMY0CFwmE9tw3z6hg6UT4COcZB2kK4fKPGQT4D8vNDQD3SggzaHoBelLr30UhiGgcGDB4ddXDNVNW58/4kbUO6uDDi0aj21KHdX4sr5s1FV4+4QDha7FclX9PXdvCzQRrUAcPj2s9jDu+kZx4AOwRIPGaVhXmhw0NCT0uOgYQzoEDjMp9ZhPtXthA6VT4COcZB2kK4fKPGQT4D8vNDQD3SggzYH8x7lEIN88dV+XPzwNSgqPQYAbX7/uO79otJjuPjha/Dl8QMdysGW4UDKVf1hSQnsUl5Lih0pV/WP2E0ZOQZ0IC3RMC80OGjoSelx0DAGdCCNkZ4TWhw09CTHQYeDdH3SFOl5oaEf6EAHTQ4d9wvDEeLL4wdw/n2T8IPzJmLO6BkY3vesFtvsPLwbCzcuxasfvRGRvyppdLBlOJD6k4GoKXTBXVAC74mWNazdnHDmdfZdAhjhv+pwDOhAWqJhXmhw0NCT0uOgYQzoQBojPSe0OGjoSY6DDgfp+qQp0vNCQz/QgQ5aHLgoFQBVNW4s2/walm1+DXm9c5HdYwBSE5NRXlWJvcf2oeDwrrhwsNitcORmwJGbAc/xKnhKq4FqL+CwwpbhiPqTQTgGdCAt0TAvNDho6EnpcdAwBnQgjZGeE1ocNPQkx0GHg3R90hTpeaGhH+hABw0OXJQKkoLDu0z5xa3dwZaZKPqLk2NAB9ISDfNCg4OGnpQeBw1jQAfSGOk5ocVBQ09yHHQ4SNcnTZGeFxr6gQ50kHLgdaGEEEIIIYQQQgghxHS4KEUIIYQQQgghhBBCTIeLUoQQQgghhBBCCCHEdCyGYRhmFnS5XEhPT/cVTzb/llbGqVrAAGABLJ1kbqlFBzpocpCuDwBGZS0AoKysDGlpaSIOgHw+AUrOB3uSDnRo6qAgo5hPdNBSnw7KHJhPAJScCzrQQUl9NQ4B5pPojc7rJGWKC9enAx20OUjXV4b4WGg4H9IO0vXpQAeliI+DhnNBB/n6dNDloATxcdBwLuhABy31tTj4QXRRildK0YEO8g7S9QGdQcm/9Mk5SNenAx1aOCjLKOZTfDtI16eDMgfmEwAl54IOdFBSX41DgPkktyjVyYa0Wdmml3Ut2QujshaWTnaR+nSggzYH6foAULa4EDjlEandKkL5BOg4H9IO0vXpQIfmqMoo5lPcO0jXp4MuB+aTDw3ngg500FJfi0Og+cQbnRNCCCGEEEIIIYQQ0+GiFCGEEEIIIYQQQmAx9zlohMjeU4oQQgghhBBCCCEyDDtehem7SjGy6DRyStxweIFqK7CnsxObspLwfG4GdmQmSmuSDgwXpUhM4jleBU9JNVDjBRKssHV2wGZyWNJBjwMh2pCeF9L16UCIXjTMCzrI1ydkQGk15q87ilFHT6PGAnxiAK8AKAeQ6gWGfO3GdSfdmLOzFO/1SsLN43phX4Yjqk4a5gUdzHfgohSJGYxaL2oKXXAXlMB7wt3i59ZuTjjzOiNhUBos9uh8M5UOehwI0Yb0vJCuTwdC9KJhXtBBvj4hdVz1eRmeWFeMao+BxQAeM4DtrWw33ABuBXDV0dP44MV9uGlcL7wyOC2iLhrmBR1kHbgoRWICT2k1KlcehFHe9mMlvSfcOL2uGFVbTyD5ir6wRXglnw56HAjRhvS8kK5PB0L0omFe0EG+PiF1XPV5GRauOYrPAVwG4ADavtF0AYDrAdwN4E2PgafWFMECAy8PTo+Ii4Z5QQd5By7BE/V4SqtR8fJ+GBVtT5DGGBW1qHh5Pzyl1XTogA6EaEN6XkjXpwMhetEwL+ggX5+QOgaWVuOJdcX4HMBFAA5/8763je3r3j8MYCSAQgBPrCvGgAj0poZ5QQcdDlyUIqoxar2oXHkQqPYCgT4IwgBQ7dvPqG0rYukQiw6EaEN6XkjXpwMhetEwL+ggX5+Qxjy+7ijcHgOXAXAB8AS4n+eb7ScAqPYYmL/uaFgeGuYFHfQ4BLUo5fF48Pvf/x4DBgxAUlISzjzzTNx7770w+NhIEiVqCl2+SwiDbTEDMMprUVPookMHcmgP5hORQHpeSNenQ+Awo4jZaJgXdJCvHwjMp/hg2FdVGHX0NF6B7yt7gS5I1eH5Zr9XAIw6ehrDjleF7KJhXtBBj0NQi1IPPfQQFixYgPnz52PXrl146KGH8Oc//xmPP/542CKEtIa7oER0fzrocmgP5hORQHpeSO9Ph8BhRhGz0TAv6CC/fyAwn+KD6btLUWMBHkPoX5eywrd/jQWYvqs0ZBcN84IOehyCutH5+++/j6lTp2LSpEkAgP79++OFF17Ali1bwhYhpDme41Wt3vU/GLwn3PAcrwr58ZV00OPgD+YTMRvpeSFdnw7BwYwiZqJhXtBBvn6gMJ/ig5FFp/FJG0/ZCxQvgI8BfGIAFx49HdIxNMwLOuhxAIJcJL3ooovw9ttv4/PPPwcA7NixA++++y4mTpzY5j5utxsul6vJi5BA8JRE7uaSdIh9B38wn4jZSM8L6fp0CI5gM4r5RMJBw7ygg3z9QGE+xQc5JW7sjtCx9gAYcjK0xQwN84IOehyAIK+Uuuuuu+ByuTBkyBDYbDZ4PB7cf//9mD59epv7zJs3D3fffXdYkiROqYnQjR2rwzgOHfQ4+IH5RExHel5I16dDUASbUcwnEhYa5gUd5OsHCPOp42MxDDi8QHmEjucC4PD6jmtYLMHtrGFe0EGPA4K8Uuqll17C888/j2XLluGjjz7Cs88+i7/85S949tln29xn7ty5KCsrq38dOnQoLGESRyRE6OGQjjCOQwc9Dn5gPhHTkZ4X0vXpEBTBZhTziYSFhnlBB/n6AcJ86vgYFguqrUBqhI6XBqDaiuAXpAAd84IOehwQ5JVSd955J+666y786Ec/AgCcc845OHDgAObNm4eZM2e2uo/T6YTT6QxLksQnts6OyBwnI/Tj0EGPgz+YT8RspOeFdH06BEewGcV8IuGgYV7QQb5+oDCf4oM9nZ0Y8nV49w+qYwiA3V1C6wEN84IOehyAIK+UOnXqFKzWprvYbDZ4vdG9pJTEJ7bMRFi7hfcLz9rNGdZN1+igx8EfzCdiNtLzQro+HYKDGUXMRMO8oIN8/UBhPsUHm7KScLYFGI7wnr53LoCzLMAHvZJCOoaGeUEHPQ5AkP04ZcoU3H///fjPf/6D/fv3Y8WKFfjb3/6G73//+2FJENIWzrzOovvTQZdDezCfiATS80J6fzoEDjOKmI2GeUEH+f0DgfkUHzyfm4EEA7gVvqfohYIXvv0TDN/xQkXDvKCDHoegFqUef/xxXHXVVbjpppuQm5uLO+64A//zP/+De++9N2wRQlojYVAaLKl2INivK1sAS6odCYPS6NCBHNqD+UQkkJ4X0vXpEDjMKGI2GuYFHeTrBwLzKT7YkZmI93ol4SoA/QDYgtzf9s1+0wC81ysJO8K4OkbDvKCDHoegFqVSU1Px6KOP4sCBAzh9+jS++OIL3HfffXA4ovs9ZxK/WOxWJF/R13fztEAnigWAw7efxR7+zdvooMehPZhPRALpeSFdnw6Bw4wiZqNhXtBBvn4gMJ/ih5vH9YLDZsFq+G5WHujClO2b7d8E4LBZcPO4XmF5aJgXdNDjEP2UIyRMbBkOpFzVH5aUwO7Lb0mxI+Wq/hG9KSQd9DgQog3peSFdnw6E6EXDvKCDfH1C6tiX4cBN43phMIBNAHp/835biwJ17/f+ZvtBAG4a1wv7ItCbGuYFHXQ4BPX0PUKksGU4kPqTgagpdMFdUALviZZPjrB2c8KZ19l3CWIU/qpEBz0OhGhDel5I16cDIXrRMC/oIF+fkDpeGZwGCww8sa4YOz0GXgHwGICPW9l2GHz3kJoG3xVSN47rhVcGR+4rpRrmBR3kHbgoRWIGi90KR24GHLkZ8Byvgqe0Gqj2Ag4rbBmOqD+ZhA66HAjRhvS8kK5PB0L0omFe0EG+PiF1vDw4Hdu6J2H+uqOYdfQ0pluATwxgDwAXfF/VywFwtsV3U/N3s5Jwy9jIXCHVHA3zgg6yDlyUIjGJLTNR/Bc3HfQ4EKIN6XkhXZ8OhOhFw7ygg3x9QvZlODDpB/0w7HgVpu8qxYVHT+MHJ91weIFqK7C7ixOLeyXh+dyMsG5qHgwa5gUdzHfgohQhhBBCCCGEEBKH7MhMxI7MnvX/bTEMGJZgH8VGSOjwy8qEEEIIIYQQQgjhghQxHS5KEUIIIYQQQgghhBDTsRiGYZhZsKysDBkZGb7/6GQzs7SPU56G/y1Rnw500OYgXb+RQ2lpKdLT02UcoCCfAFXnQ8xBuj4d6NCGg2RGMZ/ooKY+HVQ6MJ/0nAs60EG8vjIHf/lk+j2lysvLG/6j8UBJIF2fDnTQ5iBcv7y8XHRRSlU+0UFHfTrQoRGSGcV8ooPK+nRQ48B8agQd6KDJQbq+Agd/+WT6lVJerxdFRUVITU2FJYTvq7pcLvTp0weHDh1CWlpaFAzpECsO0vXpEDkHwzBQXl6OrKwsWK1y3ypmPtGhIzlI1+9IDhoyKtx8AuTPh3R9OtBBmwPzqQHpc6HBQbo+HegQaYdA88n0K6WsVit69+4d9nHS0tLETg4ddDlI16dDZBwkr5Cqg/lEh47oIF2/ozhIZ1Sk8gmQPx/S9elAB20OzKcGpM+FBgfp+nSgQyQdAskn3uicEEIIIYQQQgghhJgOF6UIIYQQQgghhBBCiOnE3KKU0+nEH//4RzidTjrEuYN0fTroctCAhnGgAx201KeDPqTHQro+HeigzUG6viY0jIW0g3R9OtBBysH0G50TQgghhBBCCCGEEBJzV0oRQgghhBBCCCGEkNiHi1KEEEIIIYQQQgghxHS4KEUIIYQQQgghhBBCTIeLUoQQQgghhBBCCCHEdGJqUWrTpk2w2WyYNGmS6bVnzZoFi8VS/+ratSsuu+wyFBQUmO5SXFyMW265BQMHDoTT6USfPn0wZcoUvP3221Gv3XgcEhIS0KNHD4wfPx7PPPMMvF5v1Os3d2j8uuyyy0yp789j7969ptQvLi7GbbfdhuzsbCQmJqJHjx4YNWoUFixYgFOnTkW9/qxZs3DllVe2eH/Dhg2wWCwoLS2NuoM2mFHMp+YeUhklnU+AbEYxn1rCfGI+NfdgPvEzlBaYT8yn5h7Mp/jKp5halFq0aBFuueUWvPPOOygqKjK9/mWXXYajR4/i6NGjePvtt2G32zF58mRTHfbv348RI0Zg3bp1ePjhh7Fz506sXr0aY8eORX5+vikOdeOwf/9+vPHGGxg7dixuu+02TJ48GbW1taY6NH698MILptT25zFgwICo1/3yyy9x7rnn4q233sIDDzyAjz/+GJs2bcKvfvUrrFq1CmvXro26A2lJvGcU86mlh2RGSeUTwIzSCPOJ+dTcg/nEfNIC84n51NyD+RRf+WSXFgiUiooKLF++HB9++CGKi4uxZMkS/OY3vzHVwel0omfPngCAnj174q677sJ3v/tdHD9+HJmZmaY43HTTTbBYLNiyZQuSk5Pr3z/rrLNw/fXXm+LQeBzOOOMMnHfeebjwwgtx8cUXY8mSJbjhhhtMdZBEyuOmm26C3W7Hhx9+2KQPBg4ciKlTp8IwDNOd4h1mFPOpLQ8pJB2YUbpgPjGf2vKQgvlE6mA+MZ/a8pCC+WQ+MXOl1EsvvYQhQ4YgJycHM2bMwDPPPCN6UioqKrB06VJkZ2eja9euptQ8efIkVq9ejfz8/CZNWkdGRoYpHq0xbtw4DBs2DK+++qqYQ7zw9ddf46233mqzDwDAYrGYbEXiPaOYT6QOZpQ+mE/MJ+KD+aQP5hPzifiI53yKmUWpRYsWYcaMGQB8l9SVlZVh48aNpjqsWrUKKSkpSElJQWpqKlauXInly5fDajVnGPfu3QvDMDBkyBBT6gXLkCFDsH//flNqNT4Xda8HHnjAlNrteVx99dVRr1nXBzk5OU3e79atW73Hr3/966h7AK2fh4kTJ5pSWxvxnlHMp6ZoyCiJfAL0ZBTzqQHmE/OpMcwn+XwCmFF1MJ+YT41hPsVnPsXE1/f27NmDLVu2YMWKFQAAu92Oa665BosWLcKYMWNM8xg7diwWLFgAACgpKcGTTz6JiRMnYsuWLejXr1/U62u/XM8wDNNWbxufizq6dOliSu32PNpa1TaDLVu2wOv1Yvr06XC73abUbO08bN68uf7DRbzAjGI+NUdDRmnKJ8D8jGI++WA+MZ+aw3xqCT9DycB8Yj41h/nUknjIp5hYlFq0aBFqa2uRlZVV/55hGHA6nZg/fz7S09NN8UhOTkZ2dnb9fz/99NNIT0/HU089hfvuuy/q9QcNGgSLxYLdu3dHvVYo7Nq1y7SbwDU/F1JIeGRnZ8NisWDPnj1N3h84cCAAICkpyTSX1v79hw8fNq2+FphRzKfmaMgoKQctGcV88sF8Yj41h/kkn08AMwpgPgHMp+Ywn+Izn9R/fa+2thbPPfcc/vrXv2L79u31rx07diArK0vkiWt1WCwWWK1WnD592pR6Xbp0wYQJE/DEE0+gsrKyxc8lHx+7bt067Ny5E9OmTRNziBe6du2K8ePHY/78+a32ATEXZpQP5hOpgxmlB+aTD+YTqYP5pAfmkw/mE6kjnvNJ/ZVSq1atQklJCWbPnt1itXzatGlYtGgRfvazn5ni4na7UVxcDMB3aef8+fNRUVGBKVOmmFIfAJ544gmMGjUK559/Pu655x7k5eWhtrYWa9aswYIFC7Br166oO9SNg8fjwbFjx7B69WrMmzcPkydPxrXXXhv1+o0dGmO329GtWzdT6kvz5JNPYtSoUfjWt76FP/3pT8jLy4PVasXWrVuxe/dujBgxQloxbmBGNcB8aunRGGYUM8psmE8NMJ9aejSG+cR8MhvmUwPMp5YejWE+xUE+GcqZPHmycfnll7f6s82bNxsAjB07dkTdY+bMmQaA+ldqaqrx7W9/23j55ZejXrs5RUVFRn5+vtGvXz/D4XAYZ5xxhnHFFVcY69evj3rtxuNgt9uNzMxM45JLLjGeeeYZw+PxRL1+c4fGr5ycHFPqN/aYOnWqqTUbU1RUZNx8883GgAEDjISEBCMlJcU4//zzjYcfftiorKyMev22/v3r1683ABglJSVRd9AAM6op8Z5PzT2kMko6nwxDNqOYTz6YT01hPjGf6uBnKHmYT01hPjGf6ojHfLIYhvK7qxFCCCGEEEIIIYSQDof6e0oRQgghhBBCCCGEkI4HF6UIIYQQQgghhBBCiOlwUYoQQgghhBBCCCGEmA4XpQghhBBCCCGEEEKI6XBRihBCCCGEEEIIIYSYDhelCCGEEEIIIYQQQojpcFGKEEIIIYQQQgghhJgOF6UIIYQQQgghhBBCiOlwUYoQQgghhBBCCCGEmA4XpWKQf/zjH0hNTUVtbW39exUVFUhISMCYMWOabLthwwZYLBZ88cUXOHnyJG655Rbk5OQgKSkJffv2xa233oqysrIm+9x6660YMWIEnE4nhg8fHpTbgAEDsHbt2hbvB1r74MGDmDRpEjp16oTu3bvjzjvvbPLvbIvTp08jOTkZe/fubfXnFoul/mW329G3b1/cfvvtcLvd9du8+uqrGD9+PDIzM5GWloaRI0fizTffbHKcBQsWIC8vD2lpafXbvPHGG4EMDSFxQbTzCQCWLFmCvLw8JCYmonv37sjPzw/Ira18AoCFCxdizJgxSEtLg8ViQWlpaYttPvroI4wfPx4ZGRno2rUr5syZg4qKioBqjx07Fk8//XSrPxszZkyTjOrRoweuvvpqHDhwoMl2gWTzSy+9hOHDh6NTp07o168fHn744YD8CIkHop1Pjedx3evFF1/06+XvM8z999+Piy66CJ06dUJGRkar22zduhUXX3wxMjIy0LlzZ0yYMAE7duzwWxtoPxv79+9f/2+x2WzIysrC7NmzUVJSUr/Nhg0bMHXqVPTq1QvJyckYPnw4nn/++SbH+fTTTzFt2rT64z366KMBuRESL0Q7n6KVEf4+P9W5tvbaunVrWLUDyaeqqirMmjUL55xzDux2O6688spWj/XEE08gNzcXSUlJyMnJwXPPPefXjUQOLkrFIGPHjkVFRQU+/PDD+vf++9//omfPnti8eTOqqqrq31+/fj369u2LM888E0VFRSgqKsJf/vIXfPLJJ1iyZAlWr16N2bNnt6hx/fXX45prrgnKq6CgACUlJRg9enSLnwVS2+PxYNKkSaiursb777+PZ599FkuWLMEf/vAHv7XXrFmDfv36ITs7u81tFi9ejKNHj2Lfvn148skn8c9//hP33Xdf/c/feecdjB8/Hq+//jq2bduGsWPHYsqUKfj444/rt+nduzcefPBBbNu2DR9++CHGjRuHqVOn4tNPPw10mAjp0EQ7n/72t7/ht7/9Le666y58+umnWLt2LSZMmODXq718AoBTp07hsssuw29+85tWf15UVIRLLrkE2dnZ2Lx5M1avXo1PP/0Us2bN8lv75MmTeO+99zBlypQ2t7nxxhtx9OhRFBUV4V//+hcOHTqEGTNmtNiuvWx+4403MH36dPzsZz/DJ598gieffBKPPPII5s+f79eRkHjAjM9PdZ816l5t/R+gxvj7DFNdXY2rr74aP//5z1v9eUVFBS677DL07dsXmzdvxrvvvovU1FRMmDABNTU17db2l40AcM899+Do0aM4ePAgnn/+ebzzzju49dZb63/+/vvvIy8vD6+88goKCgpw3XXX4dprr8WqVavqtzl16hQGDhyIBx98ED179mzXiZB4JJr5FM2M8Pf56aKLLmqSiUePHsUNN9yAAQMG4Fvf+lZYtQH/+eTxeJCUlIRbb70Vl1xySavHWLBgAebOnYs//elP+PTTT3H33XcjPz8f//73v9v1IxHEIDFJr169jHnz5tX/969+9SsjPz/fyM3NNdavX1///ve+9z1j5syZbR7npZdeMhwOh1FTU9PiZ3/84x+NYcOGBex0zz33GNdcc03A2zev/frrrxtWq9UoLi6u32bBggVGWlqa4Xa72z3W9ddfb/z6179u8+cAjBUrVjR5b/bs2cbll1/e7nGHDh1q3H333e1u07lzZ+Ppp59udxtC4olo5dPJkyeNpKQkY+3atUE7BZpP69evNwAYJSUlTd7/3//9X6N79+6Gx+Opf6+goMAAYBQWFrZ7zOeee8644IIL2vz56NGjjdtuu63Je//85z+NTp06tbp9W9n84x//2LjqqquavPfYY48ZvXv3Nrxeb7uOhMQL0fz81NpnjUDw9xmmjsWLFxvp6ekt3t+6dasBwDh48GD9e4Hmk79s7Nevn/HII480ee/ee+81hg4d2u5xL7/8cuO6664L+JiEkOjlUzQzoo62Pj81p7q62sjMzDTuuecev8eMdD7NnDnTmDp1aov3R44cadxxxx1N3rv99tuNUaNG+XUkkYFXSsUoY8eOxfr16+v/e/369RgzZgxGjx5d//7p06exefNmjB07ts3jlJWVIS0tDXa7PWynlStXYurUqQFv37z2pk2bcM4556BHjx7120yYMAEul6vdK5G8Xi9WrVoVVO3PP/8c69atwwUXXNDuccvLy9GlS5dWf+7xePDiiy+isrISI0eODLg2IR2daOXTmjVr4PV6ceTIEeTm5qJ379744Q9/iEOHDvl1CjafmuN2u+FwOGC1NvzaTEpKAgC8++67Ea198uRJvPTSS+3mU1uOiYmJTd5LSkrC4cOHW3wVkJB4Jdqfn/Lz89GtWzecf/75eOaZZ2AYRrs+oXyGaU5OTg66du2KRYsWobq6GqdPn8aiRYuQm5uL/v37t7tvsPl05MgR/Pvf//abT2VlZW1+fiKEtE608snMjPDHypUr8fXXX+O6664LaNto5FNz2vr8tGXLFr9XkpEIIb0qRkLjqaeeMpKTk42amhrD5XIZdrvd+Oqrr4xly5YZ3/ve9wzDMIy3337bAGAcOHCg1WMcP37c6Nu3r/Gb3/ym1Z8Hc6XU4cOHDYfD4Xd1vL3aN954o3HppZc22a6ystIAYLz++uttHuu9995rcQVDcwAYiYmJRnJysuF0Og0AxuTJk43q6uo293nooYeMzp07G8eOHWvyfkFBgZGcnGzYbDYjPT3d+M9//uPvn0tIXBGtfJo3b56RkJBg5OTkGKtXrzY2bdpkXHzxxUZOTk67V1MGk09t/aXvk08+Mex2u/HnP//ZcLvdxsmTJ41p06YZAIwHHnigzeNVVVUZKSkpxieffNLmNqNHjzYSEhKM5ORko1OnTgYAY/Dgwca+ffta3b6tbP7f//1fo1OnTsbatWsNj8dj7NmzxxgyZIgBwHj//ff9/tsJiQei+fnpnnvuMd59913jo48+Mh588EHD6XQaf//739v1CeQzTB1tXSllGIaxc+dO48wzzzSsVqthtVqNnJwcY//+/e0eL5Bs7Nevn+FwOIzk5GQjMTHRAGBccMEF7e6zfPlyw+FwtJl7vFKKkNaJZj5FKyPqCPRKqYkTJxoTJ070e7xo5FNbV0rNnTvX6Nmzp/Hhhx8aXq/X2Lp1q9GjRw8DgFFUVOTXlYQPr5SKUcaMGYPKykps3boV//3vfzF48GBkZmZi9OjR9d873rBhAwYOHIi+ffu22N/lcmHSpEkYOnQo/vSnP4Xts3LlSnznO99p8wac0az9r3/9C5MnT25yBUNrPPLII9i+fTt27NiBVatW4fPPP8dPf/rTVrddtmwZ7r77brz00kvo3r17k5/l5ORg+/bt2Lx5M37+859j5syZ+Oyzz8L+dxDSUYhWPnm9XtTU1OCxxx7DhAkTcOGFF+KFF15AYWFhk78sNieYfGqLs846C88++yz++te/olOnTujZsycGDBiAHj16tJs969atQ/fu3XHWWWe1e/zp06fX59O7776L7OxsXHrppSgvLw/Y8cYbb8TNN9+MyZMnw+Fw4MILL8SPfvQjAPCbj4TEC9H8/PT73/8eo0aNwrnnnotf//rX+NWvfuX3YQOBfoZpj9OnT2P27NkYNWoUPvjgA7z33ns4++yzMWnSJJw+fbrN/QLNxjvvvBPbt29HQUEB3n77bQDApEmT4PF4Wmy7fv16XHfddXjqqaf85h4hpCnRyqdoZ0SgHD58GG+++War9+MLtXYw+dQWv//97zFx4kRceOGFSEhIwNSpUzFz5kwA/PxkFhzlGCU7Oxu9e/fG+vXrsX79+vobwGVlZaFPnz54//33sX79eowbN67FvuXl5bjsssuQmpqKFStWICEhIWyflStX4oorrvC7XXu1e/bsiWPHjjXZvu6/27spZqC1e/bsiezsbOTk5GDSpEm4++67sXz58hZPu3nxxRdxww034KWXXmr1hngOhwPZ2dkYMWIE5s2bh2HDhuHvf/+73/qExAvRyqdevXoBAIYOHVr/XmZmJrp164aDBw+26RNoRvjjJz/5CYqLi3HkyBF8/fXX+NOf/oTjx49j4MCBYddOT09HdnY2srOzMWrUKCxatAiFhYVYvnx5wH4WiwUPPfQQKioqcODAARQXF+P8888HgHYdCYknzPz8dMEFF+Dw4cNNnvTbnEjk07Jly7B//34sXrwY3/72t3HhhRdi2bJl2LdvH/71r3+FXbtbt27Izs7GoEGDMG7cODz66KP149SYjRs3YsqUKXjkkUdw7bXXhvVvIiQeiVY+RTsjAmXx4sXo2rVrQMeMdD61R1JSEp555hmcOnUK+/fvx8GDB9G/f3+kpqYiMzMz4OOQ0OGiVAwzduxYbNiwARs2bGjyqNDvfe97eOONN7Bly5YW3zd2uVy49NJL4XA4sHLlyhbfnw2FiooKrF+/3u93fv3VHjlyJHbu3Imvvvqq/r01a9YgLS2tyf8JbUxhYSEOHDiA8ePHB+1ts9kAoMlfCF544QVcd911eOGFFzBp0qSAjuP1etv9wElIPBKNfBo1ahQAYM+ePfXvnTx5EidOnEC/fv1a9Qg0n4KhR48eSElJwfLly5GYmNhm/hiGgX//+98h1W4tn4LZ94wzzoDD4cALL7yAkSNH8kMVIY0w6/PT9u3b0blzZzidzlZ/Hs5nmMacOnUKVqsVFoul/r26//Z6va3uE042tpZPGzZswKRJk/DQQw9hzpw5QR+TEOIjGvlkdka0hmEYWLx4Ma699lq/C/qRzqdASUhIQO/evWGz2fDiiy+GfRUrCZzw725NxBg7dizy8/NRU1PT5FGZo0ePxs0334zq6uomoVUXWKdOncLSpUvhcrngcrkA+K42qJvEe/fuRUVFBYqLi3H69Gls374dgO/qBIfD0cJj9erVGDx4cLs3yguk9qWXXoqhQ4fipz/9Kf785z+juLgYv/vd75Cfn9/mB7p//etfuOSSS9CpUye/41VaWori4mJ4vV4UFhbinnvuweDBg5GbmwvA91eEmTNn4u9//zsuuOACFBcXA/CtnqenpwMA5s6di4kTJ6Jv374oLy/HsmXLsGHDBrz55pt+6xMST0QjnwYPHoypU6fitttuw8KFC5GWloa5c+diyJAhbd7wM5B8AoDi4mIUFxfXXzm5c+dOpKamom/fvvU3650/fz4uuugipKSkYM2aNbjzzjvx4IMPtnlp+bZt23Dq1Cl85zvf8Ttep06dqs+cY8eO4d5770ViYiIuvfTS+m38ZfOJEyfw8ssvY8yYMaiqqsLixYvxf//3f9i4caPf+oTEE9HIp3//+984duwYLrzwQiQmJmLNmjV44IEHcMcdd7TpEehnmIMHD+LkyZM4ePAgPB5P/dzPzs5GSkoKxo8fjzvvvBP5+fm45ZZb4PV68eCDD8Jut4edjYDvCozi4mIYhoFDhw7hV7/6FTIzM3HRRRcB8H1lb/Lkybjtttswbdq0+ixzOBz1+VldXV1/q4Pq6mocOXIE27dvR0pKCrKzs/06EBIvRCOfopkRgXx+Any3M9i3bx9uuOEGv2MQyXwCgM8++wzV1dU4efIkysvL6zN0+PDhAHwPwNqyZQsuuOAClJSU4G9/+xs++eQTPPvss37rkwghe0srEg779u0zABhDhgxp8v7+/fsNAEZOTk6T9+tuQNfaq/ENdUePHu13m8bMmDHD+O1vf9uua6C19+/fb0ycONFISkoyunXrZvy///f/mjxuuTnf+c53jKeeeqrd2oZhNKlnsViMXr16Gddcc43xxRdf+P13N37k6vXXX19/U73MzEzj4osvNt566y2/9QmJN6KVT2VlZcb1119vZGRkGF26dDG+//3vN3nEcXMCySfD8N08vLXaixcvrt/mpz/9qdGlSxfD4XAYeXl5xnPPPdfuMX/3u98Z06dP91u7efZ07tzZGD16tLFu3bp2t2s+PsePHzcuvPDC+humX3zxxcYHH3zgtz4h8UY08umNN94whg8fbqSkpBjJycnGsGHDjH/84x/t3sA80M8wM2fObLV240fEv/XWW8aoUaOM9PR0o3Pnzsa4ceOMTZs2tXnMQLOxX79+TWpmZmYal19+ufHxxx/79Rs9enT9NnVj3t42hJDofX6KVkYE8vnJMAzjxz/+sXHRRRf5H4AgageST61tV/eq47PPPjOGDx9uJCUlGWlpacbUqVON3bt3B+RKIoPFMPw8q5aQdqitrUWPHj3wxhtv1N+7xCxOnDiBXr164fDhw+jRo4eptQkh+pHMJwDIy8vD7373O/zwhz80vTYhRDeSn2Gks5EQohvJjGA+xSf8kiQJi5MnT+KXv/wlvv3tb4vU/tvf/sYFKUJIq0jmU3V1NaZNm4aJEyeaXpsQoh/JzzCS2UgI0Y/0/79jPsUfvFKKEEIIIYQQQgghhJgOr5QihBBCCCGEEEIIIabDRSlCCCGEEEIIIYQQYjpclCKEEEIIIYQQQgghpsNFKUIIIYQQQgghhBBiOlyUIoQQQgghhBBCCCGmw0UpQgghhBBCCCGEEGI6XJQihBBCCCGEEEIIIabDRSlCCCGEEEIIIYQQYjpclCKEEEIIIYQQQgghpsNFKUIIIYQQQgghhBBiOlyUIoQQQgghhBBCCCGmw0UpQgghhBBCCCGEEGI6XJQihBBCCCGEEEIIIabDRSlCCCGEEEIIIYQQYjp2swt6vV4UFRUhNTUVFovF7PKEEIUYhoHy8nJkZWXBapVbK2c+EUJaQ0NGMZ8IIa3BfCKEaCXQfDJ9UaqoqAh9+vQxuywhJAY4dOgQevfuLVaf+UQIaQ/JjGI+EULag/lECNGKv3wyfVEqNTW14T862cwuD5zyyNanAx20OUjXb+TQJB8EEM8nQNX5EHOQrk8HOrThIJlRzCc6qKlPB5UOzCc954IOdBCvr8zBXz6ZvihVf0lnJxvSrxtkdnm4luyFUVkLS7IdabOyTa9PBzpoc5CuDwBliwuBUx7xS76l8wnQcT6kHaTr04EOzdGQUcwnOmipTwddDswnHxrOBR3ooKW+FodA84k3OieEEEIIIYQQQgghpsNFKULCxGIY0gqEkFbg3CSEEEIIIUQ3pn99j5BYZ9jxKkzfVYqRRaeRU+KGwwtUW4E9nZ3YlJWE53MzsCMzUVqTkLiDc5MQQgghhJDYgotSJCbxHK+Cp6QaqPECCVbYOjtgi/L/2RxQWo35645i1NHTqLEACcOGA+OHAKmpcJSX45zduzFkx3bM2VmK93ol4eZxvbAvwxFVJw3k9c7FoB4DkJKYjIqqShQe24eCw7uktUgc0XxufmIArwAoB5DqBYZ87cZ1J92mzk2JjGoM56UPjgPRhnQ2aHHg3OQYEH1oyAYNDpybPswcBy5KkZjBqPWiptAFd0EJvCfcLX5u7eaEM68zEgalwWKP7DdTr/q8DE+sK4bN4QRmzULCrbcC557bYruEjz8GHnsMFyx/ER+8uA83jeuFVwanRdRFA4kJTkwbcTnmjJ6BYX2Gtvj5jkOfYeHGpXhl2+uoqml5rgiJFHVzs9pjYDGAxwxgeyvbDTeAWwFcdfR01OamZEYBnJd1cByINqSzQYsD5ybHgOhDQzZocODc9CE1DlyUIjGBp7QalSsPwiivbXMb7wk3Tq8rRtXWE0i+oi9sEboS4qrPy7BwzVFg0CBY33oL6NcP8Hpb3zgvD3jmGdj/+EdYx4/HU2v2wgIDLw9Oj4iLBs7s3h8r8p9Gny5Z8Bqtj8PZZ+Rg/vT78euJ+bhy/mx8efyAyZYkHqibm58DuAzAAbR9o8QCANcDuBvAmx4DT60piujclMwogPOyDo4D0YZ0Nmhx4NzkGBB9aMgGDQ6cmz4kx4E3Oifq8ZRWo+Ll/TAq2g6rxhgVtah4eT88pdVh1x5YWo0n1hX7FqQ++AA44wzAYgFsttZ3sNl8Pz/jDFg3bways/HEumIMiICLBs7s3h9r73gRWRk9YLFYYLO2Pg42qw0WiwVZGT3w9p3LMTCzn8mmpKNTNzc/B3ARgMPfvN/GcnH9+4cBjARQCERsbkpmFMB5WQfHgWhDOhu0OHBucgyIPjRkgwYHzk0f0uMQ9KLUO++8gylTpiArKwsWiwWvvfZaREQIaQ2j1ovKlQeBai8Q6IO0DADVvv2M2rb+L2pgPL7uKKwOp+8KqdRUICEhsB0TEoDUVFjXrIHN4cT8dUfD8tBAYoITK/KfRqozGXZbYBdZ2m12pDqT8drNi5CY4IyyIfMpnnh83VG4PQYuA+AC4AlwP883208AUO0xwp6b0hkVC/PSDGJhHJhP8YV0NmhxiIW5GW1iYQyYT/GFhmzQ4BALc9MMNIxD0ItSlZWVGDZsGJ544omwixPij5pCl+9yzmCf7G4ARnktagpdIdce9lUVRh09jYRrfuT7yl6gC1J1JCQA/fvD/sNrMOroaQw7XhWyiwamjbgcfbpkBRxWddhtdvTtegZ+cN7EKJk1wHyKD+rm5ivwfWUv0AWpOjzf7PcKEPbclMwoIDbmpRnEwjgwn+IL6WzQ4hALczPaxMIYMJ/iCw3ZoMEhFuamGWgYh6AXpSZOnIj77rsP3//+98MuTog/3AUlYvtP312KGguAW29t+x5S/vB4gFtuQY0FmL6rNGQXDcwZPaPN7xf7w+P1YM7oGRE2agnzKT6om5uPIfTvoFvh2z/cuSmZUUBszEsziIVxYD7FF9LZoMUhFuZmtImFMWA+xRcaskGDQyzMTTPQMA68pxRRi+d4VatPYAgG7wk3PCFeBTGy6DQShg33PWWvrXtI+cNmA0aMQMKw4bjw6OnQjqGAvN65GNZnaJvfL/aHzWrD8L5nIa93boTNSDwysug0PvnmKXuhXrztBfAxgE8MhDw3pTOK89IHx4FoQzobtDhwbnIMiD40ZIMGB85NH1rGIeqLUm63Gy6Xq8mLkEDwlETmJnah3gwvp8QNDBkSEQfk5GDIydh9fOigHgMicpzsCB0nUjCfYpOcEjd2R+hYe4CQ56Z0RnXUeRksHXUcmE+xi3Q2aHHoqHMzGDrqGDCfYhcN2aDBoaPOzWDRMg5RX5SaN28e0tPT6199+vSJdknSUagJ/wZ2AHw30AsSi2HA4YXv5uaRIC0NDq/vuLFISmJyRI6TGqHjRArmU+xRNzfLI3Q8FxD63BTMKKDjzstg6ajjwHyKYYSzQYtDR52bwdBRx4D5FMMoyAYNDh11bgaLlnGI+qLU3LlzUVZWVv86dOhQtEuSjkJChNrTEfxxDIsF1VYA5RH6v74uF6qtvuPGIhVVlRE5TnmEjhMpmE+xR93cjNByMdKA0OemYEYBHXdeBktHHQfmUwwjnA1aHDrq3AyGjjoGzKcYRkE2aHDoqHMzWLSMQ3C3WA8Bp9MJp7NjPC6RmIutsyMyx8kI7Th7Ojtxzu4IfUlo927s7hK786Dw2L7IHKf4y4gcJ1Iwn2KTPZ2dGPJ1ZL4OOwQIeW5KZ1RHnZfB0lHHgfkUu0hngxaHjjo3g6GjjgHzKXbRkA0aHDrq3AwWLeMQ9PJiRUUFtm/fju3btwMA9u3bh+3bt+PgwYNhiRDSHFtmIqzdwvuFZ+3mhC0zMaR9N2UloWbHduDjj31P0QsFjwfYtg01BTvwQa+k0I6hgILDu7Dj0GfweEMbB4/Xg+0HP8XOI5G6E1DrMJ/ig01ZSTjbAgxHeE/fOxfAWRaEPDelMypW5mW0iZVxYD7FD9LZoMUhVuZmNImVMWA+xQ8askGDQ6zMzWijZRyC/jz/4Ycf4txzz8W5554LALj99ttx7rnn4g9/+ENYIoS0hjOvs9j+z+dmIMEA8NhjgDXE/+trswGPP44Ew3e8WGbhxqWwWkIbB5vVhoUbl0bYqCXMp/igbm7eivCevncrEPbclMwoIDbmpRnEwjgwn+IL6WzQ4hALczPaxMIYMJ/iCw3ZoMEhFuamGWgYh6CrjxkzBoZhtHgtWbIkbBlCmpMwKA2WVDsQ7O1eLIAl1Y6EQWkh196RmYj3eiWhdvmLwIEDQE1NcAeoqQH270ftS8vxXq8k7AhjNV8Dr2x7HYdOFqHWUxvUfrWeWhz8+ghe/eiNKJk1wHyKD+rm5lUA+gEI9iG2tm/2mwaEPTclMwqIjXlpBrEwDsyn+EI6G7Q4xMLcjDaxMAbMp/hCQzZocIiFuWkGGsYh6jc6JyQcLHYrkq/o67uRXaChZQHg8O1nsYfX4jeP6wVPtRveSy/13fQ80IWpmhqgvBze8ePhqXbj5nG9wvLQQFWNG99/4gaUuysDDq1aTy3K3ZW4cv5sVNVE5h5AhAC+uemwWbAavpuVB7owZftm+zcBOGyWsOemdEZxXvrgOBBtSGeDFgfOTY4B0YeGbNDgwLnpQ8M4cFGKqMeW4UDKVf1hSQnsvvyWFDtSruof1s3v6tiX4cBN43oBhYXwXnABcOSI7wdt3WOq7v0jR3zb792Lm8b1wr4IuGjgi6/24+KHr0FR6TEAaPP7x3XvF5Uew8UPX4Mvjx8wzZHEB3VzczCATQB6f/N+W7/U6t7v/c32g4CIzU3JjAI4L+vgOBBtSGeDFgfOTY4B0YeGbNDgwLnpQ3ocov70PUIigS3DgdSfDERNoQvughJ4T7RckbV2c8KZ19l3OWgEVs/reGVwGiww8MS6L2Abmgv7D68BbrkFGDGi5cbbtwOPP47al5bDU+3GTeOz8Mrg8C8v1cSXxw/g/Psm4QfnTcSc0TMwvO9ZLbbZeXg3Fm5cilc/eqPD/BWB6KNhbhZjp8fAKwAeA/BxK9sOg+8eUtPgu0LqxnG9Ijo3JTMK4Lysg+NAtCGdDVocODc5BkQfGrJBgwPnpg/JceCiFIkZLHYrHLkZcORmwHO8Cp7SaqDaCzissGU4wnoCgz9eHpyObd2TMH/dUYx69lnUPPcsEoYNB3JygLQ0wOUC9uxBzY7tSDCAD7KScMvYAR3mCqnmVNW4sWzza1i2+TXk9c5Fdo8BSE1MRnlVJfYe24eCw7ukFUmc0Hhuzjp6GtMtwCcGsAeAC76v6uUAONviu6n5u1lJuGVsdK5elMwogPOyDo4D0YZ0Nmhx4NzkGBB9aMgGDQ6cmz6kxoGLUiQmsWUmmhJQjdmX4cCkH/TDsONVmL6rFBce3oUhBdvh8ALVVmB3Fyc+ODsDz+dmxPxNzYOh4PCuuAlqopMWc/PoafzgpLvJ3FzcK8nUuSmRUY3hvPTBcSDakM4GLQ6cmxwDog8N2aDBgXPTh5njwEUpQoJkR2YidmT2rP9vi2HAsAT76AhCSKTh3CSEEEIIISS24I3OCQkT/p9eQnTCuUkIIYQQQohuuChFCCGEEEIIIYQQQkzHYhiGYWZBl8uF9PR0X/Fk8789aJyqBQwAFsDSSebbi3SggyYH6foAYFTWAgDKysqQlib3tELpfAKUnA/2JB3o0NRBQUYxn+igpT4dlDkwnwAoORd0oIOS+mocAswn0XtK1UnKFBeuTwc6aHOQrq8M8bHQcD6kHaTr04EOShEfBw3ngg7y9emgy0EJ4uOg4VzQgQ5a6mtx8IPoohSvlKIDHeQdpOsDOoOSf+mTc5CuTwc6tHBQllHMp/h2kK5PB2UOzCcASs4FHeigpL4ahwDzSW5RqpMNabOyTS/rWrIXRmUtLJ3sIvXpQAdtDtL1AaBscSFwyiNSu1WE8gnQcT6kHaTr04EOzVGVUcynuHeQrk8HXQ7MJx8azgUd6KClvhaHQPOJNzonhBBCCCGEEEIIIabDRSlCCCGEEEIIIYQQYjpclCKEEEIIIYQQQgghpiN6o3MSGnm9czGoxwCkJCajoqoShcf2oeDwLmktU+EY+NAwDp7jVfCUVAM1XiDBCltnB2yZiaY6ED1o6EkNcF7o6AWeB9IYDT2pAc4LH9L9wPNAGiPdj1rgvPChoR/MPBdclIoREhOcmDbicswZPQPD+gxt8fMdhz7Dwo1L8cq211FV4xYwjD4cAx8axsGo9aKm0AV3QQm8J1rWsHZzwpnXGQmD0mCx84LMjo6GntQA54WOXuB5II3R0JMa4LzwId0PPA+kMdL9qAXOCx8a+kHqXHBRKgY4s3t/rMh/Gn26ZMFreFvd5uwzcjB/+v349cR8XDl/Nr48fsBky+jCMfChYRw8pdWoXHkQRnnbj/j0nnDj9LpiVG09geQr+sKW4YioA9GDhp7UAOeFjl7geSCN0dCTGuC88CHdDzwPpDHS/agFzgsfGvpB8lx03KXGDsKZ3ftj7R0vIiujBywWC2xWW6vb2aw2WCwWZGX0wNt3LsfAzH4mm0YPjoEPDePgKa1Gxcv7YVS0HVaNMSpqUfHyfnhKqyPmQPSgoSc1wHmhoxd4HkhjNPSkBjgvfEj3A88DaYx0P2qB88KHhn6QPhdBLUrNmzcP3/72t5Gamoru3bvjyiuvxJ49eyIiQlqSmODEivynkepMht0W2EVtdpsdqc5kvHbzIiQmOKNsGH04Bj40jINR60XlyoNAtRcwAt0JQLVvP6O29VX/SMF8MhcNPakB7fPCDDT0QiycB2aUeWjoSQ3EwrwwA+l+iIXzwHwyD+l+1EIszAsz0NAPGs5FUItSGzduRH5+Pj744AOsWbMGNTU1uPTSS1FZWRm2CGnJtBGXo0+XrIAbtA67zY6+Xc/AD86bGCUz8+AY+NAwDjWFLt/lnIGGVR0GYJTXoqbQFbZDezCfzEVDT2pA+7wwAw29EAvngRllHhp6UgOxMC/MQLofYuE8MJ/MQ7oftRAL88IMNPSDhnMR1KLU6tWrMWvWLJx11lkYNmwYlixZgoMHD2Lbtm1hi5CWzBk9o83vlPrD4/VgzugZETYyH46BDw3j4C4oEd3fH8wnc9HQkxrQPi/MQEMvxMJ5YEaZh4ae1EAszAszkO6HWDgPzCfzkO5HLcTCvDADDf2g4VyEdU+psrIyAECXLl3CFiFNyeudi2F9hrb5nVJ/2Kw2DO97FvJ650bYzDw4Bj40jIPneFWrT2AIBu8JNzzHq8I6RjAwn6KHhp7UQCzOi0ijoRdi9Twwo6KDhp7UQKzOi0gj3Q+xeh6YT9FBuh+1EKvzItJo6Act5yLkRSmv14tf/OIXGDVqFM4+++w2t3O73XC5XE1exD+DegyIyHGyI3QcCTgGPjSMg6ckMjexM+vGhMyn6KKhJzUQa/MiGmjohVg8D4FkFPMpNDT0pAZicV5EA+l+iMXzwHyKHtL9qIVYnBfRQEM/aDkXIS9K5efn45NPPsGLL77Y7nbz5s1Denp6/atPnz6hlowrUhKTI3Kc1AgdRwKOgQ8V41AToZsJVptzU0LmU3RR0ZMaiLF5EQ1U9EIMnodAMor5FBoqelIDMTgvooF4P8TgeWA+RQ/xftRCDM6LaKCiH5Sci5AWpW6++WasWrUK69evR+/evdvddu7cuSgrK6t/HTp0KCTReKOiKjI3FiyP0HEk4Bj4UDEOCWF907cBR4SO0w7Mp+ijoic1EEPzIlqo6IUYOw+BZhTzKTRU9KQGYmxeRAvxfoix88B8ii7i/aiFGJsX0UJFPyg5F0Hd5t0wDNxyyy1YsWIFNmzYgAED/F8q5nQ64XR2jEdXmknhsX2ROU7xlxE5jgQcAx8axsHW2RERB1tGZI7TGswn89DQkxqIhXkRbTT0Qqych2AzivkUGhp6UgOxMi+ijXQ/xMp5YD6Zg3Q/aiFW5kW00dAPWs5FUEta+fn5WLp0KZYtW4bU1FQUFxejuLgYp0+fDkuCtKTg8C7sOPQZPF5PSPt7vB5sP/gpdh7ZHWEz8+AY+NAwDrbMRFi7hffhw9rNCVtmYljHaA/mk3lo6EkNxMK8iDYaeiFWzgMzyhw09KQGYmVeRBvpfoiV88B8MgfpftRCrMyLaKOhH7Sci6AWpRYsWICysjKMGTMGvXr1qn8tX748LAnSOgs3LoXVEtqlcDarDQs3Lo2wkflwDHxoGAdnXmfR/f3BfDIXDT2pAe3zwgw09EIsnAdmlHlo6EkNxMK8MAPpfoiF88B8Mg/pftRCLMwLM9DQDxrORVAjYBhGq69Zs2aFLUJa8sq213HoZBFqPbVB7VfrqcXBr4/g1Y/eiJKZeXAMfGgYh4RBabCk2gFLkDtaAEuqHQmD0sJ2aA/mk7lo6EkNaJ8XZqChF2LhPDCjzENDT2ogFuaFGUj3QyycB+aTeUj3oxZiYV6YgYZ+0HAuYvvuYB2cqho3vv/EDSh3VwbcqLWeWpS7K3Hl/NmoqnFH2TD6cAx8aBgHi92K5Cv6+m5kF2hoWQA4fPtZ7IybjoSGntQA54WOXuB5II3R0JMa4LzwId0PPA+kMdL9qAXOCx8a+kHDuegYZ7MD88VX+3Hxw9egqPQYALT5ndO694tKj+Hih6/Bl8cPmOYYbTgGPjSMgy3DgZSr+sOSEtgzEiwpdqRc1T/mb0RIWkdDT2qA80JHL/A8kMZo6EkNcF74kO4HngfSGOl+1ALnhQ8N/SB9LoJ6+h6R4cvjB3D+fZPwg/MmYs7oGRje96wW2+w8vBsLNy7Fqx+90WFW0BvDMfChYRxsGQ6k/mQgagpdcBeUwHuiZQ1rNyeceZ19l4N2kL9kkNbR0JMa4LzQ0Qs8D6QxGnpSA5wXPqT7geeBNEa6H7XAeeFDQz9IngsuSsUIVTVuLNv8GpZtfg15vXOR3WMAUhOTUV5Vib3H9qHg8C5pxajDMfChYRwsdiscuRlw5GbAc7wKntJqoNoLOKywZThi/mkYJDg09KQGOC909ALPA2mMhp7UAOeFD+l+4HkgjZHuRy1wXvjQ0A9S54KLUjFIweFdcRNSbcEx8KFhHGyZiXHzy4L4R0NPaoDzQkcv8DyQxmjoSQ1wXviQ7geeB9IY6X7UAueFDw39YOa56JjXvxFCCCGEEEIIIYQQ1XBRihBCCCGEEEIIIYSYDhelCCGEEEIIIYQQQojpWAzDMMws6HK5kJ6e7iuebP4trYxTtYABwAJYOsncUosOdNDkIF0fAIzKWgBAWVkZ0tLSRBwA+XwClJwP9iQd6NDUQUFGMZ/ooKU+HZQ5MJ8AKDkXdKCDkvpqHALMJ9EbnddJyhQXrk8HOmhzkK6vDPGx0HA+pB2k69OBDkoRHwcN54IO8vXpoMtBCeLjoOFc0IEOWuprcfCD6KIUr5SiAx3kHaTrAzqDkn/pk3OQrk8HOrRwUJZRzKf4dpCuTwdlDswnAErOBR3ooKS+GocA80luUaqTDWmzsk0v61qyF0ZlLSyd7CL16UAHbQ7S9QGgbHEhcMojUrtVhPIJ0HE+pB2k69OBDs1RlVHMp7h3kK5PB10OzCcfGs4FHeigpb4Wh0DziTc6J4QQQgghhBBCCCGmw0UpQgghhBBiChZzn69DCCGExCzx8jtT9J5ShBBCCCGk4zLseBWm7yrFyKLTyClxw+EFqq3Ans5ObMpKwvO5GdiRmSitSQghhIgTr78zuSgVg+T1zsWgHgOQkpiMiqpKFB7bh4LDu+LKQbq+FgcNeI5XwVNSDdR4gQQrbJ0dsHXAsCSBoWFe0IHzsg6OgxwDSqsxf91RjDp6GjUWIGHYcGD8ECA1FY7ycpyzezeG7NiOOTtL8V6vJNw8rhf2ZTii6iQ9L+nQAOcmx0Ab0vNCuj4dGpCYm81/Z35iAK8AKAeQ6gWGfO3GdSfdpv7ONHMcuCgVIyQmODFtxOWYM3oGhvUZ2uLnOw59hoUbl+KVba+jqsbdIR2k62tx0IBR60VNoQvughJ4T7T8d1q7OeHM64yEQWmw2Pkt4Y6OhnlBB87LOjgO8lz1eRmeWFcMm8MJzJqFhFtvBc49t8V2CR9/DDz2GC5Y/iI+eHEfbhrXC68MTouoi/S8pEMDnJscA21Izwvp+nRoQHJu1v3OrPYYWAzgMQPY3sp2ww3gVgBXHT0dtd+ZUuPARakY4Mzu/bEi/2n06ZIFr+FtdZuzz8jB/On349cT83Hl/Nn48viBDuUgXV+LgwY8pdWoXHkQRnnbj/j0nnDj9LpiVG09geQr+sIW5ZV8IoeGeUEHzss6OA7yXPV5GRauOQoMGgTrW28B/foB3tbnBPLygGeegf2Pf4R1/Hg8tWYvLDDw8uD0iLhIz0s6NMC5yTHQhvS8kK5PhwYk52bd78zPAVwG4ADavul3AYDrAdwN4E2PgafWFEX0d6bkOHAJXjlndu+PtXe8iKyMHrBYLLBZba1uZ7PaYLFYkJXRA2/fuRwDM/t1GAfp+locNOAprUbFy/thVLQdVo0xKmpR8fJ+eEqro2xGJNAwL+jAeVkHx0GegaXVeGJdsW9B6oMPgDPOACwWwNb6nIDN5vv5GWfAunkzkJ2NJ9YVY0AEzon0vKRDA5ybHANtSM8L6fp0aEBybtb9zvwcwEUADn/zfht/xql//zCAkQAKgYj9zpTOqKAWpRYsWIC8vDykpaUhLS0NI0eOxBtvvBEREdKSxAQnVuQ/jVRnMuy2wC5qs9vsSHUm47WbFyExwRnzDtL1tThowKj1onLlQaDaCwT6IAgDQLVvP6O2rYiNDMwnc9EwL+igf16aRSyMQzxk1OPrjsLqcPqukEpNBRISAtsxIQFITYV1zRrYHE7MX3c0LA/peUmHBmJhbkabWBiDeMinOqTnhXR9OjQgPTcfX3cUbo+BywC4AHgC3M/zzfYTAFR7jLB/Z0qPAxDkolTv3r3x4IMPYtu2bfjwww8xbtw4TJ06FZ9++mnYIqQl00Zcjj5dsgKeqHXYbXb07XoGfnDexJh3kK6vxUEDNYUu3+WcwT6Z1ACM8lrUFLqi4lUH88lcNMwLOuifl2YRC+PQ0TNq2FdVGHX0NBKu+ZHvK3uBLkjVkZAA9O8P+w+vwaijpzHseFXILtLzkg4NxMLcjDaxMAYdPZ8aIz0vpOvToQHJuVn3O/MV+L6yF+iCVB2eb/Z7BQj7d6aGjApqUWrKlCm4/PLLMWjQIAwePBj3338/UlJS8MEHH4QtQloyZ/SMNr9b6w+P14M5o2fEvIN0fS0OGnAXlIju7w/mk7lomBd00D8vzSIWxqGjZ9T03aWosQC49da27yHlD48HuOUW1FiA6btKQ3aRnpd0aCAW5ma0iYUx6Oj51BjpeSFdnw4NSM7Nut+ZjyH0+ylZ4ds/3N+ZGjIq5HtKeTwevPjii6isrMTIkSPDFiFNyeudi2F9hrb53Vp/2Kw2DO97FvJ658asg3R9LQ4a8ByvavUJDMHgPeGGJ4xV/GBgPkUXDfOCDrE3L6NFLI5DR8yokUWnkTBsuO8pe23dQ8ofNhswYgQShg3HhUdPh3QI6XlJhwZicW5Gmlgcg46YT3VIzwvp+nRoQHpujiw6jU++ecpeqF9+8wL4GMAnBkL+nSk9DnUEvSi1c+dOpKSkwOl04mc/+xlWrFiBoUNbPrqxDrfbDZfL1eRF/DOox4CIHCc7jONIO0jX1+KgAU9JZG5iF+0bdjKfzEHDvKBD7MzLaBNL4xBMRsVaPuWUuIEhQyJ0sBwMORnah2TpeUmHBmJpbkaLWBqDjpxPdUjPC+n6dGhAem7mlLixOyIGwB4g5N+Z0uNQR9CLUjk5Odi+fTs2b96Mn//855g5cyY+++yzNrefN28e0tPT6199+vQJSzheSElMjshxUsM4jrSDdH0tDiqoidBNNquje7NO5pM5aJgXdEDMzMuoE0PjEExGxVI+WQwDDi98NzePBGlpcHh9xw0W8XlJhwZiaG5GjRgag46aT42RnhfS9enQCMG5Wfc7szwyBnABIf/O1JJRQS9KORwOZGdnY8SIEZg3bx6GDRuGv//9721uP3fuXJSVldW/Dh06FJZwvFBRVRmR45SHcRxpB+n6WhxUkBDyN32b4ojQcdo6PPPJFDTMCzogZuZl1ImhcQgmo2IpnwyLBdVWAOUR+ojtcqHa6jtusIjPSzo0EENzM2rE0Bh01HxqjPS8kK5Ph0YIzs2635kR+jMO0oCQf2dqyajgbnffCl6vF25325eLOZ1OOJ3hP7Ix3ig8ti8yxyn+MmYdpOtrcdCArbMjMsfJiMxxAoX5FB00zAs6xO68jDSxPA7tZVSs5dOezk6csztCX0bYvRu7u4T2b5eel3RoIJbnZqSI5THoSPlUh/S8kK5Phwak5+aezk4M+Tq8eznVMQQI+Xem9DjUEdSS1ty5c/HOO+9g//792LlzJ+bOnYsNGzZg+vTpYUmQlhQc3oUdhz6DxxvsAyJ9eLwebD/4KXYeCf0DorSDdH0tDhqwZSbC2i28Dx/Wbk7YMhMjZNQS5pN5aJgXdIiNeWkGsTIOHT2jNmUloWbHduDjj31P0QsFjwfYtg01BTvwQa+kkA4hPS/p0ECszM1oEitj0NHzqQ7peSFdnw4NSM/NTVlJONsCDEd4T987F8BZFoT8O1N6HOqPEczGX331Fa699lrk5OTg4osvxtatW/Hmm29i/PjxYUmQ1lm4cSmsltDa1Ga1YeHGpTHvIF1fi4MGnHmdRff3B/PJXDTMCzron5dmEQvj0NEz6vncDCQYAB57DLCG+BHbZgMefxwJhu94oSI9L+nQQCzMzWgTC2PQ0fOpMdLzQro+HRqQnJt1vzNvRXhP37sVCPt3poaMCqoTFi1ahP3798PtduOrr77C2rVrO2RYaeGVba/j0Mki1Hpqg9qv1lOLg18fwasfvRHzDtL1tThoIGFQGiypdiDYrytbAEuqHQmD0qLiVQfzyVw0zAs66J+XZhEL49DRM2pHZiLe65WE2uUvAgcOADU1wR2gpgbYvx+1Ly3He72SsCOMv7pKz0s6NBALczPaxMIYdPR8aoz0vJCuT4cGJOdm3e/MqwD0A2ALcn/bN/tNA8L+nakho2L4zoEdn6oaN77/xA0od1cGPGFrPbUod1fiyvmzUVUT/vdUpR2k62tx0IDFbkXyFX19N7ILNLQsABy+/Sx2xk1HQsO8oAPnZR0cBx3cPK4XPNVueC+91HfT80AXpmpqgPJyeMePh6fajZvH9QrLQ3pe0qEBzk2OgTak54V0fTo0ID03bx7XCw6bBavhu1l5oAtTtm+2fxOAw2YJ+3em9DgAXJRSzxdf7cfFD1+DotJjANDmd2/r3i8qPYaLH74GXx4/0GEcpOtrcdCALcOBlKv6w5IS2DMSLCl2pFzVP6ZvUkraRsO8oAPnZR0cB3n2ZThw07heQGEhvBdcABw54vtBW/eYqnv/yBHf9nv34qZxvbAvAudEel7SoQHOTY6BNqTnhXR9OjQgOTfrfmcOBrAJQO9v3m9rgabu/d7fbD8IiNjvTOmMCvvpeyT6fHn8AM6/bxJ+cN5EzBk9A8P7ntVim52Hd2PhxqV49aM3onJljrSDdH0tDhqwZTiQ+pOBqCl0wV1QAu+Jlv9OazcnnHmdfZeD8i98HRoN84IOnJd1cBzkeWVwGiww8MS6L2Abmgv7D68BbrkFGDGi5cbbtwOPP47al5bDU+3GTeOz8MrgyH1VSXpe0qEBzk2OgTak54V0fTo0IDk3G35nFmOnx8ArAB4D8HEr2w6D7x5S0+C7QurGcb0i+jtTchy4KBUjVNW4sWzza1i2+TXk9c5Fdo8BSE1MRnlVJfYe24eCw7s6vIN0fS0OGrDYrXDkZsCRmwHP8Sp4SquBai/gsMKW4YjpJ+WQ4NEwL+jAeVkHx0GelwenY1v3JMxfdxSjnn0WNc89i4Rhw4GcHCAtDXC5gD17ULNjOxIM4IOsJNwydkBE/trbHOl5SYcGODc5BtqQnhfS9enQgOTcbPw7c9bR05huAT4xgD0AXPB9VS8HwNkW303N381Kwi1jI3OFVHOkxoGLUjFIweFd4osf0g7S9bU4aMCWmcgPUaQeDfOCDpyXdXAc5NiX4cCkH/TDsONVmL6rFBce3oUhBdvh8ALVVmB3Fyc+ODsDz+dmhHWD1mCQnpd0aIBzk2OgDel5IV2fDg1IzM0WvzOPnsYPTrqb/M5c3CvJ1N+ZZo4DF6UIIYQQQkhU2JGZiB2ZPev/22IYMCzBPuKHEEII6fjE6+9MflmZEEIIIYSYQjx8uCaEEEIiQbz8zuSiFCGEEEIIIYQQQggxHYthGIaZBV0uF9LT033Fk83/9qBxqhYwAFgASyeZby/SgQ6aHKTrA4BRWQsAKCsrQ1pa5J4iESzS+QQoOR/sSTrQoamDgoxiPtFBS306KHNgPgFQci7oQAcl9dU4BJhPoveUqpOUKS5cnw500OYgXV8Z4mOh4XxIO0jXpwMdlCI+DhrOBR3k69NBl4MSxMdBw7mgAx201Nfi4AfRRSleKUUHOsg7SNcHdAYl/9In5yBdnw50aOGgLKOYT/HtIF2fDsocmE8AlJwLOtBBSX01DgHmk9yiVCcb0mZlm17WtWQvjMpaWDrZRerTgQ7aHKTrA0DZ4kLglEekdqsI5ROg43xIO0jXpwMdmqMqo5hPce8gXZ8OuhyYTz40nAs60EFLfS0OgeYTb3ROCCGEEEIIIYQQQkyHi1KEEEIIIYQQQgghxHS4KEUIIYQQQgghhBBCTEf0RufBktc7F4N6DEBKYjIqqipReGwfCg7vijsHz/EqeEqqgRovkGCFrbMDtsxEUx2kx0G6Ph0a0NCPGtBwLjQ4aOgHDeMg7SBdH9DRCxoctCDdE9L16UCH5kjng3R9TWjoB2kH6fp0aEDD3Iw3B/WLUokJTkwbcTnmjJ6BYX2Gtvj5jkOfYeHGpXhl2+uoqnF3WAej1ouaQhfcBSXwnmhZw9rNCWdeZyQMSoPFHp0L4KTHQbo+HRrQ0I8a0HAuNDho6AcN4yDtIF0f0NELGhy0IN0T0vXpQIfmSOeDdH1NaOgHaQfp+nRoQMPcjGcHi2EYRsSOFgAulwvp6elAJxvSrxvU7rZndu+PFflPo0+XLHgNL2xWW4ttPF4PrBYrDp0swpXzZ+PL4wfar193F/rkwO5Cr8HBU1qNypUHYZT7f6SiJdWO5Cv6wpbhiKiD9DhEoz4dgq8PRKcf657MUFZWhrS0NL/HjRbS+QTI90OwDswnHQ4dtRe0OGjIqGDyCWBPdmSHWMsnLQ7Sv6+YTw1o6Adph46aT7HoIJ0NHdkh0HxSuwR/Zvf+WHvHi8jK6AGLxdJqgwKAzWqDxWJBVkYPvH3ncgzM7NehHDyl1ah4eT+MCv/NAQBGRS0qXt4PT2l1xBykx0G6Ph0a0NCPGtBwLjQ4aOgHDeMg7SBdH9DRCxoctCDdE9L16UCH5kjng3R9TWjoB2kH6fp0aEDD3KSD0kWpxAQnVuQ/jVRnMuy2wL5haLfZkepMxms3L0JigrNDOBi1XlSuPAhUe4FAr2czAFT79jNqvWE7SI+DdH06NKChHzWg4VxocNDQDxrGQdpBuj6goxc0OGhBuiek69OBDs2Rzgfp+prQ0A/SDtL16dCAhrlJBx9hLUo9+OCDsFgs+MUvfhG2SGOmjbgcfbpkBdygddhtdvTtegZ+cN7EDuFQU+jyXT4X7BcsDcAor0VNoStsB+lxkK5PhwY09GMwMJ+i66ChHzSMg7SDdH1ARy9ocAiGaOUTIN8T0vXpQIfmSOeDdP1Q4Geo6DlI16dDAxrmJh18hLwotXXrVvzv//4v8vLywpZozpzRM+A1Qltx83g9mDN6RodwcBeUiO4PyI+DdH06NKChHwOF+RR9Bw39oGEcpB2k6wM6ekGDQ6BEM58A+Z6Qrk8HOjRHOh+k9w8WfoaKroN0fTo0oGFu0sFHSItSFRUVmD59Op566il07tw5bInG5PXOxbA+Q9v8Tqk/bFYbhvc9C3m9c2PawXO8qtU73geD94QbnuNVIe8vPQ7S9enQgIZ+DBTmU/QdNPSDhnGQdpCuD+joBQ0OgRLNfALke0K6Ph3o0BzpfJCuHyz8DBVdB+n6dGhAw9ykQwMhLUrl5+dj0qRJuOSSS/xu63a74XK5mrzaY1CPAaEotSA7jONocPCURO7GiqEiPQ7S9enQgIZ+DBTmU/QdNPSDhnGQdpCuD+joBQ0OgRLNfALke0K6Ph3o0BzpfJCuHyyBZlQs5pMGB+n6dGhAw9ykQwPBfYkTwIsvvoiPPvoIW7duDWj7efPm4e677w74+CmJycEqtUpqGMfR4ICaCN3UsDr040iPg3R9OjRCQT8GAvPJHAcN/aBhHKQdpOsDUNELKhwCINr5BMj3hHR9OtChBdL5IF0/CILJqFjMJw0O0vXp0AgNc5MO9QR1pdShQ4dw22234fnnn0diYmJA+8ydOxdlZWX1r0OHDrW7fUVVZTBKbVIexnE0OCAhQg9GdIR+HOlxkK5Ph0Yo6Ed/MJ/Mc9DQDxrGQdpBuj4AFb2gwsEPZuQTIN8T0vXpQIcWSOeDdP0ACTajYjGfNDhI16dDIzTMTTrUE9SVUtu2bcNXX32F8847r/49j8eDd955B/Pnz4fb7YbN1vS7oU6nE05n4I9sLDy2Lxilto9T/GXo+ypwsHV2RMTBlhH6caTHQbo+HRrQ0I/+YD6Z56ChHzSMg7SDdH1ARy9ocPCHGfkEyPeEdH060KE50vkgXT9Qgs2oWMwnDQ7S9enQgIa5SYcGglrSuvjii7Fz505s3769/vWtb30L06dPx/bt21t8oAqFgsO7sOPQZ/B4PSHt7/F6sP3gp9h5ZHdMO9gyE2HtFlzYN8fazQlbZmB/kW0N6XGQrk+HBjT0oz+YT+Y5aOgHDeMg7SBdH9DRCxoc/GFGPgHyPSFdnw50aI50PkjXDxR+hjLHQbo+HRrQMDfp0OgYwWycmpqKs88+u8krOTkZXbt2xdlnnx2WSGMWblwKqyW0S8BsVhsWblzaIRyceeE99SLc/QH5cZCuT4cGNPRjezCfzHXQ0A8axkHaQbo+oKMXNDi0h1n5BMj3hHR9OtChOdL5IL1/IPAzlHkO0vXp0ICGuUkHH9H9gnKIvLLtdRw6WYRaT21Q+9V6anHw6yN49aM3OoRDwqA0WFLtgCXIHS2AJdWOhEFpYTtIj4N0fTo0oKEfNaDhXGhw0NAPGsZB2kG6PqCjFzQ4aEG6J6Tr04EOzZHOB+n6mtDQD9IO0vXp0ICGuUkHH2EvSm3YsAGPPvpo2CKNqapx4/tP3IByd2XAjVrrqUW5uxJXzp+Nqhp3h3Cw2K1IvqKv78ZhgTaJBYDDt5/FHv6ao/Q4SNenQwMa+jFYmE/Rc9DQDxrGQdpBuj6goxc0OARLNPIJkO8J6fp0oENzpPNBun6o8DNUdByk69OhAQ1zkw4+VF4pBQBffLUfFz98DYpKjwFAm985rXu/qPQYLn74Gnx5/ECHcrBlOJByVX9YUgK7J70lxY6Uq/pH9IaI0uMgXZ8ODWjoRw1oOBcaHDT0g4ZxkHaQrg/o6AUNDlqQ7gnp+nSgQ3Ok80G6viY09IO0g3R9OjSgYW7SIcin75nNl8cP4Pz7JuEH503EnNEzMLzvWS222Xl4NxZuXIpXP3ojIiumGh1sGQ6k/mQgagpdcBeUwHuiZQ1rNyeceZ19l99F4S8q0uMgXZ8ODWjoRw1oOBcaHDT0g4ZxkHaQrg/o6AUNDlqQ7gnp+nSgQ3Ok80G6viY09IO0g3R9OjSgYW7Gu4PFMAwjYkcLAJfLhfT0dKCTDenXDQpq37zeucjuMQCpickor6rE3mP7UHB4V3D1l+yFUVkLS7IdabOyg9pXi4PneBU8pdVAtRdwWGHLcAR9x/tYH4dI1KdD+PWByPRj2eJC4JQHZWVlSEuTu3eCdD4B8v0QrgPzSYdDR+kFLQ4aMiqcfALYkx3JIdbzSYuD9O8r5lMDGvpB2qGj5FNHcJDOho7kEGg+qb5SqjkFh3eF1JQdzcGWmRj1R8P6Q3ocpOvToQEN/agBDedCg4OGftAwDtIO0vUBHb2gwUEL0j0hXZ8OdGiOdD5I19eEhn6QdpCuT4cGNMzNeHPouNeFEkIIIYQQQgghhBC1cFGKEEIIIYQQQgghhJgOF6UIIYQQQgghhBBCiOnI3egcgCXZ/FtaGadqAQOABbB0krmlFh3ooMlBuj4AGJW1AKDnRueQySdAyflgT9KBDk0dFGQU84kOWurTQZkD8wmAknNBBzooqa/GIcB8Er3ReZ2kTHHh+nSggzYH6frKEB8LDedD2kG6Ph3ooBTxcdBwLuggX58OuhyUID4OGs4FHeigpb4WBz+ILkrxSik60EHeQbo+oDMo+Zc+OQfp+nSgQwsHZRnFfIpvB+n6dFDmwHwCoORc0IEOSuqrcQgwn+QWpTrZkDYr2/SyriV7YVTWwtLJLlKfDnTQ5iBdHwDKFhcCpzwitVtFKJ8AHedD2kG6Ph3o0BxVGcV8insH6fp00OXAfPKh4VzQgQ5a6mtxCDSfeKNzQgghhBBCCCGEEGI6XJQihBBCCCGEEEIIIabDRSlCCCGEEEIIIYQQYjqiNzoPlrzeuRjUYwBSEpNRUVWJwmP7UHB4l6kOnuNV8JRUAzVeIMEKW2cHbJmJpjpoGAdpB+n6Whw09KMGBw2wH3xoGAc6yNcHdPSjBgctSPeEhnMhPQZ00OUg3ZPS9TXBftAxBhocpM8DwHGQcFC/KJWY4MS0EZdjzugZGNZnaIuf7zj0GRZuXIpXtr2Oqhp3VByMWi9qCl1wF5TAe6JlDWs3J5x5nZEwKA0We3QuPtMwDtIO0vW1OGjoRw0OGmA/+NAwDnSQrw/o6EcNDlqQ7gkN50J6DOigy0G6J6Xra4L9oGMMNDhInweA4yDtYDEMw4jY0QLA5XIhPT0d6GRD+nWD2t32zO79sSL/afTpkgWv4YXNamuxjcfrgdVixaGTRbhy/mx8efxA+/Xr7kKfHNhd6D2l1ahceRBGuf/HGVpS7Ui+oi9sGY6IOmgYB2mHaNSPRQcN/RgNh7onM5SVlSEtLc3vcaOFdD4B8v0QrIN0NtAhevWDddDQj9Fy0JBRweQTIN+TGvpBw7yQPg90aHR84c9QzKcG2A8dN5+CdeiovytirR+j5RBoPqldgj+ze3+sveNFZGX0gMViabU5AMBmtcFisSArowfevnM5Bmb2i5iDp7QaFS/vh1Hh/8QAgFFRi4qX98NTWh0xBw3jIO0gXV+Lg4Z+1OCgAfaDDw3jQAf5+oCOftTgoAXpntBwLqTHgA66HKR7Urq+JtgPOsZAg4P0eQA4DlocVC5KJSY4sSL/aaQ6k2G3BfYNQ7vNjlRnMl67eRESE5xhOxi1XlSuPAhUe4FAryUzAFT79jNqvWE7aBgHaQfp+locNPSjBgcNsB98aBgHOsjXB3T0owYHLUj3hIZzIT0GdNDlIN2T0vU1wX7QMQYaHKTPA8Bx0OQQ1KLUn/70J1gsliavIUOGhC3RnGkjLkefLlkBN0cddpsdfbuegR+cNzFsh5pCl+/StWC/3GgARnktagpdYTtoGAdpB+n6Whw09KMGh/ZgPgUA86nDOUjXB3T0owYHf8RLRmk4F9JjQAddDtI9KV0/EOIlnwD586FhDDQ4SJ8HgOOgySHoK6XOOussHD16tP717rvvhi3RnDmjZ8BrhLbi5vF6MGf0jLAd3AUlovsDOsZB2kG6vhYHDf2owcEfzCdz9gd0jAMd5OsDOvpRg0MgxENGaTgX0mNAB10O0j0pvX+gxEM+AfLnQ8MYaHCQPg8Ax0GTQ9CLUna7HT179qx/devWLWyJxuT1zsWwPkPb/D6nP2xWG4b3PQt5vXNDdvAcr2r1bvPB4D3hhud4Vcj7axgHaQfp+locNPSjBodAYD4FBvOpYzhI1wd09KMGh0Dp6Bml4VxIjwEddDlI96R0/WDo6PkEyJ8PDWOgwUH6PAAcB00OQAiLUoWFhcjKysLAgQMxffp0HDx4sN3t3W43XC5Xk1d7DOoxIFilVskO4zieksjd1DBUNIyDtIN0fS0OGvpRg0MgMJ+COA7zKeYdpOsDOvpRg0OgBJNRweYTIN8TGs6F9BjQQZeDdE9K1w+Gjp5PgPz50DAGGhykzwPAcdDkAAS5KHXBBRdgyZIlWL16NRYsWIB9+/bhu9/9LsrLy9vcZ968eUhPT69/9enTp90aKYnJwSi1SWo4x6mJ0A0Fq0M/joZxkHaQrq/FQUM/qnDwA/MpSJhPMe8gXR+Ain5U4RAAwWZUsPkEKOgJBedCfAzooMpBvCel6wdIXOQTIH4+NIyBBgfp8wBwHFQ5IMhFqYkTJ+Lqq69GXl4eJkyYgNdffx2lpaV46aWX2txn7ty5KCsrq38dOnSo3RoVVZXBKLVJeTjHSYjQQwkdoR9HwzhIO0jX1+KgoR9VOPiB+RQkzKeYd5CuD0BFP6pwCIBgMyrYfAIU9ISCcyE+BnRQ5SDek9L1AyQu8gkQPx8axkCDg/R5ADgOqhwABHer+WZkZGRg8ODB2Lt3b5vbOJ1OOJ2BPy6x8Ni+cJQajlP8Zcj72jo7IuJgywj9OBrGQdpBur4WBw39qMEhWJhPfo7DfIp5B+n6gI5+1OAQCv4yKth8AuR7QsO5kB4DOuhykO5J6fqh0hHzCZA/HxrGQIOD9HkAOA6aHIAQ7inVmIqKCnzxxRfo1atXWBKNKTi8CzsOfQaP1xPS/h6vB9sPfoqdR3aH7GDLTIS1W3BB2xxrNydsmYkh769hHKQdpOtrcdDQjxocgoX51DbMp47hIF0f0NGPGhxCoSNmlIZzIT0GdNDlIN2T0vVDpSPmEyB/PjSMgQYH6fMAcBw0OQBBLkrdcccd2LhxI/bv34/3338f3//+92Gz2fDjH/84LInmLNy4FFZLaOtlNqsNCzcuDdvBmddZdH9AxzhIO0jX1+KgoR81OLQH88m8/QEd40AH+fqAjn7U4OCPeMkoDedCegzooMtBuiel9w+EeMknQP58aBgDDQ7S5wHgOGhyCOosHD58GD/+8Y+Rk5ODH/7wh+jatSs++OADZGZmhi3SmFe2vY5DJ4tQ66kNar9aTy0Ofn0Er370RtgOCYPSYEm1A5Ygd7QAllQ7Egalhe2gYRykHaTra3HQ0I8aHNqD+RQAzKcO5yBdH9DRjxoc/BEvGaXhXEiPAR10OUj3pHT9QIiXfALkz4eGMdDgIH0eAI6DJoegFqVefPFFFBUVwe124/Dhw3jxxRdx5plnhi3RnKoaN77/xA0od1cG3CS1nlqUuytx5fzZqKpxh+1gsVuRfEVf3027Aj1BFgAO334We/g3DdMwDtIO0vW1OGjoRw0O7cF88rcTmE8d0EG6PqCjHzU4+CNeMkrDuZAeAzrocpDuSen6gRAv+QTInw8NY6DBQfo8ABwHTQ7RT7kQ+eKr/bj44WtQVHoMANr8vmfd+0Wlx3Dxw9fgy+MHIuZgy3Ag5ar+sKQEdj94S4odKVf1j+jNCDWMg7SDdH0tDhr6UYODBtgPPjSMAx3k6wM6+lGDgxake0LDuZAeAzrocpDuSen6mmA/6BgDDQ7S5wHgOGhxCOvpe9Hmy+MHcP59k/CD8yZizugZGN73rBbb7Dy8Gws3LsWrH70RkdXK5tgyHEj9yUDUFLrgLiiB90TLGtZuTjjzOvsufYvCXzM0jIO0g3R9LQ4a+lGDgwbYDz40jAMd5OsDOvpRg4MWpHtCw7mQHgM66HKQ7knp+ppgP+gYAw0O0ucB4DhocFC9KAX4Lqtbtvk1LNv8GvJ65yK7xwCkJiajvKoSe4/tQ8HhXVF3sNitcORmwJGbAc/xKnhKq4FqL+CwwpbhMOWJGBrGQdpBur4WBw39qMFBA+wHHxrGgQ7y9QEd/ajBQQvSPaHhXEiPAR10OUj3pHR9TbAfdIyBBgfp8wBwHKQd1C9KNabg8C5TGqI9bJmJ4r8wNIyDtIN0fS0OGvpRg4MG2A8+NIwDHeTrAzr6UYODFqR7QsO5kB4DOuhykO5J6fqaYD/oGAMNDtLnAeA4SDh03OtCCSGEEEIIIYQQQohauChFCCGEEEIIIYQQQkyHi1KEEEIIIYQQQgghxHQshmEYZhZ0uVxIT0/3FU82/5ZWxqlawABgASydZG6pRQc6aHKQrg8ARmUtAKCsrAxpaWkiDoB8PgFKzgd7kg50aOqgIKOYT3TQUp8OyhyYTwCUnAs60EFJfTUOAeaT6I3O6yRligvXpwMdtDlI11eG+FhoOB/SDtL16UAHpYiPg4ZzQQf5+nTQ5aAE8XHQcC7oQAct9bU4+EF0UYpXStGBDvIO0vUBnUHJv/TJOUjXpwMdWjgoyyjmU3w7SNengzIH5hMAJeeCDnRQUl+NQ4D5JLco1cmGtFnZppd1LdkLo7IWlk52kfp0oIM2B+n6AFC2uBA45RGp3SpC+QToOB/SDtL16UCH5qjKKOZT3DtI16eDLgfmkw8N54IOdNBSX4tDoPnEG50TQgghhBBCCCGEENPhohQhhBBCCCGEEEIIMR0uShFCCCGEEEIIIYQQ0xG90XmweI5XwVNSDdR4gQQrbJ0dsGUm0iEOHaTr00GXgwY0jAMd6KClPh30IT0W0vXpQAdtDtL1NaFhLKQdpOvTgQ6SDuoXpYxaL2oKXXAXlMB7wt3i59ZuTjjzOiNhUBos9uhc+EUHHQ7S9emgy0EDGsaBDnTQUp8O+pAeC+n6dKCDNgfp+prQMBbSDtL16UAHLQ6qF6U8pdWoXHkQRnnbjxL0nnDj9LpiVG09geQr+sKW4aBDB3SQrk8HXQ4a0DAOdKCDlvp00If0WEjXpwMdtDlI19eEhrGQdpCuTwc6aHJQuwTvKa1Gxcv7YVS0PSiNMSpqUfHyfnhKq+nQwRyk69NBl4MGNIwDHeigpT4d9CE9FtL16UAHbQ7S9TWhYSykHaTr04EO2hxULkoZtV5UrjwIVHsBI9CdAFT79jNqvXToIA7S9emgy0EDGsaBDnTQUp8O+pAeC+n6dKCDNgfp+prQMBbSDtL16UAHjQ5BL0odOXIEM2bMQNeuXZGUlIRzzjkHH374YdgijakpdPkuGwt0UOowAKO8FjWFLjp0EAfp+nTQ5eAP5hMd4s1Buj4dgiMeMkq6Ph3ooM1Bun6gxEM+aXCQrk8HOmh0CGpRqqSkBKNGjUJCQgLeeOMNfPbZZ/jrX/+Kzp07hy3SGHdBiej+dNDjIL0/HXQ5tAfziQ7x6CC9Px0CJ14ySnp/OtBBm4P0/oEQL/mkwUF6fzrQQaNDUDc6f+ihh9CnTx8sXry4/r0BAwaELdEYz/GqVu/0HgzeE254jleF/MhCOuhwkK5PB10O/mA+0SHeHKTr0yE44iGjpOvTgQ7aHKTrB0o85JMGB+n6dKCDRgcgyCulVq5ciW9961u4+uqr0b17d5x77rl46qmn2t3H7XbD5XI1ebWHpyRyN5ALeV86qHCQrk8HXQ7+YD7RId4cpOvTITiCzahg8wmQHwvp+nSggzYH6fqBEg/5pMFBuj4d6KDRAQhyUerLL7/EggULMGjQILz55pv4+c9/jltvvRXPPvtsm/vMmzcP6enp9a8+ffq0X6QmQjfzqw7jOHTQ4SBdnw66HPzAfKJD3DlI16dDUASbUUHnEyA/FtL16UAHbQ7S9QMkLvJJg4N0fTrQQaMDglyU8nq9OO+88/DAAw/g3HPPxZw5c3DjjTfiH//4R5v7zJ07F2VlZfWvQ4cOtV8kIUIPBHSEcRw66HCQrk8HXQ5+YD7RIe4cpOvTISiCzaig8wmQHwvp+nSggzYH6foBEhf5pMFBuj4d6KDRAUEuSvXq1QtDhw5t8l5ubi4OHjzY5j5OpxNpaWlNXu1h6+wIRqnt42SEfhw66HCQrk8HXQ7+YD7RId4cpOvTITiCzahg8wmQHwvp+nSggzYH6fqBEg/5pMFBuj4d6KDRAQhyUWrUqFHYs2dPk/c+//xz9OvXLyyJxtgyE2Ht5gzrGNZuzrButEUHHQ7S9emgy8EfzCc6xJuDdH06BEc8ZJR0fTrQQZuDdP1AiYd80uAgXZ8OdNDoAAS5KPXLX/4SH3zwAR544AHs3bsXy5Ytw8KFC5Gfnx+WRHOceeE9fjTc/emgx0F6fzrocmgP5hMd4tFBen86BE68ZJT0/nSggzYH6f0DIV7ySYOD9P50oINGh6AWpb797W9jxYoVeOGFF3D22Wfj3nvvxaOPPorp06eHLdKYhEFpsKTaAUuQO1oAS6odCYP8X0JKh9hwkK5PB10O7cF8okM8OkjXp0PgxEtGSdenAx20OUjXD4R4yScNDtL16UAHjQ5B35Fq8uTJ2LlzJ6qqqrBr1y7ceOONYUs0x2K3IvmKvr4bZgU6OBYADt9+Fnv4N+yigw4H6fp00OXgD+YTHeLNQbo+HYIjHjJKuj4d6KDNQbp+oMRDPmlwkK5PBzpodDAn5ULAluFAylX9YUmxB7S9JcWOlKv6R/RGgHTQ4SBdnw66HDSgYRzoQAct9emgD+mxkK5PBzpoc5CurwkNYyHtIF2fDnTQ5hBYVSFsGQ6k/mQgagpdcBeUwHvC3WIbazcnnHmdfZedReEvCXTQ4SBdnw66HDSgYRzoQAct9emgD+mxkK5PBzpoc5CurwkNYyHtIF2fDnTQ5KB6UQrwXU7myM2AIzcDnuNV8JRWA9VewGGFLcMR9adR0EGPg3R9Ouhy0ICGcaADHbTUp4M+pMdCuj4d6KDNQbq+JjSMhbSDdH060EGLg/pFqcbYMhPFw5oOOhyk69NBl4MGNIwDHeigpT4d9CE9FtL16UAHbQ7S9TWhYSykHaTr04EOkg4d97pQQgghhBBCCCGEEKIWLkoRQgghhBBCCCGEENPhohQhhBBCCCGEEEIIMR2LYRiGmQVdLhfS09N9xZPNv6WVcaoWMABYAEsnmVtq0YEOmhyk6wOAUVkLACgrK0NaWpqIAyCfT4CS88GepAMdmjooyCjmEx201KeDMgfmEwAl54IOdFBSX41DgPkkeqPzOkmZ4sL16UAHbQ7S9ZUhPhYazoe0g3R9OtBBKeLjoOFc0EG+Ph10OShBfBw0nAs60EFLfS0OfhBdlOKVUnSgg7yDdH1AZ1DyL31yDtL16UCHFg7KMor5FN8O0vXpoMyB+QRAybmgAx2U1FfjEGA+yS1KdbIhbVa26WVdS/bCqKyFpZNdpD4d6KDNQbo+AJQtLgROeURqt4pQPgE6zoe0g3R9OtChOaoyivkU9w7S9emgy4H55EPDuaADHbTU1+IQaD7xRueEEEIIIYQQQgghxHS4KEUIIYQQQgghhBBCTIeLUoQQQgghhBBCCCHEdERvdB6L5PXOxaAeA5CSmIyKqkoUHtuHgsO7THXwHK+Cp6QaqPECCVbYOjtgy0yMKwfp+nTQ5UB8MJ/ooKU+HUhzmE90oIOu+qQp0hmloR/oQAcpBy5KBUBighPTRlyOOaNnYFifoS1+vuPQZ1i4cSle2fY6qmrcUXEwar2oKXTBXVAC74mWNazdnHDmdUbCoDRY7NG5AE7aQbo+HXQ5EB/MJzpoqU8H0hzmEx3ooKs+aYp0RmnoBzrQQYMDF6X8cGb3/liR/zT6dMmC1/C2us3ZZ+Rg/vT78euJ+bhy/mx8efxARB08pdWoXHkQRnnbj1T0nnDj9LpiVG09geQr+sKW4ehQDtL16aDLgfhgPtFBS306kOYwn+hAB131SVOkM0pDP9CBDlocuATfDmd274+1d7yIrIwesFgssFltrW5ns9pgsViQldEDb9+5HAMz+0XMwVNajYqX98OoaLs5GmNU1KLi5f3wlFZ3GAfp+nTQ5UB8MJ/ooKU+HUhzmE90oIOu+qQp0hmloR/oQAdNDlyUaoPEBCdW5D+NVGcy7LbALiiz2+xIdSbjtZsXITHBGbaDUetF5cqDQLUXMALdCUC1bz+jtvVV/1hykK5PB10OxAfziQ5a6tOBNIf5RAc66KpPmiKdURr6gQ500OYQ1KJU//79YbFYWrzy8/PDFtHGtBGXo0+XrIDDqg67zY6+Xc/AD86bGLZDTaHLd/lcoM1RhwEY5bWoKXTFvIN0fTrocvBHvGQU84kOWurTIXCYT+3DfKJDR3WQrh8I8ZJPgHxGaegHOtBBm0NQi1Jbt27F0aNH619r1qwBAFx99dVhi2hjzugZbX6/2B8erwdzRs8I28FdUCK6vwYH6f3poMvBH/GSUcwnOmjZnw6Bw3zyD/OJDh3RQXr/QIiXfALkM0pDP9CBDtocglqUyszMRM+ePetfq1atwplnnonRo0eHLaKJvN65GNZnaJvfL/aHzWrD8L5nIa93bsgOnuNVrd7xPhi8J9zwHK+KWQfp+nTQ5RAI8ZBRzCc6aKlPh+BgPvmH+USHjuYgXT9Q4iGfAPmM0tAPdKCDNgcgjHtKVVdXY+nSpbj++uthsVja3M7tdsPlcjV5aWdQjwEROU52GMfxlETuxoqx6iBdnw66HIIlkIxiPoWGhn6gg3x9OoQO86l9mE906CgO0vVDoaPmEyCfURr6gQ500OYAhLEo9dprr6G0tBSzZs1qd7t58+YhPT29/tWnT59QS5pGSmJyRI6TGs5xaiJ0U8PqMI4j7SBdnw66HIIkkIxiPoWIhn6gg3x9OoQM86l9mE906DAO0vVDoKPmE6AgozT0Ax3ooM0BYSxKLVq0CBMnTkRWVla7282dOxdlZWX1r0OHDoVa0jQqqiojcpzycI6TEKEHIzrCOI60g3R9OuhyCJJAMor5FCIa+oEO8vXpEDLMp/ZhPtGhwzhI1w+BjppPgIKM0tAPdKCDNgcAwT124BsOHDiAtWvX4tVXX/W7rdPphNMZ/uN9zaTw2L7IHKf4y5D3tXV2RMTBlhH6caQdpOvTQZdDMASaUcyn0NDQD3SQr0+H0GA+BXAc5hMdOoiDdP1g6cj5BMhnlIZ+oAMdtDkAIV4ptXjxYnTv3h2TJk0Kq7hWCg7vwo5Dn8Hj9YS0v8frwfaDn2Lnkd0hO9gyE2HtFl7YW7s5YctMjFkH6fp00OUQDB05o5hPdNBSnw6hwXxqG+YTHTqag3T9YOnI+QTIZ5SGfqADHbQ5ACEsSnm9XixevBgzZ86E3R7ShVYxwcKNS2G1hHYZms1qw8KNS8N2cOZ1Ft1fg4P0/nTQ5RAI8ZBRzCc6aNmfDsHBfGof5hMdOqKD9P6BEg/5BMhnlIZ+oAMdtDkEPSPXrl2LgwcP4vrrrw+7uGZe2fY6Dp0sQq2nNqj9aj21OPj1Ebz60RthOyQMSoMl1Q60/XDD1rEAllQ7EgalxbyDdH066HIIhHjIKOYTHbTUp0NwMJ/ahvlEh47qIF0/UOIhnwD5jNLQD3SggzaHoBelLr30UhiGgcGDB4ddXDNVNW58/4kbUO6uDDi0aj21KHdX4sr5s1FV4w7bwWK3IvmKvr4bhwXaJBYADt9+Fnv4Ny6TdpCuTwddDoEQDxnFfKKDlvp0CA7mU+swn+jQkR2k6wdKPOQTIJ9RGvqBDnTQ5mDeoxxikC++2o+LH74GRaXHAKDN7x/XvV9UegwXP3wNvjx+IGIOtgwHUq7qD0tKYJfRWlLsSLmqf0RviCjtIF2fDrociA/mEx201KcDaQ7ziQ500FWfNEU6ozT0Ax3ooMmh435hOEJ8efwAzr9vEn5w3kTMGT0Dw/ue1WKbnYd3Y+HGpXj1ozci8he+5tgyHEj9yUDUFLrgLiiB90TLGtZuTjjzOvsuv4vCX1SkHaTr00GXA/HBfKKDlvp0IM1hPtGBDrrqk6ZIZ5SGfqADHbQ4cFEqAKpq3Fi2+TUs2/wa8nrnIrvHAKQmJqO8qhJ7j+1DweFdUXew2K1w5GbAkZsBz/EqeEqrgWov4LDCluEw5akc0g7S9emgy4H4YD7RQUt9OpDmMJ/oQAdd9UlTpDNKQz/QgQ4aHLgoFSQFh3eZ8iGqPWyZieK/tKQdpOvTQZcD8cF8ooOW+nQgzWE+0YEOuuqTpkhnlIZ+oAMdpBx4XSghhBBCCCGEEEIIMR0uShFCCCGEEEIIIYQQ0+GiFCGEEEIIIYQQQggxHYthGIaZBV0uF9LT033Fk82/pZVxqhYwAFgASyeZW2rRgQ6aHKTrA4BRWQsAKCsrQ1pamogDIJ9PgJLzwZ6kAx2aOijIKOYTHbTUp4MyB+YTACXngg50UFJfjUOA+SR6o/M6SZniwvXpQAdtDtL1lSE+FhrOh7SDdH060EEp4uOg4VzQQb4+HXQ5KEF8HDScCzrQQUt9LQ5+EF2U4pVSdKCDvIN0fUBnUPIvfXIO0vXpQIcWDsoyivkU3w7S9emgzIH5BEDJuaADHZTUV+MQYD7JLUp1siFtVrbpZV1L9sKorIWlk12kPh3ooM1Buj4AlC0uBE55RGq3ilA+ATrOh7SDdH060KE5qjKK+RT3DtL16aDLgfnkQ8O5oAMdtNTX4hBoPvFG54QQQgghhBBCCCHEdLgoRQghhBBCCCGEEEJMh4tShBBCCCGEEEIIIcR0RG90TmIXz/EqeEqqgRovkGCFrbMDtszEuKlPB10OhDRGQ09KO0jXpwMhraOhJ+mgw0G6PiHN0dCTdIhPBy5KkYAxar2oKXTBXVAC7wl3i59buznhzOuMhEFpsNgjfxGedH066HIgpDEaelLaQbo+HQhpHQ09SQcdDtL1CWmOhp6kAx24KEUCwlNajcqVB2GUt/1YR+8JN06vK0bV1hNIvqIvbBmODlOfDrocCGmMhp6UdpCuTwdCWkdDT9JBh4N0fUKao6En6UAHgPeUIgHgKa1Gxcv7YVS03aCNMSpqUfHyfnhKqztEfTrociCkMRp6UtpBuj4dCGkdDT1JBx0O0vUJaY6GnqQDHergohRpF6PWi8qVB4FqL2AEuhOAat9+Rq03puvTQZcDIY3R0JPSDtL16UBI62joSTrocJCuT0hzNPQkHejQmKAWpTweD37/+99jwIABSEpKwplnnol7770XhhGoPYk1agpdvkv4gj3FBmCU16Km0BXT9emgy6E9mE/xh4aelHaQrk+HwGFGxRcaepIOOhyk6wcC8ym+0NCTdKBDY4JalHrooYewYMECzJ8/H7t27cJDDz2EP//5z3j88cfDFiE6cReUxPX+dNDl0B7Mp/hDQ09KO0jvT4fAYUbFFxp6kg46HKT3DwTmU3yhoSfpQIfGBHWj8/fffx9Tp07FpEmTAAD9+/fHCy+8gC1btoQtQvThOV7V6l33g8F7wg3P8aqQHh8pXZ8Ouhz8wXyKLzT0pLSDdH06BAczKn7Q0JN00OEgXT9QmE/xg4aepAMdmhPUlVIXXXQR3n77bXz++ecAgB07duDdd9/FxIkT29zH7XbD5XI1eZHYwFMSuZs7xmJ9Ouhy8AfzKb7Q0JPSDtL16RAcwWYU8yl20dCTdNDhIF0/UJhP8YOGnqQDHZoT1JVSd911F1wuF4YMGQKbzQaPx4P7778f06dPb3OfefPm4e677w5LkghRE6EbK1aHeBzp+nTQ5eAH5lOcoaEnpR2k69MhKILNKOZTDKOhJ+mgw0G6foAwn+IIDT1JBzo0I6grpV566SU8//zzWLZsGT766CM8++yz+Mtf/oJnn322zX3mzp2LsrKy+tehQ4fCEiYmkhChhzM6QjyOdH066HLwA/MpztDQk9IO0vXpEBTBZhTzKYbR0JN00OEgXT9AmE9xhIaepAMdmhHUlVJ33nkn7rrrLvzoRz8CAJxzzjk4cOAA5s2bh5kzZ7a6j9PphNPpDEuSyGDr7IjMcTJCO450fTrocvAH8ym+0NCT0g7S9ekQHMFmFPMpdtHQk3TQ4SBdP1CYT/GDhp6kAx2aE9SS1qlTp2C1Nt3FZrPB643uJaVEBltmIqzdwvuFY+3mDPmmZ9L16aDLwR/Mp/hCQ09KO0jXp0NwMKPiBw09SQcdDtL1A4X5FD9o6Ek60KHFMYLZeMqUKbj//vvxn//8B/v378eKFSvwt7/9Dd///vfDkiB6ceZ1juv96aDLoT2YT/GHhp6UdpDenw6Bw4yKLzT0JB10OEjvHwjMp/hCQ0/SgQ6NCWpR6vHHH8dVV12Fm266Cbm5ubjjjjvwP//zP7j33nvDFiE6SRiUBkuqHbAEuaMFsKTakTAoLabr00GXQ3swn+IPDT0p7SBdnw6Bw4yKLzT0JB10OEjXDwTmU3yhoSfpQIfGBLUolZqaikcffRQHDhzA6dOn8cUXX+C+++6DwxHd7zkTOSx2K5Kv6Ou7eVmgjWoB4PDtZ7GHd9Mz6fp00OXQHsyn+ENDT0o7SNenQ+Awo+ILDT1JBx0O0vUDgfkUX2joSTrQoTHRTzkS89gyHEi5qj8sKYHdF9+SYkfKVf0jdlNG6fp00OVASGM09KS0g3R9OhDSOhp6kg46HKTrE9IcDT1JBzrUEdTT90j8YstwIPUnA1FT6IK7oATeE+4W21i7OeHM6+y7BDDCf9WRrk8HXQ6ENEZDT0o7SNenAyGto6En6aDDQbo+Ic3R0JN0oAPARSkSBBa7FY7cDDhyM+A5XgVPaTVQ7QUcVtgyHFF/Moh0fTrociCkMRp6UtpBuj4dCGkdDT1JBx0O0vUJaY6GnqQDHbgoRULClpko+otTuj4ddDkQ0hgNPSntIF2fDoS0joaepIMOB+n6hDRHQ0/SIT4deF0oIYQQQgghhBBCCDEdLkoRQgghhBBCCCGEENPhohQhhBBCCCGEEEIIMR2LYRiGmQXLysqQkZHh+49ONjNL+zjlafjfEvXpQAdtDtL1GzmUlpYiPT1dxgEK8glQdT7EHKTr04EObThIZhTziQ5q6tNBpQPzSc+5oAMdxOsrc/CXT6bf6Ly8vLzhPxoPlATS9elAB20OwvXLy8tFF6VU5RMddNSnAx0aIZlRzCc6qKxPBzUOzKdG0IEOmhyk6ytw8JdPpl8p5fV6UVRUhNTUVFgslqD3d7lc6NOnDw4dOoS0tLQoGNIhVhyk69Mhcg6GYaC8vBxZWVmwWuW+Vcx8okNHcpCu35EcNGRUuPkEyJ8P6fp0oIM2B+ZTA9LnQoODdH060CHSDoHmk+lXSlmtVvTu3Tvs46SlpYmdHDrocpCuT4fIOEheIVUH84kOHdFBun5HcZDOqEjlEyB/PqTr04EO2hyYTw1InwsNDtL16UCHSDoEkk+80TkhhBBCCCGEEEIIMR0uShFCCCGEEEIIIYQQ04m5RSmn04k//vGPcDqddIhzB+n6dNDloAEN40AHOmipTwd9SI+FdH060EGbg3R9TWgYC2kH6fp0oIOUg+k3OieEEEIIIYQQQgghJOaulCKEEEIIIYQQQgghsQ8XpQghhBBCCCGEEEKI6XBRihBCCCGEEEIIIYSYDhelCCGEEEIIIYQQQojpxNSi1KZNm2Cz2TBp0iTTa8+aNQsWi6X+1bVrV1x22WUoKCgw3aW4uBi33HILBg4cCKfTiT59+mDKlCl4++23o1678TgkJCSgR48eGD9+PJ555hl4vd6o12/u0Ph12WWXmVLfn8fevXtNqV9cXIzbbrsN2dnZSExMRI8ePTBq1CgsWLAAp06dinr9WbNm4corr2zx/oYNG2CxWFBaWhp1B20wo5hPzT2kMko6nwDZjGI+tYT5xHxq7sF84mcoLTCfmE/NPZhP8ZVPMbUotWjRItxyyy145513UFRUZHr9yy67DEePHsXRo0fx9ttvw263Y/LkyaY67N+/HyNGjMC6devw8MMPY+fOnVi9ejXGjh2L/Px8UxzqxmH//v144403MHbsWNx2222YPHkyamtrTXVo/HrhhRdMqe3PY8CAAVGv++WXX+Lcc8/FW2+9hQceeAAff/wxNm3ahF/96ldYtWoV1q5dG3UH0pJ4zyjmU0sPyYySyieAGaUR5hPzqbkH84n5pAXmE/OpuQfzKb7yyS4tECgVFRVYvnw5PvzwQxQXF2PJkiX4zW9+Y6qD0+lEz549AQA9e/bEXXfdhe9+97s4fvw4MjMzTXG46aabYLFYsGXLFiQnJ9e/f9ZZZ+H66683xaHxOJxxxhk477zzcOGFF+Liiy/GkiVLcMMNN5jqIImUx0033QS73Y4PP/ywSR8MHDgQU6dOhWEYpjvFO8wo5lNbHlJIOjCjdMF8Yj615SEF84nUwXxiPrXlIQXzyXxi5kqpl156CUOGDEFOTg5mzJiBZ555RvSkVFRUYOnSpcjOzkbXrl1NqXny5EmsXr0a+fn5TZq0joyMDFM8WmPcuHEYNmwYXn31VTGHeOHrr7/GW2+91WYfAIDFYjHZisR7RjGfSB3MKH0wn5hPxAfzSR/MJ+YT8RHP+RQzi1KLFi3CjBkzAPguqSsrK8PGjRtNdVi1ahVSUlKQkpKC1NRUrFy5EsuXL4fVas4w7t27F4ZhYMiQIabUC5YhQ4Zg//79ptRqfC7qXg888IAptdvzuPrqq6Nes64PcnJymrzfrVu3eo9f//rXUfcAWj8PEydONKW2NuI9o5hPTdGQURL5BOjJKOZTA8wn5lNjmE/y+QQwo+pgPjGfGsN8is98iomv7+3ZswdbtmzBihUrAAB2ux3XXHMNFi1ahDFjxpjmMXbsWCxYsAAAUFJSgieffBITJ07Eli1b0K9fv6jX1365nmEYpq3eNj4XdXTp0sWU2u15tLWqbQZbtmyB1+vF9OnT4Xa7TanZ2nnYvHlz/YeLeIEZxXxqjoaM0pRPgPkZxXzywXxiPjWH+dQSfoaSgfnEfGoO86kl8ZBPMbEotWjRItTW1iIrK6v+PcMw4HQ6MX/+fKSnp5vikZycjOzs7Pr/fvrpp5Geno6nnnoK9913X9TrDxo0CBaLBbt37456rVDYtWuXaTeBa34upJDwyM7OhsViwZ49e5q8P3DgQABAUlKSaS6t/fsPHz5sWn0tMKOYT83RkFFSDloyivnkg/nEfGoO80k+nwBmFMB8AphPzWE+xWc+qf/6Xm1tLZ577jn89a9/xfbt2+tfO3bsQFZWlsgT1+qwWCywWq04ffq0KfW6dOmCCRMm4IknnkBlZWWLn0s+PnbdunXYuXMnpk2bJuYQL3Tt2hXjx4/H/PnzW+0DYi7MKB/MJ1IHM0oPzCcfzCdSB/NJD8wnH8wnUkc855P6K6VWrVqFkpISzJ49u8Vq+bRp07Bo0SL87Gc/M8XF7XajuLgYgO/Szvnz56OiogJTpkwxpT4APPHEExg1ahTOP/983HPPPcjLy0NtbS3WrFmDBQsWYNeuXVF3qBsHj8eDY8eOYfXq1Zg3bx4mT56Ma6+9Nur1Gzs0xm63o1u3bqbUl+bJJ5/EqFGj8K1vfQt/+tOfkJeXB6vViq1bt2L37t0YMWKEtGLcwIxqgPnU0qMxzChmlNkwnxpgPrX0aAzziflkNsynBphPLT0aw3yKg3wylDN58mTj8ssvb/VnmzdvNgAYO3bsiLrHzJkzDQD1r9TUVOPb3/628fLLL0e9dnOKioqM/Px8o1+/fobD4TDOOOMM44orrjDWr18f9dqNx8FutxuZmZnGJZdcYjzzzDOGx+OJev3mDo1fOTk5ptRv7DF16lRTazamqKjIuPnmm40BAwYYCQkJRkpKinH++ecbDz/8sFFZWRn1+m39+9evX28AMEpKSqLuoAFmVFPiPZ+ae0hllHQ+GYZsRjGffDCfmsJ8Yj7Vwc9Q8jCfmsJ8Yj7VEY/5ZDEM5XdXI4QQQgghhBBCCCEdDvX3lCKEEEIIIYQQQgghHQ8uShFCCCGEEEIIIYQQ0+GiFCGEEEIIIYQQQggxHS5KEUIIIYQQQgghhBDT4aIUIYQQQgghhBBCCDEdLkoRQgghhBBCCCGEENPhohQhhBBCCCGEEEIIMR0uShFCCCGEEEIIIYQQ0+GiFCGEEEIIIYQQQggxHS5KxRj/+Mc/kJqaitra2vr3KioqkJCQgDFjxjTZdsOGDbBYLPjiiy9w8uRJ3HLLLcjJyUFSUhL69u2LW2+9FWVlZfXbL1myBBaLpdXXV1991a7X6dOnkZycjL1797b68/vvvx8XXXQROnXqhIyMjDaPs2TJEuTl5SExMRHdu3dHfn6+/0EBMGDAAKxdu7bVn/Xv37/+32Gz2ZCVlYXZs2ejpKSkfpsNGzZg6tSp6NWrF5KTkzF8+HA8//zzTY7z6aefYtq0afXHe/TRRwNyIySeCDWjAOB//ud/cOaZZyIpKQmZmZmYOnUqdu/e3WSfW2+9FSNGjIDT6cTw4cODcmsvJxYuXIgxY8YgLS0NFosFpaWlLbZpnCV1rwcffDDs2oFkVFVVFWbNmoVzzjkHdrsdV155ZavHev755zFs2DB06tQJvXr1wvXXX4+vv/46IEdCOjrRzqeDBw9i0qRJ6NSpE7p3744777yzSa22iNRnKAD4+uuv0bt37zZzLNjajfPObrejb9++uP322+F2u+u3efXVVzF+/HhkZmYiLS0NI0eOxJtvvtniWE888QT69++PxMREXHDBBdiyZYtfP0LihWjm09dff43LLrsMWVlZcDqd6NOnD26++Wa4XC6/XuHmUzRrB5JPR48exU9+8hMMHjwYVqsVv/jFL1o91qOPPlr//5P79OmDX/7yl6iqqvLrSCIDF6VijLFjx6KiogIffvhh/Xv//e9/0bNnT2zevLnJ5Fm/fj369u2LM888E0VFRSgqKsJf/vIXfPLJJ1iyZAlWr16N2bNn129/zTXX4OjRo01eEyZMwOjRo9G9e/d2vdasWYN+/fohOzu71Z9XV1fj6quvxs9//vM2j/G3v/0Nv/3tb3HXXXfh008/xdq1azFhwgS/Y1JQUICSkhKMHj26zW3uueceHD16FAcPHsTzzz+Pd955B7feemv9z99//33k5eXhlVdeQUFBAa677jpce+21WLVqVf02p06dwsCBA/Hggw+iZ8+efr0IiUdCzSgAGDFiBBYvXoxdu3bhzTffhGEYuPTSS+HxeJrUuP7663HNNdcE5eUvJ06dOoXLLrsMv/nNb9o9Tl2W1L1uueWWsGs3Pm5bGeXxeJCUlIRbb70Vl1xySavHeO+993Dttddi9uzZ+PTTT/F///d/2LJlC2688Ua/joTEA9HMJ4/Hg0mTJqG6uhrvv/8+nn32WSxZsgR/+MMf/HpF4jNUHbNnz0ZeXp7f7QKtDQCLFy/G0aNHsW/fPjz55JP45z//ifvuu6/+5++88w7Gjx+P119/Hdu2bcPYsWMxZcoUfPzxx/XbLF++HLfffjv++Mc/4qOPPsKwYcMwYcIEv3/0JCReiGY+Wa1WTJ06FStXrsTnn3+OJUuWYO3atfjZz37m1yvcfIpmbcB/PrndbmRmZuJ3v/sdhg0b1uoxli1bhrvuugt//OMfsWvXLixatAjLly/3+5mQRBCDxBy9evUy5s2bV//fv/rVr4z8/HwjNzfXWL9+ff373/ve94yZM2e2eZyXXnrJcDgcRk1NTas//+qrr4yEhATjueee8+t0/fXXG7/+9a/9brd48WIjPT29xfsnT540kpKSjLVr1/o9RnPuuece45prrmnz5/369TMeeeSRJu/de++9xtChQ9s97uWXX25cd911AR+TEOIjUhm1Y8cOA4Cxd+/eFj/74x//aAwbNixgJ385Ucf69esNAEZJSUmLn4U67yOdUTNnzjSmTp3a4v2HH37YGDhwYJP3HnvsMeOMM84I2pmQjkq08un11183rFarUVxcXL/NggULjLS0NMPtdrfrFO5nqDqefPJJY/To0cbbb7/dZo4FWxuAsWLFiibvzZ4927j88svbPe7QoUONu+++u/6/zz//fCM/P7/+vz0ej5GVldXkXBAS75jx+amOv//970bv3r39OkUqn6JRO9h8Gj16tHHbbbe1eD8/P98YN25ck/duv/12Y9SoUX4dSWTglVIxyNixY7F+/fr6/16/fj3GjBmD0aNH179/+vRpbN68GWPHjm3zOGVlZUhLS4Pdbm/158899xw6deqEq666ql0fr9eLVatWYerUqSH8a3ysWbMGXq8XR44cQW5uLnr37o0f/vCHOHTokN99V65cGVTtI0eO4N///jcuuOCCdrcrKytDly5dAj4uIcRHJDKqsrISixcvxoABA9CnT5+wnYLNibZ48MEH0bVrV5x77rl4+OGHA/pqTrQyqjkjR47EoUOH8Prrr8MwDBw7dgwvv/wyLr/88qCOQ0hHJlr5tGnTJpxzzjno0aNH/XYTJkyAy+XCp59+2qZPJD5DAcBnn32Ge+65B8899xys1sA+3odS+/PPP8e6devazSev14vy8vL6z1DV1dXYtm1bk6s8rVYrLrnkEmzatCng2oR0dMz6/FRUVIRXX3213Su4gcjlk1m1A8mn1rjooouwbdu2+q8Uf/nll3j99df5+clMpFfFSPA89dRTRnJyslFTU2O4XC7DbrcbX331lbFs2TLje9/7nmEYRv1fyQ4cONDqMY4fP2707dvX+M1vftNmndzcXOPnP/+5X5/33nvP6N69u+HxePxu29Yq+rx584yEhAQjJyfHWL16tbFp0ybj4osvNnJyctr9C+Phw4cNh8PR7l8D+/XrZzgcDiM5OdlITEw0ABgXXHBBu/ssX77ccDgcxieffNLmMXmlFCGtE05GPfHEE0ZycrIBwMjJyWnzr3zBXCkVSE7U0d6VUn/961+N9evXGzt27DAWLFhgZGRkGL/85S/Drh1sRrV1pZRh+K6ATUlJMex2uwHAmDJlilFdXd2uIyHxRLTy6cYbbzQuvfTSJttXVlYaAIzXX3+9TZ9IfIaqqqoy8vLyjH/+85+GYbSfY8HWBmAkJiYaycnJhtPpNAAYkydPbjdXHnroIaNz587GsWPHDMMwjCNHjhgAjPfff7/Jdnfeeadx/vnnt+tISDwR7c9PP/rRj4ykpKT6zwenT59u1ycS+RTN2sHmU1tXShmG7+qthISE+s9PP/vZz9r1I5GFV0rFIGPGjEFlZSW2bt2K//73vxg8eDAyMzMxevTo+u8cb9iwAQMHDkTfvn1b7O9yuTBp0iQMHToUf/rTn1qtsWnTJuzatavJPafa4l//+hcmT54c8F/mWsPr9aKmpgaPPfYYJkyYgAsvvBAvvPACCgsLm/zFoDkrV67Ed77zHb83/rzzzjuxfft2FBQU4O233wYATJo0qcW9agDfXyWuu+46PPXUUzjrrLNC/jcREq+Ek1HTp0/Hxx9/jI0bN2Lw4MH44Q9/GPaNJgPNCX/cfvvtGDNmDPLy8vCzn/0Mf/3rX/H44483uaFmqLWDyai2+Oyzz3DbbbfhD3/4A7Zt24bVq1dj//79Ad23gZB4QVs+ReIz1Ny5c5Gbm4sZM2ZEpfYjjzyC7du3Y8eOHVi1ahU+//xz/PSnP21122XLluHuu+/GSy+95Pd+pISQpkQ7nx555BF89NFH+Ne//oUvvvgCt99+e7s+kcinaNcOJp/aYsOGDXjggQfw5JNP4qOPPsKrr76K//znP7j33nuDOg4Jnda/t0VUk52djd69e2P9+vVNbp6blZWFPn364P3338f69esxbty4FvuWl5fjsssuQ2pqKlasWIGEhIRWazz99NMYPnw4RowY4ddn5cqVAT+Bqi169eoFABg6dGj9e5mZmejWrRsOHjzYbu0rrrjC7/G7detWf5O8QYMG4dFHH8XIkSOxfv36JpeTb9y4EVOmTMEjjzyCa6+9NtR/DiFxTTgZlZ6ejvT0dAwaNAgXXnghOnfujBUrVuDHP/5xyD6B5kSwXHDBBaitrcX+/fuRk5MTVu1AM6o95s2bh1GjRuHOO+8EAOTl5SE5ORnf/e53cd9999XnLCHxTLTyqWfPni2eJnfs2DEAaPfhKJH4DLVu3Trs3LkTL7/8MgDAMAwAvlz57W9/i7vvvjus2j179qzPp5ycHJSXl+PHP/4x7rvvviY3IH7xxRdxww034P/+7/+a5Fa3bt1gs9nqx6OOY8eO8cExhDQi2p+fevbsiZ49e2LIkCHo0qULvvvd7+L3v/99m58PIpFP0a4daD61x+9//3v89Kc/xQ033AAAOOecc1BZWYk5c+bgt7/9bUQW5Uj7cIRjlLFjx2LDhg3YsGFDk8eEfu9738Mbb7yBLVu2tPiuscvlwqWXXgqHw4GVK1ciMTGx1WNXVFTgpZdeCugqqcLCQhw4cADjx48P698zatQoAMCePXvq3zt58iROnDiBfv36tem5fv36kL7nbLPZAPi+l13Hhg0bMGnSJDz00EOYM2dO0MckhDQQSkY1xzAMGIbR7pVI/ggnJ/yxfft2WK3WNq8GiHRG+ePUqVMtPjjVHafu/6QSQqKTTyNHjsTOnTubPE1uzZo1SEtLa/IHt8ZE6jPUK6+8gh07dmD79u3Yvn07nn76aQC+J3fl5+dHvHZr+fTCCy/guuuuwwsvvIBJkyY12d7hcGDEiBH1V4ECvivk3377bYwcOTLo+oR0ZMz6/OT1egGgzW0ilU9m1+bnp9iEV0rFKGPHjkV+fj5qamqa3Chu9OjRuPnmm1FdXd0ksOoWpE6dOoWlS5fC5XLB5XIB8F2RVDfxAN9je2trawO6DPxf//oXLrnkEnTq1Knd7Q4ePIiTJ0/i4MGD8Hg82L59OwDfXwRSUlIwePBgTJ06FbfddhsWLlyItLQ0zJ07F0OGDGkzeFevXo3Bgwejf//+fj3Ly8tRXFwMwzBw6NAh/OpXv0JmZiYuuugiAL6v7E2ePBm33XYbpk2bhuLiYgC+D1KNb9T52Wef1f/vI0eOYPv27UhJSQl4JZ6QeCHYjPryyy+xfPlyXHrppcjMzMThw4fx4IMPIikpqcmNJvfu3YuKigoUFxfj9OnT9VkydOhQOByOFh6B5kRxcTGKi4uxd+9eAMDOnTuRmpqKvn37okuXLti0aVP9jUVTU1OxadMm/PKXv8SMGTPQuXPnVo8ZyYwCfF/Pq66uxsmTJ1FeXl7/bx8+fDgAYMqUKbjxxhuxYMECTJgwAUePHsUvfvELnH/++cjKyvLrQEi8EI18uvTSSzF06FD89Kc/xZ///GcUFxfjd7/7HfLz8+F0Olv1iNRnqLrHwtdx4sQJAEBubm6bXx0OtPb/Z+/cw6sqz/T9rL139k7IESRgIuGYAAEbQKwWmSmC9QB4hpYZYSpqy68VtZ1OrWXaGXvQYutMDwpji1q0RQpeIg7jiBZFcWqFKhqCChjklBCCQcgRspO99/r9sUjIOWuf1vsm+7mvK9dVw1rrvft+3/cQvqy9FgBUV1ejsrISoVAIpaWl+MlPfoKxY8eisLAQgPWRvVtvvRW/+c1vcOmll7b+DJWSkoLMzEwA1sefb731Vlx88cW45JJL8Otf/xoNDQ247bbbeq1PSCIRj3x66aWXcPz4cXz+859HWloaPvzwQ9x7772YPn16tz+jxCqf4lkb6D2fALQ61dfXo6qqCsXFxfB6va2/MLjuuuvwy1/+ElOmTMGll16K/fv349/+7d9w3XXXtfs3MokjQs+yIlFy8OBBE4A5fvz4dt8/dOhQ6wPu2tLy0Muuvg4ePNju2GnTppm33HKLLY+/+7u/Mx9//PFej7v11lu7rN329aY1NTXm7bffbmZlZZmDBg0yb7rpJvPIkSPdXnPRokXmD37wg15rjxgxol3N7Oxsc86cOeb777/fq9+MGTNaj2npeU/HEEIsws2oo0ePmrNnzzaHDBliJiUlmcOGDTNvueUWc+/eve2OmzFjhq0ca8FuTtx///1dXnf16tWmaZrmzp07zUsvvdTMzMw0k5OTzcLCQvNnP/uZ2djY2O01Y5lRXR3X8tWWRx55xJwwYYKZkpJi5uTkmAsXLjTLy8t7dSAkkYhXPh06dMicPXu2mZKSYg4ePNj8l3/5F7O5ublbj1j+DNUWOw86t1u7bT3DMMycnBxzwYIF5ieffNJ6THe53PGV9Y8++qg5fPhw0+v1mpdccom5ffv2XusTkmjEI5+2bt1qTps2rfVnmIKCAvO+++6LSUb0lk/xrG0nnzoe1/I1YsSI1j9vbm42f/SjH5ljxowxk5OTzby8PPPOO++09YIcEhsM0+Q9aSQyTpw4gZycHJSXl7d7BbITBAIBDB06FJs3b8Yll1ziaG1CSN9AMieYUYSQnpD8GUqyNiFEP8wn4jR8phSJmJMnT+KXv/ylSGCcPHkS//zP/4zPf/7zjtcmhPQNJHOCGUUI6Qnpn6GkahNC9MN8Ik7DO6UIIYQQQgghhBBCiOPwTilCCCGEEEIIIYQQ4jjclCKEEEIIIYQQQgghjsNNKUIIIYQQQgghhBDiONyUIoQQQgghhBBCCCGOw00pQgghhBBCCCGEEOI43JQihBBCCCGEEEIIIY7DTSlCCCGEEEIIIYQQ4jjclCKEEEIIIYQQQgghjsNNKUIIIYQQQgghhBDiONyUIoQQQgghhBBCCCGOw00pQgghhBBCCCGEEOI43JQihBBCCCGEEEIIIY7DTSlCCCGEEEIIIYQQ4jjclCKEEEIIIYQQQgghjuNxumAoFEJFRQXS09NhGIbT5QkhCjFNE3V1dcjNzYXLJbdXznwihHSFhoxiPhFCuoL5RAjRit18cnxTqqKiAnl5eU6XJYT0AcrKyjBs2DCx+swnQkhPSGYU84kQ0hPMJ0KIVnrLJ8c3pdLT08/9xwC30+WB00HZ+nSggzYH6fptHNrlgwDi+QSoGg8xB+n6dKBDNw6SGcV8ooOa+nRQ6cB80jMWdKCDeH1lDr3lk+ObUq23dA5wI/O2AqfLo/ap/TAbAjBSPchYnO94fTrQQZuDdH0AqFldCpwOit/yLZ1PgI7xkHaQrk8HOnREQ0Yxn+igpT4ddDkwnyw0jAUd6KClvhYHu/nEB50TQgghhBBCCCGEEMfhphQhhBBC4oZhmtIKhBBCCCFEKY5/fI8QQggh/ZdJVY1YuKca0yrOYNwpP7whoMkF7Bvow9u5KXimMAu7spOlNQkhhBBCiAK4KUUiIljViOCpJqA5BCS54B7ohdvBf2RI1weAomGFKBg6CmnJqahvbEDp8YMoKd/jqIMG2AeiDQ35IO0gUX9UdRNWbD2G6cfOoNkAPjCBDQDqAKSHgPGf+XHbST+W7K7GWzkpuGtWDg5meePqxHwi2pDOBjrocWA+EW1Irwk66HJwMqO4KUVsYwZCaC6thb/kFEIn/J3+3DXYB1/RQCQVZMDwxP6TodL1ASA5yYd5U+dgyYxFmJQ3odOf7yr7CKu2rcGGnS+hsbmzY3+BfSDa0JAP0g6S9ed/XIOVWyvRFDSxGsAjJlDcxXGTTeAeAPOPncH2dQdx56wcbBibEVMX5hPRhnQ20EGPA/OJaEN6TdBBl4NURnFTitgiWN2Ehk1HYNYFuj0mdMKPM1sr0fjOCaRePxzuGP4GXLo+AIwZMhIblz6BvEG5CJmhLo+58IJxWLHwQdw3eyluXHEHDlQdjqmDBtgHog0N+SDtIFl//sc1WLXlGD4GcA2Aw+j+gZUlAG4H8GMArwRNPL6lAgZMPDc2MyYuzCeiDelsoIMeB+YT0Yb0mqCDLgfJjOKDzkmvBKubUP/cIZj13S+Stpj1AdQ/dwjB6qZ+UR+wFumr312H3KyhMAwDbpe7y+PcLjcMw0Bu1lC8du96jM4eETMHDbAPRBsa8kHaQbL+6OomrNxaiY8BXAag/Oz3u/5R5tz3ywFMA1AKYOXWSoyKgQvziWhDOhvooMeB+US0Ib0m6KDLQTqjwt6UevPNN3HdddchNzcXhmHghRdeiIkI0YkZCKFh0xGgKQTYfYGSCaDJOs8MdPdPk75RH7BuY9y49Amk+1Lhcdu7udDj9iDdl4oX7noSyUm+qB000Bf6wHxKLDTkg7SDdP1Htx6DP2jiGgC1AII2zwuePf5qAE1BEyu2HovKg/lEtCG9Numgx4H5RLQhvSbooMtBQ0aFvSnV0NCASZMmYeXKlVEXJ/ppLq21biMM943eJmDWBdBcWtun6wPAvKlzkDco1/YibcHj9mD4eRfg5otmR+2ggb7QB+ZTYqEhH6QdJOtP+rQR04+dwQZYH9mzuyHVQvDseRsATD92BpOqGiN2YT4RbUhnAx30ODCfiDak1wQddDloyKiwN6Vmz56NBx54ADfddFPUxYl+/CWnEvp8AFgyY1G3n6vtjWAoiCUzFkXtoIG+0AfmU2KhIR+kHSTPX7i3Gs0G8AgifxaAC9b5zQawcE91xC7MJ6IN6bVNBz0OzCeiDek1QQddDhoyis+UIt0SrGrs8sn/4RA64Ucwwt9+S9cHrFdhTsqb0O3nanvD7XJj8vCJKBpWGLGDBtgHog0N+SDtIF1/WsUZfHD2LXuR3jweAvA+gA9M4AvHzkR0DeYT0Yb02qSDHgfmE9GG9Jqggy4HLRkV900pv9+P2tradl+kbxA8FbuH8PbF+gBQMHRUTBzyY3QdKfprH5hPfRcN+SDtIF1/3Ck/9sbEANgHYPzJyH4wYz4RbUivTTrocWA+EW1Irwk66HLQklFx35Ravnw5MjMzW7/y8vLiXZLEiuboH5wGwHpwW1+sDyAtOTUmCukxuo4U/bUPzKc+jIJ8EHcQrG+YJrwhoC42BqgF4A1Z1w0X5hNRh3Q20EGNA/OJqIPrkg5t0JJRcd+UWrZsGWpqalq/ysrK4l2SxIqkGE0Pb4TXka4PoL6xISYKdTG6jhT9tQ/Mpz6MgnwQdxCsbxoGmlxAemwMkAGgyWVdN1yYT0Qd0tlABzUOzCeiDq5LOrRBS0aF94j1CPD5fPD54v8qUxJ73AO9sblOVmTXka4PAKXHD8bEobTyQEyuI0V/7QPzqe+iIR+kHaTr7xvow/jPonsWQgvjAewdFNlaZD4RbUivTTrocWA+EW1Irwk66HLQklFhb6vV19ejuLgYxcXFAICDBw+iuLgYR44ciUqE6MOdnQzX4Oj+wnEN9sGdndwn6wNASfke7Cr7CMFQuC87twiGgig+8iF2H43Vk1dk6Ct9YD4lDhryQdpBuv7buSm40AAmI7q3700BMNEAtuekRHQN5hPRhvTapIMeB+YT0Yb0mqCDLgctGRX2z5HvvvsupkyZgilTpgAAvvOd72DKlCn493//96hEiE58RQMT+nwAWLVtDVxGZP/kcrvcWLVtTdQOGugLfWA+JRYa8kHaQfL8ZwqzkGQC9yC6t+/dAyDJtK4XKcwnog3ptU0HPQ7MJ6IN6TVBB10OGjIq7OqXX345TNPs9PXUU09FLUP0kVSQASPdA4T7mA8DMNI9SCrI6NP1AWDDzpdQdrICgWAgrPMCwQCOfHYUz7+3OWoHDfSFPjCfEgsN+SDtIFl/V3Yy3spJwXwAIwCE+zJh99nz5gF4KycFu6L4TR/ziWhDOhvooMeB+US0Ib0m6KDLQUNGxf1B56RvY3hcSL1+uPUANbuLxQDgtc4zPNFNMen6ANDY7MdNK7+GOn+D7cUaCAZQ52/AjSvuQGNzbJ65Ig37QLShIR+kHaTr3zUrB163gZdhPazc7saU++zxrwDwug3cNSsnKg/mE9GG9Nqkgx4H5hPRhvSaoIMuBw0ZxU0p0ivuLC/S5o+EkWbvufhGmgdp80dG9dA1TfUB4JNPD+GKhxegovo4AHT7uduW71dUH8cVDy/AgarDMXPQAPtAtKEhH6QdJOsfzPLizlk5GAvgbQDDzn6/ux8uWr4/7OzxBQDunJWDgzFwYT4RbUhnAx30ODCfiDak1wQddDlIZ1Tc375H+gfuLC/SbxmN5tJa+EtOIXSi846oa7APvqKB1m2IMdi11VQfAA5UHcYlD8zFzRfNxpIZizB5+MROx+wu34tV29bg+fc299vfbLEPRBsa8kHaQbL+hrEZMGBi5dZK7A6a2ADgEQDvd3HsJFjPkJoH6w6pr8/KwYax0d963gLziWhDOhvooMeB+US0Ib0m6KDLQTKjuClFbGN4XPAWZsFbmIVgVSOC1U1AUwjwuuDO8kb15P++UB+wbm9cu+MFrN3xAoqGFSJ/6CikJ6eirrEB+48fREn5nrg7aIB9INrQkA/SDpL1nxubiZ1DUrBi6zEsPnYGCw3gAxPYB6AW1kf1xgG40LAeav6X3BTcPTM2d0h1hPlEtCGdDXTQ48B8ItqQXhN00OUglVHclCIR4c5OdmRhaK0PWK/Q5A8P7APRh4Z8kHaQqH8wy4u5N4/ApKpGLNxTjS8cO4ObT/rhDQFNLmDvIB9W56TgmcKsqB5qHg7MJ6IN6Wyggx4H5hPRhvSaoIMuBycziptShBBCCIkZu7KTsSv7/Nb/NkwTphHua2UIIYQQQkgiwAedE0IIISRucEOKEEIIIYR0BzelCCGEEEIIIYQQQojjGKZpmk4WrK2tRWZmplU81flPD5qnA4AJwACMATKfXqQDHTQ5SNcHALMhAACoqalBRkbs3sQVLtL5BCgZD85JOtChvYOCjGI+0UFLfTooc2A+AVAyFnSgg5L6ahxs5pPoM6VaJGWKC9enAx20OUjXV4Z4LzSMh7SDdH060EEp4n3QMBZ0kK9PB10OShDvg4axoAMdtNTX4tALoptSvFOKDnSQd5CuD+gMSv6mT85Buj4d6NDJQVlGMZ8S20G6Ph2UOTCfACgZCzrQQUl9NQ4280luU2qAGxmL8x0vW/vUfpgNARgDPCL16UAHbQ7S9QGgZnUpcDooUrtLhPIJ0DEe0g7S9elAh46oyijmU8I7SNengy4H5pOFhrGgAx201NfiYDef+KBzQgghhBBCCCGEEOI43JQihBBCCCGEOI7h7PuWCOkVzklCnEf0mVKEEEIIIYSQxGBSVSMW7qnGtIozGHfKD28IaHIB+wb68HZuCp4pzMKu7GRpTZJAcE4SIg83pfogRcMKUTB0FNKSU1Hf2IDS4wdRUr5HWstRglWNCJ5qAppDQJIL7oFeuBPwLwwNc4FjQdqiYU5qgOtCRw80OBA9MJ8sJNbFqOomrNh6DNOPnUGzASRNmgxcOR5IT4e3rg6f27sX43cVY8nuaryVk4K7ZuXgYJY3rk7S+SBdP9HpOCc/MIENAOoApIeA8Z/5cdtJv2NzkvlkoWFdaBgLDX1w0oGbUn2E5CQf5k2dgyUzFmFS3oROf76r7COs2rYGG3a+hMZmv4Bh/DEDITSX1sJfcgqhE53/P7oG++ArGoikggwYnv77yVQNc4FjQdqiYU5qgOtCRw80OBA9MJ8sJNfF/I9rsHJrJdxeH7B4MZLuuQeYMqXTcUnvvw888gguXb8O29cdxJ2zcrBhbEZMXaTzQbo+sWiZk01BE6sBPGICxV0cN9kE7gEw/9iZuMxJ5pOFhnWhYSw09EHKgZtSfYAxQ0Zi49InkDcoFyEz1OUxF14wDisWPoj7Zi/FjSvuwIGqww5bxpdgdRMaNh2BWdf9ayVDJ/w4s7USje+cQOr1w+GO82/YJNAwFzgWpC0a5qQGuC509ECDA9ED88lCcl3M/7gGq7YcAwoK4Przn4ERI4BQ12OBoiLg97+H5/774brySjy+ZT8MmHhubGZMXKTzQbo+sWiZkx8DuAbAYXT/kOUSALcD+DGAV4ImHt9SEbM5yXyy0LAuNIyFhj5IOnALXjljhozEq99dh9ysoTAMA26Xu8vj3C43DMNAbtZQvHbveozOHuGwafwIVjeh/rlDMOu7XyBtMesDqH/uEILVTXE2cxYNc4FjQdqiYU5qgOtCRw80OBA9MJ8sJNfF6OomrNxaaW1Ibd8OXHABYBiAu+uxgNtt/fkFF8C1YweQn4+VWysxKgYu0vkgXZ9YtMzJjwFcBqD87Pe72SZt/X45gGkASoGYzEnmk4WGdaFhLDT0QdohrE2p5cuX4/Of/zzS09MxZMgQ3Hjjjdi3b19MREhnkpN82Lj0CaT7UuFx27upzeP2IN2XihfuehLJSb44G8YfMxBCw6YjQFMIsPsyDBNAk3WeGejur5m+hYa5oH0smE/OomFOakD7unACDT3Q4NAbzCjnYD5ZSK+LR7ceg8vrs+6QSk8HkpLsnZiUBKSnw7VlC9xeH1ZsPRaVh3QfpOvbIVHy6dGtx+APmrgGQC2AoM3zgmePvxpAU9CMak4ynyw0rAsNY6GhDxocwtqU2rZtG5YuXYrt27djy5YtaG5uxlVXXYWGhoaoRUhn5k2dg7xBubYXSQsetwfDz7sAN180O05mztFcWmvdQhju21lNwKwLoLm0Ni5eTqNhLmgfC+aTs2iYkxrQvi6cQEMPNDj0BjPKOZhPFpLrYtKnjZh+7AySFvyD9ZE9uxtSLSQlASNHwvOVBZh+7AwmVTVG7CKdD9L17ZAI+dQyJzfA+sie3Q2pFoJnz9sARDUnmU8WGtaFhrHQ0AcNDmFtSr388stYvHgxJk6ciEmTJuGpp57CkSNHsHPnzqhFSGeWzFjU7edaeyMYCmLJjEUxNnIef8kp0fO1oGEuaB8L5pOzaJiTGtC+LpxAQw80OPQGM8o5mE8Wkuti4d5qNBsA7rmn+2dI9UYwCNx9N5oNYOGe6ohdpPNB+nw7JEI+tczJRxD582tcsM6PZk4ynyw0rAsNY6GhDxoconqmVE1NDQBg0KBBUYuQ9hQNK8SkvAndfq61N9wuNyYPn4iiYYUxNnOOYFVjl0/9D4fQCT+CUfx2TQMa5kJfHAvmU/zQMCc10BfXRazR0AMNDpHAjIoPzCcL6XUxreIMkiZNtt6y190zpHrD7QamTkXSpMn4wrEzEV1Cug/S9SOlP+bTtIoz+ODsW/Yi/bBRCMD7AD4wEdGcZD5ZaFgXGsZCQx80OABRbEqFQiF8+9vfxvTp03HhhRd2e5zf70dtbW27L9I7BUNHxeQ6+TG6jgTBU7F7uGRfRsNc6GtjwXyKLxrmpAb62rqIBxp6oMEhXOxkFPMpMphPFtLrYtwpPzB+fEwcMG4cxp+M7B9N0n2Qrh8J/TWfxp3yY2+MrrUPiGhOMp8sNKwLDWOhoQ8aHIAoNqWWLl2KDz74AOvWrevxuOXLlyMzM7P1Ky8vL9KSCUVacmpMrpMeo+uI0ByjBzs29e0HCauYC31sLJhP8UXFnNRAH1sXcUFDDzQ4hImdjGI+RQbz6SyC68IwTXhDsB5uHgsyMuANWdcNG+l8kK4fAf0xn1rmZF2MrlcLRDQnmU9nUbAuVIyFgj6ocECEm1J33XUXXnzxRbz++usYNmxYj8cuW7YMNTU1rV9lZWURiSYa9Y2xebBgXYyuI0JSVJ8uPYc3RtcRQsVc6ENjwXyKPyrmpAb60LqIGxp6oMEhDOxmFPMpMphPZxFcF6ZhoMkFoC5GWwC1tWhyWdcNG+l8kK4fJv01n1rmZIy2SZEBRDQnmU9nUbAuVIyFgj6ocAAQ1qPmTdPE3XffjY0bN+KNN97AqFG9367m8/ng8/WPV1c6Senxg7G5TuWBmFxHAvdAb2yukxWb60ihYS70hbFgPjmHhjmpgb6wLuKNhh5ocLBDuBnFfIoM5pOF9LrYN9CHz+2N0Yel9u7F3kGRrQXpPkjXt0si5NO+gT6M/yy6Z+e0MB6IaE4ynyw0rAsNY6GhDxocgDDvlFq6dCnWrFmDtWvXIj09HZWVlaisrMSZM5E9fJB0T0n5Huwq+wjBULgvLLUIhoIoPvIhdh+N1aenncednQzX4Oj+wnMN9sGdnRwjIxk0zIW+MBbMJ+fQMCc10BfWRbzR0AMNDnZgRjkD88lCel28nZuC5l3FwPvvW2/Ri4RgENi5E80lu7A9JyWiS0j3Qbq+XRIhn97OTcGFBjAZ0b19bwqAiQYimpPMJwsN60LDWGjogwYHIMw1+dhjj6GmpgaXX345cnJyWr/Wr18flQTpmlXb1sBlRBabbpcbq7atibGR8/iKBoqerwUNc0H7WDCfnEXDnNSA9nXhBBp6oMGhN5hRzsF8spBcF88UZiHJBPDII4Arwi0Atxt49FEkmdb1IkU6H6TPt0Mi5FPLnLwH0b197x4gqjnJfLLQsC40jIWGPmhwCGsUTNPs8mvx4sVRi5DObNj5EspOViAQDIR1XiAYwJHPjuL59zbHycw5kgoyYKR7gHAfI2AARroHSQUZcfFyGg1zQftYMJ+cRcOc1ID2deEEGnqgwaE3mFHOwXyykFwXu7KT8VZOCgLr1wGHDwPNzeFdoLkZOHQIgWfX462cFOyK4rfw0vkgXd8OiZBPLXNyPoARANxhnu8+e948IKo5yXyy0LAuNIyFhj5ocOjDT1ft/zQ2+3HTyq+hzt9ge7EEggHU+Rtw44o70Ngcm89NS2J4XEi9frj18DS7C8UA4LXOMzz9Y4prmAscC9IWDXNSA1wXOnqgwYHogflkIb0u7pqVg2CTH6GrrrIeem53Y6q5GairQ+jKKxFs8uOuWTlReUj3Qbo+Ocdds3LgdRt4GdbDyu1uTLnPHv8KAK/biGpOMp8sNKwLDWOhoQ8aHJhyyvnk00O44uEFqKg+DgDdfu615fsV1cdxxcMLcKDqsGOO8cad5UXa/JEw0uw9l99I8yBt/sg+/fDgrtAwFzgWpC0a5qQGuC509ECDA9ED88lCcl0czPLizlk5QGkpQpdeChw9av1Bd8+Yavn+0aPW8fv3485ZOTgYAxfpfJCuTyxa5uRYAG8DaHm/YHf/IG75/rCzxxcAMZmTzCcLDetCw1ho6IO0Q1hv3yMyHKg6jEsemIubL5qNJTMWYfLwiZ2O2V2+F6u2rcHz723uNzvobXFneZF+y2g0l9bCX3IKoROd/z+6BvvgKxpo3YLYT3+rpGEucCxIWzTMSQ1wXejogQYHogfmk4XkutgwNgMGTKzc+gncEwrh+coC4O67galTOx9cXAw8+igCz65HsMmPO6/MxYaxsfvomnQ+SNcnFufmZCV2B01sAPAIgPe7OHYSrGdIzYN1h9TXZ+XEbE4ynyw0rAsNY6GhD5IO3JTqIzQ2+7F2xwtYu+MFFA0rRP7QUUhPTkVdYwP2Hz+IkvI90opxx/C44C3MgrcwC8GqRgSrm4CmEOB1wZ3l7dNvsAoHDXOBY0HaomFOaoDrQkcPNDgQPTCfLCTXxXNjM7FzSApWbD2G6U8/jeY/PI2kSZOBceOAjAygthbYtw/Nu4qRZALbc1Nw98xRMblDqiPS+SBdn1i0nZOLj53BQgP4wAT2AaiF9VG9cQAuNKyHmv8lNwV3z4zNXXttYT5ZaFgXGsZCQx+kHLgp1QcpKd+TMCHVHe7sZP7FDR1zgWNB2qJhTmqA60JHDzQ4ED0wnywk1sXBLC/m3jwCk6oasXBPNb5QvgfjS4rhDQFNLmDvIB+2X5iFZwqzonqoeThI54N0/USn05w8dgY3n/S3m5Orc1Icm5PMJwsN60LDWGjog5MO3JQihBBCCCGExJ1d2cnYlX1+638bpgnTCPeVT4TEDs5JQuThh5UJIYQQQgghjsN//BNtcE4S4jzclCKEEEIIIYQQQgghjmOYpmk6WbC2thaZmZlW8VTnPz1ong4AJgADMAbIfHqRDnTQ5CBdHwDMhgAAoKamBhkZsXvTTrhI5xOgZDw4J+lAh/YOCjKK+UQHLfXpoMyB+QRAyVjQgQ5K6qtxsJlPos+UapGUKS5cnw500OYgXV8Z4r3QMB7SDtL16UAHpYj3QcNY0EG+Ph10OShBvA8axoIOdNBSX4tDL4huSvFOKTrQQd5Buj6gMyj5mz45B+n6dKBDJwdlGcV8SmwH6fp0UObAfAKgZCzoQAcl9dU42MwnuU2pAW5kLM53vGztU/thNgRgDPCI1KcDHbQ5SNcHgJrVpcDpoEjtLhHKJ0DHeEg7SNenAx06oiqjmE8J7yBdnw66HJhPFhrGgg500FJfi4PdfOKDzgkhhBBCCCGEEEKI43BTihBCCCGEEEIIIYQ4DjelCCGEEEIIIYQQQojjiD7ovC9SNKwQBUNHIS05FfWNDSg9fhAl5Xvo4LCDdH0ACFY1IniqCWgOAUkuuAd64c5OpoOAA7HQsC7ooMNBw7qkA2mL9JqgAx06Ip0P0vVJe6THQ7o+oGNd0sFCw3xw0oGbUjZITvJh3tQ5WDJjESblTej057vKPsKqbWuwYedLaGz20yFODtL1AcAMhNBcWgt/ySmETnSu4Rrsg69oIJIKMmB44nMjIh1IWzSsCzrocNCwLulA2iK9JuhAh45I54N0fdIe6fGQrg/oWJd0sNAwH6QcDNM0zZhdzQa1tbXIzMwEBriReVuBk6Wt+i1PoU+19xT6MUNGYuPSJ5A3KBchMwS3y93pmGAoCJfhQtnJCty44g4cqDpMhxg7xKN+uA7B6iY0bDoCs673V1sa6R6kXj8c7iyveodw50I8HFrezFBTU4OMjIxerxsvpPMJ6HvrQjob6HD22v00n7Q4aMgo5hMdIqlPhzbXF/4ZivkUZwfOh7Ad+mtGanDoa/MxXg5284lb8D0wZshIvPrddcjNGgrDMLqcoADgdrlhGAZys4bitXvXY3T2CDrE0EG6PmAt0vrnDsGs732RAoBZH0D9c4cQrG6iQ4wdiIWGdUEHHQ4a1iUdSFuk1wQd6NAR6XyQrk/aIz0e0vUBHeuSDhYa5oO0Q1ibUo899hiKioqQkZGBjIwMTJs2DZs3b46JiDaSk3zYuPQJpPtS4XHb+5Sjx+1Bui8VL9z1JJKTfHSIgYN0fcC6jbFh0xGgKQTYva/QBNBknWcGQnSIkUNPMJ96pr9lAx0sNKxLOtgjUTJKek3QgQ4dkc4H6fp2SJR8AuTHQ7o+oGNd0sFCw3zQ4BDWptSwYcPw0EMPYefOnXj33Xcxa9Ys3HDDDfjwww+jFtHGvKlzkDco1/YEbcHj9mD4eRfg5otm0yEGDtL1AaC5tNa6jTHcD7qagFkXQHNpLR1i5NATzKfe6U/ZQAcLDeuSDvZIlIySXhN0oENHpPNBur4dEiWfAPnxkK4P6FiXdLDQMB80OIS1KXXddddhzpw5KCgowNixY/Hggw8iLS0N27dvj1pEG0tmLELIjGzXLxgKYsmMRXSIgYN0fQDwl5wSPZ8O9mA+2aO/ZAMdLDSsSzrYI1EySnpN0IEOHZHOB+nz7ZAo+QTIj4f0+YCOdUkHCw3zQYNDxM+UCgaDWLduHRoaGjBt2rSoRTRRNKwQk/ImdPuZ0t5wu9yYPHwiioYV0iEKB+n6gPUqzK7ePBAOoRN+BKsa6RClQzgwn7qnP2QDHSw0rEs6REZ/zSjpNUEHOnREOh+k60dCf80nQH48pOsDOtYlHSw0zAcNDkAEm1K7d+9GWloafD4fvvGNb2Djxo2YMKHzaxNb8Pv9qK2tbfelnYKho2JynfworkMH+foAEDwVuwca0iE6Bzswn+zTl7OBDhYa1iUdwiOcjGI+0YEO0TtI54N0/XDo7/kEyI+HdH1Ax7qkg4WG+aDBAYhgU2rcuHEoLi7Gjh078M1vfhO33norPvroo26PX758OTIzM1u/8vLyohJ2grTk1JhcJz2K69BBvj4AoDlGD5dsiuI6dLAN88k+fTkb6HAWDeuSDmERTkYxn+hAh+gdxPNBun4Y9Pd8AiA/HtL1oWNd0uEsCuaDCgdEsCnl9XqRn5+PqVOnYvny5Zg0aRJ+85vfdHv8smXLUFNT0/pVVlYWlbAT1Dc2xOQ6dVFchw7y9QEASRF/wrU93iiuQwf7l2c+2aYvZwMdzqJhXdIhvBJhZBTziQ50iN5BPB+k64dTop/nEwD58ZCuDx3rkg5nUTAfVDgACO9R810QCoXg93f/OUSfzwefL/rXJTpJ6fGDsblO5QE6ROEgXR8A3AO9MXFwZ0V+HTpEDvOph+v04Wygg4WGdUmH6Ogpo5hPdKBD9A7S+SBdPxr6Wz4B8uMhXR/QsS7pYKFhPmhwAMK8U2rZsmV48803cejQIezevRvLli3DG2+8gYULF0YloY2S8j3YVfYRgqFgROcHQ0EUH/kQu4/upUMUDtL1AcCdnQzX4Oj+0nUN9sGdnUyHKB16g/lkj/6QDXSw0LAu6WCfRMgo6TVBBzp0RDofpOvbJRHyCZAfD+n6gI51SQcLDfNBgwMQ5qbUp59+iq9+9asYN24crrjiCrzzzjt45ZVXcOWVV0YloZFV29bAZUR2G5rb5caqbWvoEAMH6foA4CsaKHo+HezBfLJHf8kGOlhoWJd0sEeiZJT0mqADHToinQ/S59shUfIJkB8P6fMBHeuSDhYa5oMGh7BG4cknn8ShQ4fg9/vx6aef4tVXX+2XYQUAG3a+hLKTFQgEA2GdFwgGcOSzo3j+vc10iIGDdH0ASCrIgJHuAYwwTzQAI92DpIIMOsTIoSeYT73Tn7KBDhYa1iUd7JEoGSW9JuhAh45I54N0fTskSj4B8uMhXR/QsS7pYKFhPmhwiP9T8/oojc1+3LTya6jzN9ieqIFgAHX+Bty44g40Nnf/HBs69J36AGB4XEi9frj1ADe7i9UA4LXOMzzRLzM6kLZoWBd00OGgYV3SgbRFek3QgQ4dkc4H6fqkPdLjIV0f0LEu6WChYT5ocGDK9cAnnx7CFQ8vQEX1cQDo9jOnLd+vqD6OKx5egANVh+kQQwfp+oD18La0+SNhpNl7N4CR5kHa/JExfTAlHUhbNKwLOuhw0LAu6UDaIr0m6ECHjkjng3R90h7p8ZCuD+hYl3Sw0DAfpB2ifvtef+dA1WFc8sBc3HzRbCyZsQiTh0/sdMzu8r1YtW0Nnn9vc0x2TOmgrz5gLdb0W0ajubQW/pJTCJ3oXMM12Adf0UDrNsg4/GaLDqQtGtYFHXQ4aFiXdCBtkV4TdKBDR6TzQbo+aY/0eEjXB3SsSzpYaJgPkg7clLJBY7Mfa3e8gLU7XkDRsELkDx2F9ORU1DU2YP/xgygp30MHBxyk6wPW7Y3ewix4C7MQrGpEsLoJaAoBXhfcWd64vx2FDqQjGtYFHXQ4aFiXdCBtkV4TdKBDR6TzQbo+aY/0eEjXB3SsSzpYaJgPUg7clAqTkvI9jkxKOuiuD1iv0JT+4YEOpC0a1gUddDhoWJd0IG2RXhN0oENHpPNBuj5pj/R4SNcHdKxLOlhomA9OOvC+UEIIIYQQQgghhBDiONyUIoQQQgghhBBCCCGOw00pQgghhBBCCCGEEOI4hmmappMFa2trkZmZaRVPdf6RVubpAGACMABjgMwjtehAB00O0vUBwGwIAABqamqQkZEh4gDI5xOgZDw4J+lAh/YOCjKK+UQHLfXpoMyB+QRAyVjQgQ5K6qtxsJlPog86b5GUKS5cnw500OYgXV8Z4r3QMB7SDtL16UAHpYj3QcNY0EG+Ph10OShBvA8axoIOdNBSX4tDL4huSvFOKTrQQd5Buj6gMyj5mz45B+n6dKBDJwdlGcV8SmwH6fp0UObAfAKgZCzoQAcl9dU42MwnuU2pAW5kLM53vGztU/thNgRgDPCI1KcDHbQ5SNcHgJrVpcDpoEjtLhHKJ0DHeEg7SNenAx06oiqjmE8J7yBdnw66HJhPFhrGgg500FJfi4PdfOKDzgkhhBBCCCGEEEKI43BTihBCCCGEEEIIIYQ4DjelCCGEEEIIIYQQQojjiD7oPFyKhhWiYOgopCWnor6xAaXHD6KkfA8dEtBBuj4ABKsaETzVBDSHgCQX3AO9cGcn00HAQQMa5iQd6NCChnVJB11Iz0np+nSgQ0ek80G6viY4H9iDFjT0QYODhrFw0kH9plRykg/zps7BkhmLMClvQqc/31X2EVZtW4MNO19CY7OfDv3YQbo+AJiBEJpLa+EvOYXQic41XIN98BUNRFJBBgxPfG5EpIMeNMxJOtChBQ3rkg66kJ6T0vXpQIeOSOeDdH1NcD6wBy1o6IMGBw1jIeVgmKZpxuxqNqitrUVmZiYwwI3M2wp6PHbMkJHYuPQJ5A3KRcgMwe1ydzomGArCZbhQdrICN664AweqDvdcv+Up9Kn2nkJPBx0O8agfrkOwugkNm47ArOv91ZZGugep1w+HO8ur3iHcuRAPh5Y3M9TU1CAjI6PX68YL6XwC+t66kM4GOpy9dj/NJy0OGjIqnHwC5Odkf80nDQ59LZ+0OEj/DMV8Ogfng4580vD3toY+cD7Gz8FuPqndgh8zZCRe/e465GYNhWEYXU4OAHC73DAMA7lZQ/HavesxOnsEHfqZg3R9wFqk9c8dglnf+yIFALM+gPrnDiFY3USHGDtoQMOcpAMdWtCwLumgC+k5KV2fDnToiHQ+SNfXBOcDe9CChj5ocNAwFtIOKjelkpN82Lj0CaT7UuFx2/uEocftQbovFS/c9SSSk3x06CcO0vUB6zbGhk1HgKYQYPe+QhNAk3WeGQjRIUYOGtAwJ+lAhxY0rEs66EJ6TkrXpwMdOiKdD9L1NcH5wB60oKEPGhw0jIUGh6g2pR566CEYhoFvf/vbUYu0Zd7UOcgblGt7crTgcXsw/LwLcPNFs+nQTxyk6wNAc2mtdRtjuB90NQGzLoDm0lo6xMghHJhPdEgEBw3rkg7hE698AuTnpHR9OtChI9L5IF0/Evrzz1DS48EeWGjogwYHDWOhwSHiTal33nkHv/vd71BUVBS1REeWzFiEkBnZjlswFMSSGYvo0E8cpOsDgL/klOj5dAgf5hMdEsVBw7qkQ3jEM58A+TkpXZ8OdOiIdD5Inx8u/f1nKOnxYA8sNPRBg4OGsdDgENGmVH19PRYuXIjHH38cAwcOjFqiLUXDCjEpb0K3n+fsDbfLjcnDJ6JoWCEd+riDdH3AehVmV28eCIfQCT+CVY10iNLBLswnOiSKg4Z1SYfwiGc+AfJzUro+HejQEel8kK4fLv39Zyjp8WAPLDT0QYODhrHQ4ABEuCm1dOlSzJ07F1/60pd6Pdbv96O2trbdV08UDB0ViVIn8qO4Dh10OEjXB4Dgqdg94JIO0TnYhflEh0Rx0LAu6RAe8cwnQH5OStenAx06Ip0P0vXDxW5G9cV8AuTHgz2w0NAHDQ4axkKDAwCE9wFKAOvWrcN7772Hd955x9bxy5cvx49//GPb109LTg1XqUvSo7gOHXQ4SNcHADTH6OGSTVFchw62YT7RIaEcNKxLOtgm3vkEyM9J6fp0oEMnpPNBun4YhJNRfTGfAIiPB3tgoaEPGhw0jIUKB4R5p1RZWRm+9a1v4ZlnnkFycrKtc5YtW4aamprWr7Kysh6Pr29sCEepW+qiuA4ddDhI1wcAJMXoBZXeKK5DB1swn+iQcA4a1iUdbOFEPgHyc1K6Ph3o0AnpfJCub5NwM6ov5hMA8fFgDyw09EGDg4axUOGAMO+U2rlzJz799FNcdNFFrd8LBoN48803sWLFCvj9frjd7T+X6fP54PPZf11i6fGD4Sh1f53KA5GfSwcVDtL1AcA90BsTB3dW5Nehgz2YT3RINAcN65IO9nAinwD5OSldnw506Ih0PkjXt0u4GdUX8wmQHw/2wEJDHzQ4aBgLDQ5AmHdKXXHFFdi9ezeKi4tbvy6++GIsXLgQxcXFnX6gioSS8j3YVfYRgqFgROcHQ0EUH/kQu4/upUMfd5CuDwDu7GS4Bof3l25HXIN9cGfb+804HSKH+USHRHPQsC7pYA8n8gmQn5PS9elAh45I54N0fbskys9Q0uPBHlho6IMGBw1jocEBCHNTKj09HRdeeGG7r9TUVJx33nm48MILoxJpy6pta+AyIrsFzO1yY9W2NXToJw7S9QHAVxTd20eiPZ8O9mA+0SERHTSsSzr0jlP5BMjPSen6dKBDR6TzQfp8OyTSz1DS48EeWGjogwYHDWOhwSG+H1COkA07X0LZyQoEgoGwzgsEAzjy2VE8/95mOvQTB+n6AJBUkAEj3QMYYZ5oAEa6B0kFGXSIkYMGNMxJOtChBQ3rkg66kJ6T0vXpQIeOSOeDdH1NcD6wBy1o6IMGBw1jocEh6k2pN954A7/+9a+jFmlLY7MfN638Gur8DbYnSSAYQJ2/ATeuuAONzX469BMH6foAYHhcSL1+uPUAN7uL1QDgtc4zPNHv/dIhMphPdOjvDhrWJR0iIx75BMjPSen6dKBDR6TzQbp+pPTXn6Gkx4M9sNDQBw0OGsZCg4PKO6UA4JNPD+GKhxegovo4AHT7ec+W71dUH8cVDy/AgarDdOhnDtL1AevhbWnzR8JIs/duACPNg7T5I2P6YEo66EHDnKQDHVrQsC7poAvpOSldnw506Ih0PkjX1wTnA3vQgoY+aHDQMBbSDmG9fc9pDlQdxiUPzMXNF83GkhmLMHn4xE7H7C7fi1Xb1uD59zbHZLeSDjodpOsD1mJNv2U0mktr4S85hdCJzjVcg33wFQ20boOMw2+26KAHDXOSDnRoQcO6pIMupOekdH060KEj0vkgXV8TnA/sQQsa+qDBQcNYSDqo3pQCrNvq1u54AWt3vICiYYXIHzoK6cmpqGtswP7jB1FSvocOCeIgXR+wbm/0FmbBW5iFYFUjgtVNQFMI8LrgzvLG/e0odNCFhjlJBzq0oGFd0kEX0nNSuj4d6NAR6XyQrq8Jzgf2oAUNfdDgoGEspBzUb0q1paR8jyMTgg76HaTrA9YrNKV/eKCDHjTMSTrQoQUN65IOupCek9L16UCHjkjng3R9TXA+sActaOiDBgcNY+GkQ/+9L5QQQgghhBBCCCGEqIWbUoQQQgghhBBCCCHEcbgpRQghhBBCCCGEEEIcxzBN03SyYG1tLTIzM63iqc4/0so8HQBMAAZgDJB5pBYd6KDJQbo+AJgNAQBATU0NMjIyRBwA+XwClIwH5yQd6NDeQUFGMZ/ooKU+HZQ5MJ8AKBkLOtBBSX01DjbzSfRB5y2SMsWF69OBDtocpOsrQ7wXGsZD2kG6Ph3ooBTxPmgYCzrI16eDLgcliPdBw1jQgQ5a6mtx6AXRTSneKUUHOsg7SNcHdAYlf9Mn5yBdnw506OSgLKOYT4ntIF2fDsocmE8AlIwFHeigpL4aB5v5JLcpNcCNjMX5jpetfWo/zIYAjAEekfp0oIM2B+n6AFCzuhQ4HRSp3SVC+QToGA9pB+n6dKBDR1RlFPMp4R2k69NBlwPzyULDWNCBDlrqa3Gwm0980DkhhBBCCCGEEEIIcRxuShFCCCGEEEIIIYQQx+GmFCGEEEIIIYQQQghxHNEHnYdL0bBCFAwdhbTkVNQ3NqD0+EGUlO9x1CFY1YjgqSagOQQkueAe6IU7O9lRBw19kHbgOFho6IMGBw1wPlho6IMGB+mxYA/0OGhBek5oGAvpHmhx4FhYSPdBur4mOB909ECDg/Q4AOyDhIP6TankJB/mTZ2DJTMWYVLehE5/vqvsI6zatgYbdr6ExmZ/XBzMQAjNpbXwl5xC6ETnGq7BPviKBiKpIAOGJz43n2nog7QDx8FCQx80OGiA88FCQx80OEiPBXugx0EL0nNCw1hI90CLA8fCQroP0vU1wfmgowcaHKTHAWAfpB0M0zTNmF3NBrW1tcjMzAQGuJF5W0GPx44ZMhIblz6BvEG5CJkhuF3uTscEQ0G4DBfKTlbgxhV34EDV4Z7rtzyFPtXeU+iD1U1o2HQEZl3vrzM00j1IvX443FnemDpo6IO0QzzGIVyHePQgXAcN8zEeDi1vZqipqUFGRkav140X0vkEyM+HcB2ks0GLg/Ta5HyMr4OGjAonnwD5daFhPmhYF9LjAMjnE8A+xKs+wHxqdehj86G/5lO4Dv3174q+Nh/j5WA3n9RuwY8ZMhKvfncdcrOGwjCMLicHALhdbhiGgdysoXjt3vUYnT0iZg7B6ibUP3cIZn3vAwMAZn0A9c8dQrC6KWYOGvog7cBxsNDQBw0OGuB8sNDQBw0O0mPBHuhx0IL0nNAwFtI90OLAsbCQ7oN0fU1wPujogQYH6XEA2ActDio3pZKTfNi49Amk+1Lhcdv7hKHH7UG6LxUv3PUkkpN8UTuYgRAaNh0BmkKA3XvJTABN1nlmIBS1g4Y+SDtwHCw09EGDgwY4Hyw09EGDg/RYsAd6HLQgPSc0jIV0D7Q4cCwspPsgXV8TnA86eqDBQXocAPZBk0NYm1I/+tGPYBhGu6/x48dHLdGReVPnIG9Qru3J0YLH7cHw8y7AzRfNjtqhubTWunUt3A83moBZF0BzaW3UDhr6IO3AcbDQ0AcNDj3BfLJBP1sXGhykx4I90OPQG4mSURrGQroHWhw4FhbSfZCub4dEySdAfjw09ECDg/Q4AOyDJoew75SaOHEijh071vr1l7/8JWqJjiyZsQghM7Idt2AoiCUzFkXt4C85JXo+oKMP0g4cBwsNfdDg0BvMJ2fOB3T0QYOD9FiwB3oc7JAIGaVhLKR7oMWBY2Eh3Qfp8+2SCPkEyI+Hhh5ocJAeB4B90OQQ9qaUx+PB+eef3/o1ePDgqCXaUjSsEJPyJnT7ec7ecLvcmDx8IoqGFUbsEKxq7PJp8+EQOuFHsKox4vM19EHageNgoaEPGhzswHyyR39YFxocpMeCPdDjYJf+nlEaxkK6B1ocOBYW0n2Qrh8O/T2fAPnx0NADDQ7S4wCwD5ocgAg2pUpLS5Gbm4vRo0dj4cKFOHLkSI/H+/1+1NbWtvvqiYKho8JV6pL8KK4TPBW7hxpGioY+SDtwHCw09EGDgx2YT2Fcp4+vCw0O0mPBHuhxsEs4GRVuPgHyc0LDWEj3QIsDx8JCug/S9cOhv+cTID8eGnqgwUF6HAD2QZMDEOam1KWXXoqnnnoKL7/8Mh577DEcPHgQf//3f4+6urpuz1m+fDkyMzNbv/Ly8nqskZacGo5St6RHc53mGD1QsCny62jog7gDx8FCQR9UOPQC8ylM+vi60OAgPRbsgSIHG4SbUeHmE6BgTigYC/EeKHHgWJxFug/S9W2SEPkEiI+Hhh5ocJAeB4B9UOWAMDelZs+ejS9/+csoKirC1VdfjZdeegnV1dV49tlnuz1n2bJlqKmpaf0qKyvrsUZ9Y0M4St1SF811kmL0UkJv5NfR0AdxB46DhYI+qHDoBeZTmPTxdaHBQXos2ANFDjYIN6PCzSdAwZxQMBbiPVDiwLE4i3QfpOvbJCHyCRAfDw090OAgPQ4A+6DKAUB4j5rvQFZWFsaOHYv9+/d3e4zP54PPZ/91iaXHD0ajdO46lQciPtc90BsTB3dW5NfR0AdpB46DhYY+aHAIF+ZTL9fp4+tCg4P0WLAHehwiobeMCjefAPk5oWEspHugxYFjYSHdB+n6kdIf8wmQHw8NPdDgID0OAPugyQGI4JlSbamvr8cnn3yCnJycqCTaUlK+B7vKPkIwFIzo/GAoiOIjH2L30b0RO7izk+EaHF7QdsQ12Ad3dnLE52vog7QDx8FCQx80OIQL86l7+sO60OAgPRbsgR6HSOiPGaVhLKR7oMWBY2Eh3Qfp+pHSH/MJkB8PDT3Q4CA9DgD7oMkBCHNT6rvf/S62bduGQ4cO4a9//StuuukmuN1u/OM//mNUEh1ZtW0NXEZk+2Vulxurtq2J2sFXNFD0fEBHH6QdOA4WGvqgwaEnmE/OnQ/o6IMGB+mxYA/0OPRGomSUhrGQ7oEWB46FhXQfpM+3Q6LkEyA/Hhp6oMFBehwA9kGTQ1ijUF5ejn/8x3/EuHHj8JWvfAXnnXcetm/fjuzs7KhF2rJh50soO1mBQDAQ1nmBYABHPjuK59/bHLVDUkEGjHQPYIR5ogEY6R4kFWRE7aChD9IOHAcLDX3Q4NATzCcb9LN1ocFBeizYAz0OvZEoGaVhLKR7oMWBY2Eh3Qfp+nZIlHwC5MdDQw80OEiPA8A+aHIIa1Nq3bp1qKiogN/vR3l5OdatW4cxY8ZELdGRxmY/blr5NdT5G2xPkkAwgDp/A25ccQcam/1ROxgeF1KvH249tMvuABkAvNZ5hif6h4Zp6IO0A8fBQkMfNDj0BPOpt5PQ79aFBgfpsWAP9Dj0RqJklIaxkO6BFgeOhYV0H6Tr2yFR8gmQHw8NPdDgID0OAPugySH+KRchn3x6CFc8vAAV1ccBoNvPe7Z8v6L6OK54eAEOVB2OmYM7y4u0+SNhpNl7HryR5kHa/JExfRihhj5IO3AcLDT0QYODBjgfLDT0QYOD9FiwB3octCA9JzSMhXQPtDhwLCyk+yBdXxOcDzp6oMFBehwA9kGLQ1Rv34s3B6oO45IH5uLmi2ZjyYxFmDx8Yqdjdpfvxapta/D8e5tjslvZEXeWF+m3jEZzaS38JacQOtG5hmuwD76igdatb3H4bYaGPkg7cBwsNPRBg4MGOB8sNPRBg4P0WLAHehy0ID0nNIyFdA+0OHAsLKT7IF1fE5wPOnqgwUF6HAD2QYOD6k0pwLqtbu2OF7B2xwsoGlaI/KGjkJ6cirrGBuw/fhAl5Xvi7mB4XPAWZsFbmIVgVSOC1U1AUwjwuuDO8jryRgwNfZB24DhYaOiDBgcNcD5YaOiDBgfpsWAP9DhoQXpOaBgL6R5oceBYWEj3Qbq+JjgfdPRAg4P0OADsg7SD+k2ptpSU73FkQvSEOztZ/C8MDX2QduA4WGjogwYHDXA+WGjogwYH6bFgD/Q4aEF6TmgYC+keaHHgWFhI9xHoomQAAQAASURBVEG6viY4H3T0QIOD9DgA7IOEQ/+9L5QQQgghhBBCCCGEqIWbUoQQQgghhBBCCCHEcbgpRQghhBBCCCGEEEIcxzBN03SyYG1tLTIzM63iqc4/0so8HQBMAAZgDJB5pBYd6KDJQbo+AJgNAQBATU0NMjIyRBwA+XwClIwH5yQd6NDeQUFGMZ/ooKU+HZQ5MJ8AKBkLOtBBSX01DjbzSfRB5y2SMsWF69OBDtocpOsrQ7wXGsZD2kG6Ph3ooBTxPmgYCzrI16eDLgcliPdBw1jQgQ5a6mtx6AXRTSneKUUHOsg7SNcHdAYlf9Mn5yBdnw506OSgLKOYT4ntIF2fDsocmE8AlIwFHeigpL4aB5v5JLcpNcCNjMX5jpetfWo/zIYAjAEekfp0oIM2B+n6AFCzuhQ4HRSp3SVC+QToGA9pB+n6dKBDR1RlFPMp4R2k69NBlwPzyULDWNCBDlrqa3Gwm0980DkhhBBCCCGEEEIIcRxuShFCCCGEEEIIIYQQx+GmFCGEEEIIIYQQQghxHNEHnYdLsKoRwVNNQHMISHLBPdALd3ZywjkUDStEwdBRSEtORX1jA0qPH0RJ+R5HHaT7wB7QQRsa+qDBgWvTQroPGnpAB11I90K6PiC/LgH2oQUNfZB2kK6vCQ29kHbgurRgHxLTQf2mlBkIobm0Fv6SUwid8Hf6c9dgH3xFA5FUkAHDE58bvzQ4JCf5MG/qHCyZsQiT8iZ0+vNdZR9h1bY12LDzJTQ2d3aMBdJ9YA/ooA0NfdDgwLVpId0HDT2ggy6keyFdH5BflwD70IKGPkg7SNfXhIZeSDtwXVqwD3RQvSkVrG5Cw6YjMOu6f5Vg6IQfZ7ZWovGdE0i9fjjcWd5+5zBmyEhsXPoE8gblImSGujzmwgvGYcXCB3Hf7KW4ccUdOFB1OKYO0n1gD+igDQ190ODAtWkh3QcNPaCDLqR7IV0fkF+XAPvQgoY+SDtI19eEhl5IO3BdWrAPdAAUP1MqWN2E+ucOwazvviltMesDqH/uEILVTf3KYcyQkXj1u+uQmzUUhmHA7XJ3eZzb5YZhGMjNGorX7l2P0dkjYuYg3Qf2gA7a0NAHDQ5cmxbSfdDQAzroQroX0vUB+XUJsA8taOiDtIN0fU1o6IW0A9elBftAhxZUbkqZgRAaNh0BmkKAafckAE3WeWag613WvuaQnOTDxqVPIN2XCo/b3k1tHrcH6b5UvHDXk0hO8kXtIN0H9oAO2tDQBw0OXJsW0n3Q0AM66EK6F9L1Afl1CbAPLWjog7SDdH1NaOiFtAPXpQX7QIe2hL0pdfToUSxatAjnnXceUlJS8LnPfQ7vvvtu1CJtaS6ttW4bs9uUFkzArAugubS2XzjMmzoHeYNybS/UFjxuD4afdwFuvmh21A7SfWAP6BAOzCfnHLg2LaT7oKEHdLBPImSUdH1Afl0C7EMLGvog7SBd3y6JkE8aHLguLdgHOrQlrE2pU6dOYfr06UhKSsLmzZvx0Ucf4T//8z8xcODAqEXa4i85JXq+FoclMxZ1+9na3giGglgyY1HUDtJ9YA/oYBfmk7MOXJsW0n3Q0AM62CNRMkr6fEB+XQLsQwsa+iDtIH2+HRIlnzQ4cF1asA90aEtYW5M///nPkZeXh9WrV7d+b9SoUVFLtCVY1djlk97DIXTCj2BVY8SvLNTgUDSssMu3D9jF7XJj8vCJKBpWGPFrNKX7wB7QIRyYT845cG1aSPdBQw/oYJ9EyCjp+oD8ugTYhxY09EHaQbq+XRIhnzQ4cF1asA906EhYd0pt2rQJF198Mb785S9jyJAhmDJlCh5//PEez/H7/aitrW331RPBU7F7oGDE5ypwKBgam78I8qO4jnQf2AM6hAPzyTkHrk0L6T5o6AEd7BNuRoWbT4B8L6TrA/LrEmAfWtDQB2kH6fp2SYR80uDAdWnBPtChI2FtSh04cACPPfYYCgoK8Morr+Cb3/wm7rnnHjz99NPdnrN8+XJkZma2fuXl5fVcpDlGD/NriuI6ChzSklNjopAezXWE+8Ae0CEcmE/OOXBtWoj3QUEP6GCfcDMq7HwC5HshXR8K1iXAPrSgoA/iDtL1bZIQ+aTAgevSgn2gQ0fC2pQKhUK46KKL8LOf/QxTpkzBkiVL8PWvfx2//e1vuz1n2bJlqKmpaf0qKyvruUhSjF4I6I3iOgoc6hsbYqJQF811hPvAHtAhHJhPzjlwbVqI90FBD+hgn3AzKux8AuR7IV0fCtYlwD60oKAP4g7S9W2SEPmkwIHr0oJ9oENHwjo7JycHEya0//xnYWEhjhw50u05Pp8PGRkZ7b56wj3QG45S99fJivw6GhxKjx+MiUNp5YGIz5XuA3tAh3BgPjnnwLVpId0HDT2gg33Czahw8wmQ74V0fUB+XQLsQwsa+iDtIF3fLomQTxocuC4t2Ac6dCSsTanp06dj37597b738ccfY8SIEVFJtMWdnQzXYF9U13AN9kX1oC0NDiXle7Cr7CMEQ8GIzg+Ggig+8iF2H90bsYN0H9gDOoQD88k5B65NC+k+aOgBHeyTCBklXR+QX5cA+9CChj5IO0jXt0si5JMGB65LC/aBDp2uEc7B//zP/4zt27fjZz/7Gfbv34+1a9di1apVWLp0aVQSHfEVRff60WjP1+KwatsauIzIboVzu9xYtW1N1A7SfWAP6GAX5pOzDlybFtJ90NADOtgjUTJK+nxAfl0C7EMLGvog7SB9vh0SJZ80OHBdWrAPdGhLWDPh85//PDZu3Ig//elPuPDCC/HTn/4Uv/71r7Fw4cKoRdqSVJABI90DGGGeaABGugdJBb3fQtoXHDbsfAllJysQCAbCOi8QDODIZ0fx/Hubo3aQ7gN7QAe7MJ+cdeDatJDug4Ye0MEeiZJR0vUB+XUJsA8taOiDtIN0fTskSj5pcOC6tGAf6NCWsLcnr732WuzevRuNjY3Ys2cPvv71r0ct0RHD40Lq9cOtB2bZbY4BwGudZ3iif2CXBofGZj9uWvk11PkbbC/YQDCAOn8DblxxBxqb/VE7SPeBPaBDODCfnHPg2rSQ7oOGHtDBPomQUdL1Afl1CbAPLWjog7SDdH27JEI+aXDgurRgH+jQFmdSLgLcWV6kzR8JI81j63gjzYO0+SNj+iBADQ6ffHoIVzy8ABXVxwGg28/etny/ovo4rnh4AQ5UHY6Zg3Qf2AM6aENDHzQ4cG1aSPdBQw/ooAvpXkjXB+TXJcA+tKChD9IO0vU1oaEX0g5clxbsAx1asFdVCHeWF+m3jEZzaS38JacQOtF5R9Q12Adf0UDrtrM4/CZBg8OBqsO45IG5uPmi2VgyYxEmD5/Y6Zjd5XuxatsaPP/e5pjsHHdEug/sAR20oaEPGhy4Ni2k+6ChB3TQhXQvpOsD8usSYB9a0NAHaQfp+prQ0AtpB65LC/aBDoDyTSnAup3MW5gFb2EWglWNCFY3AU0hwOuCO8sb97dRaHFobPZj7Y4XsHbHCygaVoj8oaOQnpyKusYG7D9+ECXle+LuIN0H9oAO2tDQBw0OXJsW0n3Q0AM66EK6F9L1Afl1CbAPLWjog7SDdH1NaOiFtAPXpQX7QAf1m1JtcWcni4e1BoeS8j2OLM6ekO4De0AHbWjogwYHrk0L6T5o6AEddCHdC+n6gPy6BNiHFjT0QdpBur4mNPRC2oHr0oJ9SEyH/ntfKCGEEEIIIYQQQghRCzelCCGEEEIIIYQQQojjcFOKEEIIIYQQQgghhDiOYZqm6WTB2tpaZGZmWsVTnX+klXk6AJgADMAYIPNILTrQQZODdH0AMBsCAICamhpkZGSIOADy+QQoGQ/OSTrQob2DgoxiPtFBS306KHNgPgFQMhZ0oIOS+mocbOaT6IPOWyRligvXpwMdtDlI11eGeC80jIe0g3R9OtBBKeJ90DAWdJCvTwddDkoQ74OGsaADHbTU1+LQC6KbUrxTig50kHeQrg/oDEr+pk/OQbo+HejQyUFZRjGfEttBuj4dlDkwnwAoGQs60EFJfTUONvNJblNqgBsZi/MdL1v71H6YDQEYAzwi9elAB20O0vUBoGZ1KXA6KFK7S4TyCdAxHtIO0vXpQIeOqMoo5lPCO0jXp4MuB+aThYaxoAMdtNTX4mA3n/igc0IIIYQQQgghhBDiONyUIoQQQgghhBBCCCGOw00pQgghhBBCCCGEEOI4og8674sUDStEwdBRSEtORX1jA0qPH0RJ+R46OOwQrGpE8FQT0BwCklxwD/TCnZ3sWH066HIgFtLrkg7nkF4X0vXpQDqiYV3SwULDuqCDfH3SHum1KV1fi4OGdUEH5x24KWWD5CQf5k2dgyUzFmFS3oROf76r7COs2rYGG3a+hMZmPx3i5GAGQmgurYW/5BRCJzpf3zXYB1/RQCQVZMDwxOcmQDrocSAW0uuSDueQXhfS9elAOqJhXdLBQsO6oIN8fdIe6bUpXV+Lg4Z1QQdZB25K9cKYISOxcekTyBuUi5AZ6vKYCy8YhxULH8R9s5fixhV34EDVYTrE2CFY3YSGTUdg1nX/WsnQCT/ObK1E4zsnkHr9cLizvDGrTwddDsRCel3S4RzS60K6Ph1IRzSsSzpYaFgXdJCvT9ojvTal62tx0LAu6CDvwC34HhgzZCRe/e465GYNhWEYcLvcXR7ndrlhGAZys4bitXvXY3T2CDrE0CFY3YT65w7BrO9+gbTFrA+g/rlDCFY3xaQ+HXQ5EAvpdUmHc0ivC+n6dCAd0bAu6WChYV3QQb4+aY/02pSur8VBw7qggw4Hbkp1Q3KSDxuXPoF0Xyo8bns3lHncHqT7UvHCXU8iOclHhxg4mIEQGjYdAZpCgGn3JABN1nlmoOtdfzr0TQdiIb0u6XAO6XUhXZ8OpCMa1iUdLDSsCzrI1yftkV6b0vW1OGhYF3TQ4xDWptTIkSNhGEanr6VLl0Ytoo15U+cgb1Cu7YXagsftwfDzLsDNF82mQwwcmktrrVsI7S6QFkzArAugubQ2qvp00OXQG4mSUdLrkg7nkF4X0vXpYB/mU8/0t2zQ4KBhXdBBvr4dEiWfAPm1KV1fi4OGdUEHPQ5hbUq98847OHbsWOvXli1bAABf/vKXoxbRxpIZi7r9bG1vBENBLJmxiA4xcPCXnBI9nw66HHojUTJKel3S4RzS60L6fDrYh/nUO/0pGzQ4aFgXdJA/3w6Jkk+A/NqUrq/FQcO6oIMeh7A2pbKzs3H++ee3fr344osYM2YMZsyYEbWIJoqGFWJS3oRuP1vbG26XG5OHT0TRsEI6ROEQrGrs8qn/4RA64UewqjHi8+mgx8EOiZBR0uuSDueQXhfS9ekQHsyn3ukv2aDBQcO6oIN8fbskQj4B8mtTur4WBw3rgg56HIAoninV1NSENWvW4Pbbb4dhGN0e5/f7UVtb2+5LOwVDR8XkOvlRXIcOQPBU7B4uGfG5dFDjEC52Mor5RIdoHKTXhXR9OkQO86ln+no2aHDQsC7oIF8/EvprPgHya1O6vhYHDeuCDnocgCg2pV544QVUV1dj8eLFPR63fPlyZGZmtn7l5eVFWtIx0pJTY3Kd9CiuQwcAzTF6sGNTFNehgx6HMLGTUcwnOkTjIL4upOvTIWKYTz3T17NBg4OKdUEH+foR0F/zCZBfm9L1tTioWBd00OOAKDalnnzyScyePRu5ubk9Hrds2TLU1NS0fpWVlUVa0jHqGxticp26KK5DBwBJMXo5pDeK69BBj0OY2Mko5hMdonEQXxfS9ekQMcynnunr2aDBQcW6oIN8/Qjor/kEyK9N6fpaHFSsCzrocQAQ3iP3z3L48GG8+uqreP7553s91ufzweeL/rWRTlJ6/GBsrlN5gA5ROLgHemNS350V+XXooMchHOxmFPOJDtE4SK8L6fp0iAzmk43r9PFs0OCgYV3QQb5+uPTnfALk16Z0fS0OGtYFHfQ4ABHeKbV69WoMGTIEc+fOjaq4VkrK92BX2UcIhoIRnR8MBVF85EPsPrqXDlE4uLOT4Roc3V94rsE+uLOTIz6fDnocwqE/Z5T0uqTDOaTXhXR9OkQG86l7+ks2aHDQsC7oIF8/XPpzPgHya1O6vhYHDeuCDnocgAg2pUKhEFavXo1bb70VHk9EN1r1CVZtWwOXEdltaG6XG6u2raFDDBx8RQNFz6eDLgc7JEJGSa9LOpxDel1In0+H8GA+9Ux/ygYNDhrWBR3kz7dLIuQTIL82petrcdCwLuigxyHs2fjqq6/iyJEjuP3226MurpkNO19C2ckKBIKBsM4LBAM48tlRPP/eZjrEwCGpIANGugfo/gWPXWMARroHSQUZUdWngy4HOyRCRkmvSzqcQ3pdSNenQ3gwn7qnv2WDBgcN64IO8vXtkgj5BMivTen6Whw0rAs66HEIe1PqqquugmmaGDt2bNTFNdPY7MdNK7+GOn+D7QUbCAZQ52/AjSvuQGOznw4xcDA8LqReP9x6eJrdhWIA8FrnGZ7oH95GBz0OdkiEjJJel3Q4h/S6kK5Ph/BgPnVNf8wGDQ4a1gUd5OvbJRHyCZBfm9L1tThoWBd00OPg3Ksc+iCffHoIVzy8ABXVxwGg28/etny/ovo4rnh4AQ5UHaZDDB3cWV6kzR8JI83ercRGmgdp80fG9KGQdNDjQCyk1yUdziG9LqTr04F0RMO6pIOFhnVBB/n6pD3Sa1O6vhYHDeuCDjoc+u8HhmPEgarDuOSBubj5otlYMmMRJg+f2OmY3eV7sWrbGjz/3uaY7BzToTPuLC/SbxmN5tJa+EtOIXSi8/Vdg33wFQ20bkGMw2+V6KDHgVhIr0s6nEN6XUjXpwPpiIZ1SQcLDeuCDvL1SXuk16Z0fS0OGtYFHeQduCllg8ZmP9bueAFrd7yAomGFyB86CunJqahrbMD+4wdRUr6HDg44GB4XvIVZ8BZmIVjViGB1E9AUArwuuLO8jryZhA56HIiF9Lqkwzmk14V0fTqQjmhYl3Sw0LAu6CBfn7RHem1K19fioGFd0EHWgZtSYVJSvseRxUmHnnFnJ4v/xU0HPQ7EQnpd0uEc0utCuj4dSEc0rEs6WGhYF3SQr0/aI702petrcdCwLujgvAPvCyWEEEIIIYQQQgghjsNNKUIIIYQQQgghhBDiONyUIoQQQgghhBBCCCGOY5imaTpZsLa2FpmZmVbxVOcfaWWeDgAmAAMwBsg8UosOdNDkIF0fAMyGAACgpqYGGRkZIg6AfD4BSsaDc5IOdGjvoCCjmE900FKfDsocmE8AlIwFHeigpL4aB5v5JPqg8xZJmeLC9elAB20O0vWVId4LDeMh7SBdnw50UIp4HzSMBR3k69NBl4MSxPugYSzoQAct9bU49ILophTvlKIDHeQdpOsDOoOSv+mTc5CuTwc6dHJQllHMp8R2kK5PB2UOzCcASsaCDnRQUl+Ng818ktuUGuBGxuJ8x8vWPrUfZkMAxgCPSH060EGbg3R9AKhZXQqcDorU7hKhfAJ0jIe0g3R9OtChI6oyivmU8A7S9emgy4H5ZKFhLOhABy31tTjYzSc+6JwQQgghhBBCCCGEOA43pfowhrPPqCeEEEIIIYQQQgiJGaLPlCLhMamqEQv3VGNaxRmMO+WHNwQ0uYB9A314OzcFzxRmYVd2srQmIYQQQgghhBBCSK9wU6oPMKq6CSu2HsP0Y2fQbABJkyYDV44H0tPhravD5/buxfhdxViyuxpv5aTgrlk5OJjljatTsKoRwVNNQHMISHLBPdALt4MbYtL16aDLgeihaFghCoaOQlpyKuobG1B6/CBKyvc46qBhTko7SNenA9EI84kOmhyk6xNdMJ/okMgO3JRSzvyPa7ByayXcXh+weDGS7rkHmDKl03FJ778PPPIILl2/DtvXHcSds3KwYWxGTF3MQAjNpbXwl5xC6IS/05+7BvvgKxqIpIIMGJ7YfzJUuj4ddDkQPSQn+TBv6hwsmbEIk/ImdPrzXWUfYdW2Ndiw8yU0NneeL7FAw5yUdpCuTweiEeYTHTQ5SNcnumA+0YEOFtyUUsz8j2uwassxoKAArj//GRgxAgiFuj64qAj4/e/huf9+uK68Eo9v2Q8DJp4bmxkTl2B1Exo2HYFZ1/1rHUMn/DiztRKN75xA6vXD4Y7h3VrS9emgy4HoYcyQkdi49AnkDcpFyOw6ny68YBxWLHwQ981eihtX3IEDVYdj6qBhTko7SNenA9EI84kOmhyk6xNdMJ/oQIdzcAteKaOrm7Bya6W1IbV9O3DBBYBhAG531ye43dafX3ABXDt2APn5WLm1EqOqm6J2CVY3of65QzDru5+gbTHrA6h/7hCCMaitoT4ddDkQPYwZMhKvfncdcrOGwjAMuF1d55Pb5YZhGMjNGorX7l2P0dkjYuagYU5KO0jXpwPRCPOJDpocpOsTXTCf6ECH9nBTSimPbj0Gl9dn3SGVng4kJdk7MSkJSE+Ha8sWuL0+rNh6LCoPMxBCw6YjQFMIsPuyPxNAk3WeGejmzq4+Up8OuhyIHpKTfNi49Amk+1Lhcdu76dbj9iDdl4oX7noSyUm+qB00zElpB+n6dCAaYT7RQZODdH2iC+YTHejQmbA2pYLBIP7t3/4No0aNQkpKCsaMGYOf/vSnME279sQOkz5txPRjZ5C04B+sj+zZ3ZBqISkJGDkSnq8swPRjZzCpqjFil+bSWusWvnCH2ATMugCaS2sjrq2hPh10OfQE88lZ5k2dg7xBubZ/oGrB4/Zg+HkX4OaLZkftoGFOSjtI16eDfZhRzsF8ooMmB+n6dmA+OQfziQ506ExYm1I///nP8dhjj2HFihXYs2cPfv7zn+MXv/gFHn300ahFyDkW7q1GswHgnnu6f4ZUbwSDwN13o9kAFu6pjtjFX3Iq4nP7w/l00OXQE8wnZ1kyY1G3z0DojWAoiCUzFkXtoGFOSjtIn08H+zCjnIP5RAdNDtLn24H55BzMJzrQoTNhbUr99a9/xQ033IC5c+di5MiRmD9/Pq666ir87W9/i1qEnGNaxRkkTZpsvWWvu2dI9YbbDUydiqRJk/GFY2ciukSwqrHLp+6HQ+iEH8EI79SSrk8HXQ69wXxyjqJhhZiUN6HbZyD0htvlxuThE1E0rDBiBw1zUtpBuj4dwoMZ5QzMJzpocpCubxfmkzMwn+hAh64Ja1Pqsssuw2uvvYaPP/4YALBr1y785S9/wezZ3d9G6Pf7UVtb2+6L9My4U35g/PgYXWwcxp+MbKIFT8Xu4Y59sT4ddDn0BvPJOQqGjorJdfKjuI6GOSntIF2fDuERbkYxnyKD+UQHTQ7S9e3CfHIG5hMd6NA1YX2Y9fvf/z5qa2sxfvx4uN1uBINBPPjgg1i4cGG35yxfvhw//vGPo5JMJAzThDcE6+HmsSAjA96QdV3TMMI7tzlGD1ZsivA60vXpoMuhF5hPzpGWnBqT66RHcx0Nc1LaQbo+HcIi3IxiPkUG84kOqhyk69uE+eQMzCc60KFrwrpT6tlnn8UzzzyDtWvX4r333sPTTz+N//iP/8DTTz/d7TnLli1DTU1N61dZWVlUwv0d0zDQ5AJQVxebC9bWosmF8DekACApRi9n9EZ4Hen6dNDl0AvMJ+eob2yIyXXqormOhjkp7SBdnw5hEW5GMZ8ig/lEB1UO0vVtwnxyBuYTHejQNWHdKXXvvffi+9//Pv7hH/4BAPC5z30Ohw8fxvLly3Hrrbd2eY7P54PPF/2rKxOJfQN9+NzevbG52N692Dsosv67B3pjouDOiuw60vXpoMuhN5hPzlF6/GBsrlN5IOJzNcxJaQfp+nQIj3AzivkUGcwnOmhykK5vF+aTMzCf6ECHrglrS+v06dNwudqf4na7EYr0DXGkS97OTUHzrmLg/fett+hFQjAI7NyJ5pJd2J6TEtEl3NnJcA2O7i8c12Af3NnJfbI+HXQ59AbzyTlKyvdgV9lHCIYiy6dgKIjiIx9i99HIN981zElpB+n6dAgPZpQzMJ/ooMlBur5dmE/OwHyiAx26uUY4B1933XV48MEH8b//+784dOgQNm7ciF/+8pe46aabopIg7XmmMAtJJoBHHgFcEd4K53YDjz6KJNO6XqT4igZGfG5/OJ8Ouhx6gvnkLKu2rYHLiCyf3C43Vm1bE7WDhjkp7SB9Ph3sw4xyDuYTHTQ5SJ9vB+aTczCf6ECHzoS1Ih599FHMnz8fd955JwoLC/Hd734X/+///T/89Kc/jVqEnGNXdjLeyklBYP064PBhoLk5vAs0NwOHDiHw7Hq8lZOCXVHsXCYVZMBI9wDhPpLKAIx0D5IKMiKuraE+HXQ59ATzyVk27HwJZScrEAgGwjovEAzgyGdH8fx7m6N20DAnpR2k69PBPswo52A+0UGTg3R9OzCfnIP5RAc6dCasTan09HT8+te/xuHDh3HmzBl88skneOCBB+D1xvdzzonIXbNyEGzyI3TVVdZDz+1uTDU3A3V1CF15JYJNftw1KycqD8PjQur1w62Hl9mdqAYAr3We4YnuoWfS9emgy6EnmE/O0tjsx00rv4Y6f4PtH6wCwQDq/A24ccUdaGz2R+2gYU5KO0jXp4N9mFHOwXyigyYH6fp2YD45B/OJDnToTPxTjkTEwSwv7pyVA5SWInTppcDRo9YfdPeMqZbvHz1qHb9/P+6clYODMXgwojvLi7T5I2Gk2XsuvpHmQdr8kTF7KKN0fTrociB6+OTTQ7ji4QWoqD4OAN0+I6Hl+xXVx3HFwwtwoOpwzBw0zElpB+n6dCAaYT7RQZODdH2iC+YTHejQnrDevkecZcPYDBgwsXLrJ3BPKITnKwuAu+8Gpk7tfHBxMfDoowg8ux7BJj/uvDIXG8bG7nZfd5YX6beMRnNpLfwlpxA60XmX3jXYB1/RQOsWwBj/Vke6Ph10ORA9HKg6jEsemIubL5qNJTMWYfLwiZ2O2V2+F6u2rcHz722OyW/4OqJhTko7SNenA9EI84kOmhyk6xNdMJ/oQIdzcFNKOc+NzcTOISlYsfUYpj/9NJr/8DSSJk0Gxo0DMjKA2lpg3z407ypGkglsz03B3TNHxeQOqY4YHhe8hVnwFmYhWNWIYHUT0BQCvC64s7xxfzOIdH066HIgemhs9mPtjhewdscLKBpWiPyho5CenIq6xgbsP34QJeV74u6gYU5KO0jXpwPRCPOJDpocpOsTXTCf6EAHC25K9QEOZnkx9+YRmFTViIV7qvGF8j0YX1IMbwhocgF7B/mw/cIsPFOYFdVDzcPBnZ0s+hendH066HIgeigp3+PID1E9oWFOSjtI16cD0QjziQ6aHKTrE10wn+iQyA7clOpD7MpOxq7s81v/2zBNmEa4j8knhBBCCCGEEEIIkYcfVu7DcEOKEEIIIYQQQgghfRVuShFCCCGEEEIIIYQQx+GmFCGEEEIIIYQQQghxHMM0TdPJgjU1NcjKyrL+Y4DbydIWp4Pn/rdEfTrQQZuDdP02DtXV1cjMzJRxgIJ8AlSNh5iDdH060KEbB8mMYj7RQU19Oqh0YD7pGQs60EG8vjKH3vLJ8Qed19XVnfuPto2SQLo+HeigzUG4fl1dneimlKp8ooOO+nSgQxskM4r5RAeV9emgxoH51AY60EGTg3R9BQ695ZPjd0qFQiFUVFQgPT0dRgQP6q6trUVeXh7KysqQkZERB0M69BUH6fp0iJ2DaZqoq6tDbm4uXC65TxUzn+jQnxyk6/cnBw0ZFW0+AfLjIV2fDnTQ5sB8Oof0WGhwkK5PBzrE2sFuPjl+p5TL5cKwYcOivk5GRobY4NBBl4N0fTrExkHyDqkWmE906I8O0vX7i4N0RsUqnwD58ZCuTwc6aHNgPp1Deiw0OEjXpwMdYulgJ5/4oHNCCCGEEEIIIYQQ4jjclCKEEEIIIYQQQgghjtPnNqV8Ph/uv/9++Hw+OiS4g3R9Ouhy0ICGPtCBDlrq00Ef0r2Qrk8HOmhzkK6vCQ29kHaQrk8HOkg5OP6gc0IIIYQQQgghhBBC+tydUoQQQgghhBBCCCGk78NNKUIIIYQQQgghhBDiONyUIoQQQgghhBBCCCGO06c2pd5++2243W7MnTvX8dqLFy+GYRitX+eddx6uueYalJSUOO5SWVmJu+++G6NHj4bP50NeXh6uu+46vPbaa3Gv3bYPSUlJGDp0KK688kr8/ve/RygUinv9jg5tv6655hpH6vfmsX//fkfqV1ZW4lvf+hby8/ORnJyMoUOHYvr06Xjsscdw+vTpuNdfvHgxbrzxxk7ff+ONN2AYBqqrq+PuoA1mFPOpo4dURknnEyCbUcynzjCfmE8dPZhP/BlKC8wn5lNHD+ZTYuVTn9qUevLJJ3H33XfjzTffREVFheP1r7nmGhw7dgzHjh3Da6+9Bo/Hg2uvvdZRh0OHDmHq1KnYunUrHn74YezevRsvv/wyZs6ciaVLlzri0NKHQ4cOYfPmzZg5cya+9a1v4dprr0UgEHDUoe3Xn/70J0dq9+YxatSouNc9cOAApkyZgj//+c/42c9+hvfffx9vv/02vve97+HFF1/Eq6++GncH0plEzyjmU2cPyYySyieAGaUR5hPzqaMH84n5pAXmE/OpowfzKbHyySMtYJf6+nqsX78e7777LiorK/HUU0/hX//1Xx118Pl8OP/88wEA559/Pr7//e/j7//+71FVVYXs7GxHHO68804YhoG//e1vSE1Nbf3+xIkTcfvttzvi0LYPF1xwAS666CJ84QtfwBVXXIGnnnoKX/va1xx1kETK484774TH48G7777bbh6MHj0aN9xwA/hSTedhRjGfuvOQQtKBGaUL5hPzqTsPKZhPpAXmE/OpOw8pmE/O02fulHr22Wcxfvx4jBs3DosWLcLvf/970UGpr6/HmjVrkJ+fj/POO8+RmidPnsTLL7+MpUuXtpukLWRlZTni0RWzZs3CpEmT8Pzzz4s5JAqfffYZ/vznP3c7DwDAMAyHrUiiZxTzibTAjNIH84n5RCyYT/pgPjGfiEUi51Of2ZR68sknsWjRIgDWLXU1NTXYtm2bow4vvvgi0tLSkJaWhvT0dGzatAnr16+Hy+VMG/fv3w/TNDF+/HhH6oXL+PHjcejQIUdqtR2Llq+f/exnjtTuyePLX/5y3Gu2zINx48a1+/7gwYNbPe677764ewBdj8Ps2bMdqa2NRM8o5lN7NGSURD4BejKK+XQO5hPzqS3MJ/l8AphRLTCfmE9tYT4lZj71iY/v7du3D3/729+wceNGAIDH48GCBQvw5JNP4vLLL3fMY+bMmXjssccAAKdOncJ//dd/Yfbs2fjb3/6GESNGxL2+9tv1TNN0bPe27Vi0MGjQIEdq9+TR3a62E/ztb39DKBTCwoUL4ff7HanZ1Tjs2LGj9YeLRIEZxXzqiIaM0pRPgPMZxXyyYD4xnzrCfOoMf4aSgfnEfOoI86kziZBPfWJT6sknn0QgEEBubm7r90zThM/nw4oVK5CZmemIR2pqKvLz81v/+4knnkBmZiYef/xxPPDAA3GvX1BQAMMwsHfv3rjXioQ9e/Y49hC4jmMhhYRHfn4+DMPAvn372n1/9OjRAICUlBTHXLr6/19eXu5YfS0wo5hPHdGQUVIOWjKK+WTBfGI+dYT5JJ9PADMKYD4BzKeOMJ8SM5/Uf3wvEAjgD3/4A/7zP/8TxcXFrV+7du1Cbm6uyBvXWjAMAy6XC2fOnHGk3qBBg3D11Vdj5cqVaGho6PTnkq+P3bp1K3bv3o158+aJOSQK5513Hq688kqsWLGiy3lAnIUZZcF8Ii0wo/TAfLJgPpEWmE96YD5ZMJ9IC4mcT+rvlHrxxRdx6tQp3HHHHZ12y+fNm4cnn3wS3/jGNxxx8fv9qKysBGDd2rlixQrU19fjuuuuc6Q+AKxcuRLTp0/HJZdcgp/85CcoKipCIBDAli1b8Nhjj2HPnj1xd2jpQzAYxPHjx/Hyyy9j+fLluPbaa/HVr3417vXbOrTF4/Fg8ODBjtSX5r/+678wffp0XHzxxfjRj36EoqIiuFwuvPPOO9i7dy+mTp0qrZgwMKPOwXzq7NEWZhQzymmYT+dgPnX2aAvzifnkNMynczCfOnu0hfmUAPlkKufaa68158yZ0+Wf7dixwwRg7tq1K+4et956qwmg9Ss9Pd38/Oc/bz733HNxr92RiooKc+nSpeaIESNMr9drXnDBBeb1119vvv7663Gv3bYPHo/HzM7ONr/0pS+Zv//9781gMBj3+h0d2n6NGzfOkfptPW644QZHa7aloqLCvOuuu8xRo0aZSUlJZlpamnnJJZeYDz/8sNnQ0BD3+t39/3/99ddNAOapU6fi7qABZlR7Ej2fOnpIZZR0PpmmbEYxnyyYT+1hPjGfWuDPUPIwn9rDfGI+tZCI+WSYpvKnqxFCCCGEEEIIIYSQfof6Z0oRQgghhBBCCCGEkP4HN6UIIYQQQgghhBBCiONwU4oQQgghhBBCCCGEOA43pQghhBBCCCGEEEKI43BTihBCCCGEEEIIIYQ4DjelCCGEEEIIIYQQQojjcFOKEEIIIYQQQgghhDgON6UIIYQQQgghhBBCiONwU4oQQgghhBBCCCGEOA43pfoYv/3tb5Geno5AIND6vfr6eiQlJeHyyy9vd+wbb7wBwzDwySef4OTJk7j77rsxbtw4pKSkYPjw4bjnnntQU1PT7pzXXnsNl112GdLT03H++efjvvvua1erJ0aNGoVXX321yz9btWoVLr/8cmRkZMAwDFRXV7f780OHDuGOO+7AqFGjkJKSgjFjxuD+++9HU1NT1LVHjhwJwzBgGAbcbjdyc3Nxxx134NSpU63HvPHGG7jhhhuQk5OD1NRUTJ48Gc8880y76zz//PO4+OKLkZWV1XrMH//4R1t+hCQKkWYUAPy///f/MGbMGKSkpCA7Oxs33HAD9u7d23r8rl278I//+I/Iy8tDSkoKCgsL8Zvf/MaW15kzZ5Camor9+/d3+ecPPvggLrvsMgwYMABZWVldHnPPPfdg6tSp8Pl8mDx5sq26LTiRUR9++CHmzZvXer1f//rXYTkS0t+JZz4BwJEjRzB37lwMGDAAQ4YMwb333mvrZ6hY5FO8ardkk2EY8Hg8GD58OL7zne/A7/e3HvP888/jyiuvRHZ2NjIyMjBt2jS88sor3dZ86KGHYBgGvv3tb/fqR0giE+/Maru+W77WrVvXq1dvufHee+/hyiuvRFZWFs477zwsWbIE9fX1tv4/z5w5E0888USXf3b55Ze3cx06dCi+/OUv4/Dhw63H2P1Z8Y033sBFF10En8+H/Px8PPXUU7b8SHzgplQfY+bMmaivr8e7777b+r3/+7//w/nnn48dO3agsbGx9fuvv/46hg8fjjFjxqCiogIVFRX4j//4D3zwwQd46qmn8PLLL+OOO+5oPX7Xrl2YM2cOrrnmGrz//vtYv349Nm3ahO9///u9epWUlODUqVOYMWNGl39++vRpXHPNNfjXf/3XLv987969CIVC+N3vfocPP/wQv/rVr/Db3/622+PDqQ0AP/nJT3Ds2DEcOXIEzzzzDN58803cc889rX/+17/+FUVFRdiwYQNKSkpw22234atf/SpefPHF1mMGDRqEH/zgB3j77bdbj7ntttt6/MGLkEQj0owCgKlTp2L16tXYs2cPXnnlFZimiauuugrBYBAAsHPnTgwZMgRr1qzBhx9+iB/84AdYtmwZVqxY0avXli1bMGLECOTn53f5501NTfjyl7+Mb37zmz1e5/bbb8eCBQt6rdcWpzLq9OnTGD16NB566CGcf/75YTkSkgjEM5+CwSDmzp2LpqYm/PWvf8XTTz+Np556Cv/+7//eq1e0+RTP2gCwevVqHDt2DAcPHsR//dd/4Y9//CMeeOCB1j9/8803ceWVV+Kll17Czp07MXPmTFx33XV4//33O13rnXfewe9+9zsUFRX16kZIohPPzGqhZX23fN144429evWUGxUVFfjSl76E/Px87NixAy+//DI+/PBDLF68uNfrnjx5Em+99Rauu+66bo/5+te/jmPHjqGiogL//d//jbKyMixatKj1z+38rHjw4EHMnTsXM2fORHFxMb797W/ja1/7Gv9NJ4lJ+hw5OTnm8uXLW//7e9/7nrl06VKzsLDQfP3111u//8UvftG89dZbu73Os88+a3q9XrO5udk0TdNctmyZefHFF7c7ZtOmTWZycrJZW1vbo9NPfvITc8GCBb26v/766yYA89SpU70e+4tf/MIcNWpUr8f1VnvEiBHmr371q3bf++lPf2pOmDChx+vOmTPHvO2223o8ZsqUKeYPf/jDXh0JSSRilVG7du0yAZj79+/v9pg777zTnDlzZq9Ot99+u3nffff1etzq1avNzMzMHo+5//77zUmTJvV6rRYkMqqraxJC4pdPL730kulyuczKysrWYx577DEzIyPD9Pv9PTpFm0/xrA3A3LhxY7vv3XHHHeacOXN6vO6ECRPMH//4x+2+V1dXZxYUFJhbtmwxZ8yYYX7rW9/q8RqEkPj+TNXV+rZDT7nxu9/9zhwyZIgZDAZbv1dSUmICMEtLS3u87h/+8Afz0ksv7fbPu8qNP/7xj+aAAQN6vG7HnxW/973vmRMnTmx3zIIFC8yrr766x+uQ+ME7pfogM2fOxOuvv97636+//jouv/xyzJgxo/X7Z86cwY4dOzBz5sxur1NTU4OMjAx4PB4AgN/vR3JycrtjUlJS0NjYiJ07d/botGnTJtxwww2R/l/q1m/QoEG9Hhdu7aNHj+J//ud/cOmll0Zc3zRNvPbaa9i3bx+++MUv2q5NSCIQi4xqaGjA6tWrMWrUKOTl5XVby05OhEIhvPjiizHPKLtIZBQhpGvilU9vv/02Pve5z2Ho0KGtx1199dWora3Fhx9+2K1PLPLJydoff/wxtm7d2mM+hUIh1NXVdcqnpUuXYu7cufjSl75kux4hiU68f6ZaunQpBg8ejEsuuQS///3vYZpmjz695Ybf74fX64XLdW6bISUlBQDwl7/8pcdrh/vz0smTJ/Hss8+G/fPS22+/3SmHrr76arz99tu2a5MYI70rRsLn8ccfN1NTU83m5maztrbW9Hg85qeffmquXbvW/OIXv2iapmm+9tprJgDz8OHDXV6jqqrKHD58uPmv//qvrd975ZVXTJfLZa5du9YMBAJmeXm5+fd///cmAHPt2rXd+pSXl5ter9fW3U9275QqLS01MzIyzFWrVvV4nJ3aI0aMML1er5mammomJyebAMxLL720x3PWr19ver1e84MPPmj3/erqajM1NdX0eDymz+czn3zyyR79CElEosmolStXmqmpqSYAc9y4cT3eJfXWW2+ZHo/HfOWVV3r0eeuttzr91q47Yn2nlNMZ1faavFOKkM7EK5++/vWvm1dddVW74xsaGkwA5ksvvdStTyzyKZ61AZjJyclmamqq6fP5TADmtddeazY1NXV7zs9//nNz4MCB5vHjx1u/96c//cm88MILzTNnzpim2fUdD4SQzsTzZ6qf/OQn5l/+8hfzvffeMx966CHT5/OZv/nNb3r06S03PvjgA9Pj8Zi/+MUvTL/fb548edKcN2+eCcD82c9+1u11GxsbzbS0tG5/rjFNKzeSkpLM1NRUc8CAASYAc+zYsebBgwd79O34s2JBQUEnl//93/81AZinT5/u9lokfvBOqT7I5ZdfjoaGBrzzzjv4v//7P4wdOxbZ2dmYMWNG6+eL33jjDYwePRrDhw/vdH5tbS3mzp2LCRMm4Ec/+lHr96+66io8/PDD+MY3vgGfz4exY8dizpw5ANBut7sjmzZtwt/93d91+/DNcDl69CiuueYafPnLX8bXv/71Ho+1W/vee+9FcXExSkpK8NprrwEA5s6d2+lz1YD1G4jbbrsNjz/+OCZOnNjuz9LT01FcXIx33nkHDz74IL7zne/gjTfeCOv/HyH9nWgyauHChXj//fexbds2jB07Fl/5ylfaPTOhhQ8++AA33HAD7r//flx11VU9+vz3f/83rr322h5zLF44nVGEkJ5xIp/CQTKf7Nb+1a9+heLiYuzatQsvvvgiPv74Y/zTP/1Tl8euXbsWP/7xj/Hss89iyJAhAICysjJ861vfwjPPPNPpjnxCSM/EM7P+7d/+DdOnT8eUKVNw33334Xvf+x4efvjhHn16y42JEyfi6aefxn/+539iwIABOP/88zFq1CgMHTq0x6zZunUrhgwZ0uvPNQsXLmzNo7/85S/Iz8/HVVddhbq6uk7HhvOzIpGFm1J9kPz8fAwbNgyvv/46Xn/99daH5+bm5iIvLw9//etf8frrr2PWrFmdzq2rq8M111yD9PR0bNy4EUlJSe3+/Dvf+Q6qq6tx5MgRnDhxovUWytGjR3frs2nTJlx//fUx+f9WUVGBmTNn4rLLLsOqVat6Pd5u7cGDByM/Px8FBQWYNWsWfv3rX7f2qS3btm3Dddddh1/96lf46le/2uk6LpcL+fn5mDx5Mv7lX/4F8+fPx/Lly+3/HyQkAYgmozIzM1FQUIAvfvGLeO6557B3715s3Lix3TEfffQRrrjiCixZsgQ//OEPe/WJZUaFi9MZRQjpmXjl0/nnn4/jx4+3O77lv3t68UAs8inetc8//3zk5+dj3LhxmDt3Ln784x9j/fr1nd68tW7dOnzta1/Ds88+2+6jMTt37sSnn36Kiy66CB6PBx6PB9u2bcMjjzwCj8fT5eY7IcQi3j9TteXSSy9FeXl5u7drdsRObtxyyy2orKzE0aNH8dlnn+FHP/oRqqqqYvLvyczMTOTn5yM/Px/Tp0/Hk08+idLSUqxfv77dcT39rNhdZmZkZLR+1JA4Czel+igzZ87EG2+8gTfeeKPdK0G/+MUvYvPmzfjb3/7W6XPFtbW1uOqqq+D1erFp06Zuf1tlGAZyc3ORkpKCP/3pT8jLy8NFF13U5bH19fV4/fXXY/KslqNHj+Lyyy9vfVtEb7+5i6a22+0GYH0Gu4U33ngDc+fOxc9//nMsWbLE1nVCoVCPwU1IohJJRnXENE2YptlujX344YeYOXMmbr31Vjz44IO9epSWluLw4cO48sorI/7/EikaMooQ0pl45NO0adOwe/dufPrpp63HbNmyBRkZGZgwYUKX14hVPjldu6t8+tOf/oTbbrsNf/rTnzB37tx2x19xxRXYvXs3iouLW78uvvji1jseWq5HCOmaeP1M1ZHi4mIMHDgQPp+vyz8PNzeGDh2KtLQ0rF+/HsnJyd2eZ5om/ud//idmPy/19rPitGnTWu9Kb2HLli2YNm1a2PVJbPBIC5DImDlzJpYuXYrm5uZ2rxmfMWMG7rrrLjQ1NbULp5YNqdOnT2PNmjWora1FbW0tACA7O7t1QT/88MO45ppr4HK58Pzzz+Ohhx7Cs88+2+0PDC+//DLGjh2LkSNH9uhbWVmJysrK1t+q7d69G+np6Rg+fDgGDRrUuiE1YsQI/Md//Aeqqqpaz+3ut3x2awPWHWKVlZUwTRNlZWX43ve+h+zsbFx22WUArI/DXHvttfjWt76FefPmobKyEgDg9XpbH4y3fPlyXHzxxRgzZgz8fj9eeukl/PGPf8Rjjz3Wa31CEo1wM+rAgQNYv349rrrqKmRnZ6O8vBwPPfQQUlJSWj9G/MEHH2DWrFm4+uqr8Z3vfKd1nbrdbmRnZ3fp8d///d/40pe+hAEDBvToe+TIEZw8eRJHjhxBMBhEcXExAOs3lGlpaQCA/fv3o76+HpWVlThz5kzrMRMmTIDX6+10TaczqqmpCR999FHr/z569CiKi4uRlpbW4+veCUk04pFPV111FSZMmIB/+qd/wi9+8QtUVlbihz/8IZYuXdrtP/BilU/xrA0A1dXVqKysRCgUQmlpKX7yk59g7NixKCwsBGB9ZO/WW2/Fb37zG1x66aWt+ZSSkoLMzEykp6fjwgsvbHfN1NRUnHfeeZ2+TwjpTDwy63/+539w/PhxfOELX0BycjK2bNmCn/3sZ/jud7/brYfd3FixYgUuu+wypKWlYcuWLbj33nvx0EMPdfsog507d+L06dP4u7/7u157cfr06daMOX78OH76058iOTm59eN5dn5W/MY3voEVK1bge9/7Hm6//XZs3boVzz77LP73f/+31/okTog9zYpExcGDB00A5vjx49t9/9ChQ60Ps2tLywPGu/pq+3C4mTNnmpmZmWZycrJ56aWX9viATNM0zUWLFpk/+MEPevW9//77u6y9evVq0zSth3d25xdt7REjRrS7XnZ2tjlnzhzz/fffbz3m1ltv7bL2jBkzWo/5wQ9+YObn55vJycnmwIEDzWnTppnr1q3rtT4hiUi4GXX06FFz9uzZ5pAhQ8ykpCRz2LBh5i233GLu3bu39ZjucmTEiBHdevzd3/2d+fjjj/fq210GtH3d8owZM3rN0LY4nVEtPe/pGEJIfPKp5fzZs2ebKSkp5uDBg81/+Zd/MZubm7v1iGU+xat223qGYZg5OTnmggULzE8++aT1mO6ysafX0/NB54TYJx6ZtXnzZnPy5MlmWlqamZqaak6aNMn87W9/2+OLD+zmxj/90z+ZgwYNMr1er1lUVGT+4Q9/6PH4H/7wh+bChQt7vW7HrBk4cKA5Y8YMc+vWra3H2P1Z8fXXXzcnT55ser1ec/To0a3/JiUyGKbZy3sfCemGQCCAoUOHYvPmzbjkkksSpjYhpG9w4sQJ5OTkoLy8vN2r0p2AGUUI6QnJfJKsTQjpm8QzN4qKivDDH/4QX/nKV2J6XdJ34DOlSMScPHkS//zP/4zPf/7zCVWbENI3OHnyJH75y1+K/KOLGUUI6QnpfJKqTQjpm8QrN5qamjBv3jzMnj07ptclfQveKUUIIYQQQgghhBBCHId3ShFCCCGEEEIIIYQQx+GmFCGEEEIIIYQQQghxHG5KEUIIIYQQQgghhBDH4aYUIYQQQgghhBBCCHEcbkoRQgghhBBCCCGEEMfhphQhhBBCCCGEEEIIcRxuShFCCCGEEEIIIYQQx+GmFCGEEEIIIYQQQghxHG5KEUIIIYQQQgghhBDH4aYUIYQQQgghhBBCCHEcbkoRQgghhBBCCCGEEMfhphQhhBBCCCGEEEIIcRxuShFCCCGEEEIIIYQQx+GmFCGEEEIIIYQQQghxHI/TBUOhECoqKpCeng7DMJwuTwhRiGmaqKurQ25uLlwuub1y5hMhpCs0ZBTziRDSFcwnQohW7OaT45tSFRUVyMvLc7osIaQPUFZWhmHDhonVZz4RQnpCMqOYT4SQnmA+EUK00ls+Ob4plZ6efu4/BridLg+cDsrWpwMdtDlI12/j0C4fBBDPJ0DVeIg5SNenAx26cZDMKOYTHdTUp4NKB+aTnrGgAx3E6ytz6C2fHN+Uar2lc4AbmbcVOF0etU/th9kQgJHqQcbifMfr04EO2hyk6wNAzepS4HRQ/JZv6XwCdIyHtIN0fTrQoSMaMor5RAct9emgy4H5ZKFhLOhABy31tTjYzSc+6JwQQgghhBBCCCGEOA43pQghhBBCiCMYpimtQAhRCvOBkMTE8Y/vEUIIIYSQxGBSVSMW7qnGtIozGHfKD28IaHIB+wb68HZuCp4pzMKu7GRpTUKIAMwHQgjATSkSIcGqRgRPNQHNISDJBfdAL9wO/qUhXZ8O5ygaVoiCoaOQlpyK+sYGlB4/iJLyPY46ENIWDetC2kG6vhYH5pMco6qbsGLrMUw/dgbNBpA0aTJw5XggPR3eujp8bu9ejN9VjCW7q/FWTgrumpWDg1leae24o2Fd0EGHQyLnU8d8+MAENgCoA5AeAsZ/5sdtJ/2O5kMij0cL0muCDrocnFwT3JQitjEDITSX1sJfcgqhE/5Of+4a7IOvaCCSCjJgeGL/yVDp+nQ4R3KSD/OmzsGSGYswKW9Cpz/fVfYRVm1bgw07X0Jjc2dHQmKNhnUh7SBdX4sD80me+R/XYOXWSri9PmDxYiTdcw8wZUqn45Lefx945BFcun4dtq87iDtn5WDD2AwB4/iiYV3QQYcD8+lcPjQFTawG8IgJFHdx3GQTuAfA/GNn4pYPHA/5NUEHXQ5Sa4KbUsQWweomNGw6ArMu0O0xoRN+nNlaicZ3TiD1+uFwx/A3GtL16XCOMUNGYuPSJ5A3KBchM9TlMRdeMA4rFj6I+2YvxY0r7sCBqsMxdSCkLRrWhbSDdH0tDswneeZ/XINVW44BBQVw/fnPwIgRQKjrsUBREfD738Nz//1wXXklHt+yHwZMPDc201npOKJhXdBBhwPz6Vw+fAzgGgCH0f0DjksA3A7gxwBeCZp4fEtFTPOB4yG/Juigy0FyTfBB56RXgtVNqH/uEMz67hdJW8z6AOqfO4RgdVO/qE+Hc4wZMhKvfncdcrOGwjAMuF3uLo9zu9wwDAO5WUPx2r3rMTp7RMwcCGmLhnUh7SBdX4sD80me0dVNWLm10tqQ2r4duOACwDAAd9djAbfb+vMLLoBrxw4gPx8rt1ZiVAznhSQa1gUddDgwn87lw8cALgNQfvb73WxZt36/HMA0AKVAzPKB4yG/Juigy0F6TYS9KfXmm2/iuuuuQ25uLgzDwAsvvBATEaITMxBCw6YjQFMIsPtCDBNAk3WeGejur5q+UZ8O50hO8mHj0ieQ7kuFx23vJkuP24N0XypeuOtJJCf5onboDeZTYqFhXUg7SNfX4sB80sGjW4/B5fVZd0ilpwNJSfZOTEoC0tPh2rIFbq8PK7Yei6+oA2hYF3TQ4cB8snh06zH4gyauAVALIGjzvODZ468G0BQ0o86HvjAe8UZ6TdBBl4OGNRH2plRDQwMmTZqElStXRl2c6Ke5tNa6jTDcN7SagFkXQHNpbZ+uT4dzzJs6B3mDcm2HVQsetwfDz7sAN180O2qH3mA+JRYa1oW0g3R9LQ7MJ3kmfdqI6cfOIGnBP1gf2bO7IdVCUhIwciQ8X1mA6cfOYFJVY3xEHULDuqCDDgfm07l82ADrI3t2N6RaCJ49bwMQdT70hfGIN9Jrgg66HDSsibA3pWbPno0HHngAN910U9TFiX78JacS+nw6nGPJjEXdfr64N4KhIJbMWBS1Q28wnxILDetC2kH6fC0OzCd5Fu6tRrMB4J57un+GVG8Eg8Ddd6PZABbuqY6lnuNoWBd00OHAfDqXD48g8mfHuGCdH20+9IXxiDfSa4IOuhw0rAk+U4p0S7Cqscsn/4dD6IQfwQh/myFdnw7nKBpWiEl5E7r9fHFvuF1uTB4+EUXDCiN2IKQtGtaFtIN0fS0OzCcdTKs4g6RJk6237HX3DKnecLuBqVORNGkyvnDsTEz9nETDuqCDDgfmk8W0ijP44Oxb9iL9sFEIwPsAPjARcT5wPOTXBB10OWhZE3HflPL7/aitrW33RfoGwVOxewhvX6xPh3MUDB0VE4f8GF0nVjCf+i4a1oW0g3R9LQ7MJx2MO+UHxo+P0cXGYfzJvvv6dQ3rgg46HJhPFuNO+bE3RrX3ARHnQ38dj3CQXhN00OWgZU3EfVNq+fLlyMzMbP3Ky8uLd0kSK5qjf3AaAOvBbX2xPh1aSUtOjYlCeoyuEyuYT30YBetC3EG6vhIH5pM8hmnCG4L1cPNYkJEBb8i6bp9Ewbqggw4H5tO5fKiLUe1aIOJ86K/jERZcl3Rog5Y1EfdNqWXLlqGmpqb1q6ysLN4lSaxIitH08EZ4Hen6dGilvrEhJgp1MbpOrGA+9WEUrAtxB+n6ShyYT/KYhoEmF4C6GP2zs7YWTS7run0SBeuCDjocmE/n8iFGW9bIACLOh/46HmHBdUmHNmhZE+E9Yj0CfD4ffL6+/+rMRMQ90Bub62RFdh3p+nQ4R+nxgzFxKK08EJPrxArmU99Fw7qQdpCur8WB+aSDfQN9+NzeGH1AZ+9e7B3Ud/6/d0TDuqCDDgfmk8W+gT6M/yw2H8kdD0ScD/11PMJBek3QQZeDljUR9rZafX09iouLUVxcDAA4ePAgiouLceTIkahEiD7c2clwDY7uh0LXYB/c2cl9sj4dzlFSvge7yj5CMBTuS3wtgqEgio98iN1HY/VEga5hPiUOGtaFtIN0fS0OzCcdvJ2bguZdxcD771tv0YuEYBDYuRPNJbuwPSclpn5OomFd0EGHA/PJ4u3cFFxoAJMR3dv3pgCYaCDifOgr4xFPpNcEHXQ5aFkTYefCu+++iylTpmDKlCkAgO985zuYMmUK/v3f/z0qEaITX9HAhD6fDudYtW0NXEZkP0q4XW6s2rYmaofeYD4lFhrWhbSD9PlaHJhP8jxTmIUkE8AjjwCuCP/Z6XYDjz6KJNO6Xl9Gw7qggw4H5tO5fLgH0b197x4g6nzoC+MRb6TXBB10OWhYE2FXv/zyy2GaZqevp556KmoZoo+kggwY6R4g3I9tG4CR7kFSQUafrk+Hc2zY+RLKTlYgEAyEdV4gGMCRz47i+fc2R+3QG8ynxELDupB2kK6vxYH5JM+u7GS8lZOCwPp1wOHDQHNzeBdobgYOHULg2fV4KycFu6L4za8GNKwLOuhwYD6dy4f5AEYACPfl8+6z580Dos6HvjAe8UZ6TdBBl4OGNRH3B52Tvo3hcSH1+uHWA9TsLhYDgNc6z/BEN8Wk69PhHI3Nfty08muo8zfYDq1AMIA6fwNuXHEHGpv77uu9iU40rAtpB+n6WhyYTzq4a1YOgk1+hK66ynroud2NqeZmoK4OoSuvRLDJj7tm5cRX1AE0rAs66HBgPlncNSsHXreBl2E9rNzuxpT77PGvAPC6jajzgeMhvybooMtBw5rgphTpFXeWF2nzR8JIs/dcfCPNg7T5I6N66Jqm+nQ4xyefHsIVDy9ARfVxAOj288ct36+oPo4rHl6AA1WHY+ZASFs0rAtpB+n6WhyYT/IczPLizlk5QGkpQpdeChw9av1Bd8+Yavn+0aPW8fv3485ZOTgYw3khiYZ1QQcdDsync/kwFsDbAIad/X53/xht+f6ws8cXADHLB46H/Jqggy4H6TUR97fvkf6BO8uL9FtGo7m0Fv6SUwid6Lwj6hrsg69ooHUbYgx2bTXVp8M5DlQdxiUPzMXNF83GkhmLMHn4xE7H7C7fi1Xb1uD59zb3i98oEd1oWBfSDtL1tTgwn+TZMDYDBkys3PoJ3BMK4fnKAuDuu4GpUzsfXFwMPPooAs+uR7DJjzuvzMWGsdF/FEETGtYFHXQ4MJ/a5kMldgdNbADwCID3uzh2EqxnSM2DdYfU12flxDQfOB7ya4IOuhwk1wQ3pYhtDI8L3sIseAuzEKxqRLC6CWgKAV4X3FneqJ783xfq0+Ecjc1+rN3xAtbueAFFwwqRP3QU0pNTUdfYgP3HD6KkfE/cHQhpi4Z1Ie0gXV+LA/NJnufGZmLnkBSs2HoM059+Gs1/eBpJkyYD48YBGRlAbS2wbx+adxUjyQS256bg7pmj+s0dUh3RsC7ooMOB+dQ+HxYfO4OFBvCBCewDUAvro3rjAFxoWA81/0tuCu6eGZ87KDke8muCDrocpNYEN6VIRLizkx1ZGFrr0+EcJeV7EuIvbdJ30LAupB2k62txYD7JcTDLi7k3j8CkqkYs3FONL5TvwfiSYnhDQJML2DvIh+0XZuGZwqw+/1DzcNCwLuigwyGR86lTPhw7g5tP+tvlw+qcFEfzIZHHowXpNUEHXQ5OrgluShFCCCGEkLiwKzsZu7LPb/1vwzRhGuG+ZogQ0h9hPhBCAD7onBBCCCGEOAT/wUkI6Q7mAyGJCTelCCGEEEIIIYQQQojjGKZpmk4WrK2tRWZmplU81flPD5qnA4AJwACMATKfXqQDHTQ5SNcHALMhAACoqalBRobcm5ek8wlQMh6ck3SgQ3sHBRnFfKKDlvp0UObAfAKgZCzoQAcl9dU42Mwn0WdKtUjKFBeuTwc6aHOQrq8M8V5oGA9pB+n6dKCDUsT7oGEs6CBfnw66HJQg3gcNY0EHOmipr8WhF0Q3pXinFB3oIO8gXR/QGZT8TZ+cg3R9OtChk4OyjGI+JbaDdH06KHNgPgFQMhZ0oIOS+mocbOaT3KbUADcyFuc7Xrb2qf0wGwIwBnhE6tOBDtocpOsDQM3qUuB0UKR2lwjlE6BjPKQdpOvTgQ4dUZVRzKeEd5CuTwddDswnCw1jQQc6aKmvxcFuPvFB54QQQgghhBBCCCHEcbgpRQghpF9iOPseD0IIIYQQQkiYiD5TihBCCIkVk6oasXBPNaZVnMG4U354Q0CTC9g30Ie3c1PwTGEWdmUnS2sSQgghhBBCzsJNqTAJVjUieKoJaA4BSS64B3rhdvgfOUXDClEwdBTSklNR39iA0uMHUVK+x1EHaTgOFhr6oMGBWGgYCwmHUdVNWLH1GKYfO4NmA/jABDYAqAOQHgLGf+bHbSf9WLK7Gm/lpOCuWTk4mOWNq5P0WDCf9DgQCw1zUoODhjmpwYFjIV+ftEd6PLgm9DhwLJx34KaUDcxACM2ltfCXnELohL/Tn7sG++ArGoikggwYnvh8IjI5yYd5U+dgyYxFmJQ3odOf7yr7CKu2rcGGnS+hsbmzY3+A42ChoQ8aHIiFhrGQdJj/cQ1Wbq1EU9DEagCPmEBxF8dNNoF7AMw/dgbb1x3EnbNysGFsRkxdpMeC+aTHgVhomJMaHDTMSQ0OHAv5+qQ90uPBNaHHgWMh68BNqV4IVjehYdMRmHXdv84wdMKPM1sr0fjOCaRePxzuGP8GfsyQkdi49AnkDcpFyAx1ecyFF4zDioUP4r7ZS3HjijtwoOpwTB2k4ThYaOiDBgdioWEsJB3mf1yDVVuO4WMA1wA4jO4flFgC4HYAPwbwStDE41sqYMDEc2MzY+IiPRbMJz0OxELDnNTgoGFOanDgWMjXJ+2RHg+uCT0OHAt5B27B90Cwugn1zx2CWd/9wLTFrA+g/rlDCFY3xcxhzJCRePW765CbNRSGYcDtcnd5nNvlhmEYyM0aitfuXY/R2SNi5iANx8FCQx80OBALDWMh6TC6ugkrt1biYwCXASg/+/2uf5Q49/1yANMAlAJYubUSo2LgIj0WzCc9DsRCw5zU4KBhTmpw4FjI1yftkR4Prgk9DhwLHQ5hbUotX74cn//855Geno4hQ4bgxhtvxL59+2Iiog0zEELDpiNAUwiw+wInE0CTdZ4Z6O6fRvZJTvJh49InkO5Lhcdt76Y2j9uDdF8qXrjrSSQn+aJ2kIbjYKGhDxoceoL51NtJ6Ffz4dGtx+APmrgGQC2AoM3zgmePvxpAU9DEiq3HovKQ7gPzSY9DbyRKRmmYkxocNMxJDQ4cC/n6dkiUfALkx4NrQo8Dx0KPQ1ibUtu2bcPSpUuxfft2bNmyBc3NzbjqqqvQ0NAQtYg2mktrrVvXwn2juAmYdQE0l9ZG7TBv6hzkDcq1vUha8Lg9GH7eBbj5otlRO0jDcbDQ0AcNDj3BfLJBP5kPkz5txPRjZ7AB1kf27G5ItRA8e94GANOPncGkqsaIXaTHgvmkx6E3EiWjNMxJDQ4a5qQGB46FfH07JEo+AfLjwTWhx4FjocchrE2pl19+GYsXL8bEiRMxadIkPPXUUzhy5Ah27twZtYg2/CWnRM8HgCUzFnX7udbeCIaCWDJjUdQO0nAcLDT0QYNDTzCfnDtf2mHh3mo0G8AjiPwz6C5Y5zcbwMI91RG7SI8F80mPQ28kSkZpmJMaHDTMSQ0OHAv58+2QKPkEyI8H14QeB46FHoeonilVU1MDABg0aFDUIpoIVjV2+bT5cAid8CMYxW/fi4YVYlLehG4/19obbpcbk4dPRNGwwogdpOE4WGjogwaHcGE+dU9fnw/TKs7gg7Nv2Yv0huEQgPcBfGACXzh2JqJrSPeB+aTHIRL6Y0ZpmJMaHDTMSQ0OHAv5+pHSH/MJkB8Prgk9DhwLPQ5AFJtSoVAI3/72tzF9+nRceOGF3R7n9/tRW1vb7ks7wVOxe6hhpBQMHRUTh/wYXUcCjoOFhj5ocAgH5pON6/Th+TDulB97Y2IA7AMw/mRkfxlL94H5pMchXOxkVF/MJw1zUoODhjmpwYFjIV8/EvprPgHy48E1oceBY6HHAYhiU2rp0qX44IMPsG7duh6PW758OTIzM1u/8vLyIi3pHM0xeqBgU+TXSUtOjYlCeoyuIwLHwUJBH1Q4hAHzyQZ9dD4YpglvCKiLjQFqAXhD1nXDRngsmE+KHMLETkb1xXzSMCc1OKiYkwocOBYK6kdAf80nAOLjwTWhx4FjocgBEW5K3XXXXXjxxRfx+uuvY9iwYT0eu2zZMtTU1LR+lZWVRSTqKElRfarxHN7Ir1PfGJsHC9bF6DoicBwsFPRBhYNNmE826aPzwTQMNLmA9NgYIANAk8u6btgIjwXzSZFDGNjNqL6YTxrmpAYHFXNSgQPHQkH9MOnP+QRAfDy4JvQ4cCwUOQAI61Hzpmni7rvvxsaNG/HGG29g1Kjeb1fz+Xzw+aJ/XaKTuAd6Y3OdrMivU3r8YEwcSisPxOQ6EnAcLDT0QYNDbzCfwrxOH54P+wb6MP6z6D7/3sJ4AHsHRTYHpPvAfNLjYIdwM6ov5pOGOanBQcOc1ODAsZCvb5dEyCdAfjy4JvQ4cCz0OABh3im1dOlSrFmzBmvXrkV6ejoqKytRWVmJM2cie0isVtzZyXANji5oXYN9cGcnR3x+Sfke7Cr7CMFQuC87twiGgig+8iF2H43Vk1ech+NgoaEPGhx6g/lkn74+H97OTcGFBjAZ0b19bwqAiQawPSclomtI94H5pMfBDomQURrmpAYHDXNSgwPHQr6+XRIhnwD58eCa0OPAsdDjAIT58/xjjz2GmpoaXH755cjJyWn9Wr9+fVQSGvEVDRQ9HwBWbVsDlxHZP7ncLjdWbVsTtYM0HAcLDX3Q4NATzCfnzpd2eKYwC0kmcA+ie/vePQCSTOt6kSI9FswnPQ69kSgZpWFOanDQMCc1OHAs5M+3Q6LkEyA/HlwTehw4FnocwhoF0zS7/Fq8eHHUItpIKsiAke4Bwn3MiAEY6R4kFWRE7bBh50soO1mBQDAQ1nmBYABHPjuK59/bHLWDNBwHCw190ODQE8wnG/ST+bArOxlv5aRgPoARAMJ9ma/77HnzALyVk4JdUfx2R3osmE96HHojUTJKw5zU4KBhTmpw4FjI17dDouQTID8eXBN6HDgWehyceWpeH8TwuJB6/XDroV12B8gA4LXOMzzRt7ax2Y+bVn4Ndf4G24slEAygzt+AG1fcgcbm2DxzRRKOg4WGPmhwIBYaxkLa4a5ZOfC6DbwM62Hldjem3GePfwWA123grlk5UXlI94H5pMeBWGiYkxocNMxJDQ4cC/n6pD3S48E1oceBY6HHgSnXA+4sL9Lmj4SRZu958EaaB2nzR8b0YYSffHoIVzy8ABXVxwGg28+9tny/ovo4rnh4AQ5UHY6ZgzQcBwsNfdDgQCw0jIWkw8EsL+6clYOxAN4G0PKOoO7+Umv5/rCzxxcAuHNWDg7GwEV6LJhPehyIhYY5qcFBw5zU4MCxkK9P2iM9HlwTehw4Fjocwnr7XiLizvIi/ZbRaC6thb/kFEInOu+Iugb74CsaaN36FoffZhyoOoxLHpiLmy+ajSUzFmHy8Imdjtldvhertq3B8+9t7hd3SHWE42ChoQ8aHIiFhrGQdNgwNgMGTKzcWondQRMbADwC4P0ujp0E6xlS82DdIfX1WTnYMDZ2H4mQHgvmkx4HYqFhTmpw0DAnNThwLOTrk/ZIjwfXhB4HjoW8AzelbGB4XPAWZsFbmIVgVSOC1U1AUwjwuuDO8sb9jRiAdXvh2h0vYO2OF1A0rBD5Q0chPTkVdY0N2H/8IErK98TdQRqOg4WGPmhwIBYaxkLS4bmxmdg5JAUrth7D4mNnsNAAPjCBfQBqYX1UbxyACw3roeZ/yU3B3TNjc4dUR6THgvmkx4FYaJiTGhw0zEkNDhwL+fqkPdLjwTWhx4FjIevATakwcWcni/+FUVK+JyE2oXqC42ChoQ8aHIiFhrGQcDiY5cXcm0dgUlUjFu6pxheOncHNJ/3whoAmF7B3kA+rc1LwTGFWVA81DwfpsWA+6XEgFhrmpAYHDXNSgwPHQr4+aY/0eHBN6HHgWDjvwE0pQggh/YJd2cnYlX1+638bpgnTCPdVIoQQQgghhBCn4IeVCSGE9Eu4IUUIIYQQQohuuClFCCGEEEIIIYQQQhzHME3TdLJgbW0tMjMzreKpzn960DwdAEwABmAMkPn0Ih3ooMlBuj4AmA0BAEBNTQ0yMmL3RrRwkc4nQMl4cE7SgQ7tHRRkFPOJDlrq00GZA/MJgJKxoAMdlNRX42Azn0SfKdUiKVNcuD4d6KDNQbq+MsR7oWE8pB2k69OBDkoR74OGsaCDfH066HJQgngfNIwFHeigpb4Wh14Q3ZTinVJ0oIO8g3R9QGdQ8jd9cg7S9elAh04OyjKK+ZTYDtL16aDMgfkEQMlY0IEOSuqrcbCZT3KbUgPcyFic73jZ2qf2w2wIwBjgEalPBzpoc5CuDwA1q0uB00GR2l0ilE+AjvGQdpCuTwc6dERVRjGfEt5Buj4ddDkwnyw0jAUd6KClvhYHu/nEB50TQgghhBBCCCGEEMfhphQhhBBCCCGEEEIIcRxuShFCCCGEEEIIIYQQxxF90HlfJFjViOCpJqA5BCS54B7ohTs7OeEcioYVomDoKKQlp6K+sQGlxw+ipHxPwtTX4qBhLmhwIBYaxoIOOhyYT3ociAXnpAX7YME+yNcn7ZGekxrmg3QPAPahBQ19cNKBm1I2MAMhNJfWwl9yCqET/k5/7hrsg69oIJIKMmB44nPzmQaH5CQf5k2dgyUzFmFS3oROf76r7COs2rYGG3a+hMbmzo59vb4WBw1zQYMDsdAwFnTQ4cB80uNALDgnLdgHC/ZBvj5pj/Sc1DAfpHsAsA8taOiDlAM3pXohWN2Ehk1HYNZ1/zrD0Ak/zmytROM7J5B6/XC4s7z9zmHMkJHYuPQJ5A3KRcgMdXnMhReMw4qFD+K+2Utx44o7cKDqcL+pr8VBw1zQ4EAsNIwFHXQ4MJ/0OBALzkkL9sGCfZCvT9ojPSc1zAfpHgDsQwsa+iDpwC34HghWN6H+uUMw67sfmLaY9QHUP3cIweqmfuUwZshIvPrddcjNGgrDMOB2ubs8zu1ywzAM5GYNxWv3rsfo7BH9or4WBw1zQYMDsdAwFnTQ4cB80uNALDgnLdgHC/ZBvj5pj/Sc1DAfpHsAsA8taOiDtAM3pbrBDITQsOkI0BQCTLsnAWiyzjMDXe+y9jWH5CQfNi59Aum+VHjc9m6s87g9SPel4oW7nkRykq9P19fioGEuaHAgFhrGgg46HJhPehyIBeekBftgwT7I1yftkZ6TGuaDdA8A9qEFDX3Q4BDWptRjjz2GoqIiZGRkICMjA9OmTcPmzZujltBIc2mtdeua3YFpwQTMugCaS2v7hcO8qXOQNyjX9kJtweP2YPh5F+Dmi2b36fpaHDTMBQ0OPcF8skE/mw90YD5pcuiNRMkozkkL9sGCfZCvb4dEySdAfk5qmA/SPQDYhxY09EGDQ1ibUsOGDcNDDz2EnTt34t1338WsWbNwww034MMPP4xaRBv+klOi52txWDJjUbefre2NYCiIJTMW9en6Whw0zAUNDj3BfHLufDrocWA+6XHojUTJKM5JC/bBgn2QP98OiZJPgPyc1DAfpHsAsA8taOiDBoewNqWuu+46zJkzBwUFBRg7diwefPBBpKWlYfv27VGLaCJY1djl0+bDIXTCj2BVY592KBpWiEl5E7r9bG1vuF1uTB4+EUXDCvtkfS0OGuaCBofeYD7Zpz/MBzownzQ52CERMopz0oJ9sGAf5OvbJRHyCZCfkxrmg3QPAPahBQ190OAARPFMqWAwiHXr1qGhoQHTpk3r9ji/34/a2tp2X9oJnordQw37skPB0FExcciP8DrS9bU4aJgLGhzCqsN86v06fXw+0IH5pMkh7Fo2Mqov5hPnpAX7YME+yNePqFY/zSdAfk5qmA/SPQDYhxY09EGDAxDBptTu3buRlpYGn8+Hb3zjG9i4cSMmTJjQ7fHLly9HZmZm61deXl5Uwo7QHKMHCjZFcR0FDmnJqTFRSI/wOtL1tThomAsqHGzAfAqDvj4f6MB80uRgk3Ayqi/mE+ekBftgwT4oqB8G/T2fAAVzUsF8EO8BwD60oKAPKhwQwabUuHHjUFxcjB07duCb3/wmbr31Vnz00UfdHr9s2TLU1NS0fpWVlUUl7AhJMXopoTeK6yhwqG9siIlCXYTXka6vxUHDXFDhYAPmUxj09flAB+aTJgebhJNRfTGfOCct2AcL9kFB/TDo7/kEKJiTCuaDeA8A9qEFBX1Q4QAgvEfNA/B6vcjPzwcATJ06Fe+88w5+85vf4He/+12Xx/t8Pvh80b8u0UncA72xuU5W5NfR4FB6/GBMHEorD/TJ+locNMwFDQ52YD6FcZ0+Ph/owHzS5GCXcDKqL+YT56QF+2DBPsjXD4f+nk+A/JzUMB+kewCwDy1o6IMGByCKZ0q1EAqF4PdH93Asbbizk+EaHF3Qugb74M5O7tMOJeV7sKvsIwRDwYjOD4aCKD7yIXYf3dsn62tx0DAXNDhEAvOpa/rDfKAD80mTQ6T0t4zinLRgHyzYB/n60dDf8gmQn5Ma5oN0DwD2oQUNfdDgAIS5KbVs2TK8+eabOHToEHbv3o1ly5bhjTfewMKFC6OS0IivaKDo+VocVm1bA5cR2d6l2+XGqm1r+nR9LQ4a5oIGh55gPjl3Ph30ODCf9Dj0RqJkFOekBftgwT7In2+HRMknQH5OapgP0j0A2IcWNPRBg0NYo/Dpp5/iq1/9KsaNG4crrrgC77zzDl555RVceeWVUYtoI6kgA0a6BzDCPNEAjHQPkgoy+oXDhp0voexkBQLBQFjnBYIBHPnsKJ5/b3Ofrq/FQcNc0ODQE8wnG/Sz+UAH5pMmh95IlIzinLRgHyzYB/n6dkiUfALk56SG+SDdA4B9aEFDHzQ4hLUp9eSTT+LQoUPw+/349NNP8eqrr/bLsAIAw+NC6vXDrYd22R0gA4DXOs/wRP/QMA0Ojc1+3LTya6jzN9hesIFgAHX+Bty44g40Nkd32690fS0OGuaCBoeeYD71dhL63XygA/NJk0NvJEpGcU5asA8W7IN8fTskSj4B8nNSw3yQ7gHAPrSgoQ8aHOKfcn0Yd5YXafNHwkiz9zx4I82DtPkjY/owQg0On3x6CFc8vAAV1ccBoNvP3rZ8v6L6OK54eAEOVB3uF/W1OGiYCxociIWGsaCDDgfmkx4HYsE5acE+WLAP8vVJe6TnpIb5IN0DgH34/+yde3xU9Zn/P2dmMhPIHQhIINwDRGxAabVddjdVqy3eL1jbwrZUW7YVte3+bLtsu7ttbaVdt9tuxdqiVO2iRVeUZa3UqiiuN6ooBDVokFtCAIMkmSSQSWbm+/tjTEjIZc6Zy3meZD7v12v+MJxznjfP83wfxm/OnOlCQx6kHRx/+16m4S30I+8L09BZE0SoqhHRo313RD1jAghUFMVufUvDbzM0OOxp2I+zf3wxrjprIZZVLsG8SXP6HLOzbhdWb1mLR1/flJKdY03xtTho6AUNDiSGhlrQQYcD55MeBxKDPRmDeYjBPMjHJ72R7kkN/SCdA4B56EJDHiQduCllA8vngb+8EP7yQkQa2hFp6gA6ooDfA2+h35VvxNDg0N4ZwoNbN+DBrRtQMbEcM8ZNRV52Dlra27D7yF5U1VUP6/haHDT0ggYHEkNDLeigw4HzSY8DicGejME8xGAe5OOT3kj3pIZ+kM4BwDx0oSEPUg7clHKItzhb/B8MDQ5VddWuLE6t8bU4aOgFDQ4khoZa0EGHA+eTHgcSgz0Zg3mIwTzIxye9ke5JDf0gnQOAeehCQx7cdOB9oYQQQgghhBBCCCHEdbgpRQghhBBCCCGEEEJch5tShBBCCCGEEEIIIcR1LGOMcTNgMBhEQUFBLHiO+4+0MsfDgAFgAdZImUdq0YEOmhyk4wOAaQsDAJqbm5Gfny/iAMjPJ0BJPdiTdKBDbwcFM4rziQ5a4tNBmQPnEwAltaADHZTEV+Ngcz6JPui8S1ImuHB8OtBBm4N0fGWI50JDPaQdpOPTgQ5KEc+DhlrQQT4+HXQ5KEE8DxpqQQc6aImvxSEOoptSvFOKDnSQd5COD+gclPxNn5yDdHw60KGPg7IZxfmU2Q7S8emgzIHzCYCSWtCBDkriq3GwOZ/kNqVGepG/dIbrYYP37YZpC8Ma6ROJTwc6aHOQjg8AzffWAMcjIrH7RWg+ATrqIe0gHZ8OdDgVVTOK8ynjHaTj00GXA+dTDA21oAMdtMTX4mB3PvFB54QQQgghhBBCCCHEdbgpRQghhBBCCCGEEEJch5tShBBCCCGEEEIIIcR1RB907pSKieUoGzcVudk5aG1vQ82Rvaiqq3bVIdLQjkhjB9AZBbI88Bb54S3OdtWBedCRAw0O0nXQ4qAB9kMM5iGGdB405IAOupDuSen4Whw09CTzoMNBOr4mNPSktIN0fEBHTzIPmemgflMqOyuAq+dfhGWVSzC39PQ+f76j9m2s3rIW67c9gfbOUFocTDiKzpogQlWNiB7tG8MzJoBARRGyyvJh+dJz8xnzoCMHGhyk66DFQQPshxjMQwzpPGjIAR10Id2T0vG1OGjoSeZBh4N0fE1o6ElpB+n4gI6eZB7oYBljTMquZoNgMIiCggJgpBcFXy4b9NjpY6fgseX3oHRUCaImCq/H2+eYSDQCj+VB7bF6XLHqeuxp2D94/K6n0OfYewp9pKkDbRsPwLTE/zpDK8+HnMsmwVvoT6kD85CeHAxFB+k6pMuh65sZmpubkZ+fH/e66UJ6PgHy/eDUgfMphnQeNPTCcHbQMKOczCdAvic1zEgNDpxPH15fQR6kHTifTqKhJ6Udhut8cuowXPMw1OZTuhzszie1W/DTx07B07esQ0nhOFiW1W+DAoDX44VlWSgpHIdnvv0QphVPTplDpKkDrY/sg2mNXxgAMK1htD6yD5GmjpQ5MA86cqDBQboOWhw0wH6IwTzEkM6DhhzQQRfSPSkdX4uDhp5kHnQ4SMfXhIaelHaQjg/o6EnmgQ5dqNyUys4K4LHl9yAvkAOf194nDH1eH/ICOdhw4xpkZwWSdjDhKNo2HgA6ooDde8kMgI7YeSYcTdqBedCRAw0O0nXQ4qAB9kMM5iGGdB405IAOupDuSen4Whw09CTzoMNBOr4mNPSktIN0fEBHTzIPdOhJUptSP/3pT2FZFr75zW8mLdKTq+dfhNJRJbYbtAuf14dJoyfgqrMWJu3QWROM3brm9MONBjAtYXTWBJN2YB505ECDg3QdtDg4gfOpHzifYgyjPGjIAR2ck675BMj3pHR8LQ4aepJ50OEgHT8RhvN7KGkH6fiAjp5kHujQk4Q3pV599VX89re/RUVFRdISp7KscgmiJrEdt0g0gmWVS5J2CFU1ip4PMA+AjhxocJCugxYHu3A+pfd8gHnoQjoPGnJAB2ekcz4B8j0pHV+Lg4aeZB50OEif75Th/h5K2kE6PqCjJ5kHOvQkoU2p1tZWLF68GHfffTeKioqSluhJxcRyzC09fcDPlMbD6/Fi3qQ5qJhYnrBDpKG936fNOyF6NIRIQ3vC5zMPOnKgwUG6Dloc7ML5FB/OpxhDPQ8ackAHZ6RzPgHyPSkdX4uDhp5kHnQ4SMd3ynB/DyXtIB0f0NGTzAMdTiWhTanly5fj4osvxqc+9am4x4ZCIQSDwV6vwSgbNzURpT7MSOI6kcbUPdQwUZgHHTnQ4CBdBy0OduF8snkdzqfYdYZwHjTkgA7OSOd8AuR7Ujq+FgcNPck86HCQju8UuzNqKM4nDQ7S8QEdPck80OFUnH2IE8C6devw+uuv49VXX7V1/MqVK/HDH/7Q9vVzs3OcKvVLXjLX6UzRAwU7Er8O86AjBxocpOugxsEGnE8O4HyKMZTzoCAHdLBPuucTIN+T0vG1OGjoSeZBiYN0fAc4mVFDcT5pcJCOD0BFTzIPdDgVR3dK1dbW4hvf+AYeeOABZGdn2zpnxYoVaG5u7n7V1tYOenxre5sTpQFpSeY6WSn6UkJ/4tdhHnTkQIODdB3UOMSB88khnE8xhnIeFOSADvZwYz4B8j0pHV+Lg4aeZB6UOEjHt4nTGTUU55MGB+n4AFT0JPNAh1NxdKfUtm3b8P777+Oss87q/lkkEsHzzz+PVatWIRQKwevt/dnQQCCAQMD+VzbWHNnrRGng6xzek/C53iJ/Shy8hYlfh3nQkQMNDtJ10OIQD84nh9fhfIpdZwjnQUMO6GAPN+YTIN+T0vG1OGjoSeZBh4N0fLs4nVFDcT5pcJCOD+joSeaBDqfiaEvr/PPPx86dO7F9+/bu10c/+lEsXrwY27dv7/OGKhGq6qqxo/ZtRKKRhM6PRCPYfuAt7Dy4K2EHb3E2PGOcDdpT8YwJwFts77eh/cE86MiBBgfpOmhxiAfnk304n2IM9TxoyAEd7OHGfALke1I6vhYHDT3JPOhwkI5vl0x5DyXtIB0f0NGTzAMd+lzDycF5eXk444wzer1ycnIwevRonHHGGUmJ9GT1lrXwWIndAub1eLF6y9qkHQIVyX3jRLLnA8wDoCMHGhyk66DFYTA4n9w7H2AeupDOg4Yc0CE+bs0nQL4npeNrcdDQk8yDDgfp8+2QSe+hpB2k4wM6epJ5oENP0vsB5QRZv+0J1B6rRzgSdnReOBLGgQ8O4tHXNyXtkFWWDyvPB1gOT7QAK8+HrLL8pB2YBx050OAgXQctDhpgP8RgHmJI50FDDuigC+melI6vxUFDTzIPOhyk42tCQ09KO0jHB3T0JPNAh54kvSn13HPP4Ze//GXSIj1p7wzhyju/gpZQm+1GDUfCaAm14YpV16O9M5S0g+XzIOeySbGHdtktkAXAHzvP8iW/38c86MiBBgfpOmhxcArnU8+TwPkEDLs8aMgBHRIjHfMJkO9J6fhaHDT0JPOgw0E6fqIM1/dQ0g7S8QEdPck80KEnKu+UAoD33t+H82+/FvVNRwBgwM+cdv28vukIzr/9Wuxp2J8yB2+hH7mLpsDKtfc8eCvXh9xFU1L6MELmQUcONDhI10GLgwbYDzGYhxjSedCQAzroQronpeNrcdDQk8yDDgfp+JrQ0JPSDtLxAR09yTzQoQtH377nNnsa9uPsH1+Mq85aiGWVSzBv0pw+x+ys24XVW9bi0dc3pWTH9FS8hX7kfWEaOmuCCFU1Inq0bwzPmAACFUWxW9/S8NsM5kFHDjQ4SNdBi4MG2A8xmIcY0nnQkAM66EK6J6Xja3HQ0JPMgw4H6fia0NCT0g7S8QEdPck80AFQvikFxG7te3DrBjy4dQMqJpZjxripyMvOQUt7G3Yf2Yuquuq0O1g+D/zlhfCXFyLS0I5IUwfQEQX8HngL/Wn/RgyAeQB05ECDg3QdtDhogP0Qg3mIIZ0HDTmggy6ke1I6vhYHDT3JPOhwkI6vCQ09Ke0gHR/Q0ZPMAx3Ub0r1pKqu2pWmHAxvcbb4PxjMg44caHCQroMWBw2wH2IwDzGk86AhB3TQhXRPSsfX4qChJ5kHHQ7S8TWhoSelHaTjAzp6knnITIfhe18oIYQQQgghhBBCCFELN6UIIYQQQgghhBBCiOtwU4oQQgghhBBCCCGEuI5ljDFuBgwGgygoKIgFz3H/kVbmeBgwACzAGinzSC060EGTg3R8ADBtYQBAc3Mz8vPzRRwA+fkEKKkHe5IOdOjtoGBGcT7RQUt8Oihz4HwCoKQWdKCDkvhqHGzOJ9EHnXdJygQXjk8HOmhzkI6vDPFcaKiHtIN0fDrQQSniedBQCzrIx6eDLgcliOdBQy3oQAct8bU4xEF0U4p3StGBDvIO0vEBnYOSv+mTc5COTwc69HFQNqM4nzLbQTo+HZQ5cD4BUFILOtBBSXw1Djbnk9ym1Egv8pfOcD1s8L7dMG1hWCN9IvHpQAdtDtLxAaD53hrgeEQkdr8IzSdARz2kHaTj04EOp6JqRnE+ZbyDdHw66HLgfIqhoRZ0oIOW+Foc7M4nPuicEEIIIYQQQgghhLgON6UIIYQQQgghhBBCiOtwU4oQQgghhBBCCCGEuI7og86dUjGxHGXjpiI3Owet7W2oObIXVXXVrjpEGtoRaewAOqNAlgfeIj+8xdmuOmjIg7QD6xBDQx40OGiA/RBDQx40OEjXgjnQ46AF6Z6Qjk+Hk2hYF8yDfHxNaOgHaQfp+FocNKwL5sF9B/WbUtlZAVw9/yIsq1yCuaWn9/nzHbVvY/WWtVi/7Qm0d4bS4mDCUXTWBBGqakT0aN8YnjEBBCqKkFWWD8uXnpvPNORB2oF1iKEhDxocNMB+iKEhDxocpGvBHOhx0IJ0T0jHp8NJNKwL5kE+viY09IO0g3R8LQ4a1gXzIOtgGWNMyq5mg2AwiIKCAmCkFwVfLhv02Oljp+Cx5fegdFQJoiYKr8fb55hINAKP5UHtsXpcsep67GnYP3j8rqfQ59h7Cn2kqQNtGw/AtMT/OkMrz4ecyybBW+hPqYOGPEg7pKMOTh3SkQOnDhr6MR0OXd/M0NzcjPz8/LjXTRfS8wmQ7wenDtKzQYuD9NpkP6bXQcOMcjKfAPl1oaEnh6vDUJtPAPOQrvgA51O3wxDryeE6n5w6aHjvIN0LgPx8SpeD3fmkdgt++tgpePqWdSgpHAfLsvptDgDwerywLAslhePwzLcfwrTiySlziDR1oPWRfTCt8QsDAKY1jNZH9iHS1JEyBw15kHZgHWJoyIMGBw2wH2JoyIMGB+laMAd6HLQg3RPS8elwEg3rgnmQj68JDf0g7SAdX4uDhnXBPOhwULkplZ0VwGPL70FeIAc+r71PGPq8PuQFcrDhxjXIzgok7WDCUbRtPAB0RAG795IZAB2x80w4mrSDhjxIO7AOMTTkQYODBtgPMTTkQYODdC2YAz0OWpDuCen4dDiJhnXBPMjH14SGfpB2kI6vxUHDumAe9Dg42pT6wQ9+AMuyer1mz56dtMSpXD3/IpSOKrHdHF34vD5MGj0BV521MGmHzppg7NY1px9uNIBpCaOzJpi0g4Y8SDuwDjE05EGDw2BwPtlgmK0LDQ7StWAO9DjEI1NmlHR8OpxEw7pgHuTj2yFT5pMGB+n4Whw0rAvmQY+D4zul5syZg0OHDnW/XnjhhaQlTmVZ5RJETWI7bpFoBMsqlyTtEKpqFD0f0JEHaQfWIYaGPGhwiAfnkzvnAzryoMFBuhbMgR4HO2TCjJKOT4eTaFgXzIP8+XbJhPmkwUE6vhYHDeuCedDj4HhTyufz4bTTTut+jRkzJmmJnlRMLMfc0tMH/DxnPLweL+ZNmoOKieUJO0Qa2vt92rwTokdDiDS0J3y+hjxIO7AOMTTkQYODHTif7DEc1oUGB+laMAd6HOwy3GeUdHw6nETDumAe5OM7YbjPJw0O0vG1OGhYF8yDHgcggU2pmpoalJSUYNq0aVi8eDEOHDgw6PGhUAjBYLDXazDKxk11qtQvM5K4TqQxdQ81TBQNeZB2YB1iaMiDBgc7cD45uM4QXxcaHKRrwRzocbCLkxnldD4B8j0hHZ8OJ9GwLpgH+fhOGO7zSYODdHwtDhrWBfOgxwFwuCl1zjnn4L777sOf/vQn3HXXXdi7dy/+5m/+Bi0tLQOes3LlShQUFHS/SktLB42Rm53jRGlA8pK5TmeKHijYkfh1NORB3IF1iKEgDyoc4sD55JAhvi40OEjXgjlQ5GADpzPK6XwC5HtCOj4deqBgXTAPCuLbJBPmkwYH6fhaHDSsC+ZBkQMcbkotXLgQ11xzDSoqKvDpT38aTzzxBJqamvDwww8PeM6KFSvQ3Nzc/aqtrR00Rmt7mxOlAWlJ5jpZKfpSQn/i19GQB3EH1iGGgjyocIgD55NDhvi60OAgXQvmQJGDDZzOKKfzCZDvCen4dOiBgnXBPCiIb5NMmE8aHKTja3HQsC6YB0UOAJw9av4UCgsLMXPmTOzevXvAYwKBAAIB+1+XWHNkbzJKJ69zeE/C53qL/Clx8BYmfh0NeZB2YB1iaMiDBgencD7Fuc4QXxcaHKRrwRzocUiEeDPK6XwC5HtCOj4dTqJhXTAP8vETZTjOJw0O0vG1OGhYF8yDHgcggWdK9aS1tRXvvfcexo8fn5RET6rqqrGj9m1EopGEzo9EI9h+4C3sPLgrYQdvcTY8Y5wN2lPxjAnAW5yd8Pka8iDtwDrE0JAHDQ5O4XwamOGwLjQ4SNeCOdDjkAjDcUZJx6fDSTSsC+ZBPn6iDMf5pMFBOr4WBw3rgnnQ4wA43JS65ZZbsGXLFuzbtw8vvfQSrrzySni9Xnz+859PSuJUVm9ZC4+V2H6Z1+PF6i1rk3YIVBSJng/oyIO0A+sQQ0MeNDgMBueTe+cDOvKgwUG6FsyBHod4ZMqMko5Ph5NoWBfMg/z5dsiU+aTBQTq+FgcN64J50OPgqAp1dXX4/Oc/j1mzZuGzn/0sRo8ejVdeeQXFxcVJi/Rk/bYnUHusHuFI2NF54UgYBz44iEdf35S0Q1ZZPqw8H2A5PNECrDwfssryk3bQkAdpB9YhhoY8aHAYDM4nGwyzdaHBQboWzIEeh3hkyoySjk+Hk2hYF8yDfHw7ZMp80uAgHV+Lg4Z1wTzocXC0KbVu3TrU19cjFAqhrq4O69atw/Tp05OWOJX2zhCuvPMraAm12W6ScCSMllAbrlh1Pdo7Q0k7WD4Pci6bFHtol90CWQD8sfMsX/IPDdOQB2kH1iGGhjxocBgMzqd4J2HYrQsNDtK1YA70OMQjU2aUdHw6nETDumAe5OPbIVPmkwYH6fhaHDSsC+ZBj0P6p1yCvPf+Ppx/+7WobzoCAAN+3rPr5/VNR3D+7ddiT8P+lDl4C/3IXTQFVq6958FbuT7kLpqS0ocRasiDtAPrEENDHjQ4aID9EENDHjQ4SNeCOdDjoAXpnpCOT4eTaFgXzIN8fE1o6AdpB+n4Whw0rAvmQYdDUt++l272NOzH2T++GFedtRDLKpdg3qQ5fY7ZWbcLq7esxaOvb0rJbuWpeAv9yPvCNHTWBBGqakT0aN8YnjEBBCqKYre+peG3GRryIO3AOsTQkAcNDhpgP8TQkAcNDtK1YA70OGhBuiek49PhJBrWBfMgH18TGvpB2kE6vhYHDeuCeZB3UL0pBcRuq3tw6wY8uHUDKiaWY8a4qcjLzkFLext2H9mLqrrqtDtYPg/85YXwlxci0tCOSFMH0BEF/B54C/2ufCOGhjxIO7AOMTTkQYODBtgPMTTkQYODdC2YAz0OWpDuCen4dDiJhnXBPMjH14SGfpB2kI6vxUHDumAeZB3Ub0r1pKqu2pWGGAxvcbb4Pxga8iDtwDrE0JAHDQ4aYD/E0JAHDQ7StWAO9DhoQbonpOPT4SQa1gXzIB9fExr6QdpBOr4WBw3rgnlw32H43hdKCCGEEEIIIYQQQtTCTSlCCCGEEEIIIYQQ4jrclCKEEEIIIYQQQgghrmMZY4ybAYPBIAoKCmLBc9x/pJU5HgYMAAuwRso8UosOdNDkIB0fAExbGADQ3NyM/Px8EQdAfj4BSurBnqQDHXo7KJhRnE900BKfDsocOJ8AKKkFHeigJL4aB5vzSfRB512SMsGF49OBDtocpOMrQzwXGuoh7SAdnw50UIp4HjTUgg7y8emgy0EJ4nnQUAs60EFLfC0OcRDdlOKdUnSgg7yDdHxA56Dkb/rkHKTj04EOfRyUzSjOp8x2kI5PB2UOnE8AlNSCDnRQEl+Ng835JLcpNdKL/KUzXA8bvG83TFsY1kifSHw60EGbg3R8AGi+twY4HhGJ3S9C8wnQUQ9pB+n4dKDDqaiaUZxPGe8gHZ8Ouhw4n2JoqAUd6KAlvhYHu/OJDzonhBBCCCGEEEIIIa7DTSlCCCGEEEIIIYQQ4jrclCKEEEIIIYQQQgghriP6oHOnVEwsR9m4qcjNzkFrextqjuxFVV01HQQcIg3tiDR2AJ1RIMsDb5Ef3uJs1+IzB3TQhoaepEMMDT0pnQcNOaCDLqR7Ujq+FgcNPck86HCQjq8JDT0p7SAdH9DRk8xDZjqo35TKzgrg6vkXYVnlEswtPb3Pn++ofRurt6zF+m1PoL0zRIc0OphwFJ01QYSqGhE92jeGZ0wAgYoiZJXlw/Kl/iY85oAO2tDQk3SIoaEnpfOgIQd00IV0T0rH1+KgoSeZBx0O0vE1oaEnpR2k4wM6epJ5oINljDEpu5oNgsEgCgoKgJFeFHy5bNBjp4+dgseW34PSUSWImii8Hm+fYyLRCDyWB7XH6nHFquuxp2H/4PG7nkKfY+8p9HT48PpNHWjbeACmJf7XOlp5PuRcNgneQn/KHNKRA6cO6ciBBgcNvdD1zQzNzc3Iz8+Pe910IT2fgKG3LjifYkjnYbjOJy0OGmaUk/kEyPfkcJ1PTh04nz68voI8SDtwPp1EQ09KOwzX+eTUYbjmYajNp3Q52J1Parfgp4+dgqdvWYeSwnGwLKvfBgUAr8cLy7JQUjgOz3z7IUwrnkyHFDtEmjrQ+sg+mNb4DQoApjWM1kf2IdLUkZL4zAEdtKGhJ+kQQ0NPSudBQw7ooAvpnpSOr8VBQ08yDzocpONrQkNPSjtIxwd09CTzQIcuVG5KZWcF8Njye5AXyIHPa+8Thj6vD3mBHGy4cQ2yswJ0SJGDCUfRtvEA0BEF7N5TZwB0xM4z4WhS8ZkDOmhDQ0/SIYaGnpTOg4Yc0EEX0j0pHV+Lg4aeZB50OEjH14SGnpR2kI4P6OhJ5oEOPXG8KXXw4EEsWbIEo0ePxogRI/CRj3wEr732WtIiPbl6/kUoHVViu0G78Hl9mDR6Aq46ayEdUuTQWROM3cLn9EOeBjAtYXTWBJOKzxzQwQmcT5nloKEnpfOgIQd0sE8mzCjp+FocNPQk86DDQTq+XTJhPmlwkI4P6OhJ5oEOPXG0KdXY2IgFCxYgKysLmzZtwttvv42f//znKCoqSlqkJ8sqlyBqEttxi0QjWFa5hA4pcghVNYqezxzQwS6cT5nnoKEnpfOgIQd0sEemzCjp+FocNPQk86DDQfp8O2TKfNLgIB0f0NGTzAMdeuJoU+pnP/sZSktLce+99+Lss8/G1KlTceGFF2L69OlJi3RRMbEcc0tPH/AzpfHweryYN2kOKiaW0yFJh0hDe79P3XdC9GgIkYb2hM5lDujgBM6nzHLQ0JPSedCQAzrYJxNmlHR8LQ4aepJ50OEgHd8umTCfNDhIxwd09CTzQIdTcbQptXHjRnz0ox/FNddcg7Fjx+LMM8/E3XffPeg5oVAIwWCw12swysZNdaI0IDOSuA4dYkQaU/dwx0RgDujgBM6nzHLQ0JPSedCQAzrYx+mMcjqfAPmelI6vxUFDTzIPOhyk49slE+aTBgfp+ICOnmQe6HAqjjal9uzZg7vuugtlZWV48skn8fWvfx0333wz7r///gHPWblyJQoKCrpfpaWlg8bIzc5xojQgeUlchw4f0pmiByt2JHYd5oAOTuB8yiwHDT0pngcFOaCDfZzOKKfzCZDvSen4Whw09CTzoMRBOr5NMmE+aXCQjg9ARU8yD3Q4FUebUtFoFGeddRZuu+02nHnmmVi2bBm++tWv4je/+c2A56xYsQLNzc3dr9ra2kFjtLa3OVEakJYkrkOHD8lK0Zcz+hO7DnNABydwPmWWg4aeFM+DghzQwT5OZ5TT+QTI96R0fC0OGnqSeVDiIB3fJpkwnzQ4SMcHoKInmQc6nIqjs8ePH4/TTz+918/Ky8tx4MCBAc8JBALIz8/v9RqMmiN7nSgNfJ3DexI/lw4AAG+RPyUO3sLErsMc0MEJnE+Z5aChJ6XzoCEHdLCP0xnldD4B8j0pHV+Lg4aeZB50OEjHt0smzCcNDtLxAR09yTzQ4VQcbUotWLAA77zzTq+fvfvuu5g8eXJSEj2pqqvGjtq3EYlGEjo/Eo1g+4G3sPPgLjok6eAtzoZnTCDh8wHAMyYAb3F2QucyB3RwAudTZjlo6EnpPGjIAR3skwkzSjq+FgcNPck86HCQjm+XTJhPGhyk4wM6epJ5oEOfazg5+Fvf+hZeeeUV3Hbbbdi9ezcefPBBrF69GsuXL09K4lRWb1kLj5XYLWBejxert6ylQ4ocAhXJfRVssuczB3SwC+dT5jlo6EnpPGjIAR3skSkzSjq+FgcNPck86HCQPt8OmTKfNDhIxwd09CTzQIeeOOqEj33sY3jsscfwhz/8AWeccQZuvfVW/PKXv8TixYuTFunJ+m1PoPZYPcKRsKPzwpEwDnxwEI++vokOKXLIKsuHlecDLIcnWoCV50NWWfzbeQeDOaCDXTifMs9BQ09K50FDDuhgj0yZUdLxtTho6EnmQYeDdHw7ZMp80uAgHR/Q0ZPMAx164nh78pJLLsHOnTvR3t6O6upqfPWrX01a4lTaO0O48s6voCXUZrtRw5EwWkJtuGLV9WjvDNEhRQ6Wz4OcyybFHl5mt1EtAP7YeZYvuYeeMQd0cALnU2Y5aOhJ6TxoyAEd7JMJM0o6vhYHDT3JPOhwkI5vl0yYTxocpOMDOnqSeaBDT9yZcgnw3vv7cP7t16K+6QgADPiZ066f1zcdwfm3X4s9DfvpkGIHb6EfuYumwMr12TreyvUhd9GUlD2UkTmggzY09CQdYmjoSek8aMgBHXQh3ZPS8bU4aOhJ5kGHg3R8TWjoSWkH6fiAjp5kHujQhb2oQuxp2I+zf3wxrjprIZZVLsG8SXP6HLOzbhdWb1mLR1/flJIdUzr0j7fQj7wvTENnTRChqkZEj/aN4RkTQKCiKHYLYIp/q8Mc0EEbGnqSDjE09KR0HjTkgA66kO5J6fhaHDT0JPOgw0E6viY09KS0g3R8QEdPMg90AJRvSgGxW/se3LoBD27dgIqJ5ZgxbirysnPQ0t6G3Uf2oqqumg4uOVg+D/zlhfCXFyLS0I5IUwfQEQX8HngL/Wn/ZhDmgA7a0NCTdIihoSel86AhB3TQhXRPSsfX4qChJ5kHHQ7S8TWhoSelHaTjAzp6knmgg/pNqZ5U1VW70pR0iI+3OFv0H07mgA7a0NCTdIihoSel86AhB3TQhXRPSsfX4qChJ5kHHQ7S8TWhoSelHaTjAzp6knnITIfhe18oIYQQQgghhBBCCFELN6UIIYQQQgghhBBCiOtwU4oQQgghhBBCCCGEuI5ljDFuBgwGgygoKIgFz3H/kVbmeBgwACzAGinzSC060EGTg3R8ADBtYQBAc3Mz8vPzRRwA+fkEKKkHe5IOdOjtoGBGcT7RQUt8Oihz4HwCoKQWdKCDkvhqHGzOJ9EHnXdJygQXjk8HOmhzkI6vDPFcaKiHtIN0fDrQQSniedBQCzrIx6eDLgcliOdBQy3oQAct8bU4xEF0U4p3StGBDvIO0vEBnYOSv+mTc5COTwc69HFQNqM4nzLbQTo+HZQ5cD4BUFILOtBBSXw1Djbnk9ym1Egv8pfOcD1s8L7dMG1hWCN9IvHpQAdtDtLxAaD53hrgeEQkdr8IzSdARz2kHaTj04EOp6JqRnE+ZbyDdHw66HLgfIqhoRZ0oIOW+Foc7M4nPuicEEIIIYQQQgghhLjOkNyUstx9NjshhBBCCCGEEEIISTGiz5Syy9yGdiyubsIn6k9gVmMI/ijQ4QHeKQrg5ZIReKC8EDuKs6U1CSGEEEIIIYQQQohNVG9KTW3qwKrNh7Dg0Al0WkDW3HnABbOBvDz4W1rwkV27MHvHdizb2YQXx4/AjeeNx95Cf1qdKiaWo2zcVORm56C1vQ01R/aiqq46rTHp0JdIQzsijR1AZxTI8sBb5IfX5Y1JOuhx0ID0mqCDLgfpdSEdnw76kF4X0vHpcBIN64IO8vE1oWFdSDtIx9fioGFd0MF9B7WbUovebcadmw/D6w8AS5ci6+abgTPP7HNc1htvAL/6Fc55aB1eWbcXN5w3Hutn5qfUJTsrgKvnX4RllUswt/T0Pn++o/ZtrN6yFuu3PYH2zlBKY9PhJCYcRWdNEKGqRkSP9r2+Z0wAgYoiZJXlw/Kl55OpdNDjoAHpNUEHXQ7S60I6Ph30Ib0upOPT4SQa1gUd5ONrQsO6kHaQjq/FQcO6oIOsg2WMuw9oCgaDKCgoAEZ6UfDlsn6PWfRuM1Y/dQgoK4Pnz38GJk8GolHA6+17cCQCeDzA/v2IXnABsHs3ll0wHo/MLOg/ftdT6HPsPYV++tgpeGz5PSgdVYKoicLr6esQiUbgsTyoPVaPK1Zdjz0N+wfPAR0cO0SaOtC28QBMS/yvlbTyfMi5bBK8Nu6ao4PzXkiHQ9c3MzQ3NyM/P7Wbyk6wM5+6SMeaAJzVY7g6DLX5BMivzeE6n7Q4aJhRTuYTIL8uhut80uAw1OYTHdIXH+B86nYYYu8dhut8cuowXN87DLX5lC4Hu/NJ3Rb8tKYO3Ln5cGxD6pVXgAkTAMvqf0MKiP3csoAJE+DZuhWYMQN3bj6MqU0dSbtMHzsFT9+yDiWF42BZVr8LFQC8Hi8sy0JJ4Tg88+2HMK14ctKx6XCSSFMHWh/ZB9Maf4EAgGkNo/WRfYikoAfooM9BA9Jrgg66HKTXhXR8OuhDel1Ix6fDSTSsCzrIx9eEhnUh7SAdX4uDhnVBBx0O6jal7th8CB5/IHaHVF4ekJVl78SsLCAvD56nnoLXH8CqzYeS8sjOCuCx5fcgL5ADn9fepxx9Xh/yAjnYcOMaZGcFkopPhxgmHEXbxgNARxSwe0+fAdARO8+Eo0nFp4MuBw1Irwk66HKQXhfS8emgD+l1IR2fDifRsC7oIB9fExrWhbSDdHwtDhrWBR30ODjalJoyZQosy+rzWr58edIiADD3/XYsOHQCWdd+LvaRPbsbUl1kZQFTpsD32Wux4NAJzG1oT9jl6vkXoXRUie2F2oXP68Ok0RNw1VkLE45Nh5N01gRjtxA6/ZCpAUxLGJ01waTi00GXQzzSPaMA+TVBB10O0utCOj4d7OPGfALk14V0fDqcRMO6oIN8fDtkynzS4CAdX4uDhnVBBz0OjjalXn31VRw6dKj79dRTTwEArrnmmqRFAGDxriZ0WgBuvjn2DKlEiESAm25CpwUsrm5K2GVZ5RJETWIOkWgEyyqXJBybDicJVTWKnk8HXQ7xSPeMAuTXBB10OUivC+nz6WAfN+YTIL8upOPT4SQa1gUd5M+3Q6bMJw0O0vG1OGhYF3TQ4+BoU6q4uBinnXZa9+vxxx/H9OnTUVlZmbQIAHyi/gSy5s6LfcveQM+QiofXC8yfj6y58/DxQycSukTFxHLMLT19wM/WxlXweDFv0hxUTCxP6Hw6xIg0tPf71H8nRI+GEEnijjk66HGwQ7pnlPSaoIMuB+l1IR2fDs5I93wC5NeFdHw6nETDuqCDfHy7ZMJ80uAgHV+Lg4Z1QQc9DkASz5Tq6OjA2rVrcd1118GyrAGPC4VCCAaDvV4DMasxBMyenajSKRebhdnHEktw2bipKVGYkcR16ABEGlP3cMmEz6WDGgen2JlRTuYTIL8m6KDLQXpdSMenQ+KkYz4B8utCOj4dTqJhXdBBPn4iDNf5pMFBOr4WBw3rgg56HIAkNqU2bNiApqYmLF26dNDjVq5ciYKCgu5XaWlpv8dZxsAfRezh5qkgPx/+aOy6TsnNzkmJQl4S16EDgM4UPdixI4nr0EGPg0PszCi786kL8TVBB1UO4utCOj4dEiYd8wmQXxfS8enQAw3rgg7y8RNguM4nDQ7S8bU4qFgXdNDjgCQ2pdasWYOFCxeipKRk0ONWrFiB5ubm7ldtbW2/xxnLQocHQEtLokq9CQbR4Yld1ymt7W0pUWhJ4jp0AJCVoi+H9CdxHTrocXCInRlldz51Ib4m6KDKQXxdSMenQ8KkYz4B8utCOj4deqBhXdBBPn4CDNf5pMFBOr4WBxXrgg56HAA4e+T+h+zfvx9PP/00Hn300bjHBgIBBAL2vjbynaIAPrJrVyJKfdm1C7tGJfZ1lTVH9qZEoebwnsTPpQO8Rf6UxPcWJn4dOuhxcILdGeVkPgHya4IOuhyk14V0fDokRrrmEyC/LqTj0+EkGtYFHeTjO2U4zycNDtLxtThoWBd00OMAJHin1L333ouxY8fi4osvTir4qbxcMgKdO7YDb7wR+xa9RIhEgG3b0Fm1A6+MH5HQJarqqrGj9m1Eook5RKIRbD/wFnYeTHyDjQ6AtzgbnjGJbSx24RkTgLc4O+Hz6aDHwQnpmlHSa4IOuhyk14V0fDokRrrmEyC/LqTj0+EkGtYFHeTjO2U4zycNDtLxtThoWBd00OMAJLApFY1Gce+99+JLX/oSfL6EbrQakAfKC5FlAPzqV4AnwVvAvF7gjjuQZWLXS5TVW9bCYyXm4PV4sXrL2oRj0+EkgYoi0fPpoMvBDumcUYD8mqCDLgfpdSF9Ph2cke75BMivC+n4dDiJhnVBB/nz7ZIJ80mDg3R8LQ4a1gUd9Dg47sann34aBw4cwHXXXZd08FPZUZyNF8ePQPihdcD+/UBnp7MLdHYC+/Yh/PBDeHH8COxIYsdu/bYnUHusHuFI2NF54UgYBz44iEdf35RwbDqcJKssH1aeD3D6aDALsPJ8yCrLTyo+HXQ52CGdMwqQXxN00OUgvS6k49PBGemeT4D8upCOT4eTaFgXdJCPb5dMmE8aHKTja3HQsC7ooMfB8abUhRdeCGMMZs6cmXTw/rjxvPGIdIQQvfDC2EPP7W5MdXYCLS2IXnABIh0h3Hje+KQ82jtDuPLOr6Al1GZ7wYYjYbSE2nDFquvR3hlKKj4dYlg+D3IumxR7eJrdhWIB8MfOs3zJP7yNDnoc7JDuGSW9Juigy0F6XUjHp4Mz0j2fAPl1IR2fDifRsC7oIB/fLpkwnzQ4SMfX4qBhXdBBj4N7X+Vgk72Fftxw3nigpgbRc84BDh6M/cFAz5jq+vnBg7Hjd+/GDeeNx94UPBDwvff34fzbr0V905FYqAE+e9v18/qmIzj/9muxp2F/0rHpcBJvoR+5i6bAyrV3K7GV60PuoikpfSgkHfQ4aEB6TdBBl4P0upCOTwd9SK8L6fh0OImGdUEH+fia0LAupB2k42tx0LAu6KDDIT0fGE6S9TPzYcHgzs3vwXt6OXyfvRa46SZg/vy+B2/fDtxxB8IPP4RIRwg3XFCC9TNTd5vrnob9OPvHF+OqsxZiWeUSzJs0p88xO+t2YfWWtXj09U0p2TmmQ1+8hX7kfWEaOmuCCFU1Inq07/U9YwIIVBTFbkFMw2+V6KDHQQPSa4IOuhyk14V0fDroQ3pdSMenw0k0rAs6yMfXhIZ1Ie0gHV+Lg4Z1QQd5B5WbUgDwyMwCbBs7Aqs2H8KC++9H5+/vR9bcecCsWUB+PhAMAu+8g84d25FlgFdKRuCmc6em5A6pU2nvDOHBrRvw4NYNqJhYjhnjpiIvOwct7W3YfWQvquqqUx6TDn2xfB74ywvhLy9EpKEdkaYOoCMK+D3wFvpd+WYSOuhx0ID0mqCDLgfpdSEdnw76kF4X0vHpcBIN64IO8vE1oWFdSDtIx9fioGFd0EHWQe2mFBD7KN/FV03G3IZ2LK5uwsfrqjG7ajv8UaDDA+waFcArZxTigfLCpB5q7oSqumpXFicdBsdbnC3+Dzcd9DhoQHpN0EGXg/S6kI5PB31Irwvp+HQ4iYZ1QQf5+JrQsC6kHaTja3HQsC7o4L6D6k2pLnYUZ2NH8Wnd/20ZA2M5fTw8IYQQQgghhBBCCNHCkPywMjekCCGEEEIIIYQQQoY2Q3JTihBCCCGEEEIIIYQMbSxjjHEzYDAYREFBQSx4jvufHjTHw4ABYAHWSJlPL9KBDpocpOMDgGkLAwCam5uRn5+6b890ivR8ApTUgz1JBzr0dlAwozif6KAlPh2UOXA+AVBSCzrQQUl8NQ4255PoM6W6JGWCC8enAx20OUjHV4Z4LjTUQ9pBOj4d6KAU8TxoqAUd5OPTQZeDEsTzoKEWdKCDlvhaHOIguinFO6XoQAd5B+n4gM5Byd/0yTlIx6cDHfo4KJtRnE+Z7SAdnw7KHDifACipBR3ooCS+Ggeb80luU2qkF/lLZ7geNnjfbpi2MKyRPpH4dKCDNgfp+ADQfG8NcDwiErtfhOYToKMe0g7S8elAh1NRNaM4nzLeQTo+HXQ5cD7F0FALOtBBS3wtDnbnEx90TgghhBBCCCGEEEJch5tShBBCCCGEEEIIIcR1uClFCCGEEEIIIYQQQlyHm1KEEEIIIYQQQgghxHVEv32PJEbFxHKUjZuK3OwctLa3oebIXlTVVbvqEGloR6SxA+iMAlkeeIv88BZnZ0x8OuhyIHrgfNLhIB2fDkQjnE900OQgHZ/ogvOJDpnswE2pIUJ2VgBXz78IyyqXYG7p6X3+fEft21i9ZS3Wb3sC7Z2htDiYcBSdNUGEqhoRPdo3hmdMAIGKImSV5cPypf4mPOn4dNDlQPTA+aTDQTo+HYhGOJ/ooMlBOj7RBecTHegQg5tSQ4DpY6fgseX3oHRUCaIm2u8xZ0yYhVWLf4LvLlyOK1Zdjz0N+1PqEGnqQNvGAzAt4QGPiR4N4cTmw2h/9ShyLpsEb6F/2MSngy4HogfOJx0O0vHpQDTC+UQHTQ7S8YkuOJ/oQIeTcAteOdPHTsHTt6xDSeE4WJYFr8fb73FejxeWZaGkcBye+fZDmFY8OWUOkaYOtD6yD6Z14AbtiWkNo/WRfYg0dQyL+HTQ5UD0wPmkw0E6Ph2IRjif6KDJQTo+0QXnEx3o0BtHm1KRSAT//M//jKlTp2LEiBGYPn06br31VhhjUiJDepOdFcBjy+9BXiAHPq+9m9p8Xh/yAjnYcOMaZGcFknYw4SjaNh4AOqKA3TIbAB2x80y4/53/oRKfDrocBoPzyV04n3Q4SMeng304o9yD84kOmhyk49uB88k9OJ/oQIe+ONqU+tnPfoa77roLq1atQnV1NX72s5/h3/7t33DHHXckLUL6cvX8i1A6qsT2wOrC5/Vh0ugJuOqshUk7dNYEY7fwOf03yQCmJYzOmuCQjk8HXQ6DwfnkLpxPOhyk49PBPpxR7sH5RAdNDtLx7cD55B6cT3SgQ18cbUq99NJLuPzyy3HxxRdjypQpWLRoES688EL85S9/SVqE9GVZ5ZIBP2Mcj0g0gmWVS5J2CFU1ZvT5dNDlMBicT+7C+aTDQfp8OtiHM8o9OJ/ooMlB+nw7cD65B+cTHejQF0ebUn/1V3+FZ555Bu+++y4AYMeOHXjhhRewcGHyO7akNxUTyzG39PQBP2McD6/Hi3mT5qBiYnnCDpGG9n6fuu+E6NEQIg3tQzI+HXQ5xIPzyT04n3Q4SMengzM4o9yB84kOmhyk49uF88kdOJ/oQIf+cXTf4D/+4z8iGAxi9uzZ8Hq9iEQi+MlPfoLFixcPeE4oFEIodPIvGgym/xbU4UDZuKkpuc6McVNRVVed0LmRxtQ93NFbnD3k4tNBl0M8OJ/cg/NJh4N0fDo4w+mM4nxKDM4nOmhykI5vF84nd+B8ogMd+sfRnVIPP/wwHnjgATz44IN4/fXXcf/99+Pf//3fcf/99w94zsqVK1FQUND9Ki0tTVg2k8jNzknJdfKSuU5nih6s2JHgdaTj00GXQxw4n9yD80mJg3R8OjjC6YzifEoMzic6qHKQjm8Tzid34HyiAx36x9Gm1Le//W384z/+Iz73uc/hIx/5CP7u7/4O3/rWt7By5coBz1mxYgWam5u7X7W1tUkJZwqt7W0puU5LMtfJctQeA+NP8DrS8emgyyEOnE/uwfmkxEE6Ph0c4XRGcT4lBucTHVQ5SMe3CeeTO3A+0YEO/ePo43vHjx+Hx9M7oNfrRTQ68M5YIBBAIJD8V1dmGjVH9qbmOof3JHyut8ifEgdvYWLXkY5PB10O8eB8cg/OJx0O0vHp4AynM4rzKTE4n+igyUE6vl04n9yB84kOdOgfR1tal156KX7yk5/gj3/8I/bt24fHHnsM//Ef/4Err7wyKQnSl6q6auyofRuRaCSh8yPRCLYfeAs7D+5K2MFbnA3PmOT+wfGMCST8+VLp+HTQ5RAPzif34HzS4SAdnw7O4IxyB84nOmhykI5vF84nd+B8ogMdBriGk4PvuOMOLFq0CDfccAPKy8txyy234O///u9x6623JiVB+mf1lrXwWIndCuf1eLF6y9qkHQIVRRl9Ph10OQwG55O7cD7pcJA+nw724YxyD84nOmhykD7fDpxP7sH5RAc69MXRisjLy8Mvf/lL7N+/HydOnMB7772HH//4x/D703tLaaayftsTqD1Wj3Ak7Oi8cCSMAx8cxKOvb0raIassH1aeD7AcnmgBVp4PWWX5Qzo+HXQ5DAbnk7twPulwkI5PB/twRrkH5xMdNDlIx7cD55N7cD7RgQ59Se9T80hStHeGcOWdX0FLqM324ApHwmgJteGKVdejvTMU/4Q4WD4Pci6bFHt4md1GtQD4Y+dZvuRaTDo+HXQ5ED1wPulwkI5PB6IRzic6aHKQjk90wflEBzr0hVNOOe+9vw/n334t6puOAMCAn0Hu+nl90xGcf/u12NOwP2UO3kI/chdNgZVr77n4Vq4PuYumpOyhjNLx6aDLgeiB80mHg3R8OhCNcD7RQZODdHyiC84nOtChN46+fY/IsKdhP87+8cW46qyFWFa5BPMmzelzzM66XVi9ZS0efX1TSnbQT8Vb6EfeF6ahsyaIUFUjokf7xvCMCSBQURS7BTDFv9WRjk8HXQ5ED5xPOhyk49OBaITziQ6aHKTjE11wPtGBDifhptQQob0zhAe3bsCDWzegYmI5ZoybirzsHLS0t2H3kb2oqqtOu4Pl88BfXgh/eSEiDe2INHUAHVHA74G30J/2bwaRjk8HXQ5ED5xPOhyk49OBaITziQ6aHKTjE11wPtGBDjG4KTUEqaqrdmVIDYa3OFv0H07p+HTQ5UD0wPmkw0E6Ph2IRjif6KDJQTo+0QXnEx0y2YH3hRJCCCGEEEIIIYQQ1+GmFCGEEEIIIYQQQghxHW5KEUIIIYQQQgghhBDXsYwxxs2Azc3NKCwsjP3HSK+boWMc7/GVmxLx6UAHbQ7S8Xs4NDU1oaCgQMYBCuYToKoeYg7S8elAhwEcJGcU5xMd1MSng0oHzic9taADHcTjK3OIN59cf9B5S0vLyf/omSgJpOPTgQ7aHITjt7S0iG5KqZpPdNARnw506IHkjOJ8ooPK+HRQ48D51AM60EGTg3R8BQ7x5pPrd0pFo1HU19cjLy8PlmU5Pj8YDKK0tBS1tbXIz89PgyEdhoqDdHw6pM7BGIOWlhaUlJTA45H7VDHnEx2Gk4N0/OHkoGFGJTufAPl6SMenAx20OXA+nUS6FhocpOPTgQ6pdrA7n1y/U8rj8WDixIlJXyc/P1+sOHTQ5SAdnw6pcZC8Q6oLzic6DEcH6fjDxUF6RqVqPgHy9ZCOTwc6aHPgfDqJdC00OEjHpwMdUulgZz7xQeeEEEIIIYQQQgghxHW4KUUIIYQQQgghhBBCXGfIbUoFAgH867/+KwKBAB0y3EE6Ph10OWhAQx7oQAct8emgD+lcSMenAx20OUjH14SGXEg7SMenAx2kHFx/0DkhhBBCCCGEEEIIIUPuTilCCCGEEEIIIYQQMvThphQhhBBCCCGEEEIIcR1uShFCCCGEEEIIIYQQ1xlSm1Ivv/wyvF4vLr74YtdjL126FJZldb9Gjx6Nz3zmM6iqqnLd5fDhw7jpppswbdo0BAIBlJaW4tJLL8UzzzyT9tg985CVlYVx48bhggsuwO9+9ztEo9G0xz/VoefrM5/5jCvx43ns3r3blfiHDx/GN77xDcyYMQPZ2dkYN24cFixYgLvuugvHjx9Pe/ylS5fiiiuu6PPz5557DpZloampKe0O2uCM4nw61UNqRknPJ0B2RnE+9YXzifPpVA/OJ76H0gLnE+fTqR6cT5k1n4bUptSaNWtw00034fnnn0d9fb3r8T/zmc/g0KFDOHToEJ555hn4fD5ccsklrjrs27cP8+fPx+bNm3H77bdj586d+NOf/oRzzz0Xy5cvd8WhKw/79u3Dpk2bcO655+Ib3/gGLrnkEoTDYVcder7+8Ic/uBI7nsfUqVPTHnfPnj0488wz8ec//xm33XYb3njjDbz88sv4zne+g8cffxxPP/102h1IXzJ9RnE+9fWQnFFS8wngjNII5xPn06kenE+cT1rgfOJ8OtWD8ymz5pNPWsAura2teOihh/Daa6/h8OHDuO+++/BP//RPrjoEAgGcdtppAIDTTjsN//iP/4i/+Zu/QUNDA4qLi11xuOGGG2BZFv7yl78gJyen++dz5szBdddd54pDzzxMmDABZ511Fj7+8Y/j/PPPx3333YevfOUrrjpIIuVxww03wOfz4bXXXuvVB9OmTcPll18Ofqmm+3BGcT4N5CGFpANnlC44nzifBvKQgvOJdMH5xPk0kIcUnE/uM2TulHr44Ycxe/ZszJo1C0uWLMHvfvc70aK0trZi7dq1mDFjBkaPHu1KzGPHjuFPf/oTli9f3qtJuygsLHTFoz/OO+88zJ07F48++qiYQ6bwwQcf4M9//vOAfQAAlmW5bEUyfUZxPpEuOKP0wfnE+URicD7pg/OJ84nEyOT5NGQ2pdasWYMlS5YAiN1S19zcjC1btrjq8PjjjyM3Nxe5ubnIy8vDxo0b8dBDD8HjcSeNu3fvhjEGs2fPdiWeU2bPno19+/a5EqtnLbpet912myuxB/O45ppr0h6zqw9mzZrV6+djxozp9vjud7+bdg+g/zosXLjQldjayPQZxfnUGw0zSmI+AXpmFOfTSTifOJ96wvkkP58AzqguOJ84n3rC+ZSZ82lIfHzvnXfewV/+8hc89thjAACfz4drr70Wa9aswSc/+UnXPM4991zcddddAIDGxkb8+te/xsKFC/GXv/wFkydPTnt87bfrGWNc273tWYsuRo0a5UrswTwG2tV2g7/85S+IRqNYvHgxQqGQKzH7q8PWrVu731xkCpxRnE+nomFGaZpPgPszivMpBucT59OpcD71he+hZOB84nw6Fc6nvmTCfBoSm1Jr1qxBOBxGSUlJ98+MMQgEAli1ahUKCgpc8cjJycGMGTO6//uee+5BQUEB7r77bvz4xz9Oe/yysjJYloVdu3alPVYiVFdXu/YQuFNrIYWEx4wZM2BZFt55551eP582bRoAYMSIEa659Pf3r6urcy2+FjijOJ9ORcOMknLQMqM4n2JwPnE+nQrnk/x8AjijAM4ngPPpVDifMnM+qf/4Xjgcxu9//3v8/Oc/x/bt27tfO3bsQElJicg3rnVhWRY8Hg9OnDjhSrxRo0bh05/+NO688060tbX1+XPJr4/dvHkzdu7ciauvvlrMIVMYPXo0LrjgAqxatarfPiDuwhkVg/OJdMEZpQfOpxicT6QLzic9cD7F4HwiXWTyfFJ/p9Tjjz+OxsZGXH/99X12y6+++mqsWbMGX/va11xxCYVCOHz4MIDYrZ2rVq1Ca2srLr30UlfiA8Cdd96JBQsW4Oyzz8aPfvQjVFRUIBwO46mnnsJdd92F6urqtDt05SESieDIkSP405/+hJUrV+KSSy7BF7/4xbTH7+nQE5/PhzFjxrgSX5pf//rXWLBgAT760Y/iBz/4ASoqKuDxePDqq69i165dmD9/vrRixsAZdRLOp74ePeGM4oxyG86nk3A+9fXoCecT55PbcD6dhPOpr0dPOJ8yYD4Z5VxyySXmoosu6vfPtm7dagCYHTt2pN3jS1/6kgHQ/crLyzMf+9jHzCOPPJL22KdSX19vli9fbiZPnmz8fr+ZMGGCueyyy8yzzz6b9tg98+Dz+UxxcbH51Kc+ZX73u9+ZSCSS9vinOvR8zZo1y5X4PT0uv/xyV2P2pL6+3tx4441m6tSpJisry+Tm5pqzzz7b3H777aatrS3t8Qf6+z/77LMGgGlsbEy7gwY4o3qT6fPpVA+pGSU9n4yRnVGcTzE4n3rD+cT51AXfQ8nD+dQbzifOpy4ycT5Zxih/uhohhBBCCCGEEEIIGXaof6YUIYQQQgghhBBCCBl+cFOKEEIIIYQQQgghhLgON6UIIYQQQgghhBBCiOtwU4oQQgghhBBCCCGEuA43pQghhBBCCCGEEEKI63BTihBCCCGEEEIIIYS4DjelCCGEEEIIIYQQQojrcFOKEEIIIYQQQgghhLgON6UIIYQQQgghhBBCiOtwU2oY8Jvf/AZ5eXkIh8PdP2ttbUVWVhY++clP9jr2ueeeg2VZeO+99wAAf//3f4/p06djxIgRKC4uxuWXX45du3Z1H//BBx/gM5/5DEpKShAIBFBaWoobb7wRwWAwrteJEyeQk5OD3bt39/vn7777Li6//HKMGTMG+fn5+Ou//ms8++yztv7O5557Lu65555+/+yTn/wkLMvqfo0bNw7XXHMN9u/f333Mjh078PnPfx6lpaUYMWIEysvL8Z//+Z+9rvPCCy9gwYIFGD16NEaMGIHZs2fjF7/4hS0/QkiMdM4nAHj11Vdx/vnno7CwEEVFRfj0pz+NHTt22HKbOnUqnn766T4/37dvX68Z0vP13//933Gvm+x8AoCbb74Z8+fPRyAQwLx58/q91sMPP4x58+Zh5MiRmDx5Mm6//fb4f2lCSDfpnE923mcMRLz3TwDwxz/+Eeeccw5GjBiBoqIiXHHFFbau7cb7JwAIhUL43ve+h8mTJyMQCGDKlCn43e9+Z8uREBIj3e+huvjggw8wceJEWJaFpqamuF7xZtSUKVP6vH/66U9/auvvPNB7s1Ov6/V6UVJSguuvvx6NjY3dxzz33HO4/PLLMX78eOTk5GDevHl44IEHel3nvvvu6+OXnZ1ty4+kB25KDQPOPfdctLa24rXXXuv+2f/93//htNNOw9atW9He3t7982effRaTJk3C9OnTAQDz58/Hvffei+rqajz55JMwxuDCCy9EJBIBAHg8Hlx++eXYuHEj3n33Xdx33314+umn8bWvfS2u11NPPYXJkydjxowZ/f75JZdcgnA4jM2bN2Pbtm2YO3cuLrnkEhw+fHjQ6x47dgwvvvgiLr300gGP+epXv4pDhw6hvr4e//M//4Pa2losWbKk+8+3bduGsWPHYu3atXjrrbfwve99DytWrMCqVau6j8nJycGNN96I559/HtXV1fj+97+P73//+1i9enXcvzshJEY651Nrays+85nPYNKkSdi6dSteeOEF5OXl4dOf/jQ6OzsH9aqqqkJjYyMqKyv7/FlpaSkOHTrU6/XDH/4Qubm5WLhw4aDXTcV86uK6667Dtdde2+81Nm3ahMWLF+NrX/sa3nzzTfz617/GL37xi14zjBAyOOmcT3beZwxEvPdP69evx9/93d/hy1/+Mnbs2IEXX3wRX/jCF+Je1633TwDw2c9+Fs888wzWrFmDd955B3/4wx8wa9asuI6EkJOkc0b15Prrr0dFRYVtr3gzCgB+9KMf9XofddNNN8W97mDvzU697oEDB/DAAw/g+eefx80339z95y+99BIqKiqwfv16VFVV4ctf/jK++MUv4vHHH+91nfz8/F5+p/5ykLiMIcOC8ePHm5UrV3b/93e+8x2zfPlyU15ebp599tnun//t3/6t+dKXvjTgdXbs2GEAmN27dw94zH/+53+aiRMnxnW67rrrzHe/+91+/6yhocEAMM8//3z3z4LBoAFgnnrqqUGv+/vf/96cc845A/55ZWWl+cY3vtHrZ//1X/9lRo4cOeh1b7jhBnPuuecOesyVV15plixZMugxhJDepGs+vfrqqwaAOXDgQPcxVVVVBoCpqakZ1OlHP/qRufbaa23/HebNm2euu+66uMelej7967/+q5k7d26fn3/+8583ixYt6vWzX/3qV2bixIkmGo3G9SSExHDz/ZOd9xnGDP7+qbOz00yYMMHcc889ca9zKm69f9q0aZMpKCgwH3zwgWNHQkhv0j2jfv3rX5vKykrzzDPPGACmsbExrtNgM8oYYyZPnmx+8YtfxL3OqcR7b9bfdW+99VZz+umnD3rdiy66yHz5y1/u/u97773XFBQUOPYj6YN3Sg0Tzj333F4ffXv22WfxyU9+EpWVld0/P3HiBLZu3Ypzzz2332u0tbXh3nvvxdSpU1FaWtrvMfX19Xj00UcH3cEGgGg0iscffxyXX355v38+evRozJo1C7///e/R1taGcDiM3/72txg7dizmz58/6LU3btw44HX749ixY3j44YdxzjnnDHpcc3MzRo0aNeCfv/HGG3jppZfi/t0JIb1J13yaNWsWRo8ejTVr1qCjowMnTpzAmjVrUF5ejilTpgzq5GSObNu2Ddu3b8f1118f99h0zadTCYVCfW41HzFiBOrq6vjbPkIc4Nb7JyD++wwg/vun119/HQcPHoTH48GZZ56J8ePHY+HChXjzzTfj/VVde/+0ceNGfPSjH8W//du/YcKECZg5cyZuueUWnDhxwnZsQkiMdM6ot99+Gz/60Y/w+9//Hh6PvW2BeDOqi5/+9KcYPXo0zjzzTNx+++29PoI4EE5n1MGDB/G///u/Cf0/XmtrKyZPnozS0lJcfvnleOutt2zHJWlAeleMpIa7777b5OTkmM7OThMMBo3P5zPvv/++efDBB83f/u3fGmNM9w74/v37e5175513mpycHAPAzJo1q9/f8n3uc58zI0aMMADMpZdeak6cODGoz4svvmjGjh1rIpHIgMfU1taa+fPnG8uyjNfrNePHjzevv/76oNdtb283ubm55s033xzwmMrKSpOVlWVycnLMyJEjDQAzc+ZMs3fv3kF9fT6fefLJJ/v82YQJE4zf7zcej8f86Ec/GtSPENKXdM6nnTt3munTpxuPx2M8Ho+ZNWuW2bdv36A+dXV1xu/32/ptoDHGfP3rXzfl5eVxj0vHfBroTqnf/va3ZuTIkebpp582kUjEvPPOO2b27NkGgHnppZds/b0IIel//9TFYO8zTj1usPdPf/jDHwwAM2nSJPPII4+Y1157zXz+8583o0ePHvTOJDffP3360582gUDAXHzxxWbr1q3mj3/8o5k8ebJZunTpoH93Qkhf0jWj2tvbTUVFhfmv//ovY4wxzz77rK07pez8P97Pf/5z8+yzz5odO3aYu+66yxQWFppvfetbg17XznuzyZMnG7/fb3Jyckx2drYBYM4555xBz3nooYeM3+/vNfteeuklc//995s33njDPPfcc+aSSy4x+fn5pra2dlBHkj64KTVMqKmp6f6fkT/+8Y/dtzEePHjQBAIBc+LECfPP//zPZtq0aX3ObWpqMu+++67ZsmWLufTSS81ZZ53VZ9Pp0KFDprq62vzP//yPOf30083Xv/71QX2+853vDPpRl2g0ai677DKzcOFC88ILL5ht27aZr3/962bChAmmvr5+wPOeeOKJfv8OPamsrDRLly41NTU1pqamxrzwwgvmoosuMmVlZSYYDPY5fufOnWbMmDHm1ltv7fd6e/bsMVVVVWb16tVm1KhR5sEHHxw0PiGkN+maT8ePHzdnn322+eIXv2j+8pe/mJdfftlcffXVZs6cOeb48eMD+vz617825513ni3348ePm4KCAvPv//7vcY9Nx3waaFMqGo2a73znOyY7O9t4vV5TVFRkfvCDHxgA5pVXXrH1dyOEpP/9kzHx32f0JN77pwceeMAAML/97W+7f9be3m7GjBljfvOb3wx4npvvny644AKTnZ1tmpqaun+2fv16Y1nWoLOZENKXdM2ob33rW70+Kmd3UyrejOqPNWvWGJ/PZ9rb2wc8xs57s8mTJ5vvfe97pqamxrz77rvmmWeeMeecc475q7/6KxMOh/scv3nzZjNy5Ehz//33D3rdjo4OM336dPP973/f3l+IpBxuSg0jJk6caH7yk5+YW265pdem0YwZM8wzzzxj/vqv/9p85StfGfQaoVDIjBw5ctCNl//7v/8zAAbdPJo9e7bZsGHDgH/+9NNPG4/HY5qbm3v9fMaMGb0+N30qX/va18w3v/nNQf4G/T8T4dChQwaAufvuu3v9/K233jJjx441//RP/zToNbu49dZbzcyZM20dSwg5STrm0z333NPnt3Vdx/zhD38Y8Dqf+cxnzC9/+Utb3r///e9NVlaWef/99+Mem+r5ZMzAm1JdhMNhU1dXZ0KhkHniiScMAFuuhJCTpPP9k9P3GfHeP23evNkAMP/3f//X6+dnn332oDHcfP/0xS9+0UyfPr3Xz95++20DwLz77ruDOhBC+pKOGTV37lzj8XiM1+s1Xq/XeDweA8B4vV7zL//yLwNeJ96M6o8333zTADC7du0a8Bg77836e6bUyy+/3O8ziZ977jmTk5PTawN/MBYtWmQ+97nP2TqWpB4+U2oYce655+K5557Dc8891+trQv/2b/8WmzZtwl/+8pcBP2vchYltVCIUCg14TDQaBYABj6mpqcH+/ftxwQUXDHiN48ePA0Cfzy97PJ7u6/fn9r//+7+OPmvchdfrBYBezzN46623cO655+JLX/oSfvKTn9i6TjQaHTQ3hJD+Scd8On78ODweDyzL6j6m678HmiOtra149tlnbc+RNWvW4LLLLkNxcXFct1TOJyfnTpgwAX6/H3/4wx/wiU98Iq4rIaQ36Xr/5PR9hp33T/Pnz0cgEMA777zT/bPOzk7s27cPkydPHtDNzfdPCxYsQH19PVpbW7t/9u6778Lj8WDixImOHQjJdNIxo9avX48dO3Zg+/bt2L59O+655x4AsW/3W758eb/XsDOj+mP79u3weDwYO3Zsv3/u9L1ZT/qbUc899xwuvvhi/OxnP8OyZcviXiMSiWDnzp0YP3684/gkRQhthpE08Lvf/c6MGDHC+Hw+c/jw4e6f33///SYvL6/P3U3vvfeeue2228xrr71m9u/fb1588UVz6aWXmlGjRpkjR44YY4z54x//aH73u9+ZnTt3mr1795rHH3/clJeXmwULFgzocfvtt5tLL710UNeGhgYzevRoc9VVV5nt27ebd955x9xyyy0mKyvLbN++vd9zXn31VVNUVGQ6OzsHvXZlZaX56le/ag4dOmQOHTpktm/fbq6++mqTnZ3dvUO/c+dOU1xcbJYsWdJ93KFDh3rdYbBq1SqzceNG8+6775p3333X3HPPPSYvL89873vfGzQ+IaQv6ZhP1dXVJhAImK9//evm7bffNm+++aZZsmSJKSgoGPBOzv/+7/82H/nIR2w519TUGMuyzKZNm+Iem8r51BX7jTfeMH//939vZs6cad544w3zxhtvmFAoZIyJzdC77rrLVFdXmzfeeMPcfPPNJjs722zdutXW340QcpJ0zCc77zNOxc77J2OM+cY3vmEmTJhgnnzySbNr1y5z/fXXm7Fjx5pjx471e7zb759aWlrMxIkTzaJFi8xbb71ltmzZYsrKyuLeyUEI6Z90zKhTsfPxPTsz6qWXXjK/+MUvzPbt2817771n1q5da4qLi80Xv/jFAc+x+95s8uTJ5kc/+pE5dOiQqa+vN1u3bjWVlZWmuLjYHD161Bhz8iN7K1as6DWjej5z74c//KF58sknzXvvvWe2bdtmPve5z5ns7Gzz1ltvxXUg6YGbUsOIvXv3GgBm9uzZvX6+b9++7gfc9eTgwYNm4cKFZuzYsSYrK8tMnDjRfOELX+j1P0abN282n/jEJ0xBQYHJzs42ZWVl5rvf/e6gA+uv//qv+/0Yyqm8+uqr5sILLzSjRo0yeXl55uMf/7h54oknBjz++9//vlm8eHHc61ZWVhoA3a+ioiJTWVlpNm/e3H3Mv/7rv/Y6pus1efLk7mN+9atfmTlz5piRI0ea/Px8c+aZZ5pf//rXgz7YjxDSP+mYT8YY8+c//9ksWLDAFBQUmKKiInPeeeeZl19+eUCPJUuW2N5YXrFihSktLbW15lM5n/o7ruvV9cDhhoYG8/GPf7z7gcTnn38+nyVFSIKkYz7ZeZ9xKnbfP3V0dJj/9//+nxk7dqzJy8szn/rUpwZ9gLnb75+Mif3S4FOf+pQZMWKEmThxovmHf/gHPk+KkARJ13uontjZlLIzo7Zt22bOOeec7v93LC8vN7fddtugz5Oy+95s8uTJveZOcXGxueiii8wbb7zRfcyXvvSlfmdUZWVl9zHf/OY3zaRJk4zf7zfjxo0zF110Udwv2yLpxTLGmBTffEUymKNHj2L8+PGoq6vDuHHjUnrtiooKfP/738dnP/vZlF6XEJIZhMNhjBs3Dps2bcLZZ5+d0mtzPhFCkoHvnwghmknXjErnezMydOAzpUhKOXbsGP7jP/4j5W+oOjo6cPXVV2PhwoUpvS4hJHM4duwYvvWtb+FjH/tYSq/L+UQISRa+fyKEaCZdMypd783I0IJ3ShFCCCGEEEIIIYQQ1+GdUoQQQgghhBBCCCHEdbgpRQghhBBCCCGEEEJch5tShBBCCCGEEEIIIcR1uClFCCGEEEIIIYQQQlyHm1KEEEIIIYQQQgghxHW4KUUIIYQQQgghhBBCXIebUoQQQgghhBBCCCHEdbgpRQghhBBCCCGEEEJch5tShBBCCCGEEEIIIcR1uClFCCGEEEIIIYQQQlyHm1KEEEIIIYQQQgghxHW4KUUIIYQQQgghhBBCXIebUoQQQgghhBBCCCHEdbgpRQghhBBCCCGEEEJcx+d2wGg0ivr6euTl5cGyLLfDE0IUYoxBS0sLSkpK4PHI7ZVzPhFC+kPDjOJ8IoT0B+cTIUQrdueT65tS9fX1KC0tdTssIWQIUFtbi4kTJ4rF53wihAyG5IzifCKEDAbnEyFEK/Hmk+ubUnl5eSf/Y6TX7fDA8YhsfDrQQZuDdPweDr3mgwDi8wlQVQ8xB+n4dKDDAA6SM4rziQ5q4tNBpQPnk55a0IEO4vGVOcSbT65vSnXf0jnSi4Ivl7kdHsH7dsO0hWHl+JC/dIbr8elAB20O0vEBoPneGuB4RPyWb+n5BOioh7SDdHw60OFUNMwozic6aIlPB10OnE8xNNSCDnTQEl+Lg935xAedE0IIIYQQQgghhBDX4abUEMYyRlqBEEIIIYQQQgghJCFc//geSZy5De1YXN2ET9SfwKzGEPxRoMMDvFMUwMslI/BAeSF2FGdLaxJCCCGEEEIIIYTEZUhtSkUa2hFp7AA6o0CWB94iP7wub8JUTCxH2bipyM3OQWt7G2qO7EVVXXVaY05t6sCqzYew4NAJdFpA1tx5wAWzgbw8+Fta8JFduzB7x3Ys29mEF8ePwI3njcfeQn9anSTyoCk+HXQ5aCBT5xMddDpIx6eDPqRnlIZa0IEOmhyk42tCej4B8vWQjk8HOkg6qN+UMuEoOmuCCFU1Ino01OfPPWMCCFQUIassH5YvPZ9GzM4K4Or5F2FZ5RLMLT29z5/vqH0bq7esxfptT6C9s69jMix6txl3bj4Mrz8ALF2KrJtvBs48s89xWW+8AfzqVzjnoXV4Zd1e3HDeeKyfmZ9SF8k8aIhPB10OGsj0+UQHXQ7S8emgD+kZpaEWdKCDJgfp+JqQnk+AfD2k49OBDlocLGPcfTBRMBhEQUGBrW9niDR1oG3jAZiWcNzrWnk+5Fw2Cd44dwg5fQr99LFT8Njye1A6qgRRE4XX0/frFCPRCDyWB7XH6nHFquuxp2F/ShwWvduM1U8dAsrK4Pnzn4HJk4FoFPD285WOkQjg8QD79yN6wQXA7t1YdsF4PDKzICmHLiTzkK74dHAeP10OXd/M0NzcjPz81G6mOkF6PgHy/aDBQUNPDjWH4doLWhw0zCgn8wmQfw81nPtB2mGozSc6pC8+wPnU7cB+oIMih6HWj+lysDuf1D7oPNLUgdZH9sG0xh9WAGBaw2h9ZB8iTR0pc5g+dgqevmUdSgrHwbKsfgsDAF6PF5ZloaRwHJ759kOYVjw56djTmjpw5+bDsQ2pV14BJkwALKv/DSkg9nPLAiZMgGfrVmDGDNy5+TCmpiAfknnQEJ8Ouhw0kOnziQ66HKTj00Ef0jNKQy3oQAdNDtLxNSE9nwD5ekjHpwMdtDk43pR6/vnncemll6KkpASWZWHDhg0pEemJCUfRtvEA0BEF7N7HZQB0xM4z4WjSDtlZATy2/B7kBXLg89r7lKPP60NeIAcbblyD7KxAUvHv2HwIHn8gdodUXh6QlWXvxKwsIC8PnqeegtcfwKrNh5LykM6DdHw66HKIB+fTwAzHfqCDfHw62MeN+QTIzygNtaADHTQ5SMe3Q6bMJ0C+HtLx6UAHjQ6ON6Xa2towd+5c3HnnnUkHH4jOmmDsdk6nHyw0gGkJo7MmmLTD1fMvQumoEtuF6cLn9WHS6Am46qyFCcee+347Fhw6gaxrPxf7yJ7dDakusrKAKVPg++y1WHDoBOY2tCfsIpkHDfHpoMshHpxPgzPc+oEO8vHpYB835hMgP6M01IIOdNDkIB3fDpkynwD5ekjHpwMdNDo43pRauHAhfvzjH+PKK69MOvhAhKoaRc8HgGWVSxA1ie3GR6IRLKtcknDsxbua0GkBuPnm2DOkEpKIADfdhE4LWFzdlLCLZB40xKeDLod4cD7FZzj1Ax3k49PBPm7MJ0B+RmmoBR3ooMlBOr4dMmU+AfL1kI5PBzpodFD3TKlIQ3u/38DghOjRECJJ3B1UMbEcc0tPH/CzlPHweryYN2kOKiaWJ3T+J+pPIGvuvNi37A30DKm4El5g/nxkzZ2Hjx86kdAlpPMgHZ8Ouhw0wPlEB00O0vHpoA/pGaWhFnSggyYH6fiakJ5PgHw9pOPTgQ4aHQAXNqVCoRCCwWCv12BEGlPzELtkHoZXNm5qShxmJHidWY0hYPbslDhg1izMPpbYPwDSeZCOTwddDumA84kOQ9lBOj4d0ovT+QTIzygNtaADHTQ5SMdPF0NxPgHy9ZCOTwc6aHQAXNiUWrlyJQoKCrpfpaWlg5/QmfwD7ADEHqCXILnZOSlRyEvgOpYx8EcRe7h5KsjPhz8au65TJPOgIT4ddDmkA84nOgxlB+n4dEgvjucTID6jNNSCDnTQ5CAdP10MxfkEyNdDOj4d6KDRAXBhU2rFihVobm7uftXW1g5+QlaKlPyJX6e1vS0lCi0JXMdYFjo8AFpaUuKAYBAdnth1nSKZBw3x6aDLIR1wPtFhKDtIx6dDenE8nwDxGaWhFnSggyYH6fjpYijOJ0C+HtLx6UAHjQ4A4OwR6wkQCAQQCNj/mkBvkT8lcb2FiV+n5sjelDjUHN6T0HnvFAXwkV27UuKAXbuwa1RiX9MonQfp+HTQ5ZAOOJ/oMJQdpOPTIb04nU+A/IzSUAs60EGTg3T8dDEU5xMgXw/p+HSgg0YHIIE7pVpbW7F9+3Zs374dALB3715s374dBw4cSEqkC29xNjxjEttE6cIzJgBvcXbC51fVVWNH7duIRCMJnR+JRrD9wFvYeTCxjaWXS0agc8d24I03Yt+il5BEBNi2DZ1VO/DK+BEJXUI6D9Lx6aDLwQ6cT/EZLv1AB/n4dHBGuucTID+jNNSCDnTQ5CAd3y6ZMJ8A+XpIx6cDHTQ6AAlsSr322ms488wzceaZZwIA/uEf/gFnnnkm/uVf/iUpkZ4EKopEzweA1VvWwmMldnuo1+PF6i1rE479QHkhsgyAX/0K8CR4i6rXC9xxB7JM7HqJIpkHDfHpoMshHpxP8RlO/UAH+fh0sI8b8wmQn1EaakEHOmhykI5vh0yZT4B8PaTj04EOGh0cR//kJz8JY0yf13333Ze0TBdZZfmw8nyA08cgWYCV50NWWX7SDuu3PYHaY/UIR8KOzgtHwjjwwUE8+vqmhGPvKM7Gi+NHIPzQOmD/fqCz09kFOjuBffsQfvghvDh+BHYk8RsFyTxoiE8HXQ7x4HwanOHWD3SQj08H+7gxnwD5GaWhFnSggyYH6fh2yJT5BMjXQzo+Heig0SHtDzpPBMvnQc5lk2IPsrM7tCwA/th5li/5v1Z7ZwhX3vkVtITabBcoHAmjJdSGK1Zdj/bOUFLxbzxvPCIdIUQvvDD20HO7G1OdnUBLC6IXXIBIRwg3njc+KQ/pPEjHp4MuBw1wPtFBk4N0fDroQ3pGaagFHeigyUE6viak5xMgXw/p+HSgg0YHlZtSQOwhdrmLpsDKtfcsdivXh9xFU5J6+N2pvPf+Ppx/+7WobzoCAAN+1rLr5/VNR3D+7ddiT8P+pGPvLfTjhvPGAzU1iJ5zDnDw4IfBBvi8Z9fPDx6MHb97N244bzz2piAfknnQEJ8Ouhw0kOnziQ66HKTj00Ef0jNKQy3oQAdNDtLxNSE9nwD5ekjHpwMdtDmk/dv3ksFb6EfeF6ahsyaIUFUjokf77sJ5xgQQqCiK3Q6agt3zU9nTsB9n//hiXHXWQiyrXIJ5k+b0OWZn3S6s3rIWj76+KaW/zVg/Mx8WDO7c/B68p5fD99lrgZtuAubP73vw9u3AHXcg/PBDiHSEcMMFJVg/M/lbXLuQzIOG+HTQ5aCBTJ9PdNDlIB2fDvqQnlEaakEHOmhykI6vCen5BMjXQzo+HeigycEyxpiUXc0GwWAQBQUFwEgvCr5c5ujcSEM7Ik0dQEcU8HvgLfQ7/gaG4H27YdrCsHJ8yF86w9G5AFAxsRwzxk1FXnYOWtrbsPvIXlTVVafVYWpTB1ZtPoQFh06g0wKy5s4DZs0C8vOBYBB45x107tiOLAO8UDICN50b/w6poZiHVMenQ/LxU+XQfG8NcDyC5uZm5OenbjPVKdLzCZDvBw0OGnpyqDsMl17Q4qBhRiUznwD591DDqR+kHYb6fKJD6uIDnE/dDuwHOihyGOr9mCoHu/NJ9Z1Sp+Itzk7qa0BTQVVddUILIxn2Fvpx8VWTMbehHYurm/DxumrMrtoOfxTo8AC7RgXwyhmFeKC8MKmHmjtBIg+a4tNBl4MGMnU+0UGng3R8OuhDekZpqAUd6KDJQTq+JqTnEyBfD+n4dKCDpMOQ2pTKdHYUZ2NH8Wnd/20ZA2M5/foKQgghhBBCCCGEEHnUPuicxIcbUoQQQgghhBBCCBmqcFOKEEIIIYQQQgghhLiO3IPOAVg57n960BwPAwaABVgjZT69SAc6aHKQjg8Api0MAHoedA6Z+QQoqQd7kg506O2gYEZxPtFBS3w6KHPgfAKgpBZ0oIOS+GocbM4n0WdKdUnKBBeOTwc6aHOQjq8M8VxoqIe0g3R8OtBBKeJ50FALOsjHp4MuByWI50FDLehABy3xtTjEQXRTindK0YEO8g7S8QGdg5K/6ZNzkI5PBzr0cVA2ozifMttBOj4dlDlwPgFQUgs60EFJfDUONueT3KbUSC/yl85wPWzwvt0wbWFYI30i8elAB20O0vEBoPneGuB4RCR2vwjNJ0BHPaQdpOPTgQ6nompGcT5lvIN0fDrocuB8iqGhFnSgg5b4Whzszic+6JwQQgghhBBCCCGEuA43pQghJIVY7n53BCGEEEIIIYQMWUSfKUUIIUOduQ3tWFzdhE/Un8CsxhD8UaDDA7xTFMDLJSPwQHkhdhRnS2sSQgghhBBCiDq4KeWQSEM7Io0dQGcUyPLAW+SH1+X/4ayYWI6ycVORm52D1vY21BzZi6q6alcdpPMgHR9gHTQ5SDC1qQOrNh/CgkMn0GkBbxpgPYAWAHlRYPYHIXz5WAjLdjbhxfEjcON547G30J9WJw214LrQ4cA66HHQgnQuNPSkBgfpOmhxYC3k42tCQy6ke1I6PqCjDhocWAv3HbgpZQMTjqKzJohQVSOiR0N9/twzJoBARRGyyvJh+dLzicjsrACunn8RllUuwdzS0/v8+Y7at7F6y1qs3/YE2jv7OqYC6TxIxwdYB00Okix6txl3bj6MjojBvQB+ZYDt/Rw3zwA3A1h06AReWbcXN5w3Hutn5qfURUMtuC50OLAOehy0IJ0LDT2pwUG6DlocWAv5+JrQkAvpnpSOD+iogwYH1kLWgZtScYg0daBt4wGYloG/zjB6NIQTmw+j/dWjyLlsErwpvhti+tgpeGz5PSgdVYKoifZ7zBkTZmHV4p/guwuX44pV12NPw/6UOkjnQTo+wDpocpBk0bvNWP3UIbwL4DMA9mPgh/NVAbgOwA8BPBkxuPupelgweGRmQUpcNNSC60KHA+ugx0EL0rnQ0JMaHKTroMWBtZCPrwkNuZDuSen4gI46aHBgLeQdhvcWfJJEmjrQ+sg+mNaBC9MT0xpG6yP7EGnqSJnD9LFT8PQt61BSOA6WZcHr8fZ7nNfjhWVZKCkch2e+/RCmFU9OmYN0HqTjA6yDJgdJpjV14M7Nh/EugL8CUPfhz/v/5+vkz+sAfAJADYA7Nx/G1BTkQ0MtuC50OLAOehy0IJ0LDT2pwUG6DlocWAv5+JrQkAvpnpSOD+iogwYH1kKHAzelBsCEo2jbeADoiAJ2v0zLAOiInWfCA/1vqn2yswJ4bPk9yAvkwOe1d1Obz+tDXiAHG25cg+ysQNIO0nmQjg+wDpocpLlj8yGEIgafARAEELF5XuTD4z8NoCNisGrzoaQ8NNSC60KHA+ugx0EL0rnQ0JMaHKTroMWBtZCPrwkNuZDuSen4gI46aHBgLfQ4ONqUWrlyJT72sY8hLy8PY8eOxRVXXIF33nknaQmNdNYEY7euOf12dwOYljA6a4JJO1w9/yKUjiqxvUi68Hl9mDR6Aq46a2HSDtJ5kI4PsA6aHAYj3fNp7vvtWHDoBNYj9pE9uxtSXUQ+PG89gAWHTmBuQ3vCLhpqwXWhw4F10OMQD7feQ0nnQkNPanCQroMWB9ZCPr4dMmU+AfI9KR0f0FEHDQ6shR4HR5tSW7ZswfLly/HKK6/gqaeeQmdnJy688EK0tbUlLaKNUFWj6PkAsKxyyYCfa41HJBrBssolSTtI50H6fIB10OQwGOmeT4t3NaHTAn6FxG8x9SB2fqcFLK5uSthFQy24LnQ4sA56HOLh1nso6Vxo6EkNDtJ10OLAWsifb4dMmU+AfE9Kxwd01EGDA2uhx8HR/1v96U9/wtKlSzFnzhzMnTsX9913Hw4cOIBt27YlLaKJSEN7v0+bd0L0aAiRJO6EqJhYjrmlpw/4udZ4eD1ezJs0BxUTyxN2kM6DdHyAddDkEI90z6dP1J/Amyb2LXuJ3qQaBfAGgDcN8PFDJxK6hoZacF3ocGAd9DjYwY33UNK50NCTGhyk66DFgbWQj2+XTJhPgHxPSscHdNRBgwNroccBSPKZUs3NzQCAUaNGDXhMKBRCMBjs9dJOpDF1DzVMlLJxU1PiMCOJ60jnQTo+wDpocnBKqufTrMYQdqXI7R0As48l9g+AhlpwXehwYB30OCRCvBmVyPsn6Vxo6EkNDtJ10OLAWsjHT5ThOJ8A+Z6Ujg/oqIMGB9ZCjwOQxKZUNBrFN7/5TSxYsABnnHHGgMetXLkSBQUF3a/S0tJEQ7pHZ4oeKNiR+HVys3NSopCXzHWk8yAdH6yDKgcHpHo+WcbAHwVaUuQXBOCPxq7rGAW14LrQ4cA6KHJwiJ0ZldD7J/akCgfpOmhxYC0UxE+A4TqfAPmelI4PQEUdNDiwFoockMSm1PLly/Hmm29i3bp1gx63YsUKNDc3d79qa2sTDekeWSn6UkJ/4tdpbU/NZ7hbkrmOdB6k44N1UOXggFTPJ2NZ6PAAeSnyywfQ4Yld1zEKasF1ocOBdVDk4BA7Myqh90/sSRUO0nXQ4sBaKIifAMN1PgHyPSkdH4CKOmhwYC0UOQBw9qj5D7nxxhvx+OOP4/nnn8fEiRMHPTYQCCAQSP7rEt3EW+RPzXUKE79OzZG9KXGoObwn4XOl8yAdH2AdNDnYJV3z6Z2iAGZ/kNxnrruYDWDXqMTmooZacF3ocGAd9Dg4we6MSuT9k3QuNPSkBgfpOmhxYC3k4ztlOM8nQL4npeMDOuqgwYG10OMAOLxTyhiDG2+8EY899hg2b96MqVNT81lMbXiLs+EZk9xGmmdMAN7i7ITPr6qrxo7atxGJOv3i+RiRaATbD7yFnQcTfwqOdB6k4wOsgyaHeKR7Pr1cMgJnWMA8JPfte2cCmGMBr4wfkdA1NNSC60KHA+ugx8EObryHks6Fhp7U4CBdBy0OrIV8fLtkwnwC5HtSOj6gow4aHFgLPQ6Aw/+3Wr58OdauXYsHH3wQeXl5OHz4MA4fPowTJxL7FinNBCqKRM8HgNVb1sJjJfa/v16PF6u3rE3aQToP0ucDrIMmh8FI93x6oLwQWQa4Gcl9+97NALJM7HqJoqEWXBc6HFgHPQ7xcOs9lHQuNPSkBgfpOmhxYC3kz7dDpswnQL4npeMDOuqgwYG10OPgqAp33XUXmpub8clPfhLjx4/vfj300ENJi2gjqywfVp4PcPrIFwuw8nzIKstP2mH9tidQe6we4UjY0XnhSBgHPjiIR1/flLSDdB6k4wOsgyaHwUj3fNpRnI0Xx4/AIgCTATj9Alnvh+ddDeDF8SOwI4nfKGioBdeFDgfWQY9DPNx6DyWdCw09qcFBug5aHFgL+fh2yJT5BMj3pHR8QEcdNDiwFnocHH98r7/X0qVLkxbRhuXzIOeySbGHdtktkAXAHzvP8iX/0LD2zhCuvPMraAm12V4s4UgYLaE2XLHqerR3Jv/8G+k8SMcHWAdNDoPhxny68bzx8Hst/Amxh5Xb3Zjyfnj8kwD8Xgs3njc+KQ8NteC60OHAOuhxiIdb76Gkc6GhJzU4SNdBiwNrIR/fDpkynwD5npSOD+iogwYH1kKPg3tf5TAE8Rb6kbtoCqxce8+Dt3J9yF00JaUPI3zv/X04//ZrUd90BAAG/Nxr18/rm47g/NuvxZ6G/SlzkM6DdHyAddDkIMneQj9uOG88ZgJ4GUDXI0AHGqRdP5/44fFlAG44bzz2piAfGmrBdaHDgXXQ46AF6Vxo6EkNDtJ10OLAWsjH14SGXEj3pHR8QEcdNDiwFjocEvr2vUzCW+hH3hemobMmiFBVI6JH++6IesYEEKgoit36lobfZuxp2I+zf3wxrjprIZZVLsG8SXP6HLOzbhdWb1mLR1/flJJd21ORzoN0fIB10OQgyfqZ+bBgcOfmw9gZMVgP4FcA3ujn2LmIPUPqasTukPrqeeOxfmbqbsPXUAuuCx0OrIMeBy1I50JDT2pwkK6DFgfWQj6+JjTkQronpeMDOuqgwYG1kHfgppQNLJ8H/vJC+MsLEWloR6SpA+iIAn4PvIX+tH8jBhC7vfDBrRvw4NYNqJhYjhnjpiIvOwct7W3YfWQvquqq0+4gnQfp+ADroMlBkkdmFmDb2BFYtfkQlh46gcUW8KYB3gEQROyjerMAnGHFHmr+QskI3HRuau6QOhUNteC60OHAOuhx0IJ0LjT0pAYH6TpocWAt5ONrQkMupHtSOj6gow4aHFgLWQduSjnEW5wt/g9GVV21KwtjMKTzIB0fYB00OUiwt9CPi6+ajLkN7Vhc3YSPHzqBq46F4I8CHR5g16gA7h0/Ag+UFyb1UHMnaKgF14UOB9ZBj4MWpHOhoSc1OEjXQYsDayEfXxMaciHdk9LxAR110ODAWrjvwE0pQghJgh3F2dhRfFr3f1vGwFhOv76CEEIIIYQQQjKP4fthZUIIEYAbUoQQQgghhBBiD25KEUIIIYQQQgghhBDXsYwxxs2AwWAQBQUFseA57n960BwPAwaABVgjZT69SAc6aHKQjg8Api0MAGhubkZ+fuq+nc4p0vMJUFIP9iQd6NDbQcGM4nyig5b4dFDmwPkEQEkt6EAHJfHVONicT6LPlOqSlAkuHJ8OdNDmIB1fGeK50FAPaQfp+HSgg1LE86ChFnSQj08HXQ5KEM+DhlrQgQ5a4mtxiIPophTvlKIDHeQdpOMDOgclf9Mn5yAdnw506OOgbEZxPmW2g3R8Oihz4HwCoKQWdKCDkvhqHGzOJ7lNqZFe5C+d4XrY4H27YdrCsEb6ROLTgQ7aHKTjA0DzvTXA8YhI7H4Rmk+AjnpIO0jHpwMdTkXVjOJ8yngH6fh00OXA+RRDQy3oQAct8bU42J1PfNA5IYQQQgghhBBCCHEdbkoRQgghhGQIlrvfb0MIIYQQMiiiz5QihBBCCCHpY25DOxZXN+ET9ScwqzEEfxTo8ADvFAXwcskIPFBeiB3F2dKahBBCCMlQuCnlkIqJ5SgbNxW52TlobW9DzZG9qKqrdtUh0tCOSGMH0BkFsjzwFvnhdfkNpXQemIMYGvKgwYHE0FALDQ5cm8yBJgcppjZ1YNXmQ1hw6AQ6LSBr7jzggtlAXh78LS34yK5dmL1jO5btbMKL40fgxvPGY2+hP20+GnqSDjE0rAvmQT6+JjTkQronpeNrcWAvxNCQBzcduCllg+ysAK6efxGWVS7B3NLT+/z5jtq3sXrLWqzf9gTaO0NpcTDhKDprgghVNSJ6tG8Mz5gAAhVFyCrLh+VLz6cypfPAHMTQkAcNDiSGhlpocODaZA40OUiz6N1m3Ln5MLz+ALB0KbJuvhk488w+x2W98Qbwq1/hnIfW4ZV1e3HDeeOxfmZ+yjw09CQdYmhYF8yDfHxNaMiFdE9Kx9fiwF6IoSEPUg6WMe4+XCAYDKKgoAAY6UXBl8vcDB2L3/UU+hx7T6GfPnYKHlt+D0pHlSBqovB6vH2OiUQj8Fge1B6rxxWrrseehv0pdYg0daBt4wGYlvhfqWjl+ZBz2SR44/y2c6jlIR05cOqQjhw4ddDQC+lw6PpmhubmZuTnp+5/iJwiPZ8A+X7Q4DDU5hMgn4fhOp+0OGiYUXbn06J3m7H6qUNAWRk8f/4zMHkyEI0C3r49gUgE8HiA/fsRveACYPduLLtgPB6ZWdC/wxDryeHqMNTmE8A8pCs+MLTmUxfStQDke3K4zienDhreO0j3AqBjTUj+P97w3oJPkuljp+DpW9ahpHAcLMvqt0EBwOvxwrIslBSOwzPffgjTiienzCHS1IHWR/bBtMZvDgAwrWG0PrIPkaaOlDlI54E5iKEhDxocSAwNtdDgwLXJHGhykGZaUwfu3Hw4tiH1yivAhAmAZfW/IQXEfm5ZwIQJ8GzdCsyYgTs3H8bUJHOioSfpEEPDumAe5ONrQkMupHtSOr4WB/ZCDA15kHbgptQAZGcF8Njye5AXyIHPa+9Tjj6vD3mBHGy4cQ2yswJJO5hwFG0bDwAdUcDu/WwGQEfsPBOOJu0gnQfmIIaGPGhwIDE01EKDA9cmc6DJQQN3bD4Ejz8Qu0MqLw/IyrJ3YlYWkJcHz1NPwesPYNXmQwk7aOhJOsTQsC6YB/n4mtCQC+melI6vxYG9EENDHjQ4ONqUuuuuu1BRUYH8/Hzk5+fjE5/4BDZt2pS0hEaunn8RSkeV2G7QLnxeHyaNnoCrzlqYtENnTTB2+5zTD1gawLSE0VkTTNpBOg/MQQwNedDgMBiZNJ801EKDA9cmc6DJIR7pnlFz32/HgkMnkHXt52If2bO7IdVFVhYwZQp8n70WCw6dwNyG9oQ8NPQkHWJoWBfMg3x8O7j1HkpDLqR7Ujq+Fgf2QgwNedDg4GhTauLEifjpT3+Kbdu24bXXXsN5552Hyy+/HG+99VbSItpYVrkEUZPYrl8kGsGyyiVJO4SqGkXPB+TzwBzE0JAHDQ6DkUnzSUMtNDhwbTIHmhzike4ZtXhXEzotADffHHuGVCJEIsBNN6HTAhZXNyV0CQ09SYcYGtYF8yB/vh3ceg+lIRfSPSkdX4sDeyGGhjxocHC0KXXppZfioosuQllZGWbOnImf/OQnyM3NxSuvvJK0iCYqJpZjbunpA36mNB5ejxfzJs1BxcTyhB0iDe39PvHeCdGjIUQS/E0nIJ8H5iCGhjxocIhHpswnDbXQ4MC1yRxocrBDumfUJ+pPIGvuvNi37A30DKl4eL3A/PnImjsPHz90wvHpGnqSDjE0rAvmQT6+Xdx4D6UhF9I9KR1fiwN7IYaGPGhwAJJ4plQkEsG6devQ1taGT3ziEwMeFwqFEAwGe720UzZuakquMyOJ60QaU/dgxUSRzgNzEENDHjQ4OIozjOeThlpocODaZA40OTiOZWNGOZ1PsxpDwOzZqRGcNQuzjzl/k6qhJ+kQQ8O6YB7k4ycUKw3zCdCRC+melI6vxYG9EENDHjQ4AAlsSu3cuRO5ubkIBAL42te+hsceewynn376gMevXLkSBQUF3a/S0tKkhN0gNzsnJdfJS+Y6nSl6qGFH4tcRzwNzEENBHlQ42CAT5pOKWihw4NpkDlQ52MTJjHIynyxj4I8i9nDzVJCfD380dl0naOhJOnyIgnXBPCiI74B0zaduFORCuiel42txYC98iII8qHBAAptSs2bNwvbt27F161Z8/etfx5e+9CW8/fbbAx6/YsUKNDc3d79qa2uTEnaD1va2lFynJZnrZKXoixH9iV9HPA/MQQwFeVDhYINMmE8qaqHAgWuTOVDlYBMnM8rJfDKWhQ4PgJaW1IgGg+jwxK7rBA09SYcPUbAumAcF8R2QrvnUjYJcSPekdHwtDuyFD1GQBxUOAJw9ah6A3+/HjBkzAADz58/Hq6++iv/8z//Eb3/7236PDwQCCASS/7pEN6k5sjc11zm8J+FzvUX+lDh4CxO/jnQemIMYGvKgwcEOmTCfNNRCgwPXJnOgycEuTmaU0/n0TlEAH9m1KzWiu3Zh1yjns1FDT9IhhoZ1wTzIx3dCOucToCMX0j0pHV+LA3shhoY8aHAAknimVBfRaBShUHIPx9JGVV01dtS+jUg0ktD5kWgE2w+8hZ0HE39z6C3OhmdMcv+z7BkTgLc4O+HzpfPAHMTQkAcNDokwHOeThlpocODaZA40OSRKKmfUyyUj0LljO/DGG7Fv0UuESATYtg2dVTvwyvgRjk/X0JN0iKFhXTAP8vGTIdXvoTTkQronpeNrcWAvxNCQBw0OgMNNqRUrVuD555/Hvn37sHPnTqxYsQLPPfccFi9enJSERlZvWQuPldiendfjxeota5N2CFQUiZ4PyOeBOYihIQ8aHAYjk+aThlpocODaZA40OcQj3TPqgfJCZBkAv/oV4Enwd45eL3DHHcgyseslgoaepEMMDeuCeZA/3w5uvYfSkAvpnpSOr8WBvRBDQx40ODiqwvvvv48vfvGLmDVrFs4//3y8+uqrePLJJ3HBBRckLaKN9dueQO2xeoQjYUfnhSNhHPjgIB59fVPSDlll+bDyfICzRzoAFmDl+ZBVlp+0g3QemIMYGvKgwWEwMmk+aaiFBgeuTeZAk0M80j2jdhRn48XxIxB+aB2wfz/Q2ensAp2dwL59CD/8EF4cPwI7Evytp4aepEMMDeuCeZCPbwe33kNpyIV0T0rH1+LAXoihIQ8aHBxtSq1Zswb79u1DKBTC+++/j6effnpY/g8fALR3hnDlnV9BS6jNdqOGI2G0hNpwxarr0d6Z/O2uls+DnMsmxR4cZrdJLAD+2HmWL/kHl0nngTmIoSEPGhwGI5Pmk4ZaaHDg2mQONDnEw40ZdeN54xHpCCF64YWxh57b3Zjq7ARaWhC94AJEOkK48bzxCTto6Ek6xNCwLpgH+fh2cOs9lIZcSPekdHwtDuyFGBryoMEh/VNuCPPe+/tw/u3Xor7pCAAM+JnTrp/XNx3B+bdfiz0N+1Pm4C30I3fRFFi59p5Jb+X6kLtoSkofiCidB+YghoY8aHAgMTTUQoMD1yZzoMlBmr2Fftxw3nigpgbRc84BDh6M/cFAz5jq+vnBg7Hjd+/GDeeNx94kc6KhJ+kQQ8O6YB7k42tCQy6ke1I6vhYH9kIMDXmQdnD87XuZxp6G/Tj7xxfjqrMWYlnlEsybNKfPMTvrdmH1lrV49PVNKdkxPRVvoR95X5iGzpogQlWNiB7tG8MzJoBARVHs9rs0/EZFOg/MQQwNedDgQGJoqIUGB65N5kCTgzTrZ+bDgsGdm9+D9/Ry+D57LXDTTcD8+X0P3r4duOMOhB9+CJGOEG64oATrZ6bmo0IaepIOMTSsC+ZBPr4mNORCuiel42txYC/E0JAHSQduStmgvTOEB7duwINbN6BiYjlmjJuKvOwctLS3YfeRvaiqq067g+XzwF9eCH95ISIN7Yg0dQAdUcDvgbfQ78q3ckjngTmIoSEPGhxIDA210ODAtckcaHKQ5pGZBdg2dgRWbT6EBfffj87f34+sufOAWbOA/HwgGATeeQedO7YjywCvlIzATedOTfoOqVPR0JN0iKFhXTAP8vE1oSEX0j0pHV+LA3shhoY8SDlwU8ohVXXVrjTlYHiLs8X/0ZLOA3MQQ0MeNDiQGBpqocGBa5M50OQgxd5CPy6+ajLmNrRjcXUTPl5XjdlV2+GPAh0eYNeoAF45oxAPlBcm/FBzJ2joSTrE0LAumAf5+JrQkAvpnpSOr8WBvRBDQx7cdOCmFCGEEELIMGVHcTZ2FJ/W/d+WMTCW06/YIYQQQghJD8P3w8qEEEIIIaQX3JAihBBCiCa4KUUIIYQQQgghhBBCXMcyxhg3AwaDQRQUFMSC57j/6UFzPAwYABZgjZT59CId6KDJQTo+AJi2MACgubkZ+fmp+eanRJCeT4CSerAn6UCH3g4KZhTnEx20xKeDMgfOJwBKakEHOiiJr8bB5nwSfaZUl6RMcOH4dKCDNgfp+MoQz4WGekg7SMenAx2UIp4HDbWgg3x8OuhyUIJ4HjTUgg500BJfi0McRDeleKcUHegg7yAdH9A5KPmbPjkH6fh0oEMfB2UzivMpsx2k49NBmQPnEwAltaADHZTEV+Ngcz7JbUqN9CJ/6QzXwwbv2w3TFoY10icSnw500OYgHR8Amu+tAY5HRGL3i9B8AnTUQ9pBOj4d6HAqqmYU51PGO0jHp4MuB86nGBpqQQc6aImvxcHufOKDzgkhhBBCCCGEEEKI63BTihBCCCGEEEIIIYS4DjelCCGEEEIIIYQQQojriD7o3CkVE8tRNm4qcrNz0Nrehpoje1FVV00HAYdIQzsijR1AZxTI8sBb5Ie3ONu1+MwBHbShoSc1OGjoBw0O0rXQkAM66EK6J6XjAzr6QYMDa6HDQTq+JjTkQnpdaMiBBgfpOgA68pBpDuo3pbKzArh6/kVYVrkEc0tP7/PnO2rfxuota7F+2xNo7wzRIY0OJhxFZ00QoapGRI/2jeEZE0CgoghZZfmwfKm/CY85oIM2NPSkBgcN/aDBQboWGnJAB11I96R0fEBHP2hwYC10OEjH14SGXEivCw050OAgXQdARx4y2cEyxpiUXc0GwWAQBQUFwEgvCr5cNuix08dOwWPL70HpqBJETRRej7fPMZFoBB7Lg9pj9bhi1fXY07B/8PhdT6HPsfcUejp8eP2mDrRtPADTEv9rHa08H3IumwRvoT9lDunIgVOHdORAg4OGXuj6Zobm5mbk5+fHvW66kJ5PANeF0/haHKTn9HCdT1ocNMwoJ/MJkO/J4TqfNDgMtfkE6MiDtAPn00mkawHIr4vhOp+cOgzXfyuG2nxKl4Pd+aR2C3762Cl4+pZ1KCkcB8uy+m1QAPB6vLAsCyWF4/DMtx/CtOLJdEixQ6SpA62P7INpjd+gAGBaw2h9ZB8iTR0pic8c0EEbGnpSg4OGftDgIF0LDTmggy6ke1I6PqCjHzQ4sBY6HKTja0JDLqTXhYYcaHCQrgOgIw90ULoplZ0VwGPL70FeIAc+r71PGPq8PuQFcrDhxjXIzgrQIUUOJhxF28YDQEcUsHtPnQHQETvPhKNJxWcO6KANDT2pwUFDP2hwkK6FhhzQQRfSPSkdH9DRDxocWAsdDtLxNaEhF9LrQkMONDhI1wHQkQc6xEhqU+qnP/0pLMvCN7/5zaRFenL1/ItQOqrEdoN24fP6MGn0BFx11kI6pMihsyYYu4XP6Yc8DWBawuisCSYVnzmgQ6JwPqXXQUM/aHCQroWGHNDBOemaT4B8T0rHB3T0gwYH1kKHg3T8REjXjNKQC+l1oSEHGhyk6wDoyAMdYiS8KfXqq6/it7/9LSoqKpKWOJVllUsQNYntuEWiESyrXEKHFDmEqhpFz2cO6JAInE/pd9DQDxocpGuhIQd0cEY65xMg35PS8QEd/aDBgbXQ4SB9vlPSOaM05EJ6XWjIgQYH6ToAOvJAhxgJbUq1trZi8eLFuPvuu1FUVJS0RE8qJpZjbunpA36mNB5ejxfzJs1BxcRyOiTpEGlo7/ep+06IHg0h0tCe0LnMAR0SgfMp/Q4a+kGDg3QtNOSADs5I53wC5HtSOj6gox80OLAWOhyk4zslnTNKQy6k14WGHGhwkK4DoCMPdDhJQptSy5cvx8UXX4xPfepTcY8NhUIIBoO9XoNRNm5qIkp9mJHEdegQI9KYuoc7JgJzQIdE4HxKv4OGftDgIF0LDTmggzPSOZ8A+Z6Ujg/o6AcNDqyFDgfp+E6xO6MSmU8aciG9LjTkQIODdB0AHXmgw0mcfYgTwLp16/D666/j1VdftXX8ypUr8cMf/tD29XOzc5wq9UteEtehw4d0pujBih2JXYc5oINTOJ/ccVDRDwocxGuhIAd0sE+65xMg35PS8QHo6AcFDqyFEgfp+A5wMqMSmU8aciG+LhTkQIODeB0AFXmgw0kc3SlVW1uLb3zjG3jggQeQnZ1t65wVK1agubm5+1VbWzvo8a3tbU6UBqQlievQ4UOyUvTljP7ErsMc0MEJnE/uOajoBwUO4rVQkAM62MON+QTI96R0fAA6+kGBA2uhxEE6vk2czqhE5pOGXIivCwU50OAgXgdARR7ocBJHd0pt27YN77//Ps4666zun0UiETz//PNYtWoVQqEQvN7enw0NBAIIBOx/ZWPNkb1OlAa+zuE9iZ9LBwCAt8ifEgdvYWLXYQ7o4ATOJ/ccNPSDBgfpWmjIAR3s4cZ8AuR7Ujo+oKMfNDiwFjocpOPbxemMSmQ+aciF9LrQkAMNDtJ1AHTkgQ4ncbSldf7552Pnzp3Yvn179+ujH/0oFi9ejO3bt/d5Q5UIVXXV2FH7NiLRSELnR6IRbD/wFnYe3EWHJB28xdnwjHH2D86peMYE4C2291vhU2EO6OAEzif3HDT0gwYH6VpoyAEd7OHGfALke1I6PqCjHzQ4sBY6HKTj28WNGaUhF9LrQkMONDhI1wHQkQc69LiGk4Pz8vJwxhln9Hrl5ORg9OjROOOMM5IS6cnqLWvhsRK7Bczr8WL1lrV0SJFDoCK5b95I9nzmgA524Xxy10FDP2hwkK6FhhzQIT5uzSdAviel4wM6+kGDA2uhw0H6fDu4NaM05EJ6XWjIgQYH6ToAOvJAhxjp/YBygqzf9gRqj9UjHAk7Oi8cCePABwfx6Oub6JAih6yyfFh5PsByeKIFWHk+ZJXlJxWfOaCDNjT0pAYHDf2gwUG6FhpyQAddSPekdHxARz9ocGAtdDhIx9eEhlxIrwsNOdDgIF0HQEce6BAj6U2p5557Dr/85S+TFulJe2cIV975FbSE2mw3ajgSRkuoDVesuh7tnSE6pMjB8nmQc9mk2MPL7DaqBcAfO8/yJddizAEdkoHzKX0OGvpBg4N0LTTkgA6JkY75BMj3pHR8QEc/aHBgLXQ4SMdPlHTMKA25kF4XGnKgwUG6DoCOPNAhhso7pQDgvff34fzbr0V90xEAGPAzp10/r286gvNvvxZ7GvbTIcUO3kI/chdNgZVr77n4Vq4PuYumpOyhjMwBHbShoSc1OGjoBw0O0rXQkAM66EK6J6XjAzr6QYMDa6HDQTq+JjTkQnpdaMiBBgfpOgA68kAHh9++5zZ7Gvbj7B9fjKvOWohllUswb9KcPsfsrNuF1VvW4tHXN6Vkx5QO/eMt9CPvC9PQWRNEqKoR0aN9Y3jGBBCoKIrdApji3+owB3TQhoae1OCgoR80OEjXQkMO6KAL6Z6Ujg/o6AcNDqyFDgfp+JrQkAvpdaEhBxocpOsA6MhDpjuo3pQCYrf2Pbh1Ax7cugEVE8sxY9xU5GXnoKW9DbuP7EVVXTUdXHKwfB74ywvhLy9EpKEdkaYOoCMK+D3wFvrT/s0gzAEdtKGhJzU4aOgHDQ7StdCQAzroQronpeMDOvpBgwNrocNBOr4mNORCel1oyIEGB+k6ADrykMkO6jelelJVV+1KU9IhPt7ibNF/OJkDOmhDQ09qcNDQDxocpGuhIQd00IV0T0rHB3T0gwYH1kKHg3R8TWjIhfS60JADDQ7SdQB05CHTHIbvfaGEEEIIIYQQQgghRC3clCKEEEIIIYQQQgghrsNNKUIIIYQQQgghhBDiOpYxxrgZMBgMoqCgIBY8x/1HWpnjYcAAsABrpMwjtehAB00O0vEBwLSFAQDNzc3Iz88XcQDk5xOgpB7sSTrQobeDghnF+UQHLfHpoMyB8wmAklrQgQ5K4qtxsDmfRB903iUpE1w4Ph3ooM1BOr4yxHOhoR7SDtLx6UAHpYjnQUMt6CAfnw66HJQgngcNtaADHbTE1+IQB9FNKd4pRQc6yDtIxwd0Dkr+pk/OQTo+HejQx0HZjOJ8ymwH6fh0UObA+QRASS3oQAcl8dU42JxPcptSI73IXzrD9bDB+3bDtIVhjfSJxKcDHbQ5SMcHgOZ7a4DjEZHY/SI0nwAd9ZB2kI5PBzqciqoZxfmU8Q7S8emgy4HzKYaGWtCBDlria3GwO5/4oHNCCCGEEEIIIYQQ4jrclCKEEEIIIYQQQgghrsNNKUIIIYQQQgghhBDiOqIPOndKxcRylI2bitzsHLS2t6HmyF5U1VXTIQMdIg3tiDR2AJ1RIMsDb5Ef3uJs1+ID8jkAdORBg4MGNPQDHWJo6ElpB9ZBj4MWpHtCOr4WBw09qcGBtZCPrwkNuZDuSen4gI46aHBgLdx3UL8plZ0VwNXzL8KyyiWYW3p6nz/fUfs2Vm9Zi/XbnkB7Z4gOw9jBhKPorAkiVNWI6NG+1/eMCSBQUYSssnxYvvTcBCidA0BHHjQ4aEBDP9AhhoaelHZgHfQ4aEG6J6Tja3HQ0JMaHFgL+fia0JAL6Z6Ujg/oqIMGB9ZC1sEyxpiUXc0GwWAQBQUFwEgvCr5cNuix08dOwWPL70HpqBJETRRej7fPMZFoBB7Lg9pj9bhi1fXY07B/8PhdT6HPsfcUejrocIg0daBt4wGYlvhfK2nl+ZBz2SR4C/1xj3XikI4cOHVIRx6c9kI6HLq+maG5uRn5+flxr5supOcTMPR6Uno2AMN3XUjXwamDhjmdLgcNM8rJfALk16aGntTgID0btDhI9yMgnwfOp5NI1wKQ78nhOp+GooN0LwA61oTk/+Op3YKfPnYKnr5lHUoKx8GyrH6bAwC8Hi8sy0JJ4Tg88+2HMK14Mh2GmUOkqQOtj+yDaY2/QADAtIbR+sg+RJo6UhIfkM8BoCMPGhw0oKEf6BBDQ09KO7AOehy0IN0T0vG1OGjoSQ0OrIV8fE1oyIV0T0rHB3TUQYMDa6HDQeWmVHZWAI8tvwd5gRz4vPY+Yejz+pAXyMGGG9cgOytAh2HiYMJRtG08AHREAbv39BkAHbHzTDiaVHxAPgeAjjxocNCAhn6gQwwNPSntwDrocdCCdE9Ix9fioKEnNTiwFvLxNaEhF9I9KR0f0FEHDQ6shR4HR5tSP/jBD2BZVq/X7Nmzk5Y4lavnX4TSUSW2m6MLn9eHSaMn4KqzFtJhmDh01gRjtxA6/ZCpAUxLGJ01waTiA/I5AHTkQYPDYHA+ZZ6Dhp6UdmAd9DjEI1NmlHR8LQ4aelKDA2shH98Obs0nDbmQ7knp+ICOOmhwYC30ODi+U2rOnDk4dOhQ9+uFF15IWuJUllUuQdQktuMWiUawrHIJHYaJQ6iqUfR8QD4HgI48aHCIB+dTZjlo6ElpB9ZBj4MdMmFGScfX4qChJzU4sBby59vFjfmkIRfSPSkdH9BRBw0OrIUeB8ebUj6fD6eddlr3a8yYMUlL9KRiYjnmlp4+4Oc54+H1eDFv0hxUTCynwxB3iDS09/vUfydEj4YQaWhP+HzpHAA68qDBwQ6cT5njoKEnpR1YBz0OdhnuM0o6vhYHDT2pwYG1kI/vhHTPJw25kO5J6fiAjjpocGAt9DgACWxK1dTUoKSkBNOmTcPixYtx4MCBQY8PhUIIBoO9XoNRNm6qU6V+mZHEdeigwyHSmLqHSyaKdA4AHXnQ4GAHzqfMcdDQk9IOrIMeB7s4mVFO5xMg3xPS8bU4aOhJDQ6shXx8J6R7PmnIhXRPSscHdNRBgwNroccBcLgpdc455+C+++7Dn/70J9x1113Yu3cv/uZv/gYtLS0DnrNy5UoUFBR0v0pLSweNkZud40RpQPKSuA4dlDh0pujBjh2JX0c8B4CKPKhwiAPnU2Y5qOhJYQfWQZGDDZzOKKfzCZDvCen4WhxU9KQCB9ZCQXybuDGfNORCuiel4wNQUQcNDqyFIgc43JRauHAhrrnmGlRUVODTn/40nnjiCTQ1NeHhhx8e8JwVK1agubm5+1VbWztojNb2NidKA9KSxHXooMQhK0VfDulP/DriOQBU5EGFQxw4nzLLQUVPCjuwDoocbOB0RjmdT4B8T0jH1+KgoicVOLAWCuLbxI35pCEX0j0pHR+AijpocGAtFDkAcPao+VMoLCzEzJkzsXv37gGPCQQCCATsf11izZG9ySidvM7hPYmfSwcVDt4if0riewsTv450DgAdedDg4BTOp+HtoKEnpR1YBz0OiRBvRjmdT4B8T0jH1+KgoSc1OLAW8vETJR3zSUMupHtSOj6gow4aHFgLPQ5AAs+U6klrayvee+89jB8/PimJnlTVVWNH7duIRCMJnR+JRrD9wFvYeXAXHYa4g7c4G54xzv7BOxXPmAC8xdkJny+dA0BHHjQ4OIXzaXg7aOhJaQfWQY9DIgzHGSUdX4uDhp7U4MBayMdPlHTMJw25kO5J6fiAjjpocGAt9DgADjelbrnlFmzZsgX79u3DSy+9hCuvvBJerxef//znk5I4ldVb1sJjJbZf5vV4sXrLWjoME4dARZHo+YB8DgAdedDgMBicT5nnoKEnpR1YBz0O8ciUGSUdX4uDhp7U4MBayJ9vB7fmk4ZcSPekdHxARx00OLAWehwcVaGurg6f//znMWvWLHz2s5/F6NGj8corr6C4uDhpkZ6s3/YEao/VIxwJOzovHAnjwAcH8ejrm+gwTByyyvJh5fkAy+GJFmDl+ZBVlp9UfEA+B4COPGhwGAzOp8xz0NCT0g6sgx6HeGTKjJKOr8VBQ09qcGAt5OPbwa35pCEX0j0pHR/QUQcNDqyFHgdHm1Lr1q1DfX09QqEQ6urqsG7dOkyfPj1piVNp7wzhyju/gpZQm+0mCUfCaAm14YpV16O9M0SHYeJg+TzIuWxS7OFpdheKBcAfO8/yJf/wNukcADryoMFhMDifMs9BQ09KO7AOehzikSkzSjq+FgcNPanBgbWQj28Ht+aThlxI96R0fEBHHTQ4sBZ6HNI/5RLkvff34fzbr0V90xEAGPDznl0/r286gvNvvxZ7GvbTYZg5eAv9yF00BVauvefyW7k+5C6aktKHQkrnANCRBw0OGtDQD3SIoaEnpR1YBz0OWpDuCen4Whw09KQGB9ZCPr4mNORCuiel4wM66qDBgbXQ4ZDUt++lmz0N+3H2jy/GVWctxLLKJZg3aU6fY3bW7cLqLWvx6OubUrJbSQedDt5CP/K+MA2dNUGEqhoRPdr3+p4xAQQqimK3IKbht0rSOQB05EGDgwY09AMdYmjoSWkH1kGPgxake0I6vhYHDT2pwYG1kI+vCQ25kO5J6fiAjjpocGAt5B1Ub0oBsdvqHty6AQ9u3YCKieWYMW4q8rJz0NLeht1H9qKqrpoOGeJg+TzwlxfCX16ISEM7Ik0dQEcU8HvgLfS78s0k0jkAdORBg4MGNPQDHWJo6ElpB9ZBj4MWpHtCOr4WBw09qcGBtZCPrwkNuZDuSen4gI46aHBgLWQd1G9K9aSqrtqVhqCDfgdvcbb4P9zSOQB05EGDgwY09AMdYmjoSWkH1kGPgxake0I6vhYHDT2pwYG1kI+vCQ25kO5J6fiAjjpocGAt3HcYvveFEkIIIYQQQgghhBC1cFOKEEIIIYQQQgghhLgON6UIIYQQQgghhBBCiOtYxhjjZsBgMIiCgoJY8Bz3H2lljocBA8ACrJEyj9SiAx00OUjHBwDTFgYANDc3Iz8/X8QBkJ9PgJJ6sCfpQIfeDgpmFOcTHbTEp4MyB84nAEpqQQc6KImvxsHmfBJ90HmXpExw4fh0oIM2B+n4yhDPhYZ6SDtIx6cDHZQingcNtaCDfHw66HJQgngeNNSCDnTQEl+LQxxEN6V4pxQd6CDvIB0f0Dko+Zs+OQfp+HSgQx8HZTOK8ymzHaTj00GZA+cTACW1oAMdlMRX42BzPsltSo30In/pDNfDBu/bDdMWhjXSJxKfDnTQ5iAdHwCa760BjkdEYveL0HwCdNRD2kE6Ph3ocCqqZhTnU8Y7SMengy4HzqcYGmpBBzpoia/Fwe584oPOCSGEEEIIIYQQQojrcFOKEEIIIYQQQgghhLgON6UIIYQQQgghhBBCiOuIPujcKRUTy1E2bipys3PQ2t6GmiN7UVVXTQcBh0hDOyKNHUBnFMjywFvkh7c427X4zAEdtKGhJ+kQQ0NPSjtIx6eDPqTXpnR8LQ4aepIOOhyk42tCQy6k54OGHNCBDlIO6jelsrMCuHr+RVhWuQRzS0/v8+c7at/G6i1rsX7bE2jvDNEhjQ4mHEVnTRChqkZEj/aN4RkTQKCiCFll+bB8qb8JjzmggzY09CQdYmjoSWkH6fh00If02pSOr8VBQ0/SQYeDdHxNaMiF9HzQkAM60EGDg+pNqeljp+Cx5fegdFQJoiba7zFnTJiFVYt/gu8uXI4rVl2PPQ376ZAGh0hTB9o2HoBpGfhrHaNHQzix+TDaXz2KnMsmwVvoT1l85oAO2tDQk3SIoaEnpR2k49NBH9JrUzq+FgcNPUkHHQ7S8TWhIRfS80FDDuhABy0Oarfgp4+dgqdvWYeSwnGwLAtej7ff47weLyzLQknhODzz7YcwrXgyHVLsEGnqQOsj+2BaB27QnpjWMFof2YdIU0dK4jMHdNCGhp6kQwwNPSntIB2fDvqQXpvS8bU4aOhJOuhwkI6vCQ25kJ4PGnJABzpoclC5KZWdFcBjy+9BXiAHPq+9m7l8Xh/yAjnYcOMaZGcF6JAiBxOOom3jAaAjChi7JwHoiJ1nwv3/5sEuzAEdtKGhJ+kQQ0NPSjtIx6eDPqTXpnR8LQ4aepIOOhyk42tCQy6k54OGHNCBDtocHG9KHTx4EEuWLMHo0aMxYsQIfOQjH8Frr72WtEhPrp5/EUpHldgeFF34vD5MGj0BV521kA4pcuisCcZu4bPboF0YwLSE0VkTTCo+c0AHJ3A+ZZaDhp6UdpCOTwdnZMKMko6vxUFDT9JBh4N0fLu4MZ805EJ6PmjIAR3ooM3B0aZUY2MjFixYgKysLGzatAlvv/02fv7zn6OoqChpkZ4sq1wy4Gd74xGJRrCscgkdUuQQqmoUPZ85oINdOJ8yz0FDT0o7SJ9PB/tkyoySjq/FQUNP0kGHg/T5dnBrPmnIhfR80JADOtBBm4OjLeKf/exnKC0txb333tv9s6lTpyYt0ZOKieX9fvuBXbweL+ZNmoOKieUJf40nHWJEGtr7feq+E6JHQ4g0tCf09ZHMAR2cwPmUWQ4aelLaQTo+HZyRCTNKOr4WBw09SQcdDtLx7eLGfNKQC+n5oCEHdKCDNgfA4Z1SGzduxEc/+lFcc801GDt2LM4880zcfffdg54TCoUQDAZ7vQajbFxqBuCMJK5DhxiRxtQ93DERmAM6OIHzKbMcNPSktIN0fDo4w+mMcjqfAPm1KR1fi4OGnqSDDgfp+HZxYz5pyIX0fNCQAzrQQZsD4HBTas+ePbjrrrtQVlaGJ598El//+tdx88034/777x/wnJUrV6KgoKD7VVpaOmiM3OwcJ0oDkpfEdejwIZ0perBiR2LXYQ7o4ATOp8xyUNGT0g7S8engCKczyul8AuTXpnR8LQ4qepIOOhyk49vEjfmkIRfi80FBDuhAB3UOcLgpFY1GcdZZZ+G2227DmWeeiWXLluGrX/0qfvOb3wx4zooVK9Dc3Nz9qq2tHTRGa3ubE6UBaUniOnT4kKwUfTmjP7HrMAd0cALnU2Y5qOhJaQfp+HRwhNMZ5XQ+AfJrUzq+FgcVPUkHHQ7S8W3ixnzSkAvx+aAgB3SggzoHONyUGj9+PE4/vffncMvLy3HgwIEBzwkEAsjPz+/1GoyaI3udKA18ncN7Ej+XDgAAb5E/JQ7ewsSuwxzQwQmcT5nloKEnpR2k49PBGU5nlNP5BMivTen4Whw09CQddDhIx7eLG/NJQy6k54OGHNCBDtocAIebUgsWLMA777zT62fvvvsuJk+enJRET6rqqrGj9m1EopGEzo9EI9h+4C3sPLiLDkk6eIuz4RkTSPh8APCMCST80DPmgA5O4HzKLAcNPSntIB2fDs7IhBklHV+Lg4aepIMOB+n4dnFjPmnIhfR80JADOtBBmwPgcFPqW9/6Fl555RXcdttt2L17Nx588EGsXr0ay5cvT0riVFZvWQuPldgtYF6PF6u3rKVDihwCFcl9FWyy5zMHdLAL51PmOWjoSWkH6fPpYJ9MmVHS8bU4aOhJOuhwkD7fDm7NJw25kJ4PGnJABzpoc3C0Ij/2sY/hsccewx/+8AecccYZuPXWW/HLX/4SixcvTlqkJ+u3PYHaY/UIR8KOzgtHwjjwwUE8+vomOqTIIassH1aeD7AcnmgBVp4PWWXxb+cdDOaADnbhfMo8Bw09Ke0gHZ8O9smUGSUdX4uDhp6kgw4H6fh2cGs+aciF9HzQkAM60EGbg+Nt4ksuuQQ7d+5Ee3s7qqur8dWvfjVpiVNp7wzhyju/gpZQm+2BEY6E0RJqwxWrrkd7Z4gOKXKwfB7kXDYp9vAyu41qAfDHzrN8yT30jDmggxM4nzLLQUNPSjtIx6eDMzJhRknH1+KgoSfpoMNBOr5d3JhPGnIhPR805IAOdNDm4M6US4D33t+H82+/FvVNRwBgwM/+dv28vukIzr/9Wuxp2E+HFDt4C/3IXTQFVq7P1vFWrg+5i6ak7KGMzAEdtKGhJ+kQQ0NPSjtIx6eDPqTXpnR8LQ4aepIOOhyk42tCQy6k54OGHNCBDpoc7EUVYk/Dfpz944tx1VkLsaxyCeZNmtPnmJ11u7B6y1o8+vqmlPxmiw7000EDAAB8x0lEQVT94y30I+8L09BZE0SoqhHRo31jeMYEEKgoit0CmOLf6jAHdNCGhp6kQwwNPSntIB2fDvqQXpvS8bU4aOhJOuhwkI6vCQ25kJ4PGnJABzpocVC9KQXEbrF8cOsGPLh1AyomlmPGuKnIy85BS3sbdh/Zi6q6ajq45GD5PPCXF8JfXohIQzsiTR1ARxTwe+At9Kf9m0GYAzpoQ0NP0iGGhp6UdpCOTwd9SK9N6fhaHDT0JB10OEjH14SGXEjPBw05oAMdNDio35TqSVVdtStvHugQH29xtug/nMwBHbShoSfpEENDT0o7SMengz6k16Z0fC0OGnqSDjocpONrQkMupOeDhhzQgQ5SDsP3vlBCCCGEEEIIIYQQohZuShFCCCGEEEIIIYQQ1+GmFCGEEEIIIYQQQghxHcsYY9wMGAwGUVBQEAue4/4jrczxMGAAWIA1UuaRWnSggyYH6fgAYNrCAIDm5mbk5+eLOADy8wlQUg/2JB3o0NtBwYzifKKDlvh0UObA+QRASS3oQAcl8dU42JxPog8675KUCS4cnw500OYgHV8Z4rnQUA9pB+n4dKCDUsTzoKEWdJCPTwddDkoQz4OGWtCBDlria3GIg+imFO+UogMd5B2k4wM6ByV/0yfnIB2fDnTo46BsRnE+ZbaDdHw6KHPgfAKgpBZ0oIOS+GocbM4nuU2pkV7kL53hetjgfbth2sKwRvpE4tOBDtocpOMDQPO9NcDxiEjsfhGaT4COekg7SMenAx1ORdWM4nzKeAfp+HTQ5cD5FENDLehABy3xtTjYnU980DkhhBBCCCGEEEIIcR1uShFCCCGEEEIIIYQQ1+GmFCGEEEIIIYQQQghxHW5KEUIIIYQQQgghhBDXEf32PadUTCxH2bipyM3OQWt7G2qO7EVVXTUdMtAh0tCOSGMH0BkFsjzwFvnhLc52LT4ddDloQHpN0EGXg/S6kI5PB31Irwvp+HQ4iYZ1QQf5+JrQkAtpBw2zQYODdB3oIOOgflMqOyuAq+dfhGWVSzC39PQ+f76j9m2s3rIW67c9gfbOEB2GsYMJR9FZE0SoqhHRo32v7xkTQKCiCFll+bB86bkJkA56HDQgvSbooMtBel1Ix6eDPqTXhXR8OpxEw7qgg3x8TWjIhbSDhtmgwUG6DnSQd7CMMSZlV7NBMBhEQUEBMNKLgi+XDXrs9LFT8Njye1A6qgRRE4XX4+1zTCQagcfyoPZYPa5YdT32NOwfPH7XVyPm2PtqRDrocIg0daBt4wGYlnBcVyvPh5zLJsFb6I97LB2c90I6HLq+LrS5uRn5+flxr5supOcT4Kwew9VhqM0nQH5tDtf5pMVBw4xyMp8A+XUxXOeTBoehNp/okL74wNCcT9K10OAwXOeTU4fh+t5hqPVjuhzszie1W/DTx07B07esQ0nhOFiW1e8iAQCvxwvLslBSOA7PfPshTCueTIdh5hBp6kDrI/tgWuMvEAAwrWG0PrIPkaaOlMSngy4HDUivCTrocpBeF9Lx6aAP6XUhHZ8OJ9GwLuggH18TGnIh7aBhNmhwkK4DHfQ4ONqUmjJlCizL6vNavnx5SmS6yM4K4LHl9yAvkAOf194nDH1eH/ICOdhw4xpkZwXoMEwcTDiKto0HgI4oYPeePgOgI3aeCUeTik8HXQ7xcGNGSa8JOuhykF4X0vHpYJ9MeQ8lHZ8OJ9GwLuggH98Obs0nDbmQdtAwGzQ4SNeBDrocHG1Kvfrqqzh06FD366mnngIAXHPNNUmL9OTq+RehdFSJ7UXShc/rw6TRE3DVWQvpMEwcOmuCsVsInX7I1ACmJYzOmmBS8emgyyEebswo6TVBB10O0utCOj4d7JMp76Gk49PhJBrWBR3k49vBrfmkIRfSDhpmgwYH6TrQQZeDo02p4uJinHbaad2vxx9/HNOnT0dlZWXSIj1ZVrkEUZPYjlskGsGyyiV0GCYOoapG0fPpoMshHm7MKOk1QQddDtLrQvp8OtgnU95DScenw0k0rAs6yJ9vB7fmk4ZcSDtomA0aHKTrQAddDgk/U6qjowNr167FddddB8uykhbpomJiOeaWnj7g51rj4fV4MW/SHFRMLKfDEHeINLT3+9R/J0SPhhBpaE/4fDrocXBKOmaU9Jqggy4H6XUhHZ8OiTNc30NJx6fDSTSsCzrIx0+EdM0nDbmQdtAwGzQ4SNeBDrocgCQ2pTZs2ICmpiYsXbp00ONCoRCCwWCv12CUjZuaqFIvZiRxHTrocIg0pu7hkgmfSwc1Dk6xM6M4n+iQjIP0upCOT4fEScd8AuTXhXR8OpxEw7qgg3z8REjXfNKQC2kHDbNBg4N0HeigywFIYlNqzZo1WLhwIUpKSgY9buXKlSgoKOh+lZaWDnp8bnZOokq9yEviOnRQ4tCZogc7diRxHTrocXCInRnF+USHZBzE14V0fDokTDrmEyC/LqTj06EHGtYFHeTjJ0C65pOKXAg7aJgNGhyk60AHZQ5IcFNq//79ePrpp/GVr3wl7rErVqxAc3Nz96u2tnbQ41vb2xJR6kNLEtehgxKHrIT3TP9/e/ceH0V973/8PbubbCBXLiEQCRAIl4gGlFartIZIRRHvoJ4WjlpR7BFtf7ZapfUce7zRlp6jrSAeFNFzkIIVsdQKVblZb6jcghowyC0hEIPkDtlkd+f3R5pIyG032exMltfz8djHw+zOzOedmd2P2S8z32kquhPbIYN9MgQh0B5FfyJDZzJY/rmwuj4ZOqSr+pNk/efC6vpkOIkdPhdksL5+kLqyP9liX1icwQ69wQ4ZrD4OZLBZBknBTbn/T0uWLFG/fv00ZcqUdpd1u91yuwO/bWR+8b6ORGq+nSN7O74uGWyRwdkrOiT1nUkd3w4Z7JMhGIH2KPoTGTqTwerPhdX1ydAxXdWfJOs/F1bXJ8M37PC5IIP19YPVlf3JDvvC6gx26A12yGD1cSCDvTJIHThTyu/3a8mSJbr55pvlcnVoTKtNuYV52lHwuXx+X4fW9/l92n7wM+08tIsM3TyDMzlGjr7B/Q/vVI6+bjmTYzq8PhnskyFQXdmjrP5MkMFeGaz+XFhdnwzBi/S/oayuT4Zv2OFzQQbr6wejq/uTHfaF1Rns0BvskMHq40AGe2WQOjAo9fbbb+vgwYO69dZbO1W4LYs2LZXD6NgpYE6HU4s2LSVDhGRwZ/WydH0y2CtDILq6R1n9mSCDvTJY/bmwen0yBOd0+BvK6vpk+IYdPhdksH79QIWjP9lhX1idwQ69wQ4ZrD4OZLBXhqDfjZMmTZJpmhoxYkSni7dm5ZY3VHCsSF6fN6j1vD6vDn59SK9uXUOGCMkQNTxBRrxLCvaOtIZkxLsUNTyhU/XJYK8MgejqHmX1Z4IM9spg9efC6vpkCM7p8DeU1fXJ8A07fC7IYH39QIWjP9lhX1idwQ69wQ4ZrD4OZLBXhvDMmhekmjqPrl1wmyo91QF/WLw+ryo91bpm/kzV1HnIECEZDJdDsVcNqp88LdAPiiEpun49w9X5tzgZ7JPBDqz+TJDBXhms/lxYXZ8M9mP158Lq+mT4hh0+F2Swvr6d2GFfWJ3BDr3BDhmsPg5ksFcG23a5L7/ar4nzblRRWbEktXrda8PzRWXFmjjvRu0tOUCGCMvgTIpW3LQhMuICu77diHMpbtqQkE4KSQb7ZLADqz8TZLBXBqs/F1bXJ4P9WP25sLo+Gb5hh88FGayvbyd22BdWZ7BDb7BDBquPAxnskyH0s9iF0N6SAzrv0Sm67tzJmpU9Q2MHjW62zM7CXVq0aale3bomJKO2ZLBnBmdStOJ/OFR1+RXy5JbKf7T59h193XJn9ao/BbEL/lWJDPbJYAdWfybIYK8MVn8urK5PBvux+nNhdX0yfMMOnwsyWF/fTuywL6zOYIfeYIcMVh8HMtgjg60HpaT60wuXbX5Nyza/pqyBmcpISVd8TKwqa6q1p3ifcgvzyHCaZDBcDkVnJik6M0m+khr5ymqlWr8U7ZAzKTosdyYhg30y2IHVnwky2CuD1Z8Lq+uTwX6s/lxYXZ8M37DD54IM1te3EzvsC6sz2KE32CGD1ceBDNZnsP2g1MlyC/PC8sEgg/0zOJNjLP8fNxnsk8EOrP5MkMFeGaz+XFhdnwz2Y/Xnwur6ZPiGHT4XZLC+vp3YYV9YncEOvcEOGaw+DmSwJkPknhcKAAAAAAAA22JQCgAAAAAAAGHHoBQAAAAAAADCzjBN0wxnwYqKCiUmJtYXjw3/lFbmca9kSjIko6c1U2qRgQx2ymB1fUkyq72SpPLyciUkJFiSQbK+P0k2OR68J8lAhqYZbNCj6E9ksEt9MtgsA/1Jkk2OBRnIYJP6tskQYH+ydKLzhpDWFLe4PhnIYLcMVte3Gcv3hR2Oh9UZrK5PBjLYlOX7wQ7HggzW1yeDvTLYhOX7wQ7HggxksEt9u2Roh6WDUpwpRQYyWJ/B6vqSPRsl/9JnXQar65OBDM0y2KxH0Z9O7wxW1yeDzTLQnyTZ5FiQgQw2qW+bDAH2J+sGpXo6lXBLRtjLVrywR2a1V0ZPlyX1yUAGu2Wwur4klS/Jl477LKndIov6k2SP42F1Bqvrk4EMp7JVj6I/nfYZrK5PBntloD/Vs8OxIAMZ7FLfLhkC7U9MdA4AAAAAAICwY1AKAAAAAGApI7z33wJgE5bOKQUAAAAAOP2MKanR9LwyXVB0QiNLPYr2S7UOaXcvtz5I7aGXMpO0IznG6pgAuhiDUt1Q1sBMDU9JV1xMrKpqqpVfvE+5hXlhzeArqZGvtFaq80tRDjl7RcsZxv9pWF2fDPbKAPugP9kjg9X1yQA7oj+RwU4ZrK5vpfSyWs1ff1jjD59QnSF9akorJVVKivdLo7726EfHPJq1s0zvDeihuy4eoH1J0VbH7lL0JzKczhkYlOomYqLcmjrucs3KnqExaWc2e31HwedatGmpVm55QzV1ni7JYHr9qsuvkCe3VP6jzWs4+rrlzuqlqOEJMlyhvzLU6vpksFcG2Af9yR4ZrK5PBtgR/YkMdspgdX07mPZFuRasP6Jan6klkv5oSttbWG6sKf1E0rTDJ/Th8n268+IBWjkiIbxhuxj9iQxkqMegVDcwrN8QrZr9nNJ6p8pv+ltc5qwzRmr+9Md0/+TZumb+TO0tORDSDL6yWlWvPiizsvXbOvqPenRi/RHVfHxUsVcNkjOE/6JhdX0y2CsD7IP+ZI8MVtcnA+yI/kQGO2Wwur4dTPuiXIveOqwvJF0m6YBan+A4V9Ktkv5T0t99pp59q0iGTL0yIjFMabsW/YkMZPhGZA7BR5Bh/Ybo7XuXKzUpRYZhyOlwtric0+GUYRhKTUrRuvtWaGjy4JBl8JXVquqV/TKrWn+Dnsys8qrqlf3yldVGRH0y2CsD7IP+ZI8MVtcnA+yI/kQGO2Wwur4dDC2r1YL1R/SFpAslFf7z+ZaHY755vlDSBZLyJS1Yf0TpEbBP6E9kIENTQQ1K+Xw+/fu//7vS09PVo0cPDRs2TI888ohM7pTQJWKi3Fo1+znFu2PlcgZ2UpvL6VK8O1av3bVYMVHuTmcwvX5Vrz4o1fqlQA+zKam2fj3T29r/arpHfTLYK0Nb6E/hRX+yRwar65MhcPSo8KE/kcFOGayuH4hw9Ken1h+Wx2fqMkkVknyBZvvn8pdKqvWZmr/+cMgyWYH+RAYyNBfUoNRvf/tbLVy4UPPnz1deXp5++9vf6ne/+52eeuqpTgdBc1PHXa603qkBN6wGLqdLg/qcoevOndzpDHX5FfWn8AX7/yRTMiu9qsuv6Nb1yWCvDG2hP4UX/ckeGayuT4bA0aPCh/5EBjtlsLp+ILq6P435qkbjD5/QStVfshfogFQD3z/XWylp/OETGlNSE5JcVqA/kYEMzQU1KPX+++/r6quv1pQpUzRkyBBNmzZNkyZN0kcffdTpIGhuVvaMVq8xbo/P79Os7BmdzuDJLT2t1yeDvTK0hf4UXvQne2Swen0yBI4eFT70JzLYKYPV6weiq/vT9F1lqjOkP6rjc8c4VL9+nSFNzysLSS4r0J/IQIbmguoLF154odatW6cvvvhCkrRjxw69++67mjy58yO2aCprYKbGpJ3Z6jXG7XE6nBo7aLSyBmZ2OIOvpKbFWfeD4T/qka+D/5phdX0y2CtDe+hP4UN/skcGq+uTITj0qPCgP5HBThmsrh+oru5PFxSd0Kdm/V32Onqhj1/SNkmfmtJ3Dp8ISa5woz+RgQwtC+q8wQceeEAVFRUaNWqUnE6nfD6fHnvsMU2fPr3VdTwejzyeb37RioquPwU1EgxPSQ/JdjJS0pVbmNehdX2loZvc0Zkc0+3qk8FeGdpDfwof+pM9MlhdnwzBCbZH0Z86hv5EBjtlsLp+oLq6P40s9WhliLLulnTdsc59ibYK/YkMZGhZUGdKvfzyy3rppZe0bNkybd26VS+++KJ+//vf68UXX2x1nblz5yoxMbHxkZaW1uGwp5O4mNiQbCe+M9upC9HEirUd3I7V9clgrwztoD+FD/3JJhmsrk+GoATbo+hPHUN/IoOtMlhdP0Bd2Z8M01S0X6oMUdYKSdH++u12N/QnMpChZUENSt1333164IEH9C//8i86++yz9a//+q+65557NHfu3FbXmTNnjsrLyxsfBQUFnQp8uqiqqQ7Jdio7s52ojl71fYroDm7H6vpksFeGdtCfwof+ZJMMVtcnQ1CC7VH0p46hP5HBVhmsrh+gruxPpmGo1iHFhyhrgqRaR/12uxv6ExnI0LKgLt87fvy4HI6mBZ1Op/z+1kfG3G633O7O37rydJNfvC802zmyt8PrOntFhySDM6lj27G6PhnslaE99KfwoT/ZI4PV9ckQnGB7FP2pY+hPZLBTBqvrB6qr+9PuXm6N+jo0l9yNkrSrd/fsjfQnMpChZUENaV155ZV67LHH9Le//U379+/XqlWr9N///d+69tprOxUCzeUW5mlHwefy+YO9aWo9n9+n7Qc/085DuzqcwZkcI0ffzjV9R193h68vtbo+GeyVoT30p/ChP9kjg9X1yRAcelR40J/IYKcMVtcPVFf3pw9Se+gsQxqrzt197xxJow3pwwE9QpIr3OhPZCBDK9sIZuGnnnpK06ZN05133qnMzEzde++9uuOOO/TII490KgRatmjTUjmMjrVup8OpRZuWdjqDO6vXab0+GeyVoS30p/CiP9kjg9XrkyFw9KjwoT+RwU4ZrF4/EF3dn17KTFKUKf1Enbv73k8kRZn12+uu6E9kIENzQX0i4uPj9eSTT+rAgQM6ceKEvvzySz366KOKju7aU0pPVyu3vKGCY0Xy+rxBref1eXXw60N6deuaTmeIGp4gI94lBXvZtiEZ8S5FDU/o1vXJYK8MbaE/hRf9yR4ZrK5PhsDRo8KH/kQGO2Wwun4guro/7UiO0XsDemiapMGSnEGu7/znelMlvTegh3Z08ZljXYn+RAYyNNe1s+ahU2rqPLp2wW2q9FQH3Li8Pq8qPdW6Zv5M1dR1/tptw+VQ7FWD6icvC/SNakiKrl/PcHXuLWZ1fTLYKwPsg/5kjwxW1ycD7Ij+RAY7ZbC6vl3cdfEARTsNrVX9ZOWBDkw5/7n83yVFOw3ddfGArooYFvQnMpChucjochHsy6/2a+K8G1VUVixJrV6D3PB8UVmxJs67UXtLDoQsgzMpWnHThsiIC2xefCPOpbhpQ0I2KaPV9clgrwywD/qTPTJYXZ8MsCP6ExnslMHq+nawLylad148QCMkfSBp4D+fb+3LaMPzA/+5/HBJd148QPsiYJ/Qn8hAhqaCuvserLG35IDOe3SKrjt3smZlz9DYQaObLbOzcJcWbVqqV7euCckI+qmcSdGK/+FQ1eVXyJNbKv/R5jUcfd1yZ/WqPwUwxP+qY3V9MtgrA+yD/mSPDFbXJwPsiP5EBjtlsLq+HawckSBDphasP6KdPlMrJf1R0rYWlh2j+jmkpqr+DKnbLx6glSO6/lLGcKE/kYEM32BQqpuoqfNo2ebXtGzza8oamKmMlHTFx8SqsqZae4r3Kbcwr8szGC6HojOTFJ2ZJF9JjXxltVKtX4p2yJkU3eV3BrG6PhnslQH2QX+yRwar65MBdkR/IoOdMlhd3w5eGZGoLf16aP76w7rl8AlNN6RPTWm3pArVX6o3UtJZRv2k5u+m9tDdOZFxhtSp6E9kIEM9BqW6odzCvLA0qbY4k2Ms/R+n1fXJYK8MsA/6kz0yWF2fDLAj+hMZ7JTB6vpW2pcUrSnXDdaYkhpNzyvTdw6f0HXHPIr2S7UOaVdvt5YM6KGXMpO69aTmwaA/keF0zsCgFAAAAAAgrHYkx2hHcv/Gnw3TlGkEewswAN1d5F2sDAAAAADoVhiQAk5PDEoBAAAAAAAg7AzTNM1wFiwvL1dSUlL9Dz2d4Sxd7/hJt9y0oj4ZyGC3DFbXPylDWVmZEhMTrckgG/QnyVbHw7IMVtcnAxlayWBlj6I/kcE29clgywz0J/scCzKQwfL6NsvQXn8K+5xSlZWV3/xw8o6ygtX1yUAGu2WwuH5lZaWlg1K26k9ksEd9MpDhJFb2KPoTGWxZnwy2yUB/OgkZyGCnDFbXt0GG9vpT2M+U8vv9KioqUnx8vIwOXDdcUVGhtLQ0FRQUKCEhoQsSkqG7ZLC6PhlCl8E0TVVWVio1NVUOh3VXFdOfyBBJGayuH0kZ7NCjOtufJOuPh9X1yUAGu2WgP33D6mNhhwxW1ycDGUKdIdD+FPYzpRwOhwYOHNjp7SQkJFh2cMhgrwxW1ydDaDJYeYZUA/oTGSIxg9X1IyWD1T0qVP1Jsv54WF2fDGSwWwb60zesPhZ2yGB1fTKQIZQZAulPTHQOAAAAAACAsGNQCgAAAAAAAGHX7Qal3G63HnroIbndbjKc5hmsrk8Ge2WwAzvsBzKQwS71yWA/Vu8Lq+uTgQx2y2B1fTuxw76wOoPV9clABqsyhH2icwAAAAAAAKDbnSkFAAAAAACA7o9BKQAAAAAAAIQdg1IAAAAAAAAIu241KPXBBx/I6XRqypQpYa99yy23yDCMxkefPn102WWXKTc3N+xZjhw5orvvvltDhw6V2+1WWlqarrzySq1bt67La5+8H6KiopSSkqJLLrlEzz//vPx+f5fXPzXDyY/LLrssLPXby7Fnz56w1D9y5Ih++tOfKiMjQzExMUpJSdH48eO1cOFCHT9+vMvr33LLLbrmmmuaPb9x40YZhqGysrIuz2A39Cj606k5rOpRVvcnydoeRX9qjv5Efzo1B/2Jv6Hsgv5Efzo1B/3p9OpP3WpQavHixbr77rv1zjvvqKioKOz1L7vsMh0+fFiHDx/WunXr5HK5dMUVV4Q1w/79+zVu3DitX79e8+bN086dO7V27Vrl5ORo9uzZYcnQsB/279+vNWvWKCcnRz/96U91xRVXyOv1hjXDyY8//elPYandXo709PQur7t3716dc845evPNN/X4449r27Zt+uCDD/SLX/xCr7/+ut5+++0uz4DmTvceRX9qnsPKHmVVf5LoUXZEf6I/nZqD/kR/sgv6E/3p1Bz0p9OrP7msDhCoqqoqrVixQp988omOHDmiF154Qb/85S/DmsHtdqt///6SpP79++uBBx7Q9773PZWUlCg5OTksGe68804ZhqGPPvpIsbGxjc+PHj1at956a1gynLwfzjjjDJ177rn6zne+o4kTJ+qFF17QbbfdFtYMVrIqx5133imXy6VPPvmkyftg6NChuvrqq8VNNcOPHkV/ai2HVazMQI+yF/oT/am1HFahP6EB/Yn+1FoOq9Cfwq/bnCn18ssva9SoURo5cqRmzJih559/3tKDUlVVpaVLlyojI0N9+vQJS81jx45p7dq1mj17dpM3aYOkpKSw5GjJxRdfrDFjxujVV1+1LMPp4uuvv9abb77Z6vtAkgzDCHMqnO49iv6EBvQo+6E/0Z9Qj/5kP/Qn+hPqnc79qdsMSi1evFgzZsyQVH9KXXl5uTZt2hTWDK+//rri4uIUFxen+Ph4rV69WitWrJDDEZ7duGfPHpmmqVGjRoWlXrBGjRql/fv3h6XWycei4fH444+HpXZbOa6//vour9nwPhg5cmST5/v27duY4/777+/yHFLLx2Hy5MlhqW03p3uPoj81ZYceZUV/kuzTo+hP36A/0Z9ORn+yvj9J9KgG9Cf608noT6dnf+oWl+/t3r1bH330kVatWiVJcrlcuvHGG7V48WJNmDAhbDlycnK0cOFCSVJpaamefvppTZ48WR999JEGDx7c5fXtfrqeaZphG709+Vg06N27d1hqt5WjtVHtcPjoo4/k9/s1ffp0eTyesNRs6Ths3ry58Y+L0wU9iv50Kjv0KDv1Jyn8PYr+VI/+RH86Ff2pOf6Gsgb9if50KvpTc6dDf+oWg1KLFy+W1+tVampq43Omacrtdmv+/PlKTEwMS47Y2FhlZGQ0/vzcc88pMTFRzz77rB599NEurz98+HAZhqFdu3Z1ea2OyMvLC9skcKceC6tYkSMjI0OGYWj37t1Nnh86dKgkqUePHmHL0tLvX1hYGLb6dkGPoj+dyg49yqoMdulR9Kd69Cf606noT9b3J4keJdGfJPrTqehPp2d/sv3le16vV//7v/+r//qv/9L27dsbHzt27FBqaqold1xrYBiGHA6HTpw4EZZ6vXv31qWXXqoFCxaourq62etW3j52/fr12rlzp6ZOnWpZhtNFnz59dMkll2j+/Pktvg8QXvSoevQnNKBH2Qf9qR79CQ3oT/ZBf6pHf0KD07k/2f5Mqddff12lpaWaOXNms9HyqVOnavHixfrxj38cliwej0dHjhyRVH9q5/z581VVVaUrr7wyLPUlacGCBRo/frzOO+88Pfzww8rKypLX69Vbb72lhQsXKi8vr8szNOwHn8+n4uJirV27VnPnztUVV1yhm266qcvrn5zhZC6XS3379g1Lfas9/fTTGj9+vL71rW/p17/+tbKysuRwOPTxxx9r165dGjdunNURTxv0qG/Qn5rnOBk9ih4VbvSnb9Cfmuc4Gf2J/hRu9Kdv0J+a5zgZ/ek06E+mzV1xxRXm5Zdf3uJrmzdvNiWZO3bs6PIcN998symp8REfH29++9vfNl955ZUur32qoqIic/bs2ebgwYPN6Oho84wzzjCvuuoqc8OGDV1e++T94HK5zOTkZPP73/+++fzzz5s+n6/L65+a4eTHyJEjw1L/5BxXX311WGuerKioyLzrrrvM9PR0MyoqyoyLizPPO+88c968eWZ1dXWX12/t99+wYYMpySwtLe3yDHZAj2rqdO9Pp+awqkdZ3Z9M09oeRX+qR39qiv5Ef2rA31DWoz81RX+iPzU4HfuTYZo2n10NAAAAAAAAEcf2c0oBAAAAAAAg8jAoBQAAAAAAgLBjUAoAAAAAAABhx6AUAAAAAAAAwo5BKQAAAAAAAIQdg1IAAAAAAAAIOwalAAAAAAAAEHYMSgEAAAAAACDsGJQCAAAAAABA2DEoFQGeeeYZxcfHy+v1Nj5XVVWlqKgoTZgwocmyGzdulGEY+vLLLyVJd9xxh4YNG6YePXooOTlZV199tXbt2tVknYMHD2rKlCnq2bOn+vXrp/vuu69JrdacOHFCsbGx2rNnT5vLeTwejR07VoZhaPv27QH9zunp6Xr77bdbfG3IkCEyDEOGYcjpdCo1NVUzZ85UaWlp4zK7d+9WTk6OUlJSFBMTo6FDh+rBBx9UXV1dk239+c9/1qhRoxQTE6Ozzz5bb7zxRkD5ANTr6v7U8Fk/+bF8+fJ2c7XXn6666ioNGjRIMTExGjBggP71X/9VRUVFAf3One1PGzdu1NVXX60BAwYoNjZWY8eO1UsvvdRsW2VlZZo9e7YGDBggt9utESNG0KOAIHRlf9qxY4d+8IMfKC0tTT169FBmZqb+8Ic/BJSrrf60f/9+zZw5U+np6erRo4eGDRumhx56SLW1tQFtm7+fgO6jMz2qgWmamjx5sgzD0Guvvdbkta76jvfYY4/pwgsvVM+ePZWUlBTQ79ogJydHzz33XIuvTZgwocnfeykpKbr++ut14MCBxmUC6b3vvvuuxo8frz59+qhHjx4aNWqUnnjiiaByIrQYlIoAOTk5qqqq0ieffNL43D/+8Q/1799fmzdvVk1NTePzGzZs0KBBgzRs2DBJ0rhx47RkyRLl5eXp73//u0zT1KRJk+Tz+SRJPp9PU6ZMUW1trd5//329+OKLeuGFF/Qf//Ef7eZ66623NHjwYGVkZLS53C9+8QulpqYG/Pvm5uaqtLRU2dnZrS7z8MMP6/Dhwzp48KBeeuklvfPOO/rJT37S+HpUVJRuuukmvfnmm9q9e7eefPJJPfvss3rooYcal3n//ff1gx/8QDNnztS2bdt0zTXX6JprrtGnn34acFbgdNeV/anBkiVLdPjw4cbHNddc026u9vpTTk6OXn75Ze3evVsrV67Ul19+qWnTprW73VD0p/fff19ZWVlauXKlcnNz9aMf/Ug33XSTXn/99cZlamtrdckll2j//v165ZVXtHv3bj377LM644wz2s0IoF5X9qctW7aoX79+Wrp0qT777DP96le/0pw5czR//vx2c7XVn3bt2iW/36//+Z//0WeffaYnnnhCzzzzjH75y1+2u13+fgK6l870qAZPPvmkDMNotu2u/I5XW1ur66+/Xv/2b/8W6K8qSTp27Jjee+89XXnlla0uc/vtt+vw4cMqKirSX/7yFxUUFGjGjBmNrwfSe2NjY3XXXXfpnXfeUV5enh588EE9+OCDWrRoUVB5EUImIsKAAQPMuXPnNv78i1/8wpw9e7aZmZlpbtiwofH5iy66yLz55ptb3c6OHTtMSeaePXtM0zTNN954w3Q4HOaRI0cal1m4cKGZkJBgejyeNjPdeuut5v3339/mMm+88YY5atQo87PPPjMlmdu2bWtzedM0zYcffti88cYbW3198ODB5hNPPNHkuUceecQ888wz29zuPffcY373u99t/PmGG24wp0yZ0mSZ888/37zjjjvazQjgG13Vn0zTNCWZq1atCjpTIP3pZH/5y19MwzDM2traNpfrqv50+eWXmz/60Y8af164cKE5dOjQdvMAaFtX9qdT3XnnnWZOTk67mYLtT7/73e/M9PT0dpfj7yeg++lMj9q2bZt5xhlnmIcPH27291JXf8czTdNcsmSJmZiY2O5yDf73f//XPP/881t9PTs72/zpT3/a5Ln/+7//M3v27NnmdgPpvddee605Y8aMgLMitDhTKkLk5ORow4YNjT9v2LBBEyZMUHZ2duPzJ06c0ObNm5WTk9PiNqqrq7VkyRKlp6crLS1NkvTBBx/o7LPPVkpKSuNyl156qSoqKvTZZ5+1msfv9+v111/X1Vdf3eoyxcXFuv322/V///d/6tmzZ8C/6+rVq9vc7qkOHTqkv/71rzr//PNbXWbPnj1au3Ztk389/OCDD/T973+/yXKXXnqpPvjgg4BrA+i6/tRg9uzZ6tu3r8477zw9//zzMk2zzTyB9KeTHTt2TC+99JIuvPBCRUVFtblsV/QnSSovL1fv3r2b1Lngggs0e/ZspaSk6KyzztLjjz/e7CwyAG3r6v50slM/xy0Jtj8Ful2Jv5+A7qijPer48eP64Q9/qAULFqh///7NttuV3/E6KtgedezYMb388stB/w11qm3btun9999v8yxSdDGrR8UQGs8++6wZGxtr1tXVmRUVFabL5TK/+uorc9myZeZFF11kmqZprlu3zpRkHjhwoMm6CxYsMGNjY01J5siRI5v8K9/tt99uTpo0qcny1dXVpiTzjTfeaDXPe++9Z/br18/0+Xwtvu73+83LLrvMfOSRR0zTNM19+/YFdKZUYWGhGR0dbZaWlra6zODBg83o6GgzNjbWjImJMSWZ559/fovrXHDBBabb7TYlmbNmzWqSNyoqyly2bFmT5RcsWGD269evzYwAmuqq/mSa9f/y/+6775pbt241f/Ob35hut9v8wx/+0Gae9vpTg1/84hdmz549TUnmd77zHfPo0aNtLh/q/tRgxYoVZnR0tPnpp582Pjdy5EjT7Xabt956q/nJJ5+Yy5cvN3v37m3++te/bjMjgKa6sj+d7L333jNdLpf597//vc08gfanBvn5+WZCQoK5aNGiNpfj7yege+poj5o1a5Y5c+bMxp91yplSXfUd72TBnClVU1NjxsXFNflb51TZ2dlmVFSUGRsb2/j32YgRI8x9+/a1mbe13nvGGWeY0dHRpsPhMB9++OGAcqJrcKZUhJgwYYKqq6v18ccf6x//+IdGjBih5ORkZWdnN15zvHHjRg0dOlSDBg1qsu706dO1bds2bdq0SSNGjNANN9zQ5BrljvjLX/6iK664Qg5Hy2+xp556SpWVlZozZ05Q2129erW++93vtjtp3n333aft27crNzdX69atkyRNmTKl2VkEK1as0NatW7Vs2TL97W9/0+9///ug8gBoX1f2p3//93/X+PHjdc455+j+++/XL37xC82bN6/NPO31pwb33Xeftm3bpjfffFNOp1M33XRTm2dhhbo/SfX/IvqjH/1Izz77rEaPHt34vN/vV79+/bRo0SKNGzdON954o371q1/pmWeeabM2gKbC8ffTp59+qquvvloPPfSQJk2a1GaeQPuTVH8m02WXXabrr79et99+e5vL8vcT0D11pEetXr1a69ev15NPPhnyPMH0qGCsX79e/fr1a/K3TkumT5+u7du3a8eOHXr33XeVkZGhSZMmqbKystmy7fXef/zjH/rkk0/0zDPP6Mknn9Sf/vSnkP0+CI7L6gAIjYyMDA0cOFAbNmxoMollamqq0tLS9P7772vDhg26+OKLm62bmJioxMREDR8+XN/5znfUq1cvrVq1Sj/4wQ/Uv39/ffTRR02WLy4ulqQWTwVtsHr1av3mN79p9fX169frgw8+kNvtbvL8t771LU2fPl0vvvhiq9u96qqrWt1ug759+zZOvjd8+HA9+eSTuuCCC7Rhw4Ymp5Q3nGZ/5plnyufzadasWfr5z38up9Op/v37N/6uDYqLi9v8vQE011X9qSXnn3++HnnkEXk8nmb9pUF7/alB37591bdvX40YMUKZmZlKS0vThx9+qAsuuKDV7YayP23atElXXnmlnnjiCd10001NtjFgwABFRUXJ6XQ2PpeZmakjR46otrZW0dHR7eYA0PX96fPPP9fEiRM1a9YsPfjgg+3mCbQ/FRUVKScnRxdeeGFAk/Py9xPQPXWkR61fv15ffvlls0HoqVOn6nvf+542btzYZd/xOirQHpWYmNjYozIyMrR48WINGDBAK1as0G233da4XCC9Nz09XZJ09tlnq7i4WL/+9a9b/fsSXYszpSJITk6ONm7cqI0bNza5TehFF12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\n",
      "text/plain": [
       "<Figure size 1200x4800 with 61 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def simulate_game(\n",
    "    nr_of_games: int,\n",
    "    policies: tuple[GamePolicy, GamePolicy],\n",
    "    tqdm_on: bool = False,\n",
    ") -> tuple[np.ndarray, np.ndarray]:\n",
    "    \"\"\"Simulates a stack of games.\n",
    "\n",
    "    Args:\n",
    "        nr_of_games: The number of games that should be simulated.\n",
    "        policies: The policies that should be used to simulate the game.\n",
    "        tqdm_on: Switches tqdm on.\n",
    "\n",
    "    Returns:\n",
    "        A stack of board histories and actions.\n",
    "    \"\"\"\n",
    "    board_history_stack = np.zeros((SIMULATE_TURNS, nr_of_games, 8, 8), dtype=np.int8)\n",
    "    action_history_stack = np.zeros((SIMULATE_TURNS, nr_of_games, 2), dtype=np.int8)\n",
    "    current_boards = get_new_games(nr_of_games)\n",
    "    for turn_index in tqdm(range(SIMULATE_TURNS)) if tqdm_on else range(SIMULATE_TURNS):\n",
    "        policy_index = turn_index % 2\n",
    "        policy = policies[policy_index]\n",
    "        board_history_stack[turn_index, :, :, :] = current_boards\n",
    "        if policy_index == 0:\n",
    "            current_boards = current_boards * -1\n",
    "        current_boards, action_taken = single_turn(current_boards, policy)\n",
    "        action_history_stack[turn_index, :] = action_taken\n",
    "\n",
    "        if policy_index == 0:\n",
    "            current_boards = current_boards * -1\n",
    "\n",
    "    return board_history_stack, action_history_stack\n",
    "\n",
    "\n",
    "simulation_results = simulate_game(1, (RandomPolicy(1), RandomPolicy(1)))\n",
    "_unique_bords, _unique_actions = drop_duplicate_boards(\n",
    "    simulation_results[0].reshape(-1, 8, 8), simulation_results[1].reshape(-1, 2)\n",
    ")\n",
    "plot_othello_boards(_unique_bords, actions=_unique_actions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70, 8, 8)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.reshape(simulation_results[0], (-1, 8, 8)).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70, 2)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simulation_results[1].reshape(-1, 2).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.4 s ± 267 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%timeit simulate_game(100, (RandomPolicy(1), RandomPolicy(1)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statistical examination of the natural action space and result\n",
    "As for many project some evaluation of the project is in order.\n",
    "\n",
    "1. What is the expected distribution of scores\n",
    "2. What is the expected distribution of possible actions\n",
    "\n",
    "    a. over time\n",
    "    \n",
    "    b. ober space\n",
    "\n",
    "The easiest and robustest way to analyse this is when analyzing randomly played games."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this pupose we played a sample of 10k games and saved them for later analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(70, 10000, 8, 8)\n",
      "(70, 10000, 2)\n"
     ]
    }
   ],
   "source": [
    "if not os.path.exists(\"rnd_history.npy\") and not os.path.exists(\"rnd_action.npy\"):\n",
    "    rnds = RandomPolicy(1), RandomPolicy(1)\n",
    "    simulation_results = simulate_game(10_000, rnds, tqdm_on=True)\n",
    "    _board_history, _action_history = simulation_results\n",
    "    np.save(\"rnd_history.npy\", np.astpye.astype(np.int8))\n",
    "    np.save(\"rnd_action.npy\", _action_history.astype(np.int8))\n",
    "else:\n",
    "    _board_history = np.load(\"rnd_history.npy\")\n",
    "    _action_history = np.load(\"rnd_action.npy\")\n",
    "print(_board_history.shape)\n",
    "print(_action_history.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For those 10k games the possible actions where evaluated and saved for each and every turn in the game."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70, 10000, 8, 8)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if not os.path.exists(\"turn_possible.npy\"):\n",
    "    __board_history = _board_history.copy()\n",
    "    __board_history[1::2] = __board_history[1::2] * -1\n",
    "\n",
    "    _poss_turns = get_possible_turns(\n",
    "        __board_history.reshape((-1, 8, 8)), tqdm_on=True\n",
    "    ).reshape((SIMULATE_TURNS, -1, 8, 8))\n",
    "    np.save(\"turn_possible.npy\", _poss_turns)\n",
    "    del __board_history\n",
    "_poss_turns = np.load(\"turn_possible.npy\")\n",
    "_poss_turns.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Those possible turms then where counted for all games in the history stack."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The action space size can be drawn into a histogram by turn and a curve over the mean action space size.\n",
    "This can be used to analyse in which area of the game that cant be solved abolutely."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c5f272d3d97d4277b752ac8df4ab512a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=34, description='turn', max=69), Output()), _dom_classes=('widget-intera…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_poss_turns = np.sum(_poss_turns, axis=(2, 3))\n",
    "mean_possibilitie_count = np.mean(count_poss_turns, axis=1)\n",
    "std_possibilitie_count = np.std(count_poss_turns, axis=1)\n",
    "cum_prod = count_poss_turns\n",
    "\n",
    "\n",
    "@interact(turn=(0, 69))\n",
    "def poss_turn_count(turn):\n",
    "    fig, axes = plt.subplots(2, 2, figsize=(15, 8))\n",
    "    ax1, ax2, ax3, ax4 = axes.flatten()\n",
    "    _mean_possibilitie_count = mean_possibilitie_count.copy()\n",
    "    _std_possibilitie_count = std_possibilitie_count.copy()\n",
    "    _mean_possibilitie_count[_mean_possibilitie_count <= 1] = 1\n",
    "    _std_possibilitie_count[_std_possibilitie_count <= 1] = 1\n",
    "    np.cumprod(_mean_possibilitie_count[::-1], axis=0)[::-1]\n",
    "    fig.suptitle(\n",
    "        f\"Action space size analysis\\nThe total size is estimated to be around {np.prod(_mean_possibilitie_count):.4g}\"\n",
    "    )\n",
    "    ax1.hist(count_poss_turns[turn], density=True)\n",
    "    ax1.set_title(f\"Histogram of the action space size for turn {turn}\")\n",
    "    ax1.set_xlabel(\"Action space size\")\n",
    "    ax1.set_ylabel(\"Action space size probability\")\n",
    "    ax2.set_title(f\"Mean size of the action space per turn\")\n",
    "    ax2.set_xlabel(\"Turn\")\n",
    "    ax2.set_ylabel(\"Average possible moves\")\n",
    "\n",
    "    ax2.errorbar(\n",
    "        range(70),\n",
    "        mean_possibilitie_count,\n",
    "        yerr=std_possibilitie_count,\n",
    "        label=\"Mean action space size with error bars\",\n",
    "    )\n",
    "    ax2.scatter(turn, mean_possibilitie_count[turn], marker=\"x\")\n",
    "    ax2.legend()\n",
    "\n",
    "    ax4.plot(\n",
    "        range(70),\n",
    "        np.cumprod((_mean_possibilitie_count)[::-1], axis=0)[::-1],\n",
    "        # yerr=np.cumprod(_std_possibilitie_count[::-1], axis=0)[::-1],\n",
    "    )\n",
    "    ax4.scatter(\n",
    "        turn,\n",
    "        np.cumprod(_mean_possibilitie_count[::-1], axis=0)[::-1][turn],\n",
    "        marker=\"x\",\n",
    "    )\n",
    "    ax4.set_yscale(\"log\", base=10)\n",
    "    ax4.set_xlabel(\"Turn\")\n",
    "    ax4.set_ylabel(\"Mean remaining total action space size\")\n",
    "    fig.delaxes(ax3)\n",
    "    fig.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is interesting to see that the action space for the first player (white) is much smaller than for the second palyer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total mean actionspace</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>5.687159e+18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>black</th>\n",
       "      <td>3.753117e+20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Total mean actionspace\n",
       "white            5.687159e+18\n",
       "black            3.753117e+20"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "white = mean_possibilitie_count[::2]\n",
    "black = mean_possibilitie_count[1::2]\n",
    "df = pd.DataFrame(\n",
    "    [\n",
    "        {\n",
    "            \"white\": np.prod(np.extract(white, white)),\n",
    "            \"black\": np.prod(np.extract(black, black)),\n",
    "        }\n",
    "    ],\n",
    "    index=[\"Total mean actionspace\"],\n",
    ").T\n",
    "del white, black\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5dccb2662b68447fb677365253c6b9cd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=34, description='turn', max=69), Output()), _dom_classes=('widget-intera…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "@interact(turn=(0, 69))\n",
    "def turn_distribution_heatmap(turn):\n",
    "    turn_possibility_on_field = np.mean(_poss_turns[turn], axis=0)\n",
    "\n",
    "    uniform_data = np.random.rand(10, 12)\n",
    "    sns.heatmap(\n",
    "        turn_possibility_on_field,\n",
    "        linewidth=0.5,\n",
    "        square=True,\n",
    "        annot=True,\n",
    "        xticklabels=\"ABCDEFGH\",\n",
    "        yticklabels=list(range(1, 9)),\n",
    "    )\n",
    "    plt.title(f\"Headmap of where stones can be placed on turn {turn}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(70, 10000)\n"
     ]
    }
   ],
   "source": [
    "def calculate_direct_score(board_history: np.ndarray) -> np.ndarray:\n",
    "    boards_evaluated = np.reshape(\n",
    "        evaluate_boards(np.reshape(board_history, (-1, 8, 8))), (SIMULATE_TURNS, -1)\n",
    "    )\n",
    "    direct_score = boards_evaluated - np.roll(boards_evaluated, shift=-1, axis=0)\n",
    "    direct_score[-1] = 0\n",
    "    return direct_score / 64\n",
    "\n",
    "\n",
    "assert len(calculate_direct_score(_board_history).shape) == 2\n",
    "assert calculate_direct_score(_board_history).shape[0] == SIMULATE_TURNS\n",
    "print(calculate_direct_score(_board_history).shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1e34f7bc0fea4ae28bc1394cc50393f8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=29, description='turn', max=59), Output()), _dom_classes=('widget-intera…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "score_history = calculate_direct_score(_board_history) * 64\n",
    "score_history[1::2] = score_history[1::2] * -1\n",
    "\n",
    "\n",
    "@interact(turn=(0, 59))\n",
    "def hist_direct_score(turn):\n",
    "    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5))\n",
    "    fig.suptitle(\n",
    "        f\"Action space size analysis\\nThe total size is estimated to be around {np.prod(np.extract(mean_possibilitie_count, mean_possibilitie_count)):.4g}\"\n",
    "    )\n",
    "\n",
    "    ax1.set_title(\n",
    "        f\"Histogram of scores on turn {turn} by {'white' if turn % 2 == 0 else 'black'}\"\n",
    "    )\n",
    "\n",
    "    ax1.hist(score_history[turn], density=True)\n",
    "    ax1.set_xlabel(\"Points made\")\n",
    "    ax1.set_ylabel(\"Score probability\")\n",
    "    ax2.set_title(f\"Points scored at turn\")\n",
    "    ax2.set_xlabel(\"Turn\")\n",
    "    ax2.set_ylabel(\"Average points scored\")\n",
    "\n",
    "    ax2.errorbar(\n",
    "        range(60),\n",
    "        np.mean(score_history, axis=1)[:60],\n",
    "        yerr=np.std(score_history, axis=1)[:60],\n",
    "        label=\"Mean socre at turn\",\n",
    "    )\n",
    "    ax2.scatter(turn, np.mean(score_history, axis=1)[turn], marker=\"x\", color=\"red\")\n",
    "    ax2.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ho7S0FO+//z5++ukn2UdbAPDvf/8btra2WLhwYYNjEkI0+LpYo7GvV3O5uLhg6NChePfdd3Hy5MkG60+fPt3k9leO3cbGRnrjrf++Gj16NH766SfprrfL1c/HyJEjYTQakZ6eLq2rra3FsmXL0L59ewwZMuSGHsfIkSNRW1uLlStXSm11dXXN+r4tLy9v0Obn5wfg0hxc79fpSv/7v/+Ls2fPSq83bNiAkydPSv8OgYth+4cffpA9w2jLli0Nbm23ZmzN/TlAtzee6aFWoU+fPhg6dCj8/f3RqVMn7NmzBxs2bMDUqVOlGn9/fwDA9OnTERISAltbW4wdOxYPP/ww7r//frz44osoLi5Gv379sH37dnz++eeYOXOmdLGiv78/Ro8ejZSUFPzxxx/SLeu//vorgOb9r9LBwQGDBw9GcnIyLly4AHd3d2zfvh1Hjhy5AbPSUL9+/TB+/HisWrUKFRUVGDJkCHbv3o21a9ciLCwM999/v6x+5MiR6NChA+bMmQNbW1uMHj1atr5nz554+eWXERcXh+LiYoSFhaFDhw44cuQIPvvsM0RHR2POnDnXNNbGvl7WWLFiBYKDg+Hr64vJkyfjjjvuQFlZGXJzc3H8+HH89NNPjW47adIklJeX44EHHkC3bt1w9OhRLFu2DH5+ftJZshdeeAEbNmzAY489hqeffhr+/v4oLy/H5s2bkZqain79+iE6OhrvvvsuJkyYgLy8PHh6emLDhg34/vvvkZKS0uAakn/6OB5++GHce++9iI2NRXFxMfr06YONGzdavH7sSosWLcKOHTsQGhqKHj164NSpU3jnnXfQrVs36blTPXv2hJOTE1JTU9GhQwe0a9cOer3e4vVszdGpUycEBwdj4sSJKCsrQ0pKCnr16oXJkydLNZMmTcKGDRvw4IMP4vHHH8dvv/2Gjz76SHZhsbVja+7PAbrN3fwbxojk6m9Z//HHHy2uHzJkyFVvWX/55ZfFoEGDhJOTk7C3txc+Pj7ilVdeETU1NVJNbW2tmDZtmujSpYtQqVSyW3PPnj0rZs2aJdzc3ESbNm2Et7e3WLJkiXTbcb2qqioxZcoU0alTJ9G+fXsRFhYmioqKBADZLeT1t/5autX3+PHj4tFHHxVOTk7C0dFRPPbYY6K0tLTR296v7GP8+PGiXbt2zZonSy5cuCAWLlwovLy8RJs2bYSHh4eIi4uT3Q59uSeffFIAEAaDodE+P/30UxEcHCzatWsn2rVrJ3x8fMSUKVNEUVGR1eOr19jXq/7W7iVLljTY5so5FEKI3377TURGRgqdTifatGkj3N3dxUMPPSQ2bNjQ5P43bNgghg8fLlxcXIRarRbdu3cXzzzzjDh58qSs7o8//hBTp04V7u7uQq1Wi27duonx48eLM2fOSDVlZWVi4sSJwtnZWajVauHr69vgNuqmjut6jqN+jOPGjRMODg7C0dFRjBs3TuTn51/1lvWcnBzxyCOPCDc3N6FWq4Wbm5uIiIgQv/76q6z/zz//XPTp00fY2dnJ+mzqa97YLeuffPKJiIuLEy4uLsLe3l6EhoZKj0i43NKlS4W7u7vQaDTi3nvvFXv27GnQZ1Nju/KWdSGa/3MAgJgyZUqDMTV2Kz3dOlRC8KoroutRUFCA/v3746OPPsKTTz7Z0sMhIqJG8JoeIitY+mWMKSkpsLGxueqTkImIqGXxmh4iKyQnJyMvLw/3338/7OzspFuZo6Oj4eHh0dLDIyKiJvDjLSIrZGdnY+HChfjll19w7tw5dO/eHePGjcOLL74IOzv+H4KI6FbG0ENERESKwGt6iIiISBEYeoiIiEgReBHCZcxmM0pLS9GhQ4d//NHqREREdGMIIXD27Fm4ubk1+OW+l2PouUxpaSnvwCEiIrpNHTt2DN26dWt0PUPPZeofB3/s2DE4ODi08GiIiIioOUwmEzw8PK76a10Yei5T/5GWg4MDQw8REdFt5mqXpvBCZiIiIlIEhh4iIiJSBIYeIiIiUgSGHiIiIlIEhh4iIiJSBIYeIiIiUgSGHiIiIlIEhh4iIiJSBIYeIiIiUgSGHiIiIlIEhh4iIiJSBIYeIiIiUgSGHiIiIlIEhh4iIiJSBLuWHgAR0c3iGbu1pYdgteLFoS09BKJWg2d6iIiISBEYeoiIiEgRGHqIiIhIERh6iIiISBGuKfSsWLECnp6e0Gq10Ov12L17d5P1GRkZ8PHxgVarha+vLzIzM6V1Fy5cwNy5c+Hr64t27drBzc0NkZGRKC0tlfVRXl6OJ598Eg4ODnByckJUVBTOnTsnq9m3bx/uu+8+aLVaeHh4IDk5+VoOj4iIiFohq0NPeno6YmJikJCQgL1796Jfv34ICQnBqVOnLNbv3LkTERERiIqKQn5+PsLCwhAWFobCwkIAwF9//YW9e/diwYIF2Lt3LzZu3IiioiKMGjVK1s+TTz6J/fv3Izs7G1u2bMGOHTsQHR0trTeZTBg+fDh69OiBvLw8LFmyBImJiVi1apW1h0hEREStkEoIIazZQK/XY+DAgVi+fDkAwGw2w8PDA9OmTUNsbGyD+vDwcFRVVWHLli1SW0BAAPz8/JCammpxHz/++CMGDRqEo0ePonv37jhw4AD69OmDH3/8EQMGDAAAZGVlYeTIkTh+/Djc3NywcuVKvPjiizAajVCr1QCA2NhYbNq0CQcPHmzWsZlMJjg6OqKyshIODg7WTAsR3QZ4yzpR69Tc92+rzvTU1NQgLy8PBoPhUgc2NjAYDMjNzbW4TW5urqweAEJCQhqtB4DKykqoVCo4OTlJfTg5OUmBBwAMBgNsbGywa9cuqWbw4MFS4KnfT1FREf7880+L+6murobJZJItRERE1DpZFXrOnDmDuro6uLq6ytpdXV1hNBotbmM0Gq2qP3/+PObOnYuIiAgprRmNRri4uMjq7Ozs0KlTJ6mfxvZTv86SpKQkODo6SouHh4fFOiIiIrr93VJ3b124cAGPP/44hBBYuXLlDd9fXFwcKisrpeXYsWM3fJ9ERETUMqz6NRTOzs6wtbVFWVmZrL2srAw6nc7iNjqdrln19YHn6NGj+Prrr2Wfyel0ugYXStfW1qK8vFzqp7H91K+zRKPRQKPRNHa4RERE1IpYdaZHrVbD398fOTk5UpvZbEZOTg4CAwMtbhMYGCirB4Ds7GxZfX3gOXToEL766it07ty5QR8VFRXIy8uT2r7++muYzWbo9XqpZseOHbhw4YJsP71790bHjh2tOUwiIiJqhaz+eCsmJgbvvfce1q5diwMHDuC5555DVVUVJk6cCACIjIxEXFycVD9jxgxkZWVh6dKlOHjwIBITE7Fnzx5MnToVwMXAM2bMGOzZswcff/wx6urqYDQaYTQaUVNTAwC466678OCDD2Ly5MnYvXs3vv/+e0ydOhVjx46Fm5sbAOCJJ56AWq1GVFQU9u/fj/T0dLz11luIiYm57kkiIiKi25/Vv2U9PDwcp0+fRnx8PIxGI/z8/JCVlSVdNFxSUgIbm0tZKigoCOvWrcP8+fMxb948eHt7Y9OmTejbty8A4MSJE9i8eTMAwM/PT7avb775BkOHDgUAfPzxx5g6dSqGDRsGGxsbjB49Gm+//bZU6+joiO3bt2PKlCnw9/eHs7Mz4uPjZc/yISIiIuWy+jk9rRmf00PUuvE5PUSt0w15Tg8RERHR7Yqhh4iIiBSBoYeIiIgUgaGHiIiIFIGhh4iIiBSBoYeIiIgUgaGHiIiIFIGhh4iIiBSBoYeIiIgUgaGHiIiIFIGhh4iIiBSBoYeIiIgUgaGHiIiIFIGhh4iIiBSBoYeIiIgUgaGHiIiIFIGhh4iIiBSBoYeIiIgUgaGHiIiIFIGhh4iIiBSBoYeIiIgUgaGHiIiIFIGhh4iIiBSBoYeIiIgUgaGHiIiIFIGhh4iIiBSBoYeIiIgUgaGHiIiIFIGhh4iIiBSBoYeIiIgUgaGHiIiIFIGhh4iIiBThmkLPihUr4OnpCa1WC71ej927dzdZn5GRAR8fH2i1Wvj6+iIzM1O2fuPGjRg+fDg6d+4MlUqFgoIC2fri4mKoVCqLS0ZGhlRnaX1aWtq1HCIRERG1MlaHnvT0dMTExCAhIQF79+5Fv379EBISglOnTlms37lzJyIiIhAVFYX8/HyEhYUhLCwMhYWFUk1VVRWCg4Px2muvWezDw8MDJ0+elC0LFy5E+/btMWLECFnt6tWrZXVhYWHWHiIRERG1QiohhLBmA71ej4EDB2L58uUAALPZDA8PD0ybNg2xsbEN6sPDw1FVVYUtW7ZIbQEBAfDz80Nqaqqstri4GF5eXsjPz4efn1+T4+jfvz/uuecefPDBB5cORqXCZ599ds1Bx2QywdHREZWVlXBwcLimPojo1uUZu7Wlh2C14sWhLT0Eoltec9+/rTrTU1NTg7y8PBgMhksd2NjAYDAgNzfX4ja5ubmyegAICQlptL458vLyUFBQgKioqAbrpkyZAmdnZwwaNAgffvghmsp01dXVMJlMsoWIiIhaJztris+cOYO6ujq4urrK2l1dXXHw4EGL2xiNRov1RqPRyqFe8sEHH+Cuu+5CUFCQrH3RokV44IEH0LZtW2zfvh3PP/88zp07h+nTp1vsJykpCQsXLrzmcRAp2e141oSIlM2q0HMr+Pvvv7Fu3TosWLCgwbrL2/r374+qqiosWbKk0dATFxeHmJgY6bXJZIKHh8c/P2giIiJqcVZ9vOXs7AxbW1uUlZXJ2svKyqDT6Sxuo9PprKq/mg0bNuCvv/5CZGTkVWv1ej2OHz+O6upqi+s1Gg0cHBxkCxEREbVOVoUetVoNf39/5OTkSG1msxk5OTkIDAy0uE1gYKCsHgCys7Mbrb+aDz74AKNGjUKXLl2uWltQUICOHTtCo9Fc076IiIio9bD6462YmBiMHz8eAwYMwKBBg5CSkoKqqipMnDgRABAZGQl3d3ckJSUBAGbMmIEhQ4Zg6dKlCA0NRVpaGvbs2YNVq1ZJfZaXl6OkpASlpaUAgKKiIgAXzxJdfkbo8OHD2LFjR4Pn/ADAF198gbKyMgQEBECr1SI7Oxuvvvoq5syZY+0hEhERUStkdegJDw/H6dOnER8fD6PRCD8/P2RlZUkXK5eUlMDG5tIJpKCgIKxbtw7z58/HvHnz4O3tjU2bNqFv375SzebNm6XQBABjx44FACQkJCAxMVFq//DDD9GtWzcMHz68wbjatGmDFStWYNasWRBCoFevXnjjjTcwefJkaw+RiIiIWiGrn9PTmvE5PUTNx7u3bg4+p4fo6m7Ic3qIiIiIblcMPURERKQIDD1ERESkCAw9REREpAgMPURERKQIDD1ERESkCAw9REREpAgMPURERKQIDD1ERESkCFb/GgoiIrp5bscnX/Mp0nSr4pkeIiIiUgSGHiIiIlIEhh4iIiJSBIYeIiIiUgSGHiIiIlIEhh4iIiJSBIYeIiIiUgSGHiIiIlIEhh4iIiJSBIYeIiIiUgSGHiIiIlIEhh4iIiJSBIYeIiIiUgSGHiIiIlIEhh4iIiJSBIYeIiIiUgSGHiIiIlIEhh4iIiJSBIYeIiIiUgSGHiIiIlIEhh4iIiJSBIYeIiIiUoRrCj0rVqyAp6cntFot9Ho9du/e3WR9RkYGfHx8oNVq4evri8zMTNn6jRs3Yvjw4ejcuTNUKhUKCgoa9DF06FCoVCrZ8uyzz8pqSkpKEBoairZt28LFxQUvvPACamtrr+UQiYiIqJWxOvSkp6cjJiYGCQkJ2Lt3L/r164eQkBCcOnXKYv3OnTsRERGBqKgo5OfnIywsDGFhYSgsLJRqqqqqEBwcjNdee63JfU+ePBknT56UluTkZGldXV0dQkNDUVNTg507d2Lt2rVYs2YN4uPjrT1EIiIiaoVUQghhzQZ6vR4DBw7E8uXLAQBmsxkeHh6YNm0aYmNjG9SHh4ejqqoKW7ZskdoCAgLg5+eH1NRUWW1xcTG8vLyQn58PPz8/2bqhQ4fCz88PKSkpFsf15Zdf4qGHHkJpaSlcXV0BAKmpqZg7dy5Onz4NtVp91WMzmUxwdHREZWUlHBwcrlpPpGSesVtbegh0iypeHNrSQyCFae77t1VnempqapCXlweDwXCpAxsbGAwG5ObmWtwmNzdXVg8AISEhjdY35eOPP4azszP69u2LuLg4/PXXX7L9+Pr6SoGnfj8mkwn79++32F91dTVMJpNsISIiotbJzpriM2fOoK6uThYsAMDV1RUHDx60uI3RaLRYbzQarRroE088gR49esDNzQ379u3D3LlzUVRUhI0bNza5n/p1liQlJWHhwoVWjYOIiIhuT1aFnpYUHR0t/d3X1xddu3bFsGHD8Ntvv6Fnz57X1GdcXBxiYmKk1yaTCR4eHtc9ViIiIrr1WPXxlrOzM2xtbVFWViZrLysrg06ns7iNTqezqr659Ho9AODw4cNN7qd+nSUajQYODg6yhYiIiFonq0KPWq2Gv78/cnJypDaz2YycnBwEBgZa3CYwMFBWDwDZ2dmN1jdX/W3tXbt2lfbz888/y+4iy87OhoODA/r06XNd+yIiIqLbn9Ufb8XExGD8+PEYMGAABg0ahJSUFFRVVWHixIkAgMjISLi7uyMpKQkAMGPGDAwZMgRLly5FaGgo0tLSsGfPHqxatUrqs7y8HCUlJSgtLQUAFBUVAbh4hkan0+G3337DunXrMHLkSHTu3Bn79u3DrFmzMHjwYNx9990AgOHDh6NPnz4YN24ckpOTYTQaMX/+fEyZMgUajeb6ZomIiIhue1aHnvDwcJw+fRrx8fEwGo3w8/NDVlaWdNFwSUkJbGwunUAKCgrCunXrMH/+fMybNw/e3t7YtGkT+vbtK9Vs3rxZCk0AMHbsWABAQkICEhMToVar8dVXX0kBy8PDA6NHj8b8+fOlbWxtbbFlyxY899xzCAwMRLt27TB+/HgsWrTI+lkhIiKiVsfq5/S0ZnxOD1Hz8Tk91Bg+p4duthvynB4iIiKi2xVDDxERESkCQw8REREpAkMPERERKQJDDxERESkCQw8REREpAkMPERERKQJDDxERESkCQw8REREpAkMPERERKQJDDxERESkCQw8REREpAkMPERERKQJDDxERESkCQw8REREpAkMPERERKQJDDxERESkCQw8REREpAkMPERERKQJDDxERESkCQw8REREpAkMPERERKQJDDxERESkCQw8REREpAkMPERERKQJDDxERESkCQw8REREpAkMPERERKQJDDxERESkCQw8REREpAkMPERERKQJDDxERESnCNYWeFStWwNPTE1qtFnq9Hrt3726yPiMjAz4+PtBqtfD19UVmZqZs/caNGzF8+HB07twZKpUKBQUFsvXl5eWYNm0aevfuDXt7e3Tv3h3Tp09HZWWlrE6lUjVY0tLSruUQiYiIqJWxOvSkp6cjJiYGCQkJ2Lt3L/r164eQkBCcOnXKYv3OnTsRERGBqKgo5OfnIywsDGFhYSgsLJRqqqqqEBwcjNdee81iH6WlpSgtLcXrr7+OwsJCrFmzBllZWYiKimpQu3r1apw8eVJawsLCrD1EIiIiaoVUQghhzQZ6vR4DBw7E8uXLAQBmsxkeHh6YNm0aYmNjG9SHh4ejqqoKW7ZskdoCAgLg5+eH1NRUWW1xcTG8vLyQn58PPz+/JseRkZGBp556ClVVVbCzs7t4MCoVPvvss2sOOiaTCY6OjqisrISDg8M19UGkFJ6xW1t6CHSLKl4c2tJDIIVp7vu3VWd6ampqkJeXB4PBcKkDGxsYDAbk5uZa3CY3N1dWDwAhISGN1jdX/YHVB556U6ZMgbOzMwYNGoQPP/wQTWW66upqmEwm2UJEREStk93VSy45c+YM6urq4OrqKmt3dXXFwYMHLW5jNBot1huNRiuHKh/HSy+9hOjoaFn7okWL8MADD6Bt27bYvn07nn/+eZw7dw7Tp0+32E9SUhIWLlx4zeMgIiKi24dVoedWYDKZEBoaij59+iAxMVG2bsGCBdLf+/fvj6qqKixZsqTR0BMXF4eYmBhZ3x4eHjdk3ERERNSyrPp4y9nZGba2tigrK5O1l5WVQafTWdxGp9NZVd+Us2fP4sEHH0SHDh3w2WefoU2bNk3W6/V6HD9+HNXV1RbXazQaODg4yBYiIiJqnawKPWq1Gv7+/sjJyZHazGYzcnJyEBgYaHGbwMBAWT0AZGdnN1rfGJPJhOHDh0OtVmPz5s3QarVX3aagoAAdO3aERqOxal9ERETU+lj98VZMTAzGjx+PAQMGYNCgQUhJSUFVVRUmTpwIAIiMjIS7uzuSkpIAADNmzMCQIUOwdOlShIaGIi0tDXv27MGqVaukPsvLy1FSUoLS0lIAQFFREYCLZ4l0Op0UeP766y989NFHsouOu3TpAltbW3zxxRcoKytDQEAAtFotsrOz8eqrr2LOnDnXN0NERETUKlgdesLDw3H69GnEx8fDaDTCz88PWVlZ0sXKJSUlsLG5dAIpKCgI69atw/z58zFv3jx4e3tj06ZN6Nu3r1SzefNmKTQBwNixYwEACQkJSExMxN69e7Fr1y4AQK9evWTjOXLkCDw9PdGmTRusWLECs2bNghACvXr1whtvvIHJkydbe4hERETUCln9nJ7WjM/pIWo+PqeHGsPn9NDNdkOe00NERER0u2LoISIiIkVg6CEiIiJFYOghIiIiRWDoISIiIkVg6CEiIiJFYOghIiIiRWDoISIiIkVg6CEiIiJFYOghIiIiRWDoISIiIkVg6CEiIiJFYOghIiIiRWDoISIiIkVg6CEiIiJFYOghIiIiRWDoISIiIkVg6CEiIiJFYOghIiIiRWDoISIiIkVg6CEiIiJFYOghIiIiRWDoISIiIkVg6CEiIiJFYOghIiIiRWDoISIiIkVg6CEiIiJFYOghIiIiRbBr6QEQEeAZu7Wlh0BE1OrxTA8REREpAkMPERERKQJDDxERESnCNYWeFStWwNPTE1qtFnq9Hrt3726yPiMjAz4+PtBqtfD19UVmZqZs/caNGzF8+HB07twZKpUKBQUFDfo4f/48pkyZgs6dO6N9+/YYPXo0ysrKZDUlJSUIDQ1F27Zt4eLighdeeAG1tbXXcohERETUylgdetLT0xETE4OEhATs3bsX/fr1Q0hICE6dOmWxfufOnYiIiEBUVBTy8/MRFhaGsLAwFBYWSjVVVVUIDg7Ga6+91uh+Z82ahS+++AIZGRn47rvvUFpain//+9/S+rq6OoSGhqKmpgY7d+7E2rVrsWbNGsTHx1t7iERERNQKqYQQwpoN9Ho9Bg4ciOXLlwMAzGYzPDw8MG3aNMTGxjaoDw8PR1VVFbZs2SK1BQQEwM/PD6mpqbLa4uJieHl5IT8/H35+flJ7ZWUlunTpgnXr1mHMmDEAgIMHD+Kuu+5Cbm4uAgIC8OWXX+Khhx5CaWkpXF1dAQCpqamYO3cuTp8+DbVafdVjM5lMcHR0RGVlJRwcHKyZFqLrwru3qDUpXhza0kMghWnu+7dVZ3pqamqQl5cHg8FwqQMbGxgMBuTm5lrcJjc3V1YPACEhIY3WW5KXl4cLFy7I+vHx8UH37t2lfnJzc+Hr6ysFnvr9mEwm7N+/32K/1dXVMJlMsoWIiIhaJ6tCz5kzZ1BXVycLFgDg6uoKo9FocRuj0WhVfWN9qNVqODk5NdpPY/upX2dJUlISHB0dpcXDw6PZYyIiIqLbi6Lv3oqLi0NlZaW0HDt2rKWHRERERDeIVU9kdnZ2hq2tbYO7psrKyqDT6Sxuo9PprKpvrI+amhpUVFTIzvZc3o9Op2twF1n9fhvbl0ajgUajafY4iIiI6PZl1ZketVoNf39/5OTkSG1msxk5OTkIDAy0uE1gYKCsHgCys7MbrbfE398fbdq0kfVTVFSEkpISqZ/AwED8/PPPsrvIsrOz4eDggD59+jR7X0RERNQ6Wf27t2JiYjB+/HgMGDAAgwYNQkpKCqqqqjBx4kQAQGRkJNzd3ZGUlAQAmDFjBoYMGYKlS5ciNDQUaWlp2LNnD1atWiX1WV5ejpKSEpSWlgK4GGiAi2dodDodHB0dERUVhZiYGHTq1AkODg6YNm0aAgMDERAQAAAYPnw4+vTpg3HjxiE5ORlGoxHz58/HlClTeDaHiIiIrA894eHhOH36NOLj42E0GuHn54esrCzpouGSkhLY2Fw6gRQUFIR169Zh/vz5mDdvHry9vbFp0yb07dtXqtm8ebMUmgBg7NixAICEhAQkJiYCAN58803Y2Nhg9OjRqK6uRkhICN555x1pG1tbW2zZsgXPPfccAgMD0a5dO4wfPx6LFi2y9hCJiIioFbL6OT2tGZ/TQy2Fz+mh1oTP6aGb7YY8p4eIiIjodsXQQ0RERIrA0ENERESKwNBDREREisDQQ0RERIrA0ENERESKwNBDREREisDQQ0RERIrA0ENERESKwNBDREREisDQQ0RERIrA0ENERESKwNBDREREisDQQ0RERIrA0ENERESKwNBDREREisDQQ0RERIrA0ENERESKwNBDREREisDQQ0RERIrA0ENERESKYNfSAyAiotbFM3ZrSw/BasWLQ1t6CHQT8EwPERERKQJDDxERESkCQw8REREpAkMPERERKQJDDxERESkCQw8REREpAkMPERERKQJDDxERESkCQw8REREpAkMPERERKcI1hZ4VK1bA09MTWq0Wer0eu3fvbrI+IyMDPj4+0Gq18PX1RWZmpmy9EALx8fHo2rUr7O3tYTAYcOjQIWn9t99+C5VKZXH58ccfAQDFxcUW1//www/XcohERETUylgdetLT0xETE4OEhATs3bsX/fr1Q0hICE6dOmWxfufOnYiIiEBUVBTy8/MRFhaGsLAwFBYWSjXJycl4++23kZqail27dqFdu3YICQnB+fPnAQBBQUE4efKkbJk0aRK8vLwwYMAA2f6++uorWZ2/v7+1h0hEREStkEoIIazZQK/XY+DAgVi+fDkAwGw2w8PDA9OmTUNsbGyD+vDwcFRVVWHLli1SW0BAAPz8/JCamgohBNzc3DB79mzMmTMHAFBZWQlXV1esWbMGY8eObdDnhQsX4O7ujmnTpmHBggUALp7p8fLyQn5+Pvz8/Kw5JInJZIKjoyMqKyvh4OBwTX0QXYvb8Rc0ErUm/IWjt7fmvn9bdaanpqYGeXl5MBgMlzqwsYHBYEBubq7FbXJzc2X1ABASEiLVHzlyBEajUVbj6OgIvV7faJ+bN2/GH3/8gYkTJzZYN2rUKLi4uCA4OBibN29u8niqq6thMplkCxEREbVOVoWeM2fOoK6uDq6urrJ2V1dXGI1Gi9sYjcYm6+v/tKbPDz74ACEhIejWrZvU1r59eyxduhQZGRnYunUrgoODERYW1mTwSUpKgqOjo7R4eHg0WktERES3N7uWHoC1jh8/jm3btmH9+vWydmdnZ8TExEivBw4ciNLSUixZsgSjRo2y2FdcXJxsG5PJxOBDRETUSll1psfZ2Rm2trYoKyuTtZeVlUGn01ncRqfTNVlf/2dz+1y9ejU6d+7caJC5nF6vx+HDhxtdr9Fo4ODgIFuIiIiodbIq9KjVavj7+yMnJ0dqM5vNyMnJQWBgoMVtAgMDZfUAkJ2dLdV7eXlBp9PJakwmE3bt2tWgTyEEVq9ejcjISLRp0+aq4y0oKEDXrl2bfXxERETUeln98VZMTAzGjx+PAQMGYNCgQUhJSUFVVZV0UXFkZCTc3d2RlJQEAJgxYwaGDBmCpUuXIjQ0FGlpadizZw9WrVoFAFCpVJg5cyZefvlleHt7w8vLCwsWLICbmxvCwsJk+/76669x5MgRTJo0qcG41q5dC7Vajf79+wMANm7ciA8//BDvv/++tYdIRERErZDVoSc8PBynT59GfHw8jEYj/Pz8kJWVJV2IXFJSAhubSyeQgoKCsG7dOsyfPx/z5s2Dt7c3Nm3ahL59+0o1//nPf1BVVYXo6GhUVFQgODgYWVlZ0Gq1sn1/8MEHCAoKgo+Pj8WxvfTSSzh69Cjs7Ozg4+OD9PR0jBkzxtpDJCIiolbI6uf0tGZ8Tg+1FD6nh6hl8Tk9t7cb8pweIiIiotsVQw8REREpAkMPERERKQJDDxERESkCQw8REREpAkMPERERKQJDDxERESkCQw8REREpAkMPERERKQJDDxERESkCQw8REREpAkMPERERKQJDDxERESkCQw8REREpAkMPERERKQJDDxERESkCQw8REREpAkMPERERKQJDDxERESkCQw8REREpAkMPERERKQJDDxERESkCQw8REREpAkMPERERKQJDDxERESkCQw8REREpAkMPERERKQJDDxERESkCQw8REREpAkMPERERKQJDDxERESkCQw8REREpwjWFnhUrVsDT0xNarRZ6vR67d+9usj4jIwM+Pj7QarXw9fVFZmambL0QAvHx8ejatSvs7e1hMBhw6NAhWY2npydUKpVsWbx4saxm3759uO+++6DVauHh4YHk5ORrOTwiIiJqhawOPenp6YiJiUFCQgL27t2Lfv36ISQkBKdOnbJYv3PnTkRERCAqKgr5+fkICwtDWFgYCgsLpZrk5GS8/fbbSE1Nxa5du9CuXTuEhITg/Pnzsr4WLVqEkydPSsu0adOkdSaTCcOHD0ePHj2Ql5eHJUuWIDExEatWrbL2EImIiKgVUgkhhDUb6PV6DBw4EMuXLwcAmM1meHh4YNq0aYiNjW1QHx4ejqqqKmzZskVqCwgIgJ+fH1JTUyGEgJubG2bPno05c+YAACorK+Hq6oo1a9Zg7NixAC6e6Zk5cyZmzpxpcVwrV67Eiy++CKPRCLVaDQCIjY3Fpk2bcPDgQYvbVFdXo7q6WnptMpng4eGByspKODg4WDMtRNfFM3ZrSw+BSNGKF4e29BDoOphMJjg6Ol71/duqMz01NTXIy8uDwWC41IGNDQwGA3Jzcy1uk5ubK6sHgJCQEKn+yJEjMBqNshpHR0fo9foGfS5evBidO3dG//79sWTJEtTW1sr2M3jwYCnw1O+nqKgIf/75p8WxJSUlwdHRUVo8PDyaORNERER0u7Eq9Jw5cwZ1dXVwdXWVtbu6usJoNFrcxmg0Nllf/+fV+pw+fTrS0tLwzTff4JlnnsGrr76K//znP1fdz+X7uFJcXBwqKyul5dixY40eOxEREd3e7Fp6AM0VExMj/f3uu++GWq3GM888g6SkJGg0mmvqU6PRXPO2REREdHux6kyPs7MzbG1tUVZWJmsvKyuDTqezuI1Op2uyvv5Pa/oELl5bVFtbi+Li4ib3c/k+iIiISLmsCj1qtRr+/v7IycmR2sxmM3JychAYGGhxm8DAQFk9AGRnZ0v1Xl5e0Ol0shqTyYRdu3Y12icAFBQUwMbGBi4uLtJ+duzYgQsXLsj207t3b3Ts2NGawyQiIqJWyOpb1mNiYvDee+9h7dq1OHDgAJ577jlUVVVh4sSJAIDIyEjExcVJ9TNmzEBWVhaWLl2KgwcPIjExEXv27MHUqVMBACqVCjNnzsTLL7+MzZs34+eff0ZkZCTc3NwQFhYG4OJFyikpKfjpp5/w+++/4+OPP8asWbPw1FNPSYHmiSeegFqtRlRUFPbv34/09HS89dZbso/FiIiISLmsvqYnPDwcp0+fRnx8PIxGI/z8/JCVlSVdNFxSUgIbm0tZKigoCOvWrcP8+fMxb948eHt7Y9OmTejbt69U85///AdVVVWIjo5GRUUFgoODkZWVBa1WC+DitTdpaWlITExEdXU1vLy8MGvWLFmgcXR0xPbt2zFlyhT4+/vD2dkZ8fHxiI6OvubJodsTb/8mIiJLrH5OT2vW3Pv86dbG0ENE1uJzem5vN+Q5PURERES3K4YeIiIiUgSGHiIiIlIEhh4iIiJSBIYeIiIiUgSGHiIiIlIEhh4iIiJSBIYeIiIiUgSGHiIiIlIEhh4iIiJSBIYeIiIiUgSGHiIiIlIEhh4iIiJSBIYeIiIiUgSGHiIiIlIEhh4iIiJSBIYeIiIiUgSGHiIiIlIEhh4iIiJSBIYeIiIiUgSGHiIiIlIEhh4iIiJSBIYeIiIiUgSGHiIiIlIEhh4iIiJSBIYeIiIiUgSGHiIiIlIEhh4iIiJSBIYeIiIiUgSGHiIiIlIEhh4iIiJSBIYeIiIiUoRrCj0rVqyAp6cntFot9Ho9du/e3WR9RkYGfHx8oNVq4evri8zMTNl6IQTi4+PRtWtX2Nvbw2Aw4NChQ9L64uJiREVFwcvLC/b29ujZsycSEhJQU1Mjq1GpVA2WH3744VoOkYiIiFoZq0NPeno6YmJikJCQgL1796Jfv34ICQnBqVOnLNbv3LkTERERiIqKQn5+PsLCwhAWFobCwkKpJjk5GW+//TZSU1Oxa9cutGvXDiEhITh//jwA4ODBgzCbzXj33Xexf/9+vPnmm0hNTcW8efMa7O+rr77CyZMnpcXf39/aQyQiIqJWSCWEENZsoNfrMXDgQCxfvhwAYDab4eHhgWnTpiE2NrZBfXh4OKqqqrBlyxapLSAgAH5+fkhNTYUQAm5ubpg9ezbmzJkDAKisrISrqyvWrFmDsWPHWhzHkiVLsHLlSvz+++8ALp7p8fLyQn5+Pvz8/Jp1LNXV1aiurpZem0wmeHh4oLKyEg4ODs3qg249nrFbW3oIREQ3XPHi0JYewi3DZDLB0dHxqu/fVp3pqampQV5eHgwGw6UObGxgMBiQm5trcZvc3FxZPQCEhIRI9UeOHIHRaJTVODo6Qq/XN9oncDEYderUqUH7qFGj4OLiguDgYGzevLnJ40lKSoKjo6O0eHh4NFlPREREty+rQs+ZM2dQV1cHV1dXWburqyuMRqPFbYxGY5P19X9a0+fhw4exbNkyPPPMM1Jb+/btsXTpUmRkZGDr1q0IDg5GWFhYk8EnLi4OlZWV0nLs2LFGa4mIiOj2ZtfSA7DWiRMn8OCDD+Kxxx7D5MmTpXZnZ2fExMRIrwcOHIjS0lIsWbIEo0aNstiXRqOBRqO54WMmIiKilmfVmR5nZ2fY2tqirKxM1l5WVgadTmdxG51O12R9/Z/N6bO0tBT3338/goKCsGrVqquOV6/X4/Dhw1etIyIiotbPqtCjVqvh7++PnJwcqc1sNiMnJweBgYEWtwkMDJTVA0B2drZU7+XlBZ1OJ6sxmUzYtWuXrM8TJ05g6NCh8Pf3x+rVq2Fjc/WhFxQUoGvXrtYcIhEREbVSVn+8FRMTg/Hjx2PAgAEYNGgQUlJSUFVVhYkTJwIAIiMj4e7ujqSkJADAjBkzMGTIECxduhShoaFIS0vDnj17pDM1KpUKM2fOxMsvvwxvb294eXlhwYIFcHNzQ1hYGIBLgadHjx54/fXXcfr0aWk89WeD1q5dC7Vajf79+wMANm7ciA8//BDvv//+tc8OERERtRpWh57w8HCcPn0a8fHxMBqN8PPzQ1ZWlnQhcklJiewsTFBQENatW4f58+dj3rx58Pb2xqZNm9C3b1+p5j//+Q+qqqoQHR2NiooKBAcHIysrC1qtFsDFM0OHDx/G4cOH0a1bN9l4Lr/j/qWXXsLRo0dhZ2cHHx8fpKenY8yYMdYeIhEREbVCVj+npzVr7n3+dGvjc3qISAn4nJ5LbshzeoiIiIhuVww9REREpAgMPURERKQIDD1ERESkCAw9REREpAgMPURERKQIDD1ERESkCAw9REREpAgMPURERKQIDD1ERESkCAw9REREpAgMPURERKQIDD1ERESkCAw9REREpAgMPURERKQIDD1ERESkCAw9REREpAgMPURERKQIdi09ALq1ecZubekhEBER/SN4poeIiIgUgaGHiIiIFIGhh4iIiBSBoYeIiIgUgaGHiIiIFIGhh4iIiBSBoYeIiIgUgaGHiIiIFIGhh4iIiBSBoYeIiIgUgaGHiIiIFIG/e4uIiOg2dDv+bsTixaEtuv9rOtOzYsUKeHp6QqvVQq/XY/fu3U3WZ2RkwMfHB1qtFr6+vsjMzJStF0IgPj4eXbt2hb29PQwGAw4dOiSrKS8vx5NPPgkHBwc4OTkhKioK586dk9Xs27cP9913H7RaLTw8PJCcnHwth0dEREStkNWhJz09HTExMUhISMDevXvRr18/hISE4NSpUxbrd+7ciYiICERFRSE/Px9hYWEICwtDYWGhVJOcnIy3334bqamp2LVrF9q1a4eQkBCcP39eqnnyySexf/9+ZGdnY8uWLdixYweio6Ol9SaTCcOHD0ePHj2Ql5eHJUuWIDExEatWrbL2EImIiKgVUgkhhDUb6PV6DBw4EMuXLwcAmM1meHh4YNq0aYiNjW1QHx4ejqqqKmzZskVqCwgIgJ+fH1JTUyGEgJubG2bPno05c+YAACorK+Hq6oo1a9Zg7NixOHDgAPr06YMff/wRAwYMAABkZWVh5MiROH78ONzc3LBy5Uq8+OKLMBqNUKvVAIDY2Fhs2rQJBw8ebNaxmUwmODo6orKyEg4ODtZMS6t1O54+JSKiW9ON+nirue/fVl3TU1NTg7y8PMTFxUltNjY2MBgMyM3NtbhNbm4uYmJiZG0hISHYtGkTAODIkSMwGo0wGAzSekdHR+j1euTm5mLs2LHIzc2Fk5OTFHgAwGAwwMbGBrt27cKjjz6K3NxcDB48WAo89ft57bXX8Oeff6Jjx44NxlZdXY3q6mrpdWVlJYCLk3cj9E3YdkP6JSIiuh3cqPfX+n6vdh7HqtBz5swZ1NXVwdXVVdbu6ura6NkUo9Fosd5oNErr69uaqnFxcZEP3M4OnTp1ktV4eXk16KN+naXQk5SUhIULFzZo9/DwsHgsREREdO0cU25s/2fPnoWjo2Oj6xV991ZcXJzsLJTZbEZ5eTk6d+4MlUrVgiO7xGQywcPDA8eOHeNHbk3gPDUP56l5OE/Nw3lqHs5T81zPPAkhcPbsWbi5uTVZZ1XocXZ2hq2tLcrKymTtZWVl0Ol0FrfR6XRN1tf/WVZWhq5du8pq/Pz8pJorL5Sura1FeXm5rB9L+7l8H1fSaDTQaDSyNicnJ4u1Lc3BwYH/WJqB89Q8nKfm4Tw1D+epeThPzXOt89TUGZ56Vt29pVar4e/vj5ycHKnNbDYjJycHgYGBFrcJDAyU1QNAdna2VO/l5QWdTierMZlM2LVrl1QTGBiIiooK5OXlSTVff/01zGYz9Hq9VLNjxw5cuHBBtp/evXtb/GiLiIiIFEZYKS0tTWg0GrFmzRrxyy+/iOjoaOHk5CSMRqMQQohx48aJ2NhYqf77778XdnZ24vXXXxcHDhwQCQkJok2bNuLnn3+WahYvXiycnJzE559/Lvbt2yceeeQR4eXlJf7++2+p5sEHHxT9+/cXu3btEv/973+Ft7e3iIiIkNZXVFQIV1dXMW7cOFFYWCjS0tJE27ZtxbvvvmvtId5SKisrBQBRWVnZ0kO5pXGemofz1Dycp+bhPDUP56l5bsY8WR16hBBi2bJlonv37kKtVotBgwaJH374QVo3ZMgQMX78eFn9+vXrxZ133inUarX417/+JbZu3SpbbzabxYIFC4Srq6vQaDRi2LBhoqioSFbzxx9/iIiICNG+fXvh4OAgJk6cKM6ePSur+emnn0RwcLDQaDTC3d1dLF68+FoO75Zy/vx5kZCQIM6fP9/SQ7mlcZ6ah/PUPJyn5uE8NQ/nqXluxjxZ/ZweIiIiotsRf+EoERERKQJDDxERESkCQw8REREpAkMPERERKQJDDxERESkCQ88tbuvWrdDr9bC3t0fHjh0RFhYmW19SUoLQ0FC0bdsWLi4ueOGFF1BbW9syg21h1dXV8PPzg0qlQkFBgWzdvn37cN9990Gr1cLDwwPJycktM8gWUlxcjKioKHh5ecHe3h49e/ZEQkICampqZHVKn6d6K1asgKenJ7RaLfR6PXbv3t3SQ2oxSUlJGDhwIDp06AAXFxeEhYWhqKhIVnP+/HlMmTIFnTt3Rvv27TF69OgGT8hXmsWLF0OlUmHmzJlSG+fpohMnTuCpp55C586dYW9vD19fX+zZs0daL4RAfHw8unbtCnt7exgMBhw6dOif2fkNuxmertuGDRtEx44dxcqVK0VRUZHYv3+/SE9Pl9bX1taKvn37CoPBIPLz80VmZqZwdnYWcXFxLTjqljN9+nQxYsQIAUDk5+dL7ZWVlcLV1VU8+eSTorCwUHzyySfC3t7+tn9wpTW+/PJLMWHCBLFt2zbx22+/ic8//1y4uLiI2bNnSzWcp4vS0tKEWq0WH374odi/f7+YPHmycHJyEmVlZS09tBYREhIiVq9eLQoLC0VBQYEYOXKk6N69uzh37pxU8+yzzwoPDw+Rk5Mj9uzZIwICAkRQUFALjrpl7d69W3h6eoq7775bzJgxQ2rnPAlRXl4uevToISZMmCB27dolfv/9d7Ft2zZx+PBhqWbx4sXC0dFRbNq0Sfz0009i1KhRDR5YfK0Yem5RFy5cEO7u7uL9999vtCYzM1PY2NhIT8MWQoiVK1cKBwcHUV1dfTOGecvIzMwUPj4+Yv/+/Q1CzzvvvCM6duwom5O5c+eK3r17t8BIbx3JycnCy8tLes15umjQoEFiypQp0uu6ujrh5uYmkpKSWnBUt45Tp04JAOK7774TQlx8Gn6bNm1ERkaGVHPgwAEBQOTm5rbUMFvM2bNnhbe3t8jOzhZDhgyRQg/n6aK5c+eK4ODgRtebzWah0+nEkiVLpLaKigqh0WjEJ598ct3758dbt6i9e/fixIkTsLGxQf/+/dG1a1eMGDEChYWFUk1ubi58fX3h6uoqtYWEhMBkMmH//v0tMewWUVZWhsmTJ+P//u//0LZt2wbrc3NzMXjwYKjVaqktJCQERUVF+PPPP2/mUG8plZWV6NSpk/Sa8wTU1NQgLy8PBoNBarOxsYHBYEBubm4LjuzWUVlZCQDS905eXh4uXLggmzMfHx90795dkXM2ZcoUhIaGyuYD4DzV27x5MwYMGIDHHnsMLi4u6N+/P9577z1p/ZEjR2A0GmXz5OjoCL1e/4/ME0PPLer3338HACQmJmL+/PnYsmULOnbsiKFDh6K8vBwAYDQaZYEHgPTaaDTe3AG3ECEEJkyYgGeffRYDBgywWMN5aujw4cNYtmwZnnnmGamN8wScOXMGdXV1FudBKXPQFLPZjJkzZ+Lee+9F3759AVz83lCr1XBycpLVKnHO0tLSsHfvXiQlJTVYx3m66Pfff8fKlSvh7e2Nbdu24bnnnsP06dOxdu1aAJd+1tyof4MMPTdZbGwsVCpVk8vBgwdhNpsBAC+++CJGjx4Nf39/rF69GiqVChkZGS18FDdec+dp2bJlOHv2LOLi4lp6yC2iufN0uRMnTuDBBx/EY489hsmTJ7fQyOl2NGXKFBQWFiItLa2lh3LLOXbsGGbMmIGPP/4YWq22pYdzyzKbzbjnnnvw6quvon///oiOjsbkyZORmpp6U/Zvd1P2QpLZs2djwoQJTdbccccdOHnyJACgT58+UrtGo8Edd9yBkpISAIBOp2twV0n9nQA6ne4fHPXN19x5+vrrr5GbmwuNRiNbN2DAADz55JNYu3YtdDpdgzsklDZP9UpLS3H//fcjKCgIq1atktW15nlqLmdnZ9ja2lqcB6XMQWOmTp2KLVu2YMeOHejWrZvUrtPpUFNTg4qKCtlZDKXNWV5eHk6dOoV77rlHaqurq8OOHTuwfPlybNu2jfMEoGvXrrL3NQC466678OmnnwK49LOmrKwMXbt2lWrKysrg5+d3/QO47quC6IaorKwUGo1GdiFzTU2NcHFxke6mqb+Q+fK7St59913h4OCgmN/me/ToUfHzzz9Ly7Zt2wQAsWHDBnHs2DEhxKULdGtqaqTt4uLiFHeB7vHjx4W3t7cYO3asqK2tbbCe83TRoEGDxNSpU6XXdXV1wt3dXbEXMpvNZjFlyhTh5uYmfv311wbr6y/Q3bBhg9R28OBBxV2gazKZZD+Lfv75ZzFgwADx1FNPiZ9//pnz9P9FREQ0uJB55syZIjAwUAhx6ULm119/XVpf/374T1zIzNBzC5sxY4Zwd3cX27ZtEwcPHhRRUVHCxcVFlJeXCyEu3bI+fPhwUVBQILKyskSXLl0Ue8u6EEIcOXKkwd1bFRUVwtXVVYwbN04UFhaKtLQ00bZtW0Xdin38+HHRq1cvMWzYMHH8+HFx8uRJaanHebooLS1NaDQasWbNGvHLL7+I6Oho4eTkJLtLUkmee+454ejoKL799lvZ981ff/0l1Tz77LOie/fu4uuvvxZ79uwRgYGB0puYkl1+95YQnCchLt7Ob2dnJ1555RVx6NAh8fHHH4u2bduKjz76SKpZvHixcHJyEp9//rnYt2+feOSRR3jLuhLU1NSI2bNnCxcXF9GhQwdhMBhEYWGhrKa4uFiMGDFC2NvbC2dnZzF79mxx4cKFFhpxy7MUeoQQ4qeffhLBwcFCo9EId3d3sXjx4pYZYAtZvXq1AGBxuZzS56nesmXLRPfu3YVarRaDBg0SP/zwQ0sPqcU09n2zevVqqebvv/8Wzz//vOjYsaNo27atePTRR2WBWqmuDD2cp4u++OIL0bdvX6HRaISPj49YtWqVbL3ZbBYLFiwQrq6uQqPRiGHDhomioqJ/ZN8qIYS4/g/JiIiIiG5tvHuLiIiIFIGhh4iIiBSBoYeIiIgUgaGHiIiIFIGhh4iIiBSBoYeIiIgUgaGHiIiIFIGhh4iIiBSBoYeIiIgUgaGHiIiIFIGhh4iIiBTh/wF+eLym7nDnAwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def calculate_final_evaluation_for_history(board_history: np.ndarray) -> np.ndarray:\n",
    "    final_evaluation = final_boards_evaluation(board_history[-1])\n",
    "    return final_evaluation / 64\n",
    "\n",
    "\n",
    "assert len(calculate_final_evaluation_for_history(_board_history).shape) == 1\n",
    "_final_eval = calculate_final_evaluation_for_history(_board_history)\n",
    "plt.title(\"Histogram over the score distribution\")\n",
    "plt.hist((_final_eval * 64), density=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def calculate_who_won(board_history: np.ndarray) -> np.ndarray:\n",
    "    who_won = evaluate_who_won(board_history[-1])\n",
    "    return who_won\n",
    "\n",
    "\n",
    "plt.title(\"Win distribtuion\")\n",
    "plt.bar(\n",
    "    [\"black\", \"draw\", \"white\"],\n",
    "    pd.Series(calculate_who_won(_board_history)).value_counts().sort_index() / 10000,\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def history_changed(board_history: np.ndarray) -> np.ndarray:\n",
    "    return ~np.all(\n",
    "        np.roll(board_history, shift=1, axis=0) == board_history, axis=(2, 3)\n",
    "    )\n",
    "\n",
    "\n",
    "plt.title(\"Share of turns skipped\")\n",
    "plt.plot(1 - np.mean(history_changed(_board_history), axis=1))\n",
    "plt.xlabel(\"Turn\")\n",
    "plt.ylabel(\"Factor of skipped turns\")\n",
    "plt.yscale(\"log\", base=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70, 10000)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_gamma_table(board_history, gamma_value: float):\n",
    "    unchanged = history_changed(board_history)\n",
    "    gamma_values = np.ones_like(unchanged, dtype=float)\n",
    "    gamma_values[unchanged] = gamma_value\n",
    "    return gamma_values\n",
    "\n",
    "\n",
    "get_gamma_table(_board_history, 0.8).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.09677184,  0.0037773 ,  0.12190913,  0.03519891,  0.16118614,\n",
       "        0.00617017,  0.12490022, -0.03918723,  0.14632847, -0.01240192,\n",
       "        0.1016851 ,  0.00991888,  0.1295861 , -0.03332988,  0.07552515,\n",
       "       -0.10090606,  0.14730492, -0.08930635,  0.08367957, -0.09071304,\n",
       "        0.1600462 ,  0.08287025,  0.22077531, -0.07559336,  0.1789458 ,\n",
       "        0.02836975,  0.23077469,  0.01503086,  0.13597608, -0.18159241,\n",
       "       -0.03167801, -0.23491001,  0.05792499, -0.04478127,  0.06121092,\n",
       "       -0.04067385,  0.37884519,  0.04386898,  0.17202373, -0.05840784,\n",
       "        0.0441777 , -0.14009038,  0.02019953, -0.09193809,  0.15851489,\n",
       "        0.08095611,  0.45275764,  0.13625955,  0.36563693, -0.05076633,\n",
       "        0.28810459, -0.22580677, -0.16507096, -0.5579012 , -0.033314  ,\n",
       "       -0.15883   ,  0.23115   , -0.45325   , -0.37125   , -0.58125   ,\n",
       "       -0.21875   , -0.21875   , -0.21875   , -0.21875   , -0.21875   ,\n",
       "       -0.21875   , -0.21875   , -0.21875   , -0.21875   , -0.21875   ])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def calculate_q_reword(\n",
    "    board_history: np.ndarray,\n",
    "    who_won_fraction: float = 0.2,\n",
    "    final_score_fraction=0.2,\n",
    "    gamma=0.8,\n",
    ") -> np.ndarray:\n",
    "    assert who_won_fraction + final_score_fraction <= 1\n",
    "    assert final_score_fraction >= 0\n",
    "    assert who_won_fraction >= 0\n",
    "\n",
    "    gama_table = get_gamma_table(board_history, gamma)\n",
    "    combined_score = np.zeros_like(gama_table)\n",
    "    combined_score += calculate_direct_score(board_history) * (\n",
    "        1 - who_won_fraction + final_score_fraction\n",
    "    )\n",
    "    combined_score[-1] += (\n",
    "        calculate_final_evaluation_for_history(board_history) * final_score_fraction\n",
    "    )\n",
    "    combined_score[-1] += calculate_who_won(board_history) * who_won_fraction\n",
    "    for turn in range(SIMULATE_TURNS - 1, 0, -1):\n",
    "        values = gama_table[turn] * combined_score[turn]\n",
    "        combined_score[turn - 1] += values\n",
    "\n",
    "    return combined_score\n",
    "\n",
    "\n",
    "calculate_q_reword(\n",
    "    _board_history, gamma=0.8, who_won_fraction=0, final_score_fraction=1\n",
    ")[:, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.53249554e-06, -1.91561943e-06, -2.39452428e-06, -2.99315535e-06,\n",
       "       -3.74144419e-06, -4.67680524e-06, -5.84600655e-06, -7.30750819e-06,\n",
       "       -9.13438523e-06, -1.14179815e-05, -1.42724769e-05, -1.78405962e-05,\n",
       "       -2.23007452e-05, -2.78759315e-05, -3.48449144e-05, -4.35561430e-05,\n",
       "       -5.44451787e-05, -6.80564734e-05, -8.50705917e-05, -1.06338240e-04,\n",
       "       -1.32922800e-04, -1.66153499e-04, -2.07691874e-04, -2.59614843e-04,\n",
       "       -3.24518554e-04, -4.05648192e-04, -5.07060240e-04, -6.33825300e-04,\n",
       "       -7.92281625e-04, -9.90352031e-04, -1.23794004e-03, -1.54742505e-03,\n",
       "       -1.93428131e-03, -2.41785164e-03, -3.02231455e-03, -3.77789319e-03,\n",
       "       -4.72236648e-03, -5.90295810e-03, -7.37869763e-03, -9.22337204e-03,\n",
       "       -1.15292150e-02, -1.44115188e-02, -1.80143985e-02, -2.25179981e-02,\n",
       "       -2.81474977e-02, -3.51843721e-02, -4.39804651e-02, -5.49755814e-02,\n",
       "       -6.87194767e-02, -8.58993459e-02, -1.07374182e-01, -1.34217728e-01,\n",
       "       -1.67772160e-01, -2.09715200e-01, -2.62144000e-01, -3.27680000e-01,\n",
       "       -4.09600000e-01, -5.12000000e-01, -6.40000000e-01, -8.00000000e-01,\n",
       "       -1.00000000e+00, -1.00000000e+00, -1.00000000e+00, -1.00000000e+00,\n",
       "       -1.00000000e+00, -1.00000000e+00, -1.00000000e+00, -1.00000000e+00,\n",
       "       -1.00000000e+00, -1.00000000e+00])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calculate_q_reword(\n",
    "    _board_history, gamma=0.8, who_won_fraction=1, final_score_fraction=0\n",
    ")[:, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  3.09670969,   0.12088712,   3.9011089 ,   1.12638612,\n",
       "         5.15798265,   0.19747831,   3.99684789,  -1.25394014,\n",
       "         4.68257483,  -0.39678147,   3.25402317,   0.31752896,\n",
       "         4.1469112 ,  -1.066361  ,   2.41704875,  -3.22868907,\n",
       "         4.71413867,  -2.85732667,   2.67834167,  -2.90207292,\n",
       "         5.12240885,   2.65301107,   7.06626383,  -2.41717021,\n",
       "         5.72853724,   0.91067155,   7.38833944,   0.4854243 ,\n",
       "         4.35678037,  -5.80402453,  -1.00503067,  -7.50628834,\n",
       "         1.86713958,  -1.41607552,   1.9799056 ,  -1.27511801,\n",
       "        12.15610249,   1.44512812,   5.55641015,  -1.80448732,\n",
       "         1.49439085,  -4.38201144,   0.77248571,  -2.78439287,\n",
       "         5.26950892,   2.83688614,  14.79610768,   4.7451346 ,\n",
       "        12.18141825,  -1.02322719,   9.97096602,  -6.28629248,\n",
       "        -4.1078656 , -16.384832  ,   0.76896   ,  -2.7888    ,\n",
       "        10.264     , -10.92      ,  -7.4       , -13.        ,\n",
       "         0.        ,   0.        ,   0.        ,   0.        ,\n",
       "         0.        ,   0.        ,   0.        ,   0.        ,\n",
       "         0.        ,   0.        ])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calculate_q_reword(\n",
    "    _board_history, gamma=0.8, who_won_fraction=0, final_score_fraction=0\n",
    ")[:, 0] * 64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "BATCH_SIZE = 1000\n",
    "\n",
    "\n",
    "class DQLNet(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.fc1 = nn.Linear(8 * 8 * 2, 128)\n",
    "        self.fc2 = nn.Linear(128, 1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        if isinstance(x, np.ndarray):\n",
    "            x = torch.from_numpy(x).float()\n",
    "        x = torch.flatten(x, 1)\n",
    "        x = self.fc1(x)\n",
    "        x = F.relu(x)\n",
    "        # x = self.dropout1(x)\n",
    "        x = self.fc2(x)\n",
    "        x = F.relu(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "class SymmetryMode(Enum):\n",
    "    MULTIPLY = \"MULTIPLY\"\n",
    "    BREAK_SEQUENCE = \"BREAK_SEQUENCE\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 8, 8)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def action_to_q_learning_format(board_history, action_history):\n",
    "    q_learning_format = np.zeros((SIMULATE_TURNS, board_history.shape[1], 2, 8, 8), dtype=float)\n",
    "    q_learning_format[:, :, 1, :, :] = -1\n",
    "    q_learning_format[:, :, 1, action_history[:, :, 0], action_history[:, :, 0]] = 1\n",
    "    return q_learning_format\n",
    "\n",
    "\n",
    "action_to_q_learning_format(simulated_boards, simulated_actions)[-1,0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def build_symetry_action(board_history, action_history):\n",
    "    simulated_boards2[::2] *= -1\n",
    "    q_learning_format = np.zeros((2,2,2, SIMULATE_TURNS, board_history.shape[1], 2, 8, 8))\n",
    "    q_learning_format[0, 0, 0, :, :, :, :, :] = action_to_q_learning_format(board_history, action_history)\n",
    "    q_learning_format[1, 0, 0, :, :, :, :, :] = np.transpose(q_learning_format[0, 0, 0, :, :, :, :, :], [0, 1, 2, 4, 3])\n",
    "    q_learning_format[:, 1, 0, :, :, :, :, :] = q_learning_format[:, 0, 0, :, :, :, ::-1, :]\n",
    "    q_learning_format[:, :, 1, :, :, :, :, :] = q_learning_format[:, :, 0, :, :, :, :, ::-1]\n",
    "    return q_learning_format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5600"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class QLPoicy(GamePolicy):\n",
    "    def __init__(\n",
    "        self,\n",
    "        epsilon: float,\n",
    "        neural_netwerk: DQLNet,\n",
    "        symmetry_mode: SymmetryMode,\n",
    "        gamma: float = 0.8,\n",
    "        who_won_fraction: float = 0,\n",
    "        final_score_fraction: float = 0,\n",
    "    ):\n",
    "        super().__init__(epsilon)\n",
    "        assert 0 <= gamma <= 1\n",
    "        self.gamma: str = gamma\n",
    "        self.symmetry_mode: SymmetryMode = symmetry_mode\n",
    "        self.neural_network: DQLNet = neural_netwerk\n",
    "        self.who_won_fraction: final = who_won_fraction\n",
    "        self.final_score_fraction: final = final_score_fraction\n",
    "\n",
    "    @property\n",
    "    def policy_name(self) -> str:\n",
    "        # name=\"QL-M-G08-WW00-FSF00\n",
    "        symmetry_name = {SymmetryMode.MULTIPLY: \"M\", SymmetryMode.BREAK_SEQUENCE: \"B\"}\n",
    "        g = f\"{self.gamma:.1f}\".replace(\".\", \"\")\n",
    "        ww = f\"{self.who_won_fraction:.1f}\".replace(\".\", \"\")\n",
    "        fsf = f\"{self.final_score_fraction:.1f}\".replace(\".\", \"\")\n",
    "        return f\"QL-{symmetry_name[self.symmetry_mode]}-G{g}-WW{ww}-FSF{fsf}\"\n",
    "\n",
    "    def _internal_policy(self, boards: np.ndarray) -> np.ndarray:\n",
    "        results = np.ones_like(boards, dtype=float)\n",
    "        results *= -1\n",
    "        results = torch.from_numpy(results).float()\n",
    "        q_learning_boards = np.zeros((boards.shape[0], 2, 8, 8))\n",
    "        q_learning_boards[:, 0, :, :] = boards\n",
    "        poss_turns = boards == 0  # checks where fields are empty.\n",
    "        poss_turns &= binary_dilation(boards == -1, SURROUNDING)\n",
    "        turn_possible = np.any(poss_turns, axis=0)\n",
    "        for action_x, action_y in itertools.product(range(8), range(8)):\n",
    "            if not turn_possible[action_x, action_y]:\n",
    "                continue\n",
    "            _q_learning_board = q_learning_boards[\n",
    "                poss_turns[:, action_x, action_y]\n",
    "            ].copy()\n",
    "            _q_learning_board[:, 1, action_x, action_y] = 1\n",
    "            ql_result = self.neural_network.forward(_q_learning_board)\n",
    "            results[\n",
    "                poss_turns[:, action_x, action_y], action_x, action_y\n",
    "            ] = ql_result.reshape(-1)\n",
    "        return results.cpu().detach().numpy()\n",
    "\n",
    "    def train_epoch(self, generate_data_size: int):\n",
    "        # generate trainings data\n",
    "        train_boards, train_actions = simulate_game(generate_data_size, [self] * 2)\n",
    "        q_leaning_formated_action = build_symetry_action(train_boards, train_actions)\n",
    "        q_rewords = calculate_q_reword(\n",
    "            board_history=train_boards,\n",
    "            who_won_fraction=self.who_won_fraction,\n",
    "            final_score_fraction=self.final_score_fraction,\n",
    "        )\n",
    "        if self.symmetry_mode == SymmetryMode.MULTIPLY:\n",
    "            q_rewords = np.array([q_rewords] * 8)\n",
    "        elif self.symmetry_mode == SymmetryMode.BREAK_SEQUENCE:\n",
    "            axis1 = np.random.randint(0, high=2, size=SIMULATE_TURNS, dtype=int)\n",
    "            axis2 = np.random.randint(0, high=2, size=SIMULATE_TURNS, dtype=int)\n",
    "            axis3 = np.random.randint(0, high=2, size=SIMULATE_TURNS, dtype=int)\n",
    "            q_leaning_formated_action = q_leaning_formated_action[\n",
    "                axis1, axis2, axis3, range(SIMULATE_TURNS)\n",
    "            ]\n",
    "        a1 = q_leaning_formated_action.reshape(-1, 2, BOARD_SIZE, BOARD_SIZE)\n",
    "        a2 = q_rewords.reshape(-1)\n",
    "        return a1, a2\n",
    "\n",
    "    def evaluate_model(compare_models: list[GamePolicy]):\n",
    "        for i in range(compare_models):\n",
    "            pass\n",
    "\n",
    "\n",
    "ql_policy = QLPoicy(\n",
    "    0.95,\n",
    "    neural_netwerk=DQLNet(),\n",
    "    symmetry_mode=SymmetryMode.MULTIPLY,\n",
    "    gamma=0.8,\n",
    "    who_won_fraction=0,\n",
    "    final_score_fraction=0,\n",
    ")\n",
    "a1, a2 = ql_policy.train_epoch(10)\n",
    "a1.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5600,)"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5600, 2, 8, 8)"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "700"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ql_policy = QLPoicy(\n",
    "    0.95,\n",
    "    neural_netwerk=DQLNet(),\n",
    "    symmetry_mode=SymmetryMode.BREAK_SEQUENCE,\n",
    "    gamma=0.8,\n",
    "    who_won_fraction=0,\n",
    "    final_score_fraction=0,\n",
    ")\n",
    "ql_policy.train_epoch(10)[0].shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "ename": "NotImplementedError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mNotImplementedError\u001B[0m                       Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[44], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mNotImplementedError\u001B[39;00m()\n",
      "\u001B[1;31mNotImplementedError\u001B[0m: "
     ]
    }
   ],
   "source": [
    "raise NotImplementedError()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train a model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "simulated_boards, simulated_actions = simulate_game(100, [RandomPolicy(0)] * 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 1,  1,  1, ...,  1,  1,  1],\n",
       "        [ 1, -1, -1, ...,  1,  1,  1],\n",
       "        [ 1, -1, -1, ...,  1,  1,  1],\n",
       "        ...,\n",
       "        [ 1, -1,  1, ...,  1, -1,  1],\n",
       "        [ 1, -1,  1, ..., -1,  1,  1],\n",
       "        [-1,  1,  1, ...,  1,  1,  1]],\n",
       "\n",
       "       [[ 1,  1,  1, ...,  1,  1, -1],\n",
       "        [-1, -1, -1, ..., -1, -1, -1],\n",
       "        [-1, -1,  1, ...,  1,  1, -1],\n",
       "        ...,\n",
       "        [-1, -1, -1, ..., -1,  1,  1],\n",
       "        [-1, -1, -1, ...,  1,  1,  1],\n",
       "        [-1, -1, -1, ...,  1,  1,  1]],\n",
       "\n",
       "       [[-1, -1, -1, ..., -1, -1,  1],\n",
       "        [-1,  1,  1, ..., -1,  1,  1],\n",
       "        [-1,  1,  1, ...,  1,  1,  1],\n",
       "        ...,\n",
       "        [ 1,  1,  1, ...,  1,  1,  1],\n",
       "        [ 1,  1,  1, ...,  1,  1,  1],\n",
       "        [-1, -1, -1, ...,  1,  1,  1]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[-1, -1, -1, ...,  1,  1,  1],\n",
       "        [ 1,  1,  1, ...,  1,  1,  1],\n",
       "        [ 1, -1, -1, ...,  1,  1,  1],\n",
       "        ...,\n",
       "        [-1, -1,  1, ..., -1, -1, -1],\n",
       "        [-1, -1,  1, ..., -1, -1, -1],\n",
       "        [-1, -1,  1, ..., -1, -1, -1]],\n",
       "\n",
       "       [[ 1,  1,  1, ...,  1,  1,  1],\n",
       "        [ 1,  1,  1, ...,  1,  1, -1],\n",
       "        [ 1, -1, -1, ...,  1, -1, -1],\n",
       "        ...,\n",
       "        [ 1,  1, -1, ...,  1, -1, -1],\n",
       "        [ 1,  1,  1, ..., -1, -1, -1],\n",
       "        [-1, -1, -1, ..., -1, -1, -1]],\n",
       "\n",
       "       [[ 1,  1,  1, ...,  1,  1, -1],\n",
       "        [ 1,  1,  1, ...,  1,  1, -1],\n",
       "        [ 1,  1, -1, ..., -1,  1,  1],\n",
       "        ...,\n",
       "        [-1, -1, -1, ..., -1,  1,  1],\n",
       "        [-1, -1, -1, ..., -1, -1,  1],\n",
       "        [-1, -1, -1, ..., -1, -1, -1]]], dtype=int8)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simulated_boards[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ql_policy._internal_policy(get_new_games(3))\n",
    "%timeit ql_policy.train_epoch(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "ql_policy.__dict__[\"neural_network\"].forward("
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "new_games(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "?simulate_game"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "?simulate_game"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ones = np.ones((1000, 8, 8), dtype=float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "DQLNet().forward(ones)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = torch.tensor([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])\n",
    "torch.flatten(t)\n",
    "torch.tensor([1, 2, 3, 4, 5, 6, 7, 8])\n",
    "torch.flatten(t, start_dim=1)\n",
    "torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DQLearningWinner(GamePolicy):\n",
    "\n",
    "    # network =\n",
    "\n",
    "    @property\n",
    "    def policy_name(self):\n",
    "        return \"DQL-Winner\"\n",
    "\n",
    "    def _internal_policy(boards) -> np.ndarray:\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "DQLearningWinner(0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_simple_rewords()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sources\n",
    "\n",
    "* Game rules and example board images [https://en.wikipedia.org/wiki/Reversi](https://en.wikipedia.org/wiki/Reversi)\n",
    "* Game rules and example game images [https://de.wikipedia.org/wiki/Othello_(Spiel)](https://de.wikipedia.org/wiki/Othello_(Spiel))\n",
    "* Game strategy examples [https://de.wikipedia.org/wiki/Computer-Othello](https://de.wikipedia.org/wiki/Computer-Othello)\n",
    "* Image for 8 directions [https://www.researchgate.net/journal/EURASIP-Journal-on-Image-and-Video-Processing-1687-5281](https://www.researchgate.net/journal/EURASIP-Journal-on-Image-and-Video-Processing-1687-5281)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "def sizeof_fmt(num, suffix='B'):\n",
    "    ''' by Fred Cirera,  https://stackoverflow.com/a/1094933/1870254, modified'''\n",
    "    for unit in ['','Ki','Mi','Gi','Ti','Pi','Ei','Zi']:\n",
    "        if abs(num) < 1024.0:\n",
    "            return \"%3.1f %s%s\" % (num, unit, suffix)\n",
    "        num /= 1024.0\n",
    "    return \"%.1f %s%s\" % (num, 'Yi', suffix)\n",
    "\n",
    "for name, size in sorted(((name, sys.getsizeof(value)) for name, value in list(\n",
    "                          locals().items())), key= lambda x: -x[1])[:10]:\n",
    "    print(\"{:>30}: {:>8}\".format(name, sizeof_fmt(size)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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