Q_Learning.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNW98En7JTZi5lxLGBYnhCi",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/HimalayanPanda/q-learning-exploration/blob/main/Q_Learning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Implementing Q-Learning"
      ],
      "metadata": {
        "id": "1kNOtBdPEtPr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Basic implementation of Q-Learning Principles\n",
        "To better understand the underlying logic of how Q-Learning works, I asked ChatGPT to write me a small model which outlines the basics in the implementation of q-learning.\n",
        "\n",
        "The code below is a basic implementation of q-learning principles. There is a `reward_table` for each state-action pair which defines the environment. The model then randomly gets a state and action to get the reward of the action in that state. It uses the information to update the `q_table` which tells us the best action in any given state.\n"
      ],
      "metadata": {
        "id": "SaE28rpqEHrv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "#adding a seed for consistent results when running multiple times\n",
        "np.random.seed(100)\n",
        "\n",
        "#reward table, contains rewards for each state-action pair\n",
        "reward_table = np.array([[10, -50],\n",
        "                         [-50, 5],\n",
        "                         [25, 25]])\n",
        "\n",
        "#q-table, initialized with zeros\n",
        "q_table = np.zeros((3, 2))\n",
        "\n",
        "#learning rate\n",
        "alpha = 0.5\n",
        "\n",
        "#discount rate\n",
        "gamma = 0.9\n",
        "\n",
        "print('Initial Q-Table:\\n', q_table, '\\n')\n",
        "\n",
        "for episode in range(10000):\n",
        "    #state can be 0, 1, 2\n",
        "    state = np.random.randint(0, 3)\n",
        "\n",
        "    #action can be 0, 1\n",
        "    action = np.random.randint(0, 2)\n",
        "\n",
        "    #get reward\n",
        "    reward = reward_table[state, action]\n",
        "\n",
        "    #get the maximum future reward for new_state over all actions\n",
        "    future_reward = np.max(q_table[state, :])\n",
        "\n",
        "    #apply the Q-learning formula\n",
        "    old_value = q_table[state, action]\n",
        "    q_table[state, action] = old_value + alpha * (reward + gamma * future_reward - old_value)\n",
        "\n",
        "    if episode in [10, 50, 100, 500, 1000, 2813, 2814, 5000, 9999]:\n",
        "        print('Q-Table After %d Episodes:\\n' % episode, q_table, '\\n')\n",
        "\n",
        "print('Final Q-Table:\\n', q_table)\n",
        "print(np.max(q_table[0, :]))"
      ],
      "metadata": {
        "id": "4tvgjsDSEA77",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "090711cd-4055-42f6-9012-156fd13658d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Initial Q-Table:\n",
            " [[0. 0.]\n",
            " [0. 0.]\n",
            " [0. 0.]] \n",
            "\n",
            "Q-Table After 10 Episodes:\n",
            " [[ 14.2625    -34.04125  ]\n",
            " [-25.          0.       ]\n",
            " [ 38.0609375  18.125    ]] \n",
            "\n",
            "Q-Table After 50 Episodes:\n",
            " [[ 40.12630608 -14.02222767]\n",
            " [-34.69328995  16.82897844]\n",
            " [101.33876852 114.6100845 ]] \n",
            "\n",
            "Q-Table After 100 Episodes:\n",
            " [[ 64.15140776   2.74645377]\n",
            " [-27.8913777   25.61625104]\n",
            " [173.5971731  182.26270602]] \n",
            "\n",
            "Q-Table After 500 Episodes:\n",
            " [[ 98.58401309  38.52604011]\n",
            " [ -5.68981172  49.32740622]\n",
            " [249.28784864 249.25809207]] \n",
            "\n",
            "Q-Table After 1000 Episodes:\n",
            " [[ 99.97994981  39.97633058]\n",
            " [ -5.01824622  49.98144738]\n",
            " [249.99814113 249.99820827]] \n",
            "\n",
            "Q-Table After 2813 Episodes:\n",
            " [[ 99.99999999  40.        ]\n",
            " [ -5.          50.        ]\n",
            " [250.         250.        ]] \n",
            "\n",
            "Q-Table After 2814 Episodes:\n",
            " [[100.  40.]\n",
            " [ -5.  50.]\n",
            " [250. 250.]] \n",
            "\n",
            "Q-Table After 5000 Episodes:\n",
            " [[100.  40.]\n",
            " [ -5.  50.]\n",
            " [250. 250.]] \n",
            "\n",
            "Q-Table After 9999 Episodes:\n",
            " [[100.  40.]\n",
            " [ -5.  50.]\n",
            " [250. 250.]] \n",
            "\n",
            "Final Q-Table:\n",
            " [[100.  40.]\n",
            " [ -5.  50.]\n",
            " [250. 250.]]\n",
            "99.99999999999983\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here, the final `q_table` is:\n",
        "\n",
        "[\n",
        "\n",
        "    [100.  40.]\n",
        "    [ -5.  50.]\n",
        "    [250. 250.]\n",
        "\n",
        " ]\n",
        "\n",
        "So,\n",
        "\n",
        "    Q(0) is action 0\n",
        "    Q(1) is action 1\n",
        "    Q(2) is either 0 or 1 (as the rewards are equal)\n",
        "Which reflects the `reward_table`"
      ],
      "metadata": {
        "id": "aYWiCR5Bb2kG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## AU Post\n",
        "\n",
        "Now that we understand the basics of Q-Learning, I will implement a Q-Learning solution to a more practical problem.\n",
        "\n",
        "### Scenario\n",
        "\n",
        "Australia Post is opening a new warehouse to store products and they want it to be automated. They want robots to move goods from the relevant location to the docking bay where it will be delivered to their customers.\n",
        "\n",
        "We want to use q-learning to find the shortest path to the docking bay from anywhere in the warehouse."
      ],
      "metadata": {
        "id": "FDUwTZ6MdOJq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Lets create an environment, The image below represents the floor plan of the new warehouse where the black cells are shelves where packages are stored, the grey cells are the walkways and the green cell is the docking bay.\n",
        "\n",
        "![image.png](data:image/png;base64,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)\n",
        "\n",
        "we'll number the cells from the bottom left corner. So the bottom-left corner would be (0,0) and docking bay would be at (10, 1)."
      ],
      "metadata": {
        "id": "yHfGH3Y8v5WL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#importing libraries\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "dTNq6Wq9oiP7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9AdpFVfy6ya9"
      },
      "source": [
        "env_rows = 11\n",
        "env_columns = 11\n",
        "\n",
        "#creating a 3D array to hold the q-values for state and action pairs\n",
        "#we need a 3D array because the state is a 2D grid and there are 4 actions, so we'll need a q-value for each unique combination.\n",
        "q_table = np.zeros((env_rows, env_columns, 4))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#the actions are up, left, down, right\n",
        "actions = ['up', 'left', 'down', 'right']"
      ],
      "metadata": {
        "id": "ZODyR1yg0LVC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Setting up the rewards\n",
        "\n",
        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)"
      ],
      "metadata": {
        "id": "A2f8VG-K1UuL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#initialising everything to -1\n",
        "rewards = np.full((env_rows, env_columns), -1)\n",
        "#setting the docking bay\n",
        "rewards[10, 1] = 100\n",
        "#setting the rewards for the shelves\n",
        "rewards[2,1] = -99\n",
        "rewards[3,1] = -99\n",
        "rewards[4,1] = -99\n",
        "rewards[5,1] = -99\n",
        "rewards[6,1] = -99\n",
        "rewards[7,1] = -99\n",
        "rewards[0,3] = -99\n",
        "rewards[1,3] = -99\n",
        "rewards[2,3] = -99\n",
        "rewards[6,3] = -99\n",
        "rewards[9,3] = -99\n",
        "rewards[6,4] = -99\n",
        "rewards[9,4] = -99\n",
        "rewards[3,5] = -99\n",
        "rewards[6,5] = -99\n",
        "rewards[3,6] = -99\n",
        "rewards[4,6] = -99\n",
        "rewards[5,6] = -99\n",
        "rewards[6,6] = -99\n",
        "rewards[7,6] = -99\n",
        "rewards[8,6] = -99\n",
        "rewards[3,7] = -99\n",
        "rewards[3,8] = -99\n",
        "rewards[2,9] = -99\n",
        "rewards[3,9] = -99\n",
        "rewards[4,9] = -99\n",
        "rewards[5,9] = -99\n",
        "rewards[6,9] = -99\n",
        "rewards[7,9] = -99\n",
        "rewards[8,9] = -99\n",
        "\n",
        "for row in rewards:\n",
        "  print(row)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LxSm05X30hyA",
        "outputId": "219eee99-f083-4ceb-fa70-29d76c950604"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ -1  -1  -1 -99  -1  -1  -1  -1  -1  -1  -1]\n",
            "[ -1  -1  -1 -99  -1  -1  -1  -1  -1  -1  -1]\n",
            "[ -1 -99  -1 -99  -1  -1  -1  -1  -1 -99  -1]\n",
            "[ -1 -99  -1  -1  -1 -99 -99 -99 -99 -99  -1]\n",
            "[ -1 -99  -1  -1  -1  -1 -99  -1  -1 -99  -1]\n",
            "[ -1 -99  -1  -1  -1  -1 -99  -1  -1 -99  -1]\n",
            "[ -1 -99  -1 -99 -99 -99 -99  -1  -1 -99  -1]\n",
            "[ -1 -99  -1  -1  -1  -1 -99  -1  -1 -99  -1]\n",
            "[ -1  -1  -1  -1  -1  -1 -99  -1  -1 -99  -1]\n",
            "[ -1  -1  -1 -99 -99  -1  -1  -1  -1  -1  -1]\n",
            "[ -1 100  -1  -1  -1  -1  -1  -1  -1  -1  -1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Putting code into helper functions for readability**"
      ],
      "metadata": {
        "id": "lgaTcYwqNlVb"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DnCfO5tVG0LJ"
      },
      "source": [
        "#define a function that determines if the specified location is a terminal state\n",
        "def is_terminal_state(current_row_index, current_column_index):\n",
        "  #if the reward for this location is -1, then it is not a terminal state (i.e., it is a 'white square')\n",
        "  if rewards[current_row_index, current_column_index] == -1.:\n",
        "    return False\n",
        "  else:\n",
        "    return True\n",
        "\n",
        "#define a function that will choose a random, non-terminal starting location\n",
        "def get_starting_location():\n",
        "  #get a random row and column index\n",
        "  current_row_index = np.random.randint(env_rows)\n",
        "  current_column_index = np.random.randint(env_columns)\n",
        "  #continue choosing random row and column indexes until a non-terminal state is identified\n",
        "  #(i.e., until the chosen state is a 'white square').\n",
        "  while is_terminal_state(current_row_index, current_column_index):\n",
        "    current_row_index = np.random.randint(env_rows)\n",
        "    current_column_index = np.random.randint(env_columns)\n",
        "  return current_row_index, current_column_index\n",
        "\n",
        "#define an epsilon greedy algorithm that will choose which action to take next (i.e., where to move next)\n",
        "def get_next_action(current_row_index, current_column_index, epsilon):\n",
        "  #if a randomly chosen value between 0 and 1 is less than epsilon,\n",
        "  #then choose the most promising value from the Q-table for this state.\n",
        "  if np.random.random() < epsilon:\n",
        "    return np.argmax(q_table[current_row_index, current_column_index])\n",
        "  else: #choose a random action\n",
        "    return np.random.randint(4)\n",
        "\n",
        "#define a function that will get the next location based on the chosen action\n",
        "def get_next_location(current_row_index, current_column_index, action_index):\n",
        "  new_row_index = current_row_index\n",
        "  new_column_index = current_column_index\n",
        "  if actions[action_index] == 'up' and current_row_index > 0:\n",
        "    new_row_index -= 1\n",
        "  elif actions[action_index] == 'right' and current_column_index < env_columns - 1:\n",
        "    new_column_index += 1\n",
        "  elif actions[action_index] == 'down' and current_row_index < env_rows - 1:\n",
        "    new_row_index += 1\n",
        "  elif actions[action_index] == 'left' and current_column_index > 0:\n",
        "    new_column_index -= 1\n",
        "  return new_row_index, new_column_index\n",
        "\n",
        "#Define a function that will get the shortest path between any location within the warehouse that\n",
        "#the robot is allowed to travel to the docking bay.\n",
        "def get_shortest_path(start_row_index, start_column_index):\n",
        "  #return immediately if this is an invalid starting location\n",
        "  if is_terminal_state(start_row_index, start_column_index):\n",
        "    return []\n",
        "  else: #if this is a 'legal' starting location\n",
        "    current_row_index, current_column_index = start_row_index, start_column_index\n",
        "    shortest_path = []\n",
        "    shortest_path.append([current_row_index, current_column_index])\n",
        "    #continue moving along the path until we reach the goal (i.e., the docking bay)\n",
        "    while not is_terminal_state(current_row_index, current_column_index):\n",
        "      #get the best action to take\n",
        "      action_index = get_next_action(current_row_index, current_column_index, 1.)\n",
        "      #move to the next location on the path, and add the new location to the list\n",
        "      current_row_index, current_column_index = get_next_location(current_row_index, current_column_index, action_index)\n",
        "      shortest_path.append([current_row_index, current_column_index])\n",
        "    return shortest_path"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, its time to train the model"
      ],
      "metadata": {
        "id": "8fAcBqzjOabw"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3N5BB0m0JHIn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "980af890-df7a-4f40-e291-f5bd02992885"
      },
      "source": [
        "#define training parameters\n",
        "epsilon = 0.9 #the percentage of time when we should take the best action (instead of a random action)\n",
        "discount_factor = 0.9 #discount factor for future rewards\n",
        "learning_rate = 0.9 #the rate at which the AI agent should learn\n",
        "\n",
        "#run through 10000 training episodes\n",
        "for episode in range(10000):\n",
        "\n",
        "  #get the starting location for this episode\n",
        "  row_index, column_index = get_starting_location()\n",
        "\n",
        "  #continue taking actions (i.e., moving) until we reach a terminal state\n",
        "  while not is_terminal_state(row_index, column_index):\n",
        "    #choose which action to take (i.e., where to move next)\n",
        "    action_index = get_next_action(row_index, column_index, epsilon)\n",
        "\n",
        "    #perform the chosen action, and transition to the next state\n",
        "    old_row_index, old_column_index = row_index, column_index #store the old row and column indexes\n",
        "    row_index, column_index = get_next_location(row_index, column_index, action_index)\n",
        "\n",
        "    #receive the reward for moving to the new state, and calculate the temporal difference\n",
        "    reward = rewards[row_index, column_index]\n",
        "\n",
        "    old_q_value = q_table[old_row_index, old_column_index, action_index]\n",
        "    temporal_difference = reward + (discount_factor * np.max(q_table[row_index, column_index])) - old_q_value\n",
        "\n",
        "    #update the Q-value for the previous state and action pair\n",
        "    new_q_value = old_q_value + (learning_rate * temporal_difference)\n",
        "    q_table[old_row_index, old_column_index, action_index] = new_q_value\n",
        "\n",
        "print('Training complete!')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training complete!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model Evaluation"
      ],
      "metadata": {
        "id": "Tqi3QVqEBGmI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Lets run the model in the exploitation phase with the final q-table"
      ],
      "metadata": {
        "id": "t_G8Y_FFwPZX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#creating variables for model evaluation\n",
        "cumu_reward_array = [0] * 100\n",
        "episode_length_array = [0] * 100\n",
        "\n",
        "#run through 100 episodes\n",
        "for episode in range(100):\n",
        "  #creating variables for model evaluation\n",
        "  cumu_reward = 0\n",
        "  episode_length = 0\n",
        "\n",
        "  #get the starting location for this episode\n",
        "  row_index, column_index = get_starting_location()\n",
        "\n",
        "  #continue taking actions (i.e., moving) until we reach a terminal state\n",
        "  while not is_terminal_state(row_index, column_index):\n",
        "    #incrementing the episode length\n",
        "    episode_length += 1\n",
        "    #choose which action to take\n",
        "    action_index = get_next_action(row_index, column_index, 1.1)\n",
        "\n",
        "    #perform the chosen action, and transition to the next state\n",
        "    old_row_index, old_column_index = row_index, column_index #store the old row and column indexes\n",
        "    row_index, column_index = get_next_location(row_index, column_index, action_index)\n",
        "\n",
        "    #receive the reward for moving to the new state, and calculate the temporal difference\n",
        "    reward = rewards[row_index, column_index]\n",
        "\n",
        "    #adding current reward to the cumulative reward\n",
        "    cumu_reward += reward\n",
        "\n",
        "  cumu_reward_array[episode] = cumu_reward\n",
        "  episode_length_array[episode] = episode_length\n",
        "\n",
        "print('Done!')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kgZopQdWwbDl",
        "outputId": "e3f4b4d6-6d02-47fa-bd03-1034ccd93551"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_coordinate = [ i for i in range(len(cumu_reward_array)) ]\n",
        "plt.plot(x_coordinate,cumu_reward_array)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "outputId": "3ea24d94-3dd9-4bc2-af83-6457ee279146",
        "id": "CSEFUVhRxKAV"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The model seems to be performing quite well. It has culumative rewards of over 82.5 in the final exploitation phase."
      ],
      "metadata": {
        "id": "Kw9ziC6hyCQq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_coordinate = [ i for i in range(len(episode_length_array)) ]\n",
        "plt.plot(x_coordinate,episode_length_array)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "xEbnylM1x5yk",
        "outputId": "087f6797-f9e2-4751-b847-0bf1c0d82eee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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loXIr3Xa5LwoWkWFJRxKyDEs7w9IN061fsCS7/4OmW/PjPNAl1Mg+A6lCf3pYREkoDsPSbJffQAqWdD0sCkmImW4TLnoDHpY+KkajwBYsFgD8i9jGsaZvYLEPuoTEQiLpCzC7ANguoXYPS1qmW9f1eDT/1knmYUl2/9Ntj+IjqA9IcNwgdAnF8rBwhqXMx46kyURwSagQlITS8rAEhh8O6DXLFiwWAPzFef1ocz5HP0Tzi7JE0jIFuwBYhqX94pqW6XZupQb20ul1CcVra3a9QSlYmOm2fz5DmyQUp0uISEIseTbN75FLQjm5JJRmW7OVhCwGGuzk2TDWLFjizuroJsS7iKQLC3Yxc200f5vXI60LIqPtJ4YKGC7lW++VtCTUuYel0s8FCzfd9s9xnSzDUkot44cirEsocQ+LlYQs1gpYweIzLH0gCYkMS1o5LH1EnacF8XhIi3ViaaQbxsr8LjjdLqGYOSx9fAfLTbd99BnYtrI4kzhdQjLTbVe6hNokoRbDkniXEJ1c3z/fbRTYgsUCgK9rbxjtI4ZFuNgkTXH7HhZbsLR3CaXFsDQXlXUjRT5/JXGGJZDDshY9LM3P7Hn9c2yzY2BiqBlrH5VhqdZdHoW/YbREPCzpdwkVREkoraTbLs366iVswWIBwPdrrB9tGh37wcMiXmwTZ1hq1sPCIAaspXWhZ2mk60fLfutpiqbbtZh024/tr2w7p0aaBUtUBpgZufM5B5PDRZ9h6UJwXEmQhHgOS8KSUNVKQhZrBZxhaXlY+mGWkNgVlFY0f7/chaYJMWAtrX3C0kg3EGNkJmcJ9XPBEggY649jmyXSTrUGB0ZlgJnUuG6kiFzOSe3YoqiFSULWdBsZtmCxAEBMt6OsYMk+wyLe5Sc//NDmsDC0mW5T0si5z2CsxCWhpD1E9ZhtzcEclv5dEOidfb90CjGD8ORIy2MX8fo0u+z7VwC/iEi3YJFLQuUuDD+0BYvFwKLecPmivKGVw9IPBYu4kCV911+1OSwcoiSUlhGZeVjEcC8vwU6tJLqE+plhoWbPfim82AK8riUJLVUbcCOc7zPcG9UsWLrR1qzuEmKm26RvsKiHZTBvsmzBYhE4cRjDslyLdkHoBdramhM8SV3Xs7OECNjCns8x1iNd0y3t5ACS/Q7i5rAMioeF3tn3SwecKAkB0YrNI4utlNuW5N3NpNuSIjiuWncTvcZahsViTYBewNa1ChbPi3ZB6AXEGT9JJt3SC5n1sPgL+/hQAUB6GR68S4hkZQDJLqx02yMl3dIclj5eEIKSUH8c22zfjw8V/dbmCCwwLYQBoMi6hFJlWBRdQq2CBUguz8fzvEARXXe9RFnJrMAWLBb8ol0q5DBaypOsg2zLQmKBkuRdeKUPL+ppgh0jrK00rYGQMkkISPZOOHbS7QAyLP2S11HlbEUOo6Vm0Rwli2WGxPIDIKbb9M5t9Swh/+dKQlkssvNjEGUhW7BY8DuuoUIOjuP4F4SMdwqJJ2SShQW9kNikW59t8xmW5Bc6z/MCd8KFnM+wJElxB6Y1Gy56nucFCuJ+pdw9z+tL6YAxIYW8g9Fyk6GIw7AwybvUDUmoLpeECvkcP7aTam2WMTX98t1GgS1YLPgdF6MqR0rRLwi9AGNYuK8iwUW0ItCrax2iJJQG67RYqfMFZMNoGY7jcFkoyfeLY7oVD4F+ZVjEha1f2EP2nZXyhGGJUbCsE7qEUjXdtrZZZFiA5AcgymL+++W7jQJbsFhwNoGdRGPl6BeEXoAxLIxiTVQSIhcy62HxmYjxliSU9GRswF9Uhot5PkcojfC4OEm34uft14JFXCD75S68SuSV0db1KYqcJzIsaYUSUrDXFj0sQPLx/OwaXi6kI6NmBbZgseDVOTuJRlqUa9bnCbFCgi1uaUlC/ZJVkSbaJKEU7t5EYySQTjdHLQbDIhat/boYiBJEv7CHVBJiDHCU8Djx2OpKcFxdHhwH0CyWZCWhciHXlc/WK9iCxYJX+YxhYZRr1ucJsUKCnfxJMiGWYQmCMRHcdJvCPpEXLClIQrEYFqFgsQxLV1En8soYZ1jMrk+u6/HgOO5h6cYsISJjiUg67ZbddJaL+dQCF7MAW7BYENNtq2CJeEHoFbgkVEz+jiLQJWQLFs5ETHCGJfmFbkbDsCQqCQlTl00YNLErqm8LlrpYsPTHsU3n8oyU2Q2V2WI/t1LjHqR1giSUVFuxDFWtJJSsh6Xa8BmWbmTM9Aq2YLHgJ025tfCPRrwg9ArMV8BO/iTv+m0OSxBtHpYUJaENKUtC4rabyEJi0ZrmQpcm2iShPlnUqkQSGmOStSEDfGSpGRo3MVTgx1NXPCwaSWgo4QGIbFArLVj6hT2LAluwWPCThkkro6VoF4Regd0dDrdO/mRzWIID4gYxhMkUNTK6gXcJpWi6pQxLGvS2uO0mBcugeFgqbZJQfxzXVBIaYZK1IQN8ZKkGQC41prmom0hCSeWw+B6WfCoyalZgCxYL0tYsMCwZl4TYIsLuVpK8WxTvoNcyyULN14xhSWOhYxN114/5C0saBkJx21er4a89MF1CYltznwTHUUmIS9aGDDBjWGjBUi6kz0L4kpDOdJtswVKykpDFoKPddMsYloxLQo1gd1Nabc3N1x68k98Uq2SOEOvQSEMmE42RQFqSUPC1lmvhhbn49fdtwSIskP1yF04lodGIOVFiyi1Ajqs0c1i4JKQz3SbVJUQlofTZo17BFiwWvulWYFiyHs3P2lPLaUhCAlW7ln0sjGEZIR0IaZpu2URdIC1JSPCwGHQKtTEsfboYiAVLv3wOKglFvT4dWdQVwt2P5gf8BofETLdMEirm+fv1SzEaBbZgseC6NjuJRvokOI4zLKStOSmviXjntZYLFragD5XyvOMhjc4pRt1vGOtelxBgVrCw75/N2Wq4Xl8eE2Iiar8sanTx96P5zRZ7zrDQ46oLww/9IksiCSVtuiU5LNZ0azHQEKP5x/okOI6bbkv+YZzUQipKQv24OCUFZkodKdGMhxRMt4vt1H0aHhZx26N0CQ2TSbv9KAuJbc39InVyeSVPZp0ZMixMalw/kq43SkS1rmFYkk66rbVLQv3CnkWBLVgs2iShkX4JjnODDAuQXGHRdie6lguWVuE6XMxzA2HSptvVWoNP35V1CSVZHIjbHqVLiHl4kt6mbqE9OK4/juuaTBIybmtu7z5jAwlTHX6ok4Raha9sBlAcUNNtwUpCPu666y686U1vwvbt2+E4Dm699dbA39/+9rfDcZzAv0suuST0dW+44QYcf/zxGBoawq5du/DAAw9E3TSLmBBNt2MRXfi9ghjNDyR3x2Q9LD5WCAPHLr5J7w92F1zMOzycrvlz8hKUyCqYMIns/cukOK40sn1+yNCvOSxBSSjaNHlZ91k3TLc6SYh7WFJoa7bR/ARLS0s488wzccMNNygfc8kll+DgwYP83z/+4z9qX/OLX/wirr32WvzRH/0RHnnkEZx55pm4+OKL8fzzz0fdPIsY8IPjgtOas86w+Em3KTAsbV1Ca7dgYYnHAUkoYSmBLSrrRkpwHP8Cn44kJLQ1GzEsviTBYt0Hg2Hpj88QlIRaHhbjHBa16bbnklDSptsBl4QK4Q8J4tJLL8Wll16qfUy5XMbWrVuNX/MTn/gE3v3ud+Md73gHAODTn/40/vVf/xU33XQTfvd3fzfqJlpEBFuc2dTjqLM6egV/lpB/QUiK4m5jWAaQXjUFu6g2JaF0ZrDIaHsgHUmIFVvlQg6VumvGsLQ+bz7noJzPoVp3+7RgCW5z30hCDV8SYgWACQPseZ5CEkp/llB3JSE/rdxKQhFxxx13YPPmzXjpS1+K9773vZiZmVE+tlqt4uGHH8ZFF13kb1Quh4suugj33nuv9DmVSgXz8/OBfxbxIZpuRyJSrr0C07ULOYd7K5JiWMTFqF/MiWmAe1hKNEUz2f2hKlhSkYRaF/KJ4WYIXpQuoUIu15XFzhQf/vqP8J6/fwiu4f7pxHT77R8fwpv+5m48+fxCpG3sFJ7noeYSSajlsas2wovGxUqdMw0bJGbunklCSQ8/tJJQPFxyySX4+7//e9x+++34sz/7M9x555249NJL0VDovYcPH0aj0cCWLVsCv9+yZQump6elz7n++usxOTnJ/+3cuTPpj7Gm4JtuWx6WCBeEXoItmoV8DvlcsvkgtkvIxzJhWPh+Tnh/HF1pxqdPjRQDv+fUfZKmW2HMgMmiwRafXC5bktDn73sG//HjQzg4v2r0+E6C4772/efwwwNH8Z2fdleqb8YVNP+/OfzQl4DDWGAmaxfzTsDr1tVZQhqGJbkuoXZJaBALlsiSUBh+/dd/nf//6aefjjPOOAMvetGLcMcdd+DCCy9M5D2uu+46XHvttfzn+fl5W7R0ADGaX7wglAol6fN6jQa5gynmm/S+7RJKHqsBhiUd0y1bWNjdM0MaF19W6LIxAyaSUIOwebxg6bHptuF6/LgUZwSpwI7r0VIeS9VGJJao2lpcVwxGGSQJuo2F1rleKjRlucVKHVMj6utTrS6f59MNn0eVyFgi/Gj+5JNuC/nsMIBJI/W25hNPPBEbN27Ek08+Kf37xo0bkc/ncejQocDvDx06pPTBlMtlTExMBP5ZxAf3sLSqfnZBAMBbTbMIdkIWcj7DklwOi+0SYlgmBQuV3pIcCMnulFkHCAO/E05DEmoxLFHamvM5hy9+vZ7YTIs4021hNydjrc8epRBkjJLJKIMkUSOyFTsefJ+d/rtjRaUYj1/qwiyhOjFqi0jcdNugDIuVhGJj//79mJmZwbZt26R/L5VKOOecc3D77bfz37mui9tvvx3nnXde2ptnAdIlRE7qqPM6egF2QWjedSXbvWIlIR8r1HRL7haTvINjfqlRwu4B6UhC7BhhkpBZNL+EYelxwVKNUbCwx7EFP8r5wr7v1S7fxNDvnp3npp2M1bqc5eiGh8VIEkrKw8IloTxKAywJRS5YFhcXsXv3buzevRsAsGfPHuzevRv79u3D4uIifvu3fxv33Xcf9u7di9tvvx1vfvOb8eIXvxgXX3wxf40LL7wQn/rUp/jP1157LT7zmc/gc5/7HH7yk5/gve99L5aWlnjXkEW6EE23ACKHM/UCUoYloUW03XRrC5YRYroFkjUis+OsnWFJQRJyGcPSMt1GZVgyUrDQhdxUEvIZluhTt1mB1O0EbP88d3jLu2lWFJ3yTMGKCNdL72akppWEkh5+yGYJDfa05sgeloceegivf/3r+c/MS3LVVVfhxhtvxA9+8AN87nOfw9zcHLZv3443vOEN+OhHP4py2XdoP/XUUzh8+DD/+dd+7dfwwgsv4EMf+hCmp6dx1lln4bbbbmsz4lqkA266JaFYPP46w51CvunWSXzGjWVYfNCkW1YYAskyLGwRbPewpCcJMYbFpH2fsnkesrEg0P1vLAm1pE4mh0Xp9mKLv0mBlyRk7cGmDIv/3KAsUwxEIbjI54LMXqegnU0FqSTUamtOLDiu3cMyiG3NkQuWCy64QKtd//u//3voa+zdu7ftd9dccw2uueaaqJtj0SE8zyNJt0QSKkcLZ+oFqOmWXRQaiUlC/TlzJQ1wSahUQDHnHyNJFnGLSoYlhS4hwXS7YnCX6zMsOeSc5v/3nGEhxYbptrCbE8ZQRCkE2XskJWOYQlZ0jBpmRbFtLokeFlL8VBtugF1OAmJnk4ihlIYflgq5gZaEEu8SsugvVBsuP7HKAyAJJRccZxkWhmXCsORyDnJOk0pPMouFm25LooclPUmItzVH9LA4SD7MLg7oMRrZdMsKlgifodZjSYgyLD4DHFKwKMLbaPGTxvdIr0PdTbrNW9OtxeCCVvgBhoVPRM2wJMQo15zD7/yTbmtmKfFr2cPCk25bU7ELKcg0i9x02w1JSGhrNuh6kXpYei4J0YLF1MPSYliYJBRhv7JFOAuSEL+hCrk+qXwkjuOkUgzz9yWMrFQSasnvdddLpPD3g+NsW7PFAIOZ9RwnSF2yLJYsMyxMo00jOI4tRiMt1mktR/P7DEtzkShyg3MKDEtXuoSEtmaDXJEsdgnFaWtm5/t4OX5bs0lXVZLgBUuBSkJm1yeV6Ragx1by53agsymnloSAZNrjK7zTM59qIdZr2IJljYMabunQOd+Fn92CpSZpa06OYWl1xpSj34kOGmg0P4DE5Tegy11C3HTLovkNGJbW++eymsNi2iXUYmLYZ49izOyd6bbFkuQkDEtIU4DKw0J/lwZTRjubcrl2hoVGSCQhC9EuoW5kzPQKtmBZ45AZbgFgpCUJLWa4S6hBLmRpeViYp2Ite1hWSVsz4N+ZJrlPeA6LoksoyYKxJuaw1BqhIXhZZFiielgarsfPj1jBcY3eMCx1mSRkmBNVlRh2GYopZrHoBh8CwREPq0kwLHWfSUprQGkWYAuWNY6KMEeIYaxFuWZ5YjMffph3uG6bxCJab7h8gWKF21ruEqKmW8DX5JO6g2u4Hr9rV3lYklpUaPcGy2FxvfC7bNfLoofFP9ZN9g+9kx+N0SVU65EkVJVKQszDYtrWLGFYUjSnsteU+VcYhngWS+f7s0oYFisJWQwsfIYlWLD4DEt2Cxaew0KmNSdRWNCFiLEKboIx9P0Ez/NIW3OrYEk484YWxSMpdwnR44MxLED4IpxFhqUWkWGhCyO7IYmWw9Ir063fDchg2iXE02YlklCqptuGfIYRRVJpt67rkWj+vJWELAYXslh+wHxWRy9BTbdJhiXRhYgt0oMYwmQC2kU2zGdNJWu6ZXJQIee0HYdJS0L0exwq+gbFsEWYyY/5XA7lLsS6myBqlxCTHop5x+9SMTyuPc9fFOuu19XPXpcYZ027hPhCLmNYUvWw6CUhILksFrr95YKVhCwGGKsKSYh1CWWZYam57QxLEpIQu1ulC+ha9bDQhZwdI0n7hRitP1IKGr+B5CUhukAX8g4vwsIK8ywyLFFnCfERHIU8aU03+wzid91NlkUmCZl2Meri8bvhYdFKQi3foKlhWgU6Wb45/NBKQhYDCn+OUPBQME2S7CX8pNscL1hML8A6+IPEkp8C3W9YIQwc2xdJm27ZojNWbs+xTPriG8jHyDmcQQuThLKYw0IX2orBXTo/rov5yD4k8XHdTLuVSULG05rr7cUOQzHFvJJIklCH8fyMXcs5Tba5aCUhi0GFbPAhkP1ZQp7ndzwUAtH8STAsrUW6mOcXyTXLsLQK1mHiLeGLnaI4nF2q4uhKzfg92DE2IilYCilJQvnWIL1hQx9BgGHJyHC5gOnWYFtoRyBrETaVhMTFL4pUvLBaw8xixfjxImRdQtFnCfXGdKuThJIagOiHxrUk24jfbT/BFixrHKt1n02gMA1m6hVoAdGUhJK7W6ItgmueYakGA/QAYrqV7Otq3cXPf/JO/MJffQ+u4T5TZbAAZFFJShIiMiLQnI8EhC/AbEZVPu+g1FoYei0JRfawkJsTn2Ex+47EgihKp9Cbb/hP/Je/vDM2K8PD3whLYpoTZZLD0rMuoYRMtzSDBfBZyV4X1GnAzhJa46iEMizZLFhoAVEgklASww99hiXZ1+1HMElwiDAsOtPt0ZUaDi9Wm8+tNaQyj4glxRwhwKfyqwndLdYFT8Nw6yIf5snIpIclpiQ0VMwRM7PZZxA/64rBOAOg2cHy9AtLAICZpSqOmRo2el7gvSWS0AgZHeK6njScDQhLum0+J40AQJ13hoExIp0zLMHGCSsJWQwsqBGPgntYag3jO+Vugp6MhZwT+Y5RBzqXY80zLKylmRS03HQr2Sf0rs40JXlJMUcIAGHOEmZYWscLW/iMPSxOhgqWDky3ftFpKgkJpluDcQbiNsbtKpNJQrQQ1hWbVQPTbZoMi97DkkwOi5WELNYMVsldFwWThDyv+7kLJqCSUDGfQz5Brwm9AHBvzACe/CYQU24Barptv9DTRdy0w0w1qRnwL/hJtVCLBk7GLJoyLPlcLkMelqhtzVQSivYZ2j0sZt8tZX7iFgYySWiomAMjVXQssM5LwpmInnUJJWS6rQVlL8ZKWobFYuCgMt0OF/N8UnFYmmQvwBYex2ne8fPguCQmn9ouIQ7m7RgKeFjUbBYtWEyNmYsaD4t/8U1WEmKfYcS0S4gYvNnC2WuGhS5IZkm3RBKKeL60S0Jm3y0tpKoxhwzKJCHHcYwmyus8LOUudAnpc1hYW3PCklBGcoLSgC1Y1jhWSUcMReCCkMFOIdE8ye5kkigs2AWgREKY1myXkIRh0YX0xWNYwiWhasMNnfdjAlESGjZkWBo0mj+fDdNtwMMSQRIqE4bF9WAk+YpMjKmMQbcrbgq1iiUxyWLxpRnNLKFeBccVkmFYqkLjRDHhJOoswRYsaxwqSQjIdqdQXbjrKiTIhMg8LI01Gs2/Um33sHD/g0wSavgXX1PZgDMsJXWXEJDQnCg3eOcbNYelkKEcFsoMmBUsrXOdeFgAs+wiUTYxZc/odsWVKLiHRchS8Sc2x5WE0mPKfN9NuCTUOcMieFisJGQxqFCZboFsdwqJGnEa0fzlQt4vWAbwbsUEvGAhxURe00JeCTAsZovaMpeE1F1CqveLCn7c8LbmaEm3+Sx1CVEPiwHjQUMi6QJucs7ETbpNQhLi8kouuFz5kpD6+tQr063ufRkSN90Wg5JQreElwkpmCbZgWeNgB7voYQFo2m0WJaHgBSHJ4Yf0AsA9LGvUdLss6RIqalq9Ax4Ww0J30aBLCEiG0aDzp4AIkhCRIBnrk0Y7bBREHn5ITbc5WggaMCwxc1iSkISqCpbEZ4B1HhZf3hXR6+C45HJYWp8xH5SEgMGThWzBssahiuYHzNMkewHRPJnkBGFqYlvrOSw+w+IfH7oWcvq7qF1C4qRmIEipJ2GoFr1PvulWv61+Qm6OMCy9LeSjtjXLzOSAGXMlvr5xwZJAl5BSEjJggHXm1zSZMhNJqJzQ8EM6cgEQWcnBum7ZgmWNo6IYfgjQeR0ZLFgUptsk2o9pm+Ba7xLy25p99sPUdGvKzOlmCTmOQ+YJJeBhId0+gHlbM/WwlDPjYYnaJeQzLHS/mjAfbQyLIStQDWxjwpKQwcRmWUs0g2+6Tf7cNpKEWDR/p6bbhl+Iiu9Zi7nPswpbsKxx0PkiIthsF1MvQjdRE6j9RIcf0hyWNe5hkbU1FzXyGzXdmnqf2IIzIjHdAsmGx3EpMSdKQvrXzqSHhSxG1YYb2u3jy7/Bhc3MwxKXYfEf17kkJJpuw5sC+PDDHgXHdUUSqgULloDcN2DMsC1Y1jj0ptuWITGTklDQdJukObYa6BIa3BZBE8jamnWmW7qIm+b36BgWINmJzaJZ21QS4gxL3vewuF5ygXZxIO6PMMZHzFzy83TSY1iS7RKKY7plbc3qaP40CpYoXULJRfM3Xy/IStqCxWKAsCronxSMcl3MpCQUvFOOcrcYBnoBWOsMi66tOcx0a5rfwwqWEUmXEECH1CUpCbWSbktmkhBjB3Ikmh/orSwkMjxh7bHizQntJon6XrHamhOWhEaitDXLguNSZMrMZgkl2yVEj0v+3VpJyGKQwMOkJCe0z7BkTxKqu0EvQj7tLqG1WrCwLqFAcJzaU1IJFCzhha7neVwSUjEsyUpCrQWMmW6LZm3NshwWoLeykLg/wuL5/ZuTlnQQ4S68KpjcY7U1dxjNL5pux8rh1ye2YMsZljTbms0loU67zcSkW/q+vfZZJQ1bsKxxqKL5AbNgpl6BS0KtC2jUYW468DuWfM438w6YFmwKGcPid2TJPCzRJKFK3eXFgKxLCEg2CIt6UQC/EFs1HX7YGgPBxlZkq2AJYVjqoiRkLney95ocLjZfy9R0S9uaOy1YxKTbEvPYxQyOS9F0K0rWMiSVw1KtBwtRQB/u2M+wBcsax6pgxKPglGsGJSHRdJuk16TCI8xzNjhOwrDoisOokhAthlWm2yjSRRjqAlVvnsPiM3qOk40sFnGhDS1YhI5AxhSZFBIs82WiVbDES7rtUBISio4xg5wo3SyhdIcfqpkdhuRyWPwmAQYrCVkMHFzX4ye0vK05PJipVxDbmpO8o/DbBPPIO2u8YJEwLMamWwNmbpm8Ps0GoSgmKAmJptvoSbfNbfF9Nb30sAS3OUwSqnAPi9BZZ+JhaQQLljg5LB1LQgJbYZITpeowApDq1G3d+zIwL1HHkpDQJdR8XysJWQwY6IkiK1hGDFz4vYJ4pxyWSPulB5/FPU8eNnptO63ZB8vgkTEsoUm3BouablIzf78UJKGC0NZcqevbgqmHBUAmsljEQiOcYREkoQgejqogCcXxsMSVa8MZFpPhh7LguC50CUmYHQYqCXUSoV+RpPnyYbC2YLEYFFAqckhyYo1l2cMimG51ww/3zy7jg//8A/zfX/6+0WsHkm7za5thYTJCYFozz7yRMCzkAmmSdMsWG9kcIYZkJaHgnS+VoXSLsOh94XfnWfKwhHUJCWxqpOC4lrQwGZVhSaCtWeVDYUW0igFuuB7YIar1sKTZJZRTL7GsM9P1Oju26bBWhlKC50yWYAuWNQxmwivkHH63RTESckHoJUTTrc5AOLdcAwDMLleNXpu2CXJvzICd+CaoN1xegARMt7yFPIxhCS9Y+BwhhX8FSFYSEgsPepHXFSwiw5KF8DhxkKN5Dkt0SYi918RQ83taqTVCg+qA4P7pvGAJyithXTb0/aQelm4Ex0kSdhmob7CTtNuqzsNiGRaLQYEulh8gDEsGJaGaQO3nNRQou6Ct1lwj6tUm3TZBF/AhSQ5LmOm21vBCfRW6Sc38/ZKUhAR5IZdz+MKhYw0YC5HPUMHCjtOxVhGhm9jseV6bJBQlu6gqmG7p++u30d+muHf74nfGwIpN1TFGt087SygND4smYZe/fz7Hu806Md6K05qBaC3r/QRbsKxh6GL5Ab9LKJM5LIJ5kt2FywoLuqhEucgGc1gG68Q3AStYck6QieCmW8m+rggXyLBjx8jDkiC9XRPM2oAvC2kZlkawQGbbJH7eboItRuzGQnds1xq+PCLOnImSdDtJChYTH0sSkpAq06QcgWHRmW7T6KQRvVIyOI7jG287SLvV5bBYSchiYMCDpCSx/AAw1rqQVxtuz+emiFCabmWLKLkDM7kw0Gj+Nc2wkA4ex/Ev+KamWyDcx8KMuTpJKNHgOKEdHiCtzVqGpfk8tv5kgWFhi9H4ULOI0BUsVHLwTbdRguNabGzBn1ZtIvklMa1ZJQlRH5GMOaXPo8cvQzckIdnQRYokslj0HpZsXbc7hS1Y1jBETVsEjUrP2sTmuuApKBpIQoCZVkwlobXcJeRnsASLiYJhWzMQ3im0aCAJJdnNIZvxYtLa3BDumHttum24Ht+mcc6wqLefLoj+kLzowXHFQo5720wW2VQlIXLdksk6upTb5uuZeX/iwEQSApKZJ1SR3HhaSaiFu+66C29605uwfft2OI6DW2+9lf+tVqvhd37nd3D66adjdHQU27dvx5VXXonnnntO+5of/vCH4ThO4N/JJ58c+cNYRIMu5RZonmzsbsqk46ObUA0/lDMspGAxucjWKMOilpoGHZxhKQUvE7qWyegMS2uOkM50m2SXkGC6BXyGRXdsNDyhS6jHDAtdiHwPi3pb6DHN2AZdkS+CLsDDhuMMgJQlIcIoyNglNjlc1VqcZpaOiSQEkIKlA9OtlYQ0WFpawplnnokbbrih7W/Ly8t45JFH8Id/+Id45JFH8JWvfAWPP/44fvEXfzH0dU899VQcPHiQ/7v77rujbppFRIjJlzKMGgZrdRui6VZnIKRmxLA7Gc/zAheANc2wtL7zkWKwmPDbYdU0PEMYM8c60FRzhIB0JKGiRBLSMizCLJ1e57DQ92X7TrctlXr7zUmURY0mtw6XwiU0vp0JdgmJMfeUOZEVa9W6fgBhmiyZqSSUxABEWZfQoEpC6quEApdeeikuvfRS6d8mJyfxrW99K/C7T33qUzj33HOxb98+HHvsseoNKRSwdevWqJtj0QEqIaZboGmGnF2urRmGpe5Sc2Kev75JC+eggUlCQ8KMH23SrXCBDMvwCZvUDBBJKIGFRWa6HTaY2NyWw9JrhoW8LzMs6xgW/+YkXidJlS/AOeNxBkDn0fwN1wOzp4jSjuM4KBdyqNRdqRymC40D/ELG9Zrvo0pajoOaoSRUTkISsl1CyeHo0aNwHAdTU1Paxz3xxBPYvn07TjzxRFxxxRXYt2+f8rGVSgXz8/OBfxbR4U9q1jEs2ewUYgsIuyDoQrCqEQoW+thyMYecs3YZFj82P3iZKHIjsnpfs4IgLMOHtczrGBbOBCTwHWhNtyY5LPlsBMf56a9+W7aJhyXAsMTxsBBJyIRhCXpYou+rYKdP+3JV1hSONVJkyUCloqQXdpEBVmGoQ4al3nD592cloQ6xurqK3/md38Hll1+OiYkJ5eN27dqFm2++GbfddhtuvPFG7NmzB6997WuxsLAgffz111+PyclJ/m/nzp1pfYSBhuyuSwQzQ2aPYQlS9OyuvyGThAKmW/2FiT62lF/jXUKti6joLylo5De2cEyNlACEZ/iwgkbnYUlUEnLbTbcjXOJQb6syh6VXkhC5gy8bzKTh53pMY6b/fo4RI8XQqYeF7l/Z5OOS5rPTbZaBMi9Jf4/mXUKdDUCk212SFiyWYTFCrVbDr/7qr8LzPNx4443ax1566aV461vfijPOOAMXX3wxvvnNb2Jubg5f+tKXpI+/7rrrcPToUf7v2WefTeMjDDz4XZeOYTGY19ELiMMP/bh4WZcQ9bCEDIhrPbaYd5DLOWs7h0Uy+BAgC510Xzd/t26k2WobyrBUGMNiEByXAJvBiixK/w/xgkX++i6RCQvC8MNeTWumRtSwADVA3hEYJTiOyiuRTLdE6oiTFk2/c1nMfVnzPajMuvz1SCGTNFNmKgnxtuaY70/3Ly3Aohiq+wmRPSwmYMXKM888g+985ztadkWGqakpnHTSSXjyySelfy+XyyiXy0ls6poGz2HRmm5Z2m22JKGaQO3rZv5E8bCILYJreZaQ39YcPD6KmnEFbJFYxxiWMA9LNZxhYRfiJGQ5LiWSxW+ELcA1+bY2SMaHP0uo+ZxedwmVSC6KbltWeRhie2KxrPBsfz+Pv1+UtmbKAMRhMWh8QU7iMWG+DVnKr2poIoPjOCjmHdQaXnqSkGFbsy6lWAe2T8XxKv605sG6biXOsLBi5YknnsC3v/1tbNiwIfJrLC4u4qmnnsK2bduS3jwLgrCkW8A3Q2ZtAKLKdFtreG0hUvQuJOyOWAxh0g1VHHSoGBa6r0WwRXPdaIthCZWEzJNuk6DtZR0nrCBbVRTltFjNyiwhynjoWAYGWUcgn9ZskPQaaGuO0CVEF+JYklC9/fui0MlhYR4WgEgnCabdep5HPD8hkpCBnKcDbVensJJQC4uLi9i9ezd2794NANizZw92796Nffv2oVar4Vd+5Vfw0EMP4R/+4R/QaDQwPT2N6elpVKv+4LkLL7wQn/rUp/jPH/jAB3DnnXdi7969uOeee/CWt7wF+Xwel19+eeef0EKJsBwWgIxwz1rBItwp0ztmsbZgeQxA+J1MVShYdN6YQYeSYTFIujVlWEymNXMJKkFJiN6NDoWYbmmx2u5h6Q3zSBdEHlGv7RJi8i+5C48gd1J5ZYgzUtE8LLEkoRBZR2e6Zb9TdQkB6XiRdJ1NIjpNuq1ImLPm+1pJCADw0EMP4fWvfz3/+dprrwUAXHXVVfjwhz+Mr3/96wCAs846K/C87373u7jgggsAAE899RQOHz7M/7Z//35cfvnlmJmZwaZNm/Ca17wG9913HzZt2hR18ywiQGbEE8Go+sWMdglxhoXcydQaLvI5/zPRC3lY+6B4AbAMi8zDYiAJjTLTrWHSbdckodYCGJglpPdk0GJVnPKcxhwaE1QCptt4XUKFCJ0k1EQ6Ysiw0A4W+hpRwJ6vWvh1XqKqAcvB2bsEmTL6mU0lofgFi7woKwyoJBS5YLngggu0E29NpuHu3bs38PMtt9wSdTMsEoBJDgszQ2bOdEu0WyDIsIh+k0geFuECkF/DXUK8rVlgWFQGZxoX75tu1cdNw/V4AdktSShO0i1lIPKO0NbcoztY6ikxkYTY32Q5LCZ34TV+XuSN25rFfRNnX4VLQupiLYydAdIJWKOfM0wS6jSHhQ5qpbCSkMXAoWKQdOszLNkqWETTLV2AxDvxQJdQSAS2eAEorOEuoVXe1ixKQnKGhd6lMklIl99D/S3dloQCSbdhDEvrJizngBs/e+5hkTAsWtOtJHMpCnPFDawFh8+WCmtrFrcnHUlI42FhRZbWw5J8wFpYZxNF55KQysNiJSGLlPG13Qfwme89jRv+28tx3IbR1N8vbPghQDwsGesSUrU1A+0naZBhCZGEBBMbK4Rcr8keyqa+huHoSg3vvPlB/NcztuEdrz4h8vN7hRWFx4mmCtN9IitYdIUuK2YKOUer9RcTlISkptsQD4s4+BBQSxGzS1W883MP4pdevgO/8arjOt5eFYKmW5MclvZznZ0zYcyH53kBD4tpW7O4PWlIQrouoSrZRyqk4WGhLJ6ss4mCyfFiW/P9T8/gd/75B237+Jzj1uGG//Zy/rp0UCuFDY6zSB1fffQAHjswj39+5EBX3m9VMl9ExEjGg+PYiZnLOWDXBlG+iZR02xDamjVSkykeeWYWDz8zi1se6K+8IJWHhdLcdJ9UiAF1qiUJ6aTERdIhpCsEk6TtZUPpwrpeZNktKknoP586jEf3zeGfHt7f8bbqwAuIgmPkYfEloXYPS9hdOF30ml1CZqyAaAKOUxTUDCUh6bTmkLZm+rckmbKwwDoKlYflq48ewN6ZZTy/UAn8+7fHpvHs7DJ/XFiXUK8ky7RgGZYMgVXT9z8905X3W5WMJReR1eA42Z1yIZ9Dte62Rbh3wrBQRrfuetDsKiXY92qSDJolqApaaiSk+6RKKHh23OjM2rxDqKTfqew7TmJRqUuMmCMhya0+w+I/p8hlmOBzjiw1uyGTkK90oB0wnGUwYljac1jCpBpaKJYLOQwXzSQhsYDqJOk2rEtIPvzQL+pUSIOJMPHOMKgkoQNzKwCAD7zhJLz+5M0AgPd+/hHsO7KMA7MrnIFn+1iUvQZVErIMS4bA7vAefXZOe7eUFEwkIR4cl7EuIdkiwmP0G517WEo8h6VzhoUtzFmT1cKgGt1A9zldhHhLeD7nz6AyZFh0SFISkpluh0JMpPw5eQnDIhQJM4utgiXlhYKyB1wSMhh+SO/E+ciDkP0qzvMJ8/wwsAKKkWdxPCwyzxGFUQ5LXjNYMwVzqjjnTAfVd3dgtlmwnHPcepy6fRKnbp/EcRtGAAD7W8UMoPOwWEnIImWwO5Zq3cX3nz2a+vuZ5LCMZjQ4TjZcTNW9EgiOM+wSEj0sQPwFk+3nTkbI9wIVxfFBL8R0EaITfUfL/qKmmnTNPCwjIQVLopKQZAFkxnJVwSIrjlVSBGdYUi9YGHuQI36aiMMPC2aSECvKck7zfDBNumXn0lhr/9ZdL/LU87AANp0cZsKwsH2XaJdQJEmoxbCQ7XddjxclO9YN89+z/98/6xcs1RAPy6BJQrZgyRDoBfOBPenLQrL0SxE+w5KtgkVMum3+fyvkrRNJSLgA0EUqPsPiS0Imbf9ZgWrWFPUR0uJQJgkB6oAxf1JzFyUht/24CTPdioMPAXWX0JFlVrCk+z0HJCGjaP72c51l0YQt1qIs45tu9dcEVkTQY8FkDIDuvUXoWrqpMVkFVlQkORMqmiTUXvwdXqqgWneRc4Ctk0P89zvWNRmWA7MShqWtrTn57qcswBYsGQK9YN6/50jq78dbeDVtf9zDUlPfKfcCsjvlguICHMV0y1gFdgHI5Ryf0o7Z2sy+14br9dUdz6riYshmsADBIo5GoZcLOb7Aq4pdk0nNQNJdQhLTbWvRqLvymTK6LqG2gqVLkhDtgIndJWQoG9DMl+ZrmOWwcIZlyP9+o8pCoZJQUT3TqRrBdJuGJBSWcgtQD4v//qwg2ToxFNj2Y6aaDMuBOWK6VVzDowy27CfYgiVDoBeAh5+ZTf2iZ8SwtO5+PS9bptGa0NZM/7+dYYngYeFdQu2FUFyGhe63VcVE4CxCN827IBmASEP3HMeXDtQFC2NYzAqWZCShdrqeBuPJfBlsAaIGbJWHpfuSkN8lVHc9pbxTkXyXPDgupBAXmQpfEjIzsNPvN+p+CZOE2DbFniXEE4sTZFhCOpso/GLTP+6Y4fYYIgfRnw9QD0tN/hltcJxFqnBdjy9sxbyD5WoDjx1I18diYrodLuY5wxA2yK6b4He9EklIvGOkFzOdMZH+nV4AeO5IzLsVWoiqJgJnDZ7naY+PgoRypgPyAH+hUhm22fEkBtOJKCYZHCeZolvMO/w7ljFwWoZFWBBmlrojCVHZgTJgKgZPdnOim7pNIX6vjJGqNlyt/4UtwvT7jcowhgbH8Q4pjYdFw3SUUzCnhslYFEOSpFvmUWGMCgP7+eDcKj8m1TksZhk7/QZbsGQEdFF95fHrAQAPpCgL0TkfullCjuNkslOoLqH21QwLkYRCu4TaLwDsPWIzLKRgMZlwmwXUGh4fIikOVgPkMg31sAD+QqUqdCMzLAl2CVFmznEcjGjC0Nj3LvOw0GPLdT3MLndJEiJsFpUeVAW5bDK7qc+BZr4AQUZKx7rSQaKlmBJFWJaKrq3ZZ4YMZgklmXRrIEUxyNqamSQkMixbJoZQyDmoux4Oza8CaB/WyqCb99XPsAVLRkANbK87qTn0MU0fC01W1ElCQDY7hWQ5LD4T4n+2esMNFBrGww9lDEtc0y25GGVJVtOBFnYyhiUv8QtVBQreZ1gUBUs1moel0yLA8zxpxw8ADGnC42TPkUlCC6t1/thutjUX8r5fSM2wxB9+WBOYinIhx1lXXQFOi/+4JtCOovkNmA5WhCU6/NBg6CID+z4qdZcb8g/wDqGRwGPzOQfbpoYCj/GvV+K0ZisJWaQItpCVCzmc96INAIAH9x5Jbegebe/VmW6BbHYKybIOCpK7fvFCZtqKKfOwuDE7fFb7kGFh+8lx5ObBooTNEu/2WCGimti8xHNYuiMJ0YVZnKLrdwq1H+Oy7Bba1swWmpmlCv+766U7MFMsDnVMAyDP1DENF+Om29Y+o4yUrgCnHSzcK5Kwh0Xb1iyYhWVIw+sRRxIC/P11QCEJ0d+xxyi7hAq2S8giRbAFYriUxynbJjBWLmBhtY6fHJxP5/0IfR8272I0g/OEOMMSYroV75zCPCx80SUXko49LH3IsPDBmIW8NDZfdndOZQrAP27CuoRMg+M69RlQc6m4APK0W4kpuiExeLNF0PP8goYZbhnSXCxkrAegzmKRDT80DY6rNtrTVIdD0oHptpQLOf+9kpaEiuqWbpZCrJ3WnEIOSyRJiOzT1Vbswf5W9L4oCQHAMVOt8LjWY8Tp8gxx93fWYQuWjIAVAyPFPAr5HM45bh2A9HwsfgdI+CHALuZZmickM0LKjKAiw1IVJCIRsgtAvsMuIVroZano0yHMkF2Q3J2Ld/1hUuJSxKTbmut2lGNDmbeCMEV3SMMYSGcJkfOGLZYzXSxYqgLzoJNGPM+T3ombMizVevsCPKTx/DBQA3spJUmIpdjKJaHwwkHV7dUJokhChXyOF8KrNRdHV2qckZQxLDuETiExhoHBSkIWqYJJBUxL33ViusZbk5RbhrEMzhPiptu8nmFhd3mURNIOiZNcAHwPS7yTn8pQ/ZJ2G9byzjtMNKbb0ZAuIdNZQuzC73Uos1CGTPSwjPC4+fZjXNollG8vWNoZlvTubsWWXV23TEXhVzNlrmSyDG9t1hQsdJBoapKQZo6Sv49MTLe9mSUEBMPjWIfQxrGy9Nw7Rki7FYe1MlhJyCJVMGMm09J3ndAqWPYeSSUd1SSDhWHEYJBdt1GTJJbKKG52IRsfKvLf6Yy38i6h5BiWfvGwVBSDDxlkpluRnRrVFAFA9FlCzffrpGBpbl/OQZsMOixJHOXPk3hYCvkcL4LZPhALljQHzzHWg+1rXR4JlUFlOSxhi5q/8PvPHY7AsDQlIfZeaXUJqduadbOE0vGwmEtCQDCeX5XBwrBjSmRYFF1CRBLqp3TtMNiCJSNY5R0TzZPr9GOmMFTM4chSFU8+v5j4+1UMMlgYWHT6ckYkoYbrgZ2DRYkk1CBMCDuhR0p+p4KO5ZCZbjvtEqIyQ/9IQvILIYMs6VbFsKikRLYvRg27hIDo0e4UfP6UZCEZ0gz0k2X+AO2tzWLBkmYGhngXr2MaVgnLSJkK0wRhf+FvD9sz87DkYxcGnXQJibKZDGl4WGRjQ3Rgn2G15nLmZIdEDgJIeNzsSkvqkyfdUgYwiYTorMAWLBnBiiDRlAo5vPzYpo/lvhRkodWQO2gK1u2xmBFJiF5cZJJQwAja8E/ooYL6Lpo/XnIB6DSHJdAl1DeSkP74kJpuRQ9LSW/WXozYJQR01inEvQUSk7mu60XGsADE/6BgWNKUhPzBfsx0q57YTL9LaqCmzKHuLlxWNPCuKpO25mIudmEgiy+g0M1RogMiVWBFWJIeFpMZRhRlksWiymBh2DY5DMdp7tvDi1Wf1RSTbokMNkiykC1YMoJlgWEBgHOZLJRGwUK6QMLAu4QyIgk1FObJvKSw8CnTPO/8MZGEpEm3MQoWz/OCOSw9YliePbIcqeCSBY1RFCS+nigMi+d5PsMSIgk5jhNbUqDgc4QkC8mwxpPBGLu8IzIswTk2ouk2TUlIDEXTdQmp5N+CodQmkzjMGBZ/4e5UElIt/rIAP/7cuv65QOeS0P7Z5fbZZVElIXIjxeYEyQy3QPPzbhn3s1jUSbfJyKhZgy1YMoJVwcMCALtOaOaxPLQ3+YKFLZyiu1yG0ZCZMN1GwDwZoLjbux5oASIb5S5C6mGRSE2mqDW8QKHQC4ble0+8gNf++Xfxp9/8ifFzVmvyCyGDbLgabwnnbc1qD0ul7ndrhRUs9P06uVtUhcYB/gKsmyXUzrAE786PkBwWoDuSUFsOi0wSUnQElgKLmnpbZTN5hovN70yfdOtfYzqXhEIYlobbNpzVaJZQB6bbH+4/itf82Xfxu//8w8Dvo0pCdACiHxonL1iAoCxUqcuv4/QYtwyLReJgF0oae71zffPAZHHfSeL5hebFdeNYOfSxvNsjK5IQKRzoiSljQqjGKxvlLkLbJRTjoiYyKr0oWB7dNwcAeOoFcy9UWFuzNOlWZFhKarM2LX6HDWRJU4OoDjp5QeeFcEM8LFwSWuyiJCTcxeuYBpW8Rz+P7tiWzeQZLjX/X2u6JcV/55KQKodFPafIZJYQ364YktDjhxYAAD96LjjzLaok5Kfd+l1CKkkI8NmX/bPLymh+OlHdFiwWiWOFMyz+3Sa9CCXt9A6jHin8PI1sSEL+HCEnoMnL7vqpjj6kMSa2PT6hpFuxQOmFJMRCpqLo9GEeFqnpVrijHdGYtdmxNFzMtzEXMpQknpmoqEvak/3XV3sZfIZFYCgE/8SR1o2Fab5JJxBD0cqCPEWxys8BoWChd+Ea9lA2k4f52vTFf3JdQqrFn56non/HxHTbySyh+ZUaALV3KWpb85GlKuaWm6+puy7TLBaVJETff5DmCdmCJSNY4QyL/5Wwg9Dzkr9bY+YuHfXIwKP5s8KwKO6U5QyLf0IPcWOi/CLreZ4016ATD4soh/SiYGE0c5SChe03lcdJ1kIuJt3qZgmxY8lEDgKSkYR0gV6q6cuAWkqiBctytc5ltM0tj0G6klBwUdR7WORsGfUGaRkWienWD45TXxMou9mxJKTIUinkHN5eXmkEP7sJ01HqIK9kfrVZXMwuVwM3lGFGYRHse3n6hSUAwMRQIRDDICIgCWm6+dh3O0gTm23BkhGscNOtfwEP3D2ETBmOChPqkSEsYr3bYAtIUbjj1XlYgpKQ/ARuZhY0/19muo3TJSQyLMs9kIQOCCFTJugs6ba5n3WzhHhoXEiHkPh+nUlCatOtLvE0rEuoUncx05KDSoUcJoeLgfdLA7VGcKHibc26LiHNXbjWw8LMqwEPi3qUAUOFMDvFmNKLP4JDfhw6juMz0eSzN1x/2riRhyWGJLSwWm9to4cFcm2MHBzX+l5YfIU49FCELwmtKNuagXRatnsNW7BkBGJbMxA8CJNsu/M8zw8oMpKE9Iml3QbrTMmbMCw1fw4K258qGpsWhcEclvjUqvheumTQNOC6Hp6bC46iN0G4JKQ23bLCkTIsoqTJfC1hGSwMSUhCOtNtSeNhCWVYGi6XBTaMlvji3I0uIVES0gXHyYpPk0KQRQPQBZgn3WpNtz7DUYzJUprIK7LPTo91k2j+WAxLSxICgv6lMBlLBJPqmMcs7CaSseL7jizzosxKQhZdBTfdkgUicPeQYMEyt1zj77fdpGBhXUKZkYTkXgRZXoqcYVEVLP4+lnlY4jAsoilxWTINOE0cXqxw5iNawSL3PTBw062mrZl5WOqu18buLBtmsDD4F98OGBZJOjKDThKSzRJqPsf3jbCCZd1IKfbcnCioCMVhSScJaTKXTMLjdAyLXhLy/WNxvSJ1ztqp5RWZHEbfR1ewFDXfexiYJAQEW9rjSkKsESLsJpINQKTsrazbk72/lYQsEgdbREeEuSq6dsW4YOyKal6FiKzlsNT5XVfwglCQLKLUw8IDmhT7krZAUzNvJx6Wti6hLjMsz7bkICDaMRQmCXHTrSQ4jh2zlD0R2TnTWH6GJC6+dUWhC1A/ijqHpY1hIXICW7A2jJVSmU8jop1hUUsbOrasIOn2Ur9X3KTbHJGEou2TquY7Y5Cl/NYCBYsm6TYfb7sAXxICgsbbuLOEGMJ8hcOlPDaMlgK/k7E5XO5LcO3oNWzBkhGwOxXx4A0bGx8H+yMYbgF/4ak23ESlqbhQ3SnnZYtoFIalJteDfYYl+mdnF/TxMuuq6O7+Y8UpENHD0onptrX/8jmHFzyi/8k0lp/BdFCfDlrTrZGHJXhclEmRM9tasNaPlrhHJl1JyAtsg04S0o1ZMJENuDcpbtJtIc8loaSj+dnrA8HvjhpuHUddsHRi5qYMC83giSoJieeYiUxPZaNi3mmbjUXf30bzWySOFTLzhkIXuR0XrM3VxHAL+NQ+kI2JzUrTrWSCMA1W8hMl5ftSNfk0CYZl/Vjzjqjb++8AYViiXJQrIR4WqelWMmxuTJHhYxrLz1BKRBJS362XNdJAgxcswjaR58yQgiVtSajh+mGE7V1CEg+LVhIKn0QujgEADBkWUijxwiBi0W8iCckGP4p+KuVzO5CEKMOShCTEEGa6BYJFjSrc0UpCFqmBJ90qJKEkDzqepmhQyQPNiyI7sWUdH90GuyCIngK/sJBLQkNkZocM6smnnXcJrW9RuN0OjmN5O0BEDwtjWJSSUHtxKOawAKRTSJCEWOE2YsiwJHHxbRh4WGTSgDKHhbAy7A57w2j6klBA7mjrElJH88t9DuwzRJwlpEkGZqASYVxJSOVXo5B9dpM5QoBf0MRhjqnpdjZBScjkRpKy4+oBpVYSskgJ7AIupn7KWvY6RdiALRl0mRrdRl3Rnuq3NbfPEioRSUglr8lC4wDSJdSB6Xb9SLNgWa21R4inif2EYUmyS8gk6RZQt8SzAmYscg5LArOEpF1C4QyLLoflCGdYyqkvFEFDKZslpJOE1G3NsplQImQSx3CIvFpv+KMXOpGEZBkwImTsEivAwoqGuF1CnudhXsmwRAuOo9eb4WIe60bUGSwMQYbF/Kai32ELloxgRRLND/hdGkl6WEzmVYgYydA8IXZxFeleWWERjOZnDIvKdOu3QFN0wrCsCgwLoJ9llDSoJFR3PeNiqRJmupXsEzrsjmGU34mLBUuLYelil5Cq0AX0HhZfEpIXLJWAJFT05bIYnicT0EKIbbeYukuhGn4IRPSwBJgzvYcl0HFHuoSi7hOzLqH2Ys00Hp9tl+tFO79Xao3A46np1mSbKej3csy6Ya3nxn+cLxupOvlsNL9FavCj+YWCRaLPdgoeGjcVrpUyjGUoi0V1p6wPjssbmG7lrbzczNsJw0IKlm51CtG8HQZTSSVs+GFBwnhUJQWfP7E5+JmZp8WcYen84qsqdAH9LJ56CMNSq3sBhiWJzBgdaqRLji1u2qRbzeRtoxwW2Swh1tZca0jHhlSEooq3D6chCWnamnWhceLfoxxb8yvBApwWLCadTRT0ezEx3IqPC2NYstAokRRswZIB1BouPzHbTLdF9YUoDhYrdRxtaa9RJKGRDGWxcIpeuHuSmWMDXUIFfcEituUyFDox3fJ29QJ/XZ3unyRo3g6DaeGrW+QAfdJtOVCwqBiW9mRnHZLwhegWP5O2ZjGokLMyjQYpWEqpLxSyAkJrutXIe0Y5LJq2Zs/TG31L+RxyOSd2N07VwIsiY5dqdXVxSkH3YZSbwgXSIQSAJx0D0T0s9AbJlPWm125VUZaEjJo1RC5Y7rrrLrzpTW/C9u3b4TgObr311sDfPc/Dhz70IWzbtg3Dw8O46KKL8MQTT4S+7g033IDjjz8eQ0ND2LVrFx544IGom9a3oAuosq05IQ8Lkwgmh4vGd7dAtuL5/bhuIYdFOvzQ7xKS5TVQqGKuc04Hbc185ELeKB00SdC8HQbTRXSVt3gr6GaJ/Cb1sPCJzXJJaKyLkpDWdKsJNgtjWJYrDd4xsmG0lLokJPN1mLQ1y4pPE+ZKJq/Q65SMMRSPBZNuJBl0regM0qRbw6KBvm4khqVVsLCbJFkOi7EkVAhKQiaYHC5ifKh5bqkZlnSPw14gcsGytLSEM888EzfccIP073/+53+Ov/7rv8anP/1p3H///RgdHcXFF1+M1dVV5Wt+8YtfxLXXXos/+qM/wiOPPIIzzzwTF198MZ5//vmom9eXYCd8zmk/+HjGQEI6ZJQpzRSjmrkw3YbKiyBjQqivwlgSSpJhae2voVKepIN2Zx/S9vWo7Zs63wPQLiXUG64/u4V6WBShg0uS2Vk6dDKkjkFnutUFrzUUXULsOdPzzWtbzmkuJOlLQu2FoY6J1Ztuw7eVm27J+xXzOb4gyjrfRAO7zzqZ7xM6D0iMMKCQzVGSbbMMjuPEkhuZJLSzVWCs1Br8XNcFFMoQRxKij1XeVFhJCLj00kvxJ3/yJ3jLW97S9jfP8/C//tf/wh/8wR/gzW9+M8444wz8/d//PZ577rk2JobiE5/4BN797nfjHe94B0455RR8+tOfxsjICG666aaom9eXoP4V0XCVFsMSxXALZIth4V4EkWGRtTUTX0rY8EPVqHZZIJ0p6Hdrkl2RJHhA4NQw90JFZViUkpBguqWFUEkiCakYFuOk21znkpDWdFvwzZciixPGsEwfbRYs60ZKAfkjrYVCxnjoTMP6aP52aU+ETIICSHicrGCpyQuWKEWBrH1bBpmHJYos43d1mR9bjGHZNjnM9+GR5SbLYsruMAzFkIToY0PbmteyJKTDnj17MD09jYsuuoj/bnJyErt27cK9994rfU61WsXDDz8ceE4ul8NFF12kfE6lUsH8/HzgX9bxw/1H8Xd375F2aSwrOoQA/YwQFeaWq/h/73gSzwmGSyDalGYK7kUwKFgOHl3BjXc8hbnlauhj48CfuitKQrLOFV/m4cMPlW3N+qTbThiWkVL3CxbaDVbUMAgiPM/jxZuaYQleDOnrynJYRA9L1GnNqgXv8ekF/O+7njI6P1SFrrjNIgvFClWxS4ht08FWwcKM1UlJQl///nO4/SeH2n4vC0Ub0sid2hwWSWJx2/spFuBhTaeQL8XmA9saZZ+Yxuvrhh+apM367KP5eclamieGC/x7ZwMQI0tCgYLFvBGCPVb2vQJmxagpFlZr+INbf4g//eZPpCbrbiHRgmV6ehoAsGXLlsDvt2zZwv8m4vDhw2g0GpGec/3112NycpL/27lzZwJbny4+9PXH8NFv/Bj37znS9rcVRWgcEG+W0D89vB9/ftvj+H/+4/G2v+2PMKWZYqQk7/aQ4W/vfBp/dttP8cUHn430HqZgJ2C7JNTuYaFGUJ7Dokq6rcsv7HnJUEVTBBgWgzjzJEHzdnR34CLosaYsWAQ2i72u4wSZCFl3WaXe4MbviaHwzAkAKBbkF98//eZP8Kff/Cm++9MXQl+jpmFY6GIs7qO6qq259RwxHDDOHbuIoys1vP+WR3H1Fx5pO+60HhbJsb0iGazKYDJZWrUAs2tCFEkoyj6hzIBWEpIU41UD7wt/7RhyFTPdTgwVsX606RGbaYUHRpWEpoaLyDnN8R2biN8sDCduGm0+f6Qk/XsnYwdEzK/W8fn79uHme/YatV2nBXPXZYZw3XXX4dprr+U/z8/PZ75oeX6+eTC/sFhp+5vuguLnsJgfdGwxuPepGXieFzjA4kpCY4puDxkYs0ONaElCRdFrJaFCHuz6ETatWbwr64Rh4YGApTyG2QW+ax4WvziNchcZMIGHhVK1Ls5039HjTdZd9v1nj6LW8LBxrIxtk0NGn6Wo8FocPNr8jLLzSoTOdFvIOXCcZteLWLDwLiGFJMSwoTV+IW4YGcVipQ7Xa7IjK7VGwCAv82dQWUQ852eX/UnSIkwC3Wr8uw1en4Y0niwx0yjOtGaaaC2blcMgY6FlPh/l82N8X8zDMjFc5IMI2fXOpLOJYt1oCf/vFS/H5HBJ+zlF/Mo5O+B5wMWnbpX+PcnE5RVFsGm3kWjBsnVrc8cdOnQI27Zt478/dOgQzjrrLOlzNm7ciHw+j0OHgtTnoUOH+OuJKJfLKJfNK9EsgFXkYjscQEPj2r8OnRlQBbZ4HDy6iv2zK9i53qcZfZnAnHoEKMMSXrCwE9fksXHgFywiE6Ix3RZyvPAIK1jaclg6GH7I6Pgmw9LqKumyJERNtyaFL9vmQs6RshFAu+lWFdQlS0h+YM8MAGDXCeuN79ZUCx471mTnlQid6dZxHJTyOVTqbts+Ytd7lYeFQZSEdDJLGGjM/FKlHixYpG3NzWPW9ZrHP2MWVmsNPwtorL1g8b9HA0lIYFjY8azrEmLXrzgymaydWgYZCy3bRyrEMt22jrfxoQLWCQWL6XZTXHLatvAHCRgpFXDV+ccr/55EZx3DStVtvWdvC5ZEJaETTjgBW7duxe23385/Nz8/j/vvvx/nnXee9DmlUgnnnHNO4Dmu6+L2229XPqff4LoeFloXbDFwCKCyQfvXEWdaMy1uqAS1WmvghYXmnWhUSYhdME06XNiJm1Y3jKrVUZbaGUy6bZluVW3NIdOaO8lhGS7ledG32gWGJZC3MxVVEtLH8gNq0624iI9IJCF2TO46cX34B2lBJgm5rofZ5eZnlJ1XIthiqaLqVZ1UpgwLkwaSiOan2yAa3WXTk+m20IWbJfAW8w6fGE4hiwKg8DxPGTXPj2etJJQPbGscSUgnBwGEhSZymGwfqaBLCVaBtbFPDAUZlobrgVk8wrY7bSSZdKsKNu02Iu/RxcVF7N69G7t37wbQNNru3r0b+/btg+M4eP/7348/+ZM/wde//nX88Ic/xJVXXont27fjsssu469x4YUX4lOf+hT/+dprr8VnPvMZfO5zn8NPfvITvPe978XS0hLe8Y53dPwBs4Clap0fxFqGRXIwxJklRIub+5+e4f/PpJqRUh5TBvMqKEYU3R4yMLd8WgyLynQrMizUPFou+tH8DdeTnsTqWULN140zA4jOiBrSdFUkDZq3Mz5UjHRR1uV2MIhmTVkGC+BLiUwSqjVcPPzMLADg3BMiFCwSSWh+tcYLpnkDhqXOF175na+KzVR5EsrCYri+dU4lIQnR811Ml65JGI+AaZhsPzOCrh8tSdmsokRGDb4X8ZEIn1crCSXYJRQmrci7hMzn+cSRq9jgw/EhYrpdqhp3NnUDSUpCVNruJSJLQg899BBe//rX85+Zl+Sqq67CzTffjA9+8INYWlrCe97zHszNzeE1r3kNbrvtNgwN+Vr1U089hcOHD/Off+3Xfg0vvPACPvShD2F6ehpnnXUWbrvttjYjbr+CDsmSXVhpGqoIXSCUCvRi98Ben2E5QAy3UY1TPE8jxMNSb7iYa931mvhd4kB1pyymr9bJ3U65kA8UIqu1RtvFrCrcFfLXjcmwuK7nS0JdzmER83Z4jLzBxSssNA5o39eqgkWc1vzYgaNYrjYwNVLESZvHzT4MyFRdsiDQgXMLq+HHms50C6hbg8NmCTGsbxkmE5GEyDaI6dKyDph8rpknUmt4gYWbGUFl/hUgfLGmC7BYyI9out5oYCNA94n5dUzWDSWDbKK96niUIU77LzfdDhf5dzWzVJUOpuwVkpSEVjPCsEQuWC644AJtW5PjOPjIRz6Cj3zkI8rH7N27t+1311xzDa655pqom9MXoGPIZdQ1W8BkFHwcSYhe7J6ZWcb00VVsnRyKNaWZYbTUTu3LwCh6wKyjKA7UpttgN09gABtpawaaLMK44PesKC5y+Xy8LiHaPt3tpFuxfd2/qJubbnUMiyi/qdpIRQ/LAy056JXHr49kMJR1s1BTNz3HVOCmW8X7qiShsBwWhg1tXUIdMCzk2BElIRV7UC7kUWvUAzcszHC7QeJfAcIlIVq8KXNYJDcmooE9jiTE9nsYSyLLqoqSwxLLdEskIca8HlmqBvbjIElCuuiNbqK3e3SNgN79SSUhzrDIuoTMzZIMYnFzf8vkGGdKMwPLywgLjqOLiElmSxwok26FuzhqXGTdKzyLRXdXKBYsTjyGhZoRhwp+DktazBOF2A0WxcOyqmCaKNjizfe1ysNC7sIbruf7VyLIQYBcEqLzW4xMt4rCg0Elm3GGRZwlpDDdJtFOGpCEqipJSCxY2q8VM4v+UEYZwrI62HvlnHaGSZcrJHpYOpKEQgsWdQ6LaBSWIY6HJUwSCuts6gaSDI7rWw+LRXQEGBYJdb2qzWGJIQm1HruxdVfFFok4U5oZfIZFv9gyCtrksXGhMt1yI6ik1ZZdPHgWi4Sxon4X6etGLFjYXUm50Hx/Pxm08zueMIh5O9E8LOEMixjSp6LgaZLtYqWOB/eygmVD+IcgKEqi+QMMi4EkpMrvYYjMsAiv4zMsnUtCWtOtgs2StfeyfbRhVMGwhATH6VJb/QI8/FwymQotwnSAoS7p1sR0K5Mbw8BuQieHi4GCxVTG6gaSzGFZsQzL2gH1rcjuBJc1Pe7shItTsLz2JZsA+DR8R5IQ87DUGlrzKV1E0po75N8pi4VF8OIrM9GyRVgWz08zWyhk7dImWBWYM10yaNJoY1gitTWbdAmZSULlQo7vv0eemcXCarNF92XbzP0rgPzie4QUxyaSUJjpVu1hMesSYgFeaUtCqg4YmWmYTpGWQTZ1m0I3k4ddr0zYyjjzlWqmkpCEha7GMN2aLuzVussZB8qwHF2p8X3RazkIiFckqkATu3uJ3u/VNQAqCUnbmqu+MVOEP9gruofl1S/eCAB48vlFHF6sBEy3UcEkIc/Td7nM0oKlUk8lxtm/U9ZH84vGPyCMYVG0NfPXjXbiizSqL4+kLwmJbJpuGrGISsjgQ6D9YqhiWBzHwWjrc3/np81hpq84fp2S5VC+n0QSOrJEbwQMTLeKQpdBxUKpcn/oZx0fKrSFpHWzSwiQs7EzIQVLWCGhi7gf0UXzC8V/LEmormfEGPiAWJrDEiE4jklrpgUmvekcKxcwNVIC62F4vhUb0esOISBekajCisFNTDfQ+726BhCUhGQeFjXDIjsZw8CKmy0TZZy8tXkne+9TM3yq7M4YDEtzMGPz/8WuBQrauVF3vcSmTFOEJd2KiyhlTNjEWinDojLdMoYl4okvGtV4W3PKDMtqrYHDreRXkWEx87BEMN0KOSyyQWyMnWMFS5R2ZgbZtGbKsKzUGqGLoS7ptvke8nPN7xIKPr5Mkl+p5OLPzUmmS0j0PKm8HbKJzbOGDItq3+l8JPqk2+C5VCD7xPQmxpd19PKKjD1UDWyUoRyhmAf84nisXEAh32QQWRfWodY1dtAkoWVN9EY3YQuWLmCBULrL1UYb/arTB+PMEqILNVscvrb7OTRcD6V8DhsjzKtgaN4ph3cKiXH8YV1FcaA03Sq6hEpSSUiXzilva47qYRFnRHWrrVmWtxPLw2Jiug1hWAC/YGEMX1T/CiA3EM4Ix1oYy1JT5KkwqFgodrzlNQzL+kDBEt3EKYIWHWKekU5+A4LsTLgkFNIlpGBzAP20ZjHplhYOpnf8xpIQ/9wyD4v5LCHT7aIptwxs/7KbQtM5QmkiSUlIlLd7hd7v1TUAUV8XL6w6B3a8LiH/YsEKljseb97dbp8aiu1eN+kUEheRNIy3fOquQhJid7ZieFXz/8MZlvbguCCbYAoxELBbbc2yvJ04wXHiiAIKsTjUSQej5CI3VMzh9GMmQ7dB9X4q0y0Q7mNRSYkMquA41wtva6ZdOElLQmKBq/KV6CQhlek2NDguriSk8LA0t99sv0SVhCoSScjIw9IqxkwLTJpyy8AKlkOtyd0mUlTaSFIS0kVvdBO936trAKIMJP6sMzT5J2N0D0u56BcsbLGNY7hlMOkUOrIoFCwptPCq7pR5wFvrYiXzpJQ1DIvM80Jf143oxxFnRHUr6VZmro7iYYnSJcRNtxrPAO0UOue4dbEu5nJJSChYQlqb/VyPaG3NqmnN+ZzDf5e0JES/pzaGRbEYi9tfa7h8PEMYw6L0sOgkIZO25tYxT4tE44LFkCVh53fd9fwgQ41ZWETUApMVxhPD/nHNvv9DrSG3gyYJ6cJNuwlbsHQBIqMShWGJ0t3B4C/UeWweH8KJG0f53+IYbhlGDeYJdUcSkt8p8wh9r5kyK2ZBACDzhMKNguLrRvWwiDOiRjRtoEliv2Qid6QcliimW5bDopGE6EXu3OOjy0FAe1Cd53n8WGMMTlqSUEPhmaLPWSeRhDrrEtJ4WBT+DLG9l4XGOY7fwSQiLFxMZ14d0RTgIltJ913ikhAprKtcomwYPReInsPiS0I+w8K+/0MLgy0JDZd6+7l6v1fXAESqWvyZ020aD0u17hqb1cTFgw6ZizqlmYItuLoZQYyCZtendCQhhemWXJzqrufr6JIuIakkpLg4x/awVIN3JcNdl4T87zqW6VbjYWFtm57X3C++JNT+HDZPCIg28JCCLQBsQVquNvhxftyGZkEeJgmFm27lNwesQBYZFsBf8AMMiyLPJQqoH0NMjPZZD7FLKLj9s60uqnUjJem2N18jxMNSVxcNujZ9tv1sn7Jp2EDykhCVm3x2ybytOep2+ZKQhGFpSUJZ6BJKMjjON91ahmXgwQ5w1mUjhlzpDE1UzjC5AHqe12Z4o10ZnTAsYyHzhDzP43d12yaHtY/tBGrTrX9Rbrj+TBV6QRtSJN3K9htDPkTnV0FsBWQMWq0hH76YFKSSEFvMIrU1h0tCQPNCrzPdsonNpXwOZ+2cCn1/GURJiLEr5UIOWyaa/pFQSSiEYVGZZRuadmjWWRQw3eY6v7MNMCxt0fzy41T0cvhzhNSDTsXEYhG6ADbdLCG/a8y/pkWNiq8pCjMRrFMH8D+7zwyZm25NhwT6KbftHhbW1mxi9k0bJV6MJiAJZSQ4rrfl0hoBu5BunRjCwaOrbRdWXcsYPeErdVcbl84e4z+3xbCQroxOPCxs4VHNCJpfqfOL+871wzgwtxJJEvrYv/4Yjx9axE1XvUJ7V8VNt20MC1lEXVeaXMtzWISLrGy/MeRjMizLgjeJnuwrkuGLIu54/Hl86Gs/aiuuLnjpJvz5r5ypfJ4sbydeW3O46RZoMSwNVhy2X6hZoXvWzqnYpj2RCaBm0snh5sIRKgkZMixKD4vkeexYWT/WLgm5LfZJxW7oEBh+2NYlpJglJGQ2+Sm36q7AcIaFdQlJPCwRpjUD7AajIb3j/5vbn8A9T83gs+94JX9dtt9N0mrLhVyTdasJXWsSxk9EZA8LY1iG27uETOcfdQPsOE9iWrON5l9DYGFxbAGh1LXneW3trxTFvMOZmYpBpDtlYVhxs31qGOcevx7rR0t42daJeB8CPrWvmhHE7ujGygWeS2Bquj26UsP/uXsP7vrZC3jqhSXtY1VTdwOLaMOTelJ4W7OwKFGZSzwpO026Za9Xyue4VLZq4GP52u7nsO/IMp5fqAT+femh/dosF5bBsnncX6iieFjYgifLVGGgi3694fGhdjKGhaXaXnza1tD3Vr9fUGZhGSzrx0r8Tje8S6i1mIQFxwkDInUelpO3jqNUyAXOK7q4x2VZ6PeknCUkmm6FVOywlmbAPIdFVoiOl4t8W8WiWmZ41xUGX3hgH+59egYPPzPLf1et6wtMCtG/o5LNZIjrYaFdQmJRGDUYMQ2kEc3f67Zmy7CkjNVag588x6wbxkOteHKGSt0Fs6bIqlem/VbqrlGnEFukHSd4sv79u86F63kdubzZcxcVRQi9QI6WzaY7Mzy09wjfD2FFTl1xp5zPNYs7z2MMS/tF0/ewBLeLBWxNDhdD811MIdKojuNgpFTAYqVuZLxlC/D/uPAluPjULfA84L/+zd18+2UFruf5ZmPKZsRpa9YzLEE2S9cl9Jazd+DVL96ITTHyfxhEOYEN9Vs3UuJ3umHzhPxC17yt2fM8ZZcQAHz6N87B4mo9YLoNGkzdWKySGM3veR5vUVexHmIEAh98qJjUDIQvarr24PGhAvI5Bw3Xw9xyDVsng2xwc5vIMagpjtj5wOTMsPcW0bwpqbVJQiZeEt12ycBuQGWSkPiavUSikpBNul0bYNW44wBbJ4cCvwOChjUV3RYlPI76NtgFDmgeaJ22pPEuIUURQmPAWeeGqemWzTsyeY7vRWi/KFCDrMyToipYdHkVcRmWZQmNGqW1mRW2J28dx6nbJ3HaMZN84ZZ1OQHBY4R6UNg+MLkom7Q1O45D2sg9bQ4LAGweHwocj1HBXpeZfJlXasMoYVhCPCwNRX6P+B60YKFfeV6y/cV8LlCssN8xRO0sY6Dfo5gYrfKViB6WsMGHzW3VH9u69uAcSXilQ0/pNtBt1LVQs+sgG9pJt8lIEhKKNc74pTBLiEXzyyQh8TV7CZ89S0ASygjD0vu9OuBgi854uSDV2tnCVcrnlDQiu0sxovMVxtEkwIuQEIZlA2VYDCWh+wIFi34xV812ob+rN3ymgV5s2X4Ru4R09Lk4o8gUMqNalNZmWaKmbrRA8/f+60oZFpOChefR6C9OVE7w93U6FzR6btQaLimOy5yal83polAl1jLI9hE1Wss8LDLQfJa4dLwo/9JzQmUoFWWRI8sGkpAwxFJEWMQ9K4bEOANZh57KdFtv+Azd/tnlttcwkYR8OawReI8oOSzGptvVdoZl3WjQ2JwlSajaMO8wlYF+P9bDMuDwQ4aK5MLq3wn6CYLqryISw2KQUhoXvsyjL1jWBSSh8IJlqVLHYweOBn7Woa7RpwuEDdHlsIjymm5InJ/DErVLqPk56F2JbsKtCH7skAtjOeT5rJDJ55zAIsPMh5EkoZDig0plOkkoCdDvutpweUDhhjFfEpJNQqfgpluFCVYmm9HmGdXzZGCPjdvaLB6f9JxQTSIWrxNsHyXhYVEVLOsVBUsUDwv1k8WWhASGJcosIf97N5OvOcNCbiTKhTzGSUBiFoLjKLvUSYghZYN73SVkC5aUQatxdqdMqWu/pVkt18hmZaigG0LXKVg0v4odCDAsnI0J3+aHn5kNsBdhrdAq023zd35xofewCAyL5uLeaQ4LZTqGNNkVInjew7BfsOhmIQH+QjEkfP+xZglpimiAjkJw+cU+tYKFsCK1uhtgxHxJyIxhUS1iomkVEBiWCAWL7x/oXBICgkylKhSNsVvspsXEdCsOsRQRljbLXnuGJFxTH1WwrVm+T+j5foBKQhGyVMrCZ49iuo08S4jchFJQr5CJFJU26PynuMch4BcsjpPOuhIFvd+rAw5ajU9oJCFd5VqSzMpQwaTDIy5YNL8qOI5eIEciMCzUv9J8/TBJSH2nTOf++GwTLVjkC/6shj7nbc0RaVVZuzpLB10OKT4brseHZo4H7uTkkhaDyjAbJTHZxHQL0Pk+4R6WTpEjMkvd9Tgjtm6kxO90dV1C1Dwb1tZMGQBapEZJLy1G8AzJIBaWQUlINUsoKGnpWEO+nSGpvGHMmYxhqTU8bqCXSUIi67Ra9X+ePrrKmUzTHBagXQ7T5ceIkI19UMFVnJdAcD+byFhpgx6vnYQYsu9nuJjvyIeWBGzBkjKoo3xCYg40GSoVzXSbnpcgiul2LOSxFPfvmQEQHkzH0NAsPEXiN6k02u/yuAdE2Je6i3vcLiFZICBPuw1hWBZJURvwsGhGC9D3bCtYIswSqhh2BHDDZsNLldkT369KGJYNpK1ZJwnR7y60rVkwvDJEiVPpXBISCxb/eFCabkkOixswJqu7s8KC48KkFV6wLPsFC5Wz6DaqJKHlmv/Z6q6HQ63wNd0cIxG0w6vhetwsHcnDYnB9XarWeTFGpVpAnCfV+6WVFnqdtDYvS6TtXqH3e3XAMU8c5exOMMCwGLivxbsHHVI13ZbDGJbmhWbDWMkoxh9oLrDff7bpX/m5kzYaPUc3E4aaHaXTmoVwLdm2q14z8rRmycI/zE23+s/IjpuhYk7uwVF6WOQDHOMFx5lJQjVXn3SbFOiCN0sKTO5hqdThKr4j+t2FBsdJGJZCzol0dxkWyBYGdq7LiniVt4N2CS2s+iGOoiE0ynaG+UjY+UKHntJjTOZhEd9LlEeZjyWKJEQZRPr+UaL5TYpLJjuW8rm2gn7dSLYkIcdxAjcVcSGTtnuF3u/VAccCCRliktD8So27tpkxU+e+jtYllKYkpF9sfR9I2ZgteXTfHKoNF5vHyzhl20TzOWGSkIYqZheoQDS/SVsz2XYRrGDxWkMVTSEm3QL+97wSEgIoG7DW3P4QSYhlsAgMmyxjRIaG6/GCMJLptosFy3K1wWn5DaMlfqfreequNHqHqfKilCV32roMFh1k8lIUsGKbMRhUJq3U5cc/ZWJZm/F4uaBNxy6QtmZZJ4kuEJBuH5WEaHceLfJUklBbwTLX7BSKJgn5xRp9fZOCJYp8J2tpZqAelixIQgCVbeMzLCuaJPZuwxYsKYNJQhNDBU7tu55vRl1h+qARwxJ+0PnthGl2CSlMt8wHMlLyQ+ZCig/mXzn3hPU+gxNmuuWSkI5hUXQJKdqCdZkVdLGKwrKsSvxJw4Y5LLIBawDd/jBJKLhvioZ3kaq2aBkKhM1K28MC+AvXofnmgLl8zsHEUBFDxTx/X5Xxlt5hKk23kqKuocn8MdnW2JJQ63ks40XGsIhFRIkwsbRjT7udhKWUHdthXpD1khwWFcurkoTEc4ExLHEkoUq9EXh9o6Rb7uMJP7f963k7a5U1SQjo/DgE/O/HSkJrAH5FXsRwMc8vfOz37EKkq175hcigSyjdHJbm4lltuG136svVOi8C1o+VjBkW5l/ZdeIG4pExbGsOCY6T7Qs/mt/fl3Roo+wCLw5VNEGt4XKmImC65V1CIZKQZMBac/tNCxa56bbhetrPoJupJKJAKP6025oBfxE4NM+G+pWQa303PO1WYbxlHg3HUbMlcg+L3yYeBWH5JjrQQZwbOMNCCxZ5KBpl0UwMt0Cwk0R2F14JYTkYqxBkWBjLGzwGlZKQcCzvjyEJ+VKvGyiyTGS8KMFx/nkpYVgIO5uVgoUd00l0CVlJaA3Ab2suwHEcIgs1f6+b1MwQLYclvfbSkbK/jWIhwiSVUiGH0VKeP3a52lDKKNW6i0f2NWeH7DphPS+IdMFxLjHUyRYR6quoSPwcsgV/oVLni0A4w2J2p6LKLjBNup2XtDQD7XkTIiohXUKAXhZaJcdPLmSRLpK2ZllIX9Jgi8B0i2FZT7wZE0P6AYisSFMZboEQD0vEBaiTLiH63TJfBJNJacGp87CYpNwCQR+YrK2XdQ+pIu5ZQTS3UuPbJfOONbdX3o0jxiSw1uY4khC9mTLNQoni71qoyFuaAZFhGRxJSCZt9wq2YEkZYvjX+FAw5MqkehUjt3VIk2Ep5nP85BbzVegF0nEczrAA6hbeHx44itWai/WjJbxk8xjPedGl4wbNkzJJqMUkBDpX/H1bJh4Qptkz781IKS/9HuIwLKwLKOcE74RNk25l4VSAOkeGv6/Cw0S3waRgMTl+CkR+66Yk9DwvWPwFYjyktZmPc9AsJPIclubzchHbOaPOp6Gg78+KMsawBOQO1SyhWsMogwUILqyyYMQw0y0rqDwPmGuxlKqOMZUsyY65dSPNz8okoSjBcT4L7TMsJnOEmq9vLpvoJKH1WZSECp1LQjJpu1fIxl4dYIjhX2Jr87Ikvl2EqWESgNS3kSTGFPkq4gWyXPAnE6uyWJgc9Mrj18FxHKN0XMpwSE23OXLXX2u/66cFCR8SF3Jxj+Nh8e9KCgFamrc1hzEskgFrAG3LjiYJ0X1Vaajf2zSDBQjKHt2VhJoFC23X5cylorWZLWI6aUfqYdFMataBZtREBZNUHAeYGgl6WOjCo5KEKMOiG3zYfA9H2wXHtl9VwBbzOT5yhL2n7LwDghIiBTtXXrJ5HECTYfE8T5noKwP1sFTr5s8DiIelY0kogwVLh91qAM2T6v2s5Gzs1QGGOA/GvxMUJCFtl5B/MoZBNvAvSYwohhqKi75JAcIMt7tO2ADA98jo0nHpAqBra66rPCykkBMTQVX0Ob2omzIsKuaMS0IhDMu8ohshLOnWLziC+8ZxHCPq27SlGfDZikq9wbMp0mRYClwSanpYZAyLShKqK2QUCtreyti3RswuoY4kISKp+ENEW4FodXXBTpnYmcVWm34IwwKQzBjJcWEScc/eg10DKopZVCrWiZ0LJ2wcRc5pbv/hxSpnfEw6buhnjxIaB/ifzfXCz2/WnSaThNZnUBIqJtklVOp9udD7LRhw+N0eQYZlIQrDIqGqVfAvFul8tWOKTqFZCUuh86TUGy4e2tv0r5x7wvrm48vyYkh8HoPsrpfeUcjnmTic+WGLM8tg0dHnUQuWZcVJHl0SUpluFZIQl3TajydZ267q+WEtzYBfQNACM02GpSR0CdHvSzani4JdsHVMCdt2z/MLnLB03LBtjbNQUClzRBgiWuOsQ3suDN331Jgcvq2tc0ZybJt06qwTWpvDuoRUktDEcAFbJpoT7ffPLkcqPCi7FJXtM/V3AYRhKbezDSOlPN+OzDAsCUhCPI3dmm4HA0uVOtfVKeoNl2vP7A6QdzO0ChnZRF8R7E5FnOAqQ9qSEGdYRNOtrGDReFJ+cnABi5U6xocKeFkrf4V3CWmMur6nAFJTqJRhISea4zgkfI0xLM2LkK4FNOo8IZ85C17YTIcf0nZ4iiFF8J3/vmpJx2Ris8q0KwOT32jHUzckIZk/g4+9UBS7KqMqhWzhisuwdCQJEUlFlGB1jActEA4ebfpAZEGIbdtK5m+JMDG+rlcxLBEloeFiHsdMDQNoykK1WF1CDd8obFhk0tcPW9hl870YHMfhbFNmChbJPl+tNXjRbwJ/jbKS0EDgv/2f+/Fzf/FdTsMy0FbEcW66DWrtJtVrpKRbhUM/KbCiQryT5UmxgYJFLQk9uLcpB73y+PV8MRgthRt16yFdG0Vy8VXJY2K8vWzbRURNu+UjF4RCdNiUYVF0I4RG82skHR5mpsmbMB18CPgLHWPQHCe61yMKxEUgIAkpjkuGWgTTLeAXBrq5Vdpt7ahLyF/wRwRJSMd4BAsWxkKpY/kZCnl1cWXCcrDzhpnXVeedUhKq+QvijnWtgmV2xWfFImSpVBtupPyW5uP81w9lWDTBcQCwYawc6b3ThkwS+uA//QCv/vh38NiBo0avYRmWAYLnefjxc81ul8enFwJ/Y9X4cDHPFwufuhYYFoOCxWgOTIpJtwDwok1jANB2sPt3vf4FUudJea7VuvjizWP8d0NF36irymLRZbAAfmGxrJEp2CRjtjjPSLZdhM+wRGtrFr1JpsFxvuk2eGEMG36oY0h8hkVjuuUFj7nplhlCTXMv4kK8Y94gYVhUplt23OiYkkLeP/7YueYzLNHOJ1ULrwmopDLWJgmp5Q7qU2KvYeJhoUZ1ESYJxqxwZFlGKpZXGRzHr4E5HLOOMiwRJCHCmqqGQ6rgOI5f8GrmUQFUEpKPO3j7+cfjNS/eiFeduN7ovdNGURjsWK27+I8fT6PuevjWjw8ZvYbJ+JhuwRYsHYJmeLDAI4ajEke5LwkJDIu2SyiOJJTOV8v8JvcLE5a1kpCk+JDR+o7jhE6E9u+U5Z+P/Z7mxCgZFkPTLRCdYVlVSH0sAThUEgr1sIQwLJLvX9a22/b8mnyxkYHd+bLiME05CJAwLGMxTLchhYdoTK7H7BIqdtQlRDwsgg8sbBEXj/WwtmaAMiwySShclmmThFQ5LHzfyoPjhkt5HDM1AqB5LY0kCQW6hKIxLIA8AE8GnSQEAL98zg58/jd38e6uXqMosGc/PDDHz3HWpRkGy7AMEOjQr/1zwYJFdnCPCwFXJgyLeNekQzVlDwsrWH46vcBzFwAEpucy6CQhVSsx9bHIwO4CVfo0W1joSADx4l4SGBaTzApesBguQKoEY/ZzuOlWERwXEs2vm7Rs1CUURRLiDEu6rB6DThIyNt2GyAtiUcei+aN3CXVguuXeK52HRTEPiXwHJSIpabeVs0HxTLd8AGJLWlUZ/wsKJod6JI6RSEImeSrUdBu1SwggRdeivmARuz6zDjE4jt5oPrpvzshmwK5lorzdC9iCpUPMkIr8gMCwyA7uCSHgyp/ToD4BYk1rTqlLaONYGS/aNArAb0sG/EWfdiWId4cUs2TuEAVLyFUxLPWQBUSUKcqFdplCZCn44EONQZEO+jMBG24oMixDra6hlVpDOmwOaMqMqryH0OGHJpKQAcNiZLrlHhZfEkoTYrFBjzVfEtKbbsMSa0ssMbVThiVCtocIPrQzn+Pnw0qt0RwyGVJA0BsVFuJouq2yYtyXoNSvw74Hdh6pJCHVQMhlcge/QyIJqeRfCtnwwyitxRtGzRgWVQJ1VlESCuf7n/av2ZW6ix/sD/exsGuZLnqjW7AFS4eYpQVLa8oog5hyC1CGRZSE1F9FWBw7Bb3YpYVdJzZzU1jBUm2NsweCssoYj+eXMCyKIiFsBpG/gCgkoVzQCCq76/fnCTX3p6p4oogqCa0oGBZWxHme+vtcqTX4+yglodDgOLUkpPNCxTHddksSosf0xFAhsGiLCdIiuLwQsviJfjFW6ISNKRAhUvFRwCWVYj6QGL1Sa4T6M+jxbiIHAcFxFiL8BGP1YsUC/Nhir/K98LZmQRJaJR4J1iW0WKnzERxRkm6rlGGJcDz6U6cryses1ny5SezeyyrocVhvuHj4mWaUxIkb2286VVDJ271A4leY448/Ho7jtP27+uqrpY+/+eab2x47NDSU9GalBlqRix4WGa0vtjXzbhKDaP5ISbcpMSxAc+4P4NOLbMHP5xyeegmATGxWe1hE34jYFSGiHnL35C+irbt+iTRGGZbVWoN/B3qGJV5wnEjJ0wJGFR7Hjpt8zml7vtiSLWJVEdoFmDEs7PgxymFpFY1LfF93TxJi3RgM4owuEaZDDEUWIHaXUEKmWzExuhbizyjFKVg0gxp9WUbT1jzmm249Tx7Y2HwfuSS0XGtJDsXmaIyNwnkYTRKK52FZJ/hwZGCMueMEOxqzDCoJ0SiJt73qOADAfU+H+1jY95OFgiXxvf7ggw+iQboQHnvsMfz8z/883vrWtyqfMzExgccff5z/nGanQdKgB/j00VU0XI9fFOWSkK+1N1x/BouZJGRQsEQwTcYF87H86LmjWFitcbZk3UgxcCc6pvCjrFQbfEEXL6qq6H+GUNMt97D4kpCIIW5ibvDvr5h3pGFQDHlNJ4UMqqTbfK7ZyVGtu1ipNbBO8lwqB7XLWXp5cFXT1m4y4kEV7S8D29crXWJYqCQkHjfsHKs2XKzWGm3bbzr5l7NQrX3kejE9LAlIQkzOHC0XsLBax1Kl7oeiqSShYlASMttWeXHleZ6R8ZW9T63hYaFSV3YqqiShlWpLPm1t+zFTwzhMvCQmxeIQYaHjeFjYZ5jVFCzsRmK8XIjMuPUKXBKqu9xke+7x63Hei5os+cPPzKLecLVSqfj99BKJX2E2bdqErVu38n/f+MY38KIXvQive93rlM9xHCfwnC1btiS9WamBUoh11wsE8ogpt/T/K3U3YBDUtzX7C2wYVIPHksS2yWEcu34Ergc89Mys0rSq8qPMtPZZKZ8LUN4A8b2EmG5VF7G8aASVME3UB8JM0+tD9P74Sbft32uY8XZectzwbeemW5WHRV1wqJJGKaK0xftJt93xsNDXbyt0SwWwr0/WmmpsuhW7hBodelg0mTcqiMZ5mhgdxniUA/soPIMlsK3CcUHlLF3BMlTMcybwyGJVedOk2ifixHpmvDV5bwbaSRmlu4iB7Sstw8JvJPrDvwKQfe56nBE/94T1eOmWcUwMFbBcbeCx5+a1r6GSt3uBVK8w1WoVn//85/HOd75TuxgsLi7iuOOOw86dO/HmN78ZP/rRj7SvW6lUMD8/H/jXK4gH+AHSKSQzTo6R/z+04Bc3Os9AlC4hVtSkybAAviz0wJ4jvAARY8BVfpTZVrKsrEgImz8UNnVXNILK9gOVhGZ4LL/+4s7eL3LSraZgUXX66DoRQtuamWlW8rnTMt0uV7rf1iyyB7kcydKQyEKNEO8Tg3iudZzDYsjIUVQEDwhNjA6bik0LdDbpOQyqBFpa2IYVsLS1WSVL8+neZJ94nud31LXOlR3rRvjf8znHiN2SSUJRjkcT021YS3MWwc6ZSr3BwzrPPWE9cjmHM+UPaNqbPc9Tytu9QKpXmFtvvRVzc3N4+9vfrnzMS1/6Utx000342te+hs9//vNwXRfnn38+9u/fr3zO9ddfj8nJSf5v586dKWy9GUQKcf+sb7z1UxH9AzxPLqxs3sdwMa8t6CJJQjFO1jg4lxQss5KWZoB6WIKLKy9wJJT1qCL6nyHMdMsubktVjSREjKvccBtycc9H7BLSeZPC5gnJzNoMrLCtu540Sr1iYLrV57BEb2te0viFkgQtUmXHjmhop6jxaH6ztuYq97B02iUUf1pzmRcsfhEfxh4ETbeGDItC7gwOWjQrWI4sVZXGf1n6b7XhcnMtK1iY8RYw3+/sxsT1/GM4GsMSXrD42Uj94V8B/HPmx8/NY265hpFSHqcdMwnAHzqrM97S72fg25r/7u/+Dpdeeim2b9+ufMx5552HK6+8EmeddRZe97rX4Stf+Qo2bdqEv/3bv1U+57rrrsPRo0f5v2effTaNzTeC387bvFjS1mZfEgoe4OzOmclHYWYmdqcSyXSbcsHCDvYf7J/jrFJ7pkprYRbYEl1QWzjDEma6bUlCmrv+MpGEeLdSGMMSuUtIfVfCJzYrWBL/Tk7NsAB+lxOFLqk2Sg6LzLQrgl0MGSuTtiSkY1gAfWuzSdIt0L6POMMScfghD2MzOGdF+F1CrYKFyKTV1verMqJSRjFyl5BQXLHCIueE7zfaZaNiWEoSSYgaz6mHRXxOGOh5zr5/nVFYtf0zS1Vl3ICfPt0/DAvbf6woOee4dfw8ojedqhsx2ffTS6R2hXnmmWfw7W9/G7/5m78Z6XnFYhFnn302nnzySeVjyuUyJiYmAv96BSYJnb5jCoAgCSnSStmFlQ1MDDsQeJdQw1UOBWRIe1ozw871w9g2OYRaw8PtP3keQPui72vv8oJFdkH1ixz5Yl4zbWvWMSw8s6FhlHILRPewrJJsCRGsiFF1CfmSUPuFkX4eURZquL5JMu7wwyiSkHj3q8vqSAI6DwvQnnFEYWy6TSjpls3NMTVpU4g5JjQxmrc1GzAsJoMPAXXSbZSZPFJJSGDcZJLQCmdDHP4+1MNi0iEEBAuWxRi5QGz7q3VXyXwuhMwRyiKKAqvKpHwAOHX7BEZLecyv1tvGyjDIvp9eIrUt+OxnP4vNmzfjjW98Y6TnNRoN/PCHP8S2bdtS2rJkwRa8M1o0G21t5hN3h1UMS0sSCmNY6BRZzWLjkgUrbQ+L4/ga6NOHlwC0L/qcLamKkpCuYNFH89dDzJPiXb/ew+IapdwCQN6JN/xQVrCw73ulJv+MMrM2g+M4ZJ5QcL/Sn3XDD7UMiybaX4TYWdDN4DjZ9yWmSFPUDNuT/X3U3A8Nw3ZoEb7BuRPTbbskFNYlFKetuaTysESQl2mXjYrllUlCMumUFiymhWI+53DWdbFVWEQ5HkdKeb69KllIdQOaZYjXyXNbzHjzbzmcc3zzGq6K6dddx3qBVK4wruvis5/9LK666ioUCsHF+sorr8R1113Hf/7IRz6C//iP/8DTTz+NRx55BG9729vwzDPPRGZmegGa4XH6jmbBEpSE5HfK7IBnklCYmYleMHTzhKKY5JLALnLwA2pJaKlSD9CsszpJqBQSzR/StSH+Xt8l1NAWT4HX5abbaG3Nui4h1i4oQpVyy6AagEi9KTLTLesgSdp0y9Bd0227hCfO6aJohLTDMyTlYelIEhI8LCNRuoQow2KcwyI3CIexORS0y0Zl/NdJQnRBnBgqcrYsyl09ez92s2PKzgDNG4ENhCWSQSXxZxn0uysVcjhz52Tg77R5QoaVDIXGASnksADAt7/9bezbtw/vfOc72/62b98+5AidPzs7i3e/+92Ynp7GunXrcM455+Cee+7BKaecksamJQqa4XHy1nEATUmILc6q9lQmCR1aaDIsYYtDIecg5zQNZc2LmbzCp8VM2osH4GugDKq5QPVWpDi7oLD9JjXdhjEsIRHrolSkNd3WXD4PyVQSMp0lpDvRh7npVv4Zw+K/h4pNGlfFsJTyOWlOhCoHg0I3i0iEuK+7mXQrC/mbSNJ02+EsoWQkoea20MRoth1K023re8vnHGMmQNUlVIsgCdEuGxUzU5DsE1U33THrRjB/cD7SMVUu5LBY8QuLqBLG+rESnju6qky75Wb4PuwSAoCzd061FZG0YPE8r635Qydt9wKpFCxveMMblMalO+64I/DzJz/5SXzyk59MYzNSxyw33JawbXIYjtO82BxerGK0nOd+B5Uk9Lwhw9KUAfJYqTW0HR7sziznRL8jjIMXbRrFxrESD3lqy2EhB/lSpcFPFq3pNmQxDx1+KPxeF81PPSyy4inwunGTbovtp1hYW/NCSDcCT7utywsWlX8p+eGHAsOiiW9PAgFJSDJGwfewqE23pm3Nooelm5KQ6AGhidHsWhHmYRFDHPXbyopxhYfFwJu0LtAlpJCEJAyfqpvumKlh/OTgfKTrGPvufA9LtO+Ms0SKAYjsRqJfBh8CwXOGjVShOGPHFMqFHGaWqnjqhUW8ePN44O9+nlQ2PnPvXTR9DConlAo5bJ1ojhQ4MLfCL5r5nNNWnbI7nxcW/LbmMJjME6IXum6kBVMfC9BegBTyOb7wUeOt3nSrHpgIkKRbpelWZFgk0giZeDwT0XRr4mGh2QVDkhlRvodF39as6kZQDUAMk3N4W7POdFs3l4TE4rBbktBwMS9lrvwuIYnpljNzZh4Wto9M81tEJCIJFRnD4sukprOETP0rgP/ZxOIqLPOFgk47Du0SIu+jyvhgQxCjSUJCwRLxeAzLYlnoQw8L3X+7BEYcaO6jlx/bzNu+7+l2WYhL2yk3cZgiG1vRp2DUIXPjs3a8A7MrgZ59sXhgjAu7iBoVLAYTm7sxR0gE9bHIc1WY8dYvWGYWg/st8HgSkiVDqOlW9LBoGJbFSgNHV/wQOx2iTGuu1F0wglFqujVNulV0I6jC4/yWZhXD0uo2M2FYTGYJiabbLhUsqu/KH4AoMd1yhiViW3PsaH69JPQP9z+D+xVzXETTLU2MNp3WHKVgYQyKyLDEkYRml6vEgyN0CUn2iUo65QVLJEmotZ9iSkIs+PLIsoJhWdFLtVkEKxILOYcXJiJ2naj2sfjxDJZh6XuIGR7M3b5/dllpuJX9zsTQZJJ2GyVWPSmwmRSbx8vSC4TImNQaLl+QZdknoTksISZII0modWGbPto0SDsOMKWZ1AxEa2sOyy5gd5PhkpCCYeFt2SLDok85DpOEPM+LJAmJk4/TPu6mWllHYnQ7A53TJaIR4n1iaPOwxG5rbmcTGJ58fhG//9XH8MF//oH0uaIkRBOjw4YfspuGY6ZGpH+XociGH7pyD4tJIco8RcvVBkmZlktCtYbHLQMrCo/EiZua04SnIhQH5WLwc0QtWNgN1BGFJMRCJvuJYZlsnTMvP3adcp15ZatT6Af759r+ppqJ1itko2zqU4heDM6wzK3gpJWmFii7SxYPeDOGxZ+VoUK3Um4pTtoyjr+5/GxsmZBP2PanLzcvYuykdxwEJjv7j2/ur1qjORhS/Cz1kG4P8U5YFoDGfje73FzY1o2UQu+go3hYlon5VbadQ2EMy4q6rRmgwXfB51e4JBTPw1J3PZ5qaRYcJ6f808K5x6/H9b90Os45Tn6nyG4E5LOEWotYRIalHtN0W5D4NRjYdUO1MLLvkW0LTYyuhgQnvvH0bVip1vFfXmY+j80PjhM8LHXzhX+8XEAx76DW8I8h8dylr1NreCgVHMKwBK+TrztpM/7kstPwKonvQgVVgWQKXdptte7ihRYzvG1Kfq3LIl7z4o342Fv0+3HbZPPzHJYcj7qZaL2ALVg6wBFiugX8O7+gJCRjWMSBfwlJQl2Y1CzDm85UJxmL84ToPpMtAqNkXyxV6igVgswHN90qFhDxIqWThBhM6PMoHpawVsBhTXBcreHyu5pwSUjOsKjkHLFlVwQtgIyGH7YFx6VbsORyDi4/91jl39n+kklCvpSo30Y+0Zp7WMykJBE6SYh9v0vVurQzoz2a30+MZkWF6vsZLuXxG+cdH3Fb9V1CJoWo4zhYP1ri2VKybaRFVt11UUJO6ZHI5xy87VXHRfgU7cdfVMZvvaat+eDRFXhe89ph2i6eBRTzOVyxS78fWUTAYmvSNl0/eBdXRhgWKwl1AG4ebVGJbGjXgbkVraNc1EBNZjSUQ+6Ogd5IQmEYKft3hwAC05FlKORzfPtlPpZaRIZFtoiK9Kas46R9u8xzWGTZEhQjGtMtXWzFSdYM4R4W+fuGHUOsAHIc02nN3S1YwqCThEzzVNRdQtE+m04SYtNvm3NvJPOglNOa65FYD1PwHJY2hoV1CZm9lzj8VDWtGfCzWJL0SOjezwQ60y3L1zpmargrDQ3dxMRwgV83xc+etRyW7KxsfQiVJLR/dkU7wE5sV40kCWkKFtGslwXQDAlAn3LrP0fdKZSM6VYoWJJmWDShcQANjmv/fOy4GS3llUUZS6FdbWtr1ktCsrbS4PP9gtfkotyWw9Lj6G52c7BUbSgNpKFdQkJMfcOwu0iErkuIFqqyvCHRPE8To6MYYU3BvSUqD4vhZ6cmesdpl63ouckYLN2Q0KjQMTom0ElC+1sjV45ZZ+4N6hc4juMbjoXPnjVJKDsrWx9CbM9lBctipc4j+k1MtyaSkJnptjeSkA40QwLQZ7Dw52g6hcJNt+FtzWLsvCyETASL5jcy3YaELTG9Xsew6DoRwiQhlf8kbJZQJYShEdHtpNsw0PNKLASMTbfCeRY/h0WeHgsEE45leUPt0fy+Dywq6xFlWzvpEgKCJnpZ0es4jj8GoLVfws6VKFCNAjAFu44zaYRiP2FYBhEqdinJ7ycJ2IKlA4gZHsOlPP//n07PA5D7EESZyGSBiNLW3OuFg4J7WJgkZMCwqIYmAv6FLmz4IYMJw2KiSecVnRQyMMo/FsOyqo/lB0jwXVvSbYthidklFPZ8Ed1uaw5DqeDn/ojhcXFNt2l0CdEiRc6wBCeN08RoVsQnyWYVFNtaDcl8EUHPI9X2cYNvSxJSJd3GgWoUgCkmhoq8MJ1dCsqKTBLaoehQ63eo2CV+LbMFS3+j1nClGR7sgGbTL2WS0FAxH7gImOi37K5Z3yWUQQ9LKTjfw6hg0UpC+oh1s1lC0SUh38NizrCoLsI6D4tOSmRQelhCWpJ5cFyIJGTS0gx033RrgglFpxAvdGPOEspF9C2w92m4XtuEdfq9iZ1idOI2T7olxyvrsktyMnaBy50KD4sxw+KfRyqWryjsX1a8mfj4wiAef1GPx1zOl0ZmhHj+A3PLAAaXYWEss5jyGyZvdxu9v8L0KWh7Ls3wYJ1C7EKkulOmC1KU4DjdtGbeJZSRahjwiw+xS0hXJIit0BRhpts2I6gkLp5Odg3bFvocwGyWUJguz36vk4R0DEvY8ENl0q0wiVhElMGHgCTzJgPj51Vpt2FDMxnaGRYz74sIenyJshAtUkSGhbJf7HumidFzrVb8JD0sqhlT0SUhUrAoioWi4BFaaR1zSXShdNrWDKilkQNzg82wqCWh5vdjGZY+B6MMp4aLAX1brMBVXgQqFQ1L4ttFcEnIYFpzlhiWUV58NC/S7M7FxHQr0/frIW2mbdH8CraAyh5GDAvPYTHvElIxLP7wQ7UkZORhUcwSUjEkYUVvmAdGhLivk/RVxIUq7dbYdKuQhOLOEmq+d7DIpYXqssAiUsmXnsdMJo072E8H9j2K2xnVdGtWsAQL/zD5NArEcz2q6RaQSyMN18PBuebcN1VoYb/DZ5bkklASkl0S6P0Vpk+hWnjFgkV1pzweYFjCJaGSiYellr2CZYR3OIim2/aUW/6cUrAVmiLsTrktOE41c6UYrWDhSbeKoZ4UoW3NRT8iX5SYVBO+KcoKSYibZuN6WPjzzY6fNtNtFhgWRWuz6Uyg9hyWeB6WYAuv3BwNtLOI7LvJ55wAi8iM6AxJym8FhemW7QPT9woWLGaSUJKSQ5uHJcY+ojORGA7Nr6LueijkHGwe75/QuChgHV6zCtNtEpJdEuj9FaZPoVp4xbY31cJDW5tNTlaTtmbVDI9eYkyI2j+yFD67R2yFpqiFBIC1B8fpM0kAffHEECXpNizOmn7foizkDz7UmG4VkpDp8EPXa1+cTJ4vImumW4BKQgLD4uq9TwxMQuw0hyWfc8BqHFFqocya2AnHjfPCvh0VfG5JFod+yJ18+KEpmxMw3YYwLFwSCinuo0C8OYmzj9h1aZbME2Jy0LapochMW79AZbplx6oNjutzqLwYosYpi58HgpR/Ykm3GewSGiGSkOt6/EKg9bCUg63QFI2QhceUYaGyybrR8NkgvEvIwMMSZrpttny2HivIQlEkIfFYCDXdkn0h614ZBNOtLwmJHhYz0y0bBNhplxCgzjeh37nIsIiTmhlGhRDBNCQhkXlL08PiS0IptjV3ULBQaYR3CEWYz9Rv8D930Gy8anNYBgN88KGQ4SFqnGrTrf97o7bmop7OB7LZJTRGJKH51RpfAHRFgtgKTVELofZNs0HYPh8vF4wYqUgMS8hF2HEc/jdR1lkwkIRCc1hCJCFAfhythjBDItoKlkxJQsFCwNh0K4TrxZ0lRF9LlIQoq7ZUFYtOuazbXrAkd6fPiwgxOK4era15aqTEC/GwLqGaIAkl4ZEodZh0C8gHIB7goXGD6V8BfJbZ5rAMKDjDIsRRTwwVA0WKKl49epeQgSTEu4Sy87WOlPwWZXbXElYk8FZomek2xDxpzrA038MkNI6+bhJJt4D/nYvGWyNJSDH8cDXk+y/kHL6gVBrtxSDvMoqZw5KFQpntN7FLqGY4E4h7xTr0sADqoYJ6hkUe/jgqHEtJsllhwXGmhWietAWHmW6zmHQLyKWR/bOD3dIM+J97bqUWuCmzSbcDgiMaaYMd2Lp4dXZhLeQco4tPtC6hbBxcQLDjR5y9pAJvhZYFx/EcFkMPi7K1uPk4cf6JCvkIDIvJST6syGKZj5B0KxavYbOEaNKojGGp1ORyhApZS7oF/P0mSkJRk26rdRee5/GutDgMi88mBI+ZgIdFYBFV4zVEhqUbwXEVLgmZf/Z1I839rypYCkQScl2PH8OJBMeR47aUNxsvIUImjfCU2wFmWKZa35vn+f4d+v1YhqXPwSjDDZLFlw1B1C067G+mB0L/dgn5TMLMYnhLM0CTbmWSkH4BMWZYWkWd6eRVn2EJb2teNaBRfYYlWJQtmCTdFlTBceGmWV2n0GpIjouItllCGTjumNSqkoTCFt8yye2pNbzYs4Sa7yXPN9F1CYkptwwiw5LG8EPx2GZSVpR2dSYtKGVJsk9osZ50l1BcyUzGsAx6BgvQPJ6Y35J1CtHvJ4nhlEmg91eYPoUuAI0d2DofAvub6Ynqm27DPSxZWDgYqCTG7lTCioRRzSyhMNMtpe5zjprK55KQYcESx8Oiu2tkfxOLDrOkW7kkxBgSXVuyLotl1eD5FFn2sCxUBEmISYkh3T4Bn0/DjZ10C7R3xDAEPSwqSSjEw5KoJKTPYYlSHLHzKaxLqCoULKYypA50n8XdP6I04nnemjDdAv51eUZSsGTlJjgbZVMfQjd1mElCurtk9jfzgiXYbimD6mLXS5QLOeScZivts0eaWnCYDDMqtEJT8KRb1SwhcnEtaaYOM/q4Uw/Ls0eW8e6/fyhwR8b+X8dUDEk8LK7r8c4o2Qwq8bkq062WYdFIQlGD43Kt1l3XaxWHWShYhhUMiyFTIhqTTfNbZDCRhEQPk9LDkqIkpCqs/BEBEQqWMb2HhUpCK9y/kkMugXZh+t3F3T/s2uR5wNxyFa7X/E4cB9g6OZgZLAzrR0t4+vASv34l/f0kgd5fYfoQYe255xy/DgBw2jGTytd46dZxFPMOXrZ1wug92QLbb9OaHcfhF9t9rYIl1MPCJCFZEmyLgRgtyz8jvevX7YdTtjX3+5k7prTbwl9XMUvoridewE+nF/D8QoX/q7seinkHL948pny9TeNN6vyZmWX+u6VqHezl9cFxLYal3oBHguxMJJ2iThKKmMMC+AtQVlg9tuAcXqwE9o3PsOgvvPmcw4tTWrB05mHRMCyiJKTwEbVLQil0CYnDDyPmsADAWa3z6eSt49K/yyShpPwRAYYlZsFCpZEjS1UuB20ZH8rMMZ4WxJZuv4MrO7xGdrakj0Dbc2UFy8uPXYeH/uCitg4iih3rRvDA712kZWEoTHJYuGEvQ11CQLMAWVit49moklBF9CG4mJ5vRmTvWCenZ4MFi3o/vOs1J+DNZx3DC4cwqHJY2DZeePJmXPuGk/jvt0wMYeOY+rVfefx6fOMHB3H/niO4+vXN3zHDbSmf0247Kyg8r0mtlwVPiy5HxYRhMc1hAZrTj6vIhhwEANun/Flec8s1rGsda76UGL6dpXwOK24jyLDE8rC0MxcN1wvse1ESUo3XGCEMSzHvxDKUqsCTbsXhhzEkoV995U68/uTNyvOK7hNfOk1mGaI3KJ0UFxtGSzi6UsPMUpXHVwyy4ZaB+3cWgwxLVgy3gC1YYoFRZmOa9lzdYsWwztA/AUTzsGRJEgL8AoRJQutDkmX9gYnNsDlGR07Pr6Lheijlc9ik2L/0TlhXuDmOY1ysAMTDIkTzs/EB26eGcep2NaMm4twT1gMAHt57BPWGi0I+xw23E8MF7YJEv9/Vml+whA0/BNrbdilWI7Y1A4xhabRlYPQKQ8U8No6VcXixggNzK/wc4zksBoVHqZDDSq0R8LAk1SUkdoWJxnLfOB/cn2OBgiXZ85vOEvI8jx97tRhdQgC05xXtSFquRi+QdaDneycMFJVG2JTmQTbcMviG42ZzRNZamgErCcWCycThpMEXJU1bcxY9LIBfgLDtC2VYyB0XvcAz89u2qSGlpuo4Di8ukpTGVG3NrPVanPUShpduGcfkcBFL1QZ+9Nw8AN93Ma6RgwDWstn8fyYh1Bo+G6ArOLRdQhGD4wCQfZ2dY47dDbP8DIDmsBgwLGQfJZJ0S4pDMdlYlcMiMgTUwJ20NEEXd+rR4jksqRh8XX68JcewBP1rcbGOSCPsmjPIGSwMvGBpTQQ36XbsNrJzlekj6Ay3aSFs0i6gvjvrNcQ5KGHM0lAxx+ew0As6T5wMuXiw4iLRrAqF6ZZR+mMRL7q5nINXHt9kWe7fMwPAb2meCJEJHcchrc1u67/E0R9TEqrEkIQYY5ElfX/HFCtYmsdLs9uj+TeTwoPvo4bbUQ6LLDhOLFgqdTcQ2KZiSdNkWOjrUcmTHSNJnkclKgklvCCWEvCwAP4N1exSdU1ksDDwlF+RYbEFS3/DH3zYC4ZFN0so25IQQ9h+cxxHarzdb3i3wy5WSXp5fIYluNAzSn9EkWisw66WLPTAniMA/HTWMIYF8IsK9p3TjiHd969nWGKYbluMRVY8LIC/uLAClxYMJpJQWcqwRP98JYmZlS3S1LtGj3GVcZ6yEEnva7pPauT4roWENMZ7L4kklJDkEMxhib/NNIvF9CZpEMCkeubbSXKSdlLIzlWmj8AKligelE5h0iVUzWCXENDekmnCTMlam3kegsJwy5BPQaYohJhuxyJKQgCw60S/YGm4HpeEdC3NDGJrsz9HSJ/wqc1hqccw3bYWOzY0MAtgfoMDhGFhMDLdkoIlCQ8L3dcsKHBiqMgLD3qMq4zzY4LpNkkUSTEWYFhimG5D30vSJZTUJOCAJJRAwUIlobBrziCANYkcEbqELMPS5+gFw8Lv1lyvbeYHQyWjXUL07rBcyBnFcI9IOoVMh5CxC3qShRu7poseFiYJxdHhT9k2gbFyAfOrdTw+vUAkIROGpVWwtIqMSkgsP4OJhyXKfuNtzVliWARJiC7CRpIQL+oaaBgOTZRBKgmRYX9+CrR/jKtYUuqRSlp+Y3k6QHCeUBoeloAk1PrcSd3BJ+VhYdLI3sNLWGhdf9YEw9L63LPLVXiex7+fJMYmJIXsXGX6CD0x3ZIiRHZ3XCcdDVlaPIAg+7BhtGTUkkmnPDNE9bCkwbC0FSwtSUg15FL7mvkczjmumdlz/54Z3tZs0urOPhsrMnw5R/+ZtR6WiNH8gL+QZ8nD0iYJEZnDhCmh+6gThkUmCa0Smp3JnoukU4j50MT9maaHBfALT3ptScPDQjunVqqtOTUJLYiO489l66xLqCmN/HS6aYbfMFrKlCySFtgNeK3hYX61zr+fpCS7JJCdq0wfoRemW3rRkHUK0QtNlhkW02RZVtWzgsB1SUR2CMPCfRUJLqKqpNulDu9CziU+FpNYfgaVJGTMsGii+aMUer7pNjsXNVbQHl2pYbFSD8wRMimW/bldyQTHBSUh/3ti3i465FPlYWGJ0fR1k4SsuOJtzQnKfQWJJJSk5FDOs4KlA0loxF+4gbVhuAWaxyS7js0uVbFca13brCTU32Auatngw7RQyOf43axssaFFTPYYFr9gMZ2OPCZ4WA4vVlBtuMgZRGQXUpCEVLOE2PaJPh1TvIr4WOYNBh8yiPOEOMMS8plVkpDneQNjuh0fKvK00gOzK3zhNS06igGGxSwhVwZdl9BIKc+PmcVKuCREE6PT2NdieJzneamYbosySSjJgqXY+fEo3lSthQwWBurfWbU5LIMBlgQYFoCWNHh4nIZhKeScTMx0oRgRJCGj5whdQvtb9P7WiaHQCyjPBkmhS0hMA11uMUBxC5bTj5nCUDGHmaUqHt03B0A/5ZvBZ1gagf+GS0KtmVRC0UvN3HFMt1nrTPN9LMt+yq1hpw9loVh92gnDIusSGi76ktCytEuofVvZ49OQ32h4HP1v0u/HXqve8FLpQmE3KZ0m3VKsBf8KwwbSIbUSI5cpbWTrKtMH8DzPl4QM2YKk4FPV7a3NfgZL9r5SyrCYFnlil9CBCHkI7OKbqIeFzxLyf+d5HpeEVLONwlAq5PDyY5s+loNHm2MHjCQhlsPSWuCY+TZscKGKYaFFcCyGJWPHHfWxcJbE0NfAPgvNTInV1lzw5Q+GFXLXyo6ZRWmXUPt3wB6fdJcQfU1WXNGCNtk8I78YTCPno8w9LPG3mUojwNoqWNaRtNtlg6nz3Ua2rjJ9gJVag98FmfoxkoIYwU7BipisLRxA0MNiKqOxYW+sIIiSh+CbbtOQhPx9v1rz78DFcLwoYD4WhiiSUKXNdBuvYGEFTz7nRLrYcw9Lxlg9dpw0JSEWy2+2jcwHQZmPfIwiQSbhyhmWdg+LbH+yIj4d021wW2vk+EhLElqtJb8glhIoWICgP/GYNdDSzBCQhGxbc/+DheqUCrm2Cappo1zUMCwZzWABguyDqVFZZFhYzLpJHkIxhfRVPvyQeFjonXEnJ/WuEzYEfu5IEgr5zGVVwWL4fBFZ7BICfN/B/rkV33RrKOuwz7IcYFgSkoQIq+B7WKgkJJ/WDPhFcTGFfe1va6tgaf0358STw1SgrFM6klCrYOnQKExlobXkYeGS0GJ1bcwS+vCHPwzHcQL/Tj75ZO1zvvzlL+Pkk0/G0NAQTj/9dHzzm99MerMSA81gSXJiqgl0AxB1F7peg7IPpqZbv4Oi+bmiSELptDW3m27ZnfFoKa+cbWSCs4+dCtD8JgWL39bcPBZMW5JLks4V+jpR9Wqew5LRguXA7ApvazZlSXxJyC9IY7U1yyQhWQ4LNd1qpF12TpRTYFiKQkFeTSGDBQh6ZdKRhJLZR+sCDMvaKViYZE89LAPPsJx66qk4ePAg/3f33XcrH3vPPffg8ssvx7ve9S48+uijuOyyy3DZZZfhscceS2PTOkYvMlgY9JJQdj0s1JBqLAkJHRRRJCG2iCa5L2RtzYt88GFnw9uGinmcuWOK/2wmCcUz3bLCSMmwRLw4pcFmJYFjpppM3P5ZyrAYmm5lklCMmxOZJETj6P3xE+FtzUB3JCFWXLHjI/m5RbRLKAWGpZisJDQ+VDDylA0KOMOyXE3l++kUqVxlCoUCtm7dyv9t3LhR+di/+qu/wiWXXILf/u3fxste9jJ89KMfxctf/nJ86lOfSmPTOkYvMlgYuOlW0iU0cJIQ6aDwPC+i6ZZ1CaXb1swWnzihcSJYTL/jmA1SZJ+Nfe8V4xwWedHLM1giMnRZbGsG/OPk8GKFFwRRTbfLrX2ScxCLQdN1CY0QSWiJSEJVzY3HCJeEkmd2WZH/0+kFPLT3CHY/Owcg+e+1KJOE0jDddlhAs4V7LRluAWq6rQbYwKwgmbneAp544gls374dQ0NDOO+883D99dfj2GOPlT723nvvxbXXXhv43cUXX4xbb71V+fqVSgWVSoX/PD8/n8h2m4BlsPSGYen/LiHTtmbKsMwt13h7s8kFpJgCw5IjBYvneXAcx2dYEjihzz1hA2747lMYLxeMFse2HBZDhk0VHMeeH5bjIiKNQZNJYN1IEcPFPFZqDTx7pOl/Mu30EbuE4nQIAcHFmYHetbIJ0ksGOSyAz7yx1vQkwSSUj//bTwO/T5o5K5KZXOkwLK225g4LrQ1jTWlkLflXAGK6Xazy4zZLbc2JFyy7du3CzTffjJe+9KU4ePAg/viP/xivfe1r8dhjj2F8fLzt8dPT09iyZUvgd1u2bMH09LTyPa6//nr88R//cdKbboTn5pqtp1sn9OFlacDEw5I1ah5oBnm97VXHwoGDKVMPS8mfs8LkoI1jZaOT5/Jzd6LWcPG6kzbF32gB1HTZcD0U8k7HGSwU5524ARefugWnbJs0enxbW3PUpFuh6J1bbjKHJoMXKX755cdg/+wy3nDK1kjPSxuO42DHumE88fwi9hxeAmDeDuybbpuFRMx6hZt8ZQXLUDGPXEtmMpWE3nTGdnz/2Tm86cxt8TZIgytedSyOLFcDDKID4PJz5TeacVEk3UgrKXhY3nrODswsVnDhyzZ39DoXn7oV3/3p87jiVccltGX9AZrDwq55ceakpYXEt+TSSy/l/3/GGWdg165dOO644/ClL30J73rXuxJ5j+uuuy7AyszPz2Pnzp2JvHYY2OLZi8qbXcR0c2CyyLAAwJ9cdnqkx1O6fH8EOQgALjltGy45LdmLOjVd1l0PhTxJuU3gDrFUyOFvf+MVxo9Xe1gMTbfCMTTLzeTRwhDPf/FGnP9iteTbSxzTKliemWkyLKbGWbaPVmosjDEmw8INzhJJqFQAIwGWAl1C6gGmp++YxBf/r/NibUsY3nzWMXjzWcek8toUMkkoyQXxgpduxgUv7axYAYATNo6mtq+zDBbVwb4bIFum29RLp6mpKZx00kl48sknpX/funUrDh06FPjdoUOHsHWr+o6tXC6jXO5uyixDFC9F0vDbmvvLwxIHzPeyRBiWHT3Uk+mixe5C/dC47t+BqKL5wwrWskIS6qWZPC0w+XDvTJNhMc5hYR1YVT+bJg7Y4lyXSULFPGcb2HFUb/izi7J649EpmCS0UvUHS2ZpQVzrGC8XUMw7gaTjLH0/qZ8Vi4uLeOqpp7Btm/yO97zzzsPtt98e+N23vvUtnHdeNqtbv1ul+2FC7M5P5mGpau7M+hE0h4VlsPSyvVBkWADKsPSiYGmZZ+MOPxSK3l6aydMCO16YhyWyJNQa/hYngwVQSEJ0WrOQNRQYYDogNx4imGGYDfoEgKHSYFyzBgGO47RdAwa6S+gDH/gA7rzzTuzduxf33HMP3vKWtyCfz+Pyyy8HAFx55ZW47rrr+OPf97734bbbbsNf/uVf4qc//Sk+/OEP46GHHsI111yT9KZ1jIXVGo62TrSeMizSLqHok3azDEYT1xoe9rY8CL00wNFFy+UMS3IelqjgDEs9aLqNW7AMIsPCQgZ50m1M021shkUiCS1LguOYFyowwHRAzmMR4gTrfM7JXIfZWgfNymomX3c3b0yHxK+0+/fvx+WXX46ZmRls2rQJr3nNa3Dfffdh06amAXLfvn3IkQvH+eefjy984Qv4gz/4A/ze7/0eXvKSl+DWW2/FaaedlvSmdQzGrkwOFxNpZY0KbQ5LLZ2gp16B+kJ+dmgRQG9bDHM5B44DeJ6EYYk5R6gTcNNt5OGHfnAXxUAyLMLxYsyw5Jnhm3UJJScJ0Tj6ETJ+wvM8fl4Xck6i6bJZgpiFM1zMdz2A00IPmpU1krHvJ/FV95ZbbtH+/Y477mj73Vvf+la89a1vTXpTEgfzr/TqTl8Vqw4MnoelkM+hXMihUnd9Ga7HLYaFXFPb5R6WBLuEoqLMTbdCDkvI919SdJrRBOdBgXieGptuGcPS2qdx5ggB7ZKQ53lSScj1mt/joLGkMogZMlmSGyyaoANqhzL2/QzumZECoqStpgGfYZHNEhq8i53IYvU6xMlPu20uQEl2CUWFynQbt62ZdQl1e6Bnmtg0Vg7IDaamW7aPWE5K5zksftw9K3aHivmAmXGxUicdQtlaJJKEmECbJUOnRRP0piVLoXGALVgioZcdQoC+S6g6YAwLAD5rBWjKcOM9jshmC1c2uoQESageTRKiBk/X9TC7PHiSUC7nYPuUn5dkOvxQlI469bAwhmW16u/zkdb8KZo3pEu5HRSIklDWFkSL4DUgawXl4J4ZKYDngfToTt/vEtJIQgPSJQQEu296za4A7fOElnjSbS/8TMx0G61LSCYrzq3UwPLCTIdT9gvozUXUtmb+vJgFS0GQhGjXEStmRkii8yCypCJESShLKaoWTdDBj1n7fgb3zEgB+3loXPdbmgHaJSSThFqm2wFy3FPmotf+FcAvWBjDkuQsoahgF5Jq3YXremRas1k0v+v5ZlA2bmJiqJDKYL1egha6UU23DLmYpsOSIAnJoujZsbNcbQyccV4GKwllH1mWhLKTudsH6L3ptrVINWRdQvGG12UZ9GTJFMPSWoD8ac298LD471ltuP7wQkPTLXteIZ/DzGLLcDvWmzDGNEHzkqKabhlMhyaKECUh2bA/dowvVupAi+UaJFlXhMhWZW1BtLCS0EBgtdbA4cXmnWjvTLf9Oa05LihzkYUhZOLE5p4yLGRRXak2/KTbkIKV3uEyWWgQM1gYApJQxBwWhrgeFlESkjEsNItlLUhCjhPM9chaF4pFkGHJWhfX4J4ZCeO5lhw0UspjaqQ35k/dtOZBNOxRb0gWChaxSyjJac1RUcjn+IK4sOoPzwvTnAutPBmAFCwDaLhloMdN1KRbhrgeljZJSMKwMNPtUqBLaHDOYRlo0TySsTt4C8uwDAT2EzmoV0E6qgyN5u8G7+5srEwlod74hijYwuV6HmoNly/4vWBYAL84mVup+r8LYdgcx2kzbx9ZHLwMFgbKhhq3NeeTYVjYwtxwPbiu56fcShiWpWp9IFlSGWjBkrU7eAtgaqTEb2qy9v0MzuqWMnqdwQKEJN3WB8+wN5JR02294fE4daB349eZwXZuuTkuIueYsQg8i6UlVbCU23UDWLBsnRwCqzdM25rbGZa405r996u5biDlloF1wlGGZZCM8zLQ/ZK1O3iL5nVuaripItiCpU/R6wwWgOawqLuEBunujDEXw8U81vVIhqOgOSwsg6WUz/WsSGTf9VxrvtWQYYy22No8iCm3DMV8DlsnmlkscduaO2VYgKYsRCc1M/gMS2MgjfMyWIYl+2CyUNYKysE+MxJEL6c0M+ij+QfvYsfuRHspw1HQHJalHnYIMTCG5WjLg2KamcDD49aA6RbwYwiMu4TyIsPSecFSb7hEEvIZOTaHKuBhGSCWVIZAwZKxBdGiiQ2teP6sdXEN9pmRIPbPNkfU99L8qZOEBtF0u2m8edKcuGm0x1vSBGtvbTIsrTlCPZKDAOJhaUlCQ4bfvUoSGtSChR0/pmb5XM4JFClxGZZ8zuFyVLXhEtOt/z1xhqXSGEiWVAbaJp61BdGiCaYk0LlCWYDNYTFEJiShNdbW/POnbMFHLzsNr3vJpl5vCgA5w9KLSc0Mvum2Fvg5DCWBqZvlklC2Lk5J4f0XnYRTtk/gsrOOMX5OqZBDnU1rjpnD0nxuDtW6q5aESJfQIN50yEAZrKwlqVo08YGLX4qXHzuFN56+rdebEoAtWAxQa7iYnl8FAOzoqenW97B4nheQSVgRM0gXu3Ihj9941XG93gwOP4fFxVKl2araizlCDKLp1nRoHi1YPM/jktC60d77hNLA1skhXHne8ZGeUyrkuISTj2m6BZqLc7Xuok4ZloAkRLuEGvy9BxnWw5J9HDM1jN+IeM50A7ZgMcD00VW4XvPis7GHaaCMPXG95l0+c9t7njeQbc1ZA4tor7seao0MSEKt4+HoCvOwRGvbrTZcLFbqXBoaVIYlDigL0AHBws/RWkASokm37V1Cg8SSymAlIYu4sKubAZjhdvvUEHIx9ewkQA211Hhbdz0+vG7QL3a9RMDD0mpr7qUkVBYYlrAMFgbKsDB2ZbiYt3e7BJTl6IRhKXCDsy8JjYTMEhok47wMRSsJWcTEYJ8ZCcEPjetteBm966PGW1q8DPrFrpdgC1e9QTwsmWBYmIfF1HTrD04cdMNtXNCCJW6XEOCfs3XX5QULjaNnXWZrZVozELyO9SrDyKI/MdhnRkLghtseD+DL5fw5HDSLhRYvgx461UvQWUK8S6iHHhbmWWEFiym7RiUhnnI7ZgsWioAk1IEmRCWhZYkkRBmW6hqUhGxbs0UU2NXNAAfmmi3NWUhb5a3NpFOIm/XyuZ5KVoMO1iXU8DwsZyiHhUtChgwLzfMZ5DlCnSAphoVKQqsSSYhOax7EtGoZArOErAxpEQGDfWYkhCzE8jOUJfOEWPEy6Be6XqNA25pbSbdjGchhYabZOG3NPDRuxBYsFAGGpYOChS3OTYaleczIGJZq3eUy41qShKyHxSIKrIBoADr4sNeQpd2ulYTMXoMzLA2Xm25HetnWLEgHphd/JlNUGy6XkyzDEkRyHhZ/wveKZPgh9XAwtmvQz+OAJGQZFosIGOwzIwG4roeDc80MlkxIQkWWdks9LGvDrNdrFCTBcWMZkIQYTA3XdOr3TMvDst56WAJIo0totcWEUoalVMhxxoEF+Jnm6fQrbDS/RVzYFS4ELyxWUG24yOccPkStl2AXN1mX0KBf6HqNvGT4YS+7HERGxbitOe93CR1ZqgAYzMGHnYDKFp0wLAHTbeuYEVkF5oOa4+bpwb4ss4KlVMh1JLdZrD0M9pmRAJgctHViyHjaa5qQTWy2klB3EGRYmvt/LANJt/7PHXhYbGhcAEGGpXMPS92VB8cBfmu8x7OUBvs8ZkWcNdxaRMVgnxkJgBtuMyAHAfJ5Qmsl0rvXyAXamhnD0vtZQv7PUYcfNkiX0GDG8sdF0gVLpeb6kpBwzIjhg4N+HrN9YuUgi6gY7DMjAfApzRnoEAL8tmbWGQIM5hyhLELmYelpDktM022grXnRMiwylBMrWJrPXVit89+JRa54DA16DostWCziwq5wIcjClGYKOcOyNgKneo08GX64XOl9cFy7JBRtltDCap0H4NkuoSCS87CwfV3jvxO9RmJa8qDfeLDOKdshZBEVg31mJIAsZbAAcg/LWhlL32twhqXhS0KjWZKEIs4SYhPIi3kHE0M24YAiaUmIjk8Qwx1FxmXQx2sULMNiERODfWYkgAMZmSPEIOsS4m3NA36h6zVYRPtStc6HTfZWEurMdDt9tFmwrBspwXFstwZFUjksTBKaX20PjWMQjduDzpRyScgyLBYRYVc4DTzPy6DpluWwtEtCdo5QumAL19EV34/Qy7tEsUAxZdjYcfL8QrOl2cpB7WCt3wCQ7+C8YovzfIthkR0v4niHQWdKWRFnGRaLqBjsM6NDzC7XsNzS+LdN9j6DBfCH1D0zs8R/Zz0s3QHLYWGLz2gp39PZTW0FS0SGpdGiiWzB0o7kGJbWMdPysMhYBZGlG/Qbj5O2jAMATtk+0eMtseg3WOFag8nhIr73wddjen41MzMvXnn8egBP4YE9R/jvKjUrCXUDPsPSXHx6GcsPdGC6Fe7gbcHSjuQ8LC1JaEUeGgcETbdrYYDpz520CQ/9wUU2rNAiMmzBokE+52Dn+hHsXJ8N/woAnHP8OjgOsHdmGYfmV7FlYgiVhjXddgNs4WJ3y70MjQPizxISCxa7cLQjLYZlpNh+zFCGZa2cwxvHbBu9RXSsjbNjgDAxVMQp25pU6v0tlsXPYckGCzSoYAsXk4R6ndTZHhxnmMOSFxkWu3iIKCc0rbkgeFiGpAyL/zvLklpYqGHPjj7ErhM2AADuf3oGADHdrpG7s14hL0hCvewQAiRdQqamW1ESsoMP21As+EVKoYPhhyXeWcZi+dtfix5Hg+5fsbDoBImfHddffz1e+cpXYnx8HJs3b8Zll12Gxx9/XPucm2++GY7jBP4NDWXD5JpFnHvCegDgPhY7rbk7YAVLrdE0q/YygwVojgqgxUdcSWj9iC1YRAS6hDo4rYrCk2XDMmk0vx1gamGhRuIr3J133omrr74a9913H771rW+hVqvhDW94A5aWlrTPm5iYwMGDB/m/Z555JulNGxiwguWJ5xcxs1ixww+7BNHL0GuGBQiyKqYFi7iIWtNtO4Km2/jnlTgwVfYdUdOtPYctLNRI/Ip72223BX6++eabsXnzZjz88MP4uZ/7OeXzHMfB1q1bk96cgcT60RJO2jKGnx1axIN7j/hJt/buLFWIC5cYqd4LDBXzmF+to5h3jL0WbaZbKwm1ISnTLZOEGGS+p7VourWwiIPUz46jR48CANavX6993OLiIo477jjs3LkTb37zm/GjH/1I+dhKpYL5+fnAv7UG5mO57+kjlmHpEjLJsLSKVNNYfqDdJ2EZlnaUEjLdimyWLCwtWLDYmw4LCxVSXeFc18X73/9+vPrVr8Zpp52mfNxLX/pS3HTTTfja176Gz3/+83BdF+effz72798vffz111+PyclJ/m/nzp1pfYTMgvpYWA6LNd2mC3HhGi33fnFh2StR2DVa2DoOMDVcTHy7+h1JMSyiJCTPYfF/Z89hCws1Uj07rr76ajz22GO45ZZbtI8777zzcOWVV+Kss87C6173OnzlK1/Bpk2b8Ld/+7fSx1933XU4evQo//fss8+msfmZxq5WwfKT6Xm80IpYt3dn6aKQzzDDEqEdli6Kk8PFtkXVIljUJREcxxDOsNjvwsJChdSuuNdccw2+8Y1v4K677sKOHTsiPbdYLOLss8/Gk08+Kf17uVxGuby2syM2TwzhhI2j2HN4CU8fbhqa7cUuXbQxLBkY3sa+8yhJzLRgsXKQHAGGJd+JhyWcYaFFjM1hsbBQI/Gzw/M8XHPNNfjqV7+K73znOzjhhBMiv0aj0cAPf/hDbNu2LenNGygwloXBFizpIsselijfPV1EbcqtHEEPS3JdQjLTbS7n8OLXsqQWFmokvsJdffXV+PznP48vfOELGB8fx/T0NKanp7GyssIfc+WVV+K6667jP3/kIx/Bf/zHf+Dpp5/GI488gre97W145pln8Ju/+ZtJb95A4VyxYLFdQqlCXLhkmRrdBlvgojAshXwOrPayDIscyUXzB5+r+p7YXCp702FhoUbiV9wbb7wRAHDBBRcEfv/Zz34Wb3/72wEA+/btQ45c/GdnZ/Hud78b09PTWLduHc455xzcc889OOWUU5LevIHCrhM3BH62KZnpQly4ej1LCPC9K1E8LEBzQV6tuTaWX4Gkhh+K56RqnMNYuYAXFirWdGthoUHiV1zP80Ifc8cddwR+/uQnP4lPfvKTSW/KwOOYqWEcMzWMA3NN9srq3+lCXLhGMtElFL2tGWgupM2CxXYIyZBUwdLWJaRiWErRpT0Li7UGe3b0OaiPxV7s0oW4cGWLYYlYsLQKHMuwyJFcDouZJDTKJaHeF8EWFlmFXeH6HLtOpAWLvdiliTaGJQNdQoxZicquseLWmm7loAVLZx4WM0lo1DIsFhahsGdHn+PcE3wfi5WE0kUWPSysTVYlNajAFkZrupUjl3N40dJJTo3oSZG1NQPA2FBTmrPnsIWFGr2/4lp0hOM3jOBtrzoWDdfDxJD1I6SJdoal96fPL5y+DQ/sOYK3nH1MpOdddf7x+M5Pn8crj9ePzFjLeM/PnYj9s8vYPhl/crxY5I4U5cfMW8/ZgefnV/Hzp9h5ahYWKvT+imvRERzHwZ9cdnqvN2NNoEA620r5XCY6Ol62bQJf/L/Oi/y8q84/Hledf3zyGzRA+MDFL+34NURJaKgkP2Z+7qRN+LmTNnX8fhYWg4zeX3EtLPoElGHJQoeQRfZBi9qcY6MHLCw6gT17LCwMQSPaRzMgB1lkH1QSGikV4DjxDbwWFmsdtmCxsDAEZViyMKnZIvsoEoYlauu5hYVFELZgsbAwRCFQsFiGxSIcVALKQhu8hUU/wxYsFhaGCDAsVhKyMAAtcqO2nltYWARhCxYLC0PQLiErCVmYIJ9zwGwrqgwWCwsLM9iCxcLCEHRYs2VYLEzgOA5vbbYMi4VFZ7AFi4WFIYIMiy1YLMxQbMlClmGxsOgMtmCxsDCEzWGxiAPWKWQLFguLzmALFgsLQ1AD5ZiVhCwMYSUhC4tkYAsWCwtDBBkWW7BYmIFJQrat2cKiM9iCxcLCEIEcFrv4WBiCS0KWYbGw6Ai2YLGwMETeBsdZxACThGzSrYVFZ7AFi4WFIRzH4UWLzWGxMEXBSkIWFonAFiwWFhHACxZrurUwRMl2CVlYJAJbsFhYRECBMyy2YLEwg+0SsrBIBrZgsbCIgMnhIgBg41i5x1ti0S+YsseMhUUisLeJFhYR8NeXn43n5yvYOjnU602x6BP8/htfhv/yss147Us29npTLCz6GrZgsbCIgFcev77Xm2DRZzhx0xhO3DTW682wsOh7WEnIwsLCwsLCIvOwBYuFhYWFhYVF5mELFgsLCwsLC4vMwxYsFhYWFhYWFpmHLVgsLCwsLCwsMg9bsFhYWFhYWFhkHrZgsbCwsLCwsMg8bMFiYWFhYWFhkXnYgsXCwsLCwsIi87AFi4WFhYWFhUXmYQsWCwsLCwsLi8zDFiwWFhYWFhYWmYctWCwsLCwsLCwyj4GY1ux5HgBgfn6+x1tiYWFhYWFhYQq2brN1XIeBKFgWFhYAADt37uzxllhYWFhYWFhExcLCAiYnJ7WPcTyTsibjcF0Xzz33HMbHx+E4TqKvPT8/j507d+LZZ5/FxMREoq9tEYTd192D3dfdg93X3YPd191DUvva8zwsLCxg+/btyOX0LpWBYFhyuRx27NiR6ntMTEzYE6BLsPu6e7D7unuw+7p7sPu6e0hiX4cxKwzWdGthYWFhYWGRediCxcLCwsLCwiLzsAVLCMrlMv7oj/4I5XK515sy8LD7unuw+7p7sPu6e7D7unvoxb4eCNOthYWFhYWFxWDDMiwWFhYWFhYWmYctWCwsLCwsLCwyD1uwWFhYWFhYWGQetmCxsLCwsLCwyDxswRKCG264AccffzyGhoawa9cuPPDAA73epL7G9ddfj1e+8pUYHx/H5s2bcdlll+Hxxx8PPGZ1dRVXX301NmzYgLGxMfzyL/8yDh061KMtHhx8/OMfh+M4eP/7389/Z/d1cjhw4ADe9ra3YcOGDRgeHsbpp5+Ohx56iP/d8zx86EMfwrZt2zA8PIyLLroITzzxRA+3uH/RaDTwh3/4hzjhhBMwPDyMF73oRfjoRz8amEdj93c83HXXXXjTm96E7du3w3Ec3HrrrYG/m+zXI0eO4IorrsDExASmpqbwrne9C4uLi51vnGehxC233OKVSiXvpptu8n70ox957373u72pqSnv0KFDvd60vsXFF1/sffazn/Uee+wxb/fu3d4v/MIveMcee6y3uLjIH/Nbv/Vb3s6dO73bb7/de+ihh7xXvepV3vnnn9/Dre5/PPDAA97xxx/vnXHGGd773vc+/nu7r5PBkSNHvOOOO857+9vf7t1///3e008/7f37v/+79+STT/LHfPzjH/cmJye9W2+91fv+97/v/eIv/qJ3wgkneCsrKz3c8v7Exz72MW/Dhg3eN77xDW/Pnj3el7/8ZW9sbMz7q7/6K/4Yu7/j4Zvf/Kb3+7//+95XvvIVD4D31a9+NfB3k/16ySWXeGeeeaZ33333ed/73ve8F7/4xd7ll1/e8bbZgkWDc88917v66qv5z41Gw9u+fbt3/fXX93CrBgvPP/+8B8C78847Pc/zvLm5Oa9YLHpf/vKX+WN+8pOfeAC8e++9t1eb2ddYWFjwXvKSl3jf+ta3vNe97nW8YLH7Ojn8zu/8jvea17xG+XfXdb2tW7d6f/EXf8F/Nzc355XLZe8f//Efu7GJA4U3vvGN3jvf+c7A737pl37Ju+KKKzzPs/s7KYgFi8l+/fGPf+wB8B588EH+mH/7t3/zHMfxDhw40NH2WElIgWq1iocffhgXXXQR/10ul8NFF12Ee++9t4dbNlg4evQoAGD9+vUAgIcffhi1Wi2w308++WQce+yxdr/HxNVXX403vvGNgX0K2H2dJL7+9a/jFa94Bd761rdi8+bNOPvss/GZz3yG/33Pnj2Ynp4O7OvJyUns2rXL7usYOP/883H77bfjZz/7GQDg+9//Pu6++25ceumlAOz+Tgsm+/Xee+/F1NQUXvGKV/DHXHTRRcjlcrj//vs7ev+BGH6YBg4fPoxGo4EtW7YEfr9lyxb89Kc/7dFWDRZc18X73/9+vPrVr8Zpp50GAJienkapVMLU1FTgsVu2bMH09HQPtrK/ccstt+CRRx7Bgw8+2PY3u6+Tw9NPP40bb7wR1157LX7v934PDz74IP7H//gfKJVKuOqqq/j+lF1P7L6Ojt/93d/F/Pw8Tj75ZOTzeTQaDXzsYx/DFVdcAQB2f6cEk/06PT2NzZs3B/5eKBSwfv36jve9LVgseoarr74ajz32GO6+++5eb8pA4tlnn8X73vc+fOtb38LQ0FCvN2eg4bouXvGKV+BP//RPAQBnn302HnvsMXz605/GVVdd1eOtGzx86Utfwj/8wz/gC1/4Ak499VTs3r0b73//+7F9+3a7vwcYVhJSYOPGjcjn820dE4cOHcLWrVt7tFWDg2uuuQbf+MY38N3vfhc7duzgv9+6dSuq1Srm5uYCj7f7PToefvhhPP/883j5y1+OQqGAQqGAO++8E3/913+NQqGALVu22H2dELZt24ZTTjkl8LuXvexl2LdvHwDw/WmvJ8ngt3/7t/G7v/u7+PVf/3Wcfvrp+I3f+A38z//5P3H99dcDsPs7LZjs161bt+L5558P/L1er+PIkSMd73tbsChQKpVwzjnn4Pbbb+e/c10Xt99+O84777webll/w/M8XHPNNfjqV7+K73znOzjhhBMCfz/nnHNQLBYD+/3xxx/Hvn377H6PiAsvvBA//OEPsXv3bv7vFa94Ba644gr+/3ZfJ4NXv/rVbe35P/vZz3DccccBAE444QRs3bo1sK/n5+dx//33230dA8vLy8jlgstXPp+H67oA7P5OCyb79bzzzsPc3Bwefvhh/pjvfOc7cF0Xu3bt6mwDOrLsDjhuueUWr1wuezfffLP34x//2HvPe97jTU1NedPT073etL7Fe9/7Xm9yctK74447vIMHD/J/y8vL/DG/9Vu/5R177LHed77zHe+hhx7yzjvvPO+8887r4VYPDmiXkOfZfZ0UHnjgAa9QKHgf+9jHvCeeeML7h3/4B29kZMT7/Oc/zx/z8Y9/3JuamvK+9rWveT/4wQ+8N7/5zbbNNiauuuoq75hjjuFtzV/5yle8jRs3eh/84Af5Y+z+joeFhQXv0Ucf9R599FEPgPeJT3zCe/TRR71nnnnG8zyz/XrJJZd4Z599tnf//fd7d999t/eSl7zEtjV3A3/zN3/jHXvssV6pVPLOPfdc77777uv1JvU1AEj/ffazn+WPWVlZ8f77f//v3rp167yRkRHvLW95i3fw4MHebfQAQSxY7L5ODv/yL//inXbaaV65XPZOPvlk73//7/8d+Lvrut4f/uEfelu2bPHK5bJ34YUXeo8//niPtra/MT8/773vfe/zjj32WG9oaMg78cQTvd///d/3KpUKf4zd3/Hw3e9+V3qNvuqqqzzPM9uvMzMz3uWXX+6NjY15ExMT3jve8Q5vYWGh421zPI9EA1pYWFhYWFhYZBDWw2JhYWFhYWGRediCxcLCwsLCwiLzsAWLhYWFhYWFReZhCxYLCwsLCwuLzMMWLBYWFhYWFhaZhy1YLCwsLCwsLDIPW7BYWFhYWFhYZB62YLGwsLCwsLDIPGzBYmFhYWFhYZF52ILFwsLCwsLCIvOwBYuFhYWFhYVF5mELFgsLCwsLC4vM4/8H2ayU5xk7GMQAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "get_shortest_path(9, 2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qOKKjcDXzLxg",
        "outputId": "5c9111f5-f087-4dd5-8ebb-c5219d85d9af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[9, 2], [9, 1], [10, 1]]"
            ]
          },
          "metadata": {},
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "b1Pj3m0FyXQe"
      }
    }
  ]
}