Ex9.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yy_gJ2K_QlGR"
      },
      "source": [
        "## Basic Setup"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ugp5nutQyQZi",
        "outputId": "4b4772fb-128c-41f3-c5ce-e7813453dd51"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Python Version 3.7.13\n",
            "PyTorch Version 1.10.0+cu111\n",
            "Numpy Version 1.21.5\n",
            "Pandas Version 1.3.5\n",
            "Matplotlib Version 3.2.2\n"
          ]
        }
      ],
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "import os\n",
        "import os.path as osp\n",
        "import shutil\n",
        "import sys\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as O\n",
        "import torch.nn.functional as F\n",
        "import torch.utils.data as D\n",
        "\n",
        "\n",
        "from platform import python_version\n",
        "print(\"Python Version\", python_version())\n",
        "print('PyTorch Version', torch.__version__)\n",
        "print(\"Numpy Version\", np.__version__)\n",
        "print(\"Pandas Version\", pd.__version__)\n",
        "print(\"Matplotlib Version\", matplotlib.__version__)\n",
        "\n",
        "pd.set_option('display.max_columns',None)\n",
        "pd.set_option('display.max_rows',None)\n",
        "pd.set_option('display.float_format', lambda x: '%.5f' % x)\n",
        "pd.set_option('display.date_dayfirst', True)\n",
        "pd.set_option('display.max_colwidth', None)\n",
        "\n",
        "# from IPython.core.interactiveshell import InteractiveShell\n",
        "# InteractiveShell.ast_node_interactivity = \"all\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ahwIxwNs0WHg"
      },
      "outputs": [],
      "source": [
        "# Reproduciblity\n",
        "SEED = 1000\n",
        "import random\n",
        "random.seed(SEED)\n",
        "np.random.seed(SEED)\n",
        "torch.manual_seed(SEED)\n",
        "torch.cuda.manual_seed_all(SEED)\n",
        "\n",
        "# Number of Thread\n",
        "NUM_CORES = 20\n",
        "torch.set_num_threads(NUM_CORES)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7EeAcc1d0T9z",
        "outputId": "bc9c509e-8046-4d35-a50b-9104f33ed2f8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GPU not available\n",
            "\n",
            "device = cpu\n"
          ]
        }
      ],
      "source": [
        "# Device to run on\n",
        "DEVICE = 'cuda'\n",
        "if DEVICE=='cuda':\n",
        "    if torch.cuda.is_available():\n",
        "        print('__CUDA VERSION', )\n",
        "        from subprocess import call\n",
        "        !nvcc --version\n",
        "        print('__CUDNN VERSION:', torch.backends.cudnn.version())\n",
        "        print('__Number CUDA Devices:', torch.cuda.device_count())\n",
        "        print('__Devices')\n",
        "        print('Active CUDA Device: GPU', torch.cuda.current_device())\n",
        "        print ('Available devices ', torch.cuda.device_count())\n",
        "        print ('Current cuda device ', torch.cuda.current_device())\n",
        "    else:\n",
        "        print(\"GPU not available\")\n",
        "        DEVICE = 'cpu'\n",
        "\n",
        "print(\"\\ndevice =\", DEVICE)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9A0BDLKPQ7dw"
      },
      "source": [
        "## Prepare Environment & Agent"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KkuzE0Ma81Cg"
      },
      "outputs": [],
      "source": [
        "def to_node_idx(side_length, x, y):\n",
        "    if x + (side_length*y) < 0:\n",
        "        print(\"ERROR: Node index is negative\")\n",
        "    return x + (side_length*y)\n",
        "\n",
        "def to_coord(side_length, node_idx):\n",
        "    return node_idx % side_length, node_idx // side_length"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YdnJfVT40wY2"
      },
      "outputs": [],
      "source": [
        "class Gridworld:\n",
        "    def __init__(self, side_length=20, agent_init_x=1, agent_init_y=1, num_apples=10, num_poisons=10, num_triggers=1, num_obstacles=0,\n",
        "                 apple_reward=1.0, apple_sd=0.0, poison_reward=-1.0, poison_sd=0.0, prize_reward=5.0, prize_sd=0.0, wall_reward=0.0, wall_sd=0.0,\n",
        "                 door_reward=10.0, door_sd=0.0, episode_length=6, plot_size=10):\n",
        "    \n",
        "        self.side_length = side_length\n",
        "        self.num_states = self.side_length**2\n",
        "        self.num_obstacles = num_obstacles\n",
        "        self.num_apples = num_apples\n",
        "        self.num_poisons = num_poisons\n",
        "        self.num_triggers = num_triggers\n",
        "        self.init_x = agent_init_x\n",
        "        self.init_y = agent_init_y\n",
        "\n",
        "        self.apple_reward = apple_reward\n",
        "        self.apple_sd = apple_sd\n",
        "        self.poison_reward = poison_reward\n",
        "        self.poison_sd = poison_sd\n",
        "        self.prize_reward = prize_reward\n",
        "        self.prize_sd = prize_sd\n",
        "        self.wall_reward = wall_reward\n",
        "        self.wall_sd = wall_sd\n",
        "        self.door_reward = door_reward\n",
        "        self.door_sd = door_sd\n",
        "\n",
        "        if (side_length-1)**2 < num_apples+num_poisons+num_obstacles+num_triggers+1:\n",
        "            print(\"ERROR: Increase grid size\")\n",
        "        \n",
        "        self.episode_length = episode_length\n",
        "        self.plot_size = plot_size\n",
        "        self.resetEnv()\n",
        "        self.resetTime()\n",
        "\n",
        "\n",
        "    # Reset time\n",
        "    def resetTime(self):\n",
        "        self.agent_x = self.init_x\n",
        "        self.agent_y = self.init_y\n",
        "        self.cum_reward = 0\n",
        "        self.time = 0\n",
        "\n",
        "        self.key_collected = False\n",
        "        for (x,y) in self.triggers_on:\n",
        "            self.triggers_off.add((x,y))\n",
        "        self.triggers_on = set()\n",
        "        for (x,y) in self.apples_dis:\n",
        "            self.apples_vis.add((x,y))\n",
        "        self.apples_dis = set()\n",
        "        for (x,y) in self.poisons_dis:\n",
        "            self.poisons_vis.add((x,y))\n",
        "        self.poisons_dis = set()\n",
        "        self.prize_collected = False\n",
        "        self.door = (self.side_length-1, 1)\n",
        "        if self.door in self.walls:\n",
        "            self.walls.remove(self.door)\n",
        "\n",
        "\n",
        "    # Reset the environment\n",
        "    def resetEnv(self):\n",
        "        self.walls = set([])\n",
        "        for y in range(self.side_length):\n",
        "            self.walls.add((0,y))\n",
        "            self.walls.add((self.side_length-1,y))\n",
        "        for x in range(self.side_length):\n",
        "            self.walls.add((x,0))\n",
        "            self.walls.add((x,self.side_length-1))\n",
        "\n",
        "        self.door = (self.side_length-1, 1)\n",
        "        \n",
        "        self.key_collected = False\n",
        "        self.key = (17, 3)\n",
        "\n",
        "        self.prize = tuple(np.random.randint(low=1, high=self.side_length-1, size=2))\n",
        "        while (self.prize==self.key) or (self.prize==(self.door[0]-1,self.door[1])):\n",
        "            self.prize = tuple(np.random.randint(low=1, high=self.side_length-1, size=2))\n",
        "        self.prize_collected = False\n",
        "        \n",
        "        self.triggers_off = set([])\n",
        "        self.triggers_on = set([])\n",
        "        while len(self.triggers_off) < self.num_triggers:\n",
        "            x = np.random.randint(low=max(1,self.prize[0]-3), \n",
        "                                  high=min(self.side_length-1,self.prize[0]+3))\n",
        "            y = np.random.randint(low=max(1,self.prize[1]-3), \n",
        "                                  high=min(self.side_length-1,self.prize[1]+3))\n",
        "            if ((x,y) not in self.triggers_off) and ((x,y)!=self.key) and ((x,y)!=(self.door[0]-1,self.door[1])) and ((x,y)!=self.prize):\n",
        "                self.triggers_off.add((x,y))\n",
        "        \n",
        "        self.obstacles = set([])\n",
        "        while len(self.obstacles) < self.num_obstacles:\n",
        "            x,y = np.random.randint(low=1, high=self.side_length-1, size=2)\n",
        "            if ((x,y)!=self.key) and ((x,y) not in self.triggers_off) and ((x,y) not in self.obstacles) and (((x,y)!=(self.door[0]-1,self.door[1]))) and ((x,y)!=self.prize):\n",
        "                self.obstacles.add((x,y))\n",
        "\n",
        "        self.apples_vis = set([])\n",
        "        self.apples_dis = set([])\n",
        "        while len(self.apples_vis) < self.num_apples:\n",
        "            x,y = np.random.randint(low=2, high=self.side_length-2, size=2)\n",
        "            if ((x,y)!=self.key) and ((x,y) not in self.triggers_off) and ((x,y) not in self.obstacles) and ((x,y) not in self.apples_vis) and (((x,y)!=(self.door[0]-1,self.door[1]))) and ((x,y)!=self.prize) and (not (x>=14 and y<self.door[1]+5)):\n",
        "                self.apples_vis.add((x,y))\n",
        "        \n",
        "        self.poisons_vis = set([])\n",
        "        self.poisons_dis = set([])\n",
        "        while len(self.poisons_vis) < self.num_poisons:\n",
        "            x,y = np.random.randint(low=2, high=self.side_length-2, size=2)\n",
        "            if ((x,y)!=self.key) and ((x,y) not in self.triggers_off) and ((x,y) not in self.obstacles) and ((x,y) not in self.apples_vis) and ((x,y) not in self.poisons_vis) and (((x,y)!=(self.door[0]-1,self.door[1]))) and ((x,y)!=self.prize) and (not (x>=14 and y<self.door[1]+5)):\n",
        "                self.poisons_vis.add((x,y))\n",
        "        \n",
        "        \n",
        "        self.walls.remove(self.door)\n",
        "\n",
        "        if (self.init_x,self.init_y) in self.walls:\n",
        "            print(\"ERROR: Agent initialised inside wall at (\", self.init_x, \",\", self.init_y, \")\", sep=\"\")\n",
        "\n",
        "\n",
        "    # Accept an agent's action, return new state and reward\n",
        "    # 0 = Up, 1 = Up-Right, 2 = Right, 3 = Down-Right, 4 = Down, 5 = Down-Left, 6 = Left, 7 = Up-Left\n",
        "    def step(self, action):\n",
        "        if self.time>=self.episode_length:\n",
        "            return None, None\n",
        "        self.time += 1\n",
        "        new_x,new_y = self.agent_x,self.agent_y\n",
        "        if action==0:\n",
        "            new_y += 1\n",
        "        elif action==1:\n",
        "            new_x += 1\n",
        "            new_y += 1\n",
        "        elif action==2:\n",
        "            new_x += 1\n",
        "        elif action==3:\n",
        "            new_x += 1\n",
        "            new_y -= 1\n",
        "        elif action==4:\n",
        "            new_y -= 1\n",
        "        elif action==5:\n",
        "            new_x -= 1\n",
        "            new_y -= 1\n",
        "        elif action==6:\n",
        "            new_x -= 1\n",
        "        else:\n",
        "            new_x -= 1\n",
        "            new_y += 1\n",
        "        \n",
        "        if ((new_x,new_y)==self.key) and (self.key_collected==False):\n",
        "            self.key_collected = True\n",
        "            self.cum_reward += (self.door_reward + np.random.normal(loc=0.0, scale=self.door_sd))/10.0\n",
        "        if (new_x,new_y) in self.triggers_off:\n",
        "            self.triggers_on.add((new_x,new_y))\n",
        "            self.triggers_off.remove((new_x,new_y))\n",
        "            self.cum_reward = (self.prize_reward+np.random.normal(loc=0.0, scale=self.prize_sd))/10.0\n",
        "        if (new_x,new_y) in self.poisons_vis:\n",
        "            self.cum_reward += self.poison_reward+np.random.normal(loc=0.0, scale=self.poison_sd)\n",
        "            self.poisons_vis.remove((new_x,new_y))\n",
        "            self.poisons_dis.add((new_x,new_y))\n",
        "        if (new_x,new_y) in self.apples_vis:\n",
        "            self.cum_reward += self.apple_reward+np.random.normal(loc=0.0, scale=self.apple_sd)\n",
        "            self.apples_vis.remove((new_x,new_y))\n",
        "            self.apples_dis.add((new_x,new_y))\n",
        "        if self.prize_collected==False and ((new_x,new_y)==self.prize):\n",
        "            if len(self.triggers_on)==self.num_triggers:\n",
        "                self.cum_reward += 9.0*(self.prize_reward+np.random.normal(loc=0.0, scale=self.prize_sd))/10.0\n",
        "            else:\n",
        "                self.cum_reward += (self.prize_reward+np.random.normal(loc=0.0, scale=self.prize_sd))/10.0\n",
        "            self.prize_collected = True\n",
        "        if (new_x,new_y)==self.door:\n",
        "            if self.key_collected:\n",
        "                self.cum_reward += 9.0*(self.door_reward + np.random.normal(loc=0.0, scale=self.door_sd))/10.0\n",
        "            else:\n",
        "                self.cum_reward += (self.door_reward + np.random.normal(loc=0.0, scale=self.door_sd))/10.0\n",
        "            self.walls.add(self.door)\n",
        "            self.door = (-1,-1)\n",
        "        elif ((new_x,new_y) in self.walls):\n",
        "            self.cum_reward += self.wall_reward+np.random.normal(loc=0.0, scale=self.wall_sd)\n",
        "        else:\n",
        "            if new_x<0 or new_y<0 or new_x>=self.side_length or new_y>=self.side_length:\n",
        "                print(\"ERROR: Agent about to go out of grid from (\", self.agent_x, \",\", self.agent_y, \") to (\", new_x, \",\", new_y, \")\", sep='')\n",
        "                return None, None\n",
        "            self.agent_x,self.agent_y = new_x,new_y\n",
        "\n",
        "        r = 0\n",
        "        if self.time>=self.episode_length:\n",
        "            r = self.cum_reward\n",
        "        return (self.agent_x,self.agent_y), r\n",
        "\n",
        "\n",
        "    # Return the grid\n",
        "    def getGrid(self):\n",
        "        grid = []\n",
        "        for r in range(self.side_length):\n",
        "            row = []\n",
        "            for c in range(self.side_length):\n",
        "                x,y = c,r\n",
        "                if (x,y)==(self.side_length-1, 1): # door\n",
        "                    row.append(0.7)\n",
        "                elif ((x,y) in self.walls) or ((x,y) in self.obstacles):\n",
        "                    row.append(0.0)\n",
        "                elif (x,y)==self.key:\n",
        "                    row.append(0.5)\n",
        "                elif ((x,y) in self.triggers_off) or ((x,y) in self.triggers_on):\n",
        "                    row.append(0.6)\n",
        "                elif ((x,y) in self.apples_vis) or ((x,y) in self.apples_dis):\n",
        "                    row.append(0.3)\n",
        "                elif ((x,y) in self.poisons_vis) or ((x,y) in self.poisons_dis):\n",
        "                    row.append(0.2)\n",
        "                elif (x,y)==self.prize:\n",
        "                    row.append(0.4)\n",
        "                else:\n",
        "                    row.append(0.1)\n",
        "            grid.append(row)\n",
        "        return grid\n",
        "\n",
        "\n",
        "    # Plot the grid\n",
        "    def plotGrid(self,grid):\n",
        "        cm = matplotlib.colors.ListedColormap(['black', 'white', 'blue', 'red', 'green', 'gold', 'lawngreen', 'grey', 'aqua', 'pink', 'brown'])\n",
        "        plt.figure(figsize=(self.plot_size,self.plot_size))\n",
        "        plt.pcolormesh(grid, edgecolors='black', cmap=cm, vmin=0.0, vmax=1.0)\n",
        "        plt.axes().set_aspect('equal')\n",
        "        plt.xticks([])\n",
        "        plt.yticks([])\n",
        "        # plt.axes().invert_yaxis()\n",
        "        plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 578
        },
        "id": "_XkJV62OGP39",
        "outputId": "fc71428f-912b-40ae-80a0-7401c47531f0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": 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ASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "env = Gridworld()\n",
        "env.plotGrid(env.getGrid())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3wetsrCB7vpo"
      },
      "outputs": [],
      "source": [
        "class Agent:\n",
        "    def __init__(self, num_states, num_actions=8, init_state=(1,1), buffer_limit=np.inf):\n",
        "        self.init_state = init_state\n",
        "        self.num_states = num_states\n",
        "        self.num_actions = num_actions\n",
        "        self.buffer_limit = buffer_limit\n",
        "\n",
        "        self.reset()\n",
        "        self.resetBuffer()\n",
        "        self.initializePolicy()\n",
        "    \n",
        "\n",
        "    def reset(self):\n",
        "        self.curr_exp = [[], [], []]\n",
        "        self.state = self.init_state\n",
        "\n",
        "\n",
        "    def resetBuffer(self):\n",
        "        self.experience = [[], [], []]\n",
        "\n",
        "\n",
        "    def initializePolicy(self):\n",
        "        self.policy = {}\n",
        "        random_actions = [1.0/float(self.num_actions) for a in range(self.num_actions)]\n",
        "        for s in range(self.num_states):\n",
        "            self.policy[s] = random_actions\n",
        "\n",
        "\n",
        "    # Play an episode and store experience in buffer\n",
        "    # Return this episode history as well\n",
        "    # Assume agent.reset() and env.resetTime() both have been called immediately before\n",
        "    def playEpisode(self, env):\n",
        "        state = to_node_idx(env.side_length, self.state[0], self.state[1])\n",
        "        actions = [i for i in range(self.num_actions)]\n",
        "\n",
        "        self.curr_exp[0].append(state)\n",
        "        while True:\n",
        "            a = np.random.choice(a=actions, p=self.policy[state])\n",
        "            self.state, r = env.step(a)\n",
        "            if self.state==None or r==None:\n",
        "                break\n",
        "            self.curr_exp[1].append(a)\n",
        "            self.curr_exp[2].append(r)\n",
        "            state = to_node_idx(env.side_length, self.state[0], self.state[1])\n",
        "            self.curr_exp[0].append(state)\n",
        "        \n",
        "        self.storeExperience()\n",
        "        return self.curr_exp\n",
        "\n",
        "\n",
        "    # Return an episode as 3 lists and episode index\n",
        "    def sampleEpisode(self):\n",
        "        if len(self.experience[0])==0:\n",
        "            print(\"ERROR: Agent has no experience\")\n",
        "            return None, None, None, None\n",
        "\n",
        "        episode_idx = np.random.randint(0,len(self.experience[0]))\n",
        "        return self.experience[0][episode_idx], self.experience[1][episode_idx], self.experience[2][episode_idx], episode_idx\n",
        "\n",
        "\n",
        "    # Store current experience in buffer\n",
        "    def storeExperience(self):\n",
        "        while len(self.experience[0]) >= self.buffer_limit:\n",
        "            self.experience[0] = self.experience[0][1:]\n",
        "            self.experience[1] = self.experience[1][1:]\n",
        "            self.experience[2] = self.experience[2][1:]\n",
        "\n",
        "        self.experience[0].append(self.curr_exp[0])\n",
        "        self.experience[1].append(self.curr_exp[1])\n",
        "        self.experience[2].append(self.curr_exp[2])\n",
        "\n",
        "agent = Agent(env.side_length**2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qK749LzIVfd9"
      },
      "source": [
        "## Prepare traning pipleline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pGMZ5PrNViTF"
      },
      "outputs": [],
      "source": [
        "def getLoss(pred, ground):\n",
        "    pred = torch.sum(pred, dim=1)\n",
        "    ground = torch.sum(ground, dim=1)\n",
        "    t = torch.mean(torch.square(pred - ground))\n",
        "    return t"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6XCbps7rUhJq"
      },
      "source": [
        "## Setup & Train MLP"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OZlrESmIUm8K"
      },
      "outputs": [],
      "source": [
        "class MLP(torch.nn.Module):\n",
        "    def __init__ (self, num_states, hidden_size, out_size):\n",
        "        super(MLP, self).__init__()\n",
        "        self.lin1 = torch.nn.Linear(in_features=num_states, out_features=hidden_size, bias=True)\n",
        "        self.lin2 = torch.nn.Linear(in_features=hidden_size, out_features=1, bias=True)\n",
        "        self.num_states = num_states\n",
        "\n",
        "    def forward(self, s):\n",
        "        X = torch.nn.functional.one_hot(s, num_classes=self.num_states)\n",
        "        X = X.type(torch.FloatTensor)\n",
        "        X = torch.tanh(self.lin1(X))\n",
        "        X = self.lin2(X)\n",
        "        return X\n",
        "\n",
        "\n",
        "MLPmodel = MLP(env.side_length**2, 64, 4).to(DEVICE)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LrFLbWX9VP3O"
      },
      "outputs": [],
      "source": [
        "INIT_LR = 0.1\n",
        "optimizer = torch.optim.Adam(MLPmodel.parameters(), lr=INIT_LR, betas=(0.9, 0.98), eps=1e-9, weight_decay=0, amsgrad=False)\n",
        "\n",
        "def checkpoint():\n",
        "    torch.save(MLPmodel.state_dict(), \"MLPmodel.pt\")\n",
        "    print(\"Model checkpointed!\")\n",
        "\n",
        "# Multiply LR by factor if loss has not decayed by threshold for patience epochs\n",
        "platLR = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.1, patience=10, threshold=1e-4, \n",
        "                                                    threshold_mode='abs', cooldown=1, min_lr=0, eps=1e-12, verbose=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GHbg7k2kWJ0Q",
        "outputId": "8b165e56-e67b-4c20-f15f-cc6b53a03628"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Starting to train MLP...\n",
            "\n",
            "iter : 1 | loss : 0.479969\n",
            "iter : 2 | loss : 2.894847\n",
            "Model checkpointed!\n",
            "iter : 3 | loss : 5.749211\n",
            "iter : 4 | loss : 6.203703\n",
            "iter : 5 | loss : 5.658405\n",
            "iter : 6 | loss : 5.515229\n",
            "iter : 7 | loss : 5.229844\n",
            "iter : 8 | loss : 4.738734\n",
            "iter : 9 | loss : 4.774485\n",
            "iter : 10 | loss : 5.084035\n",
            "iter : 11 | loss : 4.785698\n",
            "iter : 12 | loss : 4.493797\n",
            "iter : 13 | loss : 4.579773\n",
            "iter : 14 | loss : 4.439589\n",
            "iter : 15 | loss : 4.040085\n",
            "iter : 16 | loss : 3.915475\n",
            "iter : 17 | loss : 3.863909\n",
            "iter : 18 | loss : 3.612375\n",
            "iter : 19 | loss : 3.313018\n",
            "iter : 20 | loss : 3.181374\n",
            "iter : 21 | loss : 3.083276\n",
            "iter : 22 | loss : 2.967612\n",
            "iter : 23 | loss : 2.827046\n",
            "Model checkpointed!\n",
            "iter : 24 | loss : 2.661253\n",
            "Model checkpointed!\n",
            "iter : 25 | loss : 2.475688\n",
            "Model checkpointed!\n",
            "iter : 26 | loss : 2.279557\n",
            "Model checkpointed!\n",
            "iter : 27 | loss : 2.081063\n",
            "Model checkpointed!\n",
            "iter : 28 | loss : 1.898564\n",
            "Model checkpointed!\n",
            "iter : 29 | loss : 1.735281\n",
            "Model checkpointed!\n",
            "iter : 30 | loss : 1.593167\n",
            "Model checkpointed!\n",
            "iter : 31 | loss : 1.484776\n",
            "Model checkpointed!\n",
            "iter : 32 | loss : 1.395353\n",
            "Model checkpointed!\n",
            "iter : 33 | loss : 1.315957\n",
            "Model checkpointed!\n",
            "iter : 34 | loss : 1.251754\n",
            "Model checkpointed!\n",
            "iter : 35 | loss : 1.190299\n",
            "Model checkpointed!\n",
            "iter : 36 | loss : 1.143423\n",
            "Model checkpointed!\n",
            "iter : 37 | loss : 1.067855\n",
            "Model checkpointed!\n",
            "iter : 38 | loss : 0.988815\n",
            "Model checkpointed!\n",
            "iter : 39 | loss : 0.917821\n",
            "Model checkpointed!\n",
            "iter : 40 | loss : 0.842537\n",
            "Model checkpointed!\n",
            "iter : 41 | loss : 0.774563\n",
            "Model checkpointed!\n",
            "iter : 42 | loss : 0.711488\n",
            "Model checkpointed!\n",
            "iter : 43 | loss : 0.655622\n",
            "Model checkpointed!\n",
            "iter : 44 | loss : 0.632557\n",
            "Model checkpointed!\n",
            "iter : 45 | loss : 0.582522\n",
            "Model checkpointed!\n",
            "iter : 46 | loss : 0.53753\n",
            "Model checkpointed!\n",
            "iter : 47 | loss : 0.506054\n",
            "Model checkpointed!\n",
            "iter : 48 | loss : 0.470188\n",
            "Model checkpointed!\n",
            "iter : 49 | loss : 0.437111\n",
            "Model checkpointed!\n",
            "iter : 50 | loss : 0.407277\n",
            "Model checkpointed!\n",
            "iter : 51 | loss : 0.390621\n",
            "Model checkpointed!\n",
            "iter : 52 | loss : 0.36917\n",
            "Model checkpointed!\n",
            "iter : 53 | loss : 0.352013\n",
            "Model checkpointed!\n",
            "iter : 54 | loss : 0.328952\n",
            "Model checkpointed!\n",
            "iter : 55 | loss : 0.311036\n",
            "Model checkpointed!\n",
            "iter : 56 | loss : 0.293459\n",
            "Model checkpointed!\n",
            "iter : 57 | loss : 0.275833\n",
            "Model checkpointed!\n",
            "iter : 58 | loss : 0.260875\n",
            "Model checkpointed!\n",
            "iter : 59 | loss : 0.254976\n",
            "Model checkpointed!\n",
            "iter : 60 | loss : 0.241073\n",
            "Model checkpointed!\n",
            "iter : 61 | loss : 0.235502\n",
            "Model checkpointed!\n",
            "iter : 62 | loss : 0.224732\n",
            "Model checkpointed!\n",
            "iter : 63 | loss : 0.220935\n",
            "Model checkpointed!\n",
            "iter : 64 | loss : 0.213177\n",
            "Model checkpointed!\n",
            "iter : 65 | loss : 0.205135\n",
            "Model checkpointed!\n",
            "iter : 66 | loss : 0.196418\n",
            "Model checkpointed!\n",
            "iter : 67 | loss : 0.191115\n",
            "Model checkpointed!\n",
            "iter : 68 | loss : 0.185513\n",
            "Model checkpointed!\n",
            "iter : 69 | loss : 0.179523\n",
            "Model checkpointed!\n",
            "iter : 70 | loss : 0.173802\n",
            "Model checkpointed!\n",
            "iter : 71 | loss : 0.170479\n",
            "Model checkpointed!\n",
            "iter : 72 | loss : 0.168893\n",
            "Model checkpointed!\n",
            "iter : 73 | loss : 0.163467\n",
            "Model checkpointed!\n",
            "iter : 74 | loss : 0.15863\n",
            "Model checkpointed!\n",
            "iter : 75 | loss : 0.162681\n",
            "iter : 76 | loss : 0.157426\n",
            "Model checkpointed!\n",
            "iter : 77 | loss : 0.153459\n",
            "Model checkpointed!\n",
            "iter : 78 | loss : 0.151039\n",
            "Model checkpointed!\n",
            "iter : 79 | loss : 0.149073\n",
            "Model checkpointed!\n",
            "iter : 80 | loss : 0.152367\n"
          ]
        }
      ],
      "source": [
        "print(\"Starting to train MLP...\\n\")\n",
        "NUM_ITER = 80\n",
        "\n",
        "best_loss = np.inf\n",
        "MLPlosses = []\n",
        "MLPmodel.train()\n",
        "GAMMA = 0.1\n",
        "\n",
        "for iter in range(1, NUM_ITER+1):\n",
        "    agent.resetBuffer()\n",
        "\n",
        "    for x in range(1,env.side_length-1):\n",
        "        for y in range(1,env.side_length-1):\n",
        "            env.init_x,env.init_y = x,y\n",
        "            agent.init_state = (x,y)\n",
        "            agent.reset()\n",
        "            env.resetTime()\n",
        "            ep = agent.playEpisode(env)\n",
        "\n",
        "    states = torch.tensor(agent.experience[0]).to(DEVICE)\n",
        "    actions = torch.tensor(agent.experience[1]).to(DEVICE)\n",
        "    rewards = torch.tensor(agent.experience[2]).to(DEVICE)\n",
        "\n",
        "    optimizer.zero_grad()\n",
        "\n",
        "    pred = MLPmodel(states)\n",
        "\n",
        "    pred = pred.reshape((pred.size(0),pred.size(1)))\n",
        "    pred = pred[:,1:]\n",
        "\n",
        "    loss = getLoss(pred, rewards)\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "\n",
        "    platLR.step(loss)\n",
        "    if len(MLPlosses)>0:\n",
        "        L = (GAMMA*float(loss)) + ((1.0-GAMMA)*MLPlosses[-1])\n",
        "    else:\n",
        "        L = float(loss)\n",
        "    MLPlosses.append(L)\n",
        "\n",
        "    print(\"iter : \"+str(iter)+\" | loss : \"+str(np.around(MLPlosses[-1], 6)))\n",
        "    if iter>1 and best_loss > MLPlosses[-1]:\n",
        "        checkpoint()\n",
        "        best_loss = MLPlosses[-1]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JXifZjF9YVzu"
      },
      "source": [
        "## Plots"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MxElGJ4HXj6i",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "outputId": "1e38098b-63e9-412c-fbc7-17c99ee42d0e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(8,8))\n",
        "plt.plot([i for i in range(len(MLPlosses))], MLPlosses)\n",
        "plt.title(\"Losses vs Iteration\")\n",
        "plt.xlabel('iteration')\n",
        "plt.ylabel('loss')\n",
        "# plt.yscale('log')\n",
        "plt.grid()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oZb519bXg_tH"
      },
      "outputs": [],
      "source": [
        "MLPmodel.eval()\n",
        "def getAllRewards(model = MLPmodel):\n",
        "    s = torch.arange(start=0, end=env.side_length**2, step=1, dtype=torch.long, device=DEVICE)\n",
        "    # print(\"s.size =\", s.size())\n",
        "    s = s.reshape((1,s.size(0)))\n",
        "    # print(\"s.size =\", s.size())\n",
        "    r = model.forward(s)\n",
        "    return r\n",
        "\n",
        "\n",
        "def plotStates(grid, rewards, softmax=True):\n",
        "    matrix = np.array([[float(rewards[to_node_idx(env.side_length, i,j)].cpu().detach().numpy()) for i in range(1,env.side_length-1)] for j in range(1,env.side_length-1)])\n",
        "    cm = sns.light_palette(\"blue\", as_cmap=True)\n",
        "    x = pd.DataFrame(matrix).iloc[::-1]\n",
        "    x = x.style.background_gradient(cmap=cm, axis=None)\n",
        "    display(x)\n",
        "    return x\n",
        "\n",
        "\n",
        "def plotStatesSmall(c):\n",
        "    color_list = list(c.ctx.values())\n",
        "    color_list = sorted([str(key[0][1]) for key in color_list])\n",
        "    color_list.append('black')\n",
        "    cm = matplotlib.colors.ListedColormap(color_list)\n",
        "    color2idx = {}\n",
        "    for i,color in enumerate(color_list):\n",
        "        color2idx[color] = i\n",
        "\n",
        "    grad_plot = [[max(color2idx.values())+1 for i in range(env.side_length)] for j in range(env.side_length)]\n",
        "    data = [[0.0 for i in range(env.side_length)] for j in range(env.side_length)]\n",
        "    for key in c.ctx.keys():\n",
        "        x = int(key[0])+1\n",
        "        y = int(key[1])+1\n",
        "        color = str(c.ctx[key][0][1])\n",
        "        grad_plot[x][y] = color2idx[color]\n",
        "\n",
        "    plt.figure(figsize=(env.plot_size,env.plot_size))\n",
        "    plt.pcolormesh(grad_plot, edgecolors='black', cmap=cm, vmin=min(color2idx.values()), vmax=max(color2idx.values())+1)\n",
        "    plt.axes().set_aspect('equal')\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    plt.axes().invert_yaxis()\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UcIA-x8P82EC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "a7b2e2f7-2b6d-4db3-f1e3-467c7ac8ac45"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f0972c1dd90>"
            ],
            "text/html": [
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              "#T_a5a79_row11_col3 {\n",
              "  background-color: #c7c7f5;\n",
              "  color: #000000;\n",
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              "  background-color: #b3b3f6;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_a5a79_row11_col14 {\n",
              "  background-color: #f0f0f3;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_a5a79_row12_col1 {\n",
              "  background-color: #d3d3f4;\n",
              "  color: #000000;\n",
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              "  background-color: #2525fd;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_a5a79_row12_col11 {\n",
              "  background-color: #bdbdf5;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_a5a79_row12_col12 {\n",
              "  background-color: #a9a9f6;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_a5a79_row12_col15 {\n",
              "  background-color: #c3c3f5;\n",
              "  color: #000000;\n",
              "}\n",
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              "  background-color: #a0a0f7;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_a5a79_row14_col9 {\n",
              "  background-color: #2929fd;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_a5a79_row14_col11, #T_a5a79_row17_col13 {\n",
              "  background-color: #5858fb;\n",
              "  color: #f1f1f1;\n",
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              "#T_a5a79_row14_col15 {\n",
              "  background-color: #4747fb;\n",
              "  color: #f1f1f1;\n",
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              "#T_a5a79_row14_col17 {\n",
              "  background-color: #3838fc;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_a5a79_row15_col1, #T_a5a79_row16_col10 {\n",
              "  background-color: #5757fb;\n",
              "  color: #f1f1f1;\n",
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              "#T_a5a79_row15_col6 {\n",
              "  background-color: #4444fc;\n",
              "  color: #f1f1f1;\n",
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              "#T_a5a79_row15_col7 {\n",
              "  background-color: #1d1dfe;\n",
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              "#T_a5a79_row15_col16 {\n",
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              "#T_a5a79_row16_col6 {\n",
              "  background-color: #9b9bf7;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_a5a79_row16_col8 {\n",
              "  background-color: #3b3bfc;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_a5a79_row16_col9 {\n",
              "  background-color: #9797f7;\n",
              "  color: #f1f1f1;\n",
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              "#T_a5a79_row16_col17 {\n",
              "  background-color: #3333fc;\n",
              "  color: #f1f1f1;\n",
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              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th class=\"col_heading level0 col0\" >0</th>\n",
              "      <th class=\"col_heading level0 col1\" >1</th>\n",
              "      <th class=\"col_heading level0 col2\" >2</th>\n",
              "      <th class=\"col_heading level0 col3\" >3</th>\n",
              "      <th class=\"col_heading level0 col4\" >4</th>\n",
              "      <th class=\"col_heading level0 col5\" >5</th>\n",
              "      <th class=\"col_heading level0 col6\" >6</th>\n",
              "      <th class=\"col_heading level0 col7\" >7</th>\n",
              "      <th class=\"col_heading level0 col8\" >8</th>\n",
              "      <th class=\"col_heading level0 col9\" >9</th>\n",
              "      <th class=\"col_heading level0 col10\" >10</th>\n",
              "      <th class=\"col_heading level0 col11\" >11</th>\n",
              "      <th class=\"col_heading level0 col12\" >12</th>\n",
              "      <th class=\"col_heading level0 col13\" >13</th>\n",
              "      <th class=\"col_heading level0 col14\" >14</th>\n",
              "      <th class=\"col_heading level0 col15\" >15</th>\n",
              "      <th class=\"col_heading level0 col16\" >16</th>\n",
              "      <th class=\"col_heading level0 col17\" >17</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_a5a79_level0_row0\" class=\"row_heading level0 row0\" >17</th>\n",
              "      <td id=\"T_a5a79_row0_col0\" class=\"data row0 col0\" >-0.032129</td>\n",
              "      <td id=\"T_a5a79_row0_col1\" class=\"data row0 col1\" >0.039575</td>\n",
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              "      <td id=\"T_a5a79_row0_col3\" class=\"data row0 col3\" >0.030320</td>\n",
              "      <td id=\"T_a5a79_row0_col4\" class=\"data row0 col4\" >0.000246</td>\n",
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              "      <td id=\"T_a5a79_row0_col6\" class=\"data row0 col6\" >0.096160</td>\n",
              "      <td id=\"T_a5a79_row0_col7\" class=\"data row0 col7\" >-0.174249</td>\n",
              "      <td id=\"T_a5a79_row0_col8\" class=\"data row0 col8\" >-0.078360</td>\n",
              "      <td id=\"T_a5a79_row0_col9\" class=\"data row0 col9\" >0.071869</td>\n",
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              "      <td id=\"T_a5a79_row0_col12\" class=\"data row0 col12\" >0.164636</td>\n",
              "      <td id=\"T_a5a79_row0_col13\" class=\"data row0 col13\" >-0.091960</td>\n",
              "      <td id=\"T_a5a79_row0_col14\" class=\"data row0 col14\" >-0.081363</td>\n",
              "      <td id=\"T_a5a79_row0_col15\" class=\"data row0 col15\" >0.004314</td>\n",
              "      <td id=\"T_a5a79_row0_col16\" class=\"data row0 col16\" >0.015059</td>\n",
              "      <td id=\"T_a5a79_row0_col17\" class=\"data row0 col17\" >-0.123770</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a5a79_level0_row1\" class=\"row_heading level0 row1\" >16</th>\n",
              "      <td id=\"T_a5a79_row1_col0\" class=\"data row1 col0\" >-0.048868</td>\n",
              "      <td id=\"T_a5a79_row1_col1\" class=\"data row1 col1\" >-0.065588</td>\n",
              "      <td id=\"T_a5a79_row1_col2\" class=\"data row1 col2\" >0.016948</td>\n",
              "      <td id=\"T_a5a79_row1_col3\" class=\"data row1 col3\" >-0.011135</td>\n",
              "      <td id=\"T_a5a79_row1_col4\" class=\"data row1 col4\" >0.113069</td>\n",
              "      <td id=\"T_a5a79_row1_col5\" class=\"data row1 col5\" >-0.008983</td>\n",
              "      <td id=\"T_a5a79_row1_col6\" class=\"data row1 col6\" >0.026370</td>\n",
              "      <td id=\"T_a5a79_row1_col7\" class=\"data row1 col7\" >0.169026</td>\n",
              "      <td id=\"T_a5a79_row1_col8\" class=\"data row1 col8\" >0.010144</td>\n",
              "      <td id=\"T_a5a79_row1_col9\" class=\"data row1 col9\" >-0.155052</td>\n",
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              "      <td id=\"T_a5a79_row1_col11\" class=\"data row1 col11\" >0.176665</td>\n",
              "      <td id=\"T_a5a79_row1_col12\" class=\"data row1 col12\" >-0.133349</td>\n",
              "      <td id=\"T_a5a79_row1_col13\" class=\"data row1 col13\" >0.062274</td>\n",
              "      <td id=\"T_a5a79_row1_col14\" class=\"data row1 col14\" >0.614995</td>\n",
              "      <td id=\"T_a5a79_row1_col15\" class=\"data row1 col15\" >0.070234</td>\n",
              "      <td id=\"T_a5a79_row1_col16\" class=\"data row1 col16\" >-0.146733</td>\n",
              "      <td id=\"T_a5a79_row1_col17\" class=\"data row1 col17\" >0.041795</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a5a79_level0_row2\" class=\"row_heading level0 row2\" >15</th>\n",
              "      <td id=\"T_a5a79_row2_col0\" class=\"data row2 col0\" >0.002077</td>\n",
              "      <td id=\"T_a5a79_row2_col1\" class=\"data row2 col1\" >0.058500</td>\n",
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              "      <td id=\"T_a5a79_row2_col3\" class=\"data row2 col3\" >-0.097402</td>\n",
              "      <td id=\"T_a5a79_row2_col4\" class=\"data row2 col4\" >-0.120160</td>\n",
              "      <td id=\"T_a5a79_row2_col5\" class=\"data row2 col5\" >0.009045</td>\n",
              "      <td id=\"T_a5a79_row2_col6\" class=\"data row2 col6\" >0.029026</td>\n",
              "      <td id=\"T_a5a79_row2_col7\" class=\"data row2 col7\" >-0.000269</td>\n",
              "      <td id=\"T_a5a79_row2_col8\" class=\"data row2 col8\" >0.161481</td>\n",
              "      <td id=\"T_a5a79_row2_col9\" class=\"data row2 col9\" >0.215185</td>\n",
              "      <td id=\"T_a5a79_row2_col10\" class=\"data row2 col10\" >0.196778</td>\n",
              "      <td id=\"T_a5a79_row2_col11\" class=\"data row2 col11\" >0.199507</td>\n",
              "      <td id=\"T_a5a79_row2_col12\" class=\"data row2 col12\" >-0.169789</td>\n",
              "      <td id=\"T_a5a79_row2_col13\" class=\"data row2 col13\" >0.244060</td>\n",
              "      <td id=\"T_a5a79_row2_col14\" class=\"data row2 col14\" >-0.119426</td>\n",
              "      <td id=\"T_a5a79_row2_col15\" class=\"data row2 col15\" >-0.331369</td>\n",
              "      <td id=\"T_a5a79_row2_col16\" class=\"data row2 col16\" >0.046137</td>\n",
              "      <td id=\"T_a5a79_row2_col17\" class=\"data row2 col17\" >0.048165</td>\n",
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              "    <tr>\n",
              "      <th id=\"T_a5a79_level0_row3\" class=\"row_heading level0 row3\" >14</th>\n",
              "      <td id=\"T_a5a79_row3_col0\" class=\"data row3 col0\" >0.045044</td>\n",
              "      <td id=\"T_a5a79_row3_col1\" class=\"data row3 col1\" >-0.043932</td>\n",
              "      <td id=\"T_a5a79_row3_col2\" class=\"data row3 col2\" >0.116282</td>\n",
              "      <td id=\"T_a5a79_row3_col3\" class=\"data row3 col3\" >0.122976</td>\n",
              "      <td id=\"T_a5a79_row3_col4\" class=\"data row3 col4\" >0.096345</td>\n",
              "      <td id=\"T_a5a79_row3_col5\" class=\"data row3 col5\" >0.123174</td>\n",
              "      <td id=\"T_a5a79_row3_col6\" class=\"data row3 col6\" >-0.182235</td>\n",
              "      <td id=\"T_a5a79_row3_col7\" class=\"data row3 col7\" >0.011972</td>\n",
              "      <td id=\"T_a5a79_row3_col8\" class=\"data row3 col8\" >-0.100292</td>\n",
              "      <td id=\"T_a5a79_row3_col9\" class=\"data row3 col9\" >-0.159467</td>\n",
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              "      <td id=\"T_a5a79_row3_col14\" class=\"data row3 col14\" >-0.094747</td>\n",
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              "    <tr>\n",
              "      <th id=\"T_a5a79_level0_row4\" class=\"row_heading level0 row4\" >13</th>\n",
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              "      <td id=\"T_a5a79_row4_col6\" class=\"data row4 col6\" >-0.060471</td>\n",
              "      <td id=\"T_a5a79_row4_col7\" class=\"data row4 col7\" >0.093257</td>\n",
              "      <td id=\"T_a5a79_row4_col8\" class=\"data row4 col8\" >-0.097089</td>\n",
              "      <td id=\"T_a5a79_row4_col9\" class=\"data row4 col9\" >-0.073047</td>\n",
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              "      <td id=\"T_a5a79_row4_col11\" class=\"data row4 col11\" >-0.025229</td>\n",
              "      <td id=\"T_a5a79_row4_col12\" class=\"data row4 col12\" >0.027354</td>\n",
              "      <td id=\"T_a5a79_row4_col13\" class=\"data row4 col13\" >0.028560</td>\n",
              "      <td id=\"T_a5a79_row4_col14\" class=\"data row4 col14\" >-0.015562</td>\n",
              "      <td id=\"T_a5a79_row4_col15\" class=\"data row4 col15\" >-0.417276</td>\n",
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              "      <td id=\"T_a5a79_row4_col17\" class=\"data row4 col17\" >0.045822</td>\n",
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              "    <tr>\n",
              "      <th id=\"T_a5a79_level0_row5\" class=\"row_heading level0 row5\" >12</th>\n",
              "      <td id=\"T_a5a79_row5_col0\" class=\"data row5 col0\" >-0.008437</td>\n",
              "      <td id=\"T_a5a79_row5_col1\" class=\"data row5 col1\" >0.065293</td>\n",
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              "      <td id=\"T_a5a79_row5_col6\" class=\"data row5 col6\" >0.187642</td>\n",
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              "      <td id=\"T_a5a79_row5_col8\" class=\"data row5 col8\" >0.175797</td>\n",
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              "      <td id=\"T_a5a79_row5_col12\" class=\"data row5 col12\" >0.167958</td>\n",
              "      <td id=\"T_a5a79_row5_col13\" class=\"data row5 col13\" >0.536566</td>\n",
              "      <td id=\"T_a5a79_row5_col14\" class=\"data row5 col14\" >-0.069912</td>\n",
              "      <td id=\"T_a5a79_row5_col15\" class=\"data row5 col15\" >-0.236836</td>\n",
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              "    <tr>\n",
              "      <th id=\"T_a5a79_level0_row6\" class=\"row_heading level0 row6\" >11</th>\n",
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              "      <th id=\"T_a5a79_level0_row7\" class=\"row_heading level0 row7\" >10</th>\n",
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              "      <td id=\"T_a5a79_row7_col15\" class=\"data row7 col15\" >0.536786</td>\n",
              "      <td id=\"T_a5a79_row7_col16\" class=\"data row7 col16\" >0.070719</td>\n",
              "      <td id=\"T_a5a79_row7_col17\" class=\"data row7 col17\" >0.000782</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a5a79_level0_row8\" class=\"row_heading level0 row8\" >9</th>\n",
              "      <td id=\"T_a5a79_row8_col0\" class=\"data row8 col0\" >0.036116</td>\n",
              "      <td id=\"T_a5a79_row8_col1\" class=\"data row8 col1\" >0.087095</td>\n",
              "      <td id=\"T_a5a79_row8_col2\" class=\"data row8 col2\" >-0.146736</td>\n",
              "      <td id=\"T_a5a79_row8_col3\" class=\"data row8 col3\" >0.138970</td>\n",
              "      <td id=\"T_a5a79_row8_col4\" class=\"data row8 col4\" >0.032352</td>\n",
              "      <td id=\"T_a5a79_row8_col5\" class=\"data row8 col5\" >0.086561</td>\n",
              "      <td id=\"T_a5a79_row8_col6\" class=\"data row8 col6\" >-0.110030</td>\n",
              "      <td id=\"T_a5a79_row8_col7\" class=\"data row8 col7\" >0.090037</td>\n",
              "      <td id=\"T_a5a79_row8_col8\" class=\"data row8 col8\" >0.072027</td>\n",
              "      <td id=\"T_a5a79_row8_col9\" class=\"data row8 col9\" >-0.162989</td>\n",
              "      <td id=\"T_a5a79_row8_col10\" class=\"data row8 col10\" >-0.065628</td>\n",
              "      <td id=\"T_a5a79_row8_col11\" class=\"data row8 col11\" >-0.097751</td>\n",
              "      <td id=\"T_a5a79_row8_col12\" class=\"data row8 col12\" >-0.100677</td>\n",
              "      <td id=\"T_a5a79_row8_col13\" class=\"data row8 col13\" >-0.087504</td>\n",
              "      <td id=\"T_a5a79_row8_col14\" class=\"data row8 col14\" >0.036683</td>\n",
              "      <td id=\"T_a5a79_row8_col15\" class=\"data row8 col15\" >0.129497</td>\n",
              "      <td id=\"T_a5a79_row8_col16\" class=\"data row8 col16\" >-0.071464</td>\n",
              "      <td id=\"T_a5a79_row8_col17\" class=\"data row8 col17\" >0.040521</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a5a79_level0_row9\" class=\"row_heading level0 row9\" >8</th>\n",
              "      <td id=\"T_a5a79_row9_col0\" class=\"data row9 col0\" >0.016363</td>\n",
              "      <td id=\"T_a5a79_row9_col1\" class=\"data row9 col1\" >0.045890</td>\n",
              "      <td id=\"T_a5a79_row9_col2\" class=\"data row9 col2\" >0.039149</td>\n",
              "      <td id=\"T_a5a79_row9_col3\" class=\"data row9 col3\" >0.005904</td>\n",
              "      <td id=\"T_a5a79_row9_col4\" class=\"data row9 col4\" >-0.222678</td>\n",
              "      <td id=\"T_a5a79_row9_col5\" class=\"data row9 col5\" >-0.129157</td>\n",
              "      <td id=\"T_a5a79_row9_col6\" class=\"data row9 col6\" >-0.002938</td>\n",
              "      <td id=\"T_a5a79_row9_col7\" class=\"data row9 col7\" >0.184531</td>\n",
              "      <td id=\"T_a5a79_row9_col8\" class=\"data row9 col8\" >-0.159085</td>\n",
              "      <td id=\"T_a5a79_row9_col9\" class=\"data row9 col9\" >-0.198857</td>\n",
              "      <td id=\"T_a5a79_row9_col10\" class=\"data row9 col10\" >0.123553</td>\n",
              "      <td id=\"T_a5a79_row9_col11\" class=\"data row9 col11\" >-0.070557</td>\n",
              "      <td id=\"T_a5a79_row9_col12\" class=\"data row9 col12\" >0.099293</td>\n",
              "      <td id=\"T_a5a79_row9_col13\" class=\"data row9 col13\" >0.016519</td>\n",
              "      <td id=\"T_a5a79_row9_col14\" class=\"data row9 col14\" >0.057001</td>\n",
              "      <td id=\"T_a5a79_row9_col15\" class=\"data row9 col15\" >0.020353</td>\n",
              "      <td id=\"T_a5a79_row9_col16\" class=\"data row9 col16\" >0.000195</td>\n",
              "      <td id=\"T_a5a79_row9_col17\" class=\"data row9 col17\" >0.045183</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a5a79_level0_row10\" class=\"row_heading level0 row10\" >7</th>\n",
              "      <td id=\"T_a5a79_row10_col0\" class=\"data row10 col0\" >-0.022625</td>\n",
              "      <td id=\"T_a5a79_row10_col1\" class=\"data row10 col1\" >-0.015945</td>\n",
              "      <td id=\"T_a5a79_row10_col2\" class=\"data row10 col2\" >0.027881</td>\n",
              "      <td id=\"T_a5a79_row10_col3\" class=\"data row10 col3\" >-0.042886</td>\n",
              "      <td id=\"T_a5a79_row10_col4\" class=\"data row10 col4\" >-0.058449</td>\n",
              "      <td id=\"T_a5a79_row10_col5\" class=\"data row10 col5\" >0.065761</td>\n",
              "      <td id=\"T_a5a79_row10_col6\" class=\"data row10 col6\" >0.083530</td>\n",
              "      <td id=\"T_a5a79_row10_col7\" class=\"data row10 col7\" >0.012116</td>\n",
              "      <td id=\"T_a5a79_row10_col8\" class=\"data row10 col8\" >-0.066483</td>\n",
              "      <td id=\"T_a5a79_row10_col9\" class=\"data row10 col9\" >0.106029</td>\n",
              "      <td id=\"T_a5a79_row10_col10\" class=\"data row10 col10\" >0.098557</td>\n",
              "      <td id=\"T_a5a79_row10_col11\" class=\"data row10 col11\" >-0.061739</td>\n",
              "      <td id=\"T_a5a79_row10_col12\" class=\"data row10 col12\" >-0.244467</td>\n",
              "      <td id=\"T_a5a79_row10_col13\" class=\"data row10 col13\" >0.015724</td>\n",
              "      <td id=\"T_a5a79_row10_col14\" class=\"data row10 col14\" >-0.243557</td>\n",
              "      <td id=\"T_a5a79_row10_col15\" class=\"data row10 col15\" >0.014028</td>\n",
              "      <td id=\"T_a5a79_row10_col16\" class=\"data row10 col16\" >-0.001826</td>\n",
              "      <td id=\"T_a5a79_row10_col17\" class=\"data row10 col17\" >0.015913</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a5a79_level0_row11\" class=\"row_heading level0 row11\" >6</th>\n",
              "      <td id=\"T_a5a79_row11_col0\" class=\"data row11 col0\" >-0.085617</td>\n",
              "      <td id=\"T_a5a79_row11_col1\" class=\"data row11 col1\" >0.120585</td>\n",
              "      <td id=\"T_a5a79_row11_col2\" class=\"data row11 col2\" >-0.021824</td>\n",
              "      <td id=\"T_a5a79_row11_col3\" class=\"data row11 col3\" >-0.480067</td>\n",
              "      <td id=\"T_a5a79_row11_col4\" class=\"data row11 col4\" >0.046511</td>\n",
              "      <td id=\"T_a5a79_row11_col5\" class=\"data row11 col5\" >-0.045556</td>\n",
              "      <td id=\"T_a5a79_row11_col6\" class=\"data row11 col6\" >0.081737</td>\n",
              "      <td id=\"T_a5a79_row11_col7\" class=\"data row11 col7\" >-0.044088</td>\n",
              "      <td id=\"T_a5a79_row11_col8\" class=\"data row11 col8\" >-0.022010</td>\n",
              "      <td id=\"T_a5a79_row11_col9\" class=\"data row11 col9\" >0.069905</td>\n",
              "      <td id=\"T_a5a79_row11_col10\" class=\"data row11 col10\" >-0.203026</td>\n",
              "      <td id=\"T_a5a79_row11_col11\" class=\"data row11 col11\" >0.027543</td>\n",
              "      <td id=\"T_a5a79_row11_col12\" class=\"data row11 col12\" >-0.055873</td>\n",
              "      <td id=\"T_a5a79_row11_col13\" class=\"data row11 col13\" >-0.364355</td>\n",
              "      <td id=\"T_a5a79_row11_col14\" class=\"data row11 col14\" >-0.717350</td>\n",
              "      <td id=\"T_a5a79_row11_col15\" class=\"data row11 col15\" >0.056778</td>\n",
              "      <td id=\"T_a5a79_row11_col16\" class=\"data row11 col16\" >-0.134580</td>\n",
              "      <td id=\"T_a5a79_row11_col17\" class=\"data row11 col17\" >0.031026</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a5a79_level0_row12\" class=\"row_heading level0 row12\" >5</th>\n",
              "      <td id=\"T_a5a79_row12_col0\" class=\"data row12 col0\" >-0.168791</td>\n",
              "      <td id=\"T_a5a79_row12_col1\" class=\"data row12 col1\" >-0.549688</td>\n",
              "      <td id=\"T_a5a79_row12_col2\" class=\"data row12 col2\" >-0.156960</td>\n",
              "      <td id=\"T_a5a79_row12_col3\" class=\"data row12 col3\" >-0.095005</td>\n",
              "      <td id=\"T_a5a79_row12_col4\" class=\"data row12 col4\" >-0.249933</td>\n",
              "      <td id=\"T_a5a79_row12_col5\" class=\"data row12 col5\" >0.081426</td>\n",
              "      <td id=\"T_a5a79_row12_col6\" class=\"data row12 col6\" >-0.021257</td>\n",
              "      <td id=\"T_a5a79_row12_col7\" class=\"data row12 col7\" >0.199981</td>\n",
              "      <td id=\"T_a5a79_row12_col8\" class=\"data row12 col8\" >-0.164321</td>\n",
              "      <td id=\"T_a5a79_row12_col9\" class=\"data row12 col9\" >0.039803</td>\n",
              "      <td id=\"T_a5a79_row12_col10\" class=\"data row12 col10\" >0.440807</td>\n",
              "      <td id=\"T_a5a79_row12_col11\" class=\"data row12 col11\" >-0.421369</td>\n",
              "      <td id=\"T_a5a79_row12_col12\" class=\"data row12 col12\" >-0.308801</td>\n",
              "      <td id=\"T_a5a79_row12_col13\" class=\"data row12 col13\" >-0.031313</td>\n",
              "      <td id=\"T_a5a79_row12_col14\" class=\"data row12 col14\" >-0.202716</td>\n",
              "      <td id=\"T_a5a79_row12_col15\" class=\"data row12 col15\" >-0.460135</td>\n",
              "      <td id=\"T_a5a79_row12_col16\" class=\"data row12 col16\" >-0.088855</td>\n",
              "      <td id=\"T_a5a79_row12_col17\" class=\"data row12 col17\" >0.009845</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a5a79_level0_row13\" class=\"row_heading level0 row13\" >4</th>\n",
              "      <td id=\"T_a5a79_row13_col0\" class=\"data row13 col0\" >-0.060460</td>\n",
              "      <td id=\"T_a5a79_row13_col1\" class=\"data row13 col1\" >0.056386</td>\n",
              "      <td id=\"T_a5a79_row13_col2\" class=\"data row13 col2\" >0.087821</td>\n",
              "      <td id=\"T_a5a79_row13_col3\" class=\"data row13 col3\" >0.094874</td>\n",
              "      <td id=\"T_a5a79_row13_col4\" class=\"data row13 col4\" >-0.034030</td>\n",
              "      <td id=\"T_a5a79_row13_col5\" class=\"data row13 col5\" >0.102311</td>\n",
              "      <td id=\"T_a5a79_row13_col6\" class=\"data row13 col6\" >-0.004973</td>\n",
              "      <td id=\"T_a5a79_row13_col7\" class=\"data row13 col7\" >-0.095376</td>\n",
              "      <td id=\"T_a5a79_row13_col8\" class=\"data row13 col8\" >0.184753</td>\n",
              "      <td id=\"T_a5a79_row13_col9\" class=\"data row13 col9\" >-0.065925</td>\n",
              "      <td id=\"T_a5a79_row13_col10\" class=\"data row13 col10\" >0.040514</td>\n",
              "      <td id=\"T_a5a79_row13_col11\" class=\"data row13 col11\" >-0.064102</td>\n",
              "      <td id=\"T_a5a79_row13_col12\" class=\"data row13 col12\" >0.134541</td>\n",
              "      <td id=\"T_a5a79_row13_col13\" class=\"data row13 col13\" >-0.087841</td>\n",
              "      <td id=\"T_a5a79_row13_col14\" class=\"data row13 col14\" >-0.122896</td>\n",
              "      <td id=\"T_a5a79_row13_col15\" class=\"data row13 col15\" >0.079742</td>\n",
              "      <td id=\"T_a5a79_row13_col16\" class=\"data row13 col16\" >-0.159654</td>\n",
              "      <td id=\"T_a5a79_row13_col17\" class=\"data row13 col17\" >-0.015569</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a5a79_level0_row14\" class=\"row_heading level0 row14\" >3</th>\n",
              "      <td id=\"T_a5a79_row14_col0\" class=\"data row14 col0\" >-0.100908</td>\n",
              "      <td id=\"T_a5a79_row14_col1\" class=\"data row14 col1\" >0.004976</td>\n",
              "      <td id=\"T_a5a79_row14_col2\" class=\"data row14 col2\" >-0.184758</td>\n",
              "      <td id=\"T_a5a79_row14_col3\" class=\"data row14 col3\" >0.037017</td>\n",
              "      <td id=\"T_a5a79_row14_col4\" class=\"data row14 col4\" >-0.119446</td>\n",
              "      <td id=\"T_a5a79_row14_col5\" class=\"data row14 col5\" >0.534907</td>\n",
              "      <td id=\"T_a5a79_row14_col6\" class=\"data row14 col6\" >-0.023157</td>\n",
              "      <td id=\"T_a5a79_row14_col7\" class=\"data row14 col7\" >-0.260459</td>\n",
              "      <td id=\"T_a5a79_row14_col8\" class=\"data row14 col8\" >0.165922</td>\n",
              "      <td id=\"T_a5a79_row14_col9\" class=\"data row14 col9\" >0.421392</td>\n",
              "      <td id=\"T_a5a79_row14_col10\" class=\"data row14 col10\" >0.118272</td>\n",
              "      <td id=\"T_a5a79_row14_col11\" class=\"data row14 col11\" >0.151921</td>\n",
              "      <td id=\"T_a5a79_row14_col12\" class=\"data row14 col12\" >0.109294</td>\n",
              "      <td id=\"T_a5a79_row14_col13\" class=\"data row14 col13\" >0.026465</td>\n",
              "      <td id=\"T_a5a79_row14_col14\" class=\"data row14 col14\" >-0.192447</td>\n",
              "      <td id=\"T_a5a79_row14_col15\" class=\"data row14 col15\" >0.247820</td>\n",
              "      <td id=\"T_a5a79_row14_col16\" class=\"data row14 col16\" >0.050304</td>\n",
              "      <td id=\"T_a5a79_row14_col17\" class=\"data row14 col17\" >0.335157</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a5a79_level0_row15\" class=\"row_heading level0 row15\" >2</th>\n",
              "      <td id=\"T_a5a79_row15_col0\" class=\"data row15 col0\" >-0.160008</td>\n",
              "      <td id=\"T_a5a79_row15_col1\" class=\"data row15 col1\" >0.155496</td>\n",
              "      <td id=\"T_a5a79_row15_col2\" class=\"data row15 col2\" >0.201809</td>\n",
              "      <td id=\"T_a5a79_row15_col3\" class=\"data row15 col3\" >-0.040745</td>\n",
              "      <td id=\"T_a5a79_row15_col4\" class=\"data row15 col4\" >0.120195</td>\n",
              "      <td id=\"T_a5a79_row15_col5\" class=\"data row15 col5\" >0.195665</td>\n",
              "      <td id=\"T_a5a79_row15_col6\" class=\"data row15 col6\" >0.265798</td>\n",
              "      <td id=\"T_a5a79_row15_col7\" class=\"data row15 col7\" >0.485179</td>\n",
              "      <td id=\"T_a5a79_row15_col8\" class=\"data row15 col8\" >-0.078119</td>\n",
              "      <td id=\"T_a5a79_row15_col9\" class=\"data row15 col9\" >0.108160</td>\n",
              "      <td id=\"T_a5a79_row15_col10\" class=\"data row15 col10\" >0.060775</td>\n",
              "      <td id=\"T_a5a79_row15_col11\" class=\"data row15 col11\" >-0.146792</td>\n",
              "      <td id=\"T_a5a79_row15_col12\" class=\"data row15 col12\" >-0.003162</td>\n",
              "      <td id=\"T_a5a79_row15_col13\" class=\"data row15 col13\" >0.075565</td>\n",
              "      <td id=\"T_a5a79_row15_col14\" class=\"data row15 col14\" >-0.051350</td>\n",
              "      <td id=\"T_a5a79_row15_col15\" class=\"data row15 col15\" >0.087260</td>\n",
              "      <td id=\"T_a5a79_row15_col16\" class=\"data row15 col16\" >0.273872</td>\n",
              "      <td id=\"T_a5a79_row15_col17\" class=\"data row15 col17\" >-0.033644</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a5a79_level0_row16\" class=\"row_heading level0 row16\" >1</th>\n",
              "      <td id=\"T_a5a79_row16_col0\" class=\"data row16 col0\" >0.126660</td>\n",
              "      <td id=\"T_a5a79_row16_col1\" class=\"data row16 col1\" >0.175299</td>\n",
              "      <td id=\"T_a5a79_row16_col2\" class=\"data row16 col2\" >-0.110308</td>\n",
              "      <td id=\"T_a5a79_row16_col3\" class=\"data row16 col3\" >-0.099909</td>\n",
              "      <td id=\"T_a5a79_row16_col4\" class=\"data row16 col4\" >-0.079564</td>\n",
              "      <td id=\"T_a5a79_row16_col5\" class=\"data row16 col5\" >-0.101613</td>\n",
              "      <td id=\"T_a5a79_row16_col6\" class=\"data row16 col6\" >-0.230254</td>\n",
              "      <td id=\"T_a5a79_row16_col7\" class=\"data row16 col7\" >-0.031294</td>\n",
              "      <td id=\"T_a5a79_row16_col8\" class=\"data row16 col8\" >0.311159</td>\n",
              "      <td id=\"T_a5a79_row16_col9\" class=\"data row16 col9\" >-0.205885</td>\n",
              "      <td id=\"T_a5a79_row16_col10\" class=\"data row16 col10\" >0.159070</td>\n",
              "      <td id=\"T_a5a79_row16_col11\" class=\"data row16 col11\" >-0.090102</td>\n",
              "      <td id=\"T_a5a79_row16_col12\" class=\"data row16 col12\" >-0.050425</td>\n",
              "      <td id=\"T_a5a79_row16_col13\" class=\"data row16 col13\" >-0.037682</td>\n",
              "      <td id=\"T_a5a79_row16_col14\" class=\"data row16 col14\" >0.052415</td>\n",
              "      <td id=\"T_a5a79_row16_col15\" class=\"data row16 col15\" >-0.182434</td>\n",
              "      <td id=\"T_a5a79_row16_col16\" class=\"data row16 col16\" >0.200372</td>\n",
              "      <td id=\"T_a5a79_row16_col17\" class=\"data row16 col17\" >0.360951</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a5a79_level0_row17\" class=\"row_heading level0 row17\" >0</th>\n",
              "      <td id=\"T_a5a79_row17_col0\" class=\"data row17 col0\" >-0.142776</td>\n",
              "      <td id=\"T_a5a79_row17_col1\" class=\"data row17 col1\" >-0.158083</td>\n",
              "      <td id=\"T_a5a79_row17_col2\" class=\"data row17 col2\" >-0.037815</td>\n",
              "      <td id=\"T_a5a79_row17_col3\" class=\"data row17 col3\" >0.035730</td>\n",
              "      <td id=\"T_a5a79_row17_col4\" class=\"data row17 col4\" >0.051944</td>\n",
              "      <td id=\"T_a5a79_row17_col5\" class=\"data row17 col5\" >0.073040</td>\n",
              "      <td id=\"T_a5a79_row17_col6\" class=\"data row17 col6\" >0.093518</td>\n",
              "      <td id=\"T_a5a79_row17_col7\" class=\"data row17 col7\" >0.182158</td>\n",
              "      <td id=\"T_a5a79_row17_col8\" class=\"data row17 col8\" >-0.135439</td>\n",
              "      <td id=\"T_a5a79_row17_col9\" class=\"data row17 col9\" >0.025642</td>\n",
              "      <td id=\"T_a5a79_row17_col10\" class=\"data row17 col10\" >-0.010313</td>\n",
              "      <td id=\"T_a5a79_row17_col11\" class=\"data row17 col11\" >-0.078107</td>\n",
              "      <td id=\"T_a5a79_row17_col12\" class=\"data row17 col12\" >-0.031084</td>\n",
              "      <td id=\"T_a5a79_row17_col13\" class=\"data row17 col13\" >0.149655</td>\n",
              "      <td id=\"T_a5a79_row17_col14\" class=\"data row17 col14\" >0.001572</td>\n",
              "      <td id=\"T_a5a79_row17_col15\" class=\"data row17 col15\" >-0.048931</td>\n",
              "      <td id=\"T_a5a79_row17_col16\" class=\"data row17 col16\" >-0.000275</td>\n",
              "      <td id=\"T_a5a79_row17_col17\" class=\"data row17 col17\" >0.045003</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "R = getAllRewards()\n",
        "# print(\"r.size =\", r.size())\n",
        "R = R.resize((R.size(1)))\n",
        "c = plotStates(env.getGrid(), R, True)\n",
        "plotStatesSmall(c)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JmKU2uoc-6Du",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 578
        },
        "outputId": "2d772d15-316f-4804-d535-181e618334c0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "env.plotGrid(env.getGrid())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QcmJyQ3cPsQa"
      },
      "source": [
        "## Policy Evaluation & Improvement "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_2Vi-yC8Ppz9"
      },
      "outputs": [],
      "source": [
        "def improvePolicy(policy, Q, state, eps=0.1):\n",
        "    q = torch.tensor([Q[state][0], Q[state][1], Q[state][2], Q[state][3], Q[state][4], Q[state][5], Q[state][6], Q[state][7]])\n",
        "\n",
        "    policy[state] = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])\n",
        "    idx = 0\n",
        "    _, indices = torch.sort(q, descending=True)\n",
        "    policy[state][indices[idx]] = (1.0-eps)\n",
        "\n",
        "    num_actions = 8.0\n",
        "    policy[state][0] += (eps/num_actions)\n",
        "    policy[state][1] += (eps/num_actions)\n",
        "    policy[state][2] += (eps/num_actions)\n",
        "    policy[state][3] += (eps/num_actions)\n",
        "    policy[state][4] += (eps/num_actions)\n",
        "    policy[state][5] += (eps/num_actions)\n",
        "    policy[state][6] += (eps/num_actions)\n",
        "    policy[state][7] += (eps/num_actions)  \n",
        "    return policy"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plotPolicy(grid, plot_size, width_factor=0.075):\n",
        "    cm = matplotlib.colors.ListedColormap(['black', 'white', 'blue', 'red', 'green', 'gold', 'lawngreen', 'grey', 'aqua', 'pink', 'brown'])\n",
        "    plt.figure(figsize=(plot_size, plot_size))\n",
        "    plt.pcolormesh(grid, edgecolors='black', cmap=cm, vmin=0.0, vmax=1.0)\n",
        "    plt.axes().set_aspect('equal')\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    \n",
        "    for s in range(env.side_length**2):\n",
        "        if (to_coord(env.side_length, s) in env.walls) or (to_coord(env.side_length,s)==env.door):\n",
        "            continue\n",
        "        actions = agent.policy[s]\n",
        "        # print(\"s =\", s, \"actions =\", actions)\n",
        "        a = np.argmax(actions)\n",
        "        # print(\"s =\", s, \"actions =\", actions, \"a =\", a)\n",
        "        x,y = to_coord(env.side_length, s)\n",
        "        if a==0:\n",
        "            plt.arrow(x+0.5, y+0.7, 0.0, 0.8, width=width_factor, head_width=2.5*width_factor, head_length=2*width_factor, length_includes_head=True, color='brown')\n",
        "        elif a==1:\n",
        "            plt.arrow(x+0.7, y+0.7, 0.7, 0.7, width=width_factor, head_width=2.5*width_factor, head_length=2*width_factor, length_includes_head=True, color='brown')\n",
        "        elif a==2:\n",
        "            plt.arrow(x+0.7, y+0.5, 0.8, 0.0, width=width_factor, head_width=2.5*width_factor, head_length=2*width_factor, length_includes_head=True, color='brown')\n",
        "        elif a==3:\n",
        "            plt.arrow(x+0.7, y+0.3, 0.7, -0.7, width=width_factor, head_width=2.5*width_factor, head_length=2*width_factor, length_includes_head=True, color='brown')\n",
        "        elif a==4:\n",
        "            plt.arrow(x+0.5, y+0.3, 0.0, -0.8, width=width_factor, head_width=2.5*width_factor, head_length=2*width_factor, length_includes_head=True, color='brown')\n",
        "        elif a==5:\n",
        "            plt.arrow(x+0.3, y+0.3, -0.7, -0.7, width=width_factor, head_width=2.5*width_factor, head_length=2*width_factor, length_includes_head=True, color='brown')\n",
        "        elif a==6:\n",
        "            plt.arrow(x+0.3, y+0.5, -0.8, 0.0, width=width_factor, head_width=2.5*width_factor, head_length=2*width_factor, length_includes_head=True, color='brown')\n",
        "        else:\n",
        "            plt.arrow(x+0.3, y+0.7, -0.7, 0.7, width=width_factor, head_width=2.5*width_factor, head_length=2*width_factor, length_includes_head=True, color='brown')\n",
        "    \n",
        "    \n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "0vHyu_ueGCrK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### SARSA"
      ],
      "metadata": {
        "id": "d2lQhEy1ytj3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def SARSA(NUM_ITER = 150):\n",
        "    Q = {}\n",
        "    for i in range(1,env.side_length-1):\n",
        "        for j in range(1,env.side_length-1):\n",
        "            u = to_node_idx(env.side_length, i, j)\n",
        "            Q[u] = {}\n",
        "            Q[u][0] = torch.rand(1)\n",
        "            Q[u][1] = torch.rand(1)\n",
        "            Q[u][2] = torch.rand(1)\n",
        "            Q[u][3] = torch.rand(1)\n",
        "            Q[u][4] = torch.rand(1)\n",
        "            Q[u][5] = torch.rand(1)\n",
        "            Q[u][6] = torch.rand(1)\n",
        "            Q[u][7] = torch.rand(1)\n",
        "    agent.initializePolicy()\n",
        "    R = getAllRewards()\n",
        "    R = R.reshape((R.size(1)))\n",
        "\n",
        "\n",
        "    cum_reward_S = []\n",
        "    epsilon = 0.98\n",
        "    alpha = 0.98\n",
        "    NUM_PLAYS = (env.side_length-2)**2\n",
        "\n",
        "    for iter in range(1,NUM_ITER+1):\n",
        "        agent.resetBuffer()\n",
        "        env.episode_length = 8\n",
        "        for x in range(1,env.side_length-1):\n",
        "            for y in range(1,env.side_length-1):\n",
        "                env.init_x,env.init_y = x,y\n",
        "                agent.init_state = (x,y)\n",
        "                agent.reset()\n",
        "                env.resetTime()\n",
        "                ep = agent.playEpisode(env)\n",
        "\n",
        "        states = agent.experience[0]\n",
        "        actions = agent.experience[1]\n",
        "        rewards = agent.experience[2]\n",
        "        im_rewards = [[float(R[state]) for state in ep] for ep in states] # predicted immediate rewards\n",
        "\n",
        "        # Remove multiple rewards\n",
        "        for i,ep in enumerate(states):\n",
        "            visited_states = set()\n",
        "            for j,s in enumerate(ep):\n",
        "                if j==len(ep)-1:\n",
        "                    break\n",
        "                if s in visited_states:\n",
        "                    im_rewards[i][j+1] = 0.0\n",
        "                else:\n",
        "                    visited_states.add(s)\n",
        "        \n",
        "        for i,ep in enumerate(states):\n",
        "            for j in range(len(ep)-2):\n",
        "                s = states[i][j]\n",
        "                a = actions[i][j]\n",
        "                r = im_rewards[i][j+1]\n",
        "                sprime = states[i][j+1]\n",
        "                aprime = actions[i][j+1]\n",
        "                \n",
        "                vis_prev = False\n",
        "                for k in range(j):\n",
        "                    if states[i][k]==s and actions[i][k]==a:\n",
        "                        vis_prev = True\n",
        "                        break\n",
        "                if vis_prev:\n",
        "                    continue\n",
        "                \n",
        "                Q[s][a] += alpha*(r + Q[sprime][aprime] - Q[s][a])\n",
        "                agent.policy = improvePolicy(agent.policy, Q, s, epsilon)\n",
        "        \n",
        "        alpha *= 0.98\n",
        "        epsilon *= 0.98\n",
        "\n",
        "        rewards = torch.tensor(rewards).to(DEVICE)\n",
        "        cm = float(torch.sum(rewards))/float(NUM_PLAYS)\n",
        "        cum_reward_S.append(cm)\n",
        "\n",
        "        print(\"iter : \"+str(iter)+\" |  cum_reward : \"+str(cum_reward_S[-1]))\n",
        "\n",
        "    return cum_reward_S"
      ],
      "metadata": {
        "id": "jjPoJGZIyvnN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cum_reward_S = SARSA()"
      ],
      "metadata": {
        "id": "p8xmw_B2yvkg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ac08b3b1-2f29-468e-f054-93c633e0e031"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "iter : 1 |  cum_reward : 0.08179012345679013\n",
            "iter : 2 |  cum_reward : 0.10802469135802469\n",
            "iter : 3 |  cum_reward : 0.11419753086419752\n",
            "iter : 4 |  cum_reward : 0.09567901234567901\n",
            "iter : 5 |  cum_reward : 0.047839506172839504\n",
            "iter : 6 |  cum_reward : 0.15895061728395063\n",
            "iter : 7 |  cum_reward : 0.16666666666666666\n",
            "iter : 8 |  cum_reward : 0.1743827160493827\n",
            "iter : 9 |  cum_reward : 0.1419753086419753\n",
            "iter : 10 |  cum_reward : 0.1712962962962963\n",
            "iter : 11 |  cum_reward : 0.21141975308641975\n",
            "iter : 12 |  cum_reward : 0.2808641975308642\n",
            "iter : 13 |  cum_reward : 0.2191358024691358\n",
            "iter : 14 |  cum_reward : 0.2777777777777778\n",
            "iter : 15 |  cum_reward : 0.2700617283950617\n",
            "iter : 16 |  cum_reward : 0.3148148148148148\n",
            "iter : 17 |  cum_reward : 0.2993827160493827\n",
            "iter : 18 |  cum_reward : 0.36419753086419754\n",
            "iter : 19 |  cum_reward : 0.2654320987654321\n",
            "iter : 20 |  cum_reward : 0.4243827160493827\n",
            "iter : 21 |  cum_reward : 0.3595679012345679\n",
            "iter : 22 |  cum_reward : 0.44753086419753085\n",
            "iter : 23 |  cum_reward : 0.3904320987654321\n",
            "iter : 24 |  cum_reward : 0.3595679012345679\n",
            "iter : 25 |  cum_reward : 0.5169753086419753\n",
            "iter : 26 |  cum_reward : 0.6311728395061729\n",
            "iter : 27 |  cum_reward : 0.5246913580246914\n",
            "iter : 28 |  cum_reward : 0.45987654320987653\n",
            "iter : 29 |  cum_reward : 0.7160493827160493\n",
            "iter : 30 |  cum_reward : 0.5740740740740741\n",
            "iter : 31 |  cum_reward : 0.5262345679012346\n",
            "iter : 32 |  cum_reward : 0.6111111111111112\n",
            "iter : 33 |  cum_reward : 0.6651234567901234\n",
            "iter : 34 |  cum_reward : 0.9012345679012346\n",
            "iter : 35 |  cum_reward : 0.6296296296296297\n",
            "iter : 36 |  cum_reward : 0.5972222222222222\n",
            "iter : 37 |  cum_reward : 0.7746913580246914\n",
            "iter : 38 |  cum_reward : 0.6728395061728395\n",
            "iter : 39 |  cum_reward : 0.6280864197530864\n",
            "iter : 40 |  cum_reward : 0.7870370370370371\n",
            "iter : 41 |  cum_reward : 0.8132716049382716\n",
            "iter : 42 |  cum_reward : 0.7746913580246914\n",
            "iter : 43 |  cum_reward : 0.7623456790123457\n",
            "iter : 44 |  cum_reward : 0.7592592592592593\n",
            "iter : 45 |  cum_reward : 0.7916666666666666\n",
            "iter : 46 |  cum_reward : 0.7885802469135802\n",
            "iter : 47 |  cum_reward : 0.8580246913580247\n",
            "iter : 48 |  cum_reward : 1.0694444444444444\n",
            "iter : 49 |  cum_reward : 0.8780864197530864\n",
            "iter : 50 |  cum_reward : 0.9567901234567902\n",
            "iter : 51 |  cum_reward : 1.0\n",
            "iter : 52 |  cum_reward : 0.9459876543209876\n",
            "iter : 53 |  cum_reward : 0.9598765432098766\n",
            "iter : 54 |  cum_reward : 0.9120370370370371\n",
            "iter : 55 |  cum_reward : 0.8580246913580247\n",
            "iter : 56 |  cum_reward : 0.8888888888888888\n",
            "iter : 57 |  cum_reward : 0.8703703703703703\n",
            "iter : 58 |  cum_reward : 0.9459876543209876\n",
            "iter : 59 |  cum_reward : 1.1311728395061729\n",
            "iter : 60 |  cum_reward : 0.8765432098765432\n",
            "iter : 61 |  cum_reward : 1.1496913580246915\n",
            "iter : 62 |  cum_reward : 1.0725308641975309\n",
            "iter : 63 |  cum_reward : 1.0447530864197532\n",
            "iter : 64 |  cum_reward : 1.0570987654320987\n",
            "iter : 65 |  cum_reward : 1.1388888888888888\n",
            "iter : 66 |  cum_reward : 1.0601851851851851\n",
            "iter : 67 |  cum_reward : 1.095679012345679\n",
            "iter : 68 |  cum_reward : 1.0416666666666667\n",
            "iter : 69 |  cum_reward : 1.126543209876543\n",
            "iter : 70 |  cum_reward : 1.066358024691358\n",
            "iter : 71 |  cum_reward : 1.123456790123457\n",
            "iter : 72 |  cum_reward : 1.1126543209876543\n",
            "iter : 73 |  cum_reward : 1.1141975308641976\n",
            "iter : 74 |  cum_reward : 1.1728395061728396\n",
            "iter : 75 |  cum_reward : 1.1820987654320987\n",
            "iter : 76 |  cum_reward : 1.1975308641975309\n",
            "iter : 77 |  cum_reward : 1.2345679012345678\n",
            "iter : 78 |  cum_reward : 1.2191358024691359\n",
            "iter : 79 |  cum_reward : 1.154320987654321\n",
            "iter : 80 |  cum_reward : 1.1975308641975309\n",
            "iter : 81 |  cum_reward : 1.3132716049382716\n",
            "iter : 82 |  cum_reward : 1.2098765432098766\n",
            "iter : 83 |  cum_reward : 1.1882716049382716\n",
            "iter : 84 |  cum_reward : 1.2438271604938271\n",
            "iter : 85 |  cum_reward : 1.2469135802469136\n",
            "iter : 86 |  cum_reward : 1.3024691358024691\n",
            "iter : 87 |  cum_reward : 1.3348765432098766\n",
            "iter : 88 |  cum_reward : 1.279320987654321\n",
            "iter : 89 |  cum_reward : 1.287037037037037\n",
            "iter : 90 |  cum_reward : 1.2268518518518519\n",
            "iter : 91 |  cum_reward : 1.2978395061728396\n",
            "iter : 92 |  cum_reward : 1.3487654320987654\n",
            "iter : 93 |  cum_reward : 1.3225308641975309\n",
            "iter : 94 |  cum_reward : 1.248456790123457\n",
            "iter : 95 |  cum_reward : 1.2453703703703705\n",
            "iter : 96 |  cum_reward : 1.3179012345679013\n",
            "iter : 97 |  cum_reward : 1.3040123456790123\n",
            "iter : 98 |  cum_reward : 1.3580246913580247\n",
            "iter : 99 |  cum_reward : 1.2623456790123457\n",
            "iter : 100 |  cum_reward : 1.3009259259259258\n",
            "iter : 101 |  cum_reward : 1.3225308641975309\n",
            "iter : 102 |  cum_reward : 1.3441358024691359\n",
            "iter : 103 |  cum_reward : 1.2669753086419753\n",
            "iter : 104 |  cum_reward : 1.2885802469135803\n",
            "iter : 105 |  cum_reward : 1.4151234567901234\n",
            "iter : 106 |  cum_reward : 1.3240740740740742\n",
            "iter : 107 |  cum_reward : 1.3009259259259258\n",
            "iter : 108 |  cum_reward : 1.2962962962962963\n",
            "iter : 109 |  cum_reward : 1.4182098765432098\n",
            "iter : 110 |  cum_reward : 1.3209876543209877\n",
            "iter : 111 |  cum_reward : 1.3996913580246915\n",
            "iter : 112 |  cum_reward : 1.3719135802469136\n",
            "iter : 113 |  cum_reward : 1.3194444444444444\n",
            "iter : 114 |  cum_reward : 1.3564814814814814\n",
            "iter : 115 |  cum_reward : 1.3209876543209877\n",
            "iter : 116 |  cum_reward : 1.3302469135802468\n",
            "iter : 117 |  cum_reward : 1.3595679012345678\n",
            "iter : 118 |  cum_reward : 1.3410493827160495\n",
            "iter : 119 |  cum_reward : 1.3518518518518519\n",
            "iter : 120 |  cum_reward : 1.3225308641975309\n",
            "iter : 121 |  cum_reward : 1.3611111111111112\n",
            "iter : 122 |  cum_reward : 1.4212962962962963\n",
            "iter : 123 |  cum_reward : 1.337962962962963\n",
            "iter : 124 |  cum_reward : 1.3842592592592593\n",
            "iter : 125 |  cum_reward : 1.3950617283950617\n",
            "iter : 126 |  cum_reward : 1.3719135802469136\n",
            "iter : 127 |  cum_reward : 1.3040123456790123\n",
            "iter : 128 |  cum_reward : 1.3441358024691359\n",
            "iter : 129 |  cum_reward : 1.3703703703703705\n",
            "iter : 130 |  cum_reward : 1.3657407407407407\n",
            "iter : 131 |  cum_reward : 1.337962962962963\n",
            "iter : 132 |  cum_reward : 1.3888888888888888\n",
            "iter : 133 |  cum_reward : 1.3919753086419753\n",
            "iter : 134 |  cum_reward : 1.382716049382716\n",
            "iter : 135 |  cum_reward : 1.373456790123457\n",
            "iter : 136 |  cum_reward : 1.353395061728395\n",
            "iter : 137 |  cum_reward : 1.4027777777777777\n",
            "iter : 138 |  cum_reward : 1.345679012345679\n",
            "iter : 139 |  cum_reward : 1.345679012345679\n",
            "iter : 140 |  cum_reward : 1.3873456790123457\n",
            "iter : 141 |  cum_reward : 1.3719135802469136\n",
            "iter : 142 |  cum_reward : 1.3580246913580247\n",
            "iter : 143 |  cum_reward : 1.412037037037037\n",
            "iter : 144 |  cum_reward : 1.4012345679012346\n",
            "iter : 145 |  cum_reward : 1.404320987654321\n",
            "iter : 146 |  cum_reward : 1.3888888888888888\n",
            "iter : 147 |  cum_reward : 1.3641975308641976\n",
            "iter : 148 |  cum_reward : 1.3796296296296295\n",
            "iter : 149 |  cum_reward : 1.3503086419753085\n",
            "iter : 150 |  cum_reward : 1.412037037037037\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plotPolicy(env.getGrid(), env.plot_size)"
      ],
      "metadata": {
        "id": "uUDMpq-Fy1zG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 578
        },
        "outputId": "bdd90f84-7e41-469b-e151-d88d851aa9ff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": 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rI3NIoxG8joy7OjA/NTRgRXGx1wXxvMFdHZhQE5jWfFRVFZJ1ZE7qdIJPv3ZXByYUBSZ24EC8UFqK1y5e9Lognre4mkYdCl1IrgTmyvJybGlowIqSEp8r+3YEe9cSmX4tHkRiuoBgLYjXXiG7EqPRp8q+ngi2QnZCQgritU+wFbLzFncCM/6DD2DmeXCAT5V928NdHZhQFZh02yDeYoPBp8q+naHJYiF1ZESESEwX4UlkWJNJ0H5HTyLDmc0eF0rkOQ6c2ex1JV5PBfF4nvf4ILa/3p0FBmi/sm8wy4wnkeFY1uOqyPbrIxQFxv6d96aQXXuVfdu7PuyvB2shO3fwsA6Ebk9g7Hhb2VcISEE88QjoKdb+GEBE2WaAdFWsY6tX488PPwRrMICWSqGy1ZFJHjUKo1asEDRW2fbtyF+0CKzRCNA05FFRiMjIAGcy4YYNG1weZ+vMmeAB6CoqPApM23iNJ09i25w5sGi1AABGqUTK6NGo2r8ft+Tng7atNtuaA8uXo3znTshjYqAuKnIrMF39mfkrFs/z+OPZZ1H0009g9XowcjliBgyA+sIF9L3jDgx+4AFsuvFGaEpKIFWpcPWbbyIpN7dDscSm9YBKAKg+cAC75s0Da1s5VxoRgdhBg9BSWoq/bduG02vW4PArr4AzmZB5ww0Y8/LL2PXAA2gpLwdrMHgUmGC7Pi7++iv2PPIIelx9NSr37HErMG1jcRYLfn/oIVTt22fdR6FA4ogRqD10CMOfego9p0+/JJampAQ/Tp2KjMmTcXH7drcC0yXvoQ/7HE5MxEnbFOuBtbXIqa3Fluxs0DyPFpnMo8DYZ3rZz63hxAlsv/NOWGzlCSRKJa59/30kDB0q3LnZYulqarD1H/+Aob4ePMuCUShwxZIluOxvfxM8ljesHTQI4HnQEglm5OdDavvx6W0sP0KmWAcql2RkSkqgr65G1d69gsdylZFpOnUKTWfPwmIwXLI9azKh8dQpNJ8543MlXlcZmcr8fPAc57b7oPL336Gvq4O6uLhbZmDa4ioj03jyJEzNzSjfubOrm9cpXGVk6o4cgb6uDnoXY554nkfNwYNoKS0NqQwMAFTm5YEzmVD5++8+FbJzlZGpOXgQFr0e5bt2udynpqAAlETiUWCCEQtFoUmhQJNC0W4Gpi2uMjL7ly4VpZ2uMjL7lywRJVZ3gUhMADB43jz0njULkrAwcCYTeI6Dob7eWhZbYNInTMDI554DLZUCHAfWaIREoUDD8eOXbNtw4gRomQys0Qie40BLpbhi6VKvK/HG9O+PCZ9+6si6sAYDwHGoLSi4ZFuTRgN9TQ3AceCMRkjCwtDrllu6rcDYsYtM2rXXQhIWZn0PATSdPeux6yUYSMrNxbi33gLV6vqgpVKX6XxNSQl4nrd2f9oEZsj8+UEvMABQtW8fAIA1GkFLpYjMzva6Eq9dZOJzchwiA8DldwwAqvLzwRmN1mwsrBmwYBcYAKhXKiHhOLA0DVAUaI7DyIqKdgXGTmuRkYSFYdRzz4nW1tYiQ0ulncq4E4jEBAxVeXmw2FLrgHWV1LrCQlFiVe7Z4zQI0GIwuBx8W1tQAK5V/zrHsqjcs8enWLWHDjnFYo1GVOblXbJdXWEhGFtdEMD6a6gyP9+nWKFM9YEDjnQ3AFASCb4ZMwYtFy8CAHiLBTvvuw+Fb77ZVU3sEBV79jiNybLodCh46SUUvPSS4/+Xbt2K7bNnOxV8Y00mVLi4joINi17v+AwBgDOb0XTuHEw+VJHlzGbUFRY6BAawZrZcZbRqDh50+repqQma0tIOtLzr+Dk7G6fi4hz//jM+Hvk9esDSqngoR1GojIjw6bixAwfihg0bMHn9esTn5AjWXleEJSZi8vr1uH7NGmRPnSpqLFdU//EHvh4xwvEDk2IYfHv11bgYhBneSwcmEFyiKS2FMj7ekXIUmiEPPojizZtRe+gQjA0NsOj1qDtyBKnjxgkeq/fMmWBkMlTt3w9dZSVAUajevx+D7rvPabvq/fsBWB+Y4SkpSMrNxWUu+tk90eOqq6CtqEDV3r1QFxWBomk0nDhxyXZ1hYWw2H6Jy6KjkTh8ODJvuKHjJxlCUBSFoY8+irJt21B7+LB18LfBAElUFHjbjDaLXg9aJhP95is02VOngjUaUb1vHzRlZaBo2pqRYRhHtgA0DVoqBWs2g5ZIoEhIQNIVVyB72rSubbwANJ48CXAcGIUCNMMgbsgQpIwZA0VsrNfHkCiVGProoyjfvRv1hYVgjUZwFgvqjx1D2vjxju3MLS3Q19RYu/A4DtH9+iFlzBhE9ewpxqmJRoxejya5HLxtXAbF8wBFWf8LINxsRnJLCy7rQJl/VWamoG31hDI+HkrbuB5/E5mV5chsArYsoEyG6N69u6Q9nYEM7PVyMNTXubnW8SD//S9Sr75a1FibZ8yA+tw5DFmwAP3vustRVVGMWGatFvXHjkGZkHDJzUxdVARdVRXihgxpd8CXN/FYkwmNJ0+C53kkXH6502uGhgbUHTmC+Msvb/cGHmwDN4WOpa+tRd2RI5CqVNg9b55jbEPKlVfimrffFjSWULQd2OsKi8GAhuPHwchk2PPII9BWVACwPqSv+/hjGJuaEJ+TA1lkZLuxgMD6zNzBsSzKf/0V0X37IiI93eOASW9i8TwPXWUl6o8fR+o114CRyZxer/j9dygTExHVqxfoVusIdSSWUPg6sNdM0/ihVy+YbFkEucWCa0pKYJZIEKfTQdpOm9sO7BWTQL4Wz6xdi8OvvmqdNKBQYOC9917yQ7a9WH7E7cDekJeY02vX4vjq1W5fr7WlXBPa6RM2qdWO0eRJI0di5LJll/Qj1xw8iN8XLhQullIJVUYGrly5EtG9enncpy1d8ZDyVzwS6y/2PfMMijduBC2VYuqWLV6PbQhEiWlNbUEBfp07F6AoDH3sMfT5xz98igUE7mdGYrmJ5cM+pSoV9qamAgDGXLyINB9qbBGJscJzHDbffDOaz55FRFoabvrxR+tYSR9i+RG3EhPy3Un1R4/C2Njo9vVIm8172qY1rMGAyt9/x5YZM3Dzb785vdZ09izMWq3TOJJOxdLr0XT6NLZMn47pv/0GRUyMV/sRug/DFi1C1d69GHDPPUE/OLM1CcOGIWvKFDSdO4fet97a1c0hBBjpGg2Omkyged4ngSH8BUXTGLNyJbbPmYPRL77otcAEGiEvMbLISNAymdsPSK1WAwAi20lRg+dh0elAy2RgFAoMf+qpSzaRRkQAPO+YVtzZWBTDgJZKMXj+/HZT6ITuiUylwt+3b+/qZojCyOXLu7oJhABFLZNBa+sqU8tkiAziApBdSXSfPrhFhHIe/iTku5NYoxEaDyX2Bw0aBAA47mKKcWt+vu02gOOQddNNGPb445CpVJdsw/M8ms+f73SsPQsXQlNaisQRIzBqxQqE9+jhcXtXkO4kEitQYtnjkWuRxPIYy8vteVhnKDXaZjPG6fW4rqQENM97Nd22s91JPMeBNZkgaTWb0m2sEP/M/Ej3HRPTHp4++JbyctQWFKBq3z5c/PVXmNVqXPvee0gZM0bwWK0p/eUXAED69dd3+GIhEkNiBUosezxyLZJYHmN5uf256GgcSk621oQBIOE4qIxGNMvliDYakdLSgkSdDvF6PaQullTprMSc+OADHH3jDaiyspA8ejSScnORMGyYywkJof6Z+ZHuOyamo7AmEzbdeKPTVE9GqYSiVX0CsciYOFH0GAQCgRCMnGgzLZkDoJFKwVEUGpRKNCgU+BNAmkaDq1rV4BEKZUICaJkM6gsXoC4qwrn16xGekoKpmzcLHovQPqTYnRsYmQwZkyY5/T+eZREVIPPoTc3N+HHaNGyePl2U1a9bw1ks2D1/Pr656iqfV782a7XYcsst2DRliigViL2B53kU/fgjzqxbh+oDBwQ77on338eXl1+OCh8LAPoDXXU1vh8/Htvvukv0BSMtej22zpqFHyZNgqG+XtRYPMdh71NPYf3IkR1a/dpXTn3+Odbl5PhcBIznefyxYgW+HjEC9e10HwvBuQ0bsC4nByV+eJDWHDqEr4YNQ+Ebb4geq0UqxTd9+mB3ejrsOZVrS0uR1qoYYKZajctraiCxZ10oCjyAXl5OoPAVpzWVeB4UTaNngA8+P7Z6Nb4cOhSVturQoQSRGDdc+OEHXNyx46+CWwBi+vXzWFvBn+x+8EG0lJZCXVTkWP1aLA6/9BIq9+6FsanJsfq1N7Rejbrl4kXseuAB0droCU1JCfY//TQKVq7E7wItY3Di/fdx/J13wJnN+O0//3HUNAkUtt95J3S1tagvLHSsfi0We594Ao2nTkFXVYWts2aJKjJ/fvghSrdscVr9Wiwq8vJw5NVXwVssyHvkEa9FpvVq1Ba9HtvnzBH1+3luwwYceuEF8BYL9j75pLWAnkjUHDqEnffdB9ZoxMlPPkHRpk2ixWq9GnVleDgOJyUBACJNJoTZirQBQJjZjHSNBlyrLg4KwN7UVGhFmHETkZ7u9BxgDQYcX7VKlNWvhcC+yDBnMmH3/fdDV1XV1U0SFCIxLrjwww/4Y/lysAYDKIZxzAzq6FgYMUgZOxYUw1gXjSwuFlVkkkaNsvYj2xaN9EZkWgsMZzKBlkiQMnasKO3zBloqta5LJcCx7AJjX+snIiMDsqgoAY4sHMmjR4OxrXtVf/SoqCKTMmYMKJoGb7FAV10tqsjEDx0K2B5WYotMZGamtUI3RYE1Gr0SmdYC41jMccAAr9ZB6gh2gbHfqxTx8VDaHvZC4xAY2/IotFSKmL59RYnVWmBAUaAAJGm1brdXsixkLAsJy4LhOPAUBSPDYGt2tuAiQ1EU4oYMAcUwTotG7pw7N+BExi4w9nuVKisr5Ga6EolpQ1uBUcTF4cYffsD4jz5Cn3/9q6ub52DQvfc6r34tosikXXvtJatfexKZtgJjX416yPz5grfN37gSmIlr1ni9hL2/uOKZZ5B9003WRQFFFplet96KYY89BkahEF1kkkaMwLjVqx3rbIkpMhFpaZi0dq1VUL0QGZcCY1uNWowaHG0FRpmYiEnr1vm0ZIG3tBUYSXg4rv/0U0T36SN4rLYCY1+Nur16MOPKynDDhQsYVV4uushcsXQpJq5de8nq14EkMq4EZuKaNaItndNVEIlphSuBmfTVVwhLTETyyJEBV2xu8Lx5fhOZ9AkTvBIZdwITCqtRuxOYQPxlY1/92l8i03vmTL+JTPLIkX4TGVVmplci40lgxMjCuBMYMQoeuhOYmP79BY/lTmC8WY06zmCAymxGpkYjusio0tMRN2iQ0+rXQOCIjDuBkfq4KGYwQCTGhieBCWTciYyyA+sttUd7IqOgaSIwAYQnkZGIMEWyO4sMEZjO0xmBaYs/RMZOoIlMdxIYgEgMAGBsVFRQCowdVyKzJCvLryKTJpPh6czMkBSYqXFxQScwdtyJzOMZGX4VmUgRBsS7E5l0EcTBncgMjYjAnORkvwnMuKgovwlM37AwvwlMgoACY8edyMT7UWR6K5WCx/LE3+Pju5XAAERiMDYqCnenpAStwNhpKzIpMhmWZGX5p2upqQnP9+yJNLk8JAVmekJCUAqMHVci01OpxOMZGX7rWlqWne23jMySrCy/iczC9HRcGx3tN4GZ0+peJbbALMrI8JvALBdYYOy4EpllfhSZxZmZfhOZv8fHY1p8fLcSGCDAK/aKjV1g5DQNC8dBzbJ4pqgITRZLVzetw9gvZDlNw8xxqDSZsLy4GHoXlSs7y3CVCvNTUyGnafA8D4qiYOQ4bK6vxwbbit2BQLJMhhXZ2VAyDFpYFvedPu3VfnaBsb+X1SYTlhUXQyfCe+kv7k5OxpjoaChoGkaOw3m9Hi+WlsIiQpXP66Kj8a/kZOv3i+fRYDZjaVER1CwreKz+YWF4NCMDClv2UceyWF5cjLJWJRKEIkkqxbLsbEQwjKNyqdn2Xv6vtBRmEd5Lu8DY71VNLIslFy6gWYT30i4wrd/LFcXFKBHhvbQLTDjDgLHdP1aVl+NQOwIzKzERN9mK3n1XW9vu/SZXpcL9tnsVy/PQsCyWFhWhrtVUbaHIUijwdGYmlLbso4Hj8L+SEpy1CaEY+LEFb6MAACAASURBVPO+30W4rdjbbTMxoSgwAPB9XR021tXByHGQ0rQjIyNG19IJrRZNFotDYHieB8/z2N1FRe2EJBQFBgA+qqpCXlMTDBwHOU07MjJidC3taGrCF1VVMHIcJBSFWNvDX4yupZM6HV4uLYXB9vmEMYxoGZlqsxlH2mQ4GYrClvp6IjA+0FGB6QgHNBq8U14OI8eBoSioRMzIFBsMWFFSAr3t81HQtKgZmW4gMB4JaImxPxSF/jv//ff4T8+eTgJz9549aDSbRYvZOuMlZgye5/Fdba2TyGSpVFg/dSpMGo1gMUwtLfh2+nSkREQ4BIaiKCikUrydmwtNaWnAvI+nT5+GyrZgZ0xMTLvbH3/vPfwrM9NJYBYePgwty4bE9fFBRYWTyAyOjcWW2bNhMRoFj7W9sRFjly51iExSWBg+Gz8e+ro6wWP9qdXixk8+cRKZV3Jy0Hj6tGAxOI7DgeeewzXJyU7iTlMUHu3dG2W//iroOZ1dvx4PtLpXNbEs5ublOX48CPlXffAglvXv7yQwM777DsUGg+CxNKWleDs3F5EymUNgJrz1Fg6q1V7t/9hjjzm+L08//bRX++xXqzH+tddaiYwMy7JHIV5aDuvKTcL9FRv0WFHyjZPIrBgwADUFBYK+j0dXrcLMjAwngXmksBA6P92rAoGAlhgxaDsLyZ6BCbYxMO1hz8iIMf3a1TTq/Wo1jBznU0G8QKTtLCR7BiaYxsC0B0VRjoyMv6Zff1FV5bdZS60zMkLOWuL5S6dRFxkMaGFZnwrieUvbWUj2DIw/ZiHZMzCizEIqK8PWWbNgbG623i9sGZj0CRMEj9WWzMmTW2VkOKiYRizLnol4qfAVt4sNA50yMkLPWmo7C8megQn1MTBt6VYS42oadSh0Ibnj+7o6wevIuKsD81Z5OVaVl3tdEC8QcTWNOhS6kNzxUVWV3+rI7Ghq8tv0a3vXkpDTr10JTOzAgVheXIylRUVeF8TzFlfTqMXqQnI1jVqsLqS2AsPI5aJ1Ibnjr64luR9Extq1JPT0a1fTqLtTF1Jruo3EuKsDE6oCY0fIgnjtFbI7pNH4VNk3kHBXByZUBcZOqBbEO6nTCVZHxp3AjP/gA5h5HtVms0+VfdvDXR0YfwnM9Z9+6jeBuXLlSr8KjB2ryDzvN5ERso6Muzow3VFggG4iMcFayE4ohBAZbyvxelvZN5AIxkJ2QkEq+3rGk8C0nkbtbWXf9gjZQnZuBMYfXUjuOKCZ7DeREaogXncrZOcNAT3FWogBRO0JjH2KpD8GK3V1LFdfgPicHJhbWjD2lVdcHif/8cdBS6VoPHXKo8C0jVe2fTvyFy2yrgJO05BHRaH3rFko//VXTN6wwbG9UOfmCXVxMX6+9VZYdDpIIyNx6969KP/tNxS+9hp6XH01Tq9Z41Zguvoz81csnufxx7PPoujHHx0P67ghQyCPjkZYUhKGP/FEp+O1Pq+zX36Jgpdesn4vJRKEJSUh/brr0HT2LMZ/8EGnY9nPCQCq9u/H7nnzwBoMAABpRAQG/PvfKPrhB9zwzTcua7qc/+47nPvqK0T37YviVu9JW4FpG0tTUoKt//wnTM3NAM+DkcsxeP58nP/mG4xbtQqR2dmXxKo9fBj7lyxB5o034s8PPnArMEJcH/raWmybPRu9br0Vx1avdiswQsTieR4/33ILUsePx9l169wKTEdiHX75ZZz8+GMAwMD77kPOggVe7/vXvccaL1f1M+5PfRJy2giWp6FhY7C06EvUmXt4fUwP0ayRbOfWcOIEtt95Jyw6HQBAolTi2vffR8LQoW6PcPHXX3H0rbeQPGYMzq5b51ZguuL+4Ue65xTrlosXse/pp7ttBqYtl2RkSkpQ8tNPuLhjBzgX9RI4lkXpL7+g5Oeffa7E6yojc+rjj9FcVARNcbFIZ+g9pVu2oOnsWZz+4otumYFpi8uMzLFjKP/tNxT/9JPg8VxlZM5t2ICaP/6ASa0WNJarjMzxd96BtqICDSdOuNyn6Pvv0XDiBEo2b/apkJ2rjMyx1avRUl6Oqn37XO5TvnMn1EVFjh8YYmZgag4dgq6qCsdWrRI9A6MuKkJzURFOffxxQGVg2tI2IxPJNGBhmvdS5AuuMjK//vvfHvcp/uknNJ09i3NffUUyMC4IaYkJS0pCyujRoKXSbi8wdpxExmSCRa8HLZOh0UUBuOYzZ0BLJGD1+g5V4m0rMha9HhRNd/niaABQ/ccfAM+DtZ1/RHp6txUYO04io1SCNRjAGY0wa7XQ1dQIHq+tyFh0OjAKBeoKCwWPlTxyJK55+22HyLAGA3iOQ82hQ5dsy/M8Gk6cAM9xjjb5UonXITKRkVaRMRjAWyyozM93uX1lfr7jWqQkElG7kKr37wdnMjmyUmJ2IdUdPgyKpmHR6wUXmOi+fSFVqSBVqQRZSbu1yJh5GTbXz+n0Md3RWmRouRx9Zs3yuH3NoUMAz8Oi04GWyYjAtEHS1Q0QE1oqxbhVq3B67Vpk3nBDtxcYOxa9HlyrAc2c2YzaggLEDRrktF3N4cPgWw0m5CwW6w3JB1ij0SnLw+r1qMzPR8+bb+5g632jbMcOFL7+OmBLsVq0WmyeMQP6VhU+OYsF5pYW0DKZX9oUyFAUBbNOB77V9UFLpag7fBgZkyYJHs+i1ztdH2adDjUHD6LHVVcJHouzWJzG+XBmMyrz8jBw7lyn7dRFReDb7GdSq326Piiatnaltkrt17mQd85sRvO5c45/8yxrfViJUIQNwCXZIFaEAbx2KvPzHdkewHquQsXLnjoV2VOnCnIsOwc0k6EviwBNsShsGSfosdsSO3Agrv/sMzScOoXL/v53t9sZGhpgalU8lCf3qksI6UwMYL0B958zhwhMK2L69kVUr16gpVJIwsPBsywq8/Iu2a4qLw+c2QxJeDhoqRRRPXv6/IstLCUFcTk5YBQK6y8PqRS1Ln79igZFQVNS4pAvnmWtDw2ed5yXMj4eyaNGibLeTTASn5ODiIwMUBIJJOHhsOj1brtCOosqMxMx/fqBlskgCQ8HRdMur0UhUMTGInH4cEjCwsAolWDkcjQcP37JGIK6w4fBmUyQREQ4xuskX3mlT+MApCoVkkeNgiw6GoxcDkl4OIyNjdBVVztt13jqFHiOc1yLirg4JI0c6ehuEBKzVouWsjLHuUtVKiSPHAlpVJTgsQBrBoGWyazvt0KB+JwchKWkiBJLKI5px4ouMHZi+vdHz+nTPV5XdUeOgG99r0pMRPKoUaJJbjAS0pkYgmuybroJWTfdBNZkQuOpU6gtKIAqI+OS7XrddhsSR4xAwvDhiOnXD0wH7D9x2DBMXLMGPMeh+cIF1BYUCHEKXpM+fjySRo1C9b594FkWjFKJ3v/4B0q2bIFFp8P0Xbsgj472a5sCnb63346+t98O1mjErvnzUVdYiAgX14cQpI0fj7Tx48FZLDj65ps4+emniBk4EDzPCz54MKZfP0z45BNrxdjiYtQePuxyhp4iMRGyyEgwSiVu3L4dMlu1Z1+QR0dj3KpVAABdVRVqDx9G84ULkMfEOG0nUakQ1qMH9NXVmLplC8JFfMgzCgWGLFiA8JQUJAwdivDUVFEHaA6eNw8UgPhhwxB12WWgRFj6JNRRxMZCHhsLzmjE3375hdyrXBDys5Pao7vMPgmleL7G0lVVYdOUKWANBshjY2HWasGZTABFIXH4cI/77tq1CwBwzTXXuN2m7+23C9LPL+R7qK2owP4lS5y6DVvjzXkBQNPp0zCp1WAUCqSMGYPcpUuhiItz2sai0yH/iSesM3LcsGvXrnZjacvLoa2oAKNUIuqyyzD6f/9D1GWXedzHFR19Hy16PQrffNM6gNJoBC2VIv7yyz3u4837OOTBBy+5zjiWxdm1a3HkjTes3Wk8j/jLL/f4oPcmVva0aYJ01Qby91moeIA/4nXs3FiTCSfefx8nP/wQrMkEiqKQIMC9qt+cOUi79lqf2uKKQJqdRCQmRL+sRGKcOfXppyhYuRK0VOpyJlZnyJg82e0UdV8Q8j0s+fln7HvySUHHPFASCWiJBDdt2oTwHn9NP1UXFWHLjBnCjq+gKFA0jWvfew/Jo0b5uGvH3sdvx42DSa22Cq6ADJg7F5cvXOj0/3bcfTfqjhwRfExKwvDhuP6zzzp9nED/PgsRL5Al5sdp06C9eFHw6yNr6lRc+b//dfo4gSQxpDuJ0C3oc/vt0NXU4Oy6dQBFOWp4UBLPXwG1bbpvpJtZS5zZDEqEVZk7i70uiyQ83OXr7Z2XHZ7jHIMzaYkEiSNGXDqDi6bBsazbWPZ47cUCrAOvAWvXR0RqKiJSU9vdRyjSxo9H0Q8/OP7NKBTtfrbtXh+2X9FtSb32WtQVFoJiGPAsC1oma3ecQ3uxeIsFdDvXMyE4SBs/Hqc/+6xb3Ks6C8nEhOgvDpKJcU3zhQvYu3ixte6NxYLRzz/vcft//vOfAIC1a9e63SbxiisEGTgu5HvImc0o27HDaXZZa7w5L8BazVh94QJkkZEY+dxzblPR5b/9BrOH8vH//Oc/241Vtn07yrZtg0ShQM7DD6P3P/4BugM33c68j/XHjyP/8cet3VpyOa545hmP23vzPvYYO9ZaN6YN2ooK7H3ySdQfOwbWaMSo//7Xo4R4Eys+JwcRaWke2+wNwfJ97ky8QM7EAEDzuXPIf/xx62w5jsOo//7X4/beXB9JubmCTN0PpEwMkRgPXyCe49B87hxqCgpQlZ+P+mPHcNXrryM+J0fwWEJDJMY9PM/j/HffofaPPzD6hRdEjeULgRRLW1GB2sOHcfqLL6A+dw597rgDOQ8+2Kl47mIZGhpQd+QIijZtQmVeHpKuuMIxKLajsYCOv4+cxYJTn38OfVVVu5WKhbgWy7ZtQ8nmzRjzyisepS2Qro9Ai/XHc8+hat8+JI8ejaTcXCQMHer2Yd1ZiRkXvQE3x7+Dk7oROK4djTO6oagxp8MuLG2iWSN18NxqDx/Gzvvvh0ylwoB//xuJw4Yhqlcvl2OnumvFXpJ7dANrMuHbceMcg+7sVTv9JQUE8aAoCr1uvhm9/FSrJtj49d57UXPgAGip1FoenaY7lBHxhiOvvYaTH38MRqGwxuJ5t4OR/QUtkWDAXXf5JRZFUciYOBEZEyf6JV6oYjEYoCkuhqakBEUbN4I1GpE9bRpGPfec4LEklAVRkjpcFb0JI1Q7wFAWXDT2xjNFXwseCwB4sxm6ykocfuklgKJAS6W4effuDs0WDUXInDc30BIJEocNcwgMYJ1REDtggNt9TM3N2DZ7No6/957o7eMsFuQ9+ijyFy8GJ8Lqtm05+n//h+133tmh1a99pWTzZmy++Wa/LBpZf/w4fpw2zWXl1u5K2jXXWCut2tZ3kYaFtTuLq6P0uPpqALaxMDwPWiZDypgxHvc5tWaN4ItG1h87hppDh5yKIBJc01JWhs0334ySzZu7uikOkkeNstbW4XnHtWS/toTmjG4oLLx1/JKS0YEDgyMa4YszAkDsgAGO+ztrMAA8j8Rhw8jYp1YQiXFDXWEhqvfvdwgMAERmZ3u031/uuAP1R4/ixLvv4tjq1aK2b+/ixbi4cyfKtm1D3sMPiyoyf370EU5+/DHqjhzxefVrX6nYswf7nn4aTWfOiL76taa0FDvuugvq8+ex8777iMgAMNTX4+QnnzhdT6zBgLjBgwWPxZpMOPrWW043ZFoqRcKwYW73ufDDDyh87TU0nDghmMjUHT2KbXfcgV33349d99/f6eOFMvbVqJvOnMG+p59GxZ49Xd0kAEDC0KFO478ohsGJd9+F2TZQXEguGnuBof6KJaWMGB+7XpTVrxm5HJFZWY5/swYDqvfvF2VpjmCFSIwLag8fxs65cx1VXimJBKAoJI8e7XE/e8qdNRjw54cfiioy9pHqrMGAij17RBUZ+0OGM5uhKS4WVWQcMzR4HsbmZlFFhqJp6+h/WJdD6O4iY6ivx9ZZs6CrrgZvsThm54SnpgpeQZY1mbDz3ntRf/QoWKMRjFJpnf5uMiGmb1+3+9m/YzzLQlddLYjIsAaDozvL12U1uhN2gTHaVugGEDCVY8NTUx0zvOzrwjWfP4/tc+YILjI8GBQb+oHlaZhsi0aqmEYsy54pisgkjx5tLTlguw9b9HrsnDs3INagCwTIwN42g6HaCowkLAzXffwxag4eROo11zhZcVvMLS345fbboSkuBmc2g1EoMOCeezB43jyXsToDx7LIe/hhVOzZ47gJ9xg7FmNefRU0wwg+yOvY6tWOVXbFXga+bPt25C9aZK2RQNOQR0Vh0rp1iEhPFzxW48mT2DZnzl9Te5VKXPvuu47uk2Ab4NjRWK4EZuijj0KZkABaKkXquM6VYm89sNeVwGRPmYLLZsxA05kz6HXLLR6PdfbLL1Hw0kvWFZ9tywJMWrfOUYTP1/ex+sAB/LZgAcwaDVSZmZjqQzdJd7k+nARG4MUchTqv4p9+giI+HtX79uHUZ59Z71UyGaJ69sSETz+F1FYCQIjZSf2UBxAnrYSZl+P+1Cchp41geRoaNgZLi75EndleR6nz56YuLkb5rl1IHDECO+66y9HNK1Eqce377yNh6FBrpG46sJdITKsP3pXATPjkE8QOHOj18TyJjNAXmSeRYWzWLuR76ElkhD43TyIjdCxPItMdHlLuBKa91XV9jcfzvFuBueLZZ326MXoSGSIxwsYSU2DaxhKKwjfecCsyQk+xzlX97EFkhD23hhMnsP3OO12KTHeVGNKdZEMIgQEAaUQEJq5ZA1VWFmipVNSuJZphMObVV9Fj7FgwCoVT15IYl9jgefMw4J57rOlakbuW0idMwJUrV1oXZeQ4UbuWYvr3x/Wffuoo1tadupb8ITB2hBIYAOg9cyaGPfYYGIUCvMUiWNcSwRmxBUYsch56CP1mzxa9awmwrn79TvnzMPqhayl24EBM+OQTR/cu6VoiEgMA6K1UCiIwdtyJzN/j44VsNgD3IrMgLc2vIqMUYXE3dyKTIEI/vDuR6SvCasKBQiTD+E1gJBQlmMDYcScykSFYlbQrSJBKg1Jg7LgTGYUI9yr3IiP8vcqdyPRWKgWPFQx0e4nprVRicWamYAJjx5XITIuP95vI5EREYEFamiiDfV2JzJKsLL+JzPLsbL+JzKKMjJAUmUiGwbLsbL9lYBZlZAgqMHZcicyy7GwiMp0kQSrF8uzsoBUYO65E5unMTD+KTLbfRGZxZma3FJmAHhMjNnaBsV/QepbFipISFLeaVt1ZlDSNJVlZSJHJIKVpGDkOG+vq8H1dnWAx7FAAFqSlISciAnJbrMKWFrx58aIoBbb/Hh+PafHxkNM0zByHSpMJy4uLoec4wWMNV6kwPzUVcpoGy/PQsiyWFBWhVuDFHAEgUy7H01lZCLM9CA0ch5WlpTht64cOduwCEyuVQkJRMHIcvqiqwo6mJsFjSSgKizIy0EuphJymYeA45DU14aOqKkHjXBcdjX8lJ0NO07DwPBrMZiwtKoLaC4nvHxaG/5eejnCGQaXRiEfPnxe0bcGGXWDCGQaM7fpYVV6OQx6WlQh0bklIwA1xcVDQNEwch3KjEStKSmAQ4V6Vq1Lh/lb3Kg3LYmlREepEuFdlKRR4OjMTylb3qv+VlOBs6M2yI2Ni2uIPgQEAPcdheXExKk0mmDkOcpoWLSPDA3jz4kUUtrTAaItlz8iI0bX0fV0dNtbVwchxkNI0UmQy0TIyhzQarCovh5HjwFAUwhlGtIxMidGIFcXF0NkegAqaDpmMTCgKDADsaGrCF1VVMHIcJBSFWKmUZGQ6QCgKDABsqK3Flvp6GDgOMppGqlwuYkZGg3da3atUtu+cGBmZYoMBK0pKoG91r+puGZmAlhie50X5qykowIoBA5wEZvo336BIrxclno5l8UhhoZPIzMzIwNFVqwSPxfE8Xi0udhKZ0YmJ2L1gAViLRfB439XWYsSDDzpEJkulwvqpU2HSaASPdVCtxoS33nLcHCJlMrydmwtNaangsYoNBsz47jsnkVnWvz+qDx4U7boU+7rX19Xhs/HjkRQW5hCYMUuWYHtjo+CxLEYjtsyejSGxsQ6BGXTbbfigokK089ve2IixS5c6RCZJKsVn2dnQMwx4wO3fTgDRtve+j4ftXP2J/Zn58/rQlJbi7dxcRMpkDoGZ8NZbOKhWB/V52f/W19Rg+P33O0SmZ1QUvp0+HaaWFsFj7VerMf611xz3qhiZDO+OGoWW8nLBYxXp9Zj+zTdOIrNiwADUFBSI/pkFAgHdnSTGm9V2FpI9A1Pkh/RbGMNgSVYWslQql3VkhISmKCxIS8PoxESXdWSEZnpCgqNryVUdGSEZERnp6FpyVUdGSLIUCqeupbZ1ZIREzCmSbWch2TMw2xsbBY/lahbSL+Xl+KCiwi9TMyfExDi6liieR5jZjElFRVC46VqqDgvDb+npMDMMVEYjpvrQneSYrOuHG7uY10fbWUj2DMxBtVrwWG3x92K1tyYmOrqWXNWREZKRkZGOriWKpiGPicGkL79EeI8e7e/sI9lKpVPXUts6MkJCplh3Ea6mUYvRheQOe9eSP6Zf27uWXE2/FmOwr71ryR/Tr+1dS/6Yfm3vWgrm6deuplGL1YXkbhr1R1VVfrvx2buWGI4DT1HQSaXYmp0NA+lacomradSh0IXkDnvXkn+mX1u7lhi5HDzHwdjYiK0zZ0JbIfz0a3vXUnebft1tJMZdHRh/CYwdPcf5rY4MD7itIyOWyPirjswhjcZvdWRKjMagrSPjrg6MPwXmimefFTxWe+xoasIwIjLt4q4OTKgKjJ0NtbV+rCOjwajnn/ebyHS3OjLdQmKEKmQnFIFSEM9f069JQbyuIVgL2QlFbyIyHgnWQnZC4c+CeJmTJ/tNZLpbQbyQl5hAExg7nkTm8CuvoPjHHwWL5Ulkzn75JY6uWiVYLMC9yJTt2IG9Tz0laCx3IlOZl4ffFy6EScA+fXcic3HnTux59FE0B9DU3O4uMHbciUxFRAT2pKZC+Am2gY1JrcbvCxeiMi+vWwuMHSIywU9ID+zV1dRg0+TJ1vV34FpgunrtE1drLXEWC6J69cKN33wjaCxXay3xHAeKYXDbgQPWVZ0FjNd2rSWe4wCKwt+2bkVYcrKgsdqutQSOAyWR4Oq33kLq1VcLGqvtWksUw4DneQx58EEMuvdeQWN1lB+nTYOmpMStwAgZK++xx3Bxxw6PAtN6AUixcZxbq/93NjoaBcnJYG2DfXkANM9jUlERTAzTbQb2lu/ejd8WLABvsTi+J64Epqvvi/6O57TWklSKhOHDcd2HH4oSq+Tnn7HvySfBGo3Wwb6xsbh5925RYrVda4mRyzH1558RlpgoSCw/0j0H9krDwxGWnAyKYQImA9MWp4yMTAbWYABvsUB9/jw4gYsjucrIcCYTAEBdVCRoLODSjAzPsmDkclF+DbTNyAAAz7Ko+eMPwWO1zcjwLAtwHCr37BE8VkeJHTAAtEQiagbGTtyQIQBFgVEoAiYD05a2GRlQFCgANSFQ+8cXqv/4w3q9At06A9OW1hkZimEQN3iwaLFaZ2QoqRQx/fqJFqt1RoZiGIQlJ4syC6srkXR1A8REGh6OiWvX4uB//4v+d94ZcAJjRxoRgficHGgvXnSkt2mZDA0nTyJ+yBBBY9EMg+TRo1H+229O/7/28GFE9ewpaCwASLziCpx47z3Hvy1aLar27UPmDTcIHiumXz9r15wt8waeR2VeHoY+8ojgsSIyMqCMj0eL0Wj9VQvrrx6e4zqV0RKK0c8/D0VcHKIuuww9Z8wQNVa/O+4ALZFAX1ODIQsWBJzA2IlvU0aBpWkURUVBybKOrI1BIsGhpCQMra4OyV94VXl5QKtf6rTID9FgIuehhyCNiADHshg4d66osTInTwbFMKjetw/DFy8WNZZdZE5+8glGPPUUkZhgQx4djTEvvdTVzWgXc0sLOIsFkvBwcGYzWIMBtQUFgksMAOjr60ExDBhbVUdWr0dVfj563XKL4LEM9fWQKJXWND7DwKLToXr/fsHjAIBZq7XGMhpBy2Sw6HRoPncOrMkERiYTJSZglVCLXg/ObIb6wgVE9eolWixvoWgawx57zG/xxMz0CIWBYSDhOEdXkoWm0SSXo5GmrdkZAGaGwdmYGAysq3NbVyZYYW1jPkBRkISFgTOZIAkLE2X8R7Ay4J57/BYr4/rrkXH99X6JFTtwYFA8BztCyEtMsDD2lVfAmc1oPH0atQUFqMzLgyojQ5RYQ+bPx+AHHoC6qAjH33kHFb//3qkxKp7InDwZGZMmQVdZiXPr1+PMF18gMjtblFgxffti+q5dMDQ0oO7IEVTv34+W8nKnX55CIQ0Px9TNm2HWalF/9CiqDx5EfWEhpCqV4LEIwpCi02HGmTPQSSQojorC8bg4RBuNaJHJYLCVhGc4DoNqa1GQlAQTw+AaEWa6dRUcyyKqd29oL17E5Y8+ivTrroMiNrarm0UgdAoiMQEELZUibtAgxA0ahH6zZ3vcljUacfLTT8G6qXNzS0ICAKDwzTc9HkddVASzRoOzX34JUBSG/Oc/kLRZd4PneZz98kvoa2vdHsebeI2nT8NiNKL6wAH89tBDyF2yBIq4OI/t6wiK2FikjR+PtPHjBT92W6Th4UgePRrJo0eLHotg5fz336OltNTt645r0cMxNDIZOJpGo1KJSKMRZoYBS1GQsSxOxMeDpWkwHIdC27HcxrL919N1n/23vyEyM9PjccSm+cIF7F282DrYm+NQX1gIXWWl2+29+T7H9OuHjIkTBW8rgeALIT07yRuCdRR+zaFD+PXf/3YMzBUCRqGANCIC13/+uVMWyNzSgg2jR1tnFwkEJZWCkUpx1ZtvIsVHAQjWz6y7xrLHEyrWuiFD/hqcKgCUfbAvAJrjQpYppwAAIABJREFUwAlcR2bA3Lm4fOHCTh+no5/ZmbVrcfjll8GaTIJmJWXR0bglL6/TxwmE2UkkVsdi+ZHuOTuJ4CM8D55lBZUVT7EA+CcWgeAlgToouTOIUdSSQAgUSCYmSE3ZYjDgz/ff/2smThtesg3ieqydwZ2VeXloOnMGjFyOnrfcgssXLnQUSLLD8zxOf/aZx+4kb+I1nTmDqv37QUskSB41CrnPPgtlO+l6VwTrZ9ZdY9njCRXr7Ndfe+xOclyLHo6hkclQEREBCoDKaIRaLgdHUVCYzbAwDFiaBs3z6NPQ4LEt9qGSnq77y2bMQJQA48A685k1nTuH/EWLoCktBTgO6ddf7/G75833OaZ/f2RNmeJzW9pCMjHBG8uPuM3EEIkJoIuMY1k0nzmDmsOHUZWXh1633YbUceNEiWXn1Oef48K332L0Cy90aqqlN/Gq9u3DgaVLMeLpp9HjqqtEjSUUJJZw8dzF4nke2ooK1B4+jKq9exGekoIh//lPp2IBzsXu2tKgUOD3tDTk1NSgICnJeWBvTQ0alUqYGAbjPcgSEFzF7niOw7kNG/Dn++/juo8/RkRammixfIFIjHvURUU49MILSBo5EgnDhiF24EC3My2JxHgBkRjxYuU//jhKf/kFtEQCnmXBmc24/OGH0f+uuwSPJQaB8j6SWIEXyx7PVawz69ah8I03wJlMjin4CcOG4frPP+9ULMCzxNgpTEjAn/HxjjExgHVczN/PnvVqinUwSQyJ5f94nY1VmZ+P3fPmAbDWDuNMJsT064eJa9deUo+qu0oMGRMjIJzFgkMvvICLO3f6vC8tlTpu4vby7QnDh4vQysCn/vhx7H3qKUEXjbTo9TC3tFgHNwpE05kzyH/iCRhFWBG6uyCPjgZrMIA1GmHR6UBJJEgeM8Zv8RN0OiS3tEBiExYJy+KypibIbP9ulsmQ36NHwC8a2VJWhrzHH4euurqrmyIoHMuiYOVKlG7b1tVN6RLic3LA8zw4sxkWrRYUTYMHBB3YHuyQKdYC8vtDD6Fy716cW7/ep1LeZq0WjadOOV2YduPubjSePIkdd90F1mRCw4kTmLhmDaQREZ06pr62Fj9MnAie46DKyMBNmzZ1up1NZ85g2x13wKLXo76wEJO//rrT7exu8DyPmoICUAwD2JbYkCgUSPSjvPfQasHwPH6zjQNTWizIraoCAGikUvySnQ0LTaM2LAyTiooCsgCefTVqk1qNmv37MXnDBijj47u6WZ3Gsdbb779bS0AAfisOFyhIw8MRnpyMlosXAQCc2QxdRQX0tbUI79Gji1sXGJBMjIDUHT1qrbZrNCJ/0SKUbd/e7j5mrRbb58xB8/nz4Ewm69odEgkiL7tM1CqzgUpzURE4lgVvsThWv+5sRsas1Vq76SwW6OvqOt1Gu8CYW1rAsyy0lZWCrpbdHeB5Hgeffx4Xvv3WuhipXO5YzT1u0KCubh4AoEUmA0tRTqtfB1pGxi4wxuZm8CwLY1MTDPX1Xd2sTuO0WK1t8kJdCK7A7A2JubkATTsW0RVz9etghEiMgExauxbyqCiApr0SGVcC02/2bPSZNQv95szxY8sDh6wbb8TAf//bsWikUCIjFK0FBgAYpRLXvvsu+VXkA64EJnbgQFyxdCl63XbbJcUWu4oUrRbDWy0aGWgi01pg7Is5jnn5ZcT07dvVTesUTgJjMIBRKNDjqqtwuQhroAUDPW++GZmTJyN32TIwcjkRmTaQgb0CD4ZydWOxdy21juVOYHIeekiQdgT7YLljq1fjzw8/BGswgJZKocrKcnQt+RpLXVyMn2+9FRadDtLISNy6d6/X7Wgdy5XAXLN6NZJyc308u/ZjiU1XDex1JzDjP/jAugK5QLEA7wb2AkB1WBh+S0+HmWGgMhox9fx5p9fPRkejIDnZuv4XzyPMbHZ0LXXVwF5P9xmhY4lN61guBWbsWIx59VXQAsljMH/PSn7+GfuefBKs0QiKpiGPicGkL79EeI8eZGAvQRgi0tMxad06jxkZsQUmFBg8bx4G3HNPwGRkxBaY7oA/BEYMejc1YVgAZWTEFpiuwh8CE+xkTp6MUc8/TzIyrSASIwLuRGa4SgUFTROB8RJ3IqOk/XvZpsvlRGAEIBgFxo47kYn088M1QSoNSYGhACIwXuJOZOJttY66G0RiRMKVyPwnNRXPZ2cTgfEBVyKzJCvLbyKTLpdjSVYWEZhOwPM8ZiclBa3A2HElMsv8KDIJUimWZ2eHpMAsSEsjAuMDrkRmWXZ2txQZIjEi4iQyFAUZTSPRVrCICIz3tBWZFJnMSSzEounMGSzJykKY7UZKBMZ37F1I42Jiglpg7LQVmVibyIg9I6ilrAzLs7MRzjAhJTAcy2JBWhpyIiKIwPhIW5FRMQyWZWd3u66lgB7YGyqkyeV4/rLLQOOvAY771Wq8VV7e1U0LKv4eH49p8fGQ0zTMHIdKkwnLi4uhb2cRyWSZDCuys6FkGLSwLO47fbrdWPYMjF1gDByHl0tLcVKnE+Rcuguzk5IwLiYGCpqGieNwQa/H/0pLYfbTgGJv6B8Whv+Xno5whkGl0YhH2wzsdcV10dH4V3Iy5DQNC8+jwWzG0qIiqEWoI2PPwIQzDBiKgpHjsKq8HIc0GsFj+RN7BiYnIgJymoaR41DY0oI3L170elA2AchVqXB/airkNA2W56FhWSwtKkKdrfZSiEAG9nYVCprG/T16gOV5h8BQFIWhKhWGq1Rd3byg4vu6Omysq4OR4yClaUdGRuiuJSIwwhAMAtNRdjQ14YuqKhg5DpJWGRmhu5aIwBDa44BGg3fKy2HkODAU5cjIdJeupYCWGPuUTDH/xIxlamnBt9Ono2dUFGS2L6qJ5wGahpym8VivXijdti3ozqsr431XW+skMlkyGdZnZcFkL8ft4u80ALsuxrjZxv7XKJfjFRcC86dWGzLvodixOI7DgRUrcGNqKhQ0DUYuxwW9HktPn4aJ4wLu3Hbu3Ino6GgAQJ8+fbzeb3tjo5PIJIWF4bPx46GvqxPkPDSlpXg7NxeRMpmTwBxUqwPuPfTlj7VYsHvBAoxOTHQSmFeLi8GJfF6tz83znUCoP/98p/er1U4iEyOT4d1Ro9BSXi7ye9j1BLTEBDOuplFvrq/H4+fP+1QQj3Ap9owMw3HgaBoamQy/ZGXB3MmMTJNcjm1ZWTDbx8CQDIzP8LzradT/Ky0NyjEw7WHPyDAKBXiLBbrqamydNavTY2RcTaMOhQyMq2nU9gwMGQPTOewZme42/ZpIjAi4qwOzobYWtWZzu3VkCO3zfV0dBggoMq4E5hoiMD7hTmDGf/BBSHQhuWNHUxOGPfaYYCLjrg5MKApMj7FjSReSgBzQaLpdHRkiMQLjTSE7bwriEdpnsEAi405gkojAeI0ngQnFDExbes+cKYjIdMdCdkRghKW7FcQjEiMgvlTi9SQyxT/9hJqCgi44g+DDnchoJRKciIuDxU157BapFKdiY9FIBKbTdHeBseNOZLQVFTjx/vtuFwnV1dTgz48+grqkJOQE5vSaNVAXFZFCdn6mO4mMpKsbEEr4WonXLjL2G5ddZDizGXE5OZi4Zo0fWx+8DLatTP1nfDxYm8hs7N0bPIB4vR5Ki+WSfc7FxODPuDhQAHib6BCB6RiFr7/e7QXGTu+ZMwEABS+9BNZggK66GhsnT7bW8cjIQMakSZfsU/rzzzjy6qsofP1166DJEBEYQ2MjCl58EaAoxwrlRGD8R+bkyQDgWGvJLjJTNm6E3DaQPRQgmRgBsej14DnOp0J2rjIyPMeh8eRJ8O3UPyH8RduMDE9RoADUhIW53L4yPBygKCIwAmDWagEgaAWGlsnAGgyQhIWBUSg6fby2GRmeZQGeR9WBAy63r8zLA3jeul2ICAwA1B0+DEapBM+yRGC6iLYZGdZsDrnnCsnECMjEL77ArgceQMrYsRgyf77X+0Wkp6P3rFk49fHHsOj1AACKptF84QKie/USq7khR9+GBpyJjQVrGxPDUxQuqlRo3aHEURTORkejudVDluE4JGm1SCQC0yFG2H7p6f8/e+cdH0WZ//HPzGzPbnrvCVUQ6SABaQKWAxX1PFHB8yycenr3s2GFQ+48xe6dDdvp6XFWOD1RuihEeiihhZIQ0jdlU7bN7sz8/tidNRu2JTuzLfN+vfLixZb5PjM7O/Pe7/M836epCVNfey2qBAYAUkeOxKX//CdYqxW6ggJBtll09dU49s9/oqumBnAOam7cufO813Ech+aDB13/J2QyJA4Zgpzp0wVpRzhp3LPHdT0DANZmw8g//UkSmBDDZ2QOv/46Lnn1VaiSk8PcImGRJEZAlImJuGz16j69t3bLFrAsC0qtBmM2g7XZoN+/X5KYXtCmUsFKUZAxDBhnNsagVMKgUrluJByAPdnZIJ2/RkiWBcFxaIqLA0MQkMXwLBqxIEgSF69YEe5m9BmCIJA2apSg2+yqrYWxthYyjcaRXbXb0VVdDbvZDJla7Xqdsa7OdaMn5XKQcjnajh4F3dER9Tebhp9/BlgWIAjI4+JgN5lQv2MH4ouKwt20fkfB5Ze7ZCbWkCQmQrj8iy/QWVUFfVkZ6ktLod+3LyRxz23ejLpt2zD6wQehSEgISUyxyDCZ8Ovjx9GiVqNOq8WZxESQHAeOIGCVOU51DkB6Vxfa1GooaRoXtLYi3WRCosUi9a1KCEbiwIH49Z49aC0vR9O+fajfvh3GujrYjEY3iWFpGjKNBqRcjqG33oqM8eORNHQoKIUijK0XBoVWi4TBg5E1aRLSx49H2ujRMTUWQyIykCQmQiAIAvFFRYgvKsKAa6/1+3qO41D3449gLBaPz09wLmlQvX69z+2c+vxzNOzaheoNGzD+qadQcOWVILzM6AkVbSdOoLOqyuvzrn3zsQ0lw8BOkiAApBuNjkwLSYIEYCNJsAQBi1wOjc2GLrkcXV5KdPNLPfo6jmljxkCdluZznyT6HzKVCunjxiF93DhcuHjxec8b6+ux95lnwNI0bJ2diMvMhKm+Hqb6eo/bC+Q7nTJiBOKys4XZgSCZLU1MkAgBEb0AZCjKG/M37GiLpT9wAJtvuw2kl5tvh3M6Z3x8vN9t2fmBmWo1koYMQcnKldDm5PSqPULu21dTpzpS7F5kyrVvfrbDEgRYkgTJso4i4AQBwpmZAUGAcnYl+YKfFOvtOLJ2O/Jnz0bJc8/5aY1/ovVcDDReKGMBkX0cK1avRtkLL4C12cAxDCilEoTM929Kf99pjmEc43vef79XbfFENBzDYOMhJBVqYvM4huGHrtcFIKVMTJTCMQwIinIJSE/4tX+8Pe8J1mpF84ED2Py732HeunVhG4DHMgzsPgbZuvYt0O2RpGtMDACXHDEBFMXj5zb5Oo6shyncEhK+qP7+e8c6NM5VrxmrFbBafb4nkO+0dC5K9DckiYlS4ouLkTZqlNvo/+6UlpYCAEpKSnxup+vcOVhaWkCp1VClpMCs18PS0oKTq1djyC23CN7uQCiePx/NZWVen3ftm49t2EgSXQoFOAAUx4EhCLAEAQXDwA6AcU7BTvFy/FyxnP/6Oo55c+b43IaERE9mvvcejv/rXzj8j3+AtdkAAMnDh4PwIdaBfKeL5s4VtqESEhGO1J0UYWlTxmpFS3k59Pv3I3vqVCQNGSJaLNZux66lS3H2u+9QPH8+Kr/+Gozzpi5TqzF33Tpo0tMFiycUrlg+XlOv0WBrQQFSzGbIGAZNWi04ggDFMLiguRn1Oh26FApcV1HhO5bz30g5P6IxFh8vUrqTOI5D17lz0JeVwW4yYdCNN/Y5PR7scTTW12Pnk0+i+cABXP/zzz4H9Mbq+SF1JwkUqZ92J0kSEyFf1qPvvYcza9ei07naL2M2Y/jvf9+rejOBxjr73Xc49sEHMJw8CUImA2OxQJmYCNpg+KUQEkkie8oUTH/zzaDjCY03iemSybA3KwvNarVjUC/HgXEO7uW6fekolsW8kychZ1nI/bRXkhjh4oVbYgwnT2L/c8+h5dAhsM4CdCBJ3LBnT9gkhsdmNEIeFxeSWIEQq7G6x5MkJvhYIcSrxMT8rFLWbseJTz6BWa8Pd1N8Urd9Ozqrq8HZ7bAbjaDUaqSPHStKrJbDh9F2/DhYmgZjMoGUyaDJynKbYq2Ij4dcq3X9v+3YMVStWydKe4TCKpOhXqsFLZOBJUkwFAWl3Q4tTUPmHHtAcBx0NO2oXcFxsBMEjicn93n161DB0DSO/+tfsBoM4W5K2OmoqsLpr77q9cXa1NiIhp07YTMawVgsYKxWpAwfHvbZeAAgj4sDa7PhxCef9Hn161DBMgwqVq+GqbExJPGq1q1D27FjIYk1TLMLI+K2hySWoaICld98E5JYsUxkX7mDhLXb8eN996Hs+eexfsGCiBUZlmGgTEpySyUzVitSL7pIlHjKxES3WU2kXI4JTz2FgTfc4HpswPz5mPz88wCAlvJybFy0CDuffBKH33hDlDYJAcVxkHUvqc1xyOvsxLRz51wPyVgWV545A43dDjtBYHNBAQ6kp/dp9etQwdA0tt51Fw68+CI23HRTvxaZ9jNnsH7BAuxZsQJ7//rXXomMTK12qyZMyuXImjJFjGb2GtZmw7Z773VdqyJVZPjVqPc/9xzW/+Y3oovM4TfewM4nn8TGRYvQUl4uaqyxuk14KP9u/F/e/bg4XtwfbG3HjmHDLbdg19KlOPjqq6LGinUi86otEKb6etT99BNYmw1mvT4iRYZlGJQ+8gjqtm2D3WQCpVaDlMkQl50NmZd1f4LhyDvvoPztt8FYLCDlcsjUarA0jaShQ72+p2L1ajA0DdZqxdH33otIkTF4WI2a5DifayG1qVRoVavdVr+ONJHhBabl0CGwNhuMdXVo9LIGT3+AH7fF0jTOrFkTsMg07duHrYsXu+oqUSoVCJJE2pgxYjc5ILpqalBfWgrWZnOtfh1pIsMLTN327WBtNlgNBtRs2SJavMNvvIGj770H1moFQ9Oo6GM19EAYq9uEe3MegZK0Qk7QuCr1XdFitR07ho233gq70QiWpnH8ww9Fi9UfiKwrtsBo8/Iw4c9/dix+ZbdHnMjwAlP7ww+uBdIyJ05E6qhRKBRhlsGRd95B+VtvuQRGm5+PvDlzkD1tmtd6MwAw7rHHEF9U5FqJNtJExpPAXKjXQ223I82HxKSZzbhQr3ctGhlpItNdYBirFZRajeJrrkHe7NnhblrYGHH33UgZMcIxbsxiCUhkXALDD1qPi8OwO+6AOj0dKRdeGKqm+yS+qAjjn3zStWhkpIlMd4Hhr1U506a5ZW+FhBcY/loVX1SEcY89Jkqs7gLDcCS6mES8fE6c7Eh3gQEctblmvPOOKLH6CzE/xXrg9dcDAPY984xjkTqnyFy2enVYq6x6FJiLL8Ylr74K0k/Rq77gSWDmfPwxFAEUw5NrtZjz8cfYcMst6KyqcokMAIy45x7B29obPAkMvxr1cOcNoMPHjI8Rzc0AgKOpqWC6icycqirIw7jaqyeBKZo7F+OXLYuIMRzhglIqMfPdd7HljjvQeuSIS2QAYNwTT5x3bDwJzOwPP0TSBRdgxN13h7z9vhh0440AgP3PPw/GYnGJzGWrV0OVkhK2dnkSGDFXo+4pMLrCQsz5+GO3MXpC0VNgjEwCllauht6WJ3gsjwLz9tuijX3sL0TGT06RGXj99Rj7+OMRk5GJJoHh4UVGV1gYMRkZXwLTG0Y0N2NYc3PEZGQkgfENLzLJw4f7zMj4EphIZdCNN2LMww9HTEZGEhhhkARGPPqFxADeRSYhxFVpCSDqBIYnkkQmTyCB4fEmMuoQi4yMICSBCQBfIgMAQzSaqBMYHm8iEx+Ga1XsCowuZAJToFRKAiMi/UZiAM8i83RxcchEhgBwT05OyARmXkqKYALD401krklNFbDlvslTKrFUQIHh8SQyS0MoMjKCwJL8fElgAsSbyPwxNxeP5OdHpcDweBKZ5UVFIRMZAsD9ubkhE5hrUlNDLDA5IROYJwsLJYERkX4lMcD5IpNIUXi6uFj0riWWYXBPTg7G6nQhE5j5aWmCCgyPJ5G5KjU1JCJjqKjA0sJC1zoyQgkMT0+RyXKKjK2rS5Dte4OhaSzJz8cAtVoSmF7gSWTG63RQOcUzGgWGp6fIJMvlWF5UJHrXEsswuD83FyO12pAJzFWpqSERmHObNjkFhgyBwBzDk92vVZLAiEJEV+wVk2kJCbg1KwtKkoSdZWFgGCw9cwbtzqJoQsJnYMbqdFCSJKwsi/KuLrxSUwMxho7yAqMkSdhYFo00jeVVVTD5Gah6fVoa5jsHO/+vuRmrm5p8vl5NklhaWIgshQJy53593dyMtc7BskLDZ2D4i4KVZfF8dTWO+RGYTIUCfykqgpqi0MUwWHzihN9Y/IWVP4b1NI2nq6pgFmGwL5+BGaBWQ0mSsLAsthsM+KChQfBYsYqcILC8qAj5SqVL+mwsi2WVlTjrZ2HFSOfSxETcnJnpuFZxHFptNiyrrESHSNcqXmD4a9XBri68VlMjSn3bUH7PfsnAkGA4DkaGwdLKSuida1cJCZ+B4a9VFpbFyupqnBDox1Y/pP9W7PXGtvZ2fFhfDyvLQkaSroyM0F1L0SIwfcHMsni6qgr1NA0by0JJkqJlZPoqMH1lbXMzvm5uhpVlIe+WkRG6a0kSGGEoVquRoVC4Za04ANOTksLXKIHYbDDgk4YGx7WKIFwZGaG7liSBEQZJYEJLREsMx3Gi/v1gMLiJTJpajX9Onw5TU5Mg22fsdvz4wAO4JCPDTWCer6wEI8L+lK9ahZsLCtwE5k9lZTAyTEDvf/LJJ13H/uGHHw7oPSaGwYMHD7qJzI35+Tj0+uuC7VfbiRN4ceRIDwJjhONW5e/vBACdc8+SAnwPh7XNejeRKdTp8Pm8eaA7OwXZL7vViu8WLcKI5GQ3gXmvrk70cz9U37Hu8cTaduPevVh+wQWuLiSG42BjWShIEldkZ2P3ihVgWTaqj+OmtjY3kcnQaPDRzJkwNzcLsn3Gbse2++/HpPR0N4F5yfljK7BvTOB/h1JTcWMPgXnw4EGYArxW9eaveuNGPDxwIJQkCZCkS2CaaFrwWK1Hj2LlRRedJzDHjUbRz5FQfqcjiYiWmFDAZ2SEnn7taRo1n4EJxSwkPgMjxBgYf8i1WldGRuhZS4aKCmxcuNA1JoVSqUTNwPSEz8hQKhVYmw2dVVXYcMstQY+R8TSNms/ASGNgAsfTNOrllZU4bTb3qiBeNMBnZISefu1pGjWfgRHjBnE4NdVVl4nsloERawxM6SOPgLFaAZKEMiFBtAyMp2nUUgZGfPq9xAAOkRGyjoy3OjBidSF5mkYtVheSN/iuJSGnX3sSmOlvvhkygeFZ29yMYbffLpjIeKsDI3Uh9Q5vdWBOWyx4trrabx2ZaGSzwSBoHRlvdWDE6kLqKTA6EbuQPAnMZatXh0xgZrz9tiQwIUCSGCdCFcTzVcguVAIz5+OPQyowPGaWFayOjDeByZgwQehmB8SIe+4RRGR8FbKTCBx/hexsHBdQQbxoRKiCeL4K2YVKYOaEWGC0eVIhu1hDkphu+BKZpn37/K4eHI2VeIXGV0E8uqMDjXv2+N1GpAkMjy+Rqd+5EzbnRawnDE07bhRWq1TITgACrcTrqyCe3WxGfWlpOJovCL5EpnHPHtDt7R7fxzKM41yk6ZAUsmvUaECTpFeBEXJpj7YTJ9BVUxMSgemqrUXbsWOSwEQAET3FOhS/mPibR/dYp774wrXWEiGTQa7VgjYYcOE99+Cie+/1uq3tDz7oU2A8xeorvBh4E5i+xDr42ms48vbbAIALbrsNox96KOD39oxn6+pyrbXE2mygVCrINBpY29rw6127II+L87gdw6lT2HjzzT4F5pcbfmD7lqmowl+Kfg01ZUIXE4/FJ34OeL8cczbcj2PPyqJynQ7W1lZMfPppDLjuuvO2UPvjj9h2991QJifDbjR6FRghzw+/exXCWHw8IWLpy8qw5c47fQpMz31jrNZf1lqyWn85F1tbcc2WLdBkZPS5PeH+zE7+5z+utZYImQwKnQ7WtjaM/NOfMPzOO8/bRvOhQ9iwYAFUycmwmUxeBcYVK4j22ggCnw8dCiXDwE6SXgXG9W0O8hiumT4ddosFLE37FBghPrMtd96Jxt27QSmVPgUm3OeH2LFCiDTFujf0zMjQzgxM/Y4dPt/X8PPPIcnAAEDN1q1g7XbBMzAERYEQ4JeYp4yMtbUV8rg4tBw+7PV9rUePgqFpAMJmYGSEDTbW+0rdvaFnRsba2goAXn/ZN+3ZAxAErK2tUgYmSBr37AHnvPkFWsjOU0bG2toKmUYDfVlZKJotGj0zMta2NgBA/fbtHl+v378fpEIBS2ur6IXsWjQayFgWVplM1AwMAFhaWmA1GGDr7BS9C4njOLQcOgTObpcyMBGAJDFeGHj99cidNQuUUul6zHD8OFgfBaZmf/wx5Dqd6AIDANP+8Q9oc3IEFZjBCxZg9MMPY/TDD2PILbcEvT25Vovpb7zhuukAgN1sRtO+fV7fUzRvHobcfDNIuVwwgWmgC/B+/TKsbnoQr9W8FPT2AIfIJA8fDqLbZ+xtv+pLSwH+1xFBQJOejvFLl0oC0weG33EH8mbNgkyj6VUlXkqpxNS//x3odsztJhMad+0Sq6khY9CNNyJ76lSQ3VZrbz1yxOMv8vodO8A6fyQAgFyjQcnzz4tSibdJrQbTra4SRxCYXl0tyurw+gMH3PYfLIuSlStFGQNjrKkBa7e7/k+pVBhx772SwIQJ8e6yUQ5D06j+/nu3mxRIEu2nTiFpyBCP70koLsb1paUAQYh+g1ImJmLut98CEC61p05Lw9CFCwXZFk/V//4HjmVByGTg7HZwDIP6HTu8dssRBIFRDz6IUQ+K79C4AAAgAElEQVQ8AEKwwnIEfmyfL9C2HJj1euj37QOpULhS7rTBAEtrK1TJya7XMTSNjtOnXf8n5XJ0nj2LtuPHkTxsmKBt6g8QJImS554DOK7X58e5zZtdXYCsc4ZKw86dYjQzpNjNZtRs2gRC/kumkQPQUVmJhOLiXx5zZhB4SLkcltZWNO7ejewpUwRvV71WC47vKuU4cACqEhIwXIRlExp374adnwlEECAoCqc++wxZJSWCx9KXlbnJMMeyOPXZZxh2222Cx5LwjyQxXqAUCsz95hvo9+9H/c8/o2nvXpgbG2FuavIqMQAEvPH6Jxp+yQ9ZtAipo0ahad8+1O/YgbajR/3OoiAIwu0iEYmo09Jw5X//i+b9+1FfWgr9/v2wtLTA1NjoJjHW1lZwLAtFQgLSRo9G5uTJSB89Gok+ziF/nPnvf6HNyUH6OI9dxDFPX8+PonnzkFBU5DoXW8vLQbe3OyQ7xKuVC4lMrcav/vc/6PfvR0NpqeNa1dQEc1OTm8TYjUYwNA1SoUDamDHInjYN6aNHI0kkmbbIZCA4DkkWC3I6O5FuMiHFOZZJaIy1tQBBQJefj9xLL0X6+PFIGz1anFgNDWAsFugKCpBx8cXInDgRaWPGiBJLwj/SwN5eDIaydXUFVZApVgd59SYex7Kwm81eB/b2JlZwww4DjuaIFMT5wT/O2u3oqqnx+v4hTrE54Wdtp4233AK6sxP5c+Zg3BNPQJmY6LdtPYnWgb2BxgICOBc5DnaTSZBzMdK+0z3PRY5lceqLL7B/5UowViumvPCCT5F2nYs+Yijtdii9dA3VxcXh55wcWCkKF+n1yO/o8B7L+a+v814eFwe1c123nrQdP47tDz2Ezqoq5F92GS667z4frfb/PaMUCsRlZ3t9P8swYK1WyDQan3GAyD0/hIoVQrwO7JUkJsZPski7cQgZK9Ikxh9H3n0Xh//xD5ByzwOMOzs7AQA6nc7j8y44DnazGaRCAUoux9jHH0fxNdf0qi2SxPSfWO2nTqF0yRJ0nD0LxmyGTK32m8lynYtenucIAmq7HVedOuX2uJmisDsrCw1aLRiSBMGyoPy0t9P5r6/znrXbcX1pqaPtTuxmMw68/DJOf/GFYzAvEJBY+PueMTSNyz/7zGfGPVCi4fwIJlYI8SoxUneSRNg4/NZbqP7+e1z+2Wegug/Ki1FsXV1gbTbXeIyeqJ2DK+0BVvlkaRqszYbdy5b1WmIk+g/lq1bBcPIkOOekBHsAXTquc9HHa2gPXXBn4+NRq9O5JIkjSZ/bAABeS3yd94RM5mo/T0t5OSo++cTtsUC+O/6+Z7K4uKCXFZEIHZLESISFjrNncXTVKnAAjqxahYv+8IdwN0l0kgYPhtzHLLI25/TYJD8rL9uNRnAMA0qtRtKQIZj0zDOCtlMitrh4xQrEZWXhxL/+BYamIVOpAJL0WUrBdS762G6qBxka0tYGiuNQlpEBhiBAwJHLJH1kB9qc//o679Wpqe6zjwBkjB+PaW+9hV1PPAFbVxcYqxWkXA6qW7bGYzw/3zOZUom4rCyf25CIHKTupABTcGa9Hi2HDyNrypQ+Zw0CjUV3dKD54EEkDx/uNkhUjFh2sxkt5eVQp6UhvrCwT7ECjcfabGg7cQIsw2D/c8856sWwLCilElesWYP4goJexYq27iS/kQL8zNbMnAlbVxfGP/UUCufO7VNqV+pOit1YpqYmtJaXn3etaj9zBj/+4Q/oPHsWc/7zH6SOGOE/Vh/baqYo7MjJQZNWiwl1dRjoo9p5sMXu7GYzDv397zj+0UcY+OtfY4KfJTwi8TOL1lghRBoT4w1fH/zpr75C7Q8/QF9WBltXFziGweQXXkD+nDmCx6r98UdUf/cdGvfsgUWvBwdg2O9+h5F//KPgsVoOH0bl11+jvrQUXTU1IAgCaWPG4NL33+9TLF/xus6dw6kvvkD9jh1oP3UKBEm6UsOMxeJ4EUki5cILMeff/w7oy9HfJabr3DnIdbo+DejtbSyhkCRG3FinvvgCtdu2obmsDDZnpu6SV15B7syZrtfYurrw+cSJjowGSSJl+HBkTZmCwTfeeF6dKSEq9n45eDBsJAkOQKLViiyjEQPb2qDt0Z0qVMXejspKqNPT/Q7UjpTPLBZihRBpTExvYWga+555xq3/mFIqkXLhhaLEO/jyyzCcPOkqiiaLi0PqyJGixDrxySeo+vZbgJ9ZIJMhXaR1iWp++AFH33vvl2JvANSJibC0tIBSqRzl0uG4MQc7U6S/EGgBr/ZTpyDX6YIqqx8oxvp6MBYL4ouKRI8VSlibDfoDB5A+blzEljSwm83Y+8wzYJ2DWwGAVCiQ0iPTItdqoU5Ph7mpCQCg37cP+n37kDZmDDJEmK6fbDaj3jl4tk2tRptKBZXdjqHOCtdCE23nnqW1FebGxoALNgYDY7Wipbw8JgvyRW9xhG7wCy9uXLQIu5YuFWSbBEUhbexYUCqV6zFKqfQ59S4YsqdOdYvF0jRSR40SJVbO9OludTFkKpVoJ3fGhAlufe+kUokLbrsNk1eudD2ectFFuG77dklgBKRu+3Z8f8MN+O6669B17pyosdrPnMG6a6/Fd9dfj6a9e0WNFUpYmw3b7r0XW373O8FWv2ZtNux46CFsXLQIu5cvF6CVACgK8h6zcuRarccpyWk9vufKxEQkiHTzzzIaQXabgk0AyPCySGp/w6zX4/sbbsD6BQtwdt06UWPxa4dtvu02HHjlFVFjhYOYkBjGYsHZ77+Hft8+nP7qq6C3x0tR0969rvVFACBFpMzIkXfewYmPPwbjnDYLkoQyKSmo7gJv1Gzdip2PPw7ObgcIwrGAmcUiSobJUFGBTYsWOWLBIYEkSSJdKgwlOqVLloCxWmFtb8f6BQtEFZk9Tz/tWLPGYsHWxYtjRmTObtiAhl27wLGsa/XrYEXGZjSieuNGx7Xqyy+DbqOdpvHNnDmuNZN4vBV6yyopAalQuAbJ0l1drtWvhSbNZALJcaCcIsMRBDYVFsLQbSmX/ohZr8f6BQtgbmoCa7Oh9LHHRIvVffFTjmFw9N130VFVJVq8cBATEgMI10fHC0z31ahTR44EpVIhZ9o0QWJ058g776D8rbd+WY06Nxfq1FRkXnyx4LFqtm7FjgcfdNRUIAgoEhKQMGgQEoqL3eovCIGhogIbFy78ZTVqtRoZEycCQFDVaiUCY+Ly5Y51v1hWdJEZ++ijrvocsSQy+bNmIfWii1yLRgolMkJVB+YFxqzXux5TJiWBVCqRPXWqx/ekjR0Lzm5H7owZrkUjTY2NoohMksUCEAQyjEaXyNgoChv7sch0FxiOYUCpVBj/1FOixPK0evugBQugC3ACRbQQEwN7bUYjviwpcSzKRRC4qbw84BjdB0N5Ehh+MUfWZgv6Rt9z4NV5AuNczJFUKEAplUGJWc9YngTmsn//G5qsLBAkGfRild3jeRKY6W+8gfTx48FYrZCpVKhevx47n3oKdqMRqaNGYU6Peg+BxOqvA3sD5dymTSh95BGPq/oKHavt+HFsXLTol1V9VSrHqr7OsRbROrDX042geP58jHviCRDd1kgLNJbVYMCaGTPA0jQIisKCbmsZ+aN7LE8Co05Lw7wNGwCWdUyj9oLdWfDu5H/+g/3PP+8YlyaTQZORgctWr4YqJUWQgb0AYCcIUByHRo0G2/LzXQtCyhkGs6uqkGi1CjawN1DCNdjWk8CMfewxDLz+esFjeTxvr70W4x5/XJAf/JE0sDdmMjHB4ktgSJlM8EyFN4FRxMdDplIJepJ4ExhdQQEohULQ1ba9CUzGhAkgCMLnxVVCWPJmzULJypUhycgkDR2K2R99BJlzXFOsZGQopRIz330XycOHC56R6Su+BEamUPj9jvHXskE33ogxDz8sakZGxnEgAGSaTJhWXd1vMzJiC0x3xBaYSEOSGDh+a/sSGKHxJTBC40tghCZPqfQqMBLhwZvIpHlZ+iAY+qPIhBoS8CkwvcWbyMT7KITXV7yJTF6Mi0wCRYVMYOQE0a8EBpAkBgSAe3JyQiYw81JSQiYwo7XakArM0sJCSWAiEE8i83RRUUhFZmgAa9pEMt5E5tbMzJC1gQTw6qBBggkMjyeRWV5UFDKRWRrDIpNAUXi6uDhkAvNofn6/Ehign0sMyzC4JycHY3W6kAnM/LS0kAnMfbm5IREYQ0UFlhYWQuO86EkCE3n0FJk4isLTRUUh61p6JD8/JjMyUxMTcWtmpuhdS3aaxquDBiGp23VJCIHh6SkyyXI5lhcVwRICkdE4RcZQUSF4rHBi1uvxdHExEikqJF1Ij+bno1it7lcCA0T4wN5AUZEk3h4yBDLn4MFbjh3z+x4+AzNWp4OSJGFlWZR3deGVmhp4Xlw+OHiBUZIkbCyLRprG8qoqmLwsZR8MvMAoSRIsx8HIMFhWWYlGLwsPBgOfgeEFxsKyeKG6Gsf8LMQ2QafDndnZ0FAUKkwmLI+xaX+RylidDvfm5EBJkmCc58bSykroRTg38pVKPNXt3LCyLFZWV+N4gAtcRir8L95itRoKkoSFZfGjwYAPGxr8vjeOJPH64MGQO4//ogCuVXwGJkkmcw2QbrPb8ceTJwW/Vl2amIibMzOhJEnYOQ6tNhuWVVaio8fii0JwgUaDh/LzoXIO9jUxDJ6uqsK5bkX7ohU+A5NIUZA57y8f1tdjW3u74LE8nY/b2trwUWOj4LHCiDSwtzuSwAhDXwVGInzs6+zE67W1sLIsKIJwZWTE6FqqtlqxoqoKJucNUEmSeCQ/P+q7lmwch2erq3HGbAbNslCRpCsjIzShFBgA2Gww4JOGBlhZFjKCcGVkxOhaOmYy4YXqalh6ZGSivWtJEpjQEtESw3FcQH8dHR1QOC/CBEn6fC1jt+PHBx7AJRkZbgLzfGUlmADj9eavfNUq3FxQ4CYwfyorg5FhBI91bssWLBk0yE1gbtqwAQ00LXisthMn8OLIkecJzFGjMaD3f/7554h3dqOVlJT0KnZvz49g/mIx1t6ODjeRiVco8OaECeisrhY81lmLBdetXesmMn8eOhSNe/ZE9XGkWRbLTpxwE5krEhOxOzMTLBxTkz39tQDgb9EyH6/jANgAfDFoEJJ7CMzvy8tFuVbxf5va2txEJkOjwUczZ8Lc3Cx4rKNGoweRGYk85Qk/RyeYP/HOD1NTE/45fTrSVCo3gfnBYBA8lt1iwbc334zhycluAvPP+npRz32Oi6wOmYiWGKHxNI2az8CEYhYSn4EJxSwkPgMj1hiYnrOQpAxMdMFnZEI1/XpFVVVMzlriMzIUy4IhSZxJTMTezMzg66sA+GbQIJhlMoAgAO6XDIwQY2D8wWdkxC6IB3TPyDimhmuoLiwtXIg8ZXSNkfE0jVqsDIynadR8BibWx8D0pN9IjLc6MGJ1IXmaRi1WF5KnadRidSF5qwMjCUz0sa+zM2R1ZKqt1picfs13LSULKDKeBEYtYheSNzYbDKLXkeFxiMwbUSsy3urAhEpgiq+9tl91IXWnX0iMz0q8IsTzVgcmVAJz2b//HVKBkWYhRS9SQbzgsXEcZgokMt4EZl6IBYYnFAXxeI6ZJkalyERKIbv+SsxLjL9KvEITq4XsJIGJXXyJDN3eDrvFIlgsXyJjt1hAd3QIFiuUUH5ExiyTeRUa/nlfAiP8lSpwJJE5H4amYTUYQiIwHMvCrNf3u0q8gRLTEiMJjDBIAhP7eBOZb6+5BlsXLxY0ljeR2bBgAb658kpBY4USbyKzqaAAawYPht7LrKyz8fFYM2gQ1g4eHHECwyOJjDulS5bgmyuvDEkG5viHH2LtrFnYcNNNksB4IKYlxlhTg+r160MiMABw+PXXQyIwAHDw1VdDIjAAcPSDD2A3mwEIKzCs3R70NiSE4zyRMRhgaWlBa3k5OIG7Qj2JTEdlJewmE4x1dYLGCiWeRKZZrQbBORZB9ERDXBxAEKApKiIFhqenyJibmnD2++9FidVTZBSkGVemfCBKrL6g37cPdEcHzHq96F1Iddu3g7Pb0X76tCQwHoh6ieE4DgxNd38AjHNKsa6gAGOWLAEIQnSBAYCp//hHSAQGAKa89BLk8fGiCwwAjF2yBNrcXJAKhWACkzZmDPJnz0b+5ZdjyKJFArRSQgjyZs3CmEcfBalQABwHjmFAEATaz5wRPFbS0KGYsWoV4Cx2xtpsIGQy6MvKBI8VSniRUdvtjmNIkuAIAvVaLVjS/ZLLEAQanSLHC4yCYSJOYHh4kQFBIGPCBAy64QbRYvEiQ7MK6OlcfNywRLRYvcHc3Ay6s9Px2drtoNRqDLvjDlEEhuM4tBw+DMDx/aCUSmRNmSIJTDciumJvIG07+9132PHQQ6BUKkfGRakEY7Vi+qpVyJ48GQDQfuYMdPn5HgVG6GXZjXV1UCQkQM5fmESMZW5uBkGSUCUnix7L1tUFuqMDcdnZHp8P1/L2UizhY21cuBD6AwcAZ/aFUqkwdskSDAzihsXXOenJ3meeQcXq1a5YAFB87bW4eMWKoGIBIT6OPR5vUyrx3YAB7q9lWXAk6crQkCwLliRBcBy47jckjsP8igqoe1TJJVxPh/9c7KisdPywEaBI4i83Y8+xUmR1MLHxMLPaoGM5Izqi9fE4ntu4ET8/8QTsRqNzcwTii4ow95tvzo8U5LnYefYs1l17LZhu49JIhQK/3rULVI+p9uE470OI14q9kSj7vSJ93DjI1GpXdwdjtUKu0yF1xAjXaxKKi0PWHm83eTFQp6aGLJZcq4VcK9RFJLbgOA5Nu3cjefjwmDhGYx97DDVbtqB+xw60HT8OlqbRfPhwUBLjjaELF0KTmYn67dvRcvgwGKsVrUeOCB4n1MTTNC6urUVDXBya4uJgkcnAEoRLYACAJUnI7HbYKQoEx0FjsyHDaESm0QiVCGX+hSS+qChksVrsgV1TB6oPoonOQQcj7nWx9dgxMGYzSLkcCYMGIaukBDnTp4sSy1BRAYamQVAUtPn5yLz4YmRNnnyewPRnoj4TAwAVq1ej7MUXwZjNoNRqTHz6aRQGOEAwUn79RnOsUMeLtFgMTePT0aOhSEjAhD//GXmzZ/fpl0qk7RcAbL7jDjTu3Ini+fMx9tFHPWYYA43nLxbHceisrgZJUdDm5vYpDh+L357YeMvE9MRKkuhSKKBXq3EwIwOMMyMzsLUVp5OSYKco/ObYMVA+2hxJmRgxYvk/ip7RUgbcmvFXTEpYh81tv8YHDX/2F9ERrY/7ZuvqQkdVFZKGDPGbiQrmOHIsi5Off479zz4LgiTxm337RIvVWyIpExMTEsOxLL677joYTp5E2ujRmPXRRwEf5Ei8cURbrFDHEzLW7uXLcfrLL70+b3cOPpb5GUtFyGRgnYPuUi66CJP++tdeZ+WE3K/mQ4ewadEirwNyA90vjmUBjgOpVEKu0WDiihXInTGj1+0JRGKEIhIlhocFsG7AAHQoFJAzDBiSBOvsWuL8XLP4YfC+PrMB112HCcuWBdga70SHxHCYkvA1bs38K+SEDXKSBscRYP0O9fR97pMKBa788ktBxhn29Ti2nzqF0kcfRUdVlSPrI5P53UYg3+kR996LCwWYbRhJEhP1A3sBx3pJJStXQldQgEnPPCP6AbabTNh6111oPnRI1DgS4tN++jQ4hvH6RxEEKILw+RqOYcA6V95lLBY07dmD/82bh86zZ8O2X8a6OpByedD7BeeFk7VaYW1rw49/+APaT50K235FOySAyTU1IDkONopyDfRlnYN/ff0F8pm1nz4d3h0MIcPjduLunMehoYyQk47JHQTBgSIYP3++jyNJUTA3N4d13zYuWoQ2Z7cV4JjJ6e+7Gsj50VFZGdb9EoOoHxPDkzhoEOZ9+21IYh187TU07NyJzupqzP3mG0EGt0mEB016OgiZDATp2eetzgF1SpXK53YIinJ1ZxIkCbvJhNLHHsOcTz4JyywCVXKyY7q/l77zQPcLAFiaBiGTgVIoMPrhhxEfwjFmsUiS1YrLKiuxIzcXRrnc1bXkt0vK+a+3z4xjWWjS0wVtayRz3DgO/2n8E+anvQUZaFAkCxsrxy8db97wfe7bLRYoExKEbWwvmfr3v6N0yRJY29pcJUL8lTnw953mGCYmz4+Y6E4KhkDTfRzHwVhbC7qrCxtvuskxX1+txoWLF2P4nXcKGksIehPLajCAIMmgpoQHGs9uMsFmMgU1KDnQWAxNw9raCk1mptfXsDYbbPwsAw+kpKQAAFp8FPVibTasmT4dMrUaRVdfjdNr1oC1WiFTqzF+6VIUXXWVz3byCH1+0B0dXi98gewXAPz0f/+Hpr17kTtjBsYvXdrnz62/dyexcFTljetWG4kFUJGU5BojM7+iAqSPG1WK819fn5k8Lk7QGUOR3Z3kIFVei99nP4Yhmv34yXAVPml8xM87fJ/7pEwm2AD9YI4jQ9M4smoVjr73HgiSxDWbN/t8fSDfablOB5Kiet2WnkRSd5IkMT5OsuZDh9C0Z49j5kR5OewmEzRZWTDV17teQ6lU+NXXX0ObkxNULKHxFcvU1ISG0lLUl5aiae9emJuakDV5Mma8/bbg8eiODjT8/DMadu1C486d6Dp3Dur0dL9fyL7EYmgaDTt3Oj6z0lJ0nD4NjmVxfWlpny9KgXxmHMeh6n//Q+qoUdi4cCEser3rOblWi6vWr4cyMVGQWEIRaKyWI0dAt7cjq6Qk6Hj9SWJYAI1xcWjSaFCv1cKgVIIlCFx98qSbyLSoVFhfVASC45BssSC7qwvpJhPSTabz8gnSwF5fcBir24xa6wA00P5mTkXe98wXHWfPwlBRgfzZs0WPFSiRJDEx050kNAxNY+PCha6CXwBAKZUw1deDUqtdXQes1YrKr7/GiLvvDnOLA2ff3/6Gms2bXfsFgkDi4MGixDr56ac4+OqrrrEVgHjT0Bt378a2u+92FQ0D4KjZI/K0Z4IgUDRvHup37IBFr3dN+ZdpNLAZjajZuhUD5s8H4JA6WVycIL+GQkHK8OEBvY6xWsExDGReqtJGCgxNg7XZ+jzLKlAMKhW2FhQ4zkPnBZ9kWUcBvG5oaRokx4ElSbRoNGhVq8ERhKNOjFTRuhcQ2Nc5K9yN6BWszeYoCeLn+hRfUIB4EYuZRjsxMbBXDCiFAvlz5rgXyCNJXLV+PShnupYgSUx7800M+93vwtTKvjHguuvcuhlkcXFIHz9elFiFv/qV23gTQiZD1pQposTKGD8eyuRkN2FKHTVKlFieyJw0CVetX+9YDgKOwXiXf/45iubOBeAoH/7llCnY8cADYCO8DkhvsBoMWDd/Pv47Z45gq1+bmppw4JVXcOCVV9By9Kgg26Q7O/H9DTdg7aWXilKBuDuJFgt0Vqv7Y1breRdcJctC2f1c4DikGY1QSQIT0zBWKzb/7nf4ato0tJSXh7s5UY0kMV448s47qNmyxbU+EQAkDxsGbW4uiG6/ojWZmY61ZqIEQ0UFdjz44C83eoIAa7EgbeRIwWNZWlqw6be/dR0/EAQopRLpY8cKHouhaWxdvNitiiapUCDLWbU5FBAk6ahv0i3VGpeVBVIuB93RgW333AOOYVC3fXtMiUzpkiXoqq11W/06WM5t2IBj77+Po++8g+Pvvx/09ujOTmxcuBCdVVWwdXZiy+23B71Nb7AAduTmwiSXu7KCBMchy7mAak/S+XPWuURBq1rtWv1aIvZwW43aYsHm3/423E2KaiSJ8UDP1ag1WVkAQYiWQQgVnlajVqWkQJWeDoXAo/EtLS1Yv2ABTI2NjvVFVCpoc3PBmM1IvvBCQWMxNI2td92FlkOHXAOudfn5YGka6WPGCBqrr8ji4pA7Y4ZreYxYEpmBN9zg6B7rtvq1ECJDCNTl1l1gWJsNlEolSvVh4BeBqdNqXbOOdDQNjiCQYTJ5fE+WU2K0Npvb6teSyMQebgLjrCtV5Oxqlugb0piYHvQUGH4xR3NTE+ICGLwbqXgSmOlvvIH44mKw3RfQFABPAjPm4YdRdPXV6KqthSyAab2B4klgiubOxfilS9F2/DgShwwRLFYwkBSFyS+9hB0PPIC67dvdRGbySy9FzRgZT+RdeilKVq5E6SOPgLFaXSJz2erV0OblhbVtngRm2O23izKGzZPAZHd1YXJNDQwqFRK7rX/TnYKODiRZLIinaWzJz0erWu0SGQAY19Dgd9KwROTjSWD41agl+o6UiemGN4FRxMcjYeBAyNTqcDexT3gTmIwJE6BOTRV0oK03gRl0442QqdVIHDhQsFheBWbZMhAkieRhw7zWfwkHvMhkT5kScxmZvFmzULJypaNrVeCMTF/xKjD33CN4LF8CQwJItli8XmwpjkOS1epa/TrZbJYyMjGGL4GRVqMOjsi5wocZXwITzfgSGKGJpyivAiM0MoLwLjARfFHwJTKR2+rAiCSRUZNkyASGgG+B6Q2+REYiOpEThCQwIiJJDIB5KSkxKTB5SmVIBWZ5UVHIBGZJfn7UCQyPN5G5Pzc3ZkUmLYRVrdUkiaWFhSETmPsFEhgebyJzqyQyUYecIPBofr4kMCLS7yVmXkoK5qelxaTALC0sDInAWFpasLyoCMlyuegCw9A0luTnY4BaHZUCw+NJZEZqtbg/Nzcmu5aeLioKSUaG7uzE0sJCZCkU4nchMQzuz83FSAEFhseTyEx1ikyoigZKBAdjteLR/HwU89cqSWBEIaIr9ooNLzBKkoSNZdFI01heVQWTnzUq3hw8GPEyGUwMgz9XVaG2Rz2IcMMLjMY5WNTCsnihuhrHvMyOCAY+A5Msl0NGELCyLD5paMBmg0HwWHwGZoBaDSVJwsKy2G4w4IOGBsFjBcNHF1wAiiBgY1ncW1EBo4/zif8lP1KrhZIkYWVZHOzqwms1NVE/DmKsTod7c3KgJEkwHAcjw2BpZSX0Npvf9/kQkB8AACAASURBVF6WnIwb09OhIEmUtrfj9dpav+/hMzBZCgXkzmP5dXMz1oqwmF+oPjf+l3yxWg2F85z/0WDAhxF2zku44+lz29bWho8aG8PdtGgltlex7gt9FZhIRxKY6IID8FpNDQ52dcHKslCSpCsjE+2/1fZ1duL12lpYWRYUQSCOovB0UZEoXUuxKDAAYOM4PFtdjTNmM2iWhapbRkYiMpEEJrREtMRwHCfKX/mqVbi5oMBNYP5UVgYjwwT0/rS0NABAfHw8ysvLA44r9n61nTiBF0eOdBOYX334IY4ajYLHMjc346OZM5Gh0bgJzKa2NsFj2a1WfLdoEUYkJ7sJzHt1daIdy2A+M5mzyrNSpUJLS4vf17Mch5eqqtxEZlJ6Orbdfz8Yuz1i9qsvf3s7OjDr7393iUw8ReHNoiJ0yuXgAK9/rwLgJ+Iv8PE6DoCVJPF5YSEKewjMGr1e8P1h7HZsu/9+TEpPdxOYl6qqwIp0DGmWxbITJ9xE5orsbOxesQIsy0b1+RHqWG7xRPizEwS+zc/H8B4C88/6+pg6jpFEREuMGPSchcRnYKJ9DEzPWUh8BkasMTA9ZyGJlYHxNI2az8DEUr8ySVGujEwsTr9+vbYWFMsCBAErRWF9URG6BMjI0CSJjYWF6FQowDrHpYiVgWEZxq3OD6VSuTIwYtf5oZRKV0aGUirBWCw4s2YN9v71rxF3U+mvMAThVueH6paBiaVrVaTRryTG0zTqWOhC8jSNWqwuJG91YEIlMEVz50Z9F5I3+K6lWKwjs6+zEyUCi4wngRkWQoHJnjIlpGOX+K6l5OHDJZGJMDwJTLHUhRQS+o3EeKsDE4sCM/2NN0IqMGLNQvJWByaW4YDYLYgnoMh4E5gRIRSYyS+9FPLB1zaOw8x335VEJoLwJjDjJIEJCf1CYqRCdsETKQLTH9Ky/ir7RvPNypfI8OMKfMEhcgQmXEtFUEqlJDIRAAffAhP7V6rIIOYlRhKY4JEEJvR4E5mf/vhH/Hf2bBz/6KNwN7HPeBOZHTk5+K642Ov7duTkYGNhYb8WGB5JZMLLvowMrCsuxpaCAklgwkxMS0zH2bM4+OqrogkMF8auqB0PPRQSgQGA/c8/D1NDg+gCAwAnP/0UzQcOxIbABHl+eBKZhl27YGpoQPX69QI1Mjx4EplarRYdCgUsHgSBA1Cn1aJVpQqJwABA9fffo+aHHyJOYHg8icypTz9F09694W5azFOj06FDqUSrUikJTJiJaYnR5eWh6OqrQcpkggpMzvTp0OXnI2HAAGic061DzehHHgGlUokuMAAw/K67IIuLA6lQiCowAFA0bx7isrNByuVRKzDZl1wCXX4+UkaNgkyjCWpbvMhkTpoESqMBYzIBHIe2Y8fCKtFCkNfZiUm1tSCdIsNQFEiOQ7OHhVaNcjlYggBLki6BGdrSIprAAEDujBlIGjwYhEwWcQLD011kCJkMmZMmIW3UqHA3K6ahSRImuRyc85yVMQyKDAZJYMJERFfsFSItyrEsqjdsQFZJiUeB4W+QoUjBCh2r+fBhkBSF5GHDRI/VWV2NrtpaZE2a5PF5IeNZDQY07t6NvNmzPQpMNH9mfYnFcRz+O3s2TA0NgPM5mUaDOZ98gsTBgwWNJRYEQXgc73IwLQ1HUlMB/nPmOMRbrTDJ5WBIEhxBgGRZaGw2WGQy2J0SQXAcsrq6MN3DUgb8GSPEvtlNJtRs3Yr8yy/3KDCRcH4AjhL31Zs2oWDOHJACTF2PlP0SNV4f318XF4ftublu52Ky2YzLqqrOj+X8N9aOYxh+WHqt2BvzEuOPWP2yhu3CEGP7Fimxjn/0EarXr0fbsWMgKAqM1YpxTzyBQb/5jeCxumO3WMBYLFAmJvYpTvd4niLp1WocS0mBXqOBjXQkhuUsC5sz4+JsJFQ2G6xyOUiOA8lxSDGbMbCtDfmdnefHAv823/tmamqCJj09iL2KnPOjO+bmZigTE0E6iy72JJD9Fmq/TE1NUKel+bzpRZvEHExLw9HUVJAcBwJAstmMIoMBA9rbz4/l/DeYfWNtNtAdHVClpPh8nSQxASBJjBQrEuL151gsw6Dj9Gnoy8qQO2sW1H4ubMHEAoBt992Hxp07Me6JJ1B09dV9vnh5k5ju8FmZfIMBTVotLM6MAsWymHruHMwyGdJMJmhtNp9p+0BuHHU//YQffv97FF19NcYuWQJFQkKv9scVK8LODwD4fOJEqFJSULJyJVIuvND1uFmvx65ly1C3bRuu+PJLJA0dGnQsb9hNJpS9/DJOrl6NSX/7G4rmzRMtVm8JRmI4ABWJiTiQmQmd1YorKiuDPhd90XzwIEqXLAHd0YHrS0t9vlaSmACQJEaKFQnxpFj+aT9zBtv/9CewdrvH5ysqKgAAg/10R5kaGx0DW9VqJA4ciEnPPov4wkK319Cdndh6112gPfwS7R7PX8eXlaJAUxQoloWMZWGjKBAALmxqwvDWVtfrtuXmokOp9B7L+a+vfbObTKA7O8GxLCilEuOfegoFV17Z64uzkJ/Z6a++wtF33/X6fKCfWde5c679KrrqKox64AFU/u9/OPjSS2BsNpByOZQJCY5VxoOIlTZmDC7+y1/Oe7xm61bseuop2E0mcCwLRXw85FptULE0GRmYsWqVsF1lvXyfUSbDzuxsNGs0YEgSJMsizs9ipn7PRYJAybPPImXECLeH6c5O7F+5Eme//RaM1QqCoqDNzfUdK4DjeOHvf4+iq67yuZ1AkCQmQKLtxtFfY/U1XvPBg9CXleGC3/5W9Fh9JVpjnf3+e+x84gkwFkvQ23JBECBIEvO+/RbavDzXwx2Vlfju+uuFjQUAHAe13Y6rT550m4Gw+oILwAl8ESVIEmnjxuHS99/v1QW6L5+Zpa0NR999Fxfddx9kKpXr8e0PPojq778PvNEBQMrlIBUKMFYrOC9C21cUiYm4fscOt8dOfvYZ9qxYEfTMvJ4QMhmu37HDpwwFvK0+SIyVJLFm8GBwBCHouUfK5RizZAkGL1jg9vjaSy+FpbnZ64+QvlI4bx5Knn026O1EksR47jSVkBAZxmrFT3/8IyxtbUgaOhSZF18c7ibFFJRCAY5lIdfpPD5vcC4TkehnrAvHsrAbja5tFs6dC1VqqttrSLkcrN3uNRYfL5BRNTbnYEmKZZFqMqGktva8KZQkxzlmNHmL5fzX374xFgtYmw2UWg3ObkfT3r04+913KLzyygBa2nf2/uUvqN6wAQAw5uGHXY/LVCpQKpXXbEPAnxnDwG4ygZDJQMrlGPib36B63TpYDQZHVk2pBKlQ+NyGv1h8pqcnWZMmIfmCC9B+5gwYsxkERfmdoRfIftlNJhBk+CbTylkWg1tbcTI5GQwAEARkDON3NpK/c5GxWj0ex2F33IEDL77oKABpt0OmVoPwMsbJFcvPcWRp2k2aYwUpExOlv7QjKVZv43Esi4OvvIITn3wCxmKBOi0NV61f7zO93ddYwRKpsTiW9XlR5zgO+v37wXlZqmDGjBkAgK1bt/qMU/bCC2g7fhxxOTkoee45pF50kcfXtR496qpb5C2e70hAZUICziQmQsEwmFhfjzwPg3YBoEOhgNnHBX2G819f+6bfvx+H33gDpFyOrJIS1JWWgrVYINfpcPWGDQGXYujt+dG4ezd+uPtul0xc9tlnSBw4EABAd3Sg7fhx7/sV4Ge25c47QVIUMktKMGHZMqjT0sDQNMrffhvHP/gALMNg3OOPI76oKKhYcTk50ObknPc4x3E4vWYN9j/7LBiLBUMXLUL21KlBxVImJSFx0CCvz/eGYMbEGJRKlObkoF2pRJzNhol1dT5fH8i5mDZmjMdB2KamJuxetgwNO3c6tvX2275jBXAck4cNEzSbFUKk7iRvROpNKppi+YrHcRw6q6qgLytDfWkp9Pv2gVKrYW5oAGO1AgAolQqDb7oJox98MKhYYhApsewmE5oPHULTvn2o37EDhuPHMenZZ5E/Z47gsbpz9rvvYKyrw9Bbb/U62yXQeJ4icQDalUo0aTSo1unQrNEgxWTC7Orqvsfit+3hXDTW1EBfVoaaLVtQt2MHZEol7GazqyuMVChQcOWVmPTXvwa8X55ieYKxWvH1FVfAzK+pQxBIHDwYV3zxRUBZhkBjHXj5ZaSPH4/sKVPcHrcZjVg7cyY4jkPepZcie+pUpI0eDU1mZlD75Q1LayvK33wTg2+6yacwRdPAXgA4lpyMA+npiLPZMMBgQJrJhBSLBZSH9gsxO6nmhx/QcugQRt5/v8/XSQN7A0CSGClWb+Mdfe89HHjlFUdFUbMZACDX6WA3mwGOA8cwoJRKsHY7rv/5Z8jj4vocSwwiIRbHcfiypAQMTYO12x2Vk9VqXPLyy8i+5BJBY4mFN4nZk5GBU8nJIDkOjPNGntPRgWk1NX2P5fy3576dXrsWu558EjK1GnbnAqkyjQYMTYMgCEfXkrMy8jVbtwY0/bo3x/HUV19h99Klruq6fKwZq1Yha/JkQWN5guM4fD5+vOO7B8e+200mzHjnHWSVlAgaqzdEm8ScTkzE3sxM1+BeAkCczYa5p0+fH8v5b6wdx0iSmJiu2CsRfvIvuwwE4BIYkCQKLr8cQxcuBOXsn9VkZ2PmO+8EJDBCU7d9O76YPBnnNm0KeexAIQgCBXPnOkTAOdCPtdmQGgOVWYvb2x0L6TkFhmJZZDnH4AhNztSpoJRKl8AAQNaUKRhx992uc1Gh02H6W28FXT/GE/mzZ2PS3/7mqrTM0DSm/v3vSB8/XvBYniAIAsnDh7v+bzeZoExORtro0SGJH+kwBIHNBQXYWFAAu4+bdGq384evZVRkMHh7uYTISBIjIRoMTWPnk0+6DVSUazTIvfRStymFyoQEZEycGPL2tZ86hZ/uvx+0wYDSRx7Buc2bQ96GQGjauxdnvvrK9QsaADTp6VD4GEgbDdhIEjuzs0F2++VIcBzSut0khIJlGOxZvtztMZlGg9yZM5E2ZozbY33NbvlDodO5bZsgCOTOnAnKzyBbIcmaMsXt+2g3mVD24ov9ftFIfjXqZrUaLWo1fuo2+64n8TTtNqCXIUkcTU2FIcAxfRLCIkmMhCh4Wo2aUqlgM5kiJoMg1+kcMycIAozVitKHH444kWnauxdbFy92jdmgVCqAIJAu4lpZocBGkthQ6L4aNcWy4AgCCc6xUkLhaTVqSqWC3Wzud1mItDFjXN1m0urXDniB4VejJgDE+ygXwFfpBcc5FjCFY1bdxsJCSWTCgCQxEoLjSWCK5s7FvHXrULJyZcRkEDQZGbhs9WpHSf0IFJmeAiOLi8OcTz7BJa+9hgsXLw5z6/qOJ4EZ1tyMK86cweSaGkEvSp4EJnvKFFy9cSMm/uUviPMwuyaWSRkxAhNWrMD8LVvcVr/uryLTU2AolkWxwYAxTU0+3ze2oQEzqqsxrbpaEpkwI0mMhKB4E5jxy5ZBk5GBwiuuCHcT3dDm5UWkyHgSmNkffYSkoUORN3MmdPn5YW1fX/EmMCOamxFP08j1MU27t3gTmMkvvQRVcjIGXHNNOAYohhVKocDAa6+FIiHBtfp1fxUZbwIzrqHBb/2XJKsVWUYjMk0mSWTCjCQxEoIhIwivAhPJN4tIExlfAhPN+BIYoSEArwLjaTXq/gilVHoVmVhHHoTA9EQSmfAiSYyEIMgIAkvy86NOYHi8icxYAQpD9YahGk1MCow6xAJzf26uJDAB4E1kbvVQOyZWkBMEHhVIYHi8iUyeJDKiI0mMRNAwNI0l+fkYoFZHpcDweBKZe3NzQyYyQzUaPJKfH3MCY+vqwtIQCQwLh8CM1GolgQkQTyIzNTERt2ZmxlzXEmO14tH8fBQLKDA8nkRmqSQyohPRxe4kIh8+AzNArYaSJGFhWWw3GPBBQ4PP903Q6XBndjY0FIUKkwnLq6pC0+AASJPL8XRREbQUBZIgYGVZvF5Tg30CjtfoCS8wSmfdCRPDYEVVFaoFnqkTatQkiaWFhchSKCAnSVhZFl83N2OtiBmYkVotlM5YB7u68FpNjc/CZhdoNPi/vDzEURTqrVY85KFomVDEkSReHzwYcpIEw3FYdOyYaLF6C5+hKFaroXB+l380GPChn+9ytBCq/btAo8FD+flQdfsuP11VhXNR/l0OM1KxOwnh6avARDp6mw1LKyvRxTBgOQ5KkhQ1IyMJTPD0VWAkfsHGcXi2uhpnzGbQLAsVSboyMtFOKAXtmMmEF6qrYXFmZDRSRkZUIlpiOI4T/U+K1bc/u9WK7xYtwojkZDeBea+uLqD3f/7554h3LrJXUlISUfvGcRyaaBq3bNrkJjIPDxqE6k2bBI3TuGcP/jx0qJvAXLd2Lc5aLFF9ftCdnfh83jwU6nQugRl3331Yo9cLHoux27Ht/vsxKT3dTWBeqqoCG8D7t27d6lr5d/DgwaIex5aWFiid1YFlMllEfWYcx4FmWSw7ccJNZK7IzsbuFSvAsmxUnot2iwXf3nwzhicnuwnMBwFeq/ryd9RoxJX//KebyLw4ciTaTpwQLWY47i+RQERLjERk4mkaNZ+BiaYxMP7Q5uW5MjJizFrqOQuJz8DEwhiYDbfcgs6qKldhta+bmzHinnsEj+VpGjWfgZHGwPQNSql0ZWSiffo1Y7Viyx13oPXIEce1SqVyZWDEvlZlTpzolpGxdXVh48KFMFRUiBq3vyFJjESv8FYHJtq7kLzBdy0JPf3a0zTqWOhC8iQww26/XZQuJG91YKQupODhu5aiuY6MJ4Epnj8/pGN8+K4lfm0uSWSER5IYiYDxVcgultHbbILWkfFWByZWBSZUGRh+FlJ03GIjHxvHRW1BPG8CM+6JJ0LelmMmE6a98YYkMiIhSYxEQPgSmFjqQvKGUAXxYraQXYQIjNSFJCy+CuJFqsj4EphwXasyJ06UREYkJImR8Et/FxgebyJz8rPPsHHhQrQePerz/ZLABI8kMKEnWkRm9/LlOPjqqxEnMDySyIiDJDESftn37LNoPniwXwsMjyeR2fe3v0F/4ABqtmzx+j66sxNb7rgj5gQGAEqXLEFnZaXoAgMAR1atQu2PPwovMBF0M45EPInM6S+/xJk1a8LdNAAOuT2zdi2Of/RRRAoMjzeRkeg7ksRI+CVv1iwQJAlKqRRMYDRZWWCtVhAUhfiiIoFaGhp6igxL0wDLon7HDq/vkanVyJgwAaRCEVMCAwD5V1wBQiYTXWAAIGvyZJAUBVKhEExg1GlpYG22kJyLlFIJeVwcCIpCXHa2qLGEprvIkHI5KJUKaWPGhLtZAID2igqQMhkYiyViBYanu8hQSiVyZ80Kd5Oimoiu2BuKVCV/gkuxfNOwaxdajxzBBbfd5vGiEM371pdYdHs7vr3mGlhaWsAxDACAVChww969Xm+qrM2Gw2+9hfzZsz0KTCTsV1+p3rQJ1pYWDPrNb7zGEypW86FDqN+xA8PvusvjsY7m4xgNsRirFYfffBNFV12FhOJiUWMFyol//xsHXngBjHNwPCmX48K778aFixeLEi9QfMVq3LsXzWVlGHb77SDI4PMJ4divEOK1Yq8kMRFyQoczFseyQX+JInXfxIplt1iwdfFitJaXu27QrM2Gyz/9FEkXXCBoLDEINBbHcQDHCXJ+hGr8RCQex1iNxT/v7aYWyLVFiP3adu+9qP3hB8i0WrBWK1QpKRh2++0YfNNNosQLFKFiheo4BkokSYws1C2RiCzK334bJz/9FCV/+xsyJk4Md3OiBplKhdkffgiOZdF+5gya9+9H86FDUMdAifbu7F6+HE179mDyypVIHj483M2RiDC23XsvLC0tKHn2WbeuOI7jUPnf/2L/c89h0nPPIWfqVFHbkTdrFnRFRcgYPx6pI0c6unpjALNej93Ll6PjzBnMW7cu3M2JSKRMTBRaOeAYELbvuefAmM0en1/9n/8AABbceKPP7bSUl6Pr3DlQKhVypk3DuKeegiopqdftidbjGKuxzHo9yl58EZzd7vH5QM+Pxj17YGludoyHuvpqjH7oIcjj4nrdHikTE1mxOs6exeF//MPrgOZAz4+6n36CzWgEpVBgyKJFGHH33TDW1+PnRx+F4dQpsDYbkocOhTYvz+s2AomlzsjA6Af/n70zD2+izv/4e2Zy9qZ3KZQegNy13BYUQTkUUFRWwQNUYGVF0FUBDwQRVzm8WHVdDlcRpayouICCggj8uJFTrhZoS2lpaUuPNE0yyRy/P3LQliRNmplcndfz5HkozeTzmcm3k1e+x+f7UsAOu7gbi+c4XPjvf3Hi/ffBmkwAxyFl5Einx7hyHTMefhiJ/fu7lYs9/KknRpKYAGjQ9ig/ehS/T5tmGwMWAlIuBymTYURODqI6dXLr2EC9jsEa6/K2bTjw6qvmSccCQSoUIOVyDPnkEyS4eSOUJMa/Yp1fuxbHly2zzecSAkqlAqlUgtXrwTEMYCm3LwQESWL8gQOQC7AJa0uuo/bKFWivXkWim73VLX3Pfpk4ETV5ebbVjEKROnYsshcv9vh1/ElipOGkAIbneZAKhd3fGSyNX2VZyufkRcCZTAAAQiaDOj6+Rd+0JfwTT9sHz3G23hyCJMHodNg3Zw7u//VXUA5eWyIwIAgChIDtAwSBsORk1JeUgDEYwFkn2TbTTlyJ5ahH0RtwLIvdzz0HTUEB7v3xR0EmMzdHVKdOjerHEDJZs71QzV1H3rIHXLAh9cQE6DcpjmGQv3Gjw56Y559/HgCwfPlyp69zeetWVJ48CUqpRM8ZM9Bl0iSQMvfdNlCvY7DGYvR65G/cCN7Bt2FX20fe+vXQXr4MSq1GSGIiNAUFoBQKdJk8GZmzZrmcj9QT41+xjLW1KNi82eHvXW0fZ1atAl1TA3loKPovXIj2d98NRqfD8fffR8GPP4JjGKSMGIHYW2/1KJY6Lq7Z4RRXcfc65n7zDU58+CFYgwHR3bph5H//63JPhCfvWcWxY9g/dy4M16+DY1n0nj3b6fNduY7JQ4Y4HdpzFX/qiZEkJkBvQp7GYgwGXP/zT+R+/TWuHTqE1NGj0e+NN0SLJyStNZahqgqVJ07g2uHDKDt0CH1ffdXtYR1XY5nq61F58iTOrFqF6vPn0faOO1D822+2OViUUol7fvgBEampLsfzaAnt2rUo2LIFSYMGIaFfP8T06uWwx9DZuXEsi9oLF1Bx/DhK9+0Do9dj2KpVjb7llh06hKOLFyNxwAAk9O+P2FtvhSo62u1YQuNpLEavx86pUxGSkIDE7GzEZWUhIj29RSUTjLW1qDhxAqc++QT1JSXoPXcu0u+/v9Fzdjz1FKrPnUP6/fcjdexYtOnSxe4XJG9eQ3fj6crLseXee8FY271ajQ5jxqD69GkkZWcj3jKRWBER4XEse1SeOoXdM2aAUqnQ++WXEde7N9RxcaLEcgd/khhpOKkVcnTxYuTl5ECmUoExGMAzDExara/TknAAz/PY+sAD0BQWglIqYaqvBymXg66pESXevpdfRtH27eb2odeDZ1kU79jRaHyeNRpx+M03cfeXX4qSQ1P016+j6swZVJ87h7xvvgFjMCBl+HAMeu89l1+jeNcu7H3xRZAUBZ5lwdI0lNHRN3XTG2tqUFdYiNoLF2y9nRFpabjnhx/8rnCaO5ByOarOnEHliRMo2bMH1jMZ9sUXiO3Z0+XX2fHkk6g8fhykSgWmvh6EpchcU/Tl5TDV1eHChg24tHEjOKMR/d98E+njxgl0RuJz9N13wTRs93o9CjZuBHge1bm5uPDf/4I1GBDfrx+GrV4teHyWpmHSakFXV+PgG2+AM5mgjo3F2K1bW9RjHowETcVefUUFNAUF0FdW+iwHRq/H/73wAi5u2OCzHFwhunt3EAQBk1YLnmEgU6uReNttvk7LJ9Tm52PHk0+i+vx5X6filIiMDBAymVk2Ld+04rKyRIkVZfnGbNJqwbMs5GFh6PTII4i0TPYmFQokDR6Mrk8/LUp8e8T37Qt5aCh4loVJqwUpkyHKzYrHEampIAgCjE5nG4a1V3HW9n88D5NWC0ImQ4QX5kGIDSmTIapzZwDmD2NGrwepUCAkIcGt14nq1AmkQgHG0hYpudzudUwcONBc0ZqmwdTX2471V3iex7Fly/DHu+/ahmE7T5iA9sOHQ2bp9Qtt2xZdJk0ybxvAcea2qFCIdl4xPXqYc+F5szCSJMJSUoJybktLCQqJYQwG/G/ECGx7+GFsvucen+Xx29NPo2T3bhxdvBgXLMvd/A3WaMSl779v9O2T53nRPhD9mfrSUvz62GMo/+MPbJ80yW9Fpr64GNcOHWo0/0keFuawW9kTjHV1KNyypdGqFcZgQM9nn0VUx462/2s7eLDotT8aEpuZ2egbMc+yKNyyxa0exMtN6mxQKhWSsrNvep46Lq7RKhiWpnHt0CHUFxe3IHP/IjE7G2jwt8/SNK7u3u3y8fqKChT//nujtshzHCIzMm56bkL//pCFhNh+JigKlzZu9KtNI63wPI8jCxfiwvr1uPTddzjy9tsAgIQBA9C+wbYAocnJyHz+edtiCMB8T73y228wXL8ueF4ytRph7drdiGUwoOr0aWguXRI8VqASFBLDsyzAcWB0OtvYpS8wVFWB5ziwBgOOLVvmdyJjbzdqUi6/YfetDNZgMC9BtnzL8UeR0V65gl8mTgRdWwtwHEiFAgRJirJnjbGuDtufeKLRbtSETIaQhARBlrZ6giI8HCEJCbY9mjiTCXWFhfj18cddEpk///UvnP38c7AGA0i53NxDYDI5vI5xWVkgSNK8sobjQNfW4peJE6G9ckXoU/Mq8X372obECIoCazDg6OLFLvUe6ysq8MvEidCXl4NnWVBKJQiKQpsuXeyunInr3RuswWDbd81fd7+2CkzB5s3mYTGeh7683OHzSZkMUZ06gaAoUCoVeIaB7to1/DJxQEMJRAAAIABJREFUoigikzhwIECSICzDR9Lu140JConxF0auW2e70fqbyNgTmLQxY5C9dCkyX3ghoMf6W0pEWhqGrlhh21HW30SmqcBQSiUGLVuGbtOmoeuTTwoay57AdJsyBX3nzUOvmTMFjdVSes2cib7z5qHblCluiUxTgQlPTcXInBx0fPhhuz0IANDlySfRbdo0ZC9dCkqpDBqRicvKQsb48Ri1YQPU8fEui8xNAqNSoc9rr6HXrFnoMX263WPUcXHoMmkS7l6zptHu1/4kMk0FhlIqEdOrFwZ/8IHT43pMn45b//539J49W3SR6fiXv6Dr5MkY+u9/g1KrAUgi05CgWJ1kqq/H99nZ5gJLBIFHT592OYbQM7oN16/jl4kTobt2DTzDmGeVz56NThMm+GwlgyOBEWI3anvxxEboWOV//IHfn3nGNjmx4S7TvjovewKTvWwZ2t91l+CxHAlM092o9738Mi5v3QpSoUDWSy/hlscfdyuekNfQnpiM+PpryMPCGp2bs+e5ypUdO7B/zhzzEApJQhkZiZE5OQhr3z6g271dMXnlFXT8y18axbL7vFdfRcfx412OxdI0dk6diqozZ27aZZq09OJ4e3USx3F2BWboypWNaiAV/vQTDi9cCKa+HvH9+t00mf3C+vU4tmyZudfJ0nM5MicHqpgYwd+za4cPY9ezz9pWCcrDwjB87VpEde7calcnST0xAqOKicHInBy/6ZHxhsAEOvF9+/pVj4zYAtMQVwXG3+j57LPN9sgIITCAeV+eYOuRAcw9JSNzcpz2yAghMIB5Sf6w1avt9sj4ClcExhU6TZjglR4ZwDzP6M5//UvqkWmAJDEi4Ehk7vLypmQygpAExkUciUyKUunVPOLkcq8JjJokA1JgrDgSGTVJYlxsrCACY8WRyMTJ5QKflXdxJDJ3RkUhkqIEERgrjkRmsg82TX0qMVEQgbHiSGQiKErgzB2LTHsv36v8BUliRMKeyDyWmOg1kZERBOampEgC4wb2ROaN1FSviUycXI630tK8JjDzU1MDVmCs2BOZ9zp2xH2xsYIJjBV7IvNWWlpQiszkxEQsycgQTGCs2BOZO6KiMDkx0SvDIDzP46nERAyOihJMYKzYE5mFaWleE5n5qamtUmQkiRGRpiKjJEk8lpgo+tASazRibkoKMtRqSWDcpKnIhFAU3khNFX1oSXvlCt5KS0MoRXllCGl+aiqSFIqAFhgrTUUmkqKgtKzmEEpgrDQVmVCKwltpaUE3tKQgSYRZigIKJTBWmoqMiiRxR1SU6JN9rZN4B0dFQWVZMSWUwFhpKjLRcjkWpqV5ZWgphKIwPzW11Q0t+fXEXldRkSRW3HILZJbJg4+fO+frlBoRQVFYmJaGaLkcMoIAzXH4pqwMv4lQcdXaA5OhVkNJkjBwHPbW1OCLsjLBYwUzXUJCMCclBUrLpEMdy2JRYSGKBNw13Iq1ByaUokBZ2senxcU4KkIVZWsPTJJCATlJguY4bKqsxI8uFImckZyM7MhIGDkO68vL8UtVleD5ecKrKSnoHhpqm0Rs4nm8ePEiqkXYPLBPeDhmJCdDSZJgeR71LIv5BQWoaFA/JBBpr1DgHxkZIHFjMvaemhqsLC0VPJacIPBKSgrS1WooLPeqPTU1WCPSvcraA6OytPtLej2WFBWBaeYz8LaICDydlIQQisLZ+nr84/LlZmPdFRWFxxIToSRJMDyPKpMJCwoKoBFw13ArXUNC8HJKClQN7lVvFRbiigj3Kh8SmBN7eZ536aHRaKCwdOkSJOnycQ0Fzp1j3H3UMgymHzyIKpMJDM9DSZKYlp6OvJwcQeMwNI2tkyahZ3S0TWB6PPIIPr96VdTz89Z19Gasc/X1GL12LWhL5c4QisKyXr1Qde6coHHqiorwWf/+iFAobAJz9yef4I+6OsHPidZosGHsWKSGh9sEpu/MmdhYUeHS8RMnTABg3iV3+fLlbr9nYr5fpz79FJmxsbYPXoIgoFIqsXb0aBhFuJZ/aDS4++OPQXMcKIJAhEKBz/r3R11RkWjnKHa715WX45+33w6Kohpdx2FJSbjw7beCxzNyHBbk5iJfr4eR46AiSdzTti0OL1oEjuMEi8NxHA4tWICRbds2EpiFeXkwuRBn3bp1iLDsjXTnnXe6FHNHdTUGL1gAmuMgIwgkhITgq2HDoK+sFPw6nq2vx+g1a2BocK96PzMT1bm5ordFf8CvJSaYUMXEYEFBAapMJlFWLTVdhWTtgZGGkFpOfN++WFpUZBMZoVctNV2FZO2B8cYqJGsPTKAOITWk6SqkWoYBzXFuF8Rzl/Z3341PS0rM7SPAVy01XIVkbYtalnW7IJ67UEolFhcVIV+vF6WODM/fXAfG2gMj1BCSIzpNmIBvyspAc5xXVi29V1RkE5nWtGpJkhgvomFZLCgoEHz5tb1l1NYhJElgPOO8ToelRUWCL7+2t4xarCEke8uoXR1C8nfsLaN++dIlbKqsbFFlX3c5WleHT0tKAnr5tb1l1GtKSzH30iW3CuK1FBPPY3FRkeAF8ewJTEyvXi4NIQnFbzU1+KaszCvLr8/pdHivqKjVLb+WJMbLaFhW0DoyjurASHNghOO8TidoHRlHdWC8JTDdpkwJWoEZ8fXX0HMcfqysdLuyb0s5WlcXsHVkHNWB2V1bi1rLvcpbIuOojkxLRMaRwAxdudJrAmPlt5oar9WROafTtbo6MpLE+AChCuI5K2QnISxCFcSTCtkJgyuF7FwpiCcUgVgQz5VCdq4UxBMKZwXx3BEZZwIj9hCSI6SCeOIhSYyPcCYy5774AsU7dzo9XqrE630ciUzFiRP44513mv3AkgRGGNypxCuJjH3cqcQbKCKT+/XXKPr1V78TGCuSyIiDJDE+xJHInPjwQ5z+97+dHrv3xRclgfEBdkXmiSeQl5ODou3bHR7H6HT4ZcIErwgMAOycMiUoBebCf//rdiVeeyLz25QpouRnV2QmTACj04kSryXwPO92JV67IvPuuyj+/XfB83MkMqc/+8zpOZ1cvhz758zxS4GxYldkHn1UlFiOREYMafIlksT4GHsiw7Msai5cMG9o6QB1fDxAEJLA+ICmIgPL6pTSvXsdHkNQFBSRkSAsRbbEFBjA/KFDkKSgAqOIjASpUAA8L1jxOHdRxsQAAEg3C9k1FBmCJKGOjRUtx4YiQ5Ck+X0XoWqrJ6jatLG1D1cL2TUSGZIECAIKy9JjoWkoMqRFQFRxcQ6fb13azplMgguMPDzc/GVRqYQiMtKj1wIaiwxBklBFR3v8mo5oJDIkCVlICMgArzDdFL8ududKbrpr18xDMP/5j+1Dv+dzz6HThAlQtWnT7PH+sgvtsSVLcPG772zf2GShoeYt7Lt2tftaPMfh6JIlIGUyZL388k0C483z8nY8f4jFGAz4deJEaAoKwFkKnMlCQvCXw4cdyiRdU4P9c+ei48MP2xUYIc+LM5lwaP58RHTsiO52eh1aEouladRfvQoACE9NdUuahdzFumDLFhRt3YrsJUvsCoyzczvz+efQXLyIAW+9JcjN3FmsK7/9hovffovsJUugFGC7ESHbB6PTYf8rryB56FBkPPCAW7H0FRU48Oqr6PLkk2g7eLDHuTiLxdI0Drz2GuL69MEtTnos8jduxB/vvGO7f5JyOQYsWoS0sWPdiucIzeXLAMchJCEBspAQl49zFuvihg0o3bcPt737LmSW3hJPcBbr2uHDOL1iBW575x2EJCQIFsuLOCx2F/ASU/TLL9j70ktAk+cO/fxzJA0c2Ozx/vCBCAA7p05F+ZEjoFQqmHQ6ECSJ3nPm4JbHHhM8lhj4y3X0VixjbS02jx4NRqcDIZOBqa8HQVEYvXkzIjp0EDSWGPiifUhtUYolVqx9c+bg8k8/gZDJIFOpwBgM6Pzoo+gzd64o8Vwl2GN5EYcSI/N2JkLTfsQIxPTogetnzgAcB4KikDhwoEsC408MW70aRo0GlSdP4tQnn0B75cqN4QoJv0MRGYmH9u5F/dWrKD96FMeWLQNpqXQqISHhXUKTkhDXuzeSBg9GfN++iO7eHTLp/tkqCHiJIQgCty1ejK0PPgiWpkEqFBiwaJFPcjFqNCjYtMnh70daxj5zv/7a6evoKypgqq/H8ffegzw0FCkjR0ofjiJRnZuL8iNHHP7elfeMYxjQVVWgVCrsmTULg5YuRVTnzoLnKiEhYZ9b//53X6cg4SMCfjjJysl//hNnVqxA77lz0WXSJJePE7ILLi8nB8eWLAEcCIfBYABg3nvGGbylTDUAUGo1ojp1wpCPP4bKjcmI0nCSa+yYPBkVJ06YJyraweX3jGXBs6x5srVCgS5PPonMWbPcyqWl56W9cgWq2Fi3xtWl4aTWE0tTUICwdu3cmgMUCOelr6w075FlmewtdryWEOyxvEhgbgDpDj2mT8dtS5agcwvnkAgFz/PgjEa7DwVJQkGSDn9vffANViXxHIfrp05h35w5Pjyr4IXnefAM4/l7Zt2dlucBgkDtxYteyV9TUIDNo0fj6LvveiWeRGBRcfw4towZgzOrVvk6FUFhjUb8MmECfn30UdvEeonWScAPJ1mhFAqkjRnj0xzaDh6MihEjbppkbCXHUpHXuhuwI67u3QuTVgtKoYA6MRHa4mJUnjqF4t9/R7uhQwXPuzVzyxNPICQ+3uHvXXnPeI5D0fbtIGUyUEol+i9YgJRRowTP9aa4PI8Dr7wCnuNQ+NNPyBg/HrG9eokeVyIw4Ewm7LdMbD37+edIHTMG4SkpPs5KGM6sWgW6uhogCJz78kt0nzbN1ylJ+IigGU5qKf7Y3bdn1izUX72KThMn4ug774C1DGko27TBfb/8AnloqGCxhMIfr6O3YnEmEzYOG4bkIUPQe86cFtfOaC4Wz/OoLylBxfHjuHboEMLat8eZVavA6vUAgND27dH+rrugjIpCXO/eiOnRw1x0rYXn1Ry5a9dCf/064vv2RWxmJhTh4U7PzZNYxTt3ouzAAST074/YrCyndV78rX14KxbP86grLETF8eMoP3oUyqgoXFi/HqzBAIIkEd2zJ6K7dUNY+/aIy8pCdNeuDoeY/Om8mlJ3+TJ+fuABsDQNAKBUKvScMQOG69eR0K8fYm+91emSdk/PrfzIERT+/DMSBgxAXFaW0yXL/nwdhYjlRYJ3ibWn+GMj43keLE1j08iRMDTYqI+gKHR65BH0ff11wWIJhT9eR2/G4jnO4bwaIWLtmzMHV/fsAWc0gqAoMDQNkqLAGY03nkSSIGUy8JaKwCxNI7xDBwxYtAhxmZktOi9nbHvkEVSdOQN5aCgYgwEhCQnoNXOmw9ocnsT64x//QF5ODmQhIeZhvshIdHz4YfSaMcNuLMC/2oeYsXiOw67p01Fx/Ljtd6zRCILnwXNcwxcAYC4USCoU4IxGRGZkYNAHH9xUFsAfzssRvz72GCpPnrzR420pumesrYU8LAwsTUMVE4OuU6bYrS3j6bmdXrkSpz7+GDK1GpzJBHlICFLHjEGfV18VPJY7tFaJCZo5MY5g9Hoc/+AD1F2+7OtUXIawTA5NGDDA9q2eUqsRkZ6OiPR0n+RUdugQzv7nP16TokDDU4FxBs/zqC8uBkvTYGkajE4HUiZDfO/etmX4stBQRHftivAOHcAzDJj6evAMg7rLl2/M1xGYpEGDQJAkTFqtrYQ6Y+k1FJqE/v0hCwkBU18PzmSCsa4u6MqntxTOZIK2pASs0QhWrwer14MAkDBggK1SMKVWI/bWWxGSkADOZLJdR01hYaM5eIFA7K23IjQpyfZzaNu2aNOtGyilEiatFpzJBMP16zDV1YkSP75PH8itbdFohEmvh+7aNVFiSTRPUEsMYzDgt6efRu5XX+GXRx8NLJEhSfNS3Vtusf3cfdo0dJ440eu5lO7fj93PPos/P/kERxYuFERkKk+dwvrMTOT06oWD8+YJkGXwYtJqYWqy9w4ll2Po6tW2rmyCJJH18stIv//+RkMEJEWBrqoSJa+Efv0arYgiZTIYKipEiRWbldWo14kgSdDXr4MTSdACCaNGYx5aafB3Gdq2LYatXg2ZZeiZIAgMfPtttLvrrptWT9LV1V7N11N6z56NlJEjbT+njhmD3i+91GhrB4KioK+oEOVLV3T37o1knSAI0DU1YBv2ikp4jaCWGLqqClVnzpi/udXWBpzI+AtXduwAz3FgaRoFW7YIIjK60lKQSiV4loWmoECgTIOPRrtRG42g1GoQFIWYXr3sdunG9ekDEIR5vxmSBEvT2D9nDq7s2CF4bjG9eoExGECpVCDlcrAGA85+/jn+/Ne/BI+ljo01792kVIJSq8Hq9bi6dy/2vfhiqxYZe7tRgySROGCA3ecn9O9vHk6yiC5rMOD3Z55B+R9/eDNtwYns3Bkcw4BSqdze/dpdZCoVwlNSQMrl5rZoMOD6qVP4/a9/lUTGBwS1xIS2bYvbly83T27keUlkWkifV15BbGam+eag1wsmMhLOaSQwlt2ou0yahKRBg9DxL3+xe0ybLl0Q3b07+s6bB2VkpKgiIw8NRcrw4ejxt78hPDVVdJHp+PDDSB83Dm0HDQKlUoE1GFq1yNgTmMxZs5DQty86jB5t95i43r3RpmtX9H/zTVsvTTCIDElRSB83Dl0mTbpp92sxRKbTI4+g/fDhSBs92twWaVoSGR8R1BIDAO2GDsWg99+XRMYDKIUCQ1euREyvXpLIeAl7AtNtyhRkzpqFOz/7DCkjRtg9jlIoMOLrr9HxoYcwMidHdJEZ9N576D51KoavXSu6yPSaMQP958/HoA8+QNLgwa1aZOwJTJ9XX0WXyZNx1xdfIKF/f7vHqaKjMTInB+njxmH4mjVBJTL9FyxA5vPP23a/FlNkbnn8cQxatgz93nwTaWPGSCLjQ4JeYgBJZIRAEhnv4Uhgej77rFuvE9a+vVdEBgAU4eFeERnA/K17cCsWGUcC03H8eLdep03XrkEnMgBAKZVeERnAPB9GEhnf0iokBnAsMglulOJu7TgTGQlhUJOkIAJjxZHI9HFS06Wl+IvIBPMuY5EUJYjAWHEkMl1CQoRM2+s4ExmhcSYyMmnPO9FpNRID2BeZhWlpksi4gSOReSox0depBTxqksT81FTBBMaKPZGZkZzsVZEZ58a+X67iSGRmtWsXlCITSVF4Kz1dMIGxYk9k5qSkBK3ITBbhXuVIZOampEgiIzKtSmKAm0UmlKKwMC1NGlpyA3siMzgqCk8lJkpDSy3EWFeH+ampSFIoBBUYK01FRkmSmJGc7LWhpftiY73WI5MZFoZZ7doF1dCSvqICb6WnI4qiBBUYK01FRkmSmJOSEpRDS3dERWGyCPcqeyKToVZjbkqKNLQkIn5dsVdMssLCMLNdOyhJEhzPo55lsaCgANf8bDOx1zt0QLfQUOhYFv8pLcUBjcbXKdmQEQTmpqQgQ62GkiRh4DjsranBF2VlzR7bPzwc09q2RQhFIU+nw8LCQvET9lOsPTBJCgXkJAma47CpshI/NqjW7Ij3MjKQpFSinmXx4ZUrONeknkxT4uRyvJWWhlCKAkUQoDkOn5aU4KgIhcE8OS93IQA8364deoWFQWmJdVKrxT+LixHoNy1rD0wURUFmObc1paXYXVvb7LErbrkFYRQFPctiXkEBypr5MO2gVGJeaipCLDVXaI7D0qIinG+mXXmbifHxGGPp3dtYUYHvmqlPJCcIvJKSgnS1GgrLvWpPTQ3WuHCvaglPJyZiUFQUVJb365JejyVFRWCkL3ktpfVW7HXEca0WHxcXg+Y4kARh65GRhpZch+F5LCkqwiW9HjTHQUWSth4ZCdfw5gc9AFSYTJhfUIB6lgXL87YeGTGGlvQch7cKC1FqNMLEcVCSJO6LjRVlaIkHsLy4GKe0WtCWWNYemUDuzPdEYFrCZZrG24WF0Fl6saw9MoE+tGTieSwuKkK+Xg+j5V5l7ZERg/+UlWFfTQ0MlrZo7ZGRhpaEx68lhud5UR/H6uoaiUy4XI5P+vUzl+IWIV5LzuvOO+8EAERERGDdunWixmrJw8RxWJiX10hkRiYn49CCBeA4zuFxGzZsQIRlS4Xs7GzRr6M33zNXH7RGgw1jxyI1PLyRwGy0VBp15dG5c2cAQFRUFH7//XeXjik3Gm0iYx1amt2xI4q2bxf8HHUsixdPnECp0QhSLoeSJDEhJQWnPv1U8Fgcz+P9wsJGInNbfDx2z5oFlmECrn3oysvx5Z13Ik6laiQwu2pqXH6NNm3aAADCw8ORm5vr0jGFBsNNIvNmly64duSI31zD2bNn246bN2+eS8cYOQ4LcnMbicw9bdvi8KJFTu9VLX2svnq1kcj0jI7G1kmTwNC031xHT2P5A34tMd7A2iMjLb9uOZRCYeuRkZZfu4a9ZdRi9sA0xdoj463l128VFnpt1ZK1RyaQl1/bW0YtZg9MU6w9MsG4/NraI+ON5dfWHhlp+bV4tHqJAcwiI9WR8Qzr0JJUR6Z5HNWB8ZbAWKkwmbxWR0bPcV5bfm0dWgrUOjKO6sB4S2CsXKbpoKwjYx1a8kYdGcA8tCTVkREPSWIsSAXxPIfheakgXjMIVchOKIK1IB4PBGRBPKEK2QlFsBbEM/G81wriAZAK4omIJDENcCYyl7duhU6kmezBhLOCeLpr11C4dauvU/QZ/iYwVoJVZAKtsq+/CYyVYBUZqbJvcCBJTBPsiczPDz6IfS+/jLycHF+nFxA4EpnN996L/XPmwOhHy8S9BWs0+qXAWHEkMiV79ggey5HInPn8c8FjORKZ/Q0mhvoDxtpavxQYK45EpvLUKR9n5hmORObYsmWCx3IoMs88I3is1oQkMXZoKjKswQAAKN2/3+u5kDIZKKUSnMkEwlK7IRCwJzKswQB5SAgqT570dXpehzOZoCsrE1xgSJnMdkP0tH00FRkQBOqKijzO0R5NRQY8D83Fi6LEaioyHMNAU1AgSqyWwhgMoKurBRcYkqIgCwkBazSCID273TcVGfA8dNeueZxjSyAoCqRcDlIu97jdNxUZnuNEa4s3iYzJBK00ZcEj/LrYnTfmURCWdfv2Yu198UWU7NoFlqYBAKRcjoePHDHfdAWO5Yj6q1dReeoUCIJAu2HDXI7dklie4CietrgYm++5BzzHmf+DJNHxoYcgDwvDhW+/BVNfj/C0NPR/4w0kDBjgUSwxEDJWbX4+dk6Zgo4PP4yef/ubILFq8/NRk5cHSqFA8tChttdoDmextFeu4Lenn0bKqFG49cUXXX7N5uLZi2Wsq8POKVOgjovD7R991OK/raaxgJvPjWNZ7J89G5qCAtz1xRdQRkWJFqslVJ48id0zZiDzhRfsCkxLYlWdO4e6y5chDwtD28GDXT7OWazqc+fw+zPPoNvUqegyaZLLr9mSWI4w1tai9MABAEBSdjYUlnINnsRjaRq7pk8Hx7IYumIFZGq1y6/pbiye53Fk0SKUHTiAu7/8EiEJCaLFEgMh7glu4rDYnSQxjhq00YgN/fuDlMnA0DTAcaBUKty9Zg1ievQQNJYY+IvEnP38c5z46CNbbwwAyMPDwej1AM+bv3kqleAYBuMPHIDc+i2vBbHEQIolXDxft0UplhTL2/GCPZYXkSr2ugulUOChvXtx+/Ll6D51KlSxseBZFoyfld/2d7o+/TTGbN6Mvq+/jg733gt1fDyU0dEgLXvAAAAIAl0mT3ZJYFyFrqnB4bfegsYL2xnwHIejixej/OhR0WNJSEi0Lop37cLJf/7T12n4LTJfJyA2Jq0WVWfPOvx9V0s57WuHD9v9PSmXI/G223Duyy/BAzj3xRcIT01FSHy8GOkGHQRBICI1FRGpqch44AEA5g/9kx99hNxvvgFrMEARHo5ezz1nO0ZbUoL6khKHr9ncewYAmoICXPr+exT8+CO6PPUUejzzDCiF4qbnGaqqUOtk/NuVWJzJhNy1a3Fxwwa0Hz4cfV55RZChCgkJidaLrrwch+bPx7VDhwAAiQMHOn2+K/eq6G7dIA8LEy5JPyDoh5MOv/kmCjZtAmnnAwwAampqAJjLtjvNhWHA6PUgZDJQcjlufekldJ440a1cgrVrsSXxWJrGppEjYaiuxtAVKxr9gW4cNgwmrdbhRERX3zPOaARL06BUKiijojDovfcQl5XV6Dm/P/MMyo8eBSmz7/Mux2IYsHo9SLkclFKJ/gsWoMO99zo9pinB3j78tS1KsVpPLG/Ha2msvJwcnHj/fbAmE3iGgUytBuHgHmWluXsVZzQi7b770P/NN93KxR7+NJwU9D0xjMEAlqZtk3ObEmqZ2W5ycRdfnmHAwjzXw12JkbgBpVTi9uXLUXH8+E3fMFiaBlNf7/BYd98z1mCAvrIS+T/+eJPEMHq9eeWUQLE4yy7o59ascVtiJBqzZ+ZM1Fy8iDGbNzuUTAn/wVRfj02jRqHDPfeg72uv+TqdgObs6tU2gQHM96nmcOVexVhW2gYTQX9nSMrOxnUntQzy8vIAwLaRniO0xcW25Y8d7r0XvefMETTP1khsZiZiMzNv+v92Q4ei4tgxh8e58p6xNA26tta8pNnSc9bpkUduel7ykCEwOCn370osnudRf/WquX0oFOj86KPo2WB4TMJ9ru7daytpcO7LL9F96lQfZyTRHCc+/BAmrRaXvvsOGQ88gDZdu/o6pYDl3h9/xLGlS3H5p59s5RPC2rVzeowr96qk7GxB8/QHgn44qTlc7e77zrKML3vpUsT26iVqLCFo7V201efPY+tDDyF56FD0X7AA6rg40WKxRiO+7dsXUZ064bYlSxDVsaNosYTCn4eTGL0em0aNssklpVLhjo8/hr68HHFZWQhLSXHanR2s19HfYvEcB01+PiqOH4c6IQF7X3jB1uMdkZGBezduBOlCDZfWfq9yRuWpU7YCoeObqVPWWldozjK8AAAgAElEQVQnSRLj4htvuH4diogIj+pYBNIfjz/HczWWrrzc4wnYrsbSV1RAFRPjUUExf7yGQsZzNdaxZcuQ9803tqE5kCRU0dGga2pAymQgKQoxvXqh48MPI2XECLuxgOC7jv4S6+L336Pgf/9D1dmzIAAwNA1lZCTo6mrbc0iFAlkvvohbnnjCo1hi4C/X0VU4kwlGjQaqmBjRY7mKP0mMtMTaRVQxMS4JzNV9+6AtLvZCRt6lrqjIVlwqUPDmCjJ1XJzHFVElzBgqK0EqFLaJjKRcDplaDUqhAGswwFRfj2uHDyN/40YfZ9o6ubBuHSqOHwer14PR60GpVKBUKtv7RalUoJRK1Et7zQkCKZc3KzCtGemuKyDn167Fnueew7ZHHgmq3a9r8/Ox7ZFHsPvZZ3Fh/XpfpyMR5GQvWYLMWbNsUth+2DAMW736Rl0hmL/pS3MuvA/P82jTpQtkKpXt/zijEWM2bbIVASUoCgMWLkRvP9ufSiI4kSRGQI6/9x44o7HR7tfBwJmVK8HU14MzGnFs2TJJZCS8TmhyMkiFwvZg9XqcX7NGlN2vJezD8zyOvPkmLm/bBkanA6VWg6AohLZtC5mlRomEhLeRJEZAbreU17fufh0sItN79myEJCaCkMnAGgySyEh4HYIgkHzHHYju1g3hHTo02v1aEhnxsQpMwZYtYA0GUEolYnr0QHiHDmg/fLiv05NoxUgSIyBNd78OFpFRxcRgZE4OQhISJJGR8BnZS5di+NdfN9r9WhIZ8bErMJmZGLpyJUZv2oSsF1/0dYoSrRhJYgRGEhkJCfEgCAKK8HBJZLyEQ4FZsQKUQuGLVSoSEo2QJEYEHIlMggfLs/0BSWQk/AVJZLyDM4GRkPAHJIkRCXsiszAtLWhF5i5pw0MJL+NIZMbFxvo6taDg6cRESWAk/B5JYkSkqciEUhQWpqUF5dDSY4mJkshIeB17InNfbKwkMh7A8zyeTkzEoKgoSWAk/B6/rtgbLGSFhWFmu3ZQkiQ4nkc9y2JBQQGuWSuSBigRFimLlsshIwjQHIdvysrwm2U3VQmJljIyOhoT4uOhIEnsr63FpyUlTp+vJknMT01FkkIBOUmC5jhsqqzEj072xZKwj1VgVJbreFGvx9KiIjDNfFYsSE1F55AQ6FgWq65exWEXN02VkHABqWKvLzmu1eLj4mLQHAeSIGw9MoE+tKSxyFiVyQSG56EkSalHRsIn6DkObxUWotRohInjoCRJqUemBbRUYCQkfIVfSwzP86I/vBXrWF1dI5EJl8vxSb9+0BQWinpeAC/qQ8MyWFBwsJHITEtPR15OTsC/Z1IsYeO58/zly5dDZakKO3HCBJeO0bEsXjxxopHITEhJwalPPw2K6yhmLI7jcGj+fIxo27aJwOSB4TkX7wfWHZIjAGxw4z7inXtV03iB/p75Qyx/wK8lJtiw9sgE2/JrDRtj65GRVi1J+BJFeLitR0ZateQaPH/zMuobPTDSHBgJ/0aSGC9zXKsNyjoy1qElafm1hK+xDi1Jy6+bx57AxGRmSkNIEgGDJDE+IFgL4mlYVqojI+EX6DlOqiPTDI4EZuiKFZLASAQMksT4CGcio71yBXSArvBxVhCP0etRc/Gir1OUaCVIBfEc40xgpGXUEoGEJDE+xK7ITJyIn8aNw8HXX/d1ei3Gkcj8/MAD2PrAA2AMBl+nKNFKcCgyn33m69R8hiQwEsGEJDE+5iaR0WjAGgyoOHHC16l5hD2R0RYXg1KrUXX6tK/TkwgAeJYV5HXsiczFb78V5LUDEp5HwebNogmMnAjs+lcSgYUkMX5Au6FD0XPGDFAqFWAZi2Z0OujKynycmWeoYmJw95df2s4JPA+WplF+9KhP85Lwf9qPGIGuTz+NbtOmocvTT3v8eg1FRh4ejmGffy5AloEJQZK464svBBeY/5a/gP9VTMOW60/hdP1tAmQqIdE8fl2x1xvr0a27sPo61uZ774W2uNj27VMWGooBCxeiwz33eBSrcR0GMbF/bmdWrcLJ5ctviAyAuN69MXzt2pZH8pP3TIrlXjxvxgLsnxtrNIIzmSAPDRU9ltAIHctYVweZSgXSTtFN794//ONeJUqkAG4frsTyIg4r9sq8nUmgUrR9O6I6d0ZEhw6ivP6QTz9F2cGDKN2/H5XHj4OurkZtfr4osbxJp0ceQXhKCsoOH0bZvn3QFhejvpkS8hISYkEpFNK8DwuK8HBfpyAh4TFB3xPDsSxMTvbwiImJAQBcv37d6ev8eNdd4DkO3aZMQfe//rVFN0J3TFl37RqUbdq0+IYr9DcpJaGDjDA6eUbz17Fg82YcW7wYIcnJuP399xHTs2eLcgn2bzfBFssazx96YqRY7sWSemI8jBT07cNrOOyJCXqJOfHBBzj35ZcgKMru72nLShmlpcS5IwiSNE+EU6mgbNMG2UuWIL5PH7dyCdSbkJww4N+dbwdFME6e5dp1BMeBYxhQSiVSx45F79mzIQ8LcyufYL8xBFssazxJYgIvliQxHkYK+vbhNVrvcJKuvBw8yzpc6SAnzXObOaOzXoYbsAYDdKWl2DNzJsbv3y9Ynv6MjGAgI42QOZUYN68jTePS998jvEMHdBNg4qaEhISEROsj6CUmIi3NYS8MADCM+YNZJnN+KQiCMPcgqNWISEtD9uLFgubpz5h4BbRsFMKpaifPav468hwHUiYDz7IgFQp0nzYNXZ54QuBsJSQkJCRaC0E/nNQcrnbBfdu3L0AQ6D13LjIefBAE6f7q9ODtDgZc6aK99P33ODR/PuKysnDbu+8irH37lkUK8i7aYItljScNJwVeLGk4ycNIQd8+vEbrnRPTHK6+8RUnTiA8JQWq6GjRYwmBP0oMo9Oh8tQpJAwY4NEfQbDfGIItljWeJDGBF0uSGA8jBX378BoOJUYqducEnudRc/EieJ5H3K23eiQwYqCvrIShqsrXabiMLCQEiQMHNvsHwJlM0BQUeCkr71J3+TJYmvZ1Gs2ivXIFjF7v6zQCGpamA35TVwnnsEYjNIWFXonV8PNI4gaSxDiA53n88Y9/4Odx43BsyRK/azi1+fnYMnYstowdG1Q3Ss5kwu4ZM7Bl7FjBdr9maRqH33oLe196Cee++EKQ12wJ+Zs2YcuYMdg5dapgInPphx+w96WXcPD112HUaAR5zSs7dmDz6NHYMWkSGJ1OkNdsbbA0jZ1Tp2LLmDHI37TJ1+kEHamq03gu+SU8l/wSOijP+SQH1mjErunTsWXsWFz87jtRY/E8j2NLluDncePwxzvvCPZ5dHL5cux96SUcW7YMPMcJ8preRpIYO1gFJn/jRoDnkbduHa4dOuTrtBqx98UXYdJoGu1+HQxc+PZbXDt8GOB52+7XnlJfWor8jRtRtG0bTq9cKUCW7kPX1ODga6+B5zhUnTkjmMicXb0aRdu24fK2bag+f97j17uyYwf2z5kDnmVRk5fXqjdKbClWgak6cwY8x+Hga68F7K70/sptEdtwW6T50S9iu09yyFu3DhXHjgEch6PvviuqyFw7dAh569YBPI/8H34QTGTOrFqFom3bkPvVVwHb8ypJTBMaCox1g7TYzEzEZWX5OrVGZD7/fOPdr4NEZDqMGgV1fHyj3a+FEBmymdVnYqOIjESnCRNAqVRgaVpQkQFgt3S8u1gFhqVpgCQhDw9Hp4cfFiC71kNDgWFpGpRKhU4TJ0IRGenr1CQEJnX0aKhiY0FQFFiDQVSRicvKQmxmJiilEqzBIKjIAGjRQhV/IXAzFwF7AhPdvTuGrV5tFgY/4qbdr4NEZOztfi2UyPgSgiDQ9/XXkf7AA6KJjCc0FRhlZCRG5uS0eAVZa8SewKQ/+CD6vvaaLyZCSoiMOi4OI3NyzF+6RBYZSqnEsNWrEd29u2giE6hIEmMhkATGiiQygYW/iowkMJ4jCUzrRBIZ3yNJjIVAExgrjkQmQYDhBV/SGkVG7oMPO0lgPEdOEJLAtGL8RWRaK5LEAJiUkBCQAmPFnsgsTEsLWpG5KyrK16l5hCOReSUlxasi0yc8XBIYD5ETBF5JSZEEppXjSGSGiDAXypHITEpIEDxWINCqJYbneUxKSMCQNm0CVmCsNBWZUIqyiEzwDS09lpgYlCKTrlbjlZQUrwwt9QkPx4zkZElgPIClabySkoJ0tVoSGAm7IjM5KclrIjOkTRtMSkhodUNLfl2xV2ysAqMiSdAchwK9HouLimAK4EaQFRaGme3aQUmS4Hge9SyLBQUFuGYy+To1j4iwSFm0XA4ZQYDmOHxTVobfXFi6mqhQ4O20NKgpClqWxTO5uV7I2HUmJybijqgoqEgSRo5Dvhvt8L2MDCQplahnWXx45QrOuVDXxSowSpIEa2kj8wsKUBHgbcSbWHtg0tVqKEgSBo7D7upqfHXtmq9TaxVMjI/HmNhYAMDGigp8V1Hh44xuEElReCs9HVEUBZnls2VNaSl219YKHsvTdvh1164gCAIMz+OZ3FwY/LdWjFSxtynBKDAAcFyrxcfFxaA5DiRBNOiRCeyhJY1FxqpMJjA8DyVJBkWPDACsKSvDnpoaGDgOCpK09ciIMbQkCYznSAIj4YxalsX8/HzUsCwYjoOSJEXrkTHxPBYXFSFfr4eR46AiSVuPTGvBryWG53nBHxzH4fCiRbg3ObmJwOTCxHMw798h1kO882r4OFZX10hkwuVyfNKvHzSFhaLF9Ma51TIMph882EhkpqWnIy8nx+lxubm5CA8PBwC0adPG786L53l8cfVqI5HpHh2Nnx57DIzB4PS4zp07AwCioqLw+++/O31u0fbtmN2xYyOBeXzHDpQbjaKfnzeu4U3vmQgPhiDwU0oKut8kMKUi3zu81xa92e5bGmv27Nm24+bNm9eieGK+V7Usg/n5+xqJzN8yMnBhwwbBr5+R47AgN7eRyNybnIzDb78NjuOcHmutD6OQy6HRaFpwDX2PX0uM0PD8zcuob/TABNYcmOaw9sgE4/Jra49MsK1asvbIiLH8uukqJGsPjDQHxnVYgsDOlBRUqdVgSRJUox4YaQ6MRGNq2Thbj4w3Vi1Ze2Ra2/LrViMx9gQmunv3oBhCcsRxrTYo68hYh5aCbfk1YB5aErqOjL1l1NIQknvYE5h0aQhJohmsQ0veWH5tHVpqbXVkWoXEOBKYYatXB63AWAnWgngalg3KOjIABC2I56gOjCQwruNIYPpKAiPhArWWe5W3RKa1FcQLaolh9HqnAhNoy6hbijORYY1GcAzj6xRbRHMF8QJ1Q7PmKvta27UjrL+XCtm1HOv8F2cCIw0gSbhKcwXxhLxXNVfZN1Dvi44IWokp2b0bGwYOxJ6ZM1u1wFhxJDLbn3gC2/7yF1+n12Icicz//f3v2NC/PzQB2uPkSGR2PPkkNg4bhj8//dTucca6OmwYOBD/9/zzksB4wJ9xcdjYuTN2dOggCYyEIDgSGeu9SltSIlgsRyKzZ+ZMbBg4ECW7dwsWy9cErcSU7t8PnmFQtn9/qxcYK/ZEpvbCBdTm5we0ndsTmZJdu0DI5ag4dszX6bUYeyJTk5sLk0aDq3v22D2m8sQJkHI5ru7dKwmMB5SEhcFEkqhRqSSBkRAMeyJTsmsXSBHuVfZEpsz6uXjggKCxfEnQSoz1TWJpGoRMhvDU1FYtMFbaDR2Kge+8A0ImA3geLE1DplLh+unTvk7NI6wiowgLAwBwRiM4mkbZ/v0+zswzrCLTfvhwyCyVYQGgJi/P7jBg+R9/gKVpcJbnUQqFJDBuwgGoVSoBggBLkpCxLNprNJLASAiCVWTkoaEAzPcqlqZFEQuryISnppq/4FnuC5LE+DksTTeauMozDGovXgTtQnXX1kDJrl3gG1RmZAwGlB896sOMhKE2P/+m97hkzx78PG4cYJlDwuh0+G+fPqi9dMkXKbaY0v37G/WWEXI5vhs4ENorVwAAPMti57RpyN+4EWjw3rIGAyoDXFC9TbVKBbLBnCOGJFFqkWMJ37N1/HicX7PG9vPZVavwy4QJPszIfWouXYJRo2n0f+VHjogSi66pQe3Fi+AbfOmpzc/H+qwskDIZAICgKHw/eDCu/PabKDmISVBKTHVuLniOA6VUQh4RgaTbb0fW7NlQWcpUt3ZuefxxdJ4wARFpaSBkMhAEgfLDh32dlse06dwZmS+8gPi+fSFTq0Gp1WC0WoAgbALAMwxkajVCk5J8nK3rEASBvq+9hpR77oE6Ph6kZciMUqttMsrodCBIEkaNBpRKBUqpRExmJno8+yzi+/Tx8RkEFtctc2BIjoPaZEKKRoO+ZWVSL4yPMWm10BQWIrZXL3NPsgVCLkdsVpYPM3Of6C5d0GvWLMT16WP7e60vLRVlWF8VG4us2bORdPvtkEdE2KYTEAQBzrJKkaVpEASB6K5dBY8vNn69d1JLl4TxHIere/YgqnNnhCQlOd2M7cbvvHFq5ljeWOpmPa/mYjEGA6pOn4YqNhYRqamixxMCV2LxHAdNQQH0FRW4smMHLv3wAzjLJn2DP/gAyUOGCBZLKFyN9cOdd8Ko0aDLpEnI/eor25Bp29tvR9ennoI8LAyRHTuCpCiPYwkFQRBejQW07C+aB3ApMhJ/JCZCwXF48MIF57EaHSk2/tcWvRnr4Lx5yP/xR3R+9FEUbtkCo2UvIlVMDO775RfI1Gq34vnLe1Z+7Bh2PPEEIlJTccenn7b4PuzSfZHnoSstRU1eHuqvXcPxZcvA6vWg1Gr0mD4d3adOdSuWF3G4d1JQSow7+FuDFiySDz6kvBXP3Vim+npsGjECtEaDtoMH487PPhMtljMu//wzDi9a5PD31dXVAMxbIziDqa8Hz7Kg1GrI1Gpz74tCgbE//wx1XJxLuUgSczN1cjn2JyfbJvMSPA9ZMxviVdv+5fg9+6L0DRzU3OtmNvbw378xZ2ivXMGvjz8O1mi0+3tX2z2r14MzmUCpVCDkcnA0DYIkMfjDD5F8xx0u5yPkPX9uyjSkq50N1zZ/bjzDADwPhqZByeXo8uST6DF9OiiFwq1c3H3PeI7D1vHjUZOXh7D27TFm82bb8JKrsbyIQ4lxLWMJiQBGHhqKAW+/jSMLF6L/woU+y6M6Lw+mJuPgDQmz9Jw4e05DWL0erKX7OWvePJcFRsI+P2VkgCMIwPphQBAwOenNAoAbM2Ucv2cpyjwchBASE5jUX70KRq8HU19v9/dut3uDATAYAADx/fu7JTBCk6H+E6FUnZNnuHluNI2zq1dDX16OgW+/LUCGjiFIEtlLl+K3J59E9pIlLguMvxGYWUtIuEm7oUPRbuhQn+YgDwsDKZeDdLCjeF2d+WZo3azSIZaCVaRCAUouR5/XXkP6uHFCp9vq6F9aiqOJiWAJApxlVVJz3Pj4sv+eyQgTdFzrnhQsDwszr4IMCbH7e5fbPcxzv0AQoBQKdJo4Eb1mzhQ0V3fRsWGgCAY876hnwrVz4znONs8tMiMD3aZMEThT+0R17IiH9u71SiyxkIaTpOGkgIsXqLE4hoG2uNjh72+55RYAQG5urtPX2f744zDW1SFl5Ej0fe01KKOibL8z1ddDFhLSbHevNJxkH5qi8EdCAooiI6FgWQwvLHT6/Fts/3L8npUb24ET5PtiYLZ7ANCWlNgmkTbF1XZ/4sMPUbxzJ6I6dUL2kiWI6tSpRbkIec8PJWsQLnO26rX5c6s6exYH5s4FpVajz6uvIn3cuBYN1/jivuhFpDkxjpAkJvDitfZY+f/7H8KSkxHft/HfNM9x+H7wYHBGI6K7d0fS4MGI690bMT173jS+LkmMc8pDQqCVy5FumTzqMJbtX9L9Q+xYladOoSY3FxkPPQSCbPnCWn+755u0WuTl5CBj/HiompkX5DSSJDHNE6gSw3Ocw0YvdIMmwIEHAdhdkOl/NwZX4XnevCyvmesYiOcWKLGctWMrm0aNstWOIeVycCYT+i9ahI4PPuh2LBCEYDcrISXG5bZo71jrcwTJxL8kxpX24XIkP2r3YsXzh/fMXVz5LGttEhOUdWIacnXvXqzPysLJ5ctFj5WkyMeKW7LxYrvnQEKYTRVr8vJwfu1a5H7zjXk82AdwJhN2PfMMvh88WLDdrxmDAbnffIPza9ei+tw5QV7TETzPI3/jRpxfuzZgK1Ve2bED6zMzcfaLL5w+L3HAANu/rSs5Uu66y61Y1efOYcOAAdg/d26jooj+AM9x2D93LjYMGOB2u2EJAjs6dMDGTp1QH6CTGB1xYf16rM/MRP6mTb5OxSvoyspwfu1anF+7FvqKCl+n4xUubdyInF69BN39unjnTpxfuxaXt20T7DW9TVD3xOjKyrBp1CjbzbzLpEnIfP75Rs8R0spXdM5GCFULE6/Cae1AfFS8vMlYuPum/NtTT6H82DEQJImBb7+N1NGjXTpOSCv/4513cHHDBnAmExSRkRi5bh3CO3TwKF7RL79g/yuvgOc4xPTsiRFff+1yPu7G0hQW4udx48ADkKvVGO+GyPjDN9KGu1GTcjnu+OQTtB082O5rXN66FYfmz7cJr6M9wxzFqj53DtsnTwZTXw9SqUSP6dPR469/FeTchLiGp1euxOl//xscTUMWGorha9agTZMCXfZ6YhruRs0RBEJNJtx38aLH+fhDT8yF9etxbNkysAYDSJkMI9av97homT+0e2ccf+89nP/qKwBAt6lTkTlrltvxAqkn5vqff2L744+DYxhQKhX6vPoqOo4f3zhSC67juh49QMpk4BgGfzl0yLYVQnNIPTFeQhEZaS76pVCANRhw/quvRO2ROVV/G0y8CkrSgB5hB/FCu+c97pHheR48wzhc0eINkgYNMjfaBrtfC9EjQ8rlthoJYkPK5eBNJq/ctoSkocCAJM2F7NLTHT4/LisLjE4HZXR0o92vd06dats3xRENBQYwL8FM6NdP0PPxlIR+/Wzd6Ux9PbZPntxsj0xDgWFJEiTPo22ds2WxgUNDgSFkMqgTEhCSmOjrtLwCz7LgXVhBFgyEtm0LVVxco92vheqR4Uwmp4Ux/Z2glhiZWo2716xBZEaGV0TmXyVLcbTuTtCcsCLja5KHDLlp92uhREbCMU0FxrobdWjbtg6PCUlMxNBVq3D/r7822v26OZFpKjCUWo2hK1Ygzs/KucdlZWHoihWgLNVZmxOZpgJj3Y26z7Vr3kxbFJoKTEhCAkbm5Hg0OVTCP7FucNtw92shRSaQCWqJAcyFzrwlMjyooBWZdkOHSiLjRRwJjCu7USdlZ0OmVqPv66+7JDKOBMZf91yK79PHJZFxJDDBsBu1Q4GJifF1ahIiYd39WhKZxgS9xAD+JDKBjSQy3sETgWkIQRAORUZuGdMONIGx0pzIyCWBkQhCJJG5mUD/XHUZRyIzXoRS7Y5Fpl3AX3BHIpPgwzk7wUSf8HBBBMaKI5F5JSUFGSpVQAqMFUcik6FS4ZUgFZi7oqIkgWnlOBKZIZGRvk7NJwT6Z6pb2BOZe2JivCgyYXihXTtwTPANLS1MS5NExkP6hIdjRnKyYAJjxZ7IpKvVWJCWFrACY8WeyCxIS0NGkArMY4mJksBI2BWZyUlJrVJk/HqJtVioSBLzOnRAslIJBUnCwHHYev06vhOh3gAB4NnkZPQJD4eSJEFzHE5rtfiouBiuVOB4vUMHdAsNhY5l8Z/SUhxwcSMxb5AVFoaZ7dpBSZLgeB71LIsFBQW45qC8eEP6h4djWtu2CKEo5Ol0WNhMeXdPSFQo8HZaGtQUBS3L4plmypv7AqvAKEkSrOVazi8oQIUL19IdJicmYkhUFJQNCmYZOA5Li4qQ66M6REJwS0gI5qSkQNXgvIwch9+rq/FVEEzitQqMkiTB8DyqTCYsKCiAppWszmnKxPh4jImNBQBsrKgQ5d4dCERSFN5KT0cURUFm+XxZU1qK3c1UmrbyddeuIAgCDM/jmdxcGPysLlQDWucSa0cYOA5vX76MEpqGkeOgIkkRe2SAf5WU4GhdHWiOg5IkbT0ygX7xj2u1+Li4GDTHgSQIhFKU1CPTArwlMACwq7oaDRdT8jyPazSNfMtu2IFKvl6Pa0ZjoxoZBIDdNc72tQkMJIGRcEQty2J+fj5qWBaM5fOltfXI+PXnKM/zoj30LIs5f/7ZSGTGt2+PEx99JHgsjufxz8uXG4lM//h47Hz2WbAmk9Nj77zzTgBAREQE1q1b53JMb1xDnudxrK4Ow//1L5vIhMvl+KRfP2gKC50et2HDBkRERAAAsrOz3Yrp7rnl5ubadpFt06aNqLHcfRRt347ZHTs2EpjHd+xAueUDWchH1dmzWNqrF2SW3gqe50EQBNKjovDTY4+BMRhEO08xryFjMOCnxx5DemRko6J6cpLE0l69UHX2rKjnJea55eXkYFp6eiOBmX7wIGoZRrSY3jgvT2PNnj3bdty8efOC6tzcfdQwDKbt29dIZP6WkYELGzY0e6y15pJCLodGo3H7vPwBv5YYsZGHhtp6ZMRetURSlK1HhlKpwBoMKDt4EP/3/PNBMUfG2iMDXlq15CpNVyFZe2A8nQNjj6arkAwchyPWtuhGQTx/g6Vp7Jw6FVVnzoClaVAqFY5oNLZucVcL4vkjTVchWXtgpDkwEk1Rx8XZemRa26qlVi0xwI2hJe8svzYPLSXfeWfQiYx1aElafu0a9pZRizWEZG8Z9dKiIiwvLnarIJ6/YU9g0h98EMtLSrC0qMjlgnj+iL1l1NIQkoQzrENLrW35dauXGMAsMt6rIwNkL10atCIj1ZFpHkd1YLwlMENXrLBN4nW1IJ6/4Uhg+r72GgAgV6dzq7KvP+GoDowkMBLNUcuyra6OjCQxFrxZEI+kqKAVGakgnnOEKmTnCq4UsnNWEM9fRcaZwDTcmM7Vyr7+hFTITsJTWltBPEliGuBIZI6++y62jB2LKzt2CBbLmcjsmDwZp8FTxYMAACAASURBVD79VLBY3saRyJz46CP8dP/9fjcxTGxqL13C/0aMwOkVK0QXGMZgwKZRo/DnZ5+5XMguUETmyKJF2D1jhksCY8WRyPz52WfYNGqUX5xf+dGj2HTPPTj5z39KAiMhCI5E5tQnn2DTqFGoPHnS1ykKhiQxTbAnMpd++AGaggJc3rpV0Fh2RebQIVSeOIHCLVsEjeVt7IlM7tdfQ3vlCjQFBb5Oz6uUHToEXVkZzqxcKXoPTNWZM9CXl+Ps6tVuFbKzJzLX//wTf372maD5ecLlbdtQeuAAqs6edUlgrNgTmbOrV0NfXo7rp097K32HlOzaBe2VKzj/1VeSwEgIhj2ROffll9AWF6Nkzx5fpycYksTYwSoyEWlpIJVKMDodwPMoP3pU8FhWkWk7ZAhkISFg9XpwDIP6q1dhsnwIBSpNRYbV6wGSROXx475OzauU7dsHnmXBGgwAQUAZESHaEFL50aPgrLEAUCqVy5V4G4oMqVCAUquRdt99gufYEnRlZea/DZoGazCAUquR/sADzQqMFZvIqFQAANZgAMeyqDh2TOzUm6V0/37b3wchkyEkPl4SGAlBsIlMXJxZZPR6gOdRtm+fr1MTDEliHEDK5aCrq8E1mGxprKmBoapK+FgUBbq6GqzReOP/FApsf+IJXP/zTwAAz7I48f77ATfMZNJqwTa4hqxej/Nr1+LY0qXgLctgq8+fx/ZJkxqdv1AUbd+O3X/7G2AZwmLq6/Hzgw9CV1YmeCxHVJw4ceMHnodJpxNtSK10717wDeZVsTQNxo1CdlaR6ffGGxiZk4PI9HQx0nSbiuPHQchktp85kwl0VZVLAmOF0esbDR/xDIPSvXsFzdNdWKMRmkuXbD/zDANjXR1ASrdmR+ybPRuXNm60/Xzx229x4NVXfZiRn0MQMNXVgW8wMfz6uXP46f77b/z9EAR+mTAB5X4g9e4i/aU4gJTLkTx0KEKTkkDIZJCHhYHneVQ2/EASkLaDByO8QwcQMhlkoaFg9XroSktt36hZg8EsUP5bFtou4SkpiO/TB7KQEFBqNUiFAtqSEugrK83fCmA+N11pqSjxKUs86wc5z7KoKywEQVHNHCkM9aWlMNbUQBYSAlIuhyomBslDhkBhKfQnJDzHoerMGVAqFSiVCvKwMCTedhtCEhLceh2CIJDx4IOISE0VPMeWUnbwIFi9HrLQUJByOSIzMhA/YIBbrxGSkIDE226DPCzMdo2unzljk2lfUH3+PHiWhTwsDKRcDnVCAtrecQfkoaE+y8nfMdbVwdhg+xVjbS1MWq0PM/JvFOHhaDtkCNTx8SDlcshCQwGWhSY/39b2OZMJdYWFkCmVPs7Wffx67yRvTAC1mqizWCatFpUnT6Ly5El0mjABquho0WKxNI3rp0+j4tgxhCQm4vCbb9pEJiQpCWN//hmUQiFILCFpLh7P86i7fBkVx4+DY1mcXbkS9SUlwP+zd95hTtT5H3/PTJJJstnK7rKNbKHIAtJWOigogqAI2FHUO7Adep6nooiIgnJy/ETP8yzYThQF9U4UUEE4KcLC0pYivWxley9pk5n5/ZFMyIYkm2xmskk27+fh4WHZ5D0zmXzzyuf7KbBseVz/0UdIGDpUFC9H7fjTn1CemwvebAalUmHIM8+gz6xZkng5SldZiV8feQRNFy/ipm+/RVzfvpJ58TyPc+vWQa7RIGHwYESkpbmMVHTG/eGLV/Xhw6g5ehQJOTmI7dvX7XvAk3uxtbQU1fn5YFpb0fuee7yK6Hjj1Z6Y1lacXr0acf36IX7wYNAxMZJ5eaNA9tJVVWHj1Km2L0EytRrTfv4ZKussJbH9fFGgeRnq61Fz5AjqT58G09KCc+vWWbZnaRqZ06dj+Msve+XlR7mcnRSGmAC7yRy176WXULhpEwiStOQ2XOP0dRTFyxd561edn49f584Fz3HQTpmC0a+/LpmXrqoKm6ZOhVmvR0yfPpjy3//a2m2L7SWI53lcXL8eh5YvB2cygSBJRGVlWfKDXCg3NxeAZQyDK6Vcey0GPPqoV8fiTMEGMd56AYH7ng57+e515osvcMTa/mLo/Pnofffdkvp1VIHsxRqN2HDTTdBXVUERHY3pW7d6HAEMQ4yHCsQX3t9epuZmbJg0CcnjxmHMihWSevmijvjtXbgQpb/+iulbtkDhxcCyDi16X36Jw8uXY8p33yGmd29JvQBgz3PPoWTrVnAi5/lEarWYJkKVXBhiwl7B7MWxLH6aOROkTIYp//mPx19KOurXUQW6V8W+ffh17lyMe/tt9Jg40WsvPyoMMa4U6DcZYAk5k3K5R9tIvnp1VB1aiBgGrMnk9f5/R7x4jgPT0uJ1LkpHr2PR5s3IW7wYnMkEjmFAKhQg25nu3WTd549ycYw8xyEiORm3bNzo1bE4Uxhiwl7B7mW2brPLrBVnUvt1RMHgZWps9OpLpL2XHxWGGFcKhpss0L387RcsXob6ehxctgyXtm8HxzDIWbDAbdnsnXfeCQD49ttvXf5OTJ8+iMrM9PpYHBWGmLBXV/Tyt1+oe/lRYYhxpVC/ycILQ+d7Ve7fj7Nr12Lkq69CrtFI6uWpwhAT9goWr9ayMlQfPoyKvDxU5uUh5dprMWzRIsn8xFSoe/lRLiFG5uyHYYUVlnjqPnw4ug8f3tmHEVZYQac98+ej5JdfQCoUlqaj8B94hxUcCvk+MY0XL2LTtGko2rxZci9jQwO2zJrll4Z0HMPgt6eewu5nnpF8aCTP8zjy1lv4ZfbsNv0ZpNLFDRuw6dZbRR0aWXfiBCrz8tBSUiLac5Zs24aNt9yChvPnRXvOsCziGAaV1m/eYt1zPM/j4LJl+N+cOX7phn1u3Tr8NHMmWsvKJPeqyMvDxqlTA3ImDmsyYecTTyB3wQJwXk7iTps4ETzP2wBGFhGBJDdfCHieR/4bb2DrAw/4pXfMhfXr8eP06aKuK65UffgwNk6dKmrn+MaLF1GZl4eGs2dFe05/K6S3kwx1ddh4881gmppA0TRG/u1vSL/ppja/I2YIbuPNN6OltBSkTIa+DzyAQX/5i2Rev/31ryizzr9IGjkS495+G6RdR1MxvU589BF+/+AD8CwLjVaLSWvWXJEgK5Zf2W+/4be//AWsyQRFdDQmf/UVItPTffJquXQJG6dOBUXTkKlUuG3nTo+Px5WX/TRqeWQkbtm4EaqEBI+f1xsvKRTo20nnvvkGh15/HQRBIH3KFIxctswrL6DtufE8jwOvvIKCTZvAcxzi+vXDpC+/9PwEvPACgLNr1yL/jTfAGo1QduuGWzdvhsw6u0lsr4q8POycNw+swQCZSoUp//3vFe8Zsbw6op1PPIGK3FyAIJAydizGvPkmSLtmk668TE1N+GX2bLQUF9s6p5MKBW7dsgXqxESnXsfffx8nP/4YPMsiMiMDk9asuWILV6xzK92+HXueeQasyQQ6JsbpKBGxvBovXsTmu+4Cq9e7nIXWEa91gweDommY9XrcuW8fZGq1R48LpO2kkI7EECQJSi4HQZJgjUbsW7hQ0oiMTKWyeFmnXx+19jGQQkJFj/30a6kiMsKNzTEMWoqLJY3I2Pqo8JenX/sakeEYBpRCAXNrqyijDewBBiQJUiZr0xI/LN8llKazRqPPk6btAYY1GEAQhMeLdUdkAxiDAQRFWeY1SbTo2wMMABAUFXD3olyjAQgCrMGAst27sefpp9uNyDgCDKVUgpTLIY+IcAkwgCVSA1zuQPvL7NmSRWSEOVzgeRgbG7Fl1izJIjKkTGYrI2f1emx/9FFRIjKc2QympcWvOWtiK6Qhho6JweR160DHxvoFZBynX0sJMsOXLGk7/VpCkLnqvvsw4LHHQCmVkoNM9+HDr5h+LQbIiCVHgBGmUStjYzv70MJyIkeAoWga3QYNwnUSbfm2ARiZDOqkJEz+6iuvy4A9kSPAyDUa3PjFF9Ckporu5YtGLluGlLFjbWtVeyDjDGAGPPYYxq9ahWHtdJTNfuAB9Js717ZWSQkyyaNGYfSKFZa1iuMkBZlIrRY3rl5tgzQxQSbYFdIQAwARKSl+Axlh+rU/QEaYfu0vkOn/8MN+AxnH6deBAjKuAEaKadRh+S5XADNh1Sqvei55KlcAI8U0alcAE9Onj+hevoqkKIx5802PQMYVwPR/+GEkjRgB7Y03tut39bx5fgOZHhMn+g1kYrOzwyDjRCEPMUDXBBkpXtiuDDJhgAk+hQEmcOQOZISNNncA463CINN11CUgBnANMsMjI0X3cgUyd/iY+OlMrkDmqbQ0v4KM2ou2357KFch0b6fzrdjKiYwMA0yQaU5Skt8A5oaYGL8BTLZaHXQAI8gVyDyZloYIkhQNYAS5AhmVBGuVK5BJkGCtcgUyV0mY5xXI6jIQAzgHmcdSU/0GMlO6dfMbyAzQaPBUWprftpZezsjwG8gsycz0G8jkREbi8dTUMMAEiXiex5ykJIyJifEbwNyXlOQ3gHlWqw1KgBHkDGQGaTT4R+/eaC4qEg1gBDkDmcUZGX4DmaWZmX4Dmee02i4JMgFdYi2V4uVyLMnMRCRFgSIIGDkOH1y6hP3NzaJ7KUkSi9LTkUrTUJAkDByHn2tr8Z/qatG9CADzUlORExkJmiRh5Dj83tKCf5SWghPdDZjWrRtmJiSAJkkwHIdKkwlLCguh48R3G6LR4M9paaBJEhzPo5Vl8XJBASqtpZfulKRQ4LXMTKgoCi0si0fPnPHIUwAYmiTBWj0XFxSg2gPPsHzT5Lg43JOYCAVJIrexEe9euuTR4wSAUVrv//N6PVYUF8MsQeWFADA0ScLM86hjGLxcUIAmL3uheCIBYJTWD18dy2JpYSFKfKzc6iwRAJ5MS8MgjQY0SYLneRDWtXh9dTU21taK6jcjPh63xsfb1qpykwlLCwuhl2Ct8ue6kU7TWJSRAbW1ZN3AcVhRXIwz1r467WlNdjYIgoCZ5/HomTMwSHA9RFLXLLF2pRrrYtPMsmB5HjRJShaRMXAcXisqwiWjESaOg5IkJYvI8ADeu3QJh5qbYeQ40CRpi8hI8UJvrK3F+upqGDkOcpJEd4VCsohMfksL3ikthZHjQBIEIihK0ohMGGCCT2GACR7xAD4uKwPL8zaA4XkeVSYTNokMMADwfU0NNtTU2NaqZIVCsojMoeZmvHvpEowcB8q6VkkVkSkyGvFaYSF01vtOSZJdLiIT0BDDW29wKf5Um0x4cPv2NiDzdK9eKPz5Z9G99CyL544fbwMyd/TogSP/+IfoXhzP459FRW1AZnhiIn6dNw8sw4jut6GmBsOfftq2OGgjI/H1zTfD2Ngoutfh5mbc+N57NpCJpCj8KzMTTXI5eMDlnzMABDyNdfN7wp/iyEjMdwCY2du2ocpkkvSe9Md93xlegl97193+z9sAhKLkWe38LgcgLykJkxwAZunZs2A4TvRzOfPVV3g4K6sNwDy2bx8azWbRvcr37cPLffu2AZg7NmxAscEQ1PeHsbER626+GRE0bQMYgiCQERODnU8+CVaCa7m+uhrX/PnPtrUqIzIS306bBlNzs+heB5uaMPGdd2wgE6VQ4P3hw9FcXCy6V6HBgNvXr28DMkuys1F58GC7jxV6zyjkcjQ1NXl9fwSCAhpipFZESootIuOPqiUhIuOPqiUhIuOv8mshIuOPqiUhIgOCgImisCUzE80ifcspiYxEbmoqWJIE+MsRmHAOTGCKB3AgKQkFMTFgSRKUXQTGH1VIQgTGH1VIQgQmmHJgnMm+Cok3m2HkOJQajR73kfFFV8+bZ4vI+KNqSYjI+KNqyT4i05Wqlro0xACXt5b8UX4tbC35o/xa2FryVx8ZYWvJH+XXwtYSJTLIOAIMHd5CCmg5A5huEm4hOSujlmoLyVkZdbBvIQHOy6jXV1fjhYsXvWqI54uErSV/lF8LW0v+KL8Wtpa6Wvl1l4cYwAIy/uojY+A4v/WR4QG/NsTbWFvrtz4y+S0tGCMiyDgDmMlhgAlYuQKYCX4EmMlffeU3gLnxiy9CEmAGPPYYNtbWggc8bognhr6vqfFbH5lDzc1+6yNTZDR2uT4yYYixqis2xAv6zr4igYwrgNGEASYg5Q5gKD8CTLiRnefypJGdN519xVC4IV5oKKBLrP2RQOQ4+bO1rAxb7rkHxvp68BwHiqYx7OWXUb57N/rcey8ShgwRzYtpbcW2Bx9E44UL4EwmUEol+j7wAMx6PWKzs5E1fbpoXhzLIve553Bpxw5L/wylEkkjR6L7qFEw1tZeMXHbVz9h8jVrMICUy6HRanH144+j9H//w5gVK8Txsv67VKPBnrQ0G4QoWBajyspwPiYGo8rKYJDJsDkzE2aKgpxlceeZM6hRqXA6Lg6pzc3Yn5LiEmCEbqKdcS+Gipfg543Tmbg45CcmgiNJpDc2YsylSzgdGwsTRcEgk7kFGDFes7Ldu1H088+Iy87GkbfecgkwYlzHhvPnceydd5Bx883Y+8ILLgEmWO+P9gDG2Vq15+mnUbZ7t22tShk7FvFDh4JpacHAxx/36Xgc/Y6/9x5OfvKJba2KzMjAgEcfxaWdOzF6+XJRvZx1/h65bBnOf/stRi9ffsXEbV+86k+dwtYHH4S5tRUAQKlUGP33v6NgwwYM+stfEJ2Vha8GDAB4HqRMhttzc22DhT318qNclliHIcbJm9URZEi5HBzLoufMmRixdKmoXs5AhjObEd2zJ6Z+952oXs5Ahuc4ECSJuw4csGWqi+XnCDI8x4EgCEz7+WdEpKT47mX3M0eQASwfZteWlCDSZLoCYg4kJeFcbCxIngfnJgIThhjx/HyFmPW9e8NIUSAAtxEYMV6zHX/6E8r37AEpk4E1Gl1GYMS4jsc/+ADH333Xco2sEQdnEZhgvT9+mT0bdb//7jIC42qtcgQZnuNAymS4c/9+nz5Enfk5gozwfzO2bYPKh3YYzrwcQQY8D5KicN177yF5zBhRvRxBRljjB/31r+g3Z05IQEx4O8mJHLeWOIYBOA6V+/eL7uVsa4k3m9F08SJYk0lUL2dbS5zJBIIg0HTxoqhewJVbSzzLgqRpVOfni+7luLUE64dmpYt+CRUREQBBuAWYsAJHRoqCkaLAkaTkW0g8z6PmyBHwLOsWYMRS+e7dAMe5BZhgVmJODgiK8qoTr7OtJc7a4qC5uFj0Y3TcWuLNZlASrVWOW0vgeXAch8oDB0T3ctxa4jkOPMeh/LffRPfqLIUhxoVUCQlQp6SAtMuvaC0vB2MlWjElj4hAbN++IKxdFwGAVChQf/q06F4kRSFh6FDwdp0ZeZ6X5M0KAN0GDrQtzgBgbm1Fxb59knhFG40g7T/QCAIlkZE4kpho+xFLEMhNTkaLXfktyfNQMwxUEuQIheW9zsfEoDAqyvbvGpUKB7t3vwJWuul0kuTA6MrLLd+SrSJlMkSmp4OOixPdi+c41J86Zfs3IZOBjovzKVIZaBr01FMY8NhjGPz0016NEiApCvEOaxVBEKg5fFiKw0T84MFt1yqdTpIvrgAQ3atXm/UeHIeK3FxJvCJSU0HHxLTxqz52DHsXLrRFrXkA+5csQVNBgSTHIKXCEONChDVszXMcZBERIOVyEASB2mPHJPETIhUytRoUTYPV61EtUSIW09ICUi4HpVRCplKBNRhQLtEbyNTYCLlGA1KhsH0bqJJoYWBJEnKWBclxkLGspc+LQoHSyEiYrW9gjiRRGBMDALbf462hUSLAmjh1VZVERqJWpbJEyQC0KhS4FBkJxhqBkVk/1FgJuq0CQPXhw5atC+s9y7MsYN0KFVuNFy6AYxgQMpktH4KUySzRxBARQRDo//DDuOq++7x+LNPSAlImA6VSQaZWw6zToXzvXgmOEjA2NEAeEXF5reJ5VEjkZdbroYiKAimXW153gkDD2bOSFFuAICw9kwgCco0GhEwGzmRCwQ8/2H6FN5tR9NNPaJYg0VhqhXNi2tn7ZU0m1J08ierDh1GZl4ecBQsQlZkpiRfHsmg8dw7V+fko37MHve68E6nXXSeJF8/zaCkuxrF330XZrl3oedttGPrccx3y8sRPV1WFmvx8VFj3s4ctWuS7l4v/N5EkSiIjcSgpCRqTCTxBoJGmLW9mjoO2oQGVGg04gsC40lLEGQwuv9EHU04Mz/Mo3rIF+StX4tp//ANx/ftL5uWtvMmJaZXJsKlXLxukUByHzPp6FEdHI06vR05lJaJMJrj6mPf1NSvLzcW+F16ATKPBmOXLEdu3b5uIbBsvH69jS2kpfp07F6bmZox9803EDxoEmUoliZc3CiQvYQup5vBhlO/di/iBA3HV7NmS+ekqK3Fu3Tqc+fJLJI0ciWv/+U/JvIwNDag5ehSVBw5AV16OUa+/3uEmje15mXU6VB85gt3PPgs6OhrRWVko37PHAtEUhaSRIzHhww+98vKjXObEyPx9JMEmSqFAwuDBSBg8GP3mzJHUi6QoxPbti9i+fdFn1iy3v2s2GHDyo4/ahL3tNcu6hZL/xhtun6fx3Dkwzc04t24dOLMZg596CjIJ5m6oExOhnTwZ2smT2/3dwh9/bBNid5Tt3Nw8h14mA08QaKZpqBnGksBrhZjSqCiwJAmeIFAaGYlSNzOzhFfB3XXU3nQTug0Y4O6UJFfLpUvYt3Ahak+cAM+y+P2DDxCZnu7y9z25PzRaLXrfdZfox9qeIsxmDKyqwrGEBPAEAY3RiILYWLDW1/OiNZLmSu29ZgRFod+cOVBER7f5Oc/zuPj99zj0+utgjUYwra0o3rIFxVu2uPby4Dpm3X47op188anYtw97X3gBpqYmsEYjLu3cacmP8cEr5dpr0X34cJf/H4wiCAJR6emISk9H1syZbn+X5zicXr0aBjfzlzy5jvWnT4M1GlG+Zw92Pfkkhi1eDFV8fMdOwI3omBikXnedR19WK/bt8/n+YBkG5uZmcEYjzHq9ZYuJYUAqFBjx6qven0AAKByJCaBvHN6o6tAh/PrQQ+BETP6laBoytRo3rlmDqIwMrx4r5rn9Z8wYmBoafH4em4RjIgiQHGfbphBL2ptuwtiVK31+no5ew8KffkLeSy9ZEh9FnEJLUBRmibR96m11EgdgQ69e0MnllyvIRBIpl2PU8uVIv+mmNj/fcvfdaLhwAaxeL5oXAPR7+GEMfuqpNj/b99JLKPrpJ1s5tVhKyMnBjZ9/7vPzBOu6aGxowHfXXtsmt8VXETIZKIUC4955B8kjR3r3WBHPbcu996L26FGfn8dehEwG3mzG0AUL0Pf++z1/XDgSE1ZAiiAsuUAS5RqEJY1sCXshlEcBAJxD3xepRUg0Ed2ZXG1PhRV4IqzVjqR9Im6IiKAopI4fjz733tvZh9JhhSMxQfqNgzUacWr1apff5F577TUAwKJ2ck/Kdu1C/alToGgavWfNwsA//xkypdLtY5xJzHMr/uUXt5VZtnNz8xx6mQyF0dEgeB4ahkGzQgGOJCFjWVA8D8a6ndSvpsbtsbxm/dvddexx442Iy852+zyeyJdr2FpejrzFi1Gdnw+eZZE2YQIi3UTTPLk/NFotes6Y4fWxOJO3kZiTcXE4npgIDkCU0YgWmgZLEIhgGGQ0Nrp9bHuvGUFR6Hv//VDYVUABlute+OOPOPjqqzAbDKAUClzVzrdTT65j5vTpiHKytVd18CByn38exoYGsEYj+s2d27ZipQNeyWPGIDEnx+0xe6JgXRd5nsfZr75yu53kyXWsO3ECFXv3gpTLkTp+PK5ZtAjK2Fivj0fMc6s8eNBtBZMn58UxDE5/9hlImoYyNhaj//53JAwd6vWxBFIkJgwxQfpm9dWL5zg0XbyI31etQtlvvyFr5kzkPP+8JH48z6O1rMyS2LtvHwiZDCNeecV3Lxf/b6AoS2l19+5Qm0zgrLkUQmJvz/p6ELCU7k4uLHTvZXcOUkuM+6Nk2zbkr1yJsStXIq5fP0m9vJFXib1yOTb17NkmsTejvh4l0dGINRgwtLLSUk7vysv6d4cTe/fswb6FCyHXaDDy1VcRN2CAy2RLX69jc0kJfp07F2adDiNffx2JQ4e6bDgWSOtHsHp56lexdy8OvPYahr/8sk85RoF2HVmTCT/ffjsybr4Z2XPm+JxE7EeFIcaVAu0m84fXsXffxal//9tSQs7zYA0GaCdPxtg33xTdr2jzZux/5RVLUz2Kglmng6ZHD9zqw0wqVxBTT9PYodXCSFEgeR5m+20x+zcdz+OWCxcQ5UE+UbBBTCB6CX6eOm3v0QPl1rJTQRTLWprc8TwIa5J2VkMDhldUXOll/buj51b444/Y9+KLACz9mlijEQlDhmDiZ59d6eXjdWw4fx4/33YbAEtbeNZggCY1FTd9++0VMBOq90cgQkzYyzMvP6rrduw1NjRgz/z5qDtxQnIvjmFw4NVXUbx1q+RevkjVrRt4loVZr7e09FapkDR6tNvHFP38Mw4uW+Z1HwNlt25gDQawRiPMOh0AoPuIER0+dneScxzMJAmOJC19YQgCGpMJ2qYmUNZEP4Lj0Ku+Puga2+mrq7H7mWfQcP58Zx+KqOIBHE1IwFm7UH16UxPi9XqQ1iRl4TWUcxxY62tLAKAlGAoIwDIfzdqp29zaClIuh0yplOTDITory9LmnmVhbmmxAFiI5TaFsqoPH0buggWSDI0UU3UnTmDP/PkwilkwESAK6cReprUVv9x7L1pKS3Fpxw5M/Owzt70zfNXOJ55A5YEDuLh+PXiWvaL6IRDEsSwq9u69InnX3WDLos2bbd9MW8vKMO7tty0NuTxQ1YEDtjI+AJBFRCDJywx/T1WvVIJziLj0aG5GanMziq35DzxBOP32HsjSV1djy6xZ0FdWonzPHkz++munORbBJh6Xp1EDAEcQ6FtXh6zGRjAUhTprblY3vR5DKytR5FASXaVWgyUI0bv2RqSkQKZUwmRt/ML+yAAAIABJREFUX8AxDBrOn4ehpsanOTrORJAk4vr1Q7W1Cy1nNkNfXY2W4mLEipBnFZZ0qj1+HL8+8gg4hkH96dOYtGaNTwMcpVLdiRPY9oc/gDUaUXfihNMoXzArpCMxTGsrdBUVlm85Oh22/eEPkkZk6k+fBs8wYI1G7Fu4EEU+bJlIIWdD1Ujrnqi7Bn51v/8OwJJMXLFvH377y188isg4DlUjKMoWmhdbJZGRyE1NvTwAkudBAEjU6dpuKwWZbABTVQWe48AajdAFGYQ5kz3ACLkvtW4SymmOg9LajZmyRmTqVCr8qtWClSByET94MAiSBKlQgLeCxZZZs6CvrhbdK3nsWIAkbUm95tZWbH3wQbe9ksLqfDUVFlpmXpnNaC4sxC+zZwdcREYAGLNOB55loauokGR0TmcqeFd3D6ROTMT1n3xi64ApNchMXrvWNjQy0EDG1Xj7nOefR/Yf/+i2rHrQX/+K1AkTbIPYPAEZZ+Ptx771FjJuuQXqpCRRz80RYGiWxYTiYqQ1NyPBuoUVjGoDMCwLSqnENS++iCSJtuP8JUeAEYY5jiwvd/u47JoaDKiuRlZDg+Qgc9Xs2ci6/XbkLFgAiqYlBZkeEydCO3kyxr39NihhrQqDTMArc9o09HvoIdvQyEADGXuAAQCZSoXrP/kEartZcqGgLpHYW52fj+0PPwyztYmVTK22bS2JnQzVWlaGLffcA2N9PXiOA0XTGPm3vyH9pps6LcnLFcCMefNNj3sfcCyL3Oeew6UdO2zPkTRypG1ryd7PGcCIGWq1T+x1BjCO06jLIiKwQ6u15BrwPO714oOhsxJ7nQFMzgsvoNcdd4ju5Q8Jib2uAMZxGvWZuDjkJyaCI0mkNzZizKVLtv/jARxMSsJFu+eI0+txvfU5xH7Nzn/7ra2LLyGTQZWQgMlr10KVkCD6daw6dAjbH33U1nBPFhGBG1evRmx2dsgniQZrYq+79a6zrqMzgJnw0UeiRcHDib1+VsKQIZjw0Ud+ichEpKRg8rp1AROREQNgAMtIhNErViB1/Hi3ERmpAcZengBMMEpqgOkseQow7YkAcE1FhV8iMgDQ6847kfPCC5JHZAAgMScHE1atCkdkgkhXz5uHfnPnBkxERmqACTR1CYgBXINMRgcau7UnVyAz3M2MHilEAKIAjCB3IEMCmBEf7zeAyQlRgImmqJAEGEAcgBHkDmTkfgSZaAm6uLoCmXSaFt0rLHHkCmRUfs7Hy1AquxTAAF0IYgDnILMoPd1vIPNYaqrfQIYA8GRammgAI8gVyCzv2RO3xsf7DWAeD1GAWZqVFXIAw/M85ogIMIJcgcwCP4LM0sxMv4HMooyMMMgEsJyBzOKMDL+BTIZSiUXp6V0KYIAuBjHAlSCjoigsSk/3y9YSTZJ4LDVV8q0ljmXxZFoaBmk0ogKMIGcgk6JQgCZJEDKZtFtI27bh8dRU0CEGMPrqaizNykIMRYUcwBx45RWMFRlgBDkDmUwryLia8O6LHEEmRibD0sxMv2wtqSkKizIywltLASxHkElWKLA4I0PyraW6EyewKD0dKuv63lUABgjwxF4p1VulwoL0dCitlKxnWbxWVIRCkafKAkC8XI4lmZmIpChQBAEjx+GDS5ewv7lZdC8hAjNIowFNkjByHI62tOCfpaVeza7x1OtvWVnoQdOWxE2eh57j8NS5c2gVcZqyICECQ5MkWJ5HK8ticUEBqtsBmAEREVig1dqOcXaAfQgIEZgYioLM+pqtLi/HznZmBAWD5iQlYWxMjO1ePK/XY0VxMcztrDuT4+JwT2IiFCSJ3MZGvGuX2OtKDyYl4dqYGCitXgV6PZYXF4ORIKlyfEwMHkhKAk2SMHMcGsxmLC4oQKMEDfiuUqvxnFZrW6t0LIvXCgtRJAGkhSWOZsTH49b4eNAkCYbjUG4yYWlhIfQSrItCBEYAGAPHYXlREc6JPI29k9W1E3ud6Zxej+VFRTBYbyohIiPF1lINw+DlggI0syxYnrdFZMTeWvInwADA9Ph4dFcobHBAEAQogsCjKSmi31gdBZhAVxhgxNPqigrsamiAgeNA20VkpNha2tHQgM8rKmDkOMhI0haRkWJr6YxOhxXFxba1SojIhLeWAlff19RgQ00NjBwHOUnaIjJiby11EYBxq4COxPijLK2PWt0mImNffi22EhQKLMnMRKxCcUX5ta9yrEISAObNwkJJRsjbVyERMhlaDAbIrFtmjuXXvqpk2zbkPvecZXuAJNFkMmFxQQGqPJh9BFgG+u145BHbv+/1YutQyhJJxyokAWDG1UoPMK9ZXxYpzkvYQirYtMmynUnTOFZXh6Vnz3o8cO7MmjXIX7kSnMmE9ClTMOaNNzz2/mNKii0iQ9E04vr3x/UffwxKgg/9CbGxtoiMY/m12OobEdEmImNffi22wiXW4mhmQoItIkPKOESmmDBpWSHkat8jMnUXlNi2OB1mQ1uAOeuH3ljhEusAkhCR8Uf5tRCREbv82lkZtRCBkRpgSLkcUZmZeOr8eRxqbvaqIZ4ncgQYOjo6JCIwzsqoQyEC4wxgug0ahBXFxR2emOuNCIKwRWQopdLWav3Xhx6SJEdGiMj4o/xaiMiEy6+DR0JEhlJw4MwkmssU+OXFDDA63z56HQFGRne9CIygLg8xgAVk/NVHpoZhRO0j46oPjFRbSK76wOg4Du9dutRuHxlv5AxgJq9dG5IAk/PCCyELMBNWrZJsC8mVVldUIGvmTL+BjL/6yJzR6cJ9ZIJM39fUoN9M8UDGGcBMWNw1AQYIQ4xNwdgQz10jO38CjFCFxAMeNcTzRK4ARtOjhwRn5j+FbCM7NwDjjwiMM13z4ot+A5lwQ7yw3Onqu8QBGVcAk9C3awIMEIaYNnIFMrW//46T//63ZeCXSHIHMhd/+AGV+/e7fbxYnXg9laedeN01xGs4fx4n//3vdr1CDWBMjY04/t57aC4uDjmAOf/tt6g5dizgAAawbC2FQSasQJErkGmpkOPY1/FgGed5Jk1lCpz8IQ6158MA40xdPrHXWZKX46wlQiYDz7K46v77kfP886J6OZu1xDEMug0ciElffunyuXIXLEDJ1q0uAUbM5LXTn3+Oo2+/7RZgHP2czVriGAbgeczctQvK2FinXhV5edj5pz+5BRhvz62zE3uLNm/GnmeftTwXQbgEGMFrkW9pRB5JjMRejmHwzbBhAGCZUO4GYIQKNk/V0cRewQu4fG48z+PgsmW4uH697RjjBw/GDZ9+6vFzeuoFOJ+1dMumTZD5WPnozMvZrKWp330HTVqa6F5SKZQTe21edmx5/Jt4nFwfD9ZkSfblWcvv3PBqIRKzrwSSQ592x5mf4kCQsP2uM4AhrPnd/jwvPyqc2OuNHCMyvNkM8Dwq9u4V3ctZRIbnONSfOgXeTU8B3ro9I3UEBoBtK8ibTrzOIjI8y4JSqVCTn+/aS8h3IQjRIjCUXH75H/5/86EyLw/gefAcFzIRGACoP30apEIBjmFEj8AIlUQUTduiDR2VY0SG53mfE87dqU1EhuNs64cUcozICPdZWIErx4gMzxMgKB7VJ9VOf7/iWATAE24BpisrDDEuFD9oELqPGAHK7ttTU0EBWA/Ler1RREoKet55J0i7ElCCotB4/rzLx4xavhxpEyagx403SgowANBvzhxk//GPiB882KtOvCRF4ep58y6DCSxbdJUHDrh8TMrYsRj52muI6dNHFIBpLStD1cGDtkWeVChw7ptv2hyT1KrYt+/yPwgCqvh4ZEyd6jd/qVSVn9/mOnJmMwY+8YQoW0hZM2fi+o8/xoSPPvIp+ilIAJled9+N7sOGYcKqVT4/pzv1uvNO5CxciNi+fTH5669tX4ikkAAyUT174oZ//xuRWq1kXmGJo9431YNSXIZNzkyi/MiV6yprItBUdvlzgVJw6H51K+L7hAFGkO9NPEJUnNmMqsOHAcAWSSApCnUnTyJh8GDR/cp/+w08y0KmVsOs04FjGFTn5yOmTx+nv0/KZF6F2H3VwCee6NDjhCiEXKMBo9MBHIeK3Fy3j0mfOhXpIn3IF23ejN/ff98WgeFZFgeWLEH8wIGI7dtXFA93Ylpb0VpaCsASySIVCrRWVKC1rAzRvXpJ7i+lynfvBmcyASQJmUoF1mBA2a5dSMzJ8fm5SZlMlOexF0EQyHnuOVGf05163XGH36JtiTk5uGXDBr94heW7GopoMDoKchULs4kAz5Kou6ACz7cNFtddVIKkeLAsYYEeAqg6pQbHAlQ4BAEgDDEuRSkUuGPPHjReuIDqw4dRnpuL2uPHJdsLnPz112gtLUV1fj5Or16N5pISv+0RS6k+996LjFtuQc3Ro6g6dAgVe/ZAFhHhV//Tq1fDUFMDwPJBljZpEmpPnMDp1asx6vXXJfVnDQbQcXHgzWZcdf/9SBo1CrHZ2ZIlvNJnaSS8k4DKZ6vApIsfNbSXIjIS0b17I3nUKCQOH474wYNd5jqFFRiqPnIEJz/6CMNfeUWShnxheaakq3W4c81p1J1X4dLhCFzYFgu5igNrIiCjL6/7BAkoNCy69W5B8qAWJGTrEZ1mBBEGGJvCEONGBEkipndvxPTujd53393u75sNBpT+738u/39UVBQAoPDHH117UhSMTU1gjUac+PBDRGdmovuIEd4ffABJERWFlHHjkDJuHPDUU371limVGPX669j15z/bugs3FxWhbNcucGYzkseOdft4T16zxGHDoE5MvOLnhtpa7F+6FExLCzizGbKICLSUlqLFGplx5RX1k+vjMVxlgKnnlXBC6Akk/CsRsd/EAiwQm87A0M91yHmUxcrteUWkpLgdIDd25UrXBxpWQMnU2IhDf/87ijdvBs9xuPDf/7rdqvXkvo/KykKcBN2Cg0l1J064rVq1XcffXD+HjOZhNpBgTSQOfZKEIQ9WQhFh2WqK76PHzI/OiXnIIadwdZKImernvv4ah5YvB2mfSGqnpqYmAECU9cZ2JZ5lwVoHUVJKJVKvuw6jli/36tt7l8j498Jr11/+gtJt20BQlCXxkedB0TSIdsYitPeacQxjyUtasaLNzws2bcKBJUvAMgx4hgGpULi8Lxy9aBcjtQiOAJPM4OIPF9r8XL07AqmLUkG2kiCNJDiKA0+7vzZG6+xRl/ciz4M1mTDr6FG3z+OpvK1O8tUL8O7+4BgGTGsr6JgYyb06qo56le7Ygb0LFoA1GsGZTCDk8nbXkvbue57jIFercduuXV4dizMF81r1n9GjbVuqzmS7ju2kEbLWLSVSzkGm4DH6r6VIGdLq1bF01eqkcCRGRPEcB57nYW51fvOprcm3rv7f6XOyLMpzc8EajZ3acyPYNXzxYtQcOwZTQwN466Rh1mgE2ukX4slrxjuZXFy+ezc4lgVvTXzlTCbLYueBF9yMPiEcrGRlcvR4ogcIECA4y8JCsqTb57B4Wf52d16EhMnigaYjb72Fs2vXYtqPPyIiJaWzD0dUVe3fbwMYAOAZBuZ2Ets9ue+lmEUVbOI5ztaKw5ls19HDPFyOIWHmOVSdVHsNMV1VYYgRUd2HDUPikCEuSXjHjh0AgPHjx7t9nroTJ2DW6UDRNLRTpiDn+eehEHnidVeTsls3zPjlF5z89FOc+PBDC8AQRLvJo568ZulTplzxs1Gvv47kMWNw8LXXLP1CSBIxV13lduEXvNKvc308LeOaL/+DB1JeSgYBAjzFg401g2ylAB4w9TCBjbkSrgQV7US75xWVleX6QEJIDWfP4ty6deDNZuxbtAjXf/JJZ3zTlExDn3sOCddcg/0vvQRGrwdBEIjKyoLcTW6aJ/e9u63GrqLed92FmmPHXP6/7ToOd/0cjI5E0yXLuiBTchj5RBlSc1rEPMyQVng7yYPQorGhATVHjqD6yBFcNXs2VPHxknmZdTpsffBBtJSWYujzz6PnjBmSebEmE+pPn0b1oUOITE9H2vXXd8jLEz+e42xJ0iAI9L7rLum8eN6WJN1aUYF+c+e2KUFvKSnB3oUL0VxU1G443JPrqK+pQU1+PupOnUK/OXPalKAbGxpwaPlyFG/Zgum//OI2mdKTZndkMwnVURXU+WowCQy6v5UEUk+CJ3gYs4xgEhhoDmhQtqQMTdMaARefxVJOsXamQNpO4nkeTQUFqMnPh6mlBRfXr7e0M+B5UCoVBj/7LAyVlUgYOhTxgwe7/QIRDNtJgozNzdjxyCOoO3EC4997D0ljxriEtWA6r0D2c9bszlH1hTS2PJ+JXpPqMfi+KsiUHTuurrqdFIYYFzc0z/PIe+kllOfmwlhXB4qmYTYYMO6ttzr8Ye/uzXP8gw9Q8MMPaC0rA6lQgNXrcfW8ebh63jzRvQo3bcLJTz9F08WLNq+k0aN96p3hyq/q8GEcWbkS9adPgyBJcAwDOjYWM7dvF92r5dIl7H/lFdQeO2ZpZkYQYA0G3LV/P2Tqto2keJ4HazS220XVlRdrMiH3+edRfegQTE1Ntus4ae1adBsw4IrnMRsMHns5g5jEFd0R+b9IyKvl4GjOAi4KHqTh8l48T/HgSd4CLlZm0/fXo/L5Chivartt1hUhpv70aRx6/XXUnThhmS3GceCsW4G8XfM7SqUCZzJBplLBbDBAnZiItIkTnfarCaYPe7NOh2+GDwdJ0yBgKWPvNnAghi9ZAo3DFlownVcg+3kCMQBgNratSuqQVxeFmJAv1OIYBqdWr4auqsrrx1Xs2wdDdbUl6a+lBQRBIF6iEGrVwYPQlZWBN5vB6nSQq9Wi98kQVH/mDBovXADHMDC3toKQyZA8ZowkXrryctQeOwbWYLD1v0mQ6LxMTU2o3L8fTEsLWIMBrF6PSK32CoABLG9CX9rAm3U6VOblwVBbe/k6UhRir7rK6e/71HKeByL2RkBeLQdhJkC1UuAVPIwZRnBySxUDq2bBpDBgkhmQJhKk3vJHfVgNqi68awwAhro6VB0+DLNeD1avB2s0IiI1FRHJybZ7hJTLEZmeDlKhANPSAt5shq6qChW5uUHf8kCmVkPTowc4gwGswQCmpcXyfgny6emOqj1+HAUbN3b2YXglTwCG0ZM4+X1ch6dfh6pC+mpwDIOdjz+Oo2+9hS133+0VyHAmE+QaTZvqFTo2VpI+GBzLQhER0aZ6xWw0otvVV4vuBQAylQqk3XmRcjkShg6VxIui6TbnRalUSB49WhIvAlcmG3YfOVISL45hIFOr29wf0b16tVuB1BERJgJcFAdOdrnDJ0/yKPy6AEyKNUGTBMpfKUPjjAYb2AAAr+ABWXB/+IolgqLaJMeTcjl6zpyJEUuX2pKYI1JSMOU//2mzC0fKZFBERbWbmB0MSnJ4P4Racm714cPY9sc/Yv/LL+P4e+919uGIJkZPYuuidBxbm9ih6deu9PPtt+PbkSOx6ZZbJOlG7w+FNMToKitRvncvOIaBobbWY5BhWlrwy+zZaC4stISV1WqAICSJINhPozbr9TbA0KSmOo0g+Ko206gVCsisoXNXEQRf5DiNWqZSgec4SRIC60+dwtYHH7RVU1BKJUiFAkkS9NixTSeuqgJvNltGGhCEJNEswkhA+4gWypNKUAYKrJoFT/Iw9DM4zXfRDdUBJMApLSBDGkj0eFwL9QHx76VgUkVeHnbOm9emdQFBkk6jnQRBIK5/fxAkCZlaDdZgkHT6tT/VfcQIkAqFbZxKqE2/FkaKsEYjTn7ySciATH2BEo3FyjbTr8UAmfozZ8A0N6O5pMSvo1jEVEhDjCYtDSNfe80y9I1lPQKZNgDDMKCUSvS6+27E9Okj+rwbe4ARJj0njxmDhGHDkDl9uqhegAPAWMPm6TffjLQbbhA9guAIMHR0NPr+8Y+IzsxEZEaGqF5XAIxKhaufeALqpCTRI0xtAMY6zLHf3LmIysxEajtVZ97qMsCoLP1flByapjbC0E+PxukNTh9j6G8Ak8yg5tFqsBpLvkdXBxlHgJFrNBjw2GOISElBXP/+Th+TOWMG4vr3R/rUqZaxI0ZjSIBMYk4O1ElJuPqJJ2zzxEIJZIYtWoSozEyQcjlYgyFkQCaxnw4D7qy2DY0UE2QAl/n/QaEukdh78YcfcGDpUkvHVoqCsls3TP76a6gTE9skeTkDmH5z53Y4udZR9l7OAEbMadSOyWtXAIyH06g74ucMYMQY5ujMyxnATFi1SrR8InsvZwAj5jRq+8ReZwDTMLMelQsq26w4WdN6gi6iwWpYlL5dAt2wyw1i6LM00v+QAarFcj9xSg4l7xZDN0zXZRJ7nQHMjV980WYmWeX+/dj15JNgmpsRmZ6OaT9dbpnM8zwOLluGi+vX2yZ1x/Xvj+s//tjSLDGIE2CrDh3C9kcfBWvtcyKLiMCNq1cjNjs7qM+rvXU8EBN7PdXxb+Jxcn08WBMJUsYhMsWEScsKIVdzHUrs/WrAAIDnQcpkuD03123Zvb3Cib1+Vtb06Ri2eLHbiIzUAGMvqQHGUVIDjL2kBhh7SQ0w9pIaYOzlKcC0J2MfI4o+K+yyERlPAKY9CdOvs2bODKmIDHB5+nWoRWTkGg0mrVmDyIyMkIvIXH1XDfrNrJEsIhOM6jJn7gpkYmQyqEjSbwBDACELMDmRkX4DmHSa9hvARFOU3wBGTogDMIJcgUy2BPlWgaRstdpngBHkDmTkQd4UzxXIpAd5wm9XBBmVi9EHoa4uddbOQOa1zEwsycjwG8A8mZbmN4CZER/vV4B5PDXVbwCzKCPDbwCzNCvLbwCzQCsewAhyBjLPakMXZLLVajyr1YoCMIJcgcwCrTYkQWZRRkbIgsyMDjYrDSQ5A5nFGV0TZLrcGTuCTIxMhhSa9ssW0pNpaRik0fgNYG6Nj/fbFtLjqamgSdIvW0iLMjJsM0mk3kJampWFGIqSHGBYoxELtFpkqcQFGEGOIKMkLSBTuX+/708eQKrIy8OzWi2U1sVcDIAR5AxkMlUqLNBqQ25rSU1RWJSREZJbS7fGx4ckyCQrLCDDtHStkQUBndgrpSbExGBOcjJIuySvTbW1WOdlUzxPJERgBmk0oEkSRo7D0ZYW/LO0FFJcUAFgaJIEw3EoN5mwtLAQeo5r/8FeSojA0CQJlufRyrJYXFCAagnK9YQIjAAwBo7DiuJinNG1M+2wAxIiMDEUBZn1NVtdXo6dEjQGEyIwWSoVFCQJA8dhZ309Pq+sbPexb/TsiWSaRivL4q2SEpxq51r0oGksdriGbxQXt/u4YJAQgREARseyWFpYiJJ2ACNbrcZfe/RABEWh3GjEsxcuuP19AHgwKQnXxsRAab03CvR6LC8uBhPkDfGuUqvxnMM1fK2wEEVBDmkqksTijAwkKxSQW1+zDTU1+L6mprMPzWf5st6vsSZxm3kej545A4MEnxEiqWsn9jpKRZKYFBcHzm7B4QGMiY5GjEzc7qZhgBFHYYARRyVGI5YWFkLHto3IBPvWUkcBpqNaXVGBXQ0NMHAcaJK0RWSCfWvpjE6HFcXFtg8zISIT7FtLeo7D0sJClJtMYKyvWahEZL6vqcGGmhoYOQ5y8nJEpqtsLQX0WfI8L/ofU3Mzvp02DRmRkZBZP+jNPA+SINBNqcSnY8eitbJSFC/WbMbOJ5/EqMTENgDzZmEhOAnO7di77+IerbYNwDxz9Ch0LCu6V/HWrZjfq5dtC0kAmCqTSXSvupMnsWLgwCsA5nRrq+heuqoqfDZ+PBKUyjYAs6OhQXQvs8GAH++7D/3j4toAzGfl5R4/Rx/rNklMTAy2b9/u2WtnMFwBMi9nZ6MiL0/0cxT+SPV+5nke5fv24eW+fa8AmGKDwaPHb9++HTExMQCAPn36eOz777KyNiDTPy4OP953H8we+nbkGkp5HYU/p1tbrwCZFQMHou7kyaA+Lx3L4pmjR9uAzD1aLY69+65knv46t/XV1W1AJiMyEt9OmwZTc7PbxxHW94xCLkdTU5PX5xUICmiIEVvOyqi/r6nBR2VlXjXE80TOyqiFCIw/qpCECIy/yqilisA4K6OWKgLjrIxaqggMazTi14ceQt2JE2CNRlBKpS0C448eDEJERrg/WL0eO+bNC7ocGWdl1FJGYOxFEIQtIhNq5ddCRCYUy6+FiEyoVS0JERlKqQTHMGguLMQvs2eHfI5Ml4EYV31gvq+pwe7Gxnb7yHgjV31gpNpCclZGLdUWkqs+MP4CmAmrVvkNYHJeeMFvAJN1222SbSG5UonRiBu/+CJoQcZVHxh/AIy9VldUhGQfmTM6XUj2kRG2lkKx/Pr7mhr0mzu3S4FMl4AYTxrZedIQzxO5a2TnL4CZtGaNXwEm3MjOc7kCmGsWLhTdyxPF9OkTlCAjRiM7MRVuiBdc0nNc6PaRmTevS4FMyEOMN5143YFMRV4eDPX1br3CnXjFUVcEmE5o421TsIFMoAEMEO7sG4xy1xDPUF+Piry8zj7EDssdyFz67Tcw1rU1FBTSEGPW6bzuxOsMZH6aPh2/zpmDs19+6dZvzzPP+A1gfv/wQ78BTOn27X4DmIazZ/0GMIa6Or8BDMcwAQkwglyBTNWhQ518ZG1VefBgwAGMIHcgE6wTggW5ApmGs2c7+ch8kyuQ2XTzzdj+0EMwW++zYJQzkPlh8mTsfOwxlGzd2tmHJ5pCGmJMTU0d6sTrCDKmpiYAQPmePW4fV3nggO0DSkqAAYDy3bvBs6zkAAMAlfv2WbbCJAYYwDIaXljwpQQYANBXVsJQUyM5wACWKEzdyZPiAwxBWBZfEb7tO4IMOA7Vhw/7/LxiqiY/H7BWR4gJMKzRaJnk7uPr4RRkrK97sMsRZDiGQf2ZM518VL7LGciYGhshU6lQ9/vvnX14PskRZEwNDQCAir17O/nIxFNIQ4w6KQnXffABSIXC6068WdOnI+2GG0DZ9UeoP30anLUs1ZkmffUVFNHRSBk3TlKAAYDr3nmfjIILAAAZS0lEQVQHGq0WUZmZkgIMAAyZPx/Jo0aBjomRFGAAIHPaNGT/4Q+gaFpSgAGA2OxsjPrb30DIZJICDGBZKCeuXg2ZSiVqBGb08uUY+vzzGPbSS0gYOtTn57MHGe2UKeg3d67Pzymm+s2dC+2UKaICTMLQoRj20ksY+vzzGL18uc/PZwOZ226DTKXCxNWrJX1/+lM2kKFpZP/hD8icNq2zD0kUyTUaXPfOO+Dt1nez0RhwkciOaMCf/oSYq64CYfd5VHngAKrtvhAAQM3Ro0EZeQrojr3+qEd3NZadNZnwzbBhICnK9i2KUqkwac0axPbtK6qXFPKnl7/9wl7B5SX4he/FsFdne7nzO/beezjx/vsgKMoWDU4YOhQ3fvGF6F5SyJVXa1kZfrjxRlA0fTkiSJIAx0GmUsGs14NSqcAaDBi5bBmypk/32MuPCnfs9VaUQoFbf/4Zw5cuReaMGYhITQVrMMBQW9vZhxZWWGGFkBrOncPvH34IPnBbvncJ9X/oIdzw2We4+vHHkXjNNaBUKhit2y9SytTUhGPvvQe9RCMQIlJScMumTbhm0SJop0yBKjER4HmoEhNh1usBWHLglN26QTtpkqjeE2JiJO8GHo7EeEHKZr0eMut+sNRevipQvt2EvcJegl/4Xmwrs16PY//6F86tXQvWaMRN//mPJS/HhQYMGAAA+N1NnkZEcjLkERFeH4ujguUaSunHcxxYkwkypdLl7xjq6mCoq3P5/568ZvWnTmHfiy+ComkMefZZ9LrzTlsnXW/k1WeZTof6M2cs1XPWQpRxb7+NlLFjAVh2IpqLi10+Xjiv9rQ4IwNKgsCB5mZ8VlGBFjfpGO3IZSQmDDEh+mYN1IUh7NX1vAS/8L14WeW5udj7wgtgWlrAGgyQqdXtJhU3WQsMoqKinP4/bzYjafRoXPevf3l1LM4UDNcwEPy+v+EGS+GHi9euvddMEM9xYK3bOpFaLcasWIHoXr28OpaOnNfehQtRuHEjUq67rs19c/y993Dio49cQrVwXu0eEwAVRdnG+3xeUYFdHWsg6hJixJ12GFZYYflVpuZm/DRjBvo99BD6zJolqRfPcfjlvvsQ07s3RixdKqlXKKvq0CFsf/RREIBtC8nsQRdqYXaY2U2PD5OHHy5hiSOmtdXta+fJa2YvVq9H4/nzOPX55xjph/dYrzvuQOGPP6KnQ1GDqaUFnMkEzmRy+ji1l0UrcpIEyfN4MDm5oxDjUmGICSusINbhFStgqKnBkZUr0WPiRKgSEiTzOvf112g4exYNZ88ia+ZMJAwZIplXqIo1mZD73HMAx0EeFQXWZLL1epJFRLhNmKyurgYAJLh5jcWoUAvLcyUMGYLa48dd/r8nrxnHMOBZ1lLmr1Cg/yOPoN+cOaIfq6N4jsP+pUvBm804/PrrSBkzxhZ5icvOBh0b6/Kxwnm1pwiKAkUQMHAczut0+Ki8XJRjt1d4O8nDEFxrWRlqjx9H6vjxbcqupfASQ556Ma2tqD12DKrERET37CmpH2syoe7kSYDnffoA9MSLY1k0njsHfXU1UsaNk9SL53m0FBej4exZpF5/fYdL6z19zXRVVag5cgSKqChL4zejEYRMhuTRozH+/fdF9RKkr67GxilTbImAEampGLtyJcwGA7oNGNBurpiv20mtZWVoOH8eCYMGQREd3a4XEFjvM0FH33kHpz/7zJKHoFKh9z33oOCHH2Csq8Ndhw9D5mZtCeTz8ocX09qKmqNHEZGcjKjMTMn9xJAnXlWHD2Pb/fcjqmdPjH/33Q63sPD2vM5+9RXy33zTso2lVKL/I49gwKOPeuXVnv7VuzfkBIGPystxsLnZo8e4UDgnxpXcvfDnvvkGl7ZvR82RI2CNRnBmM8a88UaHM7gD5c1Tc/QoLnz3HSrz8tBaXg6CIJAwdChu+PRT0f2ai4pwdt06VOzdi+bCQhAkCblGg9t27RLdy9jQgDNr1qB8zx40nDkDgiRh1utxZ15eh/t0uPLiOQ6nV69G2e7dqD1+HDzLgjOZMPHzzzsMaO5es4sbNqBk61bU5OeDaW0FZzaDjo6G0W4UBqlQYOxbbyFt/HifvJxp5xNPoOy338CbzQAAiqYh12hgam4Gz3HQpKUhadQo9LrjDqctCHyFmL0vvojCTZtAAFDGxyNx2DBkTZ+OpFGjfD43X+SNV1NhIX6aMaNN915KoQDLMCDlclByOeKuvhopY8ei74MPXpHcGajnJaVXdX4+Lnz3Har270drRQUIgkD3ESMwYdUqSfzElisvQ309zqxZg4o9e1B3+jRIkgRrNOKugwc7XDzizXnpq6ux4aabbJ2vAcv6ccuGDR5BlKcQEy+Xo5VlxZjlF86J8VasyYT8//u/NvudJE2j29VXd+JRiaNz69ahYNMmQLixZDJ0HzFCEq+y337DmS++aNNUSSqvxgsX8LtDJELVvbskjcYMdXU48o9/2D7UAYCQyTrcQ8ideJ7HkZUrYbArwaTUanAMA4KiwLMsZCoVOJZF7bFjHkGMV/4ch6aLF233CyGTgWNZUAqF5QOZ59FcWIjm4mLIlEpJrkHiNdeg5JdfYNbpoKuoQOGPP0JfXe0UYgJVDWfPgqAoUBRl2UKSyQCSBEGStvyDyn37UHXwIDKmTYMqPr6zD7nTdfbLL1G0Zcvle08ub3f90FVWwlhXh9jsbH8cYofUcPYsTnzwge3fLCzRTXcAw7EsKvPy0H3ECJ8bqTYVFoKwdvvmGMayu0AQaDh/XtRmpjV+GLcR7hPjQgRFIX7IEFB25XUypRIRycmdeFTiKHns2DZvAplSKVln3IScnDZeFE0jecwYSbwitVooYmLa/CzxGqfw7rMUkZGI6dWrTfa+pkcPn0rw3an78OGWChareLMZt+/ZA01aGgALWExYtQqDnnxSdG+CJDHtp5+gnTzZ9u+h8+djyPz5bY6JIEnJXtuEIUPafMOkFAqkSOQllbSTJmHG//5nS+bleR53HzrU5kODlMsR06sXFJGRnXWYAaXkceParB+kQuE276fl0iX8fMcd2HLvvSjZts0fh9ghRWVmQuFQsdR9+HCXv8+xLPbMn48djz2GPU8/7bZzvCfqPmwY7jp4EJz1SxjPsrht1y6kTZjg0/N2hsIQ40TCNOrqQ4dsNfQAED94cCcfme8q2bYNeS+9ZPkGTZKgaBpmgwFxHtb9e6P6U6ew7cEHbW8USqkESFKS5ENhGjVj3XelaBokTSNZgm/qwjTqpoKCy99iACSNHCm6F8/zOPDKKyjdvh1mnc5yDQkCMb17W77Jd6IShg61zSUDLGC184knJJl+HZmebvvmSMhkYA0GHHv3XZxbt050L39LuG8omgbHMGgqKAiJ6de+qmjzZhxYssQScbSuVZzJhLh+/Vw+5sjKlTA2NICzJlAHIsjoqqqw5e67bZOkKZoGRdPo7mb9qMzLQ+m2beBZFmW7d4sCMqGiMMQ4SAAY+2nUCTk5kKnVSA1CSrVXybZtV0yjjunbFzG9e7tt6NQR1Z86dcU06uTRo0GQpOgThwWAsZ9GnTZxIniWFR2YBICxn0adPG4cSIVC9CiEADAFP/5oSb6jacRmZ0MRFRUQ96IqIQGq+HjEDx58efq1wYAdf/qT6CBDEAQSr7kGGq0W6u7dbSBz+P/+L+hBJnnMGMv9M27cFdOvuyrIFG3ejH0LF1qS1kkSdGwsovv0QWzfvqAUCpePG/zXv4KOjgasOSaBBjICwBhqa9usVRzLItHNWtV9xAikTZhguT8MhjDI2Cmc2GuXDOUMYIRp1Lw1B0AsL6nl6OUMYCavXQt1UhIIiupQh0hXfs4AZsKqVZe/uYt4HZ0BjDDMkTWZRPVyBjDCMEexz8sZwHQbOBATPvwQAGxeG6dORXNREeSRkbj2n/90G5J25eWp9jz7LIp+/hmkQoEhzzyDq2bPtkTZCAJNFy5g6/33g2lpsRyfUonx779vOx4xmt3xPA+OYcA0N2PLrFnQVVaCN5tBKZUYOn8+et9zT4fPraPy1svY0ID1EyaAM5lAUBRmHTsGwJKDR8rlOLhsGS6uX29Zf2gacf374/qPPwZF0wF9XmJ6OQOYyevWQZWQ4NFa1VJSgi2zZsHY2AhwHCiaxugVK9Bj4kSnflLK3ssZwAxbvBhZ06d7tFa5+3wiKapD5/XVgAEAz4OUyXB7bq7H3Z7Ds5MCUO3dIL5+QHWmXAGMpkcPS3jeR4CxlyuASczJAUEQol5HdwADQFQvdwAj9nm5AxhKoQioe5GUyUBSVJvp14A0ERnhOiu7dbMAeAhFZCiF4vL065kzu2xExhXARKSkeLxWaXr0wOS1awMqIuMOYADP1iqSojDmzTeRMnZsOCJjpzDEwNIa2R3ABLPcAYzYSqdplwAjtqIpyi3AiCk5QbgFGLHlDmACWf4AGUGhCDIA3IKM3P/ffv0qdwDjrQIJZGJkMrcA443cgUxo3x2u1eUhhgDwZFpaSAJMTmSkXwFmUUaG3wBmaVaW3wBmgVbrN4CZk5QUlAAjyBXISDHJ1hXI3OBQoRZscgUyC7TakAWZ4ZGRogGMIFcgk+PHyq8YmQyvZmaKAjCCXIHMk2lpXRJkujTEcCyLJ9PSMEijCUmAeTw11S8AU3/qFBZlZNjmaUgJMPrqaizNykKMtT+KlADDGo1YoNUiS6WSHGB4nsecpCSMiYkJWoAR5Axk5mu1fovI3JeUFJIgk6lSYYFWG3JbS8MjI/GYda0SC2AEOQOZx1NT/QIyuqoqvJqZiSi7tcpXgBHkDGQGaTR4Mi2ty20tBXRir5QSIjCDNBrQJAkjx+FoSwv+WVqKYD9JAWBokgTL82hlWSwuKEC1BI2HhAiMADAGjsOK4mKc8WCgnbcSIjAxFAWZ9TVbXV6OnSIPFAMuR2CyVCooSBIGjsPO+np8XlkpuhcAG8Aored1Xq/HiuJimNt5f77RsyeSaRqtLIu3SkpwSoLrLujx1FSMjo6GieOwrqoKW+rq3P5+D5rGYrt7w8hx+L/iYkmOMYqisCQzE3FyOWQEASPH4cuKCvyvoUF0L18UQZJ4t08fyK3vzQdOnWr3MQ90747rYmNt90aBXo/lxcVg/DT1WUoJACOsVc0si5cLCkRvkpYgl2NpZqZtlo+R4/DupUs45FsrfJcSIjBRdmvVp+Xl2C3yWuXr59ia7GwQBAEzz+PRM2dg8L2zrlQKJ/baKwww4igMMOKoowAT6CoxGrG0sBA66zdDmiQxX6uVZGupyfrhV8cwMPM8aJIMiYgMAHxeWYmd9fUwcBxokrRFZIJ9a8lfAAMA1QyDxQUFaGVZsNb7Q6qIjL8ABgB4AP8sLcXRlhYYrfeHEJEJ7rvDcwV0JEaKEjjHKiQBYN4sLJR8C0nq0j77JF4BYGZv2ybZFpJ9Eq8AMKc9HDnvjRyrkASA2SHBt2zHKiQBYD6zzpgSU45VSDaAeaAYZsqze+SNn3oiuYVGq5zFW2NKcCrRQ4B85fIxeCpnJdaeqOHsWfzn1lsvbzc6lF+LKUNtLT4YOdIWkXEsvxZbYpVYtyee5/GH5GRbRMax/FpsSb1W2SfxCgDz4PbtomwhuVNLSQnWTJxoi8g4ll/7KscqJAFgfpM4IsixLJ7OyLB9Mfc0NcLXEutXXnlFjMN3K6tHOBIDOC+jFiIwwZ4D41iFJERg/AEwlEolWQTGWRm1VBEYZ2XUQgRGaoChaPpyBMZDgAkWxfTpg6WFhX6rWhIiMqFWtSREZIK9/NqxCkmIwEgNMIAlR0aIyIhdteSsjFqqCIyjSIqyRWS6Wvl1l4EYV31gQmEL6f/bu5fYuKo7juO/ez0Pj59xYidBJrZb3EZugILSQmOIkopEqUCzIFGCsFggrGzCghUgggRlAQiDEAiikHWWKKJhUaREqCoPxZGQC9SLynIVO4llQuw8bByP7Xl0MR43pXPHM55778yZ+X4kb3LH95xjT45+Puee/2Q7Ru3VFpJTHRi/Asz2l17yLcD8cv9+T7aQnOrAVMIWkpNLCwu+Hb/ObC1V2vFrKb21ZHIdmWzHqL3aQnKS2Vpy8/i1Ux0YPwJMRmZrqdrqyFRFiMlZibfUnSuSUx0YPwOMV6eQ/KoDk6uQndtyFbKr1ACT4WcdmZlEoiLryEgytiCeUx0YPwNMxtWlJdfqyKxWyM5PKanqCuJVfIhZrRKvyfwsZFeNAcaPLSRTj1GvFQXximdiZV83C9m5JVdBvMTiouavXl31HuUUYDJWq+w7Nzm58knqlaCiQwwBxh0EmOIRYP6LIFM8k4JMOQaYDKcgc6avT6f37lUyxypROQaYDKcgc7avT6f37NGls2dL3UXXVHSImZuY0KXPP6+4ACNJ/zx2zJcAI0n/OnlyZWL0MsBI0sUzZxSbmvI8wEjS9PCwpr77zpdKvIs3b2r01CnPAkwg6d+BypQLy9L/F2QWFzV84kTR980mW5D5/tgxT9rKhxs/Pyl7kJn69ltNDw+7cn+3ZOaqcgswGdmCzI2REdnBoG6MjDh+3/hnn2nh+vWyCzAZ2YLMteW6RD+cP/8/rzV5ZaaiQ0xjR4d+9/LLkmVVVICRpIffe0+h5mbPA4wkbX/xRTV2dMgOhTwNMJL0qyee0OYdOyTL8jTASNLG7dvV88wzkmV5GmAkKbxunR4aGJBl264GmE9+M6UvO2/oy84bGmuJudBTZ3cdOqSuaFSdjz2m9l27ir7f7UEm0tqqHW+84UIvs7s9yNREItp9/LhnbeUSbGxUz9NPqysa1T3PPlv0/W4PMrIs9fT3e/r/cy12vv++gk1NZRlgMhq2bNHekydXaqukEgklEwn9ODTk+D1b+/rSZQIsq+wCTEYmyKzr7pa1XDlYkq4MDkqS7jlyRF3RqHr6+xWIRErZ1TWrijoxsxcvqr69PWuAMfnj7WPXr8uyLIWzFPRyu62luTktzc2pbuPGrNfdbC8Zj+vW5KRjMHN7bDNjY2rs7MwaYNxu66eJCUXa2rIGmJX2/+xKU7ktt+HH+15Kj82prdj0tOxgUKGmJtfakrKPbXF2VolYTJG2Ns/bcluutlKplGbHx9XU1eV5W2sRu3YtvRLjw1y1Gqf2hk+c0PcffCDd9u937NypP370keO9kktLunXlihruvLOgtrzg1Nbc5KRO/6wOjhUI6OC5cwqssfBkOdWJCXjegzLQ2NFR6i4U5PrIiGzbVnN3d87X1ba0+NQjKVhfn3chpGLZgYCnK0s/59bEn4+G9nbf2jJF7YYNvrUVamyUfPwAQL9YluXr+7hQtevXl7oLq7pr/36Fmpo0+fXXujo0pKXZWd0cHc35PXYw6BhgykXdpk16+N139cP587oyOKifLl9WKpHQzIULWr9tW6m7V7SK3k4y1eDRo/rr44/rH++8o3jM2y0CAIAUaWvTr598Urs+/FC/fe45pZJJhZubNTs+XuquFcWybXXs26cHXnlFf/r4Y6USCQXq6jR7+bJvq19eqortpFzcXO6b+OIL/f3IEcfrmYenLHuV7GhZUjKpmtpaBRsatOPNN3VHb29BfSmXJVraKrytattO8qItqXLfH7TlLD4/r7888ogWZ2YcX5PXPLxcij+VTMoOBtXT369thw8X/Bybm2M7d/SoLnz6qeP1fMdVEw6nDzNEItpw9936w+uvF7xCXE7bSazEuGhuYkJ2KJTeU83yZVlW+pfvcH3la/nNmIjFFJua0t8OH1ZserrEowOA8pZYWFB8fj7n/JrXPKz0s3mpZFKJhQUNHz9e8mP5M2Njrowrc9I0MT+vH7/5Jucf3iaoimdi/BJpa1NyaUl2IPuPdXG55kAoGMx9I8tK3ycUUqC2Vg+89prCBuwpA0Ap2cFgev7NcWQ433k4JSkVj6smHFb3oUPqPnjQza4WrH7zZl0bHnY8QZnvuKxgMF3qIRJRU2enet96y/W++okQ46Ite/bo4PLRtWyalk9fzORY6pSks089pZujo/pFNKr7n38+/TAiACCnYH29Dnz1lZLxuONr8pmH/33qlIYGBtTc3a3egQG1bN3qel8L1TswkLOQYT7jit+6pU9271agrk73v/CCug8cWP3xhjJHiHFZrhM8seW/DlY75fP7V1+VZdtqvfdeV/sGAJWuJhxWTTjseD2febgrGlVta6s69u0rm9pidiDguMov5TeuQCSih95+W5sefNDXU4FeIsSUobb77it1FwCgatW2tKjr0UdL3Q3XWbatzgobl9nrSAAAoGoRYgAAgJEIMQAAwEiEGAAAYCRCDAAAMBIhBgAAGIkQAwAAjESIAQAARirrT7EGAABVj0+xBgAAlYUQAwAAjESIAQAARiLEAAAAIxFiAACAkQgxAADASIQYAABgJEIMAAAwEiEGAAAYiRADAACMRIgBAABGIsQAAAAjEWIAAICRCDEAAMBIhBgAAGAkQgwAADASIQYAABiJEAMAAIxEiAEAAEYixAAAACMRYgAAgJEIMQAAwEiEGAAAYCRCDAAAMBIhBgAAGIkQAwAAjESIAQAARgoU+PopSeNedAQAACCLTqcLViqV8rMjAAAArmA7CQAAGIkQAwAAjESIAQAARiLEAAAAIxFiAACAkQgxAADASIQYAABgJEIMAAAwEiEGAAAY6T+hwLDJL1ZFSQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "G9mIfGDF65H9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "1OXxmcLE65FX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "6om8xnKLva7L"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "BTP2_Ex3C.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}