KarateClub_GCN.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install -q torch-scatter -f https://data.pyg.org/whl/torch-${TORCH}.html\n",
        "!pip install -q torch-sparse -f https://data.pyg.org/whl/torch-${TORCH}.html\n",
        "!pip install -q git+https://github.com/pyg-team/pytorch_geometric.git"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kekNMm4uHtQW",
        "outputId": "bbe1bd46-42a7-443c-f3ce-5ca829583be6"
      },
      "id": "kekNMm4uHtQW",
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import torch\n",
        "from torch_geometric.utils import to_networkx\n",
        "from torch_geometric.datasets import KarateClub\n",
        "from torch.nn import Linear\n",
        "from torch_geometric.nn import GCNConv\n",
        "import networkx as nx\n",
        "from sklearn.manifold import TSNE\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "DJJOmEPf7Nu4"
      },
      "id": "DJJOmEPf7Nu4",
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Loading and Understanding the Dataset\n",
        "\n",
        "The KarateClub dataset from the torch_geometric.datasets module is a popular network dataset that represents the social network of Zachary's karate club. This dataset is widely used in network science and graph analysis as a classic example for studying social interactions and community detection.\n",
        "\n",
        "The karate club dataset consists of nodes and edges, where nodes represent members of the karate club, and edges represent social connections or interactions between the members. The dataset is typically used for tasks such as community detection, where the goal is to identify subgroups or communities within the karate club based on the network structure and connections.\n",
        "\n",
        "In this dataset, there are 34 nodes (club members) and 78 edges (social connections) that capture the interactions among the members of the karate club."
      ],
      "metadata": {
        "id": "5xFWDiJnJOoH"
      },
      "id": "5xFWDiJnJOoH"
    },
    {
      "cell_type": "code",
      "source": [
        "#  Load Zachary's karate club network\n",
        "dataset = KarateClub()"
      ],
      "metadata": {
        "id": "IwSwaWlF4abc"
      },
      "id": "IwSwaWlF4abc",
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'Number of features: {dataset.num_features}')\n",
        "print(f'Number of classes: {dataset.num_classes}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0YzlXAb7-IXm",
        "outputId": "c572b1f9-35de-4799-f79d-c05ea13f9783"
      },
      "id": "0YzlXAb7-IXm",
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of features: 34\n",
            "Number of classes: 4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Basic Dataset Information\n",
        "basic information about the dataset, such as the number of features, classes, nodes, edges, average node degree, and checks for isolated nodes, self-loops, and whether the graph is undirected."
      ],
      "metadata": {
        "id": "OWtmR48YJVE1"
      },
      "id": "OWtmR48YJVE1"
    },
    {
      "cell_type": "code",
      "source": [
        "data = dataset[0]"
      ],
      "metadata": {
        "id": "_KLILEzV-Tc0"
      },
      "id": "_KLILEzV-Tc0",
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "968d661f-8a76-42a4-99e5-41ead2096016",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "968d661f-8a76-42a4-99e5-41ead2096016",
        "outputId": "76d48316-fee5-4f5e-afa6-205fa6dccdbf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of nodes: 34\n",
            "Number of edges: 156\n",
            "Average node degree: 4.59\n",
            "Contains isolated nodes: False\n",
            "Contains self-loops: False\n",
            "Is undirected: True\n"
          ]
        }
      ],
      "source": [
        "print(f'Number of nodes: {data.num_nodes}')\n",
        "print(f'Number of edges: {data.num_edges}')\n",
        "print(f'Average node degree: {data.num_edges / data.num_nodes:.2f}')\n",
        "print(f'Contains isolated nodes: {data.has_isolated_nodes()}')\n",
        "print(f'Contains self-loops: {data.has_self_loops()}')\n",
        "print(f'Is undirected: {data.is_undirected()}')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Graph Visualization\n",
        "- use NetworkX and Matplotlib to visualize the graph.\n",
        "- the graph's nodes are colored based on their classes (the club's communities), allowing to see how nodes are connected within the community structure."
      ],
      "metadata": {
        "id": "AbfMhW6eIlLe"
      },
      "id": "AbfMhW6eIlLe"
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "248bb714-2f5b-46a4-8d96-4401640c3985",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 576
        },
        "id": "248bb714-2f5b-46a4-8d96-4401640c3985",
        "outputId": "6cc45de1-28c4-4de7-b2ff-29c25ee4d7f6"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x700 with 1 Axes>"
            ],
            "image/png": 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KPy8UsgiP+Yg3sZlw7miMVmaa9eINqiHLzMwUS4gpxefzUVBQUC+27z927BgSExPx66+/SroU0kDRZF9C/qeomA9v30iERaYBqDjAlIdlAR5fiH8fvsfdoASxvgkS8cvKyhJ7H9nZ2WLvQ9z4fD7WrVuH4cOH16tbZaRuoREZQvAphJy7+QZpGQWVCjDleRaeChlpBl3bGnJTHKlRpfu+iFvpEQd12enTpxETE4Nz585JuhTSgFGQIQTAo9BEpH0sAFfjKE9fpMBYvxGM9Rtx1CKpb2rzqB3LssjLy0N2djZycnLA4/HAMAwUFBSgpqYGdXV1yMrKYs2aNfjhhx9ga2sr6ZJJA0ZBhjR4yel5CHqVymmbDAP4BrzDT0OsISsrzWnbRLwYhoGMjAz4fL5Y+5GVlRVr+1XB5/ORkJCAd+/eIT8/HwC+mu9VGsAEAgEUFBTw22+/1XidhHyOggxp8J48Ty53ZVJ1sCyQV8DD65gMtGlZ904wbujU1NTw8eNHsfaxYsUKtG7dGl26dIGtra3Eg82HDx8QGhqKoqKiLz7/rZGjVatWQUZGBkVFRVBQUBB3iYSUi4IMadBy8ooRkyC+SZchr1PRuoUWrWKqY9TV1ZGRkSG22z8CgQDR0dHw8vJCUVERlJSUYG9vj65du6JLly5wcHBAo0Y1c1uSZVm8efMGUVFRlXqetPSnkcb09HT4+/ujY8eOaNy4sThKJOSbaNUSadDexot35cjH7CJk5RaLtQ/CPQMDA7GFGIZh0KxZM9y9exfZ2dl49OgRVq5cCTU1NezZswf9+vWDhoYGbG1tMWvWLJw6dQpxcXFiqQUAIiIiKh1iPseyLPh8Ph4/foyMjAwOKyNENAwrwk9rTk4O1NTUkJ2dXWN/JRBSE3wD3uF1zEdObyv91w+OpmhpQn+p1jUBAQFiW4bdrVs3qKmpffV5lmURFRWFgIAAPHjwAAEBAWXnPRkZGaFLly5lozatW7cuGxWpqsTERISEhFSrjc/JysrCycmpzp/iTWoHUbMH3VoiDVrqx3yxhhgpBkjPLKAgUwdZWFjg8ePHnLbJMAx0dHTKDTGlj7do0QItWrTAlClTAABpaWl4+PBhWbA5d+4ceDweVFVV4eDgUBZs7O3tK7XBXlFREV68eMHJ6yrF5/Px4sULtG/fntN2CfkWCjKkQSvhCcXcA1MDfRBx0NLSQtOmTREbG8tZm9LS0mjTpk2lnqOjo4MhQ4ZgyJAhAD4d0BgYGIgHDx7gwYMH2Lp1K5YvXw5paWnY2dmVBZsuXbpAX1+/wnajoqIgEAiq83K+wrIsUlJSkJGRQfNlSI2hIEMaNClxz8FlACmxd0LExdLSEtnZ2ZzcYmIYBu3atav2bRdFRUU4OjrC0dERACAUCvH69euy21GXLl3C9u3bAQCmpqZf3I6ytLSElJRU2TJrccwDYhgG79+/pyBDagwFGdKgqarIITtPfDusskIWKkq1b78QIhoZGRm0atUKp06dgpGRUZVWn5U+p3379tDW5n4pvpSUFFq1aoVWrVrB1dUVAJCUlFQ2YhMQEAAvLy8IBAJoaGigc+fOGDhwIAwNxbPzNMuySE5OBp/Ph4wMvcUQ8aPvMtKg6WmpICk1H0IxTZRhAehqKomlbSJ+QqEQU6dOxZ07d3D16lVkZWVV+lTsRo0awdbWFqqqqmKs9EsGBgYYOXIkRo4cCQDIy8vDkydPyoLN69evoaenJ7agwbIssrOzoampKZb2CfkcBRnSoDXRVkEQmyK29qWkGOg0rvsnHDdUq1atwsWLF3Hx4kV07doV2dnZePv2LZKTk8Gy7DdDjYqKCkxMTGBkZAQpKcnudKGiooKePXuiZ8+eAIB79+4hJydHrH1SkCE1hYIMadCaGTaCorwMCou5346eYYCWzTQgL0dHFNRF58+fxx9//IHVq1dj8ODBAD7t+Nu2bVsUFxcjPT0dWVlZyMnJwcuXL6GkpAQrKyuoqamhcePGUFdXr7UbIRYXi3dvI4ZhxN4HIaUoyJAGTVpKCrYWOnj8PInzZdgsC9ha6HDbKKkRL168wMSJEzFy5MhyzxKSl5eHoaFh2TyT+fPnw9bWFuPHj6/pUmut2nwoJqlfaGdf0uC1b6ULFSU5cPnHM8MArZprQl9bhbtGSY348OEDfvzxR5iZmeHw4cMijarUxCGTXKqJc50kfXYUaTgoyJAGT1ZWGv26mnA2IsMwgKKCDLp3NOKmQVJjeDweRo0ahdzcXFy6dEnkDeakpaU535NFnNTU1MR624tl2Qo3/SOEaxRkCAFgpKeKXg5Nq90OwwCyMlIY3rsFFOTozm1ds3DhQty/fx/nzp1D06aifz/UtREZdXV1sd/6oSBDagoFGUL+p00LbfTr2gxSUkyVbjMxDKCsKIsx/S2hrUFLruuagwcPYteuXdi1a1fZZnOiqmtBRk9PT6ztN27cmM5bIjWG/mQk5DNWzbWgp6UC34B3SPmQD4bBd285CQUCSElLo7W5FhzbG0FOllYp1TUPHjzAjBkz4OrqCjc3t0o/v67dWlJUVISenh5SUsSz9YCJiYlY2iWkPDQiQ8h/NFZTgMsACwztZY5mBl8Oj0sxXx5rICMtBWl+Grb87gJzA4ZCTB0UHx+P4cOHo1OnTti5c2eV2qhrIzIAkJKSwnn4YhgGjRo1gq6uLqftEvItNCJDSDkYhoFJEzWYNFFDCU+A9IwCpGUUoriEDykpBkoKstDVVIKmuiIKC1tizW+Z2LJlS5XfCIlkFBYWYujQoZCXl8fZs2chJydXpXbqUpD5+PEjFixYgOPHj8PDwwP29vactm9rayvxDQBJw0JBhpDvkJOVRhNdVTTRLX+LeWVlZcyZMwcbNmzA8uXLoaWlVcMVkqpgWRZTp05FeHg4Hj58CB2dqu/5Iy0tXSc2gDt37hxmzpyJkpISHDlyBOPHj0dgYCA+fPjASfutW7dGo0aNOGmLEFFRbCaEA7NnzwbDMNi1a5ekSyEi2rRpE06dOoUjR47A1ta2Wm3V9hGZ1NRUjBw5EiNGjICDgwPCw8MxadIkSEtLo0OHDpwcZmltbQ1jY2MOqiWkcijIEMIBTU1NTJs2Dbt27UJeXp6kyyHfcf36dSxduhTLli3DqFGjqt2ejIxMrZzsy7IsvLy80KpVK/j7++P06dO4cOEC9PX1y64pDTMWFhZgGKbS+8soKCjAwcEBzZo147h6QkRDQYYQjri7uyM3NxcHDhyQdCnkGyIjI+Hi4oKBAwfizz//5KRNaWnpWjcik5iYiB9//BHjxo1D7969ER4ejtGjR5cbVKSkpGBmZgZHR0cYGBiUXVPetaWvU05ODi1btoSTkxMdDkkkiubIEMIRY2NjjB07Flu2bMHMmTOrPHGUiE9WVhYGDx4MQ0NDnDhxgrNJqbXp1hLLsjh8+DDc3d2hqKiICxcuYMiQISI9V1VVFXZ2dmjVqhVSU1ORlZWF7Oxs8Hg8MAwDRUVFXLt2DWlpafj7779pUi+pFRhWhO0dc3JyoKamhuzsbJrIRcg3vHr1CtbW1jhy5AgmTZok6XLIZwQCAQYOHIjHjx8jMDAQZmZmVW5LKBQiPT0dGRkZyMrKQlxcHPh8Ppo0aQI1NTWoqalBV1dX5CMOuBIbG4tp06bh5s2bmDx5MrZu3QoNDQ1O+1i/fj1Wr16NzMxMOk+JiJWo2YOCDCEc+/HHHxEVFYWXL1/SX6y1yJIlS7B582bcuHEDffr0qVIbAoEAMTExePfuHUpKSsAwzFdb/X/+OW1tbZibm6Nx48bVrv9bhEIh9u7diyVLlkBDQwP79+9H3759xdLX06dPYW9vj4cPH8LBwUEsfRACiJ496LcsIRxbsmQJXr9+jStXrki6FPI/J0+exMaNG7F58+Yqh5jMzEzcvXsXkZGRKCkpAYByzyv6/HPp6el4+PAhXr58KbZbT9HR0XB2dsasWbMwYcIEvHz5UmwhBgDatm0LVVVV3LlzR2x9EFIZNCJDiBg4OjqCx+Ph4cOHYj1lmHxfUFAQunXrhtGjR+Pw4cNV+vdISEhAaGhouSMwolJVVUWnTp04O4NIIBBgx44d8PDwgL6+Pg4cOABnZ2dO2v6eQYMGobCwELdu3aqR/kjDRCMyhEjQkiVL8PjxY9y/f1/SpTRoKSkpGDJkCGxsbLB3794qhZikpCSEhoYCKH8ERlR5eXl4/PgxeDxeldsoFR4eji5duuCXX36Bq6srnj9/XmMhBgCcnZ3x4MGDOrEJIKn/KMgQIgYDBgyAtbU1NmzYIOlSGqzi4mIMGzYMQqEQ58+fh4KCQqXbKCgoKAsx1cWyLHJzcxEeHl7lNng8HtauXQs7OztkZWUhICAA27Ztq/FJxT169EBRUREeP35co/0SUh4KMoSIAcMwWLp0Ka5fv47nz59LupwGh2VZzJw5E8+ePcOFCxdgYGBQpTbCwsKqNQpTnvj4eKSlpVX6eaGhobC3t8fy5cvh7u6O0NBQdO7cmdPaRNWmTRs0btyY5smQWoGCDCFiMnr0aDRt2pRGZSRg9+7dOHToEPbt21flQxEzMjLw8eNHzoMM8GlTPlEVFxdj+fLl6NChA/h8Ph4/fox169ZVaYSJK1JSUnBycoKfn5/EaiCkFAUZQsRERkYGv/zyC86cOYN3795JupwG4/bt21iwYAHc3d0xceLEKrfz/v17sU3Uzs7ORnZ29neve/r0Kdq1a4d169bBw8MDQUFBaN++vVhqqixnZ2c8fvwYBQUFki6FNHAUZOoZlmVRWMxHbn4JCot4YvlrkohuypQp0NDQwJYtWyRdSoMQExODUaNGoUePHtUaCRMIBEhJSRHbzw/DMEhKSqrw8cLCQixevBgODg5QUFBAcHAwVqxYUat2i3Z2dgaPx8ODBw8kXQpp4OiIgnqgqISP8Lcf8S4hG6kf8lFU8v+H18nKSEFXUwnG+o1gba4FFaXa84uwIVBSUsLcuXOxdu1aLF++HDo6OpIuqd7Kzc3F4MGD0bhxY5w5cwYyMlX/9ZabmyvWPwJYlkVWVla5jwUEBGDKlCmIi4vD2rVrsXDhwmq9FnGxsrKCjo4O7ty5g969e0u6HNKA0YhMHVbCE8DvaRw8z4TB/2k8YpNyvggxAMDjC5GQmodHYUnYf/Y5rt+LQX5h9Zd/EtHNmjUL0tLS2LVrl6RLqbeEQiEmTpyIuLg4XLp0qdrb8ufk5HBUWcX+e2spLy8Pc+fOhaOjI7S0tBAaGoolS5bUyhADfBpVcnZ2pnkyROIoyNRR8Sm5OHLxJUIj0iAQfv8vR5b99BH5PgOHL7zEm/cZNVAlAYDGjRtj+vTp2L17N3JzcyVdTr20cuVKXLp0CSdPnoSVlVW126uJAyA/7+P27dto3bo1Dh48iG3btuH+/fuwsLAQew3V1aNHDwQFBdVI8COkIhRk6qCo2Eyc/TcS+QU8VHb0m2U/jeRcvRuDZ+Gp4imQfMXd3R35+fnYv3+/pEupd86dO4c///wTq1evxqBBgyRdjsgYhkF2djamT5+OXr16wcTEBC9evMC8efMgLS0t6fJE4uzsDIFAQBs/EomiIFPHxKfk4Ordt59GWKrZln9gPF5Ff+CkLvJthoaGGDduHLZs2UK7oXLo+fPnmDhxIkaPHo1ff/2Vs3ZrYmkzy7Jo1aoVTp8+jb179+LWrVswNTUVe79cMjMzg6GhId1eIhJFQaYOKS4R4Po9bpfx3n4ci+w8emOtCYsXL0ZSUhJOnjwp6VLqhQ8fPuDHH39EixYtcPDgQU6XSqupqXHWVnkEAgEePXoEeXl57N27FxMmTKiTJ6WXzpOhjfGIJNW9n5wGLOBZAgqKKn876VsEQhY3H77nrkFSIUtLSwwZMgQbN26EUCiUdDl1Go/Hw8iRI5Gfn49Lly5xvkW/kpISZGVlOW3zcwzDIC8vD7m5uRg3bhw0NDTg6OiIFStWwN/fH0VFRWLrm2s9evRAaGgoMjIyIBQKwePxIBAIaOsHUmPo9Os6orCIB0+f5xCKMLG3KiYMsoJ2YyWxtE3+3+PHj+Hg4IDz589j6NChki6nzpo9ezY8PT1x584ddOvWTSx9hIeH4927d2J7Q+7Vqxfk5eXx6tUr+Pv7w8/PD/7+/sjIyIC8vDwcHBzg7OwMZ2dndOzYkbNTs7kkFArx8uVLHD16FE5OTl+MisnIyEBdXR2ampowNDSEoqKiBCsldZGo2YOCTB0R+DIFAcEJ1Z4XUx6GAVqba6OXQ1MxtE7+y8nJCYWFhXj8+LHYdo6tz/bv34/p06dj7969cHV1FVs/+fn5Ypn7wTAM9PT00K5du68eEwqFePHiRVmwuXv3LrKysqCoqIjOnTuXBZv27dtLdHM8lmXx7t07REdHo6SkBAKB4LsTlPX19WFlZUWBhoiMgkw9c+ZGBBLT8sTWvpKiLNxG2YitffL/bty4gQEDBsDPzw9OTk6SLqdOCQgIQI8ePfDzzz/j77//Fnt/r169QkxMDKeBU0pKCt27dxfpdphAIEBYWFhZsLl37x5ycnKgpKSErl27lgWbdu3a1dh+M/n5+QgJCalwQ7+KMAwDKSkpWFtbw8jISDzFkXqFgkw9wrIsdnuFgMcX77wKt1E2UFIU37wA8gnLsrC1tYW+vj58fX0lXU6dERcXhw4dOsDCwgK3bt0S6xwW4NMbtpubG7p16wY9PT3OJuNaW1ujWbNmVXoun89HSEhI2W2o+/fvIy8vDyoqKujWrVtZsLGzsxPLEu7s7Gw8evSo2nNgzMzM0LJlSxqRJN9EQaYeyc0vwf6zz8Xez8i+LWCkR/++NeHUqVMYO3YsQkJCYGtrK+lyar2CggJ069YNHz9+RGBgILS1tcXa35s3bzBs2DC8f/8ehw4dgpqaGvh8frXnyxgbG6N169acvYHzeDwEBweXBZuAgAAUFBSgUaNGcHR0LAs2bdq0qXawyc/PR0BAACdfBwCwsLCAmZlZtdsh9RcFmXokM6cIhy+8FHs/Q3uaw8RQvMtOySd8Ph8tWrSAvb09Tp06JelyajWWZTF27FhcvnwZDx8+hI2NeG+BXrhwAZMmTYKBgQHOnz8PKysr5OXl4fHjx9VaTWRiYgIrKyuxjkKUlJQgMDCwLNg8ePAARUVFZauiSoONtbV1pUaYWJbFw4cPkZWVxenk527duol9qTupu0TNHrT8ug6QlqqZ4VepGuqHfFrR8csvv8Db2xsxMTGSLqdW27BhA06fPo0jR46INcTw+XwsWbIEw4YNQ58+ffD06dOy4w5UVFTg5OQEY2NjAKhUGJGXl0fHjh3RqlUrsd9KkZOTQ5cuXeDh4YFbt24hKysLd+/exbx585CVlYXFixfDxsYGOjo6GD58OHbv3o1Xr159N5zExsYiMzOT0xDDMAxCQkJomTapNhqRqQMEAiF2eYWIbel1qanDWkNNtfYt8ayvCgsL0bRpU4wYMaJGJq7WRdeuXcOgQYOwbNkyrFq1Smz9pKamwsXFBffu3cOGDRvg7u5eYejIzs7G+/fvkZiYCKFQWPYhIyMDhmHK3piVlZVhYmICQ0PDWnPwY+lqOT8/P/j5+eHJkyfg8XjQ1taGk5NT2YjN5/NXWJbFrVu3xLYjdYcOHaCrqyuWtkndRreW6pkTV14hLaNQbO3LyUphlosdTb6rYWvWrMGqVasQGxtLv8z/4/Xr1+jUqROcnJxw4cIFse18++jRI4wYMQICgQBnzpxB9+7dv/uc8PBwuLq6IiMjA1OnTkWPHj0gIyMDGRkZqKqqQk1NDcrKyrX+56mgoAAPHz4sCzaBgYHg8/nQ09MrCzYdOnRAYmKiWPpnGAaampro1KmTWNondRsFmXrmXlA8gsNTOd3V9/+xMDVUx5Ce5uJonHxDZmYmjI2NMXfuXKxZs0bS5dQamZmZsLe3h6ysLB49eiSW3zssy+Kvv/6Cu7s7OnbsCG9vbxgYGHzzOTweD5s2bcLKlSthYmKCQ4cOoXPnzpzXJil5eXl48OBBWbAJDg6Gm5sbevfuLbaDLBmGQb9+/erMQZmk5tAcmXqmdQttMYUYAGCwbd0ibN++HTk5OeLqhJRDQ0MDrq6u+Ouvv+hr/z8CgQAuLi748OEDLl++LJYQk5+fj/Hjx2POnDmYOXMm/Pz8vhtiQkNDYW9vj+XLl8Pd3R2hoaH1KsQAn+YC9e3bF+vXr8eTJ0+QkZEh9pDBsix975NqoSBTR2g0UkCzJo3A9Ug1wwAKcoChrjIWLVoEQ0NDuLu74/3799x2RCq0YMECFBQUYN++fZIupVZYunQpbt68CW9vbzRv3pzz9t+8eYNOnTrh0qVLOHXqFLZv3/7NPWmKi4uxfPlydOjQAXw+H48fP8a6detq5IRsSWvUqFGN3B7Lzc0Vex+k/qIgU4f0sG/K+coilgV+7NESJ0+exLt37zBz5kwcOXIEzZs3x4gRI/DgwYM6t6qghCdAXHIOAl8m4+bD9/ANeIdbj2LxLDwViam54Atq14GNTZo0wYQJE7B161axTaisK44fP47Nmzdjy5Yt6NWrF+ftX7hwAR06dACPx8OTJ08wZsyYb17/9OlTtGvXDuvWrYOHhweCgoLQvn17zuuqrViWrZGff4FAIPY+SP1Fc2TqmOeR6bj1OJaz9tpZ6aJ7hy+3C8/Pz8fRo0exfft2REVFoUOHDnB3d8fw4cPFvptqdXzILERIRCrCoz9CIGTBMAADBixYMABKF33JyUqhtbk2bC10as0qrYiICFhZWWHfvn34+eefJV2ORAQGBqJbt25wcXHBoUOHOB0J4PP58PDwwIYNGzBs2DAcPnz4m7/LCgsLsWLFCmzZsgV2dnY4dOgQ2rRpw1k9dQXLsrh27ZrY+2ndujWaNqWz3siXaLJvPRbwLAFPX6RUu52WzTTQv5tphaM8QqEQ169fx7Zt23Dnzh0YGhpizpw5mDZtGjQ0NKrdP1f4AiEehSYh8GUKGAYizSVimE+TDLu2bYK2lrq1Yg+d4cOH4+XLlwgPD29wEx+Tk5PRvn17GBsbw9/fn9OTntPS0jBmzBjcu3cP69evx8KFC78ZkgICAjBlyhTExcVh5cqVWLhwYa1ZPi0Jt27dqtZGgKKwt7cX+27NpO6hyb71WBe7Juje3vB/b8aVe27p9W2tdL8ZYoBPh9sNHDgQt2/fRmhoKHr16oXff/8dRkZGmD17NqKioqrxKriRV1CCk1fCEfjyU7ATdRScZQGhkMW9oASc/TcSxSWSH9pesmQJ3rx5g4sXL0q6lBpVXFyMYcOGAQDOnz/PaYh59OgR2rZti1evXuH27dv45ZdfKgwxeXl5mDt3LhwdHaGlpYXQ0FAsWbKkQYcYAFBVVRX77SXa3ZdUBwWZOohhGLRrpYcJg1pBW0Ppf5/79nOEwk9v1KrKchjZtyWcOhhVahTCxsYGhw8fRmxsLBYuXAhvb2+0bNkSgwcPhp+fn0Tm0eQX8nD6RgQycqr312JiWh7O3XyDEp5kw0zHjh3h7OyMDRs21Ll5SVXFsixmzJiBkJAQXLx4Efr6+py1u3v3bnTv3h1NmzZFSEjIN/eHuX37Nlq3bo0DBw5g27ZtuH//PiwsLDippS4qKCjAuXPnMGbMGLF/PyorK0NOTk5s7ZP6j4JMHaaloYhxAy0xqm9LmBtrQFam/H9OaWkG8lIFOLjdHU42SjDSU61yn3p6eli5ciXi4uKwf/9+xMTEoEePHmjbti2OHj1aY5NVWZbFtbtvkZtfUu1l6SwLpH7Mx50ncdwUVw1LliwpOyunIdi5cycOHz6M/fv3o0OHDpy0mZ+fjwkTJmDOnDmYMWPGN5dWZ2dnY/r06ejVqxeaNWuGFy9eYN68eQ3u1h7waV7Q+fPn4eLiAh0dHYwYMQKRkZGwt7cX69ejqieBE1KK5sjUIyzLIjuvBBnZheDzhZCWkoJ6I3loNFJAcXERDAwMMH36dGzYsIHTPm/evIlt27bB19cXenp6mDVrFtzc3KClpcVZP/8VGpEmluAxpKcZTA3VOW9XVCzLom3bttDW1sa///4rsTpqwq1bt9CvXz/Mnz8fmzdv5qTNqKgoDBs2DDExMThw4ABcXFwqvPbatWtwdXVFTk4ONm3ahGnTpolt9+DaqrCwEL6+vvD29saVK1eQn58PGxsbjBo1CiNHjoS5+adNMp8/f474+HjOR2akpaXRq1evWr2IgEgOTfYlX5k/fz68vLwQHx/P6TyEUq9fv8b27dtx7NgxAMCECRMwf/78soP3uMLjCbDXOww8PvfLqBupyGHqsNYS3Vr+zJkzGDNmDIKDg9G2bVuJ1SFOb9++RYcOHdCxY0dcu3aNk7/4L168iEmTJkFPTw/nz59Hq1atyr0uIyMD8+fPx/Hjx9GvXz94enqWHQbZEBQVFX0RXvLy8tCmTZuy8NKiRYuvnlNSUgI/Pz/weDxOa2nTpk2D+tqTyqHJvuQrrq6uSE9Px4ULF8TSvqWlJTw9PREfHw8PDw9cvXoVrVq1Qr9+/fDPP/9w9tdcxLsMsYQYAMjJK0FcsmR3GR0+fDhMTU05HTmrTXJzc/Hjjz9CS0sLp06dqnaI4fP5WLp0KYYOHYpevXohMDCwwhBz/vx5WFlZ4cqVKzhy5AiuX7/eIN5Ii4qKcPHiRYwbNw7a2toYOnQoXr16hSVLliAiIgJhYWFYtmxZuSEG+HSqNpcnjzMMAy0tLRgZGX3/YkK+g4JMA2JpaQlHR0d4enqKtR8tLS0sW7YM79+/x7Fjx5CWloZ+/frB2toa+/fvR2Fh9Q6/fBn1gaNKv8YwwKvoj2JrXxQyMjJYtGgRzp49i+joaInWwjWhUIgJEyYgLi4Oly5dqvYy/rS0NPTt2xebNm3Cxo0bcfbs2XL/cktLS8OoUaMwfPhwdOrUCeHh4Zg0aVKtP9SxOoqKinDp0iWMHz8eOjo6GDp0KF68ePFFePHw8EDLli1Fak9PT4+z0VVVVVW0a9euXn/9Sc2hINPAuLm5wd/fHxEREWLvS05ODhMmTEBwcDD8/PxgZmYGV1dXGBsbY/ny5UhJqfxeOAKhEKkZBWKo9hOW/bSKSdImT54MbW1tzuaO1BZ//PEHLl++jFOnTsHS0rJabT1+/Bht27bFy5cvcfv2bSxatOirN0aWZeHl5QUrKyv4+fnh9OnTuHDhAmero2qboqIiXL58GRMmTICOjg6GDBmCsLAwLFq0CK9fv8bz588rFV7+y9TUFG3atIGUlFSVQ4i2tjYcHBxoXgzhDAWZBmbYsGHQ0tKq0XN9GIaBk5MTLl26hDdv3mDMmDHYunUrmjZtismTJyMsLEzktjKyiiAUindpcm5+CYpL+GLt43sUFBQwb948HDlypEqBrzby8fHBqlWrsHbtWvzwww9Vbqf01GpHR0c0bdoUz549g5OT01fXJSYm4scff8S4cePQu3dvhIeHY/To0fVuFKC4uBhXrlzBhAkToKurix9//BEhISH45ZdfEB4ejhcvXuD333/nbDm5sbExHB0doa6uDgAifz2lpaVhY2ODjh07UoghnKLJvg3Q4sWLceDAASQmJkJRUVEiNWRmZuLAgQPYtWsX4uPj4ezsjAULFuCHH3745sqR2KRsnLsp/o34fhpqDY1Gkj0UMCsrC8bGxpg1axbWrVsn0VqqKywsDJ07d8bgwYPh5eVV5TCRn58PNzc3nDhxAnPnzsWmTZu+2oOEZVkcPnwY7u7uUFRUxJ49ezBkyBAOXkXtUVxcjH///Rc+Pj64dOkScnJy0KpVK4wcORIjR47kfIJ9eViWRWZmJt6/f4+UlBQIheXPW1NVVUWzZs3QpEmTBr+5IKkcWrVEKhQdHQ1zc3McPXoUEydOlGgtPB4P58+fx7Zt2/DkyROYm5tj3rx5mDx5MpSVlb+6/n1iNs7fqoEgM8QaGmqSP9148eLF8PT0RFxcXJ3d/TQ9PR0dOnSApqYm7t+/DyUlpSq1ExUVheHDh+Pt27cVLq2OjY3FtGnTcPPmTUyePBlbt26tVcdpVEdxcTFu3rwJHx8fXLx4ETk5ObC0tMTo0aNrLLxUhGVZ5OfnIycnBwKBAAzDQFFREWpqahReSJXRqiVSITMzM/Tq1Uvsk35FISsri9GjR+Px48d4+PAhbG1tMXfuXBgZGWHp0qVISEj44noF+Zr5pSgvXzs2RJs/fz6Kiopqxb9VVfB4PIwcORKFhYW4cOFClUPMpUuX0L59exQXF+PJkydfhRihUIi///4b1tbWiIiIwI0bN3D48OE6H2JKSkpw7do1TJ48Gbq6uhg0aBACAwOxYMGCsnO5VqxYIdEQA3y6vaSiogIDAwMYGRnB0NAQmpqaFGJIjaARmWooLOYjOT0PqR8LkJ1bDIGQhYy0FDTVFKCjqQR9bZUKd9uVtHPnzmHEiBF4/vw5WrduLelyvvD+/Xvs2rULBw4cQEFBAUaOHIkFCxagQ4cO4PGF2O31rNq7+X6LooIMZoy2FV8HlTR9+nRcuXIF7969g4KC5EeJKmPWrFnYv38/7ty5g65du1b6+Xw+H7///jvWr1+PoUOH4siRI1/9DoqOjsbUqVNx7949uLm5YcOGDXX691RJSQlu3boFb29vXLp0CVlZWbCwsCjb56VVq1b1bp4PIeWhW0tilPIhHyGvUxH5PhNCIfvFOUcMABafVr/IyUqhtbk2bC10oKbK/QZ01cHj8WBsbIzhw4dj9+7dki6nXLm5uTh06BB27NiBd+/eoUuXLliwYAFypVsiI1s8p/EyDGDSRA1DepqLpf2qePPmDSwsLLB3715Mnz5d0uWIbN++fXB1dcW+ffswbdq0Sj8/LS0NLi4u8Pf3x/r167868FEgEGDHjh3w8PCAvr4+Dhw4AGdnZy5fQo0pKSnB7du34e3tjYsXLyIrKwstW7bEqFGjMGrUKAovpEGiICMGJTwB7gcnICwyHQwj2knLn06oZtCtbRPYWepW6qBGcfPw8MCuXbuQlJRU7nyU2kIgEODy5ctlh/kNGzcPXXqNq/zR3yIa4GgKC5PGYmm7qkaOHInQ0FBERETUiXOA7t+/jx49emD69On466+/Kv38J0+eYMSIESgpKcGZM2e+WpUUHh6OKVOm4OnTp5g3bx5Wr15dq7+Hy1MaXnx8fHDhwgVkZWWhRYsWZeHF2tqawgtp0GiODMeyc4tx7PIrPH+TDkC0EFN6nVDI4m5QQq04Yflz06ZNQ25uLk6fPi3pUr5JWloaQ4cOxb179xAUFARV2XwIBNx/HVmWhawMYG6sznnb1bVkyRJER0fj/Pnzki7lu+Li4jB8+HB07doV27dvr9RzWZbF33//jW7dusHY2PirpdU8Hg9r166FnZ0dsrKyEBAQgG3bttWZEMPj8eDr64upU6dCT08PAwYMwIMHDzB79myEhYUhIiICq1atQuvWkj0mg5C6hEZkRJCTV4xT1yNQUMSr1twMhgH0tJQxok8LyMrUjr+qf/jhB6SlpSEwMFDSpVTKdf/XeP0+j/Nf9hdObETHNkZYsWJFrfte79WrF7KyshAYGFhr3+QKCgrQtWtXZGRkICgoqFIHhxYUFMDV1bXCpdWhoaGYMmUKnj9/jkWLFmHFihV1Ys4Qj8fDnTt3ym4bZWRkwNzcvGzOS5s2bWrtvychkkQjMhwRCllc9n9b7RADfBqdSfmQD7+n8dwUxwE3NzcEBQUhODhY0qVUSp9uLaHRSIGzu0sMAxhoK2NADzvs3bsXLVu2xIkTJzg/7bc6li5diuDgYNy+fVvSpZSLZVlMmTIFkZGRuHTpUqVCTHR0NDp16oTz58/Dy8sLO3bsKAsxxcXFWL58OTp06AA+n4/Hjx9j3bp1tTrE8Hg8/Pvvv/j555+hp6eHfv36lU1GDgkJQWRkJFavXg0bGxsKMYRUEwWZ7wh6lYK0jwWcrZJh2U9nBb1PzOamwWrq378/DA0N69zyXhlpKQxybg4ZaalqhxmGAZQVZfFD9+b49ddfERERAUdHR0yYMAGOjo4IDQ3lpObq6tmzJ9q2bYv169dLupRyrV+/HmfOnMGxY8cqdcDg5cuXK1xa/fTpU7Rr1w7r1q2Dh4cHgoKC0L59e3GUX22l4WXatGnQ09ND3759cffuXbi6uiIkJARv3rzBmjVrYGtrS+GFEA5RkPmGomI+HoUmcd4uwwD+gfG14q99GRkZTJs2DV5eXsjJkeypz5WlraGEUf1aQk5WusphhmEAFSU5jOpnAVXlTyMARkZGOHPmDG7fvo2MjAy0a9cOs2fPRmZmJofVV6VWBkuXLsXt27cRFBQk0Vr+68qVK1i2bBmWL1+O4cOHi/QcgUCA3377DT/++CN69OiBwMBAWFtbAwAKCwuxePFiODg4QEFBAcHBwVixYsVXu/hKGp/Px82bNzF9+nTo6+ujb9++8PPzg6urK549e4Y3b95g7dq1FF4IESOaI/MNz8JT4R8ovttAo/q1hKGuqtjaF1ViYiKaNm2KXbt2YcaMGZIup9Jy80tw8+F7vE/KqdRqMpYFLE014dzRqMKN9ng8Hnbt2oU//vgD8vLyWL9+PX766advHqMgTgKBABYWFrC1tYWPj49Eaviv169fw97eHj179sS5c+dE+tqkp6fDxcUFfn5+Xy2tDggIwJQpUxAXF4eVK1di4cKFtWpjNT6fD39/f/j4+OD8+fP48OEDTE1Ny+a82NnZUWghhAO0/JoDRy+9xMcs8e1X0spMC306NxNL+5U1dOhQxMTEIDQ0tE7+EmZZFm9iMxH4IhlpGYVgWRZSUswXoUYo4IORkgLDSEEGeejf3RrmzUSbx5GcnIwlS5bg+PHj6NChA/766y906NBBTK/m2/bt2wc3NzdERkbC3Fyy+91kZmaiY8eOkJeXx6NHj6Cq+v1g/vnS6tOnT5ft/ZKXl4fffvsNu3fvRqdOnXDo0CHODjqsLj6fj7t378Lb27ssvJiYmJSFl7Zt29bJnxtCajOa7FtNPL5AbCEG+DQakJiaJ7b2K8vV1RXPnz/HkydPJF1KlTAMg5bNGkOdeYfNv4+FhbEcLEwaQ6exEjTVFaCrqYTWLXXRRL0ID65tx7zJznB0aI1Vq1YhPT39u+3r6+vj2LFjuH//PoqLi2Fvb49p06bhw4cPNfDqvjRx4kTo6upi06ZNNd735/h8PsaMGYOMjAxcvnz5uyGGZVns2bMH3bp1g5GREZ49e1YWYm7fvo3WrVvjwIEDZfsFSTrE8Pl83L59G25ubjAwMECvXr1w8+ZNTJkyBUFBQXj79i3Wr1+Pdu3aUYghRIJoRKYCSel5OH09Quz9zBnXtlYcYyAUCtG8eXN0794dR44ckXQ5VfbTTz/h0aNHeP369TffXF6/fo0dO3bg2LFjYFkW48ePx/z589GqVavv9sHn8+Hp6QkPDw8wDIPVq1fD1dW1Rjeq27BhA5YvX473799DX1+/xvr93C+//ILt27fjn3/+Qc+ePb95bUFBAdzc3HD8+HHMmTMHmzdvhpycHLKzs7Fo0SLs378fTk5OOHDgAJo3b15Dr+BrAoEAd+/ehY+PD86dO4f09HQ0a9YMI0eOxKhRoyi0EFKDaESmmgoL+TXST1FxzfTzPVJSUnB1dcWZM2ckPqm1qng8Hi5fvozhw4d/983G0tISe/fuRXx8PH7//Xdcu3YN1tbW6NevH/75559vTsSWkZHBrFmz8ObNGwwfPhyzZ89G+/bt8eDBA65fUoXc3NygoKBQ6Q3nuHLs2DFs2bIFW7du/W6IiY6OhoODA86dO4eTJ09i586dkJOTw7Vr19CqVSucPn0ae/fuxe3btyUSYgQCAfz8/DBz5kwYGBigZ8+euHHjBiZPnoynT58iJiYGGzduRPv27SnEEFILUZCpAAvJryiqaT/99BP4fD6OHTsm6VKq5O7du8jIyMCwYcNEfo6mpiZ+++03vH//HsePH0daWhr69esHa2tr7N+/H4WFhRU+V1tbG/v378fjx48hIyODrl27YuLEiUhOTubi5XyTmpoaZsyYgT179iArK0vs/X3u6dOnmD59OqZMmYI5c+Z889rSpdWFhYV48uQJxo4di4yMDEycOBEDBw6EtbU1Xr58CVdX1xqdQC0QCODv74+ZM2eiSZMm6NGjB65du4aJEyfiyZMnePfuHTZu3IgOHTpQeCGklqNbSxVISM2Ft2+k2Pu5fmIZTE2awszMDObm5jA3N0fz5s2hqKgo9r7LM3r0aDx//hzh4eF17hf4zJkzcf36dbx7967KtbMsi3v37mHbtm24fPkyNDU14ebmhlmzZkFPT6/C5wmFQhw6dAhLly5FSUkJVq5cidmzZ0NWVraqL+e7kpOTYWJigj/++ANLly4VWz+fS0pKQvv27dGsWTP4+flBXr78w1AFAgGWL1+OtWvXYujQoTh8+DDU1NRw/vx5zJw5E8XFxdi+fTsmTpxYY99nAoEAAQEB8Pb2xrlz55CamgpjY+Oy20YUWgipXWjVUjUVl/Dx16lQsfYh4OUj+NYeREVFISoqCrm5uQA+TVw1NDQsCzalH2ZmZmjevHmFbx5cuHPnDnr27Im7d+/C0dFRbP1wTSgUokmTJnBxccHWrVs5aTM6Oho7d+7EoUOHUFJSAhcXFyxYsAC2trYVPicjIwO///479u7dCwsLC+zatQs9evTgpJ7yuLm54eLFi3j37p3Yw29RURGcnJyQkJCAoKCgCoPd50ur161bh0WLFiE9PR2zZ8+Gj48PfvzxR+zZs6dG5vaUhpfSOS8pKSkwMjIqCy8dO3ak8EJILUVBhgMHz71Adl6xWNpmGKBFUw380P3TnACWZZGWlobo6OiyYPP5R35+/v+ex8DY2PirkGNubg4TE5NqbxjGsiwsLCzQrl07eHl5Vft11pQHDx6ga9euCAgIQJcuXThtOysrCwcOHMCuXbsQFxcHJycnuLu744cffqjwdkhISAhmz56Nhw8fYtSoUdi8eTOMjIw4rQv4FLZatmyJv/76C25ubpy3X4plWfz00084c+YM7t+/X+HuuqVLq4uLi8uWVp86dQpz584FwzDYvXs3Ro0aJdbwIBAI8ODBA/j4+ODs2bNfhJeRI0eiY8eOEtsHiBAiOgoyHHgUmoTHz5M4O57gv4b0MIOpkfp3r2NZFikpKWWh5vOwEx0djYKCAgCfJuw2bdq03JDTrFkzkW9zbNmyBb/99hsSEhKgra1dnZdYY9zd3XHq1CkkJiaK7U2Kz+fj/Pnz2LZtGx4/fgxzc3PMmzcPkyZNgoqKylfXsyyLEydOYNGiRcjNzYWHhwfc3d05H1EbPXo0goKCEBkZKbaN47Zv344FCxbgxIkTGDdu3FePsyyLvXv3Yt68eWjXrh18fHzAMAxmzJiBK1euYMyYMdi5c6fYvp+EQiEePHhQdtsoOTkZhoaGZeHF3t6ewgshdQwFGQ7kFZRg/9nnYgkyKkqy+Hl4G0hJVe8vU5ZlkZSUVO4oztu3b1FU9GkvHGlpaZiYmJTdovo85DRt2vSLN8APHz7A0NAQq1atwqJFi6pVX01gWRYmJiYYMGAA/v777xrp8/Hjx9i2bRvOnTsHVVVVTJ8+HbNnzy531CUnJwcrV67Ejh07YGpqip07d6Jfv36c1fLs2TO0a9cOp0+fxujRozlrt9TNmzfRr18/LFy4EBs3bvzq8c+XVs+ePRubN2/GyZMn4e7uDkVFRezZswdDhgzhvC6hUIiHDx+WhZekpCQ0adKkLLx06tSJwgshdRgFGY74B8YjJDyV8zVM/bqawKq5JsetfkkoFCIxMbHCkFNSUgIAkJWVLQs5pUHnwoULZSM/4pywyoXg4GC0b98et27d+u5SYK7FxsZi165d2L9/P/Lz8zFy5EgsWLAAHTt2/OraV69eYc6cOfDz88OPP/6Ibdu2wcTEhJM6+vTpgw8fPiA4OJjT2zbR0dHo2LEj7O3tcfXq1a/2yomOjsbw4cMRHR2Nffv2oWvXrpg2bRpu3ryJyZMnY+vWrdDQ0OCsntLwUnrbqDS8jBgxAqNGjaLwQkg9QkGGIzy+AEcvvUJufgknIzNCgQCxb0MxdaT9NyeNiptAIEB8fHy5c3JiYmLA4/EAfNoz5b8jOKUfhoaGteJN47fffoOnpydSUlIkFrpyc3Nx+PBh7NixAzExMejcuTMWLFiAIUOGfDHaxbIsfHx84O7ujo8fP2LJkiVYsmRJtSfqlk7S/ueff9CnT5/qvhwAn37uO3XqBIFAgCdPnkBdXf2Lxy9fvoyJEydCW1sbZ8+exYMHD7BkyRJoaGhg3759nI06CYVCPHr0qCy8JCYmwsDAoGzkxcHBoVZ8HxJCuEVBhkPpGQU4fSMCfIGwWmGGYQAleSkc3jEPz0ODsW/fPowfP567QjnC5/MRGxuLnj17QkNDA46OjmWB5927d+DzP23iJy8vj+bNm5cbcgwMDGrkzaV0cnKXLl1w6NAhsff3PQKBAFeuXMG2bdtw7949NGvWDHPnzsXUqVO/+NnJy8vD2rVrsXnzZjRp0gTbt2/H4MGDq7VsvGPHjlBVVcWdO3eq/TqEQiGGDBmCu3fv4smTJ18cF/D50uohQ4Zg+fLlmD9/Pu7duwc3Nzds2LCh2r8nhEIhHj9+DG9v77Lwoq+vXxZeOnfuTOGFkHqOggzHUj7k49y/b1DCF1QpzDAMoK4qjxF9WkKa4WPmzJk4evQoZs2aha1bt1Z7tZE47Nq1C+7u7oiLiytbKsvj8RAbG1vu7ar3799DKBQCABQVFSsMOfr6+pzd/nj16hWsra1x5coVDBw4kJM2uRIcHIzt27fj9OnTUFRUxNSpUzF37twvbie9efMG8+bNg6+vL/r164cdO3agRYsWVerv3LlzGDFiBJ48eVLura3K+P3337FmzRpcvXoVAwYMKPt8eno6xo4dizt37mD16tWQk5PD77//Dn19fRw4cKDs7KSqEAqFePLkSVl4SUhIgL6+PkaMGIGRI0eiS5cuFF4IaUAoyIhBbn4J/n34HrFJOWAYiBRoSq+zaamNbu0MISf7aY4By7Lw9PTE3Llz0a5dO5w9exZNmjQR8yuonKysLBgYGGDZsmVYtmzZd68vKSnB+/fvyw05sbGxZdv+Kysrl92u+u9tK11d3UqFnFWrVmHTpk1IT08X6/461ZGUlIS//voLe/fuRVZWFoYMGYIFCxagS5cuYBgGLMvi8uXLmD9/PhITE7Fw4UIsW7as3JVQ3yIQCGBpaYnWrVvj3LlzVa7Xx8cHo0aNwvr167FkyZKyzz99+hQjRoxAUVER1q9fj3379uHp06eYN28eVq9eDWVl5Ur3VRpefHx84OPjg4SEBOjp6ZXNeaHwQkjDRUFGTFiWRVRsJoJfpSL5w6e9XaQYQPjZV/Hz/9/cSA3tW+mhiW75JwOX7rtRUlKCM2fOwMnJScyvoHKmTJmCO3fu4O3bt9U6FLG4uBgxMTFfLR+PiopCfHx8WchRVVX9KtyU/n9tbe2vQo6trS2srKzqxJ43BQUFOHbsGLZv347IyEi0b98eCxYswMiRIyErK4vCwkJs3LgR69evh5aWFrZs2YKRI0dWKtgdOHAA06dPx+vXr9GyZUvkFZQg9WMBsvOKIRSykJWRgqa6InQaK5WF6s+FhoaiS5cu+PHHH3Hy5MmyoFUautu2bYvu3btj+/btMDExwaFDh9C5c+dKfR1Ylv0ivMTHx0NPTw/Dhw8vCy81eQAnIaR2oiBTAz5kFiI+JQepHwuQmVMEgeDTG4WWhiJ0NJXQzEANqsrfv2WUlpaGMWPG4N69e1i/fj0WLlxYa3Ybffr0Kezt7XHt2rUvbjFwqaioCG/fvv0i3JSGnYSEhLLrGjVq9EXAadSoERYtWoSDBw/ip59+qjVfs+8RCoXw9fXFtm3bcOvWLTRp0gRz5szB9OnToaGhgXfv3mHBggW4dOkSnJ2dsWvXLpFO5QY+BUbLVm0wctxsmLfujpz8krLH/juKaKirAlsLHTQ3Voe0lBTS09PRvn17aGlp4f79+1BSUkJBQQFmzJiBY8eOYfTo0YiIiMCLFy+wePFirFixAgoKCiLVxbIsnj59WhZe4uLioKurW3bbqGvXrhReCCFfoCBTx/D5fHh4eGDDhg0YMWIEDh06BFXV8kdxahLLsmjXrh0MDQ1x+fLlGu+/oKDgq5BT+vH54Yzq6urlzscxNzfndPkv1168eIHt27fj5MmTkJaWxuTJkzFv3jy0aNECN27cwLx58xATE4O5c+fijz/++ObPH8uyCItMx+3H78CyzHdvyZQGGzVVefTuZIgJLkMQGRmJwMBAGBkZ4e3btxg2bBiioqLQt29fXL16FZaWljh06FCFO/v+t57AwMCyOS+xsbHQ0dEpCy/dunWj8EIIqRAFmTrq/PnzmDx5Mpo0aYLz58/D0tJS0iXB09MTM2fOxPv378WyzX5VdezYESoqKpg5c+ZXISc1NbXsOk1NzXLn45ibm0NNTU2Cr+D/paWlYc+ePfj777+RlpaGgQMHYsGCBejcuTO2b9+OVatWQVVVFZs2bcL48eO/Gn0qKuHj8p1oJKTmVbpvhgGEQha3Lh/AMveJ6Nq1C65cuYIJEyZAVVUV8vLyiI2NhYeHB3799ddvTkxnWRZBQUHw9vaGj49PWXgpvW1E4YUQIioKMnVYZGQkhg0bhri4OBw+fBgjRoyQaD25ubkwMDCAu7s7Vq5cKdFaSiUkJMDIyAjHjx8vdwl7Tk5O2e2p/87JSU9PL7tOW1v7q7k4pR+SGBErKiqCl5cXtm/fjhcvXsDGxgbz589H165dsWzZMnh7e6NLly7YvXt32T5ERSV8eN+IxMfswmrvdWRnqY3bl/djzZo1MDMzw9u3b9G2bVscOnQIbdq0Kfc5peGl9LbR+/fvoa2tXRZeHB0dKbwQQiqNgkwdl5eXh6lTp8Lb2xu//PIL1q1bJ7ZzdETh5uaGK1euIDY2VqJ1lNq1axcWLlyItLS0rzZq+57s7OyvRnBKw87Hjx/LrtPV1S33BHIzM7NKryiqLJZlcfv2bWzbtg3Xr1+Hrq4uZs2ahVatWuH3339HREQE3Nzc8Oeff8Iv+CPiU3I4O0rj3LH1iAzzR25uLv78808sXLjwq39zlmURHBwMHx8feHt7fxFeRo4cCUdHx1rxfUIIqbsoyNQDLMti+/btWLRoEbp164bTp09DV1dXIrWEhISgbdu2uHDhgljOzaksZ2dnKCgo4MaNG5y2m5mZWe58nKioKGRlZZVdp6+vX+58nObNm0NJSYnTmiIiIrBjxw4cPXoUQqEQ48aNg5aWFvbs2YOO3QZjwKj5nPXFsiz4vGLc9FmP3Ts3f7ERHsuyePbsWdlto3fv3kFLS6ssvHTv3p3CCyGEMxRk6pF79+5h1KhRkJaWxtmzZ+Hg4CCROuzt7dG4cWPOw0NlpaenQ09PD56envj5559rpE+WZfHx48dyj3SIiopCTk5O2bVNmjSpMOSIusqnPB8/fsS+ffuwe/duJCUloUfPPug7ZjmkZWTBMBzutcIKYWqsgSE9zMGyLEJCQsrCS0xMDDQ1NcvCi5OTE4UXQohYUJCpZ5KSkjBy5EgEBgZi27ZtmDlzZo0vNz58+DCmTp2K6OhomJqa1mjfn9u/fz/c3NyQkpICbW1tidVRimVZpKenf3WbqvQjL+/TBFyGYWBkZFTufBxTU1ORN/QrKSmBj48PrtwMQgcnF25DzGc+Rl/Haa+jZeFl2LBhGDVqFIUXQkiNoCBTD5WUlGDRokXYuXMnxo8fD09PT85vY3xLQUEBDAwMMGPGDKxbt67G+v2v/v37o6ioCH5+fhKrQVQsyyI1NbXcUZzo6GgUFBQAAKSkpGBsbFzuRoAmJiblrhQ6duklPmQVAuA+0AoFAgTcPAkd1aKykZfafgo6IaR+oSBTj3l5eWHatGkwMzPD+fPn0bx58xrre+7cuThz5gzi4+Mlcj5UVlYWdHR0sHXrVsyePbvG++cSy7JITk6uMOQUFRUBAKSlpdG0adMvR3Cat8CbD1rirA6GuqoY1c/i+5cSQogYUJCp5168eIFhw4YhPT0dx48fx6BBg2qk39JDGo+fuoAWreyR+rEAHzILwBewkJZioNFIHrpayjDWa4Qmuiqc3/46ceIEJkyYgISEhFp3NhWXhEIhkpKSyg05b9++hUFTS8z+bb9Ya5CTlcIsF7s6s2MyIaR+oSDTAGRlZWHSpEm4fPkyPDw88Mcff4h9v47ouEwcOXMbjXWaofT97b/fQaU7xqqryqN9Kz1Ym2tBSoqbN8OhQ4ciJSUFjx494qS9ukggEOBhcDQCX+eKva85Y+0gW86ZTIQQIm6iZg86VrYOU1dXx4ULF7B27VqsXbsWP/zwwxf7oHCpoIiHy37RuOz3Fo11mgL4FFbKi8Gln8vKLcatx7E4fSMCmdlF1a4hLy8Pvr6+GDZsWLXbqstKSkogEAhrpC8hV5vTEEKImNDSgzpOSkoKv/76K9q3bw8XFxe0a9cO586dQ7t27TjrIzO7CN7/RKKgiPe/z1RudCX1Yz6OX3mFIT3NYaxf9RE9X19fFBUV1dsgU1xcjJSUFCQlJZX7kZycjKSkJGRmZqJ1O2dMnrNR7DXJyNDfOoSQ2o2CTD3Ru3dvBAcHY8SIEejSpQv+/vtvTJkypdrt5uQV44xvBAqL+VXeOZZlAYGAxYVbURjZryUMtKu2K+65c+dgY2NTo5ObucDj8ZCamlphQCn9+O9ompycHAwMDKCtrQ1FRUVoaGhATk4OysrKSE2KEXvd6qrykP7OwZOEECJpFGTqkaZNm+L+/fuYO3cupk6disePH2Pnzp1V3oSNZVlcv/+uWiGmrC18uk1x1f8tJg+xhlwl510UFRXh6tWrWLx4cfUK4RCfz0daWtoXoyXlfaSnp+PzqWgyMjIwMDCAvr4+DAwM4OjoWPb/ZWRkkJGRgcTERLx58wYhISEIDAwEACgqKqJNmzYYOHAgbGxtUcSwELLimYjLMIB+FQMnIYTUJAoy9YyCggL27dsHe3t7zJo1CyEhITh79iyaNm1a6bZCI9KRlFb505QrwrJAfiEP94Li0cuhWaWee+vWLeTl5WH48OGc1VMRoVCI9PT0cm/rfP6RmpoKofD/56pIS0tDT08PBgYGMDAwQKdOncr++/MPTU1NCAQCvH79GqGhoQgNDcXNmzcRGhpadgyClpYW7OzsMGbMGNja2sLOzg7m5uaQlpaGQCDAjRs3cO/Zc2gbtoK0NPc/xiwLNDdW57xdQgjhGq1aqseCg4MxfPhw5OXl4dSpU+jdu7fIzxUIhPD0eY6iYr5Yaps2og1UlUXfh2bKlCl4+PAhXr9+XeXlwKXHDFQ096T0IyUlBXz+/79uhmGgq6v7VSApHVEp/dDW1i531Vhubi7CwsIQGhqKkJAQhIaG4uXLlygpKQEANG/eHHZ2drC1tS0LLfr6+l+9zqSkJBw8eBAHDhxAXFwcevcfjn6jl1bpa/E9SgoymD7ShrPVZoQQUlmiZg8akanH2rVrh+DgYIwbNw79+vXDqlWrsHTpUkiJMO8hOi5LbCGGYYDnb9LRxU60fWB4PB4uXboENze3ckMMy7LIysr67iTZ5OTksvBQSltbuyyItG7dGn379v0qqOjq6oq0JT/LskhKSiobZSkNLdHR0QA+zXmxtraGra0tJk+eDDs7O7Rp0+abP6BCoRA3b96Ep6cnLl++DHl5ebi4uMDV1RXt2rXD2X/fIDEtj7OTr0vZt9GnEEMIqRMoyNRzmpqauHbtGlauXIlly5bh6dOnOHr0KNTU1L75vMj3GWX7wXCNZYGImI/fDTIsyyInJwfnzp1DRkYGFBQUsHHjxnKDSukuuKU0NTXLwkjLli3h7Oz81YiKrq5ulXcnFggEiIqK+iKwhIaGIi0tDcCnpfG2trYYOHBg2SiLhYWFyP2lpqbi8OHD2L9/P2JiYtC6dWvs2LED48eP/+Lfrm8XExy59BICATf/UAwD6Gkpw9ZCh5P2CCFE3OjWUgNy5coVTJgwAdra2jh//jxat25d4bWe3mHIL+RV+DgX+nZQRXpa8jcnypaeRVRKXV39u7d49PT0qnXK9H8VFBTg5cuXXwSW58+fl9VmbGz8xW0hW1tbNG3atNK3wFiWhZ+fHzw9PXHhwgVIS0tj1KhRcHV1hYODQ4XtPQ6JwcPnGdV+nQwDyMtKY+xAK6irinaAJSGEiAvdWiJfGTRoEIKCgjB8+HB06tQJBw4cgIuLy1fXFRXzxR5iAGDQ0DF49yYUAKCqqloWRIyMjGBvb18WSmbNmoUhQ4Zg165dYj8k88OHD1+NskREREAoFEJaWhqWlpawtbXFiBEjYGdnBxsbG2hqala7z6NHj2Lfvn148+YNLCwssHHjRkycOBGNGzf+5nNTUlIwZdxANG3RCb2HzoRQKBTp1uF/CQR8KCrIYmTflhRiCCF1Co3INEAFBQVwdXXFiRMnMHfuXGzatOmLWx7ZecU4eO6F2OtorlMEKzNd6OvrQ0Wl/KW+Dx48QNeuXREQEIAuXbpw1jfLsnj37t0XoSUkJASJiYkAAGVlZdjY2HwxytKqVSsoKipy1n9AQAA8PT3h4+MDlmUxYsQIuLq6wtHRUaTRnOTkZPTo0QM5OTnw8/PD2Uu3wZNvDlU1DVR208L4mBeIfHYFVy76iP2YC0IIEQWNyJAKKSkp4dixY+jUqRPmz5+P4OBgeHt7w8DAAEBl3wKrrk3r1jAx/PZcnXPnzkFPTw8ODg5V7qekpATh4eFfjLKEhoYiJycHAKCnpwdbW1tMmDChLLSYmZlVaWTjezIzM3H8+HF4enoiPDwcZmZmWL16NSZPngxtbW2R20lOToazszPy8vLg7++P3NxcrPhtLn79zQN2nX5CyOsUsCwDKYZBeX+pSDGAkAUaKcuhYxt9ZDTNR8/Vl7B69WqsWLGCuxdMCCFiRiMyDdzDhw8xcuRICAQCeHt7w9HRETy+ELtOPhN73+MGWkJXU7nCx1mWhYmJCQYMGIC///5bpDazs7O/CCshISEIDw8Hj8cDwzAwNzf/YpTF1tYWenp6XL2kCl/HkydP4OnpiTNnzoDH42HIkCFwdXVFjx49Kh2YkpKS4OzsjIKCAvj5+cHQ0BDt27eHnJwcHj9+DDk5OQwaPATqOuYYO2kGktPzkZNfApZlwbJCxMW8Rv/eXdHSRAtNDRqVjf6sXLkSf/75J+7cuYPu3buL40tBCCEioxEZIpLOnTvj2bNnGD16NHr06IFNmzZh/vz50GikgMyc6h/0WBEphoGW+rdv0zx79gyxsbHlboLHsiwSExO/uC0UGhqKd+/eAQDk5eXRunVrdOjQAdOnT4etrS3atGlT4S0sccjJycHJkyfh6emJsLAwNGvWDB4eHpgyZUqVw1NiYiKcnZ1RWFgIf39/NG/eHIsXL0ZUVBSCg4MhJyeHtLQ03Lh+Fbt370b/bqZfPP/p06ewnzwZEwYHoVmTL4968PDwgL+/P8aOHYvQ0NBKjRARQoikUJAh0NXVxa1bt/Drr7/C3d0dAQEBcBzwM4RSjSElJZ75EtqNFSEt/e2RiHPnzqFx48bo0qULXr169dX+LKVnE2loaMDOzg7Dhg0rG2WxsLAQae8XcQgODoanpye8vLxQWFiIQYMGYf369ejdu3e15p+UhpiioqKyEBMQEIDNmzdj/fr1sLa2BgD4+PiAYRiMGDHiqzYsLS0BAK9fv/7qYFFpaWmcPHkSNjY2mDx5Mq5cuSKW22uEEMIlCjIEwKdTtAcMGIDAwECcP38ewWFRmPv7IbH116ZF+X/t5+fn4/nz5wgJCcHff/8NWVlZaGholO0T06xZM9ja2mLu3LllocXIyKjKu/1yJT8/H6dOnYKnpyeCgoJgaGiIRYsWYerUqTA0NKx2+wkJCXB2dkZJSQn8/f1hamqKvLw8TJo0CZ07d8bChQvLrj158iT69u0LLS2tr9pRVVWFoaEhwsPDy+3HwMAAx44dw4ABA7Bt27Yv2iWEkNqIgoyE8AVCZGYXgccXgmEANRV5KCnK1ngd0dHROHbsGI4dO4bY2FiYmZlh7ty5uHbtGlIT30K3iSm4nv4rKyMFC5PGSE1N/Wqp85s3b8CybNmZQj179izbVM7GxgYaGhqc1lJdz58/h6enJ06cOIHc3Fz0798fly5dwoABAzgbEYqPj4ezszP4fD78/f1hYmICAFi0aBFSUlLwzz//lI30xMTE4NGjR/Dy8qqwPSsrqwqDDAD0798fv/zyC5YuXYpu3bqhY8eOnLwOQggRB5rsW4Ny80vw/E06omMzkZFT9NWuuUoKMjDSa4Q2LbVhqKsitlGGnJwceHt74+jRowgICECjRo0wevTosr/uGYZBbm4u5v2yHNadx3He/+ugy7h27gCSk5MBfBolKB1dKf24dOkStm3bhvT0dMjL1659TQoLC+Ht7Q1PT088evQIenp6mDp1Kn7++Wc0a9aM077i4uLg7OwMgUAAf3//svb/+ecf9OvXD3///TdmzJhRdv3atWuxdu1apKamQlm5/InUCxYswPXr1xEZGVlhvyUlJXB0dERaWhpCQkK+uxM0IYRwTdTsQUGmBhQW8+H/NB6vYz5+d9v/0scbqymgT+dmMNDhZnKqQCDAnTt3cOTIEVy4cAFFRUXo3bs3Jk+ejCFDhpS7PwrLsti69xJYBQNO5koIBHwkvg9H9LPzX+yEa2Ji8lX7tra2sLKy+ubIQk17/fo1PD09cfToUWRlZaF3795wdXXF4MGDISvL/WhaXFwcnJycynb8LQ0xmZmZsLa2hrW1NXx9fcsCL8uysLa2hp2dHU6cOFFhu/v27cOMGTNQUFDwzZD4/v172Nraok+fPjhz5ozEb98RQhoWWrVUS7xPzMb1++9QXPLpAMbvxcbSxzNzinD6RgTaW+uhq12TKh/gFxkZiaNHj+L48eNISEhAy5YtsXz5cowfP/67czcYhsG86YNx2OcpsgtRzTDDQl1VAbN/mwBFhSnfvPLt27cICwuDh4dHNfrjRnFxMc6dOwdPT0/cu3cP2tramD59OqZNmwYzMzOx9RsbGwtnZ2cAgL+/P5o2bVr22Jw5c5Cfn4+DBw9+ES6eP3+O8PBwbNq06ZttW1lZQSgUIioqqmyCcHmaNWuGgwcPYsSIEejZsydcXV2r+aoIIYR7FGTE6M37DFy7F1OlgxdLnxP0MgU5ucUY4GgqcpjJysrCmTNncOTIETx+/Bjq6uoYM2YMJk+ejI4dO1bqL2sZaSlMHtEBJy8FIyMPVd4CX7uxEob3agFFhe+PXJw/fx6Kioro379/pfvhSlRUFPbt24fDhw/j48ePcHJywunTpzFkyBCx3+p6//49nJ2dwTAM/P39YWxsXPbYuXPncPLkSRw/fvyrIHry5EloaWmhd+/e32y/dOVSeHj4N4MMAAwfPhwzZszA/Pnz0blz52+ez0UIIZJAQUZMElJyqxxi/utNbCaUnsahR6emFV4jEAhw8+ZNHDlyBBcvXgSPx0O/fv1w5swZDB48WORDFHk8HiIiIr7anyUrKwsdug7C0PG/QFZOHgwj9d1AJBB8GoWKeemHeZuXQFrEAHTu3Dn069evwjke4lJSUoJLly7B09MTt2/fhoaGBiZPnozp06fDwsKiRmp4//49nJycIC0tDX9/fxgZGZU9lpqaCldXVwwbNgzjxn05d0koFOLUqVMYNWrUd29zaWpqQkdH55sTfj+3detWPHjwAKNGjUJQUFCN/7sQQsi3UJARAx5PgBv3YzhtMzQyHc2N1dHU4MtJl+Hh4WW3jpKTk2FlZYVVq1Zh/Pjx0NfX/2abubm5CAsL+2J/lpcvX6KkpAQA0Lx5c9ja2mLhwoVlO+Gqa2jj+NlbSMmUQiP1T8t7pUoDDQMIhZ+Sm5ysFNJTIrFn+3Kkp8ThpzE9RVr9kpCQgCdPnuD48eOV/RJV2bt377B//34cOnQIqamp6NKlC44dO4YRI0ZwdraSqHU4OTlBVlYWfn5+X4QYlmUxffp0SEtLY+/evV+FyICAACQkJGDs2LEi9WVlZYXXr1+LdK2CggLOnDmDdu3aYc6cOTh0SHzL8gkhpLIoyIjBo7Ak5BXwyj3jpqoYBvANeI+fh7dGdnYWTp06haNHjyIwMBCNGzeGi4sLJk+ejHbt2n31JseyLJKTk7/aUC46OhoAICsrC2tra9ja2mLy5Mllu+BWtFLFbeIAhIaGYtqM+VBupIuffp4FI+NmkJZioK4qDx1NJWg3VkJSohbWLPt0COOYMWPw9u3b747iXLhwAbKyshg4cCAHX7WK8fl8XL16FZ6envjnn3/QqFEjTJgwAa6urt+93SIOMTExcHZ2hpycXNmxA587evQoLl++jIsXL5a74+7JkyfRrFkzdO7cWaT+LC0tcf/+fZHrs7CwwN9//43JkyejZ8+eX40IEUKIpNCqJY6V8ATw9A4Djy8US/uRwRdxxHMTBAIBBgwYgEmTJmHgwIFl8zYEAgGioqK+2p8lLS0NAKCmpvbFiiFbW1tYWlp+cfq1qDIzMzFx4kRcu3YNK1aswO+///7V/Jmff/4Zx48fR0lJCfbt24fRLhMR8S4DSWl5SPlYgOJiPlgA8nLS0NNUwo2rPviYGI5zPier/bUqT3x8PA4cOIADBw4gKSkJHTt2hKurK0aPHi2xWyZv376Fs7MzFBQU4OfnhyZNmnzxeFxcHFq3bo2hQ4fiyJEjXz2/pKQEenp6cHNzw9q1a0Xqc/fu3Vi4cCHy8/Mrtd/NxIkTceHCBTx79gzm5uYiP48QQiqLll9LyPM36bj1KFYsbQsFAqSnxEBPMQVjx46FqqoqXr58+UVoef78OQoKCgAARkZGXx2Q2KxZM06X0QqFQqxduxbLly9H//79cfz4cTRu3Ljs8ejoaLRs2RK6BiYYNHoeWrZ2AMMAYFHuiJVAIIC0tDSM9VXRta0h9LSqHy4EAgF8fX3h6emJa9euQUlJCePGjYOrqyvs7Oyq3X51vH37Fk5OTlBUVCw3xAiFQvTu3RtRUVF48eJFuaNkly9fxo8//oiXL1+iVatWIvV7+/Zt9OrVC5GRkWjRooXI9ebl5aFdu3ZQVlbGo0ePat0eP4SQ+oOCjIRc9X+LqLhMTib5lodlWYTc3oWQZ8GIiIiAUCiEtLQ0LCwsvggtNjY25W5RLy6+vr4YO3Ys1NXVce7cubKAIBSyWLR8J/RM7QEA0tKi/fVfGnY6tNZDJxsDyHznXKbyJCUl4dChQ9i/fz/i4uJgZ2cHNzc3uLi4QFVVtdLtcS06OhpOTk5QVlaGn58fDAwMvrpm165dmDt3Lm7duoWePXuW286YMWPw+vVrhIWFidx3cnIyDAwMcOHCBQwZMqRSdYeGhqJTp06YPn06du7cWannEkKIqGgfGQlJ/pAvthADfNrbJSuXj+7du2P+/PmwtbWFtbV1jU5KLU+/fv0QHByMESNGoHPnzti7dy/Gj5+Aa/di0MS8S6XbK/0aPn2RguT0fAzpaQZZme8fuCgUCnHr1i14enri0qVLkJOTg4uLC9zc3NC+fftas6lbVFQUnJ2doaKiAj8/v3InZr958wZLlizB7NmzKwwxubm5uHz5MlasWFGp/vX09KCuro7Xr19XOsjY2tpiy5YtmD17Nnr06FHp5xNCCJcoyHBIKGSRm18i9n42btkJq+Y1N9oiKhMTEwQEBGDWrFmYPHky3n9QhKqWabXbTUjNxWW/txja07zCvXTS0tJw+PBh7Nu3DzExMbC2tsb27dsxfvx4qKurV7sGLr158wbOzs5o1KgR7ty5U26I4fP5mDhxIgwNDbFhw4YK27p48SIKCwvh4uJSqRoYhoGlpaXIS7D/a+bMmbh9+zamTJmCtm3bfrHXDSGE1CQKMhwSCMU4FPMZvqBm+qkKRUVFHDx4EO27/IAimYr3vakMlgVik3IQ8joV7VrpffZ5Fv7+/vD09MT58+chJSWFUaNG4fjx43BwcKg1oy+fi4yMhLOzM9TV1XHnzh3o6emVe93GjRsRGBiIBw8eQElJqcL2vLy80K1btyoFCSsrK4SEhFT6ecCnIHTw4EHY2dnBxcUF/v7+YjmmgRBCvoeCDIekq3iMQGWtW7sGbFEKjIyMYGRkBENDw7L/1tHR4eRcpOrIL+RBoGAC8AXg8uTs+88SYWqkDiEvH0ePHoWnpyfevHmDli1bYuPGjZg4ceIXE41rm9IQo6GhgTt37kBXV7fc68LCwvDHH39gyZIl6NSpU4XtpaWl4ebNm/jrr7+qVI+lpSVOnTpV5d2aNTQ0cOrUKXTr1g0rVqwQecUUIYRwiYIMh6SkGKgqy4n99pKaiiwiY1MRFBSEhIQEFBcXlz0mJyeHJk2alAWb/wYdIyMjaGpqinW0IjQiDXyBEFyGGODT/JeNO72wbfVMCIVCDB8+HPv27YOjo2OtHH35XEREBJydndG4ceNvhpji4mJMmDABlpaW35334u3tDYZhMGLEiCrVZGVlhYKCAsTFxVX51G4HBwesWbMGv/76K5ydnb97PAIhhHCNggzH9LWVkVdQIrYJvwwD7Nq+AbIyn/6CZlkW6enpiI+PR3x8PBISEsr+OzY2FgEBAUhMTASfzy9rQ1FR8Ytw89+gY2RkBDU1tSqFA75AiLDIdDG9fgYaehZYtXotJk+aUO7GcLXR69ev4ezsDC0tLdy5cwc6OjoVXvvHH38gIiICQUFB313afPLkSfTv3x+amppVqsvKyqqsvqoGGQBYtGgR/Pz8MH78eISFhVV4u4wQQsSBggzHmho0wpv3mWJpm2EAA22VshDz6XMMdHR0oKOjg3bt2pX7PIFAgNTU1K+CTnx8PCIjI3Hr1i0kJydDKPz/TfxUVFS+GXSMjIygoqLyVV9JaXkoLhFw/+L/R0ZWDoNG/gRtbQ2x9cGl8PBw9OjRA9ra2rhz5843w9fDhw+xceNGrF69Gm3atPlmuzExMXj8+DFOnTpV5dqMjIygpKSE8PDwah3QKSUlhWPHjsHGxgYTJkzAP//8I/Hbm4SQhoOCDMcsmjWG/9N4sezsy7KAnWXFf81XRFpaGgYGBjAwMIC9vX251/D5fCQlJX0VdOLj4/H8+XNcu3YNqampXzxHXV39q6CjZWQLQBdc31YqJcUAqR/zYd609geZV69eoUePHtDV1cXt27e/GWLy8/MxadIkdOzYEYsWLfpu26dOnYKysjIGDRpU5fqkpKSqtXLpczo6Ojhx4gR69+6N9evX47fffqt2m4QQIgoKMhyTlZWGrYUOgl6mcHrWEsBCVUkOzY3VOW21lIyMDIyNjb+5+qW4uBhJSUlfBZ34+Hg8ffoU586dQ59hc2DXqa/IG99VlpAFPmQWiqVtLr18+RI9evSAvr4+bt++/d3NCZcsWYLExERcv379u0cGsCyLkydPYujQodU+VqEyh0d+T8+ePbFs2TIsX74c3bt3R5culd8/iBBCKouCjBh0stFH5PsM5OZzOVeGQVTIVfAGtYS0ggJXjVaKvLw8TExMYGJiUuE1F25F4l1irljrKBLjrSsulIYYAwMD3L59+7tzWG7duoW//voLu3btEun8orCwMLx+/Rpbtmypdq2Wlpa4fPkyWJblZML0ihUrcPfuXbi4uCAkJKTK83cIIURUdCNbDGRlpDGgmymnN1ekSpKxc+sqdOzYEa9eveKwZW7JVuIAwqqqqWXuVfHixQs4OzujSZMmIoWYrKws/PTTT+jZsydmzpwpUh8nT56EtrY2evXqVe16rayskJ2djZSUlGq3BXwa2fPy8kJ+fj6mTJkCEU5AIYSQaqEgIyYGOioY6NQcDFP92SIWJo0xd+pABAYGQigUon379tizZ0+tfJNQUZaDlBiXQjMM0Eil8id114Tnz5/D2dkZRkZGIoUYAJg3bx5ycnJw+PBhkSbICoVCnDp1CqNGjeJkAzpLS0sA4GSeTClDQ0McOXIEly9fprOYCCFiR0FGjMyMNTC8dwsoKsigsu/tpQHIvo0++nczgZQUg9atWyMwMBBTpkzBzJkzMXToUHz48EEstVeVrqYShGIMWCwL6GpW/0RsroWFhaFHjx5o2rQpbt26JdLGfBcvXsSxY8ewc+dOGBkZidTPvXv3kJiYiLFjx1a3ZACAqakp5OTkOA0yADBo0CDMnz8fixYtQnBwMKdtE0LI5yjIiJmxfiNMHmqNVmZan8LJdwJN6eNa6opw+cESXeyafDF3QVFREX/99RcuXbqEgIAA2NjY4M6dO2J8BZVjoPP1kmyuJcVFfLFUXNJCQ0PRo0cPNGvWTOQQk5aWhunTp+PHH3/ExIkTRe7Ly8sLzZo1g4ODQ3VKLiMjI4OWLVtyNuH3c+vXr0ebNm0wevRo5OTkcN4+IYQAFGRqhIKcDPp0bobpI23gYGMAncaK5R5+qKosB0tTTYwZYIHxg6ygp1XxyMPgwYMRFhYGCwsL9OrVC7/++it4PJ44X4ZI1FTkYaSnWukRKFGwLIv0lHf4oW93mJubY9WqVYiLi+O+o0oICQlBjx49YGpqips3b0JD4/vLwlmWhZubG1iWxb59+0SeZFtcXIyzZ89i7NixnO5kzNUS7P+Sl5fHmTNnkJaWVvZ6CSGEawwrwm+XnJwcqKmpITs7G40aNaqJuuo9gVCIrJxi8PhCSEsxUFWRg4Jc5SfKCoVCbNq0CR4eHrCzs4OXlxfMzMzEULHo3sZl4ZJftFja7tOlGTKTX+PQoUPw8fFBQUEBevXqhSlTpmDIkCFQqMEVXc+ePUOvXr1gZmaGf//9V+RTto8fP46JEyfi3LlzGDZsmMj9Xbp0CUOGDMGrV6/KduXlwsqVK/H3339/tU8QV06fPg0XFxccOHAAU6dOFUsfhJD6R9TsQSMyEiItJQVNdUXoaSlDu7FSlUIM8GlTsyVLluDhw4fIyMiAnZ0djh07JtG/fk2N1NDMoBGnozIMA+hrKcPKVBPdunXD4cOHkZycjAMHDqCwsBAuLi7Q19fHrFmzEBQUJPbXHxwcjJ49e8Lc3LxSISY+Ph5z5szB+PHjKxVigE+3lWxsbDgNMcCnEZm0tDSxzbcaM2YMfv75Z8yZM6dWr7gjhNRNFGTqiQ4dOiAkJATDhw/HpEmTMH78eGRnZ0ukFoZh0LtzM8jKSHG2BF2KYdCvq8kXt+RUVVUxZcoU3L9/H5GRkZgxYwYuXryIDh06wMbGBtu2bUN6ejpHFfy/oKAg9OrVCy1btqxUiGFZFlOnToWKigp27dpVqT5zcnJw+fJlzib5fu7zM5fEZceOHTA1NcXo0aNRUFAgtn4IIQ0PBZl6RFVVFUeOHIGXlxeuXr0KOzs7PHr0SDK1KMthaE9zSEkz1Q4zUgwwuIcZNNQqvm3UokULrF27FrGxsbh+/TpatmyJJUuWwMDAAMOGDcPVq1e/ODizqgIDA9GrVy9YWFjgn3/+gZqamsjP3bNnD27evIlDhw6JHH5KXbx4EUVFRXBxcalkxd9nbm4OaWlpsQYZJSUlnDlzBjExMZg/f77Y+iGENDwUZOohFxcXhIaGQk9PD926dcPq1ashENT8brhNdFUxsk9LKMhXfvk5AAgEfEhLAUN7mcOkiWiBQUZGBv3794ePjw+SkpKwZcsWxMTEYNCgQTAyMsKSJUsQERFR+WIAPH36FL1794aVlVWlQ0x0dDQWLVqEGTNmoE+fPpXu28vLC46OjiIv064MeXl5NG/eXCwTfj/XqlUr7Ny5E/v378eZM2fE2hchpOGgyb71GJ/Px59//ok1a9aga9euOHHihFjeCL+nsJgPvydxiHiXAYbBd49tEAj4kJaWwYtgf4Q98EFw0ONqr9IJCQnB4cOHceLECWRmZsLBwQE//fQTRo8eLdL39JMnT9CnTx9YW1vjxo0blfo5EAgEcHR0RGpqKsLCwip9PlJqaioMDAywZ88eTJ8+vVLPFdXQoUORn5+Pf//9Vyztl2JZFmPHjsW1a9cQEhKC5s2bi7U/QkjdRZN9CWRkZPDnn3/Cz88P7969g42NDc6fP1/jdSjKy2CAoykmDLKCpYkGSkqKyh777946fH4Jkt6F4u910/Di4WmEPHuKo0ePVrsGOzs77Ny5E8nJyfD29oaamhpcXV2hp6eHSZMmwd/fv8K9aR4/fow+ffqgdevW8PX1rXSY37x5Mx49eoSjR49W6ZBHb29vSEtLY8SIEZV+rqgsLS3FemupFMMw8PT0hI6ODsaMGYOSkhKx90kIqedYEWRnZ7MA2OzsbFEuJ7VQRkYGO3z4cBYAO23aNDYvL08idfzzzz+slJQ0+/BpGPsqOp0NfJnMPn2RzL54k86mfsxnFyxwZ9XU1FglJSV21qxZrJSUFKuioiKWeuPj49k1a9awZmZmLADW1NSU/fPPP9nY2Niyax4+fMiqqqqyXbt2ZXNycirdx/Pnz1k5OTl28eLFVa7T3t6eHTx4cJWfL4rjx4/X6M94YGAgKysryy5YsKBG+iOE1D2iZg8KMg2IUChk9+/fzyopKbEWFhZsSEhIjdewYMECtkmTJqxQKCz38cTERFZOTo51cHBgNTU12dmzZ7MAWDc3N7HVJBQK2Xv37rGTJ09mlZWVWYZh2N69e7MrV65kVVRU2G7durG5ubmVbre4uJi1sbFhra2t2aKioirVFh0dzQJgT58+XaXniyo4OJgFwD558kSs/Xxu27ZtLAD2ypUrNdYnIaTuEDV70K2lBoRhGPz8888IDg6GgoIC7O3tsW3bthrd7v+ff/5Bv379KpzzYmBggClTpiAyMhIZGRkwNzeHuro69u3bh5iYGLHUxDDMV3vTpKamYsWKFSgqKoKFhQUiIiIqvTfNn3/+iVevXuHYsWOQl5evUm2nTp2CiooKBg0aVKXni6ply5YAuD088nvmzZuHQYMGYdKkSUhISKixfgkh9QyXqYjUHUVFRay7uzsLgO3Xrx+bkpIi9j5jY2NZAKyPj883r4uJiWGlpaXZNm3asObm5uzZs2dZAGyHDh3EXiPLsuz9+/dZFRUVtkOHDuwvv/zCGhgYsADY1q1bs1u3bmXT0tK+28bjx49ZKSkpdtWqVVWuQygUshYWFuyECROq3EZlNGvWjF20aFGN9FXqw4cPrKGhIdutWzeWx+PVaN+EkNqNbi0Rkdy4cYPV0dFhdXR02Bs3boi1r3379rHS0tJsZmbmd6+dNGkSq6WlxQJgL126xLZv354FwF6+fFmsNd67d49VVlZmnZycyubl8Hg89vr16+yIESNYWVlZVkZGhh06dCh75cqVct988/Pz2RYtWrAdO3as1pvzs2fPWABi/3cpNWDAAHbgwIE10tfn7t27x0pJSbG///57jfdNCKm9KMgQkaWkpLD9+/dnAbDz58+v8nyO7xk2bBjbpUsXka6NiIhgGYZhTU1NWUdHR/bdu3eslJQUq6mpyZaUlIilvrt377LKysqss7Mzm5+fX+416enp7I4dO1gbGxsWAKunp8cuXryYff36ddk18+bNYxUUFL74XFUsXLiQ1dbWrrGRioULF7KmpqY10td/rVq1imUYhr19+7ZE+ieE1D4UZEilCAQCdvv27aycnBxrY2PDhoeHc9p+SUkJ26hRo0rdahk1ahSro6PDAmADAwPZWbNmsQDYpUuXcloby7Ksv78/q6SkxPbo0aPCEPNfz549Y+fMmcNqaGiwAFgHB4ey23Xbtm2rVj18Pp81MDBgZ8+eXa12KuPgwYMswzBsQUFBjfVZis/nsz169GD19PTY1NTUGu+fEFL7UJAhVRIaGspaWFiwioqK7L59+ypcXVRZ9+7dKwsklakFAKulpcW6uLiwRUVFrJqaGisjI8Ppm52fnx+rpKTE9urVS+QQ87mioiLW29ub7dWrFwuAlZKSYidMmMD6+fmxAoGgSjXduXOHBcA+fPiwSs+viocPH7IAJLKajWVZNikpidXW1mb79u1b5a8bIaT+oFVLpEpsbGwQHByMCRMmYPr06RgxYgQyMjKq3a6vry+0tLTQtm3bStUyaNAgSEtL48yZM0hNTcW+ffvA5/O/OHMoJ68Yke8zcC84Ab4B73Dj/jvceRKHF1HpSM8o+OZqIz8/PwwYMABdunTB5cuXoaSkVOnXJi8vj5EjR8LY2BjKyspwd3fHo0eP4OzsDHNzc6xatQpxcXGVatPLywsmJibo1KlTpeupKktLSwA1u3Lpc/r6+jh+/Dj++ecfbN68WSI1EELqHjqigFTo/Pnz+Pnnn6GsrIwTJ06ge/fuVW6rXbt2sLS0xIkTJyr1vCdPnqBTp05QUlLCjBkzsHnzZtja2uL58+e4cO0+ihlNJKTmAfh0QjaLT9/ODBgI//etra4qD1tLHVibaUFOVrqs7Tt37mDgwIHo1q0bLl68CEVFxSq/vitXrmDw4ME4cOAApk6dCpZlERAQgEOHDsHHxwcFBQXo1asXpkyZgiFDhkBBoeIDMIuLi6Gnp4dZs2Zh9erVVa6pKpo0aYIpU6Zg1apVNdrv55YuXYotW7bg/v37NRrkCCG1i6jZg4IM+aaEhASMHz8e9+7dw7Jly7B8+XLIyspWqo3U1FTo6enh+PHjGD9+fKVr6N27N54/f47CwkIkJCQgLiENe0/6wbSFnUhnN5VSUZJFv64mMNZvhNu3b2PgwIHo3r07Lly4UK0Q8+HDB1hbW6N9+/a4cuXKV3vk5ObmwsfHB4cPH0ZAQADU1dUxduxY/PTTT2jXrt1X11+8eBFDhw5FeHh42ShJTenVqxfU1NRw7ty5Gu33czweD927d0dSUhJCQkKgoaEhsVoIIZJDZy0RThgaGuL27dtYvXo11q1bB0dHx0pvTFd6EGFVTn0GgGXLliEtLQ35+fk4cOw87jzLhomZDQDRQwwA5BfycPbfNzh5/j4GDhwIJyenao/EsCyLmTNngsfjYf/+/eVu9KeqqoopU6bg/v37iIyMxIwZM3Dx4kV06NABNjY22LZtG9LT08uu9/Lygq2tbY2HGACwsrKqkTOXvkVWVhanTp1CdnY2fv7550pvREgIaVgoyJDvkpaWxm+//YaAgACkpqbC1tYWXl5eIj/f19cX7dq1g46OTpX67969Ozp37oz2nftAqGwJvoAFI1X5b93S98PUXEX8PGc1Lly48M1bPKI4ffo0fHx8sGfPHujr63/3+hYtWmDt2rWIjY3F9evX0bJlSyxZsgQGBgYYNmwYvL29cfnyZYwbN65adVWVpaUloqKiJH6YY9OmTXHw4EGcP38ee/bskWgthJDajYIMEVmnTp0QGhqKwYMHY9y4cZg4cSJyc3O/+RyBQFB2LEFVMQyDxb8ux9AJywCUf7RBZZm06o741MJqtZGUlIRZs2ZhzJgxGDVqVKWeKyMjg/79+8PHxwdJSUnYsmULYmJiMHr0aBQXFyMmJgYRERHVqq8qrKyswOfzER0dXeN9/9ewYcMwa9YsuLu7IzQ0VNLlEEJqKZojQ6rkxIkTmDFjBnR1deHl5YWOHTuWe11gYCA6duyIe/fuoVu3blXqi2VZ+PwTibjkbEhJSX//CSJSkJPGT0OtoahQuTk/pTUNGDAAYWFhePnyJRo3bsxJTQ4ODoiPj0dBQQEyMzPh4OCAn376CaNHjxb7z17axwK8eJOIE6cuwbZ9F8jLK0JKioF6IwXoaSnDSFcVTQ0aQUqKmzApiqKiIjg4OKCgoADBwcFQUVGpsb4JIZJFc2SIWI0fPx6hoaHQ1NREly5dsG7dOggEgq+u8/X1RaNGjaq1+uRdQjYSUvM4DTEAUMwT4OnLlCo9d//+/fD19cWBAwc4CzEpKSl4+vQpVqxYgeTkZHh7e0NNTQ2urq7Q09PDxIkT4e/vz/khn9FxmTh5NRwnrobjeVQ2Wtl1g4CVRUERH3kFPCSk5OLZq1RcuB2FA+eeI+hlCgSCmjloVEFBAWfOnEFiYiJmzZpVI30SQuoWCjKkypo3b46AgAAsXrwYy5YtQ+/evZGYmPjFNb6+vujVq1elVzp9LiQiDRUcll0tLAu8ePMBPH7l3pRjYmLg7u6OadOmYcCAAZzV4+3tDWlpaQwfPrxsb5obN24gLi4OHh4eZXvTmJmZVWlvmv8qKOLhiv9bXPZ7i7SMAgCfviblBcbSpex5BTzcC07AiavhZc8RtxYtWmDPnj04duwYjh07ViN9EkLqDrq1RDjh5+eHCRMmoLCwEAcPHsSQIUOQmZkJLS0t7N27F9OmTatSu/mFPHh6h3Fc7ZcGdjdFi2aijaoIBAI4OzsjISEBYWFhUFVV5awOe3t76Ovr4+LFi+U+XtHeND/99BOGDBlSqdVXmTlF8PknEvmFvEqt/CrFMJ/mLg3q3hzNjdUr30AVTJ48GWfPnkVwcDBatmxZI30SQiSHbi2RGuXs7IywsDA4Ojpi6NChmDFjBq5duwahUIi+fftWud3UD/kcVvk1KQZIrkQf27dvR0BAAI4cOcJpiImOjsbTp08xduzYCq9hGAbdunXD4cOHkZycjAMHDqCwsBBjx46FgYEBZs6ciaCgoO8uV87NL4G3b9VDDPBp5EYoZHHZPxqxSdlVa6SSdu/eDUNDQ4waNQpFRUU10ichpPajIEM4o6mpWbZc9siRI5gzZw5MTU1hbGxc5TZTMwrEcluplJAFUtJFCzKvXr3CsmXLsGDBAjg6OnJah5eXF1RUVDBw4ECRri9vb5pLly6hQ4cOaNOmzVd705RiWRa+Ae9QUFT1EPNle8C1uzEoLOJVv7HvUFFRgbe3NyIjI7Fw4UKx90cIqRsoyBBOMQwDNzc3BAUFIS8vD7Gxsdi1a1eVNzUrKOSVu8kcl/JFeBPm8XiYOHEiTE1NsWbNGk77Z1kWXl5eGDZsWJXOevrv3jQWFhZf7E1z5coV8Pl8AMCLqA+IT8nlJMSUKuYJcPtJ9ebriKo0pP39998S3X2YEFJ7UJAhYsGyLPh8PgYNGoS5c+di0KBB5Y4QiNgYt8V91f73L1mzZg3CwsJw7Nixam+i918hISGIjIz85m0lUVS0N83gwYNhZGSEJUuW4n4Q94GDZYE37zORkV29fXlE5ebmhuHDh2Pq1Kl4//59jfRJCKm9KMgQsfD19YWioiJOnTqFq1ev4unTp2jTpk3ZcQWiUpCTEVOFn/Uh/+0+goKCsHr1anh4eKB9+/ac93/y5Eno6OigZ8+enLWppaWFuXPnIjQ0FM+ePcPIkSNx99ELFPPEEwoZBgiLrGJQrXRfDA4cOAANDQ24uLiAxxP/bS1CSO1FQYaIha+vL5ydnaGgoIAffvgBYWFhaNOmDfr27YtFixaJvAW+dmNFCMU4ICPFMNDTUq7w8cLCQkycOBG2trZYtmwZ5/0LBAKcPn0ao0ePhoyMeEKbnZ0ddu7cCY8/d0Ck4acqYFkgIiZDLG2XR11dHadPn0ZQUBA8PDxqrF9CSO1DQYZwLi8vD/fv3//iWAJ9fX3cuHEDW7ZswY4dO+Dg4IA3b958ty3db4QMLghZFrqaFc9L8fDwQExMDI4dO1atvXAqcvfuXSQlJVX7tpIoUj8WgqsjHspTWMxHXkHNndFkb2+PtWvXYuPGjfD19a2xfgkhtQsFGcI5f39/lJSUfHW+kpSUFNzd3fH48WPk5eXBzs4Ohw4d+uZEYDUVeehpKYlt5ZK0FFPhPih3797Ftm3bsGbNGlhZWYmlfy8vL5iamsLe3l4s7Zfi8YXIzisWax8AkJ5ZM/NkSi1cuBD9+/fHxIkTkZSUVKN9E0JqBwoyhHO+vr4wNTWFmZlZuY+3bdsWwcHBcHFxwdSpUzFmzBhkZWVV2J6tha5Y5vsyDGBpqlnuPJzc3FxMnjwZXbt2xfz587nvHEBxcTHOnj2LsWPHin1lFo/39fER4lBSUjP9lJKSksLRo0chIyOD8ePHl3tMBiGkfqMgQzjn6+uLfv36ffPNWUVFBQcOHIC3tzf+/fdf2NjYICAgoNxrWzbTgKaaAuejMlIMg45t9Mt9bOHChUhPT8eRI0cgLc3tGU+lrl+/juzs7Bq5rSTuoPT//dRIN1/Q1tbGyZMn4e/vj7Vr19Z8AYQQiaIgQzgVHR2Nt2/ffnVbqSIjR45EWFgYjI2N0b17d/zxxx9le56UkpaWQv9uppzPU3VsbwR1VfmvPn/9+nXs378fW7duhampKbedfsbLywt2dnawtLQUWx+l5OWkIV0Dp1YrK3E/j0gUzs7O+P333/HHH3/g3r17EqmBECIZFGQIp3x9fSErKwtnZ2eRn2NsbAw/Pz+sWLECq1atgpOTE2JjY7+4RkdTCT06VX2H4P9q2UwDthbaX30+IyMDP//8M/r371/l86FEkZ2djStXrmDcuHFi6+NzUlIMtDREP4upqnQ0Kr+hH1d+//13dO3aFWPHjsWHDx8kVgchpGZRkCGc8vX1RdeuXaGiolKp58nIyGD58uW4d+8eEhISYGNjgzNnznxxjU1LHTh3/BRmqnMLo2WzxujXzaTc2y2zZs1CUVERDhw4INbbMRcuXEBJSQlGjx4ttj7+y1BXVay3fjTVFSArK57bcKKQkZGBl5cXioqK8NNPP1V5N2lCSN1CQYZwpqioCH5+fiLfVipPly5dEBoaiv79+2PMmDGYMmUK8vLyyh63s9TByL4toaIkV6mFxAwDyEgz6NWpKQY4mkBa6utvfW9vb5w+fRp//fUXDAwMqvwaROHl5YXu3bvD0NBQrP18rpWZllg3SW7TQkd8jYuoSZMmOHr0KK5evYrt27dLuhxCSA2gIEM4ExAQgIKCgmoFGeDTZmdeXl44fPgwvL29y1Y5lTLSU8XkH1uhs10TKCt+mpNR3kiD4H9zbRiGhZmxBgZ2bw5TI7Vy+0xOTsaMGTMwcuRIjBkzplr1f09KSgpu375dY7eVSmlpKMJAR0UsozIy0gysmjfmvuEq+OGHH+Du7o4lS5YgKChI0uUQQsSMYUUYf83JyYGamhqys7PRqFGjmqiL1EG//PILvLy8kJiYyNltmaioKLi4uOD58+dYs2YNFi5cCKnPRlOEQhbxKTlITs9Hyod8hIS9Ql5+AZo2bYb8giIw0nKQk/vybCQFeWkYaKuglZkWmhupg2GAQYMGISgoCC9fvoSWlhYntVdkx44dWLx4MVJSUqChoSHWvv4r7WMBTl4L53xkxrmjEewsdblttBpKSkrQtWtXfPz4Ec+ePYOaWvkBlhBSe4maPcR/kA1pMERZdl1Z5ubmePjwIX7//XcsXrwY//77L44dOwZ9/U/LpqWkGDQ1UENTAzUIhSxkkY+wqGzwhLKQV5Atd6imqFiAd4nZiEnIhpKCDNj8t7h27RquXLki9hADfDpbacCAATUeYoBPk6bt2+jjcVgyJ+0xDKCvrQJbC8nfVvqcnJwcTp8+DTs7O7i6uuLUqVM1tgSdEFKz6NYS4UR8fDxevXpV7dtK5ZGTk8OGDRtw8+ZNvHr1Cm3atMHVq1e/uCY7rxinb0QgMoEPBUXlT29a33jjKh2RKCjio1C6KVZs9kav3tzX/l9RUVEIDAyskb1jKtKpjQHMm1Y/RDEM0EhZDoOdmtfKkGBqaor9+/fjzJkzOHDggKTLIYSICQUZwol//vkHUlJS6NWrl9j66NWrF8LCwuDg4IBBgwZhzpw5KCwsRGZOEU5de43Uj/lVbltN2wRnfCNQWMT//sXV4OXlBVVVVQwcOFCs/XyLlBSDHxxN0aq5ZrXa0VJXxJgBllBSlMzeMaIYNWoUpk+fjrlz5+Lly5eSLocQIgY0R4ZwYsSIEUhKSsLDhw/F3hfLsvj777+xcOFCWLe2xU8LdqOEj2rP+2AYQKexEsYMsCh3VVN1sSwLCwsLODg44MiRI5y3XxVvYjNx8+F7FBXzADDfHVn5P/bOO6yps43DdxKGyBAQFBABxYkL9957W62i1i1q3aLWVu2wy9pWBbfinri1Tqx7b0HFhQgyBQFZsknO9wclnwjISgD13NfFhSYn7/sEyDm/88yMp5vVs6BJHTO1/JxUTWJiIk2aNEGhUHD79m1Kly6+XjciIiJ5J6/ao+SfhURKPGlpaZw5c0YtYaXskEgkTJ48mdu3b9O0w1ckJslVkrwqCBAWmcCth6GFXywb7t27h7e3d7GGld6nmrUR2gme/OPmjG6p/58OpBIJUqkE6TvCRktTRkO78oz+og7N61l8FCIGQEdHhz179uDn58e0adOK2xwREREVIyb7ihSamzdvEhMTU2RCJgM9Yyts7ZqrfN0b90OobmOMcZlSuR+cD3bu3En58uXp0KGDStctDIIg4Lx0MVWrVmWCQwOi45J5HZlAZEwiqakKZDIJZfS0KV9Wl7KGpZDJPg7x8j52dnasXLmSsWPH0rFjR4YMGVLcJomIiKgIUciIFBp3d3fKli1Lw4YNi3TfO16hSCSFDyllh+fT13RoqrqRCHK5nN27d+Pg4ICGRsn52Lm7u/P48WPWrl2LRCLByKAURgaqFXAlhdGjR3P27FnGjx9P48aNc5zOLiIi8nHxcd5eiZQo3N3d6dKli9qmRGdHVGwSgaFxahExggBezyNISZWrbM0LFy7w6tWrIm+ClxuLFy+mcePGtGrVqrhNUTsSiYS1a9diZmaGg4MDycnJxW2SiIiIChCFjEiheP36NXfu3CnysFJASKxa10+TKwiNKHgV1Pvs2rULW1tbGjdurLI1C4unpyfnzp1j9uzZJbJ8Wh3o6+uzZ88evLy8+Pbbb4vbHBERERUgChmRQnH69GkAunTpUqT7hr1JQKrGa69Ekp74qwqSkpI4cOAAQ4cOLVGCYcmSJVhbW9O/f//iNqVIadCgAX///TfLli3jyJEjxW2OiIhIIRGFjEihcHd3x97eHjMzsyLdNzI6EYUaByBKgDcxSSpZ68SJE8TExJSoaqWgoCB2797NjBkzSlTOTlExdepU+vbty+jRowkMDCxuc0RERAqBKGRECoxCoeDUqVNFHlaC9NCPOhEAuUI1e+zatYsGDRpQo0YNlaynClasWIGuri5jx44tblOKBYlEwqZNm9DV1WXIkCGkpam3EaKIiIj6EIWMSIHx8PAgPDy8WISMhprLgCWgkj4pMTExHDt2rEQl+cbFxbFu3TrGjx+Pvr5+cZtTbBgbG+Pm5saNGzdYsGBBcZsjIiJSQEQhI1Jg3N3d0dfXp3lz1fdyyQ3jMjpqzZERACMD7UKvc/DgQVJSUnBwcCi8USpi48aNxMfHi83hgJYtW/LLL7+wcOFCzpw5U9zmiIiIFABRyIgUGHd3dzp27IiWllaR712+bGm15sgIApQvq1vodXbu3Em7du2oUKGCCqwqPGlpabi4uDB48GAsLS2L25wSwXfffUfHjh0ZNmwYYWFhxW2OiIhIPhGFjEiBiI6O5vr168USVgKwMlfvzC+ZTIK5aeGEzKtXrzh37lyJCisdPHgQf39/Zs2aVdymlBikUinbt29HEARGjBiBQkW5USIiIkWDKGRECsTZs2eRy+V07dq1SPeVy+UcOnSIfr278OLpXRQK1TWt+/8eaTzxOM+d2zcLtc6ePXvQ1NRkwIABKrKscAiCwOLFi+nQoQP29vbFbU6JwszMjB07dnD69Gn++uuv4jZHREQkH4hCRqRAuLu7U6NGDWxsbIpkv7i4OJYtW0a1atXo378/CoWCZvYVkUpV301YKpFyePdqWrZsSZ06dTh79ix5GBKfhZ07d9KzZ08MDQ1VbmNBuHLlCrdv3xa9MTnQuXNnvvvuO77//vsimeIuIiKiGkQhI5JvBEHA3d29SMJK/v7+zJ49G0tLS2bNmkXTpk25desWV65cYbhDD6paG6HaHnMCzepV4MDe7dSpUwcvLy86depEzZo1OXr0aI6CRiEoULzznLe3N3fu3ClRvWOWLFlCzZo1iy0c+DHwyy+/0LRpU4YMGcKbN2+K2xwREZE88Pl1whIpNI8fPyYoKEitF8Tr16/j7OzMgQMHMDAw4Ouvv2bKlClUrFgx03Edm1kRHBZHYnJaoecuCQoFIUE+/Ot7ggULfuTBgwdcvHiRKVOm4OXlRZ8+fahcuTILFy6kSbcO3I0MxDc2gsD4KFL+C3GVkmlio29MkMdjLKrb0rNnz8IZpSKeP3/OkSNHWL9+PVIVlJV/qmhoaODm5oa9vT1jx47l4MGDJaobs4iISFbEM5pIvnF3d6dUqVK0adNGpeumpaWxZ88emjVrRosWLfD09GT58uUEBgby559/ZhExAKVLaTKwa3W0tTQK5ZmRSMDYUAcTrRB++WUBgwcPJjExkbZt2/LgwQP+/fdf7OzsSDLUYU/scxY9OM2ZoCe8iItQihiAJHkqT6PDiLUyotf6X1j7/BoBb4v/zt7Z2RlTU9MSlXhcUrGysmLTpk0cPnyYVatWFbc5IiIiuSAR8hD8j42NpUyZMsTExGBgoN5qEZGST+fOndHQ0ODkyZMqWS86Opr169ezYsUKAgMDad++PU5OTvTs2TPP3oOo2CSOXXhBeFRigWywtjCgR+tK6JTS5MCBAwwfPpw6depw+PBhzM3NSZKnsv/FPS6HvUBQKJDk0S4pEgSgh1UtelrVRiYp+nuHiIgIrKysmDt3Lj/88EOR7/+xMn36dNauXcuNGzeoX79+cZsjIvLZkVftIQoZkXwRHx+PsbExf/31F9OnTy/UWi9evGDZsmVs2rSJlJQUhg4dyowZMwpcUaNQCNz2CuXmgxDS5B/+s5aQ3vROgpzOLWypVaVsphDC3bt36dOnD1KplP1HDnOWcALeRiFQ8PhVXeMKTKjZCg01JCh/iF9//ZU//viDgIAATExMinTvj5nk5GRatGhBXFwcd+/e/ay7IIuIFAd51R5iaEkkX1y8eJGUlJQC58cIgsDFixfp168fVatWxc3NjZkzZxIQEMCWLVsKVRYslUpoWtecrx3s6djUCnNTXWTZtP/V1JBS0Vyfa6c2cuvUcmpXNcmSB9GwYUNu3bqFmYUFf905SUDcm0KJGICHb4LZ+PRagSqgCkpSUhIrV65k5MiRoojJJ9ra2uzevZtXr14xadKkIv29iYiI5B0x2VckX7i7u2NjY0O1atXy9bqUlBT27NmDs7MzHh4e2NnZsW7dOoYNG4aOjo5KbdTSlFGvRjnq1SiHQiEQHZdEUrKcWbNmoUhLYveuLUgkEh5dN8PZ2Y3U1FQ0NTWzrFOhQgW+3bGKf189Q1BBvqcA3IsM5PprP1qUr1z4BfPAzp07CQ8Px8nJqUj2+9SoWrUq69at46uvvqJjx46MGjWquE0SERF5D9EjI5Iv3N3d6dq1a54rOSIjI1m4cCE2NjaMGDGCcuXK4e7ujpeXF+PGjVO5iHkfqVSCcRkdLMrpoS1LJtD/udL2Xr16ERMTw9WrV7N9beDbKM6Eequ8amW3zx1iUgqWy5MfBEFg6dKl9OnTJ9/CU+T/DB06lDFjxjB58mSePHlS3OaIiIi8hyhkRPLMixcveP78eZ7CSk+fPuXrr7+mYsWK/Prrr/Tq1YtHjx7lWwipElNTU8LDw5X/r1+/Pubm5hw7dizb408FPUaC6u1MUci5GPJc5eu+j7u7O48fPxYb4KmA5cuXY21tjYODA4mJ6hehIiIieUcUMiJ55tSpU2hoaNChQ4dsnxcEgdOnT9OjRw9q1qzJP//8w7x58wgICMDV1RU7O7sitjgz7wsZqVRKjx49OH78eJZjY1OSuBsegKKQeTHZISBwNugJaWoYr/AuixcvpnHjxrRq1Uqt+3wO6OrqsnfvXp4/f87MmTOL2xwREZF3EIWMSJ5xd3enZcuWWbLHk5KS2LhxI3Xr1qVLly6EhISwZcsWXr58yffff4+pqWkxWZwZU1NTYmJiSElJUT7Wq1cvnj59io+PT6ZjH0e9UouIySBJkDN48nhu3bqllvU9PT05d+4cs2bNEhu6qYjatWuzbNky1q5dy759+4rbHBERkf8QhYxInkhOTubcuXOZwkphYWH89NNPWFlZMW7cOCpVqsS5c+fw8PBg5MiRaGtrF6PFWckQVBEREcrHOnXqhJaWVhavjP/bSGRqFACCQkFA/BuaNm1K586duXDhgkqrYpYsWYK1tXWJGVj5qTBu3DgGDRqEo6Mjfn5+xW2OiIgIopARySNXr14lPj6ebt268fDhQ8aMGYOVlRWLFy9m0KBBPHv2jCNHjtC+ffsS6wHIEDLvhpf09PRo3759ljyZoPho5Gost5UgQb+iGdra2jx48ID27dvTqlUrTpw4UWhBExQUxO7du5kxYwYaGmJhoiqRSCS4urpiYmLC4MGDM3n3REREigdRyIjkiZMnT2JoaMjs2bOpW7cu//77L7/++itBQUGsXLmSqlWrFreJuZIhZF6/fp3p8V69enHx4kViY2OVjyWlparVFolUygCHQcycOZP4+Hh0dHQICAigZ8+eNGzYkP3796NQKAq09ooVK9DV1WXs2LEqtloEoEyZMuzevRsPDw/mz59f3OaIiHz2iEJG5IPEx8ezZs0aVqxYQXR0NLGxsezatQs/Pz/mzJmDkZFRcZuYZ7LzyAD07NmT1NRUTp8+rXxMpubBihJAR7sUCxcuxNfXl0mTJhEREYGenh4xMTEMHDiQWrVqsW3bNlJT8y6q4uLiWLduHePHjxc70aqRxo0bs2jRIhYvXqyyUR0iIiIFQxQyItkSHBzM3LlzqVixIlOmTCE5OZkFCxZw8+ZNhgwZkm0DuZKOnp4e2traWYRMpUqVqFWrFseOHSMtLY379+8TExSGUECPSJ6QQFltXQDKlSvH4sWL8fX1ZfTo0QQFBWFoaIhEImHkyJFUq1aNtWvXkpSUlOuyGzduJD4+nmnTpqnPdhEA5TywESNGEBwcXNzmiIh8tohCRiQTd+/eZdiwYdjY2LBq1SpGjRrFwoULkUgkTJkypcTmv+QFiUSSqQRbEAT8/PzYs2cPpUuXZufOnejr62Nvb8+JHXvUaotCELDSN870mLm5OcuXL8fHx4dBgwbx/PlzypYti5GRERMnTqRy5cosXbqU+Pj4bNdMS0vDxcWFwYMHY2lpqVb7RdL/nrZs2YK2tjbDhg1DLn+nnD41GN5shpBZ4NsVnjeH5y3AtweEzIGo7ZAWnvPiIiIieUYcGilCWloaW9wP437/Fsn6WphUsUa7tA7amlqUK22A97U7hN5/yvG1W4t84KEqCQ8Pp1mzZhgaGlK+fHlu376trGAyNzfn1atXTJs2jYEDB1K2mg0uzy6pzRYJ8FfTLzDQyrmzsa+vL7/++ivbtm3D1NSUypUrc+vWLQwNDZkxYwZTpkzB0NBQefzevXtxcHDAw8OjUDOrRPLHpUuXaN++PT/++CM/fesAESsh4Soomym+79l75/5RryOYTAEt6yKyVkTk40Gcfi2SK7GxsTj/s4vnpVLQNTNBkMuRSGW838xWkZaGVEMDXQ0t2ltUp6tlTbRkJbsaJiEhgXv37nHr1i3lV0a5rJaWFh07dqRJkyY0adKExo0bY2RkRPny5Zk0aRK//vorgiDwy70TvEqIUXk3GalEQj3jCnxt1yZPx3t7e/PLL7+wa9cuLCwsqFatGlevXkVbW5vJkyfj5OSEqakpTZs2RV9fn7Nnz6rYYpHc+O3XBSgi1vHDJDMkEimQ12aHMkAKptPA8CuQiE5yEZEMRCEjkiP+/v64rF+Db3ktzOxrIAhCnkNGEsCklB6jqzfH1qBkNLpLS0vj8ePHmUSLl5cXcrkcHR0dGjRooBQtu3fvJiIigitXrmRZZ9iwYTx69AgPDw8Aroa+YNvzm2qxeWadjlQ3LJ+v1zx+/JgFCxawb98+bGxssLOz4+LFiygUCnr06MGBAwc4fvw4PXr0UIvNIjmgSEIIdkKIv06hcsT1u4PZryAp2TcJIiJFRV61h/iJ+Yy4fv06zs7OnPe4RU+XeZjppSeb5ifvRQAik+JZfP8MjjVa0tDUSk3W5rC/IPDy5Utu376tFC13794lISEBqVRK7dq1adKkCZMmTaJJkybUqlUrU2LyrVu3ePr0abZr9+rVi507dxIUFISlpSXNy1ficqgP/m/foFBRTxkpEhqZWuVbxADY2dmxd+9ePD09+emnnzhy5AhVq1albt26/PPPPwAcPHiQ6tWrY2trqxJ7RXJBkEPIbCSJNwvvTIlzB4kmlP8ZPuJcNBGRokb0Y37ipKWlsWfPHpo1a0aLFi14GuzPQNffKGWgB9KCnSwVCCgQWP/0Cg/fqLdaIyIigpMnT/Lzzz/Ts2dPypUrR+XKlXFwcODAgQOYm5vzyy+/cOnSJWJjY7l//z7r169n/Pjx2NvbZ6muen/e0rt07doVmUym7PIrlUgZXa05UhWNjpQiQVdTCwfbRoVax97enn/++Ydbt25ha2vLgQMHSEtLo1mzZhw9epRq1arx1Vdf8ejRIxVYLfJBondBwhWy5sEUBAFij6QLGhERkTwjhpY+UaKjo1m/fj0rVqwgMDCQ9u3b4zTTCa8KmgTER6nMw1BaQ5OfG/bGQKtUoddKSEjAw8MjU4jI19cXAGNjY2V4KCOvpVy5cvneY8OGDYwbN47U1NRsu962a9cOfX19jh49qnzscdQrVj66gEIQCpwvI0WClkyDb+p1wlJXtb13+vfvz7Fjx0hNTcXe3p6mTZty/PhxgoKC6NevH/Pnz6dRo8KJJ5FsSAmAlwMAVTZPlIBUF2yOgIZx7oeLiHzC5FV7iB6ZTwwfHx+mTp2KpaUl8+fPp0OHDnh4eHDu3DlK2VfhpQrDJABJaWm4+dzO9+vS0tJ48OABGzZsYMKECdSvXx8DAwNatWrFvHnzCAwMpG/fvri5ueHj45OtZ6YgZDTFi4yMzPb5Xr16cebMGRISEpSP2RmZM712B3Q0tJAWwDcjAQy1SzOnXmeVi5iMn8sPP/zA+fPn0dPTY926dVSoUIFZs2bh5eVF48aN6dq1K5cuqa8K67Mkajuq8cS8iwCKBIhWb/m/iMinhOiR+QQQBIFLly7h7OzMkSNHKFu2LBMnTmTSpEmYmZkBkKqQ8+3NQ8SnqWc2zIKGPTEvXSZH+/z9/TN5Wt7Na6lVq1Ymb8v7eS2q5Nq1a7Rs2ZKHDx9Su3btLM8/ffqUmjVrcuzYMXr27JnpubepSezyucPdiACkSHKdjp1xTAeLavSzsUdbDZVev/76K3/88QcBAQGYmJggCAKnT5/mhx9+4NatW7Rq1YoOHTpw+PBhHjx4QKtWrZg/fz5du3b9qHsCFTvyt+DbEYRk9awvM4LK/6bnzIiIfKaIVUufASkpKezZswdnZ2c8PDyws7PDycmJr776Ch2dzP1Jbr1+ycZn19RihxQJbS2qMvi/3I/IyMhMybi3bt1S5qVYW1tnEi0NGjRAT09PLXZlx/Pnz6lWrRrnzp2jffv2WZ4XBIGqVavSuXNn1qxZk+0aQfFRXAx5zo3XfqQo5JlemyEOdDW0aGlmSxuzqpjqqOf9JSUlYW1tTf/+/bPYKggCx48f58cff8TDw4OOHTvStWtXDhw4wM2bN2nYsCHz5s2jX79+SNU8juGTJO40vPpGvXtYboLSDdS7h4hICUYMLX3CRERE8Pvvv2NjY8OIESMoV64c7u7ueHl54ejomEXEANyLCFRRympWFAhc8n/K0KFDqVKlCiYmJnTv3p1ly5Yhl8uZOHEiR48eJTQ0lJcvX7J3715mz55NmzZtilTEQM7zljKQSCT06tWLY8eO5TiF2lLXiK+qNmGYhg17hs6ig6Y5A63tubJkEzXC5Cxs3JclzQYwoFJ9tYkYgJ07dxIeHo6Tk1OO7+Pu3bscOHCAsLAw5syZg7GxMatXr8bAwIABAwZQp04dduzYQVpammqMEgQQ1DjaoaSQ9Bj1Fn1KIPmxGtcXEfl0EIXMR8STJ0+YMGECFStW5LfffqNXr148evQId3f3XEMFvnERFDxVNXfkmlKCI8Pp3bs3u3btypLX0qtXL8qXz3/JsaopU6YMmpqaOQoZSB8iGRQUxMOHDz+4lqenJ29fhdPXvjmdrOzwPXUVzdAYypbSVXvYRqFQsGTJEvr06UO1atVyPE4ikdC/f3/u37+Pm5ubckClgYEB27Zto1KlSgwfPpzq1avj6upKcnI+QyVp4emt+IOnwYsO8LzBf19NIWA4vP4LEj3SBc6nRLI3eW96VxCkkPxcjeuLiHw6iEKmhJOR89CjRw/s7Ow4cuQI8+fPJyAgAFdXV+zs7HJdIyktlZiURLXbumb3NpydnRkyZAi2trYlMgdDIpFgYmLyQSGT4Sk6duzYB9fy8PCgZs2alCqVXrFlYGBATEyMSu3NCXd3d548ecKsWbPydLxUKmXw4MF4eXmxdetWHj58yIgRIyhdujT79++nYcOGfP3119ja2uLi4pLjPCclqcEQ8k36HKGIFRB/BeRvIEMsC8mQ9DA9aTVwNPj3h7gzhXvTJQlFHKjxxgAUoMjldyAiIgKIQqbEkpSUxMaNG6lbty5dunTh1atXbN26lZcvX/L9998rQyR5IVGuyvLQnEkqon0Ky4d6yQBoa2vTpUuXPAmZ+vXrK/+fEcstCpYsWULjxo1p1apVvl6noaHBiBEjePr0KevXr+fmzZsMHDgQLS0tTpw4QadOnZg9ezY2NjYsXLgw6/sRBIjeBy/7w9tzpFftZHxlx39ei5SX8Go2hMwGeVT+3mwJREDdM8ckYodfEZE8IgqZEkZYWBg//fQTVlZWjBs3jkqVKnH+/Hnu3bvHiBEj0NbWzveasiLyjEg/kjkxuQkZSC/DvnHjRo7HZZSPF4eQ8fT05Ny5c8yaNavAXi9NTU0cHR3x9vZm5cqVnD9/nl69eiGVSrlw4QIDBw7kl19+wcrKivnz56f/HAQBXv8Jr3//r1onP6GV/7wXb8+nh5xSwwpkd1Hz9u1b7t27x86dO/nhhx8YOHAgderUYde+C6SmqdMjIwWNgrUYEBH53Pg4rjyfAQ8ePGD06NFYWVmxePFiBg0axLNnzzhy5Ajt2rUrVJhGV1MbWRGIDMMPTHIuSeRFyPTo0QNBEDh58mS2z3t7e5OUlJRpynRRCZklS5ZgbW3NgAEDCr2WtrY2kyZNwsfHh8WLF3P8+HHat2+PQqHgypUrjBs3jmXLlmFjY8OZPX0hZnchd5RD6isIGgvy2ELbrwoEQeDVq1ecO3eO1atXM23aNLp06YKVlRX6+vo0bNiQYcOGsXHjRiIjI2ndujUVq3RGQ6bOz1Qal26GExb2cQg+EZHiRPRdFiMKhYKTJ0/i7OzM2bNnsbS05Ndff2XcuHEYGamucZpMIqVC6TIExKvPpS+VSKiga6i29VWJqalprom85cuXp0mTJhw7dowRI0ZkeT5jsOS7QsbAwIDYWPVenIOCgti9ezd///13tp2JC4qOjg4zZsxg3LhxrFq1ir/++ovNmzczYcIEbt68ya0Lq+jUQFXl+3JIDUn37pj/rqI1cyc1NRUfHx+ePn2a5Svj96ahoUGVKlWoUaMGw4YNo0aNGtSoUYPq1atTpsz/+yQpEh4iCRquVntHfL0E/+BFNGjQgO7du9O9e3eaNm2q0t+7iMingPiJKAbi4+PZtm0by5Yt49mzZzRp0gQ3NzcGDBigtkZwVQ3LERQfnWsTt4IgASrqGqEpVXfegGrIi0cG0sNLixcvJiUlBS0trUzPeXp6YmNjk0lwlilThufP1Vtpsnz5cnR1dRk7dqxa1tfV1WXOnDlMnDiRZcuWsWTJEnZs38iLs7URkCBR2d+PAuKOg3430GutojXTiY6OzlasvHjxQllmXqZMGWrWrImdnR39+/dXCpbKlSt/8DMYGRnJihUrWLFiBZe2mVLDVhtZAWeW5URamsCNB2mMcpyHmZkZly5dYs2aNfz+++8YGRnRpUsXunfvTrdu3UpEJaCISHEjCpkiJDg4mJUrV7Ju3TpiYmLo378/mzZtonnz5mqv8GlV3pazwc/UsrYAtDWvqpa11YGpqSmRkZEoFIoPNoPr1asXP/74I1euXKFDhw6Znns/0RfUH1qKi4vD1dWV8ePHo6+vr7Z9APT19fn++++ZMmUK5/+ZgaGuhxr6EEnhzfoCCRmFQkFgYGC2giU0NFR5nLW1NTVq1KBbt25KsVKjRg3Kly+f58+cIAjcvHmThQsXcuLECeRyOTKZjIsP7LGrkv2oi8KgoSHhjk8VFi5ciLa2NqNHj+b69eu8efOGkydPcvLkSUaPHo0gCKK3RkSEz1DIvE1IISIqkdQ0BVKJBH1dLcoalUKmxu6md+/exdnZmT179qCjo4OjoyNTp06lUqVKatvzfSx0DaliYIpvbIRqvTKCQCkNLRqbWqtuTTVjamqKXC4nKiqKsmXL5nicvb09FhYWHDt2LJOQEQQBDw8Ppk+fnul4dQuZjRs3Eh8fz7Rp09S2x/sYGhryRbsEhGQpqi83VkDSg/SeLNrZ98JJTEzk+fPnmYTKkydPePbsGYmJ6S0FtLW1qV69OjVq1KBNmzbUqFGDmjVrUrVqVXR1dQts3du3b9m5cyd//fWXcnipvr4+U6dOZebMmZQ1LgMBw/7r96KqnjIyKN2EGfNX4zA2lFWrVrF27VpWrlxJnz59cHJyYsGCBURERHDq1ClOnjzJ2rVr+f333zE0NMzkrckYTyIi8qnzWQiZ8KgEHjwLx/tlFInJWTuYSqUSzMrqUre6KdVsjFSSxCeXyzly5AjOzs5cvnyZSpUq8ffffzNmzJhiG/MwpEojfr/nrtpFJRK8thzmkU6VLB6Kksq73X0/JGTe7fK7dOlS5eNBQUG8efOmSD0yaWlpuLi44ODggKWlpVr2yJbUMEj2VlNPaAAZwtsLRMQaZRErT58+5eXLl8oOy6amptSoUYPGjRszYsQIpXfFysoKmUx1Yc3Hjx+zYsUKtmzZQlJSEgC2trb8+OOPDB48OHOY0ew38B9MeoC1sEJPChJtKL8AJBLMzc357bffmD9/Ptu3b8fFxYV27dpRv359nJyccHBwYNiwYcjlcu7cuaP01owZMwZBEKhfv77SW9OsWTPRWyPyyfJJz1p6m5DC6ev++AXFIJF8uLloxvOltGR0bG5NdRvjAu0ZFxfHpk2bWL58Ob6+vrRq1QonJyf69u2r0pNtQTke4MUR/wcqWUuKhAqy0uyf9BNeXl7MnTuX77//vkAl4kXJkydPsLOz49KlS7Ru/eGwxtGjR+nTpw/Pnj1TdtA9cuQIffv2JTAwMJOoWL9+PePHj0cul6t8ftHevXtxcHDg3r17RSsY316AkBlqW16uAPfLCfQan55bJJVKqVy5MjVr1swUCqpevfoHRWdhSUlJ4eDBgyxbtowbN24gkUgQBIEuXbowf/58WrdunXMo6u259P44CBRczEhBIoMKa6F0w2yPEASBU6dO4eLiwqlTpzA3N2fy5MlMmDABExMT5XHh4eFKb82pU6eIjIzE0NCQzp07K7015ubmBbRTRKTo+OyHRj73j8L9ih9pckWBuqNXtTakW8tKaGrmTXz4+/uzfPlyNmzYQEJCAgMHDsTJyYnGjRvnf3M1ohAEtjy7zs3wl4VbRy5HGpeEc7cRaAgSFi1axK+//kqNGjXYvHkzjRo1Uo3BaiAiIgJTU1MOHDhA//79P3hsQkICZcuW5ffff2fmzJkA/Pzzz6xYsYLw8PBMF7cMsREdHZ2pwqWwCIJA06ZN0dfX5+zZsypbN09EboDINaizHX9MvA5nfGdTo0YNqlSpUqRC2N/fH1dXV9auXcubN2+QSCRoaWnh6OiIk5MTtra2eVvo7SV49S0IKeT/ZyUDqS5UWAY6eROpjx49YtmyZWzfvh2AESNGMGPGDGrWrJnpuPe9Nbdv3xa9NSIfDZ/10MhHPhEcvfCC1LSCiRgAn4Bo9v/rTUrqh09K169fZ9CgQVSuXJnNmzczceJE/Pz82LVrV4kTMZBeJj2qejPa/ZecW9CQQZlk2DJ0Bn/9/gdaWlr8+OOP3LlzB01NTZo1a8b8+fPzP7eniDA2NkYqleapcql06dJ06NAhU5ffjETf9+/QM8SLqsNLV65c4fbt23keR6BShAQK/leSN8roazFgwABq1apVJCImo+1Br169qFy5Mn/++Sdv3rzBzMyMv//+m9DQUFauXJl3EQOg1wZsDkPpJv89kJcboP+O0esAlf7Js4gBqFWrFq6urgQEBDB//nyOHDmCnZ0d3bt35/Tp08pwnEwmo2nTpixYsICbN28SFhbGjh07sLOzw9XVldatW2NqasqgQYPYvHkzr169yvt7FhEpIXxyQibgVSynrr4s9DqCAKGR8Ry/6JtlCnJaWhp79uyhWbNmtGjRAk9PT1asWEFgYCCLFi0q2hyGAiCVSBlSpTFTa7VDXzN9TlBeLlUSQEMixaFyQ/7qMoxfvv9R6Z0AqFevHjdv3mTBggX8/fffNGjQgNu3b6vvjRQQqVRK2bJl8yRkIL166fLly0RHRwPppdfZhXfUJWSWLFlCzZo16datm0rXzRtFcKcuKZqQa3h4OH/++Se2trb06NGD06dPo1AoaNiwIXv37iUwMJBZs2ZhaGhYsA00y0OF1WC5IV2cKMWMhPSf47vvUxMMeoHVDrD4G2QF6xtlamrK999/z8uXL9m6dSuhoaF06dKFunXrsnHjRmWOz7vHf/XVV+zYsYPQ0FBu3rzJjBkzCAgIYOzYsVhYWFC/fn3mzZvH5cuXVTcVXUREjXxSoaWUVDlbDnsRn5iq0mG7XVvaUKuKCdHR0axfv14pWjp06ICTkxM9evRQeU5EUZEsT+N2uD/ngp8SnJB+AZYiUeYMZVQ4ldEsRTuLarQ0s6XMfx18BUFgzpw5LF68mJ07dzJ06FDlug8fPmT06NF4eHjwzTffsGDBAuVwxZJArVq16NSpE8uWLcv12ICAAKytrdmzZw+dOnWibNmyWd4vpCeJ1qpViytXrtCyZUuV2Pn8+XPlZGpHR0eVrJkvYg5B2M9qW14QQK5VE41KbmpaX+DatWusWbOGffv2KfOX5HI5X375JTNnzqRp06Zq2RtFUnpFU/ITkMcAknTBUqomaFUBqVauS+QXQRC4ePEizs7OHD16FBMTEyZOnMikSZNy7TkTERHBv//+y4kTJzh16hQRERGUKVMmU26NhYWFym0WEcmJzzJH5uKdQO49DlOpiAGQSeHF3V1s2uBKamoqQ4YMwcnJiXr16ql2o2ImNiUR/7dveJUQS4o8DQ2pFNNS+ljpGWNSSjfbZEdBEBg7dizbt2/nyJEjdO/eXflcWloaf//9NwsWLFCG3po1a1aUbylH2rdvj7m5Obt27crT8fXq1aNevXqMGjWKjh078vjx4yz5CMHBwVhaWnL8+HF69OihEjsnTZrEgQMH8Pf3Lx4hmPQUAgarbfmUVIHNB+MISvmK6dOnZ0paLQxxcXHs2LGDNWvW8PDhQ/T09IiPj0dPT48JEyYwdepUrKysVLJXSeX58+csX76czZs3k5qaytChQ5kxY0aezlsKhSJTbs2tW7cQBIF69erRvXt3evToQfPmzcXcGhG18tnlyKSmynnwLFzlIgYgLU2B98sYZs6cib+/P1u2bPnkRAyAgZYOdYwr0MWyJr2s69CtYi0amlphqqOXY8WGRCLB1dWVnj17MmDAAK5d+38bew0NDebOncu9e/fQ19enZcuWfPPNN8r+H8VJXrv7ZtCrVy9OnDjBvXv30NHRUVYwvYuqQ0sRERFs3ryZKVOmFJ83S9sWJKXVtryWpoRSRi1YunQpNjY2zJo1i5CQkAKv9/DhQyZNmoSFhQWTJ08mKCgIgHLlyrFs2TJCQkL4+++/P3kRA1C1alWl9/i3337j7Nmz2Nvb07FjR44dO4ZCkdPE8vTwa5MmTfjpp5+4ceMGr1+/ZufOndSpU4cNGzbQpk0bTExMGDhwIJs2bSrU70xEpLB8MkLm2csoUtNy/mAWBolEwsDh0/nll1/EJlPZoKGhgZubG02aNKFnz55Z5hjVqlWLa9eu8ccff7BixQrs7e0zCZ7iwNTUlNevX+f5+F69ehEZGcmZM2eoW7dutqX0urq6yGQylQmZNWvWIJFImDhxokrWKxASTSjTn7wlrxYAaRlGTtqEv78/M2bMYOPGjVSqVImJEyfy8uXLPC2RnJzMrl27aNWqFXXr1mXHjh3IZDIEQaBu3bocPnwYb29vpk6dip6ennreRwnGyMiIb775hhcvXrB7927i4+Pp3bs3NWvWZPXq1cTHx+e6homJCUOHDmX79u2EhYVx69YtZs6cSVBQEI6OjlSoUAF7e3vmzp3LpUuXSE1NLYJ3JiKSzicjZAJD41Bbl3+JhJi3KSQkih/OnNDR0eGff/6hUqVKdO3aFT8/v0zPa2hoMGfOHDw8PDAyMqJVq1bMnDmThISEYrE3vx6ZJk2aYGJiwt27d3Ps4yKRSDAwMFCJkElKSmLlypWMHDlSZeGWAmM4EAF13CRIwXAQSLUwMTHht99+w9/fnwULFrB//36qVKnCyJEjefr0abav9vPz47vvvqNixYp89dVXyvBbYmIiffr04e7du1y4cKHE9HAqbjQ1NXFwcODGjRtcu3aNevXqMXXqVCpWrMh3332n9F7lhlQqpXHjxvz4449cv349k7dm48aNtG3bFhMTE7788ks2btwoemtE1M4nI2Rehb9VS1jpXcIii+ei+7FQpkwZTp48ia6uLp07dyYsLCzLMTVr1uTq1av89ddfrFmzhnr16nH58uUit9XU1JSIiIgsFWk5IZPJ6NKlCxERER9sSKeqCdg7d+4kPDwcJyenQq9VWMKiSrH/rB4KhSo/YFLQKAfGozM9WqZMGebOnYu/vz9Llizh7Nmz2NnZMXDgQDw8PJDL5Rw9epQePXpga2vLqlWrlHOnEhMTmTVrFv7+/mzbto0GDRqo0N5Pi+bNm7N37158fX0ZM2YMa9aswcbGhiFDhnDr1q18rfWutyY0NJRbt24xa9YsgoODGTdunOitEVE7n4yQiY1PUfseMW9LZl+UkkT58uU5ffo0iYmJdO3aNVvvhEwmY/bs2Xh6elKuXDnatm3L9OnT8+TiVhWmpqakpqbmy3tSq1YtgA+GF1UxpkChULBkyRJ69+6dbS5OUXLkyBHq1KnDzN+fEZ9qiupCTAKY/QrS7PNvSpcuzfTp03nx4gXr1q3j1q1bNGjQAH19ffr06cPTp0+xsrLi7du3aGtrs27dOgICAvjtt9/Eypp8YG1tzeLFiwkKCmLp0qXcunWLpk2b0rJlS/bv35/v8uv3vTXh4eHs2rVLWQ7+vrcmODhYTe9M5HPikxEyqr1bzIpEAnK5enJwPjVsbGw4deoUAQEB9OnTJ8fk3urVq3Pp0iWWLl3K+vXrqVu3LhcvXiwSG9+dt5RXSpdOv+i+HzZ7F1UIGXd3d548ecLs2bMLtU5hiIuLw9HRkb59+9K8eXPu3vNCv8Z2kBmjEjFT/ico/eGGkRlTp8+dO8erV6/Q1NRUetD8/PwwMTHhxIkTeHl5MX78eOXvRyT/6OvrM23aNLy9vTl06BAaGhoMHDiQqlWrsnTp0gL/TZctW5YhQ4awbds2QkNDuX37NrNnzyYkJITx48djaWlJvXr1+O6777h48aLorREpEJ+MkJHJ1Nt9VBBAQ+OT+XGpndq1a3P8+HHu3LmDg4NDjnd2MpmMGTNmcP/+fSwsLGjXrh1Tp07l7du3arWvIELG29sbXV1d/v333xyPUYWQWbJkCY0bN6ZVq1aFWqegXL16FXt7e3bv3s369es5fPgw5cqVA01zsNoOWpUoWLdfWfpQRLM/oEy/HI+KiYlh5cqV1KlTh7Zt23Ljxg0aN26sTODt2LEjdnZ23L17lwULFuRagSOSd2QyGf369ePixYvcuXOHVq1a8e2331KxYkVmzJihnAJeEKRSKY0aNeKHH37g2rVrvH79Gjc3N+rVq8emTZto164dJiYmDBgwgA0bNojeGpE888lcmcuWUX956uULp7h//z5yufrmznxKNG/enAMHDnDy5EkcHR0/eLGpWrUqFy9eZNmyZWzatIm6dety/vx5tdlWECHj4eFB9erVOXv2bI5hsDJlyhQqR8bT05Nz584xa9asnIcUqomUlBTmz59PmzZtKF++PPfv38fR0TGzHZpmYO0GZb8m3TOTl1PIfx4cHXuwOQgG3bM9ytPTkwkTJlChQgVmzJiBsbExTZo04eXLl7x48YK5c+cSEBDAmTNn8PLywt3dHW1tbfr27asUXuJnU3U0bNiQ7du34+/vz9SpU9m+fTtVq1alf//+XL58Oc/5ZTlRtmxZBg8enMVb8+rVKyZMmIClpSV169bl22+/Fb01Ih/kkxEyZiZ6SNV43hcEgW9nTcHe3h4jIyM6d+7MggUL+Pfff1Xekv5Tolu3bmzbto1t27bxzTfffPDkJ5VKmTZtGg8ePMDKyooOHTowadIk4uLiVG5XxiTlvAoZuVzOgwcP6Ny5M8nJyZw7dy7b4wpbtbRkyRKsra0ZMGBAgdcoCI8fP6ZZs2b89ddf/Prrr1y6dCnnWUMSTSg7ASqfhLLjQfZuVZWEzN4aTdDrBJab0lv3a1bItFRSUhLbtm2jefPm1K9fn6NHj9KxY0eqVq3K5cuXSUpKYvPmzfj7+/Pjjz+me4ZIrxDr2rUrly5d4tKlS1hYWDBkyBBq1qzJpk2bSElRf87c54KFhQW///47gYGBrF69midPntCmTRuaNGnCzp07VSIw3vfWhIeH4+bmRv369dmyZUsWb01eK6xEPg8+GSFTqUIZ1JUmIwEqlNcnIjyUS5cuMX/+fHR0dFi5ciVdu3bFyMiIunXrMnHiRLZv386LFy8KfbfyKTFkyBCWL1/O0qVL+fPPP3M93tbWlnPnzrFy5Uq2bdtGnTp1VD71WUNDA2Nj4zwLGW9vbxISEujSpQtVq1bNNETyXQoTWgoKCmL37t1Mnz69yDqmKhQKli1bRoMGDUhKSuLmzZvMmzcvb/trlEv3zFQ+DZVOgYULlJsLpt+C2W9gvQeqXgOLP6F0A97tj/DixQu++eYbLC0tGTlyJNra2gwaNIi0tDSOHDlClSpVOHv2LJ6enowaNeqDwyRbt26Nu7s7t2/fpnbt2owdO5YqVaqwYsWKEtF88VOhdOnSTJgwgUePHnHixAmMjIwYNmwYNjY2/PHHH7x580ZlexkbGzN48GC2bt3Kq1evuHPnDt988w2hoaFMmDCBihUrKr01Fy5cEL01nzmfzIgChUJg/f4HxKup10vPNpWpXsk402OCIPD8+XOuXr3KtWvXuHbtGo8fPwbSq3datGih/GrYsGGRTPYtyfz8888sWLAAV1dXxo0bl6fX+Pr64ujoyPnz55kwYQJ//fWXyv4Ga9SoQY8ePVi6dGmux7q5uTF06FAiIyP57bff2LNnD0FBQVnCP4sWLeLvv/8mMjIy3/bMmTMHV1dXAgMDlSXF6iQoKIhRo0Zx9uxZpk+fzh9//IGOjo7a9ktLS+P48eOsXr2af//9FyMjI3r37s3bt285duwYGhoajBo1iunTpxeqWuvRo0f88ccfuLm5YWJiwsyZM5k4caLK/m4EQSAoPpqXcZEExkcRn5qMRCJBT1MbKz1jKumXxbx0GZXsVdLx8vLCxcWFHTt2IJVKGTlyJDNmzKB69epq2/PNmzecPn2akydP4u7uTlhYGPr6+nTq1Inu3bvTvXv3Ej+4VyRvfJazlu49CePCrUCVrimRgH5pLUZ/URuZLHcHVlRUFNevX1cKm5s3b5KQkICWlhaNGjWiRYsWtGzZkubNm+c6xO1TQxAEpk2bxurVq9m7d2+ewycKhQJXV1e++eYbjIyM2LBhA126dCm0Pa1bt8bGxobt27fneuycOXPYs2cP/v7+nDt3jo4dO3Lv3r0sPWXWrFnD1KlTSU1NzVeOS1xcHBUrVmT8+PH89ddf+X4v+WX37t1MnDgRXV1dtmzZQqdOndS216tXr9iwYQOurq4EBQXRpEkTWrZsiaenJ+fPn6dChQpMnTqVcePGYWxsnPuCeeTFixf8+eefbNmyBV1dXaZNm8a0adOUYcX8kiJP42qYL+dDnhGWmB7ulEkkKP47hUolEuT//buirhEdKlSnqakNso90oGx+eP36NWvXrmXVqlW8fv2aHj164OTkRMeOHdWa66VQKPDw8FDOhLpx4wYKhYLatWsrRU3Lli3R0lL9gE4R9fNZChmFQmD3ySeERSaotDneoK7VsTQr2B1yWloa9+/fVwqbq1evEhiYLrZsbW2VwqZFixbY2dl98h1IFQoFw4YNUyYBd+jQIc+vffnyJY6Ojpw9e5axY8eyZMkS5XyjgtC/f38SEhJwd3fP9djOnTujq6vL4cOHSUlJwdTUlNmzZ/PDDz9kOm7nzp0MGzaM+Pj4fJUDu7i48M033+Dn56fWu8moqCgmT56Mm5sbgwcPZvXq1RgZGal8H0EQuHDhAqtXr+bw4cNoaWkxaNAgzM3NOXjwIM+ePaNRo0bMnDmTL7/8Ek1NTZXbkEFQUBCLFy/G1dUVqVTKxIkTmTVrVr7GjTyPec2mZ9d5k5y3XkcSQAAsSpdhTPUWVNRT/c+4JJKcnIybmxvOzs48ePCAOnXq4OTkxJAhQ4pkXpjorfm0+CyFDEBUTBI7jz8mNU2hEjHTuLYZrRuq9g8/MDBQ6bW5evWqsmOpgYEBzZs3V4ajmjZtWiQhhqImJSWFPn36cPXqVc6fP0+jRo3y/FpBEFi/fj2zZ8+mTJkyuLq6Zpq4nR8mTJjAnTt3uHv3bq57litXjilTpvDTTz8BMGjQIPz9/bl582amY48dO0bv3r0JCQnB3Nw8T3akpaVRpUoVWrVqxY4dOwr0XvLCmTNnGDVqFG/fvmXNmjUMGTJE5XtER0ezdetW1q5dy9OnT6lRowZDhgxRPh4dHU2/fv1wcnKiZcuWRVqZ9fr1a1xcXFi1ahXJycmMHTuWOXPmYG1t/cHXnQp6zEE/TyRIEMjfSUX6X+LziGpNaV6+coFt/9gQBIHz58/j7OzMsWPHKFeuHJMmTWLixInKhG11o1Ao8PT0VHprrl+/LnprPjI+WyED6eMK9v/rTZq8cGKmTjUTOjWzVvvJNj4+njt37iiFzbVr14iKikIqlVK3bt1MuTY2NjZFXparDuLj4+nUqRM+Pj5cvnyZGjVq5Ov1AQEBjBs3jn///ZfRo0ezdOlSDA0N87XG999/z7Zt2wgICPjgcUFBQVSsWJHDhw/Tt29fALZt28bIkSMJDQ3NFCK8fPkybdq04cmTJ3l+T3v27GHw4MHZhqpUQWJiInPnzmXZsmV06NCBLVu2ULFiRZXucffuXdasWcOuXbtITU2lf//+dOjQgUuXLrF37150dHQYO3Ys06ZNo1KlSirdO79ER0ezcuVKXFxciImJYdiwYXz33XfZ5nW4Bz7m0EtPlew7qlqzz0rMZODt7c2yZcvYsmULcrmcr776ihkzZlCnTp0itSMqKorTp09z4sQJpbdGT08vk7dG1Z8LkcLxWQsZgIioRE5c8iUiOn9VCxJJemlnq/oVaFirfLGIBoVCgbe3dyZhkzE4z9zcPJOwadCgwUd7R/HmzRvatGlDbGwsV69ezfdJRBAENm3axMyZM9HT08PV1ZWePXvm+fXLli3ju+++IyEh4YO/56NHj9KnTx/8/f2xsrIC0su2y5cvz8aNGxk9+v/zgh48eEC9evW4ceMGTZs2zdN7yPC8qboyC+DevXsMGzYMX19fFi1axLRp05CqKGcjISGBPXv2sGbNGm7fvk3FihVxdHTE0tKSLVu2cPnyZWxsbJg2bRpjx44tceeOt2/f4urqyuLFiwkNDWXgwIHMmzePevXqAfAkKhQXr+zL7AuCFAnz6nf7bMJM7/PmzRvWr1/PihUrCA4OplOnTjg5OdGtWzeV/U3mlZy8NbVq1aJ79+706NFD9NaUAD57IQMgVyi44xXKba8wUlLlyrh1dkgk6d17rcz16dDUCuMy6qveKAiRkZHcuHFDKWxu3bpFYmIi2traNG7cOJO4yWj29jEQHBxMy5Yt0dHR4fLlywWa9BwYGMiECRM4efIkI0aMwMXFJU95H7t27eKrr74iLi4OPT29HI/79ddfcXFxISIiIpPgadGiBebm5hw4cED5mL+/v3JEQ14SkjM8OMePH6dHjx65Hp9X5HI5f/75Jz/99BO1a9dmx44dyllRhcXb25u1a9eyZcsWoqKi6Nq1K6NHjyY4OJhVq1bh6+tLy5YtcXJyom/fvkVWSl5QkpKS2LJlC3/++ScvX76kV69ezJk3l6PSMGJTEvMZTMoZKRLMShvwff3un0UCcE6kpqayb98+nJ2duXPnDtWrV2fGjBmMGDGi2MZMZHhrMnJrQkNDRW9NCUAUMu+QmqbA++UbfAKieRURT8I7JdoyqQQTIx0szfSpU9UU4yLoEKwKUlNT8fT0zJREnNHSu2rVqpmSiGvWrFnkdzz54fnz57Rs2RIbGxvOnj1boLwgQRDYunUrM2bMoHTp0qxdu5Y+ffp88DWnT5+mS5cu+Pr6fjDc0b9/f2JiYrJ4TBYuXMgff/xBRESEsrQ+KioKY2Nj9u3bx5dffpmr3f369cPb2xsvLy+V/Y58fX0ZPnw4N27c4Ntvv2XBggWFvrPM6O+yevVqzp49S9myZRkzZgy9evXiyJEjrF+/nvj4eAYNGoSTkxONG394jlJJJDU1FTc3t/Qy9CY1aDRmABI1fG5GVG1KS7Mcmg1+RgiCwNWrV3F2dubw4cMYGhoyYcIEJk+eTIUKFXJfQE0oFAru37+fyVsjl8uV3pru3bvTqlUr0VtTBIhC5gMkpaSRmqpAKpWgo62BVJ0tgYuQgIAApbC5du0anp6eyOVyypQpQ/PmzZXCpkmTJh/0QBQHHh4etGvXjiZNmnDs2LEC99wJDg5mwoQJHD9+nK+++oply5blWG7r6elJ/fr1uXnzJk2aNMlxzUqVKjFgwAAWL16c6fGMMNK///5L586dgXRPiIaGBhs2bGDs2LEftNXb25saNWrg6uqKo6NjPt9pVjJCbTNmzMDU1JRt27YVel5TcHAw69evZ/369YSEhNC8eXMmTZqEpaUlq1ev5sCBAxgYGDBhwgSmTJnySVSEpMrTmHl5L8lSQeWhZQlgqWvE9w0KlqD+qeLn58fy5cvZuHEjiYmJODg44OTkRMOGDYvbNKKiojhz5oxS2GR4azp27KgUNhkh5xJBahgk3oPkx5ASCEIKSHVAywa07aB0I5B9HH2ORCEjwtu3b7l9+7bSY3P9+nWio6ORSqXUq1dPKWxatGiBlZVVsScRX7x4ka5du9KnTx/c3NwKXIouCAI7duxg2rRpaGtrs3btWvr165fluODgYCwtLTl27FiOuTUZHpYdO3bw1VdfZdnH2tqaL774gmXLlikf19fX5+eff2bmzJkftHPixIkcPHgQf3//Qpemvn79mvHjx/PPP/8wZswYXFxcClzxplAoOHfuHGvWrOGff/6hVKlSDBs2jHHjxuHj44OzszM3b96katWqzJgxg5EjR6Krq1so+0sSqs6NyY6fGvTEQvfjuJgUJbGxsWzcuJHly5fz8uVLWrdujZOTE3369CkRrSkEQeD+/fucOHEik7fGzs5OKWpat25dPN6ahNsQtR3iL5OeRKEBZAzrlZDeyF+e/rh+dzAeBtrqa1yoCvKqPUpuvEGk0Ojp6dG+fXvmz5/PiRMniIyM5NGjR6xbt07pSRg6dCg2NjZUrFiRQYMG4eLiwq1bt4ql5Xfbtm3Zs2cPBw4cYPLkyQUe8yCRSBg+fDiPHz+madOmfPHFFwwdOpSIiIhMx2Xk43xoTMH9+/cBsLe3z3afnj17cuzYsUy25mVMQUREBFu2bGHKlCmFFjFHjx6lTp06XL16lUOHDrFx48YCiZg3b96wdOlSatSoQefOnXn27BnLly/n8ePH2Nra8sUXXzB48GBKly7N0aNHefr0KZMmTfqkRAyAX1yksmxaXbx8m//Oz58DBgYGODk54ePjw/79+1EoFPTv359q1aqxbNkytcxdyw8SiQR7e3vmzZvH5cuXiYiIYO/evTRt2pRdu3bRqVMnjI2N6devH+vWrcu1IlIlyGPh1fcQNA7ir/L/TNC0dw4SSBcx/z0edwL8h0DEclB8/HPJRCHzGSGVSrGzs8PR0ZHNmzfz7NkzXr9+zZEjRxg2bBihoaHMnTuXpk2bUqZMGdq2bcvcuXM5evRogVruF4S+ffuyYcMG1q1bl6XZXH4xNzfn8OHD7Ny5k1OnTlGrVq1Mibna2toYGBh8UMh4eHhQqlSpHFuu9+rVC19fX549e6Z8zMDAINcJ2GvWrEEikTBx4sR8vqv/8/btW8aPH0+fPn1o0qQJXl5e2XqePoQgCNy6dYvRo0dToUIFvvvuOxo1asSlS5c4dOgQT548wc7Oju+//54OHTrg4eHBuXPn6NWrV4nOuyoMAW/fqCzBNztkEgkBb1U3l+hTRCaTMWDAAK5cucKtW7do2rQps2fPxtLSklmzZvHy5cviNhEAQ0NDBg4cyKZNmwgODsbDw4P58+fz5s0bJk+ejLW1NbVq1WL27NmcPXuW5ORk1RqQ8hJeDkgXJsD/xUpuyAEFvNkMgcMh7eP+e/w0z0QiecbU1JTevXuzaNEiLl26RExMDDdu3OD333+nXLlybN26lT59+mBiYkKNGjUYM2YMGzZs4MmTJygUCrXYNHr0aP7++29+//13XFxcCrWWRCJh6NChPHr0iBYtWvDll1/i4OCgFC/lypXLVcjUrVs3x8qbDh06oKOjk2mIZG4emaSkJFauXMnIkSMLVKUFcO3aNerVq8euXbtwdXXlyJEj+Rp5ER8fz4YNG2jUqBFNmzbl/Pnz/PjjjwQEBDB+/HgWL15M9erV2b17NzNnzsTf358tW7Zk65n61IhJScx347v8oBAE3qaq+IL2CdO4cWN27dqFn58fEydOZPPmzdja2jJw4ECuXbtWYgb0Znhr5s6dy6VLl4iIiGDfvn00a9ZM6a0pW7Ysffv2Ze3atfj7+xduw5RACBwF8jdAQc/FAiT7QJBjumfnI0UUMiKZ0NLSomnTpjg5ObFv3z6Cg4Px8/Nj586ddOrUCU9PTyZMmICdnR0mJib07NmT33//nQsXLhAfn7f27Xlh9uzZfPvttzg5OeVpFlJumJmZcfDgQdzc3Dh79ix2dnbs27cPU1PTDwoZT0/PD168dXR06NixY76EzM6dOwkPD8fJySnf7yM1NZXvv/+e1q1bU65cOTw9PRk3blye85uePHnC9OnTqVChAuPHj8fc3Jxjx47x5MkTKlSoQI8ePWjfvj0+Pj64uroSEBDAL7/8kq92/h876hQxkN7mISY2hoSEBLXu86lhaWnJokWLCAwMZOXKlTx48ICWLVvSrFkzdu/eXeImYBsaGvLll1+yceNGgoOD8fT0ZP78+URFRTFlyhRsbGyws7Nj1qxZnDlzJn/eGiEVQmaCPI68e2FyQg4p/hD2SyHXKT7EZF+RfBMXF8etW7eU1VHXr18nJiYGmUyGvb19piTiwvReEASBcePGsWXLFg4fPkyvXr1UYn9YWBiTJ0/mwIEDmJubU6tWLU6fPp3luKSkJPT09Fi5ciVff/11juutW7eOyZMnEx4ejpGREQ4ODkRGRnLmzJksx2a0SK9atSr//PNPvux+8uQJw4cP5/79+yxYsIBvv/02Tz1aUlJSOHz4MGvWrOHChQuYmpoyduxYxo8fj76+PuvWrWPlypWEhobSrVs3nJyc6Ny5c7Enf6sThULBq1evePHiBT4+Prx48UL5b7PBHTFvXEdtoTN5ahrPjl/gytLNmJiYYGVlhbW1dbbfTU1NP+nfQ2FQKBScPHkSZ2dnzp49i6WlpXL4qDrmh6mS6OjoTJVQr169QldXlw4dOiiThm1sbHJeINIVIteQc2e0AmL+F+gXfiCvqhCrlkSKDIVCwePHjzN1Ivbx8QHS76LeFTb16tXL14DAtLQ0Bg0axMmTJ/n3339p3bq1yuzet28fI0aMIC0tjW3btjF48OBMF407d+7QuHHjXLv0ZowwyBjEOH78eDw8PLh9+3aWY0+cOEHPnj25ePEibdq0yZOdCoWCVatWMWfOHGxsbNixY0eeylIDAwNxdXVlw4YNhIaG0qpVKyZNmkT//v3x9fXFxcWFbdu2ATBixAimT5+OnZ1dnmz6GEhNTSUgIEApVN4VLC9evCApKUl5rKWlJba2ttja2lK2Q0NiKxoiqFE/NFEYohUQSUBAAP7+/srv/v7+JCb+vxt5qVKlshU4Gf+2tLQU+5mQ3grBxcWFnTt3oqGhwejRo5k+fTpVq1ZVz4ZCKiS/gORnoIgDJKBhAto1QLMiSPIuggVB4MGDB0pRc/XqVeRyOTVr1sxUCaVsSSGPgRedAFV7oCSgUQ4qncyX/epEFDIixcrr16+5fv26UtjcuXOH5ORkSpcuTZMmTZTCpnnz5hgbG39wraSkJHr06MG9e/e4ePGisoW8Kpg+fTobN24kPj6efv36sWbNGmUYZcOGDUyYMIG4uLhcO47Wr1+fWrVqsWPHDmbPns3Ro0czJQBn0LFjR+Li4rh582ae7rSDg4MZPXo0p0+fZurUqSxatOiDtigUCk6fPs2aNWs4evQourq6DB8+nK+//pratWtz+vRpnJ2dcXd3x8zMjClTpjBhwoQC5+oUNwkJCfj6+mbrWfH390cuT3e7a2hoYGNjQ5UqVZSCJePflSpVQkfn/528H74JZuWji2q1e379bljpZf27FwSByMisAufd769fv1YeL5FIMDc3/6BXJ78zyD5mwsLCWLNmDatXryYiIoJevXrh5OREu3btCu/ZEgRIegDReyDuNP8XEhkX/f/yVKT6UKYflPkStD48kDQ7YmJiMnlrQkJCMnlrHLqkYCzfgsq9MRlUWAm6hes/pSpEISNSokhOTubevXuZOhGHhYUBULNmTaWwadmyJdWqVcty0omNjaV9+/YEBwdz9epVbG1V0xl16dKl/PTTT2zZsoVJkyaRmprKihUrGDp0KFOmTOH8+fM8fvw413W+/+EHdp86iuvB3Ry7eoHnAX706tULfc1SWOkZU0m/LK+9/WjQoAG7d+/GwcEh1zX37NnDxIkT0dHRYfPmzR8ceRAREcHmzZtZt24dL168oG7dukyaNImhQ4eioaHBzp07cXFx4dGjR9jb2+Pk5ISDg0OBGw8WJVFRUVlESsa/Q0JClMeVLl06i0jJ+HfFihXzPCohVSFnzs2DJKSpJ+eiXCl9fmnUq8AX1sTERAIDA3MUO4GBgZnyRQwMDDJ5cd7/bm5uXiJ6tKiSpKQkdu3ahbOzM15eXtSrVw8nJycGDx5csL/51DAIWwAJ1wEZueelyAAFGA4Gk2npDekKgCAIPHz4UNm35urVq9w7ZEvtqqXU1MhVBnqdwWKRGtbOP6KQESnRCIKAn59fpk7EDx48QBAEypYtS/PmzZXCplGjRpQuXZrXr1/TunVr0tLSuHLlCubm5oW2Y/v27YwYMYLExETevn3LtGnTcHNzo3fv3oSEhFC9enV27tyZ4+uT5KlcCX3BSd8HvM3o26AQEBCQSqVIJRLk/33EFJGxPD50mrPrd1FKK+eTaUYy4K5duxg4cCBr167N1mslCAI3btxgzZo17N27F0EQGDRoEBMnTqR58+aEhYWxevVq1qxZQ2RkJL1798bJyYm2bduWqLwLQRCU+SrvCpWM71FRUcpjjY2NsxUqtra2mJmZqex9HfLz5N+gJyjUcNfrULkhHSqorxGZQqEgNDSUgICAHMVOdHS08ngNDQ0sLS2zDV1l/Ptj7RUkCAJnz57F2dmZEydOUL58eSZPnszXX3+d95l0by/Aq3kgJJP/xFpperimwkrQrpLP12YlJvo1BmFdkUjUmJCuYQaV3dW3fj4QhYzIR0dsbCw3b95Uemxu3LhBXFwcGhoa1K9fnxYtWlCtWjV+/fVXTE1NuXjxYqGT+tzd3enevTsBAQHKxORDhw4xceJEwsLCcHBwwM3NLdsL5NPoULY8u0F0SkKeLncKhQKpRIKFriFjqrfIdgryuXPnGDlyJHFxcaxatYqhQ4dm2fvt27fs3LmTNWvWcP/+fSpVqsTXX3/N6NGjMTU15f79+zg7O+Pm5oampqYyX6BKlcKfSAtKWlpapnyVd4WKr69vpgqeChUqZOtZsbW1LbIkzriUJH68e1SlXhkJEoy0S/Nzw55oyYp3kGZsbGyOQicgIIDg4OBM7RXKli2bY+jK2tr6o0hKfvr0KcuWLWPr1q0IgsCwYcOYMWPGh4epxp2BV3NID+MUVDzI0j0yFTeDdiFzdpK8IGBY4dbIC7aXQVawzuCqRBQyIh89crmcR48eZUoi9vX1BdKb+xkbGzN37lzatWv3wV4vH+Lu3bs0atSIu3fv0qBBA+XjN27coHnz5gD07NmTdevWKQfZCYLAicBHHPF/8MGJ6jmR0TV2RLWmNC9fGUh3hc+bNw9nZ2fat2/Pli1bssxvefToEWvWrGHbtm3Ex8fTq1cvJk6cqAw5HT9+HGdnZ86fP0/FihWZOnUqjo6ORXbxT0xMVOarvO9Z8ff3Jy0t3WMlk8lyzFepXLlypnyV4uRueACuT6+odM3ZdTtRtUw5la6pDlJTUwkJCckxT8ff3z+T+CxVqhQVK1bMUexUrFixxCQlR0ZG4urqysqVKwkJCaFLly44OTnRtWvXzGIs6SkEfEV67kthPSAykBmCzSGQFeIaGn8FgqcU0pY8UOk4aBbf4M4MRCEj8kkSGhrK9evX2b9/P7t370YQBARBQFdXl6ZNmypzbZo1a5anC3hAQADW1ta4u7vTtWtX5eO7d+9myJAh7Nixg2+++YaEhAScnZ0ZNWoUxwO8OBrwUCXvZ1S1Zui8imXYsGH4+Pjwxx9/MH36dGXpb0pKCgcPHmT16tVcvnyZ8uXL4+joyPjx47GysiI+Pp6tW7eybNkyvL29lT2A+vfvn6/qsLwSHR2dbWLtixcvlNPXIb2/Tk75KlZWVgUSncXBnhd3OReSNWm7IPSvZE9Xy0+jKkwQBN68efPBpOSMHDhIT0o2MzPLNnT1blJyUXp1UlJS2LdvH87Ozty9e5eaNWsyY8YMhg8fjk4pDfB3SO+vUug+LRlIQb8nmP9a8CWKSsjYHAOt4h8AKwoZkU+ef//9l169etGhQwfat2/PjRs3uHbtmrKqo1atWpmSiKtUqZLlRJmYmEjp0qXZvn07w4b932X77bffsmvXLgIDA4mKisLJyYmtW7fSb/IYyjl0VN2bEAQOj/8B81IG7Nixg9q1awPw8uVLXF1d2bhxI69fv6Zt27ZMmjSJfv36oaWlRVBQECtXrsTV1ZWYmBgGDBiAk5OT0otUcHMEwsLCsi1Z9vHx4c2b/7cyNzIyyiRU3hUs5ubmJT7UkBcUgsA+33sFFjMZHrsvbOrRreIHQhifIElJSQQGBmYbuspISk5J+f+cH319/Q9WX5mbm6tFAAuCwJUrV3B2dubw4cMYGxuzdVkXejR+gkQdlUEVt4COfb5eolAo8PHxIcD7JJ2qbVa9Te8jhpZERIqOPXv2MGTIEKZNm4azszMAvr6+ylDUtWvX8PLyQhAETExMMgmbhg0boqOjg56eHr/88kumadVdu3ZFW1ubI0eOKB87fPwYhxTBaOnrIpWpps+CIk2ORkIKSzoMRVtLi1OnTrF69WpOnDiBvr4+I0eO5Ouvv1b2d7l9+zbOzs7s27eP0qVLM27cOKZOnYq1dd7LPNPS0ggMDMxWqPj6+mbq0GxhYZFjvkpuZfOfEnfDA9jhc5OktLQ8JQALgoBUIsFAS4fR1ZpT0+jz6YycVxQKBWFhYR/06ryb6C2TybIkJb//vbBJyb6+vqxYvowZfc9Q0VxDDZVBMtDvDOY5VwXFxcXx8OFD7t+/r/x6+PAh8fHxlNKW8NajDjKZGm8SxGRfEZGiZ82aNUyaNInffvuN+fPnZ3k+OjpamUR87do1bty4wdu3b9HU1KRBgwY8efKEDh06sHr1aszNzREEgfLlyzNx4kR+/vln5TpH/B9wIsBLLd0bTLwj2fXbUl6+fEn9+vWZOHEiQ4cORVdXF7lczuHDh3F2dubq1atUqlSJGTNmMHr06BynXCclJWWbr/LixQv8/Pwy5atYW1vnmK+SW/+cz4m4lCTOBD/lUqgPCWkpSJEgvDPQQIIECaBAICEyGou3sGDwOHQ0VB/i+1yIi4v7YPXV+0nJxsbGH0xKLleuXO6ewoTb6ZOk1YYMbM8jSPXx9/fPJFju37/Pixcv0o+SyTA0NEShUBAdHa08L93aZ03Fcsmox+EpA71OYPGnOhbPN6KQEfms+PXXX/nxxx9Zs2bNB8cJQLpHwsvLSyls9u3bp3Rx29jYUL9+fQ4dOsTSpUuZNm0aMpkMuULBnJuHeJum+mF/CrmCNy/80b/8nIkTJ9KkSRMkEgmxsbFs3LiR5cuX8/LlS1q3bo2TkxN9+vRBJpMRExOTY8lycHCwcpheqVKlPpivoo5cmk+ZVIWcp9GhvIyLJOBtFHGpSUiQYKBVCms9Y2z0y7J49vecPXMGX1/fT65HS0kiLS2N4ODgbENXGd/f9TBqa2vnmKOTkZSs/XYrRK5DdbkxWZnlXJqNbo+VM9mMjIywsLBAQ0ODyMhIgoKCALC2tqZt27bKr8qVKyOJ3gHhSxEb4v0fUciIfBIIgsCMGTNYsWIFu3fvZtCgQXl+bc+ePUlLS2P8+PFcvXqVEydOKLvy6unp0axZMxr27kxUffUmv/3csBdmpQ3w8/Nj+fLlbNy4kcTERDp06ECLFi2UcfIM8RIREaF8raGhYbZelYx8FXXNDRLJnozxFv/88w99+vQpbnM+WwRBICoqKsfQVUBAAKGhoZlec3JjdTq3KIWKosdZSJMLuJ0y5Nh1M2JjY3n+/LnSC1OlShXatGmjFC7ZhozFEQVZEIWMyCeDQqFgxIgR7N27l+PHj9O5c+c8vW7UqFF4e3tz7do1AH777TeWLFnCkSNHlF6bN5YG1PiyC1IN9d1d63j4c2LlZp4/f45MJkMqlWbq0Gpubp6jZ+Vzylf5WGjatCmGhoacOnWquE0R+QBJSUkEBQUpxU2PWpsobxintv1S0wQ27o9k4k/B1KhRQyla2rRpo2zxkCuR6yByLaofGvkn6HfN/bgiIq/a4+OogRQRyQNSqZTNmzfz5s0bvvjiC86ePfvBYY8ZmJqacvXqVeX/PTw8qF+/Pq1bt1YOqVz56AJeb0LU5cxFnprGreePefnyJTVq1KBly5bUqFEjU77Kx9pd9XNl8uTJjBw5Em9vb6pVq1bc5ojkQKlSpahSpcr/G0b6/QOp6hMyUomUrl06ETrhT8qXL1+wRYzHpM97SvFDNSEwGei1K1EiJj+UDP+RiIiK0NTUZP/+/djb29OjRw+ePHmS62tMTU0JDw9X/t/T0xN7e/tMx8SmJKlNxABIZVK69u1NUlIST548YcOGDcyePZt+/fpRp04dUcR8hAwaNIiyZcuydu3a4jZFJD9I1ftZk8lkVKpsV3ARAyDRBIul6QMqKayXWAaaVlD+x0KuU3yIQkbkk6N06dIcPXqUChUq0LlzZ/z9/bMckz4jyYfNz64R3NCCAftdmHjZjWlX91DnmxEYdGzAk6hQ8hB5VQlSqRRLywpiLssnRKlSpRg7diybN2/O1AVXpIRTqgbqDVakFX5UAYCWFVhtBpkRBb+US0DLFipuBFmZwttUTIg5MiKfLCEhIbRq1QpNTU2uXLmCqakpCWkpHPV/yJVQH1IUcqRIsvQFEQQBmUSCAjCQaGIWkYKvPI5UE30kapk4CygEasgMGF+/g+h9+YTw8/PD1tYWV1dXHB0di9sckbwQvR9e/47aqoIArHb/J5hUgDwGXv8JcSfI22RuUE7nNhoNZSeAtAATwYsAMdlXRATw8fGhVatWWFpasvqf3ewNvE98akqepxoLCgGJVELC60h0TIyQqMljIggCFxe54nPqCtWrV6dBgwbUr19f+d3Q0FAt+4qon969exMUFMS9e/c+iW7HnzppSSHI/Huqb8K0Rnn1VAYl3IKo7eljDBBI9yql/fekhHSvjTz9cf1uYDRMdWJKTYhCRkTkPzw9PRn75w80mjSkwGsUZDhkfhmkUwn/B0+4d+8e9+7dw9PTk8TERAAqV66cSdw0aNCAcuVK/vBBETh58iQ9evTg2rVrhR4hIaJ6UlJSuHPnDhcvXuTixYtcvXqVLX+UpW+HMmhoqFp4SsBkOhiPUvG675AaCol3IekJpAaAkAKSUqBdGbTtoHTD/8JRJR9RyIh8tgiCwOvXr/Hy8kr/SghH2qrww/oEQVDLHbUEMCtdhp8a9Mi0vlwu59mzZ3h4eCjFjYeHh7KJVoUKFbKIG0tLS/Guv4ShUCioVq0azZo1Y8eOHcVtzmdPUlISN2/eVAqX69evk5iYiJ6eHq1ataJt27b06GBNHcO/VDxrSQJSA6h05KPORylKRCEj8lkQHR3No0eP/i9a/vvKaBZnUqki/Tb8hkRDVqIv8EOrNKatee4JgIIg4OfnpxQ2GV8ZVVcmJiaZhE2DBg2oXLmymERczCxZsoR58+YRGBgoetKKmPj4eK5fv86lS5e4ePEiN2/eJDk5GUNDQ1q3bq3s42Jvb595KGW4C0RtRaW+WPMloK/CobOfOKKQEfmkSEhI4MmTJ1kES0Yrb5lMRvXq1aldu7byq1atWhxOeIlfXGSec2KKGkGhwKSUHj837o2mtGBllIIgEBISkknYeHh4EBgYCICBgQH29vaZxE316tXVMklYJHvevHlDhQoV+PHHH5k7d25xm/NJExcXx9WrV5Uel9u3b5OWloaJiQlt2rRRds6tU6fOh8dHKJIhcDQkP6PwvVokYPAFmH28Jc7FgShkRD5KUlNT8fb2ziJYXrx4oSyFrly5cibBUrt2bapVq4a2dubMe5+Y1/z94ExxvI08IygU3F24kQ2LllK/fn2Vrh0eHp4lLOXj4wOklwbXq1dPKWzq169P7dq1s/wMRVTHmDFjOHv2rDh/ScVER0dz+fJlLl68yKVLl7h37x5yuZzy5ctnmlNUs2bN/Hsm5bEQNOE/MaPI9fAc0e8JZr+ARPy95wdRyIiUaBQKBX5+flkEy7Nnz5Rt+c3NzTOJlTp16lCzZk309PTytMf6p1e4FxGIooh6wRSEDgZW/DVmOo8ePcLFxYUJEyaoNQQWExODp6dnJnHz5MkTFAoFGhoa1K5dO5O4qVevnlgOriLu3r1Lo0aNOHz4MH379i1ucz5aIiIilMLl4sWL3L9/H0EQsLS0VLb6b9u2LdWqVVPNZ0mRkD6kMWY/6ZU/eRU0svTjTaamVwiVkPlFHxOikBEpEWSEPd4XLI8fP1Y2CTMyMsriYalVqxZly5Yt8L4KQcG0a/tIVahvgi2QHj7P57kyowLq+sod/Dx4HD169GD27NmsXLkSBwcHXF1di/RzlpCQwIMHD5TC5t69ezx8+JDU1FSkUqlYDq5CmjVrRpkyZcT5S/kgLCxMKVouXrzIo0ePgPRJ9e96XCpVqqTePLiEmxCxEpIeknO/lgyxIoBuWzCdAVo26rPpE0cUMiJFTmRkZBbB4uXlRXR0NJDecbdWrVpZRIu5ubnKT0CvEmJYcPe4Std8nzKapYiIikKjtHae+8tIACPt0oys2owFE2dw+PBhzpw5Q6tWrdi/fz9jx46lXLly7N27V+WhpvyQkpLCo0ePMoWmxHLwwrNt2zZGjhzJs2fPxPlLORAUFKQME128eFE5ib5q1aqZBixaWVkVj4HJzyD2VLqgSX4KinhAAjJDKFUbdOqCfi/QLMQIAhFAFDIiaiQuLo7Hjx9nESyhoaFA+ryjGjVqZBEsNjY2RVY9czXIm21+d9S7iSCws/8UnE/u40laNPFpKUiRICD8P7VYISCRShEQMNQqTXuLarS3qIa2TIPk5GS6d++Op6cnV65cwc7OjhcvXjBo0CAePXqEs7MzX3/9dYmpthLLwQtPUlISFStWZNiwYTg7Oxe3OSWCly9fZvK4+Pr6AmBnZ6cME7Vp0wYLC4titlSkqBGFjEihSU5O5unTp1kEy8uXLwGQSCRUqVIli2CpWrUqmpqaRW6vIAjcvXuXDRs2cCPiJU2nj1D7nrJ911m9fAVpCjlPo8Pwf/uGgLdveJuajKenJzrIGNihGzb6ZalaxhTpe3HymJgYWrduTXR0NNevX6dChQokJycrQ02DBg1i/fr1JfZzJ5aD55/vvvuOtWvXEhwc/NnlHwmCgI+PTyaPS0BAABKJhDp16ig9Lq1btxY9fCKikBHJO3K5HB8fnyyC5fnz58jl6XHgihUrZhEsNWrUoHTp0sVuu7e3N5s3b2bfvn28fPkSfX19mgzuje3wnmrf//H3a7G0qED58uUxMzPL9P27775DQ0ODEydOfHCN4OBgWrRoQZkyZbh8+TJlyqQ3yypJoab8IJaDf5jPaf6SIAg8ffo0k8fl1atXSKVS6tevr/S2tG7dGmNj4+I2V6SE8VkLmaS0VALiowh4+4a4lCQEQFdDi4p6RljpGaOn+XmWmAqCQGBgYLaJt8nJyUD6HXSdOnWyJN5mXFyLktjYWIKDgwkJCSE4OFj5lfF/Pz8/IiIiMk2olslkWFhYUK1NU2wnfKFW++SJSSRsOUNoaCihoaGEhYXx5s2bLMeZmJhkETnvf4+NjaVv377Uq1cPd3d3ZRl0RqjJy8sLFxeXEhVqyi9iOfj/eX/+klxQEJ2cSJpCjoZUhqG2DrKPsMpFoVDg5eWlFC2XLl0iPDwcmUxGo0aNlB6Xli1bFss5ReTj4rMTMoIg8Dw2nAsh3nhEBKJASB+T9d/JQBAEZVO0moZmtLeoRh1jiyyu/k+FTC363/mKi4sDQF9fP4uHpXbt2kXizk1NTSU0NDSLMHn//2/fvs30OmNjY8qXL49cLic0NJTY2FhMTU3p3r07X331FfXq1cPU1BSpVEpCWgpO1/er7T0ICgV2RubMqJu5S2dKSgqvX78mNDSURYsWcenSJWbMmJFJ7GR8z8gtyUAikSAIAoaGhjRu3Bhzc3PKly+PiYkJZ86c4fTp03Tt2pU1a9ZgbW39SYRnPtdycHd3d74c8RV/HdxGjJ4GQW+jSBP+X9arIZFiqWtIbWMLWplVwUi7eD2fOSGXy7l//75SuFy+fJk3b96gpaVFkyZNlMKlefPmeW6bICKSwWclZGJSEtn5/Bb33wQjRZJrF9eMY2z0jBldvQVmpUvee8orMTEx2bboz8hR0NbWxs7OLotgqVixosrv7AVBICoq6oPiJDg4mNevX2fyomhra2NhYUGFChWUX+/+38zMjMePH7Nt2zaOHDmCTCbjyy+/xNHRkbZt22Z6H4Ig4OnpiZubG8ENLNA1N1HPxGoB+trUpYdV7RwP+fvvv/ntt9+yCJYMkpKSMgmb0NBQzp07x549e6hSpQrly5dXPve+qJPJZB/08JiZmSn/bWho+FF5cT71cvDYlET2vLjL7dcvAQkSac6/G8l/xfoNTa1wqNwQAy2dojIzW1JTU7l3755SuFy5coXY2FhKlSpFs2bNlMKlWbNm6OgUr60iHz+fjZB5Fh3GmseXSJan5bsNvRQJEomEEdWa0qxcJTVZqBoSExOzbdGfkXcgk8moVq1aFsFSuXJlleQeJCUl8erVqxzFSca/k5KSMr2uXLlyWYTJ+/83NjbO9kIbEBDA5s2b2bRpEwEBAdSpU4dx48bx1VdfZYmne3t74+bmhpubG8+ePcPExIQB86cjNKxc6PeeHYJCYEipSrRv2iLHYzZv3syYMWNITk5GS0srz2uvWrWKKVOmsGTJEmbOnAmkz4sJCwvjzp07zJ07l4CAALp06YKlpSVhYWFKIRQaGprld6ClpZWr2Mn4rq+vXyJFT0Y5+Lvi5mMsB78XEcA275v5Pl9JkaAt02B41aY0NC26suPk5GRu376tDBNdvXqV+Ph4SpcuTcuWLZXCpXHjxp9sGFCk+PgshMyz6DCWeZ1HIRR+ks7Ias1oUV49F738kJqayvPnz7MIFh8fH6UXo1KlSlkES/Xq1Qt0IlEoFEREROTqRYmMjMz0utKlS+coTDL+b25unq8LeMb7P3r0KOvXr+fUqVPo6uoyZMgQHB0dady4caaLbGBgIHv27MHNzY179+6hr69P//79GTx4MB07diRVIjDn5iHVN8VTCLy+95jDMxfSqVMn5s2bR7t27bIIgGPHjtG7d29CQkIwNzfP1xbz5s3jjz/+YNeuXQwZMiTTcx+qahIEgbi4uEzC5n2vz7vfU1JSMq2to6OTq9jJ+F7cIZ7sysHv3btHbGwsUPLKwS+EeOP2ovAtAQbbNqK9hXp60CQmJmaZDJ2UlISBgYFyMnSbNm1o2LBhsVQminxefPJC5k1yPAvuHCdFkaaScYAS4Nt6XahkYKKC1XJHoVDw8uXLLILl6dOnObbor127NnZ2dnmONcfHx39QnISEhBASEqLcD0AqlWJmZparF8XAwEClFwRvb282bNjA1q1bef36NU2bNmXcuHE4ODhker/h4eHs378fNzc3Ll++jLa2Nr1792bIkCH06NGDUqVKZVrX3fcBh4K9VGYngoCWTIMf7Ltz8cQpFi5ciKenJ82aNWP+/Pn07NlT+XO5ceMGzZs35/79+9StWzef2wiMGjUKNzc33N3d6dChQ5ZjMqqaTE1N2bdvX76rmgRBIDo6Os+iJ6OCLQM9Pb08i573fy/qImP0xfviprjLwe+E+7P+6VWVredYoyWNTa0LvU58fDzXrl1TCpdbt26RkpKCkZFRlsnQ4nwokaLmkxYygiCwzOs8z6LDVDbVWIoEk1K6/NiwZ4GnEGeHIAi8evUqi2B59OiRskW/oaFhtpVCObXol8vlhIWFfTDEExwcnCU3w8DAIFcvSvny5YusDDYxMZEDBw6wfv16Ll26hJGRESNGjGDs2LHUqVNHeVxsbCyHDx/Gzc2N06dPA9ClSxeGDBlC3759c/ybvH//Pl8OHEit6UMwrVUFqYpOxMOqNKG1eRUg/fd78uRJfv/9d65du0bdunWZN28eX375JX5+flStWpWzZ89mK0RyIzU1ld69e3P9+nUuXbpEvXr1shxTVFVNCoWCN2/e5Cp2QkNDCQ8PR6HIPI+mTJkyuYodMzMzypUrl28vXm7kVg6ur6+fRdyoshw8JiWRH+4cJVmeppL1ALRlGvzSsBeG+UwCjo2NzTQZ+s6dO6SlpWFqaqpsPte2bVtq1679SSSTi3zcfNJC5kFkMKseX1T5uhJgQKX6dLasWaDXv3nzJttKoaioKCDdbf9+i/46deooW/QLgkBsbOwHvSjBwcGEhoZmulBoaGgoRUlOXhQLC4sSUzVw//591q9fz44dO4iJiaFDhw44OjryxRdfKO/cExMTOXHiBG5ubhw/fpykpCTatGnDkCFD+PLLLzExydlzJggCmzZtYvLkyZQrV47oxHj6rvoJXXOTQsvezhVqMqCSfRaxIAgCly5dYuHChfz7779UrVqVqVOnMm3aNPbs2cOgQYMKtN/bt29p164dISEhXL9+HWvrrHfhJa2BnlwuJyIiIk+iJyIiIsvrjY2Ncy1XNzMzw9TUtFBio6jKwdc8vsSDyGCV3XRB+o1XHWMLJtVq+8HjoqKiMg1Y9PDwQKFQYGZmlmUydEnMjRL5vPmkhYzLw3Mq9ca8i7F2aX5v3BfpBz7Ub9++zbZF/6tXr4B0YfF+i/7q1atTqlQpZcJsTmIlPj4+sz3Gxrl6UTJKjksysbGx7N69m/Xr13Pnzh3MzMwYPXo0Y8aMoUqVdO9GamoqZ86cYffu3Rw6dIi4uDgaNmzIkCFDcHBwwNLSMtd94uPjmTRpEtu2baNy5cr4+voyfvx4fl/8F5t9b+ETG55v2wW5AiQSelvXppd13VxP+Hfu3GHhwoUcOnQIgC+//JKtW7cWuHlgWFgYLVq0QEtLi6tXr+bYOKywoabiIDU1lfDw8A+KnYx/Z9wQZCCRSPLUo8fMzIyyZcvmKTSi6nLw8MQ4vr9ztNA/p5z4tVFvyuno/3+/8HAuXbqk7Jr74MEDBEGgYsWKmYRLlSpVROEiUuL5ZIVMVHIC3906rNY9ZtbpSHXD8iQnJ/Ps2bMsgsXPzw9IP5Ha2tpSrVo1KlSoQNmyZdHR0SEtLY3Xr19nEirh4eFZSo5zy0OxsLAostwCdSAIAjdu3GDDhg3s3r2bpKQkevTogaOjIz169EBTUxOFQsHVq1fZtWsX+/fvJyIigho1ajBkyBAGDx6cr8F6T548YeDAgfj4+FCmTBmSkpJYv3690huiEATOhzzjoJ8nqXI5SPjgyVwhlyOVyYgJCuWLcjVx6Ng9X+//0aNHNGrUiOTkZExMTHBycmLSpEkFagT2/PlzWrRoQbVq1Thz5kyOpa0vXrzAwcGBhw8ffvQN9N4nOTlZ2aMnJ7GT8T0j4TcDqVRKuXLl8lSu/n4VXWHKwff7enA2+KlabrqkSGhmaEmpxyFKj8vjx4+B9CqujMTctm3bYmNj88n8HYh8PnyyQuZueACuT6+obwNBIPHGE+5sOYi3t7cywdHY2Jhy5cqhq6uLTCYjJSWF6OjobMtdM0qOcxInHyo5/hSIiIhgx44dbNiwgUePHmFjY8PYsWMZNWoUlpaWCILAvXv3cHNzY8+ePQQFBWFlZcXgwYMZMmQI9erVy/fPZteuXYwbNw49PT3evHlD/fr12b17N5UrZ61E8/LxZtiC2bQe60CKRvo+EtJ7drx7wUnyD+X6lgPU1Dfln0OHC/SzqFu3Lvb29pQuXZrNmzejo6PDlClTmDFjxgfDY9lx69Yt2rdvT5cuXdi/f3+OHobk5GS++eYbVqxYUSJCTcVBYmJirmIn47GMXLUMNDU1cxU9xsbGREVF4e3trfTgZFcOXs6xJ2mlVZvz8y7R/iHsHf4N1apVyzQZumLFimrbU0SkqPhkhcyhl578G/QERe5mFwiFXE7QjfucX7BS2bY/A11d3Vy9KGZmZipPVvwYUCgUnD9/nvXr13Po0CEEQeCLL77A0dGRjh07IpVKefr0KW5ubuzevRtvb29MTU0ZNGgQQ4YMoXnz5gUKjyUlJeHk5MTatWuxsLAgJCSE2bNn8/vvv+f4e5g3bx6rV68mODiYFA0J/m/fEJH0FrmgoJRUE3PdMljpGjFmxEjc3Nw4fPgwffv2LdDPpUOHDpQrV47du3cTEhLCkiVLWLt2LQDjx49n1qxZeQqZZXD8+HH69u3L+PHjWbVq1QcF34EDBxgzZsxHFWoqDt6+fZunyq3Q0NAs5wRtbe1MicqlSpUiNTWVuLg4wt9E0sxltnoaMmYgwPwqbbGyqKC+PUREiolPVshsfnadW69fqsVVq+TNW6wfRWQRKqouOf4UCAkJYfPmzWzcuBE/Pz9q1KjBuHHjGD58OKampgQEBLB7927c3Nzw9PTEwMCA/v37M2TIEDp06FCoZE1fX18GDhzIw4cP0dXVRUNDg61bt9KjR48cX5OamkrFihUZOHAgK1asyPG44OBgqlatikwmY9iwYaxZs6ZANjo4OBAZGcmZM2eUj0VGRrJ8+XKWL19OfHw8o0aNYs6cOcpcodzYuHEjjo6O/P7778ybN++Dx74banJ2dmbixIni33AByUjGz2u5ul5FMwZuWaR2u76v352KekZq30dEpKjJq/b46MbNKgRBnRIGSJ/0PK/veDXv8vGSlpbGyZMnWb9+PcePH0dbWxsHBwe2b99OixYtCA8PZ+/evbi5uXH16lVKlSpF7969+fHHH+nevbtK8n4OHTrEqFGjkMlkyOVy7O3t2bFjBxUqfPjO9OjRo4SFhTF+/Id/v9999x16enqMGjWKtWvX8vfffxeo6svExIRnz55leqxs2bL8/PPPzJo1i7Vr17JkyRI2btzI4MGDmTt3LrVr5zz2AGDs2LEEBwczf/58LCwsGDVqVI7H2tracvXqVb755hsmT57MhQsXWL9+vTiwrwBIJBLKlClDmTJlcs3dEgSBB8F+rPa7oXa7VN7wUUTkI6Nkl7pkQ2kNTT4wmkRFe3x+oaG84Ovry/fff4+1tTV9+vQhJCSEVatW8erVK1xcXHj+/DndunXDwsKCGTNmUKZMGbZv387r16/Zu3dvpvLqgpKamsqsWbPo378/WlpaxMTE8NNPP3HmzJlcRQzAunXraN68eaY+Ne9z48YNduzYwW+//cbkyZN5+/Ytu3fvLpC9JiYmymZs72NgYMCcOXN4+fIly5cv58qVK9SpU4d+/fpx69atD677ww8/MH78eBwdHTl58uQHj9XW1mb58uXs37+fU6dO0bBhQ+7du1eg9yOSNyQSCcaGReMl0SjhFYsiIurmo/sEVNA1Qq6m/BgAmUSCleimVZKcnMyePXvo1KkTtra2rFixgn79+nHv3j0uX75M2bJlGT16NOXLl1fOFcoQN8ePH2fYsGHo6+vnvlEeCAwMpG3btri4uKCrq4uWlhbnzp3jxx9/zFNprZ+fH//+++8HvTEKhYIZM2ZQr149xo4di7W1Nd27d2fdunUFstnU1JSIiAg+FMHV0dFh8uTJ+Pj4sHnzZp48eULTpk3p3Lkz58+fz/a1EomEVatW0bNnTwYOHMjt27dztWXAgAHcu3cPQ0NDmjdvzurVqz9ol0jhMClVNH2bTItoHxGRkspHJ2Rs9LPvoaEq5IKAlZ569/gYePToEU5OTlSoUIHBgweTnJzM1q1bCQgIoGfPnixdupRy5coxaNAgAgMDWbhwIYGBgVy4cIEJEybkuyInN06dOoW9vT1eXl4oFAratWvH/fv3adv2ww3B3mXDhg2UKVPmg83pdu3axc2bN3FxcVGKowkTJnDnzp0CeTFMTExISUnJMr06OzQ1NRk1ahSPHz9m7969RERE0KFDB1q0aMGxY8eyiA4NDQ3c3NyoU6cOPXv25MWLF7nukRFqmjBhApMnT8bBwSHH6dwihUNHQxMTbfXOoyqrrYuO6EEW+cz56ISMpa4Rxmo8OWhIpNQ2tlDb+iWZ+Ph4Nm3aRIsWLahduzY7d+5k9OjRPHr0iN9++41r165ha2tLz549uXv3LnPmzMHb25vbt28zc+bMPIV28otcLueHH36gW7duyOVykpKSWLp0KUePHs2XWEpNTWXTpk0MHz48x8Z08fHxfPfdd/Tv35927dopH+/RowcVKlTA1dU13/Zn2JhTeCk7ZDIZAwcO5N69exw/fhypVErv3r2xt7dnz549mWYelS5dmqNHj2JkZETXrl15/fp1ruuLoaaiw87IHCnqiYVLJRLsjMzUsraIyMfERydkpBIJHSyqqeXUIEVCs/KVPqscGUEQuHPnDhMmTMDc3BxHR0cMDAzYu3cv//zzDwqFgi5dutCuXTvc3d1xdHTE09OTR48e8cMPP1C1alW12RYaGkrnzp35/fff0dbWpmzZsly7dg0nJ6d8V94cPXqU0NDQD4aV/vzzT8LDw/n7778zPa6hocHYsWPZuXMncXFx+drX1NQUINtW/LkhkUjo0aMHV65c4cKFC5iZmTF48GBq1qzJxo0blZOrTUxMOHXqFPHx8fTq1StP3h9IDzV5eHiIoSY10sa8qtoqLBWCQFtz9UzBFhH5mPjohAxAKzNb9DS1VS5mpBIJXS3tVLxqySQqKopVq1ZRv359GjduzIkTJ3BycuLff/+lSZMmzJs3jxYtWrBjxw6++OILrl69ip+fH4sWLSpQw7r8cvHiRezt7blx44ayJ42HhweNGjUq0Hqurq40a9YsxyRff39//v77b2bOnJltE72xY8eSkJCQ76Tfgnhk3kcikdC2bVtOnTrF7du3qV27No6Ojtja2rJ8+XISEhKwsbHhxIkTPHnyhEGDBmWaaP4hKleuLIaa1ERERAR/z/uJ0AfPUMhVW1kkRYKtvolYdi0iwkcqZHQ0tBhZrZnK73O+qGSfaW7Jp0bGYMPhw4djYWHB9OnTqVy5Mps3b2bSpEn8888/dO7cmeXLl9O6dWv+/fdfgoODWbFiBS1atCiS/iMKhYI//viD9u3bExcXhyAIrF+/nl27dhW4h1FGku+ECRNyPObbb7/F0NAwx74sVlZWBUr6zRAyBfHIZEejRo04ePAgXl5etGvXjpkzZ2JjY8Mff/xB5cqVOXjwIKdPn+brr7/Os3dFDDWplqSkJP766y9sbW3ZunUrjeQGaKp4orxEAsOrNVXpmiIiHysfpZABqGNcgc4VaqhkLQlQx9iCDhafpps2LCyMv/76ixo1atC2bVtu3LjBrFmz+OWXXwgLC2P06NH8+uuvVK9enUOHDhEWFsamTZvo3LlzoRrW5ZfIyEh69erFvHnzkMlkVKpUiTt37uDo6FgoEbVhwwYMDAxyVcaRdAAAJvNJREFUTPK9cuUKe/bsYeHChR+ssJowYQJ3797l7t27ed5bW1sbfX19lQmZDGrVqsX27dvx9vamf//+LFiwAGtray5evMjy5cvZtGkTP/30U77WFENNhUOhULBr1y5q1KjB/PnzGTFiBD4+PvwwbRb9K6m2q/IXNvaYlxZ7AYmIwEfY2fddFILA3hd3Of/Ku1Dr1Day4Gu71mhKcy/h/ViQy+WcPn2a9evXc+TIEWQyGX369MHGxgYPDw/OnTuHVCqla9euDBkyhL59+xao4ZuquHnzJl9++SWvX78mJSWF8ePH4+zsXOCJ0RmkpqZiZWXFgAEDWLlyZZbnFQoFTZo0AdJnGX1oTEJaWho2Njb07NkzX56ZypUrM2jQIBYtUl+X15CQEJYuXcratWsRBIEGDRpw5coV1q5d+0FPVHa8O6vpyy+/VFZ7ieTMpUuXmDVrFnfu3KFfv34sWrSI6tWrK58XBIEDfp6cDn5S6L06VajBl5Xqix2aRT558qo9PlqPDKTntDjYNmRktWZoSzXyVR0gRYJUIqGfTT0m1WrzyYiYgIAAFixYQKVKlejevTvPnj1j2LBhdOjQgX/++YfFixeTlpbGmjVrCA0N5dixY3z11VfFJmIEQWDZsmW0bNmS8PBwtLW12b17N+vWrSu0iIH/J/nmdDHfunUrd+/excXFJddZTxoaGjg6OrJr1658Jf2amJio3CPzPhYWFixevBh/f39mz56Nl5cXUqmUr7/+WjnbKa9khJoOHDjA6dOnadCggRhqygFvb2/69etH27ZtkUgkXLx4kUOHDmUSMZCe5zSgkj0DKtVPP/fkM8Mv4zX9K9mLIkZE5D0+ao/Mu0QlJ3A8wIvrYb6kCQqkEkmWwZJSJAgISJBgb2JJb6s6WOgaFo/BKiQ1NZWjR4+yfv16Tp06ha6uLi1btkQQBK5cuUJCQgJNmjRh8ODBDBo0SC1l0gUhJiaGMWPGcPDgQaRSKfXr12fv3r3ZJtsWlG7duhETE8P169ezPBcXF6ecGpzXJN7AwEBsbGxYs2ZNrmMOMujZsyeampocPnw4P6YXitjYWFavXs2CBQtITk6mc+fOLF26NNfxB+/j6+uLg4MDDx48EGc1vUN4eDi//PILa9eupUKFCvzxxx84ODjkafBpcHw0W7xvEPD2DdL3Jq6/T8bzVrpGjKrenAqfwPlKRCSvfLJDI3MjMS2FuxGBvIyLxC8ugtiUJAQEdDW0sdEvi7WeMQ1NrSijpVOofaKSE7gbEcDLuEhexkWSkJZeCquvWYpKBiZU0i9LIxMrdDW1VfG2ssXb25sNGzawdetWXr9+Tc2aNTExMcHLy4uoqCjs7OwYOnQogwcPxtbWVm12FARPT0/69+9PYGAgaWlpzJo1i4ULF6p0crifnx+2trZs3LiR0aNHZ3l+7ty5uLi48OzZM6ysrPK8bu/evQkJCclzrszIkSPx8fHh6tWred5DVURFRdGkSRN8fX1RKBT07duXefPmKcNpeSE5OZk5c+awfPnyzz7UlJiYyPLly1m4cCESiYT58+czderUfI/eEAQB37gILoQ851FUCPH/nT/eRVdDi1pG5rSzqEZlfRNRQIp8dny2QkbdBMdHc8T/AfcjgwCQZHNHlXEXJZNIaWJqTR/ruhiXUk0Tv8TERA4cOMD69eu5dOkSBgYGVK5cmaCgICIiIrCxsWHIkCEMGTKE2rVrl7iTnyAIbNiwgcmTJwOgp6fH9u3b6dmzp8r3mj9/PqtWrSIkJCRLmMrX15eaNWvy7bff8ssvv+Rr3WPHjtG7d2/u3LlDw4YNcz1+1qxZHDt2LMvwyKIiKiqKVq1aERYWRpkyZfD19aVTp07MmzePdu3a5flv5ODBg4wZM4ayZcuyb98+GjRooGbLSw4KhQI3NzfmzZtHSEgIkyZN4ocfflBJB2tBEIhOSeR1YhypCjmaUhnldPQx1NIpcZ9fEZGi5LPIkSlK5AoFxwMe8tu9kzyIDEYABMjWLZzxmFxQcPP1S366e4xLr3wKVQFy//59pkyZgrm5OcOHDycgIIBy5coRGxvLq1evGDp0KNevX8fX15eFCxdSp06dEncSjI+PZ/jw4YwfP560tDSaNWvGw4cP1SJiMjr5Dhs2LNtcm2+++QZTU1O+/fbbfK/drVs3LC0t85zwmzFvqbgwMjLi1KlT6OjooKenx5YtW3Idf5Ad/fv35969exgbG9O8eXNWrVr1WVQ1XbhwgSZNmjBs2DAaNWrE48ePWbZsmcrGcEgkEoy0S1PdsDy1jS2oblgeI+3SJe7zKyJSUhGFTB5IVchZ9fgiR/wfokDIV6dOBQIpCjk7fW6x0+d2lrydDxEbG4urqyuNGzfG3t6ebdu2KUMv0dHR9O7dmzNnzhAcHMyyZcto1qxZiT35PX78mPr16+Pm5oZEIuGnn37i/PnzasvXOXbsWI6dfC9cuMDBgwdZtGgRurr595TlN+nXxMSEN2/ekJaWlu+9VIWlpSUnT54kICCA7du3c+PGjVzHH2RH5cqVuXLlCl9//TVTpkxh0KBBn2wDvadPn9K3b1/at2+PTCbj8uXLHDhwQK3drEVERPKPKGRyQSEoWPfkMo+jXhV6rcuhPux98eG8CkEQuH79OmPHjsXMzIyvv/4aHx8fIL38t2PHjvzzzz+EhoayYcMGOnbsmKfJz8XJzp07adCgAb6+vpiYmHD+/Hl++ukntdq9bt06mjVrRt26dTM9LpfLmTFjBk2bNmXo0KEFXn/s2LEkJiaya9euXI/NuHN/8+ZNgfdTBbVr1+aff/7h8uXLjBkzhm7dunHlyhUuXryYafzBpk2blOMPskNbW5tly5ZlqmrKT2+dks7r16+ZPHkytWvX5sGDB+zevZsbN27QqlWr4jZNREQkG0Qhkwtng5/x8E2IyroIn3/ljed/+TXvEhERgYuLC3Z2drRo0YJdu3aRlJSETCajdevW7Nq1i9evX+Pm5kafPn3Q1lZfErGqSEpKwtHRkWHDhpGcnEynTp3w8vLK18TqgpDRyTc7b8ymTZu4f/8+y5Yty1OFSU5YWloq+8nkFl4pzLwlVdOmTRt27NiBm5sb3333HRKJhDZt2mQafzB27NhM4w9y4t1QU4sWLT76UFNiYiJ//PEHVapUYdeuXfz55588ffoUBweHEuvpFBEREYXMBwlLjOXQy/sqXVMCbPO+QXxqCgqFgrNnz/Lll19ibm7OzJkzlQmhzZs3x9XVlbCwMI4cOcKQIUOKtWFdfnnx4gUNGjRg06ZNyGQyFi9ezIkTJ5QXdXWyYcMG9PX1s3TyjYmJYf78+QwbNoymTQvf3n38+PF4eHjk6o1QxbwlVTJw4EBcXFz4+++/WbZsmfLxD40/yCl89CmEmhQKBdu3b6datWr89NNPjB07Fh8fH2bNmvVR3DCIiHzuiELmA/wb9ETld5gCkJCWwoKda7GwsKBTp04cPHiQtLQ06tevj7OzMyEhIZw7dw5HR0eMjY1Vun9RcPDgQerUqcOzZ8+oUKEC169fZ9asWYXygOSVjCTf4cOHZ8l/+e2334iPj1dZh93u3btTsWLFXJN+VT1vSRVMmzaNOXPm4OTkxN69ezM99+74gwEDBijHH3z//ffZvoePOdR07tw5GjVqxIgRI2jatCmPHz/G2dmZsmXLFrdpIiIieUQUMjmQkJbCjTC/fCX25hWFIBCiL+F1+GsqVarEr7/+yosXL7h79y7Tp0/H3Nxc5XsWBSkpKUydOpUBAwaQmJhIv3798PLyonHjxkVmQ0aS77hx4zI9/vz5c5YtW8Z3332nsgRjmUyGo6Mjbm5uxMbG5nickZERUqm0RAkZgD/++IOhQ4cyfPhwLly4kOX5ypUrs2bNGvz8/HB0dMTFxQVra2ucnJwICsoaHv2YQk1Pnjyhd+/edOzYES0tLa5cucL+/fupUqVKcZsmIiKST8Q+MjlwO9yfDU/V28BskH4VOtrnvTFZSSYgIIDevXvz4MEDNDU1WblyJePGjSvy3IJu3boRHR3NjRs3Mj3et29fPD09efr0KTo6hWuG+C5BQUFYW1uzatUqvv766xyPK1euHNOmTeP7779X2d6qICUlhZ49e3L79m0uX75MnTp1cjw2MjKS5cuXs3z5cuLj4xk1ahRz5szJcvEvTAO9VwkxvIgNxz/uDVHJCcgR0JFpUkHXEGs9Y6qVKYeWrOCDTMPCwliwYAHr16/HysqKRYsWMXDgQDEHRkSkBCI2xCsk+309OBfyFLka7yiH2jamrcXHX8p54sQJBg0aREJCAjY2Nhw5ciTfrfBVQU6dfM+cOUPnzp3ZvXs3Dg4OKt+3b9++BAQEcO/evRwviHZ2dnTp0gUXFxeV719YYmNjadu2LeHh4Vy/fp2KFSvmevzatWtZunQp4eHhDB48mLlz52b5nb/bQG/v3r05Ng9UCAJ3wv05F/IMv7hIAGQSifKzJ+H/jSdLyTRoaWZLJ4sa+WoymZCQgLOzM4sWLUJDQ4MffviByZMnizkwIiIlGLEhXiEJjI9Sq4iRSSQExUepbf2iIC0tjdmzZ9OzZ0/i4+P56quv8PLyKhYRA7Bx48YsSb5paWnMmDGDVq1aZUn+VRXjx4/H09OTO3fu5HhMcTfF+xAGBgacPHkSTU1NunXrRlTUh/8uDQwMmDNnDn5+fixfvpwrV65Qp04d+vXrx61bt5THvR9qWrlyZZZQU3jiW5Y8OMPGZ9d4Gff/8vR3P3vvNp5MkqdxPtibH+8e4+Kr57mGrhQKBVu3bqVatWr8/PPPjBs3jhcvXjBz5kxRxIiIfCKIQiYHElKT1bq+QhBIkqeqdQ91EhoaSrNmzViyZAna2trs3LmT7du3q2RidUFITU1l48aNDBs2LFOSr6urK48fP8bFxUVt4YNu3bphZWX1waRfExOTElO1lB1mZma4u7sTFhZG3759SUpKyvU1Ojo6TJ48GR8fHzZv3syTJ09o2rQpnTt35vz58wiCoKxqmjhxIlOnTmXgwIHKqqbHUa/4+d5xfGPTBZ6Qx3w0BQKpCjm7fG6z7sllUhXZN/E7e/YsDRs2ZNSoUbRo0YInT56wdOnSjzKBXkREJGdEIZMDUol6fzQSJEg/0rj8+fPnqVq1Knfv3qV69eo8evSoUM3lVEF2nXyjoqL48ccfGTVqVJ5mIhWUvCT9mpiYlFiPTAbVq1fn2LFj3Llzh2HDhuXa5TcDTU1NRo0axePHj9m7d2+W8QdaWlq4uLhw4MABzpw5Q4MGDTh66zIrH10gTSEvVEK9Z2QQ655cRi4olI89evSInj170qlTJ3R0dLh27Rp79+4tcYNTRUREVIMoZHLAuFRp1CozJFBGq3i8FwVFoVDw/fff06FDB96+fcuECRN48OBBibhAuLq60rRpU+rVq6d87OeffyY5OZmFCxeqff8xY8aQnJzMzp07s32+JIeW3qVZs2bs3r2bQ4cOMWPGjHxVHclkMgYOHMi9e/c4ceIEMpks0/iDvn374uHhQTkrSw5EPkWuKHxNoAA8fBOCe+BjQkNDmTBhAnXr1uXp06fs27ePq1ev0rx580LuIiIiUpIRhUwOWOuVRaJGKaMQBKz1Ph4Xd2RkJC1atOD3339HR0eHw4cPs3btWuXsp+Lk5cuX/K+9O4+Pqjz3AP57z5kkk21C9mSyhyUkLE0AoQ2LIFtYBBWrSC1VC1Ra9ROx7UWlLErRa0Wv1Soqttal9aq9+hGUoLQoIBFQNgEhBgJkgRC2yUIymck594+QiELCJDln5hz4ff9MZt55E0Lml3Oe93nWrl37vasx+/fvx1/+8hc8/PDDiIuL030PCQkJmDx5cpudfo1+a+lCU6ZMwQsvvIDnnnsOTzzxRIefL4TAhAkTsHHjxovGH6xfvx4znlmEgKAgQMMrkh+U7MKgMdfinXfewfLly7Fv3z7cfPPNPI1EdBVgkGlDd1uULj1kLpRmM0fTrU2bNiE9PR1btmxB3759UVRUhKlTp/p6W61aOvleeCJp3rx5SEpKQn5+vtf2MWfOHOzatQvbtm276HNRUVGor69vt+W/kcyZMwcLFy7E/Pnz8frrr3dqjUuNP5j/5DLscRwHJG0DhqIomLIoHwcPHkR+fj4LeYmuIgwybehui0ZkQMcnI3tCgkBmtzhE6LS+VlRVxZIlSzBixAhUV1cjPz8fO3fuRGJioq+31qqlk++FRb5r1qzBmjVr8OSTT8JqtXptL+PHj2+z6NdI85Y8tXjxYvzyl7/EXXfdhY8//rhLa7WMP3jwb89CVZTLP6GDJIsMJS4MSqDvrxASkXcxyLRBEgLXJWTocnNJgYpR9l46rKwdh8OBYcOGYfHixQgKCsInn3yCp59+2nCTtlevXo1jx4613lZyuVyYN28eRo4ciRtvvNGre5FlGbNnz8Zbb7110bwho81b8oQQAitWrMC4ceMwbdo0bN++vUvruZQmfOtyQOg2qkJgW9VhndYmIqNikGnHtfE9ERtog6RhnJEgkBUej/4R2rTJv9A5dyP2nz2OLypLsLnyELafLEVVfU2H28Rv3boVKSkp2Lx5MwYMGICSkhKMGTNG8/1q4YdFvs8//zyKiop0PW7dnraKfs14RQYALBYL3n77bWRlZWHixIk4dOhQp9eqqHN873SRHloa6hHR1aPzvb6vAn6SjLsycvH4zrWarCfOrzmz5xDN3mTrXE5srjyEDceLcaK+5pKPCZAtGBSVjJH2Xkhup8BYVVUsW7YMf/jDH6CqKh588EEsXbrUK8MeO6OlyHflypUAmkPC4sWLMWvWrO+dXvImu92O66+/Hi+++CLmzp3b+u9sxMGRngoODsbq1auRm5uLvLw8bN68ufXr6YjSutOXf1AXqFBxmEGG6KpjzHcoA0kJjcAdGT/u8joCzb1p7ulzLcIDun7sWlEVrCvfj99veQ//KtnRZogBAGeTG4UnSvDHHQV4ds+nOOO8uOC0trYW1157LRYsWICQkBBs2LABy5YtM2yIAS4u8l20aBEURcGjjz7q033NmTMHu3fv/l6X26CgIAQGBprq1tKFoqOjsXbtWjgcDkyePBl1dXUdXqPW5dT06ualnHM36ro+ERmPcd+lDGRITBpm9R4Ki5A69YtYgoBV9kN+v+vQq1tsl/dT3ViPJ3Z9gncObYdb9exslXL+9tK+M8ew6MvV2HmytPVz27dvR1JSEjZu3IghQ4agtLQUw4cP7/I+9fTDIt89e/ZgxYoVWLhwIWJiYny6t3HjxiElJeWiol+z9JJpS3p6Oj766CPs2bMH06dPh9vt7vgiPA1NRBpjkPHQNdEpWDRwUuuRaU8CTctjfhSZgEcGXY9eYV1/g3U01uO/d32CIzWdu0yvQIVTcWPFNxux9cRhPP744xg0aBAcDgeWLFmCwsJCjycV+9KHH37YWuSrqiruv/9+dO/eHffee6+vt9Zm0a8ZuvtezsCBA/Huu++ioKAAc+fO7VD9VYhfQGug1kuQhaeWiK42DDIdEBMYit/2H4t7+4xEVnh868cFmodAyuK7FnqSEBgUnYzf/2gsfpU5HDb/rh8DblIUPLvnU5xuqOtyjxsVwMpvNuF/3vwrbDYbCgsLsXDhQtM0EHvxxRdbi3xXrVqFdevWYfny5YZo0AcAd955JxobG/HGG2+0fsxMTfHak5eXh5UrV2LlypV45JFHPH5eUrC+DSAFBFJDzdGbiYi0w2LfDpKEQN8IO/pG2FHvbsTR2jM4Wnsade5GSBAI8QtASmgEEoPDESBr++0tKNuLUg0nZquqiglL5+GpUbch3Gb8qzAtLizydTqdeOCBBzB27FhMnjzZ11trZbfbMWXKFLy08mUMmz4Vh2tOIXHmBLgtEh7d/hFC/axIDolAamgE+obb4a/xz4refvGLX6CiogIPPfQQ7HY7Zs+efdnn2IPDIAtJ15NLaQwyRFcdc/32NJhAiz8yusUiQ4O6l8s52VCL1Uf2aLqmJMsIiLBh05mjuN7WT9O19fTKK6+0Fvk+++yzKCkpwfvvv2+oq0l1rkYM+83tCK2vwvP7NkASApakaFiEQFndWQDAAUclFFVFgGzBsLjuGJ+YhTD/QN9uvAPmz5+PsrIy3H333YiPj79skPSTZAyKTsa2qiM63WJScU10qg7rEpGR8daSSWw8VqzLuiqATyuK4FY8m3Tsay6XC6+88gpuv/121NXV4dFHH8Xdd9+NPn36+HprrXadKsPCr1ahyN8Ja1gogPPF1j8IWi1v5s4mN9aXF2Hhl6vwxYmSDvf98RUhBP785z9j6tSpuOWWW7Bly5bLPmdUfC9dQowEgb7hdkRajd0tm4i0xyBjAk2qgg3Hi3Wb/VTrdmL36XJd1tbahUW+CxYsgCzLWLJkia+3BaD5Vt2qI7vx/L4NqHM5oXbg30uBioYmN/52oBD/KN6me1GsVmRZxptvvokBAwZg0qRJKCoqavfxabYo5EQm6nIM+8a0bM3XJCLjY5AxgePnqnXtjyELgW8dJ3RbX0svvfQSBg8eDFVVsXLlSixevBiRkcaoi/jw6B6sPtp8+68rMWTD8WK8dfBL01yZCQwMxAcffICYmBjk5eXh+PHj7T5+Ro9rECBbNI0yk1P6ISG4m4YrEpFZMMiYwNFafTuiNqmqKVq7Hz58GAUFBZgzZw7y8/PRu3dvzJ0719fbAgB8c+Y4Vh39WrP1Pjv2Lb6sOqLZenqLiIhAQUEBnE4nJk2ahJqaths02vwDMTdrBKQLTvl1lgDQPyIBeUlZXVyJiMyKQcYETjbUQta5kLWqoVbX9T1V52qeF7X1xGFsPXEYX58uh6OxHkBzkW9ISAisVis+++wzPPXUU/Dz8/PxjoEGtwuvFhWi62/L3/dm8TZUn//azSA5ORlr1qxBcXExbr75ZjQ2tn0VMaNbLO7tMwoWSe7SbabsyETMyRwGWfBXGdHViqeWTKDJC7cYFJ2H+bWnprEBn1cexKbjB9sMVCGWAOxyVmL67DuxYMECTJw4EXl5eV7e6aVtqjwIR2O95hVMziY31pUfwE0mqv3o378/3n//fYwfPx6zZs3C3//+9zZPk2WGx2HRgEl4tagQxdVVEBAe1RVJEJAlCbekD8DwuB6GOq1GRN7HIGMC/pIFemcZf8n7PwpNioI1pXvxYekeqGr7b2G1bifSJgyHsMhIDXBi2c9938EXaD559J/yA7qUYStQsfH4t7g+pR/8JFmHV9DHqFGj8Nprr+G2225DQkICHnvssTYfGx0Yggf6j8GXVUfwn4oDrbc4ZSG+C/AqoChNkGQZVtmCYXE9MNqegQieUCIiMMiYgj04TLcTS0BznUFicLhu61/KyYZaPL93A8rPnfX4OZKl+c281/jheMNxAMGOGPQIi9Zph545Wnsap5wdH6DoqXNuF/afPY5+EQm6vYYepk+fjmPHjmHevHlISEjAPffc0+ZjJSEwOCYVg2NSceycAwerT+JIzSmccZ5DE1SojS689swL+PWtP8fM8VNM1zyQiPTF3wgmkBKif2v3lFB9X+NCVfW1eGLXx6h1OTu3gCRQ42rA01//G/f1HeWVhoRt6ezMK09JEDhcc9p0QQYA7r//fpSVleG+++5DfHw8pk2bdtnnxAeFIT4oDMPiurd+TFVVLP1wBiq658B/4k16bpmITIgVcibQzT8Q9qAw3QYHK1DR30tvlA1NLjz99b9R63J26SqTiub+Os/u/RSV9dXabbCDSuvO6FqIrUJFqc6n1vT0pz/9Cbfeeit+9rOfYePGjZ1aQwiB7Oxs7Ny5U9vNEdEVgUHGBIQQuM6eocvNJQEgKTjca8P2/q9kJ047uz70EjgfZhQFrx74wmcN5M65G3V9bRXo/JUrA5AkCa+++ipyc3MxZcoU7N27t1Pr5OTkMMgQ0SUxyJjE4JhU2Pysml+VUQFMTPZOe/8jNafx2bFvNQ1kClQcqjmJzZWHNFzVWMx+KicgIADvvfcekpOTkZeXh7Kysg6vkZ2djZKSEpw9e1b7DRKRqTHImESAbMEdGT/WNARIEMiJTEROZJKGq7ZtfcUBXVrTCwDryvf7pBNuqJ8Vko5BQwCmGiTZlrCwMKxZswaSJGHChAkdDiTZ2dkAgF27dmm/OSIyNQYZE+kTbsd19l6arCVBwOZvxYweg73yF3+9uxFbq47ocvpKBXDsnAOHfdCdOCU0Quc+PwLJOhd7e4vdbkdBQQHKy8txww03wOn0/JZZRkYGrFYrby8R0UUYZEzmp+kDkRub3qU1JAiE+lvx2/5jYPO3arSz9pXUnEKTjk33BASKfDAvKk3n2iIVqu6v4U2ZmZlYtWoVtmzZgpkzZ0JRPPuZsFgs6NevH3bs2KHzDonIbBhkTEYSAjN7DsG0tBzIQnTqVk3vbrF4KHs8ogNDddjhpR2tPa15C/8fOuKD0z3xQWFIDg7X7SsLDwhCTx/3ytHa0KFD8c9//hPvvvsu5s2b5/EtQZ5cIqJLYZAxISEExiVmYuGAicgMjwOAdgNNSw1HeEAQZvYcgvv6jkK3gCCv7LVFVUMtJB1zjArVZ8ewr0vQ70TZyPhekK7AOUI33HADnnvuOTzzzDNYvny5R8/Jzs7Gvn372p3hRERXHzbEM7G4oDDc13cUquprsbnyIIqrq3Ck9jScTW4AzW+EcYE2pNmiMDAqGVnh8boWpranSVV1H7Og562r9gyOScW68v2oqHNoVgMkIBAeEIRRGtVEGdHcuXNRVlaG3/3ud7Db7ZgxY0a7j8/JyYHL5cK+fftai3+JiBhkrgDRgSGYmvojAM1dUF1KExRVhZ8sG2YqcIAkQwhAx0kLCPDBvCgAkIWEuzJysXTHGs2+PhUq7sz4CQKu8Hb8S5cuRXl5Oe644w7ExsZi9OjRbT62X79+EEJgx44dDDJE1MoY73KkGSEE/GULrBY/w4QYoLmWRM/TPZIQSAzx7ryoCyUEd8OdvX6i2Xq3pA9Ar7AYzdYzKiEEXn75ZYwePRo33nhjuzUwISEh6NmzJ+tkiOh7ruw/98gw9J7lpKiqz48pD45JBQD8raiweWJzBy/PSBBQoeLW7gMxyp6hww6Nyc/PD++88w5GjhyJCRMmoLCwEKmpqRc9TlVVDBiRi+JGBwpK98GtNMFPlhEXaENKSITX676IyBiE6sGRgerqaoSFhcHhcMBms3ljX3SFaVIU/H7re7q22//jNVMQZQ3RbX1PVdQ58NcDm1FadwbifDhpT8sdt9jAUNyVkeu1cRFGU1lZiaFDh8JiseDzzz9HZGTz96HW5cTmykNYX3EAp53nADSHPiEA9YLAGBdowyh7L/w4Jg1Wi5/Pvg4i0oan2YNBhrzmgyO78dHRvZd9Y+8oCQJZ4fG4t+9ITdftiiZVwVdVR7G+ogiHak4CAGQhWguehUDrrbaEoDBcl5CBITFp8JNkX23ZEIqLi5Gbm4sePXpg3bp12F59DP978Cu4FLfHPzXBFn/c3nMIBkR5p2M1EemDQYYMx9FYjwXbPkCj0qT52r/tPwY9DVpTUlVfi8M1J3G07gxqXE6oqopgP38kB0cgNTQCsYE2089T0tLWrVsxJm88bnr6YQSk2zv8/JYrXD+JScPPew6BLBmnVoyIPOdp9mCNDHlNmH8gbu0+EK9/u1WzNQWAEfE9DRtigOZTZdGBIbgGqb7eiilkDxyAX/9rBU6JzvWLafnL7IsTJTjnbsSvsoYbqvCdiLTF/93kVUNjuyM7MlGTTrgSBGIDbbgpLVuD1cgoXv92C85aFEhy126zqQB2ny7H+4c5aJLoSsYgQ14lhMCs3kORGR7fpTAjQSDKGoJ5/UfDKrOw80qx82QptlYd0ayOSgXwcdk3OFhdpcl6RGQ8DDLkdX6SjN9kjcDYxEwItD9eoS3ZkYn4r+xxCPMP1H6D5BNNioI3i7dpPrdKgsA/ird5PNOJiMyFNTLkExZJxrS0HOREJuHtQ1+hpOYUJCGgtPFm0/K5KGsIbkrNxsDoZC/vmPS263QZql0Nmq+rQEVZ3VmU1JxCui1K8/WJyLcYZMin0m1RmJ89HqW1Z1BYeQgHq6tQVncW7vNzkyQIxAaFIj00GtdEp6B3t1ie8LlCbThWDAlCs3lVF5IgsOn4QQYZoisQgwwZQlJIOJJCBgIAFFVBQ5MbqgpYZQuPz14FFFXFweqTuoQYoPmqTJGjUpe1ici3GGTIcCQhIcji7+ttkBedbKhFo+LW9TWqGmrR4Hax6y/RFYZ/6hKRz505P3pAb47Geq+8DhF5D4MMEflcW0XeWtNzAjsR+QaDDBH5nNXinbvc3nodIvIeBhki8rn4oDDdX8MqWxDuH6T76xCRdzHIEJHPWWU/xASG6voaKSGRPLpPdAVikCEiQxgUlQyheV/f77CJItGViUGGiAxheHwPQKc+Mv6SjCHRqbqsTUS+xSBDRIYQERCM3NjuulyVGZuQyf4xRFcoBhkiMoyfpucg1C9AsygjQSAu0IaJyX00WpGIjIZBhogMI9Dij9m9h0ISXb8uI9A8aX1O5jBYJFmL7RGRATHIEJGh9OoWi19nXQtJSJA6GWckCATIFuT3uw4Jwd203SARGQqDDBEZTt8IO+Znj0NskK1Tz08JjcBDOXmcdk10FWCbSyIypOSQCCzIycO68gP4d/l+VLsaIAtxyTEDLR+PCAjG+MRMjIjvAUnw7zSiqwGDDBEZlkWSkZeUhbGJvfH1qXLsd1SipPokKutr0KQqsEgy4oNsSAuNQlZ4HDK7xUNi0zuiqwqDDBEZniwkZEclITsqyddbISKD4bVXIiIiMi0GGSIiIjItBhkiIiIyLQYZIiIiMi0GGSIiIjItBhkiIiIyLQYZIiIiMi0GGSIiIjItBhkiIiIyLQYZIiIiMi0GGSIiIjItBhkiIiIyLQYZIiIiMi0GGSIiIjItBhkiIiIyLQYZIiIiMi0GGSIiIjItBhkiIiIyLQYZIiIiMi0GGSIiIjItBhkiIiIyLQYZIiIiMi0GGSIiIjItBhkiIiIyLQYZIiIiMi0GGSIiIjItBhkiIiIyLQYZIiIiMi0GGSIiIjItBhkiIiIyLQYZIiIiMi0GGSIiIjItiycPUlUVAFBdXa3rZoiIiIiA7zJHSwZpi0dBpqamBgCQlJTUxW0RERERea6mpgZhYWFtfl6ol4s6ABRFQUVFBUJDQyGE0HSDRERERD+kqipqampgt9shSW1XwngUZIiIiIiMiMW+REREZFoMMkRERGRaDDJERERkWgwyREREZFoMMkRERGRaDDJERERkWgwyREREZFr/D0Uu/vkHfoBEAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "G = to_networkx(data, to_undirected=True)\n",
        "plt.figure(figsize=(7,7))\n",
        "plt.xticks([])\n",
        "plt.yticks([])\n",
        "nx.draw_networkx(G,\n",
        "                 pos=nx.spring_layout(G, seed=42),\n",
        "                 with_labels=False,\n",
        "                 node_color=data.y,\n",
        "                 cmap=\"Set2\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model Architecture\n",
        "- Define the GCN model architecture using PyTorch.\n",
        "- The model consists of :\n",
        "  - 2 GCN convolutional layers\n",
        "  - a linear classifier.\n",
        "\n",
        "- Use the hyperbolic tangent (tanh) activation functio"
      ],
      "metadata": {
        "id": "BEavA41CI0Qt"
      },
      "id": "BEavA41CI0Qt"
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "5e5ffdf7-d77d-4712-a976-014fd8218894",
      "metadata": {
        "id": "5e5ffdf7-d77d-4712-a976-014fd8218894"
      },
      "outputs": [],
      "source": [
        "class GCN(torch.nn.Module):\n",
        "    def __init__(self):\n",
        "        super(GCN, self).__init__()\n",
        "        torch.manual_seed(12345)\n",
        "        self.conv1 = GCNConv(dataset.num_features, 4)\n",
        "        self.conv2 = GCNConv(4, 2)\n",
        "        self.classifier = Linear(2, dataset.num_classes)\n",
        "\n",
        "    def forward(self, x, edge_index):\n",
        "        h = self.conv1(x, edge_index)\n",
        "        h = h.tanh()\n",
        "        h = self.conv2(h, edge_index)\n",
        "        h = h.tanh()  # Final GNN embedding space.\n",
        "        out = self.classifier(h)# Apply a final (linear) classifier.\n",
        "\n",
        "        return out, h"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = GCN()\n",
        "print(model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ojgmOTXI-2-2",
        "outputId": "ac1fc429-9faf-4b98-e189-97072ee455f2"
      },
      "id": "ojgmOTXI-2-2",
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GCN(\n",
            "  (conv1): GCNConv(34, 4)\n",
            "  (conv2): GCNConv(4, 2)\n",
            "  (classifier): Linear(in_features=2, out_features=4, bias=True)\n",
            ")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Training the Model:\n",
        "- define a training loop and train the model for a specified number of epochs (100 in this case).\n",
        "- use Cross-Entropy Loss and the Adam optimizer. The training loss is recorded during each epoch and plotted at the end of training."
      ],
      "metadata": {
        "id": "PPjwraNTJfG1"
      },
      "id": "PPjwraNTJfG1"
    },
    {
      "cell_type": "code",
      "source": [
        "def train(data):\n",
        "    optimizer.zero_grad()  # Clear gradients.\n",
        "    out, h = model(data.x, data.edge_index)  # Perform a single forward pass.\n",
        "    loss = criterion(out[data.train_mask], data.y[data.train_mask])  # Compute the loss solely based on the training nodes.\n",
        "    loss.backward()  # Derive gradients.\n",
        "    optimizer.step()  # Update parameters based on gradients.\n",
        "    return loss, h"
      ],
      "metadata": {
        "id": "A44mqq1EBwcs"
      },
      "id": "A44mqq1EBwcs",
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "16b19434-ab57-4838-8a85-b4edafd44c5e",
      "metadata": {
        "id": "16b19434-ab57-4838-8a85-b4edafd44c5e"
      },
      "outputs": [],
      "source": [
        "# Lists to store loss and accuracy values\n",
        "losses = []\n",
        "accuracies = []\n",
        "epochs = 100\n",
        "criterion = torch.nn.CrossEntropyLoss()  #Initialize the CrossEntropyLoss function.\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=0.01)\n",
        "# Training loop\n",
        "for epoch in range(epochs):\n",
        "    loss, h = train(data)\n",
        "    losses.append(loss.item())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "07eaf97f-4786-4ef4-afa8-b2c04a1e219b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "07eaf97f-4786-4ef4-afa8-b2c04a1e219b",
        "outputId": "0c228abd-55a0-4c15-fb82-9527b6466286"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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ZsReKjgEAcD2Sm3oIMknP3NJekpxKcCg6BgDA9Uhu6ql3x1hNvfdaxTUJd/oZFB0DAOA6JDcu0Cc5Xl8+8Wt98PtUNY1woBDnIkxTAQBQf15Nbj7//HP1799fLVu2lMlk0qJFi+y+d926dQoJCVGnTp3c1j9HBAeZ1OPKaL10xy9kUv2mqZ5auE0LtxxSznfHmaoCAMBBXk1uTp8+rZSUFE2ZMsWh+woLCzVkyBDdfPPNbuqZ8/okx9d7murEaYsenZerQdPXM1UFAICDvLoUvG/fvurbt6/D9z300EO65557FBwc7NBoj6f0SY5XRoc420Ga+wtK9OZneySp2t2Ma8P+OAAAOMbv9rl5//339f3332v27Nl64YUX6mxfWlqq0tKfdxEuKiqSJFksFlksDuzCdxHrvbU9o2ubKElRkqSk6Ai9sHRXnTsaX6y6/XHiosx65pb26t0x1qFn+St7Yg3XINaeRbw9h1h7jrti7cjzTIZh+ERRh8lk0sKFCzVgwIAa2+zdu1fXX3+9vvjiC1111VUaP368Fi1apNzc3BrvGT9+vCZMmFDl+pw5cxQZGemCntuvwpD2njRp5t4glZyTnKvMkawpz/1XVSilhU/89gEA4FYlJSW65557dPLkSUVFRdXa1m9GbsrLy3XPPfdowoQJuuqqq+y+b8yYMRo9erTtdVFRkVq3bq1evXrVGZzaWCwWZWdnKyMjQ6Ghjq2Q6rDjqB6eu1WS49NU551Pij7+wawbr0tRaoBPU9Un1nAMsfYs4u05xNpz3BVr68yLPfwmuSkuLtbGjRu1ZcsWZWVlSZIqKipkGIZCQkK0cuVK/frXv65yn9lsltlsrnI9NDTUJUF35jm3drpMISHBmvDptzpy8qzT37vwzDllztzUYI5xcNXvGepGrD2LeHsOsfYcV8fakWf5TXITFRWlbdu2Vbr2zjvvaPXq1VqwYIESExO91DPnXFh0nH/yjJ5fslM/nS5zaiTHuj/O1HuvDfgEBwCAung1uTl16pTy8vJsr/ft26fc3Fw1b95cbdq00ZgxY3To0CHNmjVLQUFBSk5OrnR/TEyMwsPDq1z3F8FBJqUltZAkRYQFa/jszTLJ8akqa/unFm7TGUuF4qJYUQUAaLi8mtxs3LhRPXv2tL221sZkZmZq5syZOnLkiA4ePOit7nmUdX+c+kxVWffHkThxHADQcHk1ubnppptU22KtmTNn1nr/+PHjNX78eNd2yotcvT8OU1UAgIbIb2puGooLp6okqV1cI6dGc6zJ0JP/3KbG4aHqfkULpqkAAA0CB2f6uPoeysmJ4wCAhobkxg+44lBOThwHADQUJDd+pD6HcnLiOACgoaDmxs/Ud38cVlQBAAIdIzd+yFp0fNu1l2nibef3+GGqCgCA80hu/Jwrpqqe/Oc2rcsrYJoKABAQSG4CACuqAAD4GclNgGBFFQAA55HcBBhWVAEAGjpWSwUgVlQBABoyRm4CFCuqAAANFclNA8CKKgBAQ0Jy00CwogoA0FCQ3DQgrKgCADQEJDcNUH2nqSRpwqffMkUFAPBJrJZqoOqzosqQdOTkWb2RvUc92karW2JzBQc5Mw4EAIDrkdw0YNYVVZIUERas4bM3yyTZvWR88po8TV6Tx3JxAIBPYVoKkuo3VUUdDgDAlzByAxvrVNX6745r5JzNKjxjses+60jPUwu36YylQnFR4UxVAQC8hpEbVFKfFVXWnY0HTV/PknEAgNeQ3KBa9ZmmkpiqAgB4D8kNamTd+O/DYd2V1TPJoXtZMg4A8BaSG9TKuqLq0Yx2im8S7tA0lXXJ+Mx1+0hwAAAeQ3IDuwQHmTSufwdJju9s/PySndTgAAA8huQGdmO5OADAH7AUHA5xdmdjlosDADyF5AYOq8/Oxtbl4pLY2RgA4BZMS6FemKoCAPgakhvUm3XJ+LP9rnboPpaLAwDcgeQGLhEcZNJ9PRJZLg4A8DqSG7gMy8UBAL6A5AYuRQ0OAMDbWC0Fl6vPcnGTztfgZHSIY5k4AMApJDdwC2eXi1trcN7I3qMebaPVLbG5u7sKAAgwTEvB7ZyZqpq8Jk+Dpq/X9S+v1oodR93YOwBAoCG5gUc4u1w8/+RZPTx3q7YeZ4oKAGAfkht4jDPLxa3TWB/vD2KpOADALiQ38ChnlosbkgrLTJq1/gAJDgCgTiQ38Dhnl4tPXLaHvXAAAHUiuYFXWGtwPhzWXVk9k+y+j71wAAB1IbmB11iXiz+a0c7uOhzOowIA1MWryc3nn3+u/v37q2XLljKZTFq0aFGt7T/++GNlZGTo0ksvVVRUlNLS0rRixQrPdBZu42gdzoV74eR8d5wkBwBQiVeTm9OnTyslJUVTpkyxq/3nn3+ujIwMLV26VJs2bVLPnj3Vv39/bdmyxc09hbvVdy8cpqkAAFZe3aG4b9++6tu3r93t33zzzUqvJ06cqMWLF+vTTz9V586dXdw7eJr12IaZ6/bp+SU77b7PWocz9d5r1Sc53o09BAD4A7+uuamoqFBxcbGaN2eL/kBRn71wqMMBAEh+frbUq6++qlOnTumuu+6qsU1paalKS0ttr4uKiiRJFotFFovF6e9tvbc+z0DNnu7bTg/P3WrXeVTSz3U4732RpyHdL+fQTSfxc+1ZxNtziLXnuCvWjjzPZBiGT/xT12QyaeHChRowYIBd7efMmaNhw4Zp8eLFSk9Pr7Hd+PHjNWHChGrvj4yMdLa78ICtx036eH+QCsscS1Sahhm6PaFCKS184kcbAOACJSUluueee3Ty5ElFRUXV2tYvk5u5c+fq/vvv1/z589WvX79a21Y3ctO6dWsVFBTUGZzaWCwWZWdnKyMjQ6GhoU4/B7UrrzC0/rsfNWfVJq08FGzXPdZU6O27U9S7Y6z7OheA+Ln2LOLtOcTac9wV66KiIkVHR9uV3PjdtNSHH36o+++/X3Pnzq0zsZEks9kss9lc5XpoaKhLgu6q56B6oZJ6XBmjn/YY+k+xWUeLSuucpjJ0PsF5cdlu9b2mFVNUTuDn2rOIt+cQa89xdawdeZZXC4pPnTql3Nxc5ebmSpL27dun3NxcHTx4UJI0ZswYDRkyxNZ+zpw5GjJkiF577TWlpqYqPz9f+fn5OnnypDe6Dw8KMknP3NJekmN74WzYd8Kt/QIA+B6vJjcbN25U586dbcu4R48erc6dO2vs2LGSpCNHjtgSHUl69913de7cOY0cOVLx8fG2r1GjRnml//Cs3h1jHd4LZ9n2I2z0BwANjFenpW666SbVVvIzc+bMSq/Xrl3r3g7B5zm6F86snAOalXNA8U3CNa5/B/bBAYAGwK/3uUHD5MxeOBy4CQANB8kN/JIz51FJbPQHAA0ByQ38lqPnUXHgJgA0DH63FBy4kLUGZ8O+E1q2/Yhm5Ryo857Ja/I0eU0edTgAEKAYuYHfCw4yKS2phfo6mKRQhwMAgYnkBgGjW2JzDtwEAJDcIHA4WmQssdkfAAQikhsEFEeLjK3W5f3I6A0ABAiSGwScPsnx+vKJX+vDYd2V1TPJrnsmr/lO17+8mvobAAgAJDcISNYi40cz2tldh0OBMQAEBpIbBDRH6nAoMAaAwEByg4DnSB0OG/0BgP8juUGDYK3DyerZ1q72k9fkadD09dThAIAfIrlBgxEcZFKPttEO3UMdDgD4H5IbNChs9AcAgY/kBg0KG/0BQOAjuUGDw0Z/ABDYSG7QILHRHwAELpIbNFhs9AcAgYnkBg0eG/0BQGAhuQHk3EZ/FBgDgG8iuQH+x9GN/pZtP8IuxgDgg0hugAs4stHfrJwD7GIMAD6I5Aa4iKMb/VFkDAC+heQGuIijG/1RZAwAvoXkBqiGoxv9UWQMAL4jxNsdAHxVn+R4ZXSI04Z9J7Rs+xHNyjlQ5z3r8n5Ut8TmCg6yd1ILAOBqjNwAtbBu9Nc3Od6u9uxiDADeR3ID2MGRImMKjAHAu0huADuwizEA+A+SG8BO7GIMAP6B5AZwALsYA4DvI7kBHMQuxgDg20huACewizEA+C6SG8AJ7GIMAL6L5AZwErsYA4BvYodioB7YxRgAfA8jN0A9sYsxAPgWkhvARdjFGAB8A8kN4CLsYgwAvoHkBnAhdjEGAO8juQFcjF2MAcC7SG4AN2AXYwDwHq8mN59//rn69++vli1bymQyadGiRXXes3btWl177bUym81q27atZs6c6fZ+As5gF2MA8A6vJjenT59WSkqKpkyZYlf7ffv2qV+/furZs6dyc3P1yCOP6P/+7/+0YsUKN/cUcBy7GAOAd3h1E7++ffuqb9++drefNm2aEhMT9dprr0mSrr76an355Zd644031Lt3b3d1E3CatcB4wqff6sjJs3W2v7DIOC2phfs7CAAByK9qbnJycpSenl7pWu/evZWTk+OlHgF1sxYYfzisu4akXW7XPRQZA4Dz/Or4hfz8fMXGxla6Fhsbq6KiIp05c0YRERFV7iktLVVpaantdVFRkSTJYrHIYrE43RfrvfV5BuwTKLHu2iZK5eXn7DqiYVbOAc3KOaC4KLOeuaW9eneMrfMeVwiUWPsL4u05xNpz3BVrR57nV8mNMyZNmqQJEyZUub5y5UpFRkbW+/nZ2dn1fgbsEwixrjCkpmHBKiyTqq/EMSpdzy86q6y5ubr/qgqltPDcKE4gxNqfEG/PIdae4+pYl5SU2N3Wr5KbuLg4HT16tNK1o0ePKioqqtpRG0kaM2aMRo8ebXtdVFSk1q1bq1evXoqKinK6LxaLRdnZ2crIyFBoaKjTz0HdAi3WoQlH9fDcrZJ+LiL+2cUJj0kmScuORurxwb9y+2GbgRZrX0e8PYdYe467Ym2debGHXyU3aWlpWrp0aaVr2dnZSktLq/Ees9kss9lc5XpoaKhLgu6q56BugRLrWztdppCQYAeLjEu15b/FHisyDpRY+wvi7TnE2nNcHWtHnuXVguJTp04pNzdXubm5ks4v9c7NzdXBgwclnR91GTJkiK39Qw89pO+//16PP/64du3apXfeeUcfffSRHn30UW90H3CaM0XG6/J+pMAYAOzg1eRm48aN6ty5szp37ixJGj16tDp37qyxY8dKko4cOWJLdCQpMTFRS5YsUXZ2tlJSUvTaa6/pb3/7G8vA4ZeCg0xKS2qhvsnxdrWfvOY7djEGADt4dVrqpptukmHU/C/R6nYfvummm7RlyxY39grwLOtOxvknz1ZTg1OZdRfjqfdeqz52JkUA0ND41T43QCByZCdjdjEGgLqR3AA+wLqTcVyT8DrbXriLMQCgKpIbwEdYi4yzera1q/2x4rpXWgFAQ0RyA/iQ4CCTerSNtqvt3qOnOKIBAKpBcgP4GGuBcV31N5PX5GnQ9PWsoAKAi5DcAD7GkQJj6ecVVCQ4AHAeyQ3ggxwtMJZYQQUAVn51/ALQkPRJjldGhzht2HdC6/J+1OQ139XY9sIVVJ46ogEAfBUjN4APs+5ifGVsY7vac0QDAJDcAH4hpnHd01MSRzQAgERyA/gFe1dQSRQYAwDJDeAHOKIBAOxHcgP4CY5oAAD7kNwAfoQjGgCgbiQ3gJ/hiAYAqB3JDeCHOKIBAGpGcgP4IY5oAICakdwAfoojGgCgehy/APgxjmgAgKoYuQH8HEc0AEBlJDdAgOCIBgA4j+QGCBAc0QAA55HcAAGCIxoA4DySGyCAcEQDAJDcAAGHIxoANHQkN0AA4ogGAA0Z+9wAAcpaYJx/8qxqS1smr8nT5DV5im8Srqf7tvNY/wDAXRi5AQKUM0c0PDx3q7Yet6c1APgukhsggDlzRMPH+4OYogLg15iWAgKco0c0FJaZtPHAT7r+qljPdRIAXIiRG6ABcPSIhq8oMAbgx0hugAbE3iMa3vn3Po5oAOC3nEpufvjhB/33v/+1vd6wYYMeeeQRvfvuuy7rGADX44gGAA2BU8nNPffcozVr1kiS8vPzlZGRoQ0bNujpp5/Wc88959IOAnAdjmgA0BA4ldxs375d3bp1kyR99NFHSk5O1ldffaUPPvhAM2fOdGX/ALgYRzQACHROJTcWi0Vms1mS9Nlnn+k3v/mNJKl9+/Y6coQhbMDXcUQDgEDmVHLTsWNHTZs2TV988YWys7PVp08fSdLhw4fVokULl3YQgHtwRAOAQOVUcvPyyy/rr3/9q2666SYNGjRIKSkpkqRPPvnENl0FwPfZW2A8eU2eBk1fzwoqAH7BqU38brrpJhUUFKioqEjNmjWzXX/ggQcUGRnpss4BcC9rgfHw2Ztlkmo9g0r6eQXV1HuvVZ/keE90EQAc5tTIzZkzZ1RaWmpLbA4cOKA333xTu3fvVkxMjEs7CMC9nDmigRVUAHyZU8nNb3/7W82aNUuSVFhYqNTUVL322msaMGCApk6d6tIOAnA/a4Hx7Pu7qler8lrbsoIKgK9zKrnZvHmzbrjhBknSggULFBsbqwMHDmjWrFn6y1/+4tIOAvCM4CCTUhObK87OmeVl249QZAzAJzlVc1NSUqLGjc+fUbNy5UrdfvvtCgoKUvfu3XXgwAGXdhCAZ0WF2tduVs4Bzco5oPgm4RrXvwM1OAB8hlMjN23bttWiRYv0ww8/aMWKFerVq5ck6dixY4qKinLoWVOmTFFCQoLCw8OVmpqqDRs21Nr+zTffVLt27RQREaHWrVvr0Ucf1dmz7MEBuEpSlKG4KLNdRzRIHNMAwPc4ldyMHTtWjz32mBISEtStWzelpaVJOj+K07lzZ7ufM2/ePI0ePVrjxo3T5s2blZKSot69e+vYsWPVtp8zZ46efPJJjRs3Tjt37tR7772nefPm6amnnnLmYwCoRpBJeuaW9pLqPqJBosgYgO9xKrm58847dfDgQW3cuFErVqywXb/55pv1xhtv2P2c119/XcOGDdPQoUPVoUMHTZs2TZGRkZoxY0a17b/66iv16NFD99xzjxISEtSrVy8NGjSoztEeAI7p3THW7hVUEkXGAHyLU8mNJMXFxalz5846fPiw7YTwbt26qX379nbdX1ZWpk2bNik9Pf3nzgQFKT09XTk5OdXec91112nTpk22ZOb777/X0qVLdcsttzj7MQDUwLqC6sNh3TUk7XK77uGYBgC+wKmC4oqKCr3wwgt67bXXdOrUKUlS48aN9cc//lFPP/20goLqzpkKCgpUXl6u2NjYStdjY2O1a9euau+55557VFBQoOuvv16GYejcuXN66KGHap2WKi0tVWlpqe11UVGRpPPnY1ksljr7WRPrvfV5BuxDrD2nulh3bROl8vJzmpVT92KBoydLdLa0TMFB9lbsNGz8bHsOsfYcd8Xakec5ldw8/fTTeu+99/TSSy+pR48ekqQvv/xS48eP19mzZ/Xiiy8689g6rV27VhMnTtQ777yj1NRU5eXladSoUXr++ef17LPPVnvPpEmTNGHChCrXV65c6ZLdlLOzs+v9DNiHWHvOxbGuMKSmYcEqLJNqq8SZuGyP3lm1W7cnVCilBfU39uJn23OItee4OtYlJSV2tzUZhuHwn0AtW7bUtGnTbKeBWy1evFgjRozQoUOH6nxGWVmZIiMjtWDBAg0YMMB2PTMzU4WFhVq8eHGVe2644QZ1795dr7zyiu3a7Nmz9cADD+jUqVPVjhhVN3LTunVrFRQUOLyy60IWi0XZ2dnKyMhQaKida2fhFGLtObXFesWOo3p47lZJtR/TYE193r47Rb07xtbSEvxsew6x9hx3xbqoqEjR0dE6efJknX9/OzVyc+LEiWpra9q3b68TJ+wrKAwLC1OXLl20atUqW3JTUVGhVatWKSsrq9p7SkpKqiQwwcHBkqSacjSz2Syz2VzlemhoqEuC7qrnoG7E2nOqi/WtnS5TSEiwJnz6rY6crLm2xtD5BOfFZbvV95pWTFHZgZ9tzyHWnuPqWDvyLKcKilNSUjR58uQq1ydPnqxrrrnG7ueMHj1a06dP19///nft3LlTw4cP1+nTpzV06FBJ0pAhQzRmzBhb+/79+2vq1KmaO3eu9u3bp+zsbD377LPq37+/LckB4D7WIuNn+11daztWTwHwJqdGbv785z+rX79++uyzz2x73OTk5OiHH37Q0qVL7X7OwIED9eOPP2rs2LHKz89Xp06dtHz5cluR8cGDByuN1DzzzDMymUx65plndOjQIV166aXq37+/22p8AFQVHGRSdOOqo6HVYfUUAG9wauTmxhtv1J49e3TbbbepsLBQhYWFuv3227Vjxw794x//cOhZWVlZOnDggEpLS/X1118rNTXV9t7atWs1c+ZM2+uQkBCNGzdOeXl5OnPmjA4ePKgpU6aoadOmznwMAE6KaWzf/jd7j57i/CkAHufUyI10vqj44hGTrVu36r333tO7775b744B8F3dEpsrvkm48k+erbW4ePKaPE1ek8f5UwA8yulN/AA0XMFBJo3r30GSfUc0cP4UAE8iuQHglD7J8XYf0cD5UwA8yelpKQDokxyvjA5x2rDvhNbl/ajJa76rse2FK6jSklp4rpMAGhyHkpvbb7+91vcLCwvr0xcAfig4yKS0pBZ2r4xal/ejuiU2Z/8bAG7jUHLTpEmTOt8fMmRIvToEwD/Zu4Jq8prv9M/NhygwBuA2DiU377//vrv6AcDP2buCSvq5wHjqvdeS4ABwOQqKAbiEIyuoKDAG4E4kNwBcxtEVVBzRAMAdSG4AuJT1/Kmsnm3tar9s+xF2MQbgUiQ3AFwuOMikHm2j7Wo7K+eABk1fr+tfXs0mfwBcguQGgFtYC4ztXfDNLsYAXIXkBoBbOHpEA0XGAFyF5AaA2zhSYCxRZAzANTh+AYBbXXhEw7LtRzQr50Cd99i72zEAVIeRGwBuZz2ioa+dG/YVFJcyNQXAaSQ3ADzG3iLj55fsZPUUAKeR3ADwGEeKjFk9BcBZJDcAPMreImNWTwFwFskNAI+z7mL8bL+ra23H6ikAzmC1FACvCA4yKbqx2a62y/43NdUtsbmCg+zdFhBAQ0VyA8BrYhrbt//NrJwDmpVzQPFNwjWufwf1sXPVFYCGiWkpAF7DEQ0A3IHkBoDXcEQDAHcguQHgVRzRAMDVqLkB4HXOHNGwLu9HCowBVIuRGwA+wdEjGiav+Y5djAFUi+QGgE9xpMiYAmMA1SG5AeBTHCkypsAYQHVIbgD4HEeKjCkwBnAxkhsAPsl6RENWz7Z2tV+2/YhyvjvOCA4AkhsAvis4yKQebaPtajsr54AGTV9PkTEAkhsAvo1djAE4iuQGgE9jF2MAjiK5AeDz2MUYgCPYoRiAX3BmF+Nl/5uaYidjoGFh5AaA33B0F2OKjIGGieQGgN+hyBhAbUhuAPgdiowB1IbkBoBfosgYQE0oKAbgt5wpMl6X9yMFxkCAY+QGgF9ztMh48prvKDAGAhzJDYCA4EiRMQXGQGDzenIzZcoUJSQkKDw8XKmpqdqwYUOt7QsLCzVy5EjFx8fLbDbrqquu0tKlSz3UWwC+ypEiYwqMgcDm1eRm3rx5Gj16tMaNG6fNmzcrJSVFvXv31rFjx6ptX1ZWpoyMDO3fv18LFizQ7t27NX36dLVq1crDPQfgixwpMqbAGAhcXi0ofv311zVs2DANHTpUkjRt2jQtWbJEM2bM0JNPPlml/YwZM3TixAl99dVXCg0NlSQlJCR4sssAfJy1yPiN7D2avCavzvbsYgwEHq+N3JSVlWnTpk1KT0//uTNBQUpPT1dOTk6193zyySdKS0vTyJEjFRsbq+TkZE2cOFHl5eWe6jYAPxAcZFKPttF2tWUXYyDweG3kpqCgQOXl5YqNja10PTY2Vrt27ar2nu+//16rV6/W4MGDtXTpUuXl5WnEiBGyWCwaN25ctfeUlpaqtLTU9rqoqEiSZLFYZLFYnO6/9d76PAP2IdaeE0ix7nxZY8VFmXW0qFT2VNVYi4zfvjtFvTvG1n2DCwRSvH0dsfYcd8XakeeZDMPwSjXd4cOH1apVK3311VdKS0uzXX/88cf173//W19//XWVe6666iqdPXtW+/btU3BwsKTzU1uvvPKKjhyp/l9c48eP14QJE6pcnzNnjiIjI130aQD4oq3HTZqxxzpAbd9exk3DpHHXlosZKsC3lJSU6J577tHJkycVFRVVa1uvjdxER0crODhYR48erXT96NGjiouLq/ae+Ph4hYaG2hIbSbr66quVn5+vsrIyhYWFVblnzJgxGj16tO11UVGRWrdurV69etUZnNpYLBZlZ2crIyPDVv8D9yDWnhNosb5F0rU7juqFpbuUX1RaZ3vJpMIy6dIO3ZWa2Nzd3Qu4ePsyYu057oq1debFHl5LbsLCwtSlSxetWrVKAwYMkCRVVFRo1apVysrKqvaeHj16aM6cOaqoqFBQ0Pl/je3Zs0fx8fHVJjaSZDabZTabq1wPDQ11SdBd9RzUjVh7TiDF+tZOl6nvNa0c2sV4/b6flNY2xmMFxoEUb19HrD3H1bF25FleXQo+evRoTZ8+XX//+9+1c+dODR8+XKdPn7atnhoyZIjGjBljaz98+HCdOHFCo0aN0p49e7RkyRJNnDhRI0eO9NZHAOAH2MUYaFi8uhR84MCB+vHHHzV27Fjl5+erU6dOWr58ua3I+ODBg7YRGklq3bq1VqxYoUcffVTXXHONWrVqpVGjRumJJ57w1kcA4EesuxjnnzxbZ5GxtcB46r3Xqo+dSREA3+D1gzOzsrJqnIZau3ZtlWtpaWlav369m3sFIBBZdzEePnuzTFKtCY6h8yXIEz79Vhkd4tgDB/AjXj9+AQA8yZldjN/I3qOc745zVAPgJ0huADQ4fZLj9eUTv1ZWz7Z2tZ+8Jo+N/gA/QnIDoEFyZBdjK04TB/wDyQ2ABstaYGxvNQ2niQP+geQGQINlLTCW7Nu/WOI0ccAfkNwAaNAcKTC+0Lq8Hxm9AXwUyQ2ABs9aYPzhsO7K6plk1z1s9Af4LpIbANDPuxg/mtHO7jocCowB30RyAwAXcKQOhwJjwDeR3ADARdjoD/BvJDcAUA02+gP8F8kNANSAjf4A/0RyAwC1YKM/wP+Q3ABALeqz0d/MdftIcAAvILkBgDo4u9Hf80t2UoMDeAHJDQDYwZmN/iRqcABvILkBADs5s9EfNTiA55HcAICDHK3DYS8cwLNIbgDACc7U4bAXDuAZJDcA4CRrHc6z/a526D5rHc6KHUfd1DOgYSO5AYB6CA4y6b4eiU7thfPisl1ihgpwPZIbAKgn5/fCKdWyH4L09b4T1OEALkRyAwAu4OxeOCsPBeneGRupwwFciOQGAFzE2b1wJPbDAVyJ5AYAXMiZvXAk9sMBXInkBgDcgDOpAO8huQEAN+FMKsA7SG4AwI04kwrwvBBvdwAAAp21DqdbYnP9c/Mh5Z88q7omnazvP7Vwm85YKhQXFa5uic0VHGTvJBfQcDFyAwAe4kwdzonTFj06L5djGwAHkNwAgAc5W4cjMVUF2IvkBgA8zFqH81Tfqxy6j+XigH1IbgDAC4KDTBrS/XI1DTPsnqKSWC4O2IPkBgC8JDjIpNsTKiTZX4NjxXJxoGYkNwDgRSktDL19dwo1OIALsRQcALysd8dY9b2mlTbsO6H8k2f0/JKd+ul0GcvFASeR3ACAD7DuhSNJEWHBGj57s0xSnQmO9PNycUmKbxKucf07qE9yvNv6Cvg6pqUAwMewXByoH5IbAPBB1uXiz/a72qH7jP99PfnPbVqXV8CKKjRIJDcA4KOCg0y6r0ei4puEO7yaqvCMRYP/9jUrqtAgkdwAgA9z5siGCzFNhYaI5AYAfFx9anCs01RPLdymhVsOKee740xVIeCxWgoA/ECf5HhldIhzeLm4FSuq0JD4xMjNlClTlJCQoPDwcKWmpmrDhg123Td37lyZTCYNGDDAvR0EAB9gXS5+27WXaeJtyZKYqgKq4/XkZt68eRo9erTGjRunzZs3KyUlRb1799axY8dqvW///v167LHHdMMNN3iopwDgO1wxVcWKKgQqryc3r7/+uoYNG6ahQ4eqQ4cOmjZtmiIjIzVjxowa7ykvL9fgwYM1YcIEXXHFFR7sLQD4Duty8Q9+n6qmEaEO38+KKgQqryY3ZWVl2rRpk9LT023XgoKClJ6erpycnBrve+655xQTE6Pf//73nugmAPis4CCTelwZrZfu+IVMYpoKkLxcUFxQUKDy8nLFxsZWuh4bG6tdu3ZVe8+XX36p9957T7m5uXZ9j9LSUpWWltpeFxUVSZIsFossFotzHf/f/Rf+P9yHWHsOsfYsV8b75nbRevvuFL2wdJfyi0rrvuECF55RdepsmeKiwtX18mYBdUYVP9ue465YO/I8v1otVVxcrN/97neaPn26oqOj7bpn0qRJmjBhQpXrK1euVGRkZL37lJ2dXe9nwD7E2nOItWe5Mt5PdJC+KzKpsExatD9Ip85J9o7nnDht0WMLtkuSmoYZuj2hQiktAqseh59tz3F1rEtKSuxuazIMw2s/uWVlZYqMjNSCBQsqrXjKzMxUYWGhFi9eXKl9bm6uOnfurODgYNu1iooKSeens3bv3q2kpKRK91Q3ctO6dWsVFBQoKirK6b5bLBZlZ2crIyNDoaGOz3XDfsTac4i1Z7k73it2HNXDc7dKsu8AzgtZ06G3705R746xtbb1B/xse467Yl1UVKTo6GidPHmyzr+/vTpyExYWpi5dumjVqlW25KaiokKrVq1SVlZWlfbt27fXtm3bKl175plnVFxcrLfeekutW7euco/ZbJbZbK5yPTQ01CVBd9VzUDdi7TnE2rPcFe9bO12mkJBgTfj0Wx05edahe63J0NhPv5XFMCkuKlzdEpv7/VQVP9ue4+pYO/Isr09LjR49WpmZmeratau6deumN998U6dPn9bQoUMlSUOGDFGrVq00adIkhYeHKzk5udL9TZs2laQq1wEAP2/+t/674xo5Z7MKzzhWB8Hmf/BHXl8KPnDgQL366qsaO3asOnXqpNzcXC1fvtxWZHzw4EEdOUIFPwA4yxUrqiRWVcF/eH3kRpKysrKqnYaSpLVr19Z678yZM13fIQAIQNaN/5yZppJ+nqp68p/b1Dg8VN2vaOH301QITD6R3AAAPKO+Z1RJP2/+xzQVfBXJDQA0MNYzqiQpIixYw2dvlkmOr6jKP3lWD83erEfTr1RC9CWKaRwYRcfwfyQ3ANCA1WeqypoMvfHZXts1RnPgC7xeUAwA8C7rGVUfDuuuN+5KUfNLwig6hl9j5AYA4LKpKoqO4QsYuQEAVGKdqoprEu70MzhxHN7EyA0AoIr6bv5nRdExvIHkBgBQrQs3/xs+e7Mk56epKDqGJzEtBQColSumqS505H+jOc9/ukM53x1XeUVgnTwO72PkBgBQpws3/ztWfFb7C0r05md7JDk+mmP13rr9em/dfkZy4HIkNwAAu1y4okqS2sU1cvoohwtRlwNXI7kBADjFVUXH1OXA1ai5AQA4zVUnjl+MuhzUB8kNAKDeXF10bPXeuv0aNH09++XAIUxLAQBcwh1Fx1bU5cARJDcAAJdxV9ExdTlwBMkNAMBtLhzNyf42XzPW7XfqzKrqMJqDmpDcAADcyjqak5bUQt0Sm7tkJEdiNAc1I7kBAHiMO+typJ9XWf2+R4LSO8QxktNAkdwAADzKXXU5F7p49+Ob20W77NnwfSQ3AACv8kRdzh96JqmwwKQW+04orW0MozkBjuQGAOB17q7L+cua7yQFa9bejdTlNAAkNwAAn0JdDuqL5AYA4HM8XZfzbL+r1ewSs44Vn2VJeQAguQEA+Dx3juYcOXlWI+ZsqXSNqSv/RnIDAPALnhjNsWLqyr+R3AAA/JI7V1lZMXXln0huAAB+y12rrC7G1JV/IbkBAAQEd6+yuhhTV76L5AYAEDA8WZdjxdSV7yG5AQAELOtoTk7eMU1f9rX+fSTY5XU5Vkxd+Y4gb3cAAAB3Cg4yKTWxuW5PMDT57hTFNQn32Pe2Hv/w1md7tDj3kHK+O67yCnekVrgQIzcAgAajd8dY9b2mla0uJ6ZxuH46Xabnl7hn6sqaxrzx2V7btbgoswZ1a6OE6EuYunITkhsAQINycV2OJPVOdu+S8gvlF5VWSnao1XE9khsAQIPnqSXl1aFWx/VIbgAAuMDFS8rdPXVVHZaZ1w/JDQAAF/H21JUVy8ydQ3IDAIAdfG3qisLkmpHcAADgIF+YuqIwuWYkNwAAOKG2qStPHP9wMUZ3fkZyAwCAi3jj+IfaNNTRHZIbAADcpLrDPD/ccFD5Rd5Jdmpadh5oCQ/JDQAAbnTxaE7Wr9t6tVbnYoE4neUTyc2UKVP0yiuvKD8/XykpKXr77bfVrVu3attOnz5ds2bN0vbt2yVJXbp00cSJE2tsDwCAL/GVZea18ffpLK8nN/PmzdPo0aM1bdo0paam6s0331Tv3r21e/duxcTEVGm/du1aDRo0SNddd53Cw8P18ssvq1evXtqxY4datWrlhU8AAED9eHOZuT38bXTH68nN66+/rmHDhmno0KGSpGnTpmnJkiWaMWOGnnzyySrtP/jgg0qv//a3v+mf//ynVq1apSFDhnikzwAAuIsvLDO3R02jO1HhwdpUYFKLfSeU1jbGKwmPV5ObsrIybdq0SWPGjLFdCwoKUnp6unJycux6RklJiSwWi5o3b+6ubgIA4FH2LDP3ZmFydSqP7gRr1t6NXjsjy6vJTUFBgcrLyxUbG1vpemxsrHbt2mXXM5544gm1bNlS6enp1b5fWlqq0tJS2+uioiJJksVikcVicbLnst1bn2fAPsTac4i1ZxFvzwmUWHdtEyUpSpL04A2Xa+OBn3SsuFQxjc06cbpME5ftVn5Rae0P8aD8k2c1fPZmvX13inp3jK37hlo48nvn9Wmp+njppZc0d+5crV27VuHh4dW2mTRpkiZMmFDl+sqVKxUZGVnvPmRnZ9f7GbAPsfYcYu1ZxNtzAjHWwZKO/+/XT3SQvisyqcgi/XhG+upYkE6WXTgtZC1Tvviae6aOjP/97zMf58qyv1z1maEqKSmxu61Xk5vo6GgFBwfr6NGjla4fPXpUcXFxtd776quv6qWXXtJnn32ma665psZ2Y8aM0ejRo22vi4qK1Lp1a/Xq1UtRUVFO991isSg7O1sZGRkKDQ11+jmoG7H2HGLtWcTbcxpqrMsrDDtGd9xdE2NSYZl0aYfuSk10voTEOvNiD68mN2FhYerSpYtWrVqlAQMGSJIqKiq0atUqZWVl1Xjfn//8Z7344otasWKFunbtWuv3MJvNMpvNVa6Hhoa65AfcVc9B3Yi15xBrzyLentPQYh0q6fqrKk8H9Uu5zCvFysdLztUr9o7c6/VpqdGjRyszM1Ndu3ZVt27d9Oabb+r06dO21VNDhgxRq1atNGnSJEnSyy+/rLFjx2rOnDlKSEhQfn6+JKlRo0Zq1KiR1z4HAAD+wFvFyjGNqy8fcQevJzcDBw7Ujz/+qLFjxyo/P1+dOnXS8uXLbUXGBw8eVFBQkK391KlTVVZWpjvvvLPSc8aNG6fx48d7susAAAQEd+6ibJIU1+T8Pjie4vXkRpKysrJqnIZau3Ztpdf79+93f4cAAGjAXDW6Y63mGde/g0f3u/GJ5AYAAPg2Z0Z34hriPjcAAMA/1TS6k5N3TCu/+Fq9bkhtmDsUAwCAwBEcZFJqYnMd32ko1YtnTQXV3QQAAMB/kNwAAICAQnIDAAACCskNAAAIKCQ3AAAgoJDcAACAgEJyAwAAAgrJDQAACCgkNwAAIKA0uB2KDcOQJBUVFdXrORaLRSUlJSoqKlJoaKgruoYaEGvPIdaeRbw9h1h7jrtibf172/r3eG0aXHJTXFwsSWrdurWXewIAABxVXFysJk2a1NrGZNiTAgWQiooKHT58WI0bN5bJ5PyZF0VFRWrdurV++OEHRUVFubCHuBix9hxi7VnE23OItee4K9aGYai4uFgtW7ZUUFDtVTUNbuQmKChIl112mcueFxUVxX8oHkKsPYdYexbx9hxi7TnuiHVdIzZWFBQDAICAQnIDAAACCsmNk8xms8aNGyez2eztrgQ8Yu05xNqziLfnEGvP8YVYN7iCYgAAENgYuQEAAAGF5AYAAAQUkhsAABBQSG4AAEBAIblx0pQpU5SQkKDw8HClpqZqw4YN3u6S35s0aZJ++ctfqnHjxoqJidGAAQO0e/fuSm3Onj2rkSNHqkWLFmrUqJHuuOMOHT161Es9DgwvvfSSTCaTHnnkEds14uxahw4d0r333qsWLVooIiJCv/jFL7Rx40bb+4ZhaOzYsYqPj1dERITS09O1d+9eL/bYP5WXl+vZZ59VYmKiIiIilJSUpOeff77SWUTE2jmff/65+vfvr5YtW8pkMmnRokWV3rcnridOnNDgwYMVFRWlpk2b6ve//71OnTrlng4bcNjcuXONsLAwY8aMGcaOHTuMYcOGGU2bNjWOHj3q7a75td69exvvv/++sX37diM3N9e45ZZbjDZt2hinTp2ytXnooYeM1q1bG6tWrTI2btxodO/e3bjuuuu82Gv/tmHDBiMhIcG45pprjFGjRtmuE2fXOXHihHH55Zcb9913n/H1118b33//vbFixQojLy/P1uall14ymjRpYixatMjYunWr8Zvf/MZITEw0zpw548We+58XX3zRaNGihfGvf/3L2LdvnzF//nyjUaNGxltvvWVrQ6yds3TpUuPpp582Pv74Y0OSsXDhwkrv2xPXPn36GCkpKcb69euNL774wmjbtq0xaNAgt/SX5MYJ3bp1M0aOHGl7XV5ebrRs2dKYNGmSF3sVeI4dO2ZIMv79738bhmEYhYWFRmhoqDF//nxbm507dxqSjJycHG91028VFxcbV155pZGdnW3ceOONtuSGOLvWE088YVx//fU1vl9RUWHExcUZr7zyiu1aYWGhYTabjQ8//NATXQwY/fr1M+6///5K126//XZj8ODBhmEQa1e5OLmxJ67ffvutIcn45ptvbG2WLVtmmEwm49ChQy7vI9NSDiorK9OmTZuUnp5uuxYUFKT09HTl5OR4sWeB5+TJk5Kk5s2bS5I2bdoki8VSKfbt27dXmzZtiL0TRo4cqX79+lWKp0ScXe2TTz5R165d9f/+3/9TTEyMOnfurOnTp9ve37dvn/Lz8yvFu0mTJkpNTSXeDrruuuu0atUq7dmzR5K0detWffnll+rbt68kYu0u9sQ1JydHTZs2VdeuXW1t0tPTFRQUpK+//trlfWpwB2fWV0FBgcrLyxUbG1vpemxsrHbt2uWlXgWeiooKPfLII+rRo4eSk5MlSfn5+QoLC1PTpk0rtY2NjVV+fr4Xeum/5s6dq82bN+ubb76p8h5xdq3vv/9eU6dO1ejRo/XUU0/pm2++0R/+8AeFhYUpMzPTFtPq/kwh3o558sknVVRUpPbt2ys4OFjl5eV68cUXNXjwYEki1m5iT1zz8/MVExNT6f2QkBA1b97cLbEnuYFPGjlypLZv364vv/zS210JOD/88INGjRql7OxshYeHe7s7Aa+iokJdu3bVxIkTJUmdO3fW9u3bNW3aNGVmZnq5d4Hlo48+0gcffKA5c+aoY8eOys3N1SOPPKKWLVsS6waGaSkHRUdHKzg4uMrKkaNHjyouLs5LvQosWVlZ+te//qU1a9bosssus12Pi4tTWVmZCgsLK7Un9o7ZtGmTjh07pmuvvVYhISEKCQnRv//9b/3lL39RSEiIYmNjibMLxcfHq0OHDpWuXX311Tp48KAk2WLKnyn196c//UlPPvmk7r77bv3iF7/Q7373Oz366KOaNGmSJGLtLvbENS4uTseOHav0/rlz53TixAm3xJ7kxkFhYWHq0qWLVq1aZbtWUVGhVatWKS0tzYs983+GYSgrK0sLFy7U6tWrlZiYWOn9Ll26KDQ0tFLsd+/erYMHDxJ7B9x8883atm2bcnNzbV9du3bV4MGDbb8mzq7To0ePKlsa7NmzR5dffrkkKTExUXFxcZXiXVRUpK+//pp4O6ikpERBQZX/WgsODlZFRYUkYu0u9sQ1LS1NhYWF2rRpk63N6tWrVVFRodTUVNd3yuUlyg3A3LlzDbPZbMycOdP49ttvjQceeMBo2rSpkZ+f7+2u+bXhw4cbTZo0MdauXWscOXLE9lVSUmJr89BDDxlt2rQxVq9ebWzcuNFIS0sz0tLSvNjrwHDhainDIM6utGHDBiMkJMR48cUXjb179xoffPCBERkZacyePdvW5qWXXjKaNm1qLF682PjPf/5j/Pa3v2V5shMyMzONVq1a2ZaCf/zxx0Z0dLTx+OOP29oQa+cUFxcbW7ZsMbZs2WJIMl5//XVjy5YtxoEDBwzDsC+uffr0MTp37mx8/fXXxpdffmlceeWVLAX3NW+//bbRpk0bIywszOjWrZuxfv16b3fJ70mq9uv999+3tTlz5owxYsQIo1mzZkZkZKRx2223GUeOHPFepwPExckNcXatTz/91EhOTjbMZrPRvn1749133630fkVFhfHss88asbGxhtlsNm6++WZj9+7dXuqt/yoqKjJGjRpltGnTxggPDzeuuOIK4+mnnzZKS0ttbYi1c9asWVPtn8+ZmZmGYdgX1+PHjxuDBg0yGjVqZERFRRlDhw41iouL3dJfk2FcsHUjAACAn6PmBgAABBSSGwAAEFBIbgAAQEAhuQEAAAGF5AYAAAQUkhsAABBQSG4AAEBAIbkB0OCZTCYtWrTI290A4CIkNwC86r777pPJZKry1adPH293DYCfCvF2BwCgT58+ev/99ytdM5vNXuoNAH/HyA0ArzObzYqLi6v01axZM0nnp4ymTp2qvn37KiIiQldccYUWLFhQ6f5t27bp17/+tSIiItSiRQs98MADOnXqVKU2M2bMUMeOHWU2mxUfH6+srKxK7xcUFOi2225TZGSkrrzySn3yySfu/dAA3IbkBoDPe/bZZ3XHHXdo69atGjx4sO6++27t3LlTknT69Gn17t1bzZo10zfffKP58+frs88+q5S8TJ06VSNHjtQDDzygbdu26ZNPPlHbtm0rfY8JEyborrvu0n/+8x/dcsstGjx4sE6cOOHRzwnARdxyHCcA2CkzM9MIDg42LrnkkkpfL774omEY50+Lf+ihhyrdk5qaagwfPtwwDMN49913jWbNmhmnTp2yvb9kyRIjKCjIyM/PNwzDMFq2bGk8/fTTNfZBkvHMM8/YXp86dcqQZCxbtsxlnxOA51BzA8DrevbsqalTp1a61rx5c9uv09LSKr2Xlpam3NxcSdLOnTuVkpKiSy65xPZ+jx49VFFRod27d8tkMunw4cO6+eaba+3DNddcY/v1JZdcoqioKB07dszZjwTAi0huAHjdJZdcUmWayFUiIiLsahcaGlrptclkUkVFhTu6BMDNqLkB4PPWr19f5fXVV18tSbr66qu1detWnT592vb+unXrFBQUpHbt2qlx48ZKSEjQqlWrPNpnAN7DyA0ArystLVV+fn6layEhIYqOjpYkzZ8/X127dtX111+vDz74QBs2bNB7770nSRo8eLDGjRunzMxMjR8/Xj/++KMefvhh/e53v1NsbKwkafz48XrooYcUExOjvn37qri4WOvWrdPDDz/s2Q8KwCNIbgB43fLlyxUfH1/pWrt27bRr1y5J51cyzZ07VyNGjFB8fLw+/PBDdejQQZIUGRmpFStWaNSoUfrlL3+pyMhI3XHHHXr99ddtz8rMzNTZs2f1xhtv6LHHHlN0dLTuvPNOz31AAB5lMgzD8HYnAKAmJpNJCxcu1IABA7zdFQB+gpobAAAQUEhuAABAQKHmBoBPY+YcgKMYuQEAAAGF5AYAAAQUkhsAABBQSG4AAEBAIbkBAAABheQGAAAEFJIbAAAQUEhuAABAQCG5AQAAAeX/A5HdYymXG2MzAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.plot(range(1, len(losses) + 1), losses, marker='o')\n",
        "plt.title('Training Loss Curve')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.grid(True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Visualizing Node Embeddings\n",
        "\n",
        "- Extract node embeddings from the trained model using the code provided earlier\n",
        "- Apply t-SNE for dimensionality reduction and create a scatter plot to visualize the embeddings in a 2D space."
      ],
      "metadata": {
        "id": "Y1zugDeiJjDu"
      },
      "id": "Y1zugDeiJjDu"
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "c3f9da86-ef1d-4f76-9a5a-b7d4b592ec44",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 889
        },
        "id": "c3f9da86-ef1d-4f76-9a5a-b7d4b592ec44",
        "outputId": "38065835-6cef-4700-a39a-02fd7a0866aa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-13-d3116166a76c>:12: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
            "  plt.scatter(reduced_embeddings[:, 0], reduced_embeddings[:, 1], c='b', cmap='viridis', s=50)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "data = dataset[0]\n",
        "out, embeddings = model(data.x, data.edge_index)\n",
        "\n",
        "# Convert embeddings to a NumPy array\n",
        "embeddings = embeddings.detach().numpy()\n",
        "\n",
        "# Apply dimensionality reduction (e.g., t-SNE) for visualization\n",
        "reduced_embeddings = TSNE(n_components=2).fit_transform(embeddings)\n",
        "\n",
        "# Create a scatter plot to visualize the embeddings\n",
        "plt.figure(figsize=(10, 10))\n",
        "plt.scatter(reduced_embeddings[:, 0], reduced_embeddings[:, 1], c='b', cmap='viridis', s=50)\n",
        "plt.title(\"Node Embeddings Visualization\")\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
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