{
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.12.12",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "kaggle": {
      "accelerator": "none",
      "dataSources": [],
      "dockerImageVersionId": 31234,
      "isInternetEnabled": true,
      "language": "python",
      "sourceType": "notebook",
      "isGpuEnabled": false
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat_minor": 0,
  "nbformat": 4,
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "title: \"[Law Graph Visualization](https://colab.research.google.com/drive/1bS6ZMkCtmqrbWvSQLC-Y6crRXwrKyYKI?usp=sharing)\"\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "X-8vTjDk_iQW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "This notebook presents an interactive visualization of the **legal knowledge graph**.  \n",
        "The graph makes explicit the structural relationships between statutory provisions that are otherwise difficult to observe through text-only retrieval.\n",
        "\n",
        "This visualization serves both as a **diagnostic tool** and as an **explanatory aid** for understanding how graph enhancement influences retrieval and downstream answer generation."
      ],
      "metadata": {
        "id": "n9Vc1VnNZtx0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Build and Load the Graph"
      ],
      "metadata": {
        "id": "WeuHEPds4etI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "After pull the project source code, the preprocessing stage constructs a legal knowledge graph to capture structural relationships:\n",
        "```python\n",
        "git clone https://github.com/Fan-Luo/Legal-RAG.git\n",
        "pip install -e .\n",
        "python -m scripts.preprocess_law\n",
        "python -m scripts.build_graph\n",
        "```"
      ],
      "metadata": {
        "id": "eMqZhFSv4etI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#| include: false\n",
        "\n",
        "!git clone https://github.com/Fan-Luo/Legal-RAG.git\n",
        "%cd Legal-RAG\n",
        "\n",
        "# For demo purpose, load preprocessed law data\n",
        "!python -m scripts.setup"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E3MdLMDwS5KJ",
        "outputId": "656e3312-b302-416d-f4ca-cbd6d5eb03d1"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'Legal-RAG'...\n",
            "remote: Enumerating objects: 751, done.\u001b[K\n",
            "remote: Counting objects: 100% (297/297), done.\u001b[K\n",
            "remote: Compressing objects: 100% (127/127), done.\u001b[K\n",
            "remote: Total 751 (delta 186), reused 234 (delta 168), pack-reused 454 (from 1)\u001b[K\n",
            "Receiving objects: 100% (751/751), 963.31 KiB | 12.51 MiB/s, done.\n",
            "Resolving deltas: 100% (378/378), done.\n",
            "/content/Legal-RAG\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#| include: false\n",
        "\n",
        "import json, os\n",
        "import networkx as nx\n",
        "import matplotlib.pyplot as plt\n",
        "from pathlib import Path\n",
        "from collections import defaultdict, deque\n",
        "from legalrag.schemas import LawNode, Neighbor\n",
        "from typing import Any, Dict, Iterable, List, Optional, Tuple\n",
        "from pathlib import Path\n",
        "import pandas as pd\n",
        "import matplotlib as mpl\n",
        "import logging\n",
        "logging.getLogger(\"matplotlib.font_manager\").setLevel(logging.ERROR)\n",
        "\n",
        "mpl.rcParams[\"font.sans-serif\"] = [\"Noto Sans CJK SC\", \"SimHei\", \"Arial Unicode MS\"]\n",
        "mpl.rcParams[\"axes.unicode_minus\"] = False\n",
        "plt.rcParams['font.family'] = 'DejaVu Sans'\n",
        "plt.rcParams['axes.unicode_minus'] = False"
      ],
      "metadata": {
        "trusted": true,
        "id": "x-d_x1Lt4etH"
      },
      "outputs": [],
      "execution_count": 2
    },
    {
      "cell_type": "markdown",
      "source": [
        "## What This Graph Represents\n",
        "\n",
        "**Nodes** correspond to individual legal provisions (e.g., articles or clauses).\n\n",
        "**Edges** encode structural relationships, capturing  referential and contextual dependencies among laws.\n",
        "\n",
        "The graph is constructed offline from preprocessed legal texts and stored as a static HTML artifact for reproducible inspection."
      ],
      "metadata": {
        "id": "Vwv7Im5ueFns"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#| code-fold: false\n",
        "\n",
        "from legalrag.config import AppConfig\n",
        "from legalrag.retrieval.graph_store import LawGraphStore\n",
        "\n",
        "cfg = AppConfig.load(None)\n",
        "store = LawGraphStore(cfg)"
      ],
      "metadata": {
        "id": "d3Xh0GkAXX-D"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#| include: false\n",
        "\n",
        "from scripts.quiet import install_quiet\n",
        "install_quiet(message_only_loggers=[\"legalrag.retrieval.graph_store\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N6SKofSshffA",
        "outputId": "bf52a399-f0ed-4841-d6b6-e0d1d9cb392f"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#| echo: false\n",
        "\n",
        "store.load()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OMBL4D8OikMY",
        "outputId": "137c5698-3d8a-484f-d493-1b400bb1700c"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Graph] Loaded 1260 law nodes\n",
            "[Graph] Built adjacency: 2640 edges\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#| echo: false\n",
        "#| code-fold: false\n",
        "\n",
        "node = store.nodes[\"142\"]\n",
        "\n",
        "print(\"Example graph node:\")\n",
        "print(f\"  Article ID   : {node.article_id}\")\n",
        "print(f\"  Article No   : {node.article_no}\")\n",
        "print(f\"  Law          : {node.law_name}\")\n",
        "print(f\"  Chapter      : {node.chapter}\")\n",
        "print(f\"  Section      : {node.section}\")\n",
        "print()\n",
        "print(\"  Neighbors:\")\n",
        "for nb in node.neighbors:\n",
        "    print(\n",
        "        f\"    - {nb.article_id:>4} | \"\n",
        "        f\"rel={nb.relation:<6} | \"\n",
        "        f\"conf={nb.conf:.2f} | \"\n",
        "        f\"evidence={nb.evidence}\"\n",
        "    )\n"
      ],
      "metadata": {
        "trusted": true,
        "id": "pwHadqOg4etJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7d47305c-b058-4a9f-a790-462a58facf6f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Example graph node:\n",
            "  Article ID   : 142\n",
            "  Article No   : 第一百四十二条\n",
            "  Law          : 中华人民共和国民法典\n",
            "  Chapter      : 六章 民事法律行为\n",
            "  Section      : 三节 民事法律行为的效力\n",
            "\n",
            "  Neighbors:\n",
            "    -  141 | rel=prev   | conf=1.00 | evidence=None\n",
            "    -  143 | rel=next   | conf=1.00 | evidence=None\n",
            "    -  466 | rel=cited  | conf=0.90 | evidence={'span': [30, 37], 'text': '第一百四十二条'}\n"
          ]
        }
      ],
      "execution_count": 6
    },
    {
      "cell_type": "code",
      "source": [
        "#| include: false\n",
        "\n",
        "neighbors = store.nodes['142'].neighbors\n",
        "neighbors"
      ],
      "metadata": {
        "trusted": true,
        "id": "rFYMziw_4etJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b4056a4a-9368-47d5-e75f-5182811e6796"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Neighbor(article_id='141', relation='prev', conf=1.0, evidence=None),\n",
              " Neighbor(article_id='143', relation='next', conf=1.0, evidence=None),\n",
              " Neighbor(article_id='466', relation='cited', conf=0.9, evidence={'span': [30, 37], 'text': '第一百四十二条'})]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "execution_count": 7
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Graph Walk Retrieval"
      ],
      "metadata": {
        "id": "gltEDKR04etK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Graph-based expansion performs a bounded breadth-first search (BFS) over the legal knowledge graph, starting from one or more seed articles. The goal is to retrieve a localized subgraph centered on the query nodes, while allowing fine-grained control over relation types, confidence thresholds, and the total number of returned nodes. The resulting nodes are used to induce a subgraph for visualization or downstream analysis."
      ],
      "metadata": {
        "id": "YDlg8amB4etK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#| echo: false\n",
        "#| code-fold: false\n",
        "\n",
        "G = nx.DiGraph()\n",
        "\n",
        "# Add nodes\n",
        "for node in store.adj.keys():\n",
        "    G.add_node(node)\n",
        "\n",
        "# Add edges with relation labels\n",
        "for src, node in store.nodes.items():\n",
        "    for e in node.neighbors:\n",
        "        G.add_edge(src, e.article_id, relation=e.relation)"
      ],
      "metadata": {
        "trusted": true,
        "id": "v5iIJMAD4etJ"
      },
      "outputs": [],
      "execution_count": 8
    },
    {
      "cell_type": "code",
      "source": [
        "#| code-summary: Perform a graph walk from seed legal articles to retrieve a localized subgraph\n",
        "#| code-fold: false\n",
        "\n",
        "start_ids = [\"582\"]\n",
        "law_nodes = store.walk(start_ids, limit = 30)\n",
        "node_ids = [node.article_id for node in law_nodes] + start_ids"
      ],
      "metadata": {
        "trusted": true,
        "id": "Po-orozT4etK"
      },
      "outputs": [],
      "execution_count": 9
    },
    {
      "cell_type": "code",
      "source": [
        "#| echo: false\n",
        "\n",
        "sub = G.subgraph(node_ids)\n",
        "plt.figure(figsize=(15, 8))\n",
        "pos = nx.spring_layout(sub, seed=42)\n",
        "nx.draw(sub, pos, with_labels=True, node_size=800, font_size=10)\n",
        "nx.draw_networkx_edge_labels(sub, pos, edge_labels=nx.get_edge_attributes(sub, \"relation\"))\n",
        "plt.show()"
      ],
      "metadata": {
        "trusted": true,
        "id": "YvCG9aY84etL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 789
        },
        "outputId": "de1cb08b-96fd-4670-a4aa-3aa6e39399a6"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x800 with 1 Axes>"
            ],
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UFxdnahYAAOBdKPABAAAAAB5lwIABSkxM1KOPPqqoqCiz40iS5s+fL4ld+AAAoHixQgcAAAAA4DEWL16sQYMGqXnz5vrll1/MjnOB5s2ba9euXYqLi1P16tXNjgMAALwABT4AAAAAwCOcPn1atWvXlq+vrxITExUUFGR2pAts27ZNrVq1UteuXbVmzRqz4wAAAC9AgQ8AAAAAcHuGYahWrVo6deqU1q5dq86dO5sd6aKaNWumPXv26PTp06patarZcQAAgIdjBz4AAAAAwO0NHjxYp06d0vjx4922vJekd999Vy6Xi134AACgWDCBDwAAAABwa0uXLlX//v3VqFEj7d271+w4f6pJkybat2+fEhISFBERYXYcAADgwSjwAQAAAABu68yZM4qMjJTValV8fLzCwsLMjvSnfvrpJ7Vr10433XSTVq1aZXYcAADgwSjwAQAAAABuq169ejp69Ki++uor9ejRw+w4f1njxo21f/9+pvABAMC/wg58AAAAAIBbGjZsmI4ePaoRI0Z4VHkvSW+//bZcLpeGDh1qdhQAAODBmMAHAAAAALidlStXqmfPnqpXr54OHz5sdpx/pGHDhjp48KDOnDmjypUrmx0HAAB4ICbwAQAAAABuJTU1Vf369ZOvr69+/PFHs+P8Y/PmzWMKHwAA/CtM4AMAAAAA3ErDhg114MABffbZZ7r99tvNjvOvNGjQQIcPH1ZSUpIqVqxodhwAAOBhmMAHAAAAALiNMWPG6MCBAxo8eLDHl/eS9NZbb8nlcun+++83OwoAAPBATOADAAAAANzCmjVr1K1bN0VGRurYsWOyWr1j5qx+/fo6duyYzp07p7CwMLPjAAAAD+Idvw0BAAAAADxaZmamevfuLR8fH8XExHhNeS9Jc+fOlWEYTOEDAIC/jQl8AAAAAIDpmjVrpt27d+vDDz9UdHS02XGKXb169XT8+HGlpKQoJCTE7DgAAMBDeM9IAwAAAADAI02cOFG7d+/WHXfc4ZXlvSTNmTNHhmFo2LBhZkcBAAAehAl8AAAAAIBpNmzYoOuvv17VqlVTXFycV63O+V916tTRyZMnlZaWpqCgILPjAAAAD+C9vxkBAAAAANxadna2evToIbvdrh9//NGry3tJevPNN2UYhoYPH252FAAA4CGYwAcAAAAAmKJVq1batm2b5s+fryFDhpgdp1TUrl1bp06dUmpqKlP4AADgT3n3eAMAAAAAwC09+eST2rZtm3r16lVmyntJmjVrlpxOp0aOHGl2FAAA4AGYwAcAAAAAlKrY2Fi1bdtWlSpVUmJiotevzvlfkZGRio+PV3p6ugIDA82OAwAA3FjZ+i0JAAAAAGCqvLw8de3aVVarVZs2bSpz5b3EFD4AAPjrmMAHAAAAAJSaqKgoxcTEaPbs2Ro9erTZcUxTs2ZNJSQkMIUPAAAuq+yNOgAAAAAATDFlyhTFxMSoe/fuZbq8l6Q33nhDTqdTDzzwgNlRAACAG2MCHwAAAABQ4nbs2KEWLVooLCxMZ86ckd1uNzuS6apXr66zZ88qIyNDAQEBZscBAABuiAl8AAAAAECJKigo0A033CCLxaIffviB8v7/mzFjhhwOB1P4AADgkpjABwAAAACUqM6dO2vdunWaPn26Hn74YbPjuJVq1aopKSmJKXwAAHBRTOADAAAAAErM66+/rnXr1qlTp06U9xfx6quvyuFwaOzYsWZHAQAAbogJfAAAAABAidi3b5+aNm2qoKAgnT17Vr6+vmZHcktVqlRRcnKysrKy5O/vb3YcAADgRpjABwAAAAAUO4fDoY4dO8rlcmnt2rWU95cxffp0pvABAMBFMYEPAAAAACh2N998s1avXq0XXnhB//3vf82O4/YiIiKUmpqqrKws+fn5mR0HAAC4CSbwAQAAAADF6q233tLq1avVrl07yvu/6JVXXlFhYaHGjx9vdhQAAOBGmMAHAAAAABSbw4cPq2HDhgoICFBSUhI73f+GK664Qunp6crKymLlEAAAkMQEPgAAAACgmBiGoQ4dOsgwDK1Zs4by/m96+eWXVVBQoAkTJpgdBQAAuAkm8AEAAAAAxeL222/XF198oSeeeELPPfec2XE8UuXKlZWRkcEUPgAAkESBDwAAAAAoBgsXLtS9996rli1b6ueffzY7jseaP3++hg4dqrFjx2rGjBlmxwEA/AsOp6HDSdnaHZ+hPfEZ2peYqcy8QhU6XfKxWRTk76NGVYLUpFqwmlYLVv3KgbLbWJiCC1HgAwAAAAD+lZMnT6pevXry8/PTmTNnFBgYaHYkj1apUiVlZWUpOztbdrvd7DgAgL/pRHKOFm89qSVbTyk73yFJslstchh/rGF/f3ugn13RbWpoYJtI1apYvlQzw33xkQ4AAAAA4B9zuVwaP368HA6Hvv76a8r7YvDiiy8qPz9fEydONDsKAOBv+CUuTYPe3aJO09dr/qYTReW9pIuW9/97e3a+Q/M3nVCn6es16N0t2nEqvaQjwwMwgQ8AAAAA+Md+e0u5bt06de7c2eQ03qNixYrKzs5mCh8APEBeoVOvfXdI8zYck9VqkfMSZf3fYbNaZBguDe9YRw91vVL+PrZiSApPxAQ+AAAAAOAfs1gskkR5X8yee+455efna9KkSWZHAQBcxu74DN00Y4PmbTwml1Qs5b3+//O4JM3beEw3z9ig3fEZxfK88DxM4AMAAAAA4IbCwsKUm5urrKwspvABwA1tOpKsoQtj5XC65CzBitVmschus+jde1qrfb2KJXYeuCcm8AEAAAAAfxkzYKXn2WefVV5enh5//HGzowAA/semI8m6972tKnQaJVreS5LT5VKh09C9723VpiPJJXouuB8m8AEAAAAAfyo5OVkVKzL1V9pCQ0OVn5+v7OxsWa3M4AGAO9gdn6F+c2NU4DRUms2q1SL52KxaOvI6Na0WXHonhqn46Q8AAAAAuKy0tDSNHDlScXFxkiTDMExOVHY89dRTys3N1RNPPGF2FACAfr1g7Zgl2+Vwukq1vJckwyUVOg2NXbJdeYXO0j05TMMEPgAAAADgsk6ePKlrrrlGvXv31nvvvWd2nDInJCREBQUFTOEDgBuYsmr/rxesNbFRtVikER3r6rGbrjIvBEoNP/kBAAAAAJfkcrkUGRmp5cuXa9u2bVqwYIEkpvBL0+TJk5Wbm6unnnrK7CgAUKb9EpemeRvMLe8lyeWS3tpwVDtOpZsbBKWCCXwAAAAAwJ/KyMjQ2LFjdfDgQa1fv17+/v5yuVyyWCxmR/N6hmEoNDRUDodDWVlZTOEDgEkGvbtFm4+lyGmYX6farBZdVydcHwxta3YUlDB+6gMAAAAA/iA7O1sxMTGSJKfTqeDgYM2ePVs5OTkaOHCgMjMzKe9LidVq1eOPP67z58/r2WefNTsOAJRJJ5JztOlIsluU95LkNFzaeCRZJ1NyzI6CEkaBDwAAAAD4g5dfflkdOnRQp06d9Nxzz2nlypUKDAzUs88+q9zcXG3cuNHsiGXKI488ogoVKuiVV15hfREAmGDx1pOyudkH1zaLRYu3xJkdAyWMAh8AAAAAUOS3LauDBg3Szp07Vb9+fW3fvl0DBw5U+/bttXbtWm3cuFHjxo1TZmam2MpaOqxWqyZNmqScnBy9+OKLZscBgDLF4TS0ZOspOd3sZ57T5dKSrXFyOPlg15uxAx8AAAAAUOT8+fMyDENnz55V3bp1JUkpKSk6f/68Zs2apYyMDH366aeqUaOGduzYYW7YMsYwDAUHB0v69ZoE7MIHgNKxPzFTN79x+W+eObKSlb5+gXKPbpPLkS97aBWF9xgvvyr1JUnnD8Yo65dVKjhzREZelqoMeUO+V9S54Dmc2WlKWzdfuSd+kasgVz5h1RXU7g6VvyrqsudePa6DrooI+ncvEm6Ln/YAAAAAAElSVlaWhg0bpo4dO6pbt266+eabdeLECYWHh6tGjRqaOnWqpk+frhMnTmjz5s2SJIfDYXLqssNqteqxxx5Tdna2pk6danYcACgzdsdnXPa4My9bZz6YKFntqnzH06py/5sK7TxUVv/AovsYhXnyq95IITfce8nnSV75qgpTT6ty38mqMnS2yl3ZTsnLp6rgzNF/lQ+ejQIfAAAAACBJ6tGjh86fP6+XX35ZM2fO1DfffKPY2NgL7uPv768KFSrI399fLpdLdrvdpLRl06RJk1S+fHlNmTKFXfgAUEr2xGfIbr30/vvMn5bKHlRRFW8ZL7+qDeQTEqFytVvIJ7RK0X0Cm3RWSPtolYtsfsnnyY/frwotexY9R0jUnbL6lVf+2SOXfIzdatEeCnyvRoEPAAAAANCyZct05swZzZ8/X127dtXrr7+ufv36qX///srLy9O2bdskSTabTZJksVhkcbOL+ZUFVqtVEydOVFZWll555RWz4wBAmbAvMVMO49JbyHMPb5FvRH2d+3yKTr0xUAnzxyprx+q/fR6/ag11fv9GOXOz5HIZytn3g1zOAvnXbHrJxzgMl/YmZP7tc8FzUOADAAAAAOTj46OaNWsqNDRUTz75pI4fP67Zs2dLkn7++Wc9//zzOnDggMkpIUlPPPGEAgIC9MILLzCFDwClIDOv8LLHC9PPKOuXr2UPq6or7nhWFVr0UNp385S9e+3fOk+l2x6Vy3Do9IxoxU27XSnfzFalPo/LJ7TqZR+Xlcc6O29GgQ8AAAAAZVhOTo4OHTqkyMhIpaSk6NVXX9Vrr72mBQsWqFKlSpKkbdu26ezZs6pevbrJaSH9OoU/YcIEZWZm6rXXXjM7DgB4vULnpafvJUkul/wi6ir0+nvkG1FXFZrfpMCruyvrl6//1nnSNyySkZejync+ryr3vKag1rfp3BdTVZB04rKPK3DyYa43o8AHAAAAgDKsb9++euedd1SzZk01btxYTz75pG6//XZFRUVJkjZu3KjJkyfrP//5jwIDA5n4dhNPP/20AgIC9Nxzz5kdBQC8no/t8ivjbIGh8gmveeFjwmvImXnuL5+jMC1RWdtXKrzHOJWr1Vy+V9RRSPu75BdRT1nbV172sb42Kl5vxn+7AAAAAFBGbdiwQenp6ZowYYJCQ0P1zDPPKCoqStu2bVPnzp3Vvn17jR49WsOGDdPtt98ul8slq5W3ke7AarVq/PjxysjI0Ouvv252HADwakH+Ppc97le9kQpTT19wW2FqvOzBlf/yOVyF+ZIki+V/fs5arZLr8t8AqODPBeW9Gb95AQAAAEAZdPjwYa1fv161atVSaGioJKlevXpauXKlhg0bpvbt26tNmzaaMWOGpk+fLkly/UmBgNL13HPPqVy5cnrmmWfMjgIAXq1RlSDZrZeewg9q3Vv5CQeVEfOJCtMSlLN3vbJ3rlZgi1uK7uPMzVLB2WMqTImTJBWmnlbB2WNyZqdJknzCq8seWkUpq2cpP+GgCtMSlbnlM+Ud36GAK6+95LntVosaVw0qplcKd2Rx8RsYAAAAAJQ548aN08yZMxUYGKilS5eqW7dul72/YRhM37uhxx57TFOnTtUbb7yhMWPGmB0HALzSJz+f0sRluy57n/NHtir9h4UqTE2QPeQKBbW+TRWa31R0PHvXd0r5+vU/PC44KlohHQZK+nVqP339QuWd3idXYa7sIVUU1LaPApt0vuy5p/Vrpv4ta/z9FwaPQIEPAAAAAGXU8uXLNXr0aNWrV0+PPvqoOnfuLH9/f0kU9p7C6XQqMDBQ5cqVU2pqqtlxAMAr7U/M1M1vbDQ7xiWtHtdBV0Uwhe+t+G0MAAAAAMqQbdu2af78+frggw905ZVXatu2bbJYLPrPf/6j9957T4mJiZJEee8hbDabHnzwQaWlpenNN980Ow4AeKX6lQMV6Oeee+Yr+NlVr1Kg2TFQgtzzf3kAAAAAgGL3008/qXfv3goICFBgYKCOHz+uUaNGad26dZo0aZJeeuklbdu2TY8++qjq169vdlz8RVOmTNG6detUowbrEwCgJNhtVkW3qaH5m07I6UbLTGwWi6Lb1JTdxofu3owVOgAAAABQRnTt2lWtW7fWf//7X6Wnp2vz5s16+OGH1ahRI33zzTd655139PLLLysmJkaVKlUyOy7+psLCQvn4+JgdAwA8UmFhob744gvl5eXJ19dXfn5+8vX1lY+Pj7Zv367vY3frYP1os2P+wQ+PdFJkeHmzY6AEMYEPAAAAAGXAqlWrFBQUpH79+qlChQqqUKGCIiIiFBERoVGjRmnx4sUaNmyY7rjjDgUHB7MD3wNR3gPAP3f8+HHdcccdlzxevXp1Nbt2kA6kGXIa5s9D26wWXVcnnPK+DOC3MQAAAADwcjExMRo/fry+//57HTp0qOh2Hx8fdejQQREREVqzZo0kKTDw1z26lPcAgLKkfv36at26tSwWyx+O1a1bVydOnNALA66V4QblvSQZLpcmdGtgdgyUAn4jAwAAAAAvd9111+mZZ55RSEiIpk6dqi+//LLomMViUeXKleVwOCT9elFUAADKmpSUFNntdv1+27jFYlFkZKR+/vln2Ww2Na8RouEd6+giHX+pskga0bGumtcIMTcISgU78AEAAACgjDh+/LiGDh2qwsJCtW7dWu3bt9fBgwc1depUbdy4UU2bNmV1jhdzuVwXnSwFgLLs2LFjGjZsmNatWyeXyyW73V70oba/v79iY2PVpEmTovvnFTp104wNOpWaa8oFbW0WqWZYgFaN6yh/Hz50Lwv4rQwAAAAAyojatWvr66+/Vps2bfT222/rvvvu04kTJ/TNN9+oadOmKigooLz3MvHx8Tp69KgkUd4DwO/8/PPPatWqlerWravvv/9eTZo00Q8//KB33nmn6D4LFiy4oLyXJH8fm2ZGt5DdZpG1lP9atVoku82qN6JbUN6XIfxmBgAAAABe7vdfvPb399f06dO1ZMkS1apVS4ZhKD09XZLk6+trUkIUt08//VStW7dW7dq11a1bN40ZM0Z79+6VJDmdTpPTAYB5Vq1apQYNGqh169batm2boqKitGfPHu3atUsdO3bUXXfdpaZNm+q///2vBgwYcNHnaFotWO/e01pWS+mV+FaLZLVYNP+e1mpaLbh0Tgq3wAodAAAAAPBiK1eu1A033KDy5cv/4diJEyc0bNgwuVwudenSRePGjVNAQIAJKVFccnNz9cADD2jBggW69957NWjQIO3cuVObNm1SXFycYmNjzY4IAKZYuHChHn/8ccXHx8tqtermm2/W3LlzVb169T/c96+uHNt0JFlDF8bK4XSV6Dodq0XysVk1/57WiqpXscTOA/dEgQ8AAAAAXur48eOqX7++KlWqpNOnT1/0ArUFBQUaP368UlJS9PHHH5uQEsVpw4YNGjx4sGbPnq1bbrml6PaUlBQ1atRIb775pvr27Sun08kFiwF4PcMw9Morr+ill15SWlqafHx8dOedd2rmzJkKDi6eKfbd8Rkas2S7TqaeV0m0rBaLVCssQG9Et2DyvoyiwAcAAAAAL2QYhqpXr64zZ85o48aNioqKuuz9c3JyLjqlD8/hcrn0zDPP6Ntvv9Xq1asVFBRUtD7JYrFo0qRJSktL09y5cyVJeXl5ys3NVWhoqJmxAaDYFRQUaNKkSZo7d67Onz+vcuXKaeTIkXrppZdKZF1cXqFTr313SPM2HpPVYpHT+Pd1q81qkeFyaUTHuhrfpT4778swu9kBAAAAAADF74477lBiYqIee+yxPy3vJVHeewGLxaK0tDRFRET8obyXpKZNm6pmzZpyOByy2+2aOHGiTpw4oS+//NLM2ABQbDIzMzVmzBgtWbJEhYWFCg0N1eTJkzVx4sQSvUi7v49Nk25uqJsaR+jVNYe08UiybBbLP1qr89vjrqsTrgndGqh5jZDiDwyPwgQ+AAAAAHiZRYsW6e6779bVV1+tHTt2mB0HpWjfvn1q0aKFYmNj1bRpU0m/fhvDarUqMzNTS5Ys0erVq/X5558rLS1N6enpql27tsmpAeDfSUhI0IgRI/T111/LMAxVrVpVzz//vIYMGWJKnhPJOfpwa5yWbI1TVr5DkmS3WuS4yGT+72+v4GdXdJuaGti2piLD+WAdv6LABwAAAAAvcvr0adWuXVu+vr5KTExUUFCQ2ZFQyoYOHSrDMPTAAw+oVatWOn/+vN555x1NmzZN2dnZGjBggGbMmCE/Pz+zowLAv7J//34NHz5cP/74o1wul+rXr6/XX39dPXr0MDuaJMnhNHTkXLZ2x2doT3yG9iZkKivPoQKnIV+bVRX87WpcNUhNqgWrabVg1asUKLut5L4pAM9EgQ8AAAAAXsIwDNWqVUunTp3S2rVr1blzZ7MjwQRpaWk6evSoatasqSVLlmjatGnKycnRiBEj9MADD6hGjRo6c+aMIiIitH//ftWtW7dEdkIDQEnZtGmTRo8erd27d0uSWrZsqTlz5qh169YmJwOKHzvwAQAAAMBLDB48WKdOndL48eMp78uw0NBQpaWlqWPHjqpQoYLuv/9+jRw5UjVq1JD068Vr27Vrp6uuukrffPONpk2bpgkTJpicGgD+3LJly/Sf//xHx48fl8Vi0Q033KC33npL9evXNzsaUGIo8AEAAADACyxdulSLFy9Wo0aN9Nprr5kdByarVauWHnzwQY0dO1bVq1eXJDkcDu3fv1+LFi1SXFyckpOTtWDBAg0ePNjktABweXPmzNHTTz+tpKQk2Ww29enTR3PmzFHlypXNjgaUOFboAAAAAICHO3PmjCIjI2W1WhUfH6+wsDCzI8HN7N27V3PnztWCBQsUFhamRx99VJMmTdKyZcvUtWtXFRYWysfHx+yYAFDEMAw9++yzeu2115SZmSk/Pz8NHjxYr7/+ugICAsyOB5QaJvABAAAAwMO1b99eBQUF+uqrryjv8QevvfaaJk+erMqVK+v111/X0KFDJUnJycl67rnn1LVrV8p7AG4jLy9PDz/8sN577z3l5eUpMDBQ//3vf/XMM8/IbqfKRNnD/+oBAAAAwIMNGzZMR48e1YgRI9SjRw+z48DNbN++XbNmzdKjjz6qyZMnF91eWFioiRMnqk6dOsrPz5evr68sFouJSQGUdampqRo9erSWLVsmh8OhSpUq6eWXX9YDDzwgq9VqdjzANKzQAQAAAAAPtXLlSvXs2VP169fXoUOHzI4DN3TkyBG1bNlSu3btUmRkpAzDoAgD4FZOnjypYcOG6bvvvpPL5VJkZKRefvll3XHHHWZHA9wCBT4AAAAAeKDU1FRVrVpVLpdLp06d4kJ+uKSoqCh1795dTz75pCQpLi5OmzZt0pw5c9SoUSN16NBB3bp1U+XKleV0OmWz2UxODKAs2L59u0aOHKnY2FhJUuPGjfXGG2+oc+fOJicD3AsfuwMAAACAB4qKilJ+fr4++ugjyntc1oIFC1S3bl1J0rfffqsOHTpo0KBBcjqdaty4sWbPnq2RI0dKEuU9gBK3Zs0aNWzYUC1btlRsbKyuvfZa7dy5U3v27KG8By6CCXwAAAAA8DBjxozRrFmzNHjwYC1cuNDsOPAQ8fHxuvHGG9WxY0c1bdpUr776qo4ePaozZ86oYcOG+vTTT9W1a1fW7AAoEYsWLdKkSZN0+vRpWa1WdevWTW+99ZZq1qxpdjTArfETGQAAAAA8yJo1azRr1ixFRkbqvffeMzsOPMjWrVuVlpamRx55RCNGjJDdbteMGTMUERGhfv366csvv5QkynsAxcYwDE2fPl3h4eG6++67debMGd11111KSUnRqlWrKO+Bv4CfygAAAADgITIzM9W7d2/5+PgoJiaGohV/y7Fjx9SmTRvVrVtXdrtdEydO1IwZM5SZmamffvpJjRo1MjsiAC/hcDg0ceJEBQUF6ZFHHlFubq7Gjh2rrKwsLV68WCEhIWZHBDwGv+0BAAAAgIdo3769cnNztXDhQlWtWtXsOPAw7du31/fff6/ExERJ0sCBA+Xv769u3bopIiJC3bt3NzkhAE+XnZ2t++67T+XLl9e0adPk4+Oj559/XtnZ2ZoxY4b8/f3Njgh4HAp8AAAAAPAAEydO1O7duzVgwABFR0ebHQceqG3btmrdurWeeuopxcXFyd/fX48++qjS0tI0cuRI1apVy+yIADzUmTNn1Lt3bwUHB+u9995TeHi43n77baWlpenxxx/nG2PAv8BFbAEAAADAzW3YsEHXX3+9qlWrpri4OIoQ/GM7duzQf//7X914440aM2aM7Ha7nE6nMjMzlZqaKovFoqpVqzIlC+AvOXjwoIYPH66NGzfK5XKpXr16mj59unr16mV2NMBrUOADAAAAgBvLzs5WRESE8vPzdeTIEUVGRpodCR7u4MGDys7OVosWLXTw4EEtWLBAP/74oxISEnT8+HHddtttevrpp9WsWTM5HA7Z7XazIwNwM5s3b9aoUaO0c+dOSdI111yj2bNnq127diYnA7wPBT4AAAAAuLFWrVpp27Ztmj9/voYMGWJ2HHiZwYMH69ixY+rQoYM6dOig8uXLa/r06Tp37pw2b95sdjwAbmb58uV6+OGHdezYMVksFnXs2FFvv/226tevb3Y0wGvxMToAAAAAuKknn3xS27ZtU+/evSnvUewmT56sZcuWadmyZeratWvRpH3t2rV188036/jx46pUqZI+++wzZWRkaMyYMSYnBmCWefPm6amnntKZM2dks9l02223ac6cOYqIiDA7GuD1WJwIAAAAAG4oNjZWzz//vCpXrqzPPvvM7DjwMrm5udqxY4cmTJigm266SXa7Xb99Qf/o0aNq1qyZYmNj1bVrV917771F+60BlB2GYei5555TSEiIRowYodTUVA0dOlTp6en6/PPPKe+BUkKBDwAAAABuJi8vT127dpXVatWmTZu4aC2KXbly5ZSamqr09PSi25xOp44fP6633npLH3/8saKjoxUZGamDBw/qk08+kcViMS8wgFKTl5enMWPGqHz58nryySflcDj02GOPKScnR++8844CAwPNjgiUKazQAQAAAAA306VLF2VmZurNN99krzBKzAsvvKAHHnhAr776qmrVqqX4+Hi988472rt3r0aNGqUJEyaoTp06kn6dxOWDJMC7paWlafTo0Vq6dKkcDofCw8M1ZcoUjR07lv//B0zERWwBAAAAwI1MmTJF//3vf9W9e3etXr3a7Djwchs2bNDIkSN14MABWa1WRUdH65lnnikq7l0uF5P3gJeLi4vT8OHDtWbNGhmGoRo1amjq1KmKjo42OxoAUeADAAAAgNvYsWOHWrRoobCwMJ05c6booqJASdm7d6+aNm2qhx56SGPHjlVkZKQkJu6BsmDXrl0aPny4tmzZIklq2LChZs6cqS5dupicDMDvUeADAAAAgBsoKCjQFVdcoczMTO3atUuNGzc2OxLKiLNnz+qKK66QRHEPlAVr167V2LFjtW/fPklS27ZtNXfuXDVv3tzcYAAuip/KAAAAAOAGbrrpJqWnp2v69OmU9yhVV1xxhQzDkCTKe8CLffTRR4qMjFTXrl21f/9+devWTSdOnNBPP/1EeQ+4MX4yAwAAAIDJXn/9da1bt06dOnXS+PHjzY6DMuj3xb3D4ZBhGEpJSTExEYDiYBiGXn/9dVWqVEnR0dFKSEjQnXfeqeTkZH3zzTdFa7MAuC9W6AAAAACAifbv368mTZooKChIZ8+ela+vr9mRUMZlZ2erZcuWCggI0C+//GJ2HAD/gMPh0OTJkzVr1ixlZ2fL399f999/v6ZNmyZ/f3+z4wH4G5jABwAAAACTOBwOdejQQS6XS99//z3lPdxCYGCgqlatqh07dmjHjh1mxwHwN+Tk5Oj+++9X+fLl9dJLL8lqteqZZ55RTk6OZs6cSXkPeCAKfAAAAAAwSc+ePZWSkqIXXnhB11xzjdlxgCILFiyQJA0dOtTcIAD+kqSkJPXp00fBwcF69913FRoaqjlz5igjI0NPPvkk17cAPBgrdAAAAADABG+99ZZGjhypdu3aKSYmxuw4wB9cf/312rBhg3bv3q0mTZqYHQfARRw+fFjDhw/XDz/8IJfLpTp16mj69Om67bbbzI4GoJhQ4AMAAABAKTt69KgaNGiggIAAJSUlsdIAbun48eOqU6eOWrZsqZ9//tnsOAB+Z8uWLRo1alTRdSqaNWumN998U1FRUSYnA1Dc+P4MAAAAAJQiwzDUvn17GYahNWvWUN7DbdWuXVvt27fXtm3btHfvXrPjAJC0cuVK1a9fX9dee6127NihDh06aN++fdq5cyflPeClKPABAAAAoBT17dtXZ86c0eOPP662bduaHQe4rN924Q8ZMsTcIEAZN3/+fFWtWlU9e/bUsWPH1KtXL50+fVobNmxQw4YNzY4HoASxQgcAAAAASsnChQt17733spIEHiUqKkoxMTHat28fRSFQigzD0EsvvaRp06YpPT1dPj4+GjhwoGbMmKGgoCCz4wEoJRT4AAAAAFAK4uLiVLduXfn5+enMmTMKDAw0OxLwlxw+fFhXXnml2rZtq59++snsOIDXKygo0MSJEzVv3jzl5uYqICBAo0eP1gsvvCBfX1+z4wEoZazQAQAAAIASZhiGrrvuOjkcDn399deU9/Aov+3b3rJliw4ePGh2HMBrpaena9CgQQoMDNSMGTNUrlw5TZs2TVlZWZo2bRrlPVBGUeADAAAAQAkbOHCg4uPj9cgjj6hjx45mxwH+tvfee0+SdN9995mcBPA+p0+fVo8ePRQeHq7FixercuXKev/995WSkqJHHnlEViv1HVCWsUIHAAAAAErQxx9/rDvvvFNNmzbVrl27zI4D/GNt27bV1q1bdfjwYdWrV8/sOIDH27Nnj4YPH67NmzdLkho0aKAZM2aoe/fuJicD4E4o8AEAAACghCQkJKhWrVqy2+1KSEhQSEiI2ZGAf2zfvn1q3LixoqKitGnTJrPjAB7rhx9+0AMPPKC9e/dKklq1aqU5c+aoVatWJicD4I74Dg4AAAAAFJOUlBQdOHBA0q9776OiolRYWKjly5dT3sPjNWrUSK1atdKPP/6oY8eOmR0H8DiffvqpatWqpU6dOmnfvn3q2rWrjh07ptjYWMp7AJdEgQ8AAAAAxWTy5Mlq3LixnnvuOQ0ZMkQnTpzQgw8+qBtvvNHsaECx+G0X/pAhQ0xOAnieuXPn6vTp0+rfv7+SkpK0Zs0a1a5d2+xYANwcK3QAAAAAoJi0atVK27ZtK/r3unXr6siRIyYmAopfy5YttX37dp04cUKRkZFmxwHcwubNmzV79mxNnjxZDRo0+MNxl8ulM2fOKDg4WAEBASYkBOCpmMAHAAAAgGJgGEbRPuPfpKamas2aNSYlAkrGb1P49957r7lBADdgGIakX/++//TTT1WnTp2L3s9isahKlSqU9wD+NibwAQAAAJRpDqehw0nZ2h2foT3xGdqXmKnMvEIVOl3ysVkU5O+jRlWC1KRasJpWC1b9yoGy2/44C3X06FHVq1fvD7dbLBYdOnTooscAT3XNNddo586dOnHihGrWrGl2HKBUZWVlad26derVq1fRbYcPH1bXrl310UcfqV27dnK5XLJYLCamBOAt7GYHAAAAAAAznEjO0eKtJ7Vk6yll5zskSXarRQ7jjzNOO06lF90e6GdXdJsaGtgmUrUqli+6z65duy54jNVqVUBAgCZOnHjJiUzAU7377rtq2bKlhgwZorVr15odByhVs2fP1pNPPqm3335bgwcPlsViUXJyssLDw5WZmSlJlPcAig0FPgAAAIAy5Ze4NE1fc0ibjiTLZrHI+bsvJV+svP/f27PzHZq/6YTe3nhc7etV1CPdGqh5jRB9/vnnRfcpX768JkyYoIceekghISEl9loAs7Ro0ULNmjXTunXrdPr0aVWvXt3sSECJ2bFjhzZu3KjWrVurefPmeuyxx+R0OvX8888rPT1d48aNU5s2bXTkyBFZrb9+Q8swjKJ/BoB/gxU6AAAAAMqEvEKnXvvukOZtOCar1SLnJcr6v8NmtcgwXBp6XaRevCtKudmZGjdunJ588kmFhYUVQ2rAff38889q3bq1unbtyrUe4JXi4uJ0//33a8uWLWrdurXi4uJUt25drVq1SpL01FNPaeHChXrqqad0zz33qHv37rr22mv13HPPsUIHQLFhAh8AAACA19sdn6ExS7brZOp5uaRiKe/1u+d5J+aEwu56RVN6Xam7b7m+WJ4bcHetWrVS06ZNtXbtWiUkJKhq1apmRwKKxfHjx1W7dm0tXrxYwcHB2rNnj2rUqKGTJ0+qdu3aeuqpp/TMM89o8uTJKleunJ555hllZGQoMDBQ5cqVY/oeQLHibxMAAAAAXm3TkWT1mxujU6m5KrnvH1vkG15Vz/90XpuOJJfUSQC38+6778rlcmnIkCFmRwH+te+++0716tXTlClTVFhYqPnz5+vuu+9WjRo19N5776l3794KDQ1VzZo1ZRiG7Ha7HnvsMfXv31/Lly/X8uXLlZaWJqvVKhZeACgurNABAAAA4LU2HUnWve9tleFyqZiG7i/LapGsFosWDGmj9vUqlvwJATfQpEkT7du3TwkJCYqIiDA7DvCX/bbmZtGiRTp9+rQOHjyoJk2aaPjw4UpKStLYsWMVHh6uH3/8US6XS6NHj9bQoUMVGhp6wfNkZmZq8eLFeuCBBzR79myNGjXKpFcEwBtR4AMAAADwSrvjM9RvbowKnEYJTt7/kdUi+disWjryOjWtFlx6JwZMsnnzZl133XW66aabinaDA+6usLBQFotFdrtdTz75pJ5//nm1adNGa9euVfny5eVyudStWzft2rVLjz76qB5++OGixx44cEDff/+9Ro8efcFzsvceQElghQ4AAAAAr5NX6NSYJdvlcLpKtbyXJMMlFToNjV2yXXmFztI9OWCCdu3aqWHDhvrmm2909uxZs+MAl5WcnKy7775b7dq107Bhw7Rnzx5NnjxZV155pYKCgmSz2SRJFotFt9xyi8LCwlSpUqWix589e1Zvv/22fvrpJ2VkZBTd/lt573A4WJ8DoFhR4AMAAADwOq99d0gnU8/LaVKJYrikE6nn9fraw6acHyhtb7/9tlwul4YOHWp2FOCi4uPjtXr1av3nP/9Renq6hg4dqm3btql///7auXOnHnzwQcXFxWn79u1Fjxk+fLi6dOmiBx54QLfddpuGDBmiRo0aacuWLXrggQcUHPx/37L6bfLebrczhQ+gWLFCBwAAAIBX+SUuTX3mxMgd3uhYLNLno6LUvEaI2VGAEtewYUMdPHhQZ8+evWBiGXAHd911lzZv3qwrr7xSH374ocLDwxUfH6/HHntMe/fuVUxMjK688kqNHDlSEyZMkJ+fX9Fjly1bpsOHD+vgwYMaOHCgunbtauIrAVDWUOADAAAA8CqD3t2izcdS5CyNq9b+CZvVouvqhOuDoW3NjgKUuE2bNqlDhw7q2bOnvvzyS7PjABc4fPiwrr32WnXq1EnLli0ruj02NlZRUVGKjY3VsmXLtGbNGs2dO1dXX321DMOQ1Xrx5RVOp7No3Q4AlCRW6AAAAADwGieSc7TpSLJblPeS5DRc2ngkWSdTcsyOApS49u3b68orr9RXX32l5ORks+MAF6hfv746d+6sjIwMnTp1quj2OnXqKCoqSl999ZVGjRqlI0eO6KuvvpLT6bxoee90/nptE8p7AKWFAh8AAACA11i89aRsbrZ72GaxaPGWOLNjAKXirbfekmEYuv/++82OAvzBo48+qtTUVK1ateqC2w8dOqTQ0FBVqVJFt9xyiyIiIi65x57iHkBpo8AHAAAA4BUcTkNLtp4y7cK1l+J0ubRka5wcTsPsKECJ69Spk+rVq6cVK1YoNTXV7Dgog/Ly8jR27FgVFBTof7dGt2zZUpUrV9aUKVO0dOlSxcXF6cMPP1S5cuV0zTXXSJIWLFig++6775KrcwCgtNnNDgAAAAAAxeFwUray8x2XPJ6+cbEyflxywW32sOqqNnyuJMmZnaa0dfOVe+IXuQpy5RNWXUHt7lD5q6KK7p+09FkVJB2XMyddNv9A+ddqrpBO98peIfyy2bLyHTpyLltXRQT9i1cIeIa5c+eqa9euGjZs2AW7xoGSlJKSotGjR+uzzz6Tw+FQ69atdffdd19wH4vFoieeeELdunXT1KlTValSJf3888968cUXde211xbdzzAMWSyWS07hA0BposAHAAAA4BV2x2f86X18KtbUFXe+8H83/G7CMnnlqzLys1W572RZA4KVs3e9kpdPlU/Ia/KNqCtJ8q/ZTMHt7pAtMEyOrBSlr3tXyV9MUcTdr/ylfBT4KAu6dOmiOnXq6IsvvlB6erpCQkLMjgQvduLECQ0bNkxr166Vy+VSZGSkXn75Zd1xxx0XvX+7du3UqlUrtW7dWnfddZdatmz5h/swfQ/AnfA3EgAAAACvsCc+Q3brn0xLWm2yBYb+338CgosO5cfvV4WWPeVXtYF8QiIUEnWnrH7llX/2SNF9gtrcJr9qV8keXFn+1Rsq6Nr+yo8/KJfz0pP/kmS3WrTnL3zAAHiLuXPnyjAMDRs2zOwo8FLbt29XmzZtVLt2bX333Xdq1KiR1q1bpxMnTlyyvJd+3WE/btw4/fTTT8rMzJQk5efnl1ZsAPjbKPABAAAAeIV9iZlyGJfff+9IS9DpWYMVP2eozn05TY6MpKJjftUa6vz+jXLmZsnlMpSz7we5nAXyr9n0os/lzM1Szt718qveUBbb5b/c7DBc2puQ+fdfFOChbrzxRtWuXVufffZZUUkKFIdvv/1WV111lVq2bKnY2Fi1a9dOu3bt0p49e9SpU6e/9Bw9e/ZUYWGhPvroIxUUFMjPz69kQwPAv0CBDwAAAMArZOYVXva4X9UGCr/lIVW+4xmFdR8tZ8ZZnVn8qIz885KkSrc9Kpfh0OkZ0YqbdrtSvpmtSn0el09o1QueJ23de4qb3lenZ0TLkZmkSn2f+Ev5svIuP6UPeJvZs2fLMAwNHz7c7CjwAosWLVKNGjXUvXt3HT58WDfffLNOnjypmJgYNW168Q9aL8XX11f333+/qlatKoeDv5sBuDeL638vyQ0AAAAAHuiG6et1PDnnL9/fyMvW6Tn3KbTz/apwdTelfjtX+YmHFHL9YNnKBen84Z+UGbtcEQOnyrdyraLHOc9nyMjLliMjSRk/LpHVL0CV+j31pxc7rF2xvNZN6PQPXx3gmWrVqqXTp08rNTVVQUFcAwJ/j2EYeu211/Tiiy8qNTVVdrtdAwYM0KxZs/71tRVcLhcXqQXgEZjABwAAAOAVfGx/r4ix+gfKJ7SaHGkJKkxLVNb2lQrvMU7lajWX7xV1FNL+LvlF1FPW9pUXPM4WECyfsGoqV/saVew1UblHf1ZBwoE/PZ+vjbdfKHtmzZolp9OpkSNHmh0FHsThcGjixIkKCgrSI488otzcXI0bN07Z2dlatGhRsVwYmfIegKfgN0gAAAAAXiHI3+dv3d8oyJUjPVG2wDC5Cn+9gKHF8j9vkaxW6XJfWnYZv/4fx+XX90hSBf/L78kHvNGtt96qmjVr6pNPPlF2drbZceDmsrOzdd9996l8+fKaNm2afHx89Pzzzys7O1uvv/46u+oBlEkU+AAAAAC8QqMqQbJbLz1Rmfb9u8qL2y1H+lnlnd6vc5+9IFmsKt/oevmEV5c9tIpSVs9SfsJBFaYlKnPLZ8o7vkMBV14rScpPOKjMbStUcPaYHBlJyj2xU8lfTpM9pIr8qjW8bDa71aLGVf9vfUh+fr5+/PFHvfTSS+rRo4deeuml4vlDANzQzJkzmcLHZSUmJqp3794KDg7We++9p/DwcL399ttKS0vT448/LquV+gpA2cUOfAAAAABe4ZOfT2nisl2XPH5u+VTln9orZ26mbAHB8qveSCEdB8sntIokqTA1XunrFyrv9D65CnNlD6mioLZ9FNiksySpIOmEUr+bp8Kk4zIK82QLDFO5Oi0UfN0A2StUvHw4l0tXpm9V3r71ysnJ0Z49e1RQUCCbzSan06k777xTS5YsKbY/C8Dd1KxZUwkJCcrIyFD58uXNjgM3ceDAAQ0fPlybNm2Sy+VSvXr19Oqrr6pnz55mRwMAt0GBDwAAAMAr7E/M1M1vbDQ7xiWde3+8ziccueixmTNn6sEHHyzlREDp+fzzz9WnTx8NHjxYCxcuNDsOTBYTE6PRo0dr586dkqRrrrlGb775pq699lqTkwGA+6HABwAAAOAVHE5DzZ9bo+x8h9lR/qCCn10/TeyoAXf011dfffWH41arVdWqVVObNm3Ut29f9e3bV76+viYkBUpO9erVdfbsWWVkZCggIMDsODDB8uXL9fDDD+vYsWOyWCzq2LGj3n77bdWvX9/saADgtlgiBgAAAMAr2G1WRbepIZvl0nvwzWCzWBTdpqbKB5TTihUrNGPGDFkslqKdzj4+PmrVqpXS09O1bNky3XXXXfLz81PFihXVuXNnTZ8+XUlJSSa/CuDfmzFjhhwOB982KYPmzZunKlWq6LbbbtPJkyd12223KTExUevXr6e8B4A/wQQ+AAAAAK9xIjlHnaavNzvGH/zwSCdFhv/f3u9vv/1Wffv2VXZ2trp3767Vq1dLktLS0vThhx9qxYoV+uWXX3Tu3Dn99patfPnyatCggbp06aLBgwerSZMmprwW4N+oVq2akpKSlJmZqXLlypkdByXIMAy98MILmj59ujIyMuTr66u7775br7/+ugIDA82OBwAegwIfAAAAgFcZ9O4WbT6WIqdh/lsdm9Wi6+qE64Ohbf9w7MCBA4qOjtb48eN1zz33XPTxDodDK1as0NKlSxUTE6NTp07J6XRK+nVyv1atWoqKitKdd96pG2+8sWiqH3BXH3/8se68804NHTpU77zzjtlxUALy8vL0n//8R++8847y8vJUvnx5jRkzRs8995zsdrvZ8QDA41DgAwAAAPAqO06l6/Y3f5Q7vNGxWKTPR0WpeY2QYnvOrVu3avHixVq3bp0OHz6svLy8/38uiyIiItSyZUvddtttGjBgAFOucEtVqlRRSkqKMjMz5e/vb3YcFJO0tDSNHj1aS5culcPhUHh4uJ544gmNHTuWDxcB4F+gwAcAAADgdaas2q95G4/JzHc7Fkkjrq+rx266qkTPExcXpw8++ECrV6/Wnj17lJ6eXnQsJCRETZo00U033aR77rlH1atXL9EswF+xePFiDRo0SCNGjNDcuXPNjoN/6eTJkxoxYoTWrFkjwzBUo0YNTZ06VdHR0WZHAwCvQIEPAAAAwOvkFTp104wNOpWaK6cJb3lsFqlmWIBWjesofx9bqZ47JydHH3/8sb744gv9/PPPOnPmTNEe/XLlyqlevXrq1KmT7r77brVu3bpUswG/iYiIUGpqqrKzs+Xr62t2HPwDO3fu1IgRI7RlyxZJUsOGDTVz5kx16dLF5GQA4F0o8AEAAAB4pd3xGeo3N0aFTkOluQ7fapF8bFYtHXmdmlYLLr0TX4JhGFqzZo0++ugj/fjjjzpx4oQKCwslSTabTTVq1FC7du3Uv39/9ezZkx3VKBWLFi3S3XffrVGjRunNN980Ow7+hrVr12rMmDHav3+/JKlt27aaO3eumjdvbm4wAPBSFPgAAAAAvNamI8m6972tMlyuUinxrRbJarFo4ZA2iqpXseRP+A/t2bNHH3zwgb777jsdPHhQOTk5kn7do1+pUiVdc8016tWrl+666y6FhISYGxZe64orrlB6erqysrKYwvcAS5Ys0aOPPqpTp07JYrGoW7dueuuttxQZGWl2NADwahT4AAAAALzapiPJGrowVg6nq0TX6fw2eT//ntZuXd5fTFJSkj744AN9/fXX2rlzp1JSUoqOVahQQQ0bNlS3bt00ePBg1a9f38Sk8CYLFy7UvffeqwcffFAzZ840Ow4uwjAMvfHGG3rhhReUnJwsu92ufv36afbs2QoLCzM7HgCUCRT4AAAAALze7vgMjVmyXSdTz5fIhW0tFqlWWIDeiG7hFmtz/q28vDx9/vnnWrZsmbZu3ar4+HgZhiFJ8vPzU+3atXX99dcrOjpaHTp0kNVqNTkxPFXlypWVkZHBFL6bcTgcmjx5smbNmqXs7Gz5+/vr/vvv17Rp0+Tv7292PAAoUyjwAQAAAJQJeYVOvfbdIc3beExWi0XOYtipY7NaZLhcGtGxrsZ3qV/qF6wtLYZh6Mcff9SHH36oH374QceOHVN+fr4kyWq1qmrVqmrTpo369Omjvn37UvDhL3v33Xd1//33a+zYsZoxY4bZccq87OxsjR8/Xh988IEKCgoUHByshx9+WE888QQf1AGASSjwAQAAAJQpv8Sl6dU1h7TxSLJsFss/Wqvz2+M61KuoCd0aqHmNkOIP6uaOHj2qhQsX6ttvv9X+/fuVmZlZdCwsLExXX321brnlFt19992qXLmyiUnh7ipWrKjs7GxlZ2dzEWWTnD17ViNHjtSKFSvkdDoVERGhp59+WiNGjDA7GgCUeRT4AAAAAMqkE8k5+nBrnD7celLZ+U5Jkt1qkeMik/m/v72Cn13RbWpqYNuaigwvX6qZ3Vl6ero+/PBDrVixQtu3b9e5c+f029vN8uXL68orr1SXLl00ePBgNW3a1OS0cCfz5s3TiBEj9NBDD+nVV181O06ZcvjwYQ0bNkwbNmyQy+VSnTp19Oqrr6p3795mRwMA/H8U+AAAAADKtGefe17Pz3xHr763VOec/tqbkKmsPIcKnIZ8bVZV8LercdUgNakWrKbVglWvUqDsNlZJ/BmHw6GvvvpKn3zyiTZv3qxTp07J4XBIknx8fBQZGamoqCgNGDBA3bt3/8frOVwulywWiyTJ6XTK5XLJarXKarWqb9++uuaaa/TEE09c9P5wH+Hh4crJyWEKv5Rs2bJFI0eO1I4dOyRJV199tWbPnq2oqChzgwEA/oACHwAAAECZdfr0adWuXVsOh0OnTp1S9erVzY7k1WJjY7V48WKtW7dOhw8fVm5uriTJYrEoIiJCLVu2VO/evXXnnXcqMDDwLz9vTk6Oypf/47chNm7cKB8fH1177bUyDIMd3m5szpw5Gj16tB555BFNmzbN7Dhea+XKlXrooYd05MgRWSwWtW/fXm+99ZYaNmxodjQAwCVQ4AMAAAAok7KystSuXTvt3btXkvTpp5+qX79+JqcqW06fPq0PPvhAq1at0u7du5Wenl50LDg4WE2aNNE777yjBg0aXHRqvqCgQOvWrdOCBQv0008/KTg4WHfeeafuvfdeRUREXHDfX375Rd27d9fWrVsVGRnJFL4bCgsLU25urrKyspjCL2bvvvuuJk+erMTERFmtVt16662aO3euqlSpYnY0AMCfYPwAAAAAQJnjcDg0YMAA7d+/X9KvE+AxMTEmpyp7qlevrkmTJmnDhg1KS0tTTk6O5s+fr169eql8+fKKiYlR5cqVL1m2v//++xo6dKjOnz+vt956S/fee6+2bt2qn376SR9++KE6duyowsJCnTlzRmvXrlVOTo5q1ar1h+dzuVxits18zz77rPLy8i5YeYR/zjAMvfjiiwoNDdX999+v5ORk3XvvvUpPT9fy5csp7wHAQzCBDwAAAKDMGTNmjGbPnn1BaduqVSvFxsaamAr/63JrbwoKCtSkSRN1795dr7zyivz8/CRJ+/btU2BgoJ5//nnt27dPmzZt0qxZszR27FiVL19eUVFR6tWrl6KjoxUaGvq3zomSFxoaqvz8fGVnZ/Pfwz9UUFCgiRMnat68ecrNzVVAQIAeeOABPf/88/L19TU7HgDgb+KnIQAAAIAy5dtvv9WsWbP+MHG9Y8cO5efnm5QKF3O5AvfkyZM6efKk7rvvvqLyXpIaNWqkmjVrau3atbr11lslSffdd5/q1KmjO+64Qy1bttS3335bdPHO5ORkff3110Uf3lzsnC6XS06ns+jfv/vuO7300kvF8RLxP5566inl5uZq8uTJZkfxOOnp6Ro0aJDKly+vGTNmqFy5cnrllVeUlZWll19+mfIeADwUE/gAAAAAypSUlBS9+OKL+u6777Rr1y5Jv5a2hmEoJiZG7dq1Mzkh/oqtW7eqXbt2SkxMVOXKlSX93yqchIQE1apVS99//706duyoPXv2qEWLFvrpp5/UokWLoufYsGGDXnjhBaWlpenkyZOyWq2aMmWK7r333kue1+FwaNiwYVq/fr2OHz8ul8vFPv1iFhISooKCAqbw/6JTp05pxIgR+uabb2QYhqpXr64XX3xRd999t9nRAADFgJ+EAAAAAMqU8PBwTZ8+Xd98840kqUOHDnr44YfVpUsXVaxY0eR0+KtcLpdq1KihNWvWSPp19Y3FYpHValVsbKwCAgLUsGFDSdLevXtltVrVuHHjom9eOJ1ORUdHq2fPnlq/fr3Onj2rN954Q5MnT9b27duLzpOcnKz58+fr/fffV3JysgoLC7Vr1y7deeedpf+iy4jJkycrNzdXTz/9tNlR3NqePXt03XXXqWbNmlq1apXq16+v1atX69SpU5T3AOBFKPABAAAAlEnvvfeeJGnixImaNm2avvvuO9WvX9/kVPirWrdurW7dumnu3LnauXOnCgoKdOjQIRUUFGj9+vVq1KiRKlWqJJfLpZiYGDVs2PCCVTvLly9XYmKiYmJi9NFHH+nkyZPq37+/rr76am3ZskWSlJSUpE6dOum1117Tiy++qBtvvFHff/+9Dh8+rM6dO0sS0/cl4KGHHlJQUJCmT58uwzDMjuN21q9fryZNmqhp06bavHmzWrdurZ9//lkHDhxQ9+7dzY4HAChmFPgAAAAAyqSVK1fKYrGoR48eZkfBP2C1WvWf//xHFStWVNu2bdWwYUO9/PLLSkpK0o8//qiOHTtK+rVg37Jlizp16iTp1xU4krRixQpdddVVqlatmmbOnKmrr75a4eHhWrt2rXbt2qXCwkKNHz9eLpdLK1eu1IEDBzRhwgSNGDFC+fn5uvbaa8166V7ParXq8ccf1/nz5/Xcc8+ZHcdtfPLJJ6pVq5ZuuOEG7du3T127dtWxY8e0detWtWzZ0ux4AIASwg58AAAAAGVSUFCQgoODderUKbOj4F8qKChQbGysnE6nmjVrpnr16umtt95S3759JUmNGzdWr169NGXKlKLH3HjjjapVq5befvttSb+uytm9e7diY2PVpUsX5efna9y4cXrggQeKduKfPXtWXbp0Ubly5RQbG8v++xJkGIZCQkJkGIYyMjL0zTffKDY2Vk8++WSZ+jM3DEOzZ8/Wc889p3Pnzslms6lPnz568803WfkFAGUEE/gAAAAAypykpCRlZWUpKirK7CgoBr6+voqKilLHjh0VEhKi5ORk9e7du+j46NGj9d5776lZs2Z6//33JUk333yzvvnmG23dulWSVLFiRd1www2aOHGiWrZsqR9//FGBgYFq2rRp0fPY7Xb5+/tfcCFclAyr1arHHntMOTk5qlGjhm655RY9/fTTSklJMTtaqXA4HHriiScUEhKisWPHKjMzU6NGjVJmZqY++eQTynsAKEMo8AEAAACUOQsWLJAkRUdHmxsEJcZutxf98wMPPKDY2FiNGzdOVuuvb4OHDRum6667Tk899ZTmzZunzZs36/PPP9emTZskSRUqVNDZs2fVuHHjouc5e/asjhw5oi5dukj64/77VatWKTs7u6RfWpmwYsUKffLJJ5KkxMTEotu9ffr+/PnzGjFihAIDA/XCCy/IYrHoqaee0vnz5/Xmm28qICDA7IgAgFLGCh0AAAAAZU779u0VExMjh8NRVOii7Pht9c2xY8f05ptvauXKlcrNzVWbNm00bNgwdevWTTt27FCLFi20du1a3XDDDcrNzdWUKVP0/PPPKz4+XlWqVLngOZ1Op0JCQpSdna3g4GA1btxYN910kwYPHqzIyEiTXqlnOnjwoK666qqLHktJSVFYWFgpJyp5ycnJGjlypL744gs5nU5VrlxZTz31lEaPHm12NACAySjwAQAAAJQ5wcHBqlChgk6fPm12FLgJl8ulxMREhYeHy8/PTzk5OZo4caK+/vprDRgwQE6nU9OnT1eTJk20a9euP+y/NwxDH3zwgT7//HPFxsYqMTFRv73d9vf3V7169dSpUycNGjRIbdu2NetlegSXy6Vp06Zp0qRJkn79s/1NamqqQkNDzYpW7I4eParhw4dr3bp1crlcqlWrll555ZWi6zcAAECBDwAAAKBMSU5OVqVKldSvXz99+umnZseBG0tJSdHixYu1ceNGdenSRR9++KH8/Py0Zs2aP72ArWEY+v777/XRRx9p48aNOnHihAoKCiRJNptN1atX17XXXqv+/furV69e8vHxKa2X5TE2bdqkvn376ty5c0UfhqSlpSkkJMTcYMUgNjZWo0aN0rZt2yRJTZs21ezZs9WhQweTkwEA3A0FPgAAAIAyZfr06XrkkUf02Wef6fbbbzc7DjxIWlqa0tPTVbt27X/0+H379umDDz7QmjVrdODAAeXk5BQdq1Spkpo3b66ePXtq4MCBXrkm5p84d+6coqOjtXbtWkkXTuA7nIYOJ2Vrd3yG9sRnaF9ipjLzClXodMnHZlGQv48aVQlSk2rBalotWPUrB8puM3dl1qpVqzRu3DgdPnxYkhQVFaW33nrrgmstAADwexT4AAAAAMqUjh07atOmTSooKLjgQqdAaUtOTtaiRYu0cuVK7dy5U8nJyUXHAgMD1bBhQ914440aPHiwGjRoYGJScxmGoe7du+u7777Tt99+q/rXXKfFW09qydZTys53SJLsVoscxh/rjd/fHuhnV3SbGhrYJlK1KpYv1dewcOFC/fe//1VCQoKsVqtuvvlmzZ07V9WrVy/VHAAAz0OBDwAAAKBMCQ4OVmBgoOLj482OAlygoKBAX3zxhZYuXaotW7YoPj5eTqdTkuTr66vatWurQ4cOio6OVqdOncrUBZhdLpead7lNlqt7Kd0/QjaLRc5/UGf89rj29SrqkW4N1LxGSPGH/f8Mw9C0adM0depUpaWlycfHR9HR0Zo5c6aCgoJK7LwAAO9CgQ8AAACgzEhNTVV4eLj69OmjZcuWmR0HuCyXy6XNmzdr8eLFWr9+vY4ePar8/HxJktVqVZUqVdSmTRvdfvvt6t+/v/z9/U1OXDLyCp167btDmrfhmCwW6SKD9n+bzWqRYbg0vGMdPdT1Svn72P79k/5/BQUFmjRpkubOnavz588rICBAI0eO1JQpU+Tr61ts5wEAlA0U+AAAAADKjFdffVUTJkzQp59+qn79+pkdB/jbjh8/rvfff1/ffPON9u7dq8zMzKJjYWFhatq0qXr06KHBgwcrIiLCxKTFY3d8hsYs2a6TqedVEu2FxSLVCgvQG9Et1LRa8L96rszMTI0ZM0ZLlixRYWGhQkND9dhjj+mRRx4pU9+WAAAULwp8AAAAAGVGp06dtGHDBvbfw2tkZmZqyZIl+vLLL7Vt2zYlJSXpt7f5AQEBuvLKK9W5c2fdfffdat68ublh/6ZNR5I1dGGsHE7XP1qX81fZLBbZbRa9e09rta9X8ZL3O3LkiKpUqaLy5S/cnx8fH68RI0Zo1apVMgxDVatW1Ysvvqh77rmnxDIDAMoOCnwAAAAAZUZISIgCAgKUkJBgdhSgRDgcDq1atUqffPKJYmJiFBcXJ4fj/1/o1W5XZGSkrrvuOg0YMEA333yz206GbzqSrHvf2yrD5SqWlTl/xmqRrBaLFgxpc9ESf/v27WrXrp369OmjJUuWSJL27t2rESNGKCYmRi6XS/Xr19frr7+uHj16lHxgAECZQYEPAAAAoExIT09XaGiobr/9dn322WdmxwFKzfbt27Vo0SJ9//33Onz4sM6fPy9JslgsuuKKK9SiRQv17t1b0dHRqlChgslpf12b029ujAqcRomszbkUq0XysVm1dOR1F6zTSU5OVvPmzRUfHy+LxaKPPvpIzz//vHbv3i1JatmypebMmaPWrVuXXlgAQJlBgQ8AAACgTJgxY4bGjx+vjz76SAMGDDA7DmCahIQEffDBB1q1apV27dqltLS0omNBQUFq0qSJunfvrnvuuUeRkZGlmi2v0KmbZmzQqdTcEl2bcylWixQZFqBV4zrK38cmp9OpG2+8URs2bJDT6Sy6n8Vi0Q033KB58+apbt26pZ4TAFB2UOADAAAAKBNuuOEG/fDDD+y/B/7H+fPntXTpUn3++eeKjY1VYmKiDMOQJPn7+6tu3bq6/vrrNWjQILVr165Es0xZtV/zNh4r1cn7/2WxSCM61tVjN12lxx57TFOnTv2f4xbt2rVLTZo0MSkhAKAsocAHAAAAUCaEhobK399fiYmJZkcB3JphGFq3bp2WLFmijRs36sSJEyooKJAk2Ww2Va9eXW3btlW/fv3Uu3dv+fr6/ulzulwunTp1SjVr1rzkfX6JS1OfOTFyh5LCIqll6jotmzf9oscnTZqkF198sXRDAQDKJPe8Wg0AAAAAFKPMzEylp6erbdu2ZkcB3J7ValWXLl30zjvv6ODBg8rPz9e+ffs0adIkXXPNNUpJSdEnn3yiO+64Q35+fqpUqZJuvPFGzZgxQ6mpqRd9zqVLlyoyMlJPPvlk0XT//5q+5pCsVktJvrS/zGU4tTH91z34FssfM8XExJR2JABAGcUEPgAAAACvN2vWLI0ZM0aLFy/WXXfdZXYcwOOlpKRo8eLFWrFihXbs2KHk5OSiY4GBgbrqqqvUtWtX3XPPPbrqqqs0cuRIzZs3Ty6XS3369NEHH3yggICAosecSM5Rp+nrTXgll7d+wvWqVTFQTqdT+fn5KigoUEFBgYKDg+Xn52d2PABAGUCBDwAAAMDrdenSRd9//73y8/P/0roPAH9PQUGBli9frmXLlumnn37S6dOniy766uvrK5fLpcLCQkm/Tvg3adJEX331lapXry5JeuHrfZq/6YQpF669FJvFoqHta+u/PRqaHQUAUIZR4AMAAADwemFhYfL19dWZM2fMjgKUGZs3b9bixYu1du1aHThw4A/HfX19NWXKFI1+4EG1mrJO2fkOE1JeXgU/u36ZfKPsNjYQAwDMQYEPAAAAwKtlZWUpKChIt956q1asWGF2HKDM+eqrr3Trrbde8nho7cYKGjD1ss/hyEpW+voFyj26TS5HvuyhVRTeY7z8qtSXJJ0/GKOsX1ap4MwRGXlZqjLkDfleUef/Hp9+VvFzh170uSve9pjKX9X+kudePa6DrooIumw+AABKit3sAAAAAABQkj744ANJ0oABA0xOApRNW7ZsKfpnu92uFi1a6IYbblDLli0VFBSkFXuTtfLcpR/vzMvWmQ8myj+ymSrf8bSsAcFypCXI6h9YdB+jME9+1RspoGF7pa6a+YfnsAVVVPUHP7jgtqwdq5W59TOVq9Pysvl3x2dQ4AMATEOBDwAAAMCrff7555KkO+64w+QkQNnUt29fBQQEqF27dmrTpo3KlSt3wfEf8/bInhInh3HxBQGZPy2VPaiiKt4yvug2n5CIC+4T2KSzpF8n7S/GYrXJFhh6wW3nD21WwFXtZfUtd9HHSJLdatGe+Az1b1njkvcBAKAkUeADAAAA8Grbt29X5cqVuXgtYJKrr75aV1999SWP70vMvGR5L0m5h7fIv3YLnft8ivJO7ZEtMFwVWvRQheY3/eNM+WeOqDDpmMK6jbrs/RyGS3sTMv/xeQAA+Le4CgsAAAAAr5Wdna3U1FS1bt3a7CgALiEzr/CyxwvTzyjrl69lD6uqK+54VhVa9FDad/OUvXvtPz5n9s5v5RNeQ/7VG/7pfbPy3O/iugCAsoMJfAAAAABea/HixZKk/v37m5wEwKUUOi89fS9JcrnkV6WeQq+/R5LkG1FXhedOKuuXrxXYtMvfPp9RmK+cfT8o5Lq/dl2MAqfxt88BAEBxYQIfAAAAgNf67LPPJHEBW8Cd+dgslz1uCwyVT3jNCx8TXkPOzMtc+fYyzh/8Ua7CfJX/i+W/r43qBABgHn4KAQAAAPBa27ZtU6VKleTv7292FACXEOTvc9njftUbqTD19AW3FabGyx5c+R+dL3vntwqo30a2gOC/dP8K/iwvAACYhwIfAAAAgFc6f/68UlJS1KpVK7OjALiMRlWCZLdeego/qHVv5SccVEbMJypMS1DO3vXK3rlagS1uKbqPMzdLBWePqTAlTpJUmHpaBWePyZmddsFzFaYlKP/UXgVe3f0vZbNbLWpcNegfvCoAAIoHHyMDAAAA8EoffvihJKlfv34mJwFwOU2qBcthXHoPvl+VK1Wpz+NK/2Gh0n9cInvIFQrtMkyBjW8ouk/u4S1K+fr1on9PXv6yJCk4KlohHQYW3Z69a41sQRXlX/uav5TNYbjUpNpfm9QHAKAkWFwu159cLQYAAAAAPM/NN9+s1atX6/z58ypXrpzZcQBcwv7ETN38xkazY1zS6nEddFUEU/gAAHOwQgcAAACAV/r5559VsWJFynvAzdWvHKhAP/dcEFDBz656lQLNjgEAKMMo8AEAAAB4nby8PCUnJ6tly5ZmRwHwJ+w2q6Lb1JDNcuk9+GawWSyKblNTdhvVCQDAPPwUAgAAAOB1lixZIon994CnGNgmUk432/DrdLk0sG1Ns2MAAMo4CnwAAAAAXufTTz+VJN11110mJwHwV9SqWF7t61WUzeoeU/g2q0Ud6lVUZHh5s6MAAMo491wyBwAAAAD/ws8//6zw8HAFBASYHQXAZWRnZ+vMmTNavny5fln+vZzXjZLcYJWO4XJpQrcGZscAAIACHwAAAIB3ycvL07lz59StWzezowD4HcMwNGrUKO3YsUOJiYk6d+6c8vLyio5XqlRJg+5/VJ8fyJKZ23QskkZ0rKvmNULMCwEAwP9HgQ8AAADAq3z88ceSpD59+picBMDvWSwWrVy5UgkJCX845ufnp/3796t8UIi2z9igU6m5puzEt1mkmmEBGt+lfqmfGwCAi2EHPgAAAACv8tv++4EDB5qcBMDvWSwWzZ0796LHFi1apPDwcPn72DQzuoXsNotKex2+1SLZbVa9Ed1C/j620j05AACXQIEPAAAAwKvExsYqLCxMgYGBZkcB8DvZ2dmaOXPmBbfZ7XbdeOON6tu3b9FtTasF6917WstqKb0S32qRrBaL5t/TWk2rBZfOSQEA+Aso8AEAAAB4jYKCAiUlJalFixZmRwHwO7NmzVLFihW1Zs0aXX311fL395ckWa1WzZkzR5b/uXBt+3oVtWBIG/nYrLKV8EVtrRbJx2bVwiFtFFWvYomeCwCAv4sCHwAAAIDX+OSTTyRJt99+u8lJAEjS4cOHdeWVV2rMmDHy8fHRJ598oh07dmjKlCmSpCeeeEJ169a96GPb16uopSOvU42wciqpDt9ikSLDArR05HWU9wAAt2Rxucy8tjsAAAAAFJ9evXppxYoVysjIUFBQkNlxgDLLMAyNGDFC7777rlwul+666y4tXLhQdrtdkuR0OvXNN9/oxhtvlI+Pz2WfK6/Qqde+O6R5G4/JarHIaRRDjWE4JYtVIzvV0/gu9dl5DwBwWxT4AAAAALxGRESECgoKlJqaanYUoMz6+uuvNXDgQKWnpysyMlIrVqxQ06ZN//Xz/hKXplfXHNLGI8myWSxy/oM647fHVbNm6uf3nlbTqhW0Zs0ahYaG/ut8AACUBAp8AAAAAF6hoKBAfn5+6ty5s9auXWt2HKDMSU9PV69evbRx40b5+PjomWee0aRJk4r9PCeSc/Th1jgt2RqnrHyHJMlutchxkcn8399ewc+u6DY1NbBtTZ1PilOjRo0kSeHh4Xr77bdZvQUAcEsU+AAAAAC8wpIlS3TXXXfpjTfe0JgxY8yOA5QpL7/8sp544gkVFhaqffv2Wr58ucLCwkr0nA6noSPnsrU7PkN74jO0NyFTWXkOFTgN+dqsquBvV+OqQWpSLVhNqwWrXqVA2W2/XgowKSlJV1xxxQXP17dvX82ePfsPtwMAYCYKfAAAAABe4bbbbtPy5cuVnp6u4OBgs+MAZcKePXvUs2dPnThxQsHBwVq0aJFuvfVWs2P9qcLCQvn6+l5wm81mU2BgoLZs2aIGDRqYlAwAgAtZzQ4AAAAAAMVhy5YtCgkJobwHSoHD4dCgQYPUrFkznTx5UkOHDlVqaqpHlPeS5OPjI39//wtuMwxDlSpVUvny5U1KBQDAH1HgAwAAAPB4DodDZ8+eVfPmzc2OAni9ZcuWKSwsTIsXL1a9evW0f/9+vfPOO7JaPatiCAoKkiRZLBZJ0l133aV9+/apevXqZsYCAOACnvXTFQAAAAAuYtmyZXK5XLrtttvMjgJ4raSkJLVt21b9+vVTfn6+ZsyYoUOHDnnsupmKFSvKYrHo7rvvlq+vr7788kvZbDazYwEAcAEKfAAAAAAe7+OPP5Yk3XPPPSYnAbzT008/rapVq2rr1q3q0qWLkpOTNXbsWLNj/Svvv/++tm3bpoULF+rFF19UVlaWHnzwQbNjAQBwAS5iCwAAAMDjVa1aVefPn1d6errZUQCv8vPPP+u2225TfHy8wsPD9fHHH6tLly5mxyoR1atXV2Jiok6fPq0qVaqYHQcAAElM4AMAAADwcA6HQ2fOnNHVV19tdhTAaxQUFKhv375q3bq1EhMT9eCDDyopKclry3vp12/yGIahvn37mh0FAIAiFPgAAAAAPNoXX3whl8ul3r17mx0F8AqLFi1SaGioPvvsMzVq1EjHjh3TzJkzPe4itX9XVFSUrr/+em3evFlr1641Ow4AAJJYoQMAAADAw/Xr10/Lli1TSkqKwsLCzI4DeKyEhATdcsst2rFjh/z9/TVz5kzdf//9ZscqVcnJyYqIiFDFihV15swZs+MAAMAEPgAAAADPtnnzZgUFBVHeA/+QYRiaOHGiatSooR07duiWW25RSkpKmSvvJalixYoaN26czp49q+eee87sOAAAMIEPAAAAwHM5HA75+vqqffv22rBhg9lxAI/z448/qk+fPkpKStIVV1yhZcuWKSoqyuxYpjIMQ+Hh4crJyVFqaqoCAwPNjgQAKMOYwAcAAADgsVasWCGXy6VevXqZHQXwKOfPn9ctt9yi9u3bKzk5WRMnTlRCQkKZL+8lyWq16p133lFhYaGio6PNjgMAKOOYwAcAAADgse644w59+umnOnfunCpWrGh2HMAjzJs3T+PGjVNeXp6aN2+ur776SlWrVjU7lttp1qyZdu/erZ07d6pZs2ZmxwEAlFEU+AAAAAA8VvXq1ZWVlaWMjAyzowBu7/jx47rlllu0f/9+BQQEaN68eRo4cKDZsdzW0aNHVb9+fdWtW1eHDx82Ow4AoIxihQ4AAAAAj2QYhhISEtS0aVOzowBuzTAMjRkzRvXq1dP+/fvVt29fpaWlUd7/ibp16yo6OlpHjhzR/PnzzY4DACijKPABAAAAeKSVK1fK5XKpZ8+eZkcB3NbatWtVqVIlzZo1S1WqVFFsbKyWLl0qX19fs6N5hPfee0/lypXT2LFj5XA4zI4DACiDKPABAAAAeKQPP/xQkjRkyBCTkwDuJzMzU127dlXXrl2VkZGhp556SqdPn1arVq3MjuZRfH19NW3aNOXk5GjkyJFmxwEAlEHswAcAAADgkWrUqKGMjAxlZmaaHQVwKzNmzNDEiRNVUFCgtm3basWKFapUqZLZsTxarVq1dOrUKR0/flw1a9Y0Ow4AoAxhAh8AAACAxzEMQ/Hx8ey/B37n4MGDql+/vsaPHy8/Pz8tXbpUP/30E+V9Mfj0009lGIb69OljdhQAQBlDgQ8AAADA46xatUoul0u33nqr2VEA0xmGofvuu08NGzbU0aNHNWjQIKWmpqpv375mR/MarVu31o033qht27bp66+/NjsOAKAMYYUOAAAAAI8THR2tjz76SImJiYqIiDA7DmCalStXatCgQcrIyFCtWrW0YsUKNWnSxOxYXik9PV2VKlVScHCwkpKSZLUyEwkAKHn8tAEAAADgcWJiYhQYGEh5jzIrNTVVHTp0UM+ePXX+/HlNnTpVx48fp7wvQSEhIZo4caJSUlL01FNPmR0HAFBGMIEPAAAAwKMYhiEfHx+1adNGmzdvNjsOUOpeeuklTZ48WQ6HQx07dtTy5csVEhJidqwywTAMVa5cWRkZGTp37hx/7gCAEscEPgAAAACPsmbNGhmGwf57lDm7du1SrVq1NGnSJAUGBurrr7/WDz/8QIlciqxWqxYsWCCHw6EBAwaYHQcAUAYwgQ8AAADAo7hcLiUkJCgsLEzlypUzOw5Q4hwOhwYPHqwlS5bIYrHo/vvv19y5c9nBbqJWrVpp27Zt2rp1q1q3bm12HACAF6PABwAAAOBxXC6XLBaL2TGAErd06VINGTJE2dnZuvLKK7Vy5UrVr1/f7FhlXlxcnGrVqqXIyEgdP37c7DgAAC/Gx/UAAAAAPA7lPbxdUlKSWrdurf79+6ugoEAzZ87UwYMHKe/dRM2aNXXPPffoxIkTmjNnjtlxAABejAl8AAAAAADcyOTJkzVlyhQ5nU7deOON+uyzzxQYGGh2LPwPh8Oh4OBguVwupaeny9fX1+xIAAAvxAQ+AAAAAABuIDY2VlWrVtXzzz+vkJAQrV27Vt9++y3lvZuy2+2aMWOGcnNzdd9995kdBwDgpZjABwAAAADARHl5eYqOjtYXX3whq9WqBx98UK+99hoXqfUQ9erV07Fjx3T06FHVrl3b7DgAAC/DbwMAAAAAAJjk/fffV1hYmL744gs1adJEx44d04wZMyjvPcjSpUvlcrl0++23mx0FAOCF+I0AAAAAgFvLzc01OwJQ7E6fPq2rr75a99xzj1wul9555x3t3r1bkZGRZkfD39S8eXP16NFDO3fu1Oeff252HACAl6HABwAAAOCWnE6nJOmFF15QRkaGyWmA4mEYhiZMmKDIyEjt2rVLvXr1UlpamoYOHWp2NPwLH3/8sXx8fDR06FAZhmF2HACAF6HABwAAAOCWbDab9u/fr5deeknBwcFyOBxyOp367TJeTqdTJ06cMDck8Dds2rRJERERevXVV1W5cmXFxMRo+fLl8vf3Nzsa/qXAwEA98cQTSktL06OPPmp2HACAF+EitgAAAADczvz58/XLL7/o3LlzSkhI0IYNG/5wn9WrV2vx4sX64IMPTEgI/HXnz59Xv379tGrVKtlsNv3nP//RlClTzI6FEnDFFVcoJSVFSUlJCgsLMzsOAMALMIEPAAAAwO2cO3dOBw4c0Keffqr9+/erc+fOGjNmjD7++GOdPn1akvTRRx8pLy/P5KTA5b311lsKCwvTqlWr1KJFC8XFxVHee7FFixbJ6XSqX79+ZkcBAHgJJvABAAAAuJ3s7GydOXNGAwYMUPfu3ZWdna2DBw8qMTFRTqdTaWlpstvtev/999WpUyez4wJ/cPz4cfXo0UMHDhxQ+fLl9fbbbys6OtrsWCgF1157rbZs2aJNmzYpKirK7DgAAA9HgQ8AAADAbW3fvl0tWrRQTk6O4uPjdfz4cZ08eVLZ2dlq2bKlOnToIKuVLxbDfRiGoQcffFBz586Vy+VS//79tWjRIvn6+podDaUkISFBNWrUUNWqVXXq1Cmz4wAAPBwFPgAAAAC34nQ6ZbPZtGTJEmVkZGjkyJF/uI9hGLJarXK5XLJYLCakBP5ozZo1uvPOO5Wamqrq1atr+fLlatGihdmxYIJRo0Zp7ty5mj59uh5++GGz4wAAPBgFPgAAAAC3NHXqVM2aNUtff/21mjZtKofDIbvdLkk6efKkXC6XatWqZW5IQFJmZqZuu+02rVu3Tna7XU8++aQmT55sdiyYyDAMBQcHy+FwKC0tTf7+/mZHAgB4KL5rCgAAAMAtTZgwQVFRUerZs6dOnDghl8ulLVu26JFHHlHDhg11/PhxsyMCev3111WpUiWtW7dO7dq1U2JiIuU9ZLVaNWfOHOXl5Wnw4MFmxwEAeDAm8AEAAAC4rcLCQvXt21dJSUmqU6eONm7cqMjISD3wwAPq16+ffHx8zI6IMmr//v269dZbdezYMQUFBWnhwoW67bbbzI4FN3PVVVfp0KFD2r9/vxo0aGB2HACAB6LABwAAAOA2fttpn5mZqfXr1+u7777Txx9/rHPnzsnPz0/ffvutOnTocMF9gdLkcDh0//336/3335ck3X333Xr33XeL1jsBv7d37141adJEjRo10t69e82OAwDwQKzQAQAAAOA2DMOQJD344IMaNWqU9u3bpw8//FDffPONWrZsqZ9//lnSrxe6pbxHafvyyy8VHh6uhQsXqnbt2tq9e7cWLlxIeY9Laty4sW6//Xbt27dPH3/8sdlxAAAeiAl8AAAAAG7njTfeUKdOndSsWbOi29566y1NnDhRc+fOVXR0tInpUNakpqaqZ8+eiomJkY+Pj6ZMmaIJEyaYHQse4vz58woLC5Ofn5/S0tJktTJLCQD46yjwAQAAALgth8NxwXTza6+9pkcffVQZGRkqV66ciclQVrzwwgt6+umn5XA41KlTJ33++ecKCQkxOxY8zMsvv6xHH31UY8aM0RtvvGF2HACAB6HABwAAAOB2Nm7cqG3btikhIUHXXnutbr/9dlksFhUUFOiLL77QHXfcYXZEeLkdO3aoV69eOnXqlEJDQ7VkyRJ1797d7FjwYFWrVtXZs2eVmJioypUrmx0HAOAh+N4WAAAAALfgdDolSfPmzdPYsWP1ySef6JVXXtHOnTtlsVi0d+9e/fLLL7r99ttNTgpvVlBQoDvvvFPXXHONTp8+rREjRig5OZnyHv/aRx99JMMw1KdPH7OjAAA8CAU+AAAAALdgs9kkSc8++6xGjBihmJgY1a1bV02aNJEk7d+/X08//bROnDhhYkp4s48//n/s3Xd4VHX+9vH3lBSSAEmoIYRepIoCoaMUabqAIB1BRAVBRUWpIkWpLiiIIiggICAgoIjA0hGkS+89lCSEkISQnpkzzx8+8FtWVJQkJ+V+XZfXwpkz59yzu0655zufswR/f3+WLFlC+fLlOXv2LF988YVmlkuaaNiwIQ0aNOCXX35h69atZscREZEsQiN0RERERETEdC6XC4vFwsmTJ2nQoAGRkZGEhYVRvnx5Tpw4QdGiRTlz5gxNmzbl+PHj5M6d2+zIko2Eh4fzzDPP8Ouvv+Lh4cGUKVPo16+f2bEkG7px4wYBAQEULFiQ0NBQs+OIiEgWoGUEIiIiIiJiOovFAvxWpAYGBnLz5k32799P0aJFKVy4MACHDx/G5XKpvJc0NWzYMIoWLcqvv/5K8+bNiYyMVHkv6aZAgQL079+fsLAwxo8fb3YcERHJArQCX0REREREMoU7H006depEqVKlCAkJwdfXlxkzZnD48GGGDx9OUFAQM2bMMDmpZAd79uyhbdu2hIeHU6BAAb777jsaNmxodizJAQzDwN/fn8TERKKjo/Hy8jI7koiIZGJagS8iIiIiIpmCxWLBYrHwzjvvsGvXLtauXct//vMfWrVqRevWrfHw8OCNN94wO6ZkcUlJSbRt25batWsTERHBW2+9RXh4uMp7yTBWq5VZs2aRkpJC165dzY4jIiKZnFbgi4iIiIhIppKamsqvv/7K4cOHuXz5MvHx8QQFBfHGG2/g5uZmdjzJwubOnUv//v1JTEykSpUqrF69mmLFipkdS3KoKlWqcOzYMY4dO0alSpXMjiMiIpmUCnwRERERETGVYRhYrVZOnTrFxIkT2b9/P8WLF8fPz4+CBQtSq1Yt6tati7+/v0ZNyD9y+fJlnnnmGY4ePUquXLmYMWMGPXv2NDuW5HBnz56lfPnylC1bltOnT5sdR0REMikV+CIiIiIiYiqHw4Hdbqdjx46Eh4dTt25dLBYL58+f58aNG6SkpHDr1i3eeustevfubXZcyUIMw2DgwIFMmzYNwzBo27YtixcvxtPT0+xoIgB07tyZJUuW8PXXX+tLJRERuS8V+CIiIiIikikEBATw7bff8sQTT9zdduHCBY4cOcK2bdvo1q0bNWrUMDGhZCU///wz7du3JzIykoCAAFauXEmtWrXMjiVyj6SkJPz8/LDb7URHR2O3282OJCIimYwuYisiIiIiIqaLjY2lbdu2JCUl3bO9VKlStG3blo8//ljlvTyQhIQEWrRowRNPPEF0dDTDhg0jNDRU5b1kSp6enkyYMIG4uDj69+9vdhwREcmEtAJfRERERERMc2f+/b59+xg0aBAOh4NJkyZRrlw58uXLZ3Y8yURcLhcWi+VP95kxYwZvvfUWycnJVK9endWrV1O4cOEMSijyzxUrVoxr165x5coVihQpYnYcERHJRLQCX0RERERETGO1/vaRZN++fVy+fJmzZ8/Sr18/BgwYwPvvv8/ixYvZvXs3CQkJJicVM0RFRVGhQgWWLVuGxWLhj9afpaam8uijj9KvXz/sdjvffvst+/fvV3kvWcbSpUsxDINnn33W7CgiIpLJaAW+iIiIiIhkCk6nk+vXr7N3717Wrl3LoUOHAIiPj+frr7/WCJ0cKDExkRdffJE9e/Zw9uxZbDbbffczDIOxY8dy/Phx5s+fj7u7ewYnFXl4TZo0YfPmzaxbt47mzZubHUdERDIJFfgiIiIiIpIpOZ1OwsPD2bNnD82bN8fb29vsSJKB7oxXunDhAg0aNKBz585Mnjz57vb/9SAjdkQys6ioKAoVKoSfnx8RERFmxxERkUxCI3RERERERCRTstlsBAYG0q5dO5X3Ocx/l/SxsbEMHTqUqVOncurUqfuW94DKe8ny/P39efvtt7lx4wajRo0yO46IiGQSWoEvIiIiIiKZQnx8PC6XCx8fH7OjSCZw5coVunTpQnx8PMWKFePHH3+kadOmrF+/3uxoIunGMAwKFCjA7du3iYyMJE+ePGZHEhERk2kFvoiIiIiIZArvvfceuXPn5ujRo2ZHEZM5nU7ef/993N3dWbduHf/+97/56KOP2LJlCzNnzgT4wwvaimRlVquVuXPnkpqaSufOnc2OIyIimYBW4IuIiIiISKZQpUoVTp8+TUpKitlRJAM5nc7fXZw2Ojqa8uXLM27cOF566SUA4uLiGDNmDLNmzeLChQv4+/ubEVckQzz22GMcOnSIAwcO8Nhjj5kdR0RETKQV+CIiIiIikimcPXuW0qVLmx1DMohhGMBv1zpITk4mIiICh8MBgK+vL8WKFSMsLOzu/j4+PnTr1g2LxcKIESMArcKX7Ov777/HYrHw3HPPmR1FRERMpgJfRERERERMd+LECZKTk2ncuLHZUSSD3LkY7eeff06lSpV49tlnadWqFRs3bsRisVC5cmUOHDjAiRMn7t7H19cXDw8PZsyYwZo1a3ThWsm2ihcvzvPPP8+FCxeYNWuW2XFERMREKvBFRERERMR0c+bMAeCFF14wN4ikqf9dIX9n1b1hGDgcDt577z0mTpzI8OHD+fzzzylTpgwvv/wye/bsoWfPnly/fp1PP/307v0vXrzIv/71L/r160dqamqGPhaRjPbll1/i5eXFW2+9dffXKSIikvOowBcREREREdOtX78eNzc3atasaXYUSUN3VshHR0cD/7fq3mq1Eh8fz3/+8x+mT59Or169KF68OAcPHsTpdBIXF0ejRo3o2LEjGzdupGzZsrRv3542bdpQqlQppk+fTps2bUx7XCIZwd3dncmTJ5OQkHD3WhAiIpLzqMAXERERERHTnTlzhpIlS5odQ9LIndXxhmHw1ltv8eSTTwKwcuVKXnjhBSIiIggJCSFXrlw88cQTjB49mmLFilG6dGl2795NkyZNAHjzzTdZu3Yt3bt3J2/evKxcuZKhQ4ea9bBEMlzfvn0pWbIk8+fPJyQkxOw4IiJiAotLV/0RERERERETnT59mkceeYS+ffsyY8YMs+PIQ5ozZw5ff/01mzZtws3NjZMnT1K9enXKly/PsWPHmDp1Kv369SM2NpYCBQqQJ08eAgMDGTduHK1atQLg/PnzbNmyhbZt25I/f36TH5GIuQ4cOED16tV57LHHOHDggNlxREQkg2kFvoiIiIiImGr27NmA5t9nFw0bNuT999/Hzc0NgPDwcJKSkjh9+jQnTpy4O78+T548DB48mISEBFavXn23vHc6ncyePZvdu3dr7rcI8Pjjj9OiRQsOHjzIqlWrzI4jIiIZTCvwRURERETEVNWqVeP48eO6KGk24HQ6sdlsAJw8eRI/Pz88PT3ZuHEjI0eOpGbNmnz99de4XC4sFgsXLlzgySefpFy5cjz55JM8+uijfPTRR1y5coWZM2fSrFkzkx+RSOYQGxtL/vz5yZ07Nzdu3Lh7PQkREcn+VOCLiIiIiIipcuXKRVBQEGfOnDE7ivxD/13cAyQmJvL4449TsmRJ1qxZA8CyZcvo1KkTmzZtolGjRnfvc+rUKSZNmsThw4dxc3OjUqVKzJw5E7vdbtbDEcmU3n//fT744AOGDBnC+PHjzY4jIiIZRAW+iIiIiIiY5ty5c5QtW5aXX36ZWbNmmR1H/oE7q+kBli9fzpIlS+jWrRt+fn40a9aMZcuW8a9//YuEhAR69+7NwYMHOXXq1N37G4aB1WolLi6OxMREChQoYNZDEcnUDMOgcOHCREdHExERgZ+fn9mRREQkA+g3VyIiIiIiYpqvvvoKgJ49e5qcRP4pi8VCfHw8HTt25KWXXqJq1arYbDYaNmxIx44deffdd4mKisLLy4v33nuPqKgo+vfvz9mzZ+nTpw/Dhg0DwMfHR+W9yJ+wWq3MmzcPh8NBhw4dzI4jIiIZRCvwRURERETENI899hjHjh3T/PssbsGCBUyfPp358+dTvnz5u9vDw8OpVKkSffv2ZezYscBvo3QGDBiAzWajWLFirFy5koIFC5oVXSTLCQ4OZt++fezevZtatWqZHUdERNKZCnwRERERETFNrly5KFq0KGfPnjU7ivwDd8bnPP/889y4cYM1a9bcvbimw+HAbrczc+ZMBgwYwK5du3jssccAOH78OE6nk6pVq5oZXyRLunr1KsWLF6do0aKEhISYHUdERNKZRuiIiIiIiIgpLly4QFJSEk888YTZUeQfujP7/urVqwQGBmK1WnE6nQB3L0Lbq1cvGjVqRO/evUlISACgUqVKKu9F/qGiRYvy4osvcvnyZT799FOz44iISDpTgS8iIiIiIqaYPXs2AC+88IK5QeShdejQgaVLl3Lt2jVsNtvd7cePH2flypVMmDCBPHnykJycbGJKkexjxowZeHt7M2jQIFJSUsyOIyIi6UgjdERERERExBTVq1fn8OHDOBwOs6PIQ4qOjqZ58+Z4eXkxffp0SpYsyfXr13nnnXfInz8/U6ZMwcfHx+yYItnKokWLyJs3L61atbr7axgREcl+VOCLiIiIiIgpvLy8CAgI4Pz582ZHkb8hISEBd3f3uyNy7ggJCaFVq1bcvHmTcuXKcfToUerUqcPixYvJmzevSWlFREREsjb7X+8iIiIiIiKSti5dukRiYqLm32chhmHQp08ftm/fztGjR393e/HixVm/fj2nTp3i9OnTjBkzhieffDLjg4qIiIhkIyrwRUREREQkw92Zf9+zZ0+Tk8iDWLNmDd26dSMmJobixYtz8+ZNChcu/Lv9AgMDCQwMpEmTJiakFJE/4nK5NGZHRCSL0kVsRUREREQkw61duxabzUaDBg3MjiJ/IiYmhoYNG/L0008TFxfHuHHjuHTp0n3LexHJfMLDwwFU3ouIZGEq8EVEREREJMOdPHmSYsWKYbXqI0lmNWnSJAoWLMj27dupX78+169fZ+jQoWbHEpEHkJiYyJQpUwgODmbv3r3Ab6vwRUQk69G7ZRERERERyVCXL18mISGBhg0bmh1F7uPYsWOULFmSwYMH4+XlxY8//sj27dvx9/c3O5qI/AXDMPjhhx9o0KABo0ePxmq1snv3bpxOJxaLBcMwzI4oIiJ/k2bgi4iIiIhIhpozZw4Azz//vMlJ5L85HA5eeOEFFi1aBEDv3r2ZNWuWfiUhkkWcPHmSQYMGcePGDYKDgxkyZAhJSUmsXbuWkydPMmPGDLMjiojIP2Bx6TdUIiIiIiKSgWrWrMmBAwdITU1VOZxJLF++nF69enH79m3Kli3Ljz/+SPny5c2OJSIPwDAM3nzzTbZu3UrVqlV55plnaNmyJXnz5gVg//79NGnShKNHj1KsWDGT04qIyN+lFfgiIiIiIpKhTpw4ofn3mURERAT/+te/2Lt3L+7u7nzyyScMGDDA7Fgi8jdYrVYeffRRChYsyPPPP0/x4sXvuf3ChQvkzZuXyMhIFfgiIlmQCnwREREREckwV69eJSEhgQYNGpgdJccbNWoUH374IU6nkyZNmrBixQry5MljdiwR+Qd69+79u20JCQnMmzePzz77jDp16vD444+bkExERB6WCnwREREREckwmn9vvv3799O2bVuuXbuGv78/S5cupUmTJmbHEpE0tHbtWubNm8f169epW7cuQ4YMMTuSiIj8Q5qBLyIiIiIiGaZWrVrs379f8+9NkJKSQteuXVm+fDlWq5V+/foxdepU/e8gko1cuHCB8ePHc+rUKfLly0elSpUoU6YM165dY+fOnTz11FPUr1+fmjVr4nQ6sdlsZkcWEZG/oHdqIiIiIiKSYY4fP05QUJBK4wy2cOFC/Pz8WL58ORUrVuTChQt8+umn+t9BJJtJTU3lwIEDNGnShMGDB2O1Wpk+fTpjxoyhffv2bNmyhW7duhEWFobNZsMwDLMji4jIX9C7NRERERERyRChoaHEx8dTv359s6PkGKGhoTz22GN0794dwzD48ssvOX78+O8uciki2UP58uWZO3cuo0aNYtOmTUyZMoUmTZrg6elJq1atWLVqFTVq1ODVV18F0Jd4IiJZgJ6pRUREREQkQ9yZf9+tWzeTk2R/hmEwaNAggoKCOHToEE8//TQ3b97kpZdeMjuaiKSzqlWrArBo0SImTZrEpEmT6N27N08//TQAL774IiEhIVy/ft3MmCIi8oBU4IuIiIiISIb46aefsFqtNG/e3Owo2dovv/xCQEAAH330EQUKFGDHjh2sXr0aLy8vs6OJSAY5d+4cuXLlokyZMgAMHTqUiIgI1q9fz4ULF/D29qZAgQImpxQRkQehAl9ERERERDLEsWPHCAwM1MiGdJKQkMDTTz9N/fr1iYyMZNCgQYSGhlKvXj2zo4lIBitTpgwpKSkcOXIEgIIFCzJixAhatGjB+PHj6dKlyz3PxZqFLyKSeemds4iIiIiIpLvw8HDi4uI0/z6dfPXVV+TLl481a9ZQrVo1rly5wsSJE/VliUgONnr0aCZPnsyBAwdwOBz06dOHfv360blzZ55//nlSU1MZOHAgCQkJeq4QEcnE9AwtIiIiIiLpbu7cuYDm36e1kJAQKlWqxMsvv4zVamXBggUcPHiQIkWKmB1NREzWrl07nn76aYYPH87GjRsB+OCDDxg5ciR58uQhPj6eb7/9lt27d5ucVERE/ozF5XK5zA4hIiIiIiLZW926ddmzZw+pqala6ZkGDMNgwIABfP755xiGQfv27Vm0aBHu7u5mRxORTOTWrVscO3aMEiVKEBgYCIDT6cRms/H222+zatUqdu/eTf78+U1OKiIif8RudgAREREREcn+jh07RpEiRVTep4FNmzbRsWNHoqKiCAwM5Pvvv6dGjRpmxxKRTChv3rxUr16dvXv34nA4yJcvH25ubsTHx3Pw4EHefPNNlfciIpmcCnwREREREUlXERER3L59m1atWpkdJUuLjY2lXbt2bNq0CZvNxsiRIxk1apTZsUQkk/Pw8KBTp054eHgQExNDzZo1OXjwIFFRUZQtW5bRo0dTrlw5ypYtS40aNXA4HNjtqotERDILPSOLiIiIiEi6ujP/vmvXriYnybqmTp3KoEGDSElJITg4mB9//JGCBQuaHUtEsgCLxcLhw4cJDw/n9u3b7Nixg59//pnGjRtjt9tZu3Yt3333HQUKFGDz5s0q70VEMhk9K4uIiIiISLr68ccfsVgsPPPMM2ZHyXJOnz7NM888w7lz5/Dx8WHRokW0b9/e7FgiksUULFiQ/PnzY7VauXnzJo8++igLFiwgICAAgISEBKKjowkJCaFIkSJs3ryZ5s2bm5xaREQANIBSRERERETS1dGjRzX//m8yDIMXX3yRChUqcP78ebp160Z0dLTKexH5x+48B//73/+mVq1aBAQE4HA4APDy8sLlctG1a1f69u3Lyy+/zI8//mhmXBER+f+0Al9ERERERNJNZGQksbGxWsn5N6xevZru3btz69YtSpQowY8//kjlypXNjiUi2cCWLVvYuXMnU6ZMAbg7LufYsWNs3LiRI0eOcOzYMcaOHUuLFi3MjCoiIv+fCnwREREREUk3d+bfd+nSxeQkmV9UVBRt2rRhx44duLm5MXHiRAYNGmR2LBHJRmrUqMFrr71GjRo1AAgPD2fdunV8//33nD59mvbt27NixQoqVqyIm5sbqampuLm5mZxaRCRns7hcLpfZIUREREREJHtq2LAhO3bsICUlRRdG/BMTJkxgxIgROBwOGjZsyA8//ICvr6/ZsUQkG7p9+za5c+fmwIEDzJkzh61bt1KsWDHeeecdGjduzL///W/mz5/PkSNHzI4qIiKowBcRERERkXSUN29efHx8uHbtmtlRMqUjR47QunVrQkJC8PX1ZeHChbRq1crsWCKSA3Tq1ImdO3cyevRoXnzxxbvbDcNg3bp1NGzYEB8fHxMTiogI6CK2IiIiIiKSTqKiooiNjaVOnTpmR8l0HA4H3bp149FHH+Xy5cu8/PLL3Lx5U+W9iGSIS5cusW/fPubPn3+3vHc6ncBvF7tt1aqVynsRkUxCBb6IiIiIiKSLr7/+GoDOnTubGyST+e677/Dz82PRokWULVuW06dPM2vWLKxWfTwTkYxRokQJihUrxldffQX8tureZrOZnEpERO5H7xBFRERERCRd/PDDD1gsFtq2bWt2lEwhIiKC4OBgOnToQEpKCp9++ilnzpyhbNmyZkcTkRzo66+/xmq1Ehsbqy8QRUQyMc3AFxERERGRdOHr64uXlxehoaFmRzHd+++/z7hx43A6nTz11FOsWLFC4ylExHQ3b94kX758ZscQEZE/YTc7gIiIiIiIZD8xMTHcunWLxo0bmx3FVPv27aNt27aEhoaSL18+li5dmuP/OxGRzEPlvYhI5qffSImIiIiISJqbN28ekHPn3ycnJ/Pss88SHBxMeHg4b7zxBhERESrvRSTTcrlcGIbB119/TUxMjNlxRETk/9MIHRERERERSXONGjVi27ZtpKSkYLfnrB/+zp8/n759+5KYmEjlypVZvXo1xYsXNzuWiMhf2rx5M02aNKFZs2b85z//MTuOiIigAl9ERERERNKBn58fnp6ehIWFmR0lw1y9epWnn36aI0eO4OnpyfTp0+ndu7fZsURE/pYaNWrw66+/snfvXmrWrGl2HBGRHE8jdEREREREJE3FxMQQExNDrVq1zI6SIQzDYODAgRQvXpwjR47QunVroqOjVd6LSJa0YsUKLBYLHTt2NDuKiIigAl9ERERERNLYggULAOjUqZPJSdLfjh07KFy4MFOmTKFgwYLs3LmTH374AU9PT7OjiYj8I8WKFeOFF17g0qVLfP7552bHERHJ8TRCR0RERERE0lSTJk3YvHkzycnJuLu7mx0nXSQkJPDcc8+xdu1abDYb7777LuPHjzc7lohImnA4HOTNmxeXy0VMTEy2fS4XEckKtAJfRERERETS1MGDBylUqFC2LXxmzpxJvnz5WLt2LY8//jiXL19WeS8i2YrdbmfatGkkJiby4osvmh1HRCRH0wp8ERERERFJM7GxseTNm5fWrVvzww8/mB0nTV28eJFWrVpx6tQpvL29+fLLL+nSpYvZsURE0k2ZMmW4cOEC58+fp2TJkmbHERHJkbQCX0RERERE0sw333wDQIcOHUxOknYMw6Bfv36ULl2aU6dO0aFDB6KiolTei0i299133+FyuXj22WfNjiIikmOpwBcRERERkTSzYsUKADp27GhykrSxYcMGChQowIwZMwgMDOTXX39l6dKl2XY8kIjIf6tWrRpPP/00hw8fZuXKlWbHERHJkTRCR0RERERE0ky+fPmw2+1cv37d7CgPJTY2lrZt27JlyxbsdjsjRozg/fffNzuWiEiGi4uLw9/fHx8fHyIjI7FatRZURCQj6VlXRERERETSRFxcHFFRUdSsWdPsKA/lk08+oUCBAmzZsoXatWsTFham8l5EciwfHx/ee+89oqOjGTx4sNlxRERyHBX4IiIiIiKSJhYuXAhk3fn3p06dokyZMrz11lt4eHiwYsUKdu3aRf78+c2OJiJiqvfff5+CBQvy8ccfc/PmTbPjiIjkKCrwRUREREQkTSxfvhyATp06mZzk73E6nfTq1YuKFSty4cIFevToQVRUlC7aKCLyXxYuXIjT6eS5554zO4qISI6iGfgiIiIiIpIm8ufPj9VqJSIiwuwoD2zVqlU8//zzxMbGUqpUKVatWkWlSpXMjiUikinVqVOH3bt3s2PHDurVq2d2HBGRHEEr8EVERERE5KElJCRw8+ZNatSoYXaUBxIVFUW9evVo06YNiYmJ/Pvf/+b8+fMq70VE/sTy5cuxWq107tzZ7CgiIjmGCnwREREREXloWWn+/dixYylUqBA7d+7kySefJCIigoEDB5odS0Qk0ytSpAivvPIKV69eZcqUKWbHERHJETRCR0REREREHlqLFi34z3/+Q2JiIp6enmbHua/Dhw/TunVrLl++jJ+fH4sXL6Z58+ZmxxIRyVIMwyBv3rykpqYSExOTaZ/zRUSyC63AFxERERGRh7Z//37y58+fKYuc1NRUOnfuTLVq1bhy5Qp9+vQhMjJS5b2IyD9gtVqZMWMGycnJ9OjRw+w4IiLZnlbgi4iIiIjIQ0lISMDb25vmzZuzbt06s+PcY8mSJfTu3Zv4+HjKly/PTz/9ROnSpc2OJSKS5T3yyCOcOXOGkydPUr58ebPjiIhkW1qBLyIiIiIiD2Xx4sUAPPfccyYn+T/h4eHUqFGDzp0743A4+Oyzzzh16pTKexGRNLJixQpcLhft2rUzO4qISLamAl9ERERERP62kydP0q1bNyZPnsycOXMA6Nq1q8mpfjN8+HCKFi3Kr7/+SvPmzYmMjKRfv35mxxIRyVYqVqzIs88+y4kTJ+5+kSsiImlPI3RERERERORv+/7773n22Wfv2dayZUuaNm1K//798fDwyPBMe/bsoW3btoSHh1OgQAG+++47GjZsmOE5RERyioSEBPz9/fHw8CA6OhqrVetERUTSmp5ZRURERETkb6tdu/bvtq1du5aBAwfy66+/ZmiWpKQk2rZtS+3atYmIiOCtt94iPDxc5b2ISDrz8vJizJgxxMbGMmDAALPjiIhkS1qBLyIiIiIi/0hQUBBXr169+3eLxcJrr73GtGnTMizDvHnzePXVV0lMTKRKlSqsXr2aYsWKZdj5RUQEAgMDCQ8PJywsjIIFC5odR0QkW9EKfBERERER+UcaNGhw989Wq5WWLVvy8ccfZ8i5L1++TNWqVXnhhRcAmDt3LkeOHFF5LyJigsWLF2MYhi5oKyKSDlTgi4iIiIjIP1K3bt27f65YsSJLlizBZrOl6zkNw+Dtt9+mZMmSHD16lDZt2hAVFXW3yBcRkYzXsGFDGjRowC+//MLmzZvNjiMikq1ohI6IiIiIiPwju3fvpk6dOri5uXHhwgWKFi2aruf7+eefee6557hx4wYBAQGsXLmSWrVqpes5RUTkwdx5bi5QoABhYWFmxxERyTa0Al9ERERERP4Ri8UCwPjx49O1vE9ISKBFixY88cQTREVFMWzYMEJDQ1Xei4hkIgUKFOC1114jPDyc8ePHmx1HRCTb0Ap8ERERERH5HYfT4GxEHEev3eLYtVucCIslNimVVKcLN5uFPJ5uuMVdZ9N3X7Nx6RwqBvpht6X9+qAZM2bw1ltvkZycTPXq1Vm9ejWFCxdO8/OIiMjDMwwDf39/EhMTiY6OxsvLy+xIIiJZngp8ERERERG561JkPAv3hrB47xXikh0A2K0WHMbvPzb893YfDztdgoPoFlycEvm9HzrH+fPnefrppzl9+jTe3t7Mnj2bTp06PfRxRUQkfS1btoyOHTvSpk0bvv/+e7PjiIhkeSrwRURERESEg5ejmbzhDDvORWKzWHD+g48Jd+5Xv0x+3mlWnmpBvn95n3nz5lGnTh3KlSsH/LZ6s1+/fsyaNQuXy0WnTp2YP38+7u7ufzuPiIiYo0qVKhw7doyjR49SuXJls+OIiGRpKvBFRERERHKwpFQnH288w6yfL2C1WnDeZ6X932WzWjAMF680LMVbTcvh6Wa7737r16+nefPmVKpUiUOHDrFp0ya6dOlCdHQ0QUFBrFq1imrVqj10HhERyVhnz56lfPnylClThjNnzpgdR0QkS1OBLyIiIiKSQx29dovXFx8gJCqB9PhUYLFACX8vpnV5nCqBee+5LSkpiQoVKhASEoLL5aJUqVJcuHABu93OqFGjGD58eNoHEhGRDNO5c2eWLFnC119/Tc+ePc2OIyKSZanAFxERERHJgXaci6T3vH04nK5/NC7nQdksFuw2C7N71qR+mfx3t48aNYoxY8bw3x9Hqlevzvr16/H390+3PCIikjGSk5Px9fXFbrcTHR2N3W43O5KISJZkNTuAiIiIiIhkrB3nInlh7l5SnUa6lvcATpeLVKfBC3P3suNcJABnzpzhww8/vKe8t1qtFC5cGD8/v3TNIyIiGcPDw4MJEyYQFxdHv379zI4jIpJlaQW+iIiIiEgOcvTaLZ77YicpTiNdxub8EasF3GxWlrxci5a1KhEeHn7P7RaLBZfLxc8//0yDBg0yLpiIiKSrYsWKce3aNUJCQihatKjZcUREshz9fklEREREJIdISnXy+uIDOJyuDC3vAQwXpDoN2k9eTfiNmwB4e3tTuHBhAgMDKVKkCIGBgZQrVy5jg4mISLpatmwZtWvXpl27duzdu9fsOCIiWY5W4IuIiIiI5BDj155k1vYLGV7e38Nl0LSohc9efgoPDw8Tg4iISEZp0qQJmzdvZt26dTRv3tzsOCIiWYoKfBERERGRHODg5WjazdhJZnjzb7HAylfrUS3I1+woIiKSAaKioihUqBB+fn5ERESYHUdEJEvRRWxFRERERHKAyRvOYLVazI4BgNViYfL602bHEBGRDOLv78/bb7/NjRs3GDVqlNlxRESyFK3AFxERERHJ5i5FxvPk5K1mx/idbe88SfF83mbHEBGRDGAYBgUKFOD27dtERkaSJ08esyOJiGQJWoEvIiIiIpLNLdwbgs2SOVbf32GzWFi457LZMUREJINYrVbmzp1LamoqnTp1MjuOiEiWoQJfRERERCQbczgNFu+9gjOT/fDW6XKxeO9lHE7D7CgiIpJBWrduTbVq1Vi3bh0HDhwwO46ISJZgNzuAiIiIiIikn7MRccQlO+57W8z2hdz6ZfE92+z+RQl85QsAnHHRRG+ZQ+Klg7hSEnHzL0qeOh3xfqQeAI6Y68Ts/JakkCMY8dHYfPzxrtSIvHU7YrG5/WW228kOzt2I45HCGqMgIpJTfP/995QsWZIOHTpw/vx5s+OIiGR6KvBFRERERLKxo9du/entbvmLUajz2P/bYP2/H+lGrp6CkRxHwfYjsHrlJf74ViJ/mIib78e4Fy5NatRVcLnI16I/dr8ipN4I4ebaT3GlJuHXuPcD51OBLyKScxQvXpznn3+e+fPnM3PmTPr06WN2JBGRTE0jdEREREREsrFj125ht/7J/HurDZuP3//945X37k3J106Su/q/8ChSHjffwvjW64zVw5vk6+cAyFWqOvmffpNcJR/HzbcwXmVrkafWsySc3vlA2exWC8f+4gsGERHJfr788ku8vLx4++23SUlJMTuOiEimpgJfRERERCQbOxEWi8P44/n3juhQrk7vwbUZvbmx6iMctyLu3uYRWIGEk9txJt7G5TKIP7ENlzMFz2JV/vB4RnIC1ly5Hyibw3BxPDT2wR+MiIhkC+7u7kyZMoWEhARefvlls+OIiGRqFpcrk13NSkRERERE0kyzT7Zx5nrcfW9LPL8fIzUJN/9AnHFR3PplMY7bNynS+zOsHl4YSXHc+GEiSRcPgtWGxc2DAm2HkKvk4/c9Xmp0KGFfv4lfoxfJXa3FA+UrXyg3/3mz4T9+fCIiknWVKlWKS5cucfHiRYoXL252HBGRTEkr8EVEREREsrFU5x+v18lVugbej9THvWBJcpWqTsEOozCS44k/tQOAmJ+/wUiKp2DnDwno+TF5arblxvcTSYm49LtjOW5HErFkJN7l6z9weQ+Q4jT+9mMSEZHs4bvvvsPlcvHss8+aHUVEJNNSgS8iIiIiko252f5k/v3/sHr64OYXiCM6lNToMG4fWE2+VgPIVaIa7oVK4Vu/Kx6Fy3D7wOp77ue4fZPri4bhEfgI/i1f+1v53G36SCIiklM9/vjjtGjRgoMHD7Jq1Sqz44iIZEp6tywiIiIiko3l8XR74H2NlEQcMWHYfPxxpSYDYLH8z0cGqxX+awqn43Yk1xcNxb1wGfI9/ebv9/8LuT3tf2t/ERHJXpYsWYKbmxsvvPAChqFfZYmI/C8V+CIiIiIi2VjFgDzYrfdfhR+9eTZJl4/iiLlO0tWT3FgxFixWvCs+gVu+otj9Ari5bjrJoadJjQ4jds8Kki4ewqtcbeD/ynt7ngL4NX4RIyEWZ1w0zrjoB8pmt1qoVCRPmj1WERHJevLkycPQoUOJjo5m6NChZscREcl0dBFbEREREZFsbOn+KwxafuS+t934YSLJV47jTIzF5pUXj6IV8W3YAze/AABSo64Rs3UeSVdP4EpNxO4bQJ5a7fCp3BiAuCMbubnmk/seu/iQ1ffd/r8+eq4qHaoH/f0HJiIi2YbL5aJQoUJER0dz/fp1/P39zY4kIpJpqMAXEREREcnGTobF0nLadrNj/KF1AxrwSGGtwhcRyenWrVtHy5YtadKkCRs3bjQ7johIpqECX0REREQkCzMMg4oVKxIXF0fhwoUJDAykcOHC+Pv7c+TIEeITErlc83UMm7vZUX8nt4edgyOewq4L2YqICBAcHMy+ffvYvXs3tWrVMjuOiEimoAJfRERERCQLc7lclClThgsXLtz39oCAAFoMmcm2cBvOTPTW32ax0Lt+SYa1qmB2FBERySSuXr1K8eLFCQwM5PLly2bHERHJFLTURUREREQkC7NYLHTv3v2+t9WrV4+rV6/yftfGmaq8B3C6XHSrVczsGCIikokULVqU3r17c+XKFaZOnWp2HBGRTEEr8EVEREREsqiFCxcyfvx4jh8/fs92m81Gw4YNWbduHe7uv43O6T57D7su3MRpmP/232a1ULdUPhb01ngEERG5l8PhwNfXF6fTSXR0NJ6enmZHEhExlVbgi4iIiIhkISEhIXTu3BkvLy+6d+/OyZMnqVevHvXq1cNms2Gz2ahcuTI//PDD3fIe4J1m5TEyQXkPYLhcDGxW3uwYIiKSCdntdj777DOSkpLo1auX2XFEREynAl9EREREJJMzDINPP/2U0qVLU6JECZYsWULevHl5//33iY+PZ8eOHQwbNgyn00nRokVZv349uXPnvucY1YJ8eaVhKSwWkx7E/2cB+jQsTbUgX3ODiIhIptWzZ0/Kli3LkiVLOHv2rNlxRERMpRE6IiIiIiKZ1LFjxxg0aBAbN24kNTUVu91OkyZNmDBhAtWqVbtnX6fTyeTJk+nQoQMlS5a87/GSUp20mPozV6ISTZmJ7zKcOGLCqRa2hqdbNCM4OJiqVave80sBERER+O01sEqVKlSuXJmjR4+aHUdExDQq8EVEREREMpGUlBTGjx/PzJkzCQsLA6BEiRIMGDCA119/HZvN9lDHP3rtFs99sZNUp0FGTtSxWgDDybWv3ybl+vm72+12O1WrVuWll17i1VdfzbhAIiKS6bVp04ZVq1axZMkSOnbsaHYcERFTqMAXEREREckE7ozB2blzJ06nE09PT5555hkmTZr0hyvq//G5zkXywty9GC5XhpT4VgtYLRbm9KxB18aPc+3atd/t0759e7777rv0DyMiIllGfHw8/v7+5MqVi6ioKKxWTYIWkZxHz3wiIiIiIiaJjY3lrbfeIn/+/DRo0IDt27dTrlw55s2bR3x8PMuWLUvz8h6gfpn8fN0rGDebFVs6D8W3WsDNZmVer2AalivIyJEjf7dPkSJFmDlzZrrmEBGRrMfb25tRo0Zx69YtBg4caHYcERFTaAW+iIiIiEgGW7VqFaNHj+bgwYO4XC5y585Nx44dGTduHAULFsywHEev3eL1xQcIiUogPT4VWCxQwt+LaV0ep0pgXgCSkpIICgoiMjLy7n65cuVi69atBAcHp30IERHJ8gICArhx4wahoaEZ+jopIpIZaAW+iIiIiEgGCA8P58UXXyR37ty0adOGgwcP8vjjj7Nq1SpiY2P56quvMryUqBKYl3UDGvJKg1JYLGCzps1qfJvVgsUCfRqWZu2AhnfLewBPT0/eeeedu39/5ZVXSElJoXbt2kybNi1Nzi8iItnLokWLcDqdPPfcc2ZHERHJcFqBLyIiIiKSTgzDYP78+UycOJFTp04BkC9fPnr27MmoUaPInTu3yQn/z8HL0UzZcIbt5yKxWSw4/8HHhDv3a1AmPwObladakO9997t16xblypWje/fuTJ48mZMnT1K3bl1iYmJo164dy5Yt05xjERG5R7169di5cyfbtm2jYcOGZscREckwKvBFRERERNLY+fPnGTx4MD/99BNJSUnYbDbq1avH2LFjqV+/vtnx/tSlyHgW7b3M4r2XuZ3sAMButeC4z9Vu/3t7bg87XYKL0a1WMYrn8/7L8yQnJ+Ph4XH370lJSdSvX59ff/2VkiVLsm/fPvLly5dGj0pERLK669evU6RIEQoXLnzfi6GLiGRXKvBFRERERNKAw+Fg2rRpTJs2jZCQEOC3i7P27duXwYMH4+7ubnLCv8fhNDh3I46j125x7NotjofGcjvJQYrTwN1mJbennUpF8lA5MC9VAvNSpoAPdtvDr5rv378/n3/+Obly5WL9+vWZ/gsPERHJOK+//jrTp09n4sSJDBo0yOw4IiIZQgW+iIiIiMhDOHjwIEOGDGHz5s04HA7c3Nxo1qwZEyZMoHLlymbHy5KWLl1Kt27dcDqdTJgwQSWNiIgAv42m8/PzIzk5maioKLy8vMyOJCKS7jRYUkRERETkb0pKSmLEiBEEBATw+OOPs379eooVK8b06dNJSkpi9erVKu8fQseOHTl58iT+/v4MHjyYZ555BsMwzI4lIiIms1qtzJw5k+TkZLp37252HBGRDKEV+CIiIiIiD2jz5s2MGDGC3bt3YxgGuXLlok2bNkyYMIHixYubHS/bSUlJ4YknnmD37t0EBQWxf/9+ChYsaHYsERExWaVKlThx4gQnTpygQoUKZscREUlXWoEvIiIiIvInYmJieP311/H396dJkybs3LmTihUr8s0335CQkMDixYtV3qcTd3d3du3axdtvv82VK1coVqwYmzZtMjuWiIiYbMWKFVgsFtq1a2d2FBGRdKcCX0RERETkPpYvX061atXw9/dn+vTpGIZBnz59uHHjBkePHqVbt25mR8wxJk+ezMqVKzEMg6eeeooPPvjA7EgiImKi8uXL0759e06dOsU333xjdhwRkXSlEToiIiIiIv9faGgoQ4YMYcWKFcTHx2OxWKhZsyajR4+mRYsWZsfL8UJCQqhZsyY3btygadOmrFu3DpvNZnYsERExQVJSEr6+vri5uXHr1i2sVq1RFZHsSc9uIiIiIpKjGYbBl19+Sfny5QkMDGTBggV4eXkxaNAg4uLi2LNnj8r7TKJ48eKEhobSsGFDNm7cSFBQENeuXTM7loiImMDT05Nx48YRFxdH//79zY4jIpJutAJfRERERHKk06dPM3jwYNatW0dycjI2m40nnniCcePGUatWLbPjyV8YPnw448aNw93dne+//56WLVuaHUlEREwQFBREaGgoV65coUiRIvz666+UKFGCfPnymR1NRCRNaAW+iIiIiOQYDoeDiRMnUqxYMR555BF++OEHChQowPjx40lKSmLTpk0q77OIsWPHsmbNGgBatWrF8OHDTU4kIiJm+PbbbzEMg2eeeYbWrVtTo0YN/v3vf5sdS0QkzWgFvoiIiIhke/v27WPo0KFs27YNh8OBu7s7LVq0YMKECVSoUMHsePIQrl69SnBwMGFhYTRs2JBNmzZht9vNjiUiIhkkPj6eihUrcvnyZaxWKy6XixdeeIE5c+aYHU1EJE1oBb6IiIiIZEsJCQkMHTqUQoUKERwczKZNmyhZsiQzZ84kMTGRH374QeV9NlC0aFEuX75M06ZN+fnnnwkMDCQkJMTsWCIikgF++eUXSpcuzZUrV4DfrmvjcrmIjo42OZmISNpRgS8iIiIi2cqGDRuoXbs2uXPnZsKECcTHx9O9e3euXLnCmTNneOWVV7Ba9TY4O7Hb7WzYsIHRo0dz48YNypYty6pVq8yOJSIi6ezYsWNcv34di8Vyz/bIyEiTEomIpD19chERERGRLC8qKopXX30VX19fmjVrxt69e6lSpQpLly4lLi6OBQsWULRoUbNjSjp7//332bBhA1arlTZt2vDOO++YHUlERNJRnz59WL9+PUWKFLmnxI+IiDAxlYhI2tIMfBERERHJspYsWcLYsWM5evQoAL6+vnTr1o0PPvgAPz8/k9OJWcLDwwkODubKlSvUqVOHrVu34u7ubnYsERFJJ3FxcQwbNoxPP/0UAA8PD5KSku7Zx+E0OBsRx9Frtzh27RYnwmKJTUol1enCzWYhj6cbFQPyUDkwL1UC81K2oA92m9a9ioj5VOCLiIiISJZy+fJlhgwZwg8//EBCQgJWq5VatWrxwQcf0KRJE7PjSSZhGAb/+te/WLNmDfny5WPPnj2ULl3a7FgiIpKOfvnlFxo3bkxKSgqhoaEEBARwKTKehXtDWLz3CnHJDgDsVgsO4/d12H9v9/Gw0yU4iG7BxSmR3ztDH4eIyH9TgS8iIiIimZ5hGHzxxRdMmTKF8+fPA1CoUCF69+7NiBEj8PT0NDmhZFaTJk1iyJAh2Gw2Fi5cSMeOHc2OJCIi6Sg0NJRKlSrxrxdex6j8NDvORWKzWHD+g/rrzv3ql8nPO83KUy3IN+0Di4j8BRX4IiIiIpJpHT9+nMGDB7NhwwZSUlKw2+00atSIcePGUaNGDbPjSRaxY8cOmjVrRmJiIv3792f69OlmRxIRkXSSlOrknXlb+Ol8MlarBed9Vtr/XTarBcNw8UrDUrzVtByebrY0SCoi8mBU4IuIiIhIppKSksKkSZP44osvuHbtGgDFixfn9ddfZ8CAAdjtdpMTSlYUGRlJzZo1uXTpEtWrV2fHjh365YaISDZz9NotXl98gJCoBNKj7bJYoIS/F9O6PE6VwLxpfwIRkftQgS8iIiIimcKuXbsYOnQoO3bswOl04uHhwdNPP82ECRMoW7as2fEkGzAMg+eee46VK1fi6+vLzp07qVChgtmxREQkDew4F0nveftwOF3/aFzOg7JZLNhtFmb3rEn9MvnT7TwiInfoctoiIiIiYpq4uDjeffddChQoQN26ddm2bRtly5Zlzpw5JCQksHz5cpX3kmasVisrVqzg448/5tatW1SpUoVvvvnG7FgiIvKQdpyL5IW5e0l1Gula3gM4XS5SnQYvzN3LjnOR6XouERHQCnwRERERMcGaNWsYOXIkv/76Ky6XCx8fH5577jnGjx9P4cKFzY4nOcCePXto3LgxCQkJ9O7dm6+++srsSCIi8g8cvXaL577YSYrTSJexOX/EagE3m5Xv+tbVOB0RSVcq8EVEREQkQ0RERDB8+HCWLl1KbGwsFouFatWq8f7779O2bVuz40kOFBMTQ3BwMGfPnqVKlSrs2rULb29vs2OJiMgDSkp10mLqz1yJSkz3lff3Y7VAcX8v1g5oqAvbiki60QgdEREREUk3hmHwzTffUKlSJQoVKsRXX32F3W5nwIABxMTEcODAAZX3YhpfX19OnTpF586dOXr0KEWKFOHIkSNmxxIRkQf08cYzhEQlmFLeAxguuBSVwCebzppyfhHJGVTgi4iIiEiau3jxIh07dsTHx4fnn3+eU6dOUb9+fbZt28bNmzf55JNPyJMnj9kxRbBarSxevJgZM2YQFxfHY489xuzZs82OJSIif+Hg5Whm/XwhQ8fm3I/LBTN/Ps+hKzHmBhGRbEsFvoiIiIikCcMwmDp1KqVKlaJUqVIsW7YMX19fRo4cSUJCAtu3b6dhw4ZmxxS5r759+7J//368vb156aWX6NGjh9mRRETkT0zecAar1WJ2DACsFguT1582O4aIZFOagS8iIiIiD+XIkSMMHjyYTZs2kZqaipubG02bNmXChAlUrVrV7Hgif0tcXBy1atXixIkTPPLII+zZs0e/FhERyWQuRcbz5OStZsf4nW3vPEnxfLqWioikLa3AFxEREZG/LSkpiVGjRlGkSBEeffRR1q1bR9GiRZk6dSpJSUmsWbNG5b1kST4+Phw/fpwXXniBU6dOERgYyK+//mp2LBER+S8L94Zgs2SO1fd32CwWFu65bHYMEcmGVOCLiIiIyAP7+eefadCgAd7e3owePZro6Gg6dOjAhQsXuHDhAm+88QZWq95iStY3d+5c5syZQ2JiIsHBwXz++edmRxIREcDhNFi894ppF679I06Xi8V7L+NwGmZHEZFsRiN0RERERORPxcbGMmLECL755huioqIAqFixIkOHDqVr164q7CVbO3bsGPXr1+fWrVt07NiRxYsX6//zIiImOhkWS8tp2/90H8ftSGK2fk3i+V9xOZKx+wWQr9WbeASUBSDh9E5uH1xLSvg5jKTbBPSahnuhUr87TvK1k0RvW0BK2GmwWHEvWIqCncZgdfP4w3OvG9CARwpr9JqIpB272QFEREREJHP64YcfGD16NIcOHcLlcpEnTx5eeuklxo4dS8GCBc2OJ5IhKleuTGhoKPXq1WPp0qX8+uuv7Nu3Dz8/P7OjiYjkSEev3frT251JcYQvGIRn8aoU7DgKq1deHNGhWD197u5jpCbhUbQiXhXqE7X20/seJ/naSa4vHUne2h3wf6oPFquNlIiLWCx//iXu0Wu3VOCLSJpSgS8iIiIid4WHhzN06FC+++474uLisFgsVK9endGjR9OqVSuz44mYwsvLi4MHD/Lqq6/yxRdfULRoUTZu3EidOnXMjiYikuMcu3YLu9WCw7j/QInY3d9hz5Of/E+/eXebm2/he/bxqdwYAEfM9T88T9Smr8hT/V/krdPh/46Tr+ifZrNbLRy7dosO1YP+6mGIiDww/fZTREREJIczDIO5c+fyyCOPEBAQwNdff42npyfvvPMOsbGx7Nu3T+W9CDBjxgwWLlxIcnIy9erVY8qUKWZHEhHJcU6Exf5heQ+QeHYP7oXLcmPleK5M60bonDe4fWjd3zqHMz6GlNDTWL18CV/wDlemdSd84RCSrhz/0/s5DBfHQ2P/1rlERP6KCnwRERGRHOr8+fO0b98eb29vXnzxRc6dO8eTTz7Jzp07uXHjBh999BE+Pj5/fSCRHKRr164cP34cX19fBg4cSJs2bTAMXbBQRCSjxCal/untqTHh3D64Brt/EQp1HEPux1sRvXEWcUc3PfA5HDHhANzasQifR5tTqONo3AuV5vq3w0mNuvan972d5Hjg84iIPAgV+CIiIiI5iMPhYPLkyZQoUYIyZcqwYsUK8uXLx4cffkhCQgJbtmzRWBCRv1C+fHlCQ0OpWbMmq1atolSpUkRGRpodS0QkR0h1/vHqewBcLjwKl8bviZ64Fy5N7mot8Hm0ObcPrnngc7hcv53D57EW+FR9CvfCpfFv+jJu/kWJO7LhT++b4tSXuiKStlTgi4iIiOQABw4coFmzZuTKlYt33nmHsLAwnnnmGY4dO8bVq1cZPnw47u7uZscUyTI8PT3Zu3cvb7zxBiEhIQQFBbFt2zazY4mIZHtuNsuf3m7z8cMtX7F775MvCGfsjQc+h83H7//f7/fHcfzFcdxtqtpEJG3pWUVEREQkm0pKSmL48OEULlyY6tWrs2HDBooXL85nn31GYmIiP/74I5UqVTI7pkiWNnXqVL777jscDgeNGjVi/PjxZkcSEcnW8ni6/entHkUrkhp19Z5tqVHXsOct+MDnsOcthM3HH8c/OE5uT/sDn0dE5EGowBcRERHJZjZt2kTdunXx9vZm3Lhx3L59m65duxISEsK5c+fo168fVqveBoqklfbt23P69Gny5cvHsGHDaNmypebii4ikk4oBebBb/3gVfp6abUgOPc2tnUtJjQ4l/vhW4g6vw+fxp+/u40y8Tcr1C6TevAxAatRVUq5fwBkXDYDFYiFPrfbE/voj8ad2kBodSszPC3BEXcWnarM/PLfdaqFSkTxp9EhFRH5jcd0Z7CUiIiIiWVZMTAzDhw9n0aJFxMTEAFClShWGDRtG586dzQ0nkkOkpKTQqFEjdu7cSWBgIPv376dw4cJmxxIRyVaW7r/CoOVH/nSfhHN7idk2j9SoUOy+hchTsy25q7W4e3vckY3cXPPJ7+6Xt14XfBt0u/v3W7uWcfvATxhJt3EvWBLfJ3vhGfTnv1786LmqdKge9PcelIjIn1CBLyIiIpKFfffdd3zwwQccPXoUl8tF3rx56dKlC2PHjsXf39/seCI50uDBg5k0aRIeHh78+OOPPPXUU2ZHEhHJNk6GxdJy2nazY/yhdQMa8EhhrcIXkbSj306LiIiIZDFXr16le/fu+Pj40KFDB44dO0ZwcDD/+c9/iImJYcaMGSrvRUw0ceJEVq1ahWEYNGvWjFGjRpkdSUQk2yhb0Acfj8w5Zz63h50yBXzMjiEi2YwKfBERERGTuVwu/upHkYZhMHPmTMqVK0dQUBALFy7E29ubIUOGcPv2bXbv3k2zZn88k1VEMta//vUvzp07R6FChRg9ejSNGzfG4XCYHUtEJMuz26x0CQ7CZvnjOfhmsFksdAkuht2mqk1E0paeVURERERM4HA4+Oabb9i/fz8WiwXLX3wInTZtGn379uXixYs0adKEvXv3cv36dcaPH4+Xl1cGpRaRv6NYsWJcvXqVRo0asWXLFoKCgrh8+bLZsUREsrxuwcVxZrKJ0E6Xi261ipkdQ0SyIRX4IiIiIhnozkr7EydOMH36dObPn49hGHzwwQesX7/+D+/Tq1cvJk6cSGJiIhs3bqRmzZoZGVtE/iG73c7mzZt5//33CQ8Pp2zZsqxevdrsWCIiWZqfm4OCrigsZI4S32a10KBMforn8zY7iohkQ7qIrYiIiIgJDMOgc+fOrFu3jsTERKpVq8ZXX33Fo48+anY0EUkn69evp3Xr1iQnJzNo0CAmTpxodiQRkUzL6XQyZcoUTp06RWRkJBEREURERHD16lVSUlLwLlaR/F0nAuaP0rFYYOWr9agW5Gt2FBHJhlTgi4iIiKQTwzB+Nx7n2rVrXLp0CYvFwttvv82pU6cYNmwYgwYNMjGpiGSUsLAwatasybVr16hXrx6bN2/G3d3d7FgiIplOamoqfn5+xMfH3/f29evXs99RlFnbL2Bms2UB+jxRmiEtHjEvhIhkaxqhIyIiIpJOrFbr72bb9+jRg88//5y6deuybNkynn32WbZu3QrwlxeyFZGsLyAggMuXL9OiRQt++eUXihYtysWLF82OJSKS6bi5ufHBBx/c97apU6fy1FNP8VbTchT39zLtgrY2C5TI58WbTcqacn4RyRlU4IuIiIikAcMwcDqd92y7fv06Y8eOZe/evXf/fvLkSV5++WUAgoKCqF69OhcuXODAgQNYLBYMw8jw7CKSsaxWK2vXrmXs2LFERkZSrlw5VqxYYXYsEZFMxeFwcOjQoXu2Wa1WqlevTv/+/QHwdLPxaZfHsdssWDO4w7dawG6zMq3L43i62TL25CKSo6jAFxEREXkId1bNW61WbLZ7P7zduHGDdevW0bRpU2bPns2OHTvIlSsXJUqUuLtP7dq1KVSoEN9++y2ACnyRHGTYsGFs3rwZu91O+/bteeutt8yOJCKSKSxcuBA/Pz/mz59P4cKF7263WCzMnTv3nvdcVQLzMrtnTayWjCvxrRawWizM6VmTKoF5M+akIpJjqcAXEREReQh3RuTs27ePbt268dRTTzFhwgSuXLlC5cqV2b59O0OHDmX27Nn06NGDxx9/nBIlSuBwOACoVKkStWvXZt26dSQlJWG32wkNDSU5OdnMhyUiGeTJJ5/k8uXLFC9enE8++YRatWqRlJRkdiwREVOcP3+eSpUq0b17d1JTU5k6dSphYWF07twZgMGDB1OlSpXf3a9+mfx83SsYN5s13cfpWC3gZrMyr1cw9crkT9dziYiALmIrIiIi8sAcDgd2u/132+fOncuoUaNo3LgxVatWZfHixeTPn5+JEyfe/ZB5/vx5ypYti8ViYcaMGbz44ot3j7V582b69OlDiRIlOH36NKVLl2b+/PkEBQVl6OMTEfMYhkG7du344Ycf8Pf3Z/fu3ZQtq5nKIpIzOBwOXnzxRb755htcLhfPPvssCxcuJFeuXABERETw5ZdfMnDgQDw9Pf/wOEev3eL1xQcIiUpIlwvbWixQwt+LaV0e18p7EckwKvBFRERE/oRhGFgslnsuRnvp0iUKFSpErly5iIiIoFOnTjz77LO88cYbAGzfvp0nn3ySwYMHM27cOJxOJ1FRUTRt2pSqVaty6NAhihUrxtChQ6lfvz4Ae/bsYfHixVSpUoXevXub8lhFxHxTpkzhnXfewWazMX/+fLp06WJ2JBGRdDVv3jz69+9PfHw8pUuXZsWKFVStWvUfHy8p1cnHG88wa/sFrBYLTuPhay+b1YLhctGnYWnebFJWM+9FJEOpwBcRERF5AMnJyXz77be8++67+Pr6UrlyZb744gtu375N/fr1uXz5MvPnz2fq1KlcvnyZFi1aMHjwYB599FGsVisLFixg3LhxHD58mDNnzjB+/HgWL17MggUL6Natm9kPT0QykV27dtG0aVMSEhLo27cvM2bMMDuSiEiaO336NG3atOH06dN4enoyefJk+vXrl2bHP3g5mikbzrD9XCQ2iwXnP6i/7tyvQZn8DGxWnmpBvmmWT0TkQanAFxEREfkTKSkpvPfeewDcvHmTJk2akC9fPnr06EH79u3p3LkzL730EufOnaNKlSp07dqV7t27ExgYCIDT6cRmszF8+HD27t3Lhg0b7h57586d1K1b15THJSKZW1RUFMHBwZw/f55q1arxyy+/4OXlZXYsEZGHlpKSQs+ePVmyZAkul4sOHTqwYMECPDw80uV8lyLjWbT3Mov3XuZ28m/XILJbLTjuszL/v7fn9rDTJbgY3WoVo3g+73TJJiLyIFTgi4iIiPyFdu3asW7dOt577z2GDRsGwKxZs1i4cCEtW7bk9OnTnD17lh07dty9j2EYLFmyhEKFChEcHEy5cuUYMmTI3TE7LpfrnrE8IiL/yzAMOnfuzLJly8ibNy+//PILlSpVMjuWiMg/9uWXX/Lmm2+SkJBA2bJlWblyZYY9rzmcBuduxHH02i2OXbvF8dBYbic5SHEauNus5Pa0U6lIHioH5qVKYF7KFPDBbrNmSDYRkT+jAl9ERETkL+zfv59nnnmG8ePH06tXLwCuXLnCSy+9RIkSJahRowZvvPEGX331FXXr1sXf359vv/2WuXPn8uabb9KuXTvOnz9PhQoVTH4kIpIVTZ8+nQEDBmC327l06RIBAQFmRxIR+VtOnDhB27ZtOXv2LLly5WLq1Km8/PLLZscSEckS7GYHEBEREclM7rcyvlq1ahQvXpyTJ0/eHYkTFBRElSpVOHz4MH379uW9995jyJAhFCpUiJCQEHLnzs3gwYNp164d7u7uKu9F5B977bXXqF27Nj///DOFChUyO46IyANLTk7m+eef57vvvgOga9euzJ07F3d3d5OTiYhkHfotkIiIiOR4hmEwf/58KlasyMaNG393u91up2PHjhw+fJjjx4/f3f7MM88QGxvLpk2bGD58OAcOHGDIkCGsW7eOCxcu0KdPH31AFZE0UaNGDd5++22sVn2EE5Gs4fPPP8fX15dly5ZRvnx5Tp48ycKFC/XeSETkb9K7PxEREcmxLl68SIcOHfD29qZnz56cOXOG5cuX33ffLl26cOPGDXbt2nV3W4MGDcifPz83b94kMTGRAgUK8Nxzz1G9evWMeggiIiIimcrRo0cpXbo0/fv3x2azMXfuXE6ePEn58uXNjiYikiVpBr6IiIjkKIZhMG3aNKZOncqlS5cAKFKkCH369GHIkCF/uiqsXbt2WK1WvvjiC/Lnzw/ArVu3yJs3b0ZEFxEREcm0kpKS6NKlC99//z0Wi4Xu3bszZ84c7HZNbxYReRh6FhUREZEc4dChQwwZMoTNmzeTmpqKm5sbrVq1YsKECVSpUuWBjvHKK6/w008/YRjG3W0q70UkM/rf63ncuX6HiEh6mDZtGoMGDSI5OZmKFSuyatUqSpcubXYsEZFsQSvwRUREJNtKSkpi3LhxfPnll4SHhwNQqlQp3nzzTfr37/+3Z0nf7wK3IiKZ0fXr10lOTub27dtUqlQJAIfDoZWwIpKmDhw4QPv27bl06RI+Pj588cUXdOvWzexYIiLZigp8ERERyXa2bt3Ke++9x65duzAMg1y5ctG6dWsmTpxI8eLFzY4nIpKu1q5dy6BBg7h+/TpFixbF39+fFStWkCdPnrtfRG7evJnGjRubHVVEsqiEhAQ6derE6tWrsVgs9OrVi5kzZ+pLQhGRdKCL2IqIiEi2EBMTwxtvvIG/vz+NGjXil19+oUKFCnzzzTckJCTw7bffqrwXkWxv8+bNtGvXjtq1azNz5kxmzpxJ7ty5qVatGmfPnsVisZCamsqQIUOYNm2a2XFFJAuaPHkyfn5+rF69mqpVq3LhwgVmz56t8l5EJJ1oBb6IiIhkaStXrmTMmDEcPnwYl8tFnjx56NSpE+PGjbt7oVkRkZzikUceoVq1anz77bd3tyUlJfHOO+8QHBxMhw4dyJUrF3DvSB3DMP72WDERyVn27dtH+/btuXLlCnny5GHWrFl06tTJ7FgiItme3qGJiIhIlhMeHs4LL7xA7ty5adeuHYcPH6ZGjRqsWbOGW7duMWvWLJX3IpLjrFmzhqioKD788EPgt1Le5XLh6elJz549ad68Oc888wzjxo0DwG63ExUVRVJSksp7EflDcXFxtGrViuDgYK5du8Yrr7xCdHS0ynsRkQyid2kiIiKSJRiGwezZs3nkkUcICAhg3rx55MqVi3fffZfY2Fj27t1Ly5YtzY4pImKaXLlyYRgGSUlJAPeU8jVr1uTTTz9l9+7dNGrUCIAbN27w4Ycf8vjjj9+9j4jIf5swYQL58uVj7dq1PPbYY1y8eJGZM2fqSz8RkQykAWUiIiKSqZ09e5bBgwezZs0akpOTsdlsNGrUiLFjx1KnTh2z44mIZBoFChQgMDCQS5cuUblyZQAsFgsA33zzDePHj2fBggXUqVOH1atX07p1a+x2O6tXr8bT0xOn04nNZjPzIYhIJrFr1y6ee+45QkNDyZs3L4sXL6Zdu3ZmxxIRyZH0lamIiIhkOg6Hg48++ojixYtTrlw5Vq5cSYECBRg3bhwJCQls3rxZ5b2IyP+oXLkybdq0oVOnTnzxxRccPnwYgAMHDtCnTx/effddunbtCkD+/PmxWCxYrVZOnjwJoPJeRIiNjeWpp56ibt26hIeH079/f6KiolTei4iYSCvwRUREJNPYv38/Q4cOZevWrTgcDtzd3WndujUTJkygQoUKZscTEcn0xowZwyOPPMKsWbMwDIPChQvTtWtXmjRpwqhRowA4c+YMPXv2pHXr1rz11lu0bt2amzdvMmbMGFwu191V+yKSs3zwwQd88MEHpKamUqNGDb7//nsCAwPNjiUikuNZXC6Xy+wQIiIiknMlJCTw4YcfMnv2bCIiIgAoW7YsAwcO5OWXX9aMVRGRfyA6Ohqr1UqHDh0ICQlh8+bNBAYGcvPmTXr27MmpU6c4fPgw3t7eXL16lSNHjtCqVau797/zMVFlvkj2t2PHDjp06EB4eDi+vr7MmzeP1q1bmx1LRET+P63AFxEREVNs2rSJ9957j71792IYBt7e3nTr1o0JEyZQtGhRs+OJiGRpfn5+ABQuXJgRI0bcXUU7efJktmzZwu7du/H29iYlJYWiRYtStGhRwsPDCQsLI0+ePJQuXRr47QLi+iJVJHuKiYmhXbt2bNmyBavVyptvvsnkyZP177yISCajFfgiIiKSYaKionjvvfdYvHgxMTExWCwWqlSpwvDhw+nYsaPZ8UREsrVjx45RtWpV5s+fT/fu3e+OyzEMgw4dOhAREUFkZCTJycn06dOHwYMHmx1ZRNLJ+++/z/jx43E4HNSuXZuVK1dSuHBhs2OJiMh9aAW+iIiIpLulS5fy4YcfcuzYMVwuF76+vrz66qt8+OGH+Pv7mx1PRCRHOHPmDBUqVKBly5bAb2Nybt++zaBBg1i5ciW//PILVatW5eeff+bFF1/E5XIxZMgQk1OLSFrasmULnTt3JiIignz58rFgwYK7zwkiIpI56XdRIiIiki6uXr1Kt27d8Pb2plOnThw/fpxatWqxfv16oqOj+fzzz1Xei4hkoCeeeAKr1Ur79u1JSEjAarWyZMkSZs+ezZNPPknXrl05fPgwLVu2ZPz48ezatYuUlJT7HsswjAxOLyIP4+bNmzRs2JDGjRtz8+ZN3nnnHSIiIlTei4hkASrwRUREJM0YhsHMmTMpV64cQUFBLFq0CB8fH4YOHcrt27fZtWsXTz31lNkxRURypHz58nH06FFKlSrF6tWrSU1NZcmSJfTv35/NmzfTo0cPmjdvzrx580hJSSEsLOy+F7F1Op2akS2SRRiGwdChQylUqBDbt2+nbt26hIWF8dFHH+nfYxGRLEIjdEREROShnTx5kiFDhrBu3TpSUlKw2+00bdqU8ePHU6NGDbPjiYjIf5kzZ87dP3t7exMVFQXA6NGjefTRR3n11VdxuVy0adPmvivt75R+d2boi0jmtGHDBrp27UpkZCT58+dn0aJFWkghIpIF6etWERER+UccDgfjx4+naNGiVKxYkVWrVlG4cGEmTZpEYmIiGzZsUHkvIpJJuVwuAOrVq0dUVBS3b98GoF27dmzdupWnnnqKLl264OHh8bv7WiwWlfcimVhERAT16tWjWbNmxMTEMGzYMK5fv67yXkQki7K47rxzExEREXkAe/bsYejQofz88884nU48PDxo1aoVEydOpGzZsmbHExGRvyEiIoImTZqQN29eJk+eTFBQEEWKFCE1NRU3N7d79jUMA6vVqvJeJJMyDINBgwbxySef4HQ6adiwIStXrtQ1h0REsjgV+CIiIvKX4uLiGDNmDF9//TU3btwAoHz58rz77rv06tVLM1RFRLK4559/nhMnTtC6dWu6detGmTJl7rnd6XRis9lMSicif2Xt2rV0796dqKgoChUqxOLFi2nUqJHZsUREJA2owBcREZE/tHbtWkaOHMn+/ftxuVz4+PjQvn17xo0bR5EiRcyOJyIiaeiXX37B29ubRx999J4V9ncuWqtV9yKZT3h4OG3btmXPnj3Y7XaGDRvG6NGjzY4lIiJpSAW+iIiI3CMyMpJhw4axZMkSYmNjsVgsVKtWjREjRvDss8+aHU9ERDLAf4/JufNnjc4RyTwMw+Dtt9/m008/xTAMGjduzPLly/H19TU7moiIpDEV+CIiIgLAwoULGT9+PMePHwfAz8+P559/ntGjR+vDoIhIDvff5b1hGLhcLo3UETHJqlWr6NmzJzExMQQEBLB06VLq169vdiwREUknGlgrIiKSg4WEhNC5c2e8vLzo3r07J0+epF69emzZsoWoqCimTp2q8l5EJIe6s9brf1feW61WunbtymuvvWZWNJEc6dq1a9SoUYM2bdoQHx/Phx9+SGhoqMp7EZFsTivwRUREchjDMPjss8/45JNPuHDhAgCFCxfmlVdeYejQoXh6epqcUEREMrPExEQqVarExYsXqV69Ojt27NBrh0g6MgyD119/nS+++ALDMGjWrBnLli0jT548ZkcTEZEMoAJfREQkhzh27BiDBg1i48aNpKamYrfbadKkCRMmTKBatWpmxxMRkSzEMAw6duzI8uXL8fPz45dffqFChQpmxxLJlpKTkylbtiyGYbBs2TLq1KljdiQREclAKvBFRESysZSUFMaPH8/MmTMJCwsDoESJEgwYMIA33ngDq1XT9ERE5J/75JNPePvtt7FarXz99dd0797d7Egi2U5qaioXLlygfPnyZkcRERETqMAXERHJhnbs2MGwYcPYuXMnTqcTT09PnnnmGSZNmkTJkiXNjiciItnI3r17adSoEQkJCbz00kt8+eWXZkcSERERyTZU4IuIiGQTsbGxjBo1ivnz53Pz5k0AKlSowJAhQ+jevbtW24uISLqJiYkhODiYs2fPUqVKFXbv3o2Xl5fZsUQyFcMwsFgs91wUWkRE5K/ok7yIiEgWt2rVKmrUqIGvry8ff/wxKSkp9O7dm+vXr3PixAl69Oih8l5ERNKVr68vp06donPnzhw9epSAgACOHDlidiyRTMPhcGC1WrFYLKSkpJgdR0REshCtwBcREcmCrl+/ztChQ/nuu++4ffs2FouFxx9/nFGjRvHMM8+YHU9ERHKwL774gv79+wMwc+ZMXnrpJZMTiWQODoeDgQMHEhkZibe3N6+//jpVqlQxO5aIiGRyWo4nIiKSRRiGwbx586hQoQKFCxdm7ty5uLu78/bbbxMbG8v+/ftV3ouIiOn69u3L/v378fb25uWXX6ZHjx5mRxLJEIZh3P3znbWSd/5z3759lC1blv379/PEE09w6dIl3n77bWbPnn3PfiIiIv/LbnYAERER+XPnz59n8ODB/PTTTyQlJWGz2WjYsCHjxo2jXr16ZscTERH5nccee4zQ0FBq1arFggUL2LdvH3v27CFPnjxmRxNJN1arlfj4eLy9ve/Oub/zn8uWLaN+/fosWLAAgLx589KjRw+CgoLo0aMHbm5upuUWEZHMTSvwRUREMiGHw8GUKVMoUaIEZcqUYfny5eTLl48xY8aQkJDAtm3bVN6LiEim5uPjw/Hjx3nhhRc4deoUgYGB7N+/3+xYIulm7dq1FC9eHACn08mYMWPYv38/DoeDkydP0rNnTyIiInjmmWd49dVXGTVqFJ9//rnKexER+VMq8EVERDKRQ4cO0bx5c3LlysXAgQMJCwvj6aef5ujRo1y9epURI0bg7u5udkwREZEHNnfuXObMmUNiYiK1atXi888/NzuSSJo4ceIEZcuWJTQ0FIDg4GC8vb1p2rQpnp6e7Nq1ixIlSmC32zl9+jRjxoyhYsWKeHh4sG/fPoYOHYqnpyc7d+4kPDzc5EcjIiKZlQp8ERERkyUlJTFixAgCAgJ47LHHWL9+PcWKFeOzzz4jMTGR1atXU7lyZbNjioiI/GO9evXi0KFD5M6dm/79+9OxY8d75oWLZEWlSpViyJAhFClSBPhtBn50dDRbtmxhwoQJrF27lvz58wMwaNAgduzYwbRp01i+fDmlS5cGYMeOHcybN+/ulwAiIiL/y+LSlVJERERMsXnzZkaMGMHu3bsxDINcuXLRpk0bJkyYcPfn1yIiItlJQkIC9erV49ChQ5QuXZp9+/bh5+dndiyRv83pdGKz2YDfFmNcvnyZMmXKMHPmTL777jucTidbt27FMAysVivXrl2jefPmBAUF8eabb1KpUiV+/vlnRo0aRa1atZg6dSr+/v4mPyoREcmMVOCLiIhkoJiYGEaMGMHChQuJjo4GoHLlygwZMoRu3bqZnE5ERCRjvPrqq3zxxRd4eXmxceNG6tSpY3YkkQdy55cjVutvAw2Sk5Pp27cvmzdv5tSpU+TKlYudO3fSqFEjZs+eTffu3XG5XFgsFs6cOcPLL7/MyZMnKVGiBBcvXmTEiBG88cYbZj4kERHJ5FTgi4iIZIDly5fz4YcfcvjwYVwuF3nz5qVz5858+OGHd39aLSIikpMsWrSIHj16YBgG//73v3n77bfNjiTyp+4U8QA7d+5k+vTpPPHEEzRu3JgaNWowbtw4+vfvT2pqKu+++y7ffvstV69exW633z1GYmIiISEhhIeHU6dOHTw8PMx6OCIikkVoBr6IiEg6CQ0NpUePHvj4+PDcc89x+PBhatasydq1a4mJieGLL75QeS8iIjlW165dOX78OH5+fgwcOJA2bdpoLr5kahaLBYfDwZtvvknTpk0pUKAAfn5+FC9enDfeeIPRo0dz+fJl3NzceOONN/D09KRfv344HA5Wr17NmDFj8PDw4JFHHuHJJ59UeS8iIg9EK/BFRETSkGEYzJkzh48++ogzZ84AUKBAAXr16sXIkSPx8vIyOaGIiEjmkpSURMOGDdm3bx/Fixdn//79+oJbMq3NmzczcOBAPv30U+rXr393e0JCAlWqVKFJkybMmjULgO+//57OnTtTvnx5Tp48yezZs3n++efNii4iIlmUCnwRETGFw2lwNiKOo9ducezaLU6ExRKblEqq04WbzUIeTzcqBuShcmBeqgTmpWxBH+y2zPvDsdOnTzNkyBDWrl1LcnIyNpuNJ554gnHjxlGrVi2z44mIiGR6AwYMYNq0aXh6erJu3TqeeOIJsyOJ/M7QoUNZu3YtO3bswMfHB/i/C9ouX76cTp06sWXLFho0aADAjh07uHr1Ku3atcPd3d3M6CIikkWpwBcRkQx1KTKehXtDWLz3CnHJDgDsVgsO4/cvR/+93cfDTpfgILoFF6dEfu8MzfxHHA4HU6ZMYfr06Vy5cgWAoKAg+vXrxzvvvHPPvFMRERH5a8uXL6dz5844nU7Gjh3L0KFDzY4kco8ePXoQHh7O+vXrMQzj7sVs4bcZ+Z06deLkyZPs2rXrbsEvIiLyMFTgi4hIhjh4OZrJG86w41wkNosF5z94+blzv/pl8vNOs/JUC/JN+6APYN++fQwdOpRt27bhcDhwd3enZcuWjB8/ngoVKpiSSUREJLu4cOECtWrVIjIykhYtWvDTTz/dU5KKmOmnn36iTZs2HDhwgKpVq97dfuXKFXbt2kWVKlVo3rw5mzdvpkyZMiYmFRGR7EIFvoiIpKukVCcfbzzDrJ8vYLVacN5npf3fZbNaMAwXrzQsxVtNy+HpZkuDpH8uISGBDz74gDlz5hAREQFA2bJleeedd3jppZdULIiIiKShlJQUGjVqxM6dOwkMDGT//v0ULlzY7FiSA90Zj3NHYmIi7du3JzQ0lPnz51O6dGkSExMZMWIEt27dYvr06fj5+WGxWExMLSIi2YkKfBERSTdHr93i9cUHCIlKID1ebSwWKOHvxbQuj1MlMG/anwDYsGEDI0aMYN++fRiGgbe3N88++yzjx4+naNGi6XJOERER+c3gwYOZNGkSHh4e/Pjjjzz11FNmR5IcxuVy/a6Mj4mJoVGjRly/fp0yZcpw9uxZAgMDWbRoEeXKlTMpqYiIZFcq8EVEJF3sOBdJ73n7cDhd/2hczoOyWSzYbRZm96xJ/TL50+SYUVFRDB8+nMWLF3Pr1i0sFgtVq1Zl+PDhdOjQIU3OISIiIg/mxx9/pH379qSmpjJy5EhGjRpldiTJQRwOBxaL5Z5V+ADh4eEcP36cY8eOUbx4cdq2bWtOQBERyfZU4IuISJrbcS6SF+buxXC5SIOJOX/JagGrxcLXvYIfqsRfsmQJY8eO5ejRowD4+vrSrVs3PvjgA/z8/NIqroiIiPxNly9fJjg4mOvXr9OoUSPWr1+vi8VLmjt27BgVKlT4XVlvGAaARiaKiIgp9OojIiJp6ui1W/Setw9nBpX3AIYLDJeL3vP2cfTarfvus3btWsqVK8eFCxfu2X7lyhW6deuGt7c3nTt35vjx49SpU4eNGzcSHR19d46piIiImKdYsWJcvXqVRo0asWXLFoKCgrh8+bLZsSSbiIuLo2XLllSpUoVp06bdLezvsFqtKu9FRMQ0egUSEZE0k5Tq5PXFB3A4Xeky8/7PGC5IdRq8sfgASanOe27bs2cP7dq14+zZs8yaNQvDMPj8888pU6YMxYoVY9GiReTOnZthw4YRHx/Pzp07adKkScY+ABEREflTdrudzZs38/777xMeHk7ZsmVZvXq12bEkixs/fjz+/v6sW7eOxx57jA4dOqisFxGRTEUjdEREJM2MX3uSWdsvZHh5/98sFujTsDRDWjwCwOnTp6lVqxa3b9/GMAzc3NywWCykpKRgt9tp1KgR48aNo0aNGuaFFhERkb9l/fr1tG7dmuTkZAYNGsTEiRPNjiRZzK5du3juuecIDQ0lb968zJkzh3bt2pkdS0RE5HdU4IuISJo4eDmadjN2khleVCwWWPlqPQpY46lZsybh4eH898tdgQIFGDx4MAMGDND8XBERkSwqLCyMmjVrcu3aNerVq8fmzZtxd3c3O5ZkcrGxsbRv356NGzditVrp168fU6dO1ap7ERHJtPQKJSIiaWLyhjNYrRazYwC/XdB21PL9lCtXjrCwsHvKe6vVSo0aNRg4cKDKexERkSwsICCAy5cv06JFC3755ReKFi3KxYsXzY4lmdiYMWPInz8/GzdupGbNmly5coVPP/1U5b2IiGRqepUSEZGHdikynh3nInFm1FVr/4LTcHHoejIp7nl+d5vL5eI///kPN2/eNCGZiIiIpCWr1cratWsZO3YskZGRlCtXjhUrVpgdSzKZ7du3ExAQwMiRI/Hx8WHVqlXs3buXIkWKmB1NRETkL2mEjoiIPLSxa04wZ8clnJnoJcWCi27VA+hd3Z/r168THh7O9evXuX79OoZhMGzYMP3MXkREJBvZunUrLVu2JCkpiTfffJOPP/7Y7EhisujoaNq1a8fWrVux2Wy8/vrrTJ48WSvuRUQkS1GBLyIiD8XhNKj2wQbikh1mR/md3B52Do54CrtNH9JERERygoiICIKDgwkJCSE4OJht27bh6elpdiwxwYgRI5gwYQIOh4PatWuzcuVKChcubHYsERGRv03Df0VE5KGcjYj7w/I+ZvtCbv2y+J5tdv+iBL7yBQDOuGiit8wh8dJBXCmJuPkXJU+djng/Uu/u/rd2LiHx/D5Srl8Em51iby154Gy3kx2cuxHHI4V/P0pHREREsp+CBQty4cIF2rVrxw8//EBgYCC7du2iXLlyZkeTDLJ582a6dOlCREQE+fLlY8GCBbRs2dLsWCIiIv+YliSKiMhDOXrt1p/e7pa/GEVfW3D3n8LdJ969LXL1FFKjrlKw/QgCen9GrnJ1iPxhIinh5+/u43I68CpfH5/H/tkHr7/KJyIiItmL1Wrl+++/59///jfR0dFUrFiRxYsX//UdJUuLjIykYcOGNGnShJs3b/Luu+8SERGh8l5ERLI8FfgiIvJQjl27hd1q+eMdrDZsPn7/949X3rs3JV87Se7q/8KjSHncfAvjW68zVg9vkq+fu7uPb4Nu5Alui3uBEn87m91q4ZgKfBERkRxp4MCB7NixAw8PD7p27Urfvn3NjiTpwDAMhg4dSuHChdm+fTv16tUjLCyMSZMmada9iIhkC3o1ExGRh3IiLBaH8ceXU3FEh3J1eg+uzejNjVUf4bgVcfc2j8AKJJzcjjPxNi6XQfyJbbicKXgWq5Im2RyGi+OhsWlyLBEREcl66taty5UrVyhdujQzZ87kscceIyEhwexYkkbWr19PoUKFmDBhAv7+/mzYsIEdO3ZQoEABs6OJiIikGRX4IiLyUGKTUv/wNo8i5cn39FsU7Dga/+b9cN66TvjCwRjJv31wLtB2MC7DwdWpXbj80bPc/M9nFGg3HDe/ImmW73ZS5ru4roiIiGQcf39/zpw5Q4cOHTh06BBFihTh+PHjZseShxAREUG9evVo3rw5MTExDBs2jPDwcJo2bWp2NBERkTSnAl9ERB5KqvOPV9/nKl0D70fq416wJLlKVadgh1EYyfHEn9oBQMzP32AkxVOw84cE9PyYPDXbcuP7iaREXEqzfClOI82OJSIiIlmT1Wpl6dKlfPrpp9y+fZtHH32UefPmmR1L/ibDMBg4cCBFihRh586dNGzYkOvXrzN27FiNyxERkWxLr3AiIvJQ3Gx/Mv/+f1g9fXDzC8QRHUpqdBi3D6wmX6sB5CpRDfdCpfCt3xWPwmW4fWB1muVzt+mlTkRERH7z2muvsWfPHjw9PXnhhRfo1auX2ZHkAa1Zs4YCBQowZcoU8ufPz+bNm9m2bRv+/v5mRxMREUlXajVEROSh5PF0e+B9jZREHDFh2Hz8caUmA2Cx/M9LkdUKrj9e1f935fa0p9mxREREJOurUaMGoaGhPPLII3z99ddUrlyZuLg4s2PJHwgLC6NWrVo8/fTTxMbGMnLkSMLDw2nUqJHZ0URERDKECnwREXkoFQPyYLfefxV+9ObZJF0+iiPmOklXT3JjxViwWPGu+ARu+Ypi9wvg5rrpJIeeJjU6jNg9K0i6eAivcrXvHsNxK4KU6xdwxN4Al0HK9QukXL+AkZL4l9nsVguViuRJs8cqIiIi2UOePHk4efIk3bt35/jx4xQpUoRDhw6ZHUv+i2EYDBgwgKJFi7J3714aN27MjRs3GDVqlNnRREREMpTF5UrDZY4iIpLjLN1/hUHLj9z3ths/TCT5ynGcibHYvPLiUbQivg174OYXAEBq1DVits4j6eoJXKmJ2H0DyFOrHT6VG989RuTqj4k/tul3xy7UZRyexav+Zb6PnqtKh+pB//DRiYiISHb35Zdf0rdvXwA+//xz+vTpY3Ii+eGHH3jhhReIiYkhICCApUuXUr9+fbNjiYiImEIFvoiIPJSTYbG0nLbd7Bh/aN2ABjxSWKvwRURE5I8dOXKEBg0aEBsbS5cuXfjmm290UVQTXL16lbZt2/Lrr7/i5ubGyJEjGT58uNmxRERETKUCX0RE/tLIkSOJj48nICCAwoULExAQQEBAAL6+vmzcvIUPT+Qm2ch8H3Jze9g5OOIp7LqQrYiIiPyFuLg46taty9GjRylbtix79+7F19fX7Fg5gmEYvPbaa8ycORPDMGjWrBnLli0jTx4twhAREVGBLyIif8owDHx9fYmLi8NqteJ0On+3T73XJhOW+xGcmeglxWax0Lt+SYa1qmB2FBEREclCXnrpJWbPno23tzebNm2iVq1aZkfK1pYvX07v3r25desWRYsWZdmyZdSuXfuv7ygiIpJDaEmiiIj8KavVyvPPP4/FYvldee/m5sZ//vMfvhnZJ1OV9wBOl4tutYqZHUNERESymK+++op58+aRlJREnTp1mDZtmtmRsqWQkBAee+wxnnvuORITE5kwYQJXrlxReS8iIvI/VOCLiMgfcjgcTJw4keXLl2MYxt3tFouFvHnzcuzYMZo1a0aJ/N7UL5Mfm9ViYtr/Y7NaaFAmP8XzeZsdRURERLKgHj16cPToUfLmzcuAAQNo3779Pe+F5J9zOBy8/PLLlCxZkkOHDtGqVStu3rzJ4MGDzY4mIiKSKanAFxGR39m5cyeNGjXC09OTIUOGEB0dTe7cuYHfVuR7enqyceNGypUrd/c+7zQrj2FkjlX4hsvFwGblzY4hIiIiWViFChUICwujevXqrFixgjJlynDz5k2zY2VpS5Yswd/fn6+++oqgoCD27t3LTz/9hI+Pj9nRREREMi0V+CIiAsCtW7cYMGAA+fLlo169emzdupXSpUszc+ZMEhMTGTduHPBbgb9q1Spq1Khxz/2rBfnySsNSWExehG8B+jQsTbUgX3ODiIiISJbn6enJ/v376devHxcvXiQoKIgdO3aYHSvLuXjxIlWrVqVz586kpKQwefJkQkJCqFmzptnRREREMj0V+CIiOdyyZcuoVq0afn5+TJs2jdTUVHr16kVYWBinT5/mlVdewWq10q1bNx577DEWLlxI06ZN73ust5qWo7i/FzaTWnybBUrk8+LNJmVNOb+IiIhkT5999hlLliwhNTWVhg0bMmnSJLMjZQkOh4NevXpRunRpjh49SuvWrYmKiuLtt982O5qIiEiWYXG5MtlVB0VEJN2FhIQwZMgQVq1aRUJCAlarlRo1ajBy5EhatWr1UMc+eu0Wz32xk1SnQUZO1LFawM1m5bu+dakSmDfjTiwiIiI5xrlz56hduzY3b97k6aefZtWqVVitWhd3P9988w19+/YlPj6eEiVKsHLlSqpVq2Z2LBERkSxHBb6ISA7hcDiYNm0an376KZcuXQKgUKFCvPjii7z33nt4eXml2bl2nIvkhbl7MVyuDCnxrRawWizM6xVMvTL50/+EIiIikmOlpKTwxBNPsHv3boKCgti/fz8FCxY0O1amcfbsWdq0acPJkyfx9PRk0qRJvP7662bHEhERybJU4IuIZHP79u1j6NChbNu2DYfDgZubG02aNGH8+PHpugpqx7lIes/bh8PpwpmOLzV3Vt7P6VlT5b2IiIhkmIEDBzJlyhQ8PT356aefaNy4sdmRTJWSksKLL77IokWLcLlcPPvssyxatAhPT0+zo4mIiGRpKvBFRLKhuLg4Ro8ezbx587hx4wYAZcqU4c033+TVV1/NsJ96H712i9cXHyAkKoH0eLWxWKCEvxfTujyusTkiIiKS4b7//ns6duyIw+FgzJgxvPfee2ZHMsXcuXN57bXXSEhIoHTp0qxcuZIqVaqYHUtERCRbUIEvIpKN/PDDD4wZM4aDBw/icrnw9vamXbt2jBs3jqJFi5qSKSnVyccbzzBr+wWsFgvONJipY7NaMFwu+jQszZtNyuLpZkuDpCIiIiJ/X0hICDVr1uTGjRs89dRTrFu3LsfMxT916hRt27bl9OnT5MqViylTptC3b1+zY4mIiGQrKvBFRLK4q1evMnToUFauXEl8fDwWi4XHH3+cESNG0KZNG7Pj3XXwcjRTNpxh+7lIbBbLPxqrc+d+DcrkZ2Cz8lQL8k37oCIiIiJ/k8PhoEmTJvz8888EBASwf/9+ihQpYnasdJOSkkKPHj1YunQpAB06dGD+/Pl4eHiYnExERCT7UYEvIpIFGYbB559/zieffML58+cBKFCgAD179mTkyJH4+PiYnPCPXYqMZ9Heyyzee5nbyQ4A7FYLjvuszP/v7bk97HQJLka3WsUons87QzOLiIiIPIjhw4czbtw43N3d+f7772nZsiUAt27d4uDBgzz55JN/+5gOp8HZiDiOXrvFsWu3OBEWS2xSKqlOF242C3k83agYkIfKgXmpEpiXsgV9sNvS7xcAs2bN4s033yQxMZFy5cqxcuVKKlasmG7nExERyelU4IuIZCGHDh1iyJAhbN68mdTUVOx2O08++STjxo2jZs2aZsf7WxxOg3M3/u/D6PHQWG4nOUhxGrjbrOT2tFOpyP99GC1TIH0/jIqIiIikhTVr1vDss8+SkpLCsGHDGD16NI0bN2b79u0cPnyYqlWrPtBxLkXGs3BvCIv3XiHubyx68PGw0yU4iG7BxSmRP+0WPRw/fpy2bdty7tw5vLy8mDZtGr17906z44uIiMj9qcAXEcnkEhIS+OCDD5g7dy7Xr18HoGTJkrzxxhu89tpr2O12kxOKiIiIyH+7evUqwcHBhIWFERgYSGhoKBaLhY4dO7J48eI/ve/By9FM3nCGHWkwdrB+mfy885BjB5OSkujevTvLly/HYrHQpUsX5s6di7u7+z8+poiIiDw4FfgiIpnUmjVrGDVqFL/++iuGYeDl5UXr1q2ZMGECxYsXNzueiIiIiPwJh8PBY489xrFjx+5us1qtnD9/nhIlSvxu/6RUJx9vPMOsny9gtVpw3mel/d9ls1owDBevNCzFW03L4elm+8N9V6xYQVRUFC+99NLdbZ9//jkDBw4kKSmJChUq8P3331OuXLmHziUiIiIPTgW+iEgmEh4eztChQ1m+fDm3b9/GYrFQtWpVhg8fTocOHcyOJyIiIiIP6NChQ9SuXZvk5OS726xWK6+++irTp0+/Z9+j127x+uIDhEQlkB6f0C0WKOHvxbQuj1MlMO/vbr9w4QIVK1YkJSWFQ4cOYRgG7dq14+LFi3h7e/PZZ5/Rs2fPtA8mIiIif0kFvoiIyQzD4Msvv2TKlCmcOXMGgHz58vH8888zevRo8uTJY3JCEREREfm7mjdvzvr167FarRiGcXe7zWYjLCyMAgUKALDjXCS95+3D4XT9o3E5D8pmsWC3WZjdsyb1y+S/u93lctGkSRO2b9+Oy+UiT548REdHY7FYeP7555k9e7ZGNoqIiJhIBb6IiEmOHTvGkCFD2LBhAykpKdhsNho2bMgHH3xAvXr1zI4nIiIiIg/h9OnTrFy5kj179rBz504iIiLu3vbII49w7Ngxdl2M5oW5ezFcLtJgYs5fslrAarHwda/guyX+/Pnzf7e6PjAwkG3btlG6dOn0DyUiIiJ/SgW+iEgGSkpKYty4cXz11VeEhYUBUKxYMfr378/bb7+t1U0iIiIi2dS1a9fYs2cP7733HidPnqTTq+9yMF8jUpxGuozN+SNWC7jZrHzXty6F3JIpXbo0cXFx9+yTP39+zp8/r1+CioiIZAIq8EVEMsCGDRt4//332bt3L4ZhkCtXLp5++mkmTJiglU0iIiIiOcymrT8zeFss0cnWdB2b80esFijm78WF6b25EnLx/7ZbrVgsFpxOJ5MmTeLdd9/N8GwiIiJyLy31FBFJJzdu3GD48OEsXbqUW7duYbFYqFy5MoMHD6Zbt25mxxMRERERk+xNLMDN5NuYtZ7OcMGlyDhul2iI/doVypQpQ1BQEAUKFCBfvnzky5ePdu3amZJNRERE7qUV+CIiacgwDObNm8ekSZM4deoUAH5+fnTp0oUPPvgAf39/kxOKiIiIiJkOXo6m3YydZIYP4hZgZb96VAvyNTuKiIiI/AGr2QFERLKD06dP07ZtW7y8vHjxxRc5e/YsDRo0YNu2bURFRfHZZ5+pvBcRERERJm84g9VqMTsGAFarhcnrT5sdQ0RERP6ERuiIiPxDKSkpTJo0iS+++IJr164BULRoUfr06cOgQYNwd3c3OaGIiIiIZCaXIuPZcS7S7Bh3OQ0X289FEnIznuL5vM2OIyIiIvehFfgiIn/T1q1badCgAV5eXowYMYLIyEjatm3LqVOnuHLlCu+9957KexERERH5nYV7Q7BZMsfq+ztsFgsL91w2O4aIiIj8ARX4IiIPICoqin79+uHv70+jRo3YsWMH5cqV4+uvvyYhIYGVK1dSvnx5s2OKiIiISCblcBos3nsFZya7DJ3T5WLx3ss4nIbZUUREROQ+NEJHROQPuFwuFv2/9u492sqCzhv4d+99DtcDB7koF7kEKGZQeEOHjJkGs0gbZcbeJbbKaFqos2bSxhxA8W5AWTrelloTjjOWLt/XSktzacvRFGeEt6wUzcRGVEABkTsHztl7v3/0DqYC3oC9OefzWYs/ePaz9/Pdi81inS+//Xt+8IPMnTs3ixYtSrVaTXNzc6ZNm5avf/3r6du3b60jAgCwl3h2xYZs2NK23cfWPPz9rJ1/6xuONfTeP4Om3ZAkWf/re7Nx0YPZ+spzqW7dnMFn3ZZil6Y3nF/evD6r778hmxcvSArFdBs1Pr2PmZZip65vm239lrYsXrkhB/Xv+R7fHQCwuyjwAd7kueeey/Tp03P33XenpaUlxWIxRx11VC699NJMnDix1vEAANgLPbF07U4fb+w7JPud/PXXDxRf/8J8tXVLug4/LF2HH5Y1D9283eev+sm3Ut6wOvudfFmq5ba8es8/59V7r02/vzrnHedT4ANA/bFCByBJW1tb5s6dm6FDh2bkyJG54447ss8+++TCCy/Mxo0b8+ijjyrvAQB4z55cujYNxZ3svy+WUmra5/Vf3Zq3PdTziBPS/GefTeeB21/Z2LrqxbT84ZfpM+kr6TxwVLoM/lB6f+L0bHrqF2lb/+rbZmsoFvLk2/wHAwBQGybwgQ5t/vz5mTVrVh5++OGUy+V06tQpxx9/fObMmZPRo0fXOh4AAO3EU8vXpa2y4/33ba8ty0vXfiGFUmM6DToo+/z5qWlo3vcdvfaWpU+n2Ll7Og84YNuxLsPGJoVCti57Jg2jxu/0+W2VahYtW/eOrgUA7FkKfKDDWbduXc4///zccsstWb16dZLkwAMPzNlnn50vf/nLKRZ9OQkAgF1rXUvrDh/rPHBU+hz31TT2HpTyhtVZO//WvPz96Rn4t9el2Lnb2752eeOaFLv3esOxQrGUYtceKW9c847yrW/Z/n5+AKC2FPhAh3H77bdn9uzZ+e1vf5tqtZoePXpk6tSpmT17dvr371/reAAAtGOt5R1P33cdcfjrv9n3A+k8cFReuv5L2fi7R9LjI8fugXTJ1nJlj1wHAHh3FPhAu7ZkyZLMmDEjd911VzZt2pRisZgjjjgiF110USZNmlTreAAAdBCNpZ3sv3+TYpemNO4zKG2vLXtH55e690rlTZP21Uo5lc3rU3rTZP6OdCr5FioA1CP/QgPtTltbW6644op84AMfyLBhw3LbbbelR48emTlzZtavX5/HHntMeQ8AwB7Vs0vjOz63snVz2tYsT6mp9zs6v/OgD6ayZWO2vLx427GWJb9JqtV02sGNb9+sRxfzfQBQj/wLDbQbCxcuzMyZM/PQQw+lra0tjY2N+dSnPpU5c+Zk7NixtY4HAEAHdvCAnvn1i2u2eyPb1x74XrqOHJeGnvumbcPqrH3k+0mhmO4H/3mSpLzhtZQ3vpbWNcuTJFtXPp9ip24p9eyXUtceaew7OF2GH5bVP7smvT/5d6lWyll93w3pdvCENPTo87bZGoqFfGhgz137hgGAXUKBD+zVNmzYkIsvvjg333xzVq5cmSQZOXJkzjrrrJxxxhluSAsAQF0YPah5u+V9krStX5VVd12e8uZ1KXVrTuf9D07/L3w7pW7NSZL1j9+TtfNv3Xb+K9+fkSTp8+mz0vThY5IkfT/ztay+/4a8ctuspFBItwPHp/cnTntH2doq1Ywe1Px+3h4AsJsUqtXqju+kA1Cn7rzzzlxyySV5/PHHU61W09TUlMmTJ2f27NnZf//9ax0PAADe4Onl6zLp6odrHWOH7j3zYzmovyl8AKg3JvCBvcZLL72UmTNn5kc/+lE2btyYQqGQQw89NOeff35OOOGEWscDAIAdOmDfpjR1bsiGLW21jvIWPTo3ZGS/plrHAAC2Q4EP1IUXX3wxy5Yty5FHHvmWx6rVal555ZUMHjw4SdKvX7+cccYZufDCC9PU5AcNAADqX0OpmCnjBmfeI8+nXEdfhC8VCpkybkgaSlZPAkA98i80UBNv3t41ceLE3Hfffds9t1AopH///pk6dWoWLFiQFStW5PLLL1feAwCwV/ncuKF1Vd4nSblazeeOHFLrGADADijwgT3u6aefzqZNm5Ikra2tSZL99ttv27Fyubzd582bNy9HHHHEngkJAAC72LC+3XP0yL4pFQu1jpIkKRUL+djIvhnap3utowAAO6DAB/ao++67L5/5zGcyY8aMJEljY2PWrl2bIUOGZOPGjUmSUqlUy4gAALDbfO3YUalU6mMKv1Kt5uxjR9U6BgCwEwp8YLe644478tWvfjX//u//nldffTXHHntsLr/88txyyy256aabkiTNzc357//+7+y33341TgsAALvX2MG9Mm3C8BRqPIRfSHLahBEZO7hXbYMAADulwAd2i2effTaHH354zjzzzKxduzbf/OY3c9xxx2XVqlWZPHlyTjvttFx55ZW59dZbkyT77rtvfve73yVJKpVKLaMDAMBu9dVjDszQ3t1SqlGLXyokw/p0y1kTD6jJ9QGAd66h1gGA9qFarebGG2/MqlWrMmvWrFxxxRUZM2ZMHnvssZRKpWzYsCHDhw/POeeck5tuuinnnntuCoVC/umf/imDBw/O8OHDs3r16iRJsej/FgEAaL+6NJZyzZRDc9INj6ZarmZPbtQpFpKGUjFXTzk0XRqtrgSAeqclA96TavWPP2UsWrQoDzzwQL72ta/lxhtvTL9+/bJx48Y899xzOfHEE9Pa2ppzzz03hxxySMrlcg466KC0tramZ8+emTNnTj784Q/niiuuyE9/+tMMGTLkDa8NAADt1ZhBzfneqUekWChkT93TtlhIioVC5p16RMYMat4zFwUA3hcFPvCubd26NYX//3Xfyy67LMcdd1yefPLJ/OhHP8ppp52W5cuXZ926dTnnnHPSr1+/PProo7ngggvyhz/8IdOnT09jY+O215o9e3a6d++exYsXZ8mSJUmy7bUBAKA9O3pk3/zr1HFpLBV3+zqdYiFpLBVz89Rx+ejIvrv1WgDArlOoGnUF3qFf/OIXOe+881Iul/OXf/mXOe+887JkyZIccsghOeGEE3LbbbdtO/eTn/xkVq1alWuuuSbjx4/fdvzJJ5/M4sWLc+KJJ6ZaraZQKOTll1/Oj3/845x66qnp2rVrLd4aAADUzBNL1+Yfbv1VlqzelN3xE3qhkAzr3S1XTznU5D0A7GVM4AM71dbWlptuuilXXnllrrjiiowbNy6TJ0/OTTfdlBNPPDEHHHBAxo8fn7a2tqxYsWLb8z796U9ny5Yt+dWvfrVtJc6zzz6b6667Lg8++GCS1yft+/fvn9NPP115DwBAhzRmUHPuPXNCpn1seAqFpLSLduqUioUUCslpE0bkZ2dOUN4DwF7IBD6wU9VqNYccckheeumlTJ06NZdffnmSZP78+TnllFMya9as9OzZMxdeeGHmzZu3bdp+/fr1ue6663Leeedl0qRJ2bhxYxYuXJiPf/zjueSSS3LIIYds91rW5wAA0JE9/sJrueL+3+fhxatSKhRSfg8/sv/P8z42sm/OPnZUxg7uteuDAgB7hAIfeFt33XVXPvvZz+amm27KKaeckiTZsmVLLr744vzwhz/Mb37zm4wYMSJnn312zjzzzBSLr3+557HHHsuCBQuyZs2afOELX8jQoUNr9TYAAGCv8fyqjfnBghdy64IXsn5LW5KkoVhIW+WtP8L/6fEenRsyZdyQfO7IIRnap/sezQwA7HoKfOBtrV+/PkcddVROOumkXHzxxduO33PPPTn11FPz7LPP5uyzz86KFSty1VVXZfjw4alUKm8o8v9HpVJJtVpNqVTak28BAAD2Sm3lShav3JAnlq7Nk0vXZtGydVnf0pat5Uo6lYrp0aUhHxrYM6MHNWfMoOaM7NeUhpJtuQDQXjTUOgBQX1pbW9PQ0PCGVTY9evTI0UcfnYULF2bZsmUZOHBgkj/utO/Vq1caGhpy/PHH54tf/GKWLFmS4cOHv6W8r1QqKRQK2y31AQCA7WsoFXNQ/545qH/PfPawwbWOAwDsYZo0IJVKJbfcckvGjBmTk046abt76M8444wsWLAg5557bp566qksW7YsP//5z3PYYYelqakpkydPzoIFC/Lxj398u9coFov22wMAAADAu2ACHzqw5557LtOnT8/dd9+dlpaWFIvF9OzZM+Vy+S0rbsaMGZNhw4bl5z//edatW5f/+q//yoABA/Ld7343SVIulzNq1KhavA0AAAAAaJfswIcOpq2tLd/61rdy/fXX54UXXkiSDBgwINOmTcuMGTPSpUuXHT732muvzX/8x3/klFNOyTHHHJPm5uY9FRsAAAAAOhwT+NBBzJ8/P7NmzcrDDz+ccrmcTp065fjjj8+cOXMyevTod/QaJ510UubNm5dVq1alubk55XI5SdyQFgAAAAB2AzvwoR1bs2ZNzjzzzPTp0ydHH310HnzwwYwYMSI33nhjNm/enJ/85CfvuLxPkv79+2f48OG57777smLFipRKJeU9AAAAAOwmCnxoh26//fZ85CMfSe/evXP11VentbU1U6dOzfLly/PMM89k2rRpKRbf21//adOmZcCAAbF9CwAAAAB2LzvwoZ1YsmRJZsyYkbvuuiubNm1KsVjM4YcfnosuuiiTJk2qdTwAAAAA4F1S4MNerK2tLVdffXWuueaaPP/880mS/fbbL1/60pcya9asdOvWrbYBAQAAAID3TIEPe6GFCxdm5syZeeihh9LW1pbGxsZMnDgxc+bMydixY2sdDwAAAADYBRpqHQB4ZzZs2JCLL744N998c1auXJkkGTlyZM4666ycccYZ73mnPQAAAABQn0zgQ5278847c8kll+Txxx9PtVpNU1NTJk+enNmzZ2f//fevdTwAAAAAYDcxgQ916KWXXsqMGTPy4x//OBs3bkyhUMihhx6a888/PyeccEKt4wEAAAAAe4AJfKgTlUol1113Xa666qo899xzSZJ+/frli1/8Yi644II0NTXVOCEAAAAAsCcp8KHGfv3rX2fGjBl54IEH0tramoaGhvzFX/xFZs+enSOOOKLW8QAAAACAGrFCB2pg06ZNufTSSzNv3rysWLEiSfKBD3wgX/nKV/L3f//3aWjwVxMAAAAAOjoT+LAH3XPPPbnooovyy1/+MpVKJd26dcsJJ5yQOXPmZOjQobWOBwAAAADUEWO+sJu9/PLLmTlzZu64446sX78+hUIhH/nIR3LeeeflpJNOqnU8AAAAAKBOmcCH3aBSqeS73/1uvv3tb+fZZ59NkvTp0yef//znc/HFF6dnz541TggAAAAA1DsFPuxCTz75ZGbMmJH7778/W7duTalUyoQJE3LZZZdl/PjxtY4HAAAAAOxFrNCB96mlpSWzZ8/Ov/zLv2T58uVJkqFDh+bv/u7v8o//+I9uSAsAAAAAvCcm8OE9uv/++3PBBRdkwYIFqVQq6dq1a4477rjMnTs3I0aMqHU8AAAAAGAvZzQY3oUVK1Zk1qxZuf3227N27doUCoWMHj06M2fOzJQpU2odDwAAAABoR0zgw9uoVCq5+eab881vfjO/+93vkiT77LNPpkyZkksvvTS9e/eucUIAAAAAoD1S4MMOPPPMM5k+fXruvffebNmyJaVSKePHj89ll12WCRMm1DoeAAAAANDOWaEDf2Lr1q2ZO3duvvOd72Tp0qVJkv333z+nn356zjnnnHTq1KnGCQEAAACAjsIEPiR58MEHc/755+c///M/Uy6X07lz50yaNClz587NqFGjah0PAAAAAOiATODTYa1evTqzZs3KrbfemjVr1iRJPvjBD2b69On5/Oc/n2KxWNuAAAAAAECHZgKfDqVSqeQHP/hB5s6dm0WLFiVJmpubc/LJJ+eyyy5L3759a5wQAAAAAOCPFPhsV1u5kmdXbMgTS9fmyaVr89TydVnX0prWcjWNpUJ6dmnMwQN6ZvSg5owZ1JwD9m1KQ6l+J9afe+65TJ8+PXfffXdaWlpSLBZz1FFH5ZJLLsnEiRNrHQ8AAAAA4C0U+LzB86s25vsLluTWBS9mw5a2JElDsZC2yls/Jn96vKlzQ6aMG5zPjRuaYX2779HMO9LW1pZvfetbuf766/PCCy8kSQYMGJBp06ZlxowZ6dKlS40TAgAAAADsmAKfJMnjL7yWb9//+zyyeFVKhULK7+Fj8T/PO3pk33zt2FEZO7jXrg/6DsyfPz/nnXdeHnnkkZTL5XTq1CnHHnts5syZk9GjR9ckEwAAAADAu6XA7+BaWsu58ue/z3d+8YcUi4WUtzNp/26VioVUKtVMmzA8Xz3mwHRpLO2CpDu3du3aXHDBBbnllluyevXqJMmBBx6Ys88+O1/+8pfdkBYAAAAA2Oso8DuwJ5auzT/c+qssWb0pu+NTUCgkw3p3y9VTDs2YQc27/gJJbr/99syePTu//e1vU61W07Nnz/zN3/xNZs+enf79+++WawIAAAAA7AkK/A7qkcWr8rc3L0xbufqe1uW8U6VCIQ2lQr536hE5emTfXfKaS5YsyfTp0/OTn/wkmzZtSrFYzOGHH56LLrookyZN2iXXAAAAAACoNQV+B/TI4lX54k0LUqlWsws25rytYiEpFgr516njdlriv/rqq1m7dm2GDx/+lsfa2tpy1VVX5dprr83zzz+fJNlvv/3ypS99KbNmzUq3bt12V3wAAAAAgJpQ4HcwTyxdm5NueDRby5XdsjZnR4qFpLFUzP85ffx21+m88sorOfLII7Np06YsXbo0jY2NSZKFCxdm5syZeeihh9LW1pbGxsZMnDgxc+bMydixY/fcGwAAAAAA2MMU+B1IS2s5n7rqF3lx9ebdujZnR4qFZGjvbvnZmRPecGPbdevW5eijj86iRYtSqVRy2223ZeHChfm3f/u3rFy5MkkycuTInHXWWTnjjDPckBYAAHSpG7UAAAmqSURBVAAA6BAU+B3InJ89ne88/Ic9Onn/ZoVCctqEEZnxqYOSJC0tLfnkJz+Z+fPnp1wuv+HcpqamTJ48OXPmzMmgQYNqERcAAAAAoGYU+B3E4y+8lr++/tHUwx92oZD86IyPZszAHjn++ONz7733vuWcefPmZerUqTVIBwAAAABQH+wi6SC+ff/vUywWah0jyR9vaHv+/34sQ4YM2W55nyQrVqzYw6kAAAAAAOqLAr8DeH7VxjyyeFXKlXqYv0/KlWqeWNmWzaXu6dOnT0aOHJl+/fq9Ybf9Aw88UMOEAAAAAAC111DrAOx+31+wJKVCoSY3rt2RYiE5+/of57zjDt52rFKpZNWqVXn55ZczcODAGqYDAAAAAKg9O/DbubZyJWMvvT8btrTVOspb9OjckMfP/0QaSr4IAgAAAADwZibw27lnV2zYaXm/5uHvZ+38W99wrKH3/hk07YYkSXnDa3ntP+Zl8/OPp7p1cxp775+ef/a/0v2gj247v7x5fVbff0M2L16QFIrpNmp8eh8zLcVOXXeabf2WtixeuSEH9e/5Pt4hAAAAAED7pMBv555YuvZtz2nsOyT7nfz11w/8yS76VT+9IpUtG7Lv35yfYrfmbFz0YFbd+Y009roynfqP+OM5P/lWyhtWZ7+TL0u13JZX7/nnvHrvten3V+e8o3wKfAAAAACAt7K7pJ17cunaNBQLOz+pWEqpaZ/Xf3Vr3vbQlqVPp8dhn0nngaPS2Kt/en305BQ7d8+WVxYnSVpXvZiWP/wyfSZ9JZ0HjkqXwR9K70+cnk1P/SJt61/d6WUbioU8+Q7+gwEAAAAAoCMygd/OPbV8XdoqO7/NQdtry/LStV9IodSYToMOyj5/fmoamvdNknQe9MFsevrhdB1xRIpdumfT0w+nWt6aLkPGJPljwV/s3D2dBxyw7fW6DBubFArZuuyZNIwav+PrVqpZtGzd+3+TAAAAAADtkAK/nVvX0rrTxzsPHJU+x301jb0HpbxhddbOvzUvf396Bv7tdSl27pZ+J07Pyju/kZeumpIUSyk0dk6/vz4vjfsMTJKUN65JsXuvN7xmoVhKsWuPlDeuedt861vq7+a6AAAAAAD1QIHfzrWWdz5933XE4a//Zt8PpPPAUXnp+i9l4+8eSY+PHJs1v7gllZaN2ffky1Lq2jObnv2vrPzxN9L/c99Ip32Hve98W8uV9/0aAAAAAADtkR347Vxj6W32379JsUtTGvcZlLbXlqX1teVZ/6ufps+nz0zXYWPTab/h6XX0Kencf2TW/+qnSZJS916pvGnSvlopp7J5fUpvmszfnk4lH0EAAAAAgO3RnrZzPbs0vqvzK1s3p23N8pSaeqfauiVJUii86WNSLCbVP072dx70wVS2bMyWlxdve7hlyW+SajWdBo562+v16OJLIAAAAAAA26PAb+cOHtAzDcUdT+G/9sD30vLCE2lb80paXno6K3/49aRQTPeD/zyNffZPwz4D8uq912bLsmfS+tryrHvsh2n571+n24FHJUka+w5Ol+GHZfXPrsmWZc+k5aWnsvq+G9Lt4Alp6NFnp9kaioV8aGDPXfp+AQAAAADai0K1Wt35knT2arf/3xfzT3f8doePr7zzG9ny4qKUN69LqVtzOu9/cHpN+EIa9xmQJGldvTRrHrw5LS89lWrr5jT0GpCeR/51mkb/5bbXKG9en9X335DNixckhUK6HTg+vT9xWoqdur5tvstP+nA+e9jg9/9GAQAAAADaGQV+O/f08nWZdPXDtY6xQ/ee+bEc1N8UPgAAAADAm1mh084dsG9TmjrX5575Hp0bMrJfU61jAAAAAADUJQV+O9dQKmbKuMEpFXa8B78WSoVCpowbkoaSjyAAAAAAwPZoTzuAz40bmnKdbUoqV6v53JFDah0DAAAAAKBuKfA7gGF9u+fokX1TKtbHFH6pWMjHRvbN0D7dax0FAAAAAKBuKfA7iK8dOyqVSn1M4Veq1Zx97KhaxwAAAAAAqGsK/A5i7OBemTZheGq9Cr+Q5LQJIzJ2cK/aBgEAAAAAqHMK/A7kq8ccmKG9u9XshralQjKsT7ecNfGAmlwfAAAAAGBvosDvQLo0lnLNlEPTUCpkT6/DLxaShlIxV085NF0aS3v24gAAAAAAeyEFfgczZlBzvnfqESkW9lyJXywkxUIh8049ImMGNe+ZiwIAAAAA7OUU+B3Q0SP75l+njktjqbjb1+kUC0ljqZibp47LR0f23a3XAgAAAABoTwrVarVa6xDUxhNL1+Yfbv1VlqzelN3xKSgUkmG9u+XqKYeavAcAAAAAeJcU+B1cS2s5V/789/nOw39IsVBIufL+Pw6lYiGVajWnTRiRsyYeYOc9AAAAAMB7oMAnSfL4C6/livt/n4cXr0qpUEj5PXws/ud5HxvZN2cfOypjB/fa9UEBAAAAADoIBT5v8PyqjfnBghdy64IXsn5LW5KkoVhI23Ym8//0eI/ODZkybkg+d+SQDO3TfY9mBgAAAABojxT4bFdbuZLFKzfkiaVr8+TStVm0bF3Wt7Rla7mSTqVienRpyIcG9szoQc0ZM6g5I/s1paHknsgAAAAAALuKAh8AAAAAAOqQkWkAAAAAAKhDCnwAAAAAAKhDCnwAAAAAAKhDCnwAAAAAAKhDCnwAAAAAAKhDCnwAAAAAAKhDCnwAAAAAAKhDCnwAAAAAAKhDCnwAAAAAAKhDCnwAAAAAAKhDCnwAAAAAAKhDCnwAAAAAAKhDCnwAAAAAAKhDCnwAAAAAAKhDCnwAAAAAAKhDCnwAAAAAAKhDCnwAAAAAAKhDCnwAAAAAAKhDCnwAAAAAAKhDCnwAAAAAAKhDCnwAAAAAAKhDCnwAAAAAAKhDCnwAAAAAAKhDCnwAAAAAAKhDCnwAAAAAAKhDCnwAAAAAAKhDCnwAAAAAAKhDCnwAAAAAAKhDCnwAAAAAAKhDCnwAAAAAAKhDCnwAAAAAAKhDCnwAAAAAAKhDCnwAAAAAAKhDCnwAAAAAAKhDCnwAAAAAAKhDCnwAAAAAAKhDCnwAAAAAAKhDCnwAAAAAAKhDCnwAAAAAAKhDCnwAAAAAAKhDCnwAAAAAAKhDCnwAAAAAAKhDCnwAAAAAAKhDCnwAAAAAAKhDCnwAAAAAAKhDCnwAAAAAAKhDCnwAAAAAAKhDCnwAAAAAAKhDCnwAAAAAAKhDCnwAAAAAAKhDCnwAAAAAAKhDCnwAAAAAAKhDCnwAAAAAAKhDCnwAAAAAAKhDCnwAAAAAAKhDCnwAAAAAAKhDCnwAAAAAAKhD/w/MCF44/gOAJAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "execution_count": 10
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Visualizing the Legal Knowledge Graph"
      ],
      "metadata": {
        "id": "4RfEvjst4etJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This section visualizes the full legal knowledge graph as an interactive network to facilitate inspection and analysis of structural relationships between legal articles. Nodes represent individual law articles, while directed edges encode relational links such as sequential ordering, citations, or doctrinal dependencies.\n",
        "\n",
        "This visualization complements quantitative retrieval metrics by providing a **qualitative view** of legal structure and coverage.\n"
      ],
      "metadata": {
        "id": "jiuFQsd14etK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#| echo: false\n",
        "#| code-fold: false\n",
        "\n",
        "from pyvis.network import Network\n",
        "import os\n",
        "\n",
        "# Create network (notebook=True for inline display in Jupyter/Quarto)\n",
        "net = Network(\n",
        "    notebook=True,\n",
        "    height=\"900px\",\n",
        "    width=\"100%\",\n",
        "    bgcolor=\"#ffffff\",\n",
        "    font_color=\"black\",\n",
        "    cdn_resources=\"in_line\"\n",
        ")\n",
        "\n",
        "# Add nodes with Chinese-friendly styling\n",
        "for n in G.nodes():\n",
        "    net.add_node(\n",
        "        n,\n",
        "        label=str(n),\n",
        "        title=f\"Article {n}\",\n",
        "        shape=\"circle\",\n",
        "        size=25,\n",
        "        color={\n",
        "            \"background\": \"#97c2fc\",\n",
        "            \"border\": \"#2b7ce9\"\n",
        "        },\n",
        "        font={\n",
        "            \"color\": \"#000000\",\n",
        "            \"size\": 14,\n",
        "            \"face\": \"Microsoft YaHei, Helvetica, Arial, sans-serif\",\n",
        "            \"align\": \"middle\",\n",
        "            \"multi\": True,\n",
        "            \"bold\": True\n",
        "        }\n",
        "    )\n",
        "\n",
        "# Add edges\n",
        "for u, v, data in G.edges(data=True):\n",
        "    net.add_edge(\n",
        "        u, v,\n",
        "        label=data.get(\"relation\", \"\"),\n",
        "        font={\"size\": 11, \"align\": \"middle\"}\n",
        "    )\n",
        "\n",
        "# Save to file (optional, for external use)\n",
        "GRAPH_PATH = \"data/graph/law_graph.html\"\n",
        "net.write_html(GRAPH_PATH)\n"
      ],
      "metadata": {
        "trusted": true,
        "id": "WqtcPn9K4etK"
      },
      "outputs": [],
      "execution_count": 11
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "<iframe\n",
        "  src=\"../data/graph/law_graph.html\"\n",
        "  width=\"100%\"\n",
        "  height=\"900\"\n",
        "  style=\"border: none;\"\n",
        "  loading=\"lazy\">\n",
        "</iframe>\n"
      ],
      "metadata": {
        "id": "fodOFCU0Ivi4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#| include: false\n",
        "\n",
        "# from IPython.display import HTML, display\n",
        "\n",
        "# with open(GRAPH_PATH, \"r\", encoding=\"utf-8\") as f:\n",
        "#     display(HTML(f.read()))"
      ],
      "metadata": {
        "trusted": true,
        "id": "E6AgOOs-4etK"
      },
      "outputs": [],
      "execution_count": 12
    }
  ]
}