CS224W_Colab_4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XuXWJLEm2UWS"
   },
   "source": [
    "# **CS224W - Colab 4**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8gzsP50bF6Gb"
   },
   "source": [
    "In this Colab, we will shift our focus from homogenous graphs to heterogeneous graphs. Heterogeneous graphs extend the traditional homogenous graphs that we have seen before by specifically incorperating different node and edge types. This additional information allows us to extend the graph neural nework models that we have worked with before. Namely, we can apply heterogenous message passing, where different message types now exist between different node, edge type relationships. \n",
    "\n",
    "At first, we will learn how to transform NetworkX graphs into DeepSNAP representations. Additionally, we will dive deeper into how DeepSNAP stores and represents heterogeneous graphs as PyTorch Tensors.\n",
    "\n",
    "Then, we will build our own heterogenous graph neural netowrk models using PyTorch Geonetric and DeepSNAP on node property prediction task. To evaluate these models, we will use our model on the heterogeneous ACM dataset.\n",
    "\n",
    "**Note**: Make sure to **sequentially run all the cells in each section**, so that the intermediate variables / packages will carry over to the next cell\n",
    "\n",
    "Have fun on Colab 4 :)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MSaetj53YnT6"
   },
   "source": [
    "# Device\n",
    "You might need to use GPU for this Colab.\n",
    "\n",
    "Please click `Runtime` and then `Change runtime type`. Then set the `hardware accelerator` to **GPU**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "67gOQITlCNQi"
   },
   "source": [
    "# Installation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_VkxWVavcGI4",
    "outputId": "68dde3e9-faa8-4e7c-bd9b-982503a9a24a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.10.0+cu111\n",
      "11.1\n"
     ]
    }
   ],
   "source": [
    "!python -c \"import torch; print(torch.__version__)\"\n",
    "!python -c \"import torch; print(torch.version.cuda)\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "10JpXxpfeP_W"
   },
   "source": [
    "### Attention! \n",
    "\n",
    "Please check the torch version and cuda version of google colab so you can install the appropriate version of torch-scatter and torch-sparse. \n",
    "\n",
    "The output from previous code chunk show that my Google Colab run with Pytorch 1.10.0 and cuda 10.2. So, my command for installing should be \n",
    "\n",
    "!pip install -q torch-scatter -f https://pytorch-geometric.com/whl/torch-1.10.0+cu111.html\n",
    "\n",
    "!pip install -q torch-sparse -f https://pytorch-geometric.com/whl/torch-1.10.0+cu111.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yI39c3egCyq3",
    "outputId": "9668de9a-118e-407b-f7e8-d5d5157157d7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[K     |████████████████████████████████| 7.9 MB 5.3 MB/s \n",
      "\u001b[K     |████████████████████████████████| 3.5 MB 5.4 MB/s \n",
      "\u001b[K     |████████████████████████████████| 407 kB 5.5 MB/s \n",
      "\u001b[?25h  Building wheel for torch-geometric (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Building wheel for deepsnap (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
     ]
    }
   ],
   "source": [
    "!pip install -q torch-scatter -f https://pytorch-geometric.com/whl/torch-1.10.0+cu111.html\n",
    "!pip install -q torch-sparse -f https://pytorch-geometric.com/whl/torch-1.10.0+cu111.html\n",
    "!pip install -q torch-geometric\n",
    "!pip install -q git+https://github.com/snap-stanford/deepsnap.git\n",
    "!pip install -U -q PyDrive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "id": "PRfgbfTjCRD_",
    "outputId": "9d85630d-a467-4051-91f2-017ac0768389"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'2.0.4'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch_geometric\n",
    "torch_geometric.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZoXlf4MtYrbz"
   },
   "source": [
    "# 1 DeepSNAP Heterogeneous Graph\n",
    "\n",
    "First, we will explore how to transform NetworkX graphs into the format supported by DeepSNAP. \n",
    "\n",
    "In order to extend the traditional DeepSNAP representation of graphs to include heterogenous graphs, we include the following graph property features:\n",
    "* `node_feature`: The feature of each node (`torch.tensor`)\n",
    "* `edge_feature`: The feautre of each edge (`torch.tensor`)\n",
    "* `node_label`: The label of each node (`int`)\n",
    "* `node_type`: The node type of each node (`string`)\n",
    "* `edge_type`: The edge type of each edge (`string`)\n",
    "\n",
    "The key new features we add are `node_type` and `edge_type`, which enables us to perform heterogenous message passing.\n",
    "\n",
    "In this first question we will work with the familiar [karate club graph](https://networkx.github.io/documentation/stable/auto_examples/graph/plot_karate_club.html) seen in assignment one. To start, since each node in the graph belongs to one of two clubs (club \"Mr. Hi\" or club \"Officer\"), we will treat the club as the `node_type`. The code below demonstrates how to differentiate the nodes in the NetworkX graph.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 535
    },
    "id": "8LQ_z5gcBVA1",
    "outputId": "ed9d2dec-7987-42c8-a205-3b04faf2e089"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pylab import *\n",
    "import networkx as nx\n",
    "from networkx.algorithms.community import greedy_modularity_communities\n",
    "import matplotlib.pyplot as plt\n",
    "import copy\n",
    "\n",
    "G = nx.karate_club_graph()\n",
    "community_map = {}\n",
    "for node in G.nodes(data=True):\n",
    "    if node[1][\"club\"] == \"Mr. Hi\":\n",
    "        community_map[node[0]] = 0\n",
    "    else:\n",
    "        community_map[node[0]] = 1\n",
    "        \n",
    "node_color = []\n",
    "color_map = {0: 0, 1: 1}\n",
    "node_color = [color_map[community_map[node]] for node in G.nodes()]\n",
    "pos = nx.spring_layout(G)\n",
    "plt.figure(figsize=(7, 7))\n",
    "nx.draw(G, pos=pos, cmap=plt.get_cmap('coolwarm'), node_color=node_color)\n",
    "show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1JpFb9fTw1lg"
   },
   "source": [
    "### Question 1.1: Assigning Node Type and Node Features (Not Specifically Graded)\n",
    "\n",
    "Using the `community_map` dictionary and graph `G` from above, add node attributes `node_type` and `node_label` to the graph G. Namely, for `node_type` assign nodes in the \"Mr. Hi\" club to a node type `n0` and nodes in club \"Officer\" a node type `n1`. \n",
    "\n",
    "Then for `node_label`, assign nodes in \"Mr. Hi\" club to a `node_label` `0` and nodes in club \"Officer\" a `node_label` of `1`.\n",
    "\n",
    "Lastly, assign every node a feature vector [1, 1, 1, 1, 1]. \n",
    "\n",
    "**Hint**: Look at the NetworkX function `nx.classes.function.set_node_attributes`.\n",
    "\n",
    "**Note**: This question is not specifically graded but is important for later questions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "zev_hMJHJXK1"
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "def assign_node_types(G, community_map):\n",
    "    # TODO: Implement this function that takes in a NetworkX graph\n",
    "    # G and community map assignment (mapping node ids --> 0/1 labels)\n",
    "    # and adds 'node_type' as a node_attribute in G.\n",
    "\n",
    "    ############# Your code here ############\n",
    "    ## (~2 line of code)\n",
    "    ## Note\n",
    "    ## 1. Look up NetworkX `nx.classes.function.set_node_attributes`\n",
    "    node_type_map = {0:'n0', 1:'n1'}\n",
    "    node_types = {node:node_type_map[community_map[node]] for node in G.nodes()}\n",
    "    nx.set_node_attributes(G, node_types, 'node_type')\n",
    "    \n",
    "    #########################################\n",
    "\n",
    "def assign_node_labels(G, community_map):\n",
    "    # TODO: Implement this function that takes in a NetworkX graph\n",
    "    # G and community map assignment (mapping node ids --> 0/1 labels)\n",
    "    # and adds 'node_label' as a node_attribute in G.\n",
    "\n",
    "    ############# Your code here ############\n",
    "    ## (~2 line of code)\n",
    "    ## Note\n",
    "    ## 1. Look up NetworkX `nx.classes.function.set_node_attributes`\n",
    "    node_label_map = {0:0, 1:1}\n",
    "    node_labels = {node:node_label_map[community_map[node]] for node in G.nodes()}\n",
    "    nx.set_node_attributes(G, node_labels, 'node_label')\n",
    "    \n",
    "    #########################################\n",
    "\n",
    "def assign_node_features(G):\n",
    "    # TODO: Implement this function that takes in a NetworkX graph\n",
    "    # G and adds 'node_feature' as a node_attribute in G. Each node\n",
    "    # in the graph has the same feature vector [1., 1., 1., 1., 1.]\n",
    "\n",
    "    ############# Your code here ############\n",
    "    ## (~2 line of code)\n",
    "    ## Note\n",
    "    ## 1. Look up NetworkX `nx.classes.function.set_node_attributes`\n",
    "    node_features = {node:[1., 1., 1., 1., 1.] for node in G.nodes()}\n",
    "    nx.set_node_attributes(G, node_features, 'node_feature')\n",
    "    \n",
    "    #########################################\n",
    "\n",
    "assign_node_types(G, community_map)\n",
    "assign_node_labels(G, community_map)\n",
    "assign_node_features(G)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mafN0P3EOhSb"
   },
   "source": [
    "### Question 1.2: Assigning Edge Types (Not Specifically Graded)\n",
    "\n",
    "Next, we will assign three different types of `edge_type` to the edges: \n",
    "* Edges within club \"Mr. Hi\": `e0`\n",
    "* Edges within club \"Officer\": `e1`\n",
    "* Edges between clubs: `e2`\n",
    "\n",
    "**Hint**: Use the `community_map` from before and `nx.classes.function.set_edge_attributes`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "rsbYWEVwSV5n"
   },
   "outputs": [],
   "source": [
    "def assign_edge_types(G, community_map):\n",
    "    # TODO: Implement this function that takes in a NetworkX graph\n",
    "    # G and community map assignment (mapping node ids --> 0/1 labels)\n",
    "    # and adds 'edge_type' as a edge_attribute in G.\n",
    "\n",
    "    ############# Your code here ############\n",
    "    ## (~5 line of code)\n",
    "    ## Note\n",
    "    ## 1. Create an edge assignment dict following rules above\n",
    "    edge_type_map = {'00':'e0', '11':'e1', '01':'e2', '10':'e2'}\n",
    "    edge_types = {}\n",
    "    \n",
    "    for edge in G.edges:\n",
    "        class_node_0 = str(community_map[edge[0]])\n",
    "        class_node_1 = str(community_map[edge[1]])\n",
    "        edge_type = class_node_0 + class_node_1\n",
    "        edge_types[edge] = edge_type_map[edge_type]\n",
    "    \n",
    "    nx.set_edge_attributes(G, edge_types, 'edge_type')\n",
    "    #########################################\n",
    "\n",
    "assign_edge_types(G, community_map)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VBsTdTPVTQ52"
   },
   "source": [
    "## Heterogeneous Graph Visualization\n",
    "\n",
    "Now we can visualize the Heterogeneous Graph we have generated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 607
    },
    "id": "n2sdufbODHtp",
    "outputId": "85c4b1c9-31d3-48ff-c40c-b8ea2705e060"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Dt3nxAvjmG2qEmQe302HM0vh/AcvfdKxtyHGDQyuvk5Da25mkUaNUbsH/1hnrnK5fz7uB0/btwI0bwNWrKLp9O+p180Lt7TsQczUAysgYSGRS2LqWQPFm9SFNG0wVFH/9BQwZAnz7LfDZZ8a1EhACGDeOiv4bNDD/GBnLBzhwYvmaRCaD1N4O2lxmWYRGC3mJ4iYaVc7Yly9DncFzSe5awgSjMVJKxglIDZxGjLDc/RsrKIj2VfP3p2Bv7Vpg/nzYFHGEa7vm1h6deQkBLFxIrQZ+/RVo18742/72G3D/Pm2twhgDwDVOrAAo1bmVUZ+eU5oaZvyefraBk2d1s43RGEXrVIOdEVklvY/D3g6VPv3IbGPMJG3GycsrbxaIq9XA4MHAtGmUNblyhVooGFPbk9/FxVEB+P79tHVKdoKmyEhgwgRgwwbAzs58Y2Qsn+HAieV7Hl94G7V/WFYNDqX2dqg09hNIrVy/IZFIUHXyKMiK6q870deoEUKg4kgLBk5pM04NGwK3b9NKu7xk9mzA2RmYOJF+XruWpp4K+pL6hw+BFi2A4sWp7YC7e/Zu/+WXtF9dHt28mTFr4QaYLN8TQuBkjc5ICgnN0e2lEqDDhd2wb9LIxCPLPk1iEo67t4YmLiHbt5XYylG6e3s08VtlhpFloUsXYPJkoGtX+rl6ddrfrG5dy41Bn7NnKeNy/TpQrlxq086HD6mgvaA6fJg24Z07F/g0Bx3P//qLbnfnjv7eTowVQgX8IxcrDCQSCeqv/wFSh+zvkyZzsINHERXsWzUHFi2yeoG4zNEBTfx+zvZjkciksCvtivq/zDHTyLKQsu1KirzUzyk6moqh16+noAkAtm0D3n234AZNWi0waxYwZgxw8GDOgqb4eMrIrV3LQRNjOnDgxAqEku1boP4vc7MVcMgcHVDuo16oEf6A3ii+/RaoXx8ICTHfQI1QsmNLNNr2o9GPRQIBO2dHtDy1E7aWLAwHUvs4pcgrgZMQFDz06pW675oQFAz4+Fh3bOYSHQ28/z5th3L1KtCypd7Ds5xsmD4daNuWAkzGWCYcOLECw21gbzT57WfISzhD5lQky+Nkjg6Q2tuh6tefov66H6iPz8qVNK0TFgbUrAksWZKuqaallX2/M1r+vR0u7zSB1N4OEtvMy+NlRRwhc7RHhVL2aJvwEI7Rry0/0Lyacdq2jTYdXrw49bJz56j+KjsF0vnF3bu0dUrlyhQ4lS2b6RCh1SL82Glc7DQEx4o1xFG72jjqWBfHy7fEvamLkPjkOXDxIrBnD/DTT5Z/DIzlE1zjxAocrUqFsMN/4/Hi9Yi9ff+/LtESaFVq2LoWh8cX3nAf+gHkxZ0z3zgxkbaW8PMDatem5dhVq1r+QaQdUvAzBK/ahog/z0IdGweJjQ3sSrmiwsgBcBvYGzavI4B69Wj66fZtoEjWQaPJjRtHz9P48fTz69eAhwdlP6xVfP34MRVFnzhBGcQUgwcDTZsWvO7Xfn6UXVuyBBg2TOchz3cdxr+T50OTkARNfGKm6yW2tpBIgBJSJRos/AoOY0aZe9SM5VscOLECTfkmCsrXURBqDeQlnGFXrjQkxjT+O3oUGDQIUChoVdYXXwAymfkHnFPLlgELFgA9ewIbN1rufidPBkqVAqZMSb2sUiXql1TdCu0dVCqgTRv63aWsogOAiAgaT1AQ4OJi+XGZg0ZD08u7d1OA7+Wl87CH36+gzaMTDfc6k0gAG5fiaPn3DjjV4T3pGNOFp+pYgWbrWgJFa3rAqW512JcvY1zQBAA9elDmon17Krb18gIeWGgPuJz47DOgQgXgyBGaarGUjDVOgHX7OX3/PQVGEyakv3zzZqr/KShB05s3QPfu1JPqypUsg6bglVsR9JOvUUETQGVgqsgYXOr0CZKfh5lyxIwVGBw4MZYVV1fKPK1eDQQGUv3OwoXUUDGvkcmATZtok91x42hPMkvIWOMEWK/O6cwZyrZt3py+IapWC6xbV3CKwm/eBJo0oWaef/5JGT8dkp+H4f60JdAkJum8XiKXo/Hu5ejw6AR6qh7ApW0zukIIKKNiETB+prkeAWP5GgdOjOkjkVDdyN27tDHq/Pn06f7uXWuPLDNPT5qeKlsWGDiQpq3MTVfGqXFj4No18993WlFR1Hpg40agTIaNef/+mwK8Fi0sOyZz2LGDemctWECF73qatob8stNge43IC9dxc/gUJP+3mfRbGg1eHz+fbr9ExhjhwIkxY1SqBFy4QDUlQUH0Jjx3rmWCk+z45hvKsCiVwHffmf/+9GWcLFU+KQRlk95/n6ZYM0ppQWDsNG1epFJRUfvMmRQIfqS/O7xWpcKT1TugVSjR4dEJeEwagXeuH0LX11fRaMdSSO1sIVQqhKzYgqjz1yA0OlaQSiR4usGC076M5RMcODFmLJmMiqHPnwfc3KiFgZcXcOuWtUeWys6Osi7PnwNbtgDHj5v3/nRlnMqWBeztgSdPzHvfKbZsAf79lxqYZvTiBa2uGzzYMmMxh7AwoHNn6nZ+5QqtoDQg6uKNdIFruQHdcbnnSJys3gnO9WrCfVhfg+fQJisQumVfrobOWEHEgRNj2VW/PtWZfPIJ8OwZNQucOZOyPHlBy5Y0VVevHk0zhpmxyFdXxgmwXJ1TYCAFs7t2UbCWka8vbbnirKP1RH5w+TK1UGjblrZRKWFcg1Nl+Jt0DS5DVm2D4mU4VFExCPv9JJwb1DbqPKrImBwNm7GCjAMnxnLC3h748Udg3z7AyYmyHtao7cnK3LnAo0f0hjtsmPmaeerKOAGWCZxUKmo7MGMG1XdlpNFQUfjo0eYdh7ls3EjtJVasAObMyVY7DK1KDSA1cEpbq6RJTIKNgY2kUwh1HtuwmbE8gAMnxnKjQwfaCLVNG1oi3qULMG0akGzc8m+zKVoU+OUX4NIlIDISWLrUPPdjzYzTzJm0oiyl+WZGx47RtGHjxuYdh6kpFFSTtWQJdbPv0yfbp5CXcIZEkvs/7zIjAyzGChMOnBjLreLFge3bqQmlVAocOAA0akRBizV17Up9qOrWpTYKV6+a/j6yyjh5eVH2zVwF4qdOUduBTZuyLvrOj/vSvXhBv7OwMOCff4BatXJ0muJN60ObrDB4nNRWDqmdbabvAdo4umRH/fvdMVYYceDEmKl89BEViru5Ua+nXr2Ar77SHVhYyk8/Ua+fiROBjz8GYmNNe/6sMk7u7jQ9+PKlae8PoAza0KFUv1S6tO5jnjyhVZAGVp/lKefOUT1Tr17UCTwndVlxccD69bDt2R1lJImAgYWE7e4eQ/f4ADi4l0XzP3zp+0puAACpnR08Jo3IwQNhrGDjLVcYMzWtFli1irpYp+zb5usLvPOOdcbz6680lhYtaBpo2zbTLc0PCqIVX0FBma/r1o2m0Xr3Ns19AZTB+vBDCk6XLcv6uO++A2JiqD4orxOCmqzOnk1ZtO7ds3/7CxeoJmr/fspYDR2KKP9z+GfdYWgMRU9ZKFq7KtrdPpqj2zJWkHHGiVmNKiYOwau24frgSfin+/9wtd843Ju6CHH3Hlt7aLkjldKWH2fPUoFy8eJA//50WUKC5cczYABQrRrV+9y4AWzdarpzOzjozjgB5qlz2rSJluUvWJD1MSoVBRH5oSg8KQn43/+oHu3ChewFTeHhVAdVpw4wYgRN6927R8HqtGkoHnwPLm2bQmpvl+1hSR3sUXe5BfqAMZYPccaJWVz8gyAELliLl37HIJFJoUlIncqS2NhAIpfBqU51VJvqgzLvdzZ+f7m8SKmkve58fYHatWkKaeNGKiq3pOfPgYYN6Q169GjqRVWjRu7PGxMDVKxI/2a0dy/Vfh08mPv7AShgat2a6pvq1s36uN9+A5Yvpy1Y8rInT4C+fen3sGEDUKSI4dtoNMBff9HxJ05Q4bi3Ny1OeP4c+PJLamGwfDnQuzc0yQpcaDcQ8fcfQ5tkuOYJoLjfs3UtVDhxIH83DWXMTDjjxCwq/I/TONesL17sOgJtsiJd0AQAQq2GNkmBmGt3cHP4ZAT4fAehycdLom1tgXnzAD8/eqOsXp36P40ZY/p6I33c3IAffqAmkbNmUb2Twrg3Ur0slXFSKqn1wKxZ+oMmIDU4zMv+/hto3pwe086dhoOm4GCafqxcmVYTdu1Kr6fNm+k8ixdTYFyrFjUDfe89QCKBzMEerU7vQqnObSB1tIdET0sDWREHyBwd0GjLElRIDAO++MJy3d8Zy0c448QsJsL/PK72HQttkvFL9WWO9ijb71002Lggf2eeAAqUJk0CTp6kN//bt6nPULdulrl/rRbo2JG2Jjl7ljJF+uqEjCEEIJfTlJNcnvm6EiUoU5RVEbexpk6l/QEPHdKfBQkMpAagz57pbohpbUJQ/68ff6R95zp2zPrY5GSqWdq4kRquDh5M2aX69VOP8fcHPvuMaulWrACqVs3ydLEBDxC8fDNe/HoUUls5jUUigVCrYVvSBR6TR8F9UG/YOBWlvf86dqQtbH74wYRPAGP5HwdOzCIUYa9xslYXaOJ1ZyckcjkabVuCYl6ecKzsjoudhiDyzGUAgMzRAXWWT0fF4f0tOWTzOXCAlsl37EhTZp070xtp8eLmv+9HjyiwOH6cpnl+/plWceWGkxNNE+laBdahAwU9uQkOT56koOHmTcMB2JQpFBAsXpzz+zOXhAQKfAIDqXFqxYq6j7t1i4KlnTuprYW3N/2u0gaCoaGZpuWMnVZTxcYjLuABVDFxkNnZwra0K5w8a2T+YBIRQYXmgwbRHo2MMQA8Vccs5OmGPQa7EGe1U7smMQmBc39GgYnx+/ShICA2FnB1pakuT0/gyBHz33f16hRcTJlC9UcjR1LQkxuOjlm3XPDyyt103Zs31Hpg0ybDQZNCQVNXn36a8/szl8ePKWB1cEjN9qUVE0N9p1LaERQvTvvSHT9O06opQZNSSdOtOqbljCV3LgqX1l4o06M9SnZqBed6NXVnc0uVojqqrVuprQVjDAAHTswChEaD4BVboU1W5HinduXraESdzyPbmZhC2bK099jo0TTd0rcvrbobMoT6FJnTF1/QfTx+DIwbRzVXuakjM1edkxAUBA0YYFzG6rffgAYNKDjMS/74A2jVirKMvr70fAH0+M6epS1xKlWi18Hs2UBICP1bpUr68/j70+M7fZqaY37/feq5zKVsWbrfVauoZQJjjAMnZn4Rx89Bq1K9/TknO7VrEpMQvGKLOYdpeRJJ6gq3f/6hOhU7O8o+7d9vvvu1saGpoClTaBk7AMyfn/PzZdUEE8jd/n0bN1JwZ+zY8lqncK2W9gwcOZKCurFj6Xf+6hV1cq9Vi37/9etTHZifH7UjyFjAHRpKjTxHjaLbHTmit5bJ5CpUoMzTggWU+WOskOPAiZldQuBTCGVq4JSjndqFQPwDHU0WC4IaNSh4eucdykKNGQN8/TW9WUZEGL59TjRsSG/on39OU3arVtEYciKrbVcAyv5ERFCxcXY8eEC1UTt3UjBpyN27VL/13nvZux9ziY0F+vUDjh6lKbcWLeh326cPtaV4+JCCkLt3qVZJ1zSkCablTKZKFco8TZ8O7Npl+ftnLA/hwImZnSYhEVq1+u3POd2pXR1nheaRlmJjQ8vMDx+mzt5NmgBlygD16gF79phnWfiMGbSy78oVYP16KgLOboAD6M84yWQ0vXTjhvHnS2k9MGcONXc0xrp1VESdcWWfNdy/Ty0CypalfkurVtFU3Lx5VL/09Cll01q1yjoIssa0nCE1atD2PZMmmTcjylgex4ETMzubokUgNcEbmo1TUROMJo9r1oyCjOLFaen9zJlU79KvH03xmJK9PQVM48dTtqtPH8pCZTdI05dxArJf5zR9Ou11Z+y0W2IiZc1GjTL+PszlwAFqRtm6NQVQ7dtTW4G//gIuXqTn18kp69uHhtKWMtaaljPE05OyaD4+9C9jhRAHTszsitbygERuY/A4fTu1AwJOIQ9oGmvPHtMHEXlJkSJUiLt6NWVdunWjKa8GDSgbZcrsU9u2tJR98mSaFgoKogaS2aEv4wRkL3A6cYKm5zZuNH5Kas8eWrFWqZJxx5uDRkNB0ZAhtOVLaCjVND17RivSDDXtTMmqlokAACAASURBVDstV7u2daflDGncmLrBDx9Ovy/GChkOnJjZuXZoAZmD4WaE+nZql9nIUEUaR3+wZ82iaYPatQt2INWjB/X0CQ6mlVkrVtDeZL17576FQFoLF9IUzIULwO7d1KH6zh3jb2+qjNObN7TCbNMmoGRJ4+/fmkXhUVHUM8rFhbJeY8YAAQHAsWO0GtCY+qy8OC1nSIsWVMz+8cfAuXPWHg1jFsUNMJlFBC5ej0dzVmWra3hajtUqoX3AUUguXqQ90H77jTIdVatS/56bN6mmpH17+mrXjn4uCISg/kRTpgBffUXZndWraZXTiBGmyUocOUKF4rdvA7/+SsHAlSv0HBvi7U0Zn5EjdV+vVgPFilFwm9U0lRDUkqFqVQoOjXX9Ok0xBgdnXo1mLkJQkLNhA02narW0BcrOndnrVh4aSq0hrlzJdhPLPOP4cWpOeuQITTMzVghwxolZRMUR/SE1YrpOF5mjA2rMnACJjQ3V4qxYQVMgW7ZQ1ikwEChXjq4rWpS2sqhdm1Yi+fhQFuXlSxM/IguSSID//Y+6RB8+TJvXbt9OwdO779KeZbnVqxc1X5w1i7I+DRtSEbAxDGWcbGyoNubmzayPWb+e+hdld3uPX36heiBLBE0vXlCBd/XqtM2JVErF6L/8Qp3AjQ2a8tO0nCFdutC0au/e+n+/jBUgHDgxi7B1LYGmh9ZB5pi9/cNkjg5wH9YXbh9n2BZEKqVVSUuXUuDg60tbfuzdS0vZx4xJndLbtYtWZ+X3QKpKFcp0dOtGtTQTJlBmrUkTYM0aynzkxvLlFIxeu0bnO3GCnk9DDNU4Afqn6+7dA6ZNo9+TMVNbKeLiKDvm7W38bbJLpaKC7969qU4pJISeo65daYrqxAnKuBgrP07LGdK7N23d0707BYGMFXA8Vccs6s2Zy7jyvg+0CgWESq33WJmjAyr5DESt+ZMhkRoZ4wsBXL1K9Rd791Imon9/4IMPKDtw+jRw6hRlbUqXTj+1V65cbh+e5dy4QR2/PT2BiRNpysfRkaaPPDxyft7t22ma7upVyiD07EmZrsqVs77NzJmULZk1K+tj1q+nQGNLhiamCgXVy/j4UDPI7Fi7lqaKfvste7czxsOHlEnZupWmD729qWYpKYnqemxsaGrO1dW48xWEaTlDtm+n3lsnT+a97u2MmRBnnJhFubZthna3jqCSzyDIijhClqGHk9TOFlJ7O5Ts1ApN9q9B7YVfGx80AfRm1LQpFTw/fkyF4wD1BerXj6Zbpk0DwsMp81SrFv1bt27+ykg1akTBTfny1Chz1iwqJm/WjKYyc5p9GjwYcHOj4KlpU6qrGjSIMi9ZyU3G6dtvaTVcdveXE8L0ReEJCRTYtW1L075aLQUB587RVOmDB/ScNGtGS/GNCZoK0rScIZ98Qq/Dzp0pM8dYAcUZJ2Y1mqRkvPQ7hujLt6B8EwVZEUc4elSA++D34VCxvGnvTAgqfN67l76SkykTNWAAvREKQauhTp1KzUiVKpU+I1XexGMyFX9/emPv25cyI2PH0uW+vjRVmV1PntDmvOfO0e179KCfs6o/WrGCunavXJn1ORUK6k315k1qwfnx41TcfuNG9lbRAcClS/RG/fAhTdvmlBA0NblhA037tWxJz2GvXoBtmnYYW7ZQYf7atRSAG8Pfn2qhPDzoOcpL/ZjMaeVKYNky+j/k5mbt0TBmeoKxwkarFeL2bSFmzBCidm0h3N2FmDhRiHPnhNBo6BiNRoibN4VYtkyIPn2EKFFCiBo1hPj0UyF27hTi+XPrPoaM3rwR4qOP6PFcuSLE8uVCuLoKsXixEGp19s+3cqUQbdrQ8/DqlRDlywtx4oTuY9evF2LECMPnbNhQiEuX6PuICCHc3ITw98/+2IQQYtgwIRYtytlthaDna/lyIerXF6JKFSHmzBHi2bPMxykUQowbR7/7u3eNO/ezZ0IMGCBE5cpCHDxIr7fCZuFCIWrWpNcOYwUMT9Wxwkcioa1Mvv+epk7+/JP68Pj40IamEyZQtiWlfmj/fuD1a8pI1KlD/3p6AjVrUl3Orl00BWhNLi40xTh9OmWI4uOpL9Pvv1MRfXaLdseOpamqtWtp65fNm4GhQ3XvnWdoVV2KlOk6ISirM2gQ0KlT9sYFUO+kAweoAWN2aLWUBRo4kLJAly5Rc8rAwNRu5Wm9egV07EhbpFy+bHj7F6WSpogLw7ScIVOm0PPcpQtlGRkrQHiqjrG07t+nwnI/PyAsjKa/+venupe0S9612sxTeyVL5o2pvWfPqKWAQkFTTP7+1NRy0iR6Q7Mxsi3EvXv0uK9dAypWpMLfgABqiZB2emz/frqfAwf0n2/1apqW8/KiYvGLF9NPhxlr+XJakbZzp3HHh4ZSU01fX+on5e1NtVwuLlnf5uJFmsYdNYqeO0PTgf7+tHVN1aqFa1pOHyHoNePvT6sPixe39ogYMw1rp7wYy7MePhRi3jwhGjcWonRpIUaPpqkllSrzsRmn9lxchKheXYhRo4TYscPyU3sajRA//SREyZJCbNwoRHCwEF260GO5dcv488yeLUSPHjTdpFQK0bw5nTetY8eE6NrV8LkuXqSpRFdXIe7fz9bDeUurFaJWLSFOn9Z/nEIhhJ+fEN270zSrj48QV68anjbTaoVYu1aIUqWEOHzY8Hh4Wk4/rVaIzz4TomVLIWJjrT0axkyCAyfGjBEYSHUbTZtSMDJypBB//knBhC4aDQUoy5cL8cEHmQOp0FDLjDsggOp4+vQRIixMiA0baPwzZ1JwYYhCIUS9ejRmIYQICqKg4urV1GNOn6Z6KEMiI4WQSIT4+eccPRQhhBCnTlHwlVWA8u+/Qnz5JQW6bdsKsWWLEAkJxp07KUkIb28h6tShoFkfhUKIBQsoCJwxQ4jExOw9jsJEo6H/L+3aGf+7YCwP4xonxoxRtSpNc12+TL14atUCZsyg3k/e3rSXnFKZerxUCtSvT/VS+/ZRbZCfH9VG+fnRdTVq0DL8nTtNu/dcWp6eNOYaNaj2pmxZmi67coWW1hvaQ87WlvoZffEFPYYqVWjV1McfUwNKgFbJGVPjNGcOdXZv3jznjyelBUHauqH4eJqGa92aapJsbICzZ6ln19Chxm0b8+wZTUvGxNA0oL4+RP7+9Ps7c6bgNLE0J6mUfm8VKlA/NYXC2iNiLFe4xomx3Hj6lAKjvXupPqp3b6qJ6tJFfxdsrZY20k2pkTp9mvoCpa2RMvVS7tOnqfbp3XdpP7j9+4Evv6Q6nhkz9I/3yy+pWHrHDvp55EgKFLduBe7epXogfQXof/1FAWarVvTcZLWvnT7h4RQABgdTvcw//1BQ5+dHfZe8vakwXi7P3nlPn6ZC5okTKTjOqpi7MDSxNCe1mp5npZJ+Z9n9PTGWR3DgxJipPH9OXaz9/KiIulcvCii6djW8j5muQMrFJTWQat/eNIFUTAxlwS5coE7PFSvSCroHD6iAOqtsUEICZVlWrKBu4gkJtNXLtGlAmzaU6QkO1n3biAjKdm3fDty6Rb2XVq/O/tgXLqTbN21KAVNyMgVLw4blrBBfCHo88+bR2Lp00X2cUklb+yxeDIwbRwXPnGHKGaWSPljY21Om1diFCozlIRw4MWYOL19SJsrPj6bGevSgIOrdd41709VqKZOTNpAqUcJ0gZSfHwUBY8ZQ8LN/P2VchgwBZs/WPcYTJ6hh5Z07gJMTNRTt1Ak4eJCmYMLCMt9GCFqSX7cusGABTW9NmUKtAIyl0VDGasAAyvD06UMZq7Ztc57xSUykadK7d+n3VKWK7uN4tZzpJSfTa6JsWWpzkWHFolatRtydR1BFRQOgfS6d6laHxBIbOTNmBA6cGDO3sDAKTPz8aJuUd9+lT93duwNFihh3DkOBVLt2mfsQGfLyJQVCb95QxqVECQoSbt5MrRnKyNubgqpVq6BJTMILn6/w+uhJKGMTIO3VA44Vy8N9yAco5uVJx//8M2WyLlygeqnYWKoLi4kxnG148oRuu2kT3TY5mYK1EiWy9zgzCg6mNhOensAvv+iugeJpOfNKTKTXf61aVP8kkSD5VQSertuNkJ+3QatSv91qSWg0kDnYo8rE4ajoPQC2JfW0kWDMAjhwYsySIiKo39HevVSj07UrBVE9e1LhtLFMFUgJQdNmM2dS8baPDwV548dThmfevPTBXVQUkmrXw+M2vRDqfwmABJqENPvUSaWQ2dvCoaIbqg7uAbcl30Ny4Xz6rV9q1qQpTU/PzONRKCiDtXEjBZkDB1KwNmcO0K1b9jcCzuivvyir9u23tB1KxmCIp+UsJy4O6NoVolkzPHKpiseL1wMSCbTJuovHpQ52gABqzv4cHpNGWHiwjKXiwIkxa3nzhoIoPz/KyHTqREFUr16As3P2zqXVUnF2SiB16hQVUKed2tMXSN2/TwFFqVKUbZLLgc8/p3Ft3Ei3BxB16SYudxsKTaIChv5wyCSAq2dVNL6wHzL7NIXnAwdS1m3YsNTL7tyh+9m+nbq6e3tTVsjBgWrHPD2pEN/JKXvPSwohqEZqxQrq9N6uXeZjeFrO4kRUFAJqtsGLOAGNWmPUbWSODqj46ceos3iqmUfHmG4cODGWF0RFUabFz4/qgDp0oIxP797U7Tq7MgZSp0/TefQFUioVZXbWrQPWrKG6pSNHKAv13nuIGfw/XOz5afoMkwFSBzu4tPJCs983pNaoLF5MU2Fz59I2MRs3UjuA4cNp6jBjwDJ7Nk0rrlmT/ecBoMzGiBEUeP32W+bHzdNyVvNo/ho8nr8WmqTkbN1O5uiAWvO/QuWxn5hpZIxljQMnxvKa6Gja1mTvXgp62ralIOq993Je32MokGrXjvrsALTdyJAhdL/LlgFaLTQTJuLEnotQaXUHFBK5HI22LUExL084VnbHxU5DEHnmMgBA6miPql94o8bMCelXsimVdN/e3pSB0lXzpFZT4faRI0CDBtl/3A8fUgDYqhX1n0q7upGn5axKFR0L/wptspya0/eaAgBZEUd0eXkRMgcDK1YZMzFugMlYXlO8OAUuhw5RNmTQIJrSq1yZVuf5+mZ/41SplKa7xo9P3YfvwAFqE3DgAG3AW60arVZ7/JgCFRsbuv7OHbzs3AdaW/1vUJEXruPm8ClIfhme7nJtYjKCl2+GdsFC2ih35UoKDu/epXqqXr2yLhQ/epRWD+YkaDpyhFolTJxIe+OlDZq4iaXVhW7d/7YAPCtZvaYAABLg5d4/zDQ6xrLGGSfG8ou4OAok9u4Fjh8HWrSgmqgPPqANhnNDq6VNfdPWSDk7Ax4ewJUrOCWvgoToRHR4dAJPVu+A25A+cKhYHhF/nsWtEV9Dq0jtmt4x+DRuDpucPjsALeq3ronyc6fSaj0PD+DYMSoU16dnT8q2DR+evccyezawYQM9Vy1bpl737Bk18+RpOasSQuDvKu2R/PxVjl9TAFCkVlW0Dzhq6eGzQo4zTozlF05OwEcfUcboxQvKDvn7U01Q5860tD5cxydzY0il1Gtp3DgKNsLCKOPVpw9iGzZDcnT820PLDeiOyz1H4mT1TnCuVxPuw/oaPL0GUgQl21EGSCKhDJeh7V5CQqjf04cfGv84oqOB99+nnlNXr6YGTUolFYc3bAjUrk3Tlu+9x0GTlSheRUD5Jurtzzl5TQFAYmAI1NmouWPMFLhtK2P5UZEilIkZMIB64hw7RgHV118DjRrR5X37UpPBnEgJpOrWRUL56pAEfAPEUvAUsmobFP9NnYT9fhLODWobdcrE4GdAZCTVOdWsCZw7RysJhdD99eOPFARFRGR9TMqXVgs8ekRd0Vu1oqm38HAKAC9epKDJzY2mOd3caBWfoXOa6kurtdx95ZOxqSLjINGo3742cvqaktrKoYqKhU0RI/YjZMxEOHBiLL9zdKQgqW9f2mz3r78oa/Ttt1TH078/0K9fzrYlAaCJT4TQat/+nPwqIvW6xCTYly9t1Hm0kVGUHZNIqOg7ORn49Vf6WdfXq1dA6dKUVcvqGImEgrz4eAqSSpembVmGDqVVgmFhFFi6udEx33+v/1ym/pJKLXt/5hhbbh+DQkENT6Oj6d+YGEhevgbStB/I6WtKaAUkNtxRnFkWB06MFSQODpSlef99esM6fpyCqJkzqTA7JYhKWUFnBBunIgaLeI0hK+kKvHxEP4SFUdfo8HDd02V791LX8VOn9J9Uo6EAcfduKvL28kpdLbdoERWG82o58xCCFimEhlKvrdDQzF/Pn1NWq0IFagPh7g40bAjbDiWhnb4uXfCUoyGo1ZCXyEG7DsZygQMnxgoqOztasdarFwUT/v40nTdnDnXy7t+fvipV0nuaIjWqQKtW6z0GoGmTlCBIaiuH1M42XYGvY7U091OmDE03BgdToXhGa9dS/yh93ryhZpoaDRV7lyqVvonl5cvcxDKnNBoKarMKhlL+dXSkbF5KUOTuDrzzDv2bcrmzc6bg2BaAs985xFwN0DsMQ68pV6+6kNnZmvzhM6YPB06MFQa2ttTKoEcPmsL6+28Kopo0oT5JAwZQJkpHEONUpxqKuJdF3MMQvXfR7u4xOFam5pLN//AFAPxdrSOSnjyHTC6Dxxfe6W+QUiCe8T4fPqQapA8+yPrObt6k6/v3B+bPpwaZH37Iq+WMoVLR4gJ9maJXrwAXl/QBkZsbtbRI+7Ouff6MVHXyKNzy/kbvMXpfU7Y2qPrgH6ppGzOGXsP23NOJmR+3I2CsMFOpqBmmnx+wbx9NqQwYQAFJtWoU2Myfj+fHLyBAVRwapeHMky5yqUDn7T9AOmBA6oWzZlEmbN689Ad/9RUgk1FBty47dtB2MKtWUfCUMi03fjxPyyUl6Q6G0l725g0tGsiYKUobEJUvT8G2GWlVKvi7tYYqKiZHt7d3K4OOD/0hOXqUuspfv05tK0aPptcuY2bCgRNjjKjVwNmzVLC9Zw/9LJEA48ZB89Vk/F2/F5Thb6i2JRtkMimqefdFNb8NlBGqWBFalQph3y3Ay237oahVFxKpBHZlS6H8B11Q+tMhkPxzKfM0m0oFTJ5MjS3376c6qcK0t1xsrO5AKO1XQkLmgCjjz2XKUGCaB0QcP4er/cZBm80tV6SO9mh+1Bcurb1SLwwMpO2CNm+mlaU+PpR5zKq5KmM5xIETY4wIQQ02582j+pY+feiN+MABoGRJxLXrgvPbTkCTkGT0KaVyG5RSx8KrfhlIevaE8s/jCOrYF0/W7YZQqzOdS2ZvC5lGjcrTP0OVCcNgU7QIXREWRj2sHB0pEzVnTsGZlkspsjaUKUopstaXKSpZMt89F893Hcbt0dONDp6kDvZovOMnlOndSfcBycmUQV2zBnjyBBg1inqeubmZcNSsMOPAibHCTqOhVWwLFtCb+LRpNFWXkpXQaoELFwA/P8Tu2odL0Q5QAYBW30kBmVSCMi52aPDVUEhnzECCkwsuxhaFUgsIjf4bS+3t4FChHFr4b4V96BMazyefUBPQJUvyz7ScriLrjAHS8+f0OHQFQml/1lFkXVC8PnUJt0d9C2XEG2gSkzNnNaVSyOztYO9eFg02zEeJlo2MO/Ht27TQYPdu2jjbx4d6h5lglSgrvDhwYqywUiiAbdsog1O6NC3r795d/5uzVov4I7/j8MRP4fLcCQISaNL8BZHY2EBiI4Nzg1qo+tVIlHl4E5LFi5Hc70Oc3fgnlJACMO7NX2Ijg52TI9pqnkI+YRywa1fempZTqagoXdeUWUpA9PJlapG1vkxRLoqsCwohBKIuXEfQTxsRcfw8tElJgEQKmYM9SvdsD49JI1C8af2cnTwujmrj1qyhvl4+PlQP5epq0sfACgcOnBgrbBISqBbkxx9pldS0abSE3MhsxqrLq/Dn4z9xsN9vCFu6Hs927obqURCK2hWBff06cJs0CkXf75F6vlu3cLb1h4hT6i6PksjlaLRtCYp5ecKxsjsudhrydk8yCQRcXRzR3DHGstNyuoqsM/785g3VC+nLFFmgyLqgEnJbICYaElMGlUJQJ/k1a4DDh6nfmY8P7ftYQLN5zPQ4cGKssIiKopVoK1cC7doB33xDLQGyIVGViGorquH3Qb+jUTmaLvEP8seCs/Ph77mIpvz27qWD+/cHBgxAtLDFpc5DaApGB4lcjkpjBiHm2h003rUMNz75Mt1mrlKZFO2uH4Jjneo5e9wZxcZmXVydEiDFx6cGQFllivJQkXWBo1RSny+l0nwBzevXVEi+di1QtCi1NBg8mL5nTA9ebsBYQffyJS3Z37iRPmGfOUNdu3NgzZU1aFWh1dugCQBiFbFwti9GXbu9vKiv0s2bVKA7aBCCXmmhSZahw6MTeLJ6B9yG9IFDxfKI+PMsbo34GlqFEiErtgDQXfskpFIEb9yLuj9O0z84IWgvPH1ZopQi64xBUKNG1Cg05ed8WGRdoMTHUwBjzt9ByZLU+uKLL6hx6tq19GHi448piKpXz3z3zfI1DpwYK6iCg4HFi6kw9pNPgBs3gIoVc3y6OEUcFl1YhL+H/p3u8lhFLJztnFMvkEgoEGnUCOpvpiGsTHNAqAAA5QZ0x+WeI6FNVqDV6V1wH9YXT9ft1nu/QqXGM9+9qPPFMEiyqilKCZDSFlmnZIpaty40RdYFRkKC5TI/UinQtSt9hYYCGzYA775LjWHHjKHGsNxYk6XBgRNjBc3du7RC7o8/qBng/ftU/J1LKy+vRGePzqhbum66yzMFTmkoXkZAYisHlBQ4hazaBsXLcABA2O8n4dygtlH3rY1PgLphY8grZsgU1a2bfkqtSJFcPEKWZ6RknCzN3Z0as377LfULW7MGmDQptbFmXliUwKyOAyfGCorLl6kH06VLqZ21i5lmA9To5GgsvbQU50ecz3SdvsBJnZCUboPg5FcRb7/XJCbBvrxxAZ3EwQGam2chdyuTzZGzfCk+3rpBsFxOXek/+AB49Aj45RcqIPfyoixUz57cWLMQ42YWjOVnQgAnTgCdO9NWKZ07A0FB1OPIREETAPx08Sf0rtEbNVxrZLpOX+Bk41TEYM8mYwi1GjbOnE0qNKyVcdKlenXqHfbsGRWPL1xI03izZ9Oef6zQ4cCJsfxIqwUOHqRPwePGAUOG0JYT48ebvCfQ68TXWH1lNWa0m6Hz+iwDJ6US9g//pX5RBqTsfJ/x+xQ2tnLIpFyXVGjkpcAphb09/T+7cIGm8V68oKni/v3pwwsvUC80OHBiLD9Rq4Ht24H69WnbkSlTqKZp2DCaXjCDxecXY0CdAahcvLLO69MFTq9fA1u3Ah9+CJQuDdnMGXCrXwWQ6f9T0+7uMXSPD4CDe1k0/8OXvq9EW2RIbWSoVFIOSfnyVKi7cye1FGAFV14MnNJq0IBW4T15Qp3IJ02ilao//UQrO1mBxn2cGMsPkpOp58yiRbQybto0oEsXs68OexX/CnVX18Utn1twd3bPfIAQGLe4PcaHVUbtS4FAQAC9kfTuDfToAZQti7h7j3Gu+QfQJhnOPOkilUrQ4fbvsC9ZHDh0CPjtN2qp0LYt0LcvtVjgDtAFy4YNVKu3YYO1R2IcISgTtWYNZaP69KFaqGbNeAVnAcQZJ8bysrg4aing4QH8/jtlm06doqXTFviDvODcAgypPyR90JScDPz5J00LVqmC7xb9A+eIWGDGDNqXbf9+YMQIoGxZAIBT7apwaloP6hzU0kohUEbEw75pI+DYMVrddOQI1ZsMGkSbEnt4UG3XmjXAq1emeeDMuvJ6xikjiYTaXmzfTsXkdevS69PLi7r0x8dbe4TMhDhwYiwvev2aAhEPD+D6dWotcPgw0KqVxYYQGhuKbbe3YWqbqRSQ+PrSKqMyZagw1s0NOHIE3efWQvjCGUC3bjr73QRHBWNi9xtQuthDk42/OFJbOYqUK4kGxZRA8eLAZ58B7dsD9+5R4fugQdRk8+VLYOxY4Nw5oHZt2j5m6VKaRmH5k7VX1eVGqVLA5MkUQM2fT8F9xYr0QePOHWuPjpkAB06M5SXPn1Mn4xo1KFi5eJE2t23QwLLjEAJbNk3Crrt1ULbT+1S/cewYBU6BgcD589Rl2dMTscq4LFfVXXh2Aa18W2HYOz44ONsDcW72kDjYGbx7mVyGYvZStLx9DLLduyjLVbkyBUNt2tCqwYQEOtjRkabsduyg52zqVHqD8vICmjalnlaPHpnwyWFml98yTrpIpfRh4sAB4NYtmk7u2pUC+507jVo0wfImDpwYywsCA4FRo2ibB4mEaoXWrQOqVbPcGJKSaBrMxwdqt/IYOGcf3nGqS72hwsOBX38Fhg6lT9RpZLWqbmfATry/+31sfG8jwhPCESwJR5/bZ3B5SDkoyheFrIhD+ulGqRRSB3sUrVsdnmvnokWNopBvWEc1Uxcv0r5lJUtSVisgAKhTh6YF05Zp2tlRj52NGymIWrAAePqU6qHq1aPmhgEBvAIqrysIgVNaFSoA339Pgf/nn1P2tmJFCvKDgqw9OpZNXBzOmDXdukVv7v7+NN302WcUHFjK8+cULB05Apw+TZv+9uqFaQ4XYFO7LmZ3nGPwFHZz7RA7NRZ2NpRJEkLg+9PfY/PNzTg88DBuvrqJmadm4tLIS4hKikKbTW3waPwjiJvBCD92GopXEZBIpbArVxplendCsUZ16MRPnlBx7ZEjlDmKi6Pl4A8eUCA3cSKwZw9QqRJtXKyvq7NWS8HXb7/Rl50drdDr2xdo0oQLePOa4cNpI+r//c/aIzGfhw+pseaWLfT6TmmsyRtH53kcODFmDRcuUCbn+nWamhs9GnByMv/9arXAtWtUL3XkCBASQvty9e5N0wouLnj45iFa+7bGo88eobh9cb2nU6gVcF7gDMV0mnZIVidjxMEReBz1GAc/PojAyED03dMXJ4edRN3SdTFk/xDUdK2J6W2nGzfe334Dvv6anidnZxr/rFnAI6TsJAAAIABJREFU+vX0/dixtD/dokUUdH79teF9xYSg5yAliFIoKIDq149qyKSciLe6/v2Bjz6ipq4FXVISZXPXrqUPMp9+Cnh7A+XKWXtkLAv8F4IxSxGCVqO1a0eb7vbqRWn6r74yb9AUH091Ft7eQPnyNN2WlAQsW0aZm507gYEDARcXAMD3p7/HpBaTDAZNQPppuvCEcHTc0hEaocGpYaeQoExA/1/7Y9sH21C3dF08eP0AxwKPYULzCcaPvV8/WjE3Zgw9f1IpFaavWEE9rbZuBW7epGxSQADg6UmF9PpIJJRlmj+fsle//07F52PHUsH7mDGUAVSpjB8nMy1LbvJrbQ4O1Ift4kVqavvsGU1DDxgA/P03TyvnRYIxZl4ajRB+fkI0bixE3bpCbN8uhEpl3vsMCRFi1SohunUTomhRITp1EmLpUiEePdJ7s4CwAFF6cWkRp4gz6m4C3wQKj+Ue4k7YHVF5WWUx/cR0odFqRFRSlKi1qpZY9c+qt8d+su8TMff03Ow/loQEet42b05/+Y0bQlSsKESdOkI0aSLE8+dC/PGHEFWrCtG3rxBPn2b/vh4+FGLBAiGaNhXC1VWI//1PiCNHhEhOzv65WM61aSPEmTPWHoX1REfT/9+6dYWoWZP+70ZGWntU7D8cODFmLkqlEJs20R++Zs2EOHCAgihzUKuFOH9eiG++EcLTU4iSJYUYOlSIX3+lP8JG6renn1hyfonRx19/cV14LPcQpRaVEltvbhVCCKFUK0XnrZ3FZ0c/e3vc/Yj7ouSikiImOcb4x5RWQAA9pgcP0l8eHi5E27ZC1KolRPnyQly9KkRSkhDff0+Bz4IFQigUObvPJ0/oDatNGyGKFxdi4EAKgOPjc3Y+ZryGDYW4ds3ao7A+rVaIs2eFGDSIXoPDhwvxzz90ObMaDpwYM7WEBCFWrKBsSKdOQpw4YZ4/dNHRFBgNHUpBRb16FDidP0+BVDZdf3FdlP+xvEhQJhh9m8+PfS7ks+XiTAhlB7RarRh9eLTovr27UGlSs2o5zjaltWaNEI0aZc7+KBRC+PgI4e4uRIkS9JwIIURgoBA9eghRu7YQJ0/m7r5fvqT779xZCGdnymht356toJRlQ7VqmYPkwi48nD4IVKlC2ev16zmItxIOnBgzlehoIebNE6JMGSH69KFPhqb26BFlQTp1oim4d9+llH5wcK5P3WtnL7Hyn5VGHavWqMWEoxOE+0/uov2m9m8vX3pxqfBc7Zkus5TrbFMKrZYClokTdV+/Zo0QLi5ClColxKxZdLxWK8T+/RTEDhokxIsXuRuDEEK8fi2Er68QPXsK4eREwdnGjXQ5M42yZWnqlWWm0dCU9Hvv0et9/Hgh7tyx9qgKFQ6cGMutsDDK9Li4CDFkiGn/iKlUQpw6JcRXX9F0VJkyQnh7UzAQZ1wdkjEuPrsoKvxUQSSrDNfyxCbHih47eohOWzqJdVfXiYF+A4UQQhx+cFiUW1JOhESFpDt+8G+Dc59tSvHmDQVBhw/rvv7UKSFKlxaiUiUhBgyg7J8Q9Ml86lTKzC1fbroas5gYIXbuFKJfP8pEdewoxM8/myZAK8yKFqXnlun35IkQ06dToNm2Lb0WuR7P7DhwYiynQkLo016JEkKMGSNEUJBpzhsZKcSOHVRTU6IETU/NmCHE5ctmq5HqvLWzWHd1ncHjnkQ/EfVW1xOfHvpUKNVKsebKGjH68Ghx8+VNUXJRSXHx2cV0x9+LuCdKLSqV+2xTWmfPUgCZVUYiOJimLatWpecuNDTNgO5RcNOwoRAXL+q+fU4lJAixb58QgwdTPUrr1kL89BO9TpjxtFohJJIcTTcXWkqlEHv30mu7dGn6kGCCLDTTjdsRMJZd9+9TY77GjWm7j3//BVavBqpUydn5hKBzLl5MrQoqVaJtVtq3pyX2169T1+GmTc3SY+h0yGkERQVheMPheo+7/PwyWm5sieENh2Ntr7WQy+SIVcRCKpHivd3vYWX3lWjh3iLdbeaemYvPW3ye5ZYsOdKmDTBuHLV00GgyX1+5Mi3tbtQIePOGWg9cvkzX1apFrQa+/ppaHYwcSfsCmoKjI21Js307dS2fNo22fmnSJLX9wcOHprmvgiwpiXpxcSNI48nl1PvqxAngzBnaoqhJE2qoeeSI7v8nLOesHbkxlm9cvUpTMqVKCTF7du6WBysUQvj7U71O1apCuLkJMXo0LX1PML44O7e0Wq14x/cdseXmFr3H/XrnV1FyUUlx8P7BdJdP+WuKcPvRTXx/6vtMtzFLtimFWi1Eu3ZCzNUzBajV0u/J1VWIYsVoGiOt6Gh6/kuXFmLdOvOteFSpaIHA2LFClCtHqx5nzhTi1i1eHaVLWBhNqbLcSUykVb3NmtH09ty5tMiB5Rp3DmdMHyGAs2epy/edO9SsctSonO3cHhFBzRkPHwaOHwdq1qQmmL170ya+Vtj24/jj45hwbALujLkDmTTzJ3whBOadnYdfrv2Cgx8fRKNyjd5epxVa1FxVE8XsiuHKqCuQZBj/J/s+QZ1SdTDtnWnmGXxoKH2q3rePOn5n5cABYMQIymD4+FD2Lm3m7tYtan6p0VDmsHFj84wXSN36Zd8+6loul1Pmq18/3volRVAQ7U8YHGztkRQc169TZ/K9e2mjYR8fymjn8PWmiIjE0w178GL3EaiiYgAByIs7o2z/d1Hp049hX660acefx3DgxJguQgBHj6ZucDt1Kk0N2dll7xx37lCq/PBh4O5dekPo1Qvo0QMoW9Z84zdqeAItNrbAFy2+wEeeH2W6XqFW4NMjn+Ju+F0cGngI5Z3Kp7t++t/Tse7aOvzQ8QeM8hqV7rr7r++j7aa2CJwQaNppuowOHaI9627coO7fWblzh553lQpo3hzYti198KvVAps30/TagAHAnDn6z2cKQtAbWsrWL0lJ6bd+KaxTVbdvA4MH0zQ1M62YGHrtr1lDr3kfH+pabuRrPfHJc/z75XxE/HkakEihTUpOd73U3g4QAiU7tUKdJd+gSPXKZngQ1sc1ToylpdEAu3cDDRsC334LTJhA9Ufe3sYFTcnJwLFjwPjxVPPUuzfw4gUwcyYFYPv2UfbDykETAPz+6HckqZIwoG7m/cBeJ75Gl21dEKeIw+nhpzMFTdtubcPOgJ1oWr4pXB1dM91+zpk5pq9t0uW99+g5HjVK/9YUnp60P12NGlTv1KIFbW2RQiql38u//1JwVacO1Spl83OlKjYeiSGhSAwJhTouXv/BEgng5UXB+f37FKi7uNBrx9298G79Eh9feLZbsbRixej1decObTB86RL9nfL2Bq5c0XvTmOt3cbZJH4QdPgFtsjJT0AQA2mQFtAolwv84jXPN+yLywnVzPRKr4owTYwBt9LptG7BwIVCmDGUeunc3LpX96hXtd3bkCO0tVa8eZTd69QLq1s2T0y9aoYXXOi/MbDcTfWr1SXfd/df30WtnL/Sv0x/zOs2DVJL+89W5p+febtw78dhETG0zFZ09Oqe7vUWyTSmSkykQGjuWNkjVR62mTZV376bfy8GDdNuMLl+mwMXJCfj5Z/o9ZkGjUOLVvj/xePF6xN97DKmtHJAAWoUSzg1qo+rkUSjzXidI5XLjH1NgYGomKiiIgsN+/YAuXbKX9cyP/vqLFkocP27tkRQO4eGAry8FUq6u9Lr/+ON0GdmERyE416If1LEGPgxkICviiNbn9sDJs4apR21VnHFihVtCArB0KVC1Kr1J+foC587RVFpWAU/KFMvs2UCzZkDt2vTHvm9f4PFjuv3UqZTlyINBEwDsv7cfMokM79d8P93lJ4JOoN3mdpj2zjQs6LwgU9D0OPJxuo1745RxmYKjOWfmYFKLSZYJmgBagbV7N2UI797Vf6yNDW0QPH8+BcvdulFmKaNmzSh4+vBDoEMHYPJkyoRkELrzEPzLtUDA2JmIC3gAoVZDk5gETUIShFqDmGt3cGvkNBwv3wqvDvob/5iqVaOVf5cv02utYUNg0SLKVA4aBPj50Wu3ICpMG/zmBaVL09+rwED6m3bgAFCxImXb790DAFztNw7quKxfbxK5HI13L0eHRyfQU/UALm2bAQA0CYm40scHBS0/w4ETK5wiI+mPRJUqwIULVCvzxx/AO+/oPj4xkTJKo0fTNMpHHwHR0cCCBUBYGLBnDzBkCFCypGUfRw5otBrMODUDczrMSVfQvf7aegzaNwh7+u/BiEYjMt0uOjkavXb1wox2M9CtWjcAQKwiFk62Tm+Puf/6Po4/Po7xzcab/4GkVasWZQs//phqhQzx9qapMXt7eoOYOpVqPtKSySiLFRBAn8rr1KHg+r83gcBF6xDg8x3UcQnQxGf9pqKJT4A6OhY3hnyJkLU7s//YKlakOq4zZ+iNrG1bYN06oHx5CtZ37KDalYKCp+qsQyajD4yHD1Ow7uwMdOyIaK9WSHr8xOC0deSF67g5fAqSX4anu1z5JgqRZy6bc+QWx4ETy/OERgN1fIJpPrW8fAlMmQJUrw6EhNCKub17da+kCg2l9HWvXvRJf8n/2TvvsKbONoz/EjaCAzfurXWDSp3gqBNnXVTFWWurdrjaarWitdqq1Vq7XNVYVNSKE7coiCJOcE9EUUFFUDYhOd8f74eIrLADnt91cQnknJM3Ecid57nf+1kiPDLHjok8nl9+gU6dwNg45+vKR7Zc2UJJ05J0r90dEEJq2qFp/HzqZ7xHe+NQ3SHVOWqNmkHbBvFBzQ/4rOVnr7//Kv5VispSUrXJ0sQy1TXynNGjRUtt6lTdjm/TBs6dE8Lkn3+gb1+IjEx9XPnysGGDqEx9/z306EHwkr+4/cPvafo80kMbG8f1GT8RsjsLlae3qVBBGHoPHRK7zvr0EdW2KlXEi97atbmXS1VQREVlb9eqTO5RrRr88AMEBXHPrCKaBOGz63j7KDW/GkP7C7vp+vwczV2XoTQxRlKrub9iA+E+55E0Kd+AaKJjubtkTUE8ijxDFk4yeknco1BuzFnGoQp2eJg15FDZlniYNsCzbhfu/705c+Pt29y7J3r3DRsKT8zFi6ItV69e8jFarWiNzJkjwhObNhXv8ocPh6AgOH5cvCjXq6e3LbjMSNQmMvfEXH7o+AMKhYKohCgGbB3Aucfn8B3rS93Sqb0IkiQxef9kjJRG/NLtlxS3vSmcCqzalIRCIYTugQPChK8LVaqIimPHjsIo26qV+L9Oiw4d4OJFNA4dufLtL+mKpvTaFiDEU8DHM9EmJmb10aXGygpGjRIVguBgcHYWj71WLbF7848/xMaEwoZccdIbtAoFIRdvpvhexUE98Os1Ds86nSneuB6VRw7I+CKSxPNjp7P+N1uPkYWTjF6hDn/J2f4T8KzXhcBf1qEOiwBJQkrUgFYiJvAhN77+mcOV2nJtxk9ImSXiXr0qWmgtW4oXmhs3hMelalVxe1QUuLuL1o21tXghiosTx4SGijbI0KFQqlSeP/b8QOWvokrxKnSs0ZHgV8G0/6c9VmZWHBpxKM3dcQC/nvkVn4c+bBm4BUOl4evva7QaYtQxFDMW1YECrTYlUaKESF3/9FN48EC3c8zNxTlTp4qKpK0t+PikfayREU+s64pzMiC9tgWAVp3I033HdVubrhQvLn5Ot20Tj2HSJCEIGzaEtm1FdfT+/dy9z7xCFk56gzr8FYq3phXcX7mR+CdPUYe/JHSfJ8WbNsj0OkojIxKehefVMvMdWTjJ6A1xj0PxbtmPZ4dOoo1PQBufkOZxmugYtLFxPPh7M369P0abkMZxZ85Av37inXfDhqLitGCBMELevw8rV0L37lCxonhn3qSJeLG8dk2YcNu3F0biIkSCJoF5J+Yxv+N8zj8+z/tr3mdIwyGs67MOY4O02417b+3lZ5+f2eu0N5XZOyohCgtjC5QKJdefXS/YatOb2NmJnXPDholddLqgUAifk6urOKdHD5HrlAZ3Fq9CExObrbYFgCYymruLV+XgAWbC26NfvvtO/Fy3bClCNn/8EW7ezPw6BYUsnPQGbVx8KuEUF/Ls9eeamFgMLTJ+EwGAUoEmC21tfUcWTjJ6QWJkFKc7jyDuUShSgm65NZqYWF6cPM+lkdOF/0mSxKymzp3FbqguXYRgmj5d5JZ8+62ICmjVSnhbxo2DR4/EtucvvhAtjiLM2gtraVC2AU+jn9LdtTu/dv+Vb9p9kyrxOwn/EH9G7xrNjiE7qFayWqrb32zTzfeaz5TWUwq22vQm06cL4/f8+Vk7r1cvkextZQVffimqUG9UNeOfvSDmXnL+U5bbFv/n5bkr+fNCYmIiROCaNaIS9fPPon3XsaPY9fn99yJwUp92PcnCSW8wLGGJNhdyxKREDUYl82mXbT4gCycZveDOT6uIe/hEtOTSID3fiDY2jqf7vXju8rPI45k0SXg9zp8X1aUJE4Sh9rPPRMjhqlXiBWT9ejEUs3jR+WXOiFh1LD94/UAdqzpM2j+J/cP28+F7H6Z7/JPIJ/TZ0oeVPVamGtybRJJwuv7sOkfuHWFiy4l5tfyso1SCSiX+v0+cyNq5DRoID5ytrTCN9+wJr14BoA4LR2mSXJ3LTtsCQGFsJEZV5CeGhmIzw8qVwhO1apUQKX36iE0PSfEHBS2i5DgC/UCjwTDgEsbGuiXYK42NXv9uvPk5gIGZCSYV9H/Hsa7IwkmmwNGq1QT9tSnd1lwS6flGNNEx3P11gxBMH38sXjBr1BAveu+/L6pL/v6iVde69Ts5yuL3s79jaGCIV5AXvmN9aWHdIt1jY9Wx9N3Sl7HNx6Y5iiWJJOGkd9WmJCpWFD8DI0ZAWFjWzi1VSuxcGzVKmMZtbODevVSaIlttC31AqRS7CpcuFbvz3NyEsHJ2FjuqvvxSbIzIzEOYF8i76gqO+HgRyzJ+PFSqhGLSJGra1UdpmvnOYfurB+gRdRmzyhWw279OfF6tEkpTE6pPHIGiCP3dLVomDplCSeiuI69N3h1vHyXoD1cqjeiHWVVrnh30xn/M12jjE7i/YgNAmr6R8GgNsS4LMOvTU+TydO4sv2v9P8Evg5l1bBbvV36ffR/tw8I4/edFK2kZuXMkdUrXYXaH2RleNzIhEqVCyZF7R/jb8e/cXnbu0L27aNuOGSOC/bKyG9LAQJiqmzYVlUxbW4zXb9S5lZwRUoIao1IlcnydXEGhEMLQxkZsQb92TeRVff658Ej16ydSyx0cxFDiPCIhLJz4J8/QPA7DMCIas7h4DEyLeEq6PhAZKcSSu7vYldmwofDI+fhArVpUCX/JzartAPCs0znFqbfnr3z9+du3JaE0Mabq+KF5t/4CQK44yRQ4D1XuaKJiXn+dLd+IkREh3y0UXo6+fWXR9H/uvLhD81XNqV6yOsecj2UomgDmeM7hUeQj1vZZm673KYlX8a94+PKhflab3uTHH4WX7fffs3f+yJHCO2doiMmwoZiVzLwaklHbAqC4TUMMzEyzt568RKEQL5xz5sClSyIFv2ZNYTCvUCE5/iAud/xZklbLs8Mn8e02iqPVOnCqw1DOBDzj5BeLOVzhfa58OZ/oO+nEQ8hkn6dPxd9KR0eoVElUZjt2FAGrJ08Kb9//PZ9GpUpQ7ZOPMDA3y/LdKM1MqTS8L6YVyub2IyhQZOEkU+DEh6QM7MuOb0SboCYhrOhsd80NvIK8aLuuLXHqOHYN3YWBMuNSedLgXvch7pgaZv6ifuP5DZ7FPNMvb1NaGBuLkEgXF9GyzQ6tWolza9WiVtg9DAwzfi7Ta1sAGFgUo9b0j7O3jvymdm0RGHvmjBBSNjYiCLZCBXByytHol8grtzhWuxPnB08m7NhptPEJJEZGk6iR0MTGoYmO4cGqLXg17825DyeiidEhEV4mfe7fF+OlOnQQnrZDh8TO04cPk9tz6Qwfb/DTDKw6tMTAXHexrzQzpWSLxjT67ftcegD6g9yqkylwJG1KH8XbvhFT63I6XUebCy2UosKGSxuYfng6PWr3AAXUL1M/w+NPPjjJ1ENT8RzpSbliuj3fO67vwLairX5Xm5KoXVu8aAwdKjxv2fHQWFvDmTNYjxjJ1Z0XstW2AFAaGVK+d6es339BU6WKaN99/rnIONu5UxjMx4wRrfEPPxTDiEtk3oIM973Eme6jhRjKwIwuqROR1Ik8O+SNT7shtPHajKGF7H/SCUkSu4nd3cXHo0diI8DXX4v/L1PdRZDCwIAW7n8SMH4WIf8dRBMXn3pE0euDFRiYm1K2Wweab1ySteHWhQS54iRT4BiXznm4pMLIEOMyRSOkMidoJS0zj85kntc8djntYt/tfczpMCfDc94e3KsL155d41bYLdr93/tQKBg+XGQZffFF9q9haorB1i3U7d8WJem8cGSA0syUxn/NR1nYM8LKlxdzGw8dEpWMfv1g69aUo1+ePUvz1Jh7D/HrNRZNdIzOO/i0cfFE374vBsam94ItI8TMqVMijqNOHSFkw8Ph11/FbuI1a0TkRhZEUxJKQ0OarfuJ94+oqPhhd5QmxhhYFkNpZooSCYNiZihNjCnfpwutPNZhs+VXlIVsHJWuFPLfXpmiQPk+nQn3vYQ2k1K80tjotbk3yTeStBNPaWRImU5t8nyt+kyMOoaRO0fyJPIJvmN9We67nAENBlDLKv18qrQG9+rCfK/52FS0oYx5Idti/PvvImbAzU0Mas4Gz2KeM+SDq0wKL0O548/R6vj+U2lmSv0fp1JxgO7Pc6HAykr4wEaOFEbjffuEuXzKFPFcf/ihMBtbWwNwfeZiEt/wNL6NwsiI5huXUMK2EebVK3O68wheePmhjYvn5fkrPDvoTbke9vn16PSfhAQxP9PdXQwrL1NGPN/btkGzZrk+HqpkyybYbFpGQlg4z4+dRh0WgbRkCcbOwyk9YSQm5dKeQFCUkCtOMgVO5RH90y/7vkFGvpH4ipZYNK2XyRWKLk8in+Cw3gFTQ1OOOh8F4K/zf/Fdh+/SPSdpcG/Xml1TDO7NjGvPrnEs8Bi1StVKlSau91haivEqkyeLbfhZJCgiiHb/tKN7re44HzpF4x+nYqCQMMig+mRgUQzDEpY0/ecnakxyzsnq9R9Ly+TRLyEhoq3n6yvCNtu2JWHeAp7uOZbp73u60SNRMdxdsjovH0HhICpKPMcffSSqf/Pni3a0tzdcvgzz5ol5m3k4U9O4dCmsB/Wk2oSPqN6gEtbNar8ToglAIeXKyHkZmZzhP/Ybgl13Zy83xsyYPU6mHG8cyRz7OQxpOCRTI3RRwj/Enz5b+jCu+Ti+6/AdCoWCaYemEZ8Yz289f0vzHEmS+HTfpzx4+YDdTrtTzKDLDKf/nGhWvhkXQy7Sr34/hjYqhFuNly0TVSdvb5232F99epXurt2Z3mY6n9t9/vr7mqfPeNLRkbu3nhKtNEFpJnYfaRMSsGxYh1rTx1OhX5ci27bQiYQEOHqUO3N/5faFe2hRZBg9kkSnwBNcGjmdF15+r7+nNDXBPmAf5jWqFMQjKTiePRM7Gt3dRahrmzaistSnj8gsK0hGjxZjqsaMKdh15BNyxUlGL6gzZ3K2wgMVRoZY1qrOyl9PsrLnSlb6raTxn41xu+KGVir6Xoi9t/bSZWMXfu7yM7PtZ6NQKHgc+Zh1F9cxs/3MdM9Lb3BvZlx9epVjgceY2GpiipErhY4vvhAtpjkZ+7+SOPXwFJ1Unfipy08pRBOAQbmyVA44hf3k/nRRBtO+hhHtj6n44PFp2p/difXgnu+2aAKxs7FHD0KUFmhJroJkK3pEqeD5kVN5uFg9IigIli8XGVq1a4vdb05OYoD1gQPCZ1bQogmgdOmsh8wWYmThJKMXmFerhN3+dRhYFNO5vKwwNsLUujx2hzZgaGpCl5pd8Bnjw7Juy1jmu4ymfzVl+7XtRVJASZLEct/ljN8znj1Oe1IkfP/o/SNjmo+homXaf1AzGtybGfO95jPl/SlYGFsUbuGkVIqxOyoVHDmS4aEetz3ot6UfG/pt4KPGH6V90P/DMo3X/E2xm1co5tgNo6chub/uQo76RUSKr7MbPaKOyOdxNflF0k64+fNF9EOLFmKW4NSpovWZ1J4rWbKgV5qSMmXg+fPMjysiyMJJRm8o2bIJbX22YmpdDgPL9LccKwyUGJibUbJlE9qfdcekrFXybQoF3Wp34/TY0/zU5Sd+8vmJ5n83Z8f1HUVGQKk1aj7b9xlrLqzh1NhTKWbJBUUEsfnKZr5u+3Wa5waEBjBm15h0B/dmxNWnV/G878nEViK3KTIhsvAKJxCzDFUqYWp++jTNQzb6b2TMrjHscdpD99rdM7/msGEiQDA+XnhMDh3K5UUXcpQpX3KyM7JGoVCkuk6hRqsVg6VnzBD5Sr16ierNsmViJ9y6dWJ3nFnWAyjzjTJl5IqTjExBYflebTrdO47NpmWENy6N1kiJYXELDEtYYljcAqWpCRUH96K1pyttjm9Kd2yFQqGgZ52e+I3zY0GnBfzg9QO2q2zZdWMXhdnWFxEXQa9Nvbj/8j6nxp6iesnqKW7/wesHJthOoGyx1Em9TyKf0Htzb37r8Vu6g3szYr7XfKa2nvo6ffxV/CssjQtBhlNGdO4shNOoUakMy8tOL2PWsVl4jvTErrKd7te0sRFjS2rWFP6TpUtzd82FmNyIDFEiYRzyWBikCysJCUJUf/opVK4sZmyamAjf3f37oj1nby/mBxYGSpd+pypOheR/ReZdQqFUUq67PQvvvWRt6xXU11QQ70YtLTCvVRWj4rqPU1EoFDjWdaRXnV7svrmb749/z3yv+cx1mEuvOr0yHSuiT9wLv4fjJkc61+jMsu7LUnmT7ry4g/sNd25Pvp3qXF0H96ZHUrVpTZ81r79XqFt1b+LiItKUly+HKVOQJImZR2ey8+ZOTo45SdUSVbN+zfLlRdCmszPMmgUXL4qxFkUwDFBXYtSgl7k2AAAgAElEQVQxPOlojcFFf4zjM37zklH0iKSVKOd9ECr8DE2aiFEhDg7CLK3Pw4GjooQvyd1deJXq1RPm7hMnROZSYeYda9XJwklGLwmKCOJZ9DNaNeuMUpHzwqhCoaBv/b70rtebnTd2ipDIE/OY6zCXHrV76L2A8nngw8BtA5nZbiaT7Sanecy8E/P43O5zSpmlfFefNLi3bum6mQ7uTY+3q02SJImKU2FIDc8MIyPYtAns7Ehs35ZPHq/iyrMreI/2zllOlYmJGPWydKkQT1evipl3VlaZn1tE0EpavIK8UPmrcL/hTodqdnyMAZCY4Xn2Vw9gXr0yAHb71wFwrHYnYh88pmxPB0x2/AkxMSLqwNNTbL+/eFHkFjk4JAsp86xvOMlVnj9P3gl3/Di0bi3CQhcvfp1rVSR4x8zhchyBjF7y17m/OPXwFKr+qjy5vlbSsuP6DuYen4uFsQUuDi50rdVVLwWUa4ArXx78kg39NtCzTs80j7n+7Dr26+258/mdVFWg7459h+d9T446H9VpBt3bXH16lU6qTtz9/O5r4RSrjsXqZytiZxWd+WEJrht5Pu1TPptnx78jd2U6EDlLHD4sXjDNzcHLCxpkboIuzNx8fpONARvZGLCRUqalcG7qjFMjJypaViRgwncEq9yR1BmLp7QwKGZGq71rsGrXIvWNMTEiNfv4cfFx6ZLwmTk4iKpU69b54xN68ECMo3F3hwsX4IMPRGWpVy/9M3XnFk+fiuHQ6aTFFzVk4SSjl/TZ3AenRk44NXbK0/vRSlq2Xd2GywkXSpmVwsXBhc41OuuFgJIkibnH57LBfwN7nPbQuHzjdI8dsn0INhVs+LpdSlP4Rv+NfH/8e3zH+eo8gy6ta9tWtGVG2xmvvxcaFUqTv5oQOi00W9fUN17GvaTPlj58/28w9hVbY7Dx39y/k3v3REswLEzsjnJ0zP37KEDCYsJwu+qGyl9F0MsghjUexogmI2haoWmK4+KfhuFt04f4p2E6j1wBUJqbUqHvBzTbsFi338/o6GQh5ekpdqfZ2CS39lq3ztbokVRIkvC0ubsLwXT/vjBz9+8vRJM+m7pzi8RE8VwmJBQt4346yMJJRu+IT4yn7OKyBH4RSGnz/Emi1Wg1uF11Y96JeZQtVpZ5DvPoWKNjvtx3WsQlxjF612gCwwPZOXQnFSzSnloOIgCzu2t37ky+QzHjZI/HyQcnGeA2gOOjjvNe2feytY4rT6/QRdWFu5/fTXHt22G36bmpZ5p+qsJGSFQI3f/tTodqHVjefgHKlq1g5kwYMSL37ywyUlQefH1FhtSsWXma7pzXJGgS8LjtgcpfxbHAY/Ss0xPnps50qdklw3ywqFuBnLZ3Qh3xCikx89BbA3MzrNq3oIX7n9kfGhsVBT4+yRWpy5fFdv+kipSdne5CSqsFP7/kAbpxcaKi2L+/CIIsLKbu3KRUKbh7951oRcvCSUbvOHz3MN8f/55TY/M/5E6j1bD5ymZcTrhQybISLg4u2FfP37lYoVGh9HPrR9USVVnfdz1mRhm/Y+23pR8dq3fki/eTh9fefXGXtuvasqHfhizNoHubIduH0KJiC6a3nZ7i++cfn2f83vGcH38+29fWB+6+uEvXf7syptkYZrafKSoZAQFit92pU3lj2tVq4auv4M8/hYhycxMBkYUESZI4+/gsKn8VblfdaFi2Ic5NnfmwwYeUME17l2taxD0O5YLTl7y8cBUpUYOUmLp1Z1DMDEmjpdqnw2iwcBoKg1ycCBAZmSykPD2FB61ly+SKlJ2d8KklkZAgjNzu7rBrl2i79e8vPmxsCrUAzhWSAjoLu9FdFyQZGT3jqwNfSfOOzyvQNag1amn9xfVSzV9rSp02dJK8g7zz5X4vh16Wqi+vLn139DtJo9VkerxfsJ9U+ZfKUqw69vX3wmPDpfor60u/+/2e47WUX1xeioqPSnXbsXvHJIf1Djm6fkFz4fEFyXqptfT3ub9T37hypSTZ2EhSfHzeLWDjRkkyNpakBg0k6dmzvLufXCIoIkha4LVAqvdbPanOijrS/BPzpcDwwBxfN/LGXSlg4vfS/uJNpH2mDSSPYo2kvcb1pSM17KV7K1VSwsvInC9eF16+lCQPD0maPl2SWraUJAsLSbK3l6QhQyTpgw8kqVQpSbKzk6RFiyTp5s38WVNhws5Okk6dKuhV5AtyxUlG76i/sj6uA1yxtbYt6KWg1qjZGLCRH7x+oLZVbVwcXGhdpXWe3NeBOwdwdndmadeljGiqW5uoh2sP+tbry4QWE16vt+emnrxX5j1+7fFrjtaTXrUJYNeNXay7tI5dQ3fl6D4KiuP3jzN422D+7PUnH773YeoDJElUEmrVSpHDFHPvIS/9r5MY8QqlmSlmlStQqo0Niuz6Oi5eFBUOhUJUM5o0yeYjyhsi4yP57/p/qPxVBIQGMLjhYJybOmNXyS7XfYCSJKGJiUUTE4dRCYuCG1MTFiZ2wm3dKqpR5cqJn4fnz4UvKqm117JloaoU5jmOjjBhQpHz7qXFO9iIldFnAsMDCY8Lp3nF5gW9FACMDIwY03wMI5qMYIP/Bpz+c6J+mfq4OLhkLRQxE373+535XvPZMWQH7aq20+kcnwc+3Hh+47V4kSSJyfsnY2xgzC/dfsnReq48vcKJ+ydY12ddmrcX5tRw9+vufLL3E9wGuqXvY1MoYO1aaN4cqWMnnmLG3cWreXnhKkojQyStVphgJTAwN6XGF6OoOmYgxmWy6O9o3hxu3xam8ZYtYeNGGDw45w8yB2i0Go4GHkXlr2Lvrb3YV7dnYsuJONZ1xMTQJPMLZBOFQoFhMXMMixVAhMDDh8k74c6fhy5dxGgTV1fh3QGIiBCp8J6e8PnncOsWvP9+cmuvZct3OqfrXQrBlCtOMnrFH2f/wO+RH+v7rS/opaRJgiaBfy7+wwLvBTQu3xgXBxdaWKexNVpHErWJTDk4hcP3DrPXaS+1rGrpfG6nDZ0Y0WQEo5uPBmC573LWXlyLzxifHIuawdsG09K6ZZrVJhD/T1eeXuGPXn/k6H7ymzUX1jDHcw57P9qLTUWbTI+P27EL34+mEmdqgSY6/egFpZkpCgU027CECv0+yPrC1GoYOlR4Z6ZNg4UL890zc+XpFVT+Klwvu2JtaY1zE2eGNhqaZgp9oUeS4Pr1ZHP3/fuiUpK0E06X/KeICBEtkWQ2v3MnZUXK1vbdElJTp4qBw9OmFfRK8hy54iSjV3jc9mBEkzzYzZRLGBsY80mLTxjVbBRrL66lv1t/mldozlyHuTq9EL/Jq/hXDN0+lARNAqfHnqakqe4ZL8cCjxH8Kvh1Sy9pcO/psadzLJquPL2CV5AX//T9J8O1F6aKkyRJLDq5iNUXVnNi1AnqlM7cwBr3OBTvz39GLRkiZSCaALSxcQBcdJ5G4z9cqDy8X9YWaGQE//0HixbB7Nmihbd7d0pzch7wNPopmy9vRhWg4mn0U4Y3Hs7hEYezvQtTr9Fq4ezZZLEUEyN2wv38s6j4ZXUnXMmSYqROnz7i6xcvwNtbiKgJE0T8RJs2yRUpW9uivdvuHQrBlCtOMnpDXGIc5RaXI+jLoFTp1/pKXGIcq8+vZpHPIlpat2Suw1yaVWiW6XlBEUE4bnakTeU2rOy5EiMD3d+ZSpJEu3/aMbHlRD5q/BEBoQF0UXVht9PubM2ge5vB2wbTqlIrprVJ/53jzKMzsTC2YGb7mTm+v7xGK2mZenAqRwOPcmD4AawtM09s1iYk4NWsNzGBD3XaLv8mSjNT7Pavw6ptNj16Bw9C374iWdrXV3hscpG4xDj23NyDKkCFd5A3fev3xbmJMw7VHTBQ5uKuNX1ArU7eCbdzJ5QokbwTztY2b6t6YWFCSHl6CjF1/z60bZtckWrevGgJqVWrhDBdvbqgV5LnFKH/NZnCzon7J2hSvkmhEU0ApoamTLabzDibcaw6v4qerj1pXaU1c+3nphtYeSb4DP3d+jOtzTS+ev+rLJtsD9w5QERcBEMaDsnx4N630aXaBKLipIsAKWjUGjWjd40m6GUQXqO9dK7qhew8QtyTp+mKJoWREc03LqGEbSPMq1fmdOcRvPDyA0T16ca3i2njtSV7i+7WTbSRWrcWg4KPHxd5QzlAkiSRxO+vYvv17dhUtMG5iTObP9ycuwnp+kB0tBCfO3fCvn1im3z//nDsmJgPl1+ULi0qWv3+X318/jy5tTd2rEgYb9s2uSLVrFnhFlLvUMWpEP8vyRQ19t/Zn+5IEX3HzMiML97/go9tP+avc3/R9d+utK/anu/tv6dhuYavj9t6dSsTPSayts9a+tTrk+71oqITuRAQwatINQDFLY1o1rgElsUMme05m3kO80jQJNB3S1/GNR+XrcG9aTHvxDymtZmWIuwyLQpDqy46IZpB2wZhqDTk0PBDmeZhvcndxavQRMVkeMyLUxcI/E2FzeblqW57efEa0XeCKFa7WpbXDUCNGiJMsHNnIaBWr4ZRo7J8mXvh99jovxFVgAoTAxNGNh2J/wR/KhevnL116SsvXiTPhDt2DFq1EmLpxx+hsp481jJlYMAA8QFiPImXl6hIjRoFwcEiPDNp1l6zZpCbuVV5zTs06FcWTjJ6g8dtD9wGuhX0MnKEuZE5U1pP4RPbT/jj7B90UnWiU41OzG4/mx03drDq/CoOjzicbjvvdmAUW9wf4unzHENDBVqN6KQrDRQkJkrUahiLZGFN3/p9+ei/j6hbui7fdfguV9aua7UJ9F84vYh9Qa9Nvahfpj6re6/OMMX6bSKv3CLqZiAAHW8fJegPVyqN6IdZVWueHfTGf8zXaOMTuL9iAwCSRpvqGpJGQ+BKFY2WZ2+oMgDFisHp0/DZZ6JCce4c/PZbpu2liLgItl3dhipAxc3nN3Fq5MTWgVuxqWijF6OEco3g4OSdcOfOCZE5YACsW1c40qvLloUPPxQfIOa9nTghKlLr1sHjx8lCqmNHEVWhz0JKrjjJyOQvd17cISohSid/UGGgmHExpredzqctP2W573JsVtlQzLgY7kPc03yMGo3Ekj9ucejEU9RqLVqtCCp+m2uXDLE0nMKQb7YRUvcJR0cdzrUXQ12rTaDfwin4VTDd/u1Grzq9+KnLT1l+fsLP+Kc4p+KgHvj1Goc2Lp42JzZTeeQAHqzKuA0nqRMJ8/TN1vpToFCIhPGWLeGTT8Tg2iNHUo0GUWvUHLp7CFWAioN3DvJBrQ+Y0WYG3Wt3z5J/Tu+5cSPZ3H33rtgJN3kydO2q2044faZcORg0SHwAhIYmC6nVq8XX7dsnt/aaNNGvuXByxUlGJn/Zf3s/3Wt3L1rviBFG3EN3D9GtVjeaV2zOwK0D6V67O7M7zH69s0urlZi18CrnLoUTH5+6evEmCsmARDU8uVkaG6NlGClyZ9fV5dDLOlebQH+F043nN+j+b3cmtpyYbpRCZqhfvkKrVr/++v7KjcQ/eQpA6D5PijdtoNN1El9FZev+02TMGPFC6eAgfE9nzyJZW3Mp5BIqfxWbr2ymllUtnJs481evvwqVTzBDJCnlTrioKOEZWrhQ7IQrytv9y5cXmV5JuV4hIUJIeXrCX3+JVl+HDskVqUaNClRISVZWaF6Eo332AsNSxVEWZr9WJhTdRyZTqNh/Zz9jmo8p6GXkKjee38BxkyMD3xvIj51/RKlQMqX1FH71/ZU269rgWNeR79p/x4GdcO5SOHGZiKY3UWpNuHEzlhVr7vDVJzmfDTXPax7T20zXqdoE+imc/B750WdzHxZ1WcSoZqOyfR2lsTEKpZKk7cZxIc9e36aJicXUWrddbkrjXH5Rb9EC7t8nsaUtUs3qjPusMl7VYUSTEXiP9tYpYqFQoFYL70/STjgLC+FXUqnEc6BPVZb8pEIFGDJEfIBo5SVVpP74Q7TJOnRIrkg1bJgvz9XL81e4t+wfnrgfBKqjqN4BrTqRYnVrUGv6x1gP7omBmY7DkwsJ7+hPoIw+EauOxfuBN11qdinopeQaR+8dxX69PbPaz2JRl0UoFeJXrbhJcWbbz+b25NtUK1GN1n92Ysvu+5mKJtsmJXH9syVHtrdjxYKmlC9rQly8lj0HnxD+Mo2eXha4HHoZ7yDv12NbdEHfksMP3z2M4yZHVvdenSPRBGBasSyKXBA9JiUtc3yNJKITonENcKXbgWFU/PgV1xtXYP2KB9xVfMm8jvMKv2iKiREiaeRIIRC+/RYqVRJtyRs3RIWpVat3VzSlhbU1ODnB33/DzZvg7w8DB4oh1QMGiIrVwIGwciVcuSKqd7lI1I27nGjqyOlOw3m8zQMpQY2EAm2CGiSJ6Jv3uPrlfA5XbM3dX9ZSlJKP5J9CmQLn+P3jNK/QPEsBkPrM6vOr+WjHR7gNdHud6v02JU1LMtdhLovqZO5RKlHckAUzG7Lm30B6Ovlw404k82aIgEKFQsGeg09ytN6sVptAVJwsjXNPGOSELVe2MNx9ODuG7KB3vd45vl65HvaQhuH7bZTGRihNjFN9DmBgaEC1Oxegbl2YMkXs9ErLtJYBWkmLZ6Ano3eNpvKyyrhedmV0s9E8mP6YJuceopg1C+VXU8DZOddfFPOFFy9EFWnAAJE4/dtvwst16RL4+QnxVL9+Qa+y8FCpkhgTs3q1GONz8aJoa166JP4tX174p37/Ha5dy9HPTLjvJU62GUTU9TtoYmJFuGgaaKJi0ETHcNvlNy5PmF1kxJPcqpMpcDxuexTaGII30Wg1fH3ka3bf3I33aG/qlq6b4fGSJLFrXxjaRLFTZtsaO3bse0S3juWpUM6UMxdesGDZDexblyXwQTSePsJ4uW7Tffa5tqFqZTMeBMeyddcjhg+silKZdX9YUrVpfd/1Op+TqE0kPjEec6OCN+Ou9FvJopOLODziME3K586AXANzM4wHtCVq85EMj7O/egDz6mKru91+MdPvWO1OxAY9QmFmSoVHF+H6Ndi7V4iAW7fEDDRHR+jRI91gy5vPb6LyV/Hv5X8pZVoK56bOLOy8kAoWFVIeOG+eaF0lVRlOndJ/g/SjR8k74fz8oFMn0YZbvVrsypLJPSpXhuHDxQeI3Kik1t7SpSLvyt4+ubVXv75OgaDRt+/j12ssmshonZeiiYnl8Za9mFQoQz2XL7P3ePQIOTlcpsCpvaI2/w3+j6YVmhb0UrJNVEIUw3YM42XcS3YM2YGVWebboV9Fqekz4jSJieJXcNsaO8JfJvDtgqskJGj58+dmbNv9iOpVzDE0VLL0z9uvz1WtbMHaTfc5ceo5xkZKtq+1w6pU1ie1D9o2iPcrvc/UNlN1Pic8NpyaK2oS/nV4lu8vt5AkibnH57LpyiYODT9EjVI1cuW6j149Yuaxmfj7HmH+ypIo4xOzfA2lqQnVJw6nwaIZKW8ICYH9+4WQOnIE3ntPiChHR8JqV2LLVTdUASoevHzAsMbDGNFkhG6/E7dvi2GzIMRIrfTnHarDXxL78Ama6FgMLYthVs0aQ8s8DsC8eTPZ3H3nDvTqJcRS164ickGmYAgKSjabHz8OsbHJGVIdO4pqaRpCyrfrSMKOn0m3YpVROKzS1AR7/32Y16ySd48rH5ArTjIFyu2w28QmxuZataAgCH4VTO/NvWleoTnbBm3D2EA3ARMVlYiRoeK1cALYvucRYS9ES8fH7wV1alpgaKAg4pU65bnRiZibiUqVgaGCqOjELAungNCALFeboOCN4Rqthkkek/B77IfPGB/KFcv5SJLohGgWn1rMb36/McF2AitdbvC03FZuu/wmWhE6ojAypFitqtSdMzn1jRUqwOjR4iM+HrXnUYJc/8BixY8kxsdSz64WfwwcQdMJX2FYLAtt0Dp14OFDEZTZoIGo6PRMruBKkkS4z3nuLl3D88M+oqWoUIBWi1adSIUBXan55RhKNM+l+XSSBOfPJ4ully9Fq2jBAlHhKMo74QoT1aqJNq+zs/j6/v3kgcULF4rWcpKIcnCAOnWIefCY8NMXMm3zpRcOK2m1BP6+kYZL9X9UU0bIwkmmQPG47UGP2j0KbQzB+cfn6bulL5NbTWZG2xlZehzGRspU1oAX4ck+mPh4DWWsjHkVqaaYecrgu2LmBsTEinEgklbCyCjrdsV5J7LubYKCFU7xifEMdx/Oi9gXeI70zPE6tJKWjf4bmXVsFu2rtefC+AtUKynSvi2njCXxVRSBy9ahiYnL9FpKUxPMa1bB7rAKA/O0U8olScLvkR8qfxVuV91o1LkRzl+tYLBBE7ocOg7/7oWpS4TAcHQU1Rldkq/NzYWXZeRI6N1btPFmzSLmfjB+jh8TFxwiBKAkoY1P6bV64raP0J2HsWxSn5Y7/8K4dDaiDBITU+6EMzcXVaV//hG+JdnUrf9Ury4SzEeNEsIoSUh5esL8+aDVElSyFpJa/N3JTjislKDm4bpt1F8wFQPTvB1gnZfIwkmmQNl/Zz/jbccX9DKyhft1d8bvHc/fjn8zoMGALJ9fvLgRGk3mnfLABzF071T+9demJkoqVTQj8IHwGCRqJEoUz9q7+IDQAHwe+qDqr8raoik44RQZH0k/t35YmVnh8ZEHJoY5+8N74v4JphyagrGBMdsGbaN1ldapjqk39wssG9blxszFJDwLfy0+3sSgmBlIEpWdB1B/0XQMi6X2GT14+YB/A/5F5a9CK2lxburMufHnqF6yevJBTVvC9OnCNH3woJizNnMmVK36uqWXoQhRKITZulUr+OILoo55cyogDPWrqHTNuyBe3DQxcbw8fxVv27609f0P0wplM38CY2Ph0CEhlvbuFWNi+vUTa2/QIG8H6MrkLQqF+P+sUUNUSCUJAgN5/P5gJE3y/MbshMMqlErCfc5TpnObvH4UeYYsnGQKjBh1DD4Pfdg6aGtBLyVLSJLE4lOLWXFmBfuH7aeFdfYGsBobKWnd0oqTZ8IyrHx7nX7OZ6NrYt+mDKfPhjF6aDXuBkbzIDgWhQJsm5Z83bbTlaRqU3YM3gUhnJ5GP6Wna09aWLfg956/Y6DM/uiJOy/uMOPwDC48ucCiLosY0nBIhpVC60E9qDiwu2h3/bKWl+euoImOQWlshEnFclSfOBzroY6pBFNkfCT/Xf8Plb+KgNAABjcczPp+67GrZJdxZdLKSmwzd3ISlRxfXyFMxo4VoYc9eggR1bUrFE/j/2HSJOKr1+J0/8moUQK6CRhJrSY+NAzfziNof9Y97apZeLgQdO7uwqdlaysqS/PnQ5XC7VuReQtJgvh4iIsTHwoF6oSUnr/shsMmhEXk+nLzE1k4yRQYnoGetLBuoVd5QJmRoEng072fciHkAr7jfHM8LNWpfxXOXQonNi79ikDEKzXfLbzKVxPqMGdKfa7diuT7xdcAMDUxYNiArL1g5aTaBPkvnO5H3Kfrxq44NXJirsPcbLd1w2PD+cHrBzb4b2Bam2m4DnDVefCvQqHAql0LrNplLJI1Wg1HA4+i8lex99ZeHKo7MKnVJHrV6ZW9CpmhIbRrJz4WLYLAQCFc1qwRlQA7u+RqVO3ar0+76x2A2tgEEtI2t6dn4JUSE4kNDiH4351UG+8kDn78WLTfdu4UIq5jRyGWVq2Sd8LlNW+Ll9jY5M/z+uv4eOFHMzUFMzPxb0zKn+HshMNKkoSUQQW0MCALJ5kCI8nfVFh4EfuCgVsHYmFsgfdobyyMc74bqXGD4pSxMiH4SSyDxp1Jcdu6zUGvPz/nH8GwT8+mOr9EcUOaN85a/lVOqk3w//BL4/wRTpdDL9NzU0++bvs1k1pNytY11Bo1f5//m/le8+lbry9XP7tKeYvymZ+YBa48vYLKX4XrZVcqWVbCuakzy7oto2wxHVpeWaFGDZg0SXxERYmqz9698NNPUKIEODqi6dqNh6vdkNIRTUmkZ+DVxsRyd+GfVH0RhGLnThGj0LOnmJXn7v7u7YTLT/Hy9m1J4iVJuCR9ZOXrEiWyd76JSaq2sGHVdmiePEvnidINhUKBUanC82Y5LWThJFMgSJKExx0P9jjtKeil6MTtsNs4bnbEsY4jP3/wc45aRW+iUCj4cVZDxk+7SGysJvMT3kCrUdKuWaMsVWByWm2C/Ks4+TzwYcDWAfza/VeGNhqa5fMlScLjtgfTDk+jcvHKuZr1BBAaFcrmK5tR+at4FvOMEU1GcGTEERqU1a1dkWMsLISnqF8/4WG6eBH27iXks+lIkdGAMlsGXoCE4BDC/QKwmj9fGNWNsx51kavkpXjJ7NiCEC9Jn6chXgqScj0deLhhByRm/LdKaWz02uOWFA6btClBm6CmVGubPF9rXiILJ5kC4WbYTRK1iTQs27Cgl5IpJ+6fYMj2IczrOC9PjOw1qhZj2bzGTJlzmdg4TaaBvgoFmJka8JlzI76cZEH0K+Eh1kU/uZxwyVG1Cf6fGm6St6nhe2/tZfSu0bgOcKVrra5ZPv9y6GWmHJpC8KtglnywhJ51eubKzs24xDh239yNyl+Fz0Mf+tbry5KuS7CvZp9rYjpbKJXCb2RrS8i1MDRBB1/flB0Dr0ah5FmjVlh98EHyN2Xx8s5T4/NRPNq0G20mwim9cNi4RyFYD+6JUfE8zg7LY2ThJFMg7L+9n561c+fFLC9Zf2k9Mw7PYNOHm/J0ll6j+iVYu9yG5X/f4cJlYZxUq1MqKCMjBQqgScMSfDW+DtWqmNPKRuxYDwoScz4zGkjuH+LPqYen2Nh/Y47W+ir+VY69XRmh8lcx4/AM9jrtxa6yXZbODY0KZbbnbHbd3MWcDnMYbzseI4Oc5QZJkiSqdP4qtl/bTgvrFjg3dcZtoFuWoxzyg4RnL1J8nS0DryQRv2IlrP4pf8VLerfJ4kUvsHyvNpYNavPywlU863ROcdvt+Stff/72bUkozUyp8WXaY6gKE7JwkikQPO54MLHlxIJeRrpoJS2zjs5i67WtnOh9Q9gAACAASURBVBh1Il/aL1WszVnq0oRnYfG4ezzmmPdTHj1JxMQErEoZ0qltWfr3sqZ82eRJ49bWIj5n0CDo0we2bhUdnLSY5zWPGW1m5HhUSl626paeWsoKvxV4jvTM0nMelxjHstPLWHp6KSObjuTGxBuUMstGHtEb3H1xl40BG9kYsBFTQ1OcmzgT8GlAnorG3EBhkFJgZMfAC6Do3Rt+mi6LF5kUNFo5l9OdR6CNzTzb7E2U5qZUHNCN4o3r5dHK8g9ZOMnkO1EJUfgG+7Jj8I6CXkqaxKhjcHZ3JjQ6lDPjzlDGvEy+3n/Z0iaMH1GD8SNq0K8fjBwmNjGlh6Ul7NkDn34q7Ch794qZqW+SW9UmyBvhJEkSXx/5mr239nJy9EmqlNBtp6AkSbhddeObI99ga22L7zhfalvVzvzEdIiIi2Dr1a2o/FXcCruFUyMntg7cik1FG72vjiZhUjHnSeoYGGBSr7ZQ5jIyb1CyZRNsXH/hwrApOosnpbkpVq1taLL6xzxeXf4gCyeZfOdY4DFaVWqV5z6Z7PAk8gl9tvShfpn6HBlxJMchizlFrdbNl2tkJOakLlggJm94eIhRaEnkVrUJcl84JWoT+XjPx1x/dh3v0d6UNtdti7tvsC9fHfyKBE0CG/ptwL66fbbuX61Rc/DuQVT+Kg7ePUjXWl35uu3XdK/dPcdtvoKgklNvQvccQxOV8RDWjAy8SmMjKvb7IKPTZd5hyvfujJ3HWs59OBGtWp3uwF+lifjjVXlYXxqumIMyIy9BIaJoPAqZQsX+2/v1MobAP8Sf3pt7M952PLPaz9KLCkNCgu6jvRQK+O47ETTdsaNo29nb5261CXJXOMWqYxmyfQgJmgSOOh/VyTMUFBHEN0e/wTvImx87/8jwJsNRKrLWQpIkiYshF1H5q9h8ZTO1rWrj3MSZvx3/znGLr6Ap27UdBmYmmQqn9Ay8sUGPsKhXA8tGdfN8rTKFF6t2LejyyIfQPce4u3g1kQE3XgslSaNFYWRI9c+GUW28E6aVcjf+o6CRhZNMviJJEvvv7MdjmEdBLyUFe27uYczuMfze83cGNxxc0Mt5jVqd9Zmozs5QqZLwPa1YAVsVLrlWbYLcE04RcRH03tybqiWq8k/ffzIdjhwZH8nCkwv5+/zfTG41mTW912TZnP3o1SNcL7ui8lcRrY7GuYkzPmN8ctTe0zcUBgbU+GIUN+atyJaB16CYObWmfZyna5QpGigNDanYvysV+3clPvQ58U/D0MYnYFSyOGbVrFEW0YHOsnCSyVeuP7+OhESDMvmUdZMJkiSx3Hc5i08tztYurrwmO8IJoHNnOHoUPhhxiZj+vvzb3zXX1pQbwulJ5BO6/duNjtU7sqz7sgwrRhqthnUX1zHn+By61epGwIQAKhWvpPN9RSdE437DHZW/inOPz/Fhgw/5s9eftK3aNsuVqsJAXGIcS2v60aJUIhXCDDLN3HkTpakJJVs1oeLA7nm4QpmiiEn5MpiUz18/aEEhCyeZfEWfYgjUGjWT90/G56EPp8eeplrJagW9pFTo6nFKi8aNofmX8/DfPYNpX5qxYkXGcQW6EpkQmSPhdDvsNt3+7cY4m3F82+7bDH8Wjtw7wpSDUyhlVoq9TnuxtbbV6T60kpbj94+j8lex6+Yu2lRpw9jmY9k1dJfOY1YKI4HhgQzcNpBapWrR79RBAj4YR9yj0NfepYxQmpli2bAOLdz/RGFQgJlUMjJ6jiycZPIVjzsefGH3RUEvg4i4CAZvG4yh0hCfMT56Oy8vKx6nt7kUcgn/MF8urnFlxFAYMAA2b87ZxAxJkoiMj8z2uJkLTy7guMkRFwcXPrZNvx104/kNph+ezrVn11j8wWL61++vk9i+8fwGG/1FhEBp89I4N3FmUZdFVLCokK31Fib23NzDuD3jmNluJp/bfY5CoaCdnzv+o2fw7NBJgDQFlNLMFCQJ6yG9aLRyLgYmBZwSLiOj58jCSSbfiIyPxO+RH51qdMrV68YnaAkJjSMqJhFjYyVlrIwpVSL9P/73wu/huMmRLjW78Eu3XzBU6u+vQXZbdSBm0s1oO4PyVmbs2wfjxwvT+J49UD6bXs0YdQwmhibZes48Az0Zsn0Ifzn+xYAGA9I8JiwmjLnH57Ll6ha+afsN2wdtz3RnY1hMGFuubEEVoOLhy4cMazyMvR/tzdXxKvpMojaR2cdm43rZlZ1DdtK6SuvXtxmVsKTFjj+JexTK/b838eCvTahfRqE0MkSboMakfBmqfz6SqmMGYly6cJviZWTyC/19xZApchwNPMr7ld/PleG4AMGPY9m6OxiPoyEoFAqUCkCCBLWW9+oVZ9iHVbCzscLAILlS4fPAh4HbBjKr/axsD43NT7IrnC6FXMI32BfXAcLbZGQE69aBi4uIK9i/H+plI4cuu/6mHdd3MGHvBNwGutGxRsdUtydoEljpt5KFJxcytOFQrk+8nmF+VnxiPB63PVAFqPAM9KRnnZ64OLjQpWYXvRbCuU1oVChO/zlhoDTg/Pjz6Q4VNq1UnvrzvqL+vK/QxCegiYrG0LIYyoKeQScjUwh5d/7CyBQ4Sf6mnBIfr8FlyQ18L7xAq5VITEw93M3/6ktu3YvC3NSAJXMbU6emBa4Brnx58EtU/VT0qKN/cQhpkV2Pk8sJF2a0nZHCz6NQwNy5UK2aiCnYvh3atcvadbMjnFadX8Xc43M5OPwgzSs2T3GbJEnsvLGT6YenU79MfbxGeaWbGC5JEn6P/FD5q3C76kajco1wburMhn4b9LbVmpd4B3nj9J8TY5qP4Xv773WelWdgYiy342RkcoAsnGTyBUmS8LjjwVetv8rRdWLjNHz29UUeBMeSkJB6qnuKY2M1xMZq+PTri7zX5yy7w8Qoj0blGuVoDflJdjxOF59c5EzwGTYN2JTm7aNHi7iCAQPg999FbIGuZEU4SZLEj94/su7SOrxGe6Xa8n/hyQWmHJxCWGwYf/b6kw9qpR24GBQRxL8B/6IKUCFJEs5NnTk3/hzVS1bXfeFFCEmS+OX0L/x86mfW911faN4EyMgUFWThJJMvXH12FSOlEfVKZ39OkVYr8c0PVwh6GEOCOnWVKT3i4rSc39GQHT950ahc1Wzff0GQnVbdPK95fN326wx3j3XtCocOgaMjPHgAU6a8DpHOEF2Fk1bS8tWBrzgedJyTo09S0TJ5BsyjV4+YdWwWB+8exMXBhTHNx6Rqr72Kf8V/1/5DFaDicuhlBjcczIZ+G7CrZKcXOzILipdxLxmzewwPXj7Ab5yfXu4ElZEp6sjCSSZf8LjtQY/aPXL0onfuUjjXbr7KUDTZNinJlE/rUL6sCdduRrJg+Q1Cn8Wj1Jji5vYKm9nZvvsCIavCKbNq05s0awanTkHPnhAUBMuWQXq70LVaicioRB48jsJCWwG1WouRUdoZSAmaBEbtHEXwq2BOjDpBSdOSgMhTWnJqCSv8VvCJ7SfcnHQzhQjTaDUcuXcEVYCKfbf24VDdgcmtJtOrTq8CH32jDwSEBjBw60C61OzCpgGb5OdERqaAUEiSpPtbdxmZbNJxQ0emtp6KY13HbF/ji+/8Oe8fke7tJYob4rbKjp9+u4mPXxjjhteg6Xsl+GT6RQCMjBRsX/s+pUsVHn+HhQWEhIh/daG/W38cqjnwxfu6Rz5ERIi2XYkS4OoK5m8EjIdHJLD74BO27g4mJlaDhAaNVouhwgj7NmUY2r8K9WsnzxyMTojmw60fYmJowpYPt2BmZIZW0vJvwL/MOjaLdlXbsbDzwhRttsuhl1H5q3C97Erl4pVxburM0EZD8324sj6z4dIGph2exvJuyxnWZFhBL0dG5p1GrjjJ5Dmv4l9x7vE5OlZPvZtKV0KexnH52ksAtq2xY8e+R3TrWJ4K5Uw5c+EFC5bdwL51WQIfROPp8xyAdZvus8+1DVUrm/EgOBaFAnYfeMxop+q58bDyhax4nLJSbXqTkiXhwAEYOxY6dRJxBaVKaVn6520OeoaiUEB8QtL7KyWgRI3EsZPPOHkmjEoVzVg4qyEmxWPotakX75V9j1W9V2GoNMQryIspB6dgqDRk68Ctr7fKh0aFsunyJlQBKp7HPGdEkxEcdT6arjH8XSUuMY7JHpPxfuDN8ZHHaViuYUEvSUbmnUcWTjJ5zpF7R2hTpU2W54q9yYWACBEr8P82Xcd2ZZk69zIJCVr+/LkZPTpXoHoVc+4EJg82jYvX8igkjhpVi/3fTC5x4tTzQiOcJClrrTpdvE3pYWwMKhXMng2t22hp38OfB4+jMmyLarXiOQ58EM2oL87xuPlcHFs4sLDzQu6F32PGkRmce3yORZ0XMbTRUOIS49h6dSsqfxU+D33oW68vS7suxaG6Q5EcfZJT7oXfY+DWgdQpXYezH5/F0sQy85NkZGTyHFk4yeQ5Hrc9chxD8CpKnSJ2YPueR4S9ECnIPn4vqFPTAkMDBRGv1CnOi4pOxNws2bgTGZ2Yo3XkJxoNKJXiIzMuPrmI3yO/LFeb3kShgPnzJe6GXOPmnSiUBhnvWkxCq4XomETKXfiWj52bMv3wdNZfWs/U1lPZ2G8jF0Iu8MneT9h+bTstrFvg3NQZt4FuORLSRZ09N/cwdvdYvuvwHZNbTX6nDfEyMvqGLJxk8hRJkjhw5wAz2s7I0XWUCgW88drxIjx5dER8vIYyVsa8ilRTzDylu7mYuQExsclDTpWF6AUoKxlOLidcsl1tepPzARE8fxmeoWhKz4CvjlPQb84ftO77ir1Oezlw9wCN/myEmZEZI5uOJODTACoXr5yj9RV13kwB3zV0V4oUcBkZGf1Aro/L5CmXn17G1NCUOlZ1cnSdkiWMMDLM+Mc18EEMtaonu6hNTZRUqmhG4IPk9l2J4oXnvYKu/qaLTy5y9vFZPrZJf/abrmz67yGxcemLphLFDVkwsyFr/g2kp5MPN+5EMm/GewBIkoJSz9tz7fFd+rr1JSIugu2Dt3Pl0yvMaDtDFk2ZEBIVwgcbP+D8k/OcH39eFk0yMnqKLJxk8pTciCEAsLOxSjMh/E28Tj+nZrVi2Lcpg7GRgtFDq3E3MJoHwbGAEFLdOxWeYa+6+ptyq9r09Hk8l66IXYvb1tjh1L8y61fYcmBLW1xmNMDYSJHCgJ+glli36T61axSjamVx35JCSxeDGQR/Fczy7suxqWgjt5l0wDvImxarWtChagf2D9uf7ugUGRmZgkcWTjJ5yv47++lZJ+djVkoUN6JtK6sMQxojXqn5buFVxo+owf7NbXmvXnG+X3zt9e1aCXp0zuZ02wJAF+GUm9UmvwsvUCqTn+AkA/6gcWeoVb0YPTpXoEbV9A34AFKiEY+ul8XIIJuTid8xJEliyaklDNw2kNW9V+PS0UXn0SkyMjIFQ+HpW8gUOiLiIrj45CIO1R1y5XpOA6pw+twLBo07k+L76zYHvf78nH8Ewz49m+pcAwPo1K4sxcwLz4+8Lh6n3Ko2AbyMVKNOTG7TZduAH5nydpm0eRn3ktG7RhP8KlhOAZeRKUTIFSeZPOPIvSO0q9ouV17UAd6rW5wuHcphnE5idXooFFDC0ojPRtXMlXXkF5lVnC48uZBr1aa0eNuAb2ZqQGycJlMDvpyomzn+If60WN2CihYV8R7tLYsmGZlChCycZPKMJH9TbjKoV13Cn5XC0EC3H10DAwXFLQxZubAZVoUoMRwyN4fnZrUJwNLCCINMsg90MeAXt5TbdBmx/tJ6umzsgouDC7/3+l0enSIjU8iQhZNMniBJEvvv7M/Vye2hodC9u4KxQxriNKAyJsZKTE3T/hE2MACtRkn1ypas/60FVSubp3mcPpNRxenCkwuce3yO8bbjc3QfL16AmxuMGgWffVyK+PicG/C7dCiXozUVVWLVsXy8+2MWnVzE8ZHH+ajxRwW9JBkZmWxQeAwfMoWKSyGXsDS2pLZV7Vy53suX0KMHDBsGkyYpgBqMGFSVQ8dDcP0vmJCncRgaKNBoJYyNlPToVJ4XIZW5d9ucsqVzZQn5TkYeJ5cTLnzT9htMDU2zdE2tFi5cgP37xceVK2BvD926gb29KWu3lkBSpD8PMMmA/9WEOsyZUp9rtyJTGfB7di48Oxfzi6QU8Lql68op4DIyhRx5yK9MnvCj9488jX7K8u7Lc3ytuDjo3h0aNYLffiPNnXWJGomYmERMjJWYmAgPTlQU1K4Nhw9D48Y5Xka+4+sLX34p/n2TC08u0Htzb+5+flcn4fT8ORw6JITSwYNQpox4Pnv0gNatxWy6n38W1SeF8Qsq178K6JYa/iZKJXTpUI45U+V5c2+y++Zuxu0ex+wOs5nUapIczyAjU8iRW3UyeUJu+ZsSE8HJCSpUgBUr0hZNAIYGCopbGr0WTQAWFjBjBnz/fY6XUSCk53HKrNqk0cCZMzB3LtjZQa1asHUrtG0Lfn5w7Rr88APcugVNmsCSJeIcS0tY9Ucp3rctobOH7E2KmRvyiXONLJ9XVEnUJvLNkW+Y5DGJ3U67mWwnj06RkSkKyK06mVwnPDacgNAA7Kvb5+g6kgQTJkBMjPDh6DKz7W0+/RSWLoXz58HWNkfLyXfS8jgleZvcBrql+P7Tp6KatH+/qC5VrCiqSgsXQrt2yS2/Fy+EaFq5Epo1E5U4X18hsj7+GAwNFWg0DfE8HkDxUlEp4gnSQ6EAczMDVixoSvmyWWsdFlVCokIYun0oxgbGnB9/Xg60lJEpQsgVJ5lc59DdQ3So1iHL/pu3+fZb4cH57z/dZ7a9jZmZuM6cOTlaSr6ToNYSHpGAoXE8cXHJW/3nHp/LN22/wRBTTp2C2bOhRQuoWxd27oSOHeHiRbh8GRYvhk6dxHP38CFMmSJal7dvw4gRQkzWqgU3bwqBaWgIJ0/CSGcDlsxtSpcOZTE2UqDVpF0lUSiEGbxKJTPWLrOlTk2LNI971/AK8sJ2lS321ezlFHAZmSKI7HGSyXVG7RxFS+uWTGw1MdvXWLoU1q4Fb28onUNzd3y8EBZbtghPj76i1UqcvRTOpv8eculKBAqFkkQNKJVaqlcpRpNWiSy8P5SO125w7JApVasKn1L37tCmTdptvWvXhH9p926xc65hQ1i0COrXF89x3brJx549C7168T/2zjusyfN743cSNgqoIKgMZ93Wvetuq3Zp6+6w+q1atWqdddVZtI46W/feq+69JyruxXAUREBURGQlISTP74/zQ2ZCEhICeD7XlUtI3vG8CfjenHEfbN4MfPwxPbd2vRJrNkfCsXgkVCoNZDIJNBoBtUagWYMS6NHZC9UrF+UUFNJcwOdenov1ndajfcX2ll4SwzBmgIUTY1I0QoPSf5WG3//8UL6YcYaT69dThOjiRcDLyzTrWrmS0n0nT5rmeKbm1r1YTJkTiCS5GvJ0Eab0qJEMqUSG5rWrYtjPpVC6tPbj+fkBs2ZRrdMvvwCtWlE67vlzYP584JNPMm5/9y49t3Il8MUXac+3bUspvK5dBd68TUZiohq2tlK4OFnDzo5Hg6QSq4hFn319EBEXgZ1dd7KhJcMUYjhVx5iUW89vwcXOxWjRdOAAMHYs1euYSjQBFG0JCQHOnjXdMU3FmUsvMWrKPbx+k6xVNAGADDaQCBmuBzzGodMhWV7XaICDB4GPPgK++44sBvz9gYgI4JtvgM6dgTt3soqmoCCKWi1alFE0BQdTqrRzZzISdS1uCx8vB3iUtGPRlI47UXdQf0V9lClahl3AGeY9gIUTY1JyM9T3wgXgf/+jtFKVKqZdl7U1ddf9/jsVnecXrt2KxdS5wVAm69/+r1RqsG1vOHYfjgBAReQbNlCH3O+/A4MHk+BRKKgg3tYWCAyk560ytYOEhJCQmjkT6NYt42vLlwN9+9L+TPasvbUW7Ta2w7TW0/B3x7/ZBZxh3gO4q44xKYcfHcbUVlMN3u/OHaBLF2DLFqBBAzMsDGSeOXMm+TpljrrkJWFhwNGjwOHDApGJQbCx0y2a6tVywYiBleDuZouA4Hj4LgjCi1dK/L3qP/wX6I7Fi6xQsSIwbx7Qrh1w+DBQpw4Vgl+4oF2EhodTKm7sWKB374yvyeUkxvz9TXTRhQy5So4hR4bg0rNLOPfjOVRzq2bpJTEMk0dwxIkxGa+TXuPBqwdo4dPCoP3++w/o2BH45x+68ZsLmYzqfPI66qRUUm3VqFFUnF2vHnD+PNCs5Vs4Oat07uvsZAXf8dWxalMIOva8hKDH8Zg2hm7SCgVw6kIUdu0CTp0CypShlNvo0cDChcChQ9pF04sX9F4PGkSPzOzcSQK2fMGai5wnPIl5gqZrmiIhOQH+P/mzaGKY9wwWTozJOP7kOFr6tDQoXREVRdGfSZMo4mRuunalaMrBg+Y9T0gIsGQJ8OWXQMmSJNacnIB16+iaN20CXsU/e5ei27mqEXp29sS6RfVwdFszTB1TFTbWErRs4oaQsEScuRSNZJXAmi2hqFjOEd6e9pDKNLBzeYayZQUGDyYrgi++oELv9joaul6/pq65nj1JzGXH0qVkUcBkZF/QPjRZ3QT/q/M/bP1mK49OYZj3EE7VMSbD0Pqm2Fi6wf/4IzBggPnWlR6pFJg2jYTaZ58ZZ6qZHQoFcO4cGVAePQq8eUPX1qsXsHZt9pYK/rfeZIh8tW7uhpFT7iE5WYOls2ujQ1sPlPVywOOQxLTzKDWIiFKgnLcjwsLliHmTgtr15PimkwMCA3O2bnj7ltbVvr12b6vbtymN19G4UrVCSYomBRNOTcC2B9uwv+d+NPZsbOklMQxjIVg4MSZBIzQ4+vgopreertf2cjlFY1q2BCZMMPPiMvHVV+SevXt37qJcjx+nCaULF6g4u0MHqtOqXVu3KFMq1dBoMuYLdx2IwOuYZADAJf8YVCpfBFYyCWLjMqbzEhJT4GBPXW1qtQTLlqvwhR7TbRITSSw2akRWBdqsl5YvT3URz/mY7wOpLuC2Vra40f8GXB1cLb0khmEsCP/XyJiEG5E0VkKfVuyUFKBHD8DbmzyF8to7USIBpk+nNBW12uu3X1IS2RmkiqXExLSI2aZNQLFiuVtXzJvkd18rlWq4FrdBXLwKjg4ZF+joIEOSnGwLHB2BsmVzPrZCQYKxUiXdM//i48nv6v59Y6+icHEu9Bx67e6FfnX74fcWv0MmZRsGhnnfYeHEmAR9h/oKQdEMlYpSWKZKlRlK+/YknrZto2677BCCBuGmCqVLl4C6dSmqtGsXRZgMFX1xcTTq5OpVKTQaCQDdVeohYUlo38b93fd2tlKUKWWPkDBK36k1Ak5FsrEMT0dyMkXWXF2BVat0v+ebN1OtlC5zzfcBIQTm+M3BvMvzsK7TOnYBZxjmHSycGJNw5PER+LbxzXG7MWPIWPHEiexHhOQVEgml637+GejePS0tlZgInD6dJpaSk0ko9e9PkRhnZ/3PkZxMM+P8/dMeoaGUxmvYUAIvDxc8i3qj8xjnL0djUJ/yaNnUFZevvUafHj54EpKIsHA5AMDeTgavMvZa909JIWEokwEbN+qOrgkBLFtGM+7eZ2IVsfhx7494nvAc/v384e3sbeklMQyTj2DhxOSaV4mvEBgdiObezXVuN3s2CZLz5ynFZGnatKHIyp9/0jDgI0doREmDBiSW9u8n+wB9okoaDdU8pRdJ9+7REN2GDWlG3rBhdLxUwXjtthfG+8bpdAuPjVNh4swHGP5zJUwaUQUBD+MxeU4AAMDWVorunTwhlWa/QI2GDCzfvqVryUmoXr1KwrFt25yvt7ByO+o2uuzogvYV22N7l+1saMkwTBZ4Vh2Tazbf3YydATuxt8derdusWUOpsYsXyW/IksTHk+/RkSPAvn1AdDTQpw/w+eckporq0WH+/HlGkXT9OuDiQiKpYUMSX3XrAkWKaD+GRiPwdZ8riI5J1r6RDmyspdizrjGcnbIqIiHIn+nBA4qcOTjkfLzevYGaNbVbFBR21txag99O/oZF7RehZ82ell4OwzD5FI44MbkmJxuCvXuBiROpsNoSokkIKnY+coQe168DjRtTVOnMGeDXX8mU8quvst8/Lo72uXYtTSjJ5SSOGjak/Rs0IL8mQ5BKJRg3rDLG+z4waOQKQLVOvbv7aBVNo0YBN29SSlQf0RQTQyLyr78MWkahQK6S45fDv+By+GV2AWcYJkc44sTkCrVGDY+/PHCz/014OWedynv2LM1AO3KExEle8fYtuXWn1irZ2JBQ6tCBip/Tpwr9/YGvv6ZUm1RKBpLpo0lhYal1SWmPcuVM1w24/9hz/LXkMdQa/cSTna0UHdt6YPjPFSHJZhGTJpEIOnMGKF5cvzXMn09Ca+NGQ1Ze8HkS8wRddnZBFdcqWPnFShSx0REiZBiGAUecmFxyPfI6PIp4ZCuabt0i0bR9u/lFkxA07y41qnTrFtC8OXXPjRlDbfiZNYZGAzx6RMXqNjZA5cqUtqtYkcRRs2YUTUpfl2Rqjh8Hxo0oBddSNtDYBMLODkhWZV/zZGcrhRBA315l0bOzZ7aiadYsGpdy7pz+oim1KHzNmtxcScFjb9Be9D/QH5NaTsLgBoOzfT8ZhmEyw8KJyRXabAgePSKzxWXLKMJjDt68oVRUalSpaFESSuPGkbFm5hRVZGRaFOnaNXoUL04iqXNnGocSEmJ4ys0YgoMpnRYYSOmxwMASuHipKYYPj8bmf58h5GkilEopZFaAtZUGxVxs0OtrL7Rv4w5Hh+x/bRcvBlasoOJ7Q67hzBkSjk2bmuji8jkpmhSMPzUe2x9sx4GeB9DIs5Gll8QwTAGCU3WMQcS+VSEunpysnYpa45MdzTD749loVbbVu20iIynaM24ceTaZCo2GIkmpUaV794AWLUgsdehAHWypvH1LfknpU24KRcZ0W4MGgJtb2j7dugH161OEyly8eUNF8hs2AGPHAkOG+dvb9QAAIABJREFU0Oy4WrWAK1co2gUAb+NUqN9AhWbNBebOsUKJYjY6IyJr1tAA4/Pn9TPETE+3biQ0Bw82+rIKDM/jn6PHvz1gb2WPTV9vYhdwhmEMhoUTkyPKZA3OXHyFzf+G4VmkHNbWUkgAJKs0SLAOw+R+rfFJCw/Y2srw5g2JmV69SDjllujotKjSsWMUIUoVSi1aAHZ2gFKZtS7p2TOgTp2MQqlsWd11SQEBQKtWVOvk5JT7tacnJQVYuZLETadOJJ5So0Lffw94eQEzZmTcx8sL+OYbYMEC3cfeupWiV2fOAB98YNi6nj8HqlUDnj41/TXnN1JdwPvX7Y+JLSayCzjDMEbBwonRyYmzLzBnySMIAa1+Q/Z2UgASDO5bEXN9PdCwIaWfjCkZUaupg+3oURJLgYEkZlIH0/r4kJt3+pTb/ftUw5QaRWrYkOqSjJm19t13VOv0+++G76uNkyeB4cPJuXvBAuDDD9Neu3SJDDiDgrJaF5QuTeuZPVv7sffuJRPPkyeBGjUMX5uvL4mmFSsM37egIITA7EuzMf/KfKzvtB6fVvzU0ktiGKYAw8KJ0cqW3c+weksolEo9W+WFFMXsvbFvu49Bo1RevqQi6SNH6F93d4ootW9P3Wt37qRZAVy/DpQokSaQGjakyJKpDDUfPSKzykePcj977tEjigTdvw/MnUuRpvRiUq2m6xg1iiJ0mSlZkkTRtGnZH//YMYpWGduxqFYD5csDe/aQ51RhJFYRi957e+NFwgvs6LqDXcAZhsk1XBzOZMuJcy8ME00AINEgSROGY2ds0aGth9bN1GpyqU6NKj16RMaTLVtSQfnTpySSfviBxpakCqSRI0louJqxLKVSJfJzmjeP0mnG8PYt7btuHdVL7dgB2GZjQL1qFQm+nlq8FtVqSkVmx7lzJJr27jW+Y/HIEcDDo/CKplvPb6HLzi7oWLEjdnbdCRuZjaWXxDBMIYAjTkwWVCoNPv/OD4lJ2keBAEC9Wi4YMbAS3N1sERAcD98FQXjxSgl7exkObmoKW5u0sFNUFEVIjhyhmqXSpSlSVKwYFUdfvw5ERGSsS2rQIOe6JHMQGkpiJDjYMJGmVpMYmjyZXMj/+IOESXbExABVq9J7Urt29tsUK0YpwxEjMj5/5Qrw5Zc0oLhNG/3Xl5nPP6caqj59jD9GfmX1zdUYe2osFndYjB41elh6OQzDFCI44sRk4dzlaOTkxejsZAXf8dUxa3EwLvm/xk/flcO0MdUwYPQtAMCp869Q1NYdR48Chw8DT55Q7ZCTExU9P3pENUgNGlAN05gxVKRsTF2SqSlbluqOZs/WXV+UntOnqY7JxYXEYZ06urefPJlEizbRBJAQyxypun2bImLr1uVOND19Cly+TNGwwoRcJcfgw4NxJfwKzv94HlXdqlp6SQzDFDLywW2KyW9s2hX2rhB856pG2H0oAp+2dodHSTtcvRkD3/lBaNnEDSFhiThzKRoAsGZLKA5tbgpvT3uEhcvxu28YIgLdYWdHnXHu7mnGkql1SfqMArEUEybQ3LYRI7RHjQAShKNGUR3WnDnkQJ5ThOzuXTIFDQzUvZ1GkzFVFxBAtV9LlgAdtU+40YsVKyjVl58/A0N5HPMYXXZ0QTW3avDv588u4AzDmAUWTkwGkpJSEBKWlOG51s3dMHLKPSQna7B0dm10aOuBsl4OeByS+G4bhVKDiCgFynk7IixcDntHOfr1V6FZU2vUr2/euiRzUKYMDb2dORNYuDDr63FxlIpbs4Zqr7Zu1V6PlB4hgKFDyZagRAnd26rVacLm8WPgk09InH3zjcGXk4HkZGD1arIvKCykuoBPbjkZgxoMYhdwhmHMBgsnJgNxCSmwtpJArU4rfdt1IAKvY5IBAJf8Y1CpfBFYySSIjVNl2DchMQUO9uSNY28vwYCfU+BZ2kyzSvKAsWOpDmnUKEovAiRm1q6l2qMOHciEs1Qp/Y+5YwcQGwsMGJDztkKQGAsLA9q1oxl0331n3LWkZ98+oEoVuraCToomBeNOjsOOgB3sAs4wTJ7AwonJkZg3ye++VirVcC1ug7h4FRwdMhoIOjrIkCRPLSgv+H/xu7sD/fuT19GyZdTJ9uuv5Ld08KDh3WyJicDo0cCmTYBMD+9FjQaQy4G2bem8/fsbdx2ZWbYMGDjQNMeyJM/jn6P7ru5wtHHEzf43UcIhhxAewzCMCWDhxGTAqag1VCk5N1qGhCWhfRv3d9/b2UpRppQ9QsIofadK0cDJqeD/eI0eTbVZoaFkUjl7NtC1q3Gdfn/+SaNoWrTQb3u1mmqtfvqJhJMpCA4mX6nOnU1zPEtxNvQsev3bCz/X/xkTW0yEVGKAcRjDMEwuKPh3NsakONjLUKmcI4IeJ+jc7vzlaAzqUx4tm7ri8rXX6NPDB09CEhEWLgcAlPVygFORgpumA4D4eDKuVCrJpDMwELC3N+5Y//1HRd137ui3fWwsRZxatSLxZCqWLwf69qWhvgURjdBgzqU57ALOMIzF4D/TmCx0+cI7xzlesXEqTJz5AP2/L4cjW5uhWmUnTJ4TAACwt5Ph228KrkOzWk1F35Ur0yy3Gzdo9t2zZ8Yfc8QIKiL39Mx524SEtK65zB5OuUEup+HCpkr55TVv5G/QeXtn7Anag2v9rrFoYhjGIrABJvMOtZpurJMmaVC29mUIpBh3HJUM3Ts0xbCh0jw3r8wtFy4Aw4ZRZGnBAvKZAqjO6cEDYMsWw4957BgweDClyHLqvJPLyT29QgUy03zyhMaimIL168kG4fBh0xwvL0l1Af+s0meY+8lcdgFnGMZicMSJgRBk2li7NkVadu2S4o/xlTM4f+uLrY0UPiU/wKiRUlSvTkaXBYHQUKBbN+paGzMGuHgxTTQBZCFw6hSJH0NITiYhNn9+zqJJqSSrgVKlqIAbMK3P0rJlNPuuICGEwKqbq/DJpk/g28YXizosYtHEMIxFYeH0nnPzJrW6Dx9OvkTnzwONGgEtm7hiUJ/ygND/R8TWRooBP5TDtg0lcfMmdZFVq0bppuTknPe3BAkJVENUrx4ZXgYGAj16ZC3+LlqUCsWnTDHs+IsXU8To8891b5eSQjPr7OwoMpQ6JNlUw4tv36aRNp99Zprj5QVJqiT03d8X8y7Pw/kfz/PoFIZh8gUsnN5TQkMpuvL55xRpuX+fRnmkFwz3b5QBkqqiaBEr2Ntrr3myt5NBo5bhs1aV0e0rKuKpVYsKoseNA/7+m3yQ8pPhokZDY0sqV6bapTt3yJtJV4Rn0CDAzw+4dUu/czx/Tgaa8+fr7sJTq8lsUy4nI00rKxJSgOmKuJctA/r1088GIT/wOOYxmqxugmR1Mvz7+fPoFIZh8g1c4/SeERMDzJhBJo5Dh1LBcpFsJlMcOUJt8NevA25uGlzyf41Nu54h+Ek8rK2lkABIVmnwQfki+LaLN5JiS2DIECnu388qPp48IVH28CFFPFautKyT+MWL1N5vbU11TI0M8ExcvBg4fhw4cCDnbX/8EShZUve8OyGoWPvxY6o9Su3aS0ykz0WtTos+GUt8PODtTTVapUvn7lh5wZ7APRhwcACmtJqCgfUHsgs4wzD5C8G8F8jlQsydK4SbmxADBggRGal92//+E6JkSSEuXMjuOCnixSuFePFKIeTylAyvdesmxLhx2R9ToxFiwQIh7OyEcHQUYuVKei4vCQ0Vont3Iby8hNi82bjzKxS0/+XLurfz8xOidGkh4uK0b6PRCDF0qBCNG2fd7tUrIUz127l0qRBff22aY5mT5JRkMerYKOEz30dcDb9q6eUwDMNkCwunQo5aLcSmTUL4+Ajx1VdCBATo3j4pSYg6dUjkGEpkpBCurkLcv699m2fPhGjWTAh7eyHq1hUiMNDw8xhKfLwQEycKUby4EFOmCJGQkLvjLV8uxMcfa39drRaiXj0hNmzQfZxx4+i9fvMm62tPn5pGOGk0QtSqJcSJE7k/ljmJiIsQH635SLTf1F5EJ0ZbejkMwzBa4RqnQszp09QZtmgR2Qzs3at7PpkQVMdTuTKl8QylVClg6lTq3NJost/G05Na/pcto9Rd3bpUByWXG36+nNBo6LqrVAFCQqhAevLk3Bdc9+lDqbXz57N/fe1aqk3SNVfO15dmxh0/Dri4ZH09Kck4d/LMXLlCx2rTJvfHMhdnQ8+i/or6+Lj8xzjU6xCPTmEYJn9jaeXGmJ67d4Xo0EGIChWE2LFD/5TU8uVCVKtGERpjSUkRomFDSsXlRFSUEJ99JkSRIkKUKSPE8ePGnzczfn60joYNc06rGcO6dUK0aJH1vX3zRgh3dyGuX9e+7/z5QlSsqDtdevOmEFJp7tf5ww9CzJmT++OYA7VGLWZemCnc57iLY4+PWXo5DMMwesHCqRARHi5E375Un7RwoRBKpf77+vtTmi0oKPfruHWLaqlevNBv+3//FaJYMSGcnITo2lWI58+NP3dYmBA9ewrh6SnExo2UNjMHKpUQH3yQNQU2bJgQ/fpp32/5ckqbPn2q+/gXLgghk+VujdHRQjg7U71UfiMmKUZ8seUL0XhVYxEWG2bp5TAMw+gNp+oKAW/fkhdRrVqAuzulwIYO1b+VPToa6NIFWLGC0nS5pXZt4IcfgFGj9Nv+668p9fXZZ5S6qlqVUnna0n3ZkZhIabjatYFKlWgg73ff5b4jTRtWVpSWnDiRUpwAWTps3kxpuOzYtAmYNg04eZK63HShUOR+7evXA198YdkOxuy4+fwm6q2oh3Iu5XDux3Pwcvay9JIYhmH0x9LKjTEepVKIRYsowvTjj1R4bSgpKUK0ayfEb7+Zdm3x8UJ4ewtx6pRh+x0+TKmukiWFqF9fiNu3dW+fWvzu6UmRppwiOaZErRaiRg0hDh6klF2bNvR5ZMeuXUJ4eAjx4IF+x963TwgbG+PXptEIUamSEBcvGn8MU6PRaMSK6yuE62xXse3eNksvh2EYxiisLC3cGMMRAvj3XyqqrlgROHGCok3GMGkSRXb++MO0ayxShDyPfv4ZuHs353EjqXToQBGzMWNorlrLluQnNWVKVr+pq1dpnIlaTds2bWraa8gJqZQiSL//TsXtL18CAwdm3e7wYSq6P3qUnNT1IbcRp9On6T3P6/dEG0mqJAw6NAjXIq/hQp8LqOJaxdJLYhiGMQpO1RUwLl6km+GMGcDSpWRUaaxo2rcP2Lgxza3a1Hz5JVC9OvDnn2nPpagFwiPlCHoUjyehCXgTm3UWi5MTper27KGOs3//pfTd/v30eng48P33NNdt0CASUJYSCJ06pXUjLlqU9X08fZqMMPftA+rU0f+4SUm5E06pc+nyg3fko9eP0GR1E6g0Klz96SqLJoZhCjTsHF5ACAoCxo6lcR++vkCvXrm7sT56BDRrRg7YhjhnG8qzZyQYDh5W4m5wJHYfikSKWgOZVAIhAFWKBpXKkft4s4YlYCXLeKdPSqKIztq1gK0tCamoKGDwYHo/snM9z2t69QIOHSJX9vQjTfz8SFjt3EmRM0NYvpxm48XFGb6e588psvX0KYlQS7I7cDd+Pvgzpraaip/r/8wu4AzDFHg4VZfPiYqiIuRdu4DffgO2bdM/7aWNxESK1kydal7RBAClSgt07PIIo6ZFwcYGSFZl1ekBD+PhOz8I1tYSzBhfAx9Wd373moMDMHcu4OwMTJ9Oa5dIgBIlcv8+mILQUODYMaBCBWDHDhrUCwA3bpBo2rjRcNEEUOrPWGG8Zg3NH7SkaFKpVRh3ahx2BezCoV6H0KBMA8sthmEYxoRwqi6fkpBA9TM1apB4CAqiLrXcioXU2Wh16lAqx5ykpGgwaspdRES/ACQiW9GUSpJcjbdxKRgx+S4u+b9+9/y1axQZ27+fBMqwYRTVWbECaNiQXrckI0fS3LvZs6kOKyWFuutSZ/J9+qlxx1UqjRvIq1bTe2Puz1YXkfGRaLOhDQJeBeBG/xssmhiGKVSwcMpnpKTQje+DD4DgYBIGf/1FERZT8M8/NOx16VLz17/MXBSMe4FxUCr19xVQKjWYNDsA5/3i0bs3DQfu1w/w9yf36+nT0wqfU1KAjh2BX34hS4a85uRJSp2OHg20bUvO6XPnkliaP5/WbizGFocfOQJ4eBhWT2VKzoScQf0V9fFJ+U9wsNdBdgFnGKbQwam6fIIQwMGDlI5zd6cIS/36pj2Hnx8JDz8/imKZk0chCTh7KRrKZO2iqV4tF4wYWAnubrYICI6H74IgvHilhFKpwfDxj/Bp07oIDgaKFs24X+3aJKTmzgXmzCHxUq0aiZWuXfOmIFqlIq+sefPSooCDBlG909KlaSk7Y1EojCvYX7Ys+84+c6MRGsy6OAsLry7Exs4b8XGFj/N+EQzDMHkAR5zyAf7+QKtWZC8wdy5FVEwtmqKiqO5l9WqqxzE32/eGIyVFu2hydrKC7/jqWLUpBB17XkLQ43hMG5PWq+9UPBEDBiZlEU2pWFvT+3XpEolONzcyo+zYEfjvP1NfTVb++Yfm7qVGlSIi0uwhTNFuYUyqLjSUZtN165b78xvCG/kbfLXtK+x/uB/X+l1j0cQwTKGGhZMFefIE6NGDnLN79wbu3KEbv6kjJikpdJ6+fYHPPzftsbMjMSkFpy++gloD7FzVCD07e2Ldono4uq0Zpo6pChtrCVo2cUNIWCLOXIpGskpgzZZQVCznCG9PewCARqPBjv3hOZ6ralUaGvzjj8Dr15TeatgQmDkTSM7qdGASXrygzsaFC+mzevkSaNeOasfWr6eonkKRu3MolYZHnFauJLd0c0cT05PqAl6hWAV2AWcY5r2AhZMFiI6mguJGjYCaNamWqW9f44qB9WHcOEonTZ5snuNn5l5gHKyt0tRf6+ZuGDnlHrr+dBUVyjqiQ1sPlPN2wOOQxHfbKJQaREQpUM7bEQAVOacvEteFTEbvp78/daOVKUO1PnXqkO+VqRk/nkbKVK1KFgSffEIpwt9+o8+0dm0SMbnBUOGUnEzRxLwqChdCYMWNFfh006f4s92fWNB+AWxkes74YRiGKcBwjVMeIpdTlGLuXIoABQQAJUua95y7dtHj+nXzCbPMxMWrMqSrdh2IwOsYCv9c8o9BpfJFYCWTIDZOlWG/hMQUONinLTIxSW3QeStUAE6dItEyfjwVbHfvTm7ks2bpX2CfkqJBWIQccfEqWFlJUMzZBqU97CCRSODvT6IsKIg8ljp0oPNMnZq2/7Rp1FX3v/8ZH/1JTjZMOO3dS0KuSh54SyapkjDw0EDciLzBLuAMw7x3sHDKA9RqGvD6+++URvLzo645cxMYSIXCR4+aritPH6TSjLnGmDdpOTOlUg3X4jaIi1fB0SGjknN0kCFJniaWjElZSiSUMuvQgaIvLi5AfDw5mM+eTY7j2o77MlqJ3YcisOdwJDQCSL2MFLWAa3Eb9PraCzMmu2PGDBmsrID27YG6dUkIpz9mnTrkZL5kif6DjjNjaMQp1Snc3Dx8/RBddnRBLfdauPrTVTjaOJr/pAzDMPkITtWZmWPH6Oa6YgXNU9u1K29EU3w81U7NmgXUq2f+86XH2ck6R9ETEpaECmXTbL/tbKUoU8oeIWHp0ndyK8ydS+m2pCTD1uDlRV2K48YBZ8+mWQS0aUPRovSo1QLzlj1Cj/5XsX1fOBKT1JDL1UhMoodSqUHEcwXmL3sCKzc/lPJ5ic6dgXLlqEg8u2udOpU6/uLjDVt3KoZEnIKCKHrZubNx59KXfwP+RbM1zTCowSBs7LyRRRPDMO8lLJzMxO3bVPsydCgZI168CDRpkjfnFoJqppo3p3/zmlpVnaDR6G4tO385GuV9HNGyqStsrCXo08MHT0ISERYuB0BRq5pVSiIsDBgxgrrm6tenUSsbN9LImJy61yQSKpa+e5eEV2IizfVr3pyGGysUJJrG+d7H4ZNRSFYJqHSYdKaoNZBINJg+LxhWDpFYvVq711L16lQwvmiR7jVqIzmZOgf1Yfly+pxtzFRipFKrMPLYSIw8PhKHex3m0SkMw7zXsHAyMU+fUuFwhw4UAbh/n/7Ny/vM/PlASAiweHHenTM9trYyfPaxB6ystF90bJwKE2c+QP/vy+HI1maoVtkJk+cEvHtdrQZOHy6NDz6guqXoaBIhFStSJKldO8DVlWqJpk0Djh8HYmOzP5e7O82LmzmT/v3yS+pgrFkTGDnpMW7ejYXCAJNOQAOF9Amu3dZdvD55MrBggfZ16UJf4SSXk5Ds18/wc+hDqgt4YHQgu4AzDMOAh/yajNhYYMYM6mwaPJjcpLV5EJmTc+eoIPrqVcDHJ+/Pn0rEczm+/+U6knUYYGpDIgHq1HRB1/YfYtEi8rX64QdyCE/vQRUZSdd55Qr9e+MGpegaN6ZHo0YU+Umf8oqJAYYPB86fB3p+K8e5W9cgkWr/FdBm0gkA7m622LW6kc7oS9++5Pc0bZph70GrVvTv2bO6t1u/nlLAhw8bdnx9OBNyBt/u/haDGgzC+I/GQyrhv7MYhmH4f8JcolRShOeDD0g83btHN0lLiKaICHKs3rDBsqIJAMqUske3L8vAztbwHzEHexlGD66Ejz6iCNGtW2Sn0Lgx8MUXwIkTlKYrXZqiebNmkcB48wbYsiWtAL9HD6BYMaB1a6p12rePIjnr11Ph9sETkdClBXIy6YyLV+H2fd2zXn7/neqgoqMNew9UKv0iTkuXmt4pXCM0mHFhBnrt7oUNnTdgYouJLJoYhmH+H444GYlGQ3/pT5hAg3j//JPGfliK5GQSCB070pryA0II/LnoIU5deKlXKkwiAeztZJg/vRaqV3bK8npSEgmjhQsplTd0KHXJOeqoUX7zhvydrlxJi0w5OQGNGmkQpfCDWqPGzlWNsPtQBD5t7Q6Pkna4ejMGvvOD0L6NBzq0dcfAMbcBUAH7oc1N0efXGwgLl0MiARrVLY65U2rqvK6BA+mcs2bl+Ba8o2FDqus6dEj7NrdukXN5SIjprCbeyN/gh70/4HXSa+zougOeTp6mOTDDMEwhgf+MNIKzZykNNG8esGYNzZWzpGgCqO29RAmKrOQXJBIJxg79AH16loWdnRT2dtn/uEmlJErKejlg5V91sxVNAHki/fQTFXv/8w91LPr4UFo0NDT7NRQrRh11kyeT/9Lr11QP1aBJQobicmNMOoUAbt3PuYBpwgRg1SpyHNcXlSrnYu/ly6m2yVSi6UbkjXcu4Gd/PMuiiWEYJhvYx8kAHjwgd+iAACo07trVuAn2pmbLFqpxuX49f6wnPRKJBN9+44Uun5fGqQuvsGlXGJ5FymFtJYFGA0ACtG7mhu6dPFGlon75TYmEomutW1O0ZckS6rj76COKQrVqpb0YXyKhtGqLhBScuAwk/r/NgbEmncnJGmg0Iot3VXo8PSky9ueflNbVh5QU3cIpLo4ingEB2rfRFyEEVt5ciQmnJ2BJxyXoWr1r7g/KMAxTSGHhpAeRkRSx2LePHKn//RewtbX0qoh794Bhw4CTJ8nsMb9iaytDx3Ye6NjOAykpGiQkqmFjLYG9vSxXre3lypFf0pQp1F02eDAVgw8dCvTqpd25WyoznUmnPssfO5YK1UeOJCGVEzlFnDZvJsfyUqVyPpYuklRJ+Pngz7j5/CYu9rmIyq6Vc3dAhmGYQk4+i0+YBiEE7jx4i1mLgzFi8l0MGX8bv//5AAdPPIdCof8Yj7g4Ku6tWZPSYA8f0ky0/CKa3r4lk8v584EPP7T0avTHykoKF2drODhYmcwPyNGRnLMfPKAU6r59lMYbOxYIC8u6vYuTdY5eU/qYdNrb6Sf8PDxoBMuMGfpdT0qK9p8zIagoPLdO4Q9fP0SjVY0gIHD1p6ssmhiGYfSgUAmnFLXA3iMR6NbPHyOn3MXBE1Hwv/kGt+69xZlL0Vi44jE+/84Pfy19hBevtI+vV6ko/VO5Mt10b92iNEt+iuhoNEDv3lS/8913ll5N/kEiIY+nAweAy5ep67FOHUqrXriQZppZ3scRDva6A645mXTKpECb5m56r23MGEqvaavHSo8u4XTlCpl3tmmj96mzsCtgF5qtaYbBDQZjQ6cN7ALOMAyjJ4VGOCXJ1fh14h38vfo/PH+hgEKhyeIsLVdooFBqcOBYJHoPuY6Ah3EZXhcC2L2bUip791Ix8fr1gLd3Hl6InsyaRcXG8+ZZeiX5l4oVKRoXGgq0bEmF5fXqAevWAcnJEnTv5AlbHXYJOZl0WllJ0e0r/QuoXV2BQYOA6dNz3lat1i6cli4FBgwwrp5NpVZhxLERGH1iNI58e4RdwBmGYQykUNgRqFQa/DLuDh79F49kHSMzMmNvJ8XS2XVQsVwR+PlRd1ZiIg2D/eQTMy44l5w8SYaQ/v761cswhEZDHXWLFpFZ5o99VPB/eAUqleEmnUIApd2LYOdqwwYBxsYClSqRz1SlStq3K1UK+PFHakJIz+vXZAL65Inhg5sj4iLQfVd3ONs5Y2PnjShuX9ywAzAMwzCFI+K0dN1/eBySoFM01avlgs1LG+DkruZY5Psh3N1sIVdoMGT8XXz9tQY9etBf8Tdu5G/RFBZGqbnNm1k0GYpUCrRvTx2I588DSYnWeBpYARIjfg2kUimuna2CsWPJQ0tfXFyomH/qVN3baYs4rV9PI2MMFU2nQ06j/sr66FCxAw70PMCiiWEYxkgKvHCSK9TYf+w5lDpGe+hygI6N1cCzfDSCgymKYypPHHOgVAJdulBnVuvWll5NwaZyZZrld+tKaVQp6w21Wv9fBVsbKWRJNVHMyRGXLwNNm1LjgL4MG0bu57qsBNRqcktPj0YDLFtmWFF4qgv4t7u/xcbOGzGhxQR2AWcYhskFBf5/0BPnXgD/X6Kxc1Uj9OzsiXWL6uHotmaYOqYqbKwlaNnEDSFhiThzKRrJKoE1W0JRsZwjvD3tIbNSI171DPZglYuGAAAgAElEQVT2lr0OfRg2jGaxjRpl6ZUUHpydAasUHxS3+QD2tlaAyF45p5p0lvdxxMp5dXH6uAsGDqQuPnd3oFkzMkPVJ/FdtCh9hpMna98mO+F05gw916SJftcWI4/Bl1u/xKFHh3Ct3zW0K99Ovx0ZhmEYrRR44bR9XwQUirRokzEO0GHhSQiLSMrztRvC2rXkWL52rX6+QYx+bNoEnDoFbN3gjqPbm+LPSVVQwYecy1NFkLWVFB+3LImls+tgw9/1Ud7HEVIp0L8/dVwC5FA+YwbQrRuNecmJwYOBS5eA27ezf12jySqcUqNN+nz+1yOvo96KeqhUvBLO9mYXcIZhGFNR4A0wX2ayFTDGAdrKSoLIKAW8y2hxS7Qwt25RK/u5czTzjDENDx4Aw4cDp0+nvq8SNG/oiuYNXQEAUS/U2LhBgiVLpDgcBVQsBfh4ZjSm9PICDh4kATZyJDmZf/ghmXG2bKn93A4O5DE1aRKN7MmMWo0MUdDnz6kpYPVq3dckhMCKGysw8cxEdgFnGIYxAwU+4pS5IDyzA7S9nQxyhVqnA7RCAezcpca6dWRDcO4czUN79gxISNAv/WIuYmKAb76h2WyWnodXmIiPp/d17lwyOM0OD3cZRo+W4vFjGrWzciU5lf/xB/DyZdp2EgmNVLl3j15Xq8mYdMIE8gTTRmrEyt8/62uZI06rV1M0S5dwTkxORO+9vbHYfzEu9rnIoolhGMYMFPiIk42NFHK5bjfwkLAktG/j/u77zA7QEgCvo2U4E0lplvSP2FjqmnJxoXRMsWIZv9b2XOr3zs7Gz4/TaKiDrlMnumkypkEIEi0ffUQmojkhk9Fn0KkTiaPFi6m4/KuvqO6sTh3azt0d2LmTRvIMGkSdj8ePA9u2kYVAZuzsgIkTKep09GjG1zSatIiTWg2sWEFu6NoIjg5Gl51dUMejDq7+dJUNLRmGYcxEgRdOnqXs8ei/BJ3bnL8cjUF9yqNlU1dcvvY6iwO01Epg5h8OKOWe/f5KJQmoVCGVWVi9eAEEBWV8LvXrhASKEugSV9qeW7yY9p81y9Tv2vvNkiX0efn5Gb5vzZokYmbOBFatIvHk40Oz8Tp3pjl533xDXY+//gocOkSmmwsXUtdm5vqkPn3Ilf7iRaB5cyD0WSIioxQo5q5G+AsZIqIccPOaPUqXThNomdkVsAsDDw3EH63/QP96/dnQkmEYxowUeAPM42dfYO6SR0iSq7FzVSPMWhyM63diAQB9e/qgTCl7TJ8XhPofumD4z5Xg4WaLgIfx8F0QhKiXSgBAzapOWDpby10pl6Sk0My77CJZur5/+ZLSSfb25NmjT3Qr83MFoVMwr/H3Bz7/nMaxZBcFMpSUFIoELVxI9U2DB5NDuSuVSeHwYaBvXxLfrVtT513m0T0rV2mwcdsrlK3yDBFRcljJJHgbBxRxBDRCQK10ROsm3pg4tgSs0g0nVqlVGHNiDPYG78XOrjtRv3T93F8QwzAMo5MCL5ySVRp8/q1fhon1huBgL8OkUVXeFQTnB0JCgMaNgV27qNBYX7GV+TmJxLj0YrFiQJEiha977/Vriv4sWEBpN1Nz6xZFCffsoajT0KFArVoknEeMoJSdoyOl8po3p30e/ZeAX3+/izdvNJDKtP8M29vJ4FTUCot8P0SZUvYIjwtH913dUcyuGDZ03sCGlgzDMHlEgRdOALB6cyi27nkGhdKw0RlSKVDS1RbbVzSCTJY/VIJcTp5AvXtT/YyxCEHHMlRspX6vVJKQMjS9WKwYpSbzm5GoRkORpurVgTlzzHuuV68onbdkCY1VGTaM3L7Pnwd69aL3d/Bg4Ps+cRg15Q7kCv1+biUSamr4cZgSw/2+w5CGQzC2+Vg2tGQYhslDCoVw0mgEfpt+Hzfvxup0EE8P3YSssHpBXZTxyB85LSEoraNQAFu2WDbik5ycUVDlJMDSfx8fTyaPxqQXixUDrK1Nfz2+vlSAffq0eY6fHSoVDY1etAiIiCCx1LMnMG0asHW7AjWbXdcZZcoegRTrt5g4rQg+r9HWLOtmGIZhtFMohBNAg36nzwvC5euvc/wL3sZGiiKOMvw9s3a+8m5asYJusleuUKqsoKJWA2/fGia20j9nZ2dcerFYsezruk6dIruA69eB0qXz/v0AgGvXKI134ADQvTugtn2IgMfPoStYVK+WC0YMrAR3N1sEBFNd3otXSthYS9CnZ1l839U77y6AYRiGAVCIhBNA5n8Xr77G5n+f4eF/CVCrBdTqtMtzsJfB1kaK7p088WX7UnAqkkehBz1ILVq+eBH44ANLr8ZyCEGdhIamFlO/BjKKKXt7KgT/+GOqN9IlwIoWNX+ULyoKWLJEjZPX/SCV6Z6vuH0FNTtc8n+Nn74rhw+rOWPAaLIqd3G2xr71TfJNiplhGOZ9ocDbEaRHIpHgo8au+KixK55FJuHU+Vd4Ga1EskqD4i7WqFPTBY3qFodUmr9uNtHRQNeuwPLl77doAki4FC1KD28jAirp67pevSI/pc8+A1q0oOeePSNz0+wEmUKh269LVwTM2Vm/ui4PD6Be0xfwC/x/49VVjbD7UAQ+be0Oj5J2uHozBr7zgzLMVwSANVtCcWhzU3h72iMsXA5lsgZXb8agaYMShr9JDMMwjNEUKuGUHq/SDvixh4+ll5EjajXVvfTqRT5ATO6wt6dHqVLA6NHksbR9u34mpCqV7rqu16+Bx4+zj3bFx1N6VZ/04qkrb7Kdr5icrMHS2bXRoa0Hynppn68YFi6HXK7GnQdvWTgxDMPkMYVWOBUUJk2ijq/p0y29ksLFnj3k4n3jhv7O7dbWgJsbPQxFo8m+riu9uAoPp38j41UZhh0ZM18RyDheiGEYhskbWDhZkH37aBjs9evkOM2YhidPgAEDaPhuiTwKyEilaRGlcuWoVuvtWxJL6R8qFSCJz6jkMs9XdC1ug7h4lc75igA1OTAMwzB5C9+uLcSjR0C/ftRlVbKkpVdTeJDLgS5dgMmTgYYNzXMOIWj4cmZRlPkBAF5egKdn2qNhQ8DusS3uBOkeHp3TfEUrmQQlXW3Nc4EMwzCMVlg4WYDERODrr8nPp1EjS6+mcDF0KFClChWFG4NGQ8X64eFUSK5NFNnZZRREnp7kBp7+e2fn7M9xN8ADIye/1GmbkdN8RUCC1s1YcTMMw+Q1LJzyGCGA/v1p9MeAAZZeTeFi3Tqyc/D3z95WQK2mgcy6okQREdTR5+mZMVrUrl3a12XK5M5nq0YVJ9hY2UAOhdZtYuNUmDjzAYb/XAmTRlRBwMN4TJ4T8O71ZHkRtG5pj8GDyTQ18/w7hmEYxjwUKh+ngsDffwOrVgF+foBD/vHeLPDcvEleTYsWATY22UeLoqKA4sWzRorSP8qUMe9w5Bs3aG5dfHIknD2eQJVi2JggALC3k2LyqKqwEq5YvJgGCffoAQwZAlSrZoZFMwzDMO9g4ZSH+PmR5YCfH1ChgqVXU3BQKoHISO1RorAwEkUuLuSDlV4IpY8alSoF2FqoLOjZM2DCBODkSWDqVOCH3gKjp9zB/aA4JKv0/xW0tZWiReMSmDSyKiT/H1Z7/pw8wJYvp1l8Q4aQmWp+mxfIMAxTGGDhZCL+e5qIF68UUCg1cLSXoay3Y4bi3agooH59url99pkFF5rPkMspPZZZDKWPGL15Q6JHW4TI15e+Xr7c0leTlfh4YPZsGvg7aBAwZgylAgEgSa7GyMl38fBJgl4zFm1tpWhQuxj+GFsNVlZZO+qSk8mCYfFiSkn+8gul8YoVM/VVMQzDvL+wcMoFSqUapy68wqZ/w/DylRIymQRCUH2NSqVBrWrO6PW1Fz6sXgyffCJBq1YUbXhfSEjIufMsPp7ET3aiKDVaVLKk9ujJwoVk6XDxIhVs5xfUamDNGurua9eOxJ2XV9btUlI0WLouBNv2RMLGBtmm7hzsZVAoAE9XL2xa5a2X872/PwmogweBbt0oClWjhimujGEY5v2GhZOR3At8i9FT70GtFjq7o+ztpIDGDtKkWjh80LZQpE+EAOLidAuiZ88oxZa5HT/zw9VVf4PKzFy+DHTqREORy5Uz7TXmhuPHgVGjKHU4bx5FGnVx4gQwdJgacxa8wo79z/DilRIKpQaqZCnkifaYNt4LH1ZzRd06Uvj5GTaWJyqKhkcvW0bdhkOHAl98wWk8hmEYY2HhZATXbr/BuD/uQ6HUr7BXo5GgmIsV1i6sl++9d/T1KBIiZ1FUvLj5hua+ekWdif/8Q0IgPxAQQILp0SNKz3XqpN/1t24N9OkD/PBD2nOvXpHQqVEDGD8e+PRTYO5c4MwZ4NAhw9eWnAz8+y9FoSIjgcGDgf/9jz4jhmEYRn9YOBnI02dJ+GnEDZ1RpuyQSQH3knbY+Hd92Npa5s/99B5Fuh42NtkXV6d/ODmZTxTlhFoNdOwI1K0LzJxpmTWk5+VLSsn9+y+JnEGD6D3UBz8/4NtvgYcPaeRLKhoNpR5HjCBX+T/+IPFTsyZFsXJTJ3f9Ogmo/fvJLHTIEKBWLeOPxzAM8z7BPk4GsnJTSI6Rpnq1XDBiYCW4u9kiIDgevguC8OKVEjFvknHi3Et8/kkpk69LraYbuC7jxvQeRekfbdpkLLZOLV7Or/zxB6UBLT3fT6EAFiygSND33wNBQYZHcGbMAH77LaNoAih9WaoUULUq1UoBJMYWLiSh066d8R2C9esD69fTz8uKFUCHDkClSpTG+/JLHv/DMAyjC444GUDsWxW+7nMFySrtwsnZyQrbVzTCrMXBuOT/Gj99Vw4fVnPGgNG3AACepeywdXnDd63k+pCSQi3nuqJEz59T95S2Vvy88CjKC44fp7TWjRuAh4dl1iAEsG0bMG4cpQtnzQIqVjT8OLdvU+ToyZPsC9sbNyZx2Lkz8Pp1mlD68kugWTMSXKZApQJ276Yo1LNnwMCBwE8/Uf0ZwzAMkxH+29IADhx//i49tXNVI+w+FIFPW7vDo6Qdrt6Mge/8ILRs4oaQsEScuRQNAFizJRSHNjeFt6c9wsLliI5JRsDDeFSv7ASA0i+ZPYoyR4xevQLc3LJGiurXT/u6dGnLeRTlFc+eUR3Q9u2WE02XLlH6TK0GNmwAWrQw/lgzZ9KxtHUDli5Ng4IrV6b0WrNm9Py8eSSqvv+etskt1tZA9+70uHmTBFSlSjQWaMgQoHbt3J+DYRimsMDCyQCu3HidwW+ndXM3jJxyD8nJGiydXRsd2nqgrJcDHockvttGodQgIkqBct6OCAuXQ6EUGD76LWIinRAeToXYHh4ZBZGPT8a5Zx4eWVM57xvJyXRjHz4caNky78//5Akwdixw9Sql13r1Mr4bEACCg6nQe/Vq7duULk2RxI8+Ai5cSBNOFSvS2J7ffiMrBlNSty6wdi0Vt69cSYX35cqRgOrcmdN4DMMw/N+gAcQnpGT4fteBCLyOSQYAXPKPQaXyRWAlkyA2TpVhu4TEFDjYpxaEC1SvoULPiSSK3N25NVwfxoyh1NHo0Xl73jdvyINp3ToSbevXm2ZUzqxZZFCpa+ZdqVIUjWzRgsb0jB2b9tr48dR1d+lSmqAyJW5udI7Ro4G9eykKNWIEpfH69aPXGYZh3kdy8Tfz+4eVLOPbFfMm+d3XSqUa9nYyyBVqODpkVEKODjIkydUAAAmAmjWlaNiQIgosmnJm507qAFu/PndRHkNQqUgsVKlCnlX379PIFFOIpqdPgX37SDjponRpEk7Nm1P3nVqd9lqRIhQVGjo04/Omxtoa6NoVOH8eOHCAIm8ffEB1Zjdvmu+8DMMw+RUWTgbgWiLnHvOQsCRUKJsWRrCzlaJMKXuEhFH6zsZWiuIuevaqMwgOpvb+nTvzZnSIECTSatQg1+2TJ6nzzJQ1VXPnUvF1Th14qcKpZEk6/717GV/v2ZOK/VO77sxN7dqUWnz0iOquOnUiUbdjBwlNhmGY9wEWTgbwxSel0qXcsuf85WiU93FEy6ausLGWoE8PHzwJSURYuBwAoNEItGjM7Ur6kJREPkN//EHda+bm5k2yZhg/ntr+jx0j3yRT8uIFsHkzpf1yIrXGCUirc0qPREJRsd9/p5RiXuHqSmnD//6j61iyhOqgfH3J4oBhGKYww8LJAJo0KAErK902ArFxKkyc+QD9vy+HI1uboVplJ0yeEwCAbnSN6hZH8WIcccoJIaiepnZtKoQ2JxERwI8/kqlmjx5kE9C+vXnONX8+FZbrE8FKrXECshdOAFCnDkV+pkwx6TL1wsoK+OYb4OxZ4PBhIDSUIlG9e5NdBMMwTGGEfZwMZO3WUGzc9QzJekyzz4ydrRR/Ta2FD6s7m35hhYxVq8hc8upVwNHRPOdISADmzAH+/hsYMICiKE5O5jkXQFGhihUpsuXjk/P2QpBVQWwszZxr2pSEVGYLsOhooFo14PRpyw/yff2a0nn//EO+YUOHkrh637tCGYYpPHDEyUB6feMNH097WMkMmzdiZytFx3YeLJr04NYtMpfctcs8okmtprqgypWBx49JyMyYYV7RBFBa7csv9RNNAAmkUqVINJUtS40ET55k3c7VFZg0Cfj1VxJblqRECeqAfPKEOvJWrKC1T5tGaUqGYZiCDgsnA7G1kWLB9A/hVcYeNjb6vX12tlK0aOKKX/sbYS/9nhEbS3VNqR1tpubkSfIqWrMG2LOH6o30FTK5ISGBrim9pYA+pKbrJBLt6ToA+PlnEiZ79uR+rabAyop8n06fBo4epXRolSpk2nntmqVXxzAMYzwsnIzA2ckaK/+qi09alYStjRR2ttm/jQ72MhQtYoWfviuL30dUgVRqoam4BQQhMtYamZLAQODzzyklN2kSCZCGDU17Dl0sXw60bk1RLkNI7awDdAsnKytg0SLyWpLLc7dWU1OzJl3/kyfAhx8C3bqR8/nmzWRsyjAMU5DgGqdckpCYgiOno/DvgQhEv0mGSiVgayNFWS8HfPuNF5o1cjU4rfe+Mncu2Q6cP2+68TGvXlHh9I4dlP4bPDjvR9MoFED58sChQ1TMbQhDhqQN4L13j8agPHqkfftu3ajOadKk3K3ZnKjVZPWweDHw4AGJ2QEDKLrGMAyT32HhxOQLLlygFJ2/v2lSZwoFRWBmzwa+/ZaERIkSuT+uMSxbRuaRhw4Zvu+MGWTA+eefgEZD9UwBAdq78p4+JeuGGzfyJgWZWx48oOL8bdso0jh0KNCokaVXxTAMox1O1TEW58ULMnNcty73N3shaAhw1arktu3nR55MlhJNKSk0XmXCBOP2T5+qk0ppvIq2dB1A79/QoXk/msZYqlcHli4lT6h69ciqoWFDmsGnVFp6dQzDMFlh4cRYFLWaRFOfPkCHDrk71uXL1LI/axYNqt27l8aDWJKtW0nMNG1q3P7pTTAB3XVOqYweTQXYZ84Yd05LUKwY1Wc9fEiGnhs30vs2aVKacGQYhskPsHBiLMrkydQxlhsDx5AQoHt3qu8ZOBC4fh1o1cpUKzQejQaYOdP4aBOQMeIE0MDf8+d172NvD/z1F0WeUlJ0b5vfkMmAL74Ajh8n4ff6NdVs9exJwpgLCxiGsTQsnBiLcegQpee2bjVu2HFsLHkG1a9PN9fgYOCHH/JuEHBO7N1LPlTt2hl/jPTu4QBZKTx5Qteui86dAXd3qq8qqFStSkaaISFU9/T990CDBsCGDZzGYxjGcuSTWwzzvvH0KdC3LxUFlyxp2L4qFd1QK1cGYmKA+/cpvePgYJ61GoMQVNg9YUJWp29DKF6c7AVSLQZsbEg8+Pnp3k8iodquadPIWbwg4+xM5p4PHwJTpwJbtgDe3sDEieQPxTAMk5ewcGLyHKUS6NqVokXNm+u/nxDUxl6zJhk9Hj9Oo1nyYxv78ePU2ffll7k7Tqp7uKF1TgAVXvfqRQKjMCCVAp99Roaa588Db9/Sz0L37sClS5zGYxgmb2DhxOQ5I0fSHLMRI/Tf584d4OOPqfD5r7+AEyfITDG/MmMG+UaZIm2YOV2nr3ACqHZs714aY1OYqFyZfKBCQ6nTsE8f6spbu5YEK8MwjLlg4cTkKVu3UsRg7Vr9UliRkZTS+/RTMn+8e5eiDrlJf5mbixeB8HCKhJiCzAXijRsDt2/rJxBcXIDp08lIszBGZJycqAg+KAjw9SUDVW9vYPx44NkzS6+OYZjCCAsnJs8IDKSb3K5ddEPXRWIi1bPUrEk1UMHBwKBBgLV13qw1N8yYAfz2G41BMQWZLQmKFAGqVSOzUH3o25dqpLZuNc168iNSKdlZHD5MwjUxkSKSXbtSdK4wikaGYSwDCycmT0hIAL75hhywa9fWvp1GQ512lStTFOHGDdrH2TnPlporbt6kqFjv3qY7ZuZUHWBYuk4mIxf1MWPocyjsfPABFcY/fQq0bAn060ejblavzn9z/BiGKXiwcGLMjhDAzz9TS3nfvtq3O32a6lRWrKCo1NatQNmyebZMkzBjBtVwmXIeXuZUHWCYcAKoDqh1a1rf+0LRosAvv9CImlmzqKHA2xsYOxYIC7P06hiGKajwrDrGaDQagRt3Y7FjXziehidBoVDDzk6Gsl4O6PaVJ+rVcoFEIsGyZcCSJcCVK9lbBgQHU9H3/fsUXeraNX/XMGkjMJAiHCEh5N9kKk6coBv/yZNpz0VHAxUqkB2Dvh5YkZFArVr0OVSsaLr1FSQePyYriw0byCR16FAyFS2IP28Mw1gGFk6MwQghsPdIJNZvD0NikhpyhTrLNvZ2UhRxtELrJj6YPrEULl2SZBl/Eh1NdUxbt1JN0JAhgJ1dHl2EGejdG6hUyfTt/w8ekJgMCMj4fLVqwKZNZIqpL7NmUev+/v2mXWNBIyGBxNPixVQ3N3QoWTfkJy8whmHyJyycGINIUQv4zg/ChSvRUCg1OW6v0UhRvaIbls2rDJmM/qxXKumGNWsWdZ5Nngy4uZl75eYlNJTSjI8f09w1UxITA5Qvn9UtfMAAEk/Dhul/LKWSXNYXLcr9bMDCgBAUyVu8mEa69OlDTQgFLUXMMEzewTVOjN4IITDn72C9RRMASKUahES8wtwlD6HRCOzcSTf78+epRufvvwu+aAKA2bOB/v1NL5oAOqZCASQlZXze0DongGqvFiwgJ+7kZNOtsaAikZA/2P79lMJUq0kAd+5Ms/L4z0qGYTLDESdGby5cicbUvwKhUGgXTfVquWDEwEpwd7NFQHA8fBcE4cUrJWyspVDGVEPS2xL46y+gTZs8XLiZef6cXLqDggwfH6Mv5ctTrVOFCmnPhYXRnL4XLwyv0fnsMyoWHzXKtOssDCQkUAp08WKyOfjlF+C770xbt8YwTMGFI06M3mzcGaZTNDk7WcF3fHWs2hSCjj0vIehxPKaNqQYASFZp4O4ThuvXC5doAoB58+jGai7RBGRvSeDtDdjb0ww3Q5k/nwrx0/tDMUSRItQFev8+ReeOHAF8fEhkhoRYenUMw1gaFk6MXoSFJ+FxaCIAYOeqRujZ2RPrFtXD0W3NMHVMVdhYS9CyiRtCwhJx5lI0klUCa7aEomI5R3h72gMAYuMT8PxF4TLSiYkhf6DRo817nuwsCQDj0nUAeR397380FobJHokEaNuWRtZcu0bfN2gAfPUVcOoUp/EY5n2FhROjF4dPRUGtTos2tW7uhpFT7qHrT1dRoawjOrT1QDlvBzwOSXy3jUKpQUSUAuW8Kceh1ggcORWV52s3J4sWUT2Ml5d5z5PZPTwVY4UTQN1/J05QbQ+jm3LlgDlzyFTzs8+oRqxGDWDp0vfDVJRhmDRYODF6ERmlgDqd68CuAxF4HZOM+IQUXPKPQaXyRWBvJ0NiUkqG/RISU+BgT0ZDarVA5IvCM4E1Pp48gcaONf+5skvVAbkTTkWLUrpuyBBybGdyxtGRmgDu3qXGhhMnKI03YgTw5ImlV8cwTF7AwonRC4Uyo1dTzJu0liylUg17OxnkCjUcHTK6MTo6yJAkT9s3O8+ngsqyZZTKqVTJ/OfSlqqrWhWIiwMiIow77rffko/RunW5Wt57h0RCxfW7d9OYHWtrGr78xRfA8eOcxmOYwgwLJ0YvnIvmPF03JCwJFcoWefe9na0UZUrZIyQsLX3n4lwApvTqgUJBReHjx+fN+bSl6iQSoHlz46NOUimlGydMAN6+zd0a31d8fMiT7OlTqn8aPZosN5Ys4TQewxRGWDgxevFhDWfY2+me7XH+cjTK+ziiZVNX2FhL0KeHD56EJCIsnArC7e2kqFW1gEzrzYE1a8gKoFatvDmftogTkLt0HUDX8fnnwLRpxh+DIdfxn34Cbt+maOTp0ySqfv2VjFEZhikcsHBi9KLdRyWRk+VXbJwKE2c+QP/vy+HI1maoVtkJk+dknBPS9qOC73apUpHh5YQJeXdObTVOQO6FEwD4+tIIksDA3B2HoShgy5Y0qPrWLbKMaNKEisqPHTNtPZlaLZCs4gI1hslL2ACT0Zu/ljzE/uPPMxSJ64uVlQSd2pfCrwPyoCDIzKxfT4/Tp/PunEJQRCM6OqsRo0oFFC9Ohpi5cS5fuBA4fBg4epSH3poauZxmMi5aRF8PGUKzDYsWNfxYT58lYcf+cJw49xJyhRoSAFKpBB9UKIJvu3ijWcMSsJLxB8gw5oIjToze9PzaC7Y2utN12rCxlqJHZzP37OcBajUwc2beRpsAEjLa6pysrYFGjWh4b24YNAgID+cBwObA3h7o25ciUKtW0cghHx8aLqyvgWnEczn6j7yJvr/ewMETz5EkV0MIQCNohmTAw3j4zg/CF9/54cBxdjZlGHPBwonRm9Ie9vjz9xqwtTXsx8bOVorZk2rAo6SdmVaWd+zZAzg7W8b93NzpOmtrijoNH07F74zpkUjos9qxA7hzhyJOzZvTwOUjR7Sn8YIfx6PvrzcQ+CgeymSN1qhvklyN+IQULFzxGEvW/luTEtUAACAASURBVGe+C2GY9xgWToxB1K3pgvnTasHRQQa7HASUna0Ujg4yLPijFmrXcMmjFZoPIYAZMyjaZIlUljkLxFNp1w6oUwf466/cH4vRjZcX1ZaFhQHdu9PPVZUqJF7Tdzg+f6HAsIl3kZik1tvmQKHUYPehCGzd/cw8i2eY9xiucWKMIj4hBYdPRmHrnmdIkquh0Qio1QIymQQSqQRFHGTo2dkLHdp6oGgRK0sv1yQcOQKMGUORAqkF/uT49VdK7wwfnvW1pCTAzQ149YpqoXJDaCh12t26ZX5HdCYNIQA/PxoufPw40KsXDRhetvkObt6N1VlUrmu49vaVDeFWwjbvLoRhCjksnJhcIYTA7ftvER4pR5KcDDA9S9vjw+rOkBSiCmMhKKozeDDQs6dl1jBrFhWHz5mT/etNmlBErHXr3J9r8mSqvdm6NffHYgwnIoIsDdauV6BsLX9Aov2/aWcnK2xf0QizFgfjkv9r/PRdOXxYzRkDRt+CjbUEPTp7of/35fJw9QxTuCkcoQDGYkgkEtSp6YI6NQt+Kk4XFy4AL14A3bpZbg2lS9OoD22kputMIZx++41cyc+fB1q0yP3xGMMoUwaYPh0o6ROJnQeo9mnnqkbYfSgCn7Z2h0dJO1y9GQPf+UEZhmsDwJotoTi0uSm8Pe0RFi7H7kMR6NvTB1ZWXJnBMKaAf5MYRg98fUlMyIxrKjQJ2rrqUjFVnRNA6b65c6ltPiUl5+0Z83D64gtoNGnRJmOGa2s0wP2guDxfO8MUVlg4MUwOXL8OBAQAP/xfe/cdX3V1/3H89b3Jzc0OO0FCSNgbhRZUQFQURFSGKNOirVIsKoqKSltQWxxoEUeLA/xpy7AKWhdq66oVUUTqYhYMhKBsMCHjJnf8/jgmEMi4Se4k7+fjkYeEfO/5foJ68+ac8/2cX4S2juo2hwP07w+ffea/oDNmjOkP9cwz/hlPai+/oOK/zLocrm1Z8GNeadBqFjnVKTiJ1OC+++C22yAmJrR1VNeOAEzIadPGbOr2B8syT3jNmQMHD/pnTKkd7wkbwutyuLb3pz5PIuIfCk4i1di40TSWvO66UFdi+ke5XNUfHOvP5TowZ/FdeSXMnu2/McV3cXE1rw3XdLi2ZUHyKfJkq0g4UHASqcb998P06fV/xN8fquseXsbfwQnM4b8rVpg2DBJcfU9vXGPri5oO1y51eenSMTkI1Yo0DApOIlX47jvTu2natFBXckxNy3UDB8LHH+Nzo0RfNGkC99xjjgdR85LgGjsynRh79W/T1R2uHWWD8wc0JzFBM04i/qI+TiJVmDoVmjY1T9SFi7FjYdQoGDeu6muyssxhvV26+O++bjf06QN33WVqkOC5atrnZOcU1um1jhgbTz58Bh2yEmu+WER8ohknkUp8/705T+zmm0NdSUU1PVkHgVmui4oyHa1vvx0KCmq+XvxnxtQOOGJq/1btiLHRv29ThSYRP1NwEqnEn/5k2g80bx7qSioKVXAqG3fAAHjgAf+PLVU7o0cjZt7QsVbhyRFjo1P7RH4/o3MAKxNpmLRUJ3KCAwegY0fTpTs9PdTVVLRkiVmGW7as6mu2bIEhQ2DnTv/fPzcXevWCzz+Htm39P75Ubc26g8x+cBPgpai48oProqMtbJbFuf2bMWt6J3ULFwkABSeRE8yebZ5cC8fGj++/b47i+OCDqq/xeiEtzYSbjAz/13DffWbsV17x/9hSPafTzXv/2c+SlTns3eckOtqcB+n1mnMjL7mwJWMubUWrlnEhrlTk1KXgJHKcvDwzk/Lpp9C+fairOdnmzTBihJlVqs7ll8Po0TBxov9rKC6Gbt1g4UIzsyWhsfuHIg4eLsHl8pCYEE2b9HgcjhCeCSTSQGgeV+Q4ZWEgHEMT1NyOoEyg9jkBxMbCI4+Y/lalOskjZFq1jKNn1xR692xMx3ZJCk0iQaLgJPKToiITCGbNCnUlVUtONoe25udXf10ggxPApZea412eeCJw9xARCUcKTiI/WbwYzjwTuncPdSVV86V7OJgN3Lm5gTtjzrJgwQKz32nv3sDcQ0QkHCk4iQAlJfDQQ+E921TGl+W66GgTAj/+OHB1dO4MkydHxp+ZiIi/KDiJAEuXmhYEffuGupKa+TLjBIFfrgPzBOJbb8HatYG9j4hIuFBwkgbP7TaH+UbKzIkvTTAhOMEpOdks1910k9l7JSJyqlNwkgZv5Upo1gzOPTfUlfjG1+DUty98+23gj0j5xS9MH6ElSwJ7HxGRcKDgJA2a12tmTGbNMhueI0HLlr4t1cXFwemnm55UXq8XjycwLdtsNnOO3Z13mj5YIiKnsuhQFyASSqtWmfA0fHioK/GdrzNOW7fn0yIrl3sfO8jdj7oBc4bZz89ozPhRrenRJRnLT2mxb1+46CL44x9h3jy/DCkiEpbUOVwaLK8X+vc3jRzHjg11Nb7bssX0Udq6tfKvb/5fPnMXbOb7vcWUlHg48f9wy4JYh43GjWK444aO9OnV2C917d1rWjl8/DF06uSXIUVEwo6CkzRYH34IU6bApk0QFUFNl/PyzKzT0aMnf+3TLw7xu/s3UOz0bae2I8bGzBs6MvS8VL/UNn8+/OtfZiYvUpY+RURqQ3ucpMGaO9fsy4mk0ASQlGT+eWL38I1b8/htLUITgLPEw7wntvLZ+kN+qe2GG2DHDnjzTb8MJyISdhScpEFau9YseU2aFOpKaq+se/jx+5y8Xi+zH9yIs4bQ1KdnI5Yu/DnvrhjAY3N7kdrcgbPEw5x5m3C56t9PICYGHn0Ubr4ZnM56DyciEnYUnKRBuu8+uP1284M+Ep3YPfyrDT/yY171J+6mJEczd1Y3Fi3J5uLxq9m8LZ97Z3YFwOPx8tGn/jmfZcgQs9fpkUf8MpyISFhRcJIG55tvzCP6114b6krq7sTu4cte3lW+RPfSon6MH5XOc4/14e0X+nPPzC7E2C0GndWc7JwCPlh9gJJSL88u20H7rAQy0uMoLHKzdEWO3+qbPx8efhh27/bbkCIiYUHBSRqcBx4wS0lxcaGupO6OX6pzub189sXhCk/PnTegObfe/Q1XXPsZ7TITGDY4jayMeLZlH+uGWez0sHtPMVkZCQB8l1PA4SMlfqmvbVuYOhXuuMMvw4mIhA0FJ2lQtm2Dd96B3/wm1JXUz/HB6ehRF7YTNriveH03Bw+VkH/Uxeq1h+jQNpG42CgKCl0Vrjta4CI+zrzYHm3j8I/VL/fVxl13wb//DatX+21IEZGQU3CSBmXePBOakpNDXUn9HN893O3xntTI8tDhYzNHTqebuNgoiordJMRXTFgJ8VEUFpnmmJZlxvKXhATz533jjeY8QBGRU4GCkzQYubmwYoU5kDbSNWkCmzfD00/Db2dF1/g0HUB2TiHtMhPLP4912GjVMo7sHLN853J5SUrw72EC48aZALV4sV+HFREJGR25Ig3Gn/4E11xjDvSNJPv2wZdfVvzIzjadzz/9FHr1srG/OJ69BwqrHeejNQf4zTVtGXR2M9Z8fpBrxrVhe3YBOblFgJl9atHM4dfaLcucYzd0KFxxBTT2T5NyEZGQUXCSU4rb7SU7p4C8/FK8XkhOspPVJoHDhyyefx6+/TbUFVbN7Ybt208OScXF5rDe008358HdeSe0agUZGWYmx7Kg8wcZPPyX/1FUXPWa2JG8Un53/wZumdqB2TM6s3FrPnMe2giYDuLjRrbGZvN/u+/TT4fRo2HOHHjsMb8PLyISVDpyRU4Jh38s4fV3fuDF13bjdHqw/bQI7fVCVJRFk/hWxNpasvgZ/86o1FVhoWmLUBaOvvrKfN68+bGQVPbRunXlx5ckJZnH/ZOTTQfwSyZ9QlFR3TYTeT02fj/9TC4aYq/nd1a5Awega1d47z3o0SMgtxARCQoFJ4l4y1/ZxTN/y8ayLJwlle/18bhtOBxeJl7eml9NzDxpM3Ug7d178izSzp3QpUvFgNSzJ6Sk+D5up07w6qvQubP5/B9vfc8Ti7fX6sgVMHudenVK58W/ZjFkCDz4YGCWM//8Z1i50oQnnWMnIpFKwUki2uOLt/PqW9/7HBZiHTYGD2zBnTd19Ht4crtNu4MTQ1JJycmzSJ07g72ekzvnnguzZ8P55x/7vT8/u51XVtXuz+Ocs5rx+xmdOXrUYvZsWLbM9LqaPJnymTt/cLmgd29T85gx/htXRCSYFJwkYq18YzcLn/uuTjMsk8ZkcPW4NnW+d0FBxaW2L780+6dSU08OSenpgZlhmTABhg+HiRMr/r6ZgduBZVHlDJzdbmEBV1yWztTJWRVC5H//a5pXOhywcCF06+a/mj/80ASyTZsgPt5/44qIBIuCk0Qkp9PNJZPWVLsZGsyhtjOu70Bqcwcbt+Qzd8Fm9u53EhNj49XnzyIpsebnI/bsOXkWKSfH7Nk5caktmP2hbrvNBLXbbz/5a+V7vl7djbPUQ9meb6/XzCKNurgVo4efRvOmle/5crvhmWfM7NAvfwm//71pK+APY8eaZcq77/bPeCIiwaTgJBHprff2MP/JbdUGp5TkaP7+dD8efHwLq9ce5NpJWfTqmsKvb/8vsQ4bU67K4soR6eXXu92wdavZqH18SHK5Tp5F6tSp/ktt9TV/PuzaVf1hum63l+92FvBjfilej5ekJDvtMxOIjvZtDW7PHrj1VtP9+4kn4JJL6l93Tg6ccQZ88QVkZtZ/PBGRYFJwkog08frP2Zlr+ha9tKgfL7+5m6HnpZLWIpbP1h9i7iObuej8NIYNTuX6mV8CZonuzaVnc83NX5CTW0RKUgzDB5zJV19Z5Uttp51mglGvXsdCUqtW4bmZeflyszn8hRcCf6933zUd17t3Ny0F0tNrfk11/vAHE1BXrPBPfSIiwaI+ThJxiovd7Pq+YrPHskNtS0o8LJx3OsMGp5HZuupDbXNyiziS52LtuhJ+3sfBL35hHpOPpKNYjj+vLtAuuAC+/to8cXf66fDb35qjVKLr+A5y223H2hMMHuzfWkVEAklHrkjEyTvqwm6v+J9uXQ61TYi3mD3HxbRp0L9/ZIUmCG5wAoiNNU0s16yBVavgZz8zncvrIi7OLDVOnw6l/jtXWEQk4BScJOJER1mcuMBcl0NtvT+NFanKDvoN9mJ7hw7wz3+aDuajR5sn8A4frv04I0ea72HhQv/XKCISKApOEnGSEqNxu/xwqG2ph5TkEO/wrofERIiKgry84N/bsswBvhs3mhq6doUlS2oX4iwLHn3U7Hfavz9wtYqI+JOCk0Qcu91G7541nxb70ZoDtG2TwKCzmxFjt0461DYrIzGigxMEf7nuRI0amY7gr75qlt4GD4YtW3x/fdeuMGmS2TMlIhIJFJwkIk24vDVxsVHVXlN2qO2Uq7J4a3l/unZKLj/U1iKK91e1ZsYMyM4ORsWB0bJlaINTmb59Ye1aGDECBgww/Z+Kinx77Zw58NprsH59YGsUEfEHtSOQiOTxeLn8l5+y/2BJzRdXIikxmifmnsVTT9p49lkYONBsVB40KDxbD1Rl4kQYNszM2oSL3bvhlltMEPrzn2Ho0Jpfs3gxPPssfPxxZP35i0jDoxkniUg2m8W9d3TF4aj9f8KOGBv33N6Fdm1tzJtnDtwdMgSuv940Zvy//4Pi4gAUHQChXqqrTKtW8OKL8Pjj5s903Dizib0611xjzvRbtiw4NYqI1JWCk0SsHl1SuHdmV2JrEZ4cMTbuuLEjfXs3Kf+9hATzA37DBnO47YsvQps2Zrmpph/4oRaOwanMsGGmqWj79uY4mieeMN3ZK2Ozmcaad9wB+fkVv+Zye3G5NTEuIuFBS3US8TZsyWPugs3s2++kpNSD54QH7izLPFHXOCWGO2/q6NPG8s2bzQ/y5cvNMSPTp5u+ReHmhRfglVfg738PdSXV27TJhNOjR+Gpp6BPn8qvmzwZWrb0Mv6qPJa/sou16w9T+tMTlPZoG317N2b8qNb07Jpc4WBiEZFgUXCSU8amrXm88I9cVq89iLPE/LCNsdvo17sx40e3pnvn2v+wPXzY7L95/HFzzMjNN8OoUXXvmO1vH31knkj7z39CXUnNvF7461/NrNKVV5o2BCkpFa9598MjzJq7haTkEkpKPSe1N7AscPwUgmdN78QZPRoF7xsQEUHBSU5RHo8Xrxei/NTg0uUyj9wvWGD2RN1wA1x7LTRpUvNrA2nbNrP5evv20NZRGwcPwl13wZtvmgOKr7jCBKJ3P9rH/Y9uKQ+9NXHE2LhreicuOKdFgCsWETlGwUmklr74wjRufP11GDsWbrrJ9CMKhYICaN7c/DPSVq5WrzbLd6edBtOmH2bBom99Dk1lHDE25s3uTp9eNS+/ioj4g4KTSB3t2QNPPmk+evUyy3hDh5qNzsGUkmJmwRpF4KpVaSksWOBl5XtriLZXf2hdn56NmHF9B1KbO9i4JZ+5Czazd7+TRsl2Xv3rWX6bXRQRqY6eqhOpo7Q0uPtuE1omTIBZs6BLF9O76OjR4NURzk/W1cRuh7MGHSQxsfqZppTkaObO6saiJdlcPH41m7flc+9MM83nLPXw6ReHglGuiIiCk0h9ORzmabD16+GZZ+D99yEzE267DXbsCPz9yw77jVRLV+yi2Gn6FLy0qB/jR6Xz3GN9ePuF/twzswsxdotBZzUnO6eAD1YfoKTUy7PLdtA+K4GM9DiKitwsXZkT4u9CRBoKBScRP7EsOOccWLkS1q0zv9enD1x+uXn6LVCL4pE841RY6GLj1oqNm84b0Jxb7/6GK679jHaZCQwbnEZWRjzbsgvKryl2eti9p5isjAQANmzJp7CoiiZRIiJ+pOAkEgCZmfDww2YZ7/zz4brrTIh6/nlwOv17r0gOTj/mu7DbK+5NWvH6bg4eKiH/qIvVaw/RoW0icbFRFBS6Klx3tMBFfJw5r9AebZGXX/0eKRERf1BwEgmgxESYNs00gPzjH82RIpmZZm/Unj3+uUfTZiV8t6OQHbsKOHykbmf3hYrH48WiYnA6dPjY9+B0uomLjaKo2E1CfMVDnRPio8pnmSwL3OouLiJBECZt/ERObTYbXHyx+di40XQl79IFLrvMdCXv3bt24zmdbt7/eD9LVuxi1/dFeD02ptwKpS4PLVvEMnFMBoMHNCc2NqrmwYLI5TK9p77+2nx8+VU0hR4PVg1/hcvOKeSi81PLP4912GjVMo7sHLN8V+rykpSktzMRCTzNOIkEWdeupoXB9u3m1yNHwsCBZm+Uy1Xz69/5YC+XTFrD/Ce3sTO30DT7xE1hkZvSUi85u4tY8NT/uOSqT3jjn6HbNb5/v9kov2AB/PKX5sialBRzhM0LL5ju61dPjia1uaPGsT5ac4C2bRIYdHYzYuwW14xrw/bsAnJyiwBIbeYgKUHBSUQCT+80IiHSpIk5fuTWW815c/Pnm1/fcAP86lfQuJKejktX7uLZZTtqbBRZVGy+/sjT29h/0Mk14zMD8B0YTqc5269sFqnso6jIHO7bsyeceSZMmQLdukFS0vGvtrAntuaJxdvLa67MkbxSfnf/Bm6Z2oHZMzqzcWs+cx7aCEBcrI2JY1rr7DoRCQo1wBQJI+vWma7kb7wB48ebruSdO5uv/evDvTzw+Nbad9d22Jjx6/YMv7BlvWrzemH3bhOKvvnmWEDatg3atj0Wknr0MP9s3dq3buZFxW4unfQJxc7afV9lYh02Xl9yNnFhtiwpIqcmBSeRMPTDD7BwITz1lNn/dOONHh59/hMKCqt/5L6q7tpxsVG8seQsHA7fwkVBAWzYcPIskt1+LCCVfXTpArGx9ft+X3w1l6f/ll3r8BTrsDHlqiyuHJFevwJERHyk4CQSxoqLYflyeOLpfTgab8UWVXVwSkmO5u9P9+PBx7eweu1Brp2URa+uKfz69v8SF2tjxtQODBucVuE1Hg9kZ588i5Sba2a6jg9IPXpAamoVN/eDBU9v441//uBzeIp12Lh0SEumT2kfuKJERE6g4CQSAa6+aV15A8iXFvXj5Td3M/S8VNJaxPLZ+kPMfWQzF52fxrDBqVw/80vABIs3l57NNTd/QU5uEemnxTNt0s8rzCB9+63ZS3XiLFKHDmZ2KZi8Xi/LXt7F4qU7sSyqXJJ0xNjweuFXkzKZMCpde5tEJKi0OVwkzBUWucneWVjh98q6a5eUeFg473SGDU4js3XV3bVzcovIyS1i5h2l9Oxhp2dPmDQJunevfBN6KFiWxcTLMxh+QUve+NcP/P0fuRQ7PVgW5OebnlixDhtjR6ZzyYUtaZQS5GQnIoKCk0jYy8svxW63cDuPTQ6XddcGyrtrR0dZHMmr2D37+O7aCfE2Fj9dSkar8A4cjVLsTBqTwYTRrdmxq5A9e0sZNQrWfGIns3U8NptmmEQkdBScRMJcZStRJ3bXbtYkhrz80mq7a4PXp6fcwoXNZtG2TQKZrWH/D5DZ2jQSFREJJb0NiYS55CQ7paU1b0XMzimkXWZi+eeVdddOSQrv2abK2GwQH2+e9BMRCTUFJ5EwFxcbRcf2iTVeV1N37czW8SRHYHACs7/p6NFQVyEiouAkEhEmXd6auLjqezCVddeeclUWby3vT9dOyeXdtePjoph4eUYwSg2IpCQFJxEJD2pHIBIBXG4vl076hPyjPhxmV4n4uCjeWHo2MfbI/LvSGWfA4sW1PwxZRMTfIvNdVKSBiY6y+O3NnXDE1P5/WUeMjTtv7BixoQm0VCci4SNy30lFGpgB/Zpx46/a1i48eW00jsvivAEtAldYEGipTkTChYKTSAQZeXEr7pnZheSk6PL+TJWJj4siKSGaWdM7sX1DOn/4QxCLDIDERNMEU0Qk1NTHSSTCDOjXjNf+1pQ1nx9kycpdbNqahz3a/B2o1OWhY7skJl3emgH9mhIdbaP3W9C/P6SlwZQpIS6+jrRUJyLhQsFJJAJFR1kMPLMZA89shtPpLt80npQYjcNRcSYqLQ3eeQfOOQdatICRI0NRcf0kJWnGSUTCg4KTSIRzOKJOCksnat8eXn8dhg2Dpk1h4MAgFecnmnESkXChPU4iDUSfPrB0KYwZA99+G+pqakebw0UkXCg4iTQgF14ICxaYmaedO0Ndje+0OVxEwoWW6kQamPHjYc8eGDoUVq82S3fhTkt1IhIuNOMk0gDdcguMGAGXXBIZh+dqqU5EwoWCk0gD9cAD0LEjjB0LpaWhrqZ6WqoTkXCh4CTSQFkWLFoEHo/p7xTOp1ZqqU5EwoWCk0gDZrfDSy/Bxo0wa1aoq6malupEJFwoOIk0cAkJ8Oab8Mor8Nhjoa6mclqqE5FwoafqRIRmzUx38f79TXfxceNCXVFFWqoTkXCh4CQiALRpA6tWwQUXmCB1wQWhrugYLdWJSLjQUp2IlOvZE1asgAkTYP36UFdzTGysefLP5Qp1JSLS0Ck4iUgF55wDTz5pejxt3x7qagzL0nKdiIQHLdWJyElGj4Z9+451F09NDXVFx4JTo0ahrkREGjIFJxGp1NSp5miWiy+GDz80+4xCSU/WiUg40FKdiFRpzhz42c9g1ChwOkNbizaIi0g4UHASkSpZFvzlLya0TJ5suoyHimacRCQcKDiJSLWiomDZMvj+e5gxI3RHs2hzuIiEAwUnEalRXBy89hq89x7MmxeaGrRUJyLhQJvDRcQnjRrB22+b7uKpqXD11cG9v5bqRCQcKDiJiM9atTLh6dxzoXlzGD48ePfWjJOIhAMt1YlIrXTuDP/4h5lx+vTT4N1Xe5xEJBwoOIlIrZ15Jjz3HIwcCZs2BeeeWqoTkXCg4CQidTJ8ODz4IAwbBrt3B/5+WqoTkXCgPU4iUmeTJ5vu4hddBB99BI0bB+5eWqoTkXCgGScRqZeZM2HwYBgxAoqKAncfLdWJSDhQcBKRerEsmD8f0tNhwgRwuQJzHy3ViUg4UHASkXqz2cxm8aNHYdq0wHQX11KdiIQDBScR8YuYGHj5ZVi3Du65x//jJyVpqU5EQk+bw0XEb5KSYNUq0108LQ2mTvXf2JpxEpFwoOAkIn6VmgrvvAMDB0KLFjB6tH/G1eZwEQkHCk4i4nft2sEbb5g2BU2bwqBB9R+zbMbJ6zUb0kVEQkF7nEQkIHr3hmXL4Ior4Ouv6z9eTIzZhF5SUv+xRETqSsFJRALmggvg8cfh4othx476j6flOhEJNS3ViUhAjR0Le/fC0KGwejU0a1b3scp6OdVnDBGR+tCMk4gE3E03mU3iw4dDQUHdx9GTdSISagpOIhIU990H3bqZPU+lpXUbQ0t1IhJqCk4iEhSWBU8/bTZ4X3tt3bqL69gVEQk1BScRCZroaHjxRdi6Fe68s/av11KdiISagpOIBFV8vOnx9Npr8MgjtXutjl0RkVDTU3UiEnRNm5ru4v37m07jEyb49jrNOIlIqCk4iUhIZGTAW2/B4MGmvcCQITW/RsFJREJNS3UiEjLdu8OKFTBxIqxbV/P1WqoTkVBTcBKRkBo4EBYtgssug//9r/prNeMkIqGmpToRCbkRI2DfPnMo8OrVkJZW+XXq4yQioabgJCJh4brr4IcfYNgw+Pe/ITm54tf37i/mUH4h+464WbPORlqLWLIyEkJTrIg0WJbXW5c2dCIi/uf1wrRpsGULrFoFdruXtf89zLKVu/h2849Ylg2nExISwO32ktYiloljWnP+gBY4YrTzQEQCT8FJRMKK220OBvZaTmKafsWBgyUUFburvD4uNoroaIuH7+5Bt07JVV4nIuIPCk4iEnZ27ipm4tT1YHMBvr1FORw2Hprdnd49Gwe2OBFp0BScRCSsFBW7uWra5+w74MTjqd1r42JtLF7Qh4xW8YEpTkQaPG0KEJGw8vb7ezjyY2m1oalPz0YsXfhz3l0xgMfm9iK1uQMAZ4mHZ5ftCE6hItIgKTiJSNjwer0sezmXYmfVN4CFUgAAA8dJREFUqSklOZq5s7qxaEk2F49fzeZt+dw7sysAHg989OlB8o6WBqtkEWlgFJxEJGx8vTGPIz+WAPDSon6MH5XOc4/14e0X+nPPzC7E2C0GndWc7JwCPlh9gJJSL88u20H7rAQy0uMAsCx44597QvltiMgpTMFJRMLG1xt/pKTk2GzTeQOac+vd33DFtZ/RLjOBYYPTyMqIZ1t2Qfk1xU4Pu/cUl/d0cjo9fPbFoaDXLiINgxpgikjY+DGvFPdxq3QrXt/NwUNmBmr12kN0aJtIdJTFkbyKS3FHC1zEx0WVf66lOhEJFM04iUjYiLFXfEs6dLik/NdOp5u42CiKit0kxEdVuC4hPorComO9nuzRemsTkcDQu4uIhI2mTWJq7ACenVNIu8zE8s9jHTZatYwjO+fY8l2zpjEBq1FEGjYFJxEJG4POaoanhtZyH605QNs2CQw6uxkxdotrxrVhe3YBOblFAMTHRXHJhS2DUa6INEAKTiISNpo1ddCnZyMsq+prjuSV8rv7NzDlqizeWt6frp2SmfPQxvKvO2Js9OvdJAjVikhDpM7hIhJW1n9zhJn3fFNtL6eqOGJsXD2uDVddkRGAykRENOMkImHmjO4pDDk3lVhH7d6e7NEWmRnxjB2ZHqDKREQUnEQkzFiWxa3Xd2BAv6Y+h6eYGBsZ6fE88oeeJz2ZJyLiT1qqE5Gw5PV6Wf5KLn99MQe3x0vRce0GysQ6bHi9MPS8VKZf1w6HI6qSkURE/EfBSUTCmsvl4ePPDrLs5V3s2FVISYkHu92iaRMHV17WiovOSyU+Xr18RSQ4FJxEREREfKTNACIiIiI+UnASERER8ZGCk4iIiIiPFJxEREREfKTgJCIiIuIjBScRERERHyk4iYiIiPhIwUlERETERwpOIiIiIj5ScBIRERHxkYKTiIiIiI8UnERERER8pOAkIiIi4iMFJxEREREfKTiJiIiI+EjBSURERMRHCk4iIiIiPlJwEhEREfGRgpOIiIiIjxScRERERHyk4CQiIiLiIwUnERERER8pOImIiIj4SMFJRERExEcKTiIiIiI+UnASERER8ZGCk4iIiIiPFJxEREREfKTgJCIiIuIjBScRERERHyk4iYiIiPhIwUlERETERwpOIiIiIj5ScBIRERHxkYKTiIiIiI8UnERERER8pOAkIiIi4iMFJxEREREfKTiJiIiI+EjBSURERMRHCk4iIiIiPlJwEhEREfGRgpOIiIiIjxScRERERHyk4CQiIiLiIwUnERERER8pOImIiIj46P8BrWs7pd2nC5gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "edge_color = {}\n",
    "for edge in G.edges():\n",
    "    n1, n2 = edge\n",
    "    edge_color[edge] = community_map[n1] if community_map[n1] == community_map[n2] else 2\n",
    "    if community_map[n1] == community_map[n2] and community_map[n1] == 0:\n",
    "        edge_color[edge] = 'blue'\n",
    "    elif community_map[n1] == community_map[n2] and community_map[n1] == 1:\n",
    "        edge_color[edge] = 'red'\n",
    "    else:\n",
    "        edge_color[edge] = 'green'\n",
    "\n",
    "G_orig = copy.deepcopy(G)\n",
    "nx.classes.function.set_edge_attributes(G, edge_color, name='color')\n",
    "colors = nx.get_edge_attributes(G,'color').values()\n",
    "labels = nx.get_node_attributes(G, 'node_type')\n",
    "plt.figure(figsize=(8, 8))\n",
    "nx.draw(G, pos=pos, cmap=plt.get_cmap('coolwarm'), node_color=node_color, edge_color=colors, labels=labels, font_color='white')\n",
    "show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QRANkF1jRxLV"
   },
   "source": [
    "which include edges within each clubs (2 types) and edges across two clubs (1 type). Different types of nodes and edges are visualized in different colors. The NetworkX object `G` in following code can be transformed into `deepsnap.hetero_graph.HeteroGraph` directly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4DW8L0hxbxw4"
   },
   "source": [
    "## Transforming to DeepSNAP representation\n",
    "\n",
    "The NetworkX object `G` in following code can be transformed into `deepsnap.hetero_graph.HeteroGraph` directly!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "-ZfHZ_eoVVGd"
   },
   "outputs": [],
   "source": [
    "from deepsnap.hetero_graph import HeteroGraph\n",
    "\n",
    "hete = HeteroGraph(G_orig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "izq4t_O9WxDH"
   },
   "source": [
    "## Question 1.3: How many nodes are of each type (10 Points)\n",
    "\n",
    "Submit your answers on Gradescope.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wRNSP6nnW78C",
    "outputId": "e2f5b707-8d4b-4b78-f05d-3ae54a5907c3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Node type n0 has 17 nodes\n",
      "Node type n1 has 17 nodes\n"
     ]
    }
   ],
   "source": [
    "def get_nodes_per_type(hete):\n",
    "    # TODO: Implement this function that takes a DeepSNAP dataset object\n",
    "    # and return the number of nodes per `node_type`.\n",
    "\n",
    "    num_nodes_n0 = 0\n",
    "    num_nodes_n1 = 0\n",
    "\n",
    "    ############# Your code here ############\n",
    "    ## (~2 line of code)\n",
    "    ## Note\n",
    "    ## 1. Colab autocomplete functionality might be useful.\n",
    "    num_nodes_n0 = len(hete.node_type['n0'])\n",
    "    num_nodes_n1 = len(hete.node_type['n1'])\n",
    "\n",
    "    #########################################\n",
    "\n",
    "    return num_nodes_n0, num_nodes_n1\n",
    "\n",
    "num_nodes_n0, num_nodes_n1 = get_nodes_per_type(hete)\n",
    "print(\"Node type n0 has {} nodes\".format(num_nodes_n0))\n",
    "print(\"Node type n1 has {} nodes\".format(num_nodes_n1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vEsHJp2ZYaE2"
   },
   "source": [
    "## Question 1.4: Message Types: How many edges are of each message type (10 Points)\n",
    "\n",
    "Submit your answers on Gradescope.\n",
    "\n",
    "When working with heterogenous graphs, as we have discussed before, we now work with heterogenous message types (i.e. different message types for `node_type` and `edge_type` combinations). For example, an edge of type `e0` connecting two nodes in club \"Mr. HI\" would have a message type of (`n0`, `e0`, `n0`). In this problem we will analyze how many edges in our graph are of each message type.\n",
    "\n",
    "**Hint**: If you want to learn more about what the different message types are try the call `hete.message_types`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qobKuqbAYvJ7",
    "outputId": "f3a69044-b5ed-43b9-9255-159ce38bed6d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Message type ('n0', 'e0', 'n0') has 35 edges\n",
      "Message type ('n0', 'e2', 'n1') has 11 edges\n",
      "Message type ('n1', 'e1', 'n1') has 32 edges\n"
     ]
    }
   ],
   "source": [
    "def get_num_message_edges(hete):\n",
    "    # TODO: Implement this function that takes a DeepSNAP dataset object\n",
    "    # and return the number of edges for each message type. \n",
    "    # You should return a list of tuples as \n",
    "    # (message_type, num_edge)\n",
    "\n",
    "    message_type_edges = []\n",
    "\n",
    "    ############# Your code here ############\n",
    "    ## (~2 line of code)\n",
    "    ## Note\n",
    "    ## 1. Colab autocomplete functionality might be useful.\n",
    "    for mes_type in hete.message_types:\n",
    "        message_type_edges.append((mes_type, len(hete.edge_type[mes_type])))\n",
    "\n",
    "    #########################################\n",
    "\n",
    "    return message_type_edges\n",
    "\n",
    "message_type_edges = get_num_message_edges(hete)\n",
    "for (message_type, num_edges) in message_type_edges:\n",
    "    print(\"Message type {} has {} edges\".format(message_type, num_edges))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bjMVik1JbJ76"
   },
   "source": [
    "## Question 1.5: Dataset Splitting: How many nodes in each dataset split? (10 Points)\n",
    "\n",
    "Submit your answers on Gradescope.\n",
    "\n",
    "DeepSNAP has built in Dataset creation and splitting methods for heterogeneous graphs. Here we will create train, validation, and test datasets for a node prediction task and inspect the resulting subgraphs. Specifically, write a function that computes the number of nodes in each dataset split.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Ct10Oh4gcqgD",
    "outputId": "33e40485-7c94-47db-c997-5d36a1db73ad"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train dataset has 12 nodes\n",
      "val dataset has 10 nodes\n",
      "test dataset has 12 nodes\n"
     ]
    }
   ],
   "source": [
    "from deepsnap.dataset import GraphDataset\n",
    "\n",
    "def compute_dataset_split_counts(datasets):\n",
    "    # TODO: Implement this function that takes a dict of datasets\n",
    "    # and returns a dict mapping dataset names to the number of labeled\n",
    "    # nodes used for supervision in that respective dataset.  \n",
    "\n",
    "    data_set_splits = {}\n",
    "\n",
    "    ############# Your code here ############\n",
    "    ## (~3 line of code)\n",
    "    ## Note\n",
    "    ## 1. DeepSNAP `node_label_index` will be helpful.\n",
    "    ## 2. Remember to count both node_types\n",
    "    for set_name in datasets.keys():\n",
    "        num_node_n0 = datasets[set_name][0].node_label_index['n0'].shape[0]\n",
    "        num_node_n1 = datasets[set_name][0].node_label_index['n1'].shape[0]\n",
    "        data_set_splits[set_name] = num_node_n0 + num_node_n1\n",
    "    #########################################\n",
    "\n",
    "    return data_set_splits\n",
    "\n",
    "\n",
    "dataset = GraphDataset([hete], task='node')\n",
    "# Splitting the dataset\n",
    "dataset_train, dataset_val, dataset_test = dataset.split(transductive=True, split_ratio=[0.4, 0.3, 0.3])\n",
    "datasets = {'train': dataset_train, 'val': dataset_val, 'test': dataset_test}\n",
    "\n",
    "data_set_splits = compute_dataset_split_counts(datasets)\n",
    "for dataset_name, num_nodes in data_set_splits.items():\n",
    "    print(\"{} dataset has {} nodes\".format(dataset_name, num_nodes))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lFY2PaDbVKe4"
   },
   "source": [
    "## DeepSNAP Dataset Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "iiyEw-agbgV8",
    "outputId": "649f47d2-761e-47df-ad6a-c8c24fef1d18"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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OIXjI6zUjPT1dP9FExih04+dHu++dO4H+/fVex6MTJyCePx9vqJmHAgAP27aFbWYm6tWsSb0jhpQuS6WU6JfJ6AY/ezZgb0+qucZgwgSqlNq5k/6enk4d5snJ9DmmQiIBvvsOOHSIHq/KkkVJSbBp2RKChAT1ygUK7t8nw1KoMRMApAIB8hIS+NRFTgHcoLymFIgmlrbqKDkZGDgQiI8n+ZJt24B69fRfhwZ1WrsmTYBly4BVq6gayVDRQRsbIDub/ty/n0JTf/5p2DkB4I8/KMF96ZKyGXDhQtrZb95s+Pn15cEDwMcHsLMDdu8G6tQpeClpyhQ09PeHMCEByMyk36lCuWDt2qLKBWoMikQgQOqkSVwMkqOEcTilRSZjbMUKxqpUYaxaNcZ27GBMLi+bz5JIGHN1ZaxuXcYOHjTsXPb2jD19Sj8nJDDWqJHh60tMpLVduqR8Lj+fMScnxq5cMfz8+hIWRmtYuJB+X8W4160bYwBjSUmMjRxJPwOMLV/O2KZNyr8DjN2/z5inZ9HnXj3ueXqW95VxzBieQ6mgiEQinD9/HkFBQdi7dy+CgoJw/vx5g1WBdcLSknSc4uJI82nqVGUFlbERCIBduyh0M3ky5XP0pXAepXlzygUZolGWn095kx9+oJyMguBgoFkz4N139T+3vjAGrFxJemnbtgHz5tHvqxhFpHAMUC7QWwqHUynhfSgVDE0z4W/evInQ0NBymQkPAGjZEvj3X2D5cuCnn0iOY/FiSvwWk2M3iDZtKOexcyfdKCMj9SthLmxQLCyUiXl9q8e+/x544w3gq6+KPr9hA/Dll/qd0xCysoBx4yhEFRVFORE1yHUc9KUNvaVwOJUS7qFUIMxlJnwRBAJKbF+4ALz5JvDjj5T4vXbNuJ8zezblAgD9VW4VApEKDBGKDAkhAcXt24tWcf33H+WXBg3S77z6cu0a0KED4OhI5d0ajAmSkyGzti4xqrcECrWC4j+/wiApHE6lhBuUCoK5zIRXS9u2FAL78ksKS7m7UyiocLWQIQgEVO6anAwcOUIVS6WlsIcCKCVYSkt6Oo2+3bOnSKIbAHXGT5hQdBhVWbNrF5V0//ADfb4q7y03V3mcmxscGjZUr1ig4NYtStQ3bEjKBWJxEUNlkBQOp1LCq7wqAAZ1uguFGDNmTPkKD8bGUr9Kbi41FG7fDnh4GOfcixeTzHpiInlFzZvr/t4PPqCbrqJr/e5dati7f1/3c8hkVN2mOFdhXrygG+7Vq3QTLmvy8kgZ+MwZ6jNp27bo64xReHD7dnq9c2dg7FhqWpw1C6mMwenOHb12lXIAaR06oFF0tDGuhFNJ4B5KBUDbTPihQ4di+vTpmD9/PhoXC3UoZsKXK25udFMdMQJ48oQ0rb78kkp2DcXPj8JWXl6UTymNB1TcQ2nalBoSMzJ0P8dPP9GN+vvvS762ezcVJ5SHMbl3D+jaldYeE1PUmKSl0TpbtSJPqkUL4Pp1YPVqMi6+vsC6dbBeuxayUqoWKJAKhbBduNBIF8OpLHCDYuaYy0z4UmNrC6xYQeEpe3vg9GkaF3vsmGHnFQop9HXyJOULSpNPKZ5DsbAoXdgrPJyaAPfsKVl0wFj5JeP/+oskVHx8yPOwt6dw1P79NAnznXdIMmXHDuDmTeB//wPWrKEw5IcfUr6ld2/DpHC+/towKRxOpYQbFDOn8Ez4zp07Y/LkyfD19cXgwYNhZWUFuVyOqKgo3L9/X+tMeJPg4UHeykcfUenvpElUalsar6A47dpRqbJMRslxXfMpxT0UgAyKLnmmp0+puW/rVkBV+DA8nLrxy1IEUi6nhslx46ggYMYMMoZffgk0aABs2UJrTEujnzt2pMq4Vq1o7sn16zQHvlB+p9RSOEIh0t3c0CQhgX6fHE4huEExc1TNhF+7di0cHR11nlFijJnwBlGtGu3ed++mnf2tW7SLDgignb0+fP899U989hnpZenSn1JYIFKBm5t2D4UxuokPGQL07av6mLLW7XryhD77zBnKIcXEUJ/LsGHkqf37L702ahQZzuhoypls3EhqANu3A2pGPjdZtgzPg4OR1qEDpAJBieovyavn0jp0QFZwMJqEhpIxHz6cGxVOEbhBMXPMZSa8UfjoI9olu7pSJdKSJTTkKimp9Oeytqab5IYN1PeiSz5FXw9lwwYgNZXyEqp48ICqoD7/XPf1l4aYGOqZsbGh8Fa3buT1rV9PxQnz5imrrx4+JOM3cCCFui5cIE9FC/V690aj6GjkJSQgddIkpHh6ItXVlf6cNAn5CQloFB1NYS5ra1IizsujaY/Fytc5ry/coJg55jIT3mjY21MH98aNQE4Oxf7d3EivS8f56wW0bw988QXtxps00Z5PKZ5DAeh9YrFKRV0AVAq9cCHpfqn7vv39yaAZWwSSMaok8/SkpsUnTwBvb6pKCwigAgBFF3x+Pikdv/MOlTLHxwNjxqjskteEXZMmaLphA94MDUXDf//Fm6GhaLphQ0kBSBsbCjXm5JBXxI0KB9ygmD2Ojo4FKsD6IpBI4PDXX7SjvXqVYvGmZsAAWssbbwA1awJ791KI5urV0p1n7lzycHr1Ao4f15xPUeWhaErM5+RQ4nvdOkDd1EuJhPIVU6aUbt2aePqUDGzduiSO+fnn5KWEh5P3UbzL/eRJCn/9/TeJO65YAZRHB7utLRAUBDx/TqFHblRee7hBMXN0yZMo5sAX/7kAS0u4BAcDmzYBffrQDnbAANJ8iokx3Y2gbl2qTFq6lHbdjo5UhTRnDnkNumBjQ6GvuXPJ65kyhfpLVKEqhwKoNyhTp1JRwYgR6j//yBHyctq102296pBKqcBg2DA636JFgIsLeSWbNlHpb3ESE+n3+NVX9LsMCVHOYikvbG0pR/P4MXlEpfUyOZUKblDMHF1nws+dOxc1atTA6NGjMXfuXNgXCr84t2kDu7Aw2uk2aEC78uxs4OxZugnUrk25jKVLaaiSsbrbdcXHB7hyhX52cKAQVrt2NPFQF9zcqN9iyxYyRuryKao8FMX7i+dRdu0iPax16zR/tqGlwrduUW9N48YUWqtdm0JrP/1EHocqTyMnh2acdOpEpcDXrwP9+plukFeVKsDhwxQ2HDeOG5XXGN4pXwEweqf8/fu0qwwKohxB9+60A375Erh4keLvHTpQ8rdbN7pxaZqRYiwYo9zArFlk4M6epR34smXa8xNiMeVU5s2jktqGDUsag127qIdj9+6izycnA126kKQKQKNy3d2pakqTYvDNm9Qxn5JSOqmVrCylDtjdu8Do0ZSH2LuXcjUHDwLvv1/yfYzRMb6+VJ68bJnqEmZTkZtLlWhNmlB5dSnzN5yKDzcoFYTSankBhcb4vprSp5LMTArbBAZS/L17d1LfrVMHuHyZvIQrV+jGqjAw7u5lO4UwJYV2ullZJAN//jx5Ap98ovl9UVF0THg4GaQVK4DBg5WvBwbSDTkwsOj7GKMhYVev0nV37gxMnEhVUpr46iv6Hn78Ufs1yeVkILdvp+bOHj1IBqVXLwprDR9O4bs9eygUWJxLl6iaLS+PcmGdO2v/TFMgElFYtUUL8hi5UXmt4AalAlEao6KTMSnO8+eU2A4KonBLhw50Q/byopt8WBgZmOhoitV360YVSB4eqm+ChiCX07TD+fPpZnvyJOUU1q9X208BgHbvd++SOnHfvuRxNWtGr4WE0PtPnCj5vo8/ppzJmTPkwR06pDmElJND6spXrgCNGqk/TtGxHhBAxQdjx5LOmWLKZUQEXd+ECVTRVTz/9egRhfGOHKEy67Fjzf8mnZNDm5LWrSn/Y+7r5RgNblAqGHrPhC8tIhGFh4KCyMi0bk2S7IMGUZglNpaMy7lzJEDYqJHSwHTrRtVbxiAxUVn++s47dKNftoxurKpu+GIxGZ7Fiymmv3MneTg2NkBoKBmoYqNsAdBN++5d6tu4fBmoVUvzujZvpu9H1QhhkYjWuWMH5TdGjKD1uroqj2GM5FCWLaPjisuYSCRUZPDjjxQSmzePDFJF4cUL8r7atSPv0lT5HU75YqJJkRwDycnJYRERESwoKIjt3buXBQUFsYiICJaTk2P8D8vLYywkhLEJExirV49G8i5ezNiNG/S6RMJYTAxjv/zC2CefMFa7NmPOzoyNH89YQACNmTUEqZRGDtety9jcuYy1b89Yjx40flcVkZGM1a/PWEYGY97ejH31FT0fFcWYm5vq92zZwpi1NWPnz2tfj1zO2DvvMHb6dNHnwsMZGzeOsZo1Gevbl7FDhxgTi0u+PyuLsSFDGHvvPdXfzenTjLVuzdhHHym/44pIVhZjnTox9uWXZTcimmNWcA+FUzqkUtrxBwXRo1o1pefSvj3tROVy4MYNpQcTFkYegiIH060bTXss7a71v/+o36FOHUpab9pEIa5vvqF5KYWZOZO62zdvBtq3h+innxAnECDj2DGIP/oItra2BfI1djY2lJSPj6e8jbZ1hYdTjuXmTfqMnTvJyxAIyBMZPVq9h3bjBoURu3cnD6Vww2lSEmltxcWRMvCAARV/Z5+VRSHTTp3oegtdj0gkQlxcHDIyMiAWi4v+TsqjCIRjdLhB4eiPXE6hr6AgSnTn55NhGTyYksaKfABjQEJCUQOTl6c0Lp6eNOZXl1i7REIltb/+SsYkJIRyP1u3UqhLwcuXQLt2SJs3DxE5OUhMSwOEQkgL/XMvCBFKJPCIi0OD8+fpZq5tdPLQoVSC/PAh9fEMG0aG5P33NRuAvXuB6dOpZ6SwTItIRKGvTZtI8HHGjKKGpqLz/DnJ7nTtCvzyC9LS09WOsS4cti2XMdYco8INCsc4MEb5AoXnkpFBelKDBlFpbXGJ9Hv3lMbl3DmqdPLwUBoYF5eSXkdh/v2XvJWWLen4JUuoF2XePOqLABC7bx9OXb8OiTZ5drkcQmtreN2+DbfBg8kzUHV9MTGUD9i1iz5z4kSSQnn1eWrJzyfP4+RJyq0omiAZo8bO2bPp2n/+uXxmqZiCZ8+Anj0R6+WFU9Wrl11hCcekcIPCKRsSEylhHRhI3km/fmRcvLxU34AfPFB6MOfOUVVZ587KJL+bW8nRtnl5wIIFFG768UdKkl++DPj7I7ZatdKXWTMGL8bgVnhw1MOH1LeyfTt9XuPG1NG/d69uJ71/nzya+vVpnYrEelwclQG/eEH9Ml276rzOikpsWBhO/f239ln2heBGpWLBDQqn7ElNpU7qoCDqp/DyorBYnz7qNaeePKFchcLA3LpVstmyalU69sIFCiG9/z7QuzfSVq5EwIABkKgIoVlaWmLw4MFwcnJCzZo1sWPHDty7d6/gdaFMhjHjx8Pp33/JiISHk6c1diwZuKZNqepNF6mV06cpnzJjBjVrWliQRMkPP9B3sWgRlQsXLxWuhGhqztX6OzHFGGuOXvACcU7Z07Ah9Xj88w95Lr16UdioYUPyXLZtoxttYerUoRv5qlWUp0lNpZtyXh7dkB0cqMHyu+8oRn/uHL3H1xcRo0dDoiGXoWnCpcTCAuE//EAJ5EGDyMPYvp2M2NGjuul2yeXkMX3+OXW+z55NciS//krl10IhFQB88cVrYUwAzWOsAS2/E1OMsebohWEythxOaalXj3Id48dTBZCikfKbbyisNWgQGZLiyVh7e+rVUPRr5OZS0+K5c9QRHxMDvPUWRB4eSHz2DNO//RbR0dFo164d7O3tkZiYiMOHD0MmkyEqKgoAVE+4tLREYosWEG3aVLLSSDFESxNPn5JXkpVFhtDJiTrkp00jI/jPP9RP8xqhGGM9ffp0/X4nUI6x5tVf5g33UDimw96eusYPHaIcytSpZCTatqUy3pUr1SsHV61K8iULFtBN+vFjYO1axLVpU1Bppe+ESzBWcmTyzZvKkl91XLpERrFVKzIiEgnlT8aOpXX+/fdrZ0yAouOn9f6dwIRjrDk6wz0UjnlQtSpVTHl7U1XU2bPkuXTuTD0dinLk1q1Vl+ba2ADu7jQy+do1AMoJlwBKNeFSyhgyjxxRjihmjPI0jo7A5MnK5xQPuZxCeVeukEG5d4+q1BITaY6Kqysl8ffsKfneyv6Qy5HRsyekb79t2O/E1GOsOTrBDQrH/LC2Jm2tj3ZA2/kAACAASURBVD8m+RFFI2Xv3lQhpmikdHMrYVwKjzouPuGyevHBVBpIsrPD3lq1YGthAUcLC7gcPgy72bNJs8zCQvmQSKixMS2NkuxpaWQ8nJ0p/1OvXtHjK9NDIiGtsYcPqUw8I4N+LvzIyIC4kBKzIb8TsxhjzdEINygc88bKSlnZtXq1spFy5EjS7VIYF3d3wMrKaKOOXzCGF69mqtwEEPrll3Bu3Lhos92dO0qvacUKmmvy5AnJz3fvbpR1mATGKAeUlkaP1FTlz4Ufz56R19aggfLRtCn11DRsSH93coLtX38Br7xGQzCbMdYctXCDwqk4WFhQ6XCHDjQM7L//qM9l2jTKwQwcCMf338dNLXIlhSdaKiZcyjQMhZICgKUl4uPjcefOHeqLSE+nkt+ZM6lnpn9/ypNMmqS5IdPUSKXkORQ2DKoMhpVVUUPRoAHltnr1UhoLBwedqtQcHR1x8+ZNjcdo+50IGIPDa1IRV5Ex43/5HI4GLCxIrqVNG+qOv3kTWLgQLr6+CJ08WeNbp06dipqvGgxHjx4NAFizZg2ysrK0fqxEIsGpY8eA8+fhNmYM8MsvwJAh9Pl16hh8WQbx4oVqT6KwwXj8mMJ2xY1F69b0p8JYlCIUpQ0XFxeEhoZqPEaX34nL1Kk0fmDSJJp7U5qhZpxygTc2cio2L14A/v4UDnvrLcDXF/vT0hBfqDGuLBBKpRhz+TKcliwxfJ68NmQyGoSmzVjIZCUNReFHw4bUsW8CD2r//v2Ij4/X+/2tWrWCz8CBpL7w22/knY4ZQ15iixbGWyjHILhB4VRMMjJIsuS334APP6TmwffeA2DYyGQF2rq3gVc3OR8fgy4DubmqDUVhg5GRQfNZtBkLe3uzVSc2+hjr27dpIxEQQKG4SZOof6m4PA+nXOEGhVOxSEyk/pT9+2lw1bffAs2blzhMn5HJhbG0tESHDh2Qnp6OoUOHIjAwsIRBEQgE+Prrr1U32zFG4SV1CW3FIzdXs6Fo0IDKpivBjbJMxljn5ZGsz5YtlPj//HMS7WzZ0kir5pQGnkPhVAxiY0mN9+xZ6gW5dYuSwmpwc3ODRCZByMkQCDT8MzekextyOeJ27IC7WFzSUDx4ANjZKXMSikenTkX/XqeO2XoVxkZhFIw6xtrGBvDxoUdCAvD77yS02bo1eS2DBlUKY1xR4AaFY74wBpw6RYYkIYFEFrdto6FeOhCDGNxqdAuD7QYjMTERMiYDk5U0DorubalUinHjxsHFxQWXLl3Sen6pXI7MhAQyCA0aUPWZwlA4OWmXtX8NcXNzg5OTk3KMNWOQFq7mMmSMdYsWwPLlNP45OJi8lmnTaMzBpEmUY+OUKdygcMwPqRQ4cIAMiUxG+ZHhw0vOVNFATn4OloYvxV+j/kK7+u0gEongH+KP1AepeK/ee3jw4EGBEKG+3dsAIO7YkUJvHJ1xcnKCj48PTWw8fRqZf/4Jca9esLW1hYODg+ETG62tSfJm6FDqFfr9dxqD0KqV0mvhPS1lAjcoHPMhN5c8kF9+Ad58k3pNevfWKyS05uIa9GjaA+3qUwWWnZ0dWEMGaXUpRvQagaCgIFx71WxnSPc2b7bTHzs7O7g3akS5j4CAsvmQ5s1pwufChaQWvWULTc0cPZpyLa8kYTjGgYtDckzP48f0H75JExJ6/OMPmuTYp49exuRJ7hOsubgGiz5YVOR58bNHeD8mHZg4EY7+/hAUGj2rDwK5HA4PHgDZ2Qad57UmJ8eoPS9qsbYmVYO//gKioshD6dGDFBh276aR0RyD4QaFYzqSkynG3bIlVUOFh5OsSqdOBp12+fnlGNJ6CJxrNafk/erVQM+e+GbISnQ5Ege0aQOX5cu1JmsVHdvFfy7A0hIuJ09S4r1PHypjzcgwaO2vHTk5OufEjEazZuT9pqQAX39NBqVRI/r5v//Kdy2VDB7y4pQ/V65QfuTkSQo7XL9OSWwjkJZ5B0n7NmG7cCjwdQvS++rTB/jqK8yc0ADvtnDHpPcmwQ6AsxY5EI3d23I5nKtXh11wMDVXnjhBTXezZpFEvbc39UWoKGnmFMIUBkWBUKjUgktKArZuBXr2JIPzxRekgMALK0oF70PhlA+MAaGhVIVz7RrtBidNomY8Q7l3DwgJAUJC8PLMX3jY3AFNP/0S6NuXmt5ehc2GHRyGwW8Phs871Iy4M3wnbv9zG0LonuxXIJRIMOboUTjVqEGTGBXd8nl5FLb780+qNHJ0VBoXF5fXpkRYZ7ZuBSIj6U9zQCKhoW9btgDR0SRCOnHiaznHRh94yItTtshkNECrY0fgf/8Dhg2joVmzZulvTCQSyrHMnk3/0d3cgAsX8PCTD9F2djXUiLpCo4HffbfIDTw7Lxs1bGpAzuSYf3Y+frj0A+q2rQsJStf8KLSygtf163BKSSEP6KOPaDjY06cURuvdm25I6enApk2ASES73WbNaDJlWBh9Lxzy7kzloahCKCTjHxJCA9Ps7WmMgrs7FQ7k5pp6hWYNNyicskEsJlmUVq1oLsicORSfHjdOv0azhw+BHTvIIDk4UIe8rS3tbB8+BHbuxIxa0RjTYwbqVFUt0pidlw2hpRDDDg7D6bunscd7D366+xPeev8tWFhZgEG7sy6USODl5ga3fftIYTghgcQWs7OpYsjfX2ksrKzoRrRyJXX4HzlCEirffEPd7+PHU+XR6zzno7yS8vrQuDHNuLl3D/D1pVL2Ro2Ar74yihx/ZYSHvDjG5dkz2pWvX0+ew+zZNB+jtKEeuZy6448fp91iYiLFt/v0IQ+gWK/IlYdX0GtPLyR8lYBq1qp3vC3Xt4SVhRU6NuyIxR8sRveA7pjbdS5GvjsSnVd3xvi64/Es7RkAmhCooEiz3b17cAoMpAICoZCu1deXfv7lFzJwYjGFwTQVFyQnk2TI4cNAXBx5OQMHUpjuVd7mtcDPj67Xz8/UK9GNlBT6HW/dSsZl0iTa5PBZ9wTjcIzB/fuMzZjBWK1ajH3+OWPXrpX+HE+fMrZvH2OjRzNWrx5jbdowNmsWY2fPMpafr/Gtfff0ZWsvrlX7+vmU88xyoSWbc2YOy8nLYR39O7If/vmBMcbYxuiNrNfuXowxxnJyclhERAQLCgpie/fuZUFBQSwiIoLl5OTQieRyxvr3Z+ybb5Qnj4hgrE4dxqpWZWzTJsZ27WLMyYmxMWMYe/hQ+3VnZjK2dSudt3p1xry8GNu4kbG0NO3vreh8+SVj69ebehWlRyJh7MgRxvr1Y6x2bbqOuDhTr8rkcIPCMYwbN8iA1KpFBiUlRff3yuWMXbnC2E8/MebhQTfTvn3pZpqUpPNpwu+Fs8arGzOxRKzy9e2Xt7N6P9djVX6swh6LHjPvfd5sVNAoJpfLWW5+LmvwSwMWkxaj+7qfPGGscWPGDh9WPpeWxpirK2PVqjE2fjxjjx4xNnMmY3XrMrZqlVaDWMCLF4wdPMjYyJH0nXbqxNiyZYzduqX7+ioSn3/O2Pbtpl6FYaSkMLZgAWMNGzLWsSNtDhQbEANQbG4CAwPZnj17WGBgYNHNjRnCQ14c/YiIoIqtmBjqJfnf/yg/oI2cHODMmYKqLAiFFObp25fkMUpZpskYQ7cd3TDedTzGuIwp8ppMLoPv3744HH8Yh4cfRrvN7fDV+1/hSsYVnBx5EjYCG6y5uAahyaE4PPxwqT4XFy7QkKfoaGrIBID8fPoeDhwg3aiQEErUT5tGgpHr11Mzna7k51NlnCI0VqsWhcW8vUmqvzJUjA0ZQrI6Q4aYeiWGI5NRKfyWLRQSHT6cQmIuLqU6TVpaGiIiIkjrDOrDr0XGUZsJ3KBwdEcupyTyzz9TA9/MmSQXrs0IJCQocyEXLlDFV58+ZERatjToxhiSEIKZp2bi2v+uwcpS2XiYJc7CiMARyJPl4cCQA7C2skbdFXXRrFYzRI6LRK0qtSDKF8F5vTP+GvUX3nV8t/QfvnIlVbCdO1d0eqC/P5VFV61KndmurmQQZsygvJJCWkYLIpEIcXFxyMjIgFgshm1ODhxTUuBy5Ajsnj1TGpdu3cx77LAmPv6YihR69TL1SoxLairJCP3+O+X7Jk0iA6Oloq00Ev86qTGXM9ygcLSTlwfs2QOsWEHJR19fagZTN+NbLKabrMKI5OaSAenThxLrRqrqkTM52v/WHvM852HQ24MKnk98mogBfwxAj6Y9sPrj1RBaCbHt8jZMPDoRiV8lommtpgCA5RHL8e/Df7F/yH49FyAnL+Wtt8i4FCY6mq43N5cMzMiRJO+xfDkl7L/+mgyyCi0wnXaoDg7wyMhAg+Bgasrr25eMi5cXGbKKgrs7bVDc3U29krJBJqNNxZYt9H9i2DAyLu3blzi0TObFlDPcoHDUk51N/xHWrKF+j9mzgQ8+UO1RpKRQt/jx4xSmaduWbnJ9+lDTXxmEZ/Zd34dVF1YhakIULF6d/5+kfzAicAQWdl+IyW40Wz42PRYf7/4Y1a2rI/nrZLq0vGw4r3NG2JgwvF3PAIHAJ0/o5rB+PTBgQNHXMjLoO7h5k0qEV68mI5ycTN7KlSv03fbrV/D96LVDdXSkJso//6TKuB49yLj06wfUrq3/tZUH7doBO3eW/RhlcyAtDdi+nTYY9epRN/7w4UD16safaGkiuEGpRJQIkdjawtHRsfRy4A8e0Hhdf38KScyaVTIOLJVSh7MiF5KeTmGLvn1pl1xHdS+IsZDIJGi9sTU29d2Ens16AgA2xmzEorBF+GPwH/ig6QcAgOTnyeiytQu+7vQ1Am8GImoCDc1aFLYIiU8TsdN7p+GLiYykG3h0NPUuFFmohPoWdu4kw3P0qDLXdOoU5VeaNQPWrkVsVpbhO9QnT8io//kn5ao6dFB26jdsaPi1Gpvmzel7eJ0kamQy4PRp2qydPQsMG4b97dsj/uFDlYeX2zhqI8ANSiXAaEm827eVeYGRI2kX3bSp8vXMTPJCQkLoP0TTpspQ1vvvqw+BlQFbLm3B/hv7ceazM5DIJJh2YhrOpZzDkeFH0Lw23Zyei5+jy9YumOw2Ga3qtsLKyJU4NfoUnr58ipbrWyJqQlTBsQazYgUJW547p3puy44dwJQp1Hl95gxNFAQo8b5uHdK2bUPAsGGQqPHkNN1U1O5Qc3PpZv3nn8CxY2S4vL3p0aqVeST1HR2Bq1fpz9eRBw8g2rYNa8RiSNXkwQweR12O8E75Ck5sbCwCAgIQHx8PqVRaxJgAKHguPj4eAQEBiI2NLXmSqCiS9vbwIJHG27cphNO4MVVxLVhABqNlS9phf/wxCTpeukTT8Tp3Lldj8lLyEovCFmFpj6V4kvsEXru9kPoiFRfGXygwEPmyfAzaPwhezb0wreM0ZImzUMOmBgDgl8hf4N3K23jGBKDO/Tp1gO+/V/36mDFUGSeRUGI+KIiet7YGZs5ExOzZWgVgUlJSEBQUVDAYTIFEIkF4eHjJN1StSp5JQACpCSxfTt6nlxcZFD8/+t3L5aW+XKNhSnFIc+CNNxDXvTtga4vp06ejc+fOmDx5Mnx9fTF48GBYWVlBLpcjKioK9+/fVz+OGkBcXFz5rVsNFbQ0hAOUPoknkUhw6tQpAIDbe+9RiePy5RTT//ZbCstIJLSrPX6cXq9Th8JYy5dT4rRwNZOJ2BCzAR0adEA162p4//f3MeTtIVj64dKCKi/GGCYcmQB7W3v84vULAKWO1yPRI2y+tBmXv7hs3EVZWtKN29WVqq769y95TPv2QHw8eXSffkqJ+aVLIXr5EolpaYCFhd4z7hMTEyESidTvUIVCyq306EHhzEuXyHMZOxbIyqLiAm9vKt0ur9+xTEYFHBWpiKAMyMjIKNgI6j2OWipFZmZmWS9VK9xDqaCkpaWpNSaWlpYYOnQopk+fjvnz56Nxobi+RCLBqZAQpHt6koDixIm0W1ZUYr35Jt0YO3YELl4k/a0VKygZbwbGJEuchZ/P/4yeTXvig4APsMBzAZZ/tLxIyfDCsIW49eQW9gzaU/C8wqAsP78cn77zKd601162W2rq1KHhYBMmUJGCKurWpZzL2LGUkO/ZE3GvDIUCxU1l7dq1BTkwXdB5h2phQV7SkiX0+/3nH/JG582jEtdRo4DAQPIeygBRUhKSpkxBSvfuSH3jDaR0746kKVMgSk4uk88zd8SFtNwU46jFYnHpx1GbgSYc91AqKBERERo9k5SUFFy8eBFDhw4t8ZpEJkN4jx7wefCAjIqVFXkhvr5kOMx4BsTKyJVoZN8ISyOW4siII+jUsKhe1o64Hdh5ZScujL+AqkLlzjc7LxuWsMT2uO24/r/rZbdAd3fy9oYPJ1VhVfkUgYA0wLp0ASZNQsa2bZC2aFHwsj4z7g3aob71Fv3ufX2puCI4mIQ9x44Funcnz6V/fzKGBpB5/DjyFy5E/cuX0RCAUBGeTUuDJDISFv7+SHV1hfX8+XDo29egz6pIFB4jXdHHUXMPpQIiEomQmJiof8zV0hKJcjlELVpQjfzdu9Qb0aePWRuTlKwU/Bz5M/Jl+bg4/mIJY3Lm7hn4/u2LkJEhcKxWNMmblZeF6PRojHUZizeqv1G2C505kwQP58zRfNzo0cDFixAXS8YWv6lY6+gZGmWH6uRE3f6nTpGX5eNDRRjNm5NxWbOG1HdLSbKfH2p5e8MpJgYCqVRpTF4hlEohkErhFBODWt7eSK4oYpGGkJAArFwJx8BACPQoFy6MQCCAg4ODkRamP9ygVEAKhzb0DY/AxgZxnTuT5Lo5VPto4cGLB+jo3xFv2r+JqAlRaGTfqMjr1zOvY0TgCBwYcgCt6rYq8f60F2mIexgHX3ffsl+spSXlo/74g3JRmmjXDrYff2yUjzX6DrVmTar2O3iQkvrffkuy7W5ulA9avJj+rqVQNNnPD06rVkEokWi94ViCRgQ4rVpV+YyKXE5KEX5+9P/O0xNITISLj4/KBtfCaB1HDej+f78M4SGvCkjhJJ4+4RHAfJJ4unAp/RIG/DEAWXlZuPzF5SKhLICMTb+9/bCm1xp4NvFUeY6YtBh4NfdCPbt65bFkCg/98QdpVMXEkNS5GhzffBM3794tUaFXnMI3EcVNRfZq9kqZ71CrVKGwV//+1IN0/jzJyfTvTyE8Ra9L585kUF+Refw4GrwyJqXB+pVReeTpiXq9exv7asqPly+Bv/+mWThHj1JD4yef0IbjvfcAS0saR/3ypcbTaBxHDWoLMHXJMMANSoWkcGjDkJirOSTxtHHgxgF8GfIl2jq0RZdGXVC/elGDmZOfg35/9MPE9hPxadtPVZ4j8WkiUrNTsbzn8vJYshIPD6rkGj6c1ANU5VNAO8vQ0FCtp9N2Uym3HapAQLtrT08anhYXR8blf/+jXqVPPiHj0qMH8hcuhJUmYyIUAnv3ktfTpAmF1cLC6GMkEojnz6f5NxWJx4+p7yc4mAoeXF3pO/nuO+oFUoGHhwc2btxYJC8a9up7AIC1a9eq/TihUIiuXbsab/0GwA1KBcRYoQ1zSOKpQ87kWBC6AAFXAvBbv98w+dhkBPkEFTlGKpdi+KHhcK3viu+7qun/AHXFO1ZzRIMaJlBmnT2bbpBz51LptQrs7Ozg7OyM+Pj4EjcOXW8qJtuhWljQDdPVFVi4kAahHT4MLFkC0ZgxqP/kifa4ekQE5WYOHizytCWANy5fhig5GXYKRWdzJSGBDMiRI9So2bMn6d39/rtOqhENGjSAl5eX3koJ5iC7AvAcSoXE0dGxoANeHdpiruaSxFOFKF+EoQeH4kzSGURPiMbea3sxs8tM1LRVTjJkjGHaiWnIl+VjU99NBVpexbn56CZOJp6EndCuoLGxXFHkU/bupeS2Gjp27gi5hX4Nhua0Q4WzMxUlREQgs18/MMXvJSmJcjBXrgDPnwP79tEoaIkEWLuWQmiK0cmFYAAyV6wo32vQBbmcyuq/+45UD17lQ+DnR/mmQ4eo6KIUEkRubm7w8vKCUI0nWxxzE4YEuEGpkOgS2pg6dSrmzp2LGjVqYPTo0Zg7dy7s7e0LXpfIJHBobn4G5d7ze3Df5g57G3v889k/uJ99HxdSL2Dq+1OLHLfqwipEpETg4NCDEFqp/w+4IGwBvu38LUQSEext7NUeV6bUq0f5lHHjgPv3S7z8XPwcY8+OxQPHBxAISxc0MLcdamGsEhOLVnMNG0Z6b02bAu++S+oBWhBKpbC6caPsFlkaXr6kUNbEiVQNN2ECbRh27CC5+s2bqVLSAM/fzc0NY8aMQatWrSAQCEpsHBXPtWrVCmPGjDErYwLwkFeFRBEi0Tc8AgCyWjJ0DOgInzY+8PPwQ+OajTUeXx6cTzmPoQeHYmaXmfim0zewsLDA92e+xw/dfiiSiD/03yGsiVqDyHGRsLdVbySuZlzFuXvnsG3ANiyNWGoaD0WBhwcwfXqJfEpqdip67+mNHk16YNXHq3D538sVeh5GYSyLScRg3TqSfgEoQa1jzscyO9vIKysFinzIkSOkwabIh/j5lZmgpZOTE3x8fArEXjPT0yEODobtoEFw0EfstRzhBqWC4uHhgTt37ugtdz1x8ERMs5+G1RdXo/2W9hjUahC+6/odmtVSnTQsa7Zf3g7fv30RMDAAvVtQEvZs0lncfXYX413HFxwXeT8S/zv+P5wefbpE6XBx5p2dB193X1QRVkFOfg6qWZtYM8rXl/IpP/wALFuGG5k30GdvH0ztMBUzu8yEhYUF3Nzc4OTkhPDwcNVinxIJYGMD55Yt0bVrV7P0TBTIixeIFFbTzc2lXb4+5ylrEhMpHxIcTCG6nj2pyMDfv8xVtAtjZ2cHd8WcmDFjyAMyc90zblAqKMZK4i39cCm+7fwt1katxfv+76P/W/3xvcf3aFGnhZYzGQeZXIbZp2fjyO0jRWaTMMbw3ZnvsOiDRQUhrcSniRh8YDB2DtwJl/qad7cxaTGITY/FviH7kJOfg6rCqkXkWUyCpSWwaxfQvj2utaqNno9/weqPV5eoTiuxQ83MpHEEVlZw+PNPuISEwO7HH3W+IZsCOZPjcTMnvBEpKNHEWBokAgFkaWnKEdGentQDo2OeQbfFymn0gCKp/vQpzbbx8yPtM3MoXqlbl7wlblA4ZYUi1GFoiKRO1TpY9MEifNPpG6yLWofOWzujd4vemNN1jsomQWORJc7C8MDhkMgkiJoQhdpVlMOggm8F46X0JYa/MxwA8Dj3Mfrs6YMFngsKPBhNzAudhzld58BWYIvHuY9Nlz8pTr16OPvjBLSZ6ofAk3vh0Xa42kOL7FAVDB1KI3O/+YYS2Xv2lKvSszZuPrqJ3Vd3Y+/1vWjWshpO6vIma2tlc621NSXr8/IAABYAHA4cIDWHc+do2mFSEtCpExmXbt1ICdvGpnQLffmSSnqDgyn8Vrs2hbK2baMZMpZmll5WGBQzr3Yzs2+NU1oUSby6b9aFDDKDkni1qtTC/O7zcWfaHbSq0wrdtnfDiMARuJFp/KRowpMEdNraCc61nHFi5IkixkQml2HOP3OwpMcSWFpYQiwVY+C+gRj09iB84faF1nOfTzmP+MfxGN+eQmUKYUhzYF3UOox+4g/p1C/h4beBmgRRSDDR0xOp7dsjxdNTtWCihQWV2P7xB4l6vvMO7ahNyIMXD7Dqwiq8t+U99NzVE3myPPzp8yf+9ruKDFdXaK1du3WLVIcbNiTJF7EYaNwYcgAPXF1h1749NYiuW0chqORkYOpUuu5vvqEw1AcfAPPnk5HIzVX9OY8fk/DpoEEkgvnzzyTjHx4O3LgBLF1KoqjmZkwApUExc/iArUrC7NOzUUVeBV7VvJQhEltbODg46J3Ee5H3AhtjNmLVxVXo1rgb5nadi3b1DR/V+vfdvzEyaCQWdV+k0kDsvLITv136DRFjI8DA8Gngp2Bg+GPwH7C00P6fvUdAD4x6dxTGuY4DAFy4fwHf/PUNLk64aPDa9UXO5PD72w9Hbx/FyZEn0bhGI6B3b2Q6OSH/xg3Uv3wZDCgSHpIIBLAA8FCdYOLt2yRGKRZTov+998rtel7kvUDQzSDsubYHMekxGNhqIEa1HYXuTboXCS1mHj+OWt7epe6UB4B8oRBZwcHaO+Wzs8lbCwsjL+bqVRop7OkJtGhBhQB//UUNmD17Ujirb1+DxS7LlZEjqcFz1ChTr0QzjFMpePvXt1l0anSZnDsnL4etPL+S1V9Znw3cN5BdSr+k13nkcjlbH7WeOa5wZGeTzqo8Jk+ax5qsacLCksMYY4z5nvZl7lvd2UvJS50+48zdM8x5nTOTyCQFz51MOMm8dnnptWZjkCfNY58Gfsrct7qzJ7lPCp5Pmj6d5QsETEZqWGofMoDlC4Usyde35Mlzchjr1IkxKyvGtmwp0+vIl+azo7eOMp+DPsz+J3s24I8BbP/1/Sw3P1fj+5J8fVmeUKjxGos/8tRdrzZkMsbOnmVs+HDG6tZlzNqaMYGAsRYtGPv6a8aOHmXs2TP9vgBTMm0aY6tXm3oVWuEGpRJw9+ld5rjCkcnksjL9HFG+iK25sIY5/eLE+u3tx6JSo3R+b540j006Mom12dCG3Xl6R+1x66PWs167ezHGGNscs5m1WNeCPRI90ukz5HI567K1C9t9ZXeR5w9cP8CGHBii81qNyfOXz9mHAR8y733eRW68Rr3JyuV0s7S0ZOyzz+imaiTkcjmLTIlkU45NYXV/rsvct7qzjdEbdf6dKEjy9WX5QqFOxrPUxuTlS8aOHWNs4kTG6tdnrHVrxvz8GLtwgb6Lly8ZCwtjbNEixj78kLFq1RhzcWFs+nTGAgMZe1S6azEJixYxNmeOqVehFW5QKgHro9azsYfHltvnvZS8ZL9G/coarmrIeu3uxSJTIjUe/0j0iHlu92T99/ZnWeIslpOTwyIiIlhgYCDbs2cPCwwMZBERESzzWSarv7I++zf9XxZyO4Q5P/sJmgAAIABJREFUrnBkCU8SdF5XyO0Q1npDayaVSYs873/Jn407PE6vazWEtOw09u6md9mUY1OKrCnj2DGWr8mYCIWMHTzIWFISvcHTs4hRyQwJUf2Bhw7Re9u0YSwry6C1xz+KZz/88wNrtrYZe2v9W2xx2GKNGwFdyAwJYSkdOjCJQMDyBYIi15z/6rmUDh3UX19hHj9mLCCAsUGDGKtRg7GuXRlbuZKx27e1vzcvj7HISMZ++omxXr3o/W3aMPa//zG2bx9jDx4YdJ1lwsaNjH3xhalXoRWeQ6kE9NrdCxPbT8Tg1oPL9XPzpHkIuBKApeFL0aJOC8zrNg9dGxeVALmeeR0D/hgAnzY+mNJqCiLPR6rurxAIIJVLkW2XjX49+8HnLx8cHn4YXRp10WktjDF08O8APw8/DGk9pMhrqy6swv2s+1jda7WBV6w7Nx/dRO89vTHZbTJ83X2LSMOkvv8+nGJi1FfECIXAlClAbCzpW40YUSCYKAeQ1qEDGkVHq35vQgIN7srPJ42stm11XnNGTgb2Xd+H3dd2IzU7FSPeGYGRbUei/Rvt1Urb6IMoORmZK1bA6sYNWGZnQ16jBmRt2sBh1izNml137ij7Q+LigA8/pMosQ/MhUimdT5GDCQ8ndYNu3ZSlyhrUosuFgweB/ftJ0sWM4QalgpOTnwOnX5yQOiPVZJVMEpkEu67uwpLwJXjT/k3M6zYP3Zt0x9HbRzH+yHis/ng1WuW30rm8WQIJ3nR9E5MGTNJ5DYfjD2Nh2EJcmnSpROJ+QegC+rP7gtJclt5EpERg8IHBWPHRCnzW7rMir4mSkmDTsiUEUimVv/76K/DZZzSC9+RJ4PPPC0pmAZBUy6hRBQYFAKQCAfISEtTffHNzqX8iNhbYupXOqYac/Bz8efNP7Lm2BxdTL+KTVp9gVNtR+KDpBxBYmrirQC4n6f8jR8iIPH5McvmffELGpKyGwcnlNOfl3DmlkbGzUxoXT0+SjymnOUIikQhxgYHIuHAB4q5dYWtrWzD7yNw65rlBqeAExwdjffR6/P3Z36ZeCiQyCfZe24sfz/0IiVwCkUSEo8OPQpAh0LsBUxdZETmTo93mdvjpw5/Qr2W/Eq/P+GsGGtZoiBmdZ5TqevThz5t/4otjX2D3oN3wau5V4vWkKVPQ0N+fqrmSkkjufeBAqtQ6f56EEn/7TfkGFQZFIhAgddIkNN2wQf1CGANmzKBS23HjgC1bCm6AEpkEp++exp5re3D89nF4vOmBUe+OwoC3BpSYNVPuiMVF+0Nq1iQDMmCA6Up6GQNu3lQamLAwWofCuHTrRmOUjWxg0tLSEBERQR69XA6pXFmArWgPcHZ2hoeHBxo0MIGStgp4Y2MF53jCcfRtYR7zt4VWQvi844O/7vyFi6kXYW9jj/nH5sPjiQfkMvXdCE2bNkWfPn1gb2+P1NRUBAcHIysrC6dOnYKTk5NWeZGDNw7CTmin9nvIEmehdb3WBl2bLmyI3oClEUtxctRJtH+jvcpjrG7cKNo5roe+lU6CiRYWwOrVVFb86adgsbGI2b8auxIDceC/A2hWqxlGtR2FNR+vKb+hY+p48oQmWwYH0zCqd98lIxIWRmW/psbCghSFW7cGJk8mA3PnjtK4LF1KhrBbN2WYrE0bg4xfbGysxk2YIlwcHx+PO3fumI2mGzcoFRjGGI4nHMesLrNMvRQA1OA2cP9ANK3ZFNenXIeNlQ3WbF2DbFk2LNVkDKpUqYJhw4bh6NGjuHXrFnr06IEhQ4Zg69atkEgkCA8Ph4+Pj9rPlMqlmB86H+t7r1cb58/OL9vGRsYYvj/zPYLigxAxNgJNazVVe2wJwUQ99a10FUxM+KAdTmybjNFfbMBbLh+i1eYpOD/uPJxrO5c4VpSUpMxtvHgBefXqlNuYPdv480ju3FGGsi5fphDdJ5+QXlU9Exs4bVhYkEy/szMw/pXO3L17yvDY2rXUdNm1q9KLaddOZ0UDbcakOBKJBKdOnQIAkxsVblAqMHEP42AntCs33S1NxKbHwnu/N7547wvM6ToHFhYWEIlEEGeKYQlLTJ8+HdHR0WjXrh3s7e2RmJiIw4cP4+2338ajR4/w33//AQBCQ0Mxa9Ys1KlTB0+ePEFiYiJEIpHaWPHea3vhYOeAns16ql1bdl52mUmv5MvyMeHIBCQ8TcD5cedRt6rm5LCxhA7lNja0U1ZhRDNFmdh/fT/2XNuD5OfJ8GnjgztXw/Dep9/iy/GbgYDOwKdKg5J5/DjyFy5E/cuX0RDFmisjI2Hh749Udc2VOi9YTjkdRVL90SPKh3z7LTUbllU+pLxo3JhyYZ+9ypmlpVFyPyyMRCXT08lb1KJHlpaWptaYWFpaYvDgwXByckLNmjWxY8cO3Lt3D4DSqOji0Zcl3KBUYMwl3LX/+n5MPTEVW/ptgffb3gXPx8XFFTmuTZs22L17N6RSKcaNGwcXFxfUq1cPGRkZBcdIJBI8e/YMDg4OePLkScF5SmhagXIBC8MWYvsn2zVWIZWV9MqLvBcYfGAwqgir4MxnZ3TKP8jatIEkMlK7YKIGfSuJQADZ8+fAm2/S/I0+fSDq2gnBaWew++puRN6PRP+3+mNB9wXo2aynMrkeFQVMm0Y5mfPngQ0bkOznhwarVsFKIlHpQyrW6RQTA5m3N5JnzECTZct0+4IU+ZAjR+hhb09eiL+/+UqcGIsGDWhUwfBXWm2ZmUoDo0GPLCIiQqNnkpKSgosXL2Lo0KElXtPFoy9ruEGpwBy7fQxLeiwx2efLmRzzz87Hrqu78Pfov0vIsmRkZBQpDY6KikJOTg4A4Pbt26hfvz6srKwgEomKvE8sFsPa2hoAxYozMzNVfv6OuB1oVqsZujXupnGdWeIsoxuUhzkP0WdPH7zf4H382udX3SuipnxON1Rt3LqlFAJ8Fc5AkybAvXskmBgSAulLERJ3r4N03mQ0jn+It1vWweJevdDqs39g946r6iTxunU0m2XkSCRHRsLpxg2dZFEsAVhKJHBatQrJgHqj8vSpMh9y+jTlQwYMIGmYli21X3dlxcEBGDyYHgB9TxERZGBmzADi4yHq3BmJXbpg+tdfq/TmZTIZoqKiAFCYVRXaPPqyhhuUCkqmKBPxj+NL9H2UFzn5Ofjsz8/wKPcRoidGw8Gu5PRHsVhc9D2vjAlAu6nq1avj5cuXsCmmFGtjY4P8/Hy15wEAsVSMxecW48DQA1rXmp2XrXEQV2m59fgWeu/pjXGu4wrCe7qs4efzP2NT7CaEt26OVldvwbJpsVzLwoXKn4u/9go5gJR272DdrbXYd30fGjdrjJFbvsPwN/vCNeoajRnu8wl5Na+8F3TvXjSkNGwYMnNy0OCLL1R7SkIhjSx2cyMj1r17QZWZ9Suj8sjTU6mxdfeuMpT177/KfMimTeafDzEVtWuToR0wgP6enY24gwcLJnqq8uYvXbqk06nVefTlATcoFZQTCSfQs1lPWFtZl/tn33t+DwP2DYDbG274Y/AfsBGolg631WGORGZmJtq1U3o2QqEQtWvXLuKVqDqP/yV/vOv4Ljo17KT1M4wZ8rpw/wK893vjpw9/wljXsVqPl8gk8P/XH4vPLYZXcy/EfREHmzZXIfP2hqUegolSoRALu+ajqW1NhI8NL5o/G9icSpAZA65fJ+OybBng40MJYoWBadoU+Zs3w0pT2C0iglSNDx4s8ZJAIoH4++8pbBYcTOGc/v1pp92zJ1DVxKXH5oBcTiFKsVjnR8ajR5C+2pyo8uZ1QZNHXx5wg1JBMVX+RDGmd7b7bEzvOF3j7tzR0RE3b94sEvYqTnx8PD766CO8/fbbuH37Njw9PZGRkVGQPxEIBHBwKOr95Epy8VPETzj26TGt65UzOUQSkVGmNQbHB2Pi0YlFpkqqgzGGw/GH4XfGD43tG+PEyBPKoWB9GyF5xgw4rVoF61IYlXyhEFcnf4Ydq/w1e0UWFtQh37YtTYl8/pzCTyEhwOLFENWpg/q3b8NSU2OlYoS0TFbi9JYA3rh2DSIPD9j99hvlQ8xoJgsAMqr5+SVv3C9fluomX+qH4vz5+ZT3srUt+qhSpeRzrx7i+vULhnmp8uZ1RZVHX15wg1IBUTSmreu9rlw/d9vlbfD72w+7vHfhY+ePtR7v4uKC0NBQjcfk5ubiwIED6NOnD7y9vZGWloZDxeQlXIr1ZmyM2YjOjTqr7fUozIu8F7AT2ukke6+J32J/w8KwhQgZGQI3J82lmRdTL2LmqZnIzsvGul7rVH5XTZYtQzKgMSGuQA7yTNJnzICbrgnxwtSsSYO5hg4F5HJkjh6Nhrdv02vDhgG9eikbK8eMKdpYqQZmYYFMuRxNu6iRxmEMkEhKfyM21iMvj0J3am7eKh/Fb/Y1a5bu/YUf1talLjqwDQqiDn0D0SUyUFZwg1IBiUiJQIvaLVC/mm5usKFI5VLMPj0bx24fQ/jYcLxV9y2d3mdnZwdnZ2fEx8djrWLH+4qwQp3fSUlJ2KCm69vZ2blIgvFF3gusiFyBM5+d0WkNhuZPGGOYd3Ye9t3Yh/Cx4Wheu7naYxOfJuK7M9/hYupFLP5gMUa/O1rj2OEmy5bhkacnxPPno/7lywBKzkMBaB6K7cKFaKJtLoguWFrCKjVV+Tl6NFYq1ml16hSVv6q7qQsE+t+QbW2B6tUpB6NlZ6/yYWNT4arIFB69JqwKeYJWVlawsrKCrJAXqcqjL0+4QamAlGe467n4OYYfGg4ZkyFqQhRqValVqvd7eHjgzp07pZJdUSCVCtGsWdGig3VR69CzWU+84/COTucwJH8ikUkw6dgk/PfoP0SOi1TbUf449zEWhy3Gnmt7MKPzDAQMDNBaQswYw+WHl7Hb8jT2+aTCdagrvo2ri6b3X0CYIyoimNjIyE2FRZor9WysBABLW1vg99/V39DNLQxm5uji0U+dOhU1a9YEAIwePRoAsGbNGmRlZRU5j6ngBqUCcuz2MewZtKfMP+f/7Z15WFT128ZvYAZRFFIUdFCDJAPRRANNBTVNTE3L3VIJNfc1izR3Mk1/lm+WWG6V5V6puGThhqGWiuKGIIugAQquICDOdt4/ngYYmGEWZhiW53NdXMycOXPOd0Y593n2xAeJ6L+jP3q36I0ve39pVLNAV1dXBAYGGtXLq06dQAwYIMGePUCnTiRuX539CqfHntb7OMYKSq40F0N/GQobKxscDzoOe9vSaZhPZU+x5uwafHHmC7zT+h3ETY3T2cYk5VEKtl/djm1Xt6FAXoCRbUbi+HvH4dnQ0+A1GovJiiudnMhCYUyCyqIvy5ov+VpJSlr0FQ0LShUj+WEysp9lo12TdmY9z5HkIxi1dxQ+e+0zjH9lfLmOpWoHoa+oFG8M2aoVZVauXQvEOq9G/5b90dJJ/3qG7GeG16Bk5mai3/Z+aNe4Hb5989tSQqoUlNh6ZSsWHF+ADq4dcGbcmTLX9CD/AX65/gu2XtmKGw9uYGirodg0YBM6Ne1k0rbw+lJYXFnWTmUUVgL/FVd6e5t1nTWR8lj0YrEYAQGWKSNQwYJSxTiUeAh9PfoaFGTOy8vDpUuXkJmZWThrXlv7a0EQsPbcWiw/tRy/DP1FZ9Ggvvj6+kIikSAqKkrrPBSA7rACAgIK20f060f9AvsOuY9Ho8JwbXq0Qec1tO1K4oNEvLHtDQS9HIRF3RaVuuAfST6CkCMhqCOug51Ddmqd1/JU9hQHEw5i69WtiEyNRB+PPpjTZQ56e/S2SKp3cZxDQmClq7iyjMJKAFRcGVI5eshVJ8pj0QcGBlq07QrA7eurHIE/B2Ky72S1FifaUGt/De0XcFX7a6lCimm/T8PfaX9j/4j9ZTY5LA8qgTtzJgv37xfg1Vft4OzsXOZ8h6l752D3/hz0E77Fhg1006wPmy5uwj9p/2DTgE069z2Xfg5v7XwLS19bivfbv6/22pXMK/j4yMdIfpSMFT1XYJDXoFJio1AqEJkaiW1Xt2Fv/F74Snwxss1IDPIaZLFZNdpIfqUt3C9eKTO7TBs6h3wx5caQBpGGjHowN2yhVCGePHuCf9L+wW/DftO5r6Htrzt374zFCYvhWMsRZ8aeQb1apvGza8Le3h5dunRBbCyQlAQM1KGNd3PvYkfCRvzz+RV8PAkIDAT27KFiY13oG0M5mHAQY8PH4vu3vlebqZKWk4aFJxbicOJhLOi6ABNfmQixTZGzSBAEXM68jG1XtmH7te1wsXfBqJdH4bMen0FSz7J3i5pQCkqsOLUCVzrl4qerYoPqYFTIxWLYFa/qZ0yOsRa9pWFBqUIcvXkUrzZ9VefF3pj218eOHENAiwB8OuLTctds6ItUqrHhailWnFqBoLZBaNm4KX77jWr1Xn2V6vQ8SndhB1BkBeVcyYGD3AF78vZodfNturgJC08sxIF3DqBj044ASIhWnlqJ7y58h0mvTMKNaTfU0o9vPb5VGFzPleZiZJuRODL6SIXMXTGWe3n3MGrvKORJ87Bz+Ulk1F1rVHFlxuzZpklhZspEIpFg+PDhhf+Xs7KyCl3Wuix6S8GCUoXQJ124rPbXKrQNtBLfFuPunbsVdrcjk+kWlLScNPx0+Sdcn0rt7W1sgC++oLlL/v7UGaR4HLKkm89KTm6pqw+vIi4uDpGRkYVuPolEgtCTofj5ys/4K/gvvOj0ImQKGdZfWI/P/voMfV7sg8uTLqOpQ1MAwKOnj/DL9V+w7eo2xGbFYkirIfjuze/QuVnnChNhY/nr1l9497d3Mfrl0VjaYyklGhhZXKl3t2HGJKgs+qoAx1CqCEpBiaarm+osrtu1axfi4+O1vl67dm3MmDFDbaBV8+bNsXnzZgCAp6dnhbW/XrmSmq6uXKl9n8kHJ8OhlgNW9iq9U0QEdWJfvZp+G2KZicQiZLlk4bTiNA69ewjO9s7YE7cHnxz7BC/UfwErX1+Jto3bokBegEMJh7Dt6jYcSzmGwBaBGNVmFN7weENrD7PKhFJQ4vOoz/HNuW/ww1s/aGwZc+/wYRQsXowmMTEQUHZxZSO2TJgyYAulihBzJwYOtRzKFJO8vLzCO3NzDbQyJboslJRHKdh9fTcSpiVofD0wkMZt9O8PJCVFw9ZWfzefXCaHY7ojvgr8CsmPkjFo9yDkSfMQ1jcMPV/oib9u/YX397+PPXF70K5JO4xsMxI/vPWDSbsWm5usvCyM3jsa+bJ8RE+ILrS0StKoTx+gTx/kpaYWTWzMyTFrcSVTPWFBqSLo4+4y90ArU6NLUJb+tRRT/abCqY6T1n1atwb270/H7t2GpVkCgI1ggyMRR3CgzgHMfH0m2rq0xY5rOzB2/1g41XbCyDYjcWXyFa0X4srMydSTGLlnJILaBuHT1z7VqyjV3s0N7lpa4DCMPrCgVBEOJhzEytfL8A3B/AOtTI1UCjhoScBKeJCAAwkHkDg9Uedxrl8/BZHI8JgRAFgJVni79tv48u8v8bjgMUa2GYnDIw/r3dqlslHcxfXj2z/iDY83LL0kpgbBglIFyMzNROLDRPg39y9zP3MOtDIHZVkoSyKXYFbHWXjO7rkyj1HczaeJ2rVrY9iwYWoxoyFDhhTGjKxgBeUDJb4c9iV6vtSz0gfXyyIrLwuj9oxCgbwAFyZcgKuDq6WXxNQwqu5fTw3i98Tf0euFXmr1D5rQd6CVi4tL4XN9B1qZA5lMc4HitaxrOJZyDDM6ztB5DJWbb+bMmejUqRMmTZqEOXPmYPDgwbCxsVGLGSkUCkRGRsLFxQVOTkVuNLGNGHUe1KnSYhKZGon269vDT+KH4+8dZzFhLELV/QuqQejbXdjFxaWw4Ekb8fHxcHZ2hpeXF2xsbPQaaGUutNWhLI5cjJDOIXoVVxZ386liRmvWrCmsOXF2dtYaM1Jh6Sl35UGhVOCzvz7DiF9HYPOAzVjWc5lRTTwZxhTw/7xKjlQhxdGbR7Gu3zqd+5proJW50OTyunjnIv7+92/8PPBnvY5R3D1nTMxI03GqCpm5mRi1dxSeyZ+xi4upFLCgVHKibkXBs6EnnO11Ww3mGGhlTqTS0i6vRScW4RP/T3TOE1EhEhW554yNGQGWnXJnDJGpkRi5ZyTG+IzBku5L2CphKgX8v7CSY+gwLX9/fyQlJUMur/ztr0taKP+k/YMrmVfK7FUmCMDly8Cff1JhoyC4wN9f+5S7rKwstG3btvC5ppiRpafcGYJCqcDyqOVYF70OW97egsAWgZZeEsMUwoJSyTmYcBC7huzSe39nZ1ckJweiWbMIWFtXzvbXKSl5WLUqCzExNrh82RrffaeEt7cCNzy/woKuC0pVoGdlAUeOFIlIvXpA797AW28B5875QBAitZ4rPj4evXr1gpeXFxISEkrFjFRYcsqdvqhcXFKFFBcmXKiUzSeZmg0LSiUm8UEi8mR58Gms38VOqQSCg4FHj3wxYQJw/LjuYj9BAGxtK6b99aFDWQgNlSImpjGAppDLyTyJiwPOnJFBwM/IaXcXLvOz4OjojD//JBG5eRN47TWqjF+8mJ5/8QWwcydga2uPAQO0dIiEfjEjS0+504cTKScwau8ojPUZi8XdF7OLi6mUcC+vSsxX/3yF2KxYbBygYxgSSBimTKGL8+HDQO3aQEZGRpntrwUBuHnTAwMHBqB/f/Pe7c6dm4rVq10hk9mg7ORCJUQiBVq3Tke/fm7o3Zs6CwsCsGsXCUl+PmBvD+TkUB+wTp3S8f33WyAIxrn5goODK03775IolAosi1qGb6O/ZRcXU+nh25xKzMGEg5jeYbrO/QQBmDsXuHCBphvWrk3b9Wl/vXevPZYvB958s2jiq6khMZFAJtOjVz2sIZdbIzZWgt69U/Hyy2746itgzRrghReAZs2Af/4BJk8Gpk6lybR797oiMjIQPXpEQKGonG4+Y8jMzcTIPSMhV8rZxcVUCdhCqaTkPMtB09VNkfFhBura1i1z3+XLge3bgZMnASftba80olAAPj7AsmU0u93UHDqUhYED62sVkx49gLAwoHlz4OxZctndvk2vicVSSCTZ6NSpERo1os84ahSwcGHR59y/Hxg/nqwypTIaf/4ZAalUBmsdFVaVacqdJo6nHMfovaMxrt04LOq2iF1cTJWA/5dWUo4kH0HnZp11isnatcD33wNRUYaLCUDzRZYtA+bPp/ntNjZGLlgDGRnAnDnS/9xcpXFyosmL778PHDgALF1Kbq1Oneh1mUwEe/sCnDkD+PoCf/9Nc1BU/P47vffQIaB9ewDwxZ9/SvD0aRQcHavOlLviqAoVv7vwHX56+yf0atHL0ktiGL1hC6WSMjZ8LNo1bofpHbW7vLZsARYsAP76C3Avx/h3QQC6dKEYzKhRxh+noICETRVMv3cvD/fv10JSkghr1wJBQcDzzwN//AG89x49Dw6mcwNAnTrA/ftAu3bAjRu0zcZGjl27nmHwYPWg+Z9/AqNHk4Xy6qu0TaEgt9iePYCnZ9WZcqfibu5djNwzEkpBiW2DtrGLi6l6CEylQ6FUCC6rXITkh8la9/ntN0Fo3FgQrl83zTlPnBAEd3dBePZM//colYIQGysIq1cLQu/eglC3riB06iQIS5YIwt9/C8KkSTcFkUgqpKQIwtmzgtCkiSDUr09rnjhREL76ShDWrRMEkjT6uXpVEAYNKnouEkmFKVNuqp336FFBaNhQEE6dUl/Pvn2C0LFjub8Ki3Ds5jFB8qVEWHR8kSBXyC29HIYxCnZ5VUIuZFxAg9oN8EL9FzS+HhEBTJpEd/peXqY5Z/fu5E7atIksFW08ekSBf5UVYm1NNSHjx1Ma73PFmgPPmWNTmBr89dfAnTu0/cABituIxcC9e+rHz86mOhMVcrkYsbFFLrPISOCdd4DffiuybFSEhVGgviqhcnGtv7AePw38Ca+/8Lqll8QwRsOCUgkpqzr+9Glg5Ehg715V3MB0LF9O0w/fe4/ScgFALgfOnSsSkOvXaYZ7797ARx8BL72kOTvs7l3g/n1rtecq8vMBiQR48KD0PBQHB+DJE/VtOTl0nKgoYOhQYPduoGtX9X0SEqiCfv9+Yz99xVPcxXVhwgU0qdfE0ktimHLBglIJOZhwEF8Gfllqe0wMMHAgsHUr4F/2aBSjeOUVuutfupRiEX/+SSN2mzcnAVm2jM5bojUWHjyglOXz54HoaPqdnw80aqQs83yxsSReKurUAVq0oO3FcXBQ4swZYPBgYMcOKnIsybp1wLhxQFVpyXXs5jGM3jsaE16ZgIVdF8LG2oTZEAxjIVhQKhl3ntzBzUc30blZZ7XtcXFA377At9/Sxd2U5OVRyvGff5Iw/PYbXbzfeouyyJoUu3HOyaFsK5VwREeT26p9e8DPDxgxAvjyS0oSmDpVgZs3ZQA0pwzv3QusWgUMGkSZWosWAVeuFAXkAUAkksHVVYG33wZ+/hl4XYNHKC+PXouJMe33Yg4USgU+PfkpNl7ciJ8H/oyeL/S09JIYxmSwoFgYVdFhZmYmCgoKkJafhhEOIyAtkEJsTxfi1FRqO7JiBV3oy4sg0IVb5cY6d46sk969gV9+oVhEkybAkCHApUu0TSUgt28DbdtSGu+bbwJLlpDbS1PdR0iIMzZu1F4tef8+fZ61a8nqOnuWBEkdK5w86YwfftAupNu2kRuueXNjv5GK4c6TOxi5ZyQAsIuLqZZw2rCFSE9Px6lTpzS2RbGytoKNtQ08PDzg6emPwYNdMWsWMG2a8edTNViMiKAfe3u6QPfuTS6kWrWAq1dJNCIjgV9/paC5tzeJh68vWSCtWmkf26uJDh3ScP68BMbNclOiWbN0rF3bTGvRpSBQgP+LL4Belbhk4+jNowjaG8QuLqZawxaKBYiOjkZEhPbGjYJSgFwpR3x8PK5dS0ZQUCCmTTOsolsqJdeUygpJSiLh6N0bmDePXo+Opkyxzz4Drl2j+IVPvW2oAAAgAElEQVSfX1HAu359crEZi0wGdO5si5gYBeRywwVFJJLj/fftyqzgP32a6l96VlLPEbu4mJoEWygVjC4x0YS+bUKSkooE5ORJoGVLumtv1YqK/i5dIgvk0iXA1bXI6vD1pWLC4vV+WVmUkhwdbXjRpCAA4eHAxx9TcN/NLRXffy+BTKZhgLzWzyzFkCEZ2L7drcz93nmHChtnzjRsjRXBnSd38O6ed2FtZY1tg7ahcd3Gll4Sw5gVFpQKJD09HVu2bNEqJu7u7ujbty8cHR2RlpaG8PBwZGdnA9DcFTcnh7KwIiJIRPLzKUvL1ZVa2cfGUpC9QQN18WjfXr1eRBuLFgG3blFFvr5ERwMffgg8fEhuKFXco0uXVJw/r2+3YTmGDMnAjh1uZZ7r7l0SvZQU/T5PRaJycU18ZSIWdF3ALi6mRsCCUoHs2rUL8fHxGl+rXbs2ZsyYgQMHDuDGjRvo0aMHmjdvjs2bNxfu4+npiRYthhdaIRcuUDFiw4bk9rlxg+IbKuHw86Nge6NGxq03O5uOf+IExVLK4vZtcqUdPw6EhgJjxgD/tc7C77+TBfHFF/ewbFkBYmKaABAKix4ByuYCAInkLkaPtsNnn+le9NKlQFoasH69cZ/PHCiUCoSeDMXmmM34eeDP6OHew9JLYpgKg2MoFUReXh6SkpIwc+ZMnDt3Dm3btoWjoyOSkpKwb98+eHl54d69e7h+/ToAIDIyEiEhIXByciqcLnjtWhI++igPtrb2yM6mNvWNG6sLiCn7HTo6kttqwQJK8dVETg5ln61fT1Xq336rXuleUABMn06ZY2+80QhvvQWkpuZh+fIs/PGHDRo2tIaDgxJubgr89ZczFi5shjFjdK9NLqdz/v67aT6rKch4koF3f3sXImsRLky4wC4upsbBglJBXLp0qfCxt7c3tm7dCrlcjrFjx8LHxweNGjVCZmZm4T4ymQyPHj2Cs7NzoaBYWQFBQZfg49MFvr6Am5v5ZpiomDoV+OorSunt2LFou1wObNxI1sgbb1CVetOmpd//v/9RmvEbbxRtc3Ozx6JF7ti4kSybmzep9cvChdBLTACK0bi7Ay+/XK6PZzKOJB9B0L4gTPadjPkB89nFxdRIWFAqiMzMzMLU4LNnzyI3NxcAkJCQgMaNG8PGxgZ5eXlq7ykoKICtbVEg28ZGjnbtsjBwYMWtu3ZtiqXMn089vASBrIKQELKODh+mgL4mbt6kwViaCg7z80kMU1NpJsonn1A/MH2pLH275Eo5QiND8f2l77F90Ha85q6hjJ9haggsKBVEQUFB4WOVmABkidSrVw9Pnz5FrRI9TWrVqgWpVKr1OBXFmDFU0b5+PRU5pqfT8379yraQZs6kfl+aCg5V2tmjBwXxJ0/Wfz2xsdQ5YNAgwz6HqVG5uMQ2YlyccBEudV0suyCGsTDGVJsxRmCno8lUVlYWXFyKLkhisRgNGjRAVlaWQccxB1lZFJuZPp16iV25ontk8P79QGIiiYUmbt8ma2f6dPoxhHXryJqx1T8L2eREJEfglQ2voKd7T/wx8g8WE4YBC0qF4eLiUjgxUBPx8fFwdnaGl5cXbGxs0K1bN2RmZhbGTwCaOOjs7FwRywUA5OYCixdTnOLVV6nFikSiu1I+P5+sk2++0XzRv3OHqv6trYEPPjBsTTk51CBy4kTD3mcq5Eo5FhxfgDHhY7B90HYs7MZV7wyjggWlgrCx8UEJ75Ua+fn52L17N3r06IE5c+bA1dUVv/76a6n9fHx8zLhKQqEANm8mAUlMpPTklSspm2vBAnq9LFasoIwzTa1QMjPJzdW9e1FasSH8/DNVxbu6Gv7e8pLxJAM9f+qJs+lncXHCRY6XMEwJuA7FzCQn00X45Elg+vRdkEo116HoQqkEmjTxxKRJw028QnWOHKG4h4MDdQ3u0KHoNUGgJozjx6u3nS9OYiLNhL90qXTWV1YWtX8ZPhzw9KRjPH2q/9oEgeph1q0jQapIIpIj8N6+9zDVbyo+8f+ErRKG0QBbKGbi7l3KQurYkS6CCQlAUJA/xIZ0ViyGUinGsmUBGDqU3D6mJjYW6NOHpjUuXkxz6ouLCUAxk88/p9efPSt9DFVMZM6c0mJy/z61nh80iLLGnj7V3KG4LCIj6T3duhn2vvKgcnGNDR+LHYN3cNU7w5QBC4qJyc4mi8Tbm4Y9xcfT87p1AVdXV3h4BEImM0xUxGIx3norEJ9/LsHhw9RaftMmuoCXl8xMike89hrVisTG0kVfW8A9IIB6g23YUPq1ffso2F6yr9bDh+T+6tcP+PRT2lZQYLighIWR4Jm79kZFek46emzpgXPp53BhwgV0d+teMSdmmCoKC4qJKCggF9GLL1JabUwMPW/YsGifO3eA2bN94e4eqJelolQCIlFRY8hRo4CMDLIkJk+mYPl/hfUGk59PXYZbtSKxu3GDhECfzKlly2hccLHsZ+TlAbNm0UW/+DEeP6ZZLq+/Tu9RicHTp4CNATf6aWnU1mX0aP3fUx7+TPoTvht90btFb/wxirO4GEYfWFDKiVwO/PADdfaNiqK+Vz/8ULr2QiqlgVXjxwPjxvkiODgYnp6eEIlEpbK/VNukUk+kpwerdRl2cKBZJcePkxupXTsSl+IX97JQKoGffqKA++XLNFzryy+pVb2+tGtHbqc1a4q2LVtGjSmLj+fNzqbmkP7+VDFf3LIw1OW1YQPw7rvqbV3MgVwpx/xj8zFu/zjsHLwT87vOh7UV/5kwjD5wUN5IVC3a582j5osrVlAwWhuTJlFcZc8e9QupamJjVlYWCgoKYGdnB2dnZ/j4+EChsIe3N2U2de9eerqjra0dLl50wbff+sDKyh7r15NoaXMJRUZSXYitLYlI586a99MHVfA9IYGC7f7+VJ+i6iX25AmJSfv2lD5cck1LllBwvUSZjUakUuD554Fjx8iiMhfpOel457d3YCeyw9ZBW+FsX3Ep2gxTHWBBMYKTJ4G5c8lt9Pnn5IIqy6+/cSOwejX1w3JwMOxc+/cDy5enY8aMU0hJKT3dUSQSQakE/v3XA0eP+kMiccUPP5DrTUV8PDV5vHqVhG/YMNPEISZMIMvm4kWad6+qKcnNpe/E25uaRWo61yefAD/+SG5AXezcSRbK8ePlX7M2/kj6A8H7gjGj4wzM9Z/LVgnDGAELigFcukQXwhs3qHX6O+/odtv8/Tfw1lvkDnvpJcPPGR0djQMHImBlJdNDBMSIiAhETIwvZs0iq+h//6ML8pw5lIFlykL7tDRK/23WjKwTsZhiKX37kqBt2KD9+/ngA2rjkpam+zwBARSfGTzYdGtXIVfKsejEIvx0+SdsH7wdXZ/vavqTMEwNgXt56cHNm9QJ9/hxapIYHq5f8DojAxg6FPj+e+PFJCIiAtbW+k53lKFv3wi0aAF8+aUvVqygzKr4ePXkAFPh6EjWx0svkZjk5wMDBlAX4LLEBKC0Y30KG69coQFab71lunWrSMtJwzu/vYM64jq4OPEiu7gYppywXV8GmZnUIsTPr6hqfNo0/cTk2TOKZ0ycSH2vDCU9PV3nqGB3d3dMnToV8+bNQ1BQEBwdHSGXy/D88xHo1i0Djo5UTxIURBdlU7N0KVkjUVGUbfb225TSvHmzbsutoEA/QQkLo+/QmKr6sjiceBi+G3zRx6MPDo88zGLCMCaABUUDOTlkkbRqRXfe8fFUjFe3rv7HmD4dcHEhi8YYTp06VaaY1K5dG8OGDcOJEyewcuVK3LlzB0OGDAFA0w/HjYvCrVvkljt1ilKMly7VXJBoDLGxlM329ddUG/L664CTE8VF9EkHlkp1i8Tjx8Du3Ya1tdeFXCnHJ0c/wYSDE7B76G7MC5jH8RKGMRHs8ipGQQEFkVesoKDyxYuUXWQo69fTRfzsWcOL94Ci6Y4AjJ7wmJSUBBubPHz3nT2CgqjNybp1JALffUe1Ifqso3hWmZ2dHVxcXNC2rQ+mTbPHokUUlI+OpuLFjz/W35KQSnU3mdyyhYotG5to8GFaThpG/DoCdW3r4uKEi2hkb+RsZIZhNMKCAmp2+PPP1FLEx4fSU1u3Nu5Yp0+TdXPqlPE1E8WnOwLGT3i8dOkSunTpgs6dgWvXqMHjl19SPUf37jSJUdOUxfT0dJw6dapQ1IpnlcXFxeHYsUi0bOmBfv38MXy4K+zsqEhy8WLKStMHXTEUpZIEcPNm/Y6ni8OJhzEmfAxmdpyJOf5z2CphGDNQowVFEOgCOG8e0KABsH07FecZS0YGpeSqCh2Npfh0R8C4CY9yuVxtlkqtWuS2GzoUGDuWrKc2beizz5pVZC2oEgG0udtU65JI4rFlSzLq1w/Ed9/5Qqkk99eZM/rVt8hkZceijh6ljLTy/HsAgEwhw8ITC7Ht6jb8MvQXBDwfUL4DMgyjlRp7m/bXX3SxWriQUmtVz43l2TNKa50yhTKrykPJqYwlJzza2tpCKpXqnPCoabqjlxdZUfPnU4bW+vU08z0yUreYlMTaWoYWLSJw5Uo07OzIQpk3T78eY7piKGFhlABRnnqZf7P/xWtbXsPlzMu4OOEiiwnDmJkaJyhXrtAF/733qGVJTIzuUba6EAS6+EkkdEEtL/pMZdRnwuOFC3YIC6MYR/FZLNbWVKNy9SpZKdnZwPTp6ThwQLuYaMooAwC5XIaIiAhkZGTgvfeoUDEiQvdnlEq1Wyi3bpHovfuu7uNo4/fE3+G30Q9vtnwTh949xPEShqkAaoygpKQAo0ZRMLp3b8rcGj3asAaF2li/nlw9P/5omgp0XdMdAd0THq2sRJBInHHpEjBuHAXPX32VGkBu2wYkJZEA7t0LrF0LvPLKKQCaxaSsjDKArKaoqCiIRJRJpo+VIpNpD8p/9x3929jbl30MjcdVyDDnyBxMPDgRvw77laveGaYCqfZ/aZmZwIwZgK8vVW8nJtLzEt4iozl9mmIT+/aZrnGhPlMZdU14lMmA06d9MHQoWWGZmeTaa9qU1tqzJxU7vvEGcOlSHl54IQkffDATnTp1wqRJkzBnzhwMHjwYNjY2ahllCoUCkZGRcHFxgZOTU+H5kpKSkJeXhyFDSEx++63s9WuLoRQUUCHolCl6f12F/Jv9L7pv6Y6rWVcRMzEG/s39DT8IwzBGU2mD8tpSVn18fGCvx61rTg5lNK1dS3e7cXGAqcexp6dTEH7LFvXeWeXF3t4eHh4eiI+Px5riLX0BnDx5svBxSkoKwsLCNB7Dy8sDzZvbIySELtJTpgDBwUDXYp1F7twBzp8HoqMvQZUDYGxGGVCUVbZ8OQX6335be5xEm6D88gtl2hn6fR5KOIRx+8fhg1c/QEiXELZKGMYCVDpB0ZWyGhkZCQ8PD/j7+8NVw2DxZ8+oluTzz8m1deEC4OZm+nUWFNAgqqlTqWbF1Pj7+yM5OVnvAHlxxGIxuncPgEQCvP8+WVFhYdThd/hwWnObNlTVPmAAIJdn4upV+p6NySgD1LPKevemos6ffqKMMk3I5ZqtxLAww+JQMoUM84/Px85rO/HrsF/ZKmEYC1KpbuOio6OxZcsWxMfHQy6Xq4kJgMJt8fHx2LJlC6KjowtfUyjIUnjpJaojOXqULmjmEBNBoIty8+bULNIcuLq6IjBQv0FcxRGLaSCX5L8+8lZW1Fp+xw5qjyKRkJurWzeqQpfJ1LPBjM0oA4qOoxoVvGQJCa8mNFkoFy5Qi399s+RuZ99Gtx+7IfZeLC5OvMhiwjAWptIIiqEpqzIZZRedPx+N/fsp9XXjRmDrVuDAAboDNxfffkuDqX74wbzjaH19fQ0SFZWYFB/IVZwmTSjek5pKWWnr1pHgJiaWnVWmT0YZoJ6d1rkz/Zt8953mY8rlpTsfh4VR9pk+iRIHEw7Cb6Mf3vZ8GwfeOYCGdczQ/ZJhGIOoFC4vfRsh9u3bF46OjkhLS0N4eDiys7Nx4EAETpyQ4PPPJXjzTfPPG4+KAkJDKavLkN5exuLr6wuJRIKoqCiNbkBVNpiHhwcCAgIKLZOyEIupwHHoUEod/vFHF9SuHad1//j4ePTq1QteXl5ISEgolVGmWodziSDVsmWUVTduXOmEBblc3UJ58IAyzhISyl57cRfXnmF70KV5OSsfGYYxGZVCUPRthHjgwAHcuHEDPXr0wJAhQ7B582bY2MgwdWoU+vcfbvZ1pqVRDGLLFqBFC7OfrhCJRILhw4eXOd1Rn0QFTbRpA3z6qQ/+7/8ite6jyijr27cvBg4ciPT0dLWMMhUls9Nefpmyyf7v/8gyKo5CoW6h/PAD0L8/Tb/Uxu3s2xjx6wjUr10fFydeZKuEYSoZFhcUUzRCTE6mlFVjL6r6oArCz5hBMQhLYG9vjy7l7UWi5bgvvuhhdEYZALRo4aHx+//0U6BjR8oyKz6TpXhQXqkkN+L27drXeODGAbx/4H181OkjfNj5Q87iYphKiMX/KrU1QlyzZk1hmrCzs7PWtFVtxzElgkAXRDc3mnxYHfH39zc4AUCFTCbGhg0BSEws/VqLFuRaW7GCnqek5GHKlBTUrn0bv/yShm7dbmP48BQ0aJCHDh00HFshQ0hECKb+PhV7h+/llGCGqcRY3EIxVSPEpKQsvPgi8Nxz+g3AMoR166h9yZkz5o/RWApXV1f4+ATizJkIiMX6pyqLRGIoFIE4d06C9u2BVatoIFbx72nBAqBLlyycOCHFlSuNATSFXE7ilZBA81sAK3TsmIbFi23Rrx/dKNx6fAsjfhsBp9pOiJkYA6c6TqUXwDBMpcHigqKrEWK9evXw9OlTnWmrUVEF+OgjGspka0vC8txz1HLEkMeOjuozTP76i9w2FRWEtxR5ecDcub4ICgKkUt3ZdkolAIjx0kuBGDLEF8OHAyNHAh99BOzaRXPsVYlh33yTiowMV9y6ZQNNRrFKXM6fl2DgQAVmz05FlzFX8f6B9xHSOQSzO81mq4RhqgAWFxR9GyG2bdu28LmmtNV+/eywaRO5p/LySFgePaLfJR+npdF8EE375OZSRtJzz1EvqaQkatuybJl+wlSnTtWzYgQBmDCBKtQ//NAXd+7ozipr3twDaWkB+OgjCVatojTkM2codfuTTwAPD+ptdv58KlavlkAm08edZg2ZzBqrV0sQHh+Ovd/sRedmevTCZximUmBxQXFxcUFcXFypIsbi6EpbLZ6yamVFlkTdupqHR+lCoaC2LXfv0kz4996jyu/ionPjhnbBksm0i44+FpKp3XX68M03NNJX5dIzJKvs44+BQ4eohuSjjyhF+MQJYPZsYOrULDx86KqnmBQhk9ki+ffJyJ6YDTQzxydmGMYcWFxQfHx8EBkZWeY++qSt6tNQUR9sbOjC/sEHlFK7fr1hFsezZ5qtItXjR4+Amzc172Nqd50+REWR9fX332RdFUefrDIbG2rfMmAAxUPWrQP69qWeYQ8eSJGZqb1KsUcPEqLmzWngV3AwcPs2vSaTibB4cYFZ2towDGMerARBn3FI5mXXrl2Ij483+v2enp4YPtx0dSjffANs2kR37GbMRC6FPu66sh4Xd9fpI0AKBQXQv/iCUqJN5a7LzQXCwvIwf34tKBSa71mcnIDkZOo1duAAtb0PCAA6dSraRySSIzHxGdzcKvAfgWEYo6kUgpKeno4tW7YY3QgxODhYrwpxfTh5kjoI//038MILJjlkhaFy1+kSoEePgIcPqWlkrVpUOW9qd92UKSnYuLEpEhPFWLsWCAoCnn8e+OMPciMGBZFFojKA6tQB7t8H2rUjlyJA2V8TJqQhLMy9wr9LhmEMx+IuL6CoEaIhvbyA0o0Qy8vt28CIEdQPrKqJCUDup/r16UcX06eTkOzbV+QmM6W7Tiq1KczeGjaMikELCkjEgoNpFPHly0Xryc8ni8Xbu0hQ5HIxYmNNMAGNYZgKoVIICoDChob6ioquRoiG8vQpuX1mzwZ69TLJISstW7eSpXD+vHrMpVYtSvUt1gdSb5RKmg9z4wYNMfu//ys68Ndf0+wVgNxbPj4kZvfuqR8jO7t0z6+cHE4XZpiqQqURFMA8jRD1QRCoy+2LL1KmUnXm8mVKODh+nCwJfVAoKOstPZ1SrtPTNT+2tQVcXelHJFIWvv/u3aJj5edTC/0HDwAHB/XzODgAT56U3KYEwzBVg0olKIB5GiGmpORh1aosxMba4MkTa9Srp4S3twIff+wMNzd7fPMNXWircyU8QHGTQYMo6UDV3j8/X7tAqB5nZVEQvWlTEgvV71atih67uqoXfk6ZokBiogyA5pTh2FiKpaioU4fatMTGFm0TiWTw9laY/otgGMYsVIqgvLk4dCgLoaFSxMQ0BiAU+vSBonYfrVrdRW6uLY4edYZ7NYr9CgJZASpRuH2bRiLXqkUioNr+9ClZDMWFoeTjxo3JRWUIN27koXXrWkhMFOH992noGQAsXkxFjx98QEWjY8dSHUtoKA39Kpnl9ccfz9CzJ2d5MUxVoNJZKKZi7txUrF7tCpms7HYfV65IIBYrsH59KlascKvYRRqJTEYxibIsi4wMoHbtIlHIzASkUooRPf980fYGDUxrlSmVNB1ywQJ7PP98GgBXAKVPcP8+MHgwsHYtxXTOnqWEiGJHgofHHYwe3QwvvUTNOd9+23BhYxim4qiWFgqJiQQymf5l52KxFLNnZ1hcVHJz1YVBk1g8eAA4O2u2JlSPXV2LChUPHqQYUXQ0WRvm4tgxICSELvqrVgFPnmRh4MD6BlfKA/TvER6ejZ49G2HfPiqATEoCxo+nNjEmCp8xDGNCqp2gHDpU9kWsrOps1UWsT58ypjwZiVJJd+Xa4hSq53K5dteT6rGLCyDS07ZMSqJxvOHh6u4kU3LlCrX1T0qiWfKDBxdZPaYU92vXqBJ/507g9dfJaunWrXrHvRimKlHtBKVDhzScPy+BJjeX7upsJfz80nHunGENpKRScjFpsyjS0shF5eCgWyyee850F8i8PPpskybRxdfU/PsvsHAhpSAvWECWg6ZeZLrcj0UoIRbLdVqKOTnAzz/TjYGVFX220aNLZ40xDFOxVCtBSUnJQ8uWFAg2vjq7qN2HINDFS1cW1KNHQJMmZYuFRKI+8tbcCAK1kxeLqeuvKe/iHz8mS2TTJrqYh4SUfTF/+BBo3/4eGjYswOXLTaA5QQJo0uQuFi+2w7hx+lmIggBERpLVcuwY8M47tB5v73J8OIZhjKZaBeVXrcoCQC2Gja3OBgQMHpyFvDx3pKfTlpIC0aYNHVv13NnZ8KaM5ubrr4G4ONOmQj97Rhfvzz8H3noLuHpVv1jGV18BvXo1wsaNQGpqUQp3UhKlcLdsqcCaNc4ID2+GHTso80ufNVtZAa+9Rj/p6dQ6v1cvoGVLEpaBAzmIzzAVSbUSlNjYonYfxlZny+Vi2NjYYO9eEoyq6EaJigKWLwf++YcyvcqLUklxi/nzgdatqT29vlbA48ckQufO0XM3N/vC3lweHiT8aWk0XnnqVGDzZuCXX2i7Ibi6AkuW0Br37qVzzppFbrjx4+l1hmHMSyW7ry4fT54UfZyS1dl161IGlT7V2XK5Nby8qqaYZGRQ+u2WLTBJXc3x44CfH1kZP/5I4myIS+nrr4H+/TX3RnN1JZfkyZP0XCSiuMiHH9K/lTGIxSRGkZFARAQVZbZpQ3PtIyPJTcYwjHmoVoJSr17ZbTpiY4Figx81VmcDVbfdh1RKF87Jk8klVx6uXqW5JuPHUwbX2bOUUWUIOTlUlT9vnubXJZKiTscq92JAAGXiLV1avvUDZE2tWwekpgLdu5MF1Lo1iVZOTvmPzzCMOtVKULy9FYUBXk3s3UsXlEGDqGJ80SJKeS2Kn1Ttdh8ffkiZbNou4PqQlgaMGUNpuX36UBxm2DDj4jBhYSRsL76o+XVXV7IkAwKKrBQA+N//gO+/p3ObAgcHEpNr12hNJ0+Si23KFNrGMIxpqFaCEhLiDE1V2SpU1dnLllFmVseOJauzAcDqv+NULX7+mbLZfvrJuASB7GyaBd+2LVkOCQnU4t7YkcS5ueQmmz9f+z4SCVkm3boBf/1VtN3FhcR+2jTTuqisrMhS2b2bhMTZGQgMpPPv2kUWHsMw5UCoZvj5/SsACoEuRYb+KAQ/v9uW/ggGExMjCA0bCsLVq4a/t6BAEL76ShCcnQVh3DhBSEszzZr+9z9BGD687H127BCEoUMF4eJFQfD0VH9NJhOEtm0FYedO06xHG1KpIOzeLQjduwtCkyaCsGiR6b4DhqlpVCsLBQAWL7aFWGycy0osliM0tAKLRUzAw4dkdX3zDbnz9EWVueXlBRw5QnUcmzaZJhsqP58aUS5YUPZ+rq5kobz8MmXkZWYWvVY8QF8yacKUiMUUdzpxgr6H+/cpiD9kCG3jID7D6E+1E5R+/Zwxe3Y6xGLD/Beqdh/maLtiLpRKYNQoqgkp7brTzokT5O778kuKVRw8aJgY6WLDBsDfX/cxJRLKSrOxof2jotRf79KF6ko+/dR0aysLb28SsVu3KDFg2jTatnYtB/EZRi8sbSKZizlzUgSxWKqH+0shiMXPhDlzUiy9ZINZtEgQunYlt40+XL0qCH37CsILL5ArSaEw/Zry88l1FBOj3761agmCUkkusmnTSu+TmUnuvGvXTL9WXSiVghAZSW65554ThEmTBOHKlYpfB8NUFaqdhaJixQo3hIc/hp9fOkQieansL5FIBpFIBj+/dISHZ1u8y7ChHDhA1sXu3bqrwdPSgHHjgJ49gd69KXtq+HDzVPdv3kx1Kz4+uvetXZtStx88ALp2Vc/0UuHsTDNUTB2g1wcrKwrY795NqeWNG1PWWteuHMRnGE1Uq15e2ije7iMnxxoODjSxMSSEJjZWNfTtIJydDaxcCaxfD0ycSPUkjo7mW9ezZ1T9vncv4Our33tatwa2b6dYjpMT1Yw0aKC+j1xOIvuitsEAAB1NSURBVPXxx9Svy5LIZPS9h4UB8fFF7fSbNrXsuhimUmBpE4kxjNxcQWjdWhDWrdO+z7NngrBmDWVujR0rCP/+WzFr+/ZbcqkZQmCgIBw+XPR43z7N+50+LQgSiSBkZ5dvjaYkNlYQpk4VhPr1BWHQIEE4dozcZAxTU6m2Lq+qSEpKHqZMSUG3brfRvn0aunW7jSlTUpCamgeAXD7jxwOvvEIt6UsiCOSK8fIC/vwTOHqUXFAVcfcslQIrVlA7e0NQ1aIA2t1eAFlkvXvTqODKQqtWFLC/dYsKQWfMoG3ffEPWIcPUNGqEy6uyc+hQFkJDpYiJaQzNrd2t0K7dXXTqZIuoKGecPl266WNkJLmElEqqNO/RoyI/AQnXrl3UP8sQ5s+ntv4LFwKnTlFDx+hozftmZZGL7Phx02almQpBoEy1sDD6HoYPpwr9Nm0svTKGqSAsbSLVdAzJRhOJpMK0aSlq7792TRD69RMEd3dB2L7dPJlbupBK6fxRUYa/NyxMECZOpMcFBYJQt64gPH5c9v5du1Z+11JGhiCEhgqCq6sg+PtTEeezZ5ZeFcOYF3Z5WZCi8bhi6C4JsoZcLsb69RLMnZuK9HSaPPnaa+RuiYujgLUl5rJs3069sfz9DX+vqhYFoP5qfn40v0YbEydSoeP27UYttcJo0oTax6SkkNW1YQN1Vl64kLLuGKY6woJiIQ4dyvpvLK72Zlk9epBQ5OWRm6d5c0Ams8Xq1RJ06XIPDRtSz61Zs+hibAkUCuqNtmiRce93dS0SFIDiKMX7epXExoY6CIeEVI04hVhMnQyOH6duBI8fU2eAQYPoOTucmeoEC4qFCA2V/jdjXTNOTsCePXRH26ABxRV27aLXZDIRGjYswIoVNIPekuzaRfUZhra2V1E8KA/QcbQF5lW8+iq11l+yxLhzWgpVwP7WLWpKOWsWJVB8/XXVEEeG0QUH5S1ASkoeWrasBblchJQUyhQKCiKXyB9/AO+9R8+Dg6n9CEAFgPfvA+3aUbt9kUiOxMRnFq2jUSgo4LxmDbVIMQa5nBIMnj6l/l1PnwKNGlFfL/syPtq9e9QW5ehRuuOviggCJSKEhVFW3rBhFMSvqp+HYdhCsQCrVmUBKNLxYcOoAtvdnS4mwcF0sbx8ueg9+flAcnLxaYnCf8exHL/9RoWSr79u/DFEoiIBAUhcfHyAv/8u+32NGlGPr6lTq67byMqKZsHs3Alcv07p3X370rYdO7gSn6l6sKBYgNhYG7XU4K+/pm67jx5RSxUfHxpZXNINkp0N1KtHj+VyMWJjtbvMzI1SSVMVFy0ybvhWcYxxewFUk/P0KbBtW/nOXxlo0oTcm6mpwAcfUOdnVRD/33/Lf/y8vDycPn0ae/bswfbt27Fnzx6cPn0aeXl55T84w/yHyNILqIk8eaKu43fvFj3Oz6cL7IMHpWfaOziot3LPybHc/UB4ONWPlHfUMFA6MN+tG/DZZ7rfpwrQv/02za03Z1uZikIkooD9oEGUkPHttzT0rHt3mjDZs6dhAp6eno5Tp04hKSkJACCXywtfi4uLQ2RkJDw8PODv7w9XU8wuYGo0bKFYgHr1dM+sj42lC4mKOnWAFi1ouwoHB93HMQeCQO6mhQvLb50ApS2UTp2AixfJ+tBFhw7Am29SA8nqhipgf/s2dQmYPZu2rVlD2WK6iI6OxpYtWxAfHw+5XK4mJgAKt8XHx2PLli2I1lZRyjB6woJiAby9FaW6H5dk716qBh80iFKCFy0CrlyhgDxAFfTe3sYNEisvBw+SqPTvb5rjFa9FAcit5+0NnDun3/uXL6e6lOIxp+pE3bpUf3P5MrBxI/DPPxRvU23TRHR0NCIiIiCTlf3/TIVMJkNERASLClMuWFAsAM2sL/vW/v59ql9YtoxiKx07lhyiZfXfcSoWlXViitiJipIuL0D/OAoANGxI8ZyqHKDXB1UQf8cOcoc1bQr060cFpcWD+Onp6QaJiQqVqGSU/MdgGD3htGEL0aFDGs6fl8A4TVfCzy8d5841M/WydHL4MPUMu3zZdFX5f/wBrF6t3gfs0CHaduyYfsdQKKg+Zfp0SrmuKcjlwP79FEu6do26J7RosQu3b8drfY+7uzv69u0LR0dHpKWlITw8HNnFMkA8PT0xfPjwilg+U81gC8VCLF5sC7HYOJeVWCxHaKidiVekm+KxE1O2eCnp8gKo/ubcOf1TZ1UB+jlz9IsvVBdUQfyjR6lBaG5uHm7eTNK6f+3atTFs2DCcOHECK1euxJ07dzBkyBC1fZKSkjj7izEKFhQL0a+fM2bPTodYbFixgVgsxezZGejTp5GZVqYdVeuQwYNNe1xNLq/nngNefFF752FN+PkBAwYY3wamquPpCQwdegm1agEzZ85Ep06dMGnSJMyZMweDBw+GjY0NvLy8cO/ePVy/fh0KhQKRkZFwcXGBk5OT2rEuXbpkoU/BVGVYUCzIihVumD07A2KxDICujC1loZhYYlyxINAskgULyBowJQ0aULp0yawuQ+IoKpYvp3YwNfV6mJmZCYWCsrm8vb2xdetWrFmzBi4uLvDx8YGzszMyVVWkoLjJo0eP4OxcFI+Ty+XIyrJs0SxTNWFBsTArVrghPPwx/PzSIRLJS2V/iUQyiEQy+PmlIzw82yJiAtCFPTOTZnyYGisrzW4vXY0iNeHkRDUsU6dS8WVNo6CgoPDx2bNnkZubi4KCAiQkJKBx48awtbVV20f1HltbW63HYRh9YUGpBPTp0wjnzjVDYuIzTJhAkxrbtaPfEyakITFRinPnmlnEzaVi6VIahiUyUylsyVoUgDKazpyhwLMhjBtH7/npJ9Otr6pgZ1cUW8vNzS18LJPJYGtrC6lUilolWlPXqlUL0hLBquLHYRh94Ur5SoSbmz3CwtwtvYxSnDpFLUHefdd859BkoTRsSC37Y2IoPqIv1tbUcPHNN4G33gLq1zftWiszLi4uiIuL0/p6VlYW2harmBWLxWjQoIGai0skEqm5wBhGX9hCYXSydCnwySc028NcaArMA8a5vQDA1xcYONDwGfdVHR8fnzJfj4+Ph7OzM7y8vGBjY4Nu3bohMzMTDx48MOg4DKMJFhSmTP75B4iPN39thyaXF2BcYF7FsmXAL79QG5eagr29PTw8PLS+np+fj927d6NHjx6YM2cOXF1d8euvv6rt4+HhAfuyZgcwjBa4sJEpk379qMXKpEnmPc/27dRpeccO9e1379Jgqnv3jMsu27yZOveePm2Z8ciWID09HVu2bDG4Uh4gF1hwcDAkEokZVsZUd2rInxhjDBcuUP+wMWPMfy5NMRSApkE6O1MVuDGMGUMpzz/+WK7lVSlcXV0RGBgIsYE+SrFYjMDAQBYTxmhYUBitLF1KbVYqYl69NpcXQHEUY91e1tZUQT9vHvDwofHrq2r4+voaJCoqMfH19TXzypjqDLu8GI1cukTTA5OTaYqiucnNJUskL69008lt24A9e2hCpLGoGkeuW1e+dVY1MjIyEBUVpXEeiui/HHAPDw8EBASwZcKUGxYURiNDhlA/rQ8+qLhzOjpSenLJNN9//wW6dMnD9u2XkJWViYKCAtjZ2RVWf+sTQH70iGaJHDoEvPKKedZfmcnLy8OlS5eQlZVV+P05Ozvr/f0xjD6woDCluHaN5sTfvEmDvSoKLy/g119pFooK1cTBa9eSYGsLKJWa77D1mTj4/ffAhg1ULFlTAvQMU5HwnxVTimXLgA8/rFgxAUrXohSfOCgSydXEBDB84mBwMLnTfvjBDItnGIYFhVEnPp66Ck+eXPHnLh6YN8fEQVUFfU0L0DNMRcGCwqixbBkwaxaNna1oVBaKOScOtm8PDB1KfckYhjEtHENhCklMBDp3pswuB4eKP/833wA3bgABAbsQH2++iYOqAP3Bg9SihWEY08AWClPI55/TCF1LiAlALq/MzLzCFFdNmGLiYP36wIoVwJQpNbPFPcOYCxYUBgCQkkKzyWfMsNwaXF0BGxuajGXuiYNBQdTscvNms30chqlxsKAwAMg6mTyZRu9aCokEEIkyC4vvzDlxUBWgX7AAKNFol2EYI+F5KDWIlJQ8rFqVhdhYGzx5Yo169ZTw9lZg9Ghn/PabPRISLLu+Jk0AQSg9cRBA4cRBGxubUu4sYycO+vjQBMp584D1603wARimhsOCUgM4dCgLoaFSxMQ0BtAUcnlRf6czZ2TYuNEKLVqk4Z9/bNGvX8UOVnr6lAopL1+mRpTPnmmfOFivXj08ffrUpBMHP/2UAvTjxgEdOpTjgzAMwy6v6s7cuakYOLA+zp+XQC4XqYkJAMjlYsjlIty4IcHAgfUxd26qWdYhCNRC5dAhYPlysgy8vIAGDYDx42mI1vPPA1KpC6ystN/nZGVlwcXFpfB5eScOPvccsHIl9fpSKIz/fAzDsKBUa+bOTcXq1RLIZGLo/qe2hkwmxurVknKLytOn1Pr++++BmTOB114DnJxojO+aNZS2278/sGsXkJ1NjSh/+omq862tK37i4OjRgJ0dzU1hGMZ42OVVTTl0KAurV7v+Jyaa6dGDAtPNmwNnz1Jrktu3bbF6tQTdut1Dnz6NyjyHIFAh4uXLRS6ry5cpY+zFF4G2bemnXz/6Xcyw0Iqzsz1sbXVPHOzbty8GDhyI9PR0tYmDggC4uxs2cdDKir6H118HBg+mWfYMwxgOFzZWUzp0SMP58xJos0ycnKiA8f33aVLi0qVAQADQqRMAKOHnl45z55oV7l9QAFy/ri4cV65QtpRKOF5+mX57eQElYuR6ExoKKBTpsLMzbuKgUinGgQPBWLBAgoEDS7fCL4tZs6h9/saNBp+WYRiwoFRLUlLy0LJlLcjlIqSkAGvXUt3F888Df/wBvPcePQ8Ophb1ADWCvH8faNeOqtVFIjnmzHmGmzftcfkydR728FAXDpXVYchFWxcbN5K1NGmSYb28gKIhUbm5vpg6FWjWjKrvX3xRv/dnZ5MY7tkDvPqqkR+AYWow7PKqhqxalQWgaeHzYcOAN94gK+P0aRISLy+yMlTk55PF4u1NggIIOH06C8HB7ggJobnuFTm5UTU5UF9RKTlx8NIl4OuvyeKaNIlSg3V1T3Z0LArQnztn3Ax7hqnJcFC+GhIba6OWzfX118CdOxQMP3CA6i/q1qU78uJkZwP16tFjuVwMQbDBe++R1VIRYgKot7D39fVFcHAwPD09IRKJCuefqFBt8/T0RHBwsNr4WrGYgvyXL5NQtmoF7N1LMZayGDWKhGfDBlN/Moap/rCFUg158kT9PuHu3aLH+flkBTx4ULpnl4MD8ORJ0fOcnIq/35BI1GeiSCQSDB8+3OiJg66uwI4dwIkTZHls2EACq80NpgrQ9+xJUysblZ2XwDBMMVhQqiH16unueBgbS7EUFXXqAC1a0HYVDg4V3zmxYUMgJwd49kzdKrK3t0cXVcDHCF57Td0NNnky8Mknmt1gL79MlsrcudTrSyVmmZnGjR9mmJoCC0o1xNtbgTNnZKWKGIuzdy+wahUwaBAVGy5aRFlbFD8BRCIZXnqp4iv9rK2Bxo3JRefmZtpj29oCH30EjBhBv1u1orqYAQNKJxYsWQJ07ZqOb789hYcPqfuxqscYAMTFxSEyMlLv8cMMUxPgGEo1JCTEGUDZqVf371PNxbJlFFvp2JEutEVYITzcGVOnAnFx5lxtaUq6vUxN06bAzp1kfcydC7z5JsVZipOYGI3Bg7fg7t34wlHDxTF0/DDD1ARYUKoh7u72aNfuLgAl3N1ppK+K0FCqDAdou5cXuX1eew24dUu1lxLt2t3BhQv2cHKi115/HQgPr5j2JK6uRaOAzUnPnhS079aNBHXxYqryV40fFgQZrPX4C9Fn/DDD1ARYUKopixfbQiw27uovFssRGmoHV1dqnnjrFjBmDLW4b9EC+N//zNvy3dwWSnFsbYGPPwZiYsgS69o1Hb//bp7xwwxT3WFBqab06+eM2bPTIRZLde9cDLFYitmzM9TartSqBYwcCfzzD/DLLxS49/CgDr0xMaZeeVEtSkXSrBmwezcQFHQKCkXZYuLu7o6pU6di3rx5CAoKgqOjIwASlaioqIpYLsNUSlhQqjErVrhh9uwMiMUyALoytpSFYrJihZvWvfz8gC1bgIQEslYGDAD8/SkmYUSnFI0Ur0WpSPLy8pCTk1Smm0vXCGJd44cZpjpjs2TJkiWWXgRjPl5//Tn4+j5EQsIjZGXZw9paAaWyqARcJJLB2lqJV165g/XrFZg8Wb9sJXt76v01YwbNaP/2W5p+mJsLeHpS4aQx5OXlITb2PO7di0ZOTgwSExPx8OFDODk5lRqiZWrOnz+P1NRUKJVKzJw5E9bW1njzzTfRs2dPNG7cGAkJCWjdujUcHBz+i7EI+PfffxEYGIjr16/j6dOnsLa2hp2dHZo3b27WtTJMZYTThmsAffo0Qp8+QGpq0cTGnBxrODjQxMaQEGe4uTXTfSANiESULTZ4MHD1KhUFenkBffoA06dTTyx9en2lp6fj1KlTSEpKgiAAjRvLkZhIr1VUim5mZqZaNpdqBLFcLsfYsWPh4+ODRo0aaR1B/ODBA73GDzNMdYUFpQbh5maPsDB3sx2/TRvgu+8oeP/jj5RN5uhIwjJiBM0c0YQqq0pbIFx1kY+Pj0dycrJazy5TUnJssLEjiPUZP8ww1RGOoTAmp3594IMPKM6ydCkN0mrenCrTb99W31eXmJTE1Cm6UikVdG7fDsTFqSteyRHEtra2kEqlOkcQ6zt+mGGqG2yhMGbD2hro25d+EhPJHdauHdV9TJsGtGyZrlNM3N3d0bdvXzg6OiItLQ3h4eHIzs5GREQEJBIJJBKJXmtRKin9+epV+rl2jX4nJ1NFfps2gLe3C6ys4iAIcq3HycrKQtu2bQuflxxBbMj4YYapbrCFwlQIL74IfPUVXdR79SI3WGjoqTLFpKyMqrJSdO/dA44fp75d48dTHMfREejalVxy2dkkclu3UpeAuDhKGf7oIx+dLev1GUFsyPhhhqlOsIXCVCh161JjxtGj87B6NQXgZ86ciXPnzqFt27ZwdHREUlIS9u3bBy8vL9y7dw/Xr18HAERGRiIkJAROTk548OABEhOTcOpUHhIS7NUsj4ICsjjatAHat6dhYq1bkyuuLOzt7eHh4YH4+Hit++gaQezhYdj4YYapTrCgMBbh8uVLsLEBVElVxmRUFRQAa9ZcQu3aXdC6NRAYSCLi6mr8FEl/f38kJydjzZo1attPnjxZ+DglJQVhYWGl3isWixEQEGDciRmmGsCCwliEkim6xmRUicVyvPtuFgYONN26XF1dERgYaPT4YX1jOgxTHeEYCmMRSqbWGptRZY4UXV9fXwQGBkIs1t7+vzglxw8zTE2FLRTGIuiTWqsro0rf4xiDr68vJBIJoqKikJRUeh6Kahyxh4cHAgIC2DJhGLCgMBbCxcUFcXFxpeaMFCc+Ph69evWCl5cXEhISSmVUmTtFV9P44dOnCyCR2KFjR93jhxmmpsGCwlgEHx8fREZGlrmProwq1XHMTfHxw7//DrRsCZRjGjHDVFusBEEQLL0Ipmaya9euMlN0deHp6Ynhw4ebcEW6+eADmvj44YcVelqGqRJwUJ6xGP7+/noHvktiqRTdevWoozLDMKVhQWEshipF11BRkcvF8Pe3TIpuvXrAkycVflqGqRKwoDAWxZgU3WfPAjFvni/y8828OA3UrcuCwjDa4KA8Y3EMTdFt3FiC4GBgyBBg3z6aC19RsMuLYbTDQXmmUlE8RbegoAB2dnZwdi6doiuTkaDUqUNNHnU1dTQV4eHA5s3A/v0Vcz6GqUqwoDBVloIC4I03gFatqDW+sf27DOHYMeCzz4ATJ8x/LoapanAMhamy2NmRpXD2LLBwYcWck11eDKMdjqEwVRoHB+CPP4CAAKBBA2D2bPOej7O8GEY7LChMladRIyAigkSlfn1gzBjznYuzvBhGOywoTLWgeXMSle7dSVTefts852GXF8NohwWFqTa89BJw8CDQpw+5wnr0MP056tYlQRGEikkCYJiqBAflmWrFK6/QfPgRI4Dz501/fJGI6l4sUVTJMJUdFhSm2tG9O7BpE9C/PxAXZ/rjs9uLYTTDLi+mWjJgAPD4MdC7NxAVBTz/vOmOrcr0cnEx3TEZpjrAgsJUW4KCgEePgF69gFOnAFPN4uLUYYbRDAsKU62ZORN4+JAq6k+cABwdy39MTh1mGM1wDIWp9ixZQhMW+/cHnj4t//E4hsIwmmFBYao9VlbAmjVAs2bAsGHUWLI8sMuLYTTDgsLUCKytgR9/pPqRMWMApdL4Y7HLi2E0w4LC1BjEYqpRuXULmDWLxMUY2OXFMJphQWFqFHXqAAcOUCpxaKhxx2CXF8NohrO8mBrHc8+pdyieMcOw99etS5ljDMOow4LC1EhcXIo6FDdoAIwapf9769UDbt8239oYpqrCgsLUWNzcgD//pCaSjo6UVqwP7PJiGM1wDIWp0bRqRVMfx44FTp7U7z2c5cUwmmFBYWo8HToAO3cCQ4cCFy/q3p+zvBhGMywoDAOgZ09g/XqgXz/gxo2y92WXF8NohmMoDPMfAwdSM8nAQGom2ayZ5v3Y5cUwmmFBYZhijB1bJCpRUUDDhqX3YZcXw2iGBYVhSvDhh8CDBzRK+NgxGiesIiUlD8uWZcHa2gbt21ujXj0lvL0V+PhjZ7i52Vtu0QxTCbASBGMbUDBM9UUQgMmTgYQE4PffgWPHshAaKkVMTGMAAuRyceG+IpEMgBXatbuLxYtt0a+fiQavMEwVgwWFYbSgUAAjRwK3b6ciOtoVMpkNys5jUUIsVmD27HSsWOFWQatkmMoDZ3kxjBZsbIDmzVMRHS2BTCaG7j8Xa8hkYqxeLcHcuakVsEKGqVywoDCMFg4dysJXX7lCJrPV+HqPHkBcHJCXBxw/DjRvTttlMlusXi3B4cP3KnC1DGN5WFAYRguhodL/3FylcXIC9uwBFi6kXmDR0cCuXUWvy2QiLF5cUEErZZjKAQsKw2ggJSUPMTGNkZJijQ8/BC5fBh4/por6WrWAQYOA2Fjg11+BZ89ozHDbtsBLL6mOYI2YmCZITc2z4KdgmIqFBYVhNLBqVRYAylcZNgx44w3A3R14+WUgOBjw9iaRUZGfDyQn0/YihP+OwzA1A65DYRgNxMbaFKYGf/01cOcObT9wAPDxoemP90qESLKzqehRhVwuRmysZpcZw1RH2EJhGA08eVL0p3H3btH2/HxqvZKbq17wCNDzki1ZcnL4T4ypOfD/dobRQL16yjJfj42lmImKOnWAFi1oe3EcHMo+DsNUJ1hQGEYD3t6K/yrgNbN3L9C6NQXna9UCFi0CrlxR71QsEsng7a2ogNUyTOWABYVhNBAS4gzASuvr9+8DgwcDy5ZRM8mOHYERI0ruZfXfcRimZsCtVxhGCx06pOH8eQmMu+9Sws8vHefOaemBzzDVELZQGEYLixfbQiw2zmUlFssRGmpn4hUxTOWGBYVhtNCvnzNmz06HWCw16H1isRSzZ2egT59GZloZw1ROWFAYpgxWrHDD7NkZEItlAHRlbCkLxYS7DTM1EY6hMIweHD58D4sXFyAmpgk0z0MB2rW7i9BQO7ZMmBoLCwrDGEBqah5WrcpCbKwNcnKs4eBAExtDQnhiI8OwoDAMwzAmgWMoDMMwjElgQWEYhmFMAgsKwzAMYxJYUBiGYRiTwILCMAzDmAQWFIZhGMYksKAwDMMwJoEFhWEYhjEJLCgMwzCMSWBBYRiGYUwCCwrDMAxjElhQGIZhGJPAgsIwDMOYBBYUhmEYxiSwoDAMwzAmgQWFYRiGMQksKAzDMIxJYEFhGIZhTAILCsMwDGMSWFAYhmEYk8CCwjAMw5gEFhSGYRjGJLCgMAzDMCaBBYVhGIYxCSwoDMMwjElgQWEYhmFMAgsKwzAMYxJYUBiGYRiTwILCMAzDmIT/Bz+OGQ3iPV5MAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from deepsnap.dataset import GraphDataset\n",
    "\n",
    "dataset = GraphDataset([hete], task='node')\n",
    "# Splitting the dataset\n",
    "dataset_train, dataset_val, dataset_test = dataset.split(transductive=True, split_ratio=[0.4, 0.3, 0.3])\n",
    "titles = ['Train', 'Validation', 'Test']\n",
    "\n",
    "for i, dataset in enumerate([dataset_train, dataset_val, dataset_test]):\n",
    "    n0 = hete._convert_to_graph_index(dataset[0].node_label_index['n0'], 'n0').tolist()\n",
    "    n1 = hete._convert_to_graph_index(dataset[0].node_label_index['n1'], 'n1').tolist()\n",
    "\n",
    "    plt.figure(figsize=(7, 7))\n",
    "    plt.title(titles[i])\n",
    "    nx.draw(G_orig, pos=pos, node_color=\"grey\", edge_color=colors, labels=labels, font_color='white')\n",
    "    nx.draw_networkx_nodes(G_orig.subgraph(n0), pos=pos, node_color=\"blue\")\n",
    "    nx.draw_networkx_nodes(G_orig.subgraph(n1), pos=pos, node_color=\"red\")\n",
    "    show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "e5LsVSRuI3hU"
   },
   "source": [
    "# 2 Heterogeneous Graph Node Property Prediction\n",
    "\n",
    "In this part we will use PyTorch Geometric and DeepSNAP to implement a GNN model for heterogeneous graph node property prediction (node classification). This part of Colab requires you having good understandings on the heterogeneous graph and how to implement the GNN layers by using PyG.\n",
    "\n",
    "At first let's take look at the general structure of a heterogeneous layer by an example.\n",
    "\n",
    "Let's assume we have a graph $G$, which contains two node types $a$ and $b$, and three message types $m_1=(a, r_1, a)$, $m_2=(a, r_2, b)$ and $m_3=(a, r_3, b)$.\n",
    "\n",
    "Thus, for $G$ a heterogeneous layer will contains three Heterogeneous GNN layers (`HeteroGNNConv` in this Colab) where each `HeteroGNNConv` layer will perform the message passing and aggregation with respect to only one message type. The overview of the heterogeneous layer is shown below:\n",
    "\n",
    "<br/>\n",
    "<center>\n",
    "<img src=\"https://web.stanford.edu/class/cs224w/images/colab4/hetero_conv.png\"/>\n",
    "</center>\n",
    "<br/>\n",
    "\n",
    "In this Colab, all the $l^{th}$ Heterogeneous GNN layers will be managed by a ($l^{th}$) Heterogeneous GNN Wrapper layer (the `HeteroGNNWrapperConv`). The $l^{th}$ Heterogeneous GNN Wrapper layer will take in the input node embeddings from $(l-1)^{th}$ layer and aggregate (across message types) the Heterogeneous GNN layers' results. For example, the wrapper layer will aggregate node type $b$'s node embeddings from Heterogeneous GNN layers for $m_2$ and $m_3$. The \"simplified\" heterogeneous layer structure is shown below:\n",
    "\n",
    "<br/>\n",
    "<center>\n",
    "<img src=\"http://web.stanford.edu/class/cs224w/images/colab4/hetero_conv_1.png\"/>\n",
    "</center>\n",
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rOTCyuRcJikS"
   },
   "source": [
    "<font color='red'>We recommend you implement the heterogeneous GNN model in following steps:</font>\n",
    "\n",
    "1. Implement the `HeteroGNNConv` first.\n",
    "2. Implement the `mean` aggregation in `HeteroGNNWrapperConv`.\n",
    "3. Implement the `generate_convs`.\n",
    "4. Implement the `HeteroGNN` model and the `train` function.\n",
    "5. Train the model with `mean` aggregation across the message types and make sure your model has reasonable performance.\n",
    "6. Implement the `attn` aggregation in `HeteroGNNWrapperConv`.\n",
    "7. Train the model with `attn` aggregation across the message types and make sure your model has reasonable performance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pkFjcktiJJLm"
   },
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "NAm9_OcJJJ-W"
   },
   "outputs": [],
   "source": [
    "import copy\n",
    "import torch\n",
    "import deepsnap\n",
    "import numpy as np\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch_geometric.nn as pyg_nn\n",
    "\n",
    "from sklearn.metrics import f1_score\n",
    "from deepsnap.hetero_gnn import forward_op\n",
    "from deepsnap.hetero_graph import HeteroGraph\n",
    "from torch_sparse import SparseTensor, matmul"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "n2prITo3JSbo"
   },
   "source": [
    "## Dataset\n",
    "\n",
    "You need to login to your Google account and enter the verification code below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "QvQwxJX4JTJX"
   },
   "outputs": [],
   "source": [
    "from pydrive.auth import GoogleAuth\n",
    "from pydrive.drive import GoogleDrive\n",
    "from google.colab import auth\n",
    "from oauth2client.client import GoogleCredentials\n",
    "\n",
    "# Authenticate and create the PyDrive client\n",
    "auth.authenticate_user()\n",
    "gauth = GoogleAuth()\n",
    "gauth.credentials = GoogleCredentials.get_application_default()\n",
    "drive = GoogleDrive(gauth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "Igoy4F_xJbVn"
   },
   "outputs": [],
   "source": [
    "id='1ivlxd6lJMcZ9taS44TMGG72x2V1GeVvk'\n",
    "downloaded = drive.CreateFile({'id': id})\n",
    "downloaded.GetContentFile('acm.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VBlboS5kJmJL"
   },
   "source": [
    "## Heterogeneous GNN Layer\n",
    "\n",
    "Now let's start working on our own implementation of a heterogeneous layer (the `HeteroGNNConv`)! Similar to what we did in Colab 3, we will implement the layer using PyTorch Geometric. In general, our heterogeneous GNN layer draws ideas from the **GraphSAGE** ([Hamilton et al. (2017)](https://arxiv.org/abs/1706.02216)).\n",
    "\n",
    "At first, let's implement the GNN layer for each message type :\n",
    "\n",
    "\\begin{equation}\n",
    "m =(s, r, d)\n",
    "\\end{equation}\n",
    "\n",
    "Each message type is a tuple containing three elements where $s$ refers to the source node type, $r$ refers to the edge (relation) type and $d$ refers to the destination node type. The update rule is very similar to that of GraphSAGE but we need to include the node types and the edge type. The update rule is described as below:\n",
    "\n",
    "\\begin{equation}\n",
    "h_v^{(l)[m]} = W^{(l)[m]} \\cdot \\text{CONCAT} \\Big( W_d^{(l)[m]} \\cdot h_v^{(l-1)}, W_s^{(l)[m]} \\cdot AGG(\\{h_u^{(l-1)}, \\forall u \\in N_{m}(v) \\})\\Big)\n",
    "\\end{equation}\n",
    "\n",
    "where $[m]$ indicates that the weight matrices or embeddings with respect to message type $m$, $W_s^{(l)[m]}$ computes the messages from neighboring nodes, $W_d^{(l)[m]}$ compute messages from the node itself, and $W^{(l)[m]}$ aggregates messages from both node types. In the equation above, $v$ has the node type $d$, and $u$ has the node type $s$.\n",
    "\n",
    "For simplicity, we use mean aggregations for $AGG$ where:\n",
    "\n",
    "\\begin{equation}\n",
    "AGG(\\{h_u^{(l-1)}, \\forall u \\in N_{m}(v) \\}) = \\frac{1}{|N_{m}(v)|} \\sum_{u\\in N_{m}(v)} h_u^{(l-1)}\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "HFFpqB99mmVQ"
   },
   "outputs": [],
   "source": [
    "import torch_scatter\n",
    "from torch_sparse import matmul"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "_Z1b0Mf8Jova"
   },
   "outputs": [],
   "source": [
    "class HeteroGNNConv(pyg_nn.MessagePassing):\n",
    "    def __init__(self, in_channels_src, in_channels_dst, out_channels):\n",
    "        super(HeteroGNNConv, self).__init__(aggr=\"mean\")\n",
    "\n",
    "        self.in_channels_src = in_channels_src\n",
    "        self.in_channels_dst = in_channels_dst\n",
    "        self.out_channels = out_channels\n",
    "\n",
    "        # To simplify implementation, please initialize both self.lin_dst\n",
    "        # and self.lin_src out_features to out_channels\n",
    "        self.lin_dst = None\n",
    "        self.lin_src = None\n",
    "\n",
    "        self.lin_update = None\n",
    "\n",
    "        ############# Your code here #############\n",
    "        ## (~3 lines of code)\n",
    "        self.lin_dst = nn.Linear(self.in_channels_dst, self.out_channels)\n",
    "        self.lin_src = nn.Linear(self.in_channels_src, self.out_channels)\n",
    "        self.lin_update = nn.Linear(self.out_channels*2, self.out_channels)\n",
    "        ##########################################\n",
    "\n",
    "    def forward(\n",
    "        self,\n",
    "        node_feature_src,\n",
    "        node_feature_dst,\n",
    "        edge_index,\n",
    "        size=None,\n",
    "        res_n_id=None,\n",
    "    ):\n",
    "        ############# Your code here #############\n",
    "        ## (~1 line of code)\n",
    "        return self.propagate(edge_index, size=size, \n",
    "        node_feature_src=node_feature_src, \n",
    "        node_feature_dst=node_feature_dst, \n",
    "        res_n_id=res_n_id)\n",
    "\n",
    "        ##########################################\n",
    "\n",
    "    def message_and_aggregate(self, edge_index, node_feature_src):\n",
    "\n",
    "        ############# Your code here #############\n",
    "        ## (~1 line of code)\n",
    "        ## Note:\n",
    "        ## 1. Different from what we implemented in Colab 3, we use message_and_aggregate\n",
    "        ## to replace the message and aggregate. The benefit is that we can avoid\n",
    "        ## materializing x_i and x_j, and make the implementation more efficient.\n",
    "        ## 2. To implement efficiently, following PyG documentation is helpful:\n",
    "        ## https://pytorch-geometric.readthedocs.io/en/latest/notes/sparse_tensor.html\n",
    "        ## 3. Here edge_index is torch_sparse SparseTensor.\n",
    "\n",
    "        out = matmul(edge_index, node_feature_src, reduce=\"mean\")\n",
    "        ##########################################\n",
    "\n",
    "        return out\n",
    "\n",
    "    def update(self, aggr_out, node_feature_dst, res_n_id):\n",
    "\n",
    "        ############# Your code here #############\n",
    "        ## (~4 lines of code)\n",
    "        aggr_out = self.lin_src(aggr_out)\n",
    "        node_feature_dst = self.lin_dst(node_feature_dst)\n",
    "\n",
    "        concat_features = torch.cat((node_feature_dst, aggr_out), dim=-1)\n",
    "        aggr_out = self.lin_update(concat_features)\n",
    "        ##########################################\n",
    "\n",
    "        return aggr_out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aKq8ScTiJthn"
   },
   "source": [
    "## Heterogeneous GNN Wrapper Layer\n",
    "\n",
    "After implementing the GNN layer for each message type, we need to somehow aggregate the the node embedding results (with respect to each message types) together. Here we will implement two types of message type level aggregation.\n",
    "\n",
    "The first one is simply the mean aggregation:\n",
    "\n",
    "\\begin{equation}\n",
    "h_v^{(l)} = \\frac{1}{M}\\sum_{m=1}^{M}h_v^{(l)[m]}\n",
    "\\end{equation}\n",
    "\n",
    "Here node $v$ has the node type $d$ and $M$ is the total number of message types that the destination node type is $d$.\n",
    "\n",
    "The other one is the semantic level attention introduced in **HAN** ([Wang et al. (2019)](https://arxiv.org/abs/1903.07293)). Instead of directly averaging on the message type aggregation results, we use attention to learn which message type result can be more important, then aggregate from all the message types. Following are the equations for semantic level attention:\n",
    "\n",
    "\\begin{equation}\n",
    "e_{m} = \\frac{1}{|V_{d}|} \\sum_{v \\in V_{d}} q_{attn}^T \\cdot tanh \\Big( W_{attn}^{(l)} \\cdot h_v^{(l)[m]} + b \\Big)\n",
    "\\end{equation}\n",
    "\n",
    "where $m$ refers to message type and $d$ refers to the destination node type. Then we can compute the attention and update the $h_v^{(l)}$:\n",
    "\n",
    "\\begin{equation}\n",
    "\\alpha_{m} = \\frac{\\exp(e_{m})}{\\sum_{m=1}^M \\exp(e_{m})}\n",
    "\\end{equation}\n",
    "\n",
    "\\begin{equation}\n",
    "h_v^{(l)} = \\sum_{m=1}^{M} \\alpha_{m} \\cdot h_v^{(l)[m]}\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "0_bun02xJwFm"
   },
   "outputs": [],
   "source": [
    "class HeteroGNNWrapperConv(deepsnap.hetero_gnn.HeteroConv):\n",
    "    def __init__(self, convs, args, aggr=\"mean\"):\n",
    "        super(HeteroGNNWrapperConv, self).__init__(convs, None)\n",
    "        self.aggr = aggr\n",
    "\n",
    "        # Map the index and message type\n",
    "        self.mapping = {}\n",
    "\n",
    "        # A numpy array that stores the final attention probability\n",
    "        self.alpha = None\n",
    "\n",
    "        self.attn_proj = None\n",
    "\n",
    "        if self.aggr == \"attn\":\n",
    "            ############# Your code here #############\n",
    "            ## (~1 line of code)\n",
    "            ## Note:\n",
    "            ## 1. Initialize self.attn_proj here.\n",
    "            ## 2. You should use nn.Sequential for self.attn_proj\n",
    "            ## 3. nn.Linear and nn.Tanh are useful.\n",
    "            ## 4. You can create a vector parameter by using:\n",
    "            ## nn.Linear(some_size, 1, bias=False)\n",
    "            ## 5. The first linear layer should have out_features as args['attn_size']\n",
    "            ## 6. You can assume we only have one \"head\" for the attention.\n",
    "            ## 7. We recommend you to implement the mean aggregation first. After \n",
    "            ## the mean aggregation works well in the training, then you can \n",
    "            ## implement this part.\n",
    "            self.attn_proj = nn.Sequential(\n",
    "                nn.Linear(args['hidden_size'], args['attn_size']),\n",
    "                nn.Tanh(),\n",
    "                nn.Linear(args['attn_size'], 1, bias=False), # q_semantic_attention\n",
    "            )\n",
    "\n",
    "            ##########################################\n",
    "    \n",
    "    def reset_parameters(self):\n",
    "        super(HeteroConvWrapper, self).reset_parameters()\n",
    "        if self.aggr == \"attn\":\n",
    "            for layer in self.attn_proj.children():\n",
    "                layer.reset_parameters()\n",
    "    \n",
    "    def forward(self, node_features, edge_indices):\n",
    "        message_type_emb = {}\n",
    "        for message_key, message_type in edge_indices.items():\n",
    "            src_type, edge_type, dst_type = message_key\n",
    "            node_feature_src = node_features[src_type]\n",
    "            node_feature_dst = node_features[dst_type]\n",
    "            edge_index = edge_indices[message_key]\n",
    "            message_type_emb[message_key] = (\n",
    "                self.convs[message_key](\n",
    "                    node_feature_src,\n",
    "                    node_feature_dst,\n",
    "                    edge_index,\n",
    "                )\n",
    "            )\n",
    "        node_emb = {dst: [] for _, _, dst in message_type_emb.keys()}\n",
    "        mapping = {}        \n",
    "        for (src, edge_type, dst), item in message_type_emb.items():\n",
    "            mapping[len(node_emb[dst])] = (src, edge_type, dst)\n",
    "            node_emb[dst].append(item)\n",
    "        self.mapping = mapping\n",
    "        for node_type, embs in node_emb.items():\n",
    "            if len(embs) == 1:\n",
    "                node_emb[node_type] = embs[0]\n",
    "            else:\n",
    "                node_emb[node_type] = self.aggregate(embs)\n",
    "        return node_emb\n",
    "    \n",
    "    def aggregate(self, xs):\n",
    "        # TODO: Implement this function that aggregates all message type results.\n",
    "        # Here, xs is a list of tensors (embeddings) with respect to message \n",
    "        # type aggregation results.\n",
    "        if self.aggr == \"mean\":\n",
    "\n",
    "            ############# Your code here #############\n",
    "            ## (~2 lines of code)\n",
    "            out = torch.mean(torch.stack(xs), dim=0)\n",
    "            return out\n",
    "            ##########################################\n",
    "\n",
    "        elif self.aggr == \"attn\":\n",
    "\n",
    "            ############# Your code here #############\n",
    "            ## (~10 lines of code)\n",
    "            ## Note:\n",
    "            ## 1. Store the value of attention alpha (as a numpy array) to self.alpha,\n",
    "            ## which has the shape (len(xs), ) self.alpha will be not be used \n",
    "            ## to backpropagate etc. in the model. We will use it to see how much \n",
    "            ## attention the layer pays on different message types.\n",
    "            ## 2. torch.softmax and torch.cat are useful.\n",
    "            ## 3. You might need to reshape the tensors by using the \n",
    "            ## `view()` function https://pytorch.org/docs/stable/tensor_view.html\n",
    "            x = self.attn_proj(torch.stack(xs, dim=0))\n",
    "            x = torch.mean(x, dim=1)\n",
    "\n",
    "            self.alpha = torch.softmax(x, dim=0)\n",
    "            self.alpha = self.alpha.detach()\n",
    "\n",
    "            out = torch.stack(xs, dim=0)\n",
    "            out = self.alpha.unsqueeze(-1) * out\n",
    "\n",
    "            out = torch.sum(out, dim=0)\n",
    "            return out\n",
    "            ##########################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tn_pnCOKJw-d"
   },
   "source": [
    "## Initialize Heterogeneous GNN Layers\n",
    "\n",
    "Now let's initialize the Heterogeneous GNN Layers. Different from homogeneous graph case, heterogeneous case can be a little bit complex.\n",
    "\n",
    "In general, we need to create a dictionary of `HeteroGNNConv` layers where the keys are message types.\n",
    "\n",
    "* To get all message types, `deepsnap.hetero_graph.HeteroGraph.message_types` is useful.\n",
    "* If we are initializing the first conv layers, we need to get the feature dimension of each node type. Using `deepsnap.hetero_graph.HeteroGraph.num_node_features(node_type)` will return the node feature dimension of `node_type`. In this function, we will set each `HeteroGNNConv` `out_channels` to be `hidden_size`.\n",
    "* If we are not initializing the first conv layers, all node types will have the same embedding dimension `hidden_size` and we still set `HeteroGNNConv` `out_channels` to be `hidden_size` for simplicity.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "hSBImHClJzf4"
   },
   "outputs": [],
   "source": [
    "def generate_convs(hetero_graph, conv, hidden_size, first_layer=False):\n",
    "    # TODO: Implement this function that returns a dictionary of `HeteroGNNConv` \n",
    "    # layers where the keys are message types. `hetero_graph` is deepsnap `HeteroGraph`\n",
    "    # object and the `conv` is the `HeteroGNNConv`.\n",
    "\n",
    "    convs = {}\n",
    "\n",
    "    ############# Your code here #############\n",
    "    ## (~9 lines of code)\n",
    "    for mes_type in hetero_graph.message_types:\n",
    "        if first_layer:\n",
    "            num_node_feat_src = hetero_graph.num_node_features(mes_type[0])\n",
    "            num_node_feat_dst = hetero_graph.num_node_features(mes_type[-1])\n",
    "\n",
    "            convs[mes_type] = conv(num_node_feat_src, num_node_feat_dst,\n",
    "                                   hidden_size)\n",
    "            \n",
    "        else:\n",
    "            convs[mes_type] = conv(hidden_size, hidden_size, hidden_size)\n",
    "    ##########################################\n",
    "    \n",
    "    return convs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "U39dX8EpJ3FG"
   },
   "source": [
    "## HeteroGNN\n",
    "\n",
    "Now we will make a simple HeteroGNN model which contains only two `HeteroGNNWrapperConv` layers.\n",
    "\n",
    "For the forward function in `HeteroGNN`, the model is going to be run as following:\n",
    "\n",
    "$\\text{self.convs1} \\rightarrow \\text{self.bns1} \\rightarrow \\text{self.relus1} \\rightarrow \\text{self.convs2} \\rightarrow \\text{self.bns2} \\rightarrow \\text{self.relus2} \\rightarrow \\text{self.post_mps}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "Soui5IiT_6r9"
   },
   "outputs": [],
   "source": [
    "from deepsnap.hetero_gnn import forward_op"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "rplknA8aJ6J5"
   },
   "outputs": [],
   "source": [
    "class HeteroGNN(torch.nn.Module):\n",
    "    def __init__(self, hetero_graph, args, aggr=\"mean\"):\n",
    "        super(HeteroGNN, self).__init__()\n",
    "\n",
    "        self.aggr = aggr\n",
    "        self.hidden_size = args['hidden_size']\n",
    "\n",
    "        self.convs1 = None\n",
    "        self.convs2 = None\n",
    "\n",
    "        self.bns1 = nn.ModuleDict()\n",
    "        self.bns2 = nn.ModuleDict()\n",
    "        self.relus1 = nn.ModuleDict()\n",
    "        self.relus2 = nn.ModuleDict()\n",
    "        self.post_mps = nn.ModuleDict()\n",
    "\n",
    "        ############# Your code here #############\n",
    "        ## (~10 lines of code)\n",
    "        ## Note:\n",
    "        ## 1. For self.convs1 and self.convs2, call generate_convs at first and then\n",
    "        ## pass the returned dictionary of `HeteroGNNConv` to `HeteroGNNWrapperConv`.\n",
    "        ## 2. For self.bns, self.relus and self.post_mps, the keys are node_types.\n",
    "        ## `deepsnap.hetero_graph.HeteroGraph.node_types` will be helpful.\n",
    "        ## 3. Initialize all batchnorms to torch.nn.BatchNorm1d(hidden_size, eps=1.0).\n",
    "        ## 4. Initialize all relus to nn.LeakyReLU().\n",
    "        ## 5. For self.post_mps, each value in the ModuleDict is a linear layer \n",
    "        ## where the `out_features` is the number of classes for that node type.\n",
    "        ## `deepsnap.hetero_graph.HeteroGraph.num_node_labels(node_type)` will be\n",
    "        ## useful.\n",
    "        self.convs1 = generate_convs(hetero_graph, HeteroGNNConv, self.hidden_size,\n",
    "                                     first_layer=True)\n",
    "        self.convs1 = HeteroGNNWrapperConv(self.convs1, args, self.aggr)\n",
    "\n",
    "        self.convs2 = generate_convs(hetero_graph, HeteroGNNConv, self.hidden_size,\n",
    "                                             first_layer=False)\n",
    "        self.convs2 = HeteroGNNWrapperConv(self.convs2, args, self.aggr)\n",
    "\n",
    "        for node_type in hetero_graph.node_types:\n",
    "            self.bns1[node_type] = torch.nn.BatchNorm1d(self.hidden_size, eps=1.0)\n",
    "            self.bns2[node_type] = torch.nn.BatchNorm1d(self.hidden_size, eps=1.0) \n",
    "            self.relus1[node_type] = nn.LeakyReLU()\n",
    "            self.relus2[node_type] = nn.LeakyReLU()\n",
    "            self.post_mps[node_type] = \\\n",
    "            nn.Linear(self.hidden_size, hetero_graph.num_node_labels(node_type))\n",
    "        ##########################################\n",
    "\n",
    "    def forward(self, node_feature, edge_index):\n",
    "        # TODO: Implement the forward function. Notice that `node_feature` is \n",
    "        # a dictionary of tensors where keys are node types and values are \n",
    "        # corresponding feature tensors. The `edge_index` is a dictionary of \n",
    "        # tensors where keys are message types and values are corresponding\n",
    "        # edge index tensors (with respect to each message type).\n",
    "\n",
    "        x = node_feature\n",
    "\n",
    "        ############# Your code here #############\n",
    "        ## (~7 lines of code)\n",
    "        ## Note:\n",
    "        ## 1. `deepsnap.hetero_gnn.forward_op` can be helpful.\n",
    "        x = self.convs1(x, edge_index)\n",
    "        x = forward_op(x, self.bns1)\n",
    "        x = forward_op(x, self.relus1)\n",
    "\n",
    "        x = self.convs2(x, edge_index)\n",
    "        x = forward_op(x, self.bns2)\n",
    "        x = forward_op(x, self.relus2)\n",
    "\n",
    "        x = forward_op(x, self.post_mps)\n",
    "        ##########################################\n",
    "        \n",
    "        return x\n",
    "\n",
    "    def loss(self, preds, y, indices):\n",
    "        \n",
    "        loss = 0\n",
    "        loss_func = F.cross_entropy\n",
    "\n",
    "        ############# Your code here #############\n",
    "        ## (~3 lines of code)\n",
    "        ## Note:\n",
    "        ## 1. For each node type in preds, accumulate computed loss to `loss`\n",
    "        ## 2. Loss need to be computed with respect to the given index\n",
    "        for node_type in preds:\n",
    "            idx = indices[node_type]\n",
    "            pred = preds[node_type][idx]\n",
    "            loss += loss_func(pred, y[node_type][idx])\n",
    "        ##########################################\n",
    "\n",
    "        return loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "o9e7q_hUJ8zB"
   },
   "source": [
    "## Training and Testing\n",
    "\n",
    "Here we provide you with the functions to train and test. You only need to implement one line of code here.\n",
    "\n",
    "**Please do not modify other parts in `train` and `test` for grading purposes.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "CI5Hl_5TJ_YL"
   },
   "outputs": [],
   "source": [
    "def train(model, optimizer, hetero_graph, train_idx):\n",
    "    model.train()\n",
    "    optimizer.zero_grad()\n",
    "    preds = model(hetero_graph.node_feature, hetero_graph.edge_index)\n",
    "\n",
    "    loss = None\n",
    "\n",
    "    ############# Your code here #############\n",
    "    ## Note:\n",
    "    ## 1. `deepsnap.hetero_graph.HeteroGraph.node_label` is useful\n",
    "    ## 2. Compute the loss here\n",
    "    # print(train_idx)\n",
    "    loss = model.loss(preds, hetero_graph.node_label, train_idx)\n",
    "\n",
    "    ##########################################\n",
    "\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    return loss.item()\n",
    "\n",
    "def test(model, graph, indices, best_model=None, best_val=0):\n",
    "    model.eval()\n",
    "    accs = []\n",
    "    for index in indices:\n",
    "        preds = model(graph.node_feature, graph.edge_index)\n",
    "        num_node_types = 0\n",
    "        micro = 0\n",
    "        macro = 0\n",
    "        for node_type in preds:\n",
    "            idx = index[node_type]\n",
    "            pred = preds[node_type][idx]\n",
    "            pred = pred.max(1)[1]\n",
    "            label_np = graph.node_label[node_type][idx].cpu().numpy()\n",
    "            pred_np = pred.cpu().numpy()\n",
    "            micro = f1_score(label_np, pred_np, average='micro')\n",
    "            macro = f1_score(label_np, pred_np, average='macro')\n",
    "            num_node_types += 1\n",
    "        # Averaging f1 score might not make sense, but in our example we only\n",
    "        # have one node type\n",
    "        micro /= num_node_types\n",
    "        macro /= num_node_types\n",
    "        accs.append((micro, macro))\n",
    "    if accs[1][0] > best_val:\n",
    "        best_val = accs[1][0]\n",
    "        best_model = copy.deepcopy(model)\n",
    "    return accs, best_model, best_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "DpNz9B5AKBUU"
   },
   "outputs": [],
   "source": [
    "# Please do not change the following parameters\n",
    "args = {\n",
    "    'device': torch.device('cuda' if torch.cuda.is_available() else 'cpu'),\n",
    "    'hidden_size': 64,\n",
    "    'epochs': 100,\n",
    "    'weight_decay': 1e-5,\n",
    "    'lr': 0.003,\n",
    "    'attn_size': 32,\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pRHbWD4hKED8"
   },
   "source": [
    "## Dataset and Preprocessing\n",
    "\n",
    "In the next, we will load the data and create a tensor backend (without a NetworkX graph) `deepsnap.hetero_graph.HeteroGraph` object.\n",
    "\n",
    "We will use the `ACM(3025)` dataset in our node property prediction task, which is proposed in **HAN** ([Wang et al. (2019)](https://arxiv.org/abs/1903.07293)) and our dataset is extracted from [DGL](https://www.dgl.ai/)'s [ACM.mat](https://data.dgl.ai/dataset/ACM.mat).\n",
    "\n",
    "The original ACM dataset has three node types and two edge (relation) types. For simplicity, we simplify the heterogeneous graph to one node type and two edge types (shown below). This means that in our heterogeneous graph, we have one node type (paper) and two message types (paper, author, paper) and (paper, subject, paper).\n",
    "\n",
    "<br/>\n",
    "<center>\n",
    "<img src=\"http://web.stanford.edu/class/cs224w/images/colab4/cs224w-acm.png\"/>\n",
    "</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "lJy03_IsKGh6",
    "outputId": "38f78a1f-37f0-4395-e3c8-ccb22b24302d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Device: cuda\n",
      "ACM heterogeneous graph: {'paper': 3025} nodes, {('paper', 'author', 'paper'): 26256, ('paper', 'subject', 'paper'): 2207736} edges\n",
      "SparseTensor(row=tensor([   0,    0,    0,  ..., 3024, 3024, 3024], device='cuda:0'),\n",
      "             col=tensor([   8,   20,   51,  ..., 2948, 2983, 2991], device='cuda:0'),\n",
      "             size=(3025, 3025), nnz=26256, density=0.29%)\n",
      "SparseTensor(row=tensor([   0,    0,    0,  ..., 3024, 3024, 3024], device='cuda:0'),\n",
      "             col=tensor([  75,  434,  534,  ..., 3020, 3021, 3022], device='cuda:0'),\n",
      "             size=(3025, 3025), nnz=2207736, density=24.13%)\n"
     ]
    }
   ],
   "source": [
    "print(\"Device: {}\".format(args['device']))\n",
    "\n",
    "# Load the data\n",
    "data = torch.load(\"acm.pkl\")\n",
    "\n",
    "# Message types\n",
    "message_type_1 = (\"paper\", \"author\", \"paper\")\n",
    "message_type_2 = (\"paper\", \"subject\", \"paper\")\n",
    "\n",
    "# Dictionary of edge indices\n",
    "edge_index = {}\n",
    "edge_index[message_type_1] = data['pap']\n",
    "edge_index[message_type_2] = data['psp']\n",
    "\n",
    "# Dictionary of node features\n",
    "node_feature = {}\n",
    "node_feature[\"paper\"] = data['feature']\n",
    "\n",
    "# Dictionary of node labels\n",
    "node_label = {}\n",
    "node_label[\"paper\"] = data['label']\n",
    "\n",
    "# Load the train, validation and test indices\n",
    "train_idx = {\"paper\": data['train_idx'].to(args['device'])}\n",
    "val_idx = {\"paper\": data['val_idx'].to(args['device'])}\n",
    "test_idx = {\"paper\": data['test_idx'].to(args['device'])}\n",
    "\n",
    "# Construct a deepsnap tensor backend HeteroGraph\n",
    "from deepsnap.hetero_graph import HeteroGraph\n",
    "\n",
    "hetero_graph = HeteroGraph(\n",
    "    node_feature=node_feature,\n",
    "    node_label=node_label,\n",
    "    edge_index=edge_index,\n",
    "    directed=True\n",
    ")\n",
    "\n",
    "print(f\"ACM heterogeneous graph: {hetero_graph.num_nodes()} nodes, {hetero_graph.num_edges()} edges\")\n",
    "\n",
    "# Node feature and node label to device\n",
    "for key in hetero_graph.node_feature:\n",
    "    hetero_graph.node_feature[key] = hetero_graph.node_feature[key].to(args['device'])\n",
    "for key in hetero_graph.node_label:\n",
    "    hetero_graph.node_label[key] = hetero_graph.node_label[key].to(args['device'])\n",
    "\n",
    "# Edge_index to sparse tensor and to device\n",
    "for key in hetero_graph.edge_index:\n",
    "    edge_index = hetero_graph.edge_index[key]\n",
    "    adj = SparseTensor(row=edge_index[0], col=edge_index[1], sparse_sizes=(hetero_graph.num_nodes('paper'), hetero_graph.num_nodes('paper')))\n",
    "    hetero_graph.edge_index[key] = adj.t().to(args['device'])\n",
    "print(hetero_graph.edge_index[message_type_1])\n",
    "print(hetero_graph.edge_index[message_type_2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hrmU5-QQKJv6"
   },
   "source": [
    "## Start Training!\n",
    "\n",
    "Now lets start training!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "y0HplV9hKMkc"
   },
   "source": [
    "## Training the Mean Aggregation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZgwfyzLbKOUw",
    "outputId": "97712cba-a173-4530-cbc5-a8ae66094331"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1: loss 1.09983, train micro 33.33%, train macro 16.67%, valid micro 33.33%, valid macro 16.67%, test micro 31.29%, test macro 15.89%\n",
      "Epoch 2: loss 1.09115, train micro 33.33%, train macro 16.67%, valid micro 33.33%, valid macro 16.67%, test micro 31.29%, test macro 15.89%\n",
      "Epoch 3: loss 1.0638, train micro 65.5%, train macro 52.44%, valid micro 64.67%, valid macro 51.73%, test micro 63.48%, test macro 51.09%\n",
      "Epoch 4: loss 1.01025, train micro 66.0%, train macro 54.03%, valid micro 66.33%, valid macro 55.04%, test micro 65.69%, test macro 54.61%\n",
      "Epoch 5: loss 0.9191, train micro 66.33%, train macro 55.17%, valid micro 66.0%, valid macro 55.07%, test micro 65.36%, test macro 54.53%\n",
      "Epoch 6: loss 0.78616, train micro 66.67%, train macro 55.97%, valid micro 65.67%, valid macro 54.83%, test micro 65.13%, test macro 54.37%\n",
      "Epoch 7: loss 0.62461, train micro 66.83%, train macro 56.33%, valid micro 65.67%, valid macro 54.83%, test micro 65.27%, test macro 54.48%\n",
      "Epoch 8: loss 0.46656, train micro 67.33%, train macro 57.31%, valid micro 66.67%, valid macro 56.05%, test micro 65.32%, test macro 54.46%\n",
      "Epoch 9: loss 0.33793, train micro 68.33%, train macro 59.1%, valid micro 66.33%, valid macro 55.73%, test micro 65.46%, test macro 54.6%\n",
      "Epoch 10: loss 0.24218, train micro 72.0%, train macro 65.85%, valid micro 67.33%, valid macro 57.86%, test micro 65.6%, test macro 54.68%\n",
      "Epoch 11: loss 0.17288, train micro 75.67%, train macro 71.62%, valid micro 70.33%, valid macro 63.58%, test micro 65.98%, test macro 55.39%\n",
      "Epoch 12: loss 0.12326, train micro 82.67%, train macro 81.15%, valid micro 77.33%, valid macro 74.66%, test micro 67.25%, test macro 58.06%\n",
      "Epoch 13: loss 0.08812, train micro 91.17%, train macro 90.84%, valid micro 84.0%, valid macro 82.89%, test micro 69.51%, test macro 62.29%\n",
      "Epoch 14: loss 0.06351, train micro 94.83%, train macro 94.73%, valid micro 89.33%, valid macro 88.91%, test micro 71.67%, test macro 66.0%\n",
      "Epoch 15: loss 0.04659, train micro 97.0%, train macro 96.97%, valid micro 91.0%, valid macro 90.68%, test micro 73.88%, test macro 69.42%\n",
      "Epoch 16: loss 0.03509, train micro 98.33%, train macro 98.33%, valid micro 91.33%, valid macro 91.06%, test micro 75.2%, test macro 71.53%\n",
      "Epoch 17: loss 0.02717, train micro 98.5%, train macro 98.5%, valid micro 92.33%, valid macro 92.13%, test micro 76.42%, test macro 73.35%\n",
      "Epoch 18: loss 0.0217, train micro 99.33%, train macro 99.33%, valid micro 93.33%, valid macro 93.19%, test micro 77.74%, test macro 75.3%\n",
      "Epoch 19: loss 0.01794, train micro 99.67%, train macro 99.67%, valid micro 95.33%, valid macro 95.29%, test micro 78.54%, test macro 76.46%\n",
      "Epoch 20: loss 0.01527, train micro 100.0%, train macro 100.0%, valid micro 95.67%, valid macro 95.64%, test micro 79.58%, test macro 77.81%\n",
      "Epoch 21: loss 0.01322, train micro 100.0%, train macro 100.0%, valid micro 96.0%, valid macro 95.98%, test micro 80.38%, test macro 78.8%\n",
      "Epoch 22: loss 0.01154, train micro 100.0%, train macro 100.0%, valid micro 96.33%, valid macro 96.32%, test micro 80.75%, test macro 79.28%\n",
      "Epoch 23: loss 0.01009, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.99%, test micro 81.22%, test macro 79.87%\n",
      "Epoch 24: loss 0.00876, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.99%, test micro 81.84%, test macro 80.66%\n",
      "Epoch 25: loss 0.0076, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.99%, test micro 82.16%, test macro 81.07%\n",
      "Epoch 26: loss 0.00664, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.99%, test micro 82.68%, test macro 81.74%\n",
      "Epoch 27: loss 0.00584, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.06%, test macro 82.2%\n",
      "Epoch 28: loss 0.00519, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.48%, test macro 82.69%\n",
      "Epoch 29: loss 0.00462, train micro 100.0%, train macro 100.0%, valid micro 98.0%, valid macro 97.99%, test micro 83.67%, test macro 82.92%\n",
      "Epoch 30: loss 0.00409, train micro 100.0%, train macro 100.0%, valid micro 98.0%, valid macro 98.0%, test micro 83.72%, test macro 82.99%\n",
      "Epoch 31: loss 0.00353, train micro 100.0%, train macro 100.0%, valid micro 98.33%, valid macro 98.33%, test micro 83.91%, test macro 83.22%\n",
      "Epoch 32: loss 0.00301, train micro 100.0%, train macro 100.0%, valid micro 98.67%, valid macro 98.67%, test micro 83.95%, test macro 83.3%\n",
      "Epoch 33: loss 0.00255, train micro 100.0%, train macro 100.0%, valid micro 98.33%, valid macro 98.33%, test micro 84.05%, test macro 83.49%\n",
      "Epoch 34: loss 0.00216, train micro 100.0%, train macro 100.0%, valid micro 98.33%, valid macro 98.33%, test micro 83.91%, test macro 83.35%\n",
      "Epoch 35: loss 0.00183, train micro 100.0%, train macro 100.0%, valid micro 98.33%, valid macro 98.33%, test micro 83.95%, test macro 83.42%\n",
      "Epoch 36: loss 0.00157, train micro 100.0%, train macro 100.0%, valid micro 98.33%, valid macro 98.33%, test micro 83.81%, test macro 83.28%\n",
      "Epoch 37: loss 0.00136, train micro 100.0%, train macro 100.0%, valid micro 98.33%, valid macro 98.33%, test micro 83.76%, test macro 83.23%\n",
      "Epoch 38: loss 0.00119, train micro 100.0%, train macro 100.0%, valid micro 98.33%, valid macro 98.33%, test micro 83.72%, test macro 83.2%\n",
      "Epoch 39: loss 0.00106, train micro 100.0%, train macro 100.0%, valid micro 98.0%, valid macro 98.0%, test micro 83.72%, test macro 83.21%\n",
      "Epoch 40: loss 0.00096, train micro 100.0%, train macro 100.0%, valid micro 98.0%, valid macro 98.0%, test micro 83.62%, test macro 83.14%\n",
      "Epoch 41: loss 0.00087, train micro 100.0%, train macro 100.0%, valid micro 98.0%, valid macro 98.0%, test micro 83.76%, test macro 83.29%\n",
      "Epoch 42: loss 0.00081, train micro 100.0%, train macro 100.0%, valid micro 98.0%, valid macro 98.0%, test micro 83.58%, test macro 83.13%\n",
      "Epoch 43: loss 0.00075, train micro 100.0%, train macro 100.0%, valid micro 98.0%, valid macro 98.0%, test micro 83.76%, test macro 83.35%\n",
      "Epoch 44: loss 0.00071, train micro 100.0%, train macro 100.0%, valid micro 98.0%, valid macro 98.0%, test micro 83.67%, test macro 83.26%\n",
      "Epoch 45: loss 0.00067, train micro 100.0%, train macro 100.0%, valid micro 98.0%, valid macro 98.0%, test micro 83.48%, test macro 83.08%\n",
      "Epoch 46: loss 0.00063, train micro 100.0%, train macro 100.0%, valid micro 97.67%, valid macro 97.66%, test micro 83.44%, test macro 83.05%\n",
      "Epoch 47: loss 0.0006, train micro 100.0%, train macro 100.0%, valid micro 97.67%, valid macro 97.66%, test micro 83.39%, test macro 83.02%\n",
      "Epoch 48: loss 0.00058, train micro 100.0%, train macro 100.0%, valid micro 97.67%, valid macro 97.66%, test micro 83.39%, test macro 83.05%\n",
      "Epoch 49: loss 0.00056, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.33%, test micro 83.2%, test macro 82.87%\n",
      "Epoch 50: loss 0.00054, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.33%, test micro 83.15%, test macro 82.84%\n",
      "Epoch 51: loss 0.00052, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.33%, test micro 83.11%, test macro 82.79%\n",
      "Epoch 52: loss 0.0005, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.33%, test micro 83.15%, test macro 82.86%\n",
      "Epoch 53: loss 0.00049, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.33%, test micro 83.11%, test macro 82.82%\n",
      "Epoch 54: loss 0.00047, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.99%, test micro 82.96%, test macro 82.69%\n",
      "Epoch 55: loss 0.00046, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.99%, test micro 83.01%, test macro 82.74%\n",
      "Epoch 56: loss 0.00045, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.99%, test micro 83.15%, test macro 82.89%\n",
      "Epoch 57: loss 0.00044, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.15%, test macro 82.9%\n",
      "Epoch 58: loss 0.00043, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.11%, test macro 82.85%\n",
      "Epoch 59: loss 0.00042, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.11%, test macro 82.86%\n",
      "Epoch 60: loss 0.00041, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.25%, test macro 83.01%\n",
      "Epoch 61: loss 0.0004, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.11%, test macro 82.86%\n",
      "Epoch 62: loss 0.00039, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.06%, test macro 82.82%\n",
      "Epoch 63: loss 0.00039, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.01%, test macro 82.78%\n",
      "Epoch 64: loss 0.00038, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.99%, test micro 83.01%, test macro 82.78%\n",
      "Epoch 65: loss 0.00037, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.99%, test micro 82.96%, test macro 82.74%\n",
      "Epoch 66: loss 0.00037, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.99%, test micro 83.01%, test macro 82.79%\n",
      "Epoch 67: loss 0.00036, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.99%, test micro 82.92%, test macro 82.7%\n",
      "Epoch 68: loss 0.00035, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.99%, test micro 82.78%, test macro 82.57%\n",
      "Epoch 69: loss 0.00035, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.99%, test micro 82.68%, test macro 82.47%\n",
      "Epoch 70: loss 0.00034, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.99%, test micro 82.68%, test macro 82.48%\n",
      "Epoch 71: loss 0.00034, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.65%, test micro 82.73%, test macro 82.53%\n",
      "Epoch 72: loss 0.00034, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 82.73%, test macro 82.54%\n",
      "Epoch 73: loss 0.00033, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 82.68%, test macro 82.49%\n",
      "Epoch 74: loss 0.00033, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 82.68%, test macro 82.48%\n",
      "Epoch 75: loss 0.00032, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 82.68%, test macro 82.48%\n",
      "Epoch 76: loss 0.00032, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 82.68%, test macro 82.48%\n",
      "Epoch 77: loss 0.00032, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 82.59%, test macro 82.39%\n",
      "Epoch 78: loss 0.00031, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 82.59%, test macro 82.39%\n",
      "Epoch 79: loss 0.00031, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 82.64%, test macro 82.44%\n",
      "Epoch 80: loss 0.0003, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 82.64%, test macro 82.44%\n",
      "Epoch 81: loss 0.0003, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 82.68%, test macro 82.49%\n",
      "Epoch 82: loss 0.0003, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 82.64%, test macro 82.45%\n",
      "Epoch 83: loss 0.0003, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 82.59%, test macro 82.41%\n",
      "Epoch 84: loss 0.00029, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 82.54%, test macro 82.36%\n",
      "Epoch 85: loss 0.00029, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 82.49%, test macro 82.31%\n",
      "Epoch 86: loss 0.00029, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 82.59%, test macro 82.41%\n",
      "Epoch 87: loss 0.00028, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 82.54%, test macro 82.36%\n",
      "Epoch 88: loss 0.00028, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 82.54%, test macro 82.35%\n",
      "Epoch 89: loss 0.00028, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 82.59%, test macro 82.4%\n",
      "Epoch 90: loss 0.00028, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 82.54%, test macro 82.36%\n",
      "Epoch 91: loss 0.00027, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 82.54%, test macro 82.36%\n",
      "Epoch 92: loss 0.00027, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 82.49%, test macro 82.31%\n",
      "Epoch 93: loss 0.00027, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 82.49%, test macro 82.31%\n",
      "Epoch 94: loss 0.00027, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 82.49%, test macro 82.31%\n",
      "Epoch 95: loss 0.00027, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 82.45%, test macro 82.26%\n",
      "Epoch 96: loss 0.00026, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 82.45%, test macro 82.26%\n",
      "Epoch 97: loss 0.00026, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 82.4%, test macro 82.21%\n",
      "Epoch 98: loss 0.00026, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 82.4%, test macro 82.21%\n",
      "Epoch 99: loss 0.00026, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 82.45%, test macro 82.26%\n",
      "Epoch 100: loss 0.00026, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 82.4%, test macro 82.21%\n",
      "Best model: train micro 100.0%, train macro 100.0%, valid micro 98.67%, valid macro 98.67%, test micro 83.95%, test macro 83.3%\n"
     ]
    }
   ],
   "source": [
    "best_model = None\n",
    "best_val = 0\n",
    "\n",
    "model = HeteroGNN(hetero_graph, args, aggr=\"mean\").to(args['device'])\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=args['lr'], weight_decay=args['weight_decay'])\n",
    "\n",
    "for epoch in range(args['epochs']):\n",
    "    loss = train(model, optimizer, hetero_graph, train_idx)\n",
    "    accs, best_model, best_val = test(model, hetero_graph, [train_idx, val_idx, test_idx], best_model, best_val)\n",
    "    print(\n",
    "        f\"Epoch {epoch + 1}: loss {round(loss, 5)}, \"\n",
    "        f\"train micro {round(accs[0][0] * 100, 2)}%, train macro {round(accs[0][1] * 100, 2)}%, \"\n",
    "        f\"valid micro {round(accs[1][0] * 100, 2)}%, valid macro {round(accs[1][1] * 100, 2)}%, \"\n",
    "        f\"test micro {round(accs[2][0] * 100, 2)}%, test macro {round(accs[2][1] * 100, 2)}%\"\n",
    "    )\n",
    "best_accs, _, _ = test(best_model, hetero_graph, [train_idx, val_idx, test_idx])\n",
    "print(\n",
    "    f\"Best model: \"\n",
    "    f\"train micro {round(best_accs[0][0] * 100, 2)}%, train macro {round(best_accs[0][1] * 100, 2)}%, \"\n",
    "    f\"valid micro {round(best_accs[1][0] * 100, 2)}%, valid macro {round(best_accs[1][1] * 100, 2)}%, \"\n",
    "    f\"test micro {round(best_accs[2][0] * 100, 2)}%, test macro {round(best_accs[2][1] * 100, 2)}%\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3oITravjOVV4"
   },
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rtkKBI_nKS1T"
   },
   "source": [
    "## Question 2.1: What is the maximum **micro** F1 score you could get for the best_model on test set when using the mean aggregation? (10 points)\n",
    "\n",
    "Submit your answers on Gradescope."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AIvw51jMKTvn"
   },
   "source": [
    "## Question 2.2: What is the maximum **macro** F1 score you could get for the best_model on test set when using the mean aggregation? (10 points)\n",
    "\n",
    "Submit your answers on Gradescope."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JBiYvwcuKd0z"
   },
   "source": [
    "## Training the Attention Aggregation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "e6na5zyQKfvi",
    "outputId": "0571122b-038b-4374-b059-4b4329e6b311"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1: loss 1.10299, train micro 33.33%, train macro 16.67%, valid micro 33.33%, valid macro 16.67%, test micro 35.81%, test macro 17.58%\n",
      "Epoch 2: loss 1.09589, train micro 33.33%, train macro 16.67%, valid micro 33.33%, valid macro 16.67%, test micro 35.81%, test macro 17.58%\n",
      "Epoch 3: loss 1.06968, train micro 35.67%, train macro 21.32%, valid micro 33.33%, valid macro 16.67%, test micro 35.91%, test macro 17.79%\n",
      "Epoch 4: loss 1.01626, train micro 63.0%, train macro 52.83%, valid micro 60.67%, valid macro 51.0%, test micro 55.06%, test macro 45.86%\n",
      "Epoch 5: loss 0.92245, train micro 66.17%, train macro 54.97%, valid micro 66.0%, valid macro 55.07%, test micro 64.33%, test macro 53.74%\n",
      "Epoch 6: loss 0.77993, train micro 66.67%, train macro 55.69%, valid micro 66.33%, valid macro 55.22%, test micro 65.27%, test macro 54.31%\n",
      "Epoch 7: loss 0.59896, train micro 67.83%, train macro 58.01%, valid micro 66.33%, valid macro 55.64%, test micro 65.55%, test macro 54.54%\n",
      "Epoch 8: loss 0.41629, train micro 70.17%, train macro 62.37%, valid micro 66.67%, valid macro 56.28%, test micro 65.6%, test macro 54.61%\n",
      "Epoch 9: loss 0.27338, train micro 73.67%, train macro 68.41%, valid micro 68.67%, valid macro 60.37%, test micro 65.69%, test macro 54.86%\n",
      "Epoch 10: loss 0.18104, train micro 77.83%, train macro 74.67%, valid micro 73.33%, valid macro 68.61%, test micro 66.45%, test macro 56.3%\n",
      "Epoch 11: loss 0.12386, train micro 85.83%, train macro 84.9%, valid micro 81.67%, valid macro 80.23%, test micro 68.56%, test macro 60.25%\n",
      "Epoch 12: loss 0.08619, train micro 92.5%, train macro 92.27%, valid micro 88.0%, valid macro 87.48%, test micro 71.15%, test macro 65.05%\n",
      "Epoch 13: loss 0.06067, train micro 95.5%, train macro 95.42%, valid micro 91.33%, valid macro 91.07%, test micro 74.35%, test macro 70.2%\n",
      "Epoch 14: loss 0.04417, train micro 97.17%, train macro 97.14%, valid micro 92.33%, valid macro 92.13%, test micro 77.08%, test macro 74.17%\n",
      "Epoch 15: loss 0.03367, train micro 98.33%, train macro 98.32%, valid micro 94.0%, valid macro 93.9%, test micro 79.58%, test macro 77.59%\n",
      "Epoch 16: loss 0.0268, train micro 99.0%, train macro 99.0%, valid micro 95.33%, valid macro 95.28%, test micro 81.08%, test macro 79.61%\n",
      "Epoch 17: loss 0.02217, train micro 99.0%, train macro 99.0%, valid micro 96.33%, valid macro 96.31%, test micro 82.4%, test macro 81.27%\n",
      "Epoch 18: loss 0.0189, train micro 99.5%, train macro 99.5%, valid micro 97.0%, valid macro 96.99%, test micro 83.29%, test macro 82.36%\n",
      "Epoch 19: loss 0.01644, train micro 99.5%, train macro 99.5%, valid micro 97.33%, valid macro 97.33%, test micro 83.91%, test macro 83.11%\n",
      "Epoch 20: loss 0.01448, train micro 99.67%, train macro 99.67%, valid micro 97.67%, valid macro 97.66%, test micro 84.24%, test macro 83.52%\n",
      "Epoch 21: loss 0.01281, train micro 99.83%, train macro 99.83%, valid micro 98.0%, valid macro 98.0%, test micro 84.42%, test macro 83.77%\n",
      "Epoch 22: loss 0.01136, train micro 100.0%, train macro 100.0%, valid micro 98.33%, valid macro 98.33%, test micro 84.75%, test macro 84.13%\n",
      "Epoch 23: loss 0.01006, train micro 100.0%, train macro 100.0%, valid micro 98.67%, valid macro 98.66%, test micro 84.8%, test macro 84.2%\n",
      "Epoch 24: loss 0.00874, train micro 100.0%, train macro 100.0%, valid micro 98.33%, valid macro 98.33%, test micro 84.94%, test macro 84.34%\n",
      "Epoch 25: loss 0.00758, train micro 100.0%, train macro 100.0%, valid micro 98.33%, valid macro 98.33%, test micro 84.89%, test macro 84.29%\n",
      "Epoch 26: loss 0.0066, train micro 100.0%, train macro 100.0%, valid micro 98.0%, valid macro 97.99%, test micro 84.89%, test macro 84.32%\n",
      "Epoch 27: loss 0.00577, train micro 100.0%, train macro 100.0%, valid micro 98.0%, valid macro 97.99%, test micro 84.99%, test macro 84.45%\n",
      "Epoch 28: loss 0.00511, train micro 100.0%, train macro 100.0%, valid micro 97.67%, valid macro 97.67%, test micro 84.8%, test macro 84.26%\n",
      "Epoch 29: loss 0.00441, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.34%, test micro 84.71%, test macro 84.18%\n",
      "Epoch 30: loss 0.00371, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 97.01%, test micro 84.33%, test macro 83.79%\n",
      "Epoch 31: loss 0.00308, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 97.01%, test micro 84.19%, test macro 83.65%\n",
      "Epoch 32: loss 0.00257, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 97.01%, test micro 84.14%, test macro 83.62%\n",
      "Epoch 33: loss 0.00216, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.34%, test micro 84.14%, test macro 83.65%\n",
      "Epoch 34: loss 0.00185, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 97.01%, test micro 84.09%, test macro 83.64%\n",
      "Epoch 35: loss 0.00159, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 97.01%, test micro 83.95%, test macro 83.49%\n",
      "Epoch 36: loss 0.00139, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 97.01%, test micro 83.81%, test macro 83.35%\n",
      "Epoch 37: loss 0.00123, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 97.01%, test micro 83.81%, test macro 83.37%\n",
      "Epoch 38: loss 0.00111, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.34%, test micro 83.76%, test macro 83.33%\n",
      "Epoch 39: loss 0.00101, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.34%, test micro 83.81%, test macro 83.4%\n",
      "Epoch 40: loss 0.00093, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.34%, test micro 83.81%, test macro 83.42%\n",
      "Epoch 41: loss 0.00086, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.34%, test micro 83.72%, test macro 83.33%\n",
      "Epoch 42: loss 0.00081, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.34%, test micro 83.58%, test macro 83.19%\n",
      "Epoch 43: loss 0.00076, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.34%, test micro 83.72%, test macro 83.36%\n",
      "Epoch 44: loss 0.00072, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.34%, test micro 83.81%, test macro 83.46%\n",
      "Epoch 45: loss 0.00069, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.67%, test micro 83.81%, test macro 83.48%\n",
      "Epoch 46: loss 0.00066, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.67%, test micro 83.67%, test macro 83.36%\n",
      "Epoch 47: loss 0.00063, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.67%, test micro 83.58%, test macro 83.28%\n",
      "Epoch 48: loss 0.00061, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.67%, test micro 83.53%, test macro 83.23%\n",
      "Epoch 49: loss 0.00058, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 97.0%, test micro 83.62%, test macro 83.33%\n",
      "Epoch 50: loss 0.00057, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 97.0%, test micro 83.62%, test macro 83.36%\n",
      "Epoch 51: loss 0.00055, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.34%, test micro 83.48%, test macro 83.23%\n",
      "Epoch 52: loss 0.00053, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 97.0%, test micro 83.48%, test macro 83.24%\n",
      "Epoch 53: loss 0.00052, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.67%, test micro 83.53%, test macro 83.29%\n",
      "Epoch 54: loss 0.0005, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.67%, test micro 83.53%, test macro 83.3%\n",
      "Epoch 55: loss 0.00049, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.67%, test micro 83.62%, test macro 83.41%\n",
      "Epoch 56: loss 0.00048, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.67%, test micro 83.76%, test macro 83.55%\n",
      "Epoch 57: loss 0.00047, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.67%, test micro 83.81%, test macro 83.6%\n",
      "Epoch 58: loss 0.00046, train micro 100.0%, train macro 100.0%, valid micro 96.33%, valid macro 96.34%, test micro 83.81%, test macro 83.61%\n",
      "Epoch 59: loss 0.00045, train micro 100.0%, train macro 100.0%, valid micro 96.33%, valid macro 96.34%, test micro 83.72%, test macro 83.51%\n",
      "Epoch 60: loss 0.00044, train micro 100.0%, train macro 100.0%, valid micro 96.33%, valid macro 96.34%, test micro 83.67%, test macro 83.47%\n",
      "Epoch 61: loss 0.00043, train micro 100.0%, train macro 100.0%, valid micro 96.33%, valid macro 96.34%, test micro 83.67%, test macro 83.48%\n",
      "Epoch 62: loss 0.00042, train micro 100.0%, train macro 100.0%, valid micro 96.33%, valid macro 96.34%, test micro 83.67%, test macro 83.49%\n",
      "Epoch 63: loss 0.00041, train micro 100.0%, train macro 100.0%, valid micro 96.33%, valid macro 96.34%, test micro 83.58%, test macro 83.41%\n",
      "Epoch 64: loss 0.0004, train micro 100.0%, train macro 100.0%, valid micro 96.33%, valid macro 96.34%, test micro 83.53%, test macro 83.37%\n",
      "Epoch 65: loss 0.0004, train micro 100.0%, train macro 100.0%, valid micro 96.33%, valid macro 96.34%, test micro 83.53%, test macro 83.38%\n",
      "Epoch 66: loss 0.00039, train micro 100.0%, train macro 100.0%, valid micro 96.33%, valid macro 96.34%, test micro 83.53%, test macro 83.38%\n",
      "Epoch 67: loss 0.00038, train micro 100.0%, train macro 100.0%, valid micro 96.33%, valid macro 96.34%, test micro 83.44%, test macro 83.28%\n",
      "Epoch 68: loss 0.00038, train micro 100.0%, train macro 100.0%, valid micro 96.33%, valid macro 96.34%, test micro 83.48%, test macro 83.34%\n",
      "Epoch 69: loss 0.00037, train micro 100.0%, train macro 100.0%, valid micro 96.33%, valid macro 96.34%, test micro 83.62%, test macro 83.49%\n",
      "Epoch 70: loss 0.00037, train micro 100.0%, train macro 100.0%, valid micro 96.33%, valid macro 96.34%, test micro 83.58%, test macro 83.44%\n",
      "Epoch 71: loss 0.00036, train micro 100.0%, train macro 100.0%, valid micro 96.0%, valid macro 96.01%, test micro 83.53%, test macro 83.4%\n",
      "Epoch 72: loss 0.00036, train micro 100.0%, train macro 100.0%, valid micro 96.0%, valid macro 96.01%, test micro 83.58%, test macro 83.46%\n",
      "Epoch 73: loss 0.00035, train micro 100.0%, train macro 100.0%, valid micro 96.0%, valid macro 96.01%, test micro 83.58%, test macro 83.46%\n",
      "Epoch 74: loss 0.00035, train micro 100.0%, train macro 100.0%, valid micro 96.0%, valid macro 96.01%, test micro 83.58%, test macro 83.46%\n",
      "Epoch 75: loss 0.00034, train micro 100.0%, train macro 100.0%, valid micro 96.0%, valid macro 96.01%, test micro 83.58%, test macro 83.46%\n",
      "Epoch 76: loss 0.00034, train micro 100.0%, train macro 100.0%, valid micro 96.0%, valid macro 96.01%, test micro 83.62%, test macro 83.51%\n",
      "Epoch 77: loss 0.00033, train micro 100.0%, train macro 100.0%, valid micro 96.0%, valid macro 96.01%, test micro 83.58%, test macro 83.47%\n",
      "Epoch 78: loss 0.00033, train micro 100.0%, train macro 100.0%, valid micro 96.0%, valid macro 96.01%, test micro 83.62%, test macro 83.52%\n",
      "Epoch 79: loss 0.00033, train micro 100.0%, train macro 100.0%, valid micro 96.0%, valid macro 96.01%, test micro 83.67%, test macro 83.57%\n",
      "Epoch 80: loss 0.00032, train micro 100.0%, train macro 100.0%, valid micro 96.0%, valid macro 96.01%, test micro 83.62%, test macro 83.53%\n",
      "Epoch 81: loss 0.00032, train micro 100.0%, train macro 100.0%, valid micro 96.0%, valid macro 96.01%, test micro 83.67%, test macro 83.58%\n",
      "Epoch 82: loss 0.00032, train micro 100.0%, train macro 100.0%, valid micro 96.0%, valid macro 96.01%, test micro 83.67%, test macro 83.58%\n",
      "Epoch 83: loss 0.00031, train micro 100.0%, train macro 100.0%, valid micro 96.0%, valid macro 96.01%, test micro 83.67%, test macro 83.58%\n",
      "Epoch 84: loss 0.00031, train micro 100.0%, train macro 100.0%, valid micro 96.0%, valid macro 96.01%, test micro 83.67%, test macro 83.58%\n",
      "Epoch 85: loss 0.00031, train micro 100.0%, train macro 100.0%, valid micro 96.0%, valid macro 96.01%, test micro 83.62%, test macro 83.53%\n",
      "Epoch 86: loss 0.0003, train micro 100.0%, train macro 100.0%, valid micro 96.0%, valid macro 96.01%, test micro 83.62%, test macro 83.53%\n",
      "Epoch 87: loss 0.0003, train micro 100.0%, train macro 100.0%, valid micro 96.0%, valid macro 96.01%, test micro 83.62%, test macro 83.53%\n",
      "Epoch 88: loss 0.0003, train micro 100.0%, train macro 100.0%, valid micro 96.0%, valid macro 96.01%, test micro 83.58%, test macro 83.49%\n",
      "Epoch 89: loss 0.0003, train micro 100.0%, train macro 100.0%, valid micro 96.0%, valid macro 96.01%, test micro 83.58%, test macro 83.49%\n",
      "Epoch 90: loss 0.00029, train micro 100.0%, train macro 100.0%, valid micro 96.0%, valid macro 96.01%, test micro 83.58%, test macro 83.49%\n",
      "Epoch 91: loss 0.00029, train micro 100.0%, train macro 100.0%, valid micro 96.0%, valid macro 96.01%, test micro 83.58%, test macro 83.49%\n",
      "Epoch 92: loss 0.00029, train micro 100.0%, train macro 100.0%, valid micro 96.0%, valid macro 96.01%, test micro 83.53%, test macro 83.44%\n",
      "Epoch 93: loss 0.00028, train micro 100.0%, train macro 100.0%, valid micro 96.0%, valid macro 96.01%, test micro 83.58%, test macro 83.49%\n",
      "Epoch 94: loss 0.00028, train micro 100.0%, train macro 100.0%, valid micro 96.0%, valid macro 96.01%, test micro 83.58%, test macro 83.49%\n",
      "Epoch 95: loss 0.00028, train micro 100.0%, train macro 100.0%, valid micro 96.0%, valid macro 96.01%, test micro 83.58%, test macro 83.49%\n",
      "Epoch 96: loss 0.00028, train micro 100.0%, train macro 100.0%, valid micro 96.0%, valid macro 96.01%, test micro 83.53%, test macro 83.44%\n",
      "Epoch 97: loss 0.00028, train micro 100.0%, train macro 100.0%, valid micro 96.0%, valid macro 96.01%, test micro 83.48%, test macro 83.4%\n",
      "Epoch 98: loss 0.00027, train micro 100.0%, train macro 100.0%, valid micro 96.0%, valid macro 96.01%, test micro 83.39%, test macro 83.3%\n",
      "Epoch 99: loss 0.00027, train micro 100.0%, train macro 100.0%, valid micro 96.0%, valid macro 96.01%, test micro 83.34%, test macro 83.25%\n",
      "Epoch 100: loss 0.00027, train micro 100.0%, train macro 100.0%, valid micro 96.0%, valid macro 96.01%, test micro 83.34%, test macro 83.25%\n",
      "Best model: train micro 100.0%, train macro 100.0%, valid micro 98.67%, valid macro 98.66%, test micro 84.8%, test macro 84.2%\n"
     ]
    }
   ],
   "source": [
    "best_model = None\n",
    "best_val = 0\n",
    "\n",
    "output_size = hetero_graph.num_node_labels('paper')\n",
    "model = HeteroGNN(hetero_graph, args, aggr=\"attn\").to(args['device'])\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=args['lr'], weight_decay=args['weight_decay'])\n",
    "\n",
    "for epoch in range(args['epochs']):\n",
    "    loss = train(model, optimizer, hetero_graph, train_idx)\n",
    "    accs, best_model, best_val = test(model, hetero_graph, [train_idx, val_idx, test_idx], best_model, best_val)\n",
    "    print(\n",
    "        f\"Epoch {epoch + 1}: loss {round(loss, 5)}, \"\n",
    "        f\"train micro {round(accs[0][0] * 100, 2)}%, train macro {round(accs[0][1] * 100, 2)}%, \"\n",
    "        f\"valid micro {round(accs[1][0] * 100, 2)}%, valid macro {round(accs[1][1] * 100, 2)}%, \"\n",
    "        f\"test micro {round(accs[2][0] * 100, 2)}%, test macro {round(accs[2][1] * 100, 2)}%\"\n",
    "    )\n",
    "best_accs, _, _ = test(best_model, hetero_graph, [train_idx, val_idx, test_idx])\n",
    "print(\n",
    "    f\"Best model: \"\n",
    "    f\"train micro {round(best_accs[0][0] * 100, 2)}%, train macro {round(best_accs[0][1] * 100, 2)}%, \"\n",
    "    f\"valid micro {round(best_accs[1][0] * 100, 2)}%, valid macro {round(best_accs[1][1] * 100, 2)}%, \"\n",
    "    f\"test micro {round(best_accs[2][0] * 100, 2)}%, test macro {round(best_accs[2][1] * 100, 2)}%\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mtAhFLQQKgbl"
   },
   "source": [
    "## Question 2.3: What is the maximum **micro** F1 score you could get for the best_model on test set when using the attention aggregation? (4 points)\n",
    "\n",
    "Submit your answers on Gradescope."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_cnsMGbsqJG_"
   },
   "source": [
    "## Question 2.4: What is the maximum **macro** F1 score you could get for the best_model on test set when using the attention aggregation? (4 points)\n",
    "\n",
    "Submit your answers on Gradescope."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aQgx5y4UqMHH"
   },
   "source": [
    "## Attention for each Message Type\n",
    "\n",
    "Through message type level attention we can learn that which message type is more important to which layer.\n",
    "\n",
    "Here we will print out and show that each layer pay how much attention on each message type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hvK58gijqN_C",
    "outputId": "39854ec1-52ed-45bc-9772-262e4df4c06f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Layer 1 has attention tensor([0.5581], device='cuda:0') on message type ('paper', 'author', 'paper')\n",
      "Layer 1 has attention tensor([0.4419], device='cuda:0') on message type ('paper', 'subject', 'paper')\n",
      "Layer 2 has attention tensor([0.4462], device='cuda:0') on message type ('paper', 'author', 'paper')\n",
      "Layer 2 has attention tensor([0.5538], device='cuda:0') on message type ('paper', 'subject', 'paper')\n"
     ]
    }
   ],
   "source": [
    "if model.convs1.alpha is not None and model.convs2.alpha is not None:\n",
    "    for idx, message_type in model.convs1.mapping.items():\n",
    "        print(f\"Layer 1 has attention {model.convs1.alpha[idx]} on message type {message_type}\")\n",
    "    for idx, message_type in model.convs2.mapping.items():\n",
    "        print(f\"Layer 2 has attention {model.convs2.alpha[idx]} on message type {message_type}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "e7JXsMTBgeOI"
   },
   "source": [
    "# Submission\n",
    "\n",
    "In order to get credit, you must go submit your answers on Gradescope.\n",
    "\n",
    "Also, you need to submit the `ipynb` file of Colab 4, by clicking `File` and `Download .ipynb`. Please make sure that your output of each cell is available in your `ipynb` file."
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "machine_shape": "hm",
   "name": "CS224W_Colab_4.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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