{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lab PyG\n",
    "\n",
    "### Antonio Longa \n",
    "### antonio.longa@unitn.it"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pytorch Geometric (PyG) \n",
    "\n",
    "Pytorch Geometric (PyG) is a geometric deep learning extension library for PyTorch. It consists of various methods for deep learning on graphs and other irregular structures. It implements plenty of graph neural networks from the literature and allows to easily prototype new ones.\n",
    "\n",
    "Adapted from tutorials and notebooks from https://github.com/rusty1s/pytorch_geometric"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating Message Passing Networks\n",
    "\n",
    "\n",
    "![title](img/img1.png)\n",
    "\n",
    "\n",
    "Graph neural networks can be defined in terms of a *neighborhood aggregation* or *message passing* scheme.\n",
    "With $\\mathbf{x}^{(k-1)}_i \\in \\mathbb{R}^F$ denoting node features of node $i$ in layer $(k-1)$ and $\\mathbf{e}_{j,i} \\in \\mathbb{R}^D$ denoting (optional) edge features from node $j$ to node $i$, message passing graph neural networks can be described as\n",
    "\n",
    "\n",
    "\n",
    "$$\n",
    "  \\mathbf{x}_i^{(k)} = \\gamma^{(k)} \\left( \\mathbf{x}_i^{(k-1)}, \\square_{j \\in \\mathcal{N}(i)} \\, \\phi^{(k)}\\left(\\mathbf{x}_i^{(k-1)}, \\mathbf{x}_j^{(k-1)},\\mathbf{e}_{j,i}\\right) \\right),\n",
    "$$\n",
    "\n",
    "where $\\square$ denotes a differentiable, permutation invariant function, *e.g.*, sum, mean or max, and $\\gamma$ and $\\phi$ denote differentiable functions such as MLPs (Multi Layer Perceptrons)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The \"MessagePassing\" Base Class\n",
    "\n",
    "PyTorch Geometric provides the `MessagePassing` base class, which helps in creating such kinds of message passing graph neural networks by automatically taking care of message propagation.\n",
    "The user only has to define the functions $\\phi$ , *i.e.* `message`, and $\\gamma$ , *i.e.* `update`, as well as the aggregation scheme to use, *i.e.* `aggr=\"add\"`, `aggr=\"mean\"` or `aggr=\"max\"`.\n",
    "\n",
    "<div>\n",
    "<img src=\"img/img2.png\" width=\"500\"/>\n",
    "</div>\n",
    "\n",
    "\n",
    "This is done with the help of the following methods:\n",
    "\n",
    "\n",
    "* `MessagePassing(aggr=\"add\", flow=\"source_to_target\", node_dim=-2)`: Defines the aggregation scheme to use (`\"add\"`, `\"mean\"` or `\"max\"`) and the flow direction of message passing (either `\"source_to_target\"` or `\"target_to_source\"`).\n",
    "  Furthermore, the `node_dim` attribute indicates along which axis to propagate.\n",
    "* `MessagePassing.propagate(edge_index, ...)`:\n",
    "  The initial call to start propagating messages.\n",
    "  Takes in the edge indices and all additional data which is needed to construct messages and to update node embeddings. \n",
    "* `MessagePassing.message(...)`: Constructs messages to node $i$ in analogy to $\\phi$ for each edge in $(j,i) \\in \\mathcal{E}$ if `flow=\"source_to_target\"` and $(i,j) \\in \\mathcal{E}$ if `flow=\"target_to_source\"`.\n",
    "* `MessagePassing.update(aggr_out, ...)`: Updates node embeddings in analogy to $\\gamma$ for each node $i \\in \\mathcal{V}$.\n",
    "  Takes in the output of aggregation as first argument and any argument which was initially passed to `MessagePassing.propagate`.\n",
    "\n",
    "Let us verify this by re-implementing two popular GNN variants, the `GCN layer from Kipf and Welling <https://arxiv.org/abs/1609.02907>`_ "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### edge index?\n",
    "\n",
    "\\begin{equation}\n",
    "Adj = \n",
    "\\begin{pmatrix}\n",
    "1 & 1 & 0\\\\\n",
    "1 & 1 & 0\\\\\n",
    "0 & 0 & 1\n",
    "\\end{pmatrix}\n",
    "\\end{equation}\n",
    "\n",
    "\\begin{equation}\n",
    "edgeIndex = \n",
    "\\begin{pmatrix}\n",
    "0 & 0 & 1 & 1 & 2\\\\\n",
    "0 & 1 & 0 & 1 & 2\n",
    "\\end{pmatrix}\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implementing the GCN Layer\n",
    "\n",
    "The GCN layer from Kipf and Welling is a popular GNN that was the first \n",
    "to bring the idea of layerwise convolutions to graphs processing. The layer\n",
    "is mathematically defined as\n",
    "\n",
    "$$\n",
    "    \\mathbf{x}_i^{(k)} = \\sum_{j \\in \\mathcal{N}(i) \\cup \\{ i \\}} \\frac{1}{\\sqrt{\\deg(i)} \\cdot \\sqrt{\\deg(j)}} \\cdot \\left( \\mathbf{\\Theta} \\cdot \\mathbf{x}_j^{(k-1)} \\right),\n",
    "$$\n",
    "\n",
    "where neighboring node features are first transformed by a weight matrix $\\mathbf{\\Theta}$, normalized by their degree, and finally summed up.\n",
    "This formula can be divided into the following steps:\n",
    "\n",
    "1. Add self-loops to the adjacency matrix.\n",
    "2. Linearly transform node feature matrix.\n",
    "3. Compute normalization coefficients.\n",
    "4. Normalize node features in $\\phi$.\n",
    "5. Sum up neighboring node features (`\"add\"` aggregation).\n",
    "\n",
    "Steps 1-3 are typically computed before message passing takes place.\n",
    "Steps 4-5 can be easily processed using the `MessagePassing` base class.\n",
    "The full layer implementation is shown below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Install required packages.\n",
    "!pip install -q torch-scatter -f https://pytorch-geometric.com/whl/torch-1.9.0+cu102.html\n",
    "!pip install -q torch-sparse -f https://pytorch-geometric.com/whl/torch-1.9.0+cu102.html\n",
    "!pip install -q torch-geometric\n",
    "\n",
    "import torch\n",
    "from torch_geometric.nn import MessagePassing\n",
    "from torch_geometric.utils import add_self_loops, degree\n",
    "\n",
    "class myGCNConv(MessagePassing):\n",
    "    def __init__(self, in_channels, out_channels):\n",
    "        super(myGCNConv, self).__init__(aggr='add')  # \"Add\" aggregation (Step 5).\n",
    "        self.lin = torch.nn.Linear(in_channels, out_channels)\n",
    "\n",
    "    def forward(self, x, edge_index):\n",
    "        # x has shape [N, in_channels]\n",
    "        # edge_index has shape [2, E] (sparse adjacency matrix as a list of edges)\n",
    "\n",
    "        # Step 1: Add self-loops to the adjacency matrix [edge list].\n",
    "        edge_index, _ = add_self_loops(edge_index, num_nodes=x.size(0))\n",
    "\n",
    "        # Step 2: Linearly transform node feature matrix.\n",
    "        x = self.lin(x)\n",
    "\n",
    "        # Step 3: Compute normalization.\n",
    "        row, col = edge_index\n",
    "        deg = degree(col, x.size(0), dtype=x.dtype)\n",
    "        deg_inv_sqrt = deg.pow(-0.5)\n",
    "        deg_inv_sqrt[deg_inv_sqrt == float('inf')] = 0\n",
    "        norm = deg_inv_sqrt[row] * deg_inv_sqrt[col]\n",
    "\n",
    "        # Step 4-5: Start propagating messages.\n",
    "        return self.propagate(edge_index, x=x, norm=norm)\n",
    "\n",
    "    def message(self, x_j, norm):\n",
    "        # x_j has shape [E, out_channels]\n",
    "\n",
    "        # Step 4: Normalize node features.\n",
    "        return norm.view(-1, 1) * x_j\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "zF5bw3m9UrMy"
   },
   "outputs": [],
   "source": [
    "# Helper function for visualization.\n",
    "%matplotlib inline\n",
    "import torch\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "def visualize(h, color, epoch=None, loss=None):\n",
    "    plt.figure(figsize=(7,7))\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "\n",
    "    if torch.is_tensor(h):\n",
    "        h = h.detach().cpu().numpy()\n",
    "        plt.scatter(h[:, 0], h[:, 1], s=140, c=color, cmap=\"Set2\")\n",
    "        if epoch is not None and loss is not None:\n",
    "            plt.xlabel(f'Epoch: {epoch}, Loss: {loss.item():.4f}', fontsize=16)\n",
    "    else:\n",
    "        nx.draw_networkx(G, pos=nx.spring_layout(G, seed=42), with_labels=False,\n",
    "                         node_color=color, cmap=\"Set2\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qoW2Z7P70LNQ"
   },
   "source": [
    "# Example: node classification\n",
    "\n",
    "Following [Kipf et al. (2017)](https://arxiv.org/abs/1609.02907), let's dive into the world of GNNs by looking at a simple graph-structured example, the well-known [**Zachary's karate club network**](https://en.wikipedia.org/wiki/Zachary%27s_karate_club). This graph describes a social network of 34 members of a karate club and documents links between members who interacted outside the club. Here, we are interested in detecting communities that arise from the member's interaction.\n",
    "\n",
    "PyTorch Geometric provides an easy access to this dataset via the [`torch_geometric.datasets`](https://pytorch-geometric.readthedocs.io/en/latest/modules/datasets.html#torch_geometric.datasets) subpackage:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "H_VTFHd0uFz6",
    "outputId": "e9472a23-e3c8-4ef2-86c1-64282d80de5d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset: KarateClub():\n",
      "======================\n",
      "Number of graphs: 1\n",
      "Number of features: 34\n",
      "Number of classes: 4\n"
     ]
    }
   ],
   "source": [
    "from torch_geometric.datasets import KarateClub\n",
    "\n",
    "dataset = KarateClub()\n",
    "print(f'Dataset: {dataset}:')\n",
    "print('======================')\n",
    "print(f'Number of graphs: {len(dataset)}')\n",
    "print(f'Number of features: {dataset.num_features}')\n",
    "print(f'Number of classes: {dataset.num_classes}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Data(x=[34, 34], edge_index=[2, 156], y=[34], train_mask=[34])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7cjjyFVnpKB0"
   },
   "source": [
    "After initializing the [`KarateClub`](https://pytorch-geometric.readthedocs.io/en/latest/modules/datasets.html#torch_geometric.datasets.KarateClub) dataset, we first can inspect some of its properties.\n",
    "For example, we can see that this dataset holds exactly **one graph**, and that each node in this dataset is assigned a **34-dimensional feature vector** (which uniquely describes the members of the karate club).\n",
    "Furthermore, the graph holds exactly **4 classes**, which represent the community each node belongs to.\n",
    "\n",
    "Let's now look at the underlying graph in more detail:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gUFSrDPxuQ23",
    "outputId": "98c222c0-d955-4607-8b0e-cb98c012cb79"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data(x=[34, 34], edge_index=[2, 156], y=[34], train_mask=[34])\n",
      "==============================================================\n",
      "Number of nodes: 34\n",
      "Number of edges: 156\n",
      "Average node degree: 4.59\n",
      "Number of training nodes: 4\n",
      "Training node label rate: 0.12\n",
      "Contains isolated nodes: False\n",
      "Contains self-loops: False\n",
      "Is undirected: True\n"
     ]
    }
   ],
   "source": [
    "data = dataset[0]  # Get the first graph object.\n",
    "\n",
    "print(data)\n",
    "print('==============================================================')\n",
    "\n",
    "# Gather some statistics about the graph.\n",
    "print(f'Number of nodes: {data.num_nodes}')\n",
    "print(f'Number of edges: {data.num_edges}')\n",
    "print(f'Average node degree: {data.num_edges / data.num_nodes:.2f}')\n",
    "print(f'Number of training nodes: {data.train_mask.sum()}')\n",
    "print(f'Training node label rate: {int(data.train_mask.sum()) / data.num_nodes:.2f}')\n",
    "print(f'Contains isolated nodes: {data.has_isolated_nodes()}')\n",
    "print(f'Contains self-loops: {data.has_self_loops()}')\n",
    "print(f'Is undirected: {data.is_undirected()}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MY4pZma9p3Ax"
   },
   "source": [
    "Each graph in PyTorch Geometric is represented by a single [`Data`](https://pytorch-geometric.readthedocs.io/en/latest/modules/data.html#torch_geometric.data.Data) object, which holds all the information to describe its graph representation.\n",
    "We can print the data object anytime via `print(data)` to receive a short summary about its attributes and their shapes:\n",
    "```\n",
    "Data(edge_index=[2, 156], x=[34, 34], y=[34], train_mask=[34])\n",
    "```\n",
    "We can see that this `data` object holds 4 attributes:\n",
    "(1) The `edge_index` property holds the information about the **graph connectivity**, *i.e.*, a tuple of source and destination node indices for each edge.\n",
    "PyG further refers to (2) **node features** as `x` (each of the 34 nodes is assigned a 34-dim feature vector), and to (3) **node labels** as `y` (each node is assigned to exactly one class).\n",
    "(4) There also exists an additional attribute called `train_mask`, which describes for which nodes we already know their community assigments.\n",
    "In total, we are only aware of the ground-truth labels of 4 nodes (one for each community), and the task is to infer the community assignment for the remaining nodes.\n",
    "\n",
    "\n",
    "We can further visualize the graph by converting it to the `networkx` library format, which implements, in addition to graph manipulation functionalities, powerful tools for visualization:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**to networkx**\n",
    "<div>\n",
    "<img src=\"img/img3.png\" width=\"500\"/>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 411
    },
    "id": "Y9MOs8iSwKFD",
    "outputId": "7bd4a2ef-08a4-458c-dfb5-09ea82fed059"
   },
   "outputs": [],
   "source": [
    "from torch_geometric.utils import to_networkx\n",
    "\n",
    "G = to_networkx(data,node_attrs=[\"x\"], to_undirected=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'x': [1.0,\n",
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       "  0.0]}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import networkx as nx\n",
    "\n",
    "G.nodes(data=True)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MUCrzb1u2jY1NltuSc8LY2JhSpUoZqEdCXhMBRcgRI4Wcnu3saFjHAlNjBcZG2fwTklSkpiTSsI4FPdvaoRA1UXJMpVIxaNAgWrVqpTkf8jb5HVDKlSun1/MVCgU2Njbig8d7TPyWCzkmk8lo7FSB8QOd6NTMGosy5hQyVWBkJKeQqQLLMuZ0bl6VFXOHI08KEW8QOpo+fTrJycm4ublpdb2RkVG+TXmp1WpWrVrFtm3bdO6DTCbLt/UfwTDE6pegM4VcjoN1KRyss56i+Prrr/j999/Zt29fHvfs/bd+/XoOHjzIlStXcnSAMT9GKA8ePGDs2LGkpKSwZs0aEhMTefHiRY5O88vlclxcXLR+rULBJEYoQq4ZNWoUly9f5s6dO/ndlffKhQsXmDFjBgcOHMjRekJeT3mlpqby22+/0axZM/r06cPFixepXbs29evXp1y5clqvpygUClxcXChdWpxRet+JgCLkmsKFC+Pq6sq8efPyuyvvjcePH9O/f3+2bt2a41K9eRlQvLy8cHFx4cKFC3h7ezN16lRNAJHL5dSvXx9HR0eKFi2KQqHINO2ZnJyMXC7HysqKFi1aULZs2Tzpt5C7xJSXkKsmTpyIra0tgYGB2NjY5Hd3CrS4uDg+/fRTvvvuO00mYG2o1WpevHhB4cKFqVq1KiEhIZQoUYIiRYoYvI8JCQn89NNPbNmyhcWLFzNkyJAs18hkMhkVK1akYsWKxMTEEBQURGJiIiqVCmNjYxYvXsycOXNylF9MKPhEQBFyVfHixRk3bhwLFy5kxYoV+d2dAkulUjF48GCaNm3KlClTtHpOYmIiT5484enTp0Bavi0nJydu3bqFJEkULVoUW1tbLCwsDHLo8NSpU4wdO5bGjRtz+/ZtrUcVxYsXp06dOhm+J5PJOHv2rAgoHxjZ29IlNGjQQPLy8srD7ggfoufPn1O9enXu3LmDpaVlfnenQPr222+5du0ax48f12phOigoiNu3bwO8dfFboVBQqFAhmjRpQqFChXTqW1RUFNOmTePkyZMsX76crl276tTOm9atW8fJkyfZsWOH3m0JeU8mk3lLktTgv98XayhCritXrhzDhg3jjz/+yO+uFEibNm1i7969uLu7axVMAgMDuXPnTpaldf9LpVKRkJDA+fPnSUpKylG/JEnC3d2dWrVqUbhwYW7fvm2QYALQrl07Tp8+bZD8X0LBIUYoQp4IDg7GycmJgIAAcRL6DZ6envTs2ZOzZ89Ss2bNd17//PlzvLy8clxgSyaTUbhwYVq1aqXV9FdoaCiTJk3i/v37rFu3jqZNm+boftqwtbXln3/+oXbt2gZvW8hdYoQi5KtKlSrRq1cvli1blt9dKTCePn1K37592bRpk1bBBNC53rskSSQlJREREfHW69RqNatXr8bJyYk6depw8+bNXAkmAG3btuXUqVO50raQP0RAEfLMt99+y7Jly3j9+nV+dyXfvX79mk8//ZRvvvmGTz75RKvnxMTEkJCQoPM9VSoVAQEB2T7u7+9P27ZtWbduHadPn2b27NmYmuZeQbR27dqJgPKBEQHFQJJTVNz2j8TzxjPOXg/iql8oj4KjUavFHHE6BwcH2rRpw+rVq/O7K/lKrVYzdOhQXFxc+OKLL7R+XmBgoE6jkzfFxcVlCuipqanMmzePJk2a0LNnTzw9PTPtysoNbdq04fz58/maf0wwLLFtWE+RUYl43wnnwZNXyGQyTa0QGWk1QOQKGXVrlMfJoSyFzURaie+++46uXbsyadKkXP30W5D98MMPvHr1it27d+coz1lUVJTei9gymYzo6GjNGRUfHx9Gjx5N2bJl8fLywtraWq/2c6J8+fJUqlQJb29vGjVqlGf3FXKPGKHo4eb952w/dJe7gS9RqqQMhackIEWpJilZxbVbYazfd5uwF2Kqx9nZGWdnZzZt2pTfXckXW7duZefOnezduzfHFRUN8UlekiSUSiUJCQl8++23fPLJJ3zxxRccPXo0T4NJOjHt9WERAUVHXnfCOe8VglIl8a4PjSqVREqqij3HH4qgAnz//ffMnz//o5vquHLlCl999RUHDhzQKdWIoSoiPnz4ECcnJ54+fcqtW7cYPnx4vmWEFgHlwyICig6CwmLxvPEMpSpn89lKpZq9J/xJTPq4a2Y3a9YMKysrdu/end9dyTNBQUH06dOH9evX67xN1hBThK9fv2bOnDl89tlnrF+/Xu8aJvpq1aoVV69ezfEZGaFgEgFFB5duPEOp0m0uW61W4+f/wsA9ev98//33/P7773ovMr8P4uPj6dGjB19++SXdunXTuR1ra2u9KyLK5XIaNWrE4cOHKVeuHM2bN+eHH37gxIkTxMfH69W2LooVK0aLFi04d+4cd+7c4fbt2/j7+xtkvUjIe+JgYw5FxSSx+eAdVDoGFACzQkaM6+eEXP7xFp6SJIkGDRowa9YsPv300/zuTq5Rq9X069ePYsWKsX79er2mllQqFcePH9eriJadnZ0mi3F8fDyenp6cO3eOs2fPcvPmTRwdHWndujWtWrWiWbNmuZJgEtL+/p8/f05AQACRkZHIZLIMU3oKhQJTU1NsbW2xsrLSO5AKhpXdwUYRUHLo9NUg/B48R5/dwMbGcrq1tKWqVXHDdew9tHfvXhYsWMCVK1c+2KqOP/74I2fOnOHUqVMGmbK6fv06z549w8go5xs05XI5bdq0wczMLMvHExISuHLlCmfPnuXcuXN4e3tTu3ZtTYBp3rw5RYsW1fcloFKp8PHxITIy8p3B0RC5yATDEyflDSQ8Ml6vYAKgUqqJjNb9gNqHolevXsTGxnLmzJn87kqu2LFjB1u3bsXDw8MgweTixYsMGDCA5OTkHAdguVxOnTp1sg0mkFa/pm3btsyePZtz587x4sUL5s2bR6FChZg/fz6WlpY0bNiQ6dOnc+jQIWJiYnL8GtRqNdeuXePFixdajbTSc5FdvHiRlJSUHN9PyFsioORQSqr+NbvVEiQn50/t74JELpczY8YMfvvtt/zuisFdu3aNqVOn8s8//+i98C1JEosWLaJPnz4sW7aMXr16UaRIEa13fcnlcmrUqJHjeu1mZma0bt2an3/+mbNnzxIZGcnChQspUqQIixcvxsrKigYNGjBt2jQOHjxIdHT0O9u8d+8e0dHROVo7kySJ5ORkrl69KtZVCjgx5ZVD2/69S8RL/UYXMqBZvYo0rCNSuaempmJnZ8fu3bs/mMNtISEhNG7cmOXLl+u9PhQdHc2IESMICwtjz549VK5cGUj75H7//n2CgoI0//8mmUyGTCajSJEi1KhRI1cqIiYnJ3Pt2jXNGszVq1ext7fXTJG1aNEiQyJQpVLJ8ePHdd6IoVAoaNy4MSVLljTUSxB0JNZQDOTw+UDuP36lVxvGRnLaN6lCDRtRQxvg77//5sSJE+zfvz+/u6K3hIQEWrZsSb9+/fj222/1asvHx4d+/frRtWtXFi1alOVByNevX7N69WoUCgXW1taYmZlhZGREqVKlsLGxoVixYnr1ISdSUlK4fv26JsBcuXIFGxsbTYCxt7cnODhYr00FlpaW1K9f34C9FnQhAoqBPHseh8cJ/wyn4nNMUjGyZw1KlMi7X/aCLDExERsbG06cOPFepzJXq9UMHDiQQoUKsWnTJp03GkiSxOrVq/nhhx9Yvnw5/fr1y/K6c+fOMWbMGJydnVmyZAkWFhb6dN/gUlNT8fLy4ty5c5w7d46+fftSvnx5vdqUy+W0b98+x1kGBMMSi/IGUqFsEQoX0icFmkTYkxvY2lblq6++4vHjxwbr2/vKzMyML774gnnz5uV3V/Qye/ZsgoODWb16tc7B5PXr1wwbNoy///6bS5cuZRlMoqOjGTduHEOHDmXhwoXs3r27wAUTAGNjY5o0acKMGTM4fPiw3sEE0gKKPhmXhdwlAkoOyWQyGtaxxEih24/OSCHn+69G4O3tjUKhwMXFhZ49e3LmzJl8XXB8/jKBYxcfs/XgXTbsu8XWg3c5ciGQ8Mi8Oew2YcIEjh49SmBgYJ7cz9B2797Nhg0b2L9/v87bW+/du0fDhg0xNjbmypUrODg4ZLpm//791K5dG5lMxu3bt+nRo4e+Xc8TarXaYFvDP7aUPe8TMeWlA0mSOHQ+kMDg6BydmDdSyGjXuAq17MpovhcfH8+WLVtYsmQJxsbGTJ06lcGDB791e6ch+T+NwvPGM2Jep6BSqzPkJZPJQCGXU9TchKbOFahWNXcrLf7444+8ePGClStX5up9DM3Ly4tPPvmEEydO4OzsrFMb27dvx9XVlfnz5zNy5MhMj4eHhzNlyhT8/PxYs2YNLVu21LPXeUuSJA4dOqR3O0ZGRjRq1EgszOczMeVlQDKZjE9aVMW2UgmMjLT7ERopZLRyqZQhmACYm5szfvx47ty5w8KFC/Hw8KBKlSrMnDmTZ8+e5Ub3gbRf8LPXgzly4TEvY5JQqtSZklxKEihVaqJikzh26QmnrjzN1VGUq6sru3fvJjQ0NNfuYWihoaH06tWLVatW6RRMkpOTmThxIrNmzeLkyZOZgokkSWzYsAFHR0fs7e3x9fV974IJpP3OGOK0u1qtzrMPW0LOiYCiI4VcTpeWNrRrVJkSRU0xMpLz3wG9XA7K1BRKFZXTp4MDTtWyP48gk8no2LEjhw4d4uLFi8TGxlKnTh0GDRrElStXDN7/iz7P8Hv4QusEl0qVmjsBkZy9HmzwvqQrU6YMn332GX/88Ueu3cOQEhMT6dmzJ+PHj6d37945fv7jx49p1qyZpk68k5NThscDAwPp0KEDy5Yt4/jx4/z222/v3WnxgIAA5s2bh4uLC+7u7nrt8AIoWbLke/cz+JiIgKIHmUxGLbsyfN6rNv06OlDDpjSWZc0pU9KMiuWLUK9GeRLCzuN1ej0Vy2ufssLBwYGlS5fy+PFjGjZsyODBg2nUqBHbtm0zyGnhoLBYbtx7jjKHO9WUKonb/pE8Dsn5CWltff3116xfv56XL1/m2j0MQZIkRo4cib29Pd9//32On3/gwAEaN27MsGHD2LNnD8WL/y8Nj1KpZPHixTRs2JDOnTtz9epVnafS8sP9+/f59ddfcXZ2pnnz5gQFBTF//nzNtK6uFAoFtra2BuypYGhiDSWXhYSE4OjoSFBQkM6J9lQqFYcOHcLNzY179+4xYcIExo0bp/MJbPfjDwgKi9PpuQAVyxVhwCfVdX7+u4wZM4aKFSvy888/59o99DVnzhz+/fdfzp49m6MpGKVSycyZM9mxYwe7du2iSZMmGR739fVl9OjRFCtWjNWrV78Xb6CSJHHnzh3c3d1xd3cnKiqKPn360LdvX5o1a5ZhqsvLy4uIiAidpk4LFSpEu3btPti8b+8TsYaST6ysrGjTpg3btm3TuQ2FQsGnn37KqVOnOHbsGEFBQVSrVo3PP/+cmzdv5qituPgUnkXoV+Qr/GU80bG5V79i+vTp/P3338TF6R70ctPevXtZs2YN+/fvz1EwCQ0NpW3btvj6+uLj45MhmCQlJTFz5kw6dOjAhAkTOHnyZIEOJpIkcfPmTX744Qdq1KhB165diYuLY/Xq1QQHB7NkyRJatmyZad3EyckJMzOzHAcFIyMjGjduLIJJAScCSh6YMGECy5cvN8iCdp06dVizZg3+/v44ODjQvXt3WrVqhYeHh1bbKe8ERKJvLyRJ4nZApJ6tZM/e3p727duzatWqXLuHrm7cuMH48ePZv38/lpbap845deoUDRo0oGPHjhw+fJgyZf63OeP8+fM4OTnx4MEDfH19GTlyZIF845QkievXr/Ptt99ib29Pnz59SE1NZcuWLTx58oTFixfTtGnTt+YYMzY2plmzZpibm2uVi0wmk2nOs+RWKn3BcMSUVx5Qq9XUqFGD9evX06xZM4O2nZqaioeHB0uWLOHZs2dMmjSJ0aNHZ7ut8ujFx9x9pP/6hEOVknRrnXufoP38/OjcuTOBgYEFZhE2LCyMRo0a8ccff9C3b1+tnqNWq/ntt99Yvnw5W7ZsoV27dprHYmNj+fbbbzlw4IAm6WNBo1aruXr1Ku7u7uzduxcTExP69etH3759cXZ21jnwqVQqHj16xJMnT1CpVJkW69NHNhUrVsTe3l7s7Cpgspvy0ufIt6AluVzOhAkTWLFihcEDirGxMQMGDGDAgAFcv36dJUuWYGNjw8CBA5k6dSo1atTIcL0hsiUDpChzN1uyo6Mj9evXZ+PGjYwfPz5X76WNpKQkevXqxejRo7UOJpGRkQwbNozXr1/j5eVFhQoVNI8dPHiQiRMn8sknn3Dnzh1KlCiRSz3POZVKhaenpyaIFC9enL59+3Lw4EHNoUp9KRQKHBwcsLe35/nz54SEhJCUlIQkSZiYmFC+fHkqVqyoU90XIf98FCMUSZKIi08hMVmJJEEhUyOKFzHJ02mFqKgobGxsePjwYa5kfn1TWFgYK1euZNWqVTg5OeHq6krnzp2Ry+Wc8HzCLX/9p6uqVy1Fl5Y2Buht9jw9PRkyZAj+/v75+sYiSRJDhw5FpVKxY8cOrf7dXLlyRRPo586dq9ndFBERwdSpU/Hx8WH16tW0adMmt7uvFaVSyYULF3B3d8fDw4Ny5crRt29f+vTpQ82aNfO7e0IB81Emh0xNVXEv8CXX70QQn5Dy/3O2Emo1FDJVUL9meWrZl6GQSd68WY0aNQoHBwe9s9BqKzk5mZ07d+Lm5kZ8fDxTpkyhUaseXPF7oVdySyMjOS3qVaRuDf1zM71L69atGT16NEOHDs31e2Xn999/x8PDg/Pnz79z6kWSJJYsWcLcuXNZu3atJn29JEls3ryZ6dOn8/nnnzNr1qx8n8ZJTU3l7NmzuLu7s2/fPipXrqwJIvb29vnaN6Fg++gCyp2ASE5dCUImI9s3TyOFHAmJZs4VqV+rfK6PWLy8vOjXrx8BAQF5WiNbkiQuXrzIkiVLuHDxEtN+80Au1z2IGilkjOvvhGkeBOLjx4/z5ZdfcuvWLa0LShnS/v37mTx5MlevXqVixYpvvTY2NpZRo0YRGBjInj17sLFJG8E9fvyYcePGERkZydq1a6lXr15edD1LKSkpnDx5End3dw4cOICdnZ0miFStWjXf+iW8Xz6qbcPXboVx6koQSpX6rZ/ElSo1KpWEp28oZ64F5XpyxgYNGlC2bFmOHj2aq/f5L5lMRosWLdizZw/Xrl4hKfopKh0T7KlUSqIi/EmIz5stvR06dMDMzIwDBw7kyf3e5Ovry5gxY9i3b987g4mvry/169enTJkyXLp0CRsbG1QqFX/++ScuLi60b9+ea9eu5UswSUpK4sCBAwwfPhwLCwt+++03HB0d8fHx4cqVK0ybNk0EE8EgPriAcj/wJVd8w7ROKQKgVKq5HfAS7zsRudizNBMnTmTFihW5fp/sVK5cmcmjemBqqtuJZRMTY16G3KR69eqsWrVK71Qa7yKTyfj+++/57bff8jQbc0REBD169GDZsmW4uLi89dr169fTvn17fv75Z1asWEGhQoXw8/OjSZMmHDhwgMuXLzN9+vQ8XQdKSEjAw8ODwYMHY2FhwR9//EHDhg25desWFy9e5IsvvtBUfxQEQ/mgAopKrebU1aAcBZN0SqWaSzefkZySu6mxBwwYwJUrV/K1DkqJYoX4tI1djlPwGylkdGtpy5I/53H06FG2bt1KgwYNuHDhQi71NE3Pnj2Ji4vj1KlTuXqfdOk7uj777DMGDBiQ7XUJCQl8/vnnLFq0iHPnzjFkyBCSk5P58ccfadeuHWPGjOHUqVN5th7x+vVrdu/eTf/+/bG0tGT58uW0bNmSBw8ecPbsWSZPnvzOkZYg6OODCiiPgmNQq3X/FCuTyQxyRuNtzMzM+Oyzz/L90J51xeL0aGuHsZEchfzta0cKuQxjIzndW9thW7kEAHXr1uX8+fN8++23DBkyhIEDBxIcnDuJI+VyOd999x2//fZbrrT/JkmSGDduHBUrVmTWrFnZXvfw4UMaN25MSkoK165do2bNmly6dAlnZ2fu3LmjmS7L7XWfmJgYtm3bRq9evahQoQIbNmygY8eOBAQEcPLkScaPH2+QwlaCoI0PKqBcvxWm1+4lpVKN1+3wXJ9aGT9+PBs2bCA5OTlX7/MuVSoU4/NetXGpbUFqSiKgQhNbJDUpyQmkJCdgKkUypGs1qloVz/B8mUzGwIEDuXfvHg4ODjg7OzNnzhwSExMN3tdBgwYRGBjI5cuXDd72mxYuXMitW7fYuHFjtsFgz549NGvWjIkTJ7J161bUajWTJk2if//+/Prrr3h4eGQ4c2JoUVFRbNq0ie7du1OpUiV27txJz549efr0KUeOHGH06NG5vjVdELLywQQUlVrN85f6lwZNSFbyOiHVAD3Knr29PU5OTri7u+fqfbRRpLAJtW2L8vs33Wnf0Irm9axo5GhJiwaV6d2xFjUtYnDfvJDaNe2ZPXs2z58/z9SGubk5s2fPxtvbG19fX2rWrMnevXsNGpiNjY2ZPn06v//+u8Ha/K8DBw7g5ubGgQMHMDc3z/R4SkoKrq6ufPvttxw9epTx48dz+PBhateuTXJyMrdv36ZPnz650rfIyEjWrVvHJ598QpUqVdi/f79mVHjw4EE+++wzUXRKyHcfTEBJTlYhVxjgBK9cRlJy7pcYnThxIsuXL8/1+2jj0KFDNGvWDMcaFWlQ24JmdSviUtsCB+vSfPppd06ePMnx48cJCQmhWrVqjBgxghs3bmRqx9raGnd3d9atW8esWbNo164dt27dMlg/P//8c65fv46fn5/B2kx369YtRo8ejYeHB1ZWVpkeDwoKomXLljx58gRvb28qVarEoEGDcHV1ZePGjaxdu9bgb+gRERGsWrWKDh06YGtry9GjRxkxYgTPnj1j3759DBkyJEPae0HIbx9MQJHJQO+sh0BKSio+N3wICwvL1amvbt26ERQUhK+vb67dQ1vu7u7069fvrdfUrl2b1atXExAQQI0aNejRowctW7Zk7969mZJStm3blps3b9K7d2/atm3LlClTePXqld79NDMz48svv2TevHl6t/Wm58+f8+mnn/LXX3/RqFGjTI8fOXIEFxcX+vTpw759+/j333+pU6cOVlZW+Pn50bZtW4P1JTQ0lGXLltG6dWuqVavGuXPnmDBhAmFhYezZs4cBAwZQtKj2tXUEIS99MAcb1WoJt63emcrY5rgdlZLD23/mtp8PCQkJ2NraYmdnh729PXZ2dpqvChUq6L3gOmfOHJ49e5avNdRfv35NxYoVefz4MaVKaV8zXqlUsm/fPtzc3AgODmby5MmMGjUqUxuRkZH8+OOPeHh48PPPPzN27Fi9DnXGxsZiY2PDlStXsLOz07mddMnJybRv355WrVrx66+/ZnhMpVIxa9YsNm7cyI4dO6hUqRLjx48nPDycdevWUb9+fb3vDxAcHMzevXtxd3fnzp07dO/enb59+9KxY8cCkxhTEN70UZyU17dwFECp4oUY0bM2kLaDJiAgIMuvmJgYbGxsMgUaOzs7rKystHrTDAsLo1atWjx58oRixYrp1W9d7d69m/Xr1+t12NLb2xs3NzcOHjzIgAEDmDp1aqb8Tzdv3sTV1ZWYmBhNrQxd/fTTT4SHh7N69Wqd24C0HV2jRo0iOjoad3f3DB8QwsPDGTx4MDKZjC1btrBnzx7mzJnD119/zbRp0/SqPAhpp+fTg4i/vz89evSgb9++tGvXDlNTU73aFoTc9lEElKehMRw480jnnV7GRnLaNa5MTdsy77w2Li6OR48eZQo0/v7+vHr1iqpVq2YKNPb29lSqVCnDAbf+/fvTqlUrJk2apFOf9dW/f386duzI6NGj9W4rPDxck5SyTp06uLq68sknn2jeqCVJYvfu3XzzzTc0adKEhQsX6nS4LjIyEgcHB27duqXXuYrFixezZcsWLl68mKHWxvnz5xk0aBCjRo2ib9++jB07FlNTU9asWYODg4PO9/P399cEkadPn9KrVy/69u1LmzZt9A5QgpCXPoqAIkkSq/f4EZ+o2y4tI4WMiYPq5vjA33/Fx8cTGBiYKdAEBATw/PlzqlSpogkykiTxzz//cPLkSaytrfP0jSUhIQFLS0sePXqUoeCTvpKTk9m1axdubm7ExcUxZcoURowYoZn7T0hIYP78+SxbtoypU6cyffr0HCdK/OqrrwD4448/dOrjoUOHGDNmDFeuXNEENbVazcKFC/nzzz9Zs2YNXl5eLF++nF9//VXnMyX37t3TBJHw8HB69+5N3759admypUjNLry3PoqAAhAUFsv+U/4oVTlbTJHUSg7t+pOfv5+k13TMuyQmJvL48WNNkPH392fr1q0ULVqUV69eUalSpQwjmvQ/V61aFRMTE4P2xcPDgxUrVnDixAmDtptOkiQ8PT1xc3Pj1KlTDB8+nMmTJ2tK2z558oRvvvmG69evs2jRIvr06aN1gs5nz55Rp04dHj58mONgeOfOHVq3bs2BAwc0ZXijoqIYPnw4kZGRTJ8+nZkzZ+Lg4MDff/+do1GQJEncvn1bU189JiZGU1+9adOmeZoUVBByy0cTUAAePH7FsUuPtQ4qRgoZzetZEfHUhxEjRjBt2jSmTZuWZ/VSli5dyqVLl9i0aRNPnjzRjGbe/AoODqZChQpZbhCwsbHRafF20KBBtG7dmnHjxuXCq8ooKCiI5cuXs27dOpo0aYKrqytt27ZFJpNx5swZpk6dSpkyZViyZAl16tTRqs2xY8diYWHB7Nmzte5HZGQkDRs25JdffmHYsGHA/7JAd+nSBUmS2L9/P25ubvTt21erfwPp9dXTg0hSUhJ9+/alb9++NGrUKF+yJAtCbvqoAgpAcHgsRy48JjlFle2airGRHCOFnA5Nq2BXOe0MQVBQEP369cPS0pKNGzfmSSW9mJgYrK2tuXfvHhYWFllek5qaypMnT7LcIPD06VPKly+fac3Gzs4OW1tbChcunKm9xMRELC0tefjwIeXKlcvtl6iRkJDA1q1bWbJkCTKZjKlTpzJkyBBMTExYtWoVv/zyC/3792f27Nnv3HUWEBBA48aNCQwMpFixYqSkqkhOSUtWWchUgbFRxtFASkoKHTp0oEmTJsybNw9JklixYgWzZs1izJgxbNu2jbZt27J48eJ33luSJLy8vDRBBNAEkQYNGhTImvCCYCgfXUCBtF/64PA4vG6H8zQsVvNLrlZLVChXhIa1LbCuWBz5f3JZJScnM23aNA4fPoy7uzt169bN9b6OGzeOypUrM3PmzBw/V6lUEhQUlOUGgcePH1OmTJlMgSYkJIR9+/Zx7ty5XHg17yZJEqdOnWLJkiVcuXKFUaNGMXHiRAoXLsyPP/6Iu7u7Zpvx29YahgwZinPD9ljYuPAyKhG5Qg5SWuaE8qXNcaltgW2lEshkaSOa58+fs2/fPhISEhgzZgx+fn7Y2Nhw584dVq9eTfv27bO9l1qt5sqVK5o1EVNTU4PUVxeE981HGVDeJEkSKakqJAlMTRRa/fLv3LmTKVOm8PvvvzNq1KhcfcO4efMmn376KY8fPzboPLtKpSIkJCTT5oCzZ88SHx+fKdi8OZ2WV1uZAwICWLZsGVu2bKF9+/a4urpSuHBhvvjiC6KionBzc6N169aZnnc/8CXHLz0mPiEB00KZR2GAJvll8qtbbFi1iEuXLhEUFETfvn0pV64c9+7dY9iwYcyePTvLdCsqlYpLly5p6quXKFFCMxIxVH11QXjffPQBRVf37t2jb9++uLi4sHz58iynjwyladOmzJgxQ1M2NrckJydjYWHB7du3kSQpQ6B588vc3DzLQGNnZ5creaNiY2PZuHEjS5cupUSJEkydOhWFQsF3331Ho0aNWLhwIVWqVAHg+u0wLt/Uvu5NakoyDWuVJuTRdb744gsqVqyITCZj7dq1meqdKJVKzp8/r6mvbmFhoalqWKNGDYO/bkF434iAoof4+HjGjRuHn58f7u7uep1FeJutW7eydevWXK/o+O+//7JgwQLOnz+f7TWSJBEeHp5loPH398fExCTLQ512dnaULl1ar0/uarWaw4cP4+bmxu3btxk5ciTJycls2LCBKVOm0HPAWM57h+V4J59apcRj06/c9b3IN998w/Tp0zXbtFNTUzlz5gzu7u7s379f1FcXhLcQAUVPkiSxatUqfvzxR1asWEHfvn0Nfo+kpCQqV66Mp6enQdKKZGfEiBHUq1ePqVOn6vR8SZJ4/vx5loHG398fuVyeZaCxt7enbNmyOQo2d+7cYenSpezatYu2bdvyOj6BNr2/x8Q0Z+dW0iUnvqZnq/LUqFGD5OTkDPXV7e3tRX11QdCCCCgGkr7FtGfPnsyfP9/gZ0O+/fZbzQG73JCSkoKlpSV+fn65Ur1PkiRevnyZ5aHOgIAAUlNTsww0dnZ2WFhYZBtsXr16xdq1azl18TZtu4/B2ES3gGJsJKO0aSRH/tnKv//+S61atejbty+9e/cWJXEFQUsioBjQq1evGD58OK9evWL37t1ZpjvXVWBgII0aNSIoKCjHp8e1cfToUebMmcOlS5cM3rY2Xr16pUlZ89/pNG2ScW7Yd4uoWP0Kk72KeIyl+XNNlUNBEHJGBBQDU6vVzJ8/Hzc3N7Zs2UKHDh0M1naXLl3oN2Awdeq35/mrBJJTVJiaKChdwoxadqUpUlj3UdGoUaOoXbs2X375pcH6aygxMTE8evQoy3WbmJgYatZ2ZuD4P5Ar9EtZIpPBlCH19E6xIwgfKxFQcsmZM2cYMmQI48eP54cfftD7VPTzlwnsP+5NTIIRpqYmGRae02u/V7YsRkNHCyqWy1ldjNTUVCwtLfHx8Xnvpnfi4uLwuxPAtQepSHqW8TFSyBjZu45egVkQPmbZBRSRnU5Pbdq0wcvLi4EDB+Lp6cnWrVt1TrR42/8Fp68Go1KZoTAi0y4mlTrt/x8/iyE4PJYmThVoUDv7dYf/Onv2LHZ2du9FMImLiyM0NJSwsDDCwsIIDQ3lxat4Stu0Q2Ek0rsLQkEkAooBVKhQgdOnT/P9999Tr149du/eTePGjXPUxp2ASE5fDdb6XIVSJXHZNwwAlzqWWj3H3d09V3anaUuSJGJiYjIEiez+LEkSlpaWFC9eHCMjI1JTU1Gq5QyZ3B59j32q1BKFTMU/fUEwNPFbZSBGRkYsWLCApk2b8umnn/Ljjz8yefJkrUYPL6ISOHUlSOtgkk6pUnPZNwzLskWwsnj79Fd6hcWrV6/m6B7akCSJqKiodwaJsLAwjIyMsLS0pEKFCpr/VqxYkWrVqhEfH8+LFy94+vQpd+/e5eHDh5iYmODk5ISzszOOjk4EvjIiWfnuPr1N+dLmYv1EEHKBCCgG1rNnT+rUqUPfvn25ePEia9eufWcNcK/b4ajUuhUFU6rUXPELpa9Ftbded/78eSpXrpyj8xXpW4CzCgxvfi8sLIxChQppgkR6oLC2tqZp06aa71laWlK4cGEePXrEzZs38fX1xdfXl127dhETE4OTkxNOTk60bduWL7/8klq1alG4cGESEhLYvXs3c+bMpmgZezr2Go9MrlvdGGMjOS61s07AKQiCfkRAyQW2trZ4enri6uqKi4sL7u7u1K5dO8trk1KUPHwaxVv2RrzTs4jXxMWnUNQ8+0Vmd3d3+vXrB6TtUHvx4sVbg0RoaCgREREUKVIkQ5CwtLTEzs6Oli1bZgoU/xUfH8+tW7fw9fVlx44d+Pr6cuvWLUqXLo2zszNOTk6MHDkSJycnrK2tM21ouHnzJqtXr2bnzp00a9aMGTNm0KFjZ9buva1zVU65XIZtpRI6PVcQhLcTASWXmJmZsXr1ajZt2kSbNm34448/NPU33vQg8BUyZIDuEUUC/B6+oLGjBc+fP88UJJ49e8bmzZuxt7dn6dKlPH/+nOLFi2cKFDVr1qRdu3aa71lYWGhVZ0WSJJ49e6YZdaT/Nzg4mBo1amiCx8CBA3F0dHxrHrC4uDh27tzJ6tWriYiIYPTo0fj5+WU469PapRInPB+DLGfTVkYKGR2aVMmUXVoQBMMQASWXffbZZ9SrV4++ffty6dIl/vrrrwxv0pHRiTleO/kvtVpi6w4PWjf8ilKlSmUIEpaWlpibm1OhQgXWrl2LpaUlFhYWOp/wT0lJ4f79+5mCh1wux9nZGWdnZ3r06MFPP/1EtWrVtCppnF5bZPXq1bi7u9OmTRtmz55Nx44ds8y8fOn0Xi5fDqBp2/5abyGW1Cqau1jjYP32OieCIOhOBJQ8UKdOHa5fv86oUaNo1qwZ7u7umrWM9IJQ+mrarCVL5iZk+QY+ZcoURo0aRaNGjXLU5qtXrzIEjZs3b/Lw4UOsra01C+XffPMNTk5Ob02bkp2YmBi2bdvG6tWriYuLY/To0dy9exdLy+x3rS1ZsoQ//viDw4cPM3POUlp0/hy5TIZayvrexkZyJEli79YFuNi6AuVz1EdBELQnAkoeKVasGLt372bJkiU0atSIdevW0b17d0xNDFP7pFhR8yyDiVqtZu/evZw9ezbb56rVah49epQhePj6+hIdHa1ZKG/WrBmTJk3SLJTrSpIkLl++zOrVq9m/fz+dOnVi0aJFtG3b9p2HQhctWsSKFSs4d+4cbm5uGKteMmVwfab/tIhK9k1QGJsjl8tQq9WkpKRSvmwxGta2wK5KSRzKjGHw4MH4+PjkaYVKQfiYiICSh2QyGa6urjRs2JABAwZw6dIlBgz/AoVcpjm0qAuFXEbZklnn/bp8+TJlypTRpNx/c6E8PYCkL5Snjzo+//xznJ2ds1wo19XLly/ZsmULa9asQalUMmbMGBYsWKD1m/vcuXPZtGkT586d48GDB+zevRtfX19SU5PZuHIed+/epXjJMiQlq5AkNdUcbHn86KFmh13nzp357LPPGDp0KEePHhV13gUhF4jUK/nk8ePHdO/enciXUXw5exfGJu9e/M6OQp6WSuTNXV6SJBEaGsrEiROJj4+ndOnS3Lx5U7NQnh48nJyc3rlQritJkjh//jyrV6/m0KFDdOvWjTFjxtCyZUutp8ckSeLnn39mz549nDp1ikKFCuHk5MSaNWvo1KkTu3btYv369Rw7dizD8+rXr8+KFSto2LCh5ntKpZJ27drRoUMHfvjhB4O+VkH4mIjUK+8gSRJBYbFERiWSnKrC2EhBsSIm2FYqYbBDcCqVitOnT7NlyxYOHDhAs2bNqFGjBg9ve1KrXhtAt91HFcqZ8/jR/UzrHTKZjLi4OAYNGkT79u358ccftV4o18fz58/ZtGkTa9euxdjYmDFjxrB06VJKlcrZgrgkSXz//fccOnSIs2fPUq5cOYYMGUKPHj3o1KkTAFu2bGHo0KGZnluzZk3u3r2bIaAYGRmxY8cO6tevT7NmzWjTpo1+L1QQhAw++oCSmKzktn8k3nfCSVWqUakl1GoJmQyMFXKOA7Xty1C3RnlKFNUth9Tt27fZsmULW7duxcLCguHDh7No0SLNdM8//57kfliyTqMUZWoyP30zGZkyVrPeMW3aNJycnHjy5AmjRo1iw4YNOvU7J9RqNadOnWLNmjUcP36cXr16sWHDBpo0aaJT9UZJkvj66685c+YMp0+fpkyZMuzcuRMfHx+8vb0BePHiBRcvXmTnzp2Znl+zZk3u3LmT6fsVKlRg8+bNDB06FB8fH8qXF4v0gmAoH3VACY+MZ++Jh6hU6kyJGCUJUv7/8Jzvg+fceviC9k2qUNNWu8SPERER7Nixg82bN/P8+XOGDh3K8ePHqVWrVqZre3RrT+nrD/H0jcTIOAfbeSUV9lYmeF85neVC+eLFizWHGXNLWFgYGzZsYO3atRQvXpwxY8awevVqSpQooXObarWaqVOncu3aNU6fPk3JkiUJCQlh6tSpHDlyRPNad+3aRbdu3ShSpEimNmrVqsWqVauybL9Dhw6MGjWKIUOGcOzYsSy3JguCkHMf7cpkeGQ8u489IDlF9c7a5Gp1WjLGk5efcuvhi2yvS0xMZNeuXXTt2pVq1apx48YNFi5cyNOnT5k3b16WwQTSFsqN1VGYqoJITU1GpXr7VmK1WgWSmlYNq9Crs0uWwUSSpFxLBqlSqTh8+DC9evWiZs2aPHnyhN27d+Pj48PEiRP1Dibjx4/Hx8eHEydOULJkSdRqNSNGjMDV1ZX69etrrt26dWuW012Q/Qgl3axZs1CpVPz6668691UQhIw+yhFKQlIqe088RJnD9B1KlcSZa0GULmFGhXJpn4rVajUXL15k8+bNeHh40KBBA4YNG8auXbsyfXJOXyj/7/bcoKAgzUK5U72mJMrKojYtg7GREcj+9+lZoZAhSRJ3blzi4ontTPS+mG1fvby8MDU1zTbliy6Cg4NZv34969atw8LCgjFjxrB58+Z35irTlkqlYtSoUTx+/Jhjx45p2l26dCkJCQl8++23mmv9/f158uQJ7du3z7KtqlWr8vz5c16/fp3lCEahULB9+3bq169P8+bNadeunUFegyB8zD7KgOL34IXOp9OVKgnPG89wrCrTrIuYm5szfPjwDClCUlNT8fPzy7A919fXF5lMptldlZ6VOKuF8tt37jN7/irsazhTr34jzAubUrp4IWraleHLC6sJfOjL5MmTWbt2LUkpSkKfvyYpWQnIMCtkhPveffTt21en9YsMr1ep5NChQ6xZswZPT08GDRrEgQMHcHZ21qvdrO7z2WefER4ezuHDhzE3Nwfgzp07/Prrr1y5cgUjo//9c926dSsDBw7M8L03KRQKqlWrxr1793BxccnyGktLS7Zs2cKwYcPw9vZ+64FKQRDe7aPbNqxWS6zc7fv/b766USlTWbVgDN27dGD48OFUrlw5U/B48OABVapU0QSP9P/m5ER5fHw8EyZM4MaNG7i7u1OtWlpG4adPn1KzZk1Kl6/C4r93EvoyFYVcpkkwKZNBfHwC1pZmdGxRi5LFc77Y//jxY9atW8eGDRuwtrZmzJgx9OvXT/NGb0ipqakMHjyYuLg49u3bh5lZ2pmalJQUGjVqxKRJkxg9erTmekmSsLOzY/fu3RmmwP5r6NChdOjQgc8+++yt9//ll184e/YsJ0+eFOspgqAFUQL4/wWGRHPoXKDO2WohLS9UYlQA187u4ubNm0RHR+Po6KgJGk5OTtSuXVuvE+Wae0kSa9asYebMmfz999/0798flVrNL4t3UrhEZYyMjJHLs34TlMvSsus6Vy9Hi/pW7wxkKSkpHDhwgDVr1uDt7c3QoUMZM2ZMtms/hpCcnMyAAQNQqVTs2bMnQ56z7777jrt377J///4Mfb98+TIjR47k7t27b31Nv/32G9HR0SxYsOCtfVCpVHTq1ImmTZsye/Zs/V+UIHzgxDmU//fiVaJewQRAJldgUrg0I0aMwMnJiapVq+bayWuZTMbYsWOpX78+/fr149IlT5p9Mo5SFg68q4SKWgK1SuLm/RckJCnp1Mw6yzdgf39/1q5dy6ZNm6hevTpjxozhn3/+0SrTsD6SkpLo06cPpqam7N69O0PCyosXL7Jx40bNNOGb0hfj3xUga9WqxZo1a97ZD4VCwbZt26hXrx7NmzenY8eOur0gQfjIfXS7vBKTUw3SjkWFSvTu3RtbW9s8SeNRv359vL29MS5Rgychr94ZTN6kVKl5+DSKa7fCNd9LSkpix44dtG3blubNm6NWqzl37hxnz55lyJAhuR5MEhIS+PTTTylatCi7du3KEExiY2MZPnw4q1evzpSaJSUlhd27dzN48OB33iP9cKM2ypcvz7Zt2/jss88IDQ3N2YsRBAH4CEcoJsaGmSO/5XuT5gvGYWVlRaVKlbCystJ8VapUifLlyxt8Pt7Y1JxKdg10yvulVKq5eisMc3kU69evZcuWLTg7OzNhwgR69Oihczp7Xbx+/Zru3btTqVIl1q9fn2lh/YsvvqB9+/Z0794903OPHj1K9erVtao8aWNjQ3h4OPHx8Vqt/bRu3ZpJkyYxaNAgTp06le2CvyAIWfvofmOKmZtibCTXe9qrYYM6dG/1GyEhIQQHBxMQEMDZs2cJDg4mJCSEV69eYWFhkSHY/PfPFhYWOQo6N+8/16vPiQmJfPXdXzSoY8WVK1ewtbXVqz1dxMbG0qVLF6pVq8bq1aszvX4PDw/Onz/PzZs3s3z+1q1bsyxUlhWFQoGDgwP3799/6+L9m7777jvOnz/PrFmzmDt3rlbPEQQhzUcXUOyrlODU1ad6tWFsJKdZfRvNWZSspKSkEBoaqgkwISEhBAYGcv78ec3/R0ZGUr58+SxHOOl/trS0xMjICLVawvfBC72yEhubmDLg82mM6u2ocxv6iI6OpnPnztStW5e///4701RheHg4EydOZN++fVmeHYmJieHYsWPZnoDPSvoBR20DikKhYOvWrdSrV48WLVrQuXNnre8lCB+7jy6gmJoYUc26FPcCX+pcx72wmTGWZd8+hWJiYoK1tTXW1tbZXpOSkkJYWJhmlBMSEkJQUBCXLl3SBJ0XL15Qrlw5qtV0pvOA71EY6Tc1FRuXgkqlRmGghJfaevXqFR07dqRZs2b89ddfmRbUJUli1KhRjBkzhiZNmmTZxt69e2nXrl2OMiPXqlVL63WUdOXKlWP79u30798fLy+vDOWHBUHI3kcXUADq1yrPwyev3plyJSspyYkkKZ8C+p9ANzExoUqVKlSpUiXba1JTUwkLC+N+wDPuBMv1qDyfRq6QkZyiorBZ3gWUFy9e0KFDBzp06MCCBQuy3J21atUqIiIi+Omnn7JtZ+vWrUyePDlH965Zsybr16/PcZ9btmyJq6srAwcO5OzZs2I9RRC08NHt8gIoW7Iwzetb5TgtvZFCRmWLIqxZOofevXsTGRmZSz38H2NjYypXrky9us4GSTsvSWkpXPJKeHg4bdq0oVu3btkGk4cPH/Ljjz+ydevWbF9jcHAwvr6+dOnSJUf312WEku7bb7+laNGionaKIGjpowwoAPVqlKexk6XWQcVIIaeqVQkGdHXi6tWr2NnZ4eTklKmwU24pbGaESocRVVYMtdPtXZ49e0br1q3p378/v/76a5bBJDU1lWHDhvHzzz9TvXr1bNvavn07ffr0yfF2ZhsbG8LCwkhISMhx/+VyOVu2bGH79u0cOnQox88XhI/NRxtQABrWseTTNraUL10YI4WMrM7JGRvJKWpuQisXK7q1skGhkGNqasrChQvZsmULY8aMwdXVlcTExFzta+FCxpQrrd/Je5VKReyLR/j4+PC2DAmGEBQURKtWrRgxYsRbp7F+++03SpYsycSJE7O9RpIkTc6tnDIyMsLe3p779+/n+LkAZcqUYceOHYwaNYqgoCCd2hCEj8VHHVAArCsWZ0i3mgzpVpM69mUoX7owJYqZUrakGfZVStKznT2j+9TBqVq5TJ+w27Zti6+vL+Hh4bi4uODr65urfXWpbYGxke5/ZSbGCkgKoW/fvjg6OrJ48WLCw8Pf/cQcevz4Ma1bt2bixInMmDEj2+uuXr3K8uXLWb9+/VtPvfv5+fH69WuaNWumU39ycsAxK82aNeOrr75i4MCBpKYa5mCsIHyIPvqAkq50CTPaN7FmSLeajOxVh2Gf1qJ7a1sqWRR965tdyZIl2blzJ9OnT6d9+/YsXrwYdU6OseeAjVUJjPQIKCWKFeKn77/i0aNHLFu2jNu3b1OjRg26deuGu7s7ycnJevcxICCA1q1b89VXX/HVV19le118fDzDhg3j77//pkKFCm9tc+vWrQwZMkTnjATvqo2ijWnTplGyZEm+//57vdoRhA+ZCCgGIJPJGD58ONeuXcPDw4OOHTvy7Nkzg99HLpfRo40dRjosqpsYy+ne2u7/25HTqlUrNmzYQHBwMP3792f58uVUrFiRSZMmcf36dZ2mxO7fv0+bNm2YOXPmO3djTZs2jcaNG7+zAJhKpWL79u0MGTIkx/1Jp8/CfDq5XM7mzZvZvXs3Bw8e1KstQfhQiYBiQFWrVuXcuXO0bt2aevXq4e7ubvB7VChXhO6t7bTeTKBWqzA2ktGngwOlskhjX6RIEYYPH87p06fx8vLCwsKCgQMHUqtWLRYsWKB1Xqvbt2/Ttm1b5syZw9ixY9967eHDhzly5AhLly59Z7tnzpzBwsKCmjVratWPrBhihAJQunRpdu7cyejRo3n6VL/DsYLwIfro0tfnlatXrzJ06FCaN2/OkiVLDFbVMF3Ey3hOXw3ixasEklNSUSgynpNQpqYA8CTAl+eBl3DfvVXrtiVJ4tKlS2zcuJG9e/fSuHFjRowYQY8ePbLcZeXr60vnzp1ZtGjRO0cSL168wMnJiR07dtCqVat39mXEiBE4OzvzxRdfaN3//1IqlRQtWpRXr15paq3oY/HixezZs4fz58/naQ40QSgosktfL0YouaRRo0bcuHEDIyMjnJ2duXz5skHbL1/anEFdavBJ0/J4X/qXIoWNMTFWYGqsoJi5CU/uX+TEnrlEPT3Pfo+dXLp0Seu2ZTIZzZs3Z+3atTx79oyhQ4eydu1aKlasyPjx47ly5YpmSszb25tOnTqxZMmSdwYTSZIYO3YsQ4cO1SqYJCQk8M8//zBw4ECt+54VIyMj7OzsdN7p9V9fffUV5cqVe+uGA0H4KEmSlO1X/fr1JUF/Hh4eUvny5aVZs2ZJqampBm1706ZNUu/evTN9/9y5c1KlSpWkcuXKSfXq1ZMqVqwoqVQqve4VFBQkzZ07V7K3t5ccHByk8ePHS6VKlZL27dun1fPXr18vOTo6SklJSVpdv2PHDqlTp0569Ph/BgwYIG3dutUgbUmSJL18+VKqUqWK1q9dED4kgJeURcwQI5Q80KtXL3x8fLh8+TLNmzcnICDAYG0fOXKETz75JNP3W7ZsiY2NDZaWlvTt25eIiAjmz5+v170qVarE999/z4MHD/jyyy/ZuHEjKSkpLF++nO3bt7/18ODjx4+ZPn06W7duxdTUVKv7bdmyhaFDh+rV53T6bh3+r1KlSrFr1y7Gjh3L48ePDdauILzXsooykhih5AqVSiW5ublJZcqUkdatWyep1Wq92lMqlVKpUqWkkJCQLB8/deqUVKlSJcne3l768ssvJRMTE+nVq1d63VOSJOnMmTNSmTJlpGPHjkkJCQmakUTJkiWl0aNHSxcvXszw2pRKpdS8eXNp0aJFWt8jIiJCKl68uBQXF6d3fyVJktzd3aUePXoYpK03/fnnn5KLi4uUnJxs8LYFoaAimxGKCCj54NatW5Kjo6PUu3dvKTIyUud2Ll26JDk6Omb7uFqtlpo1aybZ2NhI7u7uUunSpaWOHTvqfD9JkqTjx49LZcqUkU6dOpXpsZCQEGnevHlS9erVJTs7O2nOnDnSkydPpN9//11q06ZNjqbclixZIg0dOlSvvr7p7t27kr29vcHaS6dWq6WePXtKU6dONXjbglBQiYBSwCQmJkpfffWVVLFiRen48eM6tfHDDz9I33777VuvOXr0qFSxYkWpSZMm0tGjRyW5XC5duHBBp/sdOnRIKlu2rHT+/Pm3XqdWq6WrV69KEyZMkIoXLy4ZGxtLf/75p/T69Wut79WwYUPp6NGjOvUzKykpKVKhQoWkxMREg7WZ7tWrV1LVqlUld3d3g7ctCAWRCCgF1IkTJyQrKyvpyy+/zPGbXb169aRz58699Rq1Wi25uLhI5cqVky5duiS1aNFCsrS0lJRKlfQoOEpyP/ZAWrvXT1q566a0zsNPOng2QAp9HpdpOm7//v1S2bJlpcuXL2vdv4SEBKl69erSlClTpC5dukglSpSQPv/8c+ncuXNvHa08ePBAKl++vME3MNSqVUu6efOmQdtMd+3aNals2bJSQEBArrQvCAVJdgFFLMrns/bt23Pz5k2CgoJo2LAht27d0up54eHhBAYGZluMKp1MJmPWrFkYGxuzYMEC9u7dS/W67flz02UOnQvkaVgsMXHJxCemEh2bjP+TKPYcf8iGfbd5+OQVAO7u7owdO5bDhw/TuHFjrV/b999/j6OjI25ubhw6dIi7d+9Ss2ZNJk6ciJ2dHb/88kuWC9pbt25l0KBBBq9BYqgDjllxcXHhxx9/pH///iQlJeXKPQShoBMBpQAoXbo0e/bs4auvvqJt27b89ddf78wHduzYMdq1a6dVjZQuXbpQtmxZzl+4yFHPELr2nYhcYUqqMvM9JECpVBMdl8zRi49Zufk4kydP5tixYzRokOkcU7ZOnjyJu7s7K1as0ORCs7S0ZNq0ady6dYs9e/YQGRmJi4sLrVu3ZuPGjbx+/RpJkti6davBdne9yRApWN5m8uTJVK1alWnTpuXaPQShIBMBpYCQyWSMGDGCK1eusGvXLjp37vzWtCeHDx/WuthU2ijlZwaO/oWIl8nI5Np98leqJKKTC7Ny8xGcnZ21eg5AVFQUn3/+OevXr6dUqVJZ9qd+/fosXbqUZ8+eMXXqVDw8PLCysqJLly6oVKoc3U9buTlCgbTXtW7dOo4ePcru3btz7T6CUFCJgFLA2NracuHCBZo1a0a9evXYt29fpmuUSiUnTpygc+fOWrdrU6MJVeyckStyNo1kYlKIoOcSYS9ea/2ciRMn0qtXLzp06PDOa01NTenduzcHDhzgwYMHxMbGkpKSgq2tLT/99BOPHj3KUX+zE5+YimkJe+wb9GPj/ttsPnAHj5MP8X8ahVptuNowxYsXZ/fu3UyePBl/f3+DtSsI7wNRKLsAMjIyYtasWXTq1ImhQ4fy77//4ubmRpEiRYC0PGFVqlR5Z9r3dJIkcf12OMYm2h0o/C+VSo3XnQi6ty7yzmt37NjBzZs38fHxyfF9SpYsyYMHD/Dy8iI6OpqNGzfSpEkTqlWrxogRI+jXrx/FihXLUZuRUYlcuhHCk2exAFhYOfAqJknz2LOI18jlMpyrl6NhHUu96s2kq1evHj///DP9+/fn8uXLOa4yKQjvKzFCKcAaN27MjRs3kCSJunXrcvXqVSBtuiur0/HZCXsRT0KSUud+SEBgSDSJSW8vLhUcHIyrqyvbtm3TKQnjkSNHqFmzJtbW1jg7O/PXX38REhLCtGnT+Pfff6lcuTJDhw7l5MmTqFSqd7b35FkMOw7f41FwDCq1hCqLkUiqUk1yigqvO+HsOHzvna9RWxMmTMDBwYEvv/zSIO0JwvtABJQCrmjRoqxfv5558+bx6aefMnv27BwHFL+HL7JcgM8JGfDgSVS2j6vVakaMGMEXX3xBvXr1dLpHVovxJiYm9OjRg3379uHv70/Dhg2ZPn061tbWzJw5k4cPH2bZVkhEHAfOPNL6datUEq+iE9l19AGpqe8OVu8ik8lYs2YNJ0+eZMeOHXq3JwjvAxFQ3hN9+vTBx8eHkydPcuvWLcqXL6/1c2Pi9K/EqFRJxMWnZPu4m5sbSUlJTJ8+Xaf2o6OjOX78OP369cv2mrJlyzJ16lR8fHw4dOgQSUlJtGzZkqZNm7J69Wqio6MBSFWq2H/KH6UqZ0FULaX9rM5cM0zt+GLFirFnzx6mTp3KgwcPDNKmIBRkIqC8RypWrMhnn31G7dq1adasGZs2bdKqsmJO31izk5LNJ/fbt2/z22+/sWXLFp3Pjuzdu5f27dtTsmRJra53dHRk8eLFBAcH8/3333P8+HGqVKnCoEGD2HvwAjoUnARApZa49/gVySm6TxG+ydnZmV9//ZX+/fuTmJhokDYFoaASAeU9c+zYMVxdXTl16hSLFi2if//+vHr16q3PMTUxzN6LwoUyt5OcnMzQoUOZN28eNjY2Oret69kTY2NjunXrhru7O4GBgTRr3pwHQQl6TfHJZTLuPnqp8/P/a+zYsdSqVQtXV1eDtSkIBZEIKO8RpVLJyZMn6dy5M46Ojly/fp2KFSvi5OTE6dOns32eVfkiKHSoQ/8mYyM55cuYZ/r+rFmzsLa2ZuTIkTq3HRQUhJ+fn9bnarJTunRpBgz+nJJlLPRqJ1Wp5sa953q18SaZTMaqVas4d+4c27ZtM1i7glDQiIDyHrl8+TLW1tZYWloCUKhQIf766y/WrVvHsGHDmDZtGsnJmddL6jiUTduqpQcjhRzrCsUzfO/ChQts3ryZ1atXa07D62L79u307dtX6zopbxMXn4Jcv9gJpJ1bMaSiRYuyZ88evvjiC4NVjhSEgkYElPdIdsW0OnbsiK+vL4GBgTRq1CjTaXBzM2OsK+bs/MabjBQy6tUsh/yNd+rY2FiGDx/O6tWrKVeunM5tS5LEli1bGDZsmM5tvEnf3WzpstpirC9HR0d+//13+vXr99ZiZILwvhIB5T3ytnQrZcqUYe/evUyZMoXWrVuzdOnSDAv2TZwrYqTjtJeRQo6jQ9kM33N1daVjx45069ZNpzbT+fr6kpCQQNOmTfVqJ52pscIg7RgpcudXY9SoUTg7OzNlypRcaV8Q8pMIKO+J0NBQgoKCaNSoUbbXyGQyRo0ahaenJ1u3bqVLly6EhYUBUK5UYTo3r5rjoGJsJKdvp2qYFfpfEkoPDw8uXrzI4sWLdXsxb9i6dStDhgxBLjfMP8XSJcxQqfQfXZQpkTun22UyGStWrMDT05PNmzfnyj0EIb+IgPKeOHr0KB06dNBqW669vT0XL16kYcOG1K1bl3/++QcAB+tSdGtti7GR/J2L9MZGcgqZGjGwS3XKlSqs+X5YWBgTJ05ky5YtmlQwulKpVGzfvp0hQ4bo1c6bipqbULGcfv0yNpLToLZ+C/tvU6RIEfbs2cPXX3+dq9mPBSGviYDynshJdmFI2077yy+/4OHhwZdffsnYsWOJj4/HxqoEI3vXoWEdSwqZKjA2liOplShT0xazU1OTQVJSungh7KuUICT8NYn/n7ZFkiRGjRrF2LFjc1QXJTunT5+mQoUK1KhRQ++23tSgtoVeObkUChk2ViUM16Es1K5dmwULFtCvXz/i4+Nz9V6CkFdkbzsY16BBA8nLyysPuyNkJTU1lXLlynH//v0cnZBPFxsby9SpU/H09GTbtm24uLgAoFZLBIXFEhIew+79J6lsUxNlqhIT0/9N9xgpZEiAbaUSPL13kY3rluHp6alVHZZ3+eyzz6hXr57Bz2dIksTmA3eIik3OcSZhIyM5zetVpF6NnP+cdTFixAgANm7cmCf3EwRDkMlk3pIkZSqQJEYo74HLly9ja2urUzCBtBQgGzduZO7cuXTr1o25c+eiUqmQy2VIEty4H0kVm1rI5UYZggmkpVxRqSQePonitcKWOQvXGSSYxMfHc+DAAQYOHKh3W/8lk8no27EahUwU5GQ3s5FCTjXrktStrvuutZz6+++/uX79Ohs2bMizewpCbhEB5T2Q02SQ2enXrx/e3t6cPn2a1q1bc8X7IQfPBpCqVCOTv3t3lLFJIR6GKPG6E653Xw4cOEDjxo11DpLvYm5mzJBuNSlexPSdGxFk/P9Otmpl6NjUWq8zNTllbm7Onj17mD59Ordv386z+wpCbhAB5T2Q3fkTXVhZWXHixAl69h7AWe8IlDncEaVUqfG8EUpweKxe/diyZUuulPl9U1FzE4b3qEVi5C2S4l9hpJBrztLISFt8V8hl2FUpSb9ODrR2qZynwSRdzZo1Wbx4Mf369eP1a+0LmQlCQSPWUAq4Z8+e4ejoyPPnz1EoDHPGAuDs9SBu3H2u8wF6q/JF6N+5uk7PjYiIoFq1ajx79gxz88zpXAwpKSmJqlWrcuLECcpYVCX0+WsSk5UYKeQUNjPCrlKJDFui89OoUaNISUlh8+bN+RLYBEFb2a2hiIqNBdyRI0fo2LGjQYOJUqXm1sNIvbKxhEXGExOXTPGiOU+XsmvXLj799NNcDyaQds6lXr161K5dGwCLLPKRFRRLly6lUaNGrFu3jtGjR+d3dwQhx8SUVwFnyOmudAFPsy+UpS1JAt8HuiVQ1DWzcE6p1WoWLVrEN998k+v3MoTChQuzZ88evvvuO/z8/PK7O4KQYyKgFGApKSmcOnWKzp07G7TdlzFJeue8Uqslnr/MeT6qBw8eEBwcTNu2bfW6vzYOHjxIsWLFaNWqVa7fy1CqV6/OX3/9Rb9+/YiLi8vv7ghCjoiAUoB5enpib2+vV/LFrCQlG6Z4VLIOpXK3bt3KoEGDdC7ElRMLFizgm2++ee/WI4YMGUKrVq0YN26cVgXUBKGgEAGlAMuN6S4AUxPDrMeY5DARoyRJeTbddenSJcLDw+ndu3eu3ys3uLm5cefOHVavXp3fXREErYmAUoDlNN2KtkoULaRXahIAmQxKFc9ZAkVPT0/MzMyoW7euXvfWxsKFC/n6668NupkhL5mZmbFnzx5++OEHbty4kd/dEQStiIBSQAUHBxMWFqZJk2JIJc2VpKToV0BKktQ4Viv77gvfsHXrVoYNG5brU1D379/n8uXLmrQm7ysHBweWLl1K//79iY3V79yPIOQFEVAKqKNHjxp8u/CNGzcYMWIEtWvVIOa5v15tBT95QJ0aNqxbtw6V6t1rKSkpKezZs4fBgwfrdV9tLF68mIkTJ1K4cOF3X1zADRw4kPbt2zNmzBixniIUeCKgFFCGmu5SKpXs3buXli1b0qNHD2rUqEFAQACu4/roXkRKUtG/WyPq1avH+PHjKVu2LH/++aem/HCiMoW7UWF4vXiKT2QwATHP+ffwIWrVqkWVKlX0fk1vEx4ezt69e5k0aVKu3icv/fnnnzx8+JAVK1bkd1cE4a3ESfkCKCUlhXLlyuHv70/ZsjmbVkr36tUr1q1bx7Jly6hUqRKurq706tUrw+6qOwGRnLoShFKl/RZihRxuXD5MUcUL/vzzT549e8bUqVM5fPgwFjXs6POjK6mWJTCSK3izkH1CfAIVX8OXXQdQ3MRMp9ekje+//57Y2FiWLVuWa/fIDwEBATRt2pQjR45Qv379/O6O8JHL7qS8CCgF0JkzZ5gxYwZXr17N8XPv3r3LkiVL2LVrF927d8fV1fWtb0C3Hr7gzLUgrXJ6GSnkOFUri6NdEQYPHkxqaiq7d++maIniLPU5yf3YCCSZDLlR1tN0RjI5MpmMXtZOtKuoW9qWt4mLi6Nq1apcu3YNGxsbg7ef3/bs2cOMGTPw8fGhePHi+d0d4SMm0te/R3KaXVitVnPo0CE6duxIu3btsLCw4N69e2zevPmdn2brOJSlX6dqVLUqjkIuy1TJUS4DSa0iOf4F3Vrb0MqlEiVLluTff//FycmJxk2bMvfqvwQmRyMzNso2mAAoJTWpahX7n/jyzxNfrV+fttauXUu7du0+yGACadmiP/nkE0aOHCnWU4QCSYxQCqDatWuzbt26t9aPh7RP5Bs3bmTp0qUULVoUV1dXBgwYgKlpzvNrAcQnpuL38AURL+OJiorD96Y3vT7tjLE6kuFD+uHv759ph9b3h7cQYaLCyNQkR/cylisYYudCk/KGefNPTU3F1tYWDw8PGjTI9MHpg5GcnEzTpk0ZMWIEU6ZMye/uCB8pkRzyPREUFERERMRb3xQDAwNZunQpmzdvpm3btqxfv55mzZrpvR3X3MyYJk4VAHjx4gXfjOvIX3PGIElWpKSkcO/ePWrWrKm5Pjwhltjixhipc74TLVWtwj3wBo3KWSOX6T9Q3rVrF3Z2dh90MAEwNTVl9+7dNGnShMaNG+fKtnJB0JWY8ipgjhw5QqdOnTJtF5YkidOnT9OjRw8aNWqEqakpN27cYM+ePTRv3tzgZztKlSpFdHQ0SqUSmUxG9+7dOXjwYIZrTofeR5XDErtvSkpN4darUH27iiRJLFy4kOnTp+vd1vvA1taWFStWMGDAAKKi9E/0KQiGIgJKAfPfdCuJiYmsWbMGR0dHpkyZQteuXXn69Cnz5s2jcuXKudYPhUJBqVKlePnyJUCmgJKiUnI54jFqPZLgK2US8w5sw93dXauzLNk5fvw4kiTRqVMnndt43/Tp04fu3bvz+eefi/UUocAQAaUASU5O5syZM3Tq1ImQkBC+++47qlSpwoEDB/jzzz+5ffs2Y8eOzbMDe2XLluX587QU9W3atOHWrVu8ePECgNCEGOQGGBWZV7bA1dWVatWqsX79elJSUnLcxsKFC9/LJJD6WrhwIaGhobi5ueV3VwQBEAGlQLlw4QJWVlZMmjQJR0dHEhIS8PT05ODBg7Rv3z7P3zDLli2rCSCmpqa0b9+ew4cPA5CgTEGG/v1RmBjTsVMnXrx4wdy5c6latSp//vmn1qVwfXx8ePDgAQMHDtS7L+8bExMTdu3axW+//abTFnNBMDQRUAqAlJQUTRbesLAwmjVrxpMnT3Bzc8POzi7f+lWuXDlNQAH49NNPOXDgAJB2psQQZMD6devw8fGhZcuWxMfHs3btWqpUqcLPP/+smXLLzsKFC/niiy8wNi4YZXzzWtWqVVm9ejUDBgzg1atX+d0d4SMnAko+ioiI4OdffqFKlSps2rQJY2Njjhw5wtSpUylWrFh+dy/DlJdarcbe3p4jR44wceJExg4fQVy8dqOItymkMEYmk2Fra8uGDRvw8vLCxcUFtVrNwYMHsbOz48svvyQkJCTTcx8/fsyJEycYM2aM3v14n/Xs2ZPevXszYsSI/62nqBMg2h1Cv4LgURAyDsJ/hgSftHKbgpALxDmUPCZJEke8LuFx/zrqcsUxNjNFJpNhIlPw4PhF1k2fjVWRkvnex6dPnzJ9+nRCQkIoVKgQ3t7elC5dmtjYWHr27MmQoUM4YRbDy5ScV21Mp5DJaGlhz0C7zFt9/f39+fXXX/n333+pXr06d+/epVevXkyfPp3q1dNO2U+dOpXChQszb948nfvwoUhJSaFly5aMHP4JY/vIIfYQIAMp8Y2rZCArBIqSUHIElOgNMnFyQMg5kXolnymVStYd9uCK+iVy80JpBwH/syYiqdWYGBljWbgYIxyaUNG8RJ70LSIiguvXr2u+vLy8UCgUlC1bFlNTU+bMmUODBg0oU6YM8+fPJygoiL///pvzYf64B94gWa1bBUhjuYKf6nWhnFnRbK/x9/dnzpw5HD58GEdHR/z8/GjVqhUTJkygf//+3L59mwoVKuj60j8oYY+PYR71DUXMjZDL3pGfTVYICtWGiktA/v5nZRbylki9kk9evXrFggULaPFZP7zMEzEpXRyjQqaZggmATC4nVa0i6HUU828e50F0hMH7Ex0dzcmTJ/n999/p3bs3lStXpkaNGixZsgSlUsmYMWPw8fEhNDSUn376icqVK9O5c2fKlCkD/G/7sCRJNCpXVed+yJBRuUjJtwYTAHt7ezZv3oynpydWVlYAJCUl0bt3bwoVKsS9e/fEtlmApPtYKn+mWBH5u4MJgJQESX4QMgEk/WrjCEI6EVByyd27dxk/fjy2trbcjY2g7vj+yE20XzhOViv5+845QuJ1P7iWkJDApUuX+OuvvxgyZAgODg5YWVnxyy+/8OLFC/r168fp06d5+fIlx48fZ+7cufTq1QsrKytkMlmGNZR0NWrUwMTEBF9fX0wVRoyq3hRjec5PypspjBhZranW1zs4OLB582YuXbpEsWLFeP36NbVq1WLChAk0atQIDw8P1GrtsyZ/UNQpEDL+P9NbWpBSIPk+vBDbjgXDEAHFgP6bpNHS0hK/O7cp3qURKh0OACarlay5d0mra1NSUvD29mblypWMGjUKR0dHypQpwxdffMHDhw9p3749e/fuJTo6mgsXLvDHH38waNAg7Ozsst2O/N9dXkCmU/NOpa0YbNtA66AiR0ZhI2O+cmxPmUJFtHrOm6pVq0arVq1o3bo15cuXJzo6Gnt7e+bOnUvNmjXZsGGDTmdZ3muvT6QFB11IyRCzF9Q5DEaCkAWxhmIAcXFxbNiwgaVLl1K8eHFcXV3p378/pqamXIl4zPaA6zqvM5jIFXzt2B7roqU131OpVDx48CDDusft27epWrUqLi4umi9HR0edE0UCREZG4uDgkGk76pkzZ5g+fTrXr1/XfO9+dDg7Arx4lRxPqjpz+EzfZmxfvBxD7RvqFEwg7bVXr16dDRs20Lx5c+7fv8+cOXM4ceIEn376KY8ePSIgIICvv/6aMWPGYG5urtN93itP+kJKgO7Pl5lBuW+heE+DdUn4sIlF+Vzw6NEjli5dypYtW2jXrh2urq40bdo0wyf+n70PEZYQo/M9ZMioZlaKCo+iNcHjxo0blCtXLkPwqFu3LkWK6PYmnR21Wo2JiQmJiYkZznmkpqZSvnz5LBfEn8S9ZP2V44SqEzEtXJikpETKFCtB43JVaV3BgZKm+i0A7927l0WLFuHp6Znh53zv3j3mzJnDyZMn6d+/P8HBwVy+fJlJkyYxZcoUSpUqpdd9C6zkQAganLYmog8TW7Dea5g+CR88kW3YQNKTNLq5uXH58mVGjRrFzZs3qVSpUqZrXyXHE5mk31kNCYk7sRGc2+GBi4sLM2fOpEGDBnnyBimXyyldujSRkZFYWlpqvm9sbEznzp35999/GTt2bIbnWBctjfLMLaxMTPjiiy+oXLkysbGxBumPJEksWLCAGTNmZJqmq1GjBtu3b+fu3bvMmTOHK1euMHz4cB49eoSdnR2ff/45X375pWZhP0vqZHh9El5tgtSgtGkkmTEYlYUSg6FYd1C8fRNBnksNSdv6q+++BGW4QbojfNzEGoqWEhISNEkaXV1d6d69uyZJY1bBBCAuJRmFAU6UGxsbs3uvOzNnzqRjx455+mn7zfQrb8oq+3A6Hx8f6tWrR9GiRYmPj9cr8eObLly4QFRUFJ9++mm219SsWZMdO3Zw+vRpQkJCOHbsGBMmTCA5ORlHR0dGjx7Nw4cPMz5JUkPkCnjUBiLmQsrD///Er05bY0gNgcilENgeImaDWs/RgCFJiYY5qKj+yNadhFwhRijvEBwczPLly1m3bh2NGzfmr7/+om3btlrl1VJJagNku0rbYayS8mcHU7ly5TLt9ALo3Lkz48aNIyEhIUOySkmS8PHxoW7duigUCszNzYmLi6NEiRJ692XhwoVMmzYtU2r/rNSqVYudO3dy584dZs+ezdmzZ5k6dSqpqak0b96cVq1aMWPGDOrXc4TQryHh+tt3SaU/FnsIkm6D1VpQ5H02g6SkJAICArh//z4PHjzAVHmNcZ/GU1TfpSK57mttgpBOBJQsSJKEp6cnS5Ys4eTJkwwbNgxPT88c59UqbGSC2gCfHtWShKkif3JVZTdCKVmyJA0aNODkyZMZRgyhoaFIkqSZWipevDgxMTF6B5S7d+9y/fp19uzZk6Pn1apVi127dnH79m1mz57N+fPncXV1RaFQ0KNHDzbPr0ir+moUci3PYkjJkPwYQiZCpfUgz1mlSq1uIUmEh4fz4MEDTeBI/3NoaChVq1alWrVqVK9eHQfHNhQuvA3Q7yxJYLCSS5e30LFjR8qXL2+YFyJ8dERAeUNycjK7d+/Gzc2NmJgYpkyZwpo1a3TOq1XWrAgKuRz0HFxULlLKIKnidZHdCAXSpr0OHDiQIaCkT3elj+DSA4q+Fi1axJQpUyhUqJBOz69duza7d+/m1q1bzJ49mwsXLrB03gBa1L2EQp7THXipabuqondCqeE69QfSRhv+/v6ZAseDBw8wMTGhevXqVKtWjWrVqtG2bVuqVatG1apVNRskkpOTWbduHTfvJlK/lu6/yvGJcMK7IkcveTBlyhRsbW3p1KkTnTt3pkmTJh9t4k0h50RAIS31yMqVK1m5ciW1a9fm559/pkuXLsjl+q1/KGRy2lRw4FjwXZQ6TlmZKozoZFXz3RfmkuxGKJCWfXjBggWo1WrNz+rGjRvUrVtXc40hAsqzZ8/Yv38/AQF6bI39f3Xq1GHPnj3cunUL+bMxGBvptp0bKQmitkDJofCWdbL00cZ/RxoPHjzINNpo27YtEydOpFq1am9dJ/P392f27Nns2bMHlUrFTNemONdMRiHTbW3HxMSUPUciuf/gEV9//TV169blypUrfPXVVwQEBNCmTRs6d+5Mp06dsLa21ukewsfhvQgoKakq7gW+5LZ/JAlJStRqCVMTBdYVi1G3enmKF9Vt/tfHxwc3NzcOHDjAgAEDOHnyJLVq1TJo31tZ2nMs+K7Oz09JSqa6eRkD9ihnypUrx40bN7J8zNbWlpIlS+Ll5UXDhg2BtJ/p4MGDNdcYIqAsWbKE4cOHG3QzQp3qpaCQWr/dUep4SLgG5o2zHG2k/7dQoUKakUZ64PjvaONdUlJS2Lt3L7/99hv379+ncOHCuLq6MmPGDEqWKAqPu4MyghwPh2WFMC43kpOnxnLz5k2WLFnCH3/8QZ8+fVi/fj0WFhacOHGCo0eP8tNPP1GyZElNcGnVqlWeFXsT3g8FOqAkJSu54BPCvUevkMkgVfm/X5b4xFSi45LxffACyzLmtGxQCYsy716ZVCqV7N+/Hzc3N54+fcrkyZP5888/c23nVHETM1pZ2nMhPIAUdc52OxnL5Lw8c50Gk35j3bp1NG2qfaoSQ8kq/cqb0mukpAeUGzdusGjRIs3j+gaU2NhY1q5di4+Pj85tZCnupN67o9TqBM798wWjfniuGW2kT1NpO9p4l8DAQNzc3DTVLG1tbdm2bRt9+vTJuDnBag0EDQL1a7SOkrJCULgxlBoNgLOzM+vXr2f+/PmsXr2aTz75BAcHB1xdXdm4cSMymYybN29y7Ngx5s2bR//+/WnatKlmeqxGjRofXdVMIaMCe7Ax9nUyu44+ID4xFbX63b8gRgoZXVraYFc569Tvr169Yu3atfz9999UrlwZV1dXevbsiZFR7sdUtSSx8u4F7kWHaR1U1CmpGD96zrLRX+Ph4cHUqVPp168fc+fONfgBxre5cOEC3333HRcvXszy8UuXLjFx4kR8fX2JjIzE1taWqKgozRTY+PHjcXR0ZOLEiTrdf9GiRdy4cYNt27bp/Bqy9HwBRG/Xu5nIhCpEmS+iatWqBvu3lJqayr///suiRYvw9vYGoEuXLvz00084Oztn/8SUJ2m1T9Sv0zYPvI3MDIq0AYtf0s7aZNXc/4+K3NzceP78OZMnT2bkyJGaDRYxMTGcPn2aY8eOcfToUdRqtSa4tGvXziA7+4SC6b3KNpyYpGTnkfu8TkjRKpgAKFUSh88HEhSW8RDdnTt3GDduHLa2tty5c4d9+/Zx4cIF+vbtmyfBBEAukzG+ZnOalrfBSCZ/69kUY5kCY5mcHlUcubxiO7Nnz6Zv377cunWL6Oho6tSpw4kTJ/Kk3/DuEUrjxo0JCwvj6dOn3LhxA2dn5wxrT/qMUFJSUvjrr7/45ptvdHr+Wxkow26ZUsWwt7c3yL+loKAgZs6ciYWFBSNGjODu3bt8++23BAcH4+Hh8fZgAmBiDdb7oPQ4UJQC2X+no4xAZgKF6oLlfLCYm20wgbQSw4MGDeLKlSvs2LEDb29vbGxsmDRpEg8ePKB48eL06tWLlStXaoqd1a5dm7Vr11K5cmWaN2/Or7/+yvXr1z/exJ0fmQI55XXi8hMSkpQ5npFQqiT+OR3A2L51OHHiGG5ubty+fZsJEyZw//79fN0OKZfJGWTnQgerGpx+9oCLEY+QvVGVXY2EsUxB+4rVaW5hS1GTQjQ8dowWLVpQvHhxvvzySzZt2sSRI0cYPXo07du3Z9GiRZQsmbvFuN62KA+gUCjo0qULBw8eJCEhgXr16mV4vESJEjoHlB07dlCzZs13v5HqQlGatALE+k17xbyWUVyP56tUKg4fPsyyZcu4ePEiRkZGVKlShe+++44+ffpgYpLDbcmKYlBqZFoBrQRPSPAG1cu06S2j8lC0I5hkfRD3bRo1asS2bdsIDQ1lxYoVtGzZknr16uHq6krHjh2Ry+WadSJXV1cSExO5cOECR48eZcSIETx//pyOHTvSqVMnOnbsiIWFRY77IBR8BW7KKz4xlbXufqi0HJlkIqk4c2gt4U9uapI05viXMg+kqlUEv44iQZmCTAZFjApRqUgJ5P8ZvQQFBdGyZUt++uknRo4cCaQlo/zuu+/Yt28fy5Yto1evXrnWT7VajampKfHx8dn+HD08PFi5ciWlSpWiS5cuDB/+v620y5cv59atW6xYsSJH95UkiTp16vDnn3/SoUMHvV5DlhKuw7OpOU/5/oYUpRE/LY3m5mM7ZsyYQatWrbReQwgJCWHdunWsXLkSSZKIi4uja9eufP311zRq1EjnPuWVpKQkduzYgZubG0lJSUyZMoXPPvss2+nY4OBgzdTYqVOnsLa2pnPnzpqtyQXxd1TI3nuTHPKybyjXboWhUun+ydHUWGLCwPp6b/stKB4+fEjr1q1xc3OjX79+mu9fuHCBUaNG4ezszNKlS3NtBGZhYYGPj0+2lRFfv35NhQoVKFu2LP/88w+1a9fWPLZt2zb+/fdfduzYkaN7Hj58mJkzZ+Lj45M7C72SBI87///OKB3JTEm2OsLW7fuYP38+pUuXZsaMGXTv3j3Lf3sqlYrjx4+zYsUKzpw5Q6lSpYiLi2PixIlMmDCBihUr6vGC8ockSVy4cAE3NzfOnj3LiBEjmDx5MlWrZl98LTU1latXr2oCjL+/P61bt9bsHnvbc4WC4b1ZQ7n18IVewQRAJSl4FfOORcn3iIODA0eOHGHy5MkcPXpU8/0WLVrg6+uLjY0Njo6ObNmyJVeqF77tcCNAkSJFaNiwISEhIZp67+l0XUNZsGAB33zzTe7tGpLJSDQbQHKKru0roGgXTM1KMWrUKO7du8fXX3/N7NmzqVOnDps3byY1NW2dJiwsjLlz52JjY8P48eO5du0a1tbW/Pzzz4SGhvLrr7++l8EE0urjtGzZkr179+Lt7Y1cLsfFxYWePXty5syZLP89Ghsb07x5c+bMmcP169fx9/enf//+eHp60qRJE8202eHDh0lISMiHVyXoqsAFlMQkHQ+avUEug/jEDyvZnZOTE/v27WPYsGFcuHBB830zMzPmzZvH4cOHWbx4MV27diUoKMig937XOgqAo6MjRYsWzbQ4rUtAuXbtGk+ePMkwGjO0M2fO0KDtbF4nGiPp8msgN4PSozX/q1Ao6Nu3L15eXvz1119s2LABKysrnJ2dqV69Ort27SImJob69euza9cu/Pz8+Pzzz3U++V8QWVtbs3DhQp4+fUrnzp2ZNGkSzs7OrFu3jsTE7KcWy5Yty+DBg9m8eTOhoaHs3LkTCwsLFixYQPny5enYsSOLFy/mzp07otxzAVfgAoq2u7re5c0zKx+Kpk2bsn37dvr06ZPpXEb9+vW5fv06zZo1o379+ixfvtxgO2veNUIBKFasGPHx8SiVGT8Q6BJQFi5cyJdffpkrKT8SEhJwdXVl2LBhLPpjBaWd9yGTFwNyUMZYZgYVl4Nx5lHFixcv8PHxISgoCGNjYx4/fkxsbCzFihXj3LlzeHh45Git5X1kbm7O+PHjuXPnDgsXLsTDw4MqVarw/fffExIS8tbnyuVy6taty3fffcfZs2d59uwZEydOJCAggG7dulG5cmVGjx6Nu7s7UVG6l8cWckeBCyhGRvp3KTU1lZeRER/kp5kOHTqwatUqunbtyv379zM8ZmxszMyZMzl37hxbtmyhdevWmVO160CbEcqTJ08oW7Ysly9fzvD9nAaUgIAAzp49y6hRo3Tq69tcvXqVunXrEhkZiZ+fH5988klaUKiyC4wrZbHN9j9khUFREiptADNHzbclSeLMmTMMGDAABwcHDh48iFwux9zcnN9//53r169jZ2dH27ZtmT59OmFhYQZ/bQWRTCajY8eOHDp0iEuXLvH69WscHR0ZOHAgly9f1ur3s1ixYvTs2ZMVK1YQGBjIyZMncXR0ZP369VSpUoVmzZoxZ84crl27ZrAyCYLuClxAKVvSTO82VCqJwf17YmFhQc+ePZk/fz7nz5//YOZje/Xqxbx58+jYsSNPnjzJ9HjNmjW5ePEiffr0oWnTpixYsCDTyCEntBmh+Pj40LFjx0w1UnIaUP744w/GjRtn0MObKSkpzJw5kx49ejB37ly2bduW8fS6cXmw9oAKi8CsUdpZDXmR/33JTMG0OljMApvjUChtnSgyMpLFixdTvXp1JkyYQHR0NMbGxhQrVoxly5Zx7949Jk6cSIMGDdi4cSM+Pj4kJydTq1Ytxo0bZ5DcZO8Le3t7lixZwuPHj2ncuDFDhw7VbEVOSdFuelomk1GtWjWmTp3K4cOHef78OT///DPR0dF8/vnnlC9fnsGDB7Np0ybCw0XBsPxQ4HZ5BQRFceTCY52nrGSAbeUSdG9tqykDm/51+/ZtatSoQZMmTWjSpAlNmzalSpUq7+30w9KlS3Fzc+PChQsZKiq+6fHjx4wdO5ZXr16xfv16nJyccnyfVatW4eXlxZo1a7J8PDExkVKlSnHy5ElGjRqVYeSkUqkwMTEhJSXlnXVMXrx4QbVq1bh3757Bdqz5+fkxfPhwKleuzOrVq7U7/5AakVaxUf06bXrLuAKYVAb+t6tp1apVHDp0iCZNmqBUKvHy8mLIkCFMmTKFatWqvbX5Fy9esHTpUlasWEG7du2YMWOGzmdt1JJEYOwLXiUnkKpWYWZkTCXzkpQ1K2CVJf9DpVJx6NAhlixZwt27dxk/fjzjxo3T6+89fWvysWPHOHnyJNbW1pqT+02bNhVbkw3ovdk2rFZLrNx9k6Rk3YavRkZy+nZ0oELZzJ9wExMT8fb2zhBkAE2AadKkCfXr18fMTP9RUl759ddf2bVrF+fOncs2Z5QkSWzYsIEZM2Ywbtw4fvjhB0xNtU+o6eHhwebNm9m/f3+Wj1+/fp0xY8bg4+ODlZUVZ8+excHBQfN4sWLFCAoKemcqjlmzZhEeHs6qVau07lt2lEolixYt4o8//mDBggV89tlnen1wiIqKYvPmzaxatQqVSkXDhg25e/cuUVFRTJkyhc8//zzHqUbi4uJYtWoVf/75J46Ojnz33Xe0aNFCq37GpyZzKfwRJ57dJ1mdNvqUJAm5TI5KUlO5SEk6WdWkTqkKmc42FTS3b99myZIl7Nmzhx49euDq6pohY7UulEplhq3JDx48yLA12cbGxkC9/zi9NwEFwPfBc85dD0GpytkoRS6XYVGmMAM6V9fql1KSJJ4+fZohwNy9e5datWrRtGlTTZCpVKlSgR3FSJLEN998w8WLFzlx4gRFi2b/yTQ0NFSTNmPdunU0adJEq3tcvHiR6dOn4+npmeXjq1at4sqVK2zYsIFx48bh4ODA119/rXm8UqVKXLx4kSpVqmR7j/j4eKpWrcrFixczBCNd+Pv7M3z4cAoXLsyGDRuoXLmyTu1IksTly5dZtWoV//zzD23btqVkyZIcOXKE6tWr4+rqSrdu3bSqIPk2ycnJbNmyhfnz51O2bFm+++47unbtmu05Kt+XIay9fwmJtAOy2TFVGFHCxIyv6rSjhGnBzwr88uVL1qxZw99//421tbVB8+1FRkZqsiYfO3aMYsWKaYJL69atMTfXt+Tlx+W9CigAp6485U7AS62DikIuo0hhY4Z2r4mpie7/ABMSEvDy8soQZIyMjDKMYurVq1egtntKksTYsWMJDAzk0KFDb+2bJEm4u7szdepUBgwYwNy5c9/5y/TgwQO6deuGv79/lo+PGzeO2rVrM2XKFE1Sw7Nnz2oer127Ntu3b8fR0THL5wMsW7aM06dP4+Hh8fYX+xZqtZrly5fzyy+/MGvWLCZOnKjT4daYmBi2bt3KqlWrSEpKolu3boSFhXH06FH69OnD1KlT3/padKVSqdi7dy/z5s0jNTWVb7/9lgEDBmTY7Xb1+RO2+F99ayB5k1wmw9zIhJl1P6HkexBUIG10sW/fPtzc3AgODmbSpEmMHj3aYBnB1Wo1fn5+HD16lKNHj+Lt7U3jxo0102O1atUqsB8gC4r3LqBIkoTnzVC874SjUktvzetlbCSnVPFC9OngQCFTw6YnkySJx48fZwgw9+/fx9HRMUOQSS95m19UKhWDBw8mOTmZPXv2vHPL7cuXL/nyyy+5ePEiq1evpn379tleGxUVhbW1dbaL6w0bNuTPP/+kWbNmJCYmUr58eZ48eaJ5A2jWrBnz5s2jRYsWWT5fqVTi4ODAtm3btB41/VdQUBAjR44kPj6eTZs25XiUI0kS169fZ9WqVXh4eNC+fXtq167NqVOnCAwMZOLEiYwdO5YyZXK/No0kSRw/fpx58+bx5MkTpk2bxsiRIwlLjeePW6e0Dibp5DIZpU3NmVW/K8Zy/UZTec3b2xs3NzcOHjxI//79mTp1qsFrFsXGxnLmzBmOHTvGkSNHSE1N1QSX9u3b53q+vPfRexdQ0r2MTsT7bgT3A18il8tQ/39wkctlqCUJi9LmuNSxwLpCceTyvPlUER8fz/Xr1zMEmUKFCmWYJqtbt26eLwKmpKTQs2dPSpcuzaZNm7T6dH7kyBHGjx9P+/btWbx4cZbrAJIkYWJiwuvXrzOtvaSmplK8eHGeP3+u2ZnVo0cP+vfvz5AhQwDo2rUrEyZMoFu3bln2YdeuXSxbtizDgU1tSZLEpk2b+Oabb/jqq6/45ptvcjRFEhcXx/bt21m5ciUxMTEMGzYMgE2bNlGhQgVcXV3p3bt3vpXBvXLlCr///jtXr16l/9rfSC6u28jYVG7EUPuGNCxnbdgO5pH0tbX0qqqurq4Gqar6X5Ik4e/vr1l7uXDhArVr19ZMjzVo0EDvKc4PwXsbUNKlpKp48iwmQ8XGiuWLUkLHao2GJEkSjx494vLly3h6enL58mX8/f1xdnbOMIrJLheWISUkJNC5c2fq1KnDsmXLtBq6x8bGMmPGDP755x/+/vtvevbsmemaChUqcO3atUwjMT8/P/r3759hZ9fatWs5ceIEu3btAmDw4MF07dpVE2DeJEkSLi4u/PTTTxlq02sjPDyccePG8fTpUzZv3pyjaSgfHx9WrVrF7t27adOmDV27dsXLy4udO3fStWtXXF1dcXFxyVF/cpOnnw8bX95BZqT7m1nFwiX4qX4XA/Yq7yUnJ7N7927c3NyIjo7WbIgoVqxYrtwvKSmJCxcuaAJMeHg4HTp0oFOnTnTq1Cnb3ZUGo4qD2IPw+jSoYtLKTSvKQLHuULTdW8sP5Kb3PqC8b+Li4jKNYooWLZohwDg7O+fKJ9/Y2Fjatm1Lp06dmDt3rtbPO3/+PKNHj84y2aSTkxMbN27MtPtm06ZNHDt2jO3b/1esKjw8nBo1ahAREYGJiQkTJkygTp06WRbZOn36NJMmTeLOnTs5+rTp7u7O5MmTGT16ND/99JNWo8H4+Hh27NjBqlWreP78OaNHj8ba2pqtW7fi6+vLuHHjGD9+fO6/Sehg9yNvzoY9RKXHYV0TuYLpTh2pVOT9n8JJ3zDh5ubGiRMnGDZsGFOmTMHOzi5X7xsSEpIha3LlypU102PNmjUz3KxEaihEroDXxwEZSEkZH5cVBpkMivdLq7ipyLuie5B9QCmQ9VA+BEWLFqVt27a0bdsWSPsFePjwoSa4rF27lsDAQOrWras5E9OkSRODnL8oVqwYR48epWXLlhQvXpzp06dr9byWLVvi6+vLzz//jKOjI4sXL2bIkCHIZLJsDzf6+PhkCjIWFhY4ODhw4cIF2rVrR/HixYmOjs7yngsXLmTatGlaB5NXr14xZcoUvLy8+Oeff7RK9e7n58eqVavYsWMHLVq04NtvvyU0NJS///4bc3NzXF1d+eeffwrURov/uh8doVcwgbTKL4/jIj+IgCKTyWjatClNmzYlODiY5cuX06RJExo3bszUqVNp3759riysW1lZMWrUKEaNGoVSqeTatWscPXqUGTNmcP/+fVq1aqWZHrO1tdXtJkm3IWQ8qBOAbDYlSQlpf6HR2+H1SbBal3ZAN58V7A3qH5D0U74jRoxg1apV+Pn5ERoayqxZsyhatCirVq2ievXq2NjYMGTIEJYtW4a3t7cmY21OlSlThhMnTrBy5cocneswMzNj/vz5HDp0iIULF2qSTWaXfsXHxydTUS34X615yP60vJ+fH76+vgwdOlSrvh05cgRHR0fKlCnDjRs33hpMEhIS2LhxI02aNKFLly6ULVuWAwcOYGNjw7hx4zh//jxr167F29ubzz77rEAHE4BElf7JTlVqNQlKw1SqLEgqVarE77//ztOnT+nRowdfffUVtWvXZtWqVbmaHcPIyIimTZsye/Zsrl69yqNHjxg0aBDXrl2jWbNm2Nvba3Y+xsfHa9docgAEj007VJtdMHmTlAKpYRD8WdqUWD4TU14FiFqt5sGDBxmmyZ4+fUr9+vUzTJWVLVtW6zYfPXpEq1atWLhwIYMGDcpRf1JSUliwYAFubm7UqVOHLl26MG3atAz9LVGiRIYdXen8/Pzo2bMnjx49YsWKFfj5+bFy5coM1wwfPpyaNWsyY8aMt/YjLi6OadOmcezYMTZs2ECbNm2yvfbu3busWrWKbdu20ahRI8aOHUvhwoVZtmwZly5dYuTIkUyaNOmtZ2IKgtTUVJ4+fcqjR4949OgRflULIRXRL+ipU5Uket6h3KtUKlWqROXKlTVf5cqV+2C2yqbnVnNzc8PT01Pzd67reSRdpG9NTp8e8/LyolGjRprpsdq1a2f+eUvq/6/R84KcVxI1BvNGUHGZoV7CW4k1lPdUdHQ0165d0yz2X716lTJlymRIH1O7du237my6ffs27du3Z+3atdnutHqbu3fv0rlzZwBOnTqFvb09kFb4K7t8YpIkUbVqVf799198fX0zFdkKDg7G2dmZR48evfWE+fnz5xkxYgRt2rThzz//zHLxNSkpCXd3d1atWsWjR48YOXIkQ4cO5eLFiyxZsgSlUomrqytDhw4tUAfYYmJiePToEYGBgZrAkf7n0NBQKlasiK2tLTY2NhTq3ogkHXd4pTNCRvUoGckPQwgKCsrw9fr16wxB5r8Bp1KlSgXqZ6etR48esWzZMjZv3kybNm1wdXWlefPmugdPZWTaInnyI1DHg6JoWp63Yt3Syi9nIy4ujjNnzmjOviQnJ2fYmlyqVCmIvwih09Oms3QhMwHrg3ky9SUCygdCrVZz7969DKOYkJAQGjRooAkyjRs3znRe4tq1a3Tr1o3du3fTunXrHN935cqVbNy4kYCAAKZPn85XX32Fu7s7u3fvzvYw4pQpUyhXtRLla9pz4uxpvv7qK4obm+FQohzTp30DwOLFi7N8bmJiIj/88AM7d+5k5cqVdO/ePdM1Dx48YPXq1WzevJl69eoxbtw46taty5o1a1i7di0NGzbE1dU11+bT30WtVhMaGpopWKT/OTk5GRsbG2xtbTWBI/3PlStXzrBh4/qLp2zxv0qySvckn8ZyBYsa96aQIvNGkPj4eEJCMgea9K/g4GCKFCmSZbBJ/7KwsCiwW2rj4uLYtGkTS5YsoUiRIri6ujJw4EDtUxAl+sKrdZBwhbRF8jcK+MkKAWoo0h5KfQ6m9m9tSpIkAgICNKf2z58/T61atdi+wJyq5SN1fo1gAiWHQtmperShHRFQPmBRUVFcvXpVM4q5du0a5cuXz7DYX6tWLc6fP8+AAQM4dOhQjrfE7t+/n/Xr1/PXX38xduxYoqOjqVOnDjY2Nvz4448ZrlVLEnejwtjpd4kIdSKmxsYkJadQ2MwMmQxk/F975x1XZd3//+d1zmErKgjIEsWBIA7KkQvNUtGcmWJ+by21HN2plWVq427ZsCxHKjhw3JWjDCv3zG0unIADAWUICKJszrh+f3BzfuEBPHAOgvR5Ph7nEV7zw+lwXtd7S5z58XdC3/4Iv6aGgcvTp08zduxY2rRpw9KlS3F0dNTvKygoIDw8nNDQUKKiohg3bhyvvPIKKSkpJTJ+Xn/9db0lVZXk5eURGxtbQjCK/xsXF0eDBg0MxKL4ZycnJ6OFTqPT8vaJX8nTVi4GokCia6NmjGnRqVLny7JMWlpauYKTnp6Om5tbqWJT/Kqq9F5j0el07Ny5k4ULF3L+/HkmTpzIlClTys/sy1gL6cv+JyLluaIURVaCy4dgb3x6dn5+PieP7aBLo0+xUJk4w0hRD5ofNO0aRiAE5R+EVqslMjKyhBWTnJxMx44dcXR0ZPfu3WzdupVu3boZfc2jR4/y9ttv6+dYhIWFMWXKFJ5//nnWrl2rf9LLVhew4OJ+UvOy9E0LS0PWFHUhfrFZB7o1KhIVtVrNZ599RkhICIsWLSI4OFh//PXr11mxYgVr1qzB39+fSZMmMWDAALZs2cLChQtLNGk055eWLMukp6frLYsHLY309HSaNGlSqmA0bdoUW1vztTsJjz3HvsRo1HLFv3QsFEreCwjC1bae2dbzIAUFBaVaObdu3eLmzZvEx8ejUqnKFBtPT0/c3d0fWRFpVFQUixcvZv369WXXHqWvhoxQw7Td8pCsweUjsA8y/pz8y5Aw6X/BeFOQoMVpkKrWUhSC8g8nPT2dEydOcPz4ccLDw4mKiqJp06b07NlT7yrz8/MrM3332rVrBAUFERMTAxR90To6OtKpUydu3rxJWFgY/k8GMDdiB/cK84xOcbVUKBnapB0udzWMHTsWV1dXVq5ciaurK2q1mt9++43Q0FDOnz/PSy+9xKuvvkq9evUICQkhJCSE1q1b66umK+tu0Wg03Lx5s1QrIyYmBpVKVapbytvbG3d390fm5inUavjq/G6Sc+9VKIXYUqHkucb+BHmat2VJRZFlmbt375YqNsWvlJQUnJ2dSxWb4p8dHBzM6sK8e/cuq1at4vvvv8fNzY1p06YxfPhwLNTnIfH1iolJMZI1eK0Hy6bGHZ97BpKmm0FQVEUWiqJq411CUAQlWLp0KZ9//jmvv/663ppJS0ujU6dOeldZ586d9QHzzMxMGjduzP3794Gi3lmdOnUiOTmZn3/+melvvMGQ0I9R1K+DtoIZKpJWx5HPl/PGyLFMmDCBuLg4VqxYwerVq2nZsiWTJk3i+eefJzIykoULF/L7778zYsQIpk2bhr+/v1H3yMrKKjX4HRMTQ0JCAq6urqVaGd7e3jWql1O2uoD5F/aSlp9tVE8vS4WS3m4+DG3S7rHI4lKr1SQlJZUqNsUvtVpdptg0btwYDw+PSqWBa7Vafv/9dxYuXMj169c5ssGPJs7lD5YrGyXYDy4aylYOxdNDk2MPMKzjNmytTR3bLUGLM0UV9VWIEBSBAV999RVr167l0KFDNGzYkLS0NL0Vc/z4cU6fPk3jxo31gf4pU6Zw7949bG1t2bJlC8uXL2f79u0AHIuLZk3MSaRKdnp2UFrTOVVBaGgop06dYsyYMUycOJEWLVro3Vrx8fH6zrN/j6tA0dNvcnJymVlTOTk5JUTi78LRpEmTx2r4UqFWw+bYCI6l3EBCKtW1qMkroIFdHUa26PjY9u8qi3v37nHr1q0yBScxMREHB4cyBadx48Y4OTmVW0x7+fxeWqjewdLChGJSyQqa7QeFHYWFhVy5coULFy7oX2fOnCErK4u6deuiUhQQs9sLG2sThUDlDt7bTLuGEQhBEZTK7Nmz2bNnD/v37zeIPWg0Gi5evKgP9m/YsAE7Ozu6dOlCfn4+bm5uhISEYG9vz7cX9nLlXmWf5kBTUEjc8i28PGg4L7zwAnl5eaxcuZIlS5bg6empd2slJCSUamXExsZib29fZtaUi4vLY/GEXhEKtBpOpcWxP/Eq9wrz0MharBQqGtd14NauY2TFJLJ0yZLqXuYjR6vVkpKSUm4CQVZWFp6enmVmrHnbb8cyez1Q+ULQQo2SdTsasXhdAlevXqVRo0Y0aNCAwsJCEhISqFOnDr169aJnz54EBgbS0n4dUtYOjCpoLA3JBhq+CQ1GVnrNRt9KCIqgNGRZ1vfS2rlzZ7nTKgMCAvjqq6/Iy8tjxowZWFlZER8fT6sn2/HER5NNalwoAR2cvPDLkPjmm2/YvXs3LVq00LvZYmJiSE1NxdPTs1Qrw9vb26xz6B93EhMT8ff3Jy4ujnr1qi4Q/7iSm5tbpoVz69YtFszUMSDQ9M/TH0fq8eUqDZcuXcLNzY3AwEB69OhBjx49DItr86Ph1suVi9lACYuoqhGCIigTnU7HmDFjyMzMJDw8vEz3T9++fXnrrbcICgrC3d2dI0eO4OHhQfjZIxzIS0SnNM1cz05NZ/2I6bi4uNChQwdat25dQjg8PDzMMr3vn0JwcDBdu3Zl+vTp1b2Uxw755stI+edMvs5f0S4kaibRvXt3nJ2dH37CrQmQd4EKW0aSNdQbCc5vVWqdFUU0hxSUiUKhYM2aNbzwwguMHTuWH3/80SBz6Vb2XVy7tedcfhq6qxHUaeFJY6/GKBVK3Lw8UcWkUljBwU8PUs+xAbm5uTW+r9bjwtSpUxk/fjxTp041+9yQ2o6kMI+12/mpZ8H5eeNPcFsA8aNAk4rRoiJZg00AOFX/g4P4lAkAsLCwYOPGjaSmpjJlyhRkWUat03IiJZaPzmxj3vndWPdoQ1x9ia23o+jx3mTe+SucLTER3IiPQ6OpfAV3MdaWVkJMzEi3bt2ws7Nj9+7d1b2Uxw+rloCJ9TCSDVh6V+wcZV3w+hGsmhadb8w97ALBfVGV154Yg3B5CUqQlZXFs88+S/f+z6Lq34E8rbrcdh+a/EIkhYRCkpAsTDN4M+MSOfzudwQEBJR4NW3atNYF1B8VYWFhbN68mW3bqj7zp7YgyzI3Y07grnkdlcIEq1uyAu99lZtVIqshay9krAb1TZC16C0WyQqQi6ySBi+BbZei2SiPEBFDERhFYWEh+04e4+fsGFQ2VkhGukpkWTbpS99SoeT5Ju3x1lgTERFR4pWVlUW7du1KiIyvr2+1jeV9nMjLy6Nx48YcP368yodPPa7odDoiIyM5dOgQhw8f5tChQ8iyzJ7VXrRumlfJqyqLGkY2+tj0BRZchdy/QJsJWICqPtj1AotGpl+7kghBEZRArVZz/fp1Ll26xOXLl/Wvm8lJvLDuKyzt7YwWEz0yRelalcBCoeSbzs9jrTIUiTt37nDu3LkSIhMfH4+vr28JkWnbtq3I9CqFd999l8LCQr777rvqXkqNQK1WExERoRePI0eO0KBBAwIDA/VZWN7e3kh5pyBxWuUr5Rv/CFaVHLJVwxGC8g9Fo9EQExNTQjQuX77M9evXcXd3x9/fn9atW+tfqfVV/J5wqVIB9spaKTq1Bl9rR97q+pzR5+Tk5HDx4sUSInP58mU8PT0NXGYVmR9TG4mPj+eJJ54gPj7+Hym4eXl5nDx5Um+BnDhxgiZNmtCjRw+9gLi5uZV+cvpKyFhZ8V5ezh9APeM/z48bQlBqOTqdjtjYWAOL4+rVq7i4uBgIR6tWrQwaF8qyzJxTv5FRUPkpdxUVFVmrw0anYNPLM3n3rbeZPn16pV1narWa6OjoEtbMuXPnsLW1NRCZJk2a/KPiMsOGDaNfv35Mnjy5updS5dy7d49jx47pLZCIiAj8/f31Fki3bt0MBsKViSxDxhojG0T+r9uw83tQz3DcQm1CCEotQafTER8fb2BxREdH07BhwxKi0bp1a3x9fY1+Kr12L5XFl/4st0vww5Aoak+vM6Kfl1KSyLuTSZ1j13lj0mu8+OKLuLi4sHr1aoN5LpVFlmXi4uIMXGbZ2dm0b99eLzDt27ev1XGZ/fv3M23aNC5evFjrhDQtLU0vHocPH+bKlSt07NhRb3089dRTpltmeRFF1kreqf91sP/bSObieSh2vcBhPFi3Mu1ejwFCUB4zZFnm1q1bBsIRFRVFvXr1DITDz8/P5Lbtu29FEh53rrKNH/TYFBZ1lbV3dUKt0yE/IC6WCiUy0MnJi76OzejX+1lGjhzJO++8w3vvvceGDRv44Ycf6Nmzp4krKZu0tDQDkbl58yZ+fn4GcZnHcVLhg8iyjL+/P4sXL6Z3797VvRyTuHnzZokAenJyMl27dtVbIE8++aTxg7MqijoF7v8GhTdAm100pdGqFdQbDMr6VXPPGogQlBqKLMskJSUZCMfly5exs7MzEI7WrVuXOzK3Mty+fZv//ve/7LsTQ+MBPZAUpj3BZiWmML1pV1z8mrMnMYqYe3fI16q5m56Ot5snga4t6OLcVB+AT05Oplu3bsyePZtXX32VHTt2MH78eCZPnsz777//yNrDZ2dnc+HChRJCExkZSePGjUuITPv27R/LuMyyZcvYvXs34eHh1b0Uo5FlmatXr3Lo0CG9iOTm5uqtj8DAQNq2bVtjJ0XWVh47QZFlmfSCHLLVBehkGVuVJc42dVBUcVvmqkKWZVJSUkoVDgsLi1KF48GOuqaSn59PcnIySUlJ3Lp1i71793Lw4EHi4+NxdHSk1Qt9aT78WRQm/nHeuRbHiQ+W4OLion85Ozvz3XffERoaipeXl367o6MjCoWCa9euERgYyNKlSxk2bBhJSUn861//QqfT8eOPP+Lu7m6md6FiqNVqoqKiDOIyderUMRCZmh6Xyc7OxsvLi7Nnzxr2kaohaLVaLly4oBePw4cPY2NjoxePwMBAWrZsWaPf538Cj42gFGg1/JUay+6EKDIL81D+T0BkWcZCoaS3uw89GjXH3rLmVlSnpaWVEIziQDlQqnAY1eOnHDQaDSkpKSQlJZGUlERiYqL+579vy87O1scm0tPTcXBwoGvXrvTt25dmzZqR6WjNgexblZoI+HdaWDcguJE/KSkpJV6ff/45Tz/9NFlZWfpt9+/fp2HDhri4uGBjY0NERARDhw6lQ4cOODk5ceDAAbZu3cqCBQt48cUXa8STqCzLxMbG6sWlWGhyc3NLjcvUpP5jb7z5JkqPhrj16cyt7LsU6rQoJQX2ltb0cm1BV5dm2Fk8ulb+hYWFnD59Wm+BHDt2DFdX1/KbKAqqncdCUE6mxvHfayeRoMzAsIWkREamj4cvQ7zaVuuTSkZGRqnCUVhYaCAa/v7+FW6hrtPpSE9PNxCGB/+dnp5Ow4YNcXNz07/c3d31P9evX5+TJ0+yadMmYmJiGDt2LOPGjcPX1xcoqo7//fff2RT+Kw0nD0JlZcIXilrL1PbP4O9gmIb55JNPEhISUmLMamFhIWlpaXqB2b9/P8uWLWPYsGHodDpu377NjRs3uHnzJrIs07BhQxo1alTC+nFxcTHY5uTk9Mi/yFNTUw1EJiEhwSAu06ZNm2qJyxxOvs6vN85yLycbCxvDB7Li2NYTjp682LwjNqXUBJlKTk4Ox48f11sgp06dwsfHR2+BGN1EUVCt1HhB2ZcYTXjceaOm0EHRh/+Jhp683LJLlYvKvXv3DETj8uXL5OTk4OfnV0I0WrdujZubW7lrkmWZ+/fvlysUSUlJJCcnU7du3TKFovjl4uJi8OUpyzLHjx9n1apV/PrrrwQGBjJ+/HgGDBiAhYUFubm5bNu2jY0bN7Jnzx569OhBcHAwuQGNOXc3yagsrdLIz7xP9po9zJo1y2A+d1BQENOnT6d///7lXmPTpk28+eabHD58GG/vol5I6enpjB8/nri4OD799FOsrKwMLKDbt2/rf87IyKB+/foGwlOa+Dg7O1dZdld2djbnz58vITRRUVF4eXkZuMzMldn2ILIsszHmDEdTYoyqL1JJChpY2fJOuz7UszSin1Q5ZGRkcPToUb0FcunSJQICAvQWSNeuXUV7/ceQGt1t+GzazQqJCUChTsvZO7dwsLJjSJN2ZllHVlYWkZGRBsKRmZmJr6+vXjiCgoJo3bo1np6eBsKRl5fHjRs3yhWKpKQkJEkyEIdmzZrpi6zc3NxwdXWtcLPE4gB7WFgYOp2OCRMmEBkZiaurKwUFBWzfvp0NGzawY8cOOnXqRHBwMMuXL9fn5V9Pv83ZtJtQidkmShleaP0UsT0LGT58OC1atGD27Nk888wzSJKknwr5MEaOHElaWhr9+vXj6NGjODs74+joyJYtW/j++++ZMGECCxcuZOzYsWVeQ6vVcufOHQPRSUlJITIyssS/79y5g729vdHiU5EMojp16tCtWze6deum31ZYWEhUVJReZLZu3cq5c+ewt7c3EBkvLy+TH5j+iL9otJgAaGQd6fk5fHNhL+8FBGGtNF5sk5KSSqTwxsXF8dRTT9GjRw/mzZtHp06dyp25I3i8qXYLRSfrmPlXOFnqgkqdr5IUfNFpaIViKjk5OURFRRkIR1paGq1atSphbbRu3RovLy+0Wi23b99+qFWRm5uLq6urgVg8+O+6detW6vctDbVazY4dOwgLC+PgwYMMGzaMCRMm0LVrVzQaDXv37mXDhg388ccftGvXjuDgYIYPH26QqXTlyhVGjBiB67Od8OjfDZW18V+cOrUWH8dGvNn2GZSSArVazfr16/nqq6+wsbFh1qxZHD58mMaNGzNjxgyjrvnhhx+ybds2Dhw4UCIlOiIiguDgYHr06MGiRYtMdh8VuxZLE5+/Wz0pKSmkpaVhZ2f3UOEpfhn7QFBcmPqgyyw/P79EXCYgIAAfHx+j3XmJOZl8cW5XhR7WilFJCnq6tmBksydL3S/LMjdu3NALyKFDh8jIyChRgR4QEFBra3v+ydRYl9fFjERWRB8tt6NteVhISvo39uO5xm0M9uXl5REdHW1QPZ6cnEzLli31guHh4UH9+vWRJImUlJRShSIjIwMnJ6dSxeHv/3ZwcHhkcZ3o6GhWr17NunXr8Pb2ZsKECYwYMQIbGxv+/PNPNm7cSHh4OD4+PgQHB/PCCy+U2WLixx9/ZOrUqTg4OODt7c2Y+R9y8n6iUU+1moJCvOo4MLNjf4OnWZ1Ox9atW/niiy+4du0aHTt2ZMuWLUY95cuyzOTJk7l+/Trbt28vcU5WVhb//ve/OXXqFBs3bqRt27YPvZ450Ol03L1796HCk5KSQmpqKtbW1kaLz4OdCwBSUlJK1MqcO3eOxMREWrdubRCXKe38tVdPcCIlttIuTCuFivldhmOhUJZoolhsgQAlUnj9/PzE7JV/ADVWUL65sJdrJswiB7BTWTLWtgWRD6TjJiQk4OHhgbu7Ow4ODnpTOycnR29t3L59m3r16pUZnyje5uzsXCMyjLKzs9m0aRNhYWFcv36dsWPHMn78eFq2bMmRI0fYuHEjmzdvxtPTk+DgYEaOHEnjxo3LvF5ubi7Tpk1j+/bt5OfnM2fOHN566y0UCgVn0m7y8/XTpGXdw8LGyuArSVdQiKyTqZNwj/kvT0NZzheJLMvMnDmTDRs2oNPpePPNN5k4ceJDizG1Wi0jR45EqVSyfv16g/8H69atY8aMGXzyySdMnjy5RqWTyrJMZmamUeKTkpKChYWFUeJja2tLTExMCZGJjo6mSZMmJUSmVRt/vrj2Z6Wsk2JUSDSMucu58F1lN1GsQe+54NFQIwVFlmVeO7Kh0k9Pxahz89k/8xu0GVkoFAoKCgrIzMzE0tJSLwhliUWjRo2qrqrWTMiyzLFjxwgLCysRYO/fvz9nz55lw4YN/PzzzzRs2JDg4GCCg4Np1uzhXU4jIyN54YUXKCwsaiOxceNGnnyypHvj088+IyHvHk/832Bu5twlX6vGUqHEwcqOzL8uM/+NOdxJTTWqtUV4eDhr1qzh448/Zt68eezevZvJkyczbdq0cjN78vPz6d+/P35+fnz//fcGX2BXr17V/84rVqygQYMGD11LTaM4UcNY8ZEkySCrTaFQkJubq88MVLs34KnpY1CVktFVIdLu01vdoPwmioJ/FDUyKG/Kk9PfsVCpmDBlMs3sS6bOPu6dVW/fvs26desICwtDlmV9gD0pKYmNGzcyffp0bGxsGDVqFPv27aNVK+N7CK1Zs4Y33ngDa2tr+vfvz+LFiw3eL61Wy4rly/ntt98I8Asw2Ndp/Cxsra2JjY2lTRtDl+ODODk5kZaWRvv27fnpp5+IiYlh/vz5tGrVitGjRzNjxgyaNm1qcJ61tTVbtmyhZ8+efPrpp3z44Ycl9rds2ZLjx48zc+ZMAgICWL9+PV26dDH6vagJSJJEvXr1qFevHi1btiz3WFmWyc7OLlN8srOzsbW1xdLTDcxgVTt5uhHccbDJ1xHUfqpVUCRJMujzVBlsbGz4v9ED8LavmrTLR0lxgH3VqlUcOnSI559/npUrV2Jvb8+mTZsIDAxElmWCg4P5/fffadOmTYVcDtnZ2bz22mvs2rULpVLJggULGDVqVKnHbt++HTc3NwICAgz2rV69Gmtra15//XVCQkJYsmTJQ+9dLCjFNGvWjKVLl/Lhhx+ycOFCOnToQP/+/Xn33XcNBKpevXrs3LmTbt264ezsbNA119ramkWLFvHMM88wdOhQ3nzzTWbOnFkr/fmSJFG3bl3q1q1b7tCsXQmR/BZ7Hq2Jf2NaEwtdBf8cqvWvzUKh1FfCm4JW1lHHoma7rR5GdHQ0M2fOpHHjxnz11VcMGTKEvXv34uXlxcSJExk0aBAFBQVs2LCBa9euMXfuXNq2rVhh54ULFwgICGDfvn00bdqU06dPlykmACEhIUyZMsVge2ZmJu+//z6LFy9m4sSJrF+/nuzs7Ife38nJiTt37hhsb9SoEV988QU3btygTZs29O3bl4EDB3LkyBGD43bv3s0nn3zC5s2bS73HkCFDOHXqFFu3biUoKIiUlJSHrqu2Yqu0LDeuZSw2ykdXOS94vKn2x7c2Du6VHfKnx97SGifrx8+9lZ2dTVhYGN26dePpp59GkiTWrVvHwIEDWbRoEYMGDSIjI4NVq1YRGxvL119/zZNPPlnhIKgsyyxfvpzu3btz584dxo8fz5EjR0p1LxUTGxvLX3/9xciRIw32ffTRRwwZMoQnnngCDw8PevbsyU8//fTQddSvX5/s7GzUanWp++vVq8e7775LbGwsgwYN4qWXXqJ79+5s3bqV4lhfs2bN2LZtG1OmTOHAgQOlXqdx48b8+eefdO7cmYCAAPbs2fPQtdVGmtdzMtn+V0oK/BpU36hZweNFtWd53bh/h+8u7qewkjM4rBRKhjd9gp5uLcy8sqqhtAD74MGDSU9P55dffiE+Pp7hw4cTHBxM9+7dTc4su3//Pq+88gr79+/H0tKS9evXG9UWfs6cOeTn5/Ptt9+W2H758mV69epFZGSkvo5l165dzJ49mzNnzjxU7FxcXDh37hyurq4PXYNGo2Hz5s18+eWXaDQaZs2aRXBwMCqVigMHDhAcHMyuXbtKdckVs3//fsaMGcPYsWP55JNP/nE1EZ+d2c6t3MxKn6+SFHzSYRCO1o9/C3+B+SgrKF/tFkrTuo7UN6G9gwx0dmlitvVUFbdv32bevHn4+voyYcIEXF1defvtt7lz5w4zZ87k6tWrzJ07l8TERJYuXUrPnj1NFpOIiAjatGnD/v376datG5cuXTJKTAoLCwkLC2PSpEkltsuyzPTp0/nwww9LFEX26dOHe/fucfLkyYde+8E4SnmoVCqCg4M5e/Ys33zzDStXrqRFixYsWbKEzp07s3TpUp577jliYmLKvEbv3r31qbWBgYHExcUZde/HHY1Gw6pVq9gydzFyYeUHpjWzdxJiIjCaahcUSZKY0KorloqKf3laKpT8X/NOFWoN8ShRq9X89ttvDBkyBF9fX86fP8+gQYNo1KgRS5Ys4cqVK7z33nskJyezcuVK+vTpY5aGhrIss2TJEgIDA7l79y5z585ly5YtRo89DQ8Px8/PDx8fnxLbt2zZwu3btw3iKgqFgkmTJrFs2bKHXrsiglKMJEn069ePAwcOsH79evbu3Yu3tzdXrlxhxowZ9O3bl9u3b5d5vrOzM9u2bWP48OF06tSpzPhLbUCWZX1HhHXr1rF45n9wr++IshKOZQuFkuFNy7b+BIIHqXaXVzGXMpIIjTpsdL8hC4WSwV5t6OvhV8UrqzjR0dGEhYXx3//+Fy8vL3x9fbl58yZnzpyhf//+BAcHExQUVOE+XcaQmZnJSy+9xKFDh3BxceHXX3/Fz69i79HTTz/Na6+9xogRI/Tb8vLy8PPzY9WqVaVO/EtLS6NFixbcuHGjXOEaMWIEL7zwAsHBwRVa04NERkby9ddf8/vvv+Pj48P9+/c5evToQxsNnjx5klGjRhEUFMT8+fNrVV+pv/76i3feeYeMjAy+/PJLnnvuOSRJ4n5hPp9H7OS+Og9tOX/vf8dCoWSCT1cCGnpW8aoFjyM11uVVjL+DG2+1fQYn6zpYKlRlPk9ZKVTUUVnxcsunapSYZGVl6QPsxTGGVq1aERUVxf3795k0aRKJiYmsX7+eoUOHVomYnDp1Cj8/Pw4cOMCLL77IuXPnKiwmUVFRREdHM2TIkBLbv/nmGzp06FDm+FgnJycGDhzI2rVry71+ZSyU0vDz82P16tVERETQqVMnrl+/jq+vLxcvXiz3vE6dOhEREUF6ejqdO3cmKirK5LVUN9euXdML9csvv8z58+cZOHCgPp5lb2nN+08E4WZbHytF+RawlUKFlVLFa36BQkwEFabGWCjFyLLMjaw77EmI4kJGIrIMSEXbve2d6OfhSxsHN6MnNybmZLIvMZrr99PI12r0rbl7ubUkwNEDVSVcbX9f67Fjx1i1ahXh4eE0b94clUrF5cuX6dmzJ8HBwQwePNjkWe/GrOPbb7/lww8/xMLCgh9++IGBAwdW6lpvvPEGdnZ2zJ07V7/t5s2bBAQEPHTS35EjR5gwYQLR0dFlBuf/85//APDxxx9Xan1lkZqaSq9evYiJiWHQoEHMmjWLDh0MHqD0yLLMqlWrmD17Nl999RXjxo177FqIpKam8sknn7BhwwZmzJjB9OnTS+3nVYwsy1y9l8quhEiuZKZgoSiaLSQhoZVl6lpY0c/Dj87OTfTjmQWC0qiRlfKlIUkSzeydaOZXFPQt1GrQyTJWSlWF/uCj7t5mc2wEt/Puo9XpSrR3SS/I4VbOXX64dpJeri0Y6NUGiwoIS3EF+6pVq8jJyaFhw4ZotVocHBwYNWoUQ4cOfWTtPzIyMhg1ahTHjx8nICCATZs2Vbo9Rm5uLj/88ANnzpwpsf2dd95h6tSpD52c161bNywtLTlw4EC5lkxkZGSl1lcezs7ORERE0KdPH5KSkhg2bBitWrVi1qxZ9O7d2+CzI0kSr7zyCl26dCE4OJh9+/axbNmyKhd/c5Cdnc23337LokWLGDNmDNHR0UbNUpEkCZ/6LvjUd+FeYR6JOZnkagqxVKhoYGWLh139x05UBTWLGuPyKgtLpQprlUWFPugHkq6wJPIgt3LuotZpS+0VVqDVkK9Vsy/pCvPO7yFHXVjuNYsD7IMGDaJ58+YsX76cpKQkWrRowZQpU4iJiWHXrl2MGzfukYnJiRMn8PHx4ejRo8yePZtDhw6Z1Gtp48aNPPXUUyWE488//+TEiRPMnDnzoedLksSUKVPKDc43bNiw1OJGc2BlZcXWrVvJz8/npZde4v/+7/94/fXX9YF4rdYwPte6dWtOnjxJnTp1eOKJJ3jUFnlF0Gg0hIaG0rJlS6Kjozl58iTfffddpQZz1bO0wa+BKx2cvGjr6I5nnQZCTAQmU+MFpaIcu32DX2PPGd0nTK3TkpSTyYJL+0s9Jzo6mrfffhsXFxcmTZrEgQMHaNOmDdOnT+fq1ascOHCASZMmGcwWqUp0Oh1z587l6aefxsLCgj///JM5c+aY3GYkJCSkREsTjUbDtGnTmD9/frmulL/zr3/9i71795KcnFzqfnPFUMrC3t6eHTt2sGHDBnJzc7l8+TLvvfce8+bN0ycVFBSUnL1ja2tLaGgon3/+OQMGDGDBggWU5wp+1MiyTHh4OP7+/mzatIk//viDn376ST/NUiCoKdQqQckoyOGnmFNGZ4oVo5F1JOfeY2t8UUA3KyuLFStW4O/vz5NPPsnSpUtxd3dn5syZREVFcfz4caZOnWpUcZ65uXPnDj179uSzzz5jwIABXLlyxWDUbmU4e/Yst2/fLjGeNzQ0lIYNGzJ8+HCjr2Nvb8/IkSNZtWpVqfurWlCgqHhy9+7dzJ07l19++YWhQ4dy4sQJQkND+fnnn2nWrBnz588nKyurxHkjR47kr7/+Yv369QwePLjKLKmKcPToUbp3785HH33EggUL2Lt3r0FHaIGgplCrBOXPpKuVfrJU67TsS4hi4JDBODk5MXXqVHJzc5kzZw6XLl3i4sWLvPXWW3h6Vl/my6FDh2jevDlnz54lNDSUzZs3m23yY0hICBMnTtQXU6anp/Pxxx+zcOHCCrtCJk+ezPLly0t1MT0KQQHw9vZm27ZtvP766+zbtw9JkujVqxc7d+7kjz/+4NSpU3h7e/PBBx+UWE/Tpk05fPgwvr6+BAQEcPDgwSpfa2lER0czbNgwRo8ezaRJkzh79ixBQUHCLSWo0dQaQdHotBxKvo7GhM6oObm5JCkLmTp1KufPn+fGjRu899571e5a0Ol0zJkzhz59+tCoUSMuXrxY7jz1inLv3j1+/vlnJkyYoN/2wQcfMGrUKKPa0j9IQEAAbm5ubN++3WCfo6MjGRkZ6HRV38G2ffv2bNq0iVGjRpVINAgICGDDhg0cP36ctLQ0fHx8mDp1qr6K3tLSknnz5rFixQpGjRrFRx99VKo4/h1Zlom9f4ezd25xMjWOixmJ3C3IrfCak5OTmTRpEj169KBbt25cuXKFsWPH1ojhbgLBw6hxacOV5WJGIiujj5JfyVHCxXjXbci77fuaaVWmk5qaSv/+/bl06RITJ07k22+/NXs/qiVLlnDw4EE2bdoEwLlz5+jXrx/R0dGVTjBYu3YtGzduLFVU6tev/9ACSHPy66+/8u9//5tDhw7RooVhz7fk5GQWLlzIihUrGDBgAO+++y7+/v76ff/617/QaDT8+OOPeHh4lDg3V1PIsdsx7EmMJk+rRqJoJIMCCbVOS8t6zvT18KNVfZdyrYusrCy+/vprlixZwvjx45k9e/Yje38EgopS4wsbTeVuQR46MwRSMwsr/lRZVezevRtvb2+uXbvGtm3bWLx4sdnFRJblEsF4WZaZNm0an376qUnZaiNHjuTkyZPExsYa7HtUbq9inn/+eT7++GP69etXarKAq6srX375JTExMbRu3Zo+ffowePBgjh07hqurK7t376Zv37506NCBrVu36s87n57Au3+F81v8BTIL8/SZgwVaDXlaNRpZR2TmbZZFHWJuxE6y1fkG91ar1SxZsoSWLVsSHx/P2bNn+frrr4WYCB5Lao2gaHRaswiK5hG4Yh6GVqtl6tSpPPfcc7Rt25bY2FieffbZKrnXsWPHKCws5OmnnwaKUoezs7NLuL8qg42NDWPHjmX58uUG+x61oABMnDiRcePGERQURGZmZqnH1K9fn1mzZnHjxg0GDBjAmDFjCAwMZNeuXcyZM4dffvmFf//737z55pscSbzGiuijFOq0D00CKdBqSMrN5LOzO8kqLBIVWZb5+eef8fPz448//mDnzp2sXbv2obU+AkFNptYIio3KAqUZApbV3WgyKSkJf39/QkND+fTTTzl69CiOjo5Vdr9ly5YxefJkJEkiJyeHd955h0WLFpnFZz9p0iTCwsIM0nSrshalPN5//3169OjBkCFDyMvLK/M4GxsbJk+ezJUrV3jttdeYM2cO7dq14+bNm5w6dYrEwizWRh+r0AhrrSxzX53Hd5f28+ehg3Tp0oUvv/ySkJAQdu7cSbt27czxKwoE1UqtEZTGdRxMHiakQMLbvuq+vB9GeHg4zZo1IyMjg1OnTjFr1qwqzeq5c+cOW7du5aWXXgLgiy++IDAwkO7du5vl+j4+Pvj7+/Prr7+W2F4dFgoUFV4uXLiQRo0aMXr0aDSa8uNtKpWKUaNGERERwbx58wgNDaVz5864DwpEYVnxBw+tLHMrI42ZC79i2rRpnDp1imeeeaayv45AUOOoNYLiblcfZxvTUmhVCgXPuvuaaUXGo9FoePnllxkxYgT9+vUjLi7ukTyxrlmzhqFDh+Lg4EBMTAwhISHMmzfPrPeYMmUKISEhJbZVl6AAKJVK1q1bR1ZWFlOmTDEqzVySJIKCgjh48CBLf1hDjgkTcRVWFgx7fxqjR4+ulfPuBf9satUnup+HH1bKyrcnc7Kug2cd09qmqHVaCrQao+th4uPjad68OevXrycsLIwtW7Y8kpbqOp2uRDB+xowZzJgxA3d3d7PeZ8iQIVy7do3Lly/rt1WnoEBRi5bw8HAiIiL44IMPKnTuXRdbVCYmRiTm3iMl775J1xAIaiI1rjmkKQQ09GRL3HnU2tL7d5WHhULJ85UYJiTLMnHZ6exJiOJ8eiJaWUaSQJahmX1D+nn44e/gWmp35LVr1/Lqq6/i6enJ9evXH2nR5L59+6hTpw6dO3dm165dXLp0iQ0bNpj9PhYWFkyYMIGQkBAWL14MFMVQIiIizH6vilC3bl22b99O9+7dcXFxYerUqUadF5V5G62JzlUFEjfu38HFpuY3ohQIKkKtEhQLhZK32z7LZxE7ydMUGi0qlgolg73a4u9QscaK8VkZrLpylLsFuah1//9rptg4uX4/jVvRR7FQKHmxeQc6OBVl8KjVakaMGMEff/zBK6+8wtKlSx954dqyZcuYMmUKGo2GN954g++++65KZrRAUYZV+/bt+fLLL7Gzs8PJyalGtDVxdnZm165d9OjRAycnJ0aNGvXQc/I1apPvq5V15GrKb0YqEDyO1CqXF4CDtR3vBwTRwMr2ocOElJICC4WSYO8n6eNRsdhJ5N1kvrmwh5S8LAp1ZT+zFug0ZGsKWHP1BLsTIrly5QoeHh7s3r2bHTt2EBoa+sjFJDExkT///JPRo0fz/fff06RJk0rPTzEGT09Punfvzvr164Hqd3n9naZNm7J9+3amTZvGnj17Hnq80gxxD0mSUEmi8l1Q+6h1ggJFovJxh4H8q0Un3G3rY6lQYqlQ6gXEWqnCWqniabeWfPTkc3R3bV6h68dnZbAs8lCFmlCqdVp+jYlgyJsT8fDwICkpib59q6cif9WqVYwaNYrc3Fw+//xzFixYUOU9oiZPnsyyZcuQZRmHho7cK8jjbkEuBSZ2NjAHbdu25ZdffmH06NGcOnWq3GPrWZhuxSklCXvLqrEGBYLqpNa0XimPhJy7xGdl6IcJ1bcqmgVRkaFaf+c/p7dyu7JBVa2Ohd2Dq20inkajoWnTpmzdupVFixbh4ODA119/XeX31Wq1dBrUl6FzppIk51FYUICtjQ0anQ4nmzr08/Cjo5MXliYkVZjKb7/9xqRJkzh48CA+Pj6lHnP0dgwbY85QoKu8EFoqlMx/ani1/q4CgSk8NhMbqwIPuwZ42Jln6FVcVjoZBTmVPt/SwoITqbH0cmtplvVUlG3btuHp6UlBQQE7duwgOjq6yu8Ze/8Oy6OP0HHmOG7pcopcPlaWegsvJS+LjTFn2BBzmiFN2vGse6sqX1NpDBkyhLS0NIKCgjh69Gipw8o6OnmxIabyD1lKJLq6NBNiIqiV1EqXV1WyJyEKtQntWQp1WnYnRFXbAKeQkBAmTZrEtGnT+Pzzz6t85O2ljCS+vbiPjIJcZKWiTNdagU5DoU7Lb3Hn2XD9dLW9P6+88gqvvvoq/fr14+7duwb7LZUqurp4oyola88YFAoFvd2r52FCIKhqhKBUkPMZCcgmpo1mqfNJzct6+IFm5saNG5w+fZrCwkJkWTZrC/zSiMtKJzTqcIViTYU6LUdTYth5y/xz541l9uzZ9O7dm8GDB5faomVok/Y4WNmiqGDcyVKhZGDjNiJdWFBrEYJSATQ6rVmaRyolBdmagocfWEnuF+azP/EKP984yw/XTvJb3HnO3blF6IrlBAcH85///IfFixdXeaX2uqsnKjw9E4pEZevNi9wrLLvfVlUiSRLfffcdnp6eBAcHG7RosVFZMKPtszhY2RltqVgqlPR286FfBbMJBYLHCeHIrQBFdomk/8kUzNEZ+UFi7qexKyGKyxlJSJJUonmhlUJFVo9mKK+nEDR0EJ06dTL7/f/Orey7pOZnV/p8SYJDydcY5NXWjKsyHoVCwZo1axg0aBCTJk1i5cqVJdx19a1seT8giA0xZzhz5yYSGIqnDFZKFTYqC4Y1ac9TLk0f7S8hEDxihIVSASwUSsyRXauTZWxVJjSEegBZlvk97jwLLu7nQnoCGlln0Am3QKfBsq4dcmsP6v7fM8RnZZjt/qWxNzEKrQnWnFqnY3/SVbQmTOA0FUtLSzZv3szFixeZM2eOwX4blSXjfLowr/MwhjRph7N1XWyUFkWfkwI11ndzea11IF92GirERPCPQAhKBWlu72TyNZQKBY1szedH3xwbwZ7E6HILLItRWVqSr9PwzYW93MyuOlE5n55Y4fY3D6KTddzKNgyMP0rq1KnDtm3b+PXXX1mwYEGpx9iqLHnWvRWfdhzEgq4j+L5bMIGpFiT+dyet6jcSc+AF/xiEoFSQfh5+D63ALw8LhYKn3VqirGSW0IOcSbvJweRrFY5VFOo0LLi4nzwztBJ5EFmWTR7FDCAhka2uuliTsTg5ObF7927mz5/Pjz/+aNQ5AQEB1d6vTCB41AhBqSB+DVxN6mgsy9DT1XCueeWuJbMl7nylAt9QVL1/ItVwRK95qJ6036rCy8uLHTt28Oabb7Jz586HHt+yZUtSUlK4d+/eI1idQFAzEIJSQRSSxOjmHStVZW+pUNLb3Yd6luZpTx+XlU5mYW6lzy+qiYk0e82HJElYmWHypYyMnRljTaZSPCxszJgx/PXXX+Ueq1QqadOmDefOnXs0ixMIagBCUCpBQENPhjZpVyFRsVQoaefozvNN2pttHXuTois0hrY0ctSFXL9v/kaN/g1cMTVyICGZPJ/G3HTv3p2wsDCGDBny0C4DAQEBnD179hGtTCCofoSgVJJn3VsxtkVnLBXKcmMqFpISlaTgaTcfJvh0M2uA9lb2XZMdSzIyybnmH/bUx8O30r3SAFSSgp6uLVCZcI2qYtCgQXzxxRf069ePhISEMo974oknRBxF8I9C1KGYQCfnJrR1dOdkahy7EqK4V5inL3TTyTIqhYLebj70cG1uNjfX3zFHp16tTke+1vyB+SZ1HWlgZUtKJTsCSFBt/c6MYdy4caSmptKvXz8OHz6Mg4ODwTEBAQEsXLiwGlYnEFQPQlBMxFppQaBrC3o0as6d/ByyNfn6OhNnm7pmy+YqDUsTss2KUSgUJmWtlcfYFp1ZcOlAhd1ylgolT7v50MDKtkrWZS5mzpxJSkoKgwYNYs+ePdjallyvv78/169fJy8v75GMdRYIqhvh8jITkiThZFOHpnUb0szeCVfbelUqJgDONnVMvoZSknC0tjPDagxpXs+Zl1s+VeFYU4CjJ8OatKuSNZkTSZL45ptv8Pb2ZuTIkajVJS09Kysr2vXqyh/njnM85QYRd26RlCOyvgS1l3/EPJTayuW7SYRGHTHJ9WWnsuTrp56vUvGLzrzN8qgjaGVdmfUplgolMtDX3ZdBXm0eq2JAtVrN4MGDcXFxYfXq1WhkHafT4tmVEEnSvQyUCiUWFhZIUpEr1Mm6LkGefgQ09DQpziQQVBdlzUMRgvIYo5Nl3v0rnPvq/EqdbyEpCfL0Y6BXGzOvzBCtrONieiI7EyK5mX0XlaRAkkD7P/dgH/dWdHVphp1FzUkTrgg5OTk888wzdBvYD55pi1qnLXcIl5VChaVSxVtteuNmV//RLVQgMANCUGop+xOjCa9kcaOlQsncjoOxr4KEgfLIUReSrclHq9Nhq7LE3tKmwq3gayLnEmL4PuowSisLjG36ZqVU8U7bPjUuPVogKI+yBEXEUB5zern54Fu/UYVdJxYKJRN9uz9yMQGws7DExcYeN7v61K/EXJGayN2CXNbcPIvS2tJoMYGiTL1vL+6rtlb9AoE5EYLymKOQJCb6dqdNAzcsjRAViSLLZIJPV9o4uFf9Av8h7L4VSWEl58wXajXsT7xi5hUJBI8eISi1ANX/rI3RzTviYlMXS4US6YE6dQtFUYFle0cPZrbrS0BDz2pabe2jUKvhaMoNtJVsYaORdRxMvmZSu3+BoCYg6lBqCZIk0cXFmy4u3sRlpXMo+Rp38nMo1GmwU1nR3N6J7o2aUdfSurqXWus4c+emydfQIRORfosOTl5mWJFAUD0IQamFNKnrSJO6jtW9jH8M0Zm3y83oMoYCrYYrmalCUASPNcLlJRCYSLa60EzXqVz6t0BQUxCCIhCYiEphnj8jC6UochQ83ghBEQhMxNHKzuRW/QokHKyqpgWOQPCoEIIiEJhIFxdvk9vsKxUKnnJuYp4FCQTVhBAUgcBEPOs0wMnatEad7rb1aGRbz0wrEgiqByEoAoEZCPL0M6qwtDQsFSr6ebY284oEgkePEBSBwAx0dGpS6RY4bR3cCHD0qKKVCQSPDiEoAoEZUEgSr/p2p4W9k9GWiqVCiW/9Rozz6fJYtesXCMpCCIpAYCYsFEqm+veir4cvNkoLrJSl1w1bKVXYKi3o79maKX6BJgf0BYKagqiUFwjMiEJSMMirLf09WxORnsDehGjS8rNQ67RYKlQ429Slj0cr2jl4oDRT/YpAUFMQgiIQVAEqhZKOTl50FK1UBP8gxCOSQCAQCMyCEBSBQCAQmAUhKAKBQCAwC0JQBAKBQGAWhKAIBAKBwCwIQREIBAKBWRCCIhAIBAKzIARFIBAIBGZBCIpAIBAIzIIQFIFAIBCYBSEoAoFAIDALQlAEAoFAYBaEoAgEAoHALAhBEQgEAoFZEIIiEAgEArMgBEUgEAgEZkEIikAgEAjMghAUgUAgEJgFISgCgUAgMAtCUAQCgUBgFoSgCAQCgcAsCEERCAQCgVkQgiIQCAQCsyDJslz2TklKA+If3XIEAoFA8BjgJcuy04MbyxUUgUAgEAiMRbi8BAKBQGAWhKAIBAKBwCwIQREIBAKBWRCCIhAIBAKzIARFIBAIBGbh/wEWIiA6YDHYUQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "G = to_networkx(data, to_undirected=True)\n",
    "\n",
    "visualize(G, color=data.y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**What we did so far...**\n",
    "\n",
    "1) define our conv: myGCNConv  \n",
    "2) download karate dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kPbYXBn1yYIJ"
   },
   "source": [
    "## Implementing a GCN \n",
    "\n",
    "We implement a GCN as a `torch.nn.Module` class that contains a sequence of GCNConv layers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "AkQAVluLuxT_",
    "outputId": "b049292d-b2da-4926-f974-776012a3ec60"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GCN(\n",
      "  (conv1): myGCNConv()\n",
      "  (conv2): myGCNConv()\n",
      "  (conv3): myGCNConv()\n",
      "  (classifier): Linear(in_features=2, out_features=4, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from torch.nn import Linear\n",
    "#from torch_geometric.nn import GCNConv\n",
    "\n",
    "class GCN(torch.nn.Module):\n",
    "    def __init__(self):\n",
    "        super(GCN, self).__init__()\n",
    "        torch.manual_seed(112233)\n",
    "        self.conv1 = myGCNConv(dataset.num_features, 4)\n",
    "        self.conv2 = myGCNConv(4, 4)\n",
    "        self.conv3 = myGCNConv(4, 2)\n",
    "        self.classifier = Linear(2, dataset.num_classes)\n",
    "\n",
    "    def forward(self, x, edge_index):\n",
    "        h = self.conv1(x, edge_index)\n",
    "        h = h.tanh()\n",
    "        h = self.conv2(h, edge_index)\n",
    "        h = h.tanh()\n",
    "        h = self.conv3(h, edge_index)\n",
    "        h = h.tanh()  # Final GNN embedding space.\n",
    "        \n",
    "        # Apply a final (linear) classifier.\n",
    "        out = self.classifier(h)\n",
    "\n",
    "        return out, h\n",
    "\n",
    "model = GCN()\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hjsb3Fst2P8k"
   },
   "source": [
    "Here, we first initialize all of our building blocks in `__init__` and define the computation flow of our network in `forward`.\n",
    "We first define and stack **three graph convolution layers**, which corresponds to aggregating 3-hop neighborhood information around each node (all nodes up to 3 \"hops\" away).\n",
    "In addition, the `myGCNConv` layers reduce the node feature dimensionality to $2$, *i.e.*, $34 \\rightarrow 4 \\rightarrow 4 \\rightarrow 2$. Each `myGCNConv` layer is enhanced by a [tanh](https://pytorch.org/docs/stable/generated/torch.nn.Tanh.html?highlight=tanh#torch.nn.Tanh) non-linearity.\n",
    "\n",
    "After that, we apply a single linear transformation ([`torch.nn.Linear`](https://pytorch.org/docs/stable/generated/torch.nn.Linear.html?highlight=linear#torch.nn.Linear)) that acts as a classifier to map our nodes to 1 out of the 4 classes/communities.\n",
    "\n",
    "We return both the output of the final classifier as well as the final node embeddings produced by our GNN.\n",
    "We proceed to initialize our final model via `GCN()`, and printing our model produces a summary of all its used sub-modules.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Dt8fPEmc4m5l"
   },
   "source": [
    "### Embedding the Karate Club Network\n",
    "\n",
    "Let's take a look at the node embeddings produced by our GNN.\n",
    "Here, we pass in the initial node features `x` and the graph connectivity information `edge_index` to the model, and visualize its 2-dimensional embedding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 433
    },
    "id": "nwHtX5siwe2v",
    "outputId": "080bbcb1-8278-4dc5-dd4c-f163e9bb8372"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Embedding shape: [34, 2]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = GCN()\n",
    "\n",
    "out, h = model(data.x, data.edge_index)\n",
    "print(f'Embedding shape: {list(h.shape)}')\n",
    "\n",
    "visualize(h, color=data.y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([34, 4])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out #.argmax(-1)\n",
    "\n",
    "out.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1-W5Kfhu5I-P"
   },
   "source": [
    "Remarkably, even before training the weights of our model, the model produces an embedding of nodes that closely resembles the community-structure of the graph.\n",
    "Nodes of the same color (community) are already closely clustered together in the embedding space, although the weights of our model are initialized **completely at random** and we have not yet performed any training so far!\n",
    "This leads to the conclusion that GNNs introduce a strong inductive bias, leading to similar embeddings for nodes that are close to each other in the input graph.\n",
    "\n",
    "### Training on the Karate Club Network\n",
    "\n",
    "But can we do better? Let's look at an example on how to train our network parameters based on the knowledge of the community assignments of 4 nodes in the graph (one for each community):\n",
    "\n",
    "Since everything in our model is differentiable and parameterized, we can add some labels, train the model and observse how the embeddings react.\n",
    "Here, we make use of a semi-supervised or transductive learning procedure: We simply train against one node per class, but are allowed to make use of the complete input graph data.\n",
    "\n",
    "\n",
    "\n",
    "Training our model is very similar to any other PyTorch model.\n",
    "In addition to defining our network architecture, we define a loss critertion (here, [`CrossEntropyLoss`](https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html)) and initialize a stochastic gradient optimizer (here, [`Adam`](https://pytorch.org/docs/stable/optim.html?highlight=adam#torch.optim.Adam)).\n",
    "\n",
    "Note that our semi-supervised learning scenario is achieved by the following line:\n",
    "```\n",
    "loss = criterion(out[data.train_mask], data.y[data.train_mask])\n",
    "```\n",
    "While we compute node embeddings for all of our nodes, we **only make use of the training nodes for computing the loss**.\n",
    "Here, this is implemented by filtering the output of the classifier `out` and ground-truth labels `data.y` to only contain the nodes in the `train_mask`.\n",
    "\n",
    "Let us now start training and see how our node embeddings evolve over time (best experienced by explicitely running the code):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 430
    },
    "id": "etxOsz8QIbMO",
    "outputId": "a961884c-7520-460a-d591-94834563c4a2",
    "scrolled": true
   },
   "outputs": [
    {
     "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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\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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\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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\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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\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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\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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\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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\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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\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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\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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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AFwQMAMALAgYA4AUBAwDwgoABAHhBwAAAvCBgAABeEDAAAC8IGACAFwQMAMALAgYA4AUBAwDwgoABAHhBwAAAvCBgAABeEDAAAC8IGACAFwQMAMALAgYA4AUBAwDwgoABAHhBwAAAvCBgAABeEDAAAC8IGACAFwQMAMALAgYA4AUBAwDwgoABAHhBwAAAvCBgAABeEDAAAC8IGACAFwQMAMALAgYA4AUBAwDwgoABAHhBwAAAvCBgAABeEDAAAC8IGACAFwQMAMALAgYA4AUBAwDwgoABAHhBwAAAvCBgAABeEDAAAC8IGACAFwQMAMALAgYA4AUBAwDwgoABAHhBwAAAvCBgAABeEDAAAC8IGACAFwQMAMALAgYA4AUBAwDwgoABAHhBwAAAvCBgAABeEDAAAC8IGACAFwQMAMALAgYA4AUBAwDwgoABAHhBwAAAvCBgAABeEDAAAC8IGACAFwQMAMALAgYA4AUBAwDwgoABAHhBwAAAvCBgAABeEDAAAC8IGACAFwQMAMALAgYA4AUBAwDwgoABAHhBwAAAvCBgAABeEDAAAC8IGACAFwQMAMALAgYA4AUBAwDwgoABAHhBwAAAvCBgAABeEDAAAC8IGACAFwQMAMALAgYA4AUBAwDwgoABAHhBwAAAvCBgAABeEDAAAC8IGACAFwQMAMCL2EQXAAAXg9Nn+7VpxzFt39Oh/mRa0YhpTnOtVl06XS2N1TKziS6x5AgYAPDshS1H9ezLR+SclMk4ScHPPYc6dfDoGc2aXqM1Ny1UPBauQaVwPRoAmGRe3H5Mz718ROm0ey1cciVTGR1sO6MfPfWqnDt//YWMgAEAT/oHUvrFhlal0iMHRzrtdOR4lw4cOTNOlY0PAgYAPHnmxcNDHrUMJZnK6IVX2jxXNL4IGADwwDmnLbvai9qmte1sqIbJCBgA8OBQ21kVePDyOpNS6YyXeiYCAQMAHuza3zGm7WLR8Lwsh+eRAMAk0tufLnqbmU3huh6GgAEADyrLi7/M8IplTR4qmTgEDAB4sGRefVEXTsZjES2aM8VfQROAgAEAD2ZNr1F5WWFHMSbphqtmKhIJ10tyuB4NAEwSZqY7r58/6qR9xKSmaZVasbhxnCobPwQMAHgya0aN3nFz8Blj+cNlpmBYrLmpWu+9fWmozh4bxIddAoBHc1vq9Il7V2r73pN6acdxdfcmFYmYWhqrterSGWpuqArVmWO5CBgA8CwRj2rl0iatXBqus8RGE75jMgDApEDAAAC8IGAAAF4QMAAALwgYAIAXBAwAwAsCBgDgBQEDAPCCCy0B4A06daZPm3e26+TpPkVMammq1qWLG1RZHp/o0iYUAQMAY9Tbl9Sj6/fqSHuXXMa99hXJB4+e0XMvH9HyBdN085vnKBqyT0kuFAEDAGPQ25/SI49uV3fPwGvBMiiVDhZs39uhM90DetctixWJhPPzxkZyccYqALxBP3v+gLp7k+eFS65UOqPDx7q0eefx8StsEiFgAKBIvX1J7TnYqcxI6ZKVSmf0wtZjcm70tmFDwABAkXYdOFXUR+z39ad0vKPHY0WTEwEDAEXq6kkqlc4U3N5M6u5NeqxociJgAKBI8VhExX5HWBi/sXI0F98jBoA3qKWpuqjASKedmqZWeqxociJgAKBIM5uqVVFW2FUeZtKiufUqL7B9mBAwAFAkM9NN18xWLDr6OFksGtF1K1vGoarJh4ABgDFYNKdeN109R7Ho0PMx0YgpHovonlsXq76ufPwLnAQuvmM2ACiRy5c2qrmxSi+80qbdB08pYiYnKWLS5UsadeUl01VTlZjoMicMAQMAb0Dj1ErddcMCJVNp9fSmFImYKitiF+3nj+UiYACgBOKxqOpqohNdxqRCxAIAvCBgAABeEDAAAC8IGACAFwQMAMALAgYA4AUBAwDwgoABAHhBwAAAvCBgAABeEDAAAC8IGACAFwQMAMALAgYA4AUBAwDwgoABAHhBwAAAvCBgAABeEDAAAC8IGACAFwQMAMCL2EQXABQjnc7oZGefkqm0Ksrjqq8tk5lNdFkAhkDA4ILQ05fUxq3H9PLOdklOJlM641RVEdOqS2fossWNikQIGmAyIWAw6XWe6dO3Htuh/oG00hl3zrrTXQNa/0Krdu7r0D23LVEsyqgvMFnw14hJLZlM69uP71RvX+q8cBmUSmfUdqJbjz29b5yrAzASjmAwqW3f16GBZFpDR8vrUmmnva2d6jzTpym15eNSGzAZJVMZ7drfoRe3H1N3T1KRiKmlqVqrVszQjIaqca2FgMGktuGVNiVTmYLaOue0acdxrb5mjueqgMmp9dhZ/eBnr8o5d87fze79p7S39bRmTKvSO25epLJEdFzqmbAhskwmo76+PmUyhb144OKTyTh1nu0vor10+FiXx4qAyetoe5e+98RuDSTT570pc5JSqYyOtnfpu2t3KpUen9fdcTmCOXjwoLZt26ZUKjXk+rKyMi1ZskRz584dj3Jwgcg4JzPJjTY+lmO4eRogzJxzeuzpfaMGRzrjdLKzV6/sPqErljV5r8vrEUwmk9HatWu1efPmYcNFkvr7+7VlyxatW7eOIxq8JhaNKB4r7r/olJqEp2qAyetoe7e6e5MFtU2lnTZsbZMr5p3bGHkNmCeffFIDAwMFt+/q6tIzzzzjsSJcaC5d3FDw9S3xWERXXjLdc0XA5LNrf0fBc5WS1NObKmr4eay8BUxra2tR4TLozJkz6uzsLH1BuCBdecl0FXr9ZEVZTLNn1PgtCJiEevqHHyEaSiQi9Q+kPVWT04+vO966deuEbItwqasu023XzlMsOnzKmKREPKp7blvMx8bgolSRKG46PeOCvxnfvAVMMlnYeOBQOIJBrksWTtOaGxequjJ+zpyMmRSLmqY3VOmDay7R1LqKCawSmDgL50wpar6yLB5VfW2Zx4oCXAeDC8KC2VP0sVl1OtR2VvsPn1Z/Mq3qioSWzp+qqXVcWImL2+wZNSpPxJRMjT4tEYuarloxfVyO9idlwEQifIINzmdmmtNcqznNtRNdCjCpmJluv36efvCz3Uqlhz87LGJSTVVCK5c0jktd3l7JE4mxny7a3NxcwkoAIPzmNNdqzY0LFYtFhpyzjMcimjalQvfduUzxcZh/kTwewaxYsUKbNm0a07bLly8vcTUAEH4LZk/Rx95zubbuPqGXdh5XT2/wWWTTp1Vp1aUzNK+ldlxPhPEWMDNnztS2bdvU31/cudaLFi16Q0c/AHAxqygLviNp1aUzJroUvxda3nLLLSorK/xMhUWLFmnZsmUeKwIAjBevk/yRSES33Xabjh49qi1btgx54WUsFlNLS4uWLVvGkQsAhMi4nEXW3NzMxD0AXGQ4HxgA4AUBAwDwgoABAHhBwAAAvCBgAABeEDAAAC8IGACAFwQMAMALAgYA4AUBAwDwwpwb/stpzmts1i7pgL9yAAAXmLnOuSG/wayogAEAoFAMkQEAvCBgAABeEDAAAC8ImAuEmX3IzNwwt84Jru0hM2st4f3VmtlnzOxZMztpZp3Zf79zmPbXZ9f3mlmbmf2NmVUM0W6Fma01s67s/X7DzKaOscZ52X3/wFi2n0hm9k4z22RmfWZ2wMz+yMyiBWx3v5n90szazazfzPab2T+Y2exRtns8u68+N8S6+ux9nDCzbjN70swuy2uzysy+amY7zKzHzA6a2TfNbH7xjx7jiYC58LxX0rV5t1sntKLSmyPpU5LWS7pf0vsk7ZL0fTP77dyGZna5pCckHZe0RtIfSfqwpIfy2rVIWiepQtJ7JP22gv32qJldNH8HZna7pP8n6QVJd0r6WwX77M8K2HyapJ9JekDSW7Pb3C7pWTOrGaa/90taOcw6k/QjSXdI+veS3i0pLukpM5uV0/Q+SSskfTFb8x9KepOkDaOFGyaYc47bBXCT9CFJTtKiia5liNoektRawvurklQ5xPKfSTqYt+z7knZLiucs+63svnpTzrIvSOqUNCVn2Q3ZdveMocZ52W0fmOj9X2TdmyStz1v2GUkDkmaM4f5uz+6Hdw+xboqkNknvz7b5XN76d2SXr85ZViepQ9IXc5Y1DnHfcyVlJD040fuU2/C3i+ad28UiZyjtBjP7Qc5w0Jfyh43MrNnM/jE7PNFvZpvN7P4h7nO+mT2cHX7qN7O9Zva3Q7S70syezg5j7DazT47lMTjnup1zPUOs2iCpJae/uIJ3v99xziVz2n1HwQvmO3KW3S3px865zpx+fiHpYF67ksoOK72cHY46kd2PzXltPpAdsuoys9NmtsXMPpGz/mozeyL7PPZk9//fjaGW2ZKukPRI3qqHFRw53Fn8I9TJ7M/kEOv+UtJW59w/D7Pt3ZKOOOeeGlzgnDst6V+V85w459rzN3TOHZDULmnmGGrGOCFgLjxRM4vl3YZ6Hh+R9KqkexS8e/+YpC8PrjSzKgVDUHdK+m+S3ilpi6SHzezjOe3mS/q1gnf7/yPb/k8kNeT1Vyvpn7L9vkPBEMyXzWx1bqNs+D00toeuGyTtyPl9oaRySa/kNnLO9UnaI2l5ts8KSfPz22VtHWxXatn9+LCk7Qqehz9U8I5/vZlVZ9tcr2CfrVfwHLxX0v9R8O5f2XY/lZRWcBR7l6QHJcXy+ipkv67I/szfX/sk9ajA/WBmUTMryw5P/o2kbZLW5rW5XsGR5KdGqWe452TO4D4apoZLJDUp2LeYpGKjN8Eks2OIZT9WMP+Q6yfOuT/I/nutmTlJD5rZnznndimYp1isYHhiXbbdY2Y2XdLnzOxrzrm0gjCpkLTSOXck5/7/b15/NZI+Nfhu1Mx+oWCc/v2Snsppl87eipJ9sX6zgjmZQYMT9KeG2KQjZ329JBuh3dJi6xlNdtL8s5LWOefuy1m+Q9LTkj6iYE7hzZI6nXO/l7N57ov1MgX1/2fn3Oac5Q/ldVnIfh1pf53KWT+aYwrmY6TgqPLWbKhLeu3I8iuS/so5t3OUevYPsbwj+7NeUlf+SjOLSfp7BUcwXyuwZkwAjmAuPO+SdHXe7feGaPedvN+/peD5vib7+w2SDueEy6BHJDXq9Xezb5X0aF64DKUnb6ijX8HcyJzcRs65mHPuo6Pc1znM7CYFL8YPO+e+mbtq8G6H2mwM7UppqYJ32Ln1yjn3jIKPW7oxu+gFSfVm9oiZrTGzKXn3s1vB3NFXssNtQ05qF7hfS7UfbpF0naSPKjjSeiKv7v+i4E3JnxZQz1hq+d/Z/u93zg0VlpgkCJgLzyvOuQ15t1eHaHdsmN8Hx6ynSjo6xHZtOeul4J1qIacgD/WH3q9gCGvMzOxqBWca/VzBC1quwXe6Q73zrs9Zf0rBC9lo7UppsK/h9vFUSXLOrVcwLDZbwQkL7dlTdS/Prj8tabWkI5L+TtJBM3vFzN49hppG2l9TVOB+cM697Jx7zjn3dQVvQJZL+qQkmdkcSZ+W9N8llZnZlJzwGfx98JTo3KPMXPXZn+f9nzKzP5f0cUkfcc6tzV+PyYWACa/pw/x+OPuzQ9KMIbYbXDY4eXtCEzSRmr0e4qeSXlJwllL+RPIeBSG2Im+7ckkLFMwNKHvCwP78dlnLB9uV2OCL9XD7eHD/yjn3XefcjQpeWN8lqVnS44Nza865l5xz71bwYnytgsf9HTO7tMiatmZ/5u+veZIqNYb9kJ2/6ZC0KLtogYI3FY8oCIjBmyT9Qfbfg9e5bM2vJWu5grMFzxkeM7NPK5jH+l3n3MPF1orxR8CE1715v9+n4LTOX2d/Xy9plpm9Ja/dBxRcUzI4ebpW0pr8M598M7PFCq5v2StpjXOuN7+Nc25A0uOS7s2Oyw96j6QyBUc+g34k6W1mVpfTx/UKTnfNbVcqOxUcNd6Xu9DMrsv2uT5/A+dcl3PuUQXzF816fZ5jcH3KOfe8gqODiKRLiinIOXdQ0suSPpi36n4FZ4E9Vsz9ScHFq9k692QXvaTgiCv/JgWhs1rBySdSsN9nmtmNOfdXK+ntyntOzOw/SPqcpE875/5XsXVigkz0edLcCrvp9etg3qNgYjj/Fstrd1DS5yXdpmDIYkDSN3Lur0rBxYvHFFw4d4eCM56cpI/ntJun4Chmn4Iz0VYreEF6JKfNQxriOhgFFzauy1uWkvS1UR5rk4Ijjg5JbxvisZbltL1CUq+k7ymYG/hodrt/ybvPmdnHsT77WN+nYC7kV5IieY/XSfrjUWocbPfl7HOSf0soGMpxCl5Y78jW1pbd79XZ+3lQQaDcp2Be7AMKXoA3ZdevUfBi+5Hsvl+jIPTPSJpVzH7NtrtLwRuNr0i6SdLvS+qT9Pm8dp/J3ufcnGXPSPpP2efkluy2rZIOSWoYpd+hroOJSHo2u/19Cs6wW5d9/mbntBt8c/TYEP8Xlk/03ya3EZ73iS6AW4FP1OvBMdytIa/dDZJ+qOAsnA5JX5JUkXefzQpC5YSCoabNCiZO8/teKOmfc9rtlfSFnPUPqfCAcZIeGuWx3jTKY52X1/4GSc9lXyiPSfqfGvpCzcsUHBV1KxiqeUjStLw2K7J9fHKUGucV+Hzcr+CooV/BsNjDkppz7udtCoYBj2bbHFJwZlRLdv1SSd9WEPB9Cs6c+omk3yh2v+a0vSenpoMKwiSa1+aP8/e1pL9WcCr72ez/q20K3sQ0FdDneQGTXT5V0tez/0d7FFxMuzKvzUMj7Od1hTxmbhNz4/tgQsbMPiTpG5IWu6En/zGC7OnQf6rgnftQF3sCKBBzMMC5blRwdEa4AG8QF1oCOZxz+RPgAMaIITIAgBcMkQEAvCBgAABeEDAAAC8IGACAFwQMAMCL/w8KpGq83LcSWwAAAABJRU5ErkJggg==\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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\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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\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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\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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\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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\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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\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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\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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\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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\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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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import time\n",
    "#from IPython.display import Javascript  # Restrict height of output cell.\n",
    "#display(Javascript('''google.colab.output.setIframeHeight(0, true, {maxHeight: 430})'''))\n",
    "\n",
    "model = GCN()\n",
    "criterion = torch.nn.CrossEntropyLoss()  # Define loss criterion.\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.01)  # Define optimizer.\n",
    "\n",
    "def train(data):\n",
    "    optimizer.zero_grad()  # Clear gradients.\n",
    "    out, h = model(data.x, data.edge_index)  # Perform a single forward pass.\n",
    "    loss = criterion(out[data.train_mask], data.y[data.train_mask])  # Compute the loss solely based on the training nodes.\n",
    "    loss.backward()  # Derive gradients.\n",
    "    optimizer.step()  # Update parameters based on gradients.\n",
    "    return loss, h\n",
    "\n",
    "for epoch in range(401):\n",
    "    loss, h = train(data)\n",
    "    if epoch % 10 == 0:\n",
    "        visualize(h, color=data.y, epoch=epoch, loss=loss)\n",
    "        time.sleep(0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NgcpV4rjAWy-"
   },
   "source": [
    "As one can see, our 3-layer GCN model manages to linearly separating the communities and classifying most of the nodes correctly.\n",
    "\n",
    "Note that we do not need to reimplement standard GNN architectures, the library provides implementations for the most popular ones, including the GCN Layer:\n",
    "\n",
    "`torch_geometric.nn.GCNConv`\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example: graph classification \n",
    "\n",
    "Let's now have a closer look at how to apply **Graph Neural Networks (GNNs) to the task of graph classification**.\n",
    "Graph classification refers to the problem of classifiying entire graphs (in contrast to nodes), given a **dataset of graphs**, based on some structural graph properties.\n",
    "Here, we want to embed entire graphs, and we want to embed those graphs in such a way so that they are linearly separable given a task at hand."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Screen Shot 2020-08-27 at 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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most common task for graph classification is **molecular property prediction**, in which molecules are represented as graphs, and the task may be to infer whether a molecule inhibits HIV virus replication or not.\n",
    "\n",
    "The TU Dortmund University has collected a wide range of different graph classification datasets, known as the [**TUDatasets**](https://chrsmrrs.github.io/datasets/), which are also accessible via [`torch_geometric.datasets.TUDataset`](https://pytorch-geometric.readthedocs.io/en/latest/modules/datasets.html#torch_geometric.datasets.TUDataset) in PyTorch Geometric.\n",
    "Let's load and inspect one of the smaller ones, the **MUTAG dataset**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "The 'data' object was created by an older version of PyG. If this error occurred while loading an already existing dataset, remove the 'processed/' directory in the dataset's root folder and try again.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "Input \u001b[0;32mIn [25]\u001b[0m, in \u001b[0;36m<cell line: 4>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtorch_geometric\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdatasets\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m TUDataset\n\u001b[0;32m----> 4\u001b[0m dataset \u001b[38;5;241m=\u001b[39m \u001b[43mTUDataset\u001b[49m\u001b[43m(\u001b[49m\u001b[43mroot\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mdata/TUDataset\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mMUTAG\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28mprint\u001b[39m()\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDataset: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdataset\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m:\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/torch_geometric/datasets/tu_dataset.py:129\u001b[0m, in \u001b[0;36mTUDataset.__init__\u001b[0;34m(self, root, name, transform, pre_transform, pre_filter, use_node_attr, use_edge_attr, cleaned)\u001b[0m\n\u001b[1;32m    127\u001b[0m out \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mload(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprocessed_paths[\u001b[38;5;241m0\u001b[39m])\n\u001b[1;32m    128\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(out, \u001b[38;5;28mtuple\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(out) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m3\u001b[39m:\n\u001b[0;32m--> 129\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m    130\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdata\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m object was created by an older version of PyG. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    131\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIf this error occurred while loading an already existing \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    132\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdataset, remove the \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mprocessed/\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m directory in the dataset\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124ms \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    133\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mroot folder and try again.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    134\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mslices, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msizes \u001b[38;5;241m=\u001b[39m out\n\u001b[1;32m    136\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_data\u001b[38;5;241m.\u001b[39mx \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m use_node_attr:\n",
      "\u001b[0;31mRuntimeError\u001b[0m: The 'data' object was created by an older version of PyG. If this error occurred while loading an already existing dataset, remove the 'processed/' directory in the dataset's root folder and try again."
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from torch_geometric.datasets import TUDataset\n",
    "\n",
    "dataset = TUDataset(root='data/TUDataset', name='MUTAG')\n",
    "\n",
    "print()\n",
    "print(f'Dataset: {dataset}:')\n",
    "print('====================')\n",
    "print(f'Number of graphs: {len(dataset)}')\n",
    "print(f'Number of features: {dataset.num_features}')\n",
    "print(f'Number of classes: {dataset.num_classes}')\n",
    "\n",
    "data = dataset[0]  # Get the first graph object.\n",
    "\n",
    "print()\n",
    "print(data)\n",
    "print('=============================================================')\n",
    "\n",
    "# Gather some statistics about the first graph.\n",
    "print(f'Number of nodes: {data.num_nodes}')\n",
    "print(f'Number of edges: {data.num_edges}')\n",
    "print(f'Average node degree: {data.num_edges / data.num_nodes:.2f}')\n",
    "print(f'Contains isolated nodes: {data.contains_isolated_nodes()}')\n",
    "print(f'Contains self-loops: {data.contains_self_loops()}')\n",
    "print(f'Is undirected: {data.is_undirected()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataset provides **188 different graphs**, and the task is to classify each graph into **one out of two classes**.\n",
    "\n",
    "By inspecting the first graph object of the dataset, we can see that it comes with **17 nodes (with 7-dimensional feature vectors)** and **38 edges** (leading to an average node degree of 2.24).\n",
    "It also comes with exactly **one graph label** (`y=[1]`), and, in addition to previous datasets, provides addtional **4-dimensional edge features** (`edge_attr=[38, 4]`).\n",
    "However, for the sake of simplicity, we will not make use of those.\n",
    "\n",
    "PyTorch Geometric provides some useful utilities for working with graph datasets, *e.g.*, we can shuffle the dataset and use the first 150 graphs as training graphs, while using the remaining ones for testing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.manual_seed(12345)\n",
    "dataset = dataset.shuffle()\n",
    "\n",
    "train_dataset = dataset[:150]\n",
    "test_dataset = dataset[150:]\n",
    "\n",
    "print(f'Number of training graphs: {len(train_dataset)}')\n",
    "print(f'Number of test graphs: {len(test_dataset)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mini-batching of graphs\n",
    "\n",
    "Since graphs in graph classification datasets are usually small, a good idea is to **batch the graphs** before inputting them into a Graph Neural Network to guarantee full GPU utilization.\n",
    "In the image or language domain, this procedure is typically achieved by **rescaling** or **padding** each example into a set of equally-sized shapes, and examples are then grouped in an additional dimension.\n",
    "The length of this dimension is then equal to the number of examples grouped in a mini-batch and is typically referred to as the `batch_size`.\n",
    "\n",
    "**NOT A GOOD IDEA**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, for GNNs the two approaches described above are either not feasible or may result in a lot of unnecessary memory consumption.\n",
    "Therefore, PyTorch Geometric opts for another approach to achieve parallelization across a number of examples. Here, adjacency matrices are stacked in a diagonal fashion (creating a giant graph that holds multiple isolated subgraphs), and node and target features are simply concatenated in the node dimension:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Screen Shot 2020-08-27 at 13.11.53.png](data:image/png;base64,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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This procedure has some crucial advantages over other batching procedures:\n",
    "\n",
    "1. GNN operators that rely on a message passing scheme do not need to be modified since messages are not exchanged between two nodes that belong to different graphs.\n",
    "\n",
    "2. There is no computational or memory overhead since adjacency matrices are saved in a sparse fashion holding only non-zero entries, *i.e.*, the edges.\n",
    "\n",
    "PyTorch Geometric automatically takes care of **batching multiple graphs into a single giant graph** with the help of the [`torch_geometric.data.DataLoader`](https://pytorch-geometric.readthedocs.io/en/latest/modules/data.html#torch_geometric.data.DataLoader) class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch_geometric.data import DataLoader\n",
    "\n",
    "train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)\n",
    "test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)\n",
    "\n",
    "for step, data in enumerate(train_loader):\n",
    "    print(f'Step {step + 1}:')\n",
    "    print('=======')\n",
    "    print(f'Number of graphs in the current batch: {data.num_graphs}')\n",
    "    print(data)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test batch_size = 147\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# show G"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# show adj"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training a Graph Neural Network (GNN)\n",
    "\n",
    "Training a GNN for graph classification usually follows a simple recipe:\n",
    "\n",
    "1. Embed each node by performing multiple rounds of message passing\n",
    "2. Aggregate node embeddings into a unified graph embedding (**readout layer**)\n",
    "3. Train a final classifier on the graph embedding\n",
    "\n",
    "There exists multiple **readout layers** in literature, but the most common one is to simply take the average of node embeddings:\n",
    "\n",
    "$$\n",
    "\\mathbf{x}_{\\mathcal{G}} = \\frac{1}{|\\mathcal{V}|} \\sum_{v \\in \\mathcal{V}} \\mathcal{x}^{(L)}_v\n",
    "$$\n",
    "\n",
    "PyTorch Geometric provides this functionality via [`torch_geometric.nn.global_mean_pool`](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.glob.global_mean_pool), which takes in the node embeddings of all nodes in the mini-batch and the assignment vector `batch` to compute a graph embedding of size `[batch_size, hidden_channels]` for each graph in the batch.\n",
    "\n",
    "The final architecture for applying GNNs to the task of graph classification then looks as follows and allows for complete end-to-end training:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.nn import Linear\n",
    "import torch.nn.functional as F\n",
    "from torch_geometric.nn import GCNConv\n",
    "from torch_geometric.nn import global_mean_pool\n",
    "\n",
    "class GCN(torch.nn.Module):\n",
    "    def __init__(self, hidden_channels):\n",
    "        super(GCN, self).__init__()\n",
    "        torch.manual_seed(12345678)\n",
    "        self.conv1 = GCNConv(dataset.num_node_features, hidden_channels)\n",
    "        self.conv2 = GCNConv(hidden_channels, hidden_channels)\n",
    "        self.conv3 = GCNConv(hidden_channels, hidden_channels)\n",
    "        self.lin = Linear(hidden_channels, dataset.num_classes)\n",
    "\n",
    "    def forward(self, x, edge_index, batch):\n",
    "        # 1. Obtain node embeddings \n",
    "        x = self.conv1(x, edge_index)\n",
    "        x = x.relu()\n",
    "        x = self.conv2(x, edge_index)\n",
    "        x = x.relu()\n",
    "        x = self.conv3(x, edge_index)\n",
    "        \n",
    "        # 2. Readout layer\n",
    "        x = global_mean_pool(x, batch)  # [batch_size, hidden_channels]\n",
    "        \n",
    "        # 3. Apply a final classifier\n",
    "        x = F.dropout(x, p=0.5, training=self.training)\n",
    "        x = self.lin(x)\n",
    "        \n",
    "        return x\n",
    "\n",
    "model = GCN(hidden_channels=64)\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we again make use of the [`GCNConv`](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.conv.GCNConv) with $\\mathrm{ReLU}(x) = \\max(x, 0)$ activation for obtaining localized node embeddings, before we apply our final classifier on top of a graph readout layer.\n",
    "\n",
    "Let's train our network for a few epochs to see how well it performs on the training as well as test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from IPython.display import Javascript\n",
    "#display(Javascript('''google.colab.output.setIframeHeight(0, true, {maxHeight: 300})'''))\n",
    "\n",
    "model = GCN(hidden_channels=64)\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.01)\n",
    "criterion = torch.nn.CrossEntropyLoss()\n",
    "\n",
    "def train():\n",
    "    model.train()\n",
    "\n",
    "    for data in train_loader:  # Iterate in batches over the training dataset.\n",
    "         out = model(data.x, data.edge_index, data.batch)  # Perform a single forward pass.\n",
    "         loss = criterion(out, data.y)  # Compute the loss.\n",
    "         loss.backward()  # Derive gradients.\n",
    "         optimizer.step()  # Update parameters based on gradients.\n",
    "         optimizer.zero_grad()  # Clear gradients.\n",
    "\n",
    "def test(loader):\n",
    "     model.eval()\n",
    "\n",
    "     correct = 0\n",
    "     for data in loader:  # Iterate in batches over the training/test dataset.\n",
    "         out = model(data.x, data.edge_index, data.batch)  \n",
    "         pred = out.argmax(dim=1)  # Use the class with highest probability.\n",
    "         correct += int((pred == data.y).sum())  # Check against ground-truth labels.\n",
    "     return correct / len(loader.dataset)  # Derive ratio of correct predictions.\n",
    "\n",
    "\n",
    "for epoch in range(1, 51):\n",
    "    train()\n",
    "    train_acc = test(train_loader)\n",
    "    test_acc = test(test_loader)\n",
    "    if epoch % 10 == 0:\n",
    "        print(f'Epoch: {epoch:03d}, Train Acc: {train_acc:.4f}, Test Acc: {test_acc:.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As one can see, our model reaches around **76% test accuracy**.\n",
    "Reasons for the fluctations in accuracy can be explained by the rather small dataset (only 38 test graphs), and usually disappear once one applies GNNs to larger datasets.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Other datasets?\n",
    "\n",
    "\n",
    "Doc https://pytorch-geometric.readthedocs.io/en/latest/modules/datasets.html  \n",
    "OGB https://ogb.stanford.edu/docs/dataset_overview/\n",
    "\n",
    "# Other architectures?\n",
    "\n",
    "Doc https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#convolutional-layers\n",
    "\n",
    "# Other resources?\n",
    "Doc https://pytorch-geometric.readthedocs.io/en/latest/notes/colabs.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# let's move higher order [super fast]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.nn import Linear\n",
    "import torch.nn.functional as F\n",
    "from torch_geometric.nn import GraphConv, GINConv, GINEConv, GATConv # ......\n",
    "from torch_geometric.nn import global_mean_pool\n",
    "\n",
    "class GCN(torch.nn.Module):\n",
    "    def __init__(self, hidden_channels):\n",
    "        super(GCN, self).__init__()\n",
    "        torch.manual_seed(12345678)\n",
    "        self.conv1 = GraphConv(dataset.num_node_features, hidden_channels)\n",
    "        self.conv2 = GraphConv(hidden_channels, hidden_channels)\n",
    "        self.lin = Linear(hidden_channels, dataset.num_classes)\n",
    "\n",
    "    def forward(self, x, edge_index, batch):\n",
    "        # 1. Obtain node embeddings \n",
    "        x = self.conv1(x, edge_index)\n",
    "        x = x.relu()\n",
    "        x = self.conv2(x, edge_index)\n",
    "        \n",
    "        # 2. Readout layer\n",
    "        x = global_mean_pool(x, batch)  # [batch_size, hidden_channels]\n",
    "        \n",
    "        # 3. Apply a final classifier\n",
    "        x = F.dropout(x, p=0.5, training=self.training)\n",
    "        x = self.lin(x)\n",
    "        \n",
    "        return x\n",
    "\n",
    "model = GCN(hidden_channels=64)\n",
    "print(model)\n",
    "\n",
    "\n",
    "#from IPython.display import Javascript\n",
    "#display(Javascript('''google.colab.output.setIframeHeight(0, true, {maxHeight: 300})'''))\n",
    "\n",
    "model = GCN(hidden_channels=64)\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n",
    "criterion = torch.nn.CrossEntropyLoss()\n",
    "\n",
    "def train():\n",
    "    model.train()\n",
    "\n",
    "    for data in train_loader:  # Iterate in batches over the training dataset.\n",
    "         out = model(data.x, data.edge_index, data.batch)  # Perform a single forward pass.\n",
    "         loss = criterion(out, data.y)  # Compute the loss.\n",
    "         loss.backward()  # Derive gradients.\n",
    "         optimizer.step()  # Update parameters based on gradients.\n",
    "         optimizer.zero_grad()  # Clear gradients.\n",
    "\n",
    "def test(loader):\n",
    "     model.eval()\n",
    "\n",
    "     correct = 0\n",
    "     for data in loader:  # Iterate in batches over the training/test dataset.\n",
    "         out = model(data.x, data.edge_index, data.batch)  \n",
    "         pred = out.argmax(dim=1)  # Use the class with highest probability.\n",
    "         correct += int((pred == data.y).sum())  # Check against ground-truth labels.\n",
    "     return correct / len(loader.dataset)  # Derive ratio of correct predictions.\n",
    "\n",
    "\n",
    "for epoch in range(1, 51):\n",
    "    train()\n",
    "    train_acc = test(train_loader)\n",
    "    test_acc = test(test_loader)\n",
    "    if epoch % 10 == 0:\n",
    "        print(f'Epoch: {epoch:03d}, Train Acc: {train_acc:.4f}, Test Acc: {test_acc:.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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