CS224W_Colab_0.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XuXWJLEm2UWS"
   },
   "source": [
    "# **CS224W - Colab 0**\n",
    "\n",
    "Colab 0 **will not be graded**, so you don't need to hand in this notebook. That said, we highly recommend you to run this notebook, so you can get familiar with the basic concepts of graph mining and Graph Neural Networks.\n",
    "\n",
    "In this Colab, we will introduce two packages, [NetworkX](https://networkx.org/documentation/stable/) and [PyTorch Geometric](https://pytorch-geometric.readthedocs.io/en/latest/).\n",
    "\n",
    "For the PyTorch Geometric section, you don't need to understand all the details already. Concepts and implementations of graph neural network will be covered in future lectures and Colabs.\n",
    "\n",
    "Please make a copy before you proceed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AIUjFBAcdaHX"
   },
   "source": [
    "# New Section"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8gzsP50bF6Gb"
   },
   "source": [
    "\n",
    "# NetworkX Tutorial\n",
    "\n",
    "NetworkX is one of the most frequently used Python packages to create, manipulate, and mine graphs.\n",
    "\n",
    "Main parts of this tutorial are adapted from https://colab.research.google.com/github/jdwittenauer/ipython-notebooks/blob/master/notebooks/libraries/NetworkX.ipynb#scrollTo=zA1OO6huHeV6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Nwwq0nSdmsOL"
   },
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "QeqN7MHvH1OA"
   },
   "outputs": [],
   "source": [
    "# Import the NetworkX package\n",
    "import networkx as nx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MCIeGAfLfAMK"
   },
   "source": [
    "## Graph\n",
    "NetworkX provides several classes to store different types of graphs, such as directed and undirected graph. It also provides classes to create multigraphs (both directed and undirected).\n",
    "\n",
    "For more information, please refer to [NetworkX graph types](https://networkx.org/documentation/stable/reference/classes/index.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "qSI_n6P-e0PW"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n",
      "True\n",
      "{'Name': 'Bar'}\n"
     ]
    }
   ],
   "source": [
    "# Create an undirected graph G\n",
    "G = nx.Graph()\n",
    "print(G.is_directed())\n",
    "\n",
    "# Create a directed graph H\n",
    "H = nx.DiGraph()\n",
    "print(H.is_directed())\n",
    "\n",
    "# Add graph level attribute\n",
    "G.graph[\"Name\"] = \"Bar\"\n",
    "print(G.graph)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "x0pLs0-Ka9j8"
   },
   "source": [
    "## Node\n",
    "\n",
    "Nodes (with attributes) can be easily added to NetworkX graphs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "jQ8ApAL5H1GB",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Node 0 has the attributes {'feature': 0, 'label': 0}\n"
     ]
    }
   ],
   "source": [
    "# Add one node with node level attributes\n",
    "G.add_node(0, feature=0, label=0)\n",
    "\n",
    "# Get attributes of the node 0\n",
    "node_0_attr = G.nodes[0]\n",
    "print(\"Node 0 has the attributes {}\".format(node_0_attr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "btOdMYqJaKia"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, {'feature': 0, 'label': 0})\n",
      "(1, {'feature': 1, 'label': 1})\n",
      "(2, {'feature': 2, 'label': 2})\n",
      "G has 3 nodes\n"
     ]
    }
   ],
   "source": [
    "# Add multiple nodes with attributes\n",
    "G.add_nodes_from([\n",
    "  (1, {\"feature\": 1, \"label\": 1}),\n",
    "  (2, {\"feature\": 2, \"label\": 2})\n",
    "])\n",
    "\n",
    "# Loop through all the nodes\n",
    "# Set data=True will return node attributes\n",
    "for node in G.nodes(data=True):\n",
    "  print(node)\n",
    "\n",
    "# Get number of nodes\n",
    "num_nodes = G.number_of_nodes()\n",
    "print(\"G has {} nodes\".format(num_nodes))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0AdoaZPgbRis"
   },
   "source": [
    "## Edge\n",
    "\n",
    "Similar to nodes, edges (with attributes) can also be easily added to NetworkX graphs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "0szH5F6EH079"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Edge (0, 1) has the attributes {'weight': 0.5}\n"
     ]
    }
   ],
   "source": [
    "# Add one edge with edge weight 0.5\n",
    "G.add_edge(0, 1, weight=0.5)\n",
    "\n",
    "# Get attributes of the edge (0, 1)\n",
    "edge_0_1_attr = G.edges[(0, 1)]\n",
    "print(\"Edge (0, 1) has the attributes {}\".format(edge_0_1_attr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "QRTmi4EUaf_I"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, 1)\n",
      "(0, 2)\n",
      "(1, 2)\n",
      "G has 3 edges\n"
     ]
    }
   ],
   "source": [
    "# Add multiple edges with edge weights\n",
    "G.add_edges_from([\n",
    "  (1, 2, {\"weight\": 0.3}),\n",
    "  (2, 0, {\"weight\": 0.1})\n",
    "])\n",
    "\n",
    "# Loop through all the edges\n",
    "# Here there is no data=True, so only the edge will be returned\n",
    "for edge in G.edges():\n",
    "  print(edge)\n",
    "\n",
    "# Get number of edges\n",
    "num_edges = G.number_of_edges()\n",
    "print(\"G has {} edges\".format(num_edges))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9u1Utjn4bc7k"
   },
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "lykPzFOEahuP"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw the graph\n",
    "nx.draw(G, with_labels = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_Q6YTP2FDbOS"
   },
   "source": [
    "## Node Degree and Neighbor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "GFA3B6Z_DE3q"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Node 1 has degree 2\n",
      "Node 1 has neighbor 0\n",
      "Node 1 has neighbor 2\n"
     ]
    }
   ],
   "source": [
    "node_id = 1\n",
    "\n",
    "# Degree of node 1\n",
    "print(\"Node {} has degree {}\".format(node_id, G.degree[node_id]))\n",
    "\n",
    "# Get neighbor of node 1\n",
    "for neighbor in G.neighbors(node_id):\n",
    "  print(\"Node {} has neighbor {}\".format(node_id, neighbor))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4gVRVckZeSdA"
   },
   "source": [
    "## Other Functionalities\n",
    "\n",
    "NetworkX also provides plenty of useful methods to study graphs.\n",
    "\n",
    "Here is an example to get [PageRank](https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.link_analysis.pagerank_alg.pagerank.html#networkx.algorithms.link_analysis.pagerank_alg.pagerank) of nodes (we will talk about PageRank in one of the future lectures)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "_gZfQ82Wiuvv"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 0.17857162031103999,\n",
       " 1: 0.32142837968896,\n",
       " 2: 0.32142837968896,\n",
       " 3: 0.17857162031103999}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_nodes = 4\n",
    "# Create a new path like graph and change it to a directed graph\n",
    "G = nx.DiGraph(nx.path_graph(num_nodes))\n",
    "nx.draw(G, with_labels = True)\n",
    "\n",
    "# Get the PageRank\n",
    "pr = nx.pagerank(G, alpha=0.8)\n",
    "pr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RrIFCJnlvGkg"
   },
   "source": [
    "## Documentation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_7PBwhIKu3et"
   },
   "source": [
    "You can explore more NetworkX functions through its [documentation](https://networkx.org/documentation/stable/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MDcCjrW3JWzG"
   },
   "source": [
    "# PyTorch Geometric Tutorial\n",
    "\n",
    "PyTorch Geometric (PyG) is an extension library for PyTorch. It provides useful primitives to develop Graph Deep Learning models, including various graph neural network layers and a large number of benchmark datasets.\n",
    "\n",
    "Don't worry if you don't understand some concepts such as `GCNConv` -- we will cover all of them in the future lectures :)\n",
    "\n",
    "This tutorial is adapted from https://colab.research.google.com/drive/1h3-vJGRVloF5zStxL5I0rSy4ZUPNsjy8?usp=sharing#scrollTo=ci-LpZWhRJoI by [Matthias Fey](https://rusty1s.github.io/#/)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "WU7NCZtQ6msa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PyTorch has version 1.8.1\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "print(\"PyTorch has version {}\".format(torch.__version__))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "n7gMjMCT677n"
   },
   "source": [
    "## Setup\n",
    "\n",
    "The installation of PyG on Colab can be a little bit tricky. Execute the cell below -- in case of issues, more information can be found on the [PyG's installation page](https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HLxnaKsN8GVf"
   },
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "qywlcjyr8USw"
   },
   "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",
    "# Visualization function for NX graph or PyTorch tensor\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": "bbny-iTO7NQN"
   },
   "source": [
    "## Introduction\n",
    "\n",
    "Recently, deep learning on graphs has emerged to one of the hottest research fields in the deep learning community.\n",
    "Here, **Graph Neural Networks (GNNs)** aim to generalize classical deep learning concepts to irregular structured data (in contrast to images or texts) and to enable neural networks to reason about objects and their relations.\n",
    "\n",
    "This tutorial will introduce you to some fundamental concepts regarding deep learning on graphs via Graph Neural Networks based on the **[PyTorch Geometric (PyG) library](https://github.com/rusty1s/pytorch_geometric)**.\n",
    "PyTorch Geometric is an extension library to the popular deep learning framework [PyTorch](https://pytorch.org/), and consists of various methods and utilities to ease the implementation of Graph Neural Networks.\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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "g3uPffzbyqn9"
   },
   "source": [
    "## Dataset\n",
    "\n",
    "PyTorch Geometric provides an easy access to the 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": 12,
   "metadata": {
    "id": "YrpL9CtS7nx2"
   },
   "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": "markdown",
   "metadata": {
    "id": "lCeRGa2q7sdl"
   },
   "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": 13,
   "metadata": {
    "id": "sTLapYhP7uCn"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data(edge_index=[2, 156], train_mask=[34], x=[34, 34], y=[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.contains_isolated_nodes()}')\n",
    "print(f'Contains self-loops: {data.contains_self_loops()}')\n",
    "print(f'Is undirected: {data.is_undirected()}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lIzbIoc-y8J4"
   },
   "source": [
    "## Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "I5zhmKIH72Rf"
   },
   "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",
    "The `data` object also provides some **utility functions** to infer some basic properties of the underlying graph.\n",
    "For example, we can easily infer whether there exists isolated nodes in the graph (*i.e.* there exists no edge to any node), whether the graph contains self-loops (*i.e.*, $(v, v) \\in \\mathcal{E}$), or whether the graph is undirected (*i.e.*, for each edge $(v, w) \\in \\mathcal{E}$ there also exists the edge $(w, v) \\in \\mathcal{E}$)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "iFA6Xi4O79r0"
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "google.colab.output.setIframeHeight(0, true, {maxHeight: 300})"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[ 0,  1],\n",
      "        [ 0,  2],\n",
      "        [ 0,  3],\n",
      "        [ 0,  4],\n",
      "        [ 0,  5],\n",
      "        [ 0,  6],\n",
      "        [ 0,  7],\n",
      "        [ 0,  8],\n",
      "        [ 0, 10],\n",
      "        [ 0, 11],\n",
      "        [ 0, 12],\n",
      "        [ 0, 13],\n",
      "        [ 0, 17],\n",
      "        [ 0, 19],\n",
      "        [ 0, 21],\n",
      "        [ 0, 31],\n",
      "        [ 1,  0],\n",
      "        [ 1,  2],\n",
      "        [ 1,  3],\n",
      "        [ 1,  7],\n",
      "        [ 1, 13],\n",
      "        [ 1, 17],\n",
      "        [ 1, 19],\n",
      "        [ 1, 21],\n",
      "        [ 1, 30],\n",
      "        [ 2,  0],\n",
      "        [ 2,  1],\n",
      "        [ 2,  3],\n",
      "        [ 2,  7],\n",
      "        [ 2,  8],\n",
      "        [ 2,  9],\n",
      "        [ 2, 13],\n",
      "        [ 2, 27],\n",
      "        [ 2, 28],\n",
      "        [ 2, 32],\n",
      "        [ 3,  0],\n",
      "        [ 3,  1],\n",
      "        [ 3,  2],\n",
      "        [ 3,  7],\n",
      "        [ 3, 12],\n",
      "        [ 3, 13],\n",
      "        [ 4,  0],\n",
      "        [ 4,  6],\n",
      "        [ 4, 10],\n",
      "        [ 5,  0],\n",
      "        [ 5,  6],\n",
      "        [ 5, 10],\n",
      "        [ 5, 16],\n",
      "        [ 6,  0],\n",
      "        [ 6,  4],\n",
      "        [ 6,  5],\n",
      "        [ 6, 16],\n",
      "        [ 7,  0],\n",
      "        [ 7,  1],\n",
      "        [ 7,  2],\n",
      "        [ 7,  3],\n",
      "        [ 8,  0],\n",
      "        [ 8,  2],\n",
      "        [ 8, 30],\n",
      "        [ 8, 32],\n",
      "        [ 8, 33],\n",
      "        [ 9,  2],\n",
      "        [ 9, 33],\n",
      "        [10,  0],\n",
      "        [10,  4],\n",
      "        [10,  5],\n",
      "        [11,  0],\n",
      "        [12,  0],\n",
      "        [12,  3],\n",
      "        [13,  0],\n",
      "        [13,  1],\n",
      "        [13,  2],\n",
      "        [13,  3],\n",
      "        [13, 33],\n",
      "        [14, 32],\n",
      "        [14, 33],\n",
      "        [15, 32],\n",
      "        [15, 33],\n",
      "        [16,  5],\n",
      "        [16,  6],\n",
      "        [17,  0],\n",
      "        [17,  1],\n",
      "        [18, 32],\n",
      "        [18, 33],\n",
      "        [19,  0],\n",
      "        [19,  1],\n",
      "        [19, 33],\n",
      "        [20, 32],\n",
      "        [20, 33],\n",
      "        [21,  0],\n",
      "        [21,  1],\n",
      "        [22, 32],\n",
      "        [22, 33],\n",
      "        [23, 25],\n",
      "        [23, 27],\n",
      "        [23, 29],\n",
      "        [23, 32],\n",
      "        [23, 33],\n",
      "        [24, 25],\n",
      "        [24, 27],\n",
      "        [24, 31],\n",
      "        [25, 23],\n",
      "        [25, 24],\n",
      "        [25, 31],\n",
      "        [26, 29],\n",
      "        [26, 33],\n",
      "        [27,  2],\n",
      "        [27, 23],\n",
      "        [27, 24],\n",
      "        [27, 33],\n",
      "        [28,  2],\n",
      "        [28, 31],\n",
      "        [28, 33],\n",
      "        [29, 23],\n",
      "        [29, 26],\n",
      "        [29, 32],\n",
      "        [29, 33],\n",
      "        [30,  1],\n",
      "        [30,  8],\n",
      "        [30, 32],\n",
      "        [30, 33],\n",
      "        [31,  0],\n",
      "        [31, 24],\n",
      "        [31, 25],\n",
      "        [31, 28],\n",
      "        [31, 32],\n",
      "        [31, 33],\n",
      "        [32,  2],\n",
      "        [32,  8],\n",
      "        [32, 14],\n",
      "        [32, 15],\n",
      "        [32, 18],\n",
      "        [32, 20],\n",
      "        [32, 22],\n",
      "        [32, 23],\n",
      "        [32, 29],\n",
      "        [32, 30],\n",
      "        [32, 31],\n",
      "        [32, 33],\n",
      "        [33,  8],\n",
      "        [33,  9],\n",
      "        [33, 13],\n",
      "        [33, 14],\n",
      "        [33, 15],\n",
      "        [33, 18],\n",
      "        [33, 19],\n",
      "        [33, 20],\n",
      "        [33, 22],\n",
      "        [33, 23],\n",
      "        [33, 26],\n",
      "        [33, 27],\n",
      "        [33, 28],\n",
      "        [33, 29],\n",
      "        [33, 30],\n",
      "        [33, 31],\n",
      "        [33, 32]])\n"
     ]
    }
   ],
   "source": [
    "from IPython.display import Javascript  # Restrict height of output cell.\n",
    "display(Javascript('''google.colab.output.setIframeHeight(0, true, {maxHeight: 300})'''))\n",
    "\n",
    "edge_index = data.edge_index\n",
    "print(edge_index.t())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nLLsT0ROzffp"
   },
   "source": [
    "## Edge Index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wQJyi9OB8dh_"
   },
   "source": [
    "By printing `edge_index`, we can further understand how PyG represents graph connectivity internally.\n",
    "We can see that for each edge, `edge_index` holds a tuple of two node indices, where the first value describes the node index of the source node and the second value describes the node index of the destination node of an edge.\n",
    "\n",
    "This representation is known as the **COO format (coordinate format)** commonly used for representing sparse matrices.\n",
    "Instead of holding the adjacency information in a dense representation $\\mathbf{A} \\in \\{ 0, 1 \\}^{|\\mathcal{V}| \\times |\\mathcal{V}|}$, PyG represents graphs sparsely, which refers to only holding the coordinates/values for which entries in $\\mathbf{A}$ are non-zero.\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": "code",
   "execution_count": 17,
   "metadata": {
    "id": "3KfJHtlV8h3W"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kYHC5OhnYsu+bJEmMHDmS3LlzM2PGjFTvVSgU2Sug6BdF6/xjMgPQL6KT5gi6JwKKkCxjfQMmVmqGeY7c6Mu//CGgL1dQMGduJlZqSk49bWddhZQsWrSIS5cusWnTpi/u+9DT00Ol0kVGYh0x6Ri3w19beVpqX4aQIURAEVJkYpCDKZWb0754RfIa5MAwmbNODOV6xIZ+xCo4lsmVmovU9Rno6NGjzJw5k3379pE795eHFLPdkJehDWp9ay0KkMUtLNATiSKzK7FsWEiVgUKPxoXL0siyDPffB+H19jlhMZGAjDwGRlQxK8Kza7cYNX4U49v2yOrmfrPu379P79692blzJ8WLF0/TM9ktoFy7do1NK+4y00VBDkMNVhDKDCF/X523S9AdEVCENJHJZJTLZ0G5fBZJrpVpXBBjY2P27t1Lhw4dsqB137Z3797Rpk0b/v77b+rUSfv8VHaZQwkPD2fq1Kls2rSJefPmYlTAFz4cTH8ur3z9IEelDGunoD0x5CVoTSaTMXnyZGbMmEFq+5qE9IuNjaVz5860a9eOfv36pevZ7DCHcvz4cSpUqEBQUBC3b9+mZ89eyApOgdzNQJbG4VGZUdzeE9PBGdtYQWsioAg60a5dO8LDwzlx4kRWN+Wb4uLiQo4cOZg1a1a6n83KIa93797Rr18/BgwYwJIlS9i4cSMFChSIuyhTQMHfoeAvcSu2ZDn4/KMoVqlGwhAMSkOhmVBgJGTGGS2CVkRAEXRCLpczadKkLy5lFdJu6dKlnD17ls2bN6ctk+9nsiKgSJLEjh07KF++PLlz58bHx4cWLVokvVEmi0ufUnwfWLnF9VgMy8YtLTa05chFQy6+GArFt0OuHzL1PQiaE3Mogs5069aNqVOncunSpcS7t4V0O378ONOmTePixYvkyZNHozIyew4lICCA4cOH8/DhQzw8PHB0dPzyQzIZ5KgAOWYmevl6wB+EP7xLbXGS9FdF9FAEndHX12f8+PHMnDnzyzcLKXr48CE9e/Zk27ZtlCiR9LySL1LHwIdjFDI6whAnFbxbF3dmu5QxwUWtVrNixQoqVapEpUqV8PLySlswSUXDhg3F8OlXSCSHFHQqKiqKEiVKcPjwYezt7bO6OV+dkJAQatWqxbhx4xg4cGD6Ho59GXdG+/udgISkjkImUxN3RrsByPQhX3cw6ayzvRwPHz5k0KBBREdHs2rVKsqXL6+TcmNiYjAzM8PPz4/8+fPrpExBd0RySCFTGBkZMXr0aP7++++sbspXR6lU0qVLF1q2bJn+YPLhJPh1hJAtceezSxH/BROIO6M9AtSh8G4tPG0DEde0amtsbCwzZ87E0dGRTp06ceHCBZ0FEwADAwNq167N6dOndVamkPFEQBF07qeffuLYsWM8fvw4q5vyVRkzZgx6enrMmTMnfQ+GHYXASf/t6/hCRl8pOi64BIyEiKsatdPT05Pq1atz5swZPD09cXZ21mjRwJc0atRIDHt9ZURAEXQud+7cDBs2jNmzZ2d1U74aK1as4NixY2zduhU9vXSslYl+8N8Z7V84qfFzUhQEjILYV2l+JCIignHjxtGqVSt+/vlnDh06lOZd+5oQ8yhfHxFQhAzh7OyMh4cHAQEBWd2UbO/UqVP89ttv7N+/P/lz2FOj9RntG9N068mTJ7G3tycgIIDbt2/Tu3dvZBm8L6RSpUoEBweLv0NfERFQhAxhampKv379mDdvXlY3JVt7/Pgx3bt3Z8uWLZQsWTJ9D6tCIPwc2p3RvhvUKfduQkJCGDhwIH379mXBggVs3rwZc3NzDetLH7lczg8//CB6KV8REVCEDDNmzBjWrVvHmzdvsrop2dL79+9p06YNf/zxBz/8oMHmvdA9kI7zalL08XiyL+/cuZPy5ctjZGSEj48PrVu31r6udGrUqBEnT57M9HoFzYiNjclQSxL33wdy9MVdnn98T7RaiZ5MTl6DHPxgWZqa5tYY6elndTOzvcKFC9O5c2cWLlzItGnTsro52YpSqaRbt240adKEIUOGaFZI5K30z518ToqAqHtxu9b/8/LlS0aMGMG9e/fYvn07tWvX1q4OLTRs2DAhR1xGD7EJ2hM9lM9cCHzMhCu7WX73HPfeB/FRGU2sWkWkKpZXkWHsfOrF2Cu72PjoKjGqrM/kmt2NHz+epUuXEhYWltVNyVbGjRuHWq1m/vz5mhei1tHPVBUSV5xazcqVK6lYsSJ2dnZ4eXllaTABKF26NJIk8ejRoyxth5A2oofyH0mS2PbkOheCnhCjTjlDa/R/1y4F+fIkLJif7RuRS98os5r51bGxsaFZs2YsX76c8ePHZ3VzsoVVq1Zx8OBBLl++nL4VXZ9La7beL5Hn4vHjxwwaNCghwWd22ZQqk8kSlg+XLl06q5sjfIHoofxnv/+tLwaTTyklNUGRYfxz+6ToqXzBxIkT+eeff4iMjMzqpmS5M2fOMGXKFPbv30++fPm0K8ygGNqe0R4ZLTFv0XYqVaqEg4MDFy9ezDbBJJ6YR/l6iIACPP8YwtGA+2kOJvFUkkRQRBgHn9/JoJZ9GypUqECNGjVYu3ZtVjclS/n6+tK1a1c2bdqkm2/bOjijXS6DPSeiadSoEevXr6d48eL07NmTlStX8vDhw2xxvk3Dhg05deoUarWmq9mEzCKGvIDjAfdQafiXNVZSc+blQ9oUrYBCLuJzSiZNmkS3bt0YNGgQ+vrf34KGsLAw2rRpw9SpU2ncuLFuCjW0AYMSEH1Po8fVaomgjzacvbgTmUyGJEk8efKE06dPc+bMGaZNm4ZKpaJ+/fo0aNCA+vXrU6ZMmUyfHLeysqKktSnPfJZRvIgJIIE8D+SsAfqFMrUtQuq+++SQkcoYxl3ZTWw6eyefMlTo8WOpWlQtUFSHLfv2NGrUiB9//JE+ffpkdVMylUqlom3bthQrVoylS5fqtOzwoP0oXk/FSIMz2iUMkRVZkeKxupIk8fTp04QAc/r0aaKjoxMFmHLlymVsgIm8DSHriX1/ArWkwFD/v7pkeoAKjCpD/h8hZy2QiS90mSWl5JDffUC5HPSUzY+vEa3Wbh6kXN6CjKrQSEet+jadOHGCESNGcOfOHeTfUW9u7NixeHt7c+jQIZ32zm7cuEHnzp1ZP7s0te1DkKX3jPb8/dN9rK6fnx+nT59OCDIRERHUq1cvIcDY2trq5s9WUkLQdPhw5L9MAKmMIMhyxJ2pYukKch0tVBBSJbINpyA0NhKlFr2TeO9jxITzlzRs2JA8efKwZ8+erG5Kplm7di379u1j+/btOgsmkiSxYsUKmjVrxsyZM6nT8V9kuVuk74z2fL0g/6B01128eHH69u3LunXrePr0KVevXqVNmzZcv36ddu3aUbBgQTp16sSiRYu4deuWZvMekhpejv0vmETxxUwAUiREesPzfqBOR1AVdO6776EceObDfv9baDv1WMAoF39Wb6uTNn3L9u7dy/Tp07l27do3v1Ht3LlzODk5cebMGcqWLauTMsPDw/npp5/w9vbGw8ODMmXKxF2QJPhwCN6uAOXr/zY8/v+DWC3Jkcv1waA4mA6DXPV10p7PPX/+PGF47MyZM7x79y5RD8be3v7LPZg3iyBk03/BJB1khpCzDhQW6X4ymuihpCCnnj56Ouii59Az0EFrvn1t2rQhKiqKY8eOZXVTMpSfnx9dunTB3d1dZ8Hk3r171KhRAz09Pa5cufL/YAL/ndHeEqz3Ilmt5FFQKbzvxxAcaozaoBzyvO2h6CYoti3DgglAkSJF6NWrF6tWreLRo0fcunWLzp07c/fuXbp27YqZmRnt2rXjn3/+4caNG6hUn40OqMPjElamN5hAXBCNOA8xz3XzZoR0++5XednkKYBMy3xIejI5tnktdNSib5tcLmfSpEnMmDGDpk2bZnVzMsSHDx9o06YNkyZN0tl73Lx5My4uLsyaNYv+/funeN+TJ08YPNiF0NBQVq/eQoGKFXVSv6YKFy5Mjx496NGjBwCvXr3izJkznDlzBjc3NwIDA6lTp07CRH9l68cotPmeK6ng/WYwn6CjdyCkx3c/5AXw+/UDvIoI1fh5VUwsha4+Z3i/gVhYiMDyJUqlktKlS+Pu7p7lqT10TaVS0aFDBwoVKsTy5cu1HtaLjo5m9OjRHDt2DA8PDyqmECCUSiX//PMPs2bNYtKkSbi4uGi3Cz+TBAYGcvbs2YRhsgOLoXhhLeeaZDnA5jTIDXXRRCEZYsgrFc2tbDGUa/7LZ22cn2D/F5QrV47evXvzPQRhbejp6TFhwgRmzpyZ1U3RucmTJ/PhwwcWL16sdTB5+vQptWvX5vXr13h6eqYYTLy9valVqxaHDx/mypUr/Pzzz19FMAGwsLCgS5cuLFmyhDs+NylWWBdDxzJQpv3gMEF3REABqhYoSh4DI+QafADoyxX0sqvN8uXLefLkCfb29nTq1InatWuzfft2lEqRliU5P/74I15eXnh7e2d1U3Rm/fr17Ny5Ew8PD61XdO3fv59atWrRq1cvduzYkezBW1FRUUyePJmmTZsybNgwjh8/jo2NjVb1Zil1ODItd/4DcftRVB+0L0dINzHk9Z+Q6Aj+8jpEuDIGdRrTTejLFQwo40hlsyKJXlcqlezduxdXV1eePn3K8OHDGTRoEKamphnR9DQLCH/PyYAH+H18S5RKiYFcQQGj3PxgWZqyeQtm+qqrefPmce3aNbZu3Zqp9WaEixcv0r59e86cOUO5cuU0LkepVDJlyhS2bNnCtm3bcHBwSPa+s2fPMmjQIOzt7Vm0aNG3MdSqjoTHtdH8wLD/yI2hyFowFMkkM4rY2JgGoTGRzL91gpCYCKJTSfiohwyFXMFPtnWxzZd66gcvLy9cXV3Zu3cvnTt3xtnZmfLly+u66anyefeSPX43CYwMQ6VWo/5skbShQo8cCn2aWdnSwLK0Rj01TXz8+BFra2suXLjwVWeS9ff3x8HBgdWrV9OiRQuNy3n58iXdunUjZ86cbNy4ETMzsyT3hIaGMmHCBP79918WL15M+/bttWh5NiNJSI9qIUPLM15kBmB9GPTy66ZdQhJiDiUNTAxy8FvVlgwqW5vSeczRk8nJodDHUK6H0X//VkQpib50j1k1O3wxmABUrlyZdevWcf/+faysrGjatCmNGjVi3759SZdMZoBDz++w/N45noeHEKtWJQkmANEqJe9jItnt582yu2e1SkOTHrly5WLEiBHMnj07U+rLCB8/fqRt27aMGzdOq2By8uRJqlWrRpMmTTh48GCywWTv3r3Y2dkhSRI+Pj7fTDB59+4d69ato3WbNqz2CEap7V8/I3sRTLKI6KGk4m1UOC/CQ4hUxWIg1yO/YU5yRCgpU6YMvr6+5M+f/r+0MTEx7NixA1dXV96+fcvIkSPp378/efLk0Xn7TwTcZ4/fzXRlUdaXK7DLW4ghtnUzpafy7t07SpUqhbe3N0WKFPnyA9mIWq2mU6dOmJqasnLlSo2GDNVqNTNmzGDJkiW4u7snmzgyKCiIkSNH4u3tzcqVK6lfP+P2kWSWN2/esGfPHjw8PLh06RKNGjXCycmJts3tyfV2gOYnUcpyguVsMK6j2wYLiYgeigZMjYypaGpFLXNrqpgVoXhuUwoWLEjr1q1Zv369RmUaGBjQs2dPrly5wsaNG7l8+TLFixfH2dlZp6fSBYS/Z3c6gwlArFrF3fevOB/4WGdtSU3+/Pnp378/8+Z9fbubf/nlF969e8fSpUs1CiZv3ryhVatWHDlyBE9PzyTBRJIk1q5dS4UKFbCxseHmzZtfdTAJCgpi+fLlNG7cGBsbG44dO8aAAQMICAhg165d9OjRg1z5y4NhWTQ+50VuDDkdddpuIe1EQNHA0KFDWbZsmVbnM8hkMhwcHNi6dSu3bt0iV65c1K5dm9atW3Ps2DGtz6E49kLzlPwxahWHn9/NtLMwxowZw4YNGwgODs6U+nRh06ZNbN26lZ07d2JgkP6lrpcvX6Zq1apUqFCBkydPUrhw4UTXfX19adq0KYsXL+bIkSPMnDmTHDm+vsSHL1++ZPHixTRo0ICyZcty7tw5hg8fzqtXr9i2bRudO3cmV65ciR8qNBvkuSG9G45lOcBqscg6nIXET14Djo6O5MiRgxMnTuikPCsrK2bMmIG/vz/t27dnzJgx2NnZsXz5csLDw9NdXqQyFs83z5KdL0mrj7HRPAnLnA/4QoUK0aVLF1xdXTOlPm1duXKF0aNHs2/fvmTnOlIjSRKurq60bduWhQsXMnv27ERLjFUqFfPnz6dGjRo0adKEK1euULlyZV2/hQz17Nkz/vnnH2rXrk358uW5du0aP//8M69evWLTpk106NCBnDlzplyAfkEouhYU+UhbMg9Z3FBX4SVgWObLtwsZRsyhaGj58uUcPXqUXbt26bxsSZI4ffo0rq6unD9/nv79+zN8+HCKFSuWpucvBvmy9bGn1in5qxcoxsCymbOT3dfXlxo1avDkyZNk91xkF8+fP6dWrVqsWLGC1q1bp+vZsLAwBgwYgK+vLzt27KBEiRKJrt+6dYuBAweSK1cu3NzcKFmypC6bnqF8fX0T9uA8efKEdu3a4eTkRKNGjTTqwQGgfANvFsZlHUYel1X4UzJDQIob4iowKi7xpZApxByKjvXs2ZPTp0/z4sULnZctk8n44Ycf2LNnD1evXkWpVFKlShWcnJw4d+7cF4ei3kWFax1MAIIjP2pdRlqVKFGC5s2bs2zZskyrM73Cw8Np27Yto0ePTncwuXXrFtWqVcPU1JQLFy4kCiZRUVH88ssvNG7cmMGDB3PixImvIpg8fPiQmTNnUrVqVRwcHHjy5Al//fUXr169SlhCrXEwAdAzA4tpUOIEmI0EQ1vQswS9QmBQOu48F+tDUHiBCCbZhOihaGHEiBGYmpryxx9/ZHhdHz58YP369SxcuJBcuXLh4uJCt27dMDRMmq/Iw/cGxwLua12nZU4TfqvaSuty0srHx4fGjRvj6+ub+pBIFlCr1XTu3Jk8efKwZs2adE3Cr127lvHjx7NgwQJ69uyZ6Nq5c+cYNGgQdnZ2LF68mEKFsveRtnfv3k3oibx+/ZpOnTrh5OREnTp1vpp0L4L2xMbGDHDnzh2aNGmCv79/pp2TrlarOXz4MAsXLsTb25vBgwczdOjQRB9Eh5/fYa/fLa3mUABK5inAuIpNtG1yurRv357GjRszYsSITK33S6ZOncrJkyc5ceJEskE8OREREYwYMYLLly/j4eGBra1twrWwsDAmTpzI3r17WbRoER07dsyopmtFkiRu376Nh4cHHh4ehIWF4eTkhJOTEw4ODigUGq7GEr5q38SQl1qSCImO4GX4e4IiwohQxmRpe+zs7ChVqlSmnkAol8tp2bIlhw8f5tSpU7x58wZbW1t69erFtWvXALAyzoe+XLtfdD2ZHJs86Ztw1oVJkyYxZ84cYmKy9s/2U1u3bsXd3Z1du3alOZg8evQIBwcHoqOjuXr1aqJgsn//fuzs7IiNjeXOnTvZLphIksSNGzeYPHkyZcqUoW3btkRERLBmzRqePXvGggULqFOnjggmQhJfRQ/lY2wU5wOfcDzgAVGqWBQyGZIESklNidymNCtii12+QsizYLngtm3bWLFiBSdPnsz0uuOFhISwevVqFi9ejKWlJc4uzngW1eeDUvMUFnoyOdOrtSG/kbEOW5o2jRs3plevXvTt2zfT6/7c1atXad26NcePH8fe3j5Nz3h4eDBs2DCmTZvGkCFDEobHXr9+jbOzM56enri5udGwYcOMbHq6SJLEtWvXEnoicrk8oSdStWrVb/50TSF9vsohL7UksfupNydfPkAmk6WYEsRQroeBQo+htnWxyVMgU9sYExND0aJFOXXqlFZJAXVBqVSyb9++uOW3dkUo16U5kiL9QVZSq1G9fMcfdTtiZWWVAS1N3cmTJxk2bBh37tzJ0m/BAQEB1KxZk6VLl9K27ZePd46JiWH8+PHs27ePHTt2ULVqVSDuw9rd3Z1x48bRt29ffvvtt2wxR6RWqxOG43bu3EmOHDno3LkzTk5O2NvbiyAipOirG/JSS2rc7p3j9KuHKCV1qvmlotVKPsRGseD2SW6/C8jEVsbtfB84cGC2WJ2kp6dHx44dOXPmDLMGjUKtVCFpsLnRQKGHWcAHKlWqxF9//UVUlAbHsWrhhx9+IG/evOzevTtT6/1UREQE7dq1Y+TIkWkKJs+ePaN+/fo8ffqU69evJwQTPz8/mjdvzj///MOhQ4eYNWtWlgYTlUrF2bNncXZ2pkiRIgwZMgQTExMOHjzIvXv3mD59OhUrVhTBRNBItg0o255c507Iq3SlDolRq3C7dx6/D28zsGVJDR48mE2bNmm0CTGj1KpcjV9qtcFQoQ/p2PFuIFfQvWQ1Zo2dwtWrV7l+/Tp2dnbs2bMn03bOy2QyJk+ezIwZMzKtzk9JkkTfvn2xtbVl/PjxX7z/0KFD1KhRg44dO7Jnzx7y5cuHSqViwYIFVKtWjYYNG3L16lWqVKmSCa1PSqlUcuLECYYOHUrhwoVxcXGhYMGCnDhxgtu3b/Pbb79hZ2cngoigtWwZUIIiwrgQ5JvuPFQQF1Q2Pb6aAa1KWdGiRalTpw6bN2/O1Hq/pGiu/Iyv1BRjfUMMvjBJr5DJ0Zcr6F2qJrUt4vZAlChRgl27drFixQqmTJlC06ZNuXv3bmY0ndatWxMbG8uRI0cypb5PTZs2jefPn+Pm5pbqh6xKpeLXX39l0KBB7Nixg3HjxiGTybh9+zaOjo7s2bOHS5cuMWHChExbBRgv/mc3aNAgChUqxKRJkyhevDjnz5/Hy8uLKVOmULZs2Uxtk/Dty5YB5cTLB1rlyXoVEcbLcM3PiNfEsGHDWLp0aZZ8o05NkVz5+LN6W9oVrwgR0chUauSApJZQxcRCrBIDmZwfLEvze9VW1DAvnqSMxo0b4+3tTZs2bahfvz6jRo3i/fv3GdpuuVzOpEmTmDFjRobW87kdO3awZs0a9uzZg5GRUYr3BQUF0bRpUy5evMj169epW7cu0dHRTJ06lYYNGzJgwABOnjxJqVKlMq3t0dHRHDhwgH79+mFhYcEff/xBuXLluHbtGlevXmXChAlfxYZJ4euV7QJKjErJpSBfVFrsoVCp1ZzQwca+9GjSpAkfPnzgypUrmVpvWuTUM+AHi1Ls6TORrgXt6FyiKu2s7WluUYY8d16xo8to1o74hetnLqQYEPX19XF2dubu3btERUVRtmxZ3NzcMvRMly5duhAQEMC5c+cyrI5PXb9+neHDh7N3714KFiyY4n1nz56latWq1K5dm6NHj1KwYEEuXrxI5cqVuXXrVsL+ILk843+9IiMj2bNnD71798bCwoJZs2ZRuXJlbt68ycWLFxkzZgzFixfP8HYIAmTDVV4P3gex9O4ZolI5MTEtcusbMrdWJx21Km3mzp3LrVu32LBhQ6bWmxanTp1i7NixXL9+Pcm1yMhItmzZgqurKzExMTg7O9OnTx+MjVNeMuzl5YWzszPh4eEsXLiQOnUy5vwJNzc39uzZw8GDBzOk/HgvX76kZs2aLFy4kA4dOiR7j1qtZu7cucyfP59169bRvHlzPnz4wOTJk9m5cycLFy6kU6dOGT4XER4ezqFDh/Dw8ODw4cMJaXk6dOiQ7XfaC9+Gr2aV18dYLY///I+2AUkT/fr1Y//+/bx58ybT6/4SDw8PnJyckr2WI0cO+vfvj7e3N8uWLePo0aMUK1aMcePG4e/vn+wzlStX5uzZs4wfP54ePXrQo0ePDMlr9uOPP3Lz5k28vLx0Xna8yMhI2rdvz08//ZRiMAkJCaF9+/bs3r2ba9eu0bx5cw4ePEj58uWJiIjAx8cHJyenDAsmHz58YOvWrTg5OWFpaZmwj+Xhw4cJy6xFMBGyWrYLKHG/j9r/UqpUqoThmcxiampK27ZtWbt2babVmRYqlYpdu3alGFDiyWQyGjRokPChqVarqVKlCp06deLs2bNJhsNkMhndunXj3r17lCxZkooVK/Lnn3/q9GduaGjIzz//zMyZM3VW5qckSaJ///6UKlWKyZMnJ3tP/DJgGxsbzpw5g5GRET179mTkyJGsWbOG1atXa3R655eEhoayceNG2rdvj5WVFe7u7rRs2RJfX1+OHj3K4MGDMTc313m9gqCpbBdQjPXSltriS2LDI+nQoQN58+alaNGiNGzYkMGDBzNnzhx2797N7du3iYiI0Eldnxo2bBjLly/XalGBrl28eJGCBQuma4LY2tqaefPm4e/vT8OGDRk0aBBVqlRh7dq1SQKGsbEx06ZNw9PTEy8vL2xtbdm9e7fOFigMHjyY06dP8+DBA52U96m//voLX19fVq1alaR3IUkSy5Yto0WLFsyePZv58+ezfft2KlSogKWlJbdv36ZRo0Y6bU/C+eqtW1OkSBF27NhBp06d8Pf358CBA/Tv3x9TU1Od1ikIupLt5lBi1Sp+vryTaC2GrBTIqFuoFN1LVkOpVPL8+XMeP37Mo0ePePz4ccI/vr6+mJqaUrJkSUqVKkXJkiUT/rGxsSF37tzprluSJKpWrcqMGTNo3ry5xu9Bl5ydnTE3N+eXX37RuAy1Ws3Ro0dxdXXlxo0bCUkpLS0tk9x74sQJXFxcsLCwwNXVFTs7O22aD8Qt5fXz82PNmjValxVv586djB49mitXriQZLvr48SNDhgzBx8cHDw8PDAwM+Omnn3j58iWrV6+mWrUkw8caCw4OZs+ePezcuZNLly7RuHFjnJycaNWqFXny5NFZPYKgK19V6pXtvtc5/fIRKkmzb/n6cgW/VmlBwRyp/zKqVCoCAgKSBJrHjx8nHPT0aZD59J/UDoFatWoV+/btY9++fRq1X5fUajVFihThxIkTOtt3cP/+fRYtWsTmzZtp2bIlLi4u1KhRI9E9SqWSZcuWMX36dLp168Yff/xBvnz5NK7z3bt3lCxZEm9vb4oWLartW8DLy4umTZty5MiRJBsO7969m5BN19XVlTVr1jBt2jTGjBnDuHHjdLKnJDAwkN27d+Ph4YGnpyfNmzfHycmJFi1aJD0SVxCyma8qoLyJ+sjv1/8lVpNhI0nCOrcZEys306oNarWaly9fJgk08f/kzJkzSZCJ7+UYGBhQtGhRbty4keZTFjPKxYsXGTx4MD4+Pjov+/379wlJKS0sLHBxcaFTp06JPnDfvHnDr7/+yq5du5g2bRoDBw7UOD/X+PHjiYqKYuHChVq1OzAwkBo1ajB//vwk80obN25k9OjRzJkzh+rVqzNw4ED09fVZuXIlZcpod7xsQEAAu3btwsPDg1u3btGqVSs6depEs2bNskVuL0FIq68qoADseurNqZcP0r1bXh0Ti/+KPWxYuAwLC4sMaZskSQQGBiYJMvE9HX19ffT19TEzM8PJySlR0DE1Nc3UFBdjxowhT548/P777xlWh0qlYt++fSxcuJBHjx4xdOhQBg8eTIEC/0/U6e3tjYuLC2FhYSxcuJC6deumu55Xr15hZ2fH/fv3NZ6MjoqKokGDBrRs2ZKpU6cmen3UqFGcOnWKzZs3s3//fpYsWcL06dO12lPy7NmzhAOp7t27R9u2bXFycqJx48apbpwUhOzsqwsokiSx/uFlrr95luagYiDXY2i5Omxf6MbKlSvZvHkz9evXz+CWJiZJEsHBwQm5k4YPH46vr29CwAGSnbMpWbIk5ubmOg02kiRRrFgxDh06pJN5jLS4efMmCxcuZNeuXXTs2BEXF5eEtO+SJLF9+3bGjRuHo6Mjc+bMoUiRIukqf9iwYeTNm1ejHfSSJNG7d2+USiVbtmxJ+Fn7+vrSuXNnbGxsGDx4MC4uLtjY2LB06VKNsi37+vomZPB98uQJ7du3x8nJiYYNG2p3JK4gZBNfXUCBuA+AQ8/vcOj5HUBGTArnpBvK9chtYMRP5epSJFfcOP3hw4fp27cvo0ePZty4cZmya/lzjRo1YtCgQXTr1g2Iez/v3r1LdoHA48ePiYmJSXa+plSpUlhYWKQ72Fy5coW+ffty9+7dTE/8FxwcjJubG0uXLqV06dK4uLjQpk0bFAoFERERzJo1i8WLFzNq1CjGjh1Ljhw50lTu06dPqVatGr6+vqnOYyVn5syZ7Nq1i7NnzybUt2fPHgYPHsz48ePx9/fHw8MDV1dXOnfunK6f2cOHDxPOEgkICKBDhw44OTlRv379TM/jJQgZ7asMKPGiVLFcfe3HkRf3eBcdjp5MjkRcinu7fJY0syqHTZ4CST4Anj17RpcuXShYsCDr1q3TalJYEx4eHixcuJCzZ8+m6f6QkJAU52zCw8OxsbFJds7G0tIy2YA5btw4jIyMmD59uq7fWprFxsYmfEi/fv2aESNG0L9/f/LmzYufn1/C7v158+bRoUOHNH2I9+nTh3LlyjFp0qQ0t2PPnj2MHDmSK1euYGlpSWxsLJMnT2b79u2MHj2aBQsW8MMPPzB37tw0L8u9e/duQhB58+YNHTt2xMnJibp164rTDIVv2lcdUOJJkkS0WkmEMgY9mZycegbofSGLbkxMDOPGjWP//v14eHhkagrx2NhYihcvzuHDh6lQoYJWZYWGhvLkyZNk521CQ0MpUaJEoiBjY2NDv3792Lt3b5alTf/clStXWLhwIYcOHaJ79+44OztTpkwZTp48iYuLC+bm5ixYsOCLP6u7d+/SsGFDfH19idWT8fTDWyKVMShkcnLrG1HaxBzFJwH25s2bNG7cmIMHD1K9enUCAgLo1q0bhoaG5M+fH09PT1asWEGTJk1Srffz89U/fPiAk5MTnTp1wtHRMUt6wYKQFb6JgKKN7du3M3z4cP766y8GDRqUaUNAv/32G2/evGHJkiUZVsfHjx+TBJsbN25w8+ZNFAoFJUqUSHbOpmjRoujp6WVYu1Ly8uVLli1bhpubG1WqVMHFxYWGDRuycuVK/vjjD7p27coff/yR4u5zSZLo5jwEq2aOROU1Qk8uRy3FZVmQIUMGNLAsTf1CpYh5/4GaNWsya9YsunbtyvHjx+nduzd169blzJkz9OzZk+nTp6eYt0ySJLy8vBKCSGxsbMLRuNWrVxdBRPguffcBBeL2Tzg5OVGlShWWLVuWavJDXXnx4gUVKlTg2bNnGm2U1NTEiRORy+X88ssvCYsCPp+7CQoKolixYsnO2RQrVizDx/6joqISklJGRUXh7OxMq1atmDVrFjt37uT3339n8ODBiYaPopSxLL5zhqdhwcSqVchS+EDXk8W9/mzPKaoYW/Dbb7/x559/smTJEqytrQkPD2f16tVJ9s9AXBC5evVqwsS6QqFICCJVqlQRB1EJ3z0RUP4THh7OTz/9hLe3Nx4eHlrvLUiLTp060bhxY4YOHZrhdUHcB2KpUqXYvn17qsNdUVFRPH36NNlFAgEBARQpUiTZORtra2udrlaSJImzZ8/i6urK2bNn6du3Lw0bNmT27Nm8f/+ehQsXUq9ePaJUscz0OsKbqI8o07jpVYpV0siiNGtG/4avry9v375lzJgxjB8/PtF7UKvVXLp0KSGIGBsbJwQRcb66ICQmAsonJEli5cqVTJkyhSVLltClS5cMre/48eOMGTOGmzdvZsoHk7e3Nx07duTJkyca1xcTE4Ofn1+yq9GeP39OoUKFkgSakiVLUqJECa32V/j5+bFkyRLWrl1LnTp1qFixIuvWraNWrVpU+rkPL2I+pDmYxFNGx3Bz+XYMX4WyatUqypUrB8Ttnzl//jweHh7s2rULU1PThCBia2ur8XsQhG+dCCjJuH79Op07d6ZNmzbMmTMnw/YIqNVqypUrx+rVqzPs3JBP/fLLL8TExDB79uwMKT82NhZ/f/9kV6P5+flhbm6e7JyNjY1NmneEf/z4EXd3dxYuXIi+vj6lHapi4lQPPSPNkofqxapZWL87kiRx5swZPDw82L17N5aWlgkT65nRWxWEb4EIKCkICQnhxx9/JDg4mO3bt6d7o11aLViwgGvXrrFp06YMKT+eJEmULVuWjRs3Ur169QytKzmfJuP8fDXa06dPE5JxJvdPcjms1Go1x44dY6P/DYxKF0Gu0GwSXCFBzFEv9q5Yh7W1dUIQsbGx0fYtC8J3RwSUVHx6Et/69etp1ky7PGDJCQkJwdramocPH2boGRa3b9+mdevW+Pn5Zbtx//hknMktEEguGWd8L6dQ8aL8efd4uoe6PiWpJYzDYhhl3zDL86sJwtdOBJQ0OHPmDD169GDQoEH8+uuvOt+cNmDAAEqWLMnEiRP5GBtN+H97J3LpG5JDTzcrqn777Tc+fvzIvHnzdFJeZlGr1bx69SrZBQIfc+lT95efMDBO2276lOTU0+cfh846arEgfL9EQEmjV69e0b17dwwNDdm4cWOiBIfaunjtChNXLsBxQBc+xEbHbb6TQCmpsM5tRjMrW8rnL4RcpvneBjs7O1atWoWDg4PO2p3VPIP92fDgMtFS+hKFfk4hk7O0TjcdtUoQvl8pBZTM39WWzRUqVIjjx4/z66+/UrVqVbZt26b1h7MkSRx45sPhGD9Kd2lGSEwkAErV/4dwHocF8/z+BQwUCgaXq0tpk/QPi929e5fQ0FBq1qypVXszW3R0NIGBgbx8+ZJXr17x6tWrRP8dns8Imz6tMDDWLsW7IpsNAQrCt0YElGTo6ekxc+ZMHB0dad++PZMnT8bZ2VmjOYlPsybHqlUoDFNeSRatVhKtVrLQ5xQDyjhS2Sx9CwQ8PDxwcnLKNru3IyMjE4LC50Hi0/8OCwujYMGCFCpUiPz586Onp0dMTAyhoaEEBgaiNMlBKUVbrdujq+OlBUFInhjy+oL41OYlSpRg9erV6T6Sda/fTY4H3E/3uS4GcgWjKjTEJk/ah9zs7e1ZunRphi9NDg8P/2KQePnyJREREVhYWGBpaUmhQoUS/l2oUCEKFixIdHQ0QUFBPH36lJs3b3Lz5k1iY2OpVKkSFStWpGLFilSqVIk8efIw58k5VIaaf//RlyloXsSW1sW0y6kmCIKYQ9HKp4cveXh4pDnRY0h0BL9c26fx6qRCOfPwe9XWabr3wYMH/PDDD7x48ULjHsqHDx+SBIfkAkZ0dHSi4JBcwLC0tCR//vzIZDJCQ0O5desW3t7e3Lx5E29vb+7du0fhwoUTgkb8vwsXLoxMJkOlUnH48GHc3Nw4e/YsXX4bjV7VUihJ+e9ravRkcmbWaEceA+0m9gVBEHMoWjEyMmL58uW4u7vTsGFD5s6dy48//vjF506/fKhVvW+jwvH/8I5iuZNPkvipnTt30rFjxyTBRJIkwsLCUgwOn76mUqmSDQ4VK1ZM9FrevHmTHf6TJAk/Pz9u3rzJzp07E4JHcHAwFSpUoGLFilSrVo0BAwZQoUKFZPedPHv2jDVr1rB69WosLS0ZPHgwmzZtQm5kwPgruyGdPT0ABTLs8hUSwUQQMpjooaSTj48PnTp1ol69eixatCjFNCMqtZqfL+8kUhWrcV0yZFQvUJQBZWsnuSZJEu/fv08ICAMGDEg4m/zzgKFQKJLtQXz+33ny5EnzPFFkZCR37txJCBo3b97k1q1b5MqVK1GPo2LFitjY2KS6BDs2NpYDBw7g5ubGlStXEpZux5/0GO/C8wese3gFuUHavwfJgNz6RvxapSV5DMSRu4KgC6KHoiPly5fH09OTgQMH4ujoyI4dO5Ldbf0s/B2ShsMz8SQkrgf68Xr32SRBIjAwEAMDAywtLTExMeH169fkzp2bwoULU7NmzUQBQ9ssx0FBQYmGq27evMnTp08pVapUQtDo0KED9vb2mJmZpblcX19fVq1axbp16yhRogSDBw/Gw8Mj2fQsYWFhTOwxkNIt6yN3KAtp2DEvKVXIlWrGV2sjgokgZAIRUDSQO3dutm7dyuLFi3FwcGDlypW0a9cu0T3hsTH/ncyhHaUMQt6/p0SJEtSpUychSBQqVCgh/f6sWbOoXLmy1psZlUolDx8+TBI8YmNjE3oczZo1Y/z48ZQrVw5Dw/SvmoqJiWHPnj2sXLkSb29vevfuzfHjx1NNxvj+/XuaN29O5cqV6duwDf2n/EyHP8cSHPUxrgvy2TCfvlwBkkSZvObM7z6MDgsL0bRp03S3VRCE9BEBRUMymYyRI0dSvXp1unbtyoULF5gxY4bOD6ySy+XM+vvvVIeiPDw8+Pvvv9NV7ucT5Tdv3uTu3bsJE+UVK1Zk5MiRVKxYESsrK63TuDx8+JCVK1eyYcMG7OzsGDRoEB06dPhiZuJ3797RtGlTateuzYwZM6hSpQrTpk2jS812VG5Yl55/TeCNQkmUSolaqeTDmxA6VG9AHQsbcukbUmG5G927d8fT05PChQtr9R4EQUidCChaqlWrFtevX6dnz540bNiQbdu2xfUe9A20HvICMFLopfph/vTpU/z8/Khfv36y1z+dKP+01/H69euEifKqVaumOlGuqaioKHbt2oWbmxv37t2jb9++nD9/nlKlSqXp+eDgYJo0aUKTJk2YPXs2I0aMSAjgFy9eJDrwLWMdWif8fIKDgyldujRr371LeK1BgwYMHz6c7t27c/LkySw5oVIQvhfit0sHzMzMOHjwIH/99RdVq1Zl06ZNWBUtQnRUNOhrng9Mjgz7/Kl/q965cycdOnRAT0+PqKgofHx8EgWP+Iny+CGr7t27M2vWrC9OlGvjzp07rFy5kk2bNlGlShVGjBhB27Zt03U8QFBQEI0aNaJ9+/ZMnz6dw4cP8++//3Lz5k0ANm7cSK9evRIF2wIFCqCnp8erV6+wtLRMeH3SpEmcPXuWqVOnMmPGDN29UUEQEhEBRUcUCgXOzs6EhITQrFkz9PX16TNvKjK7ImiaI1dPLqeJVblkrwUFBXHz5k0WLlxIyZIlsbOzw9fXl9KlSycEj3bt2lGxYsV0TZRrKiIigh07duDm5sbTp0/p378/V69exdraOt1lvXz5kkaNGtG9e3emTp3KmzdvGDhwIBs3biRv3rzExMSwfft2kluBaGdnx927dxMFFIVCwcaNG6lSpQp169alRYsWWr1XQRCSJwKKlmJjYzl69CgbNmzg8OHDNGnShGXLlrFy5UoCTntSpHwx1BpubDQzykUho9zcvXs30XCVt7c3MTExlC1bluDgYH777TeqVq2q8US5Nry9vVm5ciVbt27FwcGB8ePH06pVK42Hlp4/f07Dhg0ZMGAAEydORJIkhgwZQrdu3fjhhx8AOHz4MLa2thQvXjzJ87a2tty5c4fGjRsnet3c3JzNmzfTpUsXPD09sbKy0qh9giCk7JsMKAHh7zkf+JjXkR+JUSvJqWdAqTwFcChog7G+9qcySpKEl5cXGzZsYMuWLZQsWZI+ffqwfPly8uXLB0Dv3r2ZMGECtzyOUrZTk3Tv8JaUKk7OWMbsw12wtLRMWJ47YsSIhInyBQsWULZsWQYMGKD1e0qPDx8+sHXrVlauXElgYCADBgzA29tb68PJ/Pz8aNiwISNGjGDMmDEAbNiwgYcPHyY6mCx+uCs5dnZ23Lp1K9lr9erVw8XFhW7dunHq1Cn09XVzZIAgCHG+mY2NkiRx481zDj734XXkB5RqNepPPsQN5AokoLKpFS2KlMfS2CTddbx48YJNmzaxYcMGIiMj6d27N71796ZkyZIpPuOx0wM379PYNHJArUjjSimlCusXUdSzsaNChQop7iOpXbs2U6ZMoWXLlul+L+klSRLXr1/Hzc2NHTt20KBBAwYNGkSzZs10Mhfz+PFjGjVqxLhx4xgxYgQQF2CqV6/O8ePHqVixIhC3hLhYsWL4+fklBO9PnTp1il9//ZXz588nW49araZVq1bY29sza9YsrdstCN+jb3pjo0pSs+HhZW68eZ5iEsb4168FP8P77QsGlq1NRdMvD3t8/PiRXbt24e7uzo0bN+jUqRMrVqygdu3aaVpK27pV3Cok1607Ke0UtxcipYzDsRGRxEZEMblue8r9kPpkfEBAAPfu3UsytKNroaGhbNq0iZUrVxIaGsrAgQO5c+dOojkKbd2/f58mTZrw66+/MnjwYCDudMc+ffowfvz4hGACcYsQGjVqlGwwgf/PoUiSlOyfj1wux93dPWE+pXXrtOVKEwThy776gCJJEmsfXOLm2xdpyugrIRGjVrHy/gWG2tbFLl/SD0aVSsXJkydxd3dn37591K1bl8GDB9OmTZtU903ET5R/OtcRP1FuZ2fH7d/c0C9dmFr9OxMhU6GQKQAJpaSmjIk5/pcusPiXP1H0uMa6detSfR+7du2iTZs26Vo5lVaSJHH58mXc3NzYvXs3TZs2Zfbs2TRq1EjnqfHv3LlD06ZN+euvv+jbt2/C6/PmzUMmkyUMfcXbuHEjzs7OKZZXoEAB5HI5gYGBFCpUKNl7zMzM2LJlCx07duTatWsULVpUJ+9FEL53X/2Q19lXj9jheyPd6eEBDOV6/Fm9TULSQB8fH9zd3dm0aRMWFhb06dOHbt26JTkDPn5H+afB4+bNm0RHRyfJnvvpRLkkSaxZs4aJEycyf/FCWrRrg0Imx1jPAAOFHsHBwRQvXpyoqCgePHiQ6lBa/fr1GTt2LG3atEn3+07Ju3fvcHd3Z+XKlcTExDBo0CB+/PHHJO9fV27evEnz5s2ZO3cuPXv2TPR648aN8fT0THT++7Nnz6hcuTIvX75MdfFB/fr1mTp1Ko0aNUq1/tmzZ7Nnzx7OnDkj5lMEIR2+yfT1kiQx6epeQmIiNHpeX6agTv6iBB69woYNGwgODqZXr1707t07IRVI/I7yT4NH/LLUz4NHWneUe3l50blzZ1q0aMHcuXMTfTiOHTsWNzc3bGxs8PLySnhdpVYTq1ZhqNAjMDAQW1tbXr169cWd5l8iSRJnz55l5cqV/Pvvv7Rq1YpBgwZRv359rXfHp+b69eu0bNmSxYsX07nz/895j4qKolq1aowfP54+ffokembWrFn4+vqyYsWKVMseOnQotra2jBw5MtX71Go1bdq0oVy5csydO1fzNyMI35lvMqA8eB/EkjtniFYrNS4jOuwjeFykV89eWFtbc/v27SQ7ysuXL58oeKQ2UZ5W79+/p2/fvrx69Yrt27cnfBMPCgqiVKlShIeHs27nNvTLF+fia19iVErkMhlqScIgSkXYJR/cpvyFgUKzUcvg4GDWr1/PqlWrUCgUDBo0iN69e2NqaqrV+0qLK1eu0KZNG9zc3Gjfvn2iaz///DP+/v7s2LEjUUCTJIkKFSqwbNky6tatm2r5ixcvxsfHh+XLl3+xLW/fvqVKlSosWrSItm21PxVSEL4H3+Sk/ImAB1oFEwBDIyOeK5Q4OTlhbGycEDS6d+/O33//TcmSJTNkR3nevHnZvXs38+bNo2bNmqxbt47mzZtTsGBB+g3/iZclTDiT6yMGrx6h+m+1muq/4B9tpCBn/YqMvbyL5kVsaVHELk29CbVazcmTJ1m5ciVHjx6lXbt2rF69GkdHxwztjXzq/PnzdOzYkbVr19KqVatE106ePMnWrVu5detWkvbcvHmT8PBwatdOmsr/c7a2tmzfvj1N7TE1NWXr1q20b98ee3v7ZPe2CIKQNl91D2Wq536CIj9oVYZMpaaSOg+dKjhQoEDaj9vVpXPnztG9e3f69+/PkHGjmXPrGB9jolCkYXOggVxBJVMr+pVxRJ5CUAgMDGTt2rWsWrWKXLlyMXjwYHr27EnevHl1/E5Sd/r0aTp37symTZuSZP99//499vb2uLm50bx58yTPjh07FiMjI/78888v1hMYGIidnR1v3rxJc6CcN28e27dv59y5cxmy0EEQviUp9VB0u2Qnk2kyEZ+EQk7h4kWzLJgA1K1bF09PTy55X2fq+V1EqpVpCiYQ9zPwfvuC7U+uJ3pdpVJx6NAhOnbsSLly5fD19WXLli14e3szfPjwTA8mx44do3Pnzmzbti3ZVPLDhw+nbdu2yQYTlUrF5s2bE03cp6ZgwYIAvH79Os3tGzNmDAULFmTChAlpfkYQhMS+6iEvI7n2zVdGx/DLhEn0O3UNKysrihQpgpWVVZL/trKySvbgJ12xsLCg66xJXHvtn+5nY9Qqzgc9obaFDbL34QlH6JqbmzNo0CDWr1+v9ZyPNg4ePEjfvn3ZtWtXsvMfW7du5fr169y4cSPZ50+dOoWlpSXlyiWf1+xzMpksIQVLfHBJyzPr1q2jSpUq1KtXjw4dOqTpOUEQ/u+rDiiFc+UlMDJMqyTxOXPkZKvbGsyUejx//pwXL17w4sULnj9/zrFjxxL++8WLFxgbGycbbD797/hDr9IrQhmD97sAkGs2l6FUqZi8YQkHfplPt27d2LNnD5UrV9aoLF3au3cvgwYNYt++fdSqVSvJ9RcvXuDs7MzBgwdTDNju7u4pplpJSfwGx4YNG6b5mfz587Nt2zbatGlDpUqVNEpsKQjfs686oDSyLMuttwFaDX0Z6elTJq8FcpmMAgUKUKVKlWTvkySJt2/fJgk6J0+eTPj/Fy9eYGRk9MWgk9yZIxcDn2h1vqMkA2M7ax76+WKWJ68WJemOh4cHw4cP5+DBg1SrlmS4FbVaTd++fRk5cmSy1wHCw8PZt28fs2fPTlfd8T2U9KpZsyaTJ0+mS5cunD9/PtOTbQrC1+yrDijWuU0xMcgRdxSsBvRkcpoULpviZPanZDIZZmZmmJmZpfjNX5Ik3r17l6hX8+LFC06fPp0oCBkYGCQJNu8cShBjpN2UlqG+Pv7RYZiRV6tydGHz5s2MGTOGI0eOUKlSpWTvWbRoEeHh4UyaNCnFcuJ7NmkduopnZ2fHzp070/VMPBcXF86cOcO4ceNYuHChRmUIwvfoqw4oMpmMdsXs2fDoika9lIiP4fiduIzUp6xOls3KZDJMTU0xNTVNlH/qU5IkERISkiToRKhikKHdJkWVJPEhNkqrMnRh/fr1TJo0iePHj1O+fPlk77l79y7Tp0/n8uXLqaa6Ty2zcGrieygp5fRKjUwmY82aNVStWpV69erh5OSU7voF4Xv0VQcUgOrmxXkS9oYLQU/SFVQM5AqcCpZnzLiBHN73L25ubpmyqU8mk5E/f37y58+Pvb19wuvjL+8iVNtgIEmoU1kGnhlWrVrF77//zsmTJylbtmyy98TExNCzZ09mzpyZanqZ169fc+HChTTvKfmUhYUFarWa4OBgjVLH5MuXj23bttGqVSsqV66MjY1NussQhO/NV71sOF5Xm6r8YFkaA/mXNyAqZHKMFHq4lG9Ik8o1E5IDVqxYkePHj2dCa5OXQ0/7vQ9ymZycOjjvRVNLlixh2rRpnDp1KsVgAvD7779jZWXFwIEDUy1v69attGnTRqOFDvErve7evZvuZ+NVr16dX3/9lS5duhAVlfU9P0HI7r6JgCKTyehoXZmRdg2wy1cIPZkcPVnit2Yo18NIoU8jyzL8XrU1JU3i9p0YGRnxzz//sHbtWvr27cuYMWOy5MOjomnhJG1OrxhlLEX0kk74Z4Z//vmHuXPncubMGUqVKpXifefPn0/YZPmloaiNGzfSu3dvjduk6cT8p0aMGIG1tTU///yzVuUIwvfgqx/y+lTpvAUpnbcgIdERXH3tx5uoj0SplOTWN6R4blMqmxVBP4VeTJMmTbh58yaDBw+mRo0abN68OcXx/4zQwLI0JwIeaF6ABJF+gdi1K0W7du3o27cv9evX13m6+eTMmjWLlStXcubMmVRTwYeFhSWcbPmlSfYHDx4kHAesqfilw9qQyWSsXr2aKlWqsG3bNrp27apVeYLwLfsmeiify2eYk2ZFbOlZqgYDyjrSxaYqNcyLpxhM4pmamuLh4cGoUaNo0KABrq6uqNWanQefXvkNjSllovlufUOFHpPa9eTBgwdUqlSJUaNGUaJECaZOncqTJ0902NLEpk+fzpo1a74YTABGjx5Nw4YNadeu3RfL3bRpE927d9f4bHrQTQ8FwMTEhB07djBixAgePXqkdXmC8K36JgOKNmQyGf379+fy5cts2bKFFi1a8PLly0ypu3OJqhhosPtfXybHOo8ZZUwKUrBgQUaPHs3NmzfZs2cPYWFhODg4ULduXVavXk1YWJhO2ipJEr/++itbt27lzJkzFC6c+gmTe/bs4fTp0/zzzz9pKlvT1V2f0kUPJV6VKlX4448/xHyKIKRCBJQUlCxZknPnzlGrVi0qV67M7t27M7zOwsZ5GWpbN02LC+IpkGGR04RhtvWSzElUqlSJBQsW8OLFC8aOHcu///5LkSJF6NWrF8ePH0el0mxDqCRJTJgwgX379nH69GksLCxSvT8wMJCffvoJd3f3NKWAuXjxIjly5NB6p3+hQoWIiYkhODhYq3LiDR06lNKlSzNq1CidlCcI3xoRUFKhr6/PH3/8we7du/n5558ZOHAgHz9qtokyrWzzFWJ0hUbk0jPEMJWzTtRKJaqYWF56+jC2QqNU7zUwMKBdu3bs3r2bx48fU6NGDcaPH0/x4sWZMmUKDx8+THP7JEli9OjRnDhxgpMnT34xqaYkSQwYMICBAwfi6OiYpjrieyfa7g2SyWQ67aXIZDJWrlzJiRMn2LJli07KFIRviQgoaeDo6Ii3tzcqlYrKlStz5cqVDK2vRB4zZtfqQN/SDqiCQpCpJYwU+uRQ6GOk0EMdoyTmjj8BK/ZyYPxslixclOayCxQogLOzMzdu3ODAgQNERUVRr149HB0dcXNz4/379yk+q1arGT58OJcuXeLEiRNp2rfj5uZGYGAgU6dOTVP7YmJi2LFjBz169EjrW0qVruZR4uXJk4cdO3bg7OzMgwdaLKIQhG+RJEkp/lO1alVJSGzHjh2Subm5NG3aNCk2NjZD61IqlZKZmZl0x/eR9DTsjfTwfZD04mOIdOnqFcnS0lIyMzOTOnXqJBkZGUnv3r3TuJ6YmBhp//79UqdOnaQ8efJI3bp1kw4fPiwplcpEbRkwYIDk6OgohYaGpqncBw8eSKamptLdu3fT3JY9e/ZI9erVS/d7SMk///wjDR8+XGflxVu+fLlkb28vRURE6LxsQcjuAE8pmZgheijp5OTkxPXr1zl9+jT169fn6dOnGVaXp6cnFhYW2FqXpHhuU0qZmFPYOC+1qtegUqVK1KhRg7x586Kvr8+PP/6ocT36+vq0bt0aDw8PfH19qVOnDr/88gtFixZl4sSJ+Pj40K9fPx4/fsyRI0fIkyfPF8tUKpX07t2b3377Lc1p50HzVCsp0XUPJd7gwYOxs7PD2dlZ52ULwlcruSgjiR7KF6lUKmnu3LmSmZmZtG7dOkmtVuu8jqlTp0rjxo1L9tqlS5ckKysrKW/evNKKFSskhUIhXbp0Saf1+/j4SGPGjJGMjIykPHnySAsWLEhzT+j333+XmjZtKqlUqjTXFxISIpmYmEghISEatjipFy9eSObm5jor71NhYWFS6dKlJXd39wwpXxCyK0QPRbfkcjk///wzx48fZ/bs2XTt2pV3797ptI5Dhw7RokWLZK/VqlULW1tb7O3tefHiBfb29nTp0gVJh7m8SpUqhZ+fH/Xr12fDhg1cvHiR4sWL06VLFw4ePIhSqUz2uatXr7J06VLWrFmTro2VHh4eNG7cWKenSVpaWhIVFcWbN290Vma83Llzs2PHDkaPHs29e/d0Xr4gfHWSizKS6KGkS0REhOTs7CwVKVJEOnHihE7KDAoKkkxMTKTo6OgU7zl//rxkZWUlmZmZSXfv3pUUCoU0e/ZsndQfFRUltWnTRmrbtq0UFRWV8Pq7d++kZcuWSTVr1pQsLCyksWPHSrdv3064/vHjR6lUqVLS9u3b011n/fr1pd27d+ui+YnUqlVLOnPmjM7Ljbdy5UqpfPnyUnh4eIbVIQjZCSn0UGRSKt9oq1WrJnl6emZedPvKHTlyhP79+9OjRw/+/PNPrQ5ncnd3Z/fu3ezatSvV+xo2bEhoaCj9+vXj0aNHrFixgqCgIExMTDSuOzIyko4dO2JsbMzmzZsxMEg+4eT9+/dZv349GzZsoFChQvTt25cbN24QGxuLu7t7uup89uwZVapUISAgQOeHWg0cOJBq1arx008/6bTceJIk0adPH/T19VmzZk2G1CEI2YlMJrsuSVKSU/HEkJcONWvWDG9vbx49ekTNmjW1mgxObbjrU1OnTuX169fMmzePWbNmYWxsrNWS2/DwcNq0aUPevHnZunVrisEEoGzZssycOZNnz54xY8YMPDw8WLduHaGhoezfv5/Y2Ng017t582acnJwy5ITEjJqYjyeTyVi2bBmXLl1i/fr1GVaPIGR7yXVbJDHkpRW1Wi25ublJpqam0qJFi9I9Ya9UKiVTU1Pp+fPnaaqrbt26UqlSpaRt27ZJ//77rySXy6WLFy8m3BMRGy0FRYRJAR/fS++jIyRVCu0JCwuT6tWrJ/Xp0yfRkuG0CA4OliwtLaX9+/dLbm5ukqOjo2Rubi6NHj1aunnz5hffg62trXT+/Pl01ZlWhw8flho2bJghZX/q9u3bkpmZmeTj45PhdQlCVkIMeWW+R48e0bNnT0xNTVm7du0XU5TEu3TpEkOGDOHWrVtpuv/48eP07dsXCwsLrl27Rp06dfB/9oyD3pc5GnAP37A36MnkyGRxpzoaKfRpXLgMdSxKkks/rkcQGhpKy5YtsbW1ZcWKFemaTJckiU6dOlGiRAnmzp2b8PrDhw/ZsGEDGzZswNTUlL59+9KjR48ku+u9vLzo2LEjvr6+Ojk583PPnz+nevXqBAYG6rzsz61du5a5c+dy9epVjc5xEYSvgRjyygKlSpXiwoULVK1alUqVKrFv3740PZfW4a54jRo1omjRorx8+ZIzZ87gunktPyyaxNJbp3gU+hqVpCZarSRKpSRWreJDbBT/PvNhwpXd7PT14u27dzRp0oSKFSumO5hA3JG/jx8/5q+//kr0eunSpfnzzz/x8/Nj7ty5eHp6UqpUKdq3b8+ePXuIiYkBdJdqJSVWVlZERETw9u3bDCn/U/369aN69eoMGzZMpyvuBOFrIHoomeT8+fP07t2bpk2bMn/+/FS/vVarVo158+ZRv379NJd/+PBhBgwYQM0OLbDs3oTYNB6HrC9TEOR9j8JPQvln/vx0f6g/ffqUGjVqcOLEiURHGqckLCwsYa7l/v37dOvWjS1btnD27Nl0bYBMr1q1ajFnzhzq1q2bYXXECw8Pp0aNGvz888/0798/w+sThMwmeihZrE6dOnh7exMZGUmVKlVIKVAHBQXx+PHjNCdSjNesWTNKVK9Ivna10xxMAGIlFfntbKg5vEe6g4lKpaJPnz5MmDAhTcEE4nJh9e/fn7Nnz3Lp0iVCQkIICwuja9euzJ8/n6CgoHS1Ia10mSTyS4yNjdmxYwcTJkzg9u3bmVKnIGQHIqBkIhMTEzZs2MC0adNo2bIlf/31V5IU8keOHKFx48bo6+unq2yZTIaDSx/0jDRYJaWn4EKQL0ER6TsrZe7cuSgUCkaPHp3+OgEbGxvkcjmzZs1i4cKF3Lp1izJlytCmTRt27txJdHS0RuV+LiQ6AvMmNfDOp2TOzWMs8jnNTl8vgiM/6KT85Nja2jJv3jy6dOmS4RmqBSG7EENeWeT58+f06dMHpVKJu7s7xYsXB6Bbt240adKEAQMGpKu8VxGh/OV1OF29k08pkFGnkA09StZI0/1eXl40bdoUT09PihUrplGd4eHhWFlZcf/+/YQjgT9+/MjOnTtZt24dt2/fplu3bvTt25eqVaumuwf1ODSYf5/d5lHoa9RqNepPHlfIZMhlcormyk/rouWxzVdIo/fwJQMGDCA6Ohp3d/cMmyMShMwmhryymSJFinDixAnatm1L9erV2bhxI7GxsRw7dozmzZunu7wTAQ9QaXFcsQqJS0FPiVYln07lU1FRUfTq1Yv58+drHEwA9u7di4ODQ6Lz5XPlysWPP/7IqVOn8PT0xNzcnK5du1K+fHnmzJnDq1ev0lT2yYAHuPqc5N77QJRS4mACcavdYtUqnoQFs+zuWfb63cyQSfRFixZx8+ZNVq1apfOyBSG7EQElC8nlcsaNG8fRo0eZMWMGLVu2xNLS8ovH6SbH++0L1Gj3gSiXyXn64cs5ryZNmoSdnZ3WWYG/lFm4ePHiTJ06lcePH7N8+XLu37+Pra0tLVu2ZPv27SkexXvq5UN2+3kTk8beWoxaxfGA++z31/18R86cOdmxYweTJ0/m5s2bOi9fELITEVCygcqVK+Pp6UlISAjPnj3jzJkz6S4jWpX2XekpkwiPjUn1jhMnTrBjxw6WLVum1RBOUFAQly5don379l+8VyaTUbduXVavXs2LFy/o0aMHbm5uFC5cmKFDh3LlypWE3sWzj+/Y+dQrzcEkXoxaxbGAe9wL0f1elbJly7JgwQK6dOnChw8ZN28jCFlNBJRsImfOnEiSxJQpU+jevTsTJ05M2KeRXYSEhNCvXz9Wr16dptMaU7Nt2zbatm1Lzpw50/WcsbExvXr14vjx43h5eWFlZUWvXr0oV64cf//9N7sfXkep4dBfjFrFgWc+Gj37JT179qR+/foMHjxY7E8RvlkioGQTgYGB+Pr6Mnr0aLy9vbl79y61atVKc1p0I0X6VoUlT5awcz45w4cPp127djRr1kzrmtzd3bUeMitatChTpkzh4cOHrFmzBt8Xz7gdEoCkxdCf38e3vInKmFVZrq6u3L17lxUrVmRI+YKQ1URAySYOHz6csFzY3NycvXv3MmTIEOrWrcvSpUu/+K22illRFFquIpIkiRJ5zJK9tmXLFry8vJg1a5ZWdUBcluKAgAAaNmyodVkQNyTm6OhI98kuGBkaaVWWWpI49+qxTtr1uRw5crBjxw5+/fVXvLy8MqQOQchKIqBkE5+nW5HJZAwZMoQLFy6wZs0a2rRpk+qmv4aFSyOXaf7HqZDJqG1hg75ckeTa8+fPcXFxwd3dPd1DVMnZtGkT3bt3R6FIWpc2XkWEEStpvtINQCWpeRkRqqMWJVW6dGkWLVpEly5dCAtL374fQcjuREDJBpRKZYrLhcuUKcPFixext7enUqVK/Pvvv8mWUTBHHooY59O4DXKZjIaWpZO8rlar6devH87OzlSrlmTZebpJksTGjRvp3bu31mV9LlKpi4UJEKWTBQ4p69atG40bN2bgwIFiPkX4poiAkg1cvnyZYsWKYWlpmex1AwMDZsyYwbZt2xgxYgTDhg0jIiIiyX29StXAUK6X7voN5Ap+sCxDgRy5k1xbuHAhERERTJw4Md3lJufixYsYGxtTsWJFnZT3qZx6KZ/dkhXlpOaff/7h0aNHLF26NMPrEoTMIgJKNpDW7ML16tXD29ub0NBQqlatyo0bNxJdL2ycl+F29TFIR1AxkCuoYlaEjsUrJbl2584d/vrrL9zd3dHTS3+gSk78ZHxG7BovkisvBskM2aWHvlyuVU8vrYyMjNixYwe///47169fz/D6BCEziICSDRw8eJCWLVum6d68efOyadMmfv31V5o3b87ff/+dKB9YmbwFGV+xCaaGxqn2VgzkCvTlCppb2dK3tEOSD/iYmBh69erFzJkzsbGx0eyNfSY6OhoPDw+tTpRMTbUCxbTc2gmSBHULldRJe76kZMmSLF26lC5duhAamnHzNoKQWURAyWKvXr3C39+fWrVqpeu5Hj16cO3aNQ4dOkSjRo149uxZwrUiufLxV/W2jCzfAPv8hZHLZCijY5Bilcgk+Bj4htIf9ZhbsyOtilVItrfw22+/UbRo0XTnFEvNoUOHKF++PEWLFtVZmZ8yUuhTs0Bx5Gje+ymXzwITgxw6bFXqOnfuTIsWLejfv7+YTxG+eiKgZLHDhw/TpEkTjYaUihUrxsmTJ2nevDnVqlVjy5YtCddkMhmlTMwZblefRY5dKO4VxI4+4xlX3BGP3uO4sf84wVEfeRkeysfYxFl9z507x7p161i5cqVOh6YyajL+U02tyqGXzgPC4hnIFbQqUl7HLfqyefPm4efnx6JFizK9bkHQJZFtOIt17tyZVq1a0bdvX63KuX79Oj179qRatWosWbIEExOTRNejoqIobFOczpNdiClRAJmeglzGuQAJpVpNsdz5aWZlSzG93FSuVImFCxfSpk0brdr0qffv31O8eHH8/PzImzevzspNjteb56x+cDFdmZcN5Ao6l6hCvUKlMrBlKXvy5AkODg4cOHCA6tWrZ0kbBCGtRLbhbEipVHL8+HGNsgt/Ln6SPnfu3FSsWJFz584lXFNLavYG+OC0aS6xpQuhnysnekaGRKliiVIpUUpqnoS9YfWDi4y/spsmPTrpNJgA7NixgyZNmmR4MAGobFaEAWUcMZArkH1hFEmODH25gq4lqmZZMIG4s2GWLVtG165dCQkJybJ2CII2REDJQpcuXaJEiRJYWFjopLycOXOybNkyFi9eTJcuXZgyZQpR0dEsvXuW84FPQCFHzyjlJbHRKiWyHAYYtKjOrbcBOmlTvC9lFta1ymZF+KVKCz54P0SmlpIsUIhflFC9QDEmVmpKnUyaiE9Np05xgbxfv35iPkX4Kokhryw0adIkFAoFf/75p87LDgoKon///hjWr0Ahh0ooSd8OcgO5gp/tG1M8t3ZJIAH8/f2pVq0aAQEBGBhk/B6PeD4+PjRp0oT7jx/h8yGIlxFx80XGegYUzJmH6gWKZcqek/SIiYmhTp06dO/eXeOTMAUho6U05KWbzQWCRg4dOsSSJUsypOyCBQuycvsmpnkeSHcwgbjMu5seX2VK5S/vj/mSzZs307lz50wNJhB3RPGIESMwMc5FbeNcmVq3pgwMDNi2bRs1a9bE0dGRmjVrZnWTBCHNxJBXFgkICOD58+cZ+oFx4uVDZBqueIK43Fgvw7XbHyFJkk4yC6fXixcv2LdvH0OHDs3UenXB2toaNzc3unbtyrt377K6OYKQZiKgZBFtlgunRZQqliuvn2p1iqNKreZEwH2t2uHl5UVUVBQODg5alZNerq6u9OnTh/z582dqvbrSvn17OnbsSN++fcV8ivDVEAEli6Q13YqmfMPeoNAi+zCAGolb77SbnI+fjM+IVCspCQ0NZc2aNV/9HMTff//N69evmTdvXlY3RRDSRASULBAbG6uz5cIpCVfGgNaJSLTLvKtUKtmyZUumD3etWLGC5s2bU6xYsUytV9fi51PmzJnDxYsXs7o5gvBFIqBkgYsXL1KyZEkKFiyYYXVok34kMc3LOXnyJEWLFqV06aRp8TNKdHQ0rq6ujBs3LtPqzEjFihVj1apVdO/enbdv32Z1cwQhVSKgZIFDhw6lORmkpoz1DdAmGMTLoaf50cKZvfcE4laU2dnZUalSpUytNyO1adOGLl260KdPH9Rq7Q4QE4SMJAJKFjh48GCGzp8A2OQpgKTl6YUKZFQ10yyRY3h4OPv27aNr165atSE91Go1c+fOZfz48ZlWZ2aZMWMG79+/Z86cOVndFEFIkQgomezFixcEBARQo0aNDKvj8ePHjB09hlu7jyEp057P6nPR0dEcdl2t0VDLnj17qF27Nubm5hrXn14HDx7EwMCARo0aZVqdmUVfX5+tW7fyzz//cP78+axujiAkSwSUTHb48GGaNWum8/PUJUni2LFjtGnTBgcHB3LmzMnCYRMwMNB8yMo4Ss3OdRuxsLCgS5cuvHjxIs3PZkZm4c/NmTOH8ePHZ+qKssxUpEgR1qxZQ48ePQgODs7q5ghCEiKgZDJdD3dFRESwYsUKypcvz+jRo2nbti3+/v7MnDkT+xKlaGxZVqNTDGMjoqgYlYO3b9/y999/c/ToUaytrWndujUPHjxI9dmgoCAuX75M27ZtNX1b6XblyhX8/f3p3LlzptWZFVq2bEmPHj3o3bu3mE8Rsh0RUDJRTEwMJ0+epFmzZlqX9ezZMyZMmECxYsU4cOAACxcu5Pbt2wwaNIicOXMm3NeueEWqFSiWrqBiKNejS4Fy/DluMr///jujR48mODiYefPmceHCBSpUqMAPP/zAtWvXCAkJ4cmTJ9y/f5+HDx/i7+/P9u3badeuXaJ2ZLQ5c+YwZsyYDNsomp38+eefhIeH8/fff2d1UwQhEZEcMhOdOnWKCRMmcPXqVY2elySJCxcu4OrqysmTJ+nTpw8jRoz44hG9kiRx5MVdDjzzQYaMaLUymZtAHRtLIZP8DClXl8LGeQkODqZTp07kz58fd3d3cufOTUxMDKtWreLo0aO0bNkSMzOzRDm6FAoF0dHRGBgY4ODgQL58GX8++6NHj3B0dOTp06fkyvV15OzS1osXL6hWrRrbt2+nXr16Wd0c4TsjzkPJBjTdHR8dHc2GDRuoVq0a/fr1o169evj5+fHPP/+k6bx3mUxG8yJ2zK3ViS42VTE3yo0M0JcrUMXEopDJsM9nyfGJ8xhVojaFjfMCUKBAAY4fP06BAgWoXbs2T58+RaVSUbZsWYYMGYKlpWWShI8qlQo9PT3UajWXL1/Gx8cnw1OHzJ8/n59++um7CSYAVlZWrFu3jh49evD69eusbo4gAKKHkqkqVKjAypUr03x+fGBgIMuXL2fFihVUqFABFxcXWrRogVyLhI/xYtUqIpQx2JWz5cq5CxS2tKRt27Z069aNHj16JLpXkiQWLVrE0qVLE9KApDVIKBQKChUqRMWKFTNksvz169eUKVOG+/fvZ+hG0exqypQpXLt2jUOHDul8oYcgpET0ULLY8+fPefXqVZqOd/X09KR3796UK1eOwMBATpw4wdGjR2nVqpVOggnE9U5MDHKQL2cu3r55A0Dbtm3Zt29fkntlMhkjR45k3rx5xMbGpqvHoVKpePXqFX5+fjpp9+cWL15M165dv8tgAvDHH38QExPDjBkzsropgiACSmY5dOhQqsuFlUol27dvp3bt2nTq1IkKFSrw5MkTli9fjq2tbYa1q0CBAglLUFu1asWRI0eIiYlJcl9wcDByuVyjSW+VSsWDBw90PvQVHh7OsmXL+Pnnn3Va7tdET0+PzZs3s3TpUk6dOpXVzRG+cyKgZJKU5k/il+VaW1uzePFiRo8ezZMnTxg/fnympF43NzdPGIMvVKgQpUuXTnQefbwnT56gUmm+SfLDhw/MnDmTgADdHS28Zs0a6tatS6lSWXcWfHZgaWnJhg0b6NWrF0FBQVndHOE7JgJKJkhuubCPjw+DBw+mZMmS3L9/n71793L27FmcnJwydenrpz0UiMsbtX///kT3REZGEhISolU9OXLkwNjYmHLlyjFw4EAePnyoVXlKpZL58+d/k2lWNNGkSRMGDBhAjx49tAr8gqANEVAywfnz5ylbtiz58+dn3759NGrUiCZNmmBlZcX9+/dZt24dVapUyZK2fdpDgf/Po3w6PPXhwwedzN1YW1uTK1cuzp07R82aNencuTPXr1/XqCwPDw+srKzSvMDhe/Dbb78hSRLTp0/P6qYI3ykRUDLBnj17yJcvH6VLl2b69On069cPf39/pk6dmuWTyZ/3UCpUqIBKpeLu3bsJrymVSp3MfygUCnx9fXF2diZnzpzcu3ePli1b0rRpU06dOpXmOiRJYvbs2aJ38hmFQsHmzZtxc3PjxIkTWd0c4TskAkoGevToEc7OzixZsgSVSoW7uztXr16lV69eSfZvZJUCBQok6qHIZLJkV3vpIs2HXC7HyMiI4cOH8+TJE3766Sf09fUJDg6mX79+1KpViz179nyxrpMnTxIZGUmrVq20btO3xsLCAnd3d3r37s2rV6+yujnCd0YEFB2LT9LYunVrateujUqlwsTEhCNHjuDo6JjtEheam5sn6qFIkkTVqlVZu3YtY8eOpX79+jRp0oSoqCit6zIyMkr03yNGjODx48f0798/YTnylClTKF++POvXryc2NvnTImfPns24ceN0toT6W9OoUSOGDBlCjx49UCqTyYogCBlE/EbqyKdJGseMGUP79u3x8/OjQoUKOt0/omuSJOHr68svv/xCs2bNMDMz45dffsHPzw9DQ0N+/fVXzp07p/WKM4VCQfHixZO8bmRkxMiRI3n8+DG9evXi/fv35M+fn6VLl1KyZEkWLlxIREREwv03b97k9u3b9OzZU6v2fOt++eUX9PT0+OOPPxJeC3wTzsFzvqzfe4dVO2+xfq8P+08/ISDoQ4ZnMxC+D2KnvJaePXvGkiVL2LVrF7179040uS6TyQgJCcHQ0JCOHTtm+U7mt2/f4unpybVr17h27Rqenp5ERUURFhbGpEmTqF69OtWrV8fCwoLOnTvTsmVL+vXrB8S9zzt37mi8gkgul9OkSRP09VNPpx8ZGYmbmxuzZs2idOnSKBQKfHx8GDFiBCNGjGDkyJFUqFCBCRMmaNSO70lQUBBVqlRhwdKNfFSb8SE8FpVazee/8vp6cnIY6VHLvhB2Jc2yXS9ayH5S2ikvAooGJEni/PnzuLq6cvnyZaZPn465uTkymSzZ8X+5XI5MJqNEiRKULl06U35hP378yPXr1xMFkODgYKpWrZoQOKpXr06RIkUwMjIiPDw80bzOhg0b2LNnD7t27QLiNiceO3ZMoyEUuVyOlZUV9vb2aX4mMjKSFStWMHv2bOzs7DA2Nub06dNER0dz48YNypUrl+52fG8kSWLj7ksEvFVjYGD0xfv19OSULpaPpo7FkctFUBFSJgKKDkRHR7N161ZcXV35+PEjo0aNolSpUsTExKRpyEChUGBmZkbVqlV1OgQWHR3NzZs3EwLHtWvXEobbPg0eZcqUSbbeQoUKcf36dSwtLRNee/PmDTY2NgQFBSXMfbx7947Lly+na4JeJpORK1cu6tSpo1EPLX4ocfbs2RgZGWFkZERQUBCdOnVi/Pjx3/2mxtScu/4Cr3uvUarS/uelp5BTxjo/TR2LiZ6KkCKRy0sLgYGB/PbbbxQrVozNmzfz559/cvv2bcqWLZvmYAJx3/LfvHnDrVu3NB6zViqV3Lp1i9WrVzN06FCqVatGvnz5GDx4MN7e3jg6OrJx40bev3/P5cuXWbRoEX369KFcuXIpBrHPV3oBmJmZYW9vnyidR/78+alevXqaA4NcLid37tw4ODhoPNyXM2dORo8ejaenJ0FBQbx9+5batWujUChwdHSkS5cueHl5aVT2t+x54Id0BxMApUrNA793PH72PmMaJnzTvv3TiLTg6emJq6sr//77L926dePkyZMJebXu3r1LVFRUugNDfLJEKysrzMzMUr1XkiQeP36cqOfh7e2NlZUV1apVo3r16vTu3ZtKlSppdZjV5yu94sUvH/40ZUyBAgWoU6cODx484PXr18n2VhQKBXK5HGtra2xsbHQyd7RhwwY6d+7MsmXLWLZsGXPmzKFOnToUK1aMNm3aUL58eSZOnEj9+vXFN2vgms+rdAeTeEqlmqu3X1GqWMafZSN8W8SQ12diY2PZvXs3CxYsICAggBEjRjBw4MBEB0VpM58Qz9zcnBo1aiT8vyRJvHjxIlHwuH79Onny5Ek0bFW1alVMTEy0eo+f6969O61bt06ycur+/fs0btyY58+fJ/shHR0dzbZt25AkCQsLC/z8/KhcuTJFixbF3NxcZ8N6UVFRWFtbc/ToUSpUqADEJYZcvnx5QmCxt7dn48aNmJqaMnHiRNq0aZNtV9ZltA/hMazZfRuVSvOVW3oKGT1b22KaN4cOWyZ8K1Ia8hI9lP+8ffuWlStXsmTJEqytrfn5559p165dsnm1AgMDtV5mGRwczMGDBxNNmqvV6oTAMXr0aKpXr465ublW9aRFSj2UMmXKkDNnTry8vJJNDWNoaMjp06epWbMm5cqVY8CAAbx48ULn7du4cSOVK1dOCCYAxsbG/Pzzz/z0008sW7aMuXPnUq9ePRwcHJg2bRqTJ09mwoQJdO/e/Ysry741933fgpargFVqiduPgmlQvahuGiV8F77Pr3Cf8PHxYdCgQZQsWZIHDx6wb98+zp49S6dOnVJM0vj8+XOtE/BFRUVx9OhRIiIi6Nu3L1evXiUoKIgDBw7w+++/06pVq0wJJpA0/Uo8mUyWbLLIT924cYMqVaqQN29eQkNDdd42tVrN3LlzGTduXLLXjY2NGTt2LE+ePKF69er8/ffflCpVitGjR7Nu3TpKlSrF4sWLE+1lSY0kSQS/i+Dpi1Ae+Yfw7FUYkVHJb7DMrt5/iEal1i6iSBK8D4vWUYuE78V32UNRqVQcOHAAV1dX7t27x9ChQ3nw4EGaP8Cjo7X/RTM0NGT48OHZYpWSubk5165dS/Za27ZtGTNmDL/99luSa9HR0Tx48IAKFSpgYGBAREQEKpVKp/tt9u/fT+7cuWnQoEGq9xkbGzNu3DiGDh3K0qVLmTJlCg0bNmTw4MFs27aN6dOn4+zszLBhw5I95z4mVsU937d4+gQSEaX8b4gv7kNZpZKwtjKhmp0FhQoYZ/s5mhilbrINxyq1T7cjfF++q4ASGhrK2rVrWbRoEaampri4uNC5c+d059XS1a5iXeTH0oWUeigAtWvXxs/Pj4CAAAoXLpzomo+PD6VKlUpYVpw7d27CwsKS/cDWVHyalbR+iOfKlYvx48czbNgwlixZgouLC40aNWL16tV4eHhQsmRJBgwYwOjRoylUqBAAfgGh7D/9BEj5Q/Txs/f4vwyjQP4cdGhUCkOD7PGrExUVxePHj3nw4AEPHjzg/v375CxQgdL2P2hdtpFh9niPwtfjuxjyik/SaG1tzeXLl9m4cSNXrlyhZ8+eGiVp1MWYvFwuzzYJIj9PYf8pPT09WrRowb///pvk2o0bN6hcuXLC/5uYmOh02OvChQsEBgbSsWPHdD+bK1cuJkyYwOPHj7G3t0/IF7Zjxw6io6Oxs7NjyJAhnLl0l32nHhOrVH/xG3msUk3gmwg2/nuPqOjMy5ElSRKBgYGcOXOGFStWMGbMGFq2bImNjQ158+bFycmJDRs2EBoaSoMGDWjRpA56Cu16UTExURz5dwcrV67k2bNnOnonwrfum/0KEp+k0dXVlWvXrjFw4EBu3bqFlZWV1mVbWFgQGhqqdQ/D1NRU67boQmo9FIg7dGvDhg0MGTIk0evx8yfxdB1Q5syZw5gxY7Q6cCx37txMnDiR4cOHs3jxYrp160bTpk05cOAAR09e4rLPO/TTsIs8nlot8TE8hp3HHtKjVTmdDn992tu4f/9+ol6Hvr4+ZcuWpUyZMpQtW5YGDRpQpkwZSpQokegLjo+PD7/88is1W4zC0MhY47Yo5Arksa85deoWU6ZMwczMjGbNmtG8eXPq1atHjhxi9ZeQ1DcXUMLDw3F3d2fhwoUoFApcXFzw8PDQ6S9AkSJFePDggVZl5MqVizx58uioRdpJbmPjp5o3b86gQYMIDw/H2Pj/H1JeXl706NEj4f91GVDu37/PxYsX2bx5s07Ky507N5MmTWLEiBEsWrSIdu3aMWLKynQFk3gqtcS70Cj8AsKwtkrfEm5JkggKCkoSMB48eEBAQADFixdPCBwNGjTgp59+okyZMql++YiKimLjxo3MnDkTf39/ChQoQL3mIcjlxmjynUcmg9yG0Xh7efL06VNGjRpFzZo1uXz5Mn/++Sfe3t7Url2b5s2b07x5c8qUKZPt55WEzPHNBBR/f3+WLFnCmjVrqFOnDkuWLKFBgwYZ8hfdwMCAggULanzeRHR0dLb6BcybNy+RkZFER0djaGiY5LqJiQk1atTg2LFjtG/fHojbsX/79m0qVqyY6L7379/rpE3z5s1j2LBhWm3YTE7u3LmZPHkyvfsOZvuRJxqXE6tUc83nVYoBJb63kVzgMDAwoEyZMol6G2XLlsXa2jpdw6kPHjxg1qxZbNmyBaVSSY0aNVi7di1169blQ3gM6/fe0agXrZDL6dy6Jj/1OouXlxeurq7MmTMHJycnli1bhpWVFSdPnuTw4cPMnz8fmUyWEFwaNmyo831Swtcj0wJKbGwsL1++JDw8nNjYWAwMDMidOzeFChXSeFXQp0kaT506xY8//sjVq1cpUaKEjlufVNmyZQkODtZoc6Oenh5du3alZ8+e/PHHHzr/0EwvmUyGmZkZwcHBKQ4Jxi8fjg8o9+/fp3Dhwol6WbrqoQQGBrJz506te4GpefQ8AoVCgTara1+9Ceex7wtePEs6TPV5b+OHH35IU2/jS6Kjo9m1axezZs3i3r17KBQKfvzxR6ZMmUKRIkUS7suTy5AOjUqx6/ijdObyktGqXomEDY2VK1dm3bp1BAUF4ebmRtOmTSlXrhwuLi4sW7YMuVzO/fv3OXz4MCtWrKBPnz5UrlyZ5s2b06xZMypXrvzdbjD9HmX4TvmwsDCePHnCq1evkMlkifZvxAeSokWLYm1tneYP1s+TNDo7O/Pjjz+SO3durdqaXu/evePKlStp3pMiSRJv377FwcGBfPny4eLigqenJ6tWraJ+/foZ3NrUVapUibVr1yaaZP+Ur68vDg4OvHr1Crlcjru7OwcOHGDr1q0J9wwbNgxbW1tGjBihVVsmT55MaGgoS5Ys0aqc1KzdfZsQLfdZREdFcGTXYpThLxMCR3yvI729jS958uQJS5YsYdWqVahUKkxNTZk0aRJ9+/ZNdTg3IOgDu088Qq2WUKayc14hlyGXy2hd3ybVYbyYmBh27NiBq6srb9++ZcSIEfTv3z+hVxIREcHZs2c5cuQIhw8f5t27dzRp0oTmzZvTtGnTTNtbJWSsLMk27O/vz507d5AkKdWltjKZDLlcTtWqVVP9C/fq1SuWLVuGm5sblSpVwtnZmebNm2fpN6DQ0NCEoJJSYIl/f3ny5OHu3bvMmDGDc+fOYWVlxb59+xg2bBht2rRh1qxZWTav0qRJE8aOHUuzZs1SvKd8+fKsWrWKWrVqMXr0aCwsLBKdSzJp0iRy5crFlClTNG7Hhw8fsLa25sqVK9jY2Ghczpcs3+5NRKR2K7XkcqhfrQiVyxXUUasSi42NZe/evbi6unL9+nUAHBwcmDJlCj/88EOah00jo2K5/egNN+4GEatUo1ZLqNQScrkMhUKGXCajcllz7MsUIFfOtK08lCSJy5cv4+rqytGjR+nZsycjR46kdOnSie7z9/dPCC4nT57ExsYmYXisVq1a310Wg29Fpqdeefr0Kffv30/TGK4kSahUKjw9PalWrVqSoHLt2jVcXV05cOAA3bt359SpU9nmPAwTExMaN27M69evefz4MWFhYYkCnFqtxsLCghIlSpA3b15q167N+/fvadKkCWfPnqVt27bUq1eP8ePHU758eZYtW5YlZ6WnlH7lU/HJImvVqoWXl1eSwGFiYsK7d++0aseqVato2LBhhgYTALkO5rBkxM036Jqfnx9ubm6sWLECuVxOZGQkAwcOxMXFRaOfSw4jfWpUKET18hb4vwwjJCyK6BgVBgYK8uYypHhhk3SffyKTyXBwcMDBwYEXL16wbNky6tSpQ/Xq1XFxcaFJkybIZDKKFSvG4MGDGTx4MLGxsVy+fJnDhw8zevRonjx5wg8//JAwPFasWLF0vzche8mQHoom52bEUygU1K9fH319fXbt2oWrqysvX75kxIgRDBgwQKeb5jJCZGQkUVFRqFQq9PT0MDY2TvZb2C+//MKhQ4c4efJkwnDByZMnGTRoEA4ODixYsOCL2Yh1adSoURQtWpQxY8akeM+lS5cYPHgwN2/eJF++fDx58iRRG5ctW4a3tzcrVqzQqA2xsbHY2Niwc+dOqlevrlEZabVx/11ev0tbOpaUREWF887/IkMHdNE6ACqVSv7991+WLVvGxYsXyZEjBzly5GDcuHFZMpyricjISDZv3oyrqyuxsbE4OzvTp0+fRCsDP/X69WuOHj3KkSNHOHLkCKampgnBpX79+mJpcjaWqeehPHz4UOM9Gmq1mu3bt1OiRAmWLl3K2LFjefz4MWPHjs32wQQgR44c5MuXDzMzM/LmzZtil3769Ok4ODjQunXrhDxTDRs25NatW5ibm1O+fHm2bt2aaWd9p6WHUqNGDV6/fs2ZM2cwMTFJEvC0nZTftm0bNjY2GR5MAMqXMkNfT7u//rmMc2GkiKJmzZp0796dmzdvpruM58+f89tvv1G0aFFGjhzJ5cuXqVmzJuvWrePp06eMGDHiqwgmEPd3f8CAAdy8eZNly5Zx9OhRihUrxrhx4/Dz80tyv7m5Ob169cLd3Z3AwEA2btxIgQIFmDFjBgULFqRZs2b8888/3Lt3T5x5/5XQeUCJjIzUathDkiRMTEzYu3cvZ86coWPHjlptbMuuZDIZCxcuxNramk6dOhETEwPE5aSaP38+e/bsYfr06bRv356XL19meHu+tBcF4nqPrVq1YsOGDclmH9YmQaQkScyZM4fx48dr9Hx62dqYavUhJZOBXUkz/pz+B76+vlSpUoUWLVrQqlUrzp07l+qzKpWKf//9N+Ecl61bt/Lx40dat27N5cuXOX78OC1btvxqV0fJZDIaNGjA7t27E7JoV61alY4dO3LmzJlkf+7xc6iTJ0/m7NmzPH/+nJ9++on79+/TvHnzhKGzXbt2ZUgSUkE3dP431t/fX+syjIyMvovVIHK5nDVr1mBkZESvXr0STerXqlWLGzduUKlSJSpWrMiqVasy9FtaWnooEDePcurUqWQDijY9lKNHj6JWq2nevLlGz6eXXCYRHfYCZWyMRs8r5LKEyfg8efIwbtw4fH19adeuHf369aNOnTr8+++/if7MXr58yfTp07G2tmbUqFHcu3cPExMTBg0ahL+/P8uWLcs2c4O6Ym1tzbx58/D396dx48YMGTKEypUrs3btWqKiolJ8zsTEhA4dOrBixQr8/Pw4evQotra2uLm5UaRIEerWrctff/2Fp6dntsmJJ2TAHMqVK1fS9MH0JTY2Nt/cL1dKoqKiaN26NcWLF2flypVJVu/cunWLAQMGkCdPHlauXJkh+2wuXrzIzz//zKVLl1K9Lzw8HBMTEzZu3Ei3bt0SXfPx8aFr167cuXMn3fU3btyYPn360KdPn3Q/m16PHj2iT58+5DHJT/u+04mKUZOeWK2nkFOjggW1Klome12pVLJz505mzpyJUqmkZcuWPHz4kNOnT1OmTBn8/PwoW7YsLi4utG3b9pvsgadErVZz9OhRXF1duXHjBoMGDWLYsGFYWib/s0xOZGRkoqXJb968oWnTpjRr1oymTZtSsGDGrLoT/i/T5lBiY3VzdkT8END3wMjIiD179nDnzh3Gjh2bpCdib2/PpUuXaNGiBTVq1GDBggVan8fyudQSRH4qZ86cKBSKZHfEa9pDuXHjBg8ePEgSoHRNrVazePFiHBwc6N69O4cO7qdnazuMc+ineZWTnkKOfWkzatoXSvkePT3q169Ply5dePPmDYsXL+bw4cNER0dTpkwZjhw58k0P56ZGLpfTvHlzDh06xNmzZ3n//j3ly5enR48eXLlyJU1l5MiRg2bNmjF//nzu3r2Lp6cn9evXZ8+ePZQpUybR0JmuPo+EtNF5QNHVL8j3tj49V65cHDx4kGPHjvHnn38mua6np8fYsWO5dOkSu3fvpnbt2hr1BFLypQSR8QICAjA0NOT8+fNJrmkaUObMmcOoUaMyNPvys2fPaNq0KRs3buTChQs4OzvH7Q3KZUjvtnaUsDJB8d++jOTo68nJYaTHDzWK0KBG0WT3gKjVao4fP07nzp0pV64cp06dwsrKirx589KnTx/q1avH0aNHOXTokJgHIO5E0MWLF+Pr60u1atXo1q0btWrVYsuWLen6Qlm0aFEGDRrEzp07CQ4OxtXVFblczpgxYyhQoECioTMhY+l8yMvHx0frP7j4nfDR0dE4ODjg6OhItWrVUlx++C0JDAykbt26jBw5Emdn52TvUavVuLm58euvv+Ls7MyECRO0/jCWJAkjIyNCQ0MTzjdJzr59+5g/fz63b98mKCgo0RcItVqNvr4+MTExaU6n8/TpU6pVq8bTp08zZFOnJEmsX7+ecePGMWbMGMaNG5fil56IyFhuPQrm1oNgIqOVqNUS+npyzPPnpFp5C4pbJr9fIzg4mLVr17Jy5UoMDQ0pW7Ys169fx9zcHBcXF5ycnBL+fG7fvs3s2bM5ePAggwcPxsXFBQsLC52/76+RSqVi//79uLq68vDhQ4YOHcqQIUMoUKCAxmW+fv2aY8eOcfjwYY4ePUq+fPkSLU3O6rRHX6tM2yn/4cMHzp07p9VEmVwux9bWlqtXr3Lp0iUuXbrE7du3KVu2bMJmKgcHB6ytrbNVkkVd8ff3p27dukybNo2+ffumeF/8SpgXL16wevVqqlVL8uebLlZWVly6dClRTqjP/fHHH0RHR3PkyBHmz5+fJGVM3rx5efr0aZqXeDs7O5MzZ07+/vtvrdqenMDAQIYMGYKfnx8bNmxIlMhSW5IkJZxPcujQIRo2bIhcLufEiRM0b94cFxcXatWqleLzT58+Zd68eWzevJlu3boxduzYTMlB97W4desWCxcuZOfOnXTo0AFnZ2cqVaqkVZlqtRpvb++EuZcbN27g6OiYkJa/XDndHkfwLcu0OZTcuXNrtW5eJpNRqFAhihcvTpcuXfjnn3+4fPkyb9++ZdGiRVhbW7N7927q1KlDoUKF6NChA7Nnz+bcuXNERkbq8J1knWLFinH06FEmTZrErl27UryvSJEi/Pvvv4wfP55WrVoxfvx4rX4GaVk6HH8GSkpnzadn2Ovt27e4u7un2BPThoeHB5UqVcLOzo6rV6/qLJi8ffuW+fPnU65cOYYPH07evHmpXr0658+fp2zZsvj4+LBly5ZUgwnErX5avHgx9+7dI1++fNSoUYOePXty69YtnbTza2dvb8+qVat49OgRJUuWpHXr1tSvX59du3ZpPH8ol8upUqUKkyZN4syZMwQEBDB06FAePnxIy5YtKVasWMLQma6yZn9vMmSnfGBgIF5eXhr9wcvlcurUqfPF4Q9Jknj27FlCD+bSpUvcuXMHW1vbRL2YYsWKfbXfOry8vGjWrBkbN26kadOmqd77+vVrnJ2duX79usbJJps1a8bo0aNTXbpbpEgRTp8+TWhoKN26dePhw4eJrtvb2+Pu7p6mD/Dp06fj5+fH6tWr093WlLx7946RI0dy7do1NmzY8MUP9rSQJIkLFy6wYsUK9u/fT/PmzSlcuDAHDhzA0NAQFxcXunfvrtXO7tDQUFasWMGCBQuoUqUKEydOpE6dOukqQ62W8H3xHq97rwn9EE2sSo2BnoIC+XNQ1a4glgVyfbW/C7GxsRmaOUOSJB4+fMjhw4c5fPgwFy5cwN7ePiHvWJUqVb7afUEZIdOTQ965c4dnz56lK6goFApsbW01zukTGRnJ9evXEwLMxYsXkclkODo6JgSYqlWrpjpHkN2cP3+eDh06sGfPHmrXrv3F+/fu3cvw4cM1SjbZq1cvmjZtmuLS3eDgYEqVKkVISAgQF1xOnDhBmTJlEu6J3x9Qr169VOuKjIykePHinD59WmfLww8dOsSgQYPo2LEjf//9t9bj4yEhIbi7u7NixQqUSiWdO3cmJCSEbdu2UbduXVxcXKhfv77OT21ct24dc+bMwdLSkkmTJtGiRYtU61CrJTx9AvG8G4hKJSV7lLG+npycRno4Vi5MuRLZ46RQTX2a269bt244OzvrfItBZGQk586d4/Dhwxw5coTXr18nWpr8vc97ZWrqFQBbW1uKFi2a5slZuVxO2bJltUoQlyNHDurUqcO4cePYtWsXr1694uLFi3Tq1Al/f39cXFwwNTWlZs2ajBo1im3btvH8+XON68sMderUYePGjXTo0AFvb+8v3t+uXTt8fHxQKpVUqFCBgwcPprmuL21u9PLyonLlyshkMmQyGW3atGHfvn2J7knrIVvr16+nRo0aOvkg+PDhA0OGDOGnn35i/fr1LFy4UONgIkkSly5dou//2jvvuKbu/f+/cgIIgsgeyhKQJYioSEWlblzUXdB6uTgQt9hq1d7e3m61Vi24wL0Vq3XjXoCAICAbRfaQsDdknfP7wy/5iQmQkLD0PB+PPB6QnPGBJOd13tvLCwMGDEBERAS8vb1hY2ODgwcPQklJCdHR0bhy5UqHDHBTVFTEihUr8OrVK6xatQpbt27FkCFDBEO0PoTLI3HlQToiE96ikc0XKSZN21XVcnA/PAdPonN7dCsTR0dHnDlzBikpKdDR0cG4cePg6uqK4OBgmRU5KikpYfLkydi9ezeSk5MRGxuLcePG4fr167C2tm7mOvuUShzaosPnoRQVFSE9PR01NTVCb3aTCamhoQELCwtoaGhIdS5xqK+vx4sXLxAeHi6wZBQUFJq5yYYOHSpycmFXcunSJaxbtw5PnjwRahHeEg8fPoS3tzdGjRqFPXv2tNlsctu2baisrMSOHTtEvr59+3YUFxdj9+7dAIDg4GBs374dISEhgm2++uorTJkyBf/6179aPA+fz4elpaVguqA0hISEwMvLC2PHjsWePXvaPS2wqqoKZ8+eRWBgIOrq6rB48WKoqKgIKrqbGh2qqKhItV5JoSgKt2/fxrZt21BYWIhNmzbBy8sLioqKIEkKVx+mI59V0+qskw+RYxIYNkgXoxz6d+DKO48P5yOtXbsWXl5eHdYDjcfj4fnz5wL3WHp6OsaOHSvIHhswYECHnLc70SXzUN6npqYGOTk5qK2tBY/Hg7y8PPr27QtjY+Mu7SpKURQyMzObxWJevXqFwYMHC1KWR44cif79u/7Ld+zYMfz0008IDQ2FkZGRWPvU1dXh+++/F3zh5s+f3+Jd9dGjR/Hs2TMcO3ZM5Ovu7u5wc3PDokWLALxzz+jq6iIzM1MwhVCcIVuXL1/Gzp07ERER0e47/IaGBnz//fc4f/48AgIC8MUXX7TrOC9evEBAQAAuX76MiRMnYu7cuUhKSsLhw4cxdOhQrF+/HpMnT+4W/vOwsDBs374dMTEx8PX1xVjXBXieVCrRRMYm5JgE5rtaQF+7cwWyI2ma4Orv749Hjx7B09MTa9eu7fDsuZKSkmapyX379hXEXj7W1OQuF5SeRG1tLaKjo5uJTO/evZtZMQ4ODh1aiNcSf/31Fw4cOIDQ0FCJWkxERkZi6dKlsLCwwP79+0W2urh+/ToOHTqEmzdvijzGwIEDce3aNdjY2Aiemz17NubMmSOwSL777jsoKyu3OGSLoih89tln2Lx5M+bMmSP2+t/nxYsX8PT0xKBBg3Dw4EGJ2/zX1NTg/PnzCAwMRHl5Oby9vTFkyBCcOXMGt2/fxsKFC7F27VpYWVm1a30dTUJCAnbs2AGz4QugptE+Xz4DgJmRGr4YZy7bxXUTcnNzsX//fhw7dgzOzs5Yt24dxo8f3+FJCSRJIj4+XhB7iYmJwciRIwWpyTY2Nj02MeJ9aEGRAoqi8ObNm2bB/jdv3sDBwaGZyOjrt9yOQ5b89NNPuHLlCh4/fixRlgubzcZvv/2GgIAAbNu2DUuWLGn24Y6MjMT69etFtsCorq5Gv379UFVV1Swudvz4cdy+fRsXL14EAOzYsQNlZWX4448/RK4hJCQES5cuRVpamtjxtSa4XC5+/fVXHDx4EH5+fvDw8JDoyxkXF4fAwEAEBQVh7NixWLp0KaqqqrB3716wWCysXbsWS5YsgZqamkTr6gryWTW4fO8V2mGcCGAyGVg+bzCUFD/erhR1dXU4c+YM/P39QRAE1q1bh6+++qrTrIbq6mo8fvxY4B7j8XgCcZk4cWKHf9YoikJ2YTVeZZWjroELigJ6K8lhoJE6zAzVJB6s1gQtKDKmpqamWeFlZGQk+vTpIxAXZ2dn2Nvbd0gLGYqi8PXXX+P58+e4d++exH79hIQEwYXz0KFDApdARkYGJk2ahMzMTKF9QkJCsHnzZqHmkcXFxbCwsEBxcTEUFBQQEBAguHCLYsaMGXBzc4OPj49Ea05KSoKnpyf09PRw5MgRsZsJ1tXVISgoCIGBgXj79i2WLVuGWbNm4fr16zh48CAsLCywfv16uLm5SSxwXUlwaCbSMqWbjinHZMBluCGGWH38nb0pisLDhw/h5+eHyMhILFu2DKtWrWq1iLcj1vD69WtBYWVYWBjs7OwE7rFhw4bJzLXK5fLxMq0YMf839vnDZA15OQIEwcAQKx0Ms9GFYi/JWmZ1epbXx06fPn0wYcIEfP/997h16xZKS0tx9+5duLq6Ijk5GYsXL4a6ujpcXFywefNmXL16FSwWSybnZjAY2LVrF6ysrDB79myw2WyJ9h88eDAiIyPh6urarNlkaw0imwoaP0RHRwfW1tZ4+vQpgNYLG5OTkwXuKnHh8/nYuXMnxo4di5UrV+LWrVtiiUliYiLWrFkDIyMjXLlyBf/9739x9epVZGdn4/PPP0dWVhaCg4Px+PFjzJo1q0eJCQBU10j2nouCx6dQW/dpZCgxGAxMnDgRN27cQEREBOrr62Fvb48vv/wSz54965SsNwaDAUtLS6xbtw7BwcEoLi7Gjz/+iMrKSixevBi6urpYuHAhTp06haKionafp66Bi7M3UxERX4j6Rp7IzD8ujwSbw8eLpCKcup6MShl8ngBaUGRG04fFy8sLgYGBSExMREFBAf773/9CWVkZgYGBsLKygqmpKb766ivs27cPsbGxIlNBxYEgCBw+fBhqampYsGCBxMeRk5PDpk2bEB4ejitXrmDMmDHIzc0Fj8cTTJB8n6aUYVE0zZoHWheUP//8E2vWrBE7CSMjIwNjx47FzZs3ER0dDW9v71ZdXA0NDTh16hScnZ0xZcoUaGpq4sWLF1iyZAn+/PNPuLm5wczMDK9fv8bRo0dl2oqls+HyZNNtuqi4FHV1dTI5Vk/B3Nwcfn5+yM7OxujRo+Hl5QVHR0ecPn1a4pszaVBUVMSkSZOwa9cuJCUlITY2FuPHj8eNGzdgbW0NBwcHbNmyBU+ePBE7NbmRw8P54FRU1DSKlfnHJynUNXBx/lYqauulv7mgXV6dCEmSePXqVbOU5dzcXAwbNqxZLEaSZnhsNhszZ86Erq4ujh8/3i6T+f1mkxwOBzExMTA3bx6stbOzw8mTJ0VaKUlJSZgxYwaysrIQHh4uEKr3KSgogJ2dHdLT0wUZYS1BURQCAgLw3//+F//5z3+wfv36Vv+u1NRUBAYG4syZM3B0dISPjw9Gjx6NkydPYt++fdDT08P69esxd+7cHtXFury8HBkZGcjMzERGRkazn6cv2IKBNiOkOj5FkXgRchlXz7+r2zEyMhI8DA0Nm/2ur6/f46w4cSFJEsHBwfDz80NSUpKgpqkrixd5PB6ioqIEsZdXr14JUpOnTJnSYmrylQfpyH1bDT4pmcVFMBjQUlfCIjebtjcGHUPptlRWVuL58+cCgXn+/Dm0tLSapSzb2tq2Ohagrq4Orq6uGDp0KPz8/NqdRZKXlwdbW1vo6uri/PnzGDZsGIB3d/6ampqoqKgQWZ9DURTMzMxw9epVEAQhcsjW5s2b0djYCD8/v1bXkJ+fj6VLl6K8vBynTp1qsfCRzWbj8uXLCAgIQHp6OpYsWYJly5aBzWbD398f58+fx7Rp07B+/XqMGCHdhbej4PP5yM/PFxKLpp/5fD7MzMwED1NTU8HPbyvlEZ3MAl+C+pMPkZcjMGOsGUz6qaK0tBS5ubnIy8tDbm6u0KO0tBT9+vUTKTZNj/bWAHUnUlJS4O/vj6CgILi5uWH9+vWC74G0UBQFHp8EkyAkDoaXlpYKUpPv3r0LVVXVZqnJysrKqKpl48TVpHZ/JuSYBL6cYgk9rba7utOC0kMgSRIpKSnNUpYLCgowfPhwgQXz2WefCaXKVlVVYdy4cZgxYwZ+/vnndp/f1dUVdnZ2OH36NBYvXoz//e9/SExMhI+PD+Li4lrcb/369dDR0YGnpydGjhyJ/Px8wWvV1dUYMGAAYmJiYGJiInJ/iqJw5swZfPPNN1i7di22bNki0pp4/fo1Dh06JOge7OPjAzc3Nzx69Ah+fn6Ii4sT3GFKMgWwo6irq0NmZqZIKyM3NxdaWlpCYtH0s6amZos3B/UNXBy+lCDxnej79FaUg8+X9mLdgHA4HBQUFIgUm6YHQRCtCk7//v17jIVYXl6OI0eOYP/+/TA0NMS6devaNRCNxyeRnlOB6KQilFW+a9xKUYCCPAEbMy04WOtAXVWyVlAkSSIhIUEgLi9evMBnn32GKXNWglA2kmj66PswGICFiQamu7Rdt0MLSg+mvLxcYMWEh4cjKioK+vr6zdxkgwYNQllZGVxcXODt7Y1vvvmmXefy9PTEhAkTMHXqVKxduxZxcXFwc3NDZWVlq00cHzx4gOPHj8PX1xd37tyBq6srevXqBX19fZw6dQqxsbE4d+6cyH2Li4uxYsUKvH79GqdOnRJyq3E4HFy5cgWBgYFITk6Gl5cXvL29oaenh5MnT2Lv3r1QUlLC+vXr4eHh0am92iiKQnFxcYtWRmVlJUxMTERaGSYmJlIV9V57lI6MvPYN6pJjMvCZfT+MsJNNqjtFUaiqqmpRbPLy8vD27Vtoa2uLFJumh4aGRreq0+DxeLh69Sr8/f2RlZWF1atXw9vbWyy3bUwKC5HxhaAoiAyMEwyAQTCgp6mMaS6m6KPcvrq2mpoaPHz4COll2mDKSVcbx2QysMp9COTlW3dv0oLyEcHn85GcnNzMiikqKoKjoyNsbGwQFBSErVu3wtfXV+Jjf/PNN9DT08OmTZsAAFevXsWiRYswdOhQ3Lp1S6idBZfLRXZ2NjIzM1FdXS10gWQymSgvL4ehoSGcnZ2F7vCuXLmCVatWwdPTEz///HMzl1pGRgYOHz6M48ePw8bGBitWrMCsWbNQUFCAffv24eTJkxg7dizWr1+PMWPGdNiFiMPhICcnR6SVkZmZCUVFxRatjH79+nVYlX1JeT3OB6e1q1JesRcTi2fbQUnCdFFp4PF4ePv2rUixafq5sbGxRbExNDSEoaFhlzV3jY2Nhb+/P65du4b58+dj3bp1sLW1FdqOoijcj8hBWlY5eC30VnsfBgPoJc+E+1QraKq17waDzeHjYNBLkFJYrMA7N+giN5s2rSZaUD5yysrKEBkZifDwcDx8+FBgxbi6ugqsGBsbmzYvbqIKEx0cHGBgYICEhAQEBARg6tSpAN7FVsLDw8Fms9tsykcQBJSUlDBy5EgoKiqisrIS69atQ3h4OE6ePCnopMzlcnH9+nUEBgYiLi4Onp6eWL58OSwsLPDkyRP4+fkhLCwMixcvxurVq1t0oUlKVVVVi1ZGYWEh+vXrJ9LKMDU17dLYQXpOBW6HZkrUy0tejoDHNCtoq3e/liA1NTXNBObDmE5+fj7U1NREik3Tzzo6Oh3aKofFYiEwMBABAQGwtrbG+vXrMX36dEHSQmhMPuJSiyUWeqVecvjXFzZQ6S25lVFbz8GxfxIl+hyIQkGewJeuVtDRbP2zQQvKJ0ZsbCwmTZoEDw8P1NTUICIiAiUlJRgxYoQg2O/k5CRUqXvs2DGEhITgxIkTAN5d4Pv27YuSkhJERkbC29sbo0ePxh9//IHExERwOByxc/gZDAYUFRXB5/OxbNkyQYt9FRUV5OTk4PDhwzh69CjMzc2xYsUKzJ07FxRF4dy5c/D39weXy8W6devwr3/9S+Jx0CRJoqCgQMi6aBIONpvdopVhbGzcrX3/WflVuPk04/+Cvi2/F3JMBuTlmZg/2RJa6l3XP08aSJIEi8VqNYGguroaBgYGLWasGRoayqTJJ4fDwd9//w0/Pz+UlZVh7dq1mD3vK1x7ktuuCzuDAQw0UseMsWZi78Pn85GRkYGX8YnIqzMEgyGdkMrLEfiXmw3UaAuF5kMiIyPh5uaGy5cvw8XFBcXFxYiMjBS4yWJiYmBkZNQsFvPmzRsEBgbi1q1bAID4+HgsWLAAKSkpAP5/s0ljY2OYmZlJ7Gbi8/l49eoVhgwZgvHjx+PWrVsIDAzE8+fPsWjRIixfvhyDBg1Cfn4+Dh48iMOHD8PR0RHr16/HpEmT2qxDycrKEhKLjIwMZGdnQ11dvUUrQ0dHp1v57iWlvpGLxPQSxKYUg88nQVIARVKCbKKaqjIoUiVY6+MOebmPM/23iYaGBuTn57eaQPB+mrSoJAJJ0qQpikJkZCT8/PzAVLXC0JHTwGinhfSuHY49lBSFXZHl5eVITExEQkIC4uPjERcXh+TkZCgpKYHJZML354vorSz+/CNRyDEZWOnhAHm51tdPC8onysOHD7FgwQLcvn1bKP2Rx+MhISGhWY+ysrIyAO9iKU0CExYWhrNnzwr2q66ubtayXlIYDAbi4+Nx4MABGBkZwcfHB/Pnz4eSkpLgi3nv3j0sWrQIa9asEbTrpygKpaWlLVoZZWVlMDY2FmllDBgwQGKrpidCkhTyWTWoqmWDyyPRS54JTTUlZL5OgKfnv/D69etu0Tm5K6EoCmVlZa0mEJSWlkJfX7/FjDVRadJcLh8HLsRJ1V9NjsnACDs9qMpXIT4+HgkJCYJHZWUlDAwMoKCggMrKShQXF2Po0KEYN24cXFxcIN/XHAmvK9qd+cdgADZmWnAdZSLGtrSgfLJcvXoVK1aswKNHj5p1ChZFVFQUZsyYgcWLFwt6lGlqasLNzU1gxTQ2NqKgoKDd7Sq4XC4KCgrg4uKCwYMHC7kOFi5ciGHDhqGoqEgonsFkMlu0MgwMDD7a4jtpoSgKw4cPx88//4zp06d39XK6PS2lSTe52XJycsBgMJoJjInFMPTSHAwK0n0Gi99m459j32HgwIHo3bs3ampqkJGRARaLBWdnZ7i4uGDMmDFwdHRslqBQW8/B0X8SpahDYWDhdBux3KG0oHzinD59Gt999x1CQkJaHQBUW1sLHR0dQfsVZ2dneHp6gsvlIjw8HNHR0fjjjz+kbt3PYDBQXFyMCxcuICwsDIqKipCXl0d5eTn09fVbjGfIaob4p8iJEydw4cIF3Llzp6uX0uMRlSZdUs1Eb207qVN3uew6BG5fjJqaGowZMwZjxoyBi4sL7O3t26yDufk0Axl5lRKLCpNgQE9bGe5TxBvZQAsKDfbv34/du3cjLCysxVb7FEVBWVkZJSUlUFJSQt++fZGbmyu4kNfX1+PJkydSj1rlcrlYunQp+vfvDzc3N4wZM0YQAO9u0zI/FppScsPCwsSe+kkjPgmvS/Akqn0B+WZQXEwZ0RfW1tYSx/W4PD7O3UpFRTVb7BRiBgNQ6a2ARW42YqeRtyQonZeETtPlrF69GlVVVZg0aRKePn0qsjiLwWBAW1sbJSUlYLPZ0NbWbmYV8Pl8EAQhtaAwmUwkJiZ2i0mYnwqKiopYtmwZ9u/f32YLHBrJUVRgvkuCkFJQ+vZRbtM13RLyckx4TLXCPw/SUVLR0GYdjByTgKqKAua7WsqkJom2UD4xKIrC5s2b8eTJEzx8+FBQqEhRFMrLy5GdnY2XL19CX18fXC4XmZmZWLx4MRoaGpCcnIy0tDQYGBhI3IJC1Do0NDQwZMiQTyJY3l3Iy8uDvb09cnJyOmzm+qdKXQMXR6Rsh0MQgL2lDsaNEG/Ed0vwSRJpmeWISixCTT1HSFjk5Qgo9ZLDcFs9DDLXlDjzj3Z50QigKErQ6qRplkt6ejo4HA74/OZt0fl8Png8HkpLS/HixQtBy21pM4W4XC5+++03pKSkwNjYGA4ODnBwcMCQIUPg4OAg8VhfGvGZN28exo0bh9WrV3f1Uj4aKisr8ezZM8Rn8dBLWV+qtOF/fzGozToQSSgqrcOb3ArU1nOB/5vYaGqohv46Ku1OlacFhaYZxcXFWLhwoSDTShyLgyAIVFRU4PXr13BwcGi3lUIQBExMTGBjYwMOh4PU1FTExcUhLi4OL1++xMuXL6GqqiokMsbGxj26VqS78OTJE6xcuRIpKSn0/7OdFBUVITQ0FKGhoQgJCUFGRgacnJwwduJMqPT7DCTVvv9rPx0VeEwVLzDeldCC8olSWVmJ5ORkJCUlITk5WfBoaGjApk2bYG1tLVEQnMvlQkFBASRJtvtiRBAExo4d2+Jcb5IkkZWV1Uxk4uLi0NjYKBCXJqGxsrKS2v32qUFRFAYPHozdu3dj0qRJXb2cbg9FUcjJyUFISAhCQkIQGhqK4uJijB49WpDCO3ToUCgoKICiKPzzIB35rBqJM63kmATcp1pCV7P7u4BpQfnIqa6ubiYYTY/q6mrY2NjA1tYWgwYNEjwIgkB8fLyQi0scOBwO8vPzYWRkJPHFnMvlorS0FAsXLpQ4dsJisQQi0yQ0BQUFGDRoUDORGTx4cItiRfOOQ4cO4datW7h27VpXL6XbQVEUUlNTBeIREhICHo8nEA8XFxfY2tq26Pbl8vgIuv0K5VUNYmd8yTEZmPG5GUwN1WT4l3QctKB8JNTW1iIlJUVIOMrLy2FlZSUkHEZGRiItiZCQEFRXV7drDSRJIi0tDTo6OtDQ0BA7nsLlctGrVy/8888/ePbsGYKCgjB48OB2raGJmpoaQRuKJpFJS0uDiYmJkMusrZbjnxJ1dXUwNjZGdHR0q3VJnwI8Hg/x8fECCyQsLAyqqqoC8RgzZgzMzc0lssh5fBLBIZnILqgCn6RanFEiL/du2NbMceYw0Os5SRK0oPQw6uvrkZqaKiQcLBYLlpaWQsJhYmIi9oW9uroaYWFhUqX+UhSFESNGIDU1FRUVFW02TyQIAjweD9988w1u376NiIgIfP311/jll1/g4+MjU18+h8NBSkqKUFxGTU1NSGRaEtxPgY0bN4LBYGDnzp1dvZROpbGxEdHR0QILJCIiAoaGhgLxGDNmDAwMDGRyrvKqRsSlspD8pgwEATDAAAUKfJKCRl9FjLDVh7mRGpjMntUOhxaUbkpjYyPS0tKEhKOgoAAWFhbNRGPQoEEwNTWVur1IUlISsrOzpToGQRCwsrJCcnIyrl69igULFoDL5Qq50JhMJnr16gUzMzMYGBjg6NGj2LFjB549e4bq6mq4u7vDzMwMhw8f7tAqeJIkkZmZKRSX4XA4QnEZS0vLTyIuk5mZiREjRgiaJX6sNHXbbrJAYmNjYWNjI7BARo8e3eHWK5fHR0U1G2wOD0wmARUleaiq9NwCXlpQuhgOh4NXr14JCUdubi7MzMyEhMPc3FzmFzWSJPHo0SNkZGTIpKDQwMAA8+bNw4EDBzBp0iSUl5ejqKgIbDYbd+7cgZOTExwcHKCurt7MCvjll19w+fJlPH36FIqKivj2229x7do1nD9/HiNHjpR6XZJQVFQkFJcpLCyEra2tUFxGmumK3RU3Nzd88cUX8Pb27uqlyIzS0lKEhYUJ4h+pqakYNmyYwAIZOXIkXYMjJbSgdBJcLhfp6elCwpGVlQUTExMh4Rg4cKDUfbHaIjc3F8ePH8fRo0ehrKyMrVu3QkNDQ+rjZmRk4OLFizh06BB0dXWbxVOmTp2KNWvWiGxESFEU1q5di6SkJNy5cweKioq4du0ali9fjg0bNuDbb7/t0o641dXVIuMypqamApFpEhpZ/B+7knv37mHTpk14+fJlj3X95efnC8QjJCQE+fn5cHZ2Flggjo6OdDsfGUMLiozh8XjIyMgQEo43b97A0NBQSDgsLS077EPN4XDw9u1bFBYWoqCgAIWFhcjNzUVUVBSSk5NRWVkJOTk5MBgMGBgYYNWqVTLp5XTt2jUkJiaiqqoKLBYLtbW10NLSgp6eHlgsFgYMGIBRo0ZBV1dX6KGuro5FixaBx+Ph4sWLYDKZyMvLw8KFC6GkpITTp09DV1dXBv8d2cBms4XiMvHx8VBXVxcSGUNDwx5zcSZJEjY2Njh06BBcXFy6ejltQlEU3rx50ywDq6mJYpMFIk4TRRrp6FRBoSgKFRUVKC4uBpvNBvCuj5Cenl6XjkttD3w+H1lZWc1EIykpCenp6dDX1xcSDisrK5m5Rvh8PoqLi1FYWNhMLN5/FBQUoKqqCnp6eujXrx+UlZVRWlqKN2/ewMTEBDNnzsS8efMEo2oZDAYiIyPBYrGkisU0zZLfsGGD4DkOh4Pi4mIUFRXht99+g7y8PBwcHMBisYQeVVVV0NTURF1dHdTU1DB27Fjo6elBS0sLERERCA0NxW+//YZZs2ZBW1u7W7alJ0kSGRkZzVxmcXFx4PP5IuMy3fFvAIB9+/bh6dOnuHjxIkoqGtDQyAVJvetNpa3RG3JdGDAmSRKJiYnNLBB5eXl8/vnnAhGxsrLqMQL+sdApgsLn85Gfny8YqSoqQNu7d2+YmZmhX79+3WrQD0mSyMnJEbI40tLSoK2tLSQc1tbW7e5B1dQ360Nh+PD3kpISaGhooF+/foJH//79hX5nMpm4cOECjh49irKyMixevBheXl7N5q1nZ2fj4sWLCAoKQmVlJXbv3i3VBa6+vh5jxoxpsWvx9u3bUVFRgR07doh8ncvloqSkBBkZGViyZAlsbGzg7OwsEJzU1FQkJiZCXl4eHA4HmpqaIi2dDx86Ojpdfnf69u1bobhMUVGRUFzGzs6uW8RlSkor8e8VmzF9rg9IEu9dnN+lu9oN1IKDtS769ul4txGXy0VMTIzAAgkLC4Ourm4zC4TumND1dLigsNlsREREoKGhoc1iOSaTCVVVVYwYMaLTZ3VTFIW8vDwh4UhJSYG6urqQcNjY2EgUwKupqWlVKJoevXv3biYMosRCT0+vxf9PU4D92LFjCA4OxpQpU7B06VKMHz9eIBQFBQX4+++/ERQUhPT0dMydOxfu7u74/PPP8fLlS7x9+7Zd/0Mej4eoqCgcPnwYq1atwtq1a4V6bx05cgTh4eE4duxYm8drqjpet24d1qxZ0+x5Ly8vlJeXw9/fHwoKCiKtnfcfZWVlUFNTE1t8Ojp+1URVVZVQXObVq1cwMzMTcpl15syX+FfFeBKdBy6HA4Ip+rNGEAwwGMAgMy2MdzISjBWWBfX19YiMjBRYIFFRUTA3N2+WwtudXJ807+hQQeFwOAgNDUVjY6PYU/wIgoCKigpGjRrVIa4AiqJQWFgo5KpKSUmBioqKSOFQU1Nr8XiNjY2COEVr7ieSJEVaEe//rq+v3+40zZycHJw4cQLHjx+Huro6li5dioULFwqCw8XFxbh06RKCgoKQmJiImTNnwt3dHRMmTGgmTmfPnoWiomK74jpsNhtTp05Ffn4+du7cicuXL8PT0xNff/01jIzedUm9du0ajh49iuvXr4t1zKysLIwZMwa7d+/Gl19+KXieJEns2bMHO3bswMGDBzF37txWj8Pn81FaWtqm8LBYLJSUlEBVVVUs8dHV1ZV5DIzNZiM5ObmZNZOQkABNTU0hkTEwMJD5XXlEfCGiE99KUM1NwEi/D74YZ95uUWlqothkgSQkJMDe3l5ggTg7O7f6PaTpHnSooISHh6OyslLiQjmCIKCnp4ehQ4dKtN/7UBQFFoslUjgUFBSaiYatrS1sbGyaZebweDywWKw2rYqamhro6em1KRaqqqoy/+I3Njbi6tWrOHbsGGJiYrBgwQIsXboUDg4OAICysjL8888/CAoKwosXLzB9+nS4u7vD1dVV6CJYV1eHtWvX4tGjRxg8eDCWLFkikYuIzWbDwMAATk5OgucKCgrw119/4ejRo/jiiy/w7bfforKyEt988w0iIiLEPnZCQgImTpyIc+fOYeLEic1ei46OhoeHB1xdXbFr1y6ZuIpIkkRZWZlY4lNcXAxlZeU2RUdPTw+6urrNRrNKuqY3b94IxWUoimoWl3FwcMDAgQPbfTOWmlGG+xHZEg+DkmMSGDRQExOcjMXansViNYt/NDVRbLJAnJycPuoamI+VDhMUaauuCYLA+PHjxfoClpSUCAlHcnIyGAyGSOEAINKKeP/3kpISaGlptRifaPpZU1Oz02M+L1++xNGjR3H+/Hk4ODhgyZIlmD17NhQVFVFVVYWrV68iKCgIz549w+TJk+Hh4YFp06a1eLFNTk7G/PnzoaSkhNzcXOzbtw9jx45FbGwsSJJs1bokCAJsNhvR0dH4/fffRW5TUVGBAwcOwN/fH3Z2dkhLS0N+fr5Ef3NISAjmzZuH4OBgDB/e/PNaVVUFHx8fpKSkICgoCNbW1hIdWxpIkkRFRYVY4sNisaCoqCi2+LR1QaUoSiguExcXh+LiYtjZ2TUTGVtb2za/SyRJIeDiSzSyJe/jBrxrsb5kth36KDd3F4pqolhSUoLRo0cLLJChQ4d2upubRvZ0mKDEx8cjLy+v3QsjCALm5ubN0ljLy8uFRCM5ORlcLldQu6Gvrw8NDQ0oKiqitrZWSDjevn0LFRWVVoPZ/fr1g66ubpcHcd+noqICZ8+exbFjx4QC7LW1tbhx4waCgoLw6NEjjB8/Hu7u7nBzc4OKikqLx6QoCsePH8emTZugr68PVVVVnDt3ThC0b2xsxKVLl6CkpARlZWWQJAmSJEEQBBgMBqqrq1FfX49ffvkFUVFRbc4qqa+vx/79+7F582a4uLhgy5YtcHV1Fdtyu3btGlasWIGQkBAMHDhQ6G85evQotm7dih07dmDx4sXdLkBLURQqKyvbFJ2ioiKwWCzIy8uLLT7vv8+VlZXN4jJxcXFIT0+Hubl5M5Gxt7dvFpfJyK1EcGgmuG1M82sJJsHA0EG6GO3Qv8Umik0WSGtNFGl6Lh0iKHw+H3fv3pV6HCxJknj48CESExORlJSEuro69O/fHxoaGoK77cbGRpSWlgoCyU2C8KFQND2nr6/fbrdDZ9MUYD969Chu376NqVOnYsmSJZgwYQLYbDaCg4Nx4cIF3Lt3D6NGjYK7uztmzpwplq+5trYWK1asQGhoKOrr67Fy5Ur88MMPzUS0qZ25n58fbGxsUF9fDx6PBzk5OfTp0wf379/HmjVr8Msvv4g9lImiKMjLy+PIkSPYtWsXmEwmtmzZgnnz5okl4EeOHMHvv/+OsLAw9OvXT+j15ORkuLu7w97eHgcPHoSqqqpY6+puUBSF6upqscWHwWC0Kjzq6uqorq5Gfn4+UlNT8fLlSyQkJEBbW1sgMJqmE9DIky4ZgcdpxK7/zoeKcu9mXXjNzMy6ncDTyJ4OEZS6ujqEhIS0qwX6+/D5fHh5eaG2thZ6enowNDRE//79WxSLj6VtQksBdmVlZdy9exdBQUG4desWhg8fDnd3d8yZM0einkPx8fGYN28eFBQUUF1djbNnz4osXgsLC8OyZcuQmpoq8mJw/vx5eHp6Ij4+XqJZ1/r6+oiJiYG+vj6Cg4Oxfft2FBYWYtOmTfDy8mpT8H///XdcuHABISEhIsWzvr4eX3/9NR48eIALFy4Iucg+NiiKQm1trVjCw2KxwOfzBdlsTZZNY2Mj5izbA0LKRBgG+Jj8mS4GWZrI4C+j6Wl0iKBUVVUhIiICPB5P2sXBwcEB+vr6H/3dTUsBdltbWzx8+BBBQUG4du0a7Ozs4O7ujrlz50qcNklRFA4dOoQtW7ZAVVUVw4cPx+HDh1tsE7Jo0SIMHz4cvr6+Qq/V19fD2toao0aNgr6+Pnbt2iX2OgYPHozTp0/D3t5e8FxYWBh27NiBFy9eYP369Vi5cmWLxa4URcHX1xdxcXG4e/dui7Ghv//+G6tXr8bWrVvh6+v70X+GxKWurk5YdIpLoGw4GQyGdG6oXvJMzBzfs1qu08iODhGU2tpahIaGSm2hMBgMuLq6dqtYhqyJi4vDsWPHBAH2pUuXws3NDc+fP8eFCxdw5coVmJubw93dHfPnz29388bq6mosW7YM4eHhqKurw44dO+Dt7d3iRba0tBQDBw5ERkaGSMH58ccfkZqaim3btmHEiBHIy8sTO8Nq/Pjx+O6774QytgAgMTERf/zxB4KDg+Ht7Q1fX1/o6ekJbUeSJBYtWoT6+npcunSpxc9IVlYWPDw8oK2tjRMnTtAz6VuAzyfhfyYWkuV2CaMgT2DORAv002k5dkfz8dKSoEh1m6KoqCh23UlrEATRbdtSSEN5eTn27duHoUOHYtasWdDU1ERUVBR++OEHhIWFwczMDJs2bYK5uTmio6MREREBX1/fdotJbGws7O3tERMTAw0NDTx79gzLly9v9Y79+PHjmDlzpkgxyc7Oxt69e7Fz506YmprC0dERf//9t9jr0dbWRklJicjX7OzscPr0acTExKCurg42NjZYuXIlMjIymm1HEAROnDiBhoYGrFixosXP24ABAxAWFiaY3vj06VOx1/kpwWQSYDKlt+BIkkJvxY/3BpCmfUglKHJyclJXsTIYjI9qyBFJkrh//z4WLFgAU1NTPHv2DNu3b8eFCxdQXV0NFxcXrFq1Cnp6eggNDUVMTAy+/fbbZm1SJIWiKOzduxfjx49HbW0tZsyYgaioqDbjHSRJIjAwECtXrhT5+saNG+Hr6ysoVly5ciUOHjwo9rq0tbVRWlra6jYmJibYu3cv0tLSoKWlBScnJyxYsAAvX74UbKOgoIDLly8jISEB33//fYvHkpeXx44dO3D48GF4eHjgxx9/lNp6/hgZaKwOab9uKr0VOqUVC03PQup8PmkHPjEYjI9iBGlOTg5+/PFHmJqaYvPmzXB2dsbVq1dhbGwMHx8feHl5oU+fPrh37x4SExPx/fffC6XEtofKykrMmTMHv//+OxQUFHDixAn4+fmJleH24MED9OnTByNGjBB67eHDh4iJicHGjRsFz02bNg35+fnNLvat0ZqF8iE6Ojr45ZdfkJWVheHDh2P69OmYOnUqnj59CoqioKKiglu3buHSpUvw9/dv9VhTpkxBbGwswsLCMH78eIlrYT52TPTkwZci7ikvR8DRTu+juQmkkR1SC4qamhqUlZXb9eEiCAKampo9tlK2sbERFy5cwKRJkzBs2DCUlZXhzz//xIwZM7B3714sWbIEDAYDV65cQVpaGn766SeJsqTaIioqCnZ2dnj+/DlsbGwQHx8vcv5ISwQEBGDFihVC7x2Px8P69euFKtLl5OSwfPlyBAQEiHV8LS0tsQWliT59+uCbb75BZmYm5s2bB29vbzg7O+PatWvQ1NTEvXv3sHPnTpw/f77V4+jr6+Pu3btwdXXF8OHDcePGDYnW8TFSW1uLn3/+GWNGOoDPqZHqWFYDevYcGJqOQWpBYTAYcHR0lDigzmAw0KtXL0H7kJ5EXFwc1qxZIxhpO23aNKxatQqPHz/Ghg0bUFdXhzNnziAjIwPbtm3DkCFDZHo3R1EUdu/ejYkTJ6K6uhobNmzA/fv3W+z8K4qCggI8efIECxcuFHrt4MGD0NPTw+zZs4VeW7ZsGYKCglBdXd3mOSSxUD6kV69eWLp0KVJTU7Fx40b88ssvsLW1xZMnT3D9+nX4+vri3r17rR6DyWTiu+++w+XLl7FmzRps2LBBME7hU4LL5SIgIAAWFhZ49eoVoqOjsdR9NOTlJP/6yzEZmDJ6AOTlPr6YJ430yKSEVUlJCaNHj0avXr3EunASBIHevXtj1KhRndbtVVqaAuwODg6YPXs25OTksHTpUpSVleGPP/5ARUUFAgICkJOTg127dmHEiBEd4hIoLy/H9OnTsW3bNmhoaODhw4fYtGmTxNXIR44cgYeHh1BNT0lJCX7++Wf4+fmJXL++vj4mTJiAs2fPtnkOcWIobcFkMjF37lxER0fD398fp0+fxsyZM+Hh4YGvvvoKUVFRbR5j1KhRiIuLQ3Z2NpydnZGeni7VmnoKFEXhypUrsLW1xaVLl3Dz5k2cPXsWAwYMgJa6EmZPHCiRqMgxGRjraISBxp3XDZmmZyHTeSgcDgeZmZnIzs4GRVEi56EwmUwMGDAAAwYM6PZpwk0V/MeOHcPt27fx+eefo1+/foiJiUFWVpagHbyLi0unZKmFh4dj7ty5YLPZmDFjBvbv39+uIk8ejwcTExMEBwdj8ODBzV7z8fGBkpIS/vrrrxb3f/jwITZs2ID4+PhWRTMpKQnu7u5ITk6WeI2tER0djR07duDBgwfg8/m4f/8+Pvvsszb3oygKBw4cwI8//gg/Pz+R1tnHwrNnz/Dtt98KUscnT54s8r0qrWjA3WdZKKtsAJ98N//kQ+TlCCj2ksPEz4wxwKBnDcij6RhaShsGRVEtPoYNG0a1Bz6fTxUUFFAvXrygwsPDqYiICCo2NpYqKiqiSJJs1zE7k6ysLOp///sfZWRkRNna2lLz5s2jnJycKHV1dWrx4sXUnTt3KA6H02nr4fP51O+//06pqKhQqqqq1Llz56Q63pUrVyhnZ2eh52NiYihdXV2qoqKizfUMHDiQCgsLa3W7t2/fUtra2tIstVXS0tKoMWPGUARBUMuWLaNyc3PF2i8uLo6ytLSkFi9eTNXW1nbY+rqC1NRUaubMmZSRkRF16tQpis/ni7VfaUU9de9ZFrXvbCy1+2Q0tetENOV3Job658FrKu9tdY/43tJ0HgBeUCI0o0MEpSPg8/lUUVERlZmZSaWnp1NZWVlUSUmJzD7oDQ0N1Llz56iJEydSGhoa1NixYylHR0eqb9++1FdffUVdv36damxslMm5JKG4uJgaN24cpaamRjk4OFCZmZlSH9PV1ZU6depUs+dIkqScnZ2pw4cPi3WMXbt2UYsWLWp1Gw6HQzGZTLEvau1l69atlKamJqWmpkZ5eXlRKSkpbe5TU1NDeXl5UZaWltTLly87dH2dQWFhIbV8+XJKW1ub2rlzJ9XQ0NDuY5EkSQsITav0WEFpaGigUlNTqTt37lC3b9+mbt26Rd24cYO6desWFRwcTN27d4/KyMhot8UQExNDrV69mtLQ0KAGDRpE2dvbU3369KHmz59PXb58maqvr5fxXyQ+T58+pbS0tCgVFRVqy5YtMrGKMjIyKC0tLaELzpkzZ6hhw4ZRPB5PrOOUlZVRffv2pUpKSlrdTl1dnSotLW33esWBJElqw4YN1IgRI6gffviB0tHRoWbPnk1FRka2ue/p06cpLS0tav/+/T3yIlpVVUV9//33lIaGBrVx40aqrKysq5dE8wnQkqB0677SLBYLjx8/RmZmJrhcLng8nqCzMUmS4PP5YLPZePXqFR4+fIjKykqxjlteXo69e/fC3t4erq6uuH//PthsNgYOHIgtW7agsLAQFy9exJw5c7pk5jefz8ePP/6IqVOnAgBu3LiBbdu2yWSORGBgIP797383q1OpqanB5s2b4e/vL3YsSENDAzNnzsTx48db3U6aTC9xYTAY+PPPP2FhYYHY2Fikp6cLWvuPHz8e9+7da7HCftGiRXj27BmOHDmCefPmoaKiokPXKis4HA727dsHCwsL5ObmIi4uDjt37myxXxsNTWfQbQXl7du3iImJAZ/Pb7M9Pp/PB4/HQ0RERIsXhKYK9vnz58PQ0BA7d+7Emzdv4OjoiP/85z8oKCjAlStX4OHh0epskY6GxWJh9OjR2LVrF8aMGYO0tDSMHTtWJsdms9k4fvw4fHx8mj3/+++/Y/z48XB2dpboeCtXrkRgYGCr7097alHaA0EQOHbsGHg8Hnx9fbF69Wqkp6djyZIl+PrrrzFs2DBcvHhRZOW8hYUFIiIiYGBgAAcHB4SHh3f4etsLRVG4ePEibGxscPPmTdy9excnT54UdDOgoelKuqWgVFdX4+XLlxLPWeHz+Xj+/DkaGxsFz2VnZ+P777+Hnp4e3N3dcfPmTTg6OuKHH35Abm4ugoOD4enp2WLH287kwYMHsLS0REJCArZv347bt29L1K6+Lf755x/Y29s3q9BPT0/HoUOHsH37domP5+TkhD59+uDBgwctbtMZFkoT8vLyuHTpElJSUrB161bIy8tj0aJFSEhIEKRCW1lZ4dChQ80+I8C7uhc/Pz/4+/tj9uzZ2LZtm9RzfmTNkydP4OTkhB07diAwMBB37txp1smZhqar6ZaC8vr163b3YGqayX369Gk4ODjAysoKf/75JwwNDfHbb78hJycHT548wbJly2R6sZYGPp+PzZs3w83NDRoaGoiKisLq1atlXsdy8OBBrFixotlzX3/9Nb799luRQ6zagsFgYMWKFa1WzsuiFkUSlJWVcevWLVy7dg179uwB8M56mTFjBp49e4bjx4/j+vXrMDU1xR9//CFUoPnFF18gJiYGt2/fhqurK4qKijpt7S2RlJSEGTNmYPHixfD19UV0dDQmTJjQ1cuioRGi2wkKh8NBcXFxu/cnSRJJSUlYvnw56urq8OuvvyIzMxMxMTFYuXIldHR0ZLha6SksLMTw4cPh7++PhQsXIjk5GYMGDZL5eZKTk/HmzRt88cUXgudu376NtLQ0kXNQxGXhwoV48uQJCgoKRL7emRZKE5qamrh79y727NmDM2fONHtt9OjRuHnzJu7cuYP4+HiYmpriu+++A4vFEmxjYGCAR48ewdnZGUOHDm2zIr+jyM/Px9KlSzFhwgRMnDgRaWlpWLhwIT1Sl6bb0u0+mbm5uVIfQ1FREc+fP8fr16+xcePGdt19dwbBwcGwtLTEmzdvcP78eRw9erTDkgACAgKwbNkyQWCfw+HA19cXf/31F3r1an/X2D59+sDDwwOHDx8W+XpnxVA+xMjICHfu3ME333yD27dvC70+ePBgnD17FlFRUaiqqoK1tTVWrVqFzMxMAO/6lv300084e/YslixZgi1btoDL5bZ53pqaGrBYLBQWFqKkpAQNDQ0Sr72yshJbt26Fvb09dHV18fr1a/j6+kr1PtHQdAbdTlDKy8ul9l3LycnJJCOqo+DxeFi1ahVmz54t6K80a9asDjtfXV0dzp07B29vb8Fz/v7+MDc3l6iZZEusXLkSR44cETm5sysslCZsbGxw9epVeHp64vnz5yK3MTU1xf79+5Gamgp1dXWMGDECCxcuRHx8PABg3LhxiIuLQ0JCAlxcXJCVlSV0DD6fj7y8PDx+/BhhYWGC7WNiYvD48WNERESgpKSkzdlBbDYbf/31FywsLFBcXIz4+Hj8/vvv3SK+R0MjDt1OUMS5CxQHDocjk+PImry8PNja2uLIkSP49ttvER0d3eEW1IULFzB69GgYGhoCeJdBt337dkGMQVrs7OxgYmIisqNvZ8dQPmTkyJE4ceIEZs6cidTU1Ba309XVxW+//YbMzEw4ODhg6tSpmD59OkJDQ6GlpYWbN29i/vz5cHJywqVLlwT7lZeX4/79+0hKSkJdXZ0g47DpQZIkysrK8OLFCzx9+lQoGQB456Y9d+4crK2t8eDBAzx69AhHjx6FgYFBh/xPaGg6im4nKLLqidUd+4RdvnwZlpaWKCkpQUhICH755ZdO8Yd/GIzfunUrli5dCgsLC5mdY8WKFSKHb3WlhdLE9OnT8ccff2DKlCnIy8trdVtVVVVs2rQJmZmZmDVrFpYsWYLRo0fj1q1b8PX1xa1bt7B582asXLkS+fn5eP78OXg8XptJJHw+H3V1dQgNDW3mBnvw4AEcHR3x119/4fjx47h58yZsbW1l8nfT0HQ23U5QZDEbpambcXeBw+HA09MTHh4eAreJOM0MZcGLFy9QVlYGV1dXAEBkZCTu37/f6uTD9jBv3jzExcXhzZs3zZ7vqhjKh3h6emLt2rWYMmUKysvL29xeUVER3t7egqSFH3/8EYMHD0ZaWhqioqJAkiSeP38uUTYiRVHgcDiIiIhAXFwcpkyZghUrVmDLli14/vw5Pv/8c2n+RBqaLqfbCYqxsbFMrJTuEojPysrCwIEDERQUhL179+LOnTtQVVXttPMHBATAx8cHBEGAJEmsXbsW27dvb1eX4tZQVFSEl5cXAgMDmz3fZKG0FT/oDDZu3Ihp06ZhxowZqK+vF2sfJpOJ+fPn48WLF9izZw9OnDiBoUOHYty4ce0avUBRFKqqqvDrr79ixowZSElJwfz58+nphzQfBTJtXy8rnjx5gtra2nbvr6urC0dHRxmuqH2cOHECPj4+0NXVxcOHD2Uy8lcSKisrMWDAALx69Qo6Ojo4duwYjhw5grCwsA5xtb158wbOzs7Izc1t1tpFWVkZLBarSzsQNEGSJBYvXozS0lJcvXq1XckbkZGRYLFYUt34KCkp0bUkND2WltrXdzsLBQDMzc3b/WVlMpkwNzeX8Yokg81mY9asWVi2bBk8PDyQkZHR6WICAKdPn8aUKVOgo6ODyspKfPfdd/D39++wuI25uTkcHByaBa2B7hFHaYIgCBw5cgQMBgPLli1rV0ahpqam1FmEHA5H7N5zNDQ9hW4pKP3794eurq7EFz4mkwlTU1Ooq8tmolxdXR1KS0vBYrFQVlYm1vjY1NRUGBsb4969e7h8+TJOnjzZJSnMFEU1C8b//PPPmDFjBoYPF56JI0tEBee7SxylCXl5eVy8eBHp6enYvHmzxPuXlJRIndpOkqRYsRwamp5E90uFwruWHkOGDEFcXByKi4vFCnwymUwYGxtLnbnE5/NRVFSEN2/eoK6urpmokSQJbW1tmJmZQV1dXcjv7efnh40bN8LKygqJiYnQ1taWai3SEBoaCgBwcXFBamoqTp8+LfPJiaJwc3PD2rVrkZCQIJgG2Z0slCZ69+6NmzdvYsyYMdDV1cXGjRvF3lcWKelNAXoamo+JbikowDvXxNChQ5GTk4M3b96Ay+WKFBYmkwklJSVYWlpCX19fqnNWVFQIMniazvXhnSiLxUJpaSlUVFTg5OQEBQUFNDQ0YPr06Xj69Ck2btyI7du3d3mQNSAgQGCd+Pr64j//+U+ntJ2Rk5ODt7c3AgICcODAAQBdX4vSEhoaGrh79y5GjRoFHR0deHp6irWfrFyGdAsVmo+NbisowDtLxcTEBMbGxigrK0NWVpageIzJZEJVVRWmpqZQU1OT+lylpaWIjo4Wyxri8/morq5GSEgI+vTpgylTpoAkSYSGhkrcAr4jKC4uxu3bt3HgwAFcv34deXl5WL16daedf9myZbCzs8OOHTvQp0+fbmmhNGFgYIA7d+5g3Lhx0NLSwrRp09rcR1FRUaippKQwmUy6lQrNR0e3FpQmGAwGtLS0oKWl1SHHr62tFVtMmqAoCvX19Xj9+jWGDBmCW7dudZval+PHj2POnDlQVFTEhg0bEBgY2KlxnP79+2Ps2LE4d+4cfHx8ul0M5UOsra1x7do1uLm54dq1axg5cmSr2xsaGqKsrKzdHbGBd58fPT29du9PQ9Md6RGC0tGkpaW16+JAEAQMDAxw+vTpbiMmJEkiMDAQQUFB2LVrF4YMGYJJkyZ1+jp8fHxw8OBBODg4CLonBwcHQ15eHgYGBjAxMemSaZgt4eTkhFOnTmH27Nl49OgRbGxsWty2KWFEGkHR0dGhLRSaj45P3okrbbt8giCQkZEhwxVJx71796Curg5dXV3s3r0bu3bt6vQ1FBUVgclkYtGiRSguLgZBEILCSjabjaysLDx+/BjPnz9vVzfejmLKlCnYtWtXmy1aCIKAiYlJu2MgTdmINDQfG5+8oOTk5Eh9jPr6elRVVclgNdITEBCAlStXYvPmzVi9ejUGDBjQqefPzMxEbGwsGhsbmxU3vg9JkiBJEqWlpQgJCUFNTU2nrrE1vvrqK2zYsAGTJ09GWVlZi9uZm5tDWVlZ4uQLJpMJQ0NDevY7zUfJJy8oxcXFUtcUUBTV6sWns8jLy0NISAgMDAzw7NkzbNmypdPPn5aWJvb/k6IocLlchIeHdytLZcOGDZg5cyamT5+Ouro6kdswmUyMHDkSysrKYlsqTCYT+vr6HTJAjYamO/DJC4os2uWTJCmztvvScOTIEXh4eGDz5s3YuXNnp8Z1OBwOEhMT2yXOPB4PCQkJHbCq9rNt2zbY2Nhg/vz5Lb63CgoKGD16NPr37w+CIFrs7sBkMiEvLw9LS0vY29t3eUo5DU1H8ckH5WX15e6smoK6ujpkZWWhsLAQPB4PFEVBTk4OmpqauH//PiZMmAA1NTV8+eWXnbKeJqSZtNlk4TU0NHSbQD2DwcChQ4cwZ84cLFmyBCdPnhT5HsvJycHe3h42NjbIz89HVlYW2Gw2SJIEj8dDbW0tJkyYAB0dHbruhOajp1s2h+xMIiMjpS66YzKZsLGxgbGxsYxWJUxtbS3i4+NRVVUFiqKEuvc2uY/KysowePBgODk5ddhaPoSiKNy/f1+qym+CIGBqagorKysZrkx66uvrMXnyZDg5OeHPP/+U6Abk7t272LFjBx49etSBK6Sh6Xx6VHPIzsTIyEjqdvkdXVNQUVGBsLAwVFRUgCRJka3gGQwGFBQUoK+vj7KyMuTn53fYej6ksrJSqhRa4J3bsK3hV11B7969cePGDdy7dw87d+6UaF8HBwfExcV1i9b9NDSdwScvKHp6elK7vbS1tTuspqCmpgaRkZEi57W3BEmSSEhIkCodWhI4HI5MXIfdIQ4lCnV1ddy5cwcHDx7EiRMnxN5PR0cHysrKyM7O7rC10dB0Jz55QSEIAsbGxlLVFJiZmcl4Vf+f2NjYdt39kyTZ7n3bcy5Z0J3v5Pv37487d+5g69atuHHjhtj7NVkpNDSfAp+8oADAwIEDoaSk1K6agv79+3dYTUFVVVWLaaviQFEU3r59K8MViUZWbV3k5Lp3joilpSWuX7+OpUuX4tmzZ2LtQwsKzacELSh4dyEbOXIklJSUJKop0NXVhZ2dXYetKzMzU6q7dj6fLzTjvSPo27evTKyUnlDs5+joiDNnzmDOnDlISkpqc/uhQ4ciNja2E1ZGQ9P10ILyfygqKmLMmDHQ09MTtAoRBZPJhJycHAYOHAgHB4cOrSl4+/at1G6g+vp6seentxd5eXno6+tL9b/oaNehLJk8eTL++usvTJ06tc1OC7SFQvMp0b19DJ2MvLw8hg4dCg6Hg9zcXGRnZ4PNZoOiKBAEARUVFZiZmUFfX7/Dawr4fL5M7voJggCHw+nwIkdTU1MUFRW1O2ajoKAgs0mbncGCBQtQUlICV1dXhIWFtdgJ28jICI2NjWCxWNDV1e3kVdLQdC60oIhAQUEB5ubmgtn0FEV1enVz0zllEaiWVdC8Nfr27QtNTU2UlpZKfD6CIGBra9vjKsjXrVsHFouFadOm4dGjR1BRURHahsFgCKyUKVOmdMEqaWg6D9rlJQZdcaFjMpkyEROKojptFsqwYcPQp08fiaw3giBgZWXVY+/ef/31V9jb22Pu3LktFnbScRSaTwVaULopDAYDffv2lclxlJWVZbCitmEymXB2doaWlhYIgmhViJviVHZ2dj26lTuDwcDBgwfRu3dveHl5ibTO6DgKzacCLSjdGDMzM6mq+KWtsWkPTCYTI0aMwKhRo9CvXz8QBAE5OTnBo2n0rYWFBSZOnAhDQ8NOW1tHIScnh3PnziE/Px9ff/11M8uSoihYW1vD2NgYMTExePHiBRISEsBisbp13Q0NTXv45Ht5dWdIksS9e/ckqpJ/H4IgMG7cuC5tuMjlclFVVQUulwuCINCrVy/07du3x8VLxKGyshKff/65oONzXl4eMjIywGazBX9/E0wmE0wmEwMGDICJiUmnjmimoZGWlnp50UH5bgxBELC2tkZKSorE2VNN44m7unuvvLx8ixlQHxtqamq4c+cOJk6cCEtLSygpKQnetw+tRD6fDz6fj/T0dOTm5mLkyJHdZow0DU17oV1e3RxjY2OYmJhI5PpiMpnQ1NSEra1tB66MRhQ6OjrYs2cPGAyGWDcBJEmioaEBYWFhaGxs7IQV0tB0HLSg9ACsra1hYWHRasEl8C5ATBAE9PX14ejoSM/f6ALi4+PB4/EkdmFxuVxERUXRcRWaHg3t8uohmJmZoX///sjJyUF2drbIeSj9+/eHqampyHoImo6HzWajqKioXXU/FEWhrq4OlZWVParAk4bmfWhB6UEoKirC0tISAwcORFVVlaCKX0FBAX379u32zRU/dtpqw9IWfD4fGRkZGD5cKNZJQ9MjoK9APRCCIOi72G5Idna21F0JiouLweVy6awvmh4J7WSnoZEBJElKNQK5CYIg0NDQIIMV0dB0PrSg0NDIAD6fL7PamvbWHdHQdDW0oNDQyABZ9V4Duv+gMRqalqAFhYZGBhAEAQUFBamPQ5Jklxej0tC0F1pQaGhkhImJidS1Pzo6OnRAnqbHQgsKDY2MMDY2lmr/njS1koZGFLSg0NDIiF69eglGSEsKg8FA7969oaamJvuF0dB0ErSg0NDIEHt7eygrK0ssKvLy8nBycvoouzDTfDrQgkJDI0OahoypqqqK1dCTIAgoKipi9OjRUFRU7IQV0tB0HHR+Ig2NjJGXl4ezszNyc3ORmZkJNpst1HmYnodC8zFCCwoNTQdAEARMTExgbGyMiooKFBYWorGxERRFoVevXtDV1YWOjg7t4qL5qKAFhYamA2EwGNDQ0ICGhkZXL4WGpsOhYyg0NDQ0NDKBFhQaGhoaGplACwoNDQ0NjUygBYWGhoaGRibQgkJDQ0NDIxNoQaGhoaGhkQm0oNDQ0NDQyARGa0OBGAxGCYCczlsODQ0NDU0PwJiiKO0Pn2xVUGhoaGhoaMSFdnnR0NDQ0MgEWlBoaGhoaGQCLSg0NDQ0NDKBFhQaGhoaGplACwoNDQ0NjUz4f+SoQ4NRTPItAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from torch_geometric.utils import to_networkx\n",
    "\n",
    "G = to_networkx(data, to_undirected=True)\n",
    "visualize(G, color=data.y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NUdHZY2u8vn3"
   },
   "source": [
    "## Implementing Graph Neural Networks\n",
    "\n",
    "After learning about PyG's data handling, it's time to implement our first Graph Neural Network!\n",
    "\n",
    "For this, we will use one of the most simple GNN operators, the **GCN layer** ([Kipf et al. (2017)](https://arxiv.org/abs/1609.02907)).\n",
    "\n",
    "PyG implements this layer via [`GCNConv`](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.conv.GCNConv), which can be executed by passing in the node feature representation `x` and the COO graph connectivity representation `edge_index`.\n",
    "\n",
    "With this, we are ready to create our first Graph Neural Network by defining our network architecture in a `torch.nn.Module` class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "6tQGQF8r8zIr"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GCN(\n",
      "  (conv1): GCNConv(34, 4)\n",
      "  (conv2): GCNConv(4, 4)\n",
      "  (conv3): GCNConv(4, 2)\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",
    "\n",
    "class GCN(torch.nn.Module):\n",
    "    def __init__(self):\n",
    "        super(GCN, self).__init__()\n",
    "        torch.manual_seed(12345)\n",
    "        self.conv1 = GCNConv(dataset.num_features, 4)\n",
    "        self.conv2 = GCNConv(4, 4)\n",
    "        self.conv3 = GCNConv(4, 2)\n",
    "        self.classifier = Linear(2, dataset.num_classes)\n",
    "\n",
    "    def forward(self, x, edge_index):\n",
    "        h = self.conv1(x, edge_index)\n",
    "        h = h.tanh()\n",
    "        h = self.conv2(h, edge_index)\n",
    "        h = h.tanh()\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": "5zgbaD5P8_M_"
   },
   "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 `GCNConv` layers reduce the node feature dimensionality to $2$, *i.e.*, $34 \\rightarrow 4 \\rightarrow 4 \\rightarrow 2$. Each `GCNConv` 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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "id": "48uhs_0j9AMX"
   },
   "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",
    "_, h = model(data.x, data.edge_index)\n",
    "print(f'Embedding shape: {list(h.shape)}')\n",
    "\n",
    "visualize(h, color=data.y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nDtJ9Zjw9I_Y"
   },
   "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 observe 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",
    "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",
    "After that, we perform multiple rounds of optimization, where each round consists of a forward and backward pass to compute the gradients of our model parameters w.r.t. to the loss derived from the forward pass.\n",
    "If you are not new to PyTorch, this scheme should appear familar to you. \n",
    "Otherwise, the PyTorch docs provide [a good introduction on how to train a neural network in PyTorch](https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html#define-a-loss-function-and-optimizer).\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": 58,
   "metadata": {
    "id": "FI3DETGi9ND6"
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "google.colab.output.setIframeHeight(0, true, {maxHeight: 430})"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "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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yAIvWzdtXynj5vfHHY5727GyXFB3J/MRbrtNtN7aruspXIu4p5hvFYp5ivqfWxmq9857Nunl7+6UblKH4OIIBsGg1N1TruvXNeu300KxTlX3PqLmhWh1r3rg4pecZ3Xbjau3ZuUqnzw9reDQl40krW+q0opUusIVAwABY1B58U4dS6U6duTAy44yyeMxT07Iq/bMHtsqb4WjH88xlwYOFQ8AAWNR839N73rxFnWeG9OzhC+ruH5fvGYWhVUN9Qnt3rdL1G1sU8+nxX2wIGACLnjFGW9Y3a8v6ZiVTGU2mAiVinmqquZTLYkbAACgrVYmYqhK8dZUDjikBAE4QMAAAJwgYAIATBAwAwAkCBgDgBAEDAHCCgAEAOEHAAACcIGAAAE4QMAAAJwgYAIATBAwAwAkCBgDgBAEDAHCCgAEAOEHAAACcIGAAAE4QMAAAJwgYAIATBAwAwAkCBgDgBAEDAHCCgAEAOBErdQEAFgdrrbq7u3X27FmlUinF43GtXr1a7e3t8jw+i6JwBAwA9fb26oUXXlAQBAqC4LLlL730km688UatXr26hBWiHBEwwBLX3d2tAwcOKAzDK56bCpuDBw8qCAKtW7duoctDGeO4F1jCMpmMnn/++RnDZbowDHXo0CFNTk4uUGWoBAQMsIR1dXUV1P7UqVOOKkElImCAJchaq97eXr3yyiuXjbnMJgxDnT592nFlqCSMwQBLTDKZ1DPPPKOxsbG8w2VKOp12VBUqEQEDLBFhGCqZTGr//v2amJiQtbbgbTBdGYUgYIAKY61Vf3+/Tpw4oaGhoUtHKYUercykubl53tvA0kHAABUklUrpmWee0ejoaFECZTrf97V58+aibhOVjYABKkQmk9ETTzyh8fHxom/bGKOGhga1trYWfduoXAQMUAH6+/v14osvOgkX3/e1bNky3XbbbTLGFH37qFwEDFDmXn31VXV2dha9S0yS4vG4du7cqVWrVjHAj4IRMEAZO3XqlLNw8TxPt99+uxobG4u+bSwNfCQBylQYhjp69KiTcJGk2tpawgXzQsAAZaqnp2fOa4hdK9/3tXv3bifbxtJBFxlQpgYGBpxMRTbGaM+ePWpqairqtrH0EDBAmSpmuMRiMdXU1Gjjxo1avXq1YjHeGjB//BUBZaq2tlae582rm8zzPO3atYv7vMAJxmCAMrVmzZprXtf3ffm+r5tuuolwgTMcwQBlqrq6WsuXL1dvb29eF66sqqpSIpFQIpHQ2rVrtXr1avm+vwCVYqkiYIAytnv3bv3whz9UMpm8asj4vq/29nbt3r2bM/GxoOgiA8pYIpHQvn371NraKs/zLguQqW6wjRs3Ei4oCY5ggDKXSCR0++23a3x8XGfOnNHo6Kg8z1NLS4vWrFnDjDCUDH95QIWora3V9ddfX+oygEvoIgMAOEHAAACcIGAAAE4QMAAAJwgYAIATBAwAwAkCBgDgBAEDAHCCEy2BHGFo9XrXRT17+IIu9I8pDK2qE752bFmu3TesUGN9ValLBMoCAQNMMzGZ0Ve/e0xDI0mlM2/cZ2UyFejg0R4dPNaje/es003XryhhlUB5IGCArEwQ6ivfOarB4UnNdA+vIIyuVvzYs11KxHzdsLl1gSsEygtjMEDWK539ujiamjFcpssEoR595rSC4NrvJAksBQQMIMlaq2cPX1Amk19oWGv12ukht0UBZY6AARSNvYyMpfJun86EOn5q0GFFQPkjYABJqUwozyvshlzJVOCoGqAyEDCApOqEf2kQP1+1NcyRAWZDwACSqqtiWtFSm3f7eMzTdmaRAbMiYICsPTvbFY/l918iEfe1flWD44qA8kbAAFlb1jdpXfsyxfzZx2Jivqd33LNJxhQ2ZgMsNQQMkGWM0bvu3awt65vl+0a5Y/7xmKdE3NdP3r9Fa1cuK02RQBlhlBKYxvc9vX3fJg0OT+qFV3p06tywgiBUfW1cN21boes2NCvm87kMyAcBA8yguaFab75tfanLAMoaH8UAAE4QMAAAJwgYAIATBAwAwAkCBgDgBAEDAHCCgAEAOEHAAACcIGAAAE4QMAAAJwgYAIATBAwAwAkCBgDgBAEDAHCCgAEAOEHAAACcIGAAAE4QMAAAJwgYAIATsVIXgGs3lk5pf88JnRjuVWCtVtU26q72zVpeXV/q0gCAgClHgQ319yee1xMXOmUkpcJAknRo4Jwe7npFWxtX6Be2vUl18arSFgpgSSNgykxorT7/8uM6OnRB6WywTAlsKEk6drFH//WFb+s/3/w21cYSl56fnJzUyZMn1d/frzAMVVtbqw0bNqi1tVXGmAX9OQBUPgKmzDxx4TUdHbpw6ahlJoENdTE1of/52o/08W13KQxDHT58WF1dXZKkMIyC6OLFi+rp6VFVVZX27NmjZcuWLcjPAGBpYJC/jFhr9Z2ul2cNlykZG+pgX5dGUpM6cOCAurq6FIbhpXCZEgSBxsfH9eSTT2p0dNRV6QCWII5giigVZPR8/xn1TYzKM0Zr65q1s2WVPFOcHO8aG9JwajLv9p4x+l7nISX6+q4IllyZTEbPP/+8brnlFo2Pj0uS6uvrVVNTM6+aASxdBEwRZMJAX3v9oB6/8JqMjJJhRkZSwo8pZjy9c/0u3bd667zHOS6mJuQbT9LcRzBSNPh/uveCNgb57Xd4eFiPPfaYPC8KxDAM1dLSoq1bt6qlpeVaywawRBEw85QJA33u0Pd0anTgskF3KykZZJSU9PWTB9U9MawPbtkzr33FPE+2gPZGUiaVlpSYq+klud1ofX19GhgY0I4dO7Rhw4YC9g5gqWMMZp6+9vqLV4RLrlQY6KnuE3qu99S89rWurllBHuMvU+Ker+VhfF77lKLQOXLkiPr6+ua9LQBLBwEzD6kgo8cvHJ81XC61DQN98/Thee2vLl6lm5avlVF+XV41flytgT+vfU4Jw1DHjh0ryrYALA0EzDy80H8m7zd7SeqbHNX58Yvz2ue71u9Swps7NBKer/dvvFl+Hm3zdfHiRY2NjRVtewAqGwEzD32To0qGmbzb+8ZT3+T8pgK31zbqkzvuUZUXkz/DpAGjKFx+smO39qzs0MaNGy8N2s+X53kaHh4uyrYAVD4CZh6MvAKOX6KBf68IZ8xvbVqpX7/lHbpn1VZVebHokZ2xtrt1nf7tjffrzWuulyR1dHQoHp//OMwUawuZZgBgKWMWWQEmg7RGUpPyjafGqhqtq29SwovlfRSTCQOtrm0qSi2t1XX66c236L0bd2soOa7AWjUlalQduzxMEomE7rzzTj311FPKZDIKgvwnCeSy1qq2tna+pQNYIgiYPLw+0qfvnHlZhwbOyTeerKxixtO+Vddlz0vJz5aGNjVXFfcNOu75aquZ/RIvdXV1uu+++9TV1aUjR45c81FIVVWVGhsbr2ldAEsPATOHb595Wd88fUjpMJBVdAkWSUop0MNdr8jzPMWMd2n51cQ9X+/uuHEBKp5ZLBZTR0eH+vv7df78+YLX931fW7Zs4aKYAPLGGMws9nd36punDymVDZdcgazSYaDQWsVnOZKJe74+vGWPNje0uSs2Txs3bpTvFzazzPd9tbe3a926dY6qAlCJOIK5itCG+uqJF/K6sGQoqy3L2jSWSalnckSxbNikw0DXNbbpXRtuXBThIknNzc2qrq7Oe7pxLBbTli1btHnzZo5eABSEgLmKQwPn8gqXKa8O9+iP7ni/BlPj6p0clW88raptLPqYy3wZY3Tbbbfp8ccfVyaTmXU8pqOjQ9u3by/aNGcASwsBcxXHL/YUFDCS9GzfKd3VvkXttYt7ILy2tlb79u3TwYMHNTg4KOmNe8T4vq+qqirt3LlTK1asKGWZAMocAXMVySD/EyinPN19Une1b3FQTfHV1NTojjvu0Pj4uM6fP69kMqlYLKbly5erubmZ7jAA80bAXEVrdV3B6/ROjjioxK3a2lpt3ry51GUAqEB0rucIwlDJIKM9ywu/NH0xztIHgErBEYyiqyI/13da3z7zsnomhmVk5HuejIxsAXdgaavmnvYAMGXJB8xgclyffvFhjaaTly75YmUVFjjAX+XFdP+abS5KBICytKQDZiKT1u+9+F0NJScUFnSvyMsZSbWxhHa2rCpecQBQ5pZ0wDx+4bhG0sl5hotRtR/Tp3a9WV4B1yUDgEq3ZAMmtFYPdx3N626UUnSU4pno8vwZGypufFlZXdfYpo9cd5uWV9c7rReXS6VSl6ZXe56n1tZWNTU1Mb0aWESWbMAMJsc1EaTzbm8l7WhepeubVioZZFQbS+im1jVqqSp8OjOuXSqV0qFDh9Td3S1jjIIgkDFGnuepqqpKO3bs0MqVK0tdJgAt4YBJBpmCpxV7xjCQX0LJZFKPP/64ksnkZZe4sdYqCAKNj4/rwIED2rVrFxfmBBaBJTtoUB+vUhDOfon96YykpkSNu4IwpwMHDlwRLrnCMNThw4c1MlJ+J70ClWbJBkxDolpr65rybh/3fN2+cqO7gjCr0dFRDQ0N5XWztDAM1dnZuQBVAZjNkg0YSXpw3XYlvPx6CZuratVR3+q4IlzNqVOn8r4Tp7VW586dm9ftoQHM35INmHQYKBVk5OcxDlPlxfSJG+5mhlIJjYyMFHSrZ2OMUqmUw4oAzGVJDvKfHh3QHx76vtI2mPWqyVVeTDWxuD65416tKaA7DcVXaLhba/lAAJTYkguYC+MX9fsvPaLJOS7HHzeefva623Rz23ouYrkINDc3q7+//9J9a+bieZ4SiYTjqgDMZsl1kX3p+LNzhosU3Qb5xEgf4bJIrF+/Pu+2nudpw4YN3IkTKLEl9T+wb3JUr4/05dU2sFZPdHfmfaY/3KqurtaqVavyCg3P89TR0eG+KACzqogusrF0Uk9e6NRj2WuL+cbT5oY2PbD2Bl3X0HapL/6l/rMq5HjEyKhzuFfbmtrdFI6C3HjjjZqcnNTQ0NBVZ4j5vq+9e/eqpoZzloBSK/uAOdh3Rn9x7CkZSalpRxuHBs7q1aFura1v1i/tuFc1sbgmgpTSNv+TKyWr8Uz+l5OBW77v67bbbtPJkyfV2dmpTCZz6cNDGIZauXKlrr/+etXXc104YDEo64A5PHBOf3Hsqat2YyXDjE6N9Otzhx7V/3PTW1XjJxQ3XgEhY1Tjx4tXMObN8zxt2rRJGzdu1NDQkJLJpHzfV2NjI4P6wCJTtmMwoQ310KtPzzlGkrGhzo9f1DM9J7WrZY1UwKC9tVZbGtvmWyocMMaoublZ7e3tamtrI1yARahsA+bI4Hmlwrlng0lR19l3ul5WW029Oupb8lrHN57e1L5Zcc+fT5kAsGSVbcA813t61pMkc/VPjuliakIf2rJXVf7sPYOejJbFq/SO9TvnWyYALFllGzBj6WRB7X3P00QmpdV1jfq3u+5XXSyhqhmuQ1blxdRaXad/v/sB1cerilUuACw5ZTvIvyxR2Jt/EIaqiUX99BuWtej/2/sTOtB3Wo+cPar+yTEZY7S6tlEPrt2unS2rKv72x5lMRv39/Uqn04rFYmptbVU8zoQGAMVTtgFza9sGHeg7k3c3WVtNvRqn3c8l4cd0x8pNumPlJlclLkqpVEpHjx7V2bNnZYy5dM2uMAy1atUqbdu2jXNIABRF2QbMDU2rVOXF8gqYhOfrwbXbF6CqxW1yclJPPPHEVW/adfbsWfX09OjOO+/UsmXLSlAhgEpStv1AnjH6uevvnHOWV8x4WlvXrL1tHQtT2CJlrdXTTz895x0h0+m09u/fz71UAMxbWQVMKsjo1Ys9OjRwVq8P9+n6phX6lzfcrSovpkRO0BhFRy5bGlfoU7veLH+JX/iwv79fExMTed1TJQgCnT9/fgGqAlDJyqKLbDg1oX86fURPdp+QMUZGkpVVzPh6y5rr9Vu3vkvP95/RD869qpH0pHzj6brGNj2wdrs6lnEXSknq7OzM+6gkCAK99tprWrt2reOqAFSyRR8wPRMj+r0Xv6vxTErBFZ++M/rWmSN6rveU/t1Nb9V9q7eWpMZyMDIyUlD78fFxR5UAWCoWdb9RKsjoMy89otF0coZwiaTDQN0TI/rTIz9c4OoAALNZFAFjrdVwakJ9k6OanHb14uf6Tmsyk9ZcowYZG+rUaL9Ojw64LbSM1dXVFdSeqcoA5qukXWSTmbSe6O7Uw12vaDSdlGc8BTbU1sYVenDtdn2362UlC7je2HfPvKJfuOFNjqsuT5s2bZr1PirT+b6vTZuW1vlBAIqvZAHTPzmmT7/4sMYyyTfu45K9jP4rQxfUOdx72f1d8vHSwNlLJw7icitWrFAikdDExMScbT3P05o1axagKgCVrCRdZJNBWp9+6WENpSauGiKFhosU3f/lRJ63RF5qjDG6/fbb57wczNRNvWKxRT//A8AiV5KA2d/9usbSSdk5R1cK93DXK0XfZqWoq6vTvn371N7eLs/z5Pv+ZV/b2tp09913q6mpqdSlAqgAC/4x1Vqr73a9fE1HKPk4MsgJgrOpqanRrbfeqmQyqZ6enksXu2xra2NgH0BRLXjAjGfSupiadLb9ue5wiUhVVZXWrVtX6jIAVLAF7yLL2ECew0H4xAz3eAEALLwFeTfuHh/Wo+eO6WB/l5KZdMFHGdGlYebmyejm5XwqB4DFwGnAhNbqK50H9ER3p8IwVHANg/oJz1dgrYLsFObZ+J6n+9duu5ZSAQBF5rSL7O86n9OT3Z1Kh8E1hYskLYtX64Obbrniasm5Ep6vu1Zu1tq65mvaDwCguJwdwZwbG9KT3SfmNehe5cf0r3fcozV1TZKRvtx5QMaYy7YZy97a+J5V1+m9G39svmUDAIrEWcA8cvaognDubq1cRlLci6kxUa1/tX1fFC6S7l51nW5evl5Pdnfqqe4TGs+klfB83bx8ne5dvVUtVYVdawsA4JazgHmhv0thAd1iCc/X5oblaq6q013tm7Vp2fIrLvlSF6/SA2u36wFufwwAi56zgEkF+V2kckqVH9endr3FUTUAgIXmbJA/4ReWXdUFtgcALG7OAmZ369q8T6iMGU97V3S4KgUAUALOAub+Ndvkm/w2b0w0CwwAUDmcBcyauibduXJTXuevvH3dTjUmuNAiAFQSpwMfH9h8q4ykJ7tPXHEmf8x4MpIeXLddb1u3w2UZAIAScBownjH64JY9um/19freuaN6oa9LqTBQtR/T3rYO3bt6q1qrOX8FACrRgkzdaq9t0Ie27NWHtuxdiN0BABaBktzREgBQ+QgYAIATBAwAwAkCBgDgBAEDAHCCgAEAOEHAAACcIGAAAE4QMAAAJwgYAIATxtr8b2tsjOmVdMpdOQCAMrPBWts20xMFBQwAAPmiiwwA4AQBAwBwgoABADhBwJQxY8zHjDH2Ko+hEtf2kDGmy8E2Z/pZ/2CGtncZY54yxkwYYy4YYz5rjLmm+3IbY+7N7uf+ef8QC8wY83FjzFFjTNIYc8wY8y8LWPejxpgDxphhY0yvMeZhY8zdOW3WGmP+2Biz3xgznn2dOq6yvY3GmK8aY4aMMWPGmO8bY27NabPMGPMVY8xr2TZDxpgfGWM+ck0vAEpqQW44BufeLyn3zTxTikIWQK+kd+csOz/9G2PMjZIelvQdSe+UtFHSpyWtkfTTC1DjomCM+bikP5P0u5IekfQWSf/NGGOstf99jnX/RXbdz0v6VUm1kv6NpIeNMXdYa1/INt0i6ackHZD0uKQHrrK9VklPSBqR9AlJ49ntfd8Ys9da+0q2aULR3+7vSjopqUrR7+xvjDFt1trPFfo6oISstTzK9CHpY5KspC2lrmWG2h6S1FWKbUr6uqTjkuLTlv1s9rW6+Rr2e2923ftL/boWUHNMUo+kv85Z/peS+qa/NldZ/ylJT+UsWyYpJel3py3zpv37F7KvU8cM2/vPioJj87RldZK6JX0lj59nv6RDpX5deRT2oItsCZjWlbbPGPMNY8yoMabfGPOnud1GxphVxpgvGmP6st0qL83UPZHt7vibbPdT0hhzwhjzhzO0+zFjzOPZ7pPjhXTRXOPPGpf044retNLTnvqKojfH9zjarzHG/Eq2GypljDlvjPkTY0xDTrtfNsa8ku26GzTGPGeM+clpzz+Y7dq7mP09HTPG/No1lHSHpDZJX8pZ/jeSWiXdNcf6CUnDOcvGJaU1rWvdWhvmWc/tko5bazunrTum6KjnncaYuXpT+lW5R+UVi4CpDL4xJpbzmOl3+yVJr0l6r6TPSfq4pEtdJcaYOkmPSXqbpP8o6SckHVLUPfEvprXbKOlHkvZJ+jVFb+j/r6TlOftrkPS/svt9j6RnJf13Y8x90xtlw++hPH/WFdnwyxhjXjXG/HtjjD/t+c2SqiUdnr6StXZSUqek7Xnup1D/VdJnFXXNvUvS7yk6wvzm1O/CGPNhSb8v6W8lvV3ShyV9VVJL9vlNkv5B0uuKuoXend1m3dROjDEd2dfrN+aoZ0f26+Gc5UeyX+d6Hf6bpPuNMT9vjGkyxqyR9CeKAuYLc6w7k0BRwOdKSqpR9Hu7JBvYMWNMa/Zv70FFf7MoJ6U+hOJx7Q+90UU20+MfZ2j3+Zz1/5Oi//hbs99/Mtvu3px2jyjqbvGz339R0qik1bPU9lB2W/dNW1al6JPon+e0zUj6Qh4/76ck/ZKkNyt6g/4fkkJJfzGtzZ3Z/f74DOs/IenRa3id79UsXWSKAiIp6aGc5R/Jrvfu7Pd/Iun5Wfbzvmz7hlnabMi+Xr82R83/Mbut6pzlsezy/5LHz/1zkian/U2dl7RnlvazdZH9nqIjoNZpyzxFXZlW0h057T85bb8pSb+4kP+3eBTnwRFMZfhJSXtyHp+aod1Xcr7/sqL/5Huz3++TdNZa+4Ocdl9S1N0y9an3AUUBdm6Ousattd+f+sZam5T0qqT10xtZa2PW2p+fY1uy1v6BtfaPrbXfs9Z+y1r7cUl/KOnnjTHXzbW+Q7cr6lLK7Y76sqIwuCf7/bOSdmdnXd1vjKnNaX9Q0RHCl40x7zPGrMjdkbX2VPb1+s2i/gQ5jDHvkfSnigb671d0VHZY0reMMTuvYZOfV/S39kVjzGZjzCpJf6RoAoYUfVCY7u8U/R2/TdJfSPpjY8wnrmG/KCECpjIcttY+l/N4bYZ23Vf5fk32a4tyZmRlXZj2vBT14eczBXlwhmVJRV1YxfK32a9T012n9tk8Q9sWSQNF3Pf07Uo5r521NqPoiG3q+S9K+leSblM0w23AGPO1qWm92d/Zg4r+X/6NpAvGmKeNMfeocFd7HaZquerrYIwxkv5c0lettb9srX3UWvuPkt6haBbYbxVajLX2hKIuwVsUddOeUzRONNXtlfva9Wb/jr9trf1FRa/HZ7JjbCgTBMzSsvIq35/Nfh2Q1D7Deu3TnpeiWUhrZmhXSlMX1etUFGI7pj9pjKmWtEnSyw72PfW6XPbaZQeuW6eet5E/s9buVTRe9VFFR49/N7WOtfb71tofl9Sk6Mgho2gcJ3d8ay5TYy07cpZPHYXO9jqslLRC0RHXJdbalKQXJd1QYC1T6/9vRX832xXNfLxFUr2kM9ba03Os/ly2be7fMBYxAmZp+amc7z+gqGvimez3j0laa4x5U067Dykag5l6U/quopk/q1wVWoAPKwqXZ6VLb4LflvRTOTOT3qdoDOgfHNTwtKJxgg/kLP9pRWMeP8hdwVo7aK39O0Xdlld0OVlrk9ba7ykau6jTG11J+dqv6IPAh3OWf0RR4D05y7qDikJ67/SFxpiEpN164wNJway1gbX2FWttpzFmtaLXaNZzcrLuUTTu13Ot+8bC40TLyrD7Kp9wn8t200x5uzHm04oCYq+kX5f0RWvt8ezzD0n6ZUlfM8b8J0XdYB+W9FZJn7DWBtl2v65okP0pY8zvKOryWKNoYL3gM66NMRlF52tcdRzGGLNBUTfJl7P7q1I09vQxSX9mp01/lfQbit70v2KM+VNJHYpOtPyqtfbAtG3eK+n7kv65tfahPEq92xjTlLMsY639hjHm9yX9B2PMmKRvKfqU/9uKJhZ8M7u/P1fUxbRf0RvlVkk/o+j3oewU7n3Z9c8oOsr5D4q6kw5Pex06Jf3mbOMw1tq0Mea/KDqx8qyiiRpvVjRw/0vZIJ56Hb4g6aPW2lh23aQx5n9I+qQxZkDSPyqa6fVJRa/lp6bvyxjzvuw/b8l+fZuJbu3Ra619LNsmrigsH1M0/XlH9mc7omhm3dS2PqFoTOsRRX9/rYo+GL1P0q9OrxtloNSzDHhc+0OzzyKzkpbntNsn6f8o+iQ4oGgQtyZnm6sUvZH3KfoU+5Kkj8yw782Kxj/6FM006pT02WnPP6QZTopU9Gn+BznLrHJmYM2wXoukbyi6H9GkohlJzyt60/NmaL9P0Rv5pKKxpj+QVJvT5h26yoyznHb3zvIaj2bbGEm/IumYoqOZ89nXt2Hadj6a/fl7sq/t64rGIBqyz9+R/f2cyT5/XtLfS7p+2jY6svv9jTz/Rj6haGJFUtGMrStmY2V/VzZnWSz72h5UFIq92dofmGH9q702P8jZ3j9mfxfJ7N/Lb8/wO7lTUcCez7abCsd3lPr/G4/CH9wPZgkwxnxM0l9Jus7OPPi/JGWPvt4taZflPwJQdIzBYCm7R9LvEC6AG4zBYMmy1uZOZgBQRHSRAQCcoIsMAOAEAQMAcIKAAQA4QcAAAJwgYAAATvz/Vve/aYovIFgAAAAASUVORK5CYII=\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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wHdi6deuCb5OAAYAKUlNT+oTI1tZWrVq1ykNtZkfAAEAF6ezsLKnDvqamRnv27PFYo5kRMABQQTZs2FDSTcHuvvvuRRlBJhEwAFBRqqurtXfvXoXh7DcHC4JAd955p6qr/czSLwYBAwAVpqWlRW9/+9vV0NBwRdAEQaDly5frzjvv1MqVKxengjnM5AeACtTU1KR7771XQ0ND6u3t1dhESqlMqKbVq9W6ZpWX+7uUioABgAo2manSkdOBTp5NKQwDSWeUjU5rXXOD9u1ar/b1yxetbgQMAFSoV7v69ciTJ5XJxldKzkZv3mTsTO9FffWbR3Xbzeu0b/f6RakffTAAUIHOnr+oR548cSlcCslkIz3zYo9e7epfwJq9iYABgAr0xKHTymTdnOUy2UiPHzoj5+YuW24EDABUmJGLSZ3rGyu6/MRkWqd7L3qsUWEEDABUmPMD4wqD4idbZrJOJ84M+avQDAgYAKgwUVR6c9fhVy8onc7OXbCMCBgAqDDLG2tUapdKFDm9emLAT4VmQMAAQIVZu7pedTWlzTLJRk4HXurxVKPCCBgAqDBmptt2tZa83NBIckFHkxEwAFCBbr6xWVWJ0nbhCz1QmYABgApkZtqwbllJy9TVJkq61P98ETAAUKFu3bG26LOYMDTdsn2N5xpdjoABgAq1qXVZfFZSRFkz066tLd7rlI+AAYAKZWb6l+/equrqULO1fCVC0/vu3qyGuqqFq5yu0aspX0xP6qWBcxrPpFSbqNJNq1q1orpusat11cYzKR0Z7Ln0fnasXKvGqtrFrhawoJxzOnl2RM+/dkEjF5MKw0CbWpdp97Y118S9SyrVquW1+uj37tSjT57QmdzlYLKRk5kUBoFWLKvWffva1ba2tP6acrimAmYgOaaHjh3SiwNnFFqgrIsu/dyxcp0+tOWtWlO38B/S1RpOTegvup7Tc/3dCs0UOafATJko0q6mNn1w861aXduw2NUEvLswOK6/+ac3NJnMKp158+q/FwbGdfDlXu3Y3KT7bu9QUMLlT/CmFY01+uB7tmnkYlJHTw1pIplRVSJQx/oVWrO6ftHqZaWMid67d687cOCAl4r0TozoM4cf0XgmNeNQutqwSj+36z5tbFzlpQ7l1Dd5Ub9++BsaS6cUFXhHJqkuUaX/uOs9Wt+wYuEreB0YGRnRyZMndfHiRQVBoKamJrW3t3u/R3kURUomk5Li+6fPde/0chsdHVVvb6+SyaSqq6u1du1aLV++eDed6huc0J/8wxGl0zNfVj4RmtrXL9f7771hQUc5Yf7M7KBzbm/B166FgMm6SA8+87CGUuNzjtOuD6r0mX3fr+rwmjr5ukzkIn3i2b/VQHJcbo53tLyqVp+67QOqChZ2J7SUTUxM6MCBAxodHVUUvblTC4K4y3Hjxo266aabLv1eLmNjY+rq6tLp06cve76trU2bN29WY2NjWbc33cjIiJ5//nmNjo7KOXdpQl0QBGpoaNDu3bsX5R7tX/zqS+ofmpyzXFUi0Hvu6NC2jqYFqBXKZbaAuSY6+V8cOKvx7MxnLvnGo7S+cfqI9zrNx0sD53QxnZwzXCQpmc3oUN+pBajV9WFyclKPP/64RkZGLgsXKT6ziKJIp0+f1sGDB8s6o7m3t1ff+c53dOrUKWWz2cse3d3devzxx9XT4+8yHUNDQ9q/f7+Gh4cVRdFl7y2KIo2Ojuqpp55Sf//C3niqt39Mw6OposqmM5GefXFhL2UCv66JgPlG98tKZjNFl/+7Uy9qJDWhyWxaJ0cH1DXSp8HkuMcalubRM0eUjIp7P8koo0eu8cCsJM8995xSqdSs4ZHNZtXX16czZ86UZZuDg4M6ePCgstlswe0655TNZnXo0CENDJT/YoPZbFZPP/20stnZr5SbzWb17LPPKpMp/v/afB051q/sLHdcnK5/aEIXx4sLJFz7FrSdaTKb1uG+bg0kx1UdhNqyokWpbFZdo6UfVf3ac1/XWCal0AKZpHSU1cbGVXrvxpu0e/WG8le+BD3jIyWVvzCx8DcCWorGx8c1ODhYVNlsNqs33nhDGzbM/2/lyJEjV5wtFRJFkY4cOaI77rhj3tvMd+7cuaK2L8Vhd/r0aXV0dJS1DjMZHS+uZWJKGJomJjNqrGdU2Xwlk0lduHBBmUxGiURCLS0tqqmpWdA6LEjAnBod0OdeeVwDqeLvwDaXodSEJCmtN4/ajo/26w9e3a871m7Wh7fspbPwOnP27NmSyk9MTOjixYvz6hsZHx/X0NBQ0eWHh4fnvc3pjh8/PufZy5RsNqvjx48vWMBUV5W2i3FOSpR4fa3ryfDwsI4dO6bz588riiKFYaj169ers7Pz0t/U+Pi4Xn75ZV24cEFmJufcpZ9r1qzRTTfdpLq6hZn24TVgIuf0R288oyd6j/nczGVSUVb7e7u0qqZB92/cuWDbzbeubrlG0nN3ak5pqfPb+Xu9mJiYKPpIXoo7v5PJ5Lx29gMDAyUdyJiZBgcHyxowExMTJZWfnCz+b3O+Nm9YoddPDFw2NHk2VYlAKxoX9ii7EkRRpOeff149PT2X9bFFUaRTp06pu7tbmzdv1vr16/XUU08pnU4XXE9PT4/6+/t1xx13eB90Innug/nTo88uaLhMSUVZ/X33S0pHC3v3tin3bdiumiJHudWECb17ww7PNbo+JBKlHy/NdwhxKYEmvdkfU06lnqkv5Jn9lo0ri57bEoamW3euZS5MAYcPH1ZPT0/Bfj7nnKIo0vHjx7V///4Zw2VKOp3WU089VfLf7tXwFjBnx4b0RG+Xr9XPyTnpub7uRdn2zU3r1ZCoLur6QNVBqLc2b/Jep+vB6tWrSwoM55yWLZvfxN2ampqSdthBEKi2trxXcVixorR5VAs5JyYITPfetlGJcPZdjZlUV5PQrm0Le62sSjA0NKTe3t6iBnEUe/CSyWTU29tbjurNylvA/OOZV5V1/hNyJskoo2MjFxZl24EF+tmb36WGRI2CGWLGZKoLq/QzN7+LOTBl0tLSUnTAmJk2bNgw7zOYlpbSdojOuZKXmcvmzZuLfh9hGGrLli1l3f5cdm5p1jvf2qZEaAWvl1WVCNRYX62PvHe7aquv3flti+XYsWNlP+vNZrM6dsx/65K3gHmu//TchTxbrCYySWqpW6ZP3PpevbVlo6qCUDVhIv4ZJJSwQLtXt+nBPe9VW8PKRavjUmNmRU+gDMNQN9xww7y3GQSB2tvbi9pmEATatGlT2Wf2r169Wg0NDXOeSZmZamtrtWbNwl6yXZL27FirH3rfTr3lhmYlQlNg8dUsVi6r0T23bdTHv+8mLafvpaDz5897We/YWPkGXc3E2+FCqoR5LT4kLFj065atqqnXj2y/U2PppF4ZPKfxTFq1iYR2rGzV8moudulDW1ubUqnUjEOHgyBQGIbat29f2UbSbN26VRcuXNDFixdnbNeemk2/ffv2smwzn5lp37592r9//4wDHYIgUE1NjW6//fZFG125emWd3v2ODt13e7vSmUhhYArnaDpD6f181xJvAVMdJpTJLO6EqX1rOhd1+1Maqmr0tjUdi12N60ZnZ6eamprU1dWlc+fOXXo+DEN1dnaqvb29rPMBwjDUO97xDh0+fPjS0ebUTmHqzKalpUV79uzxdl2y6upqvfOd71RXV5eOHz+uKIouC5KOjg5t2bJFVVULe7n2QsxM1VU0CxcrDEMvk2MXYhSZt4C5ZfUGPblInfyhBdqxqlUraxbvKqJYXCtWrNCePXu0a9cupVIpBUGg6upqb0fviURCe/fu1cTEhE6ePKnh4eFL9Whvb1+QeQeJREJbt27VjTfeqOHhYaXTaSUSCa1YsaLs113DwmltbVV3d3kHLC1UX5y3gLmvbbueOX9CmQXu6A8t0MrqOv2brfsWdLu4NoVhuGCTyiSprq7OSzNYKcxsUS5qCT86Ozt15syZsjaVVVVVLUhfnLfDmraGlbpj3ZYZR1HNJixymfx1JyxQwgLdtKpVD+55QA1VdBgCqHzLly9Xe3v7nM2rUwNX5moGra6u1u23374gZ7VexwR+ZMtemaTvnHtDs2Xv6poGjWWSyjqnxqoa3dN6o1bXNOjLR5+RnC67cGQgUxgE2rlynW5t3qSTF/uViiK11DZq35oOmsUALDk7d+6UmenEiROSVPA2FNu3b7/Ux3jkyBH19PQoCIJLl4qJokitra3asWNH2edizWRB7gfTMz6iR06/rAMXupXKhUVdolrvXLtF/7z95hlnvSezGT174YS+dfZ1jaQmVRWE2r5yre5r2671DO8FcJ2ZmJjQiRMndO7cOWWzWSUSCW3YsEGbNm26YuBKKpVSX1+fMpmMqqqq1Nzc7GWQxzV/wzEAQGW65m84BgBYeggYAIAXBAwAwAsCBgDgBQEDAPCCgAEAeEHAAAC8IGAAAF4QMAAALwgYAIAXJV0qxswuSDrprzoAgArT7pxrKfRCSQEDAECxaCIDAHhBwAAAvCBgAABeEDAVwsw+bmZuhsfQItftC2Z2uszr/F4z+2Mze93MIjN7bIZyM30uhwuU3Whmf2Fmw2Y2YmZ/ZWab5lFHZ2afvNrlF4uZ3WlmT5rZhJn1mNlnzayuiOXuN7Nv5pZJmtlpM3vIzHbOsdzXC31WZtYxy9/0ygLr2WFmf25mfbm6v2ZmP13yB4AF4/WWyfDiByRN35lnChWscN8n6RZJ35VUzP1dp38uY/kvmlm9pG9KSkr615KcpE9K+paZ7XLOXVZ+qTKzXZIelfQNSe+T1CnpNyS1SfrwHIs3SToo6fckXZC0SdLPS/qumd3snLtihKmZ/StJu+dY76clPTztudFp69mr+Pt7TNKPSBqWdKOkxjnWjUVEwFSew865o4tdiQXwo865SJLM7Ikiys/1ufyopM2Stk2VM7MXJL0h6cclfXae9a0Uv6I4iH/AOZeWJDNLSfqimX3GOXdopgWdc38i6U/ynzOzZyS9KumDkn5z2murJP1PST8j6Y9nqVOXc+67M71oZoGkL0n6J+fc9+e99K1Z1olrAE1kS0xek9FdZvY3ZnbRzPrN7HenN4OYWauZfSnX5JA0sxfM7KMF1tlpZl/OaxrpMrPfLlBuj5k9bmbjZvaGmf3E1b6PqXApo/dL+m5+CDnnjkvaL+kDZd7WJWb2gJk9lWvSGc59J9umlbk/12Q1nPu+XjOzX8p7fauZ/bWZnTezSTM7lWsqKukA0cyqJD0g6aGpcMl5SFJKV/c59Od+FjqL/oykl3LBNB/3SNqh6+cgYMkgYCpPaGaJaY9C3+NXJB2V9C8UH0X+qKTPTb1oZg2Svi3pvZJ+QXGT1IuSvmxmP5ZXrlPSM5LukvRLindQvyKpedr2lis+Sv2K4h3Vs5I+Z2b35hfKhd8Xru6tz+oJM8ua2Tkz+7yZNU17/SZJLxVY7mVJs/YhXC0ze0DS1yRdVNz89JOS3pKra1uuzGbFzUPHc2Xer3hH2pC3qq8pbsL6SUn3K26WSirv/6+ZnbAZ+qnybFHc3HjZ5+Ccm5R0TEV+DmYWmlm1md0o6X9L6tGVZzZ3SvqYpJ8qYpWfNrNMLmAfNrObp71+Z+5nrZl918zSubD9X8X0HWEROed4VMBD0scV9xsUevxdgXKfn7b8g5Kykrbmfv8PuXL3TCv3j5LOSwpzv39J8Q5y/Sx1+0JuXffmPVej+Oj2/0wrm5H0f0t8709IemyG1+5XHHjfI+leSZ9Q3H7/oqTavHIpSb9eYPlPSspc5XfiJH1yltcPKG6CS+Q91ykpLemzud8/mFvP8hnW0Zx7/f1z1OWo4iak2cq8I7euB2b4jGddftr7mvrbe0PSjmmvVysO7k/mPXfFZyWpVdLnFR8EvVPxQdDx3Pe3I6/c53PLD0j6b4rPaH5O0rikvy73/zUe5XvQB1N5vl9XdvIPFSj30LTf/1TxzvQ2Sa8rPiM545x7bFq5r0j6f4qPZl+U9B7FAXZ2jnqNO+cutYk755Jm9rrijmDlPV/Wvznn3DcUd1hP+ZaZvSjpbyR9VNIflHN7xcqdId4q6VPOuUvNR86542a2X9LduacOKw6cPzWzP5T0Hefc+bxV9UvqkvTrZrZWcdC+MX17zrkb/LyTgn5Y8RnrZsU7+kfN7E7n3Inc6/9JUp2kX5ttJc65c5Lym1EfN7OvKw6nBxV/f9KbZ2pfcc5NNR0+Zmah4s9lh3PuyDzfEzygiazyvOScOzDtUahzu3eG39tyP5sknSuwXE/e65K0WlcGWiGDBZ5LqrgRYOX2sOJRZG/Le25Q0qoCZZtUuO7ztUqSaebPuEmSct/d/Yr/L35ZUk+uGeju3OtO0rsVnzV8WtLruT6wn7yKOk29z5k+h4FiVuKcO+Kce9rFfSvvUjyS6+clyeJh3w9K+kVJNWa2Mm/I8dTv4Szr7lZ8NpX/3U318zw6rfgjuZ97iqk3Fh4Bs3StneH3M7mfA5LWFVhuXd7rktSnN0Op0uRfaO9lxf0w0+2U9IqHbQ/mtj/TZ3xpZ+6c+5Zz7gFJKyXdp7gZ8Wtm1px7vcs59zFJLYp3pt+U9Htm9t4S63RMcehf9jmYWa3is5GSPwfn3JDi5rmpM6jNig8qvqL4M5h6SPHZzqCk6X0sBVed9++X5yhb7gEhKBMCZun60LTfP6L4P+LTud+/LWmDmd0xrdwPKu6DmdrZPCLpfWbW6quiHnyf4k7yZ/Kee1jSvlynuqR4op+kO3TlHIx5c/G8moOSfiD/iN3M2hX3hTxWYJmkc+6bkv57rv6d0153zrnDkn4299RbSqxTStLXJX1o2gi0DyruMyv5c8g1221XHF5S3OR3b4GHFIfOvYoDaab1bVLcqZ//3f2D4mC8f1rxB3I/D5RabywM+mAqzy1TR7bTHMhv65f0PWb2G4oD4jZJvyzpS3nt91+Q9NOS/srMHlTcDPZDiptjftw5l82V+2XFHehPmtmnFO8c2hR3FF8xpHkuZpaR9EXn3L+bo1y73mwmWS0pMrMP5n5/1uUm9ZnZo4rnQ7wkaUJxYPycpOcl/VHeKn9f8cCGr5rZJxQfIf+qpG7FI6Hyt+1ydfx4EW9pe1698v2T4mair0n6OzP7PcVNSb+ieJLgb+a29ROK+8P+PleXZkn/RdJZSS9ZPDHytyX9meLPPlQ8kCOj+Exmqs5HJZ10zr1rjvr+V8WTVx8ys9+V1KF4ouVfOOcO5q3vY5L+UNK7nHPfzj3315IOSXpB0oikrYrnuGSm3k/ujOax6Rs1M+Xq91jec7+p+CD3KcUTN7fl3nukvP4b51y/mX1a0i+a2Ujufe9VPKrxizM0EeNasNijDHgU99Dso8icpOZp5e6S9FXFI8AGJP2upLpp62xV3O7fp/gI8QVJHy2w7S2Kh6H2SZoa0vrZvNe/IOl0geUe07TRX7m6fWGe7/fjeeV+S9IRxSOPUrm6/Q9JKwqsc5Okv1S8cxxVPBCgY1qZhtw2rhhxVmB9s30fe3NlHlC8A51QHCxfVTzZc2odt+ee6859B+ck/flUGUlrJH1R8cCM8dx3+W1J90+ry4npn/Us9b4rV6dJxX1zvyWpfobP/5685/6z4rOyoVxdXlMczh1FflbTR5H9W8XD2QcVD3ToUTzUfVuB5U3xmdvR3Pd8UvGIsqrF/r/JY+YH94NZYszs44pHgd3oOLIrmZm9R9LfStrinCvr9dWA6w19MMDl7lbc7EK4APNEHwyQxzn34GLXAVgqaCIDAHhBExkAwAsCBgDgBQEDAPCCgAEAeEHAAAC8+P9VbWj4ihyO0gAAAABJRU5ErkJggg==\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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q1avV1tY2aQDNFAEDAFeBeDyu9evXa+3atRocHFQ6nVY8Hld1tZ9b9UsEDABcVSKRiGpra2enrlmpBQBw1SFgAABeEDAAAC8IGACAFwQMAMALAgYA4AUBAwDwgoABAHhBwAAAvCBgAABeEDAAAC8IGACAFwQMAMALAgYA4AUBAwDwgoABAHhBwAAAvCBgAABeEDAAAC8IGACAFwQMAMALAgYA4AUBAwDwgoABAHhBwAAAvCBgAABeEDAAAC8IGACAFwQMAMALAgYA4AUBAwDwgoABAHhBwAAAvCBgAABeEDAAAC8IGACAFwQMAMALAgYA4AUBAwDwgoABAHhBwAAAvCBgAABeEDAAAC8IGACAFwQMAMALAgYA4AUBAwDwIjbXDQAA+JNOB3rteJ/6B5MyMzU1Vql5cbXMzHvdBAwAlKB0EOjpnSe1c//p8HnaSSZFzFRVEdMdW9u0tr3BaxsIGAAoMekg0FcfP6BT3UNKpd3F6+TUN5jUoz88ot6BhG7e2OytHYzBAECJeWrHsZzhki2VDvTMSyfVcbLfWzsIGAAoIcmxtHYf6J4yXMaFIXPCW1sIGAAoIQeO9KiQ8fuT3UMaGEp6aQsBAwAl5GzfiMZSQd7lY5GIegdGvbSFgAGAElLo7GMnyeRnyjIBAwAlZHF9lcpi+R/a00Gg+oUVXtpCwABACVmzvF5BMP0A/7i2phpVV5Z5aQsBAwAlJBoxRSL5d3k1L6rx1hYCBgBKyJEThV3Xcqij109DRMAAQEl55bVzBc0i6+kf1fDImJe2EDAAUEKGR1MFlY9ETIlk2ktbCBgAKBHOOY0lhgraJgic4vGol/YQMABQIg4fPiwbOytT/l1ktTXlzCIDAEwukUjowIEDqoyOKN9JZLFYRDdvXOqtTQQMAJSAjo4OOedkJjXV9Mk09bUwZlLjwgqtX+HvO2EIGAAoAZ2dnXIuDJWaeEJNNb0yuRzdZeGyiuiYfvot6xSN+osBvnAMAOa5RCKh4eHhi5bVlidUVXZGfaNV6ktUKR2YzKTKWFL1lcOqjCVV7mlwfxwBAwDz3EsvvZRzeSzi1Fg1pMaqS2eWRSL+O7DoIgOAeSyZTOrMmTMFb1ddXe2hNRcjYABgHuvs7JzRdpWVlUVuyaUIGACYx2Zy9iJJ8Xi8yC25FAEDAPPYwMDAjLarq6srbkNyIGAA4CrU2trqvQ4CBgDmsZl0dTU2NqqszM/tYbIRMAAwj7W2tsos/y8Yk6StW7d6as3FCBgAmMeWL19eUMBs3LhxVgb4JQIGAOa1eDyu9evXKxqd/qr8pUuXavny5bPQqhBX8gPAPLdy5UpJ0v79+yVJQXDx/cfMTMuXL9d11103q+0iYACgBKxcuVItLS3q6OjQsWPHNDY2pmg0qubmZrW3t8/KlfsTETAAUCIqKiq0du1arV27dq6bIokxGACAJwQMAMALAgYA4MWcj8Ek0in9+Mxr2ttzSmNBWo0V1bpj6WpdU1M/100DAFyGOQsY55y+3fmyvt35skymRJCSJEVkevr0q1pSuUAfvPZOLa6smasmAgAuw5wFzD8dfl4/On1YySB90fJATskgreNDvfr4zm/rQ5vfdsWHTOCc9vac1I9OH1ZfckQV0TJd39CmbU3tqoj6v98PgPwlkmm9cuSczvWNyMy0uL5Ka9vrFfP43fRXK3PO5V34xhtvdDt27LjsSvf1nNLf7H3yknDJpa26Xn98w9suu05fDvd36bN7f6BEOnX+LEyS4pGonKR3LN+kN7ddO3cNBCBJSqUDPflcp14+1C2ZKZUKL0Ysi4XBsnVDk27d3FLwfb2udmb2vHPuxlzr5iSyH+18Oa9wkaQTQz3qHOzx3KKZOdTXpf+5+3vqHxu9KFwkKRmkNRak9Y2ju/TI0V1z1EIAkpROB/rK4wf08qFupdLufLhI0lgq0Fgq0I69p/XNJ19VIR+6MbVZ7yIbTiV1sL8r7/KBpM+8/JQ+vu0d/ho1A2kX6DN7n5o2KJNBWo8d26fNjdcwcaFEjI2N6dixYzpy5IgSiYTMTAsXLtSqVau0aNGiaT8BJ5NJHTt2TP39/ZKk2tpatbW1zdoNCAs1NDSkjo4ODQ0NyczU0NCgtra2Wbnde7Fsf/G4zpwdUio9eXikUoFeO96nXQe6dP26JbPYutI16wEzkBxVLBJROh1MXzjjbHJIX331Rd23covHlhVm99njGsvzLCwdBPr34/v08+tu89wq+Nbd3a3nnntOkpROpy9a3tPTo+rqam3btk3l5eWXbBsEgfbs2aNjx46dfy5JkUhE+/fvV2trq173utflddPC2ZBMJvXCCy/o3Llzcs6d/2R/5swZ7du3T8uXL9eGDRuu+C6lVDrQzle6pgyX82VTgZ7dfUqb1i6+4t/XfDBrATM0ltQrfafVPTqodJB/uIz7zvF9evuy61QejWn3uRN67Ng+dQ6eU+CcasrK9YbmNbpj6WotiFd4aP2lnjp16JJusckEctrR1aEH1t7KL+081tPTo+eee+6iYMmWTqc1MDCg7du364477lAsduHPKwgCPfPMM+rt7b3kRoTjz48fP67BwUHdeuutikTmdsA5mUzqBz/4gUZHRy/pMhp//x0dHRoZGdHWrVuv6N/rVzt7Cyo/mkjpVPeQmhdf2ZOLZiKRSCiZTCoSiaiystL775n3gOlJDOvhAz/Wnt6Tl/U6TuHYzUvnjqt7dOiis4fe5IgeObpb3+p8WR9Yd5s2L7rmMls9vd7kSEHlAxfOjiuPzvmlR5gB55x27tw5abhklxsZGdGRI0e0evXq88sPHjyYM1yyBUGgvr4+HThwQOvXry9a22di165dOcMlWzqdVldXl44fP662trZZbF1h+oeSBX2otcw2zYv9tWm2JBIJHT16VEeOHFEymZQU3lnZzBSJRLRy5Uq1t7d76571Gl8nhvr04We/ftnhMu7xY/t1crg/Z9dUIKexIK3P7/+h9vYUp76pxCOFdWM4OZXN8adSzFxvb69GR0fzKhsEgV599cJgcRAEOnLkyJThkr1tvmV9SSQSOnPmTF6D3el0WgcPHpyFVs1cJGIKYyNPJkWu4DOyfHV2duq73/2uDhw4cD5cpPBDUBAESqVSOnjwoJ544gkNDg56aYO3I95YkNbHX/y20irejIwgj9dKO6cv7P+R95kgGxtaVGb5h0xrdZ0iRsDMV6dPn5727CXbeHeZFI7PFBoYXV35T4QpthMnThRUfnR09Px7vRI1NVQpGsk/MNKB0+KGKo8t8u/EiRPavXv3tL93zjklk0lt375dY2NjRW+HtyPe946/ojE3N5/ChlNJvez5LOaOpWvk8gzP8khM97Rt8Noe+JX9CTAfZqZUKhyjy/fMZ1wQBAVvU0wjIyMFBaKZKZFIeGzR5WlZUqPK8vy7ppc2VqtuwaWTNOaLIAi0a9eugn6GqVRKHR0dRW+Lt4B59NheXy+dl8c9118br9AbW9ZO21UWlamxolpbZmFcCP4U2kftnDs/yF/oQOp4//hcmckstrmelDAVM9PtW1vzulI/FjW9/obWWWiVPydPniy4B2dit26xePutGE4V9omv2E4M9Xmv410rtuimxe2Thkw8EtOSqgX6nY13K1bgmA2uLEuXLi3owBuLxbRgwQJJUl1dXUF/uM451dXVFdrEoqmvry/ovQZBcP69XqnWr2jULdc3TxkysWhEd9+yXG1NV/Z7mc6JEycK6s4dNzY2VvCZ+nTmxZSmqJnSBSbrbFyLGzHTz665WduWtOuxY3u1v/eUIhZR2gVaUrFA91yzQTcuXq4ywmXeq6urU2VlZV6DoeOzc8an7tbU1GjBggXq68vvQ894+bmyePFixWKxvA9SLS0t8+Kiy5s3Nmvpomo989IJneweUiwSkZNTOu20rKVWt2xqLompyTMdSzGzok8uueIDxiTd3bJOjx/fX9B2DeWz8/3TZqZ1dU1aV9eksSCt4VRS8UhMlbEr/w8Ohdm8ebOefvrpKQ+8Zqaqqiq1t7dftPzaa6/Vs88+O+0fcCQS0YYNczteZ2a69tprtXv37mlDJhqNas2aNbPUssu3rLlWy5prNTCUVN9AQmZSfW2FqipL5+91pmEfBEHRpyt76yJbXXv5k8hN0h9teZvuW3mDmitrC9ruP7TO/ndSl0WiWhivJFxKVF1dnW6++WbFYrGcXUjRaFS1tbW67bbbLlm/aNEibdq0acqxikgkoo0bN2rRokVFb3uh2tratGbNmkm7ysxM0WhU27ZtU3X17HyYK6YF1XG1LV2g1qYFJRUuUvizm8k4WlNTU9HvIuHtDObdK27QJ156bMbbRy2in197i9oy9+9698ob9L9ffiKvrq+YRXXj4uUzrhuYTGNjo970pjfpxIkTeu211zQ6OiozU11dnVatWqWGhoZJr2pva2vTggULdOjQIZ0+ffp82ARBoCVLlmjNmjVauHDhbL6dKa1evVqNjY06dOiQurq6Lnpfy5Yt08qVK1VZWTmHLUQuTU1NikQiBY3DRKNRrVq1quht8RYw7bWNuqHhGr1wrrOg7UzhmcB9K7bopiXt55dvqG/W9Y2teuns8SlDJmoR/dzaWxj3gDexWEzLli3TsmXLCt524cKF2rp1q8bGxjQ8PCxJqqqqumLHMOrr63XTTTdpbGzs/I09Kyoqrpj7peFSkUhEmzdv1vPPP5/XmMr4mGF9ffFvxuv1+2AC5/T3+7fr2e6jU5aLWuT8lbM3LlqmN7ddq9bqukvKpYNAXzr4jHZ0dSg14RqbqJlMpvev2aZtTSvybiMAlKKTJ09q586d56/czyUajWr9+vVasWLmx8ypvg9mVr5w7NRwv752ZKd2nz2utJxM0oKyCt1zzXW6fekqjQUppYJANWXleU3nPTncp+8ef0V7ek5oLEirOlauO5au0m1Nq1RddmXe8hwAZtv4V0scPXr0/L3lysrKVFtbq5aWFjU3N1/22eicBwwAoDRdcd9oCQAofQQMAMALAgYA4AUBAwDwgoABAHhBwAAAvCBgAABeEDAAAC8IGACAFwQMAMCLgm4VY2Zdkqa+cyUA4Gqy3DmX8wvACgoYAADyRRcZAMALAgYA4AUBAwDwgoCZJ8zsATNzkzx657htD5rZsSK+Xq2ZfcTMtpvZWTPrzfz/nRPKRc3sd83se2Z22swGzOwFM/tFM7vkd9vMrjOzx81sMPO6f29mDTNsY3tm339ghm9zzpjZO83sRTMbNbOjZvZHZjbtt06Z2S+Z2bfM7LiZDZnZHjP7PTOLTyj31szP5JSZJczsmJn9s5ltmOb1H83s049OWD6+r3M96ma0EzArYnPdABTs3ZImHsxTc9EQj5ZJ+lVJfy/pLyQFkt4r6Wtm9uvOuU9nylVK+iNJX5L0KUmDkt4u6QuS1kv6vfEXNLMWSU9I2i/ppyXVSfqkpG+a2e3Ouem/vLwEmNlbJX1V0t9K+h1JWyR9XNICSb8/zeYfkfQdSX8n6ayk2xX+fG5W+Hs5rkHS85L+RlKXwp/nH0h6xsw2OucumYlqZu+VdP009X9C0jcmLBuYZhvMJeccj3nwkPSAJCdp9Vy3JUfbHpR0rIivVy2pKsfy70rqyHoeldSQo9zfSRqVVJm17K8l9Uqqy1p2Z2afvmsGbWzPbPuBud7/Bbb7RUlPTlj2EUlJSUun2XZxjmUfyeyHldNsuy5T7r/mWFcv6ZTCDxFO0kdLYV/zcHSRlZqsrrQ7zexfs7qDPm1mlRPKNpvZl8ysO9OVscvM7s/xmivM7KGsLo9XzexTOcptMbMfmNmwmR00sw/O5D0454acc8M5Vu2Q1JJVLu2cO5ej3HOSyiUtylr2k5L+zTnXm7X9U5I6JL1jJu3Mh5ndb2YvZbqjujP7sXlCmfdluqwGzazfzHab2a9krb/JzL6T+TmOZPb/38ygLddI2izp4QmrHpJUJultU23vnOvKsfi5zL+t01R/NvNvrrPt/y5pj3Puy9O8BuYZAmb+iZpZbMIj18/xYUmHJL1L4af3X5L0mfGVZlYt6UmFB5UPSXqnpN2SHjKzX84qt0LSswo/7X9E0j2S/kwXH7wlqVbSP2bqfYfCA89nzOyN2YUy4ffgzN667lTYxTWdNyg8WzmZqbNS0gpJe3KUfVnSlGMDM5XZjw9J2qfw5/AHkt4q6Ukzq8mUuV3hPntS4c/gpxV28dVl1tdIekxSWuFZ7Nsk/bkmdG/nuV+vy/x70X5wzr0maVgz2w9vUNiFeWDiiswYWdzM1kj6nMKzlC9PKHO7pPdL+rU86vqEmaXMrM/MvmFmG2fQXsymuT6F4pHfQxe6yHI9vpmj3GcnbP9hhQeptZnnv54pd9eEcv8u6YykaOb5lxSObbRM0bYHM6/1xqxl5Qo/tX5+QtmUpL+dwfv/5UwdPzNNubcqPOB9OGtZS2bbD+Yo/7CkwzNoT7um6LZR2H13WtL3Jyy/PbPdb2ae/66kc1PUc2Om/KZp2jPtfpX0vsxrrc+x7lihPxdJmySNSPrCJOt3ZP2OHpR07YT1cYUB/9GsZbm6yJolfVZhSN+h8MPSawrHX64tpM08ZvfBGcz881OSbprw+O0c5f55wvP/q/CM9ebM8zslHXfOPTGh3MOSFuvCp9m3KAywE9O0a9g59/3xJ865hMJPtcuyCznnYs65X5zmtS5iZndJ+l+SvuSc+4cpym1Q+An5+wq7XebSOklLJF3UXufcDxXebukNmUXPSao3s4fN7N4cs6IOKjwb+1ymu+2aXJXNZL9ejkw339clHVY4WSCXn5V0i8Jg65f0HTNrz1r/3xRO1PjYVHU550465z7onPsX59wPnHNf0IXxsw9f1huBVwTM/LPHObdjwuNQjnKnJ3k+3lfeoEwX0gSnstZLUqMunbWWS0+OZQlJFXlsOykzu0nhzKHvSZp0SrCZrVQ4w+k1ST/lnMvu6+9VeDCqz7Fpg6Rc4ziXa3z/TbaPGyTJOfekwhlY10j6mqQuM/t3M9uUWd8n6Y2STiicldWRmR583wzaNP4zyrUf6pXnfjCzRoX72iS91TmXcyaXc26fc+7HLhxbuVtSjcJuQpnZMoXh8MeSys2sLitcx59POnXaOdcp6YcKP2DhCkXAlK6mSZ4fz/x7TtLSHNstzVovSd2afgDXi0wf+2OSdkq6zzk3Nkm5NoUzzPoVHvD6s9e7cMLAEV0Yg8i2QdLe4rX6vPH9N9k+Pn8wd859xTn3BoUH+Z9S2CX06PjYmnNup3PuPoWhdKvCs4Z/NrPXFdimlzP/XrQfMmcVVcpjP5hZrcKfSaOkNznnjk+ziSTJhZMrDklanVm0UuGHj4cVBt/4Qwq7DXsk5TPGws0Ur2AETOn6jxOev0fh2MSPM8+flNRmZq+fUO59Csdgxg82j0u6d+LMJ98yA8PfkfSqpHudcyOTlFuscNxIkt7snOue5CW/IeknzGxh1ra3S1quS6+tKIZXFJ41vid7oZndlqnziYkbOOcGnXPfVDgg3qzwIJ69PuWce0bhp/6IpGsLaZBzrkPSS5J+ZsKq+yWNSfr2VNubWZWkf1M4YeItk5w5T7Ztk8Jrkw5nFu1UeGY28SGFofNGhYE02estUzie9Wy+bcDs40LL+WezmU2cwSVJOyZ0C73dzD6pMCBulvQnCscwDmbWPyjptyT9i5l9WGE32M9IerOkX3HOpTPl/kThxYvbzezjCv/oWyXd45y7ZErzdMwsJemLU40XmNkSheESz9S/wcyyi7zonEtkZoc9pnDA/RcUBmZbVrm9WWczn1R4IP2GmX1C0kJJ/0Nh4H4tq+52hd1sf+ac+9M83tJWy30nhW8onHX3OTN7WOFBs1XheMNBhdfqyMz+XOHZ5fcVdoO1SfpNSTudc11mdq/CCQ7/mmlXdWb9gKSns9o97X7N+JDCi0s/p3C8aovCi1U/5Zwb7x6VmX0k0/5V7sKFkV+V9HqFvzfVZnZL1usedplpzGb2NUkvSNql8KxyraT/onAiwl9K589onpjYuMzP+Wj22KCZ/aXCQH1a4YWb6yT9ocIPTFOO32COzfUsAx75PTT1LDInadGEcncqHIQdVNgd82llXXiYKduscBptt8Lxkl2S7s9R9yqFB6NuhRcwHpb0V1nrH1SOCy0VHkCemLDMSXpwmvd61zTvtT1Trn2acndNeN2NCoNrSGEXzIOSGieUuU6TzDibUG66usd/HvcrPGtIKJxV95Ck5qzX+QmFIXkyU6ZT4VX2LZn16yT9k8JwGVV4gP2WpG2F7tessu/KalOHwiCJTijzp9n7OquOyR4PZJX7fYVX8vcqnP78isKzsvY82pZrFtkvKJwM0aPwTOuUwinx6+b675LH1A++D6bEmNkDCm+xssYV0IWBUObalY8p/BKlXBd7AsgTYzDAxd4g6a8JF+DyMQYDZHHOTRwABzBDdJEBALygiwwA4AUBAwDwgoABAHhBwAAAvCBgAABe/H+DxnLsuMYsNQAAAABJRU5ErkJggg==\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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Lsy7veZ5qa2sjq5+AAYA8tnHjRsVi2V+Gv6GhIbK6CRgAyGMrV65UcXHxrN1lnudpzZo1Ki6O7krLBAwA5DHP87R7926VlJRMO74Si8VUW1urbdu2RVo3l4oBgDxXWFioe+65R62trWppadHY2JiMMQqCQBUVFdq4caPq6uoivx4ZAQMAN4BYLKaGhgatX79eY2Nj8n1fiURCBQUFzuokYADgBmKMUVFR0bzUxRgMAMAJAgYA4AQBAwBwgoABADhBwAAAnCBgAABOEDAAACcIGACAEwQMAMAJAgYA4AQBAwBwgoABADhBwAAAnCBgAABOEDAAACcIGACAEwQMAMAJAgYA4AQBAwBwgoABADhBwAAAnCBgAABOEDAAACcIGACAEwQMAMAJAgYA4AQBAwBwgoABADhBwAAAnCBgAABOEDAAACcIGACAEwQMAMAJAgYA4AQBAwBwgoABADhBwAAAnCBgAABOEDAAACcIGACAEwQMAMAJAgYA4AQBAwBwgoABADhBwAAAnCBgAABOEDAAACcIGACAE/GFbgAAwK1kyteJs33q6RuRlVVNZbE2rV+uRDzmtF4CBgDyVBBY/fCVNh08dkGSlEwFkqRE3NN395zTbZtX6O7b18rzjJP6CRgAyENBYPWN506qtWNQKT+46rmJoDl47IK6+0b07gc2OQkZxmAAIA8dONKp1o6Ba8IlU8q3Ot95WfubO520gYABgDwTBFb7X+1Syrezlk35gfY3dyoIZi+bKwIGAPLM+a5B+cH0Ry6T+YHVuY6ByNtBwABAnukfHFMuByQ2sBoYGo+8HQQMAOQZzzPKZcjeGCPPMMgPAJhFbXWJbC5HMDZcJmoEDADkmRXLS1RZXph1+YqyAtVWETAAgCz8xO1rFY/NvouPx4x+4va1TtpAwABAHmpYU66VNbMflTRtqFFjfaWTNhAwAJCHXjnapc6e4VnLHTnVo56+ESdtIGAAIM/4QaAfHexQKjX7uTC+H2jvoQ4n7SBgACDPtJzrU5DlNDJrpRPnejU6noq8HQQMAOSZzu6h1y9omY2YZ3SpfzTydhAwAJBn/JyvK2YUZHHdslwRMACQZyrKChWPZX9mvh8EKistiLwdBAwA5JlbbqrK6Uz+mspiVZRmf2JmtggYAMgzJUUJbVhXqVgWPyIWj3vatX2Vk3YQMACQhx7c3aDy0oIZQyYe83TrxmptXLfcSRsIGADIQ4UFMT32SFN4JBMzV43JJOKeChIxvXnHat3/pnXO2hB3tmYAwIIqLIjp0Xs3aHg0qWOnLql3YFTxuKeVNcu0sb5SsSyuVXY9CBgAyHMlRQnd3lQ37/XSRQYAcIKAAQA4QcAAAJwgYAAAThAwAAAnCBgAgBMEDADACQIGAOAEAQMAcIKAAQA4QcAAAJwgYAAgjwVBoFQqJZvLL5BFhItdAkCe8X1f7e3tOnnypIaGhmSMkTFGq1evVmNjo8rLy+elHQQMAOSRkZER/ehHP9LY2Jh835ckWWtlrVVbW5va29u1efNmbdiwwXlb6CIDgDyRSqX04osvamRk5PVwyWStVRAEOn78uM6ePeu8PQQMAOSJc+fOaWxsbNbxFt/3dfTo0SlDKEoEDADkAWutWlpaFARB1uU7OzudtomAAYA8MDQ0pLGxsazL+76vrq4uhy0iYAAgLzQ3N+e8TCqVctCSKwgYAFjixsfHdfHixZyXSyQSDlpzBQEDAEvc6dOn57RcWVlZxC25GgEDAEtce3v7nJYrLi6OuCVXI2AAYIkbGRmZ03KFhYURt+RqBAwALHFzuc6YMUZVVVUOWnMFAQMAS9xcBuvr6urkeW4jgIABgCVu5cqVOS9z2223OWjJ1QgYAFjiGhsbZYzJunxTU5PzKcoSAQMAS15paanq6+uzCpkVK1aosbFxHlrF5foBIC9s27ZNktTW1jbtRSzXrl07L11jEwgYAMgDxhht375dDQ0NamlpUVdXl3zfVywW08qVK+f1h8YmLGjA+EGggz3n9b3219Q9elmeMWoordaDa7foprLqnPoUAQBSeXm5duzYsdDNkLSAAdN6uVefefU5jQcpjflXLrjWOzasV3s7tGZZhX516/1alihYqCYCAK7Dggzydw73638felaDydGrwkWSrKTxIKVzl3v16UPPXPP8UtA9elkn+y/qzGDPkmw/gKsFgdWp83060Nypl4906Uxb/5xObrzRLMgRzN8d36vRWXa8vg3UPTKk77Yd0zvW3TpPLZs7a61e7m7Vt1pfVdfIoOLGk5UU2EC7am/Sw/VNqikqXehmAsiBtVb7X+3US82d8n2rIAhDxfOMEnFPd962WrdtXkF3/jTmPWC6RgbUOtSbVdmk9fVs2zE9XN8kzyzeGdWBtXry+B4d6D6n8SCcvZHUlVkcP+xs0b6LZ/TRWx9QY3nNQjUTQA6stfq375/SqdZ+pfyrfyXSD6ySqUDfP3BeF3uH9dY71xMyU5j3vfbB7vMKbHY/6SlJw6lxfaftmMMWXb9vnj18VbhMFshqzE/pM69+T5fGhua5dVhMfN9Xe3u7Tp06pTNnzqivr2/WZZLJpEZGRpRMJt03MAK+72t8fDzrn+5drA4c6dKp89eGS6ZUKtDRU5fU3NIzjy1bOub9CGYwOSo/x77Lr5w+qI3ltYvy2/+on9QzbUeVnCZcMiWDQN9pO6afanzjPLQMi4nv+zp27JjOnTsnSQqCQMYYGWNUVFSkpqYm1dXVvV5+4vfST548qYGBAXmepyAIVFZWpg0bNmjVqlXOryOVi4ngPHnypIaHh2WMkbX29emxy5cvX+gm5iQIrF463KlUavaQTKUC7f1xu7ZuYObrZPMWMEPJMb3Wf0Hdo0MyCgfzc/HZ5uf1p3e+7/U3cNxPaX/3Ob3QfkIDyRHFTUy3Ll+lB9Zs1opitz+ik2nfhTMyyu5D5dtAP+hs0Xsa3qCEF3PcMiwWvu/rxRdf1ODg4FXf6icGiYeGhnTgwAE1NTWpoaFBvu/rpZdeUm9v7+snzE38HRgY0KFDh3TmzBnt2rVL8fjCn8o2NjamF198UaOjo6+3c+K1dXR06MKFC2poaNAtt9yyZHbA5zoG5OdwBDY8mlJn95BWrVjc46zWWiWTSQVBoEQioVjM7X7I+afz0uiQnj65V829nde1nqHUuE4MXNTNFbVq7m3X3xz9gayVxoIrkwWe6xjU9zqOy5NRwotpbelyPbR2i7ZVrXY2hnOi/6LGgxxmitlwm9SVzO8JT1g4hw4duiZcJguCQEePHlVFRYVOnjypS5cuTVve93319/dr37592r1794LutCfCc2ho+q5f3/d15swZxeNxbdq0aR5bN3e9A2Pyg9y+BvcNji3KgLHWqru7W0eOHNHg4OBVz9XU1GjTpk2qrq52UrfTgGkf6tMfvfwtzd55lJ3n2l6THwT6/JH/nLJLauLjEMhqLEipZeCivnisV5WFxfrNbW9RZWFJRC25wre5vTpjpFQOY1BY2sbHx9XR0ZHVeMREN1pvb++s5YMgUH9/vy5duuRs55CNtrY2jY6OzlrO932dOHFCDQ0N83KRxetljLLsl8hYxklLrk8qldLevXvV2zv1xKru7m719PRo/fr12rp1a+RfVpx14iYDX598+duRhYsUnl/yf177YVbjHRPGgpQujl7Wp378jIZT4xG2JlRbXK5YDm9KKghUWeD2Z0qxeLS2tuZUvqenJ+vBcd/31dLSMpdmRaalpWXa615NZozR+fPnHbcoGjWVxTntbK2VqisX1//rIAi0Z8+eacNlgrVWZ8+e1YkTJyJvg7OA+V7ba0oq2m/qycBXMssPc6bAWg2Mj+qZ80cjbY8k3VXXmFP32+bKOi1LuP2ZUiwes3WNXa9Lly45W/dsksmkhoeHsy7v+766uroctig6a+pKVVSQ/fhERVmBVlRF30NyPTo6OjQwMJBVWWutTpw4EflMRWcB8x/nj0S6Pk9GVlePueQiZQO90H48p4G7bKwoLlNjWY1iWRwgF3gxPby2KdL6cWNbyKnAvu/n3KWS7dHOQjPG6M071igem30XGY95umvHmnloVW5aWlpy+nwYY3I+4p6Ns4AZirg7yjNGRbHr67sNZHXucvTf+D5yy10qKyiasauswIvpwTVbdHNl3bRlkH8qKiqcztQpKFi4a/UlEomcL5dSVFTkqDXR27qxRrdtXjFjyMRjnnZtX6WN6xbXNOxkMnnNgP5sgiDQhQsXIm3H4plIP4OY8fTmukaVF1zfh9PIaMSP/mS18oIifWzH27WlcqXixlM8o8usMBZXSbxAH7jpdr2rYXvkdWNxW7t2bdY7YWNMToHkeZ7WrVt3Pc27LrFYTCtWrMip/EK2dy7uvaNeD93doKqKIsVjngoS4S0eM1qxvFiP3NuoXdtXLXQzr5FKpeY0YB/1EaazWWRzOddlOtur1uhDG3fqpQtn9Vpf15y7yaysSuJuvvGVFRTpV2+9X71jw9p74bS6R4dU4MW0qaJW26vXKLaIL3UDdxKJhOrr69Xa2jprd4Xnedq6dav27t2b9frXr19/vU28Lhs2bFBPT09WO6Z4PK6amsV3svRsNjdUaXNDlS5eGlbvQDhjrrqyeNEN6meKx+NzuhhncXG0r8lZwGyqqNXx/us/3Hp3w216eG2TjDF644p1+oeWl+a8rpjxtK7U7aHs8sISPVy/1WkdWFq2bt2qoaGhq06cnMzzPN12222qqqrSjh079PLLL88YSJ7nadu2bSosXNgJI9XV1a8H6EwhE4vFdMcddyyZEy2nsqKqZNEN5E8nkUiovLxc/f39WS/j4ojY2dfqD17n5VCMpK2Vq/T2+itzsxNeTB9q3KmCOZwFn/BiemD15kV90UzkJ8/ztGvXLt18880qKChQLBZTLBZTPB6X53mqqqrS7t27tXr1aknSypUrtXPnzinPtJ5Y7g1veIPq6+sX4uVcY+vWrbrpppvked41l6+JxWIqLCzUnXfeqcrKyoVp4A1qw4YNOV1OKJFIRH5OlcnlMGrnzp12//79WZf/4rEXte/imZwblTCe1pVW6aPbHlBB7NqDrOfaj+srp19RYK38LE5ajBmj6sJS/e7tD1/3RAHgelhrdenSJY2OjsoYo8rKSpWUTP2teGLQta2tTePj40okElq9erVWrly5qK5DNmF8fFytra26cOGCfN9XUVGR1q1bpxUruJz9QrDWau/everp6Zm1u8zzPN11112qqKjIuR5jzAFr7c4pn3MZMIG1evK1H+lHs4RM3HiKezH5NjwJ8eH6Ju2ubVRshv9E3aOX9Xz7cb3YdUrDqXFZTT3uU+jFVVdSpl+/9X6VJpbODBYAuF6+7+vAgQO6ePHitCFTUFCgXbt2zSlcpAUMmAkXRgb19TMHdbD7vHxZGUnL4oV6y5rNun/1ZvWPj2jMT2lZokDVhcvm9G0nGfh6ufucvtv2mi6NDcmT0bqyKr1tzRZtqqjlGxSAG1Z/f79Onjypixcvvn7+UmlpqW6++WbV1dVd1/5xwQMGAJCfZgqYxdeRCwDICwQMAMAJAgYA4AQBAwBwgoABADhBwAAAnCBgAABOEDAAACcIGACAEwQMAMCJnC4VY4y5KOmsu+YAAJaY9dbaKX/aNKeAAQAgW3SRAQCcIGAAAE4QMAAAJwiYJcIY82FjjJ3m1rfAbXvCGHM+wvWVG2M+box50RjTY4zpS//73VOU/cQ02+TrU5Tdaox5xhhzOb3eLxljqubYxoZ0PR+Zy/ILyRjzbmPMK8aYUWPMWWPMx4wxsSyWu9UY8wVjzAFjzLgxZtoB3PS2/qoxpt0YM2SMaTbG/JYxJj6pXLUx5jPGmFPGmBFjzGljzGeNMSsyymT9ecDicu0P3mOx+4CkyTvz1EI0xKF1kv6bpC9J+kNJgaSflvQ1Y8yvWGs/N8Uyd0vyM+5fynzSGLNa0vOSjkl6v6RKSZ+W9E1jzN3W2iDi17AoGWMekvQVSV+U9JuSdkj6pKQySf99lsXfKOkdkvZLGpO0e5o6JrZ1m6SPSuqW9BZJfyJpxUQ9JvwZxX+RdLOkj0s6KqlJ0h9I2mmM2W3DWUhz+TxgMbDWclsCN0kflmQlbVzotkzRticknY9wfcsklUzx+HclnZv02CfS2yU+yzr/XFKfpMqMx+5JL/veObSxIb3sRxZ6++fY7lckvTDpsY9LGpe0cpZlvYx//1G4+5iy3C+mt83Nkx7/sqSOjPs3p8v94qRyv5R+fHOunwdui+tGF1meyehKu8cY8/WM7qDPGWOKJ5VdZYx50hjTbYwZM8YcMsY8PsU6bzLGPGWM6UyXO2WM+cwU5XYYY/7TGDNsjDlhjPmlubwGa+2QtXZ4iqf2S1o9l3VKepekf7PW9mXU831J5yT95BzXOStjzOPGmB+nu6O609tx1aQyj6W7rC4bYwaMMYeNMf814/k7jDHPpt/HkfT2/6s5tKVe0hskPT3pqackJSS9fablbfZHeQXpvwOTHu/T1d3yM5XTRFlHnwfMAwJm6YkZY+KTblO9j09LOinpvQq/vf+CpM9PPGmMWSbpBYU7lf8h6d2SDkt6yhjzixnlbpK0T+G3/Y9LeljS/5RUM6m+ckn/kK73JyW9JOnzxpj7Mwulw++Jub103aOwi2sqrcYYPz2m8KnMME3/+yZJr06xXLPCbpnIpbfjUwq7ft4r6XckPSTpBWNMabrM3Qq32QsK34P3S/pbhV14Spf7D4Xdfx9W+H79gSZ1b2e5Xbem/161Hay1pyUNK7rt8E8Ku8U+m/5yUm6MeY+k/yLpTzPKNUv6vqTfM8bsNMaUGmPepPBz9u/W2qOz1DPT5wGLwUIfQnHL7qYrXWRT3b45Rbm/nrT87yrcSd2cvv8r6XL3TSr3HUkXJMXS95+UdFnS6hna9kR6XfdnPFYoqUfS30wqm5L0xTm8/olul5+Z9PjjCvv03ybpQYXjKuOSns0oszq97C9Nsd6nJbXMoT0NmqGLTFJMUpek5yY9fnd6uV9L3/8tSZdmqGdnuvz2Wdoz63aV9Fh6XbdM8dz5XN4XzdBFln5+o8IAmfiMBpI+PkW5ZZK+OvnzLKl4Lp8HbovrxiD/0vMeXTvI3zdFuX+cdP/LCncKb5J0XOG3vzZr7fOTyj2tcDC1SeERzdsUBlj7LO0attY+N3HHWjtmjDmucIBWGY/n/Jkzxtwn6S8lPWmt/ftJ65vc3fOsCWe0/YUx5q3W2u/kWl9ENkuqVRjsr7PW/sAYc1bSvQpf00uSlhtjnlb4Hv3AZnTjSTqh8P39gjHmcwrHT1onVzaX7epKegbYVyUNKTwi65H0gKSPGWPGrLWfyij+t5LuVDjuclTSFoVHyP9sjHmnnaJbbqbPAxaXRfOhRNZetdaezKJc1zT316T/VknqmGK5zoznJala1wbaVHqneGxMUlEWy07LGHOHwplG35OU7ZTg/yvpLyTdofCIrE/ht93lU5St0qQZZxGZ2H7TbeMqSbLWvmCM+YCkX5X0NUkyxrwg6TettYestf3pbsbfk/RXksqMMc2Sft9a+5Uc2zTxHk21HZYruu3w2wqP8NZbayfqfN6EU6H/0BjzRWtttzHmEYWzwd5qrf1uutz3jTGnJD0j6Z2SvpG54jl+HrBAGIPJX3XT3G9L/70kaeUUy63MeF4K+9LXTFHOOWPMNoXjDwclvc9am8xxFVaSbDhAfEZXxiAyNUk6MvdWTmti+023jV/fmVtr/9lae6/Cnfx7JK2S9O2JsTVr7UFr7fsUhtJuSS2S/tEYc2uObWpO/71qOxhjGiSVKLrtsE3SyYxwmbBP4WSCjRnlpPAobnI5KTyayWzn9X4eMM8ImPz1U5Puf0hhP/je9P0XJK01xtw1qdxjCsdgJnY2z0h6dPLMJ9eMMZskPSvplKRHrbUjOSz+M+m/+zIe+xdJjxhjKjLquFvS+vRzUXtN4VHjhzIfNMa8OV3n85MXsNZettZ+U9IXFIZM9aTnU9baPQqPZjxN2gHPxlp7TtKPdWX7THhcUlLSv+eyvhl0StpojJl8pLQr/bcto5wUdtvOVO56Pw9YKAs9CMQtu5uuDN6/X2Gf9eRbfFK5cwoHvB9UOA4wLulLGetbpnAspkthV8PDCmc8XXVegsKujouSTiuciXa/wh3S0xllntAU58Eo3Ik+P+mxbAajaxUecVyS9MgUr7Uwo+wrkn5D4QmAb5f0Z0rvLCetc43Co7EX0q/1gwp/emKPrj6/oyG9DT4xSxsnyn0+/Z5MvhXoykD00+k6f17hTvW4pGXp9fyBwkD5kMJxsccUzv57Jf38owoD8OfS2/5RhaE/IGltLts1Xe4dCr9ofEHSfeltNyrp05PKfTy9zvUZj5VkvL5/1pXP4/sl7cwod2f6PXhJ4Redtyg8QXJc0lczypUrDJF2Sb+cfn2/nN5G5ySV5vp54La4bgveAG5ZvlEzzyKzkmomlbtHYf/15fR/zM9p0swchd+Sn0rveMckHZL0+BR1b1A4rtGd3hm1SPqzjOefUPYBYyU9MctrvW+W19qQUfbL6fYMp9t2ROE3/Gt2Ogq7ZJ5VOPjcm2539aQyWzXNjLNJ5RqyfD8eV3jUMKZwsPspSasy1vOIwm6fjnSZVoVn2a9OP79Z0v9TGPCjCsP+W5J25bpdM8q+N6NN5xSGSWxSmU9Msa1nes1PTFr+znQ7O9Lbu1nSx6b4DNanX+/E6zutcOB/zVw+D9wW143fg8kzxpgPK5wFtslmNxkAGdLnrvyxwm/uU53cByBLjMEAV7tX0p8TLsD1Y5oykMFaO3kAHMAc0UUGAHCCLjIAgBMEDADACQIGAOAEAQMAcIKAAQA48f8BlRdbGqEhp08AAAAASUVORK5CYII=\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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wgoABAHhBwAAAvCBgAABeEDAAAC8IGACAFwQMAMALAgYA4AUBAwDwgoABAHhBwAAAvCBgAABeEDAAAC8IGACAFwQMAMALAgYA4AUBAwDwgoABAHhBwAAAvCBgAABeEDAAAC9i890AAIBfQSajQ8f79MxLp9TdO6KMc6osj2vL5Uu1+bKlqqyIe6mXgAGAEjYwlNRXH9iv4ZGUUunMueeHRlJ6eneHdrx0Sm+/Za3Wra4tet0MkQFAiRpNpvWV7+5T/+DYeeEyLgic0umMvv3YIR3v6C96/QQMAJSoXXtPa3gkJeemL5cOnB784VG5mQoWiIABgBKUyTjt2tepIJNfaIyMpXWic7CobSBgAKAEnekbURDk3yNJpTM63NZX1DYQMABQgpLpQGZW0DpjyaCobSBgAKAElSdiyhQwp2JS0U9XJmAAoATVLy5XRSKad/lo1HTZmrqitoGAAYASZGbaevVyxaL5DZPV1ZRraX1lUdtAwABAidp02VItqa1QNDJ9yMRjEb31ltai10/AAECJikUjet9bLlfTsmrFohHlzvnHYxFVlMX0Y2+9XEvritt7kbhVDACUtEQ8qvfecbk6e4a1c88pnewaUibjVFOd0LVXLtO65sWKRvz0NQgYAFgAGusr9bZb1s5pnQyRAQC8IGAAAF4QMAAAL5iDAYAFwDmnU91D6u0flRRe97K8oarg28kUgoABgBK391C3nnz+pIZH0+c9X1ke0w3XrNRV6xq81EvAAEAJe+SZNr24v0vp4MIvHDs7mNT3njyuzjMjuv265qLXzRwMAJSovYe69eIrk4fLuHSQ0YuvdGnvoe6i10/AAEAJcs7piedPKj3JVyXnSgcZPfH8Sb7REgAws46uIY3kzLlMZ2Q0rY6uoaK2gYABgBJ05uyICumPuOw6xUTAAAC8IGAAoATV15SrkCtcLLtOMREwAFCCVjZWq7yAb7QsT0S1srG6qG0gYACgBJmZrt+yUrHYzIf5WCyi67esLPpV/QQMAJSoqzc06Kq19dOGTCwW0ZVr63X1huJfzc+V/ABQosxMP3L9Gi1bUqUnXzipsWRw7loXM1NZIqrrt6zUpg0NXu5JRsAAQAkzM226bKmu3tCg9tMD6j07JkmqW1ymVcsWcbNLAMDFMTM1L69R8/K5q5M5GACAFwQMAMALAgYA4AUBAwDwgoABAHhBwAAAvCBgAABeEDAAAC8IGACAFwQMAMALAgYA4AUBAwDwgoABAHhBwAAAvCBgAABeEDAAAC8IGACAFwQMAMALAgYA4AUBAwDwgoABAHhBwAAAvCBgAABeEDAAAC8IGACAFwQMAMALAgYA4AUBAwDwgoABAHhBwAAAvCBgAABeEDAAAC8IGACAFwQMAMALAgYA4AUBAwDwgoABAHhBwAAAvCBgAABeEDAAAC8IGACAFwQMAMALAgYA4AUBAwDwgoABAHhBwAAAvCBgAABeEDAAAC8IGACAFwQMAMALAgYA4AUBAwDwgoABAHhBwAAAvCBgAABeEDAAAC8IGACAFwQMAMALAgYA4AUBAwDwgoABAHhBwAAAvCBgAABeEDAAAC8IGACAFwQMAMCL2Hw3AADgVyqV0tjYmMxMFRUVikTmpm9BwABAierp6dHBgwfV3d0tM5MkmZmam5u1du1aVVRUeK2fgAGAEnTgwAEdPHhQQRBcsOzo0aNqa2vT9u3bVVdX560NzMEAQIlpa2ubMlwkyTmndDqtp556SkNDQ97aQcAAQAlxzmnv3r1ThstEQRDo4MGD3tpCwABACeno6FAqlcq7fHt7u9LptJe2EDAAUEL27dtXUHkz08DAgJe2EDAAUCJSqZSGh4cLWieTySiTyXhpDwEDACXi8OHDs1ovkUgUuSUhAgYASkRHR8es1vN1Pcy8XgeTygTa1d2mPb0dGgtSqi+r0g3L1qq52t952QBQqpLJ5KzWi0ajRW5JaN4C5tGTr+jrR1+Qc05jmfAMBpP0g1MH1VixSL945c1aVlEzX80DgNecWCxWcMiUl5efu8q/2OZliOzbx1/S147s0miQOhcukuQkJTOBTgz16c93PaDTw/3z0TwAeE1qaGgoeJ3LLrvMQ0tCcx4wRwfO6P62PUpmpr4IyEkaDVL6+72Pyjk3d40DgDnknNPJrkHt3HNKO3Z3aO+hbo0lZ75Aciqtra0FlR+/L5kvcz5E9mD7XqWmCZdxTlLv2LAOD3RrXc1S/w2bI4HL6KWek+rI9s6WV9ZoU91KRefo7qYALg2H2/r0yDNtGhpJKcg4uYxTLBbRQ08e0xWt9XrDdauViBc2N7Jo0SLV1dWpt7c3r/LXX3+9t+ExaY4DJpUJ9MKZE8q3T5LMBHrs5IGSCBjnnB5qf1nfbd+rjMsomb2NQzwaVVSmO1Zdpbc0X6WIxzcbwKXhxf1deuSZ40oH5x8NU+nwepR9h3t0snNQH3j7lSpLFHaY3rZtmx577DGNjo5OW27z5s1asmRJYQ0v0Jx+bB5OJwtOyx1dR3VyKL80vlQ55/S5/U/qW8d3azid1GiQVkZOGTmNBWkNByl9p+0l/fO+x5VhSBAoaafPDOmRZ9ouCJeJgoxT/2BS3/3BkYK3n0gkdNttt6m5uVlmdsExt7q6WjfddJNWr15d8LYLNac9mJhFCp5TyUj64+fu10evfatWvUZPX36s44B2nWmbdt4pmQm0u+ek/vPkfr2p6Yo5bB3wKuec+vr6dPToUQ0ODsrMVFdXp5aWFlVVVWlkZERdXV0KgkDxeFyNjY3eLtIrVBAE6uvrUzqdVjweV21t7Zx9sVYhntl9SkEeV84HGadjJ/s1MJTUoqrC9nE8HteWLVu0ceNGnT59WslkUpFIREuWLFF1dfVsm16wOQuYvrFh7e3tUDwSVToo7LYETk5/+eL39Jfb71YiOn2TnXPaf/a0dnYd12BqTNXxMr2uYbWuqF3mdaxxuvZ8e4aTGsYlM4Hub9ujN668nKEyzLmRkRHt2LFDw8PD592J9+zZszp27Jii0ajS6bQikfCDopnJOafly5dr48aNKisrm5d2j42N6eDBgzp+/Pi5/3HnnCKRiFpaWrRu3TrFYpfGV18lU4EOtfUp38/ZTtLuV7p047VNs6ovFoupqenCdZ1z5/aRT973+umRft17YIdeOdt5UdsZDVLa0XlUN69YP2WZ/X2n9bn9T2o4nTzv9OenO4+qIhbXhy6/QVfULi+4buecDvV36cH2l3Wgv1PpjFN1PKFblq/XLcvXa1GifMp1D/Z3aTSd/51Nk5lA+/tO68q6wtsJzNbo6Kgef/xxJZPJC0YZxg9G4/eryr0NfEdHh86cOaObb77Z+zck5hoeHtYPf/jDSdsdBIEOHTqkkydP6qabbrokelqDwylFIqYgk1/CZDJOZ86OFKXuZDKpl19+WSdOnDjv3mONjY3auHGjqqqqilLPRF7jq22wV3+889sXHS7j7m/fO+WyPb0n9bd7HlFvcvi8cJGksUxafckR/d2eR7W750RBdY4GKf317v/U37z0sF7sOaHhdErJTFo9Y8P6zvE9+sgz/6EdnVOPk3aOFHaX0oxz6hr1c2dTYCq7d++e9CCdD+ecksmknn766Tm9rCCTyeiJJ57Q2NjYlPVmMhkNDw/PedumMpuBiWKMvHR2durBBx9UW1vbBTe27Ozs1MMPPzzr+5hNx1vAJIO0/uL5B5Uu4pvaPTo46R/JSDqlT+99fMbTn1OZQP/08g81ks7vStcgk9Endz+sQ/1dSmaCC85+S7lAqUygLx7Yoee6jk+6jUKHukySieExzJ3R0VF1dXVd1AHYOaeRkRH19PQUsWXTy/d7T5xzGhwcVF9fn/9GzWBRVSLv4TFJikVNy5dcXM+ir69PO3bsmLHc3r171d7eflF15fIWME+cPqykm/0FQ4V4qvOIXN4nPzs9cTq/MzN2dh9X21Cf0m76OaNUJtCXDjw96cRdU1VtAW2TJFNTVW0B5YGLc+rUqaJsJwgCHTlS+FlPs3Xo0KG8vrVRenW4bL7FohFdta4+756Mc9LG9YVfnT/Rc889l3fZ3bt3F7Wn5y1g7ju2u+jbrI5Pfs+cR06+ktckuhTOcTxy8pW8yj7QvlfJTH7f9BbI6fkzF6b/6up61Zfl/wlkcaJcrYv8npsOTJRMJov2fSC+vrhqMoODgwWVP3v2rKeWFGbrxuWKRWc+9IZhtEQV5bOfKh8eHi7o+2GCIFBnZ3GmNCSPATOQHivq9qJmun3FhkmX9acKmwTrT01/AZIUXrPTMZz/H+RYkNYzXccmXXZ3yxYlIjNfkZuIRPWuNVvm5Ww3LFzRaHRB/M1dCnMwklRbU6533L5u2pCJRU0rG6v1xusv7lqVtra2OVlnKpfeSeJTiFpEt05xBlnUCnsZ0Tz+mUbTqYK3OzRFqF7T0Ky3NW+cNmQSkajevOpKbWtcU1CdwMVasmRJ0U5XramZuzugF3padGVlpaeWFK6labF+4s4rtHbVYkUjpkQ8qkQ8ongsoqqKuG66dpXe86YNF30LqXzmqIqxzlS8naYct6hSRZyD+YUrblZNYvJTINcuWqoXe9rznulYm8etZ8pjcQUzzL3kqo5P/Qd/5+qr1Vxdp28ee1Edw/3nwitwGS2rWKR3rtmsLUtWFVQfUAy1tbWqqKgoeMgpVzQa1dq1a4vUqpm1trZq//79ec3DzHXb8rG0vlLv/pENGhpJ6fSZIQWBU1VlXCsaqorWoywvn/oSiqkU83ombwHz+oZmPdV1tCjbunFZqzYvmfpCoztWXamX+zrymocpi8R0x6orZyxXGUuoqapOxwfzOyumLBrT9sbp72S6qb5Jm+qbdGq4X6dGwptdLqtYpBWVi/OqA/Dl6quv1o4dO2Y9F2NmqqqqUm1tbXEbNo3m5mbt378/r7KxWEyNjY2eWzQ7VRVxrV1V62Xbzc3N2rdvX0HrFDOIvQ2R/eja1xVlO+XRuO5uuWbaMutqGrS6ul6xGYa0YhZRU1WtNuR588y3rroqr7mT8W1vql+ZV9nllTW6ZskqXbNkFeGCS0JDQ4OuvfbaWc3HmJnKy8u1ffv2OZ3Licfj2rZt24zfxhiLxbR9+/ZL8rYxvpWVlWnRokV5lx+/xU6xeNvjNYly3dW8adbrm8Lexq9uvH3KobFzZc304Y23a2VV7ZSBkIhEtaJysX7t6tvz/ie4tmGV1tY0KD5DyMQjUX3o8hsKnrMBLiUrVqzQbbfdptbW1nNBY2YqKyvTunXr1NjYqEgkomg0qmg0qlgspmg0qtWrV+uWW26Zl1vFNDQ06IYbblBNTY0ikci5/+1IJKJIJKL6+nrdfPPNczo3dKnZunVr3se8bdu2FbVuK+TMiq1bt7pnn322oAruO7Zb3zo+/SnLUYsofu7ThSmdCbSpfqXe3bJFywv4hJ/KBHry9GE90P6yziZHFDVT4JwWJ8p1R9NVunH52hnDIlcySOsz+36ofX2nlM5klJkw0zMeZj93xU26hvkTlBDnnIIgkJmd10MYGxtTT0+P0um0EomEGhoavH2fe6H6+/vV2dl5rm3Lly+/pCb251N/f78ef/zxaYdAt2/frqVLC/9qFDPb6ZzbOuky3wEjSWeTI7r/+B49furQuYsvyyIxbV7SpHeu2awl5VU6eLZL/ckRxaMxrVvUMO39vWbinFPv2LCGg6QqognVl1VedNf9+GCPHmrfp/1nTyudCbQoXq5bV2zQjctaVRGb/3scAcB0MpmMjh07pgMHDiiVSsk5p3g8rpaWFq1fv37WHxTmPWAAAKVpuoBh0gAA4AUBAwDwgoABAHhBwAAAvCBgAABeEDAAAC8IGACAFwQMAMALAgYA4AUBAwDwoqBbxZhZl6TJvxcYALAQrXHOTXqXzIICBgCAfDFEBgDwgoABAHhBwAAAvCBgXiPM7INm5qZ49M1z2z5vZu1F3ub/NLMXzazPzEbMbJ+Z/YGZVc6mXLbsu81sl5mNmtkxM/uomc3qW5YmvB/rZ/sa54OZRczsI2Z2NLsfXjCzHy1g/bz2oZn9jJntNLN+M+sys4fM7JacMo9M8zd9f07Za8zsfjMbzG7zm6+1fb8Qxea7ASjY+yTlHszT89EQz2okfU7Sfkljkm6U9HuSXi/pXYWWM7O3SPp/kj4r6TclXSvpzyQtkvQ7fl/KJeVPJP22wn20U9L7Jf2bmd3lnPvOdCvmuw/N7BclfVrSpyT9D0mV2fIPmdkNzrld2aK/rPD9m+gGSZ+Q9M0J29sg6QeSXpL0kwqPW38o6TEzu8Y511ngPsBccc7xeA08JH1QkpO0fr7bMknbPi+pfQ7q+fPsPmgotJykXZIezSn3B5KSkpaX0vsxTZsbFYbwH+U8/31JL+axfl77UNITkp7IKbcoW+7PZ6jjs9k21k947p8l9UmqnfDcKkmjkv5ivvcrj6kfDJGVmAlDN7ea2b9nhxTOmNnfm1lFTtkVZvZFM+s2s7HsUNM9k2yz1cy+ZGansuUOm9nfTFLuWjP7gZkNm9kBM/svRX55Z7I/Z+qxnVfOzJolXSPp3pxyX5IUl/S2IrXvPGYWN7OPZ4ejktmfHzez+IQyMTP7EzM7lB126jazx83s5gllPpAdlhofHtptZr80iya9RVJCF+6HeyVtMrPWaV5LIfswIak/p9ywpJSmGZbPDmu+T9K3nHM9ExZdL+lJ51zf+BPOuXaFPZq7p9oe5h8B89oTzR6UJj4mex/vlXRQ0nsk/bWkX5D0j+MLzaxK0qMKDwy/K+ndknZL+lJ2iGO8XKukHZJuVfhp9a2S/khSQ059NZL+NVvvuyQ9I+kfzewNEwtlw+/z+b7Y7OurNrM3KRxm+T8TDzR5ltuY/fnSxHWcc0cUHviuyrc9BfqCwiGiL0q6S2FP73eyz4/7HUm/IemTCgPgQwp7FPWSlA2aexW+V++W9F5Jn5FUO74BM7s9u18/OEN7NirsHRzMeX5P9ud0+6GQffgPkt5kZj9nZrVm1iTp7xQGzGenqeNuhT2dL+Q8Hyjs/eQak7TOzMqn2Sbm03x3oXjk99CrQzKTPe6bpNynctb/PYX/qJdlf/9wttztOeW+J6lTUjT7+xclDUpaOU3bPp/d1hsmPFemsCfxTzll05I+m+drvjrndX5hvF2FlJP0gezzV0yybnu+7Zni/Zh0iGxCmz6W8/xHs89vzv5+n6SvT1PPb0vqmaEtt2X360/PUO6fJJ2a5Pn12Tb91DTrFrQPJf2swiGs8fekQ9K2Gdr3gKTTkmI5z381W0d8wnOLFA6bOUkrivV/xqO4D3owrz13S9qW8/hvk5T7as7vX1HYY70u+/utkk445x7JKXevpKV69RPpHQoD7OQM7Rp2zj08/otzbkzSK5JWTyzknIs5535uhm2NO6jw9d2usJd1t8LAm225uXRr9udkw1FSGApS2NO708z+1MxuNrNETvlnJNWZ2b1mdpeZ1eZW5Jx7NLtf5/s1S5LM7F2S/l7hRP+bJL1DYc/nO2Z29RTrrMyW/RfnXO4Q6CclNUn6lJk1mdkahSd2VGeXZ4r/KlAMnEX22vOScy53iGMyp6f4vSn7s17hp8pcpyYsl6QluvCstcn0TvLcmKRZD18450YlPZv99VEz65D0OTP7W+fcUwWUG29b3STV1EnqmeT5izW+/3L3ce7+/TOFn/TvURiOg2b2NUn/3TnX7Zx71MzeJ+lXJX1DkszsUUm/6Zx7scA29UqqNTNz2W5ATlum2w957UMzM4U9pa855359vICZPShpn8Kz2CabN7lH4Qeg3OExOeceN7NfUXjyxs9mn/5etuw9M7Qb84geTOlaNsXvJ7I/eyQtn2S95ROWS1K3Xg2l+TYeIjNd/5BbbnyOYePEQmbWovAU2r3FaFyO8f2Xu4/P27/OuZRz7n855zZJWqFwPuZHFfYAlC3zNefcbQoP5Hdny90/xdzbdPYoHLpcl/P8eG91uv2Q7z5cpvBstWcmlnPOJSW9IOnKKbb/M5JecM69MNlC59w/ZLd7taTVzrk3S1op6WnnXGqadmMeETCl68dyfn+/wqGEp7O/PypplZndlFPuAwrnYMYPGA9KusvMVvhqaAHGh5UOFVLOOXdc4cHtJ3PK3aNw4vm7xWrgBI9lf74/5/nxNjySu4Jz7pRz7p8Vfjq/YCjJOTfonLtP4dDTCoW9y0Lcr/D1TrYfXnLhhP2kCtiHvQp7rtdNLJQd+rtGr37Ambhsq8KQu6D3ktOGMefcHudcm5ltUjik9o/TrYP5xRDZa881ZpZ7BpckPZszdn2nmf2lwoC4TuGFaV90zh3ILv+8pF+X9HUz+z2Fw2A/KenNkn7JORdky/2hpDslPWFmf6ZwvqNJ0ludcxec0jwTM0tL+sJ08zBmtlnSX0n6N0mHFX7qvjXb3u86554spFzW70q6z8w+LenLCi8S/Kikv3HOjQ9bKXsm1ucUnrDwSB4v6a1mdirnubPOuYfM7MuSPmZmMYXXhtwg6fclfdk5tztb338oPHA/p/DgfK3CM/U+nV3+xwp7BQ9LOqnw+o9fk/S8c64rW+Y2hWee/ex08zDOuU4z+4Skj5jZQLbOH5f0RknvnFjWzL6v8DbsE3uLM+5D59yYmX1G0ofNrEfhSQwVCk8qadHk84U/rfAkhX+ZrN1mtkrSf83uwzFJWyV9ROHJEV+e6vXiEjDfZxnwyO+h6c8iO3dR4YRyt0r6D4VngPUoHHKpyNnmCoXXMXQr/Md9UdI9k9S9TuEBpVvhfMEhSZ+YsPzzmuRCS4Wf0h/Jec5J+vwMr3WZwlOej0gaUXg22jOSfkVSWaHlJpR/j8KD+Zik4wpPu47mlPmVbBuvvIj346VsmYSkjyv8DqVU9ufHdf7ZUL8l6als20cU3pHgY+NlJL1d4dlVHdl2tyk81XflhG3cnq33g3n8HUUVhsKxCe/5e6d4747Och/GFAbK85IGJHVlt3fHJNuLZ5d/a4a/h+9N+Dvdm91vsZleL4/5ffB9MCVmwifwDS6/kwEwgZn9q8Irxu+c77YAr3UMkQHnu1UXzl8BmAUCBpjAObdqvtsAlAqGyAAAXnCaMgDACwIGAOAFAQMA8IKAAQB4QcAAALz4/5p9gGoFbEk8AAAAAElFTkSuQmCC\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",
    "    # Visualize the node embeddings every 10 epochs\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": "F2B3X6tf9YpS"
   },
   "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",
    "Furthermore, we did this all with a few lines of code, thanks to the PyTorch Geometric library which helped us out with data handling and GNN implementations.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "E9bELRjibIRO"
   },
   "source": [
    "## Documentation\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OmqyWVNObNcK"
   },
   "source": [
    "You can explore more PyG functions through its [documentation](https://pytorch-geometric.readthedocs.io/en/latest/)."
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "machine_shape": "hm",
   "name": "CS224W - Colab 0.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python [conda env:Graph_env] *",
   "language": "python",
   "name": "conda-env-Graph_env-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}