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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "#libraries\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "import os\n",
+        "import math\n",
+        "\n",
+        "import random\n",
+        "#from catboost import CatBoostRegressor\n",
+        "from lightgbm import LGBMRegressor\n",
+        "from xgboost import XGBRegressor\n",
+        "from sklearn.model_selection import RandomizedSearchCV\n",
+        "from scipy.stats import uniform\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "import time\n",
+        "from sklearn.metrics import r2_score\n",
+        "from sklearn.metrics import mean_absolute_error\n",
+        "from sklearn.metrics import mean_squared_error\n",
+        "from sklearn.metrics import max_error\n",
+        "from sklearn.feature_selection import SelectFromModel"
+      ],
+      "metadata": {
+        "id": "KGBPC8-5zbpm"
+      },
+      "execution_count": 1,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Graph Neural Network (GNN)"
+      ],
+      "metadata": {
+        "id": "iMb03_1BcYot"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Creating a Graph Neural Network (GNN) for regression tasks involves several steps, including defining the graph structure, selecting an appropriate GNN architecture, and setting up the training process. Below, I'll provide a basic example using PyTorch Geometric, a popular library for graph neural networks.\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "i6oc7bHpixbY"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "pip install torch_geometric"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "_-g1H9L0iiql",
+        "outputId": "eaa6b256-1138-4da8-b240-77e796325a82"
+      },
+      "execution_count": 24,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting torch_geometric\n",
+            "  Downloading torch_geometric-2.4.0-py3-none-any.whl (1.0 MB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m7.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from torch_geometric) (4.66.1)\n",
+            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from torch_geometric) (1.23.5)\n",
+            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from torch_geometric) (1.11.3)\n",
+            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch_geometric) (3.1.2)\n",
+            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from torch_geometric) (2.31.0)\n",
+            "Requirement already satisfied: pyparsing in /usr/local/lib/python3.10/dist-packages (from torch_geometric) (3.1.1)\n",
+            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from torch_geometric) (1.2.2)\n",
+            "Requirement already satisfied: psutil>=5.8.0 in /usr/local/lib/python3.10/dist-packages (from torch_geometric) (5.9.5)\n",
+            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch_geometric) (2.1.3)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->torch_geometric) (3.3.2)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->torch_geometric) (3.4)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->torch_geometric) (2.0.7)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->torch_geometric) (2023.7.22)\n",
+            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->torch_geometric) (1.3.2)\n",
+            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->torch_geometric) (3.2.0)\n",
+            "Installing collected packages: torch_geometric\n",
+            "Successfully installed torch_geometric-2.4.0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import torch\n",
+        "import torch.nn.functional as F\n",
+        "from torch_geometric.nn import GCNConv\n",
+        "from torch_geometric.data import Data\n",
+        "\n",
+        "# Define a simple GCN model\n",
+        "class GCN(torch.nn.Module):\n",
+        "    def __init__(self, num_node_features, hidden_channels):\n",
+        "        super(GCN, self).__init__()\n",
+        "        self.conv1 = GCNConv(num_node_features, hidden_channels)\n",
+        "        self.conv2 = GCNConv(hidden_channels, hidden_channels)\n",
+        "        self.linear = torch.nn.Linear(hidden_channels, 1)  # Output is 1 for regression\n",
+        "\n",
+        "    def forward(self, data):\n",
+        "        x, edge_index = data.x, data.edge_index\n",
+        "\n",
+        "        # Two layers of GCN\n",
+        "        x = self.conv1(x, edge_index)\n",
+        "        x = F.relu(x)\n",
+        "        x = self.conv2(x, edge_index)\n",
+        "\n",
+        "        # Global pooling (e.g., mean pooling)\n",
+        "        x = torch.mean(x, dim=0)\n",
+        "\n",
+        "        # Linear layer for regression\n",
+        "        x = self.linear(x)\n",
+        "\n",
+        "        return x\n",
+        "\n",
+        "# Example graph data (you will need to replace this with your actual graph data)\n",
+        "# Assuming a graph with 3 nodes, each node has 5 features\n",
+        "num_node_features = 5\n",
+        "num_nodes = 3\n",
+        "x = torch.randn((num_nodes, num_node_features))  # Node features\n",
+        "edge_index = torch.tensor([[0, 1, 1, 2], [1, 0, 2, 1]], dtype=torch.long)  # Edges\n",
+        "\n",
+        "# Creating a PyTorch Geometric data object\n",
+        "data = Data(x=x, edge_index=edge_index)\n",
+        "\n",
+        "# Instantiate the model\n",
+        "model = GCN(num_node_features=num_node_features, hidden_channels=16)\n",
+        "\n",
+        "# Forward pass\n",
+        "output = model(data)\n",
+        "print(output)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "WJaQHLmCieS6",
+        "outputId": "5874528b-beb0-4907-f886-e62952216176"
+      },
+      "execution_count": 25,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "tensor([-0.0136], grad_fn=<ViewBackward0>)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "DGL(Deep Graph Library) backend pytorch tensorflow mxnet"
+      ],
+      "metadata": {
+        "id": "OMJQLWoEhfg4"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "import torch\n",
+        "import torch.nn as nn\n",
+        "import torch.nn.functional as F\n",
+        "\n",
+        "import numpy as np"
+      ],
+      "metadata": {
+        "id": "u-_ACHCidWtI"
+      },
+      "execution_count": 18,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install dgl\n",
+        "import dgl\n",
+        "from dgl import DGLGraph\n",
+        "\n",
+        "dgl.load_backend('pytorch')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "geCfiSqDhP5_",
+        "outputId": "d038221e-b3f8-4592-c9ff-60fddb1c5b40"
+      },
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting dgl\n",
+            "  Downloading dgl-1.1.2-cp310-cp310-manylinux1_x86_64.whl (6.0 MB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.0/6.0 MB\u001b[0m \u001b[31m16.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: numpy>=1.14.0 in /usr/local/lib/python3.10/dist-packages (from dgl) (1.23.5)\n",
+            "Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from dgl) (1.11.3)\n",
+            "Requirement already satisfied: networkx>=2.1 in /usr/local/lib/python3.10/dist-packages (from dgl) (3.2.1)\n",
+            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.10/dist-packages (from dgl) (2.31.0)\n",
+            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from dgl) (4.66.1)\n",
+            "Requirement already satisfied: psutil>=5.8.0 in /usr/local/lib/python3.10/dist-packages (from dgl) (5.9.5)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->dgl) (3.3.2)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->dgl) (3.4)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->dgl) (2.0.7)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->dgl) (2023.7.22)\n",
+            "Installing collected packages: dgl\n",
+            "Successfully installed dgl-1.1.2\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "DGL backend not selected or invalid.  Assuming PyTorch for now.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Setting the default backend to \"pytorch\". You can change it in the ~/.dgl/config.json file or export the DGLBACKEND environment variable.  Valid options are: pytorch, mxnet, tensorflow (all lowercase)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from dgl.nn.pytorch import conv as dgl_conv\n",
+        "\n",
+        "class GraphSAGEModel(nn.Module):\n",
+        "    def __init__(self,\n",
+        "                 in_feats,\n",
+        "                 n_hidden,\n",
+        "                 out_dim,\n",
+        "                 n_layers,\n",
+        "                 activation,\n",
+        "                 dropout,\n",
+        "                 aggregator_type):\n",
+        "        super(GraphSAGEModel, self).__init__()\n",
+        "        self.layers = nn.ModuleList()\n",
+        "\n",
+        "        # input layer\n",
+        "        self.layers.append(dgl_conv.SAGEConv(in_feats, n_hidden, aggregator_type,\n",
+        "                                         feat_drop=dropout, activation=activation))\n",
+        "        # hidden layers\n",
+        "        for i in range(n_layers - 1):\n",
+        "            self.layers.append(dgl_conv.SAGEConv(n_hidden, n_hidden, aggregator_type,\n",
+        "                                             feat_drop=dropout, activation=activation))\n",
+        "        # output layer\n",
+        "        self.layers.append(dgl_conv.SAGEConv(n_hidden, out_dim, aggregator_type,\n",
+        "                                         feat_drop=dropout, activation=None))\n",
+        "\n",
+        "    def forward(self, g, features):\n",
+        "        h = features\n",
+        "        for layer in self.layers:\n",
+        "            h = layer(g, h)\n",
+        "        return h"
+      ],
+      "metadata": {
+        "id": "XbaDj5u6hcG8"
+      },
+      "execution_count": 20,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "pip install dgl\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "okfHqgJDjDRE",
+        "outputId": "e083df1c-59a6-4884-a1e8-cade1ba87661"
+      },
+      "execution_count": 26,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: dgl in /usr/local/lib/python3.10/dist-packages (1.1.2)\n",
+            "Requirement already satisfied: numpy>=1.14.0 in /usr/local/lib/python3.10/dist-packages (from dgl) (1.23.5)\n",
+            "Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from dgl) (1.11.3)\n",
+            "Requirement already satisfied: networkx>=2.1 in /usr/local/lib/python3.10/dist-packages (from dgl) (3.2.1)\n",
+            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.10/dist-packages (from dgl) (2.31.0)\n",
+            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from dgl) (4.66.1)\n",
+            "Requirement already satisfied: psutil>=5.8.0 in /usr/local/lib/python3.10/dist-packages (from dgl) (5.9.5)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->dgl) (3.3.2)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->dgl) (3.4)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->dgl) (2.0.7)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->dgl) (2023.7.22)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import networkx as nx\n",
+        "import matplotlib.pyplot as plt\n",
+        "from dgl.data import citegrh\n",
+        "import dgl\n",
+        "\n",
+        "def plot_graph(g, title):\n",
+        "    # Convert DGL graph to a NetworkX graph\n",
+        "    nx_g = g.to_networkx().to_undirected()\n",
+        "\n",
+        "    # Draw the graph\n",
+        "    plt.figure(figsize=(10, 10))\n",
+        "    nx.draw(nx_g, with_labels=True, node_size=50, node_color=\"skyblue\", linewidths=0.5)\n",
+        "    plt.title(title)\n",
+        "    plt.show()\n",
+        "\n",
+        "# Load the PubMed dataset\n",
+        "data = citegrh.load_pubmed()\n",
+        "\n",
+        "# Get the graph\n",
+        "g = data[0]\n",
+        "\n",
+        "# Plotting a small subgraph for visualization purposes\n",
+        "# Visualizing the entire graph can be very heavy and not informative due to its size\n",
+        "sub_g = dgl.node_subgraph(g, list(range(100)))  # Adjust the range for different subgraphs\n",
+        "plot_graph(sub_g, \"PubMed Dataset Sample Graph\")\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "tNJJGTf2jK3W",
+        "outputId": "1321d3a0-a22c-4f36-a539-773d7ea31c8a"
+      },
+      "execution_count": 28,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "  NumNodes: 19717\n",
+            "  NumEdges: 88651\n",
+            "  NumFeats: 500\n",
+            "  NumClasses: 3\n",
+            "  NumTrainingSamples: 60\n",
+            "  NumValidationSamples: 500\n",
+            "  NumTestSamples: 1000\n",
+            "Done loading data from cached files.\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x1000 with 1 Axes>"
+            ],
+            "image/png": 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3mDFjBlOmTKF+/fp37NetWzcuX77Mhx9+mONYamoqycnJALbvNWvWrGx9ZsyYccfn/qMiIiLo06cPLi4ujBo1ytbu5OSUY6T0s88+y/ZaALb91OPi4rK13xp9vf05DMNg5syZ2fqlpKTk2F6tYsWKeHt727aka926NT4+PkyePJnMzMwc13D7z/JO8eTm6tWrHD9+PEd7RkYG3333HWazmeDgYNv1/JHX417ZvXt3tnXtFy9eZN26dbRq1eqOI9t/5nXKzYsvvkixYsV4+eWXCQ8Pz3H8z4yc5/bzh7u/Z29tm3jL7NmzAe76uyYiIg8mjeyLiEgO/fr1Y/r06bRu3Zr+/fsTFRXFvHnzqFatGgkJCTn6BwcH06hRI1588UXS09OZMWMGRYsW5bXXXrvr9/nHP/7xu7E8//zzrFq1isGDB7N161YeffRRLBYLJ0+eZNWqVWzatIl69epRq1YtevTowdy5c4mPjycsLIzvvvuO06dP/6lrP3DgAJ9++ilWq5W4uDj27dvHmjVrMJlMLFmyJNt07fbt2zNhwgT69u1LWFgYR44cYenSpQQFBWV7zooVK+Ln58e8efPw9vbG09OTRx55hCpVqlCxYkVeffVVLl++jI+PD2vWrMmxrjo8PJwWLVrQrVs3HnroIZydnVm7di3Xrl2je/fuwM215h988AHPP/88derUoXv37gQEBHDhwgW+/vprHn30UebMmQNgK2o3fPhwWrdujZOTk+15fuvSpUvUr1+f5s2b06JFC4oXL05UVBTLly/n8OHDjBgxwjZq/0dfj3ulevXqtG7dOtvWewBvvvnmHc/5M69TbooUKcLatWvp0KEDNWvWpHv37oSGhlKoUCEuXrxo2y4xtzXzucXSpEkT3n33XTIzMylVqhSbN2/m3Llzdzzn3LlzdOzYkTZt2rB7927bVpM1a9b83e8nIiIPGDvtAiAiInZwaxuyffv2/W7fTz/91AgKCjJcXFyMWrVqGZs2bbrj1ntTp041/v3vfxtlypQxXF1djcaNG2fbCswwsm+9dzf8Zus9wzCMjIwM45133jGqVatmuLq6GoULFzbq1q1rvPnmm7Zt8AzDMFJTU43hw4cbRYsWNTw9PY0OHToYFy9e/FNb7936cnZ2NooUKWI88sgjxr/+9S/bNni3S0tLM0aOHGmUKFHCcHd3Nx599FFj9+7duW5duG7dOuOhhx4ynJ2ds23Dd/z4caNly5aGl5eX4e/vbwwYMMC2TdqtPjExMcZLL71kVKlSxfD09DR8fX2NRx55xFi1alWOmLZu3Wq0bt3a8PX1Ndzc3IyKFSsaffr0ybZFXVZWljFs2DAjICDAMJlMd92GLyEhwZg5c6bRunVro3Tp0kahQoUMb29vo2HDhsaHH35oWK3WP/163Np677PPPsv2ve70/sztvXPrffLpp58alSpVMlxdXY3atWsbW7duzfU5b22992dep7u5cuWKMWrUKOOhhx4y3N3dDVdXVyMoKMjo1atXti3z7hT/LZcuXTI6d+5s+Pn5Gb6+vkbXrl2NyMjIHO/ZW89x/Phxo0uXLoa3t7dRuHBhY+jQoUZqamq258ztd8gwDKNcuXJG7969/9D1iYhIwWcyDFVqERERkYLFZDLx0ksv3XUU3pGMHz+eN998k+jo6PtS/0BERByP1uyLiIiIiIiIOBgl+yIiIiIiIiIORsm+iIiIiIiIiIPRmn0RERERERERB6ORfREREREREREHo2RfRERERERExMEo2RcRERERERFxMEr2RURERERERByMkn0RERERERERB6NkX0RERERERMTBKNkXERERERERcTBK9kVEREREREQcjJJ9EREREREREQejZF9ERERERETEwSjZFxEREREREXEwSvZFREREREREHIySfREREREREREHo2RfRERERERExMEo2RcRERERERFxMEr2RURERERERByMkn0RERERERERB6NkX0RERERERMTBKNkXERERERERcTBK9kVEREREREQcjJJ9EREREREREQejZF9ERERERETEwSjZFxEREREREXEwSvZFREREREREHIySfREREREREREHo2RfRERERERExMEo2RcRERERERFxMEr2RURERERERByMkn0RERERERERB6NkX0RERERERMTBKNkXERERERERcTBK9kVEREREREQcjJJ9EREREREREQejZF9ERERERETEwSjZFxEREREREXEwSvZFREREREREHIySfREREREREREHo2RfRERERERExMEo2RcRERERERFxMEr2RURERERERByMkn0RERERERERB6NkX0RERGxOnTpF9+7dKV26NB4eHlSpUoUJEyaQkpICwPnz5zGZTHf8GjBggJ2vQERERACc7R2AiIiI5A8XL16kfv36+Pr6MnToUIoUKcLu3bsZN24c+/fvZ926dQQEBLBkyZIc527cuJGlS5fSqlUrO0QuIiIiv6VkX0RERABYsmQJcXFx7Nixg2rVqgEwcOBALFYrSz75hAX7zuPk5Ytb/faEBroT7OuC2WQCYPHixfj4+NChQwd7XoKIiIj8j5J9ERERASAhIQGAYsWK2dqSM61EuxTGZDZzNdOES5oFgGsRSfi4mOkR7EtCzDW2bt1Kr169cHNzs0vsIiIikp3W7IuIiAgAzZo1A6B///4cOnSIiAsXeGX2Irau+Iiw7gNwcfe09c2wGsSkWVh+Op7ly5djtVp59tln7RS5iIiI/JbJMAzD3kGIiIhI/jBx4kQmT55Mamqqre2x/i/T6qU3cu3vYjaxqE8rrl+7wqVLlzCbNY4gIiKSH2gav4iIiNiUL1+eJk2a8PTTT3Ms3Y2dWzay7aMZeBUNJKz7Czn6Xz53mqOHDvDyyy8r0RcREclHlOyLiIgIACtWrGDgwIGEh4dTunRpFp6IpfijT2BYrWyc9RY12zyFp1+RbOcc+mY1gKbwi4iI5DP6CF5EREQAmDt3LrVr16Z06dIAuDndrLRftWkbMtNSuPLLkRznHN64hhIVKlG3bt08jVVERETuTsm+iIjYzbFjx+jatStBQUF4eHjg7+9PkyZNWL9+fbZ+H374IU2bNqVYsWK4urpSoUIF+vbty/nz5+0TuIO6du0aFovF9jg00B0XswlLViYAlqysbP0vHNnP9Yvn6NajR57GKSIiIr9P0/hFRMRuIiIiSExMpHfv3pQsWZKUlBTWrFlDx44dmT9/PgMHDgTg4MGDVKhQgY4dO1K4cGHOnTvHhx9+yFdffcXhw4cpWbKkna/EMYSEhLB582bCw8MJCQkh2NcFHxczhzetxWQ2UyKkWrb+hzeuAeClvs/bI1wRERG5C1XjFxGRfMVisVC3bl3S0tI4efLkHfvt37+fevXqMWXKFP75z3/mYYSO64cffqB58+YULVqUoUOHUrRoUdatX8/mjRt5pPNzdBrznq2vs2HlrVYPUyU4iB/37LZj1CIiIpIbjeyLiEi+4uTkROkyZfhx7z6WhseRZjFwczIRGuhOsK8LZtPNdeTly5cHIC4uzn7BOpgmTZqwa9cuxo8fz9y5c7l+/ToVKlRg4sSJPDVoBAduZNp+HulHdpBwPYrnxo22d9giIiKSCyX7IiJid8nJyaSmphIfH8/qtevYsGEDNVt14mLyr2vEr0UkYU6J48mynly/cokJEyYA0KJFC3uF7ZDq16/PN998k+uxqv63PQhpTz9NDhQREcm3VKBPRETsbuTIkQQEBBAcHMy/Xh/FQ4+1pcPrb2frk2E1GNWsGkGlSxAaGsquXbuYNWsWjz/+uJ2iloJm0qRJmEwmqlevnq3darUyb948atWqhZeXF8WKFeOJJ55g165ddopURETk79PIvoiI2N2IESPo0qULB05HsHjZSgyLlazMjBz9+sxeAZnp+MdFsGnNCpKTk+0QrRREly5dYvLkyXh6euY4NmrUKKZPn85zzz3HkCFDiIuLY/78+TRt2pSdO3dSv359O0QsIiLy96hAn4iI5BtLw+O4mJzFwiFdSUuMZ8gnmzD9b43+7cp4OtPA6TrVq1dn6tSpDB061A7RSkHSvXt3oqOjsVgsxMTEcPToUQCysrLw8fGhXbt2fPbZZ7b+586dIygoiOHDhzNz5kx7hS0iIvKXaRq/iIjkG2mWm58/P9yiA5eOHSQm4swd+1WsWJHatWuzdOnSvAxRCqAffviB1atXM2PGDFtbeFw6S8Pj+PBIFKmpqbj6+WO9bfwjMDAQs9mMu7u7HSIWERH5+5Tsi4hIvuHmdHMUPzM9FYC0pIS79rtV1C+/O3DgAB07dqRIkSJ4eHhQvXp1Zs2aZe+wHggWi4Vhw4bxwgsv8PDDD2MxIDbdwlcRSVxMziIeF8pUr8tnS5cwcOqHnDxznp9//pk+ffpQuHBhBg4caO9L+MOOHTtG165dCQoKwsPDA39/f5o0acL69ettfaxWK4sXL6Zjx46UKVMGT09PqlevzsSJE0lLS7Nj9CIicq9pzb6IiNhNVFQUgYGBtsehge5Eno7lwFerKOTmTmBQCJasLDJSknD38QPAxWyifqA7e/fu5ciRI/Ts2dNO0f8xmzdvpkOHDtSuXZsxY8bg5eXFmTNnuHTpkr1DeyDMmzePiIgItmzZgtUwiErNwmLcLPh4S7eJc1n+zwEsfH0QC1+/2RYUFMTOnTsJCgqyU+R/XkREBImJifTu3ZuSJUuSkpLCmjVr6NixI/Pnz2fgwIGkpKTQt29fGjRowODBgwkMDGT37t2MGzeO7777jv/+97+5Lp0REZGCR2v2RUTEbjp37kxCQgJNmjShVKlSXLlyhbmLl3D17CnavjKBxs+9SGpiPG+3qUGNVp0IrFiFIt5e+ESfZvHixbi5ubFnzx4qVapk70vJVUJCAiEhITQMC2PKf5ayPybdtk99aKA7wb4umJVY3TfXr18nJCSEN954g5EjRxIel07bls1JirvBiM+22/olXo9iw4w3cfX0okqDppS3xvPRrGl4eHiwfft2/P397/Jd8jeLxULdunVJS0vj5MmTZGRk8NNPPxEWFpat34QJExg3bhzffvstLVu2tFO0IiJyL2lkX0RE7OaZZ55h4cKFfPDBB1y/fh1vb29q1anD06+MJ6hRazKsBoXc3KnX6TnO7d/Jse/Wk5WeRsmSJenRowejR4+mfPny9r6MO1q2bBnXrl2jTr/X+OZiCknJSTi7umM2m7kWkYSPi5kewb54FtKquvth9OjRFClShGHDhgGwLyoV62/6WLKyWPhiF4LqhtHxf9s9lvF0ZkvHNlSrVo2pU6fyzjvv5HHk946TkxOly5Thx737WBoed/PDJv+H8I9Lz/ZhU+fOnRk3bhwnTpxQsi8i4iCU7IuIiN10796d7t2752i3GgZn4jPYG5VKmsWJIePfoX6gOxUL2Ej4t1u24O7lTcSly6wb+hwxEWdwcfegdrtutBv5FhlWN5afjqdfFb8CdV0FwalTp1iwYAEzZswgMjISgEsX4slKT8eSlUls5AVcPb258stRrp0+QbtXJtjOTbMYVKpaiapVq7Jz5057XcJflpycbKtnsXrtOjZs2EDNVp24mJxl6/PbD5uuXr0KUKBnMYiISHZK9kVEJN8xm0xU8nOlkp+rvUP5W46dDMeSZeGTl3tRr1NPWg8dzdn9O9m94j+kJsbTY8oCEjKsnInPKPDXmt9cvnwZq9XK8OHDGT58eI7j77avS1iPgZR5uC4AVovFdsxWKDIzk6ysrBzn5ncjR45k/vz5AJjMZqo1b0eH/81auCXDahCTZrF92PTuu+/i4+PDE088YY+QRUTkPlCyLyIicp/EJiSSkZbCI1360PG1KQBUb9EeS2Yme9d8zOMvvo5/2YrsjUpVsn+PVa9enbVr12Zri0zO5K1xY0lLTqL9qEkULV0eS2YmAD9vXkvlR1vYCkAeOHCAX375pUBV479lxIgRdOnShQOnI1i8bCWGxUpWZkaufRMyrLw69i22bNnC3Llz8fPzy9tgRUTkvlGyLyIicp84u7oBULN152zttdo8xd41H3Ph55/wL1uRNItq5d5r/v7+dOrUKVub1TCYOXMmBlDtsba29uAGzTiwfiXpSUnUatycOCOOOXPm4O7uzogRI/I07nuhSpUqVKlShWtl4+j1yJMsHNKVT0Y8x5BPNuWotP/Ths9ZMWk8/fv358UXX7RTxCIicj+oIpCIiMh9UqRYCQC8igZma/csEgBAakIc8Ou0cbm/zCYTge7OOJlubuF4S6/pn9BmyD+5ceE0n08dzaxZs3j00UfZsWMHlStXtmPEf8+tD5EebtGBS8cOEhNxJtvxU3u2sWrsUB5u2op58+bZI0QREbmPNLIvIiJyn4SF1uXozq0kRF0hoHywrT0x+mYxNM/C/rZp45I3tn+/7TcFIA3cPL3pPmk8FedMdqhCibbaA+mpAKQlJdiOXTiyn09H9qb0QzUZMXMRzs66JRQRcTQa2RcREblPBj7fA4B965Zma9/3xaeYnZ0JqvcoPi5mKvq62CO8B9atApDPhvjRv2phng3xo5Kfa4FP9KOiorI9Dg10x8mSxYGvVlHIzZ3AoJCb/c6G8/E/elK4ZFlemL2cRmWL2CNcERG5z/QxroiIyH1St04devXpyyeLF4Eli3J1wji3fydHvv2SFv1GEFSmFD2CfQt8kin5w6BBg0hISKBJkyaUKlWKK1euMHfxEq6ePUXbVybg6uFFenISHw3tRmpCHE16vcT5XVvYfd6DH//3HqxYsSINGza085WIiMi9YDIMQ1WBRERE7pPMzEwmTZrEfz5axNWrVyhaogytnn2Bsa+9QkVfFyX6cs+sWLGChQsXcuTIEa5fv463tze16tThoc79CGrUmgyrQWzkBd5tX/eOz9G7d28WL16cd0GLiMh9o2RfRERExIHlqFHgdLNOhD5sEhFxbEr2RURERERERByMCvSJiIiIiIiIOBgl+yIiIiIiIiIORsm+iIiIiIiIiINRsi8iIiIiIiLiYJTsi4iIiIiIiDgYJfsikm8cOHCAjh07UqRIETw8PKhevTqzZs0C4Pz585hMpjt+DRgwwM7RizwY+vTpc9ffxcuXL9s7RCmg/uh7a/PmzfTv35/q1avj5ORE+fLl7Ru4iEg+5WzvAERE4ObNW4cOHahduzZjxozBy8uLM2fOcOnSJQACAgJYsmRJjvM2btzI0qVLadWqVV6HLPJAGjRoEC1btszWZhgGgwcPpnz58pQqVcpOkUlB90ffW8uWLWPlypXUqVOHkiVL2iNUEZECwWQYhmHvIETkwZaQkEBISAhhYWGsXr0as/mPTzpq2bIl+/bt49q1a7i5ud3HKEUEwGoYnI7PYF9UKmkWAzcnE6YzB3i2bQsmTZrEG2+8Ye8QxYHs2LGDxo0b8/LoN6n7/HDSLAapMVd5rEoZqvp70rFDB44ePcr58+ftHaqISL6jafwiYnfLli3j2rVrTJo0CbPZTHJyMlarFbiZWITHpbM0PI6FJ2JZGh5HeFw6VsPgypUrbN26laeeeqrAJ/qnTp2ie/fulC5dGg8PD6pUqcKECRNISUkBICUlhffff59WrVpRokQJvL29qV27Nh988AEWi8XO0cuDIjnTykcn4/gqIomLyVlEp1m4mJzF+4s+xWQy0alrd3uHKA7m40+XYjKZ8GjYwfaeS/IKYFNkOh+djMOiMSsRkTtSsi8idrdlyxZ8fHy4fPkylStXxsvLCx8fH14YNJj5h6/mSCy+ikjio5NxfLJ0OVarlWeffdbel/C3XLx4kfr167Nnzx6GDh3KjBkzaNiwIePGjaNHjx4AnD17lmHDhmEYBq+88grTpk2jQoUKDBkyhH79+tn5CuRBYDUMlp+OJybNQob11wTLkpnJoW/XUbZmKLuyCmNV8vWHbdu27Y7r0/fs2ZOt765du2jUqBEeHh4UL16c4cOHk5SUZKfI80Z6RgYrVq6ibM1QvEqUyXYsw2oQk2bhcnKWnaITEcn/tGZfROzu1KlTZGVl8eSTT9K/f3+mTJnC1q1bmTNnDvsiougxZUG2/rdu8uYtXkKJEiVo3ry5nSK/N5YsWUJcXBw7duygWrVqAAwcOBCL1cqSTz5hwb7zpGS6MWX9Tjo/WodgXxfMJhODBg2iX79+LFq0iDFjxhAcHGznKxFHdjo+g4QMa4728N1bSYm7Qa0nupCQYeVMfAaV/FztEGHBNXz4cEJDQ7O13f77fOjQIVq0aEHVqlWZPn06ly5dYtq0aZw6dYoNGzbkdbh5ZvHnX5MUd4MWT3S5Y58sq0GWVR8wiYjkRsm+iNhdUlISKSkpDB482FZ9v3rzduy/msju1R/z+Iuv41+2YrZzoiPOEHHsEH2GDP9Ta/zzo4SEBACKFStma0vOtBLtUhiT2czVTBMu7n7g7sdXEUn4uJjpEeyLZyEznTt3ZtGiRZw4cULJvtxX+6JSs43o33J44xqcnAvx8ONPkmE12BuVqmT/T2rcuDFdutw5oX3jjTcoXLgw27Ztw8fHB4Dy5cszYMAANm/e7LAFSj9eutT23roTqwHpSvZFRHJVsO+QRcQhuLu7A9imrMPNxOLh1k8BcOHnn3Kcc+ib1QBUbNE5DyK8v5o1awZA//79OXToEBEXLvDK7EVsXfERYd0H4OLuaet7a1bD8tPxWA2Dq1evAuDv72+P0OUBkmbJmVClpyRxfNtGKjV8DE+/InfsJ78vMTGRrKybU9Jvr1UyZ18Em7/9lrZdeuDl7W3r36tXL7y8vFi1apW9Qr6vkpKS2P/dhmzvrTvRyhERkdwp2RcRu7u1ddLtI9tpFgPPIgEApCbE5Tjn8MY1BJQPpkTVmnkS4/3Upk0b3nrrLb799ltq165N+XLlWDDyBcKeeYH2r07M9ZyEDCsnopOYMWMGFSpUyDEFWORec3My5Wg7vnUDmWkp1Grb5a795O769u2Lj48Pbm5uNGn2GGNXb7XVKjl69CiWrCwySj/ERyfjSM68uZTCxcWFWrVqcfDgQTtHf3988cUXZKRmf2/diUlvORGRXCnZFxG7q1u3LgCXL1+2tbk5mUiMvjlq7Vk4+6j1hSP7uX7xHDWf6OIwiUX58uVp0qQJCxYs4B+zP6Hekz3Z9tEMdq34T679M6wGw4YN4/jx48yZMwdnZ63KkvsrNNAdF3P237dDG1bj4uFJ1SatAXAxm6gf6G6P8AokFxcXnn76aWbOnMm6det46623OHj4Z955vh3njx8GIDHmGgDuRQOzzeoBKFGiBJGRkXaL/35aunQpHl5e1GzW5q79zCZwNTvGvwMiIvea7g5FxO66devG22+/zcKFC23F9kID3fn3F59idnYmqN6j2fof3rjmZp+2TztEYrFixQoGDhxIeHg4pUuXZuGJWIo/+gSG1crGWW9Rs81TOaax/vDxHLau+pi33nqLtm3b2ilyeZAE+7rg42ImJu3mVo9JsTGc3vsDNVs/hYu7BwA+LmYq+rrYM8wCJSwsjLCwMNvjKk1ak1i9JdO6NWXj7In0e38VmWlpADi73KyDcHsRRDc3N1JTU+0S+/0UHR3Nli1b6N6jB/6+Xrb3XG6czSacleyLiORKyb6I2F3t2rXp168fH330EVlZWTRt2pSt27ZxcOPnNOv7D3wCitv6Wi0Wft78BWUerkdQxYoOkVjMnTuX2rVrU7p0aeDXadBVm7Zh//oVXPnlCMGPNLX13//lcjbOmkCL7n0ZPXq0XWKWB4/ZZKJHsC/LT8eTkGHl501fYM3KotYTT+NiNtkKR5o1p/ov2xeVik/pClRt2oZj//0aq8VCITc3ALIy0gGyFUFMS0uz1TxxJCtXriQrK4vnnn2WRre9524ViLwSfozw7ZtwdTKRdiWC+Ph4Jk68ueSpZs2adOjQwZ7hi4jkG0r2RSRfmDdvHmXLlmXRokWsXbuWcuXK8c606RRp2yfbTd7pH78n6Xo07QeNdJjE4tq1axQuXNj2ODTQnWsRSViyMgGwZP26j/TxbRv4/K2XebhFe95///08j1UebJ6FzPSr4seZ+Aw+2Pw53kUDeKx5CxqU8KLi/7aElL/uVnFDv2KlsGRmkJGagrf/zVomt6bz397vypUrtponjmTp0qUEBgbSsmVLnJx+fc/tjUolzWJw7twxNr4/Jds5Y8aMAaB3795K9kVE/kfJvojkC4UKFWLcuHGMGzcuW7vVMLLd5JV5vBX/ik1zqMQiJCSEzZs3Ex4eTkhIiG269OFNazGZzZQIqQbAuf27WP6vgZSv3ZDB0xZQqbCbnSOXB5HZZKKSnyunDu61dygO59asnhuXz+Ps6oaLhyfFK1bF7OzM5eOHqdGqk61fRkYGhw4dolu3bnaM+P7YvXt3tse33nO3tnTsX3UI8/85xB6hiYgUKEr2RSRf++1NniMaNWoUGzZsoHHjxgwdOpSiRYuybv16jm/dyCOdn8MnoDixkRf55OXnMZlMNHjiSVwObmbZkV9rrNaoUYMaNWrY8SpE5M+Kjo4mICDA9jg00J2Dm/dw4vtNhDzaArPZjJu3D8H1m3Dwm89oPmAk3t7e1A90Z8mSJSQlJdG1a1c7XoGIiORnJsPQ7qQiIva2d+9exo8fz8GDB7l+/ToVKlSgV69ePDVoBAduZHJo93am9el4x/PHjRvH+PHj8y5gEfnbmjdvjru7O2FhYQQGBnLs2DE+WPAhZidnXly8gcCgEAAunzjMvL7tCAwK4bFufSiXdZ3p06fTpEkTNm3aZOerEBGR/ErJvoiIiIgdzJo1i6VLl3L69GkSEhIICAig6WPNefj5l3ErUd5WqwTg0uEf2ThrApdP/Iy3tzfdunVjypQpeHt72/EKREQkP1OyLyIiIpKP/LZWiZuTifqB7g5Vq0RERO4/JfsiIiIiIiIiDsb8+11ERETy1qRJkzCZTFSvXt3Wdv78eUwm0x2/BgwYYMeIRURERPIXVeMXEZF85dKlS0yePBlPT89s7QEBASxZsiRH/40bN7J06VJatWqVVyGKiIiI5Huaxi8iIvlK9+7diY6OxmKxEBMTw+c79rPvtrXLoYHuBN+2drlly5bs27ePa9eu4ebmZufoRURERPIHjeyLiEi+8cMPP7B69WoOHjzIkKHDiE238FVEUraq5NcikvBxMdMj2JeEmGts3bqVXr16KdEXERERuY2SfRERyRcsFgvDhg3jhRdeoFr16kSlZmExyJbow83HMWkWlp+OJ2HDcqxWK88++6ydohYRERHJn1SgT0RE8oV58+YRERHBW2+9xen4DCzWu68yS8iwMnf+h7i6uvLUU0/h7e1Nq1atOHToUI6+zZo1y7WoX5s2be7T1YiIiIjYl0b2RUTE7q5fv87YsWMZM2YMAQEBbA6Pw/o75xzY8hVnwk/i5+fH6NGjsVqtzJ07l6ZNm7J3714qV66crX/p0qWZMmVKtraSJUve4ysRERERyR+U7IuIiN2NHj2aIkWKMGzYMADSLL9fO3bjzDcBWL16NS1atADgueeeIyQkhDfeeIM1a9Zk6+/r68tzzz13jyMXERERyZ+U7IuIiF2dOnWKBQsWMGPGDCIjIwFIvJJAVno6lqxMYiMv4OrpjYdv4Wzn3bh0HjdPL1uiD1CiRAmaNG3K+q++YuHBS+DmiZuTidSsm/MEsrKySEtLw8vLK+8uUERERMQOtGZfRETs6vLly1itVoYPH06FChWoUKECL7eoycWj+4mJOMO77evy3YJp2c65cGQ/hmGA2czw4cM5ffo0AMmZVq5lOZOZkcH+n48SnWbhYnIW19MsnPwlHE9PT7y9vSlevDhjxowhMzPTHpcsIvnY/v37adOmDT4+PnetBXK7uLg4AgMDMZlMrF69Om8CFRH5HRrZFxERu6pevTpr167N1mY1DIa99gYpSUm0HzWJoqXLZzt+eOPNKfpuTmaWLl3KnDlz6NCxI01en86Zw/sBSIi+YutfuHR5ytdrRMhD1XjEDz5fs4aJEycSHh7OypUr7+v1iUjBceDAARo1akSZMmUYN27c79YCuWXs2LGkpKTkcbQiIndnMgzj9xdGioiI5LHGTZtxNvIaI1fvyLb9nrNh5a1WD1PE25Pz58/x3HPPUalSJTbtO0J8Sionf9iMJSuTbm/NpXa7rtme08VsokM5Lyr5uTJw4EA+/PBDdu/eTYMGDfL68kQkH2rXrh27d+/m1KlTFC1aFIArV64QEhLC461a8fbCZeyLSiXNYuDmZCI00J20i+HUrVOHsWPHMnbsWD777DO6dOli5ysREdHIvoiI5FNOJijs6kSHcl7sve3mOv3IDhKuRzFx3CyuXr3K1KlTbdPxSz1Uiya9h7J14Xu4eHjmeM4Mq8HeqFQq+bkycuRIPvzwQ7Zs2aJkX0QA2L59O23atLEl+nCzFkijJk1Yv/4rQk9cxeT269+WaxFJfDhoKB2e7ETjxo3tEbKIyB0p2RcRkXxp27Zttv+v5Of664GQ9vS7bVLaq6++yrFjx/juugnXspXZNHsiAP7lKub6vLcq/ZcpUwaAGzdu3OPIRaSgSk9Px93dPVub1TCIthQiKzODi+EnKFujnu3Y/k1fcPrQXnpP3IPV0N8SEclfVKBPREQKtMKFC9OoUSOCq1YD4PTeH/AtVpKA8pVy7e/mZALg7NmzAAQEBORNoCKS71WuXJk9e/ZgsVhsbcejkzj7c85aIJlpqXzz3nga9RyES0BpIpNV8FNE8hcl+yIi4hBCA9059u0XXDp2kEd7DsJsvvlPXFpSIlkZ6cDNNfv1A90xDIOJE2/OAGjdurXdYhaR/GXIkCGEh4fTv39/jh8/ztGjRxnQtzcJ0dcAyExLs/XdtmgWlqxMmvUfQYbV4ERshr3CFhHJlabxi4hIgfTDDz8wYcIEWrVqRdGiRdm9ezfLFi8mJKw5YT0G2vpFnvyZFW8MpEbrpygXVBGrD3zxxRfs3LmTgQMHUqdOHTtehYjkJ4MHD+bixYtMnTqVjz/+GIDy1WvnqAUSG3mB7Uvep+Prb+Pq4QWQrZCoiEh+oGRfREQKpFKlSuHk5MTUqVNJTEykQoUKjHvzLQLb9yMFZ9uNt1+J0gTVbsjJbd+wf00UX5jNVK1alXnz5jFw4MDf+S4i8qCZNGmSrRaIr68vP7uW4T9vjwd+rQXy7Qfv4BNQnKB6jxIbeQGA1BtRAERHR3P+/HnKli1rm2EkImIP2npPREQcitUwOBOfka2Cf/1Adyr6umA2mewdnogUMOFx6bRs1JCEmGu89vVBzGYzCwY8ybn9u+56XmxsLH5+fnkTpIhILjSyLyIiDsVsMlHJzzV7BX8Rkb9o/8a1XDx2kLYvv2kbqW815F8kx/1afd+nkJnicecYO3Ysr732Gg0bNsTTM+f2nyIieUnJvoiIiIgIOWuB7Nmzh0WLFvF469Z07PsiyVYTGVaD8rUbADeLfvq4mOkR7Mu+nT8AEBoaSqdOnex4FSIiNynZFxEREREh91ogEydO5JVXXsG5UCEtERKRAkVr9kVERERECqikpCSmTp3Kjz/+yN69e4mNjWXRokX06dMnR98TJ07w8ssvs2PHDlxcXGjXrh3Tp08nICAg7wMXkftOI/siIiIiIgVUTEwMEyZMoGzZstSsWZNt27bl2u/SpUs0adIEX19fJk+eTFJSEtOmTePIkSPs3bsXFxeXvA1cRO47JfsiIiIiIgVUiRIluHLlCsWLF+enn34iNDQ0136TJ08mOTmZ/fv3U7ZsWQDq16/P448/zuLFi7UVqYgD0uafIvlIUlIS48aNo02bNhQpUgSTycTixYtz9Nu7dy9Dhgyhbt26FCpUCJPWCoqIiDyQXF1dKV68eLY2q2EQHpfO0vA4Fp6IZWl4HKtWr6Fd+/a2RB+gZcuWhISEsGrVqrwOW0TygJJ9kXzk1lS8EydOULNmzTv2++abb/jPf/6DyWQiKCgoDyMUERGR/G77lRS+ikjiYnIW0WkWjp67yPXoKIyy1UjOtGbrW79+fQ4ePGinSEXkflKyL5KP3JqKFxERwdSpU+/Y78UXXyQ+Pp6ffvqJxx9/PA8jFBERkfzK+r+620mZVjKsv9bgToy5BoDZN4Dlp+Nt/eDmvceNGzdIT0/P22BF5L5Tsi+Sj+Q2FQ9yTsfbEu/KxXRztn+sxfFMmjQJk8lE9erVs7VnZmby5ptvEhQUhKurK0FBQUycOJGsrCw7RSoiIvnBxaTMXNsz01IBcHZxISHDypn4DNsxNzc3AFJTU+9/gCKSp1SgTySfS7dY+ehkHAkZ2T+lvxaRhI+LmUyrEn5HdOnSJSZPnoynp2eOY8899xyfffYZ/fr1o169euzZs4cxY8Zw4cIFFixYYIdoRUR+tW/fPj7++GO2bt3K+fPnKVq0KA0aNGDixImEhITY+u3du5fFixfz448/8vPPP5OVlYV2hP57jt/IfXS+kJs7AFkZGWRYDfZGpVLJzxWAtLQ0ANzd3fMmSBHJM0r2RfK5PddSqZxmydGeYTWISbNw6rZP58VxvPrqqzRo0ACLxUJMTIytfd++faxatYoxY8YwYcIEAAYPHoy/vz/Tp09n6NCh1KhRw15hi4jwzjvvsHPnTrp27UqNGjW4evUqc+bMoU6dOuzZs8c2W+lW/ZkaNWoQFBREeHi4nSMv+DLuMADg7V8M+HU6f5rl135XrlyhSJEiuLq63v8ARSRPaRq/SD6XmnX3UY4Mi0ZBHM0PP/zA6tWrmTFjhq3t1jKOaas3AdCg7VPZlnF0794dwzBYuXJlXocrIpLNK6+8QkREBLNmzeKFF15g9OjRbN++naysLN5++21bP9WfufdczLnvzuMbWALPwv5cPnEIADenX/vt3buXWrVq5UF0IpLXlOyL5HOW35nS+HvHpWCxWCwMGzaMF154gYcffhiLAbHpFltV5fiUm9Mtd964ubzjVlVlDw8PAPbv32+32EXygz59+mAyme74dfnyZVJSUnj//fdp1aoVJUqUwNvbm9q1a/PBBx9gseScSSV/TlhYGC4uLtnaKlWqRLVq1Thx4oTtw8uvbrjw+cV0wuPSNX3/HnmoyJ1H56u3aM/J7d+SHBVJ/cCbU/a/++47wsPD6dq1a16FKCJ5SNP4RUTykXnz5hEREcGWLVuwGgZRqVlYjF+nZvqXCwbg9IEf8S5RluWn4+lXxY/t27cDcPnyZbvFLpIfDBo0iJYtW2ZrMwyDwYMHU758eUqVKsXRo0cZNmwYLVq04JVXXsHHx4dNmzYxZMgQ9uzZw8cff2yn6B2XYRhcvXYNv/IhfBWRlKMGzYk4LUn7O+bMmUNcXJzt34ATP2wiPioSgLBnBuDm7UOzfiM4suVL5g/oRMCrI1idnMzUqVN5+OGH6du3rz3DF5H7RMm+iEg+cf36dcaOHcuYMWMICAggPC4dy2/WX1Zu1BK/EmX4ZsY4Crm5U75aLWbv+Ya3/u//cHZ2VjVleeA1bNiQhg0bZmvbsWMHKSkpPPvsswAUL16cI0eOUK1aNVufQYMG0a9fPxYtWsSYMWMIDg7O07gd3ZJPPyXy8mUa9h+VY115htUgNevmLCWrYWA25T4VXe5s2rRpRERE2B4f++/XHPvv1wDUbtsVN28fAkuW5rWP17N9znj+9a9/4eLiQrt27fj3v/+t9foiDkrJvkg+5/Q7Nz2/d1wKjtGjR1OkSBGGDRsGwL6oVKy/6VPI1Y0+s5ax7PUXWDrq5khMIRdXpk19l0mTJuHl5ZXHUYvkf8uWLcNkMtGg/dMsDY8jzeKEW6FSFIpLJ9jXxZZcdu7cmUWLFnHixAkl+/fQyZMneemloZSrEUqdDt3v2vdMfIatSrz8cefPn8/22GoYnInPYG9UKmkWAzcnE/UD3alYoyHmjpvtE6SI5Dkl+yL5zK2peJGRN6ffndqxiRvXbk7LuzUVLzbyIge/WQVA5P+K7UycOBGAcuXK8fzzz+d94PK3nDp1igULFjBjxgzbz/7ShXiy0tOxZGUSG3kBV09vPHwLU6xiFUZ8tp2os7+QmhBH1aoPMaBWCV5++WWaNm1q5ysRyV8yMzNZuWoVFWvX55DhT0Zylu3YrS1MewT74lnIzNWrVwHw9/e3V7gO5+rVq7Rr1w5XL296Tv0Is5PTXfvfviWc/HVmk4lKfq56LUUecCZDFVFE8pXy5ctnm4p3u9e+2k/hkmU5+9NOPhzYKdc+TZs2Zdu2bfcvQLkvtm3bxmOPPXbXPmE9BtJh1KQc7WU8nSl8ehft2rVj/vz5DBw48H6FKVLgfLl+PU927MiT/3qXBl1zX5fs7+bEc0Ee1K1Th9TUVMLDw3F21njI3xUfH0+zZs24cOECIxZ/RaHSle7Yd93br7Nn1Uf85/gN+lctnIdRiog4Lv1LJpLP/HYqHuScjlemaVNGxaZR8bbpp1KwVa9enbVr12Zri0zO5K1xY0lLTqL9qEkULV0+x3kuZhM1vKHPmDGUKFGCHj165FHEIgXDhx9/ipNzIR5+/Mk79knIsNJ70BCOHz/O119/rUT/HkhLS6NDhw6Eh4ezZcsWzhatysXbZlXcye1bwomIyN+jf81ECgBNx3N8/v7+dOrUKVub1TCYOXMmBlDtsba29mWv98fbvzjFgirjlJ7Ef75ewdmzZ/n666/x9vbO28BF8rGkpCS+/eYrKjV8DE+/Infst2XRbDZ8/BFvvfUWbdu2vWM/+WMsFgvPPPMMu3fvZt26dTRs2JCicelc+00V/tzc2hJORET+PiX7IuLQJk2axOjRo6lWrRpHjx7NtU9cXBwhISFER0fz2Wef0aVLlzyOMndmk4lAd2eSTDdH8G/dJJeqWosD65ez7/NP8PJwp3HjxixbtoxatWrZN2CRfOaLL74gPTWFWm3v/Du9/8vlbJw1gabP9GX06NF5GJ3jGjlyJF9++SUdOnTgxo0bfPrpp1gNg+ORKSRmWqnd7uae7rfXn7l8/DAAK2a/i8lkUv0ZEZF7QGv2RcRhXbp0icqVK2MymShfvvwdk/3hw4fz0UcfkZycnK+S/VvuWFVZyzhE7uqJJ55g2w87+Ne3x3Bx98hx/Pi2DSwd1ZeHmj3Ba3M+5vkqdx79lz+uWbNmfP/993c8/u9DMWRYDdWfERG5z5Tsi4jD6t69O9HR0VgsFiKvRTNu3U5bshwa6E6wrwvHjx2jdu3ajB07lrFjx+bLZF9E/rzo6GhKlixJu6e70eRfs3JMHz+3fxcfDX2Gsg/XY9DclXSuVFRLpfKAPrwUEck7msYvIg7phx9+YPXq1ezcu59eg18iMdOarTjUrS23Ph3+Dzp37kzjxo3tGK2I3GsrV64kKyuLQX2e57KLmZg0i+1YbORFPnn5eUwmE9VbdiD8v+vZfdKDH/+XbNaoUYMaNWrYK3SHpho0IiJ5R8m+iDgci8XCsGHD6N+/P0fcy5JpNfjtFKYMq8HW9Z+za9cuTp44zoU7bHcoIgXT0qVLCQwMpNXjj5NmNbH8dDwJGVYyrAaxkRdIS0oA4Mu3Xwdg4W3njhs3Tsm+iIgUeEr2RcThzJs3j4iICOav+ZpdydZc+2SmpfLNe+Np8uxgLIVLgpJ9EYeye/du2/97OkG/Kn626eMBjZrQ9JdYTR8XERGHpmRfRBzK9evXGTt2LGPGjOGM1YsMa+77Om9bNAtLViaN+/2DvVGplMrjOEUkb2n6uIiIPGjM9g5AROReGj16NEWKFGHYsGGkWXKvPxobeYHtS96n1Utv4Orhdcd+IiIiIiIFlUb2RcRhnDp1igULFjBjxgwiIyNJvJJAbGoWWenpWLIyiY28gKunN99+8A4+AcUJqvcosZEXcHN35mrUVeBmBe/z589TtmxZzGZ9HioiIiIiBZO23hMRh7Ft2zYee+yxu/YJ6zGQK+FHObd/1137xcbG4ufndw+jExERERHJOxrZFxGHUb16ddauXWt7bDUMvruUzJqZk0hPTqL9qEkULV2etKQEkuNuAOBTyEyL0p4cP3aMMWPG8Nprr9GwYUM8PT3tdRkiIiIiIn+bkn0RcRj+/v506tQpW1vrTCtblswjAaj2WFtbu4vZhI+LmR7BvngWMlOkcGEAQkNDczyHiIiIiEhBo2RfRByaZyEzJTycMRLNlPF0Js1i4OZk0pZbIiIiIuLQtGZfRERERERExMGo1LSIiIiIiB2dOnWK7t27U7p0aTw8PKhSpQoTJkwgJSXF1iczM5M333yToKAgXF1dCQoKYuLEiWRlZdkxchHJzzSNX0RERETETi5evEj9+vXx9fVl6NChFClShN27dzNu3Dj279/PunXrAHjuuef47LPP6NevH/Xq1WPPnj2MGTOGCxcusGDBAjtfhYjkR0r2RURERETsZMmSJcTFxbFjxw6qVasGwMCBA7FarXzyySfExsZy+vRpVq1axZgxY5gwYQIAgwcPxt/fn+nTpzN06FBq1KiR57GfOnWKMWPGsGPHDm7cuEHZsmXp2bMnr776Kh4eHgBs3ryZlStX8uOPP3LixAnKlCnD+fPn8zxWkQeRpvGLiIiIiNhJQkICAMWKFcvWXrx4ccxmM59HpDBt9SYAGrR9Cutt5ba6d++OYRisXLky7wL+n1szEvbs2cPQoUOZMWMGDRs2ZNy4cfTo0cPWb9myZSxbtgxfX19KliyZ53GKPMg0si8iIiIiYifNmjXjnXfeoX///rz55psULVqU//6wg9lzP6BRjwFEG67Ep6QBsPOGlciTcbZtY2+Nnu/fvz/P477TjASL1cqSTz5hwb7zOHn5Uq//64yaOoeq/p507NCBo0eP5nmsIg8qJfsiIiIiInbSpk0b3nrrLSZPnsyXX35pa3+s/8u0eukNAPzLBQNw+sCPeJcoy/LT8fSr4sf27dsBuHz5cp7HnduMhORMK9EuhTGZzVzNNOGSZgGvADZFprM7JhOLNgETyVOaxi+SD/2RqrwAGRkZTJ48mSpVquDm5kaxYsVo164dly5dslPkIiIi8meVL1+eJk2asGDBAmZ/soL6nZ5l20cz2LXiPwBUbtQSvxJl+GbGOI5+9xXnz0cwe/Ey/u///g9nZ2dSU1PzPOZmzZoB0L9/fw4dOkTEhQu8MnsRW1d8RFj3Abi4e9r6ZlgNYtIsXE7WzgEieUkj+yL5zB+typuZmUm7du3YtWsXAwYMoEaNGsTGxvLjjz8SHx9P6dKl7XwlIiIi8ntWrFjBwIEDCQ8Pp3Tp0iwNj6Nz9RZYLBY2znqLmm2ewtOvCH1mLWPZ6y+wdFRfAAq5uDJt6rtMmjQJLy+vPI/7j8xI+K0sq0GWVaP7InlFyb5IPvNHqvIWLlyY9957j++//54dO3ZQv359O0ctIiIif8XcuXOpXbu27UP6NMvNZLhq0zbsX7+CK78cIfiRphSrWIURn20n6uwvpCbEUbXqQwyoVYKXX36Zpk2b2iX2WzMSnn76aY6lu7Fzy0a2fTQDr6KBhHV/IUd/qwHpSvZF8oySfZF85o9U5bVGWnn73zNo2f5J6oWGkpWVRUZGhq1Qj4iIiBQM165do3DhwrbHbk4mACxZmf/7769T300mE8UqVgHA39OZrVu3YrVaadmyZR5GfNNvZyQsPBFL8UefwLBas81I+C0t2xfJO1qzL5LP/HYN3MWLF/l46fJsVXmPHT9ObNQVKBlCs2f64OnpiaenJzVq1GDr1q32vQARERH5w0JCQjh48CDh4eEAhAa642I2cXjTWkxmMyVCquU4x8VsooY3jBkzhhIlSmTb6i6v/HZGwq0PKao2bUNmWgpXfjmS63kmU56FKPLA08i+SD7zR9bAXb9wFoDvl87D3cePnuOm07iEB29PmUKbNm3Yt28fNWrUsEv8IiIi8seNGjWKDRs20LhxY1utnkUrv+D49i2Edn4On4DiACx7vT/e/sUpFlQZp/Qk/vP1Cs6ePcvXX3+Nt7d3nsf92xkJoYHuXItIynVGwi1mE7iale2L5BUl+yL50O1r4NLdfFjy+fpsa+DSU5IBSE9OYtiy/xJYsjSNy3nRskULgoODeffdd/n000/tfBUiIiLye5o0acKuXbsYP348c+fO5fr165QvX4FO/xhN2PNDsfyvX6mqtTiwfjn7Pv8ELw93GjduzLJly6hVq5Zd4g4JCWHz5s2Eh4cTEhJCsK8LPi7mu85IcDabcFayL5JnTIahlTMi+cmKFSvo169ftqq8F5OzWD1uGD9/+yWvf3OQsz/tYNlr/Qmq14gBC9YCUMbTmWdD/GjevDnnz5/n7Nmzdr4SERER+aushsGZ+Az2RqWSZjFwczJRP9Cdir4umPPBXPgffviB5s2bU7RoUYYOHUrRokVZt349mzdu5JHOz9FpzHsAXAk/Rvj2Tbg6mTi+cQ3RUdcYOXIkADVr1qRDhw72vAwRh6aRfZF85o9U5b01pc+raIDtvFv9AgMDOXjwYB5HLSIiIveS2WSikp8rlfxc7R1KrnKbkVChQgUmTpzIU4NGcOBGJmkWg3PnjrHx/SnZzh0zZgwAvXv3VrIvch8p2RfJZ/5IVd4ywQ/h5FyIhKgrOfpFRkYSEBBAXpk0aRKjR4+mWrVqHD161Na+efNmVq5cyY8//siJEycoU6YM58+fz7O4RERE5P6qX78+33zzTa7Hqvrf/G//qkOY/88heRiViNyiavwi+cwfqcrr6ulF5UYtufDzPqLOncLFfHNq34kTJ9i1axePP/54nsR66dIlJk+ejKenZ45jy5YtY9myZfj6+lKyZMk8iUdERERERG7Smn2RfOa3a+CKFCnCvJVfcPR/VXmf+t8auGtnf2Fur9a4enjx+PODqBfgxuzZs8nKyuLgwYOUKlXqvsfavXt3oqOjsVgsxMTEZBvZvzXDoFChQrRv356jR49qZF9EREREJI8o2RfJh/bu3cv48eM5ePCgrSpvtbbdblblNTvZ+kX98jMbZ03gws8/YTabad68OVOnTqVSpUr3PcZbH0ocPHiQYcOGERMTw+c79rPvtkJCoYHuBPu60LFDByX7IiIiIiJ5SMm+SAGRn6ryWiwW6tSpQ8OGDZk3bx6NmzbjbOQ1Rq7eQYb11z8pLmYTPi5m1rz6HCeOHVOyLyIiInZx7Ngxxo8fz/79+7l69SoeHh489NBDjBo1KkeRQKvVyvz585k/fz6//PILHh4e1KxZk/fee4+aNWva6QpE/jwV6BMpIPJTVd558+YRERHBli1bsBoGUalZWAyyJfpw83FMmoXLyVl2ilREREQEIiIiSExMpHfv3pQsWZKUlBTWrFlDx44dmT9/PgMHDrT17devH0uXLqVXr14MHTqU5ORkDh48SFRUlB2vQOTP08i+iPwp169fJyQkhDfeeIORI0cSHpdO25bNSYq7wYjPtud6zif/6EnC+V+4dCEij6MVERERyZ3FYqFu3bqkpaVx8uRJAFatWsUzzzzD559/TufOne0cocjfo5F9EflTRo8eTZEiRRg2bBgA+6JSsf7OOVYD0q36XFFERETyDycnJ0qXKcOPe/exNDyONIvBlMlTqVE3lCc7dcJqtZKamprrrkMiBYG23hORP+zUqVMsWLCA4cOHExkZyfnz57l0IYKs9HQsWZnERl4gJT4213M1h0hERETsLTk5mZiYGM6cOcM706azYcMGytRtxMXkLC7GxHH25/14B9eg/aCR+Pr64uXlRVBQEKtWrbJ36CJ/mkb2ReQPu3z5MlarleHDhzN8+PAcx99tX5ewHgPpMGpSjmN5XENQREREJIeRI0cyf/58AExmM9Wat6PD628DcOPSOQzD4OCmtZidnHjylfG0CSnO7Fmz6N69Oz4+PrRp08aO0Yv8OUr2ReQPq169OmvXrs3WFpmcyVvjxpKWnET7UZMoWrp8jvPMJnA1K9sXERER+xoxYgRdunThwOkIFi9biWGxkpWZAUB6SjIAKXE3ePHjjQTXrMcj5bzo9OSTVKhQgYkTJyrZlwJFBfpE5G+xGgZVH2lM/I3r2Qr0XQk/xokfNgJwZMMa0mKjGTlyJAA1a9bMsc2NiIiISF5ZGh7HxeQsFg7pSlpiPEM+2cTlE4d5/7nHKVyqHK+t/wmAMp7OPBviR79+/fj0009JSUnB2VnjpVIw6J0qIn+L2WQi0N2ZJBO4mE227fciT/7Mt3PfztZ3zJgxAPTu3VvJvoiIiNhNmuXm/crDLTqwdtJIYiLO4BNQHACvIgE5+gUGBpKZmUlycjK+vr55H7DIX6BkX0T+tu3fb8NqGJyJz2BvVCppFoNOPZ5n8j8GUtHXBbMW7IuIiEg+4uZ0894kMz0VgLSkBALKB+PtH0hC9JUc/SIjI3Fzc8Pb2zvvgxX5i1SNX0TuCbPJRCU/V54N8aN/1cI8G+JHJT9XJfoiIiJid1FRUdkehwa642TJ4sBXqyjk5k5gUAgADz/eifirlzm1ZxsuZhP1A92JiYlh3bp1NG/eHLNZ6ZMUHFqzLyIiIiIiDq1z584kJCTQpEkTSpUqxZUrV5i7eAlXz56i7SsTaPzciwAkXo9ido/mZKQm06r3EJpVLMb8+fO5ePEiu3fvpmbNmna+EpE/Tsm+iIiIiIg4tBUrVrBw4UKOHDnC9evX8fb2pladOjzUuR9BjVrbag4BJEZGsGHGeM7s/YHMzEwaNmzI22+/TWhoqB2vQOTPU7IvIiIiIiIPpN/WHHJzujl1XzWHxBEo2RcRERERERFxMKowISIiIiIiIuJglOyLiIiIiIiIOBgl++IwDhw4QMeOHSlSpAgeHh5Ur16dWbNm2Y43a9YMk8mU46tNmzZ2jFpEREREROTec7Z3ACL3wubNm+nQoQO1a9dmzJgxeHl5cebMGS5dupStX+nSpZkyZUq2tpIlS+ZlqCIiIiIiIvedCvRJgZeQkEBISAhhYWGsXr0aszn3CSvNmjUjJiaGo0eP5nGEIiIiIiIieUsj+1LgLVu2jGvXrjFp0iTMZjPJycm4u7uDycTp+Az2/W8rlWupWVgsBhmZmWSkp+Pl5WXv0EVERERERO4LrdmXAm/Lli34+Phw+fJlKleujJeXFz4+PjTv0Z+14TFcTM4iOs1CusXg7OlTeHp64e3tTfHixRkzZgyZmZn2vgQREREREZF7SiP7UuCdOnWKrKwsnnzySfr378+kyZP5YO1m/rt0AbFxcfSYsgCAoqXLE1SvEcWDq+JqSSN61wYmTpxIeHg4K1eutPNViIiIiIiI3Dsa2ZcCLykpiZSUFHr16sWsWbOo0aI97UZNpv7Tvfl501piLpwB4OlxM2k5aBTVW7SnWpuu/PvjVQwYMIBVq1axZ88eO1+FiIiIiPzWsWPH6Nq1K0FBQXh4eODv70+TJk1Yv359jr5z5syhatWquLq6UqpUKV555RWSk5PtELVI/qBkXwo8d3d3AHr06AHAvqhUMqwGtdo8BcCFn3/KcU6G1WBvVCojR44Ebi4FEBEREZH8JSIigsTERHr37s3MmTMZM2YMAB07dmTBggW2fq+//jrDhg2jevXqzJw5k6effprZs2fz1FNP2St0EbvTNH4p8EqWLMmxY8coVqwYAGmWmxtMeBYJACA1IS7X89IsBmXKlQHgxo0b9z9QEREREflT2rZtS9u2bbO1DR06lLp16zJ9+nQGDhzIlStXmD59Os8//zyffPKJrV9ISAjDhg1j/fr1dOjQIa9DF7E7jexLgVe3bl0ALl++DICbkwmAxOirAHgW9s/1PDcnE2fPngUgICDgfocpIiIiIveAk5MTZcqUIS4ujvC4dN5Zs4WsrCyKNW5PeFw61v/tLN69e3cAVqxYYc9wRexGyb4UeN26dQNg4cKFAIQGuuNiNrHvi08xOzsTVO9R0pISycpIt53jYjYRGuDGxIkTAWjdunXeBy4iIiIif0hycjIxMTGcOXOG9957jw0bNlCuXmO+ikjiamIqAHGGC19FJPHRyTiSM614eHgAsH//fnuGLmI3msYvBV7t2rXp168fH330EVlZWTRp0oRl67/l8KZ1NOv7D3wCinP2p52seGMgNVo/RdEyFXC1pPP5rk3s3LmTgQMHUqdOHXtfhoiIiIjcwciRI5k/fz4AZrOZ2i3b02bUFDKsBgHlKgIQcfhHKoY2IibNwvLT8ZSK+BH4dfanyIPGZBj/m+ciUoBlZmYyefJkFi1aRGRkJGXLlaNel77U7z6IDKvBjcsRbJw1gcvHDpF4PYpCTmaqVq3KgAEDGDhwICaTyd6XICIiIiJ3cPLkSS5dukRkZCSLlq7gepaZDv96F++igQDM7dWGa2dP0mHUZCqGNuLG+VNseOc1oq5ewWq1kpWVZecrEMl7SvbFYVkNgzPxGeyNSiXNYuDmZKJ+oDsVfV0wK7kXERERKZCWhscxvndn0hLjGfLJJkwmE/FRV1j+zxeIOLQXALOTE0/0GUL0kR/55ZdfiIuLs2/QInagafzisMwmE5X8XKnk52rvUERERETkHkmzGDzcogNrJ40kJuIMAeWD8Q0sweCPvibmwhkSY6LwLxtEUOmSjGlRjZCQEHuHLGIXSvZFRERERKTAcHMykZl+syhfWlJCtmP+ZSviX/bmGv7r537hypUr9OnTJ69DFMkXVI1fRERERETypaioqBxttQo7c+jrVRRycycwKPdRe2cMNsycgIeHB4MHD77fYYrkSxrZFxERERGRfGnQoEEkJCTQpEkTSpUqxdWrV1m6dCmXTp6k7SsTcPXwAmD91DfISk+nROXqWLIyObbpc84fOcDHH39M2bJl7XwVIvahAn0iIiIiIpIvrVixgoULF3LkyBGuX7+Ot7c3devWZcCLL5FQtSkJGVYyrAb7v1zOzmXzuX7xPGaziQaPPMLY0f/HY489Zu9LELEbJfsiIiIiIlLgaOclkbtTsi8iIiIiIiLiYFSgT0RERERERMTBKNkXERERERERcTBK9kVEREREREQcjJJ9EREREREREQejZF9ERERERETEwSjZF44dO0bXrl0JCgrCw8MDf39/mjRpwvr16+94TmZmJg899BAmk4lp06blYbQiIiIiIvnPn7mnPnHiBG3atMHLy4siRYrw/PPPEx0dbYeoxZE52zsAsb+IiAgSExPp3bs3JUuWJCUlhTVr1tCxY0fmz5/PwIEDc5wze/ZsLly4YIdoRURERETynz96T33p0iWaNGmCr68vkydPJikpiWnTpnHkyBH27t2Li4uLna9EHIXJMAzD3kFI/mOxWKhbty5paWmcPHky27GoqChCQkIYOXIkY8eOZerUqbz66qt2ilREREREJH+6dU+dkJLKW1/9SJrFYPlbr7Lzi+WcOHGC8uXKAbBlyxYef/zxOw60ifwVmsYvuXJycqJ0mTJcj41jaXgcC0/EsjQ8jvC4dF5//XUqV67Mc889Z+8wRURERETyrTSrCaNwcWJuxHExOYvoNAs/blpP5Uat2JLiQ3KmFYCWLVsSEhLCqlWr7ByxOBJN4xeb5ORkUlNTiY+PZ/XadWzYsIGarTpxMTnL1mffVz/w8SefsGXbD5hMJjtGKyIiIiKS/9y6p46Ni2P0f1ZydPsWHm7VCYD4qCsk3YimRNWaxKRZWH46nn5V/DCbTNSvX59vvvnGvsGLQ1GyLzYjR45k/vz5AJjMZqo1b0eH19+2HTcMgzXv/JMarTpx1v8hyhtxdopURERERCR/yu2e+sn/3VMnxlwDwNu/GAAJGVbOxGdQyc+VEiVKcOPGDdLT03F1dbVP8OJQlOyLzYgRI+jSpQsHTkeweNlKDIuVrMwM2/H9Xy7n2ukTPPvuRyRkWIm47ZiIiIiIiPx6T736wGm2rf882z11ZloqAM7/K8KXYTXYG5VKJT9X3NzcAEhNTVWyL/eE1uyLTZUqVWjZsiWlmj9Nr5nLSE9N5pMRz2EYBmlJiWyaM5HGz7+EX/FSZFgNDsek2ztkEREREZF85dY9dWiHZ+gzK/s9dSE3dwCyMn4dNEuz3KyXnpaWBoC7u3veBy0OScm+5HDrD87DLTpw6dhBYiLOsH3J+1gyM6nRuhOxkReIjbzA1cjLAMTGxnL+/HkyMjTSLyIiIiIC4OZ0s77V7ffUt6bv35rOf3u/K1euUKRIEY3qyz2jafySw60/OJnpN6cZpSUlEHf1EqkJcczo0ihH/8mTJzN58mQOHjxIrVq18jJUEREREZF8KTTQnWsRSdnuqQPKB+NZ2J/LJw4B4GI2UT/w5kj+3r17dS8t95RG9oWoqKhsj0MD3XGyZHHgq1UUcnMnMCiEsO4DeO7fH9u++kz/hAnvzQGgT58+rF27lgoVKtgjfIeTnn5ze8OSJUvi7u7OI488wrfffmvvsERERETkLn57Tx3s64KHyZLtnhqgeov2nNz+LXFXL+PjYqairwvfffcd4eHhdO3a1R6hi4MyGYZh2DsIsa/OnTuTkJBAkyZNKFWqFFeuXGHu4iVcPXuKtq9MoPFzL+Y4x9/NieZucVQMCmLq1Km8+uqrdojcMfXo0YPVq1czYsQIKlWqxOLFi9m3bx9bt26lUaOcMytERERExP5+e0999epVlny6lPBfTtJx5AQaPnvznjru6mVm92yOp7cvr70ygszUZKZOnUrp0qXZt2+fpvHLPaNkX1ixYgULFy7kyJEjXL9+HW9vb2rVqcNDnfsR1Kg1GdZf3yIuZhM+LmZ6BPsSffkCFSpUULJ/D+3du5dHHnmE1yZMocYzg0mzGJiz0hn/5KOUKl6MXbt22TtEEREREclFbvfUdevW5aWhQ6nWtA17o1JJsxi4OZkofP0cM8b/k507duDi4kK7du3497//TbFixex9GeJAlOzLHVkNgzPxGdn+MNUPdKeirwtmk8ne4Tmkl0eOYtbM93jr+9OYPbxs7dsXzeSb2RM5eeY8lYPK2TFCEREREREpCFSgT+7IbDJRyc+VSn6aSpQXrIbBpt378C9bMVuiD1DiodoAzNm4i5kvltWHLSIiIiIiclcq0CeST5yOzyA26pptS5bbeQfcbLt6JZIz8driUERERERE7k7Jvkg+sS8qlYz0NJxcXHIcK+Ryc3ZFamoqe6NS8zo0EREREREpYJTsi+QTaRaDQq5uWDJyjtxnZqQDUMjVnTSLymyIiIiIiMjdKdkXySfcnEx4+xcjMeZajmOJ0TfbfAKK4+ak9foiIiIiInJ3SvZF8onQQHdKV65OzIUzpCUlZjt28eh+AMpWfZj6ge72CE9ERERERAoQJfsi+USwrwsNn3gSq8XC3s8/sbVnZaSz/8vllKlel3Jly1DRN+eafhERERERkdtp6z2RfMJsMvF/T7dgc+sn2TRnIsk3YihapgIHvlpB7JWL9Js0mx7Bvtp2T0REREREfpfJMAxV+xLJR1JSUxn+2v+xeuUykuPjKFu5Gv837k36PNVOib6IiIiIiPwhSvZFREREREREHIzW7IuIiIiIiIg4GCX7IiIiIiIiIg5Gyb6IiIiIiIiIg1GyLyIiIiIiIuJglOyLiIiIiIiIOBgl+yIiIiIiIiIORsm+FAjbtm3DZDLl+rVnz55sfTMyMpg8eTJVqlTBzc2NYsWK0a5dOy5dumSn6EVERERERPKWs70DEPkzhg8fTmhoaLa24OBg2/9nZmbSrl07du3axYABA6hRowaxsbH8+OOPxMfHU7p06bwOWUREREREJM8p2ZcCpXHjxnTp0uWOx9977z2+//57duzYQf369fMwMhERERERkfxDyb4UOImJibi7u+PsfPPtazUMTsdn8OPVZKb8ewZ1WrbHL6QmGZmZZGVm4uHhYeeIRURERERE8pbW7EuB0rdvX3x8fHBzc+Oxxx5j+569fHQyjq8ikth/5BhxUVfwrVCF3v0H4OnphaenJzVq1GDr1q32Dl1ERERE5A/Zt28fQ4cOpVq1anh6elK2bFm6detGeHh4tn4ffvghTZs2pVixYri6ulKhQgX69u3L+fPn7RO45CsmwzAMewch8nt27drF9OnTadu2Lf7+/hw/fpxp06aRkJTM4EVfU7JKDY7992s+fbUPHn5FcPfx47F+I/AqZGbn4plERESwb98+atSoYe9LERERERG5qy5durBz5066du1KjRo1uHr1KnPmzCEpKYk9e/ZQvXp1AIYMGUJKSgoPP/wwhQsX5ty5c3z44YdYLBYOHz5MyZIl7XwlYk9K9qXA+vbAcdo9Wo/ytRvQ7/1VHPhqFZ+NfQmnQi68um4vfsVL4WI2Udd8g8frVqNbt258+umn9g5bREREROSudu3aRb169XBxcbG1nTp1iocffpguXbrc9Z52//791KtXjylTpvDPf/4zL8KVfEpr9qXAivIqSdWmbTj236+xWiwUcnMDoFzN+vgVLwVAhtXgknsAjRo1YteuXfYMV0RERETkDwkLC8vRVqlSJapVq8aho8dZGh5HmsXAzclEaKA7wb4umE0mAMqXLw9AXFxcHkYs+ZGSfSmw0iwGfsVKYcnMICM1BZ+A4gB4FQ3I0S8wMJCDBw/aI0wRERERkb8tKcPC2ctXCAyqwsXkLFv7tYgkzClxPFnWk+tXLjFhwgQAWrRoYa9QJZ9Qsi8FlpuTiRuXz+Ps6oaLhyfFgx/CybkQCVFXcvSLjIwkICDgDs8kIiIiIpJ/WQ2DEe/9h7hrV2gx6PVsxzKsBmOaVePljHQAihYtyqxZs3j88cftEarkI0r2pUCIjo7Okax7XzvFie83EfJoC8xmM66eXlRu1JKT2zcTde4UgRUq4WI2USQ2gl27djFo0CA7RS8iIiIi8tdt2neEpRNHUbZGKHU6dM9xvM/sFZCZjn9cBJvWrCA5OdkOUUp+owJ9UiA0b94cd3d3wsLCCAwM5Pjx4yxYsADDyZnBizYQGBQCwLWzvzC3V2tcPbwI6zEAT2czu1d8SFZWFgcPHqRUqVJ2vhIRERERkT/u6tWr1KzfkPTMTIZ8vNG2dDU3ZTydaeB0nerVqzN16lSGDh2ah5FKfqNkXwqEWbNmsXTpUk6fPk1CQgIBAQG0aNGCUW+MYS8BJGRYybDefCtfPnGYTbPe4sLPP+HibKZ58+ZMnTqVSpUq2fkqRERERET+uPj4eJo1a8apcxEMWLieYkGV79o/wM2J/lULExYWhmEY7N69O48ilfxIyb4UeFbD4Ex8BnujUm1VSesHulPxtqqkIiIiIiIFSVpaGq1atWL//v2M+mgtbiF1fvecMp7OPBviR+3atUlPT+f48eN5EKnkV1qzLwWe2WSikp8rlfxc7R2KiIiIiMjfZrFYeOaZZ9i9ezfr1q0jOKwpX0Uk2WayAliysshIScLdxw8AF/PNAa+9e/dy5MgRevbsaafoJb/QyL6IiIiIiEg+MmLECGbOnEmHDh3o1q0bVsPgh8gUEjOtANRu15XUxHjeblODGq06EVixCkW8vfCJPs3ixYtxc3Njz549Wsb6gFOyLyIiIiIiko80a9aM77///o7HpxyIJiszgw0z3uTc/p3ERV4gKz2NkiVL0rJlS0aPHk358uXzLmDJl5Tsi4iIiIiIFACqVSV/htneAYiIiIiIiMjvu1Wr6tkQP/pXLcyzIX5U8nO974n+sWPH6Nq1K0FBQXh4eODv70+TJk1Yv359jr6rVq2iQYMG+Pn5UbRoUZo2bcrXX399X+OT3CnZFxERERERkTuKiIggMTGR3r17M3PmTMaMGQNAx44dWbBgga3f7NmzeeaZZ/D39+ftt99mzJgxxMfH0759ez7//HN7hf/A0jT+fGbbtm089thjuR7bvXs3DRo0AGDz5s2sXLmSH3/8kRMnTlCmTBnOnz+fh5GKiIiIiMiDymKxULduXRJSUnnrqx9JsxiMbhtKQJHCHNz3I07mm+PKCQkJlCpViubNm7Nu3To7R/1g0dZ7+dTw4cMJDQ3N1hYcHGz7/2XLlrFy5Urq1KlDyZIl8zo8ERERERF5gKVZTRiFixNz6SAXk7MASEpMoHCZIBb9Ek+PYF88C5nx8fHBy8sLd3d3O0f84FGyn081btyYLl263PH45MmT+fDDDylUqBDt27fn6NGjeRidiIiIiIg8aJKTk0lNTSU2Lo7R/1nJ0e1beLhVJ9vxoLqPcvS79Xy5eB4xj7elYykX3p8zh/j4eP7xj3/YL/AHlJL9fCwxMRF3d3ecnW/+mKyGwen4DPZFpZJmccctKZnQQHe0DkNERERERO63kSNHMn/+fABMZjPVmrfjydffth3v8NpkkuNusP7dN1j/7hv8C/D39+e7776jYcOGdor6waVkP5/q27cvSUlJODk50bhxYyZMeYdffCuRkGElw/pren8tIomLSZlK+EVERERE5L4aMWIEXbp0YfWB02xb/zmGxUpWZobteCE3dwLKVcS3WAmqNG6FZ1Yqe5bP56mnnmL79u3ZliXL/acCffnMrl27mD59Om3btsXf35/jx48zbdo0EpKSGbzoa0pWqZHjnMXDexJz9iRRlyK0v6aIiIiIiNxXC0/EEp1mYeGQrqQlxjPkk02YTCYWDX0Gs5MzvWcuBSDAzYnOxQwqVapEy5YtWblypZ0jf7Bo6718JiwsjNWrV9OvXz86duzIP//5T5Zv/gFMJjbOnnjH86wGnInPuONxERERERGRe8HN6eYA48MtOnDp2EFiIs5w49J5wnf9l6pN22TrV6RIERo1asTOnTvtFe4DS8l+ARDlVZKqTdtw9qedWC2WXPsYwN6o1LwNTEREREREHjihge64mE1kpt/MP9KSEki8Hg2AYb2Zr7iYTdQPvFmBPzMzk6ysLPsE+wBTsl8ApFkM/IqVwpKZQUZqyl37iYiIiIiI3EtRUVHZHgf7uuBhsnDgq1UUcnMnMCiEomUrYDKb+XnzFxiGgY+LmYq+Lly6dInt27dTu3ZtO0X/4FKBvgLAzcnEjcvncXZ1w8XD8679RERERERE7qVBgwaRkJBAkyZNKFWqFFevXmXJp0uJ/OUkHUdOwNXDC1cPL+o92ZN9az/l4yFPM6BHV975Iom5c+eSmprKv/71L3tfxgNHI/v5THR0dI4272unOPH9Jio1aIbZnPuPzAS2aTIiIiIiIg+qbdu2YTKZcv3as2cPAOfPn79jH5PJxIABA+x8FfnLM888g9ls5oMPPuDFF19k+vTplC1TmrVffMG00a9RxtOZADcnhk58jzHvvId7eiLjR7/B5MmTqVSpEt9++y1NmjSx92U8cFSNP59p3rw57u7uhIWFERgYyPHjx1mwYAGGkzODF20gMCgEgCvhxzjxw0YADn2zmuQb0fzz1ZGYTCZq1qxJhw4d7HkZIiIiIiJ2sW3bNh577DGGDx9OaGhotmNt2rTB39+f5ORk1q5dm+PcjRs3snTpUlatWkXXrl3zKmSR+0LJfj4za9Ysli5dyunTp0lISCAgIIAWLVow6o0x7CWAhAwrGVaD/V8uZ/X44bk+R+/evVm8eHHeBi4iIiIikg/cSvY/++wzunTpYmu3Ggan4zPYF5VKmsXAzclEaKA7wb4utu2rW7Zsyb59+7h27Rpubm72ugSRe0LJfgFiNQzOxGew97Y/UPUD3al42x8oEREREZEH2e3JfuvWrXF3dyfdMLP8dLxt4OwWF7MJHxczPYJ9SYi5RunSpenVqxeLFi2y4xWI3Bsq0FeAmE0mKvm5UsnP1d6hiIiIiIjka3379iUpKQknJyeC6zak5fBxlH6oVrY+GVaDmDTLzQ8CNizHarXy7LPP2idgkXtMI/siIiIiIuIwdu3axfTp02nbti3+/v78sP9nPpj1HhmpKby46GtKVqmR4xwXs4lFfVpx/doVLl26dMei2CIFiZJ9ERERERFxWEvD4zh4IpxZ3ZtRvnYD+r2/Kkef6IgzTO/cgJdffpnp06fbIUqRe08fWYmIiIiIiMNKsxj4lw2iatM2nP1pJ1aLJUefQ9+sBtAUfnEoSvZFRERERMRhuTndLGTtV6wUlswMMlJTcvQ5vHENJSpUom7dunkdnsh9o2RfREREREQcVmigOy5mEzcun8fZ1Q0XD89sxy8c2c/1i+fo1qOHnSIUuT+U7IuIiIiIiMOIjo7O9jjY14X4s8c58f0mKjVolqP43uGNawB4qe/zeRajSF7Q1nsiIiIiIuIwnnnmGdzd3QkLCyMwMJDjx4+zYMECXNzd6fCPsdn6OhtWjm5eR/1HGlApONhOEYvcH0r2RURERETEYXTq1ImlS5cyffp0EhISCAgI4KmnnmLM2LGYAsqyNyqVNIuBm5OJ9CM7SLgexXPjRts7bJF7TlvviYiIiIiIiDgYrdkXERERERERcTBK9kVEREREREQcjJJ9EREREREREQejZF9ERERERETEwSjZFxEREREREXEwSvZFREREREREHIySfREREREREREHo2RfRERERERExMEo2RcRERERERFxMEr2RURERERERByMkn3JE3369MFkMt3x6/LlywA0a9Ys1+Nt2rSx8xWIiIiIiIgUHM72DkAeDIMGDaJly5bZ2gzDYPDgwZQvX55SpUrZ2kuXLs2UKVOy9S1ZsmSexCkiIiIi4qi2bdvGY489luux3bt306BBA1JSUli0aBHr1q3jyJEjJCUlERwczMCBAxk4cCBOTk55HLX8VUr2JU80bNiQhg0bZmvbsWMHKSkpPPvss9nafX19ee655/IyPBERERGRB8bw4cMJDQ3N1hYcHAzA2bNnGTZsGC1atOCVV17Bx8eHTZs2MWTIEPbs2cPHH39sj5DlL1CyL3azbNkyTCYT3Xv0IDwunX1RqVxJySIlw8LxmGRKu1rx8fa2d5giIiIiIg6lcePGdOnSxfbYahicjs9gU3gc0QluTFm/k86P1iHY1wWzycSgQYPo168fixYtYsyYMbYPBiR/05p9sYvMzExWrVpFg4Zh/DfNj68ikriYnEWm1eDKuTPUKFkEXx8fihUvzpgxY8jMzLR3yCIiIiIiDiMxMZGsrCySM618dDLOdj+e5u6HUbISX0Uk8dHJOJIzrQB07twZgBMnTtgzbPkTNLIvdrFp0yauX79OUMvOxKRZbO1FS5cnqF4jigdXJSM1hfCtXzFx4kTCw8NZuXKlHSMWEREREXEMffv2JSkpCScnJ4LrNqTl8HGUfqhWtj4ZVoOYNAvLT8fTr4ofV69eBcDf398OEctfYTIMw7B3EPLg6dmzJ6tXr2bMt8co5FP4jv1czCZ+fO81Vn38ka1oiIiIiIiI/Hm7du1i+vTptG3bFn9/f37Y/zMfzHqPjNQUXlz0NSWr1MhxjovZROsSLnR7rAGpqamEh4fj7Kwx44JA0/glzyUlJbFu3TqqN2p+10Qfbn6iWK/7YAC2bNmSF+GJiIiIiDiksLAwVq9eTb9+/ejYsSO1nx3Ki4s3YDKZ2Dh7Yq7nZFgNhg0bxvHjx5kzZ44S/QJEyb7kuS+++IKUlBRC23X5/c6AZ+DNbfdu3LhxP8MSEREREXmgpFkM/MsGUbVpG87+tBOrxZKjzw8fz2Hrqo956623aNu2rR2ilL9Kyb7kuaVLl+Ll5cUjLf/YH4uEyAgAAgIC7mdYIiIiIiIPFDcnEwB+xUphycwgIzUl2/H9Xy5n46wJtOjel9GjR9sjRPkblOxLnoqOjmbLli107tyZRuWK4mI22Y6lJSWSlZGerX8hE2z/aAYArVu3zstQRUREREQcWmigOy5mEzcun8fZ1Q0XD0/bsePbNvD5Wy/zcIv2vP/++3aMUv4qLbiQPLVy5UqysrJ49tlnCfZ1wcfFbKvGH3nyZ1a8MZAarZ+iaJkKZKWnEf79N5w+8CMDBw6kTp06do5eRERERKTgio6OzjZbNtjXhfizxznx/SZCHm2B2XxzLPjc/l0s/9dAytduyOBpC6hU2M1eIcvfoGr8kqcaNmzI2bNniYyMxMnJieRMK8tPx5OQYeXqxfNsnDWBS8cOkXg9CrPZTLWqVRk0cAADBw7EZDL9/jcQEREREZFcNW/eHHd3d8LCwggMDOT48eMsWLAAw8mZYR9vpHD5SsRGXmRW92ZYsjLpOmoCzYMCcXX+dUJ4jRo1qFEjZ9V+yX+U7IvdWQ2DM/EZ7I1KJc1i4OZkon6gOxV9XTArwRcRERERuSdmzZrF0qVLOX36NAkJCQQEBNCiRQvGjB2LKaAse6NSObR7O9P6dLzjc4wbN47x48fnXdDylynZFxEREREREXEwKtB3H2zbtg2TyZTr1549e3I9Jy4ujsDAQEwmE6tXr87jiEVERERERMSRqEDffTR8+HBCQ0OztQUHB+fad+zYsaSkpOR6TEREREREROTPULJ/HzVu3JguXbrYHlsNg9PxGWwKj7OtTQ8NdCftYjgffPABY8eOZezYsXaMWERERERERByBkv37LDExEXd3d9INs63qfIb11zIJ1yKS+HDQUDo82YnGjRvbMVIRERERERFxFFqzfx/17dsXHx8f3NzcqNuoKYcO7M+W6APs3/QFpw/tpf7g0VhVK1FERERERETuASX794GLiwtPP/00M2fOZN26dYz4v/FcDD/O/P4diDz5s61fZloq37w3nkY9B+ESUJrI5Ew7Ri0iIiIiIiKOQtP474OwsDDCwsJsjxOrNOHFOq2Y1b0ZG2dPpN/7qwDYtmgWlqxMmvUfQYbV4ERshr1CFhEREREREQeikf08kGYx8C8bRNWmbTj7006sFguxkRfYvuR9Wr30Bq4eXgA5pviLiIiIiIiI/BVK9vOAm5MJAL9ipbBkZpCRmsK3H7yDT0Bxguo9SmzkBWIjL5B6IwqA6Ohozp8/j9VqtWfYIiIiIiIiUkBpGn8eCA1051pEEjcun8fZ1Q0XD0/irl7i+sVzTO1QL0f/IUOGABAbG4ufn18eRysiIiIiIiIFnZL9+yA6OpqAgADb42BfF+LPHufE95sIebQFZrOZVkP+RXLcDVsfn0JmisedY+zYsbz22ms0bNgQT09Pe4QvIiIiIiIiBZyS/fvgmWeewd3dnbCwMAIDAzl+/DgLFizAxd2dDv8YC0D52g0AcDGb8HEx0yPYl307fwAgNDSUTp062St8ERERERERKeCU7N8HnTp1YunSpUyfPp2EhAQCAgJ46qmnGDN2LKaAsuyNSiXNYuDmZKJ+oDsVfV0wm0z2DltEREREREQchMkwDJWAFxEREREREXEgqsYvIiIiIiIi4mCU7IuIiIiIiMjflpSUxLhx42jTpg1FihTBZDKxePHiu56TmZnJQw89hMlkYtq0aXkT6ANCyb6IiIiIiIj8bTExMUyYMIETJ05Qs2bNP3TO7NmzuXDhwn2O7MGkAn0iIiIiIiLyt5UoUYIrV65QvHhxfvrpJ0JDQwGwGgan4zPYd1uh8tBAd3zS45gwYQKvv/46Y8eOtXP0jkcj+yIiIiIiIvK3ubq6Urx48Wxt6RYrH52M46uIJC4mZxGdZuFichZfRSTR5cVXqBRSmeeee85OETs2jeyLiIiIiIjIfbHnWiqV0yw52s/8vJ9d61bw+qffoO3h7g+N7IuIiIiIiMh9kZqVM5U3DIMv3/0XNVp1IuChekQkZtghMsenkX0RERERERG5LyxGzmR//5fLuXb6BM+++xEZVoPDsel2iMzxaWRfRERERERE8kRaUiKb5kyk8fMv4Ve8FADpVk3kvx80si8iIiIiIiJ5YvuS97FkZlKjdSdiI29uuecafw2A2NhYzp8/T8mSJXFxcbFnmA5Byb6IiIiIiIjcF04mU7bHcVcvkZoQx4wujXL0nTx5MpMnT+bgwYPUqlUrjyJ0XEr2RURERERE5L5wd86e7Id1H8BDzdraHvsUMvOwSzIvDh5Mnz59ePLJJ6lQoUJeh+mQlOw7uGPHjjF+/Hj279/P1atX8fDw4KGHHmLUqFF06NDB3uGJiIiIiIgDmTNnDnFxcURGRgJwY9937LkWSbrF4JFuL1Cqak1KVa2Ji9mEj4uZHsG+RF++OZ2/WrVqdOrUyY7ROxYl+w4uIiKCxMREevfuTcmSJUlJSWHNmjV07NiR+fPnM3DgQHuHKCIiIiIiDmLatGlERETYHn/5xVpgLQCtn+qOt1th3JxM1A90p6KvC2aTiWg7xeroTIaRy14I4lCshsHp+Az2RaWSZjFwwcrkbs0xMtM5efKkvcMTERERERGRe0wj+w4uOdPK8tPxJGRYybhtSwtzkeJcOnaI5EwrnoW0A6OIiIiIiIgjUZbnwKyGwfLT8cSkWUhKTiI59jrXL55jx6fzOLnzOyrUb8zy0/FYNblDRERERETEoWhk34Gdjs8gIcMKwNfTx7F3zccAmMxmqjVvx5Ovv01ChpUz8RlU8nO1Z6giIiIiIiJyDynZd2D7olJtU/cf7TmIh1t2ICH6Kj9vXodhsZKVmUGG1WBvVKqSfREREREREQeiAn0ObOGJWKLTLLkfG9KVtMR4hnyyiUB3Z/pXLZzH0YmIiIiIiMj9ojX7DszNyXTHYw+36MClYweJiThz134iIiIiIiJS8CjZd2Chge64mHNP5DPTUwGwJCdSP9A9L8MSERERERGR+0zJvgML9nWBxOs52i2ZmRz4ahWF3NwJrlKFir4udohORERERERE7hcV6HNgZpOJrVNf4/S1WMrWboBXQHESr0dx6Js1RJ8/RdfX3qJvzVKYTZrGLyIiIiIi4khUoM/BrVixgv8sXMjhn48Qe+M6rh5eBFWvxUsvDWVgj6eU6IuIiIiIiDggJfsiIiIiIiIiDkZr9kVEREREREQcjJJ9EREREREREQejZF9ERERERETEwSjZFxEREREREfl/9u48rMoyceP4fQ7IvigIgguiImriVuqImZoZ4YJtrqOV6YhWarZPmdm4TWW/pszMMrcUza1ySdNsMhc0XCAzLVBHk0UWFZDlcDjL7w+LYtSmRTl6+H6ui0t5zvNy7tfLZrzP+7zP62Qo+wAAAAAAOBnKPgAAAAAAToayDwAAAACAk6HsAwAAAADgZCj7AAAAAAA4Gcr+H3DgwAH169dPAQEB8vLyUlRUlGbNmlXxus1m09y5c9W2bVv5+PioTp066tWrlxITEx2YGgAAAAD+uKKiIk2ePFmxsbEKCAiQwWDQokWLLpo3fPhwGQyGi76aN29e9aGrMVdHB7jebNmyRXFxcWrXrp0mTZokHx8fHTt2TOnp6RVznnrqKb322msaNmyYHn74YeXn5+udd95Rt27dtGvXLnXs2NGBZwAAAAAAv19eXp6mTJmisLAwtWnTRtu2bbvsXHd3d7333nuVxvz9/a9yQvwSZf93KCws1P33368+ffpo9erVMhovXhhhsVj09ttvq3///lqyZEnF+IABA9S4cWMlJCRQ9gEAAABcd0JDQ5WVlaWQkBDt27dPHTp0uOxcV1dXDRs2rArT4b9R9n+HZcuWKTs7W9OnT5fRaFRxcbE8PT0lg0FHC8zam1OqwuISlZaWyr1mbdnsdhkNBklScHCwjEbjhfkAAAAAcJ1xd3dXSEjIReM2u72iD5msdh0vNMsuqdxiUWlJifz8/Ko+LLhn//fYunWr/Pz8lJGRoWbNmsnHx0d+fn7qMWSkPkrN06liiwrkpgZRN2lVwhLFz5yn746d0MGDBzV8+HDVqlVL8fHxjj4NAAAAALgiyqw2LfguXxtOFulUsUW5JquKLTaVlpTIx89f/v7+CggI0COPPKKioiJHx61WDHa73e7oENeLNm3a6OjRo5KkkSNHqmu3bnr7oy36d8K7an3H3Rryz3clSXk/HNfyv49S5ncHK45t3LixNm7cqGbNmjkkOwAAAABcKT8t4x8+fbaa9RpU6bVP35wq2e2q27y1fFylsm926v3Fi3XzzTdr27ZtcnVlgXlV4E/5dygqKlJJSYnGjBmjWbNmKTW/TH0iuqvIVKakNYt1+0PPqHZYE7l7+6hOk+YKa91ezTt1U7itQAtmvaq77rpLO3bsUO3atR19KgAAAADwp5VaLr52HDtuUsXv3YwGxY28T80iIzVx4kStXr1agwcPrsqI1RbL+H+Hn+63HzJkiCRpb06pzDa72sbeI0n64eA+WS0WzX+ovzx8fHXn319Ws+69FRF3n7Zu3apjx45p5syZDssPAAAAAFeS9X8sFDfb7ErKKdVjjz0mo9GorVu3VlEyUPZ/h7p160qS6tSpI0kyWS/8xfYOCJIklRbm68SB3co+ekQtusVWHGey2tW0aVO1aNFCu3btquLUAAAAAOA4Jqtdnp6eCgwM1NmzZx0dp9qg7P8ON910kyQpIyNDkuThcmGn/fO5pyVJ3rVq6/zZXEmSzWqtOO6neeXl5bJYLFWWFwAAAAAczcPFoPPnzysvL09BQUGOjlNtUPZ/h4EDB0qS5s+fL0nqEOwpN6NBez9eKqOrqxq3v1lBYU0kSQe3fCTpwj0qHYM9deDAAX3//fdq166dY8IDAAAAwBXm8uOjxn9SXmZSWfHPu+7/1IemTp0qu92u2NjY//4RuErYoO93aNeunUaMGKEFCxbIYrGoa9euWrb+M329ea26P/io/IJC5BcUoohO3XVg/QqVFRWp7S09lG/P1+zZs+Xp6akJEyY4+jQAAAAA4A+ZPXu28vPzlZmZKUlK27lZZ7MvrHzuPGiUSs/na9aQHmoTe4+CwiPk42rU1q93aNPGjYqNjdWdd97pyPjVCo/e+53Ky8s1Y8YMLVy4UJmZmQpr2FDt+z+ojoNHy2y78EdZbipV4tI5+nrzR8rP/EFubm665ZZbNHXqVLVt29axJwAAAAAAf1B4eLhOnjx5ydee3rBfHr7+Wvfys0r/Zp8K87JlsFkVERGhoUOH6sknn1SNGjWqOHH1Rdm/Amx2u44VmJWUUyqT1S4PlwtLVZr4u8n4X8taAAAAAMCZ0IeuTZR9AAAAAACcDBv0AQAAAADgZCj7AAAAAIDrQlFRkSZPnqzY2FgFBATIYDBo0aJFF80zGAyX/br99turPrgDsBs/AAAAAOC6kJeXpylTpigsLExt2rTRtm3bLjlvyZIlF43t27dPb7zxhmJiYq5yymsDZR8AAAAAcF0IDQ1VVlaWQkJCtG/fPnXo0OGS84YNG3bR2LZt22QwGDRkyJCrHfOaQNkHAAAAAFwX3N3dFRISctG4zW7X0QKz9v7iiQAdgj0V8eMTAcrKyrRmzRp169ZN9evXd0DyqkfZBwAAAABct8qsNi34Ll+FZpvMtp8fNpd9skh+bkYNifDXlo0blZ+fr6FDhzowadWi7AMAAAAArlt7skvVzGS9aNxssyvPZNXyowXalJAgd3d39e/f3wEJHYPd+AEAAAAA161Si/1XX885W6BPPvlEvXv3Vs2aNasm1DWAsg8AAAAAuG5Z7b9e9g98tk5lJlO1WsIvUfYBAAAAAE4sZdMaefr6qW/fvo6OUqUo+wAAAAAAp1SYe1rH9+1Ux5h+cnd3d3ScKkXZBwAAAABct1wMhsu+dnDzx7LbbBpxf/Vawi+xGz8AAAAA4Doye/Zs5efnKzMzU5KUtnOzzmZnSJI6DxolD1+/irkpm1arZnCo/trndodkdSSD3f4/djMAAAAAAOAaER4erpMnT17ytac37FetumGSpPwfjurlu6I1bsJjmvWv16oy4jWBsg8AAAAAuK7Z7HYdKzArKadUJqtdHi4GdQz2VBN/Nxl/ZZm/M6PsAwAAAADgZNigDwAAAAAAJ0PZBwAAAADAyVD2AQAAAABwMpR9AAAAAACcDGUfAAAAAAAnQ9n/g4YPHy6DwXDZr4yMDJ04ceJX54waNcrRpwEAAAAAcEKujg5wvRo9erR69uxZacxut2vMmDEKDw9XvXr1VFxcrCVLllx07KeffqqEhATFxMRUVVwAAAAAQDVisNvtdkeHcBY7d+7ULbfcounTp+u555677LyePXtq7969ys7OloeHRxUmBAAAAABUB1zZv4KWLVsmg8GgwUOGKDW/THtzSmWy2uXhYlCHYE9F+Lsp+/RpffHFF7r//vsp+gAAAACAq4Kyf4WUl5dr5cqV6hTdWf821VThySKZbT8vmsg+WSQ/N6POfLJcNptNQ4cOdWBaAAAAAIAzo+xfIZs3b9aZM2fUuOfdyjNZL3rdbLMrz2TV3EVLFBoaqh49ejggJQAAAACgOmA3/itk2bJlqlGjhpr16HfZObknj+nktym64+4BMhr5owcAAAAAXB00ziugqKhIa9euVVSXHqrhV+uy81I2rpYkNbnt7qqKBgAAAACohij7V8DHH3+skpISdejT/1fnff3pGgWFRyi0RZsqSgYAAAAAqI4o+1dAQkKCfHx89JeevS8754dv9uvMqf+oTa/+8nAxVGE6AAAAAEB1Q9n/k3Jzc7V161bdfffd6tIwUG7GSxf5rz9dI0nq0PtedQz2rMqIAAAAAIBqhrL/J61YsUIWi0VDhw5VhL+b/Nwu/iO1Wa06uOVjNWjVXo2bNFETfzcHJAUAAAAAVBeU/T8pISFBwcHB6tmzp4wGg4ZE+Ku2h0ulK/xHv/pSRWdydXNcfw2J8JfRwDJ+AAAAAMDVY7Db7XZHh3A2NrtdxwrMSsoplclql4eLQR2DPdXE342iDwAAAAC46ij7AAAAAAA4GZbxAwAAAADgZCj7AAAAAAA4Gco+AAAAAOCK2rt3r8aOHauWLVvK29tbYWFhGjhwoFJTUx0drdrgnn0AAAAAwBXVv39/7dq1SwMGDFDr1q11+vRpzZ49W0VFRdqzZ4+ioqIcHdHpUfYBAAAAAFdUYmKibrzpJv1QKu398Sll+aeO67l+N6t///5KWLrU0RGdHmUfAAAAAHBFFZfbtPxogQrNNpltP1fOt4beJqPBoMMpB+Rdg7vKryb+dAEAAAAAV4zNbtfyowXKM1krFX273a7CM7ly96ul5UcLZOO681VF2QcAAAAAXDFHC8wqNNsuGk/ZuFqFOVlqHXOXCs02HSswOyBd9UHZBwAAAABcMXtzSitd0ZeknP+kae3LzyisdQfdGDdYZptdSTmlDkpYPVD2AQAAAABXjMlaueifz8vW4kf/Kg8fPw2duUBGF5dLzsOV5eroAAAAAAAA5+HhYqj4vel8oRaOG6zS8wUaPX+9/IJCLjkPVx5X9gEAAAAAV0yHYE+5GQ0qLzNp8YShyjt5XA+8nqA6jZtVzHEzGtQx2NOBKZ0fZR8AAAAAcMVE+LvJx8Wu5X8fpR++2ae/vvKeGrbpUGmOn5tRTfzdHJSwemAZPwAAAADgijEaDPr63ak68uWnuqHbHSotyFfyJ6skSa5GydPFqHGPj5LRwDL+q8lgt/NwQwAAAADAldO9e3d9+eWXl32dGnr1UfYBAAAAAHAy3LMPAAAAAICToewDAAAAAOBkKPsAAAAAADgZyj4AAAAAwCkUFRVp8uTJio2NVUBAgAwGgxYtWnTRvHnz5qlbt26qU6eO3N3d1ahRIz344IM6ceJElWe+Wnj0HgAAAADAKeTl5WnKlCkKCwtTmzZttG3btkvOS05OVqNGjdSvXz/VqlVL//nPfzRv3jxt2LBBX3/9terWrVu1wa8Cyj4AAAAAwCmEhoYqKytLISEh2rdvnzp06HDJeXPmzLlo7K677lL79u31/vvv6+9///vVjnrVsYz/GnPgwAH169dPAQEB8vLyUlRUlGbNmlVpjtls1owZM9S8eXN5eHioTp066tOnj9LT0x2UGgAAAAAcz93dXSEhIReN2+x2peaXKSE1X/OPnFNCar5S88tk+8WT6MPDwyVJ+fn5VZT26uLK/jVky5YtiouLU7t27TRp0iT5+Pjo2LFjlUp8eXm5+vTpo8TERI0aNUqtW7fWuXPn9NVXX6mgoED169d34BkAAAAAwLWlzGrTgu/yVWi2yWz7udxnnyySsSRfd4Z560xWuqZMmSJJuu222xwV9Yqi7F8jCgsLdf/996tPnz5avXq1jMafF1389CnU3pxSffTuG/riyy/1waZ/654eN8toMDgwNQAAAABc2/Zkl6qZyXrRuNlm16TuLfWYuUySFBgYqFmzZun222+v6ohXBWX/GrFs2TJlZ2dr+vTpMhqNKi4ulqenp0qt0vKjBSo022SyWPXZ+3N1Q/fe+qF2C807lKd7wjwV5O/j6PgAAAAAcE0qtdgv+9rwNz+QystUO/+kNq/5QMXFxVWY7Orinv1rxNatW+Xn56eMjAw1a9ZMPj4+8vPzU5/7/qasgmKZbXblHP9ehbmnFdr0Bn3wj8f0yE31FVzTV61bt9YXX3zh6FMAAAAAgGuO1X75st+kQxc16XybogbEa9WqVfrHP/6h2bNnV2G6q4eyf41IS0uTxWLRnXfeqTvuuENr1qzRPUMf0JcrFmr1i+MlSWd+OC5J2rnsHR3fv0t3T3xVg/7xps6XlCo2NlYHDx505CkAAAAAwHXJZLWrSZMmateunRISEhwd54pgGf81oqioSCUlJRozZkzF7vulUT30/ZliJa1ZrNsfekZlJReWlJQVF2ncsn+rZkg9SVJ01256KuYmvfLKK1q6dKnDzgEAAAAArkceLhf2QistLVVZWZmD01wZXNm/Rnh6ekqShgwZUjFmstrVNvYeSdIPB/ephoeHJKlhm44VRV+SfOrUV5cuXZSYmFiFiQEAAADg2ufyX5uaWy0WlRbmV3zvZjSoY7CnkpKS9M0336h9+/ZVnPDq4Mr+NaJu3br69ttvVadOnYoxDxeDvAOCJEmlhfkKbNBIkuQTGFTpWA8Xg4KDg5WcnFx1gQEAAADgGjR79mzl5+crMzNTkpS2c7POZmdIkjoPGiW77HqpVxu1jrlLwU2aK8DXR8dyj2rRokXy9/fXpEmTHBn/iuHK/iVMnz5dBoNBUVFRFWMlJSV66623FBMTo9DQUPn6+qpdu3Z6++23ZbVe/BiH3+umm26SJGVkZFSMdQj2VOmZ05Ik71q1FRJxg1xca6gwJ6tizk+fQmVmZiooqPKHAAAAAABQ3bz66quaNGmS3n77bUnSgc826LM5L+mzOS+p9Hy+anh4qv1dw5Rx5Gt98e5MrZjxjD755BMNGTJE+/fvV9OmTR18BlcGZf+/pKena8aMGfL29q40fvz4cY0bN052u12PP/64Xn31VTVq1EgPP/ywRowY8affd+DAgZKk+fPnV4xF+LvpwNoEGV1d1bj9zXL39lGzLj31w8G9yvlPmiTJz80oc+YxJSYmOs3zIAEAAADgjzpx4oTsdnulL6vNptRzJrVu2lihvp56+MWX9eVX+1VUWCCz2awTJ07ovffeU3h4uKPjXzEGu/1XnkNQDQ0ePFi5ubmyWq3Ky8vToUOHJEl5eXnKzs5Wy5YtK80fMWKEFi5cqLS0NEVERPyp9x45cqQWLFiggQMHqlu3btq2bZtWrVqlXqMeU89HJspssyv7+Peac/8d8vD2Uc9ho9Um0ENz33pTFotFycnJqlev3v9+IwAAAACAU6Ps/8L27dvVo0cPJScna9y4ccrLy9OHO/drb06pTFa7PFwM6hDsqQh/Nxl/3ORh/fr16tevn9atW6e4uLg/9f7l5eWaMWOGFi5cqMzMTDVs2FCPPPKIxj/6qI4VmJX0Y46s7w5q06wp+nrvVzIajerRo4dmzpzpNMtNAAAAAAB/DmX/R1arVTfeeKOio6M1d+5c3dKtu45nZuuJ1Ttltv38R+RmNMjPzaghEf7yrmHUvHnzFB8fr8TEREVHRzvwDAAAAAAAuIDd+H80d+5cnTx5Ulu3bpXNbldOqUVWuyoVfenC93kmq5YfLdCwxl56/fXX1ahRI3Xo0MFByQEAAAAAqIyyL+nMmTN64YUXNGnSJAUFBSk1v0xW268veCg02/TA6Id1+PBhffLJJ3J15Y8SAAAAAHBtoKFKev755xUQEKBx48ZJkvbmlMr2P47ZuvBNbVq8QFOnTlXv3r2vfkgAAAAAAH6jal/209LS9O677+r1119XZmamJCn9hwJZyspktZTrXOYPcvf2lZd/rYpj9q9brk9nTVG3QQ/q+eefd1R0AAAAAAAuqdpv0Ldt2zbdeuutvzqn85B4xT01XZJ0eNsmJTz1oG7o3ktPz16s+5oHVEVMAAAAAAB+s2p/ZT8qKkofffRRpbHM4nJNnfyCTMVF6vvUdAXWD5ck/Wd/opY/G6/wdtG675/vqFOItwMSAwAAAADw66r9lf1LsdntavGXW1Rw9owmrNohSTqXeUqzBneX1VKuXhNeVFBNf3Wt6yWjwSBJat26tVq3bu3I2AAAAAAASOLK/iUZDQYFe7qqyCC5GQ0y2+w6l/mDTEWFkqR1Lz0jSZr/i2MmT55M2QcAAAAAXBO4sv8rbHa7jhWYlZRTKpPVLg8XgzoGe6qJv1vFFX0AAAAAAK41lH0AAAAAAJyM0dEBAAAAAADAlUXZBwAAAADAyVD2AQAAAABwMpR9AAAAAACcDGUfAAAAAAAnQ9kHAAAAAMDJUPYBAAAAAHAylH0AAAAAAJwMZR8AAAAAACdD2QcAAAAAwMlQ9gEAAAAAcDKUfQAAAAAAnAxlHwAAAADwpx04cED9+vVTQECAvLy8FBUVpVmzZlWak5iYqC5dusjLy0shISEaP368ioqKHJTYubk6OgAAAAAA4Pq2ZcsWxcXFqV27dpo0aZJ8fHx07NgxpaenV8xJSUnRbbfdphYtWui1115Tenq6Xn31VaWlpWnTpk0OTO+cDHa73e7oEAAAAACA61NhYaEiIyPVuXNnrV69WkbjpReQ9+7dWykpKfruu+/k5+cnSXrvvfc0atQobd68WTExMVUZ2+mxjB8AAAAA8IctW7ZM2dnZmj59uoxGo4qLi2Wz2WSz25WaX6aE1HzN3ntSWz77TL37D5GPr2/Fsffff798fHy0cuVKB56Bc6LsAwAAAAD+sK1bt8rPz08ZGRlq1qyZfHx85Ofnpx5DRuqj1DydKrbo0KFDslosMte/QQu+y1dxuU2S5ObmprZt2yo5OdnBZ+F8KPsAAAAAgD8sLS1NFotFd955p+644w6tWr1af7lrqL5csVDLXhgnSTqfly1J8gwMVp7JquVHC2T78Y7y0NBQZWZmOiy/s2KDPgAAAADAH1ZUVKSSkhKNGTNGs2bNUmp+mfpEdFeRqUxJaxbr9oeeUbnJJElydXOXJBWabTpWYFbTmu7y8PBQaWmpI0/BKXFlHwAAAADwh3l6ekqShgwZIknam1Mqs82utrH3SJJ+OLhPNTw8JEkWc5kkyWyzKynnQsE3mUwVPwNXDmUfAAAAAPCH1a1bV5JUp04dSZLJemF5vndAkCSptDBfvrUvvPbTcv5fzsvKyqr4GbhyKPsAAAAAgD/spptukiRlZGRIkjxcDJKk87mnJUnetWorpEkLGV1dlXH464rjPFwMMpvNSklJUdu2bas2dDVA2QcAAAAA/GEDBw6UJM2fP1+S1CHYU25Gg/Z+vFRGV1c1bn+zPHz9FNGxq5I3rlJZcZHcjAZ1DPbUkiVLVFRUpAEDBjjyFJwSZR8AAAAA8Ie1a9dOI0aM0LJlyzRo0CB9lvCelj0zUl9/+qG63veI/IJCJEkxjzyn0sJ8vTuqnw58uFiLZk7R2LFjFRMTo9jY2CrLm5aWpsGDB6t+/fry8vJS8+bNNWXKFJWUlFRZhqpgsNt/fN4BAAAAAAB/QHl5uWbMmKGFCxcqMzNTYQ0bqn3/B9Vx8GiZbT9XzvSvv9Kns6Yo48hB+fr6auDAgfrnP/8pX1/fKsl56tQptW7dWv7+/hozZowCAgK0e/duLVq0SP369dPatWurJEdVoOwDAAAAAK44m92uYwVmJeWUymS1y8PlwtL9Jv5uMhoMDsk0Y8YMTZw4UYcOHVLLli0rxh944AG9//77Onv2rGrVquWQbFdatVnG/3uXauTn5ys4OFgGg0GrV6+u4rQAAAAAcH0zGgxqWtNdQyNramSLWhoaWVNNa7o7rOhLUmFhoSQpKDhYqfllSkjN1/wj53TWvZaMRqNca9RwWLYrrVqU/VOnTqljx47as2ePxo4dq9dff13R0dGaPHlyxbMg/9sLL7zgdPdsAAAAAEB11r17d0lSzKAH9M7m3frm2El9vna1Plu+QF2GjNKKU+UqLrc5NuQV4uroAFVhyZIlys/P186dOyuWasTHx8tms+n999/XuXPnKi3VOHTokN5++2298MILeuGFFxwVGwAAAABwBcXccYfuHP+cNr77L339xaaK8VtHPqaYR55Tnsmq5UcLNKJ5TYeuQLgSqkXZ/2mpRp06dSqNh4SEyGg06sOTJdLpC8957BDsqUcffVR33323brnlFkfEBQAAAABcBUcLzPKuU1/h7Top6rY4efnX0vc7P9O2Ba/LJzBYnQf/TYVmm44VmNW0pruj4/4p1aLsd+/eXS+//LJGjhypf/zjHwoMDNS/t+/Um3PeVpcho5Rrd5dMVknSlnkJ2rErUSnffKucjB8cnBwAAAAAcKXMWrhUq6Y+oSc+2iP/OnUlSVG39ZXdZtOns6aqTew9Us0AJeWUXvdlv1rcsx8bG6upU6fqs88+U7t27RQWFqbhw/6qToP+pl5PTKuYV24q1brXJuvmv45WoqWWbDyoAAAAAACcxpaE+arbLKqi6P+kRbdYlZtKlPX9N5Ikk/X674LV4sq+JIWHh6tr16669957VebhpyUfrq+0VEOSti2cJaulXN1HTlCh2abM4nIHpwYAAAAAXCnnz+Sqho//ReNWS/mPv1okXbjF+3pXLcr+Bx98oPj4eKWmpqp+/fpKSM3X3VG3yWq1VizVMJcUaceSt9TvmZfk7uUjs82uI+fMjo4OAAAAALhCWjSL1PZ/b1XuyWMKatikYvzrzR/JYDQqNLKl3IwGdQz2dGDKK6NalP05c+aoXbt2ql+/vqSfl2S06Bar/es/UNb33+jAhpXyCwpR4/Y361zmhXv183NOS5Jyc3N14sQJhYWFyWisFnc+AAAAAIDTeeHZp3Xb1s16d2ScOg0aKS//Wvpuxxal7vpcHe4eJr+gEPm5GdXE383RUf+0alH2s7OzKz1a76clGb9cqpF/Ol1nTv1HM+PaX3T8ww8/LEk6d+6catasefUDAwAAAACuuO7duunf23dq7N8n6atVC1SSf0616oUp5pHn1PPB8arl4aIhEf7X/WP3pGpS9iMjI7VlyxalpqYqMjJSHYI9lX2yqNJSjZiHn1Vx/tmKY1wNBtU8c0yvT/+Hnn76aUVHR8vb29uBZwEAAAAA+LO6de6kr7/comMFZiXllMpktcvD5cLS/Sb+bk5R9CXJYLc7/5bz27dvV48ePRQYGKixY8cqICBAc1d8rEM7tqrD3cN0z6R/XXRMbQ8XNT6dott69NCqVavUv39/ByQHAAAAAOD3qxZX9rt27arExES9+OKLmjNnjs6cOaPw8Ea669Hn1fm+sbL+Yq6b0SA/N6OGRPhrb7ZzfKIDAAAAAKheqsWV/cux2e1Ov3QDAAAAAFD9VOuyDwAAAACAM+I5cgAAAAAAOBnKPgAAAAAAToayDwAAAACAk6HsAwAAAADgZCj7AAAAAAA4Gco+AAAAAABOhrIPAAAAAICToewDAAAAAOBkKPsAAAAAADgZyj4AAAAAAE6Gsg8AAAAAgJOh7AMAAAAA4GQo+wAAAAAAOBnKPgAAAAAAToayDwAAAACAk6HsAwAAAADgZCj7V0laWpoGDx6s+vXry8vLS82bN9eUKVNUUlJSMad79+4yGAwXfcXGxjowOQAAAADgeufq6ADO6NSpU+rYsaP8/f01duxYBQQEaPfu3Zo8ebL279+vtWvXVsytX7++/vnPf1Y6vm7dulUdGQAAAADgRCj7V8GSJUuUn5+vnTt3qmXLlpKk+Ph42Ww2vf/++zp37pxq1aolSfL399ewYcMcGRcAAAAAHG748OFavHjxZV9PT09XvXr1Ko3l5+crMjJSubm5WrVqlfr373+1Y143KPtXQWFhoSSpTp06lcZDQkJkNBr14ckS6bSUXWqR1WqXubxc5rIy+fj4OCIuAAAAADjc6NGj1bNnz0pjdrtdY8aMUXh4+EVFX5JeeOGFSrdK42fcs38VdO/eXZI0cuRIpaSk6NSpU1qcsFxvznlbXYaMUq7dXbkmq8qsdh0/miZvbx/5+voqJCREkyZNUnl5uWNPAAAAAACqWHR0tIYNG1bpq1GjRiopKdHQoUMvmn/o0CG9/fbbeuaZZxyQ9trHlf2rIDY2VlOnTtWMGTO0bt26ivFbRz6mmEeeq/g+sH64GrfvopCIFnK3mpSbuEnTpk1TamqqVqxY4YjoAAAAAHDNWLZsmQwGgzr1vVcJqfkyWe3ycDGoQ7CnHn30Ud1999265ZZbHB3zmkTZv0rCw8PVtWtX3XvvvSrz8NOSD9dr24LX5RMYrM6D/yZJunfyGxXz3YwGxY1+UCFPj9O8efP02GOPqVOnTo6KDwAAAAAOVV5erhUrV6pJu45KsdeWudhS8dqWeQnasStRKd98q5yMHxyY8trFMv6r4IMPPlB8fLzee+89jRo1SrX+cofufuF13dh3kD6dNVXF+WcvOsZssyspp1RPPPGEJGnr1q1VHRsAAAAArhmbPv1UZ8+cUcs77pXZZq8YLzeVat1rk3XzX0cr0VJLNrv9V35K9UXZvwrmzJmjdu3aqX79+pIkk/XCX74W3WJVbipR1vffXPI4k9WuBg0aSJLOnr34AwEAAAAAqC7mLV4qF9caanX7nZXGty2cJaulXN1HTlCh2abMYvY8uxTK/lWQnZ0tq9Va8b2Hi0GSZLWU//ir5ZLHebgYdPz4cUlSUFDQVU4JAAAAANemoqIifbZxg5pG3yrvmgEV4+cyf9COJW8p5pHn5O7lI7PNriPnzA5Meu2i7F8FkZGRSk5OVmpqqiSpQ7Cn3IwGfb35IxmMRoVGtpSp6Lws5rKKY9yMBnUI8tC0adMkSXfccYdDsgMAAACAo3388ccqKy1R2979K41/9vbL8gsKUeP2N+tc5g86l/mD8nJOS5Jyc3N14sQJ2Ww2R0S+5hjsdm5wuNK2b9+uHj16KDAwUGPHjlVAQIDmrvhYh3ZsVYe7h+meSf/S8X279MFz8Wp9xz0KbNBI7tYyZSZu1q5duxQfH6933nnH0acBAAAAAA7Rq1cvbdu+U89+9q3cPL0qxt8ddaf+sz/xV489d+6cataseZUTXvso+1dJUlKSXnzxRSUnJ+vMmTMKD2+klr0HqvN9Y2U1uuhsxkl9OmuKMr5N0fkzOarhYlSLFi00atQoxcfHy2AwOPoUAAAAAKDK5ebmqm7duupz70B1fXZWpc35TiTvqbThuavBoJpnjun16f/Q008/rejoaPXp00c1atRwRPRrCmW/Ctnsdh0rMCspp7Ti+ZAdgz3VxN9NRso9AAAAAGj27NkaN26cNm7apIyGf1GeyXrZubU9XNT4dIpu69FDq1atUv/+/S87t7pxdXSA6sRoMKhpTXc1renu6CgAAAAAcE1KSEhQcHCwYm6/XSabQcuPFqjQbKt0hd/NaJCfm1FDIvy1N5sLp5fClX0AAAAAwDWLFdJ/DGUfAAAAAAAnw6P3AAAAAABwMpR9AAAAAACcDGUfAAAAAAAnQ9kHAAAAAMDJUPYBAAAAAHAylH0AAAAAAJzMdV/2Dxw4oH79+ikgIEBeXl6KiorSrFmzKl7fsmWLRo4cqaioKLm4uCg8PNxxYQEAAAAAqAKujg7wZ2zZskVxcXFq166dJk2aJB8fHx07dkzp6ekVc5YtW6YVK1boxhtvVN26dR2YFgAAAACAqmGw2+12R4f4IwoLCxUZGanOnTtr9erVMhovvUghMzNTQUFBqlGjhvr27atDhw7pxIkTVRsWAAAAAIAqdN1e2V+2bJmys7M1ffp0GY1GFRcXy9PTUzIYdLTArL05pTJZ7fJw8VIHL5si/K/LzzQAAAAAAPjdrtt79rdu3So/Pz9lZGSoWbNm8vHxkZ+fn3oMGamPUvN0qtiiXJNVp4ot2nCySAu+y5f1+lzEAAAAAADA73Ldlv20tDRZLBbdeeeduuOOO7Rq9Wr95a6h+nLFQi17YVyluWabXXkmqzKKLQ5KCwAAAABA1blul/EXFRWppKREY8aM0axZs5SaX6Y+Ed1VZCpT0prFuv2hZ1Q7rEmlYyw2uyw2ru4DAAAAAJzbdXtl39PTU5I0ZMgQSdLenFKZbXa1jb1HkvTDwX0XHWOzS2WUfQAAAACAk7tuy/5Pj9GrU6eOJMlkvVDivQOCJEmlhfmXPI7b9gEAAAAAzu66Lfs33XSTJCkjI0OS5OFikCSdzz0tSfKuVfuSxxkMVRAOAAAAAAAHum7L/sCBAyVJ8+fPlyR1CPaUm9GgvR8vldHVVY3b33zRMUaD5G6k7QMAAAAAnNt1u0Ffu3btNGLECC1YsEAWi0Vdu3bVsvWf6evNa9X9wUflFxQiScpK/VZHtn8qSTqXfkKm84WaNm2aJKlNmzaKi4tz2DkAAAAAAHA1GOz26/cu9vLycs2YMUMLFy5UZmamwho2VPv+D6rj4NEy/7gR3/51y7X6xfGXPP6BBx7QokWLqjAxAAAAAABX33Vd9i/FZrfrWIFZSTmlMlnt8nAxqGOwp5r4u8nIDfsAAAAAgGrA6co+AAAAAADV3XW7QR8AAAAAALg0yj4AAAAAAE6Gsg8AAAAAgJOh7AMAAAAAnNb+/fsVGxsrPz8/+fr6KiYmRikpKY6OddVR9quJ4cOHy2AwXPYrIyOjYq7ZbNaMGTPUvHlzeXh4qE6dOurTp4/S09MdeAYAAAAA8PscOHBAXbp00fHjxzV58mS98MILSktLU7du3fT99987Ot5VxW781cTu3bt17NixSmN2u11jxoxReHi4vv32W0lSeXm5evfurcTERI0aNUqtW7fWuXPn9NVXX2ny5Mlq2bKlI+IDAAAAwO/Wp08f7d69W2lpaQoMDJQkZWVlKTIyUjExMVqzZo2DE149ro4OgKoRHR2t6OjoSmM7d+5USUmJ7rhnkBJS82Wy2rV1wSx9+eWX2r5jhzr95S8OSgsAAAAAf96OHTt0R2yszrj46NMfO4+Hi6fad75FGzZsUFFRkXx8fBwd86pgGX81tnhpggwGg7yi43Sq2KLsknJtXDxXN9zaW4f8IlVQalZJSYmjYwIAAADAH1JWVqYMs4s2nCzSqWKLck1WnSq26JzdTWazWXtTDjo64lVD2a+mysxmfbBipcLadJBPaANJUs7x71WYe1p1Im7QuxMfVW1/X3l7e6t169b64osvHJwYAAAAAH47m92uoPAIpaXslancUjFuKTfr5Df7JUkr96fJ5qR3tlP2q6lFH36iovyzaturf8XYmR+OS5J2LntHx/fv0r3P/59eeutdmUwmxcbG6uBB5/3UCwAAAIBzOVpgVqcBDyrv5DGtmfKoso9/r9NHj2jVpEd0Pi9bklRYXKJjBWYHJ706KPvV1OKEBLm41lCr2++sGCsrKb7wa3GR/jb3Q7WNG6z6PQdo69atstvteuWVVxwVFwAAAAB+l705pWp/73B1HzFBX2/6UK/376I3BnbVmfQT6vrAWEmS0cNbSTmlDk56dVD2q6GioiLt/3yTmkbfKu+aARXjNTw8JEkN23RUzZB6kiST1a6wsDB16dJFiYmJDskLAAAAwDlMnz5dBoNBUVFRV/29TNYLy/PvGDtRE7ce1uj56/Xoii81dulnsttskqTaDZtUzHM27MZfDX388ccyl5aobe/+lcb9gkIkST6BQRVjHi4GSVJwcLCSk5OrLiQAAAAAp5Kenq4ZM2bI29u7St7vpy4jSZ5+NRXerlPF90eTtsu/Tl0FhTetNM+ZcGW/GkpISJCXj4/adI+tNB4ScYNcXGuoMCdLkuRmNKhjsKckKTMzU0FBQRf9LAAAAAD4LZ588kl16tRJ7du3r5L36xDsKTfjxUX+4OaPlP5tsm7+62h5uLpUdB5nw5X9aiY3N1dbt27V4CFDVNvfR3kma8Vr7t4+atalp77bsUU5/0nTDS2aq4m/m44cOaLExESNHj3agckBAAAAXI9sdruWb/xcq1av1gtrvtQHM56R1WqXzW6X0XD1rqpH+Lvp9ME9+nD2K2raqbu8atbSqW/2a/+65Yrs3EOdh8TLz82oJv5uVy2DI3Flv5pZsWKFLBaLhg0dqiER/qrt4VLp066YsRNVw91DC8bco8wP5+qVl1/WbbfdpoCAAD333HMOTA4AAADgelNcbtN7357RU49PUPu7hsk9rJnKrHadL7dpwXf5Ki63XbX3NhoMuq9DpNxruGjHkre07qW/60TyV7r94Wc18o2lquPjriER/lf1AwdHMtjtTvpQQVxSdHS0jh8/rszMTLm4uMhmt+tYgVlJOaUyWe3ycDHIO+s7zZ42SXt275bRaFSPHj00c+ZMNW3a1NHxAQAAAFwnbHa7FnyXr/WL39WWt2boibVfyadWbb076k6V5J/VhFU7VNvDRSOa17yqhftSnadjsKea+Ls5bdGXWMZf7ezevbvS90aDQU1ruqtpTfefByM76a5un1VxMgAAAADO5GiBWVk5efps7svqMeoJ+dSqfdGcQrNNxwrMlfvIFXbJzlMNsIwfAAAAAHDF7c0p1SezZ8jLr6aiB//tknPMNrvTPufe0biyDwAAAAC44k4eO6qkD99X3yen6Xzu6YpxS1mZrJZyncv8Qe7evjJ5XHzFH38e9+wDAAAAAK64iUs2aMb9cb86p/OQeD384ssaGlmzakJVI1zZBwAAAABccXGd2ynztfdl+a/ry5/N+afKiovU96npCglr5LTPuXc0yj4AAAAA4Irr2LiuDsX2VZ7JWml817J3JEktb+2t2h4uTvuce0djgz4AAAAAwBVnNBg0JMJftT1c5Gas/Ig7g6TaHi5O/Zx7R+OefQAAAADAVVNdn3PvaJR9AAAAAACcDMv4AQAAAABwMpR9AAAAAACcDGUfAAAAAAAnQ9kHAAAAAMDJUPYBAAAAAHAylH0AAAAAAJwMZR8AAAAAACdD2QcAAAAAwMlQ9gEAAAAAcDKUfQAAAAAAnAxlHwAAAAAAJ0PZBwAAAADAyVD2AQAAAABwMpR9AAAAAACcDGW/iuzdu1djx45Vy5Yt5e3trbCwMA0cOFCpqakXzT1y5IhiY2Pl4+OjgIAA3XfffcrNzXVAagAAAADA9chgt9vtjg5RHfTv31+7du3SgAED1Lp1a50+fVqzZ89WUVGR9uzZo6ioKElSenq62rVrJ39/f40fP15FRUV69dVXFRYWpqSkJLm5uTn4TAAAAAAA1zrKfhVJTExU+/btK5X1tLQ0tWrVSv3799fSpUslSQ8//LAWLVqk7777TmFhYZKkrVu36vbbb9c777yj+Ph4h+QHAAAAAFw/KPsOdtNNN6nMatezK/8tk9Wux29pps63dNUnH66S0WComNesWTM1aNBAW7dudWBaAAAAAMD1gHv2HajIbNXxjCyVe9XUqWKLjv6QrsIzuXJp2FILvstXcbmtYm7Hjh2VnJzswLQAAAAAgOsFZd9BbHa7JvzrPeVnZ6nV7XdKks7nZUuSvALrKM9k1fKjBbL9uPAiNDRUZ8+eVVlZmcMyAwAAAACuD5R9B9m89xslTHtKYa076Ma4wZKkclOpJMn1x/v6C802HSswS5I8PDwkSaWlpQ5ICwAAAAC4nlD2HeD06dMa3v9Oufv4aejMBTK6uEiSanh4SpIs5gsF32yzKynnQrk3mUySJE9PTwckBgAAAABcT1wdHaC6KSgoUK9evVRcWKBR89fLLyik4jXf2nUk/bycX5JM1gvL+LOyshQQECB3d/eqDQwAAAAAuO449ZX9AwcOqF+/fgoICJCXl5eioqI0a9YsSVJJSYneeustxcTEKDQ0VL6+vmrXrp3efvttWa3Wq5LHZDIpLi5OqampevKdD1SncbNKr/sHh8q7Vm1lHEmpGPNwubAjf1JSktq2bXtVcgEAAAAAnIvTlv0tW7YoOjpaOTk5mjRpkt544w317dtX6enpkqTjx49r3Lhxstvtevzxx/Xqq6+qUaNGevjhhzVixIgrnsdqtWrQoEHavXu3Vq1apb/e0U1uRsNF86Ju66vvdnym/NMZcjMa1DHYU59//rlSU1M1YMCAK54LAAAAAOB8DHb7j9u9O5HCwkJFRkaqc+fOWr16tYzGiz/TyMvLU3Z2tlq2bFlpfMSIEVq4cKHS0tIUERFxxTJNmDBBb7zxhuLi4jRw4EDZ7HZtzyzR+R8fr9euz4Uin386Q2/+tYc8fPwUc/8YtfK26tVXX1X9+vW1d+9elvEDAAAAcArffvutXnzxRe3fv1+nT5+Wl5eXbrjhBj311FOKi4urmJeUlKRFixbpq6++0sGDB2WxWOSENfaKc8qyP3fuXD300EM6fPiwWrRooeLiYnl6espoNMpmt+togVl7c0plstrl4WJQh2BPRfi7yWgwaP369erXr5/WrVtX6S/Yn9W9e3d9+eWXl339nwdyK35/9vj3+uRfk3Qi+Su5ubmpT58++r//+z/VqVPniuUBAAAAAEfauHGjZs2apejoaNWtW1clJSVas2aNduzYoXfeeUfx8fGSpBdffFEzZsxQ69atdf78eaWmplL2fwOnLPv9+/fXZ599pjVr1uiRRx5RamqqvL29NXjoMN300GSVGtxktv182m5Gg/zcjBoS4a9li+YrPj5eiYmJio6OvupZbXa7jhWYlfSLDx86BnuqyY8fPgAAAABAdWG1WnXTTTfJZDLpu+++kyRlZ2fLz89Pnp6eGjt2rN566y3K/m/glLvxp6WlyWKx6M4779TIkSP1z3/+U1988YVmz56tvSdzNOSf71aab7bZlWeyasnhXL35+utq1KiROnToUCVZjQaDmtZ0V9OaLM8HAAAAUL25uLiofoMG+ipprxJS83+8IOquDu5GRXhQ8H8Ppyz7RUVFKikp0ZgxYyp234/q0Uf7T5/X7tWLdftDz6h2WJOLjls85SkdPnxYn3zyiVxdnfKPBgAAAACuKcXFxSotLVVBQYFWf7RWmzZtUpuYu3Sq2FIxJ/tkkfzcjCq3Ufh/K6fcjd/T01OSNGTIkIqxvTmlanXHPZKkHw7uu+iY7Ytna8+HS9T/0Ynq3bt31QQFAAAAgGruiSeeUFBQkCIiIvTsM0/phlt7K+6ZlyrN+Wk1dlqB2UEprz9OWfbr1q0rSZU2tDNZ7fIOCJIklRbmV5q/f91yfTpriv7Sf7hiRz9RZTkBAAAAoLqbMGGCPvvsM7389ntqfvNtslttspRfutSbrVzZ/62csuzfdNNNkqSMjIyKMQ8Xg87nnpYkedeqXTF+eNsmfTj1MbXs0Uf9/v6yPFzYFA8AAAAAqkrz5s3Vs2dP1etxr+5/Y5nKSov1/oRhl9yEz8rGfL+ZU5b9gQMHSpLmz59fMdYh2FP7P14qo6urGre/WZL0n/2JWv5svMLbRWvQ9LnycHVRx2BPh2QGAAAAgOrM9ONV+1a3xSn922TlnTzm4ETXN6fcha5du3YaMWKEFixYIIvFom7duumLbduU/OmH6v7go/ILCtG5zFN6/7H7ZDAYFNUzTt98tk6+NYyqXddLXxkMat26tVq3bu3oUwEAAACAauGnVdblZaWSJFNRoSPjXPecsuxL0ty5cxUWFqaFCxfqo48+UsOGDfXyq68poPdwFZptOpf5Q8VfnnUvPVNx3E9rASZPnkzZBwAAAICrJCcnR8HBwRXfdwj2VObRczqwYaVqeHgquHHkRce4GLjt+rcy2C91I4QTs9ntOlZgVlJO6Y/PbDSoY7Cnmvi7ychfHAAAAACoEnfffbcKCwvVtWtX1atXT1lZWZqzaIlOH09T78en6JZhD0mSzmWeUvLGlZKkozs/038O7tfUqVMlSQ0bNtR9993nsHO4llW7sg8AAAAAcLwPPvhA8+fP1zfffKMzZ87I19dXbW+8UTfcPUKNu9whs+1CVT2+b5fmxd91yZ/RrVs3bdu2repCX0co+wAAAACAawarsa8Myj4AAAAAAE7GKR+9BwAAAABAdUbZBwAAAADAyVD2AQAAAABwMpR9AAAAAACcDGUfAAAAACBJ2rt3r8aOHauWLVvK29tbYWFhGjhwoFJTUyvNGz58uAwGw0VfzZs3d1By/DdXRwcAAAAAAFwbXn75Ze3atUsDBgxQ69atdfr0ac2ePVs33nij9uzZo6ioqIq57u7ueu+99yod7+/vX9WRcRk8eg8AAAAAIElKTExU+/bt5ebmVjGWlpamVq1aKabf3Ro07W2ZrHYtnviI9m9Zp/Pnz8toMDgwMS6HZfwAAAAAAElS586dKxV9Saob3kR1mjRX8qHDOlVsUa7JqmKLTRab9N63Z5R1Jt8xYfGrKPsAAAAAgEuy2e1alpav/LwcefoHVHqt3FSisR0aqm7tWgoICNAjjzyioqIiByXFf+OefQAAAADAJR0tMGvbxytUmJOl28c8UzHuW7uOuj4wVnWbt5aL7Cr+ervmzJmjr7/+Wtu2bZOrK1XT0bhnHwAAAABwSTM3JWnSwJ6q07i5Rs9fL6OLyyXnNfB21cnVczRx4kQtX75cgwcPruKk+G8s4wcAAAAAXOT06dOaOXqQPHz8NHTmgssWfUkyWe167LHHZDQatXXr1ipMicthbQUAAAAAoJKCggL16tVLpecL9Lf31ssvKORX53u4GOTp6anAwECdPXu2ilLi11D2AQAAAAAVTCaT4uLilJqaqgUfbVRGneYy2y5/97eb0aCOwZ46f/688vLyFBQUVIVpcTks4wcAAAAASJKsVqsGDRqk3bt3a9WqVRpwe1f5uVWujeVlJpUV/7zrvp+bUU383TR16lTZ7XbFxsZWdWxcAlf2AQAAAACSpCeeeELr1q1TXFyczp49q2UJCTJYbDqaXapSq02teg1Q0ZkczRrSQzfG3qOwiEjdGOSpuCe3aOPGjYqNjdWdd97p6NOA2I0fAAAAAPCj7t2768svv7zs60u/P6ez5/K1YsYzSj+0X7mns2S1WhUREaGhQ4fqySefVI0aNaowMS6Hsg8AAAAAgJPhnn0AAAAAAJwMZR8AAAAAACdD2QcAAAAAwMlQ9gEAAAAAcDKUfQAAAAAAnAxlHwAAAAAAJ0PZBwAAAADAyVD2AQAAAABwMpR9AAAAAACcDGUfAAAAAAAnQ9kHAAAAAMDJUPYBAAAAAHAylH0AAAAAAJwMZR8AAAAAACdD2QcAAAAAwMlQ9q8T+/fvV2xsrPz8/OTr66uYmBilpKRcNK+8vFz/+Mc/1LhxY7m7u6tx48aaNm2aLBZL1YcGAAAAADiEwW632x0dAr/uwIEDuvnmm9WgQQONHj1aNptNc+bM0dmzZ5WUlKRmzZpVzB00aJBWrVqlESNGqH379tqzZ48WL16sUaNG6d1333XgWQAAAAAAqgpl/zrQp08f7d69W2lpaQoMDJQkZWVlKTIyUjExMVqzZo0kae/everYsaMmTZqkKVOmVBz/5JNP6rXXXlNKSopat27tkHMAAAAAAFQdlvFfB3bs2KGePXtWFH1JCg0NVddu3bR+wwbNT07X/CPn9PqHWyRJAwcNqnT84MGDZbfbtWLFiirNDQAAAABwDMr+daCsrEyenp6VxorLbcq2uKrcbNb+g4eUa7LqTFGpJGl9ZrmKy20Vc728vCRduO8fAAAAAOD8KPvXgWbNmmnPnj2yWq2SJJvdriWHc3Xs6wvlvTA3S5JUu2GEJCnlq91afrRAth/v0NixY4ckKSMjo6qjAwAAALiOFRUVafLkyYqNjVVAQIAMBoMWLVp0ybkrV65Up06dVLNmTQUGBqpbt2765JNPqjYwKlD2rwMPP/ywUlNTNXLkSB0+fFgbdyfrnWfG6HxetiSp3GSSJDXr0lM1Qxto4+uTtXPTOm3/Jk0rV67UxIkT5erqqtLSUkeeBgAAAIDrTF5enqZMmaIjR46oTZs2l5335ptvatCgQapdu7ZeeuklTZo0SQUFBerbt68+/PDDKkyMn7BB33Vi4sSJmjlzpsrLyyVJ9W5oq8joW/XF/H9p2P8tVstbe0uSso99p2XP/E05x7+XJLm7u+uVV17R9OnTFRoaesnH9QEAAADApZSVlencuXMKCQnRvn371KFDBy1cuFD3P/CAjhaYtTenVCarXc/37qCggFpK3vuVXIwXrikXFhaqXr166tGjh9auXevgM6l+uLJ/nZg+fbqys7O1Y8cOvfjxTo1d+pnstgv35ddu2KRiXp0mzTVh1Q5NWLVDzyzZqMzMTI0aNUp5eXmKjIx0VHwAAAAA1yF3d3eFhIRUGiuz2rTgu3xtOFmkU8UW5ZqsKjpfKIt3LS38vqBi/zA/Pz/5+PhctP8YqgZl/zeaPn26DAaDoqKiKo3PmDFDnTp1UlBQkDw8PNS0aVNNmDBBubm5VzxDrVq11KVLF0W0aClJOpq0Xf516ioovGmleQaDQXWaNFerjtEKCAjQF198IZvNpp49e17xTAAAAACqlz3ZpcozWWW2/bxIvPFNN+v7xH9r3aK5evPLb3T4yBE98sgjKigo0KOPPurAtNWXq6MDXA/S09M1Y8YMeXt7X/Ta/v371bZtWw0ePFi+vr46cuSI5s2bp08++UQpKSmXPObP6hDsqU/fXaL0b5PV+7F/yGi8+DMbN6NBHYM9VVpaqkmTJik0NFRDhgy54lkAAAAAVC+llovvBI97eoaK889q/SvPaf0rz+lZSbVr19bnn3+u6Ojoqg8Jyv5v8eSTT6pTp06yWq3Ky8tTan5Zxb0p9/xzvjoEeyrC301Gg0GSFB0drf79+2v9+vUaPHjwn37/7du3a8qUKYqJiVFgYKB2796tZYsWKbJzD3UeEl9p7rJnRsq3doiaNGsuq5dFCxcu1PHjx/XJJ5/I19f3T2cBAAAAUL1ZL7HtWw0PTwU1bCL/OqFqfkuMvC2l2rP8Hd1zzz3asWOHIiIiHJC0eqPs/w/bt2/X6tWrlZycrIfHjtO5Mqs2nCyqtGQl+2SR/NyMGhLhL+8aRoWHh0uS8vPzr0iGevXqycXFRTNnztT58+fVqFEjTf7HVAX3HaESuVbKEnZDW+1fv1wHPnpfGzw9dcstt2jZsmVq27btFckCAAAAAP9t2dMjZXRx1QNvJEiSgjxc9NLoIWratKkmTpyoFStWODhh9UPZ/xVWq1Xjxo3T3/72N7WMilJOqUVWuyqVa+nCBhUns/I0Nzdb7ZWj5559Vi4uLurevfsVydGkSRNt3rz5onGb3a5jBWYl/bjKwMPFoH+9+Jya/OvFilUGAAAAAHA1nU0/odTEf+vu51+rGPNwMSgg4MKeY7t27XJguuqLsv8r5s6dq5MnT2rr1q06WmCW1XbppxQWncnRjJifN+6rX7++li1bpubNm1/VfEaDQU1ruqtpTfer+j4AAAAA8BOX/7qweP7Mhc3J7TarpJ/3D5Ok8vJyWSyWqg0ISZT9yzpz5oxeeOEFTZo0SUFBQdqSmi/bZeZ6+tfSyLdXq7ysTMXHv9Wx7RtVVFRUpXkBAAAAoCp4ulYu+4FhjWQwGnVwy8fqeO8D8nNzURN/N6Wnp2vHjh3q0qWLg5JWb5T9y3j++ecVEBCgcePGSZJM1ktf1Zck1xpuivhLN0lSUEwvtRjURzfffLOCg4PVt2/fKskLAAAAAFfD7NmzlZ+fr8zMTEnS2b2fa092psqsdv1l4N/kU6u22t/5V+39aKkWP3yvRg0ZoJc/LtKcOXNUWlqqZ5991sFnUD0Z7PZLbKVYzaWlpal58+Z6/fXXFRcXJ0n6+HihXnt0hErP52vEWyvl7u0rL/9aFx3bwNtVQyNrqm7duurWrZuWL19e1fEBAAAA4IoJDw/XyZMnL/navz7/Wr6hDVTDbtXRDUu1fvliHT16VJLUoUMHTZo0SbfeemtVxsWPKPuXsG3btv/5F7LzkHjFPTW90pib0aC4hj5qWtNdAQEB6tSpkzZu3Hg1owIAAAAAcBGW8V9CVFSUPvroo0pjNrtd455+TiVFRer71HQF1g+XubRYkkFunl6SJD83o5r4u2nNmjU6d+6c2rdv74D0AAAAAIDqjiv7v8Mt3brreGa2nli9U2abXZnff6P5D/VX25i7FBYRqQ51vPVN8n4tXbpU9evX1759+xQYGOjo2AAAAACAaoYr+7+Di0Gq5e6iuIY+SsoplUdYA/3ljjidTN6lrz9ZoQ/Ly9WwYUONHTtWEydOpOgDAAAAAByCK/sAAAAAADgZo6MDAAAAAACAK4uyDwAAAACAk6HsAwAAAADgZCj7AAAAAAA4Gco+AAAAAABOhrIPAAAAAICToewDAAAAAOBkKPsAAAAAADgZyj4AAAAAAE6Gsg8AAAAAgJOh7AMAAAAA4GQo+wAAAAAAOBnKPgAAAAAAToayDwAAAACAk6HsAwAAAADgZCj7AAAAAID/af/+/YqNjZWfn598fX0VExOjlJSUS85NTExUly5d5OXlpZCQEI0fP15FRUVVG7iaM9jtdrujQwAAAAAArl0HDhzQzTffrAYNGmj06NGy2WyaM2eOzp49q6SkJDVr1qxibkpKiqKjo9WiRQvFx8crPT1dr776qm699VZt2rTJgWdRvVD2AQAAAAC/qk+fPtq9e7fS0tIUGBgoScrKylJkZKRiYmK0Zs2airm9e/dWSkqKvvvuO/n5+UmS3nvvPY0aNUqbN29WTEyMQ86humEZPwAAAADgV+3YsUM9e/asKPqSFBoaqm7dumnDhg1KTj+jhNR8zd57Uls++0y9+w+Rj69vxdz7779fPj4+WrlypSPiV0uUfQAAAADAryorK5Onp+dF424enjKbzVr0xT6dKrbo0KFDslosMte/QQu+y1dxue3CPDc3tW3bVsnJyVUdvdqi7AMAAADAnzB9+nQZDAZFRUVVGu/evbsMBsNFX7GxsQ5K+sc1a9ZMe/bskdVqrRgzlZXpy8Q9kqQz2ZmSpPN52ZIkz8Bg5ZmsWn60QLYf7xwPDQ1VZmZmFSevvij7qDJ79+7V2LFj1bJlS3l7eyssLEwDBw5UampqxRybzaZFixapX79+atCggby9vRUVFaVp06bJZDI5MD0AAABwsfT0dM2YMUPe3t6XfL1+/fpasmRJpa+nn366ilP+eQ8//LBSU1M1cuRIHT58WIcOHdK9Q+5TQe6Fcl/+47/Vf/rV1c1dklRotulYgVmS5OHhodLSUgekr55cHR0A1cfLL7+sXbt2acCAAWrdurVOnz6t2bNn68Ybb9SePXsUFRWlkpISPfjgg+rUqZPGjBmj4OBg7d69W5MnT9bnn3+uf//73zIYDI4+FQAAAECS9OSTT6pTp06yWq3Ky8u76HV/f38NGzbMAcmurDFjxujUqVOaOXOmFi9eLElqFNVOXR8Yqy/m/0tuXhc+7Kjh4SFJspjLJElmm11JOaVqWtNdJpPpkrcC4Oqg7KPKPP7441q2bJnc3NwqxgYNGqRWrVrppZde0tKlS+Xm5qZdu3apc+fOFXNGjRql8PDwisLfs2dPR8QHAAAAKtm+fbtWr16t5ORkjRs3TpKUml+mvTmlMlntyi61yGq1y1xeLnNZmXx8fByc+M+ZPn26nnzySX377bfy9/dXkmt9LZ35D0lS7YZNJEm+tetI+nk5vySZrBeW8WdlZalu3bpVnLr6Yhk/qkznzp0rFX1Jatq0qVq2bKmUQ4eVkJqvJceK9Z/aNyg1v6zi3h5JuvvuuyVJR44cqdLMAAAAwKVYrVaNGzdOf/vb39SqVStZ7dK5Mqs2nCzSqWKLck1WlVntOn40Td7ePvL19VVISIgmTZqk8vJyR8f/w2rVqqUuXbqoVatW8nAx6GjSdvnXqaug8KaSpJAmLWR0dVXG4a8rjvFwMchsNislJUVt27Z1UPLqhyv7cKgis1XHM7IU3Li5ThVbKsazTxbJz82oIRH+8q5h1OnTpyVJtWvXdlRUAAAAoMLcuXN18uRJbd26VTa7XTmlFlntF5at/ySwfrgat++ikIgWcrealJu4SdOmTVNqaqpWrFjhwPRXRu7ODUr/Nlm9H/uHjMYL15E9fP0U0bGrkjeuUo9RT8jX11cdgz21ZMkSFRUVacCAAQ5OXX0Y7PZfXD4FqpDNblf8K+9q/t/H6N4XXlf7u4ZeNKe2h4tGNK+pO2JilJSUpJMnT6pmzZpVHxYAAAD40ZkzZxQZGannnntOTzzxhFLzy9S7Zw8V5Z/VhFU7LnmMm9GguIY+mvn0OM2bN0+7d+9Wp06dqjj5H7d9+3ZNmTJFMTExCgwM1J49e7Rw4UK1iO6uwa8tlYvrz9eRM458rbkP9lFw40jdOnC4GlrO6LXXXlPXrl21efNmB55F9cIyfjjM5r3fKGHaUwpr3UE3xg2+5JxCs01PvjBVW7du1UsvvUTRBwAAgMM9//zzCggIqLhPf29OqWz/45ifNqp74oknJElbt269yimvrHr16snFxUUzZ87UI488op07d2ratGna/ukG1fFxl5vx502067Voo9HvrJGnp6fWvDJR8+bN08iRI7V69WoHnkH1wzJ+OMTp06c1vP+dcvfx09CZC2R0cbnkvH2bPtQH01/UyJEj9dBDD1VxSgAAAKCytLQ0vfvuu3r99dcrnhmf/kOBLGVlslrKdS7zB7l7+8rLv9ZFx5qsdjVo2ECSdPbs2SrN/Wc1adLkslflRzR317ECs5J+3JjQw8Wge+/qqVkP9JaRJ2k5DGUfVa6goEC9evVScWGBRs1fL7+gkEvOS9uzTStfGKtW3WI0d+7cKk4JAAAAXCwjI0M2m03jx4/X+PHjL3r9lb43qfOQeMU9Nf2i1zxcDDp+/LgkKSgo6KpnrSpGg0FNa7qraU13R0fBL1D2UaVMJpPi4uKUmpqqpxZ8JI/GzS4574dv9mvpEw+o/g1tNOGNhXJ15a8qAAAAHC8qKkofffRRpbHM4nJNnfyCTMVF6vvUdAXWD5ep6Lxc3dzk6nahALsZDeoQ5KEXHpomSbrjjjuqPDuqFzboQ5WxWq265557tHHjRq1du1YRnW/ThpNFlXYslaSc46l6529x8g0M1tiFGzSoVX0+JQQAAMA1y2a3q8VfblHB2TMVG/Qd37dLHzwXr9Z33KPABo3kbi1TZuJm7dq1S/Hx8XrnnXccnBrOjsulqDJPPPGE1q1bp7i4OJ09e1Z71q/U4cwSnS+/sJ1Juz4DVFZcpAVjB6q0MF9d739EJxK3avcJL331470+TZo0UXR0tCNPAwAAAKjEaDAo2NNVRYYLV/DNNrtqhtZXeLtOOvLFRp0/k6MaLka1aNFCc+fOVXx8vKMjoxrgyr6DpaWladKkSdq5c6fOnj2rsLAw/fWvf9WTTz4pLy8vSdKMGTO0bt06HTt2TOfPn1eDBg3Up08fTZw48bq616d79+768ssvL/v6Pw/k6lzmD3ql702XnfPAAw9o0aJFVyEdAAAA8OfY7PaLNqrrGOypJv5ubFSHKkfZd6BTp06pdevW8vf315gxYxQQEKDdu3dr0aJF6tevn9auXStJuvfeexUUFKTmzZvL19dXR44c0bx58xQcHKyUlBR5e3s7+Ez+HP5HEQAAAACuLJbxO9CSJUuUn5+vnTt3qmXLlpKk+Ph42Ww2vf/++zp37pxq1aqlNWvWXHRsdHS0+vfvr/Xr12vw4Es/o/56we6dAAAAAHBlUfYdqLCwUJJUp06dSuOhoaEyGo06WWLXxtz8iqvdHYI9FfHj1e7w8HBJUn5+fhWnBgAAAABc64yODlCdde/eXZI0cuRIpaSk6NSpU1qxYoXefvtt9RgWr3/n2XWq2KJck1U/FJXrg5ST+teO77Xliy81fvx4ubi4VPwMAAAAAAB+wj37DjZt2jTNmDFDpaWlFWO9Rz+hW0b/vdK883nZmhETVfF9/fr19X//938aOHBglWUFAAAAAFwfWMbvYOHh4eratavuvfdeBQYGatlH6/Thu6/Jxb+2Og/+W8U8T/9aGvn2apWXlSkn9Rud3LFJRUVFDkwOAAAAALhWUfYd6IMPPlB8fLxSU1NVv359SVJpVA+dKCjTp7Omqk3sPfKuGSBJcq3hpoi/dJMktegao9JbumvkkFgFBwerb9++DjsHAAAAAMC1h3v2HWjOnDlq165dRdGXJJPVrhbdYlVuKlHW999c9tiwNh0VGhqqhISEqogKAAAAALiOUPYdKDs7W1artdKYh4tBVku5JMlqsVz2WA8Xg0wmkwoKCq5qRgAAAADA9Yey70CRkZFKTk5WampqxViHYE99s/kjGYxGhUa2lLm0WObSkkrHuRkNOrfnU507d07t27ev6tgAAAAAgGscu/E70Pbt29WjRw8FBgZq7NixCgwM1PoNG/Tppk3qcPcw3TPpX8r8/hvNf6i/Wt9+p4IaNZXBYFTe918racMq1a9fX/v27VNgYKCjTwUAAAAAcA2h7DtYUlKSXnzxRSUnJ+vMmTNq1KiRhgy7X6F3xavYZtS5M3na8tYM/efAbhVkZ8pmKVd4w4bq27ePJk6cqNq1azv6FAAAAABUof3792vixIlKTEyU3W5XdHS0XnnlFbVt27ZiTklJiRYuXKi1a9fqm2++UVFRkSIiIhQfH6/4+Hi5uLg47gRQJSj71yib3a5jBWYl5ZTKZLXLw8WgjsGeauLvJqPB4Oh4AAAAABzgwIEDuvnmm9WgQQONHj1aNptNc+bM0dmzZ5WUlKRmzZpJkg4dOqTWrVvrtttuU0xMjPz8/LR582Z99NFHuv/++7V48WIHnwmuNso+AAAAAFwn+vTpo927dystLa3idt6srCxFRkYqJiZGa9askSTl5eUpOztbLVu2rHT8iBEjtHDhQqWlpSkiIqLK86PqsEEfAAAAAFwnduzYoZ49e1batys0NFTdunXThg0blJx+Rgmp+Vqb66KUGvWUml8m2y+u7959992SpCNHjlR5dlQtV0cHAAAAAAD8NmVlZfL09Lxo3M3DU2azWYu+2KfQqJsqxrNPFsnPzaghEf7yrmHU6dOnJYm9v6oBruwDAAAAwHWiWbNm2rNnj6xWa8WYqaxMXybukSSdyc6sNN9ssyvPZNXyowUylZXp9ddfV6NGjdShQ4cqzY2qR9kHAAAAgOvEww8/rNTUVI0cOVKHDx/WoUOHdO+Q+1SQmy1JKjeZLnlcodmmB0Y/rMOHD2v27NlydWWRt7Oj7AMAAADAdWLMmDF67rnntGzZMrVs2VKtWrXSkbSj6vrAWEmSm5f3JY/buvBNrVy8QFOnTlXv3r2rMjIchLIPAAAAANeR6dOnKzs7Wzt27NDBgwc1ceXnsttskqTaDZtcNH//uuX6dNYUdRv0oJ5//vmqjgsHYe0GAAAAAFxnatWqpS5dukiSDqbm62jSdvnXqaug8KaV5h3etkkfTn1MLXv00cgXX3VEVDgIV/YBAAAA4DqWu3OD0r9N1s1/HS2j8eeK95/9iVr+bLzC20Xrvn++o04hl17iD+dksNt/8dBFAAAAAMA1a/v27ZoyZYpiYmIUGBioPXv2aOHChWoR3V2DX1sqlx833juXeUqzBneX1VKuXhNeVFBNf3Wt6yWjwSBJat26tVq3bu3IU8FVxjJ+AAAAALhO1KtXTy4uLpo5c6bOnz+vRo0aadq0aRo9boLW/FCqQrNNZptd5zJ/kKmoUJK07qVnJEnzf/FzJk+eTNl3clzZBwAAAAAnYLPbdazArKScUpmsdnm4GNQx2FNN/N0qruij+qDsAwAAAADgZNigDwAAAAAAJ0PZBwAAAADAyVD2cZHhw4fLYDBc9isjI0OSNGPGDHXq1ElBQUHy8PBQ06ZNNWHCBOXm5jr4DAAAAACgeuOefVxk9+7dOnbsWKUxu92uMWPGKDw8XN9++60k6d5771VQUJCaN28uX19fHTlyRPPmzVNwcLBSUlLk7c1zPAEAAADAEXj0Hi4SHR2t6OjoSmM7d+5USUmJhg4dWjG2Zs2aSx7bv39/rV+/XoMHD77qWQEAAAAAF6Ps4zdZtmyZDAaDOvW9Vwmp+RWP8ugQ7KmIXzzKIzw8XJKUn5/vuLAAAAAAUM1R9vE/lZeXa8XKlWrSrqNS7LVlLrZUvHb6xHkZS/J1ZwMvZZw4pr///e9ycXFR9+7dHRcYAAAAAKo5yj7+p02ffqqzZ87olvhnZLZV3uLhbG62ZsRE6fEfv69fv76WLVum5s2bV31QAAAAAIAkyj5+g3mLl8rFtYZa3X7nRa95+tfSyLdXS+Vl8jn9vb7cuE5FRUUOSAkAAAAA+Am78eNXFRUVqXZwHTVq30UPvJHwq3MbeLuqUd5h3XzzzVq/fr369u1bRSkBAAAAAL9kdHQAXNs+/vhjlZWWqG3v/v9zrslqV+fOnRUaGqqEhF//YAAAAAAAcPVQ9vGrEhIS5OHloxZd7/ifcz1cLuzIbzKZVFBQcLWjAQAAAAAug7KPy8rNzdXWrVt1R1w/+Xh7V3rNXFosc2lJxfduRoM6BntqzZo1OnfunNq3b1/VcQEAAAAAP2KDPlzWihUrZLFYNHr4fcpwMyrPZK14Le+H45r/UH+1vv1OBTVqKl83Vx1MP6yEpUsVHh6uRx991IHJAQAAAKB6Y4M+XFZ0dLSOHz+uzMxMmWwGLT9aoEKzTWabXcXnzmjLWzN0Inm3Ck5nym4tV8OGDdWnTx9NnDhRtWvXdnR8AAAAAKi2KPv4zWx2u44VmJWUUyqT1S4PlwtL95v4u8loMDg6HgAAAADgR5R9AAAAAACcDBv0AQAAAADgZCj7AAAAAAA4Gco+AAAAAABOhrIPAAAAAICToewDAAAAAOBkKPsAAAAAADgZyj4AAAAAAE6Gsg8AAAAAgJOh7AMAAADANaKoqEiTJ09WbGysAgICZDAYtGjRokvOnT17tlq0aCF3d3fVq1dPjz/+uIqLi6s2MK5ZlH0AAAAAuEbk5eVpypQpOnLkiNq0aXPZec8884zGjRunqKgovfHGG7r33nv15ptv6p577qnCtLiWuTo6AAAAAADggtDQUGVlZSkkJET79u1Thw4dLpqTlZWl1157Tffdd5/ef//9ivHIyEiNGzdO69evV1xcXFXGxjWIK/sAAAAAcI1wd3dXSEjIReM2u12p+WVKSM3X1FWfyWKx6Ja+98pmt1fMGTx4sCTpgw8+qLK8uHZR9gEAAADgGlZmtWnBd/nacLJIp4otOltskiQlF9q14Lt8FZfbJEleXl6SpP379zssK64dlH0AAAAAuIbtyS5Vnskqs+3CVfyghk0kSUeTv1KeyarlRwtks9u1Y8cOSVJGRobDsuLaQdkHAAAA4LT27t2rsWPHqmXLlvL29lZYWJgGDhyo1NTUi+auXLlSnTp1Us2aNRUYGKhu3brpk08+cUDqykot9krf12vRRg2ibtKXi97UvrXLdOI/J7Rg9XqNHj1aNWrUUGlpqYOS4lpC2QcAAADgtF5++WWtWbNGt912m9544w3Fx8dr+/btuvHGG3Xo0KGKeW+++aYGDRqk2rVr66WXXtKkSZNUUFCgvn376sMPP3TgGUhWu/2isaGvLlRoZEut+cejmt7nRo0eco8GDhyodu3aycfHxwEpca0x2O2X+JsDAAAAAE4gMTFR7du3l5ubW8VYWlqaWrVqpf79+2vp0qWSLuxkX7NmTX311VcyGAySpMLCQtWrV089evTQ2rVrqzz7T7vx939xlm7qN+SSc/J+OKbzeTlq1rSpHu/aXHXr1lX9+vWVlJRUxWlxreHRewAAAACcVufOnS8aa9q0qVq2bKmUQ4eVkJovk9WunHMFqtcoQnZJhh/n+fn5ycfHR56enlWa+feoHdZEtcOaqI63qw4fPqysrCwNHz7c0bFwDWAZPwAAAIBqpchs1fGMLJV71dSpYotyTVY1vLGztn++RUOff0WH047ru+++0yOPPKKCggI9+uijDs3rYjD86utuRoPa13bX008/LS8vL40ZM6aKkuFaxpV9XGT69Ol6/vnn1bJly0r3MUkXlkE9/fTTOnDggPz8/DRw4EDNmDGD+4IAAABwXbDZ7Zrwr/eUn52l20Y/UzEe9/QMFeef1Qcz/q4PZvxdklS7dm19/vnnio6OrtKMs2fPVn5+vjIzMyVJaTs362z2hR32Ow8aJQ9fP62f+ZwsZWUKbRYlT1m18t8fKykpSYsXL1ZYWFiV5sW1iXv2UUl6erqaNWsmg8Gg8PDwSmU/JSVF0dHRatGiheLj45Wenq5XX31Vt956qzZt2uTA1AAAAMBvsynpoO65rYuCGzfX6PnrZXRxkSSVlRTp0zemyGwqUVTXO9TCs1zL3pmt3Nxc7dixQxEREVWWMTw8XCdPnrzka09v2K9adcO0f91yJS57V2dO/UdurkZ17NhREydO1K233lplOXFto+yjksGDBys3N1dWq1V5eXmVyn7v3r2VkpKi7777Tn5+fpKk9957T6NGjdLmzZsVExPjqNgAAADA/3T69Gm16RitsvJyPbz4U/kFhVS8tnDsIBldXPXAGwmSpAberupV26amTZuqZ8+eWrFihaNiS7qwIuFYgVlJOaUyWe3ycDGoY7Cnmvi7yfg/lvmjeuKefVTYvn27Vq9erddff71iLDW/TAmp+Zq996S2fPaZevcfIh9f34rX77//fvn4+GjlypUOSAwAAAD8NgUFBerVq5eKCwv04OwVlYr+2fQTSk38t1p0i60YM1ntCggIUJcuXbRr1y5HRK7EaDCoaU13DY2sqZEtamloZE01relO0cdlUfYhSbJarRo3bpz+9re/qVWrVrLapXNlVm04WaRTxRYdOnRIVotF5vo3aMF3+Sout0mS3Nzc1LZtWyUnJzv4DAAAAIBLM5lMiouLU2pqqp585wPVadys0uvnz+RKkuw2a8WYh8uFEl1eXi6LxVJ1YYErhLIPSdLcuXN18uRJTZ06VTa7XTmlFlntktl24S6P83nZkiTPwGDlmaxafrRAth/vAAkNDa3YPAQAAAC4llitVg0aNEi7d+/WqlWr9Nc7usnNWPlqeGBYIxmMRh3c8rHsdrvcjBeWyKenp2vHjh1q166dg9IDfxxlHzpz5oxeeOEFTZo0SUFBQTpaYJbVVnkrh3KTSZLk6uYuSSo023SswCxJ8vDwUGlpadWGrkLDhw+XwWC47FdGxoWdUW02m+bOnau2bdvKx8dHderUUa9evZSYmOjgMwAAAKi+nnjiCa1bt069evXS2bNntWf9Sh3+dLWSP1ml5E9WSZJ8atVW+zv/quP7dum90fdo36r5WjXn/xQdHa3S0lI9++yzDj4L4Pfj0XvQ888/r4CAAI0bN06StDenVLb/mlPDw0OSZDGXSbpwxT8pp1RNa7rLZDLJ09OzKiNXqdGjR6tnz56Vxux2u8aMGaPw8HDVq1dPkvTUU0/ptdde07Bhw/Twww8rPz9f77zzjrp166Zdu3apY8eOjogPAABQraWkpEiS1q9fr/Xr11/0ers+AyRJdz47U/WbRWnfxwla/8ZUrZfUoUMHvf/+++ratWsVJgauDMp+NZeWlqZ3331Xr7/+esVS/PQfCmQpK5PVUq5zmT/I3dtXvrXrSPp5Ob90YdMSScrKylLdunWrPnwViY6OvujZqjt37lRJSYmGDh0qSbJYLHr77bfVv39/LVmypGLegAED1LhxYyUkJFD2AQAAHGDbtm2XHL94d3tXDXxmgprMeJpN7+AUKPvVXEZGhmw2m8aPH6/x48df9PorfW9S5yHxun3MMzK6uirj8NdqHXOXpAublpjNZqWkpGjgwIFVnNyxli1bJoPBoE5971VCar4Ki0tUWloq95q1ZbPbK/4PIjg4WEaj0alXPgAAAFyPftrdvmlNd0dHAa4Kyn41FxUVpY8++qjSWGZxuaZOfkGm4iL1fWq6AuuHy8PXTxEduyp54yr1GPWEfH191THYU0uWLFFRUZEGDBjgoDOoeuXl5VqxcqWatOuoFHttmYstktzUIOomrUpYIo+m7fTkvTEyFxdq6tSpqlWrluLj4x0dGwAAAEA1YrDb7fb/PQ3Vic1uV4u/3KKCs2c0YdWOivGMI19r7oN9FNw4UrcOHK6GljN67bXX1LVrV23evNmBiavWuvXrdWe/frrz2VfUacCDFeN5PxzX8r+PUuZ3ByvGGjdurI0bN6pZs2aX+lEAAAAAcFWwGz8uYjQYFOzpKheDKj2WpF6LNhr9zhp5enpqzSsTNW/ePI0cOVKrV692YNqqN2/xUrm41lCr2++sNO7u7aM6TZqr08ARGv7aYr346ixZLBbdddddysvLc1BaAAAAANURV/ZxWRdvWnLheaNN/N2q7aYlRUVFqh1cR43ad9EDbyRUjFstFr351x5qfFNn9XvmJUlSA29XdTTkqmXLlnrsscf08ssvOyo2AAAAgGqGe/ZxWWxacrGPP/5YZaUlatu7f6XxEwd2K/voEfV5fErFmMlqV9MWTdWiRQvt2rWrqqMCAAAAqMZYxg/8DgkJCfLw8lGLrndUGj9/NleSZLNaK8Y8XC6sfigvL5fFYqm6kAAAAACqPco+8Bvl5uZq69atuiOun3y8vSu9FhTWRJJ0cMuFJxu4GS/c8nDgwAF9//33ateuXZXnBQAAAFB9sYwf+I1WrFghi8Wi0cPvU4abUXmmn6/i17uhjSI6ddeB9StUVlSktrf0UL49X7Nnz5anp6cmTJjguOAAAAAAqh026AN+o+joaB0/flyZmZky2QxafrRAhWabzLYL/wmVm0qVuHSOvt78kfIzf5Cbm5tuueUWTZ06VW3btnVseAAAAADVCmUf+IN4WgEAAACAaxVlHwAAAAAAJ8MGfQAAAAAAOBnKPgAAAAAAToayDwAAAACAk6HsAwAAAADgZCj7AAAAAAA4Gco+AAAAAABOhrIPAAAAAICToewDAAAAAOBkKPsAAAAAADgZyj4AAAAAAE6Gsg8AAAAAgJOh7AMAAAAA4GQo+wAAAAAAOBnKPgAAAAAAToayDwAAAACAk6HsAwAAAADgZCj7AAAAAAA4Gco+AAAAAABOhrIPAAAAAICToewDAAAAuKxt27bJYDBc8mvPnj2OjgfgMlwdHQAAAADAtW/8+PHq0KFDpbGIiAgHpQHwv1D28Zvs3btXixcv1hdffKETJ04oMDBQnTp10rRp0xQZGVlp7uzZs/XWW2/p+PHjql27tgYNGqSpU6fK29vbQekBAADwZ91yyy3q37+/o2MA+I0o+/hNXn75Ze3atUsDBgxQ69atdfr0ac2ePVs33nij9uzZo6ioKEnSM888o1deeUX9+/fXo48+qsOHD+vNN9/Ut99+q82bNzv4LAAAAPBHZRaX670Dp2Sr4SFv9xrqEOypCH83GQ0GR0cDcAkGu91ud3QIXPsSExPVvn17ubm5VYylpaWpVatW6t+/v5YuXaqsrCyFhYVpyJAhev/99yvmzZ49W+PGjdO6desUFxfniPgAAAD4gzZt/bd6336b3L28VVZSLKOLi8LbdVK/x15Uy3Y3aUiEv7xrsBUYcK3hv0r8Jp07d65U9CWpadOmatmypVIOHVZCar6mrvpMFotFt/S9V7ZffIY0ePBgSdIHH3xQpZkBAADw59jsdn2ZY1bUbX3V98npuu9fS3T7w8/q9NEjmj0iTgdTkrX8aEGlf/sBuDZQ9vGHFZmtOp6RpXKvmjpVbNHZYpMkKbnQrgXf5au43CZJ8vLykiTt37/fYVkBAADw+x0tMCu4ZQcNnblQ7e8aqhu6xar7g4/qoUWbZDAY9Omb01RotulYgdnRUQH8F8o+/hCb3a4J/3pP+dlZanX7nZKkoIZNJElHk79Snsla8Snvjh07JEkZGRkOywsAAIDfb29Oqcy2i6/a1w5rrBbdYnV83y6Zyi1KyimtskxFRUWaPHmyYmNjFRAQIIPBoEWLFl1yrs1m09tvv622bdvK09NTgYGB6tGjh77++us/lWH//v2KjY2Vn5+ffH19FRMTo5SUlIvmzZgxQ506dVJQUJA8PDzUtGlTTZgwQbm5uX/q/YHfgg368Ids3vuNEqY9pbDWHXRj3IVl+vVatFGDqJv05aI35RcUquYdb9GCQ6c07anxqlGjhkpLq+7/BAAAAPDnmayXX55fs049WcvNMpeWyOTtdtl5V1peXp6mTJmisLAwtWnTRtu2bbvs3BEjRighIUH333+/xo4dq+LiYiUnJysnJ+cPv/+BAwfUpUsXNWjQQJMnT5bNZtOcOXPUrVs3JSUlqVmzZhVz9+/fr7Zt22rw4MHy9fXVkSNHNG/ePH3yySdKSUnhaVW4qij7+N1Onz6t4f3vlLuPn4bOXCCji0vFa0NfXajlf/+b1vzjUUmS0cVFTzz+uL788kt9//33jooMAACAP8DD5fI77Z/NOCFXdw+5eXn/6rwrLTQ0VFlZWQoJCdG+ffvUoUOHS85buXKlFi9erA8//FB33333FXv/SZMmydPTU7t371ZgYKAkadiwYYqMjNRzzz2nNWvWVMz95e9/Eh0drf79+2v9+vUVe1sBVwNlH79LQUGBevXqpeLCAo2av15+QSGVXvcPDtWYBZ8o74djOp+Xo2ZNm+rxrs1Vt25dRUZGOig1AAAA/ogOwZ46lnxCbjUDK41npR7SkS83K/Lm2+Th6qKOwZ5Vlsnd3V0hIZX/DWqz25WaX6a9OaUyWe3ycDFo5sz/U8eOHXX33XfLZrOptLT0ilxJ37Fjh2JjYyuKvnThA4hu3bppw4YNSk4/o8MlLhU5/vsRheHh4ZKk/Pz8P50F+DXcs4/fzGQyKS4uTqmpqXrynQ9Up3Gzy86tHdZEjW6MVp2QEB0+fFhZWVnq2bNnFab9fdLS0jR48GDVr19fXl5eat68uaZMmaKSkpJK8xITE9WlSxd5eXkpJCRE48ePV1FRkYNSAwAAXF0R/m5a9uwoLRo/RF+895qSPlyiDa8+r7eH91END0/FjpskPzejmvhX3TL+S9mRVaINJ4t0qtiiXJNVadnndHD/XnlHtNZTzzwrf39/+fj4qHHjxlq5cuWfeq+ysjJ5el784Yabh6fMZrMWfbGvIsepYovWnzivN3Yf0/FTmdqxY4fGjx8vFxcXde/e/U/lAP4XruzjN7FarRo0aJB2796ttWvXKqJzN204WXTJDVt+4mY0qH1tdz1x/9Py8vLSmDFjqjDxb3fq1Cl17NhR/v7+Gjt2rAICArR7925NnjxZ+/fv19q1ayVJKSkpuu2229SiRQu99tprSk9P16uvvqq0tDRt2rTJwWcBAABw5RkNBo0cdK/eXvi+dibMlan4vLxrBqpljz7qNeYpNY6I0JAI/4qr1lXtp0f+FZXbKv279Gz6f2S32/XVxg+138VVL738smrVrKk33nhDgwcPlp+fn2JjY//QezZr1kx79uyR1WqVy4+3s5rKyvRl4h5J0pnsTIVG3fRzltxszYiJ0uM/fl+/fn0tW7ZMzZs3/0PvD/xWlH38Jk888YTWrVunuLg4nT17VnvWr9ThzBKd//Hxeu36DJAkrZ/5nCxlZQptFiVPWbXy3x8rKSlJixcvVlhYmCNP4bKWLFmi/Px87dy5Uy1btpQkxcfHy2az6f3339e5c+dUq1YtPffcc6pVq5a2bdsmPz8/SReWYY0aNUpbtmxRTEyMI08DAADgqnjqsUf1xITxOlZgVtIvlsl3DPZUk18sT3eEU0XllxwvKymWJJXkn9X4JZsV07ebmtZ0V79+/dSoUSNNmzbtD5f9hx9+WA899JBGjhypp59+WjabTc+8MEUFudmSpHKTqdJ8T/9aGvn2aqm8TD6nv9eXG9exMhRVgrKP3+SnR4msX79e69evv+j1n8p+3WatlLjsXX29aY3cXI3q2LGjPv/8c916661VGfd3KSwslCTVqVOn0nhISIiMRqM+PFmi0qOF2vLZZxr+0Hj5+PpWzLn//vv12GOPaeXKlZR9AADgtIwGg5rWdFfTmu6OjlLJ4bNllxyv4XFhmX2teg0V2vJGJeWUqmlNd/n4+CguLk5Lly6VxWKRq+vvr0NjxozRqVOnNHPmTC1evFiS1Ciqnbo+MFZfzP+X3Lwq7wvgWsNNEX/pJklq4N1bA/vcoZtvvlnBwcHq27fv735/4Lfinn38Jtu2bZPdbr/oy2qzKfWcSQ28XRXk4aK7htynbV/tU0nxeRUWFmrr1q3XdNGXVHG/1MiRI5WSkqJTp05pccJyvTnnbXUZMkq5dncdOnRIVotF5vo3aMF3+Sr+cUWDm5ub2rZtq+TkZAeeAQAAQPV0uVtKf9pE2icgSFLlRwgGBwervLxcxcXFf/h9p0+fruzsbO3YsUMHDx7UxJWfy2678O/D2g2bXPY4k9Wuzp07KzQ0VAkJCX/4/YHfgiv7+FOu1U95f4/Y2FhNnTpVM2bM0Lp16yrGbx35mGIeeU6SdD7vwrIsz8Bg5ZmsWn60QCOa15TRYFBoaKh27NjhkOwAAADVmZvx0rcQ+AWFyLd2sApzsyRVfoRgZmamPDw85PuL1Zp/RK1atdSlSxdJ0sHUfB1N2i7/OnUVFN70ssf8lMNkMqmgoOBPvT/wv3BlH9CFe++7du2qd999V2++/4E63jVU2xa8rsQP3pP0871Xrm4XPtQoNNt0rMAsSfLw8FBpaaljggMAAFRjNwRc/oJTq9vvUsHpDP3nqy8rHg2Yl5entWvXqkePHjIar1wVyt25QenfJuvmv46u+Lnm0mKZS39+spOb8cI+B2vWrNG5c+fUvn37K/b+wKVwZR/V3gcffKD4+Hilpqaqfv36SkjN191Rt8lqterTWVPVJvYe1fDwkCRZzBfuCzPb7BX3fplMpks+fgUAAABXx+zZs5Wfn6+MjAxJ0pHtm1WQkylJ6jxolDx8/dR9xKP65rO1ev/J4QrJelw1a9bU3LlzVV5erhkzZvzh996+fbumTJmimJgYBQYGas+ePVq4cKGiutymzkPiK+bl/XBc8x/qr9a336mgRk3l6+aqg+mHlbB0qcLDw/Xoo4/+uT8E4H+g7KPamzNnjtq1a6f69etL+vmerhbdYrV//QfK+v4b+da+sHnfT8v5fzkvKytLdevWreLUAAAA1derr76qkydPVnz/7b8/0bf//kSS1K73AHn4+ikwqI6eTdik3XOm6PXXX1d5ebmio6O1dOlStWnT5g+/d7169eTi4qKZM2fq/PnzFbv7jx43QWt+KFWh+cJjAP2D6yqqR18d37dTyRtWym4tV8OGDTV27FhNnDhRgYGBf/rPAfg1lH1Ue9nZ2apVq1bF9z/dS2W1lP/4q0X1b2gho6urMg5/rdYxd1XMM5vNSklJ0cCBA6s8NwAAQHV14sSJSt/b7PZLPxqwdVsZYz66ou/dpEkTbd68+ZKvjWju/nMOj2CN/+cb18QjClE9UfZR7UVGRmrLli1KTU1VZGSkOgR7Kvtkkb7e/JEMRqNCI1vKw9dPER27KnnjKvUY9YR8fX3VMdhTS5YsUVFRkQYMGODo0wAAAKi2rpVNo6+VHIAkGex2+6WfVwFUE9u3b1ePHj0UGBiosWPHKiAgQHNXfKxDO7aqw93DdM+kf0mSMo58rbkP9lFw40jdOnC4GlrO6LXXXlPXrl0v++kuAAAAADgCZR+QlJSUpBdffFHJyck6c+aMwsMbqWXvgep831hZjS4V89K//kqfzpqijCMH5evrq4EDB+qf//znn350CwAAAABcSZR94DIue+8X91wBAAAAuMZR9gEAAAAAcDJGRwcAAAAAAABXFmUfAAAAAAAnQ9kHAAAAAMDJUPYBAAAAAHAylH0AAAAAAJwMZR8AAAAAACdD2QcAAAAAwMlQ9gEAAAAAcDKUfQAAAAAAnAxlHwAAAAAAJ0PZBwAAAADAyVD2AQAAAABwMpR9wIkdOHBA/fr1U0BAgLy8vBQVFaVZs2ZVvD5jxgx16tRJQUFB8vDwUNOmTTVhwgTl5uY6MDUAALhatm3bJoPBcMmvPXv2VMyz2WyaO3eu2rZtKx8fH9WpU0e9evVSYmKiA9MD+D1cHR0AwNWxZcsWxcXFqV27dpo0aZJ8fHx07NgxpaenV8zZv3+/2rZtq8GDB8vX11dHjhzRvHnz9MknnyglJUXe3t4OPAMAAHC1jB8/Xh06dKg0FhERUfH7p556Sq+99pqGDRumhx9+WPn5+XrnnXfUrVs37dq1Sx07dqzqyAB+J4Pdbrc7OgSAK6uwsFCRkZHq3LmzVv9/e/cdHlWdvn/8PZOQTEihpdBLgADSBfKTUEIzIEVY6aLSpC4irPUrAkpTwUUBFwEFRAhIs4EKyC5IlxKQNaAhIB1CgPRkMpOZ+f0RGYnBXVxDJhnv13XlkpzzGXOfXOsyz5zPeZ4NGzAaczfx2B0O4lMsHLqWhdnmwORhoEWwD7VKeWE0GADYuHEjffr0Yc2aNQwYMMCVlyEiIiIFbOfOnbRv357169fTp08f5/Hb3yNkZFt5Krwa7Ts/xJefbHC+R/jpp58IDQ1l/PjxzJs3z1WXICJ3SXf2RdzQ6tWrSUhIYObMmRiNRjIyMrB7erP2TBqpFjsW+y+f8SWcSyfAy8jAWqXwLWGkevXqACQnJ7smvIiIiBSKtLQ0fHx8yHYYWROf4nyPYDWbsZizSDeVYdkPyc73CMHBwRiNRnx8fFwdXUTugp7ZF3FD27dvJyAggEuXLlGnTh38/PwIKlOa96f8jYysrDxrs212zl25xqL9cXyzaxfjx4/Hw8ODdu3auSa8iIiI3HNDhw4lICAAk8lEs9aRHIs54rwZUMLkQ5UGzTj0+Rq+/ngt7+yK5dh33zFkyBDKlCnDyJEjXZxeRO6Gin2ROzhy5AhdunQhICAAf39/oqKiOHbsWJ41Z8+e/c0GNwaDgREjRrgmPHDq1ClycnLo2bMnnTt3ZsGHH9Gi16N8u+EDNrwyPs/a9BvXmNGxLs9G1qNdZCTnz59n9erV1K1b10XpRURE5F7x8vKid+/ezJs3j88++4wJk17hQtwJFg/vweUfjjvX9ZuxkMBqtVj38hhe7NSIpk2aEBMTw969ewkNDXXhFYjI3dI2fpFfiYmJoXXr1lSpUoWpU6dit9tZuHAhkZGRHDx4kDp16gAQFBTEypUr871+y5YtREdHExUVVdjRndLT08nMzGT06NHMnz+f6LhkujXoiMVi4eDGFTw45gUCq9YEwKdUGYa/uwFrdjYZZ2I5vetL0tPTXZZdRERE7p2IiAgiIiKc36fVbcuY+6OYP6AdWxbMYNg/1gHg7etHSM26VG3UnFrhbfFMu86/ls+nV69e7N69m8DAQFddgojcJTXoE/mVbt26sX//fk6dOkW5cuUAuHLlCmFhYURFRbFx48b/+PpOnTpx6NAhEhISMJlMhRE5nwYNGhAbG8s333xD27ZtWXoyiUSzjZ+O7GPJiJ70nfYO93fvn+91QSYP6iWdpFWrVmzatInu3bu7IL2IiIgUllvvEdb830hi//UF0/adx+FwsODRDoQ2i+DhF14Hct8jtPW8Tv369Zk4cSJvvPGGi5OLyH+jbfwiv7J79246derkLPQBKlSoQGRkJJs3b+boxRtExyWz9GQS0XHJxCVnY//5M7MrV66wY8cOHnnkEZcV+gAVK1YEICQkBACTR24XXd+yQQBkpSbf8XUmDwMRERFUqFCB6Ojoex9UREREXOrWe4TSIZWwWS1YsjI5G7OfhPiT1Ivskmdd7dq1qVevHnv37nVVXBH5HVTsi/xKdnb2HbvMepl8sFgsfLDjMBcyckg027iQkcPmc+ks+yGZDKudjz76CLvdzqBBg1yQ/BfNmjUD4NKlSwC0CPbBy2ggLfEqAL5l8m+98zIaCA/OvW6z2UxKSkohpRURERFXufUe4eals3h6m/Aq6UvazUQA7DYbkPc9gtVqJScnx2V5ReTuqdgX+ZU6depw4MABbD//BQdgzs7mm30HALiRcDnPeovdwXWzjTXxKURHR1OhQgU6dOhQqJl/rV+/fgAsXboUgFqlvAjwMnLo01UYPT0Jbd4KS1YGlqxM52sCvIzULOXFxo0bSUpKonnz5i7JLiIiIvdOYmJinu9rlfIi5cwJTn6zldoPtMNoNBL0c1+f49s+AX55jxATE8OPP/5I06ZNCz23iPx+atAn8itjx45lzJgxDB8+nOeffx673c4LU6aRkpgAgNVsvuPrTp+K48iRI0ycOBGj0bWfozVt2pRhw4axbNkycnJyiIyMZPu/dvDdlo/pOGwCAUHlufzjv1k6pg9NonpRtVYYLUJ8GT77CKtWraJ69eo8/fTTLr0GERERKXj9+/fHx8eHiIgIgoODOXHiBEuWLMHLx4ceT08BoNJ9jan1QDtiNq3FkZXB4F4P8epHCSxYsAAfHx8mTJjg2osQkbuiBn0idzBp0iTmzJmD1WoFoEaDplT/f+3YsfQtHvv7Cuq375rvNV+/+wb/eu9NDh8+7NxG70pWq5VZs2axfPlyLl++TLVq1Rg7dizdh47l4LUsEq9f5/P5Mzh39AAJly5itVqpVq0a3bp1Y9KkSeqyKyIi4obmz59PdHQ08fHxpKamEhQURMeOHZk8ZQqGoKocvJaF2ebAaDXz3dpFfP3pBn766Se8vLxo06YN06dPp0mTJq6+DBG5Cyr2RX5DUlISsbGxlCpVioOelVk151V2Lp/HhA17CAmtk2/9mz3DKeHpyZUzcS5IKyIiIiIi8gs9sy/yG8qUKUPr1q1p2LAhJg8D8Qd3USqkIkHVa+dbe/7fR7hx4Sda9ejrgqTuITY2lr59+xIaGkrJkiUJDAykbdu2bNq0Kc86g8Hwm18PPvigi9KLiIiIiBQtemZf5C4k7tnMxdijdJ346h2fx/9uy0YAxgxxbRf+4uzcuXOkpaUxePBgKlasSGZmJhs3buThhx9m8eLFjBw5EoCVK1fme+3hw4eZN28eUVFRhR1bRERERKRI0jZ+kV/ZtWsX06ZNIyoqinLlynHgwAGWL19OvZbtGDB3FR6eeT8js9tsvNalIcGVq3Hq6EGMBoOLkrsfm81Gs2bNMJvNfH7gOw79/ByhycNAi2AfapXywmgw8OSTT7Js2TLOnz9P5cqVXR1bRERERMTldGdf5FcqVaqEh4cHc+bMIS0tjRo1ajBjxgxGPTWBjeezSLXYsdh/+Yzs7KFdpN9IZOrLL6vQL2AeHh5UrFSZvd8eYvO59Dy/94Rz6QR4GXmkiomNGzcSGRmpQl9ERERE5Gcq9kV+pWbNmmzduvWO54bV9eZ0isXZqdbkYaD3wIdZNNquQr+AZGRkkJWVRUpKCp999hlbt26hYVSvPIU+gMXu4LrZxsvLN5CcnMygQXqEQkRERETkFm3jF5EiZfTo0SxevBgAo9FIgw7d6fXy3/EJKH3H9WueH8YPu7eRcPUqpUvfeY2IiIiIyJ+N7uyLSJEyYcIE+vTpw+XLl3lreTRWm40cq+WOa83paZzY/TVN2j6oQl9ERERE5DYavSciRUrdunXp1KkTTzzxBOMWfkR2VgYfTniMO21C+v6fm8jJNtOiWx8XJBURERERKbpU7ItIkWXyMNCwYw8uxh7l+rnT+c4f+2ojJr8Awjt2cUE6EREREZGiS8W+iBRZLYJ9sFvMAJjTU/OcS028ypnDe2jUqQetKpd2QToRERERkaJLxb6IFAnXrl3Ld6xaSQNHv1hHCZMPwaFhec4d3/opDrud1g/3pWYpr8KKKSIiIiJSLKjYF5EiYdSoUXTs2JFXX32V999/nxkzZtCkcWPOn/iOXuNfwt/PP8/677ZsoHRwBV55tJvGHoqISB6nTp1iwIABVK5cmZIlS1K3bl2mTZtGZmamc43dbmfRokU0adIEPz8/QkJCeOihh9i3b58Lk4uIFByN3hORIuGjjz5i6dKl/Pvf/+bGjRv4+/vTrFkznnrqKbr36MHpFAsHr2Vhtjm4eS6e5x8KZ+LEicydO9fV0UVEpAi5cOECjRo1olSpUowePZqyZcuyf/9+PvjgAx5++GE+++wzAJ555hnmzp3LY489Rps2bUhOTmbx4sWcP3+evXv3Eh4e7uIrERH5Y1Tsi4iIiIjbmDVrFpMmTeL777+nfv36zuNPDB7Myg8/ZPHBn8Dky1Ph1Wjf+SG+/GSDc4fYTz/9RGhoKOPHj2fevHmuugQRkQKhbfwiIiIi4jZSU3MbuoaEhDiPZVjtJHqVwWA0ctVqICHdjMWcRbqpDMt+SCbDagcgODgYo9GIj4+PS7KLiBQkFfsiIiIi4jbatWsHwPDhwzl27Bjnzp/nbwuWs+OjZUQMGIGXjy8lTD5UadCMQ5+v4euP1/LOrliOffcdQ4YMoUyZMowcOdK1FyEiUgC0jV9ERERE3MqMGTOYNWsWWVlZzmPth08k6q8vOb+/fv4Ma14cweUfjjuPhYaG8uWXX1KnTp1CzSsici94ujqAiIiIiEhBql69Om3btqV3797EZpvYu30LO5e9jV+5YCIGPAmAt68fITXrUrVRc2qFt8Uz7Tr/Wj6fXr16sXv3bgIDA118FSIif4y28YuIiIiI2/joo48YOXIk77//PiNGjKBhx+70njqP+7v3Z8v86WQk38SWk8PSMX0w+fnT88U3qN+hG637D2P79u2cPn2aOXPmuPoyCtSRI0fo0qULAQEB+Pv7ExUVxbFjx/Kt0zhCEfeiYl9ERESkkO3cuRODwXDHrwMHDjjXWa1WXn31VUJDQ/H29iY0NJQZM2aQk5PjwvRF28KFC2natCmVK1cGwOSR22m/XmQXrOZMrvz4b87G7Cch/iT1Irs4X2fyMFC7dm3q1avH3r17XZL9XoiJiaF169acOXOGqVOnMmXKFE6dOkVkZCQ//vhjnrXPPfccY8aMoWHDhsydO5dnnnmGuLg4IiMjOXjwoIuuQET+V9rGLyIiIuIi48ePp0WLFnmO1apVy/nnxx57jPXr1zNs2DCaN2/OgQMHmDx5MufPn2fJkiWFHbdYSEhIoEyZMs7vWwT7kHAuHVuOFQBbTg5ZaSkA2G02ALyMBsKDczvwW61Wt/owZfLkyfj4+LB//37KlSsH5P7vKiwsjJdeeomNGzcCkJOTw7vvvkufPn1YuXKl8/V9+/YlNDSU6OhowsPDXXINIvK/UbEvIiIi4iJt2rShT58+dzx36NAh1q1bx+TJk5k2bRoAo0ePJjAwkLlz5zJu3DgaNWpUmHGLhbCwMLZt20ZcXBxhYWHUKuVFgJeR77Z+gsFopEJYfdISEwA4vu0T6rTqSICXkZqlvIiJieHHH390q278u3fvpkuXLs5CH6BChQpERkayefNmjl68wYlMD1IzMsnKysK7dCB2hwOjIXdHhMYRihRf2sYvIiIi4kJpaWl57iTbHQ7ikrN5++NtAPg90JW45GzsPw9QGjBgAA6Hg7Vr17okb1H33HPPYbPZaNOmDdOnT2fRu++y/m+PcmLHl4T3fJSAoPJUuq8xtR5oR8ymtax7figZ/1rDq6+8QqdOnfDx8WHChAmuvowCk52dfcdC3cvkg8Vi4YMdh7mQkUMKXlRp0Iz10SsZOec9fjh9luPHj2scoUgxpmJfRERExEWGDh1KQEAAJpOJ9u3bs/vAQZb9kMzmc+ncSM8dG3fdXoLN59JZ9kMyGVY7JUuWBHKbrkl+bdu2Zd++fTRr1oyFCxcyYcIEzp45w4wZM1i+ZBFVfD0JMnnw0uLVPP3SFLIvn+H/nnuWefPm0apVK/bs2eNWo/fq1KnDgQMHsP38yAKAOTubb/bl9oa4kXDZebzfjIUEVqvJ0hdGUa9WDRo3bkxMTAx79+4lNDS00LOLyB+jbfwiIiIihczLy4vevXvTtWtXAgMDOXHiBG+++SYd20UyevkXVKzbiMBquc/unzt2kLKVqnHdbGNNfArWXbsAuHTpkisvoUgLDw/nyy+/vOO5erdP1Gv0Ksx8tXBCucjYsWMZM2YMw4cP5/nnn8dut/PClGmk/Pwog9Vsdq69fRxh3QciqW5PYdn8NzWOUKSYMjgcP+8JExERERGX+TrmBN1aNad60wcY9o91WLPNzH0kghyLmZ4vzqZSvcZciY1h0xsvkJ6aQrVq1YiPj3d1bCkGJk2axJw5c7Bac5sU1mjQlOr/rx07lr7FY39fQf32XbHl5LDg0Q6ENovg4RdeB6CKryfhhkTq16/PxIkTeeONN1x5GSLyO2kbv4iIiEgRcM2vIvUiu3Dm8F7sNhslvE0Mmb+akqXKEv3cUGZ3v581k/9KjzHPUbZsWfz8/FwdWYqJmTNnkpCQwO7duzl+/DiT1v0Th90OQGC1mgB3HEdotjncchyhyJ+FtvGLiIiIFAFmm4PSIZWwWS1YsjIx+fkTUrMuE9bv5tqZH8lKTSY4tA4VSvkS/dpLREZGujqyFCNlypShdevWAByPSyb+4C5KhVQkqHptANJuJgK/jCMEMHnkduR3t3GEIn8WurMvIiIiUgSYPAzcvHQWT28TXiV9nccNBgMhNetSvekDlCxVhjOH92C32+nUqZML00pxlrhnMxdjj9Lq0VEYjbnlQFDV3Dv8x7d9AoCX0UB4sI9zHGHTpk1dlldE/je6sy8iIiJSyBITEwkKCspzzD/h7SOFJgAAKHZJREFUFCe/2UpYq47OAuzXDBYzX/3jdSpUqMDAgQMLI6oUc7t27WLatGlERUVRrlw5Dhw4wPLly2nQuiMRA38Zp3f7OMLs9HSatOlAsiOZd955x+3GEYr8WahBn4iIiEgh69ChAz4+PkRERBAcHMyJEydYsmQJDg9PRi//iuDQMABWvzAc/8DyhITWwZyRxrHPV3Pz0jm++OILOnbs6OKrkOLg9OnTjB07lpiYGNLS0qhRowaDBw9m1FMT2Hg+i1SLHYs9txywmrPYt2oh3239hOTL5/Hy8qJNmzZMnz6dJk2auPZCROR3U7EvIiIiUsjmz59PdHQ08fHxpKamEhQURMeOHXnupckcJMhZgH3zwQKOfL6GpCsX8PI20b5tG2ZMn6bCy8UOHTrEihUr2LFjB2fPnqVcuXI88MADzJgxg7CwsDxr7XY7ixcvZvHixfz444+ULFmSxo0b89Zbb9G4cWMXXcHP2RwOTqdYOHgtC7PNgckjd+t+zVJeGA0Gl2YTkT9Oxb6IiIhIEaICrOjr06cPe/fupW/fvjRq1IirV6/yzjvvkJ6ezoEDB2jQoIFz7ZAhQ4iOjuaJJ56gZcuWZGRkcPToUQYNGsSDDz7owqsQEXenYl9ERERE5HfYt28fzZs3x8vLy3ns1KlTNGzYkD59+rBq1SoA1q1bR//+/fn444/5y1/+4qq4IvInpWJfRERERKQANGvWjGybg/9b9y/MNgevDYzC19PA0UPfgsNBVlYWvr6+//1fJCJSADR6T0RERETkD0q32Dhz6QrWkqW5kJHDhevJnDl+BP9ajeg+6hlKlSqFn58foaGhrFu3ztVxReRPQMW+yJ/ckCFDMBgMv/l16dIl59p9+/bRunVrSpYsSfny5Rk/fjzp6ekuTC8iIuJ6doeDCW+9T3LCFRo+2BOAmxd/wuFwcHTrJ+z+eBU9//YKK1etIigoiAEDBrBlyxbXhhYRt6dt/CJ/cvv37+f06dN5jjkcDkaPHk316tWJjY0F4NixY7Rs2ZJ69eoxcuRILl68yJtvvkn79u356quvXBFdRESkSPjq4HEe6dia4NC6jFq6CaOHBz/F7GfJkw8DMGbFFmo1bk6Pan5U8LRSo0YN6tSpw549e1ycXETcmaerA4iIa7Vs2ZKWLVvmObZnzx4yMzMZNGiQ89hLL71EmTJl2LlzJwEBAQBUr16dESNGsG3bNqKiogo196+lp6czZ84cvv32Ww4ePEhSUhLLly9nyJAhzjV2u50PP/yQjz/+mKNHj3Lz5k1q1KjBgAEDePbZZzGZTK67ABERKZauXr3KkD498fYLYNCcZRg9PAAoYfIBoEylalRt2AyL3cHBa1kMCitNjx49WLVqFTk5OXh66u24iNwb2sYvIvmsXr0ag8HAA917Ex2XzDuHzrHt66/p2mcgfv7+znVPPPEEfn5+ReLZw+vXrzNt2jROnjz5m3OLMzMzGTp0KImJiYwePZq3336b8PBwpk6dykMPPURBbXQ6dOgQ48aNo379+vj6+lK1alX69etHXFzcb77GarVy3333YTAYePPNNwskh4iI3FspKSk89NBDZKSmMPSdtQQElXeeu/Vnv7JBzmNmW+7fM8HBwVitVjIyMgo3sIj8qeijRBHJw2q1snbdOmo2DeeYIxBLRg5nv/8eW04Olsr3seyHZAbWKoVvCSNeXl40adKEo0ePujo2FSpU4MqVK5QvX57Dhw/TokWLfGu8vLzYu3cvERERzmMjRoygevXqTJ06lX/+85906tTpD2d544037jh/+f777883f/mWBQsWcP78+T/8s0VEpHCYzWZ69OhBXFwczy37BFNonTznA4LK4x8YTGriFecxk4cBgMuXL2MymfC/7QN0EZGCpjv7IpLHV1u2cPPGDep37o3FnnsHIu16AgA+5YK5braxJj4F+893wStUqMDly5ddlvcWb29vypcvn++43eEgLjmb6LhkVp7O4KfA+4hLznbmB5yzj0+ePFkgWf72t79x7tw55s+fz5NPPsnLL7/M7t27ycnJ4fXXX8+3/tq1a0ybNo0XXnihQH6+iIjcWzabjf79+7N//37Wr1/Po50j8TIa8q1r+GAvUq5e4tSBnXgZDYQH+3D9+nU+++wzOnTogNGot+Iicu/ozr6I5PHeilV4eJZwdhMGsJrNAHh6eQOQarFzOsVC7dLemEwmsrKyXJL1v8m22Vn2QzKpFrvzgwuAhHPpBHgZnTsUrl69CkBgYGCB/Nzbdw7cUrt2berXr8+x708QHZeM2ebA5GGgRbAPr73wAnXq1OGxxx5jypQpBZJBRETunWeeeYbPP/+cHj16cPPmTQ5sWseJy5mkWe0ANO3WF4B2w57m319/RvRzQ4kaPBZqhrB48WKsViuzZs1y5SWIyJ+Ain0RcUpPT+frLzdTu2V7fEuXdR4v8XPjuhxLNoCzyVDt0t6YzWZ8fHxckve/OZCQRR2zLd9xi93h3KEwrG5pZs+eTUBAAA899NA9y3Jr/nJwaF0uZOQ4jx/avIsVH37I9p27MBjy3xUSEZGi59ixYwBs2rSJTZs25Tt/q9j3LxfMUx98wVdvv8I3qxax3WqlZcuWrFq16jf7y4iIFBQV+yLi9Omnn5KdlUmTrn3yHPcPDAF+2c4PvzQZunLlChUrViy8kL9DVs5/briXarHz7JTpbN++nYULF1K6dOl7kuP2+csdR/2yVd/hcLDxjRdpFNWLM4H3Ud2RfE9+voiIFKydO3fe8bjd4eB0ioWD17KcO7jCWzVgetfPMeoDXREpZHpQSEScoqOjMZX0o17bznmOl69ZD6OnJ5dOfOc8ZvIwYLFYOHbsGE2aNCnkpHfH9l+66x/+6mPenvkKw4cPZ8yYMfcsx9ZD/yZ6xnNUbdSC+3sMcB4/8vkaEuJP0mX8FFItds6lWe5ZhnstPT2dqVOn0qVLF8qWLYvBYOCDDz5wdSwRkUJlNBioXdqbQWGlGV6vDIPCSlO7tLcKfRFxCRX7IgJAYmIi27dvp3OPh/Hz9c1zzuQfQK3wthz9cj3ZGenOJkMrV64kPT2dvn37uij1/+7UgZ2smzKOhpFRLFq06J79nN+av2xOT2PrOzNo8/hfKV2+Eha7g++uZ9+zHPfa3Yw+FBEREZHCo238IgLA2rVrycnJYdSQx7nkZeT6r551j/rrSywa2o0lIx6mfb8h3Mi5wdy5c4mKiqJLly4uSv2/Of/vI6x6ZjCV72vMhHnL8fS8N/9XePv85RFLN+WZv7x75T+wWa006tyLpMu5I/cMSbmPSSQlJXH27FkqVqyIl5fXPclW0O5m9KGIiIiIFB4V+yIC5G7hDw4OJurBBzHbDayJT8nTxb5SvcaMWryRLfOnsXH2JPz9/Rk+fDivvfaai5P/No87bJu8diaOFU8/SpmKVXlywRpaVy17h1f+cf9t/nLy1YtkpSbzdp/W+V47a9YsZs2axdGjR4vsIxK/5u3tTXBICHHJ2Xx1Lg2A/VcziUjOplYpL21hFRERESlkKvZFBID9+/c7/+zrAcPqls7XZKh3r07MH9y1yBZu77zzDsnJyVy+fBmAU3u2cjPhEgAR/UdgMBpZNq4fWanJtH3ir5zdt539Z0vy7c/XU7NmTVq2bPmHc9w+f/mzzz6jVkQkm8+l5xn/FzFgBPe16+r83tNgINSQypSJ4xgyZAg9e/akRo0afzhLYcmw2p0fECX8vCvkZraNzb8acygiIiIihUPFvojc0a0mQ7VLe7s6yl178803OXfunPP7mK83w9ebAWjaNbevQMrV3OJ/y/zpACy97fWDBw8ukGL/buYvV6rXmEr1fnm2PdDkQQdTMlMmQv369enVq9cfzlFY7A4Ha+JT8j36AfnHHBbVD4pERERE3I1us4iI2zh79iwOhyPPl81uJy7JTKPaoYSF1mDVj0nEJZmx2e351hZU9/jb5y8//vjjDH7iCZa+OJp1k8eybvLYPGu9jAYCTR4MrFWq2BbC8SkWUi32/7gm1WLndErxnTYghe/QoUOMGzeO+vXr4+vrS9WqVenXrx9xcXF51hkMht/8evDBB12UvuiLjY2lb9++hIaGUrJkSQIDA2nbtm2+mfFDhgy54++2bt26LkouIiJ3S3f2RcStuWKHwu+avxzsQ82fn2n3rV4dx38ZF1gUHbqWlecRhTux2B0cvJZVrHaKiGu98cYb7N27l759+9KoUSOuXr3KO++8w/3338+BAwdo0KABACtXrsz32sOHDzNv3jyioqIKO3axce7cOdLS0hg8eDAVK1YkMzOTjRs38vDDD7N48WJGjhzpXOvt7c3777+f5/WlSpUq7MgiIvI7GRzF8Z2liIgUGUtPJpF42xb+iyeO8Y/HHqTPK/Np9vBA5/EgkwfD65VxRUQphvbt20fz5s3zTKQ4deoUDRs2pE+fPqxateo3X/vkk0+ybNkyzp8/T+XKlQsjrluw2Ww0a9aM1Mwspm/+FrPNwYpJf+XIts9JS0srtruPRET+rHRnX0RE/hCTx90VAHe7TgQgIiIi37HatWtTv359jn1/gui4ZOcOmRbBPs6pD9nZ2WzcuJHIyEgV+r+T2W7AUaY81y8e5UJGDgAZOXZy7PB+7A16VPCkQrnSrg0pIiJ3Tc/si4jIH9Ii2Acv438u5L2MuY8siPwR6RYbZy5dwVqyNBcyckg027iQkcPmc+ks+yGZDKudL7/8kuTkZAYNGuTquMVCRkYG169f51R8PMMmv8b3u7cTGt4mzxqrOZNxLapRMbAMZcuW5a9//Svp6en3PNvMmTMxGAzORzZuZ7FYmDVrFnXr1sVkMhESEkK3bt24ePHiPc8lIlJc6M6+iIj8IbVKeRHgZeTzDxZjTk8hNTEBgJO7tpJyLXcMYvfBo6lZqqwrY0oxZ3c4mPDW+yQnXKHjqBfynLt96sNX0dF4e3vTp08fFyUtXp555hkWL14MgMFopH6HbvR84XXnef/AENoOHkfFuo3wwEHGd7tYuHAh3333HTt37sTT8968lbx48SKzZs3C19c33zmr1Uq3bt3Yt28fI0aMoFGjRiQlJfHtt9+SkpKiHR0iIj/TM/siIvKHZVjtVKtenRuXL9zxfGzcae6rHVrIqcSdfHXwOI90bE1waF1GLd2E0cMj3xp7ZjrTOtWj60MP8fHHH7sgZfHzww8/cPHiRTbExLNz08d4lvCi50uz8S8XfMf1VXw9ObdhIZMmTWLNmjUMGDDgnuQaMGAAiYmJ2Gw2rl+/zvfff+88N3v2bF5++WX27NlDeHj4Pfn5IiLuQMW+iIgUiP82bUDkf3X16lUah7ck22pl7IotBASVv+O6w5+tZuOrT7NhwwZ69+5dyCmLt1uNNpeO7Ys5LYWxH27FcIf/boNMHjxa3YSfnx9Dhw7N16W/IOzatYsOHTpw9OhRnnrqKa5fv87He45w6FoWmVYbL3RoyAOtWrF54zrsNhsWi4WSJUsWeA4RkeJOz+yLiEiBuDXmcFBYaYbXK8OgsNLULu2tQl/+kJSUFB566CEyUlMY+s7a3yz0AY59tREf/wC6d+9eiAndw60Gmg079uBi7FGunzv9m+t8fHwoV64cN2/eLPAcNpuNp556iieffJKGDRtic0BSto3N59K5kJFD7IkTJF27AhXDaNd/CL6+vvj6+tKoUSN27NhR4HlERIozFfsiIiJSJJnNZnr06EFcXBzPLv6IkNA6v7k2NfEqZw7vITzqYby9vQsxpXu41WjTmp0FgDk9Nd+aW40209LSuH79OkFBQQWeY9GiRZw7d47p06djdzi4lpWDzZHblwHgxvkzAHwTvYgT3+7h0alzWbpsGWazmS5dunD8+PECzyQiUlyp2BcREZEix2az0b9/f/bv38/69et5tHPkf5z6cHzrpzjsdoY9oS78d+PatWt5vq9VyouSBhsxm9dRwuRDcGgY1mwz2Rm/dN0P8DJSs5QX06dPx+Fw0KVLlwLNdOPGDaZMmcLkyZMJCgoiPsWCzZ73adPszIzcf2ak8+Sij2nYbQBt/vIo27dvx+FwMHv27ALNJCJSnKkbv4iIG4mJieGVV15hz549mM1mQkNDGTlyJOPHjwdyu1jPmjWLFStWcOnSJSpVqsSwYcN48cUX71lXbZH/xTPPPMPnn39Ojx49uHnzJgc2rePE5UzSrHYAmnbrm2f9sa82UDq4Ao92e9AVcYudUaNGkZqaStu2balUqRJXr15l5apoLv/4Aw8/Mw3vkn4kXT7P/IEduL/LI1StFcb9QT70eHYbX375JV26dKFnz54Fmunll1+mbNmyPPXUUwAcupaF/VdrSphMAFRrHE7p8pWw2B0cvJbFoLCqtG7dmn379hVoJhGR4kzv7ERE3MS2bdvo0aMHTZs2ZfLkyfj5+XH69Ok8c6cfe+wx1q9fz7Bhw2jevDkHDhxg8uTJnD9/niVLlrgwvUhex44dA2DTpk1s2rQp3/nbi/3k8/FcOvkdT02YiOcduvRLfv3792fp0qW8++673LhxA39/f5o1a8Ybb7xO/cguHLyWhW9gWZq178zFI7s49sVaPrHZqFWrFrNmzeLZZ5/FaCy4DaKnTp1iyZIlvP3221y+nDuy8+L5FHKys7HlWEm6fB5vX39nzwa/cr88QmC25d79Dw4O5ujRowWWSUSkuFM3fhERN5CamkpYWBgRERFs2LDhjm/CDx06RHh4OJMnT2batGnO488++yxz587l2LFjNGrUqDBji/xumvrgnnbu3En79u3/45qIgSOJGvt/TG8fRpWGzRi1NPdDoCq+ngwKK03btm25evUqcXFxhRFZRKTI0519ERE3sHr1ahISEpg5cyZGo5GMjAx8fHzAYCA+xcKha1l8vmErAA90fQS7w+EsjAYMGMDf//531q5dq2JfirxbUx9ql1YTPnfSoEEDPvnkkzzHLmdYmT51CuaMdLo/N5Nylavj7etHndad+GH3Nq79dIrKNcMID/bh5MmT7Nu3j1GjRrnoCkREih4V+yIibmD79u0EBARw6dIlevXqRVxcHL6+vjTv3o+H/jYdRwlvUjLNAOy9aefyD8kMrFUK3xJG53zqI0eOuPISRORPLDAwkF69euU5Znc4mDdvHg6gfvuuzuNR4yYRf3AX74/6Cw8+PorsIBMLFiygbNmyvPTSS4UbXESkCFM3fhERN3Dq1ClycnLo2bMnnTt3Zv2GDfy/XoP4Zu1yVk/JbXYVWK0WAPEx33LdbGNNfAp2h4Pdu3cDcOnSJZflFxH5NaPBQLCPJx4G8kxiCAmtw1+Xfk7l2nXZuuTvvPbaa4SHh7N3714qVarkwsQiIkWLin0RETeQnp5OZmYmTzzxBPPnz6dRx+50e24W4b0Hc3zrJ1w/f5o6rTtRukIVvnx7Kt//czNnz55jwQermTRpEp6enmRlZbn6MvI4cuQIXbp0ISAgAH9/f6KiopxN237NYrEwa9Ys6tati8lkIiQkhG7duuVpTij57dy5E4PBcMevAwcOONfNmjWLBx54gKCgIEwmE7Vr12bChAkkJia6ML38Gez+ZicX4k7Qo5ofVXw9CTJ5UMXXkzFdWvH93h2kp6eTmprKp59+Su3atV0dV0SkSNE2fhERN+Dj4wPAwIEDgdyRVRa7gyZdHuHgxhWcP36Y+7vXZMj81ax+4UminxsKQAkvb96cM5uZM2fi5+fnsvy/FhMTQ+vWralSpQpTp07FbrezcOFCIiMjOXjwIHXq1HGutVqtdOvWjX379jFixAgaNWpEUlIS3377LSkpKVSuXPl/yjBkyBBWrFjxm+cvXrxIpUqV3GKc4fjx42nRokWeY7Vq1XL++ciRIzRp0oQBAwbg7+/PyZMnee+99/jiiy84duwYvr6+hR1Z/kTUp0FE5H9TPN6FiIjIf1SxYkViY2MJCQkBfhlF5Vs2dzxVVmoyACE16zJh/W6unfmRrNRk6tW7jxFNKjBx4kQiIyNdkv1OJk+ejI+PD/v376dcuXJA7tjAsLAwxj/3Ik+8udzZif3YmoV888037Nmzh/Dw8ALLMGrUKDp16pTnmMPhYPTo0VSvXt25Xdgdxhm2adOGPn36/Ob5jRs35jvWsmVL+vTpw6ZNmxgwYMC9jCciIiL/AxX7IiJuoFmzZnz99ddcunSJOnXqYPLIfb41LfEqAL5lAp1rDQYDITXrAhDo68mOHTuw2+35CltX2r17N126dHEW+gABgSHUaBbBP7d8ScQLyXiX9MNut7PkHwto3LEb9Zs2JycnB4vF4mw6+Ee0bNmSli1b5jm2Z88eMjMzGTRoEJA7znDdunV5xhmOHj2awMBA5s6dy7hx44rNhIO0tDR8fHzw9PTE7nA4pzjc+lClRbAPtW4bb1e9enUAkpOTXRdaREREfpOe2RcRcQP9+vUDYOnSpQC0CPbBy2jg0KerMHp6Etq8Vb7XeBkNNPLPvYteoUIF5yMARUF2drbz0QTI7cq9Jj4FvEzYrBYS4n8A4NqZH0lNvErZ0Ho8NGgovr6++Pr60qhRI3bs2FHguVavXo3BYOCB7r2JjkvmzV+NM7xlwIABOBwO1q5dW+AZ7oWhQ4cSEBCAyWSibbv2TNmwg83n0rmQkUOi2caFjBw2nU1j3v7TnLlwmd27dzN+/Hg8PDxo166dq+OLiIjIHejOvoiIG2jatCnDhg1j2bJl5OTk0LZtW1Zv+prvtn5Gu6FPExBUHoDVLwzHP7A8IaF18MhO5/0vPuLMmTN88cUX+Pv7u/gqflGnTh0OHDiAzWbDw8OD+BQLNzPMXPh3DACpiVcAuHH+DAB7Vi+mZEBppr31DiElSzBr1iy6dOnCoUOHCuzOutVqZe26ddRsGs4xRyCWjJxiP87Qy8uL3r1707VrVwIDA4mNjWXW7DfZ/3g3xiz/gop1f/nd3UxMYFZUA/728/eVK1dm9erV1K1b1zXhi6EjR44wadIk9u3bh8PhoGXLlsyePZsmTZrkWbdt2zbWrl3Lt99+y8mTJ6lSpQpnz551SWYRESm+VOyLiLiJRYsWUbVqVZYvX84nn3xC1WrV6P/iTMIHjMJiz73rXKleE2I2reHQxx/iV9KHNm3asHr16nzFhquNHTuWMWPGMHz4cJ5//nk+P5PMmgVzSLueAIDVnFtkZ2dm5P4zI52nVv+LyjWrMSisNB06dKBWrVrMnj2bVatWFUimr7Zs4eaNG7QZ+YLz93n7OEP/ClVZE5/CsLqli804w4iICCIiIpzf123bmbQGnXizXyRbFsxg2D/WOc/5lCrD8Hc3gDUbv6s/8s2Xn5Oenu6K2MXS72k6uXr1atauXcv9999PxYoVXZhaRESKM4PDcdu+QxERcSt2h4PTKRYO3vbsdXiwDzVve/a6qJo0aRJz5szBarUCUOm+JoS1bM+OpW/x2N9XUL99V/69/XNWPz+c0OatGbHkE4JMHgyvVwaADh06cPbsWc6cOVMgeXr06c9Xn33C/237Ht/SZQGwZpuZ+0gEORYzPV+cTfX6TQi6Gsv0554mJSWFatWqER8fXyA/vzBExyVzISOHNf83kth/fcG0fecxenjkW1fF15Ma10/QqlUrNm3aRPfu3V2Qtnjp1q0b+/fv59SpU85eFFeuXCEsLIyoqKg8TRAvX75MUFAQJUqUoHv37nz//fe6sy8iIr+b7uyLiLix4jyyaubMmTz77LPExsayJ8kAleuwdcEMAAKr1QRwPp7gVy536sCtxoQAwcHBHD16tECypKen8/WXm6ndsr2z0Aco4W0qNuMM78atKQ6lQyphs1qwZGVi8sv/eIfZ5iAiIoIKFSoQHR2tYv8u3KnpZIUKFWgbGcmmzZtZevQimHx/boZYjvLFZGyjiIgUXfqbREREiqwyZcrQunVrgpOz2XwunfiDuygVUpGg6rUBKF/rPjw8S5B67QpextxdC7fcujtaED799FOyszJp0jX/eLriMs7wbtz6sOTmpbN4epvwKun7H9eZzWZSUlIKLV9x9uumkwAZVjsJOZ5YLRaOHP+eqo2aA5BwLp0ALyMDa5VyRVQREXET6sYvIiJFXq1SXvz4z0+5GHuUVo+OwmjM/evL29ePOq07cf74ITIuxlOzlBcAJ0+eZN++fTz44IMF8vOjo6MxlfSjXtvOdzx/a5xh9aYPEFiubJEcZ3gniYmJeb5vEezD9VOxnPxmK7UfaIfRaMSSlYElK9O55taHKhs3biQpKYnmzZsXduxi6famk5D7iM3KE4mc/i63ieOtppMAFruD62Yba+JT0LOWIiLyv9KdfRERKXJ27drFtGnTiIqKoly5chw4cIDly5fToHVH2g0ahe22td2eepnTB3ezcMRfKDF+PADz58+nbNmyvPTSS384S2JiItu3b6db7374+fo6m/Pdya1xhkOK4DjDO+nfvz8+Pj5EREQQHBxMbGws7y55jxImH7o8NRmA6+fPsHRMHxo92JOgGrXx9/Lk+MUTRK9aRfXq1Xn66addfBXFw6+bTp5JNrN42vR8TSdvl2qxk2m1F3ZUERFxEyr2RUSkyKlUqRIeHh7MmTOHtLQ0atSowYwZM5gwcSIXssjTcLB3h2b027WT/3vxRWbMmIHRaKRDhw7MmTOHSpUq/eEsa9euJScnh1FDHueSl5HrZlue88VlnOGd9OrVi+joaObOnUtqaipBQUE88pe/0PDxiZgqVMdid1AquCINOnTnzOE9HN28DofNSrVq1Rg3bhyTJk3K8wy6/LbRo0dz4cIF5syZw4oVK4DcppNtB49jx9K37vjIhMXuINliy3dcRETkbqgbv4iIyH/QsmVLzpw5w+XLlzHbDayJTyHVYnfe4f/mgwXEbFpD0uULznGGr776apEbZ/h7FOcpDkVdUlISsbGx/POGAe+quU0ndy6fx4QNewgJrZNv/ZqJg0g5+6O68YuIyO+mYl9EROR3UCEsBeHWmMN/PB5F2vUEnv/iqLMXxe3W/m0QST+p2BcRkd9P2/hFRER+h+I8zlCKjhbBPmxZspKLsUfpOvHVOxb6XkYDpb08SHJBPhERKf5U7IuIiIjcY79uOrl//35Wf/ABYREdiBg4Ms/aK3GxnNy1hZKeRq6cO0NKSgozZswAoHHjxvTo0cMVlyAiIsWMtvGLiIhIsRQTE8Mrr7zCnj17MJvNhIaGMnLkSMb/PJXhdsnJyYSFhZGYmMj69evp06dPoWY9ffo0Y8eOJSYmxtl0cuBjTxDcfRiZeOaZ8nBs00esnfrUHf89gwcP5oMPPiik1CIiUpzpzr6IiEgRsnPnTtq3b3/Hc/v37+eBBx4AoF27dnzzzTf51nTu3JktW7bc04xFwbZt2+jRowdNmzZl8uTJ+Pn5cfr0aS5evHjH9VOmTCEzM7OQU/6iZs2abN26Nd/xO/WA6D1+BKsn/1U9IERE5A9RsS8iIlIEjR8/nhYtWuQ5VqtWrTzfV65cmddeey3PsYoVK97zbK6WmprKE088Qbdu3diwYYPzeXe7w0F8ioXouGRn4dwi2AfzhTjeffddpkyZwpQpU1ycPi/1gBARkXtFxb6IiEgR1KZNm/+61bxUqVI89thjhZSo6Fi9ejUJCQnMnDkTo9FIRkYGdk9v1p5JyzMWESDhXDrvjRpHj569aNOmjQtTi4iIFK78rV9FRESkSEhLSyMnJwfIvWsdl5xNdFwyS08mkZCVg8XmwGK1kp6e7uKkhWv79u0EBARw6dIl6tSpg5+fH0FlSvP+lL+RkZWVZ+2RrZ8Sf+wg4aNfxq42RSIi8ieiYl9ERKQIGjp0KAEBAZhMJtq2a8+UDTvYfC6dCxk5JJptZNscnIk/ha+vH/7+/pQvX57JkydjtVpdHf2eO3XqFDk5OfTs2ZPOnTuz4MOPaNHrUb7d8AEbXvmlOZ/VnMWXb71C60dH4RVUmcsZ7v+7ERERuUXb+EVERIoQLy8vevfuTdeuXQkMDCQ2NpZZs99k/+PdGLP8CyrWbQRAucrVCW3emvK16uFtM5O47ytmzJhBXFwca9eudfFV3Fvp6elkZmYyevRo5s+fT3RcMt0adMRisXBw4woeHPMCgVVrsnP5fGw5VtoNn4DF7uBkksXV0UVERAqNin0REZEiJCIigoiICOf3ddt2Jq1BJ97sF8mWBTMY9o91APSeOs+5xstooMeooZR//inee+89Jk6c6Oza7458fHwAGDhwIABmW+72/CZdHuHgxhWcP34YD88S7F75Dx5+4XW8S/oB5HmWX0RExN1pG7+IiEgRduhaFgGVa1AvsgtnDu/FbrPlW2OxOzh4LYtnnnkGyH2m3Z3dmjgQEhICgMkjd0Sdb9kgALJSk/n63TcICCpPaPNWJF0+T9Ll82TdvAZAYmIiZ8+exW63uyC9iIhI4dCdfRERkSLs1l3r0iGVsFktWLIyMfn533FdlWpVALh582ahZixszZo14+uvv3Y26GsR7EPCuXTSEq8C4FsmkOSrF7lx4Sfm9Gie7/Vjx44FICkpidKlSxdmdBERkUKjYl9ERKQIu3XX+uals3h6m/Aq6fub686cOQNAUFBQoeVzhX79+vH666+zdOlSOnToQK1SXgR4GTn06SqMnp6ENm9F6fKVyEj+5UOPgBJGyif/xJQpU3j++edp2bIlvr53/l2KiIi4AxX7IiIiRUhiYmKeYr1FsA9Htx3g5DdbCWvVEaPRiDk9DU8vLzy9vIHcZ/ZbBJmYMmYGAJ07d3ZJ9sLStGlThg0bxrJly8jJySEyMpLt/9rBd1s+puOwCQQElScgqDyQ+7sJ8DIysFYpDu3dBUCLFi3o1auXC69ARETk3lOxLyIiUoT0798fHx8fIiIiCA4OJjY2lneXvEcJkw9dnpoMwOUfjvPRSyNp1PkRylWpgbctm4/3bWXv3r2MHDmS+++/38VXce8tWrSIqlWrsnz5cj755BOqVavG3Llz6T50LAevZWG2OTB5GAgP9qFmKS+MBoOrI4uIiBQqg8PhUGtaERGRImL+/PlER0cTHx9PamoqQUFBRLbvQMPHJ2KqUB2L3cHNS+fYMn8al2KPkXbjGiU8jNSrV48RI0YwcuRIDCpsRURE/vRU7IuIiBQDdoeD0ykW3bUWERGRu6JiX0RERERERMTNGF0dQEREREREREQKlop9ERERERERETejYl9ERERERETEzajYFxEREREREXEzKvZFRERERERE3IyKfRERERERERE3o2JfRERERERExM2o2BcRERERERFxMyr2RURERERERNyMin0RERERERERN6NiX0RERERERMTNqNgXERERERERcTMq9kVERERERETcjIp9ERERERERETejYl9ERERERETEzajYFxEREREREXEzKvZFRERERERE3IyKfRERERERERE3o2JfRERERERExM2o2BcRERERERFxMyr2RURERERERNyMin0RERERERERN6NiX0RERERERMTNqNgXERERERERcTMq9kVERERERETcjIp9ERERERERETejYl9ERERERETEzajYFxEREREREXEzKvZFRERERERE3IyKfRERERERERE3o2JfRERERERExM2o2BcRERERERFxMyr2RURERERERNyMin0RERERERERN6NiX0RERERERMTNqNgXERERERERcTMq9kVERERERETcjIp9ERERERERETejYl9ERERERETEzajYFxEREREREXEzKvZFRERERERE3IyKfRERERERERE3o2JfRERERERExM2o2BcRERERERFxMyr2RURERERERNyMin0RERERERERN6NiX0RERERERMTNqNgXERERERERcTP/H/DCBpslw/1VAAAAAElFTkSuQmCC\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from dgl.data import citegrh\n",
+        "#load the data\n",
+        "data = citegrh.load_pubmed()\n",
+        "g = data[0]\n",
+        "\n",
+        "\n",
+        "# Extract the graph\n",
+        "g = data[0]\n",
+        "\n",
+        "# Features and labels are stored in the graph's ndata\n",
+        "features = g.ndata['feat']  # Node features\n",
+        "labels = g.ndata['label']  # Labels\n",
+        "\n",
+        "# Number of classes and input feature dimension\n",
+        "n_classes = data.num_classes\n",
+        "in_feats = features.shape[1]\n",
+        "\n",
+        "print(\"Features shape:\", features.shape)\n",
+        "print(\"Number of classes:\", n_classes)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "XzHzZR2PiK-J",
+        "outputId": "d4a763c0-3a11-41d3-8b77-5a0d66bbb343"
+      },
+      "execution_count": 30,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "  NumNodes: 19717\n",
+            "  NumEdges: 88651\n",
+            "  NumFeats: 500\n",
+            "  NumClasses: 3\n",
+            "  NumTrainingSamples: 60\n",
+            "  NumValidationSamples: 500\n",
+            "  NumTestSamples: 1000\n",
+            "Done loading data from cached files.\n",
+            "Features shape: torch.Size([19717, 500])\n",
+            "Number of classes: 3\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "GPT\n",
+        "In the code snippet you've provided, it appears that you are working with a graph dataset and trying to convert it into a DGL graph after removing self-loops using NetworkX. There are a few things to note and correct in your approach:\n",
+        "\n",
+        "1. Import DGL: Ensure that you have DGL (Deep Graph Library) imported in your script.\n",
+        "\n",
+        "2. Conversion to DGLGraph: The correct method to convert a NetworkX graph to a DGL graph is using dgl.from_networkx().\n",
+        "\n",
+        "3. Readonly Graphs: In recent versions of DGL, the concept of readonly graphs has been deprecated. Modern DGL graphs are efficient and do not require setting them to readonly mode.\n",
+        "\n",
+        "4. Removing Self-Loops: You can remove self-loops directly in DGL without using NetworkX.\n",
+        "\n",
+        "The graph is first converted to a NetworkX graph\n",
+        "Self-loops are removed using NetworkX's remove_edges_from method.\n",
+        "The modified graph is then converted back to a DGL graph using dgl.from_networkx().\n",
+        "This process will ensure that the self-loops are removed and the graph is properly formatted for use in DGL. Remember to adjust the graph loading part (citegrh.load_pubmed()) according to your actual data source and requirements."
+      ],
+      "metadata": {
+        "id": "xb5qeoLDkxrW"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import networkx as nx\n",
+        "nx_g = g.to_networkx().to_undirected()\n",
+        "\n",
+        "# Remove self-loops using NetworkX\n",
+        "nx_g.remove_edges_from(nx.selfloop_edges(nx_g))\n",
+        "\n",
+        "# Convert back to DGL graph\n",
+        "dgl_g = dgl.from_networkx(nx_g)\n",
+        "\n",
+        "# DGL handles graphs efficiently, so no need to set readonly\n",
+        "print(\"Number of nodes:\", dgl_g.number_of_nodes())\n",
+        "print(\"Number of edges:\", dgl_g.number_of_edges())"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "-HxZjcWukPv_",
+        "outputId": "161f894a-47dd-4b23-ed2d-ab79fd2ec140"
+      },
+      "execution_count": 34,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Number of nodes: 19717\n",
+            "Number of edges: 88648\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Semi-supervised node classification"
+      ],
+      "metadata": {
+        "id": "VyiW8fOmlHsi"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "n_hidden = 64\n",
+        "n_layers = 2\n",
+        "dropout = 0.5\n",
+        "aggregator_type = 'gcn'\n",
+        "\n",
+        "gconv_model = GraphSAGEModel(in_feats,\n",
+        "                             n_hidden,\n",
+        "                             n_classes,\n",
+        "                             n_layers,\n",
+        "                             F.relu,\n",
+        "                             dropout,\n",
+        "                             aggregator_type)"
+      ],
+      "metadata": {
+        "id": "YrowpTw6m9UL"
+      },
+      "execution_count": 35,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "class NodeClassification(nn.Module):\n",
+        "    def __init__(self, gconv_model, n_hidden, n_classes):\n",
+        "        super(NodeClassification, self).__init__()\n",
+        "        self.gconv_model = gconv_model\n",
+        "        self.loss_fcn = torch.nn.CrossEntropyLoss()\n",
+        "\n",
+        "    def forward(self, g, features, train_mask):\n",
+        "        logits = self.gconv_model(g, features)\n",
+        "        return self.loss_fcn(logits[train_mask], labels[train_mask])\n",
+        "\n",
+        "\n",
+        ""
+      ],
+      "metadata": {
+        "id": "cPwVJMBmnCjF"
+      },
+      "execution_count": 42,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def NCEvaluate(model, g, features, labels, test_mask):\n",
+        "    model.eval()\n",
+        "    with torch.no_grad():\n",
+        "        logits = model.gconv_model(g, features)\n",
+        "        logits = logits[test_mask]                          #Test set에서만 accuracy 계산\n",
+        "        test_labels = labels[test_mask]\n",
+        "        _, indices = torch.max(logits, dim=1)\n",
+        "        correct = torch.sum(indices == test_labels)\n",
+        "        return correct.item() * 1.0 / len(test_labels)"
+      ],
+      "metadata": {
+        "id": "5L-gTvJ4nUw_"
+      },
+      "execution_count": 44,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "train_mask = g.ndata['train_mask']\n",
+        "val_mask = g.ndata['val_mask']\n",
+        "test_mask = g.ndata['test_mask']\n",
+        "\n",
+        "# Convert to boolean tensors (if they are not already)\n",
+        "train_mask = torch.BoolTensor(train_mask)\n",
+        "val_mask = torch.BoolTensor(val_mask)\n",
+        "test_mask = torch.BoolTensor(test_mask)\n",
+        "\n",
+        "# Number of classes\n",
+        "n_classes = data.num_classes\n",
+        "\n",
+        "# Print data statistics\n",
+        "print(f\"----Data statistics------\\n\"\n",
+        "      f\"#Classes: {n_classes}\\n\"\n",
+        "      f\"#Train samples: {train_mask.sum().item()}\\n\"\n",
+        "      f\"#Val samples: {val_mask.sum().item()}\\n\"\n",
+        "      f\"#Test samples: {test_mask.sum().item()}\")\n",
+        "\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "4A9gGDTHndXd",
+        "outputId": "ccca1558-4961-49a7-942c-0bca31592d40"
+      },
+      "execution_count": 49,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "----Data statistics------\n",
+            "#Classes: 3\n",
+            "#Train samples: 60\n",
+            "#Val samples: 500\n",
+            "#Test samples: 1000\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model = NodeClassification(gconv_model, n_hidden, n_classes)\n",
+        "\n",
+        "# 하이퍼파라미터\n",
+        "weight_decay = 5e-4\n",
+        "n_epochs = 150\n",
+        "lr = 1e-3\n",
+        "\n",
+        "# 옵티마이저\n",
+        "optimizer = torch.optim.Adam(model.parameters(), lr=lr, weight_decay=weight_decay)\n",
+        "\n",
+        "dur = []\n",
+        "for epoch in range(n_epochs):\n",
+        "    model.train()\n",
+        "\n",
+        "    loss = model(g, features, train_mask)\n",
+        "    optimizer.zero_grad()\n",
+        "    loss.backward()\n",
+        "    optimizer.step()\n",
+        "\n",
+        "    acc = NCEvaluate(model, g, features, labels, val_mask)\n",
+        "    print(\"Epoch {:05d} | Loss {:.4f} | Accuracy {:.4f}\"\n",
+        "          .format(epoch, loss.item(), acc))\n",
+        "\n",
+        "acc = NCEvaluate(model, g, features, labels, test_mask)\n",
+        "print(\"\\nTest Accuracy {:.4f}\".format(acc))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "LkG7P94TonZi",
+        "outputId": "de083d75-2e20-4a40-89e9-8d452a1690c1"
+      },
+      "execution_count": 50,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 00000 | Loss 1.0979 | Accuracy 0.4320\n",
+            "Epoch 00001 | Loss 1.0969 | Accuracy 0.4360\n",
+            "Epoch 00002 | Loss 1.0949 | Accuracy 0.4500\n",
+            "Epoch 00003 | Loss 1.0924 | Accuracy 0.4740\n",
+            "Epoch 00004 | Loss 1.0935 | Accuracy 0.4820\n",
+            "Epoch 00005 | Loss 1.0902 | Accuracy 0.5380\n",
+            "Epoch 00006 | Loss 1.0876 | Accuracy 0.5820\n",
+            "Epoch 00007 | Loss 1.0891 | Accuracy 0.6080\n",
+            "Epoch 00008 | Loss 1.0835 | Accuracy 0.6480\n",
+            "Epoch 00009 | Loss 1.0837 | Accuracy 0.6660\n",
+            "Epoch 00010 | Loss 1.0816 | Accuracy 0.6780\n",
+            "Epoch 00011 | Loss 1.0791 | Accuracy 0.6840\n",
+            "Epoch 00012 | Loss 1.0761 | Accuracy 0.6880\n",
+            "Epoch 00013 | Loss 1.0729 | Accuracy 0.6940\n",
+            "Epoch 00014 | Loss 1.0741 | Accuracy 0.6980\n",
+            "Epoch 00015 | Loss 1.0681 | Accuracy 0.7000\n",
+            "Epoch 00016 | Loss 1.0670 | Accuracy 0.7020\n",
+            "Epoch 00017 | Loss 1.0624 | Accuracy 0.7120\n",
+            "Epoch 00018 | Loss 1.0615 | Accuracy 0.7160\n",
+            "Epoch 00019 | Loss 1.0549 | Accuracy 0.7120\n",
+            "Epoch 00020 | Loss 1.0482 | Accuracy 0.7080\n",
+            "Epoch 00021 | Loss 1.0476 | Accuracy 0.7120\n",
+            "Epoch 00022 | Loss 1.0417 | Accuracy 0.7160\n",
+            "Epoch 00023 | Loss 1.0382 | Accuracy 0.7160\n",
+            "Epoch 00024 | Loss 1.0348 | Accuracy 0.7160\n",
+            "Epoch 00025 | Loss 1.0309 | Accuracy 0.7160\n",
+            "Epoch 00026 | Loss 1.0193 | Accuracy 0.7180\n",
+            "Epoch 00027 | Loss 1.0234 | Accuracy 0.7140\n",
+            "Epoch 00028 | Loss 1.0131 | Accuracy 0.7160\n",
+            "Epoch 00029 | Loss 1.0141 | Accuracy 0.7120\n",
+            "Epoch 00030 | Loss 1.0039 | Accuracy 0.7140\n",
+            "Epoch 00031 | Loss 0.9990 | Accuracy 0.7180\n",
+            "Epoch 00032 | Loss 0.9921 | Accuracy 0.7180\n",
+            "Epoch 00033 | Loss 0.9829 | Accuracy 0.7220\n",
+            "Epoch 00034 | Loss 0.9895 | Accuracy 0.7280\n",
+            "Epoch 00035 | Loss 0.9750 | Accuracy 0.7300\n",
+            "Epoch 00036 | Loss 0.9573 | Accuracy 0.7300\n",
+            "Epoch 00037 | Loss 0.9626 | Accuracy 0.7320\n",
+            "Epoch 00038 | Loss 0.9487 | Accuracy 0.7300\n",
+            "Epoch 00039 | Loss 0.9490 | Accuracy 0.7300\n",
+            "Epoch 00040 | Loss 0.9382 | Accuracy 0.7340\n",
+            "Epoch 00041 | Loss 0.9323 | Accuracy 0.7320\n",
+            "Epoch 00042 | Loss 0.9293 | Accuracy 0.7340\n",
+            "Epoch 00043 | Loss 0.9113 | Accuracy 0.7340\n",
+            "Epoch 00044 | Loss 0.9107 | Accuracy 0.7320\n",
+            "Epoch 00045 | Loss 0.8901 | Accuracy 0.7260\n",
+            "Epoch 00046 | Loss 0.8887 | Accuracy 0.7280\n",
+            "Epoch 00047 | Loss 0.8951 | Accuracy 0.7300\n",
+            "Epoch 00048 | Loss 0.8792 | Accuracy 0.7280\n",
+            "Epoch 00049 | Loss 0.8570 | Accuracy 0.7320\n",
+            "Epoch 00050 | Loss 0.8493 | Accuracy 0.7300\n",
+            "Epoch 00051 | Loss 0.8431 | Accuracy 0.7340\n",
+            "Epoch 00052 | Loss 0.8373 | Accuracy 0.7340\n",
+            "Epoch 00053 | Loss 0.8227 | Accuracy 0.7340\n",
+            "Epoch 00054 | Loss 0.8218 | Accuracy 0.7360\n",
+            "Epoch 00055 | Loss 0.7964 | Accuracy 0.7360\n",
+            "Epoch 00056 | Loss 0.7811 | Accuracy 0.7360\n",
+            "Epoch 00057 | Loss 0.7657 | Accuracy 0.7360\n",
+            "Epoch 00058 | Loss 0.7756 | Accuracy 0.7360\n",
+            "Epoch 00059 | Loss 0.7320 | Accuracy 0.7400\n",
+            "Epoch 00060 | Loss 0.7283 | Accuracy 0.7380\n",
+            "Epoch 00061 | Loss 0.7461 | Accuracy 0.7380\n",
+            "Epoch 00062 | Loss 0.7240 | Accuracy 0.7380\n",
+            "Epoch 00063 | Loss 0.7193 | Accuracy 0.7360\n",
+            "Epoch 00064 | Loss 0.7277 | Accuracy 0.7380\n",
+            "Epoch 00065 | Loss 0.6954 | Accuracy 0.7380\n",
+            "Epoch 00066 | Loss 0.6688 | Accuracy 0.7400\n",
+            "Epoch 00067 | Loss 0.6744 | Accuracy 0.7380\n",
+            "Epoch 00068 | Loss 0.6390 | Accuracy 0.7360\n",
+            "Epoch 00069 | Loss 0.6328 | Accuracy 0.7400\n",
+            "Epoch 00070 | Loss 0.6474 | Accuracy 0.7400\n",
+            "Epoch 00071 | Loss 0.6062 | Accuracy 0.7420\n",
+            "Epoch 00072 | Loss 0.6322 | Accuracy 0.7420\n",
+            "Epoch 00073 | Loss 0.5747 | Accuracy 0.7440\n",
+            "Epoch 00074 | Loss 0.5510 | Accuracy 0.7460\n",
+            "Epoch 00075 | Loss 0.5761 | Accuracy 0.7480\n",
+            "Epoch 00076 | Loss 0.5773 | Accuracy 0.7480\n",
+            "Epoch 00077 | Loss 0.5761 | Accuracy 0.7520\n",
+            "Epoch 00078 | Loss 0.5775 | Accuracy 0.7520\n",
+            "Epoch 00079 | Loss 0.5599 | Accuracy 0.7460\n",
+            "Epoch 00080 | Loss 0.5519 | Accuracy 0.7420\n",
+            "Epoch 00081 | Loss 0.4884 | Accuracy 0.7440\n",
+            "Epoch 00082 | Loss 0.5112 | Accuracy 0.7460\n",
+            "Epoch 00083 | Loss 0.4710 | Accuracy 0.7440\n",
+            "Epoch 00084 | Loss 0.4825 | Accuracy 0.7460\n",
+            "Epoch 00085 | Loss 0.4710 | Accuracy 0.7460\n",
+            "Epoch 00086 | Loss 0.4621 | Accuracy 0.7460\n",
+            "Epoch 00087 | Loss 0.4684 | Accuracy 0.7500\n",
+            "Epoch 00088 | Loss 0.4186 | Accuracy 0.7540\n",
+            "Epoch 00089 | Loss 0.4686 | Accuracy 0.7560\n",
+            "Epoch 00090 | Loss 0.3866 | Accuracy 0.7580\n",
+            "Epoch 00091 | Loss 0.4166 | Accuracy 0.7600\n",
+            "Epoch 00092 | Loss 0.4463 | Accuracy 0.7600\n",
+            "Epoch 00093 | Loss 0.4001 | Accuracy 0.7640\n",
+            "Epoch 00094 | Loss 0.3844 | Accuracy 0.7640\n",
+            "Epoch 00095 | Loss 0.4127 | Accuracy 0.7640\n",
+            "Epoch 00096 | Loss 0.3996 | Accuracy 0.7640\n",
+            "Epoch 00097 | Loss 0.4043 | Accuracy 0.7640\n",
+            "Epoch 00098 | Loss 0.3729 | Accuracy 0.7640\n",
+            "Epoch 00099 | Loss 0.3834 | Accuracy 0.7660\n",
+            "Epoch 00100 | Loss 0.3314 | Accuracy 0.7660\n",
+            "Epoch 00101 | Loss 0.3511 | Accuracy 0.7680\n",
+            "Epoch 00102 | Loss 0.3219 | Accuracy 0.7700\n",
+            "Epoch 00103 | Loss 0.3444 | Accuracy 0.7740\n",
+            "Epoch 00104 | Loss 0.3081 | Accuracy 0.7780\n",
+            "Epoch 00105 | Loss 0.3289 | Accuracy 0.7800\n",
+            "Epoch 00106 | Loss 0.2955 | Accuracy 0.7800\n",
+            "Epoch 00107 | Loss 0.3103 | Accuracy 0.7800\n",
+            "Epoch 00108 | Loss 0.2994 | Accuracy 0.7800\n",
+            "Epoch 00109 | Loss 0.3154 | Accuracy 0.7800\n",
+            "Epoch 00110 | Loss 0.2822 | Accuracy 0.7800\n",
+            "Epoch 00111 | Loss 0.3091 | Accuracy 0.7840\n",
+            "Epoch 00112 | Loss 0.2721 | Accuracy 0.7820\n",
+            "Epoch 00113 | Loss 0.2541 | Accuracy 0.7820\n",
+            "Epoch 00114 | Loss 0.2909 | Accuracy 0.7780\n",
+            "Epoch 00115 | Loss 0.2625 | Accuracy 0.7760\n",
+            "Epoch 00116 | Loss 0.2974 | Accuracy 0.7760\n",
+            "Epoch 00117 | Loss 0.2660 | Accuracy 0.7720\n",
+            "Epoch 00118 | Loss 0.2453 | Accuracy 0.7720\n",
+            "Epoch 00119 | Loss 0.2407 | Accuracy 0.7760\n",
+            "Epoch 00120 | Loss 0.2361 | Accuracy 0.7780\n",
+            "Epoch 00121 | Loss 0.2045 | Accuracy 0.7800\n",
+            "Epoch 00122 | Loss 0.2358 | Accuracy 0.7820\n",
+            "Epoch 00123 | Loss 0.2342 | Accuracy 0.7840\n",
+            "Epoch 00124 | Loss 0.2118 | Accuracy 0.7920\n",
+            "Epoch 00125 | Loss 0.2362 | Accuracy 0.7940\n",
+            "Epoch 00126 | Loss 0.2220 | Accuracy 0.7880\n",
+            "Epoch 00127 | Loss 0.2017 | Accuracy 0.7900\n",
+            "Epoch 00128 | Loss 0.1898 | Accuracy 0.7900\n",
+            "Epoch 00129 | Loss 0.1868 | Accuracy 0.7900\n",
+            "Epoch 00130 | Loss 0.2087 | Accuracy 0.7900\n",
+            "Epoch 00131 | Loss 0.1879 | Accuracy 0.7880\n",
+            "Epoch 00132 | Loss 0.2142 | Accuracy 0.7920\n",
+            "Epoch 00133 | Loss 0.2013 | Accuracy 0.7860\n",
+            "Epoch 00134 | Loss 0.1978 | Accuracy 0.7860\n",
+            "Epoch 00135 | Loss 0.1746 | Accuracy 0.7800\n",
+            "Epoch 00136 | Loss 0.2065 | Accuracy 0.7800\n",
+            "Epoch 00137 | Loss 0.2072 | Accuracy 0.7780\n",
+            "Epoch 00138 | Loss 0.1703 | Accuracy 0.7840\n",
+            "Epoch 00139 | Loss 0.1735 | Accuracy 0.7840\n",
+            "Epoch 00140 | Loss 0.1638 | Accuracy 0.7860\n",
+            "Epoch 00141 | Loss 0.2032 | Accuracy 0.7840\n",
+            "Epoch 00142 | Loss 0.1953 | Accuracy 0.7900\n",
+            "Epoch 00143 | Loss 0.1687 | Accuracy 0.7820\n",
+            "Epoch 00144 | Loss 0.1650 | Accuracy 0.7820\n",
+            "Epoch 00145 | Loss 0.1612 | Accuracy 0.7800\n",
+            "Epoch 00146 | Loss 0.1403 | Accuracy 0.7800\n",
+            "Epoch 00147 | Loss 0.1463 | Accuracy 0.7820\n",
+            "Epoch 00148 | Loss 0.1432 | Accuracy 0.7840\n",
+            "Epoch 00149 | Loss 0.1385 | Accuracy 0.7860\n",
+            "\n",
+            "Test Accuracy 0.7740\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import torch\n",
+        "import torch.nn as nn\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Assuming your NodeClassification, NCEvaluate, and other setup code here\n",
+        "\n",
+        "model = NodeClassification(gconv_model, n_hidden, n_classes)\n",
+        "\n",
+        "# Hyperparameters\n",
+        "weight_decay = 5e-4\n",
+        "n_epochs = 150\n",
+        "lr = 1e-3\n",
+        "\n",
+        "# Optimizer\n",
+        "optimizer = torch.optim.Adam(model.parameters(), lr=lr, weight_decay=weight_decay)\n",
+        "\n",
+        "# For storing accuracies\n",
+        "train_accs = []\n",
+        "val_accs = []\n",
+        "\n",
+        "for epoch in range(n_epochs):\n",
+        "    model.train()\n",
+        "\n",
+        "    loss = model(g, features, train_mask)\n",
+        "    optimizer.zero_grad()\n",
+        "    loss.backward()\n",
+        "    optimizer.step()\n",
+        "\n",
+        "    # Evaluate on training set and validation set\n",
+        "    train_acc = NCEvaluate(model, g, features, labels, train_mask)\n",
+        "    val_acc = NCEvaluate(model, g, features, labels, val_mask)\n",
+        "\n",
+        "    # Store accuracies\n",
+        "    train_accs.append(train_acc)\n",
+        "    val_accs.append(val_acc)\n",
+        "\n",
+        "    print(\"Epoch {:05d} | Loss {:.4f} | Train Acc {:.4f} | Val Acc {:.4f}\"\n",
+        "          .format(epoch, loss.item(), train_acc, val_acc))\n",
+        "\n",
+        "# Final Test Accuracy\n",
+        "test_acc = NCEvaluate(model, g, features, labels, test_mask)\n",
+        "print(\"\\nTest Accuracy {:.4f}\".format(test_acc))\n",
+        "\n",
+        "# Plotting\n",
+        "plt.figure(figsize=(10, 5))\n",
+        "plt.plot(train_accs, label='Train Accuracy')\n",
+        "plt.plot(val_accs, label='Validation Accuracy')\n",
+        "plt.xlabel('Epoch')\n",
+        "plt.ylabel('Accuracy')\n",
+        "plt.title('Training and Validation Accuracy over Epochs')\n",
+        "plt.legend()\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "RQACsvAroN6D",
+        "outputId": "e64165a5-db90-4b7a-d2d0-abf90a8d5788"
+      },
+      "execution_count": 51,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 00000 | Loss 0.1155 | Train Acc 0.9833 | Val Acc 0.7900\n",
+            "Epoch 00001 | Loss 0.1230 | Train Acc 0.9833 | Val Acc 0.7820\n",
+            "Epoch 00002 | Loss 0.1697 | Train Acc 0.9833 | Val Acc 0.7820\n",
+            "Epoch 00003 | Loss 0.1495 | Train Acc 0.9833 | Val Acc 0.7820\n",
+            "Epoch 00004 | Loss 0.1286 | Train Acc 1.0000 | Val Acc 0.7780\n",
+            "Epoch 00005 | Loss 0.1459 | Train Acc 1.0000 | Val Acc 0.7800\n",
+            "Epoch 00006 | Loss 0.1251 | Train Acc 1.0000 | Val Acc 0.7820\n",
+            "Epoch 00007 | Loss 0.1284 | Train Acc 1.0000 | Val Acc 0.7840\n",
+            "Epoch 00008 | Loss 0.1410 | Train Acc 1.0000 | Val Acc 0.7800\n",
+            "Epoch 00009 | Loss 0.1326 | Train Acc 1.0000 | Val Acc 0.7800\n",
+            "Epoch 00010 | Loss 0.1468 | Train Acc 1.0000 | Val Acc 0.7820\n",
+            "Epoch 00011 | Loss 0.1185 | Train Acc 1.0000 | Val Acc 0.7860\n",
+            "Epoch 00012 | Loss 0.1139 | Train Acc 1.0000 | Val Acc 0.7880\n",
+            "Epoch 00013 | Loss 0.1051 | Train Acc 1.0000 | Val Acc 0.7860\n",
+            "Epoch 00014 | Loss 0.1121 | Train Acc 1.0000 | Val Acc 0.7840\n",
+            "Epoch 00015 | Loss 0.0996 | Train Acc 1.0000 | Val Acc 0.7800\n",
+            "Epoch 00016 | Loss 0.1353 | Train Acc 1.0000 | Val Acc 0.7780\n",
+            "Epoch 00017 | Loss 0.1108 | Train Acc 1.0000 | Val Acc 0.7820\n",
+            "Epoch 00018 | Loss 0.0989 | Train Acc 1.0000 | Val Acc 0.7820\n",
+            "Epoch 00019 | Loss 0.1258 | Train Acc 1.0000 | Val Acc 0.7780\n",
+            "Epoch 00020 | Loss 0.0771 | Train Acc 1.0000 | Val Acc 0.7760\n",
+            "Epoch 00021 | Loss 0.1021 | Train Acc 1.0000 | Val Acc 0.7760\n",
+            "Epoch 00022 | Loss 0.1002 | Train Acc 1.0000 | Val Acc 0.7760\n",
+            "Epoch 00023 | Loss 0.0890 | Train Acc 1.0000 | Val Acc 0.7740\n",
+            "Epoch 00024 | Loss 0.0975 | Train Acc 1.0000 | Val Acc 0.7780\n",
+            "Epoch 00025 | Loss 0.0777 | Train Acc 1.0000 | Val Acc 0.7780\n",
+            "Epoch 00026 | Loss 0.0822 | Train Acc 1.0000 | Val Acc 0.7780\n",
+            "Epoch 00027 | Loss 0.0858 | Train Acc 1.0000 | Val Acc 0.7780\n",
+            "Epoch 00028 | Loss 0.0920 | Train Acc 1.0000 | Val Acc 0.7760\n",
+            "Epoch 00029 | Loss 0.0752 | Train Acc 1.0000 | Val Acc 0.7820\n",
+            "Epoch 00030 | Loss 0.0720 | Train Acc 1.0000 | Val Acc 0.7820\n",
+            "Epoch 00031 | Loss 0.0693 | Train Acc 1.0000 | Val Acc 0.7780\n",
+            "Epoch 00032 | Loss 0.0867 | Train Acc 1.0000 | Val Acc 0.7800\n",
+            "Epoch 00033 | Loss 0.0618 | Train Acc 1.0000 | Val Acc 0.7820\n",
+            "Epoch 00034 | Loss 0.0779 | Train Acc 1.0000 | Val Acc 0.7800\n",
+            "Epoch 00035 | Loss 0.0795 | Train Acc 1.0000 | Val Acc 0.7780\n",
+            "Epoch 00036 | Loss 0.0876 | Train Acc 1.0000 | Val Acc 0.7760\n",
+            "Epoch 00037 | Loss 0.0699 | Train Acc 1.0000 | Val Acc 0.7740\n",
+            "Epoch 00038 | Loss 0.0852 | Train Acc 1.0000 | Val Acc 0.7680\n",
+            "Epoch 00039 | Loss 0.0781 | Train Acc 1.0000 | Val Acc 0.7740\n",
+            "Epoch 00040 | Loss 0.0651 | Train Acc 1.0000 | Val Acc 0.7760\n",
+            "Epoch 00041 | Loss 0.0762 | Train Acc 1.0000 | Val Acc 0.7760\n",
+            "Epoch 00042 | Loss 0.0624 | Train Acc 1.0000 | Val Acc 0.7760\n",
+            "Epoch 00043 | Loss 0.0666 | Train Acc 1.0000 | Val Acc 0.7760\n",
+            "Epoch 00044 | Loss 0.0800 | Train Acc 1.0000 | Val Acc 0.7760\n",
+            "Epoch 00045 | Loss 0.0669 | Train Acc 1.0000 | Val Acc 0.7760\n",
+            "Epoch 00046 | Loss 0.0754 | Train Acc 1.0000 | Val Acc 0.7740\n",
+            "Epoch 00047 | Loss 0.0716 | Train Acc 1.0000 | Val Acc 0.7700\n",
+            "Epoch 00048 | Loss 0.0625 | Train Acc 1.0000 | Val Acc 0.7700\n",
+            "Epoch 00049 | Loss 0.0543 | Train Acc 1.0000 | Val Acc 0.7740\n",
+            "Epoch 00050 | Loss 0.0798 | Train Acc 1.0000 | Val Acc 0.7740\n",
+            "Epoch 00051 | Loss 0.0670 | Train Acc 1.0000 | Val Acc 0.7720\n",
+            "Epoch 00052 | Loss 0.0675 | Train Acc 1.0000 | Val Acc 0.7720\n",
+            "Epoch 00053 | Loss 0.0790 | Train Acc 1.0000 | Val Acc 0.7740\n",
+            "Epoch 00054 | Loss 0.0757 | Train Acc 1.0000 | Val Acc 0.7760\n",
+            "Epoch 00055 | Loss 0.0587 | Train Acc 1.0000 | Val Acc 0.7800\n",
+            "Epoch 00056 | Loss 0.0528 | Train Acc 1.0000 | Val Acc 0.7800\n",
+            "Epoch 00057 | Loss 0.0602 | Train Acc 1.0000 | Val Acc 0.7820\n",
+            "Epoch 00058 | Loss 0.0493 | Train Acc 1.0000 | Val Acc 0.7820\n",
+            "Epoch 00059 | Loss 0.0677 | Train Acc 1.0000 | Val Acc 0.7840\n",
+            "Epoch 00060 | Loss 0.0506 | Train Acc 1.0000 | Val Acc 0.7840\n",
+            "Epoch 00061 | Loss 0.0630 | Train Acc 1.0000 | Val Acc 0.7800\n",
+            "Epoch 00062 | Loss 0.0639 | Train Acc 1.0000 | Val Acc 0.7820\n",
+            "Epoch 00063 | Loss 0.0522 | Train Acc 1.0000 | Val Acc 0.7780\n",
+            "Epoch 00064 | Loss 0.0607 | Train Acc 1.0000 | Val Acc 0.7780\n",
+            "Epoch 00065 | Loss 0.0594 | Train Acc 1.0000 | Val Acc 0.7700\n",
+            "Epoch 00066 | Loss 0.0618 | Train Acc 1.0000 | Val Acc 0.7700\n",
+            "Epoch 00067 | Loss 0.0539 | Train Acc 1.0000 | Val Acc 0.7720\n",
+            "Epoch 00068 | Loss 0.0469 | Train Acc 1.0000 | Val Acc 0.7720\n",
+            "Epoch 00069 | Loss 0.0499 | Train Acc 1.0000 | Val Acc 0.7740\n",
+            "Epoch 00070 | Loss 0.0434 | Train Acc 1.0000 | Val Acc 0.7740\n",
+            "Epoch 00071 | Loss 0.0634 | Train Acc 1.0000 | Val Acc 0.7740\n",
+            "Epoch 00072 | Loss 0.0550 | Train Acc 1.0000 | Val Acc 0.7740\n",
+            "Epoch 00073 | Loss 0.0510 | Train Acc 1.0000 | Val Acc 0.7740\n",
+            "Epoch 00074 | Loss 0.0416 | Train Acc 1.0000 | Val Acc 0.7720\n",
+            "Epoch 00075 | Loss 0.0630 | Train Acc 1.0000 | Val Acc 0.7700\n",
+            "Epoch 00076 | Loss 0.0498 | Train Acc 1.0000 | Val Acc 0.7760\n",
+            "Epoch 00077 | Loss 0.0471 | Train Acc 1.0000 | Val Acc 0.7800\n",
+            "Epoch 00078 | Loss 0.0389 | Train Acc 1.0000 | Val Acc 0.7780\n",
+            "Epoch 00079 | Loss 0.0472 | Train Acc 1.0000 | Val Acc 0.7780\n",
+            "Epoch 00080 | Loss 0.0539 | Train Acc 1.0000 | Val Acc 0.7760\n",
+            "Epoch 00081 | Loss 0.0441 | Train Acc 1.0000 | Val Acc 0.7760\n",
+            "Epoch 00082 | Loss 0.0541 | Train Acc 1.0000 | Val Acc 0.7720\n",
+            "Epoch 00083 | Loss 0.0637 | Train Acc 1.0000 | Val Acc 0.7760\n",
+            "Epoch 00084 | Loss 0.0517 | Train Acc 1.0000 | Val Acc 0.7720\n",
+            "Epoch 00085 | Loss 0.0434 | Train Acc 1.0000 | Val Acc 0.7720\n",
+            "Epoch 00086 | Loss 0.0516 | Train Acc 1.0000 | Val Acc 0.7740\n",
+            "Epoch 00087 | Loss 0.0412 | Train Acc 1.0000 | Val Acc 0.7740\n",
+            "Epoch 00088 | Loss 0.0414 | Train Acc 1.0000 | Val Acc 0.7740\n",
+            "Epoch 00089 | Loss 0.0503 | Train Acc 1.0000 | Val Acc 0.7740\n",
+            "Epoch 00090 | Loss 0.0625 | Train Acc 1.0000 | Val Acc 0.7760\n",
+            "Epoch 00091 | Loss 0.0363 | Train Acc 1.0000 | Val Acc 0.7760\n",
+            "Epoch 00092 | Loss 0.0418 | Train Acc 1.0000 | Val Acc 0.7720\n",
+            "Epoch 00093 | Loss 0.0420 | Train Acc 1.0000 | Val Acc 0.7700\n",
+            "Epoch 00094 | Loss 0.0315 | Train Acc 1.0000 | Val Acc 0.7700\n",
+            "Epoch 00095 | Loss 0.0443 | Train Acc 1.0000 | Val Acc 0.7680\n",
+            "Epoch 00096 | Loss 0.0533 | Train Acc 1.0000 | Val Acc 0.7720\n",
+            "Epoch 00097 | Loss 0.0413 | Train Acc 1.0000 | Val Acc 0.7720\n",
+            "Epoch 00098 | Loss 0.0490 | Train Acc 1.0000 | Val Acc 0.7720\n",
+            "Epoch 00099 | Loss 0.0333 | Train Acc 1.0000 | Val Acc 0.7720\n",
+            "Epoch 00100 | Loss 0.0453 | Train Acc 1.0000 | Val Acc 0.7720\n",
+            "Epoch 00101 | Loss 0.0591 | Train Acc 1.0000 | Val Acc 0.7720\n",
+            "Epoch 00102 | Loss 0.0371 | Train Acc 1.0000 | Val Acc 0.7700\n",
+            "Epoch 00103 | Loss 0.0324 | Train Acc 1.0000 | Val Acc 0.7720\n",
+            "Epoch 00104 | Loss 0.0476 | Train Acc 1.0000 | Val Acc 0.7740\n",
+            "Epoch 00105 | Loss 0.0324 | Train Acc 1.0000 | Val Acc 0.7740\n",
+            "Epoch 00106 | Loss 0.0338 | Train Acc 1.0000 | Val Acc 0.7740\n",
+            "Epoch 00107 | Loss 0.0358 | Train Acc 1.0000 | Val Acc 0.7740\n",
+            "Epoch 00108 | Loss 0.0325 | Train Acc 1.0000 | Val Acc 0.7720\n",
+            "Epoch 00109 | Loss 0.0409 | Train Acc 1.0000 | Val Acc 0.7740\n",
+            "Epoch 00110 | Loss 0.0397 | Train Acc 1.0000 | Val Acc 0.7740\n",
+            "Epoch 00111 | Loss 0.0426 | Train Acc 1.0000 | Val Acc 0.7700\n",
+            "Epoch 00112 | Loss 0.0416 | Train Acc 1.0000 | Val Acc 0.7720\n",
+            "Epoch 00113 | Loss 0.0341 | Train Acc 1.0000 | Val Acc 0.7720\n",
+            "Epoch 00114 | Loss 0.0272 | Train Acc 1.0000 | Val Acc 0.7720\n",
+            "Epoch 00115 | Loss 0.0423 | Train Acc 1.0000 | Val Acc 0.7760\n",
+            "Epoch 00116 | Loss 0.0335 | Train Acc 1.0000 | Val Acc 0.7760\n",
+            "Epoch 00117 | Loss 0.0355 | Train Acc 1.0000 | Val Acc 0.7760\n",
+            "Epoch 00118 | Loss 0.0448 | Train Acc 1.0000 | Val Acc 0.7760\n",
+            "Epoch 00119 | Loss 0.0394 | Train Acc 1.0000 | Val Acc 0.7740\n",
+            "Epoch 00120 | Loss 0.0281 | Train Acc 1.0000 | Val Acc 0.7740\n",
+            "Epoch 00121 | Loss 0.0416 | Train Acc 1.0000 | Val Acc 0.7780\n",
+            "Epoch 00122 | Loss 0.0358 | Train Acc 1.0000 | Val Acc 0.7780\n",
+            "Epoch 00123 | Loss 0.0393 | Train Acc 1.0000 | Val Acc 0.7800\n",
+            "Epoch 00124 | Loss 0.0280 | Train Acc 1.0000 | Val Acc 0.7780\n",
+            "Epoch 00125 | Loss 0.0427 | Train Acc 1.0000 | Val Acc 0.7780\n",
+            "Epoch 00126 | Loss 0.0247 | Train Acc 1.0000 | Val Acc 0.7780\n",
+            "Epoch 00127 | Loss 0.0352 | Train Acc 1.0000 | Val Acc 0.7780\n",
+            "Epoch 00128 | Loss 0.0387 | Train Acc 1.0000 | Val Acc 0.7780\n",
+            "Epoch 00129 | Loss 0.0351 | Train Acc 1.0000 | Val Acc 0.7860\n",
+            "Epoch 00130 | Loss 0.0552 | Train Acc 1.0000 | Val Acc 0.7860\n",
+            "Epoch 00131 | Loss 0.0364 | Train Acc 1.0000 | Val Acc 0.7800\n",
+            "Epoch 00132 | Loss 0.0381 | Train Acc 1.0000 | Val Acc 0.7780\n",
+            "Epoch 00133 | Loss 0.0391 | Train Acc 1.0000 | Val Acc 0.7800\n",
+            "Epoch 00134 | Loss 0.0504 | Train Acc 1.0000 | Val Acc 0.7760\n",
+            "Epoch 00135 | Loss 0.0259 | Train Acc 1.0000 | Val Acc 0.7760\n",
+            "Epoch 00136 | Loss 0.0306 | Train Acc 1.0000 | Val Acc 0.7800\n",
+            "Epoch 00137 | Loss 0.0318 | Train Acc 1.0000 | Val Acc 0.7780\n",
+            "Epoch 00138 | Loss 0.0395 | Train Acc 1.0000 | Val Acc 0.7760\n",
+            "Epoch 00139 | Loss 0.0311 | Train Acc 1.0000 | Val Acc 0.7760\n",
+            "Epoch 00140 | Loss 0.0394 | Train Acc 1.0000 | Val Acc 0.7780\n",
+            "Epoch 00141 | Loss 0.0323 | Train Acc 1.0000 | Val Acc 0.7780\n",
+            "Epoch 00142 | Loss 0.0430 | Train Acc 1.0000 | Val Acc 0.7760\n",
+            "Epoch 00143 | Loss 0.0324 | Train Acc 1.0000 | Val Acc 0.7780\n",
+            "Epoch 00144 | Loss 0.0404 | Train Acc 1.0000 | Val Acc 0.7760\n",
+            "Epoch 00145 | Loss 0.0317 | Train Acc 1.0000 | Val Acc 0.7800\n",
+            "Epoch 00146 | Loss 0.0300 | Train Acc 1.0000 | Val Acc 0.7840\n",
+            "Epoch 00147 | Loss 0.0308 | Train Acc 1.0000 | Val Acc 0.7840\n",
+            "Epoch 00148 | Loss 0.0229 | Train Acc 1.0000 | Val Acc 0.7880\n",
+            "Epoch 00149 | Loss 0.0616 | Train Acc 1.0000 | Val Acc 0.7840\n",
+            "\n",
+            "Test Accuracy 0.7730\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Hyper-Tunning"
+      ],
+      "metadata": {
+        "id": "VFUuYDq5urwT"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Improving the test accuracy of a Graph Neural Network (GNN) model, such as the one you've implemented for node classification, involves several strategies. Here are some approaches you can consider:\n",
+        "\n",
+        "1. **Hyperparameter Tuning**:\n",
+        "   - **Learning Rate**: Experiment with different learning rates. Sometimes a smaller learning rate can lead to better convergence.\n",
+        "   - **Number of Epochs**: Increase the number of training epochs, but be mindful of overfitting.\n",
+        "   - **Hidden Layers and Units**: Adjust the number and size of hidden layers in your GNN. Sometimes increasing the model complexity can help, provided you have enough data.\n",
+        "   - **Dropout Rate**: Experiment with different dropout rates to prevent overfitting.\n",
+        "\n",
+        "2. **Feature Engineering**:\n",
+        "   - **Node Features**: Investigate if you can include more informative or high-quality features for the nodes.\n",
+        "   - **Feature Normalization**: Standardizing or normalizing features can sometimes improve performance.\n",
+        "\n",
+        "3. **Model Architecture**:\n",
+        "   - **Different Aggregators**: If you're using GraphSAGE, try different aggregator functions (mean, LSTM, pooling).\n",
+        "   - **Advanced GNN Layers**: Experiment with more advanced GNN layers or architectures, like GAT (Graph Attention Networks) or GCN (Graph Convolutional Networks).\n",
+        "\n",
+        "4. **Regularization Techniques**:\n",
+        "   - **Weight Decay**: Adjust the weight decay parameter to regularize the training process.\n",
+        "   - **Early Stopping**: Implement early stopping to prevent overfitting.\n",
+        "\n",
+        "5. **Data Augmentation**:\n",
+        "   - **Graph Perturbation**: Slightly modify the graph structure (e.g., adding/dropping edges) as a form of data augmentation.\n",
+        "   - **Node Feature Perturbation**: Add noise or variations to node features during training.\n",
+        "\n",
+        "6. **Training Procedure**:\n",
+        "   - **Batch Training**: If not already doing so, use mini-batch training which can sometimes improve the generalization of the model.\n",
+        "   - **Learning Rate Scheduling**: Implement learning rate schedulers that adjust the learning rate during training.\n",
+        "\n",
+        "7. **Evaluation Metrics and Loss Function**:\n",
+        "   - Ensure that you're using the most appropriate evaluation metrics and loss functions for your specific problem.\n",
+        "\n",
+        "8. **Computational Resources**:\n",
+        "   - More computational resources allow for experimenting with larger models and more extensive hyperparameter tuning.\n",
+        "\n",
+        "9. **Ensemble Methods**:\n",
+        "   - Combine predictions from multiple models (ensembles) to improve accuracy.\n",
+        "\n",
+        "10. **Literature and Case Studies**:\n",
+        "    - Review current literature for similar tasks and case studies to find insights and techniques that have been effective in similar scenarios.\n",
+        "\n",
+        "Keep in mind visulisation and have other metrics rather than accuracy like F1 score would give better grasp on underfitting and overfitting issue."
+      ],
+      "metadata": {
+        "id": "oOJfFklnsSR4"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import torch\n",
+        "import torch.nn as nn\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Assuming your NodeClassification, NCEvaluate, and other setup code here\n",
+        "\n",
+        "# Define the range of hyperparameters you want to test\n",
+        "learning_rates = [1e-2, 1e-3, 1e-4]\n",
+        "weight_decays = [5e-4, 5e-3, 5e-2]\n",
+        "n_epochs = 100\n",
+        "\n",
+        "best_val_acc = 0\n",
+        "best_hyperparams = {}\n",
+        "\n",
+        "for lr in learning_rates:\n",
+        "    for weight_decay in weight_decays:\n",
+        "        print(f\"Training with lr: {lr}, weight_decay: {weight_decay}\")\n",
+        "\n",
+        "        # Initialize the model\n",
+        "        model = NodeClassification(gconv_model, n_hidden, n_classes)\n",
+        "\n",
+        "        # Optimizer with current hyperparameters\n",
+        "        optimizer = torch.optim.Adam(model.parameters(), lr=lr, weight_decay=weight_decay)\n",
+        "\n",
+        "        # Lists to store accuracies for plotting\n",
+        "        train_accs = []\n",
+        "        val_accs = []\n",
+        "\n",
+        "        for epoch in range(n_epochs):\n",
+        "            model.train()\n",
+        "            loss = model(g, features, train_mask)\n",
+        "            optimizer.zero_grad()\n",
+        "            loss.backward()\n",
+        "            optimizer.step()\n",
+        "\n",
+        "            # Evaluate on training set and validation set\n",
+        "            train_acc = NCEvaluate(model, g, features, labels, train_mask)\n",
+        "            val_acc = NCEvaluate(model, g, features, labels, val_mask)\n",
+        "\n",
+        "            # Store accuracies\n",
+        "            train_accs.append(train_acc)\n",
+        "            val_accs.append(val_acc)\n",
+        "\n",
+        "            print(\"Epoch {:05d} | Loss {:.4f} | Train Acc {:.4f} | Val Acc {:.4f}\".format(epoch, loss.item(), train_acc, val_acc))\n",
+        "\n",
+        "        # Check if this is the best model so far\n",
+        "        if val_accs[-1] > best_val_acc:\n",
+        "            best_val_acc = val_accs[-1]\n",
+        "            best_hyperparams = {'lr': lr, 'weight_decay': weight_decay}\n",
+        "            best_model = model\n",
+        "\n",
+        "        # Plotting\n",
+        "        plt.figure(figsize=(10, 5))\n",
+        "        plt.plot(train_accs, label='Train Accuracy')\n",
+        "        plt.plot(val_accs, label='Validation Accuracy')\n",
+        "        plt.xlabel('Epoch')\n",
+        "        plt.ylabel('Accuracy')\n",
+        "        plt.title(f'Training and Validation Accuracy over Epochs (lr={lr}, weight_decay={weight_decay})')\n",
+        "        plt.legend()\n",
+        "        plt.show()\n",
+        "\n",
+        "# Final evaluation with the best model\n",
+        "print(f\"Best Hyperparameters: lr={best_hyperparams['lr']}, weight_decay={best_hyperparams['weight_decay']}\")\n",
+        "test_acc = NCEvaluate(best_model, g, features, labels, test_mask)\n",
+        "print(\"\\nTest Accuracy with Best Model: {:.4f}\".format(test_acc))\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "1_WigZGTkuOw",
+        "outputId": "cc410c8b-54b8-4cb9-88d4-131e40277916"
+      },
+      "execution_count": 71,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Training with lr: 0.01, weight_decay: 0.0005\n",
+            "Epoch 00000 | Loss 0.0267 | Train Acc 0.8833 | Val Acc 0.6540\n",
+            "Epoch 00001 | Loss 0.3376 | Train Acc 1.0000 | Val Acc 0.7760\n",
+            "Epoch 00002 | Loss 0.0254 | Train Acc 0.9833 | Val Acc 0.7720\n",
+            "Epoch 00003 | Loss 0.0889 | Train Acc 0.9667 | Val Acc 0.7220\n",
+            "Epoch 00004 | Loss 0.2638 | Train Acc 0.9833 | Val Acc 0.7620\n",
+            "Epoch 00005 | Loss 0.1029 | Train Acc 0.9833 | Val Acc 0.7840\n",
+            "Epoch 00006 | Loss 0.0787 | Train Acc 1.0000 | Val Acc 0.7840\n",
+            "Epoch 00007 | Loss 0.0485 | Train Acc 1.0000 | Val Acc 0.7780\n",
+            "Epoch 00008 | Loss 0.0467 | Train Acc 1.0000 | Val Acc 0.7640\n",
+            "Epoch 00009 | Loss 0.0612 | Train Acc 0.9833 | Val Acc 0.7420\n",
+            "Epoch 00010 | Loss 0.0681 | Train Acc 0.9833 | Val Acc 0.7380\n",
+            "Epoch 00011 | Loss 0.0904 | Train Acc 1.0000 | Val Acc 0.7500\n",
+            "Epoch 00012 | Loss 0.0837 | Train Acc 1.0000 | Val Acc 0.7700\n",
+            "Epoch 00013 | Loss 0.0547 | Train Acc 1.0000 | Val Acc 0.7720\n",
+            "Epoch 00014 | Loss 0.0216 | Train Acc 1.0000 | Val Acc 0.7780\n",
+            "Epoch 00015 | Loss 0.0186 | Train Acc 0.9833 | Val Acc 0.7800\n",
+            "Epoch 00016 | Loss 0.0191 | Train Acc 0.9833 | Val Acc 0.7820\n",
+            "Epoch 00017 | Loss 0.0464 | Train Acc 0.9833 | Val Acc 0.7840\n",
+            "Epoch 00018 | Loss 0.0440 | Train Acc 0.9833 | Val Acc 0.7900\n",
+            "Epoch 00019 | Loss 0.0477 | Train Acc 0.9833 | Val Acc 0.7880\n",
+            "Epoch 00020 | Loss 0.0647 | Train Acc 0.9833 | Val Acc 0.7840\n",
+            "Epoch 00021 | Loss 0.0316 | Train Acc 1.0000 | Val Acc 0.7820\n",
+            "Epoch 00022 | Loss 0.0322 | Train Acc 1.0000 | Val Acc 0.7840\n",
+            "Epoch 00023 | Loss 0.0130 | Train Acc 1.0000 | Val Acc 0.7740\n",
+            "Epoch 00024 | Loss 0.0159 | Train Acc 1.0000 | Val Acc 0.7660\n",
+            "Epoch 00025 | Loss 0.0312 | Train Acc 1.0000 | Val Acc 0.7620\n",
+            "Epoch 00026 | Loss 0.0229 | Train Acc 1.0000 | Val Acc 0.7600\n",
+            "Epoch 00027 | Loss 0.0166 | Train Acc 1.0000 | Val Acc 0.7620\n",
+            "Epoch 00028 | Loss 0.0206 | Train Acc 1.0000 | Val Acc 0.7580\n",
+            "Epoch 00029 | Loss 0.0319 | Train Acc 1.0000 | Val Acc 0.7620\n",
+            "Epoch 00030 | Loss 0.0178 | Train Acc 1.0000 | Val Acc 0.7640\n",
+            "Epoch 00031 | Loss 0.0189 | Train Acc 1.0000 | Val Acc 0.7680\n",
+            "Epoch 00032 | Loss 0.0120 | Train Acc 1.0000 | Val Acc 0.7720\n",
+            "Epoch 00033 | Loss 0.0168 | Train Acc 1.0000 | Val Acc 0.7740\n",
+            "Epoch 00034 | Loss 0.0154 | Train Acc 1.0000 | Val Acc 0.7900\n",
+            "Epoch 00035 | Loss 0.0176 | Train Acc 1.0000 | Val Acc 0.7860\n",
+            "Epoch 00036 | Loss 0.0117 | Train Acc 1.0000 | Val Acc 0.7880\n",
+            "Epoch 00037 | Loss 0.0147 | Train Acc 1.0000 | Val Acc 0.7920\n",
+            "Epoch 00038 | Loss 0.0134 | Train Acc 1.0000 | Val Acc 0.7940\n",
+            "Epoch 00039 | Loss 0.0200 | Train Acc 1.0000 | Val Acc 0.7920\n",
+            "Epoch 00040 | Loss 0.0179 | Train Acc 1.0000 | Val Acc 0.7940\n",
+            "Epoch 00041 | Loss 0.0252 | Train Acc 1.0000 | Val Acc 0.7920\n",
+            "Epoch 00042 | Loss 0.0185 | Train Acc 1.0000 | Val Acc 0.7860\n",
+            "Epoch 00043 | Loss 0.0132 | Train Acc 1.0000 | Val Acc 0.7840\n",
+            "Epoch 00044 | Loss 0.0135 | Train Acc 1.0000 | Val Acc 0.7880\n",
+            "Epoch 00045 | Loss 0.0291 | Train Acc 1.0000 | Val Acc 0.7860\n",
+            "Epoch 00046 | Loss 0.0115 | Train Acc 1.0000 | Val Acc 0.7900\n",
+            "Epoch 00047 | Loss 0.0182 | Train Acc 1.0000 | Val Acc 0.7840\n",
+            "Epoch 00048 | Loss 0.0143 | Train Acc 1.0000 | Val Acc 0.7820\n",
+            "Epoch 00049 | Loss 0.0245 | Train Acc 1.0000 | Val Acc 0.7940\n",
+            "Epoch 00050 | Loss 0.0261 | Train Acc 1.0000 | Val Acc 0.7840\n",
+            "Epoch 00051 | Loss 0.0172 | Train Acc 1.0000 | Val Acc 0.7840\n",
+            "Epoch 00052 | Loss 0.0187 | Train Acc 1.0000 | Val Acc 0.7820\n",
+            "Epoch 00053 | Loss 0.0216 | Train Acc 1.0000 | Val Acc 0.7860\n",
+            "Epoch 00054 | Loss 0.0249 | Train Acc 1.0000 | Val Acc 0.7800\n",
+            "Epoch 00055 | Loss 0.0179 | Train Acc 1.0000 | Val Acc 0.7880\n",
+            "Epoch 00056 | Loss 0.0354 | Train Acc 1.0000 | Val Acc 0.7900\n",
+            "Epoch 00057 | Loss 0.0385 | Train Acc 1.0000 | Val Acc 0.7960\n",
+            "Epoch 00058 | Loss 0.0214 | Train Acc 1.0000 | Val Acc 0.7900\n",
+            "Epoch 00059 | Loss 0.0190 | Train Acc 1.0000 | Val Acc 0.7800\n",
+            "Epoch 00060 | Loss 0.0172 | Train Acc 1.0000 | Val Acc 0.7800\n",
+            "Epoch 00061 | Loss 0.0203 | Train Acc 1.0000 | Val Acc 0.7820\n",
+            "Epoch 00062 | Loss 0.0231 | Train Acc 1.0000 | Val Acc 0.7860\n",
+            "Epoch 00063 | Loss 0.0117 | Train Acc 1.0000 | Val Acc 0.7900\n",
+            "Epoch 00064 | Loss 0.0190 | Train Acc 1.0000 | Val Acc 0.7880\n",
+            "Epoch 00065 | Loss 0.0143 | Train Acc 1.0000 | Val Acc 0.7900\n",
+            "Epoch 00066 | Loss 0.0267 | Train Acc 1.0000 | Val Acc 0.7900\n",
+            "Epoch 00067 | Loss 0.0142 | Train Acc 1.0000 | Val Acc 0.7900\n",
+            "Epoch 00068 | Loss 0.0224 | Train Acc 1.0000 | Val Acc 0.7860\n",
+            "Epoch 00069 | Loss 0.0228 | Train Acc 1.0000 | Val Acc 0.7780\n",
+            "Epoch 00070 | Loss 0.0157 | Train Acc 1.0000 | Val Acc 0.7820\n",
+            "Epoch 00071 | Loss 0.0272 | Train Acc 1.0000 | Val Acc 0.7840\n",
+            "Epoch 00072 | Loss 0.0198 | Train Acc 1.0000 | Val Acc 0.7780\n",
+            "Epoch 00073 | Loss 0.0147 | Train Acc 1.0000 | Val Acc 0.7800\n",
+            "Epoch 00074 | Loss 0.0271 | Train Acc 1.0000 | Val Acc 0.7800\n",
+            "Epoch 00075 | Loss 0.0158 | Train Acc 1.0000 | Val Acc 0.7800\n",
+            "Epoch 00076 | Loss 0.0154 | Train Acc 1.0000 | Val Acc 0.7780\n",
+            "Epoch 00077 | Loss 0.0242 | Train Acc 1.0000 | Val Acc 0.7880\n",
+            "Epoch 00078 | Loss 0.0185 | Train Acc 1.0000 | Val Acc 0.7800\n",
+            "Epoch 00079 | Loss 0.0271 | Train Acc 1.0000 | Val Acc 0.7800\n",
+            "Epoch 00080 | Loss 0.0190 | Train Acc 1.0000 | Val Acc 0.7880\n",
+            "Epoch 00081 | Loss 0.0218 | Train Acc 1.0000 | Val Acc 0.7880\n",
+            "Epoch 00082 | Loss 0.0196 | Train Acc 1.0000 | Val Acc 0.7900\n",
+            "Epoch 00083 | Loss 0.0213 | Train Acc 1.0000 | Val Acc 0.7880\n",
+            "Epoch 00084 | Loss 0.0138 | Train Acc 1.0000 | Val Acc 0.7860\n",
+            "Epoch 00085 | Loss 0.0364 | Train Acc 1.0000 | Val Acc 0.7960\n",
+            "Epoch 00086 | Loss 0.0179 | Train Acc 1.0000 | Val Acc 0.7800\n",
+            "Epoch 00087 | Loss 0.0251 | Train Acc 1.0000 | Val Acc 0.7780\n",
+            "Epoch 00088 | Loss 0.0445 | Train Acc 1.0000 | Val Acc 0.7880\n",
+            "Epoch 00089 | Loss 0.0170 | Train Acc 1.0000 | Val Acc 0.7940\n",
+            "Epoch 00090 | Loss 0.0167 | Train Acc 1.0000 | Val Acc 0.7800\n",
+            "Epoch 00091 | Loss 0.0314 | Train Acc 1.0000 | Val Acc 0.7840\n",
+            "Epoch 00092 | Loss 0.0148 | Train Acc 1.0000 | Val Acc 0.7920\n",
+            "Epoch 00093 | Loss 0.0278 | Train Acc 1.0000 | Val Acc 0.7900\n",
+            "Epoch 00094 | Loss 0.0127 | Train Acc 1.0000 | Val Acc 0.7860\n",
+            "Epoch 00095 | Loss 0.0214 | Train Acc 1.0000 | Val Acc 0.7760\n",
+            "Epoch 00096 | Loss 0.0130 | Train Acc 1.0000 | Val Acc 0.7800\n",
+            "Epoch 00097 | Loss 0.0340 | Train Acc 1.0000 | Val Acc 0.7820\n",
+            "Epoch 00098 | Loss 0.0122 | Train Acc 1.0000 | Val Acc 0.7880\n",
+            "Epoch 00099 | Loss 0.0329 | Train Acc 1.0000 | Val Acc 0.7900\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Training with lr: 0.01, weight_decay: 0.005\n",
+            "Epoch 00000 | Loss 0.0141 | Train Acc 0.9833 | Val Acc 0.6960\n",
+            "Epoch 00001 | Loss 0.1722 | Train Acc 1.0000 | Val Acc 0.7800\n",
+            "Epoch 00002 | Loss 0.0331 | Train Acc 0.9167 | Val Acc 0.5600\n",
+            "Epoch 00003 | Loss 0.3109 | Train Acc 0.9667 | Val Acc 0.6740\n",
+            "Epoch 00004 | Loss 0.1616 | Train Acc 1.0000 | Val Acc 0.7980\n",
+            "Epoch 00005 | Loss 0.0741 | Train Acc 0.9833 | Val Acc 0.7420\n",
+            "Epoch 00006 | Loss 0.1085 | Train Acc 0.9500 | Val Acc 0.7000\n",
+            "Epoch 00007 | Loss 0.2275 | Train Acc 0.9667 | Val Acc 0.7280\n",
+            "Epoch 00008 | Loss 0.1549 | Train Acc 0.9833 | Val Acc 0.7860\n",
+            "Epoch 00009 | Loss 0.1365 | Train Acc 1.0000 | Val Acc 0.7960\n",
+            "Epoch 00010 | Loss 0.0936 | Train Acc 1.0000 | Val Acc 0.7300\n",
+            "Epoch 00011 | Loss 0.1391 | Train Acc 0.9833 | Val Acc 0.7020\n",
+            "Epoch 00012 | Loss 0.2355 | Train Acc 0.9833 | Val Acc 0.7420\n",
+            "Epoch 00013 | Loss 0.1793 | Train Acc 1.0000 | Val Acc 0.7700\n",
+            "Epoch 00014 | Loss 0.1161 | Train Acc 1.0000 | Val Acc 0.7860\n",
+            "Epoch 00015 | Loss 0.1033 | Train Acc 0.9833 | Val Acc 0.7920\n",
+            "Epoch 00016 | Loss 0.1152 | Train Acc 0.9833 | Val Acc 0.7820\n",
+            "Epoch 00017 | Loss 0.1399 | Train Acc 0.9833 | Val Acc 0.7760\n",
+            "Epoch 00018 | Loss 0.1451 | Train Acc 0.9833 | Val Acc 0.7740\n",
+            "Epoch 00019 | Loss 0.1317 | Train Acc 0.9833 | Val Acc 0.7940\n",
+            "Epoch 00020 | Loss 0.1130 | Train Acc 0.9833 | Val Acc 0.8000\n",
+            "Epoch 00021 | Loss 0.1365 | Train Acc 0.9833 | Val Acc 0.7880\n",
+            "Epoch 00022 | Loss 0.1012 | Train Acc 1.0000 | Val Acc 0.7920\n",
+            "Epoch 00023 | Loss 0.1304 | Train Acc 1.0000 | Val Acc 0.7860\n",
+            "Epoch 00024 | Loss 0.1597 | Train Acc 1.0000 | Val Acc 0.7940\n",
+            "Epoch 00025 | Loss 0.1503 | Train Acc 0.9833 | Val Acc 0.8000\n",
+            "Epoch 00026 | Loss 0.1522 | Train Acc 0.9833 | Val Acc 0.7860\n",
+            "Epoch 00027 | Loss 0.1331 | Train Acc 0.9833 | Val Acc 0.7840\n",
+            "Epoch 00028 | Loss 0.1469 | Train Acc 0.9833 | Val Acc 0.7700\n",
+            "Epoch 00029 | Loss 0.1561 | Train Acc 0.9833 | Val Acc 0.7820\n",
+            "Epoch 00030 | Loss 0.1696 | Train Acc 0.9833 | Val Acc 0.7820\n",
+            "Epoch 00031 | Loss 0.1291 | Train Acc 0.9833 | Val Acc 0.7860\n",
+            "Epoch 00032 | Loss 0.1407 | Train Acc 0.9833 | Val Acc 0.8000\n",
+            "Epoch 00033 | Loss 0.1440 | Train Acc 1.0000 | Val Acc 0.7860\n",
+            "Epoch 00034 | Loss 0.1578 | Train Acc 1.0000 | Val Acc 0.7760\n",
+            "Epoch 00035 | Loss 0.1740 | Train Acc 1.0000 | Val Acc 0.7880\n",
+            "Epoch 00036 | Loss 0.1816 | Train Acc 1.0000 | Val Acc 0.7940\n",
+            "Epoch 00037 | Loss 0.1457 | Train Acc 1.0000 | Val Acc 0.7840\n",
+            "Epoch 00038 | Loss 0.1723 | Train Acc 0.9833 | Val Acc 0.7780\n",
+            "Epoch 00039 | Loss 0.1633 | Train Acc 0.9833 | Val Acc 0.7840\n",
+            "Epoch 00040 | Loss 0.1536 | Train Acc 0.9833 | Val Acc 0.7940\n",
+            "Epoch 00041 | Loss 0.1628 | Train Acc 0.9833 | Val Acc 0.7860\n",
+            "Epoch 00042 | Loss 0.1422 | Train Acc 1.0000 | Val Acc 0.7860\n",
+            "Epoch 00043 | Loss 0.1468 | Train Acc 1.0000 | Val Acc 0.7820\n",
+            "Epoch 00044 | Loss 0.1635 | Train Acc 1.0000 | Val Acc 0.7780\n",
+            "Epoch 00045 | Loss 0.1609 | Train Acc 1.0000 | Val Acc 0.7900\n",
+            "Epoch 00046 | Loss 0.1560 | Train Acc 0.9833 | Val Acc 0.7880\n",
+            "Epoch 00047 | Loss 0.1615 | Train Acc 0.9833 | Val Acc 0.7960\n",
+            "Epoch 00048 | Loss 0.1561 | Train Acc 0.9833 | Val Acc 0.7880\n",
+            "Epoch 00049 | Loss 0.1910 | Train Acc 0.9833 | Val Acc 0.7880\n",
+            "Epoch 00050 | Loss 0.1522 | Train Acc 1.0000 | Val Acc 0.7880\n",
+            "Epoch 00051 | Loss 0.1680 | Train Acc 1.0000 | Val Acc 0.7840\n",
+            "Epoch 00052 | Loss 0.1465 | Train Acc 1.0000 | Val Acc 0.7740\n",
+            "Epoch 00053 | Loss 0.1811 | Train Acc 1.0000 | Val Acc 0.7840\n",
+            "Epoch 00054 | Loss 0.1506 | Train Acc 0.9833 | Val Acc 0.7900\n",
+            "Epoch 00055 | Loss 0.1360 | Train Acc 0.9833 | Val Acc 0.7940\n",
+            "Epoch 00056 | Loss 0.1586 | Train Acc 0.9833 | Val Acc 0.7940\n",
+            "Epoch 00057 | Loss 0.1624 | Train Acc 0.9833 | Val Acc 0.7980\n",
+            "Epoch 00058 | Loss 0.1500 | Train Acc 0.9833 | Val Acc 0.7840\n",
+            "Epoch 00059 | Loss 0.1426 | Train Acc 1.0000 | Val Acc 0.7800\n",
+            "Epoch 00060 | Loss 0.1372 | Train Acc 1.0000 | Val Acc 0.7860\n",
+            "Epoch 00061 | Loss 0.1360 | Train Acc 0.9833 | Val Acc 0.8020\n",
+            "Epoch 00062 | Loss 0.1282 | Train Acc 0.9833 | Val Acc 0.8020\n",
+            "Epoch 00063 | Loss 0.1533 | Train Acc 0.9833 | Val Acc 0.7860\n",
+            "Epoch 00064 | Loss 0.1199 | Train Acc 1.0000 | Val Acc 0.7880\n",
+            "Epoch 00065 | Loss 0.1189 | Train Acc 1.0000 | Val Acc 0.7820\n",
+            "Epoch 00066 | Loss 0.1419 | Train Acc 1.0000 | Val Acc 0.7760\n",
+            "Epoch 00067 | Loss 0.1730 | Train Acc 1.0000 | Val Acc 0.7900\n",
+            "Epoch 00068 | Loss 0.1248 | Train Acc 0.9833 | Val Acc 0.7860\n",
+            "Epoch 00069 | Loss 0.1412 | Train Acc 0.9833 | Val Acc 0.7900\n",
+            "Epoch 00070 | Loss 0.1507 | Train Acc 0.9833 | Val Acc 0.7920\n",
+            "Epoch 00071 | Loss 0.1548 | Train Acc 1.0000 | Val Acc 0.7780\n",
+            "Epoch 00072 | Loss 0.1573 | Train Acc 1.0000 | Val Acc 0.7820\n",
+            "Epoch 00073 | Loss 0.1671 | Train Acc 1.0000 | Val Acc 0.7880\n",
+            "Epoch 00074 | Loss 0.1539 | Train Acc 0.9833 | Val Acc 0.7900\n",
+            "Epoch 00075 | Loss 0.1255 | Train Acc 0.9833 | Val Acc 0.7860\n",
+            "Epoch 00076 | Loss 0.1312 | Train Acc 0.9833 | Val Acc 0.7900\n",
+            "Epoch 00077 | Loss 0.1341 | Train Acc 0.9833 | Val Acc 0.7860\n",
+            "Epoch 00078 | Loss 0.1474 | Train Acc 0.9833 | Val Acc 0.7880\n",
+            "Epoch 00079 | Loss 0.1382 | Train Acc 0.9833 | Val Acc 0.7900\n",
+            "Epoch 00080 | Loss 0.1776 | Train Acc 0.9833 | Val Acc 0.7900\n",
+            "Epoch 00081 | Loss 0.1420 | Train Acc 0.9833 | Val Acc 0.7820\n",
+            "Epoch 00082 | Loss 0.1328 | Train Acc 0.9833 | Val Acc 0.7840\n",
+            "Epoch 00083 | Loss 0.1273 | Train Acc 0.9833 | Val Acc 0.7840\n",
+            "Epoch 00084 | Loss 0.1278 | Train Acc 0.9833 | Val Acc 0.7860\n",
+            "Epoch 00085 | Loss 0.1496 | Train Acc 0.9833 | Val Acc 0.7880\n",
+            "Epoch 00086 | Loss 0.1627 | Train Acc 0.9833 | Val Acc 0.7820\n",
+            "Epoch 00087 | Loss 0.1362 | Train Acc 1.0000 | Val Acc 0.7800\n",
+            "Epoch 00088 | Loss 0.1336 | Train Acc 1.0000 | Val Acc 0.7900\n",
+            "Epoch 00089 | Loss 0.1697 | Train Acc 1.0000 | Val Acc 0.7820\n",
+            "Epoch 00090 | Loss 0.1335 | Train Acc 1.0000 | Val Acc 0.7800\n",
+            "Epoch 00091 | Loss 0.1328 | Train Acc 1.0000 | Val Acc 0.7820\n",
+            "Epoch 00092 | Loss 0.1508 | Train Acc 0.9833 | Val Acc 0.7860\n",
+            "Epoch 00093 | Loss 0.1218 | Train Acc 0.9833 | Val Acc 0.7900\n",
+            "Epoch 00094 | Loss 0.1462 | Train Acc 0.9833 | Val Acc 0.7860\n",
+            "Epoch 00095 | Loss 0.1402 | Train Acc 0.9833 | Val Acc 0.7840\n",
+            "Epoch 00096 | Loss 0.1706 | Train Acc 0.9833 | Val Acc 0.7840\n",
+            "Epoch 00097 | Loss 0.1273 | Train Acc 1.0000 | Val Acc 0.7820\n",
+            "Epoch 00098 | Loss 0.1169 | Train Acc 1.0000 | Val Acc 0.7860\n",
+            "Epoch 00099 | Loss 0.1423 | Train Acc 1.0000 | Val Acc 0.7900\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Training with lr: 0.01, weight_decay: 0.05\n",
+            "Epoch 00000 | Loss 0.1396 | Train Acc 0.8333 | Val Acc 0.7000\n",
+            "Epoch 00001 | Loss 0.4060 | Train Acc 0.9833 | Val Acc 0.7640\n",
+            "Epoch 00002 | Loss 0.3922 | Train Acc 0.9167 | Val Acc 0.6800\n",
+            "Epoch 00003 | Loss 0.4753 | Train Acc 0.9167 | Val Acc 0.6580\n",
+            "Epoch 00004 | Loss 0.5797 | Train Acc 0.9500 | Val Acc 0.7940\n",
+            "Epoch 00005 | Loss 0.5969 | Train Acc 0.9167 | Val Acc 0.7780\n",
+            "Epoch 00006 | Loss 0.6229 | Train Acc 0.8167 | Val Acc 0.7360\n",
+            "Epoch 00007 | Loss 0.7852 | Train Acc 0.8333 | Val Acc 0.7380\n",
+            "Epoch 00008 | Loss 0.8020 | Train Acc 0.8667 | Val Acc 0.7620\n",
+            "Epoch 00009 | Loss 0.8442 | Train Acc 0.8833 | Val Acc 0.7660\n",
+            "Epoch 00010 | Loss 0.8171 | Train Acc 0.8333 | Val Acc 0.7100\n",
+            "Epoch 00011 | Loss 0.8727 | Train Acc 0.6667 | Val Acc 0.5500\n",
+            "Epoch 00012 | Loss 0.8830 | Train Acc 0.6833 | Val Acc 0.5600\n",
+            "Epoch 00013 | Loss 0.9306 | Train Acc 0.7500 | Val Acc 0.6400\n",
+            "Epoch 00014 | Loss 0.9472 | Train Acc 0.8333 | Val Acc 0.7420\n",
+            "Epoch 00015 | Loss 0.9545 | Train Acc 0.8833 | Val Acc 0.7240\n",
+            "Epoch 00016 | Loss 0.9804 | Train Acc 0.8167 | Val Acc 0.6420\n",
+            "Epoch 00017 | Loss 0.9866 | Train Acc 0.6667 | Val Acc 0.5060\n",
+            "Epoch 00018 | Loss 1.0360 | Train Acc 0.5833 | Val Acc 0.4740\n",
+            "Epoch 00019 | Loss 1.0237 | Train Acc 0.6833 | Val Acc 0.5580\n",
+            "Epoch 00020 | Loss 1.0489 | Train Acc 0.7333 | Val Acc 0.6360\n",
+            "Epoch 00021 | Loss 1.0313 | Train Acc 0.7000 | Val Acc 0.6400\n",
+            "Epoch 00022 | Loss 1.0623 | Train Acc 0.6667 | Val Acc 0.5520\n",
+            "Epoch 00023 | Loss 1.0515 | Train Acc 0.6333 | Val Acc 0.5280\n",
+            "Epoch 00024 | Loss 1.0700 | Train Acc 0.6167 | Val Acc 0.5140\n",
+            "Epoch 00025 | Loss 1.0525 | Train Acc 0.3833 | Val Acc 0.2040\n",
+            "Epoch 00026 | Loss 1.0575 | Train Acc 0.3333 | Val Acc 0.1960\n",
+            "Epoch 00027 | Loss 1.0713 | Train Acc 0.3333 | Val Acc 0.1960\n",
+            "Epoch 00028 | Loss 1.0628 | Train Acc 0.3333 | Val Acc 0.1960\n",
+            "Epoch 00029 | Loss 1.0775 | Train Acc 0.4000 | Val Acc 0.2120\n",
+            "Epoch 00030 | Loss 1.0802 | Train Acc 0.5667 | Val Acc 0.3920\n",
+            "Epoch 00031 | Loss 1.0739 | Train Acc 0.6333 | Val Acc 0.5440\n",
+            "Epoch 00032 | Loss 1.0692 | Train Acc 0.6167 | Val Acc 0.5360\n",
+            "Epoch 00033 | Loss 1.0762 | Train Acc 0.5000 | Val Acc 0.4940\n",
+            "Epoch 00034 | Loss 1.0789 | Train Acc 0.4000 | Val Acc 0.4220\n",
+            "Epoch 00035 | Loss 1.0866 | Train Acc 0.3333 | Val Acc 0.4160\n",
+            "Epoch 00036 | Loss 1.0915 | Train Acc 0.3333 | Val Acc 0.4160\n",
+            "Epoch 00037 | Loss 1.0939 | Train Acc 0.3333 | Val Acc 0.4160\n",
+            "Epoch 00038 | Loss 1.1009 | Train Acc 0.3333 | Val Acc 0.4160\n",
+            "Epoch 00039 | Loss 1.0925 | Train Acc 0.3333 | Val Acc 0.4160\n",
+            "Epoch 00040 | Loss 1.0796 | Train Acc 0.3333 | Val Acc 0.4160\n",
+            "Epoch 00041 | Loss 1.0864 | Train Acc 0.6000 | Val Acc 0.5940\n",
+            "Epoch 00042 | Loss 1.0935 | Train Acc 0.3333 | Val Acc 0.3880\n",
+            "Epoch 00043 | Loss 1.0938 | Train Acc 0.3333 | Val Acc 0.3880\n",
+            "Epoch 00044 | Loss 1.1116 | Train Acc 0.3333 | Val Acc 0.3880\n",
+            "Epoch 00045 | Loss 1.0971 | Train Acc 0.3333 | Val Acc 0.3880\n",
+            "Epoch 00046 | Loss 1.0933 | Train Acc 0.3333 | Val Acc 0.3880\n",
+            "Epoch 00047 | Loss 1.0998 | Train Acc 0.3333 | Val Acc 0.3880\n",
+            "Epoch 00048 | Loss 1.0978 | Train Acc 0.3333 | Val Acc 0.3880\n",
+            "Epoch 00049 | Loss 1.0966 | Train Acc 0.3333 | Val Acc 0.3880\n",
+            "Epoch 00050 | Loss 1.0920 | Train Acc 0.3333 | Val Acc 0.3880\n",
+            "Epoch 00051 | Loss 1.0994 | Train Acc 0.3333 | Val Acc 0.3880\n",
+            "Epoch 00052 | Loss 1.1015 | Train Acc 0.3333 | Val Acc 0.1960\n",
+            "Epoch 00053 | Loss 1.0974 | Train Acc 0.3333 | Val Acc 0.1960\n",
+            "Epoch 00054 | Loss 1.0950 | Train Acc 0.3333 | Val Acc 0.1960\n",
+            "Epoch 00055 | Loss 1.0965 | Train Acc 0.3333 | Val Acc 0.1960\n",
+            "Epoch 00056 | Loss 1.1020 | Train Acc 0.3333 | Val Acc 0.1960\n",
+            "Epoch 00057 | Loss 1.0998 | Train Acc 0.3333 | Val Acc 0.1960\n",
+            "Epoch 00058 | Loss 1.0979 | Train Acc 0.3333 | Val Acc 0.1960\n",
+            "Epoch 00059 | Loss 1.1047 | Train Acc 0.3333 | Val Acc 0.1960\n",
+            "Epoch 00060 | Loss 1.1012 | Train Acc 0.3333 | Val Acc 0.4160\n",
+            "Epoch 00061 | Loss 1.1030 | Train Acc 0.3333 | Val Acc 0.4160\n",
+            "Epoch 00062 | Loss 1.0959 | Train Acc 0.3333 | Val Acc 0.4160\n",
+            "Epoch 00063 | Loss 1.1040 | Train Acc 0.3333 | Val Acc 0.4160\n",
+            "Epoch 00064 | Loss 1.0966 | Train Acc 0.3333 | Val Acc 0.4160\n",
+            "Epoch 00065 | Loss 1.1010 | Train Acc 0.3333 | Val Acc 0.4160\n",
+            "Epoch 00066 | Loss 1.1009 | Train Acc 0.3333 | Val Acc 0.4160\n",
+            "Epoch 00067 | Loss 1.1013 | Train Acc 0.3333 | Val Acc 0.3880\n",
+            "Epoch 00068 | Loss 1.0981 | Train Acc 0.3333 | Val Acc 0.3880\n",
+            "Epoch 00069 | Loss 1.1007 | Train Acc 0.3333 | Val Acc 0.3880\n",
+            "Epoch 00070 | Loss 1.0992 | Train Acc 0.3333 | Val Acc 0.3880\n",
+            "Epoch 00071 | Loss 1.1005 | Train Acc 0.3333 | Val Acc 0.3880\n",
+            "Epoch 00072 | Loss 1.1000 | Train Acc 0.3333 | Val Acc 0.3880\n",
+            "Epoch 00073 | Loss 1.1003 | Train Acc 0.3333 | Val Acc 0.3880\n",
+            "Epoch 00074 | Loss 1.0975 | Train Acc 0.3333 | Val Acc 0.3880\n",
+            "Epoch 00075 | Loss 1.0974 | Train Acc 0.3333 | Val Acc 0.3880\n",
+            "Epoch 00076 | Loss 1.0984 | Train Acc 0.3333 | Val Acc 0.3880\n",
+            "Epoch 00077 | Loss 1.1019 | Train Acc 0.3333 | Val Acc 0.3880\n",
+            "Epoch 00078 | Loss 1.0974 | Train Acc 0.3333 | Val Acc 0.3880\n",
+            "Epoch 00079 | Loss 1.0978 | Train Acc 0.3333 | Val Acc 0.3880\n",
+            "Epoch 00080 | Loss 1.0989 | Train Acc 0.3333 | Val Acc 0.3880\n",
+            "Epoch 00081 | Loss 1.0993 | Train Acc 0.5333 | Val Acc 0.6280\n",
+            "Epoch 00082 | Loss 1.0970 | Train Acc 0.3333 | Val Acc 0.4160\n",
+            "Epoch 00083 | Loss 1.0988 | Train Acc 0.3333 | Val Acc 0.4160\n",
+            "Epoch 00084 | Loss 1.0990 | Train Acc 0.3333 | Val Acc 0.4160\n",
+            "Epoch 00085 | Loss 1.0979 | Train Acc 0.3333 | Val Acc 0.4160\n",
+            "Epoch 00086 | Loss 1.0987 | Train Acc 0.3333 | Val Acc 0.4160\n",
+            "Epoch 00087 | Loss 1.0981 | Train Acc 0.3333 | Val Acc 0.4160\n",
+            "Epoch 00088 | Loss 1.0989 | Train Acc 0.3333 | Val Acc 0.4160\n",
+            "Epoch 00089 | Loss 1.0992 | Train Acc 0.3333 | Val Acc 0.4160\n",
+            "Epoch 00090 | Loss 1.0996 | Train Acc 0.3333 | Val Acc 0.4160\n",
+            "Epoch 00091 | Loss 1.0990 | Train Acc 0.3333 | Val Acc 0.4160\n",
+            "Epoch 00092 | Loss 1.0990 | Train Acc 0.3333 | Val Acc 0.3880\n",
+            "Epoch 00093 | Loss 1.0989 | Train Acc 0.3333 | Val Acc 0.3880\n",
+            "Epoch 00094 | Loss 1.0999 | Train Acc 0.3333 | Val Acc 0.3880\n",
+            "Epoch 00095 | Loss 1.0985 | Train Acc 0.3333 | Val Acc 0.3880\n",
+            "Epoch 00096 | Loss 1.0984 | Train Acc 0.3333 | Val Acc 0.3880\n",
+            "Epoch 00097 | Loss 1.0976 | Train Acc 0.3333 | Val Acc 0.3880\n",
+            "Epoch 00098 | Loss 1.0988 | Train Acc 0.3333 | Val Acc 0.3880\n",
+            "Epoch 00099 | Loss 1.0984 | Train Acc 0.3333 | Val Acc 0.3880\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Training with lr: 0.001, weight_decay: 0.0005\n",
+            "Epoch 00000 | Loss 1.0982 | Train Acc 0.3333 | Val Acc 0.4160\n",
+            "Epoch 00001 | Loss 1.0990 | Train Acc 0.3333 | Val Acc 0.4160\n",
+            "Epoch 00002 | Loss 1.0979 | Train Acc 0.3333 | Val Acc 0.4160\n",
+            "Epoch 00003 | Loss 1.0983 | Train Acc 0.3333 | Val Acc 0.4160\n",
+            "Epoch 00004 | Loss 1.0991 | Train Acc 0.3333 | Val Acc 0.4160\n",
+            "Epoch 00005 | Loss 1.0986 | Train Acc 0.3333 | Val Acc 0.3880\n",
+            "Epoch 00006 | Loss 1.0983 | Train Acc 0.3333 | Val Acc 0.3880\n",
+            "Epoch 00007 | Loss 1.0983 | Train Acc 0.3333 | Val Acc 0.3880\n",
+            "Epoch 00008 | Loss 1.0978 | Train Acc 0.3333 | Val Acc 0.3880\n",
+            "Epoch 00009 | Loss 1.0998 | Train Acc 0.4667 | Val Acc 0.4620\n",
+            "Epoch 00010 | Loss 1.0980 | Train Acc 0.7167 | Val Acc 0.5720\n",
+            "Epoch 00011 | Loss 1.0982 | Train Acc 0.4667 | Val Acc 0.2580\n",
+            "Epoch 00012 | Loss 1.0988 | Train Acc 0.5667 | Val Acc 0.3720\n",
+            "Epoch 00013 | Loss 1.0977 | Train Acc 0.5333 | Val Acc 0.3760\n",
+            "Epoch 00014 | Loss 1.0986 | Train Acc 0.5333 | Val Acc 0.3240\n",
+            "Epoch 00015 | Loss 1.0980 | Train Acc 0.6000 | Val Acc 0.4220\n",
+            "Epoch 00016 | Loss 1.0979 | Train Acc 0.7167 | Val Acc 0.5600\n",
+            "Epoch 00017 | Loss 1.0980 | Train Acc 0.7667 | Val Acc 0.6000\n",
+            "Epoch 00018 | Loss 1.0988 | Train Acc 0.8500 | Val Acc 0.7020\n",
+            "Epoch 00019 | Loss 1.0975 | Train Acc 0.8833 | Val Acc 0.7300\n",
+            "Epoch 00020 | Loss 1.0971 | Train Acc 0.8833 | Val Acc 0.7460\n",
+            "Epoch 00021 | Loss 1.0968 | Train Acc 0.8833 | Val Acc 0.7500\n",
+            "Epoch 00022 | Loss 1.0959 | Train Acc 0.9000 | Val Acc 0.7380\n",
+            "Epoch 00023 | Loss 1.0961 | Train Acc 0.9000 | Val Acc 0.7380\n",
+            "Epoch 00024 | Loss 1.0962 | Train Acc 0.9000 | Val Acc 0.7260\n",
+            "Epoch 00025 | Loss 1.0954 | Train Acc 0.9000 | Val Acc 0.7300\n",
+            "Epoch 00026 | Loss 1.0958 | Train Acc 0.8833 | Val Acc 0.7300\n",
+            "Epoch 00027 | Loss 1.0948 | Train Acc 0.8833 | Val Acc 0.7280\n",
+            "Epoch 00028 | Loss 1.0948 | Train Acc 0.8833 | Val Acc 0.7260\n",
+            "Epoch 00029 | Loss 1.0937 | Train Acc 0.8833 | Val Acc 0.7260\n",
+            "Epoch 00030 | Loss 1.0943 | Train Acc 0.8667 | Val Acc 0.7260\n",
+            "Epoch 00031 | Loss 1.0912 | Train Acc 0.8667 | Val Acc 0.7200\n",
+            "Epoch 00032 | Loss 1.0908 | Train Acc 0.8667 | Val Acc 0.7200\n",
+            "Epoch 00033 | Loss 1.0911 | Train Acc 0.8833 | Val Acc 0.7220\n",
+            "Epoch 00034 | Loss 1.0890 | Train Acc 0.8833 | Val Acc 0.7180\n",
+            "Epoch 00035 | Loss 1.0888 | Train Acc 0.8833 | Val Acc 0.7180\n",
+            "Epoch 00036 | Loss 1.0868 | Train Acc 0.8833 | Val Acc 0.7240\n",
+            "Epoch 00037 | Loss 1.0858 | Train Acc 0.8667 | Val Acc 0.7200\n",
+            "Epoch 00038 | Loss 1.0836 | Train Acc 0.8667 | Val Acc 0.7220\n",
+            "Epoch 00039 | Loss 1.0831 | Train Acc 0.8667 | Val Acc 0.7240\n",
+            "Epoch 00040 | Loss 1.0813 | Train Acc 0.8667 | Val Acc 0.7260\n",
+            "Epoch 00041 | Loss 1.0776 | Train Acc 0.8667 | Val Acc 0.7220\n",
+            "Epoch 00042 | Loss 1.0762 | Train Acc 0.8667 | Val Acc 0.7240\n",
+            "Epoch 00043 | Loss 1.0736 | Train Acc 0.8667 | Val Acc 0.7200\n",
+            "Epoch 00044 | Loss 1.0716 | Train Acc 0.8667 | Val Acc 0.7180\n",
+            "Epoch 00045 | Loss 1.0656 | Train Acc 0.8667 | Val Acc 0.7240\n",
+            "Epoch 00046 | Loss 1.0657 | Train Acc 0.8667 | Val Acc 0.7260\n",
+            "Epoch 00047 | Loss 1.0623 | Train Acc 0.8667 | Val Acc 0.7280\n",
+            "Epoch 00048 | Loss 1.0563 | Train Acc 0.8667 | Val Acc 0.7240\n",
+            "Epoch 00049 | Loss 1.0525 | Train Acc 0.8667 | Val Acc 0.7180\n",
+            "Epoch 00050 | Loss 1.0485 | Train Acc 0.8667 | Val Acc 0.7180\n",
+            "Epoch 00051 | Loss 1.0468 | Train Acc 0.8833 | Val Acc 0.7180\n",
+            "Epoch 00052 | Loss 1.0393 | Train Acc 0.8833 | Val Acc 0.7120\n",
+            "Epoch 00053 | Loss 1.0335 | Train Acc 0.8667 | Val Acc 0.7060\n",
+            "Epoch 00054 | Loss 1.0300 | Train Acc 0.8667 | Val Acc 0.7020\n",
+            "Epoch 00055 | Loss 1.0255 | Train Acc 0.8667 | Val Acc 0.7020\n",
+            "Epoch 00056 | Loss 1.0181 | Train Acc 0.8667 | Val Acc 0.7080\n",
+            "Epoch 00057 | Loss 1.0077 | Train Acc 0.8667 | Val Acc 0.7080\n",
+            "Epoch 00058 | Loss 1.0035 | Train Acc 0.8667 | Val Acc 0.7080\n",
+            "Epoch 00059 | Loss 0.9957 | Train Acc 0.8667 | Val Acc 0.7040\n",
+            "Epoch 00060 | Loss 0.9888 | Train Acc 0.8667 | Val Acc 0.7080\n",
+            "Epoch 00061 | Loss 0.9810 | Train Acc 0.8833 | Val Acc 0.7060\n",
+            "Epoch 00062 | Loss 0.9768 | Train Acc 0.8667 | Val Acc 0.7060\n",
+            "Epoch 00063 | Loss 0.9715 | Train Acc 0.8667 | Val Acc 0.6980\n",
+            "Epoch 00064 | Loss 0.9677 | Train Acc 0.8667 | Val Acc 0.6980\n",
+            "Epoch 00065 | Loss 0.9489 | Train Acc 0.8667 | Val Acc 0.6880\n",
+            "Epoch 00066 | Loss 0.9411 | Train Acc 0.8667 | Val Acc 0.6780\n",
+            "Epoch 00067 | Loss 0.9300 | Train Acc 0.8500 | Val Acc 0.6760\n",
+            "Epoch 00068 | Loss 0.9127 | Train Acc 0.8500 | Val Acc 0.6760\n",
+            "Epoch 00069 | Loss 0.9134 | Train Acc 0.8667 | Val Acc 0.6780\n",
+            "Epoch 00070 | Loss 0.8836 | Train Acc 0.8667 | Val Acc 0.6880\n",
+            "Epoch 00071 | Loss 0.8930 | Train Acc 0.8667 | Val Acc 0.6940\n",
+            "Epoch 00072 | Loss 0.8685 | Train Acc 0.8833 | Val Acc 0.6960\n",
+            "Epoch 00073 | Loss 0.8694 | Train Acc 0.9000 | Val Acc 0.7020\n",
+            "Epoch 00074 | Loss 0.8447 | Train Acc 0.9000 | Val Acc 0.7020\n",
+            "Epoch 00075 | Loss 0.8282 | Train Acc 0.9000 | Val Acc 0.6980\n",
+            "Epoch 00076 | Loss 0.8269 | Train Acc 0.9000 | Val Acc 0.7000\n",
+            "Epoch 00077 | Loss 0.8083 | Train Acc 0.9000 | Val Acc 0.7020\n",
+            "Epoch 00078 | Loss 0.7887 | Train Acc 0.9000 | Val Acc 0.6940\n",
+            "Epoch 00079 | Loss 0.7773 | Train Acc 0.9000 | Val Acc 0.6920\n",
+            "Epoch 00080 | Loss 0.7836 | Train Acc 0.8833 | Val Acc 0.6960\n",
+            "Epoch 00081 | Loss 0.7580 | Train Acc 0.8833 | Val Acc 0.6900\n",
+            "Epoch 00082 | Loss 0.7467 | Train Acc 0.8833 | Val Acc 0.6940\n",
+            "Epoch 00083 | Loss 0.7437 | Train Acc 0.8833 | Val Acc 0.6940\n",
+            "Epoch 00084 | Loss 0.7341 | Train Acc 0.8833 | Val Acc 0.7020\n",
+            "Epoch 00085 | Loss 0.7182 | Train Acc 0.9167 | Val Acc 0.7040\n",
+            "Epoch 00086 | Loss 0.6832 | Train Acc 0.9167 | Val Acc 0.7120\n",
+            "Epoch 00087 | Loss 0.6744 | Train Acc 0.9167 | Val Acc 0.7240\n",
+            "Epoch 00088 | Loss 0.6609 | Train Acc 0.9167 | Val Acc 0.7320\n",
+            "Epoch 00089 | Loss 0.6612 | Train Acc 0.9333 | Val Acc 0.7400\n",
+            "Epoch 00090 | Loss 0.6511 | Train Acc 0.9333 | Val Acc 0.7440\n",
+            "Epoch 00091 | Loss 0.6249 | Train Acc 0.9333 | Val Acc 0.7460\n",
+            "Epoch 00092 | Loss 0.6399 | Train Acc 0.9333 | Val Acc 0.7400\n",
+            "Epoch 00093 | Loss 0.6253 | Train Acc 0.9167 | Val Acc 0.7380\n",
+            "Epoch 00094 | Loss 0.6034 | Train Acc 0.9167 | Val Acc 0.7380\n",
+            "Epoch 00095 | Loss 0.5935 | Train Acc 0.9167 | Val Acc 0.7420\n",
+            "Epoch 00096 | Loss 0.5763 | Train Acc 0.9167 | Val Acc 0.7400\n",
+            "Epoch 00097 | Loss 0.5703 | Train Acc 0.9167 | Val Acc 0.7460\n",
+            "Epoch 00098 | Loss 0.5489 | Train Acc 0.9167 | Val Acc 0.7440\n",
+            "Epoch 00099 | Loss 0.5238 | Train Acc 0.9167 | Val Acc 0.7500\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Training with lr: 0.001, weight_decay: 0.005\n",
+            "Epoch 00000 | Loss 0.5156 | Train Acc 0.9167 | Val Acc 0.7480\n",
+            "Epoch 00001 | Loss 0.5165 | Train Acc 0.9167 | Val Acc 0.7540\n",
+            "Epoch 00002 | Loss 0.5430 | Train Acc 0.9167 | Val Acc 0.7520\n",
+            "Epoch 00003 | Loss 0.5132 | Train Acc 0.9000 | Val Acc 0.7500\n",
+            "Epoch 00004 | Loss 0.5147 | Train Acc 0.9000 | Val Acc 0.7500\n",
+            "Epoch 00005 | Loss 0.5080 | Train Acc 0.9000 | Val Acc 0.7500\n",
+            "Epoch 00006 | Loss 0.5068 | Train Acc 0.9000 | Val Acc 0.7500\n",
+            "Epoch 00007 | Loss 0.5142 | Train Acc 0.9000 | Val Acc 0.7500\n",
+            "Epoch 00008 | Loss 0.5263 | Train Acc 0.9167 | Val Acc 0.7480\n",
+            "Epoch 00009 | Loss 0.4984 | Train Acc 0.9167 | Val Acc 0.7520\n",
+            "Epoch 00010 | Loss 0.5128 | Train Acc 0.9167 | Val Acc 0.7540\n",
+            "Epoch 00011 | Loss 0.5046 | Train Acc 0.9167 | Val Acc 0.7520\n",
+            "Epoch 00012 | Loss 0.5451 | Train Acc 0.9167 | Val Acc 0.7560\n",
+            "Epoch 00013 | Loss 0.5116 | Train Acc 0.9167 | Val Acc 0.7560\n",
+            "Epoch 00014 | Loss 0.5082 | Train Acc 0.9167 | Val Acc 0.7560\n",
+            "Epoch 00015 | Loss 0.5244 | Train Acc 0.9167 | Val Acc 0.7560\n",
+            "Epoch 00016 | Loss 0.5277 | Train Acc 0.9167 | Val Acc 0.7560\n",
+            "Epoch 00017 | Loss 0.4976 | Train Acc 0.9167 | Val Acc 0.7560\n",
+            "Epoch 00018 | Loss 0.5134 | Train Acc 0.9167 | Val Acc 0.7560\n",
+            "Epoch 00019 | Loss 0.5236 | Train Acc 0.9167 | Val Acc 0.7540\n",
+            "Epoch 00020 | Loss 0.5187 | Train Acc 0.9167 | Val Acc 0.7540\n",
+            "Epoch 00021 | Loss 0.5264 | Train Acc 0.9167 | Val Acc 0.7540\n",
+            "Epoch 00022 | Loss 0.5002 | Train Acc 0.9167 | Val Acc 0.7560\n",
+            "Epoch 00023 | Loss 0.5102 | Train Acc 0.9167 | Val Acc 0.7560\n",
+            "Epoch 00024 | Loss 0.5083 | Train Acc 0.9167 | Val Acc 0.7600\n",
+            "Epoch 00025 | Loss 0.4990 | Train Acc 0.9167 | Val Acc 0.7600\n",
+            "Epoch 00026 | Loss 0.5107 | Train Acc 0.9167 | Val Acc 0.7600\n",
+            "Epoch 00027 | Loss 0.4975 | Train Acc 0.9167 | Val Acc 0.7620\n",
+            "Epoch 00028 | Loss 0.4800 | Train Acc 0.9167 | Val Acc 0.7640\n",
+            "Epoch 00029 | Loss 0.4951 | Train Acc 0.9167 | Val Acc 0.7580\n",
+            "Epoch 00030 | Loss 0.4940 | Train Acc 0.9333 | Val Acc 0.7580\n",
+            "Epoch 00031 | Loss 0.4928 | Train Acc 0.9333 | Val Acc 0.7600\n",
+            "Epoch 00032 | Loss 0.4979 | Train Acc 0.9333 | Val Acc 0.7640\n",
+            "Epoch 00033 | Loss 0.5203 | Train Acc 0.9333 | Val Acc 0.7680\n",
+            "Epoch 00034 | Loss 0.4990 | Train Acc 0.9333 | Val Acc 0.7700\n",
+            "Epoch 00035 | Loss 0.5148 | Train Acc 0.9333 | Val Acc 0.7700\n",
+            "Epoch 00036 | Loss 0.4775 | Train Acc 0.9333 | Val Acc 0.7740\n",
+            "Epoch 00037 | Loss 0.5038 | Train Acc 0.9333 | Val Acc 0.7700\n",
+            "Epoch 00038 | Loss 0.4893 | Train Acc 0.9333 | Val Acc 0.7740\n",
+            "Epoch 00039 | Loss 0.5086 | Train Acc 0.9333 | Val Acc 0.7700\n",
+            "Epoch 00040 | Loss 0.4781 | Train Acc 0.9333 | Val Acc 0.7720\n",
+            "Epoch 00041 | Loss 0.5036 | Train Acc 0.9333 | Val Acc 0.7720\n",
+            "Epoch 00042 | Loss 0.4916 | Train Acc 0.9167 | Val Acc 0.7680\n",
+            "Epoch 00043 | Loss 0.4929 | Train Acc 0.9167 | Val Acc 0.7680\n",
+            "Epoch 00044 | Loss 0.4793 | Train Acc 0.9167 | Val Acc 0.7680\n",
+            "Epoch 00045 | Loss 0.4863 | Train Acc 0.9167 | Val Acc 0.7680\n",
+            "Epoch 00046 | Loss 0.4663 | Train Acc 0.9167 | Val Acc 0.7720\n",
+            "Epoch 00047 | Loss 0.4642 | Train Acc 0.9167 | Val Acc 0.7700\n",
+            "Epoch 00048 | Loss 0.4566 | Train Acc 0.9333 | Val Acc 0.7780\n",
+            "Epoch 00049 | Loss 0.4805 | Train Acc 0.9333 | Val Acc 0.7760\n",
+            "Epoch 00050 | Loss 0.4598 | Train Acc 0.9333 | Val Acc 0.7760\n",
+            "Epoch 00051 | Loss 0.4739 | Train Acc 0.9500 | Val Acc 0.7760\n",
+            "Epoch 00052 | Loss 0.4595 | Train Acc 0.9500 | Val Acc 0.7760\n",
+            "Epoch 00053 | Loss 0.4503 | Train Acc 0.9333 | Val Acc 0.7760\n",
+            "Epoch 00054 | Loss 0.4581 | Train Acc 0.9333 | Val Acc 0.7820\n",
+            "Epoch 00055 | Loss 0.4599 | Train Acc 0.9500 | Val Acc 0.7760\n",
+            "Epoch 00056 | Loss 0.4611 | Train Acc 0.9500 | Val Acc 0.7800\n",
+            "Epoch 00057 | Loss 0.4441 | Train Acc 0.9333 | Val Acc 0.7820\n",
+            "Epoch 00058 | Loss 0.4749 | Train Acc 0.9333 | Val Acc 0.7820\n",
+            "Epoch 00059 | Loss 0.4559 | Train Acc 0.9333 | Val Acc 0.7800\n",
+            "Epoch 00060 | Loss 0.4536 | Train Acc 0.9333 | Val Acc 0.7800\n",
+            "Epoch 00061 | Loss 0.4433 | Train Acc 0.9333 | Val Acc 0.7860\n",
+            "Epoch 00062 | Loss 0.4493 | Train Acc 0.9500 | Val Acc 0.7860\n",
+            "Epoch 00063 | Loss 0.4559 | Train Acc 0.9500 | Val Acc 0.7840\n",
+            "Epoch 00064 | Loss 0.4302 | Train Acc 0.9500 | Val Acc 0.7820\n",
+            "Epoch 00065 | Loss 0.4640 | Train Acc 0.9500 | Val Acc 0.7840\n",
+            "Epoch 00066 | Loss 0.4397 | Train Acc 0.9333 | Val Acc 0.7780\n",
+            "Epoch 00067 | Loss 0.4405 | Train Acc 0.9500 | Val Acc 0.7780\n",
+            "Epoch 00068 | Loss 0.4413 | Train Acc 0.9500 | Val Acc 0.7780\n",
+            "Epoch 00069 | Loss 0.4674 | Train Acc 0.9500 | Val Acc 0.7780\n",
+            "Epoch 00070 | Loss 0.4283 | Train Acc 0.9500 | Val Acc 0.7800\n",
+            "Epoch 00071 | Loss 0.4410 | Train Acc 0.9500 | Val Acc 0.7800\n",
+            "Epoch 00072 | Loss 0.4214 | Train Acc 0.9500 | Val Acc 0.7840\n",
+            "Epoch 00073 | Loss 0.4101 | Train Acc 0.9500 | Val Acc 0.7820\n",
+            "Epoch 00074 | Loss 0.4358 | Train Acc 0.9333 | Val Acc 0.7880\n",
+            "Epoch 00075 | Loss 0.4267 | Train Acc 0.9333 | Val Acc 0.7920\n",
+            "Epoch 00076 | Loss 0.4439 | Train Acc 0.9333 | Val Acc 0.7920\n",
+            "Epoch 00077 | Loss 0.4059 | Train Acc 0.9333 | Val Acc 0.7920\n",
+            "Epoch 00078 | Loss 0.4002 | Train Acc 0.9333 | Val Acc 0.7880\n",
+            "Epoch 00079 | Loss 0.4394 | Train Acc 0.9333 | Val Acc 0.7900\n",
+            "Epoch 00080 | Loss 0.4181 | Train Acc 0.9333 | Val Acc 0.7920\n",
+            "Epoch 00081 | Loss 0.4203 | Train Acc 0.9333 | Val Acc 0.7960\n",
+            "Epoch 00082 | Loss 0.4062 | Train Acc 0.9333 | Val Acc 0.7920\n",
+            "Epoch 00083 | Loss 0.4240 | Train Acc 0.9333 | Val Acc 0.7900\n",
+            "Epoch 00084 | Loss 0.4010 | Train Acc 0.9500 | Val Acc 0.7860\n",
+            "Epoch 00085 | Loss 0.4081 | Train Acc 0.9500 | Val Acc 0.7860\n",
+            "Epoch 00086 | Loss 0.3879 | Train Acc 0.9500 | Val Acc 0.7900\n",
+            "Epoch 00087 | Loss 0.4083 | Train Acc 0.9333 | Val Acc 0.7940\n",
+            "Epoch 00088 | Loss 0.4137 | Train Acc 0.9500 | Val Acc 0.7920\n",
+            "Epoch 00089 | Loss 0.4143 | Train Acc 0.9500 | Val Acc 0.7940\n",
+            "Epoch 00090 | Loss 0.4022 | Train Acc 0.9500 | Val Acc 0.7920\n",
+            "Epoch 00091 | Loss 0.3977 | Train Acc 0.9500 | Val Acc 0.7900\n",
+            "Epoch 00092 | Loss 0.3843 | Train Acc 0.9500 | Val Acc 0.7900\n",
+            "Epoch 00093 | Loss 0.3901 | Train Acc 0.9333 | Val Acc 0.7940\n",
+            "Epoch 00094 | Loss 0.3857 | Train Acc 0.9333 | Val Acc 0.7960\n",
+            "Epoch 00095 | Loss 0.3895 | Train Acc 0.9333 | Val Acc 0.8000\n",
+            "Epoch 00096 | Loss 0.3885 | Train Acc 0.9333 | Val Acc 0.7960\n",
+            "Epoch 00097 | Loss 0.3624 | Train Acc 0.9333 | Val Acc 0.7980\n",
+            "Epoch 00098 | Loss 0.3691 | Train Acc 0.9333 | Val Acc 0.7940\n",
+            "Epoch 00099 | Loss 0.4169 | Train Acc 0.9333 | Val Acc 0.7940\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x500 with 1 Axes>"
+            ],
+            "image/png": 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cvXsXy5Ytw9ixY7F161YAwLJlyzBu3DhYWlpi2rRpACDeA2hj2LBh2LhxI/r3749WrVrhyJEj6Natm1q5x48fo0WLFuKPaI6Ojti3bx+GDh2K1NRUTJgwAYDic9G9e3ccPnwYffv2xSeffIJnz57h4MGDuHr1Kry8vAAA3377Ld599118+OGHyM7OxpYtW9CrVy/8/vvv4v4HDBiA4cOH4+rVq2jQoIFYlwsXLuC///7D9OnTxWVPnz5V++FDE3Nzc/E7N7/rz9fXF1KpFJcuXcJHH32U77YuXbqEWrVqqX0fKd//yMhIuLu748qVK8jNzVXbj7GxMXx8fDRe54MHD0ZaWhoMDAzQpk0bLF68WG19ZV0FQcCZM2fQvXv3Qo//jaWvJjd6/WnqdtiuXTsBgPD999+rldfUrWfkyJGCubm5kJmZKS57tRuLspudvb29SrP7nj17BADCb7/9Ji6bNWuWWp0ACMbGxsKtW7fEZZcvXxYACMuXLxeXBQYGCubm5kJsbKy47ObNm4KhoaFW3Ss1Hd/ChQsFiUQi3Lt3T+X4AAhz585VKdu4cWPB19dXfL57924BgLBo0SJxWW5urtCmTRutu+E1a9ZMqFKliiCTycRl+/fvFwAIP/zwg7jNV7sEPX36VHBychKGDBmishyvdP1QdkWJjo4WBEHRlcfY2Fjo1q2bIJfLxXJffPGFAECl22FmZqZKvQRB8V6bmJionJuCuh2+eq0oz9n8+fNVyn3wwQeCRCJRuQa0vS7yk52dLdjb2wvTpk0Tl/Xv31/w9vZWKXfkyBEBgDB+/Hi1bSjP0c2bNwWpVCr07NlT7ZzkPY+vnn+lV7t0Kt+Xt956S61Llqbr9OzZswIA4ZdffhGXzZw5UwAg7Ny5M996HzhwQAAg7Nu3T+X1Ro0aFdrtSNv3KioqSuN78vHHHwuWlpbi8Zw8eVIAIGzatEmlnPJ6z7vcw8NDACDs37+/wDoqKT+zmh7+/v5iOeV59/X1FbKzs8XlixYtEgAIe/bsEQTh5eekc+fOKu/3ihUrBADC2rVrBUFQfDarVasmeHh4CE+fPlWpU97rQtvvlE8++USwtrYutJveq27dupXv5yK/72tra2shPj5erXx+5/HVR97Pe7du3YTq1aurbSs9PV0AIEyZMqXA+ltYWKh9lwmCIPzxxx8q18HixYsFAEJMTIxa2WbNmgktWrTQuP3S6nY4ZswYwcnJSXweEhIitG3bVqhcubKwevVqQRAEITExUZBIJMK3334rCILiOqhZs6bg7++vck1kZGQI1apVEzp16iQue/X7Oi4uTjA0NBR69OihUo/Zs2erfV8r1+3YsaPKfiZOnCgYGBgIycnJ4rLidjuMjIwUAAgff/yxyvL+/furffcNHTpUcHFxERISElTK9u3bV7CxsRG/F9auXSsAEJYuXaq2v1fPV17Z2dlCgwYNhHfeeUdclpycLJiamgqTJ09WKTt+/HjBwsJCpWu38jumsEfeYxozZoxgYGCg8dw4OjoKffv21fiaUv369VXqq/Tvv/+q3Jcpr9cTJ06ole3Vq5fg7OwsPj99+rQQFBQkrFmzRtizZ4+wcOFCwd7eXjA1NRUuXryotv7Dhw8FAML//ve/Auv6pmO3Qyp1JiYmGDx4sNpyMzMz8f/Pnj1DQkIC2rRpg4yMDNy4caPQ7fbp0we2trbic+Uvf3fu3Cl03Y4dO4q/cAFAo0aNYG1tLa4rk8lw6NAh9OjRA66urmK5GjVqoGvXroVuH1A9vvT0dCQkJKBVq1YQBEHjL0mjRo1Sed6mTRuVY/nzzz9haGio8muzgYEBxo0bp1V9AOCjjz7CgwcPcOLECXFZWFgYjI2N0atXL3GbxsbGABRdUJKSksRfxfLLBpafQ4cOITs7G+PGjVPpqqn8FTIvExMT8ZdomUyGxMREWFpaonbt2kXer9Kff/4JAwMD8ZdjpU8//RSCIKgNJC7suijIvn37kJiYiH79+onL+vXrh8uXL6t0s9yxYwckEglmzZqltg3lOdq9ezfkcjlmzpyp9ut8SaZOGD58uFpCiLzXaU5ODhITE1GjRg1UqlRJ5bzv2LED3t7e6NmzZ7717tixI1xdXbFp0ybxtatXr+Kff/4p8BdaQPv3qlatWvDx8RF/WQcU18v27dsRGBgoHs+2bdtgY2ODTp06ISEhQXz4+vrC0tJSrRtttWrVitRNyNTUFAcPHlR7fPXVV2plR4wYodLKNnr0aBgaGuLPP/8E8PJzMmHCBJX3e/jw4bC2tha7F126dAnR0dGYMGECKlWqpLIPTddFYd8plSpVQnp6er7Zz/KTmJgIACrfv4UJCgrS2GtA0znU9Mj73jx//lxjkghlpslXu3a/Stv1lf/mV7aw/ZRUmzZt8PjxY0RFRQFQtHC1bdsWbdq0wcmTJwEoWsMEQRD//kVGRuLmzZvo378/EhMTxes+PT0dHTp0wIkTJ/JNSnT48GHk5uaqdXMr6G/MiBEjVK69Nm3aQCaT4d69eyU6dgDi5+PV74RX/34IgoAdO3YgMDAQgiCofN79/f2RkpIifpft2LEDDg4OGo8p73Hk/V58+vQpUlJS0KZNG5XvRBsbG7z33ntiyz6g+C7aunUrevToAQsLC7Hspk2btLrOBw4cKK7z/Plz8W/xq7S5/sriOm/VqhW2b9+OIUOG4N1338WUKVNw7tw5SCQSTJ06VW195XeEti3cbyp2O6RS5+bmpvEL5N9//8X06dNx5MgRpKamqryWkpJS6HarVq2q8lz5Idc0lqKwdZXrK9eNj4/H8+fP1bL3AOoZffITExODmTNnYu/evWp1evX4lON+8qsPoBib4+LiojbGQpvuUUp9+/ZFSEgIwsLC0L59e2RmZmLXrl3o2rWryo3U+vXr8fXXX+PGjRvIyckRl1erVk3rfSnrDEAlexigGF/x6o2bchzUqlWrEB0drdJFQ5suf/nt39XVFVZWVirLld2VXr1BKOy6KMjGjRtRrVo1mJiYiCn9vby8YG5ujk2bNmHBggUAgNu3b8PV1bXA8SG3b9+GVCpFvXr1Ct1vUWh6/54/f46FCxciNDQUsbGxKmNJ8l6nt2/fRlBQUIHbl0ql+PDDD7F69WpkZGSIx25qaioG9/kpynvVp08ffPHFF4iNjYWbmxuOHTuG+Ph49OnTRyxz8+ZNpKSkoHLlyhr3Fx8fr/K8qNe2gYEBOnbsqFXZV69/S0tLuLi4iGNtlMf26mfZ2NgY1atXF19XjknJ28UpP9p8p3z88cf49ddf0bVrV7i5uaFz587o3bs3unTpotVx5b1WCpPf+dX2HOZlZmYmjsvKS9k1Lu+Nc0nWV/6bX9nC9lNSyoDq5MmTYjfH+fPnw9HREUuWLBFfs7a2Fsce3bx5EwAK7I6ekpKiMXBWXmev/o2zs7PLN9Auyd/hwty7dw9SqVTlBzFA/XPy5MkTJCcn48cff8SPP/6ocVvKz/vt27dRu3ZtlTF8mvz++++YP38+IiMjVd7/V3/kGDhwILZu3SoGxocOHcLjx48xYMAAlXLFmY7BzMxMrXu+kjbXn66u8xo1auC9997Dzp07IZPJVH7gU35HVPT5VkuKwReVOk0f3OTkZLRr1w7W1taYO3cuvLy8YGpqiosXL2Ly5MlapQvPL6WzNjcEJVlXGzKZDJ06dUJSUhImT56MOnXqwMLCArGxsQgODlY7Pl2lp65cuTI6deqEHTt2YOXKlfjtt9/w7NkzlbE4GzduRHBwMHr06IHPPvsMlStXFpMl5B2QXNoWLFiAGTNmYMiQIZg3bx7s7OwglUoxYcIEnaWPL+51kZqait9++w2ZmZlqN9qAonXxyy+/1NkfoPzGFmj6LI4bNw6hoaGYMGECWrZsCRsbGzFdd3HO+8CBA7F48WLs3r0b/fr1Q1hYGLp37w4bG5sibys/ffr0wdSpU7Ft2zZMmDABv/76K2xsbFSCBrlcjsqVK6u0wuX1amBS1jfSuqbNd0rlypURGRmJAwcOYN++fdi3bx9CQ0MxcODAAlPIK38MKcoNdn7nNy4uTqv1bWxsxG24uLjg6NGjEARB5TP16NEjAFDpraCJi4uLWDavV9d3cXERl7u7u6uVLWjsVGlwdXVFtWrVcOLECXh6ekIQBLRs2RKOjo745JNPcO/ePZw8eRKtWrUSW0yVn9nFixfnOz63NCfgLuu/pdpQHvNHH32Ub9BZlClUTp48iXfffRdt27bFqlWr4OLiAiMjI4SGhiIsLEylrL+/P5ycnLBx40a0bdtWnGrk1R8Vnjx5otWYL0tLS/H9cXFxgUwmQ3x8vMqPSNnZ2UhMTNTqOo+NjVVbXtB1rqlsYfsBAHd3d2RnZyM9PV1ljJnyO8LBwaHQbbzJGHyRThw7dgyJiYnYuXMn2rZtKy6Pjo7WY61eqly5MkxNTTVOSlzQRMVKV65cwX///Yf169erdCMoaveevDw8PHD48GGkpaWp/PFUdknR1ocffoj9+/dj3759CAsLg7W1NQIDA8XXt2/fjurVq2Pnzp0qNzaauslpU2dA8Wts9erVxeVPnjxRu3Hbvn073n77baxZs0ZleXJyssoXd1ECGA8PDxw6dAjPnj1TaVFRdmtV1q+kdu7ciczMTKxevVrtj0xUVBSmT5+O06dP46233oKXlxcOHDiApKSkfFu/vLy8IJfLce3atQITnNja2qplw8vOztb4RzQ/27dvx6BBg/D111+LyzIzM9W26+XlhatXrxa6vQYNGqBx48bYtGkTqlSpgpiYGK0mBS3Ke1WtWjU0b94cW7duxdixY7Fz50706NFDpduMl5cXDh06hNatW+s9sLp586ZKhsa0tDQ8evQIAQEBAF4eW1RUlMrnJDs7G9HR0eKNnLIF4OrVq8VqMdLE2NgYgYGBCAwMhFwux8cff4wffvgBM2bMyLeVv2rVqjAzMyuV72vljV9hQkNDxWx7Pj4++Pnnn3H9+nWV1uHw8HDx9YL4+Pjg5MmTkMvlKt08w8PDYW5ujlq1aqls5++//1YJtB4+fIgHDx6oZJErK23atMGJEydQrVo1+Pj4wMrKCt7e3rCxscH+/ftx8eJFzJkzRyyvvEasra2LfI0or8Nbt26ptFQmJiaWqCWruD86eXh4QC6Xi61VSq/+zVNmQpTJZIUes5eXF8LDw5GTk5NvYq4dO3bA1NQUBw4cUPlOCQ0NVStrYGCA/v37Y926dfjf//6H3bt3a+ze3axZM626Ys6aNUvMYJv3+lN+Vyify+Vyra7zo0ePIjU1VSUgevVz0qBBAxgaGuLvv/9G7969xXLZ2dmIjIxUWZafO3fuwNTUVC2wV35HlHS+14qOY75IJ5RfTHl/HcvOzsaqVav0VSUVyi5Fu3fvxsOHD8Xlt27d0jjhoKb1AdXjEwRBqxTO+QkICEBubq7KxJ4ymaxIs90DQI8ePWBubo5Vq1Zh3759eP/998U+4PnVPTw8vFiThnbs2BFGRkZYvny5yvaWLVumVtbAwEDt19Jt27ap/XKn7EevTYr9gIAAyGQyrFixQmX5N998A4lEovX4vcJs3LgR1atXx6hRo/DBBx+oPCZNmgRLS0uxBSYoKAiCIKjcMCkpj79Hjx6QSqWYO3euWutT3nPk5eWlMn4PAH788UetfmFV0nTely9frraNoKAgXL58Gbt27cq33koDBgzAX3/9hWXLlsHe3l6r81zU96pPnz44d+4c1q5di4SEBJUuhwDQu3dvyGQyzJs3T21fubm5Wk/RUBp+/PFHle67q1evRm5urnhMHTt2hLGxMb777juVc7lmzRqkpKSI2dWaNGmCatWqYdmyZWr1L05Lg3LslpJUKhVbCDR1QVIyMjJC06ZN1aZGKI7ijPl67733YGRkpPL3QhAEfP/993Bzc1PJfvro0SO17tMffPABHj9+jJ07d4rLEhISsG3bNgQGBoo33PXr10edOnXUPlOrV6+GRCJRmdagrLRp0wZ3797F1q1bxW6IUqkUrVq1wtKlS5GTk6OS6dDX1xdeXl5YsmQJ0tLS1LaXNw38qzp06ABDQ0OVvzEA1D6TRWVhYVGsz5vy85E33T6g/vfDwMAAQUFB2LFjh8YfiPIec1BQEBISEjQek/IzZGBgAIlEovKe3717F7t379ZYzwEDBuDp06cYOXIk0tLSNI5vLc6Yr3feeQd2dnZq78fq1athbm6ukvUxISEBN27cQEZGhrjsgw8+gEwmU+mKmZWVhdDQUPj5+YmtuTY2NujYsSM2btyIZ8+eiWU3bNiAtLQ0lS7jmq6fy5cvY+/evejcubPaGOWIiAhIJBK0bNlS47kjBbZ8kU60atUKtra2GDRoEMaPHw+JRIINGzbotKtCYWbPno2//voLrVu3xujRo8UbwwYNGiAyMrLAdevUqQMvLy9MmjQJsbGxsLa2xo4dO0r062FgYCBat26NKVOm4O7du6hXrx527typ1fi4vCwtLdGjRw+x+8Sr6b+7d++OnTt3omfPnujWrRuio6Px/fffo169ehr/mBdEObfQwoUL0b17dwQEBODSpUvYt2+fWgtR9+7dMXfuXAwePBitWrXClStXsGnTJpWWAEARcFSqVAnff/89rKysYGFhAT8/P41jSgIDA/H2229j2rRpuHv3Lry9vfHXX39hz549mDBhgtpYguJ4+PAhjh49qjYoXMnExAT+/v7Ytm0bvvvuO7z99tsYMGAAvvvuO9y8eRNdunSBXC7HyZMn8fbbb2Ps2LGoUaMGpk2bhnnz5qFNmzZ4//33YWJiggsXLsDV1VWcL2vYsGEYNWoUgoKC0KlTJ1y+fBkHDhwoUheP7t27Y8OGDbCxsUG9evVw9uxZHDp0SG2c3WeffYbt27ejV69eGDJkCHx9fZGUlIS9e/fi+++/V5nvpn///vj888+xa9cujB49utCpH4Civ1e9e/fGpEmTMGnSJNjZ2an94t2uXTuMHDkSCxcuRGRkJDp37gwjIyPcvHkT27Ztw7fffluim+fc3Fxs3LhR42s9e/ZUGWyfnZ2NDh06oHfv3oiKisKqVavw1ltv4d133wWg+JxMnToVc+bMQZcuXfDuu++K5Zo1aybezEmlUqxevRqBgYHw8fHB4MGD4eLighs3buDff//FgQMHinQMw4YNQ1JSEt555x1UqVIF9+7dw/Lly+Hj41PoL9Xvvfcepk2bpvarelEVpwWvSpUqmDBhAhYvXoycnBw0a9YMu3fvxsmTJ7Fp0yaVVoepU6di/fr1iI6OFucA/OCDD9CiRQsMHjwY165dg4ODA1atWgWZTKb2o8jixYvx7rvvonPnzujbty+uXr2KFStWYNiwYWrnaP78+QAgJtjZsGEDTp06BQAqaceV0wocPXq00DmvlIFVVFSUOG4UANq2bYt9+/aJc1wqSaVS/Pzzz+jatSvq16+PwYMHw83NDbGxsTh69Cisra3x22+/adyXk5MTPvnkE3z99dd499130aVLF1y+fFn8vi5uC5avry9Wr16N+fPno0aNGqhcuTLeeeedQtfz8fFBv379sGrVKqSkpKBVq1Y4fPiwxt4nX331FY4ePQo/Pz8MHz4c9erVQ1JSEi5evIhDhw6J80wNHDgQv/zyC0JCQnD+/Hm0adMG6enpOHToED7++GO899576NatG5YuXYouXbqgf//+iI+Px8qVK1GjRg38888/avtu3LgxGjRogG3btqFu3boqU7koFXfM17x58zBmzBj06tUL/v7+OHnyJDZu3Igvv/xSpefEihUr1K4pPz8/9OrVC1OnTkV8fDxq1KiB9evX4+7du2o9TL788ku0atUK7dq1w4gRI/DgwQN8/fXX6Ny5s0p37j59+sDMzAytWrVC5cqVce3aNfz4448wNzfXmGzo4MGDaN26dbHHbb8xyj6hIlVU+aWar1+/vsbyp0+fFlq0aCGYmZkJrq6uwueffy6mqs6boje/1MV5Z21XwiupWvNLNT9mzBi1dV9Nzy0IgnD48GGhcePGgrGxseDl5SX8/PPPwqeffiqYmprmcxZeunbtmtCxY0fB0tJScHBwEIYPHy6mLs+bNnnQoEGChYWF2vqa6p6YmCgMGDBAsLa2FmxsbIQBAwYIly5d0jrVvJIypbKLi4vGVOYLFiwQPDw8BBMTE6Fx48bC77//rvY+CELhqeYFQRBkMpkwZ84cwcXFRTAzMxPat28vXL16Ve18Z2ZmCp9++qlYrnXr1sLZs2eFdu3aqaUp3rNnj1CvXj0x7b/y2DXV8dmzZ8LEiRMFV1dXwcjISKhZs6awePFilbTCymPR9rrI6+uvvxYACIcPH863zLp161RSi+fm5gqLFy8W6tSpIxgbGwuOjo5C165dhYiICJX11q5dKzRu3FgwMTERbG1thXbt2gkHDx4UX5fJZMLkyZMFBwcHwdzcXPD39xdu3bqVb6r5CxcuqNXt6dOnwuDBgwUHBwfB0tJS8Pf3F27cuKHxuBMTE4WxY8cKbm5ugrGxsVClShVh0KBBaumdBUEQAgICBADCmTNn8j0vr9L2vVJq3bq1AEAYNmxYvtv88ccfBV9fX8HMzEywsrISGjZsKHz++efCw4cPxTIeHh5Ct27dtK5nQanm817/yvN+/PhxYcSIEYKtra1gaWkpfPjhh0JiYqLadlesWCHUqVNHMDIyEpycnITRo0erpZQXBEE4deqU0KlTJ8HKykqwsLAQGjVqpJL2XdvvlO3btwudO3cWKleuLBgbGwtVq1YVRo4cKTx69KjQc/D48WPB0NBQ2LBhg9q50fb7uiRkMpn4PWVsbCzUr19f2Lhxo1o55XuV9ztJEAQhKSlJGDp0qGBvby+Ym5sL7dq10/j5EARB2LVrl+Dj4yOYmJgIVapUEaZPn64ydYBSQddEXp9++qkgkUiE69eva3WslStXFgAIjx8/FpedOnVKACC0adNG4zqXLl0S3n//fcHe3l4wMTERPDw8hN69e6t8T2n6vs7NzRVmzJghODs7C2ZmZsI777wjXL9+XbC3txdGjRqltu6r5+zo0aNqf8Pj4uKEbt26CVZWVgKAIqWdf/78uTB+/HjB3t5esLCwEAIDA4X79++r/e0RBMU1OWbMGMHd3V0wMjISnJ2dhQ4dOgg//vijSrmMjAxh2rRpQrVq1cRyH3zwgXD79m2xzJo1a4SaNWsKJiYmQp06dYTQ0FCNf5OVlNNHLFiwQOtj09aPP/4o1K5dW7wP+eabb9S+E5V1e3V6g+fPnwuTJk0SnJ2dBRMTE6FZs2b5Tqlx8uRJoVWrVoKpqang6OgojBkzRkhNTVUp8+233wrNmzcX7OzsBENDQ8HFxUX46KOPhJs3b6ptLzk5WTA2NhZ+/vnnkp2AN4BEEMpR0wNROdSjRw/8+++/YlYpIlLXs2dPXLlyRasxkhWVcoLnCxcuaJyA9HU3dOhQ/Pfff2Lac9JO8+bN4eHhgW3btum7KlpJTk6Gra0t5s+fL06UTKq+/fZbTJw4EXfv3tWYNfdNtGzZMixatAi3b9/W+7jb8o5jvojyeHUejZs3b+LPP/8stKsI0Zvs0aNH+OOPP9TSLVPFMmvWLFy4cAGnT5/Wd1VeG6mpqbh8+TLmzp2r76popGnuKOUYK/7d00wQBKxZswbt2rVj4PVCTk4Oli5diunTpzPw0gLHfBHlUb16dQQHB4tz7axevRrGxsb4/PPP9V01onInOjoap0+fxs8//wwjIyOMHDlS31WiMlS1alVxziDSjrW1dYHJTPRt69atWLduHQICAmBpaYlTp05h8+bN6Ny5c7HGLeWnsCkGzMzMSnV6irKQnp6OvXv34ujRo7hy5Qr27Nmj7yqVG0ZGRoiJidF3NV4bDL6I8ujSpQs2b96MuLg4mJiYoGXLlliwYIHGuZyI3nTHjx/H4MGDUbVqVaxfvx7Ozs76rhIRFUGjRo1gaGiIRYsWITU1VUzCoUwmUloKm2Jg0KBBWLduXanus7Q9efIE/fv3R6VKlfDFF1+ICXSIiopjvoiIiIiozBw6dKjA111dXVXmcCOqyBh8ERERERER6QATbhAREREREekAx3wVk1wux8OHD2FlZVXsiQiJiIiIiOj1JwgCnj17BldXV0il+bdvMfgqpocPH8Ld3V3f1SAiIiIionLi/v37qFKlSr6vM/gqJisrKwCKE2xtba3n2hARERERkb6kpqbC3d1djBHyw+CrmJRdDa2trRl8ERERERFRocORmHCDiIiIiIhIBxh8ERERERER6QCDLyIiIiIiIh3gmK8yJJPJkJOTo+9qEJUJIyMjGBgY6LsaRERERK8NBl9lJC0tDQ8ePIAgCPquClGZkEgkqFKlCiwtLfVdFSIiIqLXAoOvMiCTyfDgwQOYm5vD0dGRkzBThSMIAp48eYIHDx6gZs2abAEjIiIi0gKDrzKQk5MDQRDg6OgIMzMzfVeHqEw4Ojri7t27yMnJYfBFREREpAUm3ChDbPGiiozXNxEREVHRMPgiIiIiIiLSAQZfREREREREOqD34GvlypXw9PSEqakp/Pz8cP78+XzL5uTkYO7cufDy8oKpqSm8vb2xf/9+lTKzZ8+GRCJRedSpU0elTGZmJsaMGQN7e3tYWloiKCgIjx8/LpPje9N5enpi2bJl+q4GEREREZHe6TX42rp1K0JCQjBr1ixcvHgR3t7e8Pf3R3x8vMby06dPxw8//IDly5fj2rVrGDVqFHr27IlLly6plKtfvz4ePXokPk6dOqXy+sSJE/Hbb79h27ZtOH78OB4+fIj333+/zI7zdfBqwPrqY/bs2cXa7oULFzBixIhSqePmzZthYGCAMWPGlMr2iIiIiIh0SSLocSIqPz8/NGvWDCtWrAAAyOVyuLu7Y9y4cZgyZYpaeVdXV0ybNk3l5jsoKAhmZmbYuHEjAEXL1+7duxEZGalxnykpKXB0dERYWBg++OADAMCNGzdQt25dnD17Fi1atNCq7qmpqbCxsUFKSgqsra1VXsvMzER0dDSqVasGU1NTrbanb3FxceL/t27dipkzZyIqKkpcZmlpKc7nJAgCZDIZDA11myyzY8eOaNasGX744Qc8fPhQr+c2OzsbxsbGett/efA6XudEREREZaGg2CAvvaWaz87ORkREBKZOnSouk0ql6NixI86ePatxnaysLLWbPDMzM7WWrZs3b8LV1RWmpqZo2bIlFi5ciKpVqwIAIiIikJOTg44dO4rl69Spg6pVqxYYfGVlZSErK0t8npqaqvWxCoKA5zkyrcuXJjMjA62y0jk7O4v/t7GxgUQiEZcdO3YMb7/9Nv78809Mnz4dV65cwV9//QV3d3eEhITg3LlzSE9PR926dbFw4UKVc+vp6YkJEyZgwoQJABQtbD/99BP++OMPHDhwAG5ubvj666/x7rvvFli/6OhonDlzBjt27MDRo0exc+dO9O/fX6XM2rVr8fXXX+PWrVuws7NDUFCQGNgnJydj8uTJ2L17N1JSUlCjRg189dVX6N69u8aAfdmyZVi2bBnu3r0LAAgODkZycjKaNWuGlStXwsTEBNHR0diwYQO+/fZbREVFwcLCAu+88w6WLVuGypUri9v6999/MXnyZJw4cQKCIMDHxwfr1q1DbGwsOnTogPv376uc/wkTJiAiIgInT54s9H0joteDXC5g6s4ruPwgOd8yDdxssCioEaTS0skkevpWAv63/wayc+UaXzcxMsCMbnXR1NOuVPZXVracj8H6s/dQ2r8VGxtKMblLHbSu4aD1Opk5MozbfAn3kzJKtS5lxdTIADMD66FJVVt9V+W1kZGdi/GbL+HB0+fFWr9xVVss6NmgSBmBd0Q8wJpT0ZDrsD1EKpFg6FvVEORbRWf7JAW9BV8JCQmQyWRwcnJSWe7k5IQbN25oXMff3x9Lly5F27Zt4eXlhcOHD2Pnzp2QyV4GNn5+fli3bh1q166NR48eYc6cOWjTpg2uXr0KKysrxMXFwdjYGJUqVVLbb97Wn1ctXLgQc+bMKdaxPs+Rod7MA8Vat6SuzfWHuXHpvM1TpkzBkiVLUL16ddja2uL+/fsICAjAl19+CRMTE/zyyy8IDAxEVFSUGOxqMmfOHCxatAiLFy/G8uXL8eGHH+LevXuws8v/BiA0NBTdunWDjY0NPvroI6xZs0Yl+Fq9ejVCQkLw1VdfoWvXrkhJScHp06cBKFpUu3btimfPnmHjxo3w8vLCtWvXijw31eHDh2FtbY2DBw+Ky3JycjBv3jzUrl0b8fHxCAkJQXBwMP78808AQGxsLNq2bYv27dvjyJEjsLa2xunTp5Gbm4u2bduievXq2LBhAz777DNxe5s2bcKiRYuKVDciKt+uPUrF1r/vF1jmRtwzfNTCAz7ulUpln98fv41/HqQUWObnk9HlOvgSBAFL/opCQlp2mWz/++O3ixR8Hb0Rj4PXXq8x4mtORaNJfwZf2jp0PR6Hrmse/qKNG3HPMKiVB+o459/y8aqlB/9DbHLxgr2SWHrwPwZfevBaTbL87bffYvjw4ahTpw4kEgm8vLwwePBgrF27VizTtWtX8f+NGjWCn58fPDw88Ouvv2Lo0KHF3vfUqVMREhIiPk9NTYW7u3uxt/c6mjt3Ljp16iQ+t7Ozg7e3t/h83rx52LVrF/bu3YuxY8fmu53g4GD069cPALBgwQJ89913OH/+PLp06aKxvFwux7p167B8+XIAQN++ffHpp5+KXd4AYP78+fj000/xySefiOs1a9YMAHDo0CGcP38e169fR61atQAA1atXL/LxW1hY4Oeff1bpbjhkyBDx/9WrV8d3332HZs2aIS0tDZaWlli5ciVsbGywZcsWGBkZAYBYBwAYOnQoQkNDxeDrt99+Q2ZmJnr37l3k+hFR+RUenQQAaOphiwkda6m9vvzITYRHJyH8TmKpBF85Mjki7j0FACzp5Q1na9VeI3cT0zF991Wcv5sEQRDK7bx9t5+kIyEtGyaGUvw0sCmkpVTPuNRMTNp2GRH3niJHJoeRgXZD4JXvY0BDZ/Rv7lEqdSkrdxLSMHPPvwi/U77f4/Im/E4iACDQ2xV9mhbtPm/pwShcjElG+J0krYOvB08zEJv8HIZSCX4e1BSG0rJPx5Arl2PY+r8Rm/wcD55moIqteZnvk17SW/Dl4OAAAwMDtSyDjx8/VumClZejoyN2796NzMxMJCYmwtXVFVOmTCnwRrpSpUqoVasWbt26BUDRvS47OxvJyckqrV8F7RcATExMYGJiUoQjfMnMyADX5voXa92SMjMqWutOQZo2baryPC0tDbNnz8Yff/yBR48eITc3F8+fP0dMTEyB22nUqJH4fwsLC1hbW+ebZAUADh48iPT0dAQEBABQXDudOnXC2rVrMW/ePMTHx+Phw4fo0KGDxvUjIyNRpUoVlaCnOBo2bKg2zisiIgKzZ8/G5cuX8fTpU8jliu49MTExqFevHiIjI9GmTRsx8HpVcHAwpk+fjnPnzqFFixZYt24devfuDQsLixLVlYjKF+UNXYe6TnirpnpLy424VEXwFZ2Eke28Sry/K7EpyMiWoZK5Ed5v7KbWlbF5NTvM/+MaktKzcTM+DbWcrEq8z7IQHq04b42rVkLbWo6ltl25XMD8P64hOSMHV2JTtO6Wd+7F+9itoavG97E8aeppi/l/XEdCWhbuJKTDy9FS31V6LSgD7O6NXIr8Hkfef6oIvqITMaiVp3b7u6PYX8MqNmhfu3IhpUtPAzcbRN5XBIpVfBl86ZLesh0aGxvD19cXhw8fFpfJ5XIcPnwYLVu2LHBdU1NTuLm5ITc3Fzt27MB7772Xb9m0tDTcvn0bLi4uAABfX18YGRmp7DcqKgoxMTGF7re4JBIJzI0N9fIozV+6Xg0IJk2ahF27dmHBggU4efIkIiMj0bBhQ2RnF9w95NVARCKRiEGLJmvWrEFSUhLMzMxgaGgIQ0ND/Pnnn1i/fj3kcjnMzMwK3F9hr0ulUrWxBDk5OWrlXj3+9PR0+Pv7w9raGps2bcKFCxewa9cuABDPQWH7rly5MgIDAxEaGorHjx9j3759Kq1pRPT6k8sFnL+ruMHyq665i59fNXsAwIXoJMjkJR/3obyha+5pp3EMmbGhFL4eioBDGVCUR8rjUJ6f0iKVStD8RXdLbY//aXo2bsQ9A6AIXss7UyMDNH7Riqo8j1SwJ8+ycCs+DQDE66Mo/KorrlNla6M2lNdfaV/jhVF+F5Xnz39FpddU8yEhIfjpp5+wfv16XL9+HaNHj0Z6ejoGDx4MABg4cKBKQo7w8HDs3LkTd+7cwcmTJ9GlSxfI5XJ8/vnnYplJkybh+PHjuHv3Ls6cOYOePXvCwMBA7OZmY2ODoUOHIiQkBEePHkVERAQGDx6Mli1bap3pkBROnz6N4OBg9OzZEw0bNoSzs7OYoKK0JCYmYs+ePdiyZQsiIyPFx6VLl/D06VP89ddfsLKygqenp0pAnVejRo3w4MED/Pfffxpfd3R0RFxcnMoXZX7ZMvO6ceMGEhMT8dVXX6FNmzaoU6eOWgteo0aNcPLkSY3BnNKwYcOwdetW/Pjjj/Dy8kLr1q0L3TcRvT6iHj9DckYOzI0N0NDNRmOZeq7WsDIxxLOsXFx7qH1Cp/woW4yUN4OaKG/2yuuNuSAIeY6j9IOdvDfK2lAG0F6OFnC0Kl5PGF0TjzGaN9jaOP+i1auOsxVsLYqe0bhRFRuYGEqRmJ4tBnGFUba0lcU1XpAWys9/dPn8/Fdkeg2++vTpgyVLlmDmzJnw8fFBZGQk9u/fLybhiImJwaNHj8TymZmZmD59OurVq4eePXvCzc0Np06dUuk++ODBA/Tr1w+1a9dG7969YW9vj3PnzsHR8WV3hW+++Qbdu3dHUFAQ2rZtC2dnZ+zcuVNnx11R1KxZEzt37kRkZCQuX76M/v37F9iCVRwbNmyAvb09evfujQYNGogPb29vBAQEYM2aNQAUUwx8/fXX+O6773Dz5k1cvHhRHCPWrl07tG3bFkFBQTh48CCio6Oxb98+cYLu9u3b48mTJ1i0aBFu376NlStXYt++fYXWrWrVqjA2Nsby5ctx584d7N27F/PmzVMpM3bsWKSmpqJv3774+++/cfPmTWzYsEEljb+y9Wz+/PniDw9EVHEouxz6etjmO7bIQCpBU09FS1RJb5RzZXJcUN7QFdBCo3wtPDqx1DMJloa7iRl4nJoFYwNpmWTrUx7/33eTkCsr/G+X2ApXQEBb3rSo9rJ1ozy+x+WNGOwXs2XTxNBAvFbPaRHUPEp5jpikDEglivGgutTU0xZSCRCTlIFHKbpP9vEm02vwBShuTu/du4esrCyEh4fDz89PfO3YsWNYt26d+Lxdu3a4du0aMjMzkZCQgF9++QWurq4q29uyZQsePnyIrKwsPHjwAFu2bIGXl2r/eVNTU6xcuRJJSUlIT0/Hzp07CxzvRZotXboUtra2aNWqFQIDA+Hv748mTZqU6j7Wrl2Lnj17auw+GRQUhL179yIhIQGDBg3CsmXLsGrVKtSvXx/du3fHzZs3xbI7duxAs2bN0K9fP9SrVw+ff/65mCWzbt26WLVqFVauXAlvb2+cP38ekyZNKrRujo6OWLduHbZt24Z69erhq6++wpIlS1TK2Nvb48iRI0hLS0O7du3g6+uLn376SaXrpVQqRXBwMGQyGQYOHFjcU0VE5VS4FoEQ8PKm/lwJW6L+fZiK9GwZrEwNUdcl/0H/3u6VYGwoRUJaNm4/SS/RPsuCMmj1dreBaSmOX1aq62INK1NDpGfL8K8WrY0lvTHXh8ZVbWFkIMHj1CzcS3w90uPrU2kE2MoWrHAtuvMp99fAzQZWpprHhpcVK1Mj1He1UakH6cZrle2QdCM4OBjBwcHi8/bt22v8xczT0xNHjhxRWZZ3AmwAat0QNW0nOTk537r8888/+b7Wu3dvlayAI0eOxMiRIzWWtbOzU8mK+apRo0Zh1KhRKsu++OIL8f95fwTIq1+/fmKXVqVXj7FRo0Y4cKDgqQZiY2MREBAgjk0koopBEASxK1NhN3TKm/oLd5MglwvFnu9LGSQ097SDQQHbUI4JUiT6SESNyuUrIcPLoLVsWpoMXoz7OnwjHuHRifAuIMtkyvMcXHukCNBavEYtX2bGBvCuUgl/33uK8OhEeDowmVN+ktKzEfW45GP6FNerIntpYVkm9R3Q+1Wzw5XYFIRHJ6JHYze91OFNpPeWL6I3WUpKCk6dOoWwsDCMGzdO39UholJ2Kz4NienZMDWSolEVzeO9lBq42cDC2AApz3PExA7FofwVW5sgoUURxz3piiAIYstBWQY72h7/33eTIAhANQcLOL2Str+8K6/vcXmj/JGkZmVLOFgWf0xf46qKFuUnz7IQnVBwi3JZJZTRFq8N/WDwRaRH7733Hjp37oxRo0apzKFGRBWDctxHk6q2MDEsuOuckYEUvp4vx2EVh0yLzIp5iV2kytm4rwdPn+NhSiYMpRI08ahUZvtRHv/5uwVnmdS262h59PI95g12QUoruYupkYE4V19B5zw+NRN3EtIhkQDN9HRdNatmB4kEuJOQjvjUTL3U4U3E4ItIj44dO4aMjAx88803+q4KEZWB8CKmkRaTYBTzl+jrj1LxLDMXliaGqFfAeC+lJlVtYWwgLXdjgpTprxtVsYG5cdmNkKjnYg1LE0M8y8zF9Uf5j/sS30cdZ6QrDb4etjCUShCb/Bz3k8rPe1zelGYrVItqhY/7UgZmdZ2tYWOm2/FeSjZmRqj7YjJoBue6w+CLiIioDChSpRctjXSLPC0xxWmJUu6vqactDPPJrJiXqZEBvN1fDLovR+nIw7UcJ1dShgbSPFkmNd98pmXl4uqLhBz66h5WEubGhmhYRfke8wZbk5SMHFyPe/Eel0KA/TLFf/6f47KcRqEo8rZ+k24w+CIiIioD0QnpePIsC8aGUrEbUmEaulWCqZEUSenZuKnlPEF5FbWlLW/Z8jTuQ5eJCF4ev+abz79fdEl0tzODayWzMq9PWSjsGN90F16M6avuYIHKViUf09fkRZbJRymZuJ+kOY17UcZmlqXy+Pmv6Bh8ERERlQFlK4OPeyWtU6UbG0rh+2K+n6LeKMuLON5LKe+YoPIw7uth8nPcT3r+Yu4zHQRfeVob5RrGfZV11kVd4LivgpV2K5SZsQEaVakEADinoUUpIS1L/HGluQ6u8YIoMzvejE9DQlqWXuvypmDwRUREVAaU45ZaFLH1RnmTX9T5vv6Lf4bkjByYGxugoVvBmRXzyjsm6MFT/U+2qrwRbuCqGI9V1hq62cDc2ADJGTliqvG8zt15/eb3elVTD06oW5BzZZB10C/PBNevUmZWrONsBVsL41LbZ3HYWRijtpMVgJf1orLF4IuIiKiUKVKlF2/ckph0o4gZCJX78/WwhZEW472U8o4J0nSjqGulMdFtURgZ5N/amJGdiysPUgDov3tYSViZGqGBGyfU1SQ1Mwf/PlS8x6U5/sqvgDTu4eUsoC/KxNBUcgy+iIiISllMUgbiUjNhZCBBk6q2RVrX210xT1BCWjZuPyl4nqC8SjJOShz3UQ5++dZHWveXAa/q8Ufce4pcuQBXG1NUsX09x3sp5Q3q6aWIu08hF4CqduZwsSm999jXwxYGYouyapZJXSWU0VZ5+vy/CRh8Ualq3749JkyYID739PTEsmXLClxHIpFg9+7dJd53aW2HiKiklL92N6pSCWbG2o33UjI1MkBjcZ4g7W6UBUEQuwwV54auvGQ8i0/NRPSLuY90Md5LSXnOzr8y7i1vK5xEItFZfcoCEytodq6MkrtYmhhqbG18mp4tTqLevJy0fCnrcSPuGZ6mZ+u5NhUfgy8CAAQGBqJLly4aXzt58iQkEgn++eefIm/3woULGDFiREmrp2L27Nnw8fFRW/7o0SN07dq1VPeVn+fPn8POzg4ODg7IyuIAVSJSpbyha1HMbkwtCuiypMntJ2lISMuGqZEUjapoP95LqemLX+nvJz3Hw2T9jQlSTkpd31W3cx81qmIDUyMpEtOzcStPlsnwEr6P5Qkn1NWsLLMOttDwo4YyKU6NypZwsDQp9X0Wh6OVCWpUtgTwsn5Udhh8EQBg6NChOHjwIB48eKD2WmhoKJo2bYpGjRoVebuOjo4wNzcvjSoWytnZGSYmuvki27FjB+rXr486derovbVNEATk5ubqtQ5EpKqkE7bmbYnSZtyXMmFAk6q2MDEsWksb8GJMkKtyslX9tX4VJ1V+aTAxNBC7hyoDwMwcGS7fT9FLfcqCjZmROPE2u5cppGfl4kps6Y/3UmqhoTvfy++G8hXQl3SCd9Iegy9dEAQgO10/Dy0Ha3fv3h2Ojo5Yt26dyvK0tDRs27YNQ4cORWJiIvr16wc3NzeYm5ujYcOG2Lx5c4HbfbXb4c2bN9G2bVuYmpqiXr16OHjwoNo6kydPRq1atWBubo7q1atjxowZyMnJAQCsW7cOc+bMweXLlyGRSCCRSMQ6v9rt8MqVK3jnnXdgZmYGe3t7jBgxAmlpL3/RDA4ORo8ePbBkyRK4uLjA3t4eY8aMEfdVkDVr1uCjjz7CRx99hDVr1qi9/u+//6J79+6wtraGlZUV2rRpg9u3b4uvr127FvXr14eJiQlcXFwwduxYAMDdu3chkUgQGRkplk1OToZEIsGxY8cAAMeOHYNEIsG+ffvg6+sLExMTnDp1Crdv38Z7770HJycnWFpaolmzZjh06JBKvbKysjB58mS4u7vDxMQENWrUwJo1ayAIAmrUqIElS5aolI+MjIREIsGtW7cKPSdEpPDgaQZikxWp0pWJHIqqSVVbGBtI8Tg1C/cSMwotXxrp0AtKEKAr+hjvpfTqXFgXY54iWyaHk7UJPOx18yNiWXs5tofjvgDFmD6ZXIBbJTNUsS3997ippyLL5L3EDMSlKFobX6a1L18B/cuJoXltlLWyz+FKQE4GsMBVP/v+4iFgbFFoMUNDQwwcOBDr1q3DtGnTxL7t27Ztg0wmQ79+/ZCWlgZfX19MnjwZ1tbW+OOPPzBgwAB4eXmhefPmhe5DLpfj/fffh5OTE8LDw5GSkqIyPkzJysoK69atg6urK65cuYLhw4fDysoKn3/+Ofr06YOrV69i//79YmBhY6PexSY9PR3+/v5o2bIlLly4gPj4eAwbNgxjx45VCTCPHj0KFxcXHD16FLdu3UKfPn3g4+OD4cOH53sct2/fxtmzZ7Fz504IgoCJEyfi3r178PDwAADExsaibdu2aN++PY4cOQJra2ucPn1abJ1avXo1QkJC8NVXX6Fr165ISUnB6dOnCz1/r5oyZQqWLFmC6tWrw9bWFvfv30dAQAC+/PJLmJiY4JdffkFgYCCioqJQtWpVAMDAgQNx9uxZfPfdd/D29kZ0dDQSEhIgkUgwZMgQhIaGYtKkSeI+QkND0bZtW9SoUaPI9SN6UymDl4ZuNrAoZqp0UyMDeLvb4MLdpwiPToSnQ/7f44rMiiWfp8ivmh1+PHFHb60iCWlZuBWfBolEP2NhXp3vLG/r5es+3kvJr7od1p6OZuvGC6U9v9errEyNUN/VBldiUxAenYj2tSvj2qNUAEWfgqKsKetz7VEqUp7n6LTb75uGwReJhgwZgsWLF+P48eNo3749AMXNd1BQEGxsbGBjY6NyYz5u3DgcOHAAv/76q1bB16FDh3Djxg0cOHAArq6KYHTBggVq47SmT58u/t/T0xOTJk3Cli1b8Pnnn8PMzAyWlpYwNDSEs7NzvvsKCwtDZmYmfvnlF1hYKG5aVqxYgcDAQPzvf/+Dk5MTAMDW1hYrVqyAgYEB6tSpg27duuHw4cMFBl9r165F165dYWur+EXb398foaGhmD17NgBg5cqVsLGxwZYtW2BkpPjyqlWrlrj+/Pnz8emnn+KTTz4RlzVr1qzQ8/equXPnolOnTuJzOzs7eHt7i8/nzZuHXbt2Ye/evRg7diz+++8//Prrrzh48CA6duwIAKhevbpYPjg4GDNnzsT58+fRvHlz5OTkICwsTK01jIgKVlo3dH7V7BXB150k9GlWNd9ydxMzEP8sC8aGUvi8SNRRHE09FWOCol+MCapsbVrsbRWHMmFIbScrVDLX/dxHPi+yTD55loXohPQyvzHXB+WEvsoJdcvLmCN9Ecd7lWG3Ur9qdrgSm4Jzd5JgaWIIQQCqOVjo/PNVmMrWpqjmYIHohHT8fTcJHeo66btKFRaDL10wMle0QOlr31qqU6cOWrVqhbVr16J9+/a4desWTp48iblz5wIAZDIZFixYgF9//RWxsbHIzs5GVlaW1mO6rl+/Dnd3dzHwAoCWLVuqldu6dSu+++473L59G2lpacjNzYW1tbXWx6Hcl7e3txh4AUDr1q0hl8sRFRUlBl/169eHgcHL8REuLi64cuVKvtuVyWRYv349vv32W3HZRx99hEmTJmHmzJmQSqWIjIxEmzZtxMArr/j4eDx8+BAdOnQo0vFo0rRpU5XnaWlpmD17Nv744w88evQIubm5eP78OWJiYgAouhAaGBigXbt2Grfn6uqKbt26Ye3atWjevDl+++03ZGVloVevXiWuK9GbRNlyVNIbOr/qdlhxtPDxOcq5uXzcK8HUqOjjvZSUY4L+fZiKc9FJeNdbtz02xEmp9dQdy9TIAD7ulXA+OgknbybgUkwygIox3kvJ1sIYdZytcCPuGc5HJyGgoYu+q6Q3z7NluPwgGUDZBth+1e3x86lohEcnwspUcdtd3sZ7KflVs3vxwwODr7LEMV+6IJEouv7p41HErhJDhw7Fjh078OzZM4SGhsLLy0u8WV+8eDG+/fZbTJ48GUePHkVkZCT8/f2RnV16aUnPnj2LDz/8EAEBAfj9999x6dIlTJs2rVT3kderAZJEIoFcLs+3/IEDBxAbG4s+ffrA0NAQhoaG6Nu3L+7du4fDhw8DAMzM8p8npKDXAEAqVXwk8w6wz28MWt7AEgAmTZqEXbt2YcGCBTh58iQiIyPRsGFD8dwVtm8AGDZsGLZs2YLnz58jNDQUffr00VnCFKKK4FHKc9xLzIBUohjvURK+HrYwfDFP0P2k/Md9KbsclkY3plfHPelSeUhEoDyHa05FIytXDgdLE3g5Ft51/3XyMrHCmz2251LMU+TIBDhbm6KqXdn9nWv+okX5zpN07Lv6CED5bU3lZMu6weCLVPTu3RtSqRRhYWH45ZdfMGTIELGv++nTp/Hee+/ho48+gre3N6pXr47//vtP623XrVsX9+/fx6NHj8Rl586dUylz5swZeHh4YNq0aWjatClq1qyJe/fuqZQxNjaGTCYrdF+XL19GevrLCUpPnz4NqVSK2rVra13nV61ZswZ9+/ZFZGSkyqNv375i4o1GjRrh5MmTGoMmKysreHp6ioHaqxwdHQFA5RzlTb5RkNOnTyM4OBg9e/ZEw4YN4ezsjLt374qvN2zYEHK5HMePH893GwEBAbCwsMDq1auxf/9+DBkyRKt9E5GCMoCo72oDK9OSjZkwNzZEwxdp48/lczMkCEKpTtiqvPnKb39lJSk9G1GP9T/3kfIcxrwIdv2q2VWY8V5KLxMrvNnjvs7lGSdZlu+xjbkR6jgreu/cT1JM41BeW1OV9boSm4JnmYUnH6PiYfBFKiwtLdGnTx9MnToVjx49QnBwsPhazZo1cfDgQZw5cwbXr1/HyJEj8fjxY6233bFjR9SqVQuDBg3C5cuXcfLkSUybNk2lTM2aNRETE4MtW7bg9u3b+O6777Br1y6VMp6enoiOjkZkZCQSEhI0zrP14YcfwtTUFIMGDcLVq1dx9OhRjBs3DgMGDBC7HBbVkydP8Ntvv2HQoEFo0KCBymPgwIHYvXs3kpKSMHbsWKSmpqJv3774+++/cfPmTWzYsAFRUVEAFPOUff311/juu+9w8+ZNXLx4EcuXLwegaJ1q0aIFvvrqK1y/fh3Hjx9XGQNXkJo1a2Lnzp2IjIzE5cuX0b9/f5VWPE9PTwwaNAhDhgzB7t27ER0djWPHjuHXX38VyxgYGCA4OBhTp05FzZo1NXYLJaL8hZfyhK1+GlJV53U/6TkepWTCyEAipkovCeWYoNtP0vHkme7mMFSO96pZ2RL2ehyH1KSqLYwMXt6Il9cWipLghLoK50ohQ6i28n4fuNuZwbVS4T1R9MG1khnc7cwgF4C/7z3Vd3UqLAZfpGbo0KF4+vQp/P39VcZnTZ8+HU2aNIG/vz/at28PZ2dn9OjRQ+vtSqVS7Nq1C8+fP0fz5s0xbNgwfPnllypl3n33XUycOBFjx46Fj48Pzpw5gxkzZqiUCQoKQpcuXfD222/D0dFRY7p7c3NzHDhwAElJSWjWrBk++OADdOjQAStWrCjaychDmbxD03itDh06wMzMDBs3boS9vT2OHDmCtLQ0tGvXDr6+vvjpp5/ELo6DBg3CsmXLsGrVKtSvXx/du3fHzZs3xW2tXbsWubm58PX1xYQJEzB//nyt6rd06VLY2tqiVatWCAwMhL+/P5o0aaJSZvXq1fjggw/w8ccfo06dOhg+fLhK6yCgeP+zs7MxePDgop4iojee2HWulMYt+WmYpDUv5WTOjapUgplx8cd7KSnHBAEvAyJdKC/JLcyMDdCoSiXxeXltoSgJB0tOqJuZI0Pk/WQAurnm8k7SXZbJPUrDy67Hb+a1oQsSQZvZG0lNamoqbGxskJKSopYMIjMzE9HR0ahWrRpMTctXNhuiwpw8eRIdOnTA/fv3C2wlrMjX+cmbT/Dpr5fxPLvg7q2kTiIBgltXQ0inWoUXLqeuxqZgxC9/41lm0Scvf5aVC4kEiJzRGTbmJU/V/CwzBz5zD0ImF2BpYohXO0dl5cqRLZNjzNte+My/Ton3BwCz9/6LdWfuwthQChMD3fxGm5Ejg0wuYEX/xujeSE9Ts7yw+MANrDx6G3YWxvh7WkdIpRWr2yEATN99BRvPxej0PS5P5IKA9GwZHK1McP6LDmXetTQpPRtN5inmNV3Syxsf+FYp0/2VxPaIB5i07TIMpRKYlSCBjy590LQKZgXW13c1CowN8mK2QyICoJiA+cmTJ5g9ezZ69epV7O6ZFUFYeAziddjlqqJZeyoa49+pAcPX9KZu29/38fDFhKjF8VYNh1IJvADFPEHtazni8I14pGVpDgYNpBJ0qV96Weu6NnDG+rN3kZ0rR3Zu/gmISpuNmRFaeznobH/56dbQFT+diEbPxm4VMvACgIAGLtgUHqPz97i86dbQRSdj+uwsjPFOncqIvJ+MdrUcy3x/JdG2lgMsTQyRlpWLZ/l855Q3Wa/ZNcyWr2JiyxdVNOvWrcPQoUPh4+ODvXv3ws3NrcDyFfU6FwQBTecfQmJ6NlZ/2AR1XYo2zcGbTADw7opTeJaZi71jW6t033qddFl2AjfinmFejwZoU6NowYBEAlSxNYdBKd60y+QCHjzNQH5/rW3MjGBrUbrzYiWlZyP1uW4H3DtamRR7UurSlpkjg7GBtMIGXwCQmJZVrNbdisJAKkEVWzOdJVTJlckhEwSYGJb/1qS0rFwkvEY/QFqaGpaLOevY8kVERRIcHKySYOVNdSs+DYnp2TA1kqJDXScYG76erTf60tzTDodvxCP8TtJrGXw9Tc/GjThF1r2uDZzLxR90A6kEHva6TXduZ2EMu1IO6F4nJZkv7XVhb2mi1+QmbxpDA+lrc9NtaWIIy3LyQ0hFxLsKIqI8lBmwmlS1ZeBVDIUliCjvlAkIalS2LBeBFxERVSy8syhD7NFJFVlFvb6Vk0tWxCxnuqA8b+ejkyCTv37XSHmY6JeIiCouBl9lwMBA0V0hO/vNnT+DKj7l9a283isC1QlrefNdHPVdrWFpYojUzFzciEvVd3WK7GXKcwbfRERU+tihswwYGhrC3NwcT548gZGREaRSxrhUscjlcjx58gTm5uYwNKw4XyPRCYqJZY0NpfBxr6Tv6ryWDA2k8PWwxfH/niD8ThLqu9rou0paS3meg2uPFAFjC7Z8ERFRGag4d03liEQigYuLC6Kjo3Hv3j19V4eoTEilUlStWlVnmaJ04dyLLmc+7pXeiAH3ZcWvuh2O//cE5+4kYshb1fRdHa39fTcJggBUc7BAZeuKk8GTiIjKDwZfZcTY2Bg1a9Zk10OqsIyNjStcq66yyxlbPUpGHPd1NwlyufDapOsWu5zy/SciojLC4KsMSaXSCjX/EVFFJgjCy2QLHO9TIo2q2MDMyADJGTn4L/4Z6ji/HnOliclWON6PiIjKSMX62ZqIqJhikjIQl5oJIwMJmlS11Xd1XmtGBlI09VScQ2VAW96lZeXi6kPFeC9muiQiorKi9+Br5cqV8PT0hKmpKfz8/HD+/Pl8y+bk5GDu3Lnw8vKCqakpvL29sX//fpUyCxcuRLNmzWBlZYXKlSujR48eiIqKUinTvn17SCQSlceoUaPK5PiI6PWgDBK8q1SCmTHHe5WUsuve6zLf1993Fanxq9qZw7WSmb6rQ0REFZReg6+tW7ciJCQEs2bNwsWLF+Ht7Q1/f3/Ex8drLD99+nT88MMPWL58Oa5du4ZRo0ahZ8+euHTpkljm+PHjGDNmDM6dO4eDBw8iJycHnTt3Rnp6usq2hg8fjkePHomPRYsWlemxElH5di6aXc5Kk7Lr5vnopNdiTrhznN+LiIh0QK/B19KlSzF8+HAMHjwY9erVw/fffw9zc3OsXbtWY/kNGzbgiy++QEBAAKpXr47Ro0cjICAAX3/9tVhm//79CA4ORv369eHt7Y1169YhJiYGERERKtsyNzeHs7Oz+LC2fj3GJBBR2Xg5uS67nJWGRlVsYGIoRUJaNm4/SdN3dQrF+b2IiEgX9BZ8ZWdnIyIiAh07dnxZGakUHTt2xNmzZzWuk5WVpZbAwszMDKdOncp3PykpKQAAOzvVXzM3bdoEBwcHNGjQAFOnTkVGRkaB9c3KykJqaqrKg4gqhgdPMxCb/BwGUgl8PTjeqzSYGBqIY+fOlfNxXxnZubjyQPG3gi1fRERUlvQWfCUkJEAmk8HJyUlluZOTE+Li4jSu4+/vj6VLl+LmzZuQy+U4ePAgdu7ciUePHmksL5fLMWHCBLRu3RoNGjQQl/fv3x8bN27E0aNHMXXqVGzYsAEfffRRgfVduHAhbGxsxIe7u3sRj5iIyitlq1dDNxtYmDAJbGlRduFUpnAvryLuPUWuXIBbJTO425nruzpERFSBvVZ3Gd9++y2GDx+OOnXqQCKRwMvLC4MHD863m+KYMWNw9epVtZaxESNGiP9v2LAhXFxc0KFDB9y+fRteXl4atzV16lSEhISIz1NTUxmAEVUQ4RzvVSYUXThvIvxOIgRBKLcTcodzvBcREemI3lq+HBwcYGBggMePH6ssf/z4MZydnTWu4+joiN27dyM9PR337t3DjRs3YGlpierVq6uVHTt2LH7//XccPXoUVapUKbAufn5+AIBbt27lW8bExATW1tYqDyKqGJQtMy043qtUNa5aCcYGUsQ/y8LdxIK7dusTg28iItIVvQVfxsbG8PX1xeHDh8Vlcrkchw8fRsuWLQtc19TUFG5ubsjNzcWOHTvw3nvvia8JgoCxY8di165dOHLkCKpVq1ZoXSIjIwEALi4uxTsYInptPUp5jnuJGZBKIM5NRaXD1MgAPu6VAADn7pTPlPOZOTJcvq8c78Xgm4iIypZesx2GhITgp59+wvr163H9+nWMHj0a6enpGDx4MABg4MCBmDp1qlg+PDwcO3fuxJ07d3Dy5El06dIFcrkcn3/+uVhmzJgx2LhxI8LCwmBlZYW4uDjExcXh+fPnAIDbt29j3rx5iIiIwN27d7F3714MHDgQbdu2RaNGjXR7AohI75Rdzuq72sDK1EjPtal4xHFf5TT4uhjzFNkyOZysTeBhz/FeRERUtvQ65qtPnz548uQJZs6cibi4OPj4+GD//v1iEo6YmBhIpS/jw8zMTEyfPh137tyBpaUlAgICsGHDBlSqVEkss3r1agCKiZTzCg0NRXBwMIyNjXHo0CEsW7YM6enpcHd3R1BQEKZPn17mx0tE5Y/Y5YzjfcqEXzV7LMcthL+Y76u8jfvKO8VAeasbERFVPHpPuDF27FiMHTtW42vHjh1Ted6uXTtcu3atwO0VNpmnu7s7jh8/XqQ6ElHFpbz5bsH5ncpEE49KMDKQ4FFKJu4nPUfVcta6pAy++f4TEZEu6LXbIRGRPsWnZuJOQjokEqAZW77KhLmxIRpVqQQAOBddvroeZuXKcCkmGQCTbRARkW4w+CKiN5Yyy2FdZ2vYmHG8V1lRdukML2eTLV++n4KsXDkcLE1Q3cFC39UhIqI3AIMvInpjMcW4bvi96NIXXs5avpRJQPyq23G8FxER6QSDLyJ6Y+VNtkBlx9fDFgZSCR48fY7Y5Of6ro7o5fxuDL6JiEg3GHwR0RspMS0LN+PTAADNefNdpixNDNHAzQZA+Uk5nyOTI+LeUwAvW+aIiIjKmt6zHVLJCIKA1MzcYq9vZmQAY8PyH4PL5ALSsop/nESvOnHzCQCgtpMV7CyM9Vybiq9FNTtcvp+MU7cS0KGuk76rgysPUvA8RwY7C2PUrGyp7+oQEdEbgsHXay4rVw7vOX8Ve30rU0P8Ob4N3O3KV/rnvDJzZOj67UlEJ6TruypUAXG8l274VbfDDyfuYOfFWOy8GKvv6oiae3K8FxER6U75b/KgMvUsMxcH/o3TdzUKdOFuEgMvKhMWxgZ4z8dN39V4I7Sobo8a5ayFycRQig98q+i7GkRE9AZhy9drzsRQiptfdi3Wuj+euIPFB6IQHp2EYW2ql3LNSo8yKUIPH1cs7uWt59pQRSKVSGAgZauHLpgbG+LgxLbIlQv6roqI7z8REekag6/XnEQigZFB8W4eWnopBplfuJsEuVyAtJzehCjTU7eobg8jAzbWEr2uSvJ9RUREVBHwTvYN1tDNBubGBkjOyEHU42f6ro5GmTkyXL6fAkARfBERERERva4YfL3BjAyk8PWwBVB+0j+/6mLMU2TL5HCyNoGHfflNCkJEREREVBgGX284ZWuScrLR8ibvJLjMSEZERERErzMGX284vxeTy56PToIglJ+B8ErK8V5MB05ERERErzsGX2+4RlUqwdRIisT0bNyKT9N3dVRk5cpwKSYZgKLli4iIiIjodcbg6w1nbChFk6qKcV/nylnXw8v3U5CVK4eDpQm8HC30XR0iIiIiohJh8EViq1J5S7qhrI9fNTuO9yIiIiKi1x6DLxLHU527U77GfZ3jeC8iIiIiqkAYfBF83CvB2FCKhLQs3ElI13d1AADZuXJE3HsKgOO9iIiIiKhiYPBFMDUygI97JQAvU7vr25XYZGTmyGFrboSalS31XR0iIiIiohJj8EUAgBYvUs4rU7vr27kXQWDzanaQSjnei4iIiIhefwy+CECeyZbLybgv5aTPynoREREREb3uGHwRAKBxVVsYGUgQl5qJmKQMvdYlVyZHxF1F8MXxXkRERERUUTD4IgCAmbEBvKtUAqD/cV9XH6YiPVsGGzMj1HG20mtdiIiIiIhKC4MvEokp5/U87ks5v1czT473IiIiIqKKg8EXiV5Otqzflq+X4704vxcRERERVRwMvkjk62ELA6kEscnPcV9P475kcgEXojnei4iIiIgqHgZfJLIwMURDNxsAL1ufdO3aw1Q8y8qFlYkh6rla66UORERERERlgcEXqVCO+1KOu9I15TxjTT0VrXBERERERBUFgy9S0UI57ktPLV/KyZX9OL8XEREREVUwDL5IRVNPW0glQExSBh6lPNfpvuVyARfE+b2YbIOIiIiIKhYGX6TCytQI9V1fjPvScdbDG3HPkPI8B+bGBmjwYuwZEREREVFFoffga+XKlfD09ISpqSn8/Pxw/vz5fMvm5ORg7ty58PLygqmpKby9vbF///4ibzMzMxNjxoyBvb09LC0tERQUhMePH5f6sb2ulCnew3U839fL8V52MDLQ+6VJRERERFSq9HqHu3XrVoSEhGDWrFm4ePEivL294e/vj/j4eI3lp0+fjh9++AHLly/HtWvXMGrUKPTs2ROXLl0q0jYnTpyI3377Ddu2bcPx48fx8OFDvP/++2V+vK8Lfc33pdwfuxwSERERUUUkEQRB0NfO/fz80KxZM6xYsQIAIJfL4e7ujnHjxmHKlClq5V1dXTFt2jSMGTNGXBYUFAQzMzNs3LhRq22mpKTA0dERYWFh+OCDDwAAN27cQN26dXH27Fm0aNFCq7qnpqbCxsYGKSkpsLauWCnRU57nwGfuXxAEYPPwFrA2M9TJfgesOY+k9GzsGN0Svh4MwIiIiIjo9aBtbKCbu2oNsrOzERERgalTp4rLpFIpOnbsiLNnz2pcJysrC6ampirLzMzMcOrUKa23GRERgZycHHTs2FEsU6dOHVStWrXA4CsrKwtZWVni89TU1CIe8evDxswIdZ2tce1RKvr9dE6n+zY1kqKhWyWd7pOIiIiISBf0FnwlJCRAJpPByclJZbmTkxNu3LihcR1/f38sXboUbdu2hZeXFw4fPoydO3dCJpNpvc24uDgYGxujUqVKamXi4uLyre/ChQsxZ86coh7ma2tE2+pYfCAKOTK5zvYpkQD9mleFsSHHexERERFRxaO34Ks4vv32WwwfPhx16tSBRCKBl5cXBg8ejLVr15b5vqdOnYqQkBDxeWpqKtzd3ct8v/rSo7EbejR203c1iIiIiIgqDL01MTg4OMDAwEAty+Djx4/h7OyscR1HR0fs3r0b6enpuHfvHm7cuAFLS0tUr15d6206OzsjOzsbycnJWu8XAExMTGBtba3yICIiIiIi0pbegi9jY2P4+vri8OHD4jK5XI7Dhw+jZcuWBa5ramoKNzc35ObmYseOHXjvvfe03qavry+MjIxUykRFRSEmJqbQ/RIRERERERWXXrsdhoSEYNCgQWjatCmaN2+OZcuWIT09HYMHDwYADBw4EG5ubli4cCEAIDw8HLGxsfDx8UFsbCxmz54NuVyOzz//XOtt2tjYYOjQoQgJCYGdnR2sra0xbtw4tGzZUutMh0REREREREWl1+CrT58+ePLkCWbOnIm4uDj4+Phg//79YsKMmJgYSKUvG+cyMzMxffp03LlzB5aWlggICMCGDRtUkmcUtk0A+OabbyCVShEUFISsrCz4+/tj1apVOjtuIiIiIiJ68+h1nq/XWUWe54uIiIiIiLSnbWzAnN5EREREREQ6wOCLiIiIiIhIBxh8ERERERER6QCDLyIiIiIiIh1g8EVERERERKQDDL6IiIiIiIh0gMEXERERERGRDjD4IiIiIiIi0gEGX0RERERERDrA4IuIiIiIiEgHGHwRERERERHpAIMvIiIiIiIiHWDwRUREREREpAMMvoiIiIiIiHSAwRcREREREZEOMPgiIiIiIiLSAQZfREREREREOsDgi4iIiIiISAcYfBEREREREekAgy8iIiIiIiIdYPBFRERERESkAwy+iIiIiIiIdIDBFxERERERkQ4w+CIiIiIiItIBBl9EREREREQ6wOCLiIiIiIhIBxh8ERERERER6QCDLyIiIiIiIh1g8EVERERERKQDDL6IiIiIiIh0gMEXERERERGRDjD4IiIiIiIi0gG9B18rV66Ep6cnTE1N4efnh/PnzxdYftmyZahduzbMzMzg7u6OiRMnIjMzU3zd09MTEolE7TFmzBixTPv27dVeHzVqVJkdIxERERERkaE+d75161aEhITg+++/h5+fH5YtWwZ/f39ERUWhcuXKauXDwsIwZcoUrF27Fq1atcJ///2H4OBgSCQSLF26FABw4cIFyGQycZ2rV6+iU6dO6NWrl8q2hg8fjrlz54rPzc3Ny+goiYiIiIiI9Bx8LV26FMOHD8fgwYMBAN9//z3++OMPrF27FlOmTFErf+bMGbRu3Rr9+/cHoGjl6tevH8LDw8Uyjo6OKut89dVX8PLyQrt27VSWm5ubw9nZubQPiYiIiIiISCO9dTvMzs5GREQEOnbs+LIyUik6duyIs2fPalynVatWiIiIELsm3rlzB3/++ScCAgLy3cfGjRsxZMgQSCQSldc2bdoEBwcHNGjQAFOnTkVGRkaB9c3KykJqaqrKg4iIiIiISFt6a/lKSEiATCaDk5OTynInJyfcuHFD4zr9+/dHQkIC3nrrLQiCgNzcXIwaNQpffPGFxvK7d+9GcnIygoOD1bbj4eEBV1dX/PPPP5g8eTKioqKwc+fOfOu7cOFCzJkzp2gHSURERERE9IJeux0W1bFjx7BgwQKsWrUKfn5+uHXrFj755BPMmzcPM2bMUCu/Zs0adO3aFa6urirLR4wYIf6/YcOGcHFxQYcOHXD79m14eXlp3PfUqVMREhIiPk9NTYW7u3spHRkREREREVV0egu+HBwcYGBggMePH6ssf/z4cb5jsWbMmIEBAwZg2LBhABSBU3p6OkaMGIFp06ZBKn3Zi/LevXs4dOhQga1ZSn5+fgCAW7du5Rt8mZiYwMTERKtjIyIiIiIiepXexnwZGxvD19cXhw8fFpfJ5XIcPnwYLVu21LhORkaGSoAFAAYGBgAAQRBUloeGhqJy5cro1q1boXWJjIwEALi4uBTlEIiIiIiIiLSm126HISEhGDRoEJo2bYrmzZtj2bJlSE9PF7MfDhw4EG5ubli4cCEAIDAwEEuXLkXjxo3FboczZsxAYGCgGIQBiiAuNDQUgwYNgqGh6iHevn0bYWFhCAgIgL29Pf755x9MnDgRbdu2RaNGjXR38ERERERE9EbRa/DVp08fPHnyBDNnzkRcXBx8fHywf/9+MQlHTEyMSkvX9OnTIZFIMH36dMTGxsLR0RGBgYH48ssvVbZ76NAhxMTEYMiQIWr7NDY2xqFDh8RAz93dHUFBQZg+fXrZHiwREREREb3RJMKr/fVIK6mpqbCxsUFKSgqsra31XR0iIiIiItITbWMDvY35IiIiIiIiepMw+CIiIiIiItIBBl9EREREREQ6wOCLiIiIiIhIBxh8ERERERER6QCDLyIiIiIiIh1g8EVERERERKQDDL6IiIiIiIh0gMEXERERERGRDjD4IiIiIiIi0oEiB1+enp6YO3cuYmJiyqI+REREREREFVKRg68JEyZg586dqF69Ojp16oQtW7YgKyurLOpGRERERERUYRQr+IqMjMT58+dRt25djBs3Di4uLhg7diwuXrxYFnUkIiIiIiJ67UkEQRBKsoGcnBysWrUKkydPRk5ODho2bIjx48dj8ODBkEgkpVXPcic1NRU2NjZISUmBtbW1vqtDRERERER6om1sYFjcHeTk5GDXrl0IDQ3FwYMH0aJFCwwdOhQPHjzAF198gUOHDiEsLKy4myciIiIiIqpQihx8Xbx4EaGhodi8eTOkUikGDhyIb775BnXq1BHL9OzZE82aNSvVihIREREREb3Oihx8NWvWDJ06dcLq1avRo0cPGBkZqZWpVq0a+vbtWyoVJCIiIiIiqgiKHHzduXMHHh4eBZaxsLBAaGhosStFRERERERU0RQ522F8fDzCw8PVloeHh+Pvv/8ulUoRERERERFVNEUOvsaMGYP79++rLY+NjcWYMWNKpVJEREREREQVTZGDr2vXrqFJkyZqyxs3boxr166VSqWIiIiIiIgqmiIHXyYmJnj8+LHa8kePHsHQsNiZ64mIiIiIiCq0IgdfnTt3xtSpU5GSkiIuS05OxhdffIFOnTqVauWIiIiIiIgqiiI3VS1ZsgRt27aFh4cHGjduDACIjIyEk5MTNmzYUOoVJCIiIiIiqgiKHHy5ubnhn3/+waZNm3D58mWYmZlh8ODB6Nevn8Y5v4iIiIiIiKgYwRegmMdrxIgRpV0XIiIiIiKiCqvYGTKuXbuGmJgYZGdnqyx/9913S1wpIiIiIiKiiqbIwdedO3fQs2dPXLlyBRKJBIIgAAAkEgkAQCaTlW4NiYiIiIiIKoAiZzv85JNPUK1aNcTHx8Pc3Bz//vsvTpw4gaZNm+LYsWNlUEUiIiIiIqLXX5Fbvs6ePYsjR47AwcEBUqkUUqkUb731FhYuXIjx48fj0qVLZVFPIiIiIiKi11qRW75kMhmsrKwAAA4ODnj48CEAwMPDA1FRUaVbOyIiIiIiogqiyC1fDRo0wOXLl1GtWjX4+flh0aJFMDY2xo8//ojq1auXRR2JiIiIiIhee0UOvqZPn4709HQAwNy5c9G9e3e0adMG9vb22Lp1a6lXkIiIiIiIqCIocrdDf39/vP/++wCAGjVq4MaNG0hISEB8fDzeeeedIldg5cqV8PT0hKmpKfz8/HD+/PkCyy9btgy1a9eGmZkZ3N3dMXHiRGRmZoqvz549GxKJROVRp04dlW1kZmZizJgxsLe3h6WlJYKCgvD48eMi152IiIiIiEhbRQq+cnJyYGhoiKtXr6ost7OzE1PNF8XWrVsREhKCWbNm4eLFi/D29oa/vz/i4+M1lg8LC8OUKVMwa9YsXL9+HWvWrMHWrVvxxRdfqJSrX78+Hj16JD5OnTql8vrEiRPx22+/Ydu2bTh+/DgePnwoBpRERERERERloUjdDo2MjFC1atVSm8tr6dKlGD58OAYPHgwA+P777/HHH39g7dq1mDJlilr5M2fOoHXr1ujfvz8AwNPTE/369UN4eLhKOUNDQzg7O2vcZ0pKCtasWYOwsDCxpS40NBR169bFuXPn0KJFC43rZWVlISsrS3yemppa9AMmIiIiIqI3VpG7HU6bNg1ffPEFkpKSSrTj7OxsREREoGPHji8rI5WiY8eOOHv2rMZ1WrVqhYiICLFr4p07d/Dnn38iICBApdzNmzfh6uqK6tWr48MPP0RMTIz4WkREBHJyclT2W6dOHVStWjXf/QLAwoULYWNjIz7c3d2LddxERERERPRmKnLCjRUrVuDWrVtwdXWFh4cHLCwsVF6/ePGiVttJSEiATCaDk5OTynInJyfcuHFD4zr9+/dHQkIC3nrrLQiCgNzcXIwaNUql26Gfnx/WrVuH2rVr49GjR5gzZw7atGmDq1evwsrKCnFxcTA2NkalSpXU9hsXF5dvfadOnYqQkBDxeWpqKgMwIiIiIiLSWpGDrx49epRBNbRz7NgxLFiwAKtWrYKfnx9u3bqFTz75BPPmzcOMGTMAAF27dhXLN2rUCH5+fvDw8MCvv/6KoUOHFnvfJiYmMDExKfExEBERERHRm6nIwdesWbNKZccODg4wMDBQyzL4+PHjfMdrzZgxAwMGDMCwYcMAAA0bNkR6ejpGjBiBadOmQSpV70VZqVIl1KpVC7du3QIAODs7Izs7G8nJySqtXwXtl4iIiIiIqKSKPOartBgbG8PX1xeHDx8Wl8nlchw+fBgtW7bUuE5GRoZagGVgYAAAEARB4zppaWm4ffs2XFxcAAC+vr4wMjJS2W9UVBRiYmLy3S8REREREVFJFbnlSyqVFphWviiZEENCQjBo0CA0bdoUzZs3x7Jly5Ceni5mPxw4cCDc3NywcOFCAEBgYCCWLl2Kxo0bi90OZ8yYgcDAQDEImzRpEgIDA+Hh4YGHDx9i1qxZMDAwQL9+/QAANjY2GDp0KEJCQmBnZwdra2uMGzcOLVu2zDfTIRERERERUUkVOfjatWuXyvOcnBxcunQJ69evx5w5c4q0rT59+uDJkyeYOXMm4uLi4OPjg/3794tJOGJiYlRauqZPnw6JRILp06cjNjYWjo6OCAwMxJdffimWefDgAfr164fExEQ4Ojrirbfewrlz5+Do6CiW+eabbyCVShEUFISsrCz4+/tj1apVRT0VREREREREWpMI+fXXK6KwsDBs3boVe/bsKY3NlXupqamwsbFBSkoKrK2t9V0dIiIiIiLSE21jg1Ib89WiRQuVcVRERERERET0UqkEX8+fP8d3330HNze30tgcERERERFRhVPkMV+2trYqCTcEQcCzZ89gbm6OjRs3lmrliIiIiIiIKooiB1/ffPONSvAllUrh6OgIPz8/2NralmrliIiIiIiIKooiB1/BwcFlUA0iIiIiIqKKrchjvkJDQ7Ft2za15du2bcP69etLpVJEREREREQVTZGDr4ULF8LBwUFteeXKlbFgwYJSqRQREREREVFFU+TgKyYmBtWqVVNb7uHhgZiYmFKpFBERERERUUVT5OCrcuXK+Oeff9SWX758Gfb29qVSKSIiIiIiooqmyMFXv379MH78eBw9ehQymQwymQxHjhzBJ598gr59+5ZFHYmIiIiIiF57Rc52OG/ePNy9excdOnSAoaFidblcjoEDB3LMFxERERERUT4kgiAIxVnx5s2biIyMhJmZGRo2bAgPD4/Srlu5lpqaChsbG6SkpMDa2lrf1SEiIiIiIj3RNjYocsuXUs2aNVGzZs3irk5ERERERPRGKfKYr6CgIPzvf/9TW75o0SL06tWrVCpFRERERERU0RQ5+Dpx4gQCAgLUlnft2hUnTpwolUoRERERERFVNEUOvtLS0mBsbKy23MjICKmpqaVSKSIiIiIiooqmyMFXw4YNsXXrVrXlW7ZsQb169UqlUkRERERERBVNkRNuzJgxA++//z5u376Nd955BwBw+PBhhIWFYfv27aVeQSIiIiIiooqgyMFXYGAgdu/ejQULFmD79u0wMzODt7c3jhw5Ajs7u7KoIxERERER0Wuv2PN8KaWmpmLz5s1Ys2YNIiIiIJPJSqtu5Rrn+SIiIiIiIkD72KDIY76UTpw4gUGDBsHV1RVff/013nnnHZw7d664myMiIiIiIqrQitTtMC4uDuvWrcOaNWuQmpqK3r17IysrC7t372ayDSIiIiIiogJo3fIVGBiI2rVr459//sGyZcvw8OFDLF++vCzrRkREREREVGFo3fK1b98+jB8/HqNHj0bNmjXLsk5EREREREQVjtYtX6dOncKzZ8/g6+sLPz8/rFixAgkJCWVZNyIiIiIiogpD6+CrRYsW+Omnn/Do0SOMHDkSW7ZsgaurK+RyOQ4ePIhnz56VZT2JiIiIiIheayVKNR8VFYU1a9Zgw4YNSE5ORqdOnbB3797SrF+5xVTzREREREQE6CDVPADUrl0bixYtwoMHD7B58+aSbIqIiIiIiKhCK/Eky28qtnwRERERERGgo5YvIiIiIiIi0g6DLyIiIiIiIh1g8EVERERERKQDeg++Vq5cCU9PT5iamsLPzw/nz58vsPyyZctQu3ZtmJmZwd3dHRMnTkRmZqb4+sKFC9GsWTNYWVmhcuXK6NGjB6KiolS20b59e0gkEpXHqFGjyuT4iIiIiIiIAD0HX1u3bkVISAhmzZqFixcvwtvbG/7+/oiPj9dYPiwsDFOmTMGsWbNw/fp1rFmzBlu3bsUXX3whljl+/DjGjBmDc+fO4eDBg8jJyUHnzp2Rnp6usq3hw4fj0aNH4mPRokVleqxERERERPRmM9TnzpcuXYrhw4dj8ODBAIDvv/8ef/zxB9auXYspU6aolT9z5gxat26N/v37AwA8PT3Rr18/hIeHi2X279+vss66detQuXJlREREoG3btuJyc3NzODs7l8VhERERERERqdFby1d2djYiIiLQsWPHl5WRStGxY0ecPXtW4zqtWrVCRESE2DXxzp07+PPPPxEQEJDvflJSUgAAdnZ2Kss3bdoEBwcHNGjQAFOnTkVGRkaB9c3KykJqaqrKg4iIiIiISFt6a/lKSEiATCaDk5OTynInJyfcuHFD4zr9+/dHQkIC3nrrLQiCgNzcXIwaNUql22FecrkcEyZMQOvWrdGgQQOV7Xh4eMDV1RX//PMPJk+ejKioKOzcuTPf+i5cuBBz5swpxpESERERERHpudthUR07dgwLFizAqlWr4Ofnh1u3buGTTz7BvHnzMGPGDLXyY8aMwdWrV3Hq1CmV5SNGjBD/37BhQ7i4uKBDhw64ffs2vLy8NO576tSpCAkJEZ+npqbC3d29lI6MiIiIiIgqOr0FXw4ODjAwMMDjx49Vlj9+/DjfsVgzZszAgAEDMGzYMACKwCk9PR0jRozAtGnTIJW+7EU5duxY/P777zhx4gSqVKlSYF38/PwAALdu3co3+DIxMYGJiYnWx0dERERERJSX3sZ8GRsbw9fXF4cPHxaXyeVyHD58GC1bttS4TkZGhkqABQAGBgYAAEEQxH/Hjh2LXbt24ciRI6hWrVqhdYmMjAQAuLi4FOdQiIiIiIiICqXXbochISEYNGgQmjZtiubNm2PZsmVIT08Xsx8OHDgQbm5uWLhwIQAgMDAQS5cuRePGjcVuhzNmzEBgYKAYhI0ZMwZhYWHYs2cPrKysEBcXBwCwsbGBmZkZbt++jbCwMAQEBMDe3h7//PMPJk6ciLZt26JRo0b6ORFERERERFTh6TX46tOnD548eYKZM2ciLi4OPj4+2L9/v5iEIyYmRqWla/r06ZBIJJg+fTpiY2Ph6OiIwMBAfPnll2KZ1atXA1BMpJxXaGgogoODYWxsjEOHDomBnru7O4KCgjB9+vSyP2AiIiIiInpjSQRlfz0qktTUVNjY2CAlJQXW1tb6rg4REREREemJtrGB3sZ8ERERERERvUkYfBEREREREekAgy8iIiIiIiIdYPBFRERERESkAwy+iIiIiIiIdIDBFxERERERkQ4w+CIiIiIiItIBBl9EREREREQ6wOCLiIiIiIhIBxh8ERERERER6QCDLyIiIiIiIh1g8EVERERERKQDDL6IiIiIiIh0gMEXERERERGRDjD4IiIiIiIi0gEGX0RERERERDrA4IuIiIiIiEgHGHwRERERERHpAIMvIiIiIiIiHWDwRUREREREpAMMvoiIiIiIiHSAwRcREREREZEOMPgiIiIiIiLSAQZfREREREREOsDgi4iIiIiISAcYfBEREREREekAgy8iIiIiIiIdYPBFRERERESkAwy+iIiIiIiIdIDBFxERERERkQ4w+CIiIiIiItIBBl9EREREREQ6oPfga+XKlfD09ISpqSn8/Pxw/vz5AssvW7YMtWvXhpmZGdzd3TFx4kRkZmYWaZuZmZkYM2YM7O3tYWlpiaCgIDx+/LjUj42IiIiIiEhJr8HX1q1bERISglmzZuHixYvw9vaGv78/4uPjNZYPCwvDlClTMGvWLFy/fh1r1qzB1q1b8cUXXxRpmxMnTsRvv/2Gbdu24fjx43j48CHef//9Mj9eIiIiIiJ6c0kEQRD0tXM/Pz80a9YMK1asAADI5XK4u7tj3LhxmDJlilr5sWPH4vr16zh8+LC47NNPP0V4eDhOnTql1TZTUlLg6OiIsLAwfPDBBwCAGzduoG7dujh79ixatGihVd1TU1NhY2ODlJQUWFtbl+g8EBERERHR60vb2EBvLV/Z2dmIiIhAx44dX1ZGKkXHjh1x9uxZjeu0atUKERERYjfCO3fu4M8//0RAQIDW24yIiEBOTo5KmTp16qBq1ar57hcAsrKykJqaqvIgIiIiInrjZacD24cC6wOB1Ef6rk25ZqivHSckJEAmk8HJyUlluZOTE27cuKFxnf79+yMhIQFvvfUWBEFAbm4uRo0aJXY71GabcXFxMDY2RqVKldTKxMXF5VvfhQsXYs6cOUU9TCIiIiKiiivrGRDWB7h3WvF8XQAw6DfApop+61VO6T3hRlEcO3YMCxYswKpVq3Dx4kXs3LkTf/zxB+bNm1fm+546dSpSUlLEx/3798t8n0RERERE5VZmCrDhfUXgZWIN2LgDSXeA0ADg6T19165c0lvLl4ODAwwMDNSyDD5+/BjOzs4a15kxYwYGDBiAYcOGAQAaNmyI9PR0jBgxAtOmTdNqm87OzsjOzkZycrJK61dB+wUAExMTmJiYFOdQiYiIiIgqludPFYHXw4uAqQ0wYBdgUVnR9fBpNLCuGzBoL2BXveDtPP4XiLsK1O8JGBrrpu56pLeWL2NjY/j6+qokz5DL5Th8+DBatmypcZ2MjAxIpapVNjAwAAAIgqDVNn19fWFkZKRSJioqCjExMfnul4iIiIiIXkhPBNa/qwi8zOwU3QzdfIFK7sDgfYB9TSDlvqIFLOGm5m3EXgQ29wdWtwJ2jQB+HQjkZun2OPRAby1fABASEoJBgwahadOmaN68OZYtW4b09HQMHjwYADBw4EC4ublh4cKFAIDAwEAsXboUjRs3hp+fH27duoUZM2YgMDBQDMIK26aNjQ2GDh2KkJAQ2NnZwdraGuPGjUPLli21znRIRERERPRGSnsC/PIeEP8vYOEIDNwDONV/+bq1CzD4T0Vw9uS6IgAbtBeoXFfxesw54MRi4NahFytIAKkh8N8+YEt/oM9GwMhM54elK3oNvvr06YMnT55g5syZiIuLg4+PD/bv3y8mzIiJiVFp6Zo+fTokEgmmT5+O2NhYODo6IjAwEF9++aXW2wSAb775BlKpFEFBQcjKyoK/vz9WrVqluwMnIiIiInrdPItTBFUJUYClsyKocqytXs6yMhD8O/BLD+DxFUUXxM7zgcgw4O5JRRmJAdCoN9DmUyD1IbC5ryIg29wX6LsZMDbX6aHpil7n+XqdcZ4vIiIiIj06/S0Q/gPw9hdA44/0XZuKLTcbuBwGHF8MpD4ArN0UXQ3tvQpeLyMJ2NATeBT5cpnUCPDpD7w1EbCr9nL53dNAWG8gOw3weAvovxUwsSyTwykL2sYGDL6KicEXERERkR4IAnBsIXD8fy+XdfsaaDZMf3WqqHKeAxc3AKeXAamximWVPBQtXrae2m3jeTKwuR8QGwE0GQi0/kQxNkyT++eBjUFAVirg7gd8uE2RzOM1wOCrjDH4IiIiItIxQQAOzVYEAwDg0frl/FJdvgJajNZXzSqW7HTg77XAmeVA2oss4pbOQOvxgG8wYGxRtO3J5YA8BzDUInN4bISitSwzBXBtAgzYCZjZFvkQdI3BVxlj8EVERESkQ4IAHPgCOPdinL7/QkWwdXgOcOobxbKOc4C3Juitivm6e1qRfKJhb8C0CPeNDyIUGQVr+QOVqpZd/ZTkcuDsCkVwm5GoWGZdRXFOGw8AjEzLvg4A8OgfRVKP50mAUwOgZuf8y7r5AnW766ZeBWDwVcYYfBERERHpiFwO7PsMuPCz4nneboavdkN8exrQ7nP91FOTK9uBnSMAQQaYVlIEjH4jC27NuXcGOL4IuHNU8VxqCHj3Bd4KKXycVXHJZcDe8UDkRsVzW09FMoxGffUz/9bja8Av7wLpTwou5zsYCFymkyoVhMFXGWPwRURERKQDcjnw+yfAxV8ASIB3v1OMHXrVicXAkfmK/7f9TBGESSQ6raqayM3Ano8BQa4Itp4/VSw3tgKaDwNajgUsHBTLBAG4c0xxHMqulBIDwKkeEHflxXMp0LCXIijSlGWwuGS5wO7RwJVfFfvs+j9FUGOg18ToQFI0cHE9kJOZfxn35kCD93VXp3ww+CpjDL6IiIioQpLlADkZxVvXxLp0Ax65DNgzBri8WRF49FitaAHKz+nvgIMzFP9vNR7oNFd/AdjFXxQtSRCAJoMUrXXXfwNOLFHMkQUARuaKIMe9uaK734MLiuVSI0UGx7cmKFqgYsJfzI118MXGJUC994C2kwDnhiWrpywH2Dkc+HeXooUtaA1Qv0fJtvkGYvBVxhh8ERERUYVz86DiRlzZQlNUlespUoSXdHySICjmfDr+P0VAIjEA3v8RaPhB4eue+x7YP1nx/+rtgXaTAY9WJatPUZ3/CfhzkuL/zYYDXRcByrlr5XLFhMLHF6mmYAcAQ1NFQotW4wEbN/XtPrykCN5u/P5yWe0ARUufW5Oi1zM3G9g+WLE9qRHQez1Qp1vRt0MMvsoagy8iIiKqUG78CWwbBMiyS7YdG3fFHFB553DSllwORP2paOVRBiaGporAq9572m/n77XAn58B8lzFc4+3gHafAdXalX1L2NlVwIGpiv+3HKuYXFjTPgUBuHUYOLkESLwNePcBWo4DrJwK30fcVeDk14rWKry4la/RURGEVW2hXT1zMoFfBwI3DwAGJkCfjUCtAhJbUIEYfJUxBl9ERERUYVzbA2wfoghW6r4L9Pxe0QWtKNIeK1KEJ94CrFyB4N+1Tw4hlwHXdgMnvlbtktd0CNBqHGDlXLS6AMDTu4osiJc2KdKcA0CVZkDbz4GancomCDv1jSIVPqCYRLjDrLIN9p78pwjCrmxTJPQAAM82ioQjnm3y33d2BrD1Q+D2EcDQDOgXBni9U3b1fAMw+CpjDL6IiIioQsibja/BB0DPH4qfaOFZnCJF+JMbgKWTogWsoMQQslzg6nZFAJHwn2KZsRXQfDjQcszLZBQlkfJAMRbs4nog90XiBhdvoOtioKpfybevdHwRcPRLxf/bTQHaT9HdeLOkO4rAL3Lzy0DT3Q9wqq+5/MNIRQp7IwtFN9FqbXRTzwqMwVcZY/BFREREr73IMEVCC0EOePcH3lsBSA1Kts20J4oALP5fwNwBGLRXPQjIzVYk0Ti1VNFCBQCmNkCLjwtPw15czx4DZ5cDF9YoEoqUVouPICiCrhOLFc/fmaFIhKEPyfeB098qkn3Isgoua2wFfLRd+26KVCAGX2WMwRcRERG91iLWA799AjEbX/dlL5NClFRGErChB/DoMmBmBwzcrWhtyskELm1QBAgp9xVlze0VrVzNhhdtAuLiSk9UpFUvjbFOggAcnAmc+U7xvNM8oPX40qtrcaU+Aq7uALLTNb8ulQJ13wMca+m2XhUYg68yxuCLiIiIXlsFZeMrLc+TgY3vA7ERilYtv1GKgC8tTvG6pZMiq1/TwYCxRenuuzClkeVPEID9U4Hw1YrnXRcpWu3ojcTgq4wx+CIiIqLX0tWdisADAFqMAfy/LLuxSZmpwKZewP1zL5dZV1HMX9V4AGBkWjb71Yba/FY/A/V7areuXA78+akiqyKgaDVsOrjMqkrln7axgZ6nrSYiIiIinUl9BPw+UfH/Fh+XbeAFKLoRfrRDEeQkRQMtRgPe/QBD47Lbp7YMjID3fwYMjIF/tiqyPcpygUa9Cl5PLgN+Gw9c2ghAohgn1/gjnVSZXn8MvoiIiIjeBIIA7B0LZCYDLj5Ap7m6ycZnYgn021z2+ykOA0Ogx2pF18PIjYogUZYNNP5Qc3lZriJByT9bAIlUkRmyUW/d1pleawy+iIiISL8iw4Cbf+X/urk90HQo4FRPd3WqiCLWAbcOKZJM9PxB0fJDiuyO7y5XtMb9vRbY8zFw4w/NrXMpscCD80Xvpkj0AoMvIiIi0p+7p4HdHwMoZAj6hZ+BuoFA288UWfOoaJKigQPTFP/vMBOoXEe/9SlvpFKg21JFF8Tw74GoPwooW8wEHURg8EVERET6kvVMkfIbAlCra/7zLd07BVzbC1z/TfGo6Q+0+xyo0lSn1X1tyWWKADcnHfB4SzHWi9RJJECXrxTX4dN7+ZfzbJ3/5MVEhWDwRURERPrx13Qg+R5gUxV4/8f853jyGwHE3wBOfg1c3a6Yn+nmAaB6e6Dt54qb4aIQBCDlAWBTRTdjnsqKXK6YK6tS1YKP4+xKIOYMYGwJ9FhZ+inlKxKJBKjlr+9aUAXGTx8RERHp3s2DijFIgCIgKGxy3cp1gKCfgLF/KzLLSQ2BO8eAdQHA2q7A7SOKoKogggBE7QN+7gAsawD8EVIaR6IfOZnA5r7At42AnzsC/x3QfPzx14Ej8xT/918A2HrqtJpEpIrzfBUT5/kiIiIqpowkYFVLxWS7fqOBrl8VfRtP7wGnlynSfcuyFcvcmirGhNXyV20JksuB63uBE0uAx1dUt9NvC1C7a7EPRS+yM4At/RTBZ17OjRTHX6e7onUrN1sRaMb9o+iq2X/r693SR1SOcZLlMsbgi4iIqJi2DwGu7gAcagEjTwBGZsXfVupD4PR3QEQokJupWKYMQmp3VUyge2IJkBCleM3YEmg2DMhKVWS2s6gMfHwOsLAv+XHpQlaaosXr7knAyALo+T0Q+zdw/mfFmC4AcKwLtJ2kaPU6uQQws1Uco5WzfutOVIEx+CpjDL6IiEjvovYB1/YAnecDFg4l354gKLoDXlwPZKflU0gCeLQCmg9X3NQX1dWdwPbBgMQAGHYQcPMtUZVFafHAmeXAhTUvgxAji5f/N7EBWowC/EYB/2/vzuOqqvb/j78YZBRE5QKOqWUOOWSiSNpgcnOobg5ZGiqaZYOa6a+85b2Z3gbLbua3MjWvmt1UFMshS8twyi6KWk6laGVpKjgQgqiAnP37Y+nREyCIcA7g+/l4nMeDs/bae3+2d13x01r7s/yqmWV7H9wBx/ZA0+7Q+8OyPyt0Nh3m9oaDG8E7EKIXQd0Ic+x0Kmx8HzZNN4nlpR6YDc16Oj9ekWuIkq9SpuRLRERc6sTPMK0D5Jw2lQL7zi9+8mCzmdLa69+EI9uLdo53oEnA2g0t+qxRRjK83w7O/GEKZdz1j+LFezl/TkJ8q0HkUBOrTxXHvoe/N+9L2c5Br5nQ/IGSj6eknEmDj3vCoa3mOfothtr5JK5n0iBxBmycYv6cmz0AD8x0drQi1xwlX6VMyZeIiLiMLRdmd4WDmy623T/FFKK40uv8sNhUETz6o2mr5A9tHoGwAvbSyko3e27Z+/tB+CNw69MQEFrwvSwL5j1oNlMOawGPxue/iW1JOZMGKbugxs3gXbngfmvfgLWvgU8QPJUAgTVLL6biOp0K/+1uEmPfqtB/CdS8+fLnZGXA75uh3m3aTFnECZR8lTIlXyIi4jIb3oavx4FXANz8MCRONz8/9T9TdrwwtlzYsdC8D3TiJ9PmHQhth5g9oAqbybLZIOkLWD/x4kyZpw/cEgONugD5zMD9vgXWvAIe3vD4OghpciVPXHpyc2DmX80s2A1RZimfs5cfnjpmEsX8WLmw6iVz3C8YYpZpjymRMkjJVylT8iUiIi6R8gN8cKep8Pe390zydWEWrN5tMGDZ5fdxys2BTx8zM15gZnwih5rEyzfoymK58I7Y+olmlqUo/voytH/6yu5T2o4lwbTbIDcL7n3bzOQ5w8nf4dv/g61zzL0vp3Ko+d82pLFzYhORK6Lkq5Qp+RIREac7lw0z7jLl0i99z+vS97+6vA7tniz4/EWDYM9ycK8EHceYd6G8A64uLsuC/evMZr7phwvuV+sWuHcyuHtc3f1KQ8IU+HKMWXb55Aao1qD07pW638xebpsHthzTVrU+ePnn3z8gDLq8AcE3lF5MInJVlHyVMiVfIiLidPEvny8dXu186fBL3rFKnAFfPGuW/z3+DfzlRsdzc87CwgGw70uz9O+hj+HGu50bf1lms8Gc++C3DVA3EgZ+XvJJ4vF98M0k2LHALCcEM1t5+3NQ//ayX21RRAqk5KuUKfkSEamgUn+BlS9A7XCzFO/PFfJc5eBmmHU3WDboPQdu6u543LLgvz3glzWmfPsjX4GHpzmWfRoWRMPPq8HTF/rOg+vvcvojlHl//AZTbzVl9oNvvLr9x/7MZjv/Xtf5f3Zd38kkXddFltw9RMRllHyVMiVfIiIV1NwHzewQmL2hIh43y/j8qrkupuzTMP02UxyjeW/o9Z/8+508BO9HQtZJ6PhPuOM5yM6EeQ9d3JT34QVQ/zbnxl+efP8xLB1aete/satJuvIrEy8i5ZaSr1Km5EtEpAI6mGgq37l5QPUb4HiSafeqDG0GQ+RwqPwX58f1xWhT0TCghimHfrnNjbcvgMVDwN3TFGhY/TIcSDDVEPstgrrtnBd3efX7VjiTWvLXDbou73JQEakQylXyNWXKFN58802Sk5Np2bIl7777Lm3bts2375133sm6devytHfr1o3PP/8cALcC1kxPnDiR5557DoB69erx22+/ORyfMGECzz//fJFiVvIlIlIBfXQ//LLW7Jd137uw5zNY96YpcAFmyV7rgVAn/99RpeJUCqw8/7sp+hNoGHX5/pYFC/vD7s8wJd8tM4PX/1OzlFJEREpcUXMDTyfGlK8FCxYwatQopk2bRkREBJMnT6Zz584kJSUREhKSp/+nn35Kdna2/fuJEydo2bIlvXv3trcdOXLE4ZwVK1YwePBgevXq5dD+r3/9i8cee8z+PSDgKqs9iYhI+fXrBpN4uVeC20ebcu1N74cmf4O9K2HdRDj8HWyaaj7OFv5I4YkXmKIN906GAxsh89j5TXkXQ81WpR6iiIhcnsuTr0mTJvHYY48xaNAgAKZNm8bnn3/OrFmz8p2FqlbNcc19bGwsfn5+DslXWFiYQ5+lS5fSsWNHGjRwLBsbEBCQp29BsrKyyMq6uAdHenp6kc4TEZFywLJg9avm51sGQNXrLh5zc4NGXeHGLqZgxZZZcCbNufFVqWX2xyoq/2DoMx82/8fsqaVNeUVEygSXLjvMzs7Gz8+PRYsW0b17d3t7TEwMaWlpLF26tNBrNG/enMjISD744IN8j6ekpFC7dm3mzJnDww8/bG+vV68eZ8+eJScnh7p16/Lwww8zcuRIPD3zz0fHjRvH+PHj87Rr2aGISAXwUzx83NOUYB+xDQJrujoiEREpR4q67NDdiTHlcfz4cXJzcwkNDXVoDw0NJTk5udDzExMT2bVrF48++miBfebMmUNAQAA9e/Z0aH/66aeJjY1lzZo1PP7447z22muMHj26wOu88MILnDx50v45ePBgofGJiJQqmw2+Hm/eU0rdf2Xn7lgIMzubmZxrnWXBmvOzXm0GK/ESEZFS4/Jlh1dj5syZNG/evMDiHACzZs0iOjoaHx8fh/ZRo0bZf27RogVeXl48/vjjTJgwAW9v7zzX8fb2zrddRMQlbLmwbDhsm2u+f3gPxHwG1a8v/Nwts2D5SPPzvIfgwf9Coy6lF2tZt3clHNoKlfygw0hXRyMiIhWYS2e+goOD8fDwICUlxaE9JSWl0HexMjMziY2NZfDgwQX2+eabb0hKSrrszNgFERERnDt3jl9//bVIsYuIuEzuOVj8uEm83DygSl1IPwSzu8KxpMufu2n6xcSran3IzYYF/c5XxrsG2WwXZ73aDoHKeQs9iYiIlBSXJl9eXl60bt2a+Ph4e5vNZiM+Pp7IyMvv+B4XF0dWVhb9+vUrsM/MmTNp3bo1LVu2LDSWbdu24e7unm+FRRGRMiM3Bz4ZDDvjzD5OvWfDY6sh5CZTknx2N0j5If9zv30HVpxfXt1+BAzbDM16gS0HFsbArk8Kv3/2adi5yMye5ffZ+iGklZFl2Zkn4IclkHWq4D67l0HyTrMHVvsRTgtNRESuTS5fdjhq1ChiYmIIDw+nbdu2TJ48mczMTHv1wwEDBlCrVi0mTJjgcN7MmTPp3r071atXz/e66enpxMXF8dZbb+U5lpCQwKZNm+jYsSMBAQEkJCQwcuRI+vXrR9Wql9m4UkTElc5lQdwgSPocPLyg9xxo3M0cG7gc/tsdjmyHD++FAUugxiX/4Wn9m7D6FfPz7aOh4xhTxa/nDHOt7fPhk0fNrFrLh/LeOysDEmdAwhQ4ffzycfpUMaXNa7UuiacunrQDMOc++ONXU2q93VCIGGJiu8CWC2vP/25p9yT4Vcv3UiIiIiXF5cnXQw89xLFjxxg7dizJycncfPPNrFy50l6E48CBA7i7O07QJSUlsWHDBr766qsCrxsbG4tlWfTt2zfPMW9vb2JjYxk3bhxZWVnUr1+fkSNHOrwHJiJSpuScNRvn7vvKVOTrMxca/vXicb9qMGCZqdh3aKtJPPothlq3mARj3RumX8d/wB2XFBdy94D73wePSvDdR2Y5Y2423NLfHD/zB2z6ADa+D2fTTFvQdRDWPP84T/wEx/bAR90hehHUjSjpP4nCpf4Cc/4GJw+aZZln/oA1r8D/3jUJWLunzJ/Xrk9MrD5VIHKo8+MUEZFrjktLzZdnRS0nKSJy1bJPQ+zD8Msa8PSFvvPh+o759z2bDnMfgIObzFK6JveaWS2AqPHQ4Zn8z7PZ4ItnYctM8/3uV+FMqpntyjq/r2H1hnD7s9DsAfAo4L/dZZ2CeQ/Cb99CJX+IjoN67Yv96Ffs+D6TeGUcNvH2X2z+LNb/G47tNn0q+Zuqhns+h9Sf4a5/wu3POS9GERGpcIqaGyj5KiYlXyJSImy58OMS8z5WQeXibTmQc/p8MrMQ6nW4/DWzTpkqhr9tuNjWeQJEPnX58ywLvhxjZrkuFXKTSbqa3m9mygqTnQnz+8L+dSZZfDgWGtxZ+HlX6+geM+OXeRT+0tjMBAac38rEZoM9y83yy+QdF8/xqw4jtoN3QOnHJyIiFZaSr1Km5EtErkruOVM045u34MS+wvv7BMHDC6Buu6JdP/u0Wab4y1ro8jq0faxo51kWxI+HDW9DjZvNjFCjbuB+hfWZcs6YKoo/fQ2ePvDQXGgYdWXXuBLJu8x+Z6ePQ2gzGLAU/IPz9rMss3Rz3UQ4tAXunQzhg0ovLhERuSYo+SplSr5EpFjOZcP2efDNJEj7zbT5BJl3jpp2L3hmKaAGePld2b0syxTK8CnG31GnU02hCje3Kz/3gnNZEDcQkr4wRT0e/AgadS3+9QpyeJspNnLmD1NkpP+SwotnWJZZTnlpAQ4REZFiUvJVypR8icgVsSzYOtu8e5R+yLT5BcOtw6DNoxV32du5bFMaf/cyUxr/1uEl+6y2XPjfe5B1EmqFQ79PwDeo5K4vIiJSBEq+SpmSLxEpMsuCFX+HxOnme+Uws6dU6xjw8ndtbM5wYVPoXYtK7x512pniHsWZ5RMREblKRc0NXF5qXkSkQrPZ4PNRZtYLIGocRDwJlXxcGpZTeXhCzw/Mvl9Hfyz56wfWOj+jVrnkry0iIlKClHyJiJQWWy4sexq2fQy4wf3vQat+ro7KNdw9Cq+2KCIiUsEp+RIRKQ2552DJk7BzIbi5Q4/p0OJBV0clIiIiLqTkS0SkpOXmwCePmv273D2h13/gph6ujkpERERcTMmXiEhJOpcNiwaZDX3dK8GDc6DxPa6OSkRERMqAK9w1U0SknLAs2Pc1bPrAJETOkHPWbCy8Zzl4eEOfeUq8RERExE4zXyJSsdhskPQ5rH8Tjmw3bemH4K/jS/e+2adhQTT8vBo8faHvPLj+rtK9p4iIiJQrSr5EpGKw5Zp3rNb/+2I5c09fOHcGvv0/aNQV6rYrnXtnZ8K8h+DXb6CSPzy8AOrfVjr3EhERkXJLyw5FpHzLPQfb5sOUCFj0iEm8vALgtv8HI3+Alg8DFix+ArJOlfz9z6bDx71M4uUVAP0/VeIlIiIi+dLMl4iUT+eyYft82DAJ/vjVtPkEQbunIGII+FY1bV1fh/3r4Y/9sGos3Dup5GI4k2YSr0NbwLuKSbxqh5fc9UVERKRCUfIlIuVLzln4/r+wYTKk/27a/KpD5DBo8yj4BDr296kC3afAR/fDlpnQuBvcEHX1cZxOhf/2gCPbTKLXfwnUvPnqrysiIiIVlpIvESkfsjNhy2z43ztwKsW0VQ6D9k9D64Hg5V/wuQ3uhLaPQ+J0WDoMnkq4ODNWHJnHTTKXssskfgOWQViz4l9PRERErglKvkTEOXLOQsK7F5cIXgmbDfZ9CadPmO+BtaHDM9CqP1TyKdo1osbBz/Fw4if4YjT0mnHlcYCpoPjpEDi2B/xDIGYZhDQp3rVERETkmqLkS0RKX/ZpiH0YfllzddepWs8U0mjRBzy9ruxcLz/oPg1m3Q07F0KTe6Hp/UU///ctpnz93pXme0ANiPkMghteWRwiIiJyzVLyJSKlK+sUzO9zsQz7rcOvPHECqNYAGt8HHlfx11adNtBhJHzzFnz2DNRpBwGhlz/nt//BuokXE0c3d7ipJ0S9BEF1ix+LiIiIXHOUfIlI6TmbDnN7w8GNpgx7v0Wlt9dWUd3xPOz9ClJ2wmdPQ+fX8u+Xut9UUvztW/PdzQNa9oEOoyD4BufFKyIiIhWGki8RKR2XlmH3qQL9FkPt1q6Oysy69ZgGMzqaJYQXlhEWxL0StOpn3jGrWs8ZEYqIiEgFpeRLREre6VT4b3dTnKIslmEPawZdXofVr4AtN/8+nl5meWH7EVCllnPjExERkQpJyZeIlCyHMuzBphpg6E2ujiqvNoPNR0RERMRJlHyJ5OdgImyaDufOujqSoqlSByIeh2r1S/c+lmXKtW9fADmn8++TssuUk68cava/CmlcujGJiIiIlBNKvkT+7Kd4Uxa9vCReFyR+AM17m1Lsf7mxZK9tWZC0wpRaP/xd4f0Dap4vw67CFCIiIiIXKPkSudTeL2FBP8jNhhuioPE9ro6ocJYNklbCT6tgRyzsWAA3dYfbn7v65X42G+xeCuv/bWa0ADx9oXUM/KVR/ue4V4Ibu0Dlv1zdvUVEREQqGDfLsixXB1EepaenU6VKFU6ePElgYKCrwylYVobZ4LawvYwqqqxTZgbLP7jwvrs/g7hBYMuBxvfCA7OLtx+Vqxz6zuxftWf5xbZG90DbR8G7GGP0+D7Y8DYcTzLfvSpD28cgcljR/jxFRERErhFFzQ2UfBVTuUi+sjJg+h2Q+gs0vd/MhIQ1c3VUzpF5HBKmQOIMyM4wScjtz0KtW/Lvv+tT+ORRsHJNhbueH4BHJefGXFKSd8E3/4YflgAl8H9vnyoQ8aR5p8yv2tVfT0RERKSCUfJVyspF8rVsOHz3kWNbYUlIeZeRDP97F7bMyr8gxA1RcPtoqBtxsW37AljyhFm+16IP3D8FPCrAitxje83M1YH/mXe2rlQlX2jxILR5DHzK6BgXERERKQOUfJWyMp987f0S5j0IuMHf3oGf18APi7HPhNwQZWbC6rZzZZQl5+Tv8O3/wdY5kJtl2mrcDHeMhuo3mCRkx0IzswVQ/3bz/GkHYOkwwDIb6d73Drh7uOopRERERKQcUvJVysp08nU6Fd5vB6dSoN1Q6PKaaT+2FzZMckxC6t1mEpR6t4Gbm+tiLi5bLqwaa8rC23JMW+225pluiHJ8ptRfTBK2bf7FvheEPwLd3gJ3d+fFLiIiIiIVQlFzgzLxL80pU6ZQr149fHx8iIiIIDExscC+d955J25ubnk+99xzsSrdwIED8xzv0qWLw3VSU1OJjo4mMDCQoKAgBg8ezKlTp0rtGZ3q81Em8QpuBJ1evNj+lxuhxzQYvgVuGWCq0v36Dcy5D2Z1gX1fF295mqvknoPFj0PCeyaZqneb2Vdq8FfQ8K95k8lqDeBv78LT35uldB7epj3iCbhnkhIvERERESlVLp/5WrBgAQMGDGDatGlEREQwefJk4uLiSEpKIiQkJE//1NRUsrOz7d9PnDhBy5Yt+c9//sPAgQMBk3ylpKQwe/Zsez9vb2+qVq1q/961a1eOHDnC9OnTycnJYdCgQbRp04Z58+YVKe4yO/O1cxF8MhjcPODRry//blfaQbNU77uPLi7Vq9nKLMdr1K1sz4Tl5pgCGT8uAXdPUyCjWa8ru0ZGCvyxH+pElO1nFREREZEyrdwsO4yIiKBNmza89957ANhsNurUqcPw4cN5/vnnCz1/8uTJjB07liNHjuDv7w+Y5CstLY0lS5bke87u3btp2rQpmzdvJjw8HICVK1fSrVs3fv/9d2rWrFnofctk8pV+xCw3PJsGdzwPHV8o2nn5FakIbQaRQ8HfyXs1hbUovCz+uSxTEj7pczN79+Cc8rEfl4iIiIhUSEXNDVxa0i07O5utW7fywgsXkwR3d3eioqJISEgo0jVmzpxJnz597InXBWvXriUkJISqVaty11138corr1C9enUAEhISCAoKsideAFFRUbi7u7Np0yZ69OiR5z5ZWVlkZWXZv6enp1/Rs5Y6yzLVDc+mmUITtz9b9HMDwqDzq9BhpFnClzjDbKi75MnSirZgHl7Qqj90eAaC6uY9nnMWFvaHfV+ZZYN95polhiIiIiIiZZxLk6/jx4+Tm5tLaKjjTEdoaCh79uwp9PzExER27drFzJkzHdq7dOlCz549qV+/Pj///DNjxoyha9euJCQk4OHhQXJycp4ljZ6enlSrVo3k5OR87zVhwgTGjx9/hU/oRFs/hJ9WmYSkx/Ti7VHlHwxR4+DWp00Bi59WmYIWzpJzGo7vhS0z4bs50LIPdBgF1a83x7NPQ2xf+GUtePpC3/lwfUfnxSciIiIichXK9WZGM2fOpHnz5rRt29ahvU+fPvafmzdvTosWLbj++utZu3YtnTp1Kta9XnjhBUaNGmX/np6eTp06dYoXeElL3Q9f/sP83OlFCGl8ddfzq2aWLBZ12WJJ+nUDrJsI+9fB9x/DtnnQvLcpirFqrCkQUskfohdCvQ7Oj09EREREpJhcWt4tODgYDw8PUlJSHNpTUlIICwu77LmZmZnExsYyePDgQu/ToEEDgoOD+emnnwAICwvj6NGjDn3OnTtHampqgff19vYmMDDQ4VMm2HJhyVOQkwnXtYd2T7k6oqtTrwPELIPBq6Dh3Wbj4x0LYEZHk3h5BUD/T5V4iYiIiEi549Lky8vLi9atWxMfH29vs9lsxMfHExkZedlz4+LiyMrKol+/foXe5/fff+fEiRPUqFEDgMjISNLS0ti6dau9z+rVq7HZbERERBTzaVzkyHY4tAW8KkP39yvOBsF12kJ0HAxZC43vNW0+VWDA0oqzMbSIiIiIXFNcXu1wwYIFxMTEMH36dNq2bcvkyZNZuHAhe/bsITQ0lAEDBlCrVi0mTJjgcN5tt91GrVq1iI2NdWg/deoU48ePp1evXoSFhfHzzz8zevRoMjIy2LlzJ97eZm+nrl27kpKSwrRp0+yl5sPDw8tnqfmUHyH1Z2hyn2vjKE2p+8HLHyrn3X5ARERERMSVykW1Q4CHHnqIY8eOMXbsWJKTk7n55ptZuXKlvQjHgQMHcP/T5rdJSUls2LCBr776Ks/1PDw82LFjB3PmzCEtLY2aNWty99138/LLL9sTL4C5c+cybNgwOnXqhLu7O7169eKdd94p3YctLaFNzaciq1bf1RGIiIiIiFwVl898lVdlauZLRERERERcpqi5gUvf+RIREREREblWKPkSERERERFxAiVfIiIiIiIiTqDkS0RERERExAmUfImIiIiIiDiBki8REREREREnUPIlIiIiIiLiBEq+REREREREnEDJl4iIiIiIiBMo+RIREREREXECJV8iIiIiIiJOoORLRERERETECZR8iYiIiIiIOIGnqwMoryzLAiA9Pd3FkYiIiIiIiCtdyAku5AgFUfJVTBkZGQDUqVPHxZGIiIiIiEhZkJGRQZUqVQo87mYVlp5Jvmw2G4cPHyYgIAA3NzeXxpKenk6dOnU4ePAggYGBLo1Fyg+NGykujR0pDo0bKQ6NGykuZ48dy7LIyMigZs2auLsX/GaXZr6Kyd3dndq1a7s6DAeBgYH6i0mumMaNFJfGjhSHxo0Uh8aNFJczx87lZrwuUMENERERERERJ1DyJSIiIiIi4gRKvioAb29vXnrpJby9vV0dipQjGjdSXBo7UhwaN1IcGjdSXGV17KjghoiIiIiIiBNo5ktERERERMQJlHyJiIiIiIg4gZIvERERERERJ1DyJSIiIiIi4gRKviqAKVOmUK9ePXx8fIiIiCAxMdHVIUkZMmHCBNq0aUNAQAAhISF0796dpKQkhz5nz55l6NChVK9encqVK9OrVy9SUlJcFLGURa+//jpubm4888wz9jaNG8nPoUOH6NevH9WrV8fX15fmzZuzZcsW+3HLshg7diw1atTA19eXqKgo9u3b58KIpSzIzc3lxRdfpH79+vj6+nL99dfz8ssvc2ldOI0dWb9+Pffddx81a9bEzc2NJUuWOBwvyhhJTU0lOjqawMBAgoKCGDx4MKdOnXLaMyj5KucWLFjAqFGjeOmll/juu+9o2bIlnTt35ujRo64OTcqIdevWMXToUDZu3MiqVavIycnh7rvvJjMz095n5MiRfPbZZ8TFxbFu3ToOHz5Mz549XRi1lCWbN29m+vTptGjRwqFd40b+7I8//qB9+/ZUqlSJFStW8OOPP/LWW29RtWpVe5+JEyfyzjvvMG3aNDZt2oS/vz+dO3fm7NmzLoxcXO2NN95g6tSpvPfee+zevZs33niDiRMn8u6779r7aOxIZmYmLVu2ZMqUKfkeL8oYiY6O5ocffmDVqlUsX76c9evXM2TIEGc9AlhSrrVt29YaOnSo/Xtubq5Vs2ZNa8KECS6MSsqyo0ePWoC1bt06y7IsKy0tzapUqZIVFxdn77N7924LsBISElwVppQRGRkZVsOGDa1Vq1ZZd9xxhzVixAjLsjRuJH9///vfrQ4dOhR43GazWWFhYdabb75pb0tLS7O8vb2t+fPnOyNEKaPuuece65FHHnFo69mzpxUdHW1ZlsaO5AVYixcvtn8vyhj58ccfLcDavHmzvc+KFSssNzc369ChQ06JWzNf5Vh2djZbt24lKirK3ubu7k5UVBQJCQkujEzKspMnTwJQrVo1ALZu3UpOTo7DOGrcuDF169bVOBKGDh3KPffc4zA+QONG8rds2TLCw8Pp3bs3ISEhtGrVihkzZtiP79+/n+TkZIdxU6VKFSIiIjRurnG33nor8fHx7N27F4Dt27ezYcMGunbtCmjsSOGKMkYSEhIICgoiPDzc3icqKgp3d3c2bdrklDg9nXIXKRXHjx8nNzeX0NBQh/bQ0FD27NnjoqikLLPZbDzzzDO0b9+eZs2aAZCcnIyXlxdBQUEOfUNDQ0lOTnZBlFJWxMbG8t1337F58+Y8xzRuJD+//PILU6dOZdSoUYwZM4bNmzfz9NNP4+XlRUxMjH1s5Pd7S+Pm2vb888+Tnp5O48aN8fDwIDc3l1dffZXo6GgAjR0pVFHGSHJyMiEhIQ7HPT09qVatmtPGkZIvkWvI0KFD2bVrFxs2bHB1KFLGHTx4kBEjRrBq1Sp8fHxcHY6UEzabjfDwcF577TUAWrVqxa5du5g2bRoxMTEujk7KsoULFzJ37lzmzZvHTTfdxLZt23jmmWeoWbOmxo5UKFp2WI4FBwfj4eGRp7pYSkoKYWFhLopKyqphw4axfPly1qxZQ+3ate3tYWFhZGdnk5aW5tBf4+jatnXrVo4ePcott9yCp6cnnp6erFu3jnfeeQdPT09CQ0M1biSPGjVq0LRpU4e2Jk2acODAAQD72NDvLfmz5557jueff54+ffrQvHlz+vfvz8iRI5kwYQKgsSOFK8oYCQsLy1OU7ty5c6SmpjptHCn5Kse8vLxo3bo18fHx9jabzUZ8fDyRkZEujEzKEsuyGDZsGIsXL2b16tXUr1/f4Xjr1q2pVKmSwzhKSkriwIEDGkfXsE6dOrFz5062bdtm/4SHhxMdHW3/WeNG/qx9+/Z5trLYu3cv1113HQD169cnLCzMYdykp6ezadMmjZtr3OnTp3F3d/xnqYeHBzabDdDYkcIVZYxERkaSlpbG1q1b7X1Wr16NzWYjIiLCOYE6payHlJrY2FjL29vb+vDDD60ff/zRGjJkiBUUFGQlJye7OjQpI5588kmrSpUq1tq1a60jR47YP6dPn7b3eeKJJ6y6detaq1evtrZs2WJFRkZakZGRLoxayqJLqx1alsaN5JWYmGh5enpar776qrVv3z5r7ty5lp+fn/Xxxx/b+7z++utWUFCQtXTpUmvHjh3W/fffb9WvX986c+aMCyMXV4uJibFq1aplLV++3Nq/f7/16aefWsHBwdbo0aPtfTR2JCMjw/r++++t77//3gKsSZMmWd9//73122+/WZZVtDHSpUsXq1WrVtamTZusDRs2WA0bNrT69u3rtGdQ8lUBvPvuu1bdunUtLy8vq23bttbGjRtdHZKUIUC+n9mzZ9v7nDlzxnrqqaesqlWrWn5+flaPHj2sI0eOuC5oKZP+nHxp3Eh+PvvsM6tZs2aWt7e31bhxY+uDDz5wOG6z2awXX3zRCg0Ntby9va1OnTpZSUlJLopWyor09HRrxIgRVt26dS0fHx+rQYMG1j/+8Q8rKyvL3kdjR9asWZPvv2liYmIsyyraGDlx4oTVt29fq3LlylZgYKA1aNAgKyMjw2nP4GZZl2wdLiIiIiIiIqVC73yJiIiIiIg4gZIvERERERERJ1DyJSIiIiIi4gRKvkRERERERJxAyZeIiIiIiIgTKPkSERERERFxAiVfIiIiIiIiTqDkS0RERERExAmUfImIiDiZm5sbS5YscXUYIiLiZEq+RETkmjJw4EDc3NzyfLp06eLq0EREpILzdHUAIiIiztalSxdmz57t0Obt7e2iaERE5FqhmS8REbnmeHt7ExYW5vCpWrUqYJYETp06la5du+Lr60uDBg1YtGiRw/k7d+7krrvuwtfXl+rVqzNkyBBOnTrl0GfWrFncdNNNeHt7U6NGDYYNG+Zw/Pjx4/To0QM/Pz8aNmzIsmXLSvehRUTE5ZR8iYiI/MmLL75Ir1692L59O9HR0fTp04fdu3cDkJmZSefOnalatSqbN28mLi6Or7/+2iG5mjp1KkOHDmXIkCHs3LmTZcuWccMNNzjcY/z48Tz44IPs2LGDbt26ER0dTWpqqlOfU0REnMvNsizL1UGIiIg4y8CBA/n444/x8fFxaB8zZgxjxozBzc2NJ554gqlTp9qPtWvXjltuuYX333+fGTNm8Pe//52DBw/i7+8PwBdffMF9993H4cOHCQ0NpVatWgwaNIhXXnkl3xjc3Nz45z//ycsvvwyYhK5y5cqsWLFC756JiFRgeudLRESuOR07dnRIrgCqVatm/zkyMtLhWGRkJNu2bQNg9+7dtGzZ0p54AbRv3x6bzUZSUhJubm4cPnyYTp06XTaGFi1a2H/29/cnMDCQo0ePFveRRESkHFDyJSIi1xx/f/88ywBLiq+vb5H6VapUyeG7m5sbNputNEISEZEyQu98iYiI/MnGjRvzfG/SpAkATZo0Yfv27WRmZtqPf/vtt7i7u9OoUSMCAgKoV68e8fHxTo1ZRETKPs18iYjINScrK4vk5GSHNk9PT4KDgwGIi4sjPDycDh06MHfuXBITE5k5cyYA0dHRvPTSS8TExDBu3DiOHTvG8OHD6d+/P6GhoQCMGzeOJ554gpCQELp27UpGRgbffvstw4cPd+6DiohImaLkS0RErjkrV66kRo0aDm2NGjViz549gKlEGBsby1NPPUWNGjWYP38+TZs2BcDPz48vv/ySESNG0KZNG/z8/OjVqxeTJk2yXysmJoazZ8/y9ttv8+yzzxIcHMwDDzzgvAcUEZEySdUORURELuHm5sbixYvp3r27q0MREZEKRu98iYiIiIiIOIGSLxERERERESfQO18iIiKX0Gp8EREpLZr5EhERERERcQIlXyIiIiIiIk6g5EtERERERMQJlHyJiIiIiIg4gZIvERERERERJ1DyJSIiIiIi4gRKvkRERERERJxAyZeIiIiIiIgT/H/KEVtIlsp4OwAAAABJRU5ErkJggg==\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Training with lr: 0.001, weight_decay: 0.05\n",
+            "Epoch 00000 | Loss 0.3880 | Train Acc 0.9333 | Val Acc 0.7860\n",
+            "Epoch 00001 | Loss 0.3917 | Train Acc 0.9500 | Val Acc 0.7840\n",
+            "Epoch 00002 | Loss 0.3785 | Train Acc 0.9500 | Val Acc 0.7920\n",
+            "Epoch 00003 | Loss 0.3983 | Train Acc 0.9333 | Val Acc 0.7960\n",
+            "Epoch 00004 | Loss 0.4053 | Train Acc 0.9333 | Val Acc 0.7960\n",
+            "Epoch 00005 | Loss 0.4121 | Train Acc 0.9333 | Val Acc 0.7960\n",
+            "Epoch 00006 | Loss 0.4663 | Train Acc 0.9333 | Val Acc 0.7960\n",
+            "Epoch 00007 | Loss 0.4428 | Train Acc 0.9333 | Val Acc 0.7960\n",
+            "Epoch 00008 | Loss 0.4214 | Train Acc 0.9333 | Val Acc 0.7960\n",
+            "Epoch 00009 | Loss 0.4389 | Train Acc 0.9333 | Val Acc 0.7980\n",
+            "Epoch 00010 | Loss 0.4906 | Train Acc 0.9333 | Val Acc 0.7940\n",
+            "Epoch 00011 | Loss 0.4772 | Train Acc 0.9333 | Val Acc 0.7980\n",
+            "Epoch 00012 | Loss 0.5106 | Train Acc 0.9333 | Val Acc 0.7980\n",
+            "Epoch 00013 | Loss 0.5106 | Train Acc 0.9333 | Val Acc 0.8060\n",
+            "Epoch 00014 | Loss 0.5152 | Train Acc 0.9333 | Val Acc 0.8020\n",
+            "Epoch 00015 | Loss 0.4922 | Train Acc 0.9333 | Val Acc 0.8040\n",
+            "Epoch 00016 | Loss 0.5091 | Train Acc 0.9333 | Val Acc 0.8060\n",
+            "Epoch 00017 | Loss 0.5301 | Train Acc 0.9500 | Val Acc 0.8060\n",
+            "Epoch 00018 | Loss 0.5044 | Train Acc 0.9500 | Val Acc 0.8040\n",
+            "Epoch 00019 | Loss 0.5623 | Train Acc 0.9500 | Val Acc 0.8040\n",
+            "Epoch 00020 | Loss 0.5124 | Train Acc 0.9333 | Val Acc 0.8020\n",
+            "Epoch 00021 | Loss 0.5540 | Train Acc 0.9333 | Val Acc 0.8020\n",
+            "Epoch 00022 | Loss 0.5583 | Train Acc 0.9333 | Val Acc 0.8100\n",
+            "Epoch 00023 | Loss 0.6070 | Train Acc 0.9333 | Val Acc 0.8100\n",
+            "Epoch 00024 | Loss 0.5903 | Train Acc 0.9333 | Val Acc 0.8080\n",
+            "Epoch 00025 | Loss 0.5971 | Train Acc 0.9333 | Val Acc 0.8100\n",
+            "Epoch 00026 | Loss 0.5565 | Train Acc 0.9333 | Val Acc 0.8120\n",
+            "Epoch 00027 | Loss 0.6046 | Train Acc 0.9333 | Val Acc 0.8120\n",
+            "Epoch 00028 | Loss 0.5951 | Train Acc 0.9333 | Val Acc 0.8080\n",
+            "Epoch 00029 | Loss 0.6287 | Train Acc 0.9333 | Val Acc 0.8100\n",
+            "Epoch 00030 | Loss 0.6097 | Train Acc 0.9333 | Val Acc 0.8100\n",
+            "Epoch 00031 | Loss 0.6045 | Train Acc 0.9333 | Val Acc 0.8080\n",
+            "Epoch 00032 | Loss 0.6718 | Train Acc 0.9333 | Val Acc 0.8080\n",
+            "Epoch 00033 | Loss 0.6535 | Train Acc 0.9333 | Val Acc 0.8100\n",
+            "Epoch 00034 | Loss 0.6433 | Train Acc 0.9333 | Val Acc 0.8120\n",
+            "Epoch 00035 | Loss 0.6653 | Train Acc 0.9333 | Val Acc 0.8120\n",
+            "Epoch 00036 | Loss 0.6787 | Train Acc 0.9333 | Val Acc 0.8080\n",
+            "Epoch 00037 | Loss 0.6864 | Train Acc 0.9333 | Val Acc 0.8040\n",
+            "Epoch 00038 | Loss 0.6647 | Train Acc 0.9333 | Val Acc 0.8040\n",
+            "Epoch 00039 | Loss 0.6818 | Train Acc 0.9333 | Val Acc 0.8080\n",
+            "Epoch 00040 | Loss 0.6639 | Train Acc 0.9333 | Val Acc 0.8100\n",
+            "Epoch 00041 | Loss 0.6704 | Train Acc 0.9333 | Val Acc 0.8120\n",
+            "Epoch 00042 | Loss 0.7031 | Train Acc 0.9333 | Val Acc 0.8100\n",
+            "Epoch 00043 | Loss 0.7262 | Train Acc 0.9333 | Val Acc 0.8100\n",
+            "Epoch 00044 | Loss 0.7176 | Train Acc 0.9333 | Val Acc 0.8100\n",
+            "Epoch 00045 | Loss 0.7148 | Train Acc 0.9333 | Val Acc 0.8060\n",
+            "Epoch 00046 | Loss 0.7712 | Train Acc 0.9333 | Val Acc 0.8040\n",
+            "Epoch 00047 | Loss 0.7349 | Train Acc 0.9333 | Val Acc 0.7940\n",
+            "Epoch 00048 | Loss 0.7473 | Train Acc 0.9333 | Val Acc 0.7960\n",
+            "Epoch 00049 | Loss 0.7866 | Train Acc 0.9500 | Val Acc 0.7920\n",
+            "Epoch 00050 | Loss 0.7631 | Train Acc 0.9500 | Val Acc 0.7880\n",
+            "Epoch 00051 | Loss 0.7488 | Train Acc 0.9500 | Val Acc 0.7960\n",
+            "Epoch 00052 | Loss 0.7910 | Train Acc 0.9333 | Val Acc 0.7940\n",
+            "Epoch 00053 | Loss 0.7619 | Train Acc 0.9333 | Val Acc 0.7980\n",
+            "Epoch 00054 | Loss 0.7748 | Train Acc 0.9333 | Val Acc 0.8000\n",
+            "Epoch 00055 | Loss 0.8074 | Train Acc 0.9333 | Val Acc 0.8060\n",
+            "Epoch 00056 | Loss 0.7703 | Train Acc 0.9333 | Val Acc 0.8080\n",
+            "Epoch 00057 | Loss 0.8072 | Train Acc 0.9333 | Val Acc 0.8100\n",
+            "Epoch 00058 | Loss 0.7952 | Train Acc 0.9333 | Val Acc 0.8120\n",
+            "Epoch 00059 | Loss 0.7965 | Train Acc 0.9333 | Val Acc 0.8100\n",
+            "Epoch 00060 | Loss 0.8133 | Train Acc 0.9333 | Val Acc 0.8100\n",
+            "Epoch 00061 | Loss 0.8086 | Train Acc 0.9333 | Val Acc 0.8060\n",
+            "Epoch 00062 | Loss 0.7708 | Train Acc 0.9333 | Val Acc 0.8080\n",
+            "Epoch 00063 | Loss 0.8030 | Train Acc 0.9333 | Val Acc 0.8080\n",
+            "Epoch 00064 | Loss 0.8085 | Train Acc 0.9333 | Val Acc 0.8080\n",
+            "Epoch 00065 | Loss 0.8204 | Train Acc 0.9333 | Val Acc 0.8080\n",
+            "Epoch 00066 | Loss 0.8474 | Train Acc 0.9333 | Val Acc 0.8060\n",
+            "Epoch 00067 | Loss 0.8197 | Train Acc 0.9333 | Val Acc 0.8060\n",
+            "Epoch 00068 | Loss 0.8626 | Train Acc 0.9333 | Val Acc 0.8040\n",
+            "Epoch 00069 | Loss 0.8377 | Train Acc 0.9333 | Val Acc 0.8040\n",
+            "Epoch 00070 | Loss 0.8569 | Train Acc 0.9333 | Val Acc 0.8020\n",
+            "Epoch 00071 | Loss 0.8875 | Train Acc 0.9333 | Val Acc 0.8000\n",
+            "Epoch 00072 | Loss 0.8492 | Train Acc 0.9333 | Val Acc 0.8000\n",
+            "Epoch 00073 | Loss 0.8702 | Train Acc 0.9333 | Val Acc 0.8000\n",
+            "Epoch 00074 | Loss 0.8694 | Train Acc 0.9333 | Val Acc 0.8020\n",
+            "Epoch 00075 | Loss 0.8583 | Train Acc 0.9333 | Val Acc 0.8000\n",
+            "Epoch 00076 | Loss 0.8908 | Train Acc 0.9333 | Val Acc 0.8060\n",
+            "Epoch 00077 | Loss 0.8919 | Train Acc 0.9333 | Val Acc 0.8040\n",
+            "Epoch 00078 | Loss 0.8740 | Train Acc 0.9333 | Val Acc 0.7980\n",
+            "Epoch 00079 | Loss 0.9002 | Train Acc 0.9333 | Val Acc 0.7940\n",
+            "Epoch 00080 | Loss 0.8989 | Train Acc 0.9333 | Val Acc 0.7940\n",
+            "Epoch 00081 | Loss 0.8883 | Train Acc 0.9333 | Val Acc 0.7900\n",
+            "Epoch 00082 | Loss 0.8965 | Train Acc 0.9333 | Val Acc 0.7820\n",
+            "Epoch 00083 | Loss 0.9050 | Train Acc 0.9500 | Val Acc 0.7820\n",
+            "Epoch 00084 | Loss 0.8894 | Train Acc 0.9500 | Val Acc 0.7800\n",
+            "Epoch 00085 | Loss 0.9065 | Train Acc 0.9500 | Val Acc 0.7760\n",
+            "Epoch 00086 | Loss 0.8950 | Train Acc 0.9500 | Val Acc 0.7760\n",
+            "Epoch 00087 | Loss 0.9046 | Train Acc 0.9500 | Val Acc 0.7760\n",
+            "Epoch 00088 | Loss 0.9239 | Train Acc 0.9500 | Val Acc 0.7760\n",
+            "Epoch 00089 | Loss 0.9126 | Train Acc 0.9500 | Val Acc 0.7760\n",
+            "Epoch 00090 | Loss 0.9270 | Train Acc 0.9500 | Val Acc 0.7760\n",
+            "Epoch 00091 | Loss 0.9426 | Train Acc 0.9500 | Val Acc 0.7700\n",
+            "Epoch 00092 | Loss 0.9356 | Train Acc 0.9500 | Val Acc 0.7700\n",
+            "Epoch 00093 | Loss 0.9397 | Train Acc 0.9333 | Val Acc 0.7680\n",
+            "Epoch 00094 | Loss 0.9407 | Train Acc 0.9333 | Val Acc 0.7680\n",
+            "Epoch 00095 | Loss 0.9414 | Train Acc 0.9333 | Val Acc 0.7780\n",
+            "Epoch 00096 | Loss 0.9420 | Train Acc 0.9333 | Val Acc 0.7820\n",
+            "Epoch 00097 | Loss 0.9447 | Train Acc 0.9167 | Val Acc 0.7840\n",
+            "Epoch 00098 | Loss 0.9511 | Train Acc 0.9167 | Val Acc 0.7880\n",
+            "Epoch 00099 | Loss 0.9459 | Train Acc 0.9167 | Val Acc 0.7940\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Training with lr: 0.0001, weight_decay: 0.0005\n",
+            "Epoch 00000 | Loss 0.9429 | Train Acc 0.9167 | Val Acc 0.7920\n",
+            "Epoch 00001 | Loss 0.9405 | Train Acc 0.9167 | Val Acc 0.7900\n",
+            "Epoch 00002 | Loss 0.9596 | Train Acc 0.9167 | Val Acc 0.7860\n",
+            "Epoch 00003 | Loss 0.9562 | Train Acc 0.9167 | Val Acc 0.7880\n",
+            "Epoch 00004 | Loss 0.9326 | Train Acc 0.9167 | Val Acc 0.7880\n",
+            "Epoch 00005 | Loss 0.9541 | Train Acc 0.9167 | Val Acc 0.7860\n",
+            "Epoch 00006 | Loss 0.9391 | Train Acc 0.9167 | Val Acc 0.7820\n",
+            "Epoch 00007 | Loss 0.9466 | Train Acc 0.9167 | Val Acc 0.7840\n",
+            "Epoch 00008 | Loss 0.9409 | Train Acc 0.9167 | Val Acc 0.7820\n",
+            "Epoch 00009 | Loss 0.9450 | Train Acc 0.9167 | Val Acc 0.7780\n",
+            "Epoch 00010 | Loss 0.9349 | Train Acc 0.9333 | Val Acc 0.7780\n",
+            "Epoch 00011 | Loss 0.9316 | Train Acc 0.9333 | Val Acc 0.7760\n",
+            "Epoch 00012 | Loss 0.9536 | Train Acc 0.9333 | Val Acc 0.7740\n",
+            "Epoch 00013 | Loss 0.9471 | Train Acc 0.9333 | Val Acc 0.7740\n",
+            "Epoch 00014 | Loss 0.9487 | Train Acc 0.9333 | Val Acc 0.7700\n",
+            "Epoch 00015 | Loss 0.9207 | Train Acc 0.9333 | Val Acc 0.7700\n",
+            "Epoch 00016 | Loss 0.9444 | Train Acc 0.9333 | Val Acc 0.7680\n",
+            "Epoch 00017 | Loss 0.9317 | Train Acc 0.9333 | Val Acc 0.7680\n",
+            "Epoch 00018 | Loss 0.9298 | Train Acc 0.9333 | Val Acc 0.7680\n",
+            "Epoch 00019 | Loss 0.9499 | Train Acc 0.9333 | Val Acc 0.7660\n",
+            "Epoch 00020 | Loss 0.9550 | Train Acc 0.9333 | Val Acc 0.7640\n",
+            "Epoch 00021 | Loss 0.9429 | Train Acc 0.9333 | Val Acc 0.7620\n",
+            "Epoch 00022 | Loss 0.9456 | Train Acc 0.9333 | Val Acc 0.7600\n",
+            "Epoch 00023 | Loss 0.9278 | Train Acc 0.9333 | Val Acc 0.7540\n",
+            "Epoch 00024 | Loss 0.9211 | Train Acc 0.9333 | Val Acc 0.7520\n",
+            "Epoch 00025 | Loss 0.9080 | Train Acc 0.9333 | Val Acc 0.7520\n",
+            "Epoch 00026 | Loss 0.9245 | Train Acc 0.9333 | Val Acc 0.7500\n",
+            "Epoch 00027 | Loss 0.9265 | Train Acc 0.9333 | Val Acc 0.7480\n",
+            "Epoch 00028 | Loss 0.9092 | Train Acc 0.9333 | Val Acc 0.7500\n",
+            "Epoch 00029 | Loss 0.9385 | Train Acc 0.9167 | Val Acc 0.7480\n",
+            "Epoch 00030 | Loss 0.9327 | Train Acc 0.9167 | Val Acc 0.7480\n",
+            "Epoch 00031 | Loss 0.9198 | Train Acc 0.9167 | Val Acc 0.7460\n",
+            "Epoch 00032 | Loss 0.9273 | Train Acc 0.9167 | Val Acc 0.7460\n",
+            "Epoch 00033 | Loss 0.9201 | Train Acc 0.9167 | Val Acc 0.7460\n",
+            "Epoch 00034 | Loss 0.9088 | Train Acc 0.9167 | Val Acc 0.7420\n",
+            "Epoch 00035 | Loss 0.9307 | Train Acc 0.9167 | Val Acc 0.7400\n",
+            "Epoch 00036 | Loss 0.9151 | Train Acc 0.9167 | Val Acc 0.7340\n",
+            "Epoch 00037 | Loss 0.9267 | Train Acc 0.9167 | Val Acc 0.7340\n",
+            "Epoch 00038 | Loss 0.9030 | Train Acc 0.9167 | Val Acc 0.7320\n",
+            "Epoch 00039 | Loss 0.9268 | Train Acc 0.9167 | Val Acc 0.7320\n",
+            "Epoch 00040 | Loss 0.9276 | Train Acc 0.9167 | Val Acc 0.7320\n",
+            "Epoch 00041 | Loss 0.9001 | Train Acc 0.9167 | Val Acc 0.7320\n",
+            "Epoch 00042 | Loss 0.9151 | Train Acc 0.9167 | Val Acc 0.7300\n",
+            "Epoch 00043 | Loss 0.9243 | Train Acc 0.9167 | Val Acc 0.7300\n",
+            "Epoch 00044 | Loss 0.8968 | Train Acc 0.9167 | Val Acc 0.7340\n",
+            "Epoch 00045 | Loss 0.9227 | Train Acc 0.9167 | Val Acc 0.7360\n",
+            "Epoch 00046 | Loss 0.9087 | Train Acc 0.9167 | Val Acc 0.7360\n",
+            "Epoch 00047 | Loss 0.8939 | Train Acc 0.9167 | Val Acc 0.7360\n",
+            "Epoch 00048 | Loss 0.9037 | Train Acc 0.9167 | Val Acc 0.7380\n",
+            "Epoch 00049 | Loss 0.9096 | Train Acc 0.9167 | Val Acc 0.7400\n",
+            "Epoch 00050 | Loss 0.8988 | Train Acc 0.9167 | Val Acc 0.7400\n",
+            "Epoch 00051 | Loss 0.9004 | Train Acc 0.9167 | Val Acc 0.7360\n",
+            "Epoch 00052 | Loss 0.9193 | Train Acc 0.9167 | Val Acc 0.7360\n",
+            "Epoch 00053 | Loss 0.9120 | Train Acc 0.9167 | Val Acc 0.7360\n",
+            "Epoch 00054 | Loss 0.9228 | Train Acc 0.9167 | Val Acc 0.7360\n",
+            "Epoch 00055 | Loss 0.8892 | Train Acc 0.9167 | Val Acc 0.7360\n",
+            "Epoch 00056 | Loss 0.9085 | Train Acc 0.9167 | Val Acc 0.7400\n",
+            "Epoch 00057 | Loss 0.9089 | Train Acc 0.9167 | Val Acc 0.7400\n",
+            "Epoch 00058 | Loss 0.9082 | Train Acc 0.9167 | Val Acc 0.7400\n",
+            "Epoch 00059 | Loss 0.8939 | Train Acc 0.9167 | Val Acc 0.7380\n",
+            "Epoch 00060 | Loss 0.8927 | Train Acc 0.9167 | Val Acc 0.7380\n",
+            "Epoch 00061 | Loss 0.8838 | Train Acc 0.9167 | Val Acc 0.7380\n",
+            "Epoch 00062 | Loss 0.9060 | Train Acc 0.9167 | Val Acc 0.7400\n",
+            "Epoch 00063 | Loss 0.9034 | Train Acc 0.9167 | Val Acc 0.7400\n",
+            "Epoch 00064 | Loss 0.9089 | Train Acc 0.9167 | Val Acc 0.7400\n",
+            "Epoch 00065 | Loss 0.8751 | Train Acc 0.9167 | Val Acc 0.7400\n",
+            "Epoch 00066 | Loss 0.8824 | Train Acc 0.9167 | Val Acc 0.7340\n",
+            "Epoch 00067 | Loss 0.9145 | Train Acc 0.9167 | Val Acc 0.7300\n",
+            "Epoch 00068 | Loss 0.9010 | Train Acc 0.9167 | Val Acc 0.7300\n",
+            "Epoch 00069 | Loss 0.9045 | Train Acc 0.9167 | Val Acc 0.7280\n",
+            "Epoch 00070 | Loss 0.8935 | Train Acc 0.9167 | Val Acc 0.7280\n",
+            "Epoch 00071 | Loss 0.8776 | Train Acc 0.9167 | Val Acc 0.7280\n",
+            "Epoch 00072 | Loss 0.8853 | Train Acc 0.9167 | Val Acc 0.7260\n",
+            "Epoch 00073 | Loss 0.9089 | Train Acc 0.9167 | Val Acc 0.7280\n",
+            "Epoch 00074 | Loss 0.8858 | Train Acc 0.9167 | Val Acc 0.7280\n",
+            "Epoch 00075 | Loss 0.9009 | Train Acc 0.9167 | Val Acc 0.7280\n",
+            "Epoch 00076 | Loss 0.8872 | Train Acc 0.9167 | Val Acc 0.7280\n",
+            "Epoch 00077 | Loss 0.8937 | Train Acc 0.9167 | Val Acc 0.7260\n",
+            "Epoch 00078 | Loss 0.8875 | Train Acc 0.9167 | Val Acc 0.7260\n",
+            "Epoch 00079 | Loss 0.8922 | Train Acc 0.9167 | Val Acc 0.7260\n",
+            "Epoch 00080 | Loss 0.8873 | Train Acc 0.9167 | Val Acc 0.7240\n",
+            "Epoch 00081 | Loss 0.8772 | Train Acc 0.9167 | Val Acc 0.7260\n",
+            "Epoch 00082 | Loss 0.9029 | Train Acc 0.9167 | Val Acc 0.7260\n",
+            "Epoch 00083 | Loss 0.8831 | Train Acc 0.9167 | Val Acc 0.7260\n",
+            "Epoch 00084 | Loss 0.8790 | Train Acc 0.9167 | Val Acc 0.7280\n",
+            "Epoch 00085 | Loss 0.8783 | Train Acc 0.9167 | Val Acc 0.7280\n",
+            "Epoch 00086 | Loss 0.8897 | Train Acc 0.9167 | Val Acc 0.7280\n",
+            "Epoch 00087 | Loss 0.8776 | Train Acc 0.9167 | Val Acc 0.7280\n",
+            "Epoch 00088 | Loss 0.8497 | Train Acc 0.9167 | Val Acc 0.7280\n",
+            "Epoch 00089 | Loss 0.8811 | Train Acc 0.9167 | Val Acc 0.7280\n",
+            "Epoch 00090 | Loss 0.8694 | Train Acc 0.9167 | Val Acc 0.7280\n",
+            "Epoch 00091 | Loss 0.8940 | Train Acc 0.9167 | Val Acc 0.7320\n",
+            "Epoch 00092 | Loss 0.8839 | Train Acc 0.9167 | Val Acc 0.7340\n",
+            "Epoch 00093 | Loss 0.8761 | Train Acc 0.9167 | Val Acc 0.7340\n",
+            "Epoch 00094 | Loss 0.8755 | Train Acc 0.9167 | Val Acc 0.7340\n",
+            "Epoch 00095 | Loss 0.8723 | Train Acc 0.9167 | Val Acc 0.7340\n",
+            "Epoch 00096 | Loss 0.8667 | Train Acc 0.9167 | Val Acc 0.7360\n",
+            "Epoch 00097 | Loss 0.8563 | Train Acc 0.9167 | Val Acc 0.7360\n",
+            "Epoch 00098 | Loss 0.8617 | Train Acc 0.9167 | Val Acc 0.7360\n",
+            "Epoch 00099 | Loss 0.8645 | Train Acc 0.9167 | Val Acc 0.7360\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Training with lr: 0.0001, weight_decay: 0.005\n",
+            "Epoch 00000 | Loss 0.8667 | Train Acc 0.9167 | Val Acc 0.7360\n",
+            "Epoch 00001 | Loss 0.8593 | Train Acc 0.9167 | Val Acc 0.7340\n",
+            "Epoch 00002 | Loss 0.8527 | Train Acc 0.9167 | Val Acc 0.7340\n",
+            "Epoch 00003 | Loss 0.8547 | Train Acc 0.9167 | Val Acc 0.7340\n",
+            "Epoch 00004 | Loss 0.8761 | Train Acc 0.9167 | Val Acc 0.7320\n",
+            "Epoch 00005 | Loss 0.8483 | Train Acc 0.9167 | Val Acc 0.7320\n",
+            "Epoch 00006 | Loss 0.8784 | Train Acc 0.9167 | Val Acc 0.7320\n",
+            "Epoch 00007 | Loss 0.8604 | Train Acc 0.9167 | Val Acc 0.7320\n",
+            "Epoch 00008 | Loss 0.8676 | Train Acc 0.9167 | Val Acc 0.7340\n",
+            "Epoch 00009 | Loss 0.8580 | Train Acc 0.9167 | Val Acc 0.7320\n",
+            "Epoch 00010 | Loss 0.8614 | Train Acc 0.9167 | Val Acc 0.7340\n",
+            "Epoch 00011 | Loss 0.8611 | Train Acc 0.9167 | Val Acc 0.7360\n",
+            "Epoch 00012 | Loss 0.8556 | Train Acc 0.9167 | Val Acc 0.7380\n",
+            "Epoch 00013 | Loss 0.8623 | Train Acc 0.9167 | Val Acc 0.7380\n",
+            "Epoch 00014 | Loss 0.8419 | Train Acc 0.9167 | Val Acc 0.7400\n",
+            "Epoch 00015 | Loss 0.8657 | Train Acc 0.9167 | Val Acc 0.7400\n",
+            "Epoch 00016 | Loss 0.8370 | Train Acc 0.9167 | Val Acc 0.7400\n",
+            "Epoch 00017 | Loss 0.8510 | Train Acc 0.9167 | Val Acc 0.7400\n",
+            "Epoch 00018 | Loss 0.8346 | Train Acc 0.9167 | Val Acc 0.7400\n",
+            "Epoch 00019 | Loss 0.8431 | Train Acc 0.9167 | Val Acc 0.7400\n",
+            "Epoch 00020 | Loss 0.8400 | Train Acc 0.9167 | Val Acc 0.7400\n",
+            "Epoch 00021 | Loss 0.8411 | Train Acc 0.9167 | Val Acc 0.7400\n",
+            "Epoch 00022 | Loss 0.8479 | Train Acc 0.9167 | Val Acc 0.7400\n",
+            "Epoch 00023 | Loss 0.8537 | Train Acc 0.9167 | Val Acc 0.7400\n",
+            "Epoch 00024 | Loss 0.8441 | Train Acc 0.9167 | Val Acc 0.7420\n",
+            "Epoch 00025 | Loss 0.8407 | Train Acc 0.9167 | Val Acc 0.7420\n",
+            "Epoch 00026 | Loss 0.8257 | Train Acc 0.9167 | Val Acc 0.7420\n",
+            "Epoch 00027 | Loss 0.8386 | Train Acc 0.9167 | Val Acc 0.7400\n",
+            "Epoch 00028 | Loss 0.8433 | Train Acc 0.9167 | Val Acc 0.7420\n",
+            "Epoch 00029 | Loss 0.8353 | Train Acc 0.9167 | Val Acc 0.7420\n",
+            "Epoch 00030 | Loss 0.8551 | Train Acc 0.9167 | Val Acc 0.7420\n",
+            "Epoch 00031 | Loss 0.8457 | Train Acc 0.9167 | Val Acc 0.7420\n",
+            "Epoch 00032 | Loss 0.8187 | Train Acc 0.9167 | Val Acc 0.7420\n",
+            "Epoch 00033 | Loss 0.8070 | Train Acc 0.9167 | Val Acc 0.7420\n",
+            "Epoch 00034 | Loss 0.8169 | Train Acc 0.9167 | Val Acc 0.7420\n",
+            "Epoch 00035 | Loss 0.8509 | Train Acc 0.9167 | Val Acc 0.7400\n",
+            "Epoch 00036 | Loss 0.8310 | Train Acc 0.9167 | Val Acc 0.7400\n",
+            "Epoch 00037 | Loss 0.8238 | Train Acc 0.9167 | Val Acc 0.7400\n",
+            "Epoch 00038 | Loss 0.8198 | Train Acc 0.9167 | Val Acc 0.7400\n",
+            "Epoch 00039 | Loss 0.8318 | Train Acc 0.9167 | Val Acc 0.7360\n",
+            "Epoch 00040 | Loss 0.8048 | Train Acc 0.9167 | Val Acc 0.7360\n",
+            "Epoch 00041 | Loss 0.8290 | Train Acc 0.9167 | Val Acc 0.7360\n",
+            "Epoch 00042 | Loss 0.8106 | Train Acc 0.9167 | Val Acc 0.7340\n",
+            "Epoch 00043 | Loss 0.8310 | Train Acc 0.9167 | Val Acc 0.7320\n",
+            "Epoch 00044 | Loss 0.8355 | Train Acc 0.9167 | Val Acc 0.7300\n",
+            "Epoch 00045 | Loss 0.8280 | Train Acc 0.9167 | Val Acc 0.7300\n",
+            "Epoch 00046 | Loss 0.8296 | Train Acc 0.9167 | Val Acc 0.7320\n",
+            "Epoch 00047 | Loss 0.8147 | Train Acc 0.9167 | Val Acc 0.7320\n",
+            "Epoch 00048 | Loss 0.8497 | Train Acc 0.9167 | Val Acc 0.7320\n",
+            "Epoch 00049 | Loss 0.8369 | Train Acc 0.9167 | Val Acc 0.7320\n",
+            "Epoch 00050 | Loss 0.8356 | Train Acc 0.9167 | Val Acc 0.7340\n",
+            "Epoch 00051 | Loss 0.8240 | Train Acc 0.9167 | Val Acc 0.7340\n",
+            "Epoch 00052 | Loss 0.8067 | Train Acc 0.9167 | Val Acc 0.7360\n",
+            "Epoch 00053 | Loss 0.8283 | Train Acc 0.9167 | Val Acc 0.7360\n",
+            "Epoch 00054 | Loss 0.8310 | Train Acc 0.9167 | Val Acc 0.7380\n",
+            "Epoch 00055 | Loss 0.8194 | Train Acc 0.9167 | Val Acc 0.7420\n",
+            "Epoch 00056 | Loss 0.8090 | Train Acc 0.9167 | Val Acc 0.7440\n",
+            "Epoch 00057 | Loss 0.8325 | Train Acc 0.9167 | Val Acc 0.7500\n",
+            "Epoch 00058 | Loss 0.7958 | Train Acc 0.9167 | Val Acc 0.7500\n",
+            "Epoch 00059 | Loss 0.8071 | Train Acc 0.9167 | Val Acc 0.7500\n",
+            "Epoch 00060 | Loss 0.8200 | Train Acc 0.9167 | Val Acc 0.7480\n",
+            "Epoch 00061 | Loss 0.8320 | Train Acc 0.9167 | Val Acc 0.7480\n",
+            "Epoch 00062 | Loss 0.7945 | Train Acc 0.9333 | Val Acc 0.7480\n",
+            "Epoch 00063 | Loss 0.8326 | Train Acc 0.9333 | Val Acc 0.7480\n",
+            "Epoch 00064 | Loss 0.8052 | Train Acc 0.9333 | Val Acc 0.7480\n",
+            "Epoch 00065 | Loss 0.8039 | Train Acc 0.9333 | Val Acc 0.7480\n",
+            "Epoch 00066 | Loss 0.7960 | Train Acc 0.9333 | Val Acc 0.7480\n",
+            "Epoch 00067 | Loss 0.8057 | Train Acc 0.9333 | Val Acc 0.7460\n",
+            "Epoch 00068 | Loss 0.8071 | Train Acc 0.9333 | Val Acc 0.7440\n",
+            "Epoch 00069 | Loss 0.8149 | Train Acc 0.9333 | Val Acc 0.7460\n",
+            "Epoch 00070 | Loss 0.7978 | Train Acc 0.9333 | Val Acc 0.7480\n",
+            "Epoch 00071 | Loss 0.8042 | Train Acc 0.9333 | Val Acc 0.7500\n",
+            "Epoch 00072 | Loss 0.8027 | Train Acc 0.9333 | Val Acc 0.7500\n",
+            "Epoch 00073 | Loss 0.8075 | Train Acc 0.9333 | Val Acc 0.7500\n",
+            "Epoch 00074 | Loss 0.8146 | Train Acc 0.9333 | Val Acc 0.7500\n",
+            "Epoch 00075 | Loss 0.7858 | Train Acc 0.9333 | Val Acc 0.7480\n",
+            "Epoch 00076 | Loss 0.8011 | Train Acc 0.9333 | Val Acc 0.7500\n",
+            "Epoch 00077 | Loss 0.7910 | Train Acc 0.9333 | Val Acc 0.7500\n",
+            "Epoch 00078 | Loss 0.7921 | Train Acc 0.9333 | Val Acc 0.7480\n",
+            "Epoch 00079 | Loss 0.7881 | Train Acc 0.9333 | Val Acc 0.7480\n",
+            "Epoch 00080 | Loss 0.7888 | Train Acc 0.9333 | Val Acc 0.7460\n",
+            "Epoch 00081 | Loss 0.7905 | Train Acc 0.9333 | Val Acc 0.7460\n",
+            "Epoch 00082 | Loss 0.7939 | Train Acc 0.9333 | Val Acc 0.7480\n",
+            "Epoch 00083 | Loss 0.7963 | Train Acc 0.9333 | Val Acc 0.7460\n",
+            "Epoch 00084 | Loss 0.8011 | Train Acc 0.9333 | Val Acc 0.7460\n",
+            "Epoch 00085 | Loss 0.7805 | Train Acc 0.9333 | Val Acc 0.7480\n",
+            "Epoch 00086 | Loss 0.8063 | Train Acc 0.9333 | Val Acc 0.7480\n",
+            "Epoch 00087 | Loss 0.7920 | Train Acc 0.9333 | Val Acc 0.7480\n",
+            "Epoch 00088 | Loss 0.7893 | Train Acc 0.9167 | Val Acc 0.7480\n",
+            "Epoch 00089 | Loss 0.7948 | Train Acc 0.9167 | Val Acc 0.7480\n",
+            "Epoch 00090 | Loss 0.7946 | Train Acc 0.9167 | Val Acc 0.7480\n",
+            "Epoch 00091 | Loss 0.7748 | Train Acc 0.9167 | Val Acc 0.7480\n",
+            "Epoch 00092 | Loss 0.8006 | Train Acc 0.9167 | Val Acc 0.7480\n",
+            "Epoch 00093 | Loss 0.8140 | Train Acc 0.9167 | Val Acc 0.7480\n",
+            "Epoch 00094 | Loss 0.8047 | Train Acc 0.9167 | Val Acc 0.7480\n",
+            "Epoch 00095 | Loss 0.8049 | Train Acc 0.9333 | Val Acc 0.7500\n",
+            "Epoch 00096 | Loss 0.7781 | Train Acc 0.9333 | Val Acc 0.7500\n",
+            "Epoch 00097 | Loss 0.7955 | Train Acc 0.9333 | Val Acc 0.7500\n",
+            "Epoch 00098 | Loss 0.7908 | Train Acc 0.9333 | Val Acc 0.7500\n",
+            "Epoch 00099 | Loss 0.7807 | Train Acc 0.9333 | Val Acc 0.7500\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Training with lr: 0.0001, weight_decay: 0.05\n",
+            "Epoch 00000 | Loss 0.8013 | Train Acc 0.9333 | Val Acc 0.7500\n",
+            "Epoch 00001 | Loss 0.7859 | Train Acc 0.9333 | Val Acc 0.7500\n",
+            "Epoch 00002 | Loss 0.7735 | Train Acc 0.9333 | Val Acc 0.7500\n",
+            "Epoch 00003 | Loss 0.7843 | Train Acc 0.9333 | Val Acc 0.7500\n",
+            "Epoch 00004 | Loss 0.7912 | Train Acc 0.9333 | Val Acc 0.7500\n",
+            "Epoch 00005 | Loss 0.7838 | Train Acc 0.9333 | Val Acc 0.7520\n",
+            "Epoch 00006 | Loss 0.8072 | Train Acc 0.9333 | Val Acc 0.7500\n",
+            "Epoch 00007 | Loss 0.7774 | Train Acc 0.9333 | Val Acc 0.7520\n",
+            "Epoch 00008 | Loss 0.7881 | Train Acc 0.9333 | Val Acc 0.7540\n",
+            "Epoch 00009 | Loss 0.8084 | Train Acc 0.9333 | Val Acc 0.7540\n",
+            "Epoch 00010 | Loss 0.8081 | Train Acc 0.9333 | Val Acc 0.7560\n",
+            "Epoch 00011 | Loss 0.7843 | Train Acc 0.9333 | Val Acc 0.7560\n",
+            "Epoch 00012 | Loss 0.8017 | Train Acc 0.9333 | Val Acc 0.7560\n",
+            "Epoch 00013 | Loss 0.8052 | Train Acc 0.9333 | Val Acc 0.7540\n",
+            "Epoch 00014 | Loss 0.8131 | Train Acc 0.9333 | Val Acc 0.7540\n",
+            "Epoch 00015 | Loss 0.7696 | Train Acc 0.9333 | Val Acc 0.7540\n",
+            "Epoch 00016 | Loss 0.8134 | Train Acc 0.9333 | Val Acc 0.7540\n",
+            "Epoch 00017 | Loss 0.7930 | Train Acc 0.9333 | Val Acc 0.7540\n",
+            "Epoch 00018 | Loss 0.8100 | Train Acc 0.9333 | Val Acc 0.7540\n",
+            "Epoch 00019 | Loss 0.8187 | Train Acc 0.9333 | Val Acc 0.7540\n",
+            "Epoch 00020 | Loss 0.8011 | Train Acc 0.9333 | Val Acc 0.7540\n",
+            "Epoch 00021 | Loss 0.8127 | Train Acc 0.9333 | Val Acc 0.7560\n",
+            "Epoch 00022 | Loss 0.7924 | Train Acc 0.9333 | Val Acc 0.7540\n",
+            "Epoch 00023 | Loss 0.7983 | Train Acc 0.9333 | Val Acc 0.7560\n",
+            "Epoch 00024 | Loss 0.8099 | Train Acc 0.9333 | Val Acc 0.7560\n",
+            "Epoch 00025 | Loss 0.8139 | Train Acc 0.9333 | Val Acc 0.7560\n",
+            "Epoch 00026 | Loss 0.8102 | Train Acc 0.9333 | Val Acc 0.7560\n",
+            "Epoch 00027 | Loss 0.8267 | Train Acc 0.9333 | Val Acc 0.7560\n",
+            "Epoch 00028 | Loss 0.8042 | Train Acc 0.9333 | Val Acc 0.7540\n",
+            "Epoch 00029 | Loss 0.7953 | Train Acc 0.9333 | Val Acc 0.7540\n",
+            "Epoch 00030 | Loss 0.8248 | Train Acc 0.9333 | Val Acc 0.7560\n",
+            "Epoch 00031 | Loss 0.8212 | Train Acc 0.9333 | Val Acc 0.7560\n",
+            "Epoch 00032 | Loss 0.7952 | Train Acc 0.9333 | Val Acc 0.7560\n",
+            "Epoch 00033 | Loss 0.8208 | Train Acc 0.9333 | Val Acc 0.7560\n",
+            "Epoch 00034 | Loss 0.8154 | Train Acc 0.9333 | Val Acc 0.7560\n",
+            "Epoch 00035 | Loss 0.8085 | Train Acc 0.9333 | Val Acc 0.7560\n",
+            "Epoch 00036 | Loss 0.8120 | Train Acc 0.9333 | Val Acc 0.7560\n",
+            "Epoch 00037 | Loss 0.8196 | Train Acc 0.9333 | Val Acc 0.7560\n",
+            "Epoch 00038 | Loss 0.8195 | Train Acc 0.9333 | Val Acc 0.7560\n",
+            "Epoch 00039 | Loss 0.8200 | Train Acc 0.9333 | Val Acc 0.7560\n",
+            "Epoch 00040 | Loss 0.8199 | Train Acc 0.9333 | Val Acc 0.7560\n",
+            "Epoch 00041 | Loss 0.8122 | Train Acc 0.9333 | Val Acc 0.7540\n",
+            "Epoch 00042 | Loss 0.8355 | Train Acc 0.9333 | Val Acc 0.7540\n",
+            "Epoch 00043 | Loss 0.8419 | Train Acc 0.9333 | Val Acc 0.7540\n",
+            "Epoch 00044 | Loss 0.8370 | Train Acc 0.9333 | Val Acc 0.7540\n",
+            "Epoch 00045 | Loss 0.8243 | Train Acc 0.9333 | Val Acc 0.7540\n",
+            "Epoch 00046 | Loss 0.8220 | Train Acc 0.9333 | Val Acc 0.7540\n",
+            "Epoch 00047 | Loss 0.8236 | Train Acc 0.9333 | Val Acc 0.7520\n",
+            "Epoch 00048 | Loss 0.8270 | Train Acc 0.9333 | Val Acc 0.7520\n",
+            "Epoch 00049 | Loss 0.8175 | Train Acc 0.9333 | Val Acc 0.7520\n",
+            "Epoch 00050 | Loss 0.8420 | Train Acc 0.9333 | Val Acc 0.7520\n",
+            "Epoch 00051 | Loss 0.8313 | Train Acc 0.9333 | Val Acc 0.7540\n",
+            "Epoch 00052 | Loss 0.8088 | Train Acc 0.9333 | Val Acc 0.7600\n",
+            "Epoch 00053 | Loss 0.8363 | Train Acc 0.9333 | Val Acc 0.7580\n",
+            "Epoch 00054 | Loss 0.8305 | Train Acc 0.9333 | Val Acc 0.7600\n",
+            "Epoch 00055 | Loss 0.8441 | Train Acc 0.9333 | Val Acc 0.7600\n",
+            "Epoch 00056 | Loss 0.8330 | Train Acc 0.9333 | Val Acc 0.7600\n",
+            "Epoch 00057 | Loss 0.8297 | Train Acc 0.9333 | Val Acc 0.7580\n",
+            "Epoch 00058 | Loss 0.8599 | Train Acc 0.9333 | Val Acc 0.7600\n",
+            "Epoch 00059 | Loss 0.8548 | Train Acc 0.9333 | Val Acc 0.7600\n",
+            "Epoch 00060 | Loss 0.8484 | Train Acc 0.9333 | Val Acc 0.7600\n",
+            "Epoch 00061 | Loss 0.8299 | Train Acc 0.9333 | Val Acc 0.7600\n",
+            "Epoch 00062 | Loss 0.8538 | Train Acc 0.9333 | Val Acc 0.7600\n",
+            "Epoch 00063 | Loss 0.8509 | Train Acc 0.9333 | Val Acc 0.7600\n",
+            "Epoch 00064 | Loss 0.8435 | Train Acc 0.9333 | Val Acc 0.7600\n",
+            "Epoch 00065 | Loss 0.8602 | Train Acc 0.9333 | Val Acc 0.7580\n",
+            "Epoch 00066 | Loss 0.8513 | Train Acc 0.9333 | Val Acc 0.7580\n",
+            "Epoch 00067 | Loss 0.8283 | Train Acc 0.9333 | Val Acc 0.7560\n",
+            "Epoch 00068 | Loss 0.8369 | Train Acc 0.9333 | Val Acc 0.7580\n",
+            "Epoch 00069 | Loss 0.8502 | Train Acc 0.9333 | Val Acc 0.7600\n",
+            "Epoch 00070 | Loss 0.8456 | Train Acc 0.9333 | Val Acc 0.7600\n",
+            "Epoch 00071 | Loss 0.8738 | Train Acc 0.9333 | Val Acc 0.7600\n",
+            "Epoch 00072 | Loss 0.8423 | Train Acc 0.9333 | Val Acc 0.7600\n",
+            "Epoch 00073 | Loss 0.8352 | Train Acc 0.9333 | Val Acc 0.7600\n",
+            "Epoch 00074 | Loss 0.8559 | Train Acc 0.9333 | Val Acc 0.7600\n",
+            "Epoch 00075 | Loss 0.8344 | Train Acc 0.9333 | Val Acc 0.7600\n",
+            "Epoch 00076 | Loss 0.8748 | Train Acc 0.9333 | Val Acc 0.7600\n",
+            "Epoch 00077 | Loss 0.8837 | Train Acc 0.9333 | Val Acc 0.7600\n",
+            "Epoch 00078 | Loss 0.8660 | Train Acc 0.9333 | Val Acc 0.7580\n",
+            "Epoch 00079 | Loss 0.8455 | Train Acc 0.9333 | Val Acc 0.7580\n",
+            "Epoch 00080 | Loss 0.8588 | Train Acc 0.9333 | Val Acc 0.7580\n",
+            "Epoch 00081 | Loss 0.8687 | Train Acc 0.9333 | Val Acc 0.7600\n",
+            "Epoch 00082 | Loss 0.8641 | Train Acc 0.9333 | Val Acc 0.7600\n",
+            "Epoch 00083 | Loss 0.8833 | Train Acc 0.9333 | Val Acc 0.7580\n",
+            "Epoch 00084 | Loss 0.8553 | Train Acc 0.9333 | Val Acc 0.7580\n",
+            "Epoch 00085 | Loss 0.8664 | Train Acc 0.9333 | Val Acc 0.7580\n",
+            "Epoch 00086 | Loss 0.8602 | Train Acc 0.9333 | Val Acc 0.7580\n",
+            "Epoch 00087 | Loss 0.8609 | Train Acc 0.9333 | Val Acc 0.7580\n",
+            "Epoch 00088 | Loss 0.8708 | Train Acc 0.9333 | Val Acc 0.7580\n",
+            "Epoch 00089 | Loss 0.8629 | Train Acc 0.9333 | Val Acc 0.7580\n",
+            "Epoch 00090 | Loss 0.8566 | Train Acc 0.9333 | Val Acc 0.7580\n",
+            "Epoch 00091 | Loss 0.8731 | Train Acc 0.9333 | Val Acc 0.7580\n",
+            "Epoch 00092 | Loss 0.8654 | Train Acc 0.9333 | Val Acc 0.7580\n",
+            "Epoch 00093 | Loss 0.8848 | Train Acc 0.9333 | Val Acc 0.7580\n",
+            "Epoch 00094 | Loss 0.8564 | Train Acc 0.9333 | Val Acc 0.7580\n",
+            "Epoch 00095 | Loss 0.8647 | Train Acc 0.9333 | Val Acc 0.7580\n",
+            "Epoch 00096 | Loss 0.8606 | Train Acc 0.9333 | Val Acc 0.7580\n",
+            "Epoch 00097 | Loss 0.8703 | Train Acc 0.9333 | Val Acc 0.7580\n",
+            "Epoch 00098 | Loss 0.8817 | Train Acc 0.9333 | Val Acc 0.7580\n",
+            "Epoch 00099 | Loss 0.8832 | Train Acc 0.9333 | Val Acc 0.7580\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Best Hyperparameters: lr=0.001, weight_decay=0.005\n",
+            "\n",
+            "Test Accuracy with Best Model: 0.7030\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Link Prediction with Graph NN"
+      ],
+      "metadata": {
+        "id": "FVGSKFad2PoZ"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        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)\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "8Xhymib-2Vxm"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "The project is based on Hetionet which is a heterogeneous biomedical knowledge graph. It encodes relationships uncovered by millions of studies conducted over the last half-century into a single resource.\n",
+        "\n",
+        "I'm only using Disease, Gene and Compound meta nodes and the relationships between them.\n",
+        "\n",
+        "The target is to predict new edges between Diseases and Genes (Disease-Gene associations)\n",
+        "\n",
+        "and between those two node types (Disease, Gene) I'm only using the association edge type to make sure there is no information leak from other edge types\n",
+        "\n",
+        "I used two different approaches for encoding the graph\n",
+        "\n",
+        "building a normal homogeneous GNN and transform it with to_hetero function (basically makes a layer for every edge_type) like this tutorial\n",
+        "Used RGCN module which is based on this [paper](https://arxiv.org/abs/1703.06103)"
+      ],
+      "metadata": {
+        "id": "cIIglpBm29BF"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "F897PYQf3RNB"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file