From d631c0fb3e199262b02e0b88cc1becff077252c3 Mon Sep 17 00:00:00 2001
From: Sithu Khant <sithukhant.ml@gmail.com>
Date: Fri, 22 Sep 2023 09:20:40 +0630
Subject: [PATCH] fixed `lr` to `learning_rate`

---
 ...network_classification_in_tensorflow.ipynb | 6877 +++++++++++++++++
 1 file changed, 6877 insertions(+)
 create mode 100644 02_neural_network_classification_in_tensorflow.ipynb

diff --git a/02_neural_network_classification_in_tensorflow.ipynb b/02_neural_network_classification_in_tensorflow.ipynb
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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "machine_shape": "hm",
+      "gpuType": "T4"
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "accelerator": "GPU"
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/mrdbourke/tensorflow-deep-learning/blob/main/02_neural_network_classification_in_tensorflow.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "JZNT-Tz4ntRw"
+      },
+      "source": [
+        "# 02. Neural Network Classification with TensorFlow\n",
+        "\n",
+        "Okay, we've seen how to deal with a regression problem in TensorFlow, let's look at how we can approach a classification problem.\n",
+        "\n",
+        "A [classification problem](https://en.wikipedia.org/wiki/Statistical_classification) involves predicting whether something is one thing or another.\n",
+        "\n",
+        "For example, you might want to:\n",
+        "* Predict whether or not someone has heart disease based on their health parameters. This is called **binary classification** since there are only two options.\n",
+        "* Decide whether a photo of is of food, a person or a dog. This is called **multi-class classification** since there are more than two options.\n",
+        "* Predict what categories should be assigned to a Wikipedia article. This is called **multi-label classification** since a single article could have more than one category assigned.\n",
+        "\n",
+        "In this notebook, we're going to work through a number of different classification problems with TensorFlow. In other words, taking a set of inputs and predicting what class those set of inputs belong to.\n",
+        "\n",
+        "## What we're going to cover\n",
+        "\n",
+        "Specifically, we're going to go through doing the following with TensorFlow:\n",
+        "- Architecture of a classification model\n",
+        "- Input shapes and output shapes\n",
+        "  - `X`: features/data (inputs)\n",
+        "  - `y`: labels (outputs)\n",
+        "    - \"What class do the inputs belong to?\"\n",
+        "- Creating custom data to view and fit\n",
+        "- Steps in modelling for binary and mutliclass classification\n",
+        "  - Creating a model\n",
+        "  - Compiling a model\n",
+        "    - Defining a loss function\n",
+        "    - Setting up an optimizer\n",
+        "      - Finding the best learning rate\n",
+        "    - Creating evaluation metrics\n",
+        "  - Fitting a model (getting it to find patterns in our data)\n",
+        "  - Improving a model\n",
+        "- The power of non-linearity\n",
+        "- Evaluating classification models\n",
+        "  - Visualizng the model (\"visualize, visualize, visualize\")\n",
+        "  - Looking at training curves\n",
+        "  - Compare predictions to ground truth (using our evaluation metrics)\n",
+        "\n",
+        "## How you can use this notebook\n",
+        "\n",
+        "You can read through the descriptions and the code (it should all run, except for the cells which error on purpose), but there's a better option.\n",
+        "\n",
+        "Write all of the code yourself.\n",
+        "\n",
+        "Yes. I'm serious. Create a new notebook, and rewrite each line by yourself. Investigate it, see if you can break it, why does it break?\n",
+        "\n",
+        "You don't have to write the text descriptions but writing the code yourself is a great way to get hands-on experience.\n",
+        "\n",
+        "Don't worry if you make mistakes, we all do. The way to get better and make less mistakes is to **write more code**."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ucRDjFFCJ92X"
+      },
+      "source": [
+        "## Typical architecture of a classification neural network\n",
+        "\n",
+        "The word *typical* is on purpose.\n",
+        "\n",
+        "Because the architecture of a classification neural network can widely vary depending on the problem you're working on.\n",
+        "\n",
+        "However, there are some fundamentals all deep neural networks contain:\n",
+        "* An input layer.\n",
+        "* Some hidden layers.\n",
+        "* An output layer.\n",
+        "\n",
+        "Much of the rest is up to the data analyst creating the model.\n",
+        "\n",
+        "The following are some standard values you'll often use in your classification neural networks.\n",
+        "\n",
+        "| **Hyperparameter** | **Binary Classification** | **Multiclass classification** |\n",
+        "| --- | --- | --- |\n",
+        "| Input layer shape | Same as number of features (e.g. 5 for age, sex, height, weight, smoking status in heart disease prediction) | Same as binary classification |\n",
+        "| Hidden layer(s) | Problem specific, minimum = 1, maximum = unlimited | Same as binary classification |\n",
+        "| Neurons per hidden layer | Problem specific, generally 10 to 100 | Same as binary classification |\n",
+        "| Output layer shape | 1 (one class or the other) | 1 per class (e.g. 3 for food, person or dog photo) |\n",
+        "| Hidden activation | Usually [ReLU](https://www.kaggle.com/dansbecker/rectified-linear-units-relu-in-deep-learning) (rectified linear unit) | Same as binary classification |\n",
+        "| Output activation | [Sigmoid](https://en.wikipedia.org/wiki/Sigmoid_function) | [Softmax](https://en.wikipedia.org/wiki/Softmax_function) |\n",
+        "| Loss function | [Cross entropy](https://en.wikipedia.org/wiki/Cross_entropy#Cross-entropy_loss_function_and_logistic_regression) ([`tf.keras.losses.BinaryCrossentropy`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/BinaryCrossentropy) in TensorFlow) | Cross entropy ([`tf.keras.losses.CategoricalCrossentropy`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/CategoricalCrossentropy) in TensorFlow) |\n",
+        "| Optimizer | [SGD](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/SGD) (stochastic gradient descent), [Adam](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/Adam) | Same as binary classification |\n",
+        "\n",
+        "Table 1: Typical architecture of a classification network. Source: Adapted from page 295 of [Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow Book by Aurélien Géron](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/)\n",
+        "\n",
+        "Don't worry if not much of the above makes sense right now, we'll get plenty of experience as we go through this notebook.\n",
+        "\n",
+        "Let's start by importing TensorFlow as the common alias `tf`. For this notebook, make sure you're using version 2.x+."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "IPWQMjYwKpCH",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "80b308c9-5ad8-4c12-d46d-c4ded9de1fb3"
+      },
+      "source": [
+        "import tensorflow as tf\n",
+        "print(tf.__version__)\n",
+        "\n",
+        "import datetime\n",
+        "print(f\"Notebook last run (end-to-end): {datetime.datetime.now()}\")"
+      ],
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "2.13.0\n",
+            "Notebook last run (end-to-end): 2023-09-22 02:28:15.212411\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PEAjPGv8J2K0"
+      },
+      "source": [
+        "## Creating data to view and fit\n",
+        "\n",
+        "We could start by importing a classification dataset but let's practice making some of our own classification data.\n",
+        "\n",
+        "> 🔑 **Note:** It's a common practice to get you and model you build working on a toy (or simple) dataset before moving to your actual problem. Treat it as a rehersal experiment before the actual experiment(s).\n",
+        "\n",
+        "Since classification is predicting whether something is one thing or another, let's make some data to reflect that.\n",
+        "\n",
+        "To do so, we'll use Scikit-Learn's [`make_circles()`](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_circles.html#sklearn.datasets.make_circles) function.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "7dT80sWqLYPf"
+      },
+      "source": [
+        "from sklearn.datasets import make_circles\n",
+        "\n",
+        "# Make 1000 examples\n",
+        "n_samples = 1000\n",
+        "\n",
+        "# Create circles\n",
+        "X, y = make_circles(n_samples,\n",
+        "                    noise=0.03,\n",
+        "                    random_state=42)"
+      ],
+      "execution_count": 2,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CclQDWU8c69i"
+      },
+      "source": [
+        "Wonderful, now we've created some data, let's look at the features (`X`) and labels (`y`)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Gf47AUmxLYMj",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "e9f6392b-81fc-4b35-b9b2-8051f564c951"
+      },
+      "source": [
+        "# Check out the features\n",
+        "X"
+      ],
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[ 0.75424625,  0.23148074],\n",
+              "       [-0.75615888,  0.15325888],\n",
+              "       [-0.81539193,  0.17328203],\n",
+              "       ...,\n",
+              "       [-0.13690036, -0.81001183],\n",
+              "       [ 0.67036156, -0.76750154],\n",
+              "       [ 0.28105665,  0.96382443]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 3
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "FkgZlGstK572",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "72e57a98-72da-4f7b-bac7-dcdba10e8b10"
+      },
+      "source": [
+        "# See the first 10 labels\n",
+        "y[:10]"
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([1, 1, 1, 1, 0, 1, 1, 1, 1, 0])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DcBi6A1PeC4s"
+      },
+      "source": [
+        "Okay, we've seen some of our data and labels, how about we move towards visualizing?\n",
+        "\n",
+        "> 🔑 **Note:** One important step of starting any kind of machine learning project is to [become one with the data](https://karpathy.github.io/2019/04/25/recipe/). And one of the best ways to do this is to visualize the data you're working with as much as possible. The data explorer's motto is \"visualize, visualize, visualize\".\n",
+        "\n",
+        "We'll start with a DataFrame."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "SzQ7X9QgMGpq",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 204
+        },
+        "outputId": "0f621faa-7af7-4064-8323-2c7002f825e3"
+      },
+      "source": [
+        "# Make dataframe of features and labels\n",
+        "import pandas as pd\n",
+        "circles = pd.DataFrame({\"X0\":X[:, 0], \"X1\":X[:, 1], \"label\":y})\n",
+        "circles.head()"
+      ],
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "         X0        X1  label\n",
+              "0  0.754246  0.231481      1\n",
+              "1 -0.756159  0.153259      1\n",
+              "2 -0.815392  0.173282      1\n",
+              "3 -0.393731  0.692883      1\n",
+              "4  0.442208 -0.896723      0"
+            ],
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+              "\n",
+              "  <div id=\"df-00f67011-7c0f-4652-901a-22f7756a69e4\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
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+              "    .dataframe thead th {\n",
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+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>X0</th>\n",
+              "      <th>X1</th>\n",
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+              "      <td>0.692883</td>\n",
+              "      <td>1</td>\n",
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+              "    <tr>\n",
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+              "      <td>0</td>\n",
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+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
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+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-95b12d5f-8b3f-43c5-b0f1-499a6808fb89 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1ngNwBOxevcv"
+      },
+      "source": [
+        "What kind of labels are we dealing with?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "cfgZGtMGe0XB",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "ddb221cb-2b4d-4311-b11f-1758819ae1c0"
+      },
+      "source": [
+        "# Check out the different labels\n",
+        "circles.label.value_counts()"
+      ],
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "1    500\n",
+              "0    500\n",
+              "Name: label, dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 6
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ajz7TdQPesKt"
+      },
+      "source": [
+        "Alright, looks like we're dealing with a **binary classification** problem. It's binary because there are only two labels (0 or 1).\n",
+        "\n",
+        "If there were more label options (e.g. 0, 1, 2, 3 or 4), it would be called **multiclass classification**.\n",
+        "\n",
+        "Let's take our visualization a step further and plot our data."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "KIPTzrETMemP",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 430
+        },
+        "outputId": "f480b111-6383-4b16-f3c2-6963dfe72a33"
+      },
+      "source": [
+        "# Visualize with a plot\n",
+        "import matplotlib.pyplot as plt\n",
+        "plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.RdYlBu);"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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aRqEVgSsPu6/QKsS30nn1b5Tv3tbqGNVo5PBb33Lq2z8wpWc6ZKMzqf/2OBq+82y+Y4a0dJaF9SbzapzlysgS+DWpR+LBExYFk6RTqNCvCx3m3wgMTjx0kvXdHiE7Icl8QNPMolCDltPeo/pjw5zxsQT/ERx5ft9RAbsCwe0kJymFox/8wMLKnfjbpR7zy7fl0OtfkRkTb9f1xz/7hc0DniF+56ESttQ2kt52OJtmMqG4Fb7FkG8unY5Oy6dZFC6appn7HBVWml9V0VSN/RM+oufef/GqXvBtXOflQedVv1oULlnx17jw12KHhYtn1Qr02vsvIb075Eu59qxagVYzPqHyiN4255D0Ovyb1LU5TtbpaPTRRAZHb6PTip+pO+kpm/2N7MLBDKej707h6IdT8x27OGspGVeirbd00DAXurPyPdaMJuK23kjHNqZnsL7Ho+QkpuQ1qwTyfg52PfEmsVv3OmS/QGAvd1SROoHgdpEVm8DqtiNIO3c57wGcFR3P8U+mEfHbP/TYNhuvajcevJqmkXLyHIbkVDxDK5KTmMzB3GZ6tyGGJXcLRNLp8KpeidQztuNLFDcXqj7cn3O//5sX31AYbf76jKCOLSyeT9x/jOSbUn0LoGmkX4wi/cIV+p1ZQ/zOg1ycvQxTRhble7Wn0uAeSFbquVzbe7RIDSAbffwi3jWq0HHxj2RExZB2NhKdtyd+4XWQZJn13cfYnKPKiD4WY3AKQ+/lSUivDoT06kBWdDzn/1xo9XtrE1nGu3olUs9csLuw3uE3viawZXhe9d7If1dhV4CPHdzcc+nCrKVkxyVYnFZSZE5+/hvl2jUr9roCwa0I8SIQAHuefof0C1cKCA/NpJIdn8j2kS/RY9tswPwwOPzG16ScPGceJMt4hVa4bXEsjb94FUmS0Pt4EtS1NUtq9rAtoCSJ1jM+wb9ZfS7OWY4pPaOA7ZKi4Ne0HpWG9rQ6VdqFK3bZmX4xCkmSKNu6MWVbN7brmlxb7eJ6LIzex4smX79GleE3PCseIUF4hNwIKk6/dNWuTJ7gYmTY1Jn4COf/WlS8ar5GE/XfGYfOw529494j80qM7WsUmZNfz8gTL8b0TKfF4nhWq5j396srt1j9bJrRRNTKLU5ZVyC4FSFeBP95Mq7EcGnhWosPfM1oIn77ARL2HeXM1L859+s/+QeoKmkRjlV2lhQFt6AAMqPji+6pkWV8alWlzvOP5Hku0iOjbL/pSxKVBnWj8rD7AOi2fgZbhj5H+oUr5joqmoZmUgnq2oq2f3+JbKPSq2tAGbvMtXfcrQS2DEd2dUHNzrE6Luylx/ELr0PFgd0sZtzkkmFHF2ZJUciKtW/LsDDK1K9Fx8VT2Tp0PMaMTHOAs2T+edL5eOEXXoes2ARST18oXADIMq4BvlQe2hPFxYWchER2Pf6G7YVNKrGb9+R96Rdeh4Rdh4rnAbpO4r7jJB46iV94HVSj0WbgtGY0oWmaVc+aQFAUhHgR/OdJPHjCLgGxttNITE5qFuhbtzretUO5vGBt0Zz5sowkyzT/8d18DwYXf9+81FZLSLKEX5N6eV/7N61P/4i1XF29lWt7jyK76Anp3ZEy9WvZZUrZdk1xK1+WLCu1XFz8fAttQmgPLmV8qP7oUM78NLvQfydJUQjp24nGH79o95yugba3gjRVxcWBLaPCCOnVgUFRWzj/1yKu7TmKpNcR0qs9Ffp1RtbrMWZksr7bI8TvOpRPCEg6BVmno/0/36G4mPs8VXmgLwde+tQcY2KDm38majw1nDM/zCrW58hFzclhfdfR9D21Ev+m9biyZINlr44s49e0nhAughJBBOwK/vPILnrbg8BpwiXs1Sfouedfgjq1RCui1yWgeX26bphRIBZF7+VJpaG9rFai1VSN0JED8h2TZJmQXh2o/8Yz1H35CbuFC5iDVRt/Yl04hH84oVjNFht/8QpBnc2fNS/u4npAbJnw2rT67SOH5vOuFUqZ8DpWA2JlvY5Kg7sXzeCb0Pt4UeuZh2j1+2RaTnufSoN75HXR1nm402XdDBp/9gqe1SqBJKHzdCd01CB6HViQr1ChzsOdDoum2gzGlnQKwd1vCEW/hnWoOe6hYn8OADTITkzm8JvfcPr7mda3o1SVOuNHAWDMyOTSgjWc/XkuV9dsQ7UWPCwQ2IFIlRb85zGkpTM/qA2mjKxSWa/G2BGU69iCcm2bsLRub7tjEmQXPe3/+RbvWlXxqV3N4riUU+dY2XwopozMQmNwwl58lMafvVKsz1AYZ3+Zx/4XPsaYkpYX/6PzdCf8o4nUfm5UsedXjUauLF7P2V/mkX7xCu7BZak2ZjCVh91XJGEUtXIzG3s/af6ikO9//TefoeF744trtkPYs8WSsPcoq1sPtxrE3H3rLMq2bZr39ZqODxG32YmZP5Jk/s+K+K7+xP00//FdTn/7B4ff/hZjSnreOY+KwTT/8V0q9OnkPJsEdz2izosQLwI7yIiK4cz3s0g+EUHKiYgbAbilhOLmSqWhPbk4d4U5pdhGTEL4h89T77Wxds2dePgkux59nWv7juYd03l7UveVJ6j32tgSc+UbM7O4sng9GVdicA8OpOKArnnl9e9ELs1fze4n3yQ7IelGxpaLntTO97E3tA1u7i507hhKrx41cXe3z0NXGkSv28GmfmNRc3LyBKqkU9BMKs2mvEmtZ254WlLOXGBpLetB186m4UcTqffqk5z8ajoHCitOeF38dFn1a5G3EwX3HkK8CPEisMG+5z/i1NczrI7JLdNuT7AoYI5D0evQcgwOZXdUe2woiqsLl/5ZhSEtA9VgyFdyXvFwo8Fb/yPs5SccFh2Jh06SfCICvZcHQV1a5SsFLzBjyskhatkm0s5fZt/pFH7Yq2JwccNk0vKSacqV9eSnHwZQpXKZ221uHhlXYjg7bQ5Ryzai5hgo274ZNZ9+oMCW35Xlm9jU50mb8ynenphS022Os4WkU6g17mEavvcc84PaYsq04NGUJfwa1uG+AwuLvabg3kCIFyFe/nOoJhNoGrLOcjyAMTOLC38t5si735F5JdbmnN41q9Ds+7c59e0f5iZ1NjwjkiLT5KvXOPDSp2hGo91p05IiMyByY14qr2o0cnX1NtIvXMY1wI+QPh3Re3naNZeg6GzddpHnJhbeBFZRJMqV9WLhvw+id2Zn61Igbts+1rR70Oa4Oi88ysmvphe7TpGkKFR9uD/B3VqzY+TLNsf3Ob4c37DqxVpTcG/gyPNbZBsJ7mqiVm3hxOe/ErN+J6gaZcLrUHPscHzCaiBJEn6Nw9B7e5GdkMi6zqNIOnLa7rlTz1zEo2IwtZ55kKilG62O1ft40frPT6nYvytl2zTm0OtfcXXVVrvW0TS4OHs5YRPNRdNknY4KvTvabafAOUz/cz+yLKEWkv5rMmlcjU5l46bzdO9a4zZYV3QCWobbzAZT3Fyp/+YzlO/Rlv0vfkKyA78nBdHwqlaRrNhrdtU+yrgcTeKhk1xduQXVYCCgRUOqjRqIi59vMWwQ3OsI8SIodQwpaZz7YyEXZy/DkJyKb72a1HxqOOU6tXRoW+TUt3+wb/yHSIqSl2aadOgke55+N2+M4u5GjSfvJ+3cJZKPRzhkp6RTOP/HQsI/mkilYb3MjfcKwb9lQ7pt+gudqysA3rWq4la+nM2U5bx1FJns2ASHbBM4l5wcE/sPXLU6RlEktu+IvOvEi6zT0fC98ex+wnKNmLCXHsPF15vyPdrR+1Bbkk9EcH76fC4tWGtXN/Cb0TSN0EcGc+63f+zyPu585FUyo2LN27RoXPx7GYde+5J2c76mQt/ODq0t+O8gxIugVEmNiGRtx4fJjLq+bXO9zH7knOXUeHI4zae+U6BbcWEkn4xg3wRzeqy1ni2mzCxOfftnkSqMakYT6ReukJOYzFVL1VglSDp4ksxL0XjXqIIpO4f13cZwbd9Ru7eNNKMJj0rlHbZP4DxMdm6VGI23uRN4Eanx+DAMKWkcmvQFqsGYF9wLEDbxERrc1MxRkiTK1K1B409fpvGnL2NITeP8HwvZO+59u9Zq+N54jn/0E2d/mm19oCwh63RkxZiF+82/x6bMbDYPGkfLXz4w1x/adwydhxuVhvSkxhP341YuwC5bYrfu5fSUmcRvP4Cs11GhX2dqjXsY7xpV7LpecOciYl4EpYamqiyr14fUsxcteiSaTXmLWv+zXZNi3/MfcXrKX06pGmoN9wpBGNMzMCSlWhwj6RRqPvMgzb55g4jf/mHXY687tIbsomfQ1a24+pcpprWCoqJpGoPun8WlyGSLRQMlCV56oT0jhhXeQPJuIPtaEhf/XkbGpau4lgugyoje+domWCJ2y17WdrD9exn+0UR03p7se9aG0JEkc40dawL/eruHfB5MSULv60XXtdPxb1rf6hJHP/iBw29+k+96SacgyTLtF3wvtmbvQETArhAvdyRXV29lQ8/HrI7xrBJC/3PrbHpf1nR4kLgt+6yOKU3cQ8ox6MoWVrUeTsLuww4FPTb+/BXCXnjUqfZkZRlJTc3G29sVN7fSc7BGRaWwYPEJIiKu4eamo3OnUDp1DL0rglzn/XuUyZ9uLvScJIGrq45Vy0bj7eVaypbdfjRNY2ntnqRGXLJQ5VgmsE1jum38i0WhXciItL4FlytMiors5kqLn9+nTN0a+DWuW2C7OWrVFjb2erzwiyUJ2dWFARfW4x4UWGQbBM5HBOwK7khiNuxC0umsFtdKvxjFhdnLuLxwLUmHT6H38aLK8N5Uf3RIvgA+xc2teA3vnIwpMxuAjEtRdgsXt/JlafjeeGo8PsxpdkRGJvHTr3tZs+YsRpOKTifTs3sNnny8OZUqlmwA5Oy5h/nsy61IkjnoVZYlVq4+Q5XKZfhxSn+CgryKPLemaZw4GcfJU/G4uMi0almZwADn1o8ZPLAue/dfYc3aiHyBu4oiIcsSn03uiYe7ns1bL3DseCw6RaZN60rUq2vbc3G3I0kSraZ/zLquo9EMpnxbPJKioPN0p/nUd0m/eMW2cIFiCRcANSubndczmXzqVKPpt29QvnvbvPOnvpqeV+qgAJqGmmMg4pd51H/96WLZIbh9CM+LoNQ48MpnnPxqer4aJpbI5yqWJVwD/em28c+8lMrT389k77Pv3xHiRVIUyrZvSrcNf7Ki6SASD5ywbJck4VuvBs2nvkNgq0ZWU7sd5fSZeB57agFZWUZMphvrK4qEu7ue6T8Pplo1/2KvYzCaiIxMRlU1qlQug4uLwuatF5jwwvJCxyuKRNUqfsyZORzZSjl+S5y/kMjrb63h5KkbTRJlWaJfn9qMH9eabTsiiYlJw8/Pna6dq+HjY70pozVUVWP5ytPM/ecIZ84m4Oqio0vnajz0QDjZ2UZefGUl0TFp6BQZDQ2TSaNReDCfTe5FgJPF1J3ItQPHOfzWN0Qt2wSahqTIVBrai4bvPUfG5WgOv/kN8dsPlK5R1wvedVr6IyH3mbeC5niG26yYHdyjLV1W/VYaFgrsRGwbCfFyR3J58To2D3imSNdKioJH5fL0O7MaWVEwpKSxqFpXchKS7J7DvWIQmZdjiu2yLox2/3xL5SE9OTXlL/Y994FVUdVq+sdUGz3IqesDPDR6LqdOJxSa6qvIEvXqlmP6r0OKPL/JpPLnzIP8NesQ1xIzAfD2duH+oQ3Yvfsyx07EFrp2Lt993Ze2rSs7tGZ0TCoPPDyXtPScfIIM8leoVxSzp0Snk3nisWY89khTp1YRjrqayvCHZpOZaSzwGRVFIjTUj5kzht0V22POIPtaEtkJSbiVC8DF15tzMxawc8wkJFmyO1DdqUgSXqEV6Xd2DZIkMcerEab0TKuXlO/Zjs4rfy0lAwX24MjzWzRmFJQaIX064VG5vDm12UE0k4n085eJWrYx75jiZmfsgSzhXasqvQ8uouVvH1G2TRM8q1WkXMfm+DWpVyR7bib0kUFUGtwDgGqPDMK7VtVC55QUhTLhdagyvHex1iuMk6fiOHEy3qJ4MKkah4/GMHTE37z4ykq2bL1gVWjciqZpvPP+er79fmeecAFITc3ht+n7OHIsxup8OkVm6zbHUm5VVePTz7eQnJJdQLiYbbqxQ2cyaWgaGAwqP/y4mxl/Ovftf9bsQ2RlFRQuuWufPXuNjZvOO3XNOxlX/zL41KyKi683WbEJ7HriDdC02yNcADSNtHOXiN++39wN3FaNGFkiqEur0rFNUCII8SIoNWRFoeOSH9H7et3oDAz5/m4NSa8jZsMuACJ+/5fMKzF2Xae4utBlze/EbNxN1PJNyC56yvdoR5MvJ9F1/QwCWoVft8MsOKx1ZL4ZnY8XLX7+gFa/fpT3lq/38qT75pn5uvqaJ5UI6duJrutn2C+6HODc+US7x23afJ7xLyxn/AvLyMmxna2laRp/zznMshWFFy6z13drz1q5GIwmXn5tFRs3X7D7mpv5+de9ZGQYLJ6Pjknl9bfW0HvAH/ToPZ1nnlvM1m0XLQqwFStPFyqgcpFlidVrzpKZaWDBouO88/563v9oA6tWn8FguLc7KJ/7/d/bJ1pu4fLi9Zz/YyGZl6OtjlPcXKn2aNG9kILbjwjYFZQqfg3r0OfYMs78OJuLs5ZiSE3DN6w6lYf3Zs/Yt21PcP1JGfHrP3avKel1bOr/NEmHTuZV/IzdvIezP86m1nOj6LrxT6JXbeXCzCVkx13Dq1olQh8dwtF3pxC9emvBG7Mio7jo6blzbqFlzd3KBdB5xS+knLlA3NZ9SJJEuY7N8QqtZLfNjuLubv+vsun6A3r7jki+/X4HLz7fzuLY5OQsXnh5BfsP2hGEaQWjSSWsTuGZHfEJGcyafYjFS06SlJyFj48rvj6uXIxMLvJ6mVlGtm6/SI9uBQvK/ThtN9N+zd9hOT4hg527LtO+XRU+/7gXen1+AZuWblkIgdlLdCUqhV59Z5CaloOimMXsgkUnKFfWkynf9KVGdftqk9xtJB05bY6dv92GYC5c6V2jis2t4dCRA3ALLH78l+D2IWJeBHcEmqqyuFpX0i9GWR2XG1syz6+Z1doreUgSioc7ala2xWJ2Tb97k9rjHi5w3JiewbYHX+DK4vV5c6FpeFQqT9s5X1G2dWPb65cS6ek5dO89naws28HQN+PqorBmxRi8vFwKnNM0jceeWsCRozFWvQ62kCRwc9OxetkjeHrmX+fylWTGPLGAxMRMh7ax7GHo4Lo89XiLfIG08xce54PJG61eN+rhRkx49obnLC0th4dGz+PSZctiSpalvOS3wj6HXi/z8Qc96NypmuMf5A5n15Nvmr0vJVxzyS7syUCUJCoP7Um7ud+Ujk0CuxExL4K7DkmWqTNxjPnmU9h5RcGjYjAVB3TN+9ouNA1TeobVKrwnPv3Z3NjxFi4tXEvUis0FbZLArWzpvrUZDCaOn4jl6LGYQrdDPD1dGPlQI4fnzc4xceRo4S72AwevcvBQdLGEiznN2Pzgvlm4ZGQY+HfBMUaN+Zdr1zKcLlwA/pl/nF79ZvDmu2tJS8vBZFL54cddNq+bO+8IGRkGzp2/xsuTVtGp+69WhQuYBYvJpFn8HAaDyguvrGTqT7sBszDcuOk8Y8ctolP3X+neezqTP93E+Qv2bf/dSVQc0NWqcJF05mD73L+XJPZU55ZkCcmJWX6C24P4FxTcMdQa9zAJe45w4a/F+Ws0yDJ6Hy86Lv0pL7VY7+1pV6aRR8VgMq/GWRUvGZeiST19Id8WUNz2/eaOuDe/xV3/e2ZULOu7PULfU6tQXAt6LJyJyaTy+4z9zPz7EMkp5loybm46WreqjK+PKzk5JqpWLUO7NlU4eMi618oSlh646zacQ1GkIosXFxeF9m2rULmSL0eOxnA1Oo0e3Wtw6VIyzz6/lJTrn6ckMZk0Vqw8w4ULSTz/XJt8wcaWyMo2MX/hMaZO201Ojsmpwurn3/bSsEEQm7Zc4J/5x/LVk5m/8DgLF5/gy89625WVFXkpmchLSZw/n8jBw1eJiUmjbFlP+vWpQ4d2VdHpSufdtHyv9vg2qEXKiYiCIkaS0FSNNjO/QM3J4cyPs0k+dga9rzdVR/TBtVwgZ6bOIm7zHqfZo/fxwpCabtEDo5lUyvdq77T1BLcHsW0kKFUMKWmYsnNwDShT6FtSakQkB176lJj1OzFmZKH39aLa6EGEvfRYXjVMeyr1grkIXLUxgznx6S82Xdr3HVqEX8M6eV9vHvQ/rizdYPW6NrO+oOoDfW3aUVQ0TeONt9eycvUZa2Vjro8tWs0+RZFYtXQ0/v4Fa5S8/9EGFi89WSTx4u/vzohhDZj2615MJhVFkTGZVGRZQlFkjEa1RLwt1hg9shEz/jxYqmsWRvlgL65GpxV6TpLA1UXHiiWj8PUtvF7NmbMJfPL55kIbSeaKocaNyvPdV33x8NA71XZLZF6NZUOvx0k6fOq6V8OceSS76Gk94xObGXa7n36HiF/mlvjWk6QouJXzp/+5dSUSOC8oHqLCruCO48rSDRyb/FNeASu34EBqjXuYsBcfy/NeXJi9jB0jXwLtRpM2Q2Iyp7+fSXDXVrhfL0B15N0pdq3ZYuo7SHo9x40/WR2n8/LI16hN0zSuLNto3RWuyFxZsqFY4uXosRgWLDrOxcgkfH3d6Nm9Jp073Silv2ffFVasOmN1jkIcQ3YjyxI9u9fMJ1wyswzIkoSrq47QUP8iCwzVpPHD9S0SuNHQ0GTSMFnxgpUUsiSxd9+VUl+3MCwJFzD/G2ZlG1my/BQPPxCed1xVNaJj0rgYmciLr6wkO7vw72Huv9fBQ1d5edJKvvu6r1Pr3VjCvXw57juwkKtrtnFl0TpMmdmUCa9N6KiBdvXsCnvxUS78tQhTZlaBAHlJUUCWzJW5i6N3JXAN9KPzmt+FcLkHEOJFUOKcmvKXuVHbTZ6WrOh4Dr/1DTHrdtBpxS+knj7PjodfLHDj0kwqmprD5kHj6Hd6FbKri10VPMu2b0bFAd1QTSY8q4SQcTm60HROSZGp/sT96Dzcb6ypqjarAGuqhpqdY9OOwlBVjU8+38y8f4/lbcvIssSGjeepWSOAH6f0x8/PnQULjxdr28YSuc+y6tX8eeXF9qiqxsLFJ/jjrwNEXjLHdoQ3CGbo4HrodDIGg2NpsLIskZ5RtO9NSaFqGomJWbRuWYkduy7dbnNssvy6eNE0jbn/HOXPmQeJumpHgPp1NA2277zE2HGL+fqL3ri7lbwHRpJlQnq2J6Sn41sy3tUr02XdDLYMHkfmlRiz90bT0EwmyrZvSr3Xx7JlyHOY0jOtbgFbwiWgDOEfTKDqQ/3Qexe9TYXgzkGIF0GJkh4Zxb7xH5q/uLXnj6oRs3E3Z6bOIvl4hMVgXTQNzWjk7E9zkPS2f2QlRSagRUPAXFum/fwprOsyCmN6Zr7utAD+zRsQ/v74fNfLioJvvRpmm6zs1/g1DrNpS2H8Pecw8/49BpAnTHLfmM+dv8ZLk1bxy48Dibyc7DThUq6cJ+5ueq4lZhIc5MXggXXp368Obq46xo5bxJ69+eNlDh+N5tCRaLp3rc6adREOreXmqiMj03pqcWkjSVC2rCevvNSBkWPmkZp6Z4mrWzl5Op5ly09x8PBV/l1wvMjz7Nl7hWfHLyU01A+DQSUkxJsK5X3w9HShSePyxWql4GwCWzRkwIX1RC3fxLW9R5Fd9IT07oh/k3oA9Dm8mFPf/cXFWUvISU5F5+WJa0AZNJOJ1LMXraZG5yQkEbNhJ64BZfCuFUqZhrXt9khpmkbctn2c+30+GZejcQ8uS9WH+xPctbVdAcKCkkHEvAhKlMNvf8uxD6daLmIlSXhVq4iaYyDjkvXCUmXCa5N66gKmLBuBnhK0+Ol9ajxxf96h9ItXOPn1DC7MXIwhJR2v0ArUGPsANZ8aUagL+ey0Oex+6i3LS+gUBkZuxL18Oeu23ILRqNK7/x/EJ2RYHTfrj2FM+WEXO3dfKnZsiKJIdO4YyqeTexU49/5HG1iw6ITV6z09XUhPt+9hHxjozvChDfnejqweR9DrzXEyxblbvfV6Zwb2D+NKVArffb+DtevPlXrcjSP4+7tz7ZrtAGN7uDkwOBe9XmbwwHo8/1wbXFzu3rYGe5/7gDM//m1Xz7RcvGuH0uTzV6jQt7PVcarBwPZRLxM5e3lev7XcP8v3bEf7Bd+jc79zBODdjkiVFtwxJB89g2btAaFppEVcQrUjUC87PgmTHVs1spsbVUbkDxD0rFKBpl+9xpDYnYzIOkLfEyupM360xb3vao8NpdKQnuYvbnpDk3QKSBKtfvvIYeEC5uaJtoSLLEvs2HmJ+3rVdMrD1WTSGNCvoJcoNjbNpnAB7BYuAM8/25aWLSo6ZJ81fH1ceWliO3ZueYoVi0cVeR53dx3lg71QVY0KIT58/GFPNq97HG/vks0WKw7XrmXirBf7wn6ODAaVuf8c4ZXXVnE3v8N6Va/k8FZS6ukLbOr/NJH/rrI67tAbXxM5ZwVAntc298+ra7azd9x7RbBY4AyEeBGUGKrJhOzmarP8v6zXU7ZDc6s1ICSdgnuFcnbViWjw5jPF3teWFYW2c76ixU/v4Vu3ep4NIb070m3zX4SOHOjwnHPmHeGJpxfaHCdJ5hTp7l1rUL2af1611qIgAW1bV6Z1q4Kpt79O31fkeS2xftM56tUtR7VQP4sdpHO14M3nc/8+8sFwZv0xjE8+6sH33/Rl9fJHeGB4QyRJokwZd1xdi7bTnZlp5Olnl9Bv0F95PYg8PPQMHVSvSJ2uS4tbd1qdjabBpi0X2LuvaGn2dwJVH+rneH8yTQNNY88z76IaCt/iNKSmcXrKX5a3jlXV3IogOs5BiwXOQIgXgdOJ23GATQOeZo5rAy7OWmI9/VGSKNuxObWffdjqOE1VCe7e1q43rNCRA4pidgFkRaHGk8Ppc3QZI4zHGZFzjI6LplKuXTOH5/p7zmE++XwLmZm2Xdsmk0a9uuVwcVH46fv+NG4UUhTzAXNyxgvPt833gD52PIYHRs7Ni7txJunpOUiSxJuvd0ankwsIA1mWcHFRmPBsa8IbBucJmbphZfn4wx5MeK4NdWqXpXvXGrRuVTlfmX4XF4WO7atQHKKjU3nhlRV5AubhBxsRGOhRqECUJChX1rNY690NKIrE4qW2PXB3IqrBwNF3vy9SEC9AdmwCV1dtLfRc3PYDmDKyrF6vGU1Er9tRpLUFxUOIF4FTMKSlc2bqLJbU6cWaNiO4sni9fTcUTSNm7XaOT55G+EcTgfxVOHO3aVr+8iG1nx1pNUBOkmXKdmiGR8XgYn+eW5EVxa4AP1XVOHoshm07IvOqpWZmGpgydad968gSFSr40LKFuQ+Sv78HP33fn8CAgnVY7J3v5oaKp07H8/jYhZw+HV+k+ayhKBKhVc2Vh8MbBPPbtEG0aH5jC0mSoFWLikz/ZQijHm7Mrz8NYve2sezZPpY/fhtKj241rH6P4+LS2bHrcrFs1K7/77Mvt6KqGn5+7kz/ZTAtmuXf6nJ11THq4cY8OKJhkdcqToayIktUqVymWHPYi8mkERObXvILlQC7nnyT09/PtBqsaxVJstiSRM2xL+jc3nEC5yKyjQTFJj0yirWdRpJ+vugPlqgVm9H7etFty0xOffsnsRt3gyRRvmc76owfhX/T+gCEvfQ4xycXUrdFlkCWaXRdAN0Oli4/xRdfbc2rhAtQvrw3/XrXtsvjksv7b3fN57H44afdNuNkLKGqGmfO3BAq332/g5wcU4k00TOZNAb2vxFbUzesHD9824/4hAyuJWTgH+BRQIQpdnYUB5jzzxGH4m8soQFXo1M5eOgqTRqHEBzkzfff9uPylWROnY7HRa/QpHEInp4unI1I4Ovv7H+zvjm1PbdwoISEqmkFCgrm/r2wOdzd9Xz4XjcmvrScuPiMYgUq22OzNQ+Tqmqs2xDBL7/vIzo6FRcXHRVDvElOySYhIYMyfu4M6FuHoYPr4ePjRmJiJouWnGDP9bo6TRqHMLBfWL4eU84g+WQE56cvKN4kmoZroF/el+kXr5ARFYt7UCD+jevaVfkxoFn94tkgKBJCvAiKjCkrm6yERDYNeIaMS8XrOoyqcvHvZYR/+DztrTRMC/9gAoqrC8c/nmbOOpIADTxCgmj520eUbdu0eHYUkbn/HOHjz7YUOH71airTft1rd/VbTdNYvPQkjcLNvWAuX0nmt2LEpsiylBcnkpCQwfadJVvj5Icfd/Hpxz3zCu0BBBYiWorC8pWnrQYwS4CikzAa7XvSx8Xn9zZUrOBLxQq++Y7d2l3aGgEB7sydNZyHR/9DTGwaqno9tOK6VJQkCQ8PPaMeasS584lIMgQHeXHwUDQHrnftlmWJjh1CefaZVkjXP5UjwiVXPDlSH8hk0ujXt06h5wwGE4+PXciRozE3Hc0h4SYxnZqWww8/7ubfBcd59pmWvPfRRrKzjXl279x1mV9+28vnn9xnV9sDe7k4a2le5k9R0Xm6U6FvJxL2HuHAC58Qe1ObgoCWDSnbrinx2w8U6kWWdAoBLcMp06B2kdcXFB0hXgQOkx4ZxZF3v+fCzMVFLtRmiahlm6j1v4csnpdkmQZvj6P2hNFELduIITkVr+qVCeraGtnRoD0nkZVl5LMvC983z8XeB5CmwbIVp5jwbGt8fNxYsvQUslz0QnWqqtGxQyhAvgdOcQjwdyfBQgrv5q0X+OnnPYx7upVT1rqZ9DTrP2saUKtmIKMfbswrr6+2OV9goO14luAgL9zd9WTaUbfmo/e7s2nzBYsVdFVVIyPDQGaWkY/e757vXFxcOknJWZQt60kZXzdyckwMGjaLhGv2/5tJEvzwbX/C6pTFzU3HvH+P8tmXWwtNk775mho1Ajh+IpbY2DQ6dQzF2+tGBt7kTzffIlwKR9U0YmJTef3ttcCtlZ81cnJMTHxpBf/OGVFAIBaV7PjE4u3NAQ3eG0/SkdOs7TTSXMH3JhL2HAUJ3IMCyYqOR7spelpSZFwD/Wjz56fFWl9QdETMi8AhUiMiWdl0MOf/WOB04SLJMqZM6wFyubj4elP1wX7UfPpByvdod9uEC8CSZUXr/2MJg0Hl9JkEAKKuphR5HkWRCCnvTbcu5mwpP393G1fYZviw+haFC5gfWnPnHSUry/5tMnupXLmM1dRhc8yNH106V6NsoHVPT1CQF42ve7es4eqqY9CAMJvPyCGD69K8aUVWrTlrdayqaqxYdbrA8bJlPalZI4Ay1/sZrdsQwdXoVKs/V5Jk/sySZK7Z8t7bXWnerAJeXi7odDIPDG/IN1/0Jryh5RgwSYIzZxL4fupO3n5vPT16T2fGnwfQNLPQWrLspPUPnu+z5SXxFEDTQDWpzPvnqN3z2cKzSghYqh9liev/OIqHO40/f4U6zz/CnqffQTUYC9aiUlVzLI0sUf+dcXhUCkbSKbgFB1L3lSe57+AivEIrOenTCBxFeF4EeagmE1HLN3F5/hpyEpMxpGeSEXkVQ3IKnlUrUuOp4VyYtYScxJQiR/dbQzOZKNPwznHBZmcbWb7yNOvWR5BwLZPywV506Vyd7l2r50vZtefN1FFyY0EsNeezfq3ZU1M+2JsfvuuXV4CsbKAntWsFcqoIwbqtWlZk5IONiDifaPVNHiAtPYczZxNoUD/I4XWsMXRwPd79INbieZNJY/DAeiiKzIsT2/HKa5a9Ly9OaGt3ivRTTzRn997LRERcK/TBPLB/HV572dx3KyU126aXLT3dthdny9aLNr/PAPf1rEXNGgH061snT/jcTPt2VdHpFZ5/cRk5OQUf9LnOhNw/s7NNfDNlB3q9TM0agU4V5SZVY9PWCzw/vq1T5qs6cgCHXv/K8gBZxqdWVfwahZlrwSARu3EXhuRUfGqH4lW9Mtf2HyPxoJVMK00j83IMZRrUZmDkJqfYLXAOQrwIAMiMjmNDz8dIOnwKFLnAG01W3DUSdh0qOQNkGc9KwQR3a1NyazjA1m0XeeX1VfkCbU+djmfj5gt89e02vv+mH3VqlwXgmgOufXvw9NQTVsc89309azJr9mG7r5VliT731aZ92yp07BCKTpffVfHaKx0Y/dh8u+dSFJlpP/QnvKHZSxFx7ppd15ZE0bPe99VixarT7Nl7pVCBMHhgXRqFm70M3bvWQPsQvvhqK3HxN/59ypX15MXn29H1ujfKHry9XPl92mD++Osg8+YfJSkpC0mCRuHlGT+uNQ0b3PBsVKvqx+nT8RYf+rIkUbVyGZtr5uSYbH4PNQ3efqOz1aDnY8djGDd+icMBvz9O28MDwxs4dpEdGB3sk2UNj5AgGrz7HIff+LrAOUlRcAkoQ+fVv+FRMZh9z33A6Sl/5cXIpJyI4NK/q/Gqbl8Mzv7nP6LSwG5Os11QfER7AAGaprGq+VASD50o8Zb0lpBc9HRaNo3yd4B4OXwkmjFPzLd6w/fxcWXBvAeJjU3ngZFznba2JMGohxtTsYIP6zeeIyPDQFx8Olevptp8AMmSRJ06gfw1fZjVcXPmHeaTz63H6AA0a1qBCc+2pm7YjUrCx47HMHLMv1avc3fXsXbFGNzdnd8MMCfHxLRf9zDv36N5/YnKlfVk5EONeGB4wwLeFJNJZe/+KOLj0ylb1pOmjUMcynC6FU3TSE834Oqm5AtKzuXgoas8+qT1DJh33+pCvz6FB8jm8stve/nx5z1WY1Wqhfox7+8HrM7zv+eW3DGNKBVFoke3Gnz43o14n0uXk1m46DgXIpPw9HCha5fqtGtT2e5/I03TiPh5LkfenUJm1HWvnCQR0qcjzb57E6+qFTn59XT2Pz+58Alk2e5KgIHtmlL90SG4lQvAr3EYHiHO9SwKHHt+C/EiIHr9DtZ3fcS5k8oSqBqKpzumdPv7s5RtZ+4gG9Krg3PtsRNV1biv/x/ExVmveyFJ8OwzrThyLIZNm887rRJq29aVOXM2nti4jLwMJQfur7z3dhf69rb+YASzCPn8y20cOmLuJ6XTSfTuVZvBA+uiKBKBAZ4EBRVepXjkmH84eSquUO+CLEk8+EBDJjppa8ASOTkmLl1ORlEkKlX0LZYgcSaapvHJ51uYW0hshyRB61aV+frz3gU8YrcSF59On/5/YrQS0/H6qx0ZMqiexfNpaTl06PqL/cYXE3sy6n7/ZTDh1z1Vv8/Yz5SpO/MC0nO3O2vXCuT7b/ri729/hppqMnFt3zGMaen41ArNq/WkGo0srNSRrGgn1zWSZSoN6UHz79/Grax/3mFjZhapp84jKTI+daoh60u+m/e9hBAvQrw4xL7nP+L0lJkFou2LiltQAN61Q6k59gHKdWrBwood7X/6Xt+yajEtf2PF0mLOvCN88nnBlOfCcNHL5DjRDe7l6YKPjwsxsekWtx0a1A/iyNGYfILmerY43btVZ/L7PRwqd5+amk1Kajb+fu52e0qirqby+FMLiIlNy3tY5cZnNG9WgW+/7FPkMv73ApqmMfefo8z48wDRMebMI19fN0bc34BHRzexO/162YpTvPXuunzZZrkCoWuXanz8QQ+roi06JpXe/f8s/geyE39/d4YMqsfPv+7Nl6qd+/dxT7fk0UfMpQxWrT7DpDfXFDqPokjUq1uO338ebHfnZ0skHj7JinAbFbdzf4EcRZZwKeNDvTeepuqIPpz65g9OT52FMcX84uNaLoCwF8YQ9uJjovu0nQjxIsSLQ+x55h3O/jLPoa6shSJJ1J4wmqZfTsp3eO9zH3D6O8duopJOYeClTbgHly2eTQ4ycOhMIi8ll+qa9iJLErVrB/DMUy2Z/ucB9u03VwatXNmXh0aEX/ealM5NMjU1m4WLT7B0+SmSkjKpWNGXwQPr0qN7jUK3U/6LqKrGlagUVFUjJMS7SN+Xg4eu8sdfB9iy7SImk0b1an6MuL8hA/uH2fy3zs420qHrLxicKLCtIUnQskUlRj3ciL/nHM6LTWrWJIQHRzTM66+laRr3PziHc+cLD4DO5WYvTVFJ2HuEVc2HWrdbUYqfgGCp6qAEoaMG0er3ycUWYv8FHHl+/3dfjwR5+DUKc4rXJXTUABp/+lKB402+eAVDWjrnf59vDpgDsBFbo6ka536fT71JTxXbLnvJyjLeduFiLcNE1TROnIynYcNgfp46kJwcEyZVxc1VV+o3Rm9vV0Y+1IiRDzUq1XXvJmTZvKVVHBqFl6dReHk0TUNVNYfEqaurjv596/DvguPFssFeNA127rrE8KH1+eaLPhbHxcWl2wz8VhSJLVsvFFu8+NQORXF3s1qCQTOZKNe5JbEbdhV9IUsqTIPzMxZQbfRAgjo7v/bRfxnhyxJQ+f5eKO7FrAEiy7T8+QNkXUE9LOv1tP5tMn1OLKfepKeoNnqgzekkCZKPny2eTQ5yO7sLS5I5y8geDZLrjndxUXB304s3uv8AkiQVyav25OPN8SvjeLp9UZFliQWLC6YeHz8Ry6/T9/Hzr3vZtcd2GxFJkjDkFD95QO/tRfXHhlrsbC8pCl7VK9Nx0Q/o/UrGUy/pFM7+7LygfoEZ4Xn5D2PMzOLEZ79w+vtZmDLsD6q9FUmnUHFQd5vBab51qtPwvfGoBgPnfl9gPQ5GktF5FL+omiO4uChUC/Xj3PnEUl0XzC9unTpUY9mKUxbHSBKUD/bG18fV4hiB4GbKBnoy64/7+fDjjWzdHlni66mqxtWrqXlfJyRk8NKklRw8FI0smwvqmUyazeBeo1GlTh3nbBmHf/Q8cTsOkLj/ugfq+sKSoqDz9qD9v9+h9/ai46KprO000v74PDvRjCZSTl1w6pwCIV7+s5iystnQ41Hith/I/8t6U/Ca5KJHs9UxVZJAkqj36pNWh6WcOsepb//k0vzVmLJzcPHzIScxxeKNQjMaqTi4e6HnSpI0GyXonU1uMOPDD4Yz9snmbN5ynvQMQ6FbR5oGD45oKDwtAocICvLi26/6kpiYybkLiUhA/XrlWLHqDO9+sMGpa8mylNfHKifHxFP/W8TFyCQAmwX38uaQJLy9Xeja2f5aPNbQe3vRffNMzvw4mzNT/ybj4hV0Pl6EjhxAnQmj8axSAYBy7ZvRcfFUto98GUOiE7ePZRnXAOe0RBDcQIiX/yinvplB3Pb9BVvJ39yTxEoAr6TXoRmMuPj50vbvL/BvYjll8+rqrWzq/zSayXSjjsz1VOpC59YplGlQm/LdSzbd9lZOn44n1kaKtD24uCgYDCaLb5aSBH5l3KlUyZfQqn4MGVSXenXNNSO+/Ow+xk1YhtFoytseyo2D6dqlGsOHOb9wmOC/gZ+fO039bngzB/QL4+df9xJ1k6ekuKiqRv/rTR7Xro+w6MW09LuhKBKyLPPp5F55laGdgc7DnbCJYwibOMbquAp9OjH46lZOfjODI29+g2owFC0T6WZUFZ2XB0lHT+Nbr6Z4+XASQrzc42RcjubSwrUYU9PxrlWVCv06I+v1nJoy06J4yMOKXze4WxtCRw6g0uAeKK4uFsflJKeyZfCz5pvAzevdsrak14Fm9rj4NQqj07JppZ5e+O/CY06ZZ2D/MOb9a7mHi6bBm691ymuYeDNNm1Rgzl/38/fcI6xZe5asbCPVq/kzfFgDenavccfUMxHcGzhTIMiyRJ3agQCMf2EZ+w/Y12ne19eV5ORsXFwUenavwciHGlGjeoDT7HIUxdWFei8/QaVB3Tn20Y9cmLmk2JmYlxes5fKCtXiGViT8o4lUHWE5oFlgHyJV+h5FNRjY+9wHnJ02F9CQZBnNaMI1oAzNp73H1iHPFWt+t+BABl/dZnPcqe/+ZN/4Dy0LIQncypejYr/OKO5uVBzQlXIdW1h9O0lJyWL5qjOcP5+Ih4ee7l2r56sCW1TGPDGfQ4ejizXHgyMa8r+nW/LIY/M5d+4apltEmixLNG9WgSlf9xVCRHDb+fKbbfw9+3CBn1NHkSTo2L4qV6NTOXU6weFr/5o+lIuRSSxeeorY2DTKlfNiQN86dOlS7ban3ptycjCmphO9fifb7p/glDmbfDmJOs8/4pS57iVEnRchXtj52Guc+31+4aLBypaNI/Q/vw6vqhWtjtn20AtcnLPcZvfX4VlHrHpwclm6/CQfTN6EwWBCUWQ0TcNk0mjVshKfftQTLy/bc1jif+OXsGNn0Uqpu7vpePGFtgzqb94+S0nJ4uPPtrB67dm8vX69XmbQgLpMeLYNbm7C6Sm4/Vy6nMyQ4X9jMqoO7454eupp0jiEju2r0qplZV58ZQUnTxWvkm3uFmnun+ENgpnyTV88PYv+e+0sUk6fZ2ntXk6ZS1JkBkRuFC0GbkHUefmPkxoRybnfrPSfUTVzvRVVK1ZkfXZ8ok3xIikKEpLNG6NkR5ryjp2RvPXu+ryvjcYbtu/ec5lXXl/F99/0szlPLiaTej0Dwrx2ty7V7RYvkgSdO1WjWdMKBJXzpG3rKvlc8D4+bnz0fncmTmjL8ROxyJJEwwZB+PiUXtqqQGCLShV9+ezjXrw8aSUmk2Z3UO1z41rx0APhZGUZWbDwOKMf/YeEa0XPWMwld/3cP48ci+Hjzzbz/ju3vymiT61QPCqXJyPSvu0wa2ganJ++gHqvjXWCZf9NhHi5B4mcsxxJkdGseDuc0YAxt3+INcp3b8OFPxdZPC8pMmXbNrWrB8i0X/daLOKmqho7dl7ixMm4vI7MhZGTY+KnX/aweMkJEq5lotfLdO1cndEjG9OrR02m/bqX+HjL5flvZuJzbQgJsf52EBjgQYd2VW3OJRDcLjq2r8rifx9m/sLj7NpziWPHY63+/MuyRJ9etUm8lsljTy0gyo6moUVFVTVWrj7D+Gfb5GUx3U7KtmnMRSeIF0mWSD170aFrTDk5SJIk+iVdR2y634PkJCbbHewq27FVUxjle3e0q3R/5WH34RZcFkkpfN9aM6mEvfSYzXkSkzI5dDja6puhLMM/84/m88jczMmTcXTr9Tu/z9if95ZoMKisWnOGkWP+Yf+BKKb9MIAKVgRJbiG758e3tSlcBIK7haAgL55+qgXTfxnCc/9rbXGcLEn061ObsmU9efPddUTHpJWYcMnFZNI4eLD4gsEZlAkPM2+7OwG9r7fNMZqmcX7mYlY0GcQc1wbMdqnP6rYjuLSg8L5Q/yWE5+UexKtaJVQ7ouMlnULVh/pZ32IqBMXLgyafvWzfWDdXuqz+lXVdHyE7/nrapHZ928pootEnL1Khb2eb82Rm2v48qgoLFp1g3YZzVKlchgoVvAkJ9iEtPYcVq06Tmlp4DRdNM29BvfL6atYsf4R/5zzAth2RbNt+kWPHY7lwMYmMDHO9mzq1A3lsTFM6d6xm1+cXCO42Hn4wnPj4dP6cdQhFkdA08orLdWhfhVdf6sC589fYs/dKqdmkFTtf2TlUHtaLQ5O+KPY8mtFEleG9rY/RNPaN/9DcF+6ml9H4nYfYMngcDd55lgZvjyu2LXcrImD3HiQnKYV/g9ugZVsvMCfpdQR3b8vV5Zvsntunbg3azv4Svwa1HbMpOZXzMxZwaeFaTBlZ+DetR82nH6BM/Vr2XZ9jokvP3/JEREnx1uudGNi/br5jJpPKtWuZ6F0UyviKmBXBf4OLkUksWnKCq1dT8fNzp3evWtSvZw4wXbj4OO99uLFU7JAkWLZoJMFBtj0VpcGORydxfsaCIic9SIpMcLe2dFrxs9WsyqiVm9l43xNW5+q5ex4BzRsWyY47ERGw+x/HpYwPTb54lX3j3rc6TjMa8apawf6JZZmU42e58MdC/D57xTGbfL2p/dwoaj83yqHr8q53URjYP4zZc4/YHVToKLIsceJkPAP75z+elJTFnH+OsGjJSZKTsggM9GDwoLoMH9oAb29Rql9wb1KlchmLW0hyKdVgUhSJjh1CCwiX3BcKnV7Gr0zpthFp8eO7yDqFiF/+sd7j4GauVyJHVanQrwut//zUZrG609/PzPNQFzqlTuH0D3/T+vd7R7w4ghAv9yi1//cw0au3cWXx+sIHSBKKmwsZV2Ptn/R6ZtKJz3+jfM/2BHdr4wRL7eepx5uzY+clLkYmlYiA0TStQNGuK1EpjHliPteuZeatGR2Txo/T9rBk6Sl+mzaIgDsgkFAgKE2aNa1wcycRp5P7XA+t6scbkzrlHTcYTPw58yB/zz1CQkIGALVrBTJmdBN6dKtRQtbkR3FxoeW0D6j/xjNsuO9xUk6csypi9P6+1P7fQyge7lQc0BXfMPvaHiTuP241sUIzmri294jD9t8riIDde5g2f31GmfA6+fZLwZy+jASmzGyiljje20TSKZye8pezzLQbb29Xpv8ymIdGNMTT0/kR95pGgcyg199aS2JiZgGxpKoaUVdTmPzZZqfbIRDc6YSU96ZL52oodgav3tyx/WaHgyybvSsAiixRPtibgAAPatcK5NWXOjDjtyF5W7UGo4nnnl/G9z/uyhMuAKdOx/Pq66v5dfo+J3wy+/GsHEL915+2KlwkRabW0w/Q8L3x1Hv1SVzL+nHii9/YPvIldj3+OpcXrUU1Fh7Pp7jZ9uoq7v/dbWzhebmH0Xt70X3LTI5/+gtnpv5NTkISSBKBbRoTt2UvULSUac1oImGv5fL3JU2lSr507VyNlavPkpNT/JTvXKqF+tGsaUje12fOJnD4iOWKuyaTxsZN5/MqggoE/yXeer0z0TFpHDsem1fCILfRaMUKPvToXoNmTSpgMJr486+D7N0fBUDVKn48OKIh1ar5s2HjOTIyDIRW9aNv79r4FhJTlp1tZPbcI/w+Yz8pqdkW7fl+6i46dwylWqh/iX3mW6k0pCe+k38i5eS5AvdSSVHQl/Gm1v8eAuDinOXsGPUyqtGYt2UU8es/uJUvS8clPxLQtD5g9gDHbNiJ7GYjE1SWqTS4h/M/1F2CEC/3IFnx14j4eR4XZi8jO+4abkEB1HhyONWfHIZ7UFn2T5xM/I4Dxar1Yk81XGcz79+jfP7VVgwG57asB3NMzdQp/fPtQx87bntLTVU1Tp6OF+JF8J/D29uV334exIaN51my9CTxCRmUD/Zi4IC6tG1dOV/7i/Ztq2IwmFBVDVfXG4+dxuHlra6RnW3kf+OXcODgVbvCS56buIxqVf0ICvIirHZZKlbypUmjEHS6ktlkUFxd6LpuBlvvH0/spj1IigxIaCYTXjUq02H+FNzLlyNu+362PfhC3tb7zdlTWVfjWNVsCNWfGEbjLyaxc/QrXF6wxmJ5CTB7dHQ+XlR/fGi+47n5N/+F5o9CvNxjJB09zbrOo8hOSMpzZ2ZdjSPp4ElOfPoL9d98mpiNu4slXCRFodLg7k6y2D5WrjrD5E9Lbotm6nf9KBvome+YXm/fDU9fQjdGgeBOR69T6NGthl3xJnq94z2K/px50G7hAhAVlUpUVP4u2d7eLgwf1oCnHm9eIv3E3MoF0G3jX1w7cJzoNdvQjCYCWoUT1LlVnog4/ok5s8jax4j45R9iN+3JK16nmSzfo138fem84hfcAv1RTSbO/fYvp777k+SjZ5Bd9VTs35WwFx+9pzKRbkWkSt9DqEYji6t3J+NytNWy/y6BfuTk1lyxhiwXnEeWUFxd6HNsGV6hlYppsX1omkb/ITO5ciWlxNb46L3u9OpZM9+xuLh07uv/h9XgYDc3HWtXjMHDQ1S9FAiKS3JyFgnXMvAr446vrxs9+kznmhPaDgB4ebkw4dk2DB5Y1/ZgJ6KpKrNd6lsVI3YjSTT+4hVqPjUCnYc7qtHI1mHjubxo3fXFrntedApoGm1nf0Xloc7px1QaiFTp/yhXlm4kIzLK5ricBDuEC6DzcMOYngHS9bcVTUPn4U6HRT+UmnABOHMmoUSFCxReBKtsWU9696rF8pWnCxUwkgQj7m8ghItAUEzOX0hkytSdbNp8AVXVkCRo2iTEacIFIC0thw8mbyQ1NZvRIxs7bV5bqEajc4QLgKbhHhSIzsOcHn522hwuL1pbIO1LM5pAktj+8EsEdW6Ja4Cfc9a/gxDi5R4idvMe0Clga0tIkmzWJ5B0ClUf7o9fozBi1u8ETaNs+2aEjhqIix1lrZ1JanrhlXHtxdbHlYDwhoXvvU96pQPx8Rns3H0pLxgx98/uXWvw9FMtimWbQPBf52xEAo88Pp/sbGPeS4Kmwf4DJdMS4Pupu+jftw5+fqVTH0ZxccGrRmXSIi7ZXxfGCqen/k1gm8Z4Va3IqW/+AEtJ65qGajBwbvoCwl54tNjr3mkI8fIfRJIVNNVWuX0JzaRS86kR1HxqRKnYZYlKFXzs0VsWqVe3HMdPxBXqPVEUiXZtqxBSvnBB5u6mZ8o3fdm95zJLl58i4VoGwUFeDOgXRnjD4P9EYJxAUJJ89MkmsrKMhZYjKAlMqsqKVad5cER4icxfGLWfHcm+CR85Za74HQdY2XQIXTf9SerpCzZGS1zbd8wp695pCPFyDxHUsTmnvppuc5ymqiheHpjSMiyPMZko09CxFgAlRblyXrRrU4Ut2xzrwprLgyPC+XPmQU6cjMsTQbl/Vq3ix9tvdLF6vSxLtGpZiVYtS2+rTCD4L3AxMomDhyyXIygJZFkiOiatVNes+fQDRK3YzNWVW4o/mUnFkJzKvmc/sOlWlmQJ2eXe3NYWaRJ3CarJxJXlm9g3cTJ7x3/AxTnLMeXk304J6dMJzyohFma4gSRL1Bw7okDxuhsDzNV3Q0cOcIbpTuHFie2KdJ2bm44unarx27RBvPV6J+rXC6JcOU/qhpXjtVc68sfvQ0S/IoHgNnExMqnU11RVrdRbCsh6PR0XT6XBO89eT6cuHprJROzGXZRt19RqSrVmNFGhb6dir3cnUiri5fvvv6dq1aq4ubnRsmVLdu/ebXHs9OnTkSQp339ubv/th0vq2Yssq9ubTX2e5PSUvzgz9W+2jXieRZU7E7/7cN44Waej47Jp6MtYj9Ku/8YzNHxvPIGtwi1U35Vo/cenpR7bYo1KFX1p0dyBPkzXefaZVri4KLi66hjYvy4zfh3CyiWj+fP3oQwdXA93t3vzrUQguBvw8rSvXpSlbd2ioGnQpLH1+jIlgazX0+DtcfQ7t47yvTs6ZU41O8diMLCkKHiGVqTigK5OWetOo8TFy5w5c5g4cSJvv/02+/fvJzw8nJ49exIba7kAmI+PD1evXs377+LFom0X3AsY0tJZ12W0OdgL0AxGNIM5XiU7/hrruo4mZvNu4ncdJHbLXlzK+NDvzCpqjR+F4pFf9CnubvjWq0Hi4VNc/HspHRZPJfzD53GvYO4UiywR0rsD3bfMvCPT6x57pJlD44cOrscDw+/dOgcCwd1OwwbB+NsInHV1Vfhz+lDee7sLjRuVx83N8XoxtzJ23GJ2771c7HmKglflEDovm8bAS5to8fMHxZrr2v5j5nhduNF34foLqUfFILqs+R1Zf2++oJV4nZeWLVvSvHlzpkyZAoCqqlSqVIlnn32WV199tcD46dOnM2HCBJKSkoq03r1W5+XMj3+z55l37O+AJkHIfR1p9OlLpF+4QvzOg1w7cJyrK7YAmrmN+/X6LW5BgXRZ+zu+9WpiTM9AdtGjuJR85dyk5CwWLDzO6rVnycg0ULNGAMOG1KdFswpIksTRYzEsXHyCK1Ep+Pu5c1/PWuh0MlN+2Mnxk3EFP/It2751agcycUJbmjVx3FMjEAhKl3/mH+OjTzZZPP/k480Y+8SNrL6HRs/jRCH3AUeQJPOW8rJFo27rtrEhJY1/AloUq2jozfg3q49XtUpUHNCVSkN63pZK6MXhjqnzkpOTw759+5g0aVLeMVmW6datGzt27LB4XVpaGlWqVEFVVZo0acJHH31EvXr1Ch2bnZ1NdvaNfhcpKSVbD6S0iZy7AoupcIWhQdTKzUSt2Gw5kOt64bns+ETWdxtD/4g16L08Cx/rZM5GJPDkM4tITs7KMy8qKoX1G84xZFBdjEaVRUtO5qUjy7LEilVngPwN3W6ma+dqPPZoU/z9PPBw1+NppytaIBDcfoYOrkdaeg4//LgLk0lFUeS8TKOHHwznycea5xvv4V78x5amQVaWkcVLTjDq4dKr+XIreh8vyjSsQ+J+J2QESRLGtAzazv7qP5EFWaLiJT4+HpPJRFBQUL7jQUFBnDx5stBrateuzW+//UbDhg1JTk7m888/p02bNhw7doyKFSsWGD958mTefffdErH/TsCQku54jrCdKYaayURWTDwX/l5GjceHFcE662RnG1m24jQLFh0nOiYNfz93omNSSUvLyfeRTCbzF/8uOF7g2M3pkoV9G2RZIjY2ndo1yzrdfoFAUDo8MrIxA/uHsXL1GWJi0gjwd6dn95qULVvwpapmzUD2O9AywBKaBnv3Xbmt4gWgfM92zhEvmkbKyXOknrmAT63Q4s93h3PHpUq3bt2a1q1b533dpk0bwsLC+Omnn3j//fcLjJ80aRITJ07M+zolJYVKle6dlFbfBrVIPHTCaW7FAkgSUcs2Ol28pKZl8/T/FnP8pvTkm9vYOwtV1Th8NIYLFxOpWuXeqyIpEPxXKOPrxohhDWyOGzSgLrPnHnHKmpoGKSlZbN9xicwsA9VC/WjYoHTrN1Xo15njk39y2nxGKyUw7iVKVLwEBgaiKAoxMTH5jsfExBAcHGzXHHq9nsaNG3P27NlCz7u6uuLq6lpsW+9Uaj41nPPT55fcApqGKctym/mi8vmXWzl5Oj53iRJn774oIV4Egv8ANWsE8MRjzfj5173Fniszy0D33tPzdaoPrerH++90pW5YuWLPbw+BrRrh17guiQeO2x5sA0mvwyu04A7FvUiJZhu5uLjQtGlT1q1bl3dMVVXWrVuXz7tiDZPJxJEjRyhfvvRT2+4EJJ1S4kWGvKpXdup8165lWOwHVFIsX3mq1NYSCAS3l6efbMH773Qtcl8xSTJvOR84eDWfcAFz7ZnHxy7k3LlrzjDVDlsk2s//znJQn73z6BSqDO+Ni5+vkyy7synxVOmJEyfy888/M2PGDE6cOMHTTz9Neno6Y8aMAWDUqFH5Anrfe+89Vq9ezblz59i/fz8PP/wwFy9e5PHHHy9pU+84VKORzQOeQTUYSnQdZ4uj4yfi8mJWSouDh6K5du2/4S4VCATQ577aPDjC8VIIEqDTmYOCC/MKq6qGwWBimhM8O/biVbUiZds2KbKAkXQK7uXL0fjTl5xs2Z1LiYuX4cOH8/nnn/PWW2/RqFEjDh48yMqVK/OCeCMjI7l69UYDrsTERJ544gnCwsLo3bs3KSkpbN++nbp1S7eN+Z3AlSUbyIyKtT9NuigoMqbMLKdOebsC3ePjhXgRCP5LdO1czeFrNOC+nrVQFMs3KpNJY+36CDIzS/bF8WZqTxhdpD12xcONGk8Op+eef3AvX3CrKyvuGknHzpAVVzqepNKiVAJ2x40bx7hx4wo9t3Hjxnxff/XVV3z11VelYNWdT/zOg0h6XV5ROoeQJbuyjiTAo0KQzXGO0KB+EHq9XMAdW9KUVpdYgUBwZ1CUvkiyLHH+fOL1oFzL90hV1UhJycbdvXSKvFUa3IPQ0QM5P2Oh3dfoPN0JHTOEui89hntQYL5ziYdPcui1r4havimvoVvIfR0I/2gifuF1nGx96SN6G93BSIrikNdF0utoP38K3bfOwr+Z7ah9AE3VnN7DyMfHjQH9wpDl0nHByLJEs6YhhaZVCgSCe5f4hAx0OsceY7IsgQQmk/WXK0kC31IsYCdJEq1+m0xw9zZ2X2NMz+Tsj3+zvNEAEg/fKD+SsPcIq1sNNzeCzPXmaBpXV21ldev787WVuVsR4uUOpnzPdmhG+70uiqsLlQZ1p2zbpvTaNY8+x5fT6rePULw9LTZhDHvpMTyrOL8S7cTxbWnSqOSDrGVJQpYlxj3dqsTXEggEdxYBAR42RcitGI0q1av52dyh0TRITnbulrotJFkm5eR5h67RjCaMKWlsHTYeTdPQNI1dj72OmlOw75FmMqFmG9j1+OuUcHH9EkeIlzuYch2a49coDElnu5eHpFMI6twy3zHfsOpUGzOEXrvmUbZN/kJMel9vGn3yIo0+ftGpNoO5ON2KVae5EpXs9LlvJTjYi++/6UfDBval3gsEgnuHnt1qIFt4MbPG0uWn7Rq3dXvp99Uzpjseu6eZVFJPXyB67XYSDxwn6fApNAuiTlNVko+c5tq+o8U19bZyxxWp+69hSE3j3O/zOTdjAdlxiXhVq0iNJ4dTeVgvZL2eDounsq7LKNLORlqdRzOaqPP8I4We8w2rTvcts0g+EUHKiQh0Xh6U69Acxc359XHS0nIYO24Rx08Ur/fIrUgSBAd5UaN6ADVrBlA+2JvKlcrQtElIqW1PCQSCOws/P3eeerwZP/y026HrjEbb3hpZksjOLkK8YTHxrV+T+G37LYoPa2we+AwV+nWxa2zqmYsE2BlecCcixMttJONKDGs7PkzaOXPHaDSNzCsxxG7aQ8Qv8+i4bBqelcrT58hSLs5Zzunv/+La3mP5I9IVGUwqjT97maDO1rdOfMOq4xtWvVg2a5rGseOxHD0ei06RaNmiEpUq+uade2nSymI3TSt8XbganYavrxuDBtSlU8d7v/y1QCCwzWNjmuLmpmPqT7vJzHKe2FA1jZo1Am0PdDK1nnmIuM1FS9M2ZWQROWe5XWP1Pl5FWuNOocS7Spc2d1NX6TUdHiR+x0GLpf9d/HypPWEUNcc+gFu5AFLPXmTDfU+Qdja/K9OjSghdVv9W4v0sIiOTePWN1Zw8FZ+vk3PnjqG88HxbXn51VaFdn52JLEmomsaklzswbEj9El1LIBDcPWRnG1my7CTr1p/j1Ok4kpKLVzlcliV2bnkSnR3b9s5EU1W2PTCRyHkrSqxMht7Xi8HR20vE+14cHHl+C/Fym0g8dJIVjezI8pFlXMr40HHxD2y9fwJZMQkFgrAknYJroB99ji3D1b9MidibkJDB8IfmkJySVaAAnSxL6HQyOTkl1H+pEHSKzMqlo/D39yi1NQUCwd1Dy3Y/YTAU7540f+4Dt6XtiGoycfq7Pzn19QzSL0YB4BYUSFZMvFPmb/z5K4S98KhT5nImjjy/RcBuKZJ8/Cxnps7izNRZXJyz3L5qbqqKITmFTf3Gknk1roBwAXO8S1ZsAhG//lMCVpv5e+5hkpILCheziVqpChcAk6qxZJloCSAQCAqnWZMQlGLGw6mlXCk8F1lRqDPhEfqfW8eg6G0MjtvBwCubqfnMg0WbUJFBkpB0Ohq88yx1Jo5xrsG3ARHzUgpkRsex/aEXiVm/01wVDsmhSoqaSSUnMcX6IFXjwswl1H2pZNooLFl2qlR7FdlCls09SAQCgaAwHnownB27LhX5ep1OZvOWCwQHe+froWQwmNDp5FLpPC3Jcr7ic82/f5vqjw1lTfsHMWU4kMZtUinXsQVt53xVoJjd3YoQLyWMMT3jlqDcvP85nZwkGwKnGKSklG69A3vw9HS53SYIBII7DE3TOHU6npxsE0MG1eXfBcdRFMnhfmtGo8q3P+zku6k76dqlGoYcld17L5OZaUSvl+nVoyajRzamWqh/CX2SwvFvUo/az47kxOe/OpSRFLtpN9f2HKFC384AJB48QdSKzag5Bvyb1ad8r/bISunG9xQHIV5KmPN/LCT1zIWS7U+EuRqvb53iZRJZI6icF5GXnFO3xcVFsWubSbLioDKZNLp1LbnPKxAI7j72HYjik882czbiRh+fAH93Klcuw9mIBFJTcxyeU9Ng7bpz+Y4ZDCpLl59i1ZqzTP2uP41LoSBnLsbMLHPbGAfDVSVF4dS3fxLYuhFb759AzPqdSNe3kzSjCY/K5Wn/73d3Tfq0iHkpYc7NWMj1vaISRTOZqDF2RInNP2RQvWI1XGzZvAJzZw5n/epHWb1stNWmaLlIUuFhQbIs0bxZBRrWd25PJoFAcPey/0AUT/9vMRHn8jcgTLiWyYGDV5k4vi1t21QGsOv+YwtNg5wcE6+8vsquujHOwJiZxfruYzj20Y929a67Gc1kImbDTpbU6knMxt3Xj6l52a4Zl6NZ23nUjV2COxwhXkqYrNiEInUKdQhJotKQnlTsb19xoqIwZFA9qlcPcDgATpYlatcK5PNP7qNGjQDK+Lrh4+NGn/tqW7yByLJEhQo+fPbxfXl7zTqdnDe+ZYuKfP5Jr1LZcxYIBHcHn3+1FZOqWrzdfv3tdj79qCdffnofrVpWIqicc3qhxcdnsHVb6VTiPT75J+J3HHRYuOSiGU3kXEsGtRCxpWqY0jI49MbXxbKxtBDbRiWMV2hFc6pbYT8sTsC1XAB1xo8i7OXHkYpQJtseLkYmsXzFaerWKYtOkTh79hpGO/ZaPTz0jH64MQ89EJ4v4A3g+efacPhoNJGRyfkCgRVFwtVVxycf9qBuWDlWL3+EtesiOBtxDTc3HV06VaN2rXsj4EwgEDiHiHPXOHnKehpxcko2O3dfonPHanTqGMpzE5cRn5DhcCzMrciSxIlTcSVeOFM1Gjn9w6wSe5bkcnH2Mpp//xYufr4luk5xEeKlhKk0pKc5y6gEUDzdGXRlM7KuZP4ZjUaVyZ9uZsEic8CbJEmoqrnxV7cu1di157LFPeQKId58/kkvatUMLNRD4uvrxoxfhzBr9mH+mX+MhIQM3Nx09LmvFiMfakzlSuZfHHc3Pf363P3t2wUCQckRG5tm17i5/xxlwcITuLnp2LvvSrGFC4CGhqtLyQe6ZsUkkJOQVOLroGlE/D6fsDs8nVqIlxIiauVmDr/1Ldf2HCmR+SWdQoW+nUtMuAB8+/0OFi4+DnD9l/zGL/ra9ecsXGXmSlQqD4ycR40a/vxvbCs6tq9aYIy3lytPPd6cpx5vjtGo5gkkgUAgcIQAO4tV7t5zGU0zb007q/SDpkGHQu5vzkZ21dse5CQi/1l5x4sXEfNSApz/axEbez/JtX3HSmwNzaRabMToDJKSs5gz70ixw3UiIq7x/IvLWbbCekG50qqbIBAI7j1q1gwgtKqfzaSC3PuZM2tWVazgTY3qAU6bzxJugf74N29gX3FTQCrGi60xzfHO1qWNEC9OxpCaxu6n3jL/lpTU3qQs0Wr6xwS2DC+Z+YFt2y9iMBTf/tybxeRPNpOZaSj2fAKBQHArkiQxcULb638v3bUvX0nlSlTJ1di6mXqvjbU7AaTh++NRPNwcXkPSKfiF3/lb9UK8OJmLs5djyixeQzCryDLVxwyh2qiBJbcGkJnp3FbwGZkG1m2wvtUkEAgERaVt68p8+VlvygY6J4vIEV5/ay3RMaklvk6lgd1o+s3rNhWaW3BZwl56jOqPDTPXcnEAzWii5tMPFMfMUkGIFyeTevo8cgl2IZUUGcXDvcTmz6VaqHObkekUudTeTgQCwX+Tju2rsmzRSKZ+14+3Xu/E5Pe7lcq6h49EM3TEbI4eiynxtWo/N4oBF9bjHWa5SGdWdBwnPvmZ8A8mUCa8jrmfys0Upn2ul8EIe+kxyrZp4kSLSwYhXpyM3tcbrQR7AGkGI4GtG5XY/Lk0blSeypV9kYvZ2CwXk6ri431ntV8XCAT3Hooi07JFJQb2r0uP7jUJCfEula2krCwjE15YXuxO1vYgyTKpJ617sg+98TWLa3bHM7QiNZ68H4/K5irAOk93qj9+Pw3eeRbvmlXyxvvWq0nrPz+l0ScvlajtzkLSHK0xfIfjSEvtkiD5ZATLwnpbHiDL+NarQfKR00WaX/F0Z2jCbhTXkuvrYzKpXIxM4tSpeN79cAMmk1r8WgiyxIrFoyhbtvRdugKB4L/LrNmH+OLrbSVeKzSX7l2r079fGK1bVnLay9+tnP1lHrufeMOusZJOQTOaaPLVJGo/NypfPTBN08hJTEaSZVzKlP7z8lYceX6LVGkn41unOpWH9yZy3sqCAbvXm/U0/vQldox8mez4RIfnV3MMmDKzSkS8qKrGzL8P8efMg8QnmKPNPT30VAjx5vyFpGLN3bZNZSZ/tpno6FQCAzzo26cOnTuFoi/BLTaBQCC4f2gD9uy9wqYtF6z2S3MWa9dHsGZdBCEh3nz+cS/q1C7r9DXU7Bzrzd9uIrf8//7nJxPYunG+RA9JknD1L4OmqlxauJaz0+aQdvYiLoF+hD7cn9BRA9F73ZkvnMLzUgIYM7PY9ehrXJy9DElRkGQZ1WBA5+VBy18+pNLg7ux68k3OT1/g+OQStPz5A6o/NsypNmuaxnsfbmDRkpPFmsfLU09auiGvjoIkQXCQF1ej01BkCZOq5Z1rUD+I77/ph5eX6A4tEAhKDqNRZfHSk8yee5hz5xNxcVGoUMGHiIhrJSZoZFnC3V3HnL+GExLi3GdR3I4DrGnjWC87SadQZXhv2vz1eb7jppwctg59jitLNiApCprJlBcQ7BVakW6bZ+JRoXT6yDny/BbipSRtOXWOS/+uxpCShnetqlQZ3pusuGus7z6GtLORBZWzHb9Fkk6h4XvjqTfpKafaum//FZ54elGx5vD00FOtmj9HjtoXtCbLEl27VOOTD3sWa12BQCBwFE3TWLbiNNP/2M+582YvuKLkVhF3zhqKInH/0Aa8NLGdcya8TnZSCssb9CXzSqxDysutfFkGR20FwJiewekfZnHsox8xJBWeKSXpFAKaN6TH9tlOsdsWQrzcIeLlVlSDgaV1e5N+4UqeK68otP7jE0JHDnSeYcBrb65hzbqzxYptkWWzG9KROSQJli4cSflg7yKvKxAIBEVF0zQSrmViMqqkpefwxNMLSUnJdlohO18fVzaseSzfMZNJ5fCRGJKTs6hQwYeaNewvcnfyq+kceu1LTFlFK8kR3K0NYa8+ycEXPyHx8Em7mjz22vsv/k3rF2k9RxAxL3colxevN3tcioHsoqfS4B5OsugGFy4mFjso1xzi42Cbds3s9enb+84viiQQCO49JEkiMMDcXiAImDNzOLPnHGbJ8lMkJWVhNBavWGd6ev7inEuXn2LKDzuJjUvPO1andiCvvtSBhg2Crc51Zuos9k+cXCx7YjbsJHrdDvOboz0CTZaJ2bi7VMSLI4hU6VLAmJFJ5tVYLi9ci1TMAFWdtyc6T/v6eDiC921MY3ZGczSBQCBwBmUDPXn2f61ZvewRtm18ggb1g4qcai1JUD7khld5/sLjvPXuunzCBeD0mQSeeHoRx45b3nI35eRw6I2vi2bITWgm1aEK8BKUfJRzERDipQRJOnqarcMnMM+nCQtC2nNx1lJzMFQxyElMdpJ1ZiIvJTPt1z1ERFxz6ryOYOttQyAQCG4Her3C99/0K/I9StNg2OB6AGRmGvjy622FjlNVDZNJ5atvt1ucK2bDLnKu2XH/d3IGp6aqlO3Q3KlzOgOxbVRCxO86xLrOo1ANBrPSxfxDUFz0Ps6JDTEYTHz48SYWLz1pd7S9p6e+gAu0OCiKRNMmIYRWdW41X4FAIHAWXl4ujH2yBU+PW+zwtTpFYkD/MAA2bDpPhpX+bqqqsf/AVa5EpVChkOwku4QL4N+4Ltf2HHHY1sKQdAp+jcIIaN7AKfM5E+F5KQE0TWP7wy9iys4pVmDurUg6hdCRA5wy1yefb2HJMnNatL0eQWcKF0mC4GBv3n+7dMp3CwQCgT1omobhlvt2syYh6PWOPy6NJo2zZ81e7di4dBTF9v5TbGx6oce9qlWya01nCRcA95BytP/nW6TS7nZpB8LzUgLEbt5T7MDcW5EUBZ2nO3UmPlLsuaJjUlmw6HipbmOGVimDopOJjUvH38+dAf3CGDyoLt5eomWAQCC4/Zw6Hc/0P/ezfsM5DAaV8uW9GT60PsOHNcDVVUeTRiHs2nPZ4XnT03MACPB3tyu+L8C/8N51AS0a4lOnGimnL9gdr1JUdN6eNHz3Oao9OgQX3zszE1SIFztIOnqaM1P/Jm7bfmS9jpA+ncy9IkIKL9yTfOyM3dUPbXJ9Hs+qFWj/z7d4Va1Y7Ck3bDxffLssUK6sJ6mp2WRmmbtSu7vrGTq4Hv8b2xIXF1FNVyAQ3Hls2xHJ8y8uR9O0PIFx9Woq30zZwYZN55n6XX/GPtWiSOKlUiVfAGpU90dRLJeSkGWoU7sslSuXKfS8JEm0mPY+67uORoViCxjf8NokHzqV1z4gt0BdQKtwOi//GRc/32LNX9II8WKDU1P+Yt9zHyApct4WUOL+45z47Fc6LfuJoE4tC1yj8/RwWnR2jbEjqDSoO8FdW+frSVEc0jNykGXH6rHYgyxL1K1bjg/e6caZs/EA1KoZiLu73qnrCAQCgbPIzDIw6Y3VmExqgdu2psGRozH8/sd+xj7RnNatKrFj5yW75pVliYYNggkO8uL1t9awYtUZi1lL5uMSE55tY3XOcu2b0W3zTA68/BlxW/baZUdhVBzUnXZzvybp8CkifplHakQkrgF+VH2wL+Xv64Cs3PkvmqJInRVit+xlbYeHCj8pS+jc3eh/YT1ugf75TmXFJrCgQgc0o9G+hQrz0khQ7ZHBtPz1I6fvN65Zd5ZXXlvt1Dlz+fjDHvToVqNE5hYIBAJns2jJCd79YIPVMb6+bqxZ/ggAY8ctYv+BqzbndXPT8cdvQ/h9xn5WrTlrtehdcJAXr0/qRNvWle22O+3CZZbV64spI9O+CyRAkmj8xavUGT+a+O37STpyGsXDnZBe7XErZ3+hvJJCFKlzEie//D3PpVYAVcOYmcW53+dT96XH8w5rqoprWX9qjh3B6e9n2vbAKDLe1auQfuEyao45INbFz5c6L4yh7qtPlkigVKcOofj6upGcnOW0ORVFIjTUn84dQ502p0AgEJQ0p07Ho9PJVovRJSdnkZCQQVCQF+OebsWjT9ruS/fA8Abo9QorVp2xOEaSoGpVP+bNGuFwB2qvqhVxCwog/byVrSxJwi04EP+m9Qho3oDqjw8jKzqeZXV7k3Ly3I1hOh01nhpOky9fRXHJ32vOlJNDws5DGNMz8Amr7pTQBWcgso2sEL1uh/VsIVUjeu12VJOJiF/nsaxhP/7W1WWOa33SI6Mo36u97UVMKkgw6OpWOq/5nbqvj8W/eX2ilm1iz9PvcG3f0WJ/jtTUbDZsOsfqtWe5dDkZvV7hnTe7FHteyOvfRXiDYH78rh96/Z3vbhQIBIJc9HrFrl3+3Jg9e94nJQkCAzxZuz7CqijRNDh/PpGEhAx7zc1HtTGDrYcTaBo5Sal4Va1ItdGDMKams7bjw6SeuZB/mNHImR9msXPMazddqnHyq+ksCGnP2o4Ps7H3kyyu1o0N9z1O2nn7ts5KEuF5sYI9dVk0o4ntD0wkct7KvO0f1WAkavkmNJOKS0AZchKSrM4hyTLG1HR2P/mmWUXL5rLNCXsOE/HzXMJefpxGH7/osBfGYDDxzZQdzPv3KAbDjc/SqkUl3nqjE88/15qvvt3h0JwFbJck3n2zC3371C7WPAKBQHA76NC+Kn/OPGjxvCxJ1K4diJ+fOQuoVq1APDz0ZGRYLh2hadC8WQWWrTiNLEs2+ySlpedQtqynw7bXeuZBIqbNJTM6zuKLtpqZxZkf/+b8zMWUa9cUU1Z2Xu2xW42+OGsJdV95HJ861dj16GtcmLmkwJjoNdtZ1Wo49+2bj0fF21dgVHherFCufTMka4FLsozO28MsXCDfFpFmNJlVb2Ky1TkkRaF8z3Zs7PMUGZeu76Ne/0HP/WE88ekvnJs+3yHbNU1j0ptr+HvO4XzCBWDPvsuMeXw+fe6rTedOxd/mWbP+bLHnEAgEgttBk0blqVe3nMUaLKqm8diYpnlfR0Ym06xJiNU5fX3diIvP4MqVZJu9kfR6mXJFEC4ArgF+dNsyk4DmDa2O04wmDMlpXFmywepugqRT2DfhI+YHtSkoXHLnMpnIuZbE0Q9+KJLNzkKIFyvUnjDacjl/SULWKaScumDdj6iBpqmFj5EkkCX8GoWRfOyM5R8qSeL4Jz/jSGz1wUPRrN9wrlB3qMmkERefwd9zj/Dxhz14ZFTjfBlBOp1MqxYVcXW1vQWkqhpbt13Mq2UgEAgEdxOSJPH1572pXs2ceKEokvnWLJv/nDihLV06VeP0mXgeGj2XB0bOZfPWi1bnTE7O4n/PLWHt+nNWxymKRO9etfD0dLE6zhpeVSvSY/tsuqyfYfbaW8KenQRVJXbDLgxJqdbHGU2c/2Mhppzbd98X20ZWCOnZngbvPMuRd77LF7hrbq4o0Xb2V2wZPM7mPH5N65O4/3i+ZliSoiDJEm3nfE38jgNIOp3l7CRNI/XUeTKvxlqsLXMrS5eftFpTQFU1Zs0+TFxcOnHx6dSs4U92tonQUD/uH9qA9RsiOHQk2q61NA0yMgzF+gUUCASC20VAgAczZwxj245I1q2PICPDQGhVPwb2DyMkxIeLkUk89tQCMjPtzCC1A0Uxd7N+ZmzBchtFQQL7ukRbw4HrTZnZ5CSm4B4UWLw1i4gQLzZo8PY4ynVqwekpf+UVqavQrwu1xj2ET+1qSHodmsHaD7RG8qGTYDKBLKH388U9pCwV+3amxtgReFWtSOyWvdd/8qzjSKuB2Lh0m3VcMjMNLF56Mt+xM2cTWHm9HoG9jh53dx1lyrjZbZtAIBDcaSiKTId2VenQrmqBc9N+2UNWltFm7Iq9SECd2oF06lCNnBzntJCRXUq3npak193W6rtCvNhBUMcWBHVsUei5Cn06cWWplX1EjbwUaFQNY2oa6RFZhPTplJdyFtgqnFNfTbdqg1twIO4V7PO6gLmtuzXPiyVyfzntFS6KIjGgX5jIMhIIBPckmVkG1qyNcGpRTw04fiKO4yfi+OHHXXTrWp233+iCh4djAkTTNGI27OTc7/NJO3fJjpdp5yApMpWH9kJxu33tXUTMSzEJe+mxwiO3LaAZTZhyDGwdPgH1+jaRd+1QZFcrWy6yRO1nRzpU9bBv79pOr6B7K4oiUa6sF4/fFMwmEAgE9xKpKdkYHbjH24umXf8PWLfhHC++stKhuEbVYGDrsPGs7/oIF2cvI377gSI3AlbcHRMhkk5H/TefKdJazkKIl2JStk0TWv/xCZJeZ25OcT0I1yqqStbVOCJ++Ycj705hdcv7Ua2o5ZD7OhD20mMO2dW4UXk6dQy1qyZBUfHw0PP2G53x9/couUUEAoHgNpGRYeDU6Ti7ukEXB1XV2Ln7EvsP2q7cm8vhN7/h0nxzpfQ80XKz+HHAZH0ZH4fGV32wH75h1e2/oAQQ7QGcRObVWM7+Mo/E/ccxpmcSvWab9QvsDCrxrBJCh0VTidmwE82kEtgqnMA2Teyq+ZKTY+Krb7czf+GxAunSzkCWJdzcdMycMYwqFpqJCQQCwd2GwWjihx93M2feEbKySn4bBm5swb8xqZPNsYa0dOYHt8WUbr01gF/TeiTuO+YkC29QrnNLuq3/w+nzivYAtwH38uVo8Ob/AEg+EcGyur2tX2CnZky/GMWKRgNAlpEwp7L5NqhF+3++xaeW9RotLi4Kr7zYnrFPNGfn7ku8+8EGp/4iqqpGdraRX37by/vvdHPavAKBQHC70DSN199cwzoLpSZKClXV7G7ZkrD7sE3hAlBz7AiOTZ5G+oUrhaZKS4pC2U7NiV230yFbSyOuxhZi26gE8Kkdit7Z7cRVNa/ib8qJCNa0f4jMmHi7LvX1dUOnk0vkDcJk0li15qzTIuYFAoHgdrJvfxRr19snXJy5LS/LEiHl7cvesTu2RdVo89dnKK76AsVSJUVGdtWj8/RA52X/1r+kKAS2bmT3+JJCiJcSQDUa0UqweI9mNJGTkMiZH2bZfU1kZHKJ7dsajSppadklMrdAIBCUJouWnrB5rwzwd+e5/7VyqmfGZNIY0D/MrrF+jcKu1xuzTkCrcMq2bkzPXfOoOLg7kmJ+5EuKjGZSMWXncHXZJoz2dqbG7Jmq8dQIu8eXFEK8lADX9hzBaIdLrzhoJpVz0213Ns3Fy8vFaTUKbsXFRcHb+/alzAkEAoGzuHo11WamZlp6DpqGw52gc6v3FsbIB8OpFupv1zxu5QKoPLy3xdYzkk4hsHVj/BrWAaBMg9q0n/sNw5L3Uf2xYTcyZE2quYr8zc8GCwZKOgUkiVa/foh39cp22VmSCPHiRJJPRBC9fgcppy+Uyno515LsHtupQ6jDjR3tQVEk+vauLeq8CASCe4IAfw8UG6KkjK+7Q8JFkmDe7OF8/UXvvAaPuSiKRP165cjKNjJ77mFSUuyLe2n27Rv41KlWQGxIioxbuQDazPq8wDWmrGzO/7nQuq2yhEtAGVzLBeBZtQKyuxt6Hy8qDe5Bjx1zqPbIYLvsK2lEwK4TiNm0m/0TPyZxv5OiunMzkaxlJEkSXtUq2T1l2bKeDB1cj3n/HrU4Zd06ZYk4d40cg8kud6iiSHh7ueZrWiYQCAR3M31612bNugiL52VZon+/OrRqWYlvpuywa05Ng+EPzEWj4I3VZNI4eiyWEyfjUFWNr7/dwRuvdaJv79pW53T1L0OPnXM4O20uZ3+aTcblaFwD/an+6BBqPvMgbmULenGurt52o2iqJVtNKu1mf0VwtzZ2fbbbhRAvxSR63Q429HoMzRlbMpKEe/my1Hr2YbxqVGHbsPFWh9d4arhD07/wfFuMRpUFC48jyRKyLGEyqej1ChOebcOI+xuwZesFXrheLCnXdSpJEpqmFajY27hRCG9M6kT54NtXIlogEAicSdvWlWnSuDwHD0UX2GpXFAl/P3dGDGuAn587TRqV59CRaLsKgqo23ghz58gxmHjr3XUE+LvTupX17Rm9lydhE8cQNnGMzfUBTJn2eXWMdo67nYg6L8VA0zSW1u5JakRksRtiSXod1R4ZTNOvJqHz9EDTNHaMetnclvyWfyJJkfFvVp9uG/+yWp75wsVE5v5zlK3bLmJSNRqHl2fE/Q3w93Nn9dqzJKdkUyHEh549auDtdWOeM2cTmPn3IdZtOEdOjpEa1QMYPqwB3bpU48jRGLKyjISG+lO5kpMzqgQCgeAOICPDwPsfbWD12rP5br8NGwTx0XvdCQkxP1sSEjIYO24xEeeuOXV9WZZo2CCY36YNcuq81/YdZWWzITbH9T+3Fs8qFYhavomzP88lLSIS17L+hI4cQJUH+qJzL5ledo48v4V4KQZx2/ezpu0DxZrDJ6w6jT9/hcCWDXEN8Mt3TjWZOD75J05+OZ2cxGQAFHc3qj06hEYfv4Dey9PivBs2neOVSavRuOFByfWcTBzfhocfbISqaqxZd5a/Zh0i4tw1XFwUenavwYMjwq0WncvMMhAfn4GXlwt+ZdwtjhMIBIK7meiYVHbvuYLJpFKvbjlq1SzYQTknx8TzLy5nx65LTl9/3aoxTr/HrmgyiKTDp8yBurcgKQpBXVrRcemPbB02niuL1yMpinmsLIGq4Vu3Bl3WzyiRbtJCvJSSeLnw91K2P/hC0SeQZRq8/T8avDXO6jBTdg5Jh0+iGk2UqVcTvY+X1fHRMan0HzwTk0m1GLsy7YcB/DnzIFu2XSxwTqeT+e7rvrRsXjHf8bj4dH6ctodlK07l1XVp1rQCY59oTpPGIVZtEggEgnuRzEwDXXv9XiJ1tJYuHGl37RdrmLJzuLxwLalnLmBMz+T0DzMxZWTlqxcj6RRcA/zMcTQ/zeH4pz8XuqMgKQrlOjan67oZxbbrVkSF3VLCNdDP9iBLSBKKqws1nrjf5lDF1YWA5g3tnnr+guOoqmZRuCiKxORPN3H+QlKh541GlQkTl7Fu1aN5XU7j4tMZNeYf4hMy8u3v7j8QxZPPLOLzj3vRqaP1ir8CgUBwr3H+QmKJCBcPDz2BAcXvG3dpwRp2Pfa62Xsvy3mVdnU+XpiystByjOi8PKj26BDqvvIELmV8OP39TIuhEJrJRMz6nSQdPU2Z+rWKbV9REanSxSCoUwtcC4notoWkyChuLnRc8iPu5cs53a49+65YreliMmkWhUsu2TkmFi05kff1N9/tKCBcgOsiSePt99eTnX37S0YLBAJBaaJTnP8YlWWJwQPr4uJSvBIU0et3sGXoc3lhBze3CDCmpKHlGKn++DCGJu2l2Tdv4BESROLBExhT061PLEnErHespYCzEeKlGMh6PY0+dmDbSJbxb1aPem88Q7+zawju2triUNVkIjshEVO245V6nVXOZd16c7pgamo2q9eetRhRr2nmMRs2nnfOwgKBQHCXEFrND38/x+JSJMl6gbsyvm4MHlC3uKZx+I2vbfbRi/hlHmd/mn3jgF19EcwJK7cTIV6KSfVHh9J86jvovC0Hz+ahqriVC6ThO8/iERJU6JDshEQOvPQJ/wa04N/AVsz1DGfLsOdIPHii0PGF0aJ5Rau/GPaKm8zrrtCrV1MxGq13pdbpZM5fTLTbRoFAILgX0OsUHn6okd3jJcDb2xV/f3eqVvGlevX84QeSBEnJWQx9YDa/z9hfZLsyLkcTv+OgXWLkxGe/5vXOK9OwNoqtbCJVo1y721vfS4gXJ1Bz7AMMjt6GewXbW0BXV22x2EciK+4aq1rez8mvZmBITgPMBYMuL1zLqlb3E7PBPjfdkIH10OlkiyLFXsFcu1YAAB6eeptjVVXD08P2OIFAILjXGPVQIwYNMPclsvVyqAEpKdnEx2dw6XIKERH5X/o0zXw/VVWN737YycLFx4tkU95WkR2kX7hCemQUAHpvL6o/McwcH1MIkk4hoGU4/k3rF8kuZyHEi5PQebhjsLVPiFmMpFpoH3Dg5c9Iv3ClQAqbZjShGoxse+AFVIP16ohgrqb7+ce90OmUfB6Y3L4ar7zY3q5AsLFPtACgQogPNar7W/2l1FSNLp2r2ZxTIBAI7jVkWeKNSZ34/edBBAbaH2RrT3G7qT/tLlJfOo+KwRZ7HxXGikYDmevdmHVdRhHYpgll2zYxn7hFxGiqStr5y+x++m2ST1iuRFzSCPHiRHQe9u17yi4FPRQ5SSlcnLWk0Nx7AFSVrJh4rizbZNca7dpWYcHcBxj5UCNqVPenapUyDOwfxt9/3o/BqBKfkGFzjq3bIwFzhd2nn2ph0WMjyxK976tFxQqiaJ1AIPhvIkkS4Q3L07F9qM2u1I4QF5/BqdPxDl/n4udLmYb2ZwMZklMxpmUQu3kP20c8j2/d6rT8fTKBLcNRPK5vI0nmWi/ZsQlE/DKP5eH9ubJ0g8O2OQMhXpyEISXNZs8IABc/H7xrF0wpTouItHm9pNORfPS03TaFhPgwflxr5s4awfy5D/L6q51ITMzky6+32XX9h5M3cujwVQA6d6zGm691wkWvIEnmGJfc5mU9utXgjUmd7LZLIBAI7lUG9g+zy6PiCJGXkop0naRzvBpKbsfpsz/NQefuRthLj2HKuN4u4KY3WM1oQjOa2DJsPFmxCUWyrziIOi9O4sxPs8m5ZnuPsd5rY5ELceUpdnhtNFW1a5w1/vjrIIosYbLDDSkrEn/OOkR4w/IADBpQl66dq7F85WmOHovFZFJp17YKvXrURCmBdEGBQCC4U0hLy2Hj5vMkJWdRPsiL9u2qFprKXDesHEMG1eXfBUWLVSmM9HTHs04By558O5AUmVPfzDALIEUGUyFJG5qGmmMg4rd/qffqk0VeqygI8eIkIn6ZZ9c4jyqFV6L1qVMNr+qVSYuItHyxqlKxf5eimAeYU9v27Ltil3AB837szltKXp+/kMjCxSc4fcastFetOct33+/kuXGt6d3r9hUsEggEgpJA0zSm/3GAab/uITvbhCxLqKqGj7crr7zUnvt6FrzvTXq5IxVCfJjx10GSk4vf5NDPwVTsvOsa1TW3AjA6LmI0k0rCniPmraLChEsuqkrctn1Fsq84iNdlJ5EZFWvXuP0TPiKpkK0fSZKo/9b/rF5bvld7vGtUKZJ9uTiam2+66Yf2yNEYnnxmEWcj8jchi41L54231xY5Kl4gEAjuRC5fSebt99bz3Q87yc42C4Dc4NmU1Gxef2st6zeeK3CdLEs8MqoJq5eN5o/fhvDLjwMJrVqmSLEwOp1E0yK2X6n1zANFEi65mLeGbBQflSSHAoOdhRAvTsLeSruZUbEsb9CPk19NL3DOxc96L4fkY2fsyjayhCRJNKgfbLUGzM3kdjbN5Yuvt2EyaRYj37/4ehuZWUW3TyAQCO4ErkSl8PSzi+k/eCZLl5+yOE4Cvp2yw+JLoV6vUL9eEE0ah/DlZ73x9XGz+/6by5CB9fDxKVoXZ/+m9W+8FFtIfbaJHe+7wd3aFG3uYiDEi5Oo/qjtNuM3s3/iZK6u3prv2PFPfjbvLVog41I0lxets2v+zCwDS5ad5POvtvLd9zs4dCQaTdN46IGGdqfdqaoGmsa+A1FEXkri8JFoq9empxvYvPmCXXMLBALBnUhcXDqPPPYve/ddsTlWAyIvJXPylO1soCqVyzB31nAef7Qp7u72RWz4lXFj7YYIOnb9hZFj/mHRkhMYHPSkNHz3OdrN/RqfOoWUspBlc7fooiLL6H29qDZqYNHnKOrSpbHI999/T9WqVXFzc6Nly5bs3r3b6vh58+ZRp04d3NzcaNCgAcuXLy8NM4tFzWcetOk5yYcsc+LzX/O+NGZmEb9tv9W9RUmncHXlFptTb9sRSc8+M3j7vfXM/ecof8w8yJjH5/PokwtoHF6eWjUD7DZz/8GrPDF2IR9+bDtFW5YlYmLT7J5bIBAI7jR+/2M/SclZDmUM2RvX4u/vwdgnWjCwX5hdjpDEpCwSEjJJTcvh2PFY3v1gA889v4ycHMcEjFe1SqSfv4R068uxpllswGgTWULv5UHnFb+g9/Eq2hzFoMTFy5w5c5g4cSJvv/02+/fvJzw8nJ49exIbW3iMyPbt23nggQd47LHHOHDgAAMHDmTgwIEcPXq0pE0tFq7+Zei+cy6yq4t9F6gqMet35bkb7YoK10C1obpPnorj+ReX50WnG41q3i/h0WMxPD52YV6wrT3kXrtnr+23EFXV8PcvfhdUgUAguB2YTCqLlpxwONW5fHlvh8bf16vWzT0SHWL3niv88tteh67Z+9wHqDmGvDToPDQNJNB5eeBnT8VcCdwrBBHYpjHhH06k39k1BLZq5JAtzqLExcuXX37JE088wZgxY6hbty4//vgjHh4e/Pbbb4WO/+abb+jVqxcvvfQSYWFhvP/++zRp0oQpU6aUtKnFxrdWaOGuOQtompqXN6/z9MC7ZhWrtaU1VSWgRQOrc07/4wCaphVaUM5k0rhwMclu+xzFzU1H544Fa9gIBALB3UBmppHMTBsBqjchyxIN6wdRpXIZh9apV7ccHdtXdTj+BcxJF3P/OWq39yXlzAXitx8oKFzyJgRjWgahD/a1YzaJOs8/Qo9ts6n36pO42RnrWRKUqHjJyclh3759dOvW7caCsky3bt3YsWNHodfs2LEj33iAnj17WhyfnZ1NSkpKvv9uF6kRkSQdOmn3eL9GYUjXfYeSJFF7wmjLg2UJnYc7oQ8PsDhEVTXWbzzn9AJJ9vLMUy3w9LTT8yQQCAR3GO7uukJrtxSGLEvodDIvv9je4XUkSWLyBz24r2dNJMn+Zrm5pKRmc+myfb2LMi5G2R4kS2iYq/JaRdOIXrMNQ8rtDw8oUfESHx+PyWQiKCh/B+WgoCCio6MLvSY6Otqh8ZMnT8bX1zfvv0qVKjnH+CKQevaiQ+Nv9dLUeGoElYffZ/7ipg1RSacg63S0+/dbq3uLBoPJZvdnZ3DrL5qXpwsvTWzHQw+El/jaAoFAUFIoikyf+2rZldIc3jCY36YNom6Y9Ya8mqaxfWckE19ewYChM3lo9Dz+nHmQnBwj77/TjaULR/LqSx3wd7CWi71p1y4BZWwPUjXcgwOpPWGUTSUVvXY7G+57HNVWCnUJc9cXqZs0aRITJ07M+zolJeW2CRgXX8f2PXVe+eNDZEWh7cwvqNC3M6e/+4uko6dR3FypPKQntcePwrduDevruyiUK+tJbJztBpHFQdPg7Tc6o2kavr5utG5ZGTe3u/5HSSAQCHj0kaasWRdBRoah0OzKls0r8tqrHalU0XYvN1XVePeD9SxZdgpFkfK84idPxfHHzINMfr8b0THpuLrqqFzZl6TkLLuyQcuV9bRrfTB7+L1rViH1bCSWGtRJeh3B3VpT+f77SD4RQeRsy0kymkklfvsBrixeT6XBPeyyoSQo0SdOYGAgiqIQExOT73hMTAzBwcGFXhMcHOzQeFdXV1xdXZ1jcDHxb94At6BAsmLsaKIlSbgW4qKTZJnQh/oT+lB/h9eXJIn7h9bnhx93ozpYjM5RmjYJEY0YBQLBPUeFEB9+/3kwb76zNl8KtF4vM2xIfcY/2xq9zr6tpdlzD7NkmblOzM3b+ZoGCQkZPPnM4iLZOPKhRna3ZJEkiUafvsyWQZaLoGoGI5v6P03XdTNoO+tL4rbuI/NyjMXxSBJH3v2egJbheFQIsjyuBCnRbSMXFxeaNm3KunU3apOoqsq6deto3bp1ode0bt0633iANWvWWBx/JyErCjX/96B9gzWNKiP6ON2GB0Y0JCysrMN7qI5QsYIPFUIcSAsXCASCu4jq1fyZ9cf9/DV9KO+82YXJH3Rn9bJHePH5dnYLF1XV+HPWIafbNmhAXR4Y3tChayoN7Ebrvz6zWtPl2t6jHHr9KyRJwpiWYX1CTSPp8EkWVu7Ezsdew5SV7ZA9zqDEff0TJ05k9OjRNGvWjBYtWvD111+Tnp7OmDFjABg1ahQVKlRg8uTJAIwfP56OHTvyxRdf0KdPH2bPns3evXuZNm1aSZvqFOo8/whH3/8BzWB9PzCgZTh+jcKcvr67m55pPwzg/Y82snL1GafPD9CubRWkklRHAoFAcAdQN6yczZgWS0THpBETU7TA1tz+Sbn4+LjSsnlFhg9rQONG5Yt0/3UPCrRa00UzqZyZ+je+DWrjVs4fQ1Kq7UlVlXPTF5BzLZn286eU6nOhxMXL8OHDiYuL46233iI6OppGjRqxcuXKvKDcyMhI5JuCU9u0acOsWbN44403eO2116hZsyYLFy6kfn07ctDvAPRentR4cjhnf/zbYmqa4u5G5zWFp4o7A3d3PePHtS4x8fLP/KOMGNaAyg6mBwoEAsF/hbNn7QgfsEDFCj48P74t/L+98w6L4urC+Dszu+zSe28KCPbee8feuzFq7IkmJsbEFDWa4hdTNVFjTIyJvffeu1gRQURAqvTeYXf2fn8srCJsZZei9/c8JDJ7596zbJl3zj2FEDRp4gg726rXz0q/HQRGwKnsdSQrkeD2zC+0m1gmQ/yhc0i/8wh27bXzCFUFhmjbqa+Wk5OTA0tLS2RnZ8PComa2Nkqyc3Gux1vIevQU5SoRsQw4kRF6n/8X9p1aGdyODz46jhu3YvWeOs1xDMaNaYbFH3XV67wUCoVSl8nJKUJwSAp+33BLo5YByrCwEOHS2Rl6tAx4vHoTHn7+s/J6L1WAEXBoMG8S2q79skrzaHP9pr2NDICRpTn6XduB5ivfh7GbPNCYMxbDe/oYDAw8XC3CBQA++bgbLCzEOvfjUgbPE1y9Fq3fSSkUCqWOkp5egKVfnUPfgVswf+GxKgkXBoCjg37L7RNCUJiUZhDhUroAitMyDTO3Emh+q4EQmpmi6Rfz0PSLeZDxPFiOQ054NCL+2IW0m4FgRUZwHdwDXtNHQWRrbRAbXF0ssG3LGMyccxCJSfotKiQ11IeAQqFQ6hCZmYWYOmM/klPy9OPlZoCRIxpXfZ6XCP5mPcJ+2aLXOcvBMDD1dDHc/JVAxUs1wHIcIjbtwe25y8AwjEL9ply+jeBvNqDXqb8M1h/C2ckcObklep2T4xi0aF556jqFQqG8Sfy95Z5WwkXAMXBxtkBsJRVyOZaBl5cNhg9tqDf7itIyELxyvd7mqwwi5eE1fZRB13gVum1UDaRev4fbc5YCMlLebUcIJNm5uDhgJkqyDNPWQCLlIdWyhbo6eJ5gwrjqC8yiUCiU2ohEyuPQEe0aOfIygrW/DMb4MU3LtSLgOAb9+vpg04bhMBYL9WZj7O6TmjX+rQKNPn4HFr7V29eOel6qgeBvNgAq3tuS7Fw8+/cgGn6goreRDjxPyMG7C46iuFg/b9yyCpHvv9cRLZpRzwuFQnmzyc0pRkGBRKtzCAFGjd+J4UMbYfuWMUhKzgMvI2jSyAG2esgqepXCpFR5lpGa8h26ILKzRuPP5qDhh9P0Prc6qHipAjKeR/qtQJRk58Hcx0Op8kw6d0PtXEFL1yD46/UQmJrAc/xA+M5/C6Yeuu8hSiQ85s4/gqQkDXL1VWBkxEImkwd8OTmaYcb0NhgxTL/7sRQKhVIXMTExqlCTRRNkMoIjx0Jx6UoU/v17lEGrlZu4OKhMj9YKhkHjT2bCqX8XcGIRbNs1AyvUn5dIG+i2kY5Ebt6Hwx49cbbrJFwePBvH/AbgbLdJyAp+WmGsJm8caW4+StKzUBCbgCc/b8HxJoORditQZ/suXHqG589zqhxAVlIiAyEEMp4gKTkPK7+9hF/W3sBrlmFPoVAoankanoZde4Kwc3cQwp6mQSwWoHu3eho3SXwZnifIySnCd/+7YgBLX+AxfhBYoQo/BcuCM9awxQ4hyAoOh1PvTrDv3LrGhAtAPS868eTXLbj/4aoKx9NuBuJM5wnof3M3RHbW4IyEKNYhloXwPKSFRbg8bC5GxF0BJzLSeo4rV6N1uiOojDIBVPb/rdsD4exkpoh7yc4uQmZWIaytjGFpKa7yehQKhVKbSE3Lx2dfnsX9BwmK1iuEyDtLz5zeBteuR+s0L88T3Lodh+cJOQZruSKysULzrz9A4Kc/VniM4ViwRkLUmzICkZv3ARrcaKdcuYNdoqbgjMXwGDcQDT+aBsuG3oYwXSVUvGhJSWZ2pW8CoFR05BXgVJtRkBVXMcOHl6E4NROx+07p1KSxqFiqs3dEE9Gzect9tGrpjD/+vIMr16JBiLyTevdu9fDe3A7w8bbVaW0KhUKpTRQWSjBr7iE8T5DfiL78tRockoz//XgVnTp64Oq1GJ3XiIrKNGi/uEaLZ0JgaoKg5WtRkp6lOG7Z1BcdNn2NtJuBKlsHvIw0Nx+AvBrvs3/2I2rrIfQ6sQmOvToawHLlUPGiJTG7T0CmKvCJkKoLl1IYoQBp1+9rLF4IIbgfmIiDhx4jJCRFWfdz5esxQD1PKzT0s8epM+Eqz09LL8DUGQfAS2WKcYQA167HIOB2PP76Y4TOPUEoFAqltnDsRBhi4yqmNQNyz8nz5zlISKhatqhYbNhLMcMw8H1vMrxnjUXyhQBIsnNg4eel6K9n4uaE+x+tUpVXUilEyoPICK6OXoARz69CYFx9nnca86IlBXFJYDTsKqoPGE6ztXhehuUrL2DW3EM4fTYcKan5Wq9FCBAdk4WTp1ULlzIkEhn4V9Q6zxOUlPD4+rtLWq9PoVAotY1jJ8Kgrt9gVUIATU0EyMwsRODDRL1s8yvj+fFLuOg/A5cGz8L1CR8hYOaXiN5xFIQQGDs7yDOGdOmrKJOhJDMHsXtO6ttklVDxoiUie2uD58yXQSRSOPXtpNHY/7YF4tiJMACoUpCuNh9CZdtSMhlB2NM0hD5J1dkOCoVCqQ1kZhVWSZyoI79Aik+/OIN3Zh/E0JHbcP5ipN7XePz9n7g8ZA5Sr95VbA9lPHiMG5M/xt0FX4MQgpbfL0bjT2eDNSoNwi0VMoxAvVeIEQqQfjtI73argooXLfEcP6h62n4zDARmJsgJj0FRWobKoRIpj207Ag1vUymaPv3Y2CyD2kGhUCiGxt3VEixbDd/5ABKTcrF4yWmcPhuutzkzHz5B4JKfAKB8kdTSpsHh67Yj4cRlMCyLlqsWYWTCVXTY/B1a//QZuh9ejxFxlzRaRyF6qgkqXrTE2NkBjRbPNPxChIAvLEbgpz/gkGt3PNtyQOnQyMgMZGYVqZyOZRkIBGyVP4QMA9jaGms01tRU+ywpCoVCqU2MHN7YoNs5lfHDT9cg0bE2CyEECaev4tLQOTjg1Blnu05UecfJcByCv16PK6PnY69NOxz19UfC8UuwadcUbsP6wMjKAiZuqouSEokULoN66GSvrtCAXR1o8e2H4MQiPP7fn+CLig22Ttn2lKxEglvvfA5jFwc49+9aYZwmHyxCCHieVNn9SQiQllaodpyZqRHatXWt2mIUCoVSw/TqWR+dO7rjZkCcQbePXiYjsxC3b8ejS2dPrc4jhOD+ov8h7Jct8qq6GgggwvNID3hYbnz84fOI238GLVcvRvzBsyiIT1J6PiPgYOHnBac+moU46AvqedEBhmXRbPl8jEq+gc47f0a7P1ag/aav5W4zA7kXGZaRtxmohPr1rGGsJlqdkKoFlWnLzHfaQCSi2phCodRtOI7Fzz8MwpTJLWFs/GJrRCwW6LWB4qvoknQRt++Uonu0tlV1Xx5f9u/AT35AWoDqWBYTTxf0PPEnGLZ65QS9ulQBoYUZ6k0YrPjdxN0Z18Z+IM+DF3AAL9ObYiC8DKlX76IkMxtG1uVLSRsbCzFyRGPs2vOoUi8MxzIVsoIMBcsymDG9DaZMblkt61EoFIqhMTLisHBBZ8yZ2Q5hT9NACODna4ez5yNw+OgTg6ypS5+jJ79sAcOyIDKZ+sGaomau9htWVKmVja5Qz4secfHvhlGJ1+Azd4K8Ku5LwsWmXVN4TR+tebSrEqSFlce2vDevA5o1dQRQfgmWZWBuIYKLi3lVl9aIb1b0wbzZ7asnqJlCoVCqgYyMAmzZ+gDffX8Zx0+GoaBQArFYgJSUfAg4w1xGO3Vw12o8kcmQFvBQv8JFDQzHIfN+SLWt9zLU86JnHnz6AyL+2FXheMbdEGQ/joTA3Ax8fkHFdGuGUeulEVpZQGxvU+ljxmIhNq4bjiPHnmD/wRDEP8+GubkIQwc1xNgxTXHufAR++Pmazs9LU2jLIwqF8jpx7MQTrPz2EmQ8ARj5F9z+g4/RwMcGgwb4gTeAWLC2EkMo1LKeGMNAnt+swZcwywJEptFQVRBCNEqlNgRUvOiR2P2nEL5uR+UPEgK+oAi2HZqjKDEV+TEJYIQCgABEKoW5jycK4hLBF5dUqgAYjkWDuRNUNsIyMuIwZlQTjBnVpMJjI4c3xumzEXgUnFxua6lMMzVuZI/HoVWvy2LIEtcUCoVSndy+G49lKy68OPDSV3N4RAa273oIlgW0Kf2libyY8lYrbcyUz8swcOzVHimX7qisRWbTpgkKk9JQ+DxZ6zUqIJPBqV/nqs+jA3TbSE8QQnB/YcVmja8MQvqth+h9aSt6HP0DDT+chkYfv4NeZzZjyJOT6PTf9wDDVKiqy3AsrJr5ocnnc3S2TyQSYP1vQ/H2Wy1h9lIKs4uzOb78rCesrTRLf1YGwwCenlaKrSsKhUKp6/z9zz2V2+1paQVab5Ev+7IX/Pv5VDovy8gTMMaMrHgDqgmNFr2jXLiwLITWFpDxvF6EC8NxcOzdEdbNDRe0rHJ9omv3vlpKTk4OLC0tkZ2dDQuL6vMC5IQ9w7GGAzUa2/fKdjh0a1vu3Ogdx1CclgnC88gJi0LK5TsAITCytUKDeRPR+NNZEJqZ6sXW4mIpnifkQCjk4OpiAZZlsHjJKVy8/ExdbFalsCwDlmGw/rehsLQUIzgkGSzLoH07Nzg7mevFZgqFQqlOCosk6NJjk97mY1kGrVu5YN3aIWDAYONfd7BzdxAKCiQAAI5j0K+PDz75uBusLHXvEfR49SYEfvpj+VRploXQ3ASmXu7IehCqj6cDc7/66Hd1h9JQBl3Q5vpNt430RElWrsZjRbZWAACZRILbc5fj2eb9cm8Ly8gzlBig2bL30OC9yTCysQSrYX8jjdcXCeBVv/wbrkf3+jh/8ZlO8zVt7IC3JrfE+o0BCHz4oh4AwwB9+3hj6We9YGZGC9ZRKJS6Q5mo0AcmJkKMGdUE82a3h7C0N957czvgnWmtERKSAqlUBt8GtrCx0T7D6FUafzILzv274un6HUi/HQROLILbiL4w83bH9XELqzw/AIBlYN+ptV6Fi7ZQz4ueKExOw0HnrmojVk3cnTA85hIYhsHd97/B09+3KT2n3R8r0GDOBEOYW4HiYilGj9+J5JQ8lb2ROI6Bs7M51v48GJmZRbCzM4G5uQgT39qNtPSCCueyLIPmTR3x54YREAjoLiWFQqn95OYWY96CI1WKA2QYoH07N7z/XifUq2cFY3H1ls9/lZvTliBq22H5DbIeEJgaY1xeoF7mKkOb6ze9mugJY0c7uA3rrTYVus2aL5F2KxBXRs3H09+2qhQ7wSvXQVZNTSDTMwrRtq2rUnPKnpaDvRnWrx2Kep7WaNXSGe5ulti7LxipaRWFCyCv/hsYlIRr12MMaD2FQqHoj2UrzuNJWFqV5iAEuHP3eWkR0ZoVLgBQnJ6pN+ECANL8QqXNeasDum2kR1r9tATJV+5CkpVTqSjx+3AacsOjcXXUfHmqmhoKE1KQcTcYdh1aGMJcBY9DUzDnvcMoKpJWKHJnZmYEP187WJiL0KtnffTt7QPxK9V8Dx8NVdmigGUZHD3xBD171DeI/RQKhaIvYuOycflqtF7mkskI8vNLKnxn1gRm9dw0bhmgCaZe7jVaz4t6XvSIubcHBtzZB/fR/cu1CRA52qLBu5Ng7GiLwE9/lB/UMDK2IC7REKYq4HkZFn16CoWF0ko9J3l5JXCwN8VPqwdiyKCGlX4Is7JVN4WUyQjS0gr0ZjOFQqEYitt34vQ2l1gsgIWlSOUYQgjuPUjAvgPBOHYiTO33qa54zxijN+EChoHvvIn6mUtHal4OvmaYe3ug2961KExNx+Nv/0DEpj0oTk5H+Hol9V/UcG/hd7Dv0R4JR84j69FTcCbGcBvRF7btmulF9V67EYvk5DyVY06eDsflK9Fwc7PAqBFNMGxIeRHjYG+KmNgspVtOHMfAxZlmHVEolNqPlCea1AxVC8syGDakoSJAtzKCQ5Lx5VfnEBubrTgmELAYP6YpPljQWW2coEwiQdyBs4jcvA8FcYkwdnWE17RR8Bg7AJyRPEkiPy4Rz7YcQH5UPKxaNETWw6q3MzD38USD9yZXeZ6qQMWLgYjavB9ha/6r8jyFz5Nx0EHerZMVCkAIweNVG+HQsz267f8NIhurKs0fEpIMjmNUBukCQEGhBOER6fjfD1dw5FgoNq4bDtPSejGjRjTGL2tvKD2X5wlGDGtUJTspFAqlOmjWxKHKwoVhADtbE8x8p63SMZHPMjBr3mFIJOW9IVKpDDt2ByG/QIJlX/RSer4kLx+XBs5C6rV7AMcCvAy5YdFIPncTT9duRa8zmxG+bjseLv1VfqPLMIoYFdZICFmJ7tlUTZfOg8BY93RufUC3jQxASXYugr76Te/zyiRShdsv9eo9XB42r8oBU5yA1fiDWjbuSVgafl5zXXF81IgmSuu5MAzQs0d9tG/nViU7KRQKpTpo3MgBDf3swHG6e7Z7dK+H/zaPhp2K5oob/7oDqZSvNF6QEODQkVBERWcqPf/e+98g7eYD+S+lgbhlfY0y7gXjQv/pePjFL4CMgPAy+bWjdJxMIoXYya5ceIPGMAwce3XU/jw9Q8WLAYg/dA6yohKDrkF4HmnX7yPlyp0qzdOpg7vKYNvKkMkIjp0IQ06OfG/29p14JCRWXueG4xjMnN6aNmqkUCh1AoZh8L9v/WFlKQary8UdgI+XLRwczJQ+XlgowYWLz9SWpTh56mmljxWlZiBq6xEQJdlDhJch4/Yj5QYSgqKkNEDL736G4+A+qj9M3Jy0Os8QUPFiABLPGL4BIgAwAgFi956q0hzNmjrC3U37ejgSiQxhT9MhkfL4etUlKPuIy2TAL2tvVslGCoVCqU483C2xe/t4zJjeBo6OZjAWC+DubgkTY81SnvfuD4ZERXBsXl6J2ptGhmGQmVVY6WNpN+6DSKUa2aJ8AYAzFmuU+QqGARjAsrE32v+5smrr6gkqXvRMcUYWYvecrKbVCKR5VcviYRgGq/83QKdz//7nLi5djkJmZqHSRmMyGcG9+wmIf56tZASFQqHUPmxsTDBvdnucPPI2rl+ejcP7JmPHf2NhY62+D1x2TjFSU5V/N1tYiNR2jZbJCBwdK/fe6KO8CsNx8Jo+EgJT43IChikNMHYZ3AM2bZpA7GwPm9aN0W79V+h/a0+V4yz1BQ3Y1TNR/x5S2dFTnxAZgWUjryrP8/x5jk7n3b77HI+fpGgUmf88IRdurpY6rUOhUCi1AQ8PKwwb2hBbtwWCV+M5CQpOUpplKRIJMHigL44ef6Jy62jooMqbHtp1aC4XHLo0oyuFSHm4DOqJpkvfQ8SfuxF/6Bz4wmLYtGsG33cnwa5jS53nrg6o50XPZAU/rdAV2nAQCK0twRdXLb6mKj088vIkGt0FWFqornVAoVAodYFuXeqpFS4A8PmXZ7HvQIjSx2fPbAsLC7HSwOCyLavKMHZ2gMe4Abpfa1gWxq6OcB7QDcZO9mi2bD4G3j+EIaEn0fm/1bVeuABUvOgdgYkxoDQCRDmdt/0I9/GD5L9oHNzK4M6cZTjo0g3xh89pvWYZXvWtdT5XHQzk+8d+vnYGW4NCoVCqi5YtnNC4kb1GoSL/++GK0jpaTo7m+PfvUWjf1q3cFcPaSoxPFnXF3FntVM7dfv1XsGzaQP4lq0VCBCPgwBoJ0GXHT3pv+lud0MaMeibp/E1c6DtN6/MGh56AhW99RG7ej7Bf/0V2SDjAMnDs1QEiW2sknr4KSbaSYnKlOfx9zm+BY88OWq9NCMHEKXsQEZmhdeaRJvywyh99envrfV4KhUKpTgLuxOOff+/j9p14jcazLIOZ77TB3FntVY5LSMhBVEwWjMUCNGvmqLKw3ctIC4sQvfUwwjftQVZgqPoKuiwLjzH+8PtwGvKjn6MoOQ0mro5wHdILnLjmvePaXL+peNEzhBCc7jAWmQ8ea1yKmeFYjE4LgJHVC3tlEgnAsgplnPPkGY41Gqh8EpaFfZfW6Hdlu052hz1Nw4zZB1FcUnmbAF0wNRXik0Xd0KuHF2QyGczNRTRlmkKh1EkOHQnFym8vgmUZjW/yGAbo3dMLP+iYFKEp+THPcbheb9W2cCyafPkuRHbWePjZT/JkDwYAAViREZqvWIDGn84GIL/+FKdnQWhuCoGp8lo1+kab6zcN2NUzDMOg5/E/cWnwbGTceaSofKh0PMfBbVS/csIFAFhh+ZS8+MPnwHCs0rx+yGRIvXoXhYkpMHZ20NpuP187bN0yBn/+fQfnzkdWWcCIxRzat3XDT79ex/KVFwAAbq4WeGtSS4wZ1UTn+gkUCoVS3aSm5ePb/10CAK280yzDQCQy/GVWWqBBPySWRdqth0g6ffXFsdKnIisuQeCSn5B64wFM67ni2eb9cnHDMnAd0gvNlr0HmzZNDWO8jtCYFwMgtreBf8Be9D63Bb7zJsGmXeUvOsNxEJgZo8U3C9XOWZKZA0aDTdaSrMqLxWlC/XrWWPV1f1w5PxMnj7yNRg0129etjKIiHhcvRyEnp1hxLP55Dv73wxUsW3HeINtTFAqFYggOHw3VKT2ZlxH06lFf/wa9gqmni7xmiwqIRIrUa3dVjnl+5AKe/rbtRQkOGUHC8cs43Wk8ks4pbwFTE1DxYiAYhoFTn05o+9tSDLi9H+3//Fpejvkl7Lu2Qf+be2Dhq/7NbeblDpmabShGIICxi/Zel1cxNhbC0dEMq1f5w87WtMrzvcqJU09x4eIzvc9LoVAohiAiIkPrcziOgYeHJfLySzDnvcMYM2En3v/oOC5efgZehTdeFwQmxvB6Z7Ty7COWgcDMBHx+5UXvyvGKSiM8D8LzuD75Y3k4Qy2BbhsZGL64BHEHziAnLAr1p46EqYczTOu5wcK3Hsx9PDWaI+PBY0gLi8AIOBBJ5VUVGQEHz/EDYWSpv+7Nri4W2LV9PPYdCMaff92FVKqfDxzDALv3PULfPjSIl0Kh1H4IIVr3kXN1sYCAY7Him4tgGQYyQhAdk4Vr12PQqYM7fv5hoF63lFp8/QGSL9xC7tOocuEFDMeBAHAe0A1x+07rNrmMoDglHc+PXYL7yH76MbiKUPFiQJIv38a1Me+jOC0TjFAAEAIi5WHbvjm6H9lQ6TmZgaF4suZfJJ66BplUCshkKMlQXZ2W4TgY2VihxXcf6f05WFmKMXN6W9jamODr7y7pZU5CgIhI7e9kKBQKpbo5fDQU5y5EarVt1LaNK8QiDjcD4gAAstKTy7bLA27HY+3vN7F4UTe92WlkbYn+N3bh8febEL5xFySZOQDLwmVIT3AioypXfmcEAmSHhNca8UK3jQxEdmgkLg2YieJS4UFe6gidcT8EF/pNl4uTl3i08necbDUCUVsOoigpFSVpmRoIFxZuI/tiwO29MPVwMcyTATB8aCMMHewHAHoJthWLqW6mUCi1C0IInkVlIPBhIpKT83Dv/nOs/OaiVsJFwLGYN7s9rt2IVZr4ICMEBw4/Rl6efhv4GllZoOWqRfC/sx9d9q5B77P/wHVoL720rCEyWbVmHqmDXkEMROiPmxWek1chUh7Zj57i+dGLChUbvmkPHi3/Tas1Wv/6OepNGgqxvY1ebFYFyzL4amlvdO9aD1t3PETY01SAyAPSdNlO6tDezQBWUigUim5cuRaN39ffUniFGQCWVmJFOrEmiMUCbFw3TCPPcnExj5DHyejQ3l13o18hNyIGd95dgaSz1xXHGI7T6jkohRC4De9TxUn0B/W8GIjYPSdU1nlhOE7REVpaUIh787Xv1Bmz81i1CJcyZDKCoEdJCA5JRkkJr7NwAYB+tGgdhUKpJZw+E44PF51A5LMXooMAyMoq0tjr0rWzB65dnIlmTZ00PkefVdbyouNxpuM4JF+8VX4Nnq+6cGFZeE4cDDMv/QmtqkI9LwaAEAJpgeqobsLzkOTKK+bG7j0FWYn2UdzpAUHIjYyFubeHTnZqy7o/ArB1x0PF77oKF5YF7t5PgLW1MRo3qnp2FIVCobxKXHw2duwKwolTYSgokEAkEsDbywazZ7RFp44eiu3v4mIpvv3+MqBBg1lV3LgVh7S0Ajg4mKFlcye14xkGsHfQXzbnoxW/oyQ7V+PiqNrgNrwPOvz1rd7nrQrU82IAGIaRCwoV1WQZAQcLP3lH6KygMF0XQsTGXbqdqyWZWYXY9pJwqQqEANt2BOKtafswd/5h5OYWqz+JQqFQNORBYCLGT96N3XsfITe3BDxPUFAgwaPgZCz48DgWLDyKyGfpuHQ5Cn/+fRd5eSVV9oLIZARfLJP3mPPyskHbNq5Kmy4C8u/B+e8fRWpavuKYtLAIUduP4NGK3/Fkzb8oeJ6s0drS/ALE7DhmEOEicrBF9wO/Q6Cmjkx1Q8WLgWjw7iSVjxMpj+L0LKTdCgQrNtKqsdaLSQjSbgbqZqCWXL4SpbdUaUKgCGS7ey8BCz8+oXUaIoVCoVRGcbEUHy0+geKiystKAMDNgHiMnbgbH31yEv/8e19va997kICEhBwAwLcr+sLJUXXpirT0AqzbEAAAiNl9Agedu+DmW4sR/O0G3P9oFQ559MSd91ZUSO54leK0TJ2895rACmvnBg0VLwaiwbxJsO/aRnlVXJZF9LbDONNpPJLO3tDZX8kaCSscy7gXjLDftyF8ww7kPI3Sad6X4XkZjh3X0TukBpmM4EFgIq7fiDXI/BQK5c3i7PlIZOcUVznMQ1ceh6YCAOztTfH2lJYqx/I8wcnTT/Hs4AVcn/gRJDlyLwyRSAEZAWQyhG/YiXsfqN6yMbK2hNbl0FkGjIBTu0Pg2Ev7Zr/VQe2UVK8BnMgIvU7/jZDv/kD4uh0oyXwl5VkmAyl1ZGTefwwjG0t5af9KspNU4TK4p+LfeVFxuD7hI6TfDnrxhiQEzgO7o/PW1RDZWuv0XNb9EYD7gYk6naspCz8+galvtcS7czuA46implAouhEckqxV80R1MAxgZ2uC9IxCjebkBC/EQHJyHgQCVqXXWiKRIWjZr/KMoMrmJwThf+xCk8/mwMRNHksjzS9A1LYjiN52BMXpWTBv4Anb9s2QcSdYHqCrDpYBZAREpnoskfLwXTBF/Xw1AL1KGBCBsRgtvl6IkUnXYNnMV6nCJTyPkoxsWDVrID+gxRaSxxh/AEBxeibOdpuMjPshpZMShTcn6cx1XOg3HXyJ9jUFsrKL9BbrogqZjGDL1gf49n+XDb4WhUJ5fdHnzQ9TmmL85Wc98fuvg9WOFwhYtGr5ot6WqYmRWsFjUZyDguCwyoXLS8Tuk2enFsQn4USL4bgzbzlSr99HTmgkEo5fRvqth/Ltd008MGrWYgTyNgNtflsKu/bN1c9XA1DxUg2UpGch+9FTlVtDjICD27A+6HPhX/jMGa/x3HyhvJto+B+7UJiYWmnAFuF5ZD4IRdyBs1rbvmPnQ41iXcoi93UJ3SmDEHnb+ZfTFSkUCkUbOnVwr7LXpez7zMRYiO++6YduXeuhYwcP9O+rvMQDwzAYMawRrCxfBLb26eWl0haGARq7G6u1h+FYlGTlghCCq6MXID/muTz9ufSaovC2yGQwsjTT4BkqR+xsj3qThsL/zj74zX+rSnMZEipeqgGpJs2wGAZ8YREce3VE+w0rIHa212huoYX8jfpsy0HVW04si6j/Dmk0ZxnbdgTir3/uaTT2i897YPV3/ujY3h2eHlbwqq/bFhXLAkePP9HpXAqFQunUUf4dpCsM82LLKb9Agu07HypuqJYv7a0osFnm4CjLKOrUwQ2LFnYpN5eHhxUGDvBVWpWcEGDCu73BqPEWEYkU5t7uSL/zCOm3g1RmFckIgdDKQv0TrQSGY9Fg3kR0+vd72LZtptMc1QUVL9WAsasjBKaq1TWRSGHR2Efxu9/76vcZbdo0gbGzvE5KcXqm6sEyGYpTNPdo3HuQgJ/XaN4CvWM7d/Tt4411a4fi4N5JaN7MSWWaoAozcflKFB6Hpmh9LoVCoXAci99+HQwHe91qqLya+fg4NBXTZx1AXHw2jMVCrFszFGt/How+vbzRvJkjevfyxm+/DsHaX4ZU2mhx2ec94d9P/t3OsgwEAhYMA4hFAqxc3gfdBzaD28h+iq2ayhCYm8J9tD9SLt5SK3SkWbmKGmJawzAoSk7X7dxqhgbsVgMCYzG8Z47F09+3Vx5MxcjblXuOH6Q41PiTWYj4Y7fcPaiE5t9+qPi3qacrsrJylW9NMQxMvTQvyb9zVxA4jlHam6MMjmPQrq0bnJ3KpwRKJLzOdRNiYrPx1rR96N/XB19/1QdCofxDTYi8wm9qagFsbU3QormTXvosUSiU1ws3V0sc3DcJy1dcwLkLkVWaSyYjKCyU4K/Nd7FiWR+wLIOuXTzRtYunRueLRAJ8u7IfZs1oi3PnI5GXXwIPdyv49/OBqakRAKDV6sVIuRgg3xp6+RrBMgAB2v+xAtL8QmTcfwyiyZYYr1tZCyLlFUHBtR3qeakmmn21ABZ+9SuoZobjwLAMOm9dDYGJ3DtDCEHWo6do/etnsGzuV2EuViRC5+0/wsX/RUdSn1ljVadbEwJjJzuN7b17/7la4QLI94QXf9S1wvFGDe2rXLvl7PkI/PSrvEfHtesxGDZ6O6bPOohPPj+NGXMOYujIbbhw6VmV1qBQKK8nxmIhXF0tIBBU/TLH8wSnzoSjuFh1vRVV1PO0xsx32mLhgs4YNaKxQrgAgFl9d/jf2Qe3EX3KXSOsWzRCt4O/I+1mIA66dJM3WDRwTaz6bw0z6Pz6gnpeqgkjKwv0u74TIas2IuLP3ZBk5QIMA6f+XdD0y3mw79waAJB49jruf7gK2SHhinPNfevBrktriO1tYOFbDx7jB0FoVt4lauykPkbm+bFLaLPmSzAaRNVq4s+wtBTh379Gw6OS/eUhg/zw+/oAFJdIdf6sEQLsP/gYLVs4KSpXvkxSUi4Wf3oKq1f5ow/tlUShUF7BylKst5RpiUSG3LySSreG9IFZfXd02/cbitIyUBCbCCMrC5h5ueP6pEWI2XXc4KIFAMAy1PNCqYiRlQVafb8Yo1NvYVTyDYzLvY9eJzYphMvTddtx0X9GOeECyDuFRv93CE59O8N7xtgKwgUAUq/fB1TsmQJAflQ8ChM0iyVp385NZcwKyzIYP7ZZpcIFACwsxFj1TT+wLFulrR2el+GHn+Xel1c/u6T0Z/VPV8Hr6CalUCivL/37+eiterdQyMLczEj9wCoitrOBTesmMPNyR8aDx4jZeUwvwkVgZqJ6AMPAtpamRVcGFS81ACsQQOxgC4Gp/M1UlJaBs72m4O78lZW/SWUEREZw592vlH4QCSEaeVQ0/RBMmtBc6bYRw8jrGYwe0UTlHD2618e2LWPQr0/VvCKZmYUqzU5NK8Dd+wlVWoNCobweSKUynL8YiSVfnME3qy7ByEj1TZ0mcByDAf0bGMzroozobUdUBvJqgzSvQPUAQtBw4TS9rFUdUPFSw8gkElzs/w5Sr9xVPZAQ5EXEIu1WYKUPO3RtIy8prQITd2cYu2jWxblFc2csWdwdDINyHhh5tDyHH/83APYaRPP7+dphxbI+6Nhe82BhXUhN1TG6nkKhvDZkZBRg4pQ9WLzkNM6ci8DNW3EoLq5as0KWZWAsFmLmO231ZKXmFKWko7r6HJj71YfHuIHVs5geoDEvNUz8kQvIfBCq8fiC2ESgU6sKx12H9YaJm5O8UF2lGU1Aww+nKe+1VAnjxjRFyxZO2Ls/GPfuJ0AgYNGlsyfGjmoCFxfN6gg8Dk3B+x8eR0amBrVuXjWZAdxcLRAXn6N2rK2NGpcohUJ5rSGEYMGHx3QucskwlTumnZzMsOanwXB3s6yihZpDCEHs3pNIunBLfbl/jtU5u6jcmlJeM+99LYGKlxomZvcJMBwLouGbT2RfvvhbQUIynvy8BXEHz6AwNQNEJiv3KWQ4DoTn4TlhCHw1qB3zKr4N7PDFkp5qxxUWSnDqTDgeBSeD4xh06uiBxo3sMfe9Iygo1K3bKcex+PH7gVi46ASSknKV3oDY2BijXVvDenYoFErtJuhREkKfpOl8vrKt6YSEXEQ+y4C3l43Oc2tnB8H9D1chbM2/akuWMxwH71lj8fzIBY3jGZVRW7tHK6NuWfsaUpKepbFwETvbw6F7OwBAUWoG7i38DjE7j1Z0KzLySolGttawbtEQDd6dBLdhvbXyumjD7bvxWPTJKeTnl4DjGDBgsP/gY5ibGyG/oESnWDOGAcaMaoIGPrZY9GEXLP70lNKxixZ20Us6JIVCqbvs3P1Ip/PKvhZVFSj/ff0t9OvjXS2eicTTV+XCBVDbUkZkb4Nmy95D29+X4VjjQch7Gl3JQAYiWyuUZOYo9eIwHAfXIT2rbnw1Qr/xaxjzBp4aB2S1Wr0YrECA4owsnOk0vnLhAsiPMQxM3Z3R+8xmuI/oazDhEh2Tifc/PI6CAnnTR54nkJaKsdxc3YQLUFo2e6y8PHXvnl5Yvcof9nblt4ZsbIzx7cq+GOjvq/sToFAorwWxcVk6nScSCVQKFwCIf56DsKe6e3W04elvW8Fw6q8JTv26wP/WHhg7O4DlOAwNPYmmy+eXyypiRUbwmT0Ovc9tkdePqUx8MQwYAYcG8ybp82kYHOp5qWF8Zo1DxMbdasdxpsaI+u8wjKwsEPH3XuRFxqocT6Q8Mu4FI/3uI4P2qNi5Owg8L9N7CYL69a2x+d/78G1giyGD/NCntzd69qiPu/cTkJqaB1sbE7Rr60Y9LhQKBQBgaSFWP6gSCgs1KzyXm1ui0/zakn4nWG2ci0UTH/Q6sancMYZl0fyrBWj65TxkBYVBViKBRSNvGFnKq593O7gOV0cvgKxE8sLNxLJgjYTotv83mHm5G+T5GAoqXmoYmzZN4btgCp7+tlXlOD6/EMkXbiHp7HXNJ2cYpAcEGVS8nDsfqVElXk0pC9eJiclEbGwWjh4nWLvuFr75qi/69vFGh3Y0toVCoQAJCTl48DARhACtWjqjT29vBNyJN9h6ri7m6gfpAdZIqHZMmSCp9HyBADatK5axcB3UA8OjziNi0x4kXwwAADj2bA+fWeMUPfLqElS81ALarPkC5j4eeLz6LxQ+T1Y6Tm3UeSXoq0aAMoqqUC4bAH7/dTDMzESIiMzAn3/dQVp6PggpuzGQi6KSEh5LvjyDfxxHoVlTx6obTaFQ6izZ2UX46psLuHIlWrFrzgDo0tkDYrEARUVV+06qDHc3C40zLKu81sh+CP9jp/LO0SwDt+F9dJrb2MkezZa+h2ZL36uChbUD6nOvBTAMA7/338bwmIsYFHwMfh+8LW/IpQec+nbWyzzK8PG21bqCbtm268TxzdCpoweaN3OCjbUxUlLzle49y2QEn35+Gnl51eO6pVAotY/iYinmvHcY167FlAv3IwCu34ytUu8hVQwZ1NAg81aG7/tT5DEvSuJTOGMxPCcOrjZ7aisGFS8ZGRmYPHkyLCwsYGVlhRkzZiAvT3UxsZ49e4JhmHI/c+fONaSZtQaW42DVpAHyYxKqXpiIZeE2rDfMvT30Ypsyxo1pqnXvkPr1rPHV0t74+MOuiuj9i5ejoC6mOCk5D3PeO4Sr16Jx6Egorl6LhkRStQJUFAql7nDydDiehqeDr+Q7hxDDtP/hOAZjxzTV/8RKsGhQD90PrwdnLKooYAgBn1+I402HIvSnzXprfVAXMei20eTJk5GYmIizZ89CIpFg+vTpmD17Nnbs2KHyvFmzZmHlypWK301M3qwCZJpEmquDExvBoWd7yCQSsEL1e6i6MqB/A1y+Go1z5zVrO79uzRB07OBeLuUwOCQZNwNi1Ub8A0DokzR8sOiE4ndLSzEWLuiE4UMbaW07hUKpWxw5Fqq0mJy2sCwDmYxg+NCGOHz0idJxb09uCStL3YKBdcXFvxtGxF3Go5Xr5PGQr4g1aU4eHnz8PfiiYjT9Yl612lZbMJh4CQ0NxalTp3Dnzh20bSsvq/zbb79h0KBB+PHHH+Hi4qL0XBMTEzg51Y3OlobAqW8nxB04XaU5+IIi3P9wFRKOX0aPYxvBiYzAFxUjZvcJJJ65BiLlYdu+Obymj4LIxkrndTiOxaqv+4HnZbh4KUrteGNjYTnhcuNWLD746ARkmiiXSsjOLsKKby4CABUwFMprTlpagd68Ky1bOGHalNbo2sUTLVs4Y/VPV1FYKIVAwILnZWBZBm9Naon35nXUz4IqKEpJR15UPISWZrDw8wIAZAWFIW7faZVKLWjpGjj16QS7ji0NbmNtgyEG8jtt3rwZixYtQmZmpuKYVCqFWCzG3r17MXLkyErP69mzJ0JCQkAIgZOTE4YOHYqlS5cq9b4UFxejuLhY8XtOTg7c3d2RnZ0NC4vqCbDSN5LcPByu3weSrByNC9gphWHQdNl78Bjjjwv930FRYqq86i6R92TmREboumcNXIf0qtIyj4KTMXXGfpVjRCIO505Oh6mpvDNrSQmPAUP+RXZOUZW/kCwtxThzfCqEQsMGKFMolJpj1rxDeBCYqPVW9au0bOGEzX+OKnesoECC8xcjkZCYC0tLMfr29oadrWG9/nlRcbi/6HvEHz6vSF8296sPMAxynzzTaA5OLMKABwdh2bBqDXBrAzk5ObC0tNTo+m2wmJekpCQ4OJRPvxIIBLCxsUFSUpLS8yZNmoRt27bh4sWL+Oyzz7B161a89dZbSsevWrUKlpaWih9397qVq14ZQnMz9Dr1FwQWZuUCdxmBPIjLY+wACC3NNJuMEDz+35840WwoihJT5Yd4Xv5BkRHwRSW4Mmo+soKfVsnmpk0c0NDPrlwTx5dhWQYjhjVWCBcAuHQlClnZVRcugNwDczMgruoTUSiUWsuIYY2qLFwAwMJcVOGYiYkQQwc3xJyZ7TBhbDPDC5foeJxuLy/t//KeeW5YlMbCBQD44hLcX/idIUys1WgtXpYsWVIhoPbVnydPlO8fqmP27Nnw9/dHs2bNMHnyZPz33384ePAgIiMrj6n47LPPkJ2drfiJi3s9LmC2bZthWPgZtFr9CRy6t4Nt++ZoMHciBgcfQ9c9azAy8TrabfhKo7lkxSoydEqj3MJ+/a9K9jIMg9Wr/GFjbVwu+6hsh6hZE0e8/1559+uzqAy9FpnLyFDT8p1CodRp+vf1QeNG9lWex7wS8VLdBC75CSVZykv2awwhSDxzHflxifoxrI6gdczLokWLMG3aNJVjvLy84OTkhJSU8o2ipFIpMjIytIpn6dChAwAgIiIC3t4V3WIikQgiUc2/EQ2ByNYajRa9g0aL3qnwmMBYrLfQeiLlEbXtMBrMmwCbNrpH1bu5WmL3jgnYfzAER489QXZ2EVxcLDBmVBMMHugHI6PyWzrGYqFe7qLKcHDQ0BtFoVDqJEIhh/Vrh6Fnv791noNhUG1NFpVRkpmNuP2nlddy0RZCkB/9HKbuzvqZrw6gtXixt7eHvb165dupUydkZWXh3r17aNOmDQDgwoULkMlkCkGiCYGBgQAAZ+c350XRFGlRsfpBGiIrLsGptqPhu2AK2qz5QucGZFaWYsyY1gYzprVRO7Znj/pY8/tNndZ5FVtbE7SnnaUplDeAqt3wcCyLYUOqr25LZRTEJ+lPuJRiZFU9FYBrCwaLeWnUqBEGDBiAWbNm4fbt27h+/Trmz5+PCRMmKDKNnj9/joYNG+L27dsAgMjISHz99de4d+8eoqOjceTIEbz99tvo3r07mjdvbihT6ywFccpjh3Tl6W9b8fT3bXqftzI8PaxKO7WqHysy4sCqGNilkwftc0ShvOZkZBRg/gfHdDq3bDt7ySfdYWNT/eU3+OISZAU/RXZoJATmpnqd29y3HiybvlkNag1a52X79u2YP38++vTpA5ZlMXr0aKxdu1bxuEQiQVhYGAoK5LEKRkZGOHfuHH799Vfk5+fD3d0do0ePxpdffmlIM+skhBAkHL9kkLlDV/+FBu9OAquHejPq+PLzHrh4OQpSqeqsqulTW+P8hUiER2ZU+viRY/I4q0fByUhKkmcLDB3SEG1aOePs+Ug8CUuDibEQvXt5YfBAP5iZGVU6D4VCqZ3IZARz5x/Bs6hM9YMB9O3tjdt34pGTK/dQN2vqiJnT26BLZ09DmlkBvrgEwV+vw9N1OyDJygEAiJ3sYOLpIr8B1bFMxMu0XLVIZ295XcVgqdI1hTapVnUZaWER9pi0MNj8gx+fgGUjw6feJSblYvBw1U0pteHlAlZl/y4rRsUAAAPY2Jhg47ph8Kqvet+b52UIepSMrOxCODuZw8/X7o37gqBQqpuSEh7nLkTg4KFQpKXnw97eFN261sPRY08QoeTmpTJ8G9jCydEc9vYmGDa4IZo1q/7aYTKpFJcGzUbS+Zt6ESmV4TK4B3oe+9Mgc1c32ly/aWPGOgpbmjZtkHrYAGQSSbnf+eISJF8KgCQ7Fxa+9WHdUj8F4fRdl+XlP0fZv8uCgknpf7IyCzH/g2M4fGAyhEoaV546HY5ff7uBlNR8xTEfbxssWdwdrVspL7BIoVB0IzwiHVu3B+L02XBIJC8u9DGx2bh7L0Hr+Z6Gp+NpeDo4jsH+g48x6522mDu7XbXegDz9fTuSzl5XOYY1FkNWWKTzGu3Wf6XzuXUZKl7qKKxQCMc+HZFy8bbKVDuBuQmkudqlEAvMTGDuI3etEkIQtuZfPFqxTuHyBACRgw3a/PoF6k0cgoz7IYjefhTFaZkw8XCG9/TRMPPSrN6OrY0xfBvYIjwi3VA6rAK8jCApOQ+XLkehXx+fCo8fOfYEX319ocLxZ1EZmDv/CDZtGI4WzWkAOYWiL/7ecg/rNgQYZG6el3+xbNp8F87O5hgxrHoqcccfvYD7H61SOYYRcLBs7I3MeyE6rWHXuRVMPd7Mmyka4ViHafzJLKXCheE4mNZ3Q7s/Vlb6uFJYBt6zxkFgYozMoCe40G867n+4qpxwAYDilAzcmLQIB1y64lSbUQhb+x+idxzF41V/4ohPPwR+/rNGTcMYhsGM6W2qTbiUwXEMAm7HVzheXCzFj79cq/QcmUzuxfnpV9V3UhQKRXP+2/7AYMLlVTZvuWfwZoYp1+7i0tA5uDJsnlrPOJHyKEnP1mkdTixCh7+/1enc1wEqXuowzv26oN0fKwCWfdHMsbQ1s7GrI7rtXYOA6Z9pN6mMQGhphosDZuBki+FIPq86lVlRtVfKy394Xl7Vd9VGhK3VrPBdvz4++GB+JzAMyhW4MzR8Ja0XrlyLRl6e8qJ+MhlBcEgKomM0CxqkUCjK+XfrA/y6Vj/lEjQh/nkO4uJz1A9UQl5UHAI/+wkXB87E1dEL8Ozfg5CWbvkQQhD42U84120yEo5f1nhOU08XzSuml34/mnl7oO+Vba9FSwBdodtGdZwGcybA2b8rIjftRebDJ+CMRXAb1hseYwfiwac/QFYiUT/JKwR/9TsYruq6NuS7jfB9d5JGXa2nTmmF3r28cOjwYwSHJOOODnvc2sDzBM0rCeBLSclXBPiqIv55Nup5WhvKPArltefmrVi91XnSBolEt/oq4Rt24M78lWAYVn6TxrKIO3AGDz//Gd0PrUNe1HM8/l9p4KwW3p3Mh6Gw69IaiSeuqB3r2Ksjmnw+B469Or7xyQM02+g15oBzFxQlpdWoDf1u7IJ9p1ZanSOTEQwduQ2JSbkGsYlh5IHC5uZGyM4uAsOwsLYSY/iwhrC1McH/friqdo5WrZzx14YRb/wXCIXyMmnpBdi7PxgnTz1Fbl4J6nlaYezopujf16dCHaZ33z+K23fi9VplWx3GxkKcPzUdYrF29+2JZ6/jYv+Klc5fhhWLINOxcCjDsTCysUJJZrbq4nUMg6HhZ2Du7aHTOrWdWtGYkVLzaBuoawh4HaLoWZbB7JltDWDNiwStkhIe6emFkEoJJBIeKan52PT3Pfy85jpERuozoB48SERwSIracRTKm0J4RDrGTtiJv/+5h/jnOcjOLkLQoyR8ufwcOnbbiIlTduPQkceQlF6c7957Xq3ChWUZjBrRWGvhAgCPv//zxda8EnQVLgBAeBmK07PUFg9mWBZR/x7UeZ3XCSpeXmNM67vWrAEsC4uGXjqdOnxoI7w/vxNYlgHLMuA4RtGxul9fb9jb6VYh09hY9RZWSYlMoy9UjmNw8nTVOnFTKK8LPC/DR4tPIDevpNzn5+VyBWFP07Hy20tYsPAYSkr4qlb5V8mrsXMMAzT0tcO82e21nksmkSD5QkDVGyiqXUimfg0GyI99sxowKoPGvLzG+L47CXfeXVEjazMCDq5DesHExVHnOaZNaYXBA3xx7EQYnifkwNJChAH+vmjgY4vxk3chNU07z1L/fj44czZC7TiJmmq/gPxLOTunoldJJiO4/yCh1F4xOnV0h0hEP2aU15tbAXF4nqDZNu+duwn4+597aNHCCQ8CEzW6WRAZcRg5ojEcHUyx4c/bKClR/hkVCBgM6N8A5y8+Q2GhFM7O5hg3uinGjW0KY7H6+LtXkUl5g9XT0gWxPY21A6h4ea2pP20UIv7ai8wHjw1zl8MygIyA4ViQlzJ3GI6DsZM92v62tMpL2NubYvrU1hWOZ2Zpvh1lLBZg+rTWKCnmwbKaFbrkOEZRH6IyGAZwdS6/J3srIA7f/O8SEl76EjczNcLc2e0wcXxzGh9DeW0JDEqCQMCqbfMByLNy/vrnLjp38lApXBgG+PD9zmjcyAGNGzkotnsIGKxVEujLMMDE8c3x4ftdsHK5/GaiqhmMnFgEc996yA2PqRYR8+r36csQKY96bw03uA11Abpt9BojMBaj78Wt8Jo+GoySSrIqefViy5T+QB6c5j1jLHqd2QzPSUPBCuVfLAIzE/gueAv+d/fDxM1w5bg18WaIRBxWf+ePsyenY+b0tuAErMbfPSYmQpVfejIZwfCXil3de5CABR8eQ2Ji+bvPvPwS/PjLdWzdHqjZwhRKHSE8Ih2nz4bj6rVo8LxMq/ophMizjVR9xggBNv19F/XrW5eLU5n6Vku8/VZLRWmFl7eUhw9thAXvdlKMrYpwKUrNkHd/5nn4ffC2zvNog9DSDJyxuPJsT5aB54TBsG5Rsx2xaws02+gNoTg9E5kPQlGYlIabUxYrH8gwsGzkjYaLZyDk2w3Ii4gFAHAmYnjPGIumX80Hy3EQmJmUa9wok0ggzSuAwMKsWho6frb0DM6ei1R559a9az38+tMgxe8PgxIxfZZmwW4MA9jamiAzs7BSD8ysGW3L7Z9PeGs3noanK51PJOJw9sR02hCSUueJiEzHim8uIuTxi4B1oZAtV9Jfn3jVt8a+XRMrHH+ekIOjx58gJSUfNjbGGDzQD/XrVX1LJe7gWQR/swGZ9+VVb41srdBg7kRkhYTj+aFzuk/MMLBs2gA5jyOUelZafPcRnPt3wfXJHyM3LEp+s0jk2/A+s8ah9a+fgzN6fb9DtLl+U/HyBnLjrY8RvfO40v2TrnvXwH1Uf2TcD0FOaCTEzvaw79QKAtPqbyOvjAeBiZgxR7UQ2fDbUHRo/6JNASEEk6fuxZMwzdLHOY5Bh/buCLgdryhoZ2UlhquzBUokPExNhejf1wdNmzji7Xf2q51vxbLeGDqY3jVR6i6xsVmYPG0vCgul1ZopdOTAZLi5Whp8nSe/bsH9D1fh1f1lhmVh27EF6r09HHfnrdBq+4jhOBCeh8f4gWj3xwpcG/M+ks/fAiPgQKS84v8+s8ej3YavwLAsCCFIvXYP2cFPwRmL4TKoB8QOtoZ4yrUK2piRopL2m74BX1yCuH2n5dtJDAPC82A5Dq1//Rx8YRGOePVBfkxpoTiGgbN/V7RZ+yUsGtSrUdvLaNXSGdPfboV//ntQrqgcwzBykTKhOdq3cyt3DsMw+OWHQRg8YqtGX7w8TwBCcPbkNETHZGL3nkc4fTYCubnF4Hl5l+qHQUkaBQEyDJCeXvOp6xRKVdj4910UFVWvcAGAkyfDMctA5RPKyI95jvuL/if/5ZUbOyKTIe1WINxG9IXYwQZFycq9rC/DioRwHtAdPrPHw2VgdzAMg95ntyD5wi1EbzuCorQMmHq6wnvGGNi0aqw4j2EYOHRrC4duhn3OdRkqXt5ABMZidNu7FlmPwhCz5yQkWbkw83ZHvbeGIWbHMdx8+9PyJxCCpLM3cKbjOPjf3ldrCiTNf7cj/Pzs8d+2B3gcKm9T4Odri7cmtcRA/waVBsg6OprBxtoYaRoKiZsBcQCAyMgMnC7NVCrbRirrUl1QqL6KMSHAjVuxGD6sEaytjDVam0KpTRQWSXD2bITKQHZDISOG2ZJ6mci/98lvfpRlN8gInq7bDkag+WVTVixBl50/Q2AsVhxjGAZOfTrBqU8nFWdS1EHFyxuMVTM/WDXzU/xekpmNB4tXVzqW8Dwk2Xm4PWcZRLZWSL1xH6xAANchveA7fzIs/LSv50JkMuSERYEvKISZjyeMLM21Op9hGPTv64P+fX1QXCwFoFkgr42N5uKFECArqxD/bQss237WmfsPEjB1xn78t3kMrCzF6k+gUGoROdnFkCqJ1TA0vXrqVi9KG7JDI0HUeJQKYhLg0LM9CpNSAQ3/FvpotUKpCBUvFAXRO49BJpEqfZzwvLxRI8cqPrjhf+xE+MZd6LZvLdyG9UFRagYSjl+CJK8Alo284dirAxi24of32b8HEbxyHfKeyT0brJEQ9SYPQ8vVH0NsZ6O17drUUunRvb7K4NqX4Tj5NlRcvG6dX19GJgMSEnKx6e+7WPxR1yrPR6HoE0IIgh4l4cKlKBQVSeHjbYOB/r6KIHMLCxEEHFvtAsbZ2Ry+DewMvo7QzFSepqyqPD8A5wHdkXLptvoJORb2nVq91gG2NQkN2KUAAAqeJ+PiwJnIfqRD1ViGASsUoN5bwxH13yEQqVRRh9+0nis6b/sB9l3aKIaH/O9PPPzsp4rTCDiY1nODf8AeiGysqvBsVJOSkofBI7aqdX8zDODfrwHem9cBQ0du09v6JsZCXDz7DoRCw2dlUd5sHoem4PDRJ0hJyYONjTEGDfRD65bOFbZUs7OLsOiTk7gfmAiOY8AwDHheBpGRACuW90a/Pj4AgC+WncWZc5pvHTGMPI25rP6LpnWWyhAKWOzfM7FagnWfH7uIy0Pnqh0nsDKHbesmSL4UAKjx1HQ/sgFuQ3vry8TXHpptRMWLVuTHJeJ0+zEoSklX+2FUirI9FZYFaySA/629sG7REPlxiThcr5fSdRiOQ8OPpqHV6k90s0NDTpx6ii+Xq057NDMzwvZ/x2LLf/dx8HCoXtc/fWwq7O1N9TonhVIGz8vw9XeXcOTYE0XBxbL/d+3iidXf+b8o+kYIZsw5iEfByRVECcPIBcif64ejdSsXxMRmYfLUvVoF7VpYiDBhXHN06uCOw0dDEfI4BSIjDj2610NsXDaOHg9T9Bx7GXc3C2z+cyRsbavncyLjeZxqMwpZj56qVFgMx6LhxzPACjiErd0KaW5++QGlnumW/1uExp/ONrDVrxdUvFDxohVXx76P+EPn1LpLdYXhOLgO7YXuB9fh0crfEbxyndI6BwAgtDLHmPTblW436ZOr16Kx5IszKCyquFXWvJkjVizrA0IIRo3bqdd1GQa4fG5mhZov4RHpCHmcAqGQRYf27rCzlaemE0IQFZWJwiIJXF0tabzMG0JOThFOnHqKmNhsmJgI0a+PNxr62Wt07oaNt/HXP3crzehlWQZDBvli6ee9wHEs7j1IwKy5h5TOxbEMOrR3w+9rhgIAnoanYcU3FxH6JFUjW1iWgUjEYduWsRXqsBBCcOhIKLZuD0R0TBYAoJ6nFaZMbokRwxpVe1XqwuQ0HPX1hzQnT+U4U09XDI++AL6oGGm3g5Bw4jLSAx6CLyqBTevGaDB3Qrl4QopmUPFCxYvGFKWk46BLV5ViQi+wLMZm3cHd+V8jesdRtUJpTOYdGFkZ/vUrKeFx4eIzPHiYgKysInjVt8GgAQ3g7m4FQH4R2PzvPb1lWLAsg47t3fH7miGKY/HPs/Hl8nMIepRcbtyQQX5o2cIZ//x7D3HxOQAAjmPRv683Fi7oXMFzU1LCI+hREgoLJahf37paXO0U/UIIwd17Cdi7/xEuXooCLyMQCOR1P3ieoHtXT6z6pr/KBqOFRRL0G7gFBQXqs+Aa+tnBylKM23fj1W7nXL88q1xZgLCnabh2PQbHT4YphIcyOI5Bvz4++O7rfpU+TghBTo68K7OFhahGW2mcaDMSWfcfqxxjZGOJMekaxL1QtILWeaFoTN6zOMMLFwCQyVCSlQsja/WChBFw4Eyqx7tgZMRhgH8DDPBvUOnj6RkFpV+k+hEvMhnB5InNX8yfXoDpsw4iK6uwwrgjx57gyLEn5Y7zvAxnzkXgfmAitv0zBra2JiCEYNuOh9i85R6ySy8AANC+nRu+WNID7m7VK2JKSnicOPUUBw8/RnJKHuxsTTBsSCMMHeyntqv3m0zkswx8vOQUYl4RAi/3C7p2IxZfLD+Hn1cPVDpPUFCSRsIFgMYFGwFgyPCtGDTQDxPGNYOriwX8fO3g52uHGdPbYNXqK9h/METpVhLPE5w7H4llX/QqV+q/DIZhYGlgj2JO2DM8Xb9DHmxbWruqwdwJMKvvXm6cVWMfZAeFKb/BYlmY+9U3qK0U9dAcrjccgZkequZqcJPEiowgsrOG54TBKr0ujICD+2j/WhOh72BvCpmexd2zqEzFv3fseqi0BYEyeJ4gLS0ff/59FwDw+4YA/LL2RjnhAgD37j/H1Bn7kZikWbdffZCXV4IZcw5i5bcXEfI4GSkp+Qh9korvf7yCt6btQ0YGLdSXm1uMQ0ce4+8t93D0+BPk55cgJSUPM+YcRFyc6qw2mYzg0uUoRD7LUDrGUGX6M7OKsH3nQwwduQ179gWXe4znZWr7CEl5GXLzilWOMRTP/juEY40HI3z9DmQFhSHr4RM8+ekfHPMbgPjD5WPffOaMV+0ZlsngPqIvHnz6A66OeR8Bs75EzO4TSL/7CAXPk5WfR9ErVLy84Vg2aQBTL3f1A1/i5SaPDMeCE4nkHaZVjPecOBSZgaEoTs+CTYcWlQsehgHDcWjy2Ryt7DEkQwb56RzDrIxrN2IU/z587IlO1Up5Xu6ZiY7Jwpb/7isdk5tbjL//uaezrdryw89XFbEQZdsQRF6oGLFxWfjq6wvVZkttgxCCLVsfoO/ALVj57SX88edtLF95AX0HbsGylReQl1ei0XuB4xicvxCp9PEGPrYVeqrqm//9cAU3b8Uqfnd0MFNru1DIwsJcZFjDKiHz4RPcmrYEkMnKiRLC85BJeVwb+wHyouMVx+27tIHP3Iq9lMowsrdG4Kc/IvSHvxG3/zQi/9qL6xM+xOl2Y3DIrTvO934b6XcfGfQ5Uah4eeNhGAYeo/trNphl4dS/C7xnjIFl0wawbtUIjT+bg6HhZ9B85QeVz89x4IzFSDx9BWc7T8DlIXOQEfCw8l0YQtDww2m1qmuqk5M5TEz0u9VRUvLiCzQrq0jneYqLpTh4KERlfADPExw7EVZuTUORmVmIE6fCVW4dXLsRq5eaOXWRHbuCsPb3m5BI5K9FmbetuFiK23fiNRaxDMOorOpsZSU2uEhgWQb//PdA8fvgQX5qu0oLOBbhEZrVV9InYWu3Ki8URwiIjCDij12KQwzDoM1vX8L3g7dhZGelOC60NAcjFKAkNVNxbmWkXLmDs10nIfVG5TcVFP1AY14oyA6JkHtO1H15ymTwGO0Pn9njKzzU5PO5ENlZ49GK31GUWJqFwDCwaOiF7JDwiumESnjy679ovGS21tV2DUX88xyN4wc0gWWBZk0dFb+LRQKN2gtUBsMAWdlFYBkGMhUxOSUlPHJyixXZS4YiOCRZ0cBSFYEPE6s9DqemKSqSYuOmO3qZSyqVKe2eHP88Gyu+uVhhC1HfyGQEd+89h0TCQyjk4OJsjmlTWpUTNK9SVCzFnPcOY+d/4+DhYWVQ+9LvPkLcgbOQ5hcg7sAZldtAhOcRveMYLBp6wdm/K5Iv3caDxatR+NIWkGl9VxSnZoKoKOL5Yj4ZCJHi9uxlGPToaI0GH7/OUPFCQca9YI3qu4jsrFFv8tByxyS5eUg8cx3S3HxYt2iI4TEXkXkvBNK8AohdHXG67WitbJEVlyB6+xH4vjtZ6Zicp1GI/GsvcsKiIDQ3hfuYAXAd0hOsFj1HNLZHm4paGsFgzKgmit+EQhYoVDFcCSzLoEsnD9jbm6rt+yLgWJiZGiEruwiXr0QhN68EHu6W6NzRAwKB/pyvmn5Jv4nf5QG345CXX1LleRgAxibybuavcuDQY3z3/eVqbZoolcoUxRbnv9sR2TnFOHCo8kwdQuRC+r/tgfjys546rUdkMiScuIxnWw6gID4ZJm6O8Jo2Cs4Du4PlOJRk5+La2A+QdPb6i6azGgiOgrhE3Jr+GSotOAMgPzpBq07SkMmQHRKOjLuPYNuuufrxFK2h4oUCVqQ+OJYRcOh1+m8ITEtrj8hkuPfRKoSv217ursbY1RFd962FU9/OeLblAPhC7a/MgUt+wtPftsF9VH80mDcRJm5OiseCv1mPoKVrXrST5zhEbz8Kq+Z+6HVmM4wd9VtG3NXVApaWYmRn67698zID/Rvg4OFQGBsL0Kt7fZ1zmBhGvj2wZ1+wyhRXlgX69vHCpr/vYNvOh5BIZIou3La2Jlj+RS907eKpoxXladrUEQIBWy47pjK7W7d00ct61UFuXjHOnotEckoebKyN0b+vD6ytVTfWfPgoCXv2BSMoKAlCIYse3evrrRknATBlUgtF1tbTp2l4/CQF8c9zsHlL9W5TuLtZlsscKqvKq6qKLs8TnDj1FF8s6aG1R0JaUIjLw+ch+dxNMBwHwvPIuMshbv8ZOPbthO6HN+Dq6AVIuRQAALrVrVImUHSsKJIbGUfFi4GgdV4ouPfhd3j62zYQXsmHnQGaLn0PzVe8rzh0fdIixOw8Vvl4lsXAe/sRf/Qigleul7cL0AWWAWtkhJbffQSv6aPw/Pgl3HxrceUmchxs2jVD/xu79O6m3fjXHfz5151Kv78YBjA3E6F1axdcuRqlUelzgYCFTEZ0vkNmGMDO1gTpGYUazdG1szuu3YirdB6GYbBx/XC0aeUCiZRHcnIehAIODg6mOv0dv1l1CYeOhFZqF8sCTRo7okM7N3ACFh07uKN5U8da61bftScIv/4mj1HhOLb0wszinamtMXd2u0rt/mvzXazfeFtRzRaQe8k4jtFLFlCZY2DyxOYICkrGo5Cay275ZFFXTBhX/sK86NOTuHQpSq0ov319rtZev4DZSxH5977KlRHLwnVoLzw/fF6rOQ1Nz5Ob4DKge02bUWegReqoeNGK3MhYHG86GLISSYXtI4ZjITA3xdDwM4qGiTlPo3HMz1/lnJbNfOH77iTceXeFznct5ewwEkJoaoKSrByV8/W7vhP2nVtXeb2XkUh4fLj4JG7cjJXHl5AXFyUzMyP8uW448gskmDHnoF7XrQyGATw9rBAXn62Xwnksy6BpEwe0a+uGvfuCkZMrj5Wo52mFd6a1wZBB2lUJLSyUYP7CY3gQmKjw8JT9zcq8Mhwnv+jzPEHTJg74afVA2NvVrlYJh4+GYsU3F5U+/u7c9pg5vW25Y9dvxmLBQiWCHi+2y5SJYIbRru+Ptn2C9M3Zk9Nga1M+juqXNdexY3eQyvemvZ0JTh+fptVaRakZOOjSTfWNkC5/RANiZGOJkQnXwGng2abI0eb6TbONKDD39kDPY39CYGIs//CzrPwHgJGVJXqf/adcp+egpb+qnTP70VM49O1ULq26KpASCUoys1UKF0bAIeH4Zb2s9zJCIYdffxyElcv7oEkTB1hYiODsZI7pU1tj744J8PW1Q8sWTmjfzlVtrYuqQgj0JlwAeeBl0KNkbN5yTyFcACAmNgvLVpzHxr9eBJmmpuYj8lkGcnOVB4MaGwvxx7phWPVNP7Rr6woPd0s0aeIAkYhTeGN4nijsD32SijnvHq6WbChNkUpl+G3dLZVjNm+5j/xXYli27QgEp+L1J0Tu6arsPUKI9tfcGr9GV/IWHD6skcr3JssyGDOqqdqpZVIp4o+cx8MvfkHQ8rWI+HO3eg+uhn9EsZPhO1QDQLMV71PhYkBozAsFAODUpxNGxF9B1H+HkHr9PhiWhWPvjqg3cbAizqWMnCfPNJpTkpGNpl/Mw6OvflM+iOMAZdtVWsIwDPjiqgdFVoZAwGLIID+lngiGYfDT9wPxxbKzuHItBiwrv0ipiv/QhrJtiLffaon/tgXqZc6XeVUTlv2+cdMdODuZ4eDhUDwMSlLY0rePNxa82wkuzhWzwoQCDv79GsC/n7xq8S9rb+Dx45RKt5J4niA6JgvnLkRi0ABf/T4pHXnwMBEZmapjtYqKpLh6LRqtWrmgpEQKWxtT3H+QAF7NNp61tRhmZqIKVXTrGo6OZpXG/njVt8G0t1thSyVZRxzHoJ6nNSaOVx0DknEvGFdGvIeC+CQwQoE8nVlPfdeEluZo/+fXuDLyPUDb4pNlhbZLGy9WgGUBQsAaCdF85fvwfU950gGl6lDxQlFgZGkOvwVT4LdgispxAnPNXPxiB1s0XfYeWKEAwd9uAF/wIujV2NURHuMGgmEYPPn5nyrZXYZMIoVNmybqBxoIU1Mj/PrTYEQ+y8DlK1EoLubh4GCCb/93Raf5hEIWhMi/9Du0d8eUSS1gbiYyiHhRBssCX319sZy3oKzUe8DteGz9ZwxcXVS7d0+eeqryos4wwOkz4bVGvLxcfE0Vny97UZm1bItMHenphSgurj1eJl1gGGDiuGZKvYwL3u0IJ0dzbP73HlJS5CUSjIw4DBnkhw/md6rQkPRl8mOe43zvqZDmyysxa5IppLHdHAunfp1xZcR72rutSrekmi57F8kXbiH1qry6tXXrxvCZMwEgBEVJaTB2tofH2AEwsn6zSgHUBFS8ULTGe8ZopF1XndkgtDSDqacrGIZBk8/nwnfBW0g4cQUlWTkw83KHY++OYDn5llLKtXvIuBtcdT+4gINVcz8UJqfpPetIG7y9bODtJd9mk0h5/LL2pta1YjiOwcTxzbFwQedyx3leBgcHU8VFwdCUvSSvXph5Xt5Ib+Gi42jfzh329qYY5N8ADg5mFeZQlyJMiDyrJzu7EA8eJiEvtxj29qZo3cpFkYb7KoEPE7F910ME3I4HkRG0aO6Ejh3cIRIJIBIJ0KWTB2x1rGsT9ChJ63O0Cb7OyzOMd1AXOI6Bk5M5fLxscPV6jMrnUeZ46NGtHiZNaKF8HMNg3JimGD2yMZ5FZUIi4eHhbqVStJQRtnYrpPmF+u+3xrIw862PjPuPNYrBY42E8hjAUkzcHNFuwwq4Du4JfLUAMqlU7mUR0l5dNQUN2KVojUwqxX6HTpBk5igd03L1YjRePFOj+WL3nsS1cQv1ZJ0cqxYN0fTLefAYM0Cv8+rCT79ex67dQWq3FMpgWQZisQB7d06As1PFbZl9B4Lx3fe6eXMMAccxiuvBO1NbY96c9opMnJjYLIyduEvl9hnLMrC0ECHzlWrD5uZGmD+vI8aOLh8jse9AMFZ9fwXsSxk9ldk0cnhjfPxhVxgZaRd3NentPVo1LKyNvOwJKstQEgpYSKSycoHDzZs54vtv/ZGeUYCpMw5AJpMpDShu1NAeE8Y1w0B/X3DKKtZWkQPOXVCUZJi/vbGrAwqfp6gdV++t4aj/9nA8+/cg+PxC2HdtA7+FUxU3WxTDQbONqHgxONlhz3Cm/VhIcvIqPOY1Yww6bPpG4xTY2ANncG30Av0aWFoxuNUPn6DRxzP0O7eWZGcX4e139iMhMUfpxbYsbVQqlcHWxhi//DgITZs4VjqWEIING2/j7y33wLLKL+Cv4upijucJhm/S+OH7nTFlckvk5RVj2OjtVWqBAABTJrfEB/M7gWUZPHuWgbGTdmmUwMYwQJ/e3lj9nerMuDIIIdi99xF+WXvDYM0Nq0rZVo2VpRjZOUUVXnuGAfr388F3K/shNTUfV67FoLBQAq/61ujYwR0RkRm4d/85CAFat3JBo4b2inOvXovGZ0vPoqBAAoGABSHywOpmTR3x64+D1Na3UQaRyZD16CkkuXkw9/aAsbNDhTHZjyMQ8ddePF37n+G63GtSRRyAibszCuISFckGRMrDopE3uh9aBwtf2k3akFDxQsVLtSDJzcOzLQcR+fc+SPMKYNXCD02WzNa6KNPJViOQGfREoy8WrWEYDIs8W6HtfXWTmVWIDRtv4+jxJ4qYhwY+tpg1oy2MxQLcuh0PGS9D8+ZO6N3TS+l2ycvExWfj8NFQ3L+fgMAg1VsdXbt44Ksve6PvwC36eDoqMTc3wqF9kzFj9kFE6ykw1cHeFDPfaYuIyHTsPxiiVbbV9n/HlrtIK2PN7zfx71bl5e1rGhtrYwwe6IvRo5rC1ESIVT9cwaXLUQoPi7GxEBPGNcO82e11rpxcWCjB6bMReBqeBpFIgB7d6qFFcyeda/FEbT+CoKVrkB9V2viQYeA6pCfarPkCZvXdQQjBwy9+weNVGxWFJ2ucSnLQGY6DyN4ag4OPQWRbeWsGStWh4oWKlzpDwfNkHHIzXBEnhmPR+NPZaPHthwZbQxsKCiRISsqF2FgIZyczvRRok0h5jJ2wC88TKvfsMAywacMIODmZY8iIrVVeTxN8vK0REZmp93ltbIyRkaF51WaOYzB+bDN8/GFXxTGel+HqtRgcOPwYz5/nwNpajE4dPLDujwC926tPvv+uP/r1Kd8WICUlD2FP0yAUcmjR3ElRebc2EPb7Ntxb8HWF4wzHwcjaAgPu7kfimeu4PXtpDVinAyyLFt9+iCZLZte0Ja8t2ly/acAupUaRFujQ2EcLiIwgOzTSoGtog4mJEF5eNuoHaoFQwGH92qGYt+Ao4uKz5TEoMgCMfJth2Re90LqVCyQSHmamRnrpsaMOQwgXAFoJF0Ae15GZ9eIciYTHok9P4dr1GEVcSHQMcP9Bor5N1Rssy8Da2hg9e1TcsnBwMKs0SLqmKU7PxINF/6v0McLzKMnKwcMvfpV3XlbST8iglEUfa4NMhuith6l4qSVQ8UKpUUzdnSEwM4E0r8Ag8zMsC4GZYbsp1wZcXCywb/cEXLkSjctXo1FcIoVfAzsMH9pIkXUjELAYNbIxtm4PrPZrhb5QVaVW2XgnxxdBz+v+CMD1GzEAXmQI1ba/xcvBthzLQGwswK8/DoRQTwUfq4PoncchU5HmTKQ8Ynaf0L11SBVgBAKIHWxQmPAieFfkYAuGY1GUmKry3JIs5UkKlOqFihdKjcKJRfCeORZha7capGQo4Xl4jFEfsCmTSsGwLBi27hadFgo49OntjT69vRXHMjML8fuGWzh0OBQZmYWwtBTDykqMzEz9NJqsbggpC1olGokOnicYPMAXl69GIyYmEzt3B+ksVqrDQdC4kT04jkV0dCZMTITw798A48c2qzTrrDaT9ywODMsq75cG1Ixw4Th4TR2JdhtXIOXSbRQ8T4bY0RZOvTvi6tgPkHD8kvK4G46FhR8N2K0tUPFCqXGafbUASeduIPtxhF6DdhmOg2UTH7gM7lnp40QmQ+Tm/Qhb8y+yg8MBloWzf1c0XjwDjr066s2OmiIpORfTZx5AalqB4k4+O7sILMuo7f5cW5EX7HPD/QeJKC6WqhUTRkYcps48gPz8kiqLD44z7N+MYYCB/r6YPFF5DZW6QPbjCDz7Z79K4VIGZ2oMPr+KW8cabgGVxdo0XfYuWI6DU59O5R5vMGe86saOvExekI5SK6i7t5mU1wYjS3P0v74LTT6bA85Ut3TMMhihAIxArslt2jRBrzObK63PQGQy3Jj8MW7P+hLZIRHygzIZks5cx/k+0xCxaU+V7KgNfP3tJaSlF1QoPCbvaC2DUKj6429qUnuCP8vgeYK3JrbE8UNTMHN6W6iLdy4p4RU9iKrqNTG02BOJBBg2pKFB1zA0xRlZONdzCiQ5qosoMhwHp76d4TN7PBhda8aUeklZI83ep/bd26L/rT0w9XCp9HHnAd3hOWEwKn1TMQxchvSEuwZeXEr1QD0vlFqB0MIMLb75EM1XfoDCxFREbT+Ch5/+qPkELAuPMf1h7OwAVmQE1yG9YN+1DRiGASEEabcCEbv7BEoyc2Dm7Q6BuRlidh2Xn/vSVa3sbvH23OVw7t8Fpp6uAOSBxYlnrqMwMRXFaZkwdraDuY8nHLq3q5VbTXHx2bgZEKf0cZkMkMlkaOhnV2lBNhtrMdavHYabAXFY8/tNQ5oKkYjDgP4NcOpMuMrS+SzLoHlTR7Rv5waWZdC+vRs2bb5rUNt0gWUZMAy0SudmWQa//DgI5uYiA1pmeCL/3ofi9Ez1HlSGQbOV78OqmS9Sr91Dxt1HmgfQsixEdlbwnDAYLgO74/EPfyPlgupGmgDgNqw3zL09VJjEoNO2H2DVoiGe/LIFxSnpAOTdoX0XTEHTL+bSQnW1CCpeKLUKhmVh4uqIJp/MQub9EMTtO61h0SoCu86t0fCDqeWOSvLycXXkAiSdu/7Cvaz4v/J9BIYBIjbtQfOvFyLs138R9NVaSCu5mzSt54oOm76BU9/OlcxSc4RpWCF2yuSWaOhrj517ghATkwUbG2N07uSB/n19IBIJ4OtrB08PS3z6xRlF4TZ1tb7kbWA06/VjZMRhw2/D0LKFMz7/tAeiY7LwNDwdv627gZTUAkX1XpmMoFFDO8x/t6PixlgoMGwHb11gWQY9u9fH/Hc7YO26W7h6LVqtiDEy4vDbL4PRrq1bNVlpOJ5t3q9WuDAch57HN8K+UysAQN9LW3HUt7/66relbzy7ji3Q/fB6Raf7/NgE9eKFZZEVFKbWfpbj0GTJbDRaNB05T6MBQmDuWw+cEe0OXdugdV4otRZJXj6ujlqApLPXNRo/JOxUhQqYF/zfQdIZzc5/FecB3eDYsz0Cl/ykfBAj/zLuc+E/OHRrq9M6huDylSh8uPik2nE/rPIvF+CrjOzsIhw9EYagoCSwLAM/Pzts2xGI7OziciKlrALsN1/1wakz4bhyLUblvFv+GoXmzZwqHOd5GW7cjMXFy1G4cTMWKakvhKOnpxU+fL8zrlyNwoFDoWptV0eZGBII2CpV1nV2Nsf2f8fCylIMAEhNy8e0GQeQnJKnVMh16+qJRQu7wsO9Zhr5EUKQeu0e8iJjIbSygHP/LhCY6LZ1G7R8LYJXrlM7TuzqgFHxVxW/8yUl2C1qpvokhoFFQy90+Osb2HVqVa4+UnF2DvZbtVN9uoCDz+zxaLduuVr7KDUHLVJHxctrAyEEqVfv4sGnPyL9VmClYxiOg8ugHuhxZAMAoCglHZF/70PCqatIvXJH98U1zctlWdh1aI7+N3brvpaeyc0rRr+BW1BSonwbRihkceb4NFiWXmy1JTEpF+s2BODMuQhFPEib1i6YO6sd2rR2BSEE3/94FXv2BYNlGMhK/45lqcDvzmmPme8oF3wPAhMx573DpTE6L14DbdOlVWFmZgRnJ3MMHeyHtLQCbNv5UKsmiy/btGhhlwoNCzOzCrFteyAOHHqM7JxiGBsL0L1rPfTt443mzZxgb6dZh3Z9kfXoKR4u/RU5T56ByGQoychGSXqW4nGBuSmaLXsPDRe9o1UBxdCfN+PBou/VD2QYmNZzRc8Tf8KyoVw0ayJeGI6DfdfWEFqao/B5Ckw8nOA1fTRcBvUAy3E41/MtpFy5o3LrqdeZzXDu10Xj50Spfqh4oeLltYMQgnsffIunv21VlBEv+79dl9boefxPGFmaI/7oBVwb+4G8xoQBUq9VMSzyHMy8arYNwcv8vOY6tu98qLTR3rjRTfHp4qpXN87LK0FqWj7MzUWwe6WTMyEEZ89HYtuOQASHpIBhgFYtnTFlciv06FZP5bwTp+xBeES6TmJCE0xNhTh7YjrEYvnueXJyHoaN3gaptPLmhMpgWQaNGtpj04YRirlehRACiUQeJK2PqsraIuN5XBv3AeIPnNVofPOvP0DTL9/VaGzeszgc8emnuZosLb/vt3AqWv+0BAzL4kzn8Ui/HaR2i5jhWBBeBobjQHgeTv26oPvh9Ui7+QAX+k6v1AZGwMGqqS8G3DtQK+PTKC+g4oWKl9eWzIdPEPnXXuQ9i4ORrRXqTRoC5/5dwbAsskMjcaLFMHmdhhp4W7f83yLYdWoFu86twApqPpxMIuWxdPl5nDkXAa60A3PZ/3v1rI9VX/fXuuNyVeB5GRiGUWwtqeJpeBomvGXYjK+vlvaukN1z5Vo0Fi85BZ5/4e3hOLmn6PNPe6CkhMeW/+4jNU1eVNFYLMCI4Y3x3twOMKmF2VllBMxeikgtMugYoQCjEq9p1Mfnwac/IPSHv3X6zLX8/mM0/mQW4g6cwVVlzVlVpUKzLHxmj0f7DV/h2b8HcXvWl5CVvs/AMCBSKaxaNESvU3/B2El9fytKzULFCxUvbyS35y1H5F97tW/upmG3WU0RO9ujxbcfwnv6aL3NqSuEEAQFJ+PosSdITc2HnZ0phgzyQ8sWujfbqw6uXIvGwkUnDDa/hYUIl85W3m08ISEHew+E4PrNWMh4Gdq0dsHY0U3h420LQC7ComOyIJXK4OFuWav6CVVGUVoGDjh21u49zjBot345fOZMQGFCCgjPw9jVsdJsm3O9piDl0m2dbDOytsTIxGvgREaKmJlyDRo1+GyyRkKMTLwGkY0VilIz8GzLAWQ9egqBiRhuI/vBuV8X6nGpI1DxQsXLG8kBl65qy3u/isDMFPXfGYXwtfpvWOgzZzw4sRgFcYkQOdnCuV9XuAzsDk5EMxfU8Sg4GVNn7DfY/I4Opjh5dKr6ga8Bkf/sR8A7n2t3koCDy8AeyH0SidxwedC12Nkefu+/jUaLpoPwMhCeB2dijEsDZyLx9DWd7etzaSsce7QHAKTdDkL4uu3IuBcCzlgEM28PxO5WL2K7H9kAt6G9dbaBUjugjRkpbySyEonW5zT5ci7Sbz00gDVAxMbyAbwR63dCYGGG1j98Ap/Z4w2y5utCk8YOcHExR0JCrtIxpTsDWoc2cRyDjh1qT2ySoZFkK/8bKkXKI+HohXIF24oSU/Hw858R8u0fkObJs7+E1hawbd+8Sr0T+KJixb/t2jeHXfvmit+jth3WSLxoVk6B8jpBfWmU1wbb9s3BaFFEymVwT/gumIJEHVOpdUGak4fbc5Yh/I+dWp2XGxmLx6s34eHSXxG19RCkhXWzN5GmsCyDDxeorp0ze2Y7eNWX1/rgOHnLAwCwszMBxzFKq+/KZAQTxjWv/MHXEPNXygdoxauChBCFcAEASWYOkk5fk4sXXbZmGAZWTRoofdiuY0v1c7AsbNupSbWmvHbQbSPKa0PCycu4NEhNu3qWgVVTX/gumAKv6aOQcS8EZzqM1W6h0nTP/Kh4nW0VWpphZOJ1CIxVpynzxSUImPUlorcdAcPKLxBEIoXQ0gwd/v4OHqNf73Llp06H4/ufriI7u0hxc29iIsT8eR0wYVxzEEJw914CAu7EQSYjaNHMCV27eOLSlSh89sVZEBBFkbiywNtlX/TC8KGNaviZVR8ynscBx87lUqINASsygqy4ROPxjKC0xMHhDSrHXRwwE0nnb1Qay8ZwHNxG9kW3vWu1tpdS+6AxL1S8vJEQQvDg4+/x5Od/AI4FSl3JZQGAbdZ+Cb8FU8qdk3rjPs52maj1WsbuziiMS6ySvWZebhBaWsCucyv4vjsJlo19Koy5PvljxOw8VvEOuHTPpPfZzXDq3anCea8TEgmP6zdikZSSBxtrY3Tr6gljsfog2di4bOzdH4ybt2JBCNC2jSvGjWkKby+barDa8BBCIM0vACcWqc1uSzx7HRf7v2NYgwTy6rQh36gWIwAAhoGJqyP639wNE7eKRQpfpjApFWe7TkJeVNyL4N1St5pFQy/0vbJNUW2XUreh4oWKlzcWQgjiD53Dk1+2IO1WIBiWhVPfzmi0aHqlnaJLsnNxwKkzZEUa3jGyLGzaNEHGnUd6s5kRcCC8DG1/XwrfdycrnkfIdxsQ9OUalbbYd26Ffld3AACkhUUoSkqF0MJMoxRXSt1EkpOH0J82I3zDThSnZoARcHAfMwBNPpsN6+aVN3YM/2Mn7sz7qtLH9NLZuZRuh9bh6oj31I6z69wK3Q78DmNHO43mLcnOReSmPYj8ay8Kk9Ng4uII71lj4T1zLIRm1Vvoj2I4qHih4oWiBbfnLUfkpj3qC2QJBXDs0wk5IREoqKLXRRl9r2yHQ7e2eLTydzxa/ptG5wwMOoLwddsR9e8hRfCjY5+OaPbVAjh0bQsZzyPx1FUknrkGWYkExs4OcOjZHjatG9Mv/jpGSVYOznabhJzQyHLvV0bAyXsGnfizgieuMDkNh9y6Ky8hoKqOipb0vb4Tt97+FHnP4lQG8A64dwA2rZvoZ1HKawPNNqJQtKDV6sXIuBuMjHsh8gOlX7oMx0JoaYFmK+bD1MMFBc+Tcfe9lQazgxFwePLTZph6OOPRV79rfN5F/xkoTslQdMQGgJRLt3G+5xS0Xf8Vnvz4tzzdtbSyaRmsyAgN5k1Ei28/VPSzKc7IgjSvAGJHO5rSrQJCCJLO3UDExl3IDn0GI2sL1Js0BPWnDIfQ3Mxg6wYtXYOc0GcVhDaR8iAyguvjP8SI51fKNRKM2nIARFWtFH3dvrIsrBp5o8nncxAw44tKh5SV+afChVJVqHihvPEIzc3Q98p2RP61F+F/7ER+9HMY2VjCa+pI+M5/C8ZO9pDk5eOgUxeDVu4lUh6J524g8p8DYFgGRE03YgAAy1QQLkBp6ijD4M7c5S/SXV/JKZYVlyBszX/IuBuMJl/Ow+NVfyLlsrzYmMDcFN4zxqDp0nchsrHSx9N7bZDxPG5N/wzRWw+/KKjGMEi78QChq/9Cn0tbYVZPvx2iJbl5eLpuB56u2678PSiToTgtE/GHzsFz3CDF4ZywKPn7ycDZxG7D+8DI2hJe00cjLyoeId9seNHKo7Scv1WLhui6jwbXUqoO3TaiUDQgcvM+BMz8QuO7VLcRfZF+JwiFz1O0WocTi+A+dgBidhyrIEgqUFZmX0/VgRmWBXlJ4DAcBzNvd/S/ufuNEzBEJkPi2et4fvg8pAVFsGrmi/pTR0BsZ4PQH//Gg09WV/peYAQcLBv7YGDgYb1VMC5Ky8C57m8hJzRS7VhGKEDjT2aixTcfKo7d/eAbhK/fob7y9EtB7tpi4u6MQQ8Pw8j6RXfs7NBIRP61F7nh0RBamMFj/CBFI0UKpTLothGFomdyI2LBCAQgEqnqgQwjv3isWgQAuDhgBpLO3dSskhrLwKKRN4ysLeRxCGowsrVCSWqmBtZrBnnFRsLzyIuMw6MVv6Ptmi/1tk5tpzApFZcGzkJmYCgYAQcQgBAZHn7+M9pv+hqhP/+jVMQSKY+soDAkX7wFaV4B8mMTIbKzhuuQnhCamSLtdhCe/LIFiSevgPA87Dq1gt/CqXAd1EOpPbfnLEPu02jNjJcRcGJRuUOmHi4qhQvDcXDs1QGFCSnIfhwh95bIiMbV/9xG90enzasgtCi/XWbZyButf1qimd0UipZQzwuFogGPV2/Cw89/VhvU2z9gb7kKoUnnbuBCv+larSV2skNRUprKMSb1XNFl+486pXlri8DUGKPTAipcFHVBJpEg/sgFpN8OAisQwKl/Fzh0b1dr+iwRmQyn2oxCVvBT7XtklcKwLFiREfjCIkVvHoGpMVyG9ELs7hPleveUbac0/mwOWn73UYW58uMScdizl1bblQMfHlZkHT1Z8y/uL/xOhbEMGI5Fv2s7YdO2KRJPXkH84fNIPHMNBfFJar16PnPGo92GFbXm9aPUbajnhULRMx5jBiBwyU/KB7AsbNs3KydcAPnFR1uKktLknhemkqZ0LAvWSIjuB9ch5fJtMEINvEFVRJpfiMKEFJh5Va2kftrtIFwd8S4KE1PBCAUAAUK++wPWrRqjx5ENaut9qIIvKUHc/jOI2XUcJRnZsPCrD+9Z42DXoYVW8ySevY7MwFDlAzQog09kMrlwARSvnzS/UFHm/mVRVLY1+HjVRjh0bwuXAd3LzZVx55HGwoXhODj166wQLjlhz3D/w1Uqz+HERuiy6xfF38l1SC+4DumFEy2HoyBW9XtXYGZChQulxqDihULRADMvd3hNH4VnWw5UFBSlF7TmKz8od1jG8wj68led1xQ72KIoOR1gSuNRpDxMXBzQadsPCPzkBySdu6H6wqbHbtkCM5MqnZ8XHY8LfaeBL5DXE3lZcGU9CsP5PlMxKOioThlOhclpuNBnGrJDwhUZVWm3AhH59z40eHcS2v6+rMIFlhCChOOX8HTddmQGhoIzFsNjjD8KElLk24NSJYLQQI5qhuMQtnZrBfHCcJqX3Lfr0gpddv2i+D38j11gOFZFijQD61aN4DasT4WHRHZWat8/xi4OVLhQagwqXigUDWm34SuAZfHs730KdzuRSCE0N0X7TV/DuV+XcuPTAx6iMEG7gF0FRO6B8b+7H2nX74MvLIJlMz+AENyc+ikKYhLUTuExZgBM67si9Pu/dLMBAFgWdh1bQOxgq/scAMLW/Ae+oKjSbTci5ZH7NBqxe0+i/lvDtZ772pj3kfPkmfyX0jiNsgt2+PodMPeth4YfvOggTQjBnbnLEfHnbsW2DQA8+XkLwMgfr24IzyPt5oMKx+26tFbvXWMYdN23Bu4j+5cTExn3QlRvfRGCrKCnlT5Ub/IwJJ+/pfxclkX9Kdq/VhSKvqDihULREM7ICB03fYOmX8xF3P4zkOTkwbyBJ9xH+1fao0inbr6vIDQzgd/7bwMAoncdx41Ji9Tf/TMMvGeORYc/v0ZReiZCf9isPvjylRowCghB02UVK6am332EtJuBYDh5BWOL0uZ/+bEJiNl1HMVpmTDxcEG9iYMhsrVWnz3FsojdIxcvhBCk3biP58cvQ1YigU2bJjBr4ImUiwEgUh627ZvDsXdHMAyD9DtBSL12T+VTC139Fxy6t0VeZByElubIexaPiD/lHb9ftonwfJW6I1eVyrJwRLbW8Jw4BNFbDyu1S+xgC1lRCYhUCkb4om0CJxapfT7K4pg8JwxG6Oq/kBsRU0EAMQIOIjtr+MydoMnTolAMAhUvFIqWmNVzQ6NF6vvEmPl4Vm0hloHYyR4AIC0oxO3ZSzU7jxAUJqQg48Fj3H1vhUZZIxZ+9ZETGinPrmEYECkPVmSE9htXwsW/m2Jc3rM4XBu/EBl3g1/UjyEEzoN6wMTdCZF/7gFT6pWS8TwefLQKLb9fDEl+gWoDZDKUZOWiMDkNV4a/i/SAhy9sKfM6MIx8+4znYd7AE90O/I6kszfKeU8qozAhBadaj3pxQFX34xoSLoyAg1P/Lni25QAKE1IgsreGJDsP4Rt3IS8i9qWBFcVIUWoGbkz+GIGf/YwG705EvUlDYeruDLfhfeRbiyrWdBvRt9LHBMZi9Ln4H66N/QCp1+6Vbl8xIDwPi0be6Lb/N9pPiFKj0GwjCsWAnO0+GWk3Hqiv2VIJVs39MOjhEQDAs/8O4dbUTzU7kWXh0L0t0m4GQiaRaBT30m79cth2bIm4fachycmDRSMv1Js8DEaW5ooxRWkZONF8GIpTM7TOxDF2d0bhcxXZKwwDE08X8AWFKEnPVvv3zgsAIAAAFkhJREFUYjgOQkszeE0fjbA1/+qcGVQpZV4oHbwwnLkJ+PwijdOMFTAMWCMhZCUl8vUry2rT1B6GQYN3J6H5yvdxrNEglKRnVtyuYxgwAg4DHxyCVZMGKqfLuB+C5Au3QGQy2HVuBfsubWisC8Ug1Ipso2+//RbHjx9HYGAgjIyMkJWVpfYcQgiWL1+OTZs2ISsrC126dMGGDRvQoIHqDxeFUltpt345znSeUBrvod0FtuX/Plb8Oy8iRvPMIpkM2cHhkEmkGgfsOvXvCnNvD9i0aqx0TPj6HfIAYm0vzACkefmqbSEEBdHPNZ6P8Dwk2bnIj3muX+ECgGEZOPl3R370c40Kw5WzS8qDFcoDfssJhlLhwRmLwBeXKP4WDMfKY2xkBLLi0uagytLxNRVShCB8/Q6wAgH6nN+CC/3eQVFSqtxDRYjcDrERuu5Zo1a4AIBN6ya0nD+l1qF5KLuWlJSUYOzYsZg3b57G56xevRpr167FH3/8gYCAAJiamsLf3x9FRUWGMpNCMShWTX3hH7AXrkN7lduuMPerr/I8hx7t4DLwReaJ0MpCbY0ZQO6REDvaojgtU2ORYWRrBXNvD8Xv0vwCRPy1F5dHvosTLYfjbPfJePDpD4j4c49OwgUAJJk5sGnX7EVVYD1AeBlSrt6DmZc7GD1WbSVSHvUmD0W/q9vlKd1aICssRreD6+TP9SWMbCzRYfN3GBF3Ga2+Xwynvp3h0KM9Gn08o1xVWr1BCJ6u2waRnTWGR51Hp/++h8f4gfAY44/WPy3BiPgrcB3SS//rUijVhMG3jbZs2YKFCxeq9bwQQuDi4oJFixbh44/ld5zZ2dlwdHTEli1bMGGCZsFhdNuIUlspSstAUWIqRHbWEDvZI2jZmhf9X3hZaT8jGey7tkGPYxvLbdnkxzzH4fp91N59m3l7wLZDc8TsOKaxXTZtm2LAnf0AgPQ7QbgwYCYkGdm6PUkV9Di2Ec/+PYi4vaf0NidnLEKjxTMR/PV6vcSrMBwHkb0NhkdfACcyQsiqjXj4+c9azSGytwYjFKAoIVWR6kyI3PnS+tcv4Df/LQAAX1SMuMPncWPCh6qm0x2GQbt1y9Bg3iTDzE+h6JlasW2kLVFRUUhKSkLfvi8CyCwtLdGhQwfcvHlTY/FCodRWxHY25YIcW3y9EPXfHoHIv/YiLzIORlbm8Jw4RJFJ8zKmnq7wnjUWkZv2Kr1IN/lyHuy7tMalQbM1tokRcLDr1AoAUJSSjgv93oEkJ0+HZ6cey8beKIhLrNBDqSqwQiGCV67Ty1xgAKGVOXqd+ktRb6bxktngjEUIWrpWvvWlAcUvtWx42VtGANxb8DUEJmKk33mEqH8Pgi8s1o/tlcBwLEqyDfNaUig1Ta0RL0lJSQAAR0fHcscdHR0Vj1VGcXExiotffAHk5OQYxkAKxQBYNKiHVt8v1mhsu9+XAQSI/GtvuTozAnNTtN+4Ep4TBuNky+Hy6ryahkfwMjQoTXmN2LRHLlwM4Iy1adcUxi6OSL/1UK/zSjQUFBpBAJ85E2DdoqHiEMMwaLhwGupNHoaDLl2rHl/DMrg9dzlAiN5jdV6FSHmY+3ioH0ih1EG0inlZsmSJPA1Sxc+TJ08MZWulrFq1CpaWloofd/eqlTCnUGorrFCIDn9+jeFR59Fq9WI0WTIbHf/9HqOSrqPexCHICY1EVlCYZkG6pZ6dNr9+DsvGPgCA+INnDZMqzDDouHmVYebWUwXhMh5//yeK0jIqHBfb28BtZL+qLyAjIBJplYSLRvE9DAMjWyu4Duut8zoUSm1GK8/LokWLMG3aNJVjvLy8dDLEyUne1yQ5ORnOzs6K48nJyWjZsqXS8z777DN89NGLhmY5OTlUwFBea0w9XSutM1OUkq7ZBAzg3L8LGn08A059OysOSws1D4wv11ywNLtGmben8ZLZsGrqCwCwbOYrL+NfVdHBMDCysURJelbV5nkVXobYPSfh++7kcocfr96EuH36i9XRFaGFGfpc/A8Zd4MR/scuZD58UjGIujQwvOPf34Iz0r7dAoVSF9BKvNjb28Pe3t4ghtSvXx9OTk44f/68Qqzk5OQgICBAZcaSSCSCSFT1brcUSl3H2NVR/SAAXXb+As/xgyoct23bDDlhUcpTdV+i2/7fILQ0ByvgYNGkAe7MW47YXaUdkwmRCxmZDI0/nYUW374ISG300XTcmv6Z2vlN67mCSHl5Z+NXYeSPF6dnVnxMD+SERZX7PXrHUQR++qNB1tIGRijAgHsHYO7jCZvWTeAzezzynsXhwZIfEX/gjCK+xq5jC7T4ZiEce3WsYYspFMNhsJiX2NhYZGRkIDY2FjzPIzAwEADg4+MDMzMzAEDDhg2xatUqjBw5EgzDYOHChfjmm2/QoEED1K9fH0uXLoWLiwtGjBhhKDMplNcGiwb1YNepJdJvBylNqxZamsFteMVGfADQ4L3JiPrvkOpFGMDEzRnOA7uDKy1FL5NI4DFmACRZuch5GgWBsRgO3duh0cczKnSirj91JNJuBSJi4+5KK+OKHGzBGYuQr6R3k4mnC/zmvwXzhl64MnSualt1xMjqRZYDIUSeyVSNbQPk9VhkL7xTLAtOJET3Q+th/krVZjMvd3TbswbFGVkoiE+CyMaqSt25KZS6gsHEy7Jly/Dvv/8qfm/VSp7RcPHiRfTs2RMAEBYWhuzsFymZn3zyCfLz8zF79mxkZWWha9euOHXqFMTiin1jKBRKRVr/8jnOdZ8sv85WktHT5tcvlPazsWvfHE2Xz0fwit+VL0CAgrhEHPbshYYfvA3PSUNwaeBsZIeEK8QIw3HIDomAiZsTmnxeXmAwDIN2G1bAdWhveUfn+yHgjMVwH90fPnMn4tlfexH6w9+VCwWWhayoBH4fvI34Q+e0+bNohfeMMYp/50fHv2j6WEUYjgXDcZCVSFQMYtD/5m4knr6K5IsBAMPAqXdHeM8cq7I5psjGCiIbK73YSaHUBWh7AArlNSP15gPcfXcFMgNDFcdM3J3R8vuPUW/iELXnxx06h+BvNyDzbrDqgQwDoYWZPONHiaen886fUW/CYABAVkg4Mu+HgDUyglPfThDZWpcbyxeX4IBjZ7UNLbvuXQMTNyec6TRe7XPRFtv2zeEfsFfxe/bjCBxvMrjK8xrZWsH33Ukw8XDB7VlfVj6IZVB/ygh02vK/Kq9HodRF6mSdFwqFoh/sO7XCwAeHkBn0BPnRzyGys4Zdx5ZgVDUkfAn3EX3hPqIvZFIpUi7dxoV+0ysfSIhqocEyeLxqI2zaNkXA9M/KdX9mhAL4zBqH1j9/pqipkh8dr1a4MEIBMu4Gw320P8wbeCI3Ilbj7RyG48CJjSAtKKw0uFhkZ4XuR/8od8y0nis4EzH4AtXBzJbN/JAd/FRhi7GzA5p9NR/1pgyHrLgEQgszxd9fml+AB4tXywsTcqwibdpz3CC037hSo+dCobzpUPFCobymWDdvCOvmDdUPVAIrECBq6+FymUVaISPICgrD2U4TUJJZvmIvkUgR/scuFCamotv+38CUNiZUCyFgRUZgGAZt1y3HpYEzQWQoL2BK08C9Z41D6tU7yI96DqGlGepPGQ6/D6Yi71kcHq1ch+TzN+XPUyyC9zuj0WzFggqdkgUmxvCeMRbh63dU3puKZSGyscSAu/vACgSQSaVghcLyRQaNy297N/xgKupNHIKorYeR9ywORjaW8JwwWKM+QxQKRQ7dNqJQKEo52XYUMu+FVG2Ssi7NSuh3YxfsO7UCIQTH/PzVelP6B+yFXfvmAICkczdwb+F38vTrUiwaeqH1z0vgMrCHSrNKsnIgyc2H2MFW4f2pdFx2Ls52m4ScxxHlAqEZAQeG49DzxJ9w6t1J5VoUCkU9dNuIQqHoBSNL86pl2jCMSuHCCDhE/XsQ9p1agWEYNPl8rtJUakbAwbZDC9i+1PTQqW9nDHp0FFkPn6DgeTKMne1h3apxhfYKlWFkZVEus0jpOEtz9L+2E6E//4Pw9TtQnJoBRsDBfcwANFkyu1xFXgqFUj1Q8UKhUJTiMW4gki/e0u1kNR4XQF7CvjAxVfF7/akjkfcsDsFfr1dsV5VlMVk2aYDuB36vIEwYhoF1y0awbtlINzs1QGhhhuZfLUCz5fPBFxSCFRmBFdCvTwqlpqCfPgqFopR6k4ci5Ns/UJiQUiHmg+FYsGIRZBIpIJO9iIspFRf2XVsjOyRCZRVcRsDB5KXiegzDoPnKD+A5cQgiNu1B7tMoCC3M4TFuIFyH9KxxwcAwDASmJjVqA4VCoTEvFApFDXnP4nBpyBzkhEaCEQgARh5wa+xsjx5H/wBnLEboj5sRu+cEpAVFMG/gCd/3JsNnzgQ8+uo3hP7wl9KieQDgf3svbNs1r8ZnRKFQaiPaXL+peKFQKGohMhmSzt1A0rkbILwMdp1bwW1Yb7DC8hlChJBy2zpFqRk41XokCpNSK2YsMQw8Jw1Bl201X3qfQqHUPFS8UPFCodQaCuKTEDDrSySevqYI/OVMxPBbMAXNv1lY41tBFAqldkCzjSgUSq3BxM0JvU7+hbyoOGQGPgErMoJDtzYQmpvVtGkUCqWOQsULhUKpFszqu8Osvrv6gRQKhaIGzeqFUygUCoVCodQSqHihUCgUCoVSp6DihUKhUCgUSp2CihcKhUKhUCh1CipeKBQKhUKh1CmoeKFQKBQKhVKnoOKFQqFQKBRKnYKKFwqFQqFQKHUKKl4oFAqFQqHUKV67CrtlrZpycnJq2BIKhUKhUCiaUnbd1qTl4msnXnJzcwEA7u60DDmFQqFQKHWN3NxcWFpaqhzz2nWVlslkSEhIgLm5ORiGqWlzkJOTA3d3d8TFxdEu17Uc+lrVLejrVbegr1fdoiZeL0IIcnNz4eLiApZVHdXy2nleWJaFm5tbTZtRAQsLC/qBrSPQ16puQV+vugV9veoW1f16qfO4lEEDdikUCoVCodQpqHihUCgUCoVSp6DixcCIRCIsX74cIpGopk2hqIG+VnUL+nrVLejrVbeo7a/XaxewS6FQKBQK5fWGel4oFAqFQqHUKah4oVAoFAqFUqeg4oVCoVAoFEqdgooXCoVCoVAodQoqXvTMt99+i86dO8PExARWVlYanUMIwbJly+Ds7AxjY2P07dsX4eHhhjWUAgDIyMjA5MmTYWFhASsrK8yYMQN5eXkqz+nZsycYhin3M3fu3Gqy+M1i3bp1qFevHsRiMTp06IDbt2+rHL937140bNgQYrEYzZo1w4kTJ6rJUgqg3eu1ZcuWCp8jsVhcjda+uVy5cgVDhw6Fi4sLGIbBoUOH1J5z6dIltG7dGiKRCD4+PtiyZYvB7VQFFS96pqSkBGPHjsW8efM0Pmf16tVYu3Yt/vjjDwQEBMDU1BT+/v4oKioyoKUUAJg8eTJCQkJw9uxZHDt2DFeuXMHs2bPVnjdr1iwkJiYqflavXl0N1r5Z7N69Gx999BGWL1+O+/fvo0WLFvD390dKSkql42/cuIGJEydixowZePDgAUaMGIERI0YgODi4mi1/M9H29QLk1Vtf/hzFxMRUo8VvLvn5+WjRogXWrVun0fioqCgMHjwYvXr1QmBgIBYuXIiZM2fi9OnTBrZUBYRiEP755x9iaWmpdpxMJiNOTk7khx9+UBzLysoiIpGI7Ny504AWUh4/fkwAkDt37iiOnTx5kjAMQ54/f670vB49epAPPvigGix8s2nfvj157733FL/zPE9cXFzIqlWrKh0/btw4Mnjw4HLHOnToQObMmWNQOylytH29NP2OpBgWAOTgwYMqx3zyySekSZMm5Y6NHz+e+Pv7G9Ay1VDPSw0TFRWFpKQk9O3bV3HM0tISHTp0wM2bN2vQstefmzdvwsrKCm3btlUc69u3L1iWRUBAgMpzt2/fDjs7OzRt2hSfffYZCgoKDG3uG0VJSQnu3btX7nPBsiz69u2r9HNx8+bNcuMBwN/fn36OqgFdXi8AyMvLg6enJ9zd3TF8+HCEhIRUh7kULamNn63XrjFjXSMpKQkA4OjoWO64o6Oj4jGKYUhKSoKDg0O5YwKBADY2Nir/9pMmTYKnpydcXFwQFBSETz/9FGFhYThw4IChTX5jSEtLA8/zlX4unjx5Uuk5SUlJ9HNUQ+jyevn5+WHz5s1o3rw5srOz8eOPP6Jz584ICQmplc1132SUfbZycnJQWFgIY2PjareJel40YMmSJRUCy179UfYBpVQ/hn69Zs+eDX9/fzRr1gyTJ0/Gf//9h4MHDyIyMlKPz4JCeb3p1KkT3n77bbRs2RI9evTAgQMHYG9vj40bN9a0aZQ6APW8aMCiRYswbdo0lWO8vLx0mtvJyQkAkJycDGdnZ8Xx5ORktGzZUqc533Q0fb2cnJwqBBNKpVJkZGQoXhdN6NChAwAgIiIC3t7eWttLqYidnR04jkNycnK548nJyUpfGycnJ63GU/SHLq/XqwiFQrRq1QoRERGGMJFSBZR9tiwsLGrE6wJQ8aIR9vb2sLe3N8jc9evXh5OTE86fP68QKzk5OQgICNAqY4nyAk1fr06dOiErKwv37t1DmzZtAAAXLlyATCZTCBJNCAwMBIBy4pNSNYyMjNCmTRucP38eI0aMAADIZDKcP38e8+fPr/ScTp064fz581i4cKHi2NmzZ9GpU6dqsPjNRpfX61V4nsejR48waNAgA1pK0YVOnTpVKDtQ45+tGgsVfk2JiYkhDx48ICtWrCBmZmbkwYMH5MGDByQ3N1cxxs/Pjxw4cEDx+//+9z9iZWVFDh8+TIKCgsjw4cNJ/fr1SWFhYU08hTeKAQMGkFatWpGAgABy7do10qBBAzJx4kTF4/Hx8cTPz48EBAQQQgiJiIggK1euJHfv3iVRUVHk8OHDxMvLi3Tv3r2mnsJry65du4hIJCJbtmwhjx8/JrNnzyZWVlYkKSmJEELIlClTyJIlSxTjr1+/TgQCAfnxxx9JaGgoWb58OREKheTRo0c19RTeKLR9vVasWEFOnz5NIiMjyb1798iECROIWCwmISEhNfUU3hhyc3MV1yYA5OeffyYPHjwgMTExhBBClixZQqZMmaIY/+zZM2JiYkIWL15MQkNDybp16wjHceTUqVM19RQIFS96ZurUqQRAhZ+LFy8qxgAg//zzj+J3mUxGli5dShwdHYlIJCJ9+vQhYWFh1W/8G0h6ejqZOHEiMTMzIxYWFmT69OnlhGZUVFS51y82NpZ0796d2NjYEJFIRHx8fMjixYtJdnZ2DT2D15vffvuNeHh4ECMjI9K+fXty69YtxWM9evQgU6dOLTd+z549xNfXlxgZGZEmTZqQ48ePV7PFbzbavF4LFy5UjHV0dCSDBg0i9+/frwGr3zwuXrxY6XWq7PWZOnUq6dGjR4VzWrZsSYyMjIiXl1e5a1hNwBBCSI24fCgUCoVCoVB0gGYbUSgUCoVCqVNQ8UKhUCgUCqVOQcULhUKhUCiUOgUVLxQKhUKhUOoUVLxQKBQKhUKpU1DxQqFQKBQKpU5BxQuFQqFQKJQ6BRUvFAqFQqFQ6hRUvFAoFAqFQqlTUPFCoVAoFAqlTkHFC4VCoVAolDoFFS8UCoVCoVDqFP8HGHy3EG5f1LYAAAAASUVORK5CYII=\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_Jty66H6cbJp"
+      },
+      "source": [
+        "Nice! From the plot, can you guess what kind of model we might want to build?\n",
+        "\n",
+        "How about we try and build one to classify blue or red dots? As in, a model which is able to distinguish blue from red dots.\n",
+        "\n",
+        "> 🛠 **Practice:** Before pushing forward, you might want to spend 10 minutes playing around with the [TensorFlow Playground](https://playground.tensorflow.org/#activation=relu&batchSize=10&dataset=circle&regDataset=reg-plane&learningRate=0.03&regularizationRate=0&noise=0&networkShape=2,2&seed=0.93799&showTestData=false&discretize=false&percTrainData=50&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification&initZero=false&hideText=false&regularization_hide=true&regularizationRate_hide=true&batchSize_hide=true). Try adjusting the different hyperparameters you see and click play to see a neural network train. I think you'll find the data very similar to what we've just created."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Cv4fEHihOAq4"
+      },
+      "source": [
+        "## Input and output shapes\n",
+        "\n",
+        "One of the most common issues you'll run into when building neural networks is shape mismatches.\n",
+        "\n",
+        "More specifically, the shape of the input data and the shape of the output data.\n",
+        "\n",
+        "In our case, we want to input `X` and get our model to predict `y`.\n",
+        "\n",
+        "So let's check out the shapes of `X` and `y`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "lZxjjmsHNt_K",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "4facde92-0bb4-4d7d-9bd1-e28d65f7054e"
+      },
+      "source": [
+        "# Check the shapes of our features and labels\n",
+        "X.shape, y.shape"
+      ],
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "((1000, 2), (1000,))"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 8
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5BSq58RogJMa"
+      },
+      "source": [
+        "Hmm, where do these numbers come from?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "SPr81SsKgHvZ",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "a38bb650-8efb-4569-aa7f-a6087de6685c"
+      },
+      "source": [
+        "# Check how many samples we have\n",
+        "len(X), len(y)"
+      ],
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(1000, 1000)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XCBbrNaOgPWC"
+      },
+      "source": [
+        "So we've got as many `X` values as we do `y` values, that makes sense.\n",
+        "\n",
+        "Let's check out one example of each."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "hwrSEVubgZNb",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "712e5df3-b49e-4509-a8cb-c83df729b3ad"
+      },
+      "source": [
+        "# View the first example of features and labels\n",
+        "X[0], y[0]"
+      ],
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(array([0.75424625, 0.23148074]), 1)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 10
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OMeqY8JSgg4L"
+      },
+      "source": [
+        "Alright, so we've got two `X` features which lead to one `y` value.\n",
+        "\n",
+        "This means our neural network input shape will has to accept a tensor with at least one dimension being two and output a tensor with at least one value.\n",
+        "\n",
+        "> 🤔 **Note:** `y` having a shape of (1000,) can seem confusing. However, this is because all `y` values are actually scalars (single values) and therefore don't have a dimension. For now, think of your output shape as being at least the same value as one example of `y` (in our case, the output from our neural network has to be at least one value)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vSjUbiCBN9fc"
+      },
+      "source": [
+        "## Steps in modelling\n",
+        "\n",
+        "Now we know what data we have as well as the input and output shapes, let's see how we'd build a neural network to model it.\n",
+        "\n",
+        "In TensorFlow, there are typically 3 fundamental steps to creating and training a model.\n",
+        "\n",
+        "1. **Creating a model** - piece together the layers of a neural network yourself (using the [functional](https://www.tensorflow.org/guide/keras/functional) or [sequential API](https://www.tensorflow.org/api_docs/python/tf/keras/Sequential)) or import a previously built model (known as transfer learning).\n",
+        "2. **Compiling a model** - defining how a model's performance should be measured (loss/metrics) as well as defining how it should improve (optimizer).\n",
+        "3. **Fitting a model** - letting the model try to find patterns in the data (how does `X` get to `y`).\n",
+        "\n",
+        "Let's see these in action using the Sequential API to build a model for our regression data. And then we'll step through each."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Lt4pbcZ7OEif",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "ae93a394-a87d-4a60-ec72-3b2c882079d1"
+      },
+      "source": [
+        "# Set random seed\n",
+        "tf.random.set_seed(42)\n",
+        "\n",
+        "# 1. Create the model using the Sequential API\n",
+        "model_1 = tf.keras.Sequential([\n",
+        "  tf.keras.layers.Dense(1)\n",
+        "])\n",
+        "\n",
+        "# 2. Compile the model\n",
+        "model_1.compile(loss=tf.keras.losses.BinaryCrossentropy(), # binary since we are working with 2 clases (0 & 1)\n",
+        "                optimizer=tf.keras.optimizers.SGD(),\n",
+        "                metrics=['accuracy'])\n",
+        "\n",
+        "# 3. Fit the model\n",
+        "model_1.fit(X, y, epochs=5)"
+      ],
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/5\n",
+            "32/32 [==============================] - 1s 3ms/step - loss: 6.5220 - accuracy: 0.4770\n",
+            "Epoch 2/5\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 5.6856 - accuracy: 0.4310\n",
+            "Epoch 3/5\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 5.4554 - accuracy: 0.4520\n",
+            "Epoch 4/5\n",
+            "32/32 [==============================] - 0s 6ms/step - loss: 5.0517 - accuracy: 0.4590\n",
+            "Epoch 5/5\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 5.0146 - accuracy: 0.4610\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<keras.src.callbacks.History at 0x792aa8099300>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 11
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "oa8Tcv9ePRq6"
+      },
+      "source": [
+        "Looking at the accuracy metric, our model performs poorly (50% accuracy on a binary classification problem is the equivalent of guessing), but what if we trained it for longer?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "GqVVD_IqPHm5",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "ce314cab-1d79-41e4-9b89-2564bf35999a"
+      },
+      "source": [
+        "# Train our model for longer (more chances to look at the data)\n",
+        "model_1.fit(X, y, epochs=200, verbose=0) # set verbose=0 to remove training updates\n",
+        "model_1.evaluate(X, y)"
+      ],
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "32/32 [==============================] - 0s 2ms/step - loss: 0.6935 - accuracy: 0.5000\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[0.6934829950332642, 0.5]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 12
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "H-qaMmKrPegL"
+      },
+      "source": [
+        "Even after 200 passes of the data, it's still performing as if it's guessing.\n",
+        "\n",
+        "What if we added an extra layer and trained for a little longer?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "TED0ZkOuPklW",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "eca0d1f9-e541-4df6-9200-67a03acb9c15"
+      },
+      "source": [
+        "# Set random seed\n",
+        "tf.random.set_seed(42)\n",
+        "\n",
+        "# 1. Create the model (same as model_1 but with an extra layer)\n",
+        "model_2 = tf.keras.Sequential([\n",
+        "  tf.keras.layers.Dense(1), # add an extra layer\n",
+        "  tf.keras.layers.Dense(1)\n",
+        "])\n",
+        "\n",
+        "# 2. Compile the model\n",
+        "model_2.compile(loss=tf.keras.losses.BinaryCrossentropy(),\n",
+        "                optimizer=tf.keras.optimizers.SGD(),\n",
+        "                metrics=['accuracy'])\n",
+        "\n",
+        "# 3. Fit the model\n",
+        "model_2.fit(X, y, epochs=100, verbose=0) # set verbose=0 to make the output print less"
+      ],
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<keras.src.callbacks.History at 0x792a91e60d00>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 13
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "qFWWlhByirLa",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "845fdb32-b0a3-4810-82b0-206c53fecd03"
+      },
+      "source": [
+        "# Evaluate the model\n",
+        "model_2.evaluate(X, y)"
+      ],
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "32/32 [==============================] - 0s 2ms/step - loss: 0.6934 - accuracy: 0.4570\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[0.6933722496032715, 0.4569999873638153]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 14
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HC7vhAHKQQRY"
+      },
+      "source": [
+        "Still not even as good as guessing (~50% accuracy)... hmm...?\n",
+        "\n",
+        "Let's remind ourselves of a couple more ways we can use to improve our models."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wyTs6QgijF6_"
+      },
+      "source": [
+        "## Improving a model\n",
+        "\n",
+        "To improve our model, we can alter almost every part of the 3 steps we went through before.\n",
+        "\n",
+        "1. **Creating a model** - here you might want to add more layers, increase the number of hidden units (also called neurons) within each layer, change the activation functions of each layer.\n",
+        "2. **Compiling a model** - you might want to choose a different optimization function (such as the [Adam](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/Adam) optimizer, which is usually pretty good for many problems) or perhaps change the learning rate of the optimization function.\n",
+        "3. **Fitting a model** - perhaps you could fit a model for more epochs (leave it training for longer).\n",
+        "\n",
+        "![various options you can use to improve a neural network model](https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/images/02-improving-a-model-from-model-perspective.png)\n",
+        "*There are many different ways to potentially improve a neural network. Some of the most common include: increasing the number of layers (making the network deeper), increasing the number of hidden units (making the network wider) and changing the learning rate. Because these values are all human-changeable, they're referred to as [hyperparameters](https://en.wikipedia.org/wiki/Hyperparameter_(machine_learning)) and the practice of trying to find the best hyperparameters is referred to as [hyperparameter tuning](https://en.wikipedia.org/wiki/Hyperparameter_optimization).*\n",
+        "\n",
+        "How about we try adding more neurons, an extra layer and our friend the Adam optimizer?\n",
+        "\n",
+        "Surely doing this will result in predictions better than guessing...\n",
+        "\n",
+        "> **Note:** The following message (below this one) can be ignored if you're running TensorFlow 2.8.0+, the error seems to have been fixed.\n",
+        "\n",
+        "> **Note:** If you're using TensorFlow 2.7.0+ (but not 2.8.0+) the original code from the following cells may have caused some errors. They've since been updated to fix those errors. You can see explanations on what happened at the following resources:\n",
+        "* [Example Colab Notebook](https://colab.research.google.com/drive/1_dlrB_DJOBS9c9foYJs49I0YwN7LTakl?usp=sharing)\n",
+        "* [TensorFlow for Deep Learning GitHub Discussion on TensorFlow 2.7.0 breaking changes](https://github.com/mrdbourke/tensorflow-deep-learning/discussions/278)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "46pebMB4Qeth",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "62591f0a-8de3-45bc-b840-943e2bc589fb"
+      },
+      "source": [
+        "# Set random seed\n",
+        "tf.random.set_seed(42)\n",
+        "\n",
+        "# 1. Create the model (this time 3 layers)\n",
+        "model_3 = tf.keras.Sequential([\n",
+        "  # Before TensorFlow 2.7.0\n",
+        "  # tf.keras.layers.Dense(100), # add 100 dense neurons\n",
+        "\n",
+        "  # With TensorFlow 2.7.0\n",
+        "  # tf.keras.layers.Dense(100, input_shape=(None, 1)), # add 100 dense neurons\n",
+        "\n",
+        "  ## After TensorFlow 2.8.0 ##\n",
+        "  tf.keras.layers.Dense(100), # add 100 dense neurons\n",
+        "  tf.keras.layers.Dense(10), # add another layer with 10 neurons\n",
+        "  tf.keras.layers.Dense(1)\n",
+        "])\n",
+        "\n",
+        "# 2. Compile the model\n",
+        "model_3.compile(loss=tf.keras.losses.BinaryCrossentropy(),\n",
+        "                optimizer=tf.keras.optimizers.Adam(), # use Adam instead of SGD\n",
+        "                metrics=['accuracy'])\n",
+        "\n",
+        "# 3. Fit the model\n",
+        "model_3.fit(X, y, epochs=100, verbose=1) # fit for 100 passes of the data"
+      ],
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/100\n",
+            "32/32 [==============================] - 2s 5ms/step - loss: 3.0375 - accuracy: 0.4510\n",
+            "Epoch 2/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 0.7177 - accuracy: 0.4950\n",
+            "Epoch 3/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 0.6977 - accuracy: 0.5010\n",
+            "Epoch 4/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 0.6954 - accuracy: 0.4820\n",
+            "Epoch 5/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 0.6953 - accuracy: 0.4760\n",
+            "Epoch 6/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 0.6954 - accuracy: 0.4440\n",
+            "Epoch 7/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.6951 - accuracy: 0.5210\n",
+            "Epoch 8/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 0.6957 - accuracy: 0.5000\n",
+            "Epoch 9/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.6971 - accuracy: 0.4810\n",
+            "Epoch 10/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 0.6949 - accuracy: 0.4510\n",
+            "Epoch 11/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.6985 - accuracy: 0.4650\n",
+            "Epoch 12/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.6958 - accuracy: 0.4880\n",
+            "Epoch 13/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.6973 - accuracy: 0.4800\n",
+            "Epoch 14/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 0.6958 - accuracy: 0.5250\n",
+            "Epoch 15/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.6978 - accuracy: 0.4630\n",
+            "Epoch 16/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.6951 - accuracy: 0.4960\n",
+            "Epoch 17/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.7014 - accuracy: 0.4970\n",
+            "Epoch 18/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.6957 - accuracy: 0.4730\n",
+            "Epoch 19/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.6970 - accuracy: 0.5100\n",
+            "Epoch 20/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 0.6982 - accuracy: 0.4580\n",
+            "Epoch 21/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.6969 - accuracy: 0.4940\n",
+            "Epoch 22/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.6965 - accuracy: 0.4680\n",
+            "Epoch 23/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.6957 - accuracy: 0.5200\n",
+            "Epoch 24/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.6987 - accuracy: 0.4540\n",
+            "Epoch 25/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 0.6997 - accuracy: 0.5040\n",
+            "Epoch 26/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.6994 - accuracy: 0.4820\n",
+            "Epoch 27/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.6982 - accuracy: 0.5130\n",
+            "Epoch 28/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.6973 - accuracy: 0.4860\n",
+            "Epoch 29/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 0.6990 - accuracy: 0.5010\n",
+            "Epoch 30/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.7077 - accuracy: 0.4640\n",
+            "Epoch 31/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 0.6942 - accuracy: 0.5370\n",
+            "Epoch 32/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.6978 - accuracy: 0.4660\n",
+            "Epoch 33/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.6981 - accuracy: 0.4930\n",
+            "Epoch 34/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.7006 - accuracy: 0.4700\n",
+            "Epoch 35/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.6977 - accuracy: 0.4890\n",
+            "Epoch 36/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.6955 - accuracy: 0.4950\n",
+            "Epoch 37/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.6971 - accuracy: 0.4890\n",
+            "Epoch 38/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.6983 - accuracy: 0.4980\n",
+            "Epoch 39/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.6998 - accuracy: 0.4530\n",
+            "Epoch 40/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.6977 - accuracy: 0.4950\n",
+            "Epoch 41/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 0.6986 - accuracy: 0.5190\n",
+            "Epoch 42/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.7031 - accuracy: 0.4900\n",
+            "Epoch 43/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.6972 - accuracy: 0.5070\n",
+            "Epoch 44/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.6991 - accuracy: 0.4730\n",
+            "Epoch 45/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.7026 - accuracy: 0.4960\n",
+            "Epoch 46/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.6954 - accuracy: 0.4790\n",
+            "Epoch 47/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 0.6984 - accuracy: 0.4990\n",
+            "Epoch 48/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.6989 - accuracy: 0.5030\n",
+            "Epoch 49/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.6992 - accuracy: 0.4740\n",
+            "Epoch 50/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.7001 - accuracy: 0.4630\n",
+            "Epoch 51/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.7022 - accuracy: 0.4660\n",
+            "Epoch 52/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.6971 - accuracy: 0.5000\n",
+            "Epoch 53/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 0.6974 - accuracy: 0.4780\n",
+            "Epoch 54/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.7018 - accuracy: 0.5130\n",
+            "Epoch 55/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.6969 - accuracy: 0.5180\n",
+            "Epoch 56/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.6973 - accuracy: 0.5040\n",
+            "Epoch 57/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.6993 - accuracy: 0.4790\n",
+            "Epoch 58/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.6992 - accuracy: 0.4790\n",
+            "Epoch 59/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.7029 - accuracy: 0.4910\n",
+            "Epoch 60/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.7040 - accuracy: 0.5010\n",
+            "Epoch 61/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.7019 - accuracy: 0.4660\n",
+            "Epoch 62/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.6969 - accuracy: 0.5050\n",
+            "Epoch 63/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.6949 - accuracy: 0.4680\n",
+            "Epoch 64/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.6957 - accuracy: 0.5000\n",
+            "Epoch 65/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 0.7028 - accuracy: 0.4730\n",
+            "Epoch 66/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.6976 - accuracy: 0.5000\n",
+            "Epoch 67/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.6989 - accuracy: 0.4570\n",
+            "Epoch 68/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.6961 - accuracy: 0.5020\n",
+            "Epoch 69/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.6971 - accuracy: 0.5140\n",
+            "Epoch 70/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.7002 - accuracy: 0.4890\n",
+            "Epoch 71/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.6997 - accuracy: 0.4490\n",
+            "Epoch 72/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.6979 - accuracy: 0.4970\n",
+            "Epoch 73/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 0.6971 - accuracy: 0.5040\n",
+            "Epoch 74/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.6968 - accuracy: 0.5200\n",
+            "Epoch 75/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 0.6972 - accuracy: 0.5150\n",
+            "Epoch 76/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 0.6978 - accuracy: 0.4910\n",
+            "Epoch 77/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.6978 - accuracy: 0.5000\n",
+            "Epoch 78/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.6980 - accuracy: 0.4790\n",
+            "Epoch 79/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.7021 - accuracy: 0.5100\n",
+            "Epoch 80/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.7003 - accuracy: 0.4910\n",
+            "Epoch 81/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.6994 - accuracy: 0.4910\n",
+            "Epoch 82/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.6978 - accuracy: 0.5060\n",
+            "Epoch 83/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 0.7010 - accuracy: 0.4540\n",
+            "Epoch 84/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.6974 - accuracy: 0.5160\n",
+            "Epoch 85/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.6972 - accuracy: 0.4730\n",
+            "Epoch 86/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.7016 - accuracy: 0.4910\n",
+            "Epoch 87/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.6984 - accuracy: 0.4700\n",
+            "Epoch 88/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.6994 - accuracy: 0.4630\n",
+            "Epoch 89/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.6981 - accuracy: 0.4950\n",
+            "Epoch 90/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.6988 - accuracy: 0.4840\n",
+            "Epoch 91/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.7002 - accuracy: 0.5040\n",
+            "Epoch 92/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.7011 - accuracy: 0.4840\n",
+            "Epoch 93/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 0.6984 - accuracy: 0.4860\n",
+            "Epoch 94/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.7022 - accuracy: 0.4930\n",
+            "Epoch 95/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 0.6982 - accuracy: 0.4700\n",
+            "Epoch 96/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 0.6988 - accuracy: 0.4670\n",
+            "Epoch 97/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 0.6979 - accuracy: 0.4520\n",
+            "Epoch 98/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 0.7010 - accuracy: 0.4880\n",
+            "Epoch 99/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 0.6983 - accuracy: 0.5090\n",
+            "Epoch 100/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 0.6964 - accuracy: 0.4980\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<keras.src.callbacks.History at 0x792a917b9b10>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 15
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TJJL0YT7RVzv"
+      },
+      "source": [
+        "Still!\n",
+        "\n",
+        "We've pulled out a few tricks but our model isn't even doing better than guessing.\n",
+        "\n",
+        "Let's make some visualizations to see what's happening.\n",
+        "\n",
+        "> 🔑 **Note:** Whenever your model is performing strangely or there's something going on with your data you're not quite sure of, remember these three words: **visualize, visualize, visualize**. Inspect your data, inspect your model, inpsect your model's predictions.\n",
+        "\n",
+        "To visualize our model's predictions we're going to create a function `plot_decision_boundary()` which:\n",
+        "* Takes in a trained model, features (`X`) and labels (`y`).\n",
+        "* Creates a [meshgrid](https://numpy.org/doc/stable/reference/generated/numpy.meshgrid.html) of the different `X` values.\n",
+        "* Makes predictions across the meshgrid.\n",
+        "* Plots the predictions as well as a line between the different zones (where each unique class falls).\n",
+        "\n",
+        "If this sounds confusing, let's see it in code and then see the output.\n",
+        "\n",
+        "> 🔑 **Note:** If you're ever unsure of what a function does, try unraveling it and writing it line by line for yourself to see what it does. Break it into small parts and see what each part outputs."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "MuwGjU_XR0aG"
+      },
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "def plot_decision_boundary(model, X, y):\n",
+        "  \"\"\"\n",
+        "  Plots the decision boundary created by a model predicting on X.\n",
+        "  This function has been adapted from two phenomenal resources:\n",
+        "   1. CS231n - https://cs231n.github.io/neural-networks-case-study/\n",
+        "   2. Made with ML basics - https://github.com/GokuMohandas/MadeWithML/blob/main/notebooks/08_Neural_Networks.ipynb\n",
+        "  \"\"\"\n",
+        "  # Define the axis boundaries of the plot and create a meshgrid\n",
+        "  x_min, x_max = X[:, 0].min() - 0.1, X[:, 0].max() + 0.1\n",
+        "  y_min, y_max = X[:, 1].min() - 0.1, X[:, 1].max() + 0.1\n",
+        "  xx, yy = np.meshgrid(np.linspace(x_min, x_max, 100),\n",
+        "                       np.linspace(y_min, y_max, 100))\n",
+        "\n",
+        "  # Create X values (we're going to predict on all of these)\n",
+        "  x_in = np.c_[xx.ravel(), yy.ravel()] # stack 2D arrays together: https://numpy.org/devdocs/reference/generated/numpy.c_.html\n",
+        "\n",
+        "  # Make predictions using the trained model\n",
+        "  y_pred = model.predict(x_in)\n",
+        "\n",
+        "  # Check for multi-class\n",
+        "  if model.output_shape[-1] > 1: # checks the final dimension of the model's output shape, if this is > (greater than) 1, it's multi-class\n",
+        "    print(\"doing multiclass classification...\")\n",
+        "    # We have to reshape our predictions to get them ready for plotting\n",
+        "    y_pred = np.argmax(y_pred, axis=1).reshape(xx.shape)\n",
+        "  else:\n",
+        "    print(\"doing binary classifcation...\")\n",
+        "    y_pred = np.round(np.max(y_pred, axis=1)).reshape(xx.shape)\n",
+        "\n",
+        "  # Plot decision boundary\n",
+        "  plt.contourf(xx, yy, y_pred, cmap=plt.cm.RdYlBu, alpha=0.7)\n",
+        "  plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.RdYlBu)\n",
+        "  plt.xlim(xx.min(), xx.max())\n",
+        "  plt.ylim(yy.min(), yy.max())"
+      ],
+      "execution_count": 16,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OgekXFM2clQW"
+      },
+      "source": [
+        "Now we've got a function to plot our model's decision boundary (the cut off point its making between red and blue dots), let's try it out."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "XIyCVjolTmy4",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 464
+        },
+        "outputId": "032255d8-51f8-47f7-e0c4-52844c1c5602"
+      },
+      "source": [
+        "# Check out the predictions our model is making\n",
+        "plot_decision_boundary(model_3, X, y)"
+      ],
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "313/313 [==============================] - 1s 1ms/step\n",
+            "doing binary classifcation...\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XIzcQrknUJPK"
+      },
+      "source": [
+        "Looks like our model is trying to draw a straight line through the data.\n",
+        "\n",
+        "What's wrong with doing this?\n",
+        "\n",
+        "The main issue is our data isn't separable by a straight line.\n",
+        "\n",
+        "In a regression problem, our model might work. In fact, let's try it."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "7d9O9subUshC",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 793
+        },
+        "outputId": "cc1c5ca3-da95-4a4e-a0b3-114b78291ae3"
+      },
+      "source": [
+        "# Set random seed\n",
+        "tf.random.set_seed(42)\n",
+        "\n",
+        "# Create some regression data\n",
+        "X_regression = np.arange(0, 1000, 5)\n",
+        "y_regression = np.arange(100, 1100, 5)\n",
+        "\n",
+        "# Split it into training and test sets\n",
+        "X_reg_train = X_regression[:150]\n",
+        "X_reg_test = X_regression[150:]\n",
+        "y_reg_train = y_regression[:150]\n",
+        "y_reg_test = y_regression[150:]\n",
+        "\n",
+        "# Fit our model to the data\n",
+        "# Note: Before TensorFlow 2.7.0, this line would work\n",
+        "# model_3.fit(X_reg_train, y_reg_train, epochs=100)\n",
+        "\n",
+        "# After TensorFlow 2.7.0, see here for more: https://github.com/mrdbourke/tensorflow-deep-learning/discussions/278\n",
+        "model_3.fit(tf.expand_dims(X_reg_train, axis=-1),\n",
+        "            y_reg_train,\n",
+        "            epochs=100)"
+      ],
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/100\n"
+          ]
+        },
+        {
+          "output_type": "error",
+          "ename": "ValueError",
+          "evalue": "ignored",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-18-5dd5867236b4>\u001b[0m in \u001b[0;36m<cell line: 19>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;31m# After TensorFlow 2.7.0, see here for more: https://github.com/mrdbourke/tensorflow-deep-learning/discussions/278\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m model_3.fit(tf.expand_dims(X_reg_train, axis=-1), \n\u001b[0m\u001b[1;32m     20\u001b[0m             \u001b[0my_reg_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m             epochs=100)\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     68\u001b[0m             \u001b[0;31m# To get the full stack trace, call:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m             \u001b[0;31m# `tf.debugging.disable_traceback_filtering()`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     71\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m             \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py\u001b[0m in \u001b[0;36mtf__train_function\u001b[0;34m(iterator)\u001b[0m\n\u001b[1;32m     13\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m                     \u001b[0mdo_return\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m                     \u001b[0mretval_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mag__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconverted_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mag__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mld\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep_function\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mag__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mld\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mag__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mld\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfscope\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m                 \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m                     \u001b[0mdo_return\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mValueError\u001b[0m: in user code:\n\n    File \"/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py\", line 1338, in train_function  *\n        return step_function(self, iterator)\n    File \"/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py\", line 1322, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py\", line 1303, in run_step  **\n        outputs = model.train_step(data)\n    File \"/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py\", line 1080, in train_step\n        y_pred = self(x, training=True)\n    File \"/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\", line 70, in error_handler\n        raise e.with_traceback(filtered_tb) from None\n    File \"/usr/local/lib/python3.10/dist-packages/keras/src/engine/input_spec.py\", line 280, in assert_input_compatibility\n        raise ValueError(\n\n    ValueError: Exception encountered when calling layer 'sequential_2' (type Sequential).\n    \n    Input 0 of layer \"dense_3\" is incompatible with the layer: expected axis -1 of input shape to have value 2, but received input with shape (None, 1)\n    \n    Call arguments received by layer 'sequential_2' (type Sequential):\n      • inputs=tf.Tensor(shape=(None, 1), dtype=int64)\n      • training=True\n      • mask=None\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "lFGjyziVn_Zh",
+        "outputId": "ebee042a-f0cc-4c1c-fe4e-3065c8ad9247"
+      },
+      "source": [
+        "model_3.summary()"
+      ],
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Model: \"sequential_2\"\n",
+            "_________________________________________________________________\n",
+            " Layer (type)                Output Shape              Param #   \n",
+            "=================================================================\n",
+            " dense_3 (Dense)             (None, 100)               300       \n",
+            "                                                                 \n",
+            " dense_4 (Dense)             (None, 10)                1010      \n",
+            "                                                                 \n",
+            " dense_5 (Dense)             (None, 1)                 11        \n",
+            "                                                                 \n",
+            "=================================================================\n",
+            "Total params: 1321 (5.16 KB)\n",
+            "Trainable params: 1321 (5.16 KB)\n",
+            "Non-trainable params: 0 (0.00 Byte)\n",
+            "_________________________________________________________________\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "SMu37fayVEZI"
+      },
+      "source": [
+        "Oh wait... we compiled our model for a binary classification problem.\n",
+        "\n",
+        "No trouble, we can recreate it for a regression problem."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "OUJtmMPZVOZ9",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "4cd3683c-45bb-4ee0-bbc3-765fc4992398"
+      },
+      "source": [
+        "# Setup random seed\n",
+        "tf.random.set_seed(42)\n",
+        "\n",
+        "# Recreate the model\n",
+        "model_3 = tf.keras.Sequential([\n",
+        "  tf.keras.layers.Dense(100),\n",
+        "  tf.keras.layers.Dense(10),\n",
+        "  tf.keras.layers.Dense(1)\n",
+        "])\n",
+        "\n",
+        "# Change the loss and metrics of our compiled model\n",
+        "model_3.compile(loss=tf.keras.losses.mae, # change the loss function to be regression-specific\n",
+        "                optimizer=tf.keras.optimizers.Adam(),\n",
+        "                metrics=['mae']) # change the metric to be regression-specific\n",
+        "\n",
+        "# Fit the recompiled model\n",
+        "model_3.fit(tf.expand_dims(X_reg_train, axis=-1),\n",
+        "            y_reg_train,\n",
+        "            epochs=100)"
+      ],
+      "execution_count": 20,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/100\n",
+            "5/5 [==============================] - 1s 5ms/step - loss: 433.1354 - mae: 433.1354\n",
+            "Epoch 2/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 317.7466 - mae: 317.7466\n",
+            "Epoch 3/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 204.6052 - mae: 204.6052\n",
+            "Epoch 4/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 85.3595 - mae: 85.3595\n",
+            "Epoch 5/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 66.0646 - mae: 66.0646\n",
+            "Epoch 6/100\n",
+            "5/5 [==============================] - 0s 5ms/step - loss: 93.5545 - mae: 93.5545\n",
+            "Epoch 7/100\n",
+            "5/5 [==============================] - 0s 5ms/step - loss: 68.0031 - mae: 68.0031\n",
+            "Epoch 8/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 44.0693 - mae: 44.0693\n",
+            "Epoch 9/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 50.1791 - mae: 50.1791\n",
+            "Epoch 10/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 41.7322 - mae: 41.7322\n",
+            "Epoch 11/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 44.6526 - mae: 44.6526\n",
+            "Epoch 12/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 42.3488 - mae: 42.3488\n",
+            "Epoch 13/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 41.8704 - mae: 41.8704\n",
+            "Epoch 14/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 41.4250 - mae: 41.4250\n",
+            "Epoch 15/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 41.9893 - mae: 41.9893\n",
+            "Epoch 16/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 41.4541 - mae: 41.4541\n",
+            "Epoch 17/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 41.5821 - mae: 41.5821\n",
+            "Epoch 18/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 41.1350 - mae: 41.1350\n",
+            "Epoch 19/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 41.2003 - mae: 41.2003\n",
+            "Epoch 20/100\n",
+            "5/5 [==============================] - 0s 5ms/step - loss: 41.1228 - mae: 41.1228\n",
+            "Epoch 21/100\n",
+            "5/5 [==============================] - 0s 5ms/step - loss: 41.2605 - mae: 41.2605\n",
+            "Epoch 22/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 41.1970 - mae: 41.1970\n",
+            "Epoch 23/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 41.1297 - mae: 41.1297\n",
+            "Epoch 24/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 41.0974 - mae: 41.0974\n",
+            "Epoch 25/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 41.0470 - mae: 41.0470\n",
+            "Epoch 26/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 41.1013 - mae: 41.1013\n",
+            "Epoch 27/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 40.9046 - mae: 40.9046\n",
+            "Epoch 28/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 41.1916 - mae: 41.1916\n",
+            "Epoch 29/100\n",
+            "5/5 [==============================] - 0s 5ms/step - loss: 41.0547 - mae: 41.0547\n",
+            "Epoch 30/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 40.9510 - mae: 40.9510\n",
+            "Epoch 31/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 41.6362 - mae: 41.6362\n",
+            "Epoch 32/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 41.0832 - mae: 41.0832\n",
+            "Epoch 33/100\n",
+            "5/5 [==============================] - 0s 6ms/step - loss: 41.3475 - mae: 41.3475\n",
+            "Epoch 34/100\n",
+            "5/5 [==============================] - 0s 5ms/step - loss: 41.3667 - mae: 41.3667\n",
+            "Epoch 35/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 40.7833 - mae: 40.7833\n",
+            "Epoch 36/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 41.2256 - mae: 41.2256\n",
+            "Epoch 37/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 40.8633 - mae: 40.8633\n",
+            "Epoch 38/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 40.4946 - mae: 40.4946\n",
+            "Epoch 39/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 40.9043 - mae: 40.9043\n",
+            "Epoch 40/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 40.5589 - mae: 40.5589\n",
+            "Epoch 41/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 40.5159 - mae: 40.5159\n",
+            "Epoch 42/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 40.4418 - mae: 40.4418\n",
+            "Epoch 43/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 40.5322 - mae: 40.5322\n",
+            "Epoch 44/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 40.3204 - mae: 40.3204\n",
+            "Epoch 45/100\n",
+            "5/5 [==============================] - 0s 6ms/step - loss: 40.4601 - mae: 40.4601\n",
+            "Epoch 46/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 40.5096 - mae: 40.5096\n",
+            "Epoch 47/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 40.2794 - mae: 40.2794\n",
+            "Epoch 48/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 40.2964 - mae: 40.2964\n",
+            "Epoch 49/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 40.8208 - mae: 40.8208\n",
+            "Epoch 50/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 40.3030 - mae: 40.3030\n",
+            "Epoch 51/100\n",
+            "5/5 [==============================] - 0s 5ms/step - loss: 40.6854 - mae: 40.6854\n",
+            "Epoch 52/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 41.1628 - mae: 41.1628\n",
+            "Epoch 53/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 41.0514 - mae: 41.0514\n",
+            "Epoch 54/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 41.1284 - mae: 41.1284\n",
+            "Epoch 55/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 41.5700 - mae: 41.5700\n",
+            "Epoch 56/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 41.7255 - mae: 41.7255\n",
+            "Epoch 57/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 40.8155 - mae: 40.8155\n",
+            "Epoch 58/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 41.1133 - mae: 41.1133\n",
+            "Epoch 59/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 40.9179 - mae: 40.9179\n",
+            "Epoch 60/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 40.5121 - mae: 40.5121\n",
+            "Epoch 61/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 40.4145 - mae: 40.4145\n",
+            "Epoch 62/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 40.9465 - mae: 40.9465\n",
+            "Epoch 63/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 39.8389 - mae: 39.8389\n",
+            "Epoch 64/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 40.3521 - mae: 40.3521\n",
+            "Epoch 65/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 39.7486 - mae: 39.7486\n",
+            "Epoch 66/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 40.2865 - mae: 40.2865\n",
+            "Epoch 67/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 39.6406 - mae: 39.6406\n",
+            "Epoch 68/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 39.7547 - mae: 39.7547\n",
+            "Epoch 69/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 39.8789 - mae: 39.8789\n",
+            "Epoch 70/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 40.1463 - mae: 40.1463\n",
+            "Epoch 71/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 39.9557 - mae: 39.9557\n",
+            "Epoch 72/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 39.3120 - mae: 39.3120\n",
+            "Epoch 73/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 39.6075 - mae: 39.6075\n",
+            "Epoch 74/100\n",
+            "5/5 [==============================] - 0s 7ms/step - loss: 39.6017 - mae: 39.6017\n",
+            "Epoch 75/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 39.6259 - mae: 39.6259\n",
+            "Epoch 76/100\n",
+            "5/5 [==============================] - 0s 5ms/step - loss: 39.3090 - mae: 39.3090\n",
+            "Epoch 77/100\n",
+            "5/5 [==============================] - 0s 5ms/step - loss: 39.3305 - mae: 39.3305\n",
+            "Epoch 78/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 39.7508 - mae: 39.7508\n",
+            "Epoch 79/100\n",
+            "5/5 [==============================] - 0s 5ms/step - loss: 39.3105 - mae: 39.3105\n",
+            "Epoch 80/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 38.9500 - mae: 38.9500\n",
+            "Epoch 81/100\n",
+            "5/5 [==============================] - 0s 5ms/step - loss: 39.3868 - mae: 39.3868\n",
+            "Epoch 82/100\n",
+            "5/5 [==============================] - 0s 5ms/step - loss: 39.1880 - mae: 39.1880\n",
+            "Epoch 83/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 39.0843 - mae: 39.0843\n",
+            "Epoch 84/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 39.0106 - mae: 39.0106\n",
+            "Epoch 85/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 39.0930 - mae: 39.0930\n",
+            "Epoch 86/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 38.9551 - mae: 38.9551\n",
+            "Epoch 87/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 38.8351 - mae: 38.8351\n",
+            "Epoch 88/100\n",
+            "5/5 [==============================] - 0s 5ms/step - loss: 38.9019 - mae: 38.9019\n",
+            "Epoch 89/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 38.7894 - mae: 38.7894\n",
+            "Epoch 90/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 38.6143 - mae: 38.6143\n",
+            "Epoch 91/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 38.6522 - mae: 38.6522\n",
+            "Epoch 92/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 38.5904 - mae: 38.5904\n",
+            "Epoch 93/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 39.5002 - mae: 39.5002\n",
+            "Epoch 94/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 38.7743 - mae: 38.7743\n",
+            "Epoch 95/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 38.5620 - mae: 38.5620\n",
+            "Epoch 96/100\n",
+            "5/5 [==============================] - 0s 8ms/step - loss: 38.8502 - mae: 38.8502\n",
+            "Epoch 97/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 38.3039 - mae: 38.3039\n",
+            "Epoch 98/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 39.0067 - mae: 39.0067\n",
+            "Epoch 99/100\n",
+            "5/5 [==============================] - 0s 4ms/step - loss: 38.5113 - mae: 38.5113\n",
+            "Epoch 100/100\n",
+            "5/5 [==============================] - 0s 5ms/step - loss: 38.2988 - mae: 38.2988\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<keras.src.callbacks.History at 0x792a917e0d00>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 20
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FYHvt7fRWBUL"
+      },
+      "source": [
+        "Okay, it seems like our model is learning something (the `mae` value trends down with each epoch), let's plot its predictions."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "qUf5DL_fWMZA",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 616
+        },
+        "outputId": "0b3c1e55-ae92-4854-c44e-c382bf7345dd"
+      },
+      "source": [
+        "# Make predictions with our trained model\n",
+        "y_reg_preds = model_3.predict(y_reg_test)\n",
+        "\n",
+        "# Plot the model's predictions against our regression data\n",
+        "plt.figure(figsize=(10, 7))\n",
+        "plt.scatter(X_reg_train, y_reg_train, c='b', label='Training data')\n",
+        "plt.scatter(X_reg_test, y_reg_test, c='g', label='Testing data')\n",
+        "plt.scatter(X_reg_test, y_reg_preds.squeeze(), c='r', label='Predictions')\n",
+        "plt.legend();"
+      ],
+      "execution_count": 21,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "2/2 [==============================] - 0s 11ms/step\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x700 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "V-LGK_jQYUcA"
+      },
+      "source": [
+        "Okay, the predictions aren't perfect (if the predictions were perfect, the red would line up with the green), but they look better than complete guessing.\n",
+        "\n",
+        "So this means our model must be learning something...\n",
+        "\n",
+        "There must be something we're missing out on for our classification problem."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "RZqShArhUh6d"
+      },
+      "source": [
+        "## The missing piece: Non-linearity\n",
+        "\n",
+        "Okay, so we saw our neural network can model straight lines (with ability a little bit better than guessing).\n",
+        "\n",
+        "What about non-straight (non-linear) lines?\n",
+        "\n",
+        "If we're going to model our classification data (the red and clue circles), we're going to need some non-linear lines.\n",
+        "\n",
+        "> 🔨 **Practice:** Before we get to the next steps, I'd encourage you to play around with the [TensorFlow Playground](https://playground.tensorflow.org/#activation=linear&batchSize=1&dataset=circle&regDataset=reg-plane&learningRate=0.01&regularizationRate=0&noise=0&networkShape=1&seed=0.09561&showTestData=false&discretize=false&percTrainData=70&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification&initZero=false&hideText=false&regularizationRate_hide=true&discretize_hide=true&regularization_hide=true&dataset_hide=true&noise_hide=true&batchSize_hide=true) (check out what the data has in common with our own classification data) for 10-minutes. In particular the tab which says \"activation\". Once you're done, come back.\n",
+        "\n",
+        "Did you try out the activation options? If so, what did you find?\n",
+        "\n",
+        "If you didn't, don't worry, let's see it in code.\n",
+        "\n",
+        "We're going to replicate the neural network you can see at this link: [TensorFlow Playground](https://playground.tensorflow.org/#activation=linear&batchSize=1&dataset=circle&regDataset=reg-plane&learningRate=0.01&regularizationRate=0&noise=0&networkShape=1&seed=0.09561&showTestData=false&discretize=false&percTrainData=70&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification&initZero=false&hideText=false&regularizationRate_hide=true&discretize_hide=true&regularization_hide=true&dataset_hide=true&noise_hide=true&batchSize_hide=true).\n",
+        "\n",
+        "![simple neural net created with TensorFlow playground](https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/images/02-tensorflow-playground-simple-net-linear-activation.png)\n",
+        "*The neural network we're going to recreate with TensorFlow code. See it live at [TensorFlow Playground](https://playground.tensorflow.org/#activation=linear&batchSize=1&dataset=circle&regDataset=reg-plane&learningRate=0.01&regularizationRate=0&noise=0&networkShape=1&seed=0.09561&showTestData=false&discretize=false&percTrainData=70&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification&initZero=false&hideText=false&regularizationRate_hide=true&discretize_hide=true&regularization_hide=true&dataset_hide=true&noise_hide=true&batchSize_hide=true).*\n",
+        "\n",
+        "The main change we'll add to models we've built before is the use of the `activation` keyword.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "p54Sk5WeYue9",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "167daeff-bea5-44e5-f0c1-217768401051"
+      },
+      "source": [
+        "# Set the random seed\n",
+        "tf.random.set_seed(42)\n",
+        "\n",
+        "# Create the model\n",
+        "model_4 = tf.keras.Sequential([\n",
+        "  tf.keras.layers.Dense(1, activation=tf.keras.activations.linear), # 1 hidden layer with linear activation\n",
+        "  tf.keras.layers.Dense(1) # output layer\n",
+        "])\n",
+        "\n",
+        "# Compile the model\n",
+        "model_4.compile(loss=tf.keras.losses.binary_crossentropy,\n",
+        "                optimizer=tf.keras.optimizers.Adam(learning_rate=0.001), # note: \"lr\" used to be what was used, now \"learning_rate\" is favoured\n",
+        "                metrics=[\"accuracy\"])\n",
+        "\n",
+        "# Fit the model\n",
+        "history = model_4.fit(X, y, epochs=100)"
+      ],
+      "execution_count": 22,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/100\n",
+            "32/32 [==============================] - 2s 5ms/step - loss: 4.3862 - accuracy: 0.4760\n",
+            "Epoch 2/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 4.3044 - accuracy: 0.4750\n",
+            "Epoch 3/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 4.2339 - accuracy: 0.4740\n",
+            "Epoch 4/100\n",
+            "32/32 [==============================] - 0s 6ms/step - loss: 4.1997 - accuracy: 0.4760\n",
+            "Epoch 5/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 4.1517 - accuracy: 0.4760\n",
+            "Epoch 6/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 4.0935 - accuracy: 0.4760\n",
+            "Epoch 7/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 4.0419 - accuracy: 0.4740\n",
+            "Epoch 8/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 4.0231 - accuracy: 0.4720\n",
+            "Epoch 9/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 3.9816 - accuracy: 0.4720\n",
+            "Epoch 10/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 3.9266 - accuracy: 0.4730\n",
+            "Epoch 11/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 3.8806 - accuracy: 0.4740\n",
+            "Epoch 12/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 3.8132 - accuracy: 0.4740\n",
+            "Epoch 13/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 3.7585 - accuracy: 0.4750\n",
+            "Epoch 14/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 3.6504 - accuracy: 0.4720\n",
+            "Epoch 15/100\n",
+            "32/32 [==============================] - 0s 6ms/step - loss: 3.5273 - accuracy: 0.4740\n",
+            "Epoch 16/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 3.3597 - accuracy: 0.4730\n",
+            "Epoch 17/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 3.2630 - accuracy: 0.4740\n",
+            "Epoch 18/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 3.0368 - accuracy: 0.4730\n",
+            "Epoch 19/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 2.7837 - accuracy: 0.4750\n",
+            "Epoch 20/100\n",
+            "32/32 [==============================] - 0s 6ms/step - loss: 2.6294 - accuracy: 0.4770\n",
+            "Epoch 21/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 2.3747 - accuracy: 0.4750\n",
+            "Epoch 22/100\n",
+            "32/32 [==============================] - 0s 6ms/step - loss: 2.0878 - accuracy: 0.4780\n",
+            "Epoch 23/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 1.1282 - accuracy: 0.4820\n",
+            "Epoch 24/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 0.9154 - accuracy: 0.4840\n",
+            "Epoch 25/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 0.8836 - accuracy: 0.4830\n",
+            "Epoch 26/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 0.8647 - accuracy: 0.4840\n",
+            "Epoch 27/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 0.8500 - accuracy: 0.4870\n",
+            "Epoch 28/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 0.8377 - accuracy: 0.4860\n",
+            "Epoch 29/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 0.8278 - accuracy: 0.4850\n",
+            "Epoch 30/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 0.8189 - accuracy: 0.4850\n",
+            "Epoch 31/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 0.8111 - accuracy: 0.4840\n",
+            "Epoch 32/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 0.8040 - accuracy: 0.4840\n",
+            "Epoch 33/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 0.7979 - accuracy: 0.4830\n",
+            "Epoch 34/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 0.7923 - accuracy: 0.4830\n",
+            "Epoch 35/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 0.7871 - accuracy: 0.4820\n",
+            "Epoch 36/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 0.7824 - accuracy: 0.4820\n",
+            "Epoch 37/100\n",
+            "32/32 [==============================] - 0s 6ms/step - loss: 0.7779 - accuracy: 0.4840\n",
+            "Epoch 38/100\n",
+            "32/32 [==============================] - 0s 6ms/step - loss: 0.7737 - accuracy: 0.4850\n",
+            "Epoch 39/100\n",
+            "32/32 [==============================] - 0s 6ms/step - loss: 0.7699 - accuracy: 0.4840\n",
+            "Epoch 40/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 0.7663 - accuracy: 0.4830\n",
+            "Epoch 41/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 0.7628 - accuracy: 0.4830\n",
+            "Epoch 42/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 0.7596 - accuracy: 0.4830\n",
+            "Epoch 43/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 0.7565 - accuracy: 0.4850\n",
+            "Epoch 44/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 0.7538 - accuracy: 0.4850\n",
+            "Epoch 45/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 0.7511 - accuracy: 0.4850\n",
+            "Epoch 46/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 0.7486 - accuracy: 0.4830\n",
+            "Epoch 47/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 0.7462 - accuracy: 0.4820\n",
+            "Epoch 48/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 0.7439 - accuracy: 0.4830\n",
+            "Epoch 49/100\n",
+            "32/32 [==============================] - 0s 6ms/step - loss: 0.7417 - accuracy: 0.4820\n",
+            "Epoch 50/100\n",
+            "32/32 [==============================] - 0s 7ms/step - loss: 0.7397 - accuracy: 0.4830\n",
+            "Epoch 51/100\n",
+            "32/32 [==============================] - 0s 7ms/step - loss: 0.7377 - accuracy: 0.4830\n",
+            "Epoch 52/100\n",
+            "32/32 [==============================] - 0s 7ms/step - loss: 0.7358 - accuracy: 0.4840\n",
+            "Epoch 53/100\n",
+            "32/32 [==============================] - 0s 8ms/step - loss: 0.7340 - accuracy: 0.4840\n",
+            "Epoch 54/100\n",
+            "32/32 [==============================] - 0s 7ms/step - loss: 0.7322 - accuracy: 0.4840\n",
+            "Epoch 55/100\n",
+            "32/32 [==============================] - 0s 8ms/step - loss: 0.7306 - accuracy: 0.4820\n",
+            "Epoch 56/100\n",
+            "32/32 [==============================] - 0s 10ms/step - loss: 0.7289 - accuracy: 0.4820\n",
+            "Epoch 57/100\n",
+            "32/32 [==============================] - 0s 8ms/step - loss: 0.7275 - accuracy: 0.4820\n",
+            "Epoch 58/100\n",
+            "32/32 [==============================] - 0s 9ms/step - loss: 0.7261 - accuracy: 0.4840\n",
+            "Epoch 59/100\n",
+            "32/32 [==============================] - 0s 10ms/step - loss: 0.7248 - accuracy: 0.4850\n",
+            "Epoch 60/100\n",
+            "32/32 [==============================] - 0s 11ms/step - loss: 0.7234 - accuracy: 0.4850\n",
+            "Epoch 61/100\n",
+            "32/32 [==============================] - 0s 8ms/step - loss: 0.7221 - accuracy: 0.4850\n",
+            "Epoch 62/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 0.7210 - accuracy: 0.4850\n",
+            "Epoch 63/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 0.7198 - accuracy: 0.4850\n",
+            "Epoch 64/100\n",
+            "32/32 [==============================] - 0s 6ms/step - loss: 0.7187 - accuracy: 0.4850\n",
+            "Epoch 65/100\n",
+            "32/32 [==============================] - 0s 6ms/step - loss: 0.7177 - accuracy: 0.4850\n",
+            "Epoch 66/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 0.7167 - accuracy: 0.4850\n",
+            "Epoch 67/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 0.7157 - accuracy: 0.4850\n",
+            "Epoch 68/100\n",
+            "32/32 [==============================] - 0s 7ms/step - loss: 0.7147 - accuracy: 0.4850\n",
+            "Epoch 69/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 0.7138 - accuracy: 0.4860\n",
+            "Epoch 70/100\n",
+            "32/32 [==============================] - 0s 6ms/step - loss: 0.7129 - accuracy: 0.4860\n",
+            "Epoch 71/100\n",
+            "32/32 [==============================] - 0s 7ms/step - loss: 0.7121 - accuracy: 0.4860\n",
+            "Epoch 72/100\n",
+            "32/32 [==============================] - 0s 6ms/step - loss: 0.7114 - accuracy: 0.4870\n",
+            "Epoch 73/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 0.7106 - accuracy: 0.4880\n",
+            "Epoch 74/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 0.7099 - accuracy: 0.4880\n",
+            "Epoch 75/100\n",
+            "32/32 [==============================] - 0s 6ms/step - loss: 0.7092 - accuracy: 0.4870\n",
+            "Epoch 76/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 0.7086 - accuracy: 0.4890\n",
+            "Epoch 77/100\n",
+            "32/32 [==============================] - 0s 6ms/step - loss: 0.7079 - accuracy: 0.4870\n",
+            "Epoch 78/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 0.7073 - accuracy: 0.4870\n",
+            "Epoch 79/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 0.7066 - accuracy: 0.4860\n",
+            "Epoch 80/100\n",
+            "32/32 [==============================] - 0s 7ms/step - loss: 0.7060 - accuracy: 0.4880\n",
+            "Epoch 81/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 0.7054 - accuracy: 0.4890\n",
+            "Epoch 82/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 0.7049 - accuracy: 0.4890\n",
+            "Epoch 83/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 0.7044 - accuracy: 0.4870\n",
+            "Epoch 84/100\n",
+            "32/32 [==============================] - 0s 6ms/step - loss: 0.7039 - accuracy: 0.4870\n",
+            "Epoch 85/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 0.7034 - accuracy: 0.4880\n",
+            "Epoch 86/100\n",
+            "32/32 [==============================] - 0s 8ms/step - loss: 0.7030 - accuracy: 0.4880\n",
+            "Epoch 87/100\n",
+            "32/32 [==============================] - 0s 6ms/step - loss: 0.7025 - accuracy: 0.4880\n",
+            "Epoch 88/100\n",
+            "32/32 [==============================] - 0s 9ms/step - loss: 0.7021 - accuracy: 0.4880\n",
+            "Epoch 89/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 0.7018 - accuracy: 0.4890\n",
+            "Epoch 90/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 0.7014 - accuracy: 0.4890\n",
+            "Epoch 91/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 0.7010 - accuracy: 0.4880\n",
+            "Epoch 92/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 0.7007 - accuracy: 0.4890\n",
+            "Epoch 93/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 0.7004 - accuracy: 0.4890\n",
+            "Epoch 94/100\n",
+            "32/32 [==============================] - 0s 7ms/step - loss: 0.7000 - accuracy: 0.4880\n",
+            "Epoch 95/100\n",
+            "32/32 [==============================] - 0s 6ms/step - loss: 0.6997 - accuracy: 0.4870\n",
+            "Epoch 96/100\n",
+            "32/32 [==============================] - 0s 7ms/step - loss: 0.6993 - accuracy: 0.4890\n",
+            "Epoch 97/100\n",
+            "32/32 [==============================] - 0s 6ms/step - loss: 0.6991 - accuracy: 0.4880\n",
+            "Epoch 98/100\n",
+            "32/32 [==============================] - 0s 7ms/step - loss: 0.6988 - accuracy: 0.4880\n",
+            "Epoch 99/100\n",
+            "32/32 [==============================] - 0s 6ms/step - loss: 0.6985 - accuracy: 0.4930\n",
+            "Epoch 100/100\n",
+            "32/32 [==============================] - 0s 6ms/step - loss: 0.6982 - accuracy: 0.4940\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TTNzKBvwf2uJ"
+      },
+      "source": [
+        "Okay, our model performs a little worse than guessing.\n",
+        "\n",
+        "Let's remind ourselves what our data looks like."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "RcwI7-s1f2a4",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 430
+        },
+        "outputId": "64154248-86d9-4f91-d0a6-f3efbeb92df1"
+      },
+      "source": [
+        "# Check out our data\n",
+        "plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.RdYlBu);"
+      ],
+      "execution_count": 23,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "EmuUVtenj-tC"
+      },
+      "source": [
+        "And let's see how our model is making predictions on it."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "_vFgOI88itVD",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 464
+        },
+        "outputId": "50f08fd7-6047-4e2e-8d20-4b1bb857a4fa"
+      },
+      "source": [
+        "# Check the deicison boundary (blue is blue class, yellow is the crossover, red is red class)\n",
+        "plot_decision_boundary(model_4, X, y)"
+      ],
+      "execution_count": 24,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "313/313 [==============================] - 1s 2ms/step\n",
+            "doing binary classifcation...\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XgNyVx3XkG4K"
+      },
+      "source": [
+        "Well, it looks like we're getting a straight (linear) line prediction again.\n",
+        "\n",
+        "But our data is non-linear (not a straight line)...\n",
+        "\n",
+        "What we're going to have to do is add some non-linearity to our model.\n",
+        "\n",
+        "To do so, we'll use the `activation` parameter in on of our layers."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "utJIUKmHkbyV",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "8c118176-0353-44c0-c943-c01b67cc149d"
+      },
+      "source": [
+        "# Set random seed\n",
+        "tf.random.set_seed(42)\n",
+        "\n",
+        "# Create a model with a non-linear activation\n",
+        "model_5 = tf.keras.Sequential([\n",
+        "  tf.keras.layers.Dense(1, activation=tf.keras.activations.relu), # can also do activation='relu'\n",
+        "  tf.keras.layers.Dense(1) # output layer\n",
+        "])\n",
+        "\n",
+        "# Compile the model\n",
+        "model_5.compile(loss=tf.keras.losses.binary_crossentropy,\n",
+        "              optimizer=tf.keras.optimizers.Adam(),\n",
+        "              metrics=[\"accuracy\"])\n",
+        "\n",
+        "# Fit the model\n",
+        "history = model_5.fit(X, y, epochs=100)"
+      ],
+      "execution_count": 25,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/100\n",
+            "32/32 [==============================] - 3s 5ms/step - loss: 3.5949 - accuracy: 0.4780\n",
+            "Epoch 2/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 2.9815 - accuracy: 0.4780\n",
+            "Epoch 3/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 2.8580 - accuracy: 0.4770\n",
+            "Epoch 4/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 2.6905 - accuracy: 0.4790\n",
+            "Epoch 5/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 2.5537 - accuracy: 0.4760\n",
+            "Epoch 6/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 2.3771 - accuracy: 0.4790\n",
+            "Epoch 7/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 2.0749 - accuracy: 0.4760\n",
+            "Epoch 8/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 1.7417 - accuracy: 0.4720\n",
+            "Epoch 9/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 1.6157 - accuracy: 0.4710\n",
+            "Epoch 10/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 1.5581 - accuracy: 0.4710\n",
+            "Epoch 11/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 1.4906 - accuracy: 0.4690\n",
+            "Epoch 12/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 1.4595 - accuracy: 0.4680\n",
+            "Epoch 13/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 1.4364 - accuracy: 0.4680\n",
+            "Epoch 14/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 1.4156 - accuracy: 0.4670\n",
+            "Epoch 15/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 1.3966 - accuracy: 0.4690\n",
+            "Epoch 16/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 1.3790 - accuracy: 0.4690\n",
+            "Epoch 17/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 1.3624 - accuracy: 0.4670\n",
+            "Epoch 18/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 1.3469 - accuracy: 0.4660\n",
+            "Epoch 19/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 1.3327 - accuracy: 0.4660\n",
+            "Epoch 20/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 1.3190 - accuracy: 0.4670\n",
+            "Epoch 21/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 1.3062 - accuracy: 0.4670\n",
+            "Epoch 22/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 1.2937 - accuracy: 0.4660\n",
+            "Epoch 23/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 1.2819 - accuracy: 0.4660\n",
+            "Epoch 24/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 1.2706 - accuracy: 0.4680\n",
+            "Epoch 25/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 1.2604 - accuracy: 0.4690\n",
+            "Epoch 26/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 1.2499 - accuracy: 0.4710\n",
+            "Epoch 27/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 1.2402 - accuracy: 0.4710\n",
+            "Epoch 28/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 1.2307 - accuracy: 0.4710\n",
+            "Epoch 29/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 1.2215 - accuracy: 0.4700\n",
+            "Epoch 30/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 1.2126 - accuracy: 0.4690\n",
+            "Epoch 31/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 1.2041 - accuracy: 0.4680\n",
+            "Epoch 32/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 1.1957 - accuracy: 0.4690\n",
+            "Epoch 33/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 1.1876 - accuracy: 0.4690\n",
+            "Epoch 34/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 1.1798 - accuracy: 0.4680\n",
+            "Epoch 35/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 1.1721 - accuracy: 0.4680\n",
+            "Epoch 36/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 1.1645 - accuracy: 0.4680\n",
+            "Epoch 37/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 1.1573 - accuracy: 0.4670\n",
+            "Epoch 38/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 1.1503 - accuracy: 0.4670\n",
+            "Epoch 39/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 1.1433 - accuracy: 0.4670\n",
+            "Epoch 40/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 1.1366 - accuracy: 0.4670\n",
+            "Epoch 41/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 1.1300 - accuracy: 0.4670\n",
+            "Epoch 42/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 1.1235 - accuracy: 0.4680\n",
+            "Epoch 43/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 1.1172 - accuracy: 0.4670\n",
+            "Epoch 44/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 1.1110 - accuracy: 0.4660\n",
+            "Epoch 45/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 1.1049 - accuracy: 0.4660\n",
+            "Epoch 46/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 1.0990 - accuracy: 0.4650\n",
+            "Epoch 47/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 1.0934 - accuracy: 0.4650\n",
+            "Epoch 48/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 1.0876 - accuracy: 0.4650\n",
+            "Epoch 49/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 1.0820 - accuracy: 0.4650\n",
+            "Epoch 50/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 1.0766 - accuracy: 0.4650\n",
+            "Epoch 51/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 1.0711 - accuracy: 0.4650\n",
+            "Epoch 52/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 1.0660 - accuracy: 0.4640\n",
+            "Epoch 53/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 1.0608 - accuracy: 0.4640\n",
+            "Epoch 54/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 1.0557 - accuracy: 0.4640\n",
+            "Epoch 55/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 1.0507 - accuracy: 0.4640\n",
+            "Epoch 56/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 1.0458 - accuracy: 0.4640\n",
+            "Epoch 57/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 1.0409 - accuracy: 0.4630\n",
+            "Epoch 58/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 1.0361 - accuracy: 0.4630\n",
+            "Epoch 59/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 1.0314 - accuracy: 0.4640\n",
+            "Epoch 60/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 1.0268 - accuracy: 0.4650\n",
+            "Epoch 61/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 1.0223 - accuracy: 0.4660\n",
+            "Epoch 62/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 1.0179 - accuracy: 0.4660\n",
+            "Epoch 63/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 1.0135 - accuracy: 0.4660\n",
+            "Epoch 64/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 1.0093 - accuracy: 0.4660\n",
+            "Epoch 65/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 1.0050 - accuracy: 0.4660\n",
+            "Epoch 66/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 1.0008 - accuracy: 0.4660\n",
+            "Epoch 67/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.9966 - accuracy: 0.4650\n",
+            "Epoch 68/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.9926 - accuracy: 0.4660\n",
+            "Epoch 69/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.9885 - accuracy: 0.4660\n",
+            "Epoch 70/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.9845 - accuracy: 0.4660\n",
+            "Epoch 71/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.9805 - accuracy: 0.4660\n",
+            "Epoch 72/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.9766 - accuracy: 0.4660\n",
+            "Epoch 73/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.9726 - accuracy: 0.4660\n",
+            "Epoch 74/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.9688 - accuracy: 0.4660\n",
+            "Epoch 75/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.9651 - accuracy: 0.4660\n",
+            "Epoch 76/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.9614 - accuracy: 0.4670\n",
+            "Epoch 77/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.9577 - accuracy: 0.4670\n",
+            "Epoch 78/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.9541 - accuracy: 0.4670\n",
+            "Epoch 79/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.9505 - accuracy: 0.4670\n",
+            "Epoch 80/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.9470 - accuracy: 0.4670\n",
+            "Epoch 81/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.9435 - accuracy: 0.4670\n",
+            "Epoch 82/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 0.9401 - accuracy: 0.4670\n",
+            "Epoch 83/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.9367 - accuracy: 0.4670\n",
+            "Epoch 84/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.9333 - accuracy: 0.4670\n",
+            "Epoch 85/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.9299 - accuracy: 0.4670\n",
+            "Epoch 86/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 0.9266 - accuracy: 0.4680\n",
+            "Epoch 87/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 0.9234 - accuracy: 0.4680\n",
+            "Epoch 88/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 0.9201 - accuracy: 0.4680\n",
+            "Epoch 89/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 0.9170 - accuracy: 0.4680\n",
+            "Epoch 90/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 0.9139 - accuracy: 0.4680\n",
+            "Epoch 91/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 0.9107 - accuracy: 0.4680\n",
+            "Epoch 92/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 0.9077 - accuracy: 0.4680\n",
+            "Epoch 93/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 0.9047 - accuracy: 0.4680\n",
+            "Epoch 94/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 0.9016 - accuracy: 0.4680\n",
+            "Epoch 95/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 0.8986 - accuracy: 0.4680\n",
+            "Epoch 96/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 0.8957 - accuracy: 0.4680\n",
+            "Epoch 97/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 0.8928 - accuracy: 0.4680\n",
+            "Epoch 98/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 0.8899 - accuracy: 0.4670\n",
+            "Epoch 99/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 0.8870 - accuracy: 0.4680\n",
+            "Epoch 100/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 0.8841 - accuracy: 0.4680\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "eW3l-KicgLW8"
+      },
+      "source": [
+        "Hmm... still not learning...\n",
+        "\n",
+        "What we if increased the number of neurons and layers?\n",
+        "\n",
+        "Say, 2 hidden layers, with [ReLU](https://www.tensorflow.org/api_docs/python/tf/keras/activations/relu), pronounced \"rel-u\", (short for [rectified linear unit](https://machinelearningmastery.com/rectified-linear-activation-function-for-deep-learning-neural-networks/)), activation on the first one, and 4 neurons each?\n",
+        "\n",
+        "To see this network in action, check out the [TensorFlow Playground demo](https://playground.tensorflow.org/#activation=relu&batchSize=10&dataset=circle&regDataset=reg-plane&learningRate=0.001&regularizationRate=0&noise=0&networkShape=4,4&seed=0.93799&showTestData=false&discretize=false&percTrainData=50&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification&initZero=false&hideText=false&regularization_hide=true&regularizationRate_hide=true&batchSize_hide=true&dataset_hide=true).\n",
+        "\n",
+        "![multi-layer neural net created with TensorFlow playground](https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/images/02-tensorflow-playground-two-layer-net-relu-activation.png)\n",
+        "*The neural network we're going to recreate with TensorFlow code. See it live at [TensorFlow Playground](https://playground.tensorflow.org/#activation=relu&batchSize=10&dataset=circle&regDataset=reg-plane&learningRate=0.001&regularizationRate=0&noise=0&networkShape=4,4&seed=0.93799&showTestData=false&discretize=false&percTrainData=50&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification&initZero=false&hideText=false&regularization_hide=true&regularizationRate_hide=true&batchSize_hide=true&dataset_hide=true).*\n",
+        "\n",
+        "Let's try.\n",
+        "\n",
+        "**Note:** in the course, Daniel used `lr` instead of `learning_rate`. But for the update, we had changed to `learning_rate` instead of `lr`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "yXxtQFHwlc9w",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "cd69c341-9c95-49d4-e106-cf13499a50fb"
+      },
+      "source": [
+        "# Set random seed\n",
+        "tf.random.set_seed(42)\n",
+        "\n",
+        "# Create a model\n",
+        "model_6 = tf.keras.Sequential([\n",
+        "  tf.keras.layers.Dense(4, activation=tf.keras.activations.relu), # hidden layer 1, 4 neurons, ReLU activation\n",
+        "  tf.keras.layers.Dense(4, activation=tf.keras.activations.relu), # hidden layer 2, 4 neurons, ReLU activation\n",
+        "  tf.keras.layers.Dense(1) # ouput layer\n",
+        "])\n",
+        "\n",
+        "# Compile the model\n",
+        "model_6.compile(loss=tf.keras.losses.binary_crossentropy,\n",
+        "                optimizer=tf.keras.optimizers.Adam(learning_rate=0.001), # Adam's default learning rate is 0.001\n",
+        "                metrics=['accuracy'])\n",
+        "\n",
+        "# Fit the model\n",
+        "history = model_6.fit(X, y, epochs=100)"
+      ],
+      "execution_count": 26,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/100\n",
+            "32/32 [==============================] - 2s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 2/100\n",
+            "32/32 [==============================] - 0s 6ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 3/100\n",
+            "32/32 [==============================] - 0s 8ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 4/100\n",
+            "32/32 [==============================] - 0s 7ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 5/100\n",
+            "32/32 [==============================] - 0s 7ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 6/100\n",
+            "32/32 [==============================] - 0s 6ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 7/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 8/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 9/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 10/100\n",
+            "32/32 [==============================] - 0s 10ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 11/100\n",
+            "32/32 [==============================] - 0s 8ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 12/100\n",
+            "32/32 [==============================] - 0s 6ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 13/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 14/100\n",
+            "32/32 [==============================] - 0s 6ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 15/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 16/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 17/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 18/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 19/100\n",
+            "32/32 [==============================] - 0s 7ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 20/100\n",
+            "32/32 [==============================] - 0s 7ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 21/100\n",
+            "32/32 [==============================] - 0s 7ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 22/100\n",
+            "32/32 [==============================] - 0s 6ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 23/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 24/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 25/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 26/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 27/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 28/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 29/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 30/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 31/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 32/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 33/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 34/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 35/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 36/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 37/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 38/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 39/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 40/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 41/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 42/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 43/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 44/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 45/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 46/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 47/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 48/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 49/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 50/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 51/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 52/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 53/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 54/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 55/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 56/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 57/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 58/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 59/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 60/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 61/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 62/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 63/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 64/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 65/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 66/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 67/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 68/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 69/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 70/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 71/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 72/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 73/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 74/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 75/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 76/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 77/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 78/100\n",
+            "32/32 [==============================] - 0s 5ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 79/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 80/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 81/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 82/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 83/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 84/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 85/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 86/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 87/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 88/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 89/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 90/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 91/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 92/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 93/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 94/100\n",
+            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 95/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 96/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 97/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 98/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 99/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n",
+            "Epoch 100/100\n",
+            "32/32 [==============================] - 0s 4ms/step - loss: 7.7125 - accuracy: 0.5000\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "bJqimQ0UsiO5",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "ac338c98-5ddb-4a61-cbe2-e4365b348b9f"
+      },
+      "source": [
+        "# Evaluate the model\n",
+        "model_6.evaluate(X, y)"
+      ],
+      "execution_count": 27,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "32/32 [==============================] - 0s 3ms/step - loss: 7.7125 - accuracy: 0.5000\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[7.712474346160889, 0.5]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 27
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Dh6l8egoc-Wr"
+      },
+      "source": [
+        "We're still hitting 50% accuracy, our model is still practically as good as guessing.\n",
+        "\n",
+        "How do the predictions look?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "OtmmuzJkcclw",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 464
+        },
+        "outputId": "fbcacc96-ea40-4e25-b1ec-be68ffc2f429"
+      },
+      "source": [
+        "# Check out the predictions using 2 hidden layers\n",
+        "plot_decision_boundary(model_6, X, y)"
+      ],
+      "execution_count": 28,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "313/313 [==============================] - 1s 1ms/step\n",
+            "doing binary classifcation...\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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q2kihOJe0VDSz7587ePuLL/Hm519g6y/WU3PwFIE4J912FxsffI+9j22npbzp3BouYbjZzVqczch5Wf0+52x3suuPH3Y3W+xJl0eiYlsZ6XMz+z0eICYzjvnfXNrvcy3lTZzaUxH8eyohY/4ooEfVTucPU5SZ0cty/AvCSYhKie73KSEEBosRIbx9iq766w1M/+wc4sYknLPckIaCOtZ//x3cPdouNJ1soHzzSb/GUEtZk9/5heZtgtnFvn/uoHTDCcDbVgJden8CtQdPse3XG4J+DQpFICjPi0IBlG4s9l6AOXMBrmosp3JHGWNXjWfWPfN7eRWkLrE3dSA0gSXOyoEndtNQUNf55LAv/wxBelMCGZ8yPZ1LvrZowNyV0g0nvL14fJyno97GrC/OZ+S8LPJfOEh7jTex1BJvZfy1k8i7YcqAIYbKneVBh4qEJogZGcvC7yynqbiB4jUFNJc1YrQYyZiXxeilY/nwZ+v8ziN1yZjlOQGd0xxtYfx1kxl/3WQA3r3vFVorQ6P4Hczrrz92mr2PbeeSry4CoKTLK+IJzQfT4/QaRh317RSvKRjw8yN1Sc2+qu6cIYUilCjjRfGRp+lkAzsf2dxnE+66WBS/V0BcVjzjrp6Ix+Wh8PWjFL6Vj72xA/Demdvq2s+Zt8UUbcZjd2NNiCAhJyngct7MBaPRPTqndvvwahgEl3zlUoRhYCdtY1E9QgifHiphEDSdaGDSrdPJXpHrPZ8goJwIj93ljUUEaJUJTWCOtbDwe5chhCAhJ4nZ9/aWW2itbKYu338/pIRxSUQkRQV03rOZcONUdv1py6CO7Yk52kzc6AROH6nxP7iT0g0nmPrpWUQkRmJvtCFDJL4rdUlMRhwA5QH0vBIGQdnmYmW8KEKOMl4UH3kK38r33tn6uDM9/uoRRi4YzYYfvkfbWXfTgxIGG0QyZn+YYyxc+69busMUB57YhTBoSD/y+lFpMVzytUW47W4++H/veHMdehgfwiBAesuD/V28A6omkp1zBnNMJ9HpsX5fj3dSsMZHkL0yl3FXT8Aa37+wJUDjifqAzz9YRi8dS9Uur/dusH9roQnGrspj6qdnceA/uyl4NcD+VNLbbTpnVV5nxRfIEFSXAzSeqCNrcTbOVoff7w3SWyqvUIQaZbwoPvJU7Sjz61K3nW7nzc+/MKimeX0QoBm0kPSiybt+cq/8Cs1owN+VUmiCjLmZGCxGDBYjK3+1mqK3j3Hi3eN0NNjQjBoj52eRd/0UEnL8y/SnTEun5APf0vRSl6RM7VsFEwiZl45h3z934O5wDzhGaILV/7iZiMTIgOYM1HjSfHic/J7DoDH/W0spfOMohW/k09FgA7z5NmMuyyF9TiYd9R3s+etWdI/so6QsNIE52sK4qycAkHfdZApfPxqQh09oAo/d+36NXjKWE+8cH/TrOJuC144SkxFHRFJkQGsZrOdKofCFMl4UH3k8rgCNiBC53uPHJDDtM3PY9NDaQd2Rd+UvjF01nrzrp/R6Ln3WSI69dMjn8VKXpPdogmiKMjPxlmlMvGUaukf3VuIEUeKauXA0Bx7fhaOtf/E2oQnixiSQOH5woQOjxcjMz8/zGYKZfNuMgA0XgBGTUv17vzRB6oyMwBfa3xQGjbzrpzD+2km013pLkCNTojH0MDijUqLY/ptN3hyqTu+U9EiiM2K59HvLu1+XNSGCiTdP4+jzB/yeV+re4wES85JJmZpG7aHQdeo++NQe0udk+v389swZsjd20FzehGbUSMxJwjBAZZlCEQjq06P4yBOXFU9DUd2wJdpmrxxP5IgoMuaO8p1v0g9xWXHE54wgZ1UeSXnJfZ5PmphC/NhEmksaB9TqiM6IJaUfLRAYnKfBYDKw6PuXsfHHa/A4zhJv0wSWeCsLv7NsSJofYy4bh8Fs4MB/dtNRZ+t+3BJrYfJtMxi7Ks/H0X2JSIxk1KVjein39kKApgnGXp476DX3ms6gEZ3ev25F8uQ0Vv/zZk7tKqehqB6hCVKmppE8Ja3PezbpE9NxdbgofOOoz/NZ4q2kzRrpPbcQJOSOCKnx4mpzUrah2PcgAdkrxmGwGNn6y/W9wmfGCBPjrp7A5FunK9VexaBQInWKjyytlc1U7iqnLr+WUzuHvxt53JgEWiua0XXpV27eG+oZxcLvLfc5DsBW186G779L++m23gaZ8F60l/3sSqLTYoa4+r6017ZR+GY+pRtO4GxzYk2wMvaK8Yy7cgKWuNB8F6UuqTtaQ0eDDXOshZQp6b2E8oLB2eZgww/epbmsqdf7JDSBBE6tuAJ7TjaXjHGzKNfFIE8TFvb9YztFb/cTCur0Ji387jJGzvdq8uguD2/c9cKw556Mu3oCuddOYv0D7+Bosfc1EgWkz87k0u8t95kQrvjoEIxInTJeFB85HK0ONv5oDc0nA5CYD1Fibb9oAiHAEmv1Vi4NdC4BsaPiWfbTVVhiA/tMu2xOSj44wcl1hdgbbFjiI8i+bBxjVuZi9iMh/1HC3eGieE0BRe8ep72mFUxG8uNy2Zk6g8boZCTg0QUpMTq/vaWN8akhih0OESklx18+zLGXD+GynWklEZUazYy7LiFj7qjux1rKm3jvq68N+xqvfuwmDj+9l/IPS3x6F+fdv4SsxdnDuDLF+YpS2FUo+sHRYqd4TQFHnt0fmOZFZ2O+YProaMYgEnF1iRRgMBtY/OBKmoobcLY5aK9to/bgKdwON1HJ0Yy9Mo+xK3MxBqFUaoo0k3vNRHKvmRjwMR9FjBEmxn9sMuM/Npm9ZRpffiam0wkmeuU41bUJvvRMNE/f1Upq7Lm/3xNCMOGmqeReO4nag6dwtjmITIlmxMSUPqGmgO9PNUKW1wXQWFxP+RbfhgsCTrx7TBkviqBRxovigsXj9FC2qZjitQXYTrdjjrEweulYRi3ORgiBOcrcfcGv2lnGtt9s8vafCQQB0ekxLPrBCt66+8WAjJ1xqycwcl4WWx5eH3hPIwntNW1oBo0JN00NbG2KsPDPD7tKq/vm5uhS0O6AF/dY+PLywJslhhuD2eBNnPVBdHospigzrnbngGOEJkiekkrMyLiQVSa52p3+vzcSmkubcLU7Kd9Sgq3O+z3OXDCayBGqSkkxMMp4UQw77bVtnHy/kLZTLRitJkbOyyJtZkZQcW9Hi90b+ilp7A632Bs7OPTUXg49tRfozBOZl0Xmgix2PvJhcAqjEtqqWmmvaSPnqgkUvZk/4FCj1cic+xaSuXAMQgiW/uQK9jy6jaYTAXpsBDSXNw2YRKsIP/Vtgj2lvj1buhS8edB8XhkvgWAwGci5Mo/jrxwe0AsidUnu6olkXJLF6KU5FLx+lFO7y/E4Pd7vV1cjxwCxJkQQmxkf0Fjdo/P6nc+hu3RvJZ0uOfDvXWSvGMesL8xXCb2KflHGi2LYkFJy9NkDHH3hQKciKwgNTr5fSExmHEseXElkcv99ZM5mxx82n+nF4kOevGpHGZXbSwdd6dJ0soH47ESfYxLGjSBzgddwaSppYOODa/A4BtYk6btQepXOKoafpo7APh/N9guzS/LEW6Zx+nA19QWn+yRxIyHnqjzSO/NkkvKSWfDtpUgpqcuvpWJrCW2nWqneWxnw+UYvy2HrL9f7Hyjo1qMBet1gnFxXhKvDxeilOVRuL8Xd4SYqLYbslbnEZPjOh+ieT5fUHKiibFMx9iY7EUmRjFmew4hJqarj9QWOMl4Uw0bRm/ndGhVdcfgu1c+2qhY2PLiGVX+4zq/+Q0tFMzX7qgI6Z9ed5mDz0ss2Fns3fB+cPlxN7aFTpExLZ8fvN+NxuINrFSDoLmtVnBsSoyQCifTT1TIx8tznuwwGo8XI0oeuoOD1oxS9c6y7tUVcVjzjr5vM6OU5fS7mQgiSJ6WSPCmVlvKmgI2XsVeM58R7x3sZJQPi6+2UULGllIotpd4KMCkRQnD8lcOM/9gkpn1mjk8DxNnm4MOfraP++Onu3lBCE5SsKyJ1RgYLv7sMo1V1vL5QUcaLYljQXR6f4lpSl7RXt1K+pYQxl43zOVf13orwVgH1oP64b8MFvOGpk+uKMFqMAXXmPfvYkRdRfL/NAevyzVQ0aUSZJZfluchKOj8qdHyRECmZP9bNjpNGdNn/BVETkuumD5w3cr5jsBiZeMs0Jtw0FUeLHc2gYYo2B+SBiMmMIyYjltZTLT6/d6OXjcUSZ8VjD9KA98PZNyEFrx3FZXMx6ePTiUiI6BNyllKy9RfraSis631858+ag6fY9actLPj2spCtUTG8KONFMSzUHq7G2eZn4xfehnIGi5HqvRV4XDpxWfFkr8jFmnCmT43u0v02AhxOpC6x1bbRUFQfuFGleXMIEsYlMedLC/yPD5Cyeo3n91hYd8yE3SXISvRw0ywnV052Yg7zt/2FPWb+9EEETjcYNG+KxN82RrBsvJMfXWsjcogV2s02QWmDhskAuSkejCGOtH1hsZ1dJdFIJPIsA8YgJHGRkptmOzjdKjhUaUQCk9PdpMWdH5/DQBGa8Nn3qd9jhGDSrdPZ8fvNAw3AYDYw+baZrP/+u8PSpPTk2kJOri3EmhBB7uqJjL9uUnd+TENBne9GlrqkYmsprVUtAYegFOcXynhRDAsBCWRJOH20htpD1d1CYeVb4Mj/9jP9c3PJXe0t+43Nij9nHZz7RRNY4qyB98sxGUjMTWLs5eMZdemYkCUkflhk5HsvR6Hr4Om8+BbUGPi/tyN446CZP97aRkSIJF48ujeHs+slv7rfzG/WnJHn71ktvqnQxLdejOLPt7UTRD/GbmpbBX9ZH8HafBMe3TtBfKTObXMdfHqeg+I6A7UtgrgIyaQMD4PVO5uU4eEPt7bzw9ciabQJjJrs1nkZlajzg9U2fvNeBBsKTN3eGYFkUa6L713ZwYjo8+gzGQaylozFVm/j0JN7usMwXRitRhZ+dxk1B6roqG8f1nXZGzs49PReag6dYvH3V6CZDFRsLeluozEQQhNUbCtloqryuyBRxotiWIhICqzvTNdm03NjlMD+f+7EEmsla3E2abNGYk2I6I7bB0y4Qk26JGvJWOKy4gOaf9pnZncbYqHidKvggZejcHvolbfRdZE9XGngD+sieOCqIN+zHnh0eOuQmed3mymsNSKEZNYoN7fMdvCX9QOL5+nSW8mzq8TIvOwgEpnxGi53PhFDY7voNsgAmmwaf9to5antFtocZ6yV1Bidu5fYuXba4MI7c8e4efO+FjYWmDhWbcCgwZwxbsaneLjryWgqG7VeYSWJYGuRibv+Y+CJO9tIuEBzYgJlwg1TyJyfRfHaQhqL6zEYNdJmZZI+N5OtD6+nKRDhx3AgofbgKQrfyifv+ik4fZSFdyOEz/JxxfmNMl4Uw8KIialEJkdhOz34u7Ij/9vHqEVj0AwaWUuyKXjNd3+XbgRYYixEZ8RSf+x092OhMGS6egWNnJeFZtRInpJG3dGaAfvlGC1GxizLGfqJz+LV/RbcOgMmnOpS8PoBM04PjE/RuWqKk/ggLrRuHb7/SiQbCkzd3hMpBfvKjewp85/0aBCSNw+agzZefvluBPVtYoDXJWhz9H68plXws7ciabIJbp8/ODl8owFWTHSxYuIZ5dp/bLZScZbh0oVHCmpbNZ7cZuFrKy6sMurBEJ0ey7Q7Zvd6bMvPP6C5tPEcragTCYVv5jP+usmYosx+pRGkrhOVElh1o+L8QzWUUAwLQhNMv3PukOZoO9VKc0kjrnYnRW8fC/g4zaCx8HuXkXvNJMZcNo7UmRmMu3oii398OVEp0QGHe/ojdlQ8Sx+6oru/zrxvLPYm355duaEJNKPGwgcuwxQGef6tJwZONO1Cl4J3D5t5ZJ2V1X+K5fndga/jH5usbCgwAaLXefydswvvBT7w99mjw8PvRPBhkclvBVBvvGP/usHKaR/n0yW8c8jEN1+I4r7/RfGnD6yU1Pe/HUoJL+01+3ytuhS8dsCC2wOl9Rov7jHz7C4z+8sNnCepWWGj7VQLVbvKz4tQbke9jY76dqp2+O9Vphk1Rill3wsW5XlRDBuZC0Yz7/7F7H1sB652Z6cgFSAlmklDd/mvSnG2Oqg/XhvQ2C7isxPZ8osPcLY4EAbvBahmXxW1B08x7/7FHH/tKJXbS7s9MUIT3V4iXxvy9Lvmknv1xF7GT0RiJCt/ew0n3jnOifeO09Fgw2g1kbU4m9xrJxGbGRfwuoMhUOHgrguwW4ffro0k2iK5eqprwPHNNsFP345gc6HXcBksmpAkRfX/XjrcsC7fxDuHzTTaBMnREpcHdpUah3TONw6a+dylfb0vGwuM/Oj1KDpcZ+beVWLkvzusrJrk5Aerbb2Sm+0uaLT5v89rdwju+18U+8pNCCQIr3dqdJKHh661MTE9wD/SBUbNgcBkC4aLXX/Z6u1T5Ye866eoPl8XMMp4UQwrWYvHMnL+aKp2lNF6qgWjxUjGJVls/81GGovr/YZyIpIiKdtcHPgJNWgoquv+tacrubWyma2/3MAVv7+OmZ+/hIbCOoSAxPHJaCYD6//fO7SUN9Pr1rkz3DTlUzMZf82kfk9pjrYw8ZZpTLxlWrc2RbiZmO6h+LShV16IfySPboxg1WRXv0muNid88enoTo/E0F6DLgVXTumbX1DT4u0ZVNFo6NZZKayVQz6flJB/yoBHp9dr21xo5Dsv9VeW7j3fe0dNaFokP77W1v2MyeA1vgLxMh0o926pEtH9WS6t17jryWh+fn07y/KCC5tdCHhc+rBJFwRC7cFTAa3HEmcZlvUowoMKGymGHYPJwKhF2Uy6ZTrjr5tMdFoMY68Y73uzEZCYO4KYkXF0NASRdNrloOlnbqlL7E12Trx7nIjESEbOyyLjkiys8REYrUZGTErt98DRy3MC7kMUSsPFrTNgCOLGmc4gDRcAQU2rxsHK/qudXttv4WRd/3kewWAQkrxUNwtzeiipSthbZuCzT8RQ2ejdhs6Eh4b+nkkEmwrNXPvnWP69xYLT3RWK6kocH+gcgncOmylr0LA54bldZm5/PKZ7Vl9nBIk+QG6OR4fvvhzNb9dGdKvsN9sE/91h4f7no/jG81H860MLdW0Xnupr3OiE88ZwAbr+FD4RmsBjvzg9YR8VlOdFcV6QtXQshW/l01rR3DdUI7xGwLTPeJMEI5ODFHTzec2RFL9fyKRbp/d6eN/fd1C8pqDfuUrXnyB2ZNywNFK0u+CVfRZe2GOmssmAJiQLc9wsHe/E6faWk08d6SHWKske4eFkXfBl143tGtB3I39p72Bd6t433KB5y4xHJ3n4xFwHRbUG8lI9nKzT+MFrUZw4Hf6WCPXtgsc2W9lZYuRTlziobw/kfk3yyl4TW4vNlNafbVgN5BXyZ3R4n39+t4WkKJ2cZA/ffzUKp/vMx3N7sZF/bbHyg6ttPkN53auU0O7wfka2nDBz4rQBs1GyMMfFzFGes9OuwkbKlDSiUqJpP9028HdNQMr0DGr3V3X/jgRhFEj38Fs+UpdEp8f4H6g4b1HGi+K8wGgxsuwnq9jy8AdnVG07NzhrXARzv3IpyZPTkB6dumO1Ac0pjBrS7T83xtHU25PTWtncv+HSgyPP7WfslXlhjZl3OOG+/0VzpOrMRV6Xgg+LjHxYZKLrkioRaEIOOjF0RHT/79GpZi3IZNkuBALJjEw3RacNFNcZeehN71YzMt5DQ7uGc9iiJ94eWvvKjURbum7J/Rsaz+62ImV/1VtDtwge32LF5aHP/LoEpOQnb0aSEtvOnNH9v0luD7y418zzuy1UNnV9NiSa8OaJP7XdyvgUN7++uX1YBPSEJpj71UvZ9OO16Lrst4Hj1NtnM+GGKbTXtFJz4BQel4f40QmMmJxKW1ULu/+6jbqjPkTlQow52kxGZy8nxYWJMl4UYcfjcFO6qZiTawu7W96PXpbD2JW5mGO8cWe3w83ex7Z7DZezrg9ZS8eSOiMDgMqd5bSUNgV03jlfXsiuRz70O65rDV2UrD/RR4TrbHSXTsWWEm+4a5CU1Wt8cNxEu1OQEadz+UQn0T3kUv6yIYKjpww+LqCi+0bXu9RgL6ySjDidKSP7d59HmCWtg2xEKKGzhLr3e1jZ1OX5GN7wiDdMFXgC8FBDZb5wuLsSMgby4Ej+s9XSr/Hi9sA3X4xiR7HxrHdWdBo/3t8KTxu4+6kYnr27hahhSO1InpzGsv+7kkNP7umlbBuVFsPkW6czulMeICo1hrFX9PZ4xIyMY/43l/DW3S8OW8XSTNWt+oJHGS+KsOJosbPhh+95e/507tn2xg4OPbWHgteOsOynq4gdFc/232w80/jtrP2r4LUjGEwaUz41y1siHUAyXvKUVEYvHcvJtYVeT80Am6LQRJ9eSrY6/1o0wqAFNK4/bE74yZuRrD9uRhPeO2aPDr9/P4IvLevgE3OdtDng9QO+y3PPWpGP5/q7UHofu2+5HYE3ZPHKPm/oIcIsWZ7nYkmui3cPm4POpfEm3g60rnOV0+HVhDFoslul99wy8Bokgp0lJlo6BLERkspGjRf2mFmbb6alQ+D0+D4evFVOta2CO/8Tw98+2UbSMKj/Jo1PZtnPrqS9phXb6XZMUWbixiQElPcVkRjJ7HsXsPsvW7tbZ3QhNIFm0vA4hp6jEjkiiumfm0vmgtFDnktxblHGiyKs7PjdJlormr2/9Nw/pbfsefNP3mfeN5dwaneFz3mOv3qEsVeMpy6/JqDkwPHXTUYIweTbZrDxwff6HSM0gSnSxLirJ/R63Bxt8XuNlbrex2MTCFLCd1+KYnep96unS9G9Tzvc8Pv3IzEbYMwIT+cdeijwzmMQEiG8ib8RJvjWFTaW5rn46rNR7Czp8pJ4LcPCGgNmY6dcjQyu+ufMLOcfV0528tYhM+fOiAqcv2+2sCTXzTdfiMLTo+VDMO9sab3G55+K5guL7HS4BcnROhPTPOh4O2SHuj8UeL0rUanB55Nkr8wlIimSoy8cpD7fGxoWBo2sxWOY9PHpNJ6oZ/dftuK2uwf1ATNHm1nw3WXEZSUEvTaA5tJGTu2tRHd5iMuKJ33OqG59J8Xwo4wXRdhoKW+i5sCpAZ+XusRW107+Cwf89iHR3Tq7/rTFr2pmF12GRcqUNBZ8exk7//ghHru7W+dFeiQRiZEs+sEKIhJ7ty7IWpxN0Vv5fs8xmLu3PaXGTkNhYP660cpPP2bzOSY4JONTPSzJddPhgjFJOisnOok0w33PRLGrtGs9vcNRjs5ESu91IlADxqvn0tAeauOg6+8+2HklmQk637uyg1a7txIpsPyXc8cLe6y8dsCredM7dBicaF9Vk8aP34zi7NcbbdG5foaTzyxwEBtxfpiaaTNHkjZzJPamDlw2F9aECEwR3s9ndHos6XMyKf+wxKvmKwSWGAtul5tjLxzyO7ezzcm6b78FQFRqNOOunkDOlRMwmH1bcPamDnb8blN3zzWEd/+wxFmZ+5VLSZ+dOfQXrggaZbwowsapPRV9XMBnIwyClooW/7Fuzdu0MRAscVYSc0d0/565YDRpMzIo23ySxhP1CIMgdXoG6XMy0foROEkcP4KUaWmcPjywzH/2ylyvkm6QvHnI7FczpNWu0djubQzoDkGIQxNwWZ6LO88SbNtYYOxhuPRHl9kS+BpWTXJi0OC9o2Y8gesIDoiGRNNgYY6LdocIqBXBQNw8y4HZCL+6ycbeMif/2WZmx8nzV6RMIDsTm4f6Gei/BL3NofHMTgsbC0z88462oNpFhBtrfES/na+NVhPZK3N7PdZQVBeQ8dKT9po2Djyxm8rtZSz50eUYLP1fCt0ONxsffI/Wyhagd881R4udLT//gCUPXUHKlLSgzq8YOsrnpQgbHlcA5ZrSK9PtV6JfJ2CvS94NU/oYJcYIE2OvGM/sexcw6wvzvb2IBmg/LIRg4XeWd+q8eA0sBN1em1GLspl197yA1tKFR4dnd5l5P9/kN49FILE5BVdMcmEQQ72gSIwaXDe9r0DcPzYP3Eyx52oCRSBxuAVLx7sHmVciEUjiInQ0IYmx6Nwwy8n/Pt/Kr2+2DbpXUdfqHlkXwfdejuR4jYHZo9384VYbI+M9PdKe+1/TuWJwlV7BoUuvZ+b37/c1FC4U4sckYokL5LN8FhLqjp/myLP7BxxStrGYlvJ+5Bs6j5dScujJPcGfWzFklOdFEVKkR6d6XxXF7xfSVFwfQHM0Seq0dE682+JznL+wUhcGs4Hx1/WvfBsMpigzS39yBfX5tZRtPomjxU5EUiRjlo8jPjsxqLl0CT96I5K1RwPzGkgECZGSr63o4FClgYrGwZYse9+vn17f3ith88Rpjce3WCmsDW3Cg0RQXGdgUa7XKKhu1vpN9hV4c2+6Umm60mqsJvjFje3MH9t/ifC0TDcWg8ThGWwFlGBTgYnNhSZ+e4v3PLfNdfCbtb4u3OcyrDQ8YS2PFKzNN/GNleK88r4EiqPFTmRyFI7mQTTF1CUn3itg8idm9Ot9KV5T4Du/RkJDYR2tlc3EjAxP6w9F/yjjRRESnO1OStYVUvD6UTrqbUEl1DWeqCdlWjq1h6sHDDHlXjuJE28fw+McuOJAaIL0OZkhU7UVQjBiUmq3ByZQ3B7vS++qxFx71MTao4GHJyJMkkvHubCa4F+faeN3a628e2Qw9a7e98HVI/F3b5mBrz0bHVDFSvBIIs1eT88jt7bzpWeiOd3a9TEQ3eGyCekeHr6+nU2FJg5WeregmaPcXDnFSbSPl+n2CExGiWMIa/dIgZCS778ayVtfaeGm2U4OVxl590jvcJ5BSDxSkJXooaxhKO0RhmKACISQyDCWbXfh0QXHawxBd/0+13Q02Fj3nbfoaBh8jpi7w0VzWVOvUHMXtvr2gPYxW71NGS/DjDJeFEOm6O1jHHhiN3rP7oBB3MA1nqjHHG0hbWYG1XsqO5PihLcvEJB3/WSmfGoWutPDiXePD5gfI3XJuNUTh/ZiBomU8M5hE//60EpFp3BYUrTOHfPtrD3qP8+lJ3deasfa6aSJsUrq2gYf3dWEZGOBicsnuXC64XsvR4XJcPHOuGKCVxl2VKLOs3e38PZhM+8cNtNkE6TH6Vw33cllE1yYDHDrXCe3zu0bzhqIx7dasDnFkNcuEbQ5YM1RM9dNd/Kja21cOs7Fc7st5J8yoAmYleXmE3Md1LRo/OLdSP+T9pi9S6TP++kVZz1HQOvXhCTWKmnqGL7Ivq9VeXSvEf5+vgmXRzAx3U20BY5VGxACZma5uXKyNwkcoNEmWH/cRLNNMCJGsjzPt2E6WA78exf2xo6hR/cGUHi0xFhwNPn36FgGUXmoGBrKeFEMiZIPitj3jx1DmkPqEmebg6iUaFb96XrKt5zE2eogMimKrKVju6uBpnx6FnXHamkqaeztoelMCp708ekkB+klCQVSwg9ejeT9Y729K/VtokcuQWCVOiDIGXHGCHxln7m7rHow6JLu7snrj5toDtvFUHa2lDnzd4mywC2zndwyO3ADZSCcbnj9gCUAA7BnofbAYw0aHK40cN1078fnikkurpjUV5J/V0lwobVpI72emqaO/s/tLVaRfcJpZ4xbr8kTFyH54yfa+M2aSA5UBPP373kRDtzIMxnkgF2v95cZ+Prz0T26cEu2nzwjQKgJWHPUxJ8/iODhG9rZcdLIs7st6Dpomtfw+fV7EXx+kZ3b5ztC1rbA0WynYmvpkIXtDBYDsaPiqTtWS+EbRzm1pxLp0YkbnUDsqHhaKpp9GkfR6THEjRlc+bVi8CjjRRE09QWnOfHOceqO1WCrHZxQ29lIXVKyrojpd85l8q0z+h1jijCx/P+upOCNo5x4+zj2Tln/pNwRjP/Y5HMmPPXqflMfw8VLsLu0N7Ty761WFuW2ISU8s9MyJM0Ug4DRSd6L0sEKYw+PwGAZyDjw/v+vGyJJjpYB9eYJhro2rdPr4g/BiCidukBKtQMYEhtwHqgkwgR3LbLzteeifZxQMinDw2cX2HHpguwkDyfqDLy6z0xlk0aMVXLlZBerpzr5+2YrByqCMZ4kBg1mZ7kxGaC4TqOmxX9jTU1IFuW4qG0VRHSG/bo4Uatx7zPRZ0Vze1cvdT1nc0m+/lxUZy9U73NdFWcOt1cxWpfw2YVDSbw+Q3NZ45ANF6EJslfmUrL+BPv+saNXbl1jcT2NJ+rRjBpSlwOea8onZw5L53hFb5TxogiKw8/sI/+FgwEn0AaDx+nB2WInImngEmSj1cSkW6Yz8aZpONscGEwGjBGDL58NBY9vseI/tyGw3AddCg5XGalpERg1qGgcWlKtRwqu76w0EmJo3nWBxGqCDpf3t/6R/GVDBKsmuxigmGtQmI2BrdyoSX54jc2HAeHFowtmjvKf35Gb6iEt1kN1i7+8F8EPV7fxxkFzd75Mf+hScKjSyKhEndFJ3iv7mBF6d7iti7J6jRf2BBOK8H6+HrjKxrXTvH/vRpvga89Gcbymp9Ha83MoO9cE6wvMrC8wkxil84m5Dj41z4FRgz+siwg43Cml77otgH9+aOXGmc6Q6MoMVC0YKEITxGbFM3JeFhsfXAOcVdHY+V/drWOKNOGyubz7XmcbBiFgxl1zGbUoe0jrUAwOZbwoutHdOjUHqrwJt5rA3mCjvaYNg8XAyHlZ2Bs7yH/hIBB42XKwGK2BGSJCE1gCvy0eFLqEo1WGzp44kD3CwyXZbnoWJbg9UNsaiIER3J1Zq12EZIO/Y4GdrM6L5PRMd5AXxC68F7ykaEl6rIcjp4w+LmiCujbBvnLjgI0FB0NSlGRcsocTpweuvDIIb6LzJdluRiV4qGzq3+ugCUmMVXLZBP/eIU3AXYsc/N/bA+W9eN+b2+fZWTHRzWObIgJqp1BSb+g2XvrDnxF0NiPjdb683N7LCEqIlDz+2TY2Hjfy5/URVDUPZICdeayhXeNvG6wcO2XgB6tt7CoJ7SXC7YH3803cOGvoocSEnCSMESbcHcF7+QwWAxmXZDH7nnns/cdOv73MPG6defcvoWZ/FbrbQ1xWAmNWjOtXi0YxPCjjRQF4mxEe/M/uvuWGwmsonHjnOJpJC5vuu9AEyVPSMIWxS3MwvH7AzJ/WW2k5K0ck0iy5e7Gd2+Z6Y/fh6COnCUlytCTaKkmK0qlvDz5JNS5C586FDj4x94yLflmeiwiT9OM5ORtv88YvL7ezdLyLz/w7JqA78fq20LrRhYDb59v50RsDe+U8Em67xIEmvCXX9z4dTbuDXgaAQfOGRX51UzsD6JL14dppTmpaNP75ofUsD4Y3LPe1FbbuxGOrKUAFaIPvcVXNmt+vmUFIVk508vE5TiZnDKCp1Bl67N9w6f9vJBF8cNzM1Ex3yLVmDBrUtobGJWewGBl3VR7HXjnsc0+afuccrAkRlG86SdXuChDexqrlm09Sd6QGl93lN/ykOz10NNiY+5VLQ7J2xdBRxouC4rUF7Pnrtv6flGe8LLorBJKpAyB1yYSbpoZt/mD414cW/r65/zsqm9MrdtZmF3xhiZ1nd4W2ysAgJItzXRSdNnCkykBuioeGk2d3EB6YaIvOwzfYmNmZ99ATkwF+9rF2vvliX6n4s+kq0Z2S4eGPn2jr7kycHK1zss5/HkVSVOitulWTXZyst/PEVmuvBosGIdElfGdVBzNHefN7xqXoPPW5Vp7eaeWNA2Y6XAKzQXLlFCefnufw6fU4GyHg7sV2Lp/k5JV95u6+T5fmuLhqSu9O4EvHuyisNfh8fyJMkhl+QlYxVtnZV2rgMTowZaRnwK7g4DXCD1cF3k37DJK/b4og1FozuqSPR/F0q2DHSRNOD4xL9jB1ZADilp1M+sQMGk82ULOvqteNldC81Ypzv7KIrMXZrP/+OzQW1Xuf7BSXA+hotAV8M3boqT2kTE4lLjsRoQmV53KOUcbLRxy33cWBx3ed62VgTYigeE2B1wMzOfWcbQxHqwz8PQDl2ce3WFg50RmgSm1gCCQGAxyvNvClZ6LRhERK+slV6B+DkFwzzcklPrQ6FuW6+fG17fz0zSg8vRouev8faZaYDd4+QDfNcrByogtzj13i6qnOzkqTgZCMiJbMzAq9XogQcO9SOwtzXLy4x8KBCiMGIbkk283Nsx3kpvQ2SNLiJN+8vIP7V3Zgd4HF5A0DDZYxSTrfWOm7bPb6GU6e3G7F4e5fn0UguWW2gwg/DsYVE1y8ss+3YSzwGku+eGbnYI1rQYcr9CJ5UsKKCV4vVbsDfvmeV7yxq9IKBGOSPPxgtY2pPoyyLgwmA4u+v4KyTScpejuflvImNKOBjEtGkbt6Igk5SZRtLqahoG6ABQWxdo/k/c7eSF2aUnkfmxy0DpQiNCjj5SNO5fYyb5fWENKVzBuREklHbWDiUfbGDiq3lVKxpYTRy8Yy975LEaHM+AwAKeHbLwXer+jLz3RppoQGi0mCFNR0utV73r17t/aBLyZCeDsEB1KWfNUUN/OyW3h1v5n1x0w43YJxqR5umOlkdpbb513vZRNcPLnNw8m6/tVzQfClpbaQJuuezfRMD9MzAxclEwK/xkKoSIqW/PaWdr75QhRO9xltn65S6CXjXdyzxL9uyJzRbiamuymoNvSvUiwkq6c6SY31kaehQ/mQEr6D99YYOpPC+/M8CSG5pnPNLg989blojlb19FJ5f5Y1aHzp6Wj+fnvbgOXbPdEMGmOW5zBmeU6/zxevKfSb0xIsUpec2l1B1c5yJtw01duSxKhRur6I4jUFtNe2Y4oyM3rpWHKuzOvT/FUxdJTx8hGnraY1pJVDIyalkJSXQub8LBJyR7D1Fx9QtbMioGO7NpfSDcVEpcUMWDIdLnaXGgMWhJMIGmxDUV7ti8MlEIDez5yyU/hsYrqbo6eMPZI5vX4ZqxF+c3M7mQmBhUMSoySfu9TB5y4NrmzVZIA/3dbGd1+K4mClEYPmPb9HB6MBvnZZB6unhbZM+kJjzmg3L9zTwqv7LKw7ZsLhhuwROjfOdHDpOHdA3h8h4He3tPP157zVQl3GT1e4bEmui2+v6vA5R5emzHD0SOoiIUqSEefhYKUJg+YN52nCW921coKL73Suee1RE4cr+7/86FLg1iV/Wm/lr58cuhRDe21bSA2XLrrmPPbSIY69cghThBlXu7M7fOVqd3LspUMUvZXPkoeuIHFcXwVfxeBRxstHHJPVhAxRKkvi+BEs+dEVvVrMT7/zEuryT+NscwTloi18/SgTbpjqt119KFlztEt4K9DNPsQudb8tAgXljRrP3e31mhyrNmA0wIKxbq6Z6iRumPrSJEZJ/n57G0eqDGwsMNHh8sroXznZFZIKqYuBlBjJF5bY+UIAXpaBSIyS/PuzbWwrNrL2qFelODVO59qpTqYEkBciBMwd42ZnyWByXgaDoLEdRifCP25v5d3DZpo6BMkxOqunuhif2lN80eKz9YEuBXtKTVQ1aWTED22DMkebsdUOaQr/6F5jBei1z0ld4ra72fzT91n995sxBpolrvCLeic/4oycn8WBJ3YPeZ7o9BiW/viKPsZGdFoMK355Nfv+uZPqvZUBz+eyuag7VkvqtPQhry1QWgZQRT2faLVrpMfrfN1P7kW4EcJ/sqhi6Bg0WDTOzaJxgwvtfmqeg50lw6eD5JGCPWUmoq0dfOfK3p6h2lZBfZtGQqRORZMWUM+mquahGy+jl4yl6WSDz5sna3wEbod7UGXX/pC6xNnioPzDk2SvyA35/B9VlPHyEaajwUbJhhOYYyw4W4emejn2ivEDisVFp8ey+Icraa9to6Wsid1/3ertR+IHj314ww8pPvIHwo3WWTHj7w7ZbJAMozNKcYEzf6ybLy7p4NFNZ1fPhbNjtWR/uZGxI7yeiEMVBh7dZGV36Zn9wV+peBdR5qF/J8esGMexVw7jbHUMGD6a9InpxGXFs/7/vTvk8/WLgOq9lcp4CSHDmxGpOG+o2lXO2198iaPPHxyy4SIMgjHLx/kc017TSskHRVRsL8VgMQRU9jHcXVpnZLoYHvd6bwydommzR7sxiIE3a4OQrJrsDFlvGMVHgzsvdfDop1qZM9qFUZMI4e3+bTF2SsWGGAHonc6S7cVG7nk6ulvosQtvD1ffQdLUWJ28tKF79szRFpb9dFW3oJzo3Hu6fk7+xAzGXjGeERNTmXbH7CGfr1+kVwRUETqU5+UjSEt5E1t/uQHpGeKXqTMxbebn52GJ679kWOqS/Y/vouit/M5u0Z0aCz5OLTRB4vjkYTdeXtprITR3pIHMITv7SQqyR+j83w3ttHQI7vlvdL9JlkJ4+9Z8cl5o+sIoPlrMzPLwl7OSX9sd8Ml/xFDdGtrEc4lgcoYHlwd+9Hokuk6fz/OZ8v+BviuCz13aMaTS9p7EjornqkdvpHJ7KVW7KvA43cRmxjP28lyiUmO6x+XdMIWIEZEcemofttNtoTk5gPCuQRE6lPFykeNsc2Bv7MAcY+m+8yh8M3/AFvDBEJMRy+TbZjLq0jEDjjn8zD6K3soHCCzjXxNoRo2Zd18y5PUFQ3OHV9J+uAyXxeNcTMv0MGOUu5co189vsPGj1yNxumX3Y7qESJNXFXbsCHX3pggNURZvX6XqgNpbBIZBSHJTPbQ7BT97K4Imn13Me/dYAtFdRXfXpXY+Nn3oLQR6rc1kIGvxWLIWj/U5LmvxWEYtyqbwzXwOPrHbe7M11O1SequSSjeeYOLN0xi9LEcl7w4R9e5dpDSXNnLk2f1U7ijr/uIlT05l0q3TKd9SMuTSwQXfXcrIeaN9isk5Wx0UvHYkqHkTxyUx6575JIxNGnCMywPHThlwuAWjkzwkxwzdENte7KtfT6BIUmJ04iIkxacH0OfobG744+tsRPejH7Y8z8Wcr7Tw1iFvKakQMDPLzZWTnUSeH50TFBcRyTF6L7XioWAQ3s92Q7vgy89E49uz0puMOJ30OJ34CJ3kWInFJDlQYWB6ZuBqu6FECMH4ayeROT+LE+8VcGpPBfYGG46WoXk+O+ps7H10OwWvH2XZT1cp/ZchoIyXi5D6Y7Vs/NEab4y1x3X99NEaNj64BmEY+m5QsbWMzPljfI6p3FHmP86rQdr0DLIvH0/MyFjishIGHOrR4antFp7ZaaG5845OIFk0zs3XVnQwKnHwXgm7K3hBrt6bsiQhUvLkna20OTTufiqalo6+fXU0AQ/f0N6v4dJFjFXyiblOmBvaO0+F4mxWT3PyxsHBqvCe+Q4YNcnCHBcfFppod3Z5WwKXHKhq1uhwCRptpu68L48UjB3h4ec3tJN9jjyOkcnRTP30LKZ+ehYAa+5/neaTjUOet726la2/XM9lv7hatRkYJCph9yJD6pLtv9uEx6339a50/hoKQbrmEv9fYEezvTspbkAkGMxGMheM9mm4SAk/fSuSv220dhsu3sMFW08Y+dx/oilvGPzHeVTi4BMDI0w6n7zEwUtfbCEhCkYlevvq3DrX0V0tYdQkl0908fhnWlmQE3rpfIViMMzI9HBpjgvNR6L4QIxP8fDFJR08fkcL31jZwbZiU2cq2+Auxo0273EeKbqN/pJ6jS88FU118/lxgU+eGJpWAFKXNBTU0VA4QNsChV+U5+Uio2Z/FbbTQ1el9Icxwv9Hx5oY6Tc8JTSBNdF/W/kdJ428c7j/uIlHCtod8Lv3I/j9x32/drcOW4pMbDhuwuaCUQk6101zMnOUh5HxHm8334DCR4Lf39JKZoIkLU7v1f8HIDlG8rUVdr5ymd3bV8dIWCXzFYrBIAT8/IZ2fv52JO8dNXUmkYM/AyQhUuepu9rYWGDiK8/G0N7tJByskTGwWF27A57eYeWbV/iXVwg3WUvHUvT2sZDMJTTBqd0VJI1PDsl8HzWU8XKR0XCiLqRy/wORNnOk3zEj52ex91EDHh8NgKRHMuYy32XWAC/uNfeQxO+LRwq2nTBS3SxIi+v/tW8vNvD/Xo2i3aHR5YYSAp7abuVT8+x8d1UHX3/e23HZl6S6QXgbDy4cF0DfFYHKVVGc11hN8JOP2bhnicYHx02sPWqioMYw4HdAE5IbZzrZU2rkey9HBmTsDAWPFLxx0MzXV3ac8xuAqJTo0E0mhM+9sSetlc0Ury2kraoFg8XAyHlZjJyXhXZ26/iPEMp4ucgQmhZwMq5m1AanPWAQjL3cv9iSKcLEpE/M4NCTe/ofIGDkJVkB9fworOk/AbYnEsE3X4ji6ikuRiZ4iLZCTrKbDcfNvHPYxIGKnh9371xdRVdP77ASH9HBHz/Rzm/WRFBSb+g1c3ddODA6Sedn1wfeGFChuBAYmaBz+3wHV0xycsfjMbTa6fOdMwhJSqzOrXMcfOvFriam4Q/pdLgENqcgxnpu209YYiwhEfUEkB6d2FG+5SCklBx6ai/HXznc3VxSaILyD0uITI5iyY8uH3ZJifMFZbxcZKRMTQu4rG9QhouA+d9YQkRSYN2X866fjPToHH3uALpH934BvW1nGb10LLPvXRjQPGeHZQai6LSBP67vObjnm+F7k31im5W3v9LMs3e3cqTKQPFpjUNVRo6eMtDSoZESo/OxGU6umOTEOnyK6wrFsJIaK/nnHW089GYkhyqN9EzMnT3azYPX2LC54OAAjRXDgVGTRJjOfd8sYdDIuTKPYy8dGnLFpjHCyKhF2T7HFL5+lOOvHAbOSE10/eyot7HxwTVc+efrB1Q3v5hRxstFRmLuCOKzE729PMLAyHlZZM7PCni8EIKJN08jZ1UeZR+epKOuHVO0hVELR/cSh/LHklwXz+zUAihnPvv5wO8K2x2C3SVGFuW6u/v2XDfDhcsD64+b2Ftm5HClASnNXDHJSYQKBykuUkYl6vzzjjYKazWOVBkRwMxRbrKSvDc8R6uGL1xh0CQrJrownicRkrzrJ1O5vYzWyubBGTCdTtzZ9y7wqfXicXnIf/HggM9LXdLRYKN0YzE5V+YFv44LHGW8XGQIIVj43WW8e9+rPj0rQhPEZMbRUtYU1PyV28s49PS+oGW0zTEWxl01IahjenLTLCfP7bage8LZkwVa7L3nPlJl4FsvRtHQrmHQvBvVawfgD+si+Ol17SzKVZVDiouX3BSd3JS+JfuJUcNTuiw6lahvn3+mEanLAxsLTLx3xERzh0Z6nM4105zMGe0eFk0YU6SZ5T+/koP/2U3J+hNB5xfGZsYx9fbZZMwd5XNc3ZEanG1+5BIElG3+aBovqv7hIiQqNYa5X13kc4zUZdCGSxeFbxzF2Ta8MvUZ8ToP39COyYDP/j9DJbVHc8bKRo0vPxNNU1cJpy46xbwEHU74zktRHKo8T24HFYphJC1OMm2ke1Al1mfo71ivsJ13XkmEGX5zczu5KV5jqbZV8Ol/xfD9V6PYXOjNY1tzxMR9/4vmG89HMVy9XM3RFuZ8+VJW/PJq/4MFJE9NY/EPV3L5767l8j9cR+SIKKr3VtJ4ot6r4NsPzvYAdJ4kOIconHehojwvFylZi7OxN9g48MTu7kQv4Mz/z+SfBo3u1qnaWR5QlVAoWZzr5um7Wnlqu4U1R8043BBKL0xytM7MrDOelP/tsuB002+oSnZ2IPr3Fiu/81OerVBcjHxxqZ37/hfVby+unhg1iVsXnZ3TRed4MGpe6YKe46ZnukmK9npbpo86oyzt0WFzkZGfvRlJq6Mz2b7znHrnz+3FRn7xbiQ/vnb4kukTckaQPDWNuiM1A4eQJEy6eRop09I5taeCHX/Y3OvGMSo1mimfmkXW4t75L+YY/3FpoQmi0gIPv19MKOPlIsTV7qRieykel4cJN0+lvbaNhoI6kJLo9Bhq9p8aWq8OIQK7KwghHh2e223hv9st1LeH2mHoDUV984rejeDePmT2WeGkS69AXkuHIDbi3CcTKhTDyezRbn5xo42H3oik3Skxat5vkkcXpMd5+Nyldiak6YxJ8rChwMQbB8xUt2gkRelcPdXF5ROdFNUaKG0wEGGSXJLt7reaqKpJ4+vPRVHa4NvLKRG8c9jElAwzmQk6s7LcASf6D4VZd89j3XffxuNw92vAjLksh+SpaZRvKWH7bzb2ud9qr2ljx+82UfR2PnO/soio1GgOP32mJ5wvpC4Dqvy8GFHGy0WElJJjLx/myHP7ka7eMemIEZHMuHMuZVtKenliBnkiIpOGryeHLuHB1yJ5/1i4MuoFn13YwfK8Mz5njw7tTv9eHYmgxa6MF8VHk6XjXbz91WbezzdTUGPAaJDMy3Yzd4y7143AFZNcXDGpb0xnaqaHqZkDa53YXfDlZ6Kobgn0hkXw6zVde5O3E3teqodbZjtYNdkVFp2Y2FHxrPjVavY/vpOafVXdj5tjLIz/2GQm3DAF3elhz1+3di2rX+qPnea9r79GyqRUag4GcIOpCZInp5I+OxOA9ppWStafwHa6DVOUmVGXZpM4fsRF235AGS8XEfkvHOTI//b3+1xHnY1tv96IJT5iyCV+pigz6XN8J5uFkrVHTbx/LHylPQLJHfN7x40NGsRadVrsvnc7TUjiI1WnZ8VHF6sJrpkWHk/smqNmqpo1BhceFnh0OHrKwENvRvH793W+sbKDKyaFvnIpNjOOJQ9eju10G61VLRjMRhLHJXWLyFVsK8Vl85+QI106NQdOBXTOrEXZzL53PgjB/sd3UfjG0TPtWAQUvpFP8uRUFn5vOWZfzdQuUJTxcpHgaLFz9PmBy+q6xzUNXWJ72mdmYzAPX6Lq87stfuPqQ0EiaGjXiLL0NkKum+7kmZ2WAcuzDUKyNM/ls8miQqEIjJoWwSv7LOwuNSKlN+flQHko9hnv97fFLnjozSjeOuTit7e0h0WrKTI5msjkviq8LeVNCIOG9ITmRmfh95Yzcp5XsuLwM/sofOMoQJ8b07r8Wj78+Qcs/78rLzoPjDJeLhLKPzwZsi8GAJrw5vRKr6Kj9EiMESamf3YOYy8fH7rzBMCx6oGlyn3T9UX2f2x/sfZPzHXw5kFzv0qjmpAYDXDXpfY+xykUiuB474iJh96MRMozCfL5pwx4Qtp6wDvPnlIjf/wggu+sGr5eSQaz8YycdwhorWwGvBVJXSJ2/SF1SX1+LacPVZMyLT1k5z8fUMbLRYKtzkaPrmpDR5cs+8VVNBU34Gp3EpUSTca8LJ+iSuHCX2PqgfEe6KsnkiYks7LcxEf2fd+SYySP3d7Gd1+KoqTegEHzmlBuXZAULfn59e2MS1EhI4ViKByuNPDjN/r2SPLXDmSwSASvHzDzxSX2YctVS5+TyZFn94dsvkNP7cVlcxGdHuNXKV1ogtJNxcp4UZyfmKPNIbXsAaKSoxmRlxLSOQfD3DFutpwwMtg7ME0DXZf9dIuWSAl3XjqwTsKYJJ1n725lT5mRvaVGdAmTM9wszHGf8yZxCsXFwNM7LJ3f7OELa7g8gn3lRpaOHx5hmIScJEZMTKHu2OmQ7dPHXjpExrxRfgswpC5xtFx8HmK1/V4gSClpr22jubQRVz9lypkLxwRc/mywGs8kdvWHgNiseKwJEYNbbIj5xFwHg93YIs06v725nUgTdIlfCSRCSEwGeOg6G3NG+1bJFQLmjHbzhSV2vrjUzuJcZbgoFKFAl7Cx0BQ2L4svnMMsjr3gO8uIGRkb0jlrD1X7LcAQBkFE4vBVhw4XyvNyAVC2+ST5Lx7sFjYSBo2sxWOYfNvM7hbt0WkxZC0dS9nGYr/zjb18fHeCV79ImHDDlPMmweuSbDcT0twcqw724yq5caaTeWPdvHFfM+8dMbOnzIhHh8kZHq6d5uw3XKRQKIYHl4dO1epACG1rkJyUgUu0w4E1PoKVv7mG/OcPcOyVw0PT2urEbXOhmTR018ChI+mRwy4oOhwMy/3jX/7yF8aMGYPVamXevHns3LlzwLFPPPEEQohe/6xW63As87wk/8WD7PjdJlrKm7ofkx6dsk0nef9bb9J2qqX78TlfWkjGPN9NExPGjWDc6gnMumceQC8PjDB4/593wxSylo4N4asYOvcuDdbtKYmPkNy50HtclAVunOXk/6638Ysbbdw+36EMF4XiHGM2BNYnyWyUWI3Q1T5g6Ej2lA7/vbvRYmTq7bNZ9cjHyFoytnvPHQq+DBeEIOOSUSTmjhjyec43wv7Xe+6557j//vt59NFHmTdvHn/4wx9YtWoVx48fJyWl/3yK2NhYjh8/3v37+eIBGG6aSxs5/PQ+7y9nfV+lLnG2Olhz/xsYO8NAUSnRjF6aQ+7VEzj09D6aTtb3+WA3FtXxzr0vkz47k0XfX0HVrnKq91UidcmICSnkXD2B5EmpYX1dHh12lRipaNSINEsuzXET18OQaO4QHK3yVhpMSPMwIlqSEKkTH6HT1OHtLeSPtDidJz7TRvRH1+5VKM57hICbZjr41xarT0mC66c7uXdZB+8eNvPOYRMHK4da5yz4zZpI3B7BbZcMf2+g2FHxzPvGYuZ8aQG7/ryF8i0lobHJ6LwJld5rRNbibOZ8acFFeQ0Nu/Hyu9/9jrvvvps777wTgEcffZS33nqLxx9/nO9973v9HiOEIC0tLdxLO+858d5xv8lYHrsbj90bvLU3dFB/7DTmaDOWOOvAFrmE6r2VNBTUseLXq5l974JwLL9fNhaY+NV7EdS1ad3aLUZNcsNMJ3ct6uCvGyJ457AZl8f7ZdOEZEySh+I6Y2dDxq4vYdf/vT9NBq+a5qgED7fOcbJ6mnMIVUoKhWK4uHWuk3ePmKlq0vrkvhiEJCFK8pmFdiLNXu+pR4eDlYNP4O/JXzdYuXaa45zd5BgsRtJmjqT8w5LQTCggLiuBrMXZZC4cTVTqxdv3KKzGi9PpZM+ePTzwwAPdj2maxsqVK9m2bduAx7W1tTF69Gh0XWfWrFn8/Oc/Z/Lkyf2OdTgcOBxnLOeWlpZ+x12INBScHpQarrPN6beVutQlzjYHh57cw/xvLR3sEn2fQ8LBSgNHqoxowtuM7ZH3I7pvMLq0W9y64MU9Zt46ZMLm7O1Z0aWguM77Me29sYnuGX64up2rp7q4CG8uFIqLnhir5O+3t/GLdyPYVGDqpek0Z4yb/3eVjRHRZ/ZBUwivWi4PrMk3c+PM4e3V1pNRl45h15+2hGYyCS2VzYxdNR5TZPhUyc8Hwmq81NXV4fF4SE3tHYZITU3l2LFj/R6Tl5fH448/zrRp02hubuY3v/kNCxcu5MiRI2RmZvYZ//DDD/PQQw+FZf3nGhHmkhapSyq2leJosWOJDe2tR1Gtxg9fi6K4ztDZ3r5LfKr/pDuJ6DRcgkEghGR3qYnV04an5FGhUISexCjJr26yUd0sOFDhvSxNSvcwKrGv99hbHRiaOxWDBqeaz23poMFixBRl7reKdDDoTg+tlS0XZZ5LT867gs8FCxZwxx13MGPGDJYuXcrLL79McnIyjz32WL/jH3jgAZqbm7v/lZeXD/OKw0fazJG+S5pDgNQlbadaQzpneYPGF/4bQ0m99+OlS9Ejnh3a16NLwfv54WrYqFAohpO0OMmqyS5WTXb1a7gAZCboLMxxoYUgScSje7tW3/WfaBb/Oo7lv43jgVciOVgxfO1PgJBXA4UiEfh8J6zGy4gRIzAYDNTU1PR6vKamJuCcFpPJxMyZMykqKur3eYvFQmxsbK9/FwvZK4an1Xmo+xQ9vsWK3cmACXihxumBsobzzg5XKBRh4oerbWQm6oghGjASwfv5Zo6eMuB0e72/GwtM3P1UDM/vHr6wS+7qCQhjaPZLc4yFuFHxIZnrfCasO77ZbGb27NmsW7eu+zFd11m3bh0LFgSWJOrxeDh06BDp6ReXtHEgnHjv+JA7QPtDGARxWfEhm6/DCWuODr/o1Et7L+74rkKhOENilOTfn21l1eTBh4tFj7LrnjdaXbozv10byaHK4fHARKXGMOGGKUOfSMC41RO6u1lfzIT9dvX+++/nH//4B//5z3/Iz8/n3nvvpb29vbv66I477uiV0PuTn/yENWvWUFxczN69e/n0pz9NaWkpn//858O91PMKe2OHz4ZboUJ6JK6O0OWLNNo03AGLToUKwYbjKnSkUHyUiLbA11d2dOfUBY53vDelcOC9yqBJnts1fC3jc66cMPg0gc7D0mdnMvGmaaFb1HlM2Eulb731Vk6fPs2DDz5IdXU1M2bM4N133+1O4i0rK0PTzthQjY2N3H333VRXV5OQkMDs2bPZunUrkyZNCvdSzyvKNhWHulXRgPhr7BUM0ZZzI/zm9Fz8MV6FQtGbWKvEbAC7Oxj1XYGG9HuT5dEF208On5BdRGIkoxaNofzDkuA87gLixiQw/ppJZC0di9aj0EN361TtLKNiWynONifRaTFkX55LwtikMLyC4WVY/jL33Xcf9913X7/Pbdiwodfvv//97/n9738/DKs6v7HV27waL57gjAGhCaSUAQseWeMjQlppFBshmTvGxZ5So5+cl946LUNBE5JxycMr9a1QKM495Y0adnfw+4ce4J7jGuaboln3zKe1qoXGovqgjmspa8ZW397Lc9Ne28amh9bSVtXSrRdWqwlOvHucMZeNY/aXFvQydC40LtyVX+RYYixBdx/NvW4S1/z744y/bhLGiADCKEKQc3VeyCuaPnepvXPpgaw/BPLYUnDTrHOn06BQKM4NQ2muaND87U8Sq3F4PcmmSDOzv7QwuIOkt2XMkWf2c/xlb6qB7vKw6cdraK/2VpJ2eXK6fpZ8UHRGvf0CRRkv5ymjFo0JOlk3cVwS1lgr0z87l+ueuJUrHrluwFp/oQkSxyUx/rr+xf+GwqwsD19Y0tWLaKDXEBqDSSBZNt7JkmFqba9QKM4fRsbrmA2DMzAiTf76JAmaOwT1bcPrfWkpbRz0sUefP4DL5qRyRxltp1p9XkMK3zwaMm2Zc4EyXs4xukenem8lJ98vpGJbKW6H91YiOj3W2xwxwO+NZtJIm3VGxM9gNhCXlcCyn13JhJumYoo6U41jjDCRe+0klv7kCoyW0EYOS+s1PvdENI9tisC7+FB88eVZP71EW3TuvNTB/91gU60AFIqPIFEWuHqqc1BJu60ODX/7k0RQdHp4K3eGotHicXrY98+dlG4q9rv16i6dU3srBn2uc83wt9VUdFO2qZj9/96Fo+lMx2Sj1ciEm6cx4cYpzPnSQnSXh4qtpX7nGrd6IuaovuXCBrOBqZ+exaRbp9Na0QxATEYshhAbLXBG7Kk9aKVc/5gMkvQ4ncsnOpmU4SHKLJmU4SEML0OhUFxA3LPEzo6TRqqbtV6tBXwT+B5lHOZb/BETU8+kAw6C0vUnEEYtoONd7Reux1pt/eeIkg+K+u1n4ba7OfzfvdQdrWH2F+ez4NvLaDrZQNFb+ZRuPonu7JuYOmrRGKZ+epbP8xlMBuKzE4e05hOnNZ7dZWH9cRMOt2B0ooebZjlZPdVJbavGV56NotURKm9Lb1weqGjUeHyLlS8ssXPnQofqZaRQKEiMkvzrM238cV0E7x0xnWXADK0gwGSQTEofQmLNIIgcEcXI+aOp2lE2aJ0vGWAFaVRK9KDmPx9Qxss5wONws/9fO32Oqd5byVv3vETG3FHM+fJCEsYlcXJdUb8WeXNpE+4OF+bo8GkSfHDMxA9ejQRxRsSpqNbAL96N4JV9ZioatU6PSzgsCu+cXd/jxzZFEB8pz2kzNYVCcf6QFCV56Dob31gpeO+oic2FJvJPGWhzDGU/kliMkohzoH85+4vzaa1ooqWiedAeGH9YEyJImX7hir+qnJdzQOXOMly2ANx1Ek7trmDdd99i72M7uh87m9bKZvb/a1doF9mDqiaNH74WiS7PGC7epXiNleM1hjAaLv0h+eeHVjyhk6dRKBQXAfGRklvnOPnzbe1cPcXF0CqBBW0O7ZzsM5ZYK5f9cjXTbp9NZEoU4M1rtCZEhGybnXHXJRd0qbTyvAwDjSfqKXwzn1N7KtDdOuZoc8AxTalL2qvbfI6XuqRsczHT75wT8u7QAK/sN6NLfMSTh19Rt75NcLDSwMxRSt9FoVD0ZfkEJ8/vGZo32iDkOSsGMEWYyLthCnk3TEFKiRACl83J9t9spHpf1aDntcRbmfn5eYy6dEzoFnsOUMZLmDn5fiG7/7oVIUR3/NI9GDl+P4aO9EgaCutIn53pe+Ag2HbCn+DcuaHVfv6tSaFQnB/MHOVhcrqbY9WGQfda80i49s+x3DTLwe0LHJQ3aGw4bqLDJchM0FkxwUnUMHQQEJ0JfqZIM4sfvJzitQXs+eu2oOcxx1q4/PfXEREf0f2Y7NQTExdYEqEyXsJI08kGdv91q1dEaDi0/sN0Cvd5Kr2fGnNuWhEoFIrzl5oWwav7LWw9YaTDJYiySFrsAoEMohqpC8HpNnh0UwSPbbL2ON679/z6vQi+sbKDG4dZJDN7ZS75LxzEdro9qOOcbU5Orilg4i3TqNhaSuEbR6kvqAMgKS+Z3Gsmkrlw9AVhyCjjJYwUvpU/KIn/wSA0QcK48PSrmJzhpqxBG0Kn6DMZ/wmROo22oeXHCCEZO0JnfKoKGSkUijNsLjTywCtRePQznaK7NGAy4nVONWudif/B7D/esbKfx5we+OV7kZiNcM204TFgpC45/PReOuptwR+sS4reOUZrZTNlm0561dU7b6zrj9VSf6yWsavGM+ue+ee9AXPhZutcAJzaUzFshkvmwtFYe7gCQ8mNs5xDMlw0Af+5s4UN32zi3a+1MDvL3dmOvn8EErNB9jtGdN77fPWyDlUqrVAouimp1/jey1G4PPQKc3f9v7LJwO3zHMRYZec+EuzePNCGI/nzeivuYbqXOvTfvRx7+fCgy6gdTXbKNp0E6HeO4vcKKNtYPKQ1DgfKeAkjoezWPBBCE0SlRjPz8/PCdo6J6R4+Mcef3H9fNCExGeC3t7QzIU3vLjm8fb7Dp/tWIrhveUen5L9ECImh8+4pIUryq5vamT92eLUXFArF+c3zuy2du1P/e4tAsjbfxCv3tvCtKzqYM9r3TVTgCBptGjtLwh/I6GiwUfDakbCf58B/dof9HENFhY3CSEJOEqcPVQ/aQvaHwWxg3OqJTLhhCuaY0GeNtTngfzutvLTXTKPNa+dajODothsGEoDymibXTXfyibkOskf0NuIW5Li5Z0kHj22KwCBkt1en6/83zHTw8TlObp3rpKJRY0uR0SuKl+Th0nHuYVe8VCgU5z8fHDP1knI4G4mgqtnA0VMGpmV66HAJdpeG7hJY1xb+jal888lhyZ90NNmp3ldJ2syRYT/XYFHGS5hoKKzDYDKEzXABmHPfpWQtzg7L3C0dgi/8N5rSeq2XC7ari6sm5AAVSN7XOynDQ2aCTkJk/6//c5c6mJ7p4bndZnaXmJB4c2s+PsfB4nHu7pBQZoLOrXOVGJ1CofCNwx1YHPmrz8Z0/i/49F1fxEeG39Pe0WhDaBpyGMRnTrx7XBkvHyVcHS62/3qDtw4/jIa4wWIgY07oy6K7+MM6K2VnGS5w5us+cOm0V5DmSJWBo1UGHttk5cfX2lg5sW95+OzRbmaPVuEfhUIxdLISPRTUGPzIOvT0Fnvrj0JBpFlnXnb49zJLjLU7wTbc1BecHpbzDBblgA8x23+9gZoDp7y/hNE4zvvYFIwRprDM3WwTvHfEPIQkXW81kUTg9sAPX4tkf/nwdmZVKBQfLW6a5cS/o/vsPS00vpesRH1YmsSOWpwdlDdfGMSgX6KqNvoI0VBYR/W+qrCGigByrspj0q3Twzb/sRoDbh+x42Do8tQ8sTX0yr8KhULRxVVTnEzP9HSXRvcmvHvysWoDFY3hv5xGpUSTvWJcwOPzrp8yqJcuNEHK1PO775EyXkJI2eZir6UbZmZ+fp63Pv8CQZeC7cVG2hzneiUKheJixWSAR25t42PTnZgMwy1gKfjMv6P54Fh4vOE9mXXPfEYvz/E7zmAxkHNlHtHpMUFfL6QuGbd6wmCXOCwo4yWEOFsd4Tbwh+QGDJQJqR6MWmhfiETQPqQOrwqFQuGbCDN876oO3vpKC7++uY2Hb2hnXrZrWLqvtTkED7wSxXtHwmvAaCYDl3x1EfO+ucTnON2ls/VX6xl39QRM0eaADJiuMVPvmE3S+OSQrDdcqITdEBKRGBneEwhImzky7LHIuEjJqslO3jlsDllPI5NBEheh5PwVCkX4iYuQLMn1JtA22QQ7Tg7Hpc5brPCbNREsz3NhDvMpq/dUIgwDK7hLXdJYWE9jYT1ogrjR8TibHTha7VhirYxemoNm0ij5oKi7zUDy1DTyrptM2qzzt8qoC2W8hJDRy8dx7OXDPsf4+rD5RXoTtsKFlHC40sDafDNOtyDWKmnuGHpBoUFILp/opKjWgBCQk+zBGn7vqkKhUHDlFCd/2WDF5vRVJRkqBC12wapHYrksz8Wtcx2MTw1P5Ub98drAryW6pKW0icTxI7jq0RsxmM4UUEz+xAw8DjfCqCE0QXNJI9X7K7HGRRA3JuG8TdxVxksIic2MI3vlOE6uKxowfDTxpmmcXFc4uL4UAhqL6hm9ZOzQFtoPrXbBd1+KZE+ZCYMmvc0k6XoZA4nR9aTrBfcepyHRNHg/38zbh71CelFmyY2zHHx+kV0ZMQqFIqxEmr0q3197LhqXW/aooux/zwoFNqfGO0fMvH3YzIPX2LhqSl+piKEymDyW+mOnKdtYTPbK3F7PGSxGqnaWcei/e2kpb+5+PCYzjmm3zyLjkqyQrDmUqJyXEDPrngWMu2qC94MlzuSoaEaNybfNYMSUVExR5sFNLqFyW2loF4zX4/KtFyPZV+61ZT26wCNF511K4F+QM2GhM2aP0MCjg7NHZ+p2p+DpHRa+/lxUt+idQqFQhIsZozz87/OtfHyOg4RIHbNBkh6nE22RaH3uNEMT3vboAl3CT96M5GRd6C+1aTNHBl+4Ibzicz3xONzs+8cOtjy8vpfhAtBa2cyWh9dTuuHEUJcbcpTnJcRoRo2Zd89jwk1TqdhaiqPFTkRSJKMuHcOp3RVs+tGaIbnhPGG42u8pM7K/3JcLJJD1Cuwu2UP2yRv/9QpB9j1el4L95UZe3W/m43OUgq5CoQgvGfE6X19p5+sr7d2P1bcLntpm4bUDFmzOM+J1oUMgkLy4x8K3V3WEbNb22jbcdlfwshwSmksbsdW1E5EUSeEbRzny7H7cHQNcVzqn3/PoNjLmZWEKk7bYYFDGS5iISIwk95qJ3b93NNjY9ect3nDMYBUSBcRmJYRohWd474g3VOSrL0ggeOW5e87hf77nd1uU8aJQKM4JSVGSr6+0c99yO80dAqtJ8vQOK//aYu3Vd20oeKRgywkj3+7xWEuH4K1DZnacNOL2wMQMDzfMcJIR7z8/pvbgKT78v3WDbvyru3Xe+sKLJI1Ppv54YCq6HoeH8s0nGXvF+EGdMxwo42WYOPl+IXKoeVsSsi8LXKAoUJpsGqFplRFkDBZBeaMBtweMSoBXoVCcI4wGSIr23lR+YYmdWaPdPLfLzN4yI+0O0cObPDjcPcLmu0qMfPvFKOyu7uA6e8qMPLXNwtdW2LntkoEFsRzNdj58+AM8Ls/QoluSgA0XAGHQaC5rGsIJQ48yXsKIs9VB+ZYSOurbqdhWGpKeFOEQwYuzhsJyCSSpty9CeBN6FQqF4nxhzmg3czr7ru08aeTrz0XhkYPb4zQkE9K9c5U1aHzzhSicnt5VnF1VUH9YF0FqrM5lE/pP8D25rhCPwx12PbG+SAzm8+sOUxkvYUBKSf6LB8l//iC6R0doQyiPPgtne2hCLDYnvLbfwot7zZ2y1oOrJur9XHBoQjJ3jJsLSCxYoVB8xLgk280fb2vnvmeiBrXT6QhunuXdt5/fbcHtATlAOEog+dcWK8vzXPSXGnlqT8U5MFxAeiTpYWwEPBiU8RIGjr14iCPP7O/+PVSGC0DkiKghz9FsE3zx6WhO1mmDcIf2d/cxOOtDl4JP+nCRKhQKxfnAnNFuMhM8lDcGf8m0GCWzO704a46afObRSARFtQZONWv95r94nJ7ATuqtlwgJolPgbsTElNBMGCKUwz7EONscHH3+QFjmNsdaSJuRMeR5/u+dCErrtU63ZTCGh6/x/r4pZ543dLYeuG95B/PHqlpphUJxftAl1PniHjOv7DP3ara4aNzg9iqHW1De4J3nTEWTbwbqA5cwNimw8ugQemcik6O49P+tOO/E6pTnJQA8Djctlc0IIYgZGecz9lextRQ9NNmvfZj+mTlopqHFHU81CzYVmAahmttlyg90XE/hJ9HjUYkQMDnDQ1mDhia8dzEfn+NgWmaAdxEKhUIRZgpqNB56I4qi04Zekg8Lc1w8eI2NG2c5+d8u66DmdndWcqbH6ZQ3aD73X01IUmP6tz5yrsyjeE3BoNYQLMYIE1Nvn8WYZTkYz6MS6S6U8eIDd4eLI8/tp3hNQXcdvCnSRM5VE5j08en9GjEd9baQ5rgAGCOMTL9zLmNCUGm0u3QwhksX/o9LjNRpsJ15X6aO9PCVyzqUoaJQKM5bSuo17vlvDPbOPNmee+SOYiP3/Deaf3+mlSkZbg5XGQjGY20xSjITPOwtM2Ay+HaKGIRk6XgXcZH9j4rPTmTCjVP8tqEJFGtiBPaGDu81S5fdP0dMSmHR/1sxeEHVYUAZLwPgdrjZ8OB7NBU39BICctlcHHv5EHX5tSz58eW9ekSAN7QTtHBQfwhIHJ/MuCvzGLlgNEZLaP5UXqfQ4LLmA+HhG21EmLztBlJjdUYlhscLpVAoFKHisY1WHK7+ex95pKCsXuO1Axa+f7WNT/0rBj3AyiODkFwzzckzOy38Y3NEZ8i8/+M0IbGY4ItL7f0+38WUT88iKjWG/BcPdjdUHAyjl45lzn2XUnvwFKUbT9DR0EFEYiSjl48ldVpG8Oq9w4zKeRmAgteO0Hiivn9DREJdfg0n3jnW56nMBaOHHBsUmiAqJZpF/+8yRi/LCZnhApCX6iE8hoskI87DtEwPeWke5oxxK8NFoVCc9zR3CDYU+EukhZf3mhmbrPOXT7YFXCGZHKMzPdPNPzZHAPgUAs1J9vD321sZneR73xRCMPaK8Vz96E1MvX1WYAvpebwmmP2lBYxanM3232zk4JN76GjoIGtxNrPvnU/ajEG0HTgHKM9LP0iPTtE7x3z79yQUvXWM3GsnIYSgpaKZhoLTIARZS7Ip3VgccNKUZjagd2aRC4MgfdZIpn12LpbYwcVXfTEx3UNeqpvjNcG5Pn3jvZu47zK7KntWKBQXFLWtIoBu04LqFu+9/qwsD2OSPBTX+b98fu5SO6/uN6MJOeA5BJJRiTr/vastqHULTTBiUmpAY02RJhLHJ5MyJY2sJdns/us2avZXdYeJEHD6cDVHnz/A0p+sIiYjtvtYj9ND+ZYSavZXobs9xGcnkr0iF2tCRFDrDTXKeOkHR6sDR5Nv1x14+0u0VjSx97EdnD5S0+u5iKTIgDtHL/7+ZbRUtlDyQRGNRfVU7aqganclGZeMYvKt04nPThzU6/DosPWEkTVHzTR3CFJidK6Z5uTBa2x89okYXJ6hhI/OWGYRJvj2Fe2sGEBYSaFQKM5Xoi2B3WVGmmWP/0Mg4fcYq2Rvme9kV4mgrMFAc4fo0dw2MJLykolOj6GtutXnzbLL5qK5pJExy3M48ux+ag6c8p67K7LQ+cPe2MGmH6/hqr/cgGYy0Hiins0/fR9Hs91r6EhJxbZSjvxvPzO/MI+cVXlBrTeUKOOlHzRD4NG09T98D1drX+E4e2PgTbhaKlvY/6+dvT98UnJqVznVeytY8qPLSZ6cFvB8AA3tgq89F0VBjbHb6jdokjcOWlic6+SXN7Zz/wtD0YzxNhyLskhe/mILcZFDmEqhUCjOEelxkrxUNwW1hgHF4wxCsmrymZuzJeNdHDll8CmabjJIpmcGXl7tHkRNgxCCGZ+7hA9/vs7vWHtTBzt+v9mnBozUJbbT7ZR9WIKr3cGBJ/YgO6tne6ZQSCnZ++h2rPERjJyXFfzCQ4DKeekHc4yFuDEJvo1qTWBNiMDV6uw3L6b7MT+OjYikSA49tQepyz7zSF2iuyXbfrMxqCZcUsL9z0dxotabTNzlruyKt35YZOKDYyY+d+lAAnGSQGJeEkGbQ2P7yfOvjE6hUCgC5a5FjoFVb4XEaICPz3bg1mHDcRNHqgydW7vvffKhN6IQAeylCZE68QNUGPkjfU4mC7+7HEt8gGkG/k4jYM/ftrL/X7u6DZeBxh35377BNxoeIsp4GYC8j032/UfWJc5Wh//KIj9PJ09O9ZZhDzROShxNdqp2lfueqAe7S43kVxsHTECT0tvR9GPTHXzrchvxEb0/oBlxeg9Xqu8XoAnJ1hPKeFEoFBcuS8e7+NblNjQh0cSZvU8giTDB7z7eToRZcsfjMXz35Sg2F5r85sm4PLCjxL80hSYkN89yEITDvw8j52VxzT9vYeSC0UNPZZSguwK4WZbQXNpEW1XLEE84OFTYaACylo6lsbiewjfyzyQ1Qff/x10zkaI38wObrKd+W485cq7K8/Y+MvjWhREGQUNhHZkLRgd0uvfzTRg06TOzXQI3/i0WIaDLqWPQJLOz3DS0C6qaDQTkfZHgUCK5CoXiAueWOU4W57p4db+F/GoDRg0uGeNi9VQnkRa4/fEYSuq8FsYZw8WXpeDfitCEJDfFwyfnDb1NimbQ0MLQuNcfzrbQ9NsLFmW8DIAQgul3ziVtxkgK38qnLr8WISB5chq510wkPicpcOOlhw1gsBpJnZ7O+GsmMWJyKvv+sSOw9QRRxtPmEAE1sPZI0WttHl2ws6TnR8L/OYWAnGRVEq1QKC580uJkvzorW08YKaoNdVdlb85ghFnywTEzKyc6sQ7Bie3qcAEipH2NAiEi6dwkPCrjxQdCCNJmjSRt1sh+n0+ekkbd0ZqgROl0pwdnq5OkCSkIIUiZms6Jd477PEZ6JClTAk/YzYjXAzE7gnx8YK6brporKhSKi5d1x/x7s4NH0GoXHKgQ7C838cRWC3/5ZBupscFZHqeP1pD/4kFq9lWFcG0BICBlWnpImgUPBpXzMgQm3jQ1aDVdqUvqjtZQuaMM6HS5+fg+CE0QnR5DyrT0gM9x7VSnT8Gl0NDVXNEe9JdNoVAoLiTaHAI9TA7mrkThqiaN+1+IJphLStnmk2z4wbvUdpY+D4mg290Jpn4qeJG8UKGMlyGQOiOD2fcuACGCUyQUcPjpvaz9xuvs+etWny4+Y6SJSx+4LKj5s5J0Pj7bQXh9h4LUGJ0rJ5+beKdCoVCEm9Otgie2WiitC3XIqC8eKSiqNbC7JLCAiL2pg51//BAkoWlJE+QUCTmJJOaOGPp5B4kKGw2RsVeMJ3VGBsXvHafuWC3ONictZU2+D5LQdqo1oPkz5mSy+2/bcNtcxIyMZeyqPFKmpvltQfCNyzuItkqe3mHB4fbXEXpw1LUJvvF8FE/cGbhctkKhUFwIPLnNwt82dpYfS/xWDYUCgybZWGDikmz/VRAn1xX5LmXuxBRlwmVz+TdONEEwbh97Q+BaZuFAGS8hIColmqm3zwag/lgtHzzwTsjmLt1Q3P3/lvImKraWkrlwNPO+sQTNOLDjTBNwzxI7n5pnZ2OBiV+9G4ndHVoDxiMFx2uM7CoxMi+AL5tCoVBcCLyyz8xfNpwD+XsJHQEKldcfrw1IsyX78vEUvHpk4CGaICo1mrbq4NoTnGtU2CjEJOSOQDOF523tcg1WbCvl0H/3BnRMtAXirBK7WxCOhowGTbLumNJ5USgUFwduHf6+ORDBt8C0sIJBgt/GjF0E2gA4KjmKSR+f3nnQ2ZN4ryve9gKBvw6hiaDyMMOBMl5CjLPFEZQa7qCQUPTOMVy2wPJNats0wpX/oktod6iYkUKhuDjYX26kod3fpVEyKkEnMWowe73vvfiaqYHt68mTUv3fj0oYMSmVybfN4JKvLSImI67/pQR5eZC6ZNxVE4I7KMQo4yXE1B0LwJUXAnSnp7u5lj+8zb7CY2BoeEuzFQqF4mKg2RbIXinQBJ1GTtBlOvS9SHh//9IyO0nRgV1Axlw2DoPJMODphSZImpBM/BhvY9/Ry3JY9aePseJXq32mHPhceWdy47TPzjmnybqgjJeQYG/q4PhrR9j3zx2UbT45bOf1BChtu3CsiwhTeCwqjxRcO01VHCkUiouD5Bj/N2OakKTG6j1aCfjHoEm+fUU7oxM89GdxWI2SZ3ZaeOCVSPaV+a9uMsdYmP+tpQitb7Wr0ASWeCvzvrGk9+NCUHu4Gj2ARF8AzaRhsJ5JjU2emsbiB1d62+ecY1TC7hCQUnL02QPkv3gQKTut0mFsUhWdERvQuAgzfHahnb9tHDgBzSAkESaJzSUG6NnRn/dGcuscJ1mJyvOiUCguDqaM9DAy3kNl08BeFV0Krp/hxKDBzpMD95HrdYwOv14TxUCuebtbYHcLNhaY+OCYmTsX2vtV++1JxtxRrPz1NRx/9TDlW0qQHokp2kzOFePJvXYS1vi+e37NgaqAogNz7ltI9opcpJR4nB5v+4FBemzCgTJehsCxlw5x9PkD3b/76k8UCEITZMwdRdLEFA4+sdvHQIjNjAvIbVfTImh3CG6Y4aTdIXhquwXRqSCN8LYEGDvCzSOfaKe+TeNLz0TjcMleX0ZNSM40yZZIBFaj5Pb5dj63SKnrKhSKiwdNwFcvs/Pdl7sMDXHW85IJaR6WjncRGyHZVhxYwcKZUuv+DJ0zj3Wp+P57q5XcVA8rJvguP4rPTmTeN5ZwydcW43F5MJgNPpN5A87J7NrzhbeE2t7agTnKjDHi/CjQUMbLIHHZnOS/cDBk88WOimPuVxaRmDsCKSXNJQ29yqS7EV4jZ9Y9831+QDcXGvnXFiv5p7x/YoOQLJ/g4s+3tbG3zERlk0aURbI410WMRVLTrDFmhM6Td7by1HYL7xw24/QIjJrk8kkubrvETm2LRk2LRlyEZGGOiyhLyF6+QqFQnDcsy3Pxk+va+eV7kbQ7wKh5b+B0KZg/1s1D19owGmDuGDdfX9HBH9ZFdN/YhQpNSJ7ebvFrvHQhNIHR4v+SnjhuBPX5tX6F7RJykmgqaSD/hYNUbi9D6hKhCUbOz2LiLdO6c2nOFcp4GSRVO8vxOD1DnmfyJ2eQMjWdpLzkbmNECMHc+y4lMjmawjeO4rafyW2JHRXPrHvmezPNB+ClvWZ+9V5kr3isRwrWHzOx7YSJx25vZUySzj82W/ney1HYXd7zGjXJVVOc3LfczndWddDuEERaJKbO8Gteqk5hrcaBciNvHzYzbaSHvLShvwcKhUJxvrFqsoul45vZcNxEaYOBCJP3Zi97RG/PxW2XOEiO0fn+q6Ht8aNLwZFTRtocXsmLUJGzajwFr/nWfYkfm4ir3cmmn6xFemS3oSN1SeX2Mqp2lbPkwctJDqLnXqhRxssgsTd1IDQxJFnm6PQYJt48rV8PijBoTPnkTCbcOIXaQ9W47W6i02NIyEny6XGpbhb8Zo03znl27opHCjpckh+/HonFBEeqDPR0V7p1wRsHzewqMfLknW3ERZ55bVVNGg++HsmhSiOi058oEUxOd/PQdTZGqbwXhUJxkWE1wZVTXIBv78eBCiMGIcPSU87lGXqbaN2jc2pXOSUfnKCjwUbMyFhaK1v6jBOawBhhZPa9C9j80Fp0t+yTxyl1iZSw9dcbuPaft6CZwt86oT+U8TJIrPERQ+4nkXf9FL9CQ0ariYy5owKe89X9vk10XQqKThsZuF2AoLpF488brHz/aq/8c32b4O6nomls947v6Ro9Vm3g7qei+c+drapBo0Kh+EhSWKOFxXBJiNQ7pS4Gj7PVwaafrKWxqL7/Ku1OhCbIXDiaKZ+cSUNRPY4WH/mMUuJscVC5o4xRi7KHtL7Bcv6kDl9gZFwyCoM5eIuzq6Rt7KrxZF+eG+plkV9tGKBaqCf+NaXfPmTG3nmz8d8dFhrbRb9fTo8UtHQI/rs9EEVKhUKhuPjwppoEamQENk4TkptnOYbcN27bbzbQVNzg89Sxo+K4+rEbmf/NpUSnx9JQeBph8H1iYRDUF5we2uKGgDJeBokp0szEm6cFdYxmNpA6I4NFP1jhM+G2pbyJss0nqdhWiqPFd6nc2Xg/b4F8OXx/MN264FSzhi7htQMWn3cVHukNN7lV+otCofgIsjDHNQipOgnd/87GK10xPtUzJPWNxuJ6ag9W+40StFQ0U/B6fq/VBca5U1dXYaMhMOHmqXhcHo69dCigEJJ06yz4zrIBM8KbyxrZ87ft1B+r7X5MGDTGXJbDjLsuCSiTfO4YN1tPGP2YL4F94Awa2ByByf93uAStDkFCpAodKRSKjxZXT3Xx981WbM6+uYZ98cZuusLvZoPErcuzjhN0uODbL0XzsekOvndVx6A8MJXbywLLzZRwcm0BUz41E6PFSPLkVArfOOr7EI8kefLAhSPhRnlehoAQgimfnMk1/7wloGaMUpcUryno97mWimY++N47NJzlhpMenZPritj8k/fRXf5dG9dMc2I1gRhA+dEgBrL0eyOQZCboWE0EpCIpOu8UFAqF4qNGjFXy+4+3YzEGtl/2vIF0eujU0ep9XJcx89oBM8/tGly5kdvuhgAbOLrtbiq2ldJYXE/K1DQikiL7KPd2IwTWeCvps0cOal2hQBkvIcCaEBGw3HJTcX2/jx/8z248Dnf/FrIuqTtaQ9mHJX7nj7FKfn1zOyaty1DpQiKEZGSCTka8fyNocoYbTYDR4NU8MGgDfyENQrIo14X1/NAuUigUimFnWqaH5+9p4crJwbZLET3+9c+T2ywEeInpRXR6DDKIA3c98iHvf/NN3vz8iyRPScVgMfZvwEiJvcnOe199jZPvFyKHUVm+C2W8hAiDMbDkXVOUuc9jHQ02Tu2p8O3aE3Di3eMBnWPuGDdP39XK9TOdxFh1DJokI17ny8vsLBvvoqqpq9poICRlDQZK6r0fjzvmO0DSXSLde1kSCXxmvlLaVSgUH21SYqRfSf/gETTYNIrrgr9cD7YSyN3honxzCVGp0WRfnothgJSFtlOt7P7LVg49tXdQ5xkKyngJAW3VrXgCzFbt78PUXtPmP5IjobWqOeA1ZSXpfGdVB+9/o4Wt323mlXtbmZzh5snuqiBfrkRvS4EHXo5CSpiY7uHnN3gVJYU4k2QmhMRogJ9+zMbUTJWtq1AoFKmxknnZrqCaNgZCZUPwl2tX++Cb5kpd0lzaSOSIKFKmp/u8ZBx/5XCvXM3hQCXshoCC145AAJ65iKRIkvKS+zxutAb2ZwgkYdcXz++2BCyk5JGC4joD+ysMzBzlYVmeizfva+HV/WbWHzfR0iFIidW5foaT5XmByVcrFArFhUqbA945bGZPqRFdwqR0D9dOd5IU1ddIuW95B59/MgaX5+xE3MHT4fY/5mwCyZP0iYTCN/NxNPv2JglNUPTOcZImpAztfEGgPC9DREpJyfoTAY2NHBHVb3l03OgEIkf4lpYWmmDUojGDWWI3+8oC637ahUFI9pedMZhK6jWe223hWLWR2laNw5VGfvxGFDf8LYbj1edGZVGhUCjCze4SI9f+OY7frolgw3ETGwtMPLbJynV/juWtg32T/can6jz26TbGJHXd1cqzfgZPPw2i/RKZEo3BMrS92Z/hAl4vzXBrvijjZYjoTg8eR2Amcf3x0xx8cjfOs1x5QhPk3TTF7/FjV+UNao1dDOZr03XMidMaX302miab1/hx6wJ3Z/fT060a9z4TTVWT+jgpFIqLh4pGjd+ttfKVZ6OwOb3q4rIzuVaXArcOP3krkh0n+3rFJ6Z7eObzrfzzjlYeuKqDj8+2M1hdFLNBMi0zeNeL0WIke0XuwFVDIUTzI2oX8vMN69kuQjSzAS0Ipd3jrxzh/W+9ib2po9fjBj/9IaQuaSyqG9Qau5g5yu2zauhsPFIwdaTX7fjvLVbcnv41DHQpsDvhfztVm2mFQnHh43DDj96I5KZHY3lut6WzlLn/diqagH9v6X/vEwKmjvRw/Qwn919u58aZjs6jgrmVlKya7KS+XWMwUaBJt04nMiU6rAaM0ARps4a3bFoZL0NECEH2inFBHdNe28aev27r/l1K6RUE8plDKyh43bdoUBfVzYK/bbTy2X9Hc/vj0fzqvQhOnNb4+BwnHj3QD7D3HuOtQ2YOVhj44JgpIJXdc1Axp1AoFCHloTciee9IVzjIdxmzLgX7yk00tPuR0xfwnVUd/HB1cI1sNQFvHLRwy2OxXPVILH/Z4BXDCxRLrJUVv7iarCXZYRXEzblyQvgm74dhMV7+8pe/MGbMGKxWK/PmzWPnzp0+x7/wwgtMmDABq9XK1KlTefvtt4djmYMm72OT0YxBvJW6pGp3Oe21bQC4bC6aS5v8VC9LGovq/Yao1uWbuPHRWJ7cZiG/2khBjZFX95n55D9jOVxlIC81ENejt2mjRPDeERNf+G90QLkyHS5BgBE0hUKhOC8pqDGw7pgZGWSibZvd/3ghvEKi/7qjjcAC+ZKeChqtDo3/brdwz3+jaQ9CncISZ0XzJ+cxCMNGaAIEXPK1RcRkxAY/wRAIu/Hy3HPPcf/99/OjH/2IvXv3Mn36dFatWkVtbf9lVVu3buW2227jrrvuYt++fVx//fVcf/31HD58ONxLHTRRqTEs+sGK4A6SUJfvfQ+CERHypQVzvNrAD16LRNd7h3e6DI+/bojgeE0gFUtnjtWl6PwS+/+iWU2SIRZEKRQKxTnlncOmoMLrAAZNkhgd+D4eGyFZmOMOoJy6r9dHl4KiWgOPbwm8GW5LRTMn3y/0u437a8bYc1nGCBNZS7JZ+etryFoyNuC1hIqwGy+/+93vuPvuu7nzzjuZNGkSjz76KJGRkTz++OP9jn/kkUe48sor+fa3v83EiRP56U9/yqxZs/jzn/8c7qUOiZRp6Zhj+grQ+aQzxmKOsfitNkJAdFoMBh9l1c/stHR2zRjoAxi+mI5BSFZPdQaqRK1QKBTnJfVtWlDhb4OQrJzoIjrIlL/PLeyq4gl+X9al4JX9loA93aXri/znvEhImZru34DRIOeqCdzwzCe55GuLSchJCmwRISasxovT6WTPnj2sXLnyzAk1jZUrV7Jt27Z+j9m2bVuv8QCrVq0acLzD4aClpaXXv3NBfX4tztbgBIESc0cA3ryZcasn+HXbjbtm4oCdqAE2FPjOSxlawLPr2L5fNE1ILCb45CVKZVehUFzYJETpAd+Ede19n780MFVdj05n80aYmunhFze2e3vRIdGCNGLaHSLgCk9bnc3v7MIgsMRYkB4/I3Wo3FZKa9W5udZ2EVbjpa6uDo/HQ2pq786TqampVFdX93tMdXV1UOMffvhh4uLiuv+NGjUqNIsPkraatqDGayYDMSPjun8ft3oiyVPSBkpoJ3VGBjk+SqWlBMcwasUZNYmx07WaGCX5y21tZCYMovmGQqFQnEdcOdkVQGGDd+8bk6Tz2KdbyUryvfedOK3x4zciWfLrOJb/Np4Vv4vjd2sjmJDm4a2vNPPNKzqYNTr4hEFDgFdwc4zZr0EmdUnCuBFEpcX49dI4mu1s+MG7OFvP3Q3rBZ+h8MADD3D//fd3/97S0nJODBhTZHBdCaXe+8NuMBlY/MOVHH/lMEVvH+sWBrImRDBu9US/ScFCwMgEncpGzUfYKBQIxqW4mTXKAwJmZblZnOsimHxlhUKhOF+ZmO5h8TgnW06YBlDHlcwZ7eaeJXamjvT4NQp2lRj5xvNR6JJuo8jmFLy4x8w7h03cNMtBc4dGUhA5MyAZES0ZGR/YMVmLsil665i/KUmemkbqjHTWf/9dXG0DRxKkLrE3dXDy/ULybvCvURYOwmq8jBgxAoPBQE1NTa/Ha2pqSEtL6/eYtLS0oMZbLBYslnOvL5I6PR3NZAhYjtkU2Tc/xmAyMOnj05lw01RstW0gBJHJUWgBmtc3zXLyx3WBJ3ENBoEkPVbyzSs6/A9WKBSKC5CfXW/jR29EsuG4GYPmvR3UpdfD/fE5Tr62oiMgr0eHE773ciRunT7VSx4paLHDv7daMYieAXlvtacvBPCJuY6APS+JecmkTEvj9OGagYs+BGz5v3Ws+NVqFn53ORt/+J7vSSXkv3SI+OxEUqalD4sQXk/Cer9sNpuZPXs269at635M13XWrVvHggUL+j1mwYIFvcYDrF27dsDx5wtGq4nMBaMDHp+1ZOBun5pBIzo9lui0mIANF4AbZjgYl+IJeUOwnkhg/ljVy0ihUFy8WE3wyxtt/O/zLdwx38F10518YbGdV7/Uwv2XB2a4ALx31EybQ/gou/ZWE3mkOMvLM9Ae7n18WZ6L24LIMRRCsPA7y4kbkzDwIAn2xg72/XNnwL57V7uTTQ+tZe39b9BR3x7wekJB2MNG999/P5/5zGeYM2cOl1xyCX/4wx9ob2/nzjvvBOCOO+5g5MiRPPzwwwB87WtfY+nSpfz2t79l9erVPPvss+zevZu///3v4V7qkJn6qZmUbSr2P1BA7uqJIT9/hBn+9ql2fvhaJNuKgwtjBYZECFigjBeFQvERYGyyzheXBpaM2x/7y41oAnwoXPTDGWkKbyPdM4+NS/bwqXkOVk12BWxAdWGKMhMzMpbmksYBvS9Sl1RuK0UzBTd5S3kTG3+0hst/dx2GIBTnh0LYjZdbb72V06dP8+CDD1JdXc2MGTN49913u5Nyy8rK0LQzb9TChQt55pln+MEPfsD/+3//j9zcXF599VWmTDk3cbVgiEyJJmPuKE7tqfCpxzL19tlEp4dH0CfGKvnxtTaufCQ2DLkvAiElv30/kt/dMrxWtkKhUFxIVDVpFFQbgjRczpAe5+HyiS7MRlg0zsWENP/5Nf5oKKjzeW3qonzTyaDmlbqktbKFiq0ljF6WM9jlBcWwJOzed9993Hffff0+t2HDhj6P3XLLLdxyyy1hXlV4mHHXXOqO1+Jqc/b7IclanE3e9ZPDuob4SMnyCS42HvdXOh08OoItRUYqGjVVXaRQKBQ9sLtge7GJNw+Z2Fw4FO+3wKMLvrx88F6ffmcNZ16KEJRuLL64jJePElGpMaz89TUceGI3VTvKug0YS5yVvI9NZvzHJvvUagkVX1pqZ1eJkXa71+AINbtKjGQmBKdro1AoFBcjUsJ/d1j49xYr7c6h77cCyYigqo/8Y6trx2ULY8hfyj4Nh8OJMl7CQFRKNAu/swx7UwdtVS1oZgPOVgcn3jnO0RcOApCUl0zu6omkzR4ZFmNmVKLOP+9o497/RtNgC+38AnArp4tCoVAA8NgmK//eGmyl58BVRRK4dnrobg5dHS42/PA9HC2h9eT0RGje6tjhQhkvYcQaH4E1PoIjz+7n6HMHeuZhUXOgipr9VYy7egIzPn9JWAyYMUk6pjDkTkkEeamD6M2uUCgUFxk1LYIntgYr1yF7/Oy99xuEJCNe56rJoTNeStcX0V7dGrL5+kPqkuwVuWE9R0+UtFiYqdpV7jVcoHf1W+f/i94+Run6E2E5d4cT2kMc2dGEJHuEh6kjlfGiUCgUbxz0r157NpqAr6+w4ZX78qqVdzWDnJDm4W+faiMiyFZ5vjj5flHoJusHoQmSJiSTPiczrOfpifK8hJmuMJEv8l88SMa8UWgGDaM1NCXOp5oFX3o6mjZHl7tnMJ6d3scZhMRkhB9dY1MNGBUKhQKobDQEvbvqUvDIukiWjHcxPdNDfbvAbPBWFU3OGHpV0dl0NNpCOyEgDBpIidQlabMzmfe1RUHpkg0VZbwMEmebg6qd5ThaHUQkRpJxySiMlt5vp+7RaSys8ztX26lWXvv0swDEj01k/LWTyFo6dtChJF3CN56PpqZFY2jNGHsiGZWo890rbUxMV14XhUKhAIiyyF4pAYEiEXxYaOJQpZEnPttKamz4xEWtcRE4mkKU7yIgeXIaIyamYLQaGTkvq1efvuFCGS9BInXJ4Wf2UfDaEXS3jtAEUpcYI4xM/fQsxl19RnzO4wz+It90soGdj3xI3bFaZt0zf1AGzI5iIyfrQpHs0ttrU9qg8a0Xo3nk1jYVNlIoFB856toEbx0yU9mkEW2RXJbnYtl4Fy/sGVyLGo8UNNvgTx9E8LPrQ+8d6WLMZTkceGJ30AZWv0iwN9rIXpmLKdKEOfrctOdRxkuQ7H98Z68GV12l0O4ON/v+sRNXh5vkSalIJM7WQVi6nR+u4vcKSJ2WTubCMUFPsanQhEGTAXRGDQTR/VNK6HBKvv5cFC/f20pchKSmRVBYa8CowdSRbqLOfZsphUKhCClSwr8+tPCvLd6KIq2zF9HTO6zMyHSRl+rmeI2BwXi6PVLwwTETjTZBQmR4vC9jVuRS8PpR7I0dA4rUmWMsAXeJbq1s4e17XgK8zRwn3jyN1GnpIVtvICjjJQjaTrX47cx5+L97Q3MyTVD4Zv6gjBe7SwzawhbIzkP7/xLqUtDugP/ttFBYa2BLkbFbyddilFw/w8mXl3dgUZ8shUJxkfD0Tgv/+DCi+/ee1/9DlUayEofmifZIQVmDRkJkeDza5igzy352JR/+3zpaK5oRBu+eLT0Sg9nArHvmU/hWfsDGS0/qjtSw6dAa5n7lUsZcNi7USx8QdYkJgpPrirrDRGFHl9QdO42UMqDQkZSw9YSRF/ZY2Fdm7OyHETxZiTqlDb5DThJ4arsFKenVgsDhFrywx0zRaY0/3tqOcXhaXCgUCkXYsLvgXx8OrOHikYKT9UO/lIa7JVB0WgyrHvkY1XsrKd9yEt2jkzQhhTFLczBFmXG02Gk62RB87k7n9XD3X7aSOj2diKTh0XpRxksQ2E6fn/18dAkPvxPB6wcsnY28BhsukpQ2BJItLnDr/Vcw6VKwp9TEu0fMXDNNKfAqFIoLm60nTNj8quYOtqLTS4RJZ3eJkdNtgoU5boxhKNpxdbjIf+Egxe8d71barcuvRbp1cldPJHtFLsdeOez1vgzi5lcCxWsLmfyJGSFd90AonZcgMEWZQle84w8BSXkjAvK6vLjHzOsHvMkmQ+tlJAjsBfr+ogoheXFPCEUKFAqF4hzRaAuklGhoF4YOl+CvG618+8Vorv1zLBuOh0YyowtXh4sN33+XgteO9GoR0FFn48C/d/P/2zvv8Diqs2/fZ7ap9y7LluTee+/GYDAdQg8JhISQhFRSIF+AEEhISONNXpI3lRIgQOgQwIBx771bliVZkmVZ1eraOuf7YyTZsqQt0q5s2ee+Ll327p45c3Zn5sxvnvOULU+vwxJpZeFjl2GLDTRTcBu6pCavKkgj9o0SLwGQNS8H2dv1mECRIISgYs8JpOx5n3pbTY3guJH7i/cLVUrBsRq1ZqRQKAY+SVF9s6r4h0Bve/CsbRY8+GYE6/ODszCie3QOvraH+uJTPbo8lK4/RumGImKHxHPZ09cw5b5ZZMzIInViBkOXj2T0zRP8+xahLPx4FmrZKACSRqeQODqF2ryqfvF7qcmrYu1PPyFlfBpzH1qCObyrGj9eq1HR4I9QkGiCjguk9/h3IVvN/SmmFAqFIjTMynURHabTaO+vZ33D0vO7T8OZO6yxVwnrnE0Ojv73MAUr8rCf8qNYohDs+9dO9r24k5ZKwz0icVQKI68dQ+asIVTsOcEhP4adPC4t8MH2EmV5CQAhBPMeWkL8sETjdYhVZrtAqjpQwdY/rO+2TSCpZIInXCTeLD0mIVk8MoTVSxUKhaKfsJnhvgXe0l4E/0FNIiirM7HneOAW7NbaFj79/vsceG2Pf8IFQEpaqpo7hAtATV4lG3+1mu3PbGDTU6u9by9AM5vIuURFG523WKNtLHlyOZV7yylZV4ijwYHH6aFyb3nI9il1SdnmEhrL6rtkMsyI07GZJQ63N2ESrItLnPVvz/u6eVrgIXcKhUJxPvK5qU5cHsEzq8Nwe6AtMz4eKYiwSlqcEIqlJSNLemDh09v+d4MRXNLX1YG2zYs+Per7qwmY/YOF2GJ66S/TC5R46QVCE6ROyiB1UkbHe/tf3sWh/+xFmERI/GKEJji+uYTRN47v9H6EFa4c7+Tt3VYvlpX+WYcUSDQNHr+2haHJer/sU6FQKPqD22Y4uHK8k48PWjhRrxFpNSzMX30xilDNsVG2wO4ljScaqNh1IvgD8TGMcbdNJmN6VvD36wUlXoLEuNsnkzopg93/2EpdYW2nz0w2M7bYMFoqm3q/AyFwt3QfenzvAjubC81UNGhdoo2EkIRbZFuoXyhFjCTSJnnh7iYy45VwUSgUFw5uHdblW1hxwEJdiyAtRjJnopOMOJ2GkPnCSKZluwPaovpgRYjG0jPCJGip7v80Ikq8BJGCjw53ES4AHofbEC69KN7VjvToRKZGd/tZfITkH19s4plVYaw4aMXlMURKtE3npmlOYsN1nv403Muu2z/pW5i1LlHCRaFQXFDUNAm+9UoUR6tMaEKiS4GG5MMDVmZmuzBrEndQSrF0JjZcBpypvF8SqHa74/7fpRIvQaJwRR6l6455bSOEkXxfiNNZev3N2KtZTWTNy+7x84RIycNXtfLtS+wcq9EwaTA8xYPVDK1OeGuXjdLa7i0zXiKxA0CSEKGEi0KhuHDQJXz3tUiKqrW218b8qbc96G05ZiYuXNJo72uOrbOR3DY9cL/BhBFJ/jXsw4P02UiPJGl0SnA6CwAVbRQEpC7Z60dNI6lLRl0/jqFXjCIqPZqI5EgyZg5m4c8uY9TnxnvddsIXpmKJ8J34LSZcMmGQh7EZhnABCLfCn+9oYvJgwwSpCYlJM87cxEjJ7Fw3fQ2cEgKumaQy6ioUiguHbcfM5FWYvQgTQV2rFnA5lsvGOOgpalNDkhApuX5y4PNpXHYCCSOSvEfCChCmIN36BViirL2qwddXlOUlCNQcqcLV5N+J5mxyMvVrs4EZANQdq+XYZwW0VDURPzyRhuI6PGfEP1ujrIy/cyq5l43o0xgTIiXP3N5MQZXGpkILLjcMS/Ewe6ib334c3pYDpnd9m4QkPlIyM9vFv7faaHXBoHidhSNcqkCjQqEYsHx22NKxVOSdMz8/M6VE5+1MQpKTrPPwla3MG+bm5x9E4HQbgQ4AHl2QHqfz+5ubietlhekZ35rHZw99iKvZ2dmq3zbJC5NAuoNjJReaxpwfLsIU6sJM3aBuLUHAUe8tB0BnzGHGT667dbb/aSPFqwqMk0mXxhKSRxKVEUPupcOJzoghbXImmiV4J8bQZJ2hyZ3NkdOz3by5yxZQPyYhQRgXW1aCTkaczhefi0YI4xrx6IIom873L2vlinEq54tCoRh4VDZqvXio6yxk2go445GCiVlunry+BasZlo11MXdYPR/tt5JfacJigtm5LmbluumLYSQ6M5alv7mKQ6/vpXh1AbrLECrJo1Mw2cyc3FXW+87PInFEEinj04PWXyAo8RIEwuPDfTdqI3PmYAD2PLuN4tUFAB2h1e3/Np9spGxzCYufvMKv2kZ9ZcFwF8nROjVNwucThiYkc4e6yErQMWswPcfNcxtsbC40A6It94HRtskh+Ol7kVhMzSwdrQSMQqEYOLy7x8rGgr7cIg3ry+h0D7NzXSwY4WZEauecLVE2I4dMsIlMiWLa1+cw6Z4ZOOrtmMPMWCKtvHX7y0F1rq0+VImz2Yk1sv9r2SnxEgTihycRlR5NU3mj13aRqVHoUrLuiU85uaNn9SvbClxV7T/ZL6q2ySGYnevivT3eTkCJACYOcvPz61s6loM2HDWzo6SnImLGxfs/K8NZPNLVp6cJhUKh6C/2HDfx8w/C6Xt6CUFZncaX55+bpJ1mmxlzShRglAzQA0nJ7ieuJsc5ES/qdhIEhBBM+OI0r200i0bmnGzW/GQFJ3f6YbbToGRdUZBG2DMVDYIvPBvN+3utbfKknc7OZOmxOt+6xM4fbm3u5Mfy/l6rsYTUI4LKRo1dJUonKxSKgcFLW2xBe9iqaxFBiujsG+YwC5o5uLd8oYl+zap7JuqOEiQyZw5mxnfms+PPG/E4Oqtba4yNzBmDOfLWfuMNf05kHRpK64M/0LN49L0Iqhu7Wy4yrCYCyWv3NpCVILstEFZe3zX8ujsqGvqv2qhCoVD0Fl3C+qMWPEHK3RId1v3ceSYNrYLVRyzUNgsSoySLRziJCrIm0MwaWfNyKFlbGJR8MEITDJqb3W3B4P5AiZcgMmRhLpkzsyjdcIxjnxVQk1eJ9EhcTU6KPs0PuL+aw5Uc31yMq8lJ9eFKhBAkjkpm8LwcTEEI4yms0tjV45IPGAn/JTf9NYYIKywc4eK26Q5Gpp0WZ3ERsi1XjPerMzb8PHj0UCgUCh/oOkETLpqQXDWhZ58WXcLf1oXxr8023B7QNPDo8NRH4dw1x86X5jp8Ch/7qVYKPz5CyboiXC1OojNiyF02gkGzs9HMGlJKqg9WULK2iNaaFmOjIOR5EZpgzE0T+tZJH1DiJciYwyy01rZ0StPcF5W76VerASMFM0DRp/nseXY7c360qM/+MLtKzW3yxNvVYXzW4oSPDxrpsR+7poXLxhgOuMvGOtlU6F15R9t0pucEluZaoVAozgVmE6TF6Jxs6FtJFYEkwiq5xUuR2j+tNoRL+348bRHMTg/8dV04Lo/gvoU9R7PWHKli7U8/we1wd+S6cNTbqTpQQcGYPGY9sJCtT6+jct/JjqjWYCWoy7lsBDFZcX3vqJcon5cg42iwc/DVvUHvV3pkRzSSq8XJusc/pb74VN/6DPAE9uhGCYCfvhdB2Snj1LlklIv0WA/eroa75jpUvheFQjFg+NxU3xYPXyRGSv50ezNpsd3PjRUNgpe2nBYu3fHCJhs1zd1/7mp2su5nn3YSLnD6YbnmcBWf/uB9qg4YD9LSI41pOkiJ0NOnZAano16ixEuQKV1fhNRDnCZfGidi3tv7+9TN2AyPD6tLdwiQ8MYuw7v8ZINGg72nPiTZiR5u70Waa4VCoThXfG6Kg+EpHjSvwQje+cbi1k5L7Gfz4X7fETo6sOJA9+2K1xTganb2mF1U6hJ7bWvw6x0JCE+KIG1SRnD7DRAlXoJM+c4T/VKkSuqSkvXH0PuQKXF0uodB8YGHznmkYFOBsVT0+0/Dae2xYrXgWI2JzUXK7KJQKAYO4Vb48+1NXDneiVk7c0LvPqX/2WhC8p8d3hN/VjZoPsuymETPwQ4nth33OQ6fBPjsKjSB0AQzvjkveCUGeom6qwQRe11rULMX+kK6ddx2F9aowLLjnskT1zRz1/PRBHoWH6vR+OSguS2JU8/bmoTkjZ025gxVPi8KhWLgEBUGP7mylW8utrP/hAkpISNO59H3IjhS4f3WqUvBwXIzbh16ik6OCfddFFdKI1qpOzzByNkiBBFJEbRUN3fVZAI0k9bpATlxVDLj75xK0qj+L8R4Nkq8BJGilUd7XyCoF5ispj6HqWla75zSdAk/eSfS57YeKciv7P+6FwqFQhEMYiMkc4edfvh66sZmrvtTrF/bHq3UGJXWvXV86Wgnz270Hg/tkYJLe8hOHpcdT83hyr4tC0nJ8KtH42x0cvTDwx01+sxhZnIuHc6YWyfRUtmEq8lJeFIkUWnRvd9XkFHiJYjU5FX2384ExOUm4qi3E54Q0etu6lp765Xmv8u6zazCpBUKxYVBaowkNVqnotHXg5/kKy9E8z+3NjFlcFcrybAUnfnDnWw4aum2LIsmJItHuhiS2L34yb1sBEc/ONzLb2EgNI0hC4diiwlj9E0TaDxej5SS6MxYzG1RFtbshD7tI1Qon5cgIjTRq8i6iORILJEBWlCkkQfmv195nV1/34Lu6Z3vS3J0X5yLfX9ZTUgWjVB1jRQKxYWBJuAmL+HPpxG4dXjwzUicPaya/+yaFma2pZEwaUZSUFObj83coS4euaqlx95jh8Qz6oZxgQ6/E2NumdCRIddkMRGXk0B8bmKHcDmfOf9HOIBIGZvGia2lAW0jTEbWwxFXj2HrH9ZTsftEp881c9uaYw+GDqlLjn5wGN2lM/VrswMec26SzshUN0cqTL2IPPKOcSHC9ZODX3hMoVAo+ptWJ7y81cbrO7yHOLejS0F9q2BVnoVlY7s+xEVY4fc3N7P/hIkP91uNDLuRkivHOxmT4dunZdznpxCeFMnh1/fRWtuz0Dkbk9XE8KvHYIsJI+/t/USmRpMxbRCaZeAs8SvxEkSGLBnGvpd24XG6/Y84khJrpJWw+HAWPHopjScaqNp/EqlLEoYnEZeTwPFNxWz+7RovfUDhx0cYed1YotJjAh73/YvtfPvVSJC+Etb5j0BiNsHj1zZjM0scblSuF4VCMWBpdsDXX4riSKWp22WenjBrkv1l5m7FC4AQMD7Tw/jM1oDHJIRg2BWjGHrZCHb832aKVuZ7v/cISJ86CFtsGHlv7e/kL2Oymhh/51SGXzUaALfDjbPRgTXKijns3JQA8Ia6nQQRa6SVOT9cxIYnP0Pq0i9HKqlLBs0Z0vE6OiOG6IzOAsRe5/ukFprg2KoCxt0+OeBxz8hx86sbm3n8/Qga7AKTkH7VK+oZSVqsB4+u8dCbkUiMPpeNdXHXHHuPa7gKhUJxvvJ/a8MDFi4dhLi0mzBpmGxmhKYhvbkQCEFDaR3NO5q6iByP08Puf2ylYu8JLOEWSjccQ3okQhNkzBzM6M+NJz43MbRfJACUz0uQSZuSydLfXMXghbm+TXBCkDUv26e1pLWmxfCn8doXp+tW9IIFw9188K0Gfn5dM3fNcZAa4z1rri/K681UNooOS45HCj7cb+GLz0Zz8MTAMU0qFApFixPe3WPtlXBx64LJWaFPFRGRGIH0I/a6uaKrcDmT8m3HKVlb1JHRXeqSE1tK+OxHH1CxtzyII+4bSryEgNgh8cz41jxuePUOrn72ZpLGGDHx7fWJ2oVI2pQMpt0/12d/thib71T+0mjXFywmWDraxb0L7Dx1Yws2s7H8EzjirH/bhyiwu+DBtyLppX+xQqFQ9DuFVSbsrsCFiyYk8RE6RypMfPHZKG77ezSPvx/OgRA8wA1emOvbwCPplRVI6hLdo7PpqdXByS8TBNSyUSjRJbVHqkgYkUxYQgTOBgeaRSM8IYLsJcNIHJmM8FFAo7misa0ehXcRIXXJ4AW5QRv6qDQPf/l8E79eEc6B8uCdJhJBRYNgU4GZecNV4jqFQnGhIjFr0GgXPL/J1mG1Ka7ReH+fjTtm2vnmYnufayi1E54QwYhrx5L3Vs9lYyxR1o5cLgEjjXpKxzceY8iiob0cZfBQ4iVE1OZXs/FXq4wlnzaLi/RIwpMiGHvLJBKGJ3XZpulkI0c/PEzZpmLcdjcgcTb6PtGEJkifNoi4nODG449O9/DPu5r45wYbf1kbHrR+NSHZW6bEi0KhOP9pssMfPvOeTK470mN1qho1PDqdAiE8uvH/l7aEMShe54YgRmOO//wUNLNG3lv70T06QjMqSQtNYIsNxx5ARFJ3CJOgJq9KiZcLlcYTDax5ZEWHea197RDAXtvKmkdWsPS3V3dyzC38JJ8df97kf6lnYYgW6ZGkTclk5nfnB/U7nMmdsxxsKTKz97i5d85qZyElQXvaUCgUimBid8GxGmNZJzvRw4/fjmRvWSC3SkOqTM5ys+KA1UsEp+SFTTaum+T0WePIX4QmGHf7ZEZcPYb8/x6iuaKJ8KQI6otPcXJH/5Wu6Q+UeAkBh9/ch8fp6TbaSOoSj9ND3lv7mPYNw9/lxLZSdvxpY0D7iEiKJHPWEIYszCV+aGg9wC0mePrmZv5nZTjv7bXi1vt2pUkEkwYpq4tCoTh/aHXC39aH8dYuGy1OY44LM0vs7kDmO2POf/DyVv66LsxH1KagvN5EUbXG0OTgOQFW7D7B3n/toK6wNmh9tiM9kuSxqUHvtzcoh90go7t1StYWeg2TlrqkeE0hultHd+ts/u3agPfTUt1shK6FWLi0E26FWbluIqzG9xLCv+qq3WHWJDNzlXhRKBTnB3YX3P/vKP699bRwAdqEi99Ju8hO8PCPLzRy3WSn3w6+rc7gmaHLtpSw9mefUFcUfOGCJrDFhZE5c3Dw++4FyvISZFwtTnSXbxWtu3RcLU4q953E4+jFjVzCoTf2Menu6b0YZeBsOGrmwTcjOi5j2evlI0lipM4nBy0sGulSiesUCkVI0CWszrPw6nYrh0+a0XWIj9SZO9TNXXPspMacFiWvbrdxoNzUw7zm71wnaHJqjE435v8hiR4Oneypz/YtZB9LtJzG4/Kw7X839CXDRc8Io3zA3AeXnDdZeJXlJchYwi2Inmqgn4Ewa1girFQfquj1vkrWFPa6plEgSAn/81m7w25fnxIEVU0aj7wbyZV/jGFV3vmXuVGhUAxs3Dr8v7cieOitSHaXmrG7BE6PoKJB481dVq59JoY3d1nZX2Ziw1Ez/97qRzoKP6huEvxzg5Gy4sYpTh8PeRIJ/PS9iE61jxz1dgo/PsLhN/dRsrYQt58Pt2Wbi3sfSeQDIQSX/u5qEkcmh6T/3qCee4OMZjExeF62keSnp6UjAdHp0VQdONmncuaOejuNZfXEDo7vdR/+cPikieKa4KntdqffRrvgoTcjePqWZmapZSSFQhEkntto47OOB6MzBURb5Cfwq48iQrBnwX922Lh7joNlY528v9fC7lJzD067xnu7Ss38e5uNO2e0svf5HRz94FBHZlupS8zhZsZ/firDlo/yuueGkjqESXQKEAkWUtIl8/u5RlleQsCoG8Yb1peeXMglNJTVs/ann1C2uaRP+zr7RLWfaqV4dQFFn+ZTc6TKd8ZFP1h5KFTWESMF3u8+CQ/KU49CoVA43fDqNn8KJ4Zm0qlv1civNBmBDrc0E2Xz7h8oJby23caOP28i//2DnTLbArhb3ez62xaOfnDI6341iylUX4mo9OjQdNwHlOUlBMRkxbHw0UvZ+KtVOBocHQq6E22rPY56e6/3o1k0otKMk8rd6mLn37ZQsqazs3BMVhzT7p9D4ojemfs2Fpj515a+Ze71ntZRUFxr4u7nInn4qtaget0rFIqLjyMVJhrs/jyX+78ELtqWePzdzt2WhNakQaPD11gEsrKWY/uPem217187yV4yrKNIoqPBTtHKo5TvOI7u8hCRGNknS76X4TH08pHB77ePKMtLiEgak8pVf7+Jmd9bgC2m5wRHUpfGtdD+FwAJw5Iwh1vQ3TrrHv+0i3ABw8Kz+icrOFVQE/B3kBKeXhmM5HS+v9jhk2bueT6agip1SioUit7jR7xEwEgE372ktc2Y7l0gmDXJ4Lbis/5O6eOqD/qsX+e2uzss9ZX7yvnvvW+w7187qD5QQe2Rasq2tFnxg5hDS2iCuJwEci8bEbxOg4S6U4QQzWIicWSy76rQ0kjtnDUnO6D+bfGGsCjdcIzqQ5Xdq25dIj06e57bFlDfANuPtfu6+GN+7e6C9v8poL3u0e8+CV4mX4VCcfGRnahjEn2xQJzeVmvr54uz7dw6w8kDl7bibT40CcmlY1zEhhvbmU0wLsPd0U93CCSpssHnEr8wCVqqmmmubGL9EyvxON2dptiO+T9IxhdhEgxekMuiny3DfB6GhSrxEmIcvoRLG85mJ7O+v5DrX7kDzeqfc6wt2ljOKfz4SM/+NRgnddX+CppONvrVL8D+MhPffz3Kr7aagLlD3WdNGIFXAJMIthdbKDulTkuFQtE74iMkS0a5ellUth1jW10KLJrE6QaHG26Y4mTpaCfdKQSTkCRGSe5f3HnOv22Gw2tmcgkMzjD7tLxIXWKNtnL0g8Pobt2rSDGH90FsCBg0ZwhX/f0mZnx7HpZIa+/7CiHqLhFibHH+WRLCYo2lJbPNTOaMLL+2SZ2YAUBTeYPPwo0AzZVNfvVb3yL49quR+BehJxmd5uF3Nzfz/jcb+J9bmrhteu/9eADe2WPFdX4ULlUoFAOQb13SSkJkbxNpdl7Dd+mCV7bb+N5rkeg6/OyaFr620E5cxOn1KbMmuWysi2fvaiQpqvM+Lxnl4qapDoBOFhjR9v/LxriYf91gn1FCQggyZw6hdMMxn74t7tY+Rm8KQZif965zxflnC7rAiEyJInFUMjV51T3XLRKQc8nwjpeTvjSD4xuLvZ6g1lgbGW0ixxJhwX7Kt4XHEubf4X53r5Vmh/BSk+M0Alg+3sgtkBApmZXrJr/ShElAbyP2nt8Uxrt7rDx1YzMTBp1WMU12yK80rFLDUzxEBV4rTaFQXASkREueu7uRpz4KZ91RC/5bgbu3GEtpWIU/OmDlqglO7prj4PMzHeRXmnB7YHCi3rFUdDZCwAOXtjJlsJtXttnYe9yYw0amerhlmoPLx7kQpJE4Mpna/Oru530BuZeNQOo6ziaHn9+ll0jOy2Wiszn/R3gBMO72yax59ONuPxOawBYTRu6yMxyiBEz80nR2/2Nrtw8OJpuJ+T9ZimYyDGdZ83I4+Ooer2MQZo24XP+qTq86bPHreUVDkhwtuWJc58RIKdG6j5oevqlvFXzzlSiev7uRpCidZ1aF8/5eK06P0a/VJLlygpP7F7cS1ddgKIVCccGREi35+iI7644GsuzhJRuukLyx0xAvYPizjE73z0QsBCwZ5WLJKBe6NJ5jTZ3WPQRzf7yE9T9fSe2R6o58Le3/Zs4cTGttC+9/5fWQhUOfSfr0QaHfSR9R4qUfSBmfzpwfLmLrHzbgbnUhTBogkR5JVHo0c398CbaYMBqO17PvxZ2c2FrScYKaw8x4XB6kR2Kymsi+ZBgjrhnbESINkDIuzad4kW6d2qM1JI1K8TneFpd/oU9xETrP3N5M5FniYeEIF5FWSXMfanboUuByS57bYONolYmCKlOndWOnR/DObiND5l/vbCLi/FyWVSgU5xBzEDPZSykoqu57h1oP06stJowlv1xO5b6TlK4rwtnoIDwpgsyZQ9j+zAZaqpr7RbgA503xRW8o8dJPZM4awtWTMzm+4RinCmvRzIK0SZmkTEhHaIKqAydZ+9in6G5PpxPU7TA8ysfeOpHRN09EiK5nffWhSuNi8HZia3B8Y7Ff4iU3yUNJrYbHR/Xo39/STFZC17jEMAt8fVErv/64bxksPVKw4qChSrpzeNOloKDKxL+32rhnXohNqQqFYsAxKF4nJVqnsrEXuSi6wWoOrXoQQpA6IZ3UCekd7+371w5aqppDk8OlG0w2E9YB8DSoxEs/YraZyV4yjOwlp9/TXR52/X0bBR/mdb9R2/l64JU9DJqTTUxWXJcmrlYXQtOQXuocCSFwt7r8GucNk52sPNzzyasJyYhUD6PSet7f56Y6kcAzq8Ixdtu7icMQLT1ftLqEN3bauHuuw1vAlUKhuIhotAs+3G/h8EkT4RZJMGJTTJpk0Qj/5tBgIT06BR8f6TfhApB72ci21YHzm/N/hBcwUko2/3Ztz8LlDIQmjJDobohKi/YqXIx90WmpyRtTh7i5dIyz21BDTUjMGvxwmW8H4ZumOvnwW/VMH+Kmb/ZOb6pEUNOs0WRXykWhUMDHBy0s/0MMv/0knP/us1JcG4zbnERKuHla/1p4nc3OkBVb7A7NYmLktWP6bX99QYmXc0jVgYrTWRF9IHVJbQ9ZcrPmZfvMDSOAIYuH+rUvIeCnV7dw1xxH21PLaUamefjL55sYm+HbUc3hhqdWhLOtuHcGvkDyNITanKtQKM5/thSZefidCJwe6F3q8u6TbWoYIdLDUvqvfInu8nB8U3G/7Q8MH8vwxMh+3WdvUctG55CiT450X/eoB0zmzlpT13VObC3l2MqjmKwmdGfPgmLMzRMJT/DfB8WswX0L7Xxxtp1dpWZaXYIhCZ4eL96iao01Ryy0ugRZ8TqXjHLys/cjWH0kkDDFzlhMkJvs5kiFqcckT5qQTBjkJixUtSMVCsWA4Vcrwum9b0sPoc5AdLgxz/QXbruLtY99Ss3hSt+NBdjiwnCc6lt+LQCpD5zackq8nEOayhv9X8sUkDY5EzDWQY9+eJj9L+/ymYzIEmFhzC0TGX5170yB4VaYM7TnfTTaBY+8G8HGAguakGgC3LphcXG4+7aU8/3LWoiPlPzAS6ZfXQrumKmcdRWKi50TdYKyU72NBmqfh7vJ8YKgyQ4vbArjB34slweD3f/YSu2RKv8aS5j57fnkvbWfij3lvd6n0AQxg+N7vX1/o8TLOcQSZfUdJdSGyWom+5JhSI/Opl+v8bncJDTB5K/MIHvJcEx+lhsIFLcO330tkgMnjP51KToS/TrcfVvGibbpXDHOhdUMX1vYyp/XhGPSZEcEVPv/v7awlQXD+++JSKFQnJ/sLu397UwT3Uc0tuORgvf3Wfn2Ja1YQ3zXdDTYObaqwPeDbdtwp3x1FqkTM0idmMHef+0g7839XZpqZo2EEUnUHK7qsV+pS4Zdcf5Vj+4JJV7OIVlzs6nYdcKvtrHZ8dTkVXGqsMZvP5nGE40hEy4AG45a2FfW0ynUrsp6Y32RDE3WeW2HjSUjXdw1x8G0IW7+s8PGzhJjf1MGu/ncVAfjM1UdAYVCcXbSt8DwJlzasbsE9a2C5OjQ+tdV7jvps1QAQFhcOIseX0Z0ZmzHexPunMqwK0ZR8FEe1Ycq0cyClPHp5Cwdju7W+fQH7+NscHQVMALSpmQGXBz4XKLEyzkka14OB17Zjb221afKPpVfzYZffOZ331KXlG0qZtKXpvd1mD3y3h4rmpBeLnx/qlGfbiOQHSUJ9pWZ2Ftm4o+fhbFsjIsfL2/hsWtagjJuhUJxYVBUrVHdpBEXoTMsuS8PMv49aIVbQx8YoLv98zsJT4joJFzaiUiKZPznp3S7zSW/upI9/9xG2ZmJUMMtDLtiJGNvnTQgQqTbUeLlHGK2mVn42DLWPvoxLdXNXpeQehPn73GGdjmlolH49cTSEwKYMMjF8VMmmp0Cu0t0fHLmg8cnhyw0OyP5zeea6SZHn0KhuMjYUmTmmVVh5FWcvoUNSfSQFKVT3dSbhHS+249IdfdLKZK4bN9+J0ITxA9NDLjvyJQo5jy4mNbaFhpK6tDMGvHDkwZELaOzGXgjvsCIzojh8j9dz/GNxZRtLqaxrIGG0rq+dyzoNqFdMEmIkD4sL96Q3DnbwTcW2bG7YPkfuz5BtKNLwfqjFt7eZeH6Kf2bJEqhUJxfrM6z8OCbXSMni2s0Tj8B9nbJumcuG9M/c0/skHjvRRoxHmaHXt57/5TwhIiAok/PR0JqI6qtreWOO+4gJiaGuLg47rnnHpqamrxus2jRIoQQnf7uu+++UA7znGOymBiyMJc5P1pMzKDY4FxzEoZeMSoIHfXMFeOcAQgXiUC2lYSX3DzVyX0LjNC+DQUWmh2+l5h+tSKC1YfN7DluYnepiaa+RwYqFIoBhMMNT3wQ3iZPzp4zzvSzC3QS9W7ZNgnJNRP7L1nc1K/NxmQzI7ykDd/5l81UHazotzGdb4TU8nLHHXdQXl7OJ598gsvl4u677+bee+/l5Zdf9rrdV77yFX72s591vI6IGNgKMRBcdldQim/FDIkjfXJG3zvywpJRLp7daNRB8kfERIdJPjfVyVXjnWTGG+u6Lg98dtiM7yclwyPmR29FdrSzmiRXTXDyjUWtRIX1+esoFIrznNV5Fhrt3p65e2cF9oYmJMvHO4kN779EmLFD4rnkV8vZ96+dnNhW2m2bmrwqVv/kI+b/ZClpUzL7bWznCyGzvBw6dIiPPvqIv//978ycOZN58+bxxz/+kVdeeYUTJ7xH2ERERJCWltbxFxMTE6phnnfEZMZ6Vdv+0lBcxyfff9/wpWmj6WQjh9/cx94XdnD0w8M4m/qWH8Vqhv+9rYn0WH8czAQRVslXF9g7hIvdBd96JZJPD1nxb9Lp/ETl9Aje3m3lqy9G06xSvSgUFzwFVSbMWjBEhOzI4H3tJCffXWrkbzEsw6fbaEKSm6zznUtCn9/F4/LgaLCjt5V6iR4US+6yEd6nRgkbn1rd57l8IBIyy8umTZuIi4tj2rRpHe8tXboUTdPYsmUL119/fY/bvvTSS7z44oukpaVx9dVX8/DDD/dofXE4HDgcpw9cQ0ND8L7EOSDn0uHkv38oKH01nWxk/ROfsuTJ5ez48yZK1hUZwkgIpK6z55/bGHvrJEbeMK7batX+kBwteerGZu74h3eBqQlJ2lki589rwtpyM/RerOlSUFit8eKWML66QK0jKRQXMlaTUWMoGIzJ8PDTq1sYnGDMS0OTdV7aYmNzoRmJIClKctNUBzdPcxDKIsunCms4/MY+yjaXIHWJyWoiY+Zg6opqaTxe73N7j8PNp99/n0t/fw2W8Isn1XjIxMvJkydJSUnpvDOzmYSEBE6ePNnjdrfffjtDhgwhIyODvXv38qMf/Yi8vDzefPPNbts/+eSTPPbYY0Ed+7kkdnA8w68eQ/57B7t+6GdCuw4k1BfX8d6X/4O7xXA2MxzAjE50t86+F3ciTIKR143r9ZiHpeiMSHWTX2lC9rB8pEvBdZNOrxk3O+DtXbY+RSud2febO618eZ69T7keFArF+c2coW7+tj4YToGCRSNcHcIFYHq2m+nZbtweYzk7zELIoxvLdxxnw5OrQMoO51yP00PpuqKA+mmubGLfv3Yw5d5ZoRjmeUnAU/2DDz7YxaH27L/Dhw/3ekD33nsvy5YtY/z48dxxxx288MILvPXWWxQUFHTb/qGHHqK+vr7jr7S0+/XBgcTEu6cx/gtTsUR2lvvRGbHMeWgxk740nah0/ypEAx3CpScOvLIHd2vfPOm/vcTetqjTfSXqUWluLhl1eh8Hy83Y+1g+4EzqWjXqWlUctUJxITMmw8P4zL5WqQeQPRZzNZuMsiihFi6uFiebfrMGqeu9SoXRCQnHVh7F1dJ/TsXnmoAtLw888AB33XWX1za5ubmkpaVRWdm5qJTb7aa2tpa0tDS/9zdz5kwAjh49ytChXasi22w2bLZ+CL7vR4QQjLp+HMOvGk3V/pO4WlxEJkcSPzypY3knPCmSTU+tDsr+PA43+e8fZPRNE3vdx7RsN7+9qZkn/htBTbPAJCQ6IKVg7lAXj17dOa22JwT1v0KYTFihUJwn/PKGZq59Jhojl1tvFYYgJuzcVqIvXlOIxx68XFwep4f6Y6dIGpMatD7PZwIWL8nJySQnJ/tsN3v2bOrq6tixYwdTp04F4LPPPkPX9Q5B4g+7d+8GID09PdChDnhMFlNHMcazsZ8KrgPZ/pd3U32oklkPLOxi8fGXOUPdvHt/A5sKzBRVm7CaJXOHuslK6KpUhqV4AswR03M0kmHZ8RB9jicjhUIRepKiJIPjdQpreu/1YDFJ5p/jmmg1h6vaiyoFrc+LaQYMmYfA6NGjufzyy/nKV77C1q1b2bBhA/fffz+33norGRlGCG9ZWRmjRo1i69atABQUFPD444+zY8cOjh07xrvvvssXvvAFFixYwIQJE0I11AFJKLzLK/aUs+HJz5B98IgzazB/uJsvzHZw63Rnt8IFjAlo0QjXWd79PeE9jFqXghunXHze9grFxcjGAjOldb03swokt053nLOHHd2t92mO7QnNavIrO++FQkjzvLz00kvcf//9XHLJJWiaxo033sgf/vCHjs9dLhd5eXm0tBg1a6xWK59++ilPP/00zc3NZGVlceONN/KTn/wklMMckNQerQ56n1KXVB2ooHLfSVInhN7S9d1LW9lUaKHV5V2cCAEzs11sLrLQvZCR/HurjRN1GtuLLXh0GJvh4frJDhrtglV5FpqdgkFxOsvHO0mKupieTxSKC4fNhWYe+E9kQMYKk5B4pFG40aMLrp7o5GsL+zcy0dnoIP+/hyhccQR7XSuaRSM2OyGoVpecJcOwhDIs6jxDyFBIwHNIQ0MDsbGxVL/0MDERF2bmMqlL3rjpX3138uoGoQkGL8hlxrfnBb3v7rjyDzFUN/s2AArRHiLpReQg28ymAg3D5wYEpra8EO1n+lfm27l7jsOnQ55Hh6pGwwU5JVqqSCaFop9occLHB6zsLjWhI5g3zInLLfjlRxE4PeCfr4tk3jAXFQ0aEVYYmerh2kkOhqWEwOHOC621Lax66ENaqps7z9mBRo96wRJp4cq/fm7Ai5eGFjtJdzxOfX29z/xuqrbRAETqMiTCpb3v7vxpXC1OWmtbsURYgloTw88Cqj2GYJ/RolO6cP2M/3v0ztv+ZW04EVbJrdO798x3e+DlrTZe3W6juslQLElROjdPc3D7DAcW5RisUAQdKY1q8s9usLG5yNJmlDCu3RUHenNTFmwssCAAjxS0OuG2GUEcsJ9s+d0amquaugqVIE7h074xZ8ALl0BR4mUAopk1IpIiO2XPDRZCE4QlhHe8bipv4MAreyhZX0ibKYOI5EhGXjeOYctHIXVJxd4TNJTUYbKaSZuSSWRKlN/7y032sLu0b9Wp20YeUOu/rwvjhsnOThFQYAiXB16PZEuhudPcUt0k+PPqMHYUm/ndTc2YlYBRKIJGTbPgh69Hsv9EcG9JZ84rBVUm7n0xihe/1EhcRP8sOOx9fjtVByp9thMmDdnLEEyTzUzmrCG92nYgo8TLAGXo8lHs+9eOHtW70AQJI5OoOVQVUL9Sl2QvGorUJSVrC9nxf5vwODyd2rRUNbPrb1s49OY+kGCvbTEcU9rWZQbNHmI8CfgRtfS5KU52lvR/VshGh8aWInOXiIM3dlrbhEvXom8S2Fpk5j87bNw2QzkIKxTBwOGCLz8fRXl9aNdlPVJQ0wRv7rLypbmhvX5PFdSw5/ntVO3rOSHrmWgmgcfju113jP/ClF5nSB/IKPEyQBl2xUhK1hbSUFLXZQlJaILwxAicTYEnLLLF2Gg91cIHX33Dp2XHXtNy+sUZrlNlW0poqW5m8c8vR/OxxrJopIt5w1xsKDD7sTQUXE61dJ4spYRXt/vOGfTadiu3TvftM6NQKLxzqkXw5ReiOFHfP6ZMXcI7u219Ei+tNc2UbjiGo8FBeEI4WXNzsMUa/pUeh5stv19H2ZaSgPq0RFnR6+wBuwMMWZTLsMtHBbTNhYISLwMUc5iFxU9czu5/bqV4TdFpk6OAjBlZZMzMYtv/bAi4X0eDg61Pr+/T2KQuqc2vpnRjMUMW5npta9KMpFN/XRvG6ztttDj7TxEkRXU20zY7ocxHCKZEcKLeRKNdENOPVWYVigsNjw7feSWS46f60xNeUNPL1Xbdo7P7H1sp+CjP6EnTkLrO7n9uY+R14xh3+2S2/nEDZVsDEy5gzOd23f/cXXG5CQy/agxDFuVelFYXUOJlQGOJtDL9m/OY8MVp1OZXI3VJ/NBEwhMi+OyhD87t4DRB4cdHfIoXAIsJvrHYzj3z7OwvM/OLD8M5Uad1s3QTPMLMkn9usPHbj8OJi5BMynJz+Tj/LVU7S00sGnFuk1wpFOcjJTUab++xUlxjIswiWTDcxZJRri6O7hsLzByu6P9bUG/zu+z8y2aKPs3vWKpvf2CUHsnhN/Zx9MPDPkux9ETzyUa/25ojLCz5xRWYbBf37fvi/vYXCLaYMNKnDur0XlO5/xdDSNAlzRWBjSHMYpQZuGuOg59/EO6lpfe8MP5gd8O+MiNvzPE62H/CxItbbMSG6zS0Ch/CSfKbFRHMH9agwqcVijakNCrFP78prCO3ihDw6SErT/xXMmGQm2VjXVw2xkmYBT46YA0wy3bf0YTkqgmBL6c3nmig6JN8r216K1yAgJaL3C0uSjcWk724a7mciwk19V6gnA+q3BrVu5pTy8c7mZ3rRnSTfVcTsg81jM7sT5zx7+m/ep/Cxdimqkljc+G5/40VivOFV7bZeH5Tm++HbCvT2iZMnB7B9mIzP/8gnOv/HMPRSo26lmBEGfqPJiSRNsnnepGNu3h1AUI7P5ZnhElQc9h3BNOFjhIvFyiDZp/j0DkBg/1YMuoOswa//lwzX5rjIDrstF+K1Sy5dpKTO2b0pq6TJNwi8Z1cwb/MUZqQFFZ3VVFSQkWD4FiNxkVU4FVxkePywHMbfT2sGIKmrkVw/7+jiI+QmPwqD9KO9LOciNEWjP7bk1QmREqeua2Z1JjAl41aa1v6auxVBBn16HiBMuyKkeS/fxDp6X+nUqEJLFFWci4Z1us+LCa4d4Gdu+bYKaw24dFhSKKHKBv8wuuS0lljaRMiI1I95AW0vu59aUpKsJk7/7YfH7Tw/MYwjlaZ2r6D5PKxTr4y396rCVOhGCjsLjVT1+rfs7AuBadaoLhWa7PQeENiEjAoXuf2mQ6Wjnbyn+02/m9tGN4KtV43yUFuks7hChMmDWZku1k0wtXr/Ey2mPMnW7v0yIumcrQ3lHi5QIlMjWb29xey6ddrQpaNV7OY0F0ehCaMfbQZLWwxYcx/dGlQLnirGUalec56r92C4t0vBQSZ8To3TXUwLsPDPS9E+7lX349YEsGcoacddv+5wcZf1oZ3iCUAl0fwwT4rGwos/OMLTWTE9W9acoUilEhphDq7PFDXGrhZ4kiFoSTEWdmxz+bHy1u4asJpf5LPz3KwrdjMrhJzl2Unk5BkxOnct9BBbBCjAQfPzyHvrf1B688nPVWb1gTWSCuD5lx8SenORomXC5jMWUNY9odrOfrhYUrXH8Pd6sLj9JEJSYA12oazwXE68VybKLHG2EgYmoQ53EzqxAwGL8jBfqqVwk/yaSitw2Q1kT4ti6y52Zh675jik7gI6dfizi3T7HzvUqMAW12LCMg50KTp6Hr3/i+akMwd6mJQvCFGjlRo/GWtYQ06u71HCupb4JcfhfOHW4OfEVmh6G+khA/2WXhxS1jH0mmkNVBhfvo68eVj9vh/I0iPbWLqEGPuspjg9zc38/d1Ybyx00ZzW3qFdkvnNxfb+yRcpC45sa2Uox8epr74FCaziYyZg0mZkE7V/pN9exj0MXEJTZA0NpW6ghrcdnenfQlNoJk15jy4GJOqUaIKM15MSClZ8c13aCxv8FrNdNkfr+VUQS1Fnx6htbaF8IRIspcMC7ko8ZdjNRq3/NV70S6Af32pkRGpp8XaD9+IYM0RC74tK8ZvYzEZa/kGoq04pCAxUmf+cBfZiTpXjHPy5IfhrPbZr+TN+xrJjFfWF8XARUr4/afhvLrd5tNiEsS9MizZw0tfburyid0F+RUmPBJyk/Q+517SPTqbf7uWsk3Fpy3KGMIBAXE5CZw6WtOrvoUmMNlMRGfEcqqotsscLDSBOdzM0l9fhdAEeW8f4NhnR/E4PWhmjcELchh53ThisuL69B3PZ1RhRkW3CCGY/q25rH54BdKtd1vhdPTNEylZW0ThijwcDQ40i0biyBTicxPOC+ECkJ2os2iEk7X5lm4tKZqQzMxxdxIuAPcttLMu3+JHFXqjT5cHLh/rIL/SbJjH3dDoMBwO399rxSPhf1eFddrGW5+HK0xKvCgGNJsLzR1ZqPtHuAAIjlaZcOuGM/+ZhFlg/KBe5tXvhkP/2UvZ5mKgc/hy+//rimrJvmQYx1YeDbjv6MwYZn5vAVFp0ez+5zaKVxWgn1GZNml0ClO/NpuodOOmPeWrs5j85Rm47W5MYWY0lZehE0q8XGQkjkjmkl8uZ+8LO6jYfaLj/aj0GIZdMYoj7xygtbal42LVXTolawopWVvE3IcWd8knc6746dUtPPhWJJsLLZg0iUcXHf9OynLzxHVdl2hyk3SWjXXy4X7/QrgFkooGjRfvaeS+F6PYV2aIN48UHaZfdwAPemW1avJRDGxe22Fry+HS36E3gma7IDaEBRU9Tg/57x30vh4tCTh/lTnczPyfLCVxdEpHNtxpX5/DhDunUnWwAt2tE5cdT3RmbJdthUnzq0bcxYgSLxchcTkJLHj0UlprmmmuasYSYSEmK441D6/oJFzaMZxxJZueWs30b82jcl85HoeH6MwYspcMIzwhot+/Q7gVnr65mT3HTXyw30p1o0ZCpM4V41xMGezuse5QdqKOv0nuJIK8CjNbi8zsOd7XS0XyzJpwLGZUUUfFgGVfmekcCBcASYQttB4Op45W4/KRaE7qkoaSuoD6lXr30UHWaBuZMwcH1JfiNEq8XMSEJ0YSnhgJQH3JKaoOVPTcWBpPJpt/swZhMiwPUsKBf+9m3OenMOr6cQA4Gx201rZgibAQkRzVbVfuVheFn+ZTuCLPEE/hFgbPz2HYlaOJSvM3IsjwJ56U5WFSlv95XwwnW/8nX5Mm+WCfNQhPm8a2T68MZ2SamymDg2fqViiChZSwv8xERaNGTJhk8mB3p7T+5ybViWR4iqdLeYFg43H7t6TrcXr8TQcFQERSZO8HpegRJV4UABQFsIZ7Zu4YKWHfCzvQXR7qi09Rtrmkw3ITl5vAmJsndnq6cDTYWf2Tj2g4Xt9x8TucHo5+cJjCj48w/+GlJI9LC86X6ob5w13YzBKH2/c0LIRkVq6bigbhVz4Kf6Z2k5D8e6uNKYNbfLZVKPqKlLD7uIkjJ01YTDA9201WQvc36fX5Zn6/Mpzjp06rhLhwnXvm2blpqhMhYNoQN2uOWHop5Htf1uMHl/UmMWVgxAyK9UuUuO1uotKjaa5o8h15JCB32cigjVFxGrUIf5EjpWTfizvJf/dgn/o58O/dHN9U3OliriuqZeMvV3H0g8Md72374wYayxq6TBBSl3hcHtb/4jNczaFLTWszwzcX+zcRSim4eoKjLemcP5l5feORgs2FFr/aKhR94cAJE7f8NZr7Xozm95+G86sV4XzuLzF897VI6lo6n6+r8iw88HokZWdVeK5r1fjtJxH8bZ3hmH7zdEevLZARViM/k+B01luTkN2WATmN5IlrW5iYFXpLZXhCBBnTsvwqA9BU3kj0oK4+KmciNEF0Riw5S4cHa4iKM1Di5SKndF0Rh9/YF5zOzp6D2l7v+vtWWqqaaCpvoHz78Z6fVqSxpHRsdUFwxtMDN01zcu1EB74Eyfcva2FdvpVmR3vto+DQU9LjFiecqNNotKs85AqDqkbBunwzG46aqW/x/7zIr9T42ktRlLaJEcnpc3hLoZn7Xoqite0ZweWBX37YfZ6idv65wcaJOo3JWR7uW2CIf++i4zTtiRvvX2Tn7a838L1LW/nqAju/uL6Zd77RwIgUQ5icTv1v/JsS7eG9bzRw6ZjeFzwMlEn3TMcc7vvhQpgECcMSmfHtecRmx3creFImprPoiWVY/OhPEThq2egiRkrJ4Tf3BbR+2xuEgMKPj/ToA3M2FXtOMPzK0aEbEPDj5a3MGerid59EUNF4WsMLIZmQ6eabS+wMTfZwxf/EBjUkVAjJ0KTOT5HHajT+uSGMTw9Z8OgCgWT2UDdfmmOn2Sl4dbuN3aVmBDApy80t0x3MzHF323/7sv3ZIaWKgYNbh13FJp7dGMbOEnPH+WfWJJePc/LdS1qJ8pHC6k+rw3F56DaVgEcKiqo17vhHNOMyPWTFe/xK7f/uHiv3LbRz91wHI9M8vLzVxrZjZk5PIN1fJ+3j//XH4bw51M3N0zpbVv95VxPr8y38d7+FmiaNlGidK8c7mTPU3e9V2yNToxn9uQnsfX6713bSI2korWf6N+cxZNFQpJRUH6ygrrAWYdZInZDebfSQIngo8XIR46izU19cF/L9SF1Se7SGMD+jknQ/Hef6yqKRbhaOaKCwWqO6SSM+Qmd4it4RqXSo3ITdD9+YQJDSsPy0c6jcxNdeisLppsMcLxFsKjCzsSCKs28MmwvNbCiw8IXZdr6xyN7R58cHLbyyzcbBcuOSHpnq5tbpDi4f5+JcFMMtrNL4+KCVUy2C5CgjCkzluPGOW4eXtth4eauNupaud223bpSbOFJh4q+fbyK8hwjamibj/PEuugVldSZO1GtIacVnLS/g9Z1W4sJ1rpzgYs5QN3OGuvHohiXnhc02dpV6tzAIAW/ttnact+2YNVg00sWikaG3sNhPtVKyrpDW2lZsMTay5uUQmdL5oSoi2T8H2zPzXgkhSB6bRvLY0PnrKTqjxMtFjMfVfxEvmkkjPjfRZzuhCb/aBQshYGiyztDkrjfW0Nz0BRMyDauJLuHHb0XgcHd9Qu584zn9/3aB88KmMEaleVgy0sWTH4Xzzm5bp4q7RypNPPZ+JFuOOXn0qpZ+EzB2Fzz+fgSfHrZi0oxvoUv42/owbpjs5IHLWpVVCKMm0Ml6jUibJCteRwKPvBPBZ4ctXkWHLgX5lSbe2GXj8zO7D7mvaND8thZKv/1XBI12+P3KCP53leSOWQ7unW/HpMGcYW5qWjSf4kWXgn3Hz02iS6lL9r+0k7y3DyClRGgatPn75VwyjCn3zkJrC2dKGZ+GZta8P0QJSJmUQcm6Itx2F1Fp0SSPTfPLX0YRHJR4uYgJjw/HHG7B3RriJx5hrP/GD0skOiuWxjMijc5GSknuZSNCOx4/yUnyEGXVaXIG724rkHx0wDC/bztm5kR97yZzTUhe3mLD4YJ3dhtJ984UQO03pY/2W5icZeW6SaFzgj6Tx96LaCuVAB6980T+1i4rZg0e6IfIkfOV4hqNP60O65QdOifJw/RsFysP+5eMTEp4Y4e1R/ESFdabNWB/brptmad1wXMbbTTZBT9YZhxLk1/+L7Lfl4Ha2f/yLg6/ebqwovScFiZFK4+ieyQzvjUPMArLZl8yjMKPj/S8nK7BwX/v7uy/pwlMFhMJwxMZevkoBs0eosRMCFHPQBcxmsXEkMW5AW8nNIFom4UsEZYeHdY62ps0jv73EG987l80dRNpdCYT757exYx7rrCaYVZu974lvUUA5fXGb3fghKmTtSQQdCnYf8LMy1ttXh0nBfDvrTb6o4JZfqXGZ3nWHotfSgSv77RS1XhxTuhHKzXuei66rUTF6d/gWLXGa9vDOlUk947gRL0JTw+Ggax4ndQYD4E7sgXSXvD6ThslNca5PCnLv/0lRfX/0qG9rpW8t71UhJZQvKqAhtK6jrcmfWkGqRMzjBfdna4eugYe6BKPw03VgQo2/2YNm369Gr2ng6ToM0q8XOREJAUmFOY/spRxt09m1A3jmPGd+Vz9z5tZ8OilRKVHd73I2wIcpEenucIoquYzL8J5VifUKMIdxDEJiDnjybgvBWoB8ivNXk3/EsGxGlO/RDCtOGDtCIHteTzw6aGLM9354+9HYHfRJdS4fYknEMdwsyZ7XArcWGimokEj1CnlTELy3l7jWGbE6cwb5vZpgfnogI23dvXP8W+pbubk7jIOvb63U26q7hCa4OiHh3HbXUhpVJV2tbRZK6URXWSJ9nPcbbsq21LC4deDFMmp6IJaNrrIKd9e6l9DAUmjUkibnEna5MyOt3WPjhCCRU8so3hNEQUf5dFS1Ywl3IwtLpzGsp6XiLoj750DDL9ydIdlpztaa1uo2FOO7vIQMziOxJHJHTVDgk1VY3BvAroUXDrGmBQjrb1P2gWSlGhJZaN/zx+yLSNyZaPA5RGkROtYg3z11zb7k6QPagMI+b1QOHzSxOGK4PzgJiFZMNzVpQRGXYvgp+9FsKnQQu8Ed2DHRXLaigjw4+Ut3PuvKI535Irp7mlG8uuPw1kw3EViVO+Uu5SSU/nVVB2qBClJHJHcqW5Q08lGdv99C+U7y/z+GaQuKfgwj4IVeUSlxdB0oqGT05v0SFyNAS69Ssh//yCjbhjX4U+jCB5KvFzk+JsQTgjB+DundrxurW1h/0u7KFlb2OHYljgqmRnfmkfS6BScTQ7eu/u1gOdQe20rW/+wnqTRqQxekIMl4vTTjrvVxc6/bqZkbVEnC070oFim3z+XxJHJge3MD+IjJJqQPS6FBIZkTLobKaG8XhDWx/QPkwe7WXvEguGy1NP4JOmxOisPW3h5q43StuypkTbJ9ZMc3D3H7jPs1l+SoqTP4+2RkBQ5sEzptc2CigaNSKskK0HvsW5WO7o0osJ2lZiRwNgMT5ekcL1H4pFw+xn1sRrthjPtA/+JorimJ+EQfITo7F+TGCm5f3ErD77pzZorkNKw2Nw1J/AaX03lDWz6zRrqCmtBE4Yc0iUxWXHMemABJpuZlT/8rzGv9UYb6RjCBfpuFgWcTU5OFdWSOCL4c9PFjhIvFzlRGbE0lNb7XM6ZePc0kkanAMYE8vF338Pj6OwPUnO4ilU//pCpX59DRGJEr0OeS9Yfo2RtEbuf3cbke2aQe9kIdLfOusc/pSavqstYG8saWP3wRyz+xRUkDEvq1T574vJxTjYGMSPuwXIL975o9Bdl6+1NXGIzG8s0wo+U6wkRkl+tiODM2bzZIXh5q42NBRb+emcT0b1y8uzMFeOcPL/JuxISEgYn6BypMJGb7DmvI4+Kqg3n2vVHT/uoZCd6uGeenct6SJx2pELjR29EcqLehLltCc2tCz+Pta9jafQngE2FFsrqNP691XaGRacvlrzA8eiCpaM7/w7HT5l8in0pIa8icEtEa20Lnz30Ic7GNtGjy44zurGsnlX/70MShifhanb6Xp7uR/or9cPFhhIvFzm5lw2nbFOx1zbRg2IYdkbSuNWPrOgiXM5kx582Mv3bc3s/qLaJR3d62PHnTex9YQcp49KoPlTZfXsp0T2w97ntLHri8t7vtxsWj3SRneihtFbrMS3652faeWWbDbce2I2jydG7+jAmAW5P+ytffQgOlJs7/n8muhQcq9H4vzVhzM518dZuG8U1GhFWydLRLq6Z6CQuwv+bQE6SzhXjnHx0wNKDH44EAd95zXgyT4jUuW26gztmOs5ZFEpPHKnQuPdf0TjPCmMvrtF4+J1IKhpauXNWZ8tBeb3gay9F0eI02p95PhhZmv0VFz21Ex2f/mODIRI7O2v353KcJD5CMnVw53nArPlRSEP0LonikXcP4Gx0dCtMpC5xtbio2F0eeMchRGjCqJmkCDrn2ZSh6G9SJ2SQMT2rx7lSaIIpX5nVsZ5cfaiS1mrfRQVPbi8L2lzqanZStqXEeyNdUnWggqbyhuDstA2rGf73tiaGJhtqwaRJTMJYShJI7plr5/7FdpaNdfoRLtqdD0CgGEUiA6sv0/O4dCl4c6eVB16PYmOBmdJTJvIqTPxpTRg3/l80h8oDe0L+f8tbuGq8EzB+I7Mm26xD7TlfTo+7tlnjmdVhPPpeRDAs9EFDSviZD+faZ1aFUVrbefp8eWsYrU7RrdXhtMj09kXFWf/6M9Zz5T8kGJbi6bKENiPH5XNMuhTM6CFD9JlIKWksq6cmr4rmqiaKPsn3blEJLFgq5AhNMGjOEGwxQVqXVXRCWV4ucoQmmPWDhex5dhuFH+d3yn8QmRLF1K/NJmVCesd7RZ8c8avfqkOVZM4awoktJf1qwm2ubCIqPSaofSZHS174UhPbi82sOmyhxSUYFO/h6glOUmOM7/b1RXa2HbNQ09T1hhcsc75JSMIsErsrUPHi42bS9vnpm65ASqPW0rdeieSvdzby8UEbq/Is2F1GUr8bJjuYM9Td5eZlMcFPrmzlS3MdfHLIQl2LYGeJmSMVph6WEgSfHLRy6WgXC0f0Xw0bbxw+aSK/0vfU+PZuK99ccjrL8ft7rT6OS7v1RZ7x+kz6d9mnL5iEJDGy63U9NFln6mAXu0vN3f4WmpBEh0kuHe3d165sczEHXtkdkgzglggLpjAL9trQVXYXmsAWF8bEu6aFbB8XO0q8KDBZTEy5dxZjb51Exe4TuO1uojNiSBqb2iWKx2X3L++J7vYw+cszOJVfTWttS78JmFAVQRMCpme7mZ7d/fdPipL884uN/O+qcD5pq1EERkirR+9tsLVxM2v3IRiUoJMarbP1WP9ctroUNNjh8/+IQZenxU1lg8b6oxaWjHTy+HUt3S4BZMTpfHG2g4ZWwWvbbV59IDQh+c8O63kjXlYe9n0OSQRv7bLy7h4rUkJarN6xXOQdQUKE3kPE1cAQLmAI9PaoubP52bUtfPXFKMpOaWd46RjHOdwCv7+52auzesGKPHb+3+aQ/BxCE8QMjqPmcFWf++lpThMmQda8HCbcOYXwRP9KDSgCR4kXRQe2mDAGL/CetC5heJJPHxkwrDbhCRFc8usrOfSfvRStPNrhJxObHc+Ia8YQmRLFrn9spb7oVHDGHxdO/ND+Ky1wNsnRkseuaeG7Sw1fErMGb+y0suKAtcdK0j2hCUlqjE52ok6UzfBBmTfcxf9+FsaOYnPA/fUFw9+wa4mCVXkWfr0inCvGOUmL0UmL7Tqo4lrNpy+QLgV5J01nvPZdmsHpNva/s8SMLo2InqWjnbg9ArNJEmXz++t1YWexf9Ni8xmZlxsrNfy1nBjC5fwRKpowcsacfZx7wiQkuckeZveQwDEpSvL83Y28u8fI6VLZoBEVJlk+zsnnpjo6rJXdYT/Vyq6/bjFehOAcl9B74SIgaUwqY2+bRPFnR2ksa8ASYWHQnGwy5wyh+WQjuksnOiMGa3QfTkCFXwgpz7OsYH2koaGB2NhYql96mJgItdYYbNx2F2/d/rLPiWXeTy4hfeqgjtceh5vWU62YrCbCzyjQeGxVAdv+sD44gxMQnRlLzKBYci8bQerEjHOenvvTQxb+39u9e/p6+uYmZg/tfIMorNK47e/BXRYLFtOGuLh/sZ3R6adrZu0oNvH1l6N9bhth0YkJN/LW6FJgNUmuGOfk9pkOshM7R2vsLzPx/dcjOdWiYdIkUp4Z1Woc73EZbj4/y8HiXhT7u+J/YqjtpjCif5wvSz/+LU2ZhMRqhi/NtfPM6nC/+huR6ubpW5q7XTbqK4fe2Mf+l3aGrMp9VEYMzScbfVqCNasJW5SN1ralpbCEcIYvH83wa8ZgUjlbQkZDi52kOx6nvr6emBjv85xy2FUEhDnMwqgbx3ttE5ebSNqUzE7vmWxmotKiOwkXgMxZgzHZgmQAlNB4vJ4TW0tZ97NPWf+LlXic/Vd8sjsWjnCRHK17KQPQ9X2BZMlIZ7elCXKTdW6c4uh2O1/4n36+d+wsMfOVf0Wxt634XrMDfvNx5xDt7pG0uAQnG7SO5SWnR/DOHiuf/0c0289YJis7pXH/v6OobzXaefR2B9nO1oyD5SYefDOSv68L7An4RJ1Gq6sv4uPcCxeBYbWLC+/8u2fFe0iLOS0ENSGZP9zFs3c1cvtMBzNzXN2WmhBtNYkWjnDx9M1NPH93U0iEi72ulRPb/Eya2UuaTjT4tYQdPzSR1lOnfWJssWFED4pVwuU8QlleFAEjpeTAy7s49Mb+Lun8UyamM++hJX4LkuI1hWx9el0ohgkCci8dwdSvzQ5N/36SX6nx9ZeiaHKcjkQRGDkqrCbjRt1OuEVy0zQHX51vx9zDPKlL+Nu6MF7easPu8i8EN8ysk5Wgk19p8tm2LwgkGXE6//lqI99+NZJtx8w+9teTheD05wK4eZqDW6c7eXmrjTd3+nKM7cxf72xk4iDfInZ7sZnvvRaJsbp57kVI9xg5fqYOdrGx0HpWThXjtxqf6eGPtzURZoGqRkF1k0ZchE56rGGlOn5Ko9kpSI3RiT8jFN7phmdWh/HWLhsO9+nvP3WIix8ua+1iAfMH3aNzfGMxBR/l0XC8DrPVzKA5Qxh6xSii0gyLnLvVxa6/b6V4TYHPNP79RvtlddbryV+ZwbDlo3vYSNFXArG8KPGi6DXOJgcla4toLG8gLCaMrHnZAUf6fPbgB9QcqQ5ZTSNhElz9j5uxxZ7bc6Gm2XDw/GCflfpWQUq05LpJDq6a4KSiUaO4xoTNLJmc5SbczxIqzQ4jWdlr263sPW7uMeeLQPLFOQ50HV7aYgvgxu9LWPTMtMFOtpdYerVtdwgkZpPhC3PmjdUXJk2yZKSLJ67rHFnicMO6fAvHT2lE2iQTB7m591/R2N3nMvzYOxqSH17eyqVjnETZYF2+UZhzZ4nh/Zoe6+HmaU5unOKgL8bMJgfsKTXj8ghykz0MTuhdkjWP08OGX6ykYk95JwdXo7CrYO6DS0gZn8bqR1ZQe6T6vEos1yMClv/fjedN8dgLjUDEi3LYVfQaa5SNYctH9amPhtK6kBZjlB5J+Y7jZC8ZFrJ9+ENipOTL8xx8eV7XlOi5Np3cpMBvEJE2WDraxdgMN1/4ZzTNjq5h2hqSpGjJrdMdvLEj0IJ4vb2JS7aXBLf4nkTg8gTuT+LRjVDtM/lgn4XffRpOo13DJGSHv8yZkTE9jSK0Fpme+zdphsPr9ZNPR/jMH+5m/nA3Tjd4dAiz4LN0gT9E2WDusL5XU9/7wg4q9hpJ484UJlKXSCnZ8MtVjLtjcp8jf/oTIQSFHx9h/OennOuhXPQonxfFOUWzhn4N2e0lG/CFQHqs5O9faGJEqrE0IpAd/i0TB7v5+xcaiY+QzBvuDlpyu9Bu2xNn2/L93OqMr7zigIXH3o/sqLLtke2/ln9hzqGl++8nMKKBzqxndCZWM4RbgyNcgoWr2UnhJ0d6PlzSWFI68u6Bc7dC14uckVKXnDpaE5LhKAJDWV4U55RBs4ZQ+PGRkJqMozPOz+icYDIkUee5u5s4fNLEvjITmoDJWW5yknS2F5t5fqORXC8txkNFgxbim3Uo70aBpNk3ommmDnHj0aG+RfC7T8K9bH8+3P2N7ydO/5cwC/zyhmZykwdOjZzqQ5XovpzldYm9zh6yyKIeEYZDrqvF1VGE0RJlJXFEMid3lvne3Hw+nCcKJV4U55Rhy0cZT2ihQEBEYiQp49O7/VhKSc3hKkrWFuJodBCeEEH2kqHEZSeEZjz9wKg0D6PSjJtGdZPgi89GkVdh7ggphkDuFedL2G9nBEakjD9WJI80sgVf9nQMTY6BYGiW3DvfzpFKQ4BOyXJzxXhnn/LWnAuq83qoQ3YWvbOl9REJk+6ZQeLIZOy1LegeSXh8OC3VzXz49be8bysgdUL384mif1HiRXFOicmKY/b3F7Lx12uCUoK+g7b72pT7ZnWb68XV7GTDL1dRtf8kwiSQumF2z3/vIIMX5DD9/rloAzgs0uWBb/47iuIa44bt6TZRnC9xcv4JF01Ixqa7aXAYTs7+CKyPDwbDcTj0Qk4gmTPUzT3d+EUNJIpXF3D49X0+2wlNEJUR43f4crAYd/tkkkalAHTKgBuVHkPa1Ewqdp3ofjzCSPlwrv3nFAYD4VFEcYGTOWsIy/90PcOvHoMlKjiOntHpMcx/ZGlHojxHg53K/SepPlSJ2+5i41OrqD5YARhOvUjZMWGVrCti19+2dOmz3dGw/d/zmTVHLBRWm/ywTvSQ4hxJVrzHa5vg4X//uhTcOdvJq19p5Auz7fgnKIIhOvrBRiDgrjn20O4jxDibnWz/0ya/2kpdMvbWSYjelJg+A0uE/yVBZj6wgNE3Tejx8xnfnGdETJ51yghNoJlNzH1oCdaBZga7QFGWF8V5QWRqNJO+NJ1JX5qOq9XFmkdXGI5xAdwvJt87E2uUlciUaBJGJCGEwH6qlT3PbaNkfRG0uQxoVpP39XgJhZ/mM/rmiYQnRlC6voj89w5Rm1/dqVnCyGRGXD2GQXOGdKkBda756MDZOUC6QzA+08X+svYw69M/9tLRLh66ooVXt9v4y9pQhpkbBf7uXdDC6ztsFFSaOO3Z0XnsAsnCES7mD3chBMRHSD++YzDxbz9GNW3DkOirNIKB8bs/cW0LE/zIR3M+U7KmAN3l33cYesVIsuZmY422sf7xT9Hdfvr0tP2kI64ZS9qUTHS3h/WPr/RvWx/ziS02jEt+fSVFHx+hYEUeLVXNmMMtDJ6fw7ArR18U/nMDBSVeFOcdlnALU786m88e+sCY0PwUMFlzsjvlc7HXtfLxd9/FUd/5adanI2EbpRuP0XDsFMdWFXT7eW1eFZvz1pC7bARTvjrrvBIwp5qFXzf1aya6+NPtzazLN3OizkRkmGTuUFdH/ZkvzXUwJt3Do+9GUNeqtYkF8OY427lWDt22MZAkR0v+8YVGUmMk100yUvmX1mr87tNwNhWczl2jCYnVBMU1Jl7aYuOaiU6qGv37jv7T96WhKJvOdZOc3DTVwdu7bTy30ebDOVpi0eB3NzcxI+fcCZd2S2JfzmHp0Tny7kG/2qZMSGPyV2YChg9J6qQMyncc9+taj0qPYcpXZ3X4nkhdolk0dJdv8eNq9l7NGoz5Z8S1Yxlx7Vjfg1GcM5R4UZyXxA9NZOFjy9jy9DpaKpu8thWaIG1yRodwkR6d6sOV7Hl+RxfhEggFH+bRfLLRZ7vCFUdIGp3KkIXei1r2J2mxOofKfTu1pkTrWM1wyWg30H1I+axcNx98q4FNhWYKq0xYzZKcRA8/+28ktc2cIWYMkRETJnn6lib+vdXGioM9mdgNgfP7m5u6FOrLStD5/c3NHCzX+MHrUVQ3CXQJdregqEbjmdVhvLjZ1lYioK+Cw0h979EFFhO9yiXTjkmTvH5fY0fW2i/Pt1NWp/HxQSumDgfj04n/rCbJNROd3D7DQWZ8/0USOZsclO84ju7WcbW4KNtcTE2ekWslcWQKw68aTeaswQEJGd2t88n336O5wvu1CoCAhOHJnfp31PsXdZR72QgSRiTRWt1MY1k90ZmxCE2QOiGd8h2+I4UiklWV5wsFJV4U5y1Jo1NY/ucbKNtczPZnNuJqdXWd4ISRRXfsbZMBKFqZz/6Xd2Gvbe3bziV+CZf2MRx598B5JV6uHO/k00Pe/IeM5Zpp2f7lwDFpMG+Ym3lnJC976Z5G3thp5d09NmqbITZCcvUEJ5+b4iAxSvLYNa1kxEme3RiGSZMdTsOakAgBj1/TwvCUnm/av/04glPNnWsWgRE9VN/qT1I57wgkQxJ1xmW6WTLSRV6Fib+uC/OSM7HnjMOakCwb4+qUbt+swc+uaeHqCU7e2mWloMpEuFWyZJSL5eOcJEf3r99US00zm55aTe2R6h7bVB+upPpgBUMvH8nke2f6LWC2/2kjDcV1/g1EQv77Bw2fl9smYbKYjAcPTfh02i/8+AiFH5+OTkyZkM70b85l1I0TfIqXsPhwUidl+DdGxXmPEi+K8xqhCQbNySZ+WBKbnlrNqYIaI3pIGI624fERzPzefOKHJnLk3QPseXZ7/w9SQl1hLW6HG3Owikz2kVm5bqYOdrGr1NzN0ophXfjmkhb64isZFyG5Z56jx+gYIeC+hXYWjnDxxk4r+0+YMWuS2blurp/sJCOuZ+Fy8ISJ/Sd6/i39y1PjDYmmwR9uPW35GZvh4bXtNupb6fE3E0i0M4RYezLAockeHri0hbMRAmbkuJmRc24TJbZUNfHRN9/B4ythY5t4KPgoj8SRyQxZNNSvvot7WFrtCY/DQ97b+6krrGHeT5YyZNFQyrcfD6gPgKr9J/nswQ+45NdXkjl7CGWbi3u04Ez60nQ0k4pRuVA4P2ZahcIHkSlRXPLrK6nNr6ZyTzm6rhOfm0j6lEyEScNe18reF3ac0zG+f89rxGTGGY6I83LQ+hhF0Rc0Ab+5qZnH34/gszxrJz+UCCt8d2kLV4xz9ctYRqd7+MmVgVnCNhaaz1hqCQ3fv7S105JVXITkz3c08d3XIimvN2HSpJEJVhqWpx8sa2F4iod/b7Wx+ogFl0eQHqtz41QnN052+F2T6lyw+fdrfQuXMxFw5N2DfomXkvXHeheMJaFiTzklawsZPD+HmKw4GsvqAwqblrrEfqqV/HcPMuu789n1DxtFn+QjddlRT8kaY2Pyl2eQNS8nwAEqzmeUeFEMGIQQJI5IJnFEcpfPilcXnPPCbq5mFzX51dQcqaLo03xmfm8BziYH5jALEcmR/e7QG2GFJ29oobTWzpp8Cy0OwaB4D0tGuQjzP7r0nODyCCPdvddD2hd/F8HkwV1v5jlJOq/f18iGoxY2FJhxuQXDUjxcOd5JXNuS0BPXtSANXUM3KYTOO5orm6g5FGD9IAl1RbWUbjxG2eYSHPV2whMjyF4yjOSxqWf5q7QihOhd+gABRz84TPaSYSx87DLWPfEpdYW1CJPoOPa+rmupy456Q1Pvm83YWydxYlsp7hYXkWnRpE8ddE4fJBShQYkXxQVBY1lDrybQwUtyKfmsMHgDadt/1YEK3r/nP50+Ck+KYPSNE8i+ZBimfkyAl5Wg8/mZAyvx2dBkj59hxr0nJqz7c8WswcIRLhaO6NkyJcT5mMKve04V9L4Wz+Zfr+mwYAhNULyqgIQRyURlRNNa3YwtJoyw+PDe5z2SUF9aBxg+KUt/cxVVByoo21SMq9VFeHw4h9/c77MbV4sLV4sLa7SNsLhwci8d0bvxKAYMSrwoLghMNlPAdxOT1UTisKTgihcvtFa3sPMvmyleU8iCR5diDsD8IaXE2eBASoktJqzbrMEXEotGuIgJ02m091Q00XhXING7yQfjzZlXE5JJWW4So87vRIPBoq/nSrvlo/3f2iNV1B4JXiXoM60iQghSxqWRMi4NAI/Tw+G39vtekmrLfqu4eFC2NMUFQcaMwUamXD8xh5lZ+PgyStcfC92geqAmr5I9z/nnWCx1ScFHh/noG2/x7l2v8t7dr/HBV98g7+39ficDG4hYzfDwVS2GhUN0Pq6ibbnou5e2sniUC+2sz8dkuLGZ6fK+gVHj6UtzB5Ylqi8kjkw+b81EwiTImJ7V4+cmq8nwa/MiwIQmSJ86CFM/VKhXnD8oqaq4IEgZn0bskHgaSut6XCM32UzEDo5n0OwhZF8yDFtMWEdV2X5FQuGnR4jLScAaZSNlfBq2mK5ZbKUu2fL7tV0EVkt1M3tf2EHFnnLm/b9LLtj1/AXD3fzx1maeWR3GwfLTU1VOkt4RxXTLNCeVjYLdpWY8uuEcnJ2os/e4iQdej6ShVXSIGCnBbIKHr2xhup8h4hcCYXHhZM3N7h+hHqDjrtQlw68a7bXNyBvGU+6l2rOUklHXj/N/p4oLAiHP9yItAdLQ0EBsbCzVLz1MTEQo05orzjdaqptZ88gKmsobOybR9vX6uJwEFvz00i4i4YOvvel/Ppc2hEkEZOXx3Z9GziXDmPSl6Z1M34de38v+l3Z52RAm3DmVkRfBxF1So1HVpBEfoZOTpOOP77PdBZ8csrL92Glhc9V4J7ERA3/Kc9tdlKwrovZINUITJI9NJXP2kB59qVprW/jw62/icYTWWheZFkVLZbPfzvPTvznXr0KHxasL2PbHDcDp5at2a8z0b871KypKcf7T0GIn6Y7Hqa+vJybGeykGJV4UFxRuh5vSdUUcW1WAva6ViKRIcpYOY9CsId1Wid734k7y3trv12QrNMMhWDP7l4o8IASkjEtj/qOXopk08t45wF4/lpbCkyJY/ucbqdxfTsnaIpyNjo6okIThSR1RIR6nh5aqJoRJIzIl6oL3mbmQKd9xnM2/XYu71WVE5WDkPLLFhDH3ocUktlVMPpPNv1tL6fqikNeWNIdbsERaaa1u9tl2wc8uI3V8ut99t1Q3U/RJPlVtBVWTx6SSe9nwTpWhFQMbJV6UeFH4SUt1Mx/d/xYep8fnxB6VEU3TicCsNIEy6/sL0cwaG3+5yu9t4nITjPDSM6JCpC7JnDmYKV+dRd47+yn8OB93qxE9I8wa4fHhZC8ZRu5lIwhPiAjV17ngOVVQQ33xKTSLidQJ6Z1qa4WC2iNVfPbjDw2xffb5qglMFhOX/f5qozJyG03lDXz49bdCOq52bLFhDLtiFAde3UOPqYo1wyl34WOX9cuYFAOHQMSL8nlRXNREJEUy7ydLWf/ESjxOd8cNoV0A5Fw6nGHLR1FffIqtT68P7WA0QcFHeUYyMT9SpbdTf+wU0DUqpGxrCZX7y3G3ujtZlqRbp6WqmYOv7uHQG/uY++BikselUbquiIo9J9A9kriceHIuGa6ETQ+cKqhh+582UldY2/GeMAmGLBrK5C/PCCiSzF90t87Ov23pXrgA6BLd5eHIuweZ8tVZHW+XbS7pOJ9DTdbcbIZePpKCj/JwNNi77lMYYeZjb5sU8rEoLmyUeFFc9KSMS+PKv9xI0cp8TmwtxePyEJeTwNBlI0kYngTAzr9uCUhQ9Apd0ni8HntdYNloe7wpSSNxntdt3TobnvwMc5jZaKsJkJKyzSUcfGUPU746i9zLLs6cGVJKWiqbcLW4iEiKxBptFJmsO1bLqv/3IZ6zlg6lR1K8qoCm8gYWPrYsqI7UJ7aVsu2ZDTjrvUdJSV1ybFVBp7pEzmYnfmT86zuaYOR1Y7HFhrHo55ez8Zef0VBa32lpyxJpZeZ35pPUzdKWQhEISrwoFBjm7lE3jGfUDeO7fOZosFNzqNKvfoQm0MwaQ5ePonBFHu7WwKJaTLYAwz2DcE+SHnla5HQIISOkeMefNxEWH+41nPVC5PjGYxx8bS/1xYZVS2iCzFmDGXf7ZPY8t93weepGNEpdUn2wktINx3os1NlU3kDZ1lLcdhfRGTFkzhziNcy3YvcJNjz5md/H2eNwI906os3HKzI1Cunx4aPV1/NIwLwfLyEiOQqA6IwYLvufa6nad5KKveXobp34oQlkzurZqVihCAQlXhQKH7T7ivhDVHoMM74zj4RhSQy7YhQffPWNgPYl9TaHYLdvh+DwpEhaa1t69i0IBgIOvLI7qOJFenQcjQ40iwlr5PlXEKijwOcZPs1SN6xR5TvKfNcIEnDglV0cfmMfjScaMFlNZM4aTM6lI8h/9wBlm0tACITWZo2I2Mzke2d1K3aklOx+dltA47dEWjqc090ONye2lvreSMLwa8ZQ8MFhdI9ufHc/fdJtcWEs+eVyolKjO70vhCBlQjopE/x3ylUo/EWJF4XCB7a4cDSL7wgja7SNZX+8tsNcH5kSRWx2fIdPij+0VLVFafh4Es6cOZiwhHAKPsrzu+9e0VYxu+lkI1Fp0b7be8Hd6iLv3QMUfJiHo94OQPzQREZeN5ZBc7P7XPtJenRqjlTjanYSkRxJ7JD4gPtoKm84XZn8rN9f6tLwi/I5EGg+2dRxDN2tOiVrCo3Ky+1fUUpkW9Syq8XF1qfXoZk1suZmd+qq/tgpGkrq/B6/0AQ5S4d3vN78mzWc9JIjpZ0R145l4l3TGPO5CZRuPEbhx0eoK6r1aY2xRtu49DdXqYgfRb+jxItC4QOzzUz24mEUfZrfs3+JEIy4ekynG3B98amAhEsn2msOnu3vqAmiM2OJSI4k//1Dveu7F7ianX3bvsXJ6odXUFd0qpOl6FRhDZt/u5ZRRbWMv3Nqr/qW0ijMd/DVPdhPnfYXistNYNLd00luSzXf3XY1eVXUFdaimTVSJqRT+Em+d+fWQIxcZ7Tt6M/L9nuf286g2UM6hbG3+BFy3I7QBJZIKyOuHgNAbX415duP+9xu1A3jGHfHFMAQI0OXjaT6UCX1x055r1mkCXIvVaHKinODEi8KhR+MvmkCZZtLcDY5utzYhCaITI1i6PJRnd4/+uHhPkV5mMMsnZashCbImJmFq8nZr8IFIDyxb1FH+1/eZQi5s2+GbS8Pv7mf1IkZvVpiOPjaHg6+sqfL+3VFtax59GPmP7yU1EkZnT47VVDD1v9ZR0NpfSeRaI22nbPq5C3VzVQdrOio6wNgjbL5vX1UegxzfrSoQ0wUrynwK6FiZGp0l7w/0enew1QB0CUxWXF+j0+hCCYXZl5xhSLIRCRFsuSXV3SbACxt6iAW/+KKLv4bVftP9ulG6G51cenT1zDr+wuZ/cNFzPjufMp3llG576TPbU02E5PumUHy2NRe7x8MwZQ2NZOwuHC/t/G4PIbIa3MSdbe6vFut2vaT/8HhTu+5mp2dwm2llF36aCpv6Fa4GBsY22z73w2dHFbri0+x6v99SGNZQ0e7dpyN57bm0dnJ3RJHJPkVrp5z6XCW/fHaTmLCXtdNqPJZCJPoNrot+5JhPitFm8MtZM4e4nNsCkUoUJYXhcJPotJjWPzzy2koraM230jLnjQmlciUqG7bB8OPNiIxgrgh8TQcr+eT773rX2ZfAYPmZDP0ipGU7/Lt79C+TXdJz4RZY3zbkgIYuUaKVxdw9IPDNByvQzObyJiexfCrR6O7dfLe3M+J7cdBSiwRFnIuHU7K+HSfaemNKJ0KpJSUri8i7+0DHTlUrFFWLFG2jjIOsUPiGX7VaIYsHkrRp76XeVprWlj54Ac46u2Yw8zobonu0s+ZhcUb1mgbUkqEML5TxZ5yojNjDMdsLxSvLsQcbmHc7ZMxt5WYCIszqo97s7xIj+xWmEYkRTLm5okcfLUHYQhGPhtVyVlxjlAZdhWKELHjz5t8Why8YYm0cO2/bkMIEXBfKePTaK5oormyyXdjYTgXN1c0tSURM26ckalRzPzO/A5rk8fpYf3PV1K5t7yT2OkkHs4SQUITWKOsOBp8WzRMEWayFw6l4MM87w7LbZ+lT88CKf3y6whGSHmoMVlNhMWH01zRhDBrmCwa7la3sfTTU2K6bkgYmczIa8cSnhTBZz/8wGtbzaxx9bM3d7s8JaUk//1DHHxtD66m0z5P4QkRTLhrKoPndx8KrlD0FpVhV6E4Dxh6xUgKPz7S6+3Tp2Z1OACXrC/yXwRpUHOkyv/6SxJG3TCexFHJVO4pR/foxOUmkjIurZMvxP6Xd1G5r7xjm47NzxxXNxE6zianX+LB0+I2hEs3/Zw9XoDy7aXEDo7zL3lgb4RLLwVPb/2cPE6PISAxkge628LlAy0CWptXxaanVjPyurFkzMgyLGE9jGfk9eN69KsRbU7oQy8fSeXechwNDiISI0gem4owKY8DxbklZGfgz3/+c+bMmUNERARxcXF+bSOl5JFHHiE9PZ3w8HCWLl1Kfn5+qIaoUISUuOwEJn5pOkDghRA1waSvzOh4GUiuGXTwODwB3UAzZw0mdnA8w68ew8jrxpE0MpmG43XUFdXQUt2Eo9FO4Yq8Xt3MO6wGwa4FKdtCy0Ow/GOLsZE0undZYM1h5h6PtzXGEArCJIxlufZ2IaiTmff2AQYvzGVQm1+K0IQhOtr2O+qGcYy9dZLPfkwWE+lTB5G9eCgpE9KVcFGcF4TM8uJ0OrnpppuYPXs2//jHP/za5qmnnuIPf/gDzz//PDk5OTz88MMsW7aMgwcPEhamloAUA48RV48hJjOWw2/vp6rN0dYSZcUcZsZe29qjwBh/+2RsZzwRRyRFns4B4w0BpjAzngAy+1pjbNhijOvL2eTg4Kt7KPz4iFGs8ox++7rs0rE0FURcLS5sMTYcjY6gLQsJTTBk8VBG3zSR9+5+Dd3l3V/nbGJzEtDMGpV7yju9H5ebwNSvzcbZ4KBsSwluu4uwhAjy3zsYsHXFH4QmKPz4CAt/ehkNt06idINRdTwiMZLBC3NV3SrFgCZk4uWxxx4D4LnnnvOrvZSSp59+mp/85Cdce+21ALzwwgukpqby9ttvc+utt4ZqqApFSEmbkknalEzcDje604Ml0oq71cWm36yhYvcJ4+lbGBYKIQRjbpnIyBvGdeojd9lI9r+00+cNOmV8+umlHT9pdzh2Njn47KEPaSyr77qfINxbR1w7lrw39weUu8QfPB49eP4sQqBZTAxbPhprpJUp985k+zMbA+qi+kBFt+/XHzvFqh9/yIJHLmX8nVMoXl1I+fbSkAgXaHOCbhtLzKBYxt4yKST7USjOBeeNz0tRUREnT55k6dKlHe/FxsYyc+ZMNm3a1KN4cTgcOBynnQEbGhpCPlaFojeYbWZoi86wRFpZ8OilnCqs4fjGYlwtTiJToxmyMLfb6I9hl4/k2Mp8miuaurXWaGaNWT9cxI5nNgZ2IxeCjGlG6v8D/97dvXAJEhFJkUEXLgDulgCW1HxgspqY/8jSDkGXs3Q41igru/+5zT/LlxekbtSLWv+Lz/C4PEb4dgiWizrt8zx3UlYoest5I15OnjRM6qmpnfNSpKamdnzWHU8++WSHlUehGGjE5yYSn5vos50l0srin1/B1j+up2LXiU6fxWbHM/M78zlVUNORdt9fNLNGzqXDcdtdFK3MD5lwiRkc51e9pl4RxDHrHp3ojM5RDpmzhpA8Po1373oN2dfvIGVn/6VQigtNkDgyKYQ7UCjOHQF5Xj344IMIIbz+HT582HdHQeShhx6ivr6+46+01I8iZArFACQsPpwFj1zK5f97HVO/NpvJ987kkqeu5NLfXU3skHhO7iozIm/8QYBm0YyMrAkRNJU3+szF0msEzPnRogGRE0S6dQo/6RohJj2S6Ew/ss72J74OtS4ZfuXofhmKQtHfBDSbPPDAA9x1111e2+Tm9i72Py3NSIldUVFBevrpFOEVFRVMmjSpx+1sNhs2m/8ptBWKgU50ZizRmbFd3ve4PH5F3mhWE0MvG8HQK0Z1WBkCioY6IxcMGNYb3YvfyfjPTyY6I5bwhAjMYWbcdv+diX2NIxSWi5I1hYy5aWLHa0e9nc8e+oCmtiR55xQBsx5YQHhiJAUf5XFiS0nX37Ptd8lZOkxlwFVcsAQkXpKTk0lOTg7JQHJyckhLS2PlypUdYqWhoYEtW7bwta99LST7VCguJGIHx1O+7bjPEOl5Dy3pUusnelAstrgwHHV+LDtJmPuTJUQkRqJZTNiibWz5/VpO7jKcjyUSdCMcePydUxl57VjAqNU07MrRHH5zn0/R4StXSlhCOPbarmntg4GzqXMRyj3PbTeipM4D/5HJX55B1twcAJJGpeBxuDny7kGOfnC4I81/dGYsI64ZQ87S4X2u1K1QnK+EzI5bUlJCbW0tJSUleDwedu/eDcCwYcOIijKc4UaNGsWTTz7J9ddfjxCC73znOzzxxBMMHz68I1Q6IyOD6667LlTDVCguGHIvHc6h1/f23EAYTrPdFT/UTBrDrxrD/hd3+txPe7RUdGYswiQo+CiPhrJ6oC2niyZIHJXEtG/MIWZQXKdtx942iaaTjRzfcKxbgWKLC8dR13MIuSXSyvg7p9BS1UzeW/tDkuI/POG0w7SjwU7JugASBAaB9t9FaKJTqYCxt09i2PLOy0Amm5nRN01g1A3jsNfb0Uwa1hibEi2KC56QiZdHHnmE559/vuP15MmTAVi1ahWLFi0CIC8vj/r6+o42P/zhD2lububee++lrq6OefPm8dFHH6kcLwqFH0QkRzHxi9PY89z2rh9qhk/ajG/N63GJaOR1YzmVX03ZlhKv+3G3utj8m7WExYeTODKZss1ntdcltUeqWff4Si751fJO0VOaSWPWAwuoXDqcghV51JfUYQk3kzk7myELc/n0++973ber2UlMZqwR+ROi+3PushEd/68vqetU1LEvtP/uXoWQgHF3TiEyOYoT20rxOD3EDIolZ+nwHmtoAQiTpvK2KC4qVG0jheICo2RtIQde2U1T+WkfjeSxqYz/wlQSR3hf9pUenZL1xzj85j4aSuu8L5X48DkRmmDwghymf2selXvKKfgoj7riU5htZjJnDSb3shGdbrjlO46z/omVXsdn9JlL2pQMtvxunde2vcEabeXq525B04xYhqoDJ1n9kxW977DtN7JGWclZOpyEEUls/s1ao2LzWb+d0AQRSZFc+vursURYu+1OobiQUbWNFIqLmMELcsman0N98SlcTU4ikiOJTI32a1th0hiyMJchCw3H+3WPf0LF7vLurQW+ygnpkpJ1x3A73JRtKum0TFRfWkfe2/uZ9/8uIWW8sYzlTw4YqUuaK5vInDUES9QWXM1Ov31RjPT4wgjZ7mYbk83E4l8s7xAuYISym2xmPA4vTsYaRKXF0Frd3JGV2BYbxshrxzJs+Sg0i6mTtWvuQ2a2/mE9zkaHkWpfSqQuictNYM6PFivholD4gRIvCsUFiBCCuOyEPvXhbHJwcucJ3w29ID06ZZuMZaVOAkiXHVWqr/jTDYQnRPRYILATmsAWY8NkMTHjm3PZ8MtVXS1Aba+zlw6noaSOpvIGzOEWBs/LZujlI3E0OMh7az/HNxUjdYnJaiJn6XBGXj+OiKTITrszh1vIvWwE+f891HMklw6zv7+QuBz/fu/0aYO46h83cWJLCXXHTqGZNdKmZJIwPEn5qigUfqLEi0Kh6BZno8N3o74gwePSKfz4CGNvnUT6lExMNpP3fDO6ZPB8I9omY8ZgFv70Mva9uJPa/OqOJjFZcYy/YzIZMwZ320VEchSzvr8Q3eXB7XBjCbd4LTY47o7JnDpaTfWhyk7vt1uSJt8702/h0o7JYiJrXg5Z83IC2k6hUBgo8aJQKLrFFhMGQoQ2x7wuOb6pmLG3TsIcbmHk9eM4+MqebpsKTRCVEdNJlKRMSOeSp66k8UQD9toWbLFhRA+K9cuCoVlMWC0mn+3MNjMLHruMok/yOfrBIRrLGhBmjfSpgxhxzRiSx6T67EOhUAQXJV4UCkW3WCKtZM7I4sS20t6FCvuZRO5Mf5IxN03E2ejk6H8PdQkVjsmKZf4jl6KZu1pJojNiuqT1DyYmi4lhy0cxbPko47doS9SnUCjODUq8KBSKHhlzy0TKd5Yh0bv4fAhNYLKacNvdCJM4XR25TbQkjUrpstRyNkITxA6J7/R68pdnMOyKkRStPErzyUbMERYGzR5C2uTMwDIBh4jzYQwKxcWOEi8KhaJH4nISWPDTS9n86zXY61oRprZcJR5JVHo0c360GHeri/z/HuLEtuPobg+xg+MZtnwUgxfmsvaxT6g+UNGj5UbqkqHLRnZ5PzozlglfmBrS76ZQKAYuSrwoFAqvJI9J5cq/f47ybaXU5lcjNEHy+HRSxqd1LJ3M7CF/zOR7ZvDZgx/gcXq6ChgBg2YPIXVyRrfbKhQKRU8o8aJQKHyimTQyZw0hc1Zghf5ih8Sz5JfL2fW3LVQdqOh43xxuYfhVoxlzpkQ2hgAACRhJREFUy0TlO6JQKAJGiReFQhFSYofEs+iJy2k80UBjWT0mq4nEUSmYbWr6USgUvUPNHgqFol8IdUSQQqG4eOg5M5NCoVAoFArFeYgSLwqFQqFQKAYUSrwoFAqFQqEYUCjxolAoFAqFYkChxItCoVAoFIoBhRIvCoVCoVAoBhRKvCgUCoVCoRhQKPGiUCgUCoViQHHBJamT0qif0tjiOMcjUSgUCoVC4S/t9+32+7g3hPSn1QDi+PHjZGVlnethKBQKhUKh6AWlpaUMGjTIa5sLTrzous6JEyeIjo4+5wXfGhoayMrKorS0lJgYlRb9fEcdr4GFOl4DC3W8Bhbn4nhJKWlsbCQjIwNN8+7VcsEtG2ma5lOx9TcxMTHqYh1AqOM1sFDHa2ChjtfAor+PV2xsrF/tlMOuQqFQKBSKAYUSLwqFQqFQKAYUSryEEJvNxqOPPorNZjvXQ1H4gTpeAwt1vAYW6ngNLM7343XBOewqFAqFQqG4sFGWF4VCoVAoFAMKJV4UCoVCoVAMKJR4USgUCoVCMaBQ4kWhUCgUCsWAQomXIPPzn/+cOXPmEBERQVxcnF/bSCl55JFHSE9PJzw8nKVLl5Kfnx/agSoAqK2t5Y477iAmJoa4uDjuuecempqavG6zaNEihBCd/u67775+GvHFxTPPPEN2djZhYWHMnDmTrVu3em3/n//8h1GjRhEWFsb48eP54IMP+mmkCgjseD333HNdrqOwsLB+HO3Fzdq1a7n66qvJyMhACMHbb7/tc5vVq1czZcoUbDYbw4YN47nnngv5OHtCiZcg43Q6uemmm/ja177m9zZPPfUUf/jDH/i///s/tmzZQmRkJMuWLcNut4dwpAqAO+64gwMHDvDJJ5/w/vvvs3btWu69916f233lK1+hvLy84++pp57qh9FeXLz66qt873vf49FHH2Xnzp1MnDiRZcuWUVlZ2W37jRs3ctttt3HPPfewa9currvuOq677jr279/fzyO/OAn0eIGRvfXM66i4uLgfR3xx09zczMSJE3nmmWf8al9UVMSVV17J4sWL2b17N9/5znf48pe/zIoVK0I80h6QipDw7LPPytjYWJ/tdF2XaWlp8te//nXHe3V1ddJms8l///vfIRyh4uDBgxKQ27Zt63jvww8/lEIIWVZW1uN2CxculN/+9rf7YYQXNzNmzJDf+MY3Ol57PB6ZkZEhn3zyyW7b33zzzfLKK6/s9N7MmTPlV7/61ZCOU2EQ6PHyd45UhB5AvvXWW17b/PCHP5Rjx47t9N4tt9wily1bFsKR9YyyvJxjioqKOHnyJEuXLu14LzY2lpkzZ7Jp06ZzOLILn02bNhEXF8e0adM63lu6dCmaprFlyxav27700kskJSUxbtw4HnroIVpaWkI93IsKp9PJjh07Ol0XmqaxdOnSHq+LTZs2dWoPsGzZMnUd9QO9OV4ATU1NDBkyhKysLK699loOHDjQH8NV9ILz7fq64AozDjROnjwJQGpqaqf3U1NTOz5ThIaTJ0+SkpLS6T2z2UxCQoLX3/72229nyJAhZGRksHfvXn70ox+Rl5fHm2++GeohXzRUV1fj8Xi6vS4OHz7c7TYnT55U19E5ojfHa+TIkfzzn/9kwoQJ1NfX85vf/IY5c+Zw4MCB8664rqLn66uhoYHW1lbCw8P7dTzK8uIHDz74YBfHsrP/erpAFf1PqI/Xvffey7Jlyxg/fjx33HEHL7zwAm+99RYFBQVB/BYKxYXN7Nmz+cIXvsCkSZNYuHAhb775JsnJyfzlL38510NTDACU5cUPHnjgAe666y6vbXJzc3vVd1paGgAVFRWkp6d3vF9RUcGkSZN61efFjr/HKy0trYszodvtpra2tuO4+MPMmTMBOHr0KEOHDg14vIquJCUlYTKZqKio6PR+RUVFj8cmLS0toPaK4NGb43U2FouFyZMnc/To0VAMUdFHerq+YmJi+t3qAkq8+EVycjLJyckh6TsnJ4e0tDRWrlzZIVYaGhrYsmVLQBFLitP4e7xmz55NXV0dO3bsYOrUqQB89tln6LreIUj8Yffu3QCdxKeib1itVqZOncrKlSu57rrrANB1nZUrV3L//fd3u83s2bNZuXIl3/nOdzre++STT5g9e3Y/jPjipjfH62w8Hg/79u1j+fLlIRyporfMnj27S+qBc3p9nRM34QuY4uJiuWvXLvnYY4/JqKgouWvXLrlr1y7Z2NjY0WbkyJHyzTff7Hj9y1/+UsbFxcl33nlH7t27V1577bUyJydHtra2nouvcFFx+eWXy8mTJ8stW7bI9evXy+HDh8vbbrut4/Pjx4/LkSNHyi1btkgppTx69Kj82c9+Jrdv3y6LiorkO++8I3Nzc+WCBQvO1Ve4YHnllVekzWaTzz33nDx48KC89957ZVxcnDx58qSUUso777xTPvjggx3tN2zYIM1ms/zNb34jDx06JB999FFpsVjkvn37ztVXuKgI9Hg99thjcsWKFbKgoEDu2LFD3nrrrTIsLEweOHDgXH2Fi4rGxsaO+xMgf/e738ldu3bJ4uJiKaWUDz74oLzzzjs72hcWFsqIiAj5gx/8QB46dEg+88wz0mQyyY8++uicjF+JlyDzxS9+UQJd/latWtXRBpDPPvtsx2td1+XDDz8sU1NTpc1mk5dcconMy8vr/8FfhNTU1MjbbrtNRkVFyZiYGHn33Xd3EppFRUWdjl9JSYlcsGCBTEhIkDabTQ4bNkz+4Ac/kPX19efoG1zY/PGPf5SDBw+WVqtVzpgxQ27evLnjs4ULF8ovfvGLndq/9tprcsSIEdJqtcqxY8fK//73v/084oubQI7Xd77znY62qampcvny5XLnzp3nYNQXJ6tWrer2XtV+jL74xS/KhQsXdtlm0qRJ0mq1ytzc3E73sf5GSCnlOTH5KBQKhUKhUPQCFW2kUCgUCoViQKHEi0KhUCgUigGFEi8KhUKhUCgGFEq8KBQKhUKhGFAo8aJQKBQKhWJAocSLQqFQKBSKAYUSLwqFQqFQKAYUSrwoFAqFQqEYUCjxolAoFAqFYkChxItCoVAoFIoBhRIvCoVCoVAoBhRKvCgUCoVCoRhQ/H/lqh/SH20gfQAAAABJRU5ErkJggg==\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "q-hj-lAodLYU"
+      },
+      "source": [
+        "What gives?\n",
+        "\n",
+        "It seems like our model is the same as the one in the [TensorFlow Playground](https://playground.tensorflow.org/#activation=relu&batchSize=10&dataset=circle&regDataset=reg-plane&learningRate=0.03&regularizationRate=0&noise=0&networkShape=4,4&seed=0.93799&showTestData=false&discretize=false&percTrainData=50&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification&initZero=false&hideText=false&regularization_hide=true&regularizationRate_hide=true&batchSize_hide=true) but model it's still drawing straight lines...\n",
+        "\n",
+        "Ideally, the yellow lines go on the inside of the red circle and the blue circle.\n",
+        "\n",
+        "Okay, okay, let's model this circle once and for all.\n",
+        "\n",
+        "One more model (I promise... actually, I'm going to have to break that promise... we'll be building plenty more models).\n",
+        "\n",
+        "This time we'll change the activation function on our output layer too. Remember the architecture of a classification model? For binary classification, the output layer activation is usually the [Sigmoid activation function](https://www.tensorflow.org/api_docs/python/tf/math/sigmoid)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "zgyJ67E3corL"
+      },
+      "source": [
+        "# Set random seed\n",
+        "tf.random.set_seed(42)\n",
+        "\n",
+        "# Create a model\n",
+        "model_7 = tf.keras.Sequential([\n",
+        "  tf.keras.layers.Dense(4, activation=tf.keras.activations.relu), # hidden layer 1, ReLU activation\n",
+        "  tf.keras.layers.Dense(4, activation=tf.keras.activations.relu), # hidden layer 2, ReLU activation\n",
+        "  tf.keras.layers.Dense(1, activation=tf.keras.activations.sigmoid) # ouput layer, sigmoid activation\n",
+        "])\n",
+        "\n",
+        "# Compile the model\n",
+        "model_7.compile(loss=tf.keras.losses.binary_crossentropy,\n",
+        "                optimizer=tf.keras.optimizers.Adam(),\n",
+        "                metrics=['accuracy'])\n",
+        "\n",
+        "# Fit the model\n",
+        "history = model_7.fit(X, y, epochs=100, verbose=0)"
+      ],
+      "execution_count": 29,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "e5wpRgBVtSRK",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "3f1af78a-1623-4680-96be-c153aa1699bf"
+      },
+      "source": [
+        "# Evaluate our model\n",
+        "model_7.evaluate(X, y)"
+      ],
+      "execution_count": 30,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "32/32 [==============================] - 0s 2ms/step - loss: 0.2602 - accuracy: 0.9650\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[0.2601848840713501, 0.9649999737739563]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 30
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XsB0aZ42eIS2"
+      },
+      "source": [
+        "Woah! It looks like our model is getting some incredible results, let's check them out."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "zSoLyVMmczP4",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 464
+        },
+        "outputId": "d5b432a3-736e-4d75-d3f0-d27f1a46e6d2"
+      },
+      "source": [
+        "# View the predictions of the model with relu and sigmoid activations\n",
+        "plot_decision_boundary(model_7, X, y)"
+      ],
+      "execution_count": 31,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "313/313 [==============================] - 1s 2ms/step\n",
+            "doing binary classifcation...\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zKR2JO4PeTie"
+      },
+      "source": [
+        "Nice! It looks like our model is almost perfectly (apart from a few examples) separating the two circles.\n",
+        "\n",
+        "> 🤔 **Question:** What's wrong with the predictions we've made? Are we really evaluating our model correctly here? Hint: what data did the model learn on and what did we predict on?\n",
+        "\n",
+        "Before we answer that, it's important to recognize what we've just covered.\n",
+        "\n",
+        "> 🔑 **Note:** The combination of **linear (straight lines) and non-linear (non-straight lines) functions** is one of the key fundamentals of neural networks.\n",
+        "\n",
+        "Think of it like this:\n",
+        "\n",
+        "If I gave you an unlimited amount of straight lines and non-straight lines, what kind of patterns could you draw?\n",
+        "\n",
+        "That's essentially what neural networks do to find patterns in data.\n",
+        "\n",
+        "Now you might be thinking, \"but I haven't seen a linear function or a non-linear function before...\"\n",
+        "\n",
+        "Oh but you have.\n",
+        "\n",
+        "We've been using them the whole time.\n",
+        "\n",
+        "They're what power the layers in the models we just built.\n",
+        "\n",
+        "To get some intuition about the activation functions we've just used, let's create them and then try them on some toy data."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "qm-b9aoemQ8A",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "2f360e24-e2a6-4842-ecef-3594980edacb"
+      },
+      "source": [
+        "# Create a toy tensor (similar to the data we pass into our model)\n",
+        "A = tf.cast(tf.range(-10, 10), tf.float32)\n",
+        "A"
+      ],
+      "execution_count": 32,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<tf.Tensor: shape=(20,), dtype=float32, numpy=\n",
+              "array([-10.,  -9.,  -8.,  -7.,  -6.,  -5.,  -4.,  -3.,  -2.,  -1.,   0.,\n",
+              "         1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.],\n",
+              "      dtype=float32)>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 32
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Ti67IeWI9vnP"
+      },
+      "source": [
+        "How does this look?\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "mYWO_14e-Pf8",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 430
+        },
+        "outputId": "2b2b1435-925b-4c8b-a850-0092e51aaa84"
+      },
+      "source": [
+        "# Visualize our toy tensor\n",
+        "plt.plot(A);"
+      ],
+      "execution_count": 33,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5f9qOgTb-O7L"
+      },
+      "source": [
+        "A straight (linear) line!\n",
+        "\n",
+        "Nice, now let's recreate the [sigmoid function](https://en.wikipedia.org/wiki/Sigmoid_function) and see what it does to our data. You can also find a pre-built sigmoid function at [`tf.keras.activations.sigmoid`](https://www.tensorflow.org/api_docs/python/tf/keras/activations/sigmoid)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "corG975yiwAP",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "5af476bd-1612-4d16-96db-f093a756b171"
+      },
+      "source": [
+        "# Sigmoid - https://www.tensorflow.org/api_docs/python/tf/keras/activations/sigmoid\n",
+        "def sigmoid(x):\n",
+        "  return 1 / (1 + tf.exp(-x))\n",
+        "\n",
+        "# Use the sigmoid function on our tensor\n",
+        "sigmoid(A)"
+      ],
+      "execution_count": 34,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<tf.Tensor: shape=(20,), dtype=float32, numpy=\n",
+              "array([4.5397868e-05, 1.2339458e-04, 3.3535014e-04, 9.1105117e-04,\n",
+              "       2.4726230e-03, 6.6928510e-03, 1.7986210e-02, 4.7425874e-02,\n",
+              "       1.1920292e-01, 2.6894143e-01, 5.0000000e-01, 7.3105854e-01,\n",
+              "       8.8079703e-01, 9.5257413e-01, 9.8201376e-01, 9.9330717e-01,\n",
+              "       9.9752742e-01, 9.9908900e-01, 9.9966466e-01, 9.9987662e-01],\n",
+              "      dtype=float32)>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 34
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3EpZb0F6-cn7"
+      },
+      "source": [
+        "And how does it look?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "vz4Pr2Mk-eko",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 430
+        },
+        "outputId": "eba9913a-8fd5-4b81-d350-a842069c4670"
+      },
+      "source": [
+        "# Plot sigmoid modified tensor\n",
+        "plt.plot(sigmoid(A));"
+      ],
+      "execution_count": 35,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "EGT2qtUV-k5f"
+      },
+      "source": [
+        "A non-straight (non-linear) line!\n",
+        "\n",
+        "Okay, how about the [ReLU function](https://machinelearningmastery.com/rectified-linear-activation-function-for-deep-learning-neural-networks/#:~:text=The%20rectified%20linear%20activation%20function,otherwise%2C%20it%20will%20output%20zero.) (ReLU turns all negatives to 0 and positive numbers stay the same)?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "_PUMn--Hiwyz",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "d8469734-fd52-4c69-9ffd-5f69af64c6d1"
+      },
+      "source": [
+        "# ReLU - https://www.tensorflow.org/api_docs/python/tf/keras/activations/relu\n",
+        "def relu(x):\n",
+        "  return tf.maximum(0, x)\n",
+        "\n",
+        "# Pass toy tensor through ReLU function\n",
+        "relu(A)"
+      ],
+      "execution_count": 36,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<tf.Tensor: shape=(20,), dtype=float32, numpy=\n",
+              "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 2., 3., 4., 5., 6.,\n",
+              "       7., 8., 9.], dtype=float32)>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 36
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xAkmlNeZ-90e"
+      },
+      "source": [
+        "How does the ReLU-modified tensor look?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "iXWCO2R4mlk7",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 430
+        },
+        "outputId": "92a05241-d1d4-4657-8386-3241fb54d3a7"
+      },
+      "source": [
+        "# Plot ReLU-modified tensor\n",
+        "plt.plot(relu(A));"
+      ],
+      "execution_count": 37,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-NLdoxJr_Grk"
+      },
+      "source": [
+        "Another non-straight line!\n",
+        "\n",
+        "Well, how about TensorFlow's [linear activation function](https://www.tensorflow.org/api_docs/python/tf/keras/activations/linear)?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Cg_kBQLVoXRB",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "1ce8c3ab-f0f1-4fed-a8ff-b2096e3ee4ad"
+      },
+      "source": [
+        "# Linear - https://www.tensorflow.org/api_docs/python/tf/keras/activations/linear (returns input non-modified...)\n",
+        "tf.keras.activations.linear(A)"
+      ],
+      "execution_count": 38,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<tf.Tensor: shape=(20,), dtype=float32, numpy=\n",
+              "array([-10.,  -9.,  -8.,  -7.,  -6.,  -5.,  -4.,  -3.,  -2.,  -1.,   0.,\n",
+              "         1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.],\n",
+              "      dtype=float32)>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 38
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "d0NnpC_N_uWT"
+      },
+      "source": [
+        "Hmm, it looks like our inputs are unmodified..."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "iwlgKpco_7V2",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "254b913a-8f97-4bf0-b043-0d6664b558b0"
+      },
+      "source": [
+        "# Does the linear activation change anything?\n",
+        "A == tf.keras.activations.linear(A)"
+      ],
+      "execution_count": 39,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<tf.Tensor: shape=(20,), dtype=bool, numpy=\n",
+              "array([ True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
+              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
+              "        True,  True])>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 39
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KcCT_ipvADpR"
+      },
+      "source": [
+        "Okay, so it makes sense now the model doesn't really learn anything when using only linear activation functions, because the linear activation function doesn't change our input data in anyway.\n",
+        "\n",
+        "Where as, with our non-linear functions, our data gets manipulated. A neural network uses these kind of transformations at a large scale to figure draw patterns between its inputs and outputs.\n",
+        "\n",
+        "Now rather than dive into the guts of neural networks, we're going to keep coding applying what we've learned to different problems but if you want a more in-depth look at what's going on behind the scenes, check out the Extra Curriculum section below.\n",
+        "\n",
+        "> 📖  **Resource:** For more on activation functions, check out the [machine learning cheatsheet page](https://ml-cheatsheet.readthedocs.io/en/latest/activation_functions.html#) on them."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BPCpA90uQBjk"
+      },
+      "source": [
+        "## Evaluating and improving our classification model\n",
+        "\n",
+        "If you answered the question above, you might've picked up what we've been doing wrong.\n",
+        "\n",
+        "We've been evaluating our model on the same data it was trained on.\n",
+        "\n",
+        "A better approach would be to split our data into training, validation (optional) and test sets.\n",
+        "\n",
+        "Once we've done that, we'll train our model on the training set (let it find patterns in the data) and then see how well it learned the patterns by using it to predict values on the test set.\n",
+        "\n",
+        "Let's do it."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "NjOviFscgl4S",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "d59837ab-8322-40ef-a13c-971af874953e"
+      },
+      "source": [
+        "# How many examples are in the whole dataset?\n",
+        "len(X)"
+      ],
+      "execution_count": 40,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "1000"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 40
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "qNvKa8QrkSWR",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "1c00b1f7-8f08-49ac-aa7c-60faa1ba250d"
+      },
+      "source": [
+        "# Split data into train and test sets\n",
+        "X_train, y_train = X[:800], y[:800] # 80% of the data for the training set\n",
+        "X_test, y_test = X[800:], y[800:] # 20% of the data for the test set\n",
+        "\n",
+        "# Check the shapes of the data\n",
+        "X_train.shape, X_test.shape # 800 examples in the training set, 200 examples in the test set"
+      ],
+      "execution_count": 41,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "((800, 2), (200, 2))"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 41
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "YtNge18ahgqa"
+      },
+      "source": [
+        "Great, now we've got training and test sets, let's model the training data and evaluate what our model has learned on the test set."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "0RUOBhA1g8Kx",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "da11aa05-e7d4-425e-9f1a-e4aa9e0e08c5"
+      },
+      "source": [
+        "# Set random seed\n",
+        "tf.random.set_seed(42)\n",
+        "\n",
+        "# Create the model (same as model_7)\n",
+        "model_8 = tf.keras.Sequential([\n",
+        "  tf.keras.layers.Dense(4, activation=\"relu\"), # hidden layer 1, using \"relu\" for activation (same as tf.keras.activations.relu)\n",
+        "  tf.keras.layers.Dense(4, activation=\"relu\"),\n",
+        "  tf.keras.layers.Dense(1, activation=\"sigmoid\") # output layer, using 'sigmoid' for the output\n",
+        "])\n",
+        "\n",
+        "# Compile the model\n",
+        "model_8.compile(loss=tf.keras.losses.binary_crossentropy,\n",
+        "                optimizer=tf.keras.optimizers.Adam(learning_rate=0.01), # increase learning rate from 0.001 to 0.01 for faster learning\n",
+        "                metrics=['accuracy'])\n",
+        "\n",
+        "# Fit the model\n",
+        "history = model_8.fit(X_train, y_train, epochs=25)"
+      ],
+      "execution_count": 42,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/25\n",
+            "25/25 [==============================] - 1s 3ms/step - loss: 0.6995 - accuracy: 0.4538\n",
+            "Epoch 2/25\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.6905 - accuracy: 0.4775\n",
+            "Epoch 3/25\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.6869 - accuracy: 0.5238\n",
+            "Epoch 4/25\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.6834 - accuracy: 0.5487\n",
+            "Epoch 5/25\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.6803 - accuracy: 0.5387\n",
+            "Epoch 6/25\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.6761 - accuracy: 0.5462\n",
+            "Epoch 7/25\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.6689 - accuracy: 0.5600\n",
+            "Epoch 8/25\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.6653 - accuracy: 0.5537\n",
+            "Epoch 9/25\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.6537 - accuracy: 0.6112\n",
+            "Epoch 10/25\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.6426 - accuracy: 0.5850\n",
+            "Epoch 11/25\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.6349 - accuracy: 0.6112\n",
+            "Epoch 12/25\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.6238 - accuracy: 0.6425\n",
+            "Epoch 13/25\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.6143 - accuracy: 0.6363\n",
+            "Epoch 14/25\n",
+            "25/25 [==============================] - 0s 4ms/step - loss: 0.6030 - accuracy: 0.6513\n",
+            "Epoch 15/25\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.5989 - accuracy: 0.6562\n",
+            "Epoch 16/25\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.5870 - accuracy: 0.6800\n",
+            "Epoch 17/25\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.5775 - accuracy: 0.6862\n",
+            "Epoch 18/25\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.5716 - accuracy: 0.6787\n",
+            "Epoch 19/25\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.5687 - accuracy: 0.6850\n",
+            "Epoch 20/25\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.5730 - accuracy: 0.6888\n",
+            "Epoch 21/25\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.5639 - accuracy: 0.6900\n",
+            "Epoch 22/25\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.5539 - accuracy: 0.7000\n",
+            "Epoch 23/25\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.5498 - accuracy: 0.6938\n",
+            "Epoch 24/25\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.5456 - accuracy: 0.7100\n",
+            "Epoch 25/25\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.5420 - accuracy: 0.7075\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "aAnKqoDxloAA",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "241b49b2-401e-4a34-862b-ebd9fb16632e"
+      },
+      "source": [
+        "# Evaluate our model on the test set\n",
+        "loss, accuracy = model_8.evaluate(X_test, y_test)\n",
+        "print(f\"Model loss on the test set: {loss}\")\n",
+        "print(f\"Model accuracy on the test set: {100*accuracy:.2f}%\")"
+      ],
+      "execution_count": 43,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "7/7 [==============================] - 0s 3ms/step - loss: 0.5777 - accuracy: 0.6700\n",
+            "Model loss on the test set: 0.5776922702789307\n",
+            "Model accuracy on the test set: 67.00%\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "c1lWpsNwjD5C"
+      },
+      "source": [
+        "100% accuracy? Nice!\n",
+        "\n",
+        "Now, when we started to create `model_8` we said it was going to be the same as `model_7` but you might've found that to be a little lie.\n",
+        "\n",
+        "That's because we changed a few things:\n",
+        "* **The `activation` parameter** - We used strings (`\"relu\"` & `\"sigmoid\"`) instead of using library paths (`tf.keras.activations.relu`), in TensorFlow, they both offer the same functionality.\n",
+        "* **The `learning_rate` (also `lr`) parameter** - We increased the **learning rate** parameter in the [Adam optimizer](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/Adam) to `0.01` instead of `0.001` (an increase of 10x).\n",
+        "  * You can think of the learning rate as how quickly a model learns. The higher the learning rate, the faster the model's capacity to learn, however, there's such a thing as a *too high* learning rate, where a model tries to learn too fast and doesn't learn anything. We'll see a trick to find the ideal learning rate soon.\n",
+        "* **The number of epochs** - We lowered the number of epochs (using the `epochs` parameter) from 100 to 25 but our model still got an incredible result on both the training and test sets.\n",
+        "  * One of the reasons our model performed well in even less epochs (remember a single epoch is the model trying to learn patterns in the data by looking at it once, so 25 epochs means the model gets 25 chances) than before is because we increased the learning rate.\n",
+        "\n",
+        "We know our model is performing well based on the evaluation metrics but let's see how it performs visually.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "LRvAIBfAmtdc",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 613
+        },
+        "outputId": "cb3aeeaa-d185-4c63-8c1c-a2e01c5d2b5e"
+      },
+      "source": [
+        "# Plot the decision boundaries for the training and test sets\n",
+        "plt.figure(figsize=(12, 6))\n",
+        "plt.subplot(1, 2, 1)\n",
+        "plt.title(\"Train\")\n",
+        "plot_decision_boundary(model_8, X=X_train, y=y_train)\n",
+        "plt.subplot(1, 2, 2)\n",
+        "plt.title(\"Test\")\n",
+        "plot_decision_boundary(model_8, X=X_test, y=y_test)\n",
+        "plt.show()"
+      ],
+      "execution_count": 44,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "313/313 [==============================] - 1s 2ms/step\n",
+            "doing binary classifcation...\n",
+            "313/313 [==============================] - 1s 2ms/step\n",
+            "doing binary classifcation...\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x600 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "42KDFaHonmGN"
+      },
+      "source": [
+        "Check that out! How cool. With a few tweaks, our model is now predicting the blue and red circles almost perfectly."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "EqaFRxFaiklC"
+      },
+      "source": [
+        "### Plot the loss curves\n",
+        "\n",
+        "Looking at the plots above, we can see the outputs of our model are very good.\n",
+        "\n",
+        "But how did our model go whilst it was learning?\n",
+        "\n",
+        "As in, how did the performance change everytime the model had a chance to look at the data (once every epoch)?\n",
+        "\n",
+        "To figure this out, we can check the **loss curves** (also referred to as the **learning curves**).\n",
+        "\n",
+        "You might've seen we've been using the variable `history` when calling the `fit()` function on a model ([`fit()` returns a `History` object](https://www.tensorflow.org/api_docs/python/tf/keras/Model#fit)).\n",
+        "\n",
+        "This is where we'll get the information for how our model is performing as it learns.\n",
+        "\n",
+        "Let's see how we might use it."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "sBHuMm9mpoOz",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 824
+        },
+        "outputId": "a0d67bda-75b9-45cf-82d1-a60e39937c70"
+      },
+      "source": [
+        "# You can access the information in the history variable using the .history attribute\n",
+        "pd.DataFrame(history.history)"
+      ],
+      "execution_count": 45,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "        loss  accuracy\n",
+              "0   0.699526   0.45375\n",
+              "1   0.690490   0.47750\n",
+              "2   0.686916   0.52375\n",
+              "3   0.683355   0.54875\n",
+              "4   0.680280   0.53875\n",
+              "5   0.676074   0.54625\n",
+              "6   0.668896   0.56000\n",
+              "7   0.665313   0.55375\n",
+              "8   0.653653   0.61125\n",
+              "9   0.642607   0.58500\n",
+              "10  0.634861   0.61125\n",
+              "11  0.623793   0.64250\n",
+              "12  0.614281   0.63625\n",
+              "13  0.603010   0.65125\n",
+              "14  0.598941   0.65625\n",
+              "15  0.586963   0.68000\n",
+              "16  0.577505   0.68625\n",
+              "17  0.571609   0.67875\n",
+              "18  0.568716   0.68500\n",
+              "19  0.572996   0.68875\n",
+              "20  0.563870   0.69000\n",
+              "21  0.553868   0.70000\n",
+              "22  0.549773   0.69375\n",
+              "23  0.545600   0.71000\n",
+              "24  0.542001   0.70750"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-5e74a0e6-a037-4476-a593-078349b055f1\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>loss</th>\n",
+              "      <th>accuracy</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0.699526</td>\n",
+              "      <td>0.45375</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>0.690490</td>\n",
+              "      <td>0.47750</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>0.686916</td>\n",
+              "      <td>0.52375</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>0.683355</td>\n",
+              "      <td>0.54875</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>0.680280</td>\n",
+              "      <td>0.53875</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>0.676074</td>\n",
+              "      <td>0.54625</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>0.668896</td>\n",
+              "      <td>0.56000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>0.665313</td>\n",
+              "      <td>0.55375</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>0.653653</td>\n",
+              "      <td>0.61125</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>0.642607</td>\n",
+              "      <td>0.58500</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10</th>\n",
+              "      <td>0.634861</td>\n",
+              "      <td>0.61125</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11</th>\n",
+              "      <td>0.623793</td>\n",
+              "      <td>0.64250</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>12</th>\n",
+              "      <td>0.614281</td>\n",
+              "      <td>0.63625</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>13</th>\n",
+              "      <td>0.603010</td>\n",
+              "      <td>0.65125</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>14</th>\n",
+              "      <td>0.598941</td>\n",
+              "      <td>0.65625</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>15</th>\n",
+              "      <td>0.586963</td>\n",
+              "      <td>0.68000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>16</th>\n",
+              "      <td>0.577505</td>\n",
+              "      <td>0.68625</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>17</th>\n",
+              "      <td>0.571609</td>\n",
+              "      <td>0.67875</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>18</th>\n",
+              "      <td>0.568716</td>\n",
+              "      <td>0.68500</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>19</th>\n",
+              "      <td>0.572996</td>\n",
+              "      <td>0.68875</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>20</th>\n",
+              "      <td>0.563870</td>\n",
+              "      <td>0.69000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>21</th>\n",
+              "      <td>0.553868</td>\n",
+              "      <td>0.70000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>22</th>\n",
+              "      <td>0.549773</td>\n",
+              "      <td>0.69375</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>23</th>\n",
+              "      <td>0.545600</td>\n",
+              "      <td>0.71000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>24</th>\n",
+              "      <td>0.542001</td>\n",
+              "      <td>0.70750</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5e74a0e6-a037-4476-a593-078349b055f1')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-5e74a0e6-a037-4476-a593-078349b055f1 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-5e74a0e6-a037-4476-a593-078349b055f1');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-2da3ab8d-0cba-42e4-b6eb-072a5a587598\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2da3ab8d-0cba-42e4-b6eb-072a5a587598')\"\n",
+              "            title=\"Suggest charts.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-2da3ab8d-0cba-42e4-b6eb-072a5a587598 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 45
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mblFGnDap48J"
+      },
+      "source": [
+        "Inspecting the outputs, we can see the loss values going down and the accuracy going up.\n",
+        "\n",
+        "How's it look (visualize, visualize, visualize)?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "mqlwnZGJpYNF",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 469
+        },
+        "outputId": "0a32ef5d-1965-4a55-ecc2-a5eadc513b08"
+      },
+      "source": [
+        "# Plot the loss curves\n",
+        "pd.DataFrame(history.history).plot()\n",
+        "plt.title(\"Model_8 training curves\")"
+      ],
+      "execution_count": 46,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Text(0.5, 1.0, 'Model_8 training curves')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 46
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mofXV4znqmNh"
+      },
+      "source": [
+        "Beautiful. This is the ideal plot we'd be looking for when dealing with a classification problem, loss going down, accuracy going up.\n",
+        "\n",
+        "> 🔑 **Note:** For many problems, the loss function going down means the model is improving (the predictions it's making are getting closer to the ground truth labels)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QyZA0CYLir5-"
+      },
+      "source": [
+        "### Finding the best learning rate\n",
+        "\n",
+        "Aside from the architecture itself (the layers, number of neurons, activations, etc), the most important hyperparameter you can tune for your neural network models is the **learning rate**.\n",
+        "\n",
+        "In `model_8` you saw we lowered the Adam optimizer's learning rate from the default of `0.001` (default) to `0.01`.\n",
+        "\n",
+        "And you might be wondering why we did this.\n",
+        "\n",
+        "Put it this way, it was a lucky guess.\n",
+        "\n",
+        "I just decided to try a lower learning rate and see how the model went.\n",
+        "\n",
+        "Now you might be thinking, \"Seriously? You can do that?\"\n",
+        "\n",
+        "And the answer is yes. You can change any of the hyperparamaters of your neural networks.\n",
+        "\n",
+        "With practice, you'll start to see what kind of hyperparameters work and what don't.\n",
+        "\n",
+        "That's an important thing to understand about machine learning and deep learning in general. It's very experimental. You build a model and evaluate it, build a model and evaluate it.\n",
+        "\n",
+        "That being said, I want to introduce you a trick which will help you find the optimal learning rate (at least to begin training with) for your models going forward.\n",
+        "\n",
+        "To do so, we're going to use the following:\n",
+        "* A [learning rate **callback**](https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/LearningRateScheduler).\n",
+        "   * You can think of a callback as an extra piece of functionality you can add to your model *while* its training.\n",
+        "* Another model (we could use the same ones as above, we we're practicing building models here).\n",
+        "* A modified loss curves plot.\n",
+        "\n",
+        "We'll go through each with code, then explain what's going on.\n",
+        "\n",
+        "> 🔑 **Note:** The default hyperparameters of many neural network building blocks in TensorFlow are setup in a way which usually work right out of the box (e.g. the [Adam optimizer's](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/Adam) default settings can usually get good results on many datasets). So it's a good idea to try the defaults first, then adjust as needed."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "3g05waDawhsZ",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "0b37d756-e8e9-4329-a7c5-f459c1c338ee"
+      },
+      "source": [
+        "# Set random seed\n",
+        "tf.random.set_seed(42)\n",
+        "\n",
+        "# Create a model (same as model_8)\n",
+        "model_9 = tf.keras.Sequential([\n",
+        "  tf.keras.layers.Dense(4, activation=\"relu\"),\n",
+        "  tf.keras.layers.Dense(4, activation=\"relu\"),\n",
+        "  tf.keras.layers.Dense(1, activation=\"sigmoid\")\n",
+        "])\n",
+        "\n",
+        "# Compile the model\n",
+        "model_9.compile(loss=\"binary_crossentropy\", # we can use strings here too\n",
+        "              optimizer=\"Adam\", # same as tf.keras.optimizers.Adam() with default settings\n",
+        "              metrics=[\"accuracy\"])\n",
+        "\n",
+        "# Create a learning rate scheduler callback\n",
+        "lr_scheduler = tf.keras.callbacks.LearningRateScheduler(lambda epoch: 1e-4 * 10**(epoch/20)) # traverse a set of learning rate values starting from 1e-4, increasing by 10**(epoch/20) every epoch\n",
+        "\n",
+        "# Fit the model (passing the lr_scheduler callback)\n",
+        "history = model_9.fit(X_train,\n",
+        "                      y_train,\n",
+        "                      epochs=100,\n",
+        "                      callbacks=[lr_scheduler])"
+      ],
+      "execution_count": 47,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/100\n",
+            "25/25 [==============================] - 2s 3ms/step - loss: 0.6991 - accuracy: 0.4963 - lr: 1.0000e-04\n",
+            "Epoch 2/100\n",
+            "25/25 [==============================] - 0s 4ms/step - loss: 0.6990 - accuracy: 0.4950 - lr: 1.1220e-04\n",
+            "Epoch 3/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.6988 - accuracy: 0.4913 - lr: 1.2589e-04\n",
+            "Epoch 4/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.6986 - accuracy: 0.4938 - lr: 1.4125e-04\n",
+            "Epoch 5/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.6984 - accuracy: 0.4963 - lr: 1.5849e-04\n",
+            "Epoch 6/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.6982 - accuracy: 0.4875 - lr: 1.7783e-04\n",
+            "Epoch 7/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.6978 - accuracy: 0.4412 - lr: 1.9953e-04\n",
+            "Epoch 8/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.6976 - accuracy: 0.4575 - lr: 2.2387e-04\n",
+            "Epoch 9/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.6973 - accuracy: 0.4787 - lr: 2.5119e-04\n",
+            "Epoch 10/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.6970 - accuracy: 0.4812 - lr: 2.8184e-04\n",
+            "Epoch 11/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.6967 - accuracy: 0.4775 - lr: 3.1623e-04\n",
+            "Epoch 12/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.6963 - accuracy: 0.4688 - lr: 3.5481e-04\n",
+            "Epoch 13/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.6960 - accuracy: 0.4712 - lr: 3.9811e-04\n",
+            "Epoch 14/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.6956 - accuracy: 0.4725 - lr: 4.4668e-04\n",
+            "Epoch 15/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.6952 - accuracy: 0.4725 - lr: 5.0119e-04\n",
+            "Epoch 16/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.6947 - accuracy: 0.4762 - lr: 5.6234e-04\n",
+            "Epoch 17/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.6943 - accuracy: 0.4750 - lr: 6.3096e-04\n",
+            "Epoch 18/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.6936 - accuracy: 0.4762 - lr: 7.0795e-04\n",
+            "Epoch 19/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.6931 - accuracy: 0.4750 - lr: 7.9433e-04\n",
+            "Epoch 20/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.6924 - accuracy: 0.4800 - lr: 8.9125e-04\n",
+            "Epoch 21/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.6915 - accuracy: 0.4875 - lr: 0.0010\n",
+            "Epoch 22/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.6905 - accuracy: 0.5213 - lr: 0.0011\n",
+            "Epoch 23/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.6894 - accuracy: 0.6050 - lr: 0.0013\n",
+            "Epoch 24/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.6881 - accuracy: 0.6100 - lr: 0.0014\n",
+            "Epoch 25/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.6867 - accuracy: 0.5512 - lr: 0.0016\n",
+            "Epoch 26/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.6851 - accuracy: 0.5412 - lr: 0.0018\n",
+            "Epoch 27/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.6833 - accuracy: 0.5387 - lr: 0.0020\n",
+            "Epoch 28/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.6804 - accuracy: 0.5675 - lr: 0.0022\n",
+            "Epoch 29/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.6775 - accuracy: 0.6050 - lr: 0.0025\n",
+            "Epoch 30/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.6736 - accuracy: 0.6650 - lr: 0.0028\n",
+            "Epoch 31/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.6687 - accuracy: 0.6212 - lr: 0.0032\n",
+            "Epoch 32/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.6620 - accuracy: 0.6012 - lr: 0.0035\n",
+            "Epoch 33/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.6526 - accuracy: 0.6513 - lr: 0.0040\n",
+            "Epoch 34/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.6405 - accuracy: 0.6837 - lr: 0.0045\n",
+            "Epoch 35/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.6252 - accuracy: 0.7287 - lr: 0.0050\n",
+            "Epoch 36/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.6037 - accuracy: 0.7350 - lr: 0.0056\n",
+            "Epoch 37/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.5714 - accuracy: 0.7725 - lr: 0.0063\n",
+            "Epoch 38/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.5360 - accuracy: 0.8062 - lr: 0.0071\n",
+            "Epoch 39/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.5052 - accuracy: 0.8138 - lr: 0.0079\n",
+            "Epoch 40/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.4468 - accuracy: 0.8575 - lr: 0.0089\n",
+            "Epoch 41/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.3826 - accuracy: 0.9200 - lr: 0.0100\n",
+            "Epoch 42/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.3192 - accuracy: 0.9525 - lr: 0.0112\n",
+            "Epoch 43/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.2591 - accuracy: 0.9575 - lr: 0.0126\n",
+            "Epoch 44/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.2138 - accuracy: 0.9712 - lr: 0.0141\n",
+            "Epoch 45/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.1669 - accuracy: 0.9850 - lr: 0.0158\n",
+            "Epoch 46/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.1201 - accuracy: 0.9887 - lr: 0.0178\n",
+            "Epoch 47/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.0992 - accuracy: 0.9837 - lr: 0.0200\n",
+            "Epoch 48/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.0882 - accuracy: 0.9750 - lr: 0.0224\n",
+            "Epoch 49/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.0731 - accuracy: 0.9850 - lr: 0.0251\n",
+            "Epoch 50/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.0679 - accuracy: 0.9825 - lr: 0.0282\n",
+            "Epoch 51/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.0877 - accuracy: 0.9750 - lr: 0.0316\n",
+            "Epoch 52/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.1404 - accuracy: 0.9450 - lr: 0.0355\n",
+            "Epoch 53/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.0519 - accuracy: 0.9850 - lr: 0.0398\n",
+            "Epoch 54/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.0520 - accuracy: 0.9850 - lr: 0.0447\n",
+            "Epoch 55/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.0921 - accuracy: 0.9675 - lr: 0.0501\n",
+            "Epoch 56/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.1181 - accuracy: 0.9513 - lr: 0.0562\n",
+            "Epoch 57/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.0908 - accuracy: 0.9650 - lr: 0.0631\n",
+            "Epoch 58/100\n",
+            "25/25 [==============================] - 0s 4ms/step - loss: 0.2447 - accuracy: 0.9025 - lr: 0.0708\n",
+            "Epoch 59/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.2265 - accuracy: 0.9125 - lr: 0.0794\n",
+            "Epoch 60/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.0787 - accuracy: 0.9688 - lr: 0.0891\n",
+            "Epoch 61/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.0783 - accuracy: 0.9700 - lr: 0.1000\n",
+            "Epoch 62/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.0591 - accuracy: 0.9750 - lr: 0.1122\n",
+            "Epoch 63/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.0215 - accuracy: 0.9950 - lr: 0.1259\n",
+            "Epoch 64/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.0575 - accuracy: 0.9787 - lr: 0.1413\n",
+            "Epoch 65/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.2219 - accuracy: 0.9350 - lr: 0.1585\n",
+            "Epoch 66/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.2309 - accuracy: 0.8938 - lr: 0.1778\n",
+            "Epoch 67/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.2979 - accuracy: 0.8712 - lr: 0.1995\n",
+            "Epoch 68/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.1883 - accuracy: 0.9125 - lr: 0.2239\n",
+            "Epoch 69/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.2053 - accuracy: 0.9388 - lr: 0.2512\n",
+            "Epoch 70/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.3621 - accuracy: 0.8800 - lr: 0.2818\n",
+            "Epoch 71/100\n",
+            "25/25 [==============================] - 0s 4ms/step - loss: 0.3902 - accuracy: 0.8875 - lr: 0.3162\n",
+            "Epoch 72/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.6089 - accuracy: 0.6725 - lr: 0.3548\n",
+            "Epoch 73/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.5311 - accuracy: 0.7038 - lr: 0.3981\n",
+            "Epoch 74/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.6091 - accuracy: 0.6225 - lr: 0.4467\n",
+            "Epoch 75/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.6054 - accuracy: 0.6288 - lr: 0.5012\n",
+            "Epoch 76/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.5799 - accuracy: 0.6550 - lr: 0.5623\n",
+            "Epoch 77/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.5103 - accuracy: 0.7237 - lr: 0.6310\n",
+            "Epoch 78/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.4829 - accuracy: 0.7588 - lr: 0.7079\n",
+            "Epoch 79/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.7163 - accuracy: 0.6737 - lr: 0.7943\n",
+            "Epoch 80/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.5833 - accuracy: 0.6775 - lr: 0.8913\n",
+            "Epoch 81/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.7114 - accuracy: 0.5913 - lr: 1.0000\n",
+            "Epoch 82/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.7289 - accuracy: 0.5113 - lr: 1.1220\n",
+            "Epoch 83/100\n",
+            "25/25 [==============================] - 0s 4ms/step - loss: 0.7420 - accuracy: 0.4812 - lr: 1.2589\n",
+            "Epoch 84/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.7076 - accuracy: 0.5038 - lr: 1.4125\n",
+            "Epoch 85/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.7284 - accuracy: 0.5038 - lr: 1.5849\n",
+            "Epoch 86/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.7695 - accuracy: 0.5038 - lr: 1.7783\n",
+            "Epoch 87/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.7681 - accuracy: 0.5063 - lr: 1.9953\n",
+            "Epoch 88/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.7585 - accuracy: 0.5163 - lr: 2.2387\n",
+            "Epoch 89/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.7625 - accuracy: 0.5038 - lr: 2.5119\n",
+            "Epoch 90/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.7853 - accuracy: 0.5038 - lr: 2.8184\n",
+            "Epoch 91/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.8039 - accuracy: 0.5138 - lr: 3.1623\n",
+            "Epoch 92/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.7380 - accuracy: 0.4963 - lr: 3.5481\n",
+            "Epoch 93/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.7404 - accuracy: 0.4963 - lr: 3.9811\n",
+            "Epoch 94/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.7583 - accuracy: 0.4938 - lr: 4.4668\n",
+            "Epoch 95/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.8197 - accuracy: 0.4863 - lr: 5.0119\n",
+            "Epoch 96/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.7822 - accuracy: 0.4613 - lr: 5.6234\n",
+            "Epoch 97/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.8060 - accuracy: 0.5013 - lr: 6.3096\n",
+            "Epoch 98/100\n",
+            "25/25 [==============================] - 0s 4ms/step - loss: 0.9633 - accuracy: 0.4963 - lr: 7.0795\n",
+            "Epoch 99/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.9598 - accuracy: 0.4913 - lr: 7.9433\n",
+            "Epoch 100/100\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.8583 - accuracy: 0.4613 - lr: 8.9125\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KKln6dnbNPVR"
+      },
+      "source": [
+        "Now our model has finished training, let's have a look at the training history."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "wCd12upnyO4y",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 619
+        },
+        "outputId": "dd086608-68bc-451c-fcde-e8bd33689891"
+      },
+      "source": [
+        "# Checkout the history\n",
+        "pd.DataFrame(history.history).plot(figsize=(10,7), xlabel=\"epochs\");"
+      ],
+      "execution_count": 48,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x700 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9OkUFgZSx6hP"
+      },
+      "source": [
+        "As you you see the learning rate exponentially increases as the number of epochs increases.\n",
+        "\n",
+        "And you can see the model's accuracy goes up (and loss goes down) at a specific point when the learning rate slowly increases.\n",
+        "\n",
+        "To figure out where this infliction point is, we can plot the loss versus the log-scale learning rate."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "8fnEklbYyGBG",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 646
+        },
+        "outputId": "8749b313-abca-49d3-ffea-03f80bf28188"
+      },
+      "source": [
+        "# Plot the learning rate versus the loss\n",
+        "lrs = 1e-4 * (10 ** (np.arange(100)/20))\n",
+        "plt.figure(figsize=(10, 7))\n",
+        "plt.semilogx(lrs, history.history[\"loss\"]) # we want the x-axis (learning rate) to be log scale\n",
+        "plt.xlabel(\"Learning Rate\")\n",
+        "plt.ylabel(\"Loss\")\n",
+        "plt.title(\"Learning rate vs. loss\");"
+      ],
+      "execution_count": 49,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x700 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "w0kMFKLvOF8s"
+      },
+      "source": [
+        "To figure out the ideal value of the learning rate (at least the ideal value to *begin* training our model), the rule of thumb is to take the learning rate value where the loss is still decreasing but not quite flattened out (usually about 10x smaller than the bottom of the curve).\n",
+        "\n",
+        "In this case, our ideal learning rate ends up between `0.01` ($10^{-2}$) and `0.02`.\n",
+        "\n",
+        "![finding the ideal learning rate by plotting learning rate vs. loss](https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/images/02-learning-rate-vs-loss.png)\n",
+        "\n",
+        "*The ideal learning rate at the start of model training is somewhere just before the loss curve bottoms out (a value where the loss is still decreasing).*"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "vJo-nDw4zFfx",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "b090fcb9-1bca-4dcc-9f75-e4323229e6f2"
+      },
+      "source": [
+        "# Example of other typical learning rate values\n",
+        "10**0, 10**-1, 10**-2, 10**-3, 1e-4"
+      ],
+      "execution_count": 50,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(1, 0.1, 0.01, 0.001, 0.0001)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 50
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "iALar-WvPlEc"
+      },
+      "source": [
+        "Now we've estimated the ideal learning rate (we'll use `0.02`) for our model, let's refit it."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "EJ9wbXblzPPL",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "51f13b95-f18d-47f6-eccd-95faa2c6271a"
+      },
+      "source": [
+        "# Set the random seed\n",
+        "tf.random.set_seed(42)\n",
+        "\n",
+        "# Create the model\n",
+        "model_10 = tf.keras.Sequential([\n",
+        "  tf.keras.layers.Dense(4, activation=\"relu\"),\n",
+        "  tf.keras.layers.Dense(4, activation=\"relu\"),\n",
+        "  tf.keras.layers.Dense(1, activation=\"sigmoid\")\n",
+        "])\n",
+        "\n",
+        "# Compile the model with the ideal learning rate\n",
+        "model_10.compile(loss=\"binary_crossentropy\",\n",
+        "                optimizer=tf.keras.optimizers.Adam(learning_rate=0.02), # to adjust the learning rate, you need to use tf.keras.optimizers.Adam (not \"adam\")\n",
+        "                metrics=[\"accuracy\"])\n",
+        "\n",
+        "# Fit the model for 20 epochs (5 less than before)\n",
+        "history = model_10.fit(X_train, y_train, epochs=20)"
+      ],
+      "execution_count": 51,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/20\n",
+            "25/25 [==============================] - 2s 3ms/step - loss: 0.6854 - accuracy: 0.5437\n",
+            "Epoch 2/20\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.6722 - accuracy: 0.5512\n",
+            "Epoch 3/20\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.6509 - accuracy: 0.6513\n",
+            "Epoch 4/20\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.6020 - accuracy: 0.7325\n",
+            "Epoch 5/20\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.5539 - accuracy: 0.7513\n",
+            "Epoch 6/20\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.4862 - accuracy: 0.7950\n",
+            "Epoch 7/20\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.4454 - accuracy: 0.8175\n",
+            "Epoch 8/20\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.3984 - accuracy: 0.8537\n",
+            "Epoch 9/20\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.3532 - accuracy: 0.9000\n",
+            "Epoch 10/20\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.2956 - accuracy: 0.9325\n",
+            "Epoch 11/20\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.2648 - accuracy: 0.9375\n",
+            "Epoch 12/20\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.2355 - accuracy: 0.9500\n",
+            "Epoch 13/20\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.2201 - accuracy: 0.9463\n",
+            "Epoch 14/20\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.1936 - accuracy: 0.9663\n",
+            "Epoch 15/20\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.1854 - accuracy: 0.9575\n",
+            "Epoch 16/20\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.1656 - accuracy: 0.9688\n",
+            "Epoch 17/20\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.1540 - accuracy: 0.9675\n",
+            "Epoch 18/20\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.1469 - accuracy: 0.9688\n",
+            "Epoch 19/20\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.1450 - accuracy: 0.9613\n",
+            "Epoch 20/20\n",
+            "25/25 [==============================] - 0s 3ms/step - loss: 0.1341 - accuracy: 0.9638\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qb9xwISc0pnF"
+      },
+      "source": [
+        "Nice! With a little higher learning rate (`0.02` instead of `0.01`) we reach a higher accuracy than `model_8` in less epochs (`20` instead of `25`).\n",
+        "\n",
+        "> 🛠 **Practice:** Now you've seen an example of what can happen when you change the learning rate, try changing the learning rate value in the [TensorFlow Playground](https://playground.tensorflow.org/#activation=tanh&batchSize=10&dataset=circle&regDataset=reg-plane&learningRate=0.03&regularizationRate=0&noise=0&networkShape=4,2&seed=0.03154&showTestData=false&discretize=false&percTrainData=50&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification&initZero=false&hideText=false&regularization_hide=true&regularizationRate_hide=true&problem_hide=true) and see what happens. What happens if you increase it? What happens if you decrease it?\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "3ZCYAqitKUk2",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "0dacd73c-d0b8-4836-b28e-001b72ceac1d"
+      },
+      "source": [
+        "# Evaluate model on the test dataset\n",
+        "model_10.evaluate(X_test, y_test)"
+      ],
+      "execution_count": 52,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "7/7 [==============================] - 0s 3ms/step - loss: 0.1448 - accuracy: 0.9500\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[0.14478765428066254, 0.949999988079071]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 52
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "YWqQShzZKT7R"
+      },
+      "source": [
+        "\n",
+        "\n",
+        "Let's see how the predictions look."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "rJbzQ5kQ2HSh",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 613
+        },
+        "outputId": "0b945faf-f291-4a33-d6c8-507fe545d1a8"
+      },
+      "source": [
+        "# Plot the decision boundaries for the training and test sets\n",
+        "plt.figure(figsize=(12, 6))\n",
+        "plt.subplot(1, 2, 1)\n",
+        "plt.title(\"Train\")\n",
+        "plot_decision_boundary(model_10, X=X_train, y=y_train)\n",
+        "plt.subplot(1, 2, 2)\n",
+        "plt.title(\"Test\")\n",
+        "plot_decision_boundary(model_10, X=X_test, y=y_test)\n",
+        "plt.show()"
+      ],
+      "execution_count": 53,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "313/313 [==============================] - 1s 2ms/step\n",
+            "doing binary classifcation...\n",
+            "313/313 [==============================] - 0s 1ms/step\n",
+            "doing binary classifcation...\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x600 with 2 Axes>"
+            ],
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA+EAAAIQCAYAAADuAG/uAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOz9d5xk11kn/n/OrZxzV+ccpnt68owmSKNRsCwrGCFjMGu8BrPGsr9r/MNmF6/Z/QICFn+XaHgZB/CCgbVxAC9gW1jWaCxplDU5ds65qivnqnvP74/qru6artTd1XGe9+s1L2mqb9263dNV5z7nPOd5GOecgxBCCCGEEEIIIRtO2OoLIIQQQgghhBBC7hYUhBNCCCGEEEIIIZuEgnBCCCGEEEIIIWSTUBBOCCGEEEIIIYRsEgrCCSGEEEIIIYSQTUJBOCGEEEIIIYQQskkoCCeEEEIIIYQQQjYJBeGEEEIIIYQQQsgmoSCcEEIIIYQQQgjZJBSEE0LW5Jd+6ZfQ2Ni41ZdBCCGEEELIjkJBOCG7DGOspD8vvfTSVl8qIYQQQspgM8f+SCSC3/md36H7CELWQb7VF0AIKa9/+Id/yPr73//93+OFF15Y8XhnZ+e6Xuev//qvIUnSus5BCCGEkPXbrLEfSAfhzz77LADggQceWPf5CLkbURBOyC7zoQ99KOvvb775Jl544YUVj98pEolAq9WW/DoKhWJN10cIIYSQ8lrr2E8I2RqUjk7IXeiBBx5Ad3c3Ll68iPvvvx9arRa/+Zu/CQD413/9VzzxxBOorq6GSqVCS0sLfu/3fg+iKGad48494SMjI2CM4Y//+I/xV3/1V2hpaYFKpcKxY8fwzjvvbOa3RwghhJA7SJKEL3zhC9i7dy/UajWcTieeeeYZeL3erOMuXLiARx99FHa7HRqNBk1NTfjlX/5lAOmx3uFwAACeffbZTJr77/zO72z2t0PIjkYr4YTcpebn5/HYY4/h53/+5/GhD30ITqcTAPD1r38der0en/nMZ6DX63Hu3Dn81m/9FgKBAP7oj/6o6Hm/+c1vIhgM4plnngFjDH/4h3+I973vfRgaGqLVc0IIIWSLPPPMM/j617+Oj3zkI/jUpz6F4eFhfPGLX8Tly5fx2muvQaFQYG5uDu9+97vhcDjw3/7bf4PZbMbIyAi+973vAQAcDge+/OUv4xOf+ASefvppvO997wMA7N+/fyu/NUJ2HArCCblLzczM4Ctf+QqeeeaZrMe/+c1vQqPRZP7+8Y9/HB//+MfxpS99Cb//+78PlUpV8LxjY2Po7++HxWIBAHR0dOCpp57C888/jyeffLL83wghhBBCCnr11Vfxta99Dd/4xjfwwQ9+MPP4gw8+iPe85z347ne/iw9+8IN4/fXX4fV68eMf/xhHjx7NHPf7v//7AACdTof3v//9+MQnPoH9+/dTujsha0Tp6ITcpVQqFT7ykY+seHx5AB4MBuF2u3H69GlEIhH09PQUPe8HPvCBTAAOAKdPnwYADA0NleGqCSGEELJa3/3ud2EymfDII4/A7XZn/hw5cgR6vR4/+clPAABmsxkA8IMf/ADJZHILr5iQ3Y2CcELuUjU1NVAqlSsev3nzJp5++mmYTCYYjUY4HI7MTLff7y963vr6+qy/Lwbkd+45I4QQQsjm6O/vh9/vR0VFBRwOR9afUCiEubk5AMCZM2fwMz/zM3j22Wdht9vx1FNP4W//9m8Rj8e3+DsgZHehdHRC7lLLV7wX+Xw+nDlzBkajEb/7u7+LlpYWqNVqXLp0CZ/97GdLakkmk8lyPs45X/c1E0IIIWT1JElCRUUFvvGNb+T8+mKxNcYY/umf/glvvvkmvv/97+P555/HL//yL+NP/uRP8Oabb0Kv12/mZROya1EQTgjJeOmllzA/P4/vfe97uP/++zOPDw8Pb+FVEUIIIWQ9WlpacPbsWdx77705J+HvdOLECZw4cQL/83/+T3zzm9/EL/zCL+Bb3/oWPvrRj4IxtglXTMjuRunohJCMxVXs5avWiUQCX/rSl7bqkgghhBCyTj/3cz8HURTxe7/3eyu+lkql4PP5AKS3jt2ZuXbw4EEAyKSka7VaAMg8hxCyerQSTgjJOHXqFCwWC37xF38Rn/rUp8AYwz/8wz9QKjkhhBCyg505cwbPPPMMPv/5z+PKlSt497vfDYVCgf7+fnz3u9/Fn//5n+P9738//u7v/g5f+tKX8PTTT6OlpQXBYBB//dd/DaPRiMcffxxAejtbV1cXvv3tb6O9vR1WqxXd3d3o7u7e4u+SkJ2DgnBCSIbNZsMPfvAD/Pqv/zr+x//4H7BYLPjQhz6Ehx9+GI8++uhWXx4hhBBC1ugrX/kKjhw5gq9+9av4zd/8TcjlcjQ2NuJDH/oQ7r33XgDpYP3tt9/Gt771LczOzsJkMuGee+7BN77xDTQ1NWXO9bWvfQ2/+qu/ik9/+tNIJBL47d/+bQrCCVkFxmmJixBCCCGEEEII2RS0J5wQQgghhBBCCNkkFIQTQgghhBBCCCGbhIJwQgghhBBCCCFkk1AQTgghhBBCCCGEbBIKwgkhhBBCCCGEkE1CQTghhBBCCCGEELJJdl2fcEmSMDU1BYPBAMbYVl8OIYQQAs45gsEgqqurIQg0/10ONN4TQgjZTlYz1u+6IHxqagp1dXVbfRmEEELICuPj46itrd3qy9gVaLwnhBCyHZUy1u+6INxgMAAAXvr5X4BeqdziqyGEEEKAUCKBB771jcwYRdaPxntCCCHbyWrG+l0XhC+mpOmVShqUCSGEbCuUNl0+NN4TQgjZjkoZ62ljGiGEEEIIIYQQskkoCCeEEEIIIYQQQjYJBeGEEEIIIYQQQsgmoSCcEEIIIYQQQgjZJBSEE0IIIYQQQgghm4SCcEIIIYQQQgghZJNQEE4IIYQQQgghhGwSCsIJIYQQQgghhJBNQkE4IYQQQgghhBCySSgIJ4QQQgghhBBCNgkF4YQQQgghhBBCyCahIJwQQgghhBBCCNkk8q2+AELIzpYIx+EZmEEiHIdcrYC12Qm1WZvzWM45QtM+uPtnkAhG08e3OGFusIMJpc0Jcs4RD0QhJUUo9SrI1cpyfjuEEELIthEPxhCc8kASObRWHXROExhjma9ziYMJrMAZCCHbEQXhhJA14Zxj8u1BzN4YBwAwxsA5x/SlEViaK9B4/x4IclnmeCklYujFm/CPzwMMAAfAAN+IG2qLDu2PHYBCqyr4mp7BWUxfHkHMF0k/wABzgwM1R5vzBv6EEELITpOKJzF6vhe+EVfW42qTFtVHmxCc9mG+fwZSUoRMJYe9vQoV3XVQ6gqPo4SQ7YGCcEJIFs45ApMeuG5NIjIfgiATYG6ww9FZA5VRkzlu6sIwZq+PZz1vkXd4DlyU0PLIvsxjo6/1wj8xv3Awsv4b84XR/6Or6Hz6WNYM/3IzV0cx+c7QHRcL+EZdCEx6sOe9h6Gx6tf+jRNCCCHbgJQS0f/cFUQ8oRVfi/kjGHrx5tJkNgAxnsLsjXG4+2aw572HoDbrNveCCSGrRkE4ITsc5xz+sXm4eqYQ90cgU8hgaa6AvaMacrVideeSOEZeuQ3PwGzWAD97YxxzNyfQ/NBemBsdSMUSmL0+VuBEgG/UjYg7CK3dgEQ4Bk//bMHjo54wApMemGptK74c80dWBuDLniulRIy80oPOnz5a+jdLCCGEbEOegVlE5lcG4Fn4yr+LiRQGX7iBrvffk3dCmxCyPVAQTsgOJokSBs9eR2DckxU0R+ZDmLwwBL3TBFt7FazNFVmp4fnMXh9LB+BA9gDP08H+0Lmb6PqZexCc8oJLd94B3IExzA/MQGs3wDfiLv7NMAbvkCtnEO66PZX1/a3AgYg7mAn6CSGEkJ3K1TO1tidyjpg/guC0D8ZqS85DxEQKgSkvpKQIlVEDXYVxVwbsUkpEYMKDVCIFlV4NfZV5V36fZOeiIJyQLcQlCf7xecR8EQgyGUz1tqyU72Im3uxPB+BAzlnx0IwfoRk/pi4Moe2xg9BY8qeocUnKSi/PeQwHXLcmIVPJM3vACxyNeCAK1+3J7H3gBU7uH3Vj7tYkbK1OyJRLH08Rd6DwcxePmw/lDcIj80HEA1EIchkMVeaSJiUIIYSQzRYPRtf+ZMYQmvauCMK5JGHq4jDmbk5ASkmZx1UmDepPtcNYY137a24jnHPMXBnFzLUxSEkx87hCp0L9yTaYGx1beHWELKEWZYRsEd+oG9f+8XUMvnADkxeGMP5mP258500MvnAdYiJV9PmpWBLu3umSXisZTaDvuSsFzxtxh5CKJQufiHP4RlxQaJRFAnAAHPCPzWPstT4EJjwlBdGpeBLjr/fhxnffQtQbzjxeauX0XBViQ7N+3PreO7j9fy9g6MWbGHj+Gq5+4zVMXRouvppPCCnqlVdewXvf+15UV1eDMYZ/+Zd/Kfqcl156CYcPH4ZKpUJrayu+/vWvb/h1ErJTyBRrXyNjALiU/RjnHCOv9GDm6lhWAA4AcX8U/T+6isCkZ82vuZ2Mv9GPqYvDWQE4ACTDcQyevQHv8NwWXRkh2SgIJ2QLBCY8GDx7HanoQtC7LBb0jbnR/+9XwSUp95MBJEIxzFwbKz2I5EAqmoBnMP++bEkU834t+zgJKlPpq/VrkYol0P/vVyCl0tdkrC1tht5QZc76e2jGh74fXkbUm723TkqKmL40gtFXe4pPJhBCCgqHwzhw4AD+8i//sqTjh4eH8cQTT+DBBx/ElStX8Gu/9mv46Ec/iueff36Dr5SQncHa4kxH02vAOYeuwpj1WHjWv7TVLOeTgLHX+nb8eBj1huG6NVnwmLHX+greXxGyWTY0CKfZcUJW4pxj/M3+gvubw64Aen9wGYEJT9aguFhF/Pq33sDstQKF0fLwDOafAVabtMUHfQbIlHL0P3d11a+9KhxIRhLwDqdbs9jbqyDIC3xcMcDS5IBSr146BecYfa0vPVGR52c93zeD8FygnFdOyF3nsccew+///u/j6aefLun4r3zlK2hqasKf/MmfoLOzE5/85Cfx/ve/H3/2Z3+2wVdKyM7g6KpOb5labSDOAIVWCVNddm0VV+80UGQ/dDwQRXjWv8oX3F7cvVNFf2apWBL+8d2x6k92tg0Nwml2nNyNApMeDPz4Gi7/3Su4/HevoP9HV+Efn88E09H50FKf6wLCcwH0/+gq+p67gqlLw7j2zddw85/eTqd2r5GYyJ9urtCqYG6wFx7AeHqg3ixztybBJQ65WoGWR/blTDcHAEEuQ9WR5qzHIu4gYstS2nNiDK6ewrPmhJDyeuONN/Cud70r67FHH30Ub7zxRsHnxeNxBAKBrD+E7EZKnRptjx1YmZbO8vw/ADAGJghofmjvirEy5ouki7oUEdvE8X0jxAPR4lvf2ObexxCSz4YWZnvsscfw2GOPlXz88tlxAOjs7MSrr76KP/uzP8Ojjz66UZdJSNlMXhjCzJXRrCJkgQkPAhMe2PdUo/7e9hWp0cWEpn0ITfvWf3EMUBm1BQ+pPd6K4LQ/HayvNStNYECZ9lpHXAHc+r/voO09B6A2ayHIZTn3tUtJEYMvXEPnU0czBd1KmegA54h5SziOEFI2MzMzcDqdWY85nU4EAgFEo1FoNLm3u3z+85/Hs88+uxmXSMiW01eYsO/nT8IzMAv/xDy4KEFj1cPRWYNULIHpy6PpoqdAOhus0Y6qQ43QWPUrziVTllaIVKbY2QVLBYWshCKwC8cRssW2VXX0fLPjv/Zrv7Y1F0TIKvhGXOkAHMg5ALh7ppAIx0vee112HHDsqS54iMqgQedTRzD+Rv/S4L5aZS52FvOF0fNvFyHXKAsWlosHopi7NYGqg40AQNXPCdllPve5z+Ezn/lM5u+BQAB1dXVbeEWEbCyZUg5HVw0cXTVZj6uMGrQ+uh+peBJiPAW5WpHVUeROlqYKBCe9BV+LyYRtVSFdSolw983AdXsSiWAUgkIOa0sFKrpq83aRsTQ64C2w7Q4AwABz/cpWqIRstm0VhK9ldjwejyMej2f+TulpZLOIiRSC015IKQlqkxYz18eLzsAG1hrYloG+ygxJkhCc9kJXYYIgy70bZXFwT4Rj8A67MPHmwCZf6R14uqppMhwvepzr1lQmCDfWWMBkArhYuABLxB3E7f/7Dloe2Ze1p5wQsjEqKysxO5tdJGp2dhZGozHvKjgAqFQqqFSqjb48QnYMuUoBuUpR9DhrSwWmLg4jFUvkvUep6KopGMhvplQ8ib4fXkHUs5Q5KKUSmLs5AdftKbQ9uh+GHH3QzQ12qIyagunmtrYqKLT0OUK23vZ4t60DpaeRzcYlCZPvDKX3KxcJ8DaboJBBkMuQiiayHmdyISutnckYnPvqUX2kCSxPsRalTl20yuh2k4zEwSUJTBDSKwid1Zi7MVH0eZH5EG585020P3EIeqdpE66UkLvXyZMn8dxzz2U99sILL+DkyZNbdEWE7G4yhRztjx9E33NXsu8PGAM4h6XJgZpjzflPsMnGXu3NvXWPA1yUMPDj69j38ychV2dPQHCJQ23W5g3CjXU21J9q24hLJmTVtlWLsrXMjn/uc5+D3+/P/BkfH9+MSyV3Kc45hs7dxOz18W0XgIMBtrZK7P/gKbQ9fhD197bDvqcKAMDv6AvKRY6ZK6MY/smtvKeL+SI7rngJE1hWBdiaYy0wN9pLei6XOPqeu7LjvmdCtlooFMKVK1dw5coVAOkiq1euXMHYWLqDw+c+9zl8+MMfzhz/8Y9/HENDQ/iN3/gN9PT04Etf+hK+853v4NOf/vRWXD4hdwWNRYfunzuO+nvbYag2Q2s3wNpcgfYnDqHpob1gwvYICRaz8AplFUopEfP901mPcc4x8ML1glvpLE0O2qpGto1ttRK+ltlxSk8jmykw6YFvxL3Vl5EHQ0VXLRhjMFZbwCtNuPS3/QWf4R2aQ7CrBoZK84qvxfxFKouXSG3WQmXUQK5RgosSfKNuSEkRco0Sjj1VUBo0GH2lpyyvded+NkEmoPnhbvhG3Rg6e6Po87koYeKdQbQ83F2W6yHkbnDhwgU8+OCDmb8v7tv+xV/8RXz961/H9PR0JiAHgKamJvzwhz/Epz/9afz5n/85amtr8bWvfY0KsBKywWQKORydNXB01hQ/eIsU27u+KDDhgXNf/dLzprxFnzv59iBsrc5tM+FA7m4bGoSHQiEMDCztJ12cHbdaraivr8fnPvc5TE5O4u///u8BpGfHv/jFL+I3fuM38Mu//Ms4d+4cvvOd7+CHP/zhRl4mITlxzhFxB5EMxyFXK6CrMMF9e6p45c3NxgDGGJof2gu1ean6+eyNiZJakkxfHoXhMfOKxwVZeWaLk9EEzI0OzPfNIBVPQqFRwravDhVdNZCrlZBECRNvDUKM52+fVir/+Dyuf+sNVO6rh2NvDRhjYIxBY9aVfA7fiAupWAJytXLd10PI3eCBBx7ItGDM5etf/3rO51y+fHkDr4rczZKRBNx9UwhN+8A5oHeaYN9TBaWO6n5sd7zE4q6ppIi5GxMQkymoDBr4xlyZ9Pq8z4klEZj0ruijTshW2NAgnGbHyU7lG3Nj4q0BxP1LqckKrTL92b6NAnC5WgFbexUcndVQGbK3bJTaTzzqCeZ8XF9pApMLK1LZV0uMpzBzdTTzc0uEYpi+PAJ3zxQ6njwMlVGDuhOtGHn59rpeZ1EyHMf4m/2IeIJoOL0HjLHVFZvhQDwYoyCcEEJ2IO+wC8M/uZkVzAWnvZi+MorG0x2wtVdt4dWRYjR2Q0nHReYCiMwFigbed0qEYmu9NELKakODcJodJzuRZ2gOw+durng8GUnkOHpraB0GVB5ogKXRse5z5SvMJshlcHbXLbVdW487PwY4kIwmMfDCdXS97xhsbZUAgPE3+tNtyMqQbTDfNwNLowOmejsUWiX0lSaEZvwlPXcte8Y454i4AvAOuyAmRagMatjaKqkKKyGEbJKwK4ChczdyjjkAx8grPVDo1TDmqKy9nSXCccz3TSPmj0CQy2BusMNYa807fu9kOrsBGps+XRm9lPuAVQTgADB1cRhyjbIs90+ErMe22hNOyFaTUiLGXu3d6svIS6FTofOnj0KhKb5Ka6qzIjhVfG9VrjYfmXPU28oThOfCOWLeMEIzPhiqLLC1VcLSXAH/mBvxYAyB8XkEF6q5rwljmLs1CVN9ujBb1aFG9P/71aJPUxk1WWn9pUjFkhh88Ua6+jxjCxPzHJMXhlB1sBFVhxt35c0SIYRsF5xzTF8eKXwQA2aujO6oIHzm6igmLwwte4TB3TMFtVmL1kcPQGXY+hR7MZlCcNKbnoA2aqCrMK5rzGu8vxO9378ESZRWHWQXk4olMXT2Bpoe6IK11ZnzGM45gpNeuHomEfNFIFPKYWmqgK29sqSWcIvn8A27MHtzAhFXun2yvsoMZ3cdpcMTABSEE5LFN+pOr8RuUxV7a0sKwAHA3lGNibeHig5gtfe0rHgsMh/C+Bv9CM341nKZpWMMgUkvDFXpGyJBJsDSVAEAcHbXYuR8Lzz9M2s7N+cILwx8QLpoW82xZky+M1TgSUDlwYYVNw9ckuAZmMXcrUnEfGEwQYCpwQbn3jporHoMPH8NYXcg87rLf+TTl0cgKGSo3F8PQggh5cU5h7t3GjPXRpEIFEk15ukCXmIitW16Yhfiuj2ZY8xKDzAxfxR9z11G1/vugUyxNRW/ucQxdWkYczfGIS3buqYyaVB/qn1FsdRSaW167HnqCKYuDME35s6siAsKGaSkWI5Lx9hrvTA32ldkvkmihOFzN+EbdWdl5YXnApi+MoL2xw5CWyRlnnOO0Vd6MN8/k3WOxeJxlQfqUXNs5b0Xubts/08gQjZRzBcBBAaUWBhkMykNajj2VJd8vEwpR9MDnQXbkNWebFuRLh2ZDy7NQK/G8n1ZJe7RYix/ERYmCGg605lOib86Cu+Ia9X/LkzIDqYrDzRAoVOl958vP9XCIFl5sCGTGr9IEiUM/PjaHVVXJXgG5uAZmEXF3rqsYD+X6csjqOiqodYohBCSB+ccoWkf5vtnEA/FoFArYW2pgKnelreaNecco+d7MN+3uslaMSlu+yCcSxKmLg0XOIAjEYzBOzgL+yruDcqFc46RV27DMzC74mtxfxT9P7qKtvccWHMgrrHo0PLIPiSjCSRCMcgUcoy+2lPytrJixKQI74gLttbsMX/y7cF0AA6sSIcXEyn0/ftV7PvAiYK/P+7e6XQAfuc5Fv5/5uoYdA4jzJQSf1fb3p9AhGwyQS6UPfWpXFre1b3qmwZrixNyjRLjb/Qj5l1qOaYyalB3si1nStTYq30LKWAlvghjC703hUz6uKHKDIVWVTSVnUscWpu+4DFamx7ND+3FxNsDmL02XuJFpa/LWLvy+7O1VsJQZYG7dwq+MTfEWAqccwhyGSLuILxDczA3OiDI0jd9UxeHc7c9Wfg9mbsxXnQPu5QU4R+fz6zyE0IIWSKlRAy+eAOBcc/SJC4DvMNz0Fj1aHvPASi0K7PAfCOuVQfgTCZAri4tpXgrhWb8SEWLdw2ZH5jZkiA8PBfIGYBncGDstT7s/dnj60pNV2iUmQxAuVpRUs0YtUWXdc+TCxNYeuFlmVQ8CVfPVP4ncUCMJzE/MIOKrtrch3CO2etjOb+29OLA7I1xCsLvchSEE7KMqd5eNF15NcqVOqUyaaCxFg5W8zFWW7D3Z+5BMhJHKpaEfNmAdqeoN1x0VTcLS6eQ1xxthsqYXZ1dTKRWpKjdSaZSlDwI6Z0mzGIVQTjncO7NPUgqdSpU7K1FYNyDaCiWGdTj/ggCEx6ozVq0P34QMqUcrluTJbxW8UNKuZkihJC70eirvUsdPRYnwhf+E/WGMPDja9jz1JEVwdzczcnVFfJkDPb2qswk63aWKrFt51aNLa6eqaJZb/FAFOFZP/SV5rK8prXFCd+Iu+AxglyAzmFIB9gFro1zDoM2CrtuaSJhZioAXkIWYHhsEvZjuSdy4uFUVmed3C+enmThEl+RsUfuHtv/U4iQTaSx6KCvNK37PM79dWh+uHv1Kd35zrevft2FvRRaFTRWfcE95TFf4ZnjO8lVCrQ9fmBFAA6k0+Ebz3Smb5DuvHQGgDE0PdhZ8s2Qqc6WXgkp9mNY+DnV39tecN/W8E9uLdvHnf21mD+CgR9fR2guAClVnv1nuVZxCCHkbhcPxtIrqvniJQ5E3MGchTrDrsCqAnC5So7KgzujPkdJnTUYoNRvTQeOuL9wkLsoFigSkK6CucGeLpxa4D7Aub8+nXVW7No40PIHPwvLf/+FzB/1YydKuxCHFeqP/hSmNNW4cjmOK5fimJA7ofrIkzD+6k+X/P1sq563ZNPRSjjZtWK+MAJTPnBJgs5ugM5pAmMM8UAUrtuTCEx4wDmHrsKEiq6aTMCmtRnWt+eIMTj31cM/6i5pD7OtvQpqkya9Ap+1rxoAB+x7qmHv2Jy+pqUGxDK1HHXH22Bprij4HEtTBdofU2Dy4jDCs0s/U0OVBdVHmqB3lj7hwQQBzQ93o++5K+l95DkGWCYTYG6ww9ldC11F/nNHveHCfdQXbvoi7tw91FeNAUaqhkoIISv4Rl3FD2IM3qG5FVXNGSs9jNFVGNB0pgtK3dZXEy+FrsIIlUGDeLBAEMvTRVi3glBiMThZGWuhMEFA++MH0f+ja+kWZsu2LoADFd21qDrUCGAhJT3PajhjgPOBVrj2PgWXbOn3IdxhA/BPhS9CLkNCZ8e/Hf5jcFHM3OfNvjaMm194BQ1f+C+Q281IuX0FT6M2a/PWOiB3BwrCya6TjMQx/NLtFe251GYtLC0VmL40kn5g4XM55o9ivm8a1UeaUHWoEd7hubW/OGOwNDug0CgRC0TBBJa38Nji8UC6YJjeacLszQkEJ70LkwNGVOythanOtmntrfSVZjCZUDQdq3Jf/YoCZvkYqi3YU21BIhTLpMMrdWubudc7Tej86aOYvjIC75ArM/iaG+yoOthYtGLpIt+Iq3gKI1vYL1aGnuXgQDIcz5kxQAghdzMxkSo+VoLn3NqlrzKnJ1QLPFWQy9Dx5EFo7cb1X+wmYoyh9kQLBl+4kecAQGs3wNxo39wLW2BpqshdL2UZJhNgrF1bYbZ8FFoVOp8+isCkB75hF8SECJVRvbCgsdRetO3R/ej74ZXsSYyF8dy8vxrOv/pfePaLPchaVuccZxyV0LtnwfKtpKdEBF+7CnCevSAvSeAARn71DzF+4B7Ue94uuBBTkWe7HLl7UBBOdhUxkULPv11CIrSyTUnMH8H0xZGVT1r4oJ26OAyVUYNUfI0tyhbSwuqOtwEAZAoZeAmpWoutRfSV5rLtm1ormVKOiq4azF7Ps/eaAYJMBlt78ZX55fusAAC6xf+J3Hno6uiAulo7UkkrklERCrUAuVK2cN7Szu1hgaIF3Bk41PIoKluNmOlfxT75fJfNZmHWrQzC3eHcfUoJIeRuoDJoigTgacoc/bCde+vSxdwKqDxQv+MCcCA9htq7AJ2sBrd+Mo1UXAJbrB3LAXuDHvvfUw2FqoRMgg1g3scwc0mORDSVdxKk4YAZTsv8hry+owNAx/LMiODCnwU6wPkfGzDd58fUbT8SkRTUBgU6/vsTiD3+Ifzu10YgYwJqLdqs8879h1+C7i//CJCkFYE4B5Cw2qD0eXMG6QwAFxi0QR881Q2wTo0B0spFDWOdbdMyHMn2RUE42TWS0QR6f5A7AAdQ0mrmzNVRKDTK/OfIg8kY7B3VqD7cCLk6vffX3OjA1MUC7UUAgHOYm7ZXdczqo82I+aPwj7lXpMcLMgGt79lftFe5XTcL/Zl6yIzb88bHJtdi+ELhGwMOwHqyDo2PNOIHH/oBeGp9y+GOp/dCa08P9ovFWMRAAHh5jAJxQshdy9zogPBaX+H6Gxyw55j8NdZaUXWoAdOXR3NmLRlrrag8sDP2gC+3fAw1vRdo+5SIyVcnERgPQK6Wo/pkNYz1Wz++PnikAS//xsuIeZYKnDIZAxc5as/U4ujnTqS7zmwhK4C9y/4+2P1L+N0vDwBgKwJwAIi0tGP4P/9XVP/zN6CZXFqQEFVquB94Nype+AFYjsB6EZMk2IZ68eKv/wHq334ZnTdeh+hNTw4oNEpUdNfCua+OUtEJBeFkdxATKfT94HLxipRFRD1hOPfXY/ZakfYSy8g1SnT/3HHIFNlvJ41FB2OtFYHJPKlyjEFr169qX/RmEGQCWh7phn98Hq7bU4h5wxAUMliaHHDsqS5aLGbx5kFx32kwzdoqum+0xr1HcfnL1yBGEnmPYQJDy+d+DhqnEc4H+jFztmfNryfTKqE6dS9u/9V5DPzvVxGdDkCmUaD+qX1oOW6BfWSWAnFCyF1JppCh9kQrxl7tzXtMRXdd3u081UeaoXUYMXd9PFO8TW3RoaKrBvaOKsQDMUTmg2ACg77SXHQSeavZdbMwf/YDEAzmzGMyAE2nt+yS8rIeB5584mGMfucCRv/5MlLBGAxtFWj9yCk47m3ZtK10peqLdeJcTxwAQ4NVl/e4SEs7Bn7jWagnRqF0zUFSqRBu3QPGJTif/7eir8MA1Jg16HvoSfSfeRS/8ZgeDfwGkt9+AZ5oaVv5yO5HQTjZFdw9U4j515nmvMDc5IBncBbJSKKkyp/O7toVAfiipge7MPD8NYTnAkuz9Av/VZu1aH1k37YbpID0XjRzvR3m+tXtNVsMwEdOfQrnepPYzg0YFJ/8GLR/+MXMP8mdov/pP+Lvpg+CXZ6D4aWBokXZC0nKFPjOiT+HMDOXTnEDIEaTGPruVYz8M8PJ3zgALBSmT4TjmO+bRswfgSCXwdxgh7HWui1/TwghpBwce6rBGMPE2wMQl20JE+QCnPvrM8W28lkcrzhPF+1kgoCYP4K+565kF1plDLZWJ+pOtkGm3H63wIsB+IDiJM5diW/15ZTucCdw+D9m/noNAK5u2dUUEMf5syMFA/DlYrUNiNU2ZP7OOUdKq4M8UriTjKhUQdTq0CCXY9QTxutSFdRttWh8sA8CZb+RBdvvE4iQNXD1TJXlPEwmQGvRoePJwxh68UbR6tiGagsquuvyfl2uUqDjycPwj8/D3TeNZDgGuUYJW2tlOgVvB/QqXY1MAN6fxPmzY5Bt58BRuwdV7/tF7HnhX6AOLt2kJbQ69D/wBMaqjgEvjqL9hX+FXhTXHIRLAFIJCfLpOTCencLGRBGSxPDWn17DY185jctfHMHkheV96hncPVPpCZtHD0CVY08kIYTsBvaOKlhbnQhMeJAIxyFXyWGqt+Wd5M6FMQYsdEHp+beLEBN31HjhHPMDM4j5Imh/4iCEMlbuXq9MAC4cw7Nf7IGM7a77g+2i1AA8J8bgOfUAHOf+PW9KOhcEeE/cBy6XZ17v/NkRAI146NSn0Ii/oG1oBAAF4WSHkEQJgYl5JMJxyBRy6KvMUOpUmdXB1e7hzoUxoKbThAqTGzAB1R+shX82ivnRMLxTEQRcMSSj6T1rKp0c9QetaDxkgyArXhTF0QW0dlXc8ejWFFPZKIsp6Od6lwLwXPutgr4oZsf8SCVFaHRKVDVZoFRt0UfRmTPoP30auoFeKPw+pPQGhNr3ADI5FuuW1vZdg1BCRkQ+AgBlPP82CcY5xFgKl79yE5Pv3FHMbmEfQ8wfRd9zl7H3Z+7ZVjeNhBBSDssLeVZ0AUu3p2sr6nX15QlIiTwFw3i6v3h8rB91+yw5DtgamQC8wH5lsvXcD74b5otvQOH3rQjEuSAgpdPD9a7Hsx6nQJzkQkE42fbmbk1g4q3BFW2zmMDg6KxB5YF6CApZVgpbQTmKtzAZg8qswqH/9z5obEv7zswAFhORuMQR88TAwaGxasCEbbzKuwWE/Qfw1d7uvAF4Kini6mujcE8G053ZGMAloPfyNNr2V6Kxy7E1KdeCgHB7Z94vs2T+fePL5Upr50xA0mCAIuAvvJLOOabeKdAaj3MkgjF4BueooiohZFcpdyHPuD+O2S/eLrybjAGTIxF0/+b22Wg9IBzDuX4RxfYrk60l6g0Y/LX/jtpvfA2GvttZX4s0tmD8Qx9FyrRyckfG2EIgXp8JxN3P7aAtB6TsKAgn29r4W4OYu567SBqXOOZuTsAzNAtTnQ2ewbmie7j3f2QPJqN18PzzT8CTC0E7YzC+6zjq//BXMVW9vSqV7yTneqJ5A3DOOS69NAyvK7zwd2QmQrjE0XdlGoKcoaFj+/38Y9V16SC6QDXUdNsSO1Qe99K2f8YQbm2HbiB/saGsc5RQfX2+f4aCcELIrrG8jghk5bkljVztBxf/tfBBHAhOxzB44KNlec1yONezuv3KZOukzBaM/Of/CuXsNHRD/QDSAXi8qibvc2otWkx4Izh/dgxAPZ657zQacR4jOQJxSZTgG3UjPOcHwGCoMsFUZ6OK6rsMBeFkS3DO0ynkHFDoVCv2RiejCUy81Q/PQIHVwQWpWBJRTwhMSLfFyOfw/7MX7Lf+CEP9SbBf+E+Q3bwNiBLEjlb4nRUYCwAIFGiRQgoqlII+PxOCd65wIZOBqzOobbFBtsXtTO7kue9BGG9dy/t1LggIdXRh5JlPQzvUD/X0JLhcjmDHXjR/8X8ByF34LUuxpuULUrHSVuUJIWS7yyrkubACXA7CrBKGEo5LqdQ417N9xnwKwHeehLMKCWfpE+PZgXg3nrkPKwLx4IwPQ2dvIBVLpu9rAczdGIdCp0LLu7qhc2x9azpSHhSEk03FOYfr5iRmb4whEUp/6MiUclhbnLC2VCy0IGHo+beLpe/z5unWYnX3tmPyrcGcvUYPfLQT7b/zQXx12X5lrVuCKhREYnoAYXvhAmykuHwBOABMDXmLxpmppAT3VADOevPGXOAaBbv2w3foGEyX31mZbi4IkFQqTD39HwDGEGlpR6SlHQCgG+iFyl183z9HOgYv2seeAUpd4fZwhBCyU+SqI1IWnON+qwNajytvWC8JAsbb9uPWi4Plec0y2NaFTEnZLA/EH9pzDK0nUrC//G24w05EPSH0//tV8IXMOy4t3RgkI3H0PXcFXU8fy9uur5BkJIGIJwgGBq3DALlKUbbviawNBeFk1aSUCO/QHEKz6YrSOqcJ1uaKogWjOOcYfukWvIPZq9tiIgXX7Um4bk8CAOQaBVKx5KqvSx6fh1wJ3FkMFQyQNbdk9is7B3uw96UfQDsxmjkkWlOPmSffh1DX/lW/LikuHk2WstCLeLTEff2biTGM/8ePIe6sgv3lFyCLpouscQCh9i5Mve8/5JwJV81O521/lnV6AJH/8p9h+odvITntzn8gB+wd1Wv9LgghZNthGj0AIe8kLksmYbxxGUq3C6JGi8C+gzn3297J89hPQfeN/53zaxxIV1B/5D1U/IxsiVqLFqOeMCBTg/GlUGz6ymg6AM9TUFBKSZi9Po76e9tLfq1kJIHxN/vhHXZlVkKYwGBrq0Tt8dZt2arvbkE/ebIqwSkvBl+8kS6CtjBr6+6dxsSbA2h+eC+MNda8z/UOza0IwHNJRVcfgIMBY7cCkOI59u1y4NJv/gADP2NHjSDDwX/6mxWHqKfG0fjVL2D8wx+D/8iJ1b8+KUilkZeUca3UbNOPJJkMc4/9NFzvegLa0SGwZBJxZyWS1vx91CWlsmgAzgFE6xoR+JeXoC0UgDNAazPA3Jj79eLBKEIzfnDOoXMYobFQSiMhZGczv/0aqr73j5BHI+CCAEgSqv/p/8B7/D5Mvf9D4Ir8K3m+Y6egdLvgfP7fwAUhU9ODMwYuk2Hsl/8z4qtIIyZko0kpcSFQLnAQ55jvn0bdqbaSCtmmYgn0fP9iOvN02Q0YlzjcfdOIzIfQ8eQh6rqyRbbpHS/ZjqLeMPqfv7ZUpXzZG1pMpDDw/DXseeootDZ9zufP3ZzIWZm8LDggRvIH7xxAx09+CE04CHC+IjhinIMDqP3W1xHcewCSevWpPiS/qiYLpkd8BY+RyQU4qrf3XieuUCDc2lHSscE93Vk3f/mwVArO/psFjzHWWNH0YNeKoizJaAKj53vgH8tu46NzmtB4/x6oTbTKQwjZeczvvIG6b/zvzO1C5nOUc1jeehWycAhj/+mTmcWAFRjD3OM/jcCBw7C+9hI0o8PgcjlCnfvgOXk/UibzZnwbhJQsFU+VVBtGSkmQUhJkiuKB8/SV0Uz9pRU4EHEH4bo1Cef++jVcMVkvCsJJyWaujmbtT7kT5xwzV0fR/NDenF+PuIMbE4Bj6bT55gUZAL3HVTA9mAFAIgHTpbfgPfVAeS/wLmevMsBs18I3H8n7O9Cyz7ntirKth2gwwnP8PljfPA+WY2DlTICo0UAzPVH4RAxQGbXo++EVcEmC1m6Ao6sGGrMOvd+/hHhwZQ/y8Kwfvd+/hM6fPgqlXl2ub4kQQjaemELVv3wr73jNOIfp+mVoh/ozNTjyidXUY+rnPrwhl0nWhnMOcFCb1zvIVfKSirQymQChhHslSZTg7p0uet89d5uC8K1CQTgpCZckeIeKtADjgHfYBUmUVlQ7B1Bi9am1KfmjfCGlLR8uk0EzNQFvWa5q83CJwzUVwMyoD8m4CI1eiZoWK0y27bESyhjD4QeacOXVUXhmQpnFC87TvxbN3U40dm6/9mTrNf2+D0LpmYeh92ZmVXxxP6Ko1SK4Zy/Ml98pvFrOAdetpUA95ovAMzALtUWLeGBlAL4oFUti+JUetD7SDZmCPuoJIduHXTcL+YkPoC/WifNneyBjS/cMhp6bkIcKF0vlggDLW68WDcLJ9uGZDWGkxwX3ZACcA1qDEvXtdtS22SDLdc+4m0gS9L03oZqZBlcqEezcB0CNczf9QNshNJ45D/dzcViaHPAOz+W/VWYM9vbKklLRk5EEpGTx6v+JYAxckqj92RagOzNSEjEpFlwFz+Ac42/0gYscSr0atvZKqAzp1G5DlRmBSc+Groavd16VcQ6pTL1KkwkR4/3zmOifRyyahFwuoKrJgoYOO7SG8lW5jkeTuHBuCCFfLJPuzxgw3j+PygYz9p2syz0pUkQqKUIUJSiUcghlmLFWqOQ49nAL/PMRzI75kUqmJwuqmyxQaXZnlU6uVGLk45+G4dY1WF9/GUrXLCSNFr7D98B7z72o+advlJR+lkvMGyl6TGjKiyv/8Coce6pRfaSJqqESQrbcYmuyAeEYnv1iD4DsomwKr6foeM4kCcr5AnU0yLYy1uvG7QuTWQu9kWACPRenMDPqw5GHmyHfpfuS9bdvoOZbfwulzwu++ANgDMa9h/F64j8Aj7dleoZHPQ3wjbpzF2djgCAX4NxX2qp1Kavl6fOy/Ns6yIaiIJyURKaQgcmEpf3gBbh7pjOr3tOXR+DcV4eae1rg7K5DYMKzIddXLIzhjCHmrIJmZqrgcUySEOzat+7riUeTeOvHA4iGlvo6JxMixvvcmBiYx9GHmmGpyL13fjW4xHHh3BDC/oV2bgs/iMVBbmbUB4VKhq5jtSWf0z0dxPDNOXhmQwAAuUJAbasNTV0VUKrX/5Fhsmm3zQr9phAEBLsPIth9cMWXkkZTyT3C10zicN2aRHDKi473HqZAnBCyZXL1Br+zN7ao1RUvaskYRB0VoNwJAp4obl9Id7/JNdT55iPovzKDzqM1m3xlG0/XfxuNX/0CFm/OMlvTOEfVzctQB3x4Vf6rWOwZ3onzSMUPYOjFpT7hQPpeT6FRouWRfSW3J1NolNDa9IjMh/IfxBjM9baSVtZJ+VEQTkrCBAG2tsqF/SWlrYgvmr0+DkEuQ/WRJlQdbsT0pZGSXlNpUEOuViDuD0NaaM3ACvWZNpogDwVXpPbyhVm+qZ/9j6j+529CPTOZM/2XCwLijkqE2zpLur5Crr0+hlg4seJxzgEuclx6aRhnnu6CvITCGsulkiKmR3wI+WOQyRgUKnl6BbyAif55tOxzQqUuHnyN9blx+53JrCWIVFLCSI8L4/3zEGQMXOIwWDSob7fDWW+iD+918B07BcdPnt+U14r5I5i5Mora460A0lVTI54wGAO0dgOlrBNCNtSdAfj5syMrAnAACHbtg6RQQkiuHEMXMc7ho04mO8JYn7vwXDMHJgbm0XagctX3RNsa56j6538EwHPWhWFcgnVsEFW913FekAHoxsdOpLAPb0BXYYRvxIXwXABggL7SnA6WV5kyXnmgAUPnChR+5RzOfXWr+75I2dBdFylZ5YF6eAdnIabEVaeUz1wbg3NfXckBm1ytQMcTh6DUq9H4uAqpvSfxf09+Adzrh8BXBtmRhiaM/dInUPuPfwtD761M4M0kCSmjCRMf/E+ItHZg9Fd+Fc1/8f9B4fNmqqQv7tFNGk0Y+din0vvG1yHkj8EzU2DmEenAdnrEh7o2W8nnnRzy4NbbE5BEnp4d5bzk+ZC5iQDqWgu/VjgQTwfgwMp/Xw6IKQniQhtvrysM71wYFXUmHLivoSzp6nejWE0dfIfvgenyOzkH6bLigKtnCo6uGkxdGIZnWY0HQS7A3lGNmmPN1KqEELIhlgLwJM6fHcsZgAOApNbA9dCjqHj++zlXxLkgIF5RiUCO7KKdjHO+Kye152eCRe9VJJEj4InC6lx/huB2oZ4cL1p4lTMBbdfexGzXIZw/OwrgID52Aqh4+dsQWpywtjjXdQ2W5gpU+cLpxa/lMyEL/19/Xwf0leZ1vQZZOwrCSclUBg3anzyMobM30hWZl1fXKoKLEqYuDqfblJVAX2WGUq/OFG/5m8GDeOfD/z/s/+F34ei/mRmYJZkM3nvuw8zTH4CkUmPk//kvUM1MwXD7OlgqhVhVDYJd+zOBddJqR/9nfxeWN1+F9c1XIA/4kTIY4T1xGp4TpyFp15/eNl8kAF/Ud3kK8WgSta02qLWFV6lnx3y48cZ45u8l7c9fwBiQShQvzjHaU7yH+9IFpP8zN+7H0M1ZtO6rLP25JMvEL/wnSAolLG+9uu6aBsVISRG9P7iMZCSeNdEipSTM3ZpA2B1E++MH11RDgBBCipLJATDIigSbc+95CvJwGLZXz2V6hGOhuGXMWY2RT3wakO38CcNUMl07ZqzPjVg4CUFgcNab0LDHsWu2bZU6v8w3eiJ6kym880WPYVyC0u1CrUWLCW8EgADBYIb+TD3cz8XLch3Vh5tgqrNh7tYkwrN+AOm2p46uGmgstKVjK1EQTlZFa9Nj788dR3DSi9CsDwDgH59HxF0k8GSAb7T0IirhuQC4axDTjnbc/OtJXJmOIFlZh9lP/jo8HjfUk+OAICDS2AJRlz1zGq+sRryyOu+5JY0W8w++G/MPvrvk61mNUgPkVFLC4I1ZDN+cw8H7G+Goyd0jm3OOvivTa78eDmj0yoLHhPwxTAysbb/+WI8bzV0VKwK3ZCKF0R4XQv44lCo5nPUmWJ36Nc30p1IiPDMhpJIStAYVTDbNrlkx4HIFJj/4y3A/8G60/tHvQCjSV3y9kuE8AztPtzZz906hoqv0GgKEEFJ2goCpn/0Q5k8/BMub56Gcd0NUqxE4eAzBzu51Z6xtB8l4Cm+/MIiQf2lLmSRxzIz6MD3qw4F7G1DZYN66CywTi12L2XF/wWCcMQaDpbS9zjuFpCk+icKBTaltoHMY0XQm9z0m2ToUhJNVY4zBWGuFsdYKIN0GITIfLtq+LBEqvHd5uWQkjkv/Ogb8YAJcehFnOEeopR3jv/hxJK12JK329X4bG8ZoXcVAwgGJc1x+ZQT3PtEOnXFlT+eAJ4pIMP/euGLkChkq7gjwOefwzoUxPx2EJEqYHPKuuTZYMiEi4I3BbE8POJLEceP1MUyP+rKOG++fh0avxJEHm6EzllYdnkscA9dnMNrjhphaCk51RhU6j9XCVrl7Utfi1bXwHb8vvSK+wYF4Ia5bkxSEE0K2BSamwBUKJM1mJGwORBoad0UADgC33plEOLDyvmhxLL722ihMdi00usKT6NtdfYcdM2P+vF9nDKhqNEOp2l0hSbipFSm9oWi7PaptcPfaHZ9kZEvZ2quK5huxUlslLFo8nShl9srqhgfQ/Bf/H4RY/t7I24GlQpcOMlezUMs5RntzZwokYql1Xc+eI9VZq9ThYByv/bAX75wdxPCtOYz0uJEsIV29EL4QNHLOceHFwRUB+KJoKIG3ftyPeCyZ91ySxCGKEjjnuPHWOIZuzGUF4EB6//rFc4OYnyk8uO00M0/+DBIWWzr9covEfJFdlxZICNlZhGgEDV/5U7T94e/AcfY5WF97CVX/8m10/r+/DscLP9zYjhKbIB5NYmbMV2ztAhP9xVOatztLhR5NXRW5v8gAjU6JjsP5sxd3LJkMc4/+VN4vc0FIb4e8595NvCiynVAQTtZNV2GEqc5WMOisOdq87tdJ9wV1wfrGK+s+10ZijGHfqXoIAiu59SLnwGyewHWtbcEUShm6T9ShpsWaeSwRS+HtHw8gEohnXrccRDF9otlxP7xz4YLHJuMixvvSNxbxaBI+dwThQAyzYz689eMBvPCP13D2W9fxyr/cxtSQN+95OAeuvDKCeDR/QL/TiHoDBj/z3+E5cT8kxVKdAEmtXt2kzjowge2aVH9CyPZg181m/v/82YHCB0sSGv/qz2HovQUgPfYLogjGOZgkovIH/wzby2c38nI3nNcVLqG3arpl6G7QdrAS+07VQ29eyvaTyQU0dDhw/D1tZWl/uh3Nn34Ic+9+EkA66OZY6NgDIGUwYfiT/3VF2npfbP0desjOsDt/68mmYoyh+eG9GD3fA8/gXDpYYAyQOJhMQM2RJlTsrcXk24OrKiiWE+ewvP4y3A8+WpZrX9+lcCQTIgSBrWirYbJpcfzRNvRcmCwalC5aDGTvZLRqoNEpEA2XFmwyBuw9UYeqBnPWCrgkcdx6Z2LdK+u5XDw3BKNVU3JQP9rnhn8+AvdU/huMWKT495tKSnj9uT4cf3cr5iYCGO+fRyySgFwhQ3WTBfXt9qL74bcbUW/E1Ac+jJmnfhZK9xxmQkkE7A48KA5D+wd/sbEvzhhM9dt3qwchZOfJak3Wk54ArrXk3y+r77sF3VB/wXM6//1f4Dl1Bly5sz7fF5V6L7TDF/wzGGOobrKgqtGMRCwFSeRQaeS7vwgoY5h94n3wHrsX1jdehmp2GpJSiWD3QfgPHgWXZxflPX92BEA9nrnvNBpxHiNlKs5GticKwklZCHIZmh7ci+ojzfAOz0FMiFAa1LA2V0CmTP+aKQ1qxP3rSyVnQLq92BYSUxJGe1wY63MjHk0HtEabBvYqA/QmNZRqOQKeKEZuuZCIlx7wag2590kzxtDU7cStt0qrLM95+jnJpIh4IA6lSoZUUsLFc0MlBbZrFfRGS75hSMXFss3wJ2IpvP5cX1bKekJMF4Ub75/HkYeaYXEULnwiihLmxv0I+eOQyRkqakxZM/ZbQVJrEKttgBlAyBvB+XgV3qOQgyfLP4mSwTmc3bQfnBBSHqX2Bl/O/Pbr4AuV0PORxaIw3L6OwIEj5b7kTVFK5XPGAItjd1RIX8QYg0pTuBvMbpSocGLmqZ8reMxihfTzZ8cAdOOZ+0CB+C5HQTgpO63NAEEhg85hAFvY2xqZDyGeowBJFiG9el6MWELFyY2SSol45+wgAvPZkwmB+eiKx1arvj1/H2+lanWtWIZuzuL662OZvy9vD7lRVn3+Ml7PnXvGgfT1iCkJl14axpmnOyG/o/91LJLEeL8bEwOepeyAhSzs/iszsFXpsf/ehm1RLKbWosUEgOGDJ9F48dWS3ierQj1DCSFllh2AF+4Nvpwi4CtanJIDkAfyF/vKJ+SPYXbMj1RShEanROUWFQTTGlSwVerhmQ3lHTs5B+raKTPpbnJnIP6xEyk04g0KxHeprb+7JDuGmEjBPz4PMSlCZVDDUGUBE9JRS2Q+hIk3+xGc9mWOl2sUqNzfgIruWkxdGi4apXGlEkGDBQb3bKYY24pjmADfsVNl+55Wa+DqDAKeMheGY4DZpkV1kyXvIdMjvlWdMuzP/sDeLSlta5FKiJge9qGubWmSwzsXxsWfDK0M3pf9nDwzIVx4cRAnHm3bFilztRYtet/9Pmg9blQM3gaTMfA8WxhWg8kFWFuccO6thca6e6rNE0K21p0BeLHe4IuSRnPRlXAGIGUoveVSKini+utjmJsIAAxgYOCco+fSFNr2V6Kxy7HptTD2Hq/Dm8/3F9wiNnJrDl3H6yAIVKfjbrEUiI8COIiPnQDsL38b7rBzqy+NlBkF4aQoLkmYfGcIc7cmwcWlQVGhU6H+VDuUOhV6vn8pUyF7USqaxMRbA4h6Q/CX0CM8Llei/+GfwpFv/RU4Vtah4oxBUqswf/rBMnxXqyempHQv7TIGtIwxVDdbMhXMOedIJURInEOpkmduClyTgfK96F1ofiaIujYbOOcIeKK4cG4QUpEAlnMg6I1hatiL2tb8WQqbqbrChLc/+HE8qJlD69nvIHBzAlJSBktzBSyNdky+MwRfCe+15XhKgqnWSgE4IaRs7LpZyIzdgEyO82cHIWOs4D7w5XzHTsFy8c2Cx4hqDYJd+0s6H+ccV14ZwfxsaOEBgC8M5Fzi6LsyDUHG0LDHUdL51kISJbimgoiGEpArBDhqjdDolTj5WDve/FF/3gKjk0NeCHIBXcdom9DdZHkg/tCek2j9LID/RYH4bkNBOCmIc46RV3rgGZhd8bVkOI7BF65DaVCnA/A8Mc1830xJr6WMhKE8eQITPI6a7/4fcL4Q1DMGJkkQdXoMf/zTSJmthU+0QUL+WM6057Wob7fDWqmHxaGDUi0H5xxTQx4M33Yh5Eun7avUctR32NGwxwGp3OnHG2lx9mQbXXIsnMDMqA+D12cR8pferx4Abr8zCVulYdsUeJPJZEidPoEHfrUCiRf+PStNreWRfUiEYgjN+ME5h0KnwsCPr4EX+r1lwNzNCVia8rSQIYSQdSo1AAeAUEcXwk2t0I4O5V0Nn33PUyUXZfPMhjE/Eyp4TP+1GdS22iBbbTvVEkwNe9FzcRLJuJgeHznA3gZqW22oajQX7fAx3j+Plm7nXbmXulySCRHJRApKlXxFIV0ACPqiGO+bX/g94bBU6FHfbofRqtn8i12wGIg/+8Ue/PYnty4QT4RimLs1ifn+GYjxJBRaJewd1XB01kCupt/J9aAgnBQUcQVyBuDLJYKrC2rySanSH3beU2cQ2HcI1jfPQz05Di6TIbRnL/wHj4Erdscb3urUwVlnApCe6Oi5MImxvux+oPFYCv1XZzA3GYDOoEI4sHl7gpjA1lbJngFVjRa0dDvRd2UKc+PbYwXfPx/F1VdH1/RcSeK4+JMh3Ptkx45o3aXUq2FtTReVi3pChQNwAOBAaDaAVDwJuWp3vL8IITuYIGDkY7+G+r/7Mgw9N9OtnRYm48EEzD72U5h/4JGSTzc17ClaF0VMSnBNBlDZYM48FvBGMdbrgnsqCEniMNm0qG+3w15tKHksmB7xZtVnWZyc5jwdXM/PBIvXbOHAzKhvQ1fqN0sqKcLnjoBLHAaLBmrtxo45PlcYgzdmM51YGAOc9Wa07HNCb0qPk6O9bvRcmMz6d4iGPJgc9KD9UFX+HuebICsQ/8SxTQ/Ew+4g+n94GWJKzPzuJkJxTF0ahqtnCh1PHoLKsHUTFTsdBeGkIHfv9OZU9RIYJg7ck/mraDDC9cgTG/uaq6Q3qSGTC+teDWcCg9W5lPrrmgysCMCX87sjMNmLryKoNHLEY6l1r0DbqtIr9APXCk++5MSBpk4HdEYVGjoc2yYIX69wII5bb0/A6wojFk5CrhBQ1WhBfYcdGt32WCHPaRWN6q/+n1ehd5rg3FcPU71tR0w4EEJ2J0mrxcgnfh3qiVGYrlyAEIshabPDe/QkxFXsBQeAeCRZ0i3M8hXpiYF53HxrIuv2xz0dhHsqiJoWC/Yeryv6GSlJHD0XpgoeEwkmil4XY0AiLhb/BrYxMSWh78o0Jgbms7aCVdSZ0Hm0ZkOC8bkJP668MpJ1S8Q5MDvmw9yEH8fe1QoxJaLnwmTma8uPA4C+y9PQGdWoqF3d71w51Vq0GPWE8eyXB/DbnziGxjNvAC+PbXggLokSBp6/lhWAZ3AgGYlj6MWb2PPUEbpfWCMKwklBMX9kc6p6MYbh42ewnRNiZXIBta02jPa61hXo1rZYoVhWjXWs151JUcvH744UPe9iu7S1aOqqgM6kgtmug86owsjtuaLXlM/i7LLerAZj6eI3u8HEgCfz/2JKwshCC7SjDzXDXKQF2lZRmzSQaxRIFUl3BJBeEZ/xIzRzHaY6G5rf1b0tCtIRQu5esdoGxGob1vz8eDRZ8tioVKfHZf98BDcXWoJmDV8L/z856IXBrCm6Mj0/HVxVm9J8OMeGrxhvJEmUcOHcEHzu8Ip7CteEH353GCcfay9run0qKeLaa2M5b185T9cCuPrqCLQGZdF1puFbs1sahANAg1WHUU8Y5/pFPHTqU2jEX2x4IO4bcSEVLTBJxIGIO4iwKwB9hWnDrmM3ozssUtBij++NJlY4EDVvj+JXhbQecMJkXXuLNFuVHh1HqrMe87kiZds/rdYqii9+sqUFUqVajsMPNqH9UBVqmq3QGdO9ynUm9ZqvKeBNV49XquSoajKXvBi74yy0QLv40jBSyc1dpTh/dhhfudII+YmTsOvyZywwQUDF3tUX9PGPz2PyncH1XCIh5C612JpssPuXcK5n61orhfwxvPbD3pLqgMjk6WJpADDa4yo6bo3cdhXdshUNF1/lLgUTGCrrd26QMznkhc+1MgAH0sFvIpbCwLXSageVanrYWzhrkQOxcBKemXDRdSafK7LpY3wuDVYdzp8dAWRyyIwbPykQmPQWz6ZjQHDSu+HXsltREE4KKqlY0zqDLA7AL9OW1D90q8nlMhx7VwvaDlZCpVn9BEV9ux2yO1YXeZkicCYw2Cr1uWeTGSBXCNh3qh5tByrR3O3EwfsbcebpLjiqV36Y2ysNUK1x5v3NH/Xj7RcGEA0n0HGoGhq9cvcG4lhogbbKFnLrUWvRQsYEnD87igHFSZg/+4GCgXjl/noY61ZfzNB1ewpiYv2rOISQu0d2b3AR58+ObMnYzjnH5ZeGkUqUFjw1d1dALk8X7HJNBosGZrFIEuFg4QmGXAXAVmDFV7lbup1Z2XM7zVhf4Y4dnANTQ96yBrq++Ujx+HEV9yWiWJ6ivDtJOoux2P3pGusHEQAUhJM8EuEYpi4OYfbmeKYXeD72PdUwVC/0uF5+KAOYTIBQQrXR2LET67jazSWTC2je68SZp7vw0M9248H370X3ybqS9m3femcy84HlnQvjnReLt8oqGedQ65U49Xg72g5UQqNTQhAYlGo5GjsdOPVEB6qbLGje60Tr/ko460x5e48ygaF139rTnHyuMN7+8QAA4PijbajvsGdVndUatvE+6jWYnw5u6ustBuLPfrGnYCAuiRImLwwjOOVb9WtwUUJwevXPI4TcvbJ7g29NAA6kP5MjoURJu+mauyuyim9JBfqTL1ds7HbUGIv39+ZA+6Eq1LUtZQIuBodMYGjdX4nm7u28Ua+4UorKShJHLFzCtqkSlbpHuZRq+HKlDIpNygrdTnQ2Q/EYnHNo7YZNuZ7d6O77rSJFzd2cwPib/SWnI4emfai5pwWOzmq4bk0i5o9AUMhgbXbCvqca833TmLo4nPO5EmNIanTw3nNvGb+D9eGcwzMbQiQQh0wuwF5tzOwVW84/H8V4vxsBTxQyGUt/6BfZRx2PJOGeCYJLHJdfGSlrGy/OgaoGCxQqOZq7nWjuXnsQHQnF0Xt5el3XEosmMdrjQtvBKuw5UoO2g1WIR5IQZAwqjQIXXhyCZ7Zw25g7qdRyVDaawTnH3HgAsUjxQXsz6gpuRQu5YlVTucQxePY6AuOewicqQEptfQoeIWRjSCkRnqE5hGb8AAC90wRrSwUEeQkruDk0Pq6C4r7TONebxPmzY5BtYQrU/GyopM/+6mYL2g5UZT1mMGvgny9ch0UQWNHJZIVShoZOB4ZvzuX8OmOAxqCCs96MqkYLmrudmB3zIRFPQa1RoLLBvKNXwBcJAoNYwhgpyMr3+2Kt0GFysPDYxzlQ2WDG5JAn/70YA+pabcUnU3Yha5sTE+8MgufLAmCAQqOEqW77byXdrnb+u5uUTWjOj7FX+xD1rC4wivkiGPzxdTS/qxvtTxxa8fXKgw1IhGJw904vDYoLBbuSGh3GPvlfIGnXvs+6VJxz+N0RTI/6kEqI0JlUqGmxQrWsz6F7Ooibb41nzcgyBtS0WLHnaA1kMgGcc/RemsJoj3tNAV7QF8XQ9bny9tFmgLPOlNnTvV6D12Yhrjc1jAPjA/NoPVAJxhhkMgFaw9L1lZSqt8BebUB9hx0zo15Mj/jAOYfRqkHL/kpEQ3G4JoNIJVJIJkSkklLWZIjerEHTXgduvjkBSZRW9e+lUMnSvV0LYSi5l2gqKSIRS0GulEFZhpurxaqp5/pFoC1dNdW90DfcOzy3rgAcANTm7b9FhBCyeoEpL4bO3khvOVkIluf7pjHx1gCaH94LY83qtrAsBuBf7e3OBOCr6Q1edgv3GYU+8BnLvRJa32HPbiuW43nVzZaSxrC2/ZVIxFKYHFzWJm1hfNIaVDj6cHMmwFNrFbuiDdmdKmqNmBn1FRx7tQYVNPryZcg5G8zouTSVd/xmDNAZ1eg4XAW/O4JQILbinowxQKNXoqlr+/2bCPsPAN+/UdZzckmCf2we3lE3pKQIlVGDmqPNmHhrYOUCE0tnGzQ92FU0W5bkR0E4AQAEp33o//cr69rbMXq+F6Y624qKyowxNJzeA3tHNYIDA4hpjUjonXhN1wLZ/fdDUm98j8FELIV3XhxAyJedFtV/ZQaNnQ50HK7G/EwQF38ytOKDmPN0ZexYJInDDzRhrG8eoz3uzNdWK+SLrbvN2Z2UKjlqW1e/7zeXVFLEdJEBs1TJuAhJ4pDlmuFexee20arBpZ8MZ91TeWZCmJ8OobrJgpOPtWUqsfvdkXQVVjBYKnQw2bSQJI5ETMTI7bn06jnPfX9mtGrQ1FUBJjAoVTKYbFq89sNeREKJ/JMmHDl/9t65EHovT6dXVHK8ntWpR8s+JxhL9yn1zoUzj9d32GEpseJ6erVJDsiybwhdtyfXXOEeDNDaDNDa9MWPJYTsKFFPCAM/uro03i/7YBITKQw8fw17njpa8vu/8XEV5CdObp8AHOnP8mL3M5wDJtvK66xqMGNm1AvX5MptRowBKq0CrfsrS7oOJjB0n6hDQ4cdE4MeRENxyBUyVNabYS8lXX0XaNjjKFo3pWmvo6xtrmQyAYfONOHCi4PgEs8e61k6xfzg/Y1QKOW455EW9FyawvSwL9PNhTGgst6MPUdrtlU2goyxdPbbJ4+h9bOpsvUMjwej6P/RVcT90aX7hoX/mpsqkAzHEJ5bajlrrLWi5kgzpaKv0/b5zSJbhnOOkVdur7u4ghhPwj/qhqU59/4lXYURDU01sPz3X0C//DT+8c+vomETAnBJ4njzR32I5tlvNHLbBc453FPBggGLeyoI91QAwzfX0D97gyXjKVw8N4yue2qz9patRTyaLFuhDSawvDcZOoOqpEyCmhYLhm6k0/ly9fGcGvbCYNGgsTM9iJsduqyWYamkiAvnhla0eeM8vdJd326HUi2H2a6FMUfl+32n6vHO2cF0ynmOa91ztBpqbfYM/miPCz0Xs/vD3vl9euZC8JxNZ50s/znMjvkwM+pD6/5KtKxjX37Um7sabVEsXVW94b6ONb82IWT7mr46WvBzl3Ng5toomh/cW/Rcdt0s5Cc+gL8aPIjzZ0chY8KWB+BAOjOsWCaTXCGgst684nEmMBy8vwmD12cx1uvOFAxjjKGy0YyOQ1WrbqdlsGjQebRmVc/ZLUw2Lfadqsf1N8bScd3C797iuNfY6UBNc3kWEZazOHQ49XgHRntcmFqolq5QylDTakXjHkfm31ChkmPfyXp0HKrObEMw2jRZWZLbRbFtaGshpUT0PXcFidBCF4HFz4aF//qG51BzrBnND3cjFUtCoVVCodlddX22CgXhdzEpJcIzOIvZ6+NIBIu38CiKMUR9YVjWf6aymh7x5g3AFy2ubBfCGDB82732ftwsPcO+mjTsUi0OarfenoDFoYPerF7zuUq+vsUt8Hlu5tIzyaa8s9u1rVYM38q9V26RQiWDmJKKBusjt+fQ0GHPmRZ1443xvPv7UgkRk4MenP6pPXl7YpvtOhx/tA39V6bTEzUL9GY1WrqdsFcbIEk8M9kQ9EVXBOA55ZhQWP7/A9dmYLCoUVG7ttY0gkzAWjYU6CpMqD/ZRjPchOxCkijBN+Qq/IHKOXxDLkj3S3k/F4F0AG7+7AcwoDiJ82d7tk0ADqQ//w7c24CLLw0DfOVKKAOw/96GvIW5BIGh7UAlWrorEPDGwCUOnUlVlm1Ed6PqJgtMNg3G+ubhnkrXxTHZtahvt8NSsXHbnnRGFbruqUXXPbVZ43QuSrUcjpqt7QdeisVtaM9+eQC//Yn0NrT19Az3DM0VjQFmro6hYm8tlLrybHkkafRpcpdKRhLoe+4yYr7CxUdWh6+5oMtGGrnlKst5OAdi6+j7aXHo0HW8FvPTIYz3za/6+bYqPTwzoYL3Toyl24F03bP63tCLVBoFTDZtwcI0jAG2KkNWUJpLY2f+qq5agwrNeyswlKdoDQDsvacWN96cKLpaHo+mEA7GoTdlTz5EQnHMjvvzPo/zdKuZuYkAKhvMeY8zWjQ48mAz4tEkouEEuATMjPlw461xiMn0JEFlgxmNnRUYvFGmTAmWztJYaxBubnSkU9IL/OwEuYCK7jpozFrIlHKoTFqoTdk30ZxzhF0BxHwRCDIBxhor5NtwhYAQUpyUFDMpt4VwziElxbxB+PIA/NkvbmwAnkqKSCVFKJTykqpZL7JVGXD83a0YuDaTNVbZKg1o3e+E2V48+BNkAswldD4hxemM6i3NBthNqf8NVl2mHsxDpz6FRvzFmgNxz2DxexYxkUJwygtTvX0tl0vyoCD8LiKJEqSkCJlShqFzNxDzlzMAR3rvSIE36GL/0H7pEM7dzB8YlVssWr62FwqlDNFVPsdgUaNlXyUqaoxgAku3LZGxkluTpYuDqNC4x4H56cJF8zhPF5dbr+buClx+eaTgRbUfqkJVowU33hhbESQLMoYD9zUULVjWeqASCpUcQzdmkVzWz1WrV6LjSA0qao24/sZ4Sdcs5ajgmWtP38rvBZib8OcNwjnnGOt1Y/jWXN4sCM6B6REfZkb9YOVq/MjTbexEUVrRW/5O588O4KE9HZCfOAn7y+nUtIq9tXD3TBW84a4+0gznvrq8Xw/N+DD6am/WZB0TGOwdVag90VZwlYwQsv3IlDIwmZC/4vECJhMgU+afVNefqQeTy3GuJ75hAbh/PoKhG7OYm0jvRV2c7Gzudq6YcM3HZNPiyIPNSMRTSMRSUKrkObudELIWIX8Ms+N+pBIitHolKhstUBR435RTg1WH82dH8NDeA1Dcdxp4+RtrOk8qVto9ciq+xixQkhd9Eu1SnHOEpn1w3Z5CZD6AVDwFcfENJDCg3C2VGGCssUJtzj0QLwbgS/1Dx8rWPzTojWJq2JseYNVyVDdbYDAvBX8yGUO5PjpqWqxQaQLpdKoSf4T1HXY465ZWMwWBoeueWtwoMbhU65Q4+lAzwoEStwyU4Z+2otaEPUdr0HNhZXEvQcZw8HQjDGYNDGYNbJV6TAx4FoqhpQuL1bRYS0rbY4yhsdOB+nYbPHNhJOMpqHVKmO3aTBq73qzOFDfLJ90uZmWalCQWT2UHB8Q8EyJiSsKbP+5HyFvaz55zDl7mrl5c4kCBMT1rj9gnTy7tEYMTzQ/txdC5m+lAfPFbXPiB2DuqUNFdi3ggCtftSfhG3ZBECVqbHo7OGggKGfqfW1mskUscrttTiIdiaH33/rIW0yGEbCwmCLC1VcLdO11wL5GtrRJMKDzJxjQbV7jRPRXApZdGsPyDn3NgZtSHuYkAjj7csqoVaqVKTqnkpGxSSRHXXx9LTxAtVArnEkfPxSm0HaxCY+f2q6iej8qgTndFKnLvqNRTKnq50SfSLsQlCcMv3YZ3KE+a7wb0NJYpFWh6sCvv1+8MwMvRP1RMSbj+xhhmx/zpuALpeHHktguVDWZ0n6yDTCZArVWufR/3AsbSxTuqmyywOvXwzPVDTJZW4VyXIzisabYiGk5g8FruNCC5UgaTVYOqJgsq682QyQUIclY0oGQMMDvKsyLR0GGHo8aAif55+NwRCAKDtVKP2hZb1kqCSqNYVwExIJ3yZ6/KvQe5vr14u5iqptztYnRGdfHJEgbo87R2u/HGWMkB+EZQaUpLv8xXrAWNTuz92eNw3Z6Cf9QNSZKgtRlQ0VUDfZUZ/rF5DL14IytI90fi8I/NQ6aUFyzQFxj3wDfmhqVh59xsEEKAygP18A7OQkyJK2+8F9p2Ve6v35JrA9Jj+5VXR3Nm8XAOiKKEq+dHcP9TndQeiWw6zjkuvzwCz9xCZiJH5ndVktItbAUZQ337zkjdtndUwzdSuC6SUq+CvtK8ORd0F6EgfBeaujicPwDfIKZ6K+Sq3PtEF/uHnutNlrV9yY03xzE7lk5rXxyrF4fsmVEfGGOobrYU3NtcKqVajsMPNsM7F0YskkTrPicmBj0I++MFn6c1KLMqdS/Xuq8S9ioDRnvcmJ8OQuIcJqsW9R12VNQaV6wwqtQKOOvNmB3L3z6M8/TKe7lo9Sq0H6ou2/nWIt0uxgfXZGDF1xbbxbQdyN0uxl5tgFItRyJWYBKGA7WtKyvKx6NJzIxt3raJXOo77Jnfg2RChM8dBpc4DGbNip6qeYu1wInae1pQe09L1vExfyQdgN8ZaC/8VUwUn7gae7UP5no7rYYTsoOoDBq0P3kYQy/eQDwQzfQJB+dQ6lQwVFsx8Pw1SKIIjTWdGWOstW7a+3x6xFt4knuhlod7OrgjCmmR3cUzE4JntvDWwP4rM6htse6ILVvGWisM1RYEp715V8PrTrTROL8BKAjfZcRECnM3J8pyLrlaAX2VGb7h4oXNNJbcaWmLAXi5+4cGfVHMjPoKHpOuih4vqQ1WMVWNZlx4cTCr3YkgY9AZVQgHcgfijAFd99QW/OAy23Uw31d6Wv6eI9XwuyOIRRI5v6eWfaUVm9lJ0u1iGjF0I90uZnHvOBMYqhrNaD+Yv12MsNCj9dJLw3nP37LfuSKgBQDX1Mqgv1QyhVBypkROLF0MrqHDATEloe/KNCYG5rPqCNirDOg8VpOVhr+aYi2u25MlFWgqJBVN4PLXX4HOYYCjswbmRseOuOkg5G6ntemx92ePIzjlRWjWD3CAyRimL49ivn86czOeCKczYyzNFWh6oLNoino5+NyRkrK+fO4wBeFk000OeYv+fqaSIl751x4IAoPRpkFdmx1Wp25bBrKMMbQ8sg9jr/YuFWlb2IYoUynQcG87zI2FM94i80G4e6cR90cgUypgbnLA0mjflM+LnYyC8F0mOO2DlFrHzf8CJjDsff9xCHIB1ya9BVfFGGOwt+deiQQW940JZQvAAaT3KpfA5ypP8bmR2ytTdSSRIxyIw2TXIuSLQVz2c9cZVeg8VgNbZXnbPKk0Cpx4TxuGbs5iYsCTeU2DRY2mrgpUNW63BnHlIQgMrfsr0by3AkHfQrsYowqKEvb4OWqMOPpQM3ouTSHkW0otV6nlaNnnRG2evupiau0BqlqrgMWhw+SgJ+dALZMLqGq0oLbVgqkhLyYGPZkAWyYXUNNiRduBSjCB4eK5oZyz7vMzQbz5fD9Ovqc9axJhsVgL0FgwEPeNuMtSP4CLEkIzfoRm/NDaDWh77EDerBhCyPbBGIOxxgpjjRWJUAw3vvvWyoJtC58R3qE5qE1aVB9pQuPjKshPnMRXrjTi/NmRsmwvy76uko8s6+sSUop4NPciyMrj0gXPYpEEZsf8qG21Fl2YWS0ZY3j2z6/gtz9xCK2fTa25Z7hMIUPTg12oOdYM35gbUlKEyqiFqd5WcGKdSxxjr/Wma0wszkwwwDs8hymDBm2PH4DKULhA792MgvBdRkqVpypU3Ym2TBuihtMdGHrxZt5ja0+2Qq5euZK4UVIpEd658Ka9XjF+dwT3PtGBSCgOMSVBo1fCZNNu2IynUi3HniM1aD9YhXg0BUHG8q4E7zaCTIDJtvqJHFuVAaceb0fQF0MsnIBCKYPJrivYskRnWPvvdDSUwH1P7kHnsRqM3HbDPeUH54DepEZloxnWCn3m98Nk06HtYBWCvhgY0oXoFve3Tw558qa9cZ7uc953ZRoH7mvI+tpSIF6fNxDPVU1+vSLuIHr/7RI63ntoUz8TCCHr47o9CS4V/kyYuzmBvafUkJ/4D/irwYM4f3Z0QyqjWyrSxT4L4RywOndX1hfZGZRqxYqCtYUsBuwTAx7oTWo07ClfHZU7t6FlCrOusWe4Uq9GRVfpLW6nLg2nA3Bgxb7QeCiKvueupBf0KEMuJ/qp7DIq0/oHQ0ONBY6upV6OlqYKtD66H2pL9oCnNKjR9EDXqt6w5bCayuSbgTFgetSHiloTqhotMNs3J+VIkAnQ6JV3TQC+XowxGC0aVNSaYKnQF+0Zaqs0QLGOVjaSxHHzzQn0X5mGzxWBzxXB5KAHF84O4Z2zg0gua/chV8hgcehgduiyCsyN9xUulsI5MDvmQyJH6xAZYzh/dgzn+pMYOfUp6M9kF1rSWHUbspAU80dw9RuvYfZa/mJ6hJDtpZTMGDGRgvjgCQwoTm5YAA4AznoTFKr8LSEYA7QGFazOjavOTkg+1U2WNWeRDd+aK1jwdC3SnYYYnv3yAAaEY9CfqYddV7z393qJiRRmrxfo8sOBRDAG7/Dm1qjaSSgI32W0Nj00lvXdXOdKHTHV2dD1vmPofPoYWh/djz1PHUH3z52AtXV9VbHXYvm+7O2AA4iGChdoIzsPExj2ncjfQ7sQg1mNnouTmBr2Alg2QbzwX58rjMuvjBTdkx0qUvhv8ZzRUGLF47UWbVYgrrjvNBofX9o/XtFVU5Z09NwXBUy8PQjX7dK2jRBCtpYkljaujqfa8OwXezYsAAcAmUzAofubIMjYitR0xtKps4fub9yW+2vJ7mevMsBo06xi28SSeDSFoK/8HVcWA/Fz/WJm0n2jA/HAhGfl9pUcNrtQ9E5CQfguwxhD/b3t6cFpjeOTxpp7dpkxBq1ND1OdDTrHyurdd1r8AOiLda7tQvLYbiu/DMjZHovsfI4aI4481AyVZnUr4olYCuP983m/zjngnQvD5yq8raKU9mRAukhgLssD8X52DIr7Tmfel6Z6e/5iKwyQqeRwdNbk/nqJpi4OF01xJYRsPY1VX/yegQGXBOeGBuCLLBU6nHq8HTUt1sznm0wuoK7djlOPt0NvVm/o6xOSDxMYjjzYnOl8w9hq6hhszFYwYGkb2mYF4qV0UAGAVI5MPZJGe8J3IYVOhZpjzZi7NYlEcHUzbkwmwFam1e1Mb/CeKM6fHVuYqVs/e5UegoxlVYreSpyn0+fI7mSvMuDM013wz0cR9EYgiRJGelyIhfMPLNFwsuh5GQOmR3ywVCxNekmihJkxP6aGPen9/iX0wFXrFNCb8t+QLu4Zg0wNLLtkxhiaH+rC9OVRzN2cyBpQTbU21J1sg0KnQiIUg388/4RCIalYEoFJL0x1uYvfEULWTkykEJj0QEyIUBnV0Fea17w67OisgX8s//ucCQz6+w/DX1UD3Bpc6yWvis6oxt7jdei6pxaSyBdWxmn1m2w9pUqOY+9qgX8+gtkxP5IJEfPTQcQihcf+xa0UGyVXPRj3cxuTqak0lDARxhhURpowy4eC8F0k6g1j/I1+BKe8mccEhQzGWiusLU7oK80IjM9j5JXbedNQG+7rgEy5/l+LXL3By2oTxmGZXEBtqxWxcBKzE/6cPzPGAINFQ3vTdjnGGMx2Lcz29OqPs8GC8/96e10TQZwj024NSFdSfefFwaK95+/U1FWx5htTJgioPtKEygP1CLuC4KIEtUULpW5p0Gx5ZB9mro1h6sLQml4jFV2ZKk8IWTsuSZi6OIzZGxNZ6aBKgxr1p9rXNOllrLXC0lyRM3WUMUCukaP+Dz6BiS3YDcYYg0xOwTfZXtL3BbpMW9jpES+uvZa/FgpjgLPOBOU6as2UYjH7DajHM/edRiPOY2QDAnFDlQUKnQrJcIFzcw57R3XZX3u3oCB8l4h6w+j5t4srqqNLSRG+YRcEQYCp3gat3YD6U+1w904j4g5mjtPY9Kg52px38BYTKcz3z8AzOAsxnoLKqIF9TzVMdTawO1brNqo3+KKgLwZpHe2jStHcXYGmrgrIFTKIKQmpV4YxPx1a6g25UBlTZ1Lj8ANNNDt/lwn7YuvOxGAs3coMADjnuPTyMCJ5es7nei7nQH2HHXV5WqythiCXwVBlzv1aAkPVwQYotUqMnO9Z9T5yuZaqpBNSLpxzjJzvhad/ZsXXEsEYBp6/htZH9686EGeMoemBTqjNWszdyM6MsdbpcOzZ03C11QM926smCyHbhbPeDNuQF/PTwRVfYwyQK2VoP5QdkHKJwz0dxOSQB7FIEiq1HFVNFlTUmkrKhMul1qLFhDeyEIh345n7sCGBOBMY6k62YejsjbzHmBvt0DspUzQfCsJ3ifHX+9IBeJ4bZM/gLAJTHqQW+haCMZjqbbC1VUJj1UNdoKp61BtG33OXl54LIBaIwD8+D0O1Ba3v3gdBnt4Tvdg/dKMCcACYGfWV9Xx3EgSGxs6KzD5vmVzAkQeb4ZkNYXLQg2gkCZUq/UHpqDGu+YOS7Fzx2Pr3OHEO1DRbAaQLtQXmo0Wfo9ErwRiDyapBXbsdlorNa9Fja6+CxmbA3K0JeIfmICVLuxlX6tSIekJIRhOQq5XQWDenewAhu1HEFcwZgC839lofuj9wYtXvMyYIqD7chMr99Qi7ApBECWqTDjWVfhjrjHCt58IJ2eUEgeHwmUb0X53BeP88xNRSloqtyoDOozXQ6JcmpVNJERd/MpyuDbPY8owBcxMBGCzqdD0a9dpqIN0ZiH/sRAqNeKPsgbil0YHmh/di7PX+7Kw3xqCxaJGIJND33BUYqy2wd1RBod24VPydiILwXSAeiCI47St63PIgGpzDPz6P8FwAe546kvc5UkpE/79fQSp2xz6XhWA/OO3F2Gt9aDzTuVAAoh4DipMA4hsSgAPAzJhvXc8X5AK4KOVtc1bXYYNCmV1ojTEGW6UBtkrDul6b7A6rLdSWS3WzJVNcaHrEV/R4xtKtUVr3V67p9c7d9ANth9B45vyKnuGl0tr0aDy9B42n92D09V64b00Vfc6tf3476+9qsxY1x1pgbrCv+vUJudu5e6eWUmHySIRiCE37YKi2rOk10pkxS8/VnzFB2H8gU9+l7NvLCNklBJmAjsPVaNnnhN8dgSRx6M1qaHQrM8KuvTYGn3uhOOvi23nhvyFfDJdfGsHxR1szj9+ZdVrMYiAOCBhQnEQj3oBdN7vmHuL5WJoqYG6wIzDhQTwYQ3g2AM/QLKLe8NL3M+PD9OURND/cTWP/MhSE7zBc4vBPzGO+fwbJSAIKrbLgKnbhk6WrFo693oe2Rw/kPMQ7NIdkpMCeTg7MD8yg5lgzoANkRuPCFzbmV4tLHLESil7lY68xYs+Ralz6yRAiwURm9nHxnqa6yYL2g7R/hRRmrdBDpZEjHl39ijhjQF2bDR1HahD0RjFwfRZz4/5Snpk1s74aG1GsRVxjxdOYL4LBF66j8UwnbG1rm1Ag5G4V80cKBuCZ4wLRNQfhy9l1s5Cf+MBCdtvG9QYnZDeRK2SwVeVftAn5Y3BNBvJ+nXPAPx/B2z8egH8+As4BrUGJ+nY7attskMlW09wqfT++dH9efkwQYKq3wzfqwvgbCxXZl39M8fRWmsGzN9D500ehtVEdJYCC8B0lFU9i4EfXEHYFslJX1tXrl3MExtOzV6oclQ69o+4SzgH4JzyocuT/UOASh2sqCPdUAKIoQW9So6bZuvoCFSw9G8il0r9puUKA2aFDXZsdjhoDGGO494kOzE0EMDPmQyohQqNXobbVCpONbi5IcUxgaDtYhRtvjK/qeXqzGkcfaoZKo4B3LoQLLw5BKvF3mXMOrWHt+6vLXaxFoV7fXu/RV3thbrCXpRAkITsd5xzBSe/C+M5gqDbnbAUqU5T2fpGVoW2mXTcL82c/gAHFSZw/u7G9wQm5m8yO+0u6f/e5I5n/jwQT6Lk4hZkxH44+1FJyC9PNNH1ltOj3NXdjHI1nytu6eKeiu58dZOjFmwi7F2bO7khdWa+oN5QzCL+z0Fs+6f2huT8QwsF4ZuV58X6Cc6D/yjT2HKlBfUfpqSmMMThqjHBN+EtZDAAA7Dlak9l7u0iQCahsMKOywVzyaxOyXE2zFWJKQu/FqZIDabVWAZVGAUniuPiT4ZKft+jW25OYGfWjsdMBR83qZrXLXazF2urE3M2JNT0XALgowTM4u+5e5ITsdGFXAEPnbqZbirKFO9gL6YKpLQ93Q2XUZI61NDmKtgxkAoOx1lrwmGKWB+DPfpECcELKSUyKxXaV5OVzR9B/NX3/vJ0kI3FEXCuL0mXhHN7hOQrCF2y/aRSSU8QdTLce26Ci4Pn2mmjMOqCE/V9qc+7BOZkQ8c4LA4iG0intnC996HAO3L4wiekRb87n5tO4x1HyB5cgY3DWUWVGsjHq2+144Gf2orHTUfxgBphsWnDOcf310TWnlnvmQrj00jD6r06v+rm1Fm1mRfyrvd2QnziJxsfXVihFa1tffQQmsPSeMULuYlFvGH0/vIxEKJZ+gPPMOB/1hNHz/UtILit4ZGmugEKnKtim09FZA7lqbQWdgGUBuHBswwJwUZQwM+bDyG0XJgc9SKxxewshO5HGoAJf2y0AwIGJAQ9SJRZH3SxiidcjpSTwtcw+7EIUhO8Q3mFXScHwWjCZAH1F7kDVvqe66FSdUq+CodoC/Zl6DHb/0kLxlgHUWrSYGvIgHk0VPEX/1ZlVvSEtFTrsPV5b0rFtByozVc4J2QgKpQzth6qgN6mL9q+3Verx+g/7MDNayh7wPBbeKkM35uCeyr+nLJ/FQBwQIBjMa76MUrNk8uE8vY+MkLvZ9KURSKKUe4Kdc6RiiayME0EuQ/vjB1dWGV64P7A0OVB7vGXd18XkckCpB1D+AqujvS6c++4NXD0/it5LU7jx5jhe+t4t9FyaXHV2ECE7UVWDGYJs7ff0YkpC0Fu8owoAnD87gHM9UQj7D0B/pn7Nr1mMQqssqXicUq+mDikL6A5ohxCTqaIxOBPS+8hWFawzwLGnOu++TI1Fh4ruuoLPb7hvD5qeUENx32mc609mVU+dHCq+yh0NJRDwlPZhsqi21YZ7n+xAXZsNcsXKX2NBxtB+qAoNe0pYoSRknRhj2HeqHjKZkPft136wClfOjyIUiJXpNYGRnhJqNmwQQSGDsJ4JLs5hqi+tl7GYFBH1hhEPRGkGnewaYiIF74ircIYbB9w92V0I1CYtun/2OBru3wNjrRW6CiOsLRXoePIQmh7au60nt669NoqeC1OQxOxvmksco7fduPX22re4ELJTyBUydBxeXxHgUobCO7PfFPedXnP2WzEyhRzWFmfRGMTRScWPF9Ge8B1CZdQULUbGJb7wpiz9JtVQaU5XNi+g9ngLFFolZq6OZlVEVlt0qDvZhv0frYTivtM5e4MnS0wxW0sqmt6kRtc9tei6pxbxaBIzY34k4ymodQpU1ptpBZxsKqNVgxPvacPAtZl00ZWFt6HRpkFLdyX88+H073mZYkjOAe9sqDwnWwPGGOztVZi7NbH674kBarMOhipzwcNSsQSmLo7A3TcNLqZz95QGNSr31cPeWU2z6WRHS0YTJd1Jp2JJcM6zft8FuQz29irY26s28hLLgnMOz0wIvZcmEfQVrkExOehBwx47DGZNweMI2enq2+2QyQT0Xp5CMr6UWVbKXnEmMBjMK+s45VLuejCFVB1ugm9sHmIiufK+gKUnEKkOzBIKwncIW6sTk28PFgzEmcAQ9YRKuiHWWNMr3LZWZ9FZc8YYKvfXo2JvLUIzPoiJFJQGDbQ2PZqeUEN+4mTOABwAVFoFYpHiLcVuvTUBa6UBNqcOjhojRFHCaK8bU0NeJOMpKDUK1LZYUd9uz1lRXaVRoGEVBd4I2Qh6kxoHTzciEU8hHk1CrpBl+oPefGu87DUdtnpN2Lm/DvMDsxATqVVVmFFqVWh9976CQXQymkDPv11M75VddupEMIax1/sQ8YRQf287BeJkxyp137agkOX8PY/Mh+AZmEEqloRco4StrRIai67cl7kunHPcemcSE/2Fi8ktNzno2XZFpwjZCDUtVlQ1WTA/HUQ8moRSLYcgY7h4bjjvcxgDqhvNUKhKD+GWAvFRAN342IkUGvFG2QNxlUGNPT91GCOv9CA8m73tzlRvR+PpPdQRZRn6SewQcrUS1UeaMPnOUN5jqo82Y/KdwZLO1/Tg3lUP1oJMgLFmqeLqYv/QQu1Lalus8C9rsZBPLJLE1JAHU0MegAECY5CWFaiJR5IYvDGLiYF53PNIK7SGjUmnIaQclCo5lHcMkIlY+QsPmWzrWy3qi3WiEf8Ou24W7rBz1c9X6tTY895DGHzxJmIlFllzdNWg5mhz0YF44u1BJELxvDMN7p4pmBvtMNWWltJOyHYjVytgqLYgOF2g6CpjsLVWZj0kpUQMv3wbvkytmHS/0tlrY7C2VKDh/k4Iq+ojnNu5m+uoXbFgvH9+VQE4AETDxSfuCdktBIGt6HbS2OXAyC3XyoMZoNGr0L6GVPblgfhDe06i9QRgf/nbaxr7C1GbtNjz3sOIesOILLZcrDJDqS9t5f5usn03DpEVnPvrUXeybcXNq0wpR92pNjj31UGuLm1mXaFZe+XU5ZhcDsjUyFe8parRAp1Jtbqachzp4ix33pTwdCBz+eWRvPtCw4EYZsd8cE0Gtl3lSHJ3UyjLvz2ioWNtNQ9qLVqcPzuCcz1RjJz61LqKtajNOnS97xgUutL6hhuqzEUD8FQsCe/gbOHVdQa4bk6u5lIJ2XaqDjcW/LogY6jozi5EOvJKD3wjCzfoi5PVC+8Vz+Acxl7tXfP1LFZG72fHcP7scKa+y1pwzjF8a27Vz9uIz0pCdpL2g1XYd6oe+mUp53KFgMY9Dpx4tDVrkp9zjnAwjoAnWnQLaPo+nQEydfr+fQNpLDrY2qtga6ukADwPWgnfQRhjqNhbC/ueagQm5pGMJqHQKGGstWZmve3tVZi5NlZgVh0w1togV5d2w7xeMrmAY+9qwdXzo/DOrb8dEedAyB+Ddy4Mq1OfeTzoi+LW25PwuZZeQ5Ax1Lfb0XagsiyrAoSsR3WzBWO97jX1Bc15viYLnPVrb7+3WKwFqMcz951e1x4xxhjUZh2SkUTRHHmlrngWS8wXLloDAzzdX5mQncxQaUbzQ3sx/NLtdN2DxZiXAzKVHK2P7IPatDTBHfWG4R0qHNjO98+g6nAjVIbVZcqUuzd4JBhHbA2r2pUN5jW/JiG7AWMM1U0WVDWakYilIIkcKo18xb3s1LAXQzdmEQ7EF54HOOvNaDtQSRmjOwAF4TuQIBNgbli5AhaY9CDkCha8CWaMobrIzHu5qdQK3PNIK8b75nHrnfVXPmUMmJ8JZYLwkC+Gt54fSLd5WUYSOUZuuxAOxHHo/saSWicQslEaOhyYGPSk+4PneY8uFmTJ918A0OiVaOx0oK7Ntq790OUu1mJvr0JwsnA3BJVRA63DWPAYAECJ71V6T5PdwNJUAUO1BZ7+2fTEkpBO37Q2V0CQZ68KewZn04F6oTkqlj6u6mBjydewEb3B76yAXgq1TgFbpb74gYTcBRhjUOXJXB28PouBazNZj3EOzI754J4O4sSjrdAZaQV6O6MgfJeYvjyCqYvDBfsUy1QKND/YBV0pN8EbQGss36zc8nT0nouTEPP1WQXgmgxgbjIAZ93aVw0JWS+NXoljD7fg0kvDSMSWWg5yns7a2HeyHs56E3yuMKaGvYjHUlCp5ahutsJo1SARTYEJDCqNvGzFyO4MxNdTrMXS5MDsdQMi8/knAmuPt4IxBi5J8E94kAzHIVcpYKyzQqZYGo60Vj0EhQxSoS0ljMFYa83/dUJ2ELlKsSLtPJdULIliUThjbOG40th1s9CfqU8H4F8eQLl6g2v0SggCK7n3tyAwHH+klYotElLE/ExwRQC+iHMglRBx860J3PNI6yZfGVkNCsJ3gcCkJx2AA3nHZUtLBRrLVKxlkf5MPfqlQyUXbzFY1KsakPPhHDBZ02l20VAC8zNF2jQxYLzfTUE42XImmxZnfroTs+N+eGZD4BKH0aZFdZMl01LPUqGHpWLlSpBGvzFbSLKrph7Ex9ZYrIUJAtoeO4CRl27DPz4PMCwE3BwypRz193XA3GDHfN80Jt4ezAoSBLmAygMNqDzYAMbYQvulSswV2vPNOSq6igcthOwm6e0cRdqVcg6FdnWT3or7TuNcrwiAocFangrrcoUMVU0WTA15im7DEWQM9723A+oSa0sQcjfinKP/ykxJtRa8c2GE/DHoTStXw8/d9KOtI30f796AVmWkNBSE7zBSSkQiHIcgl0GhVYIxhrkbE0XT00LTvrLOLjc+rloYtJOZ1mT5+NwRDN+chWsyUJb9sEq1HI7adEAdDpbw4cGBUJHepIRsFkEmoKrRgqpGy4acPxFPIZUQoVTLM4F9MSuqpn4WwP9afSAuVynQ+uh+xHxh+MbmISVFqM1amBsdEGQC3L1TGD2/smiUlJIwdXEYYXcA9afaMfHWILzDOSrDApnc/Pp726G1G1Z1fYRsR/FgFIFJL7gkQWPVQ+805R2vra3OpUn3Amyta6t4vJ5CbLm0HaiEayqARLRwwShJ5IgEEtCUUDOCkLtV/9XSAvBF89NBBDxRgHOY7FrojOqFejAj2Oie4aQ4CsJ3iGQkjunLI3D3zaSLtyDd67vyQAMCUwXam2Sen0A8GM0q8LJWiwF4vt7gy00Ne3H99bF1vyaAzMra/nvrISzsBZWVuLIvU1BhNrK7eWZDGLwxC89CZshigZbm7goEPFGM9boR9MUgCAy2KgMa99izVtwXA/Fnv9iD3/7k2gNxIF0xvdKcvZompUSMvzlQ8Hn+0XncGH8zvd0kz2eaucEO57466J1LmS1SSoSUEiFTysEEeq+TnSEVT2L0lR74Rt1Zj6tMGjSe6YS+YmX2lsqggaOzGq7bU3nP6+yuW/VK+EZRaRRo3VeJW28XrgfDGDA55IWtiibWCMklHktiZJXdBnouZn9OWJw6dJ+ogycplmUbGlkfCsJ3gEQ4jp5/vYBkNLvycNQTxvBPbhXcB75c0WrDJWh8XAX5iZMLAfhoweItkWB8XQG40apJz+AtsFcZ0Lq/Eibb0uuZbBooVDIk44X2jgKVlIpOdrGZUR+uvjqa9VnAefrxmVFf1rGixOGa8GNu3I+mLgdqWm3Q6tNZNVmB+CeOrSsQv5N32FV4j/fidRf5nDLVWTMBeNgVwMzV0XQQw9Np7ba2SlQeaKCWKGRbk1Ii+p67gqhn5XaqeCCKvh9ewZ73Hs6Z7VF3sg1MEDB3ayJ9TyAwQEpXcHTuq0PNseZN+A5Kl6+laPYxQDxK/cEJyWdm1L/ubFLfXBhvPT+Ak4+1wx1PrnsbGlkfCsJ3gPHX+5CMJvOvdpf4phTk61shsutmIT/xAfzV4MGiATgAjPfPr/m16jts6Dxai3g0iUQsBaVGDlWOHuiCTEBjZwX6r0znPZcgMNS22dZ8LYRsZ4l4ammyq8TPgsWBfPiWC8O3XNDolWjqdKC2zYZaixajnjCe/fIAfvsTx9B45g3g5bF1D87xYDS7zPsazd2ahL2jGr5RNwbP3gCwtGoupSS4eqbhGZpDx5OHobGUZ28rIeU2PzCL6HyeeiYc4JKEiXcG0f7YwRVfZoKAupNtqDxQD8/QHFLRBBRaFSzNFVBott+eaqW6+K0mY4BKQ7ekhCzinMMzG8LcRACSKCEaSpThnEAynsLQjVl03VNblm1oZO0ob2+bS4RjC6s861/F9gzMrvm5y/uHlhKAA4B7Orim1xJkDO0HqwGkU9kMFk3OAHxRU5cDNS3pKslZ29lY+lyHH2iChoq9kF1qasi77mKH0VACt96ZxO13JsE5XyjMxAClHor7TpflOmVKeVk+x2LeMFLxJIbP3Uyf785Tcg4xIWLo3M2SVuAI2Qru2wWKDgIAB4KTXiRCsbyHKLQqOLvrUHOsBRV7a9cUgC+O7f3sGM6fLb7XfC0c1UbIiiwCcA5UNW1MnQxCdppoOIHXn+vDhReHMN7nxuSgp3gR4hJxDkwOeSCKEmotWsiYgGe/2IMBxUmYP/sB2HVrjxXI6lAQvs1F3OV50wGAb8xd/KAclgfgq+ofusb7X0nk8LnCJR/PGMPe47U49q4WOOvN0BlVMFg0aN1Xifuf6oStkvaYkd3LPx8p27nG++fLNtDfydzgKMt5mEyAp38W0kJtjJw4R8wbRnguUJbXJKTc4sH8wfVyhYLw9Vrz2L5KMrmAln35V9cYA0x2Ley0H5wQpFIi3jk7iLA//d7nvCzz11kkkWeKJWYF4sKxTQ3EU/Ek3L1TmL4yAnfvFFLxu2tLCuX+bHNMKF+lUl7opjWPzCAtHFv1IG12aBHyr+0GIhJOYDUJ5IwxWJ16WJ0rWzsRspsxhqLdEVZzrrE+94bcDKsMalhbnPAMrm9wl1IS5gdy90fNwoDQrD+rgBsh24VMKYeYKFwxfPG4jbCesX0tGjsdEFMSBm+k3/+LdzacAxanHgfva6D+4IQAmB72lSX1vJjlBYvv3Ia20anpnHNMXxrBzNXRdB2Yha1qY6/1ofJAA6oON94Vnwe0Er7N6RzG4oF4Kb+njEFjW12AatfNQn+mPj1If3kAQP4q6LnUtdlX9XrLyde5f52Qu4XFqS9LAA6kb4j97vKtrN+p4XQHtI51BvicI+Je21YXQrYLa4uz6NitMmqg3oC6Busd29eCMYbW/ZV44OkutB+oQm2rDQ2dDpx4TxuOPdwChYrWhAgB0qnim8E9mZ0ptrgN7dkvD2BAOAb9mfoNWxGfujCE6csjS4VYF5b6ucQxfXkEUxeGNuR1txuKdLY5uVoBa2tl0cFaY9MXPoZzOPbUrOq19Wfq073A+0UAbOENWjqjVQOtYQ17sRlgrzau/nmE3IWqGs2Ql7EF3/JJv3M3/WU7LwAIchk63nsYxjprgQso04txQF9Jq+Bke3J0VUOQywr+vlcdKv9q0GIAPnLqU2se29dDpVGgaW8Fuu6pRceh6qxuJ4QQZNLEi6lsMKO6ybzmMbPn0tSKejKLgfi5fhGK+05Df6Z+bScvIBmJY+Za4c5JM9fGkIzs/pZpFIRvI5xzcGllynjdiVZobTlWjxgDGNB4fyeaznQWHNAdXTVrSstkmvTquWyNNwKWiiKTAzlUN1qgUMrW9HqE3G3kchkOnWmCIGNY7/06Y8ikossYw/mzw+hn5d0jJggCWt+9HzXHmiG7Y/VLX2lG66MHoHOscxKOARqrbv3nIWSDKHVqtD12ADLFHSvAC+/hmmPNsLVVlvU17wzAz58d2dQAnBBSnEpbWlZIY6cDRptuzZlwybgI99TKrLLF+/3F+/9ymy+lSDQHpi4N7/riqpsShP/lX/4lGhsboVarcfz4cbz99tt5j/36178OxljWH7V6d/d7DUx60P+jq7j0Ny/h0t+8jBvfeRNzNycyhYdkSjk6njyE2hOtUBk1ANLFiSxNDux57xHY2iqhseqx56eOwFiTvcKk0ChRe6IVdSfbNv37AoCaZsuqPiDUWgX2nqjbuAsiZBeyOvU49XgHalttmSrEMtnqI3LOgfqO9DaSjayayhhD5YEG7P/gvWh/4hBaH92PjqeOQKFTYuD5awi7SiyoxoAVMw8MkKsUaH6o+67YU7ad0Fi/OvoKE/b9/EnU39sOU70NxlornPvq0f1zJ1B5oKH8r5cJwJMUgBOyTdW2FK+IpDWqYLRq4HeXXsQ4l1hk4/ee3ykRipU0Nrt7ptHzLxeQCO/eFfEN34Tz7W9/G5/5zGfwla98BcePH8cXvvAFPProo+jt7UVFRUXO5xiNRvT29mb+vptvpGavj2HircGswkrxQBTjb/TDOzyHtvccgCCXQZDL4Oyug7O7DpzznD8TjUWHtvccQCIUQzwQhSCXQWvXgwlbl/Bgduhgq9KnKy4XCcatlXocPtMEoYzF6Ai5W+iMKnTdU4uue2ohihJe/r+3IIriqs6hN6mQSiw9p9aixYQ3gme/2LMhxVoEmQBDlRliIoWe719CzBde1aSdUq+GQqNE2BUEOIcgl8HeUQXn/joodXdXQLfVaKxfG5lSDkdnDRyd6e1iMX8EYVcAYVcA+kozlDpVWV6n8XFVentZbxLnz46tObuNELKxKhvNGL49h0ggnrcqesfBKjDGEA6sL0DdiqxTuUpRcrX3iCeMvh9eRufTxyBT7L4M2Q2Pzv70T/8Uv/Irv4KPfOQj6Orqwle+8hVotVr8zd/8Td7nMMZQWVmZ+eN07s7G8eG5QDoAB3LeeIZm/Zi8MIRULJHVjqfYjYpSr4ah2gJdhXFLA3Agfa0H729ERc1SWihbdkkmmxbN3RW498kOHHu4pWgvUUJIcYloCsn46gJwAAgF4njn7GBWYZh0waaNLdYyd2ti1QE4ACSCMYTnAlAa1Oj6mXtw8MOnUXeyjQLwLUBj/frEg1H0PXcFN7/7FoZ/cgvDP7mF6996HYMv3kAqtr62PYsB+Fd7uzMBeK5CbMmECM9sCN65EFLJ1X9+EELWTyYTcOzhFpjs6UwVxpZqtcjkAvadqkdFXXp7aTKx9vepIGNw1Gz+li1Lc0XpPdc4RzwQXXdXle1qQ1fCE4kELl68iM997nOZxwRBwLve9S688cYbeZ8XCoXQ0NAASZJw+PBh/MEf/AH27t27kZe6JeZuTmTK8ufEgbkbE5i7MQGw9C9u1YEGqEzazKrPRrHrZiE/8QH0xTpx/my6fUkp4tEkXJMBiKIEnTF9Izxy24X56fS+E5lMgN6sRkWdEbUtNijVVBGVkHIr8e260sJH0c03x2F16qHRpQsrNlh1GPWEca5fxDP3nQZe/kZ5LnSB69bkuiq8J4IxDJ+7ic73HSvfRZGSbdZYH4/HEY8vrfwEArujD3wiHEfPv11EKnZHQSYO+EZciHnD2PNTR9bUriw9YVaPfnYMgJgzAE8mRPRdnsLUkDdTqEmQMdS0WNF+sAryXbgCRch2ptIocPzdrfDPRzL31HqjGpUN5qzFqvWsszV1VeR9b58/O4yH9nSi9YQP9pfL26pMY9HB3OiAb9RV8rjv7p2GY0912a5hu9jQCMjtdkMUxRWz206nEz09PTmf09HRgb/5m7/B/v374ff78cd//Mc4deoUbt68idra2hXH7+RBOTDlXcVsEOAdnIN3cC7zkNqig7O7Frb2qrKm8a2lf6iYknDr7QlMjXgLvqlEUYLfE0EyKZa074UQsnoqjQIanRLR8Nr2e3EAEwMetB1YKgy1Uemrkighud59aZwj6g0jMOmBqZY+VzbbZoz1APD5z38ezz77bNmvf6tNXx5Jr3bnGjs5EPNF4Lo9ueZ94jJj/tWuVFLE2y8MIOSPZb2+JHKM98/D747gnkdaKUuNkC1gsmkLdhCwVBgQDsyv+rx17Ta07MsdWG/0NjQAaHqgE0MvSvCPl3btqeju3Be+7T5VT548iQ9/+MM4ePAgzpw5g+9973twOBz46le/mvP4z3/+8zCZTJk/dXU7qKjXOqv+xbxhjJ7vxfC5m0u99tYpKwAvsX8olzguvTyMqeHCAfjSE4BoMI7bFybLcs2EkGyMMTR02td+Ag74XKHyXVABTGDlaUvGGHzDrjKciGyG1Y71APC5z30Ofr8/82d8fHwTr3hjSCkR8/0zRcfOuVvlGy8joTj6Lk/hrR/347Uf9CLki+WdAAh4ohjtpfcVIdtRffvqJp31ZjXufbIDXcdqCy7ebfQ2NEEuQ+uj+9F4/56SjpdrylMbY7vZ0CDcbrdDJpNhdjb7H252dhaVlaW13lAoFDh06BAGBgZyfn0nD8o6p2llZd818A67MPyTW+teTVpsX7I8AC+leupY/zw8M6u7YeccmB3zIR5d3143Qkhu9W12aPXKrb6MohhjMNevY8Igg0OkfaxbYjPGegBQqVQwGo1Zf3a6ZCQBLq5sTbriuHC8LJPtnjE/zv9rD4Zvu+BzRRCLFB+Dx3rdmVZBkihhdsyHkdtzmBiYR+LOFHpCyKYxWDRoO1hV0rH2agNOvKcNelNpNVOW9wwfOfWpDakHY22rhMqkKXqcvaO073Gn2dAgXKlU4siRI3jxxRczj0mShBdffBEnT54s6RyiKOL69euoqsr9D7CTB+WKrpp1r4Yv8g7P4do3X8PY6305e40Xc2f/0FIDcPd0ED1rXNHmHPDPR9b0XEJIYZLEEVvrJBdLp8F55zanSJNzX3kymBZbOJLNtRlj/W4llLjfuhwZI5N9bkzfWNjStopbj3g0BTElYXLIg5e+dwtXzo+i9/I0br41gZe+dxO33pnIKh5LCNk8zXsrcPB0A4y2pfGPCQxKtRxavRKOGiMOP9CEww80QSZbXdjXYNXh/NmRDQvEGWOoOdZS6ACojBpYW3Zn0c4Nr4r1mc98Br/4i7+Io0eP4p577sEXvvAFhMNhfOQjHwEAfPjDH0ZNTQ0+//nPAwB+93d/FydOnEBrayt8Ph/+6I/+CKOjo/joRz+60Ze66QzVFlR016YLr5WJ69YkpJSIxvs7V/1cmdEIyOQAWEn7P5MJEVdeGVn9RS4/xxoqOBNCiotHk5DENU7ycWC0x43hW+k0VEHGYKox4qVECg/tOYbWz6bKukdMX2mGxqZHdH4dKfB8986W7wQ01q+NQqOEzmFE2B3IHxgzBnOTY9W1XxYn1we7fwnneuJ46Z9uZrVDXY2ZUR9uvrXsXmXhHJwD433zSMZFHLiv/L3NCSHFOevNcNabkYilJ8yUGvmqA+580vGAHJDJCtaXWCtLowMN9+/B2Gt94KIEJrD0+iTn0Nr0aHlk365sTwZsQhD+gQ98AC6XC7/1W7+FmZkZHDx4ED/60Y8yBVzGxsYgLCvv5/V68Su/8iuYmZmBxWLBkSNH8Prrr6Orq2ujL3XTcVFC1Bsu+3nn+2bg3FcPjaX4SvZ6TA15IKbWN/vtdYXx/2fvv+Mjy6770Pe3z6lchcqFnDPQ6Jx7ptnDZpNDDoMoURQt+VrBvhbNd2n5mde+fPSzRY/98dPl85OuxEuaEm3LlmUFSqIoieSQHPb0TA9mumc650bOqYBCJVQOZ78/CkAjVDiV0ED3+n4+8+mZwqlTu8nCOWftvfZadW3WEo2IELKm2EJK0obUVynJ4Zn2IeyN4Ku/C7z6T0tfrEVj0iHsDhRcJb3qQCPUFbQS/rTQvX4zzjn808tYfDSLkGsFTBBgarShsrcOWqth07HVh5ow+tP72c6Gqv2NeY/hSXZbHG/9aAKh5XDe5wADLA49hu7MZz1sYdKL5h5H1iJShJDy2qsdh+ydNbA0O+AedSLiDYGJAsyNNuirTCUtPL3bMM5LlA+9S/j9fphMJtz45V+DQbW790OOvvGgPIWEGENVXz3qT7bLfotd74Tpk30YPfi/4tJAElfeGM1ZkO3W5XEszRRXjV5UCDj/2T4IwrP7S0bI03LltSGseOQ/eKs0ipx7PO0dVtjbLfjqF9rRLl2Ht0SBuHvUifE3H+X9PkEpouZQE6oONO7qm3UgFsOx//Hf4PP59tS2qd1st97vOeeYeHsA7uGFzW1IWWoZuvkDPbB1bN4r73wwjZn3RjavVDOAgaH5g72wtlbmNYatvcGlaBJDl8YL+vu09lVi7MFi1mMYA+rabNh3Mn1le0LI3jTjCeHMh9pwvltE293/At/3H5S0UvqzJp97/d6cMnkGRLzBMlby5YgFy1/OvxRFYpIJCfFoAmqtsgQjIoRs5Kgz5hWEyymytDLth63Nkqod0XEczeeuApenir4pm5sdUOrVqQKTMueGtfYKdH/iMATFs5mqRvamxfvTqQAc2PxdXv33ibcfQ2vVQ2erWP9RVV8DTPU2LA3MIrDgAxMYKmotcHTXQmWQV0hpzdYAXGQMDZUGjCoE2dlra3MHHYdqIIq5J7c4R8EtEQkh5Hm061qUPS8840ulacuTFoNCnX9QKxw4CADov5i5Ou1GZntp0t2p/ygh5bHiKX3hw2gkgboKbcmLtQiigM6PHYIyjwm5uqMtFICTXYVLHM77ubq0MCw+3F4LRmPWoeFUB3o+fQzdnzqKumOteQfgdr0TilOnMcyOrwfg9RYdBFFAXZs1Z0MWUcGg1ipR3WTGyZfb0bqvEgqVjN8xBijlHEcI2XPW4oK1OGEncUlCcMmPlQVv0V2gdhtaCX9KEtE4GGOQuxug/aMHEfWH4Lw3jVggkv1gzmFtk5+6ttYbfFg6jFe/NQCRCTlT0QGgvt2K0fsLBRd4ZwywVhmgeEYLLhDyNHHO4ZovT69vQWDrVVOBZpw/8xtoxteLXhHXmHVo+/B+DPzdzZx7w22d1TDWb68nEfEGsfhwBp7xJUiJJNRGHRy9dbB1VEMoUaEaQjKJeIOIh3M8KHIO7+Ry2cbAFApATAXvG+/lrfsq4ZzyIhZJpL1viwoBxy+0bdvXXVlnTBVLypb9xoGaJnMphk8I2UXqLTpMuoN49RsD+OoXDpe8MGsmnKcmNJ33ppCIrHZ6YYC5yYH6k23PRA0YeiJ5SlR6jex0bnOTHcY6Cyp769H3uVOwdWT54jPAUG1K9SCXIV1v8GwBOOccS7N+3HxzDO98fyDVNiWDXPu8OQda9uW3z40QIl8ptoxspatQr2eviIytrojH11fEi+UanEeuNCGlXo2ms93b9oB7J1149NfXsTQwj0QkDikhIewOYOqdQQz98DaSceppTMpLbquuQlqJFkutVeLkRzpgMKdfXU8mJNx4YxT+LVtYlGoFmrrsGc/LGGAwaWCvo1oHhDyL1nqGv/qtEYwIx8vSM3wjzjkm3x7A7LXRJwE4AHDAO7mEgb+9iai/gEKTuwwF4U+Jta0KOfPCAFjbq9Byft/6wyZjDE1nu2FtXw3E186x+qehyoS2D++XVaAo397gnHMM3JzDrbfG4ZpbQSIuZW2BJEkc1urVKrAbh8NS/+w7VQ9bdUW6txJCisQYy/iwXYxYNL4e3NdbdKuB+BQuDcehfPEsml9RF3V+z5gz557weDCKeGhz3YtYMIqxNx6kxpbm/cElP6avyttqQ0ih1EZt1slpAAADtFnuteWk0igQDmReqU8kJNx5e2Jbll7HoRrUtVkAbHvsgN6owdHzrVRglZBdTEpKmJ/wYPjOPEbvO+F357ddbS0QL1fP8I1WZj1YXqursRUHEtEEpt8bLstn7yRKR39KlDoVag41Yf72RMZjHL11aDzTue11JghoeakX1Qca4RpaQDwYgahWwtpeBYPMcv5bA/D+ixNZA3AAmBx0YWrQlfPcG7kXAug6UotEPAm3MwAOwOrQo77dBq1h91SzJeRZ1NRl39zbtwQSMQnuxcD6BFq9RYcZTwj9F6cA9OHzLwLN6MfEa4UVh0zGk/KOiyWBDZcs18Bc9u09HHCPLKD+RCsUGrr2kPJQqJWwtFXBPZJlMokDlb1Pp4r4/KQHiXiWVXgOhAMxLC8EYK95MkkuCAx9pxrR1OXAzKgb4WAMSqWIqkYzHLUVuSceCCFPjXPahwfvTSMRS65Pno3cW4DZocOhs82yiyOXYxtaOouPZjd3ltiKc/imlhELRqDSl36xYadQEP4U1RxpBhMY5u9Mgiel9S8cEwVUH2hEzZHmrO/XWg1oOCW/DdlWyhfPAkyB/ouj2wLwRDyJuXEP5ic8iEeT0OgUWHYW1tN8ZnQZL3y8C+0HqnMfTAgpmdpWKxamfFieX5F1vEIpZH9AXxUNb07rXgvEARWG2XE0ox92vbOgG7PKoEFsJUfdC8ag1KUCaS5xgAG+meWc+8i5xBFY8MHc7Mh7XITIVX+8FStznoyV/k2NNlhayrMVy3CuEcPSYVx66Ev7c/eCvDoR3qXgpiB8TYVFi55jdUWNkRCyc1zzK7jz9sT6f2+8JPlcIVy7OIrTH+uAQmaR0yeBeON6IO4qcNI9k9DyiqwuKRFPiIJwkh2XJHgnXQi5VgDGYKy1wFBjBmMMNYebUbmvHp6JJSTCMSg0Kpib7QVVNy+IuP3LG/RHcP3i6KYH7aC/8F+woC+KeDQJlYa+boTsJEFgOHKuGcN3FzAxsJQxSFWoRDR12bEw5UXCl/t3PdfvslhEH2xHTx1mr41mPoAB5mY7lgfnsfh4FrGVCJjAwGQWXSvHPnlCNlLq1Oj+1FHMXBuBZ2xp/WFSVClQua8eNYebyrJyvNaa7NJgfL0y+lZ+997fR0kIkW/o9nzGn3EOhPxRzI950NCZue7DVmvb0IBGfP7Fs0Vlv6Ujt4gqk9E+cTejqKjM/HMejF96mCosIDCAAwt3JterAGtMOogqBeydNU97qABSe0ZuvDGGqIx+wfmYHXejscNO7cgI2WGCKKDrSC1a91Vi/PESnFM+RCNxKJUCrNUVqG4yQ4pLePD+tKxVcIVShLXKULbxOrprsTw4j4g/vH0mnKX+PhFPEN7xpfWXucTBJXlp7Fpb+cZOyBqVXo3WD+5D6MAKVmY9YEoR1rYqKFTleexK1xt8a5HVRDyJUJb94BuZHU9nzzohpHQCvghWPLkn3mZG3XkF4aXehraVqdGOxYfTWbPbBKUIvWNvF4OkILyMgkt+jPz47pOVlw0rMBFvCAPfv4l9nzkJpXbn9yf640r88K9DeONOPyYn/IhUVaC+w4alGT8ioXjuE+Rp6NY8xh4s4vAHmsv6AE8ISU+pVqDzUA06D22e8HM7A7j15rjs86i1irIWYBJVCnR+4jAm3x6Ab3pzGyedrQJKvRq+qfxqUwAAGFBRbYbGlLv9IiHFinhDmL46BP+sZ/21uetjqNrfgOqDpV0Jb35FDcWp01kDcCCV0SY3E8RWTfdpQva6aFje83whz/1bA/FfP5VAM66WJBCv7K3D0sMZ8CxReGVvPQSZKfS7FQXhZTR3czxroaBkJIHBH9xC89luGKrNOzau+ysG/M7kPkRvBsB5an/YpCeCicdLOd5ZnEQsiZtvjuH0xzphMO3dPRyEPEuG72ROVUsn6I8iFk1ApS7f7UOpVaH95QOI+sNYmfeCcw69vQIqgwb3/vTdnHu/t2Gp4L7xxa6yjJeQjSLeIAb+9iaSic3ZGclYAnM3xxHxhtD8Uo+sIqq5rAXg3x49hP6LkxCZkLHNqNyPUyiFkoyNEPJ0KWXepwvdLvokEJ8EcAi/fgqwXy6+h7jaqEXLh/Zh7I2HAPiTez5L/aeh2gTOJUxdGYK6Qgtre9VTWdAsFuUGl0k8HIN/xp3zYTHqC2PwB7fhGsrvQbhQ81EV/uNEE6Jc3JTpKaP+QUlwiWPi0eLOfBghJKtwIAavK782JUAqrXWreosO/RdHcGkgjNG+Xy1Jz3C1UQt7Vw0c3bXQ2SsQXPTJWsnbtMrIGCwtlej5mWO0Ck52xNSV4VQAnuGr6h51YmXDCnmxhAozACFrAA6kenkrlNlXjhhD2oJshJC9p8Ksgc6Yu21oXau14M9ItSoVAAgQKswlufcDgKXZgX0/fwKOnjoo9WooNEroK43QWPQILPjgvD8D18AcZt4fwb0/vYKFu5PZO6TsQhSEl0kiLG/f1ZrJ/oEdaTx/2aeDBGHHgu6tOAfmJ7x77heFkGdRNJJ/ChoTWMZZ81L3DN9K7mWDCQIcvfVof/kADv79F9B6fh/URm1Jx0JIOtGVMFbmPNkn4Bmw+Hh2x8a0RhAFNHbash7DOdDYRd0DCHkWMMbQcTBzZyLGUlvM6toKD8LLSWPSofFMJw784hkc+KUXAJ7KNAIAcP5kUp5zzF4fw9LDnb+uFoOC8BKKrkQw/d4I7v7Pd/Doezfyfv9SmW/Kdr0Tb/lqkeRPN81MkjiSydwFoAgh5ZVvSjljQE2zOWMrk9SMeCoQ/4PBvpIH4jp7RSodLQcpkcTS4xmMvH4Py8MLJft8QnKJeGS08uRA2C2vVVipte2vSl+XZfX3qvNQDSyVVJSNkGdFdaMZvSfq1zPEGHuyNUWrV+H4h9uhVO3+vdX+mWUEF/1ZJzjnbo1DSsgr0rob0J7wEgku+jH0ozup//MLWeTlSKWvnyz50ACkAnDDuUaEbqgAPN0AWFQIEGW2HyCElI+uQg2TTQffsryUdM5T/YPHHy2iqcsOSeII+qNgAoPBpIEgsLIWa1Hp1TA32uGdcuW+zq7+fOb9ESg0Stg6Mq8GEFIqclvlyW3BU2qCKODoB1swM+rG1KAr1X50NQW9qdtBqeiEPIMaOmyoajRhbsyDgC8CQWBw1Blhr6koS7vEclgeXljfE55JMpaAf8YNc/PeyOahILwEpEQSI6/fKzwAXxX2BDH60/uoOdycWvEpIcO5RihfPAv9HwbgXcx/D+iaph47Jh8XUJl4FWNAfbuVir4Q8pREw3H4llNbX0w2LToOVuPGpTHZ7w+txDB0ex7Dd+ZTl7vVa55SLaKpy4GWfZWbirWc7z6N9hIVawGAhjOdCLpWEA/FZOenz90ah6XVAd/U6kw6Y6ioMcNYT9ciUlqGKhMEpQgpTd2EdQwlfUgcivQAkD/JlUpLt6Ox055K52Sg3wNCnnEqtQLNPflfdxLxJGbH3Jgd9SAWSUCjU6CuzYbaFsu2tsNDkR4040elGvIm8XBMVowVz3M78NNEQXgJeMaXUn3AS8A75YJvehntHz0IY62lJOdcw7QG9Jwx4+rfDBX0fpVWgc4DNZgd9SARyz/dgzFAoRILuggQQooTiyTw+MYsFqa86zcyxoCqRjP2narHwI05JBMSGJMX2249Jh5NYuTeAvzuEA6dbUa9RYdJdxAQNWC8NLcazjmCiz6odGrEQ/KDjthKBPf+5AqSsQQgMIADzntTUFVo0P7h/dBaqR0TKQ1BIaJyXz0W7kxmPIYJAhzdtUV/1lpv8EsD4fXWZPnaK6tghJCdFw7EcO3iCCLBJzFOaiJ/BhMDSzhxoQ1qrRIA0H9xAkAjPv/i2ZL2DF+j0Mirfq7U7Z0q6ZQTXAL+OY/83h+58FQF8bE3HkIqw77prhN1UFeo8h6uIDIcPtsMQSGgod2ac1+mQpn6am3ce6IzqnHiw+3Q7KFfEEKeBfFYEu+/PgLnhgAcSAXSzikvxh8t4YVPdqH3ZD1qWyxQaQrfH7Y448f8ROkqP6/hnGPi8mOMvfEQwaXs+8LSScYSqX+R+PoMQiwQweAPbiMWiJR4tOR5kIwlsPRoFhOXH2Pi7QEsDy9ASiRRe6T5yUr3xnslS6Wrt13og8pQXJvOtQA8V29wQggpBOccN98aQzRDD/HwShS3354AUP56MIEFL/yz7pzHiSoFjPWbi0/GglEsPZ7Fwr0peCeWwKXdU5OKVsJLQeIoKg89jWQ0Du/EEqxtxadvbqRUi2g+WQ/PoAuL035Z76kwa7D/hUZUmFPVhZt7K7Ew6UMkFNu2GsYYoNGrcOrldqx4IuvtjyyVelgq9ZTyRshTMPF4EaFANO1linMgtBLFzLAbHQersTjlQzxaXGGTyUEXaotoeZLO4sMZuEecJT0nOJCMJ+F8MI2GUx2lPTd5pnknlzD+5iNICWl9pnl5aB4z74+g7SP70fqhffBNLWPp8SzCniAEhQBLswP27jqoK4oPwBWnTlMATggpm+WFAIK+zKvZnAM+Vwi+5RBMNl3Z6sGEPUEM/eguuIyFydqjLev1NqREElPvDmF5ZCH17LO6n1yhUaLxTCcsrZVFjasUKAgvAZ29Au7R0j4cMoEhuOgveRAOpFLCD3+gBeFADPeuTMK7lHmPuCAyHLvQtqmKskqtwMmX2/Ho2gwWZzYH8o46I3pP1EOlUcJWo4SNirwQ8lRxzjE9vJx9npAD08MuVDea4JpfKfozA97SrixzzuG8P13Sc244OZaHFigIJ7IFFrwYvfjgye/UhtnoRDSO4dfuoPfnTsDcZIe5yV7Sz7brnVCc+hy+PXoI/Rcnc/YGJ4SQQizN+nNuT2MsdZzJlroGbawHAxzCr5egHszCvcknrciyqDveCkdvHYDUM8PoGw9SBa/Xr9OpPxKROMYuPUQrY7C0PN3tsRSEl4Ctoxqz10dlfUnk4kDJUtzteieARjwKdOGHf3wLIVcYHqUIw+oK9713puBzbQ/EBZHh6Adb07YxUmuVOHyuBeFgLPVeBpjtOko1J2SXiceSsla249EkFqZ8OauPyrFxn+mlhz6g4zCaz/XDVeCMeGwlgniwtPvLNkrGEuCSBCbQDi2S2/zticw/5ICU5HA+mEbjmc6Sf7bhXCNGlKcBRCkAJ4SUjdwtsVtbDq8F4oAAocIMw7nGgu/9XJLgGV3MXaiGAVX7G9azbVfmPPBPZ09fn35vGOZm+1PN0KUnjhJQaJSoPdpS2pNKHBU15qJPs9aarL/+H+Kf/MolzN1xwjfrx9yEB0O359H/NwNY8YTTvtdg0sBo1WY9v1avQnWTGdWNZgrACdmF8mkHuFyCVXDGsJ4Bk9ojNoFLw/Gi9oiVew+XoBQpACeyJCJx+Gc9OTJL+A70p6c1FEJI+RhMmpyxL+ep49JTrHZuKFwylpS3wMlTx65xDc7nXMiMB6NYmfcWNb5i0VNHkeKhKOZujcM1JP+Gq9DmCFZZqh+uqcGW/bgc1gLwe/v+CX7zy9fW93bwLVvYpWT6L7jfHcabf/UQj2/MpnqJEkL2HFEhwFplyFlMEUCqX3iRq+CcA83dqRTcUhVrURk0EJSFF4vLijHYO2vKc27yzElE5XVCkeJJcJkt9AghZLepbbFAyNE9QVQKqG40l20MokqU1cGBCQyi6skzQnQlLKvNy9MuykpBeBFW5r148BfvYf72BKI+eb23RbUCiRw97ESlAm0f2V9U65C1AHzizG/g238ykWovUMDzgCRxTA268O4PB7ft/yaE7A0tvY5S147MyFZtgKXyScuvrYG44tTpvANxQSGmWjqVOmuMAYJCQGVffYlPTJ5VSq1K1vdQoVZSIVJCyJ6lVCvQfbwu6zH7TtRv6xVeSkwQYG2vyr6qzRis7VWbstkUaqW867Tq6WYUURBeoHg4hpGf3EtVRs3j4TYZTeQ8pv3lA9DZiitothaAXxqO497bM0WdC0i1TbvbP4FwMPsEAiFk97HXGtF9NNWXuNi4QJmjfdnyQiDVi3yDJ4H4JL49egiKU6dXa1XIV3O4GRqzPueNlYlMdrCu1KrQ+fHDUFdk33ZDyBpRpUi1H8v2HWOAvZuyKwghe1tDuw0HzzZBV7E5g1dvUuPISy2oabaUfQzVB5sgZLqvs1T9quqDTZtetrZV5YzNBKUIY31pu7jki4LwArkG5yAlimvjkxYDAk5fUadY6x96aTiO/otTSMZLM06Jc8yMLJfkXISQndXU7cALH+9CfbsNeqO64NnrRCzH/mwGTDxa2vZyKhAX0H9xEiPK0zB/+XN5BeKiSoHuTx5B5b56CBnGbqgyoe3C/pznMtSY0XqhD/v/3mno7dTBgeSn9nBzatUlw0OhQqNE5b7SZ1es39sHwui/OFLy8xNCyFbVjWa8+MlunP5YB458sAWnX+nECx/vgqPOmPV9/RdHcGkgXHTPcI1Jh85XDqeykAAwxtazjJRaFTpfOQyNaXOBSkuLA6oKTdZVh+oDjRAUZdrmJhMF4QXyTrjKcl7GGBIReXvO0lm7SW/sH6orVcE0DkpJJ2QPM5g16D1Rjxc/2Q2DubBexTmLpPDU3vJYmqyftUD81W8MFByIN5zqQMdHD27a/wVgfQJzZd6Lpg90pwKkjTfg1X939Nah85VDsDQ7qBgbKYjWakDnxw9BqVt9sGRs/fulMevR9YkjT35WIunu7Vsro8djSUwOunD3nUnce3cSU0MuJEo0CU8IeX4xxmC06uCoNcJo0ebcalOKbWgb6SuN2P/3TqPtQh8q++pR2VePtrWJ9MrtkwGCQkTnK4egrlh9zlkb7uq4K/fVo/pQ07b37TQqr1mgsqyCI/WAqywwaG5+RQ3FqdPbbtLJDisGbsyVbHyEkL1PrVGUpB1ZJtcvjuL4h9qg0my+zay1L3n1GwP46heOo/3LAL4mv49oPBzDyOv3t2f4rP49nPem0HC6A/s+cxJLj2fhm14GlyToHUY4eutgqDbTXl1SNEOlCfs/dxr+WTeCi34wgaGixgx9lank369M9/aNlmb9uNs/uald0PyEF0O353HwbBMctdlXrQghpJRK3TOcCQLMzY7UdiAZ1BVa7Pv5E/BOLcM7sYRkPAmNUQtbZw20Fn1BYyg1WgYokNZqKFkf700YS+1lyJNd74Ti1Gl8e/QQ+i9ObrpJ17VaoatQFT1cxpCzZRkhZG+oabGUtVhb0BfB7cvjaStEp65NDK9+awQjwvG8VsRdg3NIxhJZxz53axyCUkTD6Q70/cIp7P97Z9D6oT5U1FgoACclwwQGU4MNtUdbUHO4uSwTPNnu7Wt8yyHcvjy+rV8vACQTEm5fnoA/QytSQggpl2K3oRWLCQIszQ60vNSL9g/vR/3J9l0TgAMUhBfM0VMrq/x9vir31a3ve5BrrRL6iPL06k1a2HSTVihFnPhwO0z21S8eK2z+gHOgodOe/xsJIbtOZb0JFRZNWeYSgdT1wusKwetK3zmiyaoHwHBpOAmmUMBwrlHWed2jizmPSUYTuP9nVzB26SFiQWqvSPYmu94J85c/l/Hevmb84WL2xxHOMfEo9+8NIYSU2logfmkgihHlaRjONe5oIL6bURBeIEO1GbaO6vQ/ZCi4vVihD4yicS3VbPsseTIhAQw4/qFWnP5YBzoOVKOh0w5rVe5Kwxs1dtpgceyeGSRCSOEEgeHY+TYYbanrRbmC8fGHTkhpVugAQGQMgALD7Ljs8yVjuTtMrPGML2Hgb29QIE72nI0B+KvfGMgYgCeTEpzT2Yu5cg4sTHoh0XYyQshTk9qa9iReIbQnvECMMTSd7YbaqIXz/vSmB0NDlRk1R1ow/uaDVJG1PO573vElxAIRqAyFFU3ayLccwtjDRSyu3qAFkaG2xQKjTYfR+05InG8bm9mug0ItwjW7sv6aWqtAY5cdokLAo2szEEQBjjojrFV6Su0kZA9TaRQ4+ZF2eJdCcE57EY8l4XEGEA4WXhxyq6XZFbz114/Qc7yuJO1M1EYt4qGovOsq54iH45i9NoqWD/Zu+lE8FIVraAERTxCCQoCp0QZTg42KtZGnbj0AF45nDcABIBaRNynFeWpCXtha0JAQQshTQUF4EZjAUHO4GVUHGhF0+iAlklCbdOul8jtfOYyRn9xFLBBNLTPJTF/3TS/D0VNX1NiWZv24fXl802tSkmNmxA2MuDO+z+sKoetILfpONiAUiEEUGfzuMB7fmIWU5OurZZMDSzCYNThyrgVaQ4mqrxNCdhxjDJZKPSyVqSwXKSlhemQZU4MuhFZiJfmMeCyJe+9OQRAYqhrNRZ3L0V2LwLxX/hs4h3tsEQ2nO6DQKAEAzvvTmLm2scUTg2twHqoKDTo+enBbuxNCACARiSHqD0NQiNCY9QVnvGWzvr1MOI5XvzWCdNltGy0vBGSdVxAZFAW2JSSEEFJ6FISXgCAKqKjdvsKjtejR9wun4J1chm96Gf6ZZcRDOR5qGTKmbsqViCdx953Jgresjz1wwucKwu0MIpmUUunsqzaeM+iL4PrFEZz5eBcUSppdJ+RZIIgCmrocaOpyQEpKGLnvxPjD0uwnHbg1h8qG4qpHW1ocWHpsRsDplZ9lxDki/hAMGhOWBuYw8/7WHsupE8UCEQz98Db2/fxJiCq6PZKU6EoYs9fG4JlYWr8JKvVqVB9ohKO3rmQZYWsB+MSZ38Cl4SQAtlo7IbPl+ZWsP19jsunKMmlACCGkMDQtWmZMEGBpcaDmUFPaQH0bDmjN+e27NpxrhHDgIC4NpKqfzo15NgXO+YrHkliY8iEWTWQ9D+dAOBjH3Jin4M8ihOxegiig42A1uo7WQlmCNNZIMA7PYnDTa/0XUwGx4tRpWcVaov4IdDZ93n2YGYCZa6OYemcw80EciIdiWB5ayOvc5NkV9Ycx8Lc3NwXgABAPRjF9dRjTV4fTdgAolPLFs4DKgP6LEzkDcACy93mbqZ4LIeQpqbfo0H9xBJcGwhAOHJRdiDVfUX8YvpllBBa84FJxC5o7gab6y8w76cLczXGE3fJSxpQ6FSrq5O+bTNc/1O0KlrX/71azY240dlHVdEKeRYwxNHc70Nhhw+yoG4+uzxZ1vkjoyX7zaoMa4zM+fOU/3MW/+z9OoCdLz3DOOWavjcJ5fzrv65uoUWDh7hS8ky5Zxy+PLKCyr17+B5Bn1tTVISSimWu7LD2ahaW1EhXV5h0d15oKswaLM76cvw/WSgrCCSFPj8gY+i9OAejDr59KoBlXMfFaaYqmhpYDmH5veNNWNYVGiaoDjaja37Br61fRSngZLQ3MYfSn92UH4ACgtVXI/rKsBeDZ+ofuhGhYfrViQsjeJIgCGjrtaOoubsJNqRIRjybw6NoM3vzuQ4xdmcbElRn86mdfx2/9mRn6L/1C2hVx572pVAAO5D3BWFFllh2AA0AiXJq98GRviwUi8E+7s3/fGMPSo+ImpvIlJSV4l4JwOwOobDDm/H3Q6JSw1VTszOAIISSNVKsyhv6Lk/j26CHZ2W+5hFwrGPi7mwgseDe9noikirJOXxkq+jPKhVbCyyQeimH63fz/jw/Me8A5zxmI2/VOKE6t9Q/dXD3V4tBjfsJbyLALotbS14iQ50XXkVr4lkPwLqXv/52NQinCaNXi/ddHEFqJbqoxIcUl/PV3xjAyZMM3LzTC/s7U+oq4lEhi/s5kwWPOJwAHAGUJulOQvU/WBDrnCLnk7csuVjIh4cH701ic9kFKrv7yMKDCosGKJ7L9DSyVybL/TOOuXQkihDw/6i06zHhC6L84ifPdp9GeJftNrsl3BlOp55mylR7PwdZRDX2lqeDPKBdaCS+R6EoES49n4bw/Dd/MMpYG58ALyAeXEhJ4jj1eufqH1rRYIO5gFdT6NuuOfRYh5OlijEFvLCxIbdtfifFHSwhuCcA3und7Gf8j/HMwnGtcnyX3z3kgxZOFDjlvjq6aHfsssnvJbVe3EwXPnNM+vPGX97Ew4X0SgAMABwLeCBRKARr95k4lFoceJz7cBmuVoezjI4QQOVIr4gJe/cYARpSnYf7y5wpeEQ8tB1KToLmylR7PFTbYMqMlzCIlYwlM9g/AM76UemF1ryIThYL2ZItKUdYNnSkUgKhBuvYlCqWIQx9oxq23xgHONz3sMgaICgFmhx6uueJm7xkDtAYValqL7/1LCNk7Cm1LaLLrMHJpPMcNE/jJD6bwj3/duP5SMrpzW160Vj0srZU79nlk99JXGiEoBEjZCp0ywNRYfE2Utcn1YekwXv29OxBZagIgtBLF0J15OKd8Gd+71gPcUadDy0vNiEeT0OpV1D6UELIrra2Iv/qNAXz1C8cLXhGPeIO5D+IcoTy2Be8kWgkvApckDP/4bqpq6vqLq38U0maMAbau2pKkjdlrKnD6ox2objKv9/YWFak9nYfONsPtLPwLuXa+CosWxy+0Q6Gg9mSEPE+qG/NP62IMmBpczt25gQPTUyubJg9VO5Qebqg1o/OVQxDomkYAiCoF7N21WY9hjMHRk/2YXNaz2zb0Bq8zazFybwH9fzeQNQBfwzmwMOmFWquEtcpAATghZFdLLSAyvPqtEYwIxzdlv8nFRHlhrLCD2cH5oJXwInjGlxBc9JfmZCx1w6/aX5qKvJxzqLVK9J6oR9/pRiQTEhQKAUxguPfuZM6U93TUWgUqG0xQKkU46oww2XW0z4yQ55BWn/8DPudA0Jdm32oaoihg46XFUG2CyqBBLCDv/YVw9Nah8Uxn2c5P9qa6Y60Iu4NYmfNsrsq/+gVtOb8P6gptwedf6w2+MQBvsuoxNejC6P38Hkg5T62cq9T0aEcI2f2arHpMuoO4NJzE+TO/gWZ8Hbg8JXtFvKLGDCawnDGNuclRiuGWHF2pi7A0MFeyVmBqoxZtF/ZDpS9uxSeZkDAxsISpQRdikVQKp9GqRUOHDcmkhBV3JO+ibYLIUNdqRcehmpL0CiaE7B3hYAzz4x5EQnEo1QrUNJthMGlgtGnhXw7ndS5RJUJrUCEcyFx9nAkMB484ADypOM0YQ8Ppdoz+9EGhf40NH4DNgRTnsLZXoeFUR/HnJs8cQSGi46MH4B5dxNLjWUS8ITBRgKXZgcp9ddCYC2/9tRaAT5z5DVwaTmItAJcknncAvj7eHdifTgghpdJk1aP/4gSA5rwDcYVaCXt3beYOFSx1Dbd3Vpd0zKVCQXgGnHNI8SSYKEDIkO4QWwmXJABv/mAvrK2VsleV15rcX3q4OUUtEU/i+sVR+N2bH4z97jAevj8DAMhn4frYh1ohigIMZg0USgq+CXmecM4xeGsekwNLqSrLq6+PPXCiqtGEtr4q3L48kdc5K+uMUCiErL3GucTxyqdbIOznwPefBN3mJgdaP7QPU+8OIRGJZ3x/NtUHGyEoRHgmliAlktBaDXD01KVm0ymrh2TABAG2jmrYOkr7IGc41wjli2dxaTCJ/osTaLKmAnrvUhCxAuogqDQKGMyFr8oTQsjT8CQQb8TnXzwLA/rhktlDvP5EG6L+MPwz7i2T7KnWqu0vH4BCszu351AQvkUylsDiwxksPppd7xVbUWtB9YFGGOs3VwEXVUoARTSaZ4CpwQ5bm/xCBGu9wf9gsA/9FycgbnhwHLnnhN+TfWUqU0XirQSRwVploAdTQp5Tw3cWUgE4APDN843OaR+4xFHTYsb8uFfW+USFgLpWK5RqEX5PGDMj7rWFaADri9Ko6rHjL99eQu++w2j/cmJTsRZLSyXMTXbMvDeCxXx6MzNAY9aj+lATRKUCNYeb5b+XkDJi2lTl8o338nissE4Azd0OWgknhOwanHN4FoNwzfkhSRwVFi2qG81pOzilroHC+jVRLkEhov0jB+CddGFpIJWtJCpFWFoqYe+uhUqvLtHfpvQoCN8gEY1j6Ae3EfYGNz1xrsx7sDLnQf2pdlT1Nay/bm2vwuw1mQXOtqats1QKetPZLtnjW+sN/u3RQ+i/OLmpNVkyIWFmZLkkK/OMAbUtVgrACXlOxSIJTDxezHwABxZn/Dj9sQ5EQnF4nDkqlDLg8LkWqDSpW07viXpU1pswOeiCbzkExgBbdQWauu0w21N7xF791kjaqqlMEMABWfvAUm9gsLZVouF0B0Ql3fLI7qfRKfN+T3WTCc29u3PfIyHk+RNaieL25QkEfJH1LFzOgcc3ZrH/dCOqGkrXt5sJDJYWBywte+saSE8kG8y8P7ItAAew/t8z742gosYCnS01S2PvrIHz3hQS0XjO4HetZRlPSlDq1XD01KGytw6iSt7/BRt7g/df3N4bPOCL5K46LMdq+gbdzAl5fi1MeXNmzTAGzI171mtPZMWBCvOTeheMMTjqjHDUGdMevlasZS0Qbz53ddMeMSaw3Fk9DDA32dH4QheU2t2ZikZIOkarFnqTGkFf7kw7QWDoPlaH+naaOCeE7A7xaALXLo4iFk5tHdt4v07GJdx5ewLHPtQKW3XFUxrh7kBB+KpEJA73iFNGw/dZNL2YWr1WaJTofOUQBr5/C1I8e/oYT0hoe3k/THU2WX3AN9oYgL/6je0B+OrQirKWCqrWKHDoAy3QV+ze9A1CSHnFIolNqeLpcA5Ew4msRdY2ioRi6yvhcmSrmlpRa8Hig5nsJ+CAvbuWAnCy5zDG0H20DjcvjWU8RqkS0XW0FjVN5ox1awgh5GmYGXEjGspeu2X47kJZg/B4KIqlgTl4xhYhJZLQmPVw9NTB1GjbNROWFISvCrlWcqc2co6VOQ845wi5VpCIxKHUqWGss8K7sVd4OowhvByEucGe99gM5xrBFApcGoimDcABQG/UQFQIBa2GK1Qi6lotsDj0cNSbaE8ZIc85lUYhayVcrVVAoRAQS+bewyoWUNxxrVjL+X0HoXzxLHD5TwAApnpbqmVZMJJ+4pQBSp0aFTWWvD+TkJ2wtr1sKNKznt226ec1FTh8rgUP35/eNilW1WhC36mG9YKpksSxNOuHc8qLREKCzqBCfZsNBnNx3VYIIaQQM6PunMf4XCGEVqLQbVj06784jvPdPWg/5YX98ndktyrbamXei5Gf3IWUlNafEWLBKPwzbpgabWj9UN/65GUyloBvenk1plPB1GCDoNiZYtQUhK/iMjdTJ2MJPPiL9xBbedKvVlSJMlqVcSSjcUxdGYJ7xIlkPAmVXg1HTy0cPbnT0rcWKggFonDNrUBKStCbNLBXV6Chw4aJgaW894W39VWhuYfSzwkhKdWNZgzcmAPPEolznqodwTkwPeTKGrQbTBroDKVbkWYCQ/tH9mPwh7eRjCXSbiGKB6N48J2rqDncDHt37a6Z+SZkU2/wbwwAYGkn1yvrjbDX9sI1t4KgPwJRFOCoM0K74XcpHIjhxqUxhFai688hjAGTAy40dNrQc6yOvvuEkB0Vk9nBJBpJrAfh9RZdahvaNwbSbkOTKx6OYeQn9yBtXZRcfU7wTS1j7sYY6k60YeHOJBbuTm46VlQpUHesFY7eurw+txAUhK/S2yuQM/8SqbR1bPlyJeVUMuWA8+FM6kuw+hmxQASz18fgGpxH1ycOQ6nLnQKejCdx5+0JOKdX25Ot3nTVOiV6j9fBuxSE1xXKPR6srmTplKhrs+Y+mBDy3FBpFGjpdWDsYebibFUNJhitWohKO2ZGlsGTma+drX3yWzDKpbUa0PtzJ7D0aAZLj+dSwfgW8VAMU+8OIR6OofZIS8Zzcc4RC0QBcKj0ajCB0ntJeWTqDZ6JIDBU1hsBbK+fICUlXH9jFJHg6paQ1V/BtceY6aFlqNQKtB/YnT1yCSHPJpVGgUQ891Y19ZYtatm2ocnlGpyHlMgely0+mk1lED3cvq0tGUtg6soQuMRR2Vcv+3MLQU8aqxQaFaxtlU+a4ZbS2sOnxNMG+dGVCMYvP855mmRCwuS1WThnNvQHXz1dNBTHnbcn0La/Cp2HazZVV9UbVevtABh7MhyDWYsTH26HUkU9wAkhm7UfrE4VaGRI9QkXnjQLr24yY/+ZRgCAvkKNIy+1bGs5snad6Txcg8p6E5xTXkwPL2Nxxp9KESsBlV6N2mOtEHKkus/fmkDUv719I5ckOO9N4f6fX8WD71zFg++8hzt//A6m3x/JeRMnJF9bA/CNvcELsTDlQzgQy7p2MPF4CQn6LhNCyiQcjGH0gRMP35/B0J15rHjCqGvNvbhntGk3paKvWduGdmk4iYkzvwHDuUbY9U7Z4/FO5tgejFSR7HQB+EYz10fTTu6XEq2Eb9BwqgPBpRVE/aFt7cQKbv3FUnshs/4fyTlWZj2IeEPQmLenpK0Zv+tEOEu1VM5ThQ5Of6wTzT0OJBMSmMAgigKkpISFKR9WvGEIAoO91gizXUdpaoSQtBhj6Dpci6YuB+YnPIiE4lCqRdQ0WaA3qiFJHLNjbkwNulItSAQGnVGNWDSBRCwJzgGtQQXPYhAj9xYgbVgpV6pFdB6uQX2brehxrsx6EA/mriK9+HgWDSfb1/+bSxxjbzyEd9K16TgpnsTi/WksD82j59PHoK7QFj1GQgBsCMDj6L84VVQADgDzk56cxyQTEpbnVlDVaC7qswghZCPOOYZuz2Pi8VJqon719fGHi7BVG6DSKBCPJjJOEnYcrMl47rVAHGhcXxF3vZb7Pg9gexp6gXhSgmdsEfbu2pKcLx1aCd9AoVGi+1NHUH2gCaL6yfyEocoEQ4059yo5A/QOIxRqJRhjUOrVqD3SAl1l+jY8W63Mbb+hNr+ihuLUafz+nWa89+PRnOfwu8MIeCNgjEGhFCGKAjjn8C2HEIskoNEqUd1khsWhpwCcEJKTRqdES28leo7VoX1/dSoAT0q49dY4Hlydht8dhpTkSMYlhPxRJKLJ9UnLcCCGpVn/pgAcAOLRJB6+N4OpIVeaT9xMZAyv/t4dDEuHYf7y57bNiIe9QVkZTIv3pzH6xgMEFlOZRMtD89sC8I2S0QQef+9GagsSIUWy650QjUZAVABQQSzB/TcelbfCHZezZY4QQvIwcm8hFYADqRbMG5J9l50B6CrU0BpSK90bs3BFhYCDLzbBXpO9MnrqGqkCRAVEo1H2arjWqi++ZRRS2X/RDfW/yoFWwrdQqJWoO96K2qMtSETjEEQBokqByXcGEVjwIeuSOAcq6qzo+mTzpjZkgZ/clfXZW4sgrVVP/fboIfRfnEQiJC8t4v57U2hot6G62YyQP4p7704h6N88g2Sp1OPAC02b0tYJIUSOkftOLM+vFH2egZuzqG2xrFd5Tme9WEuGnuGCKMjOVPJOuOAdX0LTB7pTNTpySMYSmL8zgYZTHfI+gJAS45zD6wphYdKLRCwJjV6JulYrdBVq6Awq+JdDOTsZaPXUpo8QUjqxaALjj7KkfXPAuxTEiQ+3IZnkcM35IUkcFRYtaprMWe/5xXL01MEzmrmeDVhqC3IiEs9aB4xzniq8XUYUhGfABLapv6ypwQbXwFzO9y3cmcDigynYOmtQfbARKr0GersR/hl3zgdFnf3JrNDG3uBr7Us0WgXikdyBuH85jIfLMxi6NYekxCGlab3mXQri2uvDOP2xTijV9DUghMiTTEiYlrGCLQeXgEfXZtDU7YDRqs2YnZOtWIuxPo+U9tUb7mT/gOzA3TU4j7rjbdSLmey4eDSB229PwLMYTNWNRSrpY+zBIho7bahtsWJ+wpv1HGqdEtYqQ9ZjCCEkH4vTvpxtnRkD5ie86D1Rn3PVu5QMVSbYOmuwPDSfZlCpiXtLkw1LA2l+vhEHLC2V5RnkKnqqkMnUYIPaqJWV9iglJCw9nsWjv76BiDeUez8BAzQWPfSraesbA/BXvzGw3hu8pjm/nrfxuJRKA03ze8I5EA7GMT28nNc5CSHPtxVPGIl4afZcAamb9Hs/HsY73x/E4saik1ukK9ZiZLNYmfek0s/KRIonkYjkrvJKSClxznHr8ji8S8HV/8Z6yicATA0tY9m5Akdd9ofb7qO1mzLzCCGkWLFIImfGN+ep43YaYwxNZ7tQe6x109ZiAFAZNOBA7gCcAZbWylTcV0YUhMvEBIb2lw/IaiMGAOCpVMaxSw+g1KlQd7wtw4kBQRTR8lIPGGOb+odeGoiuB+AAUNdmhVItlrSCOwXhhJB8pMusKYXQShS3L09gfiJzsan1QPxRCG9/X4G3/nAYk28PIOwO5vdhHGB5rGwLInWQIMUTDhwEAPRfHMl5rNsZgHcpe6r55IALvSfqU21G154LVv9UqkUcfLEJ1VSQjRBSYiqtIuc2GMYAtba4TFs518r0n81Qc6gJB37pBXR+/DDaXz4Ax746xFYi4NkKt63OLBjrrGj+QHdBn50PykPOg8akw77PnMDy0AKWh+cRWYlAylH1POwOYubaKBYfpN9/WFFrQePpDmjMT1ZynhRv2Rxtq9QKHL/QhpuXxhANl2Z2KRKKY37Cg6oGE6VbEkJyMpg1qdTY8sTieHRtBpUNJogZrkcigOAX/wMWH98BK2IQokqBRFjeCncynoBCQ/UzSGHWJteHpcN49VsDANj65Hom8xPenL9nXOJYng+g71QDOg5WY3HGj2QiCa1BDUedEQKtgBNCyqCqwYzH12azTspzDtTKaFWWyVo9mEsDUaDvV9Hsz79nuCAKqKgxIx6KYuT1ezmPt7Q64Oiug6HatCPFqynqypOoUqCyrx49P3scpgar7Kq8ae+kDAi5ViCq5D/cVZi1+MDP9GD/mUZYHKVJwbz37hTe+f4gQivyyv8TQp5f8WgCJnv2AKIYibgE51TmtPRO7xxqH90uKgAHYzDWW6GSmWrmGctS5IWQLLb2BgeYrNZk2Vr7rGEsVSAJANRaJRo6bGjuqUxNqlMATggpE6VKRGtflmCYAZX1RphsxT0rFNszfM3y8IKs4wxVJlTUmHesexQF4U/Tasr63M0xeCddCC75t1VIT0cQBdS2WNDYZS/ZUCKhGK6/MYpkifrrEUKeLQFfBNd+OoJ3vj8I71KobJ/DGLZ1c9jI8l4/uCDj1pXtHso5KnvrYKgy5T6NwJDMlvFEnklSUkLEF0IsEJF1X05nawDef3FCdm9wtVYpa8+lWksZGoSQndfaV4m2/VXr1ykmYP2+W91oxoEXmkryOU8C8XjBgXiq1Vj2CyoTGGJlbkm2FaWjF8FQacpeBl8Onqq+6xpMFQmYMqtw2GYFDuZ+6/RI6fZzcw5EgnEsTHpT+8sIIWRV0B/F+z8ZRiLXJB2D7KrjmXAAopj5ZqlyLYJJuScLRaUCyXgi7XhqjjRD7zBCXbGcc8xc4lDqNbkHTp4JiWgcC3cmsTQwByme6q+tNmpRtb8B9u7avFZIngTgcfRfnJIdgAOpGjC5araICgFVDbknkgghpNQYY2g/UI2GThvmJ7yIBGNQqhSobjJDb5RZP0smkTH0X5wC0LjeIcX1mvzsXVFGSzTOOYQytk5Lh1bCi2DrqIagKO3/hCFvDO9+9V30f/1HWQsShIMxuBcCJf1sAJifzFwUiRDyfBq6M5/KkskSrBpM6qIDcAAAByrrMwcWSZ0eXEYgVHe8FY6euk3XaK1Vj+aXelBzqBlA6hqea8xMYLC2lrdNCdkdEtE4Br9/C84H0+sBOABE/WFMvTuEqXeHZK+K2/VOKF88C4gK9F+cgphneqPJpkNljgC742A1xBI/gxBCSD7UGiWaux3oPlqHtv1VJQ/AgdT+8LVA/NJwHMoXz6L5FfmfY2mtzF3IZgdakm1FV+8iiCoFWs7vS+VPlnj7gOpr/wmqeCxj8ZZIsDwtcxKxZO6DCCHPjWgknuoJmuP+FfAVX1OCMcBWY4DBnHnl2Xf4eM794EwUYG2rQuOZThz8X15E3y+cQtuFPqgqNJi8PIBbf/gWHnznKrwTLjh6sreQrDnSTEXZnhNzN8YR8YUyTsy4Bubgn84zA01MfZdzFWJL58ALjahtedKadC2OF0SGriM1Jd2SRgghu9nGQPwPBvvyCsT1DiMMNebMsRoDTI02aC3la3eaDgXhRTI32tH9qSMwNzlKGoiLsSh6xx9k/LlCVfqUCcYAraH0M1iEkL0rHCj9hJ+o3HLrWb12GszanPvI/AePIWqvzLovvKqvHqIqtdtKUIhYmfNg9OID+Kbc6yuZ0ZUIZt4fQWDRj6r9DU96Ka9GOoJCQN3xVlQfLM2+NrK7JWMJuIbms2dGMIbFR7M7NiZRFLD/TCM+8DM96DpSg5beSvSerMdLP7cPzT2VO1Y8iBBCdoOtgbji1GnZgXjbh/qgtxtT/7He0jH1L4ZqM1pe6i3DiLOjPeEloHcY0XahD1IiieCiH0Ov3Sn6nFwUoXZmbiZvMGmgq1AhtFK6B2TOgfp22g9OCHmiHOmufacbEI8kMTfuRiySgEanQn27VVarRK5QYPx/+xdo+U+/DfWSExJjEDgHFCKQSKKu14Sqo63rx4c9QUy+M7j65u0RVtgdgN5egQO/9AK8k0tIROJQ6tQwN9nXA3ny7Iv4QuDJHLUGOEdw0b8zA9pAa1ChuYe2RBBCSL1FhxlPCP0XJwEcwq+fAuyXv5OzdZlCo0TXp47AP+PG8sgC4qEYVHo1bB3VqKi1PJVJTXrCKCFBIcJQY4bapEXUFy7qXIxzSEpV5p8zhrb91bh/Zaqoz9nIUWeEtcpQsvMRQvY+g0kDjV6JSDBekvMpVSLUGiUWp3wI+qPgEodGp4IgCk9Wo3OIW+0Y+sq/h/H+HSivvQeHgaF2Xx0OnQhDeDwLV/DJeZYezyJrw2Weal9Sd6IN9q7sqenk2UWryoQQsjdsDMTPd59G+5cBfC13IM4Yg6nBBlODbWcGmgMF4SXGGEPtkRaMv/ko8zECA8/S4B4AmCRhpS97ifTaFgui4TiGbs9vq/CrVImoabFgatAlY8ypSqw9x+roQYQQsgljDK37qvDo2kyWYwCVVoloKHegnkxKuPb65qKT7sUA3M4Aapot2H+mQd51SFTAf+gYZpp6keQcZy804kNdDxB/p39T1VT/rDtnQRYucQSX/DDV744bM9lZEW8Q8WgcglLcVJAtHSmZRDwUg1KXeZJ8rTXZsHQYlx5m7nlPCCHPG8453AsBzE96kYgnodGpUNdmQYVZm9d56i06TLqDgKgB43sznN2bo97lrG1ViIdjmHl/9UGTYz1IVmiVaP/IAcy8+wBBdyxtMM4FAaHmNoQbW3J+VktvJaobzZgZWYbfE4YgCrDXVqCm2QyFQoTJpsPIvYVN+zpNdt1q2ieDQiHCXlcBNRUeIoRkUN9uRSgQxcSjpW2LyoLAcOgDzRAVAm5cGkvtuc4S80rJND9cfWl+wgOjVYvmHof8sa3PiE8B6MPnXwSa0Y+JtUBcbsX2UlR2J3uKf8aNmeujCC/L7zTCkxwLdyfRcLoj7c+L6Q1OCCHPslgkgVtvjcO3HFp/lmAMmBxYQl2bFb0n6iHIzIh7FlAQXiZVfQ2wtFTCNTiHsDsIJjKY6m2wtDhQaXKh5eOn8MZ/GER0cgEAAwMHZwyMc0QrqzH1a/8P2Z+lNajQcagm7c9qWyyoaTZjxRNBPJaARq+CvoKKrxFC5GOMoetwLaobzZgedsG3HIYgMDjqjahvs0GjS03iHb/QhqFbc/C6QgV/1sTAEpq67LJT04Htgfivn0qgGVcx8VoU+ioToiuR7KvhDNDZaCvO88QzvoixNx4W9F7X4DzqTrRtq1+wOQDPvzc4IYTsVb7lEBanfUgkJOgqVKhptkClfhJmcs5x880xrHjCq/+NTX/OjrqhUAroPlq300N/aigILyOVXo3aI9tXs9du0kvNK5j8+vfQcOd96MIBJExmeE6+CO+Rk+Cq9KluyYSE+QkPFqa8SMQl6I1q1LfbYLbrMqZwMsZgtOaX5kEIIVuZbDqYbI0Zf25x6HHy5Q4EfBHcuDQmKz19q2gojuBKFAZT5jZl6WQq1hLsrYN7eCHzGxlgaXaAiQIWH8xgZcELcA59pRG2zhootZnTjsneJCWSmHh7oKj3JyIxqPSbv6NbA/B8e4MTQsheE4smcLd/Am5nMFVsnKW23A7emkfX4Ro0dacy25bnV+B3Z6+XNTW4jNa+qk3B+7Ps+fhb7iJ2vRPKF/8+Lg3G0f/uIsRjL4J9+COy3hvwRXDjjVFEw4n11/zLIcyNeVDTbEbf6UYk4knMj3sQ9EchiAIq642wVOpprzchZMdo9PL2h2ci5aiZkcnWYi3N566iCVPwHWnG/K2J7W9ggEqvgbnJjvt/dgVS4kl1bO+kC3M3x9F8rgfWtuzFXsje4hlbzLn3OxdBsblNaPMraihfPJu6t68G4IX0BieEkL1CkjhuXtqyur26tM0ljoGbcxAVAurbbZif9GatkZp6P8fitA/17fLrs1x66ENHV2oSdGM9mL2AgvCnRsjrJp1IJHHjjVHEIolNr/P1vZRexCIJuJ2BTV/wyYElVFg0OPJS63rKKCGElBND4ZN+gsigMxS++rxerAWAaEz1BK090gKNSYeFe1Pr+38FhQh7Vw3MzXYM/+hu+vocEsf4W4+g1KlRUWMueExkdwl7grIKpKbFAEOVCQr1k/vpWgD+B4N9uyIA55zTxDshpOwWZ3w5V7eH7yygttWKRCyZq0YqwIB4TP4Eaapn+AS2bkPbKygI3yPmJ7ybVsDTWV5IX1xmxRPBtZ+O4IWPd5Wl5y8hhGwkKgRUWLTrs+NyMQbUtVqhUIq5D84hvuTB4z99jOkfTyAWn4HeUYGWl3qhUCshJZNQ6tQQRAHjlx+nisllsXBnAhU1h4oeE9kdmCDkfhjMhAPVB5vW/7P5FTUUp04/9QA8Go5jatCFmVE3YpEElCoRta0WNHU5oC1iUosQQjKZG/Ns6860VSyaWiBU65Q5V8LBkdeCYbptaHspEKeIrEhSIomVeQ/8s27EQ+X7P9055S3q/eFADPPjntIMhhBCcsinwjmQCsA1ehXaD1QX/dmOkce4f/SXcf8P78M7F0JoyY+lR7N49N1rmLj8CKJKAUEUwDmHZ9SZvTI6B/yzHiSipemTTp4+U4M1Z9u6bVYXlhtOd6z3mLXrnVCcOo1vjx5C/8XJpxaAB/1RXHltCGOPFtez5eKxJKYGXXj3tUH4lgsvlEgIIZlEw3FZnUWi4Tjq26w5L7sKpYDKelNeY6i36CAyAf0XJ/Ht0UNQnDoNu96Z1zmeFloJL5CUlDB3cxxLj2YhJVZTJxhgbnKg4XQHVPrSViBPxKTcB+UwMbiE+g7qg0sIKb+aZjM8iwHMjLhzHisIDDUtFnQeqoFKU9xtSbXoRO+ffxs8mT5zyD/rwYPvvIeeTx+DUq+WnZKcjCU3pSCTvUtfZYLWZkDYHcwYjCt1KlhaKxFYSPX5NlSb4Oiug8acCrLteifMX/4cRpSn0X9xACITnkoAzjnH7bfHEY8mtj0Mc54q5nrrrXGc+9ne56r1DyGk/NRaJcDCOQNxtUYBo1WH6iYzFia9GY/rOFRTUMbu1now7V8G8LXvwBXc3fVcaCW8AFySMPr6PTjvTT0JwAGAA97JJQz87Q3EgttXxdfalwxLh/P+TJ1RjWK3eEWCtJJDCNkZjLGcabCVDUac+mgHXvrMPvSdaig6AAcA29sXwaTse8qSsQSGf3IXYIAooworExiUWgrAnxWMMbR/eH/WyfJ4KIbl4QWYGmzo/tQRNJzqWA/AgVQRIKHCjEsD0acWgAOAZzGIoC+aeYWJp3rzLk77dnRchJBnX22rJWcArlIrYK2uAADsP92Ahg7bejyz9qeoENBzvA6NnfaCx7K2In5pIAqhwgzDucydXHYLCsIL4B5xwj+bIbWbA/FwHHM3xja9vKl/6C0fJr/2Jzjy/kVY33kTYsCf8zMb2nOnceRCs+CEkJ0SjyUxej97StjitB/JpARFCWtVmG9fgyDjYhn1hRGY88LRXYusdeQYYG2v2lYNm+xtKoMGvT93HPWn2qEx6dJ+B5LRBOZvT2Dkpw/ApeKz0crB7QzknKBnLHUcIYSUUmW9CRUWbdZrUPuh6vX4QxAF9J6ox7mf7UXP8To0ddlR02JBS28lNFplwZ1R9ipKRy/A4uPZ7AdwDveoEw2nOyCqUv8TG841Yvz0P8WVf/XHMPzRX2J/MgEIIiAlUfvdP4Xrgx/Bwic+AwjpH0bNDn3ONI5cKqhXOCFkhzinvJCSuW+o1386CrVWgcZOO5p7HJAkjmRCglKtKGjiUIjJrM3BAN/MMqoPNmF5eAHxcHx7ajIDRKUCNYea8x4H2f1ElQJVfQ2I+sKI+DPvm/ZPL8M9ughbR/H1CkpN7naK5+3hlhBSfoLAcOx8K+70T8CzGFwPxjlPZZB1Hq5BQ5p2Y6JCgHshAOdqhs5awTaVRoF9J+vz3he+V1EQXoCIJ5jzGC5xRP1h6OwV6+1Lrvwf/wOa//6dJwetpUxKSdjf+BFYPI75z/xS2vMxxrD/TCM0OiWmhlybHm61BhUioXjOm3HHwd33AEEIeTZFQvHclVBXRcMJDN9dwPijRSTiqRXHVG9RK1r2VUKtkZ8KHrVXQjM3I6NJGgNPSlBqVej65FGMv/kQwUX/2o8ADmjNerSc3we1kSYwn1XJeBKuofmcKZWLD2d2ZRButOly/o5xDphs1LOcEFJ6Ko0CJz7cDt9yCM4pH5KJJHQVatS0WKBKs91rrbe4d0PByLVrWCySwO3LEzj6wRbYa4079Vd4aigILwATBAC5U9OYKKwH4N9+rx6G//FvMh+L1F5G10sfQdyWfk+EIDB0HalFa18VlhdWkIxL0FWoYHboMTPixqNrMxnPX9VkgtmuzzlmQggpBaVKzHsLzVoADqQKSk0NurAw5cOpl9uh0clrs+Q+fQ613/2T3AdyDs/EEiK+EGwdNeh85RAivhAC8z7EQhFE/RHEghFMXRmCqcEGe2cNFHlMBpC9IbYSBk/mvp+H3bszndtRZ4Raq0A0sr0w2xpRIaCm2byj4yKEPF9MNp2syb7FaR+8ruwdGwZuzeGFmgqwYoth7XK0J7wApiZ79j2EAFQG9XoRF6Y1QHnxHbBcT6SCAMuNKzk/X6kSUd1oRl2bFZZKA/zuMEbuLWQ8vqnbjgNnmjL+nBBCSq2qsfh0Ms6BWDiOB+9lnmDcKmmokLEKnpIIx7Ey78XE5cd4/Dc3oNAoEY/E4Lw3De+kC6GlFQTmvZi9Nor7f34VK3PU5vFZw0R5j0Fsw1axtd7gv3+nGf0XJ8o0sicioThG7i3gne8P4K2/fojrF0ewMOmFJHEIAsOBF5ogMLb9uYSl/jlwphEKJdU0IIQ8fTOjyzljqKAvCr87XND5+y9O4PfvNENx6jSaXyltp6pSoyC8AEqdKmfqWtX+xk0zOMKiC5xl/5+bg0Hpyd3OZ6NwMIYbb4wiFk3fjgdIzZRTUTZCyE7S6FSob7MWfR7OgeX5FYRW5O311o8M5rzWbv6A1B8RXxiD37+NhTuTTz54AymRxPBP7iG6EpF/brLrqY1aKHO1FGVsvTf4WgC+U73BPYsBvPP9AYw+cCLojyIaTsC9GMTddyZx880xJBMSrFUGnHy5A5V1m9M3bdUGnLjQjsqG52N/JSFk9wsHYrJ6i0dC+Xd0SlVIZ3umZzgF4XkKLQfgvDuV9RhBJcLeU7vpNW6qyLk5koEjoTfkNZ6pQRcSCSnzF5ohZ4ViQggph57jdahuMpfkXLnS19YwKQleyJwj54gFsgfYXJKwlKswJ9lTGGOoPpCjlQ3nqOyrh13v3BKAl7c1WSyawM03x5Hceo9f/Xf3QgADt+YAAEarFofPteD8z+/Di5/swgc/sw/HzrfBUknb0Aghu4dSRltQIJX1W4i1VmX9FycxojwN85c/t2sDcQrC8yTnAUyKJeF6PLfptdj5s2A8+74zJknwHTmZ13jmxj3ZZ5R4qo9oITNKhBCSCZc4lub8mBp0YXbMjVhkezaOIAo4+GITTr/SiaZue8E3VQA52zCtidQ2gJWrEjQHPGO782ZOCuforYOtc7Xo2sbv2eqXrvFMJ5pbIzB/+XMYUZ7ekQAcAObG3KkAPIvZUfemTDilWgG9UQOVhkr+EEJ2n9pmS85jVBoFzI7CJxDXAvFXvzGwqwNxCsLz5J+Vly6+cG8KNl1qn/ZQpAe8thpzB06AZ3iS5IzB33cIkbqGvMYTjyXlHZclXZ0QQvKxOO3D5b95hFtvjuPxjVk8uDqNt/76ER5dm4GUpsiV0aJF99E69J6sL/gz5RaW9Bw/Da5UyMl2K0gyLu+aS/YOxhiaznaj7SP7YayzQlQroNAoYW2vRPenj6HnuAKGc40YUZ7GpYHojgTgAOCc8ec8hkt8Uw/wWCSBmZFljD9axMKkF0kZRecIIWSn1LZaoNYqsk6st+2vKnob7aZAXDi+KwNxmirNl8wnu3gwCrGvEhNnfgOXBqLovziB5n/wD2H9v+agnZvZdBoGIKGvwOwv/IO8h6PWKGStcqu1VNWXEFK8xRkfbr89se11zjmmh5cRjSRw6GxT2qqmVQ0mNHbaMDW0nNdnOuqM0BrkVUeXtDrM/L1fQ8MffxucsU0FMTly1oPJSW2kVk/PIsYYzI12mBvTdScJQTSu7bfeuccmKccq+PpxSQ5J4hi8OYvp4eXUzrfVNnsKpYCuo7Wob9veq5cQQnaaQini+IV23Lw0inAwvn6tWvuzbX8VGjpKc72qt+gw6Q7i1W+N4KtfOI7mc1eBy1NwBatKcv5i0Up4nvRV8vvWTbR8BpeG4+i/OIEmqx7W9/qhnUtV+WUb/gEARTCAhj/+L/Ka6m5Q327L+lTJGOCoq6DUNEJIUTjncM37cfedyazHLU774FkMpv0ZYwzdx+pw8GwTlGp5qekqjQL78lxB9x07hYl/8iWEG5q3DqDoFfLKLfU+yLMpuhKB8/405m6OY/ahF/GnsKWrwqKVtQ3DYFLjwdUpTA0tP3mEWP0zEZfw8L2ZVEViQgjZBfRGNV78VA8Onm1CbbMFVQ0mtPZW4gM/04P2A9UlbU3WZNUDYLg0nMTEmd+A4VzjrlkRp8gsT5X76uEZXZR17L2v/Ff0f/x/hcgYWCyGqh/+dcZjGZdgGH4M/fAAgp09ssfT0GnD9LALsUgibfzOGEP7gWrZ5yOEkK2SCQl3+ifgmlvJeSxjwMzIMqxV6YtMMsZQ3WjG1KArY7C+UUuPo6BJxEBPHwI9fVC6FqEIBpCoMMEw+BD1f/7fC1sRZ4DeYYSlbXfMoJPykBJJTL4zCPeIc3WmnGFe4njc/3eo/be1wIuf2LGxNHTYMDuaZQscAyrMWnAOzE94s55r8NY8apstEGS2ZCOEkHIShNSzQHWjueyf1WTVr7aTbMb5M7+BZnx9V6yIUxCeJ0OlCcYGK/zTufeGK2/cheXoDIy9Xai4cwNiJHvPOy4IsFx7N2MQzjmHbzmMpVkfpCSH3qRGdZMZxz/chmuvj24vjMSA9oPVMFopfZIQUriH78/ANZ87AAdSyTyhlVjO4+QWaRu6O4/B2/NQaRSob7ehscsGtUb+9pq4vRJxeyUAwHP6A5BUKjj+5i+h9efR85sx2Nqr0HCmg4KYZxjnHGOXHsI3vbpqzLGenZaMJjH9lW9C9SUARvkT5cUw2XRo6rZjcsC1/Ycs9RC772Q9ZsfcYCx7Il0ilsTirH9HHngJIeRpUs/PwnT3JoRwCDG7A74jJ9FkNawG4o27JhDfkaeJb37zm2huboZGo8HJkydx7dq1rMf/5V/+Jbq7u6HRaLB//3689tprOzFM2dou7IdCxkOgJAjoGL0PAFD6fRmLsq1hkgSlL/2DYTQcx/uvj+D9nwxj/OEiJgdcePjeDN767iM8fG8mbWVicGDo9jxcc7mLuxBCSDrhQAzzEzm6MGwhJ9VcbuuytaYSsUgCYw+duPrakOye4en4jp7CW//PV+Hs2AfIKPxS2VePA790Bs3neiAqad76WRZc9MM3tZz1u6745h9CiO9canrXkVp0H63dlg3CGIMgMAzdnoN3KZh7JxsDIkHqkkIIeXYJkTCavv176Pw//w0qf/J3sPW/gdrv/im6/82XYL/0Y4gA+i9OAaICyhfPPu3hlj8I/853voMvfelL+OpXv4pbt27h4MGDePnll7G4mD6l+8qVK/jFX/xF/KN/9I9w+/ZtfPrTn8anP/1pPHjwoNxDlU0QBRjrrbkP5BxiONXbNlFRsalAUNrDBQHxCtO215MJCdcvjsK/HFo7Lfja7HxCypnS+ejaLPzuEIbvLuDxjVlMDCylD9oJIWSLxRlf3u+Rs9pW1WCCzqCS3XoMAMBTwfid/sn1a2Ah6m0GsGQSkNHKTG3QQqmVVxTuebfXJ9yXh+Zz9sJTRsPonhveoRGtVm7vduDcz/amtpatFpPhEkciLsG9GMSKJ3t/ewCpIm0qyuIghDyjJAlN//nrqHiUWvxkkgQhmQTjHEIygZq//QscePhe6lhR8xQH+kTZr8i/8zu/g3/8j/8xfu3Xfg29vb34/d//feh0OvzhH/5h2uN/7/d+Dx/96EfxL//lv0RPTw/+/b//9zhy5Ai+8Y1vlHuoeQnMe3MewzhHUptqq+PvPYikWp39eEmC9/jpba/PT3gQ9Efzrdm2LhyM4eqPhjH+0Inp4WUM3pzDW3/9CGMPnEU9yBJCnn2JuCQ7UGYM0OiVsla5BVHAsQ+1ya56voZzYMUThs8Vyut9W6nMJkgs9y1QpKKWsjwLE+6xYDRncVTOGJReea1KSykciGH0/sJqivzGAcl7P2MMlfXbJ/kJIeRZYBh6DMPIIBjP3FWi6offg5DYPRlBZQ3CY7EYbt68iQsXLjz5QEHAhQsXcPXq1bTvuXr16qbjAeDll1/OeHw0GoXf79/0T7mFXCupm7UMhoH7AOfgajUWP/LJjMdxQUCwuRWBrn3bfpa1MEseOE/Nnqf+nWP47gImB9PsNSOEkFVag0r2BKBap8SxD7VBVMi7tWgNKrzwiW4cfLEJBrP8mWnGsKk3ciG8R09CyHKzBgAILEPLKrLVszDhrtAoc1bsY5wjqa/YmQFtMDVU3L26sdMGlZomlAghzybz9SvgQvZnD0U4hMrhRzs0otzKGoS7XC4kk0lUVW3e9F5VVYWFhYW071lYWMjr+N/6rd+CyWRa/6ehoaE0g88i4JSXnskA6GamoJ0cAwC4PvQxOF/+JDgTwBmDJIjrX5hgWycmPv/PgTRfoEi4fLM2o/cWkJTZi5QQ8vypajBBVOa+VXQdqcXZT3ZDX5E942crQWCobjLDZNPllZouFZnFs9J7AOG6xuw3bc7hGpgr6nOeBzsx4Q6Uf9Ld0lqZc2VZUirh7ztY0s+VY2nWL3sybO33aO3P2lYLOo9Qaz1CyLNL6fWASdnjGQ5AHch/i1257Plp0a985Sv40pe+tP7ffr9/RwJxubggwHj/DsLNbQBjWHzlZ+F+4SVYrl2B0u2CpNHCd+g4wk0tGc+h1ijLVlAlEZewNEcVUwkh6YkKAT1H6/DgvemMxzR1O9Dc48h5rlgkgblxDwK+CESRwVFvgq3aAMaY7NVzIJXVYyq264MgYPwLX0LtN38b5vnp9G3LODBzbRSCQoSjt664z3uGZZtwHxgYSPuefCfcgdSk+6uvvlr8gNPgnMM/m71iPgewdP6jkLQ733FESsqLwNsPViO0EkU8loRWr0JdmxVGi7bMoyOEkKcrYTSBC0LWQJwBiD2FTKZMyhqE2+12iKIIp3NzU3Sn04nq6vS9q6urq/M6Xq1WQ51jr3WpGark76vijEGIb05dT5gsWPrwx2Wfo67VAt9ycfsfs4mFqUgbISSzujYrGEv1Go5Fn1wvRIWAlt5KtPZV5jzH1KALA7fmwCW+vkI3NbQMvUmNoy+1orLOiCmZ22M0OiXstcXfSJMVRgy+9HGc/LPfz5qFPHtzDLauGmpP9pSVc9LdO7GEpYczGX/OAQQ7urH40Z8pyefly2jVwDUXz7kaXt9mhVorv4UfIYQ8C7zHTsN86/2sxyTVmlRnlF2irE8UKpUKR48exRtvvLH+miRJeOONN3D69PYCZABw+vTpTccDwE9/+tOMxz8NOnsFdA55D4AsmUS0sqaoz6tpsaSKF+VTRTgPKu2eT4gghJRZbasV536uF0deakHviXocPNuElz7Ti7b9VWA58sjnxj14fGN2Q02KJ/Wvgr4o3vn+AB7dmJGV9i6IDAdfbMr5mXJMuoM4vPgQyBFcJ6MJ+Gd2vhjXXrETE+5AatLdaDRu+qdUnA+mc95jlV5Pzurp5dLQYc8agDMGVDaYKAAnhDyXVnr6EGxuzbrFbPHlT0JS7p5uJ2Wf1v/Sl76E//yf/zP+6I/+CI8fP8YXvvAFBINB/Nqv/RoA4Jd/+Zfxla98Zf34f/bP/hl+/OMf47d/+7cxMDCAf/tv/y1u3LiBL37xi+Ueal5aXuqFmKPICQfAlUp4j50q6rMUShHHL7SV5eYqKgQ4akv3IEMIeXYJAoOjzoiGDhuqG81QKHL3A08VgZzPeowkcYT8MSTj2fdzOWorcOrlDpgd+rzGnc6kO4izF5phDbiBZO66GMGl8hf93Kv2+oQ7lyQEnf6s+8EZAPWSE4qVp/M9sNdWoLbFkvZnjAFKtQLdtO+bEPK8EgRMfP6fI9jWCeDJ5XxjQwnzjfeg8+yegtRlXwL93Oc+h6WlJfzmb/4mFhYWcOjQIfz4xz9e3ws2NTUFYcOsxZkzZ/Cnf/qn+Nf/+l/jX/2rf4WOjg78zd/8Dfr6+so91LxoTDq8+Pdb8HgwgcV3x7f9fG1/4exn/j4kTfH7sbR6FRrarRi558x9cB7aD1TntReTEELy4VsOFVTTQqVRQFQIUGsUqKw3ora1dGm2M54QAI7z3WosVgIBhpwFuRbuTCK45Efr+X1QqGm1casvfelL+JVf+RUcO3YMJ06cwO/+7u9um3Cvq6vDb/3WbwFITbifO3cOv/3bv42Pf/zj+PM//3PcuHED3/72t5/mXyO3XBX1y4Qxhr7TDTCYNJh4vPRkWwhLFU/sPFybd7s/Qgh5lkg6PeZ/5hfQ/jv/AZCSADYnOKkXZnHmv/0u4l/YB9iezhg32pE85C9+8YsZV7Lfeuutba999rOfxWc/+9kyj6p4mgolzn/vV/He98J49C+/CZ3/SVGXuNWOhU/9PHyHT5Ts81QlXglnDNAa6GGSEFI+sUhhNSfi0QTOvNJbtvTasxda0Kl5DPV+Naa/L+89K3MejP70Pjo/frgk6fDPkr084c4EARqLHhFPMOMxHKnCP4mKp9drmzGGln2VaOpxYMUThpTk0BvVUFEve0IIAQBUvfY3AHja3UWCJEEdXIH0h7+P+GkVXMGqNEftHLpyl4Dtsx/BpVkHevypVLW40YRwY0vadmPFqGow4fH1J/sqi8U5cLd/Eqc+pqbqqYSQsig0iOYccM2toK7NWuIRbZYIJcBEAVxGSjo4EFjwYWXOA2Ndece1F+3lCffKffWYemcw8wGMYfns+ZLf1wshCAwm285XaCeEkN1MDPhR8ehe1vIeTJIw8l/fhdpzZsfGlcnTv5s8K5iAUEs7/AeOpNqRleFGrVIrZLUBytfE46WSn5MQQqSkhBVvGIJY2KpxIlHe1N9H/9cbuPa79+QF4GsYsDxS2m1B5OnrPiyg9uUugLFtOxM4Ywi1tMP1wY8+lbERQgjJTeH3y6phHXFHMfH2Y0S85es8JQethO8xHQerISUkTMps5ZML54Bz0gt+uoHSKwkhJROPJXHj0ij8y+GCz6GvKH6PqxAOwXTnBpR+LxL6CvgOHQWggDA1g7u/+Xf5n5ADiVCs6HGR3cOud8L85b+HaukILv7af0HL+5eh9XsBAAlDBZbPfghLH/oYuJK2bxFCyG6V1BtkH7s87IRnbBEdHz0IQ7W5fIPKgoLwPYYxhs7DNZgdcyORo5KwXJLEwflT67xCCHkGPXx/Gn534QG4WqeErbqIXuCcw3HxNVT++O/AEvFUdpIkofa7fwL1iZegeqSXn4a+EWNQ6qkA1rPEcK4RTKHAm4PA2JkLSL7ySSg9bgAccbMVEHN3ASCEkOdZJBRHLBKHSqOARvd07pEJkxmB9i7oR4fAsvV0BADOISU5Ri8+wP5fPAMhR6vScqAgvAzUC7OoeHQfLJFApLYeK70HSpqe7pz2lSwAB1JViAWBInBCSGmEgzE4p3xFnWPfiXqwIq5Ljp/+ANU//N6TFyRp/c/W99+EctyMRL4BOABwDlt75l7WZG9i2tQKisgYIAiI2+xPeUSEELL7eZeCGL67ALczsP6a2aFHx8FqWKvkr0yXyuJHfwYt3/yP612qsuJAIhKHd2IJ1radL9JGQXgJiQE/Gv7o26gYegTOGMAYmCQhbjJj+u//IwS79pXkc4L+KBhLpZIXjQENHbugTj8h5JmxPL8i6zi9UY1oOL5pUlFXoUb3sVo4ao0Ff74YDKDyx5lTzRnnSDg9GX+e+Y1ARY0FhhpzwWMjhBBCngWuOT9uvTW+rY6G1xXE9YujOHi2CdWN5h0dU7CjG1O/+gU0/M//AsRTW8eyFmoTGAILXgrC9zIhHkPrH/w21IsLAFIPeWtRssLvQ8vv/y7G/un/gVBrR9GfJSqEXC1tZWEM0OhUaOyiGX9CSOlISXlXKI1ehdMf64TbGUAinoRWr4LJriu6PoXp9nUwqUTZQmtD4YCp0Y6Wl3qofgYhhJDnmpSUcO/KVPoFwdXX7vZPYlA3h8YuOxo6bFAod2Zrj//QMTzu3ofKb//fsI8NZl215BJHIhLfkXFtRdXRS6T+7jWoF+bSPvgxzgEpieq/+8uSfJbJrkMponBrlQEnX26HSk1zMYSQ0jGYNbkPYoDBpIaoEOCoM6Km2QKzQ1+SAFfpcYMXuwWIAVqrAZX7GlBzuBm9nzmB9g/vh6ik6yUhhJDnm3Pah3g0mfO4SCiOodvzeO8nw4hFEzswshRJo8XU8Q/k3hsOwDO+hMffu46VeW/5B7YBBeEl0nztctafMwD68RGoXItFfY7bGcCtN8cLfr9CKWDfyXq8+MluHPtQW8E9fAkhJBNLpR66XJXNOTA56MK9K1MI+qMl/fykXl/0SrhSp0bnxw+h4VQ7ao+0QGvRl2h0ZLcJXJ6CtOLF+W41klzCjOfptq0hhJDdzu8O51XQOeiP4tH7M+Ub0BYznhDmO/ZBcthk1eUKLQcw9Nod+GfdOzC6FArCS0TvWZbVm672L/4Y9jd+BDHgz/szouE4br01jmShvXMZYKupgKgQkEzknr0ihJBCMMbQd6oxVfAx24WRAwsTHlz98VBRldS38h06VvQ5OOe06v2ccAWr4P3ad9Aev4qzF5ooECeEkBzyLujMU6vn4eDOtPhMco6zH2lF7x/9vyEqmbwJA84x2T8AXpKiW7lREC5DMpaA88E0Hn/vOu792RUMfP8mXEPzm4JhlpQX1BoGH6H6+3+Fnn/zv8PafymvcUyPLBcegAOpX4ApH+69O4WrPxrGldcG4XUFCz8fIYRkYKnU49iFVihV2feAcQ4kExLuvjNZshtf3GqH58QLqQKZBUqEYgg4i6vwTvYOV7AKifeu4tfb7qwG4pwCcUIIycBWU1FQgWjPYvnjjkl3EGcvNON8h4h9kddx+nPNsMjsahILRLEyV0Dh1gLQNH8OEV8IQz+8jXjoycxNPBhF0OnH8kM1PvzFIFCF1P5DGe1uGHhqPzdPou6v/ieSegN8R07IGkuxLX+2WvFEcP2nozj+4TaY7ZRqSQiRLxqJY3bEjcUZP6SkhAqLFg0dNpgdqWsJ5xwDN+Zk7RkDB0IrUXgWgyVraTL3C/8AQjwG861rkAQBAjiYwMATEphCAJcxoRnxBiEoBCjUSqiN2pKMi+xeE69F0Yyr+PyLCgB96L84hRlPCPUW3dMeGiGE7CqWSj30JjVC/mhewXi5V5k3BuDNV76OwOUphFWNaDkHxENRrMzmDrAj3hCMddayjhOgIDwrLnGM/Pge4uH0qROB5Sje+Yd/iqpv9SKsr4De75GVkr5+fgBVr30PvsPHISdPoqhV8AwkzvH4+ixOfbQDoZUYpKQErUG1YxUMCSF7j9sZ2LY1JuCLYG7cg4YOG3qO12FywJVfijkDvK5QyYJwrlBi+lf+CZYuvALx7cs4WiXBbovAGnDiYb8XgQVvzgKXU+8Orf+7zmZA7dEWmBqpm8SzLBWI9+PzLwLAAfRfnHjKIyKEkN2HMYbD51rw3o+HkIjJj09MtvJNaqaylzjOd6vRHr8K7+UpuIJPWo/J3WImiDuTKE5BeBa+6WVEV7I/RC5dncDSof8FBuRfsJwBUC85oZmdQqS+KefxFWYNIsFY/ukfLMvgeKq4wuXvPUY0nCrRLwgMta0WtB+opsJthJBNIqEYbr45tq0N2dp1aXo4tW1mftKb97nL0flrUGvD2f/4JbSuz4qvwN6lRSDPKqih5QBGXr+PprPdsHfVlH6gZNcIXJ6C5UXg/D4T+i8+7dEQQsjupNOrIAgCABmZwAww2fUwmGR0TynC2Qst6NQ8Rqz/6qYAHABMjTZ4J5ayn4ABxobyr4IDtCc8K9+UK6+nwkKfHxWBgKzjGjpsBe2/kDM7sBaAA4AkccyOuvHej4c3vU4IIdNDy5Ck7BeVuXEPeI5jtuGApbI0q+Br0qWluYJVsLRUQmPRF3TRnnpnMGN2FCGEEPK8WHYGEIvIazumUInoO9VQ5hFlZ22thEKrynzvZ4CltRIqfXknCtZQEJ6FlJRQkobcOcTNZlnH2WoqUNVoKu9gVnGeCswHbszuyOcRQvaGhSlfyS+LjAEVFi3M9tKlqU26gwD4tgAcSKWadb5yCPpK4/oAmMxKr5xzLA/Nl2yc5PkViyYQ9EcR38HeuYQQUirhgLwJaZVGgdMf7YTeqC7ziLITFCI6P3YQCvWWLN/V27++0oSmF7t2bDyUjp5FufvCcsYQqWtEtLpO1vGMMRx4oQmjFQuYHHSVZY/4pvGtthOIhuOUlk4IAYCytDfkAFQaEY+vz8BRb4Kt2gBWRG762r6wr36hHe3S9W37wgBAqVWh6xNHEFpagXdyCVJCQnQlDN/0cvZJBpZKTSekUN6lIEbvO+GaX1l/rbLeiLb91TBaqQAgIWRvyNX9ZI3JpoPWoCrzaJ6IB6LwjvsR9iSgMWnBNvQJ11oN2PfzJ7E8NI/lUSeS0QTURi3s3bWwNNs3HVtuFIRnYeuoweyNcZRjNXztjPOf+vm83icIDB2HatDaVwXPUhDhlShGHzoRDZVnJp1zIOCNUBBOCAEA6E0aRCOB0l4WObA8H8DyQgBTQ8vQm9Q4+lJrQTftGU8ISS7hq1/sThVm+dp3tgXgaxhj0Fca11fEF+5NpYLwrOSvmpO9iycSAIsA4CWtkL4068fty+Pbfn2WZv1wza3g6PnWrMUJE/EkfK4QOOeosGjp3kwIeWrstRUQRLatRsxWNc3mHRmPEApA8zv/Cd/70UUkI6nttAqtClV99aja37h+71ZolKg60IiqA407Mq5MKB09C6VOhfqTbSU7H8eT51ZJpYL36CkI8Tggs8f4RqJCgNmuS61UhxJ5723MZ5GJHjgJIWsaOmzl26Wzet6QP4rrF0eRyHPVPZ8APB1jnTX3343zHWldQp6etZ7h7fGrqz3DpZL0DE8mJNx9dzJV22XL94zzVD2Wu+9Mpq25kExIeHxjFm9+9yFuXBrDzTfH8db3HuFO/wQiIardQgjZeQqliOZuR8afMwZoDSpUNZR/K+3c7CIu/Nn/DfXf/WQ9AAeARDiG2etjGH/zYdnbo+WLVsJzqOprgEKtxNytccRWIkWda2ORciEeh/nW+7DcuIq40YSZX/qHCPTsl30uzjluvz0Bt3M1LTLP75XeqIbWoIJrbiVrsTdRIcBoo/Q4QkhKVb0JtpoKLG9IpS01zoFwMIb5cW8q6JdhxhMComGc51Pw/7/+BO/fHoGyqh4VtXxbajvnHCuzHninXJASSWhMOtg6aqCzGaB3VCC4lOHvxgCFWglLS+aHDvJsWOsZ/uunAOBQSXqGz094kIxn30YWiySwNOvf9NAqJSXcuDQKryu0+V7PgcVpH7yuEE5/tINWxQkhO679QDVi0QRmRtxgbLVTymrAozWocOx8W9lbfk26g/jw8NsQ5+aBZPprrGd8CebxJVhbK8s6lnxQEC6DraMa1vYqhFwrWJn3YvbaaMHnWnsUZJyv9/RRrPjR/Ae/h/H/7V8g2NEt6zzepRDcC4XvSwz4oggFcrc7a+iwQaGgnuGEkBQmMBw+14yh2/OYGc5dKb0Ys2NuWUH4jCcEx8PbOPrDPwULhTEiMnAJAF+GxqxD+0cOQG1MTSbGAhEM/+QeIp7gk5QgzjF7YxzmRhuCrszXVVGpQPtHD0Kga+JzYS0Q//yLCgB9RQfivuXwk4fUDBgDfK7QpiB8dtQN71L6lXjOgVg4jpF7Tuw7WV/QuAghpFBMYNh3sgENnXbMjiwjFIhBoRRR1WhCZb0JQpmzaSfdQYixCDQ/ugieIQBPDRRYfDhDQfhexBiD3mGE3mEElzjmboxl77+dz7k5B2dA9d/+BUb/xW/Kes/8hCfnzTyXXHs4rFUGtB+sLvwDCCHPJFEU0HOsDu0HquFZDEBKcmj1Srz/09H8W5NlIaf1yYwnBPPoAI7+1R+CrV6Q+YZrW8QXxuAPbqH3505AUAgY+uEdRAPh1A83XkA5h3fSlfFzBKWIrk8dhdZcugruZPdLBeL9+PyLQLGBuNxtYFuPmxrKXqeAc2Bu3I2uozU0aU4IeSqMFi2Mx3d2InCtCOuXPqTB49/KUamdA+Hl8mXwFYL2hBeg5lAT2l8+gIoay/prCo0SekdFwedknEM3PQH1wpys42PRRFEBeC5dR2tx9IMtEMucQkII2buUKhGV9SZUN5kRXImVNAAHIPv6c/zG66nAJd3Hc454KAbX4Bzco05EV8IFTZ5K8SRW5tz5v5HseROvRRF/px/nO5Q4e6ERyQJvvtZqQ877Nuep4zYK+nNvhZOSHJEg7Q0nhDwfNtaAaRIHZL1nt9W4opXwApkabDA12MAlCVzi6+mJgz+8jcCCt+AVcqXXjWh1bc7j1Fpl0SvhWXGUfQ8HIeTZ4VsOlfyapDfl7imqcy9B8Xgo53GuwXkodcW1SFkeWkBlL6X8Po8Cl6fQjK/j/JnfANCM/osTaLLm18a0st4EtVaBaCSR9hmBMUBbod5WHV0QBVktSWnSnBDyPNhahNX915eg0KqQCGdZDWds1xVVpSt2EWLBCGavj+H+n1/Fzf/6Ju79ybuph7wMD6Fynk2TOnk39bpWa1lXwtcLvhFCyFNitOZO+VUF5KWXJSIxxLPdoGWegzyfXMGqVCB+5es43yHi7IVmTLqDeZ1DEBgOn2uBQiFsT01ngEIl4vAHmrcVEnTUGXOmsqu1Cqg0lIpOCHm2rQXgZy80rXdBcYerUdWXY4Kcc1T2NezMIGWiILxAwUUfHvzF+3Den0YiEgc4EA/H4BldhKBcvRFuuGnm7HoDIGa1I1zfJOvzjVYtqpvMhQxdpt1Vxp8QsrtZHPqSTwxW1htzHhMzyNsGpNCooNJr8m7nuJFSryn8zWTP2xqIAzzvQNxk0+HMK11o7LJDoUo9KyjVIlp6KvHCx7tgMG3/jjX3OHL+bkXDCVz+m8dYmPLmNR5CCNkrNgbgv952B4n3rq63Ia3a3/ikc8nG+/zqDGbD6Q4YqsrfKi0flI5egEQkjsEf3M64/1GKJ6E2amBuqYQkBSBYLJhsOgLhu69BGQmDSdvTyhgA58d+BhDkz4vsP90AUWSYHfMAbLVOHE/teWjstGF+wiursFE6Znt+aXaEkOdbZYMJKo2i4GvORowBlipD2oBkq5DVgURPBxSDI0CWPen2rhqo9BqszHkKHpe9q6bg95JngytYBVyeQvup6/jqF0/j1W8M5F2oTWtQoftoHbqP1oHz7S30tjLZdGjbX4XR+86sx8WjSdztn4RwjqGyfnc9bBJC9ibOOdzOAGZG3AitRKFUi6hpsqC6yQxRsbNruUnOcfZCCz7fdQ/xd65i4rXo+s+YwNByfh/M40tYfDiD8PIKmJBKQa/sa1gPwBOROGLBCESlAqoKTc7rbzlREF6A8bce5SxAFPVHYKyzorXDAPOXP4cR5Wn8dkU7Tv7Pb0Hr94ILwqbNkwuf+iy8J17IaxyCKKDvdCPaDlTDOeVFLJqERqdEdZMZKrUCLb2VGHu4iKnBzBV/02ECQ13b7to3QQjZ3YTV1mU33hiDlJRyrtwxBgiK1b2uW47VVahx4IXGnJ+5Nive8+//EYZ/4SsZP0ipVUJfaUTA6YdSp0qlpeezas8YNGbdrmptQp4eV7AKhveuovPDZpy90Iorb0wWfC65D4ArnrDscw7enFtNYd9dRYgIIXtLMinhbv8klmb9m2q+LM8HMHJvAccvtEFXkbt2Symd71ZDunsXgctTAKo2/YwxBmtr5fq9OuoPY+nxLCbfHkAykVogiIee3P81Zh1qDjXD2r75PDuFgvA8JWMJ+GflVchdmfXAVdsKfO07aP8y8L+/+kH8O6sDtQMP0Dz5GCweR7S6Du7TZ5EwFx70avUqNPdsfzhUa5XoOVaH1n2VuHFpDAFvjgqrq9WF+041QK1VFjweQsjzJxyIYX7CC4Dn7IPMObD/TCOsVQZMDy9jbsyNeCwJjV6F+nYb6lotUCiz72/dmJZ2pO0OrF86iOu/dx9SPAkILHWT5RzqCg0EpYihH97Z3laSrQ5I4llbThqqTGj90D7qD06eingsicUZv+zjQ4EYvK4QLA7KaCOEFG7gxiyWZlPXnq339UgojusXR3H2U927spCzZ3wJ45cegoNnvLdHvCGMv/UI0ZUwag437+j4AArC8xb2BGWvoPDVb6wrWAXp//PnqPkHHpw91Ie3hQNY6DtSUJ/RQqi1Spz+aAec0z7MjroRDsahUAoQRQavK7T+i2WrNqB1X9W2yqyEEJKN1xXCjTdGM1ZwZgLAV39krzWipbcSlspUgNB+oBrtB6rz+rxUAM437QtLeC048EsvwDO2iLA7ACYw6KtMmL46lGpLBqS5djPY2qsRXQkjMO/N+HkqgxpKbXGV1QkpVDyW/xaPaIjalRFCChcNxzE7mn3RMRKKY/zRItr253cPL7ewJ4ixSw9lt2uZuzkOc7MDWsvOTlxSEJ6vPCoP6e0VSMaTcN6bxJ1Hs0h+4zGMAD5WW4XHB89i+vgHUG/bmYBXEAXUNFtQ02zZ9HoyKSEeTUBUiFCqaJWHEJIfKSnh9uXxrC2UuAT0HK9DXau16D1kTwLwRny+68GmfWGiUty0b3v+9gTiwWwVzTliwUjWABwA3CNO1Bxuhsa0MxOnhGykUuf/qKag+zkhpAhLc35ZIc/ofScaO+1QFnCdKpelRzPIa88ZY1h6PIvGM51lG1M6uy9/YJfTWg2pVMccmMBgqLVg6LXbmL8ziWT0yUy2OO/Evh/9FQ5874+BNEXadpIoCtDoVBSAE0IK4pz2ySrGNnhzbj07qFDbA/D+TYVZtnINzmU/IU9tG8pZMZ2lAnFCNuLhAAAJyXL2CwWgUIqobDDlbFO2RqkWYa3cvKIT8EUwN+7B/IQHEVolJ4TkkIzLi084B2bHCy94KteMJ4S1wDrpz749xzOxlF/dF84RXJLX7rSUds+0xR4hqhRwdNVi6fFs1uPqTrRh8f40Qq6V7V+E1e2HdfevI3nnCHxHTpZtvIQQUk7OaZ+s4ySJY27cg8ZOe8GftRaAn+9Q5gzAAeRYBc8HK7rHOHm2BC5PwYB+nD9zGEAz+i9OoMlavlTGtv1VcM36ZU1ktfVVre/RDPqjePj+NDyLm1upVTWasO9E/a5avSKE7B75FFxbnPahudtRtrGs1YD56he7U73BL0+ttyZLh2fJzMtEEHe+kCWthBeg7nhrakU8A1tXDUwNNjjvT2WdiZEYg+3ti2UYIeBbDuHB1Sn0/90A3vnBAAZuziLoz/7ASggh+fC5Q3BOyQvCAWDFk6M4ZBaT7iDOXmjG+Q4lmq98fbUyanaCSmaAkTOu4VDqaE84eWJrz/CzF5rz7hmeD6NFi6PnW6HSZP9Ot/ZVobErNdEVDsTw/uvD8C5tH5dz2odrF0eRSCTLMl5CyN7FOYdnMSD7SFde3wAAvIdJREFU+Gzb0Yq1sQhre/wqvF/7TtYAHAA0Fn3uDLeNGGCs2/muUDQFWgBRpUDXJw/DeX8aS49nkQinUrt0dgOqDzYjHo7i4V++n/M8AufQTU6UfHwj9xYwet+5qZ1AyB/F5IALfacbUNdK7ccIIcUJrURx7fUR+W9ghc80PwnAxfUAPNdNGABsHVVYfDiTX1paOhywte+uwjPk6VvrGd6Mr+P8md9A/0WOSXewbCvi1ioDzv1sLxanffC6gggHY0jEJShVIvRGNerbbNAankwWjdxbQCKWTL+vkwMBbwQzI+6yrmARQvae0ftOjD9aknUsY4DBrCnLONIVYZVz73f01iH4lvyOEkwQYO+qLWaoBaEgvECiUoHaIy2oOdyMZDQBJjCIKgW8ky5MXxmWfR5e4uyH+QkPRu+n9i5uvPGu/fuDq9PQG9Uw26l1CSGkcAM35yAl84huOWCvqcj7c1KrizzvABwAKvfVwzUwDymZLCoQt3XVQG3UFn4C8sxaC8TbT13HV79wHK9+awQznlDZup8IAkN1kxnVTeasxyXiScxPenMWVpoeclEQTghZF4smMPZwUfbxnAMNHbaSjyNbEdZcrK2VcI844Z/J0VKaMTAGtF3oeyrZbpSOXiTGGBQaJcTVtMf52xOyUyA4gKSohBAtPEVz0/k4l/WLM3hrviSfRwh5PkVC8fXeoXLpDCrYa9MH4dFwHGMPnbj37iQevDcN57QPkvQkevjqPzuEDuF2XgE4AKgrtOj42MH163Ohqvrqi3o/eba5glXwfu076BBu4+yFlqc9HACp31Eu5Z55Cgeo1gEh5AnnpFfWtWNNfbu1bAt7Zy+043y3FtK9u7K2oK1hgoC2D+9H1f4GCFs6sggKMbVwqlbA0V2L3p87DlND6ScR5KCV8BKKBaOpQmwyMQCKWAS67/4FAr/0y0V/fjScQMCbO6D3LgURXIlCn0fRBUIIWRPw5TdxqFSJOPLBVrA05Z0nB5YweGsutWLHUtfF2VE3tAYVjn6wNeM5Y4EIlh7Pwj22CCmehNqkg6OnFtbWSgAMvull+KZckBISKnvrEHStwD+dY1Y8o50v2EKePs552u/sXiC3FeBaATdCCAGAaCSxaTtrJkxgaD9QhZbeyp0ZWJ4EUUD9yXbUHGlGcNEPLnFoLXqoDOVJnS8EBeEllIzlbtOzFQPQ/P5buNa1H7qjh4v7/KT8wghTg0voOUarO4SQ/Aky2jSu0eiUOP1KZ9pex3PjHgzc3NBGjD/JGo8EY7h+cQSNLzRse9/KnAcjr9+DlJTW35CI+hB0+rD4cAaJSByxlUhqs9raAatBfr5p6YJCgLpi99y0SXklYwksPZ7F0uNZxAJRCAoB5hYHqvoaobNlLsj6tPjdYQS8YQiiAGuVYb1wm1avQoVFk7UYImPImdZOCHm+KNUKWf3BG9qtaN0nPzPtaRGViqdSdE0OCsJLSKUvbGWZATj2p7+Pfsu/hq11+wOnXBqtEoLANqVxZuKc9qPn2ObXJIljYdKL6WEXQv4YRKWAmiYzGjpt0FBlYELIKpNNB4VSQEJGH9HaVkvaAJxzjpG7Cxnfx3kqu8c3szm7KBGJYeT1+5C2VmNdK0K5sdfn1ieJfPeFM8DWWQNBIeb5RrIXxcMxDP3gNiK+0PprUkKCe2QR7pFFtF3YB3NT5v3TnHMo3ruBo//zL2GbnQBnDIHOHix/4AJCbZ0lHavfHcbD96fhd4fXX2MMqG21ovtYLRQKEa19VbjbP5nlLAxN3YW3DCSEPHuqG00YvDmbMxCvpSLPRaM8pBKI+sNYfDQD18Bc1tZl2QhSEg3XLq82oy+MqBBgrzXKOnbrA2wikcT1i6O4f2UK3qUQYtEEwoEYxh4t4p3vD+bVqoAQ8mwTFYLsft9jDxex4glve93vDiMso4+3b9YPJJ+s5rkG5yHtRFslltpTXntkd+zxJeU3+c4gIv4092DOAc4x9sZDxEPpCwNxznHzKz+A/l++CvvoAMRwCIpQEKZ7t9D29f8T9ouvlWycK94wrv10BP4tv1ecA7Njbtx8cxySxFHdaEbn4RoAq0kha1gqm+XQB5pQYaaCg4SQJ9Ra5Xqbw7QY4KgzwmQrT/HJzVIZxkl/fjVo9goKwouQiMYx8vo9PPiL9zB9ZRgz10cRdhcWrDJJQsvQbSQ5LyoQbz8gLzVEb9y8aj9wfRZeV5oepzzV/+/mm+OIRfNPtyeEPJvaDlTDUZe72jkDMDXk2vZ6XOb1RBQAJBOIv9MPAPBNL+czzIIwUYCtswbdnzoChUZZ9s8jT18sEIFv0pU1W4JzDtdg+sKms4+8GPnD9wAAAn8yyc2k1L/XfP+vYBh4WPQ4E/Ek7r07lerLm6H1mHcxiIVJLwCgpbcSL3yiCw2ddphsOpgdOrTtr8IHPt2DynpT0eMhhDx7ug7XoqFztVgZS03irU3kVdYZceDFxrJ+/lpvcEBCe/xq3kVZ9wpKRy+QlJQw/NodhDYG3UX2olWEQjh7oRH9F+VXANyqwqKFrcaA5fnskwENG1axopE45sY9WcefTEiYG/OguYdamRBCUitpbQeqsTSbvRgl58DijB/7Tm5+XdYWFwbUVWs3tSaTEtNFjDpFUAibsoEEhYiqg40w1lvBOIfGrC+6ojrZW1bmvbkP4oB/zoOaw82bX+Ycozd82d8qCLC99ToC3fsKGh+XOEbuL2D80SK4jPIv08Mu1LZYAAAGkwY9x+oK+lxCyPOHCQy9x+vR3OPA3JgHkVAcKrUCNc1mVFjKmz2zFoDn2xt8L6KnjAK5R50ILZc2RVtvVeBwhxJAM/ovTqDJWljJ/55j9Xjvx0MZ92tqdEp4XUFwiaO6yQz3QkBWEYalWR8F4YSQdXLbmEhpikYazJqchaPAgc+2DmyaBdfZDKlrr5yLVgYdHzuEeDiGeCgKhUYFU4MNopL2fT/XZH6f0n3nE+EYIt7sGWxMklAx+DD1OQVUXH90fQYzI/Kr+wf98vrpEkJIJjqDGu0Hqnfs8570Bn8SgMvtDb4XUTp6gVwDc7kPWiOzknBdmwHNV76O8x0izl5oxqQ7TXq4DHqjGidf7oC1Kv3+9EgojpnhZTx4bxqX/uqB7Bt7MlnkUj8h5Jmiq1DLiid0xvTVxbsO12Z8jyAwtNqjOLP8aNMsuKOnrqgAXGPWQV9phKXZgcreelhbKykAJ9A55NVT0aWZHJdk3huZJBX03V3xhPMKwAH5LcoIIWQ3eBKAN+LzXQ+e+QAcoCC8YNEVeX1yHb11cHTVwNpeBZYpGGepB0NNSycCl6fQ9uC/43x3as92ofvDDSYNjl9ow4uf7Ebf6QYoVJv/r157DpCSHG5n7hV9xgBjmVNQCCF7i0qtQHWTOWcgvuIOYWHKu+11W00FDp9rhlK9GgRvOM/R/Qp8rfkqViKbs2909gpU7S+8i0T9yfY92/uZlI/WooehypSzJfzSwDyc9zdviVDpVTm3L3AAkaoaQMj/sWtm1J334nk0HJd1byeEkKdtawAef6f/mQ/AAQrCC6ZI03InHVtHNRpf6ELLS73o+uQRaMzbqwka663o+sRhgAPT99y48u+uYPRXX8WFuRtQBYqrCKg3qhENxZGIye8hng7nQEOHrahzEEKePZ2Ha9Z7E2fCOXD3nUl4lrZn91TWm/DSz/bi4NkmONqtOP7xTvx/v/kBfPNzUzAr42nPV3eiDQ1nOqHMoy2kqFag9UIfTA10HSPpNX2gG6IqRyE+zjHz/ghcQ08KtDFBgKOnNmcAP3rsAwWNK7QSzXsBnUvAzTfHEA7k7kBACCFP01oAfr5D+dwE4ADtCS+Ytb0aczfGsh6jMmigsz+pHqx3GNH7mRMIOn0ILQfABIaKWgs0Jh1W5r0Yff0ekvHk6o18Fmp2BR8Ew9znfgWeU2cLHuvMaH5pbOm09FaWvRgDIWTv0ehUOPXRDrz/kxFEQumDZiB1WRt74MTRD7Zu+5kgCqhuNCNRocKh861oaBaBu5k/kzGGyt46OLprMf7WI3jGF7MWlqw+2ISaI80QRJp3JplpTDr0/uwxjL35EEFn9gnwuZvjsLVXr2e4VR9sgm9qGWFvcPt3UWBIHNqPicMnIXhCqLfk19pHoRRTv0B5B+IcU0MudB3JvO2DEEKepkl3EGcvNON8h7hehBV4NguxbUVPJAVydNdAVCuzznzXHGnelvbIGIOh2ozKffVw9NRBY9Ih4gth+Md3kVzrfcsBcA4mcTBJQt2f/TcYHt8veKyxSOGtxTQ6JXqP16Hj0M4VZiCE7A0BXwSPr8/gymtDWQNwILUa7ppbQSJeuh7fUlKCbyp7WykwIOQOUABOZFEZNLK2K8SDUQQWn1REF1UKdH3yCBw9dRA27McWVQp0/1wLfvG7P4MXX24rqA1pdaOpoO4rnAPzE57830gIITsgXQD+rFZCT4dWwguk0KjQ+cohDP/4LhLh2JNZ6tU/6463wt5ZI+tciw9nwKX0PT8ZAM4YKn/ydwj07C9orCqNoqCUNEFkOHyuGUZrfrP2hJBn38KUF/femcw7TTYRl1IreyUQ8YU2tRpLiwNBZypY4pKEqD8CzjnUFRoICirIRraLh+TdLxPhzRNPokqBxjOdqDveiog3BMYYNGYdBIUIfvs6Pv+iBkAf+i9OYSaPFXFHvQm6CjXCgfzT0uOx0k16EUJIqaSKT/PnNgAHKAgvis5mwP7PnYJnfAm+6WXwpASNRQ97Vy3UFemrAafjHnFmneVmnEM/PgqFz4OEyZL3OOvarBi5u5D3+ySJ49H1WbTvr0IoEINCKcJeWwGVzP3whJBnU3AlWlAALgjsSRG2NJKcA0gASY6kP3c9DNm1qhiwcHcSzgczqUlTAEwhoLKnDjWHm6knONlEZVAjuhLOufqs1KfvdS8qFdBvqbYeuDwFk/Euzh88DqAdV94YlT0eQWA49qFW3HhjFKGV/CbUpSSHa84Pe6286u+EELJTvvrPDqEj0Q9PmQLwRDS+3k5aZzNAoc5R82OH0ZNHkQSFCFtHNWwdhadrJ+Py0sXFUKigILyh3YapQRfi0UR+D80c8LlCuPnm+JPXGNDYaUfXkVoIMluvEUKeLdNDrrzfwxhQ02KGmCEtvJC0NI1ZB1GlQDKW5RrKUoWzZq9vruHBExKc96fhm3Gj+1NHICrpdkhSbJ01WJnzZj1GXaHdFmiXk1avwgsf74Jz2oeFSS9i0STCwSiiodzPD7ffnsC5n+2lCXRCyHMhEY1j5v1RuEcWwKVU4MMEBmt7FepPtu+aYJw2ye0CKhkVfiXGEDeZCzu/RoETH26HtkJ+JeGMODA16MK9dyfBi+jVSwjZuxZn/HmvgosKAa370gfVhaalCQoxd1VqjvXV73QinmDOIpvk+WJpqYTWasj6vao72bbjre4EUUBNswWHz7Xg5EfaceyDbRDE3GOQkhyzJSjQSgghu10ylsDQD25jeXh+PQAHUoUql4cXMPiD2/DPezF7YwzTV4ex+GgGiWj2mjblQkH4LmDvzlG5VBSQeOkMxuW1Jk9Lb1TjxU904ej5VtS3W4vek+mc8sHrKqyHOSFkb9t4Y5ODMeDwSy1YmvPj8Y1ZDN+dh98dBoDVIlUcX/1CO9ql63nvC6s53AxDtTnNh6b+EGWs/i0+moOULK6NI3l2CKKAzlcOoaJmNfOMYb0KuqAU0fxSDyzNjixn2BkGswYtPfLGsbxAPcMJIc8+54Pp9F0qAICnJt6Hf3gbC3ensPR4FtNXhnHvT97F4sOZHR8r5SaVGOc879lxR08dXIPziAWi2La8xABRwXDo3/4DvH4phkl3EE1WfUFjY4zBXlMBe00Feo7V4d6VKTinfLnfmMHsyDIsjsLGQgjZuyosWkTCcdkVmw1mDW68MQYu8VQwwznGHixCb9Oi9nAV/t0/34f2+FV4v/advPeFCQoRHR89CNfAHBYfziDqTwX3FTUWVO6vx+hPZHSW4Bxhd2BH04vJ7qbQKNH5yiGElgPwTbkgJZLQmPWwtDgKLuj3pM5B4R1LtlJn2Je+Vb4TZ4QQUi7rHSKSRawupsE5x9LjWXnPJpyvh1xc4pi+OgxBIcLeJa+odilQEF4CiUgMiw9n4RqcQzwUg6hSwNpehaq+BqiN23trc0lCdCUCcEBVoYFCrUTXJ45g/M2HCCysBsWrVdbVRi2OvFKNxg4nvtp1HK9+aySvqqqZCKKAnmN1WJr1Q0oWdnMO5lkghhCy93HOodWr8mqZtOJ5cqPdGAwE3WHwaS9aIlfg/f/9RcGFWQRRQOW+elTuq091mmAMjLG8tsxEvCEKwsk2OpsBOpuh6PO4glXA5Sm0n7qKSziEJJdKci83Wbc/Y2zDAJOdupwQQnaHJOc4e6ERSCYQv9JfuvPGEtu6VuRj9sYobB1VYMLOJIpTEF6k6EoYg9+/hXg4tv5QmowlsPR4FstD8+j42CEYqkwAUj1tnfemsPhwBolI6ksiqhWo7K1D9cEmdH3iCMLuAFbmvOBcgs5uhKHahBhj8H7tO2j/MvDVL57Gq98YKMnNW61V4ugHW3HzzbGCAnGlitr7EPI84Zzj8Y1ZTA8ty36PILLM1xcOjAx68fq/uo0OdWkqo268eTLGwEQBXEaquUInb0WRkEK5glUwvHcVv34KAA7l3aosHaNVB6NVixVPOHOdBp4q0EoIIU9bOXuDCxkKv8qVCMexMueFsd5akvHkQnvCizR26eGmAHwdTwXdI6/fg5RIgksSBr9/C3M3x9cDcABIRhOYvz2JoR/dhZRIQms1oLKvHlX7G1FRY15PbXcFq1KBePwqvvrF7vVZ9GJZqwx44RNdEBX5fxUqG2jViJDnAZc4YtEEXHMrsgNwlUaBpm57zgk+gXH84P/P3n+HR5be953o55zKKFQAqoBCzqnjdJye1OSwpznMoiVaa1q6e692fUVK93It29q7kiztM6afx+ul1ruPTWlX2tX6XlteyaYtUxIpxukZcqaH02k6Z+ScK+d0zv2jkApAJaCABtDv53kmoHDCWwDqnPN939/v+51pL8cwl6qMoiTC8ZVV8Mo6W+EdJQmz01KWMQieX2L+CBNXB3jwH69y799/yODb9wlMerIqMka/Hyd19Qpf7X3A+YstpFV12/fyY6+0oNHK5OqEO3SmEVOlmGQSCATPlp0U4AC+0cUV/46tksxj5FpuxEr4NggvBIgsBHNvoGZEtmd4nuCMj8hi7m3Dc34e/ufr2FtrqDnUgNG2cWZ8MeyCpRXx8xdPcPnS2LZn0RPxFLNjftKp0k2JqpyiH1wgOMjEo0lGHy8wOegmlSztGnH05WbikcJlYYoq4U1tLy4knUwze3eMhcdTpOOZfluD1YTrWDONZzp4MnUz7/6Onro9E1ki2J94R+YZfvcRoK5MyvsjCfxjizh66mg937cyqT76/ThtXOarrwEc3faKeKXNyEuf6WHo3iyzY76VFXGrw0TnURe1TUvVeIpKMp5C1siikk0gEOwqW01BKRb34CyjP3287ePoTLs3YSlE+DYITntXerdzIoFvfBH/aOFc3UQwxvyDCeYfTFB/qo2GUxtXh5bL2b76mhY4zuVLo1sae8ATZfD+LAuTgcIb50BnFH8+AsFBJRpOcO1HAyRiqZLjyABGH87Teqi24HYyKnbt1nu40slMHEnEE8q6FscDUcZ/1o+ju46mc11MXhvcdH9TdSXN57q2fH6BIOoNZwT4+g/K0tfu/llMdjOu4y0r31oW4hdeOQm0bFuImy0Gjr/ayqGzTcSjSbQ6DcaKzMRSMp5i5NE8EwMeUsk0kOkRbz9ci6u5iEoRgUAg2AbrU1B8ZRbgSlph4srm9/hS0Bp1WBrs2x9QkYhy9G2QKTErXPYQ95deajZza5TFp9Obfi/03jgAF45s7ebpmQtx7UcDLExtXYDbnRXoi4j+EQgE+5P7H45vWYADeBbC2BymgituChIfq/Jt7SRkrpXrBfha3AOz6CsNdH/mBayNVSuv68wGGs920PuFU2j04lom2DoLj6YKbjN3fyJjGriG0HvjtH34TS5061ZK07eLTq+h0mZcEeCJWIoPf9DPyKOFFQEO4HdHuPP+KMMP5rZ9ToFAIMjFpDdCWlV462t9GQG+hRSUQgQm3KTLkPXdeKZj10zZQKyEbwuz07px5ns9auFNcjFzewxHT33JkWf5UBSVez8bQ9lmXIkKTAy4qW+zbztzXCAQ7C1C/hje+fD2DqLC8MM5ul6o4/GN3CJFRiWubO0ap6TSLDyZLliNNP9wkt7Pn8LaWI2qKJmoNI1c1mur4PnFN7ZQ8EafjCaIesJUrPEeWHZMb+ObXHjl7wNtXL40uuUY0vWoisq1Hw8QC2/ycLo03IG7szjqLdgcwj1dIBCUlywBvsUY0mKIB2OFK5PzIUk0nevE2ddQzmEVRKyEbwNLYxV6izHvYrgkSxgqjVs6fiIUI+oObXF0m7M4FSAe3X5GqX8xwqPrk7z3V4/xLWzzYV0gEOwZlLTC3IS/LMeaHvbS1FlN94m63OcD/nCilWv+0o0e48EYyprVvU1RIbzGu0OSZWStRghwQdlQivRUUTZx6V8Mu9asiGs4f7FtqXdy+zz+aIpIgShRSYLx/sLtcgKBYH8TjyYJ+WMk49vXAMWwLMDPX2zdUQEOZKrZtrG2WOmy4DraXL4BFYlYCd8GkiTR8YnDPP3enUz27dqZ8KUZGdcLrcgamcCkZ0vnSCfK+2HxeyJI0tZX59eTSqT56N1hXv18byY7WCAQ7EtSyTRDD+aYHCjdhC33MRU882Gs1flW2TIXy3833cBZa4BSjE2L1dFCbwt2ElO1mdCcv0BFhoTRtnmm9/oV8cuXVMY84W2tiCfiKSYGCicZqCrbr3oRCAR7lsWZIIN3Z/G7V1tjNVqZpq5qOo/V7YhJY0aAq5y/2MpXOu+QunplxwQ4gK3FwXbETWg2gJJKI2t3t7JXrIRvE3OtjUNfPI291Zm9Ir70dzB7e5TZu2NbPr7esvkquppKQSIElBZvoqTVkv5GbU4TRnN+1+B0WmFCzKQLBPuWVDLN9bcHGX28UDYBvkw0nGBq0F3APkPCndTzOFya6DBYTWhNhV3NDVYTSqrAirlAsEVqDjcVFOBVbTVojbknqpdXxLvl27z1Gye2PaZyVbMIBIL9y9Swh5vvDmcJcIB0SmHsySIffOcxkVB8R859/mIXF/pMpK5eYfT7uc+hpBUCUx68IwtEFoNZkY7FojPpqTm0vVLyYiuayolYCS8DpupKOi8eJeYL8/R7t0lFs/uvCpZL5sBca8FgMaEqKv5JN4tPZ0gEo2gNOloe/DF9/3uCt772cb7+h0+KclVNxFPMjPqKOnfHUReNHVWoqsoH332af2MVpoY89Jzc3V4KgUBQHobuzxH0xbZVzpULnV6Dz1/csRcTeqC4VbnlG3XtkSamPxrJu23UE+bef7iCpc5GKp5Ca9RR3eHC3urYVRMWwcGkqq0GT4sD//gmK88SaA1aGl/s3NUxJWPFV9E56iyFNxIIBPuKeCzJw2uTebdJxNPc+skIr36+d9dbtFRVZf7+BDN3x1aiRQFMVWaaX+7G0lCVZ++NNL/URSqWxDs8v7oqXmSfuMagRfMMzKaFCC8js/fGSRW48ZlrrYTni3Ml11UYSCfTDL19j+C0L+uP6eGMj6GTv88bPw7x1tc+V5QQH7gzQzxa2D1Qb9TSebQWWSPjmS+uJz0RT3Pn8ijHX2lB1oiHWoFgv6CkFSYH3UUL8OV7W1NnNZND+dtsZI1E3KRFo9MUVSlm1hSesIz5I8w/mMQ9MJspH9Np0FuMJIKxvPulY0l8y1GREvhGFjBWmen5zAvoKgwFzysQ5EKSJTreOMrMrRHmH02tTrxLYG9x0vRSF4YcVW07haGi+Nz7lh7HDo5EIBA8C6aGvJlW2QKEA3E8c2EcdZW7MKpVpq4PMXd/YsPrUW+Y/h/cpftTx7E2VRd9PEmWaf/EYVxHm1h8OkM8GENr0KGi4htZyLMj1BxqfCY+MUKEl4l0IoVncK7gU6aqqugq9CQj+c1SYCnn9oOnBGd8Sztnfz8WSvHup/83Pn/PxvmLp7l8aSynEE8m0kwPewueU5Lg1OvtyBqZVDLNk48KR68sMzfu55F2kqMvtxTeWCAQ7AkioURJJejOBivth2uxOUx45kJEQrmvZY72KtBInP1kKz/994/zHtckpzluCebdJjTnZ+AHd1DSqx4cSjJNIplG0sqoxZaTLV1LY74wAz+8y6GfPyuM2gTbQtbINJ7tpP5kG+GFAGpaxVhlRm9+NhM8rmYbj65PZj4reWg7XIOlavNedYFAsH8JeIpvVV2cDuyqCI96QpsK8BVUlbEPnnL077xU0r1ZkiTMtTbMtasRzulEiieeMLFAZONigyRhsBpxHdt9UzYQPeFlIx6MFjXjFPOG0RiKm6FWFBXP0FzuFSpVJepPMvG/foev9j7g/MXWnDmj4UCsqFgyWSuvRJU8uDqRKVEtgalhL7EiJhgEAsHeoNgbnCTDxb9zjFOvt1NVa0bWyJy52EmFZUlkSMvHy/y3udtBdVcV5y+28c9+cZEGewI5z3L7z9XOo5dzf19JKwy9fT/jML3Jda5oAZ61U6ZUfavGmQLBemStBkt9Fdam6q0L8HRp993N0Oo0dB3PnUoAYK810yvayASCA0nR4lVi27HFm5Oix7j55Pvi05mCjqmJUIzgdOHFw0Jo9Fp6v3CK6g5X9jkliaqOGvq+cAptkbqs3AgRXiaK7SuUZBlHd/4b4zK6iiLcxiUY++sRlHt3udCXezZ7brw4kxZ5yZo4Gk5k9tnC53K2yHMJBIJnT0WlHoOpcFGUJEn89NsP+dn3njL2dIFUMo3JrOfVz/dy4mNt1LfaqWm00tzj5JXP9XD4xSYkSeLCERsGvcTvd1zDZciYs2TEuLoiyj/rXOCLNXnKxQDvyDypWLL8feuShDdfqZpAsIskP7gM6VRZosraDtXQfaJu5b6+loaOKs5e6NjW8QUCwd6lutiVbRUs9vK1yyxHk13oM6AEfYTeG9+wTdQXLsrJPOYvfjU/H1qjjvZPHOb4L71C15vH6HrzGMd/6RU6PnEkr2HmTiPK0cuE0V6BzmwgGc7jMihJ2FqduI42MX1rpODKjdlpJTTjy//QqWZcDvMxNeRh9HHhh0xJAmd9xqDFPZO/LDTfMVIJ4UIsEOwXJFmita+G/tszebdT0ipKWiXki/Hko2nG+928eLETg0mHq9mGq9mWd3+XIcb/1NPP7YCV6wErsbQGlyHOhWoP9YbC1TOhGd+2Ikhyoqplj4IUCLbC+qgyaOPypdEtR5VJkkTHERfNXQ5mx/3Ewgl0Bg2uFvuGSNFIMM7EgJu5CT9KWsFSZaK520lNo0W0aggE+5D6Njv9t6YLtpvJsoSrNf/9u1iKzQYvNgqs3JFhOpMeW4uzrMfcDmIlvExIkkTd8UK90CquI01Iskz3p0/kjexpOtdJZa218KqPBBX23LM4iqLSfyf/w/XK6FRo6a1Z2W8rqCpEQwlSIg5IINg3tPXV4GwozSE5Goxz72elxS9qJDhjC/D/ap7kH7WN8cv1s0UJcCi/9l7LbptmCQS5WI4qa/vwm1zo1gDqtlfEdQYtzd0Ouk/U03aodoMAn58M8MHfPGX0yQLRUIJ4NIV7Jsjt90a4+8HYDpWqCgSCnUSr1XDq9XakTSph1qIoKgO3Z7cUDbaWtQK8UDa4vbUIISxJ2EowZtuPCBFeRmoON+LsW+qvWtd3gARtHztEhTPzoGups3HkSy9iacz+AzPaK+h44wiuYy3YWhxojQX6FFRoOmLP+W3vXIhEkVElfacbsDsz/eCVtq0/lE6PePngu08JB3Yme1AgEJSXRDxVkokLZESxZy5M0BfdoVFlY6o275gSr+qo3ZHjCgRbYVmIdyk3eOvXuwCVSW95yjLXEwnGuXN5NONps+bjtfxRmxv3M/xgbkfOLRAIdpaq2kpe+3wvNmf+COOJAXfBtJN8ZAS4miXA82WDV3fUZlpuc80PSODocuVNLlFVNeMRs48R5ehlRJIkWl7toaq9hvlHU0QWAkiyjK25mprDTZiqskvKjPZMPE4qliARjqPRadFbjCulX5IsY22qzriu5yEaSJIOLMeeqVkO6fEiBXiFRU9rX83K11W1ZioseiLBrZmsJaJJPnp3iPNf6BORZQLBHmfg7iyJ2NaqV9wzQSz2jX4UGdGgQjqGmtp6uXcqlmTqo2Hc/cVV9GyFcpe8CQTbZTHsgm98i+7f1fLW187y9T98uiPnmRhwF1wBG3uySMeRWnEvFwj2IRUWA3qDtmA31+ijBZo6q7fcfnL+Yhu/dmKYxNv5BThk7rndn36B/u/fyXi9LLMUxWypr6Ll1Z5N9w3N+pi9N45/IhOtqqvQU3OokdojTWj0+0vW7q/R7gMkScLaWI21cfMSinQyjWdoDs/gLKlYEr3FhLO3HnuLY4O5m6qoRTkDDtwM4Wwdp+ulK7z1tZezMsMNxuJ+xSsOx2vex9GXWvjonaEtlaKpKsTCSeYm/NS3VZW8v0Ag2HlURWXw/ixTW5wBlyQ2jUAqti+sEKlYkiffuUk8GC2/Idsa9tuNW/D8oNy7Cy+c3bHjz08UNmBNJdP43RGqanc3R1ggEGwfVVVZnAkWLCSLBOPEIskN7So7ham6kiO/eA73wCzeoXlSiSQGawU1ffXYmp2bltEvPp1h7PKTJX+YzGvJSILpWyN4hufp/fzJZ+Z0vhXEk8cuEg9E6f/ebRJrzNti/giBCTeVdTa63jye9TAYD0aLyhMPTvtYDL8A3/gWXb8F5y+eWMkMb3BVojdqC5akN3ZsnDSoqjXz4ptd3Lk8Siyc3GSvwggRLhDsTVRV5eG1CaaGtx4BoqpsyBjerCxtKwIcYPrWyM4KcCljgPmsspwFgmdNsZPs6TWTbemUwvSIl8lBN9FwAp1eQ31bFc3dDgym/fMALBA8LxQToQw7FVWWG61Bh+toM66jhXO6Y/4IYx88yXyxfkZBhZgvzMSHA7R/4vAOjHRnELVFu4SqKDxdJ8Az38j8JzTnZ/Tyk3XfK/LDsLTdYthF6uoVvtJ5ZykzXGHaH6X7RO5INEkCS5WR2hzOxjZHBSfOtxU3jk0o5MooEAieDb6F8LYEOIDBpF1JVIC1AryFr/Y+KNgXlo90Mp0pQd/JZwIV6k607uAJBIK9TaXdmNckdhmzNTNRlYinuPqjAR5dnyTgiZKMp4kEEww9mOODv3lasreEQCDYWSRJynzOC6DRypgq9u4k2sLj6fwbqOAZni9q8XKvIET4LpBOpHjy1zfzx5ep4BtZyKz6LKG3mAqXSUqsmL0BjH4/vk6Iq1Btou90w0pphySt+sbZnBWcudC5aY7oMjZHBdbq3BnkOYcmrd64BQLB3iAWSTL8cJ4HVye2fhApE2127JWWlevKegGe/ODylgU4QCIYRSkQv7hdTI7K4lxaBYIDSkuPM/9ElwTOBstKier9K+OE/bGN26mZsvWb744UjE0VCAS7S0tPgfucBM3djj3t+xCc9hSelFdVwgv+XRlPORDl6DuMqqoMXbpPxB0qanv/uJvaI00AyBoZZ18Dc/fHc//hqaxsv8zo9+O0cYWvvqYFjnL50jgal5nXf+EwMyNeIsE4Gq2G2mbbiht6Iera7AQ8pbkgqyo0dR7seAGBYL+gqiqD92YZfjC/7WNV1ZrpOVGP3blqNrkswC9067YtwAGkXXgYiHpCpGLJwikUAsEzIh0IQDrF+YvbywzPhbPBQl2rndkx3+YbqJCMpwj5Y0iyxOJUMPfB1MxK+ey4b9MWN4FA8Gxo7KxmbtyHe3ajFpGkjC9Ux9GttY0te8Bc6DOgBH2E3hsHtnasfBQdjrKPEhX37pTHASE04yM47StuYwmUdfna9SdbqaiuzFkuVtVZu2m8zuj34yQ/uMxXex9w/mILaVVlPpKgta+GQ2eb6DlZX7QAnx7xMny/9IiS5h7Hhn5RgUDwbBh5OL9lAa7Rrl6Aqusq6T6eLcDHPGHOX2zjQreOtg+/uXQT3h4GiwmtaYcNYlRIhDdZ1RMI9gDrM8PPX2zbdmb4eiRJ4vgrLXQcc5HLFNnviXLtRwNMF9m+sjgVKLyRQCDYNWRZ4tTr7XQcqUWrW00DkTUSTV0Ozr3ZhU5fekpIuUxYi8FSZyfnRWoNFTWWgtvsFcRK+A7jHpylYC7AMioYbNnCWNbItH3iCItPpnD3z5JOZAzW9JUGao82U3ukKWecQGZF/DIXXjkJtHD5UukPxsMP5xi4M1vSPhqtTPvhWjqOiuxdgWAvkEqmGbxX2ud4LenU6vXLOxfi+uwgx15poaG9ao0A16wI8O3ehJOROCM/fUwqWkRvlyzBNsxkNDpxGxTsXRbDLnhvnDa+yYVX/j6XL6mMecJlXRGXZIlKqyH3Y4qaMWObm/CtRAjlY7fNnQQCQWFkjUz3iXo6jrkI+WKoqkqlzZglykuhnCasxVBzqIGFx1O5N5DA3uJEby7c/75XEE8fO0wykii6hkJr1GFvcQCQiMSZujGMd2Qedam/qqLWirOnDmtjNfpKY1FZfqE1N2/IX86mpBXmJvzMT/hJpRQMRm3Jxk0dR2vpOOJCoxVFFgLBXmF2zFd8KVcBlo/z4Mo4Eb2EzqQtqwBPJ1I8/e5t4qE8K9RLQqD55W4s9XaefPcWSrL0nHNjlRm9Zf/csAXPJ8tCvOulG7z162f5+h8NrsSQlovxfndega2qEPYX0WIiQaVdVMAJBHsVjUbG5tjetWOjB8zWTViLxVRdSdOLnUxeH9p4rZJAbzbkzBbfqwgRvsPoKvRFr4S3vNoLksTsvTGmboxs2CcyH2B8PkDTuS5cxwrb+cO6WfTz/who48N3xjbcvMOBGB+9M0wssrUoMgBjhY6u43VFTQ4IBILdY7M+sO2iquCbCPCH33wlU4ZWBgEOsPB4KsugcjN0FQY63ziCuTaT6nD8776Cu38W9+AsqXgSg8WErcXB1PWhvNEs9SdaxfVKsC9YDLtWYkjf+trLfP0Pn5RViIcDsaJ6KQtGnqrQ1CX6wQWCg0q5TVhLwXW8BYOtgtm7Y4TnM20vsk6Ds7eeuhda0e10C1uZESJ8h3F01eHuL1wG2n7hMPY2JxNXBlh4lKfcApi8Noi1sQpTdWVRY1gMu6gEeoyPeZeODd9PJdPcuDREvECWeD5kjcTxV8UDrUCwF1HSO+NWrFNUeoyPSVwuXxlawRgSMuXqBuuq+NDotdQebaL2aLZJpcluZvDt+6hr3//SDHrj2Q6qO3eudE4gKDc7KcQ1GpkkhatJuo67ePzRNKqqbirae0/VrzipCwSCg8VaAV4uE9ZSsbc6sbc6ScWSKOk0WqN+T7u652N/jnofUVlvx1Jvz5vD2XCmneoOF8EZX0EBDoAkFfWgWizTI17i0dSWHQWtVUbOfaqbqtryurYKBILyYNlCxGAxqDtgQ5o3ynH1xCQjhbezNlVz/O++TOPZDiz1dswuG7VHmjjyi+eoe0Hkgwv2H4thF75vfIuu5JWlGFKFSe/2s7ldLbaCnkcGk5amTgcvfrKTqprs+32FRc+xV1poOyS8YASCg0pGgJfXhHWraI069GbjvhXgIFbCdxxJkuj85DFGfvII/4QbJGmpOl0FJOpPtq48DE7dGCruoKpKcNZXtjHOjJbW972ek693YDBpmRv3Mda/SMAdRZLAWW+hpbdGiHOB4BlT31rF0L3SEw4KUdduL/sxZb2GdLxwVY7GUNztS2vUU/dCqxDdggPDYthF5dUrfOUlgBNcvjS+7RXxlh4nE/3upWeTzWk/XIskS9idZl78ZBeRUJxYOIlOr6HSXpxPjUAg2J/shAnr844Q4buARq+l61PHiXpCeIbnSSdSGCqNVHfVZXrGgchikMhCnvzNdZTzVpeMl25otDwIV7MNg0nLgysTTI94s8wS5ib8zI776T3VQNuhmrKNVyAQFCaVSuOeCZFKpDGaddQ0WVmYLG90UOfJOmChrMes7nRlHFDzLLKba6z7ygFVICg3mfSTK3z1NS1wdNtCvMJi4MTH27jz3ijKmlLzZUublh4HLb3O7H0qDVRUGrb5TgQCwV5HCPCdQYjwXcRUXUljjj7uuQcTJR3L0lhV8vnVaAhQSKtq1s26wmIgHIyXXI5usRs5cq6Z8aeLGQEOWcdYnlB/emsaa7WJaldxPewCgWDrqKrK8IN5Rh7Nk06t9kIbTFrMNkNxDsdFYnUYlq4r5cN1tJnFpzOoipLzmlR/UqxqC/YW6USKxf4Z3P0zJCMJtCY9zp46HD31aA26HTnncgzpr32yEujgw3fGtnW8mgYr5794iMlBN/OTAZS0grXaRHOPc0P5uUAgeD5Ybne50GegW72BVwjwsiFE+B7BO1LaalLNocaSts+XGd7UVc3CVPErZJV2I83dDho7qpE1EqOPC499+OGcEOECwS7w5KOpTNzQOuLRVMb7oYic32JwNln41FETyQ8uL/WFleembLCa6P70Cwz9+B7ptbFjUuZfLa/2YGtx5tpdINh1EqEYT//mNok1sXqpWJLJa0PMPZik93MnMVj3R2zXcspJ1/G6Zz0UgUCwRzh/sQsA5d7dZzySg8WOdrN7PB5++Zd/GavVit1u5+/9vb9HKJR/1eT1119HkqSsf37t135tJ4f5zFFVNdu9twCt53sx2kovOQu9N07bh9/kQreO8xfbGPOEgczsdykC+cT5Nlp6nGi0MpFgoqhYM/dMiKA3f+yQQCDYHiF/bFMBnkWZvNS+/HfadqwszVJv59jffYWWV3qwtTgwVlWgM+nRVejxDM7iHpjdMcd3gaAUVFVl8Mf3SeQwFExGEgz++F7eXmuBQCAQPH/sqAj/5V/+ZR4+fMjbb7/N3/zN3/D+++/zla98peB+v/qrv8rMzMzKP7//+7+/k8N85kiShL7Ivqr2Tx7F2duwpfMshl1rhLhmRYhLssTJ19swmosrmVucXl01L+XB4trbgzy4Os7okwUSRRgvCQSC0pgcdBd0OC4GjU7CUbc0MbfJ8T79hRZ+SfsXO9oXptFrsTRWEVkMEvNGSEYSJMNxQnN+Rt97zJPv3CQVKzwBKBDsJKE5P1FPaLX/aj2qSswXITi9PQNUgUAgEBwsdkyEP378mB/+8If8n//n/8m5c+d47bXX+IM/+AP+w3/4D0xP54/XqqiooK6ubuUfq9W6U8PcMxRbXj5x+SnTN0dIJ7YmYnMJca1Wg7W6iNV1CRRl9WEj6Ivl2TibdFJhatjL05vT/PTbj5gYLLBiJxAISiISSuTUAqXw2uf7OH2hg5Mfb6PaVYmskZBkiQqHif/2907yT//rIOH3d7YvTFUUBn54l2Q0se4bmf9EPSFGfvpox84vKJ7nueotsJR6khdJyqSjCAQCgUCwxI6J8CtXrmC32zlz5szKaxcvXkSWZa5du5Z33z/7sz/D6XRy9OhRfud3fodIJHcGZjweJxAIZP2zH6k51IjRXlHQ9jwVSzJzZ5Qn371FKp5EVRR8owvM3h1j/uEk8WDhku9lId6l3OBCtwZQGfOEsdiNhW3X1UxPOMDUkId7H5RoBLP0AK0qKo+uTTI77ittf4FAkBOtVi7LSrhOr0WSJGqbbJx9o5NPfvk4hz/dxS//41f58ktT+H//P+64MYt/3E0iGMtdPq9CYNJDzBfe0XEICvM8V70pabUYDY6a3ply9NB74yhBHxf6DGXLDBcIBIJl0qoKJCCdIh0ICFO2MrJjxmyzs7PU1tZmn0yrpbq6mtnZ2Zz7/dIv/RKtra00NDRw7949fuu3founT5/y7W9/e9Pt//k//+d8/etfL+vYnwUavZbez59k7IN+fGML+fs2VYh5wwxfekDUG86UZC6ZLU1cGcDeXkPb+T40+ty/3sWwC77xLbp+C9769bN8/Y8G0ThNBftFjRU6nPUWkok0j65Pbum9rmXg7iyuZpvIFxUIyoCr2cbMqG9bxzCYtGi0O9qplIWqKHhHFlh4PEXMF0HWaahqr81MKC7nI+VCAt+4mzq7cG5+VixXvd24cWNl0v0P/uAP+OxnP8u/+Bf/goaG3O1Ty1Vv+xlTtRlVyX/jVBUVU/XO/I0uZ4Z3vQTnL57g8qWxbWeGCwQCAWweTVYuE1bBFlbCf/u3f3tDCdn6f548ebLlAX3lK1/hU5/6FMeOHeOXf/mX+dM//VP+8i//kqGhoU23/53f+R38fv/KPxMTpUV97SW0Rj2dF49y7MsvozUV7s8OzvhWeyLXPAP4RhcyRjBKfuOixbAL3ze+RZdyg7e+1ods1ODqy+E6LIEkSxx9uRlJkpge8WaVpW+VSCBO0Ft8SbtAIMhNTZMNY8X24pBSyd0zPFNSaQZ+eI+RnzwiNOcnFUuSCMaYuz+Ob3QhvwAHQCrJ1FJQfnar6m2vUt1Ri6zT5N1G0shUd+7cg+vo9+Okrl7hK513OH+xdSWGVCAQCLbKs84GVxUV3/gi4x/2M/bBUxYeT225FXevUvJK+G/+5m/yK7/yK3m36ejooK6ujvn5+azXU6kUHo+npJnvc+fOATA4OEhnZ+eG7xsMBgyG4kzN9gtKSiEV3YbhkAqhWT/+cTf2tpq8m2atiH/tZb7+h0/QGjR4h7xEQqu9mHZnBb0nG7AvZYWGfLGCi1TFIkzaBILyIMsSh842cvu90S0fYzddnCevD60aVq09bbFDUNVMG4/gmbFbVW+QaT+Lx1ddyPdC+5ms1dB2vo/hdx/m3Kb1fG/eyrRykIkhvcJXX9MCR7l8aVysiAsEgi2RSU9Sd02AK2kF78g8i4+niQejSBqZdDyVEd3LlbKqyuS1QVrP9+3opOZuUvJdoaamhpqa/MIO4OWXX8bn83Hz5k1Onz4NwLvvvouiKCvCuhju3LkDQH19falD3be4B3I/uBSNBAtPpwuKcFgV4t2/q+X8xczN29ZgwSbJpBJpjBU6KizZEx2ypnzl48YiVv0FAkFxrLiabxGz1bjp6+kyi/NUPMnik/wmnYXQ6LXYW0Vm+E7w27/923zjG9/Iu83jx4+3fPy1PePHjh2jvr6eN954g6GhoU0n3GHvtp9VddTSpdcwfWOYiHvVkM5UbabxTMeu5dpnhPhlvvoaCCEuEAi2QqaKRuWtX++iS7mBb4cFeDqRYuCHdwnPB1Zaa7NY8+yhpBRGfvIIrUGHtal6x8a0W+zY1OyhQ4f49Kc/za/+6q/yx3/8xySTSb72ta/x5S9/eaVHbGpqijfeeIM//dM/5cUXX2RoaIg///M/57Of/SwOh4N79+7xD//hP+RjH/sYx48f36mh7jmSOfJGS0KFuL+0XG7l3l0uvHAW6OLDd4awVplyblvTYGH86eI2BwmWKuOK0ZtAINg+8ej2KktaezcKhrVlackPLm/r+MuEZv0Fe2kLoaKiqoX9JAWls9eq3iDTfvaP/tE/Wvk6EAjQ3Nxc9Dl2EluTA1uTg5gvTDKSQGvSY7RX7LrfybIQv/DKSaCFy5fGd/X8AoFg/zLpjZBWFd76Wh9dySv4vvGtHS9BH/vgKeGFpaqmIh8Jpm+OCBFeiD/7sz/ja1/7Gm+88QayLPOlL32Jb37zmyvfTyaTPH36dKUPTK/Xc+nSJf7lv/yXhMNhmpub+dKXvsTv/d7v7eQw9xxao27z2aASiQeijF/pp/lcF5JcXqMlR70Fs9VAJBjfVkl6z4nnp8JBINgN0tvoka6uq6S+vSrrtZ0qSytHL7eSSOMbWaC662CUpu0l9mLV235oPzPazRifsVFg6L1x2vgmF175+0Ably+N0rpDxnACgeBg8CwEeCIcwzsyX7LeCS8EiAejGCy5Fwv3Azsqwqurq/nzP//znN9va2vL6j9sbm7mvffe28kh7QuqOl3M3S+PwdzCwylURaX11d6yHG8ZSZI49Xo7P/ve07zRK5IMlTbjpuZrzd0OHPWWso5LIHje0WhKn3DTaGWaux10vVCHLK+u3C0L8J0oSzNWlUEUSBCY9ggR/gwRVW+lkU6miXnDIIGpyoyszW/qthUWwy4QQlwgEBTJsgA/f7F11wQ4QGDSu+UFx1Q0KUS4oPyYnRasTdUEpjzbXg0HWHw8Td3xlrL/scoaCaVA9qmq5C6PnRhwgwR9pxsAKevhXyAQlE40nOD624NFbdtxpIbqOguSJGF1mNCuEwMrfWFlnhVPhONEFgOAREWNhchicFvXue2WtAu2j6h6K0w6kWL65giLT6dRUpkqEFmnwdnbQMPpdjQFHNZLZb0Qv3xJZcwTFkJcIBBksVaAf6XzDqmrV3bNBV3ZRkWcrkJfxpE8G4QI36N0XDjC8DsPCEx5l5wB1a0/qEoZs7eGU+15N0sHApBOAemiDrs4Eyxqu0Qsd4/qRL87I8ZVMFXqaelx0tzt2NWcYoHgoPDgynjez9ta0mkVR93mlSg7UZaWjCQY/7Af39hC9rVMkkDa4vVNhQqHqKZ51oiqt/ykk2n6v3ebiCeU9XeuJNPMP5wgPOen53Mnyr4qvizEu1+7zVtfO8vX//CpMGoTCAQbOH+xna/23iP5wRVGv18GX6oiMW2lIk6CSpcNfeX+95MSSmePotFr6fr0C/T93ClqDjVQ1V5L7ZGmgnmkmyORjCTybrEYdhF6b5wu5QYX+gykVaVgzqiSKtMK1NJhoqEET29Nc/3tQVLJ4iYCBILnFVVVWZgO0H9nhqe3pum/O4NnLly0R8PyBNh60b4TAjwVS/Lkuzc3CvDMG0FeV0KvM+lpOteF1qTL67omyRKO7uLNvwSCZ8Hc/fENAnwFNdPfOP9oasfOr9y7C8D5i107dg6BQLD/Cb23u0aOSjqNVOqimwqNZzp2ZkC7jFgJ38NIkoS51oa51rbymr3NSf/37pR8LK2xcAzYZpnh+WbNd8rVPOCN8vTWNEfO7Q3XW4FgrzE77uPh1QlSya2XcimKyqPrkzy+MUlzt4Pe041M+6NZfWHlKkubvTtGIrjRF2JlLGmF2qNN2FucyDoNFY5KJFmmwmlh4Ad3UHNUArWe7yvq2iYQPCtUVWXh8VTBSo/5h5O4jjXvupv6bhKPJVFSKnqTdkveFQKB4OAwd2+cyetDJe/X9GInlXX28g/oGSBE+D7DUl+V6Ref9BS/k6quBNvHA1EWnkwTXghkekEbq3D21qM1Znor1grx8xdPcPnSWE4hXlVrpqJSTySUf5W9ZFSYHPLQc6IenUH8iQoEy4T8MR5cmcDvzl+lUgqqCuP9bnzBOA0n6rL6wspRlqak04VX+VTwDM3RdK4rS4RY6u30/txppj8azrrmmWus1J9uw9bk2Pb4BIKdJBVLkoomC26XDMdRkmk0+oN3z5sb9zH8cJ6AJxObqtHKNHZW03G0FoOYRBMItkU8mmRqyIN7Noiigt1RQXO3gwrL3k2SCM8HtiTAAQy2g9NOc/Cu9s8BDWc7Mr3ixdSdSlDVVoOpysz8w0kmrgxkfTs47WXqxjCuY82k4klCMz4A6h/+H/zif/cFuPgZLl8a31SIS5LEkZea+ejd4Uy/Xzn9kVQYebwgIswEgiVC/hhXfzRAehur3/kIzIS48F/Y+Wrvg7L2hU18OFBUHFkqmkRJpdHosm9LZqeF7k+/QDKSIBmJozXqDkQvmOD5YH2rRT6kfW5Omk4rJKIpZK20Iq6HH84zcGcme7uUwkT/IvMTfs59qhtjxeZCPJVKE3BHUVWw2I3ojeKRVSBYy/ykn7uXx1DWGJT6FsKMPl6g73QDrX2FYyafBfOPJjN+MFvIONaZDs7Enbii7UOmPxou+g/XYDHR8movC0+mNgjwtayPRBt9Z5rRS3/M6//jHFz8r/nwnbFN96t2VfLixU6e3JrGv1i+1TmAkYfz1DRaqaoRbq4CweMbU6RTOyPAISMAgv3zKK67S31h2ytDV1JpZu6Os/h0pvDGS+QTLLoK/YFwQxU8X2j0WiqcFiLuPCkAS0ZDOxFXBmtNV5Okt/DQW4h4NMnwgzkmhzwriSnWahP1bfYNAnwZVc3s9/jGJCc/nm0am04pDN6bZWLAvXLNkySoa7XTe6oBwwF6CBcItkrQF+XO+2NZppfAynXmyc1pTJV6aptsG3deYtkD5kKfATVZnKlrOQgWu5C4DoPFREWNdQdG9GwQTTn7jMX+GQITxZeixwNRHv7FNcY/6C/tREsfjtu//Zdor93Ku6m9xsxLn+rm+KstpZ2jEFJGiBdLIpYiHIwLUzfBgSMSjOOZy2HsVCZURWVhrrSJNFVVCU57mbs/zvzDSWK+MLBkxPadW8zeHi36WNZmB5IsbkmCg4freEv+z64KrmNlvn8usWy62vbhN7nQreP8xTbGPOGyHT8WSXLlhwNMDLizIksDnihPb+WfgFNVmJ8MEFtjHKukFW7+dJjRxwtZk46qCrNjPq7+aKDoBAiB4CAz9niBQg8FQw/mcn5vJ0xYi2WrjzKNL3YcKN8MsRK+z5j+aLjkfVKxwv1ouZA0Mvp//234wq8W3NZZb0GSpfLl9qqwMBUgnVLyRpa5Z4IMPZjDO595sFieMe88VofZund7YgSCYgn5dz4yRJKg0lL8SnN43s/ITx8TD0QzDuZLH3tLQxWqqhL1hko6v6XOhqqqB+oGKxAAVLXXEH2hhdm749klmEv/X3+qDXurc8fOvz4zHNq4fGm0LJnhj65Pkogmt7KotYLfHcW4VOUyNezFO7f5JIGqQjySZPD+LIfPNm39hALBPkBVVJKJNLJWQrtJlczsuL/g5y7gjhKPJjdUj6zNBt9tAQ5Q6bLiG3MXXA1f1hSSRqbllR6q2mt3aYS7gxDh+4hkJF4waqzcqGkF3c27yJ8uLOR1Bi31bXZmRrzbuiGvJ5VKk4inSMSSaLQaTJX6FWfVqWEPD65MZMUYLc+Yz08FOPfJLixVpvINRiDYAWKRBMl4GkOFDv0mZoQa7c4LU1WFl88X58EQcYd4+r07qMrSStWaz3tw2rul80/dGGbhyTRdnzyGqbpyS8cQCPYikiTReLYTa2M1848mCc74AbA02Kk93ISl3r7jY1gvxC9fUhnzhLclxKPhBAtTgW2Pbe2823j/Yt5tVRWmhjz0nmzIOzkvEOxXEvEUo48WmBh0k0pkKjurXZW0H6nFWW8BMlVoxbanpZIKhjWPwRkBrmaZsO6mAAeoPdyEbzT/Z91YbcZaX4XRXkF1p+tAmlYevHd0gEkW4bC6U8jp4sq/ek7W450LE4skyiLEZVni5jvDBH3Z8UY2h4m2w7U8vLrUy76+JUbN9JXd+3CcVz7bI1bXBHuShakAQ/fnstzOa5usdB2vy5o8sjvNaLTyzvWES1DlMnPyxVrSHxZ+qJ7+aDgjwMtcHp8IxXj6N7c5/Atnhfma4MBhaajC0lD1zM6/LMS7XrpRVAxpIZbdzreDJGWub8uE/bmjDJdR0irRcIJKm7hGCA4W8ViSaz8aJBpOZN1fPfMhPHMh+s400trrRJIkTGZ9Zrs8SLKEwZQt9TICvI2v9t4rqwlrKVgaqnAdb2HuXu5ccjWtUNVRS6Urd0/7fkdMI+4jdKZnY0qkrakirtcz6S3cL2ow6njp0900dTmQNavC11iho2ILpeGKom4Q4JApX7t7eSy/0Fch5IuV3TBOICgHU0Mebv10ZEPc2PxkgA+/38/VHw2wOBMEMpE+rX3lL1ddnpvSm/V85isn6FWuEXpvPO+seDKSwD/h3pn+dBXSyRRzDyZ34OACgWAx7CJ19Qo9xsecv9heeIc8bHtuW4K6tqos1/NiHeJFzrjgIPL4xhSxdQIcWDVb+2iK0NJEVXN3/ohOSYL6Njta3cZS9gtHMsI2Y8L6bGg820Hrx/qQc1S0xANR+r93m/DC9qtt9iriKraP0FXod38WXYK+XznBW//NIdKqUpQQ1xu1HH6xiU986QivfLaHVz/fy8e+eIhkCWYq5Vy4LmemskBQDuLRJA+vTeTdxr8Y4ea7wwzczZgbdR6rw9WyNCO8yedDkqG5J3vyKxdmq4GaJivWBgtNp+v5L996lX946nFRZWnJyA7Pmqvg7i/eUV0gEDwbbM6Kou/Vm21XaTNy6Exj1mu1jdaCx6ywGDCahUO64GARiySZK9DnLUkwsdSy0dzjwGw1bPp5kSTQ6jR0HavbodFuH0mS0Gg1KLkq/NRM2f3E1cHdHdguIsrR9xmNZ9p58l3flqz9S0YCk92MKzSNM3mF8xdPcPnSWNHla1qdJqukNl1EVvAyBpOOWKRM5feiFF2wx5ga8hT9ER5+MI/NUUFtk40XXmtlcTrIeP8iIV8MjVamttlGU0cVFdZMaaZOr2H4Qf5UgeOvthCQlsvSWkrKBt+Nvqx0IoWqKMItXSDYwxiMOuraqpgZ9eaNX3vh1RZmx/zMT2YEhtGso6XbSXOPY8MqXeuhGmbH/XnP2364RrSYCQ4cxSwYqSp4lowLtToNL36yi4fXJpifzF4ttjkqOPpyC6bKvR3rufB4KsvYdQMqhOf8xPwRjLattc3sZYQI32eYa210f+o4wz95SDpeppgOWcJQaSQRiq04m0uyRHWXi6ZzXfiSOuxXr/CVlwBOcPnSeMl9ZIqiZsWX5OP0hXamh73MjPpKfy+b4HAJkyfB3qLUXsrRxwvUNtmQJImaRiuWKiOJWBqDSbvB9bTzqIuAJ8ridDDr9WVT5sMvNm0iwC8X3RdmsJowOSqJuktzPy8FWacRAlwg2AccOtNI0BsltK5tbPl6c/SlZupaq6hrzaQmqGrG6yUXdqeZw+eaeHRtcjMjeVp6nTR2Vu/kWxIInglFTyut2VBv1HLy4+1EQwm88yFUFawOExb7/jAkjgWiRbW2xQNRIcIFewNrUzXHf+lVfKMLzD+aIjyXf9Z4heXZpqX/VnfXUd3lorLGikavJRVLEFnMPFhXOC1ojasP96Pfj9PGFb76mhY4WrIQV5TiV8EtdhOS7Ct6+1xIUibDvNIuzFsEe4ti+x6X8c6HGX44R4XFwNNb08TCq1Uipko9h8824WzIuKbKGpmTH29nZtTL+NPMirkkZ8R7a5+TkEZaEeAXunUlCfBlGk61MfT2g5L2KRoJHN17t4ROIDgIqNHMvT69zao6nV7DuTe7mOh3Mz6wSCycRJIlapustB2qyTJdkySpqMK05i4HdkcF4/2LLM4EURUVm9NMS4+TapdZrIILDiQ2Z0X+VWEyz7XVmywsmSr1mCoLT05lWkpVSMdQU2VayNsGGr2WZLiICrxN+toPAkKE71NkjUx1p4sKh4WHf3Gt4PYNZzqIecMoqTRGuxlnXz0GS/ZMmdaox9qU+0OcEeKX+eprUKoQ12hkDCYt8Wj+D71Wr0Fn0OKoszA9XHzU0doZ88wLYKjQcfzVlpWXVFXFMxvCPRvMzBZWm3A125CFwYtgl0jGU0TDCcy20k0KB+7Mbvp6NJTg5k+GOfJSE02dGaMWWZZo7KimsWPj59nvCa8I8LYPv7lkzFJaPIm9tYaWV3sY/1l/ye8jLxLIWg2uY83lPa5AIFgh9N44lVzmwisnKUdmuFanof1ILe1HalEUFUmiZKGspJWMSF+aoLRUmThyTlwHBM8PBpMOV7ON+YncfeGqWtiQLRfPOht8M6o7apm+NZJ34kFr0mGute7eoHYRIcL3OUZ7Bfa2GnxjC5v/EUtgbaym/kRrWc63LMQzN+8WLl8qzllRkiRaepwM3N1cSCyPtbnLgSxL1LXYeHxDJpUsvIJeXVeJyaxjdsxPOqVgMGlp6nbQ0uNcyVwOB2Lcem+USCC+MhOvqqAzaDj+autK9qJAsBNEwwkG7swwO+ZH3SE/h4fXJqlvrcqbnTvmCXP+YhsXujUrAryYm3DUG2bh0STe0UXUtIKp2kzNoUaqu+vwDOT5TK9n6bNXe6QJd/8s6UQKSZYyly5FRWfS0/nm8Q0ThAKBoHzsRGb4MvlKzdeTTimM9y8y3p9ZQUcCZ72F9sO1m6725UJRVOYn/EwMLBIJJtDqNNS12mnqrsZgFAZugv3D4bNNBL0xIqF41jP9SjvZ2cYtRfOtFeDPKht8M5y9DczdGyedSucU4vUvtB3Y9jQhwg8AbR8/xPA7aQKTntVP6tJ/K+vsdFw4UtbzhdbcvEuZRW/pczI77stEjq37sEkSmCwG2o/UApmV/tMXOrj2o8KuiJ7ZEDWNVt74xaOwNAMfDScIeqNotDIGk47rbw+RXOqhX6uBkvE0t34ywotvdmF3Hrx+E8GzJxpKcPVHAyTjqZ31U1Rh6MEcPSfqN/32VgW4d2Se4XcfZU6wNP7QnJ/QrJ+KmuInryStjOtoMzV9DegrjTSe6cA7srASP2Kpr8Le6jiwN1uBYC+RlRn+62f5+h8Nlk2IF0MqmebGpaFsfwwV3DNBFqeDHDrTSEtv4VjGVCrNrZ+O4F0yq8qQZPDeLKOP5znzRic2h7i3C/YHeqOWlz7dzejjeSYG3CTjaSCz2NR+uBZHXekLRhkBrmYJ8GeRDb4Zugo93Z85wcCP7mb7XC1pGNexZmqONOY+wD5HiPADgEanoetTxwnP+XEPzJKMJNCa9Di6XFTW28veP7V+Fr1YIa7Vajh7sYunt6aZHvGumsAtZYX2nW5Ap1/t+7A7zbz4ZifXfzxUcEwLUwHmJ/2YbUae3JzCPbNqGqXRSKTzmMKpqAzem+XMhY6C5xEISuXRjcmdF+BLuGeCsIkIH/OEAbVkAR7zRzICfP3gl76MLAQ37pQDNaVgbahCX5mZxZe1GhzddaL/WyB4RiyGXfCNb9H1W/DW117m63/4pGTT1a3y9Nb0pgaVy5eaxx9NUVVrzkpY2YwnN6bwzoc3/V4qqXDz3WE+9rcObZqVLBDsRXR6De2HazGYdPgXI2g0Es5GG9W1pZsMrwrw0lJQdhNzrZVjf+dl3INz+MYWUFMKpupKnH0NVDgOtrGyEOEHBEmSqKyzU1ln35XzLQvx7tduw5HzXL5U3H46vYajLzXTe7KegCeKClirTOiNm/8pmszFxysMP5on7I9viELLJ8CBldn3eCwpStcEZSUaTmxwKd9J8gn9t37jBN2py3iLFOCwFB9SjHVpMUgS/kkPloaq8hxPIBBsm2chxJOJNFNDnoLbjfcv5u0Lj0eTTI/kiUdbOtf0iJeWnsKr6gLBXmB2zMf9K+MoaXWlfXJi0IPJrOfUJ9qLLkffKMBLN2HdLTR6LbWHG6k9fHBXvTdD1P0Jngk6gxZHvQVnvSWnAAeyVrQLEfREMwJ8i5ph/OnijvXrCp5Pgt7Sosi2S7WrvKWk/rHFcmpw1BJSEgQCwe6wGHbh+8a36Epe4a2v9ZFWlSUX5Z0h4IkUVRlUaALTPRsq6jjrM5QFgr2KeybI3Q/GViJ9VXV1cj0WSXD97UHi0WSeI6yy3RQUwc4jRPhziqqo+MYXmbgywPjPnrL4ZJp0Mv2sh7UBRSleAagq2xIMww/meXprZusHEAjWUWoU2XbpOFpeo5VSPn+FUBWVCocwQBQItouSSuMemGXsg6eM/+wpnqE5lPT2JrgWwy5SV6/QlbzC+YutOyrEo6FEUdulUvmfSYp9z9v92QgEu8XAvdxGp6qaqeyYGHAXPM6qB8zaFBTBXkOUoz+HRL1hBn98j0QwllmekgBlmomrg7S9foiqtprCx/CEeHxjluj1PyFe8de0GlqRP/46irG8rsa7nfE99mSBuhYb9prdMacRHGzsTjOyRlqZ1d4yBbJDAXpP1q+kAZSLCqcFfyReltVwjU5DVXvha4tAIMhNYMrD8DsPSSdSLNeqLjyeRmvU0fnJY1S6bFs+dib95ApfeQngREkxpKWQiBeXT6zR5u/jLqYsV5J2/zlCINgK0VAC/2KBiS8VpoY9dB3P7aWyVRNWwe4jRPgBJhmJE5jyoqYVjNVmzDVWUtEk/d+7TSq+VM6irjoeK6k0w5ceUHeilag7RDKWQF9pxNlTj7WpGkmSUFWV6Y+Gmb07vmRe6AFpjKPqdZI/+R7jv/6bRFvby/Ye7M4KzFYD4cDulNFIEowPuIUIFxSNklaYGvYy3r9IOBBHliVqm2209TmxVlfQ1FnN+IC7aCFb7TKjN+oIB+KZcnYJqmrMtPY6URSVp7emiUdXH2L1Ri19pxuobyvQa52Olfzeag81ZkrSC2CutRJZDK6YLW5AgrbXDyEXeKgWCAS5ibhDDP7o3urnbE0tdiqeZOAHdzj0t85itG9dNC8L8a++pgWOc/nS6PYGvQn5YhTXYjLn92ixFfF8oKqZ6FOBYK9T7ORUMpa7QiRTvaJyoc9A2+X/RQjwPY4Q4QeQdDLF+M/68QzNZT34G6uWhHg8mVcQzN4ZW1l5iywG8Y0sYKm30/nmMdwDs8zezZS1rNz/VRUJ0MaitP5v/4LB3/0fSFm3Phu/FkmSOPpyMzcuDeVcTZQkOPxiEw+vTW77fKoKAffO9cIJ9g+qquKeCTIx4CbojaHRytQ222judmCsyDwcplMKH707jG9h1Z03rajMjnqZGfFy7JUWek42EPTFcjr4rsXmrOD0JzqQNfLKGICshIP6tioiwTjxaAq9UYvZash7zOVZcdIpkh9eLulnYGmswtFTh7s/fxZ4eD5Ax8WjhGZ8eEfmSUZWy03NLiuNpzuEIZtAsE1m74zm7oFWMxOCs/fHaTvft63zhN4bp+o1uHDEVrTpaikUGxnmrLfm/b4kSRx9aen5YM2CwlraDtUUdFgXCPYC+fyRsrYz5d/u/MV2eoyPSYAQ4HscIcIPGEpaYeAHdzPZu+tuSDFfmJi3sBAAVvdd+m9w1sfIe4+JzOU2OJFVFSkeo+rK+yx86gulDz4HdqeZc2928fTWDJ65VaM2SQJHvYXDZ5t4cG2ibOeTNbvbxyvYeyiKyv0Px5kd8y3HVQIQCsQYfTzPqY+346i30H97Gt/ixs/U8vb3r4xjc/Rx5kIH06NeBu/OZq1ir8e/GOEn//kRfWcaaOyozhkvWGExUGHJL75h+2VpkiTReLYT98Bs/pV8ScI9MEvXJ4/R/HI38WCUVDQTlWiwiAdggWC7KKk03tGF/J9DFTyDc7S+1lv2aNJyYnNUUGk3EvLHcr4fSYKmruqCx7LXmHnxzS6e3JzCt7A6ga43auk4Witc0QX7BpNZT1WtGe9COO/nvKmz8OdCsD8QIvyA4R2eJzyfQyhvp69TBf9o4bJUVBXzjStlFeEA1uoKzl7sJBpKEA0n0OpkLFUmJEnC747gmS3eRT0vEtQ05J99F+w/VFUlGk6QSqQxVugLzjgPP5hjdsy3tO/aA4GSVrn13ggvf6aHySFP3s+VBEwMLNJ3upGmTgdNnQ7mJvwMP5wj4N7cOT2VTPPgygSSJNHQvvXV461mg68n5osUvnaoatZ1x2AxCfEtEJSRVDxV1D1cTSsoqTQa3d59vJMkieOvtHDt7UGUlLLp6v6Rc80YTMVFhtocFZx7s5twIE40FEej02BzVCDvsjGmQLBdul+o58alwU0/6pIEepOOpm7RXnFQ2LtXacGWWHgyXZSJ004hAWaSjHnCtFaXv6/aVKnHVJmdHT47nr1auR0kSSp4gUsl00wPe1mYDqAqKpYqE03dDsxFrEwKdp+5cR9DD+YIeld7omubrHSfqN/U2CedUhh7spD3mEo6s1JeyHBNVWFhOkjf6dXXXM029AYN198eyrvv05vT1LXat/QguSzA3/r1LrqUG/i20RdW7ILaXl55Ewj2O1qDFkmWcvsuLCFr5X3hvWCpMvHyp3sYuj/L7Jhv5f5tr6mg86gL5xYmw81WQ8H2HIFgL1NVa+bkx9u5/+E4yUR65f6rqmC2GTn58bayG7AKnh3iN3nAiAeKWLXaSSSwdjcA6o4J8fWkEuWLVmvsrMJk1uf8vnchzK2fDJNKrkaeeOZCjD5eoPuFupWIqEQsxfSIl0gwjkYn42q2YXNU7IpQUVUVJa0iSaz0Fj+PhHwxHl6fzOrXXmZhKoB7NsSLn+zEWp3dn+hbDGf9fnMR8BSXAZ5ObTzW5JCn4MRRIp7CPROkpnFrlRlvfa2PbvUG3n/+rW31hVU4Lcg6DUq+CENJwtoker4Fgp1C1mqo6qjd4PWShQSOnvqy3GfUVAqkGKDuiEM6ZETz8VdbOfxiE/FoCq1OLnr1WyA4qNQ0Wnn9Fw4zN+En6I0iyRLOegv2GnPez/akN0JaVQEFNVqm6lDBjiJE+AFDo9OSiiZ37PgGq4l4MJr7IUCFY/9lH70/38XX/2hwx27eazFV6ss27zA95KX3ZANa3caVhGg4wc13hzeIqmUhNXB3FkOFjmQ8Rf/tGVR1dRVx9NEC9hozJz/WVrT5RqkoisrEgJvxpwtEghljLJuzgrZDNbiabc/VSuXo4wWe3prO+X1VhXRa4f6HE7zyuZ6sn82248TWEY8kufbjAQ6daVwR/NFQoqjKjVi4uDzdXCj37hbcJhGOMf9gEvfALKl4Ep1Jj7O3nprDTehMemSthpq+BuYeTOT53KvUHmna1lgFAkF+6k+04htdyORer/8sSpn7v+tY87bPsxh2wTe+RddvwfmLJ7h8aWxH7+VanWbTe65A8Lwia2Tq26oKp56sIa2qnL/YwoVuHckPLi9lgwtjtr3M87tMdkCp7qzN1ISXQpHlrrJOQ+vH+jKCZbNdJKiss1H58AFdyg3e+lofaVVZikzYORraq8u2+q8oKnPj/pWvE7EUo4/neXBlnNvvjWy6qrmWp7emeXprZkVgqeqqSPcvhvno3WGUAuWEWxp3WuHWT4d58tHUigAH8Lsj3L08Rv/tmbKfc6+gqioBb5T5ST/e+TBz4768Anx1Rwj5YxtyOXeinNG3EOHKDwYYfjgHgM6gLepzqtXv7INpxB3k0X++wdyDCVKxTGpCMpJg5s4Yj759nZg/87NpON2Opd6+8QBLkxcNp9sJTHqYvDbI3P1xkpHdiRQUCJ4njHYzPZ89ga5i6RolSSufQX2lkZ7PnSybF8Ni2EXq6hW+0nmH8xdbSavqjt/Ld5NoKEH/7Rk++JsnvP/Xj7nz/iju2eBKIoVAsJ9YNWHViWzwfYRYCT9gOPsamb0/gVpALK5s31uPRq+lwmEh4gkxd28857YtL3djqbPTcfEoY+89Wc0aX6Kqw0Xra714YhrkpVn0t772Ml//wyc7OoturNBlHCWLiIAqhkgwIyDG+xd5cnMaVVGL7jlPxnOX7KoqBJfEYl2LvSxjXWbk0QLumU3Kj5bGPPp4gWpX5ZZLm/cq7tkgT25OE/Kt9nuXuuAf8EazcuErLAaqXZVZTvzlYuDOLJVWI/VtduYn/Hm3lTXSjv6+VEVh8Ef3SCc3MXxSIRVLMvT2fQ5/6UVkrYauT72Ae2CWhUeTRL0RJFnC2lyNkkwzfXMka/fJa0MgS9haHNQdbaayzr5j70MgeJ4w19o49ndexj/hJjzvByQq62xYm3KnKWyV7Mzwo1y+NL4r1W07zcJUgDvvj2bFmsXCCeYm/NS12jh0phG9UZTFC/YH201BETw7hAg/YOhMOjRaDaliRLgEjS92ojVkbjZVai1ao47Z26Ok1/R/ak06HF11eEcXmL49SiK0eaxI3BdGVRRAs6vlbIqiZqJOyoRGJzMz6uXxjamV18o2OS7B9LC3rCJcUVTGn+Z3rpckGHuycKBE+MJUgFs/Hdnweqm/K2mTSpDaJuuOiHCAkUfznP1kF2argUgwnnO8jjoLGu3OFSv5xhaz8rw3oGac0UMzPiwNVcgamZq+Bmr6GoBMZNKjv7xB3J+jN15R8Y8u4h9dpOmlLlxHt18mKxAIMtcse6sTe+vOx29lhPhlvvoaHAQhHgnGuf3+6AaDu+Xr8OyYn9kxP5YqI22Haqlvsz9XrVyC/UW5UlAEzwYhwg8YvrHFTFlpISSwtzhXBDhk3I3rjrdQe7iRwJSXVDyJ1qBj/uEkc/cnCrquRzwhRt9/QtcnjwGZcrbKq1f4tU/agQ4+fGdsW+8tFyFfNO8KdKnUNlm59dPRsh0vCxXiZe7Zj4YSJOK5s6ch84BRrkqBvYCiqDy4Wp5seIerMuvrcCDG0x0s3/ctRkinFM680cnNd4dzTiAtTAV4eHWCIy81l/QQuDwrTjpFOpAjrhAITnspWOIhSQSmvVgaqoh6QniG5jN94xV6vMPzuQX4OiavDmJ2WsSKuECwD1kW4hdeOQm07GshPt6/WNRMbdAb4/6H4wTcEXpPNwghLtizvPUbJ+hOXcYrBPi+Q4jwA0Zw2lf4wXqJ+lPtm74uazUrM+yj7z0mOOPNfKNgVjD4xxaJB6O7mhFczh7rmiYr6ZRKNLQ9Q6ycSGCoKHeZ2/PXw7Y4HSARyz/xUAhJAkeDhYo10XJKWuHxjamCMUDbRUkrGCt0NHVV8+Rm7v71qWEvVa5KGjuqizpuMWVpUW8Yd/8M/klvEdcJlfBCkIEf3iUw6SErL6UUJJh7MCFEuECwTwm9N04b3+TCK3+fZSG+H5mb8Jd0+Rp7uoijwULNFiLTBAKBIB9ChB8wijUVqe6opcJRmXebZCSOe3C2ZI0XmPRQc6hx/cCwjw/j+tlTpFSKeF0DvlMvouq3b4JlthqLyk8thsNnGwn6tljaLmVcXvNGpqnQ2FFNNJwgFk6i02sw2wzbmmU3mfXo9BqS+c4rga1m/61a5CLkj287G77CYuDYSy0rXycTaT56Z6jo6LGtotVp0Bm0qGrhNgLItBEUI8KXy9Jeb1GQ//nvcvXtcWJJIxXOJI7uOmStzNjlp7gHZoueqEOF4JRnzddb/IGrEJjybm1fgUDwzFkMuyBLiLdx+dLorsSQlhMlVfoE4vjTRSHCBQJB2REi/IBhdlpYfFLYGdreVlNwG/+Ee0uLrP4JN5aGKoy2jOiLTPkwf+U3efnJAKoso0oSUjpN/bf/PVNf/hX8p14s/SRr0Ok11LfZmRnxbrt3O51U0G3RkfrIi5mIpofXJjf9viRlhN/Y0wW8c6ul4RVWA13HXCVFUaxF1sg09zgZfjCXeyMVWnsL/853G1VVSacUZI2MXMCl3++OZNxrFYhFi4v4yoWjrpLeUw1ZcXEProwT8O6sAAdo7q5GliUSsRSRIiougt4Y6bSCJk/me8a1WOW/PZfm0alfIu4OL61a+3APzDJ5fQhLnZ3AsqB+Bg7AO11dIBAIdpaDIMQr7QY885uYUeZCBd/CwWnlEggEewchwg8YVZ0uJq4OoqRyrIpKoDXqijJ0UZLFOayvxz/uxj/uxtbiwHXuKO9/+l+hmfBlTq8oK8lMcjxG87/9Y9JGI6HDx7d0rmV6TtbjmQsRjyQ31ReyLBVVtv7B3zwFQKOVC8aRrcdaXYG12kQ6pdB/eyZzvjV99BWWjBFXOJgd3xQJxLn3s3Hi0RRth7YmlDuO1OKZC+Jb2DxCpqmrmtqmvTOTn4inGHu8wMSge6Wfv6bJSvuhWqpqsx/ooqEEdz8Yw++OwFI63nY1pHs2xIff78fVYsNk1jM94t12eXuxtPRuwUwpz/ud9EZIqwq//XN2nnz8/0lqOVt8zQ9JTSurAvxZIFGw8kYgEOx9soT4+X/E5UvqvuoPb+5x4pkTolpwgEiXz5hYsLuInPADhkanof0ThzNKZf3CopQxX+v4xBEkufCv3mDbXl+3f9zN2//NZUJjHqT0RkErAUgSdd/5i22rKoNRx0uf7qahoyrL7Vpv1NJzsp6OY7UlHa9UAW6s0GGpMgLQ2lfD4XNNyBopSzyFA0tO2Dne6tNb0/jdEWbHfcyO+YiEis9a1mhlzrzRSfcLdRhMq3NrZquBxs5qwoE4H3z3KdcvDTI17CG9ye9jt4hHk1z9wQDDj+azDPUWpwJcf3uQ6ZHVsuVEPMX1twcJeJYmF9TyLuLOjfsZfbywawIcWMlx1xk0GM2F/QEqbcacLunLAvytr/WR+OP/jXQ4sTctAlSoPdz0rEchEAjKwLLPRI/xMecvbu4ts1dxNdlKSgmRJKhybZxATMRTREMJUrkWPASCHWatCWvyg8vPejiCLSBWwg8g9lYnvZ87yfSt0YwD8hK2Jgf1p9sxOy1FHcfaWI2uQp8/xqgAyVh+sSepKqaZSQwzU8QbtveQbjDqOPpSC72nGokE48iyhNlmRJYl0imFhckgfk9kR0RK+5Halb7uxekAD65szbn76g8Hsr521Fs4cq4Jk1lfcF+NRqbjqIv2I7UkYikSiRR3L48xNbS6AhoJxvHOhTMxWW90YjDtfhbqw2sTxCIbxeKyuH5wZZyqWjMms56JfjexaLLw76yAc/9eYnkCRJIkWvtqeJrHmA2gtW/zlfNlAX7+YitdySt8+9/deBZV5kVhb6uhqqO0iTCBQLA1VEXBO7zA7P1x4oEoskamutNF7dGmXTVN3YtIssShMw2kk2m8C+GC10xVhdZeJ/FokrkJPwFPFN9CmHAgvnK8+jY7nUddWSafAsFOIrLBDwZChB9QKuvs9Hz2BMloglQsic6kQ2ssLOTWIskSref7GPzxvR0XOLqAf9sifOVYeg02R3ZpnEYrc+ZiB0P35pgYcJe00l3fXoWqKMyNZ7uqLntbtR2qobnbAWR6nJ/eKl+8lWc2yLUfDfDSp3swFumqLkkSeqOW2++NEglsvpoeCWSyUs+92bWr0SvRUIKFqWDebVRgcsBN94l6JgeL8yXQG7Ukoru3mr0dKq3Glf9v6XHingmyOL35z8TVYqOxc6MpW0aAq5y/2MpXOu+QunqlrDF95UJr0uM62oTrWPOmeewCgaC8KKk0T797i4g7tPpaMs38w0nmH03R9alj2Jocz3CEzw5VVRm8O8vwo3lQV8MeNmVpYrf9SC2zYz4mhzyb3otURWVmxMv8hJ9zb3ZTaTdu3EggKCMiG/zgIET4AUdn0qMzlSa+12JrdtD9qReYuDJAzL/ab6zRazHXWjOxRWUgZSludX47aLUaek810HW8jpA/xtSwh4mBwiJPVVReeK2NeDTJ5KCH+Uk/iqJiqzbR3OPMEvxBXyxn7vNWUFVIxFIMP5jj8IvFT1L4FyOZHuo8x13exu7cPVMdz3yo8Ebq6nbFZqrvBwEuSeCos2CqXP08yrLEyY+3M96/yPiTRaJL/dwVFgOtfU6auxwbJklWBXgLX+19QPKDK4x+P47OtL2qlcwgKXnCrcJpIRVPgqpirrXh6KlHZ9IjyRJGm6mo1heBQFAehn/yKEuAZ6GqDP7wHsd/6VV0FVt/LtivDD2YY/jh/MrX61fBZY2Eks68aHeaaT9Uw/Sol7lxf97jqmqmhe3eh2O8/JkekSku2DGWBfhbv95Fl3IDnxDg+xohwgUFsTZVc/hvv0hkIUg8FEOj12CpryI049u2CFeBuKueWENzeQZbBBqtjM1RgWe2CEG4BoNJR+cxF53Hcl/wYuHy54urKkwNe+g91ZCzN3g985P+gilUkgTzE4FdFeFhf3F97kFvlBvvDO2rMvNCaHQa+s40bnhdliXa+mpo7XWurGbrDJq8D3LnL7bxayeGSbx9mdHvZ36mzt4GZu6Mbvnn5eiuQ2PQMv9gc3f/ZSRZWopClKh7oYWG0+3ioVMg2AMkYwn8Y4VjD6dvj9D6au+2z6dGQ4BCeq/2wawhmUgz8mA+7zZKWuXMxQ6qnGZkjYzfHSkowJdR1UySxW5PbAueH5ZTUN76Wh9dySv4vvEtIcD3OUKEC4pCkiTMtVbMtauGJpX1drQGXWYVbAtkHuNh7vNfKlAXtjPYa81FCZb1bt35iO6ACIfMw0E8miy65yydLuKNSVLRBm2qqpJKpJFkCa1uaxFuAEFfcRFg6ZRa8iTJbiNJmX8VE71V02ih91QjZmvu399yG8FWqTncyOLTaZLRZMnudTWHG2l+qQuAVDyFZ22W+NJEiN5qoqrViaqq6M0GqjvrnsvVNIFgr+LuL64Vyju8sG0RHnpvnEouc+GVk+yHqLK5CV/BhBRJgrkxPw5XpjJvashTcDJ7Pf5FIcIF5WetCWtn4kN8v/8fhQA/AAgRLtgyskam/lQbE1cGCm+cAxWoGOoncPQE7HLZqt1ZQaXdSNgfy3mT1WhlGtqLz++OFpH7vFXWroKnkmlmxnwEPVEkWcJZb8FZb1npuzVbDYUNZxSViko9qWQajVbedDUznVYYf7LIWP8i8UhmssXqMNHWV0Ndq73kFdBiy8v3AlqdjN6kIxFNotXJWKoqiEeTREMJNFoZV4ud5p5qhu/PZzm6r0WSJU58rI3aEtx4t4rOpKf386cYuvSAqCdUdBWBrNfQ/FLXStl4+8cP4TrSxGL/DPFgDK1eS1VHLbZmh+jrFgj2MPFgca1QOSNMS2C/ZYYnoqmCglpVIb4mKSMaTpRsdrmVqiAlrTA34SfoiyHLEs4GCzZHhagwEgAZAW4b7efs0DVu//OPuJlIY7SbqT2SwtFTj6zZ2rNzPBhl8ck04YUgkixhbazC0V2P1rj7hr3PK0KEC7aFs6+Bxf4Zorl60PKwrBFqfvpjnO9fwnf6JRYufLpsBm0Fzy9JnDjfyrUfD5JKpDeYriFJvHC+tfSV3x0oobZWm1aczGfHfTy4MkE6pawUEIw/XcRUqefU6+15I63W8+TmNE9uTmOq1NPa66S5x4m8JLTSKYWP3hnCt5jdWx5wR7n3s3ECnii9pxpKeh86/d6/5EhSRjyfvtCJ3Vk4+/boS82YKvWMPVkglVytLLA5Kjh0tnGDSeBOImtlLI1VxINRlGRxD9pKIs29P/+Qxhc7cfbUA5k+75Z1KQrpRIrIYhBVVTFVV27La0IgEJQfg7U453NNma7D64X45UsqY57wnhTiepO2oKCWJDCsqUbSG7Qlr4RXuUp77/OTAR5cGSeZSC+JbpWh+3NYq02c/HgbRlFt9Fwz6Y3QfPUnHP3hf0bSyChL1YsxX5jxn/XjHZ6n61PHkbWlPafOP5zMLKCteV4NTHqYvjlC58VjWJs2GsIKys/efyIW7GmmPxrekgBfZnmeV1IU7DevYrt9ndGv/APCvYfLM8ACmK1GXvlsDyOPFpga8qwIW1eLnfbDtVirS4tzsVSZdqSHWZIl3vurR6iKSnyNCdnah4NYOMGNS0McOddUckRaNJTgyc1pFqYCnHq9HVkjM3R/boMAX8vo4wUcdZU4G4pb5VUVFVOlDuZKGtqOoNHKvPyZHvzuCEP354gEV3vVq+ss9Jysx1pV3O9ekiW6jtfRfrgW70KYdErBbDHsmEuuPhhAc/s+i/F5TNEUUW8YVBVVhYEf3FkySSvtmKlYkrH3n6AqKjV92RMrSirN1I1hFp5Moy63L0gSVR01NL/ULcS4QLBHcHbXM3VtqOB2jq7ylbEuC/Gul27w1q+f5et/NMikN0JT1e5NPhaDq9nO4+tTeUvSVRUaOlYr3+pa7TmrnNYjSWBzmrHYi39m8MyFuP3eyJrzr44t6I1y/cdDvPK5nm21gB0UAp4Ik4MewoEYGq0GV7ONulZ70QsO+5FJb4TKqTGO/vA/A6zef9cQnPUxfWuUphc7iz6ub3RhtYJ13cdBSSkMvn2fwz9/FqN9b32GDyJChAu2TCqeZP5hfhOnUpAUBVSV1v/vH/Lkn/4vKIbdifowVug5dKaRvlMNpFIKGq28shpcKnUtdp58NJW1IroeSS6uj3gtAXekqDzTRDzFo+tb/524Z0Nc+/EgbYdrmBgoYPAjwdjTxaJEuKKo3Hl/lIWpwJbHVk4MJh1mqwGz1UB9m51wIE4qkcZo1hcdBbcejVbGWb89l3+dx03V1csYZ6dQtDqCh48TOHEaVatD517E/h//jKNP7yGpKm9v60ybM3l1kOpOF5qlhz4lrdD/g7uE5/3ZN2tVxTu8QHg+wKEvni45/lAgEJQfrVFHVUct3uHcBmSSRqL+ZFtZz7sYdsE3vkX372p56zfO8/V/dbesxy8HOr2G9iO1DN3PMQssgbPeklW55Ky3YK0yEfRF899/pcw95firLSWNqf9O7h5+Vc2Uw08NeWjtqynpuOVAXZrY3eqzUDnH8ejGFJMD7qyqhIWpAAN3ZzjzRieVtoMbC3du4hZoNZCrhUSFhcdTNJxqK3o1fOb2aN7vq4rK/MNJWl7tKXG0glIRIlywZfxjiyWLyUJIqooci2G7eRXvK6+X9dgFzy1L6PTbm3HWaGWOvdLC7fdHMy+s/fEslf04GywsTgVKKnEreluVrJXyrRDwRLn3wXhR5/LOh4s65tD9uT0jwJGgvs2++qUk7YmbuPOdH1D33b8Alp40ZImqm1dJfqeKib/7X9H4p/8HumgEaQediJVUGt/IPI6lsvTFp9OE53K4A6sqiVCMmdtjNL/cvWNjEggExdP28UMkwvFNP7eSRqbncyfKVo6+3+g85kJV1ExOOKuVeKoKtU1Wjr3SktWHLckSpy60c+snIwQ80U1L03V6Dc09Tlp7nSUZa4aDcfx5Ks2WmRzcugj3zocYe7qIZy5TrVhVW0lrr5NqV2XOffzuCKOPF5ib8KMqKgaTluZuJy29zm0/H22F4QfzTA64gY0/+0QsxY1LQ5z/ub4DWy2gvXE7twBfQkmmibhDVLpsBY+XCMVyRxguo6p4huaECN8Fns8rsaAspOKpnYmQkmXMQwO7LsLLRW2TjbNvdDJ4bzZLpNqdZrpfqCMWSbIwuUcE6Q4RDsQZ71/MZKqnVRLxvZPjrdHINHU5nvUwsrBf/xn13/lPS18tfaCWJri0QT+tf/JNpHQaeafz2mQpy9xp4dFU/u3VjFBvPNtRck+aQCAoP7JGpvfzJ/GPu5l/OEnMF0bWaXF011HTV/9cV61IkkT3iXpaep1Mj3iJRZLo9BrqWu05J2INRh0vfbobz2yImTEfqWQak1lPQ0cVZqux4EqxqqoszgSZGfESj6YwVuhoaK9CKrKKOrZFM9P+OzOMPMyuiJif8DM/4afjSC3dJ+o37DM77uPuB2OZx7qlW008mmLw/izTo17OfbJrWwkeaylmpT2dUhh5lLuqQ1UzQnxmxEtzj7Ms49pzKMUl2BS7UpNOFPcsVg7zRkFhhAgXbBl9pWFnMpxVSo5Y2mtUuyp58ZNdRMMJErEUeqMWkznz8JNOKzz+SEMqsb8vcpK0eXzb7LiPex+MAXvv16jRypz+RPuWS853BEXB9f2/WonsW4+kKMgom36v7KjqSim6qqrEfIVXapSUQiIcx2gT/WMCwV5AkiTsrU7srQdUmGwTg0lH++HaoreXJAlHvQVHie1GyUSaWz8ZxrcYWU18lGB6xIvVUVzvuN5Q+mP65IB7gwBfy/DDeSxVJupa7SuvxSJJ7v1sHNRNHutUiAbjPLw2wcmPt5c8nrUEPBFGHi0wP+FHWV5p73HS0rNxpd0zFyKdKixCZ8Z8B1aEpw/3IV+9DnniZCVZwlhVnCGgzmygGLdBfeWzrw58Hji4jgaCHcfW7NiZsjZVIdpWvMnEXsZk1mNzVKwIcMisxB57uXlzxbWPUNVMzNtaM5lwIMa9D8ZQ1dIF+E6lsViqjVTVmuk91cDH/tYhqmpzl+I9C0yTY+i97rx/Drv2p6KCvS1T+ihJUtGRZPIBNscRCAQlkC4uJu154M7lUXzuzETm8v1w+b8BT7QoU7HGzuIjUjPHV3l8s0AFEzDyOFukTw66s+7lG4+bcXKPhrcewzo77uPKDweYG1/NbI9HUwzeneXqjwZIxLJXaZNFLlTs9wWNfCS+9Nm8AhwJqrtcaA3FLSxoDTqq2msKPnCtN2gV7AziyUmwZWSthqZz5RXLKpDW6fGefbmsx90rxKNJZsd9KGmVYy+3oNXv74/g4L05Ln/nCSFf5sFrvN+9tQNJUF1XidlmKOPoMivfr3ymlxc/2UXboZotrSrsNJpIcX31O44EVR21WTFHthZHwRkAo70CXUV5f28CgWD/kfzgMqRTnL/Yxphnj1zXnhF+dwTPbCh3taBK3lVeSQK9UUt9exWJeKpo/53FqQBKuvC2AXc0S+S6Z4NFVTYW6wOznqyV9k3Os7zSvpYKS+HWCUmCCsvBu/9MeiOkVYXz/7dzdP+9c5tvJIHBYqLpxa6Sjt1wuj0zcb5p6R0YbRU4hQjfFfbeE6lgX+HsbUBVYeJKP2oRF/58SJpMx+utL/0K7hg0bVKtJcXjaENB0qYKlIr9U/6ajKd4dGOK2XFf1o3OUmUimIg+s3EVg86oQVIhEd98tjkaTnD97UFe+Vwv8xP+rZWgq9DaW4NnLkQksFC2Mva15XZ7lWTV3uhPt9RX0Xq+L+s119FmfKP5XfLrjmebGQkEgueP9Znh0MblS6N7MjN8N5gb9xWs+pUkqGm0sjgdRFHUlcVJVQWdUUul1cD7f/kYAK1OprHTQfvhGgym3Kue89PF+82sFfY73TpWykr7cuWgzVFBhdVAJBDPu19T9964h5aLFQF+sZXu1FW85gCJ873M3hsn7s88L8o6Dc7eeupPtKE1ltZeZ7RV0PeFU4y893hDxLCtyUHrx/qeW/PG3Ub8lAXbpqavAUeXi7kHE8w/mCQV24KRiAT1p51Y/8nvELB0cPlStju3fn6W2h99F9vt68jpNCoQ6jvK/JufJ9K5tx0cU8k0198eIhyIbZhpDno3d1zdS3QcduGeDbI4k2OmXM2UjT25OUU6X9lUARLxFM3dDkYfL2x9sOtoewbRLiWhqlRffjfzvzy7DgWd2UD3Z17YIKYr6+y0vNbL+AdPs/vIlv7fdbyF6u66ZzBigUCw11gvxC9fUhnzhJ9LIZ4vpnQFCUyVel7/0hGmRzwEvTFkjYRGIzP6eAHvmvLsVFJh/OkCs2Nezr3ZjayRGO93MzXkJhFLoTNoaeyoLro0W9ZI6AyrPdhVNeaiolBtRfayr8edrypgDd75MKb2jAiXJInDZxr56CfDOfetabTiqNtbLWbbISPAVc5fbOUrnXdIXb2CO1KHsxccPfUkw3GUtILebNiWGaqpupLDP3+W8EKAyGIQSZaw1FdlVcIJdp79XQsr2DPIWg31J9po+/ihkvftuHCEE//3j3HsZSdHYj/iQrcuq5zNODlG17/4p9hvXUNOZ24wElDZ/4iOP/gG1jsflfOtlJ2JfjchfyznzU1VMzfiZZZ1kM6gobV3Z8xGJAms1SZOvt6WszVIWnpAcDVnZuoL3UDnxv0kc6yWF8Oja5No9RoOnW1cGsCWDwVA3+kGKu1721zEcfldnJffAXK/XUWSMB3rovnnjpJ3w/VIUHu8uahNU9HcfX41fQ0c/oWzOHvr0Vca0ZkNVLU56fn8SZpe7BSr4AKBYIXFsIvQe+N0KTd469e7AJVJb2GDx4OGyawvKGhVJXOP1ek1tPbWcPSlZjqOuBh7kpmIXr//shv43Q9G+fD7/Qw/nCMeTa28PvJ4nvkik1ecDZasa3dTl6Pgqn21qxKzdWv31Hyr4Os2zPrSUW/h9OvtGM3Zq72SJNHc7eDE+dYDcw9aFeAtfLX3AamrVxj9/moVgCRJ6CuNGG0VZUsjMddYqTnUiLO3QQjwZ4BYCReUFWtjNXqLkURo46pvLtQlR+a1s+ifePUfIC3Yufn+CK/82R8jJxNI66IaJEVBBZr/3Z/wuOcQSsXenG0fH8hfzguZXvFXP9+LezbjBmq2GqhptCLLEpYqEw+uTeT9edqcFTR3Oxi4O0s8miz4s7fXmDnxsTb0Bi2nXm/n/pUJErFUVjmc1VHBifNtpJK7Y3qiKCrTw17aDtVgqtQz8nB+pf9M1khUuyozkwFFUNtsW8lW3bMPgIpC19vfK7gCHrNWcfLb36DXdA+n6mfodng151OCVGSNgF6KDJS1Gto/cRhzjYX5exObHnctqqISnPFhbdjcBMhUXUnra71FvzWBQPD8shh2wTe+RddvwVtfe5mv/+ETJr0Rmqr2TwvZdmnoqKL/7kzee7EkS9S3ZV9zJ4fcecWwqoLfnaOFTaWofnCAw2ebsr42Ww30nWnkyUcbTd0kCXQGLUdeaiKZSJNOKegNGmRN8et4dmdFUbnoNufGvxFng5WPffHQUrtaHI1WxtlgLVtc2l5gvQBPfnA5S4ALDiYH5y9YsCeQZImuTx7j6fduky4yG3qtqdNCsIbp74zw5L/5L7FOLfCJQucDSKeouv4z3K+/ueVx7xSqohILFy7PV9IqsixtuvLd2FlNdV0lk4MefAshQKLKZaaxvQokCY1WXjEcq3ZV8uDKOJ65bPMUU6Ueu7MCg0mHq8WOzWFamT12Nlj5+M8fZn7SnymHkyWcDRZsjszNMBHbpVlmKeMYC1DTYKWmwUoyniKdVtAbtEiSxEfvDuOZz1PWJkFrj5OeUxlTkdUbW2nGJbuBPDSCKeAruJ1Ro9JnfkDq6jVkVxfdn87+vpJW8I0sEJzxoqpgrrVS3VmLRpf5m9AYdaSLaBFJBIWrsUAgKA/PuxA3mHR0HnUxdH8u5zZdx+s2mIX6tmh8VgpHzjVt2lfe2uukolLP8MM5fAsZwSxrJBo7qrHXmHlwZWJlYlyjlWnsrKbjSG3eHvVloqHCrurVLnPOlXZJknDUWXDUlRYTtx9YK8AvdOs2CPBUPElg0kM6kcJgMWFpqCo6uUSwtxEiXFB2TNWVHP6Fs4x/0I9/Ir9bts5soLLOBoCqKAy+fZ/AhCdrm2J6ZSvGhtmiL/fOIoEsSytxHPnIF1diMuvpfqFw763JrOfsxS7CgRjehXAmcqrGTKUtfwmZLEvUtdipa8kYrblngvjdESx2IzZnBRUWPZHg1qNJikEC5HU/Ap1By9rb+6nX23l4fZKZEW/WdlqdTGNHNR3HXCsPNVk3tr7ylG6Vk1AgxtMitjOkohvK0tYia2Squ1xUd7k2/X6Fo5LglHfT761Fo997PyOBQLB/WSvEz188weVLY8+VEO885kKjlRm6P5flhK7VyXQdr6NlB9vNqussRILxLPFbYdHTe6qB2iZbzn1rGq3UNFpJxFKZFW+jlqlhD/c/HM96EEunFCb6F5kb93HuU91ZMazrCXqjRZXJHzrTVHCbg8haAd724TcJvTcOuFAVhckbwyw8nMwy0dNV6Gl5pWclTlSwfxEiXLAj6M1GOi4e5cl3bhL15F65XNtTOv94eoMAh2JaYCWQ9qa9gSRJ1DbbmBv35S0xs1abippNLhaz1Vhy71YykebhtQnmxv1Zr+sMmi33ep/7VBdKWiWdVrj1k5G826pq5sEhHxqtzPFXWug5Uc/CdCBTum8x4Ky3ZM0Mry/tUu7e3dL4CxGcDBKaCqE1aak+VI1GV5yQVVWVn/7jnxbeUAZLnWFbZWmOrrqCIlzWylibqrd8DoFAINiMxbCLyqtX+MpLACe4fGn8uRHikiTRfriWlh4nC1MB4rEUBpOWmgZrzkl3e405Y2K2TSoq9Zz+RDsBT5TE0nktVaai+6eXS73DwTiPbyyVqOfoUX94bYIzF3LH1U4Ne4oyoA14o3vex6XcjHnCnL/YxoVuzYoAXwy7UFWV0fee4BnaWEmRjCQYuvSAzotHhRDf5wgRLtgxZI1Mz2dOMPzOA4IzvoxWliRURUXWyjS/3E11Z2b1TlVVZm7nF2k5URV0XjfGiVFizW1lG3+5aDtUw+yYL+82HUc3X8XcLZS0wkfvDBHwbuw1247Zmt252qdvc1QQ8ORwX5VAb9BS15J7hn4txgodzV25Y0nWl3ZlZpa3TzKWYuKBj+BCDP9clKh/tcxbZ9TQfsZB2ylHwQedwasLLNwtwgVeAbOz+L+NmC+Cd2SedCKF3mKiurOWqvYapm+OkAjHcz4F1R5tXilfFwgEgnIy+v04bVzhq69pgaNbEuLagB+dZxFNOES6wkzKZidZvTMryeVGo5WLjsts6nIw/GBuW4kpGU+XjOBebivbKpMD7rwCWlXBPRMiEoznzOuOhpMF348kQSyys9V2e41cAhwgshDYVICvZfzKALYWpyhN38eIpy7BjqI16uj53Eki7iC+0UWUVBqjrYKqjtqsHMJ0Ik06VlwP+VqWS9VNwwN0/4t/yuiLH+fxZ/42OS2/nwUyNJ2oY/LubPZM8pKJlqvPSbJS90wNxLwT/pV+7LIhwYQ7vHKDcB2vJXJ1kmR04+9Z1sg0na5nOrD9vuSMAN/8xrZVlLTC0Nv3CUxurNRYJhlL0//BPB63ltZXc5uYJaMJhq49KvrcE1f6iXpCNL/UldMRNZ1MMfreE3yjC1mTXZNXB2g400H3Z15g4Id3M33fS393y09Wjt56Gk61Fz0egUAgKJWMEL/MhfNngS4+fGeoqP0Ms9PUfec/YXl4d0NVXKizh7nPf4lIR3fZx/usMFboOPZKC/d+Nr5B/EpSZoU6nVZIJXJHoGl0MvWtm5tslsrcTKCoCYHhMS/2Ruum30ugrt53cqCqEEope9dItcykVRVQudCtoUu5gW/dc8ri0xkKlQ8kw3GC015RxbaPESJcsCtUOCxUOHKXGieCWxOAyzfl5cKutuvv0VCZJvqP/wEYNp+VfVYEP9fJk6tTTPV7UBSVunY7h15posr17F3d/+pfXS94kywZFRpcJtqPr95YXrrQyuMrUzy5OkkkkEBv0tJztoHDrzaVtQytnAJcVRQeffs6cX9xf6OLj6dxdtdjrt38gcTdP1PiAGDxyTSJYJSuT72wYdZbVVWGLj0gOO1d2X45DkZVVKauDyHLMkf+9jl8owt4RxdQEikM1gqcvfVUOA+e0Y1AINj/GKcm6PhX/wNyIrFpW5p5qJ+OP/gGo1/5B4QOHd318e0U9W1VGM2ZhJCFqUwvtUYn09zloO1QDUFfjFs/GUFF3TixDxx/pSWvx0wpFPtI0HO0hq5Tm/vWTDVX8v3//Xbe/SVZ4o2/3YfJkru3/KCxIsC/8a0NzymxQLRw/T4Q3+Kzs2BvIES4YG9QxpVr/bsfYO9/yIW/+X9j6axBVVXcH43huTkOskTta53YDzeU7XwlcUEG1pbQ5S832i3+kz+P4/gW0WjAPD/Kr51Y5/b6CsD6n/902c6rRkMrJejbFeAA84+mihbgAEgSC0+mcorw4Kx/09cLEZjy4p9YxN6a3QMWmvUV7PmeujmMs6+e6k7XSguIQCAQ7GUa//3/DzmRRMohRiQyk6TN/9ef8Pif/s+gOTiPtFU1ZqpebyedVkinFHQ6zcoErMGk48U3uxi8N4t7ZjW2s9pVSdcxF1W1lWUZw5gnTN/peu79ZBQl98I7kgS/+bkA9a7NV7GV4yrj7xl43B8nvclxJAn+i5+z8A/PT5Zl3PsFJejbVIADWZWi+Sh2O8HeRPz2BHsCo82EJEtZDpDbITLl5d03/2de/u9O8tEf3sc/GlxdNlfBebiKs//gOBVOU1nOt98xS+34KPMMtKISH58k8fZH5T1uEZRLgAPM3Suxn1xViXryxcxs8W9cgoXH0xtEuHtgtmDZmpJM459wU9Veu7VzCwQCQQmoqkpoxsfC42mivjCyViYeq0dz2A8UFonGyTEqJkYLbicB2lAQ6/07BE6c2fa49xoajYxmkzxuu7OCMxc6iMeSJGIp9AZtWc1dl/uVj9sT/IMVU9WNiyUaSeWVrhCOe2+Tr6P7f3pTw2/6G3k0Y0IjqyhKJg0lrUh8+oifr3U/JfF22Ya/L8j3nFLdXot/bDHv/pJGGKrud4QIF+wJZK0GZ18DC4+mynNAFSLzUX7y21dRlqde12iUxcde3v6NK7S80o2+0ojJUYm8Ph/rOeKc3stfUYuS04u+mKC4bBQVqt2RbTl7b53yCHBVVUluwSxGzlMKWOmybZoCUHgwEPNvXGlI5jFcy97u+TK9EQgEzwZVURl9/zGewbmsCcIHfxZE/uv/B5pv/BMgv2GYcWqi+PPJGozTEwdShBfCYNRhMJZPfENGgC/3K7d9+Cf8fzoSfGPwKBJq1jOCjIpTl+CXdeOMfr+wgevvOQd4aKzkQ5+dcFpDtT7B61VeWuUYkz8q61vYJ+R+TrG316C/aSQRyn1/rznciNZQ3t+9YHcRIlywZ2g43U5w2kvMt3lJk95izBhLlYCSylFDpUIqmmD4nYcrLxmrzDS92ImtObfr9kHlDYebHyw6iStyDiGeaRhfKy2VVYevTbZXkSWVj1dvQWzuNbbQK792tVpVVcILATyDcyQjCbQm/ZarPjZzMNea9AVXwjPbiZu1QCDYeWbujGYEOGRfl1RQwlGsv/VP0Hz1dxnLZzoVS9Nc5PlUVcWbUJbEo2D7qLz1610rhmEvVLj4eucQf7Po5CO/DQUJmzbJRYebzzgXMWvy1KqvQZLgqCXEUcv2I9gOOsvpQv0/uJP93Lt0r6/udNF0tuPZDVBQFoQIF+wZtAYdvV84xcytURafTq8IaF2FHtfRZuaflK9veDNi3jCDP7pHw+l26k+27ei59hrVuhT/uGOY/3GknXBas6Q7JeSlme9fdM3yqt3H+94q3EkdZk0apy7B/zXTgIqKukaIS0tf/1cNU1i1W4832wtIkoSlvmrV9KwIZI2MvS3T95+KJxn4wV0ii8HNNy5R4Fd3biwnd3TVrT7w5hqTVsbW8vxNLgkEgt1FSaWZu59nFVtRUcJRfsUwSP1v/Fc5N0vONnLvr/8dmzYRr0NWFf7W7/4c5lOHtjJkwXrSMbqSV7L6lbvNEf6heRxFhZQqoZPUPRVCcxAxWE0ZQ9WRBTxL0aNGqyljqFpjLTrzXbB3ESJcsKfQGnQ0v9xN49kO4oEokixhsFaQiieZvF5cpMl2mb45QqXLhqUhd8RHOpHCMzhHcNYHqoq5xoqjpx5tmcvCdpOuiih/0PeEn/ns3A5YSKoyrcYYbzjc1Bkypcy/WJct9torovz7mXr6I6sO7w2GOH+nbpaztsCujn+nqD/ZVpIIV9IKD//iOrWHG/FNuInnqOyAzMp2OlFcNJ9Gr8XRU7/hdUtjFeZaK+GFQE5BX3eiVeSACwSCHSc050dJFph8VVSi3/kh3b/RlXsbJ/h//hjj376Xv2pIlqg+0ciJ44uQury1QQuyUFOpnIZhsgR6qcwurvsYJa0QdQdRFRWj3Vz2Z0BZI1Pd5aK6SxiqHkTEU5lgTyJrNZiqV81bCt7Uy8z8w8mcIjww5WHo0oOsMXlHFpj6aJi21w9T3bF/za9MGoWLDg8XHcWVkfeZI3y9a4i5uB5PUodFm6LRED9QM+SWejuNL3YyVcIkkJpW8q8GLVGsAJc0Mt2feQGdaaN5niRJdH3qOMOXHhCc8a1JGsg8KNUdb6XuhdZihy4QCARbRkkVd6+OT7jx/rM/y7tNlzONr8aAfy53G5qpUsuxE6aCxxKURrmMTQ8qqqIwc3uU+YdTK/dxSZao6qil6cUudBXPT9SaYOsIES7YF2ynj3YrBHKsfEa9YQZ/fB91kxI5VVEZ+clDdBV6LHX2HR7h3sJlSOAyHFzjr7rjLVTW2Zi+OUJo1o+qquiMOlQVUrFE2ePdspDAdawZc83mkWeQqSDp+dxJwgsBvMOZsjW9xYijuw69uXz56wKBQJAPgzW/4RoAEuht1qKEXsdnXHiG5ph/OEnMH8k8A0gSerOB2sONOHsbCMlaQqIdXLBLqIrK0NsP8E+4N7zuGZonNOuj7+fOCCEuKIgQ4YJ9gUanobrLhXtgrqABlaSRURVlR4TRzJ3RTQX4WmZvj2L5zInyn1xQdiKLQbwj86STaQwWE9Vdrk1XmwEqa230rPm9RtwhHv/ljZ0fpEpWVUg+zDXWvGJdIBAIdhJTlblgewwqOA81FHU8WavB2duAs7e47QWCncYzNLdBgK+gqiTCCaZvDtN6vm93BybYdwgRLtg31J9sxze2mCn92eTmrjXqaDrXRUWNlUd/cW1b59pMyITm/XiH5vPvqEJgykvEEyIyH0BJK5iqzFTW24WJxh4iFU8y/M7DTK+3JGUMRxWVyetDNJ5px3W8peDvKx7I3etdTjR6LfZW566cSyAQCLZLyys9PPnurZyT4fa2GqyNIt9YsD9ZeDSV31RVVXEPztF0rguNXsgsQW7EX4dg32CwGOn7udOMvveY8Hy26Ze1qZq2jx1C0kiMvPswxxGKp/ZIU9bXqViSwR/eK3r/x9/OXiE1WIy0nO8DRWWxf4ZEKIYkSVTW23F01WG0F1HCJygLqqIy+MN7hBeX/oZUdbW4QlWZujGMrNVs+BtYj6zVlGU8lsZqglO5e/BbXu1B1jy/GfYCgWB/UeG00PuFU4z/7CmRhdVkCFkrU3O4icYz7WJSWrCnSSfTJCNxZK0GvdmQ9b2oN1Sw0lJNK8SDUSoclh0cpWC/I0S4YF9htFXQ93OnibhDhBcCGSFbZ8Noq0BVFJ5891buOKgicfbVb4hzcvfPFG2itRnxYIyB79/Z8Hpozs/snTEqaq20vdZbdNmxYOsEJt2ZUsk8TN8cwdnXkFf8WurtyDrNtkwDJY1MxxtHcPfPMHN7lHR89W9MX2mk6aUuqtpq8hxBIBAI9h5mp4VDXzxD1BMi5o8ga2Qq6+27mtKgqiqBKQ/+cTepeJJ0PIWqqpmxuGw4eupzth8Jnk8S4Tgzt0ZwD86ttB6aHJXUn2ilqj1juivJMlA4Oi+znUCQGyHCBfuSCkclFY5sweodXcyadc+LlCnzzRI9ViP1x1tx9NZvmKX3ji5se8z5iMwHePzXH+HsbSAw5SEVS6I3G3H21ePorhPxUmVk/tFUwW3SiRSBSTf21twCWNZqcB1tYub22JbH0vWp42j1WlxHm6k51Ehw2ks6nkJXaaDSZROrRQKBYF9jqq58JpPL8WCUwR/dI+aLbFo67B93M31zhPbXD1O1jxNNBOUjHozy5Ds3ScVSWd5DUXeI4Xce0ng2St0LrdiaHXiG5/P6E+nMBow2UeEoyI94shccGNxPp/P36SwhaSQ6Lx7D1uxAVRSSkQSSLGUc2HOInvQuRKSpaTXTa7RENB5i4sMB5h9O0vu5k+gqDHn23n+oqoqaVpA08q6ITVVRmbjST2CyuPi1ZDRZcJv6k+3EgzE8g3OZaLACpoFraf/EYaxrYvBkjYyt2ZFnD4FAIBAUIp1M0f+9OyTCS9FmOS7LqqIy/JOH9Jozk56C55vxD55uEOBrmboxjK3ZQe3RJjxDc3mP5TrWjCSLSXRBfoQIFxwY4qF4UY7oLa/2rogdSZbRVxaOcDLZKzIz6iWIrHIRD8QYfvchvZ8/tevn3gkS4Thz9ydwP50mnUwjaWQcXS5cx1q21BuvqiqJUAwlpaA3G3IaoUzfHGbh8XTRx9WZdCv/n06miCwEUVUVU3XlSgmjJEu0ffwQzt4GFp5MEfOGkXVadCY9vlzVExLYmh1i9UUgEAh2AM/AHIlQ7mzxbCRm747R9ebxHR2TYG8TD0QJTG0eTbuCJLHweJqWV3toPd/L2OWn2ZPvS4tAjp66gp4yAgEIES44QOiMOuL+wtsZ1onumC+Ce2CGRDiO1qCjurOWihoriVAM78gC6UQKndlYOBptp3LMVZXQrJ+IO7jvTT6i3jBP/+Y26URyZcJETSss9s/iHpyj5zMvUJkjYz2dTOMdmSceiKLRabC3Ookshpi5O0bMmwmJlWSJqk4XjafbsyZXUvEkc/cnih6nRq/F2uRASaWZ+miExSdTKKmlHjBJoqq9huaXutBVGJAkCUu9HUt99rjnH0wyeWMos9ovS6iqCipUd7hoPd8rSs0FAoFgB3APzha/sariH3ejpNJlM9sU5EdJpXEPzrH4ZJp4MIpGr8XR5cLZ17jBBG23KMpLSFUJzWUeMp29DVQ4rcw/nCAw6VlJwnEda8HW4hD3d0FRCBEuODBUd9etXCBzoTXpqazLlJ2pisLYz/pxP53JzGaiAhLzDyfRGnWkYkmQQJKWxHWuUvelbRw9dSw+mSn321o5R2DSs2URnoon8Y+7SSdTGCwmrI1Vu24aoqoqw+882DxiTlUzruVv3+f4331lw8PQ4tMZJq4MoKTSK4J26sbwxnMoKp7BOQITbvp+7jQGqwkA39hiSRMkDWfaARj40T1Cs77s8aoq3pEFwnN++r54Bl3F5sY+tUebcPTU4R1ZIBGKotFpsbfXYLCYih6HQCAQCEojGU2UvE86KUT4bpCKJ+n//h2i7tDKa+l4ipk7Y8w/mKT7Mycw126MiN1xitTMa0vMjfYKDBYTqqKSjqcIzfqJ+59Se7RpqRxdGLMJ8iNEuODAUN3pYvbOWKYPLIfeajjVtnJhnLg2mBHgsGaVO/PfVCy58qW6/L3liqN1K96mqkpaXu0hlUji7p/dmdVwJJR0YTfO9aiKwuT1IRYeTWWNS2vS0/Jy95ZKopW0gn/CTTKSqRywtTiKMo4LzfgyJf05B5u5GXtHFnB016287BmcY+zykzXvqVA2iEoqnmTsg6f0fPYEwMqESjHtCo0vdlJzqJHFpzOEZnw5z5GIxJm+NULra705j6XRa3H21hc+qUAgEAjKgt5sIBEsthw9E52mNYjH4d1g9P0nRD2hjd9QIZ1KM/Cjuxz78itodLs7IVLpshf2dZHA2pjxcVHSCgM/vLvhGSEZTTB1Y5jgjI+uN48JIS7Ii7jqCA4MGp2Gns+dYOCHd4n7o2tWtzM0nO6g5lAjAMlIgoVHxfcHr0XSyLR/og9VUTHaKjA5Kpm4MpBlqpa9A0WJv7ws9SKXyuj7TzKmYetIRRMMv/uQdqC6BCG+8HiKqRvDWXFtslam7kQrdS+05i3BCs76ijIvW+yfobrTtTLZMXl9sOjxraBCcNpLPBDFYDVlStyK+B04uuuoO94CwMKjyYLncA/M0nSua9cfGAQCgUCwOc6eekKzRfSmAUjg6KkXYmkXiAej+McWc2+wNBHvGZqjpq9h9wYG6Cr0VHfU4hmey/msICGtPEPOP5zMPUlPpnJx4ck0tYdFb7ggN+KqIzhQGCwmjnzpHF1vHsPZU0d1h4uGU+0c+/Ir1J9oXdnOO5o/XiIfSjINkkR1p4sKp4W5e+O5BfgSPZ87gXPp4r0VtEYd9pbSnLPDC4FNBfhaJq4MoCrFrbDPP5xk/Gf9G/LSlZTC9Ecjm5aHZ1Hkjzs04+PBf7rK/IMJfOOLJCOllxYus9znZW91IhchlGsOr/6O8q7aL6GmlRIMgAQCgUCw01R1ujBVmQuXGEsSWoOOuhdaC2woKAcFjc+Wt5tw7/BINqf5le7M3816JAkkaPvEYfSVRlRVZf5hgUl6Mr4w6jMw8xXsH8RKuODAIckSthYnthZnzm0y5cmlRUqtJRmOA5le5YLik8ysaGimuBtQFlLmX20fP1TyTP3icq97nveYiiYITHkLRmOlEykmrw/l3Wbu3jg1hxoxWDZ3mzfXWov+eSeCMSauDhbdp5WTpf4tWauh8UwHE1cGcm5qb6vBXLPai1as0Z6sEXOZAoFAsFeQNTLdnz3B8LsPM6uVOarRKpyVtL9++JmZgT1vqEW21G2l9a4caA06er9wmsUnUyw8miIejCFpZKraa3AdbabCmfHkSceTK8+A+YgHoigpRVTKCXIiRLjguSRTnrz1GUqtUYd7YDarVzknKniG5kiECl+01ws/c62VxrOdWNY5hke9YZLRBDqjDmOVedMy8HggWtR7jAejJKOJjMNnKo3BZkKj15IIxdHqtZhdNrzD84VvoFJmtdx1rBldxcbMdWtTNfpKY96e/Q1sZxJZkrKyX2uPNKGqKtMfDa86nS+hMejQGnVEveGVmXBbqxPv8ELen6HBakKfY9JBIBAIBM8GnUlP7+dOEnGH8I8voqQUZK2MpJWRZZlKl21FVAl2B1P1JqvM65GgwlF661250Og0uI614DrWgqqqm7fYleB8LkzSBfkQIlzwXFLVXsv4hwNFz8yux9JYzaO/uFb09ql4qvBGZPrND/38aVKxJHqzYcXde5nApIfJG0NZzqJGewWNZzuwt9Zkbas1aIvqR/eOLDBxZTCn2NQadZl+dFmCfCvDKsw/mGD+wQT6SgO1R5qpPdK4soIvSRIdbxyh/3u3N4jgsiNBVWctslZGSSsrq9Wuo804e+qZujmcaSFYejvpeJLFpzMsPpmm7kQrjWc6cB1pxjs0n/c0dcdbRBSJQCAQ7FEqHJXPVNQddFRVJbIYJB6IIms1WBqqcq78VtbZMVhNxIPR3M8lKjh3uR88F7nu7Rq9FmOVeSUadfOdocJhEY77grwIES54LtHotTScaiuqlHw9eouR8Jx/1UG9CJRkuqjtDBbj5j1JZMTy8DsPNrwe80UYevsBtlYnRlsFRnsFVe21VLXX4h1ZKHjOfOYikCndD06XVkqfCMWZvDZIcMZL58WjK0LcXGPl0N86w+O/+mhHhbjOpCc45eXOv70MgKWxirrjLVgbq0mE4xlTvk1i0gBm74xlDNfOdtJ6vpexD54Ca8r6l0r8a4824RDO5wKBQCB4DgnOeBn/cCBLjMpaDbVHm2g41Z4V5wUZUdv28UP0f/9OxotmEyHeeLZjz8d4SpKE62hz/kpIFVzHmndvUIJ9iRDhgucW1/EWVBVmbo+ippWie4Abz3bsmBmXo2dzUaek0oy9/zjvvv6xRfxLq9UTHw7Q/HL30qxzbFul99vBP+5m8clMluGZ0W7G1uzEO5J/lXlTpDX/oy6705vxT3hQ0wr6SiNKKr3BzC045SU45aXpXBdRX5hC5QHJcJyRnz7CdayZw7/wIguPJjPnUFXMNVZqDzdSWW8Xq+ACgUAgeO4Iznjp//7dDc8WSiq9FBUbp+1jfRvukZUuG71fOMXU9aGsyX2D1UT9ybaseNK9jKOnjtC8PxNzu7bicOn/aw43bikCVvB8sWMi/J/9s3/G9773Pe7cuYNer8fn8xXcR1VV3nrrLf7kT/4En8/Hq6++yh/90R/R3d29U8MUPMdIkkT9iVZqDjXgHVkgGY6RTqYzmdqquqlOs9TbqWqrwVOgTHmrrC8/X8Y7skC6mNX0pUkEJZVm7PITml/tYf7+RKY//Bkx93AiS4QD1BxuKFmEN73UhZJMZ8redBrsbTVY1gnh0fcf4x6YzXmMyWuDSBqp6F7zufsTWJuqaXk1dxa4QCAQCAQ7iaqqhOcDxINRNHot1oaqZ1bqrKoq4x/0553c9wzMUtPXkOXLsozZaaHnsydIhGIkQrGV8u79NKktSRKtr/Vibaxm/uEE4fkAkPGhqT3ahL3Vua/ej+DZsGMiPJFI8Iu/+Iu8/PLL/Ot//a+L2uf3f//3+eY3v8m//bf/lvb2dv77//6/51Of+hSPHj3CaBTmR4KdQWvQZWVSVnXUMnb5aXa/jyTh6Kmj5eVuJFnG1uIoauW82NX1ZZbL1pVUGt/oIsE5H8lwnFQ0uaW88fn7E/T9/BmG33lIcNJT2s6boDFoScdTJY0l7o+STqaz+sQq6+w4+xpYfFJcVrukkXH21KPR575kpWLJTCRbgXGp6RJ+iJLE/KMprI3Vxe8jEAgEAkGZ8E+6mfhwIGsyXdZpqDveQt2J1l0Xe+H5ADF/gQhPSWLhyfSmIhwyPjnpVBqD1YSuojzu9DF/hMUn04TmA0iyhLWhCmdvA7oKfVmOvx5JkqjuqKW6o3YlikwIb0Ep7JgI//rXvw7Av/k3/6ao7VVV5V/+y3/J7/3e7/HFL34RgD/90z/F5XLxV3/1V3z5y1/eqaEKBFlU1to4/AtnCS8EiHkjyBoZS2MVOtPqhVxr0FF7pIm5+xM5j2OwmjJO4CWgM+nxDs8zevlJ0X3k+YgHoszfmyiLAIfMjV9NKyX3c6+/L0mSRMurPRhtJmbvTZCK5s8Ct7c6Cc8HMLtsOU1fIovBkiY8ikJVCc36yntMgUAgEAiKwD/hZvDH9zZMLivJNNM3R0hE4rTucqVWUZV1qkrMl21cpioKs/cmmLs3Rjqx+nxjsJpoOteFvTV3rGwh5h5MMLkca7r0swrN+pi5PUrHG0e3dexiEOJbsBX2TMDtyMgIs7OzXLx4ceU1m83GuXPnuHLlyjMcmeB5RJIkKmttOHvrqe5yZQnwZRrPdq72cC9fgJf+Y6wy0/hiZ0mrrroKPelkiuF3H5ZFgC/jHpgp27GSoXjphmqSxODb9/GNLqzMFmdelnAda+H4332Z7s++gGnZwVaSNqh27/A8Az+8y70/+xlTN4Yypi5rSITjeIbntvSeingDO3RcgUAgEAg2R1XVjDFonseIxcfTRNakpewGsrY46bB2wlxVFPp/cJfpj4azBDhkRP3Q2/eZfzi5pfH4xhYyAhyyf1YqqIrK0KUHRD27+zMSCIphz4jw2dlMH6fL5cp63eVyrXxvM+LxOIFAIOsfgWA3kGSJto/1cejnz1J7uBFbi5PqThedbx7j0BfPMHFloKTjNZzp2JJbeyGKySffUVSV4JSXoUsPGHr7Psq6WDhJlrE2VHP458/S98XTuI42rZT7r9e/Sir9/2/v3oOjqu+/gb/PXpNNstkkm2R3SciF3CAECAp5yKPiIxmIMkrVmXppLbSOttapdbQqtBUr1AEvY/9wbO04CD4zbRlxUPn9BLxQ+dn6xKgYJIRAEwzkRgJJyG7uyWa/zx9LlizJZi/ZPZtN3q+ZjObs9+z55MvZ/ZzPuXy/aPuuEd//swZCCAgh0PL1WVTv/X/o/I/n74mASRLiLIbgvy/RLPLCCy+gtLQUOp0OBoPBp3WEENi6dSvMZjOio6NRVlaGujr/vjOJZrOe1ssY6fOSvyXJ58e6gkU/LxGS0nv5kJB5dWCythNNXmdiaaqoC2j8mgtV572eK28/6fmuRaJw8asI37x5MyRJmvLn9OkphuwPgR07diA+Pt71k57OKQFIXrqkWKSvykXO2iJk3bwIhvlG9LR2eU+eYyQgvTQXusRYDHZ7ec7KT6pJruCHk7WxE63HGjy+HpOsR1pJDhwjdo+D4wFA97kO2Jo6caHqHNq+a/T7WXkAvl3gFgIphWkBvDnR3DE2Bswjjzzi8zpjY8C88cYbqKysRExMDNatW4fBwdDMPEEUaYasPt727e357CBTalRIuWawVTcSoIpSIzHHeVFNOBxTPro33qXaFoyO2NF97hI669rQ29btdgfdtYb7htDf0TP1MYAQuPy99+laieTm1zPhTz75JDZt2jRlm+zs7IACMZmc0xK0t7fDbL46TVN7ezuWLVvmcb0tW7bgiSeecP1us9lYiFPY9XX0uOaTnkp0Ygzy1hdDpVWju7HDr22MPZ891XPQ81Zko+Xrs86B3WaIiyebYC7OdLtVbaR/CN3nOmAfHoGkVKCntXvqN5EktNc0ez2z7nl9ID4tCemrclH30XcTD3auPFdmuS4LcSZDYNsgmiM4BgxR8Ck8jH8SaLtgmrciG0O9g+huuDThWEelVSP31mWugVSHegYxOuTbMUjX9+24VNvi9tibVh+N+aV50KdNHCDVYfft0T2HfRRCCD67TTOKX0V4cnIykpOTQxJIVlYWTCYTjhw54iq6bTYbKisrpzy7rtVqodUGZ2RFomCRFD4MHy4BOmMcVFo1AOeZY38U3r0SAFD/8QkMdPVdfY5aCEgKCWklOTDmmTHSN4TWbxu8hqPVRztvBRufUAMYkd0b4RDobe9GfFoSHKMONFXUoeNMq3M7Ppy4cL6JmN5AbAJIWZwGrT4ai+5cgUu1LbhY0+Ka/z3WZEBqUToM80M7mAvRXORtDBgW4URwFp0+5MSEzNAcl09FUiiQfUshei9041JtKwatfVBqVEjISkFSrsl9JhM/0vRI38RBWodsA6j76DvkrF2C+PQkt9fUOo1Ps9BoYqNYgNOME7LR0RsbG9HV1YXGxkaMjo7i+PHjAICcnBzExjoHYCooKMCOHTtw5513QpIkPP744/jjH/+I3Nxc1xRlFosFP/jBD0IVJlFI6C2JaP3G823XAAABxFkSXL/GJOuhidX69Ay3zhgHTaxz2r6Fd65Az4VuWM93wGEfRZRBh6RcE1RRzlvRU4vS0d3Y4fGWLUkhwVScCVNROga6+tBe0wRrYyeEQyA6MQYJWSlo+eqs73+8D8bOcp/7n1pc/n7cfOG+FOBjcY8N4ObHOmMnFczLM13TjilUSqQWzUfK4nSIUQckhQRJMWOGyyCadaYzBszQ0NXvR44BQ7OZOloDY77Z8zPfkgS1ToOELPmLcOfmJcRZEtyOYyajiYuCQq2c3oCzAmj84j9YfM//ciumlWoVEhakXpme1POxQPJUt88ThUnIivCtW7fi7bffdv1eXFwMAPjss89w8803AwDOnDkDq9XqavP000+jr68PDz/8MLq7u3HDDTfg8OHDnCOcIo4uOQ46Y5xz1NLJEoPkvGUrIevqwCWSJMFyXTbO/U+t1/c3F2e6rae3JEDvIREqVErk3VaMC9824NLpVlciVGiUSMo1Y96KbChVztvZYlL0yE4pnPAetpYu9LReDtpV8SiDDn2XbO4FuD8kZ6zWxk6vTcfPbR5nTkBqUfqEs+mAsx8llfy39RHNRJs3b8aLL744ZZva2loUFBTIFJFzDJixW9+J5oL0VbkY6RuCtalzwklndbQaebcuhSKIecsx6kB/Zw/EqANR8TFBmWNboVQgZdE85/gt0zDcO4jeC90Tin7L8kxYGzsxOjwy8RhFAqLidUgusExr20ShELIifM+ePV6fD7t2sAVJkrBt2zZs27YtVGERyUKSJGSvWYwz//0tRvqH3BOD5CyMc9YtgeKaEUaTck0YHbKj6UvPowTPK1mA+PnOInJ02I7OujZcPncJjpFRRCXEILnAgpgU/TVni5VIK8mB5bosDFoHIEmANl43YfueZN5UgNMHJvlbAiApFYg2xKDxi//4fyV77D0UCsxbkQ1bc5fX29By1y2BLlnvXI+3oxH5hGPAEIWfQqnAgrVF6Gm5jEtnWjFkHYBSq0JidgoSc1KhVAfnMF44HGj7rhHtJ5ucJ60BQAIMGclIK8mBNm56F8PMxZmwNndhYJrTqQ3aBiYU4dq4aBTcsRznPj+Nvnar22uGDCMybihwvz2eaIbgXkkUItq4KNfzxh1nWjHSPwylVoWkHBNSCtNct5NfK2VxGhIWpOBSbQusTZ0Y6R+CJElwjArYB4bRUnkWl061ICEzGZfOtMIxbs7N/s5edNW1ISnPhIwbCq48m36VQqWEbmw+bj9oYqKw8AfXo/1Eo9vV9ECkLnYeNA/3DfpfgEtjByVLEJ0QC9PSDFyoOuexrT4tCbpkPYtvIj9xDBiimUGSJOjTEicdmCwYhBBoOFo78c404ZyDu7etGwUbrp9WIa5QKVFw+3K0fnsOF2uaIa6ZqtRXSg9X/aPidSi4fTkGLveh/5LNOb2o2eDxOItoJmARThRCqig1zMWZbreP+0IdrYFleRbMxZloqqjDpVMtbq8P9wxOPuXHlaK28z9t0MRGwbI8K9DQJ40prSQH81YscF6Br29D85f1fg3epjPGwbw8E4DzdnxfroTrjHEYHbZDoVLCkGmEMd8CTYzzQNy8PNN5Bv+E8zY3SZKcbycEDBlGZK5exAKcKMQ4BgzRzGcfGnFeDFAr3YpTa2On50fDhHO95sp6LChbPK3tK1RKpK1cAGO+GTX7Kv1eX1JIXk9ERCfEIDohJtAQiWTFIpxoBrOe75hQgPuqvboJap0WXfVtGO4dcs3bacwzT+vWLEkhQRWlRuridOgSY9FW3QRbk/PZbLVOg6Q8M4b7BnH57EXXreIKlQLJC+fBcl2W6xb4hOwUdNZ5HoRpbFu55Us9jhwvSRLmrViAlMI0dNa3Y7h30PmsfXYKEzGRTDgGDNHMNWQbQMux751zZV856R2dGAtzcSYSspJxqbZl6pPpV66Ij/QPB+UZca0+GuoYLUb6vA9CO56xwOL3LDJEM5kkrn0wO8LZbDbEx8fjm5/8FLGa6X9ZEIXTmQ+r0NvWPb3nsK9JrmqdBnnrixEVr5tmdFcJh3O+ckmpcF15tg+NYPCyc+o0XVLshMFjhBA4/cEx9HdOPmo7AKQUpSO9JCdocRKFS+/wMK7/v7thtVqh1+vDHc6swHxPNLWBy30481/fYnTEPmmeTStZgLYTTbAPTJwa7Fp565chzjz1SOi+aq9uQnNlvfeGV45fDJlGZP2fQp/HsSEKF39yPfdmohmsr906/RHJr1l/ZGAYdYe/g3AE9kzWZCSFAgqV0u3Wb5VWjViTAbGp8ZOO3ipJEnLWLYHOGDe2wO2/xnwz0lYENvATERHRXHf+89MeC3AAaK48C1+f2JKCWACnFKbBMNnUaldi0cRGQWeMQ0JWCvJuW4bsNYtZgNOsw9vRiWayUNynIpzPlHc3diIhMzzzi45RR2tQcMd16Gm9jMvfX4R92A5tbBSS8sy8nZyIiChA/Z296Ltkm7qRBKiiNBgZGJlyfBalVgVdUlzQYpMUErJvKURXfTsu1jSjv6PnyjSiBqQsTodhvjFo2yKaqViEE81gMal69Abjavi1JAnWxo6wF+HOUCTo5yVCPy80I78SERHNNf0dPd4bCUyYRWUyqYvTg34lWpIkJOWakJRrck1ZzIFUaS7hvR1EM1hKYVporoZDwGEP3u3oRERENHP4UlwDgFKtRPYti5yPgo1f5cr/J2Qlw7R0fvADHL8pSWIBTnMOr4QTzWCGzGQYCyzoON066evqmCu3kTnEuGW+jToaZQjewGxEREQ0c8SZDT6106clIiErBYV3x+LiqWZYz3fA4XBAlxiL5EVpiJ+fxAKZKARYhBPNYJIkYf7/zkNsih7tJ5sw0NUHwFlopxSmIaUwDWLUAVtLFxx2B6LioxGdFIvqf1TAPjgy5Xsb8y1y/AlEREQkM01sFAwZRnQ3dni8o05SKpCUZwbgPDE/vzQPKM2TMUqiuYtFONEMJ0kSkvLMSMozY3TYDuEQUGpVV89MKxVIyEpxWyfjxgKc/bTaY+Kdd302NDHaEEdORERE4ZJxYwEGP6xyThc6niRBkoAFZYuhjub0fkThwCKcKIIoNb59ZA0ZRuSuW4rmynoMjEu+6hgtLMuzYMw3hypEIiIimgFUUWoU3LEcHacv4FJtC4Z7BqFQK5CQnYqUwjTOQkIURizCiWYpfVoiFs5bgYGuPgz3DUKlVSMmWe/zYC1EREQU2ZRqFVKL0pFalB7uUIhoHBbhRLOYJEnQJcVClxQb7lCIiIiIiAicooyIiIiIiIhINizCiYiIiIiIiGTCIpyIiIiIiIhIJizCiYiIiIiIiGTCIpyIiIiIiIhIJizCiYiIiIiIiGTCIpyIiIiIiIhIJizCiYiIiIiIiGTCIpyIiIiIiIhIJizCiYiIiIiIiGTCIpyIiIiIiIhIJizCiYiIiIiIiGSiCncAwSaEAAD0Dg+HORIiIiKnsZw0lqNo+pjviYhoJvEn10tilh0RNDc3Iz09PdxhEBERTdDU1IS0tLRwhzErMN8TEdFM5Euun3VFuMPhQGtrK+Li4iBJkmzbtdlsSE9PR1NTE/R6vWzbnavY3/Jif8uL/S0vOfpbCIGenh5YLBYoFHwSLBhCle8j+fMXybEDkR0/Yw+PSI4diOz4GftE/uT6WXc7ukKhCOtVBr1eH3E7YiRjf8uL/S0v9re8Qt3f8fHxIXvvuSjU+T6SP3+RHDsQ2fEz9vCI5NiByI6fsbvzNdfzdDwRERERERGRTFiEExEREREREcmERXiQaLVaPPfcc9BqteEOZU5gf8uL/S0v9re82N80XiTvD5EcOxDZ8TP28Ijk2IHIjp+xT8+sG5iNiIiIiIiIaKbilXAiIiIiIiIimbAIJyIiIiIiIpIJi3AiIiIiIiIimbAIJyIiIiIiIpIJi/AAvfDCCygtLYVOp4PBYPBpHSEEtm7dCrPZjOjoaJSVlaGuri60gc4iXV1d+NGPfgS9Xg+DwYAHH3wQvb29U65z8803Q5Ikt59f/OIXMkUcWV5//XVkZmYiKioKJSUl+Oqrr6Zsv2/fPhQUFCAqKgpFRUU4ePCgTJHODv709549eybsx1FRUTJGG9k+//xz3H777bBYLJAkCe+//77XdY4ePYrly5dDq9UiJycHe/bsCXmcJI9Q5e9AclQg/N3OuXPnJnx/jP3s27fP1W6y1/fu3RvW2AHf8nhjYyPWr18PnU6HlJQUPPXUU7Db7WGNvaurC7/61a+Qn5+P6OhozJ8/H4899hisVqtbu1D1e7BzupzHsP7E/uabb+LGG29EQkICEhISUFZWNqH9pk2bJvRxeXl52GP3JbfLXTv4E/9kn01JkrB+/XpXGzn6PlQ53t/PkN8EBWTr1q3i1VdfFU888YSIj4/3aZ2dO3eK+Ph48f7774vvvvtO3HHHHSIrK0sMDAyENthZory8XCxdulR8+eWX4l//+pfIyckR991335TrrF69Wjz00EPiwoULrh+r1SpTxJFj7969QqPRiLfeekvU1NSIhx56SBgMBtHe3j5p+y+++EIolUrx0ksviVOnTonf//73Qq1Wi+rqapkjj0z+9vfu3buFXq9324/b2tpkjjpyHTx4UPzud78T+/fvFwDEe++9N2X777//Xuh0OvHEE0+IU6dOiddee00olUpx+PBheQKmkApV/g4kRwXC3+3Y7Xa3744LFy6I559/XsTGxoqenh5XOwBi9+7dbu2CfXwSijxut9vF4sWLRVlZmaiqqhIHDx4URqNRbNmyJayxV1dXi7vuukscOHBA1NfXiyNHjojc3Fxx9913u7ULRb+HIqfLdQzrb+z333+/eP3110VVVZWora0VmzZtEvHx8aK5udnVZuPGjaK8vNytj7u6uoIadyCx+5Lb5awd/I2/s7PTLfaTJ08KpVIpdu/e7WojR9+HIsf72xeBYBE+Tbt37/YpiTscDmEymcTLL7/sWtbd3S20Wq34xz/+EcIIZ4dTp04JAOLrr792LTt06JCQJEm0tLR4XG/16tXi17/+tQwRRraVK1eKRx991PX76OiosFgsYseOHZO2/+EPfyjWr1/vtqykpET8/Oc/D2mcs4W//e3r9wx550uCfvrpp0VhYaHbsnvuuUesW7cuhJGR3IKZvwPNUf4K1naWLVsmfvazn7kt8+WzMR2hyuMHDx4UCoXCrXj5y1/+IvR6vRgaGgpr7Nd65513hEajESMjI65loej3YOd0OY9h/Y39Wna7XcTFxYm3337btWzjxo1iw4YNQY1zMsHO7XLXDtPt+z/96U8iLi5O9Pb2upbJ1fdjgpXjp9sXvuDt6DJpaGhAW1sbysrKXMvi4+NRUlKCioqKMEYWGSoqKmAwGHD99de7lpWVlUGhUKCysnLKdf/2t7/BaDRi8eLF2LJlC/r7+0MdbkQZHh7GsWPH3PZNhUKBsrIyj/tmRUWFW3sAWLduHfdlHwTS3wDQ29uLjIwMpKenY8OGDaipqZEj3DmJ+zeN50v+nk6O8kcwtnPs2DEcP34cDz744ITXHn30URiNRqxcuRJvvfUWnMe04Y99qjxeUVGBoqIipKamupatW7cONpstaN+Twfr3tVqt0Ov1UKlUbsuD2e+hyOlyHcMGmh/H6+/vx8jICBITE92WHz16FCkpKcjPz8cjjzyCzs7OoMU9ndinyu1y1g7B6Ptdu3bh3nvvRUxMjNvyUPe9v7zt78HoC1+ovDehYGhrawMAtyQx9vvYa+RZW1sbUlJS3JapVCokJiZO2X/3338/MjIyYLFYcOLECTzzzDM4c+YM9u/fH+qQI0ZHRwdGR0cn3TdPnz496TptbW3clwMUSH/n5+fjrbfewpIlS2C1WvHKK6+gtLQUNTU1SEtLkyPsOcXT/m2z2TAwMIDo6OgwRUbh4Ev+DjRHBRLLdLeza9cuLFy4EKWlpW7Lt23bhltuuQU6nQ4ff/wxfvnLX6K3txePPfZYWGP3lsc9fV7HXgtn7ON1dHRg+/btePjhh92WB7vfQ5HT5TqGDST2az3zzDOwWCxuBVR5eTnuuusuZGVl4ezZs/jtb3+LW2+9FRUVFVAqlWGL3Vtul7N2mG7ff/XVVzh58iR27drltlyOvveXtxx/+fLlae+HvmARPs7mzZvx4osvTtmmtrYWBQUFMkU0+/na54Ean+yKiopgNpuxZs0anD17FgsWLAj4fYnktGrVKqxatcr1e2lpKRYuXIi//vWv2L59exgjI5oZIj1/hzoXjhkYGMDf//53PPvssxNeG7+suLgYfX19ePnll70Wg5Gcx+Xqd5vNhvXr12PRokX4wx/+4PZaoP1OE+3cuRN79+7F0aNH3QY4u/fee13/X1RUhCVLlmDBggU4evQo1qxZE45QAcyu3L5r1y4UFRVh5cqVbstnat/PBCzCx3nyySexadOmKdtkZ2cH9N4mkwkA0N7eDrPZ7Fre3t6OZcuWBfSes4GvfW4ymXDx4kW35Xa7HV1dXa6+9UVJSQkAoL6+nkX4FUajEUqlEu3t7W7L29vbPfatyWTyqz1dFUh/X0utVqO4uBj19fWhCHHO87R/6/V6XgWfocKdv6ebo+TKhe+++y76+/vxk5/8xGvbkpISbN++HUNDQ9BqtWGPfXxcwNU8bjKZJoxaPPb59fa+csTe09OD8vJyxMXF4b333oNarZ6yva/97kkocrpcx7DTyY+vvPIKdu7ciU8//RRLliyZsm12djaMRiPq6+uDVgiGIrfLWTtMJ/6+vj7s3bsX27Zt87qdUPS9v7zleKVSOe1/S1/wmfBxkpOTUVBQMOWPRqMJ6L2zsrJgMplw5MgR1zKbzYbKykq3s2Bzja99vmrVKnR3d+PYsWOudf/5z3/C4XC4ErIvjh8/DgBuX2ZznUajwXXXXee2bzocDhw5csTjvrlq1Sq39gDwySefzOl92VeB9Pe1RkdHUV1dzf04RLh/R55w5+/p5ii5cuGuXbtwxx13IDk52Wvb48ePIyEhwWshGO48vmrVKlRXV7sVyZ988gn0ej0WLVoU1thtNhvWrl0LjUaDAwcO+DS1pK/97kkocrpcx7CB5seXXnoJ27dvx+HDh92e2/ekubkZnZ2dQc2hocjtctYO04l/3759GBoawo9//GOv2wlF3/vL2/4ejH9LnwRtiLc55vz586Kqqso1zUdVVZWoqqpym+4jPz9f7N+/3/X7zp07hcFgEB988IE4ceKE2LBhA6co80N5ebkoLi4WlZWV4t///rfIzc11mx6kublZ5Ofni8rKSiGEEPX19WLbtm3im2++EQ0NDeKDDz4Q2dnZ4qabbgrXnzBj7d27V2i1WrFnzx5x6tQp8fDDDwuDweAabfaBBx4QmzdvdrX/4osvhEqlEq+88oqora0Vzz33HKco84O//f3888+Ljz76SJw9e1YcO3ZM3HvvvSIqKkrU1NSE60+IKD09Pa7vaADi1VdfFVVVVeL8+fNCCCE2b94sHnjgAVf7selLnnrqKVFbWytef/11TlE2i4Qqf3vLUcHiby4cU1dXJyRJEocOHZrwngcOHBBvvvmmqK6uFnV1deLPf/6z0Ol0YuvWrWGN3Zc8PjZF2dq1a8Xx48fF4cOHRXJyckimKPMndqvVKkpKSkRRUZGor693m6LJbrcLIULX76HI6XIdw/ob+86dO4VGoxHvvvuuWx+PfZ57enrEb37zG1FRUSEaGhrEp59+KpYvXy5yc3PF4OBgWGP3JbfLWTv4G/+YG264Qdxzzz0TlsvV96HI8d76IhhYhAdo48aNAsCEn88++8zVBlfmfhzjcDjEs88+K1JTU4VWqxVr1qwRZ86ckT/4CNXZ2Snuu+8+ERsbK/R6vfjpT3/qdtDU0NDg9m/Q2NgobrrpJpGYmCi0Wq3IyckRTz31FOcJ9+C1114T8+fPFxqNRqxcuVJ8+eWXrtdWr14tNm7c6Nb+nXfeEXl5eUKj0YjCwkLx4YcfyhxxZPOnvx9//HFX29TUVHHbbbeJb7/9NgxRR6bPPvts0u/rsT7euHGjWL169YR1li1bJjQajcjOznb7LqfIFqr87S1HBYu/uXDMli1bRHp6uhgdHZ3wnocOHRLLli0TsbGxIiYmRixdulS88cYbk7aVM3Zf8/i5c+fErbfeKqKjo4XRaBRPPvmk2zRg4Yjd0/cOANHQ0CCECG2/Bzuny3kM60/sGRkZk/bxc889J4QQor+/X6xdu1YkJycLtVotMjIyxEMPPRTUYirQ2H3J7XLXDv7uN6dPnxYAxMcffzzhveTq+1Dl+Kn6IhgkIYI4/wQRERERERERecRnwomIiIiIiIhkwiKciIiIiIiISCYswomIiIiIiIhkwiKciIiIiIiISCYswomIiIiIiIhkwiKciIiIiIiISCYswomIiIiIiIhkwiKciIiIiIiISCYswomIiIiIiIhkwiKciIiIiIiISCYswomIiIiIiIhkwiKciIiIiIiISCb/H3vKaP9eW44ZAAAAAElFTkSuQmCC\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ppVLzAao2Hkl"
+      },
+      "source": [
+        "And as we can see, almost perfect again.\n",
+        "\n",
+        "These are the kind of experiments you'll be running often when building your own models.\n",
+        "\n",
+        "Start with default settings and see how they perform on your data.\n",
+        "\n",
+        "And if they don't perform as well as you'd like, improve them.\n",
+        "\n",
+        "Let's look at a few more ways to evaluate our classification models."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0bNdy1EbftYp"
+      },
+      "source": [
+        "### More classification evaluation methods\n",
+        "\n",
+        "Alongside the visualizations we've been making, there are a number of different evaluation metrics we can use to evaluate our classification models.\n",
+        "\n",
+        "| **Metric name/Evaluation method** | **Defintion** | **Code** |\n",
+        "| --- | --- | --- |\n",
+        "| Accuracy | Out of 100 predictions, how many does your model get correct? E.g. 95% accuracy means it gets 95/100 predictions correct. | [`sklearn.metrics.accuracy_score()`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html) or [`tf.keras.metrics.Accuracy()`](tensorflow.org/api_docs/python/tf/keras/metrics/Accuracy) |\n",
+        "| Precision | Proportion of true positives over total number of samples. Higher precision leads to less false positives (model predicts 1 when it should've been 0). | [`sklearn.metrics.precision_score()`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html) or [`tf.keras.metrics.Precision()`](tensorflow.org/api_docs/python/tf/keras/metrics/Precision) |\n",
+        "| Recall | Proportion of true positives over total number of true positives and false negatives (model predicts 0 when it should've been 1). Higher recall leads to less false negatives. | [`sklearn.metrics.recall_score()`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html) or [`tf.keras.metrics.Recall()`](tensorflow.org/api_docs/python/tf/keras/metrics/Recall) |\n",
+        "| F1-score | Combines precision and recall into one metric. 1 is best, 0 is worst. | [`sklearn.metrics.f1_score()`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html) |\n",
+        "| [Confusion matrix](https://www.dataschool.io/simple-guide-to-confusion-matrix-terminology/)  | Compares the predicted values with the true values in a tabular way, if 100% correct, all values in the matrix will be top left to bottom right (diagnol line). | Custom function or [`sklearn.metrics.plot_confusion_matrix()`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.plot_confusion_matrix.html) |\n",
+        "| Classification report | Collection of some of the main classification metrics such as precision, recall and f1-score. | [`sklearn.metrics.classification_report()`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html) |\n",
+        "\n",
+        "> 🔑 **Note:** Every classification problem will require different kinds of evaluation methods. But you should be familiar with at least the ones above.\n",
+        "\n",
+        "Let's start with accuracy.\n",
+        "\n",
+        "Because we passed `[\"accuracy\"]` to the `metrics` parameter when we compiled our model, calling `evaluate()` on it will return the loss as well as accuracy."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "LUvEwzqp4zVW",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "47f4e55c-bc2c-4894-ce61-d274d2022597"
+      },
+      "source": [
+        "# Check the accuracy of our model\n",
+        "loss, accuracy = model_10.evaluate(X_test, y_test)\n",
+        "print(f\"Model loss on test set: {loss}\")\n",
+        "print(f\"Model accuracy on test set: {(accuracy*100):.2f}%\")"
+      ],
+      "execution_count": 54,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "7/7 [==============================] - 0s 3ms/step - loss: 0.1448 - accuracy: 0.9500\n",
+            "Model loss on test set: 0.14478765428066254\n",
+            "Model accuracy on test set: 95.00%\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "a5fXPTGi5aRX"
+      },
+      "source": [
+        "How about a confusion matrix?\n",
+        "\n",
+        "![anatomy of a confusion matrix](https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/images/02-anatomy-of-a-confusion-matrix.png)\n",
+        "*Anatomy of a confusion matrix (what we're going to be creating). Correct predictions appear down the diagonal (from top left to bottom right).*\n",
+        "\n",
+        "We can make a confusion matrix using [Scikit-Learn's `confusion_matrix`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html) method."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "g_Zee4lI5vi2",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 368
+        },
+        "outputId": "a23ee0f7-d4db-4707-fc09-ccd34c779960"
+      },
+      "source": [
+        "# Create a confusion matrix\n",
+        "from sklearn.metrics import confusion_matrix\n",
+        "\n",
+        "# Make predictions\n",
+        "y_preds = model_10.predict(X_test)\n",
+        "\n",
+        "# Create confusion matrix\n",
+        "confusion_matrix(y_test, y_preds)"
+      ],
+      "execution_count": 55,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "7/7 [==============================] - 0s 2ms/step\n"
+          ]
+        },
+        {
+          "output_type": "error",
+          "ename": "ValueError",
+          "evalue": "ignored",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-55-f9843efd97f5>\u001b[0m in \u001b[0;36m<cell line: 8>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;31m# Create confusion matrix\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mconfusion_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_preds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\u001b[0m in \u001b[0;36mconfusion_matrix\u001b[0;34m(y_true, y_pred, labels, sample_weight, normalize)\u001b[0m\n\u001b[1;32m    315\u001b[0m     \u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    316\u001b[0m     \"\"\"\n\u001b[0;32m--> 317\u001b[0;31m     \u001b[0my_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    318\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0my_type\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\"binary\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"multiclass\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    319\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"%s is not supported\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0my_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\u001b[0m in \u001b[0;36m_check_targets\u001b[0;34m(y_true, y_pred)\u001b[0m\n\u001b[1;32m     93\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_type\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 95\u001b[0;31m         raise ValueError(\n\u001b[0m\u001b[1;32m     96\u001b[0m             \"Classification metrics can't handle a mix of {0} and {1} targets\".format(\n\u001b[1;32m     97\u001b[0m                 \u001b[0mtype_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtype_pred\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mValueError\u001b[0m: Classification metrics can't handle a mix of binary and continuous targets"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4GLOPxx9fx__"
+      },
+      "source": [
+        "Ahh, it seems our predictions aren't in the format they need to be.\n",
+        "\n",
+        "Let's check them out."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "aLBs249Gfu-W",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "c9724bcb-f3f7-49fa-9cbe-86dd7965abe0"
+      },
+      "source": [
+        "# View the first 10 predictions\n",
+        "y_preds[:10]"
+      ],
+      "execution_count": 56,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[0.90055966],\n",
+              "       [0.98045874],\n",
+              "       [0.99161094],\n",
+              "       [0.9773018 ],\n",
+              "       [0.75639933],\n",
+              "       [0.15442978],\n",
+              "       [0.8414854 ],\n",
+              "       [0.18507078],\n",
+              "       [0.9666112 ],\n",
+              "       [0.0190204 ]], dtype=float32)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 56
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zacw9BNegVkY"
+      },
+      "source": [
+        "What about our test labels?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "3BH8XHAegYMd",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "e0a7ed8f-97f0-465f-99ec-6d7c4748c394"
+      },
+      "source": [
+        "# View the first 10 test labels\n",
+        "y_test[:10]"
+      ],
+      "execution_count": 57,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([1, 1, 1, 1, 0, 0, 1, 0, 1, 0])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 57
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4vd8EA95ggks"
+      },
+      "source": [
+        "It looks like we need to get our predictions into the binary format (0 or 1).\n",
+        "\n",
+        "But you might be wondering, what format are they currently in?\n",
+        "\n",
+        "In their current format (`9.8526537e-01`), they're in a form called **prediction probabilities**.\n",
+        "\n",
+        "You'll see this often with the outputs of neural networks. Often they won't be exact values but more a probability of how *likely* they are to be one value or another.\n",
+        "\n",
+        "So one of the steps you'll often see after making predicitons with a neural network is converting the prediction probabilities into labels.\n",
+        "\n",
+        "In our case, since our ground truth labels (`y_test`) are binary (0 or 1), we can convert the prediction probabilities using to their binary form using [`tf.round()`](https://www.tensorflow.org/api_docs/python/tf/math/round)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "YaS_yvGA6KV3",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "41507b47-1715-455f-bfe4-f858c421437a"
+      },
+      "source": [
+        "# Convert prediction probabilities to binary format and view the first 10\n",
+        "tf.round(y_preds)[:10]"
+      ],
+      "execution_count": 58,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<tf.Tensor: shape=(10, 1), dtype=float32, numpy=\n",
+              "array([[1.],\n",
+              "       [1.],\n",
+              "       [1.],\n",
+              "       [1.],\n",
+              "       [1.],\n",
+              "       [0.],\n",
+              "       [1.],\n",
+              "       [0.],\n",
+              "       [1.],\n",
+              "       [0.]], dtype=float32)>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 58
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mLvWYgFIh_A9"
+      },
+      "source": [
+        "Wonderful! Now we can use the `confusion_matrix` function."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "GEZx1WHCiC8n",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "e69449d7-4173-4255-c10c-52edf07c5f7b"
+      },
+      "source": [
+        "# Create a confusion matrix\n",
+        "confusion_matrix(y_test, tf.round(y_preds))"
+      ],
+      "execution_count": 59,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[91, 10],\n",
+              "       [ 0, 99]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 59
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "VY1LmNeTiKHl"
+      },
+      "source": [
+        "Alright, we can see the highest numbers are down the diagonal (from top left to bottom right) so this a good sign, but the rest of the matrix doesn't really tell us much.\n",
+        "\n",
+        "How about we make a function to make our confusion matrix a little more visual?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "IIPSs9ERi78w",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 815
+        },
+        "outputId": "6d3cab59-c42c-4c70-f1cb-991d86443dd7"
+      },
+      "source": [
+        "# Note: The following confusion matrix code is a remix of Scikit-Learn's\n",
+        "# plot_confusion_matrix function - https://scikit-learn.org/stable/modules/generated/sklearn.metrics.plot_confusion_matrix.html\n",
+        "# and Made with ML's introductory notebook - https://github.com/GokuMohandas/MadeWithML/blob/main/notebooks/08_Neural_Networks.ipynb\n",
+        "import itertools\n",
+        "\n",
+        "figsize = (10, 10)\n",
+        "\n",
+        "# Create the confusion matrix\n",
+        "cm = confusion_matrix(y_test, tf.round(y_preds))\n",
+        "cm_norm = cm.astype(\"float\") / cm.sum(axis=1)[:, np.newaxis] # normalize it\n",
+        "n_classes = cm.shape[0]\n",
+        "\n",
+        "# Let's prettify it\n",
+        "fig, ax = plt.subplots(figsize=figsize)\n",
+        "# Create a matrix plot\n",
+        "cax = ax.matshow(cm, cmap=plt.cm.Blues) # https://matplotlib.org/3.2.0/api/_as_gen/matplotlib.axes.Axes.matshow.html\n",
+        "fig.colorbar(cax)\n",
+        "\n",
+        "# Create classes\n",
+        "classes = False\n",
+        "\n",
+        "if classes:\n",
+        "  labels = classes\n",
+        "else:\n",
+        "  labels = np.arange(cm.shape[0])\n",
+        "\n",
+        "# Label the axes\n",
+        "ax.set(title=\"Confusion Matrix\",\n",
+        "       xlabel=\"Predicted label\",\n",
+        "       ylabel=\"True label\",\n",
+        "       xticks=np.arange(n_classes),\n",
+        "       yticks=np.arange(n_classes),\n",
+        "       xticklabels=labels,\n",
+        "       yticklabels=labels)\n",
+        "\n",
+        "# Set x-axis labels to bottom\n",
+        "ax.xaxis.set_label_position(\"bottom\")\n",
+        "ax.xaxis.tick_bottom()\n",
+        "\n",
+        "# Adjust label size\n",
+        "ax.xaxis.label.set_size(20)\n",
+        "ax.yaxis.label.set_size(20)\n",
+        "ax.title.set_size(20)\n",
+        "\n",
+        "# Set threshold for different colors\n",
+        "threshold = (cm.max() + cm.min()) / 2.\n",
+        "\n",
+        "# Plot the text on each cell\n",
+        "for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
+        "  plt.text(j, i, f\"{cm[i, j]} ({cm_norm[i, j]*100:.1f}%)\",\n",
+        "           horizontalalignment=\"center\",\n",
+        "           color=\"white\" if cm[i, j] > threshold else \"black\",\n",
+        "           size=15)"
+      ],
+      "execution_count": 60,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x1000 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0EaKOeeNnWbq"
+      },
+      "source": [
+        "That looks much better. It seems our model has made almost perfect predictions on the test set except for two false positives (top right corner)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "ZvNQjM-rk7uE",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "61bf03b3-e350-46b4-a0e8-d1dae40b75c8"
+      },
+      "source": [
+        "# What does itertools.product do? Combines two things into each combination\n",
+        "import itertools\n",
+        "for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
+        "  print(i, j)"
+      ],
+      "execution_count": 61,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "0 0\n",
+            "0 1\n",
+            "1 0\n",
+            "1 1\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2WLKnUi6fI6B"
+      },
+      "source": [
+        "## Working with a larger example (multiclass classification)\n",
+        "\n",
+        "We've seen a binary classification example (predicting if a data point is part of a red circle or blue circle) but what if you had multiple different classes of things?\n",
+        "\n",
+        "For example, say you were a fashion company and you wanted to build a neural network to predict whether a piece of clothing was a shoe, a shirt or a jacket (3 different options).\n",
+        "\n",
+        "When you have more than two classes as an option, this is known as **multiclass classification**.\n",
+        "\n",
+        "The good news is, the things we've learned so far (with a few tweaks) can be applied to multiclass classification problems as well.\n",
+        "\n",
+        "Let's see it in action.\n",
+        "\n",
+        "To start, we'll need some data. The good thing for us is TensorFlow has a multiclass classication dataset known as [Fashion MNIST built-in](https://github.com/zalandoresearch/fashion-mnist). Meaning we can get started straight away.\n",
+        "\n",
+        "We can import it using the [`tf.keras.datasets`](https://www.tensorflow.org/api_docs/python/tf/keras/datasets) module.\n",
+        "\n",
+        "> 📖 **Resource:** The following multiclass classification problem has been adapted from the [TensorFlow classification guide](https://www.tensorflow.org/tutorials/keras/classification). A good exercise would be to once you've gone through the following example, replicate the TensorFlow guide."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "zl50sxPTqpw4",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "0c0f224d-7ca9-4b86-a248-6b4c548f46c3"
+      },
+      "source": [
+        "import tensorflow as tf\n",
+        "from tensorflow.keras.datasets import fashion_mnist\n",
+        "\n",
+        "# The data has already been sorted into training and test sets for us\n",
+        "(train_data, train_labels), (test_data, test_labels) = fashion_mnist.load_data()"
+      ],
+      "execution_count": 62,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
+            "29515/29515 [==============================] - 0s 0us/step\n",
+            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
+            "26421880/26421880 [==============================] - 0s 0us/step\n",
+            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
+            "5148/5148 [==============================] - 0s 0us/step\n",
+            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
+            "4422102/4422102 [==============================] - 0s 0us/step\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "D6qJforMrCZy"
+      },
+      "source": [
+        "Now let's check out an example."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "8PWdrQsyrBcy",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "bee22292-8e9d-42a7-be8b-8315c2851dfe"
+      },
+      "source": [
+        "# Show the first training example\n",
+        "print(f\"Training sample:\\n{train_data[0]}\\n\")\n",
+        "print(f\"Training label: {train_labels[0]}\")"
+      ],
+      "execution_count": 63,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Training sample:\n",
+            "[[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
+            "    0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
+            "    0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
+            "    0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   1   0   0  13  73   0\n",
+            "    0   1   4   0   0   0   0   1   1   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   3   0  36 136 127  62\n",
+            "   54   0   0   0   1   3   4   0   0   3]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   6   0 102 204 176 134\n",
+            "  144 123  23   0   0   0   0  12  10   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0 155 236 207 178\n",
+            "  107 156 161 109  64  23  77 130  72  15]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   1   0  69 207 223 218 216\n",
+            "  216 163 127 121 122 146 141  88 172  66]\n",
+            " [  0   0   0   0   0   0   0   0   0   1   1   1   0 200 232 232 233 229\n",
+            "  223 223 215 213 164 127 123 196 229   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0 183 225 216 223 228\n",
+            "  235 227 224 222 224 221 223 245 173   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0 193 228 218 213 198\n",
+            "  180 212 210 211 213 223 220 243 202   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   1   3   0  12 219 220 212 218 192\n",
+            "  169 227 208 218 224 212 226 197 209  52]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   6   0  99 244 222 220 218 203\n",
+            "  198 221 215 213 222 220 245 119 167  56]\n",
+            " [  0   0   0   0   0   0   0   0   0   4   0   0  55 236 228 230 228 240\n",
+            "  232 213 218 223 234 217 217 209  92   0]\n",
+            " [  0   0   1   4   6   7   2   0   0   0   0   0 237 226 217 223 222 219\n",
+            "  222 221 216 223 229 215 218 255  77   0]\n",
+            " [  0   3   0   0   0   0   0   0   0  62 145 204 228 207 213 221 218 208\n",
+            "  211 218 224 223 219 215 224 244 159   0]\n",
+            " [  0   0   0   0  18  44  82 107 189 228 220 222 217 226 200 205 211 230\n",
+            "  224 234 176 188 250 248 233 238 215   0]\n",
+            " [  0  57 187 208 224 221 224 208 204 214 208 209 200 159 245 193 206 223\n",
+            "  255 255 221 234 221 211 220 232 246   0]\n",
+            " [  3 202 228 224 221 211 211 214 205 205 205 220 240  80 150 255 229 221\n",
+            "  188 154 191 210 204 209 222 228 225   0]\n",
+            " [ 98 233 198 210 222 229 229 234 249 220 194 215 217 241  65  73 106 117\n",
+            "  168 219 221 215 217 223 223 224 229  29]\n",
+            " [ 75 204 212 204 193 205 211 225 216 185 197 206 198 213 240 195 227 245\n",
+            "  239 223 218 212 209 222 220 221 230  67]\n",
+            " [ 48 203 183 194 213 197 185 190 194 192 202 214 219 221 220 236 225 216\n",
+            "  199 206 186 181 177 172 181 205 206 115]\n",
+            " [  0 122 219 193 179 171 183 196 204 210 213 207 211 210 200 196 194 191\n",
+            "  195 191 198 192 176 156 167 177 210  92]\n",
+            " [  0   0  74 189 212 191 175 172 175 181 185 188 189 188 193 198 204 209\n",
+            "  210 210 211 188 188 194 192 216 170   0]\n",
+            " [  2   0   0   0  66 200 222 237 239 242 246 243 244 221 220 193 191 179\n",
+            "  182 182 181 176 166 168  99  58   0   0]\n",
+            " [  0   0   0   0   0   0   0  40  61  44  72  41  35   0   0   0   0   0\n",
+            "    0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
+            "    0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
+            "    0   0   0   0   0   0   0   0   0   0]]\n",
+            "\n",
+            "Training label: 9\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "MMqbRp8jrdtv"
+      },
+      "source": [
+        "Woah, we get a large list of numbers, followed (the data) by a single number (the class label).\n",
+        "\n",
+        "What about the shapes?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "gN5-jr6arj19",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "53eb0aa5-d31c-4a7b-c249-2b84da25318c"
+      },
+      "source": [
+        "# Check the shape of our data\n",
+        "train_data.shape, train_labels.shape, test_data.shape, test_labels.shape"
+      ],
+      "execution_count": 64,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "((60000, 28, 28), (60000,), (10000, 28, 28), (10000,))"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 64
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "wNfIOUUEsJRt",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "b4485b34-49e3-43f8-9c13-0e74af0121bd"
+      },
+      "source": [
+        "# Check shape of a single example\n",
+        "train_data[0].shape, train_labels[0].shape"
+      ],
+      "execution_count": 65,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "((28, 28), ())"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 65
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "r2wW0cEfsAve"
+      },
+      "source": [
+        "Okay, 60,000 training examples each with shape (28, 28) and a label each as well as 10,000 test examples of shape (28, 28).\n",
+        "\n",
+        "But these are just numbers, let's visualize."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "RmC2VsWOscKP",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 430
+        },
+        "outputId": "934d86ab-1baa-4ddb-945c-e4e5272c3545"
+      },
+      "source": [
+        "# Plot a single example\n",
+        "import matplotlib.pyplot as plt\n",
+        "plt.imshow(train_data[7]);"
+      ],
+      "execution_count": 66,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "sOqirdtfstdQ"
+      },
+      "source": [
+        "Hmm, but what about its label?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "hzTDEpaYsxga",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "15be8025-cd86-4804-b152-a8e4f1c0e899"
+      },
+      "source": [
+        "# Check our samples label\n",
+        "train_labels[7]"
+      ],
+      "execution_count": 67,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "2"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 67
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZHdVBrCUs10A"
+      },
+      "source": [
+        "It looks like our labels are in numerical form. And while this is fine for a neural network, you might want to have them in human readable form.\n",
+        "\n",
+        "Let's create a small list of the class names (we can find them on [the dataset's GitHub page](https://github.com/zalandoresearch/fashion-mnist#labels)).\n",
+        "\n",
+        "> 🔑 **Note:** Whilst this dataset has been prepared for us and ready to go, it's important to remember many datasets won't be ready to go like this one. Often you'll have to do a few preprocessing steps to have it ready to use with a neural network (we'll see more of this when we work with our own data later)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "uGOi32T8s1ai",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "da56724a-242b-4788-9f47-badbd97b2aaf"
+      },
+      "source": [
+        "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
+        "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']\n",
+        "\n",
+        "# How many classes are there (this'll be our output shape)?\n",
+        "len(class_names)"
+      ],
+      "execution_count": 68,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "10"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 68
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zOiwINQQtzys"
+      },
+      "source": [
+        "Now we have these, let's plot another example.\n",
+        "\n",
+        "> 🤔 **Question:** Pay particular attention to what the data we're working with *looks* like. Is it only straight lines? Or does it have non-straight lines as well? Do you think if we wanted to find patterns in the photos of clothes (which are actually collections of pixels), will our model need non-linearities (non-straight lines) or not?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "_qD40id2tytn",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 452
+        },
+        "outputId": "1fd64b7b-4d1b-4bb8-bf09-89c05519ed1b"
+      },
+      "source": [
+        "# Plot an example image and its label\n",
+        "plt.imshow(train_data[17], cmap=plt.cm.binary) # change the colours to black & white\n",
+        "plt.title(class_names[train_labels[17]]);"
+      ],
+      "execution_count": 69,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Rtz9__w3s4JF",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 598
+        },
+        "outputId": "b1090527-5787-4e3b-ceec-18d1753daf54"
+      },
+      "source": [
+        "# Plot multiple random images of fashion MNIST\n",
+        "import random\n",
+        "plt.figure(figsize=(7, 7))\n",
+        "for i in range(4):\n",
+        "  ax = plt.subplot(2, 2, i + 1)\n",
+        "  rand_index = random.choice(range(len(train_data)))\n",
+        "  plt.imshow(train_data[rand_index], cmap=plt.cm.binary)\n",
+        "  plt.title(class_names[train_labels[rand_index]])\n",
+        "  plt.axis(False)"
+      ],
+      "execution_count": 70,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 700x700 with 4 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TLqZif3Rv0Aq"
+      },
+      "source": [
+        "Alright, let's build a model to figure out the relationship between the pixel values and their labels.\n",
+        "\n",
+        "Since this is a multiclass classification problem, we'll need to make a few changes to our architecture (inline with Table 1 above):\n",
+        "\n",
+        "* The **input shape** will have to deal with 28x28 tensors (the height and width of our images).\n",
+        "  * We're actually going to squash the input into a tensor (vector) of shape `(784)`.\n",
+        "* The **output shape** will have to be 10 because we need our model to predict for 10 different classes.\n",
+        "  * We'll also change the `activation` parameter of our output layer to be [`\"softmax\"`](https://www.tensorflow.org/api_docs/python/tf/keras/activations/softmax) instead of `'sigmoid'`. As we'll see the `\"softmax\"` activation function outputs a series of values between 0 & 1 (the same shape as **output shape**, which together add up to ~1. The index with the highest value is predicted by the model to be the most *likely* class.\n",
+        "* We'll need to change our loss function from a binary loss function to a multiclass loss function.\n",
+        "  * More specifically, since our labels are in integer form, we'll use [`tf.keras.losses.SparseCategoricalCrossentropy()`](https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/losses/SparseCategoricalCrossentropy), if our labels were one-hot encoded (e.g. they looked something like `[0, 0, 1, 0, 0...]`), we'd use [`tf.keras.losses.CategoricalCrossentropy()`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/CategoricalCrossentropy).\n",
+        "* We'll also use the `validation_data` parameter when calling the `fit()` function. This will give us an idea of how the model performs on the test set during training.\n",
+        "\n",
+        "You ready? Let's go."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "qUFHuzIpv30K",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "ef9c8787-e5e6-4e9d-a8b3-1b94b1bdd23c"
+      },
+      "source": [
+        "# Set random seed\n",
+        "tf.random.set_seed(42)\n",
+        "\n",
+        "# Create the model\n",
+        "model_11 = tf.keras.Sequential([\n",
+        "  tf.keras.layers.Flatten(input_shape=(28, 28)), # input layer (we had to reshape 28x28 to 784, the Flatten layer does this for us)\n",
+        "  tf.keras.layers.Dense(4, activation=\"relu\"),\n",
+        "  tf.keras.layers.Dense(4, activation=\"relu\"),\n",
+        "  tf.keras.layers.Dense(10, activation=\"softmax\") # output shape is 10, activation is softmax\n",
+        "])\n",
+        "\n",
+        "# Compile the model\n",
+        "model_11.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(), # different loss function for multiclass classifcation\n",
+        "                 optimizer=tf.keras.optimizers.Adam(),\n",
+        "                 metrics=[\"accuracy\"])\n",
+        "\n",
+        "# Fit the model\n",
+        "non_norm_history = model_11.fit(train_data,\n",
+        "                                train_labels,\n",
+        "                                epochs=10,\n",
+        "                                validation_data=(test_data, test_labels)) # see how the model performs on the test set during training"
+      ],
+      "execution_count": 71,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/10\n",
+            "1875/1875 [==============================] - 8s 3ms/step - loss: 2.3523 - accuracy: 0.0988 - val_loss: 2.3027 - val_accuracy: 0.1000\n",
+            "Epoch 2/10\n",
+            "1875/1875 [==============================] - 12s 6ms/step - loss: 2.3028 - accuracy: 0.0984 - val_loss: 2.3027 - val_accuracy: 0.1000\n",
+            "Epoch 3/10\n",
+            "1875/1875 [==============================] - 11s 6ms/step - loss: 2.3027 - accuracy: 0.1002 - val_loss: 2.3027 - val_accuracy: 0.1000\n",
+            "Epoch 4/10\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3028 - accuracy: 0.0975 - val_loss: 2.3026 - val_accuracy: 0.1000\n",
+            "Epoch 5/10\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3028 - accuracy: 0.0973 - val_loss: 2.3026 - val_accuracy: 0.1000\n",
+            "Epoch 6/10\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3028 - accuracy: 0.0992 - val_loss: 2.3026 - val_accuracy: 0.1000\n",
+            "Epoch 7/10\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3028 - accuracy: 0.0987 - val_loss: 2.3026 - val_accuracy: 0.1000\n",
+            "Epoch 8/10\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3028 - accuracy: 0.0982 - val_loss: 2.3027 - val_accuracy: 0.1000\n",
+            "Epoch 9/10\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3028 - accuracy: 0.0989 - val_loss: 2.3027 - val_accuracy: 0.1000\n",
+            "Epoch 10/10\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3028 - accuracy: 0.0986 - val_loss: 2.3027 - val_accuracy: 0.1000\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "3hzYWEgoVJ_p",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "174434da-965b-41bd-e68d-dc12336faa25"
+      },
+      "source": [
+        "# Check the shapes of our model\n",
+        "# Note: the \"None\" in (None, 784) is for batch_size, we'll cover this in a later module\n",
+        "model_11.summary()"
+      ],
+      "execution_count": 72,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Model: \"sequential_11\"\n",
+            "_________________________________________________________________\n",
+            " Layer (type)                Output Shape              Param #   \n",
+            "=================================================================\n",
+            " flatten (Flatten)           (None, 784)               0         \n",
+            "                                                                 \n",
+            " dense_28 (Dense)            (None, 4)                 3140      \n",
+            "                                                                 \n",
+            " dense_29 (Dense)            (None, 4)                 20        \n",
+            "                                                                 \n",
+            " dense_30 (Dense)            (None, 10)                50        \n",
+            "                                                                 \n",
+            "=================================================================\n",
+            "Total params: 3210 (12.54 KB)\n",
+            "Trainable params: 3210 (12.54 KB)\n",
+            "Non-trainable params: 0 (0.00 Byte)\n",
+            "_________________________________________________________________\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XRfkre59zSto"
+      },
+      "source": [
+        "Alright, our model gets to about ~35% accuracy after 10 epochs using a similar style model to what we used on our binary classification problem.\n",
+        "\n",
+        "Which is better than guessing (guessing with 10 classes would result in about 10% accuracy) but we can do better.\n",
+        "\n",
+        "Do you remember when we talked about neural networks preferring numbers between 0 and 1? (if not, treat this as a reminder)\n",
+        "\n",
+        "Well, right now, the data we have isn't between 0 and 1, in other words, it's not normalized (hence why we used the `non_norm_history` variable when calling `fit()`). It's pixel values are between 0 and 255.\n",
+        "\n",
+        "Let's see."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "tGiweanwz82_",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "efca9b0f-4dfa-472c-f7b5-08328ec8509a"
+      },
+      "source": [
+        "# Check the min and max values of the training data\n",
+        "train_data.min(), train_data.max()"
+      ],
+      "execution_count": 73,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(0, 255)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 73
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "syB7LOk30H0_"
+      },
+      "source": [
+        "We can get these values between 0 and 1 by dividing the entire array by the maximum: `255.0` (dividing by a float also converts to a float).\n",
+        "\n",
+        "\n",
+        "Doing so will result in all of our data being between 0 and 1 (known as **scaling** or **normalization**)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "ABRKp5U8voV_",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "83ad0107-89ca-43de-c60e-a3c63b792cc5"
+      },
+      "source": [
+        "# Divide train and test images by the maximum value (normalize it)\n",
+        "train_data = train_data / 255.0\n",
+        "test_data = test_data / 255.0\n",
+        "\n",
+        "# Check the min and max values of the training data\n",
+        "train_data.min(), train_data.max()"
+      ],
+      "execution_count": 74,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(0.0, 1.0)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 74
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "LeYQEXOf06oo"
+      },
+      "source": [
+        "Beautiful! Now our data is between 0 and 1. Let's see what happens when we model it.\n",
+        "\n",
+        "We'll use the same model as before (`model_11`) except this time the data will be normalized."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "z1QRy7y_K_87",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "935a439f-3d29-4a07-b099-79a112e24cc7"
+      },
+      "source": [
+        "# Set random seed\n",
+        "tf.random.set_seed(42)\n",
+        "\n",
+        "# Create the model\n",
+        "model_12 = tf.keras.Sequential([\n",
+        "  tf.keras.layers.Flatten(input_shape=(28, 28)), # input layer (we had to reshape 28x28 to 784)\n",
+        "  tf.keras.layers.Dense(4, activation=\"relu\"),\n",
+        "  tf.keras.layers.Dense(4, activation=\"relu\"),\n",
+        "  tf.keras.layers.Dense(10, activation=\"softmax\") # output shape is 10, activation is softmax\n",
+        "])\n",
+        "\n",
+        "# Compile the model\n",
+        "model_12.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n",
+        "                 optimizer=tf.keras.optimizers.Adam(),\n",
+        "                 metrics=[\"accuracy\"])\n",
+        "\n",
+        "# Fit the model (to the normalized data)\n",
+        "norm_history = model_12.fit(train_data,\n",
+        "                            train_labels,\n",
+        "                            epochs=10,\n",
+        "                            validation_data=(test_data, test_labels))"
+      ],
+      "execution_count": 75,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/10\n",
+            "1875/1875 [==============================] - 8s 4ms/step - loss: 1.8464 - accuracy: 0.2271 - val_loss: 1.7161 - val_accuracy: 0.2872\n",
+            "Epoch 2/10\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 1.5953 - accuracy: 0.3389 - val_loss: 1.4544 - val_accuracy: 0.3877\n",
+            "Epoch 3/10\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 1.3598 - accuracy: 0.4270 - val_loss: 1.3162 - val_accuracy: 0.4434\n",
+            "Epoch 4/10\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 1.2956 - accuracy: 0.4432 - val_loss: 1.2835 - val_accuracy: 0.4519\n",
+            "Epoch 5/10\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 1.2660 - accuracy: 0.4532 - val_loss: 1.2655 - val_accuracy: 0.4564\n",
+            "Epoch 6/10\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 1.2464 - accuracy: 0.4603 - val_loss: 1.2475 - val_accuracy: 0.4614\n",
+            "Epoch 7/10\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 1.2327 - accuracy: 0.4625 - val_loss: 1.1558 - val_accuracy: 0.5474\n",
+            "Epoch 8/10\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.9829 - accuracy: 0.5960 - val_loss: 0.9274 - val_accuracy: 0.6240\n",
+            "Epoch 9/10\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.8707 - accuracy: 0.6505 - val_loss: 0.8581 - val_accuracy: 0.6684\n",
+            "Epoch 10/10\n",
+            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.8116 - accuracy: 0.6887 - val_loss: 0.8157 - val_accuracy: 0.6944\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "C_I2KNJiMWZ8"
+      },
+      "source": [
+        "Woah, we used the exact same model as before but we with normalized data we're now seeing a much higher accuracy value!\n",
+        "\n",
+        "Let's plot each model's history (their loss curves)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "zmRcYU7xN1wQ",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 887
+        },
+        "outputId": "8a63d7d4-f6ba-419f-c616-e1181d30d5d7"
+      },
+      "source": [
+        "import pandas as pd\n",
+        "# Plot non-normalized data loss curves\n",
+        "pd.DataFrame(non_norm_history.history).plot(title=\"Non-normalized Data\")\n",
+        "# Plot normalized data loss curves\n",
+        "pd.DataFrame(norm_history.history).plot(title=\"Normalized data\");"
+      ],
+      "execution_count": 76,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "VKm8MsACOm4j"
+      },
+      "source": [
+        "Wow. From these two plots, we can see how much quicker our model with the normalized data (`model_12`) improved than the model with the non-normalized data (`model_11`).\n",
+        "\n",
+        "> 🔑 **Note:** The same model with even *slightly* different data can produce *dramatically* different results. So when you're comparing models, it's important to make sure you're comparing them on the same criteria (e.g. same architecture but different data or same data but different architecture).\n",
+        "\n",
+        "How about we find the ideal learning rate and see what happens?\n",
+        "\n",
+        "We'll use the same architecture we've been using."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "LcR_wb4nPSb2",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "4aa1fe07-30b2-4a73-c3e2-a6fcaa922750"
+      },
+      "source": [
+        "# Set random seed\n",
+        "tf.random.set_seed(42)\n",
+        "\n",
+        "# Create the model\n",
+        "model_13 = tf.keras.Sequential([\n",
+        "  tf.keras.layers.Flatten(input_shape=(28, 28)), # input layer (we had to reshape 28x28 to 784)\n",
+        "  tf.keras.layers.Dense(4, activation=\"relu\"),\n",
+        "  tf.keras.layers.Dense(4, activation=\"relu\"),\n",
+        "  tf.keras.layers.Dense(10, activation=\"softmax\") # output shape is 10, activation is softmax\n",
+        "])\n",
+        "\n",
+        "# Compile the model\n",
+        "model_13.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n",
+        "                 optimizer=tf.keras.optimizers.Adam(),\n",
+        "                 metrics=[\"accuracy\"])\n",
+        "\n",
+        "# Create the learning rate callback\n",
+        "lr_scheduler = tf.keras.callbacks.LearningRateScheduler(lambda epoch: 1e-3 * 10**(epoch/20))\n",
+        "\n",
+        "# Fit the model\n",
+        "find_lr_history = model_13.fit(train_data,\n",
+        "                               train_labels,\n",
+        "                               epochs=40, # model already doing pretty good with current LR, probably don't need 100 epochs\n",
+        "                               validation_data=(test_data, test_labels),\n",
+        "                               callbacks=[lr_scheduler])"
+      ],
+      "execution_count": 77,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/40\n",
+            "1875/1875 [==============================] - 8s 3ms/step - loss: 1.3925 - accuracy: 0.4730 - val_loss: 0.9515 - val_accuracy: 0.6371 - lr: 0.0010\n",
+            "Epoch 2/40\n",
+            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.8161 - accuracy: 0.7099 - val_loss: 0.7533 - val_accuracy: 0.7403 - lr: 0.0011\n",
+            "Epoch 3/40\n",
+            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.6928 - accuracy: 0.7623 - val_loss: 0.6992 - val_accuracy: 0.7600 - lr: 0.0013\n",
+            "Epoch 4/40\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6512 - accuracy: 0.7755 - val_loss: 0.6705 - val_accuracy: 0.7701 - lr: 0.0014\n",
+            "Epoch 5/40\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6308 - accuracy: 0.7825 - val_loss: 0.6510 - val_accuracy: 0.7774 - lr: 0.0016\n",
+            "Epoch 6/40\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6190 - accuracy: 0.7865 - val_loss: 0.6627 - val_accuracy: 0.7766 - lr: 0.0018\n",
+            "Epoch 7/40\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6128 - accuracy: 0.7878 - val_loss: 0.6460 - val_accuracy: 0.7784 - lr: 0.0020\n",
+            "Epoch 8/40\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6086 - accuracy: 0.7894 - val_loss: 0.6337 - val_accuracy: 0.7841 - lr: 0.0022\n",
+            "Epoch 9/40\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6059 - accuracy: 0.7898 - val_loss: 0.6276 - val_accuracy: 0.7851 - lr: 0.0025\n",
+            "Epoch 10/40\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6070 - accuracy: 0.7891 - val_loss: 0.6256 - val_accuracy: 0.7878 - lr: 0.0028\n",
+            "Epoch 11/40\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6074 - accuracy: 0.7889 - val_loss: 0.6244 - val_accuracy: 0.7867 - lr: 0.0032\n",
+            "Epoch 12/40\n",
+            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.6082 - accuracy: 0.7881 - val_loss: 0.6352 - val_accuracy: 0.7870 - lr: 0.0035\n",
+            "Epoch 13/40\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6063 - accuracy: 0.7907 - val_loss: 0.6162 - val_accuracy: 0.7897 - lr: 0.0040\n",
+            "Epoch 14/40\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6032 - accuracy: 0.7907 - val_loss: 0.6255 - val_accuracy: 0.7854 - lr: 0.0045\n",
+            "Epoch 15/40\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6101 - accuracy: 0.7874 - val_loss: 0.6123 - val_accuracy: 0.7905 - lr: 0.0050\n",
+            "Epoch 16/40\n",
+            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.6109 - accuracy: 0.7890 - val_loss: 0.6253 - val_accuracy: 0.7856 - lr: 0.0056\n",
+            "Epoch 17/40\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6035 - accuracy: 0.7919 - val_loss: 0.6233 - val_accuracy: 0.7906 - lr: 0.0063\n",
+            "Epoch 18/40\n",
+            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.6066 - accuracy: 0.7906 - val_loss: 0.6254 - val_accuracy: 0.7844 - lr: 0.0071\n",
+            "Epoch 19/40\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6047 - accuracy: 0.7907 - val_loss: 0.6372 - val_accuracy: 0.7883 - lr: 0.0079\n",
+            "Epoch 20/40\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6083 - accuracy: 0.7914 - val_loss: 0.6341 - val_accuracy: 0.7869 - lr: 0.0089\n",
+            "Epoch 21/40\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6135 - accuracy: 0.7877 - val_loss: 0.6964 - val_accuracy: 0.7617 - lr: 0.0100\n",
+            "Epoch 22/40\n",
+            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.6177 - accuracy: 0.7885 - val_loss: 0.6252 - val_accuracy: 0.7902 - lr: 0.0112\n",
+            "Epoch 23/40\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6234 - accuracy: 0.7869 - val_loss: 0.6686 - val_accuracy: 0.7699 - lr: 0.0126\n",
+            "Epoch 24/40\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6250 - accuracy: 0.7872 - val_loss: 0.6393 - val_accuracy: 0.7907 - lr: 0.0141\n",
+            "Epoch 25/40\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6311 - accuracy: 0.7830 - val_loss: 0.6683 - val_accuracy: 0.7672 - lr: 0.0158\n",
+            "Epoch 26/40\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6436 - accuracy: 0.7781 - val_loss: 0.7042 - val_accuracy: 0.7593 - lr: 0.0178\n",
+            "Epoch 27/40\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6473 - accuracy: 0.7759 - val_loss: 0.7101 - val_accuracy: 0.7666 - lr: 0.0200\n",
+            "Epoch 28/40\n",
+            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.6582 - accuracy: 0.7713 - val_loss: 0.6911 - val_accuracy: 0.7679 - lr: 0.0224\n",
+            "Epoch 29/40\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6763 - accuracy: 0.7643 - val_loss: 0.7237 - val_accuracy: 0.7678 - lr: 0.0251\n",
+            "Epoch 30/40\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.7064 - accuracy: 0.7449 - val_loss: 0.6960 - val_accuracy: 0.7533 - lr: 0.0282\n",
+            "Epoch 31/40\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.7162 - accuracy: 0.7363 - val_loss: 0.8234 - val_accuracy: 0.6864 - lr: 0.0316\n",
+            "Epoch 32/40\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.7372 - accuracy: 0.7220 - val_loss: 0.6946 - val_accuracy: 0.7360 - lr: 0.0355\n",
+            "Epoch 33/40\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.7747 - accuracy: 0.7045 - val_loss: 0.8750 - val_accuracy: 0.6987 - lr: 0.0398\n",
+            "Epoch 34/40\n",
+            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.7924 - accuracy: 0.7020 - val_loss: 0.8486 - val_accuracy: 0.6599 - lr: 0.0447\n",
+            "Epoch 35/40\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.8487 - accuracy: 0.6818 - val_loss: 0.8562 - val_accuracy: 0.6873 - lr: 0.0501\n",
+            "Epoch 36/40\n",
+            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.8896 - accuracy: 0.6708 - val_loss: 0.9754 - val_accuracy: 0.6278 - lr: 0.0562\n",
+            "Epoch 37/40\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 1.0625 - accuracy: 0.6004 - val_loss: 1.0574 - val_accuracy: 0.5755 - lr: 0.0631\n",
+            "Epoch 38/40\n",
+            "1875/1875 [==============================] - 7s 4ms/step - loss: 1.0342 - accuracy: 0.5869 - val_loss: 1.2885 - val_accuracy: 0.4349 - lr: 0.0708\n",
+            "Epoch 39/40\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 1.1754 - accuracy: 0.5099 - val_loss: 1.4076 - val_accuracy: 0.3361 - lr: 0.0794\n",
+            "Epoch 40/40\n",
+            "1875/1875 [==============================] - 7s 4ms/step - loss: 1.7733 - accuracy: 0.1995 - val_loss: 1.7256 - val_accuracy: 0.1990 - lr: 0.0891\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Hi2YODTwQ1ie",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 476
+        },
+        "outputId": "0a4f582d-4fd9-4042-f075-1b2ff94903b2"
+      },
+      "source": [
+        "# Plot the learning rate decay curve\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "lrs = 1e-3 * (10**(np.arange(40)/20))\n",
+        "plt.semilogx(lrs, find_lr_history.history[\"loss\"]) # want the x-axis to be log-scale\n",
+        "plt.xlabel(\"Learning rate\")\n",
+        "plt.ylabel(\"Loss\")\n",
+        "plt.title(\"Finding the ideal learning rate\");"
+      ],
+      "execution_count": 78,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GqtOjggqWcfS"
+      },
+      "source": [
+        "In this case, it looks like somewhere close to the default learning rate of the [Adam optimizer](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/Adam) (`0.001`) is the ideal learning rate.\n",
+        "\n",
+        "Let's refit a model using the ideal learning rate."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "i9KFhAwKXjWd",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "0f9c2327-5599-45ae-b8fa-70faa4047747"
+      },
+      "source": [
+        "# Set random seed\n",
+        "tf.random.set_seed(42)\n",
+        "\n",
+        "# Create the model\n",
+        "model_14 = tf.keras.Sequential([\n",
+        "  tf.keras.layers.Flatten(input_shape=(28, 28)), # input layer (we had to reshape 28x28 to 784)\n",
+        "  tf.keras.layers.Dense(4, activation=\"relu\"),\n",
+        "  tf.keras.layers.Dense(4, activation=\"relu\"),\n",
+        "  tf.keras.layers.Dense(10, activation=\"softmax\") # output shape is 10, activation is softmax\n",
+        "])\n",
+        "\n",
+        "# Compile the model\n",
+        "model_14.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n",
+        "                 optimizer=tf.keras.optimizers.Adam(learning_rate=0.001), # ideal learning rate (same as default)\n",
+        "                 metrics=[\"accuracy\"])\n",
+        "\n",
+        "# Fit the model\n",
+        "history = model_14.fit(train_data,\n",
+        "                       train_labels,\n",
+        "                       epochs=20,\n",
+        "                       validation_data=(test_data, test_labels))"
+      ],
+      "execution_count": 80,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/20\n",
+            "1875/1875 [==============================] - 7s 3ms/step - loss: 1.2225 - accuracy: 0.5386 - val_loss: 0.7847 - val_accuracy: 0.6985\n",
+            "Epoch 2/20\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.7152 - accuracy: 0.7350 - val_loss: 0.7023 - val_accuracy: 0.7488\n",
+            "Epoch 3/20\n",
+            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.6576 - accuracy: 0.7605 - val_loss: 0.6846 - val_accuracy: 0.7479\n",
+            "Epoch 4/20\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6399 - accuracy: 0.7693 - val_loss: 0.6617 - val_accuracy: 0.7658\n",
+            "Epoch 5/20\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6300 - accuracy: 0.7738 - val_loss: 0.6688 - val_accuracy: 0.7599\n",
+            "Epoch 6/20\n",
+            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.6220 - accuracy: 0.7780 - val_loss: 0.6642 - val_accuracy: 0.7676\n",
+            "Epoch 7/20\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6167 - accuracy: 0.7804 - val_loss: 0.6805 - val_accuracy: 0.7516\n",
+            "Epoch 8/20\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6124 - accuracy: 0.7817 - val_loss: 0.6377 - val_accuracy: 0.7744\n",
+            "Epoch 9/20\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6075 - accuracy: 0.7850 - val_loss: 0.6375 - val_accuracy: 0.7729\n",
+            "Epoch 10/20\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6048 - accuracy: 0.7854 - val_loss: 0.6297 - val_accuracy: 0.7773\n",
+            "Epoch 11/20\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6026 - accuracy: 0.7867 - val_loss: 0.6266 - val_accuracy: 0.7770\n",
+            "Epoch 12/20\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.5981 - accuracy: 0.7869 - val_loss: 0.6245 - val_accuracy: 0.7802\n",
+            "Epoch 13/20\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.5966 - accuracy: 0.7895 - val_loss: 0.6226 - val_accuracy: 0.7807\n",
+            "Epoch 14/20\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.5936 - accuracy: 0.7896 - val_loss: 0.6274 - val_accuracy: 0.7814\n",
+            "Epoch 15/20\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.5910 - accuracy: 0.7907 - val_loss: 0.6416 - val_accuracy: 0.7753\n",
+            "Epoch 16/20\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.5889 - accuracy: 0.7912 - val_loss: 0.6299 - val_accuracy: 0.7783\n",
+            "Epoch 17/20\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.5858 - accuracy: 0.7924 - val_loss: 0.6321 - val_accuracy: 0.7766\n",
+            "Epoch 18/20\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.5852 - accuracy: 0.7926 - val_loss: 0.6176 - val_accuracy: 0.7806\n",
+            "Epoch 19/20\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.5835 - accuracy: 0.7939 - val_loss: 0.6175 - val_accuracy: 0.7819\n",
+            "Epoch 20/20\n",
+            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.5827 - accuracy: 0.7937 - val_loss: 0.6204 - val_accuracy: 0.7810\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1ODWXAlnWqri"
+      },
+      "source": [
+        "Now we've got a model trained with a close-to-ideal learning rate and performing pretty well, we've got a couple of options.\n",
+        "\n",
+        "We could:\n",
+        "* Evaluate its performance using other classification metrics (such as a [confusion matrix](https://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html#sphx-glr-auto-examples-model-selection-plot-confusion-matrix-py) or [classification report](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html)).\n",
+        "* Assess some of its predictions (through visualizations).\n",
+        "* Improve its accuracy (by training it for longer or changing the architecture).\n",
+        "* Save and export it for use in an application.\n",
+        "\n",
+        "Let's go through the first two options.\n",
+        "\n",
+        "First we'll create a classification matrix to visualize its predictions across the different classes."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "jK4zA47sYVp5"
+      },
+      "source": [
+        "# Note: The following confusion matrix code is a remix of Scikit-Learn's\n",
+        "# plot_confusion_matrix function - https://scikit-learn.org/stable/modules/generated/sklearn.metrics.plot_confusion_matrix.html\n",
+        "# and Made with ML's introductory notebook - https://github.com/GokuMohandas/MadeWithML/blob/main/notebooks/08_Neural_Networks.ipynb\n",
+        "import itertools\n",
+        "from sklearn.metrics import confusion_matrix\n",
+        "\n",
+        "# Our function needs a different name to sklearn's plot_confusion_matrix\n",
+        "def make_confusion_matrix(y_true, y_pred, classes=None, figsize=(10, 10), text_size=15):\n",
+        "  \"\"\"Makes a labelled confusion matrix comparing predictions and ground truth labels.\n",
+        "\n",
+        "  If classes is passed, confusion matrix will be labelled, if not, integer class values\n",
+        "  will be used.\n",
+        "\n",
+        "  Args:\n",
+        "    y_true: Array of truth labels (must be same shape as y_pred).\n",
+        "    y_pred: Array of predicted labels (must be same shape as y_true).\n",
+        "    classes: Array of class labels (e.g. string form). If `None`, integer labels are used.\n",
+        "    figsize: Size of output figure (default=(10, 10)).\n",
+        "    text_size: Size of output figure text (default=15).\n",
+        "\n",
+        "  Returns:\n",
+        "    A labelled confusion matrix plot comparing y_true and y_pred.\n",
+        "\n",
+        "  Example usage:\n",
+        "    make_confusion_matrix(y_true=test_labels, # ground truth test labels\n",
+        "                          y_pred=y_preds, # predicted labels\n",
+        "                          classes=class_names, # array of class label names\n",
+        "                          figsize=(15, 15),\n",
+        "                          text_size=10)\n",
+        "  \"\"\"\n",
+        "  # Create the confustion matrix\n",
+        "  cm = confusion_matrix(y_true, y_pred)\n",
+        "  cm_norm = cm.astype(\"float\") / cm.sum(axis=1)[:, np.newaxis] # normalize it\n",
+        "  n_classes = cm.shape[0] # find the number of classes we're dealing with\n",
+        "\n",
+        "  # Plot the figure and make it pretty\n",
+        "  fig, ax = plt.subplots(figsize=figsize)\n",
+        "  cax = ax.matshow(cm, cmap=plt.cm.Blues) # colors will represent how 'correct' a class is, darker == better\n",
+        "  fig.colorbar(cax)\n",
+        "\n",
+        "  # Are there a list of classes?\n",
+        "  if classes:\n",
+        "    labels = classes\n",
+        "  else:\n",
+        "    labels = np.arange(cm.shape[0])\n",
+        "\n",
+        "  # Label the axes\n",
+        "  ax.set(title=\"Confusion Matrix\",\n",
+        "         xlabel=\"Predicted label\",\n",
+        "         ylabel=\"True label\",\n",
+        "         xticks=np.arange(n_classes), # create enough axis slots for each class\n",
+        "         yticks=np.arange(n_classes),\n",
+        "         xticklabels=labels, # axes will labeled with class names (if they exist) or ints\n",
+        "         yticklabels=labels)\n",
+        "\n",
+        "  # Make x-axis labels appear on bottom\n",
+        "  ax.xaxis.set_label_position(\"bottom\")\n",
+        "  ax.xaxis.tick_bottom()\n",
+        "\n",
+        "  # Set the threshold for different colors\n",
+        "  threshold = (cm.max() + cm.min()) / 2.\n",
+        "\n",
+        "  # Plot the text on each cell\n",
+        "  for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
+        "    plt.text(j, i, f\"{cm[i, j]} ({cm_norm[i, j]*100:.1f}%)\",\n",
+        "             horizontalalignment=\"center\",\n",
+        "             color=\"white\" if cm[i, j] > threshold else \"black\",\n",
+        "             size=text_size)"
+      ],
+      "execution_count": 81,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Mi5kNRyZdyXA"
+      },
+      "source": [
+        "Since a confusion matrix compares the truth labels (`test_labels`) to the predicted labels, we have to make some predictions with our model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "HhxrXhrjbjja",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "ed24aa60-cbbf-44c7-8c6a-ae1f4ec1dd93"
+      },
+      "source": [
+        "# Make predictions with the most recent model\n",
+        "y_probs = model_14.predict(test_data) # \"probs\" is short for probabilities\n",
+        "\n",
+        "# View the first 5 predictions\n",
+        "y_probs[:5]"
+      ],
+      "execution_count": 82,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "313/313 [==============================] - 1s 2ms/step\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[4.5568560e-10, 1.0907908e-05, 1.9394951e-08, 2.0988197e-11,\n",
+              "        2.1145368e-09, 7.0024326e-02, 1.9955612e-08, 5.0963294e-02,\n",
+              "        1.0871998e-02, 8.6812937e-01],\n",
+              "       [3.3946631e-03, 3.5793112e-11, 8.3423096e-01, 3.0406554e-07,\n",
+              "        2.3693247e-02, 5.4171649e-18, 1.3868028e-01, 8.1107011e-30,\n",
+              "        4.5025439e-07, 1.7031652e-23],\n",
+              "       [2.0717634e-07, 9.9145997e-01, 4.7067328e-09, 9.6909743e-04,\n",
+              "        2.0993249e-04, 2.1813226e-10, 3.5861326e-06, 2.1796662e-11,\n",
+              "        7.3572104e-03, 4.8611396e-11],\n",
+              "       [8.2321691e-08, 9.9750876e-01, 3.5338010e-10, 1.1940660e-03,\n",
+              "        6.5136792e-06, 2.6947168e-08, 3.4165095e-07, 3.6558907e-07,\n",
+              "        1.2897628e-03, 7.8690885e-09],\n",
+              "       [1.4683051e-01, 9.2204486e-05, 2.8106070e-01, 1.0214634e-02,\n",
+              "        6.7037486e-02, 3.0119985e-08, 4.9274877e-01, 2.6306903e-13,\n",
+              "        2.0157008e-03, 5.4716119e-12]], dtype=float32)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 82
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "eP5zslXbf5ZY"
+      },
+      "source": [
+        "Our model outputs a list of **prediction probabilities**, meaning, it outputs a number for how likely it thinks a particular class is to be the label.\n",
+        "\n",
+        "The higher the number in the prediction probabilities list, the more likely the model believes that is the right class.\n",
+        "\n",
+        "To find the highest value we can use the [`argmax()`](https://numpy.org/doc/stable/reference/generated/numpy.argmax.html) method."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "hQvrJbbWf4Es",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "01463c1d-4944-4165-b8d9-a6c1a3cb6404"
+      },
+      "source": [
+        "# See the predicted class number and label for the first example\n",
+        "y_probs[0].argmax(), class_names[y_probs[0].argmax()]"
+      ],
+      "execution_count": 83,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(9, 'Ankle boot')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 83
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BFPgnBCghrTz"
+      },
+      "source": [
+        "Now let's do the same for all of the predictions."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "ozUpeZU6g2An",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "1f5cdee2-a436-4e69-ad46-84836e112502"
+      },
+      "source": [
+        "# Convert all of the predictions from probabilities to labels\n",
+        "y_preds = y_probs.argmax(axis=1)\n",
+        "\n",
+        "# View the first 10 prediction labels\n",
+        "y_preds[:10]"
+      ],
+      "execution_count": 84,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([9, 2, 1, 1, 6, 1, 4, 6, 5, 7])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 84
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "47g1wNk6iYTd"
+      },
+      "source": [
+        "Wonderful, now we've got our model's predictions in label form, let's create a confusion matrix to view them against the truth labels."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "FBMSVSRqcU_m",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "e3b64122-00d1-4b3f-f2e3-d93c9a1b4bac"
+      },
+      "source": [
+        "# Check out the non-prettified confusion matrix\n",
+        "from sklearn.metrics import confusion_matrix\n",
+        "confusion_matrix(y_true=test_labels,\n",
+        "                 y_pred=y_preds)"
+      ],
+      "execution_count": 85,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[696,   2,  26,  95,  11,   1, 155,   1,  13,   0],\n",
+              "       [  3, 927,   7,  36,   5,   0,   4,   4,  14,   0],\n",
+              "       [ 19,   1, 653,   8, 200,   0,  96,   0,  23,   0],\n",
+              "       [ 52,  23,   4, 784,  36,   0,  82,   3,  16,   0],\n",
+              "       [  0,   1, 117,  18, 764,   0,  76,   0,  24,   0],\n",
+              "       [  0,   0,   0,   0,   0, 915,   0,  38,  10,  37],\n",
+              "       [154,   1, 174,  43, 227,   2, 366,   2,  31,   0],\n",
+              "       [  0,   0,   0,   0,   0,  47,   0, 870,   1,  82],\n",
+              "       [  1,   6,  18,  15,  31,   7,  13,   3, 903,   3],\n",
+              "       [  0,   0,   1,   0,   0,  28,   0,  32,   7, 932]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 85
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ql-TQxuYiqPl"
+      },
+      "source": [
+        "That confusion matrix is hard to comprehend, let's make it prettier using the function we created before."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "DLr6daZAbzRi",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "outputId": "d4f3a189-3744-4d94-a8be-c34d3bd82424"
+      },
+      "source": [
+        "# Make a prettier confusion matrix\n",
+        "make_confusion_matrix(y_true=test_labels,\n",
+        "                      y_pred=y_preds,\n",
+        "                      classes=class_names,\n",
+        "                      figsize=(15, 15),\n",
+        "                      text_size=10)"
+      ],
+      "execution_count": 86,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1500x1500 with 2 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tH3FhuGEjTu9"
+      },
+      "source": [
+        "That looks much better! (one of my favourites sights in the world is a confusion matrix with dark squares down the diagonal)\n",
+        "\n",
+        "Except the results aren't as good as they could be...\n",
+        "\n",
+        "It looks like our model is getting confused between the `Shirt` and `T-shirt/top` classes (e.g. predicting `Shirt` when it's actually a `T-shirt/top`).\n",
+        "\n",
+        "> 🤔 **Question:** Does it make sense that our model is getting confused between the `Shirt` and `T-shirt/top` classes? Why do you think this might be? What's one way you could investigate?\n",
+        "\n",
+        "We've seen how our models predictions line up to the truth labels using a confusion matrix, but how about we visualize some?\n",
+        "\n",
+        "Let's create a function to plot a random image along with its prediction.\n",
+        "\n",
+        "> 🔑 **Note:** Often when working with images and other forms of visual data, it's a good idea to visualize as much as possible to develop a further understanding of the data and the outputs of your model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "2XIuAjgJri9e"
+      },
+      "source": [
+        "import random\n",
+        "\n",
+        "# Create a function for plotting a random image along with its prediction\n",
+        "def plot_random_image(model, images, true_labels, classes):\n",
+        "  \"\"\"Picks a random image, plots it and labels it with a predicted and truth label.\n",
+        "\n",
+        "  Args:\n",
+        "    model: a trained model (trained on data similar to what's in images).\n",
+        "    images: a set of random images (in tensor form).\n",
+        "    true_labels: array of ground truth labels for images.\n",
+        "    classes: array of class names for images.\n",
+        "\n",
+        "  Returns:\n",
+        "    A plot of a random image from `images` with a predicted class label from `model`\n",
+        "    as well as the truth class label from `true_labels`.\n",
+        "  \"\"\"\n",
+        "  # Setup random integer\n",
+        "  i = random.randint(0, len(images))\n",
+        "\n",
+        "  # Create predictions and targets\n",
+        "  target_image = images[i]\n",
+        "  pred_probs = model.predict(target_image.reshape(1, 28, 28)) # have to reshape to get into right size for model\n",
+        "  pred_label = classes[pred_probs.argmax()]\n",
+        "  true_label = classes[true_labels[i]]\n",
+        "\n",
+        "  # Plot the target image\n",
+        "  plt.imshow(target_image, cmap=plt.cm.binary)\n",
+        "\n",
+        "  # Change the color of the titles depending on if the prediction is right or wrong\n",
+        "  if pred_label == true_label:\n",
+        "    color = \"green\"\n",
+        "  else:\n",
+        "    color = \"red\"\n",
+        "\n",
+        "  # Add xlabel information (prediction/true label)\n",
+        "  plt.xlabel(\"Pred: {} {:2.0f}% (True: {})\".format(pred_label,\n",
+        "                                                   100*tf.reduce_max(pred_probs),\n",
+        "                                                   true_label),\n",
+        "             color=color) # set the color to green or red"
+      ],
+      "execution_count": 87,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "RAAIrpcEumyE",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 466
+        },
+        "outputId": "0efbd757-ab4a-41d6-d548-f9ad1f9613d3"
+      },
+      "source": [
+        "# Check out a random image as well as its prediction\n",
+        "plot_random_image(model=model_14,\n",
+        "                  images=test_data,\n",
+        "                  true_labels=test_labels,\n",
+        "                  classes=class_names)"
+      ],
+      "execution_count": 88,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "1/1 [==============================] - 0s 35ms/step\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FNebpPbCw52S"
+      },
+      "source": [
+        "After running the cell above a few times you'll start to get a visual understanding of the relationship between the model's predictions and the true labels.\n",
+        "\n",
+        "Did you figure out which predictions the model gets confused on?\n",
+        "\n",
+        "It seems to mix up classes which are similar, for example, `Sneaker` with `Ankle boot`.\n",
+        "\n",
+        "Looking at the images, you can see how this might be the case.\n",
+        "\n",
+        "The overall shape of a `Sneaker` and an `Ankle Boot` are similar.\n",
+        "\n",
+        "The overall shape might be one of the patterns the model has learned and so therefore when two images have a similar shape, their predictions get mixed up."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pOwM1rqhx6p3"
+      },
+      "source": [
+        "### What patterns is our model learning?\n",
+        "\n",
+        "We've been talking a lot about how a neural network finds patterns in numbers, but what exactly do these patterns look like?\n",
+        "\n",
+        "Let's crack open one of our models and find out.\n",
+        "\n",
+        "First, we'll get a list of layers in our most recent model (`model_14`) using the `layers` attribute."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "kcwMsgFuySTi",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "8c11bcec-833b-4766-c752-119ce6f91c2f"
+      },
+      "source": [
+        "# Find the layers of our most recent model\n",
+        "model_14.layers"
+      ],
+      "execution_count": 89,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[<keras.src.layers.reshaping.flatten.Flatten at 0x792a0895d0c0>,\n",
+              " <keras.src.layers.core.dense.Dense at 0x792a0de60a00>,\n",
+              " <keras.src.layers.core.dense.Dense at 0x792a0871b820>,\n",
+              " <keras.src.layers.core.dense.Dense at 0x792a0871bfd0>]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 89
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "w9si0o-h4oO1"
+      },
+      "source": [
+        "We can access a target layer using indexing."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "DXuQmsNX1mGR",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "b535751c-0d9b-4ba5-8c79-9b473460ec9d"
+      },
+      "source": [
+        "# Extract a particular layer\n",
+        "model_14.layers[1]"
+      ],
+      "execution_count": 90,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<keras.src.layers.core.dense.Dense at 0x792a0de60a00>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 90
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "W6Cftaib4uG-"
+      },
+      "source": [
+        "And we can find the patterns learned by a particular layer using the `get_weights()` method.\n",
+        "\n",
+        "The `get_weights()` method returns the **weights** (also known as a weights matrix) and biases (also known as a bias vector) of a particular layer."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "WdmZy5xi1srE",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "cda4f779-f78f-409f-b15b-6dc3fc1d70fe"
+      },
+      "source": [
+        "# Get the patterns of a layer in our network\n",
+        "weights, biases = model_14.layers[1].get_weights()\n",
+        "\n",
+        "# Shape = 1 weight matrix the size of our input data (28x28) per neuron (4)\n",
+        "weights, weights.shape"
+      ],
+      "execution_count": 91,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(array([[-0.701493  , -0.16068847, -0.05872348,  0.35484928],\n",
+              "        [-0.54165834,  0.35157567, -0.01895694,  0.56460387],\n",
+              "        [-1.4538393 ,  1.3433346 , -0.08607116, -0.41790506],\n",
+              "        ...,\n",
+              "        [-0.15071222,  0.04597449, -0.00466771, -0.7744258 ],\n",
+              "        [ 0.00729757, -0.02381918, -0.07769205, -0.417886  ],\n",
+              "        [-0.30387923,  0.33747375,  0.03100066, -0.4084278 ]],\n",
+              "       dtype=float32),\n",
+              " (784, 4))"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 91
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yCjuNSD73oG0"
+      },
+      "source": [
+        "The weights matrix is the same shape as the input data, which in our case is 784 (28x28 pixels). And there's a copy of the weights matrix for each neuron the in the selected layer (our selected layer has 4 neurons).\n",
+        "\n",
+        "Each value in the weights matrix corresponds to how a particular value in the input data influences the network's decisions.\n",
+        "\n",
+        "These values start out as random numbers (they're set by the [`kernel_initializer` parameter](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dense) when creating a layer, the default is [`\"glorot_uniform\"`](https://www.tensorflow.org/api_docs/python/tf/keras/initializers/GlorotUniform)) and are then updated to better representative values of the data (non-random) by the neural network during training.\n",
+        "\n",
+        "![neural network supervised learning weight updates](https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/images/02-fashion-mnist-learning.png)\n",
+        "*Example workflow of how a supervised neural network starts with random weights and updates them to better represent the data by looking at examples of ideal outputs.*\n",
+        "\n",
+        "Now let's check out the bias vector."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "ndG-h2yz1z2_",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "73fb4b45-2430-4087-d2c6-8b8063ad8f53"
+      },
+      "source": [
+        "# Shape = 1 bias per neuron (we use 4 neurons in the first layer)\n",
+        "biases, biases.shape"
+      ],
+      "execution_count": 92,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(array([-0.16110927,  0.28525245, -0.02173193,  0.7139954 ], dtype=float32),\n",
+              " (4,))"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 92
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3JRQFh3U374U"
+      },
+      "source": [
+        "Every neuron has a bias vector. Each of these is paired with a weight matrix.\n",
+        "\n",
+        "The bias values get initialized as zeroes by default (using the [`bias_initializer` parameter](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dense)).\n",
+        "\n",
+        "The bias vector dictates how much the patterns within the corresponding weights matrix should influence the next layer."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "_QCUb7GeSGYF",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "5df46a49-d352-46bc-80d6-b96a36e0ce17"
+      },
+      "source": [
+        "# Can now calculate the number of paramters in our model\n",
+        "model_14.summary()"
+      ],
+      "execution_count": 93,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Model: \"sequential_15\"\n",
+            "_________________________________________________________________\n",
+            " Layer (type)                Output Shape              Param #   \n",
+            "=================================================================\n",
+            " flatten_4 (Flatten)         (None, 784)               0         \n",
+            "                                                                 \n",
+            " dense_40 (Dense)            (None, 4)                 3140      \n",
+            "                                                                 \n",
+            " dense_41 (Dense)            (None, 4)                 20        \n",
+            "                                                                 \n",
+            " dense_42 (Dense)            (None, 10)                50        \n",
+            "                                                                 \n",
+            "=================================================================\n",
+            "Total params: 3210 (12.54 KB)\n",
+            "Trainable params: 3210 (12.54 KB)\n",
+            "Non-trainable params: 0 (0.00 Byte)\n",
+            "_________________________________________________________________\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "trRRZLIRyLXe"
+      },
+      "source": [
+        "Now we've built a few deep learning models, it's a good time to point out the whole concept of inputs and outputs not only relates to a model as a whole but to *every* layer within a model.\n",
+        "\n",
+        "You might've already guessed this, but starting from the input layer, each subsequent layer's input is the output of the previous layer.\n",
+        "\n",
+        "We can see this clearly using the utility [`plot_model()`](https://www.tensorflow.org/api_docs/python/tf/keras/utils/plot_model)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "YJD0GqGl3NY0",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 533
+        },
+        "outputId": "3dc9e1ec-1341-4fc6-acc2-c467a3bff76d"
+      },
+      "source": [
+        "from tensorflow.keras.utils import plot_model\n",
+        "\n",
+        "# See the inputs and outputs of each layer\n",
+        "plot_model(model_14, show_shapes=True)"
+      ],
+      "execution_count": 94,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<IPython.core.display.Image object>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 94
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OY5HO72ATJR4"
+      },
+      "source": [
+        "## How a model learns (in brief)\n",
+        "\n",
+        "Alright, we've trained a bunch of models, but we've never really discussed what's going on under the hood. So how exactly does a model learn?\n",
+        "\n",
+        "A model learns by updating and improving its weight matrices and biases values every epoch (in our case, when we call the `fit()` fucntion).\n",
+        "\n",
+        "It does so by comparing the patterns its learned between the data and labels to the actual labels.\n",
+        "\n",
+        "If the current patterns (weight matrices and bias values) don't result in a desirable decrease in the loss function (higher loss means worse predictions), the optimizer tries to steer the model to update its patterns in the right way (using the real labels as a reference).\n",
+        "\n",
+        "This process of using the real labels as a reference to improve the model's predictions is called [**backpropagation**](https://en.wikipedia.org/wiki/Backpropagation).\n",
+        "\n",
+        "In other words, data and labels pass through a model (**forward pass**) and it attempts to learn the relationship between the data and labels.\n",
+        "\n",
+        "And if this learned relationship isn't close to the actual relationship or it could be improved, the model does so by going back through itself (**backward pass**) and tweaking its weights matrices and bias values to better represent the data.\n",
+        "\n",
+        "If all of this sounds confusing (and it's fine if it does, the above is a very succinct description), check out the resources in the extra-curriculum section for more."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "LXmMG6fcpoIp"
+      },
+      "source": [
+        "## Exercises 🛠\n",
+        "\n",
+        "1. Play with neural networks in the [TensorFlow Playground](https://playground.tensorflow.org/) for 10-minutes. Especially try different values of the learning, what happens when you decrease it? What happens when you increase it?\n",
+        "2. Replicate the model pictured in the [TensorFlow Playground diagram](https://playground.tensorflow.org/#activation=relu&batchSize=10&dataset=circle&regDataset=reg-plane&learningRate=0.001&regularizationRate=0&noise=0&networkShape=6,6,6,6,6&seed=0.51287&showTestData=false&discretize=false&percTrainData=50&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification&initZero=false&hideText=false&regularization_hide=true&discretize_hide=true&regularizationRate_hide=true&percTrainData_hide=true&dataset_hide=true&problem_hide=true&noise_hide=true&batchSize_hide=true) below using TensorFlow code. Compile it using the Adam optimizer, binary crossentropy loss and accuracy metric. Once it's compiled check a summary of the model.\n",
+        "![tensorflow playground example neural network](https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/images/02-tensorflow-playground-replication-exercise.png)\n",
+        "*Try this network out for yourself on the [TensorFlow Playground website](https://playground.tensorflow.org/#activation=relu&batchSize=10&dataset=circle&regDataset=reg-plane&learningRate=0.001&regularizationRate=0&noise=0&networkShape=6,6,6,6,6&seed=0.51287&showTestData=false&discretize=false&percTrainData=50&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification&initZero=false&hideText=false&regularization_hide=true&discretize_hide=true&regularizationRate_hide=true&percTrainData_hide=true&dataset_hide=true&problem_hide=true&noise_hide=true&batchSize_hide=true). Hint: there are 5 hidden layers but the output layer isn't pictured, you'll have to decide what the output layer should be based on the input data.*\n",
+        "3. Create a classification dataset using Scikit-Learn's [`make_moons()`](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_moons.html) function, visualize it and then build a model to fit it at over 85% accuracy.\n",
+        "4. Create a function (or write code) to visualize multiple image predictions for the fashion MNIST at the same time. Plot at least three different images and their prediciton labels at the same time. Hint: see the [classifcation tutorial in the TensorFlow documentation](https://www.tensorflow.org/tutorials/keras/classification) for ideas.\n",
+        "5. Recreate [TensorFlow's](https://www.tensorflow.org/api_docs/python/tf/keras/activations/softmax) [softmax activation function](https://en.wikipedia.org/wiki/Softmax_function) in your own code. Make sure it can accept a tensor and return that tensor after having the softmax function applied to it.\n",
+        "6. Train a model to get 88%+ accuracy on the fashion MNIST test set. Plot a confusion matrix to see the results after.\n",
+        "7. Make a function to show an image of a certain class of the fashion MNIST dataset and make a prediction on it. For example, plot 3 images of the `T-shirt` class with their predictions.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "oksgPs-meGHj"
+      },
+      "source": [
+        "## Extra curriculum 📖\n",
+        "* Watch 3Blue1Brown's neural networks video 2: [*Gradient descent, how neural networks learn*](https://www.youtube.com/watch?v=IHZwWFHWa-w). After you're done, write 100 words about what you've learned.\n",
+        "  * If you haven't already, watch video 1: [*But what is a Neural Network?*](https://youtu.be/aircAruvnKk). Note the activation function they talk about at the end.\n",
+        "* Watch [MIT's introduction to deep learning lecture 1](https://youtu.be/njKP3FqW3Sk) (if you haven't already) to get an idea of the concepts behind using linear and non-linear functions.\n",
+        "* Spend 1-hour reading [Michael Nielsen's Neural Networks and Deep Learning book](http://neuralnetworksanddeeplearning.com/index.html).\n",
+        "* Read the [ML-Glossary documentation on activation functions](https://ml-cheatsheet.readthedocs.io/en/latest/activation_functions.html). Which one is your favourite?\n",
+        "  * After you've read the ML-Glossary, see which activation functions are available in TensorFlow by searching \"tensorflow activation functions\"."
+      ]
+    }
+  ]
+}
\ No newline at end of file