diff --git a/Ex_1_Tabular_Classification.ipynb b/Ex_1_Tabular_Classification.ipynb
deleted file mode 100644
index 9a966b6..0000000
--- a/Ex_1_Tabular_Classification.ipynb
+++ /dev/null
@@ -1,1402 +0,0 @@
-{
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "provenance": [],
-      "gpuType": "T4"
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    },
-    "accelerator": "GPU"
-  },
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# 1. Initializations and Dataset Download"
-      ],
-      "metadata": {
-        "id": "x15poca7jW-Z"
-      }
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 1,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "VttBdozUd2PL",
-        "outputId": "d490c112-7d71-453d-bece-1f288028a2fd"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Please provide your Kaggle credentials to download this dataset. Learn more: http://bit.ly/kaggle-creds\n",
-            "Your Kaggle username: omaratef3221\n",
-            "Your Kaggle Key: ··········\n",
-            "Downloading rice-type-classification.zip to ./rice-type-classification\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "100%|██████████| 888k/888k [00:00<00:00, 965kB/s]"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "\n"
-          ]
-        }
-      ],
-      "source": [
-        "!pip install opendatasets --quiet\n",
-        "import opendatasets as od\n",
-        "od.download(\"https://www.kaggle.com/datasets/mssmartypants/rice-type-classification\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# 2. Imports\n",
-        "Lets start by getting all our imports, keep in mind that PyTorch is not automatically detects and trains on GPU, you have to tell it to use cuda. In case you want to train on Mac Silicon replace cuda with mps."
-      ],
-      "metadata": {
-        "id": "l7fKCtFQjakg"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import torch # Torch main framework\n",
-        "import torch.nn as nn # Used for getting the NN Layers\n",
-        "from torch.optim import Adam # Adam Optimizer\n",
-        "from torch.utils.data import Dataset, DataLoader # Dataset class and DataLoader for creatning the objects\n",
-        "from torchsummary import summary # Visualize the model layers and number of parameters\n",
-        "from sklearn.model_selection import train_test_split # Split the dataset (train, validation, test)\n",
-        "from sklearn.metrics import accuracy_score # Calculate the testing Accuracy\n",
-        "import matplotlib.pyplot as plt # Plotting the training progress at the end\n",
-        "import pandas as pd # Data reading and preprocessing\n",
-        "import numpy as np # Mathematical operations\n",
-        "\n",
-        "device = 'cuda' if torch.cuda.is_available() else 'cpu' # detect the GPU if any, if not use CPU, change cuda to mps if you have a mac"
-      ],
-      "metadata": {
-        "id": "IgTlDLBpeqcq"
-      },
-      "execution_count": 2,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# 3. Dataset\n",
-        "Now lets read the dataset, this lab was developed by Google Colab, so dataset downloaded and read from the path shown below. We will be reading the dataset using pandas `read_csv` function, then we will remove the nulls/missing data from our dataframe as a filteration process, keep in mind that this process is essential as missing data will stop the code from training. You can skip dropping the missing values if you are 100% sure that there are no missing values in your data. Also, we dropped the `id` column because it will not affect our classification at all. We printed the output possibilities as we can see its a binary classification. We printed also the data shape (rows, columns) After that we printed the dataset shape and we used the `head()` function to visualize the first 5 rows, this step is optional as it just allows us to see the first 5 rows and will not affect the training process."
-      ],
-      "metadata": {
-        "id": "o4nmUYiVjfMm"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "data_df = pd.read_csv(\"/content/rice-type-classification/riceClassification.csv\") # Read the data\n",
-        "data_df.dropna(inplace = True) # Drop missing/null values\n",
-        "data_df.drop([\"id\"], axis =1, inplace = True) # Drop Id column\n",
-        "print(\"Output possibilities: \", data_df[\"Class\"].unique()) # Possible Outputs\n",
-        "print(\"Data Shape (rows, cols): \", data_df.shape) # Print data shape\n",
-        "data_df.head() # Print/visualize the first 5 rows of the data"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 242
-        },
-        "id": "_ZokM1pVfAGN",
-        "outputId": "5a19489f-db59-4a5f-9d5c-f1456d7e2cf3"
-      },
-      "execution_count": 3,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Output possibilities:  [1 0]\n",
-            "Data Shape (rows, cols):  (18185, 11)\n"
-          ]
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
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-              "0  4537        92.229316        64.012769      0.719916        4677   \n",
-              "1  2872        74.691881        51.400454      0.725553        3015   \n",
-              "2  3048        76.293164        52.043491      0.731211        3132   \n",
-              "3  3073        77.033628        51.928487      0.738639        3157   \n",
-              "4  3693        85.124785        56.374021      0.749282        3802   \n",
-              "\n",
-              "   EquivDiameter    Extent  Perimeter  Roundness  AspectRation  Class  \n",
-              "0      76.004525  0.657536    273.085   0.764510      1.440796      1  \n",
-              "1      60.471018  0.713009    208.317   0.831658      1.453137      1  \n",
-              "2      62.296341  0.759153    210.012   0.868434      1.465950      1  \n",
-              "3      62.551300  0.783529    210.657   0.870203      1.483456      1  \n",
-              "4      68.571668  0.769375    230.332   0.874743      1.510000      1  "
-            ],
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-              "      <th></th>\n",
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-              "      <td>210.012</td>\n",
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-              "      <td>1.483456</td>\n",
-              "      <td>1</td>\n",
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-              "      <td>0.749282</td>\n",
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-              "      <td>230.332</td>\n",
-              "      <td>0.874743</td>\n",
-              "      <td>1.510000</td>\n",
-              "      <td>1</td>\n",
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-              "      buttonEl.style.display =\n",
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-              "        const element = document.querySelector('#df-18fb680a-57db-4df4-920e-b09a7a4547d5');\n",
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-              "          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",
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-          "metadata": {},
-          "execution_count": 3
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-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# 4. Data Preprocessing\n",
-        "Now as you saw previously, data values are so big which may cause bad results. Its a crucial steps to normalize the dataset before we proceed. Lets normalize the dataset in the cell below."
-      ],
-      "metadata": {
-        "id": "VMzuXfJnbBpr"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "original_df = data_df.copy() # Creating a copy of the original Dataframe to use to normalize inference\n",
-        "\n",
-        "for column in data_df.columns:\n",
-        "    data_df[column] = data_df[column]/data_df[column].abs().max() # Divide by the maximum of the column which will make max value of each column is 1\n",
-        "data_df.head()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 206
-        },
-        "id": "waDVfvecMiGK",
-        "outputId": "b43f8861-309f-4196-f094-4499e7bddaef"
-      },
-      "execution_count": 4,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "       Area  MajorAxisLength  MinorAxisLength  Eccentricity  ConvexArea  \\\n",
-              "0  0.444368         0.503404         0.775435      0.744658    0.424873   \n",
-              "1  0.281293         0.407681         0.622653      0.750489    0.273892   \n",
-              "2  0.298531         0.416421         0.630442      0.756341    0.284520   \n",
-              "3  0.300979         0.420463         0.629049      0.764024    0.286791   \n",
-              "4  0.361704         0.464626         0.682901      0.775033    0.345385   \n",
-              "\n",
-              "   EquivDiameter    Extent  Perimeter  Roundness  AspectRation  Class  \n",
-              "0       0.666610  0.741661   0.537029   0.844997      0.368316    1.0  \n",
-              "1       0.530370  0.804230   0.409661   0.919215      0.371471    1.0  \n",
-              "2       0.546380  0.856278   0.412994   0.959862      0.374747    1.0  \n",
-              "3       0.548616  0.883772   0.414262   0.961818      0.379222    1.0  \n",
-              "4       0.601418  0.867808   0.452954   0.966836      0.386007    1.0  "
-            ],
-            "text/html": [
-              "\n",
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-              "    <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",
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-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>Area</th>\n",
-              "      <th>MajorAxisLength</th>\n",
-              "      <th>MinorAxisLength</th>\n",
-              "      <th>Eccentricity</th>\n",
-              "      <th>ConvexArea</th>\n",
-              "      <th>EquivDiameter</th>\n",
-              "      <th>Extent</th>\n",
-              "      <th>Perimeter</th>\n",
-              "      <th>Roundness</th>\n",
-              "      <th>AspectRation</th>\n",
-              "      <th>Class</th>\n",
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-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>0.444368</td>\n",
-              "      <td>0.503404</td>\n",
-              "      <td>0.775435</td>\n",
-              "      <td>0.744658</td>\n",
-              "      <td>0.424873</td>\n",
-              "      <td>0.666610</td>\n",
-              "      <td>0.741661</td>\n",
-              "      <td>0.537029</td>\n",
-              "      <td>0.844997</td>\n",
-              "      <td>0.368316</td>\n",
-              "      <td>1.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>0.281293</td>\n",
-              "      <td>0.407681</td>\n",
-              "      <td>0.622653</td>\n",
-              "      <td>0.750489</td>\n",
-              "      <td>0.273892</td>\n",
-              "      <td>0.530370</td>\n",
-              "      <td>0.804230</td>\n",
-              "      <td>0.409661</td>\n",
-              "      <td>0.919215</td>\n",
-              "      <td>0.371471</td>\n",
-              "      <td>1.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>0.298531</td>\n",
-              "      <td>0.416421</td>\n",
-              "      <td>0.630442</td>\n",
-              "      <td>0.756341</td>\n",
-              "      <td>0.284520</td>\n",
-              "      <td>0.546380</td>\n",
-              "      <td>0.856278</td>\n",
-              "      <td>0.412994</td>\n",
-              "      <td>0.959862</td>\n",
-              "      <td>0.374747</td>\n",
-              "      <td>1.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>0.300979</td>\n",
-              "      <td>0.420463</td>\n",
-              "      <td>0.629049</td>\n",
-              "      <td>0.764024</td>\n",
-              "      <td>0.286791</td>\n",
-              "      <td>0.548616</td>\n",
-              "      <td>0.883772</td>\n",
-              "      <td>0.414262</td>\n",
-              "      <td>0.961818</td>\n",
-              "      <td>0.379222</td>\n",
-              "      <td>1.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>0.361704</td>\n",
-              "      <td>0.464626</td>\n",
-              "      <td>0.682901</td>\n",
-              "      <td>0.775033</td>\n",
-              "      <td>0.345385</td>\n",
-              "      <td>0.601418</td>\n",
-              "      <td>0.867808</td>\n",
-              "      <td>0.452954</td>\n",
-              "      <td>0.966836</td>\n",
-              "      <td>0.386007</td>\n",
-              "      <td>1.0</td>\n",
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-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
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-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
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-              "  </style>\n",
-              "\n",
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-              "      const buttonEl =\n",
-              "        document.querySelector('#df-8b9d42cf-ecad-459e-9194-1b3d443c4f26 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-8b9d42cf-ecad-459e-9194-1b3d443c4f26');\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",
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-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
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-              "            title=\"Suggest charts\"\n",
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-              "\n",
-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
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-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
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-              "  [theme=dark] .colab-df-quickchart {\n",
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-              "    fill: var(--button-hover-fill-color);\n",
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-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
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-              "    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",
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-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
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-              "      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",
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-              "    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",
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-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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-              "  </script>\n",
-              "</div>\n",
-              "\n",
-              "    </div>\n",
-              "  </div>\n"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 4
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# 5. Data Splitting\n",
-        "We will detect the inputs and the outputs of the data which are X and Y respectively.\n",
-        "\n",
-        "Then we will split our data into the following:\n",
-        "\n",
-        "* Training Size 70%\n",
-        "* Validation Size 15%\n",
-        "* Testing Size 15%\n",
-        "\n",
-        "We will do this by splitting our data twice using the train_test_split function in sklearn the function takes inputs, outputs and the testing size. After that we will print the training, validation and testing shapes and sizes. Then we will print the new shapes of our data."
-      ],
-      "metadata": {
-        "id": "jE9tvEBqmL5E"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "X = np.array(data_df.iloc[:,:-1]) # Get the inputs, all rows and all columns except last column (output)\n",
-        "Y = np.array(data_df.iloc[:, -1]) # Get the ouputs, all rows and last column only (output column)\n",
-        "\n",
-        "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size = 0.3) # Create the training split\n",
-        "X_test, X_val, y_test, y_val = train_test_split(X_test, y_test, test_size = 0.5) # Create the validation split\n",
-        "\n",
-        "print(\"Training set is: \", X_train.shape[0], \" rows which is \", round(X_train.shape[0]/data_df.shape[0],4)*100, \"%\") # Print training shape\n",
-        "print(\"Validation set is: \",X_val.shape[0], \" rows which is \", round(X_val.shape[0]/data_df.shape[0],4)*100, \"%\") # Print validation shape\n",
-        "print(\"Testing set is: \",X_test.shape[0], \" rows which is \", round(X_test.shape[0]/data_df.shape[0],4)*100, \"%\") # Print testing shape"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "04A0-y-BfUoX",
-        "outputId": "074b7d49-2e3c-4e27-fed8-be10a4dfeba4"
-      },
-      "execution_count": 5,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Training set is:  12729  rows which is  70.0 %\n",
-            "Validation set is:  2728  rows which is  15.0 %\n",
-            "Testing set is:  2728  rows which is  15.0 %\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# 6. Dataset Object\n",
-        "Now we will create the dataset object. This part is not complex but its a bit tricky. We need to convert our dataset to PyTorch Dataset object as it will be more efficient during training, you can use the dataset as its, but lets keep things professional and efficient. First we define our class that will be taking the main Dataset class with the concept of inheritance. Let's make the concept simpler. There is a big class that PyTorch, this class has several functions inside it, we will recreate that class and modify some functions to match our needs.\n",
-        "\n",
-        "In the cell below, we rebuilt the constructor function which is `__init__`. We put X and Y as a parameters to this function which are the inputs and outputs respectively, then inside the function we define the inputs and converting it to tensors, then converting the outputs to tensors and we make the numbers as a `float32` Additionally, we moved all our data to cuda device. Then we modified the `__len__` and the `__getitem__` to match our needs which gets the specific length/shape of the data, and the data of specific row in our data respectively."
-      ],
-      "metadata": {
-        "id": "1qABq32QnQ6-"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "class dataset(Dataset):\n",
-        "    def __init__(self, X, Y):\n",
-        "        self.X = torch.tensor(X, dtype = torch.float32).to(device)\n",
-        "        self.Y = torch.tensor(Y, dtype = torch.float32).to(device)\n",
-        "\n",
-        "    def __len__(self):\n",
-        "        return len(self.X)\n",
-        "    def __getitem__(self, index):\n",
-        "        return self.X[index], self.Y[index]\n",
-        "\n",
-        "training_data = dataset(X_train, y_train)\n",
-        "validation_data = dataset(X_val, y_val)\n",
-        "testing_data = dataset(X_test, y_test)"
-      ],
-      "metadata": {
-        "id": "iju76hoWfaHe"
-      },
-      "execution_count": 6,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# 7. Training Hyper Parameters\n",
-        "Now we are setting the training hyperparameters, we defined some variables which are the batch size, number of training epochs, Hidden Neurons and learning rate."
-      ],
-      "metadata": {
-        "id": "6rm0laL0n34L"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "BATCH_SIZE = 32\n",
-        "EPOCHS = 10\n",
-        "HIDDEN_NEURONS = 10\n",
-        "LR = 1e-3"
-      ],
-      "metadata": {
-        "id": "8C6bsRjEg08S"
-      },
-      "execution_count": 7,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# 8. Data Loaders\n",
-        "This concept may seem complicated, but its very easy, PyTorch provides a class called DataLoader which allows us to create objects of it to simplify the training.\n",
-        "\n",
-        "Dataloader is an object that we can loop through it to train according to batches. When we start training, we loop through epochs, if you skip the batch size it means that the amount of training data in one batch is equal to the complete amount of training data, this method is not efficient and in most of the cases you need to train through using batches. Dataloader allows you to loop through the batches easily during the training. When you create a dataloader. You define the batch size and enable the shuffle to randomize the data and then you can loop through it in each epoch to train normally.\n",
-        "\n",
-        "In the next cell, we defined a dataloader for each of our data (training, validation and testing)."
-      ],
-      "metadata": {
-        "id": "PoeZE7D2oCe9"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "train_dataloader = DataLoader(training_data, batch_size=BATCH_SIZE, shuffle= True)\n",
-        "validation_dataloader = DataLoader(validation_data, batch_size=BATCH_SIZE, shuffle= True)\n",
-        "testing_dataloader = DataLoader(testing_data, batch_size=BATCH_SIZE, shuffle= True)"
-      ],
-      "metadata": {
-        "id": "6zs9yu6BgkR1"
-      },
-      "execution_count": 8,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# 9. Model Class\n",
-        "Creating a model in PyTorch seems not very straight forward in the beggining, but if you understand how machine learning and deep learning works, it will be easy for you to understand PyTorch structure easily.\n",
-        "\n",
-        "In the next cell we define a new class MyModel that inherits from nn.Module like we did for the dataset. Remember, in simple terms, we want to redefine some functions in the class to match our needs. In the constructor which is `__init__` and we give it the bert model. Then super`(MyModel, self).__init__()` This line calls the constructor of the parent class nn.Module to ensure it's properly initialized. Which means we have the original constructor together with our part of it!\n",
-        "\n",
-        "Then, we create our layers, a linear layer which represents the input and having the input size of 10 which is the number of columns of the input and the output of the number of hidden neurons, next layer is the output layer which have the input of hidden neurons and one output since we have a binary classification. Finally we have the activation function which is the sigmoid. Our model is pretty simple and straight forward, we are using a simple dataset and we just want to see how PyTorch can be used to build that.\n",
-        "\n",
-        "In the function forward, this function is the forward propagation of the model, how is the data flowing inside the model from the input to the output. This means we can control this completely. That's how PyTorch is so customizable! In the below cell, we define the flow as follows, starting by input layer and followed by the output layer then the activation layer."
-      ],
-      "metadata": {
-        "id": "SAbIDVA_oKpg"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "class MyModel(nn.Module):\n",
-        "    def __init__(self):\n",
-        "\n",
-        "        super(MyModel, self).__init__()\n",
-        "\n",
-        "        self.input_layer = nn.Linear(X.shape[1], HIDDEN_NEURONS)\n",
-        "        self.linear = nn.Linear(HIDDEN_NEURONS, 1)\n",
-        "        self.sigmoid = nn.Sigmoid()\n",
-        "\n",
-        "    def forward(self, x):\n",
-        "        x = self.input_layer(x)\n",
-        "        x = self.linear(x)\n",
-        "        x = self.sigmoid(x)\n",
-        "        return x"
-      ],
-      "metadata": {
-        "id": "Q0Wo9UO2gwA3"
-      },
-      "execution_count": 9,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# 10. Model Creation\n",
-        "Now lets create our model and move it to the assigned device (cuda if you have GPU or the CPU if you don't have any GPUs). Additionally, we will print the a `summary` of the model using the function summary which will take our model and the input size."
-      ],
-      "metadata": {
-        "id": "h4rlzDFNbZ0X"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "model = MyModel().to(device)\n",
-        "summary(model, (X.shape[1],))"
-      ],
-      "metadata": {
-        "id": "DRUUWbpohF7L",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "aef37c47-b246-4697-c8f8-90126128ef83"
-      },
-      "execution_count": 10,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "----------------------------------------------------------------\n",
-            "        Layer (type)               Output Shape         Param #\n",
-            "================================================================\n",
-            "            Linear-1                   [-1, 10]             110\n",
-            "            Linear-2                    [-1, 1]              11\n",
-            "           Sigmoid-3                    [-1, 1]               0\n",
-            "================================================================\n",
-            "Total params: 121\n",
-            "Trainable params: 121\n",
-            "Non-trainable params: 0\n",
-            "----------------------------------------------------------------\n",
-            "Input size (MB): 0.00\n",
-            "Forward/backward pass size (MB): 0.00\n",
-            "Params size (MB): 0.00\n",
-            "Estimated Total Size (MB): 0.00\n",
-            "----------------------------------------------------------------\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# 11. Loss and Optimizer\n",
-        "In the next cell, we will create the loss function which will be Binary Cross entropy and the optimizer `Adam` which will take the model parameters/weights and the learning rate."
-      ],
-      "metadata": {
-        "id": "wX_UTkNSb8Kf"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "criterion = nn.BCELoss()\n",
-        "optimizer = Adam(model.parameters(), lr= LR)"
-      ],
-      "metadata": {
-        "id": "LzFKRv9oiOSI"
-      },
-      "execution_count": 11,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# 12. Training\n",
-        "Now comes the exciting part. As we mentioned several times, nothing is complex here if you know how deep learning works. You just need to know PyTorch syntax. We start by initializing the for loop with the specified number of epochs. But before that we define 4 lists and inside the loop we define another 4 variables.\n",
-        "\n",
-        "## Variables:\n",
-        "* `total_acc_train`: here we will keep tracking of the training accuracy progress during each epoch, we calculate the accuracy during the batch size and we print it in the end for tracking the accuracy on each epoch.\n",
-        "\n",
-        "* `total_loss_train`: here we will keep tracking of the training loss progress during each epoch, we calculate the accuracy during the batch size and we use the loss value to optimize and modify the model parameters.\n",
-        "\n",
-        "* `total_acc_val`: here we will keep tracking of the validation accuracy progress during each epoch, we calculate the accuracy during the batch size and we print it in the end for tracking the accuracy on each epoch and help us know if there is any overfitting.\n",
-        "\n",
-        "* `total_loss_val`: here we will keep tracking of the validation loss progress during each epoch, we calculate the accuracy during the batch size.\n",
-        "\n",
-        "## Lists:\n",
-        "* `total_acc_train_plot`: We append the losses of the training accuracy to visualize them at the end.\n",
-        "\n",
-        "* `total_loss_train_plot`: We append the losses of the training to visualize them at the end.\n",
-        "\n",
-        "* `total_acc_validation_plot`: We append the losses of the validation accuracy to visualize them at the end.\n",
-        "\n",
-        "* `total_loss_validation_plot`: We append the losses of the validation to visualize them at the end.\n",
-        "\n",
-        "## Training\n",
-        "Then, we start to loop through the training dataloaders, we use the enumerate functionality to loop through data and indices at the same time. We are not using the indices here, but lets kept it just if you want to try different stuff with the loop or debug. In the second loop, we start by getting our data from the data loader, then we move the inputs and labels to the cuda device. We allow the model to make a prediction or what is called forward propagation, then we get the output of the model and compare it with our original output using the loss criteration. We use the squeeze function to modify the shape of the output and make it returns a tensor with all specified dimensions of input of size 1 removed. we add the loss amount to `total_loss_train`. Additionally, we get the accuracy by comparing the correct batch with the predicted batch and we add it to the total_acc_train. Then we do the `batch_loss.backward()` which makes the backpropagation and we use the optimizer to do a step on the weights using `optimizer.step()` and then we reset the optimizer gradients using `optimizer.zero_grad()` which is a very important step that has to be done before proceeding.\n",
-        "\n",
-        "## Validation\n",
-        "After that we exit the batch loop (train dataloader loop) and we start with the validation. Don't forget that we are still in the same epoch. In side that we start by with `torch.no_grad()` which means that we need the model to do predicitons without being trained. We just need to see the validation preformance. Then we do the same steps which are predicting and calculating loss and accuracy and storing these values.\n",
-        "\n",
-        "At the end we print after each epoch the epoch number, training loss, training accuracy, validation loss and validation accuracy. We use the printing of \"=\" signs just for making the printing output looks clean."
-      ],
-      "metadata": {
-        "id": "wvGMerrzcI-e"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "total_loss_train_plot = []\n",
-        "total_loss_validation_plot = []\n",
-        "total_acc_train_plot = []\n",
-        "total_acc_validation_plot = []\n",
-        "\n",
-        "for epoch in range(EPOCHS):\n",
-        "    total_acc_train = 0\n",
-        "    total_loss_train = 0\n",
-        "    total_acc_val = 0\n",
-        "    total_loss_val = 0\n",
-        "    ## Training and Validation\n",
-        "    for data in train_dataloader:\n",
-        "\n",
-        "        inputs, labels = data\n",
-        "\n",
-        "        prediction = model(inputs).squeeze(1)\n",
-        "\n",
-        "        batch_loss = criterion(prediction, labels)\n",
-        "\n",
-        "        total_loss_train += batch_loss.item()\n",
-        "\n",
-        "        acc = ((prediction).round() == labels).sum().item()\n",
-        "\n",
-        "        total_acc_train += acc\n",
-        "\n",
-        "        batch_loss.backward()\n",
-        "        optimizer.step()\n",
-        "        optimizer.zero_grad()\n",
-        "\n",
-        "    ## Validation\n",
-        "    with torch.no_grad():\n",
-        "        for data in validation_dataloader:\n",
-        "            inputs, labels = data\n",
-        "\n",
-        "            prediction = model(inputs).squeeze(1)\n",
-        "\n",
-        "            batch_loss = criterion(prediction, labels)\n",
-        "\n",
-        "            total_loss_val += batch_loss.item()\n",
-        "\n",
-        "            acc = ((prediction).round() == labels).sum().item()\n",
-        "\n",
-        "            total_acc_val += acc\n",
-        "\n",
-        "    total_loss_train_plot.append(round(total_loss_train/1000, 4))\n",
-        "    total_loss_validation_plot.append(round(total_loss_val/1000, 4))\n",
-        "    total_acc_train_plot.append(round(total_acc_train/(training_data.__len__())*100, 4))\n",
-        "    total_acc_validation_plot.append(round(total_acc_val/(validation_data.__len__())*100, 4))\n",
-        "\n",
-        "    print(f'''Epoch no. {epoch + 1} Train Loss: {total_loss_train/1000:.4f} Train Accuracy: {(total_acc_train/(training_data.__len__())*100):.4f} Validation Loss: {total_loss_val/1000:.4f} Validation Accuracy: {(total_acc_val/(validation_data.__len__())*100):.4f}''')\n",
-        "    print(\"=\"*50)"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "FuJVA9r3ie2k",
-        "outputId": "ae29da0d-923e-4bc5-c0d6-de4b0d017915"
-      },
-      "execution_count": 12,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Epoch no. 1 Train Loss: 0.2681 Train Accuracy: 64.4513 Validation Loss: 0.0499 Validation Accuracy: 94.4282\n",
-            "==================================================\n",
-            "Epoch no. 2 Train Loss: 0.1640 Train Accuracy: 97.4939 Validation Loss: 0.0212 Validation Accuracy: 98.7903\n",
-            "==================================================\n",
-            "Epoch no. 3 Train Loss: 0.0699 Train Accuracy: 98.3424 Validation Loss: 0.0101 Validation Accuracy: 98.9003\n",
-            "==================================================\n",
-            "Epoch no. 4 Train Loss: 0.0404 Train Accuracy: 98.4602 Validation Loss: 0.0066 Validation Accuracy: 98.8636\n",
-            "==================================================\n",
-            "Epoch no. 5 Train Loss: 0.0297 Train Accuracy: 98.5231 Validation Loss: 0.0051 Validation Accuracy: 98.9003\n",
-            "==================================================\n",
-            "Epoch no. 6 Train Loss: 0.0248 Train Accuracy: 98.5466 Validation Loss: 0.0044 Validation Accuracy: 98.9003\n",
-            "==================================================\n",
-            "Epoch no. 7 Train Loss: 0.0219 Train Accuracy: 98.5938 Validation Loss: 0.0039 Validation Accuracy: 98.9003\n",
-            "==================================================\n",
-            "Epoch no. 8 Train Loss: 0.0202 Train Accuracy: 98.6330 Validation Loss: 0.0038 Validation Accuracy: 98.9003\n",
-            "==================================================\n",
-            "Epoch no. 9 Train Loss: 0.0191 Train Accuracy: 98.6016 Validation Loss: 0.0034 Validation Accuracy: 98.9003\n",
-            "==================================================\n",
-            "Epoch no. 10 Train Loss: 0.0183 Train Accuracy: 98.6173 Validation Loss: 0.0032 Validation Accuracy: 98.9003\n",
-            "==================================================\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# 13. Testing\n",
-        "Now in this section, we will be testing our model, we will start the code by with `torch.no_grad():` which means that we are telling PyTorch that we don't want to train the model we will be using it only for testing. Then we will declare initial loss and accuracy as zeros, we will start by looping through the testing dataloader like we did before during training. Inside the loop, we got our data and we moved it to our GPU (cuda) and then we ran our model on the data and we got the predictions. After that we get the loss and then we add it to our overall loss, we do the same for accuracy, and finally we print the accuracy."
-      ],
-      "metadata": {
-        "id": "8AaERXtheCKE"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "with torch.no_grad():\n",
-        "  total_loss_test = 0\n",
-        "  total_acc_test = 0\n",
-        "  for data in testing_dataloader:\n",
-        "    inputs, labels = data\n",
-        "\n",
-        "    prediction = model(inputs).squeeze(1)\n",
-        "\n",
-        "    batch_loss_test = criterion((prediction), labels)\n",
-        "    total_loss_test += batch_loss_test.item()\n",
-        "    acc = ((prediction).round() == labels).sum().item()\n",
-        "    total_acc_test += acc\n",
-        "\n",
-        "print(f\"Accuracy Score is: {round((total_acc_test/X_test.shape[0])*100, 2)}%\")"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "vPHhFljXnoDB",
-        "outputId": "23e482f6-2741-4c9e-af94-fa5612e45980"
-      },
-      "execution_count": 14,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Accuracy Score is: 98.39%\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# 14. Plotting and Visualizations (Optional)\n",
-        "The results may not be very good, feel free to play with the layers, hyperparameters and text filteration to achieve better performance!"
-      ],
-      "metadata": {
-        "id": "plRG5lj2eFjT"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(15, 5))\n",
-        "\n",
-        "axs[0].plot(total_loss_train_plot, label='Training Loss')\n",
-        "axs[0].plot(total_loss_validation_plot, label='Validation Loss')\n",
-        "axs[0].set_title('Training and Validation Loss over Epochs')\n",
-        "axs[0].set_xlabel('Epochs')\n",
-        "axs[0].set_ylabel('Loss')\n",
-        "axs[0].set_ylim([0, 2])\n",
-        "axs[0].legend()\n",
-        "\n",
-        "axs[1].plot(total_acc_train_plot, label='Training Accuracy')\n",
-        "axs[1].plot(total_acc_validation_plot, label='Validation Accuracy')\n",
-        "axs[1].set_title('Training and Validation Accuracy over Epochs')\n",
-        "axs[1].set_xlabel('Epochs')\n",
-        "axs[1].set_ylabel('Accuracy')\n",
-        "axs[1].set_ylim([0, 100])\n",
-        "axs[1].legend()\n",
-        "\n",
-        "plt.tight_layout()\n",
-        "\n",
-        "plt.show()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 421
-        },
-        "id": "wtSp2YGUxPZ6",
-        "outputId": "6aa955ce-5d8b-451f-994e-aadb02d33677"
-      },
-      "execution_count": 16,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 1500x500 with 2 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# 15. Inference\n",
-        "Now lets see how we can use the model in inference mode, inference means making predictions like you are in production."
-      ],
-      "metadata": {
-        "id": "GVJbrC7SCldp"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "area = float(input(\"Area: \"))/original_df['Area'].abs().max()\n",
-        "MajorAxisLength = float(input(\"Major Axis Length: \"))/original_df['MajorAxisLength'].abs().max()\n",
-        "MinorAxisLength = float(input(\"Minor Axis Length: \"))/original_df['MinorAxisLength'].abs().max()\n",
-        "Eccentricity = float(input(\"Eccentricity: \"))/original_df['Eccentricity'].abs().max()\n",
-        "ConvexArea = float(input(\"Convex Area: \"))/original_df['ConvexArea'].abs().max()\n",
-        "EquivDiameter = float(input(\"EquivDiameter: \"))/original_df['EquivDiameter'].abs().max()\n",
-        "Extent = float(input(\"Extent: \"))/original_df['Extent'].abs().max()\n",
-        "Perimeter = float(input(\"Perimeter: \"))/original_df['Perimeter'].abs().max()\n",
-        "Roundness = float(input(\"Roundness: \"))/original_df['Roundness'].abs().max()\n",
-        "AspectRation = float(input(\"AspectRation: \"))/original_df['AspectRation'].abs().max()\n",
-        "\n",
-        "my_inputs = [area, MajorAxisLength, MinorAxisLength, Eccentricity, ConvexArea, EquivDiameter, Extent, Perimeter, Roundness, AspectRation]\n",
-        "\n",
-        "print(\"=\"*20)\n",
-        "model_inputs = torch.Tensor(my_inputs).to(device)\n",
-        "prediction = (model(model_inputs))\n",
-        "print(prediction)\n",
-        "print(\"Class is: \", round(prediction.item()))"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "z5WFb1OLCnNb",
-        "outputId": "d0fb5df6-392d-4c42-e7be-27f5e60bf8cb"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Area: 6431.279\n",
-            "Major Axis Length: 145.21338\n",
-            "Minor Axis Length: 56.902\n",
-            "Eccentricity: 0.919981821\n",
-            "Convex Area: 6518.93759999\n",
-            "EquivDiameter: 90.483541\n",
-            "Extent: 0.8506668\n",
-            "Perimeter: 329.972\n",
-            "Roundness: 0.742255516\n",
-            "AspectRation: 2.551696\n",
-            "====================\n",
-            "tensor([0.8427], device='cuda:0', grad_fn=<SigmoidBackward0>)\n",
-            "Class is:  1\n"
-          ]
-        }
-      ]
-    }
-  ]
-}
\ No newline at end of file
diff --git a/Ex_1_Tabular_data_classification.ipynb b/Ex_1_Tabular_data_classification.ipynb
new file mode 100644
index 0000000..803b55e
--- /dev/null
+++ b/Ex_1_Tabular_data_classification.ipynb
@@ -0,0 +1,1505 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 160
+        },
+        "id": "ZUgfsBBW-vpB",
+        "outputId": "7637155e-7a18-4ea6-9577-08fe1d2d17c1"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "\n",
+              "     <input type=\"file\" id=\"files-7b63060d-c22a-4cad-8060-174897976eef\" name=\"files[]\" multiple disabled\n",
+              "        style=\"border:none\" />\n",
+              "     <output id=\"result-7b63060d-c22a-4cad-8060-174897976eef\">\n",
+              "      Upload widget is only available when the cell has been executed in the\n",
+              "      current browser session. Please rerun this cell to enable.\n",
+              "      </output>\n",
+              "      <script>// Copyright 2017 Google LLC\n",
+              "//\n",
+              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+              "// you may not use this file except in compliance with the License.\n",
+              "// You may obtain a copy of the License at\n",
+              "//\n",
+              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
+              "//\n",
+              "// Unless required by applicable law or agreed to in writing, software\n",
+              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+              "// See the License for the specific language governing permissions and\n",
+              "// limitations under the License.\n",
+              "\n",
+              "/**\n",
+              " * @fileoverview Helpers for google.colab Python module.\n",
+              " */\n",
+              "(function(scope) {\n",
+              "function span(text, styleAttributes = {}) {\n",
+              "  const element = document.createElement('span');\n",
+              "  element.textContent = text;\n",
+              "  for (const key of Object.keys(styleAttributes)) {\n",
+              "    element.style[key] = styleAttributes[key];\n",
+              "  }\n",
+              "  return element;\n",
+              "}\n",
+              "\n",
+              "// Max number of bytes which will be uploaded at a time.\n",
+              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
+              "\n",
+              "function _uploadFiles(inputId, outputId) {\n",
+              "  const steps = uploadFilesStep(inputId, outputId);\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  // Cache steps on the outputElement to make it available for the next call\n",
+              "  // to uploadFilesContinue from Python.\n",
+              "  outputElement.steps = steps;\n",
+              "\n",
+              "  return _uploadFilesContinue(outputId);\n",
+              "}\n",
+              "\n",
+              "// This is roughly an async generator (not supported in the browser yet),\n",
+              "// where there are multiple asynchronous steps and the Python side is going\n",
+              "// to poll for completion of each step.\n",
+              "// This uses a Promise to block the python side on completion of each step,\n",
+              "// then passes the result of the previous step as the input to the next step.\n",
+              "function _uploadFilesContinue(outputId) {\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  const steps = outputElement.steps;\n",
+              "\n",
+              "  const next = steps.next(outputElement.lastPromiseValue);\n",
+              "  return Promise.resolve(next.value.promise).then((value) => {\n",
+              "    // Cache the last promise value to make it available to the next\n",
+              "    // step of the generator.\n",
+              "    outputElement.lastPromiseValue = value;\n",
+              "    return next.value.response;\n",
+              "  });\n",
+              "}\n",
+              "\n",
+              "/**\n",
+              " * Generator function which is called between each async step of the upload\n",
+              " * process.\n",
+              " * @param {string} inputId Element ID of the input file picker element.\n",
+              " * @param {string} outputId Element ID of the output display.\n",
+              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
+              " */\n",
+              "function* uploadFilesStep(inputId, outputId) {\n",
+              "  const inputElement = document.getElementById(inputId);\n",
+              "  inputElement.disabled = false;\n",
+              "\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  outputElement.innerHTML = '';\n",
+              "\n",
+              "  const pickedPromise = new Promise((resolve) => {\n",
+              "    inputElement.addEventListener('change', (e) => {\n",
+              "      resolve(e.target.files);\n",
+              "    });\n",
+              "  });\n",
+              "\n",
+              "  const cancel = document.createElement('button');\n",
+              "  inputElement.parentElement.appendChild(cancel);\n",
+              "  cancel.textContent = 'Cancel upload';\n",
+              "  const cancelPromise = new Promise((resolve) => {\n",
+              "    cancel.onclick = () => {\n",
+              "      resolve(null);\n",
+              "    };\n",
+              "  });\n",
+              "\n",
+              "  // Wait for the user to pick the files.\n",
+              "  const files = yield {\n",
+              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
+              "    response: {\n",
+              "      action: 'starting',\n",
+              "    }\n",
+              "  };\n",
+              "\n",
+              "  cancel.remove();\n",
+              "\n",
+              "  // Disable the input element since further picks are not allowed.\n",
+              "  inputElement.disabled = true;\n",
+              "\n",
+              "  if (!files) {\n",
+              "    return {\n",
+              "      response: {\n",
+              "        action: 'complete',\n",
+              "      }\n",
+              "    };\n",
+              "  }\n",
+              "\n",
+              "  for (const file of files) {\n",
+              "    const li = document.createElement('li');\n",
+              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
+              "    li.append(span(\n",
+              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
+              "        `last modified: ${\n",
+              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
+              "                                    'n/a'} - `));\n",
+              "    const percent = span('0% done');\n",
+              "    li.appendChild(percent);\n",
+              "\n",
+              "    outputElement.appendChild(li);\n",
+              "\n",
+              "    const fileDataPromise = new Promise((resolve) => {\n",
+              "      const reader = new FileReader();\n",
+              "      reader.onload = (e) => {\n",
+              "        resolve(e.target.result);\n",
+              "      };\n",
+              "      reader.readAsArrayBuffer(file);\n",
+              "    });\n",
+              "    // Wait for the data to be ready.\n",
+              "    let fileData = yield {\n",
+              "      promise: fileDataPromise,\n",
+              "      response: {\n",
+              "        action: 'continue',\n",
+              "      }\n",
+              "    };\n",
+              "\n",
+              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
+              "    let position = 0;\n",
+              "    do {\n",
+              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
+              "      const chunk = new Uint8Array(fileData, position, length);\n",
+              "      position += length;\n",
+              "\n",
+              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
+              "      yield {\n",
+              "        response: {\n",
+              "          action: 'append',\n",
+              "          file: file.name,\n",
+              "          data: base64,\n",
+              "        },\n",
+              "      };\n",
+              "\n",
+              "      let percentDone = fileData.byteLength === 0 ?\n",
+              "          100 :\n",
+              "          Math.round((position / fileData.byteLength) * 100);\n",
+              "      percent.textContent = `${percentDone}% done`;\n",
+              "\n",
+              "    } while (position < fileData.byteLength);\n",
+              "  }\n",
+              "\n",
+              "  // All done.\n",
+              "  yield {\n",
+              "    response: {\n",
+              "      action: 'complete',\n",
+              "    }\n",
+              "  };\n",
+              "}\n",
+              "\n",
+              "scope.google = scope.google || {};\n",
+              "scope.google.colab = scope.google.colab || {};\n",
+              "scope.google.colab._files = {\n",
+              "  _uploadFiles,\n",
+              "  _uploadFilesContinue,\n",
+              "};\n",
+              "})(self);\n",
+              "</script> "
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Saving kaggle.json to kaggle.json\n",
+            "Dataset URL: https://www.kaggle.com/datasets/mssmartypants/rice-type-classification\n",
+            "License(s): copyright-authors\n",
+            "Downloading rice-type-classification.zip to /content\n",
+            "  0% 0.00/888k [00:00<?, ?B/s]\n",
+            "100% 888k/888k [00:00<00:00, 325MB/s]\n"
+          ]
+        }
+      ],
+      "source": [
+        "from google.colab import files\n",
+        "files.upload()\n",
+        "\n",
+        "!mkdir -p ~/.kaggle\n",
+        "!cp kaggle.json ~/.kaggle/\n",
+        "!chmod 600 ~/.kaggle/kaggle.json\n",
+        "\n",
+        "!kaggle datasets download -d mssmartypants/rice-type-classification\n",
+        "import zipfile\n",
+        "\n",
+        "with zipfile.ZipFile(\"rice-type-classification.zip\", 'r') as zip_ref:\n",
+        "    zip_ref.extractall(\"rice-type-classification-data\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Imports"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "XXKiKpKalAsP"
+      },
+      "outputs": [],
+      "source": [
+        "import torch\n",
+        "import torch.nn as nn\n",
+        "from torch.optim import Adam\n",
+        "from torch.utils.data import Dataset, DataLoader\n",
+        "from torchsummary import summary\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.metrics import accuracy_score\n",
+        "import matplotlib.pyplot as plt\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "\n",
+        "device = 'cuda' if torch.cuda.is_available() else 'cpu'"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XX8tpt8prt9W"
+      },
+      "source": [
+        "# Dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 241
+        },
+        "id": "cuq92L2Wq4aO",
+        "outputId": "661f8dc3-7132-4aa3-99c7-bdb583114640"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Output possibilities:  [1 0]\n",
+            "Data Shape (rows, cols):  (18185, 11)\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "summary": "{\n  \"name\": \"data_df\",\n  \"rows\": 18185,\n  \"fields\": [\n    {\n      \"column\": \"Area\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1467,\n        \"min\": 2522,\n        \"max\": 10210,\n        \"num_unique_values\": 5343,\n        \"samples\": [\n          6932,\n          7847,\n          7428\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MajorAxisLength\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.376402475210195,\n        \"min\": 74.13311404,\n        \"max\": 183.2114344,\n        \"num_unique_values\": 18185,\n        \"samples\": [\n          174.6713776,\n          145.8075716,\n          158.0124457\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MinorAxisLength\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.061652559362898,\n        \"min\": 34.40989421,\n        \"max\": 82.55076212,\n        \"num_unique_values\": 18185,\n        \"samples\": [\n          69.66136016,\n          51.67327984,\n          67.8482643\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Eccentricity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.030575132488673995,\n        \"min\": 0.6766470565,\n        \"max\": 0.9667736672,\n        \"num_unique_values\": 18185,\n        \"samples\": [\n          0.9170319151,\n          0.935096321,\n          0.9031212715\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ConvexArea\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1502,\n        \"min\": 2579,\n        \"max\": 11008,\n        \"num_unique_values\": 5450,\n        \"samples\": [\n          9985,\n          4647,\n          7443\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EquivDiameter\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.90625046973775,\n        \"min\": 56.66665803,\n        \"max\": 114.0165591,\n        \"num_unique_values\": 5343,\n        \"samples\": [\n          93.94730717,\n          99.95554366,\n          97.25031279\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Extent\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.10438942430267766,\n        \"min\": 0.3832391714,\n        \"max\": 0.8865730584,\n        \"num_unique_values\": 18007,\n        \"samples\": [\n          0.4601652893,\n          0.5858939017,\n          0.5505580357\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Perimeter\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29.5006198759396,\n        \"min\": 197.015,\n        \"max\": 508.511,\n        \"num_unique_values\": 16246,\n        \"samples\": [\n          356.973,\n          374.084,\n          388.049\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Roundness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.06731016002659161,\n        \"min\": 0.1745901779,\n        \"max\": 0.9047483132,\n        \"num_unique_values\": 18184,\n        \"samples\": [\n          0.6384175711,\n          0.6501997504,\n          0.6459622092\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AspectRation\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4348364637403803,\n        \"min\": 1.358128195,\n        \"max\": 3.911844673,\n        \"num_unique_values\": 18185,\n        \"samples\": [\n          2.507435646,\n          2.82172086,\n          2.328909182\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Class\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+              "type": "dataframe",
+              "variable_name": "data_df"
+            },
+            "text/html": [
+              "\n",
+              "  <div id=\"df-d214fdee-cca7-4b14-ba29-717063cfa278\" 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>Area</th>\n",
+              "      <th>MajorAxisLength</th>\n",
+              "      <th>MinorAxisLength</th>\n",
+              "      <th>Eccentricity</th>\n",
+              "      <th>ConvexArea</th>\n",
+              "      <th>EquivDiameter</th>\n",
+              "      <th>Extent</th>\n",
+              "      <th>Perimeter</th>\n",
+              "      <th>Roundness</th>\n",
+              "      <th>AspectRation</th>\n",
+              "      <th>Class</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>4537</td>\n",
+              "      <td>92.229316</td>\n",
+              "      <td>64.012769</td>\n",
+              "      <td>0.719916</td>\n",
+              "      <td>4677</td>\n",
+              "      <td>76.004525</td>\n",
+              "      <td>0.657536</td>\n",
+              "      <td>273.085</td>\n",
+              "      <td>0.764510</td>\n",
+              "      <td>1.440796</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2872</td>\n",
+              "      <td>74.691881</td>\n",
+              "      <td>51.400454</td>\n",
+              "      <td>0.725553</td>\n",
+              "      <td>3015</td>\n",
+              "      <td>60.471018</td>\n",
+              "      <td>0.713009</td>\n",
+              "      <td>208.317</td>\n",
+              "      <td>0.831658</td>\n",
+              "      <td>1.453137</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>3048</td>\n",
+              "      <td>76.293164</td>\n",
+              "      <td>52.043491</td>\n",
+              "      <td>0.731211</td>\n",
+              "      <td>3132</td>\n",
+              "      <td>62.296341</td>\n",
+              "      <td>0.759153</td>\n",
+              "      <td>210.012</td>\n",
+              "      <td>0.868434</td>\n",
+              "      <td>1.465950</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>3073</td>\n",
+              "      <td>77.033628</td>\n",
+              "      <td>51.928487</td>\n",
+              "      <td>0.738639</td>\n",
+              "      <td>3157</td>\n",
+              "      <td>62.551300</td>\n",
+              "      <td>0.783529</td>\n",
+              "      <td>210.657</td>\n",
+              "      <td>0.870203</td>\n",
+              "      <td>1.483456</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>3693</td>\n",
+              "      <td>85.124785</td>\n",
+              "      <td>56.374021</td>\n",
+              "      <td>0.749282</td>\n",
+              "      <td>3802</td>\n",
+              "      <td>68.571668</td>\n",
+              "      <td>0.769375</td>\n",
+              "      <td>230.332</td>\n",
+              "      <td>0.874743</td>\n",
+              "      <td>1.510000</td>\n",
+              "      <td>1</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-d214fdee-cca7-4b14-ba29-717063cfa278')\"\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",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </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-d214fdee-cca7-4b14-ba29-717063cfa278 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-d214fdee-cca7-4b14-ba29-717063cfa278');\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-a03a5a11-b710-4c5e-9de3-205fa9402f2a\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a03a5a11-b710-4c5e-9de3-205fa9402f2a')\"\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",
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+              "    }\n",
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+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
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+              "      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-a03a5a11-b710-4c5e-9de3-205fa9402f2a button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "   Area  MajorAxisLength  MinorAxisLength  Eccentricity  ConvexArea  \\\n",
+              "0  4537        92.229316        64.012769      0.719916        4677   \n",
+              "1  2872        74.691881        51.400454      0.725553        3015   \n",
+              "2  3048        76.293164        52.043491      0.731211        3132   \n",
+              "3  3073        77.033628        51.928487      0.738639        3157   \n",
+              "4  3693        85.124785        56.374021      0.749282        3802   \n",
+              "\n",
+              "   EquivDiameter    Extent  Perimeter  Roundness  AspectRation  Class  \n",
+              "0      76.004525  0.657536    273.085   0.764510      1.440796      1  \n",
+              "1      60.471018  0.713009    208.317   0.831658      1.453137      1  \n",
+              "2      62.296341  0.759153    210.012   0.868434      1.465950      1  \n",
+              "3      62.551300  0.783529    210.657   0.870203      1.483456      1  \n",
+              "4      68.571668  0.769375    230.332   0.874743      1.510000      1  "
+            ]
+          },
+          "execution_count": 3,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "data_df = pd.read_csv(\"/content/rice-type-classification-data/riceClassification.csv\")\n",
+        "data_df.dropna(inplace = True)\n",
+        "data_df.drop([\"id\"], axis =1, inplace = True)\n",
+        "print(\"Output possibilities: \", data_df[\"Class\"].unique())\n",
+        "print(\"Data Shape (rows, cols): \", data_df.shape)\n",
+        "data_df.head()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vwAdqRkjsDlH"
+      },
+      "source": [
+        "# Normalize Dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "YV6NEzSFq4W1",
+        "outputId": "f577c473-a13e-47fc-8b3c-60b4e7039e6a"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "summary": "{\n  \"name\": \"data_df\",\n  \"rows\": 18185,\n  \"fields\": [\n    {\n      \"column\": \"Area\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.14370197357033015,\n        \"min\": 0.24701273261508325,\n        \"max\": 1.0,\n        \"num_unique_values\": 5343,\n        \"samples\": [\n          0.6789422135161606,\n          0.7685602350636631,\n          0.7275220372184134\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MajorAxisLength\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.06755256578685567,\n        \"min\": 0.4046314810141566,\n        \"max\": 1.0,\n        \"num_unique_values\": 18185,\n        \"samples\": [\n          0.9533868787831509,\n          0.7958431856478058,\n          0.8624595196117301\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MinorAxisLength\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.12188442966446315,\n        \"min\": 0.41683315000750715,\n        \"max\": 1.0,\n        \"num_unique_values\": 18185,\n        \"samples\": [\n          0.8438608968713904,\n          0.6259576351928172,\n          0.8218974914050132\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Eccentricity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03162594671949088,\n        \"min\": 0.6999022412967931,\n        \"max\": 1.0,\n        \"num_unique_values\": 18185,\n        \"samples\": [\n          0.948548710222876,\n          0.9672339583971655,\n          0.9341599819486684\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ConvexArea\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.13644681787780938,\n        \"min\": 0.23428415697674418,\n        \"max\": 1.0,\n        \"num_unique_values\": 5450,\n        \"samples\": [\n          0.9070675872093024,\n          0.42214752906976744,\n          0.6761446220930233\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EquivDiameter\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.08688431354124022,\n        \"min\": 0.497003755220324,\n        \"max\": 1.0,\n        \"num_unique_values\": 5343,\n        \"samples\": [\n          0.823979498342886,\n          0.876675672805846,\n          0.852949023875603\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Extent\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.11774486413005775,\n        \"min\": 0.4322702655679978,\n        \"max\": 1.0,\n        \"num_unique_values\": 18007,\n        \"samples\": [\n          0.5190382055264133,\n          0.660852364222937,\n          0.6209956759723706\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Perimeter\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0580137300391528,\n        \"min\": 0.3874350800670978,\n        \"max\": 1.0,\n        \"num_unique_values\": 16246,\n        \"samples\": [\n          0.701996613642576,\n          0.735645836569907,\n          0.7631083693371431\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Roundness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.07439655763327434,\n        \"min\": 0.19297099022212358,\n        \"max\": 1.0,\n        \"num_unique_values\": 18184,\n        \"samples\": [\n          0.7056300208419112,\n          0.7186526251707633,\n          0.7139689566430905\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AspectRation\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.11115892886588072,\n        \"min\": 0.347183569013861,\n        \"max\": 1.0,\n        \"num_unique_values\": 18185,\n        \"samples\": [\n          0.6409854827075849,\n          0.7213274288408844,\n          0.5953480714800354\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Class\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4975991120990263,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+              "type": "dataframe",
+              "variable_name": "data_df"
+            },
+            "text/html": [
+              "\n",
+              "  <div id=\"df-e732c12e-77f6-4bb0-ab08-fd7166974eb2\" 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>Area</th>\n",
+              "      <th>MajorAxisLength</th>\n",
+              "      <th>MinorAxisLength</th>\n",
+              "      <th>Eccentricity</th>\n",
+              "      <th>ConvexArea</th>\n",
+              "      <th>EquivDiameter</th>\n",
+              "      <th>Extent</th>\n",
+              "      <th>Perimeter</th>\n",
+              "      <th>Roundness</th>\n",
+              "      <th>AspectRation</th>\n",
+              "      <th>Class</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0.444368</td>\n",
+              "      <td>0.503404</td>\n",
+              "      <td>0.775435</td>\n",
+              "      <td>0.744658</td>\n",
+              "      <td>0.424873</td>\n",
+              "      <td>0.666610</td>\n",
+              "      <td>0.741661</td>\n",
+              "      <td>0.537029</td>\n",
+              "      <td>0.844997</td>\n",
+              "      <td>0.368316</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>0.281293</td>\n",
+              "      <td>0.407681</td>\n",
+              "      <td>0.622653</td>\n",
+              "      <td>0.750489</td>\n",
+              "      <td>0.273892</td>\n",
+              "      <td>0.530370</td>\n",
+              "      <td>0.804230</td>\n",
+              "      <td>0.409661</td>\n",
+              "      <td>0.919215</td>\n",
+              "      <td>0.371471</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>0.298531</td>\n",
+              "      <td>0.416421</td>\n",
+              "      <td>0.630442</td>\n",
+              "      <td>0.756341</td>\n",
+              "      <td>0.284520</td>\n",
+              "      <td>0.546380</td>\n",
+              "      <td>0.856278</td>\n",
+              "      <td>0.412994</td>\n",
+              "      <td>0.959862</td>\n",
+              "      <td>0.374747</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>0.300979</td>\n",
+              "      <td>0.420463</td>\n",
+              "      <td>0.629049</td>\n",
+              "      <td>0.764024</td>\n",
+              "      <td>0.286791</td>\n",
+              "      <td>0.548616</td>\n",
+              "      <td>0.883772</td>\n",
+              "      <td>0.414262</td>\n",
+              "      <td>0.961818</td>\n",
+              "      <td>0.379222</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>0.361704</td>\n",
+              "      <td>0.464626</td>\n",
+              "      <td>0.682901</td>\n",
+              "      <td>0.775033</td>\n",
+              "      <td>0.345385</td>\n",
+              "      <td>0.601418</td>\n",
+              "      <td>0.867808</td>\n",
+              "      <td>0.452954</td>\n",
+              "      <td>0.966836</td>\n",
+              "      <td>0.386007</td>\n",
+              "      <td>1.0</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-e732c12e-77f6-4bb0-ab08-fd7166974eb2')\"\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",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </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-e732c12e-77f6-4bb0-ab08-fd7166974eb2 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-e732c12e-77f6-4bb0-ab08-fd7166974eb2');\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-32fe2bf8-ea4e-451a-b43d-c6b6037128f1\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-32fe2bf8-ea4e-451a-b43d-c6b6037128f1')\"\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-32fe2bf8-ea4e-451a-b43d-c6b6037128f1 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "       Area  MajorAxisLength  MinorAxisLength  Eccentricity  ConvexArea  \\\n",
+              "0  0.444368         0.503404         0.775435      0.744658    0.424873   \n",
+              "1  0.281293         0.407681         0.622653      0.750489    0.273892   \n",
+              "2  0.298531         0.416421         0.630442      0.756341    0.284520   \n",
+              "3  0.300979         0.420463         0.629049      0.764024    0.286791   \n",
+              "4  0.361704         0.464626         0.682901      0.775033    0.345385   \n",
+              "\n",
+              "   EquivDiameter    Extent  Perimeter  Roundness  AspectRation  Class  \n",
+              "0       0.666610  0.741661   0.537029   0.844997      0.368316    1.0  \n",
+              "1       0.530370  0.804230   0.409661   0.919215      0.371471    1.0  \n",
+              "2       0.546380  0.856278   0.412994   0.959862      0.374747    1.0  \n",
+              "3       0.548616  0.883772   0.414262   0.961818      0.379222    1.0  \n",
+              "4       0.601418  0.867808   0.452954   0.966836      0.386007    1.0  "
+            ]
+          },
+          "execution_count": 4,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "original_df = data_df.copy()\n",
+        "\n",
+        "for column in data_df.columns:\n",
+        "  data_df[column] = data_df[column] / data_df[column].abs().max()\n",
+        "data_df.head()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "S0wx2K18s9Yb"
+      },
+      "source": [
+        "# Data Splitting"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Xw7kCFewrqc7",
+        "outputId": "688411c1-e913-499d-e491-a5f054e98fa5"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Training set is:  12729 rows, wich is:  70.0 %\n",
+            "Testing set is:  2728 rows, wich is:  15.0 %\n",
+            "Validation set is:  2728 rows, wich is:  15.0 %\n"
+          ]
+        }
+      ],
+      "source": [
+        "X = np.array(data_df.iloc[:, :-1]) # Get the input, all rows and all columns except last column (output)\n",
+        "Y = np.array(data_df.iloc[:, -1]) # Get the ouputs, all rows and last column only (output column)\n",
+        "\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.3)\n",
+        "X_test, X_val, y_test, y_val = train_test_split(X_test, y_test, test_size=0.5)\n",
+        "\n",
+        "print(\"Training set is: \", X_train.shape[0], \"rows, wich is: \", round(X_train.shape[0]/data_df.shape[0], 4)*100, \"%\")\n",
+        "print(\"Testing set is: \", X_test.shape[0], \"rows, wich is: \", round(X_test.shape[0]/data_df.shape[0], 4)*100, \"%\")\n",
+        "print(\"Validation set is: \", X_val.shape[0], \"rows, wich is: \", round(X_val.shape[0]/data_df.shape[0], 4)*100, \"%\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5fh1sW4UXAFW"
+      },
+      "source": [
+        "# Dataset Object"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "aB4Q6GT0q4UB"
+      },
+      "outputs": [],
+      "source": [
+        "class dataset(Dataset):\n",
+        "  def __init__(self, X, Y):\n",
+        "    self.X = torch.tensor(X, dtype = torch.float32).to(device)\n",
+        "    self.Y = torch.tensor(Y, dtype = torch.float32).to(device)\n",
+        "\n",
+        "  def __len__(self):\n",
+        "        return len(self.X)\n",
+        "\n",
+        "  def __getitem__(self, index):\n",
+        "        return self.X[index], self.Y[index]\n",
+        "\n",
+        "training_data = dataset(X_train, y_train)\n",
+        "validation_data = dataset(X_val, y_val)\n",
+        "testing_data = dataset(X_test, y_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "id": "eT_Uj_Vlq4RF"
+      },
+      "outputs": [],
+      "source": [
+        "# Training Hyper Parameters\n",
+        "BATCH_SIZE = 32\n",
+        "EPOCHS = 10\n",
+        "HIDDEN_NEURONS = 10\n",
+        "LR = 1e-3"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xxaGR6KGXFfx"
+      },
+      "source": [
+        "# Data Loaders"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "GI452_OGq4OW"
+      },
+      "outputs": [],
+      "source": [
+        "train_dataloader = DataLoader(training_data, batch_size=BATCH_SIZE, shuffle= True)\n",
+        "validation_dataloader = DataLoader(validation_data, batch_size=BATCH_SIZE, shuffle= True)\n",
+        "testing_dataloader = DataLoader(testing_data, batch_size=BATCH_SIZE, shuffle= True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TG7SJJOsXRMG"
+      },
+      "source": [
+        "# Model Class"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "id": "9HHvZ0d7U_N6"
+      },
+      "outputs": [],
+      "source": [
+        "class MyModel(nn.Module):\n",
+        "    def __init__(self):\n",
+        "\n",
+        "        super(MyModel, self).__init__()\n",
+        "\n",
+        "        self.input_layer = nn.Linear(X.shape[1], HIDDEN_NEURONS)\n",
+        "        self.linear = nn.Linear(HIDDEN_NEURONS, 1)\n",
+        "        self.sigmoid = nn.Sigmoid()\n",
+        "\n",
+        "    def forward(self, x):\n",
+        "        x = self.input_layer(x)\n",
+        "        x = self.linear(x)\n",
+        "        x = self.sigmoid(x)\n",
+        "        return x"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "zO6mDgdOXWZo",
+        "outputId": "cd9f2514-14af-4534-d7c1-ecb3b3abed2b"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "----------------------------------------------------------------\n",
+            "        Layer (type)               Output Shape         Param #\n",
+            "================================================================\n",
+            "            Linear-1                   [-1, 10]             110\n",
+            "            Linear-2                    [-1, 1]              11\n",
+            "           Sigmoid-3                    [-1, 1]               0\n",
+            "================================================================\n",
+            "Total params: 121\n",
+            "Trainable params: 121\n",
+            "Non-trainable params: 0\n",
+            "----------------------------------------------------------------\n",
+            "Input size (MB): 0.00\n",
+            "Forward/backward pass size (MB): 0.00\n",
+            "Params size (MB): 0.00\n",
+            "Estimated Total Size (MB): 0.00\n",
+            "----------------------------------------------------------------\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Model Creation\n",
+        "model = MyModel().to(device)\n",
+        "summary(model, (X.shape[1],))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "id": "TZWNPhJvXqID"
+      },
+      "outputs": [],
+      "source": [
+        "# Loss and Optimizer\n",
+        "criterion = nn.BCELoss()\n",
+        "optimizer = Adam(model.parameters(), lr= LR)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KSmLQzTxXxBK"
+      },
+      "source": [
+        "# Training"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "QcgyKxoXXyhB",
+        "outputId": "af11f4ec-7c4d-441d-fdc5-102f9365546b"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Epoch no. 1 Train Loss: 0.2228 Train Accuracy: 86.1183 Validation Loss: 0.0341 Validation Accuracy: 97.1408\n",
+            "==================================================\n",
+            "Epoch no. 2 Train Loss: 0.1045 Train Accuracy: 98.2167 Validation Loss: 0.0150 Validation Accuracy: 97.8739\n",
+            "==================================================\n",
+            "Epoch no. 3 Train Loss: 0.0509 Train Accuracy: 98.4838 Validation Loss: 0.0089 Validation Accuracy: 98.0938\n",
+            "==================================================\n",
+            "Epoch no. 4 Train Loss: 0.0334 Train Accuracy: 98.5388 Validation Loss: 0.0068 Validation Accuracy: 97.9106\n",
+            "==================================================\n",
+            "Epoch no. 5 Train Loss: 0.0261 Train Accuracy: 98.5781 Validation Loss: 0.0055 Validation Accuracy: 98.4604\n",
+            "==================================================\n",
+            "Epoch no. 6 Train Loss: 0.0224 Train Accuracy: 98.6723 Validation Loss: 0.0048 Validation Accuracy: 98.4604\n",
+            "==================================================\n",
+            "Epoch no. 7 Train Loss: 0.0201 Train Accuracy: 98.6409 Validation Loss: 0.0045 Validation Accuracy: 98.4604\n",
+            "==================================================\n",
+            "Epoch no. 8 Train Loss: 0.0188 Train Accuracy: 98.7430 Validation Loss: 0.0043 Validation Accuracy: 98.4971\n",
+            "==================================================\n",
+            "Epoch no. 9 Train Loss: 0.0179 Train Accuracy: 98.6409 Validation Loss: 0.0040 Validation Accuracy: 98.5337\n",
+            "==================================================\n",
+            "Epoch no. 10 Train Loss: 0.0172 Train Accuracy: 98.6566 Validation Loss: 0.0039 Validation Accuracy: 98.5704\n",
+            "==================================================\n"
+          ]
+        }
+      ],
+      "source": [
+        "total_loss_train_plot = []\n",
+        "total_loss_validation_plot = []\n",
+        "total_acc_train_plot = []\n",
+        "total_acc_validation_plot = []\n",
+        "\n",
+        "for epoch in range(EPOCHS):\n",
+        "    total_acc_train = 0\n",
+        "    total_loss_train = 0\n",
+        "    total_acc_val = 0\n",
+        "    total_loss_val = 0\n",
+        "\n",
+        "    ## Training and Validation\n",
+        "    for data in train_dataloader:\n",
+        "\n",
+        "        inputs, labels = data\n",
+        "\n",
+        "        prediction = model(inputs).squeeze(1)\n",
+        "\n",
+        "        batch_loss = criterion(prediction, labels)\n",
+        "\n",
+        "        total_loss_train += batch_loss.item()\n",
+        "\n",
+        "        acc = ((prediction).round() == labels).sum().item()\n",
+        "\n",
+        "        total_acc_train += acc\n",
+        "\n",
+        "        batch_loss.backward()\n",
+        "        optimizer.step()\n",
+        "        optimizer.zero_grad()\n",
+        "\n",
+        " ## Validation\n",
+        "    with torch.no_grad():\n",
+        "        for data in validation_dataloader:\n",
+        "            inputs, labels = data\n",
+        "\n",
+        "            prediction = model(inputs).squeeze(1)\n",
+        "\n",
+        "            batch_loss = criterion(prediction, labels)\n",
+        "\n",
+        "            total_loss_val += batch_loss.item()\n",
+        "\n",
+        "            acc = ((prediction).round() == labels).sum().item()\n",
+        "\n",
+        "            total_acc_val += acc\n",
+        "\n",
+        "    total_loss_train_plot.append(round(total_loss_train/1000, 4))\n",
+        "    total_loss_validation_plot.append(round(total_loss_val/1000, 4))\n",
+        "    total_acc_train_plot.append(round(total_acc_train/(training_data.__len__())*100, 4))\n",
+        "    total_acc_validation_plot.append(round(total_acc_val/(validation_data.__len__())*100, 4))\n",
+        "\n",
+        "    print(f'''Epoch no. {epoch + 1} Train Loss: {total_loss_train/1000:.4f} Train Accuracy: {(total_acc_train/(training_data.__len__())*100):.4f} Validation Loss: {total_loss_val/1000:.4f} Validation Accuracy: {(total_acc_val/(validation_data.__len__())*100):.4f}''')\n",
+        "    print(\"=\"*50)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PFY9tlBeD1fR"
+      },
+      "source": [
+        "# Testing"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "IX8MlHNWD09U",
+        "outputId": "c6d960d1-72bd-4988-e7d3-0b7706da5d03"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Accuracy Score is: 98.61%\n"
+          ]
+        }
+      ],
+      "source": [
+        "with torch.no_grad():\n",
+        "  total_loss_test = 0\n",
+        "  total_acc_test = 0\n",
+        "  for data in testing_dataloader:\n",
+        "    inputs, labels = data\n",
+        "\n",
+        "    prediction = model(inputs).squeeze(1)\n",
+        "\n",
+        "    batch_loss_test = criterion((prediction), labels)\n",
+        "    total_loss_test += batch_loss_test.item()\n",
+        "    acc = ((prediction).round() == labels).sum().item()\n",
+        "    total_acc_test += acc\n",
+        "\n",
+        "print(f\"Accuracy Score is: {round((total_acc_test/X_test.shape[0])*100, 2)}%\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "r3ZZ85kbD4K1"
+      },
+      "source": [
+        "# Visualizations"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 418
+        },
+        "id": "icpNx5KpD8fZ",
+        "outputId": "d604ea0d-9166-4472-8b09-6b7284b15c9b"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1500x500 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(15, 5))\n",
+        "\n",
+        "axs[0].plot(total_loss_train_plot, label='Training Loss')\n",
+        "axs[0].plot(total_loss_validation_plot, label='Validation Loss')\n",
+        "axs[0].set_title('Training and Validation Loss over Epochs')\n",
+        "axs[0].set_xlabel('Epochs')\n",
+        "axs[0].set_ylabel('Loss')\n",
+        "axs[0].set_ylim([0, 2])\n",
+        "axs[0].legend()\n",
+        "\n",
+        "axs[1].plot(total_acc_train_plot, label='Training Accuracy')\n",
+        "axs[1].plot(total_acc_validation_plot, label='Validation Accuracy')\n",
+        "axs[1].set_title('Training and Validation Accuracy over Epochs')\n",
+        "axs[1].set_xlabel('Epochs')\n",
+        "axs[1].set_ylabel('Accuracy')\n",
+        "axs[1].set_ylim([0, 100])\n",
+        "axs[1].legend()\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7JfPbcw4D_ae"
+      },
+      "source": [
+        "# Inference"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "cY14P6-ZD9-x",
+        "outputId": "8da34853-656c-480e-a460-9153c7433135"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Area: 2568\n",
+            "Major Axis Length: 89\n",
+            "Minor Axis Length: 48\n",
+            "Eccentricity: 25\n",
+            "Convex Area: 15\n",
+            "EquivDiameter: 57\n",
+            "Extent: 29\n",
+            "Perimeter: 41\n",
+            "Roundness: 29\n",
+            "AspectRation: 75\n",
+            "====================\n",
+            "tensor([1.], device='cuda:0', grad_fn=<SigmoidBackward0>)\n",
+            "Class is:  1\n"
+          ]
+        }
+      ],
+      "source": [
+        "area = float(input(\"Area: \"))/original_df['Area'].abs().max()\n",
+        "MajorAxisLength = float(input(\"Major Axis Length: \"))/original_df['MajorAxisLength'].abs().max()\n",
+        "MinorAxisLength = float(input(\"Minor Axis Length: \"))/original_df['MinorAxisLength'].abs().max()\n",
+        "Eccentricity = float(input(\"Eccentricity: \"))/original_df['Eccentricity'].abs().max()\n",
+        "ConvexArea = float(input(\"Convex Area: \"))/original_df['ConvexArea'].abs().max()\n",
+        "EquivDiameter = float(input(\"EquivDiameter: \"))/original_df['EquivDiameter'].abs().max()\n",
+        "Extent = float(input(\"Extent: \"))/original_df['Extent'].abs().max()\n",
+        "Perimeter = float(input(\"Perimeter: \"))/original_df['Perimeter'].abs().max()\n",
+        "Roundness = float(input(\"Roundness: \"))/original_df['Roundness'].abs().max()\n",
+        "AspectRation = float(input(\"AspectRation: \"))/original_df['AspectRation'].abs().max()\n",
+        "\n",
+        "my_inputs = [area, MajorAxisLength, MinorAxisLength, Eccentricity, ConvexArea, EquivDiameter, Extent, Perimeter, Roundness, AspectRation]\n",
+        "\n",
+        "print(\"=\"*20)\n",
+        "model_inputs = torch.Tensor(my_inputs).to(device)\n",
+        "prediction = (model(model_inputs))\n",
+        "print(prediction)\n",
+        "print(\"Class is: \", round(prediction.item()))\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "authorship_tag": "ABX9TyOo1GqR+btkiNz0WZRLJvzW",
+      "gpuType": "T4",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/Ex_2_Image_Classification.ipynb b/Ex_2_Image_Classification.ipynb
deleted file mode 100644
index 1cc4863..0000000
--- a/Ex_2_Image_Classification.ipynb
+++ /dev/null
@@ -1,1097 +0,0 @@
-{
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "provenance": [],
-      "gpuType": "A100",
-      "machine_shape": "hm"
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    },
-    "accelerator": "GPU"
-  },
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# 1. Initializations and Dataset Download"
-      ],
-      "metadata": {
-        "id": "ZnBAKL6Hu7iH"
-      }
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 1,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "GUbCgH6wP3zg",
-        "outputId": "4f79dc26-12bd-43a4-c88d-4543d436a327"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Please provide your Kaggle credentials to download this dataset. Learn more: http://bit.ly/kaggle-creds\n",
-            "Your Kaggle username: omaratef3221\n",
-            "Your Kaggle Key: ··········\n",
-            "Downloading animal-faces.zip to ./animal-faces\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "100%|██████████| 696M/696M [00:33<00:00, 21.5MB/s]\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "\n"
-          ]
-        }
-      ],
-      "source": [
-        "!pip install opendatasets --quiet\n",
-        "import opendatasets as od\n",
-        "od.download(\"https://www.kaggle.com/datasets/andrewmvd/animal-faces\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# 2. Imports\n",
-        "Lets start by getting all our imports, keep in mind that PyTorch is not automatically detects and trains on GPU, you have to tell it to use cuda. In case you want to train on Mac Silicon replace cuda with mps."
-      ],
-      "metadata": {
-        "id": "FuLzo_VyvD3m"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import torch # Main PyTorch Library\n",
-        "from torch import nn # Used for creating the layers and loss function\n",
-        "from torch.optim import Adam # Adam Optimizer\n",
-        "import torchvision.transforms as transforms # Transform function used to modify and preprocess all the images\n",
-        "from torch.utils.data import Dataset, DataLoader # Dataset class and DataLoader for creating the objects\n",
-        "from sklearn.preprocessing import LabelEncoder # Label Encoder to encode the classes from strings to numbers\n",
-        "import matplotlib.pyplot as plt # Used for visualizing the images and plotting the training progress\n",
-        "from PIL import Image # Used to read the images from the directory\n",
-        "import pandas as pd # Used to read/create dataframes (csv) and process tabular data\n",
-        "import numpy as np # preprocessing and numerical/mathematical operations\n",
-        "import os # Used to read the images path from the directory\n",
-        "\n",
-        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\" # detect the GPU if any, if not use CPU, change cuda to mps if you have a mac\n",
-        "print(\"Device available: \", device)"
-      ],
-      "metadata": {
-        "id": "4EeRTo8eQCOb",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "213dfdc7-f8c7-4281-824d-162100655cb8"
-      },
-      "execution_count": 3,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Device available:  cuda\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# 3. Reading Data Paths"
-      ],
-      "metadata": {
-        "id": "xrquwzbqUTDY"
-      }
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "The following code will be going through a nested directory structure where images are stored within subdirectories, each directory is representing a specific label (category). It constructs a list of the file paths for these images and their corresponding labels. Then, it creates a pandas DataFrame containing this information, which is useful for further data processing. Keep in mind that we will read all the data and then we will split it again to train, validation and testing."
-      ],
-      "metadata": {
-        "id": "Rk_Ga_5zb0AB"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "image_path = [] # Empty array where we will fill the paths of the images\n",
-        "labels = [] # Empty array where we will fill the labes of the images\n",
-        "\n",
-        "for i in os.listdir(\"/content/animal-faces/afhq/\"): # Looping through the first directory which has the train and val images\n",
-        "  for label in os.listdir(f\"/content/animal-faces/afhq/{i}\"): # Looping through the first directory which has the labels folders\n",
-        "    for image in os.listdir(f\"/content/animal-faces/afhq/{i}/{label}\"): # Looping through all the images of this label\n",
-        "      labels.append(label) # Append/Put this label in the list\n",
-        "      image_path.append(f\"/content/animal-faces/afhq/{i}/{label}/{image}\") # Append/Put this image path in the list to read it later\n",
-        "\n",
-        "data_df = pd.DataFrame(zip(image_path, labels), columns = ['image_paths', 'labels']) # Create a dataframe contains the images paths and the labels\n",
-        "data_df.head() # Visualizing the first 5 rows"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 206
-        },
-        "id": "3FbP4F6aTo4G",
-        "outputId": "6e932cc5-3232-460a-ce52-8c082b09bca4"
-      },
-      "execution_count": 4,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "                                         image_paths labels\n",
-              "0  /content/animal-faces/afhq/train/wild/flickr_w...   wild\n",
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-              "3  /content/animal-faces/afhq/train/wild/pixabay_...   wild\n",
-              "4  /content/animal-faces/afhq/train/wild/pixabay_...   wild"
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-          },
-          "metadata": {},
-          "execution_count": 4
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-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# 4. Data Split\n",
-        "Now lets split the dataset into training, validation and testing. Here we will be using it in a bit different way since we will be reading the image paths from the dataframe so better we split the dataframe directly and then read the images. We will do that by getting a random sample of 70% from our data using `data_df.sample(frac=0.7,random_state=7)` then we will create a new variable of test dataset which we will drop from it the 70% of the train which means that it will be 30% of the data which is the rest of the data using `data_df.drop(train.index)`. Then we will do the same between testing to create validation which will be 50% of the data which means the final split we have is the following:\n",
-        "\n",
-        "\n",
-        "\n",
-        "*   Training: 70%\n",
-        "*   Validation: 15%\n",
-        "*   Testing: 15%"
-      ],
-      "metadata": {
-        "id": "9eD7x_r7UX8H"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "train=data_df.sample(frac=0.7,random_state=7) # Create training of 70% of the data\n",
-        "test=data_df.drop(train.index) # Create testing by removing the 70% of the train data which will result in 30%\n",
-        "\n",
-        "val=test.sample(frac=0.5,random_state=7) # Create validation of 50% of the testing data\n",
-        "test=test.drop(val.index) # Create testing by removing the 50% of the validation data which will result in 50%"
-      ],
-      "metadata": {
-        "id": "tVXNzGhivv3E"
-      },
-      "execution_count": 5,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# 5. Preprocessing Objects\n",
-        "Now lets create some preprocessing objects, first we start by `LabelEncoder` which converts string classes to numeric for example instead of having the classes as (cat, dog) it will be (0,1) because machines understands numbers only. Next we will create an object `transform` which will help us to preprocess images by converting all images to same size, converting them to tensors of type float and you can even do more with this object. Feel free to check their documentation [here](https://pytorch.org/vision/0.9/transforms.html)."
-      ],
-      "metadata": {
-        "id": "r3mWk2pRvFNg"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "label_encoder = LabelEncoder() # Encode the string classes to numeric\n",
-        "label_encoder.fit(data_df['labels']) # Fit the label encoder on our own data\n",
-        "\n",
-        "transform = transforms.Compose([\n",
-        "    transforms.Resize((128, 128)), # One size for all images\n",
-        "    transforms.ToTensor(), # Convert images to PyTorch tensors\n",
-        "    transforms.ConvertImageDtype(torch.float) # The values are in floating point numbers\n",
-        "    ]) # Transform all images into one clear format (preprocess all images to same properties)"
-      ],
-      "metadata": {
-        "id": "pm09uRqCvEu0"
-      },
-      "execution_count": 6,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# 6. Custom Dataset Class\n",
-        "Lets create a dataset class. Our class is a customized class on how we can our own dataset. The class inherits from the main `Dataset` class from PyTorch, then we overwrite some of the existing functions in that class. We need to convert our dataset to PyTorch Dataset object as it will be more efficient during training, you can use the dataset as its, but lets keep things professional and efficient. First we define our class that will be taking the main Dataset class with the concept of inheritance as mentioned. Let's make the concept simpler. There is a big class that PyTorch, this class has several functions inside it, we will recreate that class and modify some functions to match our needs.\n",
-        "\n",
-        "In the constructor function `__init__` we are declaring and our dataframe which will have `image_path` and the `labels`. Then we are creating our own image preprocessing object which is `transform` that we talked about before. For our labels, we are declaring our labels with the label_encoder we declared before. Which means that we will convert our labels are changed from string to numeric classes using `fit_tranform`. We didn't read each image inside the constructor as reading images here will be so time consuming. However, we are reading each image, applying the preprocessing and returning it in the `__getitem__` to read this specific image with its corresponding encoded label by their index and returns them."
-      ],
-      "metadata": {
-        "id": "YAmMLxQiUbHN"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "class CustomImageDataset(Dataset):\n",
-        "    def __init__(self, dataframe, transform=None):\n",
-        "        self.dataframe = dataframe\n",
-        "        self.transform = transform\n",
-        "        self.labels = torch.tensor(label_encoder.transform(dataframe['labels'])).to(device)\n",
-        "\n",
-        "    def __len__(self):\n",
-        "        return self.dataframe.shape[0]\n",
-        "\n",
-        "    def __getitem__(self, idx):\n",
-        "        img_path = self.dataframe.iloc[idx, 0]\n",
-        "        label = self.labels[idx]\n",
-        "        image = Image.open(img_path).convert('RGB')\n",
-        "        if self.transform:\n",
-        "          image = self.transform(image).to(device)\n",
-        "\n",
-        "        return image, label"
-      ],
-      "metadata": {
-        "id": "NRaye1wmh9lb"
-      },
-      "execution_count": 7,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# 7. Create Dataset Objects\n",
-        "Now lets create 3 data objects, for training data, validation and testing respectively giving them our dataframes we created before together with the `transform` object that we created before. We will use the same `transform` object to make sure all images are preprocessed as they are."
-      ],
-      "metadata": {
-        "id": "rsttBLHSvLk2"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "train_dataset = CustomImageDataset(dataframe=train, transform=transform)\n",
-        "val_dataset = CustomImageDataset(dataframe=val, transform=transform)\n",
-        "test_dataset = CustomImageDataset(dataframe=test, transform=transform)"
-      ],
-      "metadata": {
-        "id": "y2v__bqQhrsB"
-      },
-      "execution_count": 8,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# 8. Visualize Images\n",
-        "This cell is optional and will not affect the model accuracy. Here we are just visualizing 9 random images using `matplotlib` package."
-      ],
-      "metadata": {
-        "id": "hkVIuMbGvgl7"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "n_rows = 3\n",
-        "n_cols = 3\n",
-        "f, axarr = plt.subplots(n_rows, n_cols)\n",
-        "for row in range(n_rows):\n",
-        "    for col in range(n_cols):\n",
-        "      image = Image.open(data_df.sample(n = 1)['image_paths'].iloc[0]).convert(\"RGB\")\n",
-        "      axarr[row, col].imshow(image)\n",
-        "      axarr[row, col].axis('off')\n",
-        "\n",
-        "plt.show()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 406
-        },
-        "id": "Sb-qxFFqx7Lq",
-        "outputId": "a2b19e42-a726-45f4-e6bc-5a9ee7e3e50f"
-      },
-      "execution_count": 9,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 9 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# 9. Hyperparameters\n",
-        "Now we are setting the training hyperparameters, we defined some variables which are the batch size, number of training epochs and learning rate."
-      ],
-      "metadata": {
-        "id": "YzN8rbnPvlsG"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "LR = 1e-4\n",
-        "BATCH_SIZE = 16\n",
-        "EPOCHS = 10"
-      ],
-      "metadata": {
-        "id": "REDfPqgOalbQ"
-      },
-      "execution_count": 10,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# 10. DataLoaders\n",
-        "PyTorch provides a class called DataLoader which allows us to create objects of it to simplify the training.\n",
-        "\n",
-        "Dataloader is an object that we can loop through it to train according to batches. When we start training, we loop through epochs, if you skip the batch size it means that the amount of training data in one batch is equal to the complete amount of training data, this method is not efficient and in most of the cases you need to train through using batches. Dataloader allows you to loop through the batches easily during the training. When you create a dataloader. You define the batch size and enable the `shuffle` to randomize the data and then you can loop through it in each epoch to train normally."
-      ],
-      "metadata": {
-        "id": "6b9hEynQvozM"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)\n",
-        "val_loader = DataLoader(val_dataset, batch_size=BATCH_SIZE, shuffle=True)\n",
-        "test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=True)"
-      ],
-      "metadata": {
-        "id": "OBfUiGRQwpZS"
-      },
-      "execution_count": 11,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "#11. Model\n",
-        "Now we will build our CNN Network. We will be building it by creating a class that inherits from `nn.Module` and we will create our layers. Additionally, we will overwrite the `forward` function to modify our forward propagation.\n",
-        "The model will start by having a 2-d convolution neural network that will have input channels of 3 which is the RGB input channels. Outputting 32 feature map. Then another 2-d convolution neural network taking 32 channels and outputting 64 and finally another one taking 64 channels and outputting 128 feature map.\n",
-        "Each 2-d convolution neural network will be followed by a max pooling layer of size 2x2 and a `ReLU` activation function.\n",
-        "\n",
-        "After that we will have a `flatten` layer to vectorize these feature maps followed by a linear layer which will have shape of (128, 16, 16) which means 128 feature map each one is 16x16 as the image started by 128x128 and got divided by 2 three times after each pooling layers. finally we will have an output `linear` layer with the length of the unique values of the labels."
-      ],
-      "metadata": {
-        "id": "7J7aGLC8vr2y"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "class Net(nn.Module):\n",
-        "    def __init__(self):\n",
-        "      super().__init__()\n",
-        "\n",
-        "      self.conv1 = nn.Conv2d(3, 32, kernel_size = 3, padding = 1) # First Convolution layer\n",
-        "      self.conv2 = nn.Conv2d(32, 64, kernel_size = 3, padding = 1) # Second Convolution layer\n",
-        "      self.conv3 = nn.Conv2d(64, 128, kernel_size = 3, padding = 1) # Third Convolution layer\n",
-        "      self.pooling = nn.MaxPool2d(2,2) # The pooling layer, we will be using the same layer after each conv2d.\n",
-        "      self.relu = nn.ReLU() # ReLU Activation function\n",
-        "\n",
-        "      self.flatten = nn.Flatten() # Flatten and vectorize the output feature maps that somes from the final convolution layer.\n",
-        "      self.linear = nn.Linear((128 * 16 * 16), 128) # Traditional Dense (Linear)\n",
-        "      self.output = nn.Linear(128, len(data_df['labels'].unique())) # Output Linear Layer\n",
-        "\n",
-        "\n",
-        "    def forward(self, x):\n",
-        "      x = self.conv1(x) # -> Outputs: (32, 128, 128)\n",
-        "      x = self.pooling(x)# -> Outputs: (32, 64, 64)\n",
-        "      x = self.relu(x)\n",
-        "      x = self.conv2(x) # -> Outputs: (64, 64, 64)\n",
-        "      x = self.pooling(x) # -> Outputs: (64, 32, 32)\n",
-        "      x = self.relu(x)\n",
-        "      x = self.conv3(x) # -> Outputs: (128, 32, 32)\n",
-        "      x = self.pooling(x) # -> Outputs: (128, 16, 16)\n",
-        "      x = self.relu(x)\n",
-        "      x = self.flatten(x)\n",
-        "      x = self.linear(x)\n",
-        "      x = self.output(x)\n",
-        "\n",
-        "      return x\n",
-        "\n",
-        "model = Net().to(device) # Create an instance of the model and move it to the GPU Device\n"
-      ],
-      "metadata": {
-        "id": "g50jsWB4okf-"
-      },
-      "execution_count": 12,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# 12. Model Summary\n",
-        "This cell is optional, here we show only the summary of the model and what is the output size for each layer. keep in mind that -1 here will be replaced by the batch size.\n"
-      ],
-      "metadata": {
-        "id": "bGkrTwfEvu4F"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "from torchsummary import summary\n",
-        "summary(model, input_size = (3, 128, 128))"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "T1hL4b9k0tqr",
-        "outputId": "0ba0c6c1-a90d-4f22-e973-34ca0a25e3df"
-      },
-      "execution_count": 13,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "----------------------------------------------------------------\n",
-            "        Layer (type)               Output Shape         Param #\n",
-            "================================================================\n",
-            "            Conv2d-1         [-1, 32, 128, 128]             896\n",
-            "         MaxPool2d-2           [-1, 32, 64, 64]               0\n",
-            "              ReLU-3           [-1, 32, 64, 64]               0\n",
-            "            Conv2d-4           [-1, 64, 64, 64]          18,496\n",
-            "         MaxPool2d-5           [-1, 64, 32, 32]               0\n",
-            "              ReLU-6           [-1, 64, 32, 32]               0\n",
-            "            Conv2d-7          [-1, 128, 32, 32]          73,856\n",
-            "         MaxPool2d-8          [-1, 128, 16, 16]               0\n",
-            "              ReLU-9          [-1, 128, 16, 16]               0\n",
-            "          Flatten-10                [-1, 32768]               0\n",
-            "           Linear-11                  [-1, 128]       4,194,432\n",
-            "           Linear-12                    [-1, 3]             387\n",
-            "================================================================\n",
-            "Total params: 4,288,067\n",
-            "Trainable params: 4,288,067\n",
-            "Non-trainable params: 0\n",
-            "----------------------------------------------------------------\n",
-            "Input size (MB): 0.19\n",
-            "Forward/backward pass size (MB): 10.75\n",
-            "Params size (MB): 16.36\n",
-            "Estimated Total Size (MB): 27.30\n",
-            "----------------------------------------------------------------\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# 13. Loss and Optimizer\n",
-        "In the cell below, we will create the loss function which will be the Cross Entropy loss. Also we will have create the Adam optimizer and we will provide to it the parameters of the model we created."
-      ],
-      "metadata": {
-        "id": "fnypc3arvzC8"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# Define loss function and optimizer\n",
-        "criterion = nn.CrossEntropyLoss() # Cross Entropy Loss\n",
-        "optimizer = Adam(model.parameters(), lr=LR) # Adam optimizer"
-      ],
-      "metadata": {
-        "id": "JLc90YSAbBwj"
-      },
-      "execution_count": 14,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# 14. Training\n",
-        "Now comes the training part. If you know well the training process of deep learning. You will just need to know PyTorch syntax. We start by initializing the for loop with the specified number of epochs which we defined before. But before that we define 4 lists and inside the loop we define another 4 variables.\n",
-        "\n",
-        "## Variables:\n",
-        "* `total_acc_train`: here we will keep tracking of the training accuracy progress during each epoch, we calculate the accuracy during the batch size and we print it in the end for tracking the accuracy on each epoch.\n",
-        "\n",
-        "* `total_loss_train`: here we will keep tracking of the training loss progress during each epoch, we calculate the accuracy during the batch size and we use the loss value to optimize and modify the model parameters.\n",
-        "\n",
-        "* `total_acc_val`: here we will keep tracking of the validation accuracy progress during each epoch, we calculate the accuracy during the batch size and we print it in the end for tracking the accuracy on each epoch and help us know if there is any overfitting.\n",
-        "\n",
-        "* `total_loss_val`: here we will keep tracking of the validation loss progress during each epoch, we calculate the accuracy during the batch size.\n",
-        "\n",
-        "## Lists:\n",
-        "* `total_acc_train_plot`: We append the losses of the training accuracy to visualize them at the end.\n",
-        "\n",
-        "* `total_loss_train_plot`: We append the losses of the training to visualize them at the end.\n",
-        "\n",
-        "* `total_acc_validation_plot`: We append the losses of the validation accuracy to visualize them at the end.\n",
-        "\n",
-        "* `total_loss_validation_plot`: We append the losses of the validation to visualize them at the end.\n",
-        "\n",
-        "\n",
-        "Then, we start to loop through the training dataloaders, we use the enumerate functionality to loop through data and indices at the same time. We are not using the indices here, but lets kept it just if you want to try different stuff with the loop or debug. In the second loop, we start by getting our data from the data loader, then we move the inputs and labels to the cuda device. We allow the model to make a prediction or what is called forward propagation, then we get the output of the model and compare it with our original output using the loss criteration. We add the loss amount to `total_loss_train`. Additionally, we get the accuracy by comparing the correct batch with the predicted batch and we add it to the `total_acc_train`. Then we do the `batch_loss.backward()` which makes the backpropagation and we use the optimizer to do a step on the weights using `optimizer.step()` and then we reset the optimizer gradients using `optimizer.zero_grad()` which is a very important step that has to be done before proceeding.\n",
-        "\n",
-        "After that we exit the batch loop (train dataloader loop) and we start with the validation. Don't forget that we are still in the same epoch. In side that we start by with `torch.no_grad()` so that we freeze the weights, which means that we need the model to do predicitons **without being trained**. We just need to see the validation preformance. Then we do the same steps which are predicting and calculating loss and accuracy and storing these values.\n",
-        "\n",
-        "At the end we print after each epoch the epoch number, training loss, training accuracy, validation loss and validation accuracy. Accuracies are calculated by dividing the right predictions by the complete number of samples and multiplying by 100. Losses are being divided by 1000 just to normalize the values and avoid large values.\n",
-        "\n"
-      ],
-      "metadata": {
-        "id": "ZVOrwojrv1Z4"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "total_loss_train_plot = []\n",
-        "total_loss_validation_plot = []\n",
-        "total_acc_train_plot = []\n",
-        "total_acc_validation_plot = []\n",
-        "\n",
-        "\n",
-        "for epoch in range(EPOCHS):\n",
-        "  total_acc_train = 0\n",
-        "  total_loss_train = 0\n",
-        "  total_loss_val = 0\n",
-        "  total_acc_val = 0\n",
-        "\n",
-        "  for inputs, labels in train_loader:\n",
-        "    optimizer.zero_grad()\n",
-        "    outputs = model(inputs)\n",
-        "    train_loss = criterion(outputs, labels)\n",
-        "    total_loss_train += train_loss.item()\n",
-        "    train_loss.backward()\n",
-        "\n",
-        "    train_acc = (torch.argmax(outputs, axis = 1) == labels).sum().item()\n",
-        "    total_acc_train += train_acc\n",
-        "    optimizer.step()\n",
-        "\n",
-        "  with torch.no_grad():\n",
-        "    for inputs, labels in val_loader:\n",
-        "      outputs = model(inputs)\n",
-        "      val_loss = criterion(outputs, labels)\n",
-        "      total_loss_val += val_loss.item()\n",
-        "\n",
-        "      val_acc = (torch.argmax(outputs, axis = 1) == labels).sum().item()\n",
-        "      total_acc_val += val_acc\n",
-        "\n",
-        "  total_loss_train_plot.append(round(total_loss_train/1000, 4))\n",
-        "  total_loss_validation_plot.append(round(total_loss_val/1000, 4))\n",
-        "  total_acc_train_plot.append(round(total_acc_train/(train_dataset.__len__())*100, 4))\n",
-        "  total_acc_validation_plot.append(round(total_acc_val/(val_dataset.__len__())*100, 4))\n",
-        "  print(f'''Epoch {epoch+1}/{EPOCHS}, Train Loss: {round(total_loss_train/100, 4)} Train Accuracy {round((total_acc_train)/train_dataset.__len__() * 100, 4)}\n",
-        "              Validation Loss: {round(total_loss_val/100, 4)} Validation Accuracy: {round((total_acc_val)/val_dataset.__len__() * 100, 4)}''')\n",
-        "  print(\"=\"*25)"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "PJqFH59PiOcN",
-        "outputId": "72f69991-d5bc-4018-996d-31376daacdde"
-      },
-      "execution_count": 15,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Epoch 1/10, Train Loss: 3.4491 Train Accuracy 79.8955 \n",
-            "              Validation Loss: 0.4215 Validation Accuracy: 90.0826\n",
-            "=========================\n",
-            "Epoch 2/10, Train Loss: 1.443 Train Accuracy 92.2593 \n",
-            "              Validation Loss: 0.2326 Validation Accuracy: 94.5041\n",
-            "=========================\n",
-            "Epoch 3/10, Train Loss: 0.9331 Train Accuracy 95.2263 \n",
-            "              Validation Loss: 0.1942 Validation Accuracy: 95.5372\n",
-            "=========================\n",
-            "Epoch 4/10, Train Loss: 0.6365 Train Accuracy 96.7851 \n",
-            "              Validation Loss: 0.1772 Validation Accuracy: 95.6198\n",
-            "=========================\n",
-            "Epoch 5/10, Train Loss: 0.4828 Train Accuracy 97.5999 \n",
-            "              Validation Loss: 0.1627 Validation Accuracy: 96.4463\n",
-            "=========================\n",
-            "Epoch 6/10, Train Loss: 0.3623 Train Accuracy 98.2287 \n",
-            "              Validation Loss: 0.1483 Validation Accuracy: 96.7355\n",
-            "=========================\n",
-            "Epoch 7/10, Train Loss: 0.238 Train Accuracy 98.9461 \n",
-            "              Validation Loss: 0.1718 Validation Accuracy: 96.3636\n",
-            "=========================\n",
-            "Epoch 8/10, Train Loss: 0.202 Train Accuracy 99.0435 \n",
-            "              Validation Loss: 0.183 Validation Accuracy: 96.4463\n",
-            "=========================\n",
-            "Epoch 9/10, Train Loss: 0.1247 Train Accuracy 99.4509 \n",
-            "              Validation Loss: 0.1785 Validation Accuracy: 96.4463\n",
-            "=========================\n",
-            "Epoch 10/10, Train Loss: 0.0975 Train Accuracy 99.6103 \n",
-            "              Validation Loss: 0.1823 Validation Accuracy: 96.5289\n",
-            "=========================\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# 15. Testing\n",
-        "Now in this section, we will be testing our model, we will start the code by with `torch.no_grad():` which means that we are telling PyTorch that we don't want to train the model we will be using it only for testing. Then we will declare initial loss and accuracy as zeros, we will start by looping through the testing dataloader like we did before during training. Inside the loop, we got our data and we moved it to our GPU (`cuda`) and then we ran our model on the data and we got the predictions. After that we get the loss and then we add it to our overall loss, we do the same for accuracy, and finally we print the accuracy."
-      ],
-      "metadata": {
-        "id": "YqwKmHdIv3hm"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "with torch.no_grad():\n",
-        "  total_loss_test = 0\n",
-        "  total_acc_test = 0\n",
-        "  for inputs, labels in test_loader:\n",
-        "    predictions = model(inputs)\n",
-        "\n",
-        "    acc = (torch.argmax(predictions, axis = 1) == labels).sum().item()\n",
-        "    total_acc_test += acc\n",
-        "    test_loss = criterion(predictions, labels)\n",
-        "    total_loss_test += test_loss.item()\n",
-        "\n",
-        "print(f\"Accuracy Score is: {round((total_acc_test/test_dataset.__len__()) * 100, 4)} and Loss is {round(total_loss_test/1000, 4)}\")"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "OGi1PMUCBJR0",
-        "outputId": "8ba19d26-ed9d-434c-9bb5-cb5dce54ab70"
-      },
-      "execution_count": 16,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Accuracy Score is: 96.1968 and Loss is 0.0213\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# 16. Plotting Training Progress\n",
-        "Let's plot the training and validation progress through out the `EPOCHS`. The results may not be very good, feel free to play with the layers, hyperparameters and text filteration to achieve better performance!\n"
-      ],
-      "metadata": {
-        "id": "Bg-cZwbAv5q4"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(15, 5))\n",
-        "\n",
-        "axs[0].plot(total_loss_train_plot, label='Training Loss')\n",
-        "axs[0].plot(total_loss_validation_plot, label='Validation Loss')\n",
-        "axs[0].set_title('Training and Validation Loss over Epochs')\n",
-        "axs[0].set_xlabel('Epochs')\n",
-        "axs[0].set_ylabel('Loss')\n",
-        "axs[0].legend()\n",
-        "\n",
-        "axs[1].plot(total_acc_train_plot, label='Training Accuracy')\n",
-        "axs[1].plot(total_acc_validation_plot, label='Validation Accuracy')\n",
-        "axs[1].set_title('Training and Validation Accuracy over Epochs')\n",
-        "axs[1].set_xlabel('Epochs')\n",
-        "axs[1].set_ylabel('Accuracy')\n",
-        "axs[1].legend()\n",
-        "\n",
-        "plt.tight_layout()\n",
-        "\n",
-        "plt.show()"
-      ],
-      "metadata": {
-        "id": "8EDIakLQSzYI",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 507
-        },
-        "outputId": "96bb6d7f-15ce-45d5-b412-56f9ddc570f3"
-      },
-      "execution_count": 17,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 1500x500 with 2 Axes>"
-            ],
-            "image/png": 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-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# 17. Inference\n",
-        "In the next cell, we will be doing the inference. We can get a random image from the internet and see the prediction of it. Again, the model may not be very good as the goal is to get used to PyTorch syntax. You can modify the model architecture and training loop and see the results."
-      ],
-      "metadata": {
-        "id": "NjDcYgRi5lSp"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# 1- read image\n",
-        "# 2- Transform using transform object\n",
-        "# 3- predict through the model\n",
-        "# 4- inverse transform by label encoder\n",
-        "\n",
-        "def predict_image(image_path):\n",
-        "  image = Image.open(image_path).convert('RGB')\n",
-        "  image = transform(image).to(device)\n",
-        "\n",
-        "  output = model(image.unsqueeze(0))\n",
-        "  output = torch.argmax(output, axis = 1).item()\n",
-        "  return label_encoder.inverse_transform([output])\n",
-        "\n",
-        "## Visualize the image\n",
-        "image = Image.open(\"/content/cute-photos-of-cats-looking-at-camera-1593184780.jpg\")\n",
-        "plt.imshow(image)\n",
-        "plt.show()\n",
-        "\n",
-        "\n",
-        "## Predict\n",
-        "print()\n",
-        "print(\"Prediction: \\n\")\n",
-        "predict_image(\"/content/cute-photos-of-cats-looking-at-camera-1593184780.jpg\")"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 504
-        },
-        "id": "rAsmu7rz5jzK",
-        "outputId": "b04a7eef-79ad-4721-cfd2-406c36223934"
-      },
-      "execution_count": 28,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "\n",
-            "Prediction: \n",
-            "\n"
-          ]
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "array(['cat'], dtype=object)"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 28
-        }
-      ]
-    }
-  ]
-}
\ No newline at end of file
diff --git a/Ex_2_image-classification.ipynb b/Ex_2_image-classification.ipynb
new file mode 100644
index 0000000..f3fc728
--- /dev/null
+++ b/Ex_2_image-classification.ipynb
@@ -0,0 +1,920 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "execution": {
+     "iopub.execute_input": "2025-10-24T17:51:35.264499Z",
+     "iopub.status.busy": "2025-10-24T17:51:35.263861Z",
+     "iopub.status.idle": "2025-10-24T17:51:35.269427Z",
+     "shell.execute_reply": "2025-10-24T17:51:35.268691Z",
+     "shell.execute_reply.started": "2025-10-24T17:51:35.264477Z"
+    },
+    "id": "yXHBPCbmOXCX",
+    "outputId": "7fa472ad-3f4a-42fc-9eea-a316e9ac6432",
+    "trusted": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cuda\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "from torch.optim import Adam\n",
+    "from torchvision.transforms import transforms\n",
+    "from torch.utils.data import Dataset, DataLoader\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "import matplotlib.pyplot as plt\n",
+    "from PIL import Image\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import os\n",
+    "\n",
+    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
+    "print(device)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Initialize Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 206
+    },
+    "execution": {
+     "iopub.execute_input": "2025-10-24T17:51:35.276559Z",
+     "iopub.status.busy": "2025-10-24T17:51:35.275937Z",
+     "iopub.status.idle": "2025-10-24T17:51:35.328715Z",
+     "shell.execute_reply": "2025-10-24T17:51:35.328143Z",
+     "shell.execute_reply.started": "2025-10-24T17:51:35.276542Z"
+    },
+    "id": "nILTbZAITmbm",
+    "outputId": "053775e9-7cc5-457d-c395-0426c1a24627",
+    "trusted": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<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>image_path</th>\n",
+       "      <th>labels</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>/kaggle/input/animal-faces/afhq/val/dog/flickr...</td>\n",
+       "      <td>dog</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>/kaggle/input/animal-faces/afhq/val/dog/pixaba...</td>\n",
+       "      <td>dog</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>/kaggle/input/animal-faces/afhq/val/dog/flickr...</td>\n",
+       "      <td>dog</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>/kaggle/input/animal-faces/afhq/val/dog/flickr...</td>\n",
+       "      <td>dog</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>/kaggle/input/animal-faces/afhq/val/dog/pixaba...</td>\n",
+       "      <td>dog</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                          image_path labels\n",
+       "0  /kaggle/input/animal-faces/afhq/val/dog/flickr...    dog\n",
+       "1  /kaggle/input/animal-faces/afhq/val/dog/pixaba...    dog\n",
+       "2  /kaggle/input/animal-faces/afhq/val/dog/flickr...    dog\n",
+       "3  /kaggle/input/animal-faces/afhq/val/dog/flickr...    dog\n",
+       "4  /kaggle/input/animal-faces/afhq/val/dog/pixaba...    dog"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "image_path = []\n",
+    "labels = []\n",
+    "\n",
+    "for i in os.listdir(\"/kaggle/input/animal-faces/afhq\"):\n",
+    "  for label in os.listdir(f\"/kaggle/input/animal-faces/afhq/{i}\"):\n",
+    "    for image in os.listdir(f\"/kaggle/input/animal-faces/afhq/{i}/{label}\"):\n",
+    "      image_path.append(f\"/kaggle/input/animal-faces/afhq/{i}/{label}/{image}\")\n",
+    "      labels.append(label)\n",
+    "\n",
+    "data_df = pd.DataFrame(zip(image_path, labels), columns=[\"image_path\", \"labels\"])\n",
+    "data_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-10-24T17:51:35.329910Z",
+     "iopub.status.busy": "2025-10-24T17:51:35.329632Z",
+     "iopub.status.idle": "2025-10-24T17:51:35.337906Z",
+     "shell.execute_reply": "2025-10-24T17:51:35.337216Z",
+     "shell.execute_reply.started": "2025-10-24T17:51:35.329886Z"
+    },
+    "id": "zQLi6N2wTmZG",
+    "trusted": true
+   },
+   "outputs": [],
+   "source": [
+    "#############\n",
+    "# Data Split\n",
+    "#############\n",
+    "train = data_df.sample(frac=0.7, random_state=42)\n",
+    "test = data_df.drop(train.index)\n",
+    "\n",
+    "val = test.sample(frac=0.5, random_state=42)\n",
+    "test = test.drop(val.index)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "b94v0Zt3jCJM"
+   },
+   "source": [
+    "# Preprocessing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-10-24T17:51:35.338859Z",
+     "iopub.status.busy": "2025-10-24T17:51:35.338640Z",
+     "iopub.status.idle": "2025-10-24T17:51:35.349682Z",
+     "shell.execute_reply": "2025-10-24T17:51:35.349168Z",
+     "shell.execute_reply.started": "2025-10-24T17:51:35.338846Z"
+    },
+    "id": "3myPEgFsheyy",
+    "trusted": true
+   },
+   "outputs": [],
+   "source": [
+    "label_encoder = LabelEncoder()\n",
+    "label_encoder.fit(data_df['labels'])\n",
+    "\n",
+    "# Transform all images into one clear format (preprocess all images to same properties)\n",
+    "# training\n",
+    "transform = transforms.Compose([\n",
+    "    transforms.Resize((128, 128)),\n",
+    "    transforms.RandomHorizontalFlip(),\n",
+    "    transforms.RandomRotation(10),\n",
+    "    transforms.ColorJitter(brightness=0.2, contrast=0.2),\n",
+    "    transforms.ToTensor()\n",
+    "])\n",
+    "\n",
+    "# test and val\n",
+    "transform2 = transforms.Compose([\n",
+    "  transforms.Resize((128, 128)),\n",
+    "  transforms.ToTensor(),\n",
+    "  transforms.ConvertImageDtype(torch.float)\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "-v7Ypafsm8Rw"
+   },
+   "source": [
+    "# Custom Dataset Class"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-10-24T17:51:35.351409Z",
+     "iopub.status.busy": "2025-10-24T17:51:35.351215Z",
+     "iopub.status.idle": "2025-10-24T17:51:35.363974Z",
+     "shell.execute_reply": "2025-10-24T17:51:35.363332Z",
+     "shell.execute_reply.started": "2025-10-24T17:51:35.351394Z"
+    },
+    "id": "C52lK84ShuH9",
+    "trusted": true
+   },
+   "outputs": [],
+   "source": [
+    "class CustomImageDataset(Dataset):\n",
+    "  def __init__(self, dataframe, transform = None):\n",
+    "    self.dataframe = dataframe\n",
+    "    self.transform = transform\n",
+    "    self.labels = torch.tensor(label_encoder.transform(dataframe['labels']))\n",
+    "\n",
+    "  def __len__(self):\n",
+    "    return self.dataframe.shape[0]\n",
+    "\n",
+    "  def __getitem__(self, idx):\n",
+    "    img_path = self.dataframe.iloc[idx, 0]\n",
+    "    label = self.labels[idx]\n",
+    "\n",
+    "    image = Image.open(img_path).convert('RGB')\n",
+    "\n",
+    "    if self.transform:\n",
+    "      image = self.transform(image)\n",
+    "\n",
+    "    return image, label"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-10-24T17:51:35.364807Z",
+     "iopub.status.busy": "2025-10-24T17:51:35.364560Z",
+     "iopub.status.idle": "2025-10-24T17:51:35.383981Z",
+     "shell.execute_reply": "2025-10-24T17:51:35.383297Z",
+     "shell.execute_reply.started": "2025-10-24T17:51:35.364783Z"
+    },
+    "id": "kXGYsHZam44Q",
+    "trusted": true
+   },
+   "outputs": [],
+   "source": [
+    "#########################\n",
+    "# Create Dataset Objects\n",
+    "#########################\n",
+    "train_dataset = CustomImageDataset(dataframe=train, transform=transform)\n",
+    "val_dataset = CustomImageDataset(dataframe=val, transform=transform2)\n",
+    "test_dataset = CustomImageDataset(dataframe=test, transform=transform2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "uxBXJW7Y3QFc"
+   },
+   "source": [
+    "# Visualize Images"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 406
+    },
+    "execution": {
+     "iopub.execute_input": "2025-10-24T17:51:35.384864Z",
+     "iopub.status.busy": "2025-10-24T17:51:35.384612Z",
+     "iopub.status.idle": "2025-10-24T17:51:36.005125Z",
+     "shell.execute_reply": "2025-10-24T17:51:36.004416Z",
+     "shell.execute_reply.started": "2025-10-24T17:51:35.384843Z"
+    },
+    "id": "AZ9lcDVPm41v",
+    "outputId": "d7a4db06-5ae8-4f9c-e549-f4fc20fc7ef1",
+    "trusted": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "n_rows = 3\n",
+    "n_cols = 3\n",
+    "f, axarr = plt.subplots(n_rows, n_cols)\n",
+    "for row in range(n_rows):\n",
+    "  for col in range(n_cols):\n",
+    "    image = Image.open(data_df.sample(n = 1)['image_path'].iloc[0]).convert('RGB')\n",
+    "    axarr[row, col].imshow(image)\n",
+    "    axarr[row, col].axis('off')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-10-24T17:51:36.006331Z",
+     "iopub.status.busy": "2025-10-24T17:51:36.006089Z",
+     "iopub.status.idle": "2025-10-24T17:51:36.010325Z",
+     "shell.execute_reply": "2025-10-24T17:51:36.009477Z",
+     "shell.execute_reply.started": "2025-10-24T17:51:36.006312Z"
+    },
+    "id": "6DSmM8gPI2M0",
+    "trusted": true
+   },
+   "outputs": [],
+   "source": [
+    "##################\n",
+    "# Hyperparameters\n",
+    "##################\n",
+    "LR = 1e-4\n",
+    "BATCH_SIZE = 16\n",
+    "EPOCHS = 10"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# DataLoaders"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-10-24T17:51:36.011387Z",
+     "iopub.status.busy": "2025-10-24T17:51:36.011146Z",
+     "iopub.status.idle": "2025-10-24T17:51:36.025394Z",
+     "shell.execute_reply": "2025-10-24T17:51:36.024805Z",
+     "shell.execute_reply.started": "2025-10-24T17:51:36.011366Z"
+    },
+    "id": "JRziSP-VJEml",
+    "trusted": true
+   },
+   "outputs": [],
+   "source": [
+    "train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle = True)\n",
+    "val_loader = DataLoader(val_dataset, batch_size=BATCH_SIZE, shuffle=True)\n",
+    "test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "09iCiPRXSjpR"
+   },
+   "source": [
+    "# Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-10-24T17:53:41.394355Z",
+     "iopub.status.busy": "2025-10-24T17:53:41.394122Z",
+     "iopub.status.idle": "2025-10-24T17:53:41.435869Z",
+     "shell.execute_reply": "2025-10-24T17:53:41.435137Z",
+     "shell.execute_reply.started": "2025-10-24T17:53:41.394340Z"
+    },
+    "id": "sdjF7rNyJasJ",
+    "trusted": true
+   },
+   "outputs": [],
+   "source": [
+    "class Net(nn.Module):\n",
+    "  def __init__(self):\n",
+    "    super().__init__()\n",
+    "\n",
+    "    self.conv1 = nn.Conv2d(3, 32, kernel_size=3, padding=1)\n",
+    "    self.bn1 = nn.BatchNorm2d(32)\n",
+    "    \n",
+    "    self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)\n",
+    "    self.bn2 = nn.BatchNorm2d(64)\n",
+    "    \n",
+    "    self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)\n",
+    "    self.bn3 = nn.BatchNorm2d(128)\n",
+    "    \n",
+    "    self.pooling = nn.MaxPool2d(2, 2)\n",
+    "    self.relu = nn.ReLU()\n",
+    "    self.dropout = nn.Dropout(0.5)\n",
+    "    \n",
+    "    self.flatten = nn.Flatten()\n",
+    "    self.linear = nn.Linear((128*16*16), 128)\n",
+    "    self.output = nn.Linear(128, len(data_df['labels'].unique()))\n",
+    "\n",
+    "  def forward(self, x):\n",
+    "    x = self.conv1(x)\n",
+    "    x = self.bn1(x)\n",
+    "    x = self.relu(x)\n",
+    "    x = self.pooling(x)\n",
+    "\n",
+    "    x = self.conv2(x)\n",
+    "    x = self.bn2(x)\n",
+    "    x = self.relu(x)\n",
+    "    x = self.pooling(x)\n",
+    "\n",
+    "    x = self.conv3(x)\n",
+    "    x = self.bn3(x)\n",
+    "    x = self.relu(x)\n",
+    "    x = self.pooling(x)\n",
+    "\n",
+    "    x = self.flatten(x)\n",
+    "    x = self.linear(x)\n",
+    "    x = self.relu(x)\n",
+    "    x = self.dropout(x)\n",
+    "    x = self.output(x)\n",
+    "\n",
+    "    return x\n",
+    "\n",
+    "model = Net().to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "execution": {
+     "iopub.execute_input": "2025-10-24T17:53:45.278038Z",
+     "iopub.status.busy": "2025-10-24T17:53:45.277238Z",
+     "iopub.status.idle": "2025-10-24T17:53:45.315110Z",
+     "shell.execute_reply": "2025-10-24T17:53:45.314532Z",
+     "shell.execute_reply.started": "2025-10-24T17:53:45.278007Z"
+    },
+    "id": "26UI3gTAJa1y",
+    "outputId": "36e831d6-5d6b-4528-8cc6-0e9a2f26e8c5",
+    "trusted": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------------------------------------------------------------\n",
+      "        Layer (type)               Output Shape         Param #\n",
+      "================================================================\n",
+      "            Conv2d-1         [-1, 32, 128, 128]             896\n",
+      "       BatchNorm2d-2         [-1, 32, 128, 128]              64\n",
+      "              ReLU-3         [-1, 32, 128, 128]               0\n",
+      "         MaxPool2d-4           [-1, 32, 64, 64]               0\n",
+      "            Conv2d-5           [-1, 64, 64, 64]          18,496\n",
+      "       BatchNorm2d-6           [-1, 64, 64, 64]             128\n",
+      "              ReLU-7           [-1, 64, 64, 64]               0\n",
+      "         MaxPool2d-8           [-1, 64, 32, 32]               0\n",
+      "            Conv2d-9          [-1, 128, 32, 32]          73,856\n",
+      "      BatchNorm2d-10          [-1, 128, 32, 32]             256\n",
+      "             ReLU-11          [-1, 128, 32, 32]               0\n",
+      "        MaxPool2d-12          [-1, 128, 16, 16]               0\n",
+      "          Flatten-13                [-1, 32768]               0\n",
+      "           Linear-14                  [-1, 128]       4,194,432\n",
+      "             ReLU-15                  [-1, 128]               0\n",
+      "          Dropout-16                  [-1, 128]               0\n",
+      "           Linear-17                    [-1, 3]             387\n",
+      "================================================================\n",
+      "Total params: 4,288,515\n",
+      "Trainable params: 4,288,515\n",
+      "Non-trainable params: 0\n",
+      "----------------------------------------------------------------\n",
+      "Input size (MB): 0.19\n",
+      "Forward/backward pass size (MB): 23.00\n",
+      "Params size (MB): 16.36\n",
+      "Estimated Total Size (MB): 39.55\n",
+      "----------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "################\n",
+    "# Model Summary\n",
+    "################\n",
+    "from torchsummary import summary\n",
+    "summary(model, input_size=(3, 128, 128))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-10-24T17:53:52.571337Z",
+     "iopub.status.busy": "2025-10-24T17:53:52.571093Z",
+     "iopub.status.idle": "2025-10-24T17:53:52.575915Z",
+     "shell.execute_reply": "2025-10-24T17:53:52.575161Z",
+     "shell.execute_reply.started": "2025-10-24T17:53:52.571321Z"
+    },
+    "id": "foAdLvNNZzl1",
+    "trusted": true
+   },
+   "outputs": [],
+   "source": [
+    "####################\n",
+    "#Loss and Optimizer\n",
+    "####################\n",
+    "criterion = nn.CrossEntropyLoss()\n",
+    "optimizer = Adam(model.parameters(), lr=LR, weight_decay=1e-4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "EYnn--FSaQ43"
+   },
+   "source": [
+    "# Training"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 790
+    },
+    "execution": {
+     "iopub.execute_input": "2025-10-24T17:53:55.574097Z",
+     "iopub.status.busy": "2025-10-24T17:53:55.573526Z",
+     "iopub.status.idle": "2025-10-24T18:06:35.148168Z",
+     "shell.execute_reply": "2025-10-24T18:06:35.147325Z",
+     "shell.execute_reply.started": "2025-10-24T17:53:55.574073Z"
+    },
+    "id": "cpFxWYrXaQlq",
+    "outputId": "b38f8a43-b841-4610-b520-8b32e4ea7f2a",
+    "trusted": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10, Train Loss: 3.1645 Train Accuracy 82.0565%\n",
+      "              Validation Loss: 0.353 Validation Accuracy: 91.4463%\n",
+      "============================================================\n",
+      "Epoch 2/10, Train Loss: 1.5413 Train Accuracy 92.2505%\n",
+      "              Validation Loss: 0.2357 Validation Accuracy: 94.5868%\n",
+      "============================================================\n",
+      "Epoch 3/10, Train Loss: 1.1721 Train Accuracy 94.1635%\n",
+      "              Validation Loss: 0.2132 Validation Accuracy: 95.4959%\n",
+      "============================================================\n",
+      "Epoch 4/10, Train Loss: 0.9756 Train Accuracy 94.9252%\n",
+      "              Validation Loss: 0.2341 Validation Accuracy: 94.1322%\n",
+      "============================================================\n",
+      "Epoch 5/10, Train Loss: 0.8904 Train Accuracy 95.6603%\n",
+      "              Validation Loss: 0.1679 Validation Accuracy: 95.8678%\n",
+      "============================================================\n",
+      "Epoch 6/10, Train Loss: 0.7854 Train Accuracy 95.9348%\n",
+      "              Validation Loss: 0.1935 Validation Accuracy: 95.5372%\n",
+      "============================================================\n",
+      "Epoch 7/10, Train Loss: 0.7315 Train Accuracy 96.2271%\n",
+      "              Validation Loss: 0.1686 Validation Accuracy: 96.8182%\n",
+      "============================================================\n",
+      "Epoch 8/10, Train Loss: 0.6977 Train Accuracy 96.4574%\n",
+      "              Validation Loss: 0.1495 Validation Accuracy: 96.6942%\n",
+      "============================================================\n",
+      "Epoch 9/10, Train Loss: 0.6282 Train Accuracy 96.6788%\n",
+      "              Validation Loss: 0.1693 Validation Accuracy: 96.4463%\n",
+      "============================================================\n",
+      "Epoch 10/10, Train Loss: 0.6774 Train Accuracy 96.5813%\n",
+      "              Validation Loss: 0.1492 Validation Accuracy: 97.1488%\n",
+      "============================================================\n"
+     ]
+    }
+   ],
+   "source": [
+    "total_loss_train_plot = []\n",
+    "total_loss_validation_plot = []\n",
+    "total_acc_train_plot = []\n",
+    "total_acc_validation_plot = []\n",
+    "\n",
+    "for epoch in range(EPOCHS):\n",
+    "  total_acc_train = 0\n",
+    "  total_loss_train = 0\n",
+    "  total_acc_val = 0\n",
+    "  total_loss_val = 0\n",
+    "\n",
+    "  for inputs, labels in train_loader:\n",
+    "    inputs = inputs.to(device)\n",
+    "    labels = labels.to(device)\n",
+    "      \n",
+    "    optimizer.zero_grad()\n",
+    "    outputs = model(inputs)\n",
+    "    train_loss = criterion(outputs, labels)\n",
+    "    total_loss_train += train_loss.item()\n",
+    "    train_loss.backward()\n",
+    "\n",
+    "    train_acc = (torch.argmax(outputs, axis=1) == labels).sum().item()\n",
+    "    total_acc_train += train_acc\n",
+    "    optimizer.step()\n",
+    "\n",
+    "  with torch.no_grad():\n",
+    "    for inputs, labels in val_loader:\n",
+    "      inputs = inputs.to(device)\n",
+    "      labels = labels.to(device)\n",
+    "        \n",
+    "      outputs = model(inputs)\n",
+    "      val_loss = criterion(outputs, labels)\n",
+    "      total_loss_val += val_loss.item()\n",
+    "\n",
+    "      val_acc = (torch.argmax(outputs, axis=1) == labels).sum().item()\n",
+    "      total_acc_val += val_acc\n",
+    "\n",
+    "  total_loss_train_plot.append(round(total_loss_train/1000, 4))\n",
+    "  total_loss_validation_plot.append(round(total_loss_val/1000, 4))\n",
+    "  total_acc_train_plot.append(round(total_acc_train/(train_dataset.__len__())*100, 4))\n",
+    "  total_acc_validation_plot.append(round(total_acc_val/(val_dataset.__len__())*100, 4))\n",
+    "\n",
+    "  print(f'''Epoch {epoch+1}/{EPOCHS}, Train Loss: {round(total_loss_train/100, 4)} Train Accuracy {round((total_acc_train)/train_dataset.__len__() * 100, 4)}%\n",
+    "              Validation Loss: {round(total_loss_val/100, 4)} Validation Accuracy: {round((total_acc_val)/val_dataset.__len__() * 100, 4)}%''')\n",
+    "  print(\"=\"*60)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Gc_M8OSI7B5O"
+   },
+   "source": [
+    "# Testing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-10-24T18:28:30.935014Z",
+     "iopub.status.busy": "2025-10-24T18:28:30.934592Z",
+     "iopub.status.idle": "2025-10-24T18:28:44.895926Z",
+     "shell.execute_reply": "2025-10-24T18:28:44.895156Z",
+     "shell.execute_reply.started": "2025-10-24T18:28:30.934990Z"
+    },
+    "id": "dms_ZZwa6-aX",
+    "trusted": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy Score is: 96.1141 and Loss is: 0.0173\n"
+     ]
+    }
+   ],
+   "source": [
+    "with torch.no_grad():\n",
+    "  total_loss_test = 0\n",
+    "  total_acc_test = 0\n",
+    "  for inputs, labels in test_loader:\n",
+    "    inputs = inputs.to(device)\n",
+    "    labels = labels.to(device)\n",
+    "      \n",
+    "    predictions = model(inputs)\n",
+    "\n",
+    "    test_loss = criterion(predictions, labels)\n",
+    "    total_loss_test += test_loss.item()\n",
+    "    test_acc = (torch.argmax(predictions, axis=1) == labels).sum().item()\n",
+    "    total_acc_test += test_acc\n",
+    "\n",
+    "print(f\"Accuracy Score is: {round(total_acc_test/test_dataset.__len__() * 100, 4)} and Loss is: {round(total_loss_test/1000, 4)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "eBUno38p8-ts"
+   },
+   "source": [
+    "# Plotting Training Progress"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-10-24T18:29:55.319756Z",
+     "iopub.status.busy": "2025-10-24T18:29:55.319465Z",
+     "iopub.status.idle": "2025-10-24T18:29:55.678130Z",
+     "shell.execute_reply": "2025-10-24T18:29:55.677362Z",
+     "shell.execute_reply.started": "2025-10-24T18:29:55.319735Z"
+    },
+    "id": "YFgy4bFL870I",
+    "trusted": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(15, 5))\n",
+    "\n",
+    "axs[0].plot(total_loss_train_plot, label='Training Loss')\n",
+    "axs[0].plot(total_loss_validation_plot, label='Validation Loss')\n",
+    "axs[0].set_title('Training and Validation Loss over Epochs')\n",
+    "axs[0].set_xlabel('Epochs')\n",
+    "axs[0].set_ylabel('Loss')\n",
+    "axs[0].legend()\n",
+    "\n",
+    "axs[1].plot(total_acc_train_plot, label='Training Accuracy')\n",
+    "axs[1].plot(total_acc_validation_plot, label='Validation Accuracy')\n",
+    "axs[1].set_title('Training and Validation Accuracy over Epochs')\n",
+    "axs[1].set_xlabel('Epochs')\n",
+    "axs[1].set_ylabel('Accuracy')\n",
+    "axs[1].legend()\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "yeuqIwqq9AD6"
+   },
+   "source": [
+    "# Inference"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-10-24T18:54:58.288126Z",
+     "iopub.status.busy": "2025-10-24T18:54:58.287529Z",
+     "iopub.status.idle": "2025-10-24T18:54:58.541536Z",
+     "shell.execute_reply": "2025-10-24T18:54:58.540954Z",
+     "shell.execute_reply.started": "2025-10-24T18:54:58.288104Z"
+    },
+    "id": "yZ8NArQv9Ds7",
+    "trusted": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Prediction: \n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'cat'"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "########################################\n",
+    "# 1- read image\n",
+    "# 2- Transform using transform object\n",
+    "# 3- predict through the model\n",
+    "# 4- inverse transform by label encoder\n",
+    "########################################\n",
+    "\n",
+    "def predict_image(image_path):\n",
+    "  image = Image.open(image_path).convert('RGB')\n",
+    "  image = transform2(image).unsqueeze(0).to(device)\n",
+    "\n",
+    "  output = model(image)\n",
+    "  pred = torch.argmax(output, axis=1).item()\n",
+    "  return label_encoder.inverse_transform([pred])[0]\n",
+    "\n",
+    "# Visualize the Image\n",
+    "image = Image.open('/kaggle/input/animal-photos/FELV-cat.jpg')\n",
+    "plt.imshow(image)\n",
+    "plt.show()\n",
+    "\n",
+    "# Predict\n",
+    "print()\n",
+    "print('Prediction: \\n')\n",
+    "predict_image('/kaggle/input/animal-photos/FELV-cat.jpg')"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "gpuType": "T4",
+   "provenance": []
+  },
+  "kaggle": {
+   "accelerator": "nvidiaTeslaT4",
+   "dataSources": [
+    {
+     "datasetId": 667852,
+     "sourceId": 1176357,
+     "sourceType": "datasetVersion"
+    },
+    {
+     "datasetId": 8565693,
+     "sourceId": 13491032,
+     "sourceType": "datasetVersion"
+    }
+   ],
+   "dockerImageVersionId": 31154,
+   "isGpuEnabled": true,
+   "isInternetEnabled": true,
+   "language": "python",
+   "sourceType": "notebook"
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Ex_3_Image_Classification_Pretrained.ipynb b/Ex_3_Image_Classification_Pretrained.ipynb
deleted file mode 100644
index 7692943..0000000
--- a/Ex_3_Image_Classification_Pretrained.ipynb
+++ /dev/null
@@ -1,1189 +0,0 @@
-{
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "provenance": [],
-      "collapsed_sections": [
-        "IsSuX8BmPuUY"
-      ],
-      "gpuType": "T4"
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    },
-    "accelerator": "GPU"
-  },
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# 1. Initializations and Dataset Download"
-      ],
-      "metadata": {
-        "id": "TVFbtn6DO5gL"
-      }
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "sI_QrGupQmIP"
-      },
-      "outputs": [],
-      "source": [
-        "!pip install opendatasets --quiet\n",
-        "import opendatasets as od\n",
-        "od.download(\"https://www.kaggle.com/datasets/marquis03/bean-leaf-lesions-classification\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# 2. Imports\n",
-        "Lets start by getting all our imports, keep in mind that PyTorch is not automatically detects and trains on GPU, you have to tell it to use cuda. In case you want to train on Mac Silicon replace cuda with mps."
-      ],
-      "metadata": {
-        "id": "YuZrb1IKPDn_"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import torch # Main PyTorch Library\n",
-        "from torch import nn # Used for creating the layers and loss function\n",
-        "from torch.optim import Adam # Adam Optimizer\n",
-        "import torchvision.transforms as transforms # Transform function used to modify and preprocess all the images\n",
-        "from torchvision import models # import pretrained models in PyTorch library\n",
-        "from torch.utils.data import Dataset, DataLoader # Dataset class and DataLoader for creating the objects\n",
-        "from sklearn.preprocessing import LabelEncoder # Label Encoder to encode the classes from strings to numbers\n",
-        "import matplotlib.pyplot as plt # Used for visualizing the images and plotting the training progress\n",
-        "from PIL import Image # Used to read the images from the directory\n",
-        "import pandas as pd # Used to read/create dataframes (csv) and process tabular data\n",
-        "import numpy as np # preprocessing and numerical/mathematical operations\n",
-        "import os # Used to read the images path from the directory\n",
-        "\n",
-        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\" # detect the GPU if any, if not use CPU, change cuda to mps if you have a mac"
-      ],
-      "metadata": {
-        "id": "uPbJhUNxX-yg"
-      },
-      "execution_count": 255,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# 3. Reading Data Paths\n",
-        "The following code will be going through a nested directory structure where images are stored within subdirectories, each directory is representing a specific label (category). It constructs a list of the file paths for these images and their corresponding labels. Then, it creates a pandas DataFrame containing this information, which is useful for further data processing. Keep in mind that we will read all the data and then we will split it again to train, validation and testing."
-      ],
-      "metadata": {
-        "id": "CELvNP_ePJfH"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "train_df = pd.read_csv(\"/content/bean-leaf-lesions-classification/train.csv\")\n",
-        "val_df = pd.read_csv(\"/content/bean-leaf-lesions-classification/val.csv\")\n",
-        "\n",
-        "data_df = pd.concat([train_df, val_df], ignore_index=True)\n",
-        "\n",
-        "data_df[\"image:FILE\"] = \"/content/bean-leaf-lesions-classification/\" + data_df[\"image:FILE\"]\n",
-        "\n",
-        "print(\"Data shape is: \", data_df.shape)\n",
-        "print()\n",
-        "data_df.head()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 241
-        },
-        "id": "3-hfl-lkYNu1",
-        "outputId": "ba8c9e8c-f5d7-4a65-d393-5e4ae7d8c8c9"
-      },
-      "execution_count": 212,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Data shape is:  (1167, 2)\n",
-            "\n"
-          ]
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "                                          image:FILE  category\n",
-              "0  /content/bean-leaf-lesions-classification/trai...         0\n",
-              "1  /content/bean-leaf-lesions-classification/trai...         0\n",
-              "2  /content/bean-leaf-lesions-classification/trai...         0\n",
-              "3  /content/bean-leaf-lesions-classification/trai...         0\n",
-              "4  /content/bean-leaf-lesions-classification/trai...         0"
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-20284c7a-74a1-4afd-85b9-149b8d9ee317\" 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",
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-              "      <th></th>\n",
-              "      <th>image:FILE</th>\n",
-              "      <th>category</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
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-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>/content/bean-leaf-lesions-classification/trai...</td>\n",
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-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>/content/bean-leaf-lesions-classification/trai...</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>/content/bean-leaf-lesions-classification/trai...</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>/content/bean-leaf-lesions-classification/trai...</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>/content/bean-leaf-lesions-classification/trai...</td>\n",
-              "      <td>0</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-20284c7a-74a1-4afd-85b9-149b8d9ee317')\"\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-20284c7a-74a1-4afd-85b9-149b8d9ee317 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-20284c7a-74a1-4afd-85b9-149b8d9ee317');\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-2e799fb3-0e61-4bd2-9342-b80095b1ed5b\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2e799fb3-0e61-4bd2-9342-b80095b1ed5b')\"\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-2e799fb3-0e61-4bd2-9342-b80095b1ed5b 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": 212
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# 4. Data Inspection"
-      ],
-      "metadata": {
-        "id": "IsSuX8BmPuUY"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "print(\"Classes are: \")\n",
-        "print(data_df[\"category\"].unique())\n",
-        "print()\n",
-        "print(\"Classes ditrubution are: \")\n",
-        "print(data_df[\"category\"].value_counts())"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "fdRSVG1cZCzC",
-        "outputId": "f44deac4-08e5-488d-a13e-bfaf7206dae9"
-      },
-      "execution_count": 213,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Classes are: \n",
-            "[0 1 2]\n",
-            "\n",
-            "Classes ditrubution are: \n",
-            "2    393\n",
-            "1    389\n",
-            "0    385\n",
-            "Name: category, dtype: int64\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# 5. Data Split\n",
-        "Now lets split the dataset into training and testing. Here we will be using it in a bit different way since we will be reading the image paths from the dataframe so better we split the dataframe directly and then read the images. We will do that by getting a random sample of 70% from our data using data_df.sample(frac=0.7,random_state=7) then we will create a new variable of test dataset which we will drop from it the 70% of the train which means that it will be 30% of the data which is the rest of the data using data_df.drop(train.index). The final split we have is the following:\n",
-        "\n",
-        "* Training: 70%\n",
-        "* Testing: 30%"
-      ],
-      "metadata": {
-        "id": "iuKUy94YPNhr"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "train=data_df.sample(frac=0.7,random_state=7) # Create training of 70% of the data\n",
-        "test=data_df.drop(train.index) # Create testing by removing the 70% of the train data which will result in 30%"
-      ],
-      "metadata": {
-        "id": "2op-PmFYZ3xN"
-      },
-      "execution_count": 214,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# 6. Preprocessing Objects\n",
-        "Now lets create some preprocessing objects, first we start by LabelEncoder which converts string classes to numeric for example instead of having the classes as (cat, dog) it will be (0,1) because machines understands numbers only. Next we will create an object transform which will help us to preprocess images by converting all images to same size, converting them to tensors of type float and you can even do more with this object. Feel free to check their documentation [here](https://pytorch.org/vision/0.9/transforms.html)."
-      ],
-      "metadata": {
-        "id": "0ruB8x_uUuT1"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "label_encoder = LabelEncoder() # Encode the string classes to numeric\n",
-        "\n",
-        "transform = transforms.Compose([\n",
-        "    transforms.Resize((128, 128)), # One size for all images\n",
-        "    transforms.ToTensor(), # Convert images to PyTorch tensors\n",
-        "    transforms.ConvertImageDtype(torch.float), # The values are in floating point numbers\n",
-        "    ]) # Transform all images into one clear format (preprocess all images to same properties)"
-      ],
-      "metadata": {
-        "id": "oZD-O96KaDpf"
-      },
-      "execution_count": 215,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# 7. Custom Dataset Class\n",
-        "Lets create a dataset class. Our class is a customized class on how we can our own dataset. The class inherits from the main Dataset class from PyTorch, then we overwrite some of the existing functions in that class. We need to convert our dataset to PyTorch `Dataset` object as it will be more efficient during training, you can use the dataset as its, but lets keep things professional and efficient. First we define our class that will be taking the main `Dataset` class with the concept of inheritance as mentioned. Let's make the concept simpler. There is a big class that PyTorch, this class has several functions inside it, we will recreate that class and modify some functions to match our needs.\n",
-        "\n",
-        "In the constructor function `__init__` we are declaring and our dataframe which will have image_path and the labels. Then we are creating our own image preprocessing object which is transform that we talked about before. For our labels, we are declaring our labels with the label_encoder we declared before. Which means that we will convert our labels are changed from string to numeric classes using `fit_tranform`. We didn't read each image inside the constructor as reading images here will be so time consuming. However, we are reading each image, applying the preprocessing and returning it in the `__getitem__` to read this specific image with its corresponding encoded label by their index and returns them."
-      ],
-      "metadata": {
-        "id": "khRlv-PWU_NG"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "class CustomImageDataset(Dataset):\n",
-        "  def __init__(self, dataframe, transform=None):\n",
-        "    self.dataframe = dataframe\n",
-        "    self.transform = transform\n",
-        "    self.labels = torch.tensor(label_encoder.fit_transform(dataframe['category'])).to(device)\n",
-        "\n",
-        "  def __len__(self):\n",
-        "    return self.dataframe.shape[0]\n",
-        "\n",
-        "  def __getitem__(self, idx):\n",
-        "    img_path = self.dataframe.iloc[idx, 0]\n",
-        "    label = self.labels[idx]\n",
-        "    image = Image.open(img_path).convert('RGB')\n",
-        "    if self.transform:\n",
-        "      image = (self.transform(image)/255).to(device)\n",
-        "\n",
-        "    return image, label"
-      ],
-      "metadata": {
-        "id": "yam-TqxtaGUF"
-      },
-      "execution_count": 216,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# 8. Create Dataset Objects\n",
-        "Now lets create 2 data objects, for training data and testing respectively giving them our dataframes we created before together with the transform object that we created before. We will use the same transform object to make sure all images are preprocessed as they are."
-      ],
-      "metadata": {
-        "id": "bl3y6UYnVz-7"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "train_dataset = CustomImageDataset(dataframe=train, transform=transform)\n",
-        "test_dataset = CustomImageDataset(dataframe=test, transform=transform)"
-      ],
-      "metadata": {
-        "id": "1XfAysFmaQZF"
-      },
-      "execution_count": 217,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# 9. Visualize Images\n",
-        "This cell is optional and will not affect the model accuracy. Here we are just visualizing 9 random images using matplotlib package."
-      ],
-      "metadata": {
-        "id": "g-yR_iOHV8Q0"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "n_rows = 3\n",
-        "n_cols = 3\n",
-        "f, axarr = plt.subplots(n_rows, n_cols)\n",
-        "for row in range(n_rows):\n",
-        "    for col in range(n_cols):\n",
-        "        image = train_dataset[np.random.randint(0,train_dataset.__len__())][0].cpu()\n",
-        "        axarr[row, col].imshow((image*255).squeeze().permute(1,2,0))\n",
-        "        axarr[row, col].axis('off')\n",
-        "\n",
-        "plt.tight_layout()\n",
-        "plt.show()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 486
-        },
-        "id": "PcSuqE76aXx6",
-        "outputId": "20174f5d-5e12-4941-88a1-fd2b8a90dbf8"
-      },
-      "execution_count": 218,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 9 Axes>"
-            ],
-            "image/png": 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tMwxeRXoGxnBkHjIlBpUzRZOxZWL0CZIgBU+2iiwFQuhpKkTASI2kJsVAGiN9dAgZiSqSTKILjjElCqOpKKkAxkjWGWEFslAgJDYrksgoIxFKQJIIoRBZQcogQUmJMgWFrvBBQjSIZICEJJMTGG1RtSIRGHVLKHtk45ECSgL1OSKKhnW5waoCoac1VFCGqCUQELHGjyWjSAjrUUmRomDoRlzMqCiRQkKRiTiEHLG2QlcGKw05JASwms9QwiCZRuzKKkpdM6oTmB4lwn/I+fb3C6eSsnzNauMY3gq2TxX98ZLmekkuHUK9I4czrr2EfEOSNbtz5u3eU1FwVd+gRIUIB6x3hAfN/nAkuYFn8Uv62x0XX9wwv14wbDtCqfB1ZowJd5AoamIxZ/SS+zuP0XNyvWBMLclHuncn2rND6kR9IdDlB2ThoB3YHe8ZTKTtB4LLBPWG3X3m9s2e9hjpXWT0DucLyqJk8AE3jnR+xIeATpGTVCyrimfXa65XDVWXqOYlonvi3eOBsQKfElpIZrOauqlIKeFigJiw1mKUYdwFxJD44ueXxO7A9psdWQiWNytefnWNNpo3bx+Y1S3EPSEMIE6EWqBsTdGsaHQNWhErRVOvUBkEiTZmejMQVWYfe/pxRKUWYmYYTqTBcZ5tafs9QT5iNo6kAr5d4p8KBtfS90fMUDIX19TFDJcjmISMiRgyPgW2pyO7845uGPDRMb/wfPXH4OeJD+dH3H7x+67W3znutgfWmzWLeU2KMGvqaRJrFIMbmc8adFFyuVjx+rffcDyfePnsGZvlEqkUpjAU1tD3PafjkcPpyPF8orCa1byhkpqUEtfX1yTv0ErStmdE36O1RpN5vlmymleMo0dk0EpR2wLqAmEV1ihKrSm15kPbczi/5qd/+sdcPLtg1x5o3YiSmpfrK+To0GNkJWoWqiGNkePhzG53ZLNaEoKnJ2FNgZCaECMxZdaLJeLVZ0ijGVLi2Wefs1rM0S4QuxbI9P3AGAJaK+qypF4tsBdrQo6QAsqU0/SejJSKpqkpr6+pqopT1/P9u3cc256//M23HNqOqDTPvtiQjCQJwGpSjri+JXuPlNM7SJclpWhIIfB0/4Q/tWxv7zk/HTBS0VQ1OXn6rsNqg6hrhBR47wFIKRNTJoRIygJtLKuihAyjH6mbhpQCwTuqwnK5XjM2M0IMWK1wDt58/4G/+He/4Wf/9H/6t9bUD1/r1A3CWpyIBHoSEe/AtxKyIQ2KKKG2DUkETqpHloqhFwzjiJEJIQE93WSquUTphLLQn+DcBU5dS5EU7hTJg0CZGqtmFFKR/EA3dIynkTEk0BmnA70YGaNmiAO4SIiaqphjRaYNgcN4JErY6CUmK1zbQCjAR6QIODxDyLg8okyB0BpFRgqJEQU5a7wMpJiIIeG8J/hEEqCSRjiQWmCFwUVPFhHvA2GAlBPJZYQHjcIgICcQCpEyUjCtkkQNMRERxAwhwRASWnmGYSCOnpKEERYXA10c8YxYmaibyHIemIuMHgusr5GiIMlEBILWJKOQZJSwdGdDnwaC7yErYsx4H3FDxCSBKEtMqbGzhDKggoAAIgqEmhq8aYoiiWmaLGUZMJVB2gVBGGT6w1/rbN+11J/dY1aJ/uhx3Q0qbiicRuQWw46aM912oHcFJ3Vk+/SANSU//vzH/MnnP8EMAx++/5pzN+D3kWPvMFIi5xn31NKpHbnOiLnkJAf6oUOeJY37DCEkVAV5dKwPV1TzGWnXMnZbCumZFRliRM0SxWzk3W87kpesloK4iISg2D4YztueLN4yniS+lRhpWMxmjM6jFCghKIwhSRjx4AUqS5p5w+pyibKSIY4s5zNWl3O29ztW6znNrMTFiE8BrSXWGISSyBDwEq70hrKZIWYDJh9Rg2S5VMz/ZMkvqjXVvELbGccHh2wV0bQkdkiz4zCe8BaOcsSnRMqe2hQIKoLp8WOiaioKXbL1j2zbA/vQ4cRIU9RoJfF9RqHJMjO2FhcvEFkxPI6EzlLqFXlsifGA1gPZDIicyH0kdRGhJTlBDAEQ7I899/sdkcCzStKNmmDhfN7x9HT8fZfr7xyH/ZExRgKZBLTjSHAeKQQhBGII5DzQq5YUIqfziTfv3zGb12wuL8hSsj+c8CGgJGwWM0qrubu/Z1ZZnhtNfzqRo4ccsVpjpCSnSCUNlbUUxkCG4+mIkor1YolWkpATMTgkicpoCmtYLGbs+57z2FFXM4YYuVhdspAKGyKndkd/7hlkZhQD292Bvu1o2x5B5vmzK6qqoiorfExsn54YfUAKuNisOZ3PWG0oZw1n7zl9eE/uOmZVTWUtyU7fB9NUUJc8nY5o69EIJIKQIghFZTWz5QJZNwitcG7gYjFjtV6zPRzYD47nXz6jd57h2FKVBXVRYvqe/WHPGAK2sEhrmDcz5JNlez5jckLGzHBqcf1IkgqrNSlPVIScJYfjEWMNOUPOGaUkGUHKCSUFdVkgyCgp0FVJBoZuIGewWmFkwawsUdpgrEFKicqauqh/UE394ObEzhTGKkoMzidicsRB4pJBKUvO4Iho4VE20pQCPS9YLhf4UdCYBqM1fYicY482BqUiMQhUtBRJM/YRnMA6jdWWRd0wLyqkAJcTcYgkJZFIcnJ0wRGqRO8Htl0kDgMhGyoDRpaonAku0o0tK2PJKHxvEaEmE1CqJ4sBHyPgAYcxFTNVICPkEEk5I6QElck5ARKRBRZBTKATkAU5C7KUIAUpZmLnGbwnxoSRmsZItJAgJTEJUo4gMllIclIINMiMUBl0xMWI84nzuZ0+h8loGUBEsvSEEAltwOqRxWygqBQ2RsZtIEVNyJEQI0mMFKVnNUvYnNnuIfVw6gNuSMQgMMqShcNmhY4GnCS7jJCZsoHoM/mkkVmRSUghSWSkmgi6SYw4kZBSU5g56v8PmpNjKzjfB4p1RNYdqjnTt5ah1QjpUJVC2ILQKWabl1RLy+X2AzfrC15tLtBDwB8HsoPooT1H/KAo65JSFMgoSSeHkhpROFw44jtH0xnqOEdpQxol5ZB5ri5QydDuW+rUUc09m0Lj5RyxDDjd8u5NwD9ec20vIbWETsNB4B56os/M6oarq5qBwGOWdHagqhXZJ4YgiV4ilKYqLNYYNpcLqkXJmDwlFcuLDUG0tO6ELTXWWsjTRUQqgZjoSiAh58jFxYrPFzVj8Q3Lq3uEDFxeaXTpqRc9WgXafUYAZQE5GPxZsRMt274jaEmWEqEjpjySc4lkTtcN2NywqNbE5DgfzzwedrR6pJhLpE2YwlLLhnh0DEdPcivm62syCzp5Qs7vsGYk3ErysEZnTRQWnzICiW9HUpORCKQQ1E3FxcWaJMB5h0XRbzVuTLQ+04+/52L9/wFmQvF0v6UNA8ZYnPe4EMgpT7yEwrJ/2nP77gNt31PVNSEl3t7eThPemAjeEWNgNpujkawWS2KIE8EzePZPD7SHE4UxCKPRWmFMQV0WaCmRUtKPAz4EhBYAJDLeOfowcjqeaE9n2tOJwmhuVpdknTkcdsgEcgwYpRiOJ46PO/b7I33b0Y+O4/nM1WpNBt7e3lLVJevFklnd8HQ4sNvvCSmzXK4ojOFpN/273e558/49x/s7LuqGUhmaskJpTVaSrBX704nbuyeunj1Dx4ipFIW15NJiJGgl0NIQAaMUL68uuby6YlbXXOfExc0VbdfRuYGFKbg0Fjd49h9ueTzs0Voxm81gc8F+8Nzf3jErKyQCLQWF0RTGcG5bqrKcuIZ9RzcMlEx1jhCEEJFKoY3GSknOmeg9ZWERShJTQilJaQxCgkRMFIeiJOVE1/dYpaiK+Q+qqR/cnCQbCbmn0jOkN7SDYyQjSoUuNFFGMo4YPFpn5lXFfKGw1UB0niLPkBj23Ql3GHBdwovIOGS00zRRM9MFMmkymqqsmM9qjJE455BZYGWF1hqXAr0bCV6gC0OKkUN3Zhx6RNKgNNZAKQvqbMljRsgRhMT3FrJCaI2QBikjKUaykrjsIPegC0QGF0acS+QsiTnjciDm6cC0QkDMkCUhZVxKeCEAiYiZ6BM+BIICbSXSSLSUZAFJQBaJKCIISURDmh52EJ6oAj4lXBdoTz1aCQwKVMLWGWsSzkfaYcSYnkXpKWSN0JGgO0JKhMhEDBYjpR1ZbyLGRoYqMbSB7tYRjgKdS+qiImAwUlNmSz5BNwpM45gtA8pmglT4MRJjREqByFPzIqUgpcToeowqKKsGK4p//9Pt7xkcBcfbkhtdsaktdmXYnRznbSCrgFjAeacpU8l6tmb1+XM+378ld2fCbsvd0yNiyAxupO8jY4CmWVEog8wGpTREEENG+IjNCeEFVdZYoVHS4OKIJkzEPwLGjyyrQFGPWKsRdUWaOQ7O4IdLFlf/kBcvL9k+/oanuwMyVJTAYt3w4maDTvC6e+KeRMyBpDJGamapIA+JsjHILKiKkrJU9GPHGDLrxQI7L7jdfqB1PciSsqrRXiBSQpCI3uFj4jyMdMNAVddcLwvmlx3FxT3d0aHkhqE/0w5bskj4bsbuaDiMGSMqjLAce8HeKRSWWWGorKAoAJcZgiP5Ab1YEgrJ9unMh+Oe7Xgmi4jxBnfuEQmKWYGuNO68A/VAufZoWWNKTWZPDj32vCQdLzBxQVlUiEGjDSgfJqKlFOAlXXdkNhc08w3BQ4gJ30ZcGtFzy81XF7/nav3dYyYV39w9sD0fqWYNgczgE0hFU9eUVYWPW97d3qG05vMXL2nqmnHsORyPWGNIztH3Hd55rC25vLqiLEs2myVd33L/4Q4hBNVmgxICqzSlLSiNJcYICMZhRKtpCpBTBCRSKdrTgfdvP/D+zTt2uz3FouHls5doEnFwSJfY3z1AWTD0HU/bPfePWx6etgyjQ0rJzcUl1hrevN/x9Xev2SyWbJYrlnVDWiayFCyWS8iZsV/iR0cdM8YFKmVYzOZUVYPR09Qk5ER3OLPb7TBZonIm+UBGMLqAmZWQpwZYIgDBermgLkuaqqIuDGVZoI1hmzN9iiznc+qiYn9uMT5RJYk7j3ifOQfB8Tzwzet3NHWDlpKmtGwWMxazmpgifGzAh3HEfmxaSIAQxJiQUmK1RquJf+JzoiwLyOBjQJYFWk38TCEERmsEGeccGSAnRPrvmRArdMQNnloViBjxXSBljUSSpCTogJKZlAQyVdTKUNFiODDERNu2kDRj8CSXGR34JBn6QBUjtVLMygZtNd6BVApBJkZPjI6YIkJYjDYkBzpabCgxUUPoGIfM0INB44XA6YjSgnk5I0bQaLLscQzknNGpQqDRUhEEpCzIPhG7SJJ5Ik8FzzBEspBEYPR+IhVJgbUFEkg54nPCx0BUoBDkDAhQWiIKhSo1qGnNE3LG50xSEXIgCUXOihgT0Tlibkl+ItG6U0QOYKxGC4kyApkyOUcyAZ895y5yoCDlJa5QjDHi8YSYSCliCEgcKTmGOOB1wiwTdg9GWGrVUKqaLDNKGLQwpDEx9go3JDwOawVCwDAOuJQoCkkhDQlFiArnFWMKJDHgazUpkf7AYXUNh2vm4YZZFrR6pG4UCo03nqHu6PIZQmD3+Aaztmgf6NoTj68fKY41CzUjIggpo+uaF1/8mPbhkWPfsW5WJCkQWWKpmeeMzw4tp2MqJocPHUk5KCKBgd4PiNxD9MQoEd6BDyzKZ1z/6IpXn/2CIDuwmd3TAzIuWK4VP/3Rms1ixrvvn3g8Htj3kzIlJsHMFizqGaU/k2cZ143YJIjjwPHokULTXfRs+y0HfyJmgZCasiogeVKIxBBIKTH4wO54wmdBO7zDbDQvvnxgyEcGL2m3BU8fRnbnkVwMmLpnt42ce8X68jlXsxV6u0BjmJkZ19LyjIJ5KPFO4r0DI4k1HEXHbbvjvm3xEoTPtE8OnxP1MmJnmtUXC7rTE6F+TRde00gHLBjZI0yPXipMV1GEmpktEVEQU6SqS8qLGTJlzCg5nb+jmJ+5vFpj1IrTIbA/7InKs7hYc/FHX/2+y/V3jlN74v72nv3pRN2c6UJg3w8UdcPnr15R1zWrq0suhxGZ4fpyw6yuGMeK/f6AGwZyCHTnDh8S189qZqsFCCiKiu+/+Za7D3c8v7nks2fPsMYghUCQCd4TU0KmRE5patZTJHiPMBJta0KI3N098vrdB5KSXFcripkldj11Umy3e46PT+S6QimJd57j8czh1JJSnlRgwWGqkpgS797fsn31ii+ePed6ueJmvUEKUFoxjAONeU5wnvl8xbwoOLcnZs2MeTMn5zRNJg4tD09POD/y2Rc/QlqLMpYQM8dzx2a1xFpDiUJ/fKk0pUVKPU0UlWZdl9N3SwpMVVFZS3COoeuY24LNswXD4Ig5EXNCZIlWBmss86amKgzKGELM5Ojp226a9htDVVcU2kwT+JQwWiOVnCY5aiLZSsBIRcqZlCVKSUSe3t8pJYbR4WPEOT9Jk2Nk9MMPqqkfzjkpoA0SHz0xZbQqEbLAmBKhBdpkqkZTJ4FNFSJOMtR8rGn7kcfHA8MY0UWBKmpk0EQ3dbrSZURlkdpgS43S4BwMg0OIRCQyeI/4yI5PCUo1eRt4NxAOkRBLpNdYVSNSTTdqrLFY2WBsiRIRn7pJvYJFUvyN+CWmRPBAEkhVEBQEOeJywpNJMU2yaCQyJWQWqCzQWhKyYAwOLQTGWKzVKDF9RpUNWUmQGXIkJjlxRpxH2Yy2gBCMKTM4j88j0nhkcuiYqITG1kuMtmiliCRC6/CjRxhDoSLRwWksUaImGIlsNMKASGHakefM0AkePygCirOIiMZhUsGyXFMxQ2aFMBIpNEoqEBmLISlgdAgMIiVIIKQCU5ClZQiJkxf0QRCjxIUBpSD9sJXi32tcmmcU5SULX3H4/o7jKaJ1yWJTk2aBczEwpD1V17B//Wu69p7t3T0xBsY+ohEoZbBYlNaY9ZxX/+AXfP/rX3P+9oksQa/myNWMolSo05b+8EQYRnrX4bppBG43ifKq5fzwwP1TJOwddmYJaSDKM5cvZ/yjf/hHPFs+I4ue1/ffMbvw/PgfJ/YPHVrXlIvI3fbE19/t+fA00I6B9aJmWVes6yXrzRXLeObs9oRsyWMijJ4QEi6P7I47htsdYzrT9SNWK/rzHj0KcsykGFFaUZgC03cMrePUnXg6j1yKE+O5oJA3lOkC2bYMt5Eeg1lkxjh+XCMGhFWsqzVKVazVnAsxYzHUKCdA9JR1pO07TsdbXNTc77fsTwPVsiS0mTwKkJp6XmLLGXKuKRqJkwF1KhAhkpOhZU0xLyk2BcoOyPFEHufEIxOB3UoWny0JKVPoNa9e3pPrM4tlxiSF2DqG+xNdF6ivSuTs912tv3v85vU7tuczgw+oKDh3jt3+xAqNMZZmNmN1/YzN1TXvvv2WrjvjxjOzpqa0hhACOQuKekZd1ywXMw7HI4+7HW4Y+e13bzgeT5RVMVk55EzXtYxSTryWlKapSlnR9xP5OWbIyVNUFpQGpfDAbDXn+uUz3DigsqQ/95z3R8qiJPgIOaOkRGsNSFKOpJg5nlrKoialTFNPfjzkTE5xukiTSSmQoqcQfPyZAs2CrrDUszmLxZLRO87vep62e/puYLVaMK8KinrOeragG0aeX1xwubnEigx9CyGglJqal5CQQlBqgwB88DRFAXVN7xy3D0887XYUpaWqCtabFWXd0I+OstizrAsuNxvmTU1ZFgzOc3f/wNPTIzl6mqahKmuMUuSUyHm66Fpj0Gq6eIoMhTUYpZACQowIAVJOtAuEIAuQChQgxESqTzEQ/A8TTPzg5sRIQc6SoU8Ir6mrGmUsQimyyEhTUBWB0B3JLsGpol4ocipJaUPMilN7RnSa9bqiVCU+CYiCYYBYGGxZUi8s4+hJbSQ6AVmSw7RaUVqDkBijKGSBTIpTPNN2CSMBESeSaigpyzk2N4hgECYTciRmSc6SkAI5dISQadue82kgBYFMAlU2DGrE6UBSBmEzOkGh9PSwQgKfMIKJ6KcVJmqiBowEGchiQESHigKZLZmMAhCTIkgQERGUUAhlcDmTciIHgRAGlTyr2lCVJYYZihofEiEnEA1ZzulFi8wtUXhMUhPJFoUxmlxFEh7/kTPjY41xNX40eD9Ad0QFPRVb0Ig8NSVKT7JMBBipiSohjcWIGqUNzA29iASrGFKkjZ5eBKLKSCmJo6bdBVx3+vc72f4ewt9rbLR8+8s33N1/oDRz6pnAHkfUAvI8472kTA22h7F/pBQFwwAXiw2iV6TM9My0Aa0YteTZL35GtTlRtQH5Yob64hJVg3zTTjyUMXBsD3THAWMVulDcHt7w5tszu4eCpDO6igwhELQjGUHfJxZfrDh0e+r1DfX8gqI8YO2ZD9/3/Lu/uGV7b9k/RRA16EzCEqOlrpfUVUHuRlSumV/NkCjc6Bj8QDCR1fWC++4th4cTx13HZl7iesf+6UTOmWbWcHmxoVnXFCvBw4cD5TlTzArO+0ghb1iufwqFpy4zLy5ekNFAxquebuw4vj3zdP/AsplTxkBReELo6aVA2IyverAjse2REQpvqOoFy41HScE4KOabhuVyzuJ6TjGvSPSch0w8WUTxGf44RymIixcwz8jlDr3u0R7kQTO296ghURYXiHlEFQVpZrGnAmFeUJY3qGyo6m9YXjxRnApS6dgfv/99l+vvHH/266952u3oXeQnixUnN62mr64u+MUf/QhTap52R95+/x2HpydeXF2y3iwYxp6QA3GYpgxXNzc0VUVZGOyV5cOHB8aU6UKkdY6nw5H98ciqqTFaM7iRGBOC6bK4PRx5etoym1X0ztHKxM28oqpKnj9/hkuZ9c2a9eWC2+++44sf/ZiDO7BeragKSz90HA97yrLi+vKK/bnn3LYopTFlgy1LXr58ybqumTc1IXiObYsP03RACJACrDXMazuZlylJXZWUhUGQOJ+PvH//ntu7e66vLvn8xTOaqmIxX/Ly4pJx6LherRlDYBgHeh9xzmGt4Xq9wRpDFpnUe7r+THQeoyZ7jpOP5ASb5Yariw2lnVYsRllmZUlZaKQQzJqaGCLaWEpbsN3u2R+OXF9sOBzPdN1AWRYoJWnqCi0lShZIqTh3Le35OK2YqprCKKKUhBgQgCktIMjeE1zEe4/3juADhf24KvoB+MHNye1DQCAphcVai5YakQX4jMwCrRX+1HM6tqTeURUeWykwGSU0q9kGmStC8FS6QIgCETztacCfBSvTsVgXlAhCCtPLOgtCyHg/kS3LYnrYWmqUklg0ehToCE4VRB3wKeCTxEcQRDQe5zwhOILrp6ZCBgZ/pu8jQ+/xOSCVoClXLOsVUkFmpE/TzbAyitIoSmEnp0s9kWNjDowxkHKeSKVGEoQnqYFMS3KCOFSIZFDKIORk6JaA4EbymEjSkVJGxkgRBCoIpMxUC5hJMU2LoiAmgckSaTRRGsY0MZ+zGJEpomQmZDgdexgdOUTGMaKFpCwMytSIYJFnQ0wKW4/T38cZlNcQA4Lw0c9FTmRdJbBVQakqziGSKsmQHFG2hOw55ZY2dsgcmWlLWS7w3YiPf/hSYnfwSGEQg2VlnzGr5hgNbmjp+5GwzyQ7R61nKBtJA9RVSR4VZW0ZcfjgcCkg00fjvzjQLDdUTc3w5nuc/A3j8G/IwaFDhqRJKOqLJYvrNR/uv+e3H554eNhx+03mYr1kNStJxkE4M5rAYbjjv/nz/4rj+QN1M6dazeEMh23Bm6/veXpv2T0MPN31+CEzny/ZrNZEH4gpEZH4viceO1QCO7O8/OJLdKHYHe45jEeO44EPt3c8vj9xPgisi7SFp3eJ3eHI0gWKSoNyCON5/rMlL6sb1EYz7u5xY8HuMNCfHPO64aKpGbvI+XBmNl+jlor74wN323u62JKKiLCRnEaS0ujKYpsSMQjSrgMEy+WcF4uC1dxz3G5593BCLOcsPntONS9o2y3eDDzdB847zfU//YpTOGNlJEvFoXX0qcOYEcMRW/ya9JM3JB+R55c8HTzJrlBjzWlwePlIXI009QxXjEShMWWFHz2+/8Nv1l/ffsB5j4/QduN0JlqJKTwpbRm2kYfvn3i4/cCHt+85Pm35z/7z/4SvvvyK8+nMd9+95nw8TKuG1RwpM0rAsxdXrNaX7Lsj//bP/oL3T0/861/+Ej8ObBZzYgzEmCffpbJkGD0RwW9fv+P28ZHrrz5j/dlzKmm43qyZbxbMlw1v37zm2cU1N1fPWRZLRI7EOHI+dwztir4dgVsqa3CjoqpLosgcz2d++uOv+Nmrz2mUIaU4GSDmjJACpQSzqpouiTmzOx3ZHw8UheXUHRFSce4GEIL1ek1T13TdgJaa1cuG+fqCWVxQacvTdouIkWK+JJMJKXA4n6YJUWURITMM/eQQKyCnxLypEULiRoeWmRg889WaEBO3T48cjieeX19T2YJqUSKU5ulwhJxYzmZsVitSDJRlSSRDzsSQSDIjnac/ntnu90gpmM+hd47Re6q6wXyUewcfEGJyZR/HcfJGkoqqKVgsGuazHzZK/MHNyegkKQ9kKRncQHYZKyylKtBakpwgKTBpTkKhk+X4IZKNIiomZ1UpyVLQ9yMuJA79yHFoOez9NGEwnt3ZoJOGUeL7QAqZomoo6wIxTYvIJJKYdluF0gjVMCrDoHtC7Bh7j+hHTGlADITk6KUj5wjZIAvQTaZpBEVvCQhcdlQkGq0wwqKCINMRsyebQFKSHCWSiawYoselSBcdUmQIkRjB65FceJKMuAjOeayQLI2igI/jx0mSdd53hNNErjPCUApDIQtMpVk0k/xYZksYNDkLYgiTPBhBNhZtFBqLGntkcgw+cns8M7ge9XEyUxcVWoBMHnwgxARYskyIOpLUSHSBOAaMtohSEWPEJUdgIAkJoqQlclaZsxiIweHdyHns6caeQgtsWbBSFUHZSa//B45FVWO1YVE8Qyn98Tbj6eOJNrYMytOKlpjj1JCqgdOpJfWSbCyXF9cUs4Y2DYhtgSeSwpmud3TnMzr+FsITOl9RqmfIpsY3PaUS/OJPf8bLn1zz21/9v/lX/8//FtvVrF4aNs+X2EWiyyfudh1ujLgxst1v+f5bwbqYsbgqSNkTYqTfL9l+CMhkuV5aUj2x7U2hadYr8J6+H1EuM7cFVVVSzReYumZ33PHdm7egIYSA7mtqaRiUp+8Tb+529F1Hdx7wMSFJLI8l159dsflig7ySdEPk+HWJOIGYOUyhsEaTXCS4HpRn86Lgp3/yBbL5U/7Vv/g3/Ju//kt67eizJgRLSpaiFRRJs1BrisWculmSiKx1RFw5Urnl//F/G7nfPtHsr+mdYfvhW+rVgKojl5drpNzyV3/xDaZc8KN/8ILVzZpCrNFqRo6asX1HHLcMw5GZEKxtwfH1DniPuHmNXd8yxsBwsMRUoPUMZeYk/URRu993uf7OcTy2IBXWGr7+9V+yWNSsr2tc3vLrX/8Fbit59+HI4uKKbuM47XcMw8jh8ZG2O2O1pCpKZM6EELi8vmS3e6I9n7m6ueG/+C/+M5qq5Jtf/5bXH245ns589eolL6+uaGwxuXE7T1NVKKXpxoFy7Ll6dk1Oid35hCkNX1xfUFtBra8pdU3fdUgDvusYx47dbofrHSlOjUZT13T9wDCM3H245VwU/OnPfsrz6yvGruV8dggpaeoaJQQ5BJqqJKXIeRj5+vX37A9HCmtQWuFdoK4aVrM5V+s1hbUIMouqoCkM2XuG7sy5n7y8tDYgMjFF+vPA/dMTwQeWixllUSCYlE5td2JwHm0rQsz0w0CIgaau6PqBY9ty7nv60fP69Vus1NgLzTgM7Pc7cvDM64roRxaLOXVVYYSkrGvOfUvOkOKk6luvN5OsOHqUlFxd3+C953Q+4ZwjhABC4nxgGAMxZaxWICXD6HDu6QfV1A9uToaxI6UAUiB6QR4ga4GuNFpNREArLEoWRBFQSjJ0jgSgBNkIpJ0Y7ufzwOkcaGMgBckwJu63J8rC4H3FzGh0ECA0tjZTV2YFksmuOsk0SRMBqQQmWwIT4WeM4+QKGBRFHuizx6dILif2sOsdImnm1tLMLaLWdH7koX+kHw8kuSKSMSqwKQ0yRfp0ohdnlFpSsYKsCDHjRGIUAQkM55EQPBSgFwpMhQ+JISmSElQ5ogjkIDDCYAr90cQsTisVKTEiU1uLVNMNPJeCSk4GaFKDExBSZIwjQxhIRNCRrEZcDuQik06e6BVWFTSFpbE1loLgEi46UGBsRU4KlwK4DpEKENP4ccoPSkQ8XozEkNmfB04dnHUipolR7ntPdx4Yxp6kE52IlGUmeYPUf/iE2K+/+Q2fPbthUc+pCo0w06QpCUNKhjhG3NETBs2OjlBEnl9dMJc10hQsZ2uKumDVRNY/rXg6/xYz/xd0YeTw0JP6jtvfZF798QuuX1aUJKK+RxQP7Ic7xOsbxsOBV882cDry4vKSZD27cCAMAestTazIocMMAnEAX2Qe2z1j6wgq0u0Uu7uRAsXlcsV82VDWM4QuAYkTibYd0fWM+bLAaEFZW3b3H3j37h33Hx6JObIoLM+qK3J/5iHvOXYjRmoKU2JrwRAiT/szUsJ1TMTtyLgNRJ2olSTPmRx2DciQ0VqxWs4IymBmI/qi5eVPn7N8+c/o/ssj//qXf8nQCYysuXz2jPV8gUpQ6oJxlnDjSDseSSmSzi3lywM/+WeJ+W3F6qZm0RTcXCwxJZSXCV15Pvzyrzm9zuz6e8b9kfXljOXFBYura1RtCF1AH17SLP6Y57/4OceHO+qLQL0sSMsloqywqsQPjuPpnsHdksv3k1/FdAr+QaOoCra7E1kyvTCJDM5x9+HI+CRYlys2iwU//slXvHr1kn/9r/8lunCTvcThzMPDE9YakneE4DFa8fL5S+7utnz39TdcX13yi5/8mC+fveDr777j3fsPeGWQVcW+PbPf7Xn1/DllUVAXBpkDTWVYLGe44Ggaw+ZKgXjPt99/T/sQsHnF3Yc9MQaeX12jrOX2w56723vqsmQ+m/OzL7/gx199wdPTEy8vr5hVJS8uL7l/ePj4whXYjzbubduiyayaipAFu/2e2/sHHh6fUNoyn81YzmdUVUFTFRhjKaqacRgQKWGFIPQtw/lE23YIISYzNDHFSMicWTYNCMnNxQVt13E+n/AkusERYkQqT13VzGYNQkiElOwOe4RSVEXB/d0WI2BwI6euY/COYegn11o5ZeYIrVB1SVlWlGoixuY0ycKV1JOpGwlygTWWcRz55tvvEBLqpsZISfRhomfEiBQACe8cIU4qnx+CHy4lHiLIKX9mXswx5cSWliojNZOkNOdJqSI/NhClRuXJNjiEkZQ9Q/Ds9x3Hk8IljaJC5ECMiZwkha2o5zVVaTFaIbMke0lOafITIeMJKDGi5OQ5klOeRtBissqPKdN7RzwllM5UUlM4Q1EUKJNRZAoHU21JTLZULHHR4fG42BHCyHI+50LVHHzAhUmJFEUiizD95YJERwEecjQYpUFNo7SUBBJNYQRiHBhcIBkwQlLokkJKmllBFnn6nTmihKC0BdJmonZk5QjRoyjBABaii5y7M/thjygjykROxwNyTFx9tkLazDwXrMycxhQUHxnWSQWUERAlRMiDIGcFQX3szqfGxPHRR0V6gojEweNjpg+Z0xAm5VMSCA8MmXgKDNLRC0lUGSM0Of2w4vv7DK0UznlSFYlEYu4Z8hGvAgiFDJJFXjCbbbgNdziRsLXh8vkVSShEgBiO+OFIsGdWLwJ9vmOOQdz03P4WuruCe/cNN+JIsoL+8IjPT3zz+g2n+xIOV4jB8PR4xpYRvYBedVR2zrOLOc4VdG5L7jXGLYm1opVHHu47ynJJERuuyojKik1VsZot8Ei2xyOdD2hlgIyLA0EZMgLhOtr9gXA+YjOMIaOsIvnE/tDSdQPBDeicKKyBnFmWJZvlgovFnNhHuocBWxi0heV1hZpL3t3d4QfFSl/RWAs+4n3icDvwZ//3v+ZXf/01F883VCZSOMPV6oYXN6+YLVZkB25wBBcY4kBzCX/6j16xOx/48z/7a0bnWbyUzK5qlkvDvDakcUk/JrbjG+7uztzeKq4+2zB8uye3ibNrOXx3plndcv3jms0zjV1rXn31JZcvZ4zhPW72hjhr8ZzojiMcZ1Thilr9GMWRMLbsj0/0vvt9l+vvHOvNjME52rZluZmxvm7o2pHt3Y600Xz5Ry+QQGxPvHj5jLsvP8OpxIfHRzoXGEMmhB6r9gTvGPqeUluWVcnDhw/86ukJawr+6I9+wX/8T/4JX7y6Z9FU/PTHPyKMI//in/9XbM9HpFRIIvdPj5xyIOYBUyXadGT73ZGH91u+/809p22gUSXzekFVFAQfp4nEcsnzP/kF/TDy9LTFu5GXL1/wfLngcrnGWs35sCc3DR0ghGC7O+DGkdIonl9uUFIgUCxnc4KLSAQ3VxeTdYW2E4+ldxTWUpUdhdYsC4uuSqQusTEi23ZSMKkEQvB0OPLweM96PkOIzNP2iRgTTT0jBI8PiafHRx73k5FdP4xorXl2fYnWmtl8zu3jE1rC9WYz+bmMA4Pzk2matSipuLzcgBR0fcf1zQ2bZkZ3PNIeT5TWMGtmCG0YnePcdby/f+D93R1VVfDi6hlKq2liEhwxTOrCsiyQcpJCa2sYhx9m/PODm5Nnsw1ZCGTW1LLGSA0iEsLIOPY4N6BLiymn9cvoe0KOFFZTFTUxKs6947iP3N317PcJredsLjes5x5TQ30hkLOBvHCIhUapEroSv9ekKCi0RShNjIGua6eMDW1IOeGZ3FnjGBnGEWLClAvqqsI6wdzUFEqjpEBZjy7OIDrGoSC4kjIvqWymKCWjbBnGIzp7ZFTTuAaFzxmyx0qJNIYmgw1gKourMgMeJzz5I4NbK4UxEpwmtp6jPzCrBatljSKjlEbYTFZpMjWLCi00UoIqDZ060Y63WHkGbYlCM/rAEM504xFyQPSJ+3cnrCy4fmkoy4CSnoJIXYAuBFEGAiNCJkxoMGNNTgknHFklRJrY7i4OkzxPZ5JJIAzO+4+7tEToHKTJRdRmjVZgC9ASVmpGmRrqoiGL/5Dj7e8XLjeXNFWN1JqY0vTdMGaSvneJuVqyXGqGGBhPnqFIbNsnvrz4Mc3FJcOp5fbXv2V/d0+dSlazDUn9AlVdUH72LTc3c/6jf/IVcXcL5+/49q+3vHvnKGvJs1eG0GXUPqI7gxU1w3mSaPpixC4q6rJEyZocEzJZTKgY2zPBJopSohBcztYs06QwmM0087lF2hnaFOzbgXF0CJVJztGeWpbr5d+w9yfLcUuQkgScT2f6vkcDQmj8mCYnTyOxRmOtIeRE24/M5vVEtJvXPP9qQZuO/Oa3A7HTnO2AahbYqsZqAWNg/2HLw92J3TvH/tgjfEHXe9qup+sCvo8IFxHJEUXPUPXk3V9xdi2DOdJvJcUmUs9HgthybAtslii55vD0lseHjF2Cz0fmi8lIclYrFlcr5rOKynisPFBcOEJd0Q4SbR2lKnn17AohJW/f3PKwbQmArgpEXjArZlxc/5jd8Q/fhU2qzOXVnPyYWV9ZlPaMfU937jkpy+lw4MvPXlJoSTif8G7k3buB0iekz5QKVrMFpTW8v3+gHd7RnU48e3bF0J35/t0HqqrhxfMXfPHqJb/48VcEP7J7uMO5AZkjt7f3lLZCykybHavnNVG/Y9fv6I8OzjX5MMOmyOn8np17otInnl1dET66oLa3PaW10yrkeKS0Gt+1rOZz6rokp8RmuUBIyfF0JqbEODrIEWlrUoocj8eJl6kkP/3qR9ze34EU7M9nng5nBjdCTmipaKqS55cbmp/9jLEbMHWBKWrKckbfDXRDy6lruX14oO9a1vMZMSeGMaC1YblaIQRUXUNA0Q0DHx4f+e72HWM/IJRls1pw7B55++49nz17zuVmAznTtx1SaxZNQ2UMzjlqY1itJ18Z5QZkWWKVIVU1QojJ+bdtyWSM1SyXcyBjrWboB4RWeD8Rg+uqRGsFQpBixFrLMLqPZqZ/O35wc1LYKewrY5BJopSYZKcIxjEQciKMkSyn8VBwkTG6yfXPe2L0DKNHJMOi2aBEIoySUlnKzZzqClYLg6CnVwNCWCQLTJSoKMlRTLyVj+ZnYZzGTElPEq4sMyIp6CZraqkjZWloTIVwkVpWk9GN1CjlEHrEC48nk5PECIPUGdsMRHPCfHSc9tFNZmpBQlAkJjdXi6KUmrrU6NLSypHRecIY8N6TcyDLQKEMTVEhoqETjkprNIKcJna3LQVqPrnT+lbhncQgyDLSMnKWe+aVYl4tcW1B6zKD7yYClpeIaLChpilqOGtknmTLSTu8EXjVE8RkMa+lQYaGQhYkwKsE0oGY5NIp5kmujMfngUxiDNP/IY0JukxpJAtdUusSaSVpniEmRITYJgY/TAZif+BYzuZUZTl5zqSIiAoZDSaXGCWxYuIJ9d2ZU9uzH3sELef2idXzJeaqYnDXyKVBFYHHN++5P5yYrZaUxYzFQtMe7km7J06vD7z5TcfxbLh4Nse1BenssM5SyBqfhonIGSPJKXw3MuREYQyL+QpGRW0quuggzjF2IGfFYjZDWEkIAVtMtt5IgZUSjcAWFTJ7SploygqJ5tz2dEMgKQtGEIYTGMl6veKZi1yh2R2O5JwprcVoOXkvJDh1npQCtihQQ8YuLjkfjpz6FjlYKj0nxch52NLIBiMt0lpihsM2cLs9EURgezjjA5hsUbnEjRGRI9ZEhHb4O0c39ngi7dEwGstVbeikJ/g7GnPB5uKG0sDX3wjcMfHyJqNqx0pbuiNYqagqRaEV1hmqc4EoWg5yz+7hV1gsf/z5l7yon9HMZqzlA/92+IZfvX7Dw917XPYsZ5p/8g//lGezz37f5fo7RwwjpTU8u1qxWdZ05x7fThefGDxuHMgxTT4abcscw6EdeH/7ASslwXny6FFK893rt+xOU4BmM6vZrC84tQNPTzvevnlDdzxwsVrw4uaa5WLOfuen2hWZD0+3NJuKqx9X1MuBc/eO8ZixbslN8xmri0tuljskiv1+S/ZhmoKOI/vTmdOpw3+022+KgufXFxzbFiEkVd2jteTu4Y7j8YzRmrKafFGMlggBMSZc9owhcDh2zKqSRV2RpOJhd2S7PzCMHsh475BSMI6OF9c3ZGORhUFkplDLoiKEgECwaBrWszlSKPquo5rP+PKrH7FZrxE50h2PlIVldzoTc+bUdnRtT13Xk19JytxsLri5usRaTYwRkzVKT6nNGosWghQCOmcuFguc91ijKIzBFobjueVwOJJTZL2cM6sqGlswr0oO5yOjD4iUCN6jlGS5XmFswbnt6LxHBM/oBgr937NaZ5STL4fvBwgJo8qPyhWJLgwKyDKTY2TsR5wPxBwZ80jL5FytlGLRrFluBG3fcf/2RCEkq8UV9SxP5FYKEgbtDaQSBoNKatJOx4wUidpYZNlAhCQyY5zM0bJTMGhMMFM+jJ6aH1UIrJo02VobEoIwNiRdYIyemgEimQg6EHMiOE30EaEyMhuKpMlJIzLEFIhqkt5aKaFISNODOpNjD95PoX5ZoYViUVvKpmQcAzlJlIiEmOlCz+gSeXAMaWRoBTpG5kqjgucsMp1sqaoRO0u4sSJHjYyCKtforCm0pVzWNEVBHYppRaYdaM8oR2IOeB8gSyozx5BBZgKJTg0kMWClxUtIUTJ6T5tavOnQQuDbiCotpa25mJUYIZiraRdZ2Em5RYLu1DH0I9FPxOA/dBRWI8Q0RchMoVi9czg8GsNxPDHGQKtGYulQDIhskDJQmUTIEdFkYhIcDye+//oDDx86SjGwvlB0n2v2tyMcBkQqcSLhiZw7T7mVLCmxuUBLgRQWWyiCHoGIsQpbT5K9HAQ+B5INlKFBuopDvieVUK012lp8FxBJkF0E6amtpVcjs2Y+SeJFxJYlERhcIKsCVVRI3yHkwBgSCdgs5jTNHJEi0ihuLtaTDwITNywET9eeOPUdaRgpd3nyPBI1i2KD0RU+O5IIxDhME0qtCDlwbnu2w5lQRLrgWUlNGiMpjPS9Zww9pghUS4ltDeddiZSCw2lPvlD4rsbFAaUE9WKGNjWP97e0u4imAh+Zm4xuCoRTyFhDqIipAG+RUePPFvkiMIQTPiTk9ee024Gx7QkMCBLjOHI4HWn9yPEs2My/J3UP8D//vZbr7xwheATTWlolje8EQzfxDs5q4N3dA+RJUmusQSvDP/zZH/PPHx7Z7fZ45wCJlorTuWMYPX3bctjvOXeOly9f0jQN4zjy5t073rz+nvtn1/z8Zz9mtZjz4sVzPuxuGVWPvpyS7Ge2wZ8C1ViyNBcs7IJSKp5vNpg/+iOc96QwvUiPxyNSKI6ngf2pJwVPoTV1WbNaLFFCYKRg1jQcjyek0pR1PfmqjAPn4xnvA7OqQRpJ142kDIXR04TVFhTFjpQzg3OTIWeISCF42h/51Tff8h9tn3hmC5RUVKVFrNdU1mCUpDKGcRg4HI4gJc+fPef64hJdWAiebAtYzPExUmrJZlZzuZhRFwajFUppfFMjgafddrICkZOPScoZMpPthPPEGNBJkWMiDANKKaqixGhFZQ3jOKK1RCjJ+dzy/dt3jN4BYgr0NAZrp8TooijIyMnWIkXO5477rv1BNfXDr7h1QbGq6V5/IHUelTOxUCSRkGb6NVlkUgjkBIUu0BmGEIgISlPQVCX13KLrSDtK8BmdZ2zqBSINyDFii5JMBU4hvIVgUcqglJis07PAKoU2IK0hikyRpikNKdPoCjXTqPm0bhApYXWFkAKpFFJJctIk34CAslYUc0emoxs+5tkMkq7PyDCtpUpdomUBSk8+KVlO/BaViDni00iyW6ryhC0cwxFcb1DZ0ChNM5NUBZRO4Tsg5o/KoZFejvSDp3UBN0KRBXI0qC5yJiNnCltGjBmxWlMazaIoQVoUGqMlM2WpS42VGpEivshEe8Jbh7IZ5QXuZBjjZH0/iB4nHW064oLDjBYRNCkqzn7glE5gR2pj8R24lJnZkrKWyAxF1ugkUVli1dR952ryGghhsvL/Q4fLHhnBGENZWKLOnN1He3804zCyPR5pjSdaj9aJ0Xv2+z37u7dIkxjHM4fhyONhT+ci1ljwnsf3R7wyiFxQNA2qKWjKAXe7Z+h6Dsc0pbxKjRYCKS3JJEYGgpzWiVMswkRsdiLjh8hCLKdnF/cM0aMaKLUkjZmxc/RxICNoZkvWdcF8URPGiTOmq4rt8cR59Gg1OVnaImG0JfmBJDJXFxcU1vD2bkrEXqwXzGtLyiM+dpOs8SIzWxzoOk/rFed9waIuaEpDEomU1RQQJjSIie/io//Y5EhOfUtMYNQUtyCUIupETgqlE3ZVYOuG7q6jEoKFmTMIh8o1TXlF1cxZzjfELNk+jYShwarMeXemqQSNnqHnJSnU4C02lVS5pggWmSxlFnTnN2zv93xobjE/L9k/3uHzCdePVAoKIehypj0Efv3L94jDD7sp/n1GhL8xCvMuTJNdIZBSkYVid+7x4ZbPn91QpunsnM8abFFxaj9MtTHzoAVCTuTNZV2Dc7j2ROhnvLy+IsTE4+7A+9sPbA979sc9X756znxmWT0zjLZntrQ08oILc0PX9ph5SW1rSJnT8YTSisvFAmstUgo+3N1x1w+sF0ue9md2hxPxo9Psatbwo88+ozCaed2grZ3iSwo7uaYaTTfAuRvoupG6qHl2eQFSk0ViGEbKsmRMmVPb4j6mERe2QkqN95Op6bv7e77+9V+zaRpk1fwNVyOMPZJMCh7nRlJOGGWpimqyux+Hj1Eikhih7Xr6YaAwivlsRtcPbLeOWVNTGD1Jg+PUkPx3DrtCTHzRcRyBTEzgQ6LtB/zdA2UxmegppRB5CqwdRkfnPE/7A8du+GhVP7lXKyXR2vxN4GNZWOq6Zrvbs90dPkZD/u344SZsRcXsYsnx/j3h5HBZAZN3RwZCiGTB34Q8lUWBD0z5GnoyFKtLg60F2Y4kadhcrZBpjjYGH8aPaYeTr0dOkhQlIqvJU0VkZE4fHfkSSki0MVglqbTEJ4eRkxtdUplYOBwjWXi0rEBMnWEIYaKQMKllCitZrSNJJMJjYn8WnI4a5zy1kshcIJNFCY2UhpgFLmd8DrgEOUVG10MaaGpPOfN0WtAeDSJoiqwR2hN1QiSFKTRCgbSBKAIxelI3+UmgBKSIC4lwDPRkVrMaKx05ZEQSlIXCzDUqlMioSTkRFZRWTX+ToPAqM+JRImBrgUoSNwrGVmBEwHPGi54xdYyjwPcRlSUxJrpxnFY5SaFlMSUxdwmURKLQcooCUEIisiS6BFKgpKYoJTpB7Pt/v5Pt7yF8YyBGbGGZrQu61OMfRobRo2yBUQXSC6SxXN48x6szxw9Hbt/eUXOimQVOfmB/Gum6zOx6Rv2yIia4/eYDp33P8qJg9bIklga3kAxp4PS+m9KrhSRLIAmMMQQdaPuOWHq8F7SnaUImtECJgq7vKeyCpSqoxZzBHxj6kVIVIDIhBoZxGnk15eQ4WZQSISb7amU07TBw6nuKEhprUVpON6ismJUF68UM5xxjiJx7h5ORxYsCXY30viNnz+XasNns2T44Xn/fMI6GLn5czRoPJoGQUxBc39F1LcF7lrOaPjreb58IIdO3I0NVMrOGprQYk1FzQ3NdYi7mFEtYy5bFWHGmYnlxw8XzLzC2IuWR4XxExIYir2mHM4PXjMmw3lR8dnPN0AmOTyO5j2gxOWIqabHK0u4j561g99Dz4ieBb968pxseqZsVdVOxdDMwmlEpjncdcv+HP0rMZCJTCvXoHdoqrq83EMV0ZsdE5xy9c0hrAMnDwz3eRwbnESkwjo4QE5lMWVjWsxkqRmop2d1+YGYVi8UKN6s4zRoOhz0fbm9x4cSLL2ao9Zm5kVzpZ2zUcypRU9qW0lqklJzOZ9qux8dAWRaUtmA+a3h4fOLb1++4WG9QZBZNhddT6m5TFlyulszqmmPb8f72jt45dvs9XT9gTYEPgZQymczDdkdpLfWsxgdH1480dcXj/sD94xMpBF5cXvLy2bMpE2d/Yhx7UvT88le/5nqz5rOXXxAz7A4HDrstbdsyjD2jG1HG4EPg7uGBi+USweQWG2JmezzxsN1zPHekGFD9yIe7B0Y/8tnNDTcXGwpjpm3BRxlyztPnjjl+5Ik0aKUBQe883755S1VYrq82FGYyV0tAROBC5Nx2XKzXJDI5BIrCTInp5I/0hpaiqCg+WvMjFevV6gfV1A+fnAwRGR31RpNUoNIZ9XEV4MeJ6ZzFNJkwhf4YxiUQ0qIKjS1AGjdNG4TDRUfWhgT0eSDlgBKR1veEkCEWWCGQSqNVnPZCk3k9wefJsVQK+Bisp4Ulp4IcAkEkRhnxOZDtiMoZJSQpTesUIad0YJkzIiekiRNPI2ZGpwixIOeEtSUyG5ybosCVhJgFfZq4KpWsEAhEsuR+RpYa2XSUGrIpiFESved8dqgAJldYIzCzRFGOjC4gB5BSUUg1WXwjIXrOR0dUEjksCKeBUxKMR0kkomeOkgLVW/wIjo8+/PK/I7aCywV6sIw2IHSaRDoBxpgQRAbbEVRCyRlIDWni9OQs0NlSC0sjG6TxhJDJceKyqGJaF5g87UZJmZAiicm6WMgpX+EPHXFTkEdHEILRDNw9PfL24REXJM+vGq7mS4ywFK82rP70kmN/z9PiAzL0FDJx2j2yPR0ZA9iqYH61wlSSiGKjVzz92/f4vkWaQJQaqQpspWmWDTNZgTGkBGmcyKlCRrxz6FJMUehdx5AcqippyhIXMqPNNMsZcWjZHY/s789UswYrLbGEybZYoUtLFoGEQ1cFYeiJfoTkETqR7bSudX7yNDBSkESmGzpCCAzOT5EOs8TVTwrqS00XBSFaNrOSi+aOct1x99hyvvMM4Yy1All7ooj03uNOnnj2DL2jsBWLRc1h6KaU8S7wEI6olaa6KCiMIikQhQAZKSrD1fM1hT4SfeI5nzHbvKJZv8Q5z/bxSL87U4SCcqxpz2eMMchosVLy2csZ52MgDh3n0dM7gfOTbTjjgvYUKe0lutxw+/jAv/v6HUmMfP7VitlqzXVZc5EDSjV87d7y5s3977tcf+fIySOUIuXI4EaqsuKzZ9eUsmT7uPu4CpG44CipWc1mtOczAMZaCIJT2+F8oHeOZVNNDXM/oCV4H9g+PCBSxg8dtZHYzQJpIuUqMRQPpHxi7T/jyjynzDXH43laV8Qp32kYR7bHE4fTkaqaSOMJaAfH/tRzOL3FasVnz66nCUCO9G7k/f0DUhu+e3/Lr7/+hm7oGccR8XGNURjNZr3icr0m5cT7uw9cpQu6vmccp8bl/d09fT9QKsGPXlzzpz//CUobtocjznmOXc/bt2/5r//Nn/OPxoBWmu/fvqPve5qmQirFqR/x55Zx9OzOLV9+/jmzsiD4wKnteNjtaIeRkARtN/K4O3L78IDRgs+fP8NagzVyis0oq4nM60aGcSSEQF1XrFerKVzXe7KQvHt4IqfAGAJXmw1VWZIQjB/N76QQNGXB6B2jj8iPRqUpJmLOtP2AbDs26zU3VxdT8N8P7Dp+cHPizh3H257lVUn9fER6R3eA/lwghMZahVIFIiWk8FMgmQoooaccmSqhikySHucH+mHg3HqEkGitUDKi7CTzEFIhMMisps4uT+F7SgukMB/zXwRxUlvjpmAcBu8ZfSDkQCogysDAGS8XGFmShSCLKQfho9s9fgzs9j3OO06tYYwKZSqkyCit8f1kfiOyoioESEkkoLRAIFFqkmD5Ec69pN2ZaSQdBWH0xHGk6wTCKJoG0iIxpoF87ni4P/N0iPgwSX4ba1k2JaMc8cOIbWpUX3AcR8LgEVmiqh5bjWghp/DDpBDKko0hCUhRkU2FxsKY6R6Hj34lEukndZFWGucUoQJT2Knx8CDiNAFRVlGWDcYWKBVJIWCEQMVEXSiaqkBGQ3TxYyhUJpERIiMylPYPP5X4aXeLzAp0zdun97z+8IbDydHM1gQU1bJiM2948fPPmP1owRhmLH7xc5Tw7A/v+fO/+mukkVyuzsTqSBA9bTdDmJp6YQjLBTefzYF7jneB4bxmZpdcfXmFURadgXMmPPZTb+kShSqJ3k28KpHxvqeLHWG0iKSQ1lIt5zyc3+LHwKBGYhGoTI2cSUQZCAgoMohAyANNsyImR3c4sKo09WLNzju2+z3HfUd37IkxkoLnsq1ZFCU5BzaLmqvrGbYxYEpQc/ph5NuHgcdHwdUKtN3x+u1rqmrDly+vObcdT7c79vszwkNjCkLM1M2SejlHPO4gKHw3JaLHDMZK6mpqrnvhOD11rFcXzD9fsB0XOL/n2bpAVAqfW9rjgd27d2zfP0Bbo33mxfwZbmwRTpC7god3Z/oh0vYRJwRGQ46ZMWaKMbGsFzT6gv0IX//5t3zYHlk9m5GbmtnVDVfFHK0jUib8XvDLf/nu91ytv3skF5FWkmUkpkwIkNJI+ngpLIqC5zdXXCwmdcf19RVjiHz++ecczyfc0DN0/fRyjYm+G3ja7th8+QVNVVAMI7P5jNIaTqdAe3hiYKC8ENg6cs4dpVsys8/pTp6T27I9HJg3DaOS9OPAd+8+8KvffscwjtxcXfHq1ef0IRNSJks5TQGWC64vLymt4enpia9fv+evv3uLlJpj1/O4PUwkVQExTTwKBdwcj1glefHsht1uSxSZ9txSmInQvd3tp3NUC2QKiOgoC8lnlzNmZcNhcGiReX93x7/6iz/ncrUBJGVdUTUNIUTafuTu8ZG2m5Lq//hnP+XVsxv6oefpcODcdZOCZ7n6/5D3Z722ZWl6HvaMdnar3e3pIyIjMyuzOlKkSIESQFgwIMv2D/C9f4Bv/OcEwfaNLcikyGJVVmVlFxEn4nS736ud3Wh9MXcmeccgwHKhggPYwLk4WGs3Y83xje973+clZdgdjpRlybPTJS+fPYMYCM5RNgWVUghrUCIT/UT0lVLSDj2Dc4zDwOAcFxeXbLZbPt4+UlUNJ+tTfIw83O0JzvHs8pJj3/OweZy0mzkjlaIoCkJKHNsOUmZeV5wv56x+/Ib77e577anv79YpCsQYKKloZokw9tBlZFlSmnJiW4xqEq1mj5SZEDzOuwncIkqEEsSU8QGIhugCiBEVDZ6AlAZjK0pdoFLxZK2dxDxSJhKerDNSVwQf8TGQVMLHgeAS0U18FSklwkzOiRQcnewxaoYpJqcRMU2sDwEuRra7gf1TrHRWBm0MQk6/3N4HDv2AzgqrptGUFkDOiJjJIpLE5OhxIeOjRKGfsv48IjH9zMmBUaRe4tsRf0wct5bQp6njVEqM0Ghv8VEwNwtKU1N4hQ9zcurBeJKYMoKSEAgrkVlDFjhtyBqkmbQ+xEz2if0use89KFgVEiUkeIjhye8rHaGPpCiQSVFphbKaalaCkRRCUlIi+oTvekgRKeSkLxIg5NTaz2JKaFZozH8BxUl/HChUiVt4RqGYLy6oq0hTzTmZLdA6sd8+8u3/6yuu/h93PP/yGf+n/+G/5fRyQbF8wX9zfsbBb/h0/2t+/btfsH+IaJ2oFwfSwfJf/dnPEMWe795+4PpXR3YPA89eK1791y+Yv3yGz4n4+Mj+r75hJnqiyzTNnE3/QNe5KawyTkI06QOFtHS7jpvigX3XQ5L0secoj2gJqtHYYrLKj8OICIkqFiAmXdU49NRFiWs7drd33D7s6PuA83AYR1wa0GvHcJRIo5BS87u//Mj97YbyUiAWgT4NPH460L3v+emXisZYlBacna+Yryvu392w23SIqFk1DWUJu+Oe7z585P7Q8fHqgeihKkuUkehSgo5II6i0AC1wSpDDSLvpEPoULWs6p9jHO1R/zXFzx+32muPOofsVEsv57AzbnOBtplIln77ecLPZsPU9yMyqCpzPL1jOF6QwPc/6NNAPHXf9SNYSXUaqOnJxdsLpyU8IIbJ5/EQWVzTNDz8JU2aB8IkYEkpLkpBsdo+Uesbq5ISffvljXr96PjFMtluKcsb1Zsfnn33G7fUNm8c7REqMzjM6T1VaLs/PeXZxTnQ95ycrqnpGion65XN0nbgPN4h1R6xGYi95ffo5F9VLbj498O03b3l2eo6ZL4kpcr/Z8uH6lpvNjtF5fMqcP3uBLQvW8zkXZ6e87Xo+3T9yaFtmVUHOk7YipQlZ0bkpYTfDU7xDnLDyObM7dtzcPVCXJb33fPjmW16cX/L8+XOO3UjfD8SYCEJw6Dq2hyMxZhQJfEQi+bMfv+HHb17y7tM1UglWyyWD9zxsHvF+or1+9uY1799/4pu33/A//c//Mz/50RcoMxFxfUjELNBKUxjN5ekJ1iguVnOaomAcJ/lF3/coISaphTHo+Zyh8AwhcOx7Hh83SCkotKEpCh6F4n67oS7vWS2WlFVFUZQs5zOEFLRu5OFxw8+//BKpptetygoXAk05WZCVUmy3O0xpePPF93Ovff/ipCzQyuCPim2Q+CjxXiGNQOlE7APZR2o7QyMR0hNKiQ8Dfswcjg7pMjE43OAhGFDgcsCFQHKJVWGpa0shC3QqJgGrmCzLOSfGdKCPOwwFNlW45MhqgHJElZqiajC5ImVw3mPSHDPLKC1gcPgw3exlBhCklBn6kcMQOHQRoSJS9sQM6Ck92A+JEMIU3JcjhdYYVZBJCCbRnjSZ0mjyEJA5IUVAoxBymq3aKqPqDp8GlGwQoUQrQbFMnJ0lAgE3CrJPtO2UBbTWNU0xn6zMg0XrBCqhqDC+QdI8qaw9UtSAmMixbmDXHslCIlOJi4bRGbIKpAoSgcO24+BGZqngxNSM2XEcR2JyxJyJOZAsyCSZGUWjawpTIFSm7yKCHqsKJOpJZCxIMk8FUYL/AvSwfP3pO5pmSZ1LTsslZyczfDdQqYqFahgOHcf9wO3Nnr/8cMV2TLx+9b9w/mB4+dmfc3L+ErUVfHdt2P6VJXpDuS45fGyZVYmf/+PnxCSo3BnuLnIvFDUed7MhrRqq85LN8Zq8Glg+f8HhU0u3yyzVnL5vn7qIER8noZotNTNrGPc7tBAUhWHvjtz7W6LpKTQg3ZTQHaFkxWnzGX50BO+RSkESNLrki9MLLudr2mHkfrfn492Wh+NA8lA3M9puZH8YcL3j5mqLnUsWrxpWzxYUXcn9x4G3R8WPvjzni8/WhNHxN7/4W7buQNKJ9WLBumkQKfC4hdu7PQ9f3ZKV4dWPLnn+2dnkKIgBp1qOOWOERBnQdWJ0W565Gin3eJPY7DMPrkPYLfvNI5u9Q/kl56ahrmvOzmusMLzbXEO94Nn5TznRkV3sGMeBdBjo7w7cf7pFNoK+G0iVwNeK2Ysz6lRR2Ed8d89he0XdvMaUZ8zWM978vOKzb3742Trz2RT2GGKetH0y0Q+OYpZYzStWjSWLxNkXr+nvG7r7PaW2LOYLvvzyczbzkru7e3JKHI4tVVFQGMO8KghasNtu2e/3nJ+e08wUp7UlIdj6kd1u4KQ4oYgzjvdb9tst7z984GK5ptSamCfnSYoRpGA/9Ixjx/uvf8tMTJfPRGYMgfbJbWUkzKqSqixBSIbecWg7lDaTxjKGP1BVpRCklHBxynZr+x43OlJK/M3f/oYxTMVLypnz0zPOz04xRpNypCwKpJk0Md6NED0niwYfEh+urtgep0Imxci8qYHpUjhk+F/+3V/zzfsPvHr2jLKqpg4QgrIo0FoTguP5yZI3z5+jpJgEtjEz9AM+BHgCteUnfcmn2xsetluOhwP/6Gd/TN3UDCnjPl5zc/+AG3q0Vlhb8PH6isJMbhypNSEEHjaPCCk4Pz2dEp2lBAnDMNB2kbqpOXYDh+PvuPyTf/kf3VPfuzgRJIoqkxk47jyDm0RrgiNa9sgkyH6ipZoM1UJRzi1CKFIUjNkz9g7nwA9TuJ2QAhcGQsjUxZJqMaOYZ6RPyAAiTSpiFAilyU7jY+I43lNpDToS9REzG2lqQylmiKEh7GuKUSMLQf3ckckMnzTD3pG9ngqHp0yCPnj6fWA4OMpKYgqFkposC4SU1EtJfXpCgcVmTakLCqVxYSQmCCkTUsIUsCwlZR0pbIBQ4HsNSTNbClbPBu7uWh4eDUWpsbMCYwKm8QwhsrtX9A+gc8ZqjREFIAgiI7Qhk3BjwASDlc2UB+T2yCSoZ4YsNSk7nBsY3IAqa7SaFNx+DFNQHz1lKej9SN/2FFEiraBWJRiNzxEnA9EIEILQe9qjR8uEKhuaWUNCTgUIadoVOZFjfiL8JmLMxPjDp7AN3cCL0xekMRJwPAwtJgmqytC3LUPf8bg9EnJi7BK/+LfvWD+/4n/34sdgtnTbzO/+7Vu++TfXnMnP+ek/ecUg77GzJd5d89f/6/+bZ+cnmNFQ+5K1ssx0Dfsj73/zCy7zGh33zOeG7bhDLBPu0GHljJQcWTi0VPgx4t1AkgU+OIYw0rnj9O/kcE2JnZeUYhK4RetIqxZ3BO934CXjwyM+JarTgkpaCic4iQ0hwbKxzEqNMM+ZXdZU3nBiOjaHPS+erakrTUiTUFLcBla65rOf/SnWpokknWDf9TwOe8ZioK4KhIm46FDeYFgwrwTWNNRVzaIo+PHnF5x8eUnvdnTtV/j0iNZLpDjhcXNFTD3vH+6IVULXNdtjx+12g04jhZtzSc3yfM3Z6pQvf/6ai7NT3v32gY3pWX55yusf/QShYPN4y/Wna26/vmXjBvbHHaflnCJoDm7Lvk8oveLsdE3YJz7+asPx49+y/2nm8s1PUbKm0iN/9Mef/X1v17/z9frFC95ffSL3jowkpoR3Hj+OuKGn3e0ZvJ9cXIsFduE5fPzIoe85WcxYl6+4PFlxul7w9bfvcM5hSkMSgvvNDmM0Y3Dcbm4plWBX3nHf7tgdewygpWLzacfXH77DOc9iNkdJMR1wIvPs8hJdVpRNw/jr3zD0I/044p4cRmF0U2fDR4YQGXLEhUA1OpRUhJT+INYFwUTmnL5iynRPnJSH7Z5xGHh5ccnl+TkPux137z8iJSQlUMbQjZEPtxuGoUcryWcvX1AXBSHDV9++4/b+nsF5rh/3jCGhtKYoLHq7Z7/fczx2uBhBKO63ewafEFKymDWcn5wiTEbEwLKpuTg9oypLpIT98ThdqbWeukLeYcRTZ98HFrMFs9mM+/s7+qElCrjf99zcPdCPjnEc6Ye/Zb2cc35ywvnpGd040HctLy/OeX55weCn3K7D8YhQU42QU2Qxa3jx7JIxJK4f/jNn64zuQK0lQRwYCXih6VMguH7qRggJIdPlgdrOUbrCM5AE0wEZB5JMaC3RVYUSeuIv+EgkM19W1GsNusOFhI+QnSKGhNQSWUgSAaManByRlcNaCNaAORLLI94cELYgiQqdJsxumzJhKBHKo2aZ7EtykFPB5BPHwdG2ntALFss5z88vIGU8MNBD6amKAhMLGAUyg5AZrTU2a1KGQUHWkdlJ4Pxyj5GJ0M7JvgAGdvcl99/NOfaC4Qhlo/EyIVWm1I6m7BBRUuaM1eA6iWsLRhK+VEilkUEgk8amAktJjoEQRqyVJD2yiQdG6RiMJ5SQRSL0A93xyNB2U6KmjygbsVLz5tlzTNYYbSmspTQwCEevA15ncgQxSKSbOABRQ9BPljUxzRVBTX79OLmOwJNNAvvDF8S+Obng/t1ErTx7VtG2Pdf7PTdqx+XijFpq2kNPF1qaCrIXNHPB/f4d7leBuK/57V/uuL+FL9+8ZLY0fP7mOTkr7q/2vL/7yMPvHjG6QPgVC21pmoY0c2zCI5/ePdI0ip/+/Md0h8Tj2yPDmBgPHTlkKl1grIIwkpzgdLGi7Vq+u7umsQYjJFKW9LeJ+3bPcl5TnMygsHgRsM2M+bpieHAYDRLL+rNL7GVFd9jhrg+oXeRczyiXCX1a0JeR6sry6vSEZ18s+eJnlzw+3vH2qxuur3a4wdMYw6ye4UKPQOBTZOw9phEEDZ7A3rVEMlWakUzk5Zs1qrGU1qJjZPvpE7HomL+cU56t0UgSiuPBc78LbN8+sniRufjzz/j20y1fv/2AaHsugmSFJTaJ+kWJWQSkafn49YH9teZ0fcn6vCL0O97/+h2/+Ou/5turDyQHJ3rJ+mSFFRKSR/pEfnQok7i92RAeOuzBsvv2yFe//P8gm3+FiJr+ITD2Gf7vf9879u92vb54xuN+h/OBHDNKCCSSHCd2hhscc2sxx4G7zQ4fAkZB2x347ad3qJxYrxacna4ZvePu/oFiNqc+u+DqV79hXtUkAp3YY6rAvm/ZHjuIE4nU9AusrKiaiFYjKUW2hz23m4cJn45gURS8Pj3l8OIFv3n7nvvNjvZ45OTkjBTD1KXXiuQFXT9yiGFy7FQ1Zor9JfqIlJMzMvHkRCLjYuL6fsN+N4l8P3z8xN3dHUVZcrla8uriGYeu5/TkFKUMu/2Bh+2Wfhj57dffcn6yJivNr776lt45juNA5wJNUfHi/JTPnj/j7PyCd9fX/Ku/+HeEEFg2DXVhefbsgtlshsyCRV3x/OKUy7M1dVVTaM3wey4JcGw7lFAY82TDTwmpNednp6xXS1IMfFdXPDw+ErPgN19/x9X1Pd0wELwjecflyZIvXj/nZHUyueqCx2rJoqkxwXN1c0/KE/W861pSCFycnlBZzWw2YzVrvtee+t7Fyb49oI4aWXaE5IhG4wmMZFSahDUJj+88WWp0jPTjI/4gSF5OGQEKEFPnwmo5uRmqKWhILiLC7gghMvrpYa6zRSmJSJGh64gyYCtFXc1Yzh1GFrhQ4foZIdww6D1ZddhqQ63XpGHN/r5CSji/cGgSh8fMuIsQLQKBEZpKV1RlQS0rKlGga0kSmTZGnIioDFoJsoXoHaNISCMngmWSE6ApJXofOXYKEw2MlsIGpDkSY8HmvuRm5xDCEkbIeIQInMTEyWVisQDlIYeEk5CEZ3xq39mssdJQqkwpNVoaIiV2VqGspwtHHrodeVEwVJoQIwye3I+oFFk1NaackN0mZhpd8Hx1hpQKJJRmykAqs6GQnkFEQkgIqSnr5skepqYsHtRElJWTzuTJ3U2WiWh7UjUg0w9fc1InS58Nr1bn1FnxadfzcL9HG0eRC8rljJdfrFg8K6m+6fn6beQ4SH77XU88fqC/h+2twLDiYbzjF7/7lpehwHjD9oMjjHM2h56sMqoU+LCnHXsWpyWLouF46JDKsj/uSRh66RCFZSEXLHSFzIE+dNzrI/eblm4Y+Xj9wM1uT2kMK1uxXJSENnD7+IBbDZyzQgzQ7QzH1HH++Z7u5kh/GKhmK7pdh34zx1wsCcuI+/ZAus8Ik3msNqA1n714ThNrzk5OWJ+vQHtu77aYRyBPYYBCKMKQ2G33iCQppWIhG6JPHIaBQxhxRqDqmvqspj43UEU2+w/Eg+Di9AWOgbdvP2HLTKklboRDADuzPHu1ACVIvWdWN9TLFXKxRPSJ3c2RfteSFxZ/M/Ld+19zIl6i3Qmz9RqrFe9/+Tve/fITu087cp8ptKXWGkuiPwyMY0QIzam+YHvbMZqB0A2MrWfsPcdhoO0TrgPRS1T64XcSf/LFjzmOA389/mYa26c0hb8JR/CBoqow1vL+4we+/fQOrRX/+Gd/irUVb7+DD58+MtyNXFyc89lnn6FsybcfrlidnjObz3l8eECYTFp72ujZHT3OQaEE+8fA9uoGOTzw5etXfP76FTf393z19jvevv/IvK5ZLxes5nM0GYMgBY8wkpPFgpPFgkKp6QxLcTJhCInPmWM/ANAUBVpM2WhKTCyPmCHkqYOM1JChd1PobYmhH0batufiZM1nLy/RZmJ/kMGcrbHlT0kI7jdbru/u+be/+Gs2x4ExBfb9ADnx+vyMf/LHP+FPf/oTrC0otOTd+/doMsvFgvefrri6ueZPFz/hT/7kj1nUNTIHtACV0xQ7EhNaGYwxFDYissAaS8pTSKZKkVldUhWG7bZFIHFZ8PHde24fHnDOIRJPSA9JDAHvBpwbEUJSWIuPgY83N5NhJAYygsPQc3v3gB9GfEgcjiMnqxlCav70e+yp712clHWFzAoVe6yIZOERyhNExkWLQkHwiCDRauDQjjg3kPM0U9PWAhkpNKWZTfTH6EilRpWaVOwIrieNBoEFM20UmTIpRJx3Tzd6gYkS5ywhFaSkCKnA5xnBRsT8CDVgNNmJKUPGd/QuYaQgMrEfZFBoYTFlw4mdYZKmNJaqstjLSDGX5LtI3mdUFCiZp26eZiqy9OT2TkkyhjS1/dpMcDUFJYUqGFxLjJLtMRGkQpVzrDQUKk0jqZhIfY3brUDCOCiiE+QkkAUoK4nZE2OeMPsiYaWaZrrZE2RGJk8fPC4kRh9J1uKjp4iJxhbkuiBHTVlWzMoKjaI0hqZqEMqQRUKR0QhUlkghsWLiTpAzWgqijKScMUIhgJgT3oepKImBTEAUntCM+KKlGO1/4qPtH96a6ZrmtOb1yRlD7EFOEeHH7sCybPizP3vDT/90zTdv/w3KFJjLxDcPGTVKlOuJR0cuS9anJfnUMPSJX//NJxgVytWMR8VhGFGN5qSeozMTuG/MrGZzyqJC+ER1bEhHyXmh0f/4nJlaw2PPeL2hve3ptgOPj0cO+8B+3+LjBHQqsuFEKGqjESJipSB2Pe6Q6PeZMfQcqp65naNnJUkIjvd78ieLUJJYRvSzgvpsyfrsBUXaEo8erhTKGQ53I1//2ytGN+D3msrMsQKsqCBkSCBSpCxrlos590eJ84nWe3KMCAPaCuQc/NzTD1uwHfOzBUL2nF2coseK47YjjppZNWdxZlDVBu3uOBwGsr3EdYI/evGcar2mvXpk133EhogtCtrgsQZiIzlZNoTqwN/8m7ds3j4ytppZUTOvFsyqGU1pCeIIpiVLj1Ylwi65e7wj6mFKdnWekBI+JuI46dtCzHj3971b/+7X7755x2q5wlhF9J6UnlDu41OUgHcYs8AUlrPzk6kTkRyxT7i+Y9E0lOVky1UkTpcL+q7j8fYaoyar+mI1Y1j07AfH0CaUlMQAjx9Huk89f/LZF3z++iVGCgqtmS8WvP90w8ebOxZNxdl6hdKax90Wcma9XHG+PkEbw3q1Rl1dI0TGakWyBnIipUkTWSiNkCCI5CQJMeJjmooTMQXiKjF1jKcosgxK07uer9+9Y72YcXpyghQSITKVVawWDdpYcop8/fY7Du2AT4nx6dlaKEWhJJVSFGRE8JzOK/7JT37Ecj7jarPjV19/w/12xLlAVVp++vlrFnWNMRot5CRA9gOahqIsWTSzfz+VAgoy9axmfbIipshxGJDa8uH6nr/821+zazvcONF/hQAtJg1maUtOVmtCCLTD8IRVE1hbUDczvPdIo9HG4n3gsD/yy9/+gkJODtH//f/1//Yf3VP/CYJYO2XadIosBEk6Yh5xOTEyIr0i9gE1WnQekTuFRKNkgUAhs0YpjbUldTVHp4jvI0lktDUEJXAukZ2fbL4SpFBIqSaBqpoSiUOMiAD7Y0TkgUxLCnukSmhdIEWYBKmFQZAQe8fhuqc7eObljFllqYrJ5ZKjmlqQ2lDqAqMVphComSDXHfV5JiRBOERSSEjElMRsNFILUphSip3whJwR3uB8xMhEoQdUnxgOJe1e0NSCVdVQiAKjADNtQCs19BCmmFpEmj50KElpFeLY0vUDzkClFeYJvOZywuWIjwPjGJDJoKMBp5DOUrjAQlZUsxIlyimrwZjJgmws1hZPUBwIbiQFD3FKeYaEkJMfXQpFCAEf/OTIYcrfkXJqaYY4EBnQ9slNFSG6H/5Yp6ksfnR8uHqHy5Hd7jg5DXxm3x943D7w9a8P/O6bPe/8yL7IzJo1Lz87odSPtLtbtvvIoEYOCmZmxuZhx2F7xKqRpT5nebqmnE0wKj8OjH7AjJpZarg4acgpUmBR2nL/7h227Khen9ONgevDPQ9XdzxuWx53PUpFqrLgYlbhxp4UA+M4cLY8Yz2bk0Ng6HpGnxA+Y5Ng93iAGdiiYDZfEDTknceLkVQ6RFlgLmcUpxXVEQZ3JKeMQNAdHYedxwfPcfAT4C1DYTNaK6q6ZBgN9w93zFcrlCkpVEWtB0prWDYVRQ3BBLq+p0yCF68/w2jDfHFCtS6xY4myA0MfEHXB4kLj07fkuGW+UBx3VxxvBKs3n1OYhsfdR5KbHrSHuyNRei4uZqQ5dHLHv/vFr2kPHS8W52QyEkElC9bLJfN1xZ7IsGun7rAT7NsrvBsZ/UR5Vk+p7NN5JZ6wCmKyZ//A11/96itevrlkXi8JXcDlSTAaU6DvO+7ub6isJgXP6WKOFhDc8Adsg1UKqwwygYqRZVVwqAqEn7K/hFLkwk4YB2oqmTEiYZJGyIiuegojcX1HOZvRVBXn6zWDj3z3/iMf7x+5ftyCEBNKPWfquqapa5KQlIVFCgEpIZ7osNYYfHiip4ZAluBzmtyKMAEwyQghCSKghERKiYuSQ99z37WQM4WAwhacn2yorOXZxTnr1YrROd5/uuZvfvc1v337jjFMLiIyzMuCVxdnrJqaFCakfGEMlTXMKsuiLmhdTVkUdP0wcU7uH/jixXOqkxKr9aSrdH6iwuaM81NB1Q4dhS0wSmELS86Zh8cdm8Oe69sHbh92fPPdBw7dwDB6ckwoKSanT4rsDkceNxtOT07RxuC8oxsGThZzyqpCKE1LR1OWWG3wIZEiJKn4cHeHLb7f5fX7Q9hCnG5d2ZCFmg5HEQkqEHQg+oyPYFJiFhI2NBggSzEJgQIYqSnUlNZICgimQ3gEos+MfsAHR6ErKrugEHLKaWCajaXsyDmAmgRXMQQijhwCNgmUMzAafBtxCmIUeOcZdp48ZvRMUJ9oVKHRQoHW5ARkiDJiCkU9nxH1QLQ3NEaQQ6INitxKFBIhJ9yvQBJzxGWHTx0RhRLm6QPpiDlikiD4Bi1qat1QKovEoBUkNR32Khmy44lkG0AlUpQwNVeolaJvJCEHIlMCbMwZlyNddgw5MPSB8eiRSVIahQ0ltRAsVEltKowqJzu2kEg5IaVlzuTgEVrBE+U3poyPkTENpBQxQk9Oqafk2ZDC1M0yE54454wnEemJciAFiKMkHX/4V0VVSu62B777dEO2mjY5sAIjgRy4v7ljc59QqxkcBnw3MOw69neK3PT4IeJdwKeW+7hh8JrDAYYOBjtydrbk4uyMcThQ2gIrJqu5iInh0VHMS2ZljRwkMiraxyOfuiPdEDjeH7j5cMv+/kAICS01KWZmZc2y0TyOAwORYwzsgqfICRkyCMvpScNKJLpdhxsd92HLyXpNpSCS0NnD0YNPSCsQNuJDhxo8tk8YIanmDX3b0R46XAiMwZP8FG8gCoFSEiUlznt2Xcv4FKCmlWVRzbGFZDYviMbTtjuy87x5/Yx1sWQYRqKIHPoRY2bMFhWiGCgaQzU3hL5mHAtEHlFm5Ms/+inLizdc39yR9nusjBipiIPDGovpLLEbuT7u+d13N+RSsnpxiSgEoxtJfcCMEj9WfOqu2O82FLrCdyP3m0dEmdGlxCSNioIUQEQ/pZJniTGG4b8A99rDviV/vKcoNWRFik8Ie5mJMXI4HHl4eMAaw2m1nCzZWoLIVJVls2u53e2pmobz9YL+eOD69p6266irAltVCG0pJOiioHUKGxNzUzObB66Ot9w+bpAp8yd/9FOqsuJUKIQyPO4ObI5Hhq4jA1opSlsgtSTGCBqsMVhjSBl8jMQ8CV+lUMTkcTEAUyd+UpuIP9DRU04EMjmLJxQ8U1elSygBhZD8zdv3nD9u+aPP3vDaWBCS6/tH/vJvf82vv3nH47HFxYhPGSUFhRJcrhe8vjjn7PQEhOBwOLA9TBC59XLO2WrBy4sLvPOQEzd3j3y4umbRzDhdL0kpQYaqqElZcrPZc/P4yMN2S1VYqqKgrCYq9e5wZH88cuh6dseOu82WnCcyspCSnDMgyUIy+sS+7dkdW5CSq+tr5vMJb59SIkRHjJGcJuumErCaN/z8J19yvZiz3X8/99r3Lk76tiVGQSJPN3cyqZgyb5RyBDWga0NRZVSINHVJKTKeQMgahMQIgSQRQk9+qoiTBM+Umuv0joPb0eiGZgm1MTBMWThynA5syoyqI6EbcC6QgJzlFO7Qg8SQhoQ7Tvj7caOo4hwtLZUo0bFEBAWAUCDM9AuN0pNLg6gmvQdimObjS4UYLGNURD+BxnLOpJhJwhMZyW6qLik0ShUkIYkpoLKgsCVGz6l0NcHj8tP7SjHRCxOkLEhSkFUm5Uhwmb4P5FpTNTVZKY6HI94lvPQkmfCMBBUYZcZLQR5BikRVK1ayotEWI9RUYCAhTQ8KLTVSiGluSCL6qUuVnyp2mQQyTrc/aRQoic8BLxNFEggJWmuU0fjkSXkKX8wykUZJ6BS+Df9pT7Z/gEs0mn3o2bmR5cWc88sZC7fFPfbYcSImFoXizY/O8V953EdHZuTYOVo7TuM6kYmi536M3B0k/n6kKjJ1pSiWc4qqpD8+0jONN4XMiCzIY0IYgUbRdsOEoR4Du4c9Kkl0NmQn8AEEiuW8pu1GCquxWhOywCvBUWY+bHccW89CWU6aGfWsxpQCEQKuz4SYGJxjs7nH47k4WbM4a0hWELWjPbxHOIcMGhUcsAKbpziEQ3zKDnEIAlpoSH4KGPMBHyJZax67njpDqSSFshM63yj2oaPfHylLSeoj213LZtuST46oVcH52TSGLCUoKRhGQUrPAIEb77Hlktc/+oxlc8m4uWduM6MBmRJSKuamRseS2ELyhpmY46tMrAV5SIx5ZBgd4ThAVHzYXOEGz4u6xPlIDJn5zDLajjJJ8lHhXEaGRCUli9qSsuQx/vCLdSQcDh0plxjb4EMm53HqNEuBkJKQQPhE8gmjDYUtuNtu2R727NqeQ0gsq4LTwtA+OjbHIx9v7qnLkueX55wIxbJaMwwddalYmIqVmbEzHcdDz8N2x+1mz/runh+/+ZyTVUMWkrq0GK0JOqKkwliNRPC42XB9d8f5xRlSCLQyU4dGiD/kzyAgI6ZgWZg6KwiUmDAXMU0k1OnxKZ7GNhLxNAJPKTNKuNkdUGSKqqBpavph5N3Ha755/4nHw+8Lk+k9lZCE4Bn7jtVixmI+59B2XN3c8HBoeXd9w3q5ZLlYcTKreahKumHg0HXcb3Zs9odJBCye8nMyHNqeX719z7fX1+z2e7RSVGVBVdbEmDgcWno3EvOEnXchoLRCK0WMkZjS0zhD4UJisz/ysNsSYubj1Q0/Xy449v3k8oxp6iqFKbZBa0VTGarinLP1iqub70dM/v6C2P1xCuUSE6I4KoWQltJOQUBCRnRjqI2icIFqnlhVgt5F+k4TPAgVCbknjpHsIlraKaNGjKiqJbMl8ECse1hmsIG0XxLahigUWWawnmQGxrCj6wRKVUih8DlNWo1kiZ3C7SxEg3EFq3qBUQXaJszvx0M4sgJBiUKjTUTNEr68J+oRnStUnmE01HVB6hIuHEEFpJATBVEOxOix0RD8FKKkjAGlyGI6oK0pqUQxdRXilHIsjCSRCD5NJDihSCLj0sAYBtyg2O9GKjlDraZiZ/Qjoj+g7IgpM0F7opREDKqy2HIK4mukZWYrallMILg05R2RJ/i/eOLGxKfNMyaPknJScMtpXCXFpM0xxhC1wIeOkAOVNggkQgh8SriUppEbFuVBBoM/CIbhh58lYs8M62GJn0ee//EbVp+VdLsr7n99R/d+Gm2UM4tV8EwtaUNLGjM+JHqliEKBjiQX2N94ukdQSVC+UNjKgs4MXUvuHR0eZJ70QNlQFhVNMUMmSXffIbWmpGCpSgonqcqKQzmAmmzfWimkAmTG5wSFpZxJVA2b+5Z2GAizBZWxHLuOVdmwWM3QM8PoEi4Fhm5PzI7kLEW9gqrg0B847r9Bzo8s5i/Ja0GKBaPq8CGiK0OlBKkdIYPVgOiIUTKOHhczQxbcjwNliiyNplYKo/RUiEUoY8HcVIRW4naOh+ue9mpk9cUMM2gKZVAzQ4iJw2OLrEq0fUXyhiArxjgg0oH1QrJeGe77jB+m33UaoI0RpQoW9Rk/W8N+dqAk48dEHiMhOVxOWApO56cEm8idQKGYz2uqJfSdR2c9gQ1DotCCua24rJf0Y6TrH/+ed+vf/ZI6k1Om60fmy5pKZAaRp0JQCbQ2GGMRMRJD5Gx1QgyBu7sH3l/dkFBkaxl8z6bfM8iIqgqSEriY2B5b5kXF5XLNURrKmWGuK4KL9H1g3jQ0dUNTFBzaifOzXK5gt0VmqIyh1BprC1JO7A5Hrm7u+M3bb2nqakIhIDDGot2EZs85TToLrSbAWQhTyB6SKDOk+ARiE08FiZzoqNqghPwPCpepdVZXBfOqREtB20/WXK00Z8vlZO/VakqF73q69sjtwwP74/FJMPvAu0+f2Hc9D9sty7rmzYtIXWiWs4bRO1yI7NuWh+2GqjTM6gqtDfvuyHdXd/zyq7fc73aEpy6QMYaiaKdiyAdCTqT8dE6JqfBXerq8Z/IfEtgH57l72LBYzEhZMgzjBGMbRuJT+nBmKuyMNsg8MQOkkNSlYbX4z+zWaTuPloqm1ORckkMmjQKtLcZW6GhRMlEkgy0Vsh4wK407jsQhk2Q5jQ9ynGAzXiC1xBMZ7AGTd2A8dWOwNjHGPdsxIqLDK0esaoyUDBzojkdclwiuQFvzRKBNKJHQIqNyRsYCne0U7mSWU0qr7IjCk9VE6QhxcqTMbEVZC8yiJ9Y70DUinZFCMVnjpEbZAWED2QxobRBB0I2OGCSlnJM1+AzRCSJhCvGz0x83p8ToAlmAUXIixT6JSgUKLRVZBvrYc4gdQVQcsidFh42a1kV8EEjh6MWeIEYyMI4Fva9p7Iq6rlBjptQWpTRCimkMlQVTQtwk1JJSIaREPDkIJFNhosXEd9Fao7IhyYxQEkfESgm/HwvxVJjlMCVSZ4XyBaIvSFGRhpExjP8Jj7V/mEuetXz56oTTQbA4LzGVJR4teVCk0aONYjz0fPfr98zDklJppEnEWrFc1RzCgcO+pd8l2ErmFCxWBadzUCZy7HY0tmGuC4aQOcaBMY4UeEplppFDmnQ/1hhmZoFSJV4Kuu5IN+6JyiG1oueIKBNJjvhkKRcl5UlJzCNBeuSTJqL3joftjrqyvLg4o1I17bGnHTpGLwlJMd637PUdalbjOJLKkVT2iEJQvjjBLhvYw+63O5qyRlUFEHBKY6wgpj3BJQYfOXQjt9uWh+QwoyIUhlFI3DgyHy11abion3F+eUa5KkkXkVEK7r6+ot4kDkOLlwXNSqBbSEOHnwnkYoHvLHd5x/bxV9zNG0RMZOsRNiPGSXPW7z0hjxgUs0LyvD6jJjM+jKhDZvQSqTWrRcPnn33Oyxc/Yv945Bf/21/R4hHasncb4hBpZIWdFwx6gtadFBXrqkEJh1Xq73ez/v9hZeFJZNyYsaOmKAukTIgUKSoLJOI4YrWENIUpuuARKNyYKAqLEZLxsOOd3yIlRJlo5iUXyzPQktPTE2pTMJ8bSmXZ7vZ89e4Dd49bcvT82c9+xsXZKb959y0tiSEGRu+xWrGeNWgzdfBvHzcMg0OSefvhIz96+QKtpuLCSD2NQWVCCzGRsPPkUHGJaZwDfxiZKKmw1qLVpPEwakpqz0B4olHGGDhbNLy5vKQpC1KIVIXlzYvnZKaE7fVySbNcsDu2vP34ka/fvScMLdd392z2R65uH7h93LLvO6SYkpSXdcWL8zNiFhz7nkPb8en6lsoomtLQ1CU+Rq4ft/zVb37Lp9t7BGCsIWdIKTKOw79Htqjfx8VMIy3v/X/QPZqKrJgiznuObcdhf0Cbkvms+cPrDjE9CcHjlKXkA10/kMk0dUM39Ox32++1p753cZKDwdYVTV0TRkF2iTAGlNQYKkwuUS5TyQKpA8fhQLxXDH3COYlVYMz0Ic1ZkFFIOTlEQhuJXiOL51gVofPsN54MWDVi1I48O6JURu5HZF9zalb4uUUKjVUSRUKqgDDTqEcmS84SLaYqNmXB6CLaZJpSPrWrJ8dN0EzunmLAkxFBkEVHEN/hhUHZBWpmqKQkykg1k5AK2qsSJzRSTQIpmSZBnIkSnQWlUUzTm4jQAh0lKk5tTiSEIQKTYCkkx5gcxxxItYFsoJCEGAj9kaoeaOoAOGIcGQc4Pnr2XaA+n2NVgYgJm80fIHNSCYw0CGEm3D4Z1PQ9KTmlPUskWuk/iLmSnG79SQBaEIHSWITPxJAQWmG1RqtEQpCSIGVFTtA7N7Xxhf++2+of7LqPf42M023DP97hhoL3f7Vj+3bkzCw4XS0Y45H9fj91G5VjcVkilhK9soTHI7styKA5bWqen5zy6uUCp1red1ds728oZnNeVuecNjWHu488PmwwWOI+s7k/0DQNC9WQhWLWLAjtkffvrxjygA8jzUrTnJUUazGBm7YdpS6xSXD3cUMOidNiRr1WWC2IyTO6zObxQHaRRbWgmZXMmoKwbXExoIUktTva7ZbOjch5Q51qvFHIeYGpl2incb5nVha0bY+PEVPWSJE4HD1unMCD3eBojwOtH7ClZa4kbXKEsUP4giovmdka3Wtu9lte/fNnXJw85y/fvmf7mwd+cnnJbNWghcKMmnmqJp3L9ZbtYcu34w1ROEotqaoCayRJCkxVsqpXlGvDMXSE2BGPR4wq0aOZWtg+I2NPtSh5+fIFf/7zP+FHX37J0AUIjn/9b/+Kvh3Y7TwCzcmzFS9OzjjsD9xc3SFj4GGz4XE34v0P//NQmoIhTGF43dAhTU3TNKxmNctZDX0mKVBaMQw9n26uWSxXPH/xnHYMKKmYzWZ4PNeHWz7d33D/MHB5dsI//0f/iGwkZVnx+LBh2O0ZB8fD/sjVZkfnHIXVPLRHxKyief2axdk54/FIRrBezCkLw74fuLp/ZHM4IqSCFBnGkaZumBcl1mhiCuQUUVIAkpSnsFkhBDlP4liRI1oqCmupiwKrzTQ+kWIyMzyNeUIEKQWz0vJPf/ZTfvbZa15cXLBcLEFOobZlYZmVJYU1LJZLRh/44sUFnz17xjfffjNZflOevl85jYtWi4aT9YrXz59xcXpKVc/Ytx3vr24Z3cjDds/945bVaok2FVd3j3y8vplAitY+6b0CIUSE+L1YW2CxGKvJKVAUBW3X4dy/H0lOWsv0pPgWCCWp64J5XaFJNIVlOZvTHVu6YUBoRd+PbLcHQhgxl5Mj9nS9/l576nsXJ+t6hTZTLk2ICZEMta4oZY3WlmQrsvQYDUPqkaUmRkHXJ3JIWAkiTQIbLTRST6KgAs2cGXm0mDgFzfnUchxbXApQg1llhN3RzAOr8gyz/wybF7igSTFSTCB5snAE1RMI5CBxKSM0GK3RlLhhJMQBksVkickRLxLJBsQykWd7xJgo1AxZaJAGgQdzT66h6DVpKCarmOzQlJQYpIqEEFHCYE1BicEIAcqTcUQxgnzq8BhFIBFkopOekCPKA0/t4xAFuZAs13MMgUPYY+zI4sTRzCIi1SQ3o93D4cGTu0zcR1zyyDajGklZ28mLL6bgZkSY2nViejhLMkEJktQIHycmQZ6Si5OCLPKUAp0VfXAM40h2mcFFbCkoFeQciNGRQ4IgSUGSgiD5hC1++O6Eh11Eyi2iN5RE7t/f83DVQirR85LyZM5h2xJjYj/uGO3IXTgiDwa3h74P6GRZ1SW1rFjNSpbna7ZJslAtrh/Z+57u8IF0DDxujvRjoNaCgQ03hwOilLxYn7Aqa2ZFTT9OURHOCwYPuUoYH0ktiKhYLOYcHzs2jwO7+551M6OuC0QK9MeRqAWySLRph/TTGG+MIxBpD3v60VPVFSpFZArImGnyKRf2jOX6gmhqSqtYXliG5xvi3cju+prDoaOZz2jmNV3reP/+gd5HohA0JnHsPcor4hCwM8vpomJhLcJLusPAL3/3ga/ubvjvFn/OF//sM5rLhrfv7xgWGdeA7CMpSpQElSXIwOG2Z7xNXD47ISvP9vpAKCO6tJwUBWYlqM8Ft/sH2tvIRXGO8hC6OOkjRk8bBuLg+HB/RfFriYtbLs9f80d//GP+9ldfcXWzYZEX2FKyrlasmiUmKdqyY78def/pnrZP9CH/fW/Xv/O1XKwIcYN3npgDPgdCVk+iysk8kKTASaa8sr1jzJJnz5/xTxcLhm6YxKcxUEqFSYYvnhX8i//mn/DF6ze8e/eO4dCyns3ZCoUVisVnFS+EZB9Gjm3L764/8dD1/PTNF6xfPGf48J7Ts1NW84b7hwfat9+SQuBktSCmwOF4JBD47tMHFvMVbugJzk2W/ZzxKdI7j38ac5Cn7nNpDKUpmD1RuHOekP0pJnKaAJ1SSoJzFBl+9uZH/Nd/8nNOFgvqokRrzbEf2O4PCCFomhprDbPZHDOMRD9wuZ7h+3P6rp+S3k9XQGJRWz57dsGrixPW84rT1ZyqabjbbTgcD+z3U0DgV999YNd2LJdrPl3dYKSitBYXIn0/4GOYxMBPuTdaaVL05JB4ebZmuVhyv91x6B27/R54Ao9qhTFq+vm8ZzGvOVssWC7m2KJCSIWeT3q5GCOr+YLnFxc4N1BXFYN30yX5e6zvX5wsKnrX4/xABprZknndYAtJUJ7BBGgEKXQcHkYSFefzJbXtUYWmKgSCQIigK4tAE0JEJkEjSnyUlNEiZCSnQI3ARBDdVDGqakZRRyq7RtcV2SlEzFgxAW+s0iRpGMigOmSpkUnQdo6QEnVtMUETnCIdS0ppqYRDFCNqtcUv9nh9R2MlUkmiWJJzQokRGZYENNk6rKpR44zH63eIfWKOQBnLmBIpayQaI/UE6VFMTqY8cPQjOQ0UqUT2MKqenRvIUWKTxOZMgaUWEucFEc/YthzcEWFHNIFw1E/dIIHCcrowlDnRiIoi6MltZAsqWyKFIKdITI6UwtQ5kpY+a1CJaAVKWOrWk/qRGANBTALIICNJRMKYOMaA8FOOUC9hyJ4xOUIaSeOIdgqbCrQoUNKggsL5H749wfGazccDz87P6EbJ9jCii4LLH7/ki5/+hPPXZ4S3gu3fXE+ofwmbg6ORAiUNp3aBXVrKecU6L7BK0Y+P9MVINavoh46h89S5JPpMd0zEEVIFbXJ0fcAIy/440B8dTTkyYRABqShUSfABdx/JneR8vub58pzr4chqljnWeyyGsrAMQ6aZlbx+3vDyckmlaopmgVoOpJPvGPtHTvUlRXqFtoZBtFSzwPKypLQVJM3y5BXFeo0Re4q0Ra3vOTy2eHXE1hqjEu3jNXfvvyNGy+rZJYvFgvPNjsvdgeMwMuwdofOUZycUVcmH21t2R8+720cOzvGv/p/fMHuxRi8sXsA+Bk7mFn2xwNSKSnlUzAyy5M3pFzz75JhpQRKe2/2OT/4RhKAWFdpL3DCwPM1EPJ/aT5yLU8pa040j7djR+pFUJD7dbXh4/8j12498+cVPEaZhYRZUyRA9aARpl7je3dEeWvpjZOgzSVjG6BjdD79zUpSWZl5zOO6m7oJI+BC4f9hw/+mebtcjhODF8wueP3tGYSpan/h0c8fpaomQoBSUVc0r+5znzy9YX54yO1ny9e0tv/7tt6TuwHJWY4oCLzVZSERRYaTE73vGOCI19N0evzV0m3u6/RatNAfvOKaArDW6NHgROLsosRh+d/0R/eEjj9stfd/h4wSp9ynhgqcbR2Kc8ptmdcGssFRFidGTwWESsiaUUkhlSDnTdh0xOv7Jn/4x/5f/8f/A2XrJ/nCcuog5s5zPKKyl73vaQ0soLc+fvWC1PMELwbe//A3fvnvH6XLJrK6ZVZaff/6S5XzGm2fPJtK6Nmzbju2xo57VzJYzutExjp5P91s+PWxomgYXIeYJ8OlDJJOANJ1RMRG8x4fAMBkOsSny+bML/smf/7ccxsD/+m/+gk/X14icmFUFJ6sFq9kcqzXzuqYw02hLAIUxqKqmDJ6U4uTeCR7QdF3H/eaRi4uz77Wnvr+V2Ex/MC0tVkJVlE9+5YALI0GGyYoVFZUska5CxQWXZyvWJwnUyOPW87ibUolFnihy1lRUdk6RHSWZjJ/U7rokW0XIE9MvO09350FbpPcQAilIhDTTjR8JKLSyyNojqwRDRLQJUgbpKIxBojDZoNHYeaa56AnrB2TRIk1ACI3jhjE6LCVFeI7en2CcJaoRZi3F6o6qkNzhiDuLzHai3Kbp9TNT1e2IRAPMDEmPDPsByEgnOA4dzg1IXRCFwT/NM63QFFESQ6Q/OoyTE1W2Neh1gakkVo8UamSxykRTUuc1hWiQUaK1QhBQ0pDFFC6VUwQkUoCLjkGDMIY6ayAilZoiwHNgEIFWDAzCE0TEKsNc1whlySlNGT9uR9seCJ2joEIVNVpqrIQTtSbkxX/Sg+0f4lotaz5eWXprOF2csb40LD5f8MWPfsLZi+e07ZHNL+/Z7wfm0tIPklgYlosVKktqaZmebYGuO/DY9WxuH1HnFbPZEhMla7tiQYNYZWbJ0vVT8ujxMM2JVRIMrQNbMD+bcfv4wN3DkaawnC4bVuszhNXs2z1lslRoaEeyd/zoZM355TNmM4m0Oy7fSF6+qQhj5NNXe0yjUJcf2PYfERQsV3NMXEBpSVZgKk9Oieubd7TXPcOyY/3yNZYDxDsO+hPxORzvWzZXI3qEeZ1YryL+4chXv2yx1YLkA855rFXU0iAFhMGxc5FP9zu+vdqyHxM+w91f3SHqv+TVTxfML2qe/bMf8+V/+6cUc89x80va9reossRUP2MWVsTzPfrjjnGbiT4TQ6S0JZWsiXvN+++23HLg7GcN980Ovw1cpDX90NPHnrJSyFkBStHvOn75797y7rstL3/0Y6pc0VQ1ogCiwHURUpyE7wGOxx6jBF++Pud4HP6+t+vf+frizWu++/gegeN47HHHAZdGtFBYoTG2wirD5uHIom559cU5h+OBru+wVUHK0O22DH1HSpGybOiQrKoTlp//CZ+VF3z39Ve8/fgec9hSykRpBNYorIDzFHjx4nTKZjrsmTc1Lke2RvDtzR3vPt5wtT3QxhGRRpSVnCwahC7p/cDjzR3hqQMSYnwCXaapi/zkOCqsoTSa0hiMUkAmxECK8SmVXZCiZ3Cebhx4db7mf/zv/gVfXJ4CArNYII3GWk1OkVJn5qVhaCqOXcfXX3/N7OSC3374xL/+1Vd0xwNDAG4fCGPPZ8/O+eL5BdpIdseWD1f3fHp4YMyR5nTNduh4aPf4IaCeRvs+9mgzxayElLB2su1PTqNI348Mw4BPcQrMlZK7Y8uvvvmGLAWnZxd88fI5vmvRWnF+djKdoznz4vKSy9Nzoh8JPmCswTwFI7ZtxzAMbPd77h8egMzrV684Wa8w8vtpsL53cZJUJghIITyNAqZKTMiMz082qI1DJUmp51PRkDwuSDbHnjEcOXaZLih8yKSgMbKBp3AmmSRZZVCSlCc0ujQGGy0Jg0+CoY8ICzUCwuRO0fL3nY5pY0klWa4txfmB3e7IcCzIwTO4DlMKTCXJPpJEQs8cctHjCRTK4IdIkoaUPTiBrc4w6TkFDVoZUIEhXeH4DXm5Z/bjOcP7hu4WxjFSCIWQgpgTIUeiToQMMYEuNMulQUSFG/xEY03g3YiPnjQkkgvYQrOcW2QyLIqSej6jFIIYIkM/opRnsQjMT/Yo0xJ3L5DbEpUNEksIaeKPxIhQCmEsWtlJ0MRk7xtjZJ5LGlkjrYYkEMkjEoTYsw89rRwolKIyZgpHMhEjICXBGKZDVZiANAnsZK8zRrO0DVJ9/5r3H+r6+eWMUv+cq4/XvP/VBypbs7wsUXXPrrtiHAWjh0/vIy9XDat5geKRShR0Q8fD8cisLlhUCyKCQzswILGjoCWzeRyJsqNZzhBZ0PvApu0n0bVQFEWJi5GcBEWpSWIa7dlS8pPPn3O+muN85GG3Z3e7J9YV6RjYPe6RxnL+7AWvP3uOC+84f3nP4rRlvy/Y3SyJNCR5QOwLztM/R/k13mdcgE17zYO/JerI4qTi7AvLj+cV++0nrn+xx1aB3I/cfBhod4G79y3Hh4yMkraXzMo5i6bkJ9pyfb+l95lGGZazZuqZi0wpJFZqXp6uOHQOv+8pEtSFRt2MpNmBf/RfveRn/+w19bLl+PhLhs3fIoqeLhpcC8Z8ySAyjVVUqxmz9MBq0GQpkYWmOl3SrCL64ZFy3iKWlu1tIu8yy9lEsPb4CTVeVtQvKvKyYfXygture+ZjgzUFVmmk1JN90juEyBil8S7ROkdZan702eXf93b9u19D5MXZJcH1eOcYOkcKkLVEaEGpDK/PL1nMZ7ih4/bmhsfdFlsaRteREHRuspcLJMkLzIuaP/vjP2W2PiFmiRunENfddssxjOzHnrDbIXxHJeG0mnO+vuDZ61e0sefkR885069Jv/gN397d0AdPeKLNp6gYg6IyGqkM/8P/+f/Ip+tr/vW//guO/Q6fEiFF4u+Th9V01hTaoJXBp6kz5IOfHDkpE9JASpOYVEvB5WLBvCoojUFoDdLTeU9/HBFiom8jn/LKYsKFwP377/iLX/wt292BY9uy2x/REtZNRT963n244uPtPe3g+PrDNTfbLfXJkou6xPsJTa+1pjDFJHIVAv80vhn9iBKWMIzYwlAXBSKLyZ3zH0Qs5AzfXd1xfb+lKivquubNi0t+9kc/IQPXV1fIHDhdLyiriq0fiT4S8wAkiqrCFpqUDfN5g9aTxtN5j29HQvWfGcJ2dI4xjSgNi7llVlcYWSAihCDpk59IcAlIEj+OjEOPVJGYO3zySGVQuiDlibtRlJIURgbnqKRFaoUyFsGIxyOJUxZClAgUgUzne6BiLucYmcnaMcpAipM33ahERcTYI/W8xZaKYTuQjGe5KijqRL+LIBJyNYLuMU7gu0gsI1FGZBvJrcDrTFLXjIWiVpmy6lFqg9AtSeypCk1xMQn4+rYmxEkYmiSIAopy6tj4MaKERiZoD3vi9DdEBolRAmU0MSaGPoKPKDkl/BalYlkVzHRF9uCTQ1QdRTVQVD0IT1YFWItEE/30IUFMPJI4vQ0hR3xwjMnjsicmsLXFrk/JhcffXSFGRwwRNwbaMNKbnqK2hKDocwI3ToVWkAx9R44JYzVFqSm0oAg8WZcTQvzwxzr/7n/6isWPz3nz6jWPeQ9DxO03vP/dHZ0DLRvc/sDZbIb2MGxblquSKmkShpwi496z2W8o9ZzPX79hM265j498+PaGw31HawK3Hw/UpaFqDPPzBaMH1XmqmCBGUk6MwXHftuhZyaqpEIVk0x65v9uz3XUYozBSkUNi6HvGznF1t8HFQDfesO0GLjYFtT6lsqfkShKOAnE45+KLLxGi5DFeI4sRfUhUveYwjOwOW07enPD8J894Nez57V/cUaQXnL9cscoP/HZzxTePRz5+OlLogmdnK6wuKGzFrFSsZiMrIVAIbFFwtd0RkSxnluVshg+Jxf2GkCLPz0748tkFJ4satQzUXyxpakd7/x3j7beYfoBUYeqS1OyI+R6zeMHs8jmrtuMz1fDx6pa37x6JBKpnNf/4Rz/mp/JnjLzl7nrHrb+hBKJSyEpjhMU2hnpdUC3WrNYr7u/2XP/iLV4E1uUcgWB3aKmbgsWyRqZIWRiqqmAMERf8pPv6ga+ZLDC1ZVisycmxSZGxT39weSgDdWU5Xc4wpytCjqxPZpRVye7YMvqJXLrZd4SUWD2vmZ2tyVqjC8PZ2YKvf/XA+/dfMwwjta1orMVWC6QynNY1L05POH12Rq8dI5m43yH6QO6n9F+hJMk7cohYYyf2Vdb8y3/xz1muVvzir35BCIFZUzOGwKHvkQmklIicn4wEk0i2947eB1wM02jkiQUiEdTGcjKf80evX3OxmoIMNQLvJwpzOw70w4BzAzFlxpCnkU/w3O2P3D08TCMSrTiZ11yerHh2dsLri3PWqwXd6BjuNgw+0rmI63p43NKPDlsUeBfohn5y5DBxSzJTcjyzBv/kugk+TERaQDIB5CTgciIC7RgYQkfvA8ZMycjWWnIKLBbTCOn/+xd/yeN+S1MW/OiLH03v6T1VXRKCw7kRpRRKGQ5tixSJy+b7dda/v1sHqHTB/NRw+ZmmbBKhH+k3knSUFL4kp8AgJp90d+zZt0cSEa2hqDS2zMgQEGiKQqJlJo8jOSpUMeXzyJQphZ3GE2LyjysEVhYgRg5uwMURygVYQScPjMlNrxENpddsHyPHEFHaMI4DYxIkNEMCP0qSdTSLQLUayLJjVkYikeNgUbLAGGABUXdI4cm2ZRQDTieS8AQZKFWBSQKWD5zrGhkDcacQFGQtkXUml0fGtiN2UChNHgPp6MleETLopNFCo4QikaiKSWw0MxXZSppVg80C2coJdS/shHrOS3TIZC9J4zMy+olOKCfAkJR4JkV1CoFhHDgOe/rU04VI10EbDYUqiELjxx7Z9bixpx87snRYHVHJMwYIUoBIBPdklR4iKhoKYyhVSSE1VkqEllNi5/ds2/1DXp9+vaU/CC6+WFFq2PU7tpuOnDMyaWTasdAFp5+fk3tJYQvKGua6oVQdPQPBdaTkMDqijKIQBWmfJyhehqEPGKmpKoktDdJIco4oqxEpc7Fasz+2fHjYs2tbnj8/pbEFn+423N7u2B8cSkien8yobMGr8xNKrbjZHjlutvT7Pft2x9WnkZ98ecJPflpgq4FhlxFuSc6au0/XCA1FXSBPBPOFRYwVwkVGRlz2bPdHTuaJZpn59m823HzYYmKBHAtmquZyZSjKCmsV1w97dq1jPZvR1DVGa45tz7Efud0cGXxEJkg+0A0DCsG6qThtCk6akqY0VOuC5nzB0B3Yv2tx9yU2nmNiTXFekoxjQCFsZGQ6RMr5jOKF5bJ5BhjmZ6eszl8xLwTH4wWPb/+SZt0ykrgbWo59T1CReWWI48D+4Y5xTNx89cD9hwND5QlzT9t6bjZ7mrrgZFmhpWBsAzJJLs9OCSnTjj98zUlVaipb8uLkgtENDK0niA6Y0OdNXXJxsuL5xQkpTUnzs5lGq5L7Tcv22HP0oIaRpql5+eqS2+srXu52LOYz5lZTEnH7A8fdjqQ1yWhKrTlbL/nxT7/g8sU5x3Hg9vqefntgVTQc9kc+fPzEod2jBcxMwbK0KKl4/HDDcrHkRGsOd/eokHh9dsqr58/pnOO3373nejMV8aSElgohpuLEx6lzElIipd9D2CZHS1EYXp2f8ic/+px5U5Ol4Dg4tvsDScCxd9w+bGm7jpAzn+7uORxbRu+52+zICOZFQTmvOGkqLk9WvDg/5dnZyeSOSXNaF1jMGu52e7p9yzB0hDDtOyJ/yD5LKZFyRMipgzI6zzg6nNGUtkRq/UT/zoicn9xG088Sntgmgw9c3T9y7DuqoqQuLdvdgU83D9xutnSD49nZCqk+cbd5pFCaWVOyXMyoqpLsI6NrKazh9PSU+fI/s1sn4ahNQVVqZguFaQZ2OdCi6J1FBEOWkiwFQxw5DEc2xxatLU1VUOSp+NBCIYT5gyBWC/XUGhXkLEkRNCWGNIGntEShp24AliwLDDVGC3J5JNkDMQeENWgPRghE9oSxJYXMOAbaPqCExT9EtDWsnwf0qiMXLYIBJSOlWxHuGmIqMecLOn3LGA8UGtAtURyACcCWABcVkUyWB8zC0Dz3tLEndgKURZWZXCTS0ZM8JAaEFJS2JCiwSmNQFFIjkyKKTFYRoSQma8ysYP5yTR4DzvXTKCpEQpcJFMjhBCksOtQT3TXliVdiJlvzBF+b1OM5Tm6imCI2C4xSiOOR9sN7klCIMCKiY3AD3g1IE9A5kfyIdx5lFFVdoNVkJav0lNejskD6CY0eREAZ+fSeP3wiZqVqTvKC8k6xp8f7HqEds7miKRKhd2hvSAkCEVPVLBYldZ7CF21WDBK6PtIeWobxmj0j92PPpnc4Is4JThYGURmOMZKdozIFJ89XDG7keOw5dtNsdz6b0Whw2y2PdxsetgM+CeaVniBtZTFxD+YNxhYkFKNzlMqiMgipGIaR4ThScYJ5Uu8/bq9ZnFcIs2BMPX3aQQGzeYPNBjeMXH+1ZawF7hjptx1OFNTS0IiKNxcXrBaJzge2hwObbuBxDFQnK86fnXE89uw2O6KPWKNpypJCK8Z+QAn48RfPma/nmBRoCoMuMsd+B3vB9thx/9UO0WdWiwVl1ZCTYvOpJzU9ovyIqQakOWUoakS1oG4yOWRkaUjJ49vM9uaR47jloI887AaMszTLOatn5yzXlqE7cPvukbvuwHjnSC6xo8P3gRgk285xGEYOY4+RCtdlgoOHNjKOnrL44RfrvR8otGZZ1jxbXuKGhHeR3jmEkswXcy6enfL61Tlu8Hy8feS7D3doZXnYHfn4sOHu0FLOa55fnpDcyM3jA+1hz+BOcY8PfHj7loebO/b7A7016ItTXr55yZc/+Zz5esnt45abD9cc7rfYBKaJ3Dw8st8fIUcKIzFa8Wp9wvlyzV/+zS8xcXKe5fCkHYmBVV1yMp+z3R/Zti1pmOBjpS0QUuHCZMM1SlMWmpgSo/eEGNFC0BjNSVNxsVpQWMNhGPl4+0DbdlTzhpvNlr/95jsetlsisDu2hBgZfZj+j5E8e/mci/WSyijWizlVWdINPUNw2LJh3/XoopxG6N4Tes8QHDJLrDIYoyELtFIoJeido3fHiduV8ySA9RGt9ZQDmKcu18R7EVOnlSn8NaeEj5HNoaMdHLYz3O/2SCGfxluZ+azm6vqGJKCYGaSSzGYzTk9PJg1K12OtpZ7NGOP3y1773sVJzA6QDENgtzXYFGj7TB9hJCNiwKAQMjP4kZ44kRV1gdQlCIWS05xLy4lPkpniyBtt0Uo9ecknhr9OCqMFykzUvZQyWWhyqrBU2CqR1lukusZ3HiVqdL1gZhoWMyhmPW3fcvNOcthnKjFxThZnFlVFRHEkiQ4p4pQZEhRzK+nEAalKtNT4mLBUlFRI0ZHkNF8UyYIv8eOSYDtSGVAnParLjOOISA2FLpH2ibInBTFHYgSlJl98WVbYIlNkiXSa2E85QD5G8IKcwYWIEODFJGpNIT5lOWgICmMNhcwQAykntMko9B8SM0Ehc8JISaXsRLCVisIaRBK4riUlMOWEo0/ZUAlLxpFymsBVASJx+nsoQ1QRw0QdzXIa4/iUiNqjhUSmPKXO/sDX+mTGwlaUQTA4QykacmmpjKcsPWKWyT1sN3B0EnpF3VhmpaaImTAagioobCYnh8/QuoFD7xiGSFSTm7uoS7KGIWecmwrMZWkoFpLycoa8V2y6DpPBtwPd4Ygf3cTdUWIKqCRzHD2f7jcQPIumISXF/TiyqAvW5zWlFdzf9IROsygdy4WhtBPpONtIG7dsH/cMnadZLKmrBuE69nc9yicCgUJFaiayso6KolSkpDkMOx63e9oQuPjic05/8hmvv/yMclmzGhz6L3/J+7/6FYVWXKwXLOuC5D1CSp69eU69LLl69x2qNChjOO6OhG89h9bBPmOVpbAV9brBFY5Vtmz6B7ROFBmyKOhbw8Ou5+Fhj5aZ8/MlrttyfX3k4907xuaAtxKzbjhdP+P0+TOaWuO2D9OI7W5EthEGmNUlWz+wPQwgJgddiIFj57DaooTBkfnd1ZZh9Lw5r/++t+vf+Qo5sVzOEEmgdQlC4mPg5vEBBFSzisvnZxR1yXbX8e3VLd9++IgWE636Zn9AVjXPV0u0yFx/vKUqZohxxHnP4bBn/7jFjyM5JeaLGa8/e8Wrz19TFCXb+y3bqwdy61gXcyqj0drw6vISCoG+E2x1i86KUkEpMj998ZLz0xW2sAyPO9xTwu7d4yNVWZNTQgmJEpIsJmI2CGLOVMZSlxVIgQthioWIaQLMSUGhFEZr2n7g9nE7FSIp8di1fPPxmne3dzzu9k/P6ggCvI8QI0HmyUHjPYXRhAybw5F+6BFKIW3JzeOWx8OB9BRSCBrxlBWUM4BAyieGlRbEcZhGOSlOGDkxUVujn4TtOSeEnJhVpbVorUkhPXFeMiFNF92YMyFP54gxU1NBG0Eza7g4PUFbS91UzKqC1XKJteWEwlcKpST5D9/vf3x97+LE6ExmZPCR7cFgomT0hjFqvARJQIRMEG6y4UVH0hJbWcqqxGqwuqCyNaUqUFmRUkRJTaEtSigQMKaRmBJaGDQGq6cfyD1x+pVSaClRpUfOWiSP+L4lqIJQHRDNinK+YDGzZPZkBClljDL4NFDNBcp6Uh4RwoPI+CwIyTE/lRi5RcgRMxjCsaasLHXZYApPlJo+KFQ6g+GS/rZAzx9Jp9eoImDOD4h2wB8jMUmUgPwUBhYSjGOiTlDZAqMMajYidcD0ZhrZZEFK4xTAN0Tc3WHSXIeIzDwF9MmJH5CYxkEik5OYcPZqRAVL8pmYQMlpllhoDWWJ8hKloDYW7xLOu6mVh0Yr9fRhy6iYceL3ACJIXmC9pNYlDjd9AJV+IvwGxuTx2ZOzpHgKF/yhr3KuGMeerg0MMVHMFmgLmoHU7klFj5UZpWGMDhNHpDYgMsMw8LA7cOhHQGC0JYqIE4kYMyoIsgJtJaYweD8wpoQfI8mN7PYHzt8s+JN/9kdsdxt2hwPD1tN3Ay5GIlMqushTe3fbDZAzu53ii4tzmrpiGAJVYTg7nXF6UXDod1xfH4guM9QHlJDY0zmlXUOGftix2bUEN+mkBJnj3nP8lPDDSKMzejklsEY/FdbGSA7twPXdhl3bc/bZS/78v/8XLP74R9SrBa0fuahmVHMLqaW727AqLXNjyeNUzEPk4bCh047nK4V0EjkK8n2kzJJCaaTVVHPL8y/PsDNJ2zf8b799xxBGfIbtLsLNwMe393y6uaVsJHxxhi0K7m9HnPbIuWS9mnE2X3D2/HNsPWO8e2B3tWH/aU/qoJEVeqYZ6DhsA4dxICSP0Yri6WHuYkIbiFKy6x0uJPrww49zkEoxWyxILmGKikhkDAOJTNsNHLueYXDss+RX33zg12/fc3V/j5WCoiwR1nBxeUbTVFxf3XD18ZY/+vzH7G5uOX31nGHscMGhtaIuLVVpUGTisWPoPdol5tlQrWfMm4YUpviQ2axmPitRZO7NDpIgP+khf/zmFa+eXaKVZn84UFqLVIbr+0esPdIP7g9ajCkYNeJSIMTIsqyZlSXu924dqaa04iQQAkbn2R87bN3xsNniY+LxeOTjzS0f7x7Ydz0uRpzzk6MSJsgl04jlbrtjGD2LRcO26xFMgYRlVTL4Ldvj9LoxRSQgpUZrxeAmLpULYXLOpET20I/jH8Y8Rhu0Nkg5FTMxp2nUJuTkTFJqEgA/QehCjIQwZWtPJhRFXVc0TcOsKpjVJa+fX3CyXjGGSFNVWK1wzhHDhMqXmSfjS0Tm7/d5+N7FyXJZkeKRbD1Jenqv6AdB59UUVkQkeMeQOjrfcXAdSpWYStPMCiohqa2lNMX0QPGAVCAVOfJEnMtPqYYBJwVF1CSdCdHRO0eQk5AnyoDKmegApdASQuoZk+cYPfpY4oYTjkeFUI7lcqIUOjyLk4g0DnJAi4SQkTEqRtkjbcQIhZYd+RjRXULKEikMSs4QSmHQ2PgShhe4+5Yig58/IKXEzhzVucellnaU+CTp9p7jNuGTJntJKQXEzKhH4rqDKqOExviSIpYgBC5PqZjDPpAiFEKhrSLnhI8ByEg1wXNkVoSccbIHHCYKVJTIPCW/KimJUqBURsspUVPZCb3vkkMIhfOeLDLqKbY85imXJ+YIMZECGC8pTYFgigm3UiO1ISfJkDw+haduV02hiu/9UPuHugZGDt2Rdu+wwnA2P6W2Fq0qQqfZbDfo2YKZzjzySFEYnj07ob974OPtLW8/bLg/jqAU86ZAlomjHEkepBeIPIHwBudwzpFFwhpFISGPPSfrZ7z8ySUnnPLh3Xu++4tPZCXJaGgVYsjICIRM3zt0gqKuWS2XpAztOLBazvnRm5eUlcH3CryHNHXaRBYIoSjsnOw7wpAZjomoYXfoiLct3bZDjuBdwM5LCBqlI13fs9t6EIKbxwODDzx785w//+//KT/6l/+IW8KEnvcRsTyhfL3mT//lHzPc3JG6HsaEGBKhC3T0DGbg9OUlTTmDredkNaeqLdIIDgfHPgwkHVhe1pydrfh41eOz535/JGePOUbk1tM/HMiuhUoy7MFlTRlqTi5XHNMOWSdEoUF4NjefOH7zifbjA6mNqOkGRrYSlxJGGpSODL2nMoJ1XeN95OE4sGlbInKa2cvMrv/ha05sUeKfcO1SKubVjM9fvMLYgrcfPnJ3v+F3X3/gxfkFX737wKe7ezo34pTEScGLizOWyzn7/Z6v337AOc++P3D16QOv/+hzdo+P7LqWzBRm1x97br67whw9P3n5movlimpdoJTEKMmxbSfThTLMTMlpOUfWU7bVyBSRMGsqThdz+hCRAj579gylNLvdjhQThdGUVjOG6RkbU2R0Yeqm/P5gf/ryIRDz1BWQQrM9HHl3fcN8fUpC0MfI7z584v2nKw7d8OQGSk8WZJ5wF0+heSGQDh2HwfHYdZTWUFUVzWJOCJnH7Z79oaMfPVo+UWvTdCZEYAiBGDyFCURliDmTExhtp47q0/cvgPQfRpoIgZJTEr1gusiSElYZhAOtJFVRsFjMuDg95fL8lNP1kllToZUgxoARiZmduvdd22G0ZjmfYwo72a8FdO3xe+2p7w9hW9e0w4GRgUigHSTHLjM6SHFKIpYh4VNE6ClATqkpIt1YyUyVzMuSojL/PlcmgZJ6siHjUVkgkGSpcDnSho5AwrmRcYigLOTEGEcyiujmqOoVTd5Peok+sDtYHAqVZ4Q8x5QdMyv+f+T9SdOlR5qeiV0+vtOZvjkmRAAJIDMrs+Zis8Wmmq2FzCQz/Qv9L61kvVIvZNKqZaYlpW6a2Cx2kcWsHJAIBGL65jO8k49a+AG4JBaVlkbQzbBBBAIRcfy4P/48933dNJWhOjHUywlUoJaJTmWSFCUDwUKfHet8hZYSvb4n6x3KtMxzXcLClCCqA07elUvbTqi22BNV7ohY2s1U2pC3W/bXMD4EhsdAShGra2QnycEz7x3p4FCVIYqM0hlZKRrbYFNijh4tZXH+yPx9crBMoHOp6zQlgTbg2U8DQiSWscOIEtCopS45O5TXmxPlAw8iMydPlCWUa0ozwUdsLhYvmRQqKXLIiCkgpULmUlQ1xqCsKenLWeJ9Ik+5IPpFJCuPkD9+K/GYHAdGmrphpRa0VQUxklWmqhuq6OjMKbazPNYzra44PV3w1c0b3tzc8fq2536MuJypdz2bE4NZZpxPpFEwkxDS8yFsUSrx7MUJT56vqQw0RvPy55f0fo9YtPziv/0ZyQdkKI6A/GZLerMnD7CuCrXVasnTkxNSDPz67Uf62fGzzz6haZeslytyLCDDnDyrRVta2FogdCkSxGjwU0J2hvFxYryfSD6zXq45Pd1QY3HO0a0ko58Z/YHtYUDqii9//orP/slPefXPf86hEfT3PcOw5XLxhJAnxjTSmcRq05I3HZ6EOwzYceTTl2vM05phMLhvZ9Rqy+KyY1UtUFrw7v0j413PEHd8+/4btje3fPvtV+xuB7KWPBxggSLPB6qLxKVtqFtNpyviI+Q+YK4H6uyJ1cDYjoxi4ubdI+HjAXOAJlumGLnfbtlNnupE0JiKy9MV223PurE8O1vhfWL0mevdDi8gy+Lw2E4//uLk5Nkl8/E8kRGapqNdLrB1i0vw5t17rg97lsslGImqNIbC1sjWIJuah/2Bmw/XTFHQnZ7gu5p6YWH3yIevX/Nwv2PuPVZI1psll6tTzhdrzrolJ10RWPsYECLTu5HpMOGHkcMUiBH8HOiHEe8dPjgedg/oTz9lVbU8vbyiXU4IqVi3LZUxTN4TQiRnQcgZLUELh5SKRGYOZZyehSABMWUqJWiqmpAy94/bMkbRhm/ef8NX377j5uHh+5BBRMboEh0SjqLVkCMiZrKkWJQBpKA1GlM3PB56Pt7el2BCSmGRj6OXkh4cvxfmFolERiuFMbYYJYIn5zI2EkKAKG4khECJAnYjiyOCv4xyFosO2dbH7nrFxemGLz59yeefviJHz/6wIx/R+Gcna85Ozo4JyfdIIWiampRKYyGRmeYflr32wzUnbmae9+zmLYdtYPAWHyTRO4KP6KRpTENTL9goXTJtpMDIRAgOdI2tDKZVzDmSQ4Ypg8hEI0pAUyzts6wUQXhgJPiZaZzxk0bLEiUdRaRrFhi5QlQWKTuEG/FjIkeDqhZUVUXdrlg2AfKI1hNPrgTRZnzOLFLNIq9wCbwIZGNJscLnxDhPWN2S24oxJUS1BTmSmAnRUYmEPb0jdQO7SmHqGh01JieoHe25w3nBeK8wuaVVprhYTI2xhtpqhEqo6LCTRApBIpFVgIKQKWMTJF5Fgo4EFYgpoESiUoVWqINEJgEq4t2AzhqtNFqZIo5VumQyIGlyBSqTsmd2gSRANZIsIzlmUlYIU7z8iEScAiqDNaXzEiPM40xrLdaWEEAXMjKDEZoYNHFMuDwhzI9fc7I5q0ki0I6WpWyorWHInnlytKbA1kQSrJYLLlZrlLKMuwmhNFIruqbGi0jvXJnbCk2tJE6WEWZKAmTCpwBG0J2s+OQvXtFdaFy/xVU9uzlR2YnzT9b80//Tn3K2WJHHib/7n37H3/7L18QHxauzC2oD97s9da152G95PPTUpqI1thSwUrFeLakqSLHYxLOQ9L6HgwcPrT5BujvauqYxNW7VMvmJ2ghOViv8qLj+0LN9vcPWBrvoWFaapz97zmd/9SWLlxeMNjEdZkI/UJuKuu7YTzdImbn/6ob7f/+ax3Hm7IsnfPLFGctLw+KZY9SRMG64/LOn9L7n8f0NLRpjQfs9my6gVoLEA1Zbnp4vef2+pYqCw33Fh0dPYKY6U1TKkoYiyj+RG2xbISeJyg3v33r29kBYbOk/7skPkYXd0C0bHsWBh+GAi5E2Nez3Hu8yMiv2h5FbKVDKkGIZpUoBgoyUFFH9j3ydfHqOlZDmQOwjClk+i7MlZr2kWS6YpgE6zasvP8XXkr7vkdrQrDpSyhz6Pc1qwfMvPuPFy+esmo46OPLdHYd3tyifuVgsOek6vnz1CVdnp7TGUlflTHLe43OkXa14+9XvmfsRbSuQEkzFznm+ef+eSij+4hc/Z557bndbnl08Y06Jf3j9hof7ey5PT7g6O2OcJw5DTxaCh8OIUbKwQRDMLuCdL90EY77vpCyM4fnpKV9++gmfvfyEx13Pb958y29ff3O0TAficSze2opl26KVLmOv4MluLiJWKb+3YVtr0FrzuN1yfXv3H4uREJiSR0tdvrMcXTopFbGutVTGUlVVCeKTklQXKu08zxhTfl0AIcvnpbUmeE9WJXm+sIdkgXQe2S7aaNqu4XH7wLu3b1jXlr/5q7/k7PSsPITnom1RsoQkxuBJUO4ZMuIHjv1/eHEyR4KD7YOj9xIXIIWJFGdyLvjaZdPSKYsLmdO2pq4UkYDIoCizLGSB1QTlELXEh0iUSyY8bYLgPEEKbKcxViFzxu8cu/2MQTDPE1EntLQYUwOWLCwqSqyoMaZGZYhjxvuIXXVoK1gsJJ3SxLkmmgNir+gPKxINahNRi3cIkXE5IIxkijWKGlKPVDOVlSixInjFxIJoEwe5ZQ6RC/kE76BRNcJIkho4eaqpOGObFsgMaSohiYumYXNiac8FcrEnRIefJmbh8GomTRp8C16x7Qei0ohFxlWeutIsak1tFDpImCQ+B+7nG27nb7ioL8CsEVoiKAh8kYsw1ggNuiodFBkRHbjR4XNEKo2MmRg8ShqstMhaH0mJkfkoonLBlzYkZc7pQ/H2V1ZjbAc5Y5JEph//afw47nm871ksFrRdg9GS6AO77Z7dmLDrihBmDqmnkRVpinzz6/dcPtvw1z/7gp89D9zvZ17fP7KbJy4vVuzDju3jyLprUFKy7XuUAC1g2t7jpwXrJxdEEXm8/i2NW6Pdhr3QnD05Q+aZN//6NTf//hp9kJxvNjy9PCGkkTf3Hzk8jnz57Cn/ZLPhbN3x8sUzFm1DCo50PDCTNETpyEkeD9A12gh8CthU4R8dWpaQs6axrNoOYuTdN++5fei52W5BCWRl+Sf/xz/ji3/+E+yTJ9y7iu0+k+diR786vWScZvZ+oK0lp1dX6A8D4fUH8usd/ZBw3cDDdc/JLz9n9eoFddty+/orPoQ9fZq50geqjWOzrom6xsiZdtHi1IHLJy2/+/cedb/g8P7Ax37i5JOWxSLi+gMiOT57dcInn79g7Gce372ldxk516QpYb1huVxx0m7wMjP4HXscyWT2+5HdNvBwiFgFjYFdP6OVJCSwCnJMxAzGCH78Cix4e/0fqIym0oaq7lCyJhtJZWrOV6dsnq2wlcIYeO4jv8yfM049UoL3M9PokKJCm5oEzLPn8cM7dBCo9inPn11gjObF6SWXmxMqrQnekVNGCMlhmpjnmbZr+Orr1/gQqJuW0c24KXA49DxsD1zf71k1FTEmFssTrh/u+MmXP+f3H6/5X371D4gQOPQH7u7uqKuKRdPgQmY3TOwOe1pbsW5bLs9OOVmvOD/ZoLXh9nHLx7tbOq350y9+wsXFOa/fvufj3QO3+wPjPNNUFeqY/GuVoq4qlJSlO5MKryjEWEBvWmFUceoslwuc8+z7gVpbxjjj/URIgdbUSKnw0ROcg5zpqrroZ0pjBCkSWktAoKTCSUkWxXShhUQpSRJFpDo7BxRAnFQGqRQPux3Olf29WS643+74+9/8lrvrG6Sf+e/+q7/AKhjGkX2/ZwqJb9+9Z54mlouOpisg0tLliYQUftCe+sG3SD9G/GxRaUmnLZ2s0LZCCE0SEmsqTtsGCyTRUlcNQnocIyCprUKKoh8JeSLKCVMZHh4ecH5GcrzUcsT7jPRlfi4qi2wUaeshSxbdEp9nxn7GNIpKCpS26Lqhth1aGvzgCANk55kPE2bdUC1GXLNFq0B2Ffv7hsO1INaPVJVDLwayWDEnRatqUJHJb0tOjrTElAnZMkZJEgNWQMwSq05xXhL0gLDxGD+/QCiDrC2mzdSVwVQNi7ZFLzN6MyE2O8Rij4k1oc9Mh549W4zY0IiXhJQJOhFF4jAMzOPI+ckG2TQIJcgy4pPjMQ18qG6Z8gHbniDFTJKKhCMiiEGSJkk+zsxHMbFXB0Yx0eeJNMK6qqh0jc0axZFVohU6RGY3o9PM7COeoiRXxmIo7BNJgnz8DLTBUKxsP/a1u5tofMsqtTA5opJYa7g6e4oMivf379n2A6vFkqZpESZz+/GeRlg63ZD0SKUmlpUkZEnwjsfHA37KdJWmtGMF1kjOnxh+8kvP6fOvcPE3+BxIQtHJKz5/umE/vGPYv+ZXv93y9m8TdTzlal1TGY21xR54eVlB0GQSwzTw9GpNjBMPDxPxmA0iZUapguwe55GQApVMVKuOeb+nk5ZZlE5OzJFxmhmHiYMckTry9PkVVy8u+ebDRy5+8YLFz0841Aa/j8wxMyXJHEaWmyWL5RIZR1y/ZB4fiS6iteXF+QVt09B1LV7OHMYeaZ6QneObf/+Bf/j/ved+tjQrz6E6cL5ekptzPm4jH19/RMr32CvHIBJfXWcefrXHz5KsInLnyRNM944QPO50JFWJuVX87uYjbrHF7DP6YNmsl6zqFqkVLjlyK6kbxaI2xDFSrxVaeB4PHh9BK446IUFTS2pUacvHjK5+mDvhP+f1uPs3FJwekAWSGq2WWL2mrjqqSpcbCoFLPS7uGKaiCVIqkWNCySLUhCLBMCuNMi238sDqM8HJqzM6VkgH19d3bB92VMrw9OyMpeww2hStXcqcLpe8u77m5atPST6w3i2wUnHWLWjrmpcvPz3+zj0fd49s9weMlCzWK2xVcZhmHnZ7Gmu5urjkyfkZv/rdVxAj/91f/xl/8Sc/Y71coBA87PZ8e33D/vkVZ5sNq0XH9e0do5uJwP3jjsHPxzFRoNIW0TSkkBnDyOAd/Tji83Hs4WZylLTLJUjB/XbHOM0oqTHWgjwS1KUmZoFVCtsaUi6XvsgFTe+PwtM0z1hbkXNCk1g0NRkKOmCeqKzF2lI4CSFAljTlVddxv90yjhPdoqM2lhACv3/9Lb/7KmCl4PPnT+hd5Fdfv+X3bz/igoeYEGQuL89JQrLdHwihpBlvVgue/mNn6zzOmTQ1dMYeHQeJHCVKGazRWGVKzoAUaCGYg0eohF6UWRdEhnFXugRyRgkBRiNFJg0HhKyIUoKATMS7iWlS1DYiF6CXifHxgFAF932/2/PoRlZJUVUCa0s+jNAVVpdgpiwMUWW0iCi9I5gHxhiZD0sCFfaFR50diDrgIvi8I+UVBcauyQSS0CQlSVnjY41IEasSKe4Ah8YwHQyq7snSY6Un5B6nMmoFJy9PkUkiZaBuD9huJiqBY0AwYOpHNC127rD9FSqssKpmYo80isp2pCSRQWKpUUETs2OMew5h5DYP5HXmyclLVu6KPChGX5TZU4jErCBXKKGQUZJixs+JbKFWhoykpqK1LUaYQrhNmRiLeEwISasbUuhJHsyxcMkUrVGOGZEFCkllLI21VObHfxifzB1nJ+es6w4dQWaBSBKhLdkYjKhZthHTWn79/i37h4Gz1YoLYdDHyAfnJqZDz/3jnmRL9kX0iZtxh60MP/vLV3z6iwuefblkcXaHN6/ZxS0hC8yyYZAH/uHd39OEB5Q6sK4c+rMTNuMpDWuMTRibCN5gxDmHXeLf/fpblFQ8udjQWEUMkZRyQXTLDDKAifiYSVFwu3vLKE+QyTJOntthhzaCyihEzmSpyLXER8/dzS2LruLFX77g019+yrJeQa6QqsJnS8oTUiQ6o3l3+wbbWepagmiZdKarDUtdU2lTKFBUtCiG61tu/vZXpNeG1X7Nb756S970bI1kf7mgedJwEA4vmgKhuq9J3YHP/izAPtC/r7k6PeHysqWtasZF4GEcmfLEh9s3xCqgVxNiM+N+H1hFS5sgDRO7ac92nBEu8uXmgs2i5f27e+xG83ST+PXbj7gYqUw5B50PjM5jKs3poqa1kurkx8858XGHyIoUQzkfcib7a3I2sNdICW6emUaPsYKqNcQUUUoSgiOFWAilsrhdBEWPcReKW1BKDbEi+YrOnlKrU2YZyQG6tmHR1AD0Y48Pnq7r+PnPvqRdLPm3f/u/8viwY7lY8Cdf/IRxGBj7AyFmEon/17/8H3j7/paUAtvdnm3eEVO5YBdVRWstL19+wv/mr/6c3f0d/9u/+SvmY2DfyekpWmtWbctqscSYEnDXGMO/+K/+Kb9994Gvr28ZDnuMVmw2G2pbARLnAy5G9uPAYRhQuuyTkAPtMby173tGH4qLTwb248Bu6EFkGlNBzqzajsoaIBFjIsaIjIUPJgTIo57EalW0l3WD3e65Z4/37ntwXFWVLsw0Fi1dzBmZEuenJyxXhYY8jgOHQ2AOiawVb++39H/3D8h/+D3vbu/JIXK5WfHsyQVDSLiv35CC53S94vmTS1bL7gd3En9wcbLD01YtnTZYI2ibwDzl0v61mrqyaBmJGkQUiFgsR0aXhEo3TeTgiCqQdLFP1rJmvUgYJ4heElwgRE8mYITGO4scBTFrhJ6JOFIOBBztWtFWFVZDzpnJlx+TFqSwKFk+jCASjek5WVwzyTfM0WIWBlUwb5iqIaFBSLJvcOKe0e9o9AYlmhInP5dLvqiZJUI5wjzTmo42VsxyT1IOmQu+WEtFNh4pBiplMFkVut7pzKobefywItyvMVmi1zeIrKnDCWla4kME6/FhRMUa70ce/J4pzdSxpnWaEANTDIxqImRHchCz5jAk0r4v4LWg6KcATYVeabSVWJdJU2baTTQnhtp0qE5TixojiocfchFrkamUREpJre2xvRnhyN8AyMfdU6ptUWJFRbFN/9jXZb1mUXd0TQtHvPNu2vPN62+4edwhjUS3il9df83Hmx5LjQtw9bng2RefEPotD9+AyiPVPBJywhhJDDNzhMurDf/0X/ySp79YwdITGImuQbkeokDoM2a9QVAxHZbI21s++/SU5z95yfyuY94lTO3JjLz5zSPSG6xMaAlPLk5oG40PE26KSKGwxiKlRqiMl1NJfA0KUwti6jFNzX70vP12y/nZkuWmZZ5GbqdHhtNElStcDix/9oqn/7tP2FQnCL3kkQJwSnlCJI+VAqthZWuSkpwvKrz13D2rOKuesAqG6XFge/2Inx2RSPrgSdeRcT9iVctPf3JJrBydsSSpsLbi2fMVFy8aLJqbX90RxyVyvce/6hlNy+VqTV1JsssQIjFmxlTEjiIobm9m1uszlnjWzaLEOMwTk0vYVCzLNYLYT9Q5se4aahNZWM12SmijWSw6Uoo000xXGyorQUn29+Mfe7v+wdfswM8TWgtUFsToKWxITwiaFDLkgNGJuqkIfsSlRJwUfvaQI1qXXBilTHGUDZ7gSp6XkgKZPOM4kuOeRj9ysXjCq4tnrM9O6B92kDP9MNLPM29v7zg/3SA/XOPnmZAj727v2O323N3foa0Bqbl9eOTd7TXee1xMJWcsZ5Qqp1w/7bjfH/jdx48Yq/nk/Iy/+/tfEVLEWEt1c8ft3T05R549ecJq0dLVFVqAJLGymk/OT+mM4ursnH4ceNgfOEwjw+zYzyPDOBRApihslhQci7qhtRYhFU1dNCEg2A09h6GnqRuWdUNtKyQQfCi5bjGTUrF2I4og1ihJ1zQE58g5sWosp6tnfHt9zc39A1ZLXl2c8tMvvyQIydevv+HrN2/ZDQMXp2ecnp5gtYSccAbOlw113aAkxRFVVQitEbri/uGBwXnefLwFIYk+UhmF0hWnPnL7sKOe/pEFsX7OyFYj6hpTV9RaU/uEm0srqdKSRSVBw4wmjgHpAnkKCJWRSZAwyKQgANkQnWAeA9McyTSFMJskiQK9yV4gxopaaVxMJJNYtS1YQ9VIlnaNSpop9IxpSxSOrB1ZDwgZSCIhQ0ua9pANmTOEbghSE0VPjoIcO4iSRIAgqCqBFD2WQCUXxCjxUcLRWpXTxLb/gFaG1i4wtxOWJXHhmMVEiBWJJZkZJQW6yoR2QI0COWzAavAN001DnBTZQ86WNK4BC3JgDjPzwROCINaRh37HKBy2UugUsTkzRsdBTuzcyGEaEdWSKo+4wVGbhqZqwSRGCVPwyBRRVCgBViq00LR1RU2DCgbvQiEfplTEUUpjK11mklJipMVmiDkdI7cBvhNLCXIuQVkuZvJ/AVyH5kpy0m5QSVEpg5LwMOzYb/fsH/dUjWUpWpKDcQioKtN7x1e/f4tLkc2nF7z4659jP72Af/dbPv7uHXlOGCEIOiPyxP3Xv8GEBatPzqiuzpB1hZIX5ADN8lMwJ+QgaMwF7eoTPvvsEuMFb+7e8v7NA6qNVE2inwJm0XHx5Smrz1ew98zDSEyKummx0iJEQeP7EPE5IQwkKcFp3OQZtrfMo+NssWKpLCKUQ/AwTDxmx1I0nL56weK/vsAtA3ObmKNkNyrCOBODQ2TFSXfBYtkQZASZuLSRxzcPhAjX799z7yW1sCQtCAGii+SbQJ0WyHZEdonn5hSchpwZbY890SyvOlLQ5J3n/NUnXK0v+P3bN1zH/4BoDMbC0E/EOTNMM/t5T/BLThYr5pD55vczp53iVdWgmyVzcAx+y8N2IPnIWddxetqis6C7OKVddHy4uafRkoMQhBjwYaKxhqqpSkCqkCWh9vDDDuP/nFc/eKbJYy0INZFTJiWD8wI/B4zMrNeKqikak7F3TEHgvMcaSWUUxMQ0F9BlTILgI11jj6JKmFzAhxKsl9xAeHxX6NhCYJxkd7vFuZmD83z9zTc83C/48tVLqrpmf3PPw3ZHjInRR8ZhZHKefpwJMTP7wHx05pijpgrA56KtO+wPxJy4u3ugQfLll59ze3PDYZi5f3yksgalDZv1iidnp9zd3jL7nnka+ezJJV++eEZdVWz7nq/ff2T3/gMP/YFxngo3pVuwWizLI1sV8GhIGRE92ihqU6irWrQ0xiCVPnZ3BDEVMWyOGZkLfE2IojqMKX2frxNTgpS4ub3hk2fP+OLFc87XS4J3vLg658tPnzHOidpa3t89sBTw6bMrFm1Lip7NouX85ITVeonUin0/cPewpR9HUoqcr5fIV89KLlDdkHLm/mHLh5tbvvl4w+3jlkVbsVgu+N//n//Te+oHFydW2iO11KEzGJFJWiKkLYRE5wguFGjNsj2+v0vCZAbQApmPAUqpoI39bmCeXYE8hYEsDXVVY3VzHMWUSzRPEA4ZGUvaoqw0WXjSsX1lpEQYCwZ0E7HdPVomtKgJtxVKCCZVcUhLguxIKKKaiSKWrF4BUhiSOqBEoFFr2ukc4TSTmqjlAVRG5sDkBmRQLJtTtDCYricEQZKSTIULApcCQuYyExQJs/FUQjDcSOJ0yri3xFATHgX91OERTG7G+QkhI2H0bA8z8zQxpsSd2+JspLOalSyvMU/AhYybE+6QCAJEq2kag5UV2hpiDBA8aY7HL1vJKWpNRQoRmQtOXwqNSO4YbiWI32UyIMCIQhRMEZFlsRQrdaQJlr0hBN/752NO/0V0TuKZZxz35Emzi+VVIaXm+dUlUghuH3sGEUhCEn3mYR7Qg2QYPft9z8XDI6sXZzRPl/ziv/kTzj9ZEfeecfCMU+DP/+SUFyee/cOB61+N2PsN1JJhUCilefLK0G5m5ps9j28ck7K8UxN3bz/y5vdv0Y3hyWcXrK+WLB8DYTCcnp6z+Lhg9/qOx0MmZ0XbLLHGklJm9hNhLq6BOTtMtaDxHX4IuGnEJMFytaIxkhhnhmFk6D0par78yxdc/rPPOdQjIWrwgWHyON9gREetKkSGKSakkwilQIxspUNQ0cqO39/v2N/sOV2fs1luEJWkVTVhnhERRBUJdsRQo0RTvu9nktBOjP2WBZYnm1MuX53SoElMvPn2Izd54uFhz+3HibauaNeC1UKySzP3v/+Wd7c7+t1E7j1JHNjrHjULDoeJcXY0RlOvNK0xzONM7wJt1/D86hSkJr3+yH4aqZTk2cUJq7alH3qCCgQB3e7HT4jtp8g0KnzyVFUssEifmadCmbZ1JBM59IngMxLFOFIOD5OIAXIA58C5SIiFe2VkLo+04AjhP0LO4pG3dLO/xdYNz0+fk1rN3eM9376/pj/0nK1W7EbHu7sHbh4e8SFQGUtd1yhrielAVcHkPTEXpEQMCWEUIhbdS0iJnCFET4wRpTWbiwtutzs+3twzu4ALicnP/Oqrr3nY7vjTn37Bsm1wwWOU4GqzIuTM9f1D0WdWFsjMrui9GmO5WJ+ilKQfepQs2WsuJSQFIKqVIn1HaU2J0Y0gJG1lMbrEuwBEX7AVIpccekFCScvsPfPsUUJgleTj/T2XJyc8uzjHWsNy0RBD4nG35/31NUYKPvnkBZ89f8KitiyamkXXUdcVQhURLykxDANCZrwPSCG4ONsUdD6CEALr1vLs6pTD5BiGAVLE/0Az5w8nxApFjBEnHDp4IgNZWAQNImqCCwwilKTURiGqhBKRmDVBSsgRGWakD8QYOUwzSQl0BqUkIjlCckcwjSWpVDouMmKUZdmelA5HyIQ545MnpAM6aqRJyBqaytKuesTiFrylTmcMs4CFYJKJMRtEaFG5ReMQoi801uzJ2aLkjJUtKdRMuw4zO0SlqDpFUhM+DUCmrc7RYoGLGVE5nDqQqIlJk7Ik5WLDVdKUhF4NYqXx04TYnxCyxdYd0Rv2+8Q2HOjTgM8emxVhKPhyoWDsR4TIxMHjtzNezCgr8BREuXYGNUUmK+mXkk3TUscagSLGgJuPmTp1SZTslMGIzJQndKDwaLIo6HmtMVKWLZ0SMhcir8gQYyDFAEKW+W8u47ScE5IyDEqpQPT+S/AnXP38BYtpxcM3d9y/vUPMms6UufO+H7jfO5wvWO8MLLqWk1XH7EaG/Uh/s0eGCH7k/E/OufxnP6XSFf2c2R22LOIj735zi0yKam3x/ZbHr2ceP7iiReoV1WcN8fpAvoabQyC6PeP0QFBb6uUSvRaoswrTCNSdQQ5Q+YZFfYKrQWmDsXWxGKbCPZBK4WMiEHFhIAQJLlKZimW3KJDEkPEe/CzIQdMuNzz7J5+hP92wjyMhe1ysOQyBw+EWKyXrpqEymtn34AQpa0IayVlSr5f0vud6GHh8f83wMCJeJS6fnUIK4AVGa4YsSJMgC80UMgcJXijG/YD/uGU51Rz0yP7FxOWJZru9Y9lWjHUmSMl6UYiZwUW0rojDzO3hNQ+HES2gbiqEhzlFFkKyMAqbDYum4mK1ZN11TKbiIDycVSSXePmT59TrDW/fvkPJxPmy4WS1YGgUExPNYsm2mf7Y2/UPvh53kRhk6XrkokcLLhJ9hiwQBEJOpKzwc0BLmCaJMuXClVmQgyAGQQiZmCPKKg5DJMYyriBnxNFyIDKIVLR/yl4jbcWy6Vg+OcVsd0z3D7y5uePt3QO7fqAfRnLOhSkSCuQyHR9jVlvqKpbMtOxJWeBiIoSATwkhBDFEYvSgDff7A+M4cuhHhJBIJYkxcfu4536342G/48XVBS8uLvji5SfknHl9fcvd62/Y9SPbYWR2HiVLvMmibZFCFKdMzjRVRWOLZTnliAsJjuGRU4z4VHJwKqUxxiDE8byWkiTL35BSAilUgXVqzeQCgfKgFFmw7Ue0UpydnnK22SAk7IaR37x+Qz8c+POffs7paslm2dFaxdl6gzK6hAcGj/cFSW9N4ZbZo1h2mmamqRCpc84supYn5+dIY3jYbhE5k8Q/spW4UhqhBEIlcg54FxDCIaVHxQqJIuWC2A3DgWpp8HXEJ03ICrxDhBHpZqJPjGEmJUlnGiotaSWMYULqALoiyEAQkSRqTqoLTswpIgh2+y0+BnzyyJwx0qDRaFlRWYG1GR8tcWiRoqWxgWRGHJmcLDlptDRYJZEpErNjUI6YoBIWEdfkuCTpjBAjSieEtETKSENIizIrQk7E6IkohJbl0E6RRGlDKiGQQpOEw8fAiCeuPVntyS4gZIsRFukNMSeinBEykOdCzDV1RVdZpNGoMOBuPO7elRftUoMGrQwNmtElhimjgqeKgUVWSDQiKtIUS7FUWWpr6URNrcAmidYaLcsXvWiRJVoVam/ZxBktS5hfygJyJIsy1MmpiJfFcaN9V6iQSgfhx74+//JL9CQRDGwfrlEYtNIM00w/BRKS2Tl8jBijaBrNsqtZd5Z5nMnOEYaEnGChT7h43hGzxERF2l2z/eYtu/DAk80liMzhes/Dm57+PtMuLHm/Y3g7MPZbpMrIJrHb7pjnGRUy+SCY7wPToif6GbVtkV7SyA67KtTjFEMRSPuC4IYyzhFSYk3N4TATxYSOgmW3wvjA9d0j0ziXMR6azdUFV3/9iuazE0LVIifL7G4JIRDzcVwUM8lHpFElWRlPCOCzYAqGQx749uEjD1NPVSfOruDqlWR9Gvnw+obdw0DbNEQdqNQCKRqu00zfLqBZcthP3P/2HfnDHXK64fRpx5/8/BK/mxgfEnJSLLWlXkm2+55DPzApOLjAQyy6EmUVXV2xVpJGKC5tRx48Yz/R1ZaTRYdRhkmBObFc/NXnTL3DjBmzaFDM7Hdbpnnk/rHE0UuT6FYas/jxd07cCCAIUpGjLNqNJNEIyttU4UaJ1AaZNUSJVQII5CRIWR7F+MezJAVi0PhcBPo5Q06F5PrdPyDIybEdtqjHGn1ZsTxf8Sy84OFw4OPNA9657x9N+RgdnDOlMDkaMCpbxLkpJZRQJe8m+O/zZPKR8VFi3yRv3n8gH7vDlS3FQUyZyQfGeeJmt+Xt9TX1X/8l/81f/DkuBd4/7plD4uZxy/Yw4JyjtVURogvFrj+UR6Q2dHVDbQwpZXw63qnJIaQkZaiqqhQgosSYHHvchBjLxEKW0Y5SEqEkPpZiLIuSCzSHUArIxx3V+w/UTcWnn76iWi7RX79hQ82TkzXLrmXRthgtCSnhZ8ccAy54Qky42eOdK1wVazDGMk9z6TApUZADGSqtadqOcegRIqPND3Nz/uBbpKstSEgiI3DHqtMjcySLgKgWJQk3gR9mhBVMyjOlRE4S5UbyuAfnkOijXUkijaK2DUtpGVImVjPZZOYQ8SGRkkEoQa0tSgi8NqgEWlgqZVnaBVVjUAuJ6QQSQzpE8t5gTmsWz3t2cVdaXFkDpajSSqKlJQpBIOJTQLEix4aEwixLJyCkGVSLUCu0zKRskMqQ6MlIfJYYUXzzPgaktCiZkRRtRmnPOXya0DUYdSBtIykYtF6gdUSlhFIgTcHHhyyJWtHYlmbdIQfDtJ8RQyTOCVqBNgqOoYI2O4bRMUVHP0k2ZkElLRpNhS5FXJLoLNBaIaui7FYalIwFYZxFiREgluJKagTy2C7MaCHBCByeEB0iaTTm+PMEIheAXpHL/vjdCeH9TH9wTL0j5fL6ikjmBFkZmtbi04zMkfWiZlFbZEws2wqbPdkOmI2gvkrQXZfX3cGTtWW3/x19+IC6ANTE/DEzf4iIXrCoNCfrBitgfzswiR2JmauLS+Y+IrPCssCMa/JHyawc2gAPGVEJhAJdGZaiYzocGOaB2QeyKBBALwJJAVmhosbphNCKXT+w60ce+5H7XY+ymtNnpzz95TMu//oFvjLIVKMQuPDInHq0qDHaYgIYCrdBVRUhSzyCGDLT6LkfHvjweEN3Yvny1SU//9kV5xcX7O9mXD/wcH/NTfAsTtd89vIFAx0PSqIvr6g3LRwCw+4dh7sRIWA4aJa7DrtLHG5GbKqopcJP5Xtyd5jZSsejcfQ5MbtMcoloPJVpqZXhZLkmyJlKaBZNsVk+7Hre7/dEUfGzV5esFkv8xy3662tO/YCLE7c3W8bxEUVm2Rm6qqOy9R95t/7hV1uJIxZdH3lJFEjeUVSfki6EdqNQlUCJglRPWZTjIkmiL/RqjiNlKSQiK+RxbBxy0YRkmcmiXMKJEjS773f084itGhaXGz756WdEIdnelbBAQrnEcz7mTqXiYklCYIzCBg3JomUqdljS0eVSug0TRw1UztxutyUfTSqkLNltPkQmH5hCpHcT277nsw8fmdxcgKBVKURm7xndTIgRow2VsfTzRD9PtJVl2S4KowSOf8bSKcrHQkmpoj8RQhwzb4q+T6sy2RAUMa/8LvssBg5DGQHlVIjF5REtCNHxzYePnJyu+eX6z1idnPD86pxGlvy12hraui6dl3k+hhxGhnEmxYjzASEltVZIrVgslsxVRfAeqUrGmpSyaFKEZBgntJb4Hzj3/8HFSdMZfJgL+590pL8KsooEMROkJQcIcyC6TL8bGZmZ8WgENkXyPCFiptaG2jRIpaiqmkV9wlJLJip6e0tQHusMcbYYv8R4A6LkFqzaBkSDzw1SaRrbYiqBaAGdCXODfzzBoFlctthuIu1nJh2IR/GUEBByRRSGLDUxSrIa8DFilWdOD7TmnMPkmcJIqxdUuj2O9TRZlOTHhEYKS8gzISlSUqVaFBIpAhBBRKTI1EZQC4FJiUpp9qJH6Ig2M8r7IjI1Cq0sQil8CAQDQklq3fLs/BS3G6kqg5SCnCUpCMiKprYc4kxF+bMlUcYsWiiWVUtKEZMVeYqE7AkikHVCKvCMSGtQpiI7SQ6JnGJBGCOIAlARaTOogGdgchkTa2RWyGPWhRQKhUYlcQxs+3Gvv/1//C3z6JlwuAiLOiOyY4pQLzowmSgm/CSobcvpco0MER0jugN17ug+m5Ev99yIG/w7YNZIVdEPD8wukp3g+uaRs/4Z67SmXkzYVtKtKtIIISliLRlmSVis8cljTyVre0LnFwgnybeSZrMiJ4WbZnzeo5RBBYi+ZEwFPxFEIIrIKEbG5EhBUgnNfpqIUXPz4ZF0DHVUtcGcLTj580+4/JuXhEXi4CS11hilkXbDdhhJaWIcAkiJqBoyhhAkQhly8gyHA85IduOBbDTPv7zgy6eSZ1cb8myZH2a0b8hR8e033/KJbqjaJVvTYe2K1ZMLjFWMqw3Lpy9YLE9pzzbUZxusUsyPxS56tVqjUma/Hzj0PbeHA3fSMS1LympyMO4ju37g5MQirSBuZIFLVjXSWB4PM99+vOPr2wfCneAXt9e8evWE5ekJ47JD1CVyYxwjwffUlcaYjAuOnH/8Y86zUwlZkqJk7EFrj60o9lhpSEmX8Y4EqRJKQpzLiEGoXBLXk0CqwtkQolyu+sheEkLhfSDEdDyaioYOBKTEPE88PD6gpKWuGz796ad0iwVvfvsNtx8+MuwPOOeJx6TdFDPqOzu8EFTWoKVgcqE8+I6CWK01MZUsqn1fCMHD7I6heqWLLEWBiY7O4Y4Xv4+R3797x9fv3vOnX37BsuvQSuFjxIWSGp+JGFOYIloKVm3DyXJRujchEshHyvDReIBACSAVZP6xkVMKNijj9JyLq1RLshS4OZa05OPYXWh5rM5KYTanxMOh59t37zkbRs4WLU+fXDINjhg8IUW0MDR1jdSaYXb0/YCSkqZpWFUWpRTeOVbLjursBDeX8NrZOaaphD7OR2S/1pb4Ay+IH+7WweHiTIgBCMWIewwMyirimcsmlAmhBfPkGd2IZ8ZJQdQlyVQLgUBRYZFCIaRF66bYqKTAh76MNWSFqdYYuUS7GiUMWoFSFq0rkoQoCsZ3zo7kPCEG5t4zbwObU4NqFbIRrFUL+ZYkFCkCsXQ0klDkZAneEKUk5C1GVyixJ1KRciDljAsTWahjMJ5ljnsyE1FYLBYREjEMxUIcZxIF8SulJMUyYqmVZSkq4jSTqamTRiwcSc5UwaNjJivBlH3JFokJkTxJZYxWnJ1uSE1LTAVj76aiLreVojttmXxgJQzrZomVFTmWL35JfC6OG0IuF48IiCqTMjgGutOK2lgYNXEQJBdJKZZ2Zk4kNSGayCx7gnYQG5gFzAIRShEDx6IMQYlQ/nGv968fsdowRUdaLBA2c9g98PHmgd0wIfOMzhkRBCpnUhOptUYRaDaW7pMKcRmYVYlnEElikmJ/e2DaZ24+ZPyd4DQIOgvrumXd1YhmJolIGitWJxvu9YFu3bCd9yQNrCGJxLyfaOcli2FF1gqdLVJIhuGAVpZK2oKSFgmjM5Mb2fUDg/KMqUARU9A83NzjBjC+4uXzJwQpOLGw+vk5V3/1Cb6r2O4fMCIRUiEp180F/v53KFFeoFk5nEgkLyBq6kaSiYQ007UbUp8IWTOLwDdf37H93RY1r5geQCTF6ByJiuX6HF9BPm85X7WYrsKHCXsiufyrZyyaNbZqmW4fGX/1O4btHd264/zylJOqRhrFg5u59jMmBkiRECAc40TSELkXA6PRPDtzWJEZxpnbh+KqeOgHptmTe7h994arn3+CXT1l8dlTzLJBVZbVYomaEm3TsD3cMLmZh48/LOjsP+dVdYLoMs4lVFVe70nKcs6G70SaGik1+Ri0F6dMpowfcggkn0hJIaUuMoGUgEglFZW2RFW+byEXqSe5RHMIFCkFdrtHKrOk6xZIDa++/ITTkw2//fvf8Ob3b3i83+KDKyOO8qwiHsfxCoWSitqWkXXS8uhMLBk4lTVMvowtpuzpqlgydY7FQEzpaAYoIYBkwce7R/7Nf/gHnl5eFBaYsSWn5/hzvgsLrIzhfLVm1bZoIUtBrANzHximiZgzRim0UuQkyCmjbcFlxHx0L5V5WBm3xkycUymAMjRtQwiJx+0O5lLcVceRUkbw9dv3zMPEzz77lL/85c+xTcvjFDhME9k5zpcrurZhdoHD0BNiZNE1VJWlqizaGIJW+GliUVUsVquicfHhaA3XZCGwxpBzLjbnH7B+cHEyTDOH2SFipqnVkSciSTERfSSksaDLQy68baGQySBmR0gzopLUjaWqKxpZUefm+JfekK1j9gMmSpTXKFqQFVkoyAV7S8qFsyHT9xTCkD2OmZgcSXp8cCU+O9S0ylL5jipVTBEO/S2TvEPLhkpfoYTCiUDKAiWWpCSIMZJNjbFrxrAniB6jDJWQVEkh0AQx4fMBwYyLEa1XoAyKCiknUgoIDDnrIibMVfmAhEUKg6y/4Xa6QfafIaRljjM+gZEaIWzJM8iCKmhMhCgSui3gO6Uz3mXmmMko6koShCOKQLNaUAnLUnforBFSoE1JJ9ayhEtBIqSAUbYkWKZAYiKrgNCeJEqrPfhU8n2MJKvAWD8glnvGacQLgxEakSCFTAyFKIrIRbgbUulQ/cjX6+2expQso+efP8cuNB/2j7x9vOfQz9iUqJJgVTdUwvOb129orGVVG7pJcSFbzMEy6B1BetrOAi3uw8jdtxM3D44cMs1Cks8Vi3VHu4KgNT4n6uWazYszFqtMvlTcv7/hxJzgnWe42ROHAS2WNLJBDIocE9QCjcFNE0ILcgwIUQTpUiim2XM93eNUJAQPLrOqWpZ0zENidjN9Jbn45Us2PztnTIF5H5FKkHMk58zD4Zbz0ysWy6e8e/tt2cvLFT4JJjeByOznHZOf2KxaQuyRsuiUtg9g3yvyKGh1RqoiMB9mR73o2M8Hvrr5Bw7TB7Yo1peXVCuFMffY2lGbFn97z/h3v0HeP+K0INnMjpmNaWjamtWio7M7Dv3MMguSSAw5sc+Z4AOPuwFnNc55lsuWvp/YjwMpBxZNRW0VwkbC21vmjx8ZnKXWFi1guVlSX07ULlGtLQy3DHe3PNwMf+zt+gdf82zx44iQElMBVDivCDEhRIQIISSmeSDEhNEaIRRKFK6IkcVJKJKELIr2jYRJGRUSNiXSMSjPpcicIQhB5lig5EKsDmFivVpzenZKyomua6lqi1CS+T/8FhcDKpauwXdBdClnYnQoWUZIWqnSdfCB0TtiTJBBiZJ3EwEfi0VXKgHfjbTFcWZEpjYVWRr+9te/Yb3sePLkOUfJCwhBU1UYbYpZSSq6uiXFYnN3KRfYWn9g8r4YH2TRAxpdHr3a++8MOiipUEJirClicTcxTyXE0B4D/8ocC5wvXf+QZiBRW41Wkuv9Af/114xh5pOnT+iWK6zVJDKBjEuZ3VjYLNoY2mMmkMgZIwTalsyeFAIYg5ZwttmAFPjgOQwjQwzFdfSPLYh1UyJFhUmSCouVEZEcaS4Vb0AgUsANjm30xNqSgkA6SZ0lbaWopUDnRHABJwJds2Z5skSd3ODdPXG/RESNSg0+QsiezIRVDTmXfI/S3iphQmMamfJIEh4hI0JmFrrhZHFG6zXzt5pBGJL8CSL3LM4/QpPI8qgliQYjGky1ZAwGraE1HVPck0XpWBhV0coW4STjtOVQPSCMppKCSgpSLsAqowyIgZg9ggjCEwEpq2KmzpY9W1oraZ7eIcwpWlfcPSQOLjJlSZWBWGxsp3bBgoYpZg5kkixfTEsCXYROWmv2Q2ToZ9a6Y2U65JxxoaeqKows4l+tKqQoglaL+V5HEnMgizXi4PAykaeIdxzJsAKpJWDI3iBQ5PoewQDjQJwVedSFXnushDOJ6AOj+/G7E353+8DFZokWkuU4FKhYimiZMAKCLyi73eQxWh6dBiXbQqeK/oOg/xYeJ0tUisXacr5Z0JozqniHzY944QuLwffM1LSyxYoGUmKKEwe3J+VM/26gG05o4opTaZlZ4uSMSYCbi+A6S6QAN3iG6cCQD2QiMSaE0Oi6ZaMjh9iXcayNTCmgKs3FpsXtBdEonv7lC9rPz0hNTcwUkJufsboBmYne0R/uWbRXPHumif0eEUvQp6olKc+EkFl0BiMnDoeB4B3Oe7BL2tZSOV9mk5Uki4Qykq6y3O223H67ZyRyHwSb83NOniwRdQAXWcw74jai7ntkVrx48hPGOXDnJtL+AVs3rM8uWN4cuL4/YIRk09S83+0ZXPmchBBsFhX77SNxHkHC81cbzi9rxoeZuRfMfoJvJt79j/8OqX9DqzXLjSWHicP9jml06FOJeXlPff7AJ1dnf+zt+gdfu10oF2eW5DniY7kIRc5oXcSuIhrwihwC/eDJRKQu4/paS2TO5OBBCGxTXvZoiQtl76aUkT4TUykoshBkmb4XgKYUuH98z69/Y3mye87l5QXLRcuzV1ecnG948uIp//Zf/Vtu3l0jEBwOe4TI+OAhcUwgTmghEUISMwih0Ep+j1nIgDqKTfUx0TeldDQQUKzIWbCoGmqj2Q4z/+ZXv+XldiCGwKpt0FKhtS4Ub+eY5pnHwwF1dP1kAaN39PN0DAFUxJwAiaRYiZ2fEGTapi5FgoBEZphnfIhkIXHO4UMkJUuCouNRunTFSRglqauKrCQP48h+dhADP335isvNCXcPD+x2O3KX2I0TD8PI3f09jRacbVasl4Ua29gSO7BZrRmnkXB0DolUkpObqqaqaupxZJ4nhPphBPEfXJxIDa1V6GgxQpOnwDzPzG7Cp8wsMyMTOZQPlJgIs8N6WC2WtE1GCE8KgRwdSYXjX3wEHQl2B2JP9E8IU8UUBlKcESIRckuOioBHiRpxhEWNyTNnT5YJ7SVLaVjnlkZaFm1FCooUNMP2QDIt3XoiTomJopEx6opGnhDIaB1QEsY04HyNyBsyPSEckCrT1YqqzkgEUddoUSxdiUhiYI4lZ2Z2oFVCyIDPCYkmBYdhQppiAVtf1vjmA8pKrs4N02vF23eRfjtRVy1CO6Q8oLNBZ0GcHC4eOxQShFIooRBZIpJB+4rOVzTKEIMrrppYlNkChVIJqTSgirAqf2f7zWgs7BVRZEiFY2KMQehjvo6QyKTwQ0X9dIesM/5GMz4InAsQjoFOSJSWCJmBH7/o5OEQWKygtpL3797w5quR24+PPD5OpAydVZw0DYdh5P4wooGnJw0vn18wTQO3j3s+bgf2IaEay5wlcZ4hHXicesYh8+RVy+VJy8VZCyJwc3NPdgLVGHo78HC9Ze3WiEHTcY6kwamAapbUC4uJlkp0aFEuBCUCjZYkZeingWq9QDUNqTXEKiEOsPYrVP/A7CdSEvTTyPr5p7TPVoRnHeOpYhYjwSVE01GZmrGfSFEy+J5+nEnzDFVEiCWbboOWgawUwixBaCKKfuwZ5nukzqATylqCsoQWxAgiRZzy7MPA+nyNRNKHzN45TrqO6rQlrloGJ0mjxsiGVXvG5vkT3KGHX39DexDkned3H97yYdNxdfEUpw26qtHSElyij4k4F8WlFpJF2/DLz15y0lYlxCzOnDyzPPtzy+9/fc/v/uUWkyp+0l3i3ozsh2s25zW9nxCnI+IF2HqJbZeMU8TfCy7qH5Yl8p/z2vcSSfi+S5KP4wYhJcELYkyk4PFzpq4tSujiGMkJP0e8KwJUSXEAujFgjWSmdC0GGRBZFGYT38WcJJAJZQ2yMPkQZB7urzHGYGtLTJGTkzWXz885vdhwcrriX/6//yde/+516RoKSVVVeF8eVjnnYo44prvr42V+GEdCKMDRGAMxBmpbgvGilKVpkjNVSlRGseoalm3Drh94d78jHC9qqySyrpmdZ5xn5hCYvCeRqawu0SE5MTkHOdHYmmXbkYHDPNFPY9F1Aau6obb1Uajq2e4O9NMIFCRASqVw2w4Hcs7H+wBOlkusNRhdIkcQEqUiq67lk+fP2SwXuHFCS8X56RmrxYKz8wsehpmPNx/ROXB2coIQpZujjAXAKMWsNIdhoqtrqqr8ex8CIQaMMczec+wf/SfXDy5ORu1IsgBcVIIqCoyo0dYwi4jLgcehJ82Zs2bNWVUzpqMzYL1AVJHITJ0FVa5LJ6Lfwm0PeUdvblBBoIJFZkkkEMWMzxnnKypREVMkR4OUkSlNpFBGIKaBkzWsG4c4DMy7HWb5gpwdw85BJzAY7l8rTq5GFqeeQVUEv8JlR7aSoAMhRXIOaGkgwIwgu5r9Q0N2S0zX0p6e4buCaQ55y+T3CKE5zB7FVKLCVYORMMUZnwJZlqq7FYJOJpSoCG3GyQNKVVy9alGV5ON1cT6dLiWr2pHvBuKjxu0PDCFwUCBrg1IaGQXCZ+beI6NApWIrC1lS2wojDRKFPrYqyxcqknIqxYcqs1qJJOUy0iqyqaJDELKo4hEGmSwcKvLDKd40xP0GlWuslsw+MMw9SSYa01DpCtP9+K3EFRk/zbh5pq4tWskiFF0ERE7IJNhPc8mgSPDlJ095/uyUwzjy/vqRwUfikUZpMkiX2M8OKRKVlSyqlg01r55fsFA1jApVSfRSYzqNkBUn5+ecL66YG1c6Fc9fEv1A1VT45NBIFrphe33D21//ivFwYNUu2CyWpBC4v9tjPl1jn57gzcgke8RDQ+Mc9n6kq1uUEnRmhb0SuMsdybTI+gTTrMnKMA8Du8cDQicau0SjMOkOHm8Y4ilqtUQvNFl3qNShksZ7T1s1uLig3+4ZhkhdtSSX8EYTVMBNE3fjlht3z2JlsbpFiIb+8EizXlOdNfRtg12csVqfseqWWNuQs0Y97rCPnvr2gA8Tp7rj7uOBf/v13zF62PcOnwIpJ+bgiSRqLamUoKkMVhdceltZulph14pt1SNeQfpt4N1/mJFac3FWcfKywTx39O0dgxwQSVHtHfUu0HJKd7jE6ss/9nb9g69xzGiV0VoT5kSKkHMEWQoAkQQpFk1KFgGtVRnr5IhQipxluUBT0Xl8B4LUx055lnCcoCAzCFcMATkBOiFNZpgmgku0TeZiLTjpTlmt1zS1RWRJ29T88s9/StfW/N//b/8jb79+AzEhkRhVYkdCKNSmFDOTc4QUmb1j1w+44BFHvlPIifSdli9mYiyFTW0MUkqsMVhbUflICIHb+y1NY4udF4HPqQioQ3FZppyJIqGOyb05R6zW1MaWrCn+ozunnNXFyZMF7IeB+8Pue06KUQqjFVVTMYfAOE4orbHGHKFuhU/ynRPJx6Kt1Ebx2ZfP+cmfvWJ4mOh3RQS8XizQqnwAddvRAou6K7h8ACFwzpGFRJsKvz/QTxPaGozVCDIiJ0KM2Lrm8WH7g/bUD75FDlPPwzhgc8VGVHTJsqwaamuxcqZKEZEDYUoYLTiVDb3JpKQQThJQJGOxWtNoie0S4zQRvaLOFTIJHh73LNUdy0UmR8HkIilKUoykGPCzxwiNUwllHZXNSJPpLicWVyO6Hklzpskn2PYDMrxFPLU85hOSd8yvn9B/TNh4h1/tGORHhJhpxQsqfYFzW3yYWNZPCcwMbiDmTMARco/MvgSjSQdZUwlDpVtyCkdBcKBSXbHPiUSjLRmDkhO1bLDRkvqJuJxxOtH7LYGIWCy4fHnCyWrB/QeJ9DWVsozeo5JgIQvBc5CJkDJRBESKmESxBwtLJQy1sFhlimtGmtJ+FLIQXFOGnMgpkNEIqYpdLSdSEMTE8QtSwrCIDiM1RgpSSPhZMI412RraqqW2NbnTaB3QIRKEQ4r5KCD78bsTLheW/jASs6BXPcoIksiFIWIsRhj291tm76mUoK4UDw+PTMcXmEiZRinWtmLR1Sy6Gjd7EInVyYovnjxlsWlQTaapOvTCEnxk8COP7oH3wy1fv3/PCb/Hz5nV+oJ52vH86XP248R2t2Wx3NA9O2UWgt9+/Zpvv/mW1WpBbSzT7FgtWubf/Bb3rcI3gZw8dkg0tPz8+c8QtcENEyHMPH+yQjWGfUrswkDqBVp2SCRCarybqFXLqrVsxAXt5Yph1MQwcRgG9o89IvesmycYa5jmUJgNzYpFjiXgcpyx04h49LgQGGLAt4p7H1jUC+pXr3iyMKicefP+G96+fkez8by0S5YnLaZZQsrELhHqFud3LLuWbtFy+rjn73/3NdM4UJFZVJopJ3wqksonm45XF6f0Q8/cT+xjRmnFctMgNolrf8dBjrQvLNe/6Xnsd5z9fMP0PHJQO7bvR7bXCes1F5WlXZ7y9ItfMkw7Pt68+2Nv1z/8ShXBZ6LMpADRF7uv0rK4tWLBTEQf8EKRhaYoOABZRhcy5++dkFJKBEcuCYIUNNFHks/4yTPvImFKhQvS1owE7rc93kXqauDjuz3/y//8d5xfnfP5T3/Cn/zip3zy8imr9ZI//YsvUVrzP/z3/0++/eqbY2aaKA5Mqb4fe/gYmJxjdI4sipxEitJZjkddiNYGHyIhBpSQNMawWHQsuxYlBYumYtluOFkumd3MzXbHw2FkmD3D7Jj8ETxKJnmPwqOVZNHUWG0xSjO5YzEdE0ZKdGULU4pMjP5I6C5j/1XT0jUN1mqU1uzHCS1k0YgIUXQpIRXXrSyRFTGXMb4LgdvdI2GZEVXFbvuA84HDNDKmzP2hZ7vdEqzhZNlhlC4u8OOvMRx69n1PTsUq3A8DdbQopVFCUWn1Pffph6wfDmFDI31i8j0PaWIWDUJo0IZMQKREKy1tp1hXDcJHdNZkZVHCILNgnotobnHa8ux5x/3DHe8/TIhJIdQZRs5cPBs5Wb7jcatwNw3OVyQlMbYlZ4+QEBjQNlK3npOne+pNYtYzu3SAJhG0ZRY1da0YnGPne4xqsU8qHr5JxHdFFKXPM8a2SGEZ3J5+vAaRuR7eMM87jDEoGxCnE9pUVI3G65mEZzc7WrOmTS3bx4GmswThj/qSVIBmUhePfvZoaUF6hJTMRKKomaLE54mURwgSEQN1uiRPG0IPdcrUraaWASkOqOSZRSbliCRRS6jbCisburqma1pEyqSYSOn4ZVLHIHNZkj1VVmVOSpmxphSJmZJdVL59RDIpRrKEnBXzPDINPVkbkJDbSBYFvqMUVFmhosG5wOgHRP7xc06UsgyTY9V2LG1Hyo6QI4JCkhIyo6UoDpoEfT/w5Oqcs1XLzeMDMQnWywXWFIZIdJ6nZxsuL1cE1bNatzx9cso8T+weRz7e3nIYJoL0TGbkNt4RYsLqwEl3xaZrmccDr7/6NTJLNifnLJqWYbtnd3eHSJEnT884uzzj0Pds3+6wdUfVVHw49FzveoKMLLzkXHeIWmCNYkJSf36CpWV+PPCQJ3Yqok2mrTTWVixXa7b7j0gDa6NI3xz4ttriTVO4PUJhVYtRC3x0uHGm7WwJU8iS2jZcnF5AcNhwh3p0LMcFPkYGOfHN7pqpveL52QlRTfzP/+r/w/tvvsV7z+L8A0lNoOHy/BVWaGLMDCnibm+4WnS0Xcfp6YovwjPODiNjTBy853GcuN73jGJCScmzy1M0pwzTyOwj94+PbK2gvZSE5sAwjUirWa4sVxcr5rXn/c0es02obQWHBEIR6tKJ1AE+/dM/4fB+98fern/wJYUmY8nRobVCK47i0JKXhoIsM4ZiFY7pmJ+Wj48ZQSlOSlZdSXLPguQywWXG/YifI252uNmTfWbTLbg8O2e5aPnm7XumMTJOM0ZolAgkZt599YZ3r9/yd//673j16jm/+NOf85d/8xf8xV//DbfXB/77/8v/lehi6SyHIwdFStQxtNaF8mMhhmLXPepbEILRe8I8H/VkRdjaGINV5nvwmxaSdVPzi598Ckrx69dvSG8/oLTmZLMk5lyoq8eMnRIcK7/XZaSUGOYJF3zppFiDFAKtDauuAylI08TKN0hZAv6MVuWhGSK1UtDWaCno2hZtNLtDzzBOJDLKaLQxaFXcSftpYiITazj57JI3/+vX9CGR/Mjtxxs0idVygQCcm5mdo6o0KWf2fc84jrR1hZAlNymnhNElZNeH8ndo9T96cSKopeGQR+YQUUIxxAkRI6RjS0sbqlVNpUug3XcKalX2JjFFiJIYBd5lUjhqNnKDjWcszcyy8yzXRei3vVeMo2fHiKwajJQgISRPYsZ0d+zMDUnVjMkyhtLOUiLi0sw+VpDB1IbsPepEobJH5QHdOVRt0UrhcsBFyao+JzDQzwGtWhSJxki0cTi5LwmpsQSjRZYksWIfd+TGIBT42aGUJUuIRWNdYDTCEJLBZY8VCZklKmmEOyFiQGtsVWEWCu2WVNVLDrd3gENpiTeSxjYELzAio7SgVpKFVlhpyNmgjcVogyKTQmG6lKlvEWKWIKjCqRBKoTI4XIHx5MwUIakiBINMjkUclpRjjp45liyjrCYYZxxTGS9JQ4pFyOXx7KYBGX/8Y50YI5VSnK+XvHzyBB9nbodHDnEipojv92xqweVnF+wPE2E7c7vd8vR0zflyTdPUdF3NMI3s+h5T11y+eMazlxd8+/BbrvcfULcTnWiQc4KQyvdFCKQ0nC42TLFHyIheZqLOfP31W66uLjg7WRPcyPb6PbVtwDsuLy8xXYOtJJuTJTlGus0Jc5Lspy0fb7dM3rFUlsE4dHXg+csrqhdr/JMlW2+52d3QW0eoDTlOjG5HFgtq29ELRT8fmBdL2qdXdARcCvjJ4UJkdlO5uIShWyyJUZMFDPPAanlKzYIhbdnKgKhhXVsWs2VtWtZnS+rTln4eue9veHvzvjAtQsYzkW1GKEPymsq2VEIxa4lpa4y1SK2wSqCsYXRbHg4jQYBRmro2haCsyvjg7GTNwnd8uL1nmgM8RsS1JAjPEB3TNrDatFTnmjfDI7vJ8XRc8MyeIFaJECJWavCO2+tvab5Ys29+/MVJDPF7NDwyHvWEGrImUiy3SkiELpe7i4mcC0RNRMjxSGMNmRwEUwgkB24I9IeJeQ4IoQrLI343qs6Mw4TMgmn2Rbx6pF4/2ax59uw5H25ueX9zw8c3H9jfb7n9cMvD3Z5/9i/+OV/89EtsXbHvtxR8SMnWUaq4HYUowleEwMeAliV1Pabj+EVIUo4oITBKHdPbW5qm4Wi6LFlOdcP5ZsPpyQmb5YqnFxe4GGmalsHNvLu54e2Ha0bnjoTt0lPKokQBCKUY51JAW2MQ5KOTJh1JKGCMRkpBzLEkQHMU7mpJo0oHRkmBDw6fStZabSuU0d8hrYgSkjU4qXHBUS8s9cmC/rbHKIuVgs5WtHXFd/iI4nSKSDRd11FbU7K6csJ7zxRmXCgannmeqZr6+N/+p9cPv0ViwgiJTLmAaMSM8j05FNRupS1dralqjUViZot0jigEttKgY2nRSZic5/464eZMY1qsblCp0EfT4AmtRuQZlTVhCgxpxMqBWtXIIMjCUy1GRHdPYOawN3ihSVqX1360+Dzjk8EogyIitCZlD+sBIyas9cfkx1iw+bIj51Lu18ZSwPAZlWe0mJBqIssyZ8xxRMgFiSVebJnY0bsbVDJUtsEIQcrzcXYoEMIwBhBSkwkYYYgZtFpiEUidaKTB5hphGiq1IjaOx/4DKktCKsKjNhqQxS/eVYbWlvnm5Is6WwiOeQ0a+R2y+bt+JMcnydHtlkWhM3oyPmdChoCErEkxE+cIOIzVxBxISpWwrSwRLuPkiNUaJVJR4QsI2dH7gXH88VuJtVRcbVacdjVhHggpoBF0dY0wCdFJ/vzPP+HP/uuX3H645Xf/6lvyQbFZ1SwqS9uUwpmHwGaxYHPxhOevXlGvKtxd4G68xY0TTVySfCkoF4uOLDOTcJhKsdCaWgqETtyOtxg6us0pIXnm/RavZ0Z94PrDewSK9eqUx5v3JNcT58A0RUzX0lQNOkjGR8dMxDWRy6cb2p9cIF4ueNCgbI2aWpYqI5QmUDQCAtBKU1ULPt7e8l4ZTptzDsOEkeXV6YttDVtZjKhKMz9lxjThg6MxHW6O7IeJPnqUFXSVJI8JfGC5XOC15PW3b/hw94H9YWb2GZUScXTk63uS+4phKzi/eEZT1cjsefHkAjsHnHf0/chhf2DoB+bJkaTCywA68uzTFZ2zVLXF2nIx5ZwxVpOTYL5xzNFRXwjOrjpOnlzS9w7/kOh0x8uLF1zGijCNR5toh9EGl0d++/bfcR8e/si79Q+/SsdBQZZ4H4rbSiSULEVJ4YFQDp581I0g4djp9S4S51j0KkGSjgxL7xL94AkhUllFZTTGFqeMkophmpicY5gdUmoqrejqGk0mzBPRe6KPeD9z8IG3s2caZg67A599/nlhcBydOEKIAjw7ak5iKuOaEGNxoIiy50OMxQSQM1oprC6FSVdVdG1DZUtsw9nJhl989ilPNitePrlitVqyWi5YLzp8CLRNg4uRq9MTNt2S1x/es9sfkMe8nvIQj0cUvWL2Hn8U3Kqj42dOBXKY+C6sFZSi/LmkOMashFLwpCK+FUrS2rY4ho5ANqUVpqppVl2hpkfwOiNXFel2i0ZztuiojC4IkXwcDeXv3J3l4V4s4iXfR2vF7GPp+giJ1EWkq+Q/slsnzgEVEtpnYgJlRUmsNQZrW1pl6JoK25UkXCUUbWXJMqBMJNsIlBAnMoQZTK7LB4kip0wIS/p7RwwC50fmQyI5IHsmP+JmTyUM68XM6uyAWAwoV6OnM4JaMtczyOmY6ZFRChCRlDxWKVIQKG1I2eDTAZE9kQpoEMyIlNBqPvrdLQaJd7EIHCVkJCnXJAoIbfQ7ZJoJecfsB9b6BZVZoqUgJANEFBIjDGNySGHIaJIQZJHQViLpkGrEyogNkhAj/f491UqSw8ScLFlX1FRYWQ7OqjLU1qBV6Yuoo8gq5YQLGaVEUY8h4HtS8LE44bu0TRgjzBxzK6QkCQgc48pDJkdPlEePXC4kQuENjgA2EpEYdOHbpFgIwiLQH1XtP+blfWBR1TRakX0Ju5q8I1eSxVnNyy/O+G//D3/KF798xrvfvEHczvh9ZnViuXxyQqUVD+97xslwLk+4enLFouvY7rY83Gx53A98uJ3Jw5bKVHz64ornJx1Kw9btGJ2nqStqnXBDEdadXD5hSgfmxwfkDKlekmLk7u6OpltTHQZ2j1ukd7gxEOnZ2JpVXbFuGx63A5OLOATdxZrmJ+f0J5J+GNAyQFMh/Z4cPQn9PW055UTTrllUAx+u37I3PVCxrCRaG2zVoUyDFhqRNf3YF/oknqbuyFniomf2AWxNPjGMDuZ7zzSO5LplNzh+98077h9u8GMADKYSaJHhkJjnR+b4EWzHo9pR6QqxXpFuH4kh4qeZ2miuTjesfOQwO673OxqbefG8hZ0iZl/SZ1MRpndLRa4jcemxm8jly5qn5xec1S/4zb/+wOl9xdXZK150Txlv7o9wrppF1yKUYseeh9t3LNbdH3u7/sGXiOXCURhEFBCLhk2ohDIKTTlGUi5PdOlL5zzOiWmccaMnzIkcvou/KMVC0bBptC7MjtoaKiXp6orNeoVWmtu7uwImkxlri9BznCe+ffeO+/2BYZwQooxLwuy4e/+RvxsnPnz7gXlyAEXXmBIISYgBH0rA3eDKSCWlRBQl1FRICDEgEFit6WxFU1W0VUVlyrlcWcNPnl3yNz/7nJPVitVyga0qlBlIKeGcK8j5nECcsmxbTtZLXr97z+zKHhzcTHYzRivqui5EVzcfx0sF4ZCPsEyULPEQ6rvIEXE8y2MprnAIBD4VQ4S2JXbkuwKrqiq6RcvZ5RlJFMhdSBm5MHTrDjsXWq5WikzpHqVccPqCohEie0CXnoqU38cLZF8Et0oX7H79j52tI3xCz5kuW6zI2KqhaTrauqXRNbXQ1NKgRGZOJTugbUC3CfQIVYYQ8aOmSpZWV2hRkMXjGHEJUoQhRg69IoSW0IOlAHPGYWDuExfLhtXqQFMPaN3CrsYc1gSxIMcHRmaSrFG6KQVFcojjRS1zhdE1LiRGvyWzRWmNVpuSQ2M8SXjm1BPThBOaOY/oHI/BaBByJuSJnGZyruPK4QABAABJREFU3qPzQGJAZUOjOpTQR9dLVQSoOSHQSBw5BqReHBMkHUoHDAZkQDCDDsj2Hlc90J2v6cyB/a5GUVHrCplL3q/WGq0LVC3FEn4ohSCliPMOZXShLh5nuN/Z875LC44xMUeYYmYkocjHjksqAini8eI5QvCQxBgQqRxCLiW8gGkSWKALAh0T3gWUUUj147cSj7Ontp7KGFormeaJOHtyEixMx89+/pQXn53ihomHb3c0uWWxgcsvK158uSF7cN5RXxuadsFq1TEe9jzcXLO/Lbk024eBqZ95cl5RNy3L5RKlE3M/IA6JWjeEsKMSC84WNbqFQ3hHVhOmahhCIhzm4nLIiYfbG1IsLVapS0BljpG2NnRNjdGKOWTQCr1umKrMGALjMLJ1mbrNTN4zp0RWiqRNASwmRVW1PLv6lLH/Nfc319T1CQ1tIStLjVLmSLEsL9HZD0gtuTq7OIqxC1xxuVhjOhgGz6gy85yYe8FDHrn+cI30A5dVx5wC0oBVmSZITkTLc92yCZnfbB+o2w2uqQq/BEFTV3Rdw5VQuBh52B+ot4r2QvHiswUPd47p/USyHXVrOGuWLKXFt1viqqe+spw/XXKxWrNKS9bNlhddxyenT2j0kv1hxzyX8M1xmlBGE4WnMppXm8//yLv1D7/C3pOOrkQtLFpIUojl5a6KVlArQWsbtK7x3nM4HPC9w02BGEAkWQTWlIcSCKRU1FWFErJ06JWirjSffPKcVy8/wU2Ow6GntdP3hcs4zUzOkVJif+jxIVNbi8pAiMQQOTzseP/xhsmVh5SP5RKPgHOFLzQH/33InRDi+3BTIRQpZawqv6fGVnRVTdeUNOFF07JZtvzk6RMu1ysWiyVV06KsxWfBOpfzYppGog801nK22vD8/IyrkzXbfuB+v+f1u/cMbipxMUrRNg1KCUQqeWhT8PhYcuGMLRRdIeWRVFsIsRFAa1wqdxFSoGUR0JYzXiKkQmvFZr3g8skZMRZHkFCgao1etrhpX8YeCFIsobtSFS6QFKUwLQVJ6fKU/0OmNhYpNJP3eOdZrtZ8n5r4n1g/uDipMRit2NQGHzJSG7SsKIbVjBTFx56G7zDVxVlijcfUAqEVIik0ikpKpC4q33kKHAZHkAmlAhGP84GcJFY3VF3FOM/c32+ZD4lnZ5Jm9Yh/zNThJcmNTNuA9w4fINpENAMy2rLZs0CJmpgNUthjO3CJ54QQJ6I4IMQDS/sSLSyHkIhpIqc9XsLMhMmmCLREJqSRmCaslGg1MYaR2U9o0WFyJqWROTtcyghhICasCgWXnEsuUYw7pCxAuZQrclQlt8QcUE8ObM4bpH6kWwW4MfTfalSs0MfEY3mchwpRFO05y5JVEcEHWfQuKUEqrxWE/D6BM6ZECJ55LJvFiUSlVQlvEolMAdphElIrjAClDQFJDJ5YGvoEF5hdxOWIFBUWEBTvvNE/bPP957yMkWglOV0tITmkKAmlq5OGzz8949WrFW7Y8/U/fMPf/3/f8Xz1Gc1acfFJh+0Eu9tikzeNQSZNCDO31++ZpoFGGi5PLlmmkRDg5fMnPHtywWrZkkVkxQphFKvFmncPjtPTS9rlCd/ef8XTn7Xoasn4EOi/3ZJcZNmuSBJmN4CCKDS2begWLf9/8v7kWbPjTtPEHp/O/E13ihETwSRZmcUaurpaaqtWqxfSQlpIf6+00UJSmyRrSS2rzGSSSRIghoi4cYdvOqOPWvgHZi9RZlWdlig3g2EBswAicO457v573+dp1y0u5oxAzE4HkBIvI/vjnlkXpHHE2xlTbzGqQAkLRiBVZLLDhUTZUjc7fvX5v+Lvw2/ph56UPOPiGXygrlc0VY1C0zU1k1uQUmCKkhQE0TuIEaMKhNIsXcPU1ixPCj8rlugpUuJVveIXm2vePxx4nCa6tuKT9YrPmituux3LaPn9uw9U64DrAvOyoBB5pFQYpNTUQN3W3L3acv2moP1c8bAMPH51Ztu2rN/suKleM8uRIXxgSiVCzRS6YD55/MMRJs2bu2uaUqCM5upqi4oz9tzTzyfKWCO1oF4KXpb/GXBO3p/RJssdC6XRpSEFWGafRaESmrrm9tWWTXfFUjji+IHx6KhNTd3lMad3gZjk5XY3kDcoBVJJFJJSa+5e3PDrf/Uvub3a8fvf/A4RIq+ur7jZ7Tge9vTjxLDMf5btaRlzM/Hy0U0psXjLaP2lKSRJQhKSZ/Eub8B93kSHP498VN54KZW9Opd2jNEaoxR1WbJdrbjebHh5fc2L7ZqfvXlDVdUonduRMSWaqqKpasZp4mkfsd5TG8O6rpGypjKS2Qc+PB+Y7cJ5mZgvbJAs4jN47y8CQXepFIvst0mR4LOMTyiFEPkD/wNfJIaQXUdCII2hNOZyi2JYrWvefvqC1aplmZecbysUSSWWFBinmVoZzGWDppXBGI2/BF6987l0Uao/h5qFyKMdpQU2Rfb7A912y7mfftQz9aM3J03T0m0rulYxHSPBGpIUuOjw1uJkRv5KJ9HSYExJCp6wlGhhUF5T+pT70iID1KzLOFxrI2Vd45MEoSibCNLhZ/A+71ilzCS6uzcG0cwc/lCjO8Pda8041RyeDN5ZKCpUowneE5NFxRIpAi4tGaCGQFIg4xXEmeCfSeJIb99R6TcIcUuMitnusemMDT2dbihkkTXwQbMud9TGodOMpMQHRVO8QIiC2Y3MYSJIUKYgRUXwGikUWtXMHgIWE8mWZT9i/URKA1J4Cm3o6ox2i0ly/WqB8z3yvLsk2Mnp8IvyW2hJyGRitClISuFFRCaQKlOLQVwU44koIsEv9KcDwzwTjaHYrmhagdQCFzXWS6IFlQQiZhfCYiQ+auaUN2stEZMCxEDwjtmBT/n3rNRPf6yTUmTdNNxs1zwfHllsPr18+uUNf/kvX1EXka/+/R/5//73f4TTDe2nt2xeS4RxvPv6iYevLcf7wLjMTP0B//CBP334nq6oeX295ma14tOrt1RVhVISYxTeZo17V2zZddf5ZKi3DH6gTiWVs2jTEncxk5NPnsbXdLT5hCgVy+JptyuuTU0KjiVYnPVIRM4wGcHuaoNZaY7TE8mseF3UtAOk2ZJEfsHpokLIkmU8s6QemWoWH1nXV/zqn/1b3HJgGN7z/vBM8ILgC5YFtCzxi0WZiZc3nzG7BeEc8zRkvX1IlF2NfnFD/7jj+Q9fUZXQKcFfXV9RhcB46Dk+nxlFYNOtWHUbuqZFeMv5/oH04SNyiISNJRU1NgkKo6lUjTZFZmhIRfRw+NYyL4r2L9es/5s7yrZEdw1OapoUWIctbn7FPD4xnu6Z9jPjHz5Spjvurm8QpsYPkuOHM9HOoC1LWIjOIp1BecXjV9//Yz+u/8mXHy2iMEThsBGsXgiQbx1Syg0UmYiLp1CK1VVH9I7j8fjnk7pEUNUVbbtCSUk/DJkM7ixKZank7d0t/+6/+3f88ssvePjmW6b9gbvtlk9evuDnn3/G/+83v0Hqgt999w2TtQhkJjSTsm34Yvd1LpHIbBMtJVHnG2VJyI4wmYm1KqUsKxQCqXL2T0qBkpkbUlzMwm1Vcrfd8ssvPuNXX3zBuipZNQ2LdVRNi5Awnfu8YSgKBHnDFIIneo8vCpSWFFIiFLy+2rH8/OcEqfjq++8YxxGtBFprqqqiiok2RhIw2BkfA7P3pBhQl02TSvHPgr7juSeliDGKqq5YtQ3r9Yp21bDddXzy9gWfff4aJyLBRlZFDVXB7EDVhvVmhQlc/D35BmW5wNWIiaqqqOuaojDM84zzmSNEBKUL2qpinwKnxwdMUfyoZ+pHb07azQ0v39YIMZGcQ8oWISRzmBmX3FCwImeHy1VD13T5ZJ/mLIiTCiVdloBxYg4eRyAVkkpUaFlyWHpmO7HrDLoUDNPAvu9h0Rgj2K4N608kyzrg3vTsxyem70tM3+JDi40Tp/6IcwNRSMpSsynfQIqYVJBwaNkgadHlDQbF4mEJAzZNRKZ8mtU7Gtlh0hntP2BkQus6b7JQNFLj3RmpK0TqSUlSlw0kj0wVVZEDuT66y0kzj0pmn6hUg5aaQpSE1CNVoNQgqS5jFAW0SFZoMxHVA6uX0LuBMJcQdZbrcXE6XAKuMeV5qRAZUayFQoQEIV6uIxNcqq6FhsIIxOgoEdTlTLmeEYVHRlAW3BxJY3ZjuBCYVSKYHHZLRJQQlDE/oOPs8ZMnRksj1pj44wJP/5SXCJdbKcRl/hq4utvx5tMdyTl+83/9yPGj52d3/0vkJuLPA8/fzHz9zbecnnvO92D7bBnVFVgCuvNsVg1VWVHqmtvra0xpsHbGLpZxmrDOURQlsTIIDcs08Lh/5nB6oAiJ1dRyGkaG0lK9LRG1Yvxm5PnjiZgcXVfiphkZs7l0Hgam2VEZyd31mnMI7F53tHcFooyIyqJ1RMcSVjUmOJR32DkS0oy1gbHvqQpBZVqe948sPlIpz7p2aGXxXrDYiRAtKfV463nV3CKSYRoHnMtHBqk1IS7M9ogyifUnd7z++Dn7r7+jwnFVFPz98yOjd/jbmnmeeRaBJyJrEYh2QelIKyQv2o5CSMIyM4UIVJglh/XSpaappEEhUEsiHQRmU3MOFpYjpuiQqqAwZa5BpxFxSszpwPrLmhdXHTrdMD0p7vcD++cDVvXUnaQQijIa2nKLFgXHb38cdOqf8nrc7zFFkT/YSmOVy3j5lDAiY9K1TBwe74l2YrPdkEJg1TZMs2O52IC7smKzXlEVhnXXAZLDYc/ruzt+9a/+BX/xl3/Jz778HHE6ovcH/uovfs7Hhyc+f/2Cm92OefmCL3/2c6b/e+Sbd++w1uYMUfph5CTQIt/2G6kpyxqhBIs7Arkp80M4Vkl5kbfmMofROjtupKLQ2dTbVtUF1lfy6nrHP//yC65Xq0sd3VLoAu8yo8R7S1FWefSyBLzzGKkoi1woWJwjhIDSBpUsd9sVf/nZJ6yrksfDnnGxRGD2Hp0SpjBEEWHIFmSdMptFkZtoufSQ9QG3r67ZXa15cbvj1ScvefnqmnbVkGQGj6oLhl/6fEO1aVvO0hNipFxVmFEQTmO+jVc6e4emkZQSZaNRF7pvcP7iDJLZxTXNnIZnjv2Zpq7xMWCnH+ea+vFunWnm+CGRosAvebZXmZq62bBeJaapZ5onhIRSFXkupQVFKTGqRqKw7sTsejwOXZRoVRJ0ZqUEF6EwPB8mxo8LWsDz08Dz00RdVXz6yZov/nVJujuy+IXypUKlR+LxBbGfsftIkJG2egnrnPlQ6RFILJFLFz0SGCkkaNGgxQ6Uwvo/koTk7PaUusUHhxYGkRwpOKxzhCnlMZGcmAsBIuDkzBJPrMpfEOKAEJ6UNEJUVKZDCol3jnF5z+gXNAVNo3DBYYNF4KgKmfHeRGAFYsUSAwKLjU8s7pFG3TH6J3RsqOnyWAeZhYhkwJqLPrsdUpYZSqNz3S2G7NDJ5xikCCgt2Kw76qbK4qoigPcklUjlgKgnpE7Mc8MsDU6ClSHbQFNEiohS2dYQYtaYa6PBF0grWemffgDwk9srxvOBv/7t3/F8PjM6z126hhNMTlIP13SdoTMNUTqMVNglMB8Dy9nyYvOa1Ysbkg9MtuccT4RloupgpVskJrs3pol+ODEMI8uSJWTaTOz0mhAs/XjMwdhU0XQ1RVXg45EpLDjhsXpknh3vPj7x6ecNq89XuAN4NG13xek8ILBZ3mUU5VXB7tcbvDozz56yqBmExlwrhAycHkaOdkGYgFYVKhqsXfhw+o6m2bJud2yaNSpZCHuMSsx+xvlETJoUI9vVDm0a/vjN92zWWxKJ2Y0k6TE2YJSgqSs213eIv3B89Xd/i/cLm+6Ob4975kIilWEWluNx4f505DdFxedVw/UESElXVazbNe7ycZJKEaJnnHoSAmkK6mZFqRUox/Kto3//juVnC2lnMWVLoQ3aWAQj/jQSHx3rIvDql4bN5kQpttz/TvO3v33g3emJeZrZli3XTUW9atBVwTwvlKvtP/LT+p9+PfcjMCKEoNY5sC+k5AdpXV8unKaJQgqe90euTyfqumbTdlxvCkJMeYOiDUWZT+CL7TmfzrRVyX/9X/1b/t3/8f9As73OybltT9l1NOsNL77+E6uqQgjBz95+yv/w298SYqKoKiKC6Bz5MJfyiCkl2qqkKhsWFymLilC2jAmkDBfuShbWpSRQJo/TC60pdN7QFkZTF4ZV29BUFdVlXLNuWoxSdG0HCLz3nIf+4sxJ7JoGaQwqBOqmxphc8Y1C0s8zwzRgtGZxno+HAzYEPn/9gn/2xackIZit46k/Zxhngu+fPvL+cGCxFnUZ01SFYbdpuX15xc3LHZ9++obr13cIDZAoKo1UcB5G+tECiTJBtJYiGSpdEUzOemqjUEYSxUJZVWjyAbhrJFfb7Q/OgH/YyKXEsiy5yZfA6IKm5tJOza3Dp4fHH/VM/fgqsYvMzw4pa2RZEFTCCotQCikN0mpKU10ETh4RHGVboLRitlkwZkNk9pKQFJXqKJtN5njME3GZsrZ6KXg8HphPFjsEhEhUdeT2TnJ35Yj3hlX1r+mN58n8jrh5xqxnfFfgeoiTQ1RgzAYXwGhPjImUPMgKnyQxThg8UmhCcihaXDjTVg1bI3E4ztMjszvi/RERFeJ0Q3X+NJ+MPjsxi48c3DcodpRFQ0hHhBhwKaPlC5F754gSoV4Rlj2VHpA8URdtNmIGjxSJFHKdFwGllkz+GSk0Nkg8G2wPYkqI5BEqXrIm5PKNUAiRkFIDCR1llk/9+WjvkcIThScEl5PoIfsbpMzyQLsk3KxJhSJ2klgp8JaEZBKOQVmECsiYEF7mXIIGrwShyIHHTlXUsSQt2eHzU1//7b/+F3x8vOfhaY+Rmuu2oYsV7BWEnNiP3jOeTtTtiqKsaYsV3WrN8/MHlCppmw11XaOk4DjtGX8/cBwGrrpIW+RNrFCB7mZFsSkYDz12ihRVjTYKO48UsuBxf+Lu0xds2ga3H1Abh9ELdozMD4k0whd/eccX/6pg3t8Ty5rb21cc759ojKIrNwjds7kq2P5iR7wib2ZTxmarWfF8HBGdBpOvu22YcVFgVE3ZrKG3fPX7b3j7xjKXM3URKM2ETbnVFXy+bbveXrFqO/phQemSU3/OG3MBSVhKk7kN82Q5TWeGMCO7DvnoOQxZnjb5RJK5tZAi+GjBLlSLo5Mdq6ahahua9epyYxj/wSkVchU0AcSAlAUKhZsTyMjw/Tui3UMJMiqMAh1BPBt0r1j/QiOdxfV7gpj593/zxO+/eqYoCl7f/gxpBcNhT0wDRbui2d3+Z2CaAin1pQ+YiAIC+QAFgpAki0vMdoEQkFLw3C9oKTHGUBY6KzWExtQtHkVXVzw/7wnO0RYrnvd7vvn9H/jiVwVV24IpWKLgw/OeP/7pW+62K96+eMFsLYfjGefzrUQyJreEIqQY8Unho6MoK3abFd9+/5H0g5AveVxM1Kag0hoXfhjxqIyMj4HqgpTPRciIDZ4yBKqyZLfd0rUdImVYnA8JFzzTODLNM9v1im1KcBlzlWWZyech8O75kffPz0zTxKuXtxyHha/e3ePdwtu7a27fvma73uBjZNc3eSNSlqw/rvj2uOd4PnF3e82vfvlzfvnLL3j72Q2rqxqhBdNiOc0zx/OZrmtAWKZloR/HnB8V2fztJ0sRDM1VxxgS0zLjgyMGQfIOGQUpxUvMQhJDpCjyO+L5eU/wEVMUfw7F5pIFl2ZpR1NXCGUI4cf9RPwHZE5KmMA5C8ohdGDxMLkBGQ0i5VpVKQ1GFUQz461ltpL5B2uxBkSdRw2+Qes1uhAUpaBEE4YT4pxwwkMB27alrQ3rleL1zRbxaFm+umX75c+x2xP9dI9NAZUSygT0VaIQFlhygMls8M6jZElIAzCiUiLEBR8UQTQoUdHoa9LsKO09W/cSr9cktWHyiskHVAhUvqKKDVosjNMDx/QOKQyr8hUuWlwsEMGjRIVRDUIIXHCARKmSdfsazYCLv8d4jxYqI9BEC0IRw4RLM9MyUyRNIStsOIN74vywo1x07ofnWQJJZMLrD1eRP7RxtISwOKyf0FqjjMgo5iWbXyOJVEXk2mLHxNOD43yaiQRkrajmhrrboWOA4BCqJ8qJVHq0Tchk0FTIqAnOET10dc3rmxcYX/H8/gDuP+i99k9yvby+YbdqeXV9xvlEeWGXtE1F3dSZIRAvplupQSQSkkq1rJsNsxuxdkQgaLuOTz/7AtNp/k///f+Z7+Mz3d0OSvj5v/7nvPjyDc/393z3d19x/HgiBEjesttsiSrwvB+wc+TsBuR5QZWKqqsgOdIY6TYdLz/9BE6eq/Ytxe6O/sNEg2T74kUOy20E+oUkfLrlOJ05nBLn4Dn6hTEqinpFSf7IiwuXYvYBKWfW6w2fffY5V+srvv7qj/iuZ7fdUaI4TDOT9XRtw3a1QwjFeXC4EEmxwJgSUxRE57A+4oPl+fTMMnvG4cw0HGiv1zx/eOD3x33GZ8A/6OdTxs83yvCyaHlb72jbirrIv+4wDiTvCTbTKZXSFKZE6XyTcj49o6UkxfwxLec17pxItwF2gnGZGb6d8X+K3K1bpjcCN0eej+9o1onxDBtZMbmS1EeE0dRtRyLwcDjyer2hWf/0rcR1UeaPucykVBIQE6UxtFVNYQyQK7SJjC54HkeUdBTy4gMzBrk4+mGiqavLTUU+6Hzz9VcoEfCP96y3W/q+54+//yN//de/4ft3H3h7u+N/8Zeesqr41eefczqP/OGbby7v/8wvSVGQksK5QD/OBOsRwKZrKTUEL9Eyt09+GJXnsGwWFxYmZzlIgslafIjM08Rt2/HFq5d8+eY1RslcfvCecb4EP0UGnhltWC4W4nHOGgujFVEryq7FTCNojdQlMPPy5oquqbhadRRFSb8sLHZBSsmqqigKTVcZPv/8NXevf82/+S/+S7747Eu6VYuTA8/zPYf+iJ0sw3mkXjUYAzJFercQoyORCEEgfaKWDW29obi55mB7QozUpszS0NJQK42IMEwj4zhSVzWnfsisLevyzxeR8lJptt5n23hKaKOZlpnj6QHnfpy1/kdvTkxdELxDCkeUHhcseImJNYXIVSRiRGgBSuEQ+BgpVEGpFUFGvHLIJDFSoKXH988IWeCkZ/IT/Tgwn/P44+p2y7pp0dGxqQIvX1SMw4S4AitnPIK2ekuyI35KSJWQpUNKi5QzIj3QLwNK7RitI3Gg1NmGqWQiRIdII4VJGJ2DU+7jlv2HhdVbhW9PGO0oC01YZmT3QLGL+Lpnb59wUbJrPqWWMQdxpaaQLQaFjPrCRBlIweJkQiRJaWoWu+VsnxBElC5RUiJEJg6JmIg2Eo4toz7QVw+kZHn1Fyv0emJ5GBG+vvTYL6hncrBL8QPy2MFFspTLXOoC89Moc5FaFTPlDqhg+Wg59ANRepKLbMqFblvTNZGYIm0BrYP3J8cyKIxToC4tKAoakWiSwcSM669MefE+/LTX4XigLA1d0xKSpGlb1quWqjIXM3MOib365AWHw4HD45BbTinSjz2H0wNtu6E0BUvfc0yR9XrL6xc3KNVy8+oapRxjnPjbv/kN7/7wHccPDxA8SgtKaZgjvN8/83g+UzztudnmWXWaNCYI2ltP/cuAjokmVOhlTUnN8f0jflxoioJZzfhG4EvB0Iy4IaJUiSlqqhBJWjP2R8bpSLtM7GSD9dl/UpmOuighOiIj1y9v2Wxv8KeB1eJw9sTa1HTrDnTL4hI6gZSOuvaIssD/T2jCPnrG50emecY6SVnkivO5X/A2Y7xDiiQluBAnch0S0ELQ6Iqu67hqWuLsOLtnnHdI8ng1IRCVQJeCQglszHPzJUnqpqFUFdFuWE4VsXUM1Z5+9AwPAvFkODvB/Z88QZz43R8GVp3CzDu21TVtMswhy9g211dUbc3sPbLseP/++R/tOf2fa0khuV03GKNZbCZUK8BISfKOJfj8XooBpMJZCwiqssbofMusy5Kqrqmbhs16neV0wO3Vhn/zz/+SN3c3uPHMEh1+WigEvHn1gtvrK95cX/Hq7oYUPC+qlmleWOaJbz/CNM2kGPA+6wWkMkgh2W23vH35mlc3r7j/+C1fffcn+gv3BHKzMTdTNFerFaYsmJaZGBJSKlSKlKZkvWq53q5p69wstSlxniaWZWZTt5moGnOr5nQ+s1hLSpHSGFLMm6/SlHR1Tawz9n3V1tzuNpm4qjNp3MdAFIm60mxfrNDril98ueLT4i/YrK/YVtcoL4jLhNOW4/nIOA4In5Ap0dSGXSlp5I513fFBPXE49Vkr43I4uOoaxmDxyRFxWBfw/cQmZU/b4hbmacY5m9kwPrJbb1jdtHzcPxNjzMWRmDgPIyEG6qrKEYeQvwue/8gQtrIrSZWHKZ96dCwQQaPQCCIuJrxUaOHQ0bN4DxLKJDBCoVRkKrIgToeQcegWwiyZB8s4jkxh5jguyKpCFetcIXOJSmi0KAld4sRf4807nGrx8Z62rkmpogxZCGYRJC8yqCd1PE2PeBHR0mPjQkiSKAsKVaCwpDASZSIKQyxuOSWHt9/TmwmfNPPck8KIM5JBe47jniXUbNdvMSaBnlHBYyKkQ0k8lsAKvZMUV4GoemZ3IkSLlRVKG2p9RwoeoyAGx+ImFr9gw8A8e9YW6sIT4oKjINSO7nYiTiOh3yCSuIB4MvxGSo2UCqFyi8QUJfKyOQkhElIm+UUh8THhJgGjQRBpm4rNJrCIkVg4trcLLz9b2G0dzmmSlNzYwPS15OPZ4IXGOYu0iTIVlKlAL4r5ebmk16sczv2Jr/OUyZTnceLQz5RVy88+/YS3r2+plCCEhWGeeP/+ESEE3nmkydfX65srUhcxdYmqDGkR2OgYDgsvty/Z3t5SiMS7D9/y//m//C2Hw0hlNTUKKULme0jD2c6893sG5fj2cE9TvcCoQHVlKW8HUjcwWE1hr9iVNaWqOX48I2XB9nqLqRPj9pmhSex94OE84/eOtpEcziOyLGlLg9lcUZqKKyco3gdQntBFJpMD1jFEhunMYB12CrSqZW4EJmWfR6w6Fm+wczY0RyVJQZKSJUSbN9JBIVKkKla01RUJyTgeeX73juVjj7CKl6st4/HAnM+muVaPxCSJ7x0fz0deqTV3620OgTtPWRSEJEiLw0fP0mcSbV0WtHWZSZo+hyUlkkJVgOJsT0zzQpg9DSVlt6FUiv2HZ6yeOI8C4WtWNOioLswHkGWJrGuSKXEefvf3f8/j4/t/7Mf1P/lKiQu8UmR6qSID6eoaIWCcJq62a56OJ47DlPlYZYMpzZ/fF13Xsd1uuL664vXrlzw8PDD1I3e3V7y8vcpMEa0xhWG72fDq5QsWmzOBdWHoygrb90zTRPQT/+IXX/LLn33Oqe953h94fD7wfDgxTgNKgneWq82Gf/7LX9I1kufjM/38xDTPjDbXn6uqzLI8CedhyLeMMZFipLzwpJ5PJ769/8Druxs2qw0fj0dmu9BqA3VDoXUmaAN+npFKYlRBAo7zjPORWQj+eP+ReZlYNy13uw26NMjSQKnyOFVpalVQbTvS9Qq5rqiTpzGaTnT4xzPzMCJrAWtJYyS+MFi/ILWCEGjYkvpAXWlUSMQlEJeAFgbT1rgCXFzwbiEGRwqJ6APWJvAzfskSxKau2aw3mUqj9MVsHLPWxrrcPC0MSlbZkqw0RaGoq5r7/Y8jJv94QqwKNLuFMB/wR0XqOwgKpTTaKBbvWfyCC5EKibmc5gkCoXJTxCfHLCwievzosC7ivSaMnuU009uZRSjqpsEvCTvP6OBRrcTHGVkpDurAo38iDTWmcCgKKrFFxgZCIqrcjVfSUhWSSggWHwk+s0faokNpzeVRyT84PjAtM01XEl4rHtwj5/5MWWuU8iidkEogZJ0DrNQk4SFZiGX2zWDQusCdOogdqQlENFKsaF2FEQspHUkp4pKFmAFSColSDaWo0QoiA7N6wIlACIqr5pY4rbCUxGqEaSa57vJq5nJ7ciHx+USMPzgsMs7Yh0CKMdf1iJnuqjxxJqPwS0l3LSi0QBjFahsxtcNJR28diYjRIFLCuYAkoFOixFDJmjKVFEkjfZGZAUqTwk9/rrNYD1rweDiw7yeUGrJkUUZeXm8QOD4+vOd0mvj8i8/RAlyckaqk3qwp3qww65KyLBEegos8fnjH8Ligx56Fkd+/+z33hxGTKqRoslAxRVCBJcykRuCT5/rtDbpLNG872nWJKif6k+fpa8u4L2mUQO6eKMKR43PP1dUV680WuXJMzcKxsMwyQK0IQjJFR5IGlwKLOxOFQaYCWRa0V5oYBL2Y8HGmaiQ2GmabOB2e6M89Xdmg2g7DxMJ84ZdsqMoGhGCcB+Y4s1kVEDzncU9/6qnK3ICYo6Pfn9i/e8/5+3uKh8BOlNRVQ/h4QkpAZpOrShETQC2ZiGnKPLIRMV3AWTKHwhEc+omn45lpcWzXHV9+8QmbboVzLiPribmGKSD5gJsswpHdJIUmhcThKbCUgbZcUeot8xTQMmGjZ3+emI9nyr6nahqWxbE/3CNM/4/9uP4nX11VXUSnks/f3vLy9ppKKUSMucnpLG9fvuAP33zHn94/8OLuBSTBHDzjPJM5Hpq6LHIGRQruNmtWr17yL/7ZX3B3fU302QlDTBeOV0SmSPKW3s0ZMBg9UURurnZcdysKY5jmhWPfc/904Pv7Bx6fn5ntRIqJYezxfuTl9Y7P37zCuoANz5lIWxqMKYgCxnnOuUnnccHTlhVGZUryeVr47TffIaTm09evGcaRTdew3u5QOitSFufwKeG8y7chlcYBc5zZ2xwyH8oIdUHa1IgXa8z1lrI2yAKklqA1Za5aMoqMhVBlgZCZ9qoqQQoS6ybsyeLbQEwZSxGExAWwKaJMYLYT87ywzI6UFGVdI2qDxeUDbfRAfueHxTM5gcDgU3YaCaMQQmJdroOPw5hvmbQmIbA+IGWkKmQGx4lECh6tDa9vftyY80dvTuZ5pHnRI6r3JG8YDj3uvKIxW7quzVc5KZKSpiwMBZLgZqYwk6TA4gnJ4/D4xeKHDJIJUmEuDoA5gmg7dLNG6ArpI4VRyNKyjx8ZvSWYHf10xtuF0nsSE6VwVKmGmEFnlUp4JhyPKG2oqLAX2mkpDIqFhAckEY2PAq0lyvS4bU0aW4RdmF2PigulyWHTQnU05YYpOLyYkD6RnCboQFQR2Vjk2qHSBPWCSwMqGUyqUKpkTAeEWFAqEkJkdh7vU5ZgkfMjLi4kNdNoxU7dsLIrDl9DXEWUHBFqQdjID6HTP9eIYzaheu8uYLYsVSSF3O0P/uLIcWgJy+JJEmLp0F2m05IkS5S8fwAbE6ezQ6vEyhimg0T0Hq0CTVFSqppaNZSiRKNRUpONCvnk9FNfpihYNSU7OyOVQumCrirQIiFiQAgodUlbQwqeJCUhWOwUc8sqSERUUAuWZOndiCscz/bMt3+8pykLHoYD0RoUkn5eOLtIXRt2NyW71w3li4rn04ztDEt74rweEDtNaWrGfsPpWTG9F3gT2IcDV21DDAsyOOz5gFxH9n7mgGNKCi8UloCzE94nXLKXcaFgiZZzoSmuEzjQ5wh2IQRBEpKyanlZrZnqHm8dUgWSAl0pZAFCWEJMEC43FaogRNC6ojQNvpwBx+m8cHg68/TdR6YPT4jB0o4FqqtxvWcZ8wZc1omkQMaEnhNyhqAS85IJuGWZnSnz4tFaE9LCw6Hnfn8iQhaQCUVRVkit8MFnCJ2QKK2JzrOc8uigqaEqF4iScW9Ypop1vSGlkkXNHJaR4BTxQqKeF09gzh+GkGm3P/V1tWrR2qCUZNO1XG3WFFJexjeJpr7hxdUVQz+yalf87PPPedofePf4jCkMTVXx+sUtdVMzLzM4x8/fvubtyxdcX+3yR0/pfLhzjnGc8M4hBcSYsN4TQo+KgeB8FrQ6x6quqLoun951BgBebzo+Pj2hlGKaZr7+5iu+eHPHX335ObvNhndPmQFlnWeYZ4Z5oU/gLi4ZhPgzpbssC8qyJEjFNx8fcNHz6YuXXG+2dE2NuHiFZmszjbzUmFWF7CqQCbWGNtUIZ6nFNej8a3ddg24rkiRzq4zJz2phmJwjLBapFY3O36Y5TmAWUuMJPmGBJQoCioggScHkI++OTyQ34t3MMM2IJClMRVHVoCRJ5JhC9I7oPHayKJeQaApTICIEl2+rHp+fEFIxDCOkRNe2mVw79MSYaNuGpq6pC0O8EHiVEmjzH5lz4uYFn0ZS8UxqwBYVg9/hg8YUJUrkhkpKCqk1pBzCnJeFICQpaWKILCGHgXQokLIkpECKAaklWlWY1Zqu27KqWrokqOWI6B45pYE5OoRZUyWIwUJciMlhYw56GmpMqCFAP57w4oQTK0RqkLqiSAW1SMAJL3IeI5Ln1UWpicESpKCoaurqFbN9YvFnREhIrSmNojIq/zmICFaQnACpidLj9YC8CSAGQumxcQSyqE9IAWKmRAMVc5xZfGJ0CzZasodHoFNBW7ZsakmttoRjwrkJRaIoElJ7JDGfooW4NBbSZYQTiD63dLJ0MOdG0uUviGgJksASl1wRrh22GLEuEGbDsYclRM6zY14cm7pi0SViMFRuxkRFWVQUoqSQhkIWaKlyxiamLPtSP/3NyfpmS1NJ3jaauznzbNqmYdXUKJXns7vtFbtdJvIG5/+8kRQ2EY8JuZ/xKnI/PPK07Lm+WZNC5PnpiZNW+ABtW6NmlcdIk2MjBXf1llef3LL+ixW7Zeab4ZFxsdzPI8fTyLpZM7mE84YCTadLwmxJTcWqrikVLMOR4A2noDjFyBIl1iciiUor3Dgjhac0HVEaQhLsbcAaR6UVSSmSS0zThKhKtAxURqPqmkUYUhzwyZM9sjY3ZSL4WRB9Sdts8T7gRSCiKOqCeVh4/PjMh28/cv74jBwX6qCxY2CIMzba3FpbUn6OK4HxEjNLmGGSno9PR8LPPsWUFfM0gQCpVJaNngcO40LblGh9qdmTaLZrvHMMxxM+OMo6IpeRNM3U65Krq0ShJ4wqKY87zPcV82Q4LCNOJqbUkzAYk+vHIXCZuwdiUPSHH2dh/ae8mjKPlpVW2Hnm6fE5e1hSQmuNVAUPhxMIySevXvLyekdVGXRZEaTg7uaKF1dXnPqev//qK/pzT/3ZJ2zaBgUkIXAx0J/z2GYYBlKMrLuW7XpNTJFlmhgHy9P+wPcfPjCvtxRFgURyHka8zxwhrURu8yhF6FY87fdUOvHJy1u+ePOKl3cvmK2jH0f25zPDONNPM0/nnmPfc5qmPJbXmk3XcH11jVQKu8w457had9xs15TGEC9GY1Ma9LpGdobUasZCssiEFJpGS6S1bIoSoSVjP6DrKv+5pYSTEi8lIoL0CW8jwUVSowgIUnBMdsbPQz5MlAVeapx1RCQRRQDcbDnZATf34APCSypTUVZNvhnkB3r4TPQLeIghUaSERqAAZQyiKgneMy8L2khIsOpatpsth/5M8J6iKGiqMmMOjMlyxxCIKYuDf8z60ZsTGRLBWoJxiNJRbiNhXqF/GA9IgxL5BZf8gpMLXo54OeG8RvoGZMJHxxgmKmHo6hVxngmxp6gVK11QtyVrremUoZaSqnCoNuFURBmBTDOr1mNETYq5xuTshIqSJl1j5mum08hxOSE7yxQXXILKKOpC5yS51CAiPkkEHiN8ToLHSEoWiWZT79jUBbO9JriFUhmEyLC5mCZcCohUgYZCVVhGXHKobiGlzFIIKeClJRqPTIkqRcq5xCdNLbO5MpqYwUBolKvQc8dKVqz0GSsiczWjPwtQJtJS5v9jEogicwQugLWU8gnizzrqJP78z2LKH514cWol5YksLCYxqIXz1NPvI/5kCE6whIXejwgBnepIsaIUJRT5BaRVicRAVGT0bB4r+RiQQmHMTx/Cpu9qnFqobMXGrihEcdHE/8OHKFcQI9GnPDoQApVUruH5wLJYztPA4/49T/Mzop/pdMXr9pbjeKKQkq5riEaw8gqtPVppvJX09x61SRR3LV04MDvN3i705xN2nlkOgugNV9WGVVMzTUeejkdu19fMbkY3ilFVjLT0dmCx+XRfmIKuapBLYggTWoFNkuAFziXGWdAWCiXyaHPqzxi5sCyBwZ1ITiJEgcAT0oB2EIsKTIUWEm8jiBXee1z0LHHCLjNCLCzTQn/omYaRKCOqVjBJwhR4Oh3QhaJ1+UVrLJhCUEYNARYfsdKzP/csPgIyazBIhBRYnGVxAR+yXdYojbOW2Vpu1q8IwWGXiRQHdq8903lk3Htubmp2N9CfR5wOXL25oqx3/OGPz0xjT9CGeK2x58j4PJDG/LIOMeGcRQhBUzT/OA/p/4xLhoiIAUhM/cgyL0ip8w2DMZyHGSkEhVYUVc237+/RZcFmvaLpWt68vKVQiv58RiTozyee93turzZZQiclj0977j/cY50lxURdGqrSgIRKV9h5JiD507sPPH18YteucS4w25HzOKKlpKkqik3BtDhO5xOvb14wTAu/+/pbtNZsVys2Xcdm1bBbt7y+vcb7wLA43j0+8eHxmT++/4CzMzdXW1Z1xaZtKIqCGBtu16t8YyIFU/AUTQUIio2BbYVfGyYcS/KgNEEknLf080DhHUobhnHGlCVSKgot0VqypMiyWIS7xBaURqgcMo0/4OyXGS0LlNS4mAgxZkAk4F1G8y/WEh2kBRpdUFQ1ps4h/hg88zwxTwtGigxyMwKNQ8SIXWakyJRcLSXaaKyLWG+RoiEkeDqeQOQRXQyOcRyQdZOr4mTxYlL/kcV/SSSsBa81Emi7GrPt4Fyio0QLhRaCkCaUGtHljGktZbCcjlkoFqXKG4woWSZHmQIiaQpTUerE2ig6IzHjAMcejETfemR5YuBEKTSV9kgChgKlA1LUUEQaDPW4wn3omN8lmrd39PVXJCFZQsxk1VIz45HRImSm2onkMCzEAIVuUJSYqoTYI0RiVdV4rYhuoV8OyOTzTZDTSF0jpcMAsxcEla+aERcxEqBCwMhIEQqqXhEODbESNC8LCqMwKebmAxVyqRhOAncQxFUiFCPRzISVyJ6eOVNhhQAhM3U3Xrr48AP25FKDE4DP814XA1PyTMmzhIUaT0iO8zJymkYeD2cO9wHRK0qhSCYQ1URVFRQXC6VLDtMUVLrEYPJIIuVKZxAQQ/53oURO6v7E18f6yPZK4T7MxKnJp0aVb7NiugSRvQciRudbwx9usUKILHbmcDoxLjONqQh1h+sXinXDJ1dvEeFbCqkwssSVkZefrtCxyDNiAg/fDfzpfc/NJyuKK8Wb3TXVynAKmSQrRaDcSKRwPO4PPB/PrLca0wrMEthdXXEsCryqmE8wjgtKQioCYyzpqjXHpw8cxYAXkURuUbiYONlIKQ1Jr1j2J5ZwRNU1rdmgZEuwkVIFgst8v8lKzrMnpZDhV3VJPx6QWiKloS5qlsVSyJr1epNhWXNN6BeU8xgdmEZLWWg20mCKApUi0gpqWeJUYC/HjN/RipgiLjpMmaubIUVMWVDXhi4UdE1NWVW4CMMwc+5HZjfjRGTz2vPqFzPmZCjvG5qmJDnBuz/OLPLEZ79SvPzlF4TW0//mwD5KirsV6V3i8P7A8DCiEBgpkQqkUVTVj7vG/qe8aimJIkPXRIwkD1FmpUVKIjdckCzS03/7Pm/kjaIsStZdS7/Pm08fIlebDYtdskA1Se4fHjmOIx/uHzjsD3Rdx2a9oq4KrHXsn/aEkDiczmy3W969v6cSiuvNNt+yzDNSZrBfVVZIrVmvLQ9PT/TjQNPW3D/A83mkbVcYYzJ401maukLXFVdrSVOXrNoGGyzrpuaTN6+5f8igz6YwXG9veHVzzWbVcXALJ2u53txitjVOJ2gMM55xmRBKUxSSeepZnGd2nmlccuOxW6OqhsUIogalMpE6XkSvWhqatiYIMmJfQGU0tlCEmG/tfMo5w5hym9O5heA9zkXwUImKqukQlcHKnN2JIZJiQGtN09SIJPHLAD7hfcSHhZQtnZm2azTDMDFOI9Nqxf27D/zNb3/HJy9uLq0dR1NWhKsrirLMmRsX0NWPu1n/0ZuTMY4wGWCH1AHhWqQrwYWcozASU5YUtWT35kTZnBAy4dxM/E4wPo6ce0UvPJPz+Bl06imDolSSrhBs24ZttWE6WoZpRkmQIuLEmcCEVuny4mlRcnWpYWVsb/Se5fDMvJ+QqaVMO8b4ERsXYgi0naYoLnrsKCAswISUCS8EpSwAj6oE3j/go6XQDSJ6rJ/wMSJ0DbJCRkVlK8o24PoJpSWoFUYZUpqJYkRIj/ACPZa0tKxYc/xa0s8N7a9GrNzj4oiNM95LbBJoGRF3ikqUCA2FMSzOU6kCaxu8VxAz10EKdQEEcVGLAz7/PVxyJpG8YVjCQu8mDn7h43yibSXbTjOLmbHvmY8egmBdF2zLClk4Fp3QRbZMBwmpgEJqalViQpHBPDERU0DEfHOC+EGd/R/yWvunudQraK8ldnDY/YCyIFIDRZFJvSHPw3+4SdFaE2MghCwUA8Gq7VivVkgDzVLg3ILwkcn12VbtoFE1MSVWuqGqGsIqssTA0zjy7pt3EDTv/p/v2H5aUf7MkrrE3XbF6y9WFHPBH/4fR/5f/+O3zHPkZ+Wa5nZDOnvu7ZGzgGbbcKM2PNwLxvMZP/csZ4vZXLEuOw7OMYUl15dTg8DgYiQmT6ElbdHx/v03mF3HUgXc+ZlKNLQ7xfN5ol9mFk4U9YarqxuKqqWfR7q2ZZlnzscTRMFpOLCqWrYvd2xerUjLwnI/MM97YnHGrGpeX2/5+v0zUkqM04zDgpcLSuXasVSS63WNtTPnPlLorG8/nTLobb1umK1jf+qZFse6a3hxteVhf+L7h3uUcfy3//sd98czdolUeoWYK9xYsK4grJ/59Be3vHh7xYsvr4jrkn//OPAUIrIOtG2FaSNtadhsKoZ5YT8szOHHic7+Ka/r3TV2sbjg8ZE/o+sJEcjVcyEjKYmcv4r5RtHOnvNp4P2HJ7RR1HXFumkoy4L+PLM/9Pzuqz9wPJ2IITEMA0ZKRNuSfCSFDFL4+ttvwBh+/otburpDBM9iJ/phxAVPaQo0+RC1+EA/TyTgeb+nbVt+/rMv+HB/j5SSpm4otaI2Bdu6RRvDMC+oFHl7vePTm3/Lq6sdzWrFw/5ASom2vtyWzCNj8DzrwHzdYK6LbHnXhk1VsNJ5sD9YyzjmPIsNgdk67Lhws71me32L1JJEwAaPCHkkjFJ45yiKBlEWOVMo8oFHkGGePiZQAh9hsYEkYJxGvLdEnw8VUmiuNhtSIZhYctFBZDZJ1eT3TYoJb/M4miRQ2kDK1mYhQZCIMVJVBZu0ZfGR3331NcEHyqLCXpQUyhTYkBjPI0sM9P2AUj9u2/GjNyfH/ozoCur2FlN6VAKzSSgzUQiPMRZEi5MLGIWVE+c54m2LKwRsHH4YmKbEEmuiLOiPC6WAcl0hgoYEWmq0jFRlR1FXlM1IqgswBm9PeC+QxrPEkcnPKJkoRabArrcVN1fXGFr2fcKFlyziSNeVII6clyMqJztQSiJlIoaJEAK7YkuZCuK8kETMAqt0hNjnq7eoGMeJuCRas0bNR6JxhPEKpa9Z3Qr6w0gyEVNDoSPBCk7fVCz31+xnjZYb5puecvVMlInBB6YQiPFSv9MGoS2BBamhBFJKTKEnyhqiIWFAXYSCl5uLGANCJlShCC5nePKHMGYirHd463HnkeFhj9tqumZLuRK01CRfYjY5m1ALiQ+SwQeSz8Ai0QgKVRKCZHGRGH1G45NI0V/GR/kjLJLIrOKf+KrWgiH0hMYzTCfacWHTRNqm/fNHMcWACzG/WCLEmPv/8cI8qKoSpQQQaYprYsi1Whsdp35gvz9gi0TdrBE6sQTL5GbuTwe++fDMHBK3rwoejpbf/N+eWf1GU+8S/6v/3efcfLlmP/Z8/+HAx/3Eq3XHp6+3VMLQHz3nQmGVw9oTwVTcvL6D6Zrz4z2n84D1iYoabwdMtcIHxThNlKVmWSzzPFEKTWNqXlx/wTkeWK9XXL19hTwH5PiBpZ85zjNVt2JVK9rKUxiHMp6mmihVItjI88Mho3tkzDVQAXVds35TsvaC84eZwSZ2mx3vnwemJY9rg7/QKpWiNIZNVVEZw7sPj/yzn/+MQmsmb/nwcCYoWHVrlgXun/acpyxLS1Hw+PTE6EZe3JX83V+f4TczOE2t13R1S2EqZKEom4VlDuwf98RC80EMfGeP+Kh4+/KOL9QLltszkz0i6onj04FjmFHNT3+s86u/+iv605nn+48M03Qx+kJMmUXDpT2V9+oCefGCJZGZQCHmm0a7OIbTgFKKjx+f+NPX37HqWpq6RpciW6PXK6rSIKXEhcjHw4Hff/stq67l3fc7YorM48T+dMp4A6HRSiIvYLCH5ycQgrevXtGfTmzahqurK4SS/Omb7wgJvnj9grvdlqoqCCkhBNxuthRlQaE0SkCjJT979QKE5DSPPJ9PeAmsSzZvXuBsz/PjA1fXt3gVCN7S6haRJKOzCCXRSnMeJsZ+xgjNzYsXKGPyxk6KC5n28p71AZIgEhnmgRgjJ2dRySGi5zxMpFSAFiwpYJeFECPEgFYSTUJ5SdnUxEIQVOb46ktZxMdwQekK3OxIPuEWh5wd/twjQ6RtapqmwWhDXdW4lBjfvSOFhV+8veN690uasrxUixPWBw7DyKkfOY0zX3/1R169+HGW7h+9OTk8j8hNSalrVBcwmwl9s1DOC3KWhHnkcBzpe0M4VsRGMZMo2RELMOojhekRHuxRkpYKPYAoYXNnWIBDv9DvHyljRVdvqMuGQi15F64azqNDlTM+jIyzw5gtlc5ivagNoyzwAtL8wOkxobcb1uXMyT8SSAixolR5B+jjRKEchTbIUDIcPKvwC8LY4PQzcfcdkYjzER8KFCWVAStHzn6kKBeaVCKvVgijcLxH1yNOehYR8SlgjGR7UxGeFsZHy1A45K9Gntw7ztZiTJevwYXCx5RhbiIitcQniQoBZMS7QKki41mgJ0WREiL6DBTShhQtyIAQl7FPEvljGDzHQ8/zsScpwaZeoe8UvlzQhcXrBVdMyFpSCI2IknMfmZcIuqSpKgQakTTSXyqvKWG0QEp1kWRJpFA5lJvyZir8ZwDs/ni4R4pImkfmwaK8oY0BGwMSSanzDZK3gXnKREQhBOLyktRKYEzmQkipMUYyzXmj2RQdn96WlJR8fDry/nDP7N5xc73js09estts+O1X71jf3vKX/+bXvH9+5NxblntHpUsKWWKXkcf7D4zngU9f7fjVp7e8+XzFx68P3LUv2e8c96czjRLEaDEqsFq1vNq9YDf1DOfI+Smx9J5SB6q643gc6ONCSBFkRLaa7mpDq1fw1Tu+++3vcZ9Z7nYv0GjevH3Fy7olGoGPIz71iODYNZrnqefYG5wTiK6iwkBRsFnXKKnyx2qZKK9Kttcdx6dH/ub337LEhJAKieD2qsEuC+dxJrgsKTucR+4/PnO12XC32/KnD4/88f6ZN29fIEKin2c8UJSG2S48nfbUjeGquOZXX76mjIG26nKrQpeAYloWTv3CYTjzbX9GtBoha35zPHN0iarakgqHahRiszCNe6gHXr9quP/DzMfhp18l3q5W3Lx4yc3dS/bPz5yfnxjPPdZ5IpKkFJDdKkLmVl+ICY8n+fTny1aRMiQhkZinnEPa749UhWbV1qy7DpEEyzSjlKAsCtabDf/1f/lvkALauqYqC56eHpFac7XpKExxqbfmza/Whu2qZdW0bOuSp+cnSin48s0bDsczX333HXaZIcEw5axMaQrats08S5Hfg4dzj9EqjxkRrNuOpYTptkCuNPogELqiatcc/JlxHAllQb0uqFD0pwGfJMs4ExbLv/n1v+L66oa9PdGP0+WWKY+IvbMQAnXRUFQFQuRxfQZ2SKZpYZoiSkeEiMSUtfTJB4zQxOhY+onr9hqXHN47nPPEBPXFDTTPCyllaF3wCTfNuHnBeNis12DzmKYqakLMb/k/fPc9+8cnfvnl57RVRVFWTNNEchYtJYN1vPtwz1ffvePUj1yvOz558/ZHPVM/enOyVmvS2eCPBYdwJBYTlYqIc2C8d7he4VNL3d7iRYPqau66QDEuHB9zlz1aB4tHnix1Wnh1dcur7YZVayijYnQ1KWW1eVmUqChIsyCOmlQtmHLN7BZu9QqtBHs34VnQKFRyhDSy+ICIkbtXa+rGMp4TzkbWTb5qGrzKVl4FP1BWS1OCbHj+5kj4eqL5JNFuYBKAqQhS4WYBs2X2I0kLrjY/Z1tv2IcHPi4fEWKh0j2CmOmpAkQhcasHxm7AqhVL2+CqPX7xuGhQPmLUjBIXTomIWYsdHDK9ALFhljMFW5a5JziDIOV5tpT4kAjkSrAsdSb0BnE5oQgSWcL0/HxgWiyrbYuoJZaZ8XEiTBNWLZSqYdxbDntHsoaiKenuCsq1ItnIcl4glSgURilKVVBX9Z+v57K+5AcyIMBPH8L27sMHtFbok+fabGjLFmMMWitMUyFk4vT0DM4TXEAApq5RSmFMHkdaZ3He/9n/4rzPt2g6IEKgrVpi3HM6HyjKBpESj49PHIaeddvwv/nf/jds1y3jeaRVgqvbDWNceP/tM1FZlrPlk9cbitstL+9WDGGESqHeCIqbFX6KnI4eZMQYhw8ThoLoI0WpufnsMx4PTxwf9wR1QFcNUte0VYvWkojnOD8yqhO7l1uW/jV//Pff8nBz5NPbDkTP6Ca8LJEaah1p0ozzhtMsGJfEPDtcnLnqaqQWhAjBx5w58Av1qqKvJEEonM+hdch+khAyZVN3EqMVTWU4nXt2XW5vzPPM3331DX/6uGeyC8l5Ph57ZhtoioK6Mrx+seK/+he/5Ha3pjQKby2IhF1m5jnTeGOCfvB8996hT5HVrzrGc6JSG17ovBmN9oxTDnnt2b1Zc3V1zTQN8O6J2P/0fx6WwzPRWqqy4ctf/ZLpeOT5/iPHw5FpnPImRUi44POEECgFOkm88nlEDBf6tbgE6PJoyIVInB3z4tkfespCs2obVm1NZTTD4ch5s2K9XXPa54/tMEyM44TYrhFS0g8TwzQBgrauuNnu0EIyxsRulwWw5/HAeZ556gf6aeL5dKJrGrbrNW9ubrlGEKLP5FqtCSHmzYlzKK2gK/G3Jb2xTI8fcVGh6opB5PD74BZ6b4lhwdslS3Q9xMXzxc1Lvrx9mW8bdMUpnAGBv9iM7bJAgropmH1EpEBwOegdnc2HRGNyvRoQSuGdJTiH0YI4Q2PWRCWQpUYQ0FEhkkAlQXLhYrEPyOhw1pKCo60162gIg0UKxX4c+dP9R2II7K52fPj4QFtmTcQ8TpRlSdU0SJ1Dr5t1x1/+/As+ef2K/Wlgmad8Q/Mj1o/enGyrOo9DzpLn48I5nWk3gk3TIDcbVFwjFigLgQgLle9oYmJyA8M8ME6OuCg6Ldm+Nry66Xj1ymDMTJwDzXKLOa4QpabRJoOPpEHKNTZuGcKfiCRkbLj/ekG0iVgLJqAQTa4FxowaLpuKUfSkOJNMi3Qqc/6jJOCZXE+hZupCUlLi0kgwjuJK0T1obm8FD3HgPHrGpJi8oUgt1+WaN9tbVquCrfwCmRSz/5bJj0il8WhsmHMjISWUCBSM1J9HaqOQZcEpDRzPHiUXlHGUBTRakaJgdBmg5hOYoLH9CXEdGYcJZwYafUtpDDJJJAotBYuz2LhQmJyZCXIg6Iz5dspRdJrtrsY/Lnx8fCBVmnMcELWjvYLdbUWrGnySTET6xTPEAb2JdGWNlpJiktRC0ugOI3UevV1eIH8+8QiJECoLB/8zWPZpxKrI9dLRiRpNwrsZqyVhjPSnE2mxrKoKpXNl3caAj4lhHpjmARftJflucIv98yguCZBRUpSGpmm5vRKURYkQkcf9EakU/8Vf/ZJf/exz/v5v/paf3d7wx3Fmtguzndk/SUK9gF0wOtGuHbtfGkz3gs3LF8TS8Xd/+I61ecnx+T11FVASxnMA4bjbdlTaM9rIdvuC/sN3YAIhWFzyCBEoU0mMjsNpRFAzVZGf/8tfc/PyM77++rek2UNbU5iOlBQ+LNiYGFAM1mBDTakKZD1jrcMYyRIc3i4Eb5FJsuu23K62LDcnZvfxB1ksAMYoJregpORq23K7XefgXjhRaMmyOJ79QPSOlZGsjGb0eRx91bV8/uqWVZODqiLBdrPmfDjSn3ti9OiioFqtSSQen498PJ5YloJ3Xx/BHvn057e0AV7pNd6Dt4AFLxJWJHwFrz7d8c/SyPT//unj66MdOfcT7fYGtdrQdhtMUbF7sTCdBo6PT4zjjPcBF/KpWwFR6gzrVNkXllLepMQUL8LWnGWLidw6vDjFpmlmv5dUhaGpK9p+4P7DR4Zp4eH+I9PlYymQ3D8+c+oHqrKgKSvcPDOcTyglMcqwOMf/8Le/4e+//ZY/fvuOxVoqozn3A0pJ6rri/vaJX37xBetVS03Cnk5s1+v8TdQGWZdMteKsBZQtpayZzz3KGI5Dj3MRayODnVGK/Hv2nuDgenPFi7sr0EvmhuyPmQuSMh5CpEhjSkpTURc1k83h1slmP06wcx6RhokYYs4hkohuwS0WMcLKNOiyZPAzCEEQ+dctlaRAIKwjLo5hznj66+trpEjYp55d06C0JEaYnaUuS7TK4durzZZGCrabLSkJ2qbGhkh/YZ+UOhdPurJk9arLlW+7/Khn6sfj60tDqmFcZg77CVdGmo3BdCVluWGZrrGTQ/gZMSemg+A07ZnjA7Ya8SnmJHJVsSobrq9K9G5iXM54alpd0cUGFRRaJgyKQpQkEQlzjZ+hWgeqpmb2NVNvGU9HUmFxRjBHQUBQiURYLCUOIRI2LtSiYxoPLMmClLRNgdElQnhi9PTLBFFRXy+8+vUv6b/fI/UdVt2TVESnQHGh3TV1SRILp/j3RB+Y3YSSEFiYXGT2LS6AFIFCBqbgOLozmyvFrltR1oaHoWFwENJICgtzCigMtWgoRMVa7NiJKw43D7iqpzI97hwQbo1KDULIXB1OkegdAk8KiaBnxvIDZ/nIHB2hDiSRKNFsRU06Cs7WEqynqASGwEpK1oWhvF7BpuD+8cT9+YhdJtwoWddbNrsVcVKopPONllSky20P5FsaRK68CS/5h0van+56VSusXnhVGzZKokaHRGd6aQKtNHWXMxDeWRwJ6zzTMJGiy1mTJDkNE9N8wOg8x1ZaUzY1Hz7uKYoSVEnXKWLwHM4Dp3nh5YsXvH35EuNmVmLmyxcN+wfJ89nxi1+/4Yt/vSJUB8annrpcsd3cIm5L6psGtfL0/cCH/TMeQ7dqEOGEsx6fCgKex+OEtQI79Ny1HUoInJcED8iAFzNVEamMIEVFUgUYwe+++XtWZcfnP39L6c68Gw7My5koZHanGIVNmphKhnnAhwPg2WrD7WaLqmti8jQictW0rOo15+eevuoQZPCg0ZrCZNzfsjhsCCQp8SLx8LSnHy3rtuHDw5F+mtnttqzXHSF4Vs2Oty/u2Kxa7q431GWJVIrNdpWx2uMD/+Nf/w5RCF6+esGb1QoEnJaR3k2sVhsOw8y3fzgRB/iXv35JoxV6rumfLURFaMH5iY+nA+VVx+dfbhH6p99eAxieD8So2NzdUdU1ZWEIvqJRhqosMil1dvSnM9OQs34xRAK55RPFBXmQIKHwMRJjRAouN2aQLk9CSpmqbH0OlR7PJ5qyQGvDqmtZ3IKUiaYuMOU1QmWfjwBWbcPt1Q6tFedh4MPDAw/PB+Yl01vrqkAAznnmxTIFz+ItSkt2q1Vu5LQNbVWii5IxBj4eHnk8e2Rc5/yWgXrVsAwLISViCCihcubM50wgwVPokt2LO8ZC8dX5PSKB9Qmls2wwOEcpJKuqpa4avIZ8DIVhymOYRCQKgS5KkvCXMKslLC4LbYuGzbrB2xmnobcW5xyKhCoNQQmSSzifKLqGdtOiEdjJEpfI8dzj5sDxeKIxBS9vbjBlxXcfP6IIfPLmU4qq5MPjE/00MS0WaSSvX76kMob+dCY4j/UBUuKT2//ImZMgE0l5vLIUXcNqW7LZJYyRhPPCdOiZT4GkEoyeIPdM/iO6tehK0pUSs1ZUJlHIGTcLPn4Px+NIWULZWVbGozQEnwhpxqUELqDnls5vEe6JtBiullcMJ8XcP+O3zwzFiUU4kpDgPJYIZUmhNdumw+iGoVeclwOVCZQpYRewAqLO0r0pnlhwcPXAKnUIc8fbumS2ME052OUJPKt3pOBYQkQimUNAiFzZGpbIYD0hJUoTUfKHj7dBiRWlXliZHll7et3gYosXkiAdUihKIrXyyPNMkieS6lmWZwqZqKcrCrsleM0ilkwWiQkpE0oXOUGdFEooXFwY3BEqR7nSFG3DnDT27FjcwrqriGpid1tw+9bQ1AkjIsJJQt3gHwI2WZKL6BJaUxKjZBwD3nvKpiCl/AMSEZcqcw67hch/FuI/1faItHBK96TVSFusKZcN07SANTS6RFdZKy6FoFQXMWZdoFTFYmfOh56P+yMewetXdyz9mdo0eCH508dHFg9KJDZNRaUVw2SZl1wHjCmBP/LJJwV2ge1ac/3Jil//rz/j5pclx0nzIQo8BaKRHJc9yU6EoWF/7jFaEJQl6DXng+J8OBJlT1EbfGEoyy1tZyjqit1qx9P5kaLR2BBxbmGaLEFrnJMMc08hI0JCUyjWmxvEELD7eybvUUpmDoRqOC8WG6Y8wtQKOxwx5RWLG1FaURhNqaEQM4v37IcjS7QXsJ+grksKBSJKJHkn6GfPw3LiPFpKrZAk9uee07iw3nVcNx33949oIXh5veX2akNZFAilKMsCozXTPKFN3kSN1nKaR9Z9j1SC0c3MaUGW2eOzPgqaITB8OPHi7RXV3OKdRBQVpog4MdEvjzweFsq6oln99CFsQuWRwvH5mZtppus6JIkoBKo0GflgCpKA3c0V0zAznU70hxPz4vJHO6ULaj23faRKpBCJZFGrlAKp8kbmEnDLGbeUmGzE2gmjFsrCcLVZkVJm2uw2HQk4HU9oElerltvdFqE18+K52u3wUnPoh5yzkzLTti8MEec9S+/4/bffcrtZs121vLy+Ignol4X3w5mvhwP3foS95uVw4tWrO4SEcZ5ouw1COsZ5yrwj8limlIbN9gpVGCKR42miEIJus8YEmJwlxki6VHltiCzB5mB98CigUIZkJLMbL46iSPQBP3uUT3T1hq6t8cqx7jJfS44Gbz0IsupBaXQt6YgEGZEpID2ES37x4+Oe43EgBk9zm5tE/TwxzzOfv37DdrthGCfWbUPXNiQFddfQtB2nY4+PUBQVMc20VRYl/pj146vE3iGCwymPrgqqSlPqiAgBOwXcNAMZQiVEJDIihaARLyhFiakTZTmh9ICdF5aDJh3uaOdrqlShWSOURJlEFJYg7OWDa2Ap0P0NvtrDIPHfSQrTstks6KsRKktINSE2eAxROnQpKVTCKImUC22n8LGgSBNtlIgYmWSuvemUCAHObuE7/j5X0UTHripwfc3DtxGrEvrtzBQHlmVhjgohSlIIGLXgomR2Ae8SSUZ89Ew2o767eIUaNwx7sKpBlSCFRcuIrD22XEhSIJUipolJTgS5x6WFFGfipKimN5i4wfrIwojRUGiJShpUPplpX1KJHevo0bpjUT2xHPFmxg4ef3B0Vc3tbsUcj7z9vOXmrUaVAhXBHQP6lCi0hKAplEFLQwgCMCxuxtqA0QZ9SZTnVDnZ7yAUQkmi+3HXdv+U13GYETLh3cQQLRs50+mJUm1p9BV12WVGjAClDVpnRLeLkSRhGAzHcSIIRRQSXZTMos/WUh84DiPHySNSIrjItq2y00JItNaYxmBqx/H+SIiRL37xmvZty8tPGjY7iWwMpwlOZkCtDOtNwWbd0DVbvBRU+sgcHMM4oYuaphWcTz2nYaJuPLoTtLXiri3Qr26YwpkJS0qgjURoiUvZoZFCwrqJoiqYppEPH+5ZicQyTNiwULUlCIkLliQk1o7E6NClojAS3RQsyWNsbt31JILwTOeF5/2ew+GIEGCtQ8gaHwIi5Tr2drOmKQ19P1Aqzd3VjqouOAwzTVMjlaZbrTBKUSjFdtXRVFVG1OdjJ5OdOfuRqOHlm1seznusXzic9phC5//uMmGuNN25Jh0912XJ4fHMN8tHmmXJMs5iYekdp/LMslmgdBgfsctP/+bExTyOHPuR8dwTtzuKQhNFDrdqmZ9bpRSxrNlcXePPa/bNE/15ZBwm7LLgQsALCFxuR6QgxYCSCkFmRwkh8wgo5pFPSpGQskU4pISLjlLD/cc9f/Pb3/Pi7i4jKJylrArKwmCXBW8dj6cTh+OZ/fMzRsKr2xusDzyfTvgYcTFkZhSJx74nicTTqefmasYJwdl5llrTbW6Y5p7H/Z6v//gdi3W8eXNHEuS2ZMo04ighek8hJG27oVltCDIiCBjAjAGpFqqmYCERvScsHo1AFgUhemJ0KAmlUSiy1y6GlDN/y0xaPEYUbDZXFFVNUh4bZiahiDFQaYOXkiTztCFIgTSKQmi8W/A+axdkABUV66bFqDwC7Vbry7s+0NUV21VHWZQM40hTaa6vVqiixCeYRsvHhz37/Z6rzRp9UQh4/x85cxKVImJZXMRFhRwl5VmD8TjrCViqakVdKEqtEaKh9C3r8TXGlpjGkuQD1gZ8HzF2h+EKXa4pRUMxK2QhkCpgVCCqEeRCdAY3NcR+h18XCGFZZI8XgeJmoN1aOiNh2DDPO0YFvj6D8QQmRr8gYiIFA7KCWUJI+DQxioD3ikaDVIY5wPfuyFUxsVMOfE08BcRBo2uDiFm0tkTL4kwOPImFEBeWkBH9RVHggyP4BSnApIpKt1SNRJ1ahu8hSImVJ2IxU30SkTcWWzh8giU4hIp4J1FCsNhI2N/S2k8RYoWTEZcSSQqUzDcWQoCIQFSo1NCJO4qy46yfGcRHXPFMup3pEtTzil3VEJNit1tTt0BhEYsmxALtJXWMaCJVKtCiIcSCJEqi8ARnmRZLJURuUZFddKRcHQs//UsTAA5HzctVRTp47BBR25bN6o5NdUUVVpSxRiaB0BJTNrlOnBwqJbzI7SjZjwR1ZLGOacovhWnOzgylZObXxISPEZ8SCEGhNXVdgoTvvvnA0zeP2KDZvdyQdDaMihipKsXqpkQ2sGk119tr1utrStPhIuj4HjsForR0bUVbr2mrmvE8MNmJ+XBCnSKTCzi1RifJMi9EkW/qYlQkodDG5H82zPglMEawMqFrQ1WUpBRRRZ5xe7dAkngfMwzbR6qiQlcdQRjiJUu1EFis43w8cXw8cnw4Iy+hYe8y5VIgUEISYqIoCtZd3rwMs8X53Dy62V3lfIxR7G6uUAKkkJd6ZeY02GXACscoLc2uofjUUJ4NSWhkq5HaYExNcS4oCo1UNbpMlNrwOPbI5YRPGiUVcUk45XAq4GeN62DBsvz09+pYF/CQ21PPe6aba8pidWkQpsz8AVRMCAXNeoWoCsqiYOgH+lPP+XhiHEdmHwgpEUJ2hOWCQQ7HinRhYF9qM+ky+gk+ZhaHzGTuyXq8G5jmP/H+cU9VGrqmJu62uPjMu8c9Pgl++6dveXx4QBeGV3c3FEXJ+8cnfAjM3jN5l8PshWGOkcM08/XHe4zR3N7e4I2iefOSVy82dHam/djy7ffvef/9B0qjWa1X+XsQM4uE6Ineo6qGsmn+TIjVwrMqFLKPzO+O1G82CBFRKaGUQiudx+UJdIqIC9RxsSOLXQjRErwjLA4TFU3TUDY1VgQWP5NSxIeESIlSSoyCqPKfqVKKrqoolWAYLL1N2CWQfGStS3bXHYvzPJ16jsNIipFVW/PiakvXVEhJbk6ZMtN0557TOLM/9RyOR5QQVGWJFJJhnJh/5A/Ej96cmK5jFha7ZJmWtxKTKugMzmc/TK1WGEpM0sjwEu0aGnud2wlpYJxmliWgbEkVtsxk06JMbb4BiBbvIkJFIh7vLW4JuEUhxQo/7Viq7xC3j0SvqRtHUFMG/NgChpJYTizFhEh5lzo6dyEUapQoEJSc/MQxDAwC0Cp3wGUiRknvBUoFisVijx65j6i6Q3WakLLozydJmLLwrOpAEVkCFLq6GCIjzmVDZ6NX1LVGyieaOTH+sWbqM6NErRXFXSCFiRQjFphDoE6JuGi8Ddi+puh/DnJHALyKRARRaBKayKUZFDM3gKTRqiKkgEwFKRii1Mha0L1O1ItDLY46rhBziz1EZGWQk0GcK5qQoLAZRR8U0jaEmF/WZVljkXl2aB1K60yqJRGjx3tPvBBsf+rr+VRwrTv0eaEbGm6bT3ml3rKq14gkCDaQLtRc6wPeOVQBSMXoHMdp5jjNTNaRQsQujhhhcYGbzSYH/aLEO4+48BxijJR1RdVW9OeBr7/9juF44vNf3rBaS7778Ey1FehbSeigqGqkkbSNwVQ1pthSKsEqQeUMhERZCbRxGYRYt+w2Fc+HM9NBYWfLd9/1hFWgKgo4JlCRqCwugTSSqqyoiiw+Ow4j1i6IusKSKOoyQwCTI3qXR4E2IYRCyQJipBAmayOEQEiBMpKYPMs0M59H/H7B7S2rumJVG/rFMk2ZEWG05jiMaCUpjcSlxOPzgbow/Mu/+IIv375knM60VUFdlDlkGCOL87jR4rxFiISoJKEI2NYxFgt27SnKgtBkTX2xbunerRFDpJgNgQofAjGCkQqj8sh3wefb4tSiJ4nqF6waWOxPPyQ+LxfrbvAcn544n26pmxLzP23fhCw3TYCWElOUyBWURUFdldRVwel8ph9G5iUzT6IPJETOmqRLCF8mRL4yIcT8s+VcQKkK+UNN+SJUfe4nTuNEaSS7zSaP3WPOPuii4vl4ZJpndnXNtluRpCTF3JxzIf/sSpHQoiAmweg83z09URlDt+roNte0uxazrjBeUVclUku++uZb3n33gZ/9vEIKmVuc0ZPsgiL/3Mgij2OcX9Ai0VQFQqUs2xtGnEmIFCiKBlNW+BTwwSGjw7uFOcE0LoRgScGDd6ioqYoWU1SMbuZsR1LwFIXE+SwlpUxUMgsGlZQ0Vcltd4URkX1YmMYJ77LrrSpKtmWDdZFhsZyWmRgDq6ahqQpMaYhKIozChoAdJibrmULAieyt2jQtXdcx9iOzs/zYz8OP35yUNYuTxJDBSWlxTKKgQBEDLG5i8TO+6Sh0RS0qKrFGqwIjIbiEtlvKpUalEh8T/fRM1Ri07Ci0Bhlxy0xMC4ubWZwnhKyhr1QN06ec5COifmDbGpLUTAtIlahFRGoPYmKOPd5ryqLOJ/qoSFQ4IRBaYZ1gDBIldWZNyPwgQoaZjYvj+ZiYfxPZ1ZrtnUJtE0ELVGrRQRGdoq0KrhuZjaapJGLwcabQEZUKpC1oyhptZmbxSNuNtLdfkFpP70b03YBvB0IIhFESRCRqwegtUzhhrac5fUklvmQJCRt7bMwnBKUMAoFIoJKElO2XIIgxsPgMqRKpArvO4C89IGtL9DMirJmfE/NeZPFTKNG2plGSsgv5vykBVmJ9BGUpjc4VZufyy5mUDcTyUtvJKd1LZfCnvR5OARNnrkbJL7o7ruoXlDRElyslkfwSHYeRcT6zBEu7W6G15nDqOZxPLHagKiWFrOnqjhnBchhAmxwg1xIl8qa4nxYSiXLVYeqS/fOBp8cBWsfLX0sKLPZ3M9M8EQZBlNl1JNyCTYJh6amlZ9tJ1jGw/f+T9x+9tmXpeib2DDvdMnttc1yYDJfmOhIkCyWJKAjVEtSsjhpq6T+qIwES1JCEKghVNLokL5mZN11kxLHbLDfN8GqMFXnZ4ylArIsbnIFAnIg4du0xx/jG973v8/qGjgWtEpJIyFWMKIxge9PRtFuW2bMsJ5qusG57jkfHkiZSCsS8IBNEodDrjtvtDfbBcBxHQphYhCYVzxQCrniMNVhtKSKhZSEmQdYGIxXj/Ijtrth0Gi080zziTkfUvGAOET0XdpsVvZX88tu3XCY6UAQuRJ6OZ5QUjC4QcqaXkqtVz9WqY7ft6s0difOOkguhVNfE4heubq7oBosSHmkV9JpZe07CsRcjVnWYjWG1rEjfTcTRkUoiK8dmrbiRBl0iixY4LcnK0CSDTRb3cESLgFE//lTi8TwTMsSUOB32HB6fGFYDfdtc5jOZmANFgNCaEhNSiFq8aI3oO4wxtENPezpzPo+c9idyjJfg0ssYh0vnRF6cPSnhfSDGgrYFrWvmmJCqckKoBVOMnpj2nKYZIQXXuyu2245vPv+Mw3HP8XTmw4cP2L7Hx+r8zPniGFLUgkZAQnIOHl8S1jZc311jNj1eZGIKaCO5e3nLEgPffvtHDqcT61JDMUsKiFxYDStW6zVCQgoO7xcSiqfoGYxFbA1j9Jydp2RQQ0ss5ZIPNZPTQvCemGu3MIUFQsQWTd8PtMOaJAvTfKoJ4QiyyPX75oRUFQzZ6RZrNGtj2NoVJXicbGr3FYFtLSrKSga3GqmfM15tMFrRrVZQCv7ioHp3OHF+OtAPPco2FGPR3YCQAi8Ebx+fOD4+YbXi+nr3UWvq4wWx+xNCBZpYBUkuBM7HCUVL29WZes4eIRO6NHT0tKpDWAkyIWNPnzQNgSUvjNOexWeapmBsQdhIZCTkM37KPD0ujOeAtZrdTYMWPcp9zqbdk1e/RMkFtRiS16QOlIzozqO0owjPkhbSMjKYFq8U0zJWf7rpaHN7wdafQS0UMklKtIhIBKVYvIVsG85jC7PE7hwYj+kkV2aFt4ksZkLMuCgpWZLKAqLexoxqGczAVrcU+YGIJAvN7qVg1Y+8jvdMeo/TgeIU+azJWRK2jqk4rEkMyx138i/Rec3eHzgvEykLunYgKUtQAplBZoHMmeAiSUW8nHFhrqOA2COjBJdINpC2lpwUJUjwkuhA6wajO4SyCAFSK4wWgMT5xBhOlJQxqkUIgTGGUhKUS55O/gFLXW+/McX/2ZvbP7QnusiEZytXNKsB3bWgaq5LShkfAyF6lhiYs2chIZJHzCNaKn72zU+AT3l8f890dAztioOStKczomk4LgspKwZr69g0ZbRR6MZgjOJ8OiDazFf/6x3dF5F/+/94zzx3tSvgI3lK5GVGLzOLFAgxM48HxKstbWm5KRvM6UCUjthohC0ICTlIZNFIJZG9Yv18RYgLixfc3T3jw/1bjsHhU6ydIVeQuaBy4ubFjo274g/fvoWSkCKjrKSRukZRkGiMhOhxyRPSQLNd4fOCFQtbAzlMnPYnxJjZLYLp7URYArP1kCU+pAvducphZYGUEiHUjggIuqZhNXR13lhqoWytRRqFCBHZCPqrLbJrWW3XHB7ecLhfuHE37BrFffrA4/ieOQWKVPR6xZ3esV0PHE8z2x20zzWboWeXJPMhsg8tttsyesX0fkKcEv4IbelZf/LjbyWO5/FPibPlPPF0f0+/HhC7LfYyfhGiaqas0aTgKUYjRcEoQaECCZXWNF3HsF7VrKeUKFPNfMmX0SaU+qXN6ZIXE8hFXEYXdRQiyqVIyVX3lUthdh7vPZvVwKu7W376xefEFHj9XnI4Hnnz4Z5uGDhPEy4EUkpIWS8bPkSUUaAglUzX9/SbFc3VitwYooi4AoubmZaFq92GWF7w4eE92jxHyZYSEkO3ZndzS7/qCWmBUhBIhJCMbsHlhG4bpDKEoJC5kGJCBo9ICVkg5Mo/meaZ5Kv9vi11hLTabqGRLMGhtKErCnHJ2SkJhNCkJBmTp6ZyZhwnxnxPyonH/ZnzIdBt11igwvYrGE/J2kmcY2R6PNBoxfXNNd/98QP/7//Pv657X64ZPT7lS5hozRaTBWSK/Pzrn/Dq888+ak19PL7+zVv0tt7kVlIhree8LBxOBaM2XG16GiXptx293qKLqRZTBBSFoEGbDmUgpQkTEo1PaGfohoa2kcwi4EtER0trVtDCanPNs7sXGG0hF9T5F4TuA8v5Dem9IgeNvSuc0pnIzLirnA9BRQP3puAnR5s1Js8IEVCmgSDIi2JyEVci7aDoB4NKUKSh38FqW8gPVcSXrMDFBXd+TTIRUyymaJayZoqamJfa+RAQfaZxklZsGMyaedkzp4V59JT5ibl/h28OFCcoUaMMDLeRRhWiVjzNUKZrXkz/nNZ/wsNp5Ol0wIe50jEHgZaa1LeVOVLkxYyXiMnX4s87opII2dGVFhkN02xYzplMR2dWWNNhZaaRLUqampKcMyXXjBwhVE1vjgnTSIzReFeBYlorcpKkS4ESUiZHgTb2ohr/cT/fNBu+Xn3KM7tmW1b408K41IJtcY5xnBjdjM+FOTnaTc9df0e7XbFebxhWK6bDgdQt9Kpjnhw+zFztBg7nPS5c7IfZ1TwlqVivB26u10BiGo98/Vc3fPFPDP/q3/yWf/H/OvHp7jO6cSQ/nAkE7EmzGlcs6QrbXvMuHUl3ihdfbHk59HTvPvB9fCL3hu2VpR8kEY1LBiULMkYWl1icZhkjQ+lQagVuomY8NqhiUEkyHxNRTJjGsH01YJNieTqjoiMRKFohW4XWlunYEsY911dbpM2sRP310nxAjpFV0RSZUQ8j8hBJIfJ+f8YoiFQtjpQSrSRdo2mtJad6MLkERptq6RY1KiDFQNP29G2PDIGEpN9d0+22HPYPaNMgjeX9H+8R20h3bbDA2XmOS+Axnjlz5styy2ef7/jz/2bN7U8Vq1Zgi+Dhuyf+xX//gV/9euTd95n5lLjZrLm9vcWFE3358Vvr52VBKInLER8iT/f3rLYVM4+2VTMkBEkqWiGg5Bq7QaWtCikQRaIlaGsQEsrtNSKX2vUIHkW55HlVkW1OkRBqwSGVraPlUEALpKgHqhSy/txCkksipcDpPPLb3/yWbd/RtIrN0LFdrXj7/p7DNDL6QIgXsBqCOQaWxTOIrtr9Vcvm+hZ9dYW3mlwSPgWWsBBDpGstCVjHnpQHpCzM88y627C5vkU1hnk+E/1CFgUpJefziUJCF4tUCeETKWZaqbFSsm0blB54OD0S/EhJkUbCvDhsFOw2VzRdz1wCca6fV4gRJQzrfuA8jTRdT3SB6CJGSaY54EXAjZ693BNi4P5+BrPmqreog0PEghOeaVn49sMjv/v+Ow6HA6uu45vPPqM3FlLVbP7+97+jMZZntzc0tqXTNayza1tiTKyHjpvrHefHj+P+fHRxgtKcz5Vst2pXrIeWZq1Aa6zuWbc9VtVWsktnpGgRqanAGZORWoHsiEWwHBxuzDSxpdUNzIKsBEFXq5QSDV3f0lrN5uqKm+s7Ssyc94+YeUPxP2d6kHDWrD+dGJeF8yRIJhBWJ6KMuAjHWSCiYv/Xluapxb7ItNeS4DrUtKIRkdQZ7LDnaqM4zBODvkIqSUgzzlnCfsFKS2M6lG0QvgbvqgRPv1vjtopyHfB5wZeMsQorLWrU+KfEvo2c51vspxvmjSOsE4tsGf9oCL9b07TQfvWAfHFPyBETO+7CTxDHf8qWbzj5M4fDieAD625F07QgJXH2FSVvWlpdoWha1/TncZnZh4XS9LRC0ynD1rY0qeF4HJnPC+VGYHYdbaer+p26kedU6+RSqko+xMAyj8xzotEaIy9x1/nvNtw6Tk74UHOCGtN89LL6h/r8RK/4yXDHRvW42dek0aFSLxc3M55PxAK66fjis1estgPb9QatFd3QI4pAZo2WDR6H7hu+fvYVx/OJX/3+j7TKslkP5BiYljoWud5tuL5ao8isB0O/Ufz2r1/zr/5ve8Kxx7Wed9+eOb5/wrnMM/GCzeo5rd2gxAofT7z/3czhXWFaMpvJ8m2Gh/3Ehw8L7WBYDR2dGUBeinEpWCZDOAeaneTq+Q3+jeP84Z4gPKUkzuFM03VoV3UkumnZdS3XK80NDU9z4cF5TmNEiRYp1nx6+4znL+84xwkJxLSwp6FrbnB+Ynz9nvL7meQV1iq01aRUMKoWzyJXQXjbWLabFSkVTovDR09rFJvNCn3BlndDTxaCca5psLsXL7n5/CdM45mn1/+OaX5gTB+YwoGBDc9f7jjeP3J/OpGWTHCZU1xIm0zXD9zsdnStxPbPaUTHq88nTm//lve//Q3fHx/xU+bQZO6etdx+MrD7qv37Xq7/2Z+QIqJIlhzJ3mPPZ54eHrFNQ1mvaYyBS1EZQ8BqRcypwtWkpN2sMbngTtXNmYJHCcEw9Fxt1xXOF/IldLKOIEOMeOeJMWKVIXoH2qCErenw1MJHlqpRUUiKUCQK390/8fTf/4/8xU9/wmevXrAeGlZ9Q0tDPE+MMVJSxIcqrvUpo2Kk1Zr19S33MfMvf/cHPlUTnbMEEQgxV1S8MBymESEkQ9/hlpnkMlerG5YYWEaPKJEcA3NYgFwT7LVCCEHyAaMUbpmRuid3NUjUx4nz+YkQJkryMAZu7Zr17Q32asthOjCOZ2KuHYvqb8o8Pk0s0WOsJYYAuWCGllhy5ZbFDLGQQmFY7Xjx6hlu3uOOjpQUTdOBqs6lzTDwzatP+PyTT1h1LU/nkafjATeN/KOvvuQf/+WfE4JHlELbdxhrazFZwGiFm8+suu6j1tTHFycGZM60raRvDKt+QG0kM555nJmOBtN3xLIwl+/xWdOWW2RSaFFYoud09ixjBFdosuJqfYsUgvN5xBpwesaVitBu7AACgh+ZxwNXm2vkRVTTup/RD5/hb/8G+/nfsE+OmC3GCLIUhKixZcAiSE8GGQW3w0vsU4/80LJuOuwtpOsjBxM5h5Gn/R6UJRFAghIa2TVsrxRqKkgnac2OISRMPNE+F3Q/b/nOH0lGEZNiOQeEy2zsmutnazYvBCY4VuOG5i4wrd+zTwdSEmw+uSKdLO7xQJ4CJRtKXCPGrxmmf4LkWf3QhSCngBGKzWrLensFSrBEzxwC8eyZjURYg9KKTvXY1GC0IrVdva24iEyFVvXYocfLhU2/orUtxjQ1ayh6oMAF8JZSIZdILpHGKk6nmeP+yHpYo3S9jWQkCIXW8oKa9gQfkP8FaE6ateXXT7/lhX3BVm0oPuDcE8PQsBp6jBQ4n1itrzFKI13EC8fj8UQ3dFASYZ45HU48Hs/YruX12zfcPzzy7jixeM+uX9E3DZLCatVxu+5QMXB4v6+ZIH975P39THzX8mx7w/PNmuUI7vszURTytSSuIrrt6TbXHO+fEM7jx4VGN/z59hWzS6x6T9kYpJHIJDFC0fmJdWeZuxXySiMEPE0zxzSxvnuGVD0fPrxjPI90m4bttuN2tUUvgtdPBzK16zK/cyw2o64FQgXCFFBBsL6+AuFYbXpK7umwZO9ZomN6OnP87YHjLw+I0vKzn3zNV5++QJo1RQoO+yOPTyce9g+cxyPv3jxWYFdM7IaGl7cbRMkIqaptMUSmeSLnyPPPv+Dm1QuUUBw/PLA/vec37/+aefXE5mXD3TdrzI3ik+YGJSOng8NNkvEt/PbfPxCeHbj5+R13cYW2AjM94/zdwuvfCrbijp9/3nEfD6QXkZt/qnjx8jnIj99m/6E+qQKOCKWwhICZJ/YPD1hj0EpSug4BtKZauJMPVcMRMhjD6WGPdwthmsmxpnfPy4wPdbTSd23Ng1li7ZLlXAMovSeXerhH72oyfK46CClA5B9QkZcuPhIlDAhwPvAv/90veTqNfP3F5/zZN18ijOV3b94x//5bns5nJudYfCRTmJi5vr1hd3PHh/2e37/+lrz+OZ+tn9OuWlSjIdaL2vX2mmVZSD6glaXvDLppqoQgBoJ3xOhRujqRBtugrMblRKQQc8QajdGVEaS1JYVAbw3zqQLWRBBsnt1S2ob7+cDkK91YpJooPPsFJWXtoBiNUprzPIKQ1XmZqiMnhyriGtYbum3LPO8p04nvfv+Wp9CyGtYsMfH49MS6seyGDisl+3HkD999R86Fz1/c8eWnr9BG4+aJ26stXd+ThCSXjJUCqxX6ek3bflwQ5ke/NevbFygxVm0HhkZLlFbEKDm6hcN8prWWvpGgE3FZWNyAjg1IXVv9yaPJKGXISfD0NNN3HUJm3PmMWjmSDWR/RgtNo4ZqvXQj82wwumFYr5iXkZQj/eqWKLe0zTvQ7zFGgCy4pMhOYRRoMdN83rE5Go5/07E8SFgdOF0/4voJGQ0mviDFBbFOHOOIC5leN6juezZfKMz3O/J5zRI69s1CafaolDjEwCkXvDf4AkoNrFTH1XpHN1giDpkWuud7vJxIKHwOjD7Rt571X7b0aSKJgCx3rPyfoc8/g2Vd550mMax6Pn31kpQTTdchtSSUVMcpMXI6TTzFA9thYLfrq6e4KHrdErJCiPoijAeH9BqRFUJIYqpzSKNzRc9LQ84BnwLex0owFAUjYbvZViuYlCglSCX/CYBV26wZJDSt4YfR0o/9ufnzn/Krv/lrXu/fEETCZFuDr9qeYd3Tti3j6DCNwWjF7vaWFDP3b14ji6NtCkYGrPY0MpLnmeU4IbJGZsW67ditB7765DklBZQuaAvJTQxS8ezqmpI9D4vj1W7N559+QqMK5znzXBTa65ZXX93y/Nk1fdkismQaz1ghuVnfkaOgbXp+3mpK/j0f0p5iJbQ9XbfhWdqQGsVvp0QMJ7I7czpFdrst65vqCMjLFW/ezixHx305sN1ZvvzpZ+SrFWmK2KOCzyVCTVjtGbzkPCWaCGvX4p1AtabaD/PCPJ+Z9we258Jf3n7GzT//K7768md89cVnXO2uMd2WogxSN8RQOJ32vP72D/ztr3/J92++JaeZ1aphPXQYY0FIlCp451lmx/p2x82nn4CA17//DafDA6fxjLKZn3x9xRc//xrsNUtJrPst+TZi5chkBcyJ+19+z7NPO37xi8JqtWF/SLz57Wum14LT44z3kW6l+fzuhv6LDncs/O2v3/Lh/sT/6X/7971i//M+OScyoqZqp8jiA+N5ZP/0hLYGdqUWKsHjphHd9WQpiM4TphHRNBfrfGY6H1nGkWVxxAIhBFKqVGWlROVwxIB3C9F7hDHEFFDKUPu48cLCF3/K18ii2pJ/ENVqJZGyIRXPr7/9nvePT9xebfjHf/XnbDZbQs4cxpHTsgAFJQUvb2/5Z3/1V4QED6/fIK2kZEfd8yLTPONmh0Jhp4nD+VTdNb6wu31OSI48R3IJxFyLrJIltm2IJ8dw3bAkT44ZnwIWjZaC43jgw/Edzo+Ukgizo80Nm90tobOMceY8H4nBkXPVTQpZL+rjdK5Bo6JeUEWBkDzBL+QUERla07DadHSDYjw/MYZIv0Q+29wx6DVPpz1hmVk1lrvtFV3TMi8zh2kkl0RrLM+f3TGFyNv77ykxsN2s6ZVCi9qFFyJjjSSRCPnjBOIfXZw8371iWR6IZY9QkVIWZOnoTU/YNiwN5CZWe6HpIFs0HUZ1SCkpCRqZETKwxMT945GHd2e+ePk5P/lsx5Id874QMLTUmGqXMq2qP9YtI0EEpNWc5ZFxOTNMA/r8BcNaoc17hBhJIiNVAjvTpBusgZYZp/+AX3+F3QrUdqRZr1DqJU2CqBfE48Tr43dM0jP0DZs2YQSEDGaTQDrmLHjwkXs/0rhCipIgq0gqx0DDhUGhFw7+BBKurCaJiSUWQm7x0dFoS2MKIR8IOWHdDc3hzzDHP8PSk5pE8pAISK0YtiuSqJkT3lfOgw+BxTmexifGZcKXDcP6E3JJ9WV2C4uPJCJunDm/n3DnSEHy6avnWKPQUtZ+m6idDwGIkpEkCgkK5CIrR0VJbNMAEuLFt38BLKVccczCGDCA+/FbJ+dx5Iu7ax7nPTl7lO4wQ0uxGlfqDBaT8ULQtj0lFvx5ZtOt6FeWYa1oGkH0ieunkePTRKMVT2cHRXC36vizb76k0YoUA1KBMaKmGStD33XMy8Kzm2cM/YrWGryb0JuObiNph4E+DyynglATyi/kGPnjuw80n2x59smX3L56Rft0z+GPZ47nM8fVDF1imjwPWaMawyIi60YQ/jbzaug4nmfehUQrBoZVz9pu+cP7N+gx0inD8m0m2g6WwIbCSZyRJdB+EMjXgp3dcPPNFvvK805lxsliBOR5QsTMdkn81F7xz37+M768/ZJhc43tBmS/ptih8kdsRysUq82GZzfP+MU3P2X/4TVv3vye949vEaZ+Zk8P9yhjaNdbbq6vefHlFwjTcHzaV4um0rxY33EnLXKamP5a4+KEtwl1K2g3V6w2HcV6Fn/i2TeCn/ysYXt9Qze8hGFFUo57PfL24Dg8HMhImmZg+pXnzbf3ROVwH7kZ/0N/cinEVNOhXYpM3mGOJ6SQlJTY3d5ifCSaWC2lJRNCJEmJyoniHfNxz3jYs4wz8+IIMV1GOKmm6OZUtSZ+wfuFnKrgMnpH0ylEDhAVQtYRScWgSn5onMhL6KCUEnXRKecc2Z8mpmnGGMvd8zt264Hn11ecnUNoz3a74ZMXL6r4dNXTWMUUHCVnUkhE4XB+qSwSpVBG0VnL6WnmenPNbnuFu6QFL26BHLGX3JlxOtHLhlQiKToarWlMQ1oS5MDsJ47TkRAWVMyorFntNrRXKx7cgdlPuPmMLIkSI0tMl8KnauAKgnmemcOZlPKFWq2ATKMNSQYOhz3HpwMxgkqKsijuNneULLheb3l+dY2SisZWorKLgZISrTE0Xcc416/Fpy9f0FnDs9sbtNaEmHAxMC+O+8cnQgy0bcPnH7GePro4URGkWSGtRMo9Ivrq/y6iAqP6zKQmrGgZZEtREXSFUGWAIrBSgxRMeeE4TXz/4Z6cLMOmZX01kI7nuqhayXkaiWfPYDRKNjSkepgHx6IfCVERRsUyBQQ77m427F4ckf07Sl4IcyanBTXd0dtAXj8iPv+eIq4RoacLNzRxILgT4XCiObXYbUvsC53xyChQSiO85fSHxPjhyLSZEJ8A2nBOgcY2lOxxISED3DUdVhZ8mPDZoTR4KZAUXLFgDVrUQEAfAzlFzHJDe/oZ+vgVxIbcZpwMzD5jssZqRZaKWBLeObz3lFwwytCZRN9oppg5ujOP44mUA0s+EUTElQRK0A2W7cuOEgRFKm53N3U8I2UVDKZILHWW3xhDZw0pZ3wI+BgJMeBDQOiaupuFrJ2VkpFSgpQsIRDiXNNG/2dva//wnv37I+tSuGo2rO0WTVNFnc7jY8YoWQsLkRG54OcJQcQYyzL7C7vB0Laa3e2AVhmtE1e7NZ/HO9q2ZTUMvH77hncfHtHWcnez5Xrb0zW2wv5y4ebZDa0xpBjJRdeAvbhgaWlMhzEtRSo261s2fs95DLx7PDC8yEzf/oF5/8DNrLhza6boiSYTpMMjkT7X2HVVHTBCCWS2PL1zJLdw1XbsXmw4L45xPnA47VEbjUrgJkBIzkvCqkA/tfSzQbWS7lNF+6LQjwIZFJ2ykCP77x95drb8/PolP7l6wabrMUqhlKZkQY4R2ha0oSiNTBqVBX2XsDfP6a2i7zq+f/89p/lY9QbaQtOyvr5jmRzj05EcAylF/DTRKktKa5anKn4sIeOj5/jbibDOhBuIQ2BoIj/9pzv+/M+fE8WOOWeUEvQ3He2nCX5puN8vPNyP5N89kDRc3Vp2rzpMv/n7Xq7/2Z+Ucx1HxEAuiZgFSwjoeQbqJUaZBqUNxhoKBakUaEWmEKcT8/6J836PWxaiC0zTjI8RhLh0euuIJsaIc7XYlkhETGhRA+x0yjVUj4KUopq1LmJcQXXwSFELFCHq+B6hiEIQo+d3f/yONw8PzDFwPI9IIbneXvHq5XOaruG0TLw9PPHd23fYzuK9I4Zwsf3LSzBmzWXvm5b1TcPV9Q7ZCLa2peQMqx7nHZObUdrQ6AZRMlP0aF0dTCVHtJE1uTkVdCokB61oubnaMYWF+29/w3E+k3IiRXehs5eK3c+5ioILqKCJbiHGWsituhYpFD4uxOCZlwXnPcFHZFZsUsdNd8fj8cQfvnsNMfHp82dcX12hlEIqhZSSdd8ztB0ISasNWmmurrbknCi54BZHutjAKQIpDVpX0OLHPB9dnCz+xCgK2Rak9jQqoNOCWzyTc4wiIfGYsmFFA0mgSgTpaxur+LpoRSHrguoFuS28PT/Sfqd5GdbodaDYBZEDh/mIO3eUdsXgPHOIBBZyt5DaBak6QiqMIZOdBdcT3S3D7Q3t+h4l7mlVZv71FUkkzF9mljYixULKkTiNTM4ST4bzXtJ/vqXTb0m5hXgilISJ4A6asA/kuGBUgyoFSwZVkNIjk6cRhUZt2A6SxgYoEZ3rIZ3GyLlEZhPhQj4UKZFjog+3rJc/oxm/ovgVrhTGEDnjiCHT5IwtGaEMzjvC4sghXcYr1TEz9ANjcjxNJ759fI/UkXZdCMmz5ExjO7qhY7tZY7EIaWhNi5SmviilEIKvOUZktBSVSCglIUUQpVbAKbOEhLoYcbTUF0dP5arkDM57jK0F6I/9efPtAwcEr/ordFedaVIUjJR0xmAbg7aG83gmLQulaQgxsz+NNVtDNmx2lm7dMo1n3t2/ZT4vXN+8pG07Vn0d7aWYuN8fcbmwxFRxVNsBozUCgRbqkjsTq7Qhi8rzOB3pV1fY9VCt/yXz/KuvMM2K6eQ5nx9I00xyC6Jkttmij4o37oC8MqAFWkmQmcEOxK1HigBoVqsWfdUw9AZi5Fl+zvvfF/ZvPoB84pu/uCatBtRpJmcNYiGqSBoa1K1kryaufEtvDDe7NXFUuA+PfD5bftE859PmmhaNSJkSIyV4CpIcc9VvFBDakHKBWMWFWiv6YcPtLhD8QlEQpeQ8L6AU03QmOocS0LZVx5OCx4da8Oes6LuGeRpxc+HxceHNfziwFwvDJ5K/+Ec9P//yjlWzYl48Rk5oZZGiwy2Z6DWt7bnZSmhgFEvd85yt79GP/Emp4tljStUVkwUuRoRwNZiuFIRUaGtQRqGXytoIMRC9x48jy/mMc74WOjHiZkfMCZDkP42QCz4mfAjklGonkUJnFZaMpqDE34Eg665W/k7AL6gWYyEu31YoBNJI0iViwZ1GQo5YKXl+fUV/taXtB1xcePf2Aw/HM7Pz7BoJqhBLICy1W4QEmR3ZRUyWDNoSsie4CR0luYAosmJ6yBzP5+qA7RpkEYQUSZfPM6dMKAIVYKVa1uuBIgReZxbvyCHSFcW0eLyPXAD5FEDJ2jTQymCEQdtEUoJVK3nxbM3i4d2j4zhFUhTIrCBEZCiIDPfLkRDu8YvjejXUnKCpJg1v1muMrRckIQXOeciRrmuRwHGaKBRKrtlG8pJVJFV12NUO/H/6+eji5Bj2nELGSEMDZCXxOTDHyu2XWqBFJobEQrm4OjIhT0Q5kkQgZHABXEnoTrJ9tqp4eukZw4mVjRR7JtuE7iJxyfgS+HA4cTodMD2sbwwiGYwUSA1atSwEHg+R89myfrrm7m7N9rZDmj3TacJJRR8z5+hAZhqtwUaE1zRcIXSDb5+IMmHdirA4VOcQNqPbQvdZJOSR2Tqizqy1ZBIgZMYqgSkd627N0IDUHoOgKT06N2RZ8OWRXDw5REQ0NLIjeUE7f4WZPyO7FTEVUomcFs8pTXRBY6Rmjo5YHME5iAWJIKpCKbFuAtSOxzg55pS4um2QnaCMEUpBISpi3DSo3ECS5FIThXNKf6L1yctsMKXEcmHQp1hjtaWut405R5AFK1QVeUpJ+VO0eRWdlRxJ+sdvnTwdJsaY0bOmUR1D22Btjzb1ULfWXDY+hSiCcXS8e3zg+zcfaBvDZ9yxvdtwXiK/f/2B//CHN1wNW250gxAKicRHh9W6isanmeY88ux6XcV500wIESEKUghiCggpGIYBlwfmZeFwuEcNAqklj6cHNrfXdNsrnh7+CNkR3EKKubogxgiLIHcCukIOhSQ8XScoZ4/VEmMUrZaYZPHFEkVFYKuN5frlDctvZ8Z3E+7zB1bX1YUXESw+MlForxu6ZxpXHE9PhZgcOrbIp47+j5GvxDM+754xiIbiIkl5oAq0RSygIcsZEQJCmSonCAEZPKSIpNBqw7YbGMPMwzSitaJpNNN4Is4LQ99jzKYW1Qgyif35iPeZa7FmXGa+/f4d7w4nHk4LxxAIQRFetsioGQ+BHI/I4MgusH+v+Nt/feD4MNP1HcOmQbYFdZZ898dHpHMM6x+/ey2mRMyJEOOli5KRoq5P8cMeAVUz52eMLJAzyXvCsuBnR/ChJviKH5yCifxDGnGph10sMDlPDAFRBFpKVkPLdjWAr+GAlZhfyOS/o8pS/xayivULVPeQELU7iAB9GVNHf+n0K4TWmMsFbBxH9k97Fh+QUtG2BmVldR2lyviAgtSKNQ2PxxPNjSLJDApcDBfkQq44flHwMWJ11ceIlCFHpKwXDx8jbgkMqmPVNKRcOMaJOSaWUrUwXVe5R00w5BIuI/bal5UCjNRoLcghkZPAklDBc/hwYv90ZFwCOQlKEsTJ0xXL0Z1xywFN5tl2zYubGzarFQJorEGrGq/RttWJU8+BqilSShBzZpoXlKideCkl8dIBs439IQruP/l8dHESZSEagc4Klp4kIj7NnJaZJc10XctgG8pZEJE1obYEMIlsR3yeWbLEF0tWIG2h3Wo6sWalFLJPmK0EK6sdsTXItnA4PvFhf8IvC9f0bK8GdNaInCoxUw9EfeY8z7Vd7gfMskK5LxDPj4TtEadHlnhijoWUZ2YhiSUDmkEmdNMQw5FiLH07MDt3gQYlsAXRBBCJ5BUia3otEWh89hSv2chrroc12pyJOWNiRx+usKwoDZzlTMRgS8f5EYy+xSwbxPw5s+/JKV0ANoljmogEulSr68VFFu+rfVkqAoWQIyFHco6QMkZaetMRZMR2GtsLSrS0xtDZNY1qQShCzKQl0hiQulIblRR1sVGTl1MupFjnkrkkGi1pWkNJmZNzFCWw2laRM5kYAy7UWyIqEHWk2B//TVFLgReZOQa8C6y0wTQVMGWsIseED47oPLObeHP/xPf3TzwejlijQQnUIMkm8cvffcd39yNNc0culTfjva85O0rTdy12cighUcaQC5yXGS1UDcQLkRgTbWdoe0suVxg1IY0ilomcEh8OT5zizNresMxnbN+yhIB3mWWOvL+fGcdAd21gp8krg20VrzYC/d4jEdhnhSAzUwo4B6nURoZsC6sXhufphoc/3LN8d+bVKrNebTi7mYclkmSi3QjadcS7xPksWY6e8OuZ3WPPV/KWT25vWasOYq5djgvlU6aCDILSSIpUICJC1VBEgicFR8mJkmtnqTUNvW74w+N3DDc7coj4xRHnGY1gthMgWOal6rbOJ97e70n6BVorluBqnopSiCWxnAqHD47vvh85HBVLydidJbsDj3/IvP3diGFg9XyF6SUpBeZYMExMh4T4yKCzf8iPj3UE7GOoN2YSMktkBF/4U0hofp+YpzONkn+CrCUfSCGScqlFgqxk11qQVCErpaIOlpSYF0fOYJXGqopfv9ld0UrFMjq8j8SUCKXUi5io0oIL+QRRLsnpQl0AaAJQFAlK5/p9k4ASKSkR5xmZM9lXgiuiUqW6xiIv0Mmca85ZoQqwXYR5ntmZHUlSNR5CkHONBrmEkKCFRGtJCK4GruaCyBFZCjqWGkciFchEKp6QJ+bF47xDZ0WrJEaDMBKhGhK5/vlDRfRX/k/do1OAc4Ywe759/cjTOBNSNVCIJClLLc561dZIASV4eX3D3W5HY5v66ckKyxMXe3DJ5U9ft5AzJdaikgLWGBpr69c956r/alvSR3YSP7o4kc1QK7BYyF4QssRHX6s45Rmajta2uKwgC3xeSCmhOosQmSXNzEkhlEHqKpAqTUSHWlVnI9CrFtMWpqczIWa8cIzCE4SmGRqaztDYtsLAgsdPkawzSkmGVUERGQSoaHGPN5juFXH7Lb59iy8NSQhCWgglUnKdpedmoTEJLTWr3GJXglJ6QgiEyXH2BVIhJU0SFpqCzIoOzTR75NxjNwPa1Eo9ZSjOIt2KwsA8Lbhi0deGgY44Dej0Kcy3xLBiTIGQw0WvlQm52ssEgpzA+4hbPKuhrxZivzDFhUhCItBFYGzD9dUtZ3+CXOeofWNo7ZZOrzGlRSZBDDV0qijNpQMJyD9VvkKAFAZFxidPToWm0Wz7HiU0++LxWiK1oSCq8K0EFrEQjCMphzeRKH/8ELbrYcuTO2KNwWhDqzta06AvabdhqZ2NFCKn05E/vv6eD8eZKCCKyNGPfDjuWbLj3cOBKUpOU8ItmX5oOE8z7x/3AHTdwNU6su67qnMqgpQKbWvIOeN8xPlIRJAECBq26wHRi0oLTgtzWJgPJ0YcvdYczgvLUshZ8eEw8fZ+ZpojbdHoTUPqFbaB2y7DvhAWaK4i9zmz+ED0miQqIKptNaaNXP/0js/sC+6aFX928xW36x3T6okPj/cs8YRuR6J84HQ4k/aF9J3n9PsTz8WOT/7qZ6xUh4gVmhaSQEpdyaGpbr4FBap2pESMiBghLOTgqxODeghqrem0QcaIkpLj077qtcaJ5TxzPp2RUrFME9McOE2O7x/uEZ3k+e01TdewFprZnUkxcz4nvn87Y/59giw4PHk2dy27TU86gXMB2SvkaqDZdpTYsnaSZy8vLrn841dh+RhYfAV/CQqp1G5DAgIXpwyFdI4sy4yVCmvUJQzvUohw0Yb8QIEVVPHG5f/HUhidJ4SAlroGOZKRFDbbDZ88e8H+/pHj4yPjOONiIgpBEjV9twhZxf8I8qWrLAqXMEGJEoUidD3spUAkQSqREhKlLLRCsN3tOLqFWGr4nZSCnCIFhRAaSq7creWMNgbRGhbvMUqiZL3klZRAJERKmCSwTUPOAUFLDAEfHbpIjGjQTYNQMKUJnxYSgXgpyAUGn2a0Slgh2QxrkoL9eeQYXE0TCdUdtCyOFAqzS7h54eHpzLLUlHEp6jRCBE32C7cvdtzsbijB8eruOZthhZDV4RlTIocabbJME0oqJrfUbonzSKMYp4lV1zF0PVqrCrTLGa01KedLVMx/+vl4zYlURJmIZaLkQpMLAo1WDaZxqNailK0wsAwhLvV2n1qKb5jGgguF1gqEyhRbMB0s48TTybFbbbmVK9pWERXMMZNFot+BvTM0smHdWHQngEQpkukcWdKJ1W3m9oVhPSga36NOO0QamPeRUT0nby1FbVFmoYgzRmb6rsH0hrBEpEw0zYI4SybOCAQyXzEvR5bg4GTR4wZrO/SzSFB1k8pzz5W+Q7YSX8Z6oFMwJuG0JwXJ2+8PHKOkbwUyanbqG/T8Cc4plpiYnSOmXMMHtaQrCllkXQylEFyo7puc8TEyuRkXXaUgasOSEj5lmqZDLTPLfiS2mkY3tO2KVm3QXtWWJxGpBFJe4ERKk4WgdljrPFeKH+aVEFwieiALuqZnlSLnlCiljuayyHhTiDKTRMTJyDlGnP/xa05uhxtmN1FkZlo8i824kCnnuYaKSYnVGqs1t1Jwt39iCY5kDNubnhc/ueH6ZltTO6VGSMv+MLMskdjB/eHM71+/xxjL9W7Lal1oGltnyFojGpBKc5pH9qcTznsW72gaxW47cL3dICScQ2Qpkdkngjsy9Ctc0jz88cRquGHdrziejwQHazPQLBL1WrGIwOwCD1Nms2/ppWDIkXdzIPqISBqRJSlrlLTc9gM/u/uGLz7/S7569gt2mxs0GXwgLjOLO3AcX/PHp7/Bv/sly9vvib8a2R01X/7iM7bdClWghEhN9kjIkCgiIoug5IrRQsiaPpszxS0QXcXUC1HHAdS1rIDddkNWkml/IHhPCLEGpeWMthXQ5kIiBolA8ds/vOXhsCCEZFkC+2muB27I/PGPiWPwTHPmfB8YhomffaO52rQ0wjCfHUk8kouk769QNmLWmp01dZz6I3989MzBE1JESoEsCZlFRcfny8UtZkKSKB/QQmJ0BelVimv9eSrVtcLDEEAWNSZBCOYYmZ2/aK0KhUwIHuccXT9wfXvL0HV0jebpYV+DZkPCl0Ks0XsUccm+KlXAWvUntRMii0Ch/kTHllKSiiblRMkZqyTrxtK1Bjt02LuWxkqk/GGMXQghEXNEKMVmuwElmZepBsxaSU6RlCJSZKSPqLmgOw9GE1JicTNpcTSyp912JJFIolqpT34hplQLKVnJtKk4WgrPug0v11eURvPHAtPxjI/VvZNKJPjAMjlO+4VldvjFIRLVzEKmBEi+kFzCKsXQGM5uomksjTWgFG4cccFX2m6A43iia7s6ztKa0+mMkNURZXXtrLuQiTnWblHwFQD6kcTkjy5O3scTXZEUHEUqBApVNA0dQhbA4nMGJFpoVLumyIBqO7LUUI4ImVENoAttsRRhoFhGv2cJmZIk6/6K1c0z1PxImN/g8siwabm6WbFqGsQcCEtCWk0SCyEtFEzNHdEa4TWgSDlwmo7sp5nwvWJ9c42+O9F0ktYW1kJji8L7jBMzoj+RO00gYdoGodrajhRH+pVEu0Teg2LAfj5zkI/c9tc8G16RhwUt1yz+CZ8PnKXDlXuyNJRXml4YBvGcbfgGM98iabCNotMRKyRzCCQBUgmMHFBCkUohLgGRa2foPE34VMOnetthjCKVzH5+ZIqRm0ZDSsQl4ttCf1dv8TIXREi1clUCkTMpeoSsgLeQMuQqTMs5oWShNbIWkcUwzzMfHg9kqSlaIjNM0ZGAIhMRj8sR5zLLmJhdovDjh7BNeaFJmqcPRx6XPe+GJ15sr7hZb9itt1zv1jTWXCiXK/6xbdhuvyM0krtP7thuNxUN/bSwFh1OFFTOuMXxx9cTf3j7jvf7M6thQDX1tj90DSFE1GDIQnKeHb/6/Xc87A8oKTkvC0Zr7m56fIp0W4MzM8EEFu94ej/yaE9MY0LsJSo2hBPY0vDpzQsGY8ghMp3PiN9k+rDiNJ2RqWDahbOfOU+RaZGUrPC+sLiCGlb8N3/+F/y3q/+Klze/oFntQDWgJaWvAtZVjNz4b/j09s/4svkl/9f/8H/m94czP//8M17ePcdIASmSCghlQGjyJUNEmhrtLmMkn44krauOwTvIqYofZc12Kgj8suCWESMFPhcaqfApY4wht4X9aeLN2w+8ezoScuH59ZpPrp/xu9dvOZ8ch8Uz+YBWmmHTEFJ1H5z2Eu8KboZljPw6HFg3I1+83PHy+Q1lhnd/POOWE8fHGRdGtn2H/C/ASrzEyBQc6RL+mX4YnacagJmRqFKQItUiQMoaNaA0SinUBWkvKH/XOaFArsWNE3Ba3KUwkSiRIQaiSAw5oZVGiELfNejnzxk2W06HM6dTBan5WOMNYr6MbaiFSRXv18tURCCVrs4iUbV5iUwqGeoQG7c4ur7l1W5HvqoFllAQU6yORgSNsTTKsN1ckaijcu89soBS1aWncsRa6Iwka8HJLySXwWd6taUZViwknD9jJHTWsOpWHMax2rBlzcyRQtG1gld3WwYaEooWBSHgponia97RNM4c9xPHwwwxV0syglZbhLREX7uUTa9ptabXhm57hdKSrKiwOKPQSeFj5dgorWgbS0GiraW3DeniYkUI9ucTMSW6tkWpiztKqY9Orf/o4qQrnis2CG1RJMTi8fNMygUrt7SuJcUZ7zzaZKzdMKnA5CK2kZRuhRIRMViMzUijMEtDWvU0i+bh/gPnp4x4tkaWBkOGec90OMMcsCqzei5o1hHRuDpTPHwgOkH2K+L5imnWcBIov4C2JCQmQjlBnBSr8AnNVURvHonlnimf8VGge0OMUHSm02vwkXYFzijc/bo6Igbw5zPTm4XhWlE2PZ/efsJd+ylj/IBoElpL9nNBFWj1QLErStoizmvWp1fY5ZpmY0AGln0gL4JGWaRQl4RfiTUWYywxBcY0odYD+InTea4I4MbQKI3KkHwVAiITWguuNht81FjT0HYDVui6CBMoBFwgalYrQF3Gchkh1EVIFZAxYqRms10zrFY8PR3wMXGeFpxSnErEi4QRmjTPxDKSVUQEgZ0ENjagf/xt7NVVSz40vHl6YAyJ+yfH+bigPpHsVps/ZYBIKQkh0TQNP/3ma5yOCKHIh+q+6lB89eoTNs0eGQLL+ZH3T0fuj2MNxrSWw/HEvMysVx1932K0IQjP77/7jnePD7x89gyjFKt5orGavlMX11RhDjOnecT5yK7fscTIs5/c8errW8K9p29uEbeb2g0pAUrh8HTg/Yd77v/NE6vPtrRXK76d3/LwqxNnUVC2wg7nyROPiY2/4a/ab3iun9PZK4Qe6gE1R4oWiFXNySqjoJGSz77o+D/+H674nzb/F/L4xNAZGqOqsl/UbqwyFrQhKw3UgyKESA4BWTKSTI7hwnHIZFHdIFnAMk+Mxz3j6YB3nlJKtZ4iaPoBG+Dw+p73+xMoxfXVmq8//5x5iSw58e27M2rd8PyzG7TKLHFEWsHT48J0CHhfnR4hKcY5kzCYZsC0kuXwxOvv3vHu9Znb6xU25Tp++pE/LlWdRx2RcBnDpCqYL4KUK3dEAkrUwFIhBaEUdEwo8QOKpP4EpVxUGaV2eafgCblgtKYgEDmRE1gpK7ukZEqsyPu2sTR9x/pqyxIi43nifDhxOp6Y56WyPoSqv85Fj5JLQUuFkBdnEBWtLykYBYhCSKFyo4Ln9fvXvHz+CqU1MRdizhdKdqkhqdogW4EvkdY2iBgwUuLDzLI4VEqoRtIMhrMfOR0jKkk+ffkZUje8e3rDGCZSiihRGI2+jEgSzi8YWSGj3gViU1CtIS3wcP/Eu/cPnE4ji/dElxjnBe888+SIMVFilQdrpYgZSgBTDNfbNXfbNZ+9eM5uvaVvDeu7G24+/wRrDOE0cX7YczwcmS4MGpRmPM/oGCkxsl0PaGMYl4WuaUBKpsVxfNpzs10TL9qij3k+ujj5s/YlhoZxmSgp1LlRMZA0TBpPJDLh0sK7UyIfbnEWUIKXz3dos0JmByiUFhSdyDFyOp84nWbIPXne4Pcr8rjgjo7iCjJZ4iw43id0SuxWAicCeiu4+3LFw3cLJVnicQNFMR9nUgnIvqqJt62ia1QV1wWBPSqEu4LhM2zv8OZEEI+c/UhWM5pMnxrKnDHF0nDH/KZl3Cek8ZibQlAj2x6IZ47LL5k8zPMbJjxWX6P1NUJe04Q71P0O7jt0WWF7y/bZqgpHw55lWRBS09mmpl9eQjeFKShd6JLElB6sRklNShmhVBUllTqTXakBrWvuzWbTMwVFIjMdZsop09PQiKoRydRNUpnaspyjYwke27TYziKLJbuZnBJuCRgr0baliIxKgvGw51gmmmFgsLX4m7wj68BVt6VrV8xj4PxfwGZ82z8nNR5bNCdXgV63Vzs+efGMzbqr/ICSsbZmEQkJSIWfAnE6o0V1ilAy665l/eIFuiQaa7i9vcL+4Tve3T+xshnvzqyHjufPrxlWHcF7Hh8PyJz486+/YJ49rz888vx6y1c/ecV61aJ7xTke2D884M+JXBSfvvyMXracliNdVOz6HZvVjmb9ioeHI4/vvkPhGKxg1WrOo2D823vOfKBsFdcvNzy/FaRWErpMurL0Nw3/q6u/4Mubn7C6+gS6LaLtQQg0mRyh5Fp8iyIopxOKyPb5S/43//v/HX/z//y/I/xEDgbTDihjUdoitUXohqwUscIqyDmSoyPOCyXX7JOaVhvJZFA1N2V2C8enPfunJ74/f8d6s2a96gn5csNfJowWfPJsS9s1lBT4V//mVzycRvpVw3bdItcKu8rIJtKkjJWKRnXIlFBoTqNjnheKVjwcFu5c4mbVkrLgeFyYlsA0RXI8k/yP/32YXD3069C9um1kgSKq1kOIfGGLyJpQewn7i7Le4CWX6uTiq0mX4iTBxQFUuxkpZ6SqLrBSwGSQGeZpwseI1gZ1sQqbrmW9sVxf7ziPE09PR86HE35e6qEeI7GUat2t/p1aqACpqIv+pYAspBxQytJ1K2KaSDmQUgEuIMuUKCmRU6FEMOuruqeHyhKJ0RF8IeQ6hlcJJpeIYWFxIOm5u33O07LnaTrVZHupyKEw+4XT2dfxUoESI1JaQo5oU0iNJTnLdJz47v07vnv/jvvjqfJOQqrjreliN87Vqlx9nwKZDTppWqW42214cXfHJ19+CUViFNz89Ge0ux3x8ZHTYeS0PxFCrHwrUdkzfddWh5QWhBxxLuFjREhJihnvA5KacTR0A237cVlTH12cNMnglzNxmmh1gykDQkoWf+R4PqFLpt15hIgspyfefjjgU2H34hPOtzs6JDbUFpmXiSWccLPATC1XqsDQE0+R/Xf72gbNkfWqR2lBVgWjLR/e7fnulwtJw83XVzz77JaXXyTKucX4TJ4yPi+ckkfHAcM1vbUMQpNi4LQspNSwUgM2d6RFI+KC4YnN1UvEbsLLB3pl2P+uUE7X+MeGdCy0ttBdR/Tzhfau0Gwdx+WBAxNeGhrzGZLqtDBph3m6Id+vkGNHo3oarRGl4CdPjIE0l5rCqRTWGKSSNdBKepptADVzKiC8pNc9fdvWLIlccxBEKvjsySmy7lt2TU8rLVkZFu9g9LV92WhKqylSkPMFH+wcbSdoeosOMwWHKOoyb7WQA+dpQSyBIjUZgScSimPXWDbtQKOrTdrIniXNyFjIUiB0Icfp43e1f6DP//d/+ms+u3rGP/+rf8S8LFhtuNtdsRk6vL+4rxbHMAyYxtbOnC84D227QuVIigGlNdZ2yJwo0dG0Fm0lf/nzr/n6S8c0Tbx/OLDZXrFqWw6Pe3IINI3l66++4DTN/Po3f2DyiX/0i2/YrlY0jaaUjAyCQXaIzvJ6emA/Hlj3Hd1Rseo6Nqs16/WWZrPBdDc4n/j2l/8SI2vsvJU1niLGSPjgCPeZSCAbyBY2z3f8+T/9p/zXX//XXG1eQrdB2Bb0ZVspEtFKUBqxzMTTmXzcY8IZNQx0N7d89vOf892/+B+IuaCRGGXr4aIUSAnSAtQC5AJOy4sjBEeJgRQ9OYVK1yyRUAqTm9mfzpzGkdVmjWkbbNejcmH//omSMj959bxu8kryNM78hz+85d3hxDAZ2kEhZWQ6Z660ojOZZXIsIxDg5nrFi5cd277jzR8PfPf6A+dl4fmrLT4sHMaZeQ68jUf61pD/S+Cc5EvH4/LvkYoWEZRLp6tqSaqBu3ZQKDWvTV0AafUpiFJtxwlq8ZBrAaOUvHQ2wMWIKQKMJqfIYf/EabzGrDcoKVFCYEQtHEQp9I2l+eQF4ovPCMvC+eHA8XRinGZiTORLcRFLJXGHSzckl0DJkZAE66sNStU/q0ejjKkGCApKFISoVmdtOnY3t7S2ARHJYSakRCDjU6oOopJJLoIwrOyGbn3FwR2YCWAEIkeUEgy6oW8N+MrJciUzTkuFYRaJyA43Ff7mb37Fu7fv2C8zY64U8ZQgJ8E0RcgSpSwUjRQJpQxds+LLlz/l1c0r1l3Hp8+f0w8DQiluX31C2w0sxyOP3/+Kp/v3LMcjq7Zjs92SS2FeFmJM+MVxc3vD4+GRtx8+/OnPV4SoBXwKvLzbMfQ9WSruD08ftaY+ujh5+90bxtHRt2tW2xZjBTE7UvS4aUKsLKY3uMcRksMYgRwGEgfev4W+BStGjFno0HRWQJLY0LARguPhQBCKsWSMrhkLuhgaETgvEww9tmsZn0YEkmUKHO8Lu6s17abBlgbZizrqDg6jB1bKojxkZVBKsdIGbTRN2yKVISfIsyT7DXncUN4nhq2n7xZ0LEixJl0ZXBcZ4xOTf4c6OPr2BmO2rPqWDshJkwt08wY5Scy5Rx5b8ihBFsSFSxJ95vwhUFIhLRlRgJxIoZCLIgEuBOKUaNeZ9joQ9zN+ahBJMYeFIDJGW6xQdElx1Qx0usMISWChqMzKdlytOkSCkMCnTE6F2TtCDpi2RWRFq1paG8jV51Ftm1KRhSCUUMVouZ4R1mjWQ8/QtLS6RSFIyiD6DTY3pCAuNwPw+ce/Gf/81efcbHe0SvLm7X0d36TC/dOR0zgiEGyHFUJqsvdMPtLvBkRKFeIlRLWqSlEx0H2LKJqYEk3X07SF1i1k57nqe4a2odeavm2wmw22tZi2pd8f+ItfOI7HM9fbNW3ToBuDTx5RNK3sEDowJIPfj3h35tbu2HUb1usNbb9CG4npWtIXXzJNE7/5d/+SaTqSlSCWhCuRFFMtdrMkzRnbWL748mv++S/+W15e3aKaFULbOtL74ZASEqxFSImKhmItDljmMyaeMVcv2L38lPvNBvfwUNX8tkXZ9gKpqo9A1PckOkKYcfNECoHg6j9TrA4H52d8CozTxOM0IzYbXrx8WbkPBcbxyIfHe9497Om7jtvdhv39idf788WBUZh9wgwFpRQlZb64fcWnd2t+/dvv+DdvHjkdM8NNRgqH1pa+MTwcFpb3B4pJDCvFatMgomZeMsezo7U/fg0WF6HpfywnKNQCJeXatwVBKoWUqXRqUh2bXOypP3zB/6RJkIIIpBRppan6OVkhlkoIlLYoowjR8/j0wDi/ZN33NFKSLr8DEWsKMkLQ9kPVQ3Qdu6sdMRfO40jwnmWaCc5VAXSK+BBxy4KbJ2KQrDZb+vUaRCYmSzaZYViRtSGUWnjE2iakaxuC/GF9eowoFKWIRRPCgkx1tCKKYb2743q74/G8r8OkHDFSVrptWBAojFTYRqEbwzEGfElIk8k+41zhzeGMyifmPHEOyyUdnqoJm+tefHu3Zeg75jng5kh0mc5s6LoNV7fPefHsGc+e3XK12zLu90z7J+bzWLuTKbC9WrMaOqILnHyglIxzFbUfReHf//pXWGv45NVLBOC9Z3GeNHQYJVl3DdZoQo4Y8f9nt87r14+8/TCxXWXklyvublfYBlZ9pUa2g2boArlbMDcWvV24XzyPj68Jywc264b11mHaEe0UK7lFvGm5ygNx8rjzjLnKmJyRSWBMg9ENM5l5PJMmj9oatncrlJA07UA4FR6PC926sNkput5gRcN60rT0EArORaxqUFqhZEI3hiRhPM8cnkYe74+MR8c8z0zLQtcKNr2lkYqhd2x3A8NGoxpHjmeiP/H4OpGfPkX2t9i2p0RNiaBDjwgakTRkSbF1Dq6EpOSKFBapZmRKKdG6pnmGWEdcLibO84w8Zp592nJ1p/HLjHOOkCDiyVKBrtY6vKAzAxrN8enEzEI/tBitoIALkePZMwdIUuCCx8WFfpU5jxEdBUYXusFQ1KWhKTUpgUsXcFKWaCnoug4tNFKApmZVCKGxuseIhqgTSiTImuFjKTv/gB9bwHaKx+XEr95/T/aCVddjtaK1hrurLUJrXBEXcJGg6VqUlpQ809mGxtrL5whaC8BgbIttDfP5hKfQtQ19P9D1A21b06aVVEhZhYR92/DNTz4nhIBtWlRjkY1GpISMCpMbdLZ8vrWUObEza262W/p+Rdf2GKtRWqBl5nrd8s3XX5HczN/+8t/z/Yc/Xlq1tX2bLlH1mcLz21f87OtvuF2vq6hO2VrFwuVUquh7IWsHpAiJlAYlLcHX+bS5axDNwPrFC84f3qHcgvIObECrjFCCUjIhOMJ8xo9nlvHIMp6r6HWe8c4RQiREx+IXZj8zOcc5JT774idIpZimEUFBa8WLu2sA9seR9/cHQik8HE/MLqIAq/Ulzyix3zt+85s9eZTkpWfcP3CaPXE8oDuJNR26lQhZM1WCDwRfM6caq+lbQd+sce7HL4j9QcT6Q/5vBUBeZK2i8kZKgVj+o+5KofKVcroEif5daVMhYqJSZwU1MyxDEzONUFUfIqol2EXPOJ45n0/E3Y4QAkplUoqUVGmzTdvTCEmTUxXBGk0W0DVbMhBjIiyOxTvCJfH4dDgync+Yvuf5F1/QrnoKmTSdKXnhOJx5EiMxi8v7aNClMHQtyIRPjhgWSkgsMeNcJPiIxbDpNwzDmmQEr+cHYqlAzJJSPRNSLdyQCSkEThbO0TN5h08OpQoxOLyLtELj3IRUFm0hpEDJEqMEagj0g+L6ZoVfEjpIms2WcIzcv93z4dv/kX/7r/4dm+0VN89u+fnPfsrnn33G5mpDYzUyCZQcmA41VuDwdGCZFqy1KCGI0hNypGks2/WKVlWLeLPbIqTCOYdzVS7RNg22Vex2w0etqY8uTh6fjnz/5siHNuKV4NmyReuL9Uh2dMJiy0xnevp1gxYHxrd7REjs2hW3m5ZhW0h6YZ4dH94+Id6uidKSU6gK4ksLbugGhtUKrQtdI1GyMMXEsmT6zboCXtqe5BQyKoRXHB8cJ7Fg0PSipVEGH6opUUhFLoVx9BzfHtjvDxyejpwOZ6ZxIYe/e0nOsvAkBVJUIuaw0Xzydc/2c2ivPcIu5PPCh99KhuYnNH1LEZZV12CNRRRRxXsFYsmoi2Ut5XQJnqrCEqkUSEWIF2hQTvjgmY57pJaMK4M1LcllfDgRSSQVSbIQRBVruVIzdpQwCBaIEpU1Eo2LMC6J/egZl3BBSwdiDDWnpO/IrrqFhBgorUGpeGGtRFyun4ss9feZo8QoU9uk1MBPIaCIAjLXGw01yC6XH7+VeNhtSc9bfvNvf8P3T0/4KWOkZmgaXtzecHV1xegdiMR6tUVJQ992pFQQtmCbhuYyq00xkBNIrZFaEb0npQSlhgZqY7DGVhjTRf1fYkJcmA9KGRT6oiiU5Au8zBfP0Z0xqaEvA62VbNoV1hi6vr84HCrsT5RIS+a6N3z1yStYqh7pN69/x1ISRVVqZSmFrtM8f3bLF5+8opUSYwxC1ZEmBUqqXcEixeXqnBCxIIqiFIlPBdsOZN2CbGhWV8RcWMYRaQZQHUlUoTgy4ecz8/6R+XxkOu1rYu0815utCzgfcNHhg8elwJwT+mqDbhti8JQciaHOwDfDgJaaxuz5w+v3fHd/4PE8kQsYrZCyoDDM58R5ivzbh3u+/fZEZxQ+ZIaNwnSFTrVs+i2j3bPZtEzzTIyBGDIlFkoq9F3Ppy+veHg4/T2v1v/8j7jYuP/uWlKBZPVbda9IuQpd/9QkKdWZ80NZ84PFVIhL/g0/uFsqgTrWxQWAKgKVBUsCk2D2jsP+wPLcYztdf/UQCN5XxIXWKCUgR4gVEqekREpFLAVVMlIBWiJiJuaEkYLdzTWfffUVm7tbdFu7gGWaKGHkTX5Xpy3LVLNsSiWn9n3ln7ilEFwixIALkeAS63bDzfqWxrYsxeOoazeGSKstVjbM04yP/tJxy8QQL3lCnpzLBbCmyB7UhQWUisIvkfEcWJaAEJWf0ncd/WDJCebR4RcwcaHMhTA6TqeZWU8ICldXax4/PJBdRGjNNE3kEnn+/I5PXj1ntVkx3Gxw4xk/zrizqxckaVDrFe0wYMzlrHWe87xwPJ44jydyCjWXR8PsFv67j0jp/ujiZBhWaDMhrGTE83Z8ws8zYo7crVdsblrySSO8QUpFpxR9LrQL3G5anl0bRBcZQ0MJhdOTJ+9HhBLsNi1d39JqTWctrW0qI8JKWrPGKHg6TYgc0XbA5YzQHRqJNpJGCnx01WdfIkoKGt0SQyYGxZvXe86HI9PpjHcecsCQuGs00q7qTVRQUcm5EGKscBqtUUaQZxBOsWrXiFViiiPvH8/MPKHbSNdv6e860OrS7gqMiyelwno9oLRG5Ipx5odX9kJNzKWq1o3UaCURrPAxsP9wwIo603STI6t0EbUmAoEkC8lIGmWhKKRpWLcN66bFqPrjsiwom2mlJpeMWwoqg0wJSkRaQYmFZYm11arqGCpe/lJSoXIFFeVUCbU5xxo//idZPpAzQhVkmytY6CPbdv+QH/HpltN14f10ZPKeGCuQLpXMMC84Hxk2PXrTYxuLDgkZ63jN2AakIl4IvXVjL0hxAZB5j0BhbVMzSS7ivxgTQtbC1lwKi1IK6XKwJjJB1CySxXumcWEeA5KGVdfTCkXf9tiL1a9Qg9ByTpToST4gQ2TbKF7d7gjha0a/8O+//wMzdcPTUvD8xTWff/qKm+sdWoAyFqF0rVjThWpFFT/Wa2mGmP8Eu6IAuqUIRRYaZVdkFOfDiYwhojAJlI81kXYaGfdPnPePnE97pnFkcZ5l8ZdwyoTPiZAjnoxaDew++YTFB4pwlQkhMimVatvPmd1mRcqJwxx4e5oQErRRrIaeRlmOoyO5S8quz8TO0HcGNWRkK7nbPGO3uWW7umOze+Rvf/M7cplY9YabqzUqSKb5iWHjEfLjBID/oB+lL4VJrpqRUi7LoCLijRCUmCsZ9XKBE8hLJtXfFTIg6shGShAFecl3yj9sN1kQSmYudXSRBYgksd7z9PTEaRrp2hYRK1cj50LbN5jGIC6i6pIu5FVtKCkhUqli1uDraGdZmMcZKSW32y3btqVN1ZAhhKBYi5CZbex5FGem7IhJgrJ0TUPbdfjkSCHhfaWlyijYdlvWqyt0Y0CDERoXArlmfzAtM522WG2Q5GqvpxDjQvQ1xVleaLlhSVhRibDTODNPAeciKUm6vqPrNMZqShEEVzhPE8fjiMyGddOxXg+YO829OjLNC3GZKd5zc3XF7voW3TRM08S79+/49a9/x+9+93tutgNXq5beGvrWsr5do6ttDSEUp2lmdoH3jw+czicOhyPeVUTFvEx1THdhdf13H7GkPr44WXUMfUO7arlet+gG9lOoaYj02Kwxk8DlhWRmYp6QztGVhk3XsNkonHScZ0OeM3l2lBCxbeH6yiKzpm0VXWexugqnlFQYc1ECC4uIkYDG50BMVWxnikIlQSNaMpLoI8sS8cuZ48mxjJkwOZI7I3OgMZJNb1l3A53VGK3QWiEkFFFvh85XRbXQGqkUwkRsjKijQxSw0bDbXdHoGxAaJQUpBtxSC47FB4IPSClrp1uAUj+8HBWz/cPNQemqO5AXm11jDYufOU1nsq/z2uASSWayFERzCR0UFnEpXjyg2oaha+ilrWwACkIlTJPRTYWrLUoSVECqDCmRZEVFhxgoGRCFWCJeBIpKDLpDZllZKFJRRN14pMyUlJEIlBRkNEIV6AudUNj044dOya9umNIDnljBQ5fDPqZCSAWlLVdX1wy3G3JcEGGhzA6Zq8sqpBq/IIyiaRTNAMHNzKdYdT7K0tiuJo6mhCgXcqasuqBK9a03p2leSNYS8BRryc7jTxN5hD4NXDUrBt3SmAbbdJimoVT9fC2Qgyc6T/KeFAIie3pbuNt2fHp7w28/fM/TPOFLYdO3vHjxjE9evqRruwrQEgqosRP8QH9UBkQdL5Iu/z1mRKoFrFQGLm4MlCUVxX5/YnGRwSfssCBNRy4Zv8yMxwOn/RPn6cw4L8yLx8dESJFQMoEqyG42A8+//IKr21sW5wgxYEwDomYVTVNNyV2tem6utqyGQwUTljqQ6NqWVWNYFkuKGSfSxXIqWPc9q62iGQZe3LzESEMic7dbkX5yhcuS1VZzs2volOLhqbDaKq6vdn8/i/R/wadrbdWN5Fwvepc0WmMMN1dbnt/d8ofX7/jj2zeEUNlKQojL+oF6acsXCJu8aFgAJZG6drYpgpJqZy5eOraJSEognKA97Lm7v2fV90QKJUbadsBYjRCijnkue++fBlClzplETihKxeo3lk5rbNuyu94yGIHKgTRWvpPWBpEjvZC0UmAUBCUqmbtt0BKCz/jZ4efAYDu2676mMitJLg6BpkhNKnXslFOEnC5nRgW+KSVJucY0NEIghSbrchGDg9CScCG/SmlZrTuklmgD1gBZEEOpNu+QSVHQC80Xty+47a9wt5H9OHE4nXHOse4aSvS8+/CWm9vnrLcbmrZhtVpxeLwnTI7D4jhK6FvD9W6LQpJ85ml/IJTEMAyE7FjcDBRWQ4s1hpTWSKU4nI/o2XzUmvro4iTJifVKs920PF/1GAttcARjuN70rEyDFoKoPZMeAY8QnrbtsUZdWmqFshSU06x0i1pZrjYtQ2dQwjKsWtreoLVAiEgukiI02rS0ncE5zzzPFJ8pjSA3mtln8pzQSZK9Yj46jscFd5pZ5kSYFjpRGJrEdl3HL+uhrUFtunrHlVYIBegKhvMRktDotkcbDSRymSnhRDmcEeHI3XZN119jmo6cKrKfXKPDZS7oUlBSIEUtWIRSCCVJEcIPFbCoycKlqIvHX6Jlg7YWoat9OMRIyboyAzIEIlkFlJKoAHMEpQ3dqsF2LRpDdLHm9SAootJejVLI3OCKpIhEJtaNu7GoIhClJoq6EFiyo+iI7RQkgQ91PhxToTEKq37YUOqGIakCNKkinbWU8OPnnJS7Nep0xgwNQiu0qrEB8pJhNIWMR9DHQi/tn6zziCr2lhcyptGGfm24+VTgw8zhfcbPhbB4lG4gXDZPIf6UpqouHbqSa9jWw+Oetw9PJFtoGCBlxMljQ8tWG9amRRaFNi3GXm7xFwR+To4cqnMgh0BOnpQWCgtWe7ad4HpoebeMLDnzcrPik1evuL26QYmqpxK5dkxKyiCqm6ye9pfNv8IqLgnDkRIzUllKyuRYQ9xcSDw+HTGHE9M0Y7sjSre1OAlV5DqOI5N3jItn9oFQasp3EoBVDLsNzz57yd2nLy9jhHgJpazvWskFrSTqh3creUJOWKOIPl5GaYGhtyjRYa3k6eyYLoF0Shmuhy1Iw3z2uOMDi5vZ3a159nzNaUnMfuLd+z1WKWIs7DYN18+e/X0u1f9Fnn/2l39Ws1xiRCuFvOwRFQp4zWeffsJvv3vN//Cv/zXff/8O76rAWhRxsR9XF08dw9elkwUoLZHNZb/JghwKRV7iPYQgAIWECB5xPtJ9/x2NtWz7hrbtaJSsou4wIy9ViVZVtwcFbRRKKEzJ1IzAul6lEFhrabsWbQRCCXwWhJgutuhCoxQDli5rkkmQJY0SFL8Qx5nz/ozRLatmTd/3ZHkBy5Fr6jAChaw4+ORplCKlgAsOEsgsySkgYkAX0ELiSryMDyU5LDgf0NpguyqaFbJQSqKU6hjMucaSNE1D8ZLP+xu+efaKTTcghKzU3cXhnWcJiWU68d39E99/95btzY6758+5e/aM5zfXCL9weHri/ft3PD48MZ5mbNsx+8S3f/g9203H55+95Pp6zdV2wGhDY5q6R6QCQvL24T3n+fxRa+qjixNnntjsBnadZWMs2gjs5oqyyqy7gaZp0QY6PRDsGal79OoAU8CFiWnUxBxRS2aImna4ol8NlZBXBLurK9brNbZta/pkSiTviBTU5WYmiyCNCyKDRuGKYn88w4cF6yV5yYz7iXCOyNLRtT19Exls4qo37LYtXdvS2QZrTN2olEJqRSZXnYiU6GGg77b0my1N11XlfnSE+Yg/PzG7BxwB6QJts6LZ7FBKk0Im+sisRmYxEnMke0eSFbudROGQErFkekBREJfQpIxAKInWFi0aGgopZAoKIQ35IkLNqRBcJsqAdBG8xzQNnWhqy1wKsijEkgklEkvkh4RNrQ1RV6ucVYoSoO86bFGQCiJASBKZJORamKRcCCmRlnzBLnfYtkVKRcqxtu2lpGRJSbWDkPSPP1snykJrG1prUVKjjaQzTaWa5szDeMY+3LN/euRnn39K27RE7yiXWbySEq00Shu01XRb2GwVw11HnAqHD57gEucPAXnJ5ZCydtmEFJRL8qgxNbvi17//DnnVsIoLPYZNNKxlw6AadKm0VZAgNNF7pMo12TrES2FRtS8lOUJciKl2P0ueWbcaIyVKSl7d3fDq9jmd7SghIppCjpkSEkJfuiZS/Z0otvzHBUoixUjMAqEtOQTSNLIcnxjHmYf9CCnQnyea5lKciKrdmkNg8YElJUYXcDnXA01JTN+wvrni7pPn3L14htSSFAJGX5KLi6BIidY1tFNpjfOZx8OZkDN3Nyu+f1cTXQWBzz/fYJTkzfsjv/vuQHis/IqUMr3tOBxHfvv+DVoITFPYftYSEzweAu/enzmPjhKgsxaZItfrH3+x/t/8V/+ElCIpRhrbMvQd1tbjRStFt+rZ3l6xsGAVvH9/5DQtxFQpsJdJIFJcmEDUQ9VYhenr51eyIOua6ityqV/Xy7VuyYVH58hv3pBz5rMXz3j18iVtCkgHNiqMUlhjUVZXorhRNE1zCZj8Af52cQNdihgjBaoxiK7HtA3h0iElRVTpuImSOCsUp5r5gyDOC+eHMzjY7K5oh44oqsbFaItPGR8iIif0pdskSiLHzBwK0zKTU/6TDVuFCo3TRjMmhw+JGDNJZIy1tE1DlomYF5KP5PyDmQGiS6SYaLDc9mt+8fJzroc1jVEY09ClTOx6RCmcJsfjOHJlDX94/Zbvv/+WzfUN33z9U774/FN2t8+R/z/y/qvXtm0908OeFnsYecaVdjiRZJFUuVQluwTLgiXBVwZ85Z/h/+Yb23c2IFiGqkTDZImpeNLOK800Uk8t+qL1tXhkG6oloFjEOW7A2hvYe64x5xyj9d6/9n3v+7y2Zt8NPB1P8HRmvdU8nHoeng40lcKNns16zWq5oGpqpJTl/hAiOWb6sCSbf89unWYnC3Y9lUpTC42qinvDGoMLiTFl/MqSzAZRJ+TyjuHdnv3JoOslTZtptMILjbQLLrc3+KkHoVisrrDVkiTkLBKNRCLSBSpjsbLYZ9d1U6hzU2J4fWL/5p5wN2BHiYkFr77QlvVmRWUtIngaG7haNSxaS2UtRlvM3DURQpFEKU5O/YBpJav1lrpZ0yyXNIs1ul2WMLfzEW9XtHbD1J/ojo+c3z+QLhXry1vqVY3IgvVyzdCd2B8eOJ97gs9Uq5Y+BV6PZ5Q2KFNhQ5zTOAFdTqEzaBCfKCJaWVwG2ZUQrZwUeYqEPELMVKZCZoGcfOmIWFmqc1FEqlllkizZFlKrgiAGalNGVpWusEpDyEjhi6MoZ6QUWFERSKXrlRMQCcnjfHGKxFRYilpkhIhk6fFmYtC//wLA5Cc4Ddg+sTYN1mhkFviQSu7GuuE8nPGj57Dd0d7ssKp0yIL3xBhKyJeE/ux4enJsNyfa6yWb1Zbn08T7H3qmYSQPS4y0KFXaoSkGQvZooz6Crc7jhBgE/v0BW60KL0VS0rWlplzqmuALETIxIYQmTn4OVkuE6AhhxI0dp+7E037P4XSkL/NKtosFX95cs1kuS0T8NJHrptx4fEToUIoSMz+M03xUzeW0mGaKqEeRhSWOI/504Pz0wPHU89h7gptoxoQ1AWUG8gc7KeBCYsiBKSY8RRPWrluuXtxw+/IZzaJlcgFcad23TUtG4J0ro1WjCK4wS479yL7vaFeW2+WKYRg4nB0vnm/5L/43/5x2pfjbv/4N+s+/J3+l2Z9cGTvkxKI1uDzg8Gy3FfWryP27gYfhxGM34iZwXeYpOrR8QMnv/4Pvz//Qa+jOc+iiQQhRuherJZW1JJGZsmP0J168uuJi9c/5u7/5hr/76nuejj0hBkQqHVgxj3MEgMgoBU1ri4YuFYp3DkUbWLQj5e+RISE4Bc/X7+8QxtAsVzTWYkSD1Q26sti6wdgCpXSpWGoFsoTUUsZHRsgCApQGmhX5Yge7Lapd0FR1qaCCBzdg+ifaw3sW4h1PwxMuj0xjJDnNy2efo9YGhwcpS3M9ZqYgGH0mhwmBK3qvWISsSklG15Nnqnelit5s8KFcBNJilCmjm0pQqWI17kaPm0pIbAihUHBDsUdF5xEi8kfPXvF8c/nxklRKUykFzuG9wxrLbllycW63W17fP/D64Ynvv/oNwTtePH/Jar3i+uVnpZAfO4wCkTyruuLZ7oJXl1dYZQjnif404ElEUQ5RTV1Rb2sGO33Snvp0CBstAmgrS06ZEIuYzXvPdD7Su44xRzZfPqO63JJVpFpvkfbM+TRyubtgu1qRjOfdQ8c4JkKAmDXr5QVGNwXeEiFLgzCQ44jPJSQqKYsRFWttGAZP9+aB/btHctexiJKFMdSVRkuJqRrW2yV+7GlWFavGsl20aCXQataZGI1UmohkpGJKCpcDy2pFpVsa02ByeeAInzGmRm8adLVE6zsqs6IyNd3pgcPdO9wwcf3qx7TtCnRVZqkpIlNm3/egoU8DbuoxuiYrgxCKkFwR7gVVAhN1QAlBCpGcA1oGVo3CR+jHgE4ZnUtwFVqx3myolKSRijQFRhdK+zAFlAYtBSkFBh8RQWB1xcK21NogJ0/wkZAK1RChsdqiEGitsbZmmEYEoJXAGFMqYe+J3hXHjpZFoCuOOPues72n4/F/8s3td225/QHxmweexSW3n1/h3MTTcU9SgtuXtzy/vUSdJ64XG5qmQQqDqVpIriDXYyGcepeRo2TqQStLlPfsxyNXVzdcV4Kn1xL/aFGpQslyuXpHcZ8gUbpisynMncFnWmG5aGoWUpeb7AwzU3LmPWSBG/oyy0aWuPrgSJQ2sPcTQ3/m8emBh8OBt0977k5nupz4yeWOm80O4TOunxC2IYdcrtmUyT6Uu55Ufy98Lbnq5ByJyeP8VDQmIZDHI9P5gJ8cvU8cY8KFTJ8jOmak84UwKgVpDsKcUiQpgbCK7c0Frz5/yeZyC0JwPJ/x3hO8J5G5vCwdnDQLGbXSnP3I3eOe4zSBVfSPHbUR/OjlFe8fjvzpn37JT/7wR9x8sebzP7rh2Y++YvvffMtf/MVb3r97zw/v7/nJT2/544tL9JVj8cKyfiEJbeLaZeRWMPRwuBcc32Te3x8hfvWPulf/Q6wQAz5E4ug4nDsOpxOrdslqucC2kiEdeTzcY/SW/9l//Kf8i5/9R/zf/tWf8X//sz/nYX8ghrl1Mlt5PvREUkxYrQml8YbUhQskZuKoBKw0aGFQSbHQFbt2wXa1pK1Mga/VNcJoXIxMfYfoC64+zSpbKRUilUmkEZJNs0JtVtjNFfrVC+T1DhY1yhSdFDmTlQZlaJTFyBqbS4fyvr8na83LH13jlWeUpVNYugcB4kjwidE7ghvJ3jPFQqu1qiJME9mXTqNSFltVKFMOFtpalNG4POHCQM5FiC9ShiSKjiVFgk+4qcDRVCHK8fz2mj/+7CeIXAwfIUZCKnqxlAp7pqQOJ4yUXK+XbFdLPn/2jIfjmXc/fMfb779nc3nJ7bNnvPzsC1Sc6I9PBOf48fUf8sXLZ1zsdiShOXYDj+/v+Or197x5fI/Pgc12ycvnO66uPk2D9cnFyfBect20xOT45u2BSEUSmjGMjPlMtoHOD1zcC14+e0mI0J8tQmgeH46szSXresXQDXz7wzti1ESVeHZ5zfXFBbWxJU3UNNSrhpQm+vGIixNJKE5jwHWBNz888PD6ERk8m0ZhdIUxkrpqMFojpaJerLC15TzuaeqKRauxRqNUae0qbZDa4JOgSxa5fo5BcGkrnl1/xmp9gWlahLEIVXJnwjiRlULXG9Rtg3t8j1QabRqEthwP97z76hfcfvZTFqv13KJMNJUiJsOx7zCN4qZuUGhMLiFI1hh8CPgUyHGO9VagZluokJoqJZbWUOuquDb8RIgBU7dcrK8QMhNdJPtE9AXl7ZLHyUDSGZGKzkEIQW0sTdUgckaRmILn6XHP8XCmqipWy4Z6Fgq3taWuDC648pyJhdUixcyg0BlvBkZ7oNNv6PU7JrFndOP/5Jvb79rq/vZXLO40F6sL7HaBNxPbVFMtLFfLLfacWDRbCAIo9kklZSlylaAnE3woo0WhGY4T9++PXH+hyKKjHx4gLdDSEFHEkIkEEJlEIIlIQpOEZLe74k9+/GNev7njcnnBQipSEIQsUDITvANVHBTOTZyfnoh5pmImmHyPn2+Q0zQxjRMPhyfujie+vt/zZop4Y/ji2Q2LZsXoc0lgFqZ0QIQqTZIYS45aKA4FEWceT4pFMO4cU0yoakEYHaE7MU0DPif6mOhSYkoRLTIql7RYqSQ5ZaLIJURNZuqm4uXnL3j56kWZ5edE1w2cTiekVlTGkGKi6/si2lbltYJ3eAKLbUOtlhz6kfvHO3744Q1/+h+94It/csMf/Cc39NnRDbC9vOWf/rOa6Wx5803H2zf3fHv3xMVPF5gvM/lSMJjA+PCOaezQraO5BLECtRJsVob2UFH9/jvriXlOH44R7wOn45m34T1KS6rGUC8Mty+eUW231HXL0iz43/0X/2uCD/y3f/FveDwcS7EwO3xAkEUqaIIIqiphfxpJVRlqJVlUlt1iweV6x7Ze0aiabbsoKboIrLYIBKP3DN7P5U6pl4UQxDkEUIqyx0iJha5ZrK7QV88wL16hdluENhAEORX4mIjFfZajJ4eAFpZVe4GbAn3nGYY990/vMWvJFANTCISYyJK5YxpK2rwrGi9lTTkcZsG2uYTlFT4EbF1hrCQXEASQGcae0fdISoBrDokkNcfzgFCKujFYa5lMYMga6RIvVhv+s5//U1pTl6JPaqIoRPIw86xizvgYiClSVTXOTdS2Qrc1lZZc7Vb8cHfP999+xde//AW7yyv+8Oc/49XtS7ZNy/OrS2zT8vbxka+++4F3Dw+8ebzHy8zzz1/x+Zdf8nj3hj//q/+Opk78l//b/8O/c099cnFyeXmJpufb797x1V3PwWuwFdVK07RgpKfaChJHurcV3emRqra8ePkS93CktTtSV+OOR1brBfVmwWZbs1vVWJMRGpyP2CSQogKlccHx5vWR9z88sn84w5RZN5qdyazaOSxMCrQ2GGPQUqGNpd2sEarCnRTLZoHVGWXMXJwohFL4mBmyRa5uuXz5Jd3b77h88Tmr1TWmWiBsA7Yu4xYEIgRScMSQQDdUN5/hhUbbFmkMWhv2T+95evsV5M+o5rwcrSVtYxj8QK1alpUlhYzUFi0FWWusNogYkCqj24SygtQLclCkGHEuY2RVUlVzIqiSXVGZBpWKcyPEvuSL6NLKn5Kjw5NTEWSuVitqWaNTCcr6QNVMYYLkENnRHXsII2K9RMmGEHwRYVKEvRGJcxOVMGgjcHT0PHAWd3TyCZfG4lhxn6bG/l1ev/6bv6Yadiyudvz8j16ye16T7IiSnlWUNE8C/b4lHCGMnuG8h7jCaYhxIuSItAptNaqSCKl4ethTX0q2uzWj7wgdeG9wriRxp5xQNmKagFKJHPXsPBCEs+f5YsV2e0V3GPCi8EdS8ggvmLwn47i/vytclZzLzxFLFs3kxhIEOY6cu4F9P/L6cOatjzxkeLnb8cXtC6RqOLrIFoloVrDckOuWKCQilQ4KucQrZFnAftF5pmFg8IFcLTBkwjTi+h7nPb0bOY4jQ0hMMWFF6dRJUbgZcc6REhZ2l2v+6I//kN1uhxSzuFUrVqsVz5/dMo0jzjtCiGhbIUQJAsxEUIJnnz+j3ay4f3rk7q/+hsvrmudfWF79L9b86F/ckuKe/8v/9f/EpXjOH/z8ZzSbBo+j3o3UO8H9YeLXx3dMP3T4+8xqYWmtxABDgqkcYjFWcLmzrM2Ghfo06NTv8oox45zHTRPjOBFCwLlQaKUZjNG8f3vi2Y9fMS5PfHlxy8V2y3/1v/yXZAF/9pd/zf3DEynnMt4BEBkNNAFeXG1Z1g3bdsXK1NRKYYTEqNLtFUKXUXmznMF9jsmPgJyZTHIWo2fSnFAtlSRmkKIkzZAFQwwMMpPqCmkt2cePnRxyQqSZPZsShIiI5aCghWGz2NIPI/vTCREU1jSoKmKnESEtXT/Sh4xBsmha6o0BJYkyo0XJTLPGMmVPN50RRAp0WeBjZhzGIqbHMo095MKumnIgyzKaTclBEIiUqaXkZrPlP/3pn7CqGowxRc8SMqdhKt0nVeIAYoykGIoQORVbuFYKYw3GaMbJ8fn1FVebFd5HppB5+/oHlJR8dnvJd4czf/Ov/p88Pj1x7I4kAy++/JyXX3xBRvGrv/sVv/m3f4Mb93BhP2lPfXJxkuwZZx8xt3s2NnO410y6wmwW5CrgziPXu0sW25bp6YnzDwd2P76mWlgq2+H8SHA1l+sLnr28JNWZlam4qWoaXdT0ycE4TLx9/Q3ffXfP3bsnfD9Sq8RuUXF507JuDToHjLUoWfQuUsjidFEK2ywwTUs/eMLkkNJgKllcKUohJXjnObuEWK+5uHlJPHfslmvWqx26WSKqFqoWjIU5FVVAAfXEQJj983J3Q3p4izWXNCSEiDw9PbJ/eMv28hrTtqBAJk8THKeuY1E1KF2Q8jEWfYdRhaWYdAKbSEYSKe1N5yJ976ibCqMlUkpUVWyb0cHxsKeqWhKZyTukEkxhxKvCDZA+IXOZp+qqqOilAKUtywSjgLYy3F7umKaBGOMMLJJMznPuJkJI1HUJ1BoGV4oVabHGIPKynCSkAX0i6QkT8//ITvr9WKs/eMnbb9/TreCF3pN9hdSGtllBtcAsFIEzBIehxBKcxJ5oMmoZsTKiU4U1kuoio9YdsU6M+YkuJFSsef8NdH5J0GewmnapWFwMuPSO0+OISH+ATEu+/uWvyePA1cWO9fYKTMKHACIQw0gMnugCk584dWNBdCfHOA1FqR88o3cM00g3TpyGiafRs4+ZQwYvJX/4/BlXVy8YJ8X7xw7qjpvPNbvtJapdzCMqh0qyjJ9yBh+K22YYGceRJCTVYkUcDoShx00j3nuGYeDY94wpztGUmUREzVHtUisW65bnL5/xxeevaNu2dHiCo10sZjgdKK1ptUZNE13fM/lIio6q0oQUy+/78J42jWQduf2y5rOffclD947mx5apjeyWO37+v7K8/rM9//2f/1uayy3ipeSP//c/YfPmgr/4r7/ieN+zT4mhipxsZLmUbJYVZIGPkAaoRoXuDd3TgN3+/hfrIc6jgZAL+t1NH4uTEDOMgmPX8f7dHXVl+ebZM7548ZLLqwv+8//kf441lv/XX/4VT/s9VkrWTcXtxZoX15dc7zYsmooYM5kSP5JjLAm7CKaS+ch58uhdAyLh4sxAYS4kUirjxVT+uBipKouUqmi2hQCpGL3j3fu3VKsNpl3QskWJD0rd/FvW4wTeF+1JCmRRkrIbW1FXDen0yDgF1rcbYImxFaYfaCaHVYZaKWQKZJkYRWaYHDlHhuQIcwRIJTNaGlKY8EMBvWWhiE4QQ2G/jD6hgRx84fkEiesDTJln1YZ/+aM/4qpdk3NmcD1ozelcaOi1tR9Fy1GAqgqx+tQP1E2NT0VjhchYozHWUhldiNa25pu7R775/nts27JdbVldXvN0ekJVmX/2L/8F6901v/nFt/zVn/8bTvsH1ivL1dUFxv57thKfvWOzXfPjzzZsHg/EX3lOaUGz3VDJSFQJHxL745Hu/sC7bx4YT57Pf3LBetEg5QmnMlW1wNiKxlpuVlsujcINZx6eRu4e4btvnzg+dqgs2NaGzfMV61pSaaitojKKnARtu0CqXGKZKX55rS22XiGrJcEd8d2JlC3KNqV1lj0ueI6nHrnYsdpcIGNE+onFdo02LUJXUNVQ16BN4Xt8mIXORYoOkWkcyQKSEWhRUy8vyX5i6Se67sBJwu7mFlVV5JCp6oqHxwfstCArQ9eNQC6IYFEYFsklxidHFAnhLUZYcs5IJUBlXO6Z5kJAS0sg4GNinBzojMsOg8SLhJCKVdYYkdFJIKdITBPCWJIqTBWrLVSJFB1aCdrGFtpAEnRj5O3dka+/eUMIGSlht2khZzabBVe7FYuFpjU7TFqj4pnWnJnyRJ5+//vYuy9+zvL5FbaCepGJFOdBPo2ku0wcDAsHwmRc4+mfQ7qq2V0s2FaRdH/H8N2J8zki2obtZ5q8UWQlOMaO8Kbi6Z1lcZVRm56FWqDJ7O8PvPvuCe+hUk+YpPDnjs9uLlkulggkn//sp4wx8vj6W8b7EZUlD/sz09Tx8PTE2U1MYWKYJsbJMwbP4B29cwze04XEOWVOGUbgdrXiZ89uWK4vGI6R43HiL375NffdxL/0iT/4oz9ks1yidE2cZuhZSHgfGYYe50dyEkhtwDmiG4iuI4aJceg4ns4cR1e6DlIilEBVGt1W7C5WPH9xzbMXz2nqBWQIMSKURM3WzxgjKWUm55BaoStLqxVDNzC6CXtj8dOJdOrowoEuZy6er/j5P19TLyWvf9GVU+NZopRmebtg+3M4/63h3ZsR1we2f9zwoz95wehG/tX/8ResDhoayCLhjomhHjECtAPrKy7YsmvXJC1YLtt/7O36H2AV/g5SFsF/KqOPmIu+oRiHywNnmjzD+B0/vLljtWpZr5YsK8u//Kf/hOgmlMgYpWms/hiMmoVEGFmMF8OElpqcC+unNhaMwQjJ435fCMB1XXQlueTVFCt5IoVAjhFJ2UOVlQgtCwXbJ5JSnLozb958R7Vc8aJpkUrPeH7mkWVGxAB+IruJFBw+eUYRCHFCo8lR8M1Xr/lyWaHqjA6JpMs9N4XEmBxaZbIqFuYwRYZxwChDCCBQjH7AhwmyJ3mP84lh9KSgMKrCOYeMBjdNJa/NT8QhsRA1X14+54+ff8m2WhBD4aeMPiFUxLmpkG6BGGLp3mqDjxGlDS5G+qc9y8WydPUpOrKU58ypnBiGAZkTaex5+8P3TDe33Dx/RWUFuoWrFy/4y//+F/z5f/cXPLy/QxAxWuHcRNc/fdKO+vRsnbeP3B8XfKE3YFuW6554DuRzRx8d4/nIvvPYNpPGAdFMeHnGDxuuVjdUK4G2hQUhhEG6CjlpUkrsn8785V8dePsWZFJsWsPl0rJdWBaVpbYaJcPMP5HkmLDGAJGqKe0qpRRKWaRuQFuWywYjE93hxGa3JQpNTon98YmAYtWu0ULB1NM2C7StC+VSaTCmFCZKF2GfKLwQPvjwjaZSmjgKnKnI0wltq+JICgPejfjhRHfUVIuGnH1BI5Nwrkc1LVKmmfY50xClIAdBjsWmlpFkmVFaslhV6DYRGPDTgHM1YVrhpszoE152BSshHQtladoFQmq0K2RGWTKyywy//BiklNCz8nt0jpASSpXOTAyRvht4enykH84cTyPHQ0dbKzbrCsQNy4XG2paYJF6AUi1L2cLpwDiDrn6f1zf/57/j5T+/Qb1QnKaRRi+IR8/5zTv6O4nlgrpdQAXHq8TdpqNqBcoYUhfxrz3iXlJnw3gHexOpfgShdSjpSH5biuJ6YLE1cMo8fufY/2Cx+We0tcGNmfP9PZ/dXLJplyizRFQVlUq0mwtySPzm3QPHt68RWvL+6RGfIu8Pe87TyBg8ow/0LjCEgEsJnzNjhh6YAITg589vudleQSqi6PViySGe+O77NxyP/zV/84uv+PLLH7NdrUrAV31AaUMWgqa2xaUkAikE4tAThhOuOzANHV3XcToPHCbHIMBWhuVuxcvnN7x8dct2t2S5aAqnIcHQjyipUaiPUSwFrlXm51oKRFW6qrWSVFiqW4m68FwuXtKsfoJdVqjWIhRM08Qtz/j+3Tvi/lB+63TD/XvPd7868MOvnjiFM9vfLPn5f/6KixdrvvjTLaevOuTQ45JgUyk2QkHI+B50aFkuV6y3O0jQNr//UEI1M5yELB0snQtR1YRETGkWWxbMmpDgpwkfE72b2J/PLOqKxaKlbZYYJQt3KQd8SEwxUNU1xlZoI0k+4GMusQ5zPlVAIKsaoyu8G4sxQlv6oXTkEKKIP3Nhh5AjOErmWs7klPBpThMPnqf9E4v3b1juLtitLkp3PobSLUkRvCf5ieRdsVCnmaudAyILKmUZjhP7+yOb5wuiKHs0k4kuzRrAXMYwFHpxCIEUPDHKuQgo2TQpTpCLLiSTITu8i7RVy9R7oleEMVAlzRebK35684rn2ysqWdy0QkiUqch+IPhY+C91g9WmFPUp4HImKUlCUtUtF+uKSGboOiprCSEwThPGaOq6xmeP945d07Bbtvzq179CmIrrqw2fPXvBuRvBRy6XS6QPSJG5vNhS26pIIz5hfXJxcjwG+jdHBg/rW02KitT3nI8dk3OM4Uy9rdne1Eg9sl4vaO2K7DytXbDdLAjKc+p79t2IGCPCVdxPjq9+/cjbdxGTLJtWcrW2rBtLYySVAquLrsJYgdEVOUWa2iKUoFk0GFuhlEZJg1A1KAs5sFjW3L3+iuXlBVuzZjgNnM4Ty6sbTLWABMYWV4pWReeCnguUGe2ahQChCk2w0ISLqHzGjrsxIKcT1lYoq9BVRdU0TKc9+8d3bNUVUlJm/yIxuo7FosLaItJTqnArSvVQihEhSl6ClJosAnadqS6KnsT0ktNd4nic6M+JU/A4FdEhU5nMsjYFLETR2MhU5vaSTJ5tpyJDEgKlBCmWO7ybPFkUcFxKmRwDVgsut0s2q5anRtE0mt22ZbNeYKwhZokPiWwFVdOU13Unuu7TIDu/y+tZs0X3Gn/06LXGETm82TN+e+LKb1AXl4hY8o8e05GD72iGTKsM6j4hDwbrFUpbCJL+vWDUgnTpsXWk0hPrZ5ExHTl+p8gPEB4rkm+wpkJlTTjv2dQVu+2uhNDpJXa5wTQVicSyXXD1/HOO+yNv33yNS4mH44k+Ju76kZN3hbkQU6Gs5hJ17wGHIJHZNi1/8PwzFotLiKBTZKEkoqkZVMCPE6+/f0MSmme3L2mqiuvnV9TKUGtNSLnEvydf2tPTQHBTCbQcB8ah59T3DDGiW8uPvnjJz37yGc9uL1kuW5TRaGXmSHuP1ILoY+kWZgoxd85hSTkjk2YaJ+R2iRMCGSSRTK4Efg12YxBzdLsPI53vCHjGrud0dpz2kh/6nq//9Ui8szR1w8pck06Cr/8fj6hbz9XzLdo51MmQs6QWhnXToINkEoE8GNq6LSdi71CL5h93s/4HWGLWbDAH/JUusyigVymQWUKKhFiE+SLGItCOEj/rU8bJ09cVTV0SbNM8hvF+ZJkUrayKRb6qiCkxOE83TCyXS9qmZRhGUnTYuuU8jqQ00NQtMU3EnFCzgSzmTPRF3CqFRCqNUpo8Q44DmW7oeXi4Y3P/nlrXIA3STYjoEGl2231AAgSPi4ExeoY04dxUrPVj4u7rd7TrV8hKovQcVZHjPNKftTphAlWy3WKKePeBf1LYJymJEnzoA250BJeRWRCmgOt76qR5tr7lRzcvuFnsqFURAmfAmIoYA26a5qiMctiuTIWQitPUc3/Y043D7MbUhL7n4qc/4f39HcPQE3MRDut5pHPuOs7jhHcTt5cXtLXh1W7Lb96849f7e1yaWFUNL3c7Xv6zJSEElFJsVkuMVCX65BPWJxcn798NdMfM/hBYv69YbC1hCggPLbIAoYisWoNqW4aTI/YRkxXLuqGyNVPM7AfP3cOR2GnGe0F8mnCdYF01bNuKhRFslxW1MUhKMFTZ7aVA0MZS1xprJNJobFN/LEykNCWeXUtytty8esnp/He8++FbQrihPx/Q1mKrBVIoRE5YU6GVKThtZcDoMsCcOyWFCCQ+tvWyYJZ7g8iJ6Bx+OCFyEV8pbbB1i+qODP2RsbfopiGEMlMMoeCIpVblZxDlIRBzIon8MYwqxhIPLrVCCU0OAlJDnmQh5M75PzF4EgGbDCtVszR1KXhC/u0RKULpj66FMned31aYgxEL3TbP9tO6rthsVuULpGC7bVkuW2pri+U7S0KKSCuxtSnVde/QQqLN77/m5E16R3pv2aqG7ghSaETWLG6vWVYbhNKE3hOC49Q/MSoPGWJskXuLdVVJd86CHCCcFPGHBbHfUL2SLC+2hC6zfy3Yf5/g4NDRoHUFupyodEhsdxvqusXoMlZR2s7ug0glM9v1ksvrW969/Zan45GzD5wmx5Aip1hcMomM0ApRKUxtaVuLri2LVc1NrPns+obKLhBZUEuQVlArS1g0hCzwPsPQ4U97pGsYlwZDIkZD0oIoIqSJNA34qZ/TVCfGfmAcRobJ0bYVn7+45SdfvuL5zSXtoialyP54pKob2kVLu2wK9j85Qhw4n3u01lTWlLwha9CmYhgmgoKwrDEZ0pSJe0k2Ac+B/tyhhMAYEKlnISW5zwxu4u7BM77t2X8debn9jKvtluDgYX/k6c2e8NXE8x83tKnl/cOAC/AwTNybyMVywcq0yEpzmCLUCiElIf3+QwknVyysMUJKhT6cE3xob+U8AxpF6VKknMgxFwpqKsTU4D3j5BjG4hSRstwbvZvop8DKR9pFmM+ImcmHoosbJq4uBW3TcOz6knYsMkPfcz53xUVEotKaWit8FrgQCbEQhKU2NM2i3OslIBWT9zzuH1m8+Z7lYoWoVhjvkanA03IoncAQPc47xuAYgqePJR07jA41RrpTz3mzpt1ViJUha/H3kLRcuCfR50JnzpkUIsE5cgwlXT6UoL+SJTUSXcSPntbWaKlZxYof7W74yfULdos1SqjCOCmWJPzsDlJS0diKwXsSgnPXM+XE4/FEPw3U1mCE4N3DPdk5Tt2J4+lEY2duzYyRyCkiRMZqxcV6XQqOquLVZTGovDvuudxtiUOJw9jUNbvtDcbO2tCcqZtPy5r65OLk/r4nTnA+e7opsBozWgqW0rBdtVy1BrWF1aIpp7GHiXDvePFsw2qxREiJ7zLDKXDaj6STxviOykl2bctuXbNa1MgYi4VJKpQonQ1jTHEBiNIyrNumMEsqU/JvpEIqjZQFHy5kxhjN7tkLbroD796fef82IUjcPN8VdkmM2FqhlS6zcKXnUY75WJDMkvGP7eMPl1oGxEwSNFrSeYdjQGuLmLUvRltIiaHvqLRkciOTm9C2LsRbIWZLfyFPphRBzA8JWWxtOSekVKTREkPGR8U0CuLgUQK0KoC02lbsmiWbuqVRFp9K5hHJotBIKWeyqEQKVWx/MRFyBCGRuhRk0SdyKp0cOSfXeh+ZnKNuLZvtGu/g7m5PSh2LlWG1bZHJklwm+0hlFTu7/NRt9Tu73ts9KSnCaUmdMn7KyGrJaiuJ+kyKAzdCY1HEYcDJCe1B9oFmXGKlIqu5nTwVcbSfajTPEBcwWsO0z4x3S2KXSV4gRMZoDUriup6NraibJVrX2LropaQt9neZBVomWpO52i14+fw5h27PD8cjJzcVwuosKN9cLLn4bEX7YoHermiWC4SVXDQt9d+cWTWGnAJCGKzSyCpjU0RKjdAVPmRsJVjXAm0yKo6oYEhpIqhMFkXXlF1PmAb8NOKGsThrQlEz/vynX3L9/JamNnjvcGPRlpyOJ/ppRNWWVi+R2qAs1Mslkw8lz0UpspRFLIgihcR0PBNq5jucYDoIcgpMdxNxGhE5stm1LHeWZlqhDgtkHugfBoxfslgIpIKnw4HjvmTySCJMkemHQGUr/L3hNAW6LpLxnJaJ3TIh0YzDkc1p4otnG9zvf62OD38/OsmUw48QxYUGgjSHAQIf/x3TvDdyLriGmZXifcBpj9IKxGxxzRNTTKy8/3g7TjnjY6IbRiYfePHyFbZpOB8PKFUOfl13JoRQyNlVhW4btDF0fYf3Hh8CSpdngDE1pVsuiTlz6s68ffeG9e4SfampY0KlWIoT74vj0ZdU4dE7+uDo/cTgBvwwkQePGCLp3qOpiMceJxxRS5KRJMlHrgtIUvJ4H4lThBxJLpIjhYjrwHjDwiy5uLxE+kjoAkp5fnL5kufbK2KMc2bXTJHORZyMEFijP8ZfgKAbe8YQ8G7CSEFb2XLPENAsF0gpWG9WiJipqqoUJmT8rN9pa4vWhVqdUsQoye1uQ7usuPnicx7f3TPEhLVmFh5LvI+Qi5D3U9YnFyfdqdDrEJnUafxd+aVkI7leam6urrGXEarMcD4xDgnXQcYSokKMkLoMB9BHhZ0sSwS7dcvloma5XGFthZ8cQpQ3UhtZ2ni2IsWAUSVISpsKqUoaKlIUwZI0SFVi1nOKKAFV03L98hWH8zccTj1ts0QbS/YOmRJWF3SxUIrZGsCHIUhZvzXH+bBy/qgAFzFQGU0vwI8dVBGlmrl7UwqloR/IxjAMA8M4sru4RinFh0NFmguTTAIlEDVIW+BCcYLkMmmQJAQ+JnIosCGrM94EagKb1YJdtaLRFSQYhhGXHBUCLYvgNotyeig3j7JxYwIpBVkoXIBT7woLpSrV8ugiPgpcyEifiAieuo7ffPeOyXk2u5arYc3lENgtW2otsdZQV9tP3Va/s+v5z79gyiMmJzj2HB7OdE8d5nBmt6yYtoaL2w3LtGYxWvpuwg4lj8NEg0KRZMlzid7jfSBKhfVrxqeO/eOJuG8I+xrhMoJCTA5+AicwCVabNda2KD1n5ugKVTelyA4JlRMmO1ZV5tXtFefuBe8Pe/Z+QDSaz18suVivef7yiqs/3mJ+vCQuNphqwTSNLJ8S4psyMhn6I1rF+VorCcpGCiojkY1FaMOiMdSLhnrZUNeqzOODL3P0MJHdGTec8WOPn506PiWqZcvF1TVoSd93OCcQuZ27IpqoBC4E3t0/YmdXwbKukFoXvQmCkCLeJeI4lpPpOBJHwbiyuD7Sux49eoQITOcJ3w2MdSQ8v6SqNix5Qb1JWC3Z6me8/fXA0+uex3cPnA4TWit2mxapBafDyJAiOE0YAykJfMg8PU2MfTmhx5A59xPXl0uU+/+DlG4hEFohUwSfkUohc0aEOI985jDAIn4rXVtRAkrL06rcX6KI86gkIJRAIkiiwFFj15FyRs9hgWIW4IacGZ6eyNryoy+/RArozh1VVezCUz9zmlLpxO/WK4TS9KcOp8rrGNOwEKY81CmANjdNPD09cnf3jmW9IgmNzqEkHgdP9B4XHJN3RVDuJ3o3Mkw9vp8Ig0MGUJOk9RXTeWLoOwYC0Uh0Y9lsFihblQI8K4ysCKqmtrIU8MPAImvaaoVdVay3N/zkZ/+E73/xa77a/5p1VXG13mGsZei64owSzNlbCa0M1thyjeSEVLoUkrKkP7e2vEdWaWKIrJctu9WGmDLGWHwcUUoRYjk8FwyNLEwxpXAhFAFwjggpWdUNi7YhX+xoUkLFElwaY2ScPEIK0vBpmsRPLk6Cy8hakpUg+cj4MKAR2DUM2wUiLckuQ+Xx/YTrM1Eo9v2Z1dOJ2lj8U0DtYTfV7KolF23LZtlQKYOtWkIqtLosDcpKjBFl5qwN0hqMyti6Rik7B4vNqcFSIaQumzUXgmAORfRkqro89IVA2RrvHU1t0EqjVbGSCaFAqJIJMl9IpSuWf7ss4WPBlz+krMY530zgvEcZjTSQJYg58nvoe5LWHI9HxsmhrCLlNF8E86vL0qwRVUYsEuiITCCQjJNHoVEChNZkJWeaYsJoWJgF62ZFpSqU0KSYcH3AyYCyESsziXJqkRmKqz0jKO3F0QcOx5637/Y8PB1Kxbxq0Upy/3jicOowWrFaV9jlgofDibvDgW5wPJw77h9PPLtY8ZPPrrm93sxK/U/eVr+z6z/6J/+CLj+SUs/jr7/i8e2RPEWwFmkMdqPYflbRPMGNu8SMDSoqGm1JMuNzQMyfxYc4MKkUbvL07weG3iH7GpNESXpVRQjnziMyGC7XO+pmgdENUtcIVbqIQkqQEpFDmY/HCYOnNYnnF2v+yRefkfeS6Xnmj/7TCz7bXdK2K9KNpdsYzrHMotUA/S/vWE8GKl1uwGPZ78YItBJkYzFaYW1J/Y3eUzcVy/UaYyS+i4TRkbwnTANxPDF1R1x3xIcJHwNT8NjGEtLE0HnICasN5ITWilW1YsqJ86nn9ev3/PTHn9M2TRHbrlakXAR9YRgIUwfOE1OiHyd0tHTR4x73JO2olwa5rvG1xDnF/r0kO8vyZsHu9jn1q5GfrRoWYsNfCs/j454x9wQR8VEShoRcgAxgQ4F6RR9Lp1OWh58Cdm1FYyt6F+gGhwvy37GbfvfXB1geiDkWJM+dXzGHXM73upxAyI8POZgfpDNJOFE0b2HG08t5tC6UImVP6jqs1iip0MagzRzpkBLv3r5jt92yXi45ns6484SZU9+nyeFDJAtBuyqOUec9k0uApLYdShuM1qQ4az+8J4bA48MD2/UluWoxOSJimgvuMtKZvGP0js5N9G5gnAbcOOHHctjLPmC1xbDE9yP9cSDlxHJnebVZIan4dt+xXbSo9Y5RCnabFWHsefPttzQZXl1esV5vWT1/Rbt7xi/HX/L+8YmrV69Kho3RuJiYfIG1kRNaqUJNV2p2spWk8Jg8RmmUlKQP1HRrOYeuZIUZy/dv3yNSZLtczmO3gFKyXJvzRCHGUnhW1uJdQUxoIXF9T9tYRFPTPx04Oo9SCud8KXiS+6Q99clPkRwghIQUmuA9YcokpUkLEFngzw7vJxojyJ1DjJJFvSiJt72nHydO92dUn3m1WnG1WrCoaipbYYyFuuXUQcwRoWpMBdbmOeFy3vBEbNWQKfHRCEOmFBdSqgKl8SNxKgmUMXjO+xP7w5GYC8J4HHp2uytstSjVPh/ErvLvBRq5PLw/QJQ/9E8+ijRSJvtCB4whEIOfU4cNylYfCYQ5JYa+Z8qCp6cDqmnAanwOmFScQCWFU6KsQCwg2eKU0UFhvUKLVCiBM4Uzz/PUlAOVhEoprKpQsohrs88QJS5lUB6Jo8LwwWuUc0JJRZKS7jzwsD/wzbfv+fq7txxOZ2ylWa8WKKF4e7/n8XCkqiwXF0vOrqjSq4WlD47TMHA+d/hx4GJtudg2CGkh/v4HnYUfTjQ3Dao1+LrhtlpxZSo2zy+4frHh9mJBY1tEDuyyYhW2xBCxURNVmdvKWRgmpMJaiUuOvuvIE+hpR6tWaKNI2RGyI4aIQbJplyyXG4yuENqiTTUXJoboAiIkkhtJfoDoyWGE1KG05+rFkj/+0y9p/2XD6hXUUpNShdctPmvCqNBZwKPAfz2SvCVqRd2uSzEVHERPGEdC15N9JmWNros+KcWIBMQcTEiOxOAIfiRMI2N/pj8dcH7AeYfzDlGB9yfapqapWrQ0JR5elKyid+/veXv3gA+J4cUNQiuc91S2QVqDD5GJiDQgEpyOI51z7JYrvJ4QS8Huy2vyTc1oSiZKc7GkfqtZmmvCRiKanqqONE3Fi5slIlke7ye604TWcO4iT8OZum34/LNn7MyShx8eef90KAm1BmSCi6Xli5stq0XLYQo8do5z//vfOSlMsvLwN7YCDyrGEm6pVNHSRUHMczEy34uEEDNIsKycP4x9SsHyQcwvM2UMmiHGVMabUqFMOXgVMq3j66+/5k//5I9RWjMcOzzl5xqnEroptOL1u3es66Jp6t1EzlCpI1oVJwo5FZGrL9fcYf/E/rBHL0o3UqREDqVw8cEVQ4h39G5i8APT1DMNE24cqYwhjhPJx2KPrlpWtUdrybPtJVuzoJt8kTcsW2LSRGkRuWK3WbNv9lTRs2gXaF3T9Z6/+Lf/LX/zt3/L4djRDRNn56iV4uw8wzBilKI2Fq0sIJi8I2Xo+mF27hi867HWYqu6PGOlwiiNqSwuBB4f9zy72LGs21KwpUhtSh5dTJmUQBo7ZymBaRqkUCSRcIcjixfPcdYyhcA0jB/HSUopmurfM+eEJIi+zA79GEkhYxcWU9UoXSNixeVyh9KOo79nHSVX22tulxfEc+L8eKKKmeeXay7WLeu2RouiE9F2gcPSrFqmyWO0RcuEUqkIaVQBrCllybrkcghVElalNkAmfZgFhmLxKqjuwK9//Q39ULzc0zCybnbYaolWVYnrntXlosi4S5R2qURgfqTPl0355wf4jivtvRgcwXmUrZF2ibSLOR567kxMJcTw3Ds+/+wLQgJERAldZMRZFJG7TGSdGN1E30WUlwzjQKUkZtGQQkkIzvOVrJSmriqkVOhQEixRnpQnhBVMfeQ4DnR6YFW1LG1TbvooSDD5icNpz/v377h7eE837PFhYoqZ83QkJYGbEikHzl3g3A+8u3vg9sUF7W5B5TpyTKBLN+08jnTdvPnF7z906t1/87fUf3jN8z/eULnIjza3XFa31IuG/rHj9O6RH9aB5xdXUEWqUZb2tSjdOSUlxs4Xe4w4N3LcHxiSp17usLKlMgZlE0obpi7hh0QjDItmRWUbTL1GtyuEqshZIbQmjRNp6PFjj+s7xqln8gNTGojNhP2DLaufeabdxF2skKkm+AqcBQr2nkFz+LsneB9ROhDzwEpWVEoUi6ZtMKslKUSmc8+0v8NVjvViQXaOOA5gDX5y+NHh3YQbBlzf4SZXiLfB40LEp8Srz1+wuVowOcdh3zP2CR88vhvwKSJTZNPWXN/e4qeRU3Ql4E/2BCmIJlFXgmplcF2P0bCsK0y7RElBtdswNJLBeUKYEDJSrSFWgrMcmJqEVAGjW7rpxNOpY7HYslzUXNxUvPqixjnNX//1HVpafnb7gt1yjegmHhea3nvQktoqLjYVi0YDmbpp0R6++/r3P/hPKtBSFyR8iMXtOHePP8Q3fDQV/H0LmvzB9DCTXMl5PoTFIhiFkjUWI1Jk1Fy85AxZOPLc3XaTIyTJ3f093/3wmkrCMAyM44RSqlBrUylkhMg8xUhOxR6bXEDnAhqbf0KmcWKcJoLzHPZPHPZPqCSoCuSBPCcw++DwzjE6R+8HBj/hgqPvSminQeCmQszVqoz6JQIlJEYZYoZxmmiaCjd2rCtL1Jb7+yfqVy9YLLfk856n45nj2yd+9fqeX379DWPf0x1PDIcDF+sFpl5w6DrGYcAqTWurMi1QCqUEIUSO3cB60bBdrVjWNbv1CoSkGwoPSNcWpTTnw5HPnt3Q1pYooT+fEGL+OKVEm5L5I6RkGiaEFChTMvei98jJQYrUi5b1doPTZg4gLdpHhPj/2j//v9YnFydaVyQybvKEqYhsRE740TF0E+3LZ7y4fk4vH3m2/RGbkFCp5eGHA2IM7FrL5bZhs2pYLlqaqkbmoqXIwiCSZLna4KaBelGhTQLpSEKidFPCj2Q5SYkIMXqEmTsCKZWZdgjlpJgCzk38+qs3vHm3R1ULfMz0p5761U+wuvkYE58i5QqIxapbrhBKd0TOpz/E7HxJEAM4h3C+jI6CR5HRApSuQdqPrxET9MPE0fVcPH/BYn1JN3R4JdC1QYtMCAmXAyl7gp8Yx5HuJKm1xeaihxFZzZ2iYkMLOYJS2KpB5YwII8JGWD0i5B5TS0wWPL090WdIm4DRmma+K6T5Al20FS9eXHNxseHUdZy7M11ynGWgGx1p70hnT99PDD5x6j3ffHvHTQxsNg3X2yVuKBkRx9OZw3lJXZeC6fd99W8eeXv/wGrxU2I3IOnBNbRdS5tX7E8jeYw8+Ds2ly02L1C9wbvZ8qos2lhkCaNBqoCUkla1BG/JQpG15+pnGWsj978I+LFis9jRtCukrsDUVIsNbnDYqi2qlBwJbiSEkZhd6boox3Ed6L9ck75Yk9SIpyZmS441iiUywbl/4vB4ZLHf4n8z8OL6x6zX17x7/S3D8A5DYtXW7DZrKmuoW81y2TD0A3UjWS71R51JzIGxH/BDX/6MPW50TJMrzIuU8TEyBE9EokzN49t7uuNETgrvE957VuslX37xOeeu5+FwYr1ds1rXfPX1N/zww2uC0Vy8vOLy+pqmUbjHXLD9jxMuCIb7nse714iFIF8r5K1Eb8GryJQzSfVElqgES3tD7xOPTz3hLnE6dEQxolaKZ+s12+ufMO57GtdTnw0/vblgkT0YweXNlmmMTGOm1g1aG1SSvFhsubm6/sferv/gS0pBjKGMp1Uq91cpQCu0NZg8O3LE3Iee70UCSpDeh4OgEPNI6IPxoHTTQSCjADmLZykUZDeVSJIQEz6VNPdf/epX3Ox2nE9n+mEkUcZEKRfhblNX9KcjWuQi3pWZc9fRNjValU7A5Cb8NOEmR6dOHB4f0VlRKYkik1P8mCzuvWNyA0MYcamAMQ+HI955gtb4sbjTjITgHK4fELUhpkA39Hg/USnYLtdUlaGfeo7Hnl+FhPIOes/TdOD9/shf/eJbDueOxmj6YeQuBf71v/lrXILTMOJ9oLaGRV3EvWouCNKcoXO5WfHls1s+e35DPkBWCucDMTjquuVp6PjFt1/zBy8/Q2qNrixZFOF8TmXsppVCCUEIoRA3EKXAqirapsGROD0eWO4uWF9e0odYnp9AyPm3q9P/0fXpnRNRsi6ST5Cgbgx1pdAqolQso4ecUGSsMfQh8vDmiWWWXLclQ2e9bFi0C6ytkDNnw5oFUVRYvUIvFwhxQQpjiVcvwmCUyniRyCIzPDzQNEuUTAgzW3pTKuKrVKrqkBJvHx75u19/i6w3oCuGLrBYrzGmQWQBIc/FCIgsi+IqzcrplMmFKP8/0MKSUilMvC8Qnmkk+g5CR5QaEDOUx89BSgkXM4vrK158+RMg470jxYwSikoUiqYLExFPVplKViW4LEEKAlkX73nO5f1nFoGVoiXDfHGYJqLWT6jFt1RRI6sl4zni+qp44kNgEBNhFsNKpVgtV2hTU7Ue1dbQGJAjsg1UItAcBMuzIQwD+3PHL7+6493+zOnxxO31K15eb8l+5OnpkRgnjqcTF5sNTfP7P2P/yc8+49dvvub1X32NSJEXi0t0bUkiYpRlrbcs7ZLxcOLkn/AmUrkVMVAC9maoUZ5byErBolkxDT3d/gC2xawiwfTcf9/DacN6cclitcPahiw0xESYRnRVl9h5KcghEv3E2B9wrmNSjsOzxOF5zePG0CiPkBofJeehpxvO6DzQ6pY4JcL9xPlv33LNFauLFxghefXqFd35iBYw9R1v3t+xamqurq5YLFuqpmRbpRiR1qK0oD/sSdMAYSL6ieBKB6XvepwbSNkzTSMJuH84MnhHiqUr411EykxdWZSQnA5HpsnzcHfH6mrN4mLLbjhi24rTMDF2A4fv7qmfXbJtNoSYeHe8Z+xG4rsz+c1I3VjSIRIfM+EyIq8yq+cL8rLh6DSTC3z/0HFV13THgadf3uNCQpmG/VMiuBOfvdgy6UjeD7SLHa8+v+U//s8+49nLC1YLyW9+fce//lff8v13HavNlsFHfvnLX/D89uYfe7v+gy8lxaxnKE4dJcEoQTYKsimdkKSJOeJCcehkMh8QTx/cmGXMI2YV1t/fgkuAZIk1QBSNiYgQg8S7MpaPGWISHN2JNE2kHBl9mLUmABIfyut6H/FpwmpDlgmXPfv9gZQylbGEGBidJ7iJETg8PWKEobHmY8ZOTCWyYXIjU5yIIhMEPB2OPD0+Qcw47xmHgbHrEckzDT3eO5CJYRzYVpbL7RoBVLrh8XRidB5/OPHNt6/xwSP8iEye0Uf2pw7vHGmamJwjJc9f/+abEmQYBVJrmqbm1JduDUhS8PNnpDmdzhijEVqwXCzppwkfEsvFAtE73t894PqBtmmQqiAtSoBi+Sy0EBSeaEILSc5hBokqZEoFsCYEcX7/ZWPRyxbhPCFEknfI9GnPh08uTmxjCMFT6QqpBc3CslxZNtcLmpuGTg4kmdguV7z/zVuO3525rhdcLlvWbcWirWmaGmstVVWgaUIahGpo1xcIuyCrhmk48Xj3hB8H6toi2oroI1W7wPuBOI0MWdA2hqnrCd6XDiIJOc+4n/ZH/vbvvsNlw7pecB4Dpz5wc7mGpMlRkrIiCwMzBlkkUYqTBDlmMPLvuyjMXZNU5o1FDOtJYSROJ5IfqVbPUPWSMJXMkGkqGQn1xYb15y/JAsapJ6tEMpkpjyWEKpckES0LDK6uaipjmZzncDqRk8T7TAgeEBhjMbpCUTQ1McwgoCFiosIYhW4cq8sndlcNw9MaI22JyvYBnydiTGgJOQmOp45j6tnHjqdwZlKlsJJWsFi3WNlysdmxnjqkVlw87TmcexoT2awUy2rJ9YXkPATiCF3Xsah//3Hd1//lDfaXA8dvRvyo2a6es6suqLQlJk9kIodEHhWPdx2mCqyXc/s6ZFIYSLF0+MZpoK4sN1c7rK25XKsZCOX47q+eMOcVW7tl0W6oqgVStyXLKMUSHy9zCaXMEIKnH86cuwOd7jhceO6eB/pVKDk1ynLXDQgzB6dJjcxFHKdchb2rmd72iMsV0+iLYyIlrLJoo2gWC/w00R329D+849kzwcXVFcoonHPU00h/hGnoynhnZvtM0+xU6M841yM0jCGSpUQYRRYSbQzKapaLgsjOMZQ2vCwHn93FGkxmtNC8fEb35h3T+cThbs/bb17zm7/+JZvlkle3L2iUYXA9DYGr6yL6dTGwvzswnk60ZJ7dRoz2fHMeQTTl8JM1WSy42+95uHM83fWkmPjyy4rW1FCN6I2g3g5sv9RsbwZ023Eee4Tu2G06Ht8nHu6feHP3SA4jX/3m1//Y2/UffGmpyphcFC2JzBklBEqKoq1KnhQ1ygdUFrOFOH4sQj4aET54BIT8OF7/WKjkSE7yQ7ulUFNjmmXlM1xNaCZfxvtCiAIYnIFvQhY35jQ5gnfIFElClRFNUpzTQEyZtm0RwDSNROcwGfruxFFVOKORKX3snLgw4ZInKYhSMEyO+8cnun7A6pI4359PPD3cMy4qghs59R1TF5mk4A/aBcPksFXNw/mB8+lMSJlFzugcOIwjbhoglA72ue8RqaTJx5QwqXQ+pNIzjj7AOOJVse4qUfQ+apZU9pPj7bv3LGvDerFgt1ziYrnG6qbh1cvnDP0ZIWAYetqmQdc1Rusy0lUln604mhJ1U+FjnD+rArqTZHLw9Pd3qKZF1zV+CuSUZl7Kp3XWP7k4EY1AOIFNsgQXWcXtbsmXr665vF6iZaDfH3n85szhN2debdZcb5csm5ralkRdW1mquik6FWMQaIReULclBTgmQdNolIL98cBwhnFRxklNu8S7ASUytk5EL1FSlDfOKiQFBzw5z1ffvef+OFI1KxJw7j3v9h03t5GIJktLVhVZGbLQBalMSXIkhGIrzsyO4rkNFeMc9BQhJnKYcG7ADR3OJ9rFLTFbQugIzjOOJWANqQpaOweyBiUkGYfQEpljCZ5SMwFXa6wx1FWNlIrRTaVDIhRSFIFYmpXYOYmi+chlDth1J4bvBQt3Q7t1BDcilaJuLZVaYkUNoSjhXSpUwuACb+/ecZc7/IWhrxNhthxHl/Fjj0Rhq5q63fCjtuXVMLE/PrK5NDy7SbSVx7lMN+6Y3AJCZtK//2CHy+aaFE/sdi2hzRhTIZF45xjceUZTZypraeQCLS3Hfs80RtbNhugTXdfh3FiYPa0ihoRRgspYXHS4Yyb2Dev1JW29RcqKlCQiS5SyOD8g+p5kMllIhtOemEf68YmTPnB/MfD+Cs4mkb2jEZlzyLNFU2Nly5QmBJp4TrjverqvB/qjI7p3vBPveHl1RcrQ1JZGJFQMaG3ZXj0nh8DpPDL033F1fUV1s2D0A6Iv4Cg/jqQw4aahFOzjwDiNjNOA1NC7iWrdslmvZraJo20txtqZ+lrOa0pK6maB7PZIEekenjCrLZeffYatWtbtI+484IcRQsDIMkpQw8C6tjRSzwGHCa0rmlaxXEhebhpWa0jnR74ezrTLLVcryeQkLkjevesZnkaeXa/Z1Eu++80jInW8Wj9H0zCNR/ZPByZXc7Fdsrkw7HY125UgS4MUmcXiBb/49Xf/2Nv1H3wJIQpUUECWJdIiz2WDnAsPgSzCWJHmz1cC+e+D9357zXqT/2Hrmo9mhcLmLA/ID1+byEQSIcSPCPiYCj4/JSAWN5jIEVJCZYGMGSUScR43dV0/60MkKQQI5Zkxnjt6oXFSInIixVjw+tmTZBmP+Jw5njr2hyOj92QhUF5gxoHD4xMpLwlh4Dz1nEjsn+45ecflYkXKguP5zPl8RltDQPAwTJzdfN+Pnq4rGVCkxDQ5og+ISoOxRB+IqXwOdbI083SisEkiVVWew5VWrBY1u+WKRVOeM41QbNYbNpsN3Tjy7nTicrPhcrebpQQlv0rPI6LyqZXcJBcS4zjRNnWhheeMUgKVFdl7hHLIusasGjgl2rYq2INPWJ9cnDhb4qbrVtJIw+2q5Y9evODnn79ktbGcDnve/uoHcpd5tlhxs1myXC6pK0tty3y9nIwqtJmhUcqgbIPQgpwDImaUzCijiNFz3B/oe0PVVpzPT1Ace1jb0bQ1Sik4njCVQqlMiJGn48B3r98jlMEYy+gyh97TBcFjNzEJSa4baBqSrQiyuGZyBpFimWt+6JDkWecRE/gAPpJjIseI9xOj6+mGkVxtEdWG4ALT0NN3J07HPf3YQV3YIz5mjKmxQpNdRsYS6KeMQkk9I5QNQmkSxWbXtgtiFiihEKWbyDSny+YMehacZQRaaPYPgruHyObW0Cwc7SqiVw4ZHbE3zO7q2UkBHs+UB0Y/4PqEJ5IspCwLfbEbYUqQJVe7SxoUJgtqe8GLLyXPXwaMjjw+Zdy7yBQVer0krn7/i5PHP7uj6VfkrJGyDFRDCsToCTGwalclnTQGtrsLMnB66OnOA1rV+MlzPJ2wWnN7eUFTGbxz5Jho2xoBhC6yqi5o6w3WLpCqFCfZB8CREnRDR7USpdWbR06n9xzFnu45DDctzgSUFIxBso/FuWCqBuEhBoFIGj96Tt/28FVkq2+5/cmSfuh5fP0df/W3f8Nys+X25jlCL6mtRGuNnPM6qu0GUuR4OJB84PbzL5hyIPpQZu5jhxvP+PFETI5IwseEnyamHPhs95LKVkgEfuyYxhLqpqpqPhMU4eHj4UA2CZsU91/9QFR3tC+fgbAsVzukXhCqEZkTi7qh9w73+MTSNoiqkDmPsee1O/Jw38MEX00b/uRPXiLklnU78XzTcL3TfP3Ne+6+vqc/Drx63vAHP1qzqDTHQ8WyXtPKK8aDpVrA49M7lN5z11YoseF4lhwOA8dO0rQVRMHt7bN/5N36D7+yKKOZDzqMECIhFE2dd4E4H+rK2Hw+DgoxMzl+y4PwW68n5tdk/n85lcPZB3hlEc+W4iPPupUkRBHkigKxTBlSysVMAJATilRI2QJMKvkeaR7v5wRpHEvXh4wRghAD0zgySI2YOwaJTBDFECC0IsZAP448Hg7sux4ByFheQ+WIPD6RVSAkzzl7eivxYeB43/N+/4RIivvHfaHAzvf0MsIvUNA0C3gvVgtyitxNI0PwpQBJHqlkgWzO8SPO+1mvk1BC4mVhmZjaYLVm0TZsN9tCMPYRISihfENXCpE57HByjqqqEEhSKod0qWUpvIQmAsZWZAQ+BKSSGKlmJpogeodXAts2SCB4z/oTgzA/vTgRBWwmlUJluNpseXV7zbPtjhg87+8c+RS4WizYrSzrRY2tDG1do3RJPdSmxpgGbZvCZ6hsycGZPwAfe2QO1FYgpac7PXE4JBbrJeSEqSq8cxhjqccWY3RhmRiNVIJucjwcBkYPtrIkYfBJMzhBkIb3x46TjwRjSXVL1MVqlWJB74v8AShZElSlLNCg7D3ZFZ1JDr5YIocT59OBp+PA9uWPiNSM53f0+0f2D/c8PT0xeE9zc0GWgsl5vJdz4JOGSiJMKUiU0gV/jiZFcCkW0ao0xbaWKXA5CefzkTfvH5h8YLdds90ssEZQ1RXryqBSIOERZmTRZip1Io+WMUnyWBE8jG4g6yIAXu8KgfQ4eIT3eAuxVagI0ylw6CfaLFhoixUWk0WBYxlN2xrqhSSIibf3jjfvvkeuWy62i0/dVr+za7gbsNUaWxfV+ug7fBoLw0YptK7Jc5zBdt1y//AIUbJZ7zDa0p3OVFZzfXHJxW5XcNtDh3eeFA3TNGFNzWq5RQrLOHi8G1E6o3TGTa6cCv1AGhLT2BPjhKsn3K3mfKvpawEpIOeoAoFByhkr7hMxRHIWhGPAv50E4lMAAQAASURBVA3oJ420FUbXLBca/eoLjoc9j/f3+Devce6Ky4sdSjclnE3k4s7SErO9QOTI4/t7mqbFKoF3I8G5ojfxE867OfxNEnLCpUg9a9CssqSQGfqRw/GAMvM9Q2pyTLx995bNszXT45nzoScJzckHQsyoMdJSYjKstcVRh8CfB7oVDLXnvEq8bU58dzhwf+6JD/AYRvpK8dOfrqkrTZAZNw1Ylbl+taG63HB1ocEkDo8nRFzT6kuINePkkXeW96cKdI8Qj1gT6A8Ljt3E6zcn1ptVwYKbTzsp/i4vHwPEckAslNdCPQ4h4ILDeY9zfh5PzyBLEh+gliXGg48iEwmziDITconi+CCehQJ5+JA6nGaRZeks89EZwuzO+VD95FxiQrzIH10sMeYSADuzoAqxm/IMKHoBEpExTDAVXEVMiSRAGPWxg/NhDP94PDF5j5ZlpKUQpRjqOpAJLyOjFThjCFniRS56wigZ8UQj0LZh0bYoIRj6iWM3MIwOcqKtNMu6gosdk7/HhYLzt9KicnHlkIvD6QPLyhiN8554PFKpzHL5HFs39MNETLFg5aUi9j0Ai6bFh2J60drMLqNigElzACG5WEW0NpASMUUSAiP1zK8pTqHyMURSDNhlzbQvkR6fsj65OPHekxwluVFqmrqmqRq648jj3QPjfuCiabjarVkvLG1dzzk4NYiixrfVAmMblC6gF2Es0jQgDSkGokjIHNAysWgU1iRO9we6ri/BcrIrbaqYaZdrmrZGi4SUEpcz5ynQuQTSEj1MKdD7zHFKnFxguH/iF9+8Zr27QugGq0DOQDWdi1VKmwJjM4slqqqARIqFaZJjJAWH64+c7r/j/euvOTw9IJpbRv8tw9MPdE+v2T++4bHb41eaeqPKzNOByqqIBlWmqhWoYsPNSFKWhXuSS55ESqnYpVOar1dJjJmuG3j99j2PxyM31xf81H7GUtYYFWgbsFYTjCuQrKyJzpKdhqjIWRJSYIol2jtKgagtK7WkdQLvMsdxYL/vCMGTTiMpRVq1g35kShOyapAi050F+ydDGwXnwdONE6MfSW6Ew+FTt9Xv7Dp2I61eIWMZB6aUC4skR0RSDH4ixURl9ZxJkVksVkWA5hyLuqK1lu16iTEWKaDJCaMUWmvCMVEpixsjcRwR2aPUhK1bqqa4DMbujCAwxo6YHH06Mz1L7G8U+zYzCYWQDRqBTiBFQ8iOFEdEAkIgeQiPHn0yVGKBEArvPFIKrK3ZXd2idcX58MQPd295Oj3x8vY5NxcXLCtD8EWgWNdViWyPHgi4ccS7gqr3zuF8CXfzLuB8ga8Fykmr2OPzfJMsJ7Djsec8DlS2YmFrRjfQeoXSEi0lh1MPPmJNRZ4C56kUYdasiKkEo1Va49eZYefZN5FzknipCV7izp7H88DXb+549WWFrRsehkQIGdbXvPxnl6xDQiqPiBPtlceea9QkGU4TyUF31Hz3TaaPAaUDVp9pTUNdtwhOnM4D7ZXBuekfebf+wy8/OciZMGe5FJutx7nCAXE+4EKcBbNzts6HIoSCU/iYB5c/2I5zsQr/VkHywUb80fGR57/PB35KEcuqDwrOXB7+eaapfHjdGGPB1IuivfuIdPjQypm/X0FNlNFICh6BIJYfsNybXcDHyKnvOJxODNOHz1riUxGPCgTZJ0KXiBU4Y0hZzXOrhJsf5HZlSR6qZknbrNBkpqnwVvpxIOdETIU5UkTHCp/9jCLwKFXeW0zGKPWx4AvzMwRZeGGbzQozd3vKfwfnHSlGlCnSgpgzOQSWc7GfZzNVLD/yx7E1c+cqZUgUWntK5b1WunT3Y0oEN+G1xi4XnM8dF5+wpz5dcyIgiWLXCqpEqz8+nTg/BqbuzLoyXK9bNssFy7bBWlOis02N0BptG2y9RJmqJEFqQxYl/I65vVc2Q0CLyGrVsNstOez3vHvYw3kqr2kVkws8ngPr3Zpq/oz7kOg8uFReT8lMSBOdg/vTxHEMxJj4s7/8t1hd4c6O9WKJFqLg7P2EEqkkNyKwTYu2tvBH8twiTIHgevrDPfdvv+Xu3dviuvAKrSWue6A/P3Dq9vTGoy/XtItIFSGniI4KIws9ETIhxYLqF2JmnuQ5QrGkVSrBDGqTpAwhlimskpkYRg6nJ079DqUFPozY7NGyRQtDHBzjKHF+gRgXpFGTYuG6GCWJHoIrMd8JSgdHG46DY3ycCGLAqMh2VXO9XqHGjBsHcgxUS0PXS978kAnfD9wdjnz/Zs+5B7uQ3L0/feq2+p1dZzfhKaApJQQiS8I8h0bA43Rko5coKelOHdPoOPcjQkmsViwWNU1lqUyJNVdKI6uaqjKcj2dEVJhqwf7+gEgUno2xVM1INfUoo+iPT+TkSUoQRWC8DvQXln0lGHLpuBmhQSiUyMSQkUKjZAsp43NmPD7h7h31tKaxDVorckykWAqgqqow2x1aCs79kcNxT347R75fXaLbqjgvEgihiKGklSY3EN3INPYFSjWNjNN8gvYTLgbqVYOxmhgCOSZCjAyT4zyOHKeBp/4AMnOz2rBYVVhrIMGiKXEAySdWTem8HtyRcz/QLpYYWbqhTdty1A7fwGgzEYVqbaE044gR+nPPMI5I0xK8ZLSWRbOgepHx04CPE0IE7E3Gjop0EKS3E4eHnu4u8f0PZw7jgNKBSsNnz+B6u+Xm0vH67rEAq37/p5yFdyEgpkBwvoig3VQC65zDuThrP4qxIOXIh3DVD+7i/8/1sWidj2f5g4/nA+vkwxfNHZaPDE0KKv9DYfLxxeYv+BC8l6QuaHxZHvblGfSh0CljpCzm7ossXZc8B7WmnMsY3zu6cSqskGks9/A5yb4kwhfxYpKJKSeSMiRRjBt6dij55MkClJWlWBcClwIxJUbvGKaRMbiSmYUgT7P7LfryO86/r8pqDsotuUUf2DIppdkdq8ozVEq0klTWFNnC/HekFOV5qDWT9xhRXDgfislycBez7rF0vmbGb2HSpETICZEThIzU82gnRWJIBDGhlivC9GnF+icXJ01ToRcSMWVE1PRu4O79PRst2C0s19slm0Xh6ld1U+xKyqCUxTYLVNWgbF0w8wiE0uRUrJDKWkieHCaSHxDZYa1kuW5ZrmrevN/zeHI0iyWmMeQM3Xhk8Jm61gQBZw+9V4Ssiwp8fpiPIXMaA52LhAj/9jffFjDOsePzm2e0TYNIETEN6BSQJLRQ2KoqszVVNmbKieBGhvOBp4e33D3ecToPZCk5HHu0iATfM00n+jCQLjXLaslkE7WtiaFHTplGFKoeKuNdJMkIOmMkMCcTI+Qc/14eSgJBjKVCbZqa57dXaJMZwsTxdCaGSKUDyynThJqkWpJ2ZQ7pa8RkkUmgRUYahRINVgrS1JFCEetOMTJ0HW/f3vP+4QGzzFy/XPDZswuu1huSjnQpMfkRITM5G/aPgddv93zz/Xv25zNqobi+bAi++9Rt9Tu7QspM0WF1EYiTJSKASIKkMk/TiSQUdbLE88j94yOHfuKzly9YNbZoN+Q8Jxbio4UyRNg/7FnWG1arC4Z9z2H/hPMOqRRVbbC1Lg91X5T8SUBsM/IPtpwa6JEEL8qJU0S8yKQkCMmVdGoMKQkg4g4j0+OEnRaIhkKBnemd5Ez2E9E5Vk3LctHwoCTD6cTbd68ROVK/eomxprTHlWbyHZ0fIRWS7NB39EPH6Aa8GwihQKp8DNxstmilcZMrvBcB3TDw9uGJLgZiJchM5Mpzub1ASIvrA4tFS4xwPo9YpTCVYhoth9PAsRuoY+nohKjoHxzVomUtJFE6el2+Xs5RWkpkTr0nWkFSEpcrgqwRKoP0kDxBKnoh8K3GGIGVibd/uefbXz9y6gYG78hkKiPYrTIvr2qe31zxdDzwdOxoqt//sc6H7lDOsXTKnJ//zNC9GEm55IhJMetGUkHHizn7S/yWcydRChCRUhnniAKhRHxgds9FxodxzgdWSgLE3HkuRBLSXKAIMikmRJZzgSJnrUyRw5SiokA5U4ZIJojyPWVRTBBSJuTEmEoScTdO9MOEc2Hu/pXvJOYCIZBBCoJUSKsQtuQCyRhJlO+ZcypdbAFSmpLXE4sTb0wBtEIZg6osddOiYqAb+5J3A/PYX2KMmrPTMiHEYrIwmg/5glKK0jEJodBq64owk32FKLl1TVMzhlIYaV00lynPnwGihDnObimRJVJKlJAEF4gpM7mJ5aLBu8DkI0WvXA7i5InJWFTbfNKe+vTipG64uNniR0e99+gcqAjsFktutks2iwVNXdG0LdrUJfNAFoCabTZIUxXniVQzeFWQQyiWYZEIccT3R8J4IrqBFBxKChbLiuWy4jiMPOwPWF/myjElHp8eqdsFThv6qJhCSZNMpHnjJkLi7y+MnDkNA3/z9bf4yXE+dby4uqGpKnQMSD8hYqQ2NXrwZJHKg1hkQhjpuwNP+wce93s655lCxJjEME3I7Ah+YAojXgWs0eS6YOQnk0h1LBWnB6ssUgv8MBbSZeWQRqI+QIjmC6lck7K4Z3I5WQopWa/WSCl4OB04Hk50xzO7pWFpa5giKQtEVSO1RgaDypLKZqqmtPeG3qAFOB0KuyIJuuR4Gvec+keG4Yy1hm215XazZrtYIa1kvWo493uyKjkLWST6wXHaD8SYEHVEKM3u4vf/ZiwRnIcjjZIEIchEjCg5FmMOjGHgfRDsRItVksPpQLPYUGuDApTQKCWRQs4z65Hz+Ux3Hhi6kU21oalbbj77MVP8DU9vXjMORyAhZOGAKC3wbiLkwPqfXiIvd/SiZC/FWE6oQXhSLicnqTJTGIk+MQ0ZFSz21NLvHV3foYMuieCmCFQ/CL9TCEhVIWTierOlN4bj0xNv3/5AU2nM7W0x8vtyY3fe4cczxIlhHDgPPcPYk8JExuOTxwWPrSzTNBFCLJiBpsFUhhQCfvI0zYLVpubmdsOiWvF4P2JMi12uWWlDzHuUEhgtaWvL8dxx9/BAVbcM01hOs/nMTy+WXNY1tcxkbXALg9wY2tay2VhSasiqJSXB5AJSTCzbgvX2Lpb8FApHyXrJRdKMPnG3P5bT/ezI80FwOE/sTyOrpuLmascvvnnHuRv/kXfrP/wKH8iwpJJZMyf+pli6bDklYo4zmj7P3ZLSnftgBS5DlfIY/JC1M//nuVguQtoPhfMHxH2G+XQvSlGRYmFfiUI0T3PS+gedS+llzK8v5EfRbJKpaCnm1xQ5IZIEoRAxF7hnivTB0cWJkYhLiSASSYFIc39HzCMiAUFlopWoRqJqhZQClYGQi2tIqvn3iyRBAZNmQTeV61YYRbtZYnwAIVkuFlilmMKEcweCCx+7RGTQdnaH+mLdTbFk7AgKi6YymqaqsMagtSWEQPQekRPWKJaLlspHlCo6lRB86fAjSD5QC4GxH0i6FDoskoQHIQjBsVheMfRTsWznhJLFxyxjJA49sfn3XJxMh57Yblgky/O64sfbJS+2Sy7XC7bLJU3doq3GVi3GNmUkIiRKV6VLMlvASqqpmD+MhJIJPxwJbsAPJ+I0leC+OUHS2prNZslpgMf3e45PHlvXaCUI0dMhCVYRhSHlD4mSqVStOZMic4ZOQoiygffjwN/88D3785mfvTjx2c0tq7pC+gmdBC6GYmvBA4mUPP1w4uH4wOP5yBQjMZVK14eATIEcHTGUzUslqdYVooHH/WuO/RMbtaKSmuwSOSbquiZHSrIlM7xOGhR6vqDSx/eKBDFExr5nf9hzPJ0ZJscQygZs24plW2G1mkdTEls1IBQ5SYyG1XZie30mZ8fT+yUx1CglSKGcWupKcnu9QpHZrxdUWrKza1TQJXehNhipMa3h6XDPFBK5UqyuFrzwF5ATwToWjaBtxb9rO/3OL9/1pEbTh4HzMKC1oK0rGmuwwjOZBQ954ph7rqs1X37+RfnctaSxFUabgoNWgpgS948PfPXVt+QguFksyCmAn7i8esbu2XOevb/j7Q+vuX/3mrt333I6PuKCIwlgKVj8+AVTqzG2xfmR0TtENhhVzzP8TPATzntSEpzOZ/L7wOL9GjVaDk8PnA97ri5uuby8IseECIUhpLRCJI9ICWMUy8tLNss1h6d73vzwPSomLi8u8TGhRQQUQkvG0eFCwPmIC4GcIpnIFEKhOxuJiw5ta1TbQoblZserl9D1PUEkrq5ajNIczoVJoZqGgCTP40mRE1JI1qsVPsJvvn/N3dOe/dAz+oFnn0l0vWBhVzTdmQ0TegGpLUnI9XqDWu/Q7Q4XHOPU4+NAyGU8HKNgHH3R0gRPPmfC444qLZFaQy4PvnITz7x7OOB94PnFmtWyRWvD/fH4j71d/8FXpJQWIXhi/gDdzn+vdxClm1DGDCVXSs65OnHudIt5rCLmYUFB1lOErYjfGtv8fbfkgxAWfsv1wwe5SbEQp5wRSRSaqSiEWSvVByPzx9HNx8wfihYmZfA5EWP+aFQ4+4lzmijct+JKsW3p5ucYETGWIkgKolVQzZRVI4vGJZVyTEsBWRamVipODIFAJtA54SdHknIupiJCF9orIqMqy/Xzl/R9T4oBNcNHfQzoXOJeVFYz1bwIeIUQ836F2lZIpUFKpuDpp7kDmUqhqCuLCoE4DiXjataUjM6RcmapFKq8eYQcCRSNkdEl3C/GSFUbQippxEpAVVsQME1Fn/gp65OLk8MPBza+4cvbG35+teKLmy2Xu1WZnZuKypSTutIVtlqUnqlUKFshjS06E1VShAFSKgVAihNh7BGxYOBjcMV14Hr6fmAYIj4oXEhE4NQPHB97UEXYs2gawBJTqYA/zOCgVNOz+BujJclFIoGU4OwyXz8+8TSMfPv+PZ9fX3O1WLDWFaMrwU4iO3zynMaOfX+i88O8WRNSqhkdH0qhFUI58eVEtTTI2xo2lmkamM4TRkkaWROcIgSPd4mqtizqmkwsRYooia8x5o/BgQhJShEfJmLsqUxitZQIlRAu0FRLPnv+gv83e/+xbGmSZWlin/KfnXMuM+rmJEhG8swiLWgAUo0etgjmgAieACNMeoAx8BB4C4jgFTColgJpkZbM6srMiExn4e7GLj3sJ0ox0GOehQnKqgRdFeF11yTC3c3uIVd/1a1rr7V26xwFT041kEcbV1XTQmF0wa08w5Mjxt5gO8viX3D9AFkmpMg0SvBsdcmL1SUPu5lliRQlGCfJdAZGVTGi0ppYJIewkCx0T8/4fDPgj0fujg8cHx4Q4u5jl9XvLeYlsd8v7MNIjIGucbzqLtmctayGgc/UBW/2Ww4TTKnQtgP6pE9JuZwOfoHMsirdUyDFmcG0tBKs1pToUXlhNfRcPv9j/vif/RPmeebmzbd88+t/zZdffcn1fov5wlBedkRhyHGi5EhOkWlaSPGIdY7WWOoFJrA/jMRJMn97pB9XPDl/gjMNd/e33Ny/Q8hIpx2t1DjXE8KCEFX1L2ICPIN1DC8+YZ6O/PDD99y+/oGf//xnXF5cEsNELoJIrHTuKcPCB0/KHp8il5fPWW3OKUIQhWK/BJAa4VqGs4JpLD5PrFZtnZGyL8QlkMaAM460zFWjphTTWOemWKv57MVT/vbLL/HzniefF/7pf9OzehnYxwPnItMtIyod6LUmzBNBvmBWGmRGO4dRmuP8gPdbRIFpOXL/cIMonsFaWtOie8nm1Tn9ty1+TPUQlIqUI5OfeXu3cBgPPNms2U8j/9+zzX+aUEoT/HK6cGVy4sQg1BZNLid+pIA+JYLlXNsS8sPQ1fyBETmZdk66D05seEGeJqx/aOfwo97p3/pbnEqOmiRbMunkCJKcYtdzImdxeqlTINjJdVMnJldHSir5lFHFaerwxFIiwtSEbiVOCkH5YUCtOek8oEiQRp8sRvJUcImTNqu+VVHEidFUKFFD5Oboa8chBfbbGeUsprGkEpmOE+M0cqkEw+qMYbWieF/v0IU6kT6EHwX4SkiMrBqWJWXuHvZsh4H9snC736LGaht2xjB0PVJJpqW+fkwJrQ2F6nyaZ8/heGCeZ8iZ1jmM0RQKUgrapmYTzbIaSNq251gm/OJxpqPrHMdp5rA7sqT4UWvqo4uTje34rBv4xWrg5fkZZ+s1w3qgbS1GKKztsLZFKI3WDrStIh1tUaZBCk6bZqi2peTx8564zOQ4Q/Tk7MkiENPCMk/stwfev3vgu9e3vLnbsZsjY8gcfe3RnfUbomwpRdfFdlrINVHy9FCcFp4oAi0NIkMWdcpvQPDgI+PtHW/v7rgcep51PUPT0QhBzIGdnxjDTBYFqSs1qISsbaIU+RD/k2Ids52IdINFDgJfEkoYbAcsgTAtrOQGc+o7aqVriypGpmXBp0AudbFqqYkpUqa5tnSiR8nIeq25tOeUsmFZJnKWDF2HlBYfLal4BBohNNpQA+VEYYmS3b6nX0VMl5BtpGhFNzQ41bCylsGtWHzCyD33x4lD8My5sJRMKYoYI2OqtPVRZOawEHPGJIkMmd1uYSxH/uyPfvqzdf7u2+/4wy9e0UjJImdeXK744z99yufPL0hLYR4FjXJ8fXzPt9//gEXy6dPnWCcxpzkVwdeWRpGStltzsd5w3rRcbS4YujVN29D2lsZJnFO4oUe/eMbnv/iCv/wv/hlTnDhOD/x6/H/xV/IbfJgAi5KSdApH8jGwG+9pTI9V0PcacuL4bkvz0DCPC8a1PD17xno44+31d3z73Ze0SvPi/CkQaFxXe80kREk/shVGWdqNZXO2Js8z3371N+zuL9icnZPjiPeJeZ5Y/ExKgUzGp8gcEtdT4TMvyX5PEAvNsGJYW7p+xcPi8SkxDC297RCzIvgDjbIYXWMDkjW1uJM18bOkSNcPPHv6lOfPn+KNxD1/y8PZ1/y/v7umWMsvf/YpL19dsDJ7XD5w90Pgt1+/ZToozn71BUV2tH2PVIWctogyw/KAiUcun15yfvmU1g1YOp7/UnD+/ive/pt78hKxRlJQLCGdPmM9Khtj8PGnr4hVUhCVoGRxCnusDLGUEqXrHhRCTVVNgKamE38QlwpZrSDlVMTkU0endn/qhTOJD86QxIe+yQc9Sf2zpwKHTB0cKKh25dN/zQWhasopJ/alhmJ+KGeqti8Sf7Qo+5QYQ+S4LPjkEQqskBghKCpVoakUSCV/1E1pI5Ef5r5RTQ1G29pQOunLPuhf0odXlrUVZBEoIVgNjof7A/cPe9qhZ7VZ0TQtyzRzc33NPM04Z9griVSKuGSCr20U6yxaqiprSLnqZYQkFLjb7fj6+x9wznB5tmHoGoy2QGWqxmkkF7DG1enGtuFhWri53zJNR8rQM3Qdzp4IH1WD95xVGKMxptBYQ+MMgkKKkWVeSDHhfUQrw+b8/KPWlCgfOLFHPOIRj3jEIx7xiN8B/PQntD3iEY94xCMe8YjfKzwWJ494xCMe8YhHPOJ3Co/FySMe8YhHPOIRj/idwmNx8ohHPOIRj3jEI36n8FicPOIRj3jEIx7xiN8pPBYnj3jEIx7xiEc84ncKj8XJIx7xiEc84hGP+J3CY3HyiEc84hGPeMQjfqfwWJw84hGPeMQjHvGI3yk8FiePeMQjHvGIRzzidwqPxckjHvGIRzziEY/4ncJjcfKIRzziEY94xCN+p/BYnDziEY94xCMe8YjfKTwWJ494xCMe8YhHPOJ3Cvpj/+D/9n/3hzAl7r8/sDt6dvuJFAqNdayGltXaoTaGsYEQA/GwoIVGtgbfS1rluFwcF97w7GrgL/5Xrzh/rvib/8eXvMsLza+gXZ2xpILQgkwhZgXCYFWhLFs4Lpz3r9BPnzGLhRAP2OzJ+3vef/0D023kydXnXH32M4603Bw9Y9ZIZemt5dnFJVIEttM7bvbXHJeFkqDMnk4pVkVjbhbCfoSDQAnDfeO5tQtJZNZecnmwtKNl3kWilYTPLzhcOGg0faPI+2vmd+/QEzy9fMnFZ095//Att7svOXvl+OLl/xIxDvzN//2vWMaF1RfP6F9c4lYO1WX24Yb54Z4un1Hap9ztt7S7Axs/gYrsG8nOSmYSZvT095Fn7RntszPU1QXi6RlCSa7ffsmb139NyXuGdYNtDUuCkFoCA+fdiudXT/HLjhgOdO2arj1n6FvCeOSrv/mOr/76Pc5sGD79lHE8ovuGuN8z391TIky3O9Iyoa8s4oUjslCQZGX5v/6f/5//U67b/+T4P/23/weUUJQUiCmSYqDkRM4ZgcBoTUyJtu2w1jBNI8dxz3jY09gGZx1aa5RWGK0RGQ7HI1kUjFHkmKGANpoQI4fjxO5wZJpnKAUhCqu24eriglRgyYWuaWi1pgDTvBBCIKXIw25LjJlf/vKXrFYD0zSRUkZKgTYWISVaKpSUQKGkRC6FVAqTnzG24dnL5/h5YXv3gFQKoQTLPOPnhZILKEl/ecUv/vxPsecdetUilWEeF3776y/54e9+Qz4eeHp5xvnFBa/v7vG9Rm46lHXkHLm/fYcdPRfF0WnLojMP7Ln1t+TjBGMgMlNswq4daYzkJSJVQhtBkYYlwv14YNU7/vlf/BlNP3C9vcMvC313he2uiGhm7wkBYi4sIQHQOoczDh8D0zySo8dIUDlyfLjn/ZvXGGWxZyvWz69AWIoyGGPQxlIAJTXONThrUEJQiCht+T/+r//b/5TL9X9y/O//xZ9xfr4i+5klBKRUzItnCRGpJYLMfppwquH55TmX5wNKSt68veXL798x54KyhjkGbnZ75uAZGsuzzQaREkIqtJKs2pbdODKHiM+Jow9MMXKxGtgMPV/98JYsQErFn/wvfs76Vx0zC3eHI6FkhlWDazS7eWL//p6rNrM6W7h8eUXbXPDuzcTX395yd9gRSiD5jEHy5Gzg5599ylQWdvOWrm+5OOv4ZNVxeHfP33/9jh/uRkqEQcM/faX55WXGhBVf3rX89c0drDLaN8w/aM4ayc//RDLbkb/6mxFtNZ887Xn3w8xxVLRPA6KJZOo+gBBIBSqDkpBPtIJMAIKUBU4rOmWw0TLtYJwLn/3iJffXt9yFI5/+4RNW1rOkHTcPE94XhmaF2Q3M/yARh8LFZ5r3/TviKnD1XBCKx1pIwbDbZfb7wnyE6KFdC7rnhSIzCkijwChQCpRwSH+OSmdYM2BUy243cXv/gNSC/9v/5e/+nWvqo4uT6Y3n5vYef8gsMdWHOiaO85Fp8RS9plspci5MdxNyypgzi/QFVzJSLhy8Z5lgnCbKv0oovXD9Zkv5eUt+8GyUoWvPkSnhl3s0kk51pP2e8XaHCR379kA4eryesUIwrDo27Rn9WeH67sj8PbxZDoybRDCOIAoSzxIDh9mglcInyRxhDIHWOl68fM6maZl/uGY8vEVJWL08pySDj4IoNCnBkCWDamjPO9S5wlhDSDPd1pNci5KaeYE8CeQxE9lxVAXPHjqPlxa16sG1PPnz56hSkKuG0kqKLnhi/W5dT9RnHEJhLwU7kzmIRGsFUUEio1KmDYJzb2lZ4PY7rm++Yb5rcRdnjIc9YsqoJEAkpGzYrJ9hmitCMTQycTb0jHnmOIFBISUUEuSEyYI2W8oCKWSGiwvC61vEb+8w4wIys1KQBssiCsf7kSV6DtOCPTv7D9nffq9gtaIUyBlEKQjqHqLV6aBHIAUQPT55oGCVYh8yS/Zo7TBSI6UiZ0gxYa1DaEFKASEEjetAFHIqWKXpmwar1ekdFKZ54u31Dev1GtUoYCGqQhCZ3BZc12GkpV0N7B+2LMuMVnVXEwJSKSghsMbWgzRnckrEEDDWsvgZciLHwO31NVJIjFbkGIk+QSlorckl0XYdT58+QWiJbCxFibqhto5Xf/orLp9ecfMP33C8eVcLuI2lrB1JSbSWTA8H0rwgQiGKxFHMHJm5Y8eYZpxNxCmwfRiZw8IlA6thAK0pJaCNRCmDS/X72ax6dtuZ+90IRmJMg5QJnbd0znLeGRCWmAVLgJwlFEnJgk4ZlC/s/JH9PJG8J84LfX/G0ycvKL3Ca4HUHdY6lBRIKZBKobTBaIkxoIBSBJn0H319/sdGYwXjPKMFLD6gZKZrGrQKXG937JYFazWr3iEEbO93dG3Lamj57JNLru93+FCwxmA3A0kIpmXh+m7PLz57zngc0cpgJJwPDdtx4bD1THPANo6m6Wid5awx7ENCWYVZSca4Zy4BYyVOO6RQzGPksJs4HjwX1pBi5Hi4R2AQQlJyIc6FkDOCAgp8TNzst7Qri7aGKCL3xwNSGC4ayy+eNKyYOYwFJwWDlixeEMM590vik198imoE119NXO92pGnhcm/Q64TIEls0yML7bws3P0ycvzA8/UVLc54oJBQCIyRLCCQBxig2rDlez/gRSihsXvSIoBhvM/Mh029W/OnLv+Rf3/2ALJp4c+B2fsunr3r2DwIfA8d5xqaM6VvE0XF4n3HPWs5/HskiIopAYnh6+ceomDnc3xDjHojEMcFOoNpIVAXjBCILcshonchpZtUJXj1/wvnZMwQNf/+bbynl49bURxcn45sDh93C5BNogQ+ZlDK1aiskD7ZY4uIJh4IYM0oHrFR0SqHI+JAZl8SYR3bbA33bYJWivcmIc8fRJqxSaAp5zKyd44VZsdtH9M7gg+K4G3n/7h3KKjbDQH7hcJcS1Uhib9nNinBc0BaEhULBWIOisHhP0itCGihiQSqJFJCB7TxxDAu7cYdMI6mHzZNnNKJnWTKTh7mR7JIl2AFpW9wY0D/c0x0DqVUINCyZdJSEu8D2bsfudku8CqRniThnHg47svbkpxqN5GxY0fcbsrLs5pGYE/s4E4pCiULnJIeUmaQFa+sNfV7oAmyyZdVI2mGknO0J95HruwPSb2mzQQRLGCNiFvTmjKtPfoFdX1JEQecHVi7SDZJ4U1he7+FcIs9bwuxJCXrbUaJmfH+HSSvOksLIgaIV65ct/XOLN5G7GNkqjQ+Z7bste53/gza43yeM+x1SanKOHA9HcsloLTFKk4nExZNSYPILS4wopXDWYKxGCkUphUJGSlP/jsqklDBOE73GZ48QAqCyLErTti2pFJC1kAgx4n2gXXXE1nMT7llywBdwsuPcbFgVRYqevm9QUrDME23TsiyeTKmbTwZhDBIoqRYo4zSRS0ag8POClJq266Ak0qmASYBQEqUUkNg+3HLIMxue016cY02hSIWQkubynKfWMr87J+4fkCJVlkhI4jIzHnaomFBIokhMJbANOyYxQU6kEIkhEY6FFCXzzrMaMqYxlCQRpUCENEcYBe3FmhI79oeRKDPCCJSZMXrGyIJSEm0URkm0lGjjkMagjCEnEF3GmYHMBiU0oiiUUFjnuPd7cs5o6ehdg5QgpOTDnitLRiMxSpKywKeffnGSUmZ7vOd+P3Ox6tl0ikIEWchS8O5hy5IKN7sDv3j+jKuhI48jpRSMEHxydUYp4GPGx0wSmXc3DyTT8ObdDZ1p2LSWkGJlZoRk3TY429AOK5YwsdsuvLy44Hp7wMvM9maH6RSLThQj0UIgc6bEQpkL8xTwwVKKIMWZebqnaS4xWpJiJsWCVFAUCClAS+boQQliDkgaohQkaVl3lvVaEDooApKHd1zSnV9w+aTFrQXJ77j79QGLomsNiMSyRIyWXF30DMbyl7/6A/6H7TeYpInvJY2T5LjQKMWTJy3/+qvXbMfE2mnKJNB+4Ppui7KS5xc9N7cTr7874pqWV09afv2bN7z77R377YR7suOXfyk4vo8s7zW7tDCKgC2CwbfEmPDHPb/6C01RiRBBCsFZ94w/+PRf8HQzEPz/yDj+G4rYIbLncB0wvaB5VkAUmk4QRoBM8DPfvPuOb35zy7PNM371xR/wxz/7Jd3aftSa+uji5KzvQBhCLCQSu/3ImD25QKcta9fS6xZMwzLAsuxJu0CzcrzabBjahsM08+5uy/GwoJSkKYbWOtwIw2GFvOrQNAgFyhSaJGCbKXuQOI5pz+1yIG1WnD37lH5Y413i3e6Gw/t79oeA1AajQEyepBIYgWkGyIUSK+2eo8CUlk6DloFxWkglE7RlbBx5t0fc7cC0yPUKnKZoSWoNkzAkMnb/gHy3I98/IFqLnBdyOqJjpLUO02hKKKToyT6iJ41Smt3dDaG1pAQr12GHns3ZOUJb1OTwYWHajmR/RBvLIDPFCnyR+FwoPuHmzBAkg9C4NiHdTEgjjdK40pCDQ6ARjUYVh0mCNm/o9BqlHJQAc2C6uWdZJsJ95PBu4u13e/SVRbWKKWTE5Yp2UtiDp7+dOBMOOwz4wbD62QrzDII/EPeJUgydNZgOGLf/YTvc7xG6puF4nPApoLWuxUZJLH6pB7ePkDPjMjMuC0IInHM0jWPddyitKbmQYkAJgVYaSmE+zqScKaWQckKKQimV6TBGo6UklUwKns5arNXQCvaM3OcZZS3WdVjVQJL4yVNKxmmLsQZyIcZIKRkpJQKQUmKMxRmLkgLvFx52D+RQkEZDicQYkBK0MoiSyUAKHu89WtWW0P31e3iwRDJXbYuQFiUUQkCRAjl09OYl874lv/8WRABgHo/kFGiVRCtNTIEpjixiBhmQpRCmQjgu9M6ikKgoKRFko0EZckikkEipkINimj3KBXISlKgQxRKzIkbJIgVCSaQXlBSQaUbkPUIElJFIZdFuhWlWGDtgjEUCKSWUdcg0Y2Vm1bY0jalFntT4kEg5UZAUFEUY0unA/clDSTrjWCzsx4Xb3R4pBFoplpiwyrLEhe04882ba+TTS15enmGUYvaZLAAkIkHJEUXh8ydXpAIJaFsLqeBnj5SCKQTOVwNX51esNmu+/u4blmWis5ZPr855s91yfHtgOOvJK0giEpMn50SOMM8LCElMilw0uXj8PNI0LdZmjJGELEAAAlJOxBRRWrBMAdtKQDD6BYNFBION9WLthoZdXnE3Kd6+/56cA65LrIaezz97yssLwTzfsoQbUhFYaWmdZNpJ/vSf/Cnarnj/7i3v395z+73n1actZ4Pl+vuR699m+vOe401gfzfx8vyc8ZjRRRCKpVjB5ecdn356xroT/PDl1/jbSKcVL18qTJlhWtGZWP9/ihRVsOvC1dpSnKGcTUyhkJOg05oz93Ma9ZwydDx78jl3d/dcX49kPCkUbBaoTiOaiDEZaQWEDCIx7TO77yNBwuHvDuirv+XsWc//5r/5dy+pjy5OLoeBVSsx1jHOE7d6y+44E1KisYZeGXpRbzBLFmhlsEKxUg3n3ZrzVU9nJsIUkL6walrOu4b+vEO0hbXs0GYgKoMXmSw7poMn3k9Mh0hw4IfCrDXN5SVs1pSuJ+jEvDvwdqfxxfLkxQtMO8ASwC+Vbi0NMUeyLyACYZqQKdIbg6Awh5HcWDCW9vlz3OUakyWy72n6jlIkIkuyMIAihUhiJocZaRVcOuIA3nuyzsgzhzMCNQIkcrsQ0YhZM2/3jFEilGFwDShNkrUnqxV0WnMeJBwSUzkwihGlI2hFIiMDWAyN0egkoCyInGFv6XxLrxomrynGgClY4ehnSTsb9DagXCLlyPGbA/6HB3xIHKfIYR94HUbGd4X2rGXoN/RDgzCKTRSsErRFIgxkYxhlZjku7JaZg4cIyGnBTJH1Iv4DdrffM+SCkJLO9vVmnzM5Z1JOGOOgK5QYKaMkCUHJYIylcS3GWIwyVIlH1XgIpVBaggeJQJ0KiVIyMUagvl4ukpAS0S/kEEkKMIbUaNrujK7t6w1xycg5oJJiNayQRZClxMdIWJZToSEpOeL9jFISY3TVVLgGZxqWZU/0nuM0IfyMD57OOYzSKCkxJ32LlIKUMjFElJDsbu5pXx4RrsUqhUKe+uZAa7H2kvMSCHfvyCEwTyOiFKyr7E3KhRIFMov63PnCvAuoCEZrvM+Y7JDBIIpGCAWlkEuglFSZnCxQMrNue9IU6cyAsg0ziV2cGWMEqQFLieCnSJxmlCw8eb7Brc/Rtq2MitKUktFSI5VBSklnLI1VKCnIWVCQCCkQ1LZSSKKuCcRH09i/z8gp0rkGqxu208j11rP4iFGCxlo+e9oz+4WHccQqg7GGtnUoWbURS4iEVPVa666lcYbgA841CKPJEg6HA8qqyjg3K85WazZDTymJp6uOvRJ1PSqBEpL5GMh7sBtNNhmfIiF65jEye09KkckHhFSV9SqF4CeMBmsV4yIo5Nq+TZnxOCG8QBlDihBCRupMcgrRtlhxhbI9c8yMD0em68RUFqQ74n1meij0xmFUIqoHQkgMbsPV56/w+S1Xz14xHve8enbG+y+/QZeCiNBoRV4y3381sm6u+NM/+5Rf/+3f8va15+32wDHCumm5PSZCFjx9vuLFq4H9ww1L3BG9pnSZ7kqwvxaYW4OVhsF6slRo6+iMplVHuPLMeiEl0EKjDgM6PmVaLL5IhmHFZjXw9nViDgslJZCK+UEiB4FQhdbUwkKq2u4OU2YbR467A/E2I3+t/n+upQ/46OJk0zi8r2K9TrS0KI6tx8eIVpKzoWOlHIsoNE2PMgNKaobW0rYNWhucjKxMi+wlm25gM3TYjWPeRNIQSeGOaZ+J0qCKhZgY54V58hSZUFcr7LCGbiAIgSfVfr/uceuXmI3DPXuBcA4zBcr9Du09YlYswbPIBSUK+7fvYJxYNw5rBAsz4qpBtobu4pyVOEOkjHOa9XqgSeACRDQxQUgJkSJaCtg0TJeGqc8UBRSJHAxuZelGTZc12UWOccsSj/hpxlNIQuLNisVH5hBRElKIaF9YHwVmX3hYJhITU5MIjSQDJhWUa1FtT1kiYTuivYXZYXNL32hShiAU0oAuCRMEcknE7x6QuZBEZvxuxN9kchGkIIhRkL1k9jNhKahNgzlfaKXDGUUjJbIIIhm0Yt4Hxt3MkgtFa0ROlNuJ8jCjRfz3391+zxBTwhiDlIIsClLKSm/8iEKKEYREG0s63Z6dsT8e7kKIyoxQW4tCyPozhcA0jnmcmKaJZZ4B0FqjrcFIhdCG47KgnQOhWDlHIzK6SMroyUcPAVwzsFmtiDGxxELIgA8IQClFyoVpGokxklLCGUtOCR88RZx0I9ogRGFZPMkHrKki0CJq2zSmRAkFpRRSSeZxYj6M2E1Anv6czBlBJgmQ1rI+f0KIiet3P5BiwEqJVIKiUmWKlEQtBhUy82EmHOvrpiRRGHq9oi0txCqgN8JgS4M2GasLRklKTKwaR1gUG9XRNwNTXtAiIVPhECJLyOScWCLEqNl0DcP6CtesqlhYCPKJulJSk0vGaFM/F5JcFD5GCokiBEJUTU9KVauglcK4j95mf39RBFpKrJIIHE5tSBlyEbV1oxVPzwaexoGHQ2UH99OEMxKkwBmLM/WZsEbTWENjDVJqlhAoqWCUIsvEum/p257OOVQJ+JBojaY4x5v7B/ZxZvKJLATz9YwbXF2nIpFiIYZa8Occ8WEBYRFFQZEEn9BaY82JVUTSGwOpMI0zprc0Q0PXabRzGOfQTYcdzrEJxtmze/0D4T4QdhbWoJTmyfknXPTPEDGxO75hjhFl6wHeDpmz5gVn7oK7b2+5XF/y5Lwn5sBw1tA3jsN+RpuOf/5f/HN++RfnfPfm1xQLDzHSXPY8e7lBSYG1iq6THI4j799u0U3EPtX0L+F48ExbRRd7tCksLLRti+4sykyI/ohvAj6CRHPVbnD7M453gWK3FKXZbt9zOFwTwpEQAhSBXzLqaFASTJ+QrqA0ZFHIGZZYSNnTWclyCMToP2pJffRT02mLihGFoDWOVlhWJhBJGK1Zd1WhnqTgSdOi0BxHj+s0UktiyUip2PQDvetonGPoG8yqxV8cOTZblnnLdEhINdCZM2RU5JyJIaO8pWsv8c6yFFWFZrlQBFjXcf7MkYVDNA1eCDCaZDXcH3HLxBxGvF0ISjO+ewe3O7qmxXYGoTOhaMzTFdJqihRoA9Iqmr7DFYkNkIUhZ8GCwMctBonvHUsjiabQNmuUNuQC0gvadcsLsyGWyNtbmH1lW0LJxJCIciRtFlIbkE5BiIjDgjkW3FgYvABhmcOR7TgRP9wKjUE2glIUYZHYuadETcmStkiKKowCEgm5RMokiATm+y05e3wGf72QFlE31CixwnBpBSYqSoCzWTOMgtYWdMrElMFZ5pTIqbDcLcQlgCoIlyjzQrie8OOMb3/6xcl6vUZIwXg8Eoui3pEFUgkEgpwTShvUMNCllhgi87ygtUYqidK1QKFkCoVUwGqN0QUh6qGWc8J7jz+5SeohCdZIWlc3XDd0FAWpJMYlkr1HB8hzocRCEJ5pGpHSoKXGWksJkZwiQmpKCgQfmOeFaZpreyknpJJo65Bao06tnJQiMXjmpbIoSmsKgpACJWX6YYAMOQX87kh6EkguIU7to6oLSVAkRhnOzy85bO84IrESMpEiM0kkSgaSJOwz08MCEZRzpCi52vSc9yussvg8kUvECIUqjlYrFpWhRMLkEU1CK41C4CRorRDSYaJAxpm7WLVntu+JxnJxvqIfeqSxgCTnDNRLmTWaOcy1MJOWIhRICxRyrrfEXGLd61RtaUgBjfrpFyfGdvgUGDrDWjs6KykIYobD5Jm8x1pFYwwpHdlPM5BpnUKiGFxDY5taqOdcReKlkP1MCAFrLb1r0Aic02ipIEVCSiyh6qBSSRyniSlGjHS02hC3HnFbUI0mtlBKppRIKQJEIaRALg6yJsd6nrRNoe8k+73CSrhqFXeTJ6ZCqy3GFdpOY5yhaTu6doXUa477wM32NWLKyLljc7Zib2c+f/VH/PKzP0Xnmbevf2CbUhWmZJiXkeNyyy9+/l8yPdxjxAPLXnN20ZKYOb9yaKOIJfPZL57yX/5X/4wx/Rbdas5frrjbJYRRuEYiZMI0sISRh+9nbl8fePYcLj8RWKO4fxdRRaIQUAQygUbiXCYNE6kN+FAouTKo55whl46H21t25WuOYeLd+2949/5bQlzIlW4ixIxaJFpb/CEQVcHKhBIFJQuoQpQJ2UsaqWn7/z9rTox2SGexRgOShXr7QhpaZxnaBimgKEnbO6AyAdaYSncW6FzD4FpCjORSMK2jbTumNjLimUMke42NEkVGIpFe4bLFsaIxF0Rn0an+XKctThdUCQhRSFkyLRMxRHKGnBbkeKAbJ2xamGwhG0W438P9yGIjelIUnZmXTBYZc9ly1IrGKErIhAyttVhrkbpBK0NQhlvx/sfbsJgLrWs4W51hnWOej6Q0YhWsNz0hRd57S5o1JSmkL+hF4rYZdTsjugWUQ/hA2U2IMZEDWBxraVjiwuG4J0eP1Bq0JVtDjoI8QpgdoAghoFNmZSSSwnHylBDIQuH7SJIj8mbE7zNhFysld7rptcbglKE/OUjO1Rld7shTgqMnlAxSsz9OxJSYlwk/eaKApCGOnrgPxBjI+adfnFxdXhKDJ4WAjJnFL6Rc190HZaSzlrZxJ2suWDOTShWRSymRUhBj1ZYYpVDKkEOEnAjLXNs5QqKMrboNUVhiIMdMKxqctSipKCXjjwthtyPHgjENokhSSRzGkcPxyNCt2GzOMVKSjeUQAiJ4cqyFTwyRaZpPjhxN6xqsFSipMK6yKSkFFgpTDMQQEEiEUuRUyDEhK5WCzoKwP5BCIOWMlOXH4kSeipciBU3XcHX1lOnhFhFHiowkEev3GCNpSUwPnuUQaxtTOYZmxSdXVxhRb9xKSBZZmSUpBKZYSkyElMkapu5I61agCllElIIWS0qCqAtxUGAVm37N4he6VYPUCgl1n6JU9kNqGueYwkgBCgqhLFo7mgIlZwQw+wUfPBKJyZIUofbvftro12d89/q3CKfotcIvkRgLBUUutW1zuH0glcx2nGmcRkrB7D0CiRSq7m1AypmQCw/7HVLAqmtpnCWdWqmTD+TkMScR8vY4MsXAcVlw1nGx2qC1AwpLithsUNKxbwJLLoCvj6gQJy2JoCRJigllM21bONsUwqxpcuG8EdxOGaUU1gqEzFAKlIQEjDXMPnJ7c8Pu+o4X9pxkCy8/XXE/L/zFH/zPaJXl1//wL/n111+zpBktTwyOzSxmwSbJbfyW1CW+/YdAlIJPXjRszjseFknTOp4+e4pbZf7mf/wK3Tasn2ju9lvur/fcDIpuJWmMYHwoPLyfWLYzn3zuGJ7D/F1DniRuU230xcAiE+gZ0SRkNxJygaQwyqBxxAfNdLfnrjtyGO+52d7wsLvFe0/JUKianJJh8ZGsO8qtQk1Ak2lcZOgTq01kWiKiS2yetHz+2dlHramPLk6UsYiSEULUTbQIPsi8tNRIIckUZBHIAig4O1tjjK03ipRw2lbb4DzVRSkVhEK3WGaxhuTo9Aq3aNhnSAVBQ6cdxqxAdRghEFbS9QNt00OaOB5u8CGwJME4B1JISG0oKUJJNNuRVBIl10M23h0pR8+ygE6a0hTytMcLhWkUeXCMOTLPgaZxmIuOoe2wpkVJRWwD90oyzQG5VbQT2GBxMiHMATk9wOFAzo7dJAiNZNQTudW0aVN/m2SGxaHfTiT5QA4NyS/k7Yz0mVxAney9a2E4P2oIBYqiPESmeU8WmjIWCAlCJOSIpoom9ZgQ3iNspJxLpnXEp4DYFtiJqkUMGSUVWhtUVPgQkamglUDISAlz1RL4gLOG8WFie/eAz5HFB5bZk3NBKgkRQqiCQhE+rqf4+wy/eKalfj9K1EMylVgPtJjQ2iCbKhQtgJEKrTXjOBFiZJlnyomNEKIWATEEMgVKIS61F25dUzVFRpJEIoaFsCT8dktG4PqmHrw5MW1nlsUzygNKaRrXYo1lmiZK2mGtwbkWZw0Ph8JyOFZNiNI0jUJ4T0qZpmlpmwYBGG3oVysW7znsd+RS6ppRGmMMCEHOmiXnU16KpshCmhdKiKSYQCQydV+o6qqC0goEbC4umJ89Zdq/Y0mJFCPZJ8IU8KPHL4mcBMJqjO25unqGNQZJte4iNBlJ1AulheINcilVMyYtfprpNz2NbZBYKIJUErEIZCPZdBphFI22NNhTJodmnn19z1pShEBKjZSG0fuTBduhEOQScUZWm6vS+ODYj5LFT6SwYKwk//TNOrRDww/Xt7x7uOeTywvM6dCqQnHq9x8D7x4eKAJ244jSBpELUiicixTlSSmxeE8umRASRit6JYkCbndbbnZ7jn6htY5N1yIQ3O6O3Ox2pAy/ePWSJ+uOIiT73YHzszXWCeIMspNMzYodB2KM1amZalQCMdX3KgpCRoahoLOiWeqfKQKMkUitkcJQiqJkweF4RKktaSrcP9zW9pAeoD0w9IVPnv0pOUX+u3/1r/j+5it2ZaRrGwwWlQI5L+ATb//h7zmuXpPaM96PkZVtePbqgs6tMAdL3wlEl/kf/u5f8vfffoO2lpvX9+zfbImpMMeASQKXDcdtYHs90Q0Sc2a4PYJ1a8Z2hFVD7iyLnPCm4DYBhkAshZzkaZ0LymL5/u3I3fYd20PikCKhFFIuJ8akSsiKAEohpYBOBSs2lFkSkqTVI8Nw5OyqwC5hWsXTV2ue/sJ81Jr66OIkUWj7pr7xXLBSk3OmpIwSqhYrJWOkhJDICTarNSUVwrygUZgTvZliYfEJpRJhO8OSOe+u2FBwQiGXeiDmXB0KtjOUvuWhBGY/Uawki4Y5TizTjmXxxJSqkygmJBIyCDTZWUZGkveEw0RaIvIYKRFiKYQpozLo5ClqRH2RIHnGacQvB5yUrLsNQ6cQotoJs6hK7VIgbRdMAfGQ2f/2lqg8sg0MvURLz+19YFwpbtsRYQ2fPP+Emcjhbg/34G/3pHmBYwNWksaIQaC0RGWJsRqyY48jaUOyCpkk7E4ivCKYQ2A5BISs1miZC6F4hIb+vMe9dIzSMz/MiCKR2UBKlCXhdNU9xBSZwkKMAVUC6ZhorMVoR8qBFBM+RJxyUCS5FGLK9eYgDOiCQbFedRTz03cn3D08MPsFozVWqZM4NCOFAG3QUiFLIedcC5STKlLr2qqc5wngpDGR5JiYpwljVNVwCEnbNaScWGTBG8FSAjEIGpM4vr1jCp5OJ/p1x+F+5H57ZJ5mtJQoJXF2Yb1a0XUdYQmM00yMCWUcbdMgaU6i2IxWBqkUKecaCndq13R9z9nZBUsI5FLwS7UYS+pn0lrTaf2jY8mfBLTOacopmK6UTBaKQhWJQqk6BKWQGJ598ik37xb83ch8XDjuZ8IYWY6BFAvGNjRNh7Ft1X50/SlLBvCeWAoogdsYrpqX+MNMmveEHNjlWyY/MpuF4AWhwCxhcYrcWoSqBRhKVLo7ZEY/g1QgRdWXSUWMhfd3t9zvjxjX0VmIMVBKIcuak1KKw2jDxfqMxTfsj1tSDPx7bLO/t8jek3LhOM20h5GrVV8Jo1M44MV6gzaa87sb3tze88P1LW9v7jlfr7ja9MzLwna7JeWCMbq6J1NtL97sd5yvNwQfefuw47AsrLsWpRS9bZBoyAajJDElvnz9mtEHlpA56zsuhx43WuxiOTtruKNhYl+LkSzIobLISkkEklwi1iXW5wUzKb55E6sLLENKEoFBa01jHRmJ9zPRF5x1WKeJ+4mu9ahi2ZwN/Mu/+u94/+6WfQosqrBuLSt7TiBwTO9xRdPiiM7y5qGQCGzWHV3fE0bPYCXn/Yp3/sjN3W3tjM6e5WFGJTBWcXVpuHpmWMbC7W5CGcGzX/XsXeC71yNSJFZPqsNye9iSFk/fR6TL9fkpqrKBWHwEXRpeT9cc9VQLF1FZUSEqQ/kBRVTyXYrMZlD82Z//CZ01NE6z333H9uZ71qvEPAlePr/i0184Dvz2o9bUxz81KXP+xRVCC+7f3taNyVahUC6F7fZAEYWhdTjTMh0XEDMKSc7Vj3UcZ5ZlZjcemJdAXDKtM6gGBrHCKYvKgCwsra4plkVQlGBuC5NaOC535FBQjcDKBu8XspBEEZnDSJwTrV7jdEtKniln7qcDaZzRc0EHiUuKAGQBMZ4q5yzQTaHMC5OcmO62GH/A6Ia8OeLbCYVCGENImRATIWSIEodGS0OcAlpX8WjbgSyF4zKy05kxepqkaVeCy1cvueGG6e6G7f6APICeHaZrYB8pcxWqWiFZrQeO+0I8RLKumQl+XpBLtbO2jSXMHhD0XUPIkYe7HeM8sflsYHg20D9vKMcdu+tAOBaYQUZJXgKOmrERc0YUMMpiRG2ZWa1BQAyBxrbYtiXkRGssyXVMZj7R9oIQA6ZvOVsNIH769oTZL5RSEEKgrUEbjQm1YJenIjH62vbRTUPJ9QZpra3aFCFODh2FKKeWhxCkHKEUxnFkqJYaUmtZnGYKMxlwRjDEQn64pxs6xu2Ru7e3kKBtewTilFZbiCEipeTs/AJJ7bcHP2ObDmdqSm2K8ZRsWzOBjDGMx5GcqttCkVEClDj9anNByIKWVfsSciEL8CEyz3PVgZl6Azt9uJMds1ByLWjzEqpQVmowK4bVJ1y/fmB8uCNOIKLCipYXTy4431ywWa/pm4bVumezOUNJzXQ4sLu7RwTwUpLkQtELF+drdvcef4wYOsZpz3W8wblLdLchNxqhC8pIIJNL1ecMpsPHiUbXnBkfA0hJkYp5ntlODxSpaW0LJROCr4ebgqgUIXucLjhr0MZhTMM0LRzS8p92sf5HwHF/h8+B/RIQ93tEprqZTgfZ7iDpnePMOVafvOTp2QVvrq9Z9R2Xmw2NMZRyinoomWMIfPPmHcfDzBIDD8cFow1jiBSlmEJk9onLoWXzfMPnL5/z9du3fP3+DdvDyGDbysy1HSULetuzaS5IUiCFZ0q3HGQmC0EJhZhAa0PJEIPEuojpEjkLtmPV3ImQiTGRS2SJkJeqNNPaopWj6RxqLOgg6AfFm9sbJgnff/c941iYTSTLQiye9RPH6tklv/n7W/AwzQe0blH7jv/5nz/h8kpyOEi6AbrVnsk/MO8h3wXkbcY01ckjhaTpNE+vNGfnmq/e7clFcPmzDf4s8+YmcpgjpRwQA3CcIQguVgqjq74kel3DN5OC7EgJChK91vWSH0HJ2hVJqZ6bnIoSJFgLQyt4+nzi4nJEig1aatp4gbeeq67l2S8VL/4gcV9+ABk+ak19dHEiMtx8/55IDaexyqG1IZd4Eiw1GGNobLUNam3wS8QZQ9t1CCHx3hNFpncbNsqAB6UKmNqzLyXRCItuDFIUluDxIeCNJq879nKhyIgqGbEc8WXBx1xV8znhp4Xx4UjSGXvRQEoEH7jfHUj7mTPT0irLYAvFSGznIMPd+3tyUfRREG4PLHceszvwxUozXO8Yj18xv97RXV3Sr1fkORCWSMj5ROcXRPQYrVBawJyZryFIwRwzZUmsEpilcJzeY7KBaSLuA8UXJLDcL5SbCTlGGhTOWJ48v0LLUj/TXJNhfcnMc6BoCecgnWTFhgZdbXIxQc6sbEMvFU6CVQWbMmqXsVNPo3vQgpAUrbJIpWisqNkzztHaFqMMOReWZaGzmmHoUErjfSDESMqZtWtACIqAkCNN0xBPOR8/dQipKCkSQ6A4hxKiCiVPabGySiIIMSKkQp4cPU5baBIxZxbvq1YlJow2OOuY5sQ4zSzzzDRPpLZBt8/AtGipmWPhfj6wMprzl89omobj7QF5Yhm10mhjUSdGJOfMfntECkPftQhhq9YlBEIBTvkNOZ+uRSngg8cHT/AL33z1JS8/8axWa3QpWG3QXU/jDE3TkIvEz5WR895jtMa1DbZpazUD5FzIoraEpVKIVAPnso8IBSVmbHvBi5e/IhwzN8fXaCG4en7JqxeveP7iBau+B0q9pcZcf07jkOuBZtEcs2IbJ663v2W39Ey7mZALurUodwZtC/1AMpakC0VRc2aoWTPntsWkgnaWMS2knOqNUEqUrJcPKTSmJBqpsbZq6ZRWzGEhpirkTTnjY8Qoh3UrUi68v3/7n3Cl/sfBu5trlhgxQrJqHRebASslh3Hkfn9kCQXOJCUF/DJjteH5kzP2x4lxmemHjnW/IQbP2+tr3lzf8P5hiy/8KDw3SpNyDf90RnNoF45hJufEPIV608yS52eX/Pmnn7Lbb5mCp181nF8OaFW4uznQHQwv5gteuweCBaElNUjbIE5yBakTs0osGbCGtWpJZMLiOc6FSHUXFRRCLAytoW9aFjVzvYyE5Z6baeTv37+mMStCE5nKQi4CHwI3D79lQuB0Iew1Yxtos+asd/z5H2+4eL4wTi3uouP99prrb96x6gRXnzrkzy1fvXnP4Yun/OarG/oLyX468u1fTYwHS/OkJ54nbh4eTtSGIMXI7CPOa86sQ6uIELK6qRI1aa4YKG3V4yhJUQllQZyeY1EEOZWqSpAgtWC1Fnzxc82rT2HVz8jyJbvtHyJlDyiuzjZcPrsgqYkfdv8GqQMxfZwG66OLk0POqO1SK7WmQWlDSqnOqIix9qhFwYfA9nDkZrujawY+efoUOU+V/uS0KQiBkBIrFKTEMi8ctzNKKFZdT6MFiyosBRYFUyOJslCQ9G6FXRbOSsMiBJNKhFztjGFa2L6942F/g/xCYHvDvDsS955zueKyGbBSEVUNlGqMI0vB3ozsDjPl7oD+hwVIOOCs3yCPhesf7tnOW4r9DjM4nDOk3Qg+I0omiNpgHZxBOlgyLF6hsiJ6T/Cgi0RJxRxn3m6/ImoggAwCYaruAyEoeUQZzfOnT7g8G1iWI2f9GSE4OEyoGLGNJT6xmJ+tMK7luT/H7TzHODLoC9bXD0gfKTmgt6CcwN4bzg/nDO4M1xpCTkTjcNogpaLrGkpJhFC/G2M1KInpO5KfMRK0slhlQcl6jqWEkALjLEpLhITdw5bDbvz32NZ+P6GExLmGkiLLOJ5cIFVUF2OssfDWYPVJmS7AGntKhi1oJRHWQK6ia0phCQEhJJv1hlU/sDscOCqFaRpoDNEXpLVkBkQraYaOVmu69w/MuyNa1/hteZqRI5VCuQYhFMfdHiMVq82aoe8ZjyOlJKRSOKWqiLxkKILow4/k1zhNfPfbb3jx4iUCSd915BwYuoau7ziOnpgyrZK0jUUWsG2DuTw/2ZwFOVVHkhCyCuhKqg1rqHktKVKkZHjyhBeLx2IxKfPJ8+ecnW1wRjMvI1oKSowYVT/n7nhkd2qvRRkRWuPLSAZyUZQI50+eMbWGWRSCUkiRIGZEqsWkURqlJXqJhDKSLJVVlbJu3qfRJsYZzCJRouZo5JLQ0qC0QgaBVbIyVTETyXiZkEJhmxVt+9NnTpzpeXV1RWcNnbE4WcWmKRciAi8lP9zc8fnzK4yC7262vNntEFJhDhPf3TxgjMTq6lK7P45MqV48W+uY5plCPtn3E0VkbncP+GXhsl/xdHPGHz57xYvjwtn5mjQdsabnmzcT/+bLr/nbb37Lqu1x2pFS4Gm/IvqZgyyISbC6XJGKJItEqaoApigYQ2bJGaMtTzZXPEw78pKrpdg1GNtgXIMUmvmYGOXM0t5R8p79GMg+44wgqUSOBXViIktYKBra3pGLwzeJXgtefS75uy//lpeL4Ozc8N0/OGZh+NUftfzsE4vfZ+6Obzm/yVx/I3lRWs4/l7y5P+KL5NWfn1NW8Nu7a5aYKAlSqe6/FEHmyryWIshJg4gImaqrIVtCqpkvRUzYNhMz+NPylVQS1BrBaiO5eKp5+kywXkU2A3RtIS1bjrsD89Sxu3+A3QM308KXr69597Dnk886Ns8/ru3/8W2dz9Zc/+YtNmpMWFBCooQ8BU9l4jyhpaDRmvvdxDhGztZNtfFJReMsMUeW44FxPDILxco0OGNqp0toShEIoVFCQ1gQRdCt1rjLnsk1lFagkuf4+nsevvkefbGheXKGMYrkA4ye+e2Wh68fCLeeF798Tt6O9KLlyeqSzlQBlkFXanoBkTMr1YCTpBQpt4nOaETJvP76gFaKyQsmn/AlIOQRqzSNEvTKoE4x1X3jOOs7oqiH1Th74jwzH0eWacG6k40za8SUMdZijQIkJQA5I4VENh1PPnvGJ5+94Prvv+T+/oHLiyd0myv62x2HeWZeSfYvNPuLwqwNWy9YLwmZBZ3RrC+vyHPgeNxT3gryQdCGHoXDmmp97BqL7AeUqtkNzpmTPkIRYiKWRL8aGC6u2N9cM9/fk0qoWVpF1pEAVtcWBoLkq4Mn+8qW/dRRTgd5iKkq/wQICrnkKgynWoqlrM6cmkwcmObaCrM/Bpn9o2i2MhqlZqdIwTD0WGsoxpKUptja/snWMTQN2iiETxjXY1334Y2d8lMkQtbclLbtGMcj03Rk6HsUAiUF41QFzY1zp+CyWnDqk7vEqO70/hTLPGGMRRuD1gata/BZCDXMChRCCrSSVSBtLbkIZBEoo6s+K9WZXKJQdU6+6qTapjkxFIonn36KU47dm3fs9yOXFxdYozlOnsNuZHt3z/n5Ba5pOByPpFLohh7pDIGF60Og2DpiY9OsyEZxfX9HNgJpFDnXOITOdpALiERjNfN8YBah0vTWAZnZZ2IqNcNDSMbpQDq1p9q+pyy+FqUlI0Q+iYNLZXNLjchPObB27j/VMv2PBm0UvbacdT1aQgiRkiRD29P19TnxSB62RwqZ3TIzhqpTkVLVp0Vwyv+BKQRA0LYN5+szxukNKWdUCkAmxcy8FLyv6ceXZF5fv+f+cOTrt2/IOSApONtRhGI7TRwXz9B0bPoOqxvctuf+5sD+unAhNPK5BxXqMxxqZk8u9Xybpz0pBq6unlN0Ydruaxhgv2DSwtBe0vYbZrfwenuDoSAWxaXtWPUDaYbGGNCBTV9om1RnuLVnHKXmILZ80fbo5cib95b3e4loRy7PZv74Vxf8/MU5bbdhtRHYMeLzAz+8e8/lizOGpzOxgW7dsJiRt/uZyS/EWFsxJRW8SLgAnXZ1XlapWipxEuCXKEmpOvysKyh35NXnBS0N45iRSlKCwChB14HtEloHZJG0JjNoSfIQfSD6CSEyIS7sx4ntbeDX/3BkLpkiI9Z9+nFr6mMXn7tsUVcN77+5YXe7J6XaW/1gudNScbnZ8OrJU/p2xYsnn9A25jRMrKq2ydBKix0q66IQrFY9m9VTpnmueSbanhrbhUZblHGU4phmyTQu+P2e8HrPfDzAYeZMGpqLDXGSdNvCMBuS7pGHhLr1bHSPvVpxtjqn0xpCoPiIkRKnqojvrJvZHUcO04iQAqs14zwTjpkoEj4WyAInVXU8KENrLa3WKCFwVjF0ddKsSopWRCIJLwumaVk3LdadhqsJiZYCawxKybo/Com2hvbpmvM/+ZzhFy/I0bP9zW/YLYGhFC7PLxDSYOPE/bqwP4sEMyOsYNtK2IN+v2cTNzw5u6K0hantK/0mJEFE2i6hbZ1CG8Jy0j7IGv5mLdZY8nA6REoVLs67W7KfKSXhEyze10M1VLeG1vrHTbmkqiHI6advJU4pkU9BbDlXl45INXDJaIMSkEsmLrGyhMagpMRaS0w1l4Fc0EbXCHpRBwV6UUgp40MgBl+dL8tEMvVGo2UV3xolCMeROHk+/fwL8rRw3G2JIaCUxjlX54EUoNQCpFAj5w/7PYtfmOep2gEB5yxK1vh4pdQpQK5qRursHAjeE0Kg7zuMdcSUWRZfnUanlNxjCHRG0ZkqgM25oGNCSlWj74WAHJEpIpSk6dqT7L9mhWSj6Z9dorViub7l7vqW/VbS9B1KWcISefv99ySq1q1dDbihRxmFihq3dOzmW1ynuFyd8z56dssOP6XKfhiDQNSBZ1pzthmYcyK2ipgFqQRynGr2ialFWNu0ONuAVPgQyCwUoZBCMUUwUuCUqLojo0FmfIjVCl4Kqvm4XIffZ0xz5LzvaIysiapKYXSduxRKQqbIMLRkCocxMc6B2ddIiaGzNNZQUsanQEiFImVtnYmavN00DTHV56z8mCsjQGp8Kvxwfc92POBL1azknCEXPnnScrnZMIQWKDTG1suV1lhtkFHx0j0h/MNEZ3rKpScRgETOkbgESoxQMvOy8Pb+HReXT9B6IISFeJzxi4BlRHcNq2EFUnIMM61NCNGQl8SzYcM/+eM/Yc733Oy/ZzfdYRrDi88+Yb5K3N4HhG+gPGfz7Bn//V//9xgXWP2Rxe8e+OE3I8q+4uknf0nXf86Ze+C//q+/Q7g9b8f3rD5NHHPi5j6x3UX8cmq/iFqDS6noGk3rODlrZW35FFVTlqUjJ0WRkSVMODkzDBHXFFYbECVDBGPA2qohk0UiR8tVm9EiEWShuITUR969+57DdKQ4S2kUi6jptUW1/Mmf/YuPWlMfXZx8/TffoiJIrXBdS0qgEIhSMEJhZPWSUxRd29K6aj0utYOHFBLbGIzReF+HeGlj+BCPKZEoCcs0gTgN65OK7DPldsLdZ467e7a3N4iSaVcrYgG3C/TZo/aJZdJ43dN0CqcMm6PgctPRDw2da2h0FbSqlaLvOxpr8dPEw/0DTkmcEpTTbdZI0FqSS32QfJI108AZnDU4q1GiMghSpEr15lqAdcriOk3pOQkhy+kGreqhRN3wa88PlDN0Ly9Y/ckr9M8umEwheYl+ek6vDbnvSFpgGoMpidx4FhUpIoEsJBnJWjI0AxfDOX3TVdeFtafFWYgxIaWolLuzHPY7xv2B4AOlQAwJawVNW6n4aZoZD3um6QAIlLL4UC3EQ9ehlaHkQjgJLoWQ1fWAQcufvndSCFHFsFoTY3WtSSGqxVXU0KMPbMG/nR6rpSTGwLLMCAQNTf17Hxw9ouoYbNMQc0JS8IcDS0pkVwOZjFTkZeb2hx9waP75f/VPyYvnq7/9G2QpNG1L0za1KA2xakGMxlkLQhCCJ3h/mqqcWJYFKGilkaI6iEAQY8DHiD+Op9RMizEGpeokZR8ilPo95JQJ0bObjnC+pu9bULXFVIBUThNeE5RUdWNIiQ+hzvbRNZAuxdpeUquOlsLtt98x7Xf8/A9+iZYgtWG33ZHJmLahSFUvFrstYZmZ44HkEk0X0VYQ/AK6kBZP8pFEQqtaKGZR8DlBhlBqvopWqq7pXBCSqjc52aada2j7HqstxjooVWu1+JmQTuyZLKc9MSOMOjmgfvrPg5YCIQvHuQrFQ8xs1mtWQ0fwC2OurZLeaTpnWbJnTpk5hhpCmCtjEU/sWi4fLgCZQC3+RBGELPCxisulrEFtB++5P9Rp1VorrFUkUQg58XAcaYImpVALYJcZupZwSp21SvLk8or5sMPsDGGYyO1MypEUEn4PeZ5AFqSRKA1zPNC7DZ1eUQzYtqd1A5nCcToglCWHubpgejiGHSI0fP/9t/SDYnCKmBQhGhqzxl1kLp+85Mr9jLv3ku9++GtkUchx4O4byVezR7sdS/h7zs4PPH9yQfbwi89WlMHxVAykcuSrN1te7+5A+DqbK0Gmfpcyw7qXaDUhhEGWfNKZVFVrlakLBI5UwslhCIWMc5AngTIFaWpQnRQSjUBnRdhnylkiA0qJ0xTzG0xzgW1XvH9/Xd15RvHsxWf0Z08/bk197OK7++09rbFoIdlsVhhp0UhEKvUGKDWNrv9On1wI5UTZ6ZOuQak6jZUSUErVjWCZiU1D41qW4JkWj0ShUBht0c4grWFaJnzU+GixbUs2kiV7+n3CHUfifsLtC+ey43zd0uuGoW3ohGVoWozSGFmjxF3XsDrf0LUN8zjW4V/UiOUiFYKM0xLXKFKKjFMgRAnSoLTEmg8qdIGRgnSafxKKgFTQUtE6hza69tlLpc6kNvgQqr1Q1IhroTXNJxe4X72Az87JnWWeRlCG6emKuHHscoLxiEqBInJ1dpTaOtBZoJeImgKrYc0wDKfI7X9UvgtVg7SgKqzrRNU6HTflmlpa7a0zIYaa7OgjORa0rLNWtGlBHFFanUah63rTzRkhTzHdp3bOsvz0BbGNc4QTuyBKLVYqg5QpOVPUP/6zzJllnkmyai5yiqQYKbnmfpSc61wbrWuHSEqMVZhQC1ATIks4UNwCrWMphf31DXc/vOHVs1dcPX3OZhhYDntu3r6p9l7XorSu6a9SorX60e6bUxWn1t9X1YIoUZ9Xn9OP4UopZ1IpFGoOizgVUGEJCER1MAiBlOBMtVLbbqB/9hTZNMSUKSdBcG1xKTjNKRGlfk+xnCLwjaz5SB+sxlohVx3u4pyb2xvub+/oug7XtXR+hVCyujgedhwOew7bLVYK+lUHETKJbbhjv8wkTs8NoiaEkiii3s6XuIAyUDKCdBq4WN1yWp8yfArM88Qyj/R9j5ICSanMlFCgDaVEYkqQ0mmbr5eyGOvz91PH+aalFHjYThhraXpLO3QIJVBB0nc94zLXUQ8ahs7RHm2Nc4ixts/glKNRi3Uja/S9tQZxmuJdUD9mbQhRrekpUxOXT8FsSwgIWYdN7ueZKUqsEjitiTlzOOVs5Vy1guM4QohM7xLt0JKUYBGeciy4B0mXF3ZiQgNWiTpPR3oau+E4Lyz+wNJlrG5IOeCGFUuIJAKhUXi/sEyJ++++xWnNq081XW9JU+H6+g1Pnj9DK8Wzl19Qyj1OH1FpIU+JXXb4g0FYgXGCh+u33L6+RpC4+PkXiIuBMB8we0cj16RwzzLXdlmqcVoIIWgaxXpVW2eI+r8l1kCyQm27VqJVgCo0asXd9ztefFZLF1EEStWgwRQFWQhyEQhT8MowTorZS6x8wu2NpCTJan1F02zYXS9cbVYo1/Py1c/513/zm49aUx9dnOQl13kbWtP0ls5anDRoJKqISglLhTvNRbBGk3NlQ7SqH77kf5wfoqVEiWrhIxe0NiyxahqUMRjb4BqHaxuk1ohcoF2jViCUYokekwpNkVWfcgy0SeOsobGWzjU4o1Gl0Jj6MRX15qqlJPvAmBJ+nqDUFpVzLUpbYljQUmKMOE10zcQkSLmmdDrnKDmRYq7pn2SKkBQKSkis0ThXU2X1aS5JKdXhsXhfp9bmRJYC/ewM9bMnxJcrYiMxOQGZJUxsnSfZBH4hjoF1BlUkXZCsokLITI9ifZD0k6BrWqSumRn5FIwTY0QqhbGV1i+x2lVF5hSmVQeZIauKvPhaOCqhcbb7kcWS0iIKxGSw2pzmjJQf4731idaGmp75U0ddAwVSQiv1Y9pkzhmRC3M5Rc7nQoRK8VuLIJNTQlC1IZRqrw2+FnRCasRpU3bOkZOvSbSxjopf9nvG48j9+xtYFuyzGoX/+R/9Edfffs34cM+yeCgFqzVafBjQVzf/UgoheRCCxrp//J2dROX5FJoVU6xFdyoUIU7Pb0EoTinAiQ+z/D7MnpFKM2zWdFeXoHVlK0JAyIw6JQ8LIRFS1XC2kqvm4HSREVqjjSGn/GNbafX8KVfHI3FeiHJCtw2rszXaGMbjxDhNTBzRSPq248nFU+Y8MuZbHsIDx5AIhFMmS21dSVmQqlBEvaWbfBo+qwQxJ+ZQL0i5aKTIeB+gJMbj8ceZSJl8Suf9kP/wj5cQcSLKQoxMIf4YWvVTxsXlwDRFlNvQrDqUKiS/UHzAGMPiA4dpZNpXJ1RIBR8D2hpyAB9q8NoHvZQU1eadSl1ldZJ2qQnG5pSxlev3nXKibxqerK+wSvHdzQ3j6XVLKSy+uhuN1hQBy1yTraXU9NZwd/vAurWYZNA7Q1pqPEUTDKvY0A6wH1/X11cS5RSByFICucA8eeYYabpIqy396pzxIKB4onIEGQhzZlkiMR3wtuGLFxs6pbi9vca1HfvDNSTJ9XcL2o/M00g8ZsIYSRKEVqw6h1Ee14yYdcR2BWEX4jJz+3rhbhuZDp4Qa4hnzlUh4bTlyWWHayMiGxAKIQpZAVmQUxVzC7FQSsaawrp9zm/+eodOivMrWV11k6YS/wZjunphNY6YW8ad4GG3oMrAzc0t5xcvefHi5zSuQ4eCWjxJDBQ0v/77X3/Umvro4qScREikQjEJqQtWK9qT40NJgda1ONG6Dm/6IAikZPKpkjO6huVokSkpkJX6cWNMMUOptzh5GsNOzoR5JvuIQuJMUwWFUeJUi5OGSKAY0BiQmq5t6lC+EisHIyUxRnyucz5MjMyHEe890ziSYsRai3MN2li8EqRUEwSrXqAOegunOO7WaXKSRAIx1xuYURKjavSvc5amcThrUKdI5hrC9UH4qMhaIq96xOdnTE96ZidROVFyQgvJw8M7Rv9QA54IKJlZSYuNmk0UiNAwKUubHCtfhYrKGTweKKRyenjTPzYe5emWz+mhVlJhjSWVRNVicxKlKZy1CKFO7Ysq9mycJUZxmgtTb/kfKGslxWmdgP3PQACoTsFrKQa01ghZKJHTQVTtpCXXuS4f4uqFEMRY11HJHzbiqiNJORFDxNjTgZcLRmtiSViqE0qGRBhnwv2eMi50zjJ0HeTEMHRcnm0wWrPb7k4ajw/6FFWTZ0WdcUOueg2tags251zfU6lzfer0Y3HKcKji8JryHEmyTkr+YFMWSiN0LTpQkvZ8DdacguTK6QCphHHOCimrUy8ARtTi32hbXS/6VCirTC15I6prefazzwnv3oNfuH+4p3Eta2urqDwm+qbFCcF6vcIYSwmZmBv2cU866VmMMrVYM1W4W3MaE0rUvS2fDr76diUpC1SGlD58NzUyYV4mhBE0uj4XMaVqbZZ1gq0QlWkqpZz+W03d/aljvemRDlbtRQ1QO+5Ii0fEmvtznEfeP9yzW2Z8qq21yUdcWzVOIimoHe9TyFf5ca1Wll0S51zbi7kQUyDnehk0Jybrk6sLNIIUEt/f3dG6hsG5qoWaJ6bF18NaKtatQwjJahgIPiJQDG3HdB94GI8gwTWarndcXr7gt4c7ihCYxiKNImWYwow4hReGkFjmGdMpXNdjXSH6hI+CLDwpz3hRoyDe3i60xvPyvOE4zdze3PHbN+/ZPnzH/rsNeuvYHRZEUCx4ki6IovEHSddapITzoUU8vQC9IF3D7fbA2zcHxtFDqcMqC1Vg3LeWy4sGIQ4ILNUypxAik5E1QDAnilgoItJ3ZzT2kpsfEoTC+kpQtGBOEEMVh3etxfU9/TAwrK7QboWPR/Y374lR8/T551xdvECpjHxxgYyB7Vbx/f0D+8P+o9bUxzMnKVJSDVfSQmGEorOOdT/QNw6tFVLVtNEcY53JodWp31yQSuOcQYlabExhxiiLUjX0xS9VRNk37Wn4WFXzB78wTVMNv4mnVFIhaG1H0zVIBFF5tPIsykORtNbil5l5Gmlc/XkxJlKsh0VtNxVyzKfXLTV+WmsotftmjUUZfeqZZ2TwpJLIoepolKoMj5U1AM5IiVUW1zS0TR2CqFXVHfDjzakGdtE7xJMVfHbG4UxyVPWwKElQcqQTGv/+juJHkpaIkyhRO4OVGqcUjWxJRiGLwjgFCoJIzHFEFRBJIpE1ml4prNL10IGT3TWdNoLK+IgCRuoftRS5nGLZlTlR7ZUerP36Uy9dlJN2ubYGKmVeZ0381FFK3XSTEEglEaf2RBK1TfPBMiyUwtrqkCo5EULVW+WcKo2qFVK4GraWEmFZkKr+HqCWjEYpBBJpNLoT2CLZuAYhJC9evKA3iuXhjnLquadcmOb5NHunBrIJqeszKmuxLqRAyH8smlKMKHEqYIUApeqQMNeihSCGpY6dOE4/Dl9OqSCkou07lDaYrqE5W1FEDSnUplp+q+6qalKkrMF+qRSsFD8yESkXcoiE088up155zIHubMAZyfT2LW+++gpra/DbNE4cD0cMsOlajJJs727Rsro8bsdbROuwwmJ1g9IKbSqLVNmjU17N6TCsDJCicT2iiJoZI2sxk07ZDiFFTKqhdVllfFxOjAk/FncgTwxURgtB959BsS6URjlNEpn97S1+uyN7X1t2JbObJranoXwhZ2QSp+TdqjmxWpFkfXaUqOvCKkUSmd5Z1sPAu5s7+rY5sTATpWQarU9ygcy8zDih+PTpFUJkohB88ewFQ9/zD99/z9evX5NSpj9bc3l+Tm8cm6HHx8jbmzucNjw8jNy/3YMs5C4Qzxp+/vIT1mNH6TTrrmUigZLMOVOYa9ZJgTAHjuXI4EwVvs+JZRYIcQZmSyLWwbExc/8wYnOobaBmx/4Y2HQCcZT4fcHPmVXbYY0k60wAIlB6RW4U61cX2HVHAbwN5MYze0nw4sPdEykFRiu6QWI15GDQwpKRlKQoJVGyAtSpqK4X57YdGA8LKQl++3Vg+TUEBIuoTkQtZ4weUe6BzWbF8+eBF58pNmeW/cOOy/Nz1pvL6uZbRoTKSKe4396wfTiePsm/Gx9dnGil0aoq143SNK6hbTuaxmJtdQcoLZinAyFEbN+RRR1H3g4ruvUGoyXHh/fc3F5zPC48f/aKzWrNEhMhJ5SydF1bb6Q51VCxJTCO1WgtRRWmiVJom5bWNSx+PvXRTzNNlEDkusmnVKnzkqvb5sNB/YE2TimidS1KGutQQhHicgqVM9iuIVOIOaCkxhWLFLJmeohT+0lbYlqqa8c5utWKxlqkEORYXRkfBqQJozFdi3i5ZnrWsreJMac60wBBlhkrM3bv2WwlB2M4ykIrNOe2oe8bdJYEI8mtJfcNQUpm5xHbGTF7SvboJGiEQ2nD6ZJYCwhqPkdMgRBCHeZ2coz8o4akttlyrFkCJdVbsxASpWqxRhH/1ryQOtellIzWVYtT8k//pphTFW4aZ9DKEGNtldWEkVq8pFizLj6IkT/oO6Sq+g15uh3mHEmnuRW17VP7DEpSxdnakE+6qM45OmcR4pzjvPDJZy857xwP331LOB5pm5ZhNQCCpnFYY5iXSIwL1jmkFChpTpqRQAyx3lyr96E+L1UpW/85JcppLedUKfKU63TsDzO1lKx6Izt06FVHUgKrFK5pqmA41HkpMWVS9JVlyTUSu4iMX5ZTgVC/m3JaU+RMKQmNJbWW6SQ6zynz+s1bbu7uGNqO5+fnlJzZ3d0ii+D8/IJjnPC7SJA1T0ZribIapTW5RMj1Fi5PjF/991THoPjHghJJde5og2tbYoqIJEinXBjvZ5wSWONAyEp7C4kWEqXqZNbuP4Ni/f4wsr3dI5KgtTX36OBhe5yYloVcCo11HENiDgUpI52rFyJKnbckRNWJaClpraG1rjp0lpmkNBfOcb7uefewY3/S0wFo6gXh6zfvWPc9P//kJV88f8pv37/nxctnfPbqJZnC3f09S44UIUlF4RrLEjwP+xEBGC25Wq+4ub/n9f0t+/HIbu6YlozfwbnuuZp67spI6CQhL3giMQbIGiEMy5TJYURoR06JLCzCDpjB0cSGcrhDxUhOkvGYsa7Ob5oWifeK/e1I6wdWsmfd9UiduF8OXI8LsUhCY3j+zPLssw1S19cWUlC6SHtp0UmTx9qmVEpgrKDvJaIURJZI7aqRQmgoNa7/9DiSS83aOh48b3/zG477wP1tZj5Uq7haC3JbCCUzpYUyLtze7vju27ec//03/PGvXpCWHX/4yy+wAubjFikjFMnd7oG/+pu/QmgHH6nB+ngrsagiVVUETlvONmecbVZIEjFHHK72dK0iLR5p6jwK1XSsr54gNWxv3vNwc4tUlvW6p3UdUDBWEOZI8BEvJYXI7rBnt6+FTj6JpRpXg8JESbSuATLTNOGXBQq1p1gy01Sjt4euo2tblFAYY8klIqgD6mIMpFQTL7u2rdkSudrUlhiwTiOtIKlMCIUUK/VrdLUTSykxpsUYy3Is5BAQuVBCwOeTnkCqSqUrDasW+WQgv7Lct4V9rpa5nARaaoTIpDijFJht4JNygUySm/FIVyznssU0lr1J3JvMwQa8SkQKxhZW6UhfCqZxZCPwh4IKC0bWabcSdWJJOLUTVJ2+miuDUttyNb45pVg3cAR+Wcg51+A99UFTUgc5lg+WPU6tI+RpoNtPfwqrbRzTcUSLarOTnDIaamcSLSVFCkpJp+F+qtqtKacCodpOlRCEsDBO9Qa2GYY6oyYEkILW1qTe6BdCTFhX7a0+BEoO5OhxSrGfZwRgrWGzGmqPPiXmeWJaZh72B54ZV/VDVPZAIE4ThX3dYJHVQYcgztVNo7Qih6ret1qj+lWVesoqfJeyal5c02KvLhCtI4YFoywlVW1BLgVrDE2jK3MUqwtDilNb8JRqK2UVzH84dEqpoWaHwx6rNc3VU158vue3v/mSu4ctTy+v+Nmnn0KKvPvhe8I88eLqCkkdzNjIAS8EyrmqZ1HVvRZTdRZoIcg5EYUkI1CiDjEtFGY/1mKxSLJN9F1Loy3348h9CnS5xeo6Ldo5TWMUS8yEVCdTa6Uxuo6UaP4zKE6WnOidglCjFXRjOdusOZ8924cdMQb6tsGoB/bBs4RUgzCl5un5hhgD2/2RmDJ939K7ht00cr/f0zQtrrE83ayYfcQqjdN1nlvfGC7aDiXh18cj77Z7pvkrPr96ytXqgj/4wz8mHPf0SvKzq0sOfqZpGoxTvL695fb+nouu42yz4bc/vObZkys+e/aEIgoP4wGV4fb9PWTJ9N2Rwyg4O28Y+8AyJEITyRJCqm40Zzv84jEio7SsrVr1/yHvz3pty9LzTOwZ/WxWt5vTRpyIzGAmkxTJkmzYgm34whe+9V/0PzDqLxgwXDBKcskUi6LIZGZGd/rdrrVmN1pfjHlO6k5RBUICQzNBEBnMc7hzr7nm/Mb7ve/zWpKwKHtFu3eI4YjMntaICoYLVyzzEVngeX/BN19/zcXHG27ngbt5YvaRlASu17z4pWV3kQlRMi1ncpkpKXP5omH2ieYsUFNdiiklkCKz6QpCjytFLQEBoSZKUSAsiAXwCKlxzZaUM99+98DN+8R8X/uHioGcBTJLxCaTZSHJtQImJeKHW1Q4s9/sON5MmPiWdr+l3fV4X7h/HHh4GIAB1f4TF//94voFfdvVYqvDgcuLCyiZ8/GRZZrqvl0Uxlx3ZMP5keA9Tdtx/zquvJNEZ3tku8FoiZXg40giVxJjUZSUGWfPzcc7Ho5nxCpDx8WTNoXZz3g/0rc9RSQej6faeqkNZi0jTCngrKPv2npiklXq9r6iuoUAYyx96z4PGjnFasoik40gtoLcFbKVyORq14wpGCRSi7rPs9UU2bkW4erLuxr/JMVIaC3sGvLlhuWgOXWJE3Ol7gWIAZx2FCKhhGq+Owd4yLTF8OTc0KW1GXXbct4abnXgPo8sea51mWtY22w9Y8wsWtKJDQep2J0ENq7TsZAIsXobYo23VpJolVZDDGgtMMbhmqbG7kohxaWCuYytLdIrF0PKGlX71DoqlaivO2FQ5ufPdZjHGXKp0VcSShusq2bO6Ku3SlB9WMZYcqrG5KIUOQUEZU04aaQopFgqgPAT3VWaz307heppEOT1M5f4ZWGZJn7313/Dk6ZBpUhGMC0zHz+8r2bSfovWhpALPmeCgO1hjzOW5Tzgh2oGD74aYZGSkOpqTmhDSIkwTnTWkEtdVTWuoWnbyuhJiSUEfM7oRrN/eU22Bhk9y+whZOQ6hAlVDY7Vq2MJciEnX+FOoiYsqnjMel9CEXVg1qL+s2Q1T775hrube5xr+PXXX6Ny4fF0rIO3EIzLghiO/Hj7AbXt2O52pEYTUi0urEOkAmOrn0ZpwK5qYQRRCa8xRJCqFgzquhZtXYvfZGL0kARFCEJZGERkToGCWjegAojElS66LD9/JXHXNOu9I2oKTVd1XQqFnyZOMdBbx19+/QVDWPj9u49kodm3PfvGse8d4TpwHCd8hiVEphCYY8GPC2/vbri5u2HT9ry4uODZfsvjeUAryaunV5Qc+fhwxLqO3ig2TvPy+Qu++uIZf/f/e48pkb/4kxc0rWVe4OY88cPNHa+eXfHsck9JgnnxPD6euTrs+YsvX5FL4rDrGYcZaerB/PbxyMO7mY/jPfMmsDyDsLdEq9cY+YRAEvNEt9kxPNTDidQWkBhzQHSOOB959CfYjFw+v+DruGPXCJ49f0qeFn797Ir/3eWvGMPMtx8/8IfjR4btRLtJ/OPfBb7+sy1CpvW7G5FNZLODrl1RA7nG/EHWSplVAc+ikGpkEFkMpViWmBh9TRfZ5UQrHY9vCufbUoMogB8jjOBmSZslZi+Iqv7fTCPpjeXVqy/ZX/+C22PCtolGWgoNIXpCqu/F6COM/8TdOt+8fMnF/lAd9xJca4k5Y1OPdo5tv0EZCDT4PGJVy7bf4lwH60M1BI/3nhQSWrqacomJEBLCOrYXB6w2DI9HjLHs9/saj217UvBM48DtMYFoSaXw/sMN729u6ZsOvdvStg7bVDlWCEHT2tV4KGtDJoJS6j7fGvM5USNF3XuH1DDGiU4aSi/JFmRr0RhaBY0u2FzlYXTV3UvIFF39AwgBnaVcNsRrR9hrBid5iJEoZig12YP8lJAQUMTa2lpBRfPtmY+/u2PwEq01m13L3BXudoVbE7hPEyGNKBEwIrHCKpgsRKsYiuBUFsqhp1GGflSoKNcIWOUIiLKaXNeHfgh57YQpNV2lZV1HpLRyL+p+WEiFUpoYF+Qn/HiswLCUU2W2qJ9/AytA1/VVGQgzfvbkXP07Ka2JHSGx1q1rgxo5jWGpTBOlGIaR25ubyqNRmqZp6buqHKRSzbRd01b6ckzrnxP44JnmidMwMg0z9/aO3//DP9D3LU1/4OrpU06nM6IUtv2mLo6MBGdpdzt017KEwEzBdC3FBzRVwSsIWIdWv3pQquSb6xpKKVLJTMtESomSC8oahHOUqx0L69QtJY2zSG2r6pMiYZ5qlFjUBExKGZGq8vZ4PmEbS9s0aFsTFjFUX4c1BqGrQlNyxjeGX/7VXzC9+8jj+czjzQ15XmitwW62+BgI80S53OCeXaD3G+YcUKka9ZcwE3Osg4mQUOrfrY1EaIUPIynHlU8imKYFsYBeBMsS6e2O/npDFBlE5vH0kdH7+vAXEiMEVivU2lwrVV3x/dwvawXLWPAh4azDOINSta1WkMkhcnHYsdm02OHMl4c9zjYMwwg5cnf0nKaRx3lijomwclG2mw4BLDGypML9cEsB/uzVl/zy+VOWaSaGiLUtv/niS2Yf0Eaw32z4zV/8OdkvGAJKwjgFzrPnNC6kLPj1V1/wq1cvIGaGeeGV1TRa0zpLjJ5xnkjLhJOCxhSatmOKgT/cfCBKSc+e5TQx50LuBcqaWiIrAkJEXHdme7lhGstnmnooIE1DUSsR19zzdvyRV988x57gMh+Y354pMVFipJeaX1zvefJryfTFibfjwMe7hGgliz8SYyGFup6/O79n8I9IndFrc7zTEqMEJaU1RgwySwoGqVoGD7ePA8M8oIwmnD3Tt49MbwIq1YNvKbWv+MlhS2MEy7gQdWT3XNBtDb/46isYI//6//h/5eP9wI+vv+Nvf/cD5vu3OLeykXTHV3/yp3x48455+Gn1Jj/5TbLfNIgSatkXmUhAtg3b/orkA8nPBD8jW8n2cseluUBHCVKSwoJfAjEHYklIqZHasdttSdFzOp3JRhNlJMnEKY2E6NFC07vKK0lWcjrfs4QFZy1+nri9e8Avid5Wj0SIYcVgV3hSRUgrtLSUIjDmkws8f46+Sln3lcZYTNGorCGNJFnNsilFlJJ1LWKqCiGtQjem+g6kgr5B9g1+p5guJI9tZDYRoTIhZcZUT6VaVSl5XjwhKYSo3hopy7oqyWylozdtlVWUoFQ2FSyhTshilQwFqKIo61pGNhuk3lLfDwunXNi4RLeA9gViqSfhEFG6mitrErYQYx1Mqhcn1L9P15SJUoZCjYl+AoWJVT1hlQ7tipyWejXdlp9/dtIYzeQ93tff6TjOKFVTTtbatXso1KSGEGx3e4KfGM9Hclp/n9rUyKxUq8EWyKF+trqaMZcQmZaFlPKaVK2+oNa1KFHjuUUospBkQSX2QmUraE0B7o8PDD5ynCae+ufYpuHh+IiRElUgzwsagbWuFlnGRMkRSWHbd3jva/onZYZpYlkWjNFY51CuwT6/onv1BVmCsR1xfCTHsMbrK+sjhEhtWYk1Vi8EqkQaY9lsNzRtU5H4632IVlAUxtrq36EglCRFsBc7bIj89g9/4OMPP7C1lmeXF1UFNRrZWZ58dYXXmSSq3G5Xw37MGl0KEkkpYl0jhbpqCv7z0E4BKzVd39CoqpA+DreY2FJ0oViN0Ypdf8U4PzLMR4TIaGvJwLhUqqg1ctU2f95XYEE3UEZFjolxmEmuVpMctjta55CycHt3R8qFJ9stfd+S9xuGeeHd+cRiBaloxmlh8akOq6ryN/wcsNqiW4szlhwCp1PiPE4gBV2JtEYRfAQ0JcGzZ9d899vfcry95fb+kd+/u2XwgYvNht/84gu0LDycZnSBP7x9QyiJ6+2e508u2F709MqxLJ40K8Zp5s3dI//zt98y58Tl/pKH4UxSCdEoCLISvGX97ucSmIYBvZl4+fIlnXvG6zdvV4VQUoQhcSAuDv945vmTFt0UWjrcLBEJvJ9JbiG9GBi3t3x//MD9HHnyq0uyXcglE0LCCoVp1FoDIBEi1zK/LNBKINWMcIEcJSIFRBEY03F/DLz7cOS0Kqh5XigPjpu/OyNLQQvw66NcK4E0ieQExWbcQbLdtfzVX/wVl7styzixudwSpOXbH35gmDzMAXFe6vm5gHANT778ihT+iQ2xUqjVDKoZhiOPHx5Ba9x2g3aWIhO+TKR5wbaah3OkFVucdVASQpSaO9eKnAVFGSICdHXLnx7PeJ3QW4PqDJdPL1FBoAWV/LhMPBzPLEtk22/JKdMZx7bb0a0PitVz/5l8OU+Rvu0RCqyrcdtKHayrHW0Uxuo1abIyCgJ0wlJ8YpoSc/IUFYnGsGhNVBLpIzJ7mk2H+fqK8ULzICL3eSKaVOO/USBijWwpQ+2rSSBV7dNJPrFpXI0x5mqYlCikzFhniTFh29rd08RCMwy4MJOcpDiHVBYtFCwLaR7R7Z4BBzlWWqNIeJ2ZRUTHgoyglcH2fU2SSdZEQcFah1+W6nMQYBAoVvlb1bVYbaAuqy/nUyKlxpUL9e8rMZBJ/01wHT58+ICgdtRoo1YPklkpuYCAru3WNWNmt+2IXjKdj3jvAWibBlt3Yxit8ctMYO3nSanG5qn8FK00xrafAXvL4utJLFYEOEphrPnsMxlOZ4KPKK0Ii2c8jzx+uGEYR/7FX/0VL1++JK++lDgtTKczw1Q5HkZpFGCtZr/peTgmfKzh3s41FFeVSWEN6mJH+8Uz0BofZnwYKGElJpdqPlRSoVuz8lvAhxrPlaxpsRwpKdcVUwqEVP1OajUa1xVSJqVK7RxIbJ4cePXrP0GVQjgd8TEihMA5U4cYURuHEXXw/lQT/UktVEoRU17HhoLIEaFrW7OW1XsVs6fkWuKYkRQHSRVSSXjvyXn9XQlFYxyzP3IeJqQwaO2wqiCNIeefP5QwionuskXrHWkqxJyRq7FZSsl+u2VeJnabHcM4sd12LL7GrKsRuR4EkQVjazNVozVG1jbv/a6h1QqtNF8/e8K+7bi7f+D+eMJozaI1xmq2+w2Hix3/8q/+nDgNvH/zFrJHakXbOA67Lc8v99zc3jDMnovtgetDz2G34/WHG974O96eb9leOq6eNHTOEEbL27uBKSUi8OL6mugjXWM56/ip+PczpkEIvd5DBj8ENi8OnE5j/a5+sucJAUVT2CLGjjcfHf/dq6c8vIncf/jIrusIasE3Dwzqgbv5yO154HGEwyEgjanGaynYbhzLOFSMx7oS1VqihSUlycNjYX9NxcT6GYSmiJYsIsqBnmE5evx95vy9JywZucb/ZanGfLeRlE2ibAvSFlSnudw/5enVc958/y2/+Vf/W4RtuXi6YX95wfE84P1MRUJXVTbnRImlohJ+wvWThxMhJKappkzpDdo6Qg4sy4lYTK2dlpmJkWGeuf8oadKe3WbLvmuRlHUAAAqkGJmmGVSCNb7UWsnCTIp+Jc3WzpGYEtNSY8LOWIxSNFaxfdZjrK0o7pyw1mK0JviFlPl8GjPWsNl0Na6YUzXhlYyQBanqi1ysIDlrDFY6op9rbToriCpXmJLQltxp9NWO8fklj71gVonT4sFolKqegZQzs494H9hu2toxEQLJZ3JRaGHWG7WgdU0EkKrSpI3BdRJpBEEkBrvwId3z+njPLDLO9Vy0F+zMHnEOTJMniJnUVcVIKYMsCnQg4asnRVuscas0n0isIDAKWq2dLrmsJuH6RSs5g0g1aSRZIR71xZQzq8GT+oGWDGmNUPLTbr5/ztfF4YIUA9M8EpZYlbe1t8V7XyFmQdE0DZC4v/1A17VYVw2nn4aW4APRe/yyEKLn8XhkCR4poNEN292Wvu/RStYHjjI1shwTbeugVEDVMk1cX17htEIWWOalnvoKdQ1TBH6eef/uPbuLC7569RWbrkUKQbQO2zjCPOPHkbSECjAUonKAQl3h9F0LQtQm8pRQraN5donc98QCTddSAhSjCL5GdeVa01CvutJqVtWy+IWUIzF4hhhQpSCkRDduVUwKy+KRK0NJCAmy8paWkrj44jkqJo4/viYvC23ryBSO44D1PWbTMMewYrVrCqimzipMS1LXlyVXLk1e7+aUandOKIW81O9EaxtyCWQf8UpRrGJaAtPKaZFSYlRLDGfmOOHyaijOia79+UeJz/GBvreIqeCEo6Wu4WKo8DRrNDlmBr/Qtx3bvufq6ZZSYJ4jUWWmO88wzZXCrSwKiSShpeZqu+HFfoNEImLh/Hik0ZJfv3qJ0orTMPP6/QceTyMfPt7w1S9+wXgauL975PH4SNs2/OaL57SNwxnDeB7IGRqjabRh3/c4Y/jtj2/5eHskbVueXEtslxjuc/VIZcOrFy/Z2AZN4dvje5KsQ7AoCU3BAVnWMITVjk3XImTP/cNr5sXX6L6udOEKJTVsuiu6zTXnAks+8fc/vuFw6di+SmRde4ps09CGwMMpoHSDFC1KTOx3DUJFvJG0W0fbG+xQWIIgrKGPN+8lqjH0LlDEgDZ7ztNMIGJtrQbwoRBOMB4rfZwCSkm+fPEMnyd850l9ItlEkTX2//79O/76f5p48uQF0hr8NKMwXG4v+Kg+4P25PgfTyvgq9X2ay08LTPz04UQJ7KYj6nq6kboSYYsFYQBRR8IU4eF4JN7Ds6arJzFrKDGSE5g1DpiCx5MpMlFqYxzI2niqsqDtHI2sD98UE0IotLbElGmMpm0MrMjoso6j1eSpUKpK623XrKwHSQzLaopdGMex7rODJsYaJTZaIwDvQ+31MQbjEioXYq49DWiNfrJHfXlJuGh5pHDv58pNMRap6vicVoNpCKG2cS4RJanemiwpKdE5SxEZbQRG14RSliCcpdlozJQIYuFUJsZNBt1SHs6chyPzFGmzqt0d84T2E5wUmxLBdBQnkQpaUdBSoJTEYJCiRrjqgF+BejklSgwYKeBTgmRtkkZU/4FYNSkpJUVpINa/RMhqICaxztkV3//fAK677ztS8IzDqTrydS0pk6LGf6snJ+OXGQQMw8T93X0FFbp2rQ6odFkvBSHU9aKUUx3OrUXqqsRotSKwlwVtKygwxlh5P11XacsFxuORME0rhrru/31KDNOMUQZRIC2e199+T/aRX/7iazabHoTAOIdrHKXvGO6PxGkgpcTiq/9CSsnj8YRPkZATetOzu9hhLg/rynTl+qyKRS7Uh2MJlYsjBLHUDpXPULJc1RNSIglq9F3/UcmsiH2N97X7R/4nCP3Fe5wxbJ5dI2NgWX+352lYDx/rqjSXqpjw6WGdPt//IdafR1DwMVVrEBCiryspoasxeeVoKKVqv5URRBJyReDnktZuMIdRmRROjOMJqRQ5u8/FiT/nS3YTPp8x6kDy9XdlnaNxjpxqTDg1gesnFyhj0QoGf2J3uECIhV3f0J4NnCGFjNaZIiBHsNbS24ar/Z6+cSzTwjTPFApK1l4npxWvnl2Sax4OJ2F6OHK17+kbg7WGvnO0zgGSF08uQTzUJ5uAcZ7JMdNpy6Hpudp17K4MWc8MJC4udjx//g2372+4+/CBLEU95IlCFAVDwoiEE4KsFf1mz5cvv+T6+oIf375mnCrsU1hdden18Ce1oO/3PH/5JzzefM8T2+IuNuTLI9/dnLlqr+mswzQLO9FwmiRaNVzsnkFRkCZSmUlJEnxlc7e6PouFljiteTwPvHkDTy8VV71iXjw3dx6fC4RCHhP5WAiPgrxUzowzmq7rsJuGWAJZJlIq5KkmDosULNHzcbnn4b3k8fbfoaOk+MjpNJGXGSsCPk48TFNdMQGqz8SfGOb8ycOJaRqKLIzTSEweQaUuSlWr2StyPSHOkO4Fvey53B3Y9X2Va0MgrVnrnDIlC5IQIFNtgdSCrApSqM8ofGM0EkUxtVV1s+krl0GA0lU2TjnjXLfmumUtLGpqyZBcjachRuZlrN0hvkrIBQi+ytrF2ZX7kJi9p4hC0zW0/YaUMkuO4CQ82cCrC+aLhkUL4pLISSFUfXmnXActH3xtrAyRFApBJaTRlKxYfKQ3Dq0UUaxfQFZzrJBgFKqrFNsYAkuOLCWjjKXvaoNqfeDCEmdEnsAUtIzouKzD1AR2oS2SxnRYs0GmCuPKuZCpMWByIaynfKkqpMsovUK61GeviVJ6NRXXxE/WikAiEGsLaAadwJTas6LMz/9h/HB8JHtfsfOlck/8MmO0XnkwgpLi51WZ1pZlriqAFNVYXKPYFcim1njx5cVlbQY2hsZZlK4FizFGQkzIlDGxmm6VFDhjqp+iwHA8kUKgcRakZJzH+mdWA3hVITN58QynE/cP9xhjkFKQ1oZl17bE2TOfj3yKhueUyBTmGIgpg7O4J1e0z5+QtSaGgFWSmEtlIAuQog6zMSZKyjWFJESlxa4G0ZwiQtf2WZRCm1pMWNbvUjWqV5jUZ3qt+PQ9V0hlMLstuoC3lvPtHcfTiWbbYIRYyccFJSokK8dUDzPUhFn67Lla1nLG+tmKVZ2SUq5pKUhk5ugJeSHngDEWpWRdu6X0aRGK1pam6chTIJX6PZv88l/pLv0vd8l2oLBZ4YINWlusM5ScmMLENM5YVw+MfllYlsD9w5lSHPM8My2RuJLGFPJzLchhe8WLw5brXce8LLTOstl2dK0hxIj3ER8ST652q6dQkLNgeLxnOJ7pWsth30OpvqdpnPEJ5rUM893dHUNYuNj0CApXu57GazqlSVHirSCoyFW747Dt+fjdjzSd4TRMZKXAaKSsq2xfMr2VvHpyzS9/+Wu63SWn+cj94z0xhJUlJD4D+0quIMfd4UC72fD9D5G9MVz/esPvv3/Ph3ee3RcKaQqRRN8rLi8N0zKQwoxrO0KOeD+RUvWbNEbQ2YIU1e/07ErjS4ttNIdeI4Pk5jQzx1JLdadIGhLSazphUH0BUQt9U8qcziPHMLPEUEsEV4aVawTtXnE3RJbxhre/PaFzwQjq4Vtr2kazsCByYOsarISHcWQRP82D9dOjFVIyDQPjPLLMNXXgjMXIldAX60PPjIqLcsFhf8F+0yNKZhynCqRSGiEqmEyK+tAV1NVKkKlKxYtGxow3YITECIsQkrZrySkRfJXGcokIZVHGUFnUpQ4aOZFLdSbHkJjXxlU/z3WoWfHyqVDbWWOFUGmdKRKEkgglkcogjEUYQeoEXDjS9QbfO0aR8CkSi8AZRxCJmJaqfuS0+kvqyU0gSSFTdDXTphjptm1tb1wfbJ/MkEoqipEEAzpFwqeX2Vyju12SRGlJSqCFZl5msh8rMK5UOJQWmRTfIuQtRjmMfYE0lhI1af2ZckkrTl98riDXSv0RsibWB3UBEH+kfOaIUAqpJdEUHmXmWCKkTB8lrhRUyjTypxme/jlfjw8PqFIHjaIEKea6DsiZrlX18y2rCyqDsY7NZlfv93XNkVJFXgfvaRpHTNUEWBtzazuxgppqW4mzOSWi8IiS13WdxmpN8L4OGFqx227Z9WdO55GQA5tNT9e2n3tGshD0bcs8VxWxdXUdklICY3DOoa2tJFsg5Apis12HVQpzcaB/+Ry92RByTWsVWfW1RoGT1YgnpCbmsiZ78tqtI1BaVq/IkpFmjQ0XVoS8WP99LXoTQqDU2ub9qfxtjT1nCkGC2Xa0on7HrTY0yjA8HNFuW/+8AE1ZywbrcJLXqHwpEOKCtRVnnkta0QMBWU0ElJLIRRFTZJongq4vBqUkKdUaCCEMgrR2v1hsbjidH1lkIP03kNYRekErCRK0kjjnsM4QgieXwrwsCCXo244lJaZpRqEJk+f27pGPpxPJQ6sb5rIghMRpw8vLSy5ajdOS43EghMBu07PpbE2oNYVl9lwcNszzSE6CzWbDeThxPp+4utzRd5ZpXJimhWEKzDFxniaca5lDpAwT17stndNEo3BWIVJheYQgG5SGy/0Fp9s75uGMuuzZvzjw/v0bULF2Q8naaN9uO754/oRvfvEL0B2Pf3jkfB5QStYDKGtFwnpPdE3DsydP6/uqZCYNbt/w8W1mGjTSSKTJUARKZjabQoxnjg/v2MotMQSmOZKipOss19carTzDKBBasr0A7Rwx1e/ReRQMYa0bibW7JwyZNKpaNSDAR0HMtZl7yZEphdq8XoOhlZ+CJGnJ6UNARJh4RBTojGDjLGHJxCkQZERbya6xaJO4Ww8HP+X6ycNJTJmwLCzDwjzPGKvQRn7us5GqJl060bLf7tlsdyglWZYJv3iUqisb1r4NiVgtvAKyIMyBSEGEFXWNZEGRqFFL0TqEKJVUpw0pSZRRGOfqLy0mcozEUCBVs6b3nvN4rq2Wou65nXUgFfPs1wKzjNIaU3JdHVmDbhpE4yjbhnSwcG0pe0cyGu8z3ieWWFWY1jiWsBBiAHIl24ZSqa9CgmL9ZxEpDU7bOhzliIQV7vaJwgpZZca8kMcz8zRhO02fJWLxhCXR50JSAhUjcZoJy0QokVgy1rZgYFETVp9Azwi7A+lJxda21ZXaK4UmA1nkivV29cSPUHz2Kwkook7LMdS0lbUOhMKbxL1e+CDqWmvbOmwBFT3b8vOviM8x1k4YpRApfa53jzISU0QXWT1LQiDXE1PXtOvwXE9NKa8uByFWJaVKzF3fr8WNcVWrat+NTn8sE1x8raavzbhrMRqF1jj2my2Xh4nzMKGl4mq3q7RY5xjGmeM0VTOuMcRlwZeK2f+kbtimoe06hvOALwndttjG0XQdom3RVwfErg4mIda+qeJDVX8UuCKIQhNWtk4ReU0Z5XVVWHlQGokogiV4YgKdqm/sE7V1TTYDtSvExwAUQkykXMsNRSm1zqFz9FcXXA0D0zzw9vvXXLZf4DYtWWSQnxADnwUS4qeagZIJYUbRUEplnaQUqyJSJEKDMZUSK6h03JQh5khOsXZq6aqAxsy6PpWEmMh5pml+/twfkQXKarIoFFEb52KKtfBSCIxRBD+j9IZ+03P3cKzdY4I6sGfBxtVOtBA9jXG8vLrkxaHHTyM5VgDmtCw1mh47LvY7Wlc/12lauL0/EULm6RNFThFEzUmlUpOKs6/JtywEm7bDuYbr/R4KbPuGvjH4ZaFNioXI8phAtVy2Ha1reHz/Bu894wn+7F//Ce+6ibc3C6SINYbDfs/TJ09odluS0fRtV1chKWGsxi/pM4GbFeh5sT/w7MkzEhFjDLf3dzTvQU4bGhfRzZo+jaam23TkYqeI4YFxlKS8EFIGFJuto1WOfR+5f4TjLIlCM42SYQ4UMvNQ8EGiYyIviXguLA+wHBPMhXlOTAEyBacVca4mZbF6RZUQtE7SNYo8ZXRa/Yhrn0/OhdZajKjd3DprWOPOuQ3kVNbv9H/++snDSQixfiF9Jvpc/SYBUogUI+tuVjvaVtJox6brKCRK9BTrUKqi3guFrMr6Q1YJ1y+e4GMFgEmL0lVxCL6eOoSFZZqAgrK186YWeMnPcm1OkZQrUvqPp/7q81BKc3HY0ThD8J5pmjkPZ4ZpWgvOQFuNM6oWmbWOdN0RnvcsB0N0kiwlIRdCyviYmJfIME2o/o8UUEpeCwKrQ1mpTzHCzOJnWifZb9bfCxklAbOim6utiojgnGfSdCKHhMbRCgmxmpayqP0eeV6I48wiPEMY6HPDpZZIbYm6R6r9Sig15Fy7chbhGcSZojOd2kKArDO0At/VanuVRS3govaOiJWiOc9DPQWliIqWWBILI49qZJEwmJo4KWHiIH/+BkAt1FruFlGyFsUVIMbENE8oUYcIrfVn0F9ZQWaUNcUgJE3Tkk31RSmnCWtbr9WKKdThxrUdWkBJFVe9LAvnpa7jQvD4UNMIWtaB2CiNM4Zt39NYw7bvaFvHfndgaGfCzUemaeLy8gJSZjkPtUtnXYuKNdoupMBow8XTazbbLdJZct8yO8NErij7FJFSVW6JEJRSyw2XnJmKIuS6umS9j0rJaKUIFKSzpFITDCkVRMz4Us3AUn7ykFUVTwjJstT4JKU2I9fyysp/0Uaj9xua7ZaHhweGuyP6xrEz12RtoBRi/k9ObWtPVCCipMb7GYGikMg5UITAx0RJBWklTtbiQOdqAlEKSUiRmALTciLGavqL8Y+0ZC01KXviz184qcN0hug9KkXmeSTFRFg8JWecUcxzJOVC29XE4DTWwe36sKdvW87jzIfHhFOKi03PL19ccWgtd2nGOUPXWVKoRYsxJE7HM7lviTFx93DEh3p/LfE9VkmaruX93SMPQy2hTaXgtKbrW7abnnGaeL7fMPqEsIogC9IpdBEsU8A/BBoarkzP6ThUkrhS3L27Iy0TL391zXfLHeMpcTgcePX1Vzx98YxgFbfnR2bv+eGH39f1oig1vZYqa0uqgnOGy8trGtsR8oLRit/+7ffYt5Fnh2cszRnX1HJQpyy7HmIWlI1EtzDGtFZJaIQEKzu03bKxkhQ9948LS7Tc3BaiVAQBaVzIS4bsmU+J4a6wPEpa2bC77Li5PyJ0rlR2uTIfw6cXgqBpJJtOYSXMvnDoHdOyICNEWUsbY070reHQ95AFp2lkSQtChrqdMP/Ehtibj7csMdbNqlCIrCAKsi9kuSBlwVmHyPWX2W97hCicQiClmYrKzp9NRLIVZFH37RnYmB0Gu0pGa2V7jEgF1ijWgmKijzQOXNOsXI5UVzprtFUqgZI1wSNETTdIKZFasSwTD7f3DNPMeZ4Z5qUOBDnTtpZ2v8EceuSra5aXW8amrAGaeqLMJbIkzxI9PkXGZaZvbP1ihppsSSnWc7JU61qHGucMlU6571uGMNXCJ1un4pRFlfxyIYhE2GpaK3FCk1NhujkyhglfJkqbKSXih5mQPEczcutPXO16rrYNqusRscP7iSmDOVrKKMkx8WjOfC+/JdnApbpmJ7YIpTm3I7Px9NKxL3u62KO8xeS1L2SOeL9QyMRc78IcAkENzPpUO09kxgpHKCOTnP+XPdn+GV7GWAoVLJjS2jbsl897ZKX+E7y7rHHguO5r83pil0LhbAMlV2ZOykTvOc3D2hZc0ys5pmq8TLV3RyvFZrOpu/px5Owcu76na5uqBpYCQq6AwdUTISol2DrLrt9yM5xJKRLnpVZSWItVGikEPicehxEl4OLqkrZrkVoinGYxhTEvLLmsDb4rpMkoUs48zgmnJbEUhmUBBK6xaFO9Kd4HQogEIeo6xAek1AiZSSmRYqjDiarQN6UUrnFUw3tgmZa6dtJqTTCtXJcUCUqiLg/k1z/y8uKa0zAznI5sDwfmGPAxVrVj9UkJWQfKrmsRIleOkBTMvkaZtTIYa3G6JZfMeTwSiWyans66OuhkEELjQ8JqWQf0tSeoGpknwvLzT69ZB0JW/lLjaoGqkpCUqIqXlMyz592bG4wuXB52PD6csdZy+/BYacBK02rH88Ml33z1kutdg1GSZ1c7xrl+Jk2rcXo1d6dMKvWg62ePbR05J4Yx8G4caezA7emBOSwc+g1fP3vKy+eXOKtIMaOlYNMaHqYz//63t8zB0xrLi+sDuUSGZWEZMhNvubq6onGGb758QWMN8+s70qtaUbDfb3nx5Qu+/OordtstyzIynG95f3Pk7W9/j9z1REIdUNZngBTQuoariyuG8cjgT+R0Ikwf+fjjid/86/8zat9ysZ1QztO2li+/chQGzsPM+1Pk48NH+tbStx2UQqMu2W8O3MePnB9+5OG7zHQz8ubHBbt3+ASaQL9dSCZwviucbgRNavjFV1/w66+/5N/+9X/gdljQUnA6zSRVVfMVNstmo+gahR8yccl0G8lXhyvu7h95nCa0UtydJ47DTIiZy01H1xgQkXOcEKq+D3/K9ZOHk/fv3iO0Ytu06+lKkqeFsmSUU2x6jTD1S+jDwvD4QNc5rFNI3SKVJZTCNA6MS33waafQTlJ0ZvELWjiUtqgiMNqBrScjsRr2pKyxv0/yElKQRYGSKolQrwi8Aj5EEIrD4UChrnge7+45HY+186aGmsjUpELbd9jrLfrPXnC8almUQFAfjohCLIE5eiY/EVIh5QjkuorJtXdHS10jx7lm93PO9ecSipwzzhh88AhVX17IUocxWQvDEhmsYLIRR6ZMnuVxxocJZKTgWU6eKApFFhaTeEgzPmacA7cbEeoOlRyUPXlQLKeWMhQQkUUFPqoz7/nIRnzk1/tf0esdfwhvuEm3NErxpLnkSXzClb/kMF8gS42RaqkRWoJVdY0gImMZmMsJnyUiFGJe8HnEp5//w3iz6ZmXiZwzyzKR40oJVlVtAoXSlpASj6fTWqxY5dJSKt6+dQ3OumoanAZi8XUNJBUhJoyxpJSZpoGkauLAWIf3vsaWhWAYx2pIffGcy8O2DudLNW0uy0hMmU3fI5XCx8A0L3RNy8u+Q0qNNaIWenYtTdegVqldtQ1xnrFtg1CSSKEYyTkGTr5KyVLWMtBUPsEVFb5kTnOgrJwSZ6sRt35/JY21hOA5jTW9sDUK7arBWsg/qj5C6ap0rOugT/+dawdWNcXW02haPTyCKMAeNrjrC26/f48PM+d3Z3IJtP2BWCCtfjQK+CXUP18sxkiGeUSWynIqpZrCM+BTQAmBkAbvPbP3KFkwRlOIUGI9dJW8NntX+N2m7zAmMvufVhH/z/m6eGbIx4DuJa1qCVP93TaNQcnMcK4pqkZLBJmucbgniizgYThxHma00Vzue6RUPLvYkP3CFAJd62hLfR47a1GixvULlUFjjaJrnnEcBo5jJCt4c3fLr754xaatoQaFJi6Fu7sRaSQhLGwazTLV+PLvf3zNUjJWG24fjyhV1fRtv2GZTvztD2/YNB3fPH1KZxz+7Zl3NwNqCy++esrV1Z6MJ6fEs8NLlvGeu4dviSFUICEFpRUpVZim0g0Xh0ueP39eC3PngYd3f8OTfeJwecFGRr7+xZ7QeyadMV1LzoZxgG9/PzKrDbbfIY3lPM+U4NFa08UN4aPn/Pc3vP63j5xPR+Y5Ud7NzCFy9dKw3UKaC/MDmKRpDXy8veHtzT0f7iceT2eePz1w/XRXDeM+0WqBNgntMk5bitH8MD0wzJEXB1cp4otks+2Zpql+Nl1ic11XrvdRcF5OOCWQ8p/Yc/LkyROavq9MfiXRZnXx54LrOg77A0ZIlnJiiZ77+wf8bNlsWpzVeAmp75AvNjB3zI+P6PMIg2dIZ7Tu6Nq+NrWWSrrUuoLfpKwnpBQ9lMD5eIMP3bq+qZnrUo8wIGTtdhASacA6XZNCOZCEwDQtm7Yj5oQbLVJrLp9f0/36BfzlS45by1xK3XEaSVp9JPV/CjFl5mVBCoMxLTEXlrgQSljhUgtaW7SW+CUye4/NFl0E275nmBdyKihNXaskaudNEcyh+ha2TUPQApUSWkqSqiRZMRfy5GuCpymMfSAryW7q2D1saV8YxvRIyg02X6DjDlUahKovsk5seaG+wIuMkJlZw9lM3BZBtBtmE/ioBjKWzm45RFlBckqgjKYoKIfa1TIqsZY0giSTi68k4DRB/PkPJ9YoQqhJk65rPiPXqzrgmeaZnCNd163m44p0R1QPUk55NQvmz0bVFANCKdpug1KKxjarX6rgvUcpTeMcTYyM48DDh/ecTieM0bjWsdtvMNpgjKXreuCWDze3fLy95+qw47DfcZ5mmm5Dt99yf3dH13akkklTIWtRO1HajidPn3JzcwNI5iVQnGZJC0c/g9S0bVtN40iWeVqhZIUYE865auzVlS0SY12nCFjbfTt00xCWhUYkrGtxq9pYjxfr/ZOql2aaJqQUlZiZE3ntdKqrH+pzYE0KSa159ue/4t3ja4a7W7CK890jWjqKUoRUPTICUVNK2RNlBiWglJWGWzebaanGTKurqlQ/h4nsPcNQaJ3DNRYhBK1z2NU/EUrCpwCZ+owoP3+DuBM7RpmZw5mrTUMpmse7O6QoOFtfM9ZqlKy/4/M0rGtOw/XFntYa3t88MIwLlxc74rxgrWI+zci+Z7PrGE4TRRqUa7FM5PlEEQVtDTkI9ocDp8nz8eMHKmk48Itn15gvn9E1DdMS+Mc3b3l9d4+Wir/86jn7xvKL50/xITOVTJGFOcwM04wtgiw837274XGaAEnIgr/86inJzxzHW7789W/44td/wubygDYGK1tyisTbM/OU8Z1jmRfsysDSpmLst9uey6tLxnkg+oFv//5/4HTzj/zixRc0wwbrB+bJc39/RwqRZQ68/f1Ijp4Xr54xqAP3bEl5ZlkCxScWW/gPf/OO1//mI+//caKNmn5nuROB4+xpnGZ7pSjaMz5K8AWVE6TEsgQehpFxjmw7RycLYR54/vwCPQucSgQ1M/pMOAdETPS94cf7gTf3R+YQEE0htxPba7Cdom0TD/KeeQl4EcBmZBHknxhe+8nDSS5Var14ckXbtQQ/MZ1OzONEGkcWKdH9BrPt4NCzLAvHoZIqNxdbzItr9JdPaS5ayAvT3S3x2zeIH27YDJJts2PnGkzRLLOvAKmsV7CVXsl7NX54PJ0QpxNN29S+mlBjkNY6hAQpFW6VuIUQtH3LPNa9nbZ1qPJhwXaOw9cvuPjLXyK+ueJRpRoXjJlN01OoLaMxx9UlUqFS200PKPLZc//wgNvU9FCKmZwLsoJvSVqQlsxwPvHNi1cYZUgich4Gtp1FW8myhFrLLqtuVhB4reDlgW4+YqXieHxgGRZE1mQvib6QQ0aIQiMU9kHiT5KzU/CypQhJfFiIJw+5QfcKGomyjoO6JmHQVmKE5gGPlrXmXkhTHTJSQ2NQWWOKJehA0IkoC+c+4AVMxiDTljZUamz9+RPOO0z5b6FfJ62vUME4LeuaBhAFpSVt6wghcTqd6LserXSFAqr6p1JKpJTXdVA1Z3sf0Equa8BAlBX0UHItrCxFEJe61mi7jmfPX7C/vMQ6y7SM3D08cH15jWtaDocLnk4Lw+K5vbvj/d09YwgoY9lctUwh8vb2hpfPn7NvNvjgiceMdQ1N07Hpe+alsoEmVcgqc3c6UaSg6wxQmUGUyi1a5gWpFdttHawyeaW+1tWJEGJVYNZ/DswhEgnYLKq/ZvWYKFUJrkpViJ21hmme8X7CaEXXd8RUmKaReV4opSqYJVcybdc0fPHrP+Xm//u2ekeOnkmd6K52bPsepCKkzLyMaF1N9yWBFBZrastqWluaSy6EZUJqS9MYWtNy2O6w1qxlpVBKQFJ7deqsJBCqNrbW5onjf4X787/s5d8/pdgjm71lGk9kL1YOiaRrW+y1QhvFw8MD2m74/R9eIyVoKXj+7JLDfktGsA+Ri11PTJnr/YHGac7zwA+//4Gr3Y4nrSHEjGo0VvXc3d7x/tvXZAFXlztUyXz95JpfrOp0LgmB5uHxxOubW/7w4T1308QXl0/wCZIUXO43/KvuG358d8OcMj8+BFzr2HQbYqqKh9aKRht8Cry+PXK/nDkdRvLda+Rbyy5OGNfRmIad2bC9esbtP/xH3KaFya+r3IxzlV308sULXn35FSUkvv2Pf8Ptj/+enVZIMldfXdCLyF36gY+3N0zvK7r/6YsD/+J/8w3SWY5vLTknxuHMPC/1PbCxuGZBKsUSFB+PC0Uk+nZfT8EqkAnECYZ7SINAZUghIETAGsnzrWHTO8RcuL+fUdvIq5eX3IdbPj6MfPzomU/VE7fpG/Z7wdyf2F1J2saQQ+b8kMhnxbbvOI+Jd+OEe1rpyiUp5nc/7Z76yW+RprWU5Hm8/cDjXTXO6VKhLSFG2r4nNIr0bMOwtUwxwnnBTB512CBfPUNeHcBITFHQKdK2QVwc6H48IwePnwKIxHA+E2LCNQ1tLzFWkvJKrhOCbtMjpMYaTYmBJGI9PKVU+ShNQ7vd83h3RxgGsqvKwWazJYTAHCe6l1v633xJ8ycvGDaWpApLgiVDivWzlFqgdCbFis0OvhrgCqpitMcTMc5s1RYvFUUYRDGUKCqBtUSsTEw5Y40i5gUhJoyJxAIyidpoK3QFmBWF1ZKSCsvWIRqNzJByZJlm/LJgpGbfG3CSYBNT9viUYNQc/wGapUX1Ev8hoIMnXCbkhSRdGGYjIBW6bChhovGCZ6nDx8SNCCBVjbQlSVSFZGCIJ/6w/J6xDOwun3EXAkNK7MwzumZH4w6EnAhlBiYmEZiPP3+uAysILIS4JlDUCoQCyFhTe0GG4UyINfkxzzPOucr/SNXZrpSicQ5ZqOjpFUbmXEOOC5la/RBTZF4806Loclox+QVnVo5mhNv7E03b03VbXNOw2264OOw4zzP35zMlRP7ln/0Zfb9hmCb+8s//nJRqUVseC/M0czqeqvriHLv9juM0ovcbTqLCCGMM+PSIkHqFHTqMsTjRfma2lFKTSFpptK7fPbFGhIOveHofYj3xqExOC2Ed1mpKuA5o2ujKfFm5LzrHz8TonOuDvmlsjRjnUmOVy4SPgWa75+mLr3j/4TU5JVh8pd+KjNIWhMJqTUyBFGuSQwiDM4ZSApQ1BVEkigolLKka6o/DLbvSkVHkUiP45DqsaqkQymJk9fuEFOH881cS5zuF+0IwpUd+/7s7lkFzub9g2zSM55GuMeSS+OH1DWjNixfXkBOXu25d3Su6pxdIKXGNI8nE25sfuPilovGKJ/OOX33zDcGPvH77kX/49g2j9zy5uODi+romZpTG7R1SwmHf8XA68/H2zDwHfIhM3oMo7PuGJ5sOUSL/9j98z7/8yz+lVY5YBKdh/swVOsdTTXBSeHrYcbU98P3b1+S00DqH9orpzT1vlsTy1cSzV19jDi2oxHdvf+B0eqCEXFvJSy3y00ry5RdP+OUvXtDIwpvf/o5/+Lf/hk078+zFlieXLc+vMw+ne47v7vBHuHmvarx3lpzTmR/vRjZXL9k+k/T9hov9JTJERMlcXrcsL3d8+MdHVKmr1zieeXboeYxH/JCQEcIZ4lL7cxSsRaWSy0tNvyuMY6GNlvPs+XAeuRdnvCn01y1mWxAmcXiuuGot55PHP2TGe5BJUUbJbrvFpoa5zBhbIaMlg9Ua++ynjR0/nRBLIS6hJlSk4eKwZ9N3tNZyGgfOy4jcXxFeNhxFYvQC4Sy92tAc9oR9jzHVAEoxYCXyQiNtD4eB8N1b8ut7ltPMOI61g6aALALN2hlQIqZtiCnil4W4VBe0kNXsJ0VldBTqw9AaQ5ICvwSELGAV5tmG9suvES/3pH3HoAWhJFKULDFTaSRV5a0FTp9Q7YBUGNMwzZFpDijtCGFhmSJWVANfUNU/UkpGycLGFXrhyDESkqSxBiEzPkSyWBMSop4+SxGIIskkZpX44fYdF8KibWXJkAWNNrjWoRpL0pklBKY2IpLCZYt6L8CCWEz9eZZAmSawGaELpmSSgykn1Cw4pC1L2XAWJ5YUPkOvRCuZlef703v+LvyIahTXes/Hh5FhXiAbvuo7Wr1lkIJHaRnn6qA/3d//r3rA/XO6ptmvDB2Bc2vaRkmE0eRcEfWu2dD3Wx4f7pimkWkWtbHV2MrtEFS5V4hV9avI75wrhCysfTE1Fh9qz4ys6yMtK9k4pnpzjuPE23fvUcby4nklI7d9h2taun5Du78ErbBdX1cj88Km3+AOHdJoYow8PD5SHu4pZIx1nP3CXZy4PnzJOVQOUcyxRnhlgVTLNpWyKK1JKUCpqTPnmrVLJ1ODwyvHBEnjGrTNxCVAmpEClJQ423weZpA1Pi2osLR5WUhLrD6PkoDVO7KSiYWsHjQfAjkLEpLLp19ye39HDAN+WkgaGquw1lbFNQWcMmSlCbmupVJJUCJKfcoxK0SpvjMpa+v2snjul3u6rsE0LVNYEEWQrcVajcgRUqSxCmc1rTX/le7S/3LXP/7dB77SmmU+8vpmYHwUhAj9q6/Y7Dfc336s6qLRDOPEohXPnlyy2bakmNDGIYQgxIU5DdAs7P+7idILHv5dC9nyeDwSZ4+zDfvdluHDLY/HMxebnt2uY5kW2tauniFwxrLtGqSSTH7hcT6hpWTXNjw9VAXtfpn4d3//O/7k2RfcnY6Ekth1LTthsY3EbSzt2XJ8XLh7eKR3PVvbkAUwwuHKkMSR44cTw90PhLlBFEO/NPTHkaIzfhcRRqJsw9XFBV9/8ZR9l3n7+/+Z3/6Pf0vjE0a0pCXh45GzhG/fnTi/6cjnhvkhU/YtZv8lx1iY8hmdBRfOUgQ8HB+wuaUxguk0cXscKaVw2FpA83iamOYBoQXnD9A5RfaZEEFqybZv2W1aZJtZxJHT+8zOdFxsDCkGHoYH9JXg6plBmEKImTAWptPE8U0knAXF14Z6csJIC6Lh49lzyiNlU9aUnCIrVbcFP+H6ycNJ3/ecjyeUNOy3e/q2rw/H6JnmgcFHGvmS0ihyDpUbICWl66AzZF0NqKKsVdFFIrSBDSQr8PpAZET+sCCCpJGOrm1wjcFoiRYCVnpsje1WuIyU1bVNrt0wcfFVlTCaEGrxkOkc5skW+cWe9KQn7AyzElU2l5K8wqaEKOTk2W82lFJBallKYhYsMbOEGiOOueDTAgJiSTycFy62G7QsZA0pVnS4QbCzjs1hg6cmi5QoSBRWK0LM1dBrPpWRAUUS0oJpBQMBcT/SWIsUiq7pqkchZEKcK6hLCDbaIrTGaIsWuu7kjUQ4idg6xK5Hdhb8zDSd+LDcIlqBdg2XY+ZCdhxw3MeAUJLWGfRWM6TEXZxJWbJvdgiviF5B0TUpwgnjLMlopjxz//jA/Yc3FKb/FY+3f17Xj2/fIZSmaeqwXDKotZE0hFDz/psdF5sDKXrmacT7hbhWpadYScJDKThrcbZdabCJZRyIKaFNZeLkEBCJ+gLMiRQCQUhSFCAKWgnapuHt+3fc3t2x3WwwFxf0fc/19TXnaUHZBu0cyzDSak2vNWVZUG2DyhmnFFZKpmGs0DAheXd6pH/1hA/HO5aYca5BaVl5Hqm+3AMBZ9f2b1VBZf2mxxhbcQAhUFbeS1nBZyJVsioFRC4oqylKrNRV9dmHk1ONXaecmecJKyoxJsVEyqEOEKKWCeZUSDGila4MIQlNs2HTbViOA+dxQMxjVTRl9aZ8IiOnlJEKUs6EHFEIKJKS6/CVokcLg1MapS3WtDRaIIlEH/ChrnUUmVzWKocc8VNCSFOZTj/zqxNbljsP1nPYbonDwt39IyF8i9OKYRjQQvJn33yFFql+R4Dz7PHzgmKh6xp0J8jOM13dkroj6fsNenbcnQZ+9/3fVqS6rQj2xjiM1ozzwsP9A33XsaSMQKJKxppaCGuMxlrNixixVrHb7ei05ePjiUJmCoEp1p9JqMx2v2H7ymFfZHK3II8g30mef3zGC7Fj2zr+5vb3dHvBrM/gMzZpZCpMjydOp8KfXX3Nl82Ov/7xd8iU6K5aRBH02SPnE9P7I/e//4GP//iWlD27Jw2/edZhNoFv3x/5/VtF8gfstufpb655/s0zdk+3+CzYnWfux+9YloBUVVHt3Y5hHvjw8MDDuFY4pEhrFZdPDgzTwkOJDHOmcY4gE9YVpK5JwmEeMK6gbMKfJFL1jMPAHDxNUTz9whFz4uGd53QTCaNAJEtJmvG8kGJmjnW9+mxnWebAmGZSF1BdJhWwjaPbd9x8ePhJ99RPHk4UAqM0ptNoXeFqpRiKzjRPNOrCoK8Xgnxcu24qwVUbgVag1nbQKu/WE5UgEVJgKQtzmwhPBS5ItFDoZHGNw2pTjW7V60rJ1XR4Op/J08x2u6FztnbB5FqIFkvCLxOZhL3eYr64pnx5IBwswSoWUQhrooYEYZXYK6GgEixDiIRcEb4hQggw+8S8BIKPFUojMqzNvORMYwy5rIV/MZOywEnHpTmwxKU+ota0QU61pC+VTAgCLUWNfa6VvkFmzK5DHwdKKjjb0Di1SuapxiFL3eMLV+uytdQo5Sii0gpDD/7akJ44Su9YlsTNuxN/uPsDXWrZHr4EldmUhmt1wJgW0Rj27RblWsY5kFzmWl7xxF6QU0NnJSLOJGl4ILLkkbDAeXzkeHfDPD2wPfz88fXGNkitaBoHOSNFQevqmbC5gvXiMjKngJaCru0xKwyQlX+i1uiuMWuXkzbkHJnnCQVoqTHKkotAtjUhF0JN9MzLzDAOCFnYbvZQMq11TMPA8fFI4xq6ruNyv681CrmglKknGyGRpqZ+zvf31bi6BDR1UB/OA8IausMO3becpxmEpOslTeMQUpNKJV1KoGlatHE1VROXSpUNa/9SEaRc79mS6/dsXGZSyUigVQUjNUtKpJzXFI5Y/Tj1+yWg/tytWdNtCplKBX4V6veP6jWTCpC1YFAlybY7cLN8YBkTLAnkETS4/abG/VcqrMgFqyQh1i4kUVStnS9VEZNCEaJnDiNGWKQ0NMaipUbHam6eckRHgzWOzloarQk5M59//sP6sycb5OaB8SzZ6JayteQk6ZyhsYZGS1Ks6lPbWaIv/PD+PaP3bLqWrWuINqBdZrl6ZGxPqKPCv5bsiuV0ulnhnZ8ajOu6sypgHudqgEJrg5ICI2qEv8Q/qo6bxnJ1eInWjpvbe14+vcbHmZQ1Omd+9fUz1GWBp4lynYj9whyHit/fzNhiCLc9Zg9baTgeFcMHyaHdcNF2pJwY5zOmZHaNxWXYG8uNf0BlcEZyvPsDv1/esNEWf/RcPTF0WwtGM9zAjzeex7LlKDYU13H17Jonf/oNz189Q0uBXyIPH//AsizIyVPKgs6Ktm0Yw8CQZhblUU7Q2YZXz664aDpev3/gdH+DFfB0u+XV3nDzOPDxcWCKlRpttEBYaux/SjSmxYvM/mnDNCYe3kQebhPzuaztwh6jFAJVQzIlo9fDy2maSI0HW1NsKUqGY2aZ/WcG2X/u+umE2KV+wAAxaUpOJCL6AM0Tg30uCJsHxuWBEC2kDrnGAhvjMEp/jvBWHHQkZs+SJuYwMuWJ0EbyLrEZBdprlJAVPFUSrP0hKUaGaeDu/gHVdvS7DcpanKldJX5N5mAzZtdjvnxCfrFn2ViSkcRc6+grt7OsFEdBjDWy2DcOgSSXT/XusPjCvGSmORJ8QgtNYyQheRrbQqkMBUomr91B0QeEUBjdE7MGkYippjFirmRY5yys+YS6PqqRakHGaIW73NE/SET6hP6u4K8iyudkUpY1USQAnSVKWaQyFCEYTGAmEYonkzhz4qhmkhK03Z7D9TVqUvgx0EqLbjqkdVhpq0y3BKzUKNlSloJQgk2/QS4aoRReS5Y0sIwz59MjfjyhTfUC/Nyvru0QqrIZpKrqhTbmc6w1x4hMicV7iqjGTocjxMBwPkMp1U+ha5N2TaZVKmzbWEqqEXRyXj/qWgwokiTFxDRNnE5npKppLy0l236DoDCNE+NYaw20lGyaZgXo1UK2Cl0tlJDqajTV71ZrDF4bhrRQlGD/4prQKpooSTmvqxYwRtRBptQEglmL7T6VRM6TRyCxuhrakQqR5dopUq0mi5+r6fSTkVV+yuisCHspiSS8D5QcaZxd2SdVJVn/o3U4Wf+VqEMhQPA1CdW4HoIiTPXApFRhaRVYjWlafIi1byjmalbOGakqGl+p+tAtSGQRpBiZ55mQUy0SdQ3WWIx2hBCrt0UXlFoR30rWJNtPjSf8M75e/dpQOocIG26/FzxYmIa8JrgCc/CEkHmcFpyWLNHzeB75cHqg9y3Xz3v8VlE2M7M6o6Kgv98h5wYvAk3jaFqHNZrzONXOKClprK6VCtoQQ6LkgJCQ1yRhLpFp9oSY2PQtu36DVpKcNmx3Bzat5eFhQCvF4euO8eqRcz8x6Yk5LYzLxMM4MqcFbY4Ud4VymfIe5IOjHQxd6nGlxQuPyJ62rYW2Nw/HqhBFKCWhTGI+n5gQtLue/sqidcNwH2nGjvDWcOskR7clyno4f/blC549f8mm2TAPZ969fsPvf/d73MHjxUSjCxfba4yRFAmBxCICQtcAyONxoVW2ggAr35Fpmvnml095cXngzc0DHx7PBOtRrUcqQdsqnrcHvCi4ppBF4ubtzPkhkrzACYN0kmWuCnHFCki2vatloyFi2oLoEqhCihA8zOeE0YEau/jPXz95OFnmqfZ3FOj7LUpZiohkFRGdILnE6I+c4kxmXwcAo3BaY3SDVHrFolf6XCbh08wURpZ4xqeBgEfLKsfZrBGpljWlFImxYq9jCNzd3+FT5OlhR3+5QbW2pkxKXcMIa5F9i352RXp6wLeaOUGOn8rDKiiu5LJKyKuMm8BpR8mSUtTn1Y6PhRCBKDBo+qZF60KeEs40gMHHmcmniirPIHJGa0XfNsScKqslF0wWiFz7VSRypchW/kX0sQKLcqLtDepqS3sDOtSixJxr56ZQNfJZhxPBXOqLR0aJEWvMGwgZ9BCYHz1zLozxiACeXnzNl89/xX63I5ZbJn+ufRjaQNbkuaKN8QmbDKcQGc9nzMbQby9AV+k858jiZ4bhXAcTmdGdxv5UPvE/48vaiiSXYo3HWr12PpXa0ZJX3rOolFVjDaUUUqmf4Sc8ek6x+jSKg5xIKazER1VZOSmCoBpN1yOHD9X7tfiANlUy75zD6Aoc9MEzDENt2paS6D05Vsib1qZ6W0RZU0C150pKjVGK1lpShrzrobWVoKr1mnCL60sXpBKIdTqIMZKpf7cUghRiraqAFeFfDcNybSe21uGDq+uSPJFzJKzJFy3UOqT8kQ4r13biGBMxBkqp3JQ/9uPktUSx8v9LiWQfSaKglUNmQQm1KFGETDxPYDRaWbKqBZeIUge4nEmidovUT1BRytqLUtYW71BLHUuK5Kat5mRR18s5BURRpATzittv5M8/vfbqXwimSXB1dc377cJ3/3Hm3Y8zx+PMuARO80IskG8E+67hPI70O8v2YAntxOl6JuwzQc2EMbItO7qTwwpHShFrdTVRU8nLaYURallLHVOI9XNSVFXBGsbJkwgM40xGooxh9pFNaznsKiR021hKl5C9JV55hu3AbGrR3bgkzmPiNIaKjFAeXKFExXyXaLyjtwptJbNbmFVNehqnWOaF4/2Zx2mkdDWs4ZxESYvWEe0qdTUWzXgjONgd81yIzx0RhZCCJ08vefb8Cmvg9vY979++5be/+y0fPn7gm8MlWlr2WvKk3RFFXUlRYJ4iykOjNR/vzkxLINdWF0IsfHw4cRxmvrzcI/IOIRNHmfHNQtGC3dbwvNtxl8/4CO++H7n/4CmpYLVc12UOK+vv04eFq8sdJXv8Eii6IDoQLtfOLg9+hDgXhIy45qfdUz99OEkeZVSVP52j7VsSieB83ctOiagj0GL0gRwWBL7+PxCSUgH8a6FXIhOZw8BpumcOAylMpGWhyRKlqjkw5YqcXvyCWM2D8zQyjAPbi2uuXl5jnvd4k4lLLd5jK5EXPXK7J+0PTKKerGIS9WFua4MyVIATJVFyNdQ2VmONJKS6IwspV6BarsqEkbr+by0pKlJkRhdNFgatJD7P5FhXYL21dM6itcDnSG1HlRhZS+GSEhSqOlPlyYwoGVGoJMtiKJc96iLTnCIl1cRGAYSu6xyQtem51O4WWeoJT+SCJGEltF4Qx0iyhVYasuxR7YHD/msKnnN4QxALVjpkKaQUiHMkjpmwBObiWUrAeBC+4IwjqxqfDctA9CM5TQgVaVuFbjTK//x53cZUM6qU4o99LaXGZHNaB+oCxqzt2koRUzVxNs6RpCLGuFJLEz4mNDWC7pNH6Jrg8iGuK9A69NT+o1rYWCs6BDEkJhZUiPRdRwyRaZyQ1O9cDAGtVY39rqec2ilTGMcBHzTWNZQVQNb1Lel6yykHRj9TAK1MHaBjLTLUWtRG7RyhVKVBVIETrUS9x3Mk5poEUNJQFFXjW3H0zlkIC34JzD6hbcFiEWsXT0XiFxpnEUoSQo34VgO8IJcasY5r4acqEmsdMUussRRRkK7CD42s/S5KZMKwEBLYpsF0TfWxkOv6ilKjxDnXARyJKBqNWssNM0LEul6OhWWpCaRSEikulW1CQiiHTAKNYkv3X/NW/S9yvfyV4e23kYuXW9r9jiSPeCHxP2b8Q6anYQieP7x7w3azwW0FF18brq8NJz0yMnLMC2nM6KRRvq7IjDGEsNRmeV8HXWdtPVCu5n3We1lpRdNY5nmgbfs1WVYV8pQiS4xMMSJnSKV2UVmjaHct9/LIg7hjzAMyAyhSTkxzIYQ6pgYiQ5qJSz3EdVqjesl5c2boRxYV8Cnj5IY8ZIIv1cNUBDlAZy3owuhPPJ49YnGkO0uvesiFj34hqC1SBra7li9ePcG1hXcfv+e7777nhx/ecHw8Y7VktzlweGrYloVt2/GRe6QEMQuWu4Q4JayRKCO4P5/pe4OUdQ06+8Lrd3eoBMsyMy4DqfNIXUALNp1GuAjTxLtvz3x8uxBDQUsIZAQRLTK7rkOJBWs1Xz654NsfXtdC4M4gra+Q01SIiyROIBP4JeF+YtXUTx5Oyqaj63vSEggl4HSP2++Rnaa0BdEppFV0RiGkZlYPiJIoqRrOlITa0JopyRPDmeN4w91wyxICJSYI1VT44BSUQicSKtSEgDEabS3CB66un7K9PDCZwNB4JhtJasHqQne1wV1vyd2+xoJzdf2nlPExE8hr6V757PGoa6ZM33UrrXUmpECIua5QVhkqC0GIAenBthWNX0p9SQlhSTEQvMcKwWG7Y9v1TCESyShR6kNZCpxWtRsl1+ZkIQpGCUQRaCEp2tQptzOEq55NnBA+VfVEFNZesdrq7EEnQSrVkKjKJ0NhRJBxQpKFQxnNxoCKmbsQGP2CJrJkSVIGHwsiBQSaLDKznHmMZz6M9zjbshVXyEkhQsQagSczh5HiB5RIqB5sr6ox8fTzl7HzmlopRVUGx1JP+KzNukZrlhDxMQELzbruUULSWEfWhriaYkOqSHVhawfMOE2gA13XcRzO+Hlm03V0bYcyisYF+rZDS41UEqMVx/MZ8oLRGudaRJEVFe9nYozsdlukEKtXKZEoGGNIpTAOZ1xKONchnKN5esmwUeAXSgZKwTqDdQ5jaqxWIBGiajkxpbVOPdIahVaC2a8As0+9QuvKKsaMXxYWPyNItCphXS0a1Np9pskuvrY1W2MoRpNzLQBVStZiRV37UlQSFF/W72D1mmitMKJ6ULSyXFxeMB/vSWWpz4El4c8jgRuetRbtLFKwqjFr2eKqm+Tk66pAmKqullhj0mu8+xPnRMkKdSs5sZxnVCzYoIlxxoif/3ByeHbgdDrSXV5x/fU1V18WXv3FxN//u3d8/7dvSafIjx9v+Mebtywi8Ce/2fPw7AOjPDH5hWmO5FQwWtJKTfSFxjSgDafTPW3jaJykpICWjr6tiRkhCloKrJCch5HoF8ISoMDlYYdzDT54bu+PKOfY7nYc7x9493jL1ZcHfvEnL8la8ePDOz76W8YQ0UZjtKzoCB9XA4Ak5ogXHikVm7ZH5sK0HTi2D9zJM5mCMYrcGIIMeJdQpRJWSzLI4ni4X7g/eTCSnSwcZMPSwr//7Xe0zy8xpuAawctfXNJfCo7jLb//ww98+4fXzFNYCxAl/WaH66oJfNR57aaC5UNi+uBJ58CsPAqBMoUlzrhG0ha9slsMt6eJx+HIKZ0xutCkGvU1rcCLI8f3A/evF6KvPRUZiGvZpjBw2bVsjaE4ye3dPeO8oHuHbS25BOICwQviWBChbhRSfSH/pHvqJw8n9i/+DLRGzBPzeUK0jvJ0h3q2R7UaREYhkdqRS8TaTZ0YUfgQ6ss5R9I4EMcjp3DL3fSGox8QGIzsaLdbCpoP1nM/eLZD4jIktk6x3Wxp2w1Nt+PKqmr+aRVnm3gsE8jAVb+nuXiObrfEXFCikh5zrA9PresJqZpn1/guUIiE5Cm5qUkAXx3fZTW7WmXAZEoO4CS2rTJu02wYxpl5mpFSsfhq+JG2rlx8gXNINNbW3h0St8cjL692aEX9ElHXNT7XL9mnxlYfIt5YTheWi3NB60TOER89PldMsiwSg6EphpDi5y6XrDK5swgFQkZKW9MJyhfMAoO/4a2yLJsDWjmiiBzPZ0rKWGFxspDxhDISxyPb6DBRc76/Q5iAfbZnJ1bjIonMQhI1xRTOE9vh5+85OR3PNRWgFbnUygIlRC2HMwYpFEpSE2qr1woqw0QIKCGiNLSqoSl1aM1ri2vMka5v6XeOH384k3ykcxZB9Wa0jcNcXayDT1VunLGf1x9t29N1HVWprC262liUFCzTUr0Zop42m7ZFGI3Uhqwl0UpMbwnZ07Yt2rXkUMsGE5kiHAhNoZCLwIcEuUbu267WVGgSKoNSZsXP1+E/plSL20KorcwpUoRgWQInP9I2G6SQFfGdY/XxaLGm0hQheKQUtT5jjR8brbG2qd6xECovJSeiBKcrJ+XlL3/J+e6Wu9MHQir4Bc6D5zQt2K2lv+6x1lUzbqpDPrnyjfyyEEMiSEfrOkCyzCNFC6yuv9NC/e9IFjhp6KWh8Y5wu1BSwIufvyH24f6a7UGiTUvOls3VU/70yYFn3yz89uu/42/+n/8fnriOX11c8uJfbVi6G949PBASBJ9JoXxW3JSBNCVef7zD34HM8HTfYYwk+szNzZHJR4awMC4LG+f48vqKJ08ucdZwsd+hJDVROM3YRvP1q+e0bU/Ihbv7O/JBcfF/es501fD4cEvoDPHGcnqYWeIA1HDFMnugwje1kCxm4t27j3z15IDPI3+b3nHMI0F8Uoszj+nMUU3IFwp9q5mXgNQtommJwhOLI82RhOLC7flh+kDeKTZXhmIW7EXP9ZMOYwUf391x+/GeZao8nhAiUhdSgePxiFIJ1URAc3oTOX+3EI/VjnCelhp/l6CFxJjM6t8nE7k7DjyOE0kXZKpBlItux8ZtuP/HBQZXbQIyU9a1awac0Xz55MCf/+I593eP/P7jAz/e3CKdROjIvJxxQRFmxTRXT6cqVa3VqrZ8/5TrJw8n/sU1UYBWF4iUmSiUjcF2haRjjU4aW9WEDLnoyl8IC2FYKEdPvL0jvrkhn0/M+4VxdyKIiNUtzm44bDc0tmVuJk7ijrwsbI2i2/YorSs2XpnaFYJl2RY8C9oYLp884cnlU3TTs8RMJtI4jUEQChhdQJXaaZ4FRci1B7h6UJQAiSQnRc6aEH2FKEVPKQKlBbpkcigIXU9N4+yJsbBxHed5oWRJSgVrGqxpCTFXjLXPNLLuJotUnKeZUqirHCEQotTTYix17RRAC4HSmnOzMLaKflyQOaOzoCRJKKm2sgpF21hYS8yCLeinHbzoSDJzvHvLh/MH4ljYp4Y+FvYpc/fuO/L2RLNpyCyEkuoAJCLnacI/PiK852t3ySbuiI8Tw9uPuDRwkAr5xCK2LYGR6eHI6fRIPkXymLmyX//0p9o/16vUzqf6ks1r+3BllqRSUwRaa7Q1WGNrSWXOaydO9UiIJNYq8vpyG9fW3a7tubo+cDhseHj2hOP9cVUsKs9Dlk8G0oy1Lc41aFWVhLr+KWvUdoW5pYT3y5oOq/e8VpolBKQ2XGx3aGuIVjNtHLOCEitbZAkJUVg9KpE5CKZVFTHaYrSl6bpK48y1Y0asAPppHD8X9xldoWd2a6pXKlfCbJpOxJxouw4lFNMwEuNC6wxt05GL4Hg60TYtTitSqYeLGDyJUlk+uTAvAZELfDJC5gqHFFKw+/IZ2++vuZtuWcbaaquLwmTB8PoOZCTue6D2JMWoKs051wJF5RRGuuoFIuJzBcEh/DqY1LizWnkrjTRsjaRpHaSOcfz5d+v89//3/zf9Zs//5f/2BXY/sj9Ium7L4fqa3/wfDHI/8nff/U+0Zs+H4SMffjwyzBkf6kncKFlNnQLGOTCcJsa379imLV88ucY2hqbRHONIv2lIw8Rxzjhjefn0CmdUZf74CWd7Fu9xvcMpXTvZSmE6n8nA1fWOL3/ziuZPt/zh9fd8eHhf4Ws0GO2ZlsA8T8SQVuK3QKSMUJDaiGrhvMyc+4F7P7KUtCIyqoIXJg8CXCdAOeTYkXcHpmZHOGSQCekLo7d8l44EHfjlb/ao7GkXx5PtJVem43ga+PDmLY9398Sw2gtkJsWZjx/e8fRVT99vqn/tJEi/D5i7jEmCsMJEQykYI7AGvM9IDcZIljCSEZ+TnV3f8vTpFtsovvvuluO3mXHMxFJ9JmSqzaIUhnnhu/cfyERSlPx4+wC9RjWSNEQKiqXAEgUpATnXFbhZO+3SP7Eh1okKhqoY90jnBP0mY9vIlASxWLRSCAJSeJ50GlHg3SnxuIycX/9I+t2PNLcLTWNYCOTOU7RfvR8Wo5/SNg3bZsvO9aDvaeREPBduhgcSgm67w5epmv9SYd/u2Dy/pO9rYVSkqkZKaGKqBqBh8hjTUH/DAkUldNYJQeBDZJxmrrptNdmtfpRCXk9EpZrejCHEyDjONEZhTKm0y0KFNImC1ZrOOpw25OhxyhCCZ8yJXhqs0SQKPgSMtfXEHOO6YqofnFGa3lUoUbSSx13BjZpNVhDrOkcKQcl1qAEQVmAvG+KLDn/dElvFHCbuzhM/DK9J0aPap1zJnpdxTxhu8emBbC+IwqNVonWFtlXkxnJaFHHUtKJHhML98SPNxnF5+QR32OK3gpInrLTsdAePnmGKsJOIi/3/sifbP8Nrt99UlS3FNYQmazrLGIyqqO6wdu0sU23l1tahUiUN51RPI7Xor3ootP4EZ6tAss2259VXX3LvbhlOZx6PR3JONbngap+P1YoUfX0RFkHwHh88KRWapjYTJxUJIRByRhtb/Ry58DjP/PjxPYfdnosn19j9AXW5wYeZKa7rC20RQpLizOwnvK/QtaYxdTgxFb4WI2hd1TRpDJ0y5DxQRDXwqpgwWuN9IYsKM4OCUpIQPcfTI36OWNuw2XS41oIyOGtBKaZ5wpmuek1qIVV94UwzISUQ1Wy7hIUcQ+UfKYVqFDjN5auXvLt/zRgnGqkQGrQqhDjj749oJ9FNjQbP84JXC9pYjFPEnAlxoaSIVInVOQMFYvRAQco60YWUeRwXwsnTz4arqw1t+/Nfc873njf/+JHQ/xue/+kV//IvrzDummF+yz+8+R/593f/hnt7xxCOvP3wwP0prQWqNR6s11V1jLn6E0KkswaTDAp4//rDqnZ5DoeevnVc73rujyd+9/p7tt2GVhu6xtI0hbgsjNPC9dWBEjJ+9kghafuO7tCxC5ZXl1/x7MkT5uXEP/zuW/7+D99VUKCyDGpgGEai97VPTgKlcE5n7vQtyxneyFtOevnsHUSIal8QtcojU5hcRnUti4Hv371HS4Xd7FBSVWVfJHq5YzmN2KPh5dDR/+1I/McfmdNC/njELhnnJFqDVbL6bIaJ401i3zecHyaGfxdY/rGwix1iA0MM+CZireDpk55M4If3Jx4fFxTQ71qSSCwh4i5bXn59iesi7+4+8OjhuBSWWJEYRsKmaeuB3Ed8gpvzzNm/pWk7FlMoSM4PHuUL223PcZqZY0QK6KyjaRzn4JnHmfgThcSfPJw8HU9McSQIz3k5c/Xqgl8+vcJtJTfnxP1ZE6mGsgsnaWXk/bsHjueB+/GO8c1r7O0JM1VzrVeespkI/RGRFViHoE6gRQhM32KeSzAn7j4+Mu8LZtuQGs3DvLDJhesvXnD5ckcwCp8LOdXYpdSClCrEKSdBTrXTI68rHS01xqrPZltK/mNbpFKQEil6jNH0pqmG31SjlI3TnM8TxbiKp7cCH0YSkVwCioxVEqEyIXpSqQ2tuayYpjwD4Kxm9r7imq3CGFh8IMZCSrUcyxlDsYbTXtENkWaUOASqKCRi7WeZKWT0VqJ/1THvDJNM+JA4n2b8GGhSTRvMfiKUFhUt2gvG5Gmkxu1aRAlIUQPWTjVsts+ZReZUPA+nR06bmd5sOW4Tqm/JWjKeb1hyoO07Gtswh4JPBezPP50gxJpYkZoSCilmhnlgBLq2pevaWgUQIyllrHFQCvM8A7mSYFcMuxRrn4yscVulJfMwEXNPd2h4fNCUU02TfIoQC2C32yGlZvGRaZno2rYWA6q2/j2qrj1qBUKl+FAEuRRItZBvWjz+fCQcenqxQYdp9YUlRM7Mc23x7puGiAZj67pHq9rMHXJVFIRCCA0ofCyUNVk2zRMlFxpb/So5Z07jwDiOaC3pZMEpxc5aVL/DdT3a6qpKicpIcbbCtuZlRKlKf5ZIpFZYqyjLQk4R13Q1ukyh5ECKFel9ngbstsM0DgxkESkiI7TEtQ5rDWWJLKQav9KV3hvTQoqCmIBUgW8yJ3IJSJErul4qck5IWRUAmTPKQ/KeaDViKyjl5z+cTEXy4n9/xXH/SD4bfjFNcPMH/u77f8O//Yf/Fzene2LOzHPg8ZjwIdfsWalerZxhnHJ9hkeBk6Xqb7GymzZ7Tdc32ONM21gOu46UItN85sfXDwyD50+/fEnyEV8mtISLbYefF5IQ2N7hlEEJSVwCH393w8f/xyP6rzS+UXzx1TfY7SV//df/nhQeoKvfn3msdOFcCjlBVhD6QHGJvFYw5FSj7GpdsQpRVsq4JGF49YsrkJJ5SChpVmVzqYZrIcjSkFqL2yhm3XD+fkS/T8RZ4G4m+mVkf3HJ1y+eszGaWSV8I0jW8PH7R777H97SvpH0QfHs6sDTqz2mMXz1zRO+edXz9//xP/L33/2IetGSnu+qKVU6fBBsX1zinhgwI9//+BahMi63GJEIpYY7Sinsthu896Q8kUgkAcXqGpgImeEcKRGsFGSp/v/k/dmvJVmW3on99mhmZ7yTzzHmnJVVLFaRzVEkwR4EAdKbnvT36UH9JKAhQYKgBthki90kq4qVrCGzKjMjMwb38OGO555zbNijHpa5Z7+1F1BdhQwaEAiEx/V77znHbO+11/q+30dVoGd20KJxtKuW/k4KI94PEPtXwNefDFRzoNQeGyZYOaruaGzD+TJScs/1wWLMmrOu5cWLX/LHnz/nZggc+zvcceRsvcCcdhiV0KaymkaCqmxPF2zcElffOmlErR9TIsWJZDJh7TBrTXCKzfmWswePcesFQcukJiWxFZo5oRTMvIhJQrFYDCshJbKpaC0Kf43wHRZnZyyahjGlGaploAgToplxyCFnKpHGFFStAprSihoFPFNrYdlYvDWkWphyJJZIVRLUF5Om5EQtkUdnJ1Ar9/1IKoa2sXMEfCLnimmaGf+t6JvM9Tqj90e2wdGqBgWkHCg1oTSszjvqJpNrYryPjEMi9xNnyXO2/QjjNIf9SNonXsV77tKArprp6p6Lk4/oNgu0UYQhkPsRMyaUr0ydYq8dZa+I9yPHcaQbA4tuC9Ez9pJm620LdWA8jgzpmw9hU1psjKXM9lwUfT8wjiNjmLDOsl6taLuF8IFqZZqCpAtrNTuz8szByGjtMNbQNB25JI7HiVRArxvU0hOvKtZ7zpZLlNLEEGm8pyjNcnvCs+2HpGlkHAbpLCDFEwqappntwPKMpJLZH+85HHaslx2rRw/ZfPQMfbZiiBPkytnJuWiutKFqLSNFPzDO4tcwk1+NMTRa3DFvwwCnIOPKzjtUSgQipQrDR2vLZrXGGkMcB8iB5XLBZnNKHyLHMNFPR7QWOJ1znmqc5A3RMYzH2aGtZXMQgwHeeRnDWI1BQkrXK+lu7Q89Tbvg5OED9uGWvr/HKIdvHN1CRLgpii6GRuI3cxaRoURRzHReJTbRXCWTJCbhOKgq+hQFtMZzsmpplopQJj6/f80xfPM1J3xk2J1fk2rEhsTPf/Xn9OmGL68/43K/5zBE9odACuL4encVMG7uAlf5c281XWd58miJfqXpDz2jrmxO1zy4WNAuWozKkDXf+egJHzy+oFbFk4tTvn5xDUYOp9UYpqlnHBMUTde2WOcZY2B9uuZuuOGT33kGrvKTn/+Uxm9YLTf09wem4Yiuha71LF1DzhVrC4u84Cxs+Wq6YioSVum0lntHFeEHVbC+oesWfPLtb/O9H37K9eUt/T7T9wMhDcQMIWpKUlAUWcGD733A937wMe2oefWfnvPFv/2a07Qi3SRev3rF7u6ei5MN7sRzUk7Il/DTf/UV+qby8GTNxUnHo8ennD89YX3W0raKNAXaVUfbGfpwhDxxfrEhTY4Pn3xIrzLPb59zc/eG6jMWjaqGpbc8PDnlq6+vCLWwuz8QYmLMBazCOwnmHPuRGgseqd+rgZvDAXJm0zg670gUbu53pBAlPdy8HzH5vYuTq5N7bo4voSbWm47JDdwO12A81hQaHWmVofOWdZO5vvucL65esJ8KJY6cr05Q549IiyV9uCcOt4SsWdkNZ+YcH7xQH1NCGU3NhfHYk27vsaHCcoF1S548fMJ2I0msUVWRkGQltt9aZheBEvU+CqWl1fyW/mqNFQtmnk87RmbGMUYO05zhUxTWeDIwpkngTFUxxUgfIrpqYgRrGkqucooqiVzFjVRqhqLnELRECQHvHcdpwGhovWecZoW1fptcKrAasDQWusYD8jtqqwhbw7BTdDFjYoBaiHGS4LamIR4j5Xmk9BF9iDRK0VZFOzhyMVRrUGPL/bjjNgT2ZNqpwJc3qIunbM6XqNYxMjI1CtUcaduCXxvGZInO0eZEO4yo3YGkFGoohF3l+rgjjCPT2BNLZv3w8fveVr+xV4jiIDPWYhDHijWSs6O1ese1MU4UznGKcwcuUyvkKEnEpRSU0qxWjq5rULmSYiCUSsgFq2Hz5JxpCOxfXUMVroo1TopXrdmen7LqWu7jhNZy74qyRLQd0xSIJUrXpEAsiSlOVK3pths2Hz2mOd0QjaI1HWYpYXY5JhKQaiElcb2laSSVgteGrmlYLDq0lSIt5UwMGaWsFETGslga1HDkMBw49oW2WZBSRhuL7xaUITKmRGGiXSw4Wy3nzJwsic05SWbPbP9vfMcw9ihdMVbSymsuxFKJJUCseGexWsa5qkqycNaFzek5m90pNUn6szaaUpSMF1Kl6IRTBmPqWzWaZAhJ71MEycpgqkIXMCFjvKEqhdEKQuK4PzDOPKFUIkUVuvY9wQ6/wZd+Bn0eyFNl0pFfXX3OkK+5PdxwPI7iPoyVaSrkLAfK2VVOLnWmZ/POmq9RbE4WnOkzpjByOO7J0z27w4F4JQ2u7WrJg4tzNmdr+uORxdbzgX/C1Ztbvn59y4urL3n84IyHp2fUDP0YOB4PpBRZnXlUe2AKe85PnjKFwu76wOPzU1wOmJwYBgszTK+xDfvDLfmm5XhtOdRIXhSikQLFNzK6wArAs2k6Pvn4A374o0/wi8pm+wGGlpBEL0YpHPf33N7uuLmduN33PH36IX65xq7g7Pee8uXnbxhvCyGJpmo/3dGXkWdPH1JjYPk18FVPNJ5XNzuG20turz+n+RX4DprOYp0nRNkrFIXhEHB+w9/7p/+MqjQ//vGPuX11BZuEcVIcvnrTE3aV373Y8snDE3CWGBO7YaKEiVASZU5RV6nOAEeD6gz9FAghsnKW00WLMoqbIERoyqx5af+a3Tq38Zrb/ppH6zM+OXvMtvVYnYjTwFRGxmnEZcfWr2ibBYodMe4ZQkGlhD65wG5X6NUGoiIcJvb7A6aAuptYlx63yBJQZyTOfHfYEcYDG7PgpF3z+OFTTk7O59arIqUsRNbZ3mSVxmpxEoQ0oRA8eFWa4zCy7tp3qamlSltRaU1WMKZMf3XFhw8e4K2hKFkcjZmBbWoWFlXBMPfjRCmw6RYYEh5LUlIYhVTQer5pVSPlZJFCqjqLVo4xFIy1OFsEt601tdrZWTTbUpHFuVYYNaR1SzpG0hilbaiVwKEyDJcT02UkxYIvUGcAly+eWDNJK2oCmw2LarnLjuEwoY6BdDegktjF+imwn/Z427NearpW0XpN0yg2LSxioL58zeHmmtFXQp+5ebnn7uoG7xW6s/h1+qusa7+RV82JPC8yMSUJNdOaxuvZXgrHY0+IEqaYc0Fbg0aYNsvNBq0UKYY5tTgxHo8YBSlOBKAPETOBahwnHzyAWrh58Zrj4YjzDavVipQjVzdX6NPTuYtjqVVgZIVKyFFyNqpQhscwEsmEUmDRcvLJB7QPzynOQhbQW611frYkSyqXgtWWRbcipISKEe+c2HxLIQTp/uRccbahmbVUwinRczcokw4H7g/3aDuyXCzRczZULIXD4cgQAl3X0jRe7JzeQNJMkwjIjbZoVei6jmEaCeMoUQDGSaEyHw5CiFQrG11JZeaqKBbLFavlhvG4o+Qs6eNRYG4oWC5XrE9WjHngMBxJKss6UMSNRSnkaqjVoIymmSr0oJeKrmuZysCU0jtqbS6V1WLJxfrh3/bt+r/51adeNt7qSbVyf9zRx3v6MTKMmRArIVZSZb4/5e9prQX3oCpqtq1nXai6sNw6OqNxteV82bBYNWCekJE1edG2rBdbrGrZX94yjT1T36NKZLNccL9fkkYZ0Z2crtlWGMaJaQq01jApzUJVbBw535yRJ8v5+TlnD844vVjy+vmX3N7sGVRlTBHrFoRGAGlP/QWjmkhlxLaeR88ec3J+xuVtT3/MdOsVH376bTITyhiMUlhd6JZLnBOm1MXJGZ982NEPiRcvv2TdbfB6iVVwfHPPeAOHQ+I4JUKR96TqTCVx/dNrLo5n2Cok9eNhJKoeZYV3pbLGJhjHnqZb0y09sVZO/Sm//4//K3Ix/NEf/ic++9WvCHrCL4uksdRKnCr9VHhzfc+D1RLfOJrTFfHNDbtxwBSNUVDJJCVdSjFbV7aNJ8TMqnFYZ5lKYipVNJ5z18Tav+bi5NDviXGiZmiVY6ksuQQiIpLNtWBsxpgDlcKqS9h6pMZETYWcRY/iXEM1a2KZKMNhJs9WYoospsIywziMXF1fc3d3Q6tg+eCMR0+fsdxsSG/JrqmSkkjTSi1iKbQWrbSkhCLdEmZcdk5RKJwUlHnr267UnIlJEmDHOYzKGoOuCaVEcFiLxLmbCo3RhCJakylEDuPxHe215oKeGSVTSISacVpm8UZJ+nAqVcZFRpgJWmuUlnqrzgyWOJNxvZ/JlCJEIK8UuSnUfUZlsUlXZC6vRkPKwmpQGHSqoEQ7UGqahcwVVQ2L6GiD5RADcUr0d3v6+wP9oHh5fUN/3LF2iTRULuyaj89POFeV4q6YLkfurgf21jKdrYljIR0znWrYrlf0aSIex7/SwvabeYlTJ86ZMd57vBMLdakIEXnO0Hk7ZlFaY5QRsXVVAhBLsvGpCjEGqrV474kzubhEJLdn07H64IEs/K9uqKlQqgii99c3eG2xM2tkHGUzr3MHEZCOoJJCJeVEUprth49YP3kIXUvI4mzRWGGhZCm40OCNQ2t5ba1vxb0wU2tzFhic9Za28fMIpFBToWoLSCZQ61voCuV45Hg8EmOgWyxoSmLRdCgVZ62VfD8dIhUpMFJJaCSLp5aKMhrvGsZ0FG2LyqQk2VRCppUCME6JFDNGy6KrYhEwXTWULJqfXArKaFbbNScXJ/jWYaMj50wfjmJ3LuK0UqVSdSHNAslF20h0wAKcchQS1Xq0mQFxAZqmY705/Zu/Pf+Gr2GUVOfGWhKF++HI7f2emCIlF8ZJnDlvC/dSwRjRbokrSronFSUcDQsnTzp+63e+j1m2rC88TWfQtkNyFETjUZPicHkghyPL9YL1Q8Nt85KLCbrOyfjUyMnfGIN3jhgzfT9y8nTL6cU5xnS4bsP1zT2HYcd64/nwW1tOtht+9dkdzy8zfa5YbVHbgleGT9tPuLraEUrm9OE5jz96hPYNdRew1nP+4JRmJSPAmCq+AWtBEagpUZXF2xWL9pxll1h3K0iGu5dXXD5/zZ/+q5/y5idvoJe9ySpD6xt8huPze7zqeB13PHt4QeMdn798TSqR7YNTPvrknG61oKbMy6++YsqFnB1PPv6AB09/wP2u5+bFFa+/fEV/mHAnUjDUUmS9qeCtpEorbZlCEGRCzOgs+6Mzmilk0uyeQ4HTilXTsstHSskcpolJVRn3FNlzFUpixt/jeu/iRIKWLGMaOY4HVqajqkBUmaw1RVu0gjEeeLO753Z3Reh3mEFmauG4ZxoOrLYPaN2C3J7Q+D1hLAw1kdPEYn9PsqKW398fWDRLnj19yJPHz1ifbsFqOfFVJaruXGXLnVHxZo6XrxWsduLcqQqrK11j0apSlEJTqbMIK8/jH6oiV9iPIxfrNaYoqIWSEdto1RgqVVeSqngDpjNMU0JhQImC32sn/IeUKBpyFaR9rRlnNAZFyWAbQyTOqv/6jgJa5/BCiiInTS0GY0CbQllo6qqgD6CHt8F/hao11WiZi0fRw9ScCXPBFVIiUCne0ZyfsFWKq9sD/VhQWdPf9Fy+uOTWZm7GHqMTqw4ap3iw3nJxesL4YMmbRebnh9d8/cUtBy1p0SlGXFGcnpyyPd3y5vUl8fY/g+JEC149ZbHRvyUJa20k1G9GySsFYRJeTpG5HUZryuzaESiRcE7CFIk5sexajHdgLVoZaqxUq3BnG06spVksOF7eEYfANMk/++NA54WmOU0TtRQJvzMyoqn1rShWg7E0Z2s2HzzEr5eEAiVJOKDSCqUEsFaoEsegEPFeVXOxJaOWnJHun7bzOElYBooMWqN0JeUIBaz1rNaOqg2oA8M0Mhz2+MYIOdk5lDbkUgn9IN0eJEzRzIGKRs/JxnEe0WrzDvUfi2D0rTHvZtpK6TmmIs0LbyRNiRQyOYlbyhhDu2rZnG1pFwuJmlAGbxtiHolFvRs/iMspU2om5kywGm3BmIbxMBFmC6nzDmsbkp9dW/Wb30kUDVLFGZjiyDANHPuJGEWLE5IISufcSwqz80QJKLPUtwc05P22ImJ9/OyUZGHzdMvyZI02nWROIUVODgnb3REOBzgY+lApD045f/iExRevuPzyFX0vOj5FmSMPMsXCWCqvf3Gk+sj6wlIOPTe7A9YuOHlU2LQKasftcMeUFCprVFsZ1UifI1TD9vSMi8cXLDcL+lEOKl3nOD33oAaoUVx7zmKrIoYRpxy2LOhDoE+3jP3EdEz0Vzs++7O/ZNjd8/znz9ldDmz9EmcMXnecrdYsWgGBXt4PXE0T3/toxTBOpFpZb9acnm4x2nB7u+d+P/HyzcSUKp98+7tcfPCMr77e8bM//znLXDlfdjh3ytAeoBnIWhyjVsno5eHJipPO8fLmns43eGXorEU7ec5BAnm1kmJDsuoilUosmRAKycnhRJPlzPB+dQnwVyhOnG0pJTHEI2/uX+L1Fu8NSUOtcq5JJXA/jByGG3765Vfc7w7o2KCKYbjbcbi7YX3ykEVzyqJZs+xOGI4DORaG3HN59YZ+DHjTcLY84dmjxzx++IjFogMtiyVai85j7oy8Df2qlXewJ7nFjSQZV+mhLBfzqS6bd+jjOkezO+NpnLTuru/3nC1XOGVRRFIuc66GwJaYORJaVfzMrIgpkasETjljJU+gSlLt3L+RClNJrskUBtrOiAC2CPPibUu6FnDKYrQjZyhvuy9AVIq4dnBQmJykDV8TyUAmohqFioo0JmJM7MeB4zQxpUxGYddLnmwuUKtTvvryc+yU8NUw7gauvr5kvypkD22n6Zzh8fmWDx6fs2nXTJNn7O8I/jW3Y+A+Bloj+HxXFZvVksY47FjJ0zcfX4+aN3E0SgnrZCxB3GCuzinAmpwzYRpJMQrR1Bi0d+h5YwWJItAaYoykGSSm1h3FG6o2lDFBquiFxZ+vsW2DbjxvfvmC+2HEWMcYI9ZaYi5y+jSCzW+8R2tDTNIpiKmgtyvWHz2EZcNUigjK57wSYefM4ZylkIr8Wa4KMHirsEZRq0Q+WOPQc3cx5SSOoxmPX7J0Q4wyAgE0lqW2WN/SH+8J00jXGCqFkKI4GNIckKnAWYPRDmckINM5ud+mXr7Oa4WxHmWthG3mLKnNRSId/hcfFhol2pthIk9ZQhCd8B0WJytMa5lSJMRAKRGtDM602Jzn7pM4AVORDJeYJ/a6YmtEF8O0P5LGEes1DQhR18m4NqRvfrGeskRsaDQhTYQUSCUTo6xpb7vYWkm3W2vwXtbyOpOGFQhvSsmh7u7+jrsXn4GpaPWIpv1t/GI1E0YlJ800sLpwhE8nXvzBZ+wuj5w/esbFJ9/ml19ecXk/oXIVdPw8Umq8IRnoryP7q2uahSecD/QcuU43NGXBxQJOTiNPHy757Mt7pgE8iv1Y6FXPz26+Zoiah4/O2Z6vBX4ZxDp7em5pF5FajlAzMRQGXYmDZrqf0FNDuLvn7uVIf5/p7yfGQ6C/umV8c8fFuqOpwjTRBlrtcRjW3ZLtqmE/HLjb3xCzHI4u7/bc9wNn5ycMfeD68oovXl6yGwqm2fDxt7/L5tGnfPn8kn/zr/89N9f3fPrglGdnp2ybjmJ7JlMlMT0rdKksOsdmYbnYNhyOjlXriIuOFBNjSUTkUGXmg0BFurTiHhVZQlYCUNQKmNekgnTJ3ud67+Kk8wtKCQzjnq9uv0KpI2ebE5zv0FggM8Ujx3zkxZuXfPbymsOgWCTQUTOVHfc3lyxPLrBNi29bVt2anb0lqUgKmSEeWPo1Tz54yMfPPuLkZCtx7lrC7kquUjXrLFwIbUi1ztMuPZcOUp6XWt/BoFJJWGvehR+BZORQIjVLTkpjYdUWdrsd4zTRNg1OO4rJTHlOZK4FtHRa9LwxWTSBQKoBEbDO46IZH6+EsoWxRtgLNTHGkUV00oXJb/Uvcyu9GDbtEq0tqUZpjyOb2FAyU6PJS4fqK5QsduiaGeOB5XKJ8obYTwxTYAqJcYpMKVKVQiVHqQG3dLim0LpMqy0hD8R9xHWWxhp8qajJs25blktHCJmpN/RDZYiJrC1jjMT7I9YpFgtP01nICVUq7n0RgL/B11uKqNa/Fp7GFKFWSSfWwiMIYUJVyaPxzqK0Q5vZMlzBWI+2mpozrc8UazHWwLKjODtze5R08goUp2HV0H1wgT0cyff3NM7LPaSY59vgjJLfEWQ0AYAiWc3yyQX+wRnHGEjjiELiHXKtqFzJNUk+UJUuXgWqNnPH0bz7vjEXUokoma8KMViVOVdHfqLRBt94jDHkAsZ4us6y7DpqGVF5IKTKMPaESeBu1hq89yy7jqb1kpNTQRmFKhljNNM4oI1F6cy89uG9JcVCTHKoyDGiqsIbTy2V6TAw9SM1ZrQB3zq67QLlLMM0zsVYEhdVVZjsMMlSopy2jTbUXFEYcQeqKE9myiIMRKOVpqZMGieMlRBO881/HGbatmiOcj7K/TR3RKSdz68LkKpwjaJtYRhk/KiqjBNMnQF+U+AXn3/GxYsrPvzonGaRmC5+hPGnGId0JRB6qzENfrHizZsX+Nhx9uQpN7cH/viPf0p/N7BpO7w2OKPonAYa7vuRdVmAgrWxHL7oubk/cBkGwu2I7w36E0NSFTs62qjwWjHuFYyG5y/vaU7XrM/WdIuWnBU5FdpWc/HIobUkJxulmYbIeOzJ9xP7L0eOX8Prz4+8+PySEhJeWVbtgsNx5IdPn/Jsfcp+OtK2UVyfJZNU5Wo4sk8T94cjQ8w4qzC6suoaXt/ecXd/YBgO3N0fuDsW2pMzPv3+D/g7f/dHfPbLv+Tf/Pf/I7v7nn6qvL67J9eMW1eSDeQqnf88KEwV48jNYceDEy+hflWmD6umoUzS5c/OSUJ3lpF1jAVnNRpB7Bsv+2yKCUXFeckrqu+ZC/v+ELYcSaowUtiFI6/uBVV/Yj1OQykTUzywO17x5ZsbbofCmDR5jKyix+jCdLzn6volSStOTs4xWrFoOmrIqNrzaHPB7/7o93j09BnGibAvIwuctHk1Jf96ll6LoqApaRbyiW9YxihzdVarqPlzElJsSpKB4azFGaipSmppSnitaRrLECe6thN6KwWlMlpJASGXhBeSDV63TKpQa/+uIDHaoIoip4w2ombWGHLMKFNoO8dUZlvVXEJJu0tYF945JKlWJO2lVJm958KgFMFUfE2EODLWyKQz/dQz5cxSdyRVqdbg6Oi0RqeRSgLV8/rqTwhnCftoz+nKozEMIRH1wLo6umTIh8x0n+gfRsLjQC6JA4UhFw5TJhXRu1ivaDeO1aah3WhqVSyeLf4qt9Vv7NW0LSmI7XaKgSlMol5XihbomoacIpRC0zRIyi5YZwVqVjLWOZz3lFLYHa4ljXrRYhqH2a7QzlC13P8myxiyKi2b9NLw4AcfYRrH7a9e4qumFpmrZ1WZUmSYpl/3UZXGdwuaByc0T84pyuBtSwkS3KmUjFBKqdQsLBFVwbmGXApaC2nTaIVWlZwi4zRgnMY6O5fphpwLKU1zDo/HWSe23yq0zVrFPeebdkbfjzjrOFmtUastznusc1gr45n8Vpcz05ljTqA0rWvEeadEu1NqwSpF4x1GC3xxjAlnHbYovDLEKTCNI5VK07W4ZUtQhTL2UCuxJvmMikIVi5kMda9IU6Y0CdqKRpKXZYQLnfF0tRWBoBZsf+0zsU/URsSy8M231qM1JZU54JWZfSOfu6RKzQWKUpQKi07RWIhWY7Os0SQlXCoKKWW+uNux2AUWHh7+4HeJYyT0PX65xNgE6g4YKVWRUiBnxUc//CHXV1f82R/8Cdevrlh1K6wW9NLJusF7w/2hZ7VqcU1Ls7BMYWTVeepdT39d+fLzK375HwuPn7YsTgzjqNndK4bDiA6Ok3bN+GLHhz/a0i0MlcqUMkVnLh52rJaalCamEXI0hOPA5es3hFd7pl8Ujq8MN3eJ/jCx8pYPLs5pveMnd3dMcaLxDp8cy8Zyve8Zeyn+Y6yElAUd0TakGvns5Qsebk44XTbc745EAtY7Pv7OJ/zg7/xdPvjWR/zb/+Hf8Af/7g+IYS4SDAwxcBcqS6cxSykY6qTIPSx9xzQFLmPP9KvA+WbD4ThBUawWHetly34cOMTEMQXu+xFjLLFEtDWQE1aDaxp0LhzGwNw5AFWxzV+zldjdXdF1E32ZSNWSWBAQu61TiZwnYhzYH48MpSWZBaMKtG3LZnHKYtlRupZcJo6HW1IMGOtw2rBdbVm0p/zOD36L7cUF1nmoMv/WRlp4MuKY54UVrJKbX9WC0RqjtLgfZrfBW4pjrmItdM4SpjhbdxNTiBglJ7QQ0iwuBdMYvrq9olaL0fbdz0wlYqqh8Z6QJ4wx1JypVTMMkSlEPOCMo2iLyQaqm2fVmZgUzoHTGmUVqURiSjgjWSFvrZLGVKybUzNLRStLThVlDShLnyJXw5Fxv6f0E7kRFiFR2qP3eUC5ijIemxVtEUx3ZSL5RNgeMKeR07MVeVKUXUW/Hhj2AX2jyFeVfhdJNfLV8pbzR6c8OFuQdCTZBmxH40bOTjX+3OHXCtdVlA8Ya/FVE81fYbD4G3qFEAjTRAgTcbYHdot2ztTRklKb85wtIyM+re3ssMpM40jOGW0MtRaGYYCSsd5hNyua1YJqZeGLXhamWjIkjXKGQqZ2lu23noDRDF++IScBooVYmEYBk5nZtTWEgeV6yfnHj+hrxM84feskSl7EpBZt3FwsC5ekFObTcJ7ZH4lSMsZ6XM3EHCghYlSha5csFh0lC/8EJaOqWutML5o7l1VxHHpymnB15KRzrNoWXCMC75wFR69EqKe0QVfFOE0cx4kYAkvrUEqRUe9GqKVWpnGU+9A7OudFxJcqrbGYxtGsOsxC0rOzSoS+l78/4/elxGo4bU9IJTEeBvRekztFOStzwKgcbladY2NXnORTDuxJMeCLlQwYE1HW4ZoF/xlgf6hVSf6KAt94NtsVQ5gIIVHT/6J9ohW2Ae8U01BIUTrRMjWs71xOJMUYDJ+9nnjyInL28y+5CGspDvVT8B5lVihdCNOBV89f8Sd//DM6f47vNjz//Gs26w2EidZ6nK3EGsko4qLBL5aoWgnTkTROPHj8EJNecH91xdD39HeFqxcH6eRpS8yGxhisiuTNgo+//4iLDzohCOfIFEZx4XULhl2k7wPT1HN7eeTq9SUxHNnUhi51qJTxJXLWei6Wa55u1ry4uuakW7K0DlszH51f0BxFr3mZJoY+icVaKxpnKDlyjIlhuiXGzAcPH2OGhtxovvvb3+X7v/0D7u6P/N/+r/8tX/ziS9C/ht5RZYwbTSabjNEFVRVp1MQ+s7SGRrXEZNkdAnf7K2KBzluatmHRNYw5Qow4Y2aNY2Sx0CwXnn4vh3inNSVBn2ZcR03YFoz/ax7rXB4HwnhLxXN68jGb7UPMomFSiZpHaWcqzThpTLNBmVuMShiviLaQlh7TtrIQGkVVhZQjjVny7Y8/5cNHz1BWYuXTJKAq7ySrRAY3moK00QV3LNW1zLc0WhsZtaiKVUWEfRqmKuF6MU94ozEaQspMOTCmglUWa1qckdyR1jpqs+DF1RWPtmcCgpsdGW8no9ZavMokk+inI6hILZHNYolzhj5ncomkkskZrAzdyCEzUHCdIdcoeSRI9DpVz9j6Fu0MIUVB4mtmaqyWQkUFhhyxKUtx1RjajeeQNEFXigGFx1ZHChligqxQaLSvVF9YnZyjugXjfU+8PqDvKmYnZM/dEDgOiqQTn5VXnK47nvzDx5Kg2XjOlyv6B4l9HSlNwmqNVxpSxTYO0zRMKbz3ovabeqUoLgStREfi5wwdqgjWSsk454RZEic0mjGM1MNBlOvMrfBasdYIC0PBlDONs9A4YilgJIk6mkpJBROVZOQoZMTpYf3xA9rW8/ovvyAf7yWNOEYR4E6BYgxm3bH5wccMutIfe3IBWwxmuSBbTaqFEoR8ypxF8jZUUCjKwnJ5G5dgjcVnQwyVtm3YrLdUJd2zt46gmBKzUYlUytxhiqQ0L15Wc9JaxunA67s7ktbYRYttPM44nHUUJ2LzKQa0tSy6BUPM0l3UasbXJ6wzOGuxxlArHPZ7TIVF02KMRRvLeruln0457O9IITDGkYTY8ksFi8FWOD/Z8umDD9lxQ+0GTE7c5VvGOFGdwPOc0jBFWucxQdPvI69fX9K1lmcfP2L54IxgMxP1nVvrm3wpZMRQ9IjzsDYNh77lcBgJMYkSdj45oxTDQeyloRaKmh08yNeoItlivrOYtuNP/nTH1esf88//m1P6XmObn80Y+lN0u+J+p/nZT+75Dz/+nL43/Nf/zb/k9//Jf8FP/vRzajiwOWtp1wuUd/Rj4vnza0y64dHJgtXCs91sWG8voMr942eWvlKGqcDtPuCM6FsOMbAz1/yL3/kRy03LMB0Ydz31mKkHxau/uOF4fWCYeia3Z8gDzlcar4g50KpODCFWiOiPT9d85+MnrNolh5B4cNKhs+KTx4+4GNe4N88x+h591tKpjuP+SOcUb/Y7IbaXwtWhx612/N4/+h6f/uARh+PI//iv/3v+4x/+kvvdiKJKF9QqXNMK50tl/FqDTcSxUCZF/6ZSo0G7wnefPWHqC1f3O17ub8lUYkl8fXvNi2vFOIlA/sHJkpOuYTcO5Al2kxz0nQY3jiysw66X3B2OWAvdWpP0++0P7+/WOdni6CihZbH4hGaxQXtFqIEp7bElkLMnlQUpVVya+HDbsl2sybQEJ8p1yd8pUALb1Sm//Z0f8eDkoSzU2oiAVYsLp6iCQuLZy6wlebth+waZPQYwWWFVBUSVn+b5O1VyPKyWsUnJCYPGqkpUMhLSStM4jSLiqsbjWXaeyxi5P+5Yd2vpuuSJlCJDHkkl0jr7jgpYCeIY0nKaE91LBpXm0YxFIZtQHyKhz/iG+f/LAq6VxRvPerlhyNM7C7GxIjYb5vh4rQtTzZiUaJKioWWx2VK1pX99JVYv62aHgeZtWGb1CtoqYsHRoTqLwhLuM9PtRNhFxkkIutqBc47DZeCP/9+/IHx1zdNPzwgF8u0o4W5asWZFOxpM0HQXGxYnJ7gcGfpvPhHToCU4qwqrw2jZHLUys2slo5Cu3TCMjOPANA3kDF3b0rUd1r9N95XN3HlH27YsHpxSFIQsRXZBRpIhZVQtdM5RtICq3mZAtQ+3PFYf8+Inv+K4v5fuhdYCCFs0nP/wU8qqYQwi9jSxYiMEFaBzFFWZsqQFU+Ue6rqOkKKwWmYWjyriKNNUWXiWSyKVfX+cHRfS7aFklBaIlULROs+iW5CLdDhEJZloa8HfF07dArtdYTcd1Six98dMyJmcA8f+ILgC5XD1reDXY11DP46EOGGNwc4W7m6xIB0HQghoXTFtw+n5OYfdFfurS1II1ChKTQ102uIxqKL51pNPKbmyu7tjOOxJYWSKIxMjC9fKqNYwI8otQx+4P/ZMqrI8WaLPV4RWCrKq5932G34pJYfBlI4oo1m0DQ/PThj7zJvhdj71y9caBWGqZCrVSM3yaxuDFCaddzzcnPAvf/tf8Is/+oL/37/793z24r/jg7OG3/297/Lx9z9FKcPdq2su3+z57OWRB6sVv/zFV/y3d/8PvvvJp3zrW89IyUnwZT/B7cC4H7j95df0CYaLjmQLre34re6EKYKzCxaNwTeOVCDsjqyciHcP08QhJL7/u0/JKfD5H92zuz2y8g1rv2D/puenf/Y5MUpcgV4Vlo8siwvNA++pWjHlTOfg0eMzlDbs7u/5f/27H4NSvLze4a3DGSlOPnh4zhQdtRiyjoxRxpbb9ZpjSrTjgGkW/Oj3vsU//a++zTje8gf//t/x2U+vuLkOgsuf9z+UvO81CTu+3RrsIoMppFER7hTTTqEiJFs4aVu6zkEauT0aocOGyqQLi9ZysV2xaTq+8+FThinyr//0zwm5EpN0z7QreOfYLheifYyR5VJhTjP79+wkvndxYtyaZeuYRpnl+igLWKmFKVpKcNTQkXRHf/+GZ9sl33v6iKY5ZdQrjsozJfGlt67hyfljvvPpD9iuTqkotDZ4NFOVOPeM8ARCUTgjbed58EEsiToVGXdgJM1yVvyHIquNRrgMWiH2QGMYxpHWeBR1jkTPaK2xC0NGc7+/n9XcjkXnuL7Z0VhDqyQ6uhpDyNLVGcNITFFOw7WgZ0GesoJirloWcKtFIDQOR7Tt5o5RoamOEqNAabTBG0/rPbEkxhhQyMkyRsHT55IJCWwtaF0Za6RMmXqfmW4iaiOk2m3pmMaMXrQsTjfkuOQu3HC32DE+sCS/II0Dm4PDZAWdQ512tMXS2Eoqhewq2VaG40i6q/z0397xyz/aU1VhryvNpmGZl/i9Ie2jxAG0lqZZ0XrDdvHNPymWIiMZNVtm7VuN1AzsSikyjSOzsx/vHSVHtK7YRux4SskkXhW5H1GKbBS1s8RaZTQyq7iNUmhryEkYOFhxrpSUaKwDpVidn/Dsw2cslGPsB2LJZGdoP3rE8skDlDVsF0vpQ4YKU6LkCagyGrUSmWC0Ffz3DD7rnOIw9EwhUmsR6JlzYCxpmrg/HlFas+gWGO/ItRCjgHhmvIEwXowRq7WW8VHJinE80Cw9SnVMpjAMB8pb+e5MtPVNw+nJBTd3N8S+Z3NyJsLXPGG0ZtE0LBvHEAfGEFHVzt2shlIL3ntQYL1j0a1YugVqLOiQUVlBkve4OMX2ZMuyXfD8+QuGw/HXQLsMKik2XkTn/TiQjUJhOeYJs7I8enDB5mwFbSVRyLVQckQUWt/syyhN4zcYDHGa0M2Ck8Wa43Ji7w6EGt+5NhYLIyLPDKUosqoinKViUXinWSwdrtX86U//nB//4edc7RPHMHG567mvLzjqE046x5tfvuLNmz3b1Zb/47/4B3z24p7/6Y/+E08Xp1y5jpevXrFqPZuuo3OOTdPy3Y+e8sXXO1xtuXnzijfc8/PP/z9kpdG1crZZs9kuKSXjVWXoR/pJxNWtsrhd5It//TVT1NzuDqQHa8Kq8Ob1jv6YWSw6cpwou8TuAPfPoVxUvvW0pQ4TQ79nKBOHoLg6TIyhcH5ywdnWsh8Giq788tUbvr65k8RhFWlaCZrth8pXd3ecXHT8H/7lP+Hv/ZNPMO6S//l/+Ak//oM39LcTNc9htVomBZIwysyYEQdQGgvhFoxXTD2Mu4qumsYaHm/P6Izl8ekS7x9jvefFzR1vDgemnLAGNouWDx9ecLZe8Px4LfygIpILpRS1GI7HiZrqrPtM9KliS6bav+axTtOuqAqMz9wfepRpRXSqJsYpECOEoNiFys3dgQd2wcV6y2a7pjdbjmpJzA6jGk6Wpzx+8CHeLlHVSgZOVmyK5qgKwUJWYjuqSOaCtU7oqRVqyeKbL2BVoehIIpOogtIGQPJ03vrYVMroPBcNSuGsLDimFkwBWxqMapjCSKZw7A+M8ch+LCybcxrrKVMCZalKWpGNd/TTQD9OeGfYLBuc1QSlmdBMUyXEJB+cMsRcSTlhjdBt5VmVosIaySeJKWOVlsDAUihFsPljCHRNByi0N3Srjq5UjC6kHBgXE+tTh7oxeGVpfMumW5C8JS4Du03kuM1MqtJkgw4Ty8WSzY82lEcT+S9ucTsJ3Yo1M6bEqCZiiISpUEbheGy3DXkyEBRlyJQhCXH01YD9NnRPOor95p8UAYx3GK3JKaJmK3uKkRACKQVxeCkRbBstTA3nxckjXZT8liRCKYWoYPv4jDprSpQx6FJJUVql1Coq+RxRGGKcRBXvLMZotNWsPjjFdZb96zv644Darmg+eETSc2ZMjCjtCLWAB10bmRmX9O73TUpmxClLsJmthdYZQfXP3BRVJSfKuIKxAW0MpVb64UhVGqeN6A2qMJfS3CGqs5NQKy3Bf6bQq8zl7pYpJqrmXQepaxe0bcMUMlOQBOzttqFtW0IVu77wlyrWG0yxhGmk6kwqhbZK35VcCMjm2LgFtnqON0fSGPBG450lpcQw9Dw4P+f18+eMe9EbJOAwBcYho6tl3BXa84auXdNZz34/UVvP+faCZuHQXlFVnjkxBZMrznR/y3fq//ZXTvJZ6NYwpcx+39P6FlMdNrZkLZuSUuAMRD0vzUUKlHeBAQaUU5hWkdXEy8trKuCdIQP7pPiLz99webXjo0cXtLbl8vLIsnvAcMicrc/47uNnrDvP+nTFf/iLPYfdnkfbDU/PzzhbL1kuW777rY5hzHz26iW71JNi4Wy7lS6bkXDHKWXGmOhDprGejx4+5M31DjM17A971men9FpAlqkrmMaAAe89hxBRpmEc5TD79WXhuDuyNJ5hiOz6HVMqM1fEokrl248e8cHjFX/2iy+5HDOb9ZZl48lxYioHSjPSPtY8evoB/+X/6YdYl/j5j/+Cn/3ha+5fV46vC0a3WAtFD+DmIMsZV1BLYc4qROVKvCsEBSlBjRptFKtlR9N13B4DMew42674/gfPWHhPfpG5Go7ElHl1e8f9oeeL5pK7/ZExl/lzEhOK0SJ+7qMEynarlrSayCpS/rqD/2KqFC22LecqpSjGYSKVPVPYEeJEPx64vNwTjpbmfMN2uWC7sMK/qA2uecB6/ZDGrUE1pGoYUyWWSFcN0TmEZVrFy16ly5DyREHhtMMbRy2ZWOYOhHdzkaRn9oki5UwIPaVUlnZBpzRLlfHas5siqUprWWuFUYZcxGFjEOqgiPhgs1hRgbuxZ9PAyjeMKRKJaO3op0lGJ1phamW96EAb7uJIntOQwxQwpgpgKiaxiZZExZKLYuk86+UC7xqmlJhiwFnwrVBlYyyzSExSi63TNMuG1alhg4IamfzEzt+zPmtxk6cmAyHR7+5IJjGsI2HhSKYjZk12BuUt0QOdxmeLWhp0r2itY2WdHNhXmWka6Y+BaYporWgXLakqxhQINmFbQ0vGThb1VU+7WWI23/zFeAwBby2r7YbpsKc/HpimkTQnxRktUCKFqNZzEhBXqXWOb3/LE6kC6tMW4z3LsxOyqqSSZGPVCt04bKmQM6lmceUoRas1xnqh/6bEmJJoIs5aWneGmzKpacjeE4JonDAOZsS6s1bGjjOzRcI2I86pWTdXqTVjZgeNmh1ztdZ5pKnRytHYhsPUM5aCd57lconWBudlRBRS5jiOjGOgvLXll5lTsWxYnzzk5ORshgoqjNPigJgiY+iFwRITZRpZnZ5w3x/ACuzOOoVVBVuKANi6BTf7e3l/2hUxF6x3uLbFosjrEW09/TCRp4liDCVlWbyV5vr6hsP+QAWO08h+ODKECV0tbbHkXSXpjOksuVU0bUvbLCiqSmGiCyklVEmQE65Y4vjNTyW2ytEqx8p6tO2JqScmaL3lfLHiZpwYVEArJa6TXImlzlEdMrF/++9cCmMIBB1Z+o7vPn3M9d0dt/2eMYoGcNcnXlz3nCw0q8WWUhS/+PwltIaPnj0iTYUvPn/Fyekpr6/v+OzrN4xDJD0452K7JdbKi8tbgg7YdaH0cD8eaZ0VcWhMjH1iGCLealaN5be+/SnkX3Bzf4s2jpube5bLlUDJSsVYkRxo1KyJSqzvjAQsAAEAAElEQVTqklDFQXN7HEmuYJUTqGaccEbzbLvl08drtqcNn7+8JBTYHw4snePbzy54uO14s7/hV8Nz7CcTj74V+ewXv+JP/+1L0q1iqzZsrebKHIRnpSWNPJYs53Oroc6/m5ZiUFWoSb+zemslSeKpJK53d/j1KS4X7uoR6yzNrMVRSmCloRRKTYTYsx8n4TrpKiOkCqUkplDk+ysB7nk/O2rTX7Nbp1bhOTBz9ePQc3izZ+rvyHoimshu2HN7PaLUEuuWaOMw1bAyC5aLx9jlI4zfkKslRZnVK/PrxOBkJXshzhRYlIxrahXAUlEVpYTyZ42j1oIxyB2NRlVh/FOrsEHKbHtNkbUCV5UA0+aFuBRQunDX34Oq+MaQMPRTpGhN0zSUKnkz/XBP5x2L1lKZAE1jPVdhR4wTjROdRyxSyIWQmEISIaqSBTdnCWFz82vOCXTrsaoViE2N5FLRWaGrWIjHEMlF7Hcx5ndxAcppjBG3UtWGqgqjj6zJ4ipy0urPVqNah27lpGqqJZbKYGEMEeMVyitM55jKAFOiaxzee8zCsNlsyaeF8S3tUYmQ01bNSCQYQ1MzGkN5GejNLYuPtn/lxe037Wq7likG7m5uyVGcOzFMpJzRSmO8xzfNjLa3qLaZxz4VpZK4d2aHicbgfAdtg1stGEhMMeJmHLv0oQpGFaqW56PUCsbJCCQWYYS8PbEY0CuHWrYo44T8GCrj2OPajpqFuEqVLiJKdCEGTa7S/UkpY42hbVu8taIVSaJvkQgUNbtpLK1vOA4HtFI0vqHkTEoCpHLO0TiHMYZl21GrJBcrpDhriDP8LGO9x1rppBYKymTCnIWSUuB00WGbBh1hnEZ57qxYtNGKqj3GeDbLFXmaxI7cWJz3cw9W0a3XrM9OWXYLMI5F27A7HAhjkK5XjDTOiYtKCxHWKE1nHaZqVNLoCRHg6gbnnHSElBwgBDw3QU7UqbK/3dP3h7/Ve/Vv4nIYlrpjqQ3VJFo/YW0l0ZDXmhQcuUS0gWkqxHmJt1rhrHRRqGCdwnsoWTKSni3WPPn4Af3DCz57+ZwXl7f0IZAKHPrApq08eXLBdrlEOw0+sXYdR5UYp4HvffyI1aLhcLPnfNHywYMzrHYcDyMPTha8HjTXagBn8KPCuA6lDffHid0hkKui0Rq04z/+9OfsDjeMKbFebKkpAZIJ1DqP8YrNcs/CGTrXUWvi4tEFP//6C5yzrB+dc7g5MAwTpRYa3/D05Izf//RDWhPZPmlhtcaNK9QrgwqZ/XSH6yeOY0+uis1ixfOf72n2jrJfkmLCrxsapfjgbM3XN3sO/UDV0jWptZJzBamV3lGfjdEzJfmt8F29i13RS3hycUpXy4wy0IRjZkqZmKpEsFBxHrarBZvVks9fXwqPbIabKoQ1lnKVfbtW1FSlefCeTML3F8QOhVozIfWkkthf7bn/ekc8RFSjCYvEfR6IqWHZNCiruBsnrNtwfvoEu3pCtkti1fMCrTCmSr5CUZQEMWSSERqlpFOKy6TCuzcvFcFsO21YupZGQ9WKUmWcU0qmloJTDmUduVRiKdzlKuyQosBKaqyeN9pYIjGPWL8i10BFtAFaMxc8hj6OHKeJVdRs1xY1t6itVdSp0jmLQTGVKg9aKdSS36VsKjUPqXJFnP+Fxjha2wJmDvjL8nXKEUKBWXhKnil8RXD6RStoJBhNJ6Es+urJFrRzuGJpuhZtNN5DbRMTEz2VoCsxBaYY0TXjTaYah+0abAc2y2suSUSNJQuSvWkaNKKB0RS8thQncLy3kLh8zIzPjww333wipjESrJWmUVDxerbizvh36zxt2yKHlpkSPDMKtJ7HjW/t7gViyfjWUYy4XSqQUkEZZtHsHK5XhNyqSgJjZwz+BLXgrAEtbJA601VTVkwhyN/LmWkYUMx02mLFxq7EAcfM3MlFdBreOqyTIkZo1bLYSbdH3gc5+Wo2i45QpHgJUfgq3juxHqPRytD6VgL4uoVg/ccBxkiIUozFUpiSdCLVDKqzxqJcJZHxviGUjLGOhVLUFFG1UHIRzLzJFFWw1uGVkhwuKqlEyCK4d06zPt1yfnHO8eqGRddJVydGUimUKFRcrbXEwmtN6wQJkCnYxmO9xzftu0DDUiK2EShfzRFdKlZ5Cpk+ZvL0nn3s3+hLNqyURlIeUHak6IDzHacnLWZcYwMczcghyJjTOo01c4d8Dl90XtN1Bp0NZqfRwbLctDw43bJYejbLS67v7rm+35NSohawzlFVZrHwpFTkMzZejAnjyCcPt+iLLZ3WLFtHyZpFZznLjtthxz5MHMzExWZDVxqudkdu9gN9yKgq319Vw7FPHMZIqhkfI03bEKeRtlnx4OwEfOVwuUdNcJgGUFKsOqXojOU73/8WX/3qFW+ev5Gkeusw2nC12xHLwEm7J20Mm/UFLRtevbji69sbru8t1SjstmO6UVz+fGTaXaFncrg6z5ydn7BdOxKKLy/vOMbCmIqkausKTqNnW5RSsxJOzbBd3slSZsJ6oTUaPSejVxKHcWLKv+6EyDoh2pZ112EUTPktxkP21fkHzWGChXwjxUl5zzSH9y5Orn55TRlHjM34pWF/tWd/eSDfFZQ2lK0mtQa3aFm0FqUrx+h5tPwIs3wKbiPAtCyobz3fzqVKx6OqylQyRQkR8939Ps/kjVJkia6bg7gUTddilMyX8+zSyW8tkBSc9dSQyVlxnwWzDpqUqjRmqppTihNKFbEW5kitCaMrBmkxemNIznMYeu76kYJh1cgbb11EkVn6Fm89fczv8ni0lJHElFDGifZFWcrsGT9drWhdQ6lC1ExFUNnGCpZYVdGiCNZefg+lkRj7zqDWHnPMqBRZpSVKW7T35Apjk7FKujD+UGlLwS4zmEjOI6UkvDXz76gw1rLoDE3RqMy7TSjFQlUiPBYdj0YBjW8w3pGqjCCmKQizYIDD9Tf/pCijGyUWd1XJRcOcFWKMkTswZ6oy73gn8/orOpVcZivx26yRim0dqYpewWgt93TMs/OMd6yQUqHEiDWOKU2Ukmi8kXk98xOjNFWJpcQYg1IarzVTmCR0EimeS5XkYGWMhBUCNWfpnCl5DWXWPolGQMYWqchCZbWmdZZlsyD0d0xTkfRgLZbekDKVjDUCk6sFtLUyes0RV6tMcJkXxlJRSkOe2RnyC7FqW8ndGXuappWTqrXUKl2akBMlF5SSBdmB2FQlBherNGY+9LTbNWePH3G4vmUKE84aVosFvTGytuQIFGrVGKQXXlRBOUN7sqDddLjGoq0llwR67mSljMoVlYVNVGNiuWxw5uRv6Sb9m7tSiUx6wpTEmCMqRmyKeJdp1pUHaonfbfjqEJiMHM9EdDcHkmohAKMqOVV8NazyApecZJEZxQePH9D5lruTPZd39+z7gfVySdstGMcjy66lBkngNiiW1jL0A92iYbVZ4OZ9oaiKsYpcHCfdkvW0Yq8nujPLJ4tzTm+XNC/vuNoJnO9staLxLbGt3I87aqqMacIWAVi2XUPOhfWi4bd+5xnD7ZGf/uXAmBQ397dyb4TK0i/4zne/Q6cbdpc3ANz3R/bHO4LKnBjNiWs4MZ4tDY1yHEIGp1ktPMpqXv38juEq0x8msAXvNLuD6EQ2rWG97ljsR45JtIhWyzRB9Pay1uu5o0JVc14VJMqM4lCMITDFRBpHDlNkufTcHHuOUySV+u6AVZWibRxLZ7FaMdVfFyVVyaI1G2zJUyXLwOG983Xeuzh59fNXxOOeZtmwPF9zPCTGfabuAzorbO1ozjx25Wg1dNZzcvItthffpfotCU2uilL1XBBUrIGYK7UaiilEJSdLpd7Ou5mBUEo28DRzRozFzuOSKRdCSaScBEY2j3hSiWJBzoqaDQVDnE98IUv3RdJVlaibjWKcZpR3mSt7rYlhQmtD6zUxGfoQuN73TEGxbh2aCV0DrbNorYk5ElOWUDEtIWkpScej1jqD42C7XLDoltINyoWiJHSpFHm9MgcU/UwtojOwzmEMBAqHtnLcVpqcyVGx0lu0seiTTkKXDOg+oPtMDgnVT9gywuJITkeUNhjTyUagFMoamsbQZQMJci0z0K4Qc0FlcMajG9EpaCs21VQLYxilRVgBbVDlPwPOSc4CEpyLx1zK/PkiJ8E0Mc7kUaUFJW+MkTA+PWPly9vRolBWfesF9mcVeu6m1BTnED2oc8ESa6WmhM6JHDNNa3F2ttszPwLIM6SN0FRrqajq0VrjXEPM5ddcLG3nAkaKhDCnA2vmrColWTrkSk4inM21ilNJaQmezEAW16y2Fm0MU0pSxBkDVFIOxJQpE6I7IWLJaOPx3mGtm9k/QsKMMUmaeJjw3QlTqXPgYCS97VShKUoj8LaCKnlGzANOMnnyXHwppUlK4VZLto8f4r74knG3QytYLRegNTEExpqEp6SV3OcGlNE065bV+YZm1QqAUUu2SM4StGmKxmRH6TPD/RGtEs3K0K1Xf8N359/CpSuTmnBaEac6n8KhEtBNYXEBW99yqA1lhCkXhjTfX0bRdNKFVhVKrNhqeKhPOV+ssdahnMcZy8l6RWMM29Wa6e3o0Tcc7u45WTrw8/gxSze7bRfooghDJM/Paj+M3B0O7MfA1d2eNrcs7ZK7sqe7eMLp+gFkgykSQbHuWqERV9GSxFIIOXEYe3JRHIeB65tbNg/P+b1/8iEx3DPYI88/C9zf3IqOJWZuXt7w27/3e2ybNb/6i19wdXXN9bBjTIGsoRwMNhTCeEWXnvBofcIDbcFrqs9cHe959eVOurEKckqoWrm62TOmytmmIeQoHX8k28saWTPK7PK0s4Mnzeu1eAlnK/js6BmmzBjFyn/XD4w1c3vs6adAmIsTicewLBcND9crThYNsR8oRcvYV+l3mXHzkjgXKor0nua19y5O+pt74jRwHAp3x0o8TOSbA35ILJwHA7Zd4k8Nfip8dP4Jn376d9FuQ66itM5zuFhKcupEGUqWFbEa4ZrYWV38VqgDUHOlZMn5CHnCOUfnW6Y8MeaJMlffUg2Kaz6kRCgjKUkbqWk6Yk4SjFZl0y1KBLEJqEkWz843lCriIFSVMc9cyHRe3EJTMtzcT0whs15YVo2hc4qYAkMcCHGcbwaZ51s1p8JmySRYtUu6ZskQM7kKbK0qhdYOo+R1iFYFCTk0Cq0rhYQylqoUR5O4Mpk2BRbG409WAuU6Edtz7idqHSBWyAoTCs0QcX7CMGJMO2+kwqbwjcO3BhdFOV+VAN1CTZRY8MVgsegqJ36lpM1dSsZo0R1I8FwV6+Y3/BrGkaHvMRo0svk5azFKxGClRNnoJiluvRN9QtN4tDcoZTBGoc08vvQW0zqhKBY1j4pkTKkqsvnOAvFcRKhak2RGNc6iVJKvK1XSh1USITWCYldKbM/eO5qmwWPk8SqSbv3WnRNTYhxHUsl4A9pY2RzeVTwKYyxudu/UClOMqJJZ+yV9ziSFPGsx4qyZRXKJMWammAghQS44U4km4bQTe/Kc5FxSJIbIOPToWli1LalUjJWzr0aYRUPNsyDdYFwDOpNTJMeEbTuYLcsC/pLlJFexfa8envHoow+4/CwQxhGNzMVTybNzztB4h24ddBa78CxOljTLVqz9SYTMtSZqzthqaUqDHiHsE3YAbRxTHGH9nkfF3+CraZ2EXNZCzpVZBkRJEFSilB3teuTDdkH7xnE4FO5JJJsoPuNaaDyorNFjw8N4ziP3kLbx2Lbl9NFTLl+8hFLFoVUrq7bFO0sYJwxiu/de1ihtNd5bulXH7n5kt+sJuXAMkVfXN3z55orL3Q6nDN/7+Ft8tHzCz+9/yR9//StW45p+JzkxF5sFNRdCmMA1WKPRqhJzJZuKc56YonB2Ws32g4bzpw8Jiz3/4f95yZ9e3fLk4pQ4ZXaXV5RYePT0Q968uePnXzwna0ekYLuWhIz2qw2EceKjBxesVx13eeJX15e8enVDDpFIFhdgEY5XqwOuKxyPgf4w0o+D6LGMLPB5joHOFrxV1Hk8I11b6XSYtx2WnME1pFzZbFaMJfPF5TWHSZKPY5oNGkbMGId+5OHHT/n2wzOGr14zaTnUFhQpiUOQeRqitFj1cng/N+f7FyfXVwx9RLsltZEALbMPWCXtTTUGbG/x44Kz1QN+94f/jOy2TDPooCILYJF0MllishUbI+K9lo6BnmsSOZlTpNpyMyOhxFGSeu3bJNMiwrfKO4IsFaaSZyZK4P5wYOFbrG0xKaKUIZvCmAPjNKCrcERyzXib0dqQYiCFiTy3jI2SdvzSW6CSXMNhSmijeLB5yKOTBxwnxTjMWHIEVlUQEFOIAasdrTU8PDsnF8WURyoRZ+QESpUQwlIKaE2IEW8E9zVqeX2Yt225RG4UxRiycaTWilgxHdG9Ql2P6LGgvQZrsdGx0g2j9SRjqMphqsUmg0tasNvWYqy016tVTI1iAPR9wCLZPq5KRY5SVDSmSjVe7dzNyr/WI3yTr3EYOR6P86l7ycK3klINlKzfdf/KzPzox5HVjHTPuYiY1MmmXxSohccuW4Y6EScJD2yUYZpEJF2yZuYhCx49JUpMKG/J04BWiaotzOO3XCpTmggpv+OKaGMxyEioqCJp1UXGrHUWxdVaMUoSur1taJpGnpdcqKrgGknWzlVYLtIVTDhn8c5yv7tinwPOtlil6ZoGZw05iQ9PazmrxZjJMbFcaUKcOBwHlBH0fCmZceiJ48CybfFNx+1uR6awXC/Rdn5GKmjzVr8zx0BoRcwJow0y0ZHxrJoVl0pLWqpuHRfPntK/fkONUX6/IuOcpvEsFg3Ga2gsZtnQnCxo19189hHAXkkTtRZau0BPmnA7oMdMawzNeklIhXxMpPrN55y4lDBuHtZXWbNVAV0lW6ymQlI962XkO99Zw91D9leGfe3p24FjHki54LPnpJ7zLfct1I3nq+eXrB9qls/g8y9f8OGTc5arJdc3B+6udyw6z4PzLZv1khgCw/HAaruisZYxZY5TFgPAmLne91wee97c3bMbJJixZLjc3fLIP8SNDZ9dvYHxmgf1lO9ePOGTR+c44DiO/OrljpSS2PerxvsG37Qcx4Hdbs/17T03+w0rc8a3f+9T7q/hp3/yGuc7to3l1eHAZ3/5C3709/4+T7/9MV8+/4KSC7tp5OLZI0x7C+Y5rYHD0PP5yxu2pwuOOvH8csebNwesVTgMbWs5jgm1sCzPWk5WK9Jd5T70cyzK3NWrYH1DcpHoCuE2SYGhft0xtQZBSlYJtF22lvXCs+4aUp74yYuRKc+ADjWjAUolxMLdfmQaIz/66An9lHm1v6efIkOScbQ2aj6YQ50DUt/3eu/iZJuPXF/2FDPhlrM1cVJMVjO5Sms1bXasRs3f/wf/GLU5YQzzyAZEnITAepx3OGWALC0q6jz7ktNdiJkQEwUhb6qqZWZsLC4XdArUGNDe4aqfW1MZo+Vn1AKda8kFgoLjOPD5q6/54Owh2+6UYzxymO4pKcgJbJpoGsOYJrpssVhi0uQiLdtSBdZWayXlSIojRoF3mj5W3tyOfP+hZwoijvTWMmXFOA3UmrEWXC082J6glOHYB5IuVCPYe43BIcRZUUOLiv1ddH1N5JxY+CVGeSqJlGB/d4CbIy0rdNuxs7fspz02ObrQ0YRGNianMM6wtC255nms1tHmFSpq2h7anaIeC2HMxBjpDYyuoWiNmiI1ZAEmWYtprIwaapmj5T26GkrNWKMw1r33DfibehkDXSNjCGPUrH1QM7/D45uO5VI2/pTlfjZGgvOMcTjrsV7cNqFm7OmKajJNFZtlzUgCdk7orChR9CbJzXZVMjVGOlraGmiWntEaxiiha6WAKlqoqc5ilHRKaq30xwPKNQyxEJIIZUFON04rWu9QVqOUnUed0pFRQNWGkut80JCxlDaWWArHMtG2nsP9SCqJdrkkJhHxem9ZdyuWGHnOqyKmiXK8phRovCUpzRgncow4Bcv1mm6xQlnHydmWfhrESTRNeO9pWicF4Ayd0kazWizonEcpQwoJ0hzmp5Qkphov8DwUywennH3wlOF4YJoGsDLKtQsHC4teNPhFg2lnuOJ8CtWzaLCWTOfXNLHh9vqW8e7AuvGstwsWywZUx8uvLxlD/7d2n/5NXZtDAw9aTJeo8RadEm1b8K3QdCnzc6IiQ71lcRp4tDxn/bIjDluCrgyhYKcFD/VDzu0ZdaNYftqS2jV/9If/iXI/8fC8sOgMq3XDi9eX/OTnVzw4P+Wjx2dsFh3WGsI0klPD5nTDz37xgucvb9FaEXNk7I+cLJd8+8MPWK4a/vDHf8bXN2/mDLMMp4q0yuimcnO44/WfXvLb3/uIx49P+J/+7DPRPaqK9y2lasBQi+JwDFxe7bm6vWfbtzi34uP/4lt8+99f8fxPdpy1Df/8f/d3uToO3B8u2Zxs+Af/9HfZ7w782c9+yenFlu5cc3XzAod0bHa7I1OovD6MvHq9F+1jzgw5oxvNydMVzUnH4c3A1XFkPAaUtpyuV+z7I1OK4tQphVAqxctnUZMUkaVkcQ0ahdFvgXiapvVsNy03t3f89MuvuRvm4kQr+QznEY01MvZMuXJ2vuIfff8T+lj55deXfHlzx+Vhz5CCTDbqrCuj8lZS+r92vXdx8n/+v/yI/+7//mf85V+OHIaBkARKlJpK8IZzs2JpF/z+3/+nPPj2t7mfMrUaKlmyaahoI/k3zCMVRZ7VwqIlkY3OYI1DaZlPV0STIfZDWHWe7aKjT3tKTVjboZGQvVyklVyLtOTKJO2/9arjanfg8zfP+fjiiRQBIdKHI1McSSli7BKvHMvFir4PWN+xH4LoTcxyBthEiYlPQWbdpWCMY0qJYZy4vn4j59YSSEOPykLTPF+esFkuOAyJIWeG6YD1mkYLfryEjDIV4+2s4QiAovMGrRKpBIwyONOisZQqRNscI/V1gnGknETaNtGxpGZLqZqqJBPIjh6rHTZVdOmw9xU1SFprLBFTLV016KiIWdqyNWXMnVBu3VhptEZbEU0qJQ6ht6IqCuSUyTnhuw7rvvnFibMO1VS5nxXEmEi6iKMpGYw1eGsxxmKtx9q3othEzlGK3awoVqMWLW7Tcby/RZdExKMWGu0VycpI0aBnt1YkTpEpBEqFk80WpRzFSwcixYrxDcpoTJWRk3UGsriFYpEgwn63k24mipJme761uK7BGE0cDvSp4LXGWyMk2qbBeemaxZgIKZNVZYyBKQSc0my7hvPtCUOUkMAQI94bdIEUpIuUoujJwjjiECfRPgbpNEwJrw3dcolvWsacqeOR5aJlszpBK/WuyxFjkm6p8RhjCCVRh15C/gyAsGK8tRjnUMaQq4zEvJW8oYvvfMqrL58zHe/RVu5xu2nptkuaZYdtLNVAQUIOKQl0RaFozQm3X92xzAMdGtt2rLcLVqdLaol437E5OcMev/nPw+2PO8qjwLMfLViphuRGChLtZatkoSkj/02BnTkyNiPLT9Z0u0dMz5esdisatWG9OsUvN/zqFz/lyQeP8J3h8ssv+PazZ4R+5FAl1mHdtmwWCxpr506XonEtbdNydbVnf/cSYmC9akAZnF+x3qy5uTlicmbTdPzD3/99/u2f/il/8fJrlo3jbLPlyJ69uWdz3rJ2J/y7P/0Vm+cr2vWKurujAOuLE+72R1JJNF6hquZ4nLi+2fNg19G0K9xizT/93/+Q/++Xf0jYj9ynPYsPG16//oJ667ELx5/97C+4u9pzqAMPVY8xUJIiKRFu3xx6bt7cMPRHEaPninEWd7bh9NmWX/3kJWkfORpx087lEkVVihFqa0gTY64iCDezpnPmfAnfpJKyIs/OvjfqwJ9/8RVhyvzq6laE5VV0KVorzFxgWAN9GDnmwt0QOd92PNSGzogIf4yBkKPsF2p2wSkZLb3P9d7FyebbH/Hp77zhq6+fU+6lbYfSVF2ZssyTv/PD7/PDf/736WfWBySBwCikH/RWGVwqJScaK7bbioyFW2+xWgQ1rfMYVcRTXWXGLLHpE6ONEnVepbVassYai65Ca7C+QauC8pbj0EtfJSVe725ZGE3jFPvDLWOa2KzXLE6XpFq4299zPwgSu6hCyJFpCJysT3G+YYh7hpTRdk3rHGM4UOjxHrQJrL0jKjml+jlMIlbNmBTHu55cpVNkjdgtaxVro9GKqt+mtmZpsWkz/3dinBLb5QldIyfCmMW5RNPRnJ3S7EElOO03In4ExhRJLpOM6GQS4mLwUbE+LtG9RgWoGHCGrmnRq4bcFcI4YKcRP9uyfbcUS6eS4gSYmR2FaZpEwY2IsHKM8/jhm31NU8RZR9tIIZCSiDdLlViEMWRGbdist1irEY/V7IIqEJLEl2vrMZ2lJzMZg2s9cSqQEvatFU9DsSK6pSqsE/aISZVD3zNS6VRLSTPO3ljU20TjMZD6gsFgu4YhJ0qFxrdoLeNWY7yE4+k5ATwEnHGodAQ8wzCyWi3R1sh9iwiCxykxxSzPctPQNi0ZGfPl/shxGPFNg1ISvCmHE4EeximggcZa+jER+pFaYLtYsVlt8G1LohDGHjRMAdrGyCIJNL5Dl8Q4Bfqhp9SKs5pWG5rGEnOmbRu8MzjvJWQuZYHK5YrSmWos9nTL4x9+n6//zUusMSzPNqwvNjhnMd6iG3FBlRxIcRIhb9ao2PDVL97w5S++YOsaHp6e0LSWpnVMSaGVJ44Dxv1nEEkMPPrWp1xdvyBdFYpX+EWDNhnJO8vMUwE5oVfpbvc5E/0dzebA4nunLHZPqa8sMTZsH3zC4w8eMSX4yR/8CR89fch67TnuD+Ts2KyWfPjkjMfnK5RSNI3n7vbA5oOHVAz7Q09FsVov6NORv/zqDbd9z9OHJ3zvkw/4xRdf86/+51/x9NEzHp0/w64ueP7lFzz//Bb/GPoHGbW+5aPNkuXdgp9//hWnJyd466hGUYtiHEeCKpysF7TWQoS7m8AUHa417O6PbL51xtl3l0w/i1z+4pKVP+Nu3PHyxQ6KxlnN95485sbfowioUrm+GhinI5v1irv7A8fdQE3yrLrW8eDjC04vOr7489cMdz2pwFALrddMQcY21mkaZ6gIC0xnSD2kWrBaQ3mLuGDm80DnJAbier/jy9sbwljJCrpGvjDEQski7i9Vok76ceIPf/YLHm4W/OjDJ3SNZUp5piHNBGCFdIy1mUfd77c/vL9bZ7ene7Tm4ZMlT9YbYiwcp8gYE1Y7Pnn6hH/4X/5jaCH3MspIFfTsTChVeCZqNlZXKlYblEpMOVMwpFgJIcg4x3q0tqiS0aqIdiOPNIuWoMTB4LSlZJmBhTDN1E1LLWVWKmfWnefNfodDs9msuezv2HjHxfoc3yyYamUMA2pGrh/7EavAKIXTlmoLOU8YlXEmYY1k9yQVqA5UtWxWLefbLevVBZ+/vub+EDkMGqUT3kd0LugKU6kEozFa442IfnOG6O0Mp5HxjcTEi5ioKOHWxqmQdcWaggGM1ZjOordL9H5C9RllFdY22Mai7MSkAtlHxiYSmgidRusOugqvR9xeY7JoCoxv8G0rqbPzfN7GRJmhYq5p0WomBOb8TilutSZrQ1bCn6iILfobfynQRroVOhusc6I5UQoo0tkoVZxYNbwLA3NGMNHFaKo16KXHbDzHNJG1YQhSTHqqWNKVkBg1CqUlqDJXcfgcwp6N61DeM4ZE7I8svPBocpGCxKmGsD+gW01pFd42FJUxxs7CWumk1CosjpzL/PxYTK2EOLDslhgrLrZcypxOLDPrVddI7lVJaCWbESgenp3w6uqGfT9QGSXQcNGhjMZbS2MbSpqwpWfjPHZ7Trdco6yEEGqjWTiLbx3TJPlVOQ1obanIQqmNpDlrrenHgTSOZOsofkmsUFOSsVkpqKqZvaviWLIWow1ZVR5862Me/vIp1RVWFxucd0ChzvqYWjI5BPIUqSPE+8x+d8PrF1ekDHdxYIqRhXfs90fubu9oOkvbNXjVMQ7f/CBMPwYaFzheT3z9lebB2SMefKzoHh9R6wNZBYpKcr8BysEM1yGR6P019cGAbd+gbp5wM52zefaYP/8Pf8zHz85YnSpoDKv8jBefv2Z/fYdHYdGcnC4xFrplx/3ulrvdwBDh8vqOb310QbPq2Gw3tIslD7Yb7o8TD043aNuyXXb85KsXjCHxd77/PXbXl3RdoVc9V9OeX9rnPDk75+xwwu3hbna4eG7v7il5ttVWja0S17C/Hbi9vEPTQm0IG/j0Hz+kWxr84Pnqcs94GLjb9Qz7iUcXG44Y7KagiVg0j1drthdbXn5+w4l1/PC3v0/btrza3/M87jCLhs/+03PGfZBR8eyozEX0YJhE1oXiwFlDSAmOFZNBL0WjVSfB1pciXRCFJpRAnW3CX1/uCaGgjVBdOy/Mo6hF7KqUIoTIGBJf3+2IObFqGtCG55c3XO2P9CnOuUlF1gsUvrWk97TrvHdx8vUXr1Eanj5eU69XhAnc/sh9HTl/8Ih//M/+EasPzulDwZiCd4aa3y5+4j6oSoMSC5NRkiDqnCJNSdw4teK8I8TMMAWZh5eKt06EqFaRCVQstSpiyJIGq6C8I8VmphQIsbBZefIwMk0TZyfnHIYjb+4OHPdHyJZz0yIG5EQaknSDtOI49Dw9fcSUA6gso58icdpGa0IaUToQcqKmDKklZk2omj4HptwTktgkNyrxYCoMjeOLOlGVB52xvmEKgZQM1IrToleoWRTYMYw0jeR+lBpxxuK0EzbGHI021ELfOpZxJN2PBF/IRuFaz3SS6deJQ1c5WMWkFEVF9DTidKJbO9zC0GaDC8KdKMeBkhIpBUqS12ateWfxrLzN0JANqlYZy3lv0VbN9jGw+ZsvAGzdDCmzbyH1era/i83sLXk1RoksjzGSc+aYI8pYVNNg3QLlG9CBmgIoi9HyPcYwRyMoJSA0o6jMo5KQGEbJ8IlKbJbDMJKOI+umxXqHsZ6aoeYJ3RiSqcQ0EULGWUc/DmIFnuvIPHNYNAbvPJ33OFXog4hUY5Lk5JwSFTDGYbQDqoDWapRRoKrYriOT2Cw6VKkcp4lhPDCEAe8avO9wxkkoptKslmtabUl1TlFGSNA1Z5y2LNZbAUBOvYQNJuEZ5dnS1zQtbduSxh7Pr4sQpTQxF0xVqFKwzsIsyitZju9TybjO8OlvfZ9Xb77AdmKDzrHIIp4r5ISZQA2eu9f3hPsRMJx0LRsvvierlDiYnCUoS8UzhUJnItZ/88c6L28/p7fXbNMDPmw/pR0c018E8tdbNs8K7VmPPb8jdXuiku5blcagpP/mQlJH/HpksR4JxrAMPyI19yQcemMZrgxXV0cm0xGTos2JlYXruyO7PuKdZXd3x/PXN2Q8T87OKBju9z3eaM7XCy62a2pNpElG07e7O37wwQNu9z3kwPd++F26VvP54TmH48BO3VNcxS49cV/o2pZFt+LVzQ7qnGwJKGsx1kJxWBTOFqagiEFx/ukT9F1P+1rxnWBINjIMe66yppbMoRs5WUVpf+N5dHHGA7tl9bBhHCIxJQ5pYvt4xXhIfP6zG8Ix0zrLZiFgw36cmFKmGk3bOIyrWCeOU4zGNoj+xIvdv4gcS/hsVmG1aKr6cQAqY5T8OguopGisGE9Sqkwh4ayR2BZnAMV+DLw+9ByHkbt+IORMRdE5x8J7UizcHHvClN4Fe/6vXe9dnJTrEecVC63pS0IlsKVwtl7zvd/6Lt/+/e8RdKWkSuvldKiLomDEQQPvFgStNFkX7oaeZZWI+VBlBAQzX8CIvXdKksWx7SzeGZJBbJEoppyocaR1DmsAymxLrkwhME6FfuzROqF0ELtlkQX3btjRdFpat0Yx9QO39zsab2iMxTpLyhGHYUoTMUXw86hqbtGPY4KU2T7cEpLimAP9NJJrRFux2Y6psrBLzl3Lm+mGXhlKKUJonRNgw5ywumhaSYRNhbeDkjFO1BqlE1WiFHtqpotqxV4nFjpTp5GgJ9JGUx8rjqvM0ERGlYm1kmoh5YgJgfVtwh0NbaPF6uktKhlqkkW7pEKOiZwy1lh4C+CqUljWIrbuUvO7dt1bi3MFVHpPBOBv8NW8zZtRAjETUJ4stkpbbGNhFn3GKCd/pTXWCGK9Nha7aiit5hh6sV8aCaIkJ3KxoBrJZCoVpQV3rzU0jYxglIIUA6WH/fHA0llQmpgzMQ2kkKgpoTzcTwdyqCgsdtZ5xTBRasW7BqN/TWJONTFMgW7mmIwpUguizZqZLUIDDdQZDWctgCblzH0/CGs2KRrvCTHQ9yOByhhGdD/QWI8ic9oofLPmZrcjpCzvkTU0Xp7BmiCEyrJt6NqWMRY5g6hKeYuajGXudBpa03AIkRQydQZFLdp5DZpzT1SppCA05mIqRSvOHj3gdv+aapCsLqQ4UqFik8LFjhIVSWfUdonzlvPTrXRfZg1MMZVsDLgW6xuG4UgqmbOT07+NW/Rv9LrWr0m9ohwjF06xWC9Zrx+SScTnR5bTU+ruDn16R1lfcjT3pJogQ85zPpmtFJdJ9p5J/5TQXXP+9xTqfkOtnkZ3nHUKZT3mdEXXOJwu3N3u+frFDVpVlNHc9wNJVc5r4eZ+xGrLpmuJMfLi8hJnDY9O1py3K3bhyJBHLjYbSIWw73lzeeQmjHjbYvXErd3j1p62bzlpNhjgfNEQ5jFuyZIUbheW1WrJ+uyEgUhRCqs9i9MLmu9N7PdvUFHTZPAlourINGROzhcoI2Moz5phslxdDTx/ccn1fkdtDSePTrioW9bRc2I1sXWoWtkfJrTWdM6iiLOLrpJSIQ1ZQIYotIVi5cBUV1CnArHK/y8CIdfzc2W0IldNo5lJ6XX+fBSMlWkSnEHjjPxZlbykL67uJOGmSudz3XRsuiXbrsEo+PMvXnBIcrB9n+u9i5OlXaISeFW5no4MQ8FZxyc//C5/51/8PezFikTFammHhplnUgGULJpaVxlAKchVLHZ1qmw7j6oThUxGgbIzDtxgc0HXKiphLQ4CUw3TFAQuUyLJCLBMPgZpxSpV5xEFWJVIqafkEW9h0JUhHhmTY9tt2A8Du+Ml/Xhku3rGerERwa9xqOiwypNqZgqRkCfxtDvpYOSUWLk1uSr6IGj/qsTTXSr02vJVrnxkHNY08vp1fXcizimQU0FVKYZiyrJBkDFJSyiiE0iVRLfL2KUyp8ZS2C1BLyrN44b0oWPcFnqTmagScliK5H+kTEkFlwxN1HRe9DElFUqIkOT75yxwpKpEG2GrRpUZdawUKUv4VlUFlHSTlHo7Wxfh8zf9qiimGEhposy0V6uNFC3Wiq8f4cFoLeMJ33ZUBDBQFw6WlmgqMRcp3GPElIS1jsa4OVJe9FlUZpS9/KwYMjFFcobGOJrGSrFktcyDk/BLjPPoWmhLZAoTJSWm/oBydkZLS0Gh547P2wLFGfDaYKncx0CaMk4bbCut41LE9YWCt72jUqvMm0uGonBGiqt2sURpzXE4chxHpjIwKI3XivP2FG09zaLgS3n3842eR4hFOnQhJ0zVxBTIdX7PymxvrEoCz5QmKi8jX2tE5mbMbM/P8+hp7qzM3BZrDLUm/GKB7zqmcpTuUIyUKTPtE+WYIEIYI9M0kUuWTJj/P3n/1WxJlqZnYs+SLrY6IlTK0lUt0egGMMBgSI4RHJrxT/OCnJvhYAYNFNiiqrIrK2WIE0ftvV0tyYvPI2puaJYwa6s2JPymIisjTuQ52335J973eY2hass4z0xxwnWO/mJPv9li+xanIJ5OTPH7j69fdCYed/SpYcmZ0QfiCp7svCUfHW6+5PiycjKQrxrS/kw0I8rlFU0gfCXrEn03wvZr1BNHcxgZv35KHBJ5sDRNpXeObWPxjcEZReMstzeP+K7n8nHkd2/u+fz1DTFC4xRtY9Heko00X+cx4xpD3Wls1kzHM7vNhrenE9+8vWOuicsXB55eXvLr4QtKX+CqoJMhnBeeX+4Z5pExRpSuWJtpW4vvFLPKhFgxKTAPGYzHPG1RH28Yjw/MQ8Kj6VvFFBJ9nzGukOYG7y/5+vMH2rnh9f2JUWW6rmUqipdfH4mnkfMwrSRzTb/d0zrPvvNYRFNyfz7z9nRcYxgE4UGBtFRqqti9JbtK9QpT1tXlyuEqQC2COmicliFBKQI+VRpnKnaVQDgreW1URVbIICHKKrv3ls5brncd1/0Wb8Td8/ff3vD29N0I4t+5OFHBMz8uEJysVHLhxacv+Pl/96c8+/lHLFpGRNokCgJnMlbC7YSf8I7bL1WYUqIezkWRkrgRQpxXFXFLrfKf5rShUQarKkU5VPk/YNRLEshLLVJ9V7F4Uougw40mpiROH62Y40hGVjKpKnKdMbqh5oHWe1q/oWt2GO15OJ3Y9htCkSycUquA3YICLKFM5DTSGs/G9ywxcR5PzMv4vqNTShOV4Q2FGgPFN5CWlfUgroOS03vOSSwzMYkau9TKFIR8e709CO22CihKwDoyZkdXwlOLaTrKtaUeMrNJzCUT3o3ia4IqRFGbEtkZht3MvC1oZzCLwWDQyYgQ2ESCka7A5kKLwVQB1hUDxVYyRfaaSgkxtqi1EH03VP9+X0sUIWYugRwl5bfxzft/rxQova5KrMVYT9N2FFUlpM9rqgVFQVdFyFlEsjXTKYtzeoV9ifsslCq3vVpBRgi4L1fRKLXGysTPmNWuJ9NHR8XrwvXlhqIP3D1MnOcBVF5jE+QeUSseslBxRtNisDi6AiomcdI5vxa6QSCGZcUDaC06pDW8TZeK0xZtDakolHG0ncYax6YT0FvMGVUCnffElDBayfSNVURXqxR9tZBLZlwWAdOt4vqU4rpaFGBhYxzOWklLXUW5jX1HbU6SR2RE8CsQKoVzInzQylCtwjUNy/lIWQI1FM5vR4bbgTwlaq4sYSElKXJMrcKWQPNwHpjTzCZ3tF2LKUnWW40jjoabtzf/BHfoH/baXlzQ8owneYdepFmcphmtNduuRxWNzh5OGxrVcB46lq1j+3GLbs9kE0lkZLguAK+UE9pHZhfRzzJqucC8vmLrW/abFqMqaUk0zvHk6sBwnnh1d+RweUCfJx5qojpDbDOTFWp1LWCVIZ0r45uRHDJPtzvqHBjSwtvjidMyooxmQ8uTZs/tdMNtHcibhbu7I3lIUCOppPcE73bjOVx1tHvDnIO8A2Mgxkr2ERpF9/GW8urE/EUgxYTpQDcZpRJ5MuRzjzk0nB/u8I0nO83mYk/fd+hJEYeF4+nEkoXv5W1DjpFA5S4t9KtLMsa8ygYNKUlOVMkCcyyjwANxmsuPt9Q5s9zNqCiBgGp15mit2XUdMcvn4m2zEqXfZXjJpKWuDcW7OIuYKjEWqIm780RjGza+Y9t1fHS1Z5gi4/TdUrq/c3EyHSEOipykODhcX/Pjf/4LPvrzH6C2jhQrFhEIsdqOrJYiAvm/hDpKRWsRhWrJcyaGgtMOo9buXkfJyCmaxli21ssHqNSquZBo5poV2niBgElUqqx1ShLXiIY5zIzLjFKZKZxZ4oxz0Dcb4TPkyKa1tN2BkDQpFgKBcRnx3ksoYElSVeaKrg6UIcQIpfLksMO7hoclEUKhFskKMuuLpNRCdHBTFlrVAkHCDpWkLVcy3sqkZQ4r2wVNqQKpSjlitVuzWERDVmshlyhjUVWJBwMHy2Bncp6IUTJNQsrkInoAiLha0Klw7jRHM5Jbg7KatvNsuhbvLelcGENkVIEK7ILnaWywSlOdJtrCoionNTPriMOxiS0+rTj0dXf5fb9yjrJSqb/v4GstkuqbE1pr7BoCWGqh5kKOGdMYAek5cT7ZIiC7oipjmEGDSoWqIhUkl0kZKJLUKjkk9X3sQF4LVkelARyapVRUzThV2ejKhTU8vexxF1f8yt5T7yHVvBaUksSb1oTSSkUVTVKQlKExhkYpFq1JNUvuTM3vp0JGSTr2u8CvxmiMshilKQqylmRSrQzdZo8zFsmtEWBgZw3HaZCRM7+nQ5ciP0uZFAk5WhuNb7o1UVkRdCYmIbRSK9YYllrItaCrJtUqYsEY5WtL1YNG4aylqEpdpz9FK7z11JCoUyKcM8PtyPlhoMTfU6UpWQTp6/lm0DRGY5VnZz1bZWhippxOxJwx79EA3+9LWcPTH215Xi9YXs6kUXQJ3jv6vsU7g/eGrs1snebh28D8tueD/TVOn0l+IrtANolAQc+gdIJ+YckJv3+g+XFG9Y582jMnTzxlwpzoNx3KaurG8+qbG55+dM3FD55wnmaoBW8TVmV0KdQk0o48J96+PnIaAsMh0KxT4ilIfozOmuPDyKZzbEvLvT6TXeLsTpRSCY/jSncWQrEyms2uo9maVYsngNGYIsssxb/bbVEvLLHLDK8XZp0ofWUJBjd72rolPCzEOcIWuqsN7abhQrewJG7jmSUFCpIt99H1NcP5xN1wIpdKe9hDqYQwC0kaOTO0E0SHKglVZDVjFDx9vkVrxcme0I+VXdtyHkemmEhV4ldSjjTW0fmeeZHEbUkUl/e6hJFWlpBl47GuhWIqHKfApg1MMTLNlsYWPrzcMX/HIMzvXJzcPSRa54lEMC0//fM/5if/8k/on18w14pSZrWXSnUiCv6VGV0lq0ZZOXi0encIaVASggaarhE+QF4D0bTSNNbgnZbUUczaSQXBeq+iWlXlsAPIWcbdNReWZWZcBhIRs/qVjdH0Tc9+c4lSlofzxPW2w9uG03AkpkWcDHUh5nnlUkjHpFB4Z4kZCg2aDU/3T4gFxhCpOJztpbOUxQsxL/jGoqogvUtWNGiUkpDDkqVAUcqA+n02S83vEmglplprEVQapWAFqSkt+SsjldYsnMIDcw6kyHvMuaCDM4qIWcOdxk4xekvUXsBbKrPpCm0rXcbjEDjliELxzBYuxw1OKUpvGW1mjvBYZs55oKXBoFFF7Fl6tYJ/3y+rNX3bSmGyqkqtte+FYmj9ngkTpoFS07rK63CtRXUGaxp0tjgyxWROCC22VGET5PIuoVtT15yospKMJW03S8ZLSmgl4jWHJqlMKhFnCpet56O+Z2MN3dbz1cZjxxaDxlWFNZElzJJjswrYrVYoa4ne46qiCQuneSRkweUb5HBz1tGIj56UjTAWtMYaJXRlyhoABko7lHEULSA6Z8AUMGEixYVcRExd1ntWgGcCvDIonHErZ6VBK4MxHl8qIQXSErAVVKnv0QXv7PR6DS806+SxlIx3FmvFDplTohrI2ov7byykY2Z+mClzRiNFWckiQPTOrDA9Q+skqyiXTM2JrW/ZaoddMsNpZJpGuu2W/Xb3T3af/qGu+4cHDocDz68PmEEiQ1rt6bsObx2KClpjjBFdn7KEeYN9dUU7XkEbKW4hbTJ5W6mpYMaRob+FmCibzOIXysUN96dAvrskPrZY03HVbVmmmdwY2ucbytbw6dU1t68eCaczvlou2w2Hrscqw7hkHqbIy9ePTHHi5f0jfs2c0WsoZy2Zt3dHYgzUvqI2CrYFdcjUY2F5EE2Vcxq7hkPaRtN2hqoq2mpUdqRFmpjJaYamQT/z+OcN+SvFdM7UVjFHxwWX7Lae11+9JVdN9vJz2lTNpbYEm/hiOnOeo3BKiuGD60u2Ly7569/8Duc8P/3g6UoojwxxIeZ1yloKm86TYmVJoiGxVbNTmt2TDdukSSXw/HDB8eh4mAPHJXIaF7QubNsGa5xIKNYVttGFWlYNS1kNnlo0iNoqYqkrkFJMKqdhQG0sl4eWH6sn3+me+s7FyWcvHzjsNjhlufzwY/74v//nPP3xxwSjyUmtxUkQ+yMygpbcnIrVUk2ZFXmuqqIWTVzFOChICqaiaHWH1+DXFA2jIdeFpUj6cAgJraSrEqbEO5Gt/F0pRUoWLcSyTMzThHOWbbvHx14SkZUhF8cSIsfxTAgLT/cZTQSVOY2PWG0BscvWnIkpoLRBe/lh15RJKdKYltM8MKZJotmVUD4qosMw2tI1LVA5DzMhFvq2w2IZl0E+TC3JtaWAtY6cpDNNOdJah0ITYyCkGVZtg7UebRxTPjKFBd9oUkiMy5llFraA0hpjRfDrNHij6L0lGivW4NpQMQLBUoW5i2RdOZvCMERUTJx1ZXBboumYN5q50ZTg0aWnHTKuaHCKkkVo+G4c/32/Gu9wzok7ycoLF1itxIpURNT8jh9SSxHoYK2YRjETKdXTWC8OnJxk0pYrtS4UpUA7jGukU0qJWtfnLBemJZDKGv7nN2CFzGuMoTGaqhKaiPeWftsRpoF4OrKEsApsNaWK62a7kcyaXFY4k6ooo8hAWpLYd2Ok1CgdodY4Y2mcA6UJMbHEJOsdrQlAWV15FCWUYq3IWVaFIVViDNgy8cwbrBaIXakyctZK9CNWG0zn8NbTWiPcoBSpZPKa5K2wtN5gcqGmLJ+JUaSSxHGGoOuNka+jGi9WboBaVt2WAlvQWVHPiXyM5HPAK01xVlx1OaOspmtarDbolXGTciaESM2RaC05SFaQx0i0h7Zs/hsoTuah8PU333Jx2PP04ytpuoa8NlKKEDPzkplDQOuGH330jGFcSCGgpwYVO1KOMCnh5ZhEfXhkPhbsrUM3itGPxE2kdjP35oYhH7jYXtFcXXP7+mvU+cyV9/RJ0VKZjwvdYrg67Hl62LDf9Wz2W/zukiEkznPkfviMOcxUKzoyqkAHO+9onSXEzPHtgs4e1S6UtuA+MORJkxbJrjJao5uK30O3b5lVIaNRzqNtRKmE1oWqC82TnuufX3HxzVvuP1sowTDbwMXTC5pSqAFefPScF5884e03d1w5Sw0jb+7fMoSJJWY2TcPlrmeYJv7iJz9hPE8o6zj0Hl0jm41jky11yYSQQVWcVXTGo+ZI1Zpd6+nOmrYpjEvicRhIIdO5ltY4goWvhnu6VtO6wnE4cxxG5hjfn3F51aa8I0fkJP9sjMaskNV5nhlmT7/tcTtD3WdU/Ed267w+H7kbRp7tLvjv/2//A09+8oLsDSEXCmtQGeJIUNqsHVBZhWOrGJZ3CA0thxwiDK0KklIUpRiLdIC9c+ybDq0Lp+WItxaVxInjjCIWzbJIFoZeo+JDTMScaJ1jHEfGKfD8+hMepolpGogrp0GrSlazTDKoPE4TT/YbNt7weH9HqYnriw+p1UApaC3TkxgGUgGU2DjzPGGVIRZNxaAtoII4Caq4erSulPju15mqEjk7VFUYHI2zoGX8Z4xoZpYYqcpSiuWwO6CxULWsr1Re8b/itgjLgtPg7JZtdwnaMZeFcR6oaRVSVoOyBm001jVU36IrhIyECsiTSa6RqiO6y5i0MC9n7vPMF8bie8fSOUoj3fIuHbimE8V26ylDJt+O6CG/H/F/ny9jnEwKrKVt3Hv+S12LElWq6J1iZN01EpeFoipsG4YkE5e2yUSbeQyCm6a+C8VUdF1DromSROhtjMQGFGXpUOSSIAasswQD5xzQaYGqUMqiteGcFa/mhbbxvL078vZhohZLyYlYZK1hrQFlkLga0bPkWAQglCu+6Xl6CY/nOxSRg/f0/YagDHfDTFy5Bcs4CxreiP5IqMbynJcSV0ZKJef6PoBQWwFEGaS42VpLoxSmQtGa5GSNvISFSqUYAbHloqWLSxlXoVXiyCkpsCxR6NFrPte269m0naQ6IwVYKgWz2uBLilDhdHtifHvEo7GIw+0dUygmCDlSVYu1dhVWKWKtDPNMTZGuaQRMGCUBd/fsKdvLJ+wO+3+iu/QPeN0ZZgK/+d3ndH/S8cFfPMPcFPLbgIoa5zw1R6Z5IYSFRGTTNZzHwjAVHs+DrBdNYvl6Rn2Q0T6A8Uxfawwb7tkSGoU7jHSHRLoI/OqLv+ezv/07njcNf/WjT3lze8//8+//BoWmMw0fPL3mibHUDNN5IWM5bOCDZ5f8P/7dv6XfbPl///VfM5Ug4n7garfjctdTCzycZg7acWg7Xo+vmC5GfviLJ2Tn+Oaz0yo6rzTXCv+hJTSVOVcKki0jYXmJaYqQKp1p6F9sufrRFa9eLWTVEOrIMZ/YnC1/9KMPefHHHzO9PXFxceB8PPL127e8PD6Cgl3v+flHH/BXP/0xv/ryNZ99fcMHTy/49Zev+fzVI3OciWuMSOOMPG+pkFLio+snHObIWEXMWgZxpZmxsiyBh9NMQZNSWNfRlSVmbh5P5DUzLWU5i0qRCWkuMhFWWtac2ii0AlMlpwtTcZ1m96zBPVWc7VuO43czTPwXBP+NbNuWw5MtH/74B9hNL8FaaHIVK6Wlvmftl5RxdoWpGb2m7UJZd9GoTOtFOBtjlYME6Wj0egCHWjElg5asmZwDtQbQBpUDVkesl91XikmYDkqcPamMjHHkYtMR48hpusM3HdY6UsoiT1FrcYRiihqL4snFJVVZYgrc3L8RL7uyVKXJRTOeHwjpLVplLtwebz3JtZiSOZ1nYhJtQa4JtTqXlphwVrHMgRQSqt+DNpSkmBfJma9KrwQ9SWdWulIIlFxYYiYTkUwd0TMY9CoKjHjbEbPG+wMHt2XvRUSojaMqS66yCtJ1IurEXIpkHhSxgZaMBDKuC1mtCn1r0bOjhMwbzmjdYYrB5Q1UxRaDweM2De6DjaQ1v7KUr86Ux+//jt35RgTDzoGCEBfmaSJGgdah15dnyTir36eXvrl7Q9MfyNaR0sJ9OAk3Jkgh+Q4O1q7iWlPlmSrrYRGz2MmbtsGajjiPoktSFizEmlHriiSmyhAzb6eEN5o5wTBXvK0oLWBApTVGSWEqFnZxuMQYJbwNRVDy+3Zdi8OwAVQMTCtgUZ5hWQO+c8EYY9BIPEMMYnFUWqG1XePnC8Mw0vWtYNusRRtZX+VSxW2nMnPK1FzpGkeMC8M4UQto24iJOQYaDMpWzkFWYRiDt4Zd36OdR2R8Suiu6p2zTwCIVJmOlrDw8ndfs5xmVOPItcg0MyVKlOlIAWpfKCS0tcxhpm07PnjylHE40lhLzZkpTwSjsI3FhRNm+f5PEuvRs9k7jjcDn33xD9ifZS4vtrjZoE+SW6O15sn1BTlJPlLTKE5j5nScGQaZjj9OAzfjPe1jy5N/vqeka8adJSTNzf09529vWI739G3hyfWGNitObwYOP/wRz6/3DMXx25f/Kz//9AP+3b/5ZwyPI8O4MI4LT68u+cGLPZ7IZ3/zt5im4cOP93yYD+jG8/D1kbZ4nl1dYr1jOAeaXGlq4aLpmM8dTax8/MMtV/0HHMod33x7x+aDlg/+6Bn6yZZjjgx5QqlCVj0lF6YwE4Kidg5aT7Pf8fGf/QjnnxBODa8ffsvFhefSbPjTv/wJf/03f8ebv3/NNCTeHgcel3ENj1S0rqV3DRvnaaj88tdf0G8Mr2+PzClircI7LeqtWrBG3qsaw9PtBVdPe+6GhYc6oY2h8R3X2ZAOhaIMrx+O3NfAlCLegFox91XLc26VEmFtKaTV+SC9qF7R9PJ3t41ls+nYXXakPvPL+9+SzpGf/LnFfvzduD/fuTjZVcdB9fzVv/4rmmdbZoVYUGVBjEamB6nolY+h0VYOyZQlZbcUmRCAWL/euRmUE4iRjNXWHVYuFJ3QRrqgtKziw/VwVqaIRdI5ybhZHTzLMjLPA+f5kVASqBljRBTa2QO1GlKdZW+MISnDptlT2fLy4Q2Nz3g1ECmcp0cUirbtadseYwzx2EO6xDYznzx/gfOWMZ85TzfcH++oBWIU5oW1RpKOvadWyTDZ9VfkrClkprBQcma7aWm9Y44T83zGakdMM1ZlDt1B8PDGE7PCOREU5pxAaXrX47QhpIDOkapFjtp2W7r+QMGxxErMmZwChYRhgfTIvDyS6oTKIoakFAnz00YEfq2nzolzgkzCkTE5oc+J/HYizBXrK1p16MsOt7kgbDS8HP6LDrb/Gq8lLIJ+ziLIzDmwLIEYk5BcjWEYJ9FgbDcoq4gxilbIKslSshqnDCVDsl5WlAWsNqQin5k1ErZlkJwjYxTKOQEznSZqjHJ/oQgpY4zBNU6oyVHcXtoYlLKiogdClXskp4JxhiUVWU1U0ZHEFAkhEYII15x1bFtP3+2Zp0du48yyzCxFE6sEn1WqRE4YzXE8iRVYC7OIsrJxKOQCCrHhNiqRsmWcAynHNbvDrHA7jeiMxQpcS2FrHIbC3TJx+3hCKcWh69jst+w2WzaqUBIsOUqAaMqEeaRrGlntaE0qRSYiKwBurcxJ50B8PLLve7ICk8U7X7VF68SSxE01TAvtXr5PpRS6Vi53O5wWm/OYMxHF4zBxvrnhhz/6iPGs/8nu0z/U9TiM2Fc7Dh/sufv8kV+ef8UPPnjBU3uJDQ4fG7peQhcrlcYaYopYZ+g6R9c5xpTJc0O+vkJfbLl/VSlD4IvPfs3dm1vCNGJMxWoIaMJdYNM4Lpuej188Z3/Y81G75/lhz59+dM3BJ8a68DdffEGs8C+3jh/5yv3NPd98/YbP7+94tX/E2Iqd4X/4d7/gkycf8NtfveQ//vVvuXl1gqxZYqBvDJeXHeGzyn/khu2c+OPtC+yzxPanW/afHAiNIyWRIaiqKRpIkTAvtJuOpDJRBYy17D++YH+xZ3lbUb+8ZYkT/YsD//P/9rd89p/+gYNtCbnwMM88zkESfpVh03meXl/y5esbbk4PDOHM7VLBWOYFKJlOadyKHdBKQG9Uw5OrA3vAkVHRs7nYYTM47/j42SXTFLjabPnlV19Ra0CZSkwFbRW6gGN1C67Axlr/j9YHzZISKRWuD3uun3ZEG3gd3jIvAZzi+uDQTqH5bkGY37k4aZzjg48+4Bf/+o8xfSPJqbzb3VbJ4kAR0ooPW5/7asRmmFJGGy3pnqXKHr2m9xZjo9J7QiVKUynydZXC2IYhzswpS6eDJqcFrUVjknOSUL4oB2zMiZQlUOw4HKm1sOlaYp5xpl2V/5XOeXbNHkXD4/lEpPL2zSMfXbcoFrZty3Z7jdaOeRmZywllEta2qGL54PpT5vjA24cbbu5vqdVLIq13GOVRuDWYTMBxru+wTjPFGWIhp0Ctdd23a0LSaLXHkEh1wCBpkUuZZTQ4z3LDIVTOlDOdd0LNLRVdF2YSCiu70KbDOo9xlqocCge5UkuicT01dwzjW+r5EYZAHSMKMN5jWoNWAmDragHj6fwWjYOUSUNkOi9gCzxx+L1CdQb9UUvefv8JsbWWVUSahBNQRICsjJbJloHLww5QLMskSak50TiPtUZIqBkaLFY1zA3cxZElzAxTxnmHbTxGCb235vWzcQJAMxT6RmBvdc6EXJlLxs4iRMU4lLJYpUgxgtXvD5JSVrFprZA0tWq0cmsqtkzUSq50XYexFlUNEShhJmXDw1yYs0wPFJBDomtbEb1T2XUdS4jMyyK2xbU4gXdxFgprHdttg1pf6k1raZpOIIN1FU9qC1l4PksIJKWw1tNt9xTjSfNEawyxJu6XE855UoElJ6zyEmpYMjnO2NbjrCdUTUjC/tEalFHs2p6bz/6B8eHE9mLPPA4orelaj4qZOgnl1zlLyJnTOHFhNH2/EQF+CRjvuD0P3D4eiSWhvKHrO+bjxPnt6Z/gDv3DXsks3D6AVk+p1rPczyw333C6GPmoeYZGcR4TwxQJIdI4zTAuNF2L3nQMSvFYK8uuMh4HXv7dbynDRKsi490dyySFcqkK5w0XbcvGN+IKy/DbL75lHif63Y6ff/CMb17e86Of/ZCPfnLB376651dfvuLf/+p3LDFz0XaMS+bl8sDwGPjLD37Cz/6nj9BaMf32kfNvbynHBUUm1IAygLLMU0AB9ltLaBIv4yP/p//Ln6M+KbxsjszhzBICIS50bS8TBNuitgeyjczzPSFqYrund3t81+CvCldPG375N7/j7m7k8e/PPN/u+OOff8ir+IiaGzZjZFkqXisapfjr26/Qc+Y8nsm6YNWajdM0DPPEMMu0dgkJpTT7jcc7eBzObPqWx2liXiSosxjD/bhQUuHJxZ5d3/Ph5TXf3N/w0YsnvLk7cjcM4uJbg//qqiusvN9uUqtsSgBCCnzz7UK7A9cX2gqpKMKD4u0XG3T5R17rbJqWf/E//ne0z6+ZUZQqEexlVbSWanDWYeu4EkxX2FpOsps3SjJj1t38CkOXHfea3muUJsvOB2MM3ii21mDslrw8sBThLQg+vdBZKzHoaJYlscTAEheGcMJ6i6uam/tbGtuI3dIJjdZYh7MNWnUsC8R4JtWFEAdKDRynLfsGnl/uuD3d8Dg9oIzB2A5lI9N0xgO9bak0XPQbvP4AY3pSEh5KrpVCkmnIMtE2jiVOss4pQdYsRIx3KK0IKTCHtAqyNLU2fPL8Od5alqwFtqVlRC0jw4hVgveuZRJKJSNzWdB4UspY17JzrajlK6gMVWtKsSgsXdNgZ8P07UR5NQsrZtdQLzwBg2kctjEwjiynieImtCn4jUc96RmnM7c39zh/x757wcX1E2oqBPP9X+soNG4VoDpnV0HpCiBb3Wp9tyEXwT5LWrbHthrVeMY8y3Ng5X7MIYHOpCxUXt91a2AgoGSKkGOkROm8tNaEskYuFEgxMy4Bm1v2bo9zBjBULcTXeRmICbxv10kEEsAX5/U7evd3sTKKHEbbNaQwkLKshhrX401gCgtzmtDK0FqPtkrWkDGjrZUu2RqmcYI1UuH3cDqFV5rGFCCjlPAthnkQQR2aosz7lYvRmiWJgwylSVmCyy43G/rGEXMk5pm2Zoxr0UqzzDPGWLx21JwIIUt+kLaYFSQYchSwHJnzN69prSXGTAyZpjO0bQcm044zvfd4o7HWsaTINI1sNzswvG+idtsNcwic55G237DZi2Zgb7//k5P+Ao43kbulwEmzvMzsusrhE4X+pKEqxXmYefnmnsvDgVoU08byZVhYQiQlTVcUFygucmbjNbPqefMw8NMf/pyv39wwzjOtVvzw2RWN0cxL4nGceHs8Y6zn6ulTPvv113z89CnDOPC737zi04+v+cVPP6T7ZMub+cz/dvcV3Wj4Fz//CFTlMuxpnKY7WabXR/wx0SQoITFNC9pZdvsNYZ4Z5oVaDA/fDCyc2P2rlvqhIu4zIU4oIq2DkC3TMlGVwykv0E0NRkmzOocZqqGainaKJz8+8PztNV/86lt+/MGH/PjPr7A/zTzdb7loDhTjUNWii6ZmwzxVGODmN3d8/h++YXj5ACpgqsMkzbwE5iXJO6OR3KthyXz+7SvKkyd8+eaBm2F8zwRzxnHY7EhUeu8ZpxHj4PF4L8yUygqT/P3nLRiB3+eovQMBNF0jYaAhkVE0SWixtXiWB8XvXo8c4z/y5OSjH/6An//rPyV5jTJqFZW+g4JBrgWSRtWIMuLzfof0NiutEapwSgrEIuAubYQvYJQmrZYkZwyda2gdZCKnaWSICwWwRkK5lHakVGidOCRUzYQ0MqaJOQWc9kzLmVzB2lZirY1mSQuNcVitVsS9uB5iDtSk2G6fyAcbJ5oGeusoviXURC4zlYgzmp1raEzPVLaEsjBG8dGHuLDEBaUr6MwYj5Ra6K1lGUeKcjhtSCXjnUMbTSkyDislUaoSXkrOGGUZQyAjKx1ljLBRShQBI5E5BtwaUhhSomSxYoeasLahbTa4TYs1mmI0Bb3u3Y2wONo98+KII9A6qvdkZSCCTxmVEuPjkbPXMhFyHc61xF0lPaks5wUIPNx7BuXxVFT4bjfff82XMRZrLblENKsA1KjVNSbBealWus2GbtNzd3fH+XzEuIalLMwlEFMmxsrGST5MjtP6Zz3DeKZrHMZZwBIrpKopKVOWZWVzFAkBjLAxnt47mqZFoYjLTAa08fTbPT5X5mVmCQvDMEOV76Gqd8F/0vVIPpAAFevq3ilaeAapGgoFawytdZRQ8M7jG09IC9SMM5ZYgjj1UGw3e5SSvbckkhYqhbw69Fqr8EazzJGqtIhaqxR/2jqMER6J8R6zQMzSQZuipJlByeqlwrIEbJWiTCyUEd90KBxzjoRYUCpQc0JVIVx3ypJujzzd9Fz+/Ofc3N0TVgvzOAcp8rWmbxriMlNNofF2zZqSNZs1jlorewWlbLGjpXpB7d/eHcnN9784qaXSbDN3t69p7YbWdOybngu3p8dTU8Jbz4cfPeM2z8SnHY85cv/yiD1G9kVz5QwXrWF72fPp9Q/5//zy16Q0c7HZkC4WlLrik6cX/PyjS46PZ16/PfLi8kC37dg0LQ/3j8zTQrmq/OJPPubNq1t+8+W3qK1hqAsXl3v+8s9+RroZeHn/FaOf6POWjw8HuodKWQxZww8//RC927L59hVBR/71//kX/PY3L/nP/+FLYimMg+iSuqcbbt1IJhL1gmLAO8/OXjMMlRozCyOzGlGqiPjbGKxxWAdVBUJWdBvND3/6lFev3nD98wP1Ty3hIguUrmQkLsVQcHjbcLjeUBNsPvyUj//qh0yvJu6+fsv9V4+8+ocb6utHWclqSClzPleMhZflzHFYOA4Ls5LAP03FmsScE/ejkVRhLVyvIRaWLHERYsMXgCqs71wl7+8YJRtPwgDze6RADKL/2u0a9q7nSbtjjAvfnNrvdE995+Lkn/3bv6R52rGsB2KpoIwkGNaSqWRSVThlqXlGa/v7gB+lhEaZ1yqryug710KMhXep4rkoEeEVha5QSmIsM2NMVJ3wSjHME03j0XVlHHiN11bG0lmTk0YVs1Z0BqsMtcjhmEslBtlzKi2jcq0WliSH/b7vmZMi6MCcFA8T8vVosSqQ80gtJ7TWXHeXmOrka5aFVCPkxJLmNUUWcg6Uso4jCWijSLHgO0daCn23QSHY31rFhWONJlaYlyhgG61YSmIp4igoOaNXZkvMEV0TXePxKuC7Lbq0hFCYl8QwnGmbR5zt0KaTQ2QtEuXGzMQ5MI8jw3ImLpmyaKKq5CXji+HyYovq5aCf50dUmXG1Z66F4E+k3YLylmleWB5O9Mbgy8z3/aolr5oIYdPElNcEZzGSpywP6YurK7qmYRpHYpxJrWaME6lmjHI460FbllpwrhGbXizUkohaOAVVFXJabfjaU3OQVekSmVPA5srTy2co36CcQq+6Ie882nlqkVBJ71rhmCBo/RiF1lpWyuO7GHSxExtUragi++yiFEVpDAqvFPu2o/WeKSeG+Uyl4p0UL1QklwaDaGYrZY1EkBtPSVRFLVQkQNB7Cdx7NzbOqy091UwMouNpmpY6K8I0s2l7CekMC6lmnBOh7TIHnPMyjbVGBOTKsiwJhTiTvHM0rsWg4LQQXt3y7HBgd3mJ/eIr7o4PzEtEkSRZOslaOqSMNSI+bn1DztKd1lpxzrHte5z3KHPi9eOJIQRKjIzl+89Mnk+V/mDInWIZFrqLHaUq7k8T225gnGdq5/EfbzltN5zOM+evH4k3I3EIHHZb+usdfdPgvCXXzNPrC27OC892LX/xs1/w+vU9pTq++OYtrYbnh05IwK3n6vLAVzWiauDudM+H9oJYM9/e3LGjo+0N7VLhGHC7jOoDm3vLh+aAmgpTmjmdIndTYkwzu8sdf7pveHu64dkHGuMuefW7R85jIAyBH/zZC5789IrQaZTRWAwxaEJSGGfZbbfUBep0lHeCTtgC1voVFpow2qJqS8kF/7TlZ//qE/TTyrmbZdKtjbCBSiWlhKKgjcUpTSBBl+k2HVcfXPPJn12SjomXf/uWv/mfP+N3f/MVtlZAU3Ihp4zvJXW+Kgn700rswKkWxhg4L5U2revdWFDaYJ2nkBCOYV0z1uoKWuX9usd7RSmQUlrZRuLUUStGIYbAohPPr5+w6PvvdE995+LkT/7Nn1EbKCWK6kEZNEK3FPvhelhrLcFaOpCzTEFqXR09q/pfKzDqXaCjkkMrv4M1yTcfU4ISiHWS4LFa3vVepBhJMa+HWGEsiTEEllgoRbNxnlZVYkR4KCmSkyXWhTjPqFwxVgKI5jTJisV6DBG96lq8a0nVM4ZMTpHWOZzr8HqkqMiha5mWmfvlxBQGQhZEfiGtXVsm5IVcE251J1SVUVqJo0M3pLTiw9dCyii1ci5AFSQmICbmJGGCyqxK6Sovr1qkoq5ZVlbaeKIyJJ3RRm6U0/ERq1s2WwNVYEhVZZmwTCPnL16y3ByJp4UpTISaWFIiTJHWNey2DX7XocpCjGcgkEsgxIkQH6l1wZUe0obzNHJOEau+/8WJtkCVaZe2dnXoADULtyRFTK3c3t6y7Trh/ThDMJUxTGSgt4Z25Xmc4iTuq6QwWfQoQSVh6xh5bgQZr1DarIJTg3YOXwrOe7KuYj2u4JXFAjEE5lhEM1Iqjfc43wq9MgaWeVmR9JWQBBiV3x0wJWMx1JRIqpKVoXUNbdfgTcNpHjiOA6nGdQKoSOvBZRRolam6oowBLQVKXQWxZp0w5epJKHlOi3RcKEVGtCYCfpbDQqmM0oZ919M1LUvNxBUhXmqlWElerbA6fiCGiFGWUhLWKElCtw5dNfU0883f/gbzcGL36afv8fiNNWKvR6yTqax6OS1xGEtYqP1GHIJVXhwlZVRbMdrgjYEsgDyU4vsfgwmt0Til2W8dY1Bc9C0pZb68feAuzeiDpz0cME3g+GakfDsyfvlIOM8cfEN3KevsZQkM04z3Ev5Ya8KReLrp+fifPePz333DQ000q0icUvDGYLyjbRw//eQZ7tCxvfIcNp9w8eSAbguntPB4zrx9OFEuB8YmcFl3XJkNr96cqYyQK6NS3A5n1PlI0hDawmMdMJcFv9X0dLx4pviTf/sJmw/2RJ2oZRb7vWrIxUMpGJ0oygqbKy6UGshGGvScM9lorG2xpiWj0Z3no198SFAnzmlEYYQInaNgOaoMAlLWzEtmWmQto6zFe0+7adlsO1prefz2ga9//YrzecYZhbOa1jf81S9+yDAdeX2cOS/LGpEhTttCxZpVYhAWUkx471GqMk2BGGViqlDMS2CYZ4lTQSCRzslKVFcBOZYi7ryy/jqrxHEeKY+KmL7b++E7Fyfbj7bcp5FUCso4tDasjd067QBiASMrg5qjhJcrJYmdRYixRltJMhS5mrxwSYDQRRWVXGCJmaQXcg3EImJAuxJhp3kmpUTTyE78PE/MMVCqcBu0hd7KwzKrsuKlHSUnrGqIMTPnI7mKE0gbRcizsEWUom82KOVk2lMVS1JULMoYnNnT9pFnl9fiPsgJsoiStKlopItOKZCTuHGctcQ4y55OW8Is0dsxJ+YoNFmt3tk/o1iJUVjjmVMQLYhx2HW8LpA52cVTFEtIFGPQGVmr1YzRFYx41udpxPvtatUslLwwjifC7T3DN68oDxNmqOipUGIgr44oYxS1ZhIRXTI1iDU2xcQSR2oNNN7QOoulQBzRWrgQ3/dLKUkFZj1slJLVZS4STdx4h7OG8XgkDAMhBgKZqURCDuRc8LGSo0LrTMkLygLaoYzGVUPOcYWvyWSjZnlRG+0wyoBbX6QxkyiEvE4jUsQpIVeWKF1/VOCdx0h2AgDaOBovxUJVCpcS1jhJHzZqvber6KRQaGPxtsE1Hk3GqAWrFEuS4iYjXAertRwM1kgMRZZJZkxZDlqqFHStEfH6iqlXtVCzsElKkSlOygmjPajKNM8Y4HK7W4M4F3GhFUHUR53ke1nhUFkp0fI4aBuHtaKLM1XDGBm+ecubz7+mzhNOaXb3DwzjQOecEJFrWadX0hDIqisRYuI0jhJMaoQYW4vkAGljULXQec8UJUPov4Xrz//8Bb/57C2awovrSy53LUErbvPIfVvonxqijsQvJvx9ZD8ZNk3HVBUfXh14ctjgjObueOb1/QP73ZaQE84YHh5PTOOCa3uolbZxkDPDOKOV4lnf0fQ926srDJUPPr0kqImL62suLvaY3vPlzVuSPpNazaMbyCdLf9/x5MWOm+WRxyHQWsM4L4zTTCyBKUdCn3g7WrYHRWwS8wyf/uULtj/Ykp1mTjOxPFJqoNCAsVQVSXUk05GrIoVCSgFjDTkbUspkp3BVUcqCLh5ywTWaznRMeSJliEXynGoROYNVFpIhlUCIEaMtVh4voqkiXYgJFSJGC4Op7xoOh5Zt27G97DBtYUIRHzOlphU8hnCuciYtSRhdRaYtxrDakz1d09F3G5aYuLm9Y44B6ru0dLXm4skZGJLQmkuu5FSpHhKBhzESyz9yKvGsA3OWHsAqEfxR1j1UVvJijJVERbuGVsNcAmJ7rStT4B05TvP701ZCALUSYU7OVRTPJaNVINeJSMIoTe9a6dBqIZeCMQ01S/x5SiKsjTGSjCJo2QnnFDlcbDFuw3H01LwwL0dinjAmYXUllcASJ6K25Gy46A4si6TzalUwxlCAORUa03K92/HkcM2cWrriUcrj8kLViYAIHUH25kaDRRNyxmqHwkjQGpLPEmMApd4HlOUcqRSMEtdTrVLUWWPl5VfzGgFgqCpLgZUqc2rwNVPzgq/iiqjeo2mEn1IKyghpdDg/8PBwx3z/QJpHXMrYpGiLE3eFVihvaduGMC6ox4LtHF5ppilSdKWEhK6V3rZcNAecazHGsttsMPa7+dj/a74kh0YAgrLjzGvgY6H1jk3fYq3n8TSyzDOxJlJTOaeRkCMkcdiE1YTfKI3yirqxZFVxKHxWAvvT4l4RMqvDmHc+uUSnNROZOS4suaCNFK0lJaqWzt05i7WarutJuUrWTBEWjlErZl9rGuNxrqWWhDZaphKr4l9ps0YsQCgFckEZy7brWdJCypm8rri0lSiLgmTZSP4QIHWb7KQR3kgsiVIiqsgYWRBwoleRYLFKfLcazQVvHcZoUkzYmClR2CXVCD5fr5k31jmMEq6E84bWe7QSUThzILx95PGbV4znM+TMl998S+edkKy1MBvE1q1prKM2YGslRdGqLVGCB723qJyxxsi0TItW5mK/4xwCaOHUfN+v/+n//ue8fvW/cHc70V972FbcvuHQOhad2FhPuFlwN5ErPJ1xNNc9IUaudy29t+QMIRXOY+Q4P2KM4tnVFedp5ouX9/DqTpycpeCtwTiPt5qmbwnzwu6wI2Nw2wtKdORqWYYzVjVsmis+/cET8rbw669uSLc96uzY9i23D4/UGmnbluH2nnFe2F501Kx4mI5MYYM1lbop+K7h8ufXjFahSqbWRMlZ9JJaY0ylEsklk1f9k9MbitJr9hbr75cXuDOG1uzlPTonLjaOzm04xSRN9CJ6DucsvijJx8kVbQ2qrkV/KTgyy7lw8w93jG9HLi+27PaKrrH0jaWg+c23r3HAeRhIcVkTttfGB0VMsCwzfjWOzDWsJhKDbzwlS/5bzHVN/paoGqFjV4E7okg1k2ZZ6wiyoIKHprOy6Tj9I2frvIN2aaskcVVVUhXcti0CiMJIgJk2Du8c03wjuousoBp5cHWh1oQEWqyodyvjH60sBRinEzXNGB1IdSHVgtWG4iPONxgLMUFVa5dUNTEXzstCioFt41nyTK4DIQX67Q5j9wwRSqiEpEE1ONcBkZgXjLbkksg1UJiJaSHEvHaqrN2TEGgbvcHRcEZYLl4Z8qIJaWSJ0gkqbTDWQ8nkrHHekasixoBWkpqcYoGCUGRLIBZxucS0sGsbclnWl95CTGqdQsmoWyGI7lwmnDLMSQifOWS2fUv1lup7rN1idY+xFqMhLTOPd295PD5S4oJuFMZqrAVvGtAOpyvViIRxGRfsSXG5vyB6RR0GQoBwzOQlUEtLt2+4uLxgu92w32xJ9fu/Y6dKEqdaC0uFCMeMtWw2LZu+A2V5PA3kFFEWdGeF/lsKRmmUMSgsVnusMoQ4o1JhUTIe9VnWMsWA9gbfNDj/DuMu3dhUYE6SlZFSxivRVCgNiQzeyyTAOpQx5LxQa5KiuBaUa6RJyLJarFqhtBO3mbIoZyTwUmnSapuNIaIrNFbTNi2bpZGRbU74Zs2bSZmSMhjzXtmPlRWW1wqvNbWuqcZKui4le2EpNpBsLWcdS4yEZabRjsvDBZDpMGxKQyya6A1104ITa7O1jqbtVteRx1ogZQn1G2by/ZnpzS3zMIBStG1HyonzNEHONN5jnaWUKjECSuG1QntHWAXDznmUQizcyygHuJUp4s439Jueh/FEYoet//9uou/P9S/+xR/xH//6a/6X//A7Ts3C1FtMr+jblidlS/NYGO5mLjcXOAXzkuicpZN8EmLMoDQXmy32Y8fXb244jhPXF3vuQuY3X7yhbxXPnzxBW0PbbdkcDFolTuPE3dtHfvCjj3j+gx+BMzjdc7p75O7NA8OXN1z94BM++eEHHOMd9VWkPfW03nN7OvPrL7+lbRwfPrvCGL3alRussTT1gUxiTIHtB47D0+eoQ8NSNZ5K5y2t3RKKYikSBZJLlWa5JLyy9O0FRvcERlCBXJKQW0tGk1BOXuJziizRsG2fkPPAeTkSpnWCGAv4hCoZiqLVLcUUec/NhUrD7W/OfPu3rxlPicNhK9qvGpiGgbvHheEc6bymrknmgDxo1HUSL0GG8s6TJjqnSmMayIbH08D9cMMUJcurb7UA35xbDRaGmAvKGKzP6xZl1ZDlgreeUhTD+fid7qnvXpyEtAr+9BrolSgIDjojqxSjFVVVYgpUv0VXyxJHcq44Iy4CWMc9RcnhgSantI7EZTye80AOZ6wXUmYqAnBblkxOC7W+myQY6cSU7OdSSuskp9AZBXUWVoGxTPGEtTPGLxibSEmxBEspGtSBQ9sQ60hJC7WMFM7MYSZFGeEbZ6m6rkj+K5ZQOE4jpxBEFZ0jw/TIabyj6orW9r2QrlhLUzypKKZp4nJ7TYpSdTpjULrK6iRnJEuycrnbowyoHFA6yki8yrpHYahVSadoG9y6MlO2oaoG125QVoNrV2tlg1ZWyJhFhJuS0Fokbbg1BEB5B84IZyUs5BjR3qGKprUt+0OHsQ3Hh4G4PJLnTD4tjPdnNv2O3FSOy0xI3/9RttIaqyXbKedI23paL/ZfYzQ5V0JcyClijQanmXRCO0frZILY+g5tW+ZSqTlRk0E50Flhmw6TZuw0oZym1Q7nRL+V1pVDyDAlARA2zuKcwThN1oohRXQqOC+CtSUnmUiUitOaoirWKBG55YRkoFdJBJZWilKTsIpW9kgulWUR27q3DVWLILz1Hed5QhuPs5ZlmdDaoqqWyZKq7+FNlIJOlewUySuKcvRNg2qb99MIkNszlYpSltY76rywNQ2t9wzjkWZS9NHT+568a0nbjmSEa6LQGOcpKVJrROHRVREeR+Y3d+TzSM0F33VsLw4QIqoabNUs8yTU/piJSVAHzloaZ3CmwTmH9+LmCSkyLYHHccJrw8W2x3VimaYW2tbxdHvN+f78T3mr/kGuvnX8j//Xf8bvjgO3VIaaaE4zz8KWT7pLcp4wNnGxaVctiXR8mncUU6ngmsbzUWu52Hn+46+/4u//4WucbWkbzbPrJ3S20u86UIVYCiUn7m4f0cqwTDOn8x23b+7Yty8o04SxljifuHv9LarLhDJj58q2a+hsx7//28/55e++5l/+4kf0nefHH3/A8s0Nj48Tps1cXnTEElA188GfXGC7hmA0WjtQgaoiymp0teisyUkKeWM8WrVUEnNayC6BrvLix+N9i3eWzrd0zvN4vCOoCV0z3vVsXcd9HdDVUpOs8qvKOG+YiZzDAiFjTEtrNyx3R774X7/h7u8emB9nwrKQc8Y7jVWKmATBcX+ecVbRFDkztJbP4Z3zrDpHiJEQRSivlKLdekosqFJorQZliPmdRkwGBKlKMvHdaaHZOLqtRmlQRWHWzKzTMGJ0z+n83VRY37k4QVkBhuWCVgWrBTFt7AplWYV4JReMssxJ4d0T4vwSzUKjK9YAKhNzIMaKUQ2mNiIANYp5noUOaSKxDCi1Z9tfU2goRZTFWssayOiKroWUFlCe1h/Y9Y7zPPJ2Dgy5MsYDnd9wd3rLkhJ961FqQDNLdoBSRDI5QuM7QlA4HF7PRJUo6Z5c5cMoxaGqljGcbVDW4dsWz0wII3OYOI1HlhjFRozgwFXV+K5BmcKyDLR+y8XhKZ9/9S1LXsS8oA2oBq0cWkessTw5PEVVRU6RGEYBXlXRDaC0fN9V1kC6ehorxVq/3WGbTgSN2qGUWUfywphp2o7D/sB4PpNyxWlDebphUoUlJ8K0EIeZkmSkd3HRk+eRl599ye75BappabMhNS2hUURTeX37ltN5ZHO9Z7KB+N9ALnEFhmEghpmuadC6o+k6GudIOTLNEzmDtx5MZVYJ5TSmyGrPOZlkzDmQa5IJnG9ESKo9MRaS0+i2xTUOX0ANhVgjdaPEdYInqUqcI1kXioI5LIRQ0Ws8b2MXDttKcTKd9LalUfDkYkPTNtydA+OS1mc4r3AyTXrHL1pXKimlVdOVaLxFa5hjkCKr2XC5zZyWkWVaqKqgtXSOpZb3956IXc17Nb/RhmmeiEWKNpQilUzOImzPBWJeaGzD88un7JoN4zzS2A7mkVIzZuupXhqmd4epACDTGlBWUbESHkbe/OYL5tt7sQErGWH3Xcf26gpNpZbK6eGRaRhJUQIBSy7EIviArm2IOZNKAKS7pILThtY7mnWducwz5EjbNswozuPNP81N+ge8tHX87Gef8PM//px//x+/5uGLE+NZ0Wwrm+eO67bl2cUFu61HW83pFHBWmFdjKCgQqOC6sosxMRwnurbl4TjSrCRspRH9hrccp5kwBw4XW7ZNQ4iRqiesgq//89/ygx+/wOx3PN9tUb3h8f6Br3/7LfOkSHri/mHiP332BdVo3tyf+N3rE9dXVzTmlq9fP8Km8uQDT2kTxu3ZXOx5XCIpJQxwnh9An3BGYW2H1q3k0SwB1ibTKcsyz8xhxPiKUYbGOrw2WFUoJZDjgimKECPnNFLTG673e642LVrBnLOEtzqZtpYgsERFIcdIrCPHzx94+ZvXHF+eqVnezaBIc10DQwtVV0piZQVVapXUbhkOijXfasOCbEVqqe/Xs7tth3eWTYoMceZuGFcEASgyVC1QQxTLHLGNo9/21AhpDnitCQEejvco893WnN+5OCklo5QWTYSKEoe8kldkMiTZM0pZfGPIJFT1fLz/GUu85RjumOMZZTQxB8ZpZtNdYqp08FmJdTgDrx++YTg+cr2L2GaD8VuoRsK6lF5zdgopR4wxEjZohDOhtCZkwxIyMfWMofB2+AptA+UuYpXDmS21wBxmMolEYoyFccqM54kPL56wcdf46w+JaeI0vWVOZ0ouWFocllNYyHgqkdP8yN3xHqUNKWtUlRTKWPOq90iQKvtmw4dPfszt6QhqwlsZd5UaQStyFTKnSok4J6puiHnPdncBQJgDOQbQM6UGyJFaMl3XkmJZhZnSIYuVe+1O1JoIXarY3LoNb+bCeBox2472gz3WW/RpIL5+ECGmtmRTya5y6HacziOvv7pheT3glMG+aMjXhkUX9Jxoq6Uplm9f31DN93+OXVdHWc6if1qWBa0VAzCMI9M4opRM+KwxLDWhq8M7CfOjQk1FIGPIeke4QDNRWTKW4iqqsyw5sKCwTlN1psRZGLGr2+2da60oCafTSgTauVSmZSKEhW3fs9n0NLXw4WHLp08v1zUozCEyJhGUr14aoLLMsmY01lBIwjixwjgqNYFSVGUY54DGrE4yOdDmZRLhrdJoZXBrOjZGsWiNiomuKvrtRsStOYplGEMpmRhHKWyyQWVw/Z6JjLZO1ryXPUvITCZTalzTzaVgyCGAkUFNZz3p/sjn//sveXz1hhQDZYUcGCUH4Nh27Pd7mrbFb3rRjhiZyuaSmeaJh9ORFFu0VnhjZKqSIp01+O0GRSWEhVJktWVVw/bymvnhxPdfcQLtfo/RC3969YS/vf2cN7eRJVS+PL9mGhZ+8enHXDQeGyomianh6sOnhGi5uT3hrSWVynmYME5zfXngL37xA1rvqUbx5TdveHt/RlHwTcv++sAwJW5e3tPoBy72PcUonvxoh58L83jm819/yf0Y0Rc9H/z8haz9rMe0lrthwJvE1cWGj55e8+HVgY+fXdBZ+KNPnpDLxDfzLdkljLfsL54xazC9weCYxkxAUbIlA56C0YFcYJpmQhwwZsOh9jhjGFIlzYXOWUlxV5k5C6wsKsOm3fN00+NsYRgmHseJq+0WlOZ+HpiL6Lp0VRKsmcJ7B984Zr7+1Vvm44JBUZAJqFpXku81r7Ws69lK1YVcV+qrEo1VrLIHEXG6wmhRtp3nUf7eoohxYQkLADEXlgBdK/ESKWesrpKPV6pY+XNmmhfSrLDKYIym7fx3uqe+c3FSa8X5FpPEGmg04ghZKzSqYkmR1jfrwxhJqfJYQNs9hcAUXhPrmVgiMVS2m0tCmqEWbEGSf2tkzCOneMRNle28wSvp+KqqpCJuHmcNIc30pkHrImrpuuBtoVB4HAa87TnPM0OIvDhseHbdMi+RcY4clxPzvGCMI5dCTgvjkoCG7fZD7k9HUtVYNjRui7dnnJ44NIpD3zMXAU89Tje8OX3FHCdCmokh4K0RIZHSXPTXfPjkY0rWpKwYp4lxOZNX0RRqNUhnCQvMJdJYhXGGWIUHIY6LSI4BpTK1BlIWsXFjDZ1qWGokhRmtkwhqjcG8A61Z2QdKZhF4a2g0cNmh9x3NZsum63ny5JL7fsNb+5bh/gQ1YhqD7jy2FqbjQn4MIsp80kO0xBgocyZsE2Oc8Nr+l9xW/9Ve0zzhnMNZeTBjTGi9UJFU6ZALJSWsFUZHplJOsHvWk+3qoFnkHlAaKBCLIg9CRZ10JNaKqRWjFEst6JLxWaZ3SlXSUoSFUypFW0HnryRHJVtDUqnMsdDMhWgSTa9oSTx8/ZpzyUw5E/Oasp1lapKNZgly0BpjKamSk0xBtFJrKN6ax1TBoLjc7fDO8M3ta7lnc8D6Bq21iOcRp1dWGmrBa3AKhjAxlyjW4STTCmsUOWuWKdAow+GwA+8Yl4lWG5QuzLaiDMxxogyL2KMVGKOZlwmnPTvbc755zbd/82vevrkRrY/ROGuxdo1hWCbOy1HCy85nlnkmTBObtqXzHUYpXNNStOE4z4RF/t1+uyMuC5u2ETFwFTJ0UYq8CohzKsyPj1z1m3/KW/UPc3mF3bb883/1M/79v/+c33115O4oBGHQmJdveH71nNe3j/zg+VOss7x89ZJaFbv9JdM08+r2nloru/2G7W6D1oXdtsUaeOhbHoaZ50/3bHYXKO+JNXP9ZMPH11fstx1jSHz+qzvaqvjko2e8eXvP6fTIxbOWr795yf3rB8I54LuOznk+//xLUInjcMcff/qC3balhMjt7T1zmNg/b7j4xHN41qO85/E8kIl425BKJpJWIWlmZEHrBYVD6w5nC6a2WGVoveXQ7ylUtMoUFZlSomSDKk7CQ63DNxkD7A8HpqUwxopvGpo4cZoWotCBSEnSvY2ykCrp9cLx2yPzaUEXIabL4y+6QWdFFF+KJaZMCJmUeD8xqbWKxmylvXaNgaoo6/OUcmbJCz94+pQcWr66eUtImVgV4yRJ6mJzrngvf0dcMuMw4azhsGu5aFv+6Kcf8PLVHb/87PV3uqW+81tEqUrMYR1hSfIgrPkcRUY6m74Rl0BR5ATTcGZpOlzdYu0126o4DV8yLPcY64j5Aa0bcqooZ8EUwhwlRybORO9RNVLKTKmSweGc2JhLjSi9Bg+uzH/vLMZq0jxjTV27vUDb7Fiy4cuX32B1oWCY48wUFrzZ0LU7lHFsu8rm4sAYjsQ6YKwlpMyUxbq8cYYn7QZjO5Y08vrhGz77+j8zxAFnHI3reLK75GL7lNbv0NrTWPGxn5cT5yViKczhSFULxjliZh1jJ1JaqCT6/RZjFGPKaJsE3lVkf97oFkVPwQgDoBpqNjitMDrTNoAusu9bx+nv4G5ikdDYRrN5usMWTwKc91jf4LWmXBfm88x8njBKs9t0uBWVHlLBWEurPO6+ko8LOkUwCl8BP3FpGpT6/hcnFNj0PUI9LqJYXwXazjVo49FA4y3DII4QPVbsrMk7w+ISaIstQFXUqoljoaOHlFA5kmqh7zqMVixxYUkRles6AQOjKo3XUIUTot5pV2ohF0XNWhwrGFTTkJRiSZnPX77l0DTo3jOESdZ7RgvVsWSWMJOzgNhikoLrHQulFHH7lCQOuta3NH3PjMLphqvdgdv7N/S+o2laIpUQAjUVTLFkVYTbYhRVOUJZWELCOk3bWHKulAyt9bSdp7UN2hnuTveUWkjGo2oSLYs2MjFauTJlJSXnZUKFM/F0w81nX3M+PhKmmYJoGrbbLfvdgRQC82AwzuK8E5jkArZtyFSmuEgOWBZtFk1DSInj+YxB0XpP3/frOZGYloWiNWglvJjzQJlnnl5e/xPeqH+YS5kKjefqk6f81b/5BZ99fuT+4ZW4w3Lk5uEebMuhbfndmzu2RlFyxBvD9nDBftdhTOX+8cSrm0fceaYuiU2fsDXx4fUFf/KLA4cXTzjfPXB3GlG6kHTleJpQMbHbeK4PB54+vaRrPE+eXKE3Demy59f/8BXffvYNXhuSKmz2e7ANCs3DtPD/+k+/5PKzHfum4+X9mblfePai5fBpg+oaXp/OPE6VnBWbLkHNhGUWG7xSWKNpvNh9cylgLUYZVFLEZWFKC9WplZSe1m2DxeFQaOZlYMwnUBlndni7YwwBnxWt3bBv4H46o53FGUvKhWGYMakyfH6mvE3oLIpE5x3eWQGu1bI2LJVYJZS2aRzeeOZFVrqpVIzRGCuNRi0rFylL0VG8RqmK9ZoXl5coMtM3ryleE2ImxLhWOki+ltNSiKVK1zccdh3bqnn96pZPPnzB33325jvdU999raMCtXgJCdNQ1GoP5F2bllBESTKskgHQNB5jC1qNVFqMvmDfO3yzYw63HB++Yk4JpTyt1wKpSppUZ0KNnJYz4/zAvuup2tC1e4xFvOFYnGlkvVMqRjusKcQwUVKh962ksqaZq8NTlrjwMMHT7Y7Oamq1bJtrNs0zUoYhDxidmBlZlkdSzrhmQ1hOxDRRahRFdnJMYebV/Q33j3c82X7MB82GvtljVItSjlTWjtZUQp7prV0hdYWkCjkVrJVgshxmSY5VBms6KgVvOkotjPNJXhSri0HYE6MQ/0qi5oK2FxRtSHkRq6lrRAekC7JwK5Sa37NUJOdI8l10FAurUWuxqSrOK3yr5cGPmbBIQCMFjC6ovYW2xVpHn8HRyQN2NtgiuPH1CfxeX9vdDucMOQVyUZSciSGsfBiLsxajHZvtln6z53x8FOJiaUixMKpAUBWcg2opM+ve3YulOCdKlsUcWVKhM4kcE2FZCCS8MyRbSVos51UBMcM6OdNKU6ylbRqM02CUsEiM4QioKEm7OSYkEHnNyqiSEq5iIZVIWjVEIUi2jbcO33XCTKmS3ZOWQmsd+90VW+t5HI8M80RRrATaCgWBBSophJSCxmgmgwSBRunqSoLGNOy3e5q2Z1hmVC3keSKqBWEiKZQSy/L7HrFAowqHrEgv7wjjTGOgax1TnJmWQAiVsCzEZoEVrmatpdTKoe94sd2QKry5vyOWhLctnW+x2jBOE8s8stluudzv6TcdTduIC2+YwAqVtiLnkFXw9HCQVez3/dINaDDK88/+9U/43//D53zz7S3HY0RpgU0ej3f89Md/wsPLV6Sx8OJyL8Ayq9Gqst80tI3F3p8YY8L3Lf2mxVtL17fsXzwnDSfCOLEMEyUrYjXcno788OOfsGktMQQe7x85G800L2zanq9e3WGnyEeXV4SQ+Pr+gWF+oGlbxpLJKMZ5ISuFso7qNBcfep78sEFvPVFtcW3Di31PyoWYIsPpSIqAMSInIJPSSFXCMglpxhCgerGek96/O2vNKCpeSwyCMZZcF6ZJ6OXOiKi+ZEuumk3b8qLf4s+ON6cHYilQFd5qppcD5k3BLWIFzqWQdRbgWxGDSOcd1ipiEhBh12gaq1ArJK4mCcG0Rq/umhWeaoThFGMlxcTvvn3L/sef8MMPPySEwtf3j8wuMqdIWf9MrgiQsIrwvu8su72jD4Xbu8jF1RN+8uMffqdb6r9gcrJGmCshymXimiLsMcqsYrpEUVCrQykHtpLKLB1POFNrK5h5dY01HTu/JQ8vUTqwhCPnUqjaCmdBCzJ9mI+Y0eKqonVXGOsxymOUI6VEzBGlLLUaCe2KiVIU6EpWCzGNxHRinB+pOaNVw6ZpaF3PmApjfBR7p9EiLgoBgyWGmTE8MC5HShGoTWN7GvMUaw90jWO/uWC3OzDFSE6grCHkhVAWqqr41SFTUmYOM1YLVEcj+7w5zSglALpc5X+pBa0PFBzGtDjnKGFhHM9oFbAqr/jjijUGaxuGZZZiZU3P1IoV3LXyaFDvR2/OWIyVAiIVaJ1FI9HvTmt0Y+kai62KaciczURngGnGu4r6qGNpDKlCE2A/G3bRs7U91nisdf9NcE5SKXhl5blIkXmZyUV4F3VFrhcrdFGrJdzPGIcvjvw4YxFLMMZidYPH44wRFohWYAs1ZQn7K4m6huHlommLkdVKKuR1PpipRFVoqyeXBE5TWoPylsa71ZIOVWmERG1FSW8bvHbYKuCzkCLzvEjMu5KCOJVE07a0jV9dCkamKFUK4FqqdFslM6cVYjgPFFWJOa3rHxHcGrM+E7GiGyAnYOUURTkIdbFY59DOcs4zQWV839O4FrQm5yDo7LpC75D7uteO/Zzh+EgyjrrRdF3FTw7daO7uj9ItjmdUrXhrV3fRzHa3xxtHYzS6CII/RoHSGWdxxuFSpHOezju2u63cBykRqkJZCzGS16BCYwyqVFrvZIX0vb+ugYzSiacvPD/5+Sf83f/3W4bjHSlEck1id48TP/vZp/zu19+yP+y5OGzp+o4cZsgZpTXbbc88zLx++5Zvb9/SGscf/+wH6KahjAN5iXileHLYst33zHcnQsqkc+Srr9/w8QfPaL1hWhaOp4Hp9siL3Y7tk467+zPetzzOM9/e3nEfZ/CGvmk4lcqWyv6p4/pHHdvnPbPuqWojjbYrWDJ5Tjjv0ZMlpolUMoqEpojmCWGsaMT5po1bs+iyvCdTlHgHDd430uDoAapIIcZ5EESH7lBU5jTRaMNus6doOM0z5zHRY7j51Q35zcy+bTElMMcsz0YpqzsWQkiAGCZyCUxzpnpLyoKXt+bddN0IeyiJmFepgnV6naDIWvOzb1/ywydX/PSTD9ltd/z911+hjCIsa+aVkg2G1Q6P4fmu5Sc/3xPHkfuHxHD/kn/zFz/4TnfUdy9OisVq2WbFuGbJoIRa+k4/oRVKr551CiXPaAIqZWKqVIzs6GKEYqk5spSG680TSpGvUWuiYcI1GuIMc4Yuk32kuii7ttXdg8rCNTDQeMMctGR4lEJIkdP5EY2mZIU3DfvLaxSe0xSxusjNEMeVmgopVnq3pWRHMo4Qz8AGpRaMyezaDZebC0IyVNejfCEU8XPnHAlhpBIpiEMhF8Fpp1xW6qYIBb1uiEsGLYderYmYAqmIetq3LUMYyWVimBZyiSxxRNWCNQWFAN16vyNjiWlBG0cqUQ7UYldhrF6R9dKVK0HUYq0VoW4uRJ0xWipdBTTWcmgarpTl/jhgpgnTGYyt1N4RG8WsEzVDMArrFHut8aaltS3Oedmhfs+veZ7IcREruwbnHLZK4T5NI8fTWTqTlGm8W63gib4qsrIYY2isx5oGax1OudUxUlZ7POAKyVmxEZbfh+aVld6YV2dLzAlKEWhasSv/RuizZc6oeabaSjWK1FhoLEUr5ikLHFDJyxQKVju2m4bokxSrVjPHBeOsfM11vaOUEJuNUtSQKHMgmkrsDEPJdF3HUmZiiiKxXROJU4oYJI+k1MIUg1BtVyGtQdG3HX3fk5Ug7DXiNsCp92JvCfmQaHinHHZRlJtHXr98w/T4IN0gak2BDYyLODtiyCzLyDwHNm3H5X5HzQkjTwsgmUNKaayVbCLlHBgLymCtI9TK2/NA6z2Nt2AdJS2EKKJo2wgjw3kPVeI2vv/XDoUUrr5N/Pinf8TPfvgt+mxoXCP3CoYXXcPV1rP98z/ly89+y7REnlwnOm/ETaLEzvr67oZXb+8YxonGWdq+47DdoFJimmfQlsvtFrfpuXcd397cYoFusyfGwjTOPA4jymqu9nuoiuNpZgpi7DiOCw/zSKJikmJhodm3bF5YfvDHW9pPLUOrmarHVIMmsYQzVctasuka+rjheF5IIaA14qg0Thx6ymNrA0GCXKOS59cowTjUUkWWrZCCJS9ApNTCnEVX0lnBFdaY8VGJ1qndSmKmy7z+z2+4/fsH/FzZeU/nzcrCEhbPuzVsBUqI5IKkmaciLk+lsUZAoe/0Y+LaAVZzd5U6HaUh5szNw5mNbbje7Xl+2HD32LPUSmwLd2fh/XxwdUFjGgqZg2/RFWYy8zJz8+prfP1H5pysVA2UdlBFKawk1Us6u6JXPPUiTgIWSp5ROogKc71KkVj4qgzjMpBTZq8vqRgZC1bFwV+y7S4hz7gaoRbCPBC0I0VD5z1OQ+saURgrYZ0swTHZhpAzxymQcsO229LYLVppWr/hPAemZcbpRGPB68oSB2LKdP4aoyyJim8cmQ5tPLUmvMlsmx27bs8UA1VDVZGQReEMsMQZ50RwV4pwLkqJbFtHYw25JKyzpCR4fozwMhQWRcUooe61zjOnmZAmpuVMylEKEBTGyA1mTIOxHSFHoZFXcUbM44L2BdsK4l4pS6kNtTpqLfIz1hqVCzXIuqcaBdVRshSbfeu52rVgzhAqikzYabLXxFVjoapCa4fxYgd3OJnKaC3BhN/zy1lJx4bfA7lKyWsYnLjK5mXhzc1bNrsdTbvB+hZsS+NEBOesw64/M6VlesXa76h1OladW1X5rGRkIReXIiLqmJLsfKvYClNK5JxW9Pv6e5ck4YG6Ul2gNJbZZIZayRRcEdGtah2m8xQrgk7jnOyRbQG1FghlVVUrWauoqlApo3Mlq8K0TFgqh75h3+9QSrOktDpkxB1QV61aKJE5JQoVUxVWW5pNR2N7CpUlygTSGrc6ZxJUgQZSFawZRGaayW8H5tf3TMejCGsL5BjXQxh0rnTGo2zmNE+EENn4BmecjLLXLJwpZoZlEfR3I6h+ZbSAD60h1cowzZgQudhscV7+fIgLOQes8SvJucjnV5GcsO/95YEOsCid+fCjT/jTX/yEfWxpTYOqitY2HA4NmUi/bzg/ew7LwDIvAq9UhaQsD+fE67s7jtPEFAKhVD7/9jU//fRDdo3Y3B8eT5xSxbQTX72+4auvvuEnHz7j0HZ4BVpVHoaFx3HkyWGH1Zq748jDMFMUDLOkdisl3rQcI9a3bD50+E870qVjrppUPDUXap3IjFRVqVi0lrVjzplh1JQVoFmqkkm0hrIi3DFa6MpEcs4YNN40ONOijQEVaFSlKE+sco/J6lORi2RixZwJcaHrt1xvL1hu73n11zdMdwtYw1LlPmct8uv6vUnFoaVAqfm9u7WWgtISwFvhvRVfv3P4rE5ACfZdA35XVJE2ik1n6bYNNb/gNAfabc+vfvuS3aanWWFsQ0icjwv6xvAQE+dQGKaZx7vvtub87pqTKrbHikGrZp0O1PU/WKOqZg4DOQXatkUjL8eUE7VmrJEkVFUTisQSZqb5SGsOzCVQSxbxZjW02rNGHpNTocQZtGEJM1ZbLIrOdux9TxY1B944yJoUM2OcxK2iW5TyVNbUWJ8QMVJmKRldDZ3rqaoyMWNUJZSBVIX2qU0mV/X+kFHS23JaHhjSwrJE0jrGVcoS0ozWIoSMMRBSwDv7Pm+jFolXn2Mkk8SPvhIF5es7SWOOhSVNTNMjMYo9UStFYxt63+Bsg1ItqShinmmsI2dZqZ1PM0sp9PsW5zPVGbTyWOMp1WBWeFs8zizfPtBdbeSAX/H41Rq0t3RXWw4vAmaqLDkyqSDgJFUxquKNY287dqqjLR02W+mIYdUBfL8vY81KRVQ416xi2IxZs6cKBrsEMoZme0Hf72ibFu8c3nucsVj9jpos41fR1K76n3cnAe/C8PQKGoxrEJ2hpMyyzEzLArWSbCDFSHk3VSmZlCM5J0lRXqMe6riO2dfRLTGRS0G1Dr3vYdNgGk/RmlQSVYsNveaC5HvzvsDIVVFyIKtEzGIvNG1DQuF8zwZFnQfGZRTOim9WFH8m5EhIWSZPytI3G3y3pWIIKYH0He+DQqFIXZQLtmpc0qjTTLg5Em4fKdOCtZa220DKTMNALAWrRSiYlSJXjVYLqciKyfkGlS0xJh5jJKeAcsIwMt6vwWiVQkZZS6iVOUZ26+coK0xFDBG1rlqNUsQimADrHE3X/5Pco3/YqwPkZ0GNHPZbPv3kOeXtzHLK1Ax90xOmmWoT1ZywppKVrDHvwoljmAiL4eE+MsaFVJHiT2vuT2e+fH3DTz56ylIUN49n8uNEu91yezrz6u6Rw7ajNQ697aglMyyJ2+PIpu3o1uklVC73O+aUGdPClCNaK7QxXHzc03zUMG0sSTtyaVA4YgrEMpP1TAWsNrSNxnWeQ72glso4n0gpAoWMrLBUjuQsRNZCWnWCCaMcjW9omw5tK4VJ3K+mBTzkWc5i8tqQRCqZqAopt+yaDW5OTDcTOUFWwlFyWvEu+uGdE0epKmtihListJz/aAnxWzc/YvNfYWyiP1xZREax+nEBccm2jaVvHTvvyekCfRzAWjZtx6ZpqTEzxkCKieFUmCjcpYk5Sk5XiP/IELZaxTaVSwRlKRlKmlFoNC2lFMb5EW96UQjrjLVQknmfECqu6Qo5scwnjIK+38mHVmfZJVdNLIqYB0Ic0Dmy0Za935BKxSpDzqCqojMtupEk1LYW8Z+HgZvjwn6jKUUzjEepSNNMqh6lisB/ikbpBme3OHpcPXGOZ5Y8SGpklD1byoqcCtpaqFshV+ZMXGaoEMKM956cA7VEqL9PkzRovJU8k7B21DllwXarJNV1FYtmrsKXKOvhSyqkqPC6A12xuqGxW7z1Yn+uSGdZVthVSnjtmIfMcTgTlki/aWl6UNXhrGTf1GpQVVOOgfnXbzEfKvxFwc5gthm16Sho7EVH//EePynMEhmGE5SKV5Ih0ZuWg+7ZKU+TLbr+Xi+h39/K398rp4z1gm1+x5NRWqONxruOnevZoHG+pWk7nHFYLWK3d2s1o0WMrNeX5zsugVkdVjL1VSi9RhekRIiGUgq+a0kxYq1Z9WCZqJXEPNgiwV0lkpKh5EQpMiXLa1BeyhlbqoSQJUFp5yVT5oTatNhDT91ksArTeOIq0FVKrRAz4SSknFlqIpOF+Gw0xhmWXERr41p8CsxLXQNAjVB01zRUQXgbmqahbTdUI7yLqjWmGiChJcoYXdd111Jos6KLinxM5McZNUWMUpKp4wQYqRrHUgslZWrJxJyIKUn3pw2pyBLWe8c0Jd6eHrC68uT5M2zj5SBdIiHE3wsXlUIZQ994Nn2H88274xyjjUwPlcYZaUq6zXalcH6/r1o9oNcieCLMA0pD9ZrjMpJCZloi8zSw3TfsGsPweE8tFeU9d/HEzenE9ADnYyEVRUXj1zdUTIXffv2S3aajaMucheHjtx0vnl9wvL9jGGc2H3d0246bN7coBftNJyRUJSLT1lsuNj3nacFrSygJbeDykx0v/uwa97wnWE3IilI1WlmUgZQdJUlRrl0jDYUutG3LZrMlxJl5mikZTJEJoa1QqyGFhVBmYo0iSXBGNExOk9REKjMGjVF+xQoIZR0MtSj5dcooPMMy45Sia+HiSc/8GN8jAwSoptZi5N09Kb8uVUTy77SIZs2eq+sqx2pNWZlJ6t39+u4YX7EECnBW0zmLNZaQCrfngdtx4uE0cj8J/2TjvWw3rCKUxPG0cJwnYSAhxc53uf4LCLFZ9Cb5TKkNJWVSnrBKMLgxzZzPtzy/PrCECWUyzoE2rG4R9fsDs0jc2WZzIYm9ZSKXgZAHnPakFFnSwhJmaspgGrYhoGwQi5a1UBZKXrC6Y+M2km6aZrxJWB1orcNsFafzkZB6nBG7XyqSuOiUozU7NDuOwwMlGqzz1PhAKYNMr7WnVFnaOLtl2/Q07sB2u0e7RdY0w4mqC3OeaJ2ic0IAtbqhrj/eOQzieoiFuERQYvGy2kMVERlV4uS9b9l3LbFUYrMD/Erl7MgR6ZJ1QRtI6f9H3p/0aHKuaZrY9Y42fIO7h8fEIHkOeYbMrFRWdQ0tQVADAnqt36pdo1daaNGl1qbRXUNWZWWeyjOQQcbk0zeY2Ttr8Zg7c1dMIVGFwzQgwCkY4fG52WvPcN/XrVHNk2pArW4c0yzzsZDnM0u/sNlG1DNNZwas7VZ7sWHQPepToiwn7BW094l6kXGvNfrS03xH20V0D671XB0d9S5C0TRl8MoxNEuHxlWNamvl3X6o1H/KV21yeOYKLRW0lQfWGFnVdL0UIc55JOpSrf8saxyjRXzGqvWxZn0UlaLzHfYxb0atWTO1EGsjt0hDViDKqaf00JSEN2CUopW2/nvpkqo2EllAo1k5YEPM+CaCzlxE75Xryl+5nahTRO0Hhud7MFLEqHU/3lpFGfk9chJyrLHmSWjdal4R+5beG/phIJeZ43khxzOdbTjrsdrilMIqje83FKUlXXgtImqt9E4LAyZXTFH4aumCossamxpVe9R2i2qVeZ7JKbIsC6ZUvHMUrTmXSEiRWCshJaBhreU4TfSHA29eviKkxBQjm76jHzY0JTC90/FEToWu69DWUWtl6DqwimrAdl5cWJ1Hm7U40pqu67C+w3XSFPzUr4/v3qHQ5BRI8y1vf/NXvPvuA3OtlMEw5UCqkX6nmEvk0u64uPScTwtvj+/5fv7Ew6EQjo4QKinJWqymLPRRFB8eDvzu7Tt+/uUb/KbnPghb6rNh4JdfPOfu5kDfK7S1TCFztenZDnbt/kXjcZhn0seP3B4n+s5TqHQXhp//i+dc/HyH2nQkJICw1oQzjqHv8X5PjIHUsoAAc6LUilMDY9cT+4ElTCxpQamE6SrWanQ1qNIoQSCHfTcydBusc2QKqQotJaeMbRPWykq2FnFMOu1WN08jtcYUF0wp9Nc9v/hXrwgPC9OdTO1y40k3xfo+sE4y11oWwWspjbqe1UrL+kejMMqQW5X4F7WucRDMBWtBYZRi9I79ZsBby6fbA3/5+7fcx0RMlSVFOqfxVvHsYkdJiVOM5GrZUDkvgRAybfPjnoe/x+SkUhFgU20LKTZM85ILU48s8YGYTmjdaLWgaaQQUCoTcxEMbo20kqBprBklgbh+orRIo9I7QyqBVBYJJzJK8nuUoHaJB6IJ2DIQ8pmbU6R3F1xvP6dzPTrAEg/UthBjpOs0m7EQcqRpy3E+YtAMtkergZwttSSWUjlPsNlcsfU9mntKnXhYDtQaqDScGehsJztIJQc8ysktXyvbfhDAzmMisZacj1ozKWY0hpwzS0poIy+dtFJua8u0ltENdv0zKppQGrUZKaZyxbnGkhdCXnBeo5WnNk0toJ1HGzGbWmMgQT0XlhgpzNhXjR0bsCO1l+XLdrfl4uKSPBX65DEPlXKc0cHQFYu76KCPVAuDdWzHHbYsqPsqgqxHa7uSm9asVXutTczuP/HLDCPNWqzxq3bEYLVZBWYr8/Vxv7u2Ct45nJNUXbu6VkoutNpwxsoBoA299TgrSdKsos6SISETm5giZk0K1UqKoJJkPN2KUCP5O/THsir3VZNfX2kkKVdpspepiTjfMjmv9uJaCR8n1Fxx+4H9tqOOlkOKLHkRYGCr1CarSuecRKE36Y1qBWU1MSHi7X5PK5nzfECXwGAHtNZs+w5nB7T1LDHI+VIzJUk4ZqoWpR01VHb0tPsJkkJ3A01p0Jp+M4pF+d5wvL9Hx/ikf1pi5LDM5JzQRjM6S+stqcHxdOZ4eEB99gpjFb7zNG1wvmeJgfN5IiwzznkqcDydaK1ydXWJ2jq4HGi9I1dFd7lH03DW0UqBoFlaweSI+zuau5/q9T/9P/9nFI0yL+TzgeV4T8kF50S30zXNcV7Yv7lG5chZz+xe9RzygZtwz8O0sEyWaUocTzOdczijSFF0cqVUQo785rt3skrTCm01H27uUaWw7z2TM/ztN9/xq19s2F1dcDoeOB8DSzwDisN54f404XvLn339OVobfvfuPf2fdzz71R619QSgqIr1Tqy8OXNeZqyJ9K6hSiETxLFfNBiNc5rLyx3GND7d3xDyiZoqTRmc6uiUSBWs79lu9vRjh7KKQkLToWoh5kTMGW8RTpJVNApKeTrXUbXGuEbn1kxvPfKzf/Uz7r8/8fbffmI+JlppGLNOUJCzWBv1tGrPeXXXNSitgQdtgdIoUcCKj5qwslJltRaXrlINqxX7zcD1xZZxcFhvOIXAlLJEpBjFzflIrYXrZztKahxCRF9s+frLL/gP//u/5eF85vnlP3BxUmsG1cSamxI5weX4movxkhjuOZ0/cjH0pHKHdR2qiqU3lkyqkdKaJJnqLd5tCQVimvCmx1u3TjQKtIzzDtssKQols2kPdmDfdZzbmXnN04l1pi8nqq4837yhtcoSzsQS8LYTSxZRKs+yyL7fbDDNMYdMzPc4Y1FO40bPXDKmOHbDGwZzphUH8Z6qEi93lzzbX3GORx7mM3NaCLkyhQWrDYnGs97itSI1y1J/EBkJ/bUSsxRdS8j4zkoRVld9izL0fmAcnnG/yIsot4WGItdGrJBZ0KZgtENrQeSX0khVSL1FVbreYYtGvZ/ZVIsJlXp7IqRb+tyhn1dU5xi3A1efXRHfz2Sj2fYjlIZdDP6+UpyjdQOpk4q7U5baKZROqCJMDNWkplaAUY+ofI225u9xrP1xXv1mT9/3srpAxviPegPRSOi/M2bVtCq0R2uMFAhqHbtajULJC7CKw8QAVmmUNesqBli1KLUU0hLQWoTX1Io1lmwtrfAEDQMRSKuVqNoqsgISeS2dEzmv05raLMkYbDZkU9DaUlpdCZTQbgL6lNEby9hVmlEsKCrraspoocJWWXBJVpaka6cGnevo/MhOg6ZS04QiiYNu3FGV53Q+M80zDYRrYTu863DG4pvh/HDP/bvvyNOMMobNdkc/9EI7doZuu+P1uOdqtyctM6fTkdvDA3OMpFrJDUypbMYR23VMKZGrZxh6lmWRPBwUqVSaUhgn7iLf9xjnmXJmcZX+zY7rX37F5uUlxjm0thhj2cc3sDqEVG3kJVJLxTmLKj99DdbHDzMFSS0/vvuACgtGKbqh4rrC6bzw/e2Zm3vNdpdQKvDy+hmHIfNwmJmWSlmk2ek7J04oqzHjyHmZKEZxnhY+tQX97Xs6K8DAqhV2mtkMFmU8f/XN93zz6YG/+PWv+e3b9/z2/QfmnOmsY+sl3+fXX75h21ve393w5Z/s6P/VSHnhOddKyEHiHywo01FqZQlHtJ6xakYbTdMzrXr53lsBnBlrubQX+M5zd3fD6XxHq5mXG8Pz1y+5NtCcozUjwXoliMjaGDq/xbpCikkmkWVFaSiDKuvkR4trU1WPdlBcT/figj/7H/8J4fRveffXd6RFsm4e7ZmtiQPVGi0RGlkytMQ9pyk1Eas4VXXUGAwy5FM87l5kjSvn0fVm5Nk4YppEY1xc7Nl3HceYaKViUISYOOrIX/3he2LOqM7zYjuy2e9pxnCaC/fTP7AgdikNTVt32JVnm2t22xc05Si5cXH5T9ikW6b8EXTFsaXrHTenJOrhXBm7PbvxktwKMT7gdJEPQjtaWWitkHOkKTlMlfJsxmc4u2FCw7xgdWRpC7N2eNuhOPJwPnBY7jDK03VbNsM1hUxMFWVgNJ7O9Si34+EQmHIgs4BpNNOD0hQzo03H2F3xMM/U2NN3X3Kxfc7GRz6/eMO2v+BUFppu5DZRqmSMuM5ijWIKhaVqms5kGiFnlhhJKWBapWXF2G+l4GgLuS7kFig5Y3F4d4nRGw4hssSIcPo0zltKjZJnQqG0AtXQdMU4TU2iCk+6UrVmdAN5memiZmcGhtTR/T6Q7r9HvXjAvt6yGzTl2ZbDTSSGSPCOfd/TO4tNoCZNrzfMVNK04GLDlIFiNNRGWUeHj8LNWmSdo60kJv/Ur/1uL2wALQWJTCT0KkJT6x53Tc3WskozSuOtXUPw5MWuUGht0MaScmIlG1JSfgqx08iYVa/gspIzcV5IviPHIHTf1b7d2lqgqCo+wFUY10pZLbnqh4IYeBTBaWMwQFIK1TSlFTpjn8TNrTbS3Qx5Znsxsr3aMdnCIU7MOaCNXgV3Zv35AiQ21sjXXETwd7V/DvM9XbN0dDSjOSwRKmyHS3w3sO1Hdq7Hxsbh/Se+/5vf8u4P3zJNJ168eA5K8fbtW4bNlhcvnvPy+TXe92jn2HUvIS5sdjsycJoDLhtOc6DzDt2J2NcbzX6/ZbfbYoxDLQFjJCZgbhE/dnTPd2A0w8WOq97jdh7rFdaPYDQNLVOnVkQQY8Vx551nvBgIKbGkQPtHsOb81//z/wvtB/7iVz/DpcrpnIi50h4SZbWvfro78OGvv8EpoXc/e7mlvSqc5kCeLB4HSqIgOmdlmjad0avgwVqh704hMNqB3TiQdOUUZv797w64rJ9e+Kk2YgNrHTvr+Oz6iue7PZ31HM9n/o/ffEf3uuPzP7+mPINZRw5LJqSINRpnFVoZWpWVSqHRVEdnLNthwKqOkJUQYqtMjjGWYdzidEenBw7HI4uO2L0hO71OWg373YDVmhQD5+VIKglnHb2VzJlaZepuqmj5So0sKRLKhDMLnZvp/MxurHRvRr78v33Ow2EhfzdDaLSV7GqUwntx2SxF4Yx7CvespUjelSpECqgqAn/VfmCaIUaYQWte73f8yeevudoMnM+Rvz5+olEFlaDFyHL9bMd5XgipMC2Boe8orfG3f/s78ttvmPOEU405/Lhi/UcXJ1ZrSi4M/SVGjygMc44418BZ7qYDlg0Xw5bj6fdUe2RBU3VAkUglcFrEUFh1odQjVje6bs95KhSht2GdgMdKltj5WiPH6cASz+z0yM8unqOrISnFlDKbYSDqxF38wOBHhh10sfL24y2leVpVHJbA5a4jhXvmZSEVMNpilaKWmZADxnbs+j0hFpoqVG14mANaZ0bXY7DMy5Gb40c+HT4Cjc9e/CmHwxmrLcYUDvOZUpQQA1Ull0xKMyUXnl99xt1hZp5WRHFfGbsN02KocWLTb7jeX5NLY8mZmGQvroxazaWG1jQ5Jxry0lK6yo/2mPrqMMqh69rNoxm0Y+M6LI56aNTjRP1+5vqrF+w2L/luiJymifP5TO8Mo/bYbFDHRj1F2ulMnRPNjWA6dNXi1HqSNa1/90Oxve4pf9qXX4PhvF3Fdo/FhlLiFDOSrcTaqTjnV0Gs/DxrzNNUBSXPlnWit9LAmuongkGR7st90BCoVcpY62hF/uq1BdOgNpoxorCvYJRb8zN+0LcpY2TKwpPWjfZogUThtMGi5BCrRVy7pVDOEypXPBVXMl3X6DrPSVcSTSYUuWCtXe28haIVTWQzAj3rPJcXr/CxUIvhYrjgaj+IOC8DQe7RePOWuw83PNzesZxnTE7oCrefbnj58iWXux23hyPzPLNMM69fv2K3v6CzVlZiRuOco+97ltpoTPheGpFpXkhF0pWVkSlJVQqz7Xn12SXd5zu63YbX9gV15VO0VuUDpUkxkhuDNvgVGyCMJ0UumXOeUWUtUr2W//cnfjkqp4dbPr51XPQbtn7EjcLzmWPi4/09g650WjH2HVe7Le6F54P5SFs6RjuK4ywkjDXMMTMtD1il6KwFGs/2G2LMvLjc8idffMbLyy2Vyu185g/v7rgYtnz8+InBdzzc3HA5Dnzx/Ipt59lvB0LO/O13H/n9tzfkK8XP/4miPofZwjEktHY4q6m5kXLF6oQxCqiEECWttzZSBqsyyhoRq2tHYxVho9lstuw3e6bzQjnd8+lwy1mB8T2d73FaBPHeeZwfUNWS8wKsOhXlgA5dHC1VllREcKzWwD4Nc5mp04HOwvM/e8GfHif++v/9HcuHyNZYOiWC3i9fv+R3370nu4JpjVI1MUsulVKgiujUbO8YdA+tEmJcdXKwcZY3l3t++eYNKWa++f4WaPSdQdvGz14/47+7vKS3nqvtlvtp5jhlPt7c0Izm0zTx8f6G2AraZXRVKwftv3z96OLE2ZHOeVpzEmWeI01lam5Mc2ReJq4uX7KkjNKf4V3kOH9DSDcUIjEtKLNbw7HAWUvnezQGrQpNXa3jqAWjK9okalnIOaJaQytLMz3NXLJRik/zR5LWDL5hvYcGIRyIObPEezJB4o9aZimROY+0vELjWl7TSQ1d17MdtvTdliUE5jiT04zSmsxMWibuleXWWc7dzPcfv2HOM5+/+IoSC/N5Yhg3xBIpKyZeOuhCqROUxqtnX3B/PDGnA6nNaNOouawfv2McrrjcXLAZBm6nIzGfWdKCToq+98QSxcaYFwlgVL3wS6p8kwsymRq0k2DGc8Epg+8cyhuyU1S7Yr4L2NlS3wnLYesHXNMECqUIf6UVRVkExd4pIw6p2oQK/Fh4SDgE7VHGva51ylNc90/7csZglcJZD6in0ak1Vtw4SiYJVmtaa3jnJYARLdh0JfZFvWpS9GMA11pC1Foep7OyFiziyKpFdE0NyClRcqIvVTqlBlYpstJo3cTeXIp8b7SiFYEDslKVHpVBSrXVqgvQ1kxiweEnKqkUaozkJYDWwmNZMnoujLYxbjrKoEmjg7FHe0eISVaNJULLMuK3jo1z+Jw53bwjxczQHVGt57u3HyjTwqAMwzrFaTlhW8NoGPuO7TCQUqKFhd57tt5xnmZuP7zH0vDW4XZbjPfcfvrIw+GA1orOObbjwH6zYeg9IUBYu77TPHM5DgyfP+Py+ZbNiy3KitNGplBFaDZKCdsHTWmiOUs50/uBfnSc54lzXChUlJEVnGoNvYaq/dSvX372mr/55luohd04crW/YLPpqbUwzRMvrnYcTydeXBz4w/c3hDJzX+4oGXz2mGJIOVHkwxXNoFvXETEKnCw2OuekAM+FtGTc2PPq80s+zTP/6Te/Y3SKr798RZgjc23cnk/cnSC8SxzmhalE+ueWL/77AfeqYgdPsgOX4wWtKmquInKvwimJKZNSJNUmGVehgDc0p2gpCY5eV5TXOO9WobuYBnoG0jKTEgQMJWQmfcZa+XPJJLTiHHSdXXlgBWeEfZVyJKdKyEVorqqhlWhHUo4s7YH9xrDZ9XzxFy9YPkUe/uOZN90ll/3It+8fUNrTdT33xweck4rEarEY1xWDr9cdUC6rZnG911/sR37x4prPn19Tc2OZMyHL1GW38fzq65foDCgt+VitMD2c+O7myLf3t9zNM+ecUIPictvTKcvpZlqBj//l68evdRZJvLWmB2UBEd1RK0Y3Nv2A1YpzXdDaElqPNm/oChyX77ANBmPorSKqjMJCcyxxIc4njLuiICTWsJxpTQBmoLB6pO9fc7n7kqwUJs+MWA5xIi4aI58QNCtZB85SEXBUbZWQghxqTVFUoukFaxVejxijiLVwPN+vcdcPGJMJS6ZkcSR0pgcgpkbXXzNoz278jCmceVjuMYPlfP5EqzOjH1DNUXLFYRm3e3JpHMMRraWbVFghfLYojgTtyDlwmO64O94ypyOxFAwbfN1JZkPRlFJxtpeU21pQSmii3qlVV1PRs8IdCn3z7LY7+qGjtMqcE602dGk4b5jnBd3AaYPpBnojmSBqJVnU2ggporVmHLfMKQKyPnqiCT46c1ZAz5NC9h/BGNuu2TVKGzrf0TmL0UqmGM49fT7WWhG8aoPVGqU0rerVWr+q5hGmWK4V8/j51rayhPg74LXVcaMFrR5LpoRAygmr16IHmepoxPKeG0KkbFJIGvWYdSM2Q61XNk2VfbVuDW1WAV1bxXXrBq+shY7oYhSqgo6ga4FjpuubCKk3UPoNURVwPVY1sVeGSL49cloeyPGAcxoVKofvb7j55h0lFvabgTr0aK1JqUrYpDLsRod3npISKIilyESwl0IunSdOd3fyGew2KKMl3XVZSOXI5WZD7zuM1gx9j8Fzahk9WtybC/pdT7MGXMfjTKm2ug6wpHtWta2rLk0GtsNIaonj4USqa9S8ku+xNrK6a61RQvhvcIf+172GbsNnL18KPFBEaIQqjev5OOMVGDRfv/6MP/nFV3zSt/xvH/8j5ZPhdDNjKDijhI/VClo3dmNPZw0ozxQyN/cnUIa+d1xsB2IqvP9wz5vLkX/2z7/m5uEjJiROYeLjw5F3tyfmkNagWjmbdq96Xv3TnrybuLp8jekHqrVYs2ZLPZJCtKEZKFECTwuGznvGTsSqFaAZeS5WpEQpSc7mookRylIZQsb5kdEbCqxmkbo+h4VlCRyPC76TqIi+M3Su4OxGeGINYiqkGlE60kKioMlF0+tCmo+c2sxFt+WzP3tON1t2x46NceSS+Mu//cBm4yWgrxSMWd8d/ABaE4SBJlOorVGqNASlKm5PM3enb+X9WSvboedyN+K9YzkHeuvou57+YkSVxovLLaE23h/uqCuif7fxvLnashwLiykruPS/fP14tw6LwNaQfJqqyvoyWjubzqEodM5iTUfMgSV5dsOfo9UrrDrTu0xRgVIjqAB0K1mvQSvkGkQpnMQ6uxmeY/QGo0fAEXLk5vwJwkc+H+UAmkLgoS4AaNVh7UhTYI2SipsAiPMn1QwlolRBGSdshjpRW2EJGWUc1lU6p4X52hSpZKxSwlxojl0n044wzxxOn9h3nlE5THdB715I/keTHWvKAs06LrfiZLJN/tyt0Vql5ECpBb8d6IcdmQ5rNQN7Ordgjac3DouhKkuxrK6nilJZXlilse0HrLG0UsRxMyUuui3XF1eg4RwWtJHPuDeGse/lxZoqzgoBszTJKRGdphAOjRaXVkqSNL2S6YTTQnvicqj1PpCvS/+jgLANXb/SXc06BZS1jdYGs+py9CqsbKXKTpp1tbL+/XpeCty3rtAjpShrNSBdeyNXQdTnWslAqpV5WdDOkM5n+s1Itfap4DFNSSFeG6quWPbVEq+sxTx2TY94JcX6fdQScb8WMErLBMMUZA+dC9Z2mDV2QfQlIsamQUsac8oQKtWlH6YxtVJTJM4TKZ5hE7l6vWUct4SbzEOc2Q9bggoYpckri6Q1IVTWKs6n3jt050ErliVgdMRpLWGFynB3+8DN7R0vri/ZjhvGYXxa41jfo73HWIXtLZttx3Z0dBcjZrOGLYIU1k2R82MxKC/KphSlNZaYcUaSjWPOnMOZZtTK9xFt0SNVs7QqsuGfvj6cm+OE9Z4cEzfTiT/c3OL8ICuZFLgcBowzvHt/y5//8z/h04ffUEPDJCl+YxaLvDPQaclJojVyzWuv09bUX8Xge8a+Z3fpeGUM+6s9tS98/fMX/OG3N3y8P/P77284p4zWlqYEHrZ73vH8Vz3d55VuMzLs92SU6IJylNy3oqCZ9VlwDF5DKxRVMLbJRBIht+ZcUFV4PlU1YhL8fqmWlBW6OiyKWCpLiGAVlCzTzCoroKaUCOUdqFaY5oV5iXhTGTrk3F3PfCG4Nqx1bPsNo+1pNbOEgFEW98xw8dUG843m4YNMiuacuHA7BueYcl71gI82BvlcaXLWNCUwIb0ivo/zwhQWvDF8fr3nixfPeH6xYXRmbWw1m2GQYUSV6Iurq0v8ZkNslfHTLZFEPxrspLm/icQZyo+sOn50cZLziUaB6rB2RDJbPAaD1g6tLKEVBt9jjGIJkU2/Z+gvKM1RyolYZ1D3qDbTWqTUIyHNpNpo1VNKeAoQ9PQYPWBNT6ORy0RMM1P6RKuJpTYuxysoJ05ZREW0SCka48BrmGukkRm84ONrCpQW0Q3SioRXqqJ1o7aGU1taS+RcsUp252RF5zxDN2LMDu88KSsMDrcZKK3g7UjfMqlUYmqgK7kllrQQYiCkGaWquJEolCZJwykHrHZ0bsTakSlBbnatXD1GSyS1tpsVZ57RRuBtel0a1lboTI8qgK4wBYaq2Y/SKcYYMU3T257WCoM2dNqvB7qi3/S4wbNMM3GF6LCCfKwTLUSMGZyMLBvQHltphdBv19TRUoSAqNVP/zQeOknl9StQza06E70WZ42GRmOaFHSPhUBb12FPQp21NnhMxy2liIW4id5DgGkCTUutUpr8fYwBOyviPBGmieLsalO2Io2olZV9LXECSoGyPELdWqtPGhbWL0kpEec+/kurNaUZVJFRbs15XVUZnF2x7+tqkdbQWqObxMS3UiUnhRW/nRpkhUaCCZWuGC8daMmFwXfiUFLtafBWm8I7R62FrnMrJ8YJElwplNF4X1HaUhocTmdiiixT5Gp3iTKWc0iEUtBWYS10lwPu2Qj7gb7v0EYRa0Ih4uFSioDsWkGpgqditCIbzVwa3o1shy3HcGbJUSyt2vCo6NFa1nm1SZPVyFj/o4/ZP9qrXDbYaj797sD0TnhIr5+/5GI74AbL4BylKWIr/O27P3CTjkx3GRss49ARQiGXSKgFZxyjH0ghUnKV1YoEs0uWURWonlEOSiY+nKFoLIpXX33GN//pA8ZadJEcHJTC9Jr+hcK9ztS+sb24AueJWbPUQEOaVqrBKI8163paI/AyVsdlzGvRLNMWHuGIWf57aYrSJM6lWwt+o7WYPWKC0iTyoYEx0HUO5w3WNnTNhKzWTLZCiQlvHL3TGNuB8dK8OinOrHHU1phDIMYCVuNeD+hYWe6OxCrT1YfTiaG3pKlQEMFtW0NzHhH3bUUPPLp5lIIlCxNot9/x2bNLtq6jxgZWM44dNckkVRk5I2KO5NzonOXnr59zsRmoVHznqBXe7GZ+//GO33z77Y+6p/4ek5NKShO6SYdmdI9qGo0RyFCTuGRrFKUs6KbZjZdobei6gfOSOS8JGOm9AXUkpzMpn0m1oaqhtUBKUTqX0hNzEOZCk8OjtYi3Ang6pInLYcA7T6dGJDTJSkGjNRuvqa1xnCLKCW+hZINRwypyEmhba3Kje+2pJRDzmVQz1hqsqmiVpTgZ9mizRylPqRWjHN53HKYT2hoqMKeZqgraFFJaOAfhK7Rm0VqEcwpPrY1SZBW1GzYMvqe0SqqJVGbxm9ODgqUEOuskPoCK4Cl+0A00oFQth6hR2NIYV4hXWSI1ZazSWOeoNaNyW00cmmY0ftxiO0MMUQqP2kQ+AiijpeMvGm3tSrdt65BbOltJpJWbm5VzUutPP0vEeykenTbrnvkRZiRajdrEsttWqBGr+FW1Vfi5FjCtQFu5BI/NTGuV2sq6+lsha1VydFLO5BzJYSYqmcyF+UyOBjVuwK7fnyLBeCK7l7WONuKcaSWLfUaLVfnx+yffQv0E0pOCWJRFJQZZ4a5MG7MSN1mzOVRdI9eNEG+rCGlETKsLloZTUKolasj5SK2RVLLQV9dDPKYguq01W8Q6WaN47zHG4JwnlYIxDuc1tlW0NqRc8c7SdSKCVcZCa8TSOCyBcVT0o0Nd9dTLDjX4FaomaeCPOH5VMsrYJ6u3fD/EAdEZz36zEyZFlbyixz70sQhrTYJI1Uqr/SG9+ad9DV/3HB5mHsoMBl5cbNmNlpoTSWmy09wcT3w6HQkxUI0iHGGeg6zZjKJUmcIOnWH0lqwVD6czISVCBW8szhjRTmgl0L6SyZM4Le9vAxc/u2D7vOfucMIV0cpV3RieW7pXlcVNdHUDtmcuEmcgtFS7rqaNrHQQDUbOEhwZ60TTjdY0xlq8VXgrpOGiDYFCTvLzS13vFyuC8xQic5xJNWGVwVuP8xbrV+uuErqy0nrVdSpKEThhZy290RRl0Fbju57BOZxu5AoZTWc6EkkyvfYKXkby95VsMlU1jsvE5aZj8IZTzOv0XRqQp/mJ7KlEu7YK8BuN3kih8Xy3p6XKPCXJ2lF2bUAKKjX0mrdVWsVg2Pcd5EIIEdMM1nsuXo546/n08fZH3VM/PvhPdSglQLJcBJmrkcwLbdQ6AtZAIaaZwV3QWUdTmd4bSvXMwZBSh7OjvHzxOBNodYE2yeQjB4zx1LYwB6nGUp7XirlK3kZT5CbYaLvaMxua0jxaj6CM2AatQNlKbcyxkCtYLRWn0UmonK1gSHSu4xSWNRlW4FioiqExdAN9vyeUjpALsVaWOosDKR3ByxRkyifhVpRKSDMxLTKe5ocO2Bu/ThjA2YH9uMNazRTPhCw6G6UMpQrop9RFGDFaCkGNkoyVKrkLNE0q4J38vp11DF4618dJiHbmSSIkDbvGdR1phW2VWXD9rUnl3CoSbWQ0ymp0lvUCTV52tSG4faVoaxXekJ8vCc8//RRWawS0Jl3Q+sGusC2zWn7bGs6HAqXtSoTlcZJKq1V0DSjBuZe6JolWahEoUq6FXDI5JxHmhYUcA2m9VxUQlhmDuIRY98lSJK7f9HUFZ42Mn0vJT0wVxapBWYtL/s5Lmbqulla2imoN9Qh4a03+/3VSoAwrdE7+LE081KBl0lbyKixMUeyL5YBWFe8Nm+2GvFR0TuSSJIlYyT0vya2KptY0Yv1DTIJRRpDYVWiexij6fqDrOpBZCOcQCKWwHTvMs5G276idQWtWAGIRQTAK2VzKGkwZ+bVLewxzc3jXU2vmOB2opoGS6ZZqTb42tRactLWBM2js2oj8tK95Xsi3iXCq7AbP9pkllYnDoaL1QFGOw7xgO03/ynPzTaNFQ1giqgWMcbSisM6w3XQ8G0c626FWx1gKFY1m1/dcbEdyLXy8P5JLZbcf8bljWTTbmPn5n1/z7dv3qCgvXTdqNp8puleN7EHZgVgtOa7ZM06mmpL91qBViQSpjVwgpso5BJRWONuJpktBKRmtLNZ50AIkNbqtkwlFpzwmF0xTqGpwSmOtwXsrIE3Leg9VSm4kpXAIJFHunRUlsJ7DygqfRBlNblEgnspiXYcdB+ISabrQrhTm9YC79JjTQkyZKSR2vcdnTRZw0vq4/zA9lUJakCFaaZwxfHa55/NnVzjjSCWxpMxpCeQKF5ueaUmkmLncyZpbm0SKQXhXSL7UEheUifTeM1rN16+e/6h76u8xb3RYO1DqTM2RppJ0Zk2i97QuNGXXvavwRlARo6UrGzpLLAOnqbEkoA70fqQzlp2PhHwk14jVAWc8WmtyCdQ000qiKOkkvRro7MCue4b3F5DviQSUFiFVU2kdKjicaigdKC1RMcScqNpBs5RqpXlkPZCUR2uDN2L9yzXQdGI3duyHPdp4phA5xTMxFxHZ5kCqgaYUsU7EfKYznlYTMZ2pTQS9Ic2kEqW20nbNTQBvO7ztKA2WFCglYbS8rGoJxNDWl4jBarcKaSspL6Qq4qvOdKAsWWUMMNiOzmnRMFTkYM9rwdBAKYOyBtf31BCYp5mas7hwmoj50Os7yshLtsZMTHk15IjLobYV8LWOAmtrT+Tcon76REzRccgKQiBbokt4FJipKi6b2mQyotcf6rEQaPJclFpXqq4VHdIqfs15LUrWHykFcgiUIBHtOSWh0HrHMk3SKDhLKx7zuFpSSkL2lMJosXW2R9ghIBWrevpaWgO0TANakZdCLZmSEmFZ5N+VDCWjakEVhTZi3VXrJEVpwfQrbWSKYrWIBFslxUCYT7DMAjqzhsvLLf2bK443J85nEZaSHy3ZdnXGQMgVbzU5i2CvotBGnBuphHXaqJ70UKWJVue4nOm2PePLPfqyh96gHp/xXKi6YZVMPyRgUe5nayxKGSoN1TQGS6Xw6XRDrhm1CggfCz+tDNYaeYbkEySXBa2lgfqpXx///S1d7uAESRce4gOVwlw1OlRSU2y85c2La8bPR373lx/Z+YERiQRwztOsAi2S1HHjuN5cMFjFNx8Mv/1wZHCeV1cXPN9tablyd3+SaX3nyEtis7tCp8LP/+Sa//3/sJwXKQy2Lxzdy0Z3qRn7Labfr+tzQ2kRCCjk/OSR2K28PMcYgRHWNQlbSYLxQqW2JHZgJ3Zi6yzWGpqyGGXRBewcuNhsUeNA0xV0Qem2ZqolaGp9PholVZp6LGwVTleiktVgI0nuXFEkU0CJQFWbhm4R7RytdpRUMDvP9ivNyw8zh2PicHtmSYV9r+mdJeVGVPUpcFQmDSJyb+tZYLTixcWOr1+/pFtRBw2ZiJ6WBeMML57viCGxLIEwdE+6mFISWmuM1bjOs0wL59PEYgPOGr5+c/2j7qkfT4ilgbLCCGlZfnOVyKWRySgdUaqnd5d03VZGwWqRnXITxPm272i1cppO5FpYUo9tz/nyzec8TB/xVqYupVRqCVQdufAKjAUlXvPNcMnQXWGsJaYHcpRvpFWVQpDDwCgoltwKvYVJJ4xulHLGmi2lBXKpxCIVq2mWVAq5OVKxqNYwruJd4dXFnmEYmdLMKcxMeSbGKPbospBaJWZFLDOlihUulZlYjuuoGGKepVjShtoWcpvQaDo/UrWIUZWyq6YjkOKZzvZkmThjtUyGYinye+coIVZ2j1E9NGGjuFzomqV3Bp1ZRZF1tS2Le0AbTa6NJUYBptVCaTLaF3urRhlFRULiYlwoMbPEJIewFWGgdIyrIwr1g9Nn0MR/BIJYtb7UH60sf0fu+jQdqbmsCcKOojI6y0uQVXfSVvFxXR0KrawY+ZyJMZJyEjthEet7SZGSEjVnCf3TGlMNp9NJnkcn6HSjpSBQ2qCtFRHsymVpRYofswLi4FFkLZZZWdWuhVWt1JyJIZBCoOVMyxlKQq3TMb1akq2R3bO1VgIRrcFaObC1tVTVSDnReUMqBwoaY2C72/B68zPuLu55//1b2r0hp7SuSURU3oCYEsk1YonEuICCoR+fmDLOe4mMKHXVc2UelolIZP96T/dipPWaomWaJVsnjdMarVhXL6L1yVRUNWw7EZrHkjgtZ4mzoGKNEZZKXtOh25pAvD7D8pkWSl3TnttPvzgxwTCXwOVuRDWoc8M+q2ibePj+hnQ/sXv1imo9m80rXg3vsC8VXitAo61DGcvt8ci7h4/cTAdMs3z1+iVaab75eGLjey7GDQpZH7568QyU5nyeuTndMOx2qHTCY/nizy54uLmlWti8MpiLiuos4/aSYgZSlbljyplWpSDxSKNRaWizFtuAt4bBdcK7obAsM2VKaKPIvmFcQ+u0au2kOHHOoyt0qtI7S3VIcndN1IpYlnORCaBROOswRgTsrVSoWopvIJVCIaOqJhPFBWcylkzXMrEFaAmjLrFuwBnN5ssNnTLMU6L+m+8pc8LajlpmOqspUaInWMXGj5o4rWWd6pzhyxfPebW/oMaI7TTaNPabnkpjzplYKptxw+kwc3t7wDtD7y3D2K96yIxqRbJ9msJ5S99ZNmP/o+6pH12ceGtJRWHdDvIq8GwyIm5N1jreSeeSy0xrYFoQlb/WWLUGYnlDygZUodSZ1hxz6rmfLBfDjpw2HOczRu0Zux5rFUubOM/3KDzKX3IujSXcE8Inupq4cBt65WhtJteA1oDqqLmyGy6J1fDx/l40AsZhlKbWRCszOWWU6SlkCT5aFdStNFJJxDFzWE6gG4mGNg3rNFRNrwZUipQWiPlEqZmQrAh7awQqpWVx6bQqI8rHG78aNv2egmGpUcZ0SRDGVslYWtHWaVWhmUhImZAjXnsGf4FVW3JJMvnRCodlozs6I0nFj92kTLPEz15bI8bEtCx0g9iS26qTUKK0JdciD0StzJOwVVKRX5PMCqQSfFdFcN926BlfX6K+GLiZfv/jT7U/0kvs1PVpDaLWrls67bo6YGR1k2sUHYOWQlqpp3xu6RaLJieZUMQYyCFKyNi64sg1SdBYSpQUyTFScoauI6fE/d0d3nu6vhf7shEdmHGyN1ZNCseqkSlZyjQvbh7dhAXUiuiJ0FK8tvq4c0+EeSYsC1YUo1BkoiIBhgjBE3EaOedwncd3XsS5RsbQ64qbNvSM24FYQNWC6wzX22uudldcXl7w7bdv+XRzwzRN1BRFCOs90/nEYZ4Iy4JWCmvNigeoKK3ox4HWBoGyhci5NN7efcRdjoyfX9E2jmY0GoHmoSSsUKGESK0UZrVtz3GmWtiqDXOM3J7uCWUBrbHWraFoBhDiZy1tTV6vEiulGiEFYo0iEH8qA3+61zwlnu02vHp9yWYc6EZLtAc+lfcc9mc2aaAPln73jJ+/+D8Rrz8yNNj0lnE7cJxm7h8mbJX7dQkn3vOR+LYxdjvGfqCh+f7DLW+/v6Hzjv3oGXoxEPzthxvOBZ6NPf/0f7jml/98w2/+8pZ01tjLihkUWnc0PKEoEXsqeYZrUeSmiGERMTkVpRLGeDo/YJRF1co0P8i9gmTQOKxY+rOcAzKx1JQqej2nPV715PnMXT1STVzXoFbcc1Wo4U1nmpMJijGiD/RqoNedmBxaZs6F1BpZZZTKVBJWFZKaaRichcGL3lJrixksV7+45i8M+GaI7wPlFLg/nOkHR6qVJTVSybRWVyH3Sq5WitE6RuWocUEbaUrNqu16OM188+kTqRR+/eYV49hRaiEsQaQYaPb7Hc4bTEgMXjMOG1YLm9gGf8T1o4uTwRQamlodqAyIxetxJFVbRKsge27tUFWC0JQyVMUT00OrjHOV0zKTY+bF/mfEOFNbIGZJajwuR7zt0M6LfqOcmNOD2BdVI6aTaDFK5lwDznouzQW2Wg7pjtwWIGFMh8qFvgetK05bcszMJZNyIq++784bFBWlI7kuaLxYaFtkTo6YBRHe1Kq7WNcWtcgNG1Mi5FmEe1pTCrRqYAVudVYzL2es7eR/p7HrR8ZuIFCJaSHlRYqQ2uhGSyqCxc85kLOM02uObHxP7y5orSfESN9LQVFzRZ/BRXlIZO9QxBKnq+gf1pVCLQW0xjlDN27JIRKmiVYrMQo6vzT5udMSBdjTFCEKpjxn6TzbmkFlR497sUH9+jnv/A2/S3d/r4Ptj/EqMaNUoynpwJpqT9ob2vrSg3V1swLVNDRERFppT3TYnAulyK42LgsxLlIYlDUxOCdyCoSwsARxgKWcZU1ZKw8PD/jeM2y3oqPyonuo64vbOBGH1gYlRQnkXIXVFbWup37gFtUiCdmy6w9SnMRANYZKfRJ4aq0wRq9rHYVzAqCzxq3o7yprpMz6+cge1Q8bXHqGbwOqDvhuoNsODOOO/eU1796/4+3bbzg8PDB0Pd531Osr3r37wOl04nK/Z7MZV01AIueMxWKdoynFaVn4/uN70qD46udfoTaGisbicNoJO6bJsyYpswIQjJPAvrwfeba7oNbC/XxgqRnvRli5MylFSXRVhpSEYKpWSkYoEzEnSitoq1ex8U9fEPvixXM5+3LBYahTheaxdaCpI2l/RGvP5Wbk5fXPuLu8JD0c6TcdbgcqFErMXO82fP31a95+ese///A7fvP+t3zhX/AXv/4Vf/3bbzgsM8ZYnu8v+NWXr9n0jt9/954lFT7dn9h7D0f47Jc9X/5Fz9v/FHCdZ7PZYPsNSVkqRqaaeWazHXD9TlZQdqAWoK6xE04KBVaackwJ8bsohnFgGEess9TVel5ypqRCFqAJ2nqql+Z8jomSs6R3r9Z71Rq2GVrLTDGhjMJZjZMuhkyi1kjVjWbses4UwUIglvc5JgpgbYB6ZugM1koem7aG/c82/Mn/+AX/5n/6K27fn0gqU5aENpYWRUSrtFpdeApv4GfPr/jTL7/g5y9fskwnDnPim28+8fxyu04SK4cp8PbDHV999pLN0JFLpht6llne8/f39wydY7vt2WwHShGRcGmFzXb7o+6pH1+cdD05NjQe042QF1pZKDWQSZAL1YnwD2VQDDQ1oA2i5KWRayKVRNORxoJSHu9Gzum4jn81YTmT0pmxG7DeEOvMkg5M8cCrq89oVUZFDssxJ2KeoVp6s2Xjdwx14RQPMnJjEfZJqzgl6ZJKVwqQShRBE4pYFKVGCgE0LMssO2grWhmtKjEdmVNijomUJYNBIePbzm/AdJTSWHIipcojZ0ZSHSu6GVlZIS/2F5dfSHLzcqblCdUimoxzDu8qsU3U5jHGMfYd3l6iNh6aeOhDyhgrYW4pS2T2OClsqiis7My1+UFcuX4PhPAqcLBh2NGNO6INclAvs1hW1zhwcYdIAQdS3MSS1064rfkLjWYb5yHz28Pf8Hb+htx++tCpaT7jrMcqKy9e1olSaxTEiVWLjPVrq+iqyFn0C+LmWYuDWokhQG3EVUtSmiDSU8mklCgpEIMUJzEGYlwIYSLVTKuV43ymL2ktgA1d62SNo/U6pcuomFAocpDfw1qDstLRN9pal4j9WBxgjVYyKSyE6UwIC8058hpiKWRgWVdohVg3labVSgoLKcl/dt7Qjz3jZqQbR3Tfi+sBGWnXlCmpEcsMTbHb7ej6nu12y/vvvyfMy7oHr1xdPeP27p5piYybDcYZcpXCrlaJdch11ZHsHC9//Qa1NRQFnZJAxZzjU2HY1FpgqyaTXz/gVcf15RUpBe7ODyw5iVi5VZppTyuATjvArJEEijnNhCKON6cNg3NoYyhK/aMQxF7tRkpqvLy8YJkm3n26ZRg8m+01Rg2EcIe+OmBevuOvvvlf+d3bt7zeXWL2HdGeyCQpcoESMq+vX/A3337Hy89hzPD6sqP/+g3PX2y4fn5BjIVpmvnuww1/++49U0q8eX7Fy6uRd78/8vP/bs+v/sWGZYLLZ9e4vuOYC/OyYJ3GmwFdIjkXdMk4BxcXW5Ry0Mwa1ppkvZ0U/bjFnk+Umhj3O7bDSCmRlht979hvR6xxKBw5axQOh8eEwPkwM7odsS2rPklRKWjVGG2H04pDOhOiTEGMG6Vp7rw4lcJCLhFlDEaDdRbnDE53qHGkKUtpGtsUikSphaU0KhrnFJe/3vGz/+EF79/d44tlWSJeQ99ZUJCL2OV3Y8ev3jznT169piXNzc0DrRameUaphrPiltoPHa+vLtmMHTkmvr09cnF5gbMVb83qDNSMY0+rjbBEjLW4oWOJgfojCeI/PlsHhypRvOXKkbVD02HajKpntC7UkrCmUeqM1SIwq7SVXQK5NEoWEd7oN2x2L8V5UgrO92K/Pd3S9R19P1BLYFpumcOJ1qDze2JunJYzUzhwf3hgv7lm6K84Z8Fsj37HqGRHR6s41UE1XG+3HA+3hDgT8kJtAkPLzZDygnWKlI4YI6M6vboDRt+z6TfE5qiq4lQku0gdNMdzpmrFeT5ivGLJZ3LOooI2gu4utdKKotYBmsWoE88vrnl28YZzqhjvGMyVaEtUo/OeViu9SyhtUbrD6oEUFTGLe6LUjAKs0+SWyTnjc+GqDKiq12ybR4aGekKkP4qdaq2EeeLDd28x1pOBWpPwp2QRQEoCeWut0tadudYO3TfMlaWYRjsVWBrNKOa2oJzhuvuMZT79/U+3P7Lr+HDPZn8hqw2rsUq8aq1W0fCU9bOWxZdMDUqFKqsf6hrgtxaCWkEI89P3Nldx6OQkK54YFlKKLEESd0/zRJtncaREgflN04RWihAWmWA4h7F2LS5FY5FDpJSCshpbxFr8uHLhyVIsK50UI8t0YjqfSDEK7VbLyFrZR2GpwKjQmRIrFAGn9X3Hdrvl8vqK3eUlw26PHzfo9WuqOQtREy12zFJYwiIBZ8ssLgWt+PjhA8eHB2prjMPIsN1xPp44TwvKKFKMtCbpv6VWlFEUC5fXz9GjoaqGReOUQhtZX2qMrLlUQxspKFvKDG7Dm+vP+O7DOx6WI1lVjDWrNVicU95Ymra4NbF2CmdcEZeTNR6toFs1Kbk1tLUY/dPnnNx+umO32/D+9pbj4cRut+X1q2f0neE8OR7mxqfTB7rrb7h9dyJRGPaevJ25fbglLI1x3DEMm5Udo/m//8WfM+sDd+U9dx++51df/ZLP3jzjt2+/55QS2mtCl5l9Qo0a6zRLKtx/iHz+hxf87IufsfyrB4IfmK0ix0xqjTAl/BoqNccZuwT63rDbW7RVqCbNXCyRlCulyPfzcn/JYTpCKYRpEr6T0tQSySnTeYezYlyorRFTpA+Fcejp+x3FCHizZik4pnkiTAtRNezgabZI81AyyjTmOZBaoSmzTjobUEmxUqumaFkxVp0FK1EgzDOlRZSR8MSx70RI/Kdbfvnfv+S3/8sNdcksUYL/OudwuuKN5XrY8YvrV2yt5XbOfPv9R16/2HExOr54dUUrYov+8uUlX335ku1mw/OLC37/zXccz2d2g6NpzTiM3D8cmaaZcTMy9D2tVRHFpko9/rjm9Uc/Ncas0Bk6QkiUEsk0seZqTcn3VBZIDkxF6QmjveR5oFB4et/jnaNkhdeNzbAh54XDw5GMQ5GIZWEcnpHqQi0ztQYB1uBJKbOEI/ene5wfePn8V9AUxzATwpHBWD7bXbLtrlnSraT2+gGcJdwF0JdYg1BkdaUCtWgoFucs2/EFWjUuN3LoWt3YDc9otSckETppl0jrKieWRCwTtc6YXGltedrBF/JKmNTUptfRn+bN1Zd88eIrHubCKc+kVqhV07LB4tC2ZwpndNM479FWk0ohpUIpTQLVaDgjyuocA4rGkC0XZVj1Dm21qv5QoT46RIXHpaipkspEZaIZQ+4aqpPCJq+iy1KzCDurUGJzLrhtR381wqsN53Mgvj+TYmCqR7S74rMXv2IOP/1O8Xi4RztLo2GzEZ2HUk+MmFrrWqBUcmu4TkuI3uojbqUJx6RWmm4C8MuBXGXqUmpatSaRFCNhWZiXheP5zO3hgWWeQWmMdyxFCsu4zJxLwTr3Q2GiVjGsFcpqDkE6+lpRGWkc8iNITT1ZilNOLPPEw+nIcZ4oAjcQRk+ulNJ+WB+vBNhGxfeecbPjYrfnYn/BfnfFsNnj+l4E2Kv12HQDnd+DcQItSxkfA30KLDFg+xGsk3TvGAlLEOz3KkBtq93RrDiA1kQsy2DoLve0jcdbT2kVvyK7S20Y58WqX8QZZbUV0uW44WJ7zXe377kPD2C0QPSUxmphO1EbTju0tjwcD1SVxSpdxA0luUqiG2hATQK66tzw3+Qe/a95ff7FK54/2/HwcMZ6zc9+9pr9fiOFdcvUs+ez7VeUDxPt9cSf/cuf0fcGnlde7y85zoF8V1E1UEvj08MB03v24w7dR/SfOj7/8hV/+b/9hn/3V3/gYrNhM3reHW55ON1TquLjPJHLnpeXl/z1//cTl//XC7YvB5acWGKgWY1HgzXoImeytR6ZCmtCSKic0BhKaYKtbw1rtJBTjWE5zUxhxlgRYQ/9QOcdVE0rheoi1orbFG3IOXI43nCbFppVOAXeKpRZn70GS4jUGPFWYxnYdJd0fiCSRTIQF1JJKN2wbnVhRo3RFWtA20zvPE73GOdX2nehxMApZxazMFrHL/7Pb5jeNcJ/+EicIjGlJzfZxjleX1zglUARd6PnxfMdu/3A1W6Ud4kGazxQMFbhVOR0uENR2Iye58+vAEVYErvtlhCFiZJzIoZMLo3bmxuG/sc9Dz9+reN7pjiJ+4CMroWsChmFUhZttyxpJpUjxlmyEjKpUR7vttTWkZtMMZLKxBrQEZwpGBNY4gK6MWxGCUtTef3mZUoNyJi8oJXncv8ZpRhSySzxgVwWvO0w/pKp9rDMjHpDLSfwkVQnqlkoaiJWL5HUj+ikR+dEBVM82ihqkfF7jAsxNpRLLDkwl8KSZs7xRC4BkchGjBZBI62Jin91wAjSXXQGpSSgcrl5zmGqnOJCbFl4IrpS9Ur+LI3WJhIGqzpSibK3X79OVms2a7JtzgLAuk4dfRTtiTEGY/WTtevx+qFWUatTREIDa1fJr3rSoAgPs6izY0E3JZOAXJ6qm/IQOf/ugFWKuIXzvpJCIrlMzRNbZfH9j9sp/jFfVSuOpwO5ZNzfnVJoEYCK4r6t4+Eiwtf2KCYWAmReRaitIROSsMjKRClaycQUiSmwhMfC5MT9wwN3hwOlFLz3qKUQYmS0liXJVETHdXJinSRmO6GrRg05Lviux4SFrBS1ZmoW/QUrLbI2KU7meeLheOS8THhtZGzdGjklUkpYV4C8BooZhr5ju9+z3ewYxw19N2CVgZSpS5Tbt7PizHAd2veCyWygW8bjsK7HdgnjJUCwKng4HDmd32G1YbfbMs0LGLsKWkVnl1UhqobbdejRoL3HaUdvNKkFmlVU/Zi8bKk1EULAbzpGt+fq4pK377/nbnmgtUxve6y28vnJbpqUE8f5hFEa74Ui25QSR9Rq3855FQs3sKaj6/ofnSXyx3z94fYdfW/BKIbNwHEO/P77D7z97gPTeaJzhlevntMlhX0J+nPL7d0DlMK20/grR1dlFVJa5eFw5ub9PZuvv2RnvuDj4Xv+l//wr6nB8tUXz/HN8fHuwPv3J27OET90OCNBf//pu2+5NFumk4W949wKsa7us5XcS5bVdNThyb6caltD+STGwZmOwXdoLVNONfTsxh3ztIDVaGMprbKEBRUayoK1IpZuTPRuYJ8sJTXO50jW0DmF9xprqzBWrMP7ATCQMxu3oeu36E7jdGHTOXw/Mi8L07IQQ4ZmVsv/IlpKC6Yahq7hvKM3FoHKVWqT1W4uFXPhuP51z/vfQYuGnIs09NeX/PKzl1xvRj59uGUz9rx45hl7i25wdbEhpoXTOdF1Hc4qpvPE4WEhpcTFs5EYE8t04tn1c4a+pyQp/ofVmfPh+09cXGy4eLahln/g4D8XZY96ipGqI81WcekoC9pRm8a6Z9BO5DxRW1ghT4aQAkqNNNXRtKOpRlWR03xmv9ngnCfXREwLfdejtGhJUppJOaKUxZmR3l9QaiWWQC4LMR+Bwm68prNbWoOHMHGuZy5VYqMdWMVoFZcbzaf7QCmQYiWlglIG5xyoQiVSq0U3hyJTcsTWTAhnCrBkCKUQ8kSuC3M4g4r0TqOaQTeLMb0QEXMhSXwCtWRSzMSY+Pz6c8b+GQ9LIK/2w9Z+cHAoU8h1wrhESBGPXbleWrpd8srXEAFfXcPaXC1cxw61iHX1MecD1ilJayvRtT69EMsjtrhTLM80x33iZDI5Ltgh0k+FLhpUUWIbVoqGJqeGOhbC2wfOLxTnIVO2YDeDrPtqFdjYT/zqLy+YHg6U40FAgF2H9X6dWqyPVWu0VcPTHjOIENhRfkwaXv9aYiSH8MSLqSUTloW4LCzzxHk6cn84cHt4YAoLzloR6UVZT2pjhGTJI+XR4IyTAiUZgVypRkmBocrUg7ZmahT5/iqlBQFQG7FkTtPE7emBWLIQWq3g71vJpJjQNojGoreMm5HNZmTcbOn6Aes6lHU81sNKG4zp0DhUlR8kDVVBK9QsuH+lPU5ZQCZN2/0Fz1++4vu331FSQStN13eUnGjVYKyIXZW1qB7yxqKc3NvWKcmH0pbAet+39rSydbrnaveCcRj5ePee2Cac0Wg9SMq0lryqECNNN5YYyBQ657AKYQwZQy51zRkCXStdbXQGMJVYJpT2/1Xvzf8W17OvL6ml8eH9HdMcWGLk/v5ELYVnF1v2Y8/pYeJqf839/ZnlVxnvOk5l4kZNpBrxxaOPntNDoFa4uLzg3YcDTcGcKu/qHZu95fOdx0UHupFrI8RG1RkzWKaYOJjA5uWO45CJc2WumVREP9Kqpq3hjLlEQprJLeGMrGe9s4ydp+stzncobSglonWjHzv0s2tyipzzLGL1lnFG46x9ggQqbTDa40yHV4Zuf4m+2JNtRWiwSdY7RcJoU6lY4+j7DZtxj+06Yj2T8kxDgi673oDpKVkD+ilsVTUhLGvV05ohl0RqmYaEthojtGStNCFpXv7qNbvjhvDdgkqNwRpe7DY8v9xScyHOAwqLs5rnVxs2u0GmJHRoLTpErS1d19PIeG/xzrEdN5Qk50JKMiEOMbGEWfQxDsJyRlv3D59KHN8l+jeOYCLFWen416lAU5JTihoxSlJYU56Y2ySppzVgdMbaEW09kosRoRgR0mWDsYkcF2pTMnHRgJNwsRALrQo8DWTUXWpEaxi6C8nfaZVYFqblnpwmsnEM3cCYLW4YiENiP95TmiKt2QWtJapKWC2ThkeiZ8mRlgujtzxKBguZmCfmeE9MCyXldefY8Mpi7QUxRVIJQu4rUIqiJHn5bPqeV1efEWujaEVrbtV/JBQFY2THrlST8CtklFyqKAuNVihdsEqC0GoTTLJRjSE39kGtILX6VIQAq4NkdZI8OjEQ90TsKqeryv0ucFsDUxJhoesL241mlyw2NFQF5z21QioZvCZViNURtSc6hTMdtvXkonA//RU7/XZLKYXp/oF5mrHe0fUDXd/juk7EqJq1+CiQ0ioi5Sm+oJRCSYkcIy3nVUBbxIWSI/M0E+ZJbLTHI3cPDzycJLFaaUuNYj1WWhNrpYSAWm2BxhisLjiTsfZR11GhJmIpuLA8FUe0JtyTVZtUWmXJmfvzmcN8xqn1ObVWVjtNbOW1FXxn2W5HNrsN47jBdyPO91jXyQrFOrTt5JCsDtU8SnnACtSqNFoWi2Fbi3SBQWmskkPw4tk1+6tnvP3d75mXGWeNMIra6njqFK1XMCroNE1VnDWgoahKQZxPAsMTt93YjTzbPsM6y/u7t+Q00/c91sjkKqXInCdZbeomGWIGCULUsnYzxqxFHegGRlkGp9jQcFSiLuRWEYLMT/vSLxR37498f/eJsGQ6N/D5F1/w8tkFXjXeff9BqNVL5sP7mc3tR3bzjtRBsZnJnej6jgs6tu6C01EatONp4nRe6HvH6SCRAQd/ANdQHpzXKA0xZKxVhNT44mrk4udXxEFTjcHJjUBMeZ0eSy5ZLpkQEzEHQmsy/dxZ1GBpSpNrWS2/BUUl1QXnDdtxw3Q3kVMCo2nOorWRKAvrBa1gVix+bVgLY29o3pJrJKZCQzRSMWVKbpASfjeAgtSisI9Y6cdN7iCtNdo5aoHUxM7sbY+3Dq97WVc2aayLzMTRKqBJktWmNmyuPFd/4uk3hT5ZTG04LcVVTJEX1xcsS6FqzbsPN7x2lr7rpAm2MiG1zlBLxWQhWstKq+Gs43g68+7myBITvbdse8c4eoZO5CACgvxxz8OPfo18+I/f8+blz+itkTjzqgXlXurTC1CEfQPeabSqlBqoNZDTAtZjbE9rWfJCapBHWBVyE2tyaTO5SuditEcpS62y1qCJSCrlQKlhfeHvsdoRUiDXSK3iXa8YPs0TXc78wr9g0BeUPrMbekLLhCKOGqFCthVZX6HJikaCm1hHexqtG0YVrBY1MtXh+x5nO4zSODWQq+WcZdJTWlz1IYpSNUZ7Xj17g7UDh7iwvqao9RH/rTC6YfWaOmkGVG6UInu+WgsxB4xm3WcLUlwpi2mVy6AZonydSv3dqcqaftwELFQFz0KphaQqhyHxfX/ivi6cpkDKhZIKtjWy77C2p68aUxVk8fM3B+ZZj34xYC88xgvtJBT5XGRK89MfYxvn6TZb4rJwur+nTme6YWHIG/oyykHnzA8Fbw1Peo7aJBOn5ERJWfQapaw/T0jIIcwsy8L5fOJwPHB3OHJzOPAwzeKiklEYuUScMrQQHtEqYoM0EtBntSj8ldIrfE1hY5DVUi5rkrTgspVaWS0N5pw4zBMxRi6GjRQmRtahVWts59jut1xeXbLbbemHDa7f4LqRzncSQqgfQ0E9unnhFGkPWpxkYgtqkFljAFYoVKtQCqqCMY5+HHn9+Ru++/ZbKg3vnYzUayJ7RRsNdavRXgTdBovSilgSzSIOniqWyUbBGsV+M2Jdx83xhhhnxn4gZxFBKq2prazddlm1J+C9o1RZDU0xUJSiUxpvvegAaqPpRtGA0ix19W39IwjCfNAHWgdzFzk+BLqo2G0r3nRcbUd24wW1VI7ne0oc+HT7wLQUxnGkmYLaKMISuQlHyI0wFy57y+Wmg1qwxvDlxSuMqZS0cK8fONsCvmKtXh2SCm0MF59f4K9Hoi8iZtZgixCFcwaKpoZKToUYCq3KBsBYR2uaVBotpjWh18h5VgV65vVAP4z0p4E4nyi1kBskpTEmUx5xjK6SjSYVTU0LxxihU1T9GFEh69NSQFfN4D2Dc6SykGoVVhdtbQSkVEEpKZJyIpaKs46mxWKsWBuL2shAqZI8rFTDKhHMohTZOsx1ozsVNpPDFhHxpyThnF3vmcLMt5/u+f3bD1xcX0nY4drsKmXIuTHPkfN5plSxVkcNu+2WlDPzsjCHSO+3OG+fyMvaSJMTlvij7qkfXZy8+5u3PP+LV9jnhmakaitViQOnNWqRUKS2YtZHb8h1JsZ7UPMKIloj4HOg5EgtmZjPVBKtJaCg1thxeXnLr0trWK1oLdKIWKvQxeF0JyTGEkRXUkX30Sgcwh02O352+YohGnrfM3rPUAvHJcuesQrmvVX5yrRpaJVR6vHQ9hjdQJV1uqHp1IDRDm87nOmp2UPVhHAkl5naEq3lNRRW0ZRm8Ft2m2vOSbrWvArpQPIiZJQh0dvSlWlSzBJdrVfHR2o4KzdnWztGoxS+VK4Xj8msEB+zIrhFb5Jrke7RNqJtzPYRsJa512c+xFse4pmYM7U0aq64rPEZCiIebEoLtCdH7OhpnYLOgBF7pgZJ5SxrpME/gk6RKp3WsNvycHfL+XhgXiax/paC7ztc8TKNKHV1wFT551oFRx8jNUsInyQQizsqLoFpOrOEhfvjAx/v7vh0/8DhNLGkKFC8x4agZbJqpJXQ+1SgZHHnmHV9B3JPGCOPfGlFsnz4O3RIpVdAmSLWQkgZqxTGOcmaUYA19JuRy2fPuH7+nIv9Bb7vsb7H+AHnB6zzq0PIIBLEDnBSmBgnSP1Vz0QpqLIydtfCpFVJt1ZFLPjaGJ5dX3NxdUlMQoFWgp8g+UYdNfRyEEs+SyeFy6rpWv/00uBg6d2Ac5bDcs/d8YZdv0Ubz3m6YckB5x2GdVopCUqAEHetUphmoc2kGBncSOc6lnmRyalqBKuxtiNjhZuifvqjxGM64Tcd7pUl3c7MhzPDp1suXcdVN/CLr37OZr/lD2+/48MtnJZvKXnGJI9yiW5w5IvG3enIPB+5bHvG/opqYD/2QuB1lqYK9+mO+/LAomfakLFexK1KG/bPBzZvNgTbVstqFgihkhWnNkpIq7VgtcObHtcZeu8xTtGMiNMLVSJRlFkni7IuLaphx4H9/opcKuc4CbAwV5YQ0VlS2WvO4DR96yA1zjmSQwZb1vWuhiJPyOg79psNvjOkEgghrs14QxlpaNTKRqmtUFbWUM2FhOgRjXHC8VKKXPUamwGaShHlO6plsvakjWHuE/oYGYpkVS0hcp4CxmmO54XvP96SWqXv3PpMQk5FGuNWmaaJw2kmpYL3Via2RjhDV/uRS0Z2u4Gh8/Do0kVJUOOP1GD96Kfm5v0n3v/ld7z4v7ym9Vl8/03in3VTgMVYDwaW6YTue6wbaa3H6hlUolEpZaHk8womGsj5jNFQdUUpi7WDjKyLrG6g0ekOUyAsJ3Aa70ZSDpyne7z3NCIxnYQjksXCq1RCdRui0cznIyjFYDucmnCmoEwhRnEqeOWIuWKdEuhNzdTW8O5S9vqlEKrs8nItNNNwnUU3S0yKnCIhn8htkhu6Cim3tYbTnotxRyqw1CjQrir5JdaIGLKsQUxVd1hrmKeJmkVYGVLGedDaimVzfcFYZdC1chEVF7NhZeCvOG1Wm3RmUYngC6GvnG3imBdyabhQRQvUCrVGQjxTWkVXTVd6fAQdkPA2Zyg0coOiCuH2SAsTrVfUwaA2DjMYmiu0FkB1f7+T7Y/wakUsv77v2V5d8XB/y/HhQE5RUq7zgO96sd+iUbWSchFoYS0ifk1JyKw1r2K9Ri2J+XzmdD5zXCY+3N7w7tMNNw8HsdxbSUF+FHO0JnMqVVft0tMi8jHIq67PmnA9VMorNFF4JbmJxqQUGWEbrVdGjvxa2tqnwkRZx/bigpevX3P9/Dm73Y5+2Ii+xHUo26G0lfgJpSTsUHcos/43a4E1yZq2Tk3qmgElX7UUJmnN76noXNc4CcP+Ys/93S21FaLKJK9InaJ6AVrpJimxSovrTFstMCwksl5jGewGqz2H84GPh4+kEplLYF4yoYrjqK0l22NQY0M9daVWaazxbLsNtSY2roPWWNJCynkdW69uP9PRmpCif+pXrhljFf6FZvjkUWiutgM7byjLzOHjDdvNjhcvX/PCnLmfv6cf1uwiKlUrxquegiKle/ZhR5gWTg+Br7+4xlvD8RzQSuH9NV11lPqRh02g2ypKBt9ZXnx1gbmyRMSqXnJcJ5aPLCIlVnKl2Y17uqHHdBpnoZJJpdBUXvX/lZyrTMe9x2qZelpn2O73cp8eK6FkGkJcRjeRJDQt6AjsmtsWqS2RY5J3Q1FYLPtuy8U44jtLUhnXGWxR5FCk3Vjx9mqdaFIUnetQulJDIcfCY0yJthK9kNojcFM2EpCwymP8TLWOYMHsFNNNhqXimrB4UsqEXAkh0VnDi89fshs9VrgVxCRNlFYN7yT/6HRs6wS2cDydcdby7HJLP3RoY8hJcAk00bmF8/R0Rv2Xrh9dnPzhm2/gf9Vsv7jEfmmoWrD1WsmIFStI6FhPLOmA7y25KVLz9G6z5vAcJBAvz2ilKGhoAWske0BIletoLUvgmWkKWzUmNFzzNGUJ4Z5pes8Szmy2G0I8sSwPpJQxDOz7K7rdz7nYPOesKnU5sOs3PBuuuJ1Okl9Q00pktWjDSkZ9jDvXDN2G7bAnlsqUE6FFGfGuZE2tHFaPQKSqzLJMlCoZKbkkYsloLLtuw/X+BQENqlJVluC/JHkKtRapcLWkC+dZPlejDXlNDe66lfLaFNb0OOdRVdEtkednT7+s4W2GVf8DTRVmHbm3Zx6GxKHP3JvIuUWUMuyjpzs6rrmilkJeTqSa8M2zz57t4mlTI3QVd7Ul5sQpn5lTINVGnQolBIxVjM+39J9fYAcj063201/rtPVAUq2x3e+4fPGceTpzOh6AxtDEeq2NQSPdV671aU2XYhCU/CNZcp2chLAwn07cnk98Ojzw8faG+9PEOQRZ+WkZ1z59Ha1KwN4KGTPqMS/jhyKlIQLAR6y+fvz364u3tEaqki9jm/AUnLJ4IzbkXBtNa549f86XP/8519fXjOOI9z3aebAdGE/VwgjKJUI3SAia6cB6lFkThbV0uCgtIWfaoKog8WkFHguTVtG1wvoZqdbERqygOk12lmgzxa3utdxQRkbzMQcBFHadrM1KRTXDbtyxHTaclyPv799TLXSuI9VIqk3iBVBQKsrqJ30Q68fZ1vyhnCNbN+DcSFOVOR1FfGsFq2CNwmrwXjOlRio/boz9x3xVLU5DMzqef7XDj4aL6Kglcjjck1Lg4f6WpAxpp4mxUgbBsVMyhYjtK35sqE3g4/yR6/KCOWYwhtJAG0eMgav9jp3ZwINi6hLpaiaFyv5qw5d/9hI2GqUbWldybjLdL3WdrMsK3dPjvUMB1URiC7SSyRXUGuGujcZpK5Enq42eBtiGHSz7tqNRuD+dmJM0mLaz9EOHM55Oj9ikCfHEeTmztEBtYmqw2uK8Z/S94N9NYQ4zrjOMY0d10szkJunITwnwVfLPet+JZCtVVAPdFCkk5roQahKHZcs4KwW7sWZl9czMTTPuPXVIlLnhMXSdA6WYlsjl5QZM49X1HqeNCOaNphs7FA1rYFM7LhsctzM1yRTXeUNYMt5J+rJShlAez0Ehyc/zjP2HFsQeTg98+MO3/O2//iv+9P/xT/FXvcQrK0tFi1CSSMkncZz4a6GMlkCuZU1Z3LLpPPgNU7wnoWDVoCjVQFVKCyg0MU+ktNArj689Y1GYmDjGA+flPTndUUuiZo3XHd34Bqu3eHeFdRvJAJhO3JcjPt3ypatc7K/4fJs4T5nDMROawtkeYy298qRc1/wMQ9Ga4xzwylHt6mxBYaxD46ENxNRQRrFMC6dpgTVXoTQRUG2HgdfXn2GchxxxulHbTMkzJTu89SKqMxrV5NAzK+DpFGeEkqLo9Gad5HhQmlIrPhYuJsPFg6LGStYNowzNQG6V5CrTpvHOJt6pE+eWiLWQyRjtaE5x0Vk63bNTGT1lWkq4xTHEgb55stOk6572+Y75MPNwuifaBrpQ4yJTkmTIc6LNGVKipJnsN3//0+2P7JIpxzouRbF/ds35eOBmmTmejtKv5IxSCqv8CmQTfHsp9Sm8r6REXn+kmjjPC/cPD3x3d8vHh3tKTqQqUKjW1FOBo1ZeyuMIRVKPmzxH64pE0o/lHrJK0ZShtCrpqyiKUqj1Xn38dZQRbZfVEplujafrRr7+6pf8+k9+zbNnz+iHEWsdzVjyqplqrUEKhJTQOaKNZXCO5hzNWpoRbL9Ev8tI+ymuvQKpCH21ZCqF1h4TkeP6Yqloq4g6sfSwuEZGMk20Nrg1HTmtWURWK2Jc6FxHrpbdZkvfex6mO+5PdyvF18ifV1lB0q/fV6VBWUNbdT21VlRJoj2pGkehGC3uqBJIaNAOaxRKVXRroAylyoSr5J8+Mbk2KW6rDtgLTw0NPYOpmmmKfLw7sN0MdJuBf/dXv4GfJdTz5+RSyaHgXGKqGZstvu94P595efmKf/bmlzy/2HL76YHX19ekEPBWOv3P2kty00zhG46bhYvPtmw+33C0geN5ouksWVFFSwwWSnRHrdHIhBB4WB7QPmGM6PREiZhRFKy1dH4AlSl5zb7RDlQl1YZxjs1mQ8oFWqBp0BRKDuLSKwrXJL9MKSmgdVX0zrMdt2zHDd3gqLbgO4NrHTknxkGSvUMxLEkRUyWmQs6NWirWSiFAk7BPi1nz4mQTUHISmYDVdM7RdyOd73FOhN6FwqQaFxcas2hMlCJMG+E0TfczD8cjn73YE3MEDN9+8z3H88yLZzs+f/2CoR85Hk84Z+m2nloyyhgeHu7EedRtKCVjdERpy93DmeO8cLEd/uHXOs9fPWdg4Hf/5m94/otXXP/L19hBPN5lFYI1IilNdNZgNbRW6DsrnXZrNBzOXTP2b9iWhZhOLPnIaTkKFdCIXkRaOvkDxJa55cChN1xWQ00Tjsb19g39eE2uPUp1gs5GUTHEHLk7fc8SzhRmYvjEOHbsyyU/u/w1tw+N92ph1hOxQC7QmsS6W9VQJqM1NDIpLuSiaN5ibY/CYlyP1hDiQq6Nu8MdKS+0LImo2mgu91u+uP6Cq90zjnGhMpPqwhRPpFpxbkdWEa0QiF0Ri/S+FzCdsRnTega3w6qejGCGG4kWF9wxs7/3lGMmtrbCojSlNySTOXaJ713gXVm4SzL1wUiqpsGTlOHkHcEYvNrx4uRQSxKCaKjEjSa+2MDne+LW8PBw5n2+4/rZjouXz8W2UBtd1zNePsPuR87lQNEVW5b/P463P67rKbW3rl2D9Vw+e858OnPz/r1wTGKUzArjRAOkZbRccqGtsLuYwsrTiZyXhY+nI28/feJhkvGn1ayBlDwRfAHRFcnfyNehCs2IFVg96UxkSmKM0GvLOnOR8kVGxjRFW6ctWqkVx/74w7LbbvkX/+yf8etf/YLdboftO5pWxFpptaCaRjVhuaQUICwSF9GPFKVhTZvFGLCa5gxKC0hKJk9ALrQkbiUR11dKzaQi06W6Bg0qbwid4uACBVmJ6qYxWii4tRbmMIkovoFxFatHvvj8awqN37//z9weP6KMwncDDViWhd51aFjR4oamjVA5jUJVid9wyrJxHbpW5nAmt0KoBVUlONMpgzGeWjNzipRmcLWRkrCafurXeY50XtNMgx7GNzv0vYKHho2GkAv3H+8wdw/s1YYrL+c2xlCbrHdiSlQqbmcxW8O/++YtU1/5l7/6U16/fs00nRl6j1Gw1IrXnq70lKMCb9h+NRKHQlaVJUZSzWjtsNZjlAHk5V5blvO6QKuZnDI1K6ztcIY1B6qhqWiiCLSbR68OyhgCpYgayXaWi8sdzlnO00RJCSxYu3KPiqaEjAqFXjv2lzt22y2+71FO0UxiIaKaZhw3nOezEGKtwhjDYHqMs+hYYK6SUr4Gf7am8b6nsyPeeuHyeMeFafhOywTDGBqOUhJaFzrfEUMkkwlbjT/AiGH0A6XrsD5yXiL/5FdfYJTir3/zjqI133z/HqVgGLs15NJweXnFMombECUi/NevrpnOkW/ffsDYhlKQs7x3tIZxGMTl9COuH12cfP3lLxicJ9fI8bc3bJ6NqC8d2i1SMWorB4K2KOdkBFqi6CMB3YzoSYwnVE2tG4y7YOsSlg88LN9jJe9IbqK22v+aJefKs90zhv0zWntGLI2UoeoerTXbbkdnPLEs3M+fuD+/4+Pd7xnHnjneY7xh1paHMPHS7fj6+pfMsfGfP/1nzkWcPxVwBrQFrcUx0dQW5awwJZSm5nVbZgqpSN7A3eEjx+MNFtk3AlyOl7y5ekM/XvH2cBRAle3WULSe0k040zPHjMNjmyerhh86rnZXxFRJRdGKkCZTEeV4KZkWI+Mhcn3QXC6WSmKKgd5acmtMe8dtt3BjJz7GEw9xIZWIthmnNJ3ucHUgVk90A0kLodQNPd1iqJ0i9I3wfCC/6GEAlWZCOuOfeYbPL9i+ecWwu2AcdljbURosYYZTEP6F+ekfxuVpr/tEtqPfbNlePePh7o7Tw4GwBLzrMWadvKlHLH0mrwnUSwxMYeG8LDyEM6euoj8buCwe3UTUp7QmhUwJCZ2bkF1TfWKIVBp5Fa09EorVKrSWFU6jwBPwDZ4kK7K6AFDqSW9itMFZz8XlJf/qX/xzfvWLX+GdE2F0LBQDyriVjioTv1SqdGwpYSn0y5lNOOPrXgI4ncF4A495PrWtNuJCTeJcosooupSwTlwDtWZKycScaU4T+9Ux1KRMk+NC6Lri2ssoJYJ9Wx274Rm27/ib3/9HHs4HmnJyRikrRN9VPE6tsOYNNckWwJmOzm3RTl5sx2Ui1oj1jhwa/bBhDrMI9m23WjnL6pZIFK2QT/en79aJecF2Hl0VRSdmd2bYWi7VlqtuZPCedzf3nEOgRsXpXWX3eY/RjpxuRYdhE4mEdparn2t+8/bA/+dvFu7uM//817+EtEDLdK6TM7tWam4Y1dMPiu1nA0EHKeStkxV5SsRFSL1Ka5zv8L1Dr5wgG85MqeCMp2ktwX7rmt3pRqcznbdgHKlAWMKam2awnUW5hnGK0fYMfUcOkSnOxLjIitXteHWxw4+W1hnc4NHePonMS4FcK00bnO/YGsvpfAvlEUIqxhOrPG7sYCjEOHNeJkouLNOE7QxbswrRrQeHxKeQeczPfmQuWaewXQcV8hbmIWOOZ8qSyKUSVk7JF6+fc/z/sfcnS5psa3oe9nyrc/e/iyab3Z5z6lShCoUCSJEUKclo0kz3oHvQUKaB7kFzaqyh7kGaaAajCZJIgKgCClV1mt1lExnxN+6+2k+D9ecGxAmyYOTpGK9ZpqXtHRYZGbF8+de8zdOJw27g7fHE/YsDmxAIVnh4/56j8ThrGYK/8rEa1nUS7zjKjzSyVntK+v3NAT4cmS8z0/hpvj+fXJwsPzywOMPd6xfk08I3//XfcR8/Y/sP98TQ9eOignO+k2DyypojTbsHSrC+rx3onSBXWZ+qZZy+oBlDbt8SnJA1Y13DVMESuNvf8PndTylVeHd8+2MM/cZYgh+5xEdOpY9YT+sDT6df0vREI2El8+rmC6JkfnV6B8XwcvMFP9m85Dh8Dzmy2kouSsmtu32K/rjD308bzBBYm5CLpdGNqHbbkZiPpO9PNM0krRjT2PiRl/tbxrBlzivi++edsyGYPWIsg28Eb8A0SnKkaq7BSnBa+Ld296kSxk7nUUBKIRwjd99H9heHjL2oiGntapvU+GG58L2/cKqJVeu1cFQ8yt54hrZB6pazdVTjaXVlbplkC7s9tMFwksJyU7BjJljHaAz3n7/ALJ5jidxYz7C9w4dtP4KloJJQa6ll+f97Yf+horTSX/btOo3QvmIYt1s2hxvevXlDO58J4WplLXpVqhVyu5LPUibGxJIza82sg3LzJ1+w/WzPYMEZIWu3uK+pQqyY3G3n5yVx+nAiPs60OXU/nVYx1ymaiF7dUPsF0csj6XkyfSlEk559pXQTt2B7Nohzgbu7O/6L//l/zs9+8vW1gNEfuSr1OrWpTaklXe3ur3lU3tEqfDg/YZ/eYnc7/GGLHw2EvgLqDN4r+TXnaxfYDeFaiZQ892BOqZRrcVJqtwAwwaKpfy+72VznWJXWPUWsc9fxdmK/fc2rV5/xN9/8DbVcJyTWIiitlV6EmG5Pvh1GsipzzThr8NYw+oHgdywx8vbpHVgYh4FWCi5MXaHWlGnYwdVRWmh467saqinBj5/cKf4+w1+jHMQI3hiMK6xh5vHiMVnYjAN/9PVn/JtvvuXN0wMP7wWTdkzNoU16au3HVaWstAG++k8m/ts3R/6f/91f8de//oG/+Opz7jeBkivb7cQwBo5LBGP44qevCIeBp5TIH0UDzpFT7k1yU8B2RWJtBC8EOzG4iZgztUFcLiANb/vUoalStRHLQpFCNQ5lRBgYxgEXQMiIgSEMjDceycoyR2KMpCWzE2W7C2CFaAzVCU3oRVBp3euoFMoaiWbtBoZGiCn396TYq62GuUrtDdZ5NtMOJotksGr6xLA2SqRzOQ3XFWNh+DgsaIU0KKMbUXGIE8wo6Naxlx1OHD+8e+Lp/MgLUS6hYHxPHTelEYzBO98LIBTn3dVwTTjPsX9dw4A2xZvGOAZK6cn2rTWcE+Z1xvtPO1Ofbl8/Bp4eP5C2nsvlQi7KMs98WX/G9s/3zK5HhTd6MugS03X1LQx+w+D3GBOo0kPOrO3jWG1CjBXrXvLyZkPjwiU+EvzA4AK78RVjuKGoJ0viHD/gbe9M1gRG9uR8JueZNc1c1keQBWsTtcLN/gVjmEh15WE94ovScqLVlVfbgMSJb06PlNwYcLSYSFJxEgijg5bIJfZwPBypGIwNPF0WzpcHLpczQgLTGF3g61dfs9u8YK3Cqp0A+3FX6XwnCx7XylYnShOKlk7GA8T18KZSG84ElrjifMDYhKhyszS2bwvjQ4EKS10x3lG0Qut5Lss680EfiGN/+WirOLVsqmdfBqRZsnVgBpDSfWRypOTS478DLA2yqQQnbLaO0QbKaJkuhU3wTNs9znblRWsf982AdpUC5dN2ir/P6PvpTjCVJpRae+6RGMK0QXzgw4cnnFtw7upI2bohW9VGqfpjunVshTIadj99yXC/ZSmJ0rq3RuuCE0QMxku/UKxl+3rH9LM7NFZ0yYyXSn1YOR/PPDydiLET7+w1BdXQw+n6Gqfb738Uynp6jox3jiEMfPbiJf/Jf/Qf89nr1131YrtDsTEWjKViqCrU689dW1chXRMvwTjieuHdwzvstMOOI2YwbMdNv2SLoLVS1oTm3G3+W6OVSE4LJSeqFpJUkvYU4KqFOS04Jxz8QCwFUYMVRy2FnBNIQYzDiePl7Wtu93f81d/9Sx7nh+7Qa1wvxq5rItPDwnBi+si0NYw0vHHcbg9shj3HS+Tx9Nij3sO+c4xM3+MfL48MYSSEQM2pr8X8CAglLn3tKxmVP/znwbrQLReujtS5NYzN5KnSkkLsDV/OmS9e3/EmnlCNLElZc8Jo7UpHKsb15nV65fjpP7nhv/7FBx6WlaxghsBm6lyKpgLWcff6Bvf5hmQMTTyD9wyjsK8jce1TSm2tF0FqMeIYrvEGrXjyOV79ukCMYKfuUJxroyoM4rFhwPmJMewIdouagrEVwfWp41XuK067IeVgud8o++IomllrYslKTr3AMMZijSEY6QrNFK/NujKNDmREc+53a9OuXi2JnMuVlNtJruMQ8M2Ry0JtCTVdwq9FyaUXJNEWxtCl8aU1YildcCKWKg1JEXsp3GwOxHXhPM+8eXribbrw3b/+ASvCly9fMo1DD0UsGR88MWeWObKs3c5/UxuX9WMWHqhYihYul7UnsztLVeXD8fJJZ+qTi5N5vvRwrMMtac1QK/P3J978s294mV7j/2yEADnH/g20juA8zm9wfgcmXOVcpXcVpVAUgpkQ7HV8BZvhwBRGbsY7tHlEtjR6aumSZ3I50dl+oGRqmym6sNYzl3RiTXN3vaRgzYQxgfOysKQLtUAMhrktGImMAcbaSUwi/YIutV4nF5BCD7xTEZqB0lK3/W7CaT3z/ul7lnzuTggqvLr5nBBuiSosNVFb70hrUdRWvO+dWmtdqmakZzE0vT486q5ZONJ9X6TLTEsr3KzC7tvI+FCwEYpWWlswqXfJTboTeE+nXTjHgh1Ggt8QfMCp7btCD9U2oL8MtCb8JeMuFWyXZxpv2ISR28OO/e2eXAtLnQn7kWl7xzhtsLab61jT1SBWHNIccSkE+4dPAOxy3E6G1as99kePE7EWO46cU8Tk3lUifUWn7d8aFq65EEuhSAUxpDWiD0d0MMgwUPqOpsvHRRArFBHEdg8Gi8ENjo317L3HjXDcXljTL4n5CXUgQw+R8NV35QofuSodPTqhu7UOzvP65Uv+4s//nPu7W7h2wsZ51FjyNZCiO2y6zm1pDTHuOtoTxLo+tVEhppXH928I04ibepTEdpqQItSk/Z5Au69QSd37JSdKTeSWiRRKF3iSamHJC8ZWTO8NMdLvhVR6c+B9l3HebV7y5Rc/4a9/+Zc8rh/AmKsRlKJaENNwtnNFmiqpVJTMbtwzWoPYxuPlzGlZQS3jGAgEnAukkthNW2qu7KdNfzFZg5ZueocqKa+UsqKt4IPvHe8fOG53X3KOb2lkrFzvp7rgcAzGk9eMEUcRJaaFaXQYKd0LqhoQj9qKSv0xZG6pK7c/mdjcGA7uwPfvn9gFy5ev7xEVjqeFx8vMaVc4bA9Ua3sQ3+QRs6It9HVbXzBcQyt7Yd2tKpUwBqYwUi4zGM+0GRkmj/eKMa4rdoYN1g8YExjCBiuW2gpGFGscrUGMkUterhNGjxeH931luJSVC4liHLV1WTuau4O0E5xVxDuQnt8kmM4QKEpO3atKsT/6f+VcKSnjQ8X47korfrgGi7Zu6Gkd1jlqcbQau+eP2Ot7o9KaxUiDQTBbRz5XHh+PeOs4HDbMUrj76p71h5X56UKMkegtg59wwSNiSTkxbSbUGN4/HlmXd7y6P/SoixCoV2+UUiqlNjabkdvDDcfjp6XW/z3cgRRxlsu69GwPExBV4vsL53/1gRv/ktu/uENMRKTbqO/GDZiBhumuUNJAM6XM1HLpKZ/THcFMNGNZ4sKaLKIZ0e4u6F3vdiIVkxLOQi0ZI6Hrxluj1KWHA0pBqddQsgFjPTEuFE14OzEMe2ITHmNib5XBByYCm9lxvPS4eloD6QeltB7c17ISyVQUZKChLGnuRF5piBi+uPuSw/YVuQlJl04+FIuIR4xeTakqKXdZoTVXdnWrIL077QqL3lUbb2jSOTebuTH9aoFfnylz6WFOxmKaYqoQvENtr8i9HxjZk00DN2DcBGYgY1EKQ+j+HFojNS8MURkiDMmgrsKguO3A5sWeu/tbrHfUVXFD96AZwx4jHmnyY9KxRXDi8WYkV9/Nhv7AUVK++tNoJ7fWQk6JkiLaGn4I2HFAa6NKl9EpratQtBe9S87EkrtZ1LlbxsuDxQyOcT8y7Cf8NnQisxNwHx1fr8m3sTJmuKkDNna+iDEXBmfYbwbs1mA2gqjhpm3xzbA73NE5XV1CXFp3mljXxOACf/STn/Dy7hZnu/kZ1tFEKNr5GV0WTTeWk67sUZE+YaEXYMZacArVcV7OuPdv8NNIGAa8GSDRiYlCvxNauRozZnLJ5JLIWshSyVJJtYcfrqlPIxKVWrsCKaZI1oT13eV28ltevXzNr7//Oz5c3hNr7qo4Y7t5o3SbOmcdPoyc5wUwOBy7Ycs8z3w4PpC1MAzdcFGb9mLcVpw2VBPOdM+fVhXNqV+kDXJOpBKBHiJn6JPiP3Tc3/2U+HCh5iO19bWbqjLrwrfLW8z7wM5s+HCaWXPl85/eIa3hRBhkQirUqmTT123GCk0z427gP/9ffc7L4+fchg2j6Wc1rV3F5ifXz/jkwViCM4gu1HyhNICAM9eFpJHuclw+ktg902bH/e0rgjtTDIg3GKc0qV3mbrqUvuWCNULShLOZqiuacu8d1Fz9hARjO69Qq3BJCUmJc06cSYgHc8254eqnk6pSrz461vRJZw+ehWA9xVRSbt2Dyna16Gg9a+qrXAA1fVXUv47retR0l1lrgdauvEqF0u9rKw4xEA0kC5FGjl3BdvNiz/DnO95fLvzwdMQlZT/BZU0c55UwDNzut3jp5m8fDSabEWLp78mmfWVlxLDZjJwvM6U2ximwbeMnnalPLk5efP6KuCbmZUav7o6tZMoC69sz9i8tw27D7o+2MDZSKjTx1x1i7jbeBtDSiW+tSxxVF1I6UekyxzlnlviExfJi9znboe/CXC2sArthoqR4XX1ktC2gK4aIISFERCqop6aKC47B7diHkUbjki4saUad53YKbNyWm/HCMSTOsVIbYOX6tbV+kIzBag/Sq9LDlUpJlJwQGvtxy4vblzQsOXdyrbH2GjDWrs6fDtRixRGc6Rk319Tlj1EDQu6KjGtuzuAs09I4fBMZv1vQS0Fruzofdr8LVSFTMWpoEWy0bDc7slGq6TkmKo5ekggOwUlF6kqojeFUCRFCFSRY3Najdxv8zYDYzpFopfaR9ZWTYE3/t11znfsqRzrbfRgP1PxpY7vfZ8R5odYC2s3T6tWvpOREKblLXJ3rGTTtSljV7sybSibl0uWBrf14aWiucOnrvXJaWccLfhu6SdTkumHUNGGnwGQCsq7ctcCWiaVmxDm0NbbDwHYT8AfP6hPrmrjxe26HHbv9bZ/2INfCvl1XM8o0Try8f9HdMl0nthvv+8rD2s5ZUfpapPUwQ+M9jV4YizGIvXqY0FDrSCXxeHzCvvke5xytQLDTv7ViqZWa1v9ecVLIFLJpJCJL6qnMscReABrFmqFfgNdO0KklyMjt9sC8PvF4ed99VPhI9L0qF1RRrX1yiWHym+6MSzfHOy5HzusF4y2m5f5SEIc1Sq2ZXFOXhbZKbhlnDEG69LM16S7AfFRV9XXt5P7wHWJzjtSWSSXjTW+0nDXIAMVXxHTPHzH9TOx3W6IslJagNrR1bUyz0nNNaWjLZBv5+k9v+fz9jl275d2bxx+Ln9Iq1Slu65EATQsxJnxeENZuhmeV/FHJqYZaDaXUTprG4mRkM25opXIusfO7SsVa7QpQEXLrzqiGTLWVEAQoffJA57L06If+HMUSKVnRWLClEquytgY14VyPQOm9aLne9Z3eINZ2ebzt70ZjDd47Ys7ENUJt+GDwpseqNCxWbPepkvrvhMn26Yl1fWXZVEm5ILbb8Xce4lXBL40aLHYKyFIQaxj3Bveqfw1PKeJWUM4M3pNyf0fN68rtduxk2FKu7y1483BiGj2bMRBWCwjGCuN2YI4r0zbgu+rl34tPTyX2A2nNlFiY/Nj3zNLliCVmzt89Yv+55278it1nE2cnxFJxolwZg/Q/1quXwIBBiOlMiheaerCBXBbWfEJVGfLEjd4xmR1WAtE44jRRneN4+YDUTNOEtIQ3BWwBD7b1fbhpoNUiToFuLZ/rhVhXrA4M1jG4wNYF9lN/kVyK9kmHPIzK/wABAABJREFUaUAGCtYOBBxZpbt75pmYZrRWgrHc37zA+kAu2mWZdEt8le5Eq1poWGJu3QRIDLXlvqK/Sjib9odt9B7Xtz9MUdj/sLL9ISELVCxN9EePDaNKrd21ULCUKjSrbFxgCY21WlTkSnnU/pBpwUmkxAV3MbingqV1e2EFNYZmC6ku1Lnn/5R8DaSzFWcEbw3OdU18J7I1VCrGCm6YKPUPf3KynE+0rkXs07XWx8a5JFKK5JzRes3QaN1MqbZKKrlLTa//70fy8PX50EZ/YedKvUTi0eKCwQdHmlbitJI2E80HpqyEw5ZmLLHlru6uje04sr/ZEe4CH8qRN+mBMA5M045hGDrvRCHI1dUSQwgDm82WaeqJwiYErmmUPxbBP/qncA2SVKUW4GrWZmxPP/4oVm4YKpY1Zj58eN+Js7lyOLxidBO0hsZIWVdqiZQSKSWTW6FQiS2x1E6sv6wzlzSTaqY2iw0fc7YyRoTBjuyHPa1lHp/eUrQTJYdgGAeHC+46Cb2+dNRCE4J1PfLeGlLLpJZ76Os11M/aPqVCWvdykn4/pJwpZAZxjHTr/9UApnvNtFo6N8hPvNwefjuH9DeI94+/JpVzV0up7y9fr1inuDuHTZ7zD0sP0xPY7basMpPySh8eFyqdj/VR3KS1sRJJ44K9TehRCdPE8tQ9m+aSmZ0S9iPioNVMIWFkYbR9Mlda6dOJJl2yrPY6RGnkVlDbPXFEDDn3sycO1F+VmdfQvdauq9VrErxz4Pw1pfuqLm1NyVer9laBLJTa79DuvijXSJF+Riz5+sBz9cQxXYl2Vd19NAMQ6TSDHCtVE+r6mtJJgNZYykwk9e8d/RIppuHVoKb//Sk3pIKz3Qm99nkNBWDw+N2AzIZMYfjcs7oZO8DaMvGc+HC6MA1dxh3s1Q5Au3uulZ5cr9bx5uGJ3XYCVaqzNG0MU2C6GQgHBy8r5vI/sEPs6eFIrRnXpCcYqmKd7VHx1lJK5fiLB9wwsG+fM74OLNIj1SmNRrkyiMs1oM+RS4G89m5GBC0FbY3BDcQSWfOZc3rql4tYqAtBE+oaMz3LomnFmoo30uObgVmVmPtKJueFoldXWCv0COrKUpRTFByeEcsuWJaorKaPtiwNkUJtK56u4eaqTljTiWW+IE252d6x2d6zaqPKNdCsdh8MjNKs9irdCHNKWBWCG2gK3hl+9PvUbkJnTScpjUm5+SGy+3bFX3qYYX94u1EUKHI1AEMtTZW1Qrso0yYwOUvS1mXITfqYtWViSQgLEhPyTtCjoqOSSyTnRpwX2mkh2MJhO5BLI6alPzjtyOQ3bIYJe/UD6GzNnvSKqZ3A+T+BTjEu8zWZt/34sleFXBMxLizLTE75qs6pfU1RemcZc+nEUPqq5N96ufbfjUqXEVd6mFyqZFPJT5HFXbh4z8Uatj7gkgU39DK6FVIuDOPAi/t7xruRdCogD32aoXJNRO7TnhAC1nm8GximDcM04kLoacJXHkmtXYUk1C4VFOmmgXwMy+xER7nG0EP3TVFj+8rTKKiwrIn2/m2fGDbg8AqTMy2u1LRQ6/oj36RoIWthbZGlrMRaOcWZY5yZW8ZZ21OD44yKst8cuNne4pzl8fKGqCvlyoUZvGUYQjegSwmRnnvjjcPQC5xLOmHDgGdgmAaqSThrCc7hXbgWGwlrlMF2s6ps+0oiOLC1F2s9zK2AZoTMaAfup1uG9RPlCb/HuCzvaCbBlYtlzdWvxihhH9hNlh/mJ+Z3mYrp5mtXbxOtArVnIVXbeqEBaFVUCo+ceBoeiXVg3N6xvPvAh9PMpSVkG9jdevD0yZZGjOn5S810Pl+r1yIBfpykmeuqp1Fp0nOTuoK8Xj2BAJXur/MxnNVZkEIuDVWDlf55ml6zblpfb/TUXQM4VPtz15+Nq9qtVYwUrClY9MpD6Tl1TjxVC+aqXupZod0xPNZCuubujM7h1KGaWdLM0roAxRr7UYvXp1G2cwKLCqZcXZddnzLV1kuUagM6GOzO0UxEviysnGlmoGjlaVkouRJmyxQcu2nEra7nTIlhOw5dHHDN/IkpEpNHtJvFhclhDsLm5YhOmbx+WvP66YTY0xnnLJvN5mrj238F55k2W1LNxCVz/LsTKQVe/i++xL7wRNNoOfVxmcko12A8IsvSX/zBD71YyT251duhB4OVxjw/ccZATrx5/Des+YKzQooz6gdaa3jbqzmDxalAq4gWjqcFGSqtQkyCcaaT6UqhxpVLnAl5wouwU8MZJUohFe1dozZay32fSA9dKiX1kL8UGd3I69ufUPDduVMFVdfHwFYRKZ1wan2vfltCtHumxBzpeQyNmjvJ0HmPNgdLZPc+cfjVwrB2mV29dhY99ZfexOp1KmWgSCO2xiABUy2DKoPpcfE9Xr6TpcT2itk3RZ9mytFCFYrJrKpcckZK5cZ/zv3ucz6cnzjPJ6xYLvOJ1jJjsHh/B/jr96b2kCqn1NSzh/7QUbX1wqOWnqjLdXxaMmtcOV/OxJRItRBL7pLgWqi1dvMwrlMz+vRRfvRW68WqNfZahBqMuUqCVWkZtCQWVaIsHM8Lzg+M08R58JhacbstxvluKNb0x2mHoKSUe+dmDF6FEALDuCFMG2zwiOu7e706flbNiOtmZ6qK1NrDJc01w4lu4a/XKVBpDeNcz+QRQaq9vrgz6xp5ePcDxnSR84CFvFLzSi1rz/e4BvalmlhrJGojojysZ+a0klUZvWNdZ0pL7DZ77g/3DMPA+6fviW1BbCcODs4weYMPnmVRrHi89Xjj+8S3Zc7rEVzDeKW0xDgFqnqsCE4M9kp4Fs1shhFjHGvt/DkLFBqXbuhAw1wTnpXReW7CjiEGvvmXv/ytnNHfJMzHFcWVeyS+0cjE0idI9682fP2fbSjtkV//y8i37x6Q+04k12K7W7IWJAimdQJrLTBYIdvMg3tkQJjOwpIrD+eZ6Cr32w1+H0gGUurk02RX1ipg29U5ecQ737mE2OvEujd4apQmBu8Dow+Umvo51Eap4FwPBRwHjxsNrSlx7eZ6pSWCD9cEYXp2lVHoW7/rZsH3FWBO3aDNgnUF4yoSup9KqRUx3S9EjekRCbV2+qPphVPwgZILMa1kaQSBwdquszN9ytRa53oKfb1UcsWYgvOhT+yxPVzWKsbRC8kGiUr0FX9vGO4dp+kdS1wREzDBUPkYb9GDe6t2a/1aG6c58uLmwGEXMNp4fbcj5UYsncS+H7fY24C8tNS7iGbl9G75pDP1ycVJrRU/DAzTRG0NsZ2UKWJpapg29133LdDeN+pfLtz8owPLXeTi+kVeUiKVuUtYuXqjmA3aHNYJnsAQDohRslnJZSXGmceSeXr8lnfnb7ofiFHEbkEdQqZ7KJmreZRD6YZnta59eCWOZY4YzbS0IKtiY0NFWVxCxLOxwl0bKEtmzgUNSnYzLQwUm1hKZGmFVBq1rQTfeLl7ifET65WborRrBg6AR8UDHm8t67oi0r9GpKEmkhuIenzY4u22T1zOF25/WNn+7QVfevfaaMxpudri99H6x5VAE6HlRstd/jyOE8UYQonsmhCcY5XKqkqVLkn1bWSyjqwX5ktCUsPsG2IHDrd33H31BV989g+RvOfN/IGm4Dw0Vt69f4uRhc/0T9hsXiDWY1zn6Ejpw/x/x+LrDxbGuh40d+Vf1dYv5lIKa0yc5oUlxy7ba4WPNuutKaIfU6W70sqaznkworjrxxkxvYNCcKa/9H4sTPW6VgG0KFoilxhZTC905hiJKNN+4JQvaLG01sfFVQuIJbjOHxrCgP/oxQLXy6cXM8YYUk4YhGBDz8gwV9n4dTKrrcI1Vdj67n8g0v2O1Jhr0GHrBXDOzPOFh/ffog1udrf43O2+a849qE17gbLWyKqF4hxzuvBufkANDHbAB8cSG5txx+evviKEwLun7/mwPDBOI60q1oJHuBsG7DSgWSkiNApLvtAoeD+AN6Ddrlw109q1S76qeyrl6kHUX1IVyGt371UV1k47YnTXLKKmNOPZD1tu2PHur7/n+Pbxt3FEf6OoNVOp5FQxXjCuFwCtKbkV5pJ49ZnnP/rfbDh+eOJpPXGjFiFRjCe2zn0KxnSncFFqq6COMDhO9UzZeObvHFUN0zBwc+t58eU9dutYqDS15CjUALl17p9zjnFQgreI2q4eqRHrTA/lrHIl4JouKa+texiJ/liEexsw1lFyRozgroXCx2eK6+SkFycG63q6tymO0TmMHblQmZN2Y9ECySumdQ+qog3jG8Z0t9oq9erL4n+07zMWpsn1ANickKzsNhPeBdJSyVpJKfUwzY9y+aZdcuxhCiOT7888vk80jViCDoiEruDcVuLuqT8fzWKNYENf4ei1QKm5XhPqK3Na8caztkosE1/ev2AcR1Tg6XLhaVmo4tjdCOZGwAMPypD/BybEYnvccUOvrGGHdSPD5pZxe8D5kY3p7pLBGcLi0H+RuP2Tic0XG47jineBmLfkslLbjAuezXQP1TMMI3UtUIW5PnJOZ07zAzVHNm7bSYZ2R3A9yXVJKy1eENOozmK56TkIpuvrnQqDqTweTxgnrI8Lw+rZpcAX4XO+2H3F/e6ecQi9E01n3p/e8qv1LX/37tc8mhP6ciabkdyUxS+skllSY1kzk99yd3hB1EhVQ6lgsIz+39kR1sqyJlIdWJeMCw23qbRU2LiJadgjTGizkCv2MrN7l/F/eyGcM80KqdZrwFMlaUHluk4REOuo0glJQZVxMBAzOSWMiwR7Ja94xXgoppPMzCkjKbNeTjydFsQIh+3Eqxdf8dkf/ymH118Sj41/8y//Oz6k73AvLHawTJMl5sivv/8rTpcjn332J9zefYn3Ux9BtsZ17f4Hj5jKtUh3PVW9JGrJrDlzmheezmeWkjvZVQRvXackWyjUK5ejs/OdsTjbi5Nge9CYkW5S6IwlONvFkPqx8O3hgar86FJ7VbijqqR55dvl+6ufiWKt8ObygbJt7A8HNpMj+EAIgeDDlS/SuSUlVVpw2BDIrWJ8X0GuKWLt9bJstRcvItgrCbAK5LT2AkXBev/jOe3qoF70pJKI745cLWLYmUDNkZquY/16LUwo1GBYLXx/esNT+kCjsglblnVBjOOL1z/BOcubD9/wcP4BE1y3nKd1rwlV9s7jhg2rjzysJy75gorifUBzIaYeEuitvaZDC8F7gg2IONKVb9VlxIY1R6qCtY5cFGcC0OPiPxrGbc3ArR54+Js3XN49cthsfhtH9DeKWgpqOx9HbG9Quj8HYBvNFIwX/vgvAk5u+W//aSZMntUkpNIDIZsDcm+6tCHXZF+TLbk2bu5GFpfww8Q/ePk125cT6Vb5IIUmirOBYCeWtIJUrHOoMcSYKLl1+3k6mbXWXjRb8Xjv8SEwXJOHUwXE4txA8J7WlHVZqZoJwTMEx7TZUXOhlJXUUpfoi7uuNxtWDU4EZ5RxO+D9hlgrS4q02rmMSp+mXkeQCK1zLkv/+lS7sZm93g1iKuMo+GHLzm5x3mIHx8HvcKMn5YZRrl4knfLgrGMcBzbbDZtpoJa+Bsoto7X7yfitJQyCTjPz8p5MpbUbjBq8M0zBdcVqrqQCqSrOdjL/YLuI5d1RCNPK+c2Z2/0Wa4Xp9Ybwk5H8Qkmh4C9w/NvIznwaB+vT1zo5sx88x8cPGOMYpi2b7S3bm9dM4x7vQ9/T2p7KCYpV0F8WhtVy+/UGu3U9B8fuMVcX1lwylcbl8sBghBIbKV/IdSHnRMkFJXGaVyCwlx2HIFR9IKVT31FnJa0rZ/PYqzxJ1FSYL5XjuyOTD7yWHV+Fn/NPfv5f8Edf/AU3h8/wYcQYJceVuJw4nx75x4/v+eHNN/zV3/03/OL7vyVdnjjfJJZdooy9epTmub95TaZbkrfrWD/YAVqhilLVdI5BXmhppdQMGbZuxNctxu3JzVFRfClMp8j2m4Xhh5W8flRSrIhYjO9Kgly77FREwDlUhFQyEgsvDgeseC7HJ9K+4V9uQBrtMlNTxKWKtkQlcXlz4nJUIitP64lgB14fPufrP/5z7j/7E47nmX/zL/45f/lP/1/YG8u23NKiZ/PCsd1ZHvLML779l7x9956f/uQv+OqrP8G4gdoKpfSx6B86Ukp8lNdLn+WDs6Rarr466UoW6x9vrmoWjOny3da9TT5O05w4vLedEC1gncP7/msIAcs1Q6fVHy/v1trVN+VKyK0FMDT6JKcHBnZeUJoj38/vOV4u/Nkf/5wQ+qUsznQZujWIOKo2LvNMaIrzHh88aYl4b69rqdLN2GzPolIRvA89BZWV2gph2ELtEyC5vg4+7sFrVdbLTJB3OBcw+3vkan1faiLVRNRCHRzNeT48vuXX73/djQZREGVeT3z9+o8Yh4EfPvyKN0/foVYYjLsuryzGDkDlUg35w8I3794QWwZrcL67fMY4Xz0nLDFGvA3XlOROIgzGMTRLLRZK45Iz57TgBgcVauM6qc2cL0esVO7GLTe64/LrRz58++Y6+frDt68v5RqYKF25Ulv/eYn0zDSRQnDd9uDP/mc3PMaFHy7dycYOjqlNxEXJeaXainNKcBatirQB2zISlM++2pO/C3x4uvDQZvw2kF5YGBzT5CkaWI4BxOL9gHX0iZh2SXc3P5Orcsh3jx9ajyXwju0w9QgDa3HBY61cgyfzj9MDJ43NYGimPzO2mR6DgAMc1gU2YWKrIJczD5f3sN0wbnqmDjr0VUnNpLzirwZwWle8Cinrldx6lQBfHZ7FdAsKbwZ2mz1RYS6JJgU3ePxgGa5KHrRSNVO1pyVjFbWl30NJrnwa+scMlrZRknlANZMLfRqqXVX05f0N37cjTSONRv4YHt6gmsbbpyNU5d3l3LloP7zj7rMdf/Szzxm+nKibbs6Wf7VS3yq1fZrPiej/FLzGn/GMZzzjGc94xu8NPk1w/IxnPOMZz3jGM57xG8JzcfKMZzzjGc94xjN+p/BcnDzjGc94xjOe8YzfKTwXJ894xjOe8YxnPON3Cs/FyTOe8YxnPOMZz/idwnNx8oxnPOMZz3jGM36n8FycPOMZz3jGM57xjN8pPBcnz3jGM57xjGc843cKz8XJM57xjGc84xnP+J3Cc3HyjGc84xnPeMYzfqfwXJw84xnPeMYznvGM3yk8FyfPeMYznvGMZzzjdwrPxckznvGMZzzjGc/4ncJzcfKMZzzjGc94xjN+p+A+9QP/L//V/47gLfd3d0wby6n8wGl9IM6VfFbmJaHGIm5Ai+V2uuWLn3+Bvc9k+4CWM3Uu1DWh7UJJkQ/v4Rc/XIhkJASaDgx+w+20Y+cCm1EY7yr29kRsC0tuYMG7gWEUjL3QzgU+OMa6IRhP0cpKxW/BT5X55HnzdmA5D+yZ+HJzIKhhuRTOcyNmBaN8/qcTX/8vH0kOinjevHvL0t6w33/OsPlTKnesqVJiJVSPpjOaz4SXW1aOKBVKYUg3iDhqKOhgEbdBFExJYBsKxHQmloCSaC1h3IBjYv125pt/+gv2o/LFzwLbQ+K4/opUL8xL49vvMhodP/38ln/w8xfsdxtqKbx5OPMv/uqB2uAv/vzn3N284Jd/c+Ev/9kTD99FKIoxhqZCKlBUabWw3RtevdpwmLaMMkATkIyYhoilVks1MH1ueP0XI5HGP/tv/hUfTg98drNjcI6cKhID/vKC8xv41S8fOJ8y//f/x7/4H/HY/vbxX/6fPBTLn/3pT5h2A01WUj4S44XLUvjwobJcGtYACsHBbm84Lw2qZecnJhmRKtRWEL9iyJyOhffvwQosD/DqM8tuJ7SLwYcNhy8mwr4ig+EvfznTamAII5vdhsN+wJhKzCulJGgNaxxh2uLChMHR5jOGghqgZkyBYg1zieSSCc6yGw0vbyo3+1ek+TP+9tu/gqGx2b7o3YyeGTcw7TylJdJieHqsrGs/3xiPYcCJ0DRT20LVGSUhpqFkVCulgsdxf/iMmi3HeSa3igooimqjaaLmwmHzCu8PxJxQbdSaaBoRUWoraHNswguc25FypJWIFXBWQBsxzWAEcLQmaCtMPvLHPwn88as/4/s3lW9+eMPx+5lf//Mnylq4eeX58o8H7l8bpFWWNRFTYDnuWJaF6RbaLhL2E8M4Us+Fcm7MT5mH7z+wHyv/6Odwczfwf/zfH397h/U3gP/1/+E/Y9f23Bzu8JPHrYZxNoQM2ipUxaX+8x5CwDmDERAjWGsRa8gezkPl/bTwwR8xKTHWAGLZrcL4mDGXioyGZQunSbkEw0zl9OGBeDphJhi2gY0JTNHgs8U1i3MjwQ+41OAYsStICGRTeVjPXOKCdwFrPVIEWw3eWcY7y/5PHOYA6zdKeWuoqiz7QgmKUcN0mLh9vWG3BY0n1mXBHScODwcOm6+4+Yf/AP/HXxAnx9oypWZaK9RWyC2jrbD3gfN8YrOxTB5iKVxyJVZIRQFhtBZfCuu3j5z+v7/CPq5s7ib8FHAxEZzy+id7Xv7kgN1aPsyP/N3b7/lXDw+s5ZFX/h2jJOYlM7o9N4cdyMCHJ8MPj8LqBsbXW47zkdPDt7x2jt1+x9kPNLelNc/5fOZ0fkLmys1xy+1yw627xW6F+pmljQZEaKVRTyvtnKAqxhk0F/wjDG3L//n/+n/7956pTy5OpiHw4sUd22mk5gVWSJfCaT6TFjBlYtpsCDtHTUKOM09vf2AzGOxtxO4azUaiHonLI0YsLtyzCZanDx9YPjR2+4lD2BPYIBmWdeXp+EB+8w4dK2aYCBuDbCOmFExpBA5s9y8IzRGXC6fHC0teebEJHHYju1FwLnA87hjWgMcz4vECwVcKEO4s9396RMbAZvyMVCuaV+LxEUsktfc0C8Zu8BtPyxXMyFxPlNJofksqM3m5cLCNXAvn8wWzNg5j4uXujjB5TvEdj+sjMSmpbsg6I6IMWJy3DDeBm58caE9HTucjS15Zk6W0wGleuZwqQSxCI+cTDx8emNfGsnqMCoED8/sK6xOXx5WcV0or1GoxTRhC4PX9nmEIaCmYoTFMBoPQVHBuoKonpZVaEsYWZKvMLZHZsQKndGGNjZQdQqCVgiQDS0Wq58XhBYep/Qdfcr83aEpaCu/eHPl8uENcw4gBHMucOZ8aJcPoQQssCVJuiIFWKzUt1LGxDQNOgeZBLEYizmbEwHTjcTs4vLzl8PVLFMs5HzldzrzYGn72hfKrbxKNgdN5YV0j1sJmM2CMUHSllkxbZzzKbnPA73bMlydEle24pa0JEcX7XtjXVqjNoKJYv/L2/CuSKDVGnD9ye3NgnLaonEjlCTGWaWcJA+Tk+eH7hXVNBAdJpH/OWkFBrAGtiFHECKJQNPH+/B274cD9zYF1hfOSiCVRWqZRsTicm4CCkEAEawVRh1DRZlAF0YpowQmo9aCZVhK1VYLzgHCZC8dzpLXC/SGwHAObr1/wGH/JnOB0XPnsZzvu7nZM+8JmOjPZGVHLMB1Y0sDNtpBzo2yE92tlHEc0N2hKqwnVlcMLy2Qqr27hxWH5rR7V3wR2r/b4IhQ/U41FrCU6z+QCow+4rLSH2M9jM6g6EMGIRUTACjJ5dGcoXqltxrhKbJFoKnkXCK2yqUpYFb1UbDDYnUeC0i5KOysjgd12w8aN7KsjFEtujVQtyyjYG4vxHvuosFRaKVhj2BxG3N6TaeTHgp6EoRo8BpMEMyuhwODB7j3jvWMVg0bBDo4k8FQbJTfSUtjGxE0YuP1Hf4L9B59zNomYZ5oquVSUhnOKl4p3DtGK8w7FMqdMrkLT3tkojTWunOKKzSt6PtF2loeHhf1gCbkwnFZe3Axsd8L9S8FtG+1JMQ+RNR6JMbPKwDhu2W0jIVjG3YR3e0pR3r25ML85E5dG9BXsRHENGR2D8zT68KCahkpBKITqIYJYwalFFkMuStaZlGZyWskWdPAEAofmGa18ctHxycXJi/tbNlvhMr/l+OHEZV5IzWLahLOKqKeVBlmZvGOZLzw+nah3lnGsSISyQlwMcxTIlRILZSnobBnNxNZtGRhgFUAQPKgjFaFWwaun1kxrEXswhGDZ7QMH5whiWc6GpQiX9xDXwvm4MngYnVKdQbVQ8koRMOIZNx67g/3PG/Iys7SKSytiYDNMpHDAiUc0Y9oRrQtrbRRj2W6+QM2B78+PLGnGucxeDM5YvBswwWO94XbYsLOBpN9Bm7nzt+RWeSwzcbngxgOD3+IHCxQOXwineeT8sJJKxo3KWpXzYmllojnL07nyy2/OLGkmJiGYHVphtxVg4e3bM7/6u8S7HxJptQzeMg2B/WbL/c2BzeRpNVI0gxcGHxj9iDEj61opDbQVmDLuXnloDyy/fEPY7nh5P/D6dkcwIyVVanZ4MzEOt/j7Le6lp9X/gNvt9w3Sf6vFInVELLQWQS3GeGou1AxFwTnITckRrOu71Ngqta1g4OXNntFsiWtDZaZwpJbC7dd3NI7sbre8vNuRc2R5N3M5zbQLfH1voDrenxtLBHCIsTgXcK53pTEuLDGSFYIbGcOGaXOgpEijIc5gUFzzOAPaMsY2kIXajozThlA8qgNLTMjpgvEDYXSgQi6F2jKiHucsX3215/EhMx/BOIcznmYcaEAlo1JRrdSWQCMNEJRzfGItka2/5Xa/4bIEjheotTFMW5pWalnJJWPkem1JQ1VBFQM4EbyTPlnRjHeu/wxyoxRlt5tYZCbnglhDa5bjMfGvfvE3vHmMgOXFy5FRDevpCWmRg0t4KVySJyKkekHzkSZCYWIz7mgRyIqVhvMZMStiEnbXeJMNjx9+Kyf0NwpzZ2ipkVLCNIv6QMVhBJwFaY3BWpwaWmtQG9Z6rDUYIzQaaEGqIpqwpYExrKaSyagqRWEQB1XQtaJrg5gpkijHhaFYblxgFw0ba7mxE+IdswhVQAHvFP9ygA3U9wsUxY3AVlBT0EfFJsMkA9vNxLTz4CBrwewsjI0SCnWoIIEGZDLzmpBqkOKwbYeMN9z+6T/B/clrzvZMrImlJEAwYghOcFahFLwYUkkUYE6Zuq4MWHSA3CqxVpbWWErG5JXd1ChBubxS6l1kPMKdbfjB4IJSWSmxspwX4pyYMLy6uePzyTG4SOYRxWHMDdkcmEmc48p8WtluLIf7O5LdkpZHLqsBZxAtNFWsKNtpxFvLlomREd88MhdCaYivxBBJurC0laQVmsfKljEHRrW0T+xdP7k4wc1cYua0PvFUz5xjgmJxYWTcKM0UNCdqFdQI6lZWWXBFSOeMZEHyQCtKq5ZUlDWtXJZCS4bgDUNRhtQIAt4IIgaxATfskS3IuGFZIsv7ytCE3QvPMFaG4Yg3IK5wWwyljsS18MMPmTFYgo2wzkj2UAzVVsQ77Ab8i0jZPVCbIa0DuA8MYSRMAzv3BTZYskZqXaA0yAMalNouDF7YBAunRo4VNw6Ya8cmZqFZz9qUGt+SypGd97zQDcZY4mHkBxk5EigFFl2gJvwW9ncT1SnaKmEvPF6U82poqhQVzhFSqyy5YWXEjQe248C4Udw2Y5sjhJGbjcdOge20ZRo2BDeyGTzBGcR6qhqq9Ke2AblW1pKJraCm4MeE2zY4J47vP7ArhV0IjGOgZVgbSBjZ2hfsx5cMZcQYR8nl7325/b7hfFFGt+X+9VeoCyAeaKhUrM39JVmUhtIQam+soYJxfW2jtZFSRk3j7tXIt7964HJZaLUiqpR6BlNwpmLMjPMzY1g5lsbljXA/Cl9/Fnj11Q3fv83k0sfXzhmaZqQpxgpalXm+YIzHW4+jMY4DJWcqSsqFlCuoYfQ7tgNYc6HVBTGGNRaC3zGEDSkXHp8yu2oRG1iWI0jFmcbgYBwDr17veJMXUiyoUdBMqZGmGWOlF3ZisM4zWY+gVK20WpjTBwaX2I1bBrfjEh1giPGCakbE0FrpXbfpTYwxFm89Q/DXFU/BioBapuFAnk/M84lSLywpIlbw3mGsUG3hKZ6hjbgVLt8lPpwSfqxMU4WSWXLllGDNR7xZKBSy2eKsJ7QJXSB4RWxE3MpmzBhVbvbSv/+9kv2DxlIWtIFrgYDFBKXaQiwKObGJhr0b2QyBGCPWWobgwYBqg1LhnPFrZS8Jb4Q8QDQFi2GTHP7YMBclR6Uk7S95hKkJOTpG47gzA4OANwXTEtoUKwMbF3BW8LbCqCSfEW9YG0SbyVLxJ8s2bpjcyGYYGPces7EsNrNKho2Spa/ES0pUMloNIgbNjaqGIIHb28/56ef/iOn1V5zMQqwzSsFJQRFEhVYgJ0WrUkxhzpFiHVkFUNaYMNbRRDDGMQZBBMQJu/3E8fGBnFcWc+LgPYdxQ6FxPi/wXWRJmR/eXLg8NV7vbvnqsxte7hzHy/d8+6bx+CGi45m2HXlaDBcTYMhYY5jGiWAtyzmzPpQ+4bEFtGAaODVsdOQQbvCjQeaKqeCKAd9wg8XYQIiNoSqOwLZ4hmKZ3ED9xMfhk4uT9w+/pqKoFeqQqVOkXiyqhiEYzBTJqVLXkWoyZr9g/FNfCZwipgihBVAhlkzSQDKNFgQXPKNxDKURmhJcw6gi2piGgen2humVoN7x9DjxdBxxaaUsK+v5iJSKNw3U4IaR7WEgP3ZeiW8DdmdwtlKwlFaBCBO4A+jtkdW+I6c9and4A9ZZxo0QL1ByotUVraB5hBywvqA6U2siuIbd7bBimUQIzpB0JbcVLeBdoGlh8AN771h/dWH9Jdy83rFRYR2heaU5xdjAsJnwh8LjsVBbIriK8VCboVVDUeUyVxZbaQK7ybHdHbgZXhGGSLiNhG2A2fAigC2O8HGXeh2hilG0WQwWqnK5LORzxviRVpVSMmuZWc8XgiukNWOd4LNChaIV1f5gSbNY4xmMZ3AOwVHbH35xshyVm882jLstVZRSwJobjAGtK9YKQxCqVmrVfztNsoIAxoAoiGk0IlXP3Nx5vvsmE1PFGWW5RIZReXz7gXY5s9lnxhDZj4KLjvubG8bPR87GIENjWaDVTMqVNUZiXrlcIikVai3klLGtsPEBYy2lZErK5JKxxvJif8+ru3uUB6rpP+MxgGWlJDDDhBFLTjDPfU3oGGltxrnOHWhaEVOY9kJtmRIriiIYVC25FJTWP84VtDasMag2RJRGIdVKk0jwG+6HifNc+tRG+veub+AVUaWWgsESwoAxjloazjisGdhtX7DbvmAzrnz3wy+Y1wdyUZzrvAfvDWGCgkOK58PfvCe9LSAGHyrSKv0HV2lVqemCHRpRA+P+SzQ12grD4BCbaC2imhhsYzKwE3BFQf/w15yXtzNiHcFa8J2vYQO0tbIskbYIPlaqRFAYg0OkUpvSaqM1xRqY1OLMQLKWx3SBljEYdnlizAGpilIx3oIIgzcE8WyvRe40CWaXKNvMnA1tzRAbg1i2ZiBYQ5ZE9Zl8aGRtpAZEy0Y33PkDm+2IHyzu4NAdREnkFCnes7hGMwVqwygEHMEGKkJRw25zy09/8hdMh9ck3yhkqjacAed7odpWJT1k2rkS9gPrrpBpQOk8LS/kqleuhsNbwYiiTYgYYmnkprhSWZYLSSeymTgtGflOcQ/Kckk8fJhZpHE3TezCiDfKMq88PmbevVeSvdAOgWwnqrM0ZzgdL/DGMW4EfxHKU5/8TqNBm5Jzw6hh9JaNc4QQ0JyuDYdSNLHWRDEVa4QNga1uGJrHV4OIYI1+0pn65OIkpRn1jdYcxUSay2QqrRgEiw2VrI2cDHgw04z4R9YWWdaKbxPWGmiFVFayDuA2+N2IE8ummL7Pc6CuQjM4LJuw4e7Gs7/LFNtHZyWO0AzxtPKUI5ewgGScDYzO4cKE92CrwZaBYALiHOosObf+cnaVOiYYz1STyS2DRJwxnQcShBAbdVnRNWN0i5MDTUbW9sQ6P2K9JbgdugU9xk64cwVrwONRMxDMFi+GySmDTaxl5vS+4TXQxoSaRikZNQZvPCIV9Ymn9cLj45FN7MTBdRVyEVAl54qxBT8KbmcJXpgGhxFhMIFxt8G/ckQMGhtqlArUBC3rtQBRSm2kCmtsxJKxvtFUOZ4vPM1HytMZ8653ofevHMPtSMkjpSoqlZqh1UaisNrUuwIqK+t/0AX3+4SSYBw3OG+pKtcdce/G/VAZBqFIRNJ1KlEVBKTRuQkC1gjGKqUlzusTu92O2/uB776JpKKIVIbBEOfKQmXaFu5uGntnmE/KZqdst0rRle0uYqUAgVQFuRSOl4XTvFBTQ6SxxkzLhd3GYY2Qa6XUhmrjZtowDge2U2RODxTNVBXG0NhNhaYJPwpVhdoMtTlK8Uxhok+M+iolZ+2rVwvjziA4Uiy01vkF2qDWggKtNZo0jOnzJWvBGqWRKRSqVqag7DYjEIgp0bRdybJKKw1p4J3DG49WMGLxw4bd5p7t9gXeTzi/A2t4eBi5XN5R6opxgnOWIVhENuTHzPm7GRMFOwCq0BobD8EbLll5XArnFYqz3Iy3xGXGjSP+sKPqDvIWywUjM74uSItIrjT9tMv49xnyDszGUDYZDAxhyzhtwWWW80rVXuiVbLnxW7S1TlxW0CaIEZx1BOcIRnFkLmXFrFDiSmmeFkek9MmDNQZjBeMNk7e07UA2DXMH9dYRx0xdLcwNnxWvyoDBI6gWrFHENoo2yI5N8dzojhu/Yxw9dieYW6FOjTEKYW1UMlWVTMWKwxiHkQG1HjGO7eaWzz/7Obvbz6gipLxiXKVqb7btdUqdL5n1+xVOYKwnhky1FWs7mUEEmjWUqnjAWEVqw+VGq5a1KMZM7HWilojDURp8WBOnHBGr5LmwnBLFN+xD4nK3EE8rv/71I48PhVQGsthOXDV9vavOss4R+XDGrI5NMYQaGJplYwMtNWysYAzOGKy3+ODRAmq48suUErvQw6F4AkN1hOyg9vXYp84RP7k48S5gxsxcoJYrXyQbKhlbQLA0M1L9Dhcq3gbAYkQ6J8HeMdhArRdsidR67bC2AIo5rzTjyLaA8wSxTGZgvzXsNkrwT5RypjWLM3u831LaTLosLAsUFB/gbu/YTYFtLaQlITUizWOdYkOhFIv3Hhss1SgVQWUC51G5IExUrTjrGb2nmAS6xZR7vNzSPKx6JuXI6KdOPCQyxzPBOkbnCUHYe0+zjsEZvNlQykI0lfHWsflCmUvmokcKnloNLfWRX00GDcJiKg/nypwUqUJalZI7QRBJGFOx150+Wsn5AsnivGeYHMF42tCINlFc32fmk0FXg2ZhniOXtVAwpNK77Iayxszj+cwpnmk2YVQZnWUaDVImBjeylJkm5cdpTE6VS5rJUmmiRDf//W+33zMYPIebu646wJCyUmoFmRi3r7nVkRIjZY1cTmdO51N/sTZoDYxc1xsKrVVSiSjCV19vePvmTForrYK3jsPuwGaM7LaFVzfg75UPT42qF9LaqAZMy1DOWOfZjJ6UFdU+FQGgQa2VucyYq4FAbkpt4J3BuIDYI2s5kfQBFaXRMLYwjRWkYcfCmhNSAfXAgDaPouSSaJJRPFot3jnGyWFF+tRmrddizCAYGgbF9M+jDdVejDTpExRopDKTSuFmuulE/JpZS+urstqwQHADwY0YGXBuwrqRMGzYbW4ZwghGMM7w8tVLNmPg+OQ5nn4g5hlnYQwT89Exf/8BjYqhk3a1gRQYRPr0SECrcEqKmYSaIt4PjLefU8OEkXs0L8iwYqYLNT6S6xFNT2j7w38eNrqjlUasEcUyDBu2+z3ZnjkPhegjrcHYDNY5Wmuk1HpDIwZrHGLMdVUHpkEohs3qOD0tPOULSRtj8wRj8d5eCxqL9Ra1YEah3Fp0MohaWlMIBrsLuOZAoVSltgZXjgtUpho4lIltG5lCYLP3+NtG2xWSy0wibC6GlpRYlSqKeKFYYQW88ex2r3j18mfc3n9FwlBqorWCkUrTihHBIahq30B4i52E5nvziDTQ1p+NqrRqyBm09skFtSCx4SMkCQzjDWa6JfuGNEt0MLf1xzWxyYrmhjZheXsh2EgpM3/9N4+kagm7LcYNoJ3DIwhiAxogV6gRfAgMHrxaLBZp/d/QgoHJwmgRGcB7THLomgg5MGaLUcWgSLuu7JKj1f5ci9hPOlOfXJxoMbRkQRytRGrs5ZJxpk8ty0BwN9TNDcYopqwYKn6cORwcQfaYJsRUGNuIKUqtoF5ZNXJZKrVaNsFigsEGyzgExg1Yl0nlPafTieOHAWHi7vaGUjc8nmZSU0QC3gf8OLG9nRh3idKOpMf3FDJidjQruHDHdjdh98rqoTZPK4ILI7WeUS39ofET1t1gB4e0ETNPaFKaqxjrcWYk5kTRR7QqaahItWRdCKbivVKlT5NQx2U+kdKZlzuY/gTePTxxTkdMeImVoasKSoMccNuB8XZCvh0JzqClIjmiMYOrqG2oAq2PQLs8eUHSwLgGqjZiSpzqmaM+kGXpFb7usGVEsnC5dA5RE0csiTXNxJw4XzKnJVKsMm4Cox0QLaxzo8wON9GLJJuYDhNeJlpz1LkQMSztQpr+sGWTALvdgdsXt31No2CtEFOfiPgwcfNihGoocWW7O6Lff8v5cqRpo9brWkcErVBLL1pyWbi927DdW0qqGAfD4Hn9+Q3BHzmMC/dbZRwb46A8HjNvfzAcTSA3iGvDuZUwJahgKTgLGOVjjTI4wRlIqjTtUtv9JnA4OKxfmPOMmsbHBUrTjDHSJcKqqCagYk3FUIlF6fzGSlV7LZgFayzK9ay6CKbLmrGK2NpfHFgMDkRpzXeirNbOQbiufmItPJUnbvYwDoa1VLSVrk5zA2PweD8Q/I5puiMMO6wNOGsQ03fotQFGONxsce4VKhk5N0avbP2G02MlvZmx9G7cDdr/nD05Qu++hP1kuFTF+UBajoybVww3L1gqgCJmQENDt42SXmHLBVneIcvb3/wB/Q3j5rOXXNZH1jZjtL9YrEA2SnWV5BKjDDgTsNfquDVoaC9VjdKaUmultYLWTChwk0dyTJwviUIEZzCDxYkgzmKcxYrpqkSpRNepgXIxMBdUri/SDKVmSims9UJsC1kT1gg73XFrtkw+ELxjd+/xN5noEqWuBAejs+RYmCo0EVRhtZVsG9P2wOsXP+PFi6+pYoglARkBcq6Iua4uMX3FOTr8lx5boYYV6xqG2isREWptpCS06jCu4Ypial/F5yK4YcDf7tA6I21BaaSmzLVyzgu55c7FEYdRy+lpZp4j6Tzz3a8zNgi7FzBJ/97hr381XcRhJOG8J2wGRFdqq6ilk5W9xewCbD3qBDUGHS02GxyNUAO7NrGmSq0RLbUrtNQAgvGOTx0kfnJx8vB0QVHMtKOZwDhY/H7ETQ69PrzBOZCGYUOQz9FWGDZP+M1M0zNpLRiN+GZQMRix1GqopaBq+ngoJTQYCCNqGpVGzJG6LsxPieXRMe0u7PYTTTK5Goa2wQ1C2Do2+8B4cKhWdrly1A8oCylmYtmwcROb7QY5XKghoVpoGgFPa5l5fYM2y3Y8sA0D3kcKK5VIkQ01BBjA1g1P57cgH5jCBkLB60TWheP8Dlsb+BuyTlgCaS4s+si6PeJt47xfseYVk5vwFZaUqbYgwSGD4cX9hu8kMPkAEklLJrY+oTLeIqb2y75fAyCGcWvYHTy1KpGF9+Utb5fvqUtka+7YpZGggkjDWe1kXuswqWHMiPce0QRiURs43AYO45bTcsLISjwZLkvk0iIMiWHXCxhTRkgOrY7j+sClPP69LrbfR/z0j37CsAuUllEFax3GNHJOIBV1FmcteMfuxQvcOPHtr/+Oy/mItoR2Lie1KMuihEGZJkVl5e7WsZ4yzl8JrVIQqXhjGIywC5nBK8ul8a9/mfhhjlTnGMaJaSpMNWNo7Ee4TIZ5bVRRrIXd2NU5UTtNczt4Xr0YOBwa4gqtKYJFNXUCHoVgPaU5arW0ct14mEqlv1CcG+jzI4uIwTuPKiw5sq4rqRXEOxCDUhCtaM1dLWTcjxwRZwIqvRDSqrRSqCWRS+bx6cTtboeVgtHKFLZ4HzCuMk7KdrvBug3WBKxxOCpSF3IulNooKIqlGctm/wprLRtzZGe3+OOFsThWyRiBMBlGK0jrvjTWFpwXboLhIVq22wlBOS5HXo4O2sCyLLTry0WsR9xE5Z7Ni6+x83e/5dP6Pz5uf/oa/T5xeTqiS2T58B5bMg3FGsfoPQczcPAToQqm0s/DRzKWgNIouVJypNaGacpUA1u7I8mlrwRFESdYb7DOYKzBWIuo0qQxl0hbIWSBpqgp1BqpGWwTsktc6plFFpSKZ+KF2XIzbbsnkPe4SXC+kWqhxj7Rtp6+CsyGTet3rkGw0y1fv/gZn928pmphiSs2CEhFmqHVXoBp6UV3aY3YGi4YjFFSWdCaEaMYhFKUlCoxNjZ+4mCErTasWprfcnYOFWEYtzgMD+9+IJuZcDNQyaznRooVgmcMDk2NHAvffZip5wJp17lfs8ddPIgBU1BVrFosMPjGbivYXeViVmo1OPq6zbsBt/M4PyBrppYzaB9QlLzSSsUUhxQHqaAFslaMyThvMX64+rb8+/HJxUmWlZgb9hwIm4GbFxO3n71ke7el6Jn5+IZRzljOnOcdZtyh6jFSqPUJNTNmUEbnsMHDWSgEnIzc3jvMuCWoo8mCMZmmkaKBJSnzQyMTuJyVNAsyPBFbwZhMSQVtgTB6toNnGAxiCqoVY/tuOsaMrB7KS8TuOZ36WLhsA7Jv2GFB9Yg3A6kMOKnshiObMYBmLrIgfofbWUqN5NZwds/9zWc8Pv4blvMTTZSdvWF0lhA81UbmErlkw7ye8WpZ5+/ZSuee5Ny4u3nJ7fZrBu9J7chxOfK0nBCnvHq94WYzYosF2wiTY2gF58ENndjqXaAWR2uWECzDUBBZSFlY2omlHqmp4nXLzt1xb28ZtltqLtCUGCPihM000nSDiGFZM8fTTKyRzc4TBkc2K1UTxnhybqzVoU25nCobm9kN3W+gLYbNZWI9j3//2+33DD/9sz9CfAYMNLDe9a4sZWKu5OYZB4f1jsvlzLiZ+Nkf/wPefvsrjo/vqCX1VVBTCtLl65OgDV7dO+b3jkvLLEvk+x/ecn9bITbsKvz8J5bNBkpsaC7U2jjOQnmAzc5xfzew3VR2m8LLu8b3bxLFNIIRNmFiibGvaYyw33lCSDQarVmsOJy15Jo74dkqU/CsxRFjRFu9cpg+drmdZyQI1vTCJZVC93hLXZIulUIll0gpfXQttY/lU24UqRiTyEBVRXCMjExxg6yWunc8pZnL+cLkYBduGcMOGRvJfMD5R6w7ILKlNEAESsZLorXMEiNFhUov5q3x3N3tufOOKd+wy5Evb+5493TilM9YL1hrmFNBMmykr55qtew2B8bB8f7pkVMyfPmzM5vDHq1CzoVcI04s1hpUlUXOsIm/1bP6m4AF9uOefL4QLyfi+oCZC2G/Y++3TPs9n9cbDjXA5TrGM/TtSqtoS6Q1Uj8qYSpgLFYsN9stqLLGgjfmOpkTrAgGUO0FixkNzjuMt5hWaFmRWPDFEa7ryyICTVCxYByuDTgspjkESymNdS5UTeTaaElQr9gJrBqGJ8s+DYg49OaGVz//RxzuX6JGSXXGiGJEOecFWx3BDIg2mlTmUllzwWEYDN3zRxQFalXWXCkF1lVx1jEYIZTCUAyD8ZQQiGIJCDZU9rdbnt4eeFguECzGjgS3xUpgGwIb67FPmbpkvBfMweD2Yx8MTAPWT7RYqGnBlEKwgTAouyBMttKGmWOd+xrIjEyDYTCN0TVsLuQPmXY8IU2oxl7Pf6M20GyxbcTTV0IlV3LNRCDWTyOIf3JxcnuYOM4rtYBYMGNhvBFuX73AuVfEfWYjv2AwR371gxD1a4xZaClRr5cwUpDrvjhFy3rqbq+b7ZbNdIBaWcq5KxzsQDEFMKwX4fg0UlZPE4vyxHl+guSIxwnVgBMwNhLbQmjd5yHFyHL0mPkVW/8lm5sDN58pdqwkPCuB7AvSDpiysh1vmQ57SvoFO/eWICtmfEEqlpL7Xn5QJaYjsSam6Qvc5gXnU6YaGNvI2BIBwwLUItA8m/GAdYYqR57mB3Ze2I9fsNl8zjTdMkjA15E6WZKeCcZzM97x5U8jD3+14Ictmy937NYzKV9orWLwBDdgpSFywfiVNVdO351Yl5HVJqYBNuaGTbtnx0uGNmHEUMTRtltijKxrBCs46/E2oM6SvKO1wjonni69y7i5m9jd7WmLIa4rag2KJWrBmgVcnyA4gY3e/j2utd9PDNsNazoTBoc1Bitd6r2mzPEyE4YRb7fUK8djCA4ZPJ9//TOsNTx+eEdKK60qmhWzwOUirFtlnArDaFgusJ4aD+uCNuHXR+X4fePPf+r43/6XAyaubES5kZUWBmLYM88LKTXcYAhhwjnH/Z1wU8DSsNHh3crIGedhMzm86x4dQud+fOxr9PrbNE3U1aPLwvV10BUzVzlv074GyklprRFcJdVM0UhrDW2tr6+qkkpBm3RuR+1keiMGqYIx/fP6bCiPSvl+QSRRv460KXHOhq/vv+LPX/+cc/3Am/wrUjtR1eN4T5AtOQVKESgzRleaKIU+ybDSpyqGhrWZMHrIW7bTDW0qxJQoYUZ9JUplnITDix33d5XjceHDOrGZNixx5sPjE+q2nJ7eMU6vGMOGnBNlWSil33OqmdPxn5P1r39bx/Q3hstff4u1jo3Zsh0FQ0ZEaFoQcUx+YIPHNyGj1NrASHeGzYmcrh4g3TYW5+11fZLZBos1IyWCFel+NmJwGCwGjMFOjrIXZFjJ7YJoAWkE9fjmcQSMGCyNUEZa8RQjrLXxplzIBUL1BAEqDJtKrpCj0kJDbhSZFDkCzTANN3z2R/+Y7Wdfkg2kthCc9hV4jlALVMFWcHhiq0RttCqMzjMK1ArBjKSS+0r/uuoMQXG2rxi9m7DNkzCs3rGWRs3KQCO0zCt7Tzk9YgGfDK5NROewQbBi2OeRmxzIRgmbCe8COSfWWllrpeTeLFhjkdYIKrgE+phQAymunHLDhMTFW8a2sI8Ju+6xZ0M795VQo5FawYWxO403QzCWjQ9YNaSUWfJKu0TkI+nt34NPN2HbvcLbmTkqyUbm9YGHpxk7ZXa7G4yNVHOm2LfsbzwbcSzxiErGBkucN8yzI+aBnA15iSznzFxWOCTssKISucgjxhqGsEFDpfnuvzC4F4xjANMJc/Eho+c9smzBNS5PM4+XM9kvjAfDfgPz8Ynzg2cr90yfb7n7o8ZwCxiHZJDmcbpF1OEL3FjF2b9ksx1hvaPGAa17tnGkJShlZmqOYTNyMkqqC+ICu7s91Iw/OSQZYquczZFLrayt776321sG94JqMqXMDNNrVCutzVRNpHQhlwsBj7TM2jLbl5bHfw37ac/2iw3n+I7HY6OVhf0YuL/ZMo5gwxmVTFkm1jmwoBjnCTLhEDbimIxiczfSMU2uhz+wnFfWnFGdybny4bhwvKzUrtckayXs4fZ2210xY0My5BJZFiEbQ7MzFGUTR3xypOUP34VNpa8zrBkAyKVgzIA1gVYumGAY/cSHyyPWGrwzVBVsGHn5+VdM08j7t284n49X9RTMc6NRMUER19n7r+5vuDlYYlz4/sPCr79vvH+feTo2/tO/EF7vlM2QGNeZt9Vh/JY1JpY1seaGtdfLRyzYgTmvKJbbwyu+/MyDOdN0wWJxxlJqRtSAWpS+YhnHkVgdzllatdTWVV1NG6il1AZt5GbzBbtxx5Ie+O78LU0aoJTcyEmpVen7LHpBI3RpZVNMd5lHV+HyyxW+Wbk1A/svBh7TgmwVZ5V/+OVP+KPDS/7u/MD7lrsCRGHJR5p5R8oDDoPRTvg1dqAhV0nwAtolrMZEkA0fJGH/4T0vDyMf/t9/iWmVhZkwCDcv7mlVeXp3odoNanYUrczziVYU6yrz+S2if4q3wnYaKXVgXmfUVmAl1Q8g+bd1TH9j0DczZRdIQyV4MKOBCcRWpBg0KvESIRtqaZ0UbjpJO5cGGJy1OG+6+68xXf2BwXlh9AM6mP7ioxPKjTWINYi1GONoVGRdyLog0vC2K2SsdBWXGEfTQmqGpN0wrbbGqpYlVtpcKbUg0VI3lipCyZZqG7U26lDJo8fsdnz5j/8x46sXVN/Vp5UFyGitaFOC9RjniHGFrAQvtKoY4xBV1tK6QtN7gnEMwVIaxNxIOeENOOs5YXkSoWqjxoWmirMeA+S24tbG67TBpsglZaytLLVz7TdV2BvPuHWE+xsOn78Czbz55jvev30iLwtDEIa7DcZDSQmrDSkOv3qYPdtUmOORkjNpKNhWkGQJ64aQPFX7M55S5hJnTIqUpqSyshkCwRv8MDF6hysBbd0l+1PwycXJ+Zg5XRaSaci2IrVxeXqP6iNPkwE94/yR7VgYjTAMJ8rlPecLjGyo60BePCVvkOrxJTFoxZgtVMNpmRFbIDimzcA0eqxt/SBXxxQmvA2sOdHO95RV0TTSxVaVWCrH4jjVSn1zZjspkwSEO3b7F/ibzGWxZDdSTEL1jPOFYQrdEKgpQb4j6Ab98CXruxsuT3A5B2JTcMoYApuwpV5Wzudf0FyiHGbyGNGYkKcD5vEA7Wvc4ZabV47tYWIpZ3JdieWRIpXb3ReE4Q5jHOf11KcgYtkPB2LLHNcH5iqE14pMmcd3Z2ZZmdsHcpu53cMXr5UXNytWGlUK0QqzGzvfxC6EOjC2PYMENkNgCoLJQhXHfPWasN4wp0pqkFLi3cOZt+9n5lgJg+NwGLnZHxgmQ7sYvvvbB5JJuAlOl0p5mtFNog0FJ41X5iUHd4/4P/w8yZhWoF9aIp3kpdUyhC37bf9vpSSWuGCkYMUwDiPBB3KCzW6LNZ8RvON4fECbsi6VmBJbD5sAeQBkAWNZLoXLSakRzlH5//yLwuNbw3/6HxsO+8a4WTCz5bu8RcKGmDxrvlDrivfdWXmOC03BqGUIgjF9sqCtm8RZa7A2dCt4kf5Sv7JJRIVcG6UCzWDxjHZCzMA5rUzDwM1+INdHTuktueY+Fq+9iWxVQcEZQ3COlDMinfdi5DqqL5b6VqlvhMtD4Rs9cjMJ411jGoT9jTKOv+TX5x94s3zAOmFsnqwZrQupnki5UXGMfo9xAWM9OZ2pNQJKM4YQYBgsxm7ADDy5yP7LwO7dK95/+5YsmbvbF8zHyvn7wrAZmV4OROneM6O1fPnZQBPFmhOlHrE6Erxj9CPrupLjAvYDqkf4RALg7zPCZsK+CsiQWNvM/4+8P+2xJMnS9MDnyKqqdzEzdw+PpTKzq7J6JdlNggRn5tv89fkLnAYx3ewma83MiPDNzO69qirr4Qe5kTPfGNUoVCNjBHDAEQazMDcTFT1yzvs+7253xDp8XXB7oGzK5z3hCkwmEMSCguqA8TljBiOIRu0FI5afPKeCIdiIsYbeG3T9Y/+uaacXcEk4vgjf2AOrj6RjZps3JBrYxqhRTCebSpZODkIQx5QtoQVc7hhN0IW2C0WEFka2WO6d8tHRoie8f+RX/82/wr07kTSR90LWhJE2xkxiCH4U9XvZaCiLRA4Ip9nirCOVzN4ragy5NQz3yhzBSCOEu5vFGJpCKnV0OFQx0rhtX0ja8KLgN8Q2nCv40DnOhnOwzMUyrQ5XPd0YDsfI07uJy76zyQvX7RlJYGPALIouHS0Z6RZ/C7jsaa+C3ZVZh4vNG89cHac1MDXLZBzMoCp0UW5ppeQ8QAB9aCR3HT93Iww30tHgH5aftad+dnHSOqhVXIRwcOAG4v10UMx04fXlwuuPjS84Jjxfv/0aGwytfeZ1E9rqKbun1TFqnJiYjhNOjjRbWdnBGHw4E7yA6dTa6Clguh9tJ5TYPTmd6VuhdFBbaL1zSZXntXNtSvaN6yz82dszX739jqdv4PFrx+Wq7GUj2x3jNsR6rLU0qVQrpPoV+fk9178LlM8TvTvEdSZfB3L55mnrjDbhnfnXfGLl1v83LocXZhNwNXDUB5w506dX8nHl2VaCW9hL5ZJvmFbx85HtdmX3O90YvJk52AOhVdb6Pbfyw7B2uVdy/MJf/fuN+ned6m8cTg37bWSbGy9lsDNMEOx5JuvKJpVkDK47oiyc5ERUi5ROzo19v3HbE7UpW97oUnHesicl5UZVQ5gcx5PnzdOR03EZjo9aue0VmTt+9rTNsaUd8o49jgfKhU5vypfLLz9LxJqIMTB5RyqF2kCMZZoOGBy39cbrbbBwvPWoseT6E+FU2VLCese7b7/h9LDw8vkDWhOfPiYOe8Q8W05nzxQ8Rivp2kiX0QpG4VrhP/6uY4Llf/53hmXq/Dpu3JLhizjmOWIsXG7PlHyjdcHcXQNxOnA4OJwXau2o2DsAjTsgzmDwQ6DYDaXeED2ODqPxLPOB6BdSbXz6/MKeds5nofGF5+sra94HwyQrtQ0dCgpGHN6GP7aRjbNj/l8roXjO14XLH1b25wFcU+8wh8hXX3vK9JljVN69OfLDy87l9kJuGSPjFi1SsaZgxKMaUOPGx/vgKniErh4ngSko0cG2Qi3QWkGPZ+JfviPUd/RV+PKHHcnCw+PCdJ4pPfH8/IVoLfMjLG8d62rweMr1FZEj1s/4MBHjxPPlR7r7W+CXrzcBkF9NlFNBPaTc2WuB3vHVsCdlRYnnZVzCrMeox247Zu+jWyJDsNTaICrLmBtixeMlEmwcnTbtCIpl7KsKdCsDDZE7cTeY4CB21pMnzwbZKuv2ClWwJjC5BbVgrcdvHvsyNqnxDidDCPv6ulODDBvyNGPagcVPfPebf8XjN9+x1jQC/LQO/ctPCZ+mjZdwF6xpxGkmJugp002n9Ubpw8JssXfH0hiNAlhnkM69m9THc1Qr0juTs3hvWKZ5uDv3nXj2vL5mXnimh87hwfPuvPDGTNgXuP5t43qrPP/hI7m+crHPrPYz8W0nrBNx8uN3FjIlVtQIzhjyHxrXz4m9ZGx0xO4Im8HtSt0yOys2Ki54ug4L+MPhRNHO3lZ6iIRlwU4TVRXNleYr7tdH+nfzz9pTP59zEizeAr7hjKG3jmkWLxbvJnY58XxxlNsR9Y4y+yG6s5Fm42CgGEfuitZBTF0OB5w9kfpK7BVawzSLlkbpBVstrswEM2O8gZZod+9276O11U2h9MSeE2WviApeAlEj5+M3vPuLJ8xxxk8PzO7GSidtO9oyaITmECnghGYCJhj0AVq54L0Q32SaT6RkqC8T9VNi7gdKKxy/ifDtA32vyK0jOdAS1FrJZmdzz3z0GzkEul6JduHd6Q3HeaHVzpYTe8+IvbK7KxGoutHU4Y1hiW/xD5/Y90TJ0ELF+MJlVz5dDXsKUDtuUnzPXNtHShOie8OkimvD/r1nQ2mFy3rlcrlSExh1pDVzmgJiPbfbhooSJ8E6CLFhQueWVrZt43RcmOZlFJQBHg6Vh0MkPnbiU2fygTmf2T4Zbtdf/oEsYtAGqYGxkcNkxyikGYqdKOXKur1iLIgJOOcpJeG0Y31Ad0+jMU0zj/PCPE3cXj+wv75yvUXmYrGL4+uv3tDKB7Lr7AdwFV63Tq5jbPG//23DG+F/+DfC46HyVdh42Tq3HlEMzgZSGhZg72UwTqSAHaK7tKc/dkhAURXERGpd6TSCs9R2w5nIm/k983yimcLn6yc+fPrCtjbOx5k5Wkrd2PPOeit3ezH3kc2wFjvj7wThPiyTvSPd4JPn9BJ5XGc8jkt/JvWVx/cH/vm/fE84bTzziRhgOoC+7oj2IaY03BkRd9edVHp3XLaXgbi/P0tGR1GofcdMlpmF6ya0vWC1Ap1+6Lz/N78lf3hDf/2Mo3E4W0IsbLcBa1RTWa+N0/nAyZz4+/+0sn7zmff/8i3GR6x1TJPH7zt7/wzyky37l70u9kraE657eoFaR9G37hndDRM7p2PnsByYD4ZdAu5Twn0oaFJQg+idICoW7X0YzZ0jWI+3w5mj9yA80KFlEkEXTztN9BuwXbHdYruha6MGIUth74neYNYDkQVzx8iLQGtDjG2c0FqjqZKwdByqEcETl4lv/vIvePfbX5N9Jsi96MbQBVrvlJqpWsdzZyasNDCNPkHTTqn1PhIvFO0YM4wNxjqsudt6gdoauShuqH1xKNYZpgjOjeKmdfDRI65wJfNidoLACeEY4HHxoLC7MjhCm3D5/YVyzMynwPRVx64BWz1dGs0MrWHVEYak2ql7pyUGmXyHkgt9rZgKF7szx8BhWZjijLOewzIPuGRWegT3tKCzo5ZET4UcC9vxM93/PNTEzy5Otmum2Mo8T8xTpNYrqom8j/lg0EeelplUT1ij5P0y2sCyUNWRRUlSSKJUHfhdawrqKjkP8p4lYo3Bh4TzGckd2zu2K6qVPSW2tKP93vb6iaPjDD445nlkXRAC87xwPL1FDgvucKLahS5Cb43oI8YGvDvSdaJWJZXEwT0hvtMef6Cale47u7/RRLDxjF0O1LBy+bHiCdjDj2T5SIgG3yKzO2FaIF87OUWKHtG3nWv5gjMr7w9/yTI/oOY6RIg4KI1byzzXDU9ndp1pOtKKkNLMw1cnTqfC6y2DOIyrFFd46Ru3FfrNsJyEKd5IcsX7haANmws5JfK2UpKy5o3bfmHbdqQEKGPUcJgDDUMInmUJOFcxpo8gu1rRbmilM88zwTtEd4L1vHt7ZJ6Vx/fK0zeGw7RQng/8oXZup+M/4Fj701zaxq1uCgcMgjN2uPL8mFGXdOX5+j0qI7VTsHcWSEOMwTk/uv2iw849HbB1x1bBrtNI1+2K70pZO+8fDV8/GG5Z+P5j4+MHJeXhRssJPvwAp+/gSMVm2FujG4uRwDIdSeWK2Ebrja53PzD34D4a1jtMF+gFNXJPqu4IjVZ1tGZtoGrltl54ebmxb5XgPcdjoGnly/ON19t4CbQ2XBTGCE4szriRKdIbXYfFtHeLvwnTD4K+dm4t4YznzZsFa5T5vXC5fqbXHfdeCc5hfSGV/S5Q4Y+EUVVFJFNzImcZaAM/YW3ESmAKM613TN84eMN5OtBePbkU3i4zGYezgbhYll/db9RtZ7ve+PjjC6/P29DwhEAgEB491sPHv/nEohOn774izOH+otlw7orNCW0/FX6/7HW9bKgTLB7HgtTEvl/JW8EUjwsTVSqpbazZ0GNlChUzj46JZhlmb2MGowbFWou1Dnf/b9ZCN0JxnaKVXCpVDeodcrQYE5huR5rv3MxK2zp7yXeqMYhxGIVWKzVXrO48tAnbKx1Dsx68xUbP4gPi3dCzRM/Dr7/h9JdfczPXUeT+FAPSKrUPi3BVQ+vD2hy8p/dOQwduQOzoRvaGiMdaQMwoXFVHF6grmBFm6Z29E+HHhWLg6yupNLI29loJvaPbjZfbym46URx+t/hd8EFQHZeT6BXfLV2UiZnKTnE7Mkf6LtRe0TLOpbIq25eMZDPGsXm4m1yMeLFo6GCGlkfF0hm6H+cdIYzkb5WJ5Dayv7HPkJc+yOau8dJvXD/fftae+vnFSSlseyMsBmsCEoVqMqlY7PrIrCeObybaMlP2Qi6FnI40K1SpbCWTu1JESNLYckW2C3OBXhvqwB2FKXb8VFGbqVLIq7LfBhZ7zaPN5P1EK5WeGjFEgnfMISAaKNUQFsebt488vDvSxRHckdx3Sod9y2xtx8WJ3vywSzJhZCHXA6o/0v0H2hlSHXhtbxeisxB2MBm1lXJb6PaFNX0kdcHWd1gTMOIxtmC9HeMWV5hM4WE+cJ5njK9UKqHfG+ch0ppjyxutbgMzXy1gmecD77/9isevMrfbC6IeamO/ZV5o2NYwyWCCHbcP6wl+JorFqIHWybWw5sqaVnJpw2Z8TUhV3p3PwICqGeA8RzT6IU40YH3AGMeb8yOPpyO322Wg/a3FzYIxGa2N0B1Pi6ASSW8M2+sv/6YoDBHeHI/knAaZkeF6sTIC55xMNK0YE+40dB2CNhMQcbRaaK2hzuH8hNiZ5gvz6Ylb+ghp58P3n8gvjUUdp1NjOXcej3B7B3mHrsJxgjl0Wh9USdMF2w1qBGs6zoG1B1K5AkOsbM1gE/SmVAYp1ukQHLZesdaiddwkVRXrGz8+f6C2MU9vxTP7M4eDZ4qelG88v2Zy7Xe1vg4dibi7w2cUO711lIaoxVeP/r5Tf4SXLeFd5/GdcHg0qLVkLTz//cb8rSJd8d7QGRj+UqAo6JgMIaI4J2Pk5N4ADDtmCHd+RUQweLUc5oZ2Yb1eMLVjzAlxFfEdaRVnKgGD6EQryiY3NgJmNpxPJ2ZA5Ubign9b6OGF1/V7zE0I0VPbJ4zcmN2CkSPW+v9a2/SfbOUfC+7gSH2n+0yrDS0N22A2gUOIOFHaXqlpQ72iyeDwxNkjtqO13zUbDnOnCY9R4AAHWmfoprP7ysVcSb5iehjjwWZw2phigAmaLfQEbW80a0Zx4B0iw/qvuSO5M/WI855qOkYstnqsBMQFrA+E5cDh67dMvznTZoYEoHe0DZmBs2Nva5fhZGXYnJumwfZQR0JpTfB6t+hbxdm74FxHEd/vInPLSP5tvd9R9gN0akXoTcits7VCqiMvyynk2hADVi16sySjrKliisdoJE5CK4xA3t5xq7lzZRytjXyhjTKe7RdD+VJxLeK841AXRJTZzcwh4DAjUPTu1HPO4py/52opqiOKYrM7F0msRuhh2PcxFtTha/xZe+pnFyc9NnqBddvhteKWFZk26J6a4eROPD2ccG8ML59ufP/Rsl6Foo3mOlWgY+hGqXRKqbh1xflIMA6JjXDc8UumayKVxJo662UlfQzUZEgoSQvToYCCNHiUyOw8wc447RTfObw58tW3b/GHSBbLnhNdb9Tds14zV7PjTCAKaKt4F5jtib0VkB3MCjaCjEwPY4+IDXTZafNKf/pMtTdyfyXt14G+r0pOQ1Bljhn3mOBxxU2Vt+HIwxxxvrC3Mm7TRDAjRfYYHJNLtHSlt42SOzHM+OAwp4nzt5Eff2foBGxW5DbcF5OfkHGdoKUwuBp1wZlIdDPq48Ak10boHtM9SROpXJmdZZoCxghtz4h2zktkiQHv3GicGoMLE3FeUDr79YrFoQWyqXTq0BA1kLUze8EEy/mbnzdT/FNeUzgiGFpr1DZ0T0Ysxnqim7BYlvDIa/5Ea42s+W7X9gQ7sctOaetIBvZmcASmEx8ur3yyBbMYvIPX2w17Weh1Ie+ZunXsqXCYO6cFJg/HMBoJzxtcNsV3OHhD8m7YNWl4H0Fn1lQAwVuHdEWMRVujtTG7N9gBQcNQGxRrkVaIrrHlnesmoONQtWLweRRJtTikOax2tA/xq/zReDzEi/2uIzDd4TUyXyPlS6XdhNIbOlfyIaNTJd0S+bVjGoOSjOKCpWtnL41UII/3wh//WDred9BMbWUECOYh6c3V44xlXgzeB0oZYYvFbnyfvlCbY6uFII4ZS+4bW828SqYcM8bI0AMsI1vlo9kQKqfvYNWNLb0wpWUkyfKCNZ3ozog4/qjs/CWvz2NMUm2iTxmHcFCPUVgILHLPVGoGn4XZwNwMAUeMEeMULRVRgw8e5w3i3HCZtUGMHeBCodC4mJ19KgTpBGeRXpEG6qcRBNkiS50wHWowZG9QY0Y8gqmIKlE9xzaDU65sqGloHgnDzsLh4cDDr74hfn0iHwpV1zvUbRCOvYwuidE2niUBYyy1d0rPeFMw2lAVtFS0QS0VbaMIUZExMTA/beIBNfRmdERrF8TYO0xuPHMNRlaTjBR5rXV8H93iuqFmuGbFXRqBgDaHcyPTrnVoWWl7p3eho6MDpY3iOt0b/EVwyeDEjC6hjL/H6AnB480olOT+R+9dn66dkiu1VZJNbKZyM42EwbSOloIYh3ce/5Pg9/9i/Xx8/ZwxsXG7Ja4fK/OjcrCjSuytomTmoMyHxHq9UvrGbUuse0aiwcwOGx0qHbGNKo29FroXnAuovaJmZW8b25rZVtheDduls10rJVmqFTZ2vGZ8mAjiiTQO3TO7QAhK88rx/Ux4gmpWxB5Y6yvRGgxxcBOQEU5WEloH1dPHQJR7OBuO6B1iD9w2QdQOuqRaKkr1N8wZWtqwadw6tRpK3nBecY8ZeZuRBWZ3Zo6WOSSa7tQsiI10t6Bq6TLhrePgZkw4kvLKVq84O7GlTG2Z068Dh//N03bHHCceDvD4ZJhnx96UTSvaPXQlVzNuiy6i3mEtxDBEt9VaWnXknDktgcN5ptZKuVZqzRynmYfDzDJPqIysOh8XTAjcbitznDBGkaK0LlTjaMWg2dNuhtOhkWylPvzyU1iXeKTVyp42Wm+j/WwDwQec9YgaHo/vef7wgVI2mvXMccKagLczwe0UswL1fnvqTNOBiuNvt0+8iZZ5jvQMi12Y0wP2tZNWR1uv9HllOlWmaWRvpCQ8r8q+QtTOYvuIzbgrB0WUECdy3gfaXsbNz/yEkFe5Y+MBPKojaNI7h+njNiR2UF6rKqUWWs3sObOniLeW4BZ8938U/mmr/KSyHb6c0W0KWJY9EL8ItRmubhRj4Q30mNmuheuPo2g5PBhcUDKKc2YcfrmRmlIBe5+YKCNAcSs/st4+klsZzJbWgBEqNznL0/ye4N5TcsdOO5+uH/nxchl73RjeL2/p4vmyP/OSruy10iPgB4H0Ym44W5lC5xyF95Pn48URfAMZX6frdcQU6E8vnF/+8xB3g62OWju2WYKzOHEYaSObpTaqA+MMc3GcWmDCDdu3CNbe94eMEbOJBmLEGUe53CipYVRRC9koWYTNKdkVot0xzVCNITrPXBzH4shtpBAnYzFOyYyxZm0ZUPABdY6mleoY0SDJEIrhdDjx9Kt3LL86k2Oh1Cv0MfrQu8J7FBBKbo1W+xDx6igaWu9UGtILWIfrhlbLmEaKjLGnDAK6NXaMdsakcnQg+oiFUNUxCh6TSsRYvLNQO/Weyh3E4qvFdaEWWLVjTWe2HXcvdLSPoMxSCznfAXN0SqvUrqixuGAJ2eGawRvFWoNx0xireTt4TvfARRFB+08C5nEhKa2QayXbRnce4zxeBCkVUqbbijlZxP68Yv3nd064Uc1GQkbY2C3ipwOzCahacr2y3UBsYk/P5LJSWiMVBVFiFLyxiDHYYlCrFOlUr+MQrY3tNZPKyuU1U/aIqRHfJky0ZKPsdyX1vidyUZLr46DtDXcIHCfHdFLi08ruXkAizgdaT7jpLWFZqDOsty9s/YYWsOpQUVDlcX7EyAHVt5yDQ2zkw3bjVl7I3dIl03QFK8QpDoz9Gnm+NejgJ0N8dMhDph0NMZyxbsKZhJgdUxvBvKH1TjOGrobaC46CcZZl8hzCmUsubDWzpUrXwvnPFh7fT+RPwuM58u03lq++6bhZ+bRlfrgW1pK4tYppHW8OuFbodb2jggU3BfxscNNEiDMhOuLBUq6FtSauacMgHKdI9BbrDN5avAF6wUnh7eM0XmJdKXh2ClU7rQtbFrokXuSG+PoPPtz+1NbkIqnrsAQyOA3aM7kUestYZh7PX+M+/Udyu2HotOZpxWDdPWPEOYwDRMg9441jCgd+ePkI0RH6whQX3LIQesSuBkmWrXr2TclyIUwwe+F5Uy6bMoswtSG+25qizo5QP2nUIizzmd52jNohlPtJeCgGqw1zT04WcWh39GbuqtP7ySmWOE340LjerrxuI6NpCpFDDEQf4N4ZFVMx6B9Tm0UtTj2LiUy100qCWQlLJ74zuCD0a0e+GMwlUFplrYWTVQZry5BKYSud0u7Ii8bQ7RioNbOWj1xvjVwZRZEOtoozgj9EYvwKH4TrZeOSXvhw+8xLW3HW8nA4cwyWL9cLH25f2HseOHqxiHVY5/Dec4yOh9nzMHVaDzAFmm+ofBoxFH28AIwB5+zQlv3C15PxdBxJwBrPLEIwDVy9J0mDUYP3wiSGKTsmcdA72saFUQSMFbppqBXaJHSxtAvkokivNNPJtqPOUU1hI7NqxzM6BNItcRemvTF1JU/KHfmH6dBzp+ZMb4XNW15lx4gB7+izwxwmHuav+Ob9n7G8fWD3mb3cUEa6sLSBrhcZ2pWR7K0DuKbQpdP7GBtWHXh+bYUlHEklYZ3QnRn7GcF0xdZR0HQdnZIiht7vAXkyiqHOsON6+SkcsdPqXb+GwTWLbw6TBwJ/Nw1xhWABFVoZ3Z5eO60yBMgimNYxu8V1T4ieaB0inWDB+xHBYa1gjAHunzPEMDQaVdsoqNCBJXACweKCY7aOop3arhQy3Y3cvCw/j4P185+asrOVF3KZsPaAmECtE3WfaerYcuf77ZnpoLzeKnmrOCxLdBAFY/ugBqpiW4XakKhUk1hbR6rCOrGtlXQVjEZmf2DxB6z11A676VzKxJcvH7leE7tR1FV022nrxLdfH3h/tEjIKErwY3SS2sqlTszBY73DiYf8irjOYX7ivBxxumGd8m75lv2Tw10bhwdgLuTtI5e0UrVjjRL8mbV9xoTOwc0EZ5AeOESPOxmYHWpnCp2tKmIEox6TA4f4xLb9AUq/47odRgOdETnvNKLtmVQSy3LCy0INyvld5Pm5UfbOunW2rMRJ2c3KtX/mmq/U3tA6uia9Cj05ShphS9Oh4uZArKCxge2Iz5S9kHplLw3bM1/MSkqFECzHwwHvPKLweIbjeQShpZtj3SM2J/aeUCq9Zm6a+FKfMf9/EBE/qLCO4CJbunBLF7hHCsz+wGk6clweeZif+P3z5xF8J5Eg4OwMashlR/tOlwFmyr1xmI+IGnbtfH9ZeXM4cvzKUWZL/9DZPnW2V2E1E1vbwFa6wpdNiAf4ZlGoY5xx1UZpd1GfNVgznEWTHLHiUN3Hi12HBdcZj3YzdFhmiBPRAclSGeFspSpdLPMUOdsTnca27dQ9UXsnOgaHwliWaWKJkd5gWytlVXwdwYCb7NT3Oz0rhweHlYp8ttjVs6jBzMqtZfaYKbYRrWCtI5U+uiZ3i3JvwIiIohswMubvZvwYhiPCCEaEh2XmuEysNfF/fPwdf/PD91QGp+HgA988viGlxI/Xz6RWRsCnDVg7QkXjFHg4ThwnQ84XPuSNvXaq3elth/rTSEzvdN171/WX3zjhq/OZZCvJOyQIgYbVQc7OVBBwTjBBMQu46rBdxstyr+NnFey4rEqjqCHXwiwzc+9YGxDbyWFjnzKb30fIaM0D2y4zva60snKsnZgNjcxeC+veyNVQu9JzHQ7H3tCyU+zOZCYWH7F25u37b/izb3/D+XSiSUVKgyo4MyGtk0vDeENrZSRV6zAPSBv5OVjFi6IGQjjQeianTLCWagva7xYzNXgMblP0JdHOliZ9JBcHRqihMSP7Ru5C8p6pLaGa6LVQc2aKHucDslVMs6MMM2NcVGtHRrgPrVaMcVhr8T6OeIU7nK5XxRVLVEP0AXuCuIzEe60dqiJ9wBJVB0BPRam93YGMQm33LlAQ+uzorlN6p5ZKopLmijt7/GEaP6efsX52cWJMRVtlshPLdMIHjyeizbCnwvpcea2O5XBEZYYdgt4I052sx4bWEZA2GyGJu7f9BvHVS4CycKgT0VScdczxXpzgqL2xk5m9Z8mdH/Nnshq8txjttGLw4cDxKVK9x8S3NDx7alRRGrDmxuROPBy+Yp4B0wZ+u2Wke9QUyuffk3//wI9/feNXfzlx+FY5u5Wt3Eh6wPjI5fZC7RnnI5NVrOlMrqBhJ00V6zzBPmBQinll3V5R15icB5OIMdJ0J7VK4TBgPX3HGY9rDmyito0dcDGwxJk33018+U8vfPih8OXa+MNnZXqC1Vy51hWh01TBNzbZcOLpxdIA5wxmWmlnGUI1kzj4hWhm4m4JwTG5wOIjyzThnaO1RsqFWHe8h8c3nbffJLaL58v3wrYp0oTAGNX1NmBAp2jp9Zc/Y08l0buyxAPOKOvzhcvtFcRwmE70anhYvuLrp7/g+89/A87i7CDK1jKIoVYiuXb2WkEqxhiO8cxheSL1Z7pt3GTn9+GZ9W1mlkC5FV4/JnJV9uq4bp1P7zs6Ke/Pwx3/8CC8S5bn1bG3IWoVcajxVKnYEBGGvbFTUTPU9LWNePcYLEYcQkCk03UD7QQ3ipWURyZTCJaHhwVr4fq6URtEZxEnYySpFWzntBw5m8j2Uql/n8laqY+J9r7RbONqMj4Z4jkgBnQ2hDTi2Q9PHpZ6n3EbahNyVuquNAFxYIdJABFw/p74DFiVESDYR1r08RAxxvDh+ZXff/pE6oWulmgD3z19RW/w958+ULRziAemEPBTwE4BcYZSd9aW6C1yWRPrVqm94uw956oNLoWYO8EUaK1j9Zfv1nk8LeS5sb2ztJNQy420FUpujHilgShoUWmzQdv4Xe4Ibe2YbmhTZ5+GGyWnRsmNp35i5ogPEeMau9/YdeOSn7m1C6pKiBY1MzmlwQOqJwyeC4XnsrNfGCO2Dh7wg6RCsDBZw6lZQnOcv/ma7777LctpQSVT643cNjKdNTVc6xjDPUxSCc6iFGorqAjYuz1edKQmk3EOvEx4YHKGRMXaMeuU1w5/WzBfGtNvzyzGUF82yncBcwRjFbTRdeg81Axt1rYleoc4L6CZLAU7wWoz9EoUh+OArYKU4W4YbjshWE8MQz9VaqWVyhxGLl0IBjsZ7PtI+PaEtYHyZaN+WGHtmNrpfehKujb0bpLX1oYxIgh2suhsSHLjmtIoZlylO4s4T4hHhH/kbJ3X9hlFiTIRe8Rnx+QDjrs3vDu8PePsI0LmfBAO0XHtP/Dh9ROXdcPFwOE0yI1T9IhYDnHh7cMDpkB9LcgSkKbQBW8PTP4EGLZyo9WMdYF4OkOvVK04Z2hFeDg8cjwckV5wxmLcxJpf2OptxDrT8V5oZUNbGhVf7xhTqbVQL+OH+3g48nASvqQdKY843ZktRGvYsiVnj5Mn9vqJvV75Uj/TSuYbmZjVozViNTLZBWs8QQyEmVItz72T999xnk8Ed6TdXrjVF0KceIgHQo2EMnEKB67ayTWymoXr7SPhSVgeApePyvULrFvFfCioM5ye3nN6MGx5w4nl2I6IGNxiMXPHuESTTE4rW93wGI72DcvpSO8zv3594uvlkaObOC0LRiwpF2otowuiSilKrQWM0I2l9p/yMcAaJc6dp28WptOZy+WXj69f9yuqcLBHnF14PL4fB+L6heeXH7m8vvL1OfP1269wTKzbFZkMx/lpJLFK53A88+if0J55uXwg1cJeCsvpiXbNWCsD2tQaL3YjPTWmKXB+eiD/4Ya2zOu1UKLwZoLSlA8rtHvOhTalZoOE4WoZc5BOk0TFoL3f7YsjzM9Yg7ZOaUpwBmc83EeOzjimYBDSuA2lhGIIVng8LWjt5FTIfczQxVqstdQubL0zTcL05Fh/qGzfX1hOndQy6gzNCDlm0pJx31icOnwPhGpGcbSA8eDtgDiuN6VmcHfR/3AdjX9ec+ADpBtoH2I9DPhgOE6OfdsGunvfmbxlCTPvz+84TgsfX16Zj2ee4sRs4wju052kiZoa67YBlUOZUMwfRb692TFm6oOQZ36qjhjuCmm//E6iMRUOwJNDz5YtX3kNV/bLFftqWXLkMJ2Y7ILplr1UsI7rMn5n0irJVy52Zy2JrhCq4aABWEYmWymgjWlTjjounMl2etlQHO/3hTcsnMMjPQh1u9KKIgqug+/C7DwmWHbNd5w8RCJfvf8V7/7yt9g5sted1G7s9cbaMrVBy5WpCOoFjULtildDLoXSEmI81gaMKFI7vg+DxLXuPE5vWOvOxkie91bxZhTWHBRdZtrDASMGbRYVRVsZjiUxg5Uk4xO6Lhhjue0763ahv27svdDDRvcJ1TLGZU3xqzD3CW8mvJvH17sXFFUbtWYEZY4O70ZGWJUGs4fvTpjDTPwQYa+QE70bVM0gwnbubh3BqI6xsLVIE/JW8CazULFisS1SnEGrw7SO6//IqcTf/fO37C+Z/HtF14w9KMFAdBEzH4AI6qisAxXtlOnkoDsuV8vaPC179mzwi8XOhpbr4KGEwOIi8dyZbKelRrkFLBYfBbUbNf3Adf2INUckerzpuGqYcEznRx6fvmXylfX6O+yykuon7DQxma/YS+KweHCN1jLaBCcL3WSyJgqGECtZzAges42v/uzMbj6xpr/li/07XrfGy3oitZkY36HuyHV9Ju8VqZZyDFyu36POsUxvqUGIrhFCZDn8mo+XH2ntCgSQiaJKnBb26xfK/syrfsK6yuYjr/sLfv4O16G3jKpjOp1Y3jrkrz+SnxNmV5bFclg879oDj+7Ec72BrSwx0E2hu4rxQ5CXcqHdEl1XTu4B2RrP299yWSdSPjK5A0u0zGGIHntvaO1YA85P3F6Uy/U6rJtVsMGwiMMYxS3C4Z3y7rvIw+kI/fAPPtz+1FYqF6wZRYdzgdo6ISx8NR2HKK40Wm283l759t2f8R/+5t+DGj4//0DNN/byjNpMjJYYI/O8kC6fQBLLcmTdPuGN53x4ILiJ0/IWOtzM98S/uPHf/ff/At06/8v/8b/y93/1if2DEv9SsI/wqUCWIcg0YqilImSg4f10Lx7MmHmLp+u4XalWhIJ302j/WkduO4rig8E7wTtLatBbHzdi5xA6x+OBz/lCyopUvaPFK00qaoUewB4s4d86zn95RtsL12tHXUeCZwoTuWd2ClCwdifMhjgJU7BYIyMMblfKyIjDjakt7d4haUAxjIOfMbH6yQfxdLDMwZBTptTMNI15+uNh4XjwbLrBDFEdZVuH40c6mySabZj77F2bJeUBbXPeI8ZS82h3GxniTiP3F0CpQxTofvnutdXs3IKwG0AdGxuveiGXG8fbgr8tTGKZDFinrL6yToVruVF0R2jUUtj2REmdkCYWTngzHJ7aCqqNoJ63+ojHsMjEpa/kUoi28c6dOXKmd0dRWOqBr/bRpVBh6DNsRKYZypW0vtCkcP7NV/zZ/+NfURfY243cM5VModFpw/7vIdeOwVFyxYkFrXgRugnU+/juYCwPzbD/5535mwV7NBxC5LW8oKbjTECbkKvgjGLfKzUGNoFCh28NTQeWIFoz7PPaqHWntnEhz63e0Rgri5tZnaM7w/H4RJwCrhvkRdm2Rt9XJpTlcBjuORGUMaadDpFpjvTa6eXuQlLgUtDXNLSYW0Zq+f8GfYrF2cEvsveRqRq9A8cGhn/JwjsbyMYNoXqrXKm86Er+8gUj/8idk7dfH7jGnT98+Mx2eSY1QTljzHvmcMSqpaYEWmmy0/qG6xDckXcnQ5DMplCcG3HVDmyHba/D2nsuhFNBJWNNxHHC9gkJjRxe2Pzv+CH/DtrMQ/wKpgm5Grx43j2+5+3bb+iHj7yEC1v5QE5vBgkwFUxq3ORGPCnOHEa7ql7Zy5fRpi2BN4/fcPQP/P7/80p4vhLffOQ2/TUf1x+5AbkdSUC1FqTiCCzHd3hvWfrEKTyi2VBk2OGkW3oRbvkFrVeaOnrveH/mkq/IXYQ7BaGuK+vrC1vsiHNoV96J52mueLdw2wLdR95+Z8i/Sxxaw0rjdIo8Ph55eveEXxZaE/b5md1dqX3oe5zrEAymHSF3Ji38xdNblvrEf/jPv+Nv/urvePl+4Y17j7wZN2TnBkCoqgw7Wxe6WtI6k3pGXcX2UTELoFpZt52PP75imvL2YfoHH25/auu2feG4PFL6ypb7uCEbh8OjfTzI02HG2hPfvvk3/O9/9Z95ff4Dzi1YE1mWd3gPyo7RCmpwLXJ7fmFZznz91bd47+HeAs1t4xAfmZb3/N3v/1c+7f+e//a7X9Nc4/K7Di/CX73CV38Bx6eOTtCD4OPI86h3jXKtiVItrU2gDRScDRgsrRRa0aHw94VoJ3K+jrFP7wQfEDKtjYPINEuvjPAyPwICr9eVru2P37fujd6UFhvWeHqtHJcAn078+J9W3v1baFNGehjJvrVQaqYzgHWlgebhpPHGUM3Gu7eOa+5I7EMP2+8zHYAGfoKnw/i8PnR8vDkL3hguuVFrZ44Tp+XAcV7Y+s6HlxdebzvneCSqp9tGjzpusaroXfTY+p39Ig3nPDV1ttuG84Hg/fg2dADEMBEjM88v+z/t5vyvsF6fMvUoFAuiFtNnYj/jsBztxFlm4g2sq9h3kdux8cqN6/ZKSiu2VAIQjMVL5CAH3uqJYzcYGt0OtEGJllINvkycuxC6I5EQ5xFv2Esiv1wxxTHlhqsWtaM7U7ST1xv7pyuv6wWl4P7yW37zP/1z0pzZ9kRuhayZKommmVKHuNog2DhE0aEP99eabqRaULUjY2vP6AtwPbDcjmy/3zj89w5jKnGGQ4ijk1hGQnevGckb2Xe6GNQ6RCpeoFZDqQ1oqDZKraSWWfPGmm507UQb0DUTbpXTwfPm8MTp3RuiiciUqddX8pd1dGrvVqDWGkkyt6mikyPaQCgGWQ2mjDgO7VB+94X6Y6WuCSlldIW6g/4Ty2VQbY0xd3DcTxynkcI8IZheKBSSqWx5J+V9FEbh5+2pn12cvFxWSjO4s8VIJtlCmR8Ib9/yuHyH7HD5+In+5RknjW53ttyR7IADzlr6trFtiWwFGwPReKa+EO3EtIyI67QaYo3EHpFmqHsett/eqFW5rTf23XDITxzCQjwseD+zrxtVbsh5xs5vOfANIXxNdcryECgWQjdEA8YHrj0gxRLtwjw/0PozL9eNXhzrnonyme/XT/yQNtZuEcmIJqpUXMwE41CzUconup44mhtxUd4sZ47mO4K+ofRE2jfWS0Ymj7Oe1+0zvV+JPhLMxHl+j1/ecdl+5MP6AyVX3j/8BsVTciGvV4oZsfbn7zLTvzhT5wOtJJo0XJzxbqEWUN2o7gu7PkPvHOKCDRGdAhiLLRMxNUwTTIeFNxwItHnAelKt3PYN79sg/7mhFs+lYazgxJNKppZK1THmEe2wNtKPG8bslD9fSI9f4P/2X3DC/Qmtoiu5yrBi10LH49xM04p2xYjhmnZm14i68Ku3v+XT9jcoAyQYmuF0eGSeTog0rAS+fvxntCJsdeVvfvh/gx06eCuKFUuar0zxzOn0hr//8B/Zded8Wnj89g0fX164/Ci01Dk9GaZHob0V+lvFHOCwTLQOt+1KSgZj3tLqjjDQ9koYluIOpjssA3r10juYgJWZOXic20dhDfcGsaWp0FWZ5onbbaPVPOiZ4uhiKKUBK1bGgXszhq/ePvIv/u/fUMxHkiRaz0gxeDMQ8E33QautA4PvrCK9U/PO6bGxiBshl1nR7jDG0GRYGWcn/PnXB+Zq+dg2XlpljoJ0j3TP4uHohDl6Ut153XdEJowZAr8t74gbzgSM0roOfUofIkDVxmEaAabb5YqIw2JHrWd0vMDcTAxnareI+eWPddq3E7t+ImXF652g3Ca6NPr0EzfJU9540mMjB6UXoFlcdRyy46ENB9vu7Cj2muBSG/ugKyUIl2PnlhLSEjY3pA3sfW2dnZ2Dt0yzoaeKNMHjhihXlZqVcm1cbyvrtjEdAn/5b/5bePTkmul3S/OEpRpP7gAD5NR7wYsjWKH0wXgq2ugygjGddtgK+kkxaWIKES2KrpaL3WACFxhdxddGvyriDfWwIMZiZYxcUXBiEOPQNroX3lnEK1UtogHtlVo2HoJHU6V+VtyXitGE18x0cLgiZHFIcNDk/pyPgv8mmd/LjYtWJvW8NQtPMQ5jhxdWv/O5PrOmF9Zy42AC7/07jnrC9iG6VYad+CeGzxDFjo5qu2s8i+2stnBzmWwVpxELNPuP3Dn59OUZKx53DATptBY5Lr/iq6/+DV+9/RW0Rjj8HZ/rRl0bWMdar+NG2QOpCvuqrNdKEcN8Bn+aOLpIbGCTh2SoLzriWHvHkRHTiTHwaE98ZW/0tlJyQeLOtEwsy4FpWrBOMIeOPzv6acHZjkhjKxYbGkd3IqjD9Eqn4O5ui71d2FuhG1jrJ3SxTPJAePsND+Edmlf23kAtLSuv643X2wsrCWcT3mzM8R25ZF63HzjuC30+sbgjXQqT89TSKKURpgMhWpxEgq1EDKYlmg3Y+YmzNfReOS+/wbBC+UTRirqvuZbv0XDDPzwQryfaFsf3bQ1VO9t+A71gzBdUXomzwc2dZuuo7k1gNo5o3/D5I3z49Ey6PPJ2eeLoPL0KXgTsXS8gBodgdLyAtTe8c0x+hlKGKKroPU9i7AdwfP4oOP3lR8Tf1i/QK8II9OttZ5cN1UFODH6mVOh+uBC++/o3vP7t73nNz3QVShFyrWiCXCpdh+XUyWlYkM1E0xvBxhG3IGZAk/pGnCdiPHDNK80pb//H9zy9e6L99YqumX5LbJrpHvpJ6BGCbZxPC/PsKUlHp7PWuzTCjrRiUXxwDNmgxZpK9BOVTi1KcPEnChRYobbKbet4H4a9EIc1jp53HINpYa1BZPAgmoCxI7fjUjYezxMP8Ykvz5/GQd8TKoZgZ1Qdub/eBdx3B0yvpDoSyDGjmzLNAW/PdAwv+8cRNSGGd3HirQ/4puzrDZql7BZpntmBGCGXxOfrhSpmCCaN0szgw0hvmA2aDLt46hVjBzxKO5TcCdYQ3DQEkNaOGby1hGnhsDzi/IFcO0v75T8P/nxk3V5Jty/ozTO7E9Gc0ekwRKje0SfPfgLChA8HHF+g3PDOcXSBB3vAecvNVRKNNW04hVgVaYGulsEXGdAxf3e8oJ2eM5WdPg2qdekZKSM1qqsOO3Pv2GAIbuHwOPEXv/0XfPuvfzvcJNrp2lgsiCpVBHF+UJ8VmlYcSu95iLZVmKJhTxkUwuzxZsGVSv1ho1EQm5jczKd8AwviOt5EvFrq1unF0Q+WrhXRMkCC1mCtQwzUqnQqlZE1V1vBWouznt5WZq5cPhX0JrRquGwX6o+Nw3FmFo/bIJiA2HEmqVUwhl3gxWS+9A0vnm4FsZYzEXzjd/rMX7e/57l8JNedX/HEWR44WcF7hxqltk5tyqANtXtXZiSQF9Opi5KDcqPyIoU9CNM0g1j0Z74ffnZxkldliZYuO6lvmPpA0AOzORL9gsTG8iawXzv7l0peLdICEgwtd+pQc+DxmGJwq2E5Oc6LIwJm8xgO+AI9jawCsZ0YHS4cscsDZflCNRvXaWc5Gp6O73grj0zNY2JF3jr0cWEzma7gfUeqkDYhHiYu9RPaKr1B7dC7R1u5dyFm9pwIj0f62bMFMPGBxR3xvVJ7owfw0yN2ncj5I+l5R6wlhgXrZoJ9R9PCS/7MtRXUdFSGOyG/GqYyY6fhCxdVrO2Uug+YjjMY7/FN6OmZff8R5EZp4OzGnm/MMmHnBaJBs2L9qKibr3QS0WZsTJjY8LNifGXXOtgaeIIPRHfglhJfPrxiesFIZIoBt0w4O+LsjRhKytSU8fdCRWGQCpuyXu8dsLLTpRJmx8PTzOPTwvHkiL/8ETvBR2ovWOOYwgnUjHFhS1zXF2ptlNLJsrOaC4/hyGQXfnz9PSrwXAu5GEK4o9DshHZLcA3nB0+m1kzJG3syGGOBkarqfcTZiZQqdnqDPz/iZwenhe3vPlDqTpk6fVZqFSRDvYtrQ/DEEPHOUbLBiBmI7MGsHAWEd4NbQCaGQMBQa2KKAy2uulPzOEwNQu9C8H4UN95h9gEfU8YB9pOVF2n0O44tl8zzqoh4pjBT9xtKp2tCGc4mWMjtBgpqx3ilN0NK0I1C6HQp5H4ZBbUXYhFMU3ItPD1+jWPm+z1Dc+yb/P+0yoWX9cZ13/BhZstlANfCaNl7a8mpsJdM0kYXcAolV4J31NypuiGYP06UvHiO4cz59I759EDqhbLfiMvP7GP/CS91nnl+i+7KvDvOzeG8o9ApptGiUCcli2ORJ4LMbPUFSh0pgd2B6Yg34CpbTexm6LE6lVoVTZb5arHFIjVitNEQqjpMKmy3leY6oc/UNePKyLzirrWIsxsQsMXyeDjxr//dv8McLLmtNCqlFaITnGn0QUkjOkfTjtbRFXDGM3tLbnUA59zgkJSeqEaZnwx+cSQZOUyrLRQa3vhhf28VoqW/cWRjSBZqU5zV++gShOGa69roLbPVRGmZ1pRShW29YdvOOzvQDtgw4GuvmfXllTXsPC4zT8uRKQ6HWjOwm8LVNy7a2HtHxWFtxNiI6jA5VNO5SmWrkPGICFGOhDbIu6pC09HNrHUUdNaBE4sx0JyDqaPnoScjGaQ4Wm5skjAhDETBz1g/P5V4fcBUyLLiysSsZ9wK+6cPPOtKcTdu2+9o8QvymOi5Y3TBOaW0FRws88LRTFAM3SSO0bAshXaDvAaiDXduf6Ub6BbwFuMNxi2IQpcEvuJmy/nwnmldSJcbwTXmIDTjMFRKm3B2wuhOa8qermOTdQcoxsIkC8a8GemrZsHNEyoFIZAxmD6Ud84GlEzThI+Bsz0g2uh+grTz4N+xoUxesHS6bKyq7LmS6w50qBOhWI5LYGOAuooOC5yKQzRjtA6HjxOmKXBrlazKZf2ElsqDe0OcTiSfyD2BbYhXdrdT5YVluSLLoHLmojRrsMax6ITPCyHNSJyotpOspeeMZ2OJ013wODZyb5VcM+t2ITrPEhcEQ62FnCrbmri8XlEah1Pg8WHizTeeN18JFqFr/i843v60Vi2ZW1pBrxznByZ/IPiIsZ6uR0QjMhmcRKzOePG8efiWv/vxr3jdLvS2ssyO80NkOcwEL/ek3p11v2Al4M2J1jI11zvYCdCEsUrrnp4XNhU+rK8E65iPlvDPJtp1G/RjwFRBGyP6PCUmEeZpRs1dbS9xEDrlXlDIGNX02tg0UXonOIdqAireGawRchmwKSMGoWKNQZzgnBkjXIbADjGjuBUG9I02xKKtQVFe1spD9HgfqD3RpVF0H8AuO9FKH0GFrWAQWhVyEooMsFfwHWVHmxCc4Ww9Ux2Hnw+e22WnNkM0gV4MTZVmGg0dBQ6WUjvlTpLtOm6FpXe2lEi1gIyCRcRCbagx+OgpuYIZAZCzjbxbzjwdHrBxpsgQCVpTWaZfPoStpIr1B+bQiGvC5USvG7tWtt6pzqJ4XJ2ZE0ylkK6ZwwZNDb01ctsRsXSb6XWjq6M7T8+NXjuSGjY7DmJoTcm1UmqllkG5XvfE5pSjsfg+Cnrr3L1rAH2GfgbzaHn77TvO7898+fxKDgni6K6UJhRGfk6XEUZY2gjcs+qIzhH86P4ZMzxbtVVabyCwz44eLd4IBiXXncIodKQp2kcIqE6WCvQ+smqst5h7po72Ru0NYyzaPVozrY5cql4ads2cnOFYOwfjEW9ZVcm1UfeKKZnCYI10P/D4SuNK4oNkXqXgTOBkLUcTeSiBuQpWG9V0pmnmKbwj6IzWwlHOWAn0bsDKiM5C+EnKIsYgxgzooIWyKGXZqJpxDWLxrNvKdrsis2Oef55h4ucXJ69PVF2RYHgKj5zcV8wlsH/+wFb+mpv7nlv6zGQMRmEtQt3DoL+WjkGY4gCrue6oBA4zRL9zqY2aQFyjpREa1XXEQ3fTqd6SG3y5dZ5fKs7NPMpfMu/fUi6d6+UTy7nhtFNTpftILuMwyfl1zMJygj7RisX4QgiG4C3ePlDuuQi9FVrdMF7oeHK7YI1HdKiQDR2RgnfgTSDYQN8nvHheyhd8X1kw7GSuWtja4CB47zhMnoOLHNwBY4ScMqlUGhlnBUdGe0XCARXPNB/opWJdxJeMWsMiB8IUKDFTe4NcMR6S3jDhhfnhgsaOvsDtAv5kmZeJyAlXDtjsx+Z2MD3OrN3QC4jRgaU3jNurrcixgq2joBOHVH/XUsAUIjUWnBPO55nzwRFDAruRkyXvv3zqVK+ZfVvZcyGXjWU6keoyOgfGE4Nn8ge8LEifkC4EB+f5DS+vN0qqZBWSB+sKYnasRHoXkm7crjuqBivzHaQ0gr5KHbcpEUOrlnUvXHXFWsObOHN2ghwPyEVhK1hxcPbktnPb03AeuDAYJ/crkDFjbt75KTPDIL1zS4VuOjF0xFZEOs4J3tuB7L5jupGCayM51pjRwt9LHR9D78C6keWhOsRzKtB7ITfPJWVOywSlU3uh90rpO8ZavF3Y63BzGGP+aN3NfdjYx94F7X1Mm5zlYZr56nziNWf++uVK6obJeKKbEYXSK3vZUBko8dLukDA73DYpVfY0Um+FMarpVah9kIBb6VhxdDtye45h4pvjgV8/HJij5crOS92ZesFoovxM6+Sf8uqXggSD0YnqMi/1ypZXVq1UcRgTMM0jvWBqIPTAcktIMWQjaOskzVAMIo3QG9UqzWRSsshuYas41/HRUaWRSialTN4q+dIoSbBBkCDMUxhdX2cw1oAbGgiM4qeJN+/esped188r03s3ohuUoUtpFZWGWKFoZS+FXBqLM8hk7inEQ/QtOsYa1nasF0TaSA5uDt9gKyupZ+qeMEbu+iQHcheV69BvmJ8K4zo6i+Z+sVQx2NawWiltp5XM1JRfPx5Y2o5TIRpLjx4MNF9xdWjeWuuUNMBvxTZublibxQpv3BHrLHMVjhnmPC7sUg3v6kwQy0UnsmS8jRRrKdFhTQQMmipsFWkNg4J2xBqsVzQKmUzuG5UxirOpY9aMS+B/JmniZxcn7fNMKcrh4Q1v33zL2T7i1NLzjZw+s+vfs6Zn0v6Appnrs1BTGqFDIlhrCNYxWUc0EVwk+Ibr/R6O5Ki5crtcWddMa6N7MCLWByr95Qvs6cBvvv2X/PnyPxFukcv6THGfyPOFm+mk5ND+yF4SjcRaN1K9MbFgywOtDN5/iMMaiIxDZ2uvbOkVr0o3CSRT+wvdzaAZUSEYoWmm1YSS6eNiiNNEays2VwwL4waaicZwDDPGep6WM0tzeAlEq6jLaN3ZywYmjZhwsYgYcqtEF4gm4mzgtEzUcsHngmsOdxDECXUzg19hR/6IGmVNhpeL4fUmHNVxsBFvAk5HgFTdbtjYOD5M9ByQNBJbnRXs/TZppkY4dBBLWxv9krFXQyCM36OLnJcZ68BHi5VG2xN5S+x7Yvt5idh/0iu6iSU09v0T6/5CbjtbCmMk4CKHOaNTxgSDNnA4pnjgz77+ZzxfvtDLlV5hvRVK31n3hJUZozPJCc+fnukIUxxtUJGhhG91WLmNMeMmxuB7qHaaVbI64vTI1CbcdsPkThVLJrPv+e7gWinTI70p2gsDgG3udkEGrrobUq3Y6MehYwVrdEClnCMGQBkz99ZobUS9/4Rs73lwRgBw4+uOA3ikE6vcU4qBS8pMy4T3E1IYeqZ7gRLcGSMBazLOuTv4yYF2SmkYo3griDJgWb3Q5s5yOvL7Hy/8/rZSG3ipHE+dIAHZLCU3ciljXKsDjW/MyArZ1sKeBqDRO4d0Q62N3Bs+DPFrb4K3gdl6vjsc+e1j4OtjRWwjFEVKo0mh9cbr7ZfP/fGfG2J2dIZdOqvPbJLvmUUgVmhaSG1lK41YF8xtx5WGiiHRSRawikMJOkLl9p64roJsjnZtBGuJ3dPcyIWhCaZaTHM4LBFPFMfk/CAim5Hqq1Ho0TL7yMP0FQ/hRKo7Pli8N2wp37VXSs4VlYLzQm3j4uz7hE0GCY29JdaSaL0ggDMdMYqzAJV6B1hq99RS6FoQNVjnRpyB2CEmFegdGopqGcVJB28d1gwx/BjlDoaK3Lkkj5Pn23nGXxv0RhAPXgizpx86JndCHXEAvXdqhSojxiTeC/WDLMNtqYXA+N6td3ggFMNUYC7KZhzWC20xlPOMHM6I8ZAbbquwZ8xlpW37HV1vELWwmbuQPA3daYZTmpl1ZjL/yJ2T/QZWTxz5ikN/S+zTsJH2jsiRGg7k8srttVOeI7UZamuYrizRYa3ByUg5cM7jncW0htlhMdBspOVOT2N2pmqJdsKpxzRBmiPWJ747/Jp/+6v/J795+DNey9/xogXz5kJ78/dcfWVvR1QLqke0H7HuhGiitxXfZoI7cpgf8AGQjdp3SrtwK19Q25llId8uGFkpfqP2K3Q/hExmcB+2smPNGIsIFh+fiC6gNrJLBKOc3UTwy9AG1ExwlsqV1i3btmGN5+D8EFmRRpHjRqy4MwU44cRR+UJpicYr4jv+YPFPBn9ylFVpBaIsmPLElw8bH/YXPrw2ercswdIj1JBRWzG+09IN0y0hLByOM8RIJOKxmHvLu4dMP26wJPRaUW24NjPJBN3R73bKASxWurQB9+mOxkdKv/7DTrY/weXMwtNpppTMLb/QWhqp1owslm3feLGfef9GmewbxCwYmfjmq1/zux/+mpeXV7ZtJxUhFEsogFqkgTOFkhqNSusJI2CNYK3HiUfdwKm3Wqg14/wBZww+Hpin80hzdYljvcJ+5Tlv2IMn50IplXXd2JdEsGOcY/0oPkQH7t3JoP42VawA0kf7FghuBI91HCKOPdVxqDcAwTgdbV619N5GMGgfxcsIdxvwsnYPBKxtRMU/rzvvjgvBgbRE63UEsmkhhoBzDufNCEVDhkC9KdvWKUZHUqoqxTVwF863z3xKidob27WzXV4QAnM40FOjp86eM7VXrPWIMaDKvmbS1sdz7QyiQmt9BLUpOHEcpyNOLMF7vokTf36c+CZuHOyOsQavDl8Le7/h/cT0M4mYf8rr8dnQpbHlym3JFG9x/sTReiY7mKxr3eklcTE7NEXrTl0rvRmaFeRoqYuCHSOMagytVKpuo9jNndl55uyHtVcskwu4aBnkeMvsLZMfL1Qro7NGhHoUzHnh7dN73py+w2boj475KXDZX9lvCR8njB+i6K4dWy2xeqIJLH1m//6KaZV6WrnmG8ZACINyrtrvBXq9B+JBKh6nSjSeGGaC91jjEMaFvaEUbah0tOfRpVbzx+dCjAUMnYoYIfpAbMrXOOQTrKvDWUGsQ6XhLZjJYGYwSXHV4IwbQm4Ph2DweCYx+O5omqkG+iJUsaOr0zpT6ZhNsLvl4D2qihwidlng8TQCGVUwqSEvV1rK1GtHG+MdXsGVCUtB7Y1eCiYLczlx5sgi/8jFyRSPPB7e8HR8ILhwzwcAqkf3GTgje8a3mRifsN6RdMNLYw553ISSUHok2hmD4LrDmgNqKhmwJnA6PCAacC5yOh84HSPeK5MuxOUtv/nt/8C7t18TTOfj73Yu+Ur1editjAczgSpzONKZ8CZyOi6YvFI+24H1bQ/0lGmSwICzlknDEN1tmfJZOJ0iPVj2emNPrwNgYx25N1RGkmuXDY9gTON0nDHL+Z43kIjGYc1xCPj2C+XWh0Bs/RGtlRhPiJ04xEhHqTiwcVTQrbH3SmbDT5ZUobZCY0flij1F3KOS/j4hBRYJtOsjP/648oe0cSs7YVZKKez7RqdgQ8OGilUh2re4OOG8oxeHzQbZR2R565mUrtwun9jKC9KVh/CG8/Erjm6C5qkZSinkWqi9Y43DEOkpIlrw8ZfPdXhed07zwtdvf8XzJXLbn0k1gViCn7DG8fLyTNr+M7/9zX+D9ROp7WgXjvMjwfzA3kawnFah7p3gDTEeMSq0Wiil0EsbYz/r8G5kbdSmdB23eCMjQ+q4nDnGmafDmS6WPdzovdNeEr2sLNOMsUraN9KeqbUwxWG8aa0hWMQ4rASseJKuiImgQ5zuJNzTuj3WjFRXYwRjxy2w9U6uGX8PRQsuUGu+MxDqGK3qyPgBpavSqhKNpbVCLpWXNfN2mZknz75vlNbIdeMwBbxzQ1Db+yhcWqO3UTGNUfgYSSVtSGr8+z98jzeecAioCJfXxut+47Kt7PsIDmw07tdLfjJH96Ysk8dZS61jbGWsuWsNPKdl4SEcqL0y24V3Ht6GG5O5oW2jFoXmmBRSvzFF5bs38b/qXv2nWOc6kc1O3le67FgRpmXhPB+Zp0ChY24WwaHOcesFqGhuaFWMiwQzEw8g80pqlayOujaupVNKJwSHtQ5vFYcMRLwYkI5gmGPkuEwEI7g2VMpVEj1W9sWyPL7hzdPXPLkTKzeaNTSTUTrRjygW44RgFzqdkIVDEs6HhfRxpf5tw2wReZcodqV5pVSw9ifXm6B3zlVvHadn6J1pnpnmgMUMHgijy921U7QNMbcztFxAlXbvHIbgBpwuFdre8d0St4A8ez5eNl4+VXyNw1nZGtr7PUHYY43BFcHLyNZywYCJ9LpDG2GlzSk5CHsYTrVF4ZCUngqmClE8Pth7B8mha4MwOkxiR9J0vybqNdNWpZRGKYVSG6VauvUQzRhTZUFaBBOQnxlv8rOLkzdP7zhOY07VVanaRmwyQk2R2t6h7YjcwU9xMgTtOClEk0m3zp7v2Ou8UUNj8ZbJOUQtwTB0FTFi7UhSXeaZaZ6Y5oidAm+OgSkqP17/PcSVeqjIImSXUeNx8oBwIroDc3jklhRVT0tKKQy1OBtb/TS6NnZDTMZIxbXKl+sL1Mbbt39OPL7D+45nJZVX1u3Gbd9pXVEKr6+v7HnlzXSmmsqWXjD9xrQ8gmnjRVR2VD0mem7XL5juCOKZ/QO3cuHaXplsJVqLsQ4RiH5mMguZiZQ3et0o7WW4emoB3ziez4S3gtrE9rlR9kK1jkv2XLJnNwZ/ahS/sttGFYPQCF44xwcme2KajrS5D5fC80p+EfraqSmz5yu3fuO2XwmL5egVo4LpnVYLrSt7SVz2G6VknLHEODZdcYbE9g8+3P7UlpssW1kJLfB0+obT8sDl9pnbfh0vO6vEJXC5fuB/+Q//L6I9c4iPzG5GUeY5su5Xaqvo3v4oXIURcmaC4Xg4EYIlBk9wHrqQc4E09D7LPCESmd2Jx+Utb6cjhznSnWdtZ9LDzL46ykUpsnOYF1ptpFTQXvHWYpjuhb2hqwMJ1Nq4rDdCOGHI9FJo9w5I13aPd+/0WqE1zF1r0jvkAq0rp8PE621oozrjY2jH6E/WzuFYMzryeFQ7a0pEFzm6AQJUhFYh18SDHTe4Xu+AN+kjIVt1dG3M0MOICKXB665Ek3Gu4efA1CyHh5nrWrncrpTeEDcuJtJ06G9EmKKldoCGdzJG0Axkt/cBHx0mCmf/yP75Rog70WTQnVZ+yhupeDqmVXLPOLP8V9un/1QriAz7bVdcUgqNUle20rF9xkTDpB1XPeoOdFswGAqFvY4XXjCOeVHkBKKG0hxX3bjmxO2pEgm4MCNYQjaDzNt2ki3UqNhzw5wFWxz+dYyok6u0UJifnnj77pHlEMl+R0/2TjW1WOvxHsAOAmsdHTPpHbtn8h9uxBx4PMzcrhPT5vnuNyd+rK+s7TrAoc5gfcRaj1WPtWC7Ya8rgYZSR0qxWsR6VBjMHNGhKxFhvI6F6/qCsRXnGq4n5GNH/hDxaYbN8OXaMCHy8ceNXz3NTLYSiLRW0K1jahtpwsaiztxtzCB7hb2ANOpUKd6SnGcNd7OUt5gdzNpwCnZy+CeDRWCv1N+9wOeEOU7D0bc16peV/pqhCcPGA3VX0t4pVjA9DjlEB2cCXYX2M5Mwf3ZxMk+R2nf2JDRnccEOZkHvlGKgnvHmwKYvpLqOZF5p5FJJzZC3wJ48dVPK9cpuHXkOnJaFGIefvNZC1X53EtxvZm7CHc/oWflB/4rbj8/EN4XK73n/zb9mboabdWyl4VJBqJT+md4qnQO5GI7TE1kumDmifaLUFdWE1T7mm/ZAcJ7JzJye3nJ2C9u2st5e6LEhznA6PPFwCnc3y07KjZf1C0sdgehXbThVTL0OIVMt3LYXOnA4fMPx8ETKG9Ist9sVDonX9QOrCF8dv+YQZnIfUCmxgviEmonL/hkh4m1ATcKZwOwd9lTxx8b1h87r5Uo2ws1UChZ7nLDTTjaFlUrEYtTQm8GTmTXju6G5wmo/8+n6TJZK74InYm3EuSPOFsQWvAv0zPj/rEquwmte+bI/k9LGLIGn5YEQAy9tJ/v6Dz/d/sSWttHCL1S0Oyb/wNunE4f0wqfnH/n04/dUGnFyPJ6+4ZunfzEwlR2CdZS9crtsvOYLYgRrA3FZOJynuzVV8dEwLR4rjskuvH/8c56Of8a6XbjcvpDrZ3789Nes5UpMhebfYPKR4/SeaQm81kIPC9EdSdcrYjrLPKPdsueVfR+FhjDR75qW6DuVzlYuRN9x9oh2Q5g6tWWcBSOgbbSxS7lb46On9SGqa62jTuhqSBVEdGDdrQ6mPDLSXBkkXQFKHQ6FF1nxx5lghFRu1AbSN441oiwj56QJoo7hyWkY03FuYNE7d/t0BdxwBc2T8PBwwHhDqpmtFYwzxOBxYkfgYVNK2VHDiDhmNIYxjHwQ4/B+JBQbb+kdfvzd9/zPXy8sc8X0TimCdvMTxRvHxJc94JY3//Qb9J94tdJBhCiBh3DAucytZl6uz7yun1m85WAjsju4Nvxmsc+OflHK7UaRFfUJ+xCYjnCYJ7xauK6k0kito9NIvNVuaTRWk1iHFwZxljxfufqGrQem7ilSR1jsdOC797/lzXd/zu4sGwn1I+NGN6W8tmGKiJCkUikEnzE+09aEXsDJCWsq3K7Eo8PPjsU0NCkld/ZtZ+sJNZ5lecAaIZhG7htaEt4mtAlYj2W4+pwxqB3Pwp7K0OeEI7OcqO2Z1q9MulMv0P/giP3EFBY0Ch++fMTayDQHDjLRSyOVRMqJnjolFNoB6vHuSmuZsm90KVRXeTE72XlqnGizI8wTMs/UvVJen9FtxR0807IQNoski9s67pqQL+OyUttPKc/D5o8RXIjY5rBN8F2w2dDF05viTMQFx8/Nwfz5Hjcn9NzpxiM+YPzIVemtYZswEzE98enLlef9C60ciCeLSBnW1h7oNmKDwRcd8+4gI1RrTww1ygDoLN4TrCfEiDlF9jfC9+4Hfn/7PdVfcOXG1C3OXNBvAjYfyNrYsyLmhlODlp2K4O2vyE2GVbAletuobR85IiJYt1NaYTEz5+kRVeH10zOnuGDszGv5wl5uOD8R4mlQMQ14azkskZh2ak/c+hWTHJd8pZUNo0oIJ6w/UcqNU/iO0G7s+wvWTojtPB0fWOyJh/gGZ2ek1nHj0IxtL1QtVFV6TdzWK1B58F8z+QVz+D1PXxfc61teX1aet8Ql7VgUq4aaYHuFuil9UpaDxUSPYUGJtA22nrnZTzyvH7msKz4HHuQdRzdjvGNTM8LjemO9JcrzRuqFtRW+5Ff2sOEPBu1wWy98eRG+//yMP/0XnW9/Uqv0eg+ndLReSFXprWIQ3j19RTCev/7dX1PKRtmFVhxvzl9j3YSxwvHxzOPrA7nt1N4oqZF9Zl4sYisqHW9mrJk4zUda7Vy2T0zhwOn4yPF44LZ5Pnz5G1JOfE4vVFf43fXveEg/EJbTHcB3o9ZEqzpCyYxwOh2wBgb8vWHEDqeQjH2tegPxiGTgE2ICYmaM+CGck4w1HWeGI652paWM6x1rG9qU0jrWekytdB0uFxRq6Thn7iLfMR0uZaDtMSOMbK8B692wZbeMaKO0Qm8e1YAM9BbtDq4yTrFBR9ckDZHwGB5B8PC8Vzh2jq4RYiNGoavD4aBBTpXUGsYbgg+gQil9BJypYJ3iPUyT4By03vjb//w75LbxzdOZh2PjdoFaLa2PI7VZ6MFCeI97ev9fa5v+k60QHpiDI+RXgl6wupLllVddaa0Qw4GH8yN2El7//pnb3yR4tZQCJRVKL6y9E3xjco75m84UdqpJbAI+HrDTzIM4HrpBgpKmxlV3OobDtAA7dcuYFIg6obWAwsSJxT0g3lP8gKYZNbRVybeEOPCTsJc09HVyw22fKeVGFUf87i3L0wP9Wgm3hJpCnoHmeDs9Mrs3bNvOx5crl5zYrhemgyf4yJuHM26OGKCSya3QdMU0h5qAYih90H9GMN+KDx7lBNVwjI2Hd8r+xdGeh735eH5AoiU8NuaemdNMo1OLRzah553kC5c58XyoFFfxpTBrp8TMc1t5paI9IGVFkmM2B7CWXnYu/gP1sDEtM2+C8GZfOBIJNmBU6F1H7loD7XKHA9yDL0Vx0RBx2PpT4mij2kbrI8GYO8zw/2r9/M6Jn6lYgpsIzg8U7r7fRW8OVNi+fCF93nGzR1WoMnzgYgwmDOBTCJ5FHQcX8JOl9p3bJTH7hWVasAS88QMcE4RXeeE5/YHn/oxbKu60kMtGEc8zN2Iw3C6Vl3RFg3BYHvDxiDNvRxKjP7KVFzoWg0VMpbeNUne0d0JXZndArKekzMvrB7RcKfaJkjY2uSB+2CG1Xkl158v1lZfbBSONJ+eJFF7XZ0pXrMlMvnAMDt8rpjUmd8a2HR8cHz5fWdOOlszD+YnZH2lyxPQZp4LzYwY68il+4OgOrGUna0O0/Z/k/UmPbUmWZokt6U9zG1V9qs86Nw/38MxIZrJYLJCTAsgBfwL/eAEESBRREZkZHt6YP3uNNrc550i3hQO55gBHZUUEIxCWx0cOM7xG7TYie3/fWqzblbO6Evwz778N2I+BwQTU+UI5N3Qt1GzYPkHxK2Zf8SawV3tGvWM09+zcHeTA8uWFU03ImhmzZtSBIBZZKiVGRCdqWjiVT1x/hHw2fGmvvOqFqDcGD1/d7znYCb4Yvny6cnot2O2XT2ErVZDSUL4gRhAytSSkVAbvePfwjt0w8Z//6++5nK6k+I9U2fjm/d/hzICzM87ONLEsS0RrwQWDsyPWa5Ry6NYnLM/bM1rBaq7EfGE/PhLshNFwPL4jlsx1XUkqY73h3FZ2zeJ0D84Og+P01qit9TaELoR5xjpNTQ7VOt1XKYGaWbYr03DsY213wrqGsl0YZm0ADTF3s7cPNxtwhVLKbRKjqSURnKNKB5nVW3/4r8jrm5SQpihNUUvP3AiNy7JgxpEp7Nnil7/+zI3SOOOxuvbAIKqPcegfmEA/kTQoANIZX9fcuJTMI7DbO751mreTsKyZVhWVXm2G7jzRymKVRTuDdd2u7JzFeM/kZz7/6YW//ONnfvdgMSpzPp9Zl0bMDgGKNqzKsw473PGJP708/8u9MP+VHj+OjPMOlkLKb0CEEtEpIblAEZqt5CTEt43r60Zbe41LG4OfNXanqRnWHzNWhPFROA4O9c2RPHuSXLDXN1x7Igw7kgjnUxf/jZPiYAf2WXHMFlthVJ5fH3/D01d/w3x8IGqhloKI0HKhpYbdGzYd2VKlVU2umVYTqTg078CPuHHkbBbUFHm4G2hqB65yBHa+N12Utxysw6fKEjckg/UD1Sf249Rlqg3WLZFrh5KmnCg1Ymzo9fpWaKrnQZwDpUZCGHj8jcU+COc/CF/+S6PV3uzcfTMwBMX29wt1HFnsxLIJeYlssvHFLHzOK1uO7HLj2zQjqbHVhugRUZ5Uc5+qmjOv4QvGVIpc2PSGyVdUthxaf88ro6CCqg3VOnrCao02jrYJuTSqKmilGIzQTJ84atWnqSl3aaFu/8yZk9EYilhaLSzbleV6ZVlXlNbsphlnLela8UyMo0aHQipn4rbimutAbGUwzhPMzG7YMQ4OkSuTiajmGEzoIy86Q+DMhU/lhbUstHhl2K9El9F2YRwaMw+QoemEHSzj7sh+/prgjmi1x/k7TuvHW6C1UsuFXs8qBOcZ/R1j2GGtIpaVZjSjnVj9C6/yZ9IWwTmMseTtgqRnRC9kFPvjiKNwVAcO9j2Hu56faalSXxbMSeF8YDuuLPkDW9mY3Humac/n9EdcHXB6AhTn9RXagrczvvbJB8OeyWtM8RznJ5R/Zc0/IrlQxHBwj4S7HZcxsF4z3huOcyfsRhVwKExQHPaNp/s9j0/3hDFQMcBKPCXiJSJvI2N9wuqGVx7iQN6EKIlqoQ2w8ErdGpfk+cPymS9tY3dUfGcNUzDsxogqM8f7QBWFH37m3O7f8NNSu634ep0VNLV1/oHKBk3lbj7yf/qP/wPnLfHl7ZWX1y/8/fP/xN3hsQPvLIxhZFlWBKG1TIxnCsI8TXjnmecdwR5waiCmhcv6Ss5/5jA9sJ/21NZDp/vdDt0a8zCzG3ccpyODdWzlzAsLx+PM6XLpZlF6DrTU3A/Cllt2IyO6g62M3RjcAG3E6X4wEDLeWYbQvTOldI6JVqBtB322JjTVK5HWBmqDUgH6h5lWiibdaKqx0DTBjjeuUaPJTXJWhMO4w+h+81L0dWcIHthudtVex1TqJ6vy7SYnjdr46+8XU8Wi+HIp1J1lFwLTLnNZFpbUW0n90ORQzeOtx/h+UFKq48SNtZhmkKw5P2+01DiOnsubofmRUhMVRTSGiw2kccbujlzTK8v6h3+9F+q/0PPx5U+My0BjI7mFrCNGW3bsiJeN5YcLf6gbkht1KdSrwaE67XhvaV816kMmmozeFOqzRezIcO/56ptH9D1crlfOzxdincgaZIvslNC8ZnCCcZZpCAzaooLmsH/k/be/Ibzb8VYinz6sLLFzfuZ7y/4RsjlzuSSaGC7rlVIaTllqNV1kl0GrxKiE49a/sF82C98NZLeSX694D3r07GaNDopiA7IIWsGSFhbVybcNDdqile3gNQVNW5xxxBSJWShmY3AGJTC4gFIbZlTMo4Zi+PKnTI6Rysb45Khe8fmfrqyusUyWk125cGJJr0QdyTRUg10LHPOAJEPUO7SyLDUjpfQVq+nY9BA6T2tojrxsSNsokol5o8aMqaZfCho01W7oAcMQPE0qtRW0NX1K0rhNSBVSBa81WQyVf2ZC7HJ543pdeuc/Z17f3lhjxnlPzQWjFHHrIzajbf/LNqF2GC8owWlwyuJCYJgOOA1p25iHGaM9pjkQS2ua6oXVXnlTn4iyIeuJYkBZBdJ3Xav+BIxIULg24IymycIaI4VnfCt4NNNwJIz3rPETcXtFN4NpAVUcURIX3ihaQBzewra8domgdYzDA9YHYl1Z05m1XslVYc3GqIU53LHpC+/uhG8Hy/bDnrfP70jP4A6Nw+PKJ/+Fqxg+vH2g+B0Pd08c3MTdvMOakVotpTZqWVk3gaJRopl3O1oK5JzRFWJd+o3a3RPciLeGpoXSKpdt5eW0YKxinBR37wzhGHCTYT/u2O0m/MFyrSvX9ZUNoaG54wEjnloSKW3k2G8QXk/Y5tlwbPZMdBfS7FAUxgx7Y5irwWyFxDO0FXs03A+e4ZcvJUZqwWqH1vSAAT/V9jTWWLSxbK3j4o/DDh8C7+7vkKqQAtsasdpwmCdayZzThXa7TQ1DI8sVyQm1ZvCVu/vf8aun35Hyxo9f/onr9YRRfbXijcOpif20v31YaAY38H73iLRHtnzhZL6gDUAj50SpFmcFbTuDSIqmSCPmSmFAiaaqK7ZZtJpv1eCEMx3YVkujFukZEgF0P6zR2i1zUQGD1H5o0LrDplrrww0FFElI7tMT6ZJunOttn1IgF8E7R5Paf85KcE7jnSP/VNvufyWa0Lktlc5V0rdsTGs4C7lmarKsL5nZCXfDyPFuJH46o6CLOF3o5FprcKPHKoNF43UPF1Ituhiedgfsu8Rvn44EOwOGqi4sWrhYQwwzbryjSOP1/AeUe/tXeIX+yz6v6yt5CzijyL7S0LidR+/2WNlYzs+sLxvt2qdcuoENjjA4zDtD/jay3l2oeUO9DoT1wPbFgLdMj41RZ6yDZhuXuhIRVKjs7wMNjZhCyQkRTfEVOzaO779mGGfUKKgpEqRixaGVwQ5wFyzSDF/KwhYhlUTMiopBl9qhmFqhxKBE2H+ptHgkXVfECXm/oVpGCQRTcQoGI0TXyL7X62szbAKylf420Z2SLK3TZ0UKa8xkiTRx5KYwZgPpSokTEYN0CBqB6gd0VoSwx7iRl3jhVcPJXFhb4yyvvLXPrHIGFEMbOLiJg5nQybNlIdODq64KXvp72CrDTk8c9MAgjpYrskRIik0nUkq4opnawKhHrAudPHsLt1qr8MFRaw/fmmZ6BfmmxyhKo1sjaI+y7me9pn724eRqPvNxfSFd++1huW4oZdlbRb0l6BvSR7zKoFE0bSCMxFSwAlp1o2HNQhEwKHKE0XucCSgMiKU0RRkidVhQLnWHjA4srbBTA7UqclJUu9GUBQbySYg/LFhXMO+FrAu7Kuzm7wlm7ByI8Y61VdKWuv65CTGf2eqlJ5OHvk6a48wlbQzzjHG2N3raitGVQU0EE7iuV7bacOMO568M7gv7APvpHU0H3krGkFGst5ucZmkX1ph52j8x2R2qKCSDweC1o6rKwomGYraW0EAZz5oyKV2IbUOrxrpl3i4G+0Wzbp6Ppyt/+PCZ6zVxdxg5vPd89auBcOcoTdG0Zo2RGjeK20i8slHRZs9gPU52VCqrsRiXqDfpVFwar03xxWSMSex2wuNsGa6KSQV8HpHFci2VXJ5pu8r86BnsL5/rkGr3rXCjq/arwu09IIXSoFaNaEWLiXH02OCwasLqgbhlTv4Vp1VvCrxmlroRk8YUhXECZJbtQs4Kw4BTgYfDV0zf/Edezp94u/xITYnRO47Wc6dnmoGrRHIRLjkjkruTI66cXi943zHZIt12ZZRGq34gaFLILXeGibb9FkTrGanbZtlb029D9KmEUgahZ0hb0x3PrfvhRKvbr38LwIromwzQ4r1nSUvPo7TWzca5r560slQ0sVbGYSDFiFYNqJQa/0qi1XIj2qI7W6IItTSa6lMTUaANWNVhWi0bighvW6Ukw8O046unR06XiNIa6wNlW2gIWimsNQRjMaIpb5XlhzMu39bS0xOPh4lhUDQ8SUbOaGLY4eYj2lo+Pv/IJlea+uW/HzaVsNnQkqJsqk+gBk+dLfJOMNXSsobSMMnh7UAII3YH7T6TjmfW8ZWiE3oUxhIwq6F+scT9lZjOWF1wNaCr6sI51ycSttpurK6CqkILnnH/wLgPhFDQQ2OeFKIVuXarerCBcbNcVKP9FFKyUFLqU71QaW2ltYpTHq8s9qx5+eMFc/DYwZCUIreKKQW1VUwTmtHoLOjq0CUwOIO1jlQatVVSyTRKr0A3Rau317RLiAEpDicBLwZXLaIq55ioJoEt8JUmv26sXzT+x5F6nBB3IrGy5EiUDW0cc7jH4dg3x11y7Jqltsa1ZWLKKAyB2ht4WlDGYcyEsXtcUpiSMWWg1MpVr1zrhWA0D/qA073hZ4wF6SsekUrVGpQBKyjVRZh96uhoaFKqnUKt/5kzJ+1po/DG+QcoxRN1Y9QG4w27eWIXLIJwJhOnwtbOJGnU5noAiZ6M101RU2FbNswQMHagtR6M0gqMVShnWe3KVV/JqiLKYqfAco28Xa8M1mK1orSENj3kF18Tlz8n/KCYdhWG2pkMxpLqwrZ+wauRWjJJErSMatCoOBXQ4nrV0QZ2wyODs7RRSJIRieiWGc2AsQc0Azs7oWtlsh5rviBtIYljf4g8fOfws6Ls4NmtvFyuRBlQzmJyw+uAYHk9v2JZ2PsHBmfwTaOHGSd3WJNI8TOoxpaviHTfEKohWyOeFdsXRZGRf/zzP/LhecHT2O88w95x93VgftQkEbYtkdPC28uVFqRj+iNoaSglHcSlHcpPWGvYUmGLkfOy8dwWnnXkYTLcjSPGG77IBtFhmEEcMV+IEpl2meO3FfffgIUV1d9kSpvbqqKPBKQ1qiRqKihjaMr0QEYqzMNwIz9WdNBo38PlzgfCMHB6XVmvgnGdCJuVdOOtVuj2EasMkhOD3/W8hoK3yzPNbhxGC9qjrcFrodnMaX1jyws5r5R1o142Fqex3iFNoZXFaIPVlmYqqEKuFWM9SjeU8hhzY9iUK+o2/ahSUQjOQlOG2mWnKNUzHj32oShVbtVaBa2hW7+hGW1v+G99+7X6vy8Nchas0RRjSUWYXZcNGt3R97lkFBajFFX12Ylq6jY5UT3ESjfJ/rS+0q2j31KsKG2RJpzyihbL3f7Abh+IMWOsJYweXQvHMPcpT4F0bSwfMvUzOOlMleNxRsSySOvVbQNFe9x4h7UD5/WN83pC+0zjl4+vL/tG3gQ2iFGQTWNKr8YaB+5Oo5tFzQ19cbjsO9n3rpDvE2VuNO9oDdIAWyn4VGETlueVS3zDDw1fAiULlQKmv4Z8Meza2KeA8453D3/DN49f8bjTqHqlejpvS2ecEaLSDNYiMfFGolSDM5pgBPG1e3F063zWphmMwyhNva/EOeHuZkqAagxVHCuFXBL6ppWgWaQoEMVspU/6lafcVo6l5b5ebb31pQRGB0uMeAn4l4A5C2ZQsDPk0XMyQq0r9W7D+Cvx6riuA+ZemN4H1reVZcv4otjrO2Y9EcQwpsoYK74IW67ophhar/1KrehaaVoQK8RaWXPFRIXbCiobmjUko7moSqQwqkKi4m5iRAN45/rAQKW+6tatgx2txTnXpyzW42ujpErJ/8xV4uHesQ+WrDLpRTCn3rpxg2G3G7mbQ9dKt8TZL2xSew0Qw6gMsw14ZdCl78ByykStcUqTG5AyRgnBeYoTVr1wrSuRDCSMBT80LusZrXa4NhJLJWjp6HUv2Kli584j0L5hTKO2xFZeSfkTY3sH2iE6kerWT/F0X0DLhlwXqstMyrMPR071Y/+iKRWqxTKiqkNRGZztdUSlkKq5bo4XnXHuFf/txvRYOZG4xDdOceWSP7MfZw7+gFYDVVuybpRyxVXwuuC1Z3YHQrvjsv2ZrBOizqz1gqaQaiXYitMQlCbV/gb48OnC67Xwfu9oShFTI5eAspbBd3V3KVfW11fqmyb4Gb1oWtYUMqolLB5tDFoZoFuYCxlsZDTCYzjw3fgOO4JZrlxyD+2mcjPJOkFZS5g8tv3ycd1Iu00c+k61SaVJ++v/r7dJCiSmEFi3lcENaF27AqGZHoqTRm4aawO6aUoS0iq3NUa/+YtNrPrMm7a0KozugA0BaJyXC1tbYKtc84JzFuUsrs6I6wdzyRdIkaFCMh0rLXKTkJl+s1O63Q4AXeyndcPrCW8GVLPEnAle3aBtchOd9RVOJ0W37jFRitRBIf0A0uhNvNu6Sd+ycCnnvt65ETFp6nbwabefZ99TV6k3Bw63lVCjtR7C163/kFqj/31ufx6NYnCO435g3S7k2FDSkJtFVitLLpm3y0pthsN+RxgGjIVRT7AVHqdH0lJ5O1+Jb5ESLWE0eGnYCFYapyXDKph5JfmCCSM+BGJpnM5XiiS01H4z/4U/5b0jLpBeCpIqthls61K7RkOsJb/3cCeYs8ZfevapPgjtweJ398CeXDaULmQHi8q40ideS66suhJEsaTYjcACKItkj22W3Xzk+P33vL/7LY/7wMAn8rYgVqHJmFZoxqCcp1B5U8JbFpRyODTOwWgUwo16LAFqYzA9KHvdVczfDtRRk2xBtAU7UltXoLRa0VVQSCcg68ZgKk0iqQm0fhiPOSJUjHK0orHNYpvHNsFmaH9OqLeM3hvqJBSrEG8pc4CpooYT5mlHDBeoEXPUTKthOQk+NnbKcdcCXjRaMkbARCA3duIxGGItrCl2X5ppPcu4rawRzGKpa6UWwFiUdTg1Y0vp0l4lt+Br61BFY/tFvwFNMF5hQ8du/KTsVtp0Ym9TUH+eGPbnH07yjmM4oJ4W6uBIo4Jr6DIkJyz5wrItJBq1dTGWKwNGB+bJsRsMtihUbn0M3hrrurC2Tk61aJwC0xoXdWVVF3KLpJpRKuLFsJ96jbBVoRTXq4VW4ezA8auZcFcwPuHnGWUVW/qEUx5hobVMbZHJ72mSSPlELBGDw9hKjppU3kCvuPn+tnczVBSpCrp5mnLkfKW2lSyZQQd+dVBomdkynFJhtBvav3KSE5+2zOdNU7RiTRcO44799EhVAbRh3t8jeaGUjaVFtN5jzUDOr1yXz5hpptmGdpkkC2teepXTrvixoeeAaRFvHCkKMmty0nz4w0oLwlfRsHtIGPOGqmdMWYnnwDCOhBRIoigaEgnXFPoW6CzSBVl+NNwHw9Fovho8j2EmjAYmx+e4sSRhWTbELgxOiJvj+aNmHH75RMyaM+JCh5HdAl9NBGcdzgdy7uGwWgtKTYgYllgY0Hhr+mrC9NdXrqC1ZwgDy7ZQolCcwlhFu01lqmRKWcn12kGItSJSqKLYUmSLn/kiK+Ng8c4z1hnnA0onVD6hU2Y2mnE3E+1tlSK5QwKVxujW3VduomExCryecHhaq8S89dvQ7e8vqt1w2316IVUwtjdbtDREBK06YBCxiK4Yo5BWKfWnA0X/gEa1G6itX1yQiqbTLotkimSCGEQ0TWlSiVjjqE1TWhem1Vb/OqFQSuGt5et3j7y+KF6WlVSlK8ikobXBaUMuifN1oVJ5ON71SrV1fw3tD1qzrsJ1rQw7j6Wit4Sziqojl9S4bgvj+IoD7sY7jFHEayInQfe/yi0Q/Mt+5N3IyS3IdiUsir11ONPQ6sbDEaijoswapka5L2ijqBOo/USwEzYLNa+QN1qNXFrBmcrReIIe2GTjlDOn6wZNMY8ebx2lNLbWuDve8c2vf8cwDzR1Zd0u3ZxtDJq+TklNszXd+TxNk5rFuApZGFzXMpRWaEpTi6O2jG4rsJG8Yfx+pFExreGaomIRBqQZpAZiiiipWNUve1iDtEypF7JYclLkXBAqVYFpFqsnWlYEGnIu5LdMuClB6kulXhTVBtTXHvMrIUvFvatEdyHnFas8YVDsG+S1MKWFIF1FUYZG9Q63CToqfNGkXGmlUkuX9nnVNTSmJCQ1ytVjiqG2gqcxqglv7jGlMleDxWJsRwGo1oPxPxGbqxSG2TMOodu+Y0VyRqqmNFBVUOrnvSF+Pufky4AZZnxLSNCEudP6BmMpXHg9vfH8fCJVR5hHcBpHIAyBebJ4A630+mUrDYUQ48aSE3707MOA0YZcK+f6xsWcSSqTa4/PjbKDmjmMA7XOpBLYTY5gDUEHjJpo/krRC6IbXg09xFfPHQ7vRpQqGG8Z5b5jvFkoupDbRm6NKCeC0RiVCGbHrH9FjF/Y6meq6pZepYSaO3xq7wdMEfCWaA7EtnEpmbhtfDhtfFgzS/Zos+f97oE5fIUd7rAYSrmiMczzEVU9NV5IKmH1hYVPbOaKv3jMqBmNInIGk7AmgH6j2BPh+BXv3mv+0+++4+PzG00grnBdIq+nK88fFe+/jzx8dWber+wCcDX4HNBmhzIQ7UCqmpRT99JaoXmB1HBa40IAFWkxspwWpnDPwMTOgeTMtsHWFKLhfCr8+PvE/jDA//3/l4+4fztPa7cUJq2PZum00ioFR8D7gZiXm3Ojf22mWqnLSrYbzg0YI1hDn8wJ7KYd67pSc7sBmzobxGjF4D3GamJZyG3FVo8SjbMOTSeqdvJqw4YREwas05jWiNcLzjT86OAwMgSFC+BMQSuh5IVSF5RSHMZ7cg1oemix5oywUFoC7aAV1M3uWvJttKv7+L5K9+c4C6U2nGkMg0XpQpQCuk9spJQekBWhtIwxGqMNtd0mRhQUFWcHausHlyyNKrrL4PLKFALOQMutT3F+8qRJPzitMdJo/M1377HtI+YSUUmzlts4WzucHRCV2OLKp+fMMExMw8BI4Mty4mAPmNFhN4MOipQzdlQQKuIKdYaY3lBFGMwDzj1QsmFbNmoS2k0QWOvPq07+m36C4ZULybxxnD2jtWQjZJW52itRNkgCeHLQXIcFFRzODH39tqyopRLOMGwjtTiuJKrXNAxDmoiqcV4WTq8V3yxDtLTBIc4jw8Tu3de4yZDaK0pHVOivUV+haY0oT26WtQjbVpDc814rK5VOL+78q0prjUK/HAiJyQvN3Ng/aiVohW2w3qaEShuUzCg9QYEJjaQzz+tGbCtbg1iEVgODmjtjS1usCXgdMFogn5FLhDtNPUBRGfcGnBvLS6YQmB8OzHYgj4aLvhCl52Lu7ibUl5HrS0TFSK4N5SxpdMgw4K4VtSWW08ayJWIrYOTGG2yoAWxTGDTu9vNSzqO1Yq4Krwac6n48j8He1tlK6Z/a+zdnkO4kZeuoOSGpXxoUllwrJWes/XmjxJ99OEmvmRwadSqI2rChEQbP4BNNIrqsyGvi/FZ5eUsYo/DWsTv0nTmbRpdOmpPWHS5KGk40ruqeR9GWVIXLlklGo+0ehyaWyuXaKDUiKmHciFWBUlaQTFWFUi5s+UJV155raRpnJqCw5k80BoIdseUVpTRuUAQCW1r6B6EvHI5PTOM9Vi9EFNSJSXUC5prPpHTBKsth+BpzMOxaY4lXTm+fyYPB6QsXl2ii+LJoXs/doHq/e2I/3yHegS1MSjAOYl6RmJnCnnB4R82ZJb/ykv5AsZaSLGGZaS4zjIK2jsO8ZzcbJpXZPQTu7zWXdeT/8b+MxJzwKeGDxpWB8ixsIVEGMIPGN09tB0iKcTCkpkhYlFNsbaW1yOA0xjZIhbZmdm5Eo4nnxl/OC7bdkaKD4vAKRjOSt8L2alg34eWDx7Zffl2n1kopmRYVzjmc1r1C1yoxbThnqKUS/G3kiRDThrUDcSu4fMGgGIbC4oUaOxXzOo5sZbmpIQStDE0KtVTcNLCbjkBlSytbWpjHmd34jsPujlyurPHC/eGRITisXgmmoWYN72ZiGngphuAMh6DQNeI11HxmWc6kpjkcNmY7UEq3VBdZSNsr2L7yAQNN/7Vdoxp9vOsMQj+c9QNVwzuDiAAVavefGGV6PkQqoBAE1RRWgTYWbdQtrFsxppFzD+622n9/rRXKNWrbCNpRdUMJtyyLIUmGJgiVv3z+yNN/+I6vvx3xLxb1RWhbY60NUYXgBrYtk2qHHRZZEDSvOfK2FaZwxo4K4zXgYFPUElFDAn8h6QVjKq69537/dwR/5OX0mT9+/JEPXz5jvTAETa6//Gq9bbpDGgbDZjRnFVnNwtoiqysYrQhZ4ZtD3MBSE21bGHSjrBs6w30cuE87DnVENUvSmYvLpCyo6EmtsF0TQ9wxYDtyQjvc/sDT97/lu9/+mq2e0Sax5oipBSkRa3Y97IkgJdHyisrQVk1wjqwbmcJFoJbSVQw0SqlYEQY3MPpEqStKNarqq0ZvLUX19l0T3fNPqlGVolaoKfFaN0yYsHbqzBwXsHbAaEeTDgfNkvGqMiihHh1taFQy26UxB4O7g8EVasi0l0hdFOrOoO8FnEOaMAyaHAyLUj2rZkufSBYDRlNcbxjmlNFVMbuJpmGTK1sptNpwtYsBlTSqAes7dM1smkHrnonsloeesTN9zUrrhFhtDA6PtZ4milYFLTfDp0hnlwmk688jiP/sw4mplpGJMM+0+RVDZOc8u6EBGbsrnK+Vj38ppOI5HCYGZ/FK4YvBJYvTA3aYsNqhlGFQIzsU1tq+K7eezRbO+cqnlxN+1+mYEhcu6zNXWSmq0njm3c5h2so4DWjr+w0ld29I0RtuHijlmTlERq+ppJ7FyD9Q5A1tLM4d2Zl7nLO4sCcXcFojCFtrZN5QpnEYJ2gLn08X1mqIV8O0gyEEkjuSGEh1oUjrnogCrYzc+3fMwwPH3XvET1zKKzH/yOB2jK7bgOfwPdiRtb5RzYZTDpMUWz5jwoHlzSD5jcIJ7Q2xjryu8JrfGNLveWgfGMbE948zP740vnt/YD8PeK/YHRvhELC1sb5ZbJth+7pDqkwhboVNEgVLSgvNFOadZ3+0WKt5e7Y87GecapxL4/JpYzsZ/LjHjAFrN6Su1Gy5bCPylmmrYl1++e0EuWUYUAVvLVI7L0AaiJK+49b6Bh7rExaF7lIval9PKsXufsfDwx3n1ytvX1756vGOP3/abreNhqLQmmHdEka9oTVM44Qxjsv6ytt1YYkVHzzjMPLp5QNaZ75+ODDvG/O4Mk1CLcLLG7x9BKM83gpVDM5YpGyUVhENiVdEzjh7QFrlvH3Ejd142uWXpoeBW7fC9tJBF+dZbTHasuREQ9OaI9V8a/MYmnSOiTEeEelhVdXhjD3D09sLWmlG75lHzdaEWG4HIdXNzM5CyRGnze33dezDHYOb+ePnP4BpOAPLsvD2ujC4yK+/HnDGUJ8jJaa+jpPMGA5c3jZqjoyDolxXWjPEcmWrnciQU6S1xmAnxsEiVneOkPJ8dXzid3/zn9jf77iklcu2ckmVrCa2rfDlvHV41S/9SVdm5znMjwxJ4VJnY2wxUpzDjTPiIGqLD3ccF4fbLhRJvJS1S2DtzctmDFP0ZONI5sLndMY0TVKemSN31mGlUVTBj3u++u7f8e//9r8DlbmmBW0zUhM7awnzjhIdcRPecuIUI7VlgjG0cWSl9GOOtiwps5WC0v0F16vAiqUJkle0q+zGGdpEZEPKCuRO7WiOhkfQiPSJ/7vdnsk/UqT/s1x7DhNtaFoDgqagWkFaQlRGvbdY1VBFiFazLpoRwzwa2izkKbK8vTGejxzsA24wXIYTq0TyIqTciAjJNUJTmLWgtgTqZjb2gXcPBx7ff0Muwo8vH/i4fWDLG+qq6D08i1UOnQUEWtI00wWYRusOVTW94cdNHFqkIk3QtpuUS+lqDGt7LiyXRGk9j6fNP3OV2KsZP3j8saCfBBUEpytOX6jlwlhXHr5y1HWHNg/c3x97tSontG44bbAuoLXtu10TsNYRrGZ0HuM81VoiF0IzHMyew/7Iu8MD17zw9x9e+fD6Ci0hvkFbuJ9HnGhK23eXgdsoTWPsjDFH3u0fGXSvcyVe8O6ICLxeM6Vc8doyD9/20eO2YnB00KRiqR9orfCw/zWTDexGGFzm45cz19MXnB7w4Z4iiWv6xGgbOSpQgck9crf7DuMONJOxgyXjGM3E6/UjxR7YqmJUhlIWUrqwlS9otTC4A989/O84LW98On3gUv6Itw1EY9vEy5r4eFpR5cLRNHKp+Ok3/Oq7J/70+X/G+cbDu8DdQ+PrX4GbJl4umS9fPNdtJjChgHWJXJZEMRDp0jXtBPcgvHsIPBw0+2HHaA8E77k/Wl78FYomjBYTAu1quGwJE4TZjJiyZ6Ihh1++W6fTTTU1FXCF4B003c2+zqNoaO0xRqGNRo0jqvU3dq6ZenszV6URB/M7zzg/8vqXF6aTJ9aNanqSX2uLiOOyJnL7xJw9TTSnywmlHVs888PHf+Lr99+yPxxZrp/4+OWF66miJHJ/cF1foDWj0rzGK945dDMo3Vi2yJIEM3gaUPWVa3pFqYYKFa08Ir11o9F9EnSDLCnTfTqlSs/B2BtNVjTaOkpOlFr6basT0/oBJ1doPURsncNaTaoRpRVWK5zJGCM4Lz3jgQIE7xreKTapKN2wxnCcDrzff0cpjT+03xNcD6pnqXz5tPK77444eebr+4FL0Syv5oYLt5iquZse+fD2I8F1/MGWEsbEXvkUTcmNWuAlPYOqjGEgaMMxaO4f7ihq5nzRnLbEmrqfJ7gjdjRoJ8S8/Gu/XP///qT0Aec8kx7ZV4PTjlPTXEoiXRekVpqFKPA+HtjXmSlWttZ/5kYZnHLEQbiEQo2auCXeypVSV7AaZ3fMccecNMSM3w883v+G33z/nzBak1Imi+noimZxN5FevQqn58jrkkne4B8H1NhoCa6pQb0JLRUorfvUUIG33XzcqqGwQ5UK2nb4oPakqik1k1Mipo1cz4g0jPZM+sg1C34cEG1orU8c2639KdzgxrpiTEVjKcnRRlC5MGSNKI3MnuIFpYQ2NFKN2K/6BCN/OeOOBwqF12dF+ehZN8XVFfRomTGMbw3/5XLLcnUWkNGW+/t7jPXgFPFjRpZnWqwYrzF+oDXBRGGHZlIG1+i1Ya0wWv9/hV1FSp+cOEPYjzSjSDFRtowBtO3toBw74sD9TBDWzz6cSGukmKnrglsyTlmKgxgj6/XKJWbs/cSvh0dMuwMseUksJ7heVkT67qk2wWCYxh3jOLCfB5QOaDURk+F8iphx5v23A2GGygUzCN9++4g9N+LpMy/xjeUaccXRUsbuBKu7YXSYJ4y9R5qj0AFl5/VCbj8yDw20xZuBvXnH2L5CLgZlr0g701r3Y2h7YR4qk3/AO4u1vWL8aL5hcl/zNr2gpHJnH6nqQm6BQfbsdt/QlCEpTfMjbhrBDFy2FxoOawOPd/9HJC3kUmkmI+oFhWZSgWBGcs2U9MbO7TD73/G5/BdO6UfytnHv73sgUSlED1QqUQeWZce3T+94up8Z9oX7byK//feBx68bOTuu/2Xi8kVxfjMcZmEeB4ZwpIU9i7qQ1aVzY5bKy5crhylx3AWs1SzbCl5zfHePIbCdImHWKOsw2VJcJbYNmmaUHbN1WO//t3yu/Zt8RuswVhOGCafajYzaDwBC154b1YV2To+kktFKY40jBN+dPLVzD1Lpq0M3GL752/eo0PiH3/9Xtq2iMxSfOewHQvBIzVzOW6/jJgHda70XKfz5L5V5HGg18ely4WNOlGvj+lEzGMPhMXK4m7CPmmgLgynknNC2YpvqE5AmXUrmWudziCWVyjSEWx24opWgdW8lAMhtbdFUp80itR+qEBBBqnSYleqV31w3tNI0GsYolO6/jnMDVXrdkc2QXit16I2lYNUt53NrCRnIkrAqcL97YDcf+dOHP/WgrjSK6SbUT88nnvYzfmeZx4XHaeBtGTlnhfcO2zwle47TI8vyShsU1qhei07dSSRFoZTtE5DWQDKiPdnt+MNr5fXvPwKFJGe00Tw+/gbFRC6JFC88HN7967xI/wWfaBM1KKQI2ozs9UgwRyYpbOsLNZ/IVbGVwtviqWrPpa5skvBu5qh37JvFVk30hmVXOL2c+fLxmbEWUNIPizmQrxqTRp6++Rt++/1/x3G3w7pIKgpXJ3LVbOsGqueR2iWTtoy2Crt3JAw1O5BGk40knTtiVMVrsHa45bgUWmdaS6SscCi+vP2FrRTC7kgshuCP+DCjTcXd5HstZ5QynGrmkPv6Eu16rsQZqgio3k51tYtVShOioXukXhvqQ0BPhu2hsQy94Se54JUl3Fni5UJ4OFDTGWmGizSCCZgwd6GralQNOIU5ZUzWkMBjSOvK2+cfmfYzRjKz9hQ9kVtBNYctDZUzxATGo+cRYw0/CTv7yqaC7nkTuUn/5vsjfh5Y16ULBJdMbg1rBRsC+3lPrOWGEfhff37+4URH1njh+sMb+jWxPxwYpgFpmmWrbLdErjEZyoIkQy0aox0hDJSSiDlScv+PLVZhBo0Uw6fPV8LTQm6Fqo44NZG3E1E9U8yKDAoxhSGsjF8p5nrPclZIVLAkmrqCt2QRLrIwDDPIwsfrf2Xvd0hb0BaSJISCbg38N0h7peQF6zS5PEMtTP5bJv3AIIYaV87Lc9+lWYs3Ox7vAnc7D1tlV/cQ7nH7Hb5qdDLoaWLFsGVBUqLZjWBmUs1oNVCqJgwz6/JMLpElR1qtzPaICV9RauYaP5O2V8b6Lbvrr3HDnjT8mcnuwTbG0TH498xuwpZK3CJ7J/yP/t9xev0zY8i0knn9rLm8aZ5/sKwvgbgoomnc70bujg+E2lXXYguLZL5cV/74p4W4WL55nFkvhc9f3gjHwNP9ylB26ATKNXQTUIkwgDOaZU2k9oZRhum/AXz9NB8RVdHOkHNP+zedUUjPVdA6sVQ1JAspblirEFVBjZ0S1hreerwfyTWxxTdWNL/5d38LWvGPf/qvpFooWdi2K95X9rsBJZ7reaOVRiwZpxWi+m2xaiGlhZIThoYyMB4Us9/x9a9/xbSznNNHRAQXFKVuncFjZpRtRFnxyuKd9HZRGEjpQpaC1vc02g1Q1mu/NM0NWYLzikZFqYYPHppQaqH8JB1UHdymm9yw9I4s9dbIaShlUHi8jMjnxpfrwvw7hX0STBFabT04Kx2wllshmLmjuLcrX5ZPNN3R9VEaAyNWB378cmG2Gi1gsnAcRkyYSelK0QXmjllXTThf33qQ2IAxPeantOvU3RCwzJSyor3GhJFrqvixr/Mui2LdNhQXpnEiDCNDGBn8Lz+DtTVFzYLXDu7vcPMTozOo9Ij68gPx0zMmKrI2nGrhZC4MzlNOwnTJBFf7CiAJtQl5puPc0YgNNF/AZXKpiNU8zI88/uY73n33xOgbVTbQlRoT1IgiohWc/rIxas9052keYlOkXDu0rWSEQpVIKwblHUYZrNZUEVIpWCN4wF8dMjo+v50prnK5vOC0x1vb15m+Mz1kzWyrUHIi6sjr9oJ3AcfQ7dxG33QXjaAaQxNU0lycUHxlRCEXMKcRfS1IEMR5iB5/6h8bMTREwzoWokvocGDaD2ip3F0fURfF87lTuzUjQXtCUWgN7i50zlGOrNdGzZnRWvQ4k6SSdecDedNZM6fzhW0tTMPIMAwMwfeWjlZYbai0jhEYA9PxgPWOuGVU69/9rQolNZoUxp0nGEuK2896Tf3sw4k9Wpq3XJMnXSzLNhCtIqnC2hrVNLQuONerV7aNTGHCBEPJnpRXtqRIWaNFEVzAGANVY0bP9GvLS/wLcipsm2JLzyh/QtlKzgqrPLvhkfP2D4TgGPUD3hwodWEwMGhL3gJlS1zkA4dx5jjuOIwPXJLpo0F6ENerkSqJpF6QUDBN4+0OO/TmxbLq7hQJFmpPdZfiaFqTp4gMZ4ICyQkUDPM7sBrrCio4QuvV0Wt85VqeyVw7+0PtQCcua6WKdDhWXTrASgfU9kopP1LNivJH0rqR2gvxuuKOhiyKmAXVGlVXYGEygv3+wvhw4KvDwPl/0py+CG3ViECOmrwF7qYdYzBkqbycImKuKA/aKywGpxVWwxIbHz9Ezs+F67nycoGnr2euy58ZysioBg7LgWkaEZUZtOFxPqAeDO47S00bS/zli84yQpMCtdNEU44IghGDiEFphSjFEEZqzeSccD7QWu2j4JxptWKdRVvTE+7mjuvlhYrw27/9LcpW/ukPfyJLplUhx41sG1YFahWc75j1uG23UKgQlwtSUye7ajBe4bzFDB5zN7LUlZfrmTs98G5SN0ryinYer0Pni7SCsd0oLq3cVjGABu9nDmNldAtxu/SDBx2x6b3FWFi3zGACkhsiN/WwdIS9tRZppR9yzEBJK013PL1WnVu0144gitePZ64N9oOm7RtZWv95dS490ndexFQ5lTfO2xlrFXMY2I0z76Z7dpNncom9z9g24reCatJH0iaQ4gVpQq6FKUws65WSIjoYlOs03IbujBajiWuhtoxtjefLM8GPqKvDmICygWEy5LhxelvIX94Io2UYf96O/d/0UwJ5S0yHkd3ukWn/Dq0MBcPeRHzM2EtF9p48RbJTODzHZLkvBr8lNq4UDersqAaySrTgkb3H7cB7hZ37WvDp6XuO3z+A2RAp5BJ74zJe0MDkDK5V1KRQImxRuG5CtArBkrNQJGJ2Go2lKYtUj7695nPtfBpDR0isY+FSI1Xfk+MX1usLDw8PiGyd9VQLFSGlTBODtSOjnSmiSKlS9bXDU7XCGoPTGkOXuVIbYhONCiuEdwOswKlgzxlTGu1Nww+N5XIle0UbZ4a/uyMfVnZuRAXD+JuRIYP+p0T5+Eq7Ci4IulkMCmdNP2Bbg1QhrxGRim0NpR3BBYrTSMkE4xmOltU7Upbu26oF1xwW1XMn1iIiFEmoKuRaIffGjjaOZm6fkXQGzLas1CaQ/5k5J+E44mYhLnvW50i8Rra4cC4nzlypXtjtRuzdgVF5VO4fJLthQLxhTYJzQs6qVyXFEHNDbMEeC5srZFMo/pX4Uol5weSEtQ1tAihLjK8YbtVLzh1TbRrFbGwUQBH0SDVnhuA4+CdySTg9UrNhzWe80TgduipdR4o0jFh0A2c15/UjLTaq3WFjpmaB6CBpsiTS6YQeEo/hiFOZl9OfmMvf4MLXoBxaFkQKGssw3JNzpOYLqTxT1Cs1ayAwhffM87fkemaNz6zbC0VfcK5hzJE1CzF+xIQza3kBPeONQ6phi5GtnIlWUYaG068w3mO+cYQnR3vW5Kuj5katGoNjDh6L5sPziX/4w5/B/MD7byYevrNUlWibMFqLeE9aEudViKtiMjOTCfjQ2MqJdTkTJXLUB8bgGH1gP+4Y9yPzvUK5Fz49//JdIoqK0rdQpzW0dDOeNkFaw9hOItVaMKo7pKxVDMOEalBKopYeHI0GkJkheAR4vb4xhx2PXz0hInz+8pFYNqDrDmIpxJwRrRDJhGAwTaHq1gOnrUvHDAqaYY1dQLa7nvHesURh9g2tKq0ZrLGI6h8iVvf6sDXjDQlfMGbsEyFjccbzsH/kbl55Xa/dBqx720bp3krqjBRHVAprLE16ffenQ4A0wepGa5ngPVtZQQlKaR72O747vmN879kfBp4/fub0Xy7s/qaybJkmmcEpaoNsOh47lcTpeiVLIehOcJUM5zXineLO9cZTzg6qQXLlki+gApIdTUBLRSl43L/n+fKZpiqlNSx9MmiNweI4rxvNFEww1JpQ4ri0Be8rzhmMBh88Cri+XKlLIpVf/iRxjq5L5Aqs24azF6x21Cyo2tdnVmK3q7uCmEZpFhVGlECMC9d8JpWGrzt8HbCDw+8MZnSYAEMwTLPn8f13PL77LW4XWPOFRu4skdYPyh7bv/TXwu7getty3Sgp0pqhbIHtVFFeoQeFVg5RliKFJn1aonWXSNL6F7mKtTfF8Oz9N3jlKHElqiveBXITLuuJkjKzeiTryjAeUMr3tQcFtGD1zfsklRZBLmBpBAOFhk6V5BLpaUNNEQw9G2IK8XJGXhP26IhY9FvB7i3OwW50WKOQWjEHy+gG4mmBUWhWsCb0RqwCe6v9Z8k9WwI451DekzRUNK5V/GgYw0CVRmvdY9VFfkBrpC31ye6NgH16OeG9J6fcJypa0Yy+hYuFGhNFCuVnEsR/Pr5eeagZx0QU4RJX1hzZpLCmzPm8cIkRYwLvnWY0h55ilopqDacdxmmqGUi6knIf45oAdv/GKpbTUimtkLVHzIxiQDfBta4wr6QOrmr0imONHZpTBNsinonJj6zmhcqFKiN6m0iqoJRi8HeMPuAIbCVS8tK/SETh9EiwB3beUW3CezANVCpUU5DQQ1s7c4fVFc9AbcJZvXE6/yNmW7DVMniNcQETBDMEJjfiVOOqux472NZ5InlD7Ih3e5zxlHwh1zNbrbSkexvEraR6opgVFZ5Q2mMRnPJUIltNmM0wuHcdqDWcmb7xyFnTUvceGKXR2oOybDGzrQtfTm+sOaPCnvFhjwlCCAbrPdZVFtPQxfDN08Rxt0cZYTFvtDmSTCX5yBYW/HjP6O8Y3MigHV6thMNKtb/8D+O+sqh9BorCh4DWhlwLtXbsPFqRc8eiI5BjwhiH1r43V36y4LV2A6pZvA+k3OGDg7V89f6JafQ8v34mbgspCyX3dDxaM1jF0/3A5W2hbBXV+uGgR1cdjQ7EqyJs15WcMkpsr94qIQrdfaGgtoRV/cOn3RDbIuC9xyhAaYoUBrdnF+YejDPS1emuZ0hyqSh63mMtC0Lq/Hg6G0Zu9ODaoEnGqoAzAa0Tu3Hg/jAT9h7dDDs/4x4tp9cL59fPnM+Z2Rt0UYxW45piMA5VK6kuQKfe5lTIdeWiKvm6Mh/3JN06WVQPaFGUdUNMx907F/oNrwmtKvbDkUs+USSD7VViYxzu1jLIWQilYwJ+Iubm1BEC1nbxo3WWaRxZ1xMp/vJ1DvdlRN9+Dq9vf+F6ecGpEdUUNS6IK5ixEm3rvqdcupusdPdRs5GFjdgElxS7zaBVIODQ2vQLX23sdjsen75nmPY3nQdsrWe+pCa8BtaC+IJ3GhcMW+26EucqVWCrmWqEQXsmdkQqSSqlFmgVaxRW9xaZwVCrwsjA6BxugCaRwJGcYctX6pRp1v7VODya3U/+S4I3/b0lvdHmFRgKlUxpFVzrhZHYcMaTwkrSlbaD6gVpusd6Q0GGjNkr/HuHOirquGLsCCpjVYVWyAJqAL8byTVik8agUU6hTW/jKG6DQ6WQG8sFo/sKWm68FwUajfUG1/rngEhf2dKEnPPNGdfQ1qCs7dwjieSSkVp7q+92OPnpgCK1c59+zvOzDydxzcR0ZssbaRHyprru2Q6EWtlihQjb9UqaZ6zvB4pOtNXYFnpKXzeMRGpZwVisNbTcf/01wiZLH5Mx4iUgsbMJaNKhSaYQiEQUlUophYrvHwi6/x4xOZZtxZgX/FVRvaCnEWcHtA0IAaM9Q1HUfMXWQrADpYIXi3KCMmdqzYhpSDBk6RkC0xStGErTJFVYJPJ6fUX0ZwKWw3hHGI84teLViDUNqzPzsEeKwbmF2mBZIlt+xqkZZwacnyGuLMuVU40MYULrRmkNbwNaj7fGAhhtaVhaS2hzpMmBS76QywvuzlPsCNcuFHQ/7UJFo5eNQcPdPCJXIcdE2jIPj4Fh30+5y6VweQaTR371/h1P7weuy8o//viCKgUbKpiIGgs6dMCcQlG2ynJZ0cfIuPt5Yqd/y0+92TibdKmcswbnHRRNKb2K6KzDaINSjSF4svRbhmvSQ5tFUNpQK4Bgav9ytVrd2j6WYep+m+Atb6dXXk8nsiwdL+80T3ear5+Ef7oWVGuoqpCiadoiLiDGoa0iWMgxs62RcZhxXtPINNUPkr1N07rsT3U6pLGN1kyX/GlLE0Uq/Ys2uIHBBZJklKZr47WiRhjNwJIjqUSkyQ0/r7oYELkdfhqNSpWMNwO7ceAwDzhriTWTybSh4obAYe9pbzcM/Oq5/kHj95rDNLGzA2vL0FYUPby61huZt2XkqvmS4N00UQEzdSaNkg3tBGMDTQGmW4BqyTgXCLLjGs8kqShVMbqgVMFaCxJwOqC1RymPpl8marmZhFpF68Y4eGgT63b5V3iF/ss+O9VhgOecWGXhkgSSR+uAtoCrYBUYi6uFeIlI2kgKLtqCFjZrSVLIVFTM+OKw5wZjl7Wm4Bmf3hHGQ/+1qGijKU1Tal8dOtSNct1oth+yUyuI0aA6TTbpjJ5czwIVQZtCKwmtpOsRapfUeuuwOFIDNQ4E7/BeiKUieIzM1FrJ60YxK7kJpk79sknrU1QSPYnV14miBCMZcpeDFgcYQWeoSpO973ySKFRjKVojaOzBoH8bULHij4o2CZtONGNJUolV0Kqyrq3ntnYDm7FQGsreoJH0A5OI9MnGTQtRkf5dJ/29bVDUVim3VU29HSi01X062KDVShNQpteDaR19IFKhVloVbkotuN3BUH3V3f65xX9bfOXEB9Z8oi6evEA1DhW6j2CYR4xvmFpphV73NQbaTyQ5dXORNKwGTQRlCf7I9WUhXV+pWpF8IrZ604U5tiKINLIrMDqMcVizdFmRGroLRLku9FIGy8ikvuKc/kAyG6ZuOH9ETJdK5ZJQxuDdzOAHWvaIShQq2/bMmBvarqT4pY/OGSgMpJIpqdBKYyAwmMAQLMHfMaFYpVFrYqkXtpgw7ZUgE8EFRlPxfupCNXXprxDfSHmlpoo4MKr2LyYGarmycMLq1L/k9Aw4BNU/7Kk9eIggNLZSWHOjtitqXKmhA+2G4BingNGWuFaaVAZjeXfYk2uhxESthfunHfffebCGy2vjNAhc99w/3nH/lcYvjT+/aJpSBKNRAeadZgqCLZmyJOKiqDVTDo3h7pfPdShS0Ep1yFipFAQXPN7bG6So4qzGWkdNCe89rfagXam1I9crqCxkI0gTnG3ELWFMwWiHtP6Bqo1m3pm+x+U2mjWZ+53lficcpoxvDVsMtnqWoolKIxgEBd7gB8cW+67362/uGYZMaZduT1by11pgkYKSvjpVuh9UaBXVOqektkxpEWMMU5i45EuXR5oeejVoLI63eOlNiZsz5yeKZD9f//RBqbEaHg8H3h33CJlShWVd0UBTDWMNenLshxllVkwKtL+MpE+Fr75/YFKByBWtEoZelZSiMLrXWU0znJbIZCx27EFeh+kOL6uxQbPFRJGK0w40pJqw1mPTQMxXokoo3bNE0oTgHFYblO6GZbn9jaR1+BZNsLpirGaaD/w3oNbBKWFWgBSQSL0m0mVBtYDZDeiDQw0Dvjn8uVHOCzU39FBJXlHRlBaQYiEpYmzoUvH2lrdSEfV0x274BoxDWkHfWl5VpMNnW1+lioGCJUthlYJG05qn1p4Jqa3hxwHtPWk5gREaCa0NSkm3XYtBoVFGY5WmGtX5RapgHeQm6KbR5UDJmrq9UOqGUyPGQTUKY0HINKnk2qeareUexE2Qxf51eoeFSGcj6xapqfT2n1H9cHdnaKNH0sJmIlkqzViUzTTdWGtH0W+5N3J80BinUJvc/Fb9OxhUR/O3Rmk9WC7cPFjSqeDGGopppFYREWJK1CZ44xmVxtOnStr06nVfZQu4rqVQhb+qIspPFwXp9ufa2o3x8r/+/OzDSW5vRHnhmt+occd6MeTaMBPsjzP3dxPWVNZ1xaZANpVk+q7L6T4ObfSxrlIGZwKiPMEdWJ436uWMnhXqAdzQbiPiRNaJ0Tc0EZWESSVaK0DE2h2zvSPWxJYjjUww99wPv6atJ6wp2MFhpwNrzf22KwqtCtIq2nmcO5B15nR+oZU3Rhuo9FBht0lWYrkScwERrHZY1WmzyhjeHb7jK/+eVDyn88p1O5PaGSkL6IhWe2zTtLaRUYg64Z1H2xlyodZI0ZGm+i11mB95CAe+nP6RTX3BYFHtgUoX8omK1LpQZaO1yEv6MyIjtQ4MY4bBYh8Ucxu5m+/wwZFiZtk2YonEXKm5j9dKKdigODxY7t9ZBI2pBnUxxDyzXgc+f27E5Mibw2jHOFr294q7J09wA/EM5xhZI6QsbJ8brv3yOSc5R5Q2WKW7dVhplAHnAs5ZtiV1346CnG+EzDCyxpWce624I9z7bcM0oZZMyf3AqIxFNhiCx5qRlATjJp4eLcc5MNkLj3eF8/mELGAumilPWD9RHWwlkW9TCqP74SDmzDwOPNzv8XZDiiLVchOj9Rqjoh/gte7iTK1v2tTWuSRaFXJdUKoxhQGz0q9fqhMoRztQinTfRuvtnBveBK3617RW/crmjeVx/8Df/up7xmHk+fUj1/VCuonBlAZdNUZnvBPWmLh3B755/y2nD2eG5dgvFiZiDXgUSTRDGNmFPU4NeBz1uvCyrUy6MgWNVhPeGRJ9HaAa/SAlhiJCJt9sq6HfIMtGLT0vs2wbu1lTJPaadC9Ed7BUU+RSEATt+8XMGMdh/8uvEutWGHxF20otQmwdHS+poTAMux0+7HFVU9Yr+aJpWmN3ji0Yogh1hfamaG+KutLD5j8WWCqMhqdff89ueuptbjKqFaoUigilNVRTCJrqFE08UgrGaJy+ZcGKQNU9d2gDW400G3uV3VVEMk4pvAvda3OTTmplEN1dVKpk0BFjcm+bJt8vzMlQ08bAjGkV4wTvfD/8S6LUhIiQyEgRWrHQQOP61C3nPkk0K7pdqKXS3IA2dGJ3g9Qg6n7A0Xic1fhQME7INy+Vth6MopLxwWBLz0w52w/URndRn249DK+VRmkwSqPoMQCUIjtNDJ20nl13Winb8fWuGax0iacoENOog0Ymg4kCsfWLQmsglVoirfYVVRUB+888OcE0jFK0VrmUM8+X3gQ5suPdw8xumJG4sm6C1JGKRnxDDwarHU01SkmUWvsNPEysOfL8+ieQwFQOfVxkKs16klhOpbCVRFIRq8/outKUZtYZjHSzqe6j1GAHjBq7SEwMd+EbmilcTGXNzxjlqdKw9ohWgVIip/yCUhmjDgwh8Pa6cpGIYiOYA6P3rLJh2oaVm7GVG8tCNX54/SOpKt49HDmMXzOMmi1fuWwfyPWENrbfsBioSrjGj4juaG2tE1Y3YjohknCu1w0bV+ZxBvY8nz8wuB3B3bHUlS2/0ohIKxRJ1Jq6floLRidMEZaqOT5pxjYx+xl9s9BqY3qFjMyyZmpVTPuRw35GZUs9e2pT5DPUTSFJ8fyW+dOfN0pduSyNx6eRp8c9X33fuH/yOLvj8uJpuXFZMrVk4ibk51/+jj2XfptWqgcBizRc66hm1QwlCyVmrBqR3NBOYZ3DqELNBWVdNxjfwqsglBopLeG0JqYIqmDsiDUTDU1t/QvzcX7gcfao+oHnD5XTD5btB4d72rEcoFqhLY2WujtDK02JBecM4+SoBYZ5JFWNMg1IN3Nuu/Vu6DXBKjhr+s669T9ja525YF1gHkfMm0JUp7dSFF55zjWilYXWLcVN9S9xpYHWMMpglOV+2vMffvN37MYDtQnTMLPFhW3bbrtsjaqdX2S04XxNtCfN7/7H33D+0yshW/7w+z/yljbce8dswBvL+4evGc2etFW0BGILyKopbaXYRPP9z6AIaGUJznC6vJLqlXoLC1SVcHZgr3fkYkkxklWkiZCLdGaGdaS8IaKxVrpjqLWuHdAGRPCuModfvghTG2E+FmaXkEtiuTaWJqStIEUxHA6E+4AtlXKt2GzAKEJz4FzPoFwL7XNBvak+vdCdjzOrkeM33/I3f/e/RztHlggIumWaykj9SfhIX1UaS6rpVt0VIoUsQsVirQcyur6xbhsZwVSoGkoWRGl8ALTcMij9C72S+wH7htqxWIzuLRmjMm4OuM3CWtnUGe8NtVxYS68qxxQp0l1Zug1oBnTr78VEpsYrwTgKMChNyxsoh9RC0reuvrIodUBKwg8HtFEUuaCkYJTFaAe2E85ba/hpYKgQvMOZ20WKPrx0xiDN0HS7vVZ/Iiv+pKhSXHThZAviK845BgO6QsiqyzyBYmAZIO0CGENYEyYmWqW7tHr4gloKOTVEQXDTz3pN/ezDyXUrqBncKLQx4d4N+BKYJo+2mrg1ymrICdaUGK3CTUM/hWpDvVExay0UKcQUec0fWcIPuPzA3r5n3Af0t4qqG+dzYzkJkgpbyaiyom1CMEyDAylcY6b5DYWQS4IWySaxdwdmN3OVF5SFWDPeTGgCgqHkV1SLFFkpNfF42DHsZ6z+NcvyA4OdGXkky0IuK4Oz3M2POH3AtB02W0IybOkTf/zxL/zhpHiYPnG/+wrnPaU1UqlIXLD6irMzftihCFyXz0RnCPaE1Z5SCzm/orPDqBFBk/2F3TRjGdmZ36BVYEknSnqjykqRSqp90mJtYOf2PO2f+PPn3+PnQBmvPF9eKBt43amfVgXu7jzKHqjGMZeZeTJw9nz+e0v6DGYSYo6krcPrSh14fTnx+npGqcz9YcSaEWczphl0NaimaCZRpwvKb0x3jVh//pn33+rjtEWkp96tdTSkG2iz9BCysv3DBI3GUHJFO4VWgSobWtu+lqsJqaVnTJylIRQBlCA/2YB16EFbERCDREMtgaN5x+f/14XP/7ly/O0D8Z2lDBnjFIM1cO23Kas113XlcO/xo+F8XZmnEVA3bkj3cPQ2Qu1VR9WXhrUk8KavTZu+GWZXtLWMg8d28CVaK6zxnJeFreYOomtQMjfyZg/jaekHk7v9Pd9//SsO012/0dIILuBuX/AKhe3LciQLURW8FwoZPZ749rcD1z9ldFGEy8g0ONxD4E2gXQOv6YzOYBbPH//nNyYd+Xf/4Z5ff/tE3mnq9Y0Uc+fHuLE3Lkh9dQtoGlk2JrfjMD6ybiufL58ZBnsLHHfKrNxqlaX2n6HTulvXRTAanBbqz1TE/1t+zBixxyvavDHnyiiW022NmeNCfXmmDg4nDYmZVhW5CuYiuFEzNIMqGokaWwyDCgzKM4WBp+++5u/+b/8X7NORlWufksvGYBJWl84TMtC0+iumHVM6Rl4iMW2kqCnFd3qvXdlLwlwSz8azeLp8koBRmjX16Yi2GqMstfZVukEYnKdVRXozuFWxmxXsoFiB0LDKUs5X4mUh18y1atwN6R7MA0bPfT1YbmRaXSgq02z36XSvjaMNAw3pUM52q+4qwejANN4jFKzVrNFTa8K7QlX9QtTOAbcGBizTAMFarDEglfbTmkVrjJJOtb79T5C/BmFd0VAqL/GVN70x2JF3wxEjGqcNYME5SjAsu0DSGnNa0VuC1DEJYoSsK0W128pTep6o/DNXiX98Sbw7Tjw+fs18rKwni6yOFiGvC8/XiMZiwi3Zb8B4i3KmW0Wr9HGnUiCN63oi7yrX44ZXv2f5p4X2PNIeHPYomNC433vmegS7BxNY20diyZxOFeO7MCmmz+jbtETTqPaCVn2/eMk/ErylrGCGbxjCiNaCMxNNHKZ5dmNlGjQpveD8zJ37j+hyxZRCrCtaKbyd0cqx5kRrr+i08l79Gm9HVNaUVfM5/8jb9YXD7p7RDzg7EfxEvXlL8rbctvFHSBGnBvbDgYUrp/WNVi3DdIc2niIrp7c/drGeP4A78dXgucuPPTGtHdoOWGPxVqHFE/PK/d5R6sopR14/JabPF56mdxyPe7z3TMGgdUAQkuxQCMvLled1hQrH7yJujF2mpiyIZpwMf/m4Ercrhy+W3ZypKfMyJYxuXKLmw/nMh+UzKUfup/HWYPllP4fDDqUd17crrdU+Pg4OY02nigLXZQHlyCWDauQlsp/uSSlRk2CNwiiNbYpgLN5aTJgotfWKvHRxWdULUgsi0lHYxnLeLMUeuPvud/iHBf2VZmmJWnp+ZBgc3nryplivG85VQtA0Bdc18vyiuZsc0M3JSEPdqr80RWv9YCXSD8HaGmKJGNd/DaUFp20/pOmKqY4tClEyRXqDx/T4K9DQwOgs97uZp/0jTUakNE7XCyllYuqekloFMF3hju6UTiVs6QxWCE5TZWOJC8P9noffzMiPEWuBqqnnyOUvJ5a3leWceZgUM4bdVwPceRalesakZeZ5ZHnb2PLSXUZK4ZzpoWXTaKaw5mdq3eFMoJWMlEoTjTH939PWA4qcNkQa2ru/ZoN6zk6jfvmqKfTuCxIWrMmMwRCUQkkj204+vcQ3Xv604oDpohii61f0LTNWhw8asqVpzeA8Ozuynwfu7o/8zf/1v2f620cWTiArmrXnP4zHa0ctG43bOk1ZNP0irCigDNqMGNvpplpW9suZ9v+8Yk8D47czfKeYw0wIvhPPU6bkgqoK7yveACKUpjhvF3jOyP9bc6d3FIQ0Zur7hH9f8Mrx3t2zGsdqEjszISpQm6GpqU8lJCOm3i6m/bWoWu3tHSnUNtOUodqVajakNmriNoksoMCEPj3XZkTpidoglUzeGuUtM1wMxzBhaoGbKkPR+qEEbheifrFQTXUcvVa3Yk1jxvI1M2vbWMtKLYXUKpuqaJtZvabsDXYIWD/hzhG7ZCbpbdWqEkUiuXS0vbGW6jTrtnFa4896Tf3sw8kPzyscAsf7R45PmjgVtrdKPEWu+cKyXBmG7sNRCjSZtb7hpKK1pqjau7k0aGfM/T+Rd28UJ8QV/K6fvte10kTwtq86psH2QJAcuB8ql/JGWkFU4LA/UosQtGVAY/SNuVA2mpkhTSznV/bDN9hph0ZT2hXVGkEf2DmPtxHkhFIbqD21alLJrOuPLPmVcX4ANZNrr42ta2QUjzoYvv3Vnp3691SOpLqQykoVKDnxsr5Q2dhNAy0ncoNd+Jr3D7/rrpxiwFRGHRiGPVtaeLn8mUTFaIM3Fdvu2TDU8hecOzGOQ6+hAtJuwUhXua5/5vmcgEIrBjfumd478nMm14r3gf1+RmtNyoXSEs/nDWiEQTBjZToWjk9nzG5jXQYuymKS5zBNfP3txOv1QlErz58t69kSPPixIF4Ra6NsjnhunNJPzIxf9nONK84I6EZOGYOhpYYWi9WO4APn9dwBRTdMWSsVI5W7ccaahnMGa2estZ0PkCO7sOutNJW5bhdaU1hnGYKnlAGRirTCWjv88P53O+7UjtP1lVA1bdPkXFDoXn3VwsKKGyxNKtQ+sv38+Yx/V1Cu9AyX6u27JjezamuUqjFO+pexNqS49g99ZcEotNMM3rPViNGOJZ+o0npV8WYsHj1YDcHCbmzsp41pPEN1fHj9zB9//GP/gap6a/FwC6gK3meshsl73h12tJrwds+HN+GSG2PwqO8e+NXXhlmuvPywkv5JUGfPvO35tG48Pk18+3f3fKk/kGrkf/nwD1ztFXU4oOyANFjSFcmtN/+kS0hEV5wFRSPnE5JnDrsjr8sXtLOUNaG0EFzPEmk3YB1oo2nK9NCm6s0Rr375k0Q9vfbcHAb6pzGiM9kL1RmUONJrIq2CrgHfNKFZVIESC2YweOmsjmnw3I8jd/cHvv7vf8fu//A9m4kMpjBdV+LpTLKaiuZCRiShtWWtCUXoYMGUehC5KaQaDBav+6qo6pXx3z+xt+9xCq5jxQ5Q1UJVgjEBmieXyLUmqjdQC8ZOXWNwGHF/5ygoTGnYrHsm5RrRUTG9DTz95h3n+wsrirM4lqjYcqG0hUZE6doPPORuO26FUgqzOJyGZAKxVnJZge12sT3gzUxTutfcZYGW0c5j7YhRHjNoyqPHTQE5O/I/rLStO6isNj174kzP7dxswj8FtsX0zFXPwjR2DHxj7ihOsVqNFUdRlbOtZB+JXJjKxPtUCC8Ve157rMI7tFH4oqFoUs4gvR+jrfrZAfGf/a758MMLRg/YLDzdH7rNNGuUrgw7h/OKyU087O5wYol5o6mNqBPOa6RpVPFIEWLdkPuCvo8cg+fyF8P1unI8evb3muoTNW/EZJFssK4y2cDQvqZqodgrRS/k2voHsBGaEarqIJ7MPbp5duEdb1q42Ezbfo8SQy4bk50Is8LogrRO16TNnOJHWhV29h0P978hrCtL2ni+vAERHzSjtxztHd4EnuPv2UymqcayXnomRGmsmWlNcb5cSNsrxzCxmx652+9J9WNnPDTpsqicbyJEh7c7Xt8+kG3jzsPj/Gtel8/4qWGtJ0qhlUKtC615vJnZkvC2vmL9HVYFrIa4vfHuV98Qn2f82tcHWgvOKbTV7NvAJVlSXtjde6Yn4f77hfndiWYyORus0xA806D46ts75rbS1kIQhbcB5zxWG0pR+KVTY/dKoa+KTa3/Wz7X/k0+koTqBOsCKRVEFLpppDQSBWtNN3rmyj4ckJpxIWBVwTmLSLkdNBSNjspeUwQPynhQnfBbJdEQBr9nv7/DGEcqG2594+38icu2sJsmDocHrucrKliMEmoGqgJiB8EZizG3lYlSaGvJteBMxdLDalJad3HcrKOtQi2GpITLZe3o7JRIEbTu05DZT0i0LHmhmUaODSUwBhhG1Q9DAkoUNcPpXPj88sJyvVBFobRlnEIPwGeIsddAtdGkXLnWhPKWZgxz2BOCx7c3cjN8vF6xfsAOA9cf33j7zxvyo8NgGbVisIV4PnP6sRELvOkr23GhHAu4xDAkhmHgtJz6f1TVfUE9F9cQWg8t6+5omfyey+XCui4YD1NQSEmstTBNDmsspfY1dG9cKbJWTGH+V3yl/ss8NRtyFrZqSF8025umJYc1HkPAFku9bJhz46AHdn5ksh4QZAVTLME7QrCMs+c477n//ise/of/yKaFrSxYU9jHBi8NOTbyEElsGKuIpVGyRpoC5fB2RkSRS6/0liZ0zbDhIiN2P3DwloqgauW6XbG6MbiB1CCnipKBoD0tZrIIpvXqe2FjuLeYYBh0Qb8U/NXiTwF5UVw+KJbnyPB/BnW3YJRBK41IouSEsKK4UMuVkgqp9i9s0RrlPY0LuVYETasB0wJOe4KfCQGM3bhGRZERkY0WC1JXjPbUdcQZz/+HvD/rsSTLrjTBb59Jhjuoqk0+RDAiSCarClUPBRT6/7/WQ6PR6GpkJ5NFJhmMCJ9s0OHeKyJn3P1wxDwqs/vB2CCymp7iUKi5qtqgV0XO2Wfvtb51/sYx3Bv4aBh+AhGDs73j5/2AiIXd+lt2kJqq9kBT2QX0NVFouDARjEFLI/tEcpFVC20VTFlpqWBeDC1m4p1DphmuG1IrRi2m/dmxM0vAfOGY88s7J989MYwj2/WRT8czr86vOB5PuMHSbM++wRQMmUA/DS7pSpIVAZwGBjeixRHXCbd+y8OvDlz8R+bzie0MJcCy3Kj1ipFMkHsm9yvCOOLkxvX6B9ow4ndQlaXiTaBoJKsgalhjpuqNwTwy6ZmalI/re2JujO7A/fjA4CY0V5b1QuOCjJ5riqTygrNH7uZfY2zj5A/M5cS6HEjpiVKv1NLFWs1eMU5Y48J1u1KqJcWC0Bi9ZXYTp4dfM3jlLoxgRqx9phmltRmMxyM402eG1gpvzneMvvKnD088HH7LOL6j5CeWBZ5vG8Ero+8CY3DEIv0mLndY4/pGVjp7Y3zdCN943I+O2io5bxjbbWTD4DkdjzxdMhIWHr5ZOL/bsIPheh15frJcrhFrI+Y04euRo77BSKEumXUpVDdQc+gb6GaYdeJwPKDecOPpX766/Ru7tmUjzILzinGOWgqlVJxznaJYC3fTjBNL8AI+EPNCsxa1vo9oSsVRUDciuxBtqwmtkRobMWeUQiqZhgFr+yLfCvfne+Zp5OOHP3CLC/M4cfcwEaNQkse0mbQmnup7hsnivOsZMc7inKGlwssl8/rOMg8WlW7ntPTQu9Ya6D5aUWGLmTB4fPUEG3qCd/Uc/IGnNbNsmZIUr33TDr6TJyt9RLQlw7p17QmYP7eSRcklY4z05GMRgncYa3/G3TsCy2ZYmvLhsnIaPe+3xGW9YnJDxDI+P2Cr5XxnuLwseD9xCkdGUeJlI8tGOkaumolLxbSNEl5ww4HRj6zx0pWOrROoShFKMb2TpILTRtLEt29/zT9995+oqVJFUSmYIN2104RW6FRp7eMv4zxL/LKgs3/L19P/8zWEgrVCvUB9UkJ03E0D1s+4ZqAGZhc42MDg+tajpiJW+th5HJhCYBhHDm9fcfqf/x3PB4uydqpogXJwmN8eaC2RWiZrZ06p8QQ30pojaaWU1m2wfR7StRs5Y5ow+olmKhe94I2l5UheE3IcEKu98ysNYwZGf0TEoOsT23IhtoXYFjYvpNw4BM/QBtxqGa+G4xYoNeBvno//1xf4HxP5vOCMELRRaZS20eqClghVMfVAxiK2knQDHFuOVATPxGzvmcNEcytJLwz5yuCOuHpg0wr6so9lbdd+GQFZWSRiHhy8hM4W8QGVHsfwOXizl+D9tm+1d0yt7eC4YpQWHMM4YEXRFjvZWhrSFFMEvW7kG+TmWXQDrQzGYmtEUoRaqK5bqyUrHospXxbn8MXFyf/+Hz6xvIy8fWP46k2hvnUYmTg5j1jXpdIuI1MkLxHNnnRrtGtBD45DcLjB9wwAPXE8jeRwJZlEu79y/uuZ73+4kZYVNxWmqTGYgrSNYhLNPnFLj6hm/HRHKSPTKL0FXio5K86emaczTSy+GcZ4Ys4vfEovvDrccXd6hzdHIHJNz2heMbJhzcbkZ0Z/h/evuhi0LF3hHAYObuakM04bLReGEggCrs6chE6FtRP2fkBUO8/EeTCVwXfqZk6JnL/D+wNbs5RiaQLGCl4c3nomb0Ebf/FwzxjekIm4YWV2J2KeuiWuvHBtG1UL6Ia0yuAeqHv6q2FgmofO1fi6ki8ry0Vh6xucmEDTEWd76JrKFTOsNFXWy8SHD4Hvf1QeXy44ZzkPFlsczhwYx0TVkdvqWJ8dm1hckD6WmAOH04FmFfS/AXeCjJRYcMbhXJ+fiwjOOMbgGZ1HgLgmUosEP5CWiBSh0TUqpfUZcaXDwJyxxJxwvgu8U9p6l6X2LltDcdZ1d0+shCHw8HDPur6wxUoqMPqRu/MdmjyP6ydqyzgf8GFApBNoVYXgRz48OiYzElQJQ8bbhojbnToWa3pIWlIhasWPDmMqg6NTWVPDSGNZFmqE2Ql+ULT1kL4qhpINuRhK7Vk4BkErBLGYICCuA9+sw9O7NmI8zgVEhFRWWk2UIng/8fGl8DevD1Td2NLK5CfkcML89p7x/hX1ujJ9WCiXjL9knBbqvHEbbtz8SqYSaiDUiXxNxFw7NbMaaNqhWrmhpsOlOk/TUFG2GhnbyLevv+UPH75jaRvD4BnEoc2Qc+vOHWt/Lq4+M15+6dcP/5vBuMA4BjwGjY1JYWTAuwnTQJ1hMp7J9AK+1YoJvWAevGceB8ZxZHi44/A//JryzZEimZoj1iglNx6tctPa7yEzMQ6WKo4qvUOipSI1oWSSaneMtLwfYAteAt47sLnbnltjXW8YnaAZtrVQSsOqxXqPWiHWTPXgzICtjtm8ptVISS8sWjq99W3PO5NnYS6BVCPrc8Z/vVLCY3doquJaIeeMqODdwCCOxESUgS0vJHq6X9WG1oHgJpxYal2prDTT7b+khVKlwzgZKa1ijMd6T8OyNsX7QD1PrJNinKf5gMkdhtjYic1oB6PttNhcWx8Z7Rbh++nM29OBJa1c0guLth7qp9BqY8gFVyDZypMrqE0c0pVBC9ZX7KHzbSxCebyiTwuiX8bB+uLi5OVDJN0Kjx8sL1838jphOeLMkXAQ1FpaAHPs/mlKV7SXTbhl0KFh5txFgGZksifq84G7169o84XiX5izoTwa0trBN5tdie17tFTmYcGZxlaFvF7IzdBqYCuN2hyqkHKhpWvPCQBaGngY3lJGZTpPO+vh1hHaUjFO8c0y54lxuqdI6m3Z+ox1/Wbfyg0DDNYjIlRZ8GbEuNcc5wfG9nU/JSGo7Y4KlR4SZkgoN9RMTNNrWD/RUiXHhTUrzTSsrwzO4AhsUSjLyqivMNaz5ieKPHfhUnMgllI9qXThoNHuGgmD53J75rbeGAfFO+XSCvM9mHtPvVW2LWHUY2yhSulBbM5SRbhd4LZaXq6Op4vn+da4xBU1N2qeeDudcdYj9oXD0eNK4FqUdSvUpARvCYNDpLdOg/nlB50N4cDt+kKWxjDavaMlWNs3WExvpeZSaAmc74r5pj2vhibwGaK3RYxrBOc7Fr91/o1SacVyCDPGGnLrmO3gPDkXjCgueEiCt4FWTV+wvCDSBYGCdNaQ66F+2iwW4eBnXsyJUpTjOGLswkt8odkeRMZ+bttSww+Kd4GaK81nvFdqurHeNmpccE07llu6Q6U2KEWoVUipjzD760FnoihUqYgaVHsgIJ8XLKOoJKpW0EYjk6oSasX5kVtMxDTzfIssMcOwsOhMHQfEQ7uzyJsT9XIhf7xwu7yw2Y1VCnmDGh3GOfBCS6WnOOdGa+wCS0PKW4cjas/rMnTEuBW4rit3wxGHIdZMAQ7e4Xy3LWnTvgbU1vUX4hjtL18hHq/dxdGixzjHwG67XR1uB3apd1gVHF2HhLd4Z3+msY7eM90dmH73lvC718TQ2Tgp1b72WseSEreojMYRvEW8UNsOgGwJweJMYtUrKg5jfCc0qyXQi8iUC74VVFdShuAnrD0gxmHUIFLBgojS2oq2gjcdHmb8EW0TqUREJkxbKXXbY1qgFhDvMdI43s/Yt5EX3SitdEJ0jj0uRQK+9C6NsY4hPDDqicvmyPWCF9M5XvZIaI56WUgx0+5BpgCxjx97VMjcx8Cxv57TEAhjwIvFqSd/uOE2yzAMjGMvDjUX0pbIuo+WXSdBS1NKzhQpxADT5DnNM8YJN42UnCA3jkawVZmc5zAMrKaig9IGT7WC+gDGI8OMcxNtSbTnpQvK65cpxL+4OGkZltYoTdnSQlqfKZtnWxMPX1uGOyBAtobp2CjrrpMgsW29orVkggdjFcmW9N0D3jnk/oUqG4fXjvEwEl8Mls6HuJUXUtyoesN7y9YseKHoRsuGqoZbyuRWEBMBR05deexL4GjvkCzk5YrXEWcDzVpS2/M4GoxyYMvP4ByioSdN5gshGEqpaFWS7FRIKsFYbvnGpuB9wBhLcELOC066x76kxCAFXGEzjSoNZ1+BbTRJGK/c0o0tdTtzbfQOSpkYuaexoVwxdEKuNbZbHI1ndAOoIG3EDRmxEe/B115AXW4r0WasGTje32NvB8xqsdbS9mOc9wPH4wM3Mp8+WOKWeH4qXNdGEsjeUIiY7cZ9OCNiqUWYDoHj3QxFaWw0Mm4wDLPDemgJyL98ASDicG4ibYnBd5dHb332AMBune0ji5YaZRi6vbZ1HkYn/XYBt4hQS2UtleAdrVZS3ciaaQqhecJe2PT7vAv0TIFSC6kUvAtYYxn93MP2NDMePKdyYEsRZ22PP2gBp8LL5YXrbWG5ZkYxzJNjk4BMFSOC81BrIUyBUiKT8WjbOp24VZZb4eXTlUrFZqWV1oPLPIjth5JUum7GiPZujxho3RFQW0NzH4MpvUAQka772Om5/TXqJ7yCI9fKD49PnIdAdQE3GSqFW7523H3LPRPIN9bxiWX8SDURNwTkeURzw4slhJHWhHWNVIE1FqzvYChRcFYARy3aEd37f7V1TMJoM6/v3vL9p++pRVluiVb7a9bz5Ds7whjB0rDml2/XGezI6D3HMBCMoCgeRXy30tog1EFoFUzZC1UnWAyDCYxhYJhHwrs7wm9eoQehEnuhTiWnSm2Wvq9ZEOkI9lrJ2hlBVTNGAsbQ17raC2FrLDhDlUbOkIvicUx24DiOJO/ZciWXBioY85n2Q+8AttgPAq7HFtTqKF2ygaqjacCK4o3QjLL5jJ8tw9d3lNnQ6tpHhiVhSDgVQu10bcVQVMEX1FuGesKJJ1iPMzNeJnhpmJ8Mc/a0e0+mUI3+nO7diiBmoDXTmSZDPyxa6Swl/UoYPg7M7kDwHiuGGhOqnVuE0Z694z1yy7SLUp1QXhuWk4JZWUksWlhrRQzMOA5NmRGCszTX/4xslTiC9R7cgDUDWi0qvYuFE6T8K2frKL3NmTI8PxVqfGa7FZ4uV769HXjz65nXzXF7U/FDJNFoahFxqHbBoCIMs2WYK+iGXo7UHw3Vu57M6grD2WC9IqmxXSp5SyxxJZcF7w1iRgY/AAkWh3UHTBv6xu2E0QeCcXipmFCptcJmWa9PuOqQGarPxHKl5LWfZu2CaOIYvkXrBBUoFW+6hVLpC5RK66cAsdzixofbI8O4MPiBu+mA5gVjDv2mrYmsEaWQnUPF44zByYCh4q1jlECLnlw+spQbkxqG6z2SR1RuuDnSZEPpEdzoPj0zfb6IBqrZWNNCUYP3ve1uSmOwIyE0hnMjXDxa+sm5toI4g7EO3wbSMvPpVmnFs10jyzWRaHA0NFtY9MbzfGH2Dk/D3o8c5yNaKqlWMg1/tLhZcK7flL7+8rN1Sq1YH6DV3uLMBTUNldbTOKUneCqNlCoxJrTWbrO1pms6Ph+oG7AncNTaWQQK5JzBQWoZbYYKP1sGZc9ZymXZFfaZ4MxexApqDOEwcTYVdzO0Jli1BA3cnp748OknLtul52YkGAeHnSqHV3B/Z/d8KcU5oWShacex90ygTMkCyTAYYWiWLedde9M38VTYxYn87N5RlR32JnT3stBst1Gq0mmYZkdsV+3hiJ9bzgK5NFKMfFwj+MDofHcVlYi2vJNcV2pZSfFKs5FwnhjdkXZrMDeCG8nR8vhxJZ8M2SRSKgQjnXhZeyKtE4MAtXQLpjFK3gvBywYPh1e8Or7icX1irZlWwA8N6yzW7ZoatThj0PrL15yc5yPzODAHh2kVBPzkmO8GxjsHrnGNCy8vEV1BjEWNIOoZw8ToB8LDkfDNPbyeySaTc2c6NRqxdMaVtX08mGpDY0VtpWive/tstZOJrZl7TMT+nHWrcaGooq0fLEY34JzScqY+bzTvsYNiTKU0Q1NPE0Mj0So4M3bdHRVM3fNoKlUVUUPwDvdN71cyG/RuIBtHk0YfJaS+PmtFa6TJCqWncee2Ulr/HoM9MbkR53zHw48Nd5xxzdNGy9V2kXxl2aF/FdWClRFnLM6CkHrhEQzua8Umod7gGnNn8ajSnEMmBWfRk6EdArpk2vvutCqvlWQzecvEWkgU8AYnFq8OrwVvhHkyqO+k7AwYE7Cmxw+4smFaBtPwr2a0VOrjlxkmvvyIK6a/aaVm5SVvxDVzuS1c1hOpvGGUifV1xZ8aJSdsm7t1SRzBe473M69/PTHfNZ6+u2KM5/aktJAT42gAAQAASURBVLcj2SZaVLLLZF17ZLU7Eqztwk/6qT/sG4C3gq0G07ooz1vF2MJhMMxhpBJ7mNRWmKYH2vZMiwslZWJLQOPgAyfn8RLxRngzv+Fla2QuqASsGfqpx+ruE68MGFyzGHGUEinbD7TqceaMrw1DQW0AD6Vp93rXhPGOWFcShTVdKGoRe8T7GdGErha5nJD3b9Ay0mIm/GZidR87HtvcgULOidpih1OxkHJEi6eVnsMQ/MpxaNx7x8EqLq493TgbYmzkVvDzQKmN27bxck28XxI2GIIZUdMTWwHc5KjSeH65YKep45DtyDTPbNOKGQBVqmsket7K+XSH/W+gjV1r7iO1sVsXc6moVMT2qPDaKt657iRYEnHdcM5SW8VWs9sc2UOxenCgNQ5apeREcIGoa8/laBmtrhOHbeidhNZPPWu84oPtM+daWNPaF7We1ocdAwfTqFHZLo26Lnz48J6X5dbnzU34eIm0y4pxyv3qEbWc74Vx6vBE6wwpRw4+0GpBtXJ/OHJ8daakzC324M9Se6RFLIXWm0I7arz/os+4+6Bkr/gptQeuGWcQI/2k1/6cvgPSu6sCpXR4VKOj9jvoTkE3SkzkuFDK0sFxQBhnJn/GJM/hqIyvAiZPfP8PNy4/pN0lVNAGKRWcCR1zvocgqmgvNvcfVs8QUZbUg0Jf371m2Va2slJMpTWwvuKDxQoYumXzFpf/+jfof+Xr4XTubB2jtJKxznJ+mHn17Ynjm0BqK+8/NmLMlGYZ3ECrDds81g3YecK/u8O8O1GCUjSTd1dHa7VvtNLHgYVE/czuaEoThzcB6cIvclZKtb1wqI3WCtr6yF6rQejBqbEltvhCS4p5qcidRaZMlkhqSmkeEY9qRdRDTnuYX0Ok55spldQ6pTb4kflXB+pgyA5Wzf3vkjNKQk3qhTiFYgPFeiiRSiW2QsqRVsdOMTcO4wTjGuZeYB6hBGRUnBrQRjWtP2CuM0u86xEapSVq7mOm5izTCeK8sv5QaVmZbGDwlsFb7ByoByivLPUUqKnRgmHbrmxhRfNC3kMK1Su+GSY1hKyIVmywTK8C0YJeug3fVLClYdeExATOYe9n3HyPFsjLl6Emvrg4EbPnU1eg9Xbnliu1RlDhHA781ZuB+jyy1YbLwrhbTq2x+INw+tYw/arhBsHeHPrhRtlWRCxpM2gyEOBGxErhZIWH6YhLSh4MxjWSJLIsHL1FDoa8q429m/C+4t2Cdb0KXXJGRThMv0Y4E813GKkM1jFORx7mE7OxpNtCadCKIevGmn8glYx3xw6D2hNJTVO0nTB24BAGvgoPbHrdo0cWqtGdhBvAOUwQSq6s6akvrBJQjWwpsuYbKo94GzjKxNx+hX95INzeMODJj2fk2yPNr1R9gdYhXKnkHl+thWu+UVFm47DNYr3B2SsBwZZGzRtVX6gy87IpLy+9KzLqzJYra61sNbOkBEY4jSPmMGC0UlslmAkJI5oUNzgmG5BqyBFyySSNxLZRIxQUMYF3h1ecjtO/eHH7t3ZJ7ZujsWYP9dq1Bq0htoPHWmt421XxKSdEQhcuu771VtUefmctBrDawFS2tHLyZ8Zw4Lb0eIPutHF74dGLBmWjtojTCWcDsTRKW3uHy1igQWsY2zjenUjXZ/74w5+4xmtHVlfb5SViqC0Rt0T9qKCGXEbevg24sYty45poRbEGgoWvHk4cp7d8elx4n278sC4ssaA7+4X6Oam0d3g6ErsD1trnyqzFjhhvPUoe6GPUJntgIFjrejfUwhq3PbTTMogl1kqskZyvlNQPHooSwoT1gVodLXomAvfvjkynA+uT8PRdYx6Hnog+DqTSA+GiVKyVvTBhh7U0mtI3ub2gUoTL9sToB14dH/jpJVNL+fl7FFGqs1RpLCny/Pz8X/PW/D/lOh1GnO1xDBjpxFVjkQot9lwb2ZSxBex0IDhPWrYuEG8VPY+0r87ks6Nqj+gQ07tnuXZmSWuVnqTdx6b9mRBQQyqA9WgppKI9rK51zZWodM2eOpxY1BletpVUCtVEDtZjJ0O1fWwatVJqT+ZFKlBxxiBlRcVRqYgRvOv6qn6LV1KL3IqQXadEV3UMZu5nEOmFd5Oyu2QM1Qxo6FKJsimtOiY5cQgHmhWwPW9HLbRBUN9JzLZ0AdfkZ3TPAGpVMTbQqKy5u42sWJacSUE4nrXLDq6WYCeKN/jDiB0C6izNSB9VHTztVWB9bFzLFU0vWC00a6lqsNngosGUTuMlGMpsuYlAtIzFcaiGw6aMt4aNigQwR8WMgeoc1f5rZ+sIvT3ZFJogO7wlb43bpXB9qdh2YJJXDEk5WOEwjljrMBba+RP5/onHYUF0wzyAnBVvT7zEym294atnCJ7DfMBZmBaHWTzj84g/FtppJYZKKivBWWjdrlfKyiQjzjlqTay6EkshVrAmkDVynn7NCyvWVNxhwk+ewTqG6pjcgToFfnj5Zy71AyoLy1aZholxHDkET7CxEwPjPa7OtPLC64dK8SfKZnBWWLcraUsYb8BrX6iMJcbG2h6x9kjTig8n8vrSeRXOMbQRczlxsA/c/82JdgMNA+Z1w7lvsavtVXXLTKGLt3Jp5Ga5bCuLPOHyytv7Bw6jY45n6ndvaEUxc4awUnxgaRtrU25rIzahWY+b73g1BJItOOtormBISA0cj2dOh3e0lHAmYZxQs3KJiduS2VIfXYk3PT7eWppWWv3lQ9iMNGpOpNThQs4JrVYqrTtqjPk5mnycRq7XpcMIa+n0VNe/xhqDt12vEuOVMPVMm5QHrHeAJZeKLQUxiYYSwsjgPTG+YIyQS2XwgSVesUGh2q7cF0tpmdJgHi0Pbx/4/k/vqcsVRalNQF2PU29d8InCx08bL5dMyYbf/C4gtmK9J+aItULZLZq+RrJmrnlj1YQ63UPE9lBQK4i6LtxrPSfFGLt3RgpYRRGkGQS/sxAspvVxmNqeC6RNoTpEuqYqYPnv3r7mh6f3fPf4TNou1Ko4NxHGEw3PtkUkKUfv+M2vvsHNgZecsW9GfvO/HPCvB/54/RPeG46HiecXJeXKwQ+0z5uaMajpgl5t/Hwoq9IQKXx6fs9ffvXXLFvkaXuktILzvcUtCKUocV3J7Zf/PFgDRgVruggVGtst8T4l7I+KaiHm0sfN44SoglpEwE8D9psz+cGTdMOUinc91XfdIpXWR4RVcOJ371Tbu3BK0Y01F6RaMJmqe7xHbcz+yOD62L+g1FaI+cYtR1QmtM0UVsJry22LmOK7hdj18WjRDZWKto3FQSu5h9dau49jeqlhxdCqEq/KdVlwh4A1007b7h0NMDS1XWgtgPG9yxMFTcKrw2uCPSIWKhXdO3Vt/36F3ihBuk7EQs+tqtCykFtFWg819CZ07o4WYlmYD4n5PNI+9S5Pstr1UFopEXQxyDZizgPWQguNWCLJROykWAv+1rA3oUbhqorzlYbwkhbeOyVbz6iOuRmG1nBFITUoCo8rJin1lr+YmPzlmhPVTlNqbZ9zff44pNR4eol8elxIq3Kwvchw1iLOoW6hnr6jHH5gsQtinjm/q7yStyx/Z2ibY7AHlm2jpIINGyGA9zNsAfMHg5yE9S8uuLddnW2MozRhcJ1Gh1RyE1pztKqUBtecMfKMG54x5Q1n81uW8MxSLpTnK6MR7vXAfHW4YQRe0LySpfa23nZjLVfioEy+4rHUGhh5oLWFbfsBbTMxeaql5/eMiayPpK2SdSQM77ib/4rr7crjyw31mZkbU7DknHH1wOhfM72eMOGRdP8JH++J8YlN3gPKNHvC1rU7xSaKbohxvBu+4bBdebp9j/WdX+LsSLmB/mHm7nDg7dc3yqvCyyUSHwu5tE4K9SN+DAQ/MvqZarpzJKUNcQ0tCzYYDsFjsGRNVJtQZyg3oVaL7lkr0ugjnyBsZSU/bl96W/2bveZp4PaydMCYCrl3lLv7wBpAKbn0ILFx6OyAfrSmlT7GGX0Hd/VTVR8jrHEjDCOpFMYhcHd6xcv1hRQTiuC1J4gOwTP6gZorMQmRSkwZD6AZLwFnPa02bjGCSYzW8zf/w28wfz/y4/tP3RmjhlpiH6BIL6iaNqDy4adnXp+O3D0YhmFgiyuV3rH4cX3i+8tK2qB5wY8Bal+kg3Oo3wFmTcjZ9oXT9Of2UlbUVGzrxYpgKLlzQpxYxBhEbPcyNe2ZHFYZ5wM5J6Jm/vThj7z/9J5b3FDncOPUdVlmJK6ReI0cCHz9zSvMZHh/fWapivWN+W7mzd/ccfnTJz5uCWsNh3Hk8eVK9BlQjBWcFYwIxSi1CoXeOFboNnzdeFw/8vr+ntuPK6XcugNQBKMQt7R3e375mhMn/f4xxuGs9BGeVkoupNj2F86hQVjrgmmKw+BGz+l37yhfH1mDklvGUrE4oBFz7d0BbcSUMYUeVaBdZ6VSKJppP0ceRJwVnDScGzmO3Tofyw49bN1gIA5iqQxuJq4LEgrBHZn8q46CkPYzUE+lksrGWp6J5YVUbgQrfX8TR26CloCTew7DHX58R/MAHZlftQdGWmsQ49CifTwqDVrAtsAhzARz6EG2Tru4rPmuy9KGmq5zoRVKYddodWffbS207BlNQOnrtTUNI5FgancRvhSsdkt/ypHJhd7tvSXycyG9b+hPN/zDhD8ZvFZ8c8h0ZDqNeCuoWajXyFYzqRbENjYRKp6LBhwTcxVG77DnjkqoqWByZVgNGivmlvhSffiXd05q3W+I/1xpK3T184fHK3/7D3/im7f3HH77muJ6VSmlEIefaMP3NPmRljvqqd56W/j1X01cPl3xl2O3CcaI3SrrDEkTU21IHTHRYvON2UZaU9a1cTgoxmTWVGnZMIXSWQvuQHADU6s4Zyk18ni5ILnxuHyCITENE0YOtCAs4SdiSkSzcp7veV4iozHkWFi3SIyVi4XZeL4aD9RgyGFjFYPPnnXbSCbifRdHldx5Ed6PaLli5Z6Hu1+jcuPH5/9AaU94JxyGM2+mbzhPI5Wf+Jjf0zYluFfYo5DyC3ndUMDdTvim1KmQQt3Fxr3N9/o8Y9uRtBVu9YlxeWAOFe4y21iormDuILXE5blhR8PDw8hpclSFJWYqpc/YW+46h5i5xifkUgnVYeaEuReetifKSyAtPX8CrZQ1sV4qSiCnSrp98V31b/aKS+oW7suC4AHprWjdBcvWEHPtLq59wy+14Jzrr9mW2GplGAf80AmpPgzcloRzEMuGNZbBjxymA6XS+QJVkJQwVjkeTt2d4w7ELXM/Tz/P6Y0o1ijjMJFrJOWVlCNGMn/537+lqvDTx0+7pqTRivaifu9Y2P3th9+/QD1z91bwdu5BbkVQI7xo47tPK0spXJZIbYpVw+ZSj5xAMfLnLo5RwRnLaZy5piu1y+z7QovFuh7NUPbxmDV0urRkYsn4QRmGwCUtXNcXSqm9NY4HDVjxaCuUdSWo4S9/9xXHh5HvHj/xEiPNWoJW1DSaBTt6TLIYLO7QU7nXfOsnx5qJO4rfmP3kq59FuqA7xfbD8j1fn37Fm/M9LyuITwS7Z+0gHA6BWn/5pJPDPPXAyj1IzljBWE8z2t2IMSJ1V67uJFJvHe4woV+NbGOmZ5kbcmvkZSXXRC3QtKLSSCWhmnHV4G3PXbLSE+JVM01M50xVwyGc8TaAa/31N707qLSu3VIlbRWfLLN7xeQDRkMXnOel2/jpKboNqNUzmdf4cGLTZ0p5IaUFuNHUEiRgbD+kqoW+HkAqK5CwDrbahexNDW1/nkc7MMxnSlGyZJSI2cXgPc1QQMI+0l9616U6aBbbHLEUSu45Wt4FtFW2eCHnijUZS+HUJj793wq/ym84BsFOEEaPcQOlObQUNEbYEvnTRp3Bh8RwEvzbA/MwYWxlszeyRmot1NxIZLap0dRRmmVqnlKETRrTYHGTx6wVJGOlYUrGVBD3ZWXHlxcnrSeH/meXETAGNcI1Fv7+j+/59vc/8s1X95xOvSXabISHSHg90cJMWjPt5S3p8i3t2w379g98dQiE9YCTwu9/euLypL09NQv2HDn+O2F6qIzHr1jdSmuRuHVxU82Z0feAskEMk/fklqA1jv5AroXrdmPwB2Z3ZI4PrLwn1Suicb9xr6SqDOOZ6fCACxvBPLBtG0tciaVSK8wyM/KKGBv/fP3Ac7pxEIdvihthCwvBnzF6QsQAhaovxBRJ2yfmwze8bt/w46d/pDrLw/G+R28jzNOJwyHweNu4rAuVC8HtVksV6rD2eaWt1L2B1eTCmi40TdwPjnksjMNbhleC++0nxApPqbK+zGx65HhvqNeIFc+QoH288HjbeEyRTOvsB1PRmnAUvIM1PbMsiktKvQ/k2hjrkdEfcO5MVoOYRI3KTQplTJT/BqzE2xrBC8Y7civU0k/HTjxGbRfq1cotJo7Ook1pFCoGZx21/FkFP9TaGQTT3B06DVBhXTdUDVM4MOi+yJOIrVCTkGx3Ad3fBXB9RCjVkcuCkvuIyQXevnrLEp9xdiJtme8e/8i73xxoeuDx0wtLKt3y3KsEnAgHa3hrlboVPvzzM6Z4Xn3rqW0BGsYFkhYet40weBBDrr3ABfYNau+uoYg0VAS0Elzg3fgVH64fgJ4I20oPV+9z/l60oBaqoZmM8Q5jFCHj7BEZzmztyhK7cNJapTTFqTKK4dtv3vLtV6/53/7T77lWJbWCdX2xd8b38UzrBd6aIojyZhyoxfPyciWmTGvdHqcKRrqOwpku+tMG0Kja+O7jd/zmq98wHV/xdPvEEjeaNJyzXez7yzevdSt7g1wq4i32PCIPM2USUrqSnp+w18yhWFwRHB5rLeHuQJ4aSSKl9v2kKbTSf/7WgtGGWAh+pLTx5/wjbf2ZyJq7k7FZfAiMfsTLtPvfMqoFdV3knFPmtl1IacUQMGZgHs48X58JtmK9pe4wviqNYgopZ4I9cAj3GDFE95o1LuTPgtr4TAgHgj3wsizEfMGO3eprTaOxoFlJJXficB2YzB3H4TWzfyA3JconIILp+ABt0rOe6N+7mK5xa0WIW6bWgvOua1Bc57toE8QEwKKasdZz8JC/b9SPnundwGA8wds+jvIz3p16rtTliXT9xHK90R4zZjKEpuibAVcM6RbJzxVdGqwVWQv4SgyRdt7QEEguECdLWTZsLhzciH8YydnQaJStIknxX8jB+vJd5HPH5Ge4W5/J4gxYQxXhaU38w58+8lffvzB54TBW/KlSbSYQ8PU17mmgffdrpvMb6vjvuelKCis2NL76naVNAz/8qCwvmetWOJ4N828NZfiJrXmW2tjajSRCrYmCwfsJUcOWV7JuWAsHumgqyB2DcdTtBm5gMhO1wlYvGG+5Pxx5dfiGSOBlW3j/9D2Da4RZGSbhcHiFlTMGhyuCjx41lnf2v2OKN7x01oWhIlUxLiBGye2FtDxTaIgErDUs6QPT8ci39q9xxXCevyZ4i7ZMjhVGYQh3KIGqyjAIL9snjBgymdQStVaCGxicQ1qD5ikJzPgWlQ+o1V7A6JXYMi2M6OlIeCe8vsycyj22CTUmPj5duFw2LsvCNWWyheYazlbmQTCHwGAtMkDWQt0C4i3TPDHJHbEWWrLEutA0453gJrOfmn/ZVy4brZnOHJE+E2YX3jmxOA8aPFurpK2ncKpIT+msnfJorO2bemkgpT9i6ogxMk93tGaIS8O1CJJJulJNxRqhpJ7BU3Jl3TZauYGZUaHrgDBY3XUw6kg54dzIw/0DVRsfPnzHr3/7DXlVLteVVD/rRYTZG/6nX535bVDuh5m//dMT2+ON9mZEpt4hai2hOIqm/aQ6EdOFqgq1Fxyyu232rWQXBipNE0YdX999xSU+c40rUuhz9W5z6RtUU0wS1De8raS8IjKAGUhN2ZLr7o/cMCYjVZmt4RwmJgff/eEDz5fYoVCG3T4EJWdSjMRtodAwToGMWGVwwpgdW0401V0b0f9NqooxdM2dkS5uLo3SIu8ff2AeJkqCl1vCBCG42oWx7ZfPOSmlICoEJ8idp3w1s74ZuYXCy6asIWPHlTfrwKtb3/CssbgxkG134yigrYu0jXGdkWP6a971Wp0d1IPvlNgErR7DwNEYiII7DmAyVuqe7WJoWnqOU7VIMYwyMU8PGBytKI+x59+klvFaiMvKukRwBn8IePHM04SV7hYLFsSNpOxxzlPcwMH2WIe4ZiYfSJpIecHaBLKRo0XawOjOzMMDg50x4shSUGuxOlGo5Lh2h1jbN1pbsU4xRiAJKTZagqyJIi94n0EsWnt8iRWDtUrwwjRYvGaSbnzzf/k10+uB4U+e8j4Rt265x/XcO0tFa6Xm0sdIG31PShuyFLZPN9L7jfZcaUulrhUG0AxSZXfkWuxgoAgtV5pttMHS1BBrN1mMgyEwftE99S/bRuT/4xc/2yExQiqF795/4u/+8TtGCm/uPCdvoV55uXzCtcp8m3uMtBNu0fOcM5EF4+7IbuX8pjF4S9sOaDtimdhqINcJbGTyA5dkwQx9nmUrYgzOTtRWiXljVIO3xz575gXkzHBwNLkx1JmQ39HsgJ8X5rEQ9YmtCLlmclo4hAOl3vDOoTxT6cFPWyz45QBuRAM8HL8m2Dfksu4zVljzM2KUQY4sceYSXygtIP7IbXthe/oTd/7MafyG2CpFE80XiknkmNFmEWsItlDrC0FgzRulQVXXQVw1gSkIluAHJveaKoW1CVIctSk5NZwZcXKHmiPiBZmvGPMAsW8XYmXP3Skst5WlZdQ0hMJjq1ymkb/86g3ng0eDxWRDXZVtKiTduG2Ja1ppPnF8sJy/mTE18vLxl685URqtVrQVwvDZQdPt5krtsKdcsMZiUEorDONMK11zUjV3aqzxqCo5Z6wxDMNMLlAyTKEvtKVu3c7elFIqzZkOOIwZbcqatv7viZngHCl2u2IInuD3FGugtsoWV+4OJ4RvqVvid3/1F6gK37//kdwS1sA4Gqwq//xT5G/+x1/x3gVe7kaaSzh3RWh9wxXFB4eizOPE4+XapQWl/Yx+F2l0PGznJEG36sa2Qa2MYeCy3P5srW790GNsf193sqxqp85uceP+FMk5orV3WK32wPNBlFd3nq9fn/Bm5N//3R+4qZLjTuHco9tFDFUMYi0xrtAafh9DZ+miVovdx3Hs3aCGEaEVRWxnogRrYR/jbfnaGTPN0LIh10ZzEKX0cccv/Eo54qxhnGb0LDxOVz60J55vG5flwi09UeuNJx34a/fA1zx0h1oQrM2MzhCbJdZG0YIxXR8y2D4y7ZwtxfmeAdNqBTVgAoPzDE0pP90YTspzy8SiGG3czzPOWEYLZcfkr3GkNEPJGdEFtRWxnrReSeVGqRt2nAjhjHeWBizLM01S56Jo6xqkKsRcqCURdGROyrAsLLnwuK4kEndhYjIn7sIdgzvi3RFrhj4ebYmisQf8SdeYGCZifuncFtc7JiqdBm5ao20ZW8EHCzKj7YZnxLkR60Blo+pKqxnNltyEepwod4Ef9MaDP8K2kbeEWIWs/VBUcneZDZCBXCOxVlJKyKXRniJcC+w0ZdVOdbbiCTIz+nPvgOqGPQh1U1apFCM068gVqq+E4KB+GWri/4czrvz8ri8o2rUoDWqpPD6/8Hf//APHYUbMK4J3WLGITp1c92agHJ7ZzIVLWilhZNUbVh2lgQngDoXmb2gzOH2Dl29YCzSeOkq6eJxvzNPEPA148V3ZTEPskcEcGJlINneMPRVspWjGyEzgDSkL18vKNZWODW8Nl8Df5h6frSvBnljjyhqvpFQZdOLeHIntyksplJfCaRJUE6IZZ2ZieiTrDWMDMIAc+fj4E2G6YIxlNEfu/Tu8mchsaI1EIskU1GivzlvkEn9AtIJarmvC2gNVDVUh177geWuhgJcBobFsCy/rhraGt3DwlcnN2FbRPSvi1gply1jpp8IUN663lVtORNOx2zVm8pLYTOYgAfv1sdNyc2WjUtrCElde1gvFbZyPA4c3Zw6v4PH9ytN/A1yHss+vP3cG+gyz34M9ADCgtR/WZU8Yho7xTinuKPedPiqG2hqlNoIa7g6verdFCynden6RcRhx3SW3h3zlUnDG05pSGwyW3WIs1KaknFG1ONfzNHoQupBbZppmNAzIVPkr8yt8MHz//gcwGVHD9x8ydS38r79/Yfz2AU6FHOzPiG9VAWsx3hDXwhBGBNsx3qaTQJM0jBFkdxb0AqUXG9ZWSqtM5sAUBpa47QqUnU1n2K28pgstcZTkqDlDBSkNzQVTG8NgOEyGwygcZ8+b16/4/XePfFxvFOnhmEYsqXQK6Dg0jHGIcXjvuh5nH+O4wSIKp+OMuWaWJZJaxdhuDzVGeofQdT2K8woNsnarMzUwupHrtlCqIrts4Jd+NatsNmIs5KZ8vF35cV14KZm401fTtlHXKyetnM1EkNBdY6UbGErrYX3QKK2DKps2jPRoAGPoeVQ5E3NCmkDtKdt+EI5fTxRdSXUjZYNRy5QLQWqXnLTcbePVUYqQkwIWazqmfnKOmMC4A1oC3DLGV3Ts627NjZQ3GssO7BNopsc14CgmEEchG8spnHE2cLCHDmfzvXuu0jPVtEHN2gMNpYExOOuxbexiXF0omnqyOYJKxbvG3aveiduaQWTAmCMt9TVINVLz0inmYUT9Cd3/3ug3yIrLhjEVBjPgnKcp5NbBQsYa6tmxhEpZC9kVtlzRUhiydpQGSjNQRShVcQS8jN05pJEmiWHyuCkgdkBC6EGEe4ezXQtr/rLD67/ISvyf////4QNNQQtala01/vj+E8fpjvFu5s2xMp0sZpgYfWAIgXpa0Fy43m6oKFmUqlu/SVQ6Ztj2dEQvJ4I+QFwomgkUXh0f8OGZJjdKLYzuVU/DZKOS0OKpOpK8I+u2i4haX+soGPHkOvKyGtpamMeGKw35o2V+f6D8ReT414amC8qAtTPzMDNzj29nPsV/4iVfMObcBcEaGa1wCILQ2NKNrBcGf8aYEecGnl8+cRhPvDt+g/dHzGBw0k/MMd1QWTHWMviAbSvyIpRL4Ph2hinTdGDNqRdp5jNBs+KaZRhPZBJrMhg7dBZF6G3TmBOUZxwTNljEB0zwjCGgJnKaVz5dNlxJFEs3rUkj1UpNjcfbymFzPJynvrFgSQku6wuX/Ak7VJjOVFu5XDMfPj1zjfmLb6t/q1erwOdEz6IY6QtVg33MoLTaUOl0Ua2fP747nGwHiVELdqeg1qosa+TuMDF4T0pXco20Ip11QF8QRKDkSimFcJgIdiLWhVK6UNbaHU4msiPhDdZ4YqqorzuDwhCcYW2R6bXnN/5b/GT56f13BK8EEcZ3Z+pXd8Q7oXmLtd12rFr7odWAcQaVihXL4AIlL/y8WGif87PD10z/QKfY72ObXDOH+UjMhVQLVuzPjAxrOvocBNkdesYoXgIxXtHcdj6MYhVO/siv3/yO+8Nr/tcf/p41dfZIygVr+6Kec2W5JawLGAc2gC9mT4BulNjR+QbLNHU31bImthi78NIozncXD/TXoHOH6KfM3b1lVKD0zyO/fEHs03hjszcmE3DR7ffupQMEsXgZCHnkrimDGbrNXhpxTVCOdNB/pmp3S8lOF867iNx+7qC1XYukvUPcQZeQaAx30sn2t/05obHVTJNMVSG1Thp2wdIQ4prJtTCwMUjBypFzeM1SM9d6RUtE8XgbGJyh6UCTA5vCGi/dOFB6l3QKDglHwvGE8Q4VhzSP8xYb+sYvewuwad6h0KZ32lpCVbCu02JFbIe31c45yVLBKsEZjCnkWjDV43aeSkw9ZZjmkXrsYDs9YcZjd525SGp9tBxdJmg/MAl2byw0au0wuXXIXE6FNlQKSsMhDWrLNAqYhjqluQYOhuOAnwzVLDsaw+LnIz4cenincxgDU7K0ZaP8lMg5fdE99S8oTmQ/6e0ngc9vqn9+o1MgL9eVP3x84nfyCrlTTMioZAqFJAXxFmMTbVm5rBsSFCsLoxe0TSg96diae5w74evI6E7UtmLNM4NzDO7AJWeibmyyMBmPGEFbI8UbKESUIrUHsYnHy4A3DgOM5cy0veFxu9I0MRdleg64H++wqWDnC+XbG2oTRjqZc8DimShZuMUr3je0dhqiGqHWK4dhINeZuD6ytCe8OzNPEzUfufffcBi+xk8F53tC5uANKTdijrS0kV4SfLC4T+8Yk+U0NfT+E9lBMB5HYUmFUgvBWCZjydrHQ2GccaJYqRjp1NK4RgyW+2HGz4bhYInRMR5nrHe8ib06lsVxZWMrEauNEitprTwtK8fVcygjuTTmaSAVS82FVDekZC43x48/BUzJPD3eqPrLJ8Sep0NPO219YzZiKXT8uTS6HmjXafVxT8/6sNLF29A6R0NKh7EZhzWenCspRaxWYlr30ZvCDgErtXb3SG4/235bU1qrfXTkxk5l1bxTYmU/dQqlNKoK3noMineO7AcSkfHO8iv3huMRTHrmTQjoacLcC8VmRHryeC2BWj3eZcQ0nDW7vkSZQuBWll02v897d5ia0lCVfeH9vCYKRfvvO85HbtsNFcGI7Yhs6/DWkksv9qxVvHbR73pNnWVigGwZ68Dh7g1/8fav+dPH7/jup2eWWABljQUjDWsrqgnYCMEzHx1H6xn8CE1ILVMb1FppFJzbXQ1mxBtHTKlb6ouizuyFV+8S5dyIuSE14/Zk51wKtUsCfvHXh7vEY3nk0CwP2wmhMavtbp0+/SJI4J2beeCAq4HWoGwFzfQg0s+F/G4xL7swFXpHvZR+z4h4rO3ap9JapxJnCL5bZayZ8bantKciFGMorZKK0GrvQuScoHaezWAHpFkytn+9OpoKRSreC47O7DA3oVSP8RN+GolaUBKtFpwZEeNBhGGcwHhqEowTxHXnDwK1FVrduS0YrDgET24btWUMhqr7CHfTHufgoJryZ8YJijEJNFOToZUJHwZUA1Zs78DYERHXiyActZa+N7pCUsVVS61KqoWtJVbNXCSymsxmImncqFUZ5YxnRP1GGhQ1ieYKzTTcIeAfLIyF3BLruiBuxpwtdgyI83ux1bAIbtwzkb7QvfblhFj9s7zkc1jZn8c6+1ufoVBL5RYj4V6x8wul5Z8Xz6QWP8yIFQ6z4fGxUjbDsI9/nDki1mLxGHNGjacB0zB014jpUdPyueKUla3GfU7ZRYeqCRXTE2CrIs6x1f65Ki8MBkZ/z/30llwvNH3mzVE4vrWYD0fsiyf9vyzpWSmnjE4JGzYkFIbgeD2d2cqVVgx+Cj07QTeMwHGasfaBRuVxuXBNL0zjia9Ov+Lgv8H4AeMKxhSMsTjvCE7xBsq6kr9vjD++ZbgeGMeGyf/ES9vQnZ8xOkfJji11ap+bDiRNIBErFZqh1Ma6rlAbtVgGv4uSh06tbabRfG8rDqPjfDqSDNgKIYNr0JLScmRNleutEVeDtY27k8WqUGJmi6nHAWw3bh/B7RwB4375q/HD4UjVRtW6G9kM6uhuF2/xQQgcdtiZdreJtfjQqaW5ZbS1XYOind1gG956RBPbupJrxhrfuQj0vJ3aj/VU2zNfWm2ktJLyihdD0B5WWWvPmqq1d3esaQyhz6aNdoicMZ5xPPV2umSmu4Hj/DUh3TEZ5ZONFPsZINaj0tetUTMMQxeZ9s4MfVQURmSf6PUG8GdK7C4nadoLFO3MFxF6Fo2F0zyjrbGVBK2PQ6wVjFXIDeMEbCVIwGEosVBS7VkibuAwvuI8vkXE8u//7p/4+Lj1QDWBmBShInQUvUihTco4CkYC1niM1U4BbUrLG9oqTXIvirxjwILxeCyW3rVsdR9XRCXH1vkTpaIiOOeIWrpG5ZePOWF9fWS5PpOuG6ZuDD4QhgOzq0Tp7pJBDFPyTHXGqu9FdGpIrhjdi88+uEcwxNZdOK0WSmnkPem51s4PUeqeAN3ZV1uBUpT6eQSUK0vqY8WilZKVln2/J4vyulm2odtwU+3I+q0mrBq8PfTwQNnY2sJkKpaKpAFbAtPxyM0GGoFWNkwnDHWnKA6zJxur9A5P04KR7i4tu6XaiMMYi5VAq5mmK5VGVSVXpeaGcw5jO5uqtrzDHUGk53Cl1aE14ELr3n8BsQ1CJFMpUfekb6WWSpSK1EZdMlpgo5BmyHeeEgasbxiXSG2hSmOWEScHog9sUyT7RisFOyj+bGhzpcpKzIl8XYgI9a7CgZ/p0F0jrzhnMK4zoL7k+hcE/+2t2s/24c9Qtp+5JwLSxTWiAlgeXg9gI41Iq4aaG6U0itkI4jnNntNquWwBL3e00tDQoVRlv0lN7e1v57quBEasuyPGj7QWMK6CWJY9mdjaDnKqtuLE7WOWTCyFTTa8MZwCDH5knkbe8Su0jXxzVwj1xvpPgpSR9cdv0KcZ/yoi54afD7j7B8JXlm/uBmb/jts2UE8jS7ngUA7B4dzGwRoqd+QKny43bIPXx3fEYok8oSWjGrG2v3xWDIfwGglKqvBq+C2mFsy8cPMVsdJtqEUJErgLMyMWI57ZnSmmAJlte8EQiCmzLBvBDAzeUsnc0jM0SzaN5h1JKkva2GKk5oppyiQW7wM+gBkFKZaYCnkz5M1zA16mSC2Fy+XGbctkGs5GamjcHweGcab88oGY+CEgteKdp1WhpIJznhB6GzN4YQ6OhpJLxZuAquC9wbba3SC7yNKI6aJuhfM4MA7C46p44/Zic0fj0/qibIXSEtb1jJoUV3KLiPFssedqGNtPZa1ALZFiMuPdgeACtWzgusjQOcs0TOTsEAzeG+7u3qEtIev3GM1d96GgRVjX3K2WnwMG90NLKpnJHkFlF7BKz5r53ERT9nThBq7TNb0TxsHhff+zBxeIuRM4ta+y+xiodSKnaYwh8OZ8T7p7zW2JDDZwN9/x+v4Nfh743//wD/ztP/6RdetFGdIRTSL6c8dmR21grO2Y+9LFwsZYrKFvgK2hBaotVDIVsGMP6zRqiCmDdh5QXCs1A1VptZNInelBp7nqHuz4y77s+R0H60j1J17ywnhomMnTjEVzxsTIIH0ER5HeUdKKrAm/daej+LaPPyyt7OWtso9CC6lEahNqZT8UtI6j1x5qupXGFuMeyleJuYBaxHR8njaLqMPoxGgs73zlo20sresojFT82KgRHDNOJrb8iaIr+ASHhh0KeY2kkkitdfNBSyStpGSJmkgxY+weHSEBKw5M7+C3Ir24MgbnKs44MIIzQi7ac3G0UFpnJYmnx0sUQ2qCdUpMBeeVUgoxhx6RYdqe69VorlJsZstKzh0o6IOjtF4gNc3kpRf3ZTJwPmJ/e2Y8emxdKC+RdckYyQR7wzRDobFKZvUZXGFUQx0rsS2kFdK1wHNEq6XebbRDRq0DQ89JKgmvFetsf22+4PoXCGLl58IE+DOQrfea/jzvEQMEnBk4nAzNgHoAj5ZdDKSWWCJOF85z4TjPkGYuy0LWZ1I1ZJkxZk+3RBjtgcP4DaneKLHDzqrS03WDQayhRiWWRFYl1sI0jMzujrRkBjMzhsAUAoOzYBIWx71/BcmyLd+RHwfSrTGr493DW7Df4KxQbo20eoRAuX+kHH4gnBpy/oZNBu7cHUPLxPhHtvgTqUPLOc8zngdme2QcZ7blwhIv1GZRb5GWMAYGNzC5gL9X3G8F/riiqYOg2n5ia02pxRGMY/JwFyaMOZCzIdUnWtt27c+VnDJOPUObmGwAs7HkFSkVGR3HhzucOIpJbHWlbhFS5nTwWD8QbWGSxCCFT9cbtcC6KO1auN5+IpN43m6sFUQtIoI9KucwMo1nbmv8l6xr/yYvDbC8rKCdJbJJRmrCpa6ZGIyl5oT1HnGyp0gLxhqOxyNTUwwweIs37mdM92kYcBbs/dtOFxWh1EqqiVRTF01bR20ZEFptlNzTr7UFtnVFgGBGhJ6Om0tCJXFZXzjiEBW8up3wm1GFaTgS/AEQNq0c7JE7heflJ8RWpAm3aySlhg2eqlBqX+z6BtGwphcxDbCyH1J2G45CR3G3nkAcBs8weoahB5YtpSLO4qylaNsFww1FcN53C72x3B9O/PWvf8uvwhtulxUxFj96qi38tPzI3/3xP/G4XPmcCdjt3Z8HTdrPUwWibeTSD0BNO/RNtRLEMjpPrF17VbTuybONnk67InTwm7ET1npEMlAQ9m5WM+SsWLWU2L4Y1/1v+RImpvkbTLUs6U9sfsWZSsiGYSkMK9ylgSmGfmJPGbRhsjBdEho9xSnNSN/EbQ/1Ky0zT/2gR9uoKVG00ai7siCg1VOyklokl4g1nZmTq+LFYJrDmRHrRsSNoCO2Cbd6ZamJpWZ8gCl0ZP6mlpgaVMGaewwjy/o9BGUaM80LS4us60qsjVQaxVhSmlhbBIlgYz+Aisf7uSe2N4vREVXXn1vp2hOzI+prK6gaWi1IAzsaxDVaEXIaiGqwvhcuKoUtd96Qcx5jpGPupRfHJfdDUWs77QOlaCN422t+oxgP4W7Evjtj3pyRyeOS47D+xE2VahUrCWUlp0xZNppkxCvNK5mMLjfiCvmxMX9SBhrmFNF5owAaKrlGaoqMW2Fu2nO9vuD68rHOz2OcPyPs9wQv+gq0i1CsR82AHRx62FiaYbmBpacZUyC4RNYPeJ8R2bgf7tDHA+tjJU7PYEckGLKNVHnG2JExPHAIX1Guz2St4BaaWak1IsUx+CPOTFxuV2IqYLpg0LqBrx/eIPZIgz0uenfxWIN6S9YAuZ/gwmHE3zw+eMbzCRcMSCOqcrORa37h8fYT48GSdSVyBjnh3FteYuHWMkUjWguema9Pv8WaQHGV43lEbiux3FhoODvh3UBLGzE9Egz4V5bqDFZmdLlyLRFhwpqIc5DqSkyZ0Qzch1d4uivoJcbuFjGJQ5g5H94xiMOGlSiFJQLSOL8asJywSZhOhiE7pBkOaWM4eMIwUItyXSI/fLiwLJmn28If//iBWgvRFOTcF5JaGqYUTs7wMM4c/MD96cQcjl96W/2bvQobIVgut4U6dRz2bpTtj0YRaisMu+OkAtuamO2I3aGBht4cqAJP25WmyqqVVjP3d2+ItTJPB17d3fXFoaw430chqWzEElFkT8sVPIGtJBrdplxyIsXeuRzmmdZ6keMYKCpYhdt2JcaFEA4cCF3852cycDy8xVjLLX4glpXL7Uqjds2KCMY0jFWM9Jn6YZ5w1pG0IqZrCLp9+POhpXd6jDF4ZzC2z89L7TOeMDiUkVSkC1+94ELnMKTUqAkepjPaMqfXM9NpZs0rT7cPfPfjn/inH7/jh083aumiVqm9INIKtclnWVy3Jbve5q6161JEaif7SmdrWNc7NxbpgX5tH1WJUEqi1IiWLlJuVqiiewIKfQSxCYNz2FRx4ZcviN0uz/jxAMajYkhpRSuc08ybOHPYDvhtRlbLWheyRJoASYifAvbtPeo9OKHZ2ju62sF91vck+tGPBDPRpFvlBcERsBiW/EJqK6UUjLVM3jMMI6M/E2TE2/7vWlPhumwspfLcNnKOhBBwdG6NkRnvOzwPbTS11HJgnn9H3T5yKz9QTcGGrxmnEy1GWt0Y/ZnD+KprEGdHyQu5bD+TkmvNpJJw4vFmwNA7mk0LVSNi4t5tDFgdGLyjStl1NZCqUqvDa0BJFC6UIszGY4xStOuvSlVa1m6YqIJYwbpum26lj5xP40w4KG0A/XqmvTpSXefKSIWalLI0NqNsS2RQQR4Lw1PuI6FB0YNSzgWuSv3QyD9Wyuoxk+kaU3OjxY16KBSJaM7URcmbYNK/MiFW+VyM6K4/+S8fuD8XKqoVNza28YXn8omjH6i8sGmk5kaQnthqvDIEQ3wKjH9/Dz9UzNsJ822Hu6kVrFWMr4ipnMcDmiPf5d0BICdKjjgjiM0E47if7slBMM4zhZnJCI2VLd3AjFg/o2SqGLI2buUFgyeYA2Z+Zr4/cAjvON294np9JpaGn6T7NIfEtUR+eGyMq3KaM+ojixSyKJ9S5elacQbuwwOH8Bc4e0DHiiExmsQh3LHGVzzd3mMYOtrbGFBD1kwzlnr/ETP8RPkJ6mEkypVUM846Ys7ctkxzR16PbxgHj7cVPweavuDCgKql1UhtH2gI0gLOGFQiIgmXGrIqgkWGkfE041ogDA5rXCfQiiVcbvgAPvaiKGshGYHqqAVKbDgauvMD7g6Br17N1PzLL06qRIZx4nqBvKTuvnJ93NkFr58D/DpjQ2xnQfhseruTvvCWfTzigiHlToBVLxQprC2hzWM0E8tKzgteOl3zOB2ZOLFtNx5Od6DgveP5duEWV6QKpUZiupJbIpbKYT6S6xPzeMZkT8k3lngFo8SSqesNZwtuvTFOBx4OB0bumYcj3//4D7RW+gbcevFRayGXSlELKOva83+6zubzK9VdQ8aANXvmSquI6XH0qfSSrqcoK2MY8NYhpjH4sIPqBKeemnvuyv/jb//vPa02VV5uNz48Xfl0WdlyolbIGWrujqr68+S5c4g+H6Nq2RkOcuR0PFCCcFueeqH3WUAZe8KyDd2lU/dgHaG7hLBd12Y9aKrdOVIgJ2hREAej6xvLL/1K10dCS3B7xOUVo8pcLKfqmNuAK5a6QrltRJd4uY9sQ8Pd4HBRjs+Wcb7HIF1bISAYjPcYGkgjt4IiGAvWWowIUrsDC4GaNnwbuBtfEVyguaEflqv0pr5NYDeMrwxYpAzIWpDmMKPF+K6JQgVveoFcauE5rzzfVs7+gdM48/7yI9vtJ5o1RAQyiO1ZUs4EhmGmhbEnVbfu0OtAiw5xc2IQ0w8tpWZyLhQKSOniUX+kivR1WBtNusbDacASqNlBG5ls4+iP4IQihpwTpdYOYBTbQzYFUtzIJTIOB7wPGPa5++TQY6A50Ni5Q+XlxvLjjeWlj2BdjGhW5ptjfnHEDRabyfcNqx6TDeFHwT4ZNMKWC6skZLviPyjmWBjGhvOCyxa9KXX7Mjfnv4AQu682+wP+Mxnp58/v72uGvDDYyPEwcDgImGdaW3GjotGyroL1gZIyt7Xg64V2uyGfPPX5jPOFYVL8nMBdqcawqcWvEZd/wMnCUjacFbycsWlBZcN4xzwMDOGO53hF9dJtWwLOBkq9oq3DfGLeiCVhEGZ3zzS9ZkhvCOMDD+/eoWVlPDvyuCKvIjJYXCucveHX7muqZN6cD2zZ86enT+T4E+iZ0X7DyQ7cDW/ww0NPiZUN5Qb0xdxI4OH4QMorW3xG6Z2fMEAqP3FNH5jCzPiXR1pzlE2gCN6vzAb88EAw33KJjY/57xnPoFJxXjH+RkyVoiOtFIz2qOvaLLRGuiXckigJfnpc+P2ffiTHhfN5ZJ4nxGRyzVy2K2u94qbG7Ax2NFQCj1vmtiQ0K1Ia1gmD90zj2Fk1a2Fdf/lW4lLBmczhdOT5+YrkXmy0/TkxRpCdjQBKE+1QMVVMKYgoJjgwjaqAM6zXyOjASNiZD5VSN6pkCpEqqYfpUdmaQ5rlui3Y8dhdPH7AO7j3M8Po8N6Q0x1PTxfWWClbo5QNrcpwmrjcPhLrhWk4EFzPWWptI0uhxQqqPMzv0Lzw9u63tGx4WV6ACpo7UTJ11k7JlRr4GTHQye9/FsS1uoulBRRPrb24CEE76t7VzhCZAto8OeWunTGNahLDNHA6v+b7P974j3//j8SSqFXZYiPXPlH+rOHaHdo/u4E+X3uNws9yuWoZ/IwPM6OfMOp4WZ72kMtCs11wTFL2CNjelSmQEuT0GbanvRgqSisgTfAWmhRSo4/Df+GXaTfW9YW2fGLIjVlnhuIwFa51wawGu3ooljgUnsfI9lA43AuhenywBOuppj8rRjrbxzlDaYVc+jjAGL+Te3cdHhnxMLvAQc6YR8/d3WsWmxHpqfVdOpsQCs43XM1gIuIr44+ZIRy52IqaREyVNX7mAik1J6gwjxPjeOJ6szj5ikNovKyfqPnC0U0M9ADNahsx71EJpt/bTfsIylu3wxQzWiPURK2CM4HCQKsez4C6itqVoml3tnQHjiCgG9YqKVukCivdiDH47iitpg8XBd2FwD3mRcT3z7fSkUyqlJRJTxfS7UrZMvm6kC431tsjBsNYPdPNcmgDow6gsJKxOVGXynwZcNWh1ZJs40ZkuS1oa5R4YHp2ONfwg+JOA24ItKjkL6zV/3VB4/pZmZ5wUikxsiyW4/HY58dSGMYD3g7c1pVmLKlk8uGZ8GZh+9OR4bUn3T2xtkR9PiLSEJdBMrYd0bai3DhP92zLTxzsQF0gbw1zNhRbqOnHrhXJUI3r3vfWCDagupFaItVGq0Jwnod55uyPqAyUXz/y4fwTrWSSFT4tH9B246Cv8O6eJf9EyY/krGyA9UdEhY/P77kPE98ef8PD4YgYZUld/zEOpuOMFWDCSWMcFDmcKfU129ZY04Xb+iPKBR+OGKM8rX8g1ZHRvSUEg/CCNmE090zziSwXoits+QW0cLYnagps24aqYtmhV7XicmCK9+jmebleeHkp/O3vP/D3//RH7ibLtk145zGui5hi2TqDQCvhqNhZe/bFUpBUcaVHhFsHYgWtluXFUOKNp/TTv+pt9f+PV62GtSneGeZpZIsbKWasMx1C1hrGWoxz5FSwuK6nSAljAkp33VjfNTvGOWptbOvGfPTk2pklqoUYb1RNqGmo7Poqane35JUlPqFYrHo2TdSaWDfHxMRoj5yngdejoVrB+AEXYBgsjhMvV6GkPq9HMk0L3veFuXM7AofhSGLk4dVf8+1XgVY3Hm/v+fHxT1zKB6B3VKzzHMIIGWrpVs4+7d01H6aH5pVa2WLGWUurpXdUqhCGAWuULSZSS1hv8UNgMBNjmDhw4D/96T8AileD2bOAOhyqc9uawpqU8l92d/+PJoFdClMLIA41vasyng4kMutTIqe224qFHOmo+rYXIbkXOKn20NOW2YXH/AzeMx4QYTo53n5191/rtvw/7RpL48oLajaCCfjscGr7KDFnbLG9y2oMSRrWC4fjxHF02MV1IafuDq/dcDEOHrHKlkp38IinNtP1P3X/Wnr462BHTocj491MMhW0oGp7HlXr3X5pDTUZzI1SK4fBMf/34+7kKbhmKVtCatdEWLGMwwkZLJYBFUMMQm4G0cbb1/foduGowmA9hUIIDjcaalsxVqjZd8F16W03rZkmkVYj2lXUGByoEOSeIQQ2XdH9JpO944h2IrW1QtGuq/QcyNqwmqmlQIGmBna6sjTLViNKw4vDmEIiUn0HDd4+rFx+3Ii5oKlX9KqFOVh8OOCzYdaxp78JnW/SLBSHNOWw9qweBsOlRpw41rihJVNTQrzF4/HN4NKIlZFSMq19mWPiXwZh08/vFP6/Vj9dlyKqbJeN5bKyTolLih0YYyyjV46z4MMdTy83qJ4fLzes/0Q5C/mrG/FUSMFBOzHaNxyHmXvA28AtH7l8+iMnk7EaSYA/HRjtiPcJkdLr6bxh7ZmnpTKGhjEwMuKdMvozxzDtkKeGI6MlkQbD9cHwPt9IfCK3C2awTOZM0zO1jNzymZ+2D1SJPL54UnoBcXw9/gXvTr9lngNuuKBsGBW21ZLiDZkzWU5M1jOIksszJV0xTHhjUL8gRak4co4s+RnTznj3mnW7IJJY4kYwZ4bpQDOFrB8Y/AZi8PKK0d5R9cppuiPdApEnxBYkW9pPnvb8mof5W340P/L+00d+eP5EcYrzlsE5WqmkrBjf556SExZLOBiYoV4Lp8Fy92rqjoUYqa4QZs9wmJjHM+O94B9++Z2TnOouQiuM40BpqQtPtWHEodqIsTJg0KqUlH7OY6n7pl1b56JYKxgjHKaJ6+3SaY4CJSWCHSk1U2qPfBcxWCc0FB8mclGWZcGHsQOspO1IokqtjYyQtPC8XRiPd0iB0QQajeDPvHt4BaKdG4GyxkjMkfW2UHTvjJzANE8YD6gdGGfPb+5/y9vXf8WP7/8THz59z8ePj4zW4yUQyBBsXyCNIZdCyamLTqVn7mhTthgxrmFtoOF2aUoj+O4kQpTgPbObOE33/ON//BNriWQru4MJ2q6tE6WLc42jmUKrhc/xJEoP7utDad2bOz0ELq43rHeIybTSO5vaPGnpeVe1KKVC0f77GrKPd0wvYIr0wiQrtI61F9vTlB9en3j11ZFp+uWPdU6PSjhMOH+k2YZWQ2yFIpViEi7AYQx44wiD5Xx/hoNFWyJeC9VE5KiYwVFr7Z1F02itdHvwfvpvtbBuabfnVppmjBRwER3gRZ4ww0A1E6KOLUVqa3jTGVelNrYcqSVx9I5NKiKeQziBGbCniZS6cwaVPf3Y9HFdbQyTw4RDDxxsGyEEZjtgqnLJN9ZlReOGdQ1nBtAJJzPWun2sqNRWiC1SawIyUpRgznjvkdCwpVKLw3DqWpW6gEkgSq7S4Wlicd7Qdipra3UXnPdnwe6OmFoECLjgO+wRZZMKW2J93igpY7ThrHRhLa6T1kXwoYMXSy6UUlBjGEdL2DOmBg0EccjQB53GVry11FYJPhBcYAgOZw00KKnTfXP+Vy5O/Dz1TJCc+Xl4+19cIoqYrngua+akr/j29YGbPpHNhviCdRkxT4w+8FUYSNvX3K4ryyEy/rvC1V7ZaqakAYNF8Ax1pHjL5F5z55RDeE/gCcKNSy0gmbsB5mAparkVpcQbW7sQ3DsOIRDCRMB39LQ97gDNjlumCal0Sut0PmOWVzw9P/PTemWYlFeDYdZXzOq4NwMyvuL72w+8XCLokW9OX/NqfsM8z/ixz6lrFcQZxuORnAeWfGFriTBlsGNPOm6J2c94C956vL3jEhu5bMQMaKbkH/HOMTjDUO6Y7FssA0u6srYVayNBhOAe0ChdfKSBdWk0p+Btx/1vJ4b1NRIGxA80B9NdYLgbeC3K3TBxvUZu10TNDe8tWIM2IdPnqePpyP35FbO9o5XElp7IsjIdPIfTTqA1hnE6felt9W/20lZRLE066G8+ziyLsG0rpWiPZXeWnHfhnjGoNIRGrbmjomtncFjXZ8Tz+cB1ve26jA4tK6n0ULvPOodaSdtKCxU7DMyHI2lbsF7JOZFyTyfuNvo+7mu28rg9cRc6KRY3YqrHVsscTmgFxDCGmcm9ArpzZYu3jhPXyOW28PHlx9759B7v+kL11be/5Vdf/4a8Lnz/h9/zH3//DxSTcU5w1iJGGQdLNoF1ix1H3vmXfaPYKkZqR9xXxTnPMDicE1LNlJbx9p4PP33gaflAlfpnV6DpdmFV+piodl6GccLBWUpVttoZD5+ZkRhBTD9G5Zx5eb5Q1JBzZLslbrdKid1GrIWOLW9711A+U237z7/kvftSu33aDrInERusgzAp0wGc/+VnTaVtxajjNM0sZmG1mWoqRTKlRkavhJPjPPfibTzPME5cHi+sHxbylqj3CTlJjxkxwpYytSUqPeG6pERcFlLaGVH0nBs1QlHBMJHVoEWJbUWr/mwR/3MSriDN78RmwzQfGX0gFlhrpGGpdDdQU6Umz1QGcosk8g4V66TWSuaWFyYqRzMyjzPGH/csprrnRHW6aqk7vE8qhdyZLcXiZSLYwOROkAW5Nuzo906JdP/JrqBotD0pu6ISSDmibqXl9LPGSfcmQt1DKw0j3o2MwRBsZrSCHxyFijeChK7pMlZ3t52htd5pGVwXKBdXiVtEUYy1yM45c9bhXD9UzALWVLzrqc7G2B3Xv4d+1toTloXenf+C64uLE+sNqhXJ/7nU5PMlBrwTzkfP+RiYR8HdLPPNcwz33PyFjRUAYzKYBWMKrsHkPOPbgH9TSdFBFXIZOo63WIoGYh0Zi+E0jpyHd2wFKg3v4G6Eh+lKMCupjRzGd6TpLde4kGXGMRKKxw30BUYNVjNmb6k1k2lDV2fn+gl1cDd/RaxwXT+SakAHj7WNYBtDO/L/Ju+/mmTJsixN7NuHKTEzJ5cETVZZNU1mpEcg84JH/Hn8hBYAU9XdlZUkyGVOzExVD914OOo3qiEDmWhISjUqWkUyIiPiuru5meo5++y91reO9nccnGMeT7y+ueM4Hal6JedPHe6gvvvQNWLMLWN4hdGFmD5R+YTYAiRye0/VTK2ZXA2Qmd2BWgyfzs9kqRymkZQrB3/LEE69XSYOo19S64WaE2trrJeP6HBhDPc0X1ENlAiSHME5cIV/+st7Hh6vnGtlvBm5nSfeiuVkPcEvrOWRy7YhErg9DYzWEkPDzoHjzYkb/wazBeK6UCUgIeMny2EY0epYL5X0h18+dcpau2+KjWo7cMl6h82BGBPGlk5yFYMa2FLCBkupFbRisbjd4tNUqaI4gekws6wLKtJP4tahTT9Dp5r2GIamgsjSn8vYU12bNqw1n2FNQBenW4uzA5fLhXGaUGkMw4GiGeeG3tnp7QWC7ZuxEeX+9gscfZO+Da+4xDNrulLzFcGQS2CzoT9HYePrb2bmf3A81bQHDbY+ujIwBIswsPUZyecNp1bt3TrbmMSgwLolcixY7/D+wPffPfL9xx9wzjBMgXLp+Ot/riHpv6t2MB799OisMCioEZr2QgO6LKZ3yhvPTyuP50zauySt9kLG9AqmOyj2AuVFXNsqO3ivf79xtIxj6IAp6Voj55X5EPAmoVz+he7K/36XaoTUkKnih0wOCazQEuhzJS2JqBbrYHAWXzKy3rAsB+zWfU66VaT2z7FRibW/xS/pxNZarAv42q36JwxWHU/uSqyZ3EZyy1zSGRCsBJwNOPHUVmltoZFpVKwdcOEWwwBbQduV1NYeull7DptqRdSyFYitr7GdHmwRLN2ftVEEilpStUR0F+v2Zz/XTMmJVisl99wrMZaRG7wdGYe5W+et0Eql1NKJ0AKYgkrqQvQqaPN9XCqNYAJWPBUlbkrLBtsU5wRvuiMo+IC1AbEWZzPORI4CiOmho4OgxiKDoMHQYqFFhQSU/pA45wBBw26GsWZH33dOkPc9uHQMinUNm2zvLO6CYiP6+bDVi7qOC/g5189369Q+K/r/dOns0R04L7y6Gfl3v33Ll68m4hapa+bpvDDNgsjYT4uiPYXRQCoRUYM/wni4oeo7DjUT8oTojLaBmvtMeZChf+hu4M3xt/zx4UrFMtrKTdg4uhUrBmctdgAVJfgjDxH0AiIBPynNRKquKAmldwmWfAHx/WRaDI4TN8M9wcxc1jsmM3IINzhvwByZ9MQb10ONvBOGoTB4oTQLzVGkB64hgVxBNDMG+uJbR+Il40PCh4iYTNFMrJWYu+J/9CcGb3B25BLPiNsIzXLyNyQcta1gI95VnFXUvCIVQ7EVaxylxR78tnbh4Wga4/1KLD/wl7+PxDKyWWEcB758fccXbmQWz3xYkeB4eDrjneHm5oAdPdEKz2XFNSHkCHk/ydsB66YO1vEDiyoxX0jPv/zipAFoH9HUWnuGju36EyuGddtIKSPSMK7j3VvuJ3xUEWvRJmjewSAOihamaQCjrFvsGyQBrW03qXbNht3x6K31kxwGMEprBWvBiNvhZ50lYY3l1e0b3n94Ry2ZtC9QxlpSjdD6yUYp1FDIJWONdHgTDm8DYj2nwz2n6Yhq6cRa21OYm0bEbsxz46vTHeeHld1420c5pmPJh2AwZiCmuG86vRjIKfe00p3ollMjJmFk5HEt/OWHH9jywjg6DuPIOFpqKRjd02p3ytnLuKa+hKWp9tGOdlqlNXv0ofaio8TG+SnSTKPmXbeyo5wqPS6A1ouTph3m9oJ2av0sw2Hy3N8dCGPAhcC2FZbLFeeVaWqI5Bd85S/6smrwzWJdwc4FO1TUGJoDyZDWzOP5zEEsdhzYni5UM5CjxyWPqMLScNUR3EhuG9oU4/qYVLA9gNGAdZ09KvvJfZTWuyoldSGtDEzjhGjvCgh7+notFC00FQ7+1Dst9GlAMzvavvZntNTSIyC0UTWz1siaG7V2rD50DP5oFfETO3oYYzt6orQCKizrpSfI76LwYCcG2wsTayzG7CL1VmkGspTeLdLu3Gts/bls5rOmSQHrPd6NPeHYHrrt3RqcEazpXctxCPhgd53XCsZwGG+oY6WODV9KRyDcBFowrE9Lt5pZizUdlPjSc7S7a+4lsbRnbDVUdS86DG6n1xrTtTIdfNgfmIrB0JOMP5tr/k+un12ctNyD/V6OKS9FycvNcpwcv/vmFf+Xf/dr3twGHp+fCerZjCO1DRO7cBJn+qnvJb+gZqoD2640nrHO4tTibERkpWQHRTBtoHnD5iJH95r77VvOesXJE1oXUq44V8EuaKs9lMjdEbJgB/AmgxZquVKBQgZN5NK4xhUjA3M4MstIqEdGbjgcKvNocFkJ0sA3zDAztJlsE8Vk1BoqhSU9o7qBqUjnYKLiKNqwRHJLOKsE4wjDDGx4TRQqUTOxZrZSaE1QWcitcZwDVXwXD/obDDdspRHLE9ZfqFpx0gj2jlYzfnghhubuVGgexaBOKbJR/VPX3owT1Y+cppE39/fcOs+glnEeMd4yjwO1NO7vjpzujyQL758euZyf0XhFimCNo7Wee5KzoNWwFaHkfSb6C79qKaB9821qd82n9hOJnWjaaZW9gdF67hNgdmCZ0f6wGtMf9lZrX3SsYZoHSi2klPeQrIb01ModYGT2MPCKEbDOUUrGOY80wflOINOWUVEUQwgDIQz7SMmR4sowDPvcW4FGboqWRsqpF/nJshawNmAlMIaBwVqc8agaSlvJaWVLz6TtgaeUWXLcFyb5nA3atIewGQMhOHIpO7fxn60nL7TZ2rsW3npqdHz37gOPywVve7De4C3j6Ehr6ej//av7X3Vvu/fRS+WnYkKlc2X6ZkVPe1W6rsSwL7L9db9MrXunpP+DqHSbq7beaq+9I3R3P/HVN29x3qMI56crWjemgzKOBTFdJP1Lv1zxjGbA1M7pqS1TsZ1JYiq5ZZ6eE/Y6kAdLul6pzeL9AUlgaoOtYqpggkWqwRmLNqVScRaMaYTB9DW2QbHsZoddM1cbwXi8DwzjSCmFqrXvMVp6V61WRB3BD9Rayay0tuKNR3cEvbMDykQuiR6BXfEmkyn72KO77VpNeOPwckCw1JZIpVA0klKEZokxYYNgMYxuZhruGOwBsBhRrCs4lKSFsmdVhXDgmgqlbTSNu6W4H+pFBKN71LWAtY5p9AgeK3ZXfRacpY8YjfTsNw3katnUEkbFjx6S9Nwn6yhicNrXFuv697W2h272UY75PFpS3cdpTbDaMHvHU1S7S1FArPlnz3gH6ikdzaz/R6OX/6N76ufefLW89ER/KkqM6x2TwRve3M383W++5Pe/+pLDoEyTw9qJ5g3RLaR8xSdD8PsCog1jPTmtRG2U9oDzgjeW1iKFTxiTUBMRW9mkUe1rlha5NwOvx68o60cqV3LOLKbgJCOSsXXDyohIQlshjB5rly6gKqUXSVKp2uu5gz9hcZzcPYfgkC2gNVHsitjrzkUQsjaQCXUHconE8kQsSraCNQVY+0Nk+4ZVdN9wnCW3C5iKs4H5FChRKZshYdhorDkRS0Ob5VKugHAzCa+Oludn4RTegPUUPdPaQk3dMtzziB5Zc8STkZKJq0NNv7m8V5wt5BRBI3eHExVBauD+eMPtfIMXRRqM3nLbDpRY2LaN42nky69vSdI7NO+WTpQFJbfGFitLLmgYcHogakCiZXb+/+t99Iu59g2r08mlB3a1hkgjeM84j+RaOp1UFd0LmL5h/yTOZH+Yc84YY6mqOBGmeeh02Jy7VkV2eJmxNFVSzr3AMX3ElFK/15vsNt49cK+faoRUMqfjicvygKiiraDVUUvsJxyBKoqWFyKqUiUiDXJeMcaQq+eqfWSitZJKL0yW7Uy8XDAZfrg+0Kxid+FrX5t6GixWsSJ4H8h7USRCL4RsX3BbFYId0Ww6v+TxAXG9q1EF1jXhZvs57VehFxwCKga7/86l1c/vRdsLjO4E+ekj3OtGdrTFPwNN/rORUeu2VWlgtSPpX77ffPDcvz4xn0asGzrULUes8UyHhneFSumn6V/45UogmADn1AP75kYJnSJaUqKmSj4r77aNTQImB4wuHEbTtQ1qMZkeVyK9mDdqUJloLe0MkIqxQpCGweKkM2csBqvdxSMYjHTGT9XcBaja6csqYDB4ehTKliOlrrRaCGKpzaAqODt1BpR23YlB8AZomaSK0YIxDZWNIEJrA6U1at16OrhkalWMjjg74I1HsHh3gw8HMEKrCUSwtrtxaqo07Z1vb/ohs7RGqbv2pb1kDjmCDJSitFAxdGCgwX4WvIJibBeX6y7K6hq3ytMWuQ8wBot4evcjgSkN3wzGuV4Aie1j130c03S31e8ift2dcq1pPzjBrkPZ73W3B3zuHRbM3m3S3i39WffUz735dJ8fG9Nbn9aC9YL3wmn2fP3mjt9+8yWvb28xEvtM2QwssbANiYULbTUcZGCePWIXmrQ9PM1AiT351DgaG7SESO2jGhlJqsTi0VYQ+yN38hqpprexbY/fTgpaGgOFyVVEC1v6hC0OO4wwDzgzYuxuT9aAVccYDlhNODxiE6vJrDlxLQu1Xji1O7yObOVKTU+4UBFr8dWwxWeqFqaDQWXtUOWaaQq5hj5jZEakoi2RbeRKBLGkZFhz42IKW2u01oPa1lwYg6PmlXFsvD7eY82RaDYaF0QKORuW1O10tX0ip41BhFkc+SIY7wm3jWFMOMlQOhxvcp7rlvcHbuhAtdbDsbu7ovvz1TT8QTi9GdliYnlyHMLIeNNbeNdYWB+ulC2CWgZ1eCZM8ZzsL9+dYKSn8fZFT/cNq/f7DYodHJNOxKULyV5CsGrriGlaQ1pHm5udO9C38/7nrLUcb44s1xXVRi2128NNH5mVqngTunjN905V7t5YjO2vT7SHng3WkdLKYR4RPRFzFxm2uhfr7IEzTTpMqvWuzGV9ZHQDIoUqmZwgpcK2ROKaiHljS1dqyUxuIFhHsb0g7xMW+azbUAWaQY0wDCN1Sft/2xfTvdurYmk1cH5e+O7de2qruNo7JK30VFcrsb/fLwcleCE7YOgBfilW2ssL2YsN2fU9yv41sjsbRGimv4bPzZy96/NSYLXWOSYvkC5D4+5+4u7VqfM2csWoEELFO8WPikiBXVz5S79GHbDZUx8UvQTMUTGH2nORFsFePWVRtkuj1cxtGBmdxZaGtUpA8M3gEZrtRWVpDW8PeDy1Xmk1dR0IPYE4uP5BterIMffRTROIjZiu+LAH1L6MEaQ/V6N0+3DMiSr9mXlxBVnpMEDV3q3so1WLUY9zntoUNYJQEQ00STzFyETDimNgZB5uMcMEbUSbEILvQtvceoq5reDrT+nDWoGKE6i68LytxFxojT7Oqa1nPWnDicPaQNm1J60UlIwzDTV2HxX18Uv+3OHpI1orylYyKQQGZ7oZpHSGlkUJJqCD++y6F2OQ2l/nTwW7dg0cvUDs/2oX00rHKCCgxu4Q+Z6cvpf9PdPK/JWLE7sXJp9HOUZwDsZgeHVz4DffvOXbL19zc3NDy1dqM1gzk5dn5JBRMkvsM3orA8Ns+tijrRhxNAqIoSks0WGweDdgg8dbS6uO2BQrgWwWVnyHTBkhmtIzYkoEGptkcndnY2QkXTZsPnQ6rCsYjQQqwYEXgzcJaZ0OeY0blxqJGZYSafnKwBEnAmRUHlGN+HZkYODkD5AcJZ3JHqpmtrqQaqO2oXvsQ8PYDCZTSofvDPXIcDhwWTNbzKgLzMPYiZNmwWujpsyKYTZHUt245iubPu+tvsZSu62xpApNSaXSdGaut0zzSPDXfoNJr+ANJ845kNYNrGNdI4+cGbTrAYoxxFhR8dgRYjNct0KJleV5Q2vl9Zc33NweeHxa+2khV7CeEcj0wLbbMP3c2+pf7dXank3hQxfzae2pu8iesCr40eGdJW2JWvscuqeK9vGJWMU2etuTn04ZpTRELM4aDocZVNnWjZILtWRqybS9YyJANYacC955Kj1YDNOw0rfs1ipWhJIq94e3XJYr1+25d6yNQDMYHNY6ZF+Yt+XC5fpI8JZhBhOUUjp0LEVIWyPn1Dsg2ruDIp2GaWS37upebMnLeGXf2PcZtWrHRcW0u3uKQwg8fox898Mnckm9o1PhpekhpnczvFiMNzSr+wmzsy+MGoJx6Khctm5Z7EMrdrgXuwh5LzxaF86afVH9XJxo/1nQ/7knxTZyFKyxHE6O041nHB3WjcT1SslnhAVxG2IKWQvG2c9gvl/yNcmEVKHGitWJITp8LLjBYVNn/fjW2LRh8BzDwHEIhOAZvCWEbmJ1rcMMa2mU4NnS2mMgfN8TYtmgNQYjFAqp9nFBcwVvSgfmZUOqveq3VgBHq3tSt3S3WlMQM8BurFjLSivKYEfwXVNVakKxaPMMdkBJnV2EIFJoLDQiS7qAOF6NdwR7i5nuaM2RoiHWSGkFMbvOTBu11S52pfQR2J5sbl1GudL0uj//AL5392oXPgU/4ukI/7IfLJp2+qwxjuADDtcP/K0Xftb73unRLjgog9AcOGt6QviuI3kRr+493a42EYPmvFfxvVCD3bJszK7l6gGiPUFZ9k7P/sz1P931Z9qL+2Z+XrH+s4uTaTCfhS7QC5QpWN7enfg3v/mWf/+3v+Xt/SuGMJI0M46GaTpiylek8iNlOuPtSk4b10ukIIzjLdIqqS6dZKeV0R9ROWJEaNWwrgHjLRG4bGcGN5LSSmPjLnzFw+ZY9XVv6ekDQSINw6qeaTpwf/o9z+0jz/WBsl3IsnI3eZytqGyU5mglYOMFYiOLY7VPbArNNMYgXaSVTtjFslxWrCakrrQMVg6QDae/u8fcjGys+DJ3bUiCogbvb9jyO2qteAfBTThVxhC5tzMmOaL24qWWxHHIHC+BIiPvF8NxfktNjygrSGXLjSVGtlQoxXSAlTW8cTMHe+T+7oixiWqVWjyqHhtnbLylZHi+RLIWqAMmKTdDwNmJHCPrupGz0PD88KdHLo8ro/OszxveOg6nW+7e3oO78OHjSnhKCIa5Fc7xicvzE2X85VuJY0lU47Cun8ycdZ8Lj7glrNspsWKYjzNaC4+Pz10MvmfMaGm0l/GEdk2GlkasiWGcu2BWG8E7DsfTLiDNxLzRaiWmtbsR6GLuVrTbBDOkFnHGIGGgla2POnJmawujGximt2xxpV4LZavkZUVbwQyVZgrX7UxukS+/OmGCI+/t3U69zaQce2ggvVhwYrlcL2jtCa/dj9NoRroORmzf7UV3lf9AylvX4EinuU7uyPlT5ocfP3Benvtas49cSjdE4dUw+Znj4dBPxa2RcmaNkboVhjQQ7MB5OnfOUK0k04hVPxcpu3yHTkXvEDo39p8l/1UhpftYgB3I9iLirYyTJ+WN7979kdvTDZYCsoKJNKnkBooFHdBfPvaHoK6vKUiniaaAZKH6yqobqUWaVdLQOwx3xwOHMWCtwRrTD7+1YWrFGcsgvifzSqWQaLlHjMx2QnxE2FjWJ84xUsQgFgYHTQPCyBRmisnE2lBNUA2DHBnCLRVDTaV38tSSi6J6gFqp6mkSKbpSSh/P5ALBGryzrGsll4obCmq3PUW4kTn1hGotGAvOj+DNXlAnoCAydFZL67BDbKKw0aR3Y1QitV2pLe+Hhm6/xRic93gduB3uEBzNKORC09gBdpKoZFrODIw417uqYiDn2LN26O4bOzVM6M4ZNS+uNOUlofIl7qH/vR8wtPWcLOsc4vaOsSrWObzzeN/ztpCuPelmQ/PZdty0kUvtnaefKRH/2cXJF6dAbbkLYQDrDF+8uuHf/+3v+F//7d/x2199zekw7QK9wM10xA8T3ow8LF/i7UfcVMkWLtfMtlTEPHMYDKkGCgHrVmL+RCsTWRWwWPOGyoQNJ24OA2M4YcwdPj8QWuG23bIJZJ1o9Rb0GbWZc7myLD8QYsIdXtNaIsczS3xGtCe9DlaZOXCTT6z/+8TT38NmIPz7L2j3P7KaJ/wQoBX0k0O/f4t5ONBS5RCOmOoYp5HH7YJ+mWi3Z5rtSZSn8cTdNFNqJdaIHw4YmUFWYvmRrUUum++FiinEbSXRxcUmK/HPML7xvLl7xXGsNCOcr4ZlU0o1eJ3xAmbwBGN5NRy5l5lwCyU8UdKCNMVoIBjLbA/kdCKz8JCvfHy/cP+U4e1bjm9eYQDnPaVUWstQFdMc7TGTbK+jY4P/8of3/Pn9mU+fLvzjf/6Oh49nTocDX6llzY33Hz/izM+D7PxrvmppGNNwxr+w2vtIrFVyKYhxGGM7F0MbwQWONzcsy0YpBWccqh3hLtKFcF2e0lkGRmy3DVJp0u2JYgXjLIfxSCk7e2DnGdRWiGlDEaZxZHRHnHiCmUBhuSxoqjxdP7CcI2VrXC8b29JBTKqN4+3I179+xfFuJFfD8XBiOnhUlLRmnBlJOfUoCl8wTsgFUIP3njWv/XfIu6Ni19OYfYGCvT6RxjAMlBoRozhnCM5So+Xj4weWeMa7bv190X70da+zFeIauT7HXfezjwByQ6ISdOD+/sCr+4nzsrBdMh+WK4+aSW0/GXaXdn89KC32Asj6z9aez9eL+FV367CBfZ4vhNHhBaQs4CKY0pHlajDiQC2aA9TxX/Te/O9xvYhPrTOYJl0ntXfkqoEmDWcth8lineN0mJjHvv04cWAsmhvbZaPWQJsNJMEAVgxqKtfYmPyBICNND1g5MbqVtVxpMaG5h0QOYQCbOKdHYuzk0smecM7S2kLSzO7bYo9u/OyurCp9RKQZ1UKtldIE1KPqUKRTWktCW6eNa7UY73s6edpY1kdctQxhRKzDOAt1LwKoiO5ZaiVRuVJKBvHkupJr6uMQqd30rHtacVMMAzU1KAk/7TZg7nsaMZncNiq1d2j3DqWo0Joh5oYxHi+7CUVd7yy5Phtt0Lvgn8Wq3cHH/rwg0vktwSHThPp9xLWPfEzbTQK75gXorCcbMMbtZoCyr41/ZSvxzeRIuZL2Huuruxv+w//0e/63/+Xf8rtff8vhMCMouUTCNDKMMyIWj+FV/QpNV1b/ARkyB5+I8Zk1brRSEecwtuPQty0ym4J3FuyA9xuHOeLsr8jNUdqZNcU+4qkbkw40DWBm1B+o+pbIH9nKO0ztJ1ObItaCDwMHvSHmB67pyuBG7ocJyZYgdxideOM9v311gl+/5oP5gWUzTOuXuMNAuVe8v4HHwPZ4YbCetTXMl5U2nanmStaVrJGe69Rb5tY+4KhUXalNucYexR7shLEZKwlfas8JwWC1Yr9NXOvEXRiI9h/xc+S197zWA6FM2DLR3EANidFuTG3iuv2IO+ZuPQNcmnBYvIKsgbpNhEnR0fFpO5POlVkNkwr5ujGOEy5Y/GCQ1piGgZbq3sLzJDX88Z/+zNN64dPDlY8fL5TSWLZKLP1kWlT5eL7+3NvqX+0VnOs22Vp3MSef/aWyOzr6LlhRa4glIw6OtxPbspJSpTXBSLc5gvSAL9c3yG3r3Q7nLa0KtfQwNO884rpozg59AZjHiSEEtCmtNKQ2jPMYdSwPK08fH3h6f+Z6XtCUu7aDPqrQPVzMBuHmfuTr3x4JsyGcByQo2EzLlcF4lktiXa8Y1yFLW6y00mFOwfdWeq27bXoPyevCWIOiNOlz8doKY/BsO0l1dBOvD1/yX/7wjsflGXVtx413BU4uyuANHjgmAc1cYiGr9BFAVWpRpAlJzlzOK18OE//h97/l/Cmy/OMfuKrsGPE9AHDvAO/uY/La12HvO5/hZY0W27kltL0jZIUQBKPCgOM4gfUbpXWnnciAlwkvM6UK2yqgf92UkP9/vPrG1v9/q5lCQ4pFm+2b4D7uG7wnOMdo+5im1Eqz/TkoMZOfruRzhdnjTMBgsGrJ+8nd2ECqmTUmSgVjT3iZUFtQTbtLzCKM6AVGN3F7fMvoeze36ELOPaizFUdJEaTrg5YqUAzaCqVlcon9EOsDJkzUBpkNAQY/4PxErhWKMLkTxo64wTCdekYZpm/eMa19NNMSxjqQxpYWnpcnSt1618FlMInSQHqUMaqGRifherEMZqCKwlARB5ApbQPtRcjgRlQaSk/N9m7nnIgB52iVntGj3XiB9FGu9Q7EkG13CPYuSwNjabX2IsNYqoVyM7G9vmE5HqnicKUyrpFyvsLzmbATZwGMVpp2ansf9bQ+5tG/cufEyK4AFsPgHL/95kv+/e//ht98+zXH44y1touK6ByGF3mfwfPafEN8qsSL0A4/whQ5TpZlG/j0+IxaGOdefZaWezBFsxgZKBnePf3APMw4uSfmK4/xE602kiu89V/T6u4Btxl1mdocxoyILN0BURPOjQTruB2+JpY7zsulEyRb4GncmP72keGrZ4JTLq8C6j5S2bpDxj3Dqw3/VWVqr7DvZz79vxxP75+Zf1cY/+0HtvDAuXygagEzIHaj1jPSRrb8jPeRKpklV2LxiLSeZ1IzB6sc1NOi5yMbFzUEDnzz+iuK+YGqD0zGoS5hTWOcwGlj04XncqZqJHGghYa1K641mh1wwUJyaDyh6Z5WXRco1crhMPCKmfvTBC3x+GlBAT86poPneBoYj0cGd4PoQK2GJWa+/uoL7usttzdXbo4X1jVSSuHj45nnmJhvAsn88k+KznXbHQq1VpC+AAdj+0OtStq2DjHaFZjioNWCDw7vB4wxlFLYtkzKGS/d5WSdw4ihlNbZPvSugZFOeHXiiGkBGsZCzolaGs4ENGkvSB4eqdeeuZFtIeZMjgnZdR+6Cz07v7HjxrUCu5PBBkMlU3LDElgXum7AB4pWUuwJvM46Ru9ZY2GwnmmaKbUR4wpa90LoJ3BTZ7wIW4yAMPqRL+++4S//9IEPjw9sOf0z7YciFoIXnIC/CPLYuH3rsEG51EbMSk2gqYtuq6lctsqxGtw3yjgP+FEYpRM0U7Gk3HomTlO8E+z+81rsr01MH+kI/ASj3cmyzgiHyfHttzOHqWcflWqw9obAgGFAdKLGQIqlW4zaz1uM/zVfhc7xoDVqLRSn1AA1dDOFCZYwWwZjMdIL4tKgaSWhqDNkiV2TWKce3GgzgvaumARCCDQyKrk7RV23FytKqZV13bidb4iS0K3x68OvaA6yMRTN++do8AyksgGVm4PnuhZaUSY7YPyRphVtG6VEUuyBgVoT5/hAlpXBB0SnDvN0hm7trx3NXlb8ZNBBdweNQ70B1zfnkiOpbKzlQmoNrRPzeEfOz1RWsIJScQ48HiOWZgy+TngZwBlwtRc+petWxHT0vEjpgEAFoQtjc9364Ucsg8xYUVrO1GIpuWGk7Swm6ZwU58gt7eDHvSB0vktfLaRxIB0OrMcT1TlcU+KxYY4HxsFRPz0SlhV5IRWW0h1BrVGa7oL0n+de+/lWYgoYwxg837x+xd/99ld88+VbDtO432zKVivnnAhi8Kp4azAIB3Pky/AlNiWePymbF8rrHxhvhVxHPp4b4hVHwBrLVjO5CJREbJlkHdvoOExQ0z2TCSTzCR0tSSwDRwzKxifW8kQkg3Us20IVmOwNea0cDmBlYxxmnPmKZUk8Xb/nu/wjVgxf3o7cjJUfUubxqZCL40Ym7uinoDMfGMa/583dhP93A/f//i1xeOISI4tpiD1gWyTV3FM0ZV9YzR2tnLnED12o6x3UCBdFrwHnPW51mO8N4/GA+cLy9d2/4zD+meSe+wL3wy3eC9wFsjdsbWMrmai1L662UDViSmKUwGDASHfSsAlp23jaGo/bA0GUL7684ZvjK16HmaAOq10vUalkSZxjxI6Gw/EWpyPXJSPGcXN3z9ASbjwyzSeWZeHxfOGxRVbt8914+eXjup2zLMvS7YnW0l0ZglrbrYGqlNbZCsbsZNPSsKaHnL0YZMZxwDvHFhPW2N5J3E+aqWZK3Mc5YlArFCrNNEpqvetSKjGulCe4fH/l8ucr7ppwTjE3Bu4cYg3jNOy0yl3kpi84xZ1L06CscPmUYVoJB9AqiA4si3JdFnB9vJGyEFOjFhiCw7uJH99/INbMHDy3xxNPqqzbToTe8fGGXgg4K6ScOY0Dr6bXfPfnR/7y4QNrSjTtbkCh69o6VArCAvwXIA94tby+Ee6C4WnNLLESC1yXRsvgktCOhvPtin0duHcTpTbOuxXVGsG8bIr7yMjQOyd5VayHzxXL3tX2HiZv+OJ+5s0rQeY+GkAtVgZsG9Diehe3OXKpPZ3Y9Hn8L/1KvuEBj6HikQm2G+Vy7BvdrRtx0UOhQwUNiLUULVztlSe/skplLHe8LgcGO+xMjd6lcpY+bhZAKs69zPoqOUdKXplHx3xQnq/P/X4QaFsh+4Z4g7cWIxaaxTIjmO5QscI8T6ypccmRrSSM7MJc67gZbpCa0Nq4FIVWeL5+wNguSjfFM4bAjT/18M0tYk3PhartpaGkXcsifRRUcsEVCGLY0qXnvNl7cl0Q6ZqyJhUjQwe2aaDuTkARgxFDrt2AILb2Q3FTaumjrYZA6wL3Zc14NzCEDneMS+aQO9yxtxN073Z0Dom1Zsfu7zoT2ztc2irULsBt0rsgxQjqLPUwk3uEDqYpdtt6Z7kDmTpk7mUM9DPdaz+7OCkIPjj+9lff8h/+ze/5u9/9hrev7whhQKz0ICZ6IJdvDStmhx4p0pSZI2/4hkknFj3xqayM/oq5bUw7+VSxxPiOq2SUitZGaoZUC2t+z5LgOMDd8StifM/aNkxbuNETploylSiJpaw4V3poWmpUbzlMJwZv8f5K0zNqwQ8TR32LMxNbfuT90yfOqXb0/vgK2w6M7YapvkYEgjNw+ET1F2T8iNhHzmsh15FUtHvea0KB4A4YewItOCcc598Swhuetydift/ZIxdD/vtb1njD2Bz6AOEm8O3rt4yuYd1jD6ASS3RCc4aYEjF3T7wxAiaw5TORFUNgOwuvxpHJebQJpgGpkM4Ly3Lhen1kNI3b48QXr2duZcRm19uUYSRr5Rwf2coZJFHKlZrh8XHl4/PCqgUNQhg90/2IO3riaAkt4gehtdRbgr/w6zCOGGBZ132uW7vYVQxNZH8oXzyshhQzzjm0CaKG2gpJu+3UOIPHIQjee9wOQJLdXtvJj70DU7eC5orkxvoQWX9ceXXMbOfM5Z2SL7WzQw4DbTJUydgmWBzzYeLpvO1sAvZVUzDaxaY0paaGmxylFgYXKBs8Pj3iJ0Gs6R2TulNoXedLbFvhsmzglJQKlo3BD8QYP4PN+k/qVhljYRpHjv4t//iPn3i8PHPdYncpdZEB1vaTsRc4Nc8X8chaG6dh5k4cHz4+kEalNChRyYuwPhjW54ZpSp0jNn/H7a8Ghi8m2taj3K0IUsEUwW9gmsF4haBQd7ZG7GByvEGCIF7xc+P+Fm4PEUGpuYsEa7MIsdNIm0fU0tTuM3ZBrCfHXz7nRG4t5qkgzTJNB8K94Xy6cvERVXpeUjK00jeqG3WMpfFQnvlLe8eHfCGr4VXMDMst/jT16lR7wq5zltIa7WV29JlQ2u3wwQaOhxExEe8FPRguaWUwjloTtRWKs3g7dAYW3XqaisOPR7a4E1l1QUykaDcNGOMRa2nqMP7EfZjYasRpxUghpWtPSDagridzh6NHTKOpdNaRaRirtLqR6kIsmVYaYxUOTnnPtbOScpcBICO1RVJrWBoj3T9TdOs6wrpnPkkHozYtXUYghrYzkQBqUgTHJDODmfDGs+WNYRNeKommjayKbYoYS/3nzjLp3ciyY/VLKdTVwLohxwk1hoJBxVKMRaYRdzoyXFYk9Qw+1Z/+1z8z4K/dOTnNB968uuV//rvf82//9m959eoWHwK6+5uN1S4Csp7B+O5cgD6rbRUjhskfcMHj7UCqz3D5CydXsA7yXKntjqdzIkVlq7GDlFRQo1SBSibWd3xaE6nCbIb9V3CUIqQSyXJB6Um9zjliK1SpNMlc0spBEntIIljlON9wCl+R88Ky/olr+p7HvBCGhdFBLYG0RVxp2GHFThu1rawmsWpjUeFaIkYNVSeCmRnshLM3qBmpeqHUrTtr6khtZ7RCSwFfZsp2Iv04o6WHtfnDkdP1Ncsf/oJ+NcO0sthMPDx14qLxYBxWO/K5kTF298/j8W7A+4bWQl4tcm2YB0UuhqMIZpwIt57SKqNW5qE7J1yYUeeJcaNhsUxIGYirQi0s28L75w9cTMXbgTt7xJiBXJS1FRLdzYH1zPeHn3tb/au9Gn38MowT1tieubHrsWS3qbaqWCMEGyg54ZzDGUdOcU8j7ouvau/ElFT25wlKif370qDmbhVZoXxMxA9XaozUUlneZ+Z7kKkxneB46kyDGpTsFRXpLIWihDFwc2u5XFZaN1bwQgiBLsJrZOwuzk2pcLlUjLU4b/eTFEBPSPbG45zlul66O6eZ3d3QCKPHWNlt1n1hMgLO9TFHqwf+858/8Pj4hNhCoyc179q7PddPCKvh9Tryd8Nb3N+O/OXje55l4Tkktj1rKDSLLaaPk1QpRslj4b1pLE+Jb+YBvzlCqWhSdIOyGOLaCbvhvmf/pK1hFJqRDtsqgmm9dX+aHK/vIUvvxmozbItQ427zpDuHam20nTdjjTCOlnX75XcSm99psCKYwdNmQwmQaBRjONvelTW+CzIlNWpybHtatTO2a0WiUC4ZXim4jsAX03db3W3xXcpVKVr33JnKPMx4YzoXx3m2lLjGFbVzP7lL11AY1ymq2touYm80iTTZsLbiXT9Y1FywKN55imZS2TDWYU2gFddF4FYY3C1eG0c/gbWU2pBSMUZ7hEgrII2WIykvnY7cLMZ66qCcKVQpGK1ocVjnULWYdkRKJNgJL1N3iUmm5gSlIq4nA1P7etFdMKVzROhBhz3mou8J1lgwDa2KixZaB7Z13VxH9hv7AjXc83D46Xyle1wDa8adF8JxQq2jmL0PIoYqQvWeYi1WQHY1uWr7XJi8aNx+zvWzi5N/8ze/5tfffMXvfv0tp9MRY92Oof8JHGXEMLjAEPqbUVtFa4fmGKcMs0VGz8QJ1t9xWStrfMDphTadaeo5zgeMayw5k5ohV/aU1yM5G5w0tsszltfk5qi9SwfVohu0FrE2IkTUrNRaSfW5K/9bo2SHsZ4+YF8wZiaME0MQTuHXXPPIY3rguj5TZWXgBi0OoiDVIq6hzpCwZBEMtzj6icybI8EGghsRBlJt5FbJZSXbC1uCta5QJ9hbvm70VBVKhHArzN8mpuNHnv8fK+tf7ohvoX1xRY8r4rtDpFFoewhVqqXPU7Hchhu8ZJo+ownC9jU8vaZ8HJGrYaLg/czhMFJywhTB1Z6PoiVTWuN6ufLp04VtLTgpDL5yOihVBDt71MFqGiZuXFPk8rTy48cnzo+XnkdhlWb+B+Cc7HNuYzqMzVoDdK2Ile4Gk9023AP+Kg5LMIGivdXdpAtnrRGM7Keh3Dsw0irOC6Y1pGWsKnWt5PeR8x+23pE79UVkOQth7Cc3daDBgpXP55Om3WZsq/DNl1/wcTzz8PGZWl9SffcFuSo5FyYs1sD1krhcNtxgaWrIpVFK7STUovigeCuktPVCRHvYX2kVu2ds9KCzzkEJQ2AKjrpZvvv+zPsPT0BlkP4id3YUpoG5CnqB+BHWWrl+GZlu4MlfeJSNqy09+FMNp7G/77FFDgfIHswR0twtNtctoY+KjbYHyxVFc+2pPK4LXTNKMXvyswEnYKriovD6/o5fvX0L80c+1o1sGpqEqn2MU9UghB4ZUbsYuNWuUwnDyK5e/EVfl5o4+j6+W2xkbcrSEsVCFWHrNzqDWBw9PrcqWA2c5A7vFA2BwRwI0cPakJPt4wA6UCy3vu4ZEXZpZX/+jEO1xxu4YDrME0VMIdXGIB6D7SnFNaOm7GMPi3OGykYj0dT0tOmSabXzWKxYYlp72ncYP6MZxFlUGk0dqn28ojRK6wnBaiql9rGUdroITj3WeKKvJAqlVUQSjmWfMhSMd1S1tDgw2yMHf7OnLSsisb9OEqVd0QaOAW+G3rlttT9r4vASCNbjrcF6IUl//6wY/CZI2Qu03R2oL1BIYz6j6l/GUZ//KoIpir9GxucLOIO6gSq21wG1IilRtVCkGwNetCcvjeRafxKb/59dP784+dvf8auvv+LudMA60ysupHujXyAuCN56nPH7XCzv5EWDH5X5NjPcZFI18PA1Ni7kpxWJFfPqGeszx+MrjjNc4sJWITdoRTByy1I8kz9RcsC2W1opNBNpavEmE9QRlqmjttcMtxW1qYvm8hFnLUWk47tNFwI1Ekv5AWs2DuPIcXiLKwfm7T1aNuZywK4T7clR4mvKA6gvNCLD68BwvMf7ROfWe1S2PneTSmuJsmtQSlu5ph46GPTIWjeMi4zDgAmBUkFfJdy3TxQr2OWG+HzAXm5w60L49gm931jtxlpjd01po0p3UM3uiCNAXUm5Ih+PmKfXmMe36LMgNeFEuv7BeKYQKKnSUiPVFesaVSzr44X3f3ng3ceF0hyHeeRvfyMcbiem48RjUR7PFx62Z9q1cnnaeHi6sqWEHx0yW3L95Y91jHG02k/EVQSxHd3srfs8vpChq+CdEYZh7rTJ1ot4g+laor3DUmuhxEaJlaIR0Ur2dkeDZKxUiI1SMylVJCv20EcOcVNMNSTA4PCm2/c+E2db7wiYAsdh5vTbO4x8x6dPT5SyN8pVe5eyNzB2aFvpBZgKKafudqk/WZ2NWrYtEVPuCxrAZ5oIu/Ooz6xDcHg3QHFcnhYu5zOllm4Z3qPhrYATwUXBPkH7BPXSeAiR/7J8xFbhsV25SCW/KGbcfjgZFLWNwXR6tQkgoWtRVxeJpZBSFxAzKGXspF71Sh76AQgB47sI1zaB2DUBY/KYaMhTzy0qtXQWUHMgvvNMcLQXtqb0gMZalVKE4X8A7s8HuVBvDgyiJD2zlsKqive9k+7ajkUvMBVPSIJkxbeBg/PMziNuwGtgXD3mqcLo9+iCQsylT0ib6Xk6ol3MucvFU06sOXHC925NrVRq78wwMvuA1EpMC8Y2FIc3E8IVw7VzgKrrjisFIwHUkXOl7mTa1jpWTEQRmykkSonkBgdnGZ1HtVKqIDQMhlKFVAtQGZzDuI7Tj21BpTC4gqkJawe2fVPX5hEJTP6EM0dUOrXViwdRco1IC7RGTx+2A2I6V2h/O7oVRQJzhWYSm6kUNqYUsFeH5oq2/X61XTPaWsY025PQX6Ic9ilDpienqyp2SwzPF5oUsgSscWCk61ZS7kGak8NIRdb2ElK1Z1a1v35x8tXbt9zd3DAEhzXy2YK3y+r3m0VwYj7Di7rCvbeZ3Vjw04YfMuSZcTgy13sGnUAMa3rGjwPHwZFQikRazngjeAk4KkM+YmVGhiPShFg3QlgR84h1whSBdIdeWrcW2ginzJYb29UQhsC1ZYxN3b5lA0kTa3xP8IWqM9a8IQTHV/MNUgfao0ffKTyO+OUb1LyimcRgF25vBDfN5PZAEddJlS1RFbxzNIkgCUXZypVU152u63jO1x4x7QL2cIO9HeG3C+mw8Ph+AHtiIDDmkfK4wc2RfPjE1X9gyyupVawVrFi8wGhHWt2oW0LOI+67r6mXN0idcLbhDhYtASswDLanW6ZE2hoxZ5xXjPG0LbE+r/z4wwNbMbx9faJ8eY80j9bC9Wnh++8+sjwtmAw1K1tMNKP44BkOA/IzI7H/NV/OBqQZciu9ZayKGN9Ns3tSsTGGwVtElXk4UHJjjSuagNWiWclUGoW4bdSs1FhpKYNUrO+5OMa0rsEQpYiig1JWxW6COiEVZWggwSAuoGZAPwfvac8eaYJWZbls/PbbL7sLoCrPjxdKri/lFNBPTimnPUyzh3/Vmmn7YiZAcD0v5PHySCoV7+3+WndYo3StjDGG4D3BD5AdT8+Rjw+POKe4nctWq/bANyBYIRSBrB3vfYI1FP6Yr7TYCCMk07tMIpDorXFxUE/9tdlGb89bcJOhukI6FDZbe5aR1R6IqIJxvZhpZ8EijJNFTIMstAxgeH64svzTxsFX/J1FsyGt9I6q9iRWdHeSK10nIUJthW2LvDrc/ve4Rf9Frw/2jL0/YrWxLFeqgxBmbuYZZz1kQZbKEDPhopgrtLSj6I3HtQGXAiYZbG5Ym6nHgNztwMLWacLSWhdImby7QIRW6fbitFGqJZNYtpWYI9oEL4JzEwbDFq80iVg34saBohueRwwWMSesdQQxmNat8aWlLoh2po+A2Lp7RwpQaJJpxiKWDjjLFrWGeUfnP1xTh6zVRNSKa43KArJgbWPySsBgw4CSKdkgzTO4A1ZGiprdjNI1IR3gGPAudE3hDjEcQne0IY1CpjaFatBiyC1SfKGahl4VzZYCGBrGCM0bqoEaC5J2qrJ1VGfA2x5O2/oz40rF14pcKzYv2KyIdcjskbAXNUFQF2i2PxRSG6YqYhRsz+n5OdfPLk7mcQKkw2r2hvFL8ugLK/8FuIR0YK2RvT0k0LSQYq9+a/FIC1huCcMt4fUz2R3IphHLJyIPVF27KFb7zBstqCwUk7GyYH1mtBFne0ieMBPyBG3g+kPCyQH3tePcIKnhoplgDhQFvGE2J7x4Yj2jkhERLukMZJyxlEEJJtJyxS433LaRm/kWVeX58kibR4J7xE5wuZxZi9l5F70tX9hAl92o2bimJ0qDgx+p2u1fBaWMYL8YsG8m9NeJS/OYOhNG8JPgRoO9NaxjJJpKbl2AJHYHnVVPEEFqI5UV83HE/fiGY/wd0/gWP+x/VitaGrMfMChPjxdKyZQq1Gb6fN0Jkx+5mSeOg8UY5c3tyGEylHjl+fzIxw8PfPj+E9fnldkPzGNgmrpycTwEDscZ4375bWzU9IWh9TlvF8V2xHvLjZJKL+AzuD0zKS+J7ZxIz4n4VCi5oa5STexFiyoWodWG80KOdU/05TPQTDJU1xklsjT8jVDpgkMdZC9ObF8Q5Sc7bBVl08rj05kvzgu/+eprNDX+UP7E8/O1h3ntY53aLA/PV5atEoLfqan9d+unLelgrD087WUeIzvpsyv9u8XTO8sYJqwOXK+JD58eiK3gxDAOQi5KLSAJWobm+0lODtB81wdWget5H7k4qE5pItgKufXXbD34wGdtSzCGrSjD6PCDIbxx1Et349TY30eq4A8weYgLuNFx8CNNGnmXXm6xck6XznL57YSpgqb9hantmTt1z2Jpu4bocw9JSKkDsX7pV2mJNhgKlYRlGEbm+cQURqzpVluhEGLGrkrJQs1tL04gNMW0LvCsthfS7SRwCNjQHVFa2z5+yZQW+zNQ7T4q6Lj5p1iIbd1Tw4XJzZzCkSmMfbRSAqlW7OhppZJMxEtEayH4oWegZQNtL3RNbx1YJ1SJ5LyAVF6w7SGA2zVj1gx4kzGD5Tg2Yt0oXMD2bqgVodZKle46cuIYKvhocM5zmBwVT8sDk7unNUuRTKPs3UwPGIzrpNe2j0iqNnA9DFNkJ7A2aKZytY01Z0otOAyuzNT5xNYKbcsYVdooFG965EWtXbMVDGn0lMEQaeSquNSYtsZYFVtK74isqT94YUZG20Mua9tT1hWcdCAjvcOZA2zur8w5sUZorfWH0fQ01JdFaSc99+wKazoRz3R1tZZuU9LVUPKEMRZjJswQOJ2+5sPH9z0lt0XW/I6yPqC2jypiUXSPQA+cGXwEgVoXrvkjtSwYyRzDiAn3+OmeehggbNyPihuV5DyleNzYcGPgMNxhww2Ih5aYrJJYaeVMbYmqlcuaWKJiSRy2wGvprTOLAau4AHoPcWrkkliAqIlJRpwZdjFUIuUIIlStqPSo+ZiVpV2o2qvkdpqo9yPbTWbzT1izcfM3ijl69J9ueVgXyuHCevgzm2SigtaecSDGU7Lv7osC9vIF5j8dee1/y+uvv8G4SpFENaajt2PfsHJunJfEZSsMwWN9JxYijnm2fP3Fa0pVYil8880bDoeRD89PvP/wwKcPjyznlXSNDJPiD57jzYibAsPpgA0j+X8AXncpCTHS3R9NccZCUXLLaG60tdI2Ia4LNgrPayQ+Ry6PG/k50ajYW8Hcgw49G6brTjoYrU/tXk4Y0i2YLy48o6iFGHfLrbO8eX3Dh7qxlZ6f8bmooX9dR4E3TIo8vH/mm7df8fvf/IpcCvXPP3A+L8RSeXq44k/KugE41hhxrqeT1vxiNXR72GHZOyC2P9d0FpKz9nOO0BCOODlwOW88PF0Q6/ECVOVmFJ6fM2VT2gVaVq4GVgf7pATnu2ljuOvvS9ynRtIUk4VA75BUVVKDJsIw9HFWScrt7ciX90fcV56H7xa+/4cLz+832tZd9taCKIw3jrtv7zjc31JzYHUbS13Rmmg5QayUpJisiA4YMVS0A6uq7njvXpB8PhlKdzqcz8//sjfnf4frK3/LpI7VNMZwYB6ODGZEiumGiFTwKWNtg5uBVgz5GjGpYlqFmNC4axKcdFGpLwz3d9RZCeGGwU3YYWRrF3JOn91mrRZoDS+W0c+EMjHPE84NHU6GsLTYA1rNyCk7UlJWSV0kSunFtY00KVTpI85eHTtybMRsaRKpbFTNvRjR/ky1asnlFmMbg/aifNVIkYjYlS1eEaOMrltySwMY0OyIz0L7sWC/malfV6waJn+Hk4nYAT77VKJ3YHnJh1JorRfQTZWtFNTur4d9BKXStXGmYXAYbTQZWe4G4mwJmyC5kq2SbSMgDMVSnGMJhuuoPLvIUq40Nm483KmBAtb0Mag9BoIfqNYRmsVnhctKypFiG7iuTTNa2VzleS4s419ZEDsOAxiD8RYXOkSMnZVQEbCm2ySNxQyut29SRQ1YsUjz1DiQSutfMUUCA19+8b/w7tMfWdYPXO4vNI1gXzJ8eoVlLTgqJV7J7UeMmUA3cr5gjSMbwyYrzY64rzy3bUQ/LsijZ2pHzJueGJz4iKOgtUMmDBkhMcmM2TwhPLPYRlTP03UlYBi3Cc2BnApLPdOksdkr5fSBfEisQK6WVC8gW49sR0C7eKtpQokEM3YPfD6TciSIMPuvCa+/oQ4Tmz2zNYc2RzWR09snXHNsbePD8Ee29kheDa16vDiC9ZRUiGvBx8B4uUG+P/EqfY01M0YVZxKqKw1BnEOLsFxX4jXx7v1HfvzxzOs3b5gOA170M9nUOceruxNN4HiYac3y/nHjux+feXi8QCocR8/daeIwerwzjENgHubeYm2//OJkuTwi4hiGgLZGXjNxy9So3VXzrrF9l9kuibpVBmPwwksEFhyACdTvSvsdsPnSaaS+dEwAdpHlS7iqCFioa3eKiINtU5JxnVogu8i19a9lHzcISqqVdw+PfHs+c//qhr/5/W9p1vOnP37P9fnKeq58/POVEoTpxmBMJW4VMf4z2dEZRy6ZJT5jXMFa1xNg1ewUz067OE4nXJv59HHh8fnchYMKRj3BWu4GwdqFkrcOUWygBUrspywNUKrgwp7Vsf8ezvXQUeu7g6Pa3k0SC84L4+ypsQsn37458H/7v/6vfPP2nj/+ww/83/M/8B+/+4FrKpgbMCPUGU5fjvz+f/sd4+0Nxp6QOpAXJZ4L14/PPJ//Qh0+EtMZSsVZR2qVVk1/n5vQhB3a1jkU2rrF9NPTw7/4/fkvfX359jesXinb0gtpWg+oFJBasSnhRTCvbpHhSFoilw8RvUbCophV0AVUDcY7TKmYvDG8CYS/C6S8UOh8E+Msx3CP0unAahRnPNctYmToBT65a55MBqdcrmfO5zPz6nh1NRhdiffKWRLeZN6cvugI9xqptXcPWwFSwW+WMs5UowQjqBlRVZwYnBU8nrGNDE8VHjNmDWwHpQwblguGDZGBS936IVENrUK+VvR9YP7LRLiZiTFizIC2katmxHY3Un9uLI3WdUzNINJ2i/4LhK6TmdHeYbSmH6QxDZcNNMFWS66wyQIz2KlBqqSSya0xTo6bZJGY2dbEY0n8aC9c0hNSC0lHTJtADdb2z6FrhArtWhGfEeepwBoMF1dpVAYpWCk8yMqPZiHLX7k4GcZO/fRh2HkNpXvH95Af3a1CRpW0dpyzIHhrEe1o+lZrb3trbzvlIhymkS+Gb3j/cI/kE1oCxihWVwxdHEQ0ODHYbSWbSDIrg1iO/kjzrScB5ydyazhTme4npBm2GviUz6RtwQ+Gy1aJVKxcCN7jbKOVFbLDPE7cvT6i4UfCbUCZkM0RLifWp0Z9eMZLxR6E8K1heRV5n55oeWD0J7b0gTVuOOf7wlQLuSZyW6kFbke4Lu/JpcNoRnuDG95wbZFL+kRy6w7sKqyp8uN2oegn1GWuckaKAgpJCalScbgwcWMmvjgcOSyCe/sOfRLUGq5r5Wg93ttdsGxpxrKUQo7KaGa0Lfz5x0fmm4E3t0fenKZuaa1KrJWYEufrSq4r37174sNTpiXhNA188eaG25sRBNZcuZw30mogK0nTf+PS9q/vckU5DJYxeN5/fOD8uOIQxuJY/1J5/M8rNvf1wQL2s2FXMQPIQdBBe4BW/9e0/SOGXVMB3V2yK90B2NknPaW0j5Pe/BoubSMxUaVvypbunmmNvRzqkeu1weNy5U8/vONwmDiOM3/3298wDzN//qfvePz4kbIWwtCgRIpmajEYa/vYyTjEKHVvFfvBdlKn9O6P955SV+5Pr7HtyI/fPfD8eCGtmXStcG1IE159Y7j/3S2H6Ug5VDZTWRfFJMXubpdYuqbEBZDwUyu47iK9RBe8Kr1riwokWHah7XS0fPnFHa/uj1i1fPjTmQ9/PJOuFRPA3SjmXhjvA3pT2UJ3ZFziJ5zM+NuZcHKEr15xqzOP54k///nvSdeF4zRhtLNOXgB5tene7u/ps2EY2LQnpf/Sr/D2S57OP/J8OVO3wmY3vA8Ya7AIg1Hk5sj09mtsONIeP/J8/UDURGiKX3vXSYrFI4TaCFVIf//E3Tev2A6RJl3LJXsuTiqZnCvWesZhIrYNbYl5DohkhqHrTy7bhU/LJ9ISCe1EmUeGa2X885mnY8G9eYUvE0mvPY/GdCeO2oYfjqjpwLLBD9ycDoASU+1jlVYpVUlRyZ9A/xgYbk5cny7InDj8dqa6QAmGOGastZ1p5C3qDWa02NcT+a4CBt+OPaTSp+4qktxddbWiNVFqBQm00qUIw+BQbeQCrQkWxdILttoiGwu1CpJhTjNarlxrppUdjFgh18zWCs54ShiYn5X2nInhwrP/xKIrBseoR2KrZLVUqxjnOtMMaE15Xh/Q40i7OXH2jmcqrUUmkwmm8rhceNgeyX9tzokYgzMWJ6A198TGVmhaqbHbKlGwzlNbptbEOB+ww9gzj4xBsJ+FY9Ld2Fg1WG/5+ovf8P31zzysjeHQGEaL9bmL1zgQzD2pPFN9ZAqGyTqUkYteEanczyOv5gOujiwPIxdfeVyuPMqZlJWjCQyucb1eGIcZMZFYc89GUDD3jRXDqXmETGkXJr1h1KG/btNQn8mHjXjIXCalythpm6wcxtc8PH/gspxxthJCV5n3KHtDbVeUxrJWDvbA3fQbju7EJa/EyyOf4qcee+0rTQOXDZBGEFiXCq27bGYzMoTAPFjmyRFMAT4xzsLpkImPE/b9a0wZKUCukLSRUGIRHpZI/LTg1HKcDzy+f8AasMcjkx85jEeOh3uOp42H52f+8u4d7x4vnHMjzCeGw8TtjePtqxPHKZBbwVxWzk+JsqykLZNe9DC/5GuznILn3gX8ceRgGiZb4vdweZfxhR1i1Iv2VGofrTgYRwuTdsHYS0GyMwDQvVvS9g7KXmSI7FyD/dLWxxEkiMbQmqe++GB3XHrPp/mpI2P3712L8u7dA79+/ZbT3cidCQxffMnBDvwThofz++7e0gL0kU3Tzis5TQM5RdZtATq51vvOGGF3ytyf7iGO/OEffuT505V8TZRrQRIcB8P9F56/+XcThxGeo+H+ZuZ7PfeE5peiCoW9GxLtrlur/FejKvjpn1+KuhdLdKpQmvCf/vOZ9O7vqR/PPH94JG4LZlR0Uvw9MAttEJ5j4j/96Q98K98yj6/I+UwsK86OfdavyjjcIDqS0hOrbnh76F0bevfLCGD2z7yBsTuj438AfP3x+CVLTtT6Bz5ePyGmd1OnceAwjJ1/0QImF0y+8unxA4+XZ4xtuLsZrO26kcdC2RJZAPVcHjZunhrTrSG23K321VCBXCu1sh+OM2HaoyRcNx6IqeR65hI/Ia1yd3zNaXjN1pRFHLdm5JsThHvP6K5IS5SyMkhFaUSx5CrgCqIN5x3IAHvRkLX20a7r+quqihRlbiNiXvPpqTLbA6dvlId4JdnuWKm1QzTVNvJYeZArB3vgxBdoc2wSSXEls2Ls7uhre8fVKaVkdA+HqlHxznU0gfYQUWv7g1819kiXBqd8Q7g6nvPC2i4oytQGfAm0XEm68GwL2Q/cDwFXKnF7pvoHZGiE+ch0cIzTjDWBUhspFXLKGK0smnhfnxDvGaWSmNikF0lGC14KITdICyn/lQWxVlxfFGoh1UhMEZXaT1GtUEoFHEtaMQLT4BGtoBVrA9Z1z7qgaKnQCqbmPvoJK4c7+Gb+ku2j4fHjA58oMDT8JMzzRpo/kMfI1jwpGbIbcWGi5ELwlapwXp7QbWXb7qjWERs8p4p4y+A6twNRqmRUArVZrjFjjSL6yKXBZG85hRnVDdsOSAM3XglfJcLrRHl95uN05aE1VDrLpTVlEM+rmzuqvOa6XXi+fiCljYN4bK08kpFmuPff8uXxfyJMip8/csfCkJU361vWZeKaVopJTFMmhIkxVIhKMAFrR0QSzTxSzSNZFcWyZsvaMrdxJETlizBh3dCFwDFySZkqBWQg4/l4vmJT4zCOzAZGeuHT1eyFx+eNj09nrsvKx4eNP37/yI+XK8UL929Hbu8HfOgcgcEq90fP0VpahKcFruMvH8J2vizYGrmfXvHtqwOn0ZOfPe/zRr4+d/1IA2TPbumBFzQLbQRcH4nKS2HywhopL5kh/ee0l8qiG2n6v1eoA9gsaBHWGDCHnpjaN+69DdM6yKyL+7rY1qmgGJYl8ac/v8Oknpb6HBeezhdK7awI4xqx0iN4+ythcAEnELXQqLu+hB0+lhnHmbvDES+WD+dL16BJn4PPN8L9K8eX3zjCAD+8v/Dh+4pW4e6NR45CXRtr2ZHyplNi7cv7qPSuEftb0ZuvP70/wOcKBdAm5Fz5f/6nH/lD+YRZIsE26qjIN4qbgGMPqiQWYmvkjxvz4YE4J+bpHmdGDBHjOq9iXRLaPE09z+eFu9PE4ANbK3vM0k98CGdtF+cawbpfvnttiQu2KEeZWMTSJBO0Mktl9kIylR+ennm8PHGwPQPqFOaeBC2WtFtztSZc6S7EVAS9Zt7//TvefHHPMFmSrdTmabVrvPpuvwdeBsX7gmjFYKllI2+f8C0zHY/M/ogR5bxFylFY5plgM9f1PXmIiK0YKtSIbcpgJ5ovKDsMtEWu+crhIFhfOjxDDSE5ZoR59pTDRtRPhN9X/O1I/cJR55U15b5nKgiuF9A5UlvGqcPbL9nqSKyVZhzB3aA1kNNKqf3PKRWk34tWHU3rnlHk6JSC/lza2hgcjA5UC6jhGAOHR4MU21OCVZmqw1ePQXhUuOgT26DU6cRhDLgnw+vhHnM3c/jinrv7Ww7z3D+vLXO5nLmeH4jLM0kTzRuOIaCmIlS8c1QstfXOyk0deLUGYn76WffUz++cSEeTYxqtJWxQ/BhQqT16Pa2U5oipMYaJw2HAe4/dCbKlKCklUoq01nDGUVVZk1LzRvLwav6C9eAI08C5BdZ6JeXIw6fI43rFTR1ctlZY5IqYRHAVI8rztRFtYAoDHDNtsQQGWPtiXIqSsVxbZjYXxBqcCZzmWyb/hpSvxO3C06pct0gQx1FeEWTGnxI6fuR8/57naeNqDc50e6C1nXMw0PUka43k2kVSmi3TYLAZPiyVebjhmzf/BrGw2R9p8o7b0DiYgbENPFnHK3fCDY3n9NzzOeSCcw2DJdYjS+yzXGuVMAqTF56L5ZM29GHi19c3xCWjsrEW4Xnd2ErBWI8zSvADN3e3mDVxd7whxcK6dctda0JMGz+8e8eff/xAabBumZgyKa/YYBhPM+PJ4WkEC4dx4DDfMbiBlCqflsan+stfjF/djRyN5WYacbZSnOUvH698+OHpMwzppShwu+rSAszA0NA9sbTtIxtt/bT9mXr/GRfSCxOVFxIj2AD+XqhJyNUw2om2E5lFFfsTQan/eTqy3Uv/RgLk0vjzuweeLxth9PjRgzRkEhywtgzG4gfLtkZo4Lzlulxp0pinsFuce3fgEDyvbu7R1ng6n6lYXr2auRsFYy1h2p/Tp8zjfyk8PfZxyN3bgeJ7OvEgDUm7GLGXWV0cvIuBxXUGCfRxSu+YyOfa5KVgEelFjQjEWmhWsKPhmnq7yU4wHntR9Or1HWE68Z/+9D1P55WP7zb0tUf0yuCVVCPLJfLp0xNPD2e2bWXwhlbg+Xnh7sbjHOT6kxDWCIhRWuu6AGd/+dk6f/in/8h4TRzPGV9HWqlQVrxExDU28ZzXK5ErbrzhdnyNmTw1J3Lsh0Y7GnSQPtrbQzW3LfHwnz9w+o1l+I0l+EqxAfzQtXSq2AYqDm2Z0TVOXrnGC+tWCGXizfAF82GAoKwlUUzmU1n446cz397+iloPPC6ZcVZKXagt4d2Bgz9QpZHriuJZs6GW3uIcfGX0HsMIxiPq0NFhvKN4xXzrWOvCJivxsrDEhAo93bdp76RWQ7AHbsIdqXiqVdLL0KMYah2xDARfgUgsC2u+0lrZC/XevWgtg1aCs4RqORTPfRAO943FZR6ujfgpM73buG2eu/GOrJmaCs0o9Thymu645sg1fuTJPKOHE4GR27s3mK9uCXc3yDRQvOvckyGSW2aNwuPlirrKNN+gYUJMwOauQTPiwQXEKgfn+FZeY8tfuXNSpGCsodKwg2UcB9xgWbeVmBLX65UUldYsDMI6DDhnUIG8beTcSClSa8KKgmQuCmgFr5RNiAZkC4yHI9vhmegb4+jx4lnTAslhhyMxL1TZeiiRsR1KVS2bKOuQGUxgcJb70y1PaeFdOXNZQUZPq4ZL61TV02g5jQ7vlK2sjEPAmyOj8RzaxpTucS7Q3EIazjwPj3xsldRmgitUNowOnTEiC1u+sJREVcFaxQbLWiJkwyw3fHn8Wxih2UdKekDylTVBTkqshRIiYdzAbIRh6VyJJqjv9jNXD9QFrrHDoOyoGKeMAYZ8Yr6+QZ89MSeqGnL1SDKMBIIJkEG1cZoOqFimeearr77gcrlymCeGEHAO3ry6I5fKh4cnzrUQRsOtCww3gftXBw63E2MpTAh385G3b15ze39HrI3x04X0/pfvTviff33PjZx4ekz85+8+8PGydK7ACfyTpS0NjFJVuqtDgADmAOp716RVetdk/55CDx1F9xHG3i0R89Pfu3VQwQhJlGkM4MxPdYzp1Gb2n9noX+ew+0be5/WqSiyZpwVCLbicgIpKhAnWLTENw+fXZN3ItkXUdJ2J2R1gIoY5BF4fb6Fanq4XjFhOo+GL+xP3x1c8nN/zp+8/8vFjoWyKt555qNzcH7n96obH+EQrO1RLtAPj9lFwq9oX0N25ZGV3MenLO7Zzp/Zi5OUS6fjtThRtuNH3kdaWqfRws/v7E7//3d9wXRUj36PaOD8ueBzLQ6KUj8RdHBhj1zcgYO3Q5V8x8fR85nA4IrLP5aR/BoruGrI9Z+kXfrXnR9xzJWyF0CxUj5pCNkocBBk8d+MtQwOvjRovtGyxseET2GKo6iAodoDQQhd8loJZle1/X5kPN8hd1/0Zk1FbKU6wrVKr0rZCPTem1wPVP5MZ8cuJ6fHA8XVAXkewG4NtYDZOr040J7R1YnCedX3PlitjOODDLYOzqC542ciSyDoiMnTyr2v9XqSRKeSW2CRRhooOG3awLNdGaomtbD0WVDteP7dKaQ3LwCm8wjCy1AJk6KlOHbMvhhgLUhRrPMKBKQTQTl7PJXNwpieHp8rNMCJnwX3X0DVzeb1w+Va4ZIPZHNsCocCRA8VVrnVhNSDGcxomGAKPiyeuF0pT7OSxpxmmCbUjRR2t7Hk8rR+WmkIuiUYm6NRtyHZ/znLuuWHOY0fDMFvmYpnczyOI/+ziJNVG8J7WCs46jHMUZQ+m8wzjASOJmhRLpZVEyV0dnMtKSo1SOrjG251TQI9p9s3hthGTC+OmDO2AMVCnQnKPjFNjnh05T1hzRCvk3HpGTm5sWXGiBAumOoKbmLzBGMvbdMfHj1dK7jfEbCeM9zixODeDNVzSJ3K7oGpwNEoTSgsUwJ4S2WdWZ1gJ5JooKLGeEZOxksGvtJZYS2VNHY08OkcIju2SERl4ffoSH2bO+okgz4ht5GK4qOAtVNs6WEgEpNA0IzgO45Hc1p4z1GJv3avgrSFYhebx+YT7eAfvZqI2Bi+doVEFKTvcynSsem6tA3dsH0mdTjdMw4R1FucD3lreiOzjg8Iar2jwHDkQDp7DaeRwmghbwS6FtFSWS+Z0Yxj8iNUrmpf/1rXtX90V85XvnhJ//4cn3j1fWWthnBx3rwZuzcTDP26US0Fs36iagL81cFCqbTsbpX+vF2NapzLq531XOmD2c5ECP2lYgH7KvAlU02mU8gJBFGi0PtnZZyCNXbBOZ1f2MVJ/JpuAqQUjFRsKZmeFGLMnXnvfRzeasYaewdEtKgx+5PXNa0ILXNdEkIHDSRCbSesjT9eBH3+IfP+nTmi9vw/89ld3rEtlur1DDoHLD1fiknoXRvrsvtUeGKq6u/WcdAqldJpr3YW+n0Un8s+GOmbXqLRdg9IqTWwfIeR9zGW7QPnx6cyPH54pNQJC2gqfPjzvYuKuX2naMFY43nuOp4Hj4YbnDyvrU2bbEt4XrA9YY3qrfS+kXtKfP3/Qv+DrrY6EmlhrphZhbDMmjGxWqHZgdiMHo8h6pqTIWgquWHwRpBg0GXSVHhOiDjA7Ut3gK7T3je3vN/yvA/ZXHpl7WF1LdCprEGw6oH9MlMsdp99BmB3WToyPQhCD4rBimELg9c09SGBZrxQrzObIkG9xJuBD2O+3DakZ0dzJsWIR42hI7+4JlNIoTSmqe46QUmzFK2AqJVdiTuTSoAoqref/MHLjT/g2saFk6cwRaLttOO9W4X5KEbEgtruIWu9CGqmIWKwrhKDMtx6bFbWZUjOXVHi6NGx4RbUnliFDjkjuAaRJIVWlpIotA6fxDvGV5cnStoIbLSr7w6RCU0Ot/fWnkiklovQxWokrOQ0UfyDrSEqZtEZccLh5xriAuZ0J04wMNz/rnvpvoGUZxHtaLlRgy0ouhRgFYw9M88gYEjUmUMFZoZbIGhNLbJTS45eDF7wxiBWscYQQ8D6gKogdGd0NxhtOnPDN8mP8BzZ5j5+ErtN+4DhZ1Iz9JnaRKo5gPHMYGH3H56tuMGTubkbunmeeSxfOGpt2fLiltUapW5/naaHQWEtkawrtjkDGzlfKHCnNgI4E8VjjiE2wztNK4Wk5dzcEFn0RLZqO8z+OdwT3irvjl0Q2LukDk6wMojh3AnFkSahGUEOqGVM3aq04sQQqYjJKoeiFcTgS7EhwwigZXQPT+Rvm8xvWbaPODjOOtKgdTb63+GsueB9wxnbB1L5BOuewxlFrd1sEaziMAbk70UrCusbaElkrUSrFe5wVBuvQBsuSiPGB0izjfODj+weW6/m/aWH713j9+OmZdz9Wvn9aSaVbRpMW9Oh5+8WBN28m/vT3Zx7e9SRRgmBPXSsiAubl8P/PRzht77BIl3qY7kt/aRDwMuJhN6a4g+COhkbDaC8+pUtNeneEnzbsnmOiuNpFpi+vAUBbxQXPPI5gI2uJjN5jbb9/vA1sZUPsnsWDwVph9gP3pzuCDtRNofRRVYwFf+gnyU/PV96dNy6pUgscEaa7I8c3FjPPFCMcloktbTgEV3sxUWt/Y/qMvhclBsVaS5X62XK9vx27aHj/vYT/6ndXbZRWsOIwxoD2e/3jp4WHp7+wpo0qihihFWWNnaFhjcW9JHAfA6e7gXl2jMPMFGZ+qA8sz4ktZgbpEEalFyjQPzd5sVf9wq/j1UC1XOjiex88zI42OcR7vHFI3Sitp81rlh6vYE1/PmqP0mhFaEYpAiY3jDZEPJIs9XvwCnaqOAs1G8zVErynToKEE3rKtMeJcQscbitMBTcpzivRdxSDkcYcPEUbxVd0tpStcPD3OPOKrBs5PlNtwpuu8+gWsQUjDdVAqQrNotXRmu/8r94fobReOFQWthzJJUIztCJUbTjjOYQjoz+izZDrRqV0XZjpx4fWCrX0IFEjtu8vjf3n9MOjdYHaoFLQUdGxMnwBVpTlE6RqEHNktLeoc6zDRtsibSkYtT1TSholFowa5PVEMIE2zMhRIASK9i6IMWsn8oqQWyaXjRo3TC147bDBsqwUFqoLsFbqcoViKVZJvrJ4Sx0mdBh+1j318wWxYSCMQ5+Vt0ZMiW2rlALgMcbjQ8BLpJaEMYZSMtdl4xobqoZh8D3ZcVf8NePBTjTjemCWdPpSWgotzjj7FuOf2E4XrvXKYDbUrNyf3jLYE2UxNNsors+SmykkKqksGElYbVh3xxfzDe2aqC6TTMQ0Ry2CxhXFEZzHaNkVzgVnTCfn1cy1XqjhSrULUvtCbYynVcHaTG6F66YY0zBOdx1iJ1+O5sD94WuGcIMMBi9XxhQptRMCw3CLGEcu3ZKtexaD0Z4UeTAGUyPGFqxpCIXTNOClW6WleOT6BafLrxjKiWg+ot4gfuz5DalgdodVKZVNC2oCtYL3nmB9V443YYsJWzLOjMzjgDuOwB3H08BWMktKPKSVB5vQVDG1Q+C2VljOC4UP+PnM94/vWHX9/2V9+1d1vX+I/PCcydIdOGI7e6SYin8Fv/nbI3dfwD/+R8sP/7SSfKX67kIx+0aqu9jzJfFTlc/jG+M+J8b/VxvtyyVGcQeww/7NUKTRF1Lgcyz5PgKRvUg1gGn9ayyyO4KUIThub2YUw3Z+wnkDWnHiybW7F6w1NKlYA8dp4CYcmezEes2U1EMu11o4nyNDq9gAmynUofbCLCoRIcvEfHeiuorTxnwYWJYBWROt2Z7AquVzh+jFYq2qmNaLCLuPuHphonue1T8b7fyzpgqi3Y4pihVBjZBy4+Fxo7EyHC26g9/EvNQSOzH54DndD8yngfno8f9v8v7sx7IsS+/EfmtPZ7j32uTuER5TZWZVFkeRbHWDAvTAB+lFT/qbJQhoiIIaarLYrJFVOURGhM9mdocz7GFtPexjHklAYjsbRBEZPEA4EIC7mV27++699lrf9/uc0DnD+NmOtBTifE9RZZ5m+qFvF5an90PkkxNY/9Cf+rA2+60Uciice0FHyME0AXNKSG4milpa4nPwFsOW15QbSTt1UOym1TLQA9UIFoeslvquIoeM7Qw1OKx0bSQxCise/0vL/Dul73qcn4myglNWk1jUktQQtVJU0BqxphUutVSc7fDVo7GyRoM6IEBzyyhGBXETopFSA+I6hKY5zLVS2BDzFQqFtZyZl4VcFEePwRJsR3A7vN1TKqi03B3Rhp2vWJzY5oat7SbReC6NuizyhO4ozcJOocpCITElwQVDeCYUAXPacXB7fAmkFClmQSUR59SE8EYoUkh2oWqidgupXyl9xXcDJQvrspDjjF8muv0OQmhjrLSQ1xVZC30JRA2YGWpeMXbGr4Is2jL4wswqmXuJBHYY7T9pTX1yceJCwPpAMIaUUkMJayTlsiWrtpuDcT1aMlkrBYja/qsVTBHWZMilNrtxjnS+grGsayLnzHyZOU0TU16ZZCFdjZiffYF88QE7nNCasexw7BtMTVeMLMzlwilmqlS8BW8UXzyjLNwerniMZx4orLU0mmpVQlXEKtYIWSPOgDFK74Vd9pASl9M9uXtEhgTWQd21xagTpaxoXbHVUbSQY2qLPhqsBML+msHfIS4x1/d4f+bWO05LZc3aGDFlJZVEqQbFkdTgjcOWyuA9okqqDmuaenxnDgTbUVZLXQbC6WeE6RklZUQ8pVRKaSKFmFKrsIOjGphiK4KoMIw93nvmeaFo5Xi6ICjeXHMYPM479uOOru9ZS8LOC+timGolxwUpFWssIQTWUrisCyoXpu6MGz7Nx/6H/BwvyprYsqNaN8IEIWrhMc7YK88//peB5587/s3/0/D9+wuLlI/Ii8p2C9oKk49unK1jYsyPnQB96pZsh7TQDlHfG6TFuzSdw6ZheZoDtZvWlgpeW4elSEU2saHIj5pbrdoEnLZlYGgtmOowONb4iLUWZw3GCmMfOPQ7fO24nFcuc4O95+3WnBPkszAcDM5DNypdrpiuIF6YiuFgdogkhBkfLNc3u48kmNbFa4AteLIOty5KSgVrDN40yFyhkvmPfz8fx2L8OBaruaJbRhEibeav4HqDC5ZMbu+hlzb61zZCa5Rbz9D71kkRi7MWHxzPn1/z4c2ZtNJEw2IYRos1Ft16VyKC+W8glThPEaziO8EMcOlWVrMiJdOp4krBSKJWIcWMXwVvOoxW0hQpq1K8IfWwuAKl0LkO7+zvFe2CUYPcG+rOIs8dsnNwMDgnRE10XxpcPzMeCiUslLiStRBzZtbAXCxrVpTG7VEFoxY/DOSSyUURsTjZbYnymUKjnVszYOSRoiuq0ro3pemhMpVsKl5sc4rlzJIaJbwUASn0ZuDQ3SCmJ1Uh1YgxFeeEHIW1KKkkQvCktDSAnTQnXwvmU6xoc+bUTBvWJiwJzSvTpQIBuwTy5PDaE5aOOkfMsmByIRRDKELdtCNIRm1mni7EkyFKRqonIMS1cLycSaXpzw7xDrsbSbaSdUZjRhJ09BzcNRKhu1Q6G+nVM6gjm9YVk6rM68K5XHDp04IwP10Qq1sypEh7Y7HbrlHQktEsiOtwxpNqoObCWmBJliUq67ryWGIDsmUl5tJU2tW2qrAkqmbWGDlOF07TzJoK4RD46vQ5f7T/kuz/ism9Z10qmlecBIx5DrVV3VELpaaG7raGoR/xHrxaOrsjLwsXzYhJGJMZ1OCdoeqK1sxgDRiYSyZXT2cqWhZSXjHaOiNGEpaBykLOC1oyQ+hZUmUpFcmGMe6488955l40nLCZkHrmPN3Td44QdlQyJWdynck1ojhKhqVMqEuMxrZslDqgm1uh12v2do9LPeX8DHt8gT4cWC6ZpGv7sE2JIgm/WRnnZaHSMww9KkLOtA+4dY0VgBJT5Hg+EpcVNNO5JpbVjwbUivEOitAVw+A6wmajiwaKRk7TBXUr/ReOw834v2mD+0N6UjYIpQk4ndlGJUqhMqfEu9NMkMoXvwyMNwf+zZ/Br769sM5lK0qkuYulOVDqZhN2bitKzI+HrNm2oacxUBNlNouqsa1z00YZQt2oZE/W5Kfugqkbj8NUrJHf68i0vzNNK7VG/BiptlCojG5gXi8Y05ABnbeNWzGMlATvzuetiGjtXmMabsCYRotOa/v6xni6rv3ghcwPb1/hvOP6tgGtRITr60Mb3dgLqm3TX2r6CJITtpENIFWxCMY2xnMjMbcXqroVdtss7KmRotpCFo03rTNlYLe3jIeBYtsoyiCIa8FkmusWlBgZho6b20DvXaOCisdKT/CG0JuGURCIMWKM4DuP2HYztSKI+ekXJ9EkbKl4AsEUFhI2V0xM1FqINSKuXURz1dYNDkKnhmwqxSmxK8SuMJt2ERRnyMFSc5vVdaNnNw5YsaS3GzL5m5U5FLriEAw+WIbPFhwTGIeobxfK4j5mX9VicNIRVbHi6bsRRVnrQm7zPBw9rkqDUdaMYlDpMKVRzLUUSIUSE5toi0ojj5dUyGYmp7YuUyqIyYQQqPi2ViU304QTUmyfbYMjAXOM23moWCcYqdRSUC0/rqnaRv3UBaFgY8ZnS82etPTobJBUWS8TcszYXAm9JWTXWGW2aWWMESQo2c8cdWJdVnzcNS7WmnmY7sk5UuaRLgl+jeQe1LSOjxGD8wMudbhpwZ+VPlS6UBFrN/F9YyuxKpd0ocZP42B98qdmnS5M09QqSS2QE1VjK05qYZkTD+uEtztSTKxp5XicWZZMiq0iVW23CGsaM7MWaZVxmluctLdcDyPP9geOlzP3j2cuU+Txzxce63O+/lf/gnn417xL3zOGgaHb0/tftnmySYx9s1YaXfDOYI0yL0c0XXM3fM7r00TOCfGFzgk1V+Ylo552w9GK9WCrodaRagx2V8g1oaXgskFqYc0zVSu5JEoFZ5VQWv6I1MDL8QVf33xFfxW4T49M88qiEwuVuSQMLf5dZEcWT8RRagubstVgsocAD3FFysBlARsHurhj6EZ29iVhec46GU7zhWVeKCx0QxNVkgudC/S9ZYmJlJXROrrOwZIxatoICTAh4KvQd4F1njidTnzoAzdUfOib4EosFoMD9tazCzu6GohrZZkfuJwmXt+/o54SL8drXPjpd04M0goEL23UaZ4iF2CJlR/eZI4/KN98lXj53PB/+D/2DNfC3/7VheMxbwyUrTOwHZbWteIEfrTE1u2vCYKp9aPO4vc7Lb6DWmRrLxdK1h/HRDz95dahUaOI6JZaLO07aUVLK+6zLhSTsXag0OIXrBUQZeh7xq4jpcRlTsQC4DHOUhWStq6GFUuphZJaAWGNEBygljUlHh4fmOeJl58/5+52D1j6vsfY2rRaxrX2+Knxc8tGvxTZiNxsmp0K3rZ9RGNpxRhP9VttP/c2DqraXFMqlRBgNwS+fnnD/nDgV2/eYMUSXAvBLNp+J7VWSoTjw0I/Tnz1s5ZY3tkdru44pTOJiWpawSMIa4ysaaUfAv0w4F1AfxQN/WSft7sjQwyM3YAJht5aurW5RlJJzKwUWzDDCOOWlncobV/aB07HRFoWVBZMaCTyIIY6A8WCCm4M9IeRUiv5fEHXiPFCufasweAMpBJxEik1U0rHGgsJQ94+Q8E7vLNQ/baXJyBTecR6SLmwCoh2dHWkt1+RUoAameYF1w9Y+xQ5EXHSby66SMwXpAZSnVnKiZwyVRVTHQd3jTc9SXOLZ9mIzVJbEZtUUQymOtaYqdWyrCvGNIinFbNNb5toPnHGkjClsMuG8dERck+qhktcWadMnjNuEfqLxy4gfUCldRulA7xQHeROyIOgfdO8eDw1F2K+kOpj01DFJhA2zjXrNolqShs9pYqZQB8STAa9qiSfcdXi1WBXgy6CmSxR4NxdPmlNfXJxMq0XcolM5zNxiTjTUhljVB5PKw8PC/MZpFq0JDofENtaoaNY8AaVlmgsCFUbxMlLxXYj7QbVXAK+81z3nmfjjtPpwul05O2/f4VLd/zs//Kv+I38v4j5zFrfbTCahHUzXUgcggX1lJqYl4XRGforpe8G7k43nB4mxMA4SEv+DcquqwxeWGalzIKWHulHziXxKsJFK9bCLkDvFV0umGyxnTIXaSdLMoyMXF9/zvPDZ4wHMN17/DzRzYKUG9bVMc+P7GrFaqDrRsRGYpnJWpvQiUSpFhXH+7hynk+kJXNHx5huMNNzkB1SFImKc4L3YMQyjAHrt2wFgd3Ot4OqWqxWAhUxhbguVGebH914/OD5/AvPzfUBitJ1AYzH+A5jHTVmBvXc+h1JPbvQ4W1H8pW4FJ7tH1mnQFwq9ncVpp8+vt4GwSk0QFMrIKyzWG833Ybjd/924YebzL/4V5bPPq/8i38W2Pd7/v3/MvH+PpG2C8RTp+RjDAT8x//VpieRCuYp/ViFUA2qkGMrLqj6MX9Gi24VTvsaTVpXoQjqKmpAKR+TxXGZ6iJqMiIWI0JKZ5xrw5bDOLLrOqZlIlFQK2332Ai0bLbfWton3GAopVAxiFi8bzdAxJJy6wO9fvuGaTrx2cvnaAYxln7f411PcBGRTKqtI5gTUH/MD6lVmgOigjMGrYaYCr83CMKYTa9SwLgtZdZWbq97/uRnX/IPf/HH/PXf/R1LipTSDqZSGpa8aN0sy8I8wf27hZubQvd8B9VTS+EyH1G7gA1U+zS+aa97XVtfMuy7zYXx035+2D/QZcftcEN36LGup57XJqaMqQlLSWjO0PuW7XJduLruubIHzh8K8sMjNfX42xsON7dogvO395xfX+hV6GxGzExZE3lesVaRfSBMAbuv2CzEyYBNOCaKXJijspYBNsq3GKGIQQhILZS1sapwlyZmzYU1FUqKrLXSl44r+xlpORPTG4rtKOoa76arqFkQEqIJUoUSULmQmJjWzDplDu6Gq3BDRkn5QsnaOqHSupjyJHbaZpLeCNaMpCUS1wuaK95ajBWMq/gKOU/Ukrg9B/a/7vGPFjNC3SU8SpwSac0sYhCvMEG9VPzQgVW4Arl1pM6QsSjNqGIrWNOhKOQFUwveGLzS5iXGIlWo54UYJ7IvjFfX+METtcXWuOb337KVLJ122NnSL4Zr7yn/pTsnj1NlOs08vj8xnVfWJZPX3DajrRviradzDustXQiI63HObfHVBrG+zdpLblAnKxgpOFGMpc21xYO2zJHD0PH53Z5lvuLh/sTlNw+c/m+B//3/9f/Mt+5/5NXyPVP527bxGiEuMBhwAm4wTZxGYuY1IoUvn90gZuKkR5KLJFUkgXSWlDPOC9e553r+U67cVzz0bxEcok14pAa8MZACD6+U+FxZqRAmbsM1d/03XF0/R+3C7+bfsJxf4YNhv3vGIIHd2nO+VPZrh5E7pjwxpYlEJVXDlBuO+coFDJaUAsH17HfC3fIN9rjHWkcY2+9aBodIIKVETpG1JA77wNA1saOIw1RLSgbnHME6Qm+ZShNVGWPJcQVn6IaePnStXSnS4mBthwkdlpVA5doaYlpxVprt2Fle3AnWCJ8/uwJTceJ/TGX9CT+l1m3U0pwk1pkNVKhYqXTBsszw9ncrs3H80/+h8vOvlV/+sce7K/78LyZ+eL18xL4jW1GxtUaexi0VNvtNc+KUBDGC9ZaSHTm3sWib05RNcmI27HX9mJb3hMVzRppguzZVvzOy6WVKw3RbJfiAkFApOGO5u7mh94Y5zVSbcVJxVbC0ALOSMxXfOjG1dSFrFaRacmrfX8RgndBjgDZ6NQLLUricM0NvqMbQ0rYheM/Q98RlxoVWgDvrUFVKFmJqIl5nXQMUroXTeaVoK9hbIdI2+lo3/YoKN7c9/+Qff81/94//McEe+J/+3b9rAlYxrdtDsxk/uX+rNjHtdE68ezVxe/0F1u5J+cz9+R0SSgupW1zLOhLBGNMYLVo4nh7/m+CcSN8xryvomV22dF1H3SklR0QNXe3wvmPZRuedC3S+w4eRob/CqHIXHTUJ3d0L9lfXxHWlflg4nh+5z4Jm4WLPhGrojMUHixzB/FAIO5gejly9DOSHwHIaybsjUT4w14RsuOGERauhcmy1ew7U6qg206IfdBupGGAh6oTMlcO7js/CDW/0gZNeUO2JsyJmwjuPQ7d4hcJST8x6ZFkMkgPBdkzTheoLKk0jFXOhaMIKeOPBeSorzjpyVqT2eNchEhqS3lScqRSdqVG50muuT4Hwa7DvLNYpuazUBKE6anYImdkksi8Eb5qTLwiLKItrcELjDFUsTrqmnU8FU+GAbc5ZzSwlttRvq4Q+4HyPToU4n3HecnX3GfntTCyKDYoPzbmUa0FjQSfws8epRdTiavikNfXJxcm//n/8OZIVUyGEHmdHHBXnoPOe4FtCoxWB0ja1ilCL4sQh0mwI1llM59E4IyJ47zE10vmPnepm9X2qFK1gbnf8/KsvmOYz7159y+n/Dr/4P/33uN7zuL4hxoVytshsIQu5WIqv2EGIV5X6fKXv3nO920G6Y71fuKSIDEJwlVQKSaFXIby+5nb5CqzF7BRXEyaVps8QJZqCXnnOp8hpbUKokoWvbw98dntL6GCqEacGnw90uYKfyDxSreGrl46bMnI+W5CRUjJjragK53iiSmTsGja7Wz2j/SPMhzvsQ+DgBq4PHZ231LWNcIwNeNOxMHFZznSHnufXV1wNgbQK5wfD6dGwLJC18SJ8L60Vro1j0bgWFuNa2mTdFGilKEYLRoQWFWMYuh3WNUumamUY4KZW+r7f5qOGafnpc07yluGCgeA8Ylqfw5hNz7NZ/qqF+/vMv/s3ynQ2/Mk38M1Xlt3uwF/9jefXv72wLPnj4q+/NwEQ+JgoXFVIEZa5UtUweou1jo+E1PqRkNIcQTRHizUG83t2H7PxQuymrC1SwSjVKGIqpgoOIenM0Dnu+juWs/Kbx2MrxcQwDEIYKp1vrzUZx7LqEw6hzd7hY/dhizbEGIM1js4LMdePv7PHhzP9YAhDGx9hFONrS3xuDvs2ZxfY7fZcLpl4ngidIE1ggg+W3dhzvkw/vlhqK4Ke3D0K495zezPQeeH+8cg5ZaxxVEvLKEktadEY+TGuT5uu5nxaWSe4HXvW/IFqMtaB6StxBlLrGYk0QbEi/xEY7qf8PN//jGxP5PcP6Hkh3zrYe/K+R1HcpPgsjFiEQJcD5jhQZOBygcsxsswGH0Y61yO1nSXDYSTd7NB7mOYV63u6rsWhiAGdlPRdZLzbMYSOm84RV+V3vxpZGDB/fIPZLUx54rIsLHmh2XNXDoOjuhPJdSxLwYlga8E0zydiE76CnCrnv0jcXb3kxReB1b4mWWXRuTlsuor4zJIjaZ05xROln8irZeRANYXHx0dyStiDJdlM0tQ0Vs6waguutNYQTE+pCd9lXAWJlpxadpVU4eBGdrPQvRPCQ8FmwV/10Ctqm+7FrBW/CjW20W3WghVH6Bzl4IiaOdaVy4cJC+yGnv5moO89sRasF0YGrhhIVnmrj8QkTHvB7z2d68lLIM1gfRPrLtORai74zmGDb3tR1nYWi5A6h/cddego5dPOh08uTq7MFX5srAHzFAJoDcEZOm9bdLRvt8d1WtsuJYI6h7UOIw3e4ELAe0ucWgHjnWnqYFuxpuAM+M7hvd3Czgwh9BjnKDLy+Ys9b3/3G/L/GPkH//Kfoc/+ivu3b7j/fsf83YGyQo7NBz7cQPfP3+F+9gbfz+y6CV0H9h8GTnHiUhNrhCXBTQ9mtZRf7yl9wNwU0nrC2sQQDFEhl8LjoqyxMBnIGQY78o39jMEMTPlbzrowlQkls7MDAwNSH7mshdD3mKQc8wOzazwJtUJJBbJy7QwSCjG9Jy2Ga/uSq9MvWN5lgnEMux7XCbVkShJyblkepjp604PJpCUyz2dur6/oBg8ixAzTWlg2kq48iSO04q2j6wI+9ICguVByaSTDuAm/RIlpRQDfD/jQUxViXii1YnzAGddEyLueg/3sP3dv+4N7UmqpoM55gu+oRsHkpueooMWyLs2dglSmU+Uv/33hclz5h78UPv/Mcbjy3N0d+Mu/nHg4x83hsT2bpqIVHlthMlXS2kZ2JHC2iTdrlY8FwFP3oolpt6wds1ltjcF5w77rSEWZSmbVgtmEecY4qErMK7vg+er2K37zl0fevzszlULe3A1OKuMgXN8F9rcV/IqzhsHtOU8LFUWkxVZUI78XPti6Qk2X8lRMGbQKp1NkbxyhM61IcoIPHmc9RvLmXFKctXTe4dyCtbJ1X237PigxuaYrMz/qdUp5Eslu3BQJxGz53etXTGkBGh6h1Wum/S63EbNSN91LYT4vfP/b7/GS+e7136BkpDaOh+uaQwNaAZjzpu0B/ltIJZa3F/apUh8ckgWPJYtn6YXYS2NJnRLd2TBYjz1W1vnMh6Hw3lbuL2cuqdJf3+I1YK8yVZRQLUPfs+qCl8Cuv6LvHFYKgiIFOArxd8rN/7DH7ireWYafC6w9cR+IRojFgg6YGsk6oRIppWDMyrzOLLFjH3qsRHqzol6xFfRDQf/a8cx/RffMEG88z+fPOF0u1FU5mIHxYhgtODXEqbQOTyhYMZhRmNOF6e1KPEfsC4WrSjGJWhMmGUoFbMUSwN4BEXGGQTzOOgIjbg3IfcKdMkO01JipteBGhzGWahTbAnYg0gSwU91ShwuqE8udkPuO5D1JPCk6igpDGLF2QOOMngvrCo81YWuh9FDFENeMcZHeZMQp604pAm5QJnlPGk7wWcWKxXSCE9DVojKi3YFFLDGtLPXEw3r/SWvqk4uTZ/sd4+CpmlrL1DQxq7cGa9mipmWr/jwptvGAH7rNpghiPa7rG33UOYbgUU103mONEpyjD5ZuCPjQENltUxKM73BeWHc79ruRD+++o/7Nwk5fEj9AWQcO4QYbWupjlcr4eWR99opZCrVmzvF71HzBLniG6JlTogp01jAYKN9a0ruO+DIz9ILKsrWnm0NA1VCqoDj6oTKcBr7kl3yV7yBNPDy+4lhOVDszeIO3YG3ClowtQl0zUx7QUJjLSqkrxrQWvGSP1R2aEpJW7vhHdB/ukFmxtRAlMrHSc4WjwXdiyWisqFaCHVrRtz5wOSamq5nQL0wFzsUyFSHVxhywPrQbaqp0xm/0wQZFqo080G7itZJTbDa+raCpYpoK24BGYV2VeU3MKTOnyG5NPL+5+8/b2f4An8biEFQUFSV0obWLJWKMxapHU0FCGydnraQJfvXrzLIu/Gn0fPlZ4OffOLzs+atfXXjzbmkZO0+FCYAaUoR1VmIEyjb2SS135kmn0gS0skG/NtWFNJhV04TAzhp+sb/i2eGaZY28X2Zer8cGj6ulsRwMBGf5+sVLQjowpMg3hx2//vCWlGNT3qvhssJyynx4Xzg8c+xuDUMnjCEwx/npbrJdUlokoYhQpd2InW2b5lNrYVkyLrQ4CucMJghoz/Vw4JIeGntIKmucKGrpe3C+jRSNCCIGZwww8ng8NXu3NCGsbh0uMa0AW1bl2zdv+dsffoua5jjAAnmj6pam7QFBpLWumo4k8uHtO2I8kuwEodKHHq0O7SppypTcxtOQm1ZIBK3pv8IK/ft9PjsZXDLkOKCpUt817LwZpYlka0dcJ8rjiiTF5kq+JM4mkq2yakFUMMcz9Wypu4Fqm85hmB2+Bjpx2KKQM2Jbp8Fbh62W/K4S30I9ZKLJ6DcBFDILEhUvHd4f2GOpZJZ8pOpMip4Y74mLMmeDcwZb7WbLV/YHh/sl2PkB882AQdmtz3jZf4kPlrEY+irYopS0EvOFc3rGcXnkWCeWHHgIJ+bvPjSu0SEh/TbCvSgmGeTaI53FVEsnjrGM9JPDLRaOgjkm7FJwCaQUZCfYfSBLQccmOt8ajmiRLUqhILkQCgTxXGzi7CNrmJmDYXUC+x3ejkDPPCfyh4l0mkkK837F7irRL0RNRJ0pi6Fbjoi1JLeQ+xkzQNo7whBgusGslppA1gylIs5R9gNrVT68e83pfP8j/fF/5fnk4uT57cA4OLQ4rDQbsWxWSEPdihOD7xzaG9YlUqmE3rcNAoPrdojrievcipO+wxiLqc0WNvSBvnf0XUfoQuMwqGKcw/ddY5h0Dh32uC6wLo+s3w50cWAUWIxSYmxqbFcxh0waZuYFdhbWmKCujPuRmxrJMUGfuPIVvxh4fcXePseNnsWdmOQB3xu8eExqh1DMQkwFl/Z8Xv6Yu8fPuLrb0e0NvnuGXQPL5Ui+X7jUgtxF1KbGNEkWbxI1NeFcMCAsGAJZe+alImbgyvwSff+CdIIwFOzuRDVHSrBEWzHqKcZSjFCNbUlwNSMIGh3nx4X348xwBVO0TOpZxLJIE2+ONhCw1JTbDXfrmChsh2M73az8GF9vNweFiKNsNtVc4PG08PbdPZdlYllXak08f3b7v2mD+0N6tFSwTWvigm18kNJ8IqhQVohLoRvawdiorbCule9fJZaknE6Vrz+Dz18YXAhcXXl+/ZsLcyxtPpwgrpW0VnJuYwk254muSk4F682PWTOmjVTMBvmoImzO4iZDV6UvlevQbok+BObHhUcbG7HZGryHL56/4Hp3y/tvJ4hNX1O0UWaf3C9ZIabKnGCZlWVVwpcd17sDmgt5++w2IUbdit1NDC9bcJ9YnkYwJcM8J0Joo1yhhaQdxium+yPGttl7Q3qDsQZnm+ZENtyaNW2kKzQ0v7Vt7GM2m7cxBmsdj+eJt4+PHOeJok+zNIMY20SWWxqjartUbb9MalYux4nj+ZFuD4dnntrBftgRVIiXlq0l2g7O5sgoiFv/vpfn3/vzvF6zxJmCpYqSVkWS0i+eYTcwBMclGR7XjC+KUbZwu9o4Stay73qG2tGdFHNpHS1E6NUgtmvvc05UYxFrMaaNkaVa6gUuf5UYxp71s5XsbXOZlIWuq4h1VDVYmu8+TYFpUYx+xtXwgsG0rmUuD6R8YY0ZSxuF8GyGPHHtnvGPun/A3c0/4io8w+FYHx9YP7wjn05Umyh+ZY1npuXEtN5zPF94rcr68MCb6YgJpXG4gsfZgb47MFxdE/oDkoXdyePeKBKbqybNmXwuuFUQ9UitZKOEvaEOlrVTiq7YGsE2wCMC1TQ9iRUBC/PekW8Nk6/MEplyhOCwVkhJ0ctMPU7IkshVsS8s9sqSqlJWJZdCXC9cLo84H9AayZooOdHRMVw9o7/d0SVPPS3k92fqulCtxXpHL4bBdJRquLl69klr6pOLk8FDZ9vN2Vrfbge1UrVQa6F9uAXvWpvJQMsQsGb7RfX43a4FBS2RofMM+77l8MQFI0IXHF3XNdKs9Zs1s4kv2ZD3xjTBpQ+BFG9Z1uftF1bPuHpivhyJcWZZIv3RkI8HYs4ctCfIiDNXFD+i3pGTckkP1FWxU8eQbhntgPSV2b4nyhkvGVCKQmrRBEjsee5f8sw+57a7xh9WukPkulfqNLCsB1SEaidq/cA0P5BswrsGLIlJ2Lldm79WS44O4jX7eovUij50zA8VUwxmmLDdEeyFYgxT6cm5x9aAdSNOBmJJpDg1eFVu8/xXrxf6Sai2Z0mGuVSOKRJNpBZHZ0asdxQRcpXm/qFBu4rmZnMTA2JbR0Va0JtWRdNMLpn7hxO/+e4Vv/32B3JOXO87jBQenvDdP+VHaEJSJzz5a4xtN3hTW1cprUpXmgW4GWdaEREXePe2kNfI5Vj5+gvHs2eG2+eecdjzl39z4XIupLmJX8uGdODJRqsVXStlVcSZ9t03fYcY2XQPT/qPVqRUgVjhcVnghzeoEaKDtTSuiQh4b7na94z9QFY4niZOa2TKmXVzyMgTzba0jh0qRK08vlmx9YT7yjdbbhcQE6kpUso2wqm6dYVaF042eMlH2FxW4lpapIJtnR9rW5ZJrtuBJq5h4mu78CBmE+C2gtp7QwieNa2tiDG1uR9sG2eWCveniTlOmyixUksmx01DVNt7qPIRo4YgdFYa22ct5ASHTrjVlgA9OIsbHefOMU9PRFCDSMH6gv/pY3+wSShrxkjAeYPTQgfYaumSp6sOWwfEtwtRFShSmPKCKdA7z94N9LZrwMCsTZQsrQg1XXODme3SZNviaUyrom0dvs2Uf5+wc0e9LZRuwdiCM0K1jUFkTUWMErwwG4eIp+sDRU9YcfjyDMuOTgSpHi2FuZzZacd1+orP3M95vvuG3f4Z1jnK+IJ195z53RuW+w/ourIP1xzCxDIfOMwP7NeO4cbzW3nDeTljoqXb7/DdiA97XL8D31ONYjyISe1z3Qt5XMh9Ih0L+ZiQZVuRV8DOsGhCFqWjELpGXzdOwSpqWrZT8ULaO+JowTvQgqatc4/Ba8BJRlxlJVGAbggM11eQOrIKi0nMcWW+nBi6EeeUmit5zaxrJgWhDzvU9GjqKWOAvCDVNheeGg410MvIre4+aU19cnHSOYOjgt34DraJ9IpuoC7bADjOGmpVQrCkLISw4dKHHaEfWZYLaOMAeN/824rSe0PXW0JwOGMx1jYxnzXNzgrt9m49VVrrOQ8Vn/f44YZwOeHDO5wTzlPTXvT+hu7c0emZ67ynd3uc66nq2Wtizxc8prfoeaKbIHDTNnkbUXvE20hwiVyEGCvz2eAZuOuf8/nhiut9xa4PrDevMX5FZcUZx9jdIjQvf+lfoGlg4i1oRKS1iC+53fd62yPlBpP2lCTgFtKcWGZDNR7yI6FMrRVeHalkWBe6lOiq4K1FfWZZG3/GOo/TkTgvTR/gPVUtJSeWFFkNzOtM7TrEOVKplJyb/dg0h0eV+nFUYKUdflmV4+NMTEquyrwsvHl/z9/+5jtevX7PrvPcHDqu9jt246fhif+gH7OB0AzU2rgEjewqP85+pQmdaxHE6kdbb62tyP3wobCulXlVflEd3/yR8Kd/Yqk68jd/vfDmMaFtwrZNP1pxQGpW4XSuMPye2lXZnFIKph2yT0z3Wg1LrbxbV5bTSqlK3QcWl8hUAm0tBRcotTDnlcu6ch8XFs1kLRsKfwsNRD+KPatCulQ+6IK1Rw43PYd+4DAMIC2rppTfk6l+FO/+qBZ9KlByUnJq4xBEsdZijWx5Na0rYmy7AT99nSrm41jLeWEcPfkct69ZcVZaZ8g5shbOy0LMkU1mQtkgblXrJhQ3UO1miG43aqOCTeDOyt7DLiq7peIGDysE49iNnnUxzEts9nKjuL7id5af+jNPE8u0MPYtK807WgFPRUpGSyWYwL47bG82LMtMNCsudAzdwOA7gg8YDFlza5wYh/f+Y0I1dcuf2bz3qlBFMWLQtZB/t+DXSn1WMc8FuQ3QV3KVFl5JxEnCmrVh69WDKMZlgvP4aJBkqSYgKXA+zyzFc2efcy2fcxie0cuIU4ckAekYDs/xpiPYnvX+Hl1WQt/T+YHgdox+x95ecT3e8i6943RaWVWQLmA7h3QV6St412z8o8WoI+0gU1iGmeoSvihuBVeEkhs2f80R5qZdNN3m0rMCphV/yUAOnugt2Qje9ThprrdzfiTHiWIU8bU5eYgkhJwzTkau+2usjpA6SvxAyZW0JPzgsMWjrOS5MMnawkMNUAbM0GNNwZZ2ITNzZF8Ey8D4iX6JTy5O+r5D9OmG1W5mSRtDVIzgfIN8OWdYlhVjLCF4rPUY5+iGA8YFKI/YWgidxRoahE0C4xDoOo81bZOxxjR6ndngS1tLFuPAGmwf2vzNe6wfsKHf3pSM31cO9cD+7gXiO5xUfBkJbsTUgLWBMhQWPzHnI0s6kt3MfLWwnheqJCyZ3lQMypqFEh2hDNwMt7y8fcFhbzDyvnnuw/ctQj4XjHjsUNBgUFOwfsfobqj5zLrSsnmYOC8rhQEb7ujLnrjMnJc3dH3ByBXGdURRVr1Qo9LXXXNI5R4TLayKElHngEzZ8nrGYU+lkmsCqVS11Kx4TQTTDgNZE3WjMOZSSCljKw1Q5Ft728jmPDCmtdIV7h+O/PDqnqiV02Xi9YcPvH7/nnVZcXbHsmY+e97x/Pmnte3+kJ9aBWNkG6M0RkGltmL6CXfdtbTSkpsu6/fKiPYeFeF4UmJMrGsl58rLF4F/8IsOSiVHeP++EOOWYqxQI5SlYoqQLxl/Z1oq8SbmfKIlm8oWGNZw2kglUTll6NSxG69YRYGlMVowmGooqbZwNmBNmUuOzX2jLXbz6Wf/fVdRBaiGdVI+/LCQZsUUx+Gub1oRI5QnyusTEK4qrdhgmw9v7iFtgZVN6Npqq8565rK0sSKKSGn8la0Do08iG1GMEYbBsybfivWt+1oVnLNgWqGtPBVtQsUhoh/FzLW07pMVaWFupZCzYhbwF6UbK3WqxJOQbeR8f6a6AqGy3ztMaGm0WEVCBfffQHEyT+SUkb5Zvq1tn4lSIqVkCi27xhnXgKrG4myiDz3OWfow0Pnu45jOlK1rtcWiPGkPq25jwm3Upk/KcQO5VEw25FdKug/U+57u6x3+tiCh5f4UGyl+BZ1x1lAIzUVjDI6MyxecJmJciRclnmZKtdweXnDrnnE13LHr9lhx5NzC+Spg/Uh/+xlWAtPbN9hScH7E2p6uG+j7A0N/zdXpiu8eX/H9m/dM6Ug/Fvr9SHdwuH2gcwFfBowN9NvZWkJk2UXikjCLIGrBQ3VQtSCliVNTzS3R2FSiSUxmJXaB8GyP7ByqR0xO7HaHxleZMqteUI1kYyi+skohrop5PLJ0Z3a75+zsHXUfqLlnXRNSA6IOpx05zWRJzHmmLFNL57Z7guvp9w5XFVkXzBQJReiKx5VPE4h/cnES+p5aEmghhDbvQxrEyViL8w4fHM5Zci5oEVzoKFhCfyD0AzlGNEasQBcCRgS3zaR81/JcnpIZmxi29bKNCNY6xNmtOLEYF3AmI0WbSFcqznl2+1tGt6fre8Jwg3M9wdm2UPoeF3qs70GEohktiRgn1suFh/4V9x9+x8W+h+UGGx45lxPTVPF55O7wnJvDM3a7ATUTc35NVwImVsywUk2klkCJSlJYbSLWI87eYiRhlh4bDkgtBFH29o4rOyLLmWl+Axpx5oDvBb0+Y0pzf6AdVkeCXNPVHtFAzRDLQsktMLBxSwx9NwKCC46ilXVdKelCJ3DrA6VafGlQHTRRspLiypoyJXhq7dp7Yz2G1go3knGbZfW7H15zf0k8Xi4cpzNrXvHWMsfMm/f3PL8b6MKn+dj/oJ9tTyxFG+PEGqxTnDNQIOaEDE2nEVfo3YaM3yivT1VK1cp0qXw7J44fKr/8heVPfmH4J/8kYAL81V+svH1VWS8VXaDO4JMwdpYhV1yqzK40yyxu46U014g8/VllKyy2lOFhx8+++SUP8yPLNDXUeDWQW+4VtJyekpXaICqbWHqjrD61fzYYyNP/kgzru8z6LsFcyDog4yazlvbvPyJut+JGnv5tfQKmVUquqGtdD9XMEHbM09p+Aint8JKNIvs01qF1ALUarHcMQ8+ytANI0XZZcr7ZhWnx70KmahtBGinbDVx+HBPVpo1JWqBqQ9L3QlGlRMjHSkor3314RyyOl1+P3L30uLFnSYlcWrCb/vTNOtRa8N7jfSCErqXSl/RRbygIuSjLNm4LzmHFsBt2OOfwLmDN5uqkYqr7SDhWbfC0StNhbAPSbUSomyi8dRPFAOpJF4vkga4eGBch7xNTmJjdhewriQC5UhDq2j47QsIsiaFAnSbW0wmzFG7cF3zVf8mz/eccdrcM3QDScpastnC8NSeqcfjrO9wa0ccTxlqGEHDetxG8H3C2a3KCdxfWtxdscOxuRvazZbg4umAbqqMfAM/kd7yj44NxLMOJcAN9Dfj9nhLaZ6VaqCRyThjXdGdnmXlwC3rl+fzrF/TWoG/eczmeCdazG66Q4Y6HuVLKQqKR0tUa0joj75X76XuWYcFf7WEIHLo7Otuy9GqqrZtyMeRppe4jZYz4LhN6wThHts384sRg/UwwnmDA/V7H9D/1fHJxIq4S+oGyroi1WGfpjMMHNkS1xQWPFsW7juoaBG0YDvjhAGKI8wnNkeA8fT+gOSO1Epynqja3QW3OELMVKGzWZeMciG17m7Nby7yQ88oyn5hOD+Sc6ccrhusbfNfjfd8cRc5iwoALe1zXYX2HWNfAPCJkTaTLhcP1Z9zefsbD8RWnacdJdqzlt7i00vcDt1dXjIddS3A8Z+SiLN9F+v1I/6fviSFxeh+4fL8jiaG8eGQZJnaDI58v5Pcd6yiULvOz/We8DJZQv+USz2iAvvZ03qM2gkvoBMtk6cKAVEuKiq8WLx6VRqLUnBDrCF2gaiGliHMeY2279VHbmE2E4EITNNPeu5yUHFeqFkrNLGsEStMM2Q4xruGMpd3Ob6/37PYdv3nzgfvjRCHTdYEhBKxAzCvrElnXn747oeVFKJg2U7Xe0PVC5yyyCKUmbCeUBOtUmy7UbTqVDbr2lLGTc0VL5Xyf+fB24nIZ+Gf/wvKP/omyPwT+/b8RfvgPmeVYsSrc7Dxf3AyMo+GYC6sq2UBh25ypmNJgZNa0zomlbRKdg93Vnn7Y82K/492bH9C6YKtQ1YI4cq6YFUps8+12RjwVF3Wz5Db7bQtPq2i0lEshlUQ3wuNDJLuF3V2PGxppWGsbOxVlG5a0g8Zsp0stLSw0xYqzFfEGI8JuuOK8XjCmfgxrE9MsyM7b1tWthcfHhbUo3rf9JcUFcdA5RxcGhn6g0rRTpg0PMDTeRKppuxhBtZWiZcu+KhskT8hGyUFIl1a01KkVnuukXGLkcgpcPQuY8JSrY1vX8b8BCFu7bAb6vicE37oS5ak8bknTa1w4Xk44a5FuaMWib0n1xjoQg1Zt2imeOnL1o4gaYz5qqAA+ZjRs2jiMgrVkBmLtuQl7xtxxeOzwi6G4RDILyS6s7sJSI2sp1Cd+ck1I6SAX9tPCbh6ocuDl/p/w9eHnXI3P6LoR5zyI2bSVtiHoFaZ1YdWM2d9R1oIsC9YFJBxad85asJ6X0sazgwksc+Qq9Fx3I0PosaZxhnwVHIE7OfC53HDyzzkNjxRX8LaH3rOwMpkzF/uBOT6S5oi6lZKVR5l5ZyesG7nrAiUVyrxwnt5To3J7+zWuC3QyMGukSEGCwTgDuVAuMM2PTPVMsQaz3+MPB6zvoApFC7Va4slxmR+5+fyA7QJoG4dKtRQ1ZGMwNmC6HrMbmibmCWXxv/J8cnGy2+3xwZI2VLqI4LZNRbUBYtr/KmIs3ncg0PUdYi1FK2gTOVkbEPEMY8BbS1mXpooWQWybK6s2mLvzbgMt2W1WWfEukNJMWi48fHjH+fKICx2H2xf0h1tscE1Ut8GXXPCIc9uYo/7YxjYe4wPBOnS4YdzfMA57hmHPzfSCD5eXyPorRv+G1B0JQ2QIM2YZiN8fuPxlZH4t7P+Bof/8TO0TcvbUJaC+kHNG48pS3tLPA/k0kE6Br756wf/upuNmvKdmeJeuqNOBtRwopTLp3AKj5p7pHtwodKOyykJwA0PwSAksqaJFmsTAOlQKRdfWTSo9zlg6F5DQo74hkuNaIQtONjtwTlTAuSaUyrkwzwtahD702K5r+h9rePbsin/4J19yulwoZaUgHA4jV/uBzlt2weF9z+Px07IT/pCf0HUguX0QrRCCwbqCmEItlukSG2TXQVk3IXXbAzEbEKxoO6ifbtZiKue58G/+7MLjueOf//eWl19VjLPcDsLpV5Vu7rgbd1wNHYaMzTP3WskeiklofTpLK7YkAoo1AdQ3AamrXNYTr978wN2Lz/HWs+8GsKbZ06fM/Zpx0aFbnHil/Yyl2YWoWwFUI+hUWc+F9ZwgwuE5+ANIp6zzSnlV2F9Xwr5vzjLjNjZLi6t4kp2azXYsYjHWYzewXc6KFCG4gXHomKaFNSWyzXjrMGKxGzae6tCS8DuLc54YO4TCbhzp+x0KLDET06YvUdNs+VvnRiRvyPzYNA8baVfM1i0xlUsV7OBJD5GcICnUTU+zrJHjSfFUqq2IE6pp3bGf+tOFDmNC04NstnFr2/tgMKhm1lJR28ZcXdfjrcU5izGtm1W2Qrdu50Q7xFrXHG9RazG5YGvFOttYGqqUquSSNpZWYNIeF/aMfbvsBesJ4pHSo2lsGjFN6LoQlyOqcRNXW8S2QihVYa6e2t3w+fXPue5v6LsdwfcY2/ALRhSxoC7ThQ4tynLKLArj7TPiq99hY2pdbNMhjsb8Mg2eKFV4+/oVTgsmRUx2TQoBeFo8hsPSm8A+9Lz0n1G0dS6LVkpViq2cb2beTK94c/4ty3pmtpmjs5ydMqTEh+9fkeNCOp0RVS6P91AsfjywlJmkmW7wOG+xYQJ7QYwj7HZoLlweHlgeH5Cux7oApZGXDy9uQQPLScl3Qq0e3YJ8NStVlGoNVRyp7yn7juwLBP9Ja+rTOyfisM5j+i2waBOmCZWSMikWVBsd0QeDs7ZVS9K4DctlZj4dWS8nkrMIdhO7GsI4NsGab7NKUUVoBZBKm1nXWtrc2zim6ZEYJx7u35ByZbx+xri7wYehBZidT6gz7dY/7LBeWqqwZNjEoSAN3GSkfd0qODdw/eIb+t0Vl/s39KFjf37Gh/SGU/wtKf0KyT8Qj5nlb/8Y3nyBWVbC2tGfT1ztI+7LCXmmPK6wzhFz9hz211x3v+D1eoCT58svBq5d5Lq/oayRSzVw7piOhtk9EruFGIX8MCCLsrRGSvtjXDA+0kmgSrPUQRsXGOMwlvbatOKtpzqgVGr1DX1vKpoUMNjOUcqOOa6IMdRiyTFzWSYms9KFwOFwYBwHOm8Yh44//qOvMFQ+uxm4rAvWerQK81rI6vlwykyfCNn5Q36a/KmFVRZdyeo2QGG7Ac5HZTqBCxUfmn6i1gbu09w0EFo2DJmD0Ak+wDgY+kG4f0z8u/8588XXhuGq8uKXwrMbC6+E7gQmZ949TvzwcGK2Fb0x5KCorR+5HdZWgiaC1xaRUByTGK57OMmZ+28L2SuhdyxZOV9iw8wby82+I7gWhqlPbiFVKE2lX1bIl0I+K2Vtdwd3BeEWwkG4urGURZneZE6vJ5xbCVcdt7+4JfVKTG1z1dqcP4LZRO/grMV7RyVRasZKYB8OfH5zxWseMetMIjbrZ47MS9Oa+N5hg6HrHd5a5OZA5yzWe4zxxFJwFYpm8lqomzunlkJOE2tqll/rm0aolLKNxCquthZ6LkoNod3mFdQ0R5ApkGLhfKw4FfxoMFbpBtv2mJ/4c3V9Rc6l/a6cwxkhrhEt+SP/yleHXy19N9D1OwwNmNZynDIlZbRuMSZh0+nUdqCr0qCQG1qCajbNUNMENbt+IKWA3d00aGXXEfymWUGgass5UqAItVpEwqYFExC3GQHard+YkX54xnW3p3c9vhuw3rciRuw2omw6D2sdXTdwVS1mzbgBuq+/Yvr2O/waccZQTY9IoMOy3xXWm5llOjM9Hpt7ZhWwjbDqTGgAQixG3GYQ8VBbyCZU4pYJ5VPHYbjly8MveTAnvk0/8Pj4HzDrhTzPPMy/RqzBLIrLEPvMnI/EOTIvM6Uq3t7SDdfEMYI/UoH+MFKlsKwT+XFGzwsxT40jtuvg2QHfe/r9CMZRq5CrwWqLi3A2U4uQraGEjnrYYVzC+//C+HpjLFJ7VFvbKPQe5yxSG7bLGAc0QWyt2m7ixpOBWjLL6ZHz/XvWyyP9ftfU+sbhLYBig8GExt6w1n38QIsRsObjRlTXhePlkXmdMSEw3tyyPzzDamWdTsRlbqK460MDwBRQNXjXhEkmDBg/NHHulvNQS6am2LgfxhC6A+5FaIux29Odd4yz5/GHSLy5p+pr5uM7yumGWitxFYz+EcsH4ZwfeLgIl/uRMH3D3fAVV+VrXr1V/u7Pf0t5OLLvem72X+DVomtlfkzMjxPH+wfiAMGMdKmBi3ynqCbK7CimojVRh4Vd7TAbq6Tv+m0TtducXElrbrHaWxtclQbI85Zc8+YmsOz3e+qliSBTVi6Xmfv7M0tM9H3PMB4Zh5Hnd1ccdiPW93z15UsOh5GYEvOc+N0P7/nN737D2+MJpHFhfvKPa0nEXR8QI+SUwFQG65p7JWZqaURXzeAMhADBNMhX3iJ1fAfdDrq+FTyHXeXqANO5UpfK3/1bxQXL3TfC4VnBDjPn7zLv/xa+e7VwjKUJDKMge4MGpXpFvBBplku/F571jro63kwzv9OVz54nZhWe9R3nc+a0ZtRarg49X35+x3Xv0Q8XXuUzJRV0gXypxKlQcwvVMw7CHuQFuL3QX7fX8vlXnhcvhPvvCtP3mfl1xmvGvhDcl5Hrb66Y1kQ8r8xroZqW6G2MELwlOLOhCLbfkTRBvqEgNdPbwN72nONE1tLi2J3FbzqGwQW6rsG/QFrSbIytMCwFNDW9CZmYM8aBk6YpibE2gbPUZsfUH51r+Ha7f9QV7tgOCWAFc2kjq+lcKFPF9sL+ym2jtf9Ka/Tv8RnGA/NlomoT0MeUSU92budb1kpJOITOOp7iFooqKk3Y+uSassHjfEeznz2N+xUpZSsudCscDaoC1VHFEmXAhTsOuyvSPFMLWGndtdbNsZvDrLZumZaWOdUipVuXC4haKMXguoHD7kDvLM52W/ddNthh/dgBZIussBWGweH7Siozkwql70kPD02H5yxiPC5Y+h3s48J6cyGezyzTqTkvuy1KxHiMCRgcVjzOeKzx1LK5ccQgNgCWEBJLjJgZgrvDmY4oM8f8PatM1GAx0sMS0CWRZSF3F5wrZI2kmFgugZv9LcN4wF8HNEfYQbe/4qa38N0987sjxIi3MPRNYxot+MFTNFPKgperZmoxTVRQtuRlMQ57uMK4CsvySWvq0zsnNEvfmhJSm+VOnMF6g/Oh3UK24DORZgczFqwLxCVR05G4nIgpMYhQykothmzbfFJcQGvFdaHJZXTzT0pbgJoLOSbuH9+R6spw84zx7mu64YDGheX8gbzOWOcQ40jqGPyew/6abn+FHUck9EgYENu1nfVpsqkFNQspP1LWc9MEqNB1O9wzgw8e8yC4SXh864nu/8NqjhQRbq4d49WeN7/5gtPlBbEINhqucgvtK4+WV98m3uQztcCH45E/+7eVUnu++OIKKcLpXvnwfuWULrig9HpDx3OG3jLLiaIZk0Ibr2Kxpm8L2AobOg0R04L7qqA5Y8U22ymbyp0NaGebTbsJEc1Hi6qxwjD2LEtmSfe8envfrGoJ5jXy8rM7vnh+x93NyH4fsL5n3w0MY8F0le5q5nHumS6J5fzTb2Mbvx3QBro+EGNpI28cWi3eNKRA2wQrpcCizbjhAK/gO8NwLbixWVm0Vi5TpUToRGCBy2vhfJ95/yvDiz+xPP+5YL+IVGOJK6Qf2gFep0rJir+zhIPdgi8N4gwUywt3yy9/9hX/+td/wdvpTHVK1JnjAmvMJFXGvef6auDZ9YH+lLCPCS6ZfCzM59r2cC/4A4QrwY3bBdaycRUgVeH9faTbCS/+xLK/cfzwF8qHv1OKZrxZ0XKm2x1ACv2hJyeaiL4qoXMEZ3AZ7AJD7dAgvHt4h+2uCeKwMeCdwzvHUlMrUDYhr3OeXizeFGKpzGuDL64xgjR3oTEKoV2IbLFUC04ry1SadVsLm8nnoytJ5MdOVzYGtYBrgLpqKyG0XCKtzXUVj5V1SsRc+eLr4b/KGv37fF798BaqMPY7lro08wRKNUKMkVwionDVjQRr2yyMjcbrLGIdvm/dM+c9xlkstnVGaMwn7y1VW+7RE8EaEaoLFBlx4YpxbKOcOM3EVckdZNvWrtl0TaggOKq4jY7dNsrWzWyhfBpGfL+jCx5vLMYExPqPNuimn9pSsIVm3bce7wTJiRgTH16/pcYV7z06T/g+IKZDnMeGkW64alKC3Z77V98zhhENmdIpGhpssUojQVeVFgpaZTvsLQ6LSstL63QTscWJXap8le44ua/5bfwPGLu2Aiy4xnqRdqZZZ7exC1SN7dzwjt2LPTWAv7mm213hr6+ozmN7yJfW2eoP1/h9YJ3OaF5Js7Ke3tP3e0ztqKXZoJvTtmEBsgGMQ+KncbA+uTjxnZB1bVoSzVhrcN7jui2NdEtGLdu8MK0rNSqimRIXrKw4k5nyyvl05HJ8pB8GFMF0fZvFb3ZL3RptqqVVzMawrhP3H94QdWZ/c83+9gsOL74BTZznB4RCNzQnjnM9w+6K4XDNMByQvke6sV1dbQDxzb2w1SbVVEzXZpM19qTpxDodSWnFd55h94yPRKuLZT1bvv7HP7D/l8ovXp6Z33h++OsXcIzIMrE+vOPxcibbDvE7slYedCKukRozHz488md/8Rv+/LvAsibynOi98uy540U3UBfHvAjDMBJ8JuaFPAk1CU4CBE/xsuWHFOI60QeHtR2im2iYLRBuWxxam2bAVG1t0rrpHjAollzAu8D+4PjiZfuHr16/5+27B94fJ377wysO+z0vn93wj375Bc9vDxip+B4+//qaP/nvPkc6y/lh5cMP7z51Wf3hPrbF6y3zzPwoGAd+EBYqsjbt1eHGEVMmNpMbdds/cqpQWuOuzE046ToIoVlXy6rcH2G6h8tDK2xiUda/qZwuhmc/Mxw+L/zpjeG7vxQevitohDBahrsO2RnEN+FgMUK0hneaeO4K/+Cf/pzlr/6SoivrHFkfHhBjKEYwaklD4f35PQ9/95bf/vqe8zFjA3z2R8Ltzy3sK+dJuUzaOhFPFme2vTE2euz3vwPrCi++tOyfWd59bZleG4wo88OMvWl48tC3NnfJiePx2BKBJZAfF3g9cdjvKc877k2lUBiHHn+G/OaCCcLQHRh2nyGd5336Fq1zg76lTK4JYxydN3gTsNuNM6sSi1IqWE2kkul9QPOZsum4VFq6tKUlTwueUmGRiJQ2piulUKmoATUNSPnkOqoKJVXOHzKXm5/+WEezYowHmlgSmmg5lcyyzqCVPnj6PmCN/cil0dp+j2KbcUGM/SjoNyKoZkzdkqJNbY4cNoaPGCoWlYD4kX4YGfuOzgoXcRxPFyyFeuhRZzBaEdWNllNbZY1toyfX2ltZC6sKKgHvPMEajAttlGQMuvF4qv7YVWsyh9oufWKQHDFF2fcD9/MFtc0CX5Yz3jR2jxGL8wP9cGB3dcf7H35gOp/phytKypSYsaYg4j6OgJuYfhsnbTZr2aIhrAerBadKXyt35YafDT9j0gce83uUBRVP9zzQ3+6Q4BAMnQkk3yNiqWnF9p7d4ZrkBWyPVoMJjvHFATsk0mQxFTp/aOfopb0/Oq/MvMEag6aJ4HYglugca/B4Z+hLpV+VUD7NWv+f0TkpWGfABGo19EPf4pZrs+nZrql4PbBOE943MVMukZwu1LJgiZiysp4zD+/eMux3WHPFOmnrwLg2H6xPOS61ohuH4/HDe9Z4YXc9MF7fsbt+TjeMxPM9taQ2c+52dOOBrtvR93t8v8P4EUwHpgfjEWmagB+9jJsavDrE9W2sI01RLOd71mVpm5ME+uGGqhmtyri74epnHvI9l4tBqqN3C7AyOmV1lbeXM4/TheOUeFjbvHQXwIfAu/cn7t/OnOYJK5WXz3bcPP+MztwgsfD2zTsufmJ8JqiDUiqiBlHTbJ622euM90h+6lQUqHYTS5UGCXOt6DMYUspAbd0laV2XZY0cjxMPx0cMwtD1dMHy8rMbNM9cphNLEu6PE3NKXJaVYbBbnlJiKIbrL3qunj/HOE8wM7789PvYffDElMGA61ugnhhBk4G5Ml8S4iB0hiFUUq5IAVkb8TUvkKbK9FhapkyAMArdaAi9xfcVvFJdG5FnC1IqD/fKqsrN2XD3pfDVP4f+M8v770rbYMcKoR0M0HbSKPA+L/z597/jxfORz55fscYTh137K9jClJXjw8r59RkzCXpZyaIML4Uvfuk4fKaEq/bz7BbD/Qfh8b60sVX50TjR7NUwT5X7d3B1UG5uhMM/9aRvAvO98Ph+xbsVf+3JpekUsq4YzdjVYFhbyew9vutQH9hxQEkolbxWLm/PzBl21yNX3zxjd/OM4jLv468p1hLCnuCVujq+/av3zPGCC4Xh4NgfRvp+2HKhWtZK42gkjE1NI2EsVQpPOVNUxYnBiCMt2oToG9uG0sTM1BYX0G7SAJWcKm+/++mndD+5xFRbAJ15iibQQi2pRQEU19ax+dE+3hw5NM3hRgtui6kxaKiCNW5LPK8tBVibmLqIUMQjbqDrBvZ9z9j1eOe42e94eFiZphVnKhocTsC2L41F0JLRnDfnlqdQWTSzmoAYj3mi07pmnJANSFnrk2usoEWppcm6jX1C9oFopfOBPvTMMVGtI09H1ArBbU5I2+HDnm64xnc7lsuRdV5w3YqxC852WNc1GrLSLuqt/tvgiq1oaYL6JiR3vhA0M9qB5/6OL8wXXM6PrCWjvnD3s88Rb7icJow1DF1PwJOmRDye8XbA9p4shporKhFcxXeCNQMlgBSDZ0eeKzYrXTVogpoTVT5QisIuIlgKBbHC4Ab2peMQLV3+tDX1ycWJc66128RQa24dE9PeqG0C19IRc9kSTkPrFGjCiWJIoAslTqwZzo8954f3OAO17+jwZDXgAxk23kAlxsTx8ZF5uTDsesarW/bXL9rhupxIy4laFGsHuv6Kvr8mhAHv++ZSkA3c1hioG7HhP/pYtT+lUmvLXZAguKoMKLUajg8PrHHFGkforhjTSl0Nj//hiEnXlMmw2/f03rIGQxcMxVpOZea4JlKeeby/sERlGR1JhdWunM1MqgviK+eT8rvfGvoSeHl9YB+EaT6Tlx22C1htWSPBOBwei8f5QO8HdMnE9QhzbHqHXLCim07IIKZ1T5KpTVhswVRLiZmUV2K8sE5HrIHBK8H2uF744vmeoYObmx1//Xc/8GGaSfHEuw/veHY7cHXVIz6g4lnmQloW5scT8/38qcvqD/axXpFUsUGaXsRWbHUMWUATOw/HtTk1QgdOQJxpyPcCXgxD5+l7R87Kw8PCdK4EX7i6cYxXlq5PcLfZVZdKXBvULSVhOSune+HlLwzPv6h0O8Px1BKz21i+WfCfMpGs9/TXAzUrgwj9GFhnJWXbMqt8hq60DWYUdNPAjM8hPC/YfcX4ClYY94I4wQXD4/vK5fx7rqPt0CHB/ftK78EUuDkouzFTVyX+NjLpiRfjM6pXKgVRxa2KSSt+b7BiSR2kPRSXuepHEvdUt5J7x8M0UxaLNzPx/IDbQTEz1TQEd+d6gu94/H7iu798pLiF7qZyqBbvBYdjeV14+x9m3M7jbzx+t0P8QqU0EN3mAtINSldp3ZRiNwmlaWGZapSMfMxPerrctktPG+/81J+SM87ZBgXbwJllQ/A6a1uEAoUYV3JuDBNrLMZtnZKnC+NWCFZls2ELxss2gm5snSpQcBQ6jN0x9HvGrmPf9YQuYC1oZ1i8Y5kvQKYMHZ3z+C0Nu1DRHKk5bxk9SiyFKUHyniBN99RGTH4jk2+iFNhylxSt7TWKsW2+qQXKxuLBYq0HW0iposuKD2N7bbX9e8RgbaDr97z/8JZ5OmPDiLUB5xMmNLMGSPtepWwXj3b2ltJSycW6j0Jd6zxBe3Zlz2f9F7w6vybpB65vr7i62vPw6sj6biLcDPi7oZHH7yNxPoOvGNdjQlv7VbV1eUVw6lEVJDWKbjktuKXiRFAriDqGZBjXSu8L3oBoxhVlLMpYEl3xBP4LW4mNMVjnsM6gBTANvgaVmktrc6Fba1NBLMZUrCk4V7A2Y21BJBHXzHR65Hg/0HUOqSOiAedd+/emtexKzlzOZy7nEyFYhv2Bq7uX+G6PlpV4OrGcjqBCGA503TWdP+BtwBqPiKfFjVo2g+X2av5/tVmbTbmd3K0at73SF1imhfkyE0vEB7CuI6x7zo8r1l4zeIPzQnYOJw0V1K0ZiCzrjLHCbjDUkrnMC2tJmB24O8vt7Y7DaLBZWS8XXv/uHdehZ3c94rpKNR1BukabNYIzDlc9joEuDHjTc5nPvP1wwhpL8APWWoIXpLRwNYs8XaJbG7qWZn+sFWcNYxfQfUs13u8GgndoqvibHVdXA4erAyKVt9OZtGY6I6xxJZceVcf5MVHLPbpG4mXm4d35U5fVH+yTSzuKvLMficm98QRjSV7Z3XiW+8yy6BYj0C47Kk0Ma4FxZ/nyyz3eG37723vevlspuXJ5VJa5UrRifBupihHiIi0EcIX1WJneV+IJXv6pcPjcYEfl8liYJ0VLE0WIqTSDQRMRJqmEzhOGgjmvLHND7ItxOG9xu4q5FuL1FocwFIyvrbvjKnbTOgcrdNYQfEXeKqeHNs5R2To9CtMqvH5bGyBQCteHiozgxsLDu5mr54XuxgEVpzRuEpUORQPkfeHsLjjj6DslrjNVhHDl+PznLwnpmjHcMAw7clkozPggWNsiGNDAm1/9wPGHBXNV0FBxgzKPK0468gLvf3UmJoPsHLdfO7rbiusszvtGeLW55UqhqBYaSrlSctMB1NpAXmglb50jaL8HAUTb6OKn/oTQ4V1ouDVphy/UNvp3hpwiJWdSVsS1EZg1Dicd1vlmKN86Kaa2boSWFtao2pg7RhoXKKmj2rFRWfuB/dAz9j1D30YxxoDphXoYeNTEvC6sMdN7xxgcvROsaPP3l0Q7wzKrwkU91Vp6DM6aNqqxDrZRuGgTHhQtHyMP2KzTUKmloCVvI3XT1o5WUkxoLlto6lP3hbY/m0DoD6ypMl/O+O6KLkSKj+Q1tnMssHG/QKrbMu3g6Vx74r/UzRJtrSPYwLW/4a67Yw0zVy/uqGtheXOkXBbqoQNp7p9yKaz3MyrgTcVe95jOYgrYCCaBTBVOBZ0bIqOkFXFQe6EE8NXSOccOx5gNo3G4VGEp2BRxpYDJqPwXHus0Z4vZgsXa4f6xCyGt5QRP1Vy7RwiKGMWI4ryhGxyhF/S08vD4gO8tXe/RvKBdTzd0uNIh3lOqMF1mpvMZa6EfR/r9NePVZ6SUyPM96XIirUro7wjhQHA7vHRY2cY3OD4WJlsWw39q+vvxdYmhGofYARsK4+5AXFbO55ZkbABnHIEBbwd6b5BNz6HZ47YsiHlJvH84cX2945d/9ILLaeY3rz5wXlfG24EXnx/4+usDd1cBWRKnNxdqFGZVhl1HGDxpBW97JOjWevaICpLBegEK8zrx4f4REMZdYhhHsviPG2rwP5K/VAprWiixtaidM+zGDnTEWkPfN3CdurZBi0DoDkylMJZb5vPK5d2Z42km503ImApLZ7EbVOt4+um3sXNswLM2P3ZUVazfXAN9YvxiQPvEhzcr86U054ltNsZ1AVblSGbfRQ6H0ESylsbV0HbQlyzUUnG+YpwQhlZgxgVyrCRtP4cbPHdfDdhuwhrFucq6Cloq1kHoDVUza1w53I74fYfKghkq+azkaDFV8CL43rAbHfNxYlkrCJQIksEHmuCPNq7vnND3ELzwBnh82Lgf2twtqVbOM7y9h24Uhp3gRsOXPztgjMcbh62GSmNXeCs4r8S6EquyesXUyLUb28y8Lu3S03d89cc/Y1+/wqYRbMfFnTDlB7xtQmADTB8W3v3mgfVYcA7MCstSmaaMt5GCZ14S5/eVYkHVM16EcXT0g8W40kZrtungxMLhtt3krbWMvcci7XMQ4eGY+fCYOa8/UnTbfvhfb53+fT3DsG86nNxGx1rK1pFrdtgcEym1AFVnN6qrlq0DUZtYdbP7NsBXq1Ss2JaYXivFepbiUDPi3Z6u6xm60ECQQ0/wvt3wjeACmH2HsCffF86XiXlamIPhMAZ6b7BbAF4tSjWWicBie0xuI7oGW2tgQsRsrq32c5WsW5bV1iGjCco0JzRn0O0cEUuMieV8IiBU03Kg2PbK1s23uDCQMkzzTL8upJjJOSHLAk/xGN6hWjCm6d1Ua4O8Gdm6TD82LqFFvgQTuPE3nMIR5z3T+wdYI16a5qdobeObpJRLRpmpXhg6j6kFWQvMlTpnOK/ocaXMGa0V7Sv1uUNHR6ml4RKcwaphUMOuGHw0lBXI+nHCkvm0av3TOyfWbhNWg1i7dd/qZq0yH311IgK1UHJqxcmGF/aho9+NDPsL7uHC/eMF+15wwVPSTBkHchoJw4DpOtZUuEwLtSrjfkc3DvT7O0zYkU/fk07vWM8T2Bu82xPcAW/6DUO8tdiQbRhqPs7o/lPVyUeS41b1Ig5jAl2/43CdqTVzPl3IJUEtLSQtJWSLczdbK5cNkrOmyDQvfPHyOX/68+cslwvzsiBH5dnzA3/0xUu+enHD7b7HFWUe2qG/WuEhFzpxrS2qSnh6PVWajU4iJVoqkZwnoJBiYTbNRVUtaDWbMr2l5yoNPx7zTF6E3bjHGSEE24KhciGXsuGnfSuXtRKC0I8Dvvac1wun5ZHT4xHLPS8v1/iq+KsR11nCMDZA2U/8qaVpTcT8OBtXhUWV2Rb8ned6FGwP735IzMdMzkCGtMHL3p4jl4fCMLR497VU/Ai+3zpxuQHcmpi2YiytQDHbxrJpjUr2dP1Nc5HoBYMSQiXGre2MNpumagsa6y05Kt2u4o9KyrSsGSuEwXN327OUyrt5Zq2GeIHFNZaHUcG1BAk6L+xH2PVCZyrUwsOpXUaftARFK/MsnB4Ny03HFy+veX73grtbePs4seSE1tQKZk0YUVLV1l3yDufA+8xaLlSa4LRKAq+IGpxpwK1swS4eYxzWWKQYXv/VWy5vJsraRMplhjTBFDJWVjRCRsApVmiBm2foqBBTy2KRSrECDvbXwmd7wXRwe+X42Tc9u+Dahap4fvu7lf/3Xxz56++mTfAJoafZJ3/ij3OBWiKVQtHSsA/bhitIsxbnQqWNzJ1tbklD/ShWqii1lq0rUJFqmhXVCEUca3UkerpwoOuGBlhzjuDbhdAZs4E+myYueMN+35NyIqfI6bRwXJvF+bDr6IxSUoaiFBEmC0kEs1WTzoXWWXgqTFolsnV12kWj1u2MqdIS3VNqhZluZ6Wx5HUlnu7pr/dgbSt0N9FN3QoWsZ6slXldWZeFlCIpZ8RGTLaYbMBUwDTxdTUbKbnloD3B2Z6qk0o7m50Y9nbHzu+Z15WyLHTetY6HVEpuUgDjBRcsumrD+RePnAp8mGFWdFHqJZIvkZIUgqHuPbU30AE1Upwle4smgbnCmhqeY3PSYSw414q3T1lTn7z6pHn8DYKznpybVUw2rkYTBZV2SHvQGLeWVwUxuNDT7faMh5ndYeHDufDweMa4DkGpOZLSylAiLIE5NZHRbj/QjwOh3zFcvWjBYMuFslwoSRn6PV13TfBjm69/zNrYFo3ZugZPAsH/P9XJj4XJk9z+6YPlsG6g3zUl97I8cp7eYSXTuZ776ZHOgiGjeUXTSkqxdSw6y83tyM31yNVhj0O52Xf4IHzx5ec8v3lOqI46O5w17HpDLPBwuXB6c2JnV0YXUFGittwJ3wUkWIqAXQ0BwZrK3d0t0/GM1oypCUNAjEXFfgTjZG0bQE6ZNVb2uxaw2HeBHAOnfCHFRPSKdZ5ULVozqkrWynRa+eH7B169vudyuhBMZeiE8vKGvu/Y7wb6/RVJf/pBZ2KkaXrEtvGYMeRUiLEQtWIlYYbCs18E3N7y+u8W1nNFq+JsJUkDsj2eCsfzJsh04FUIVfGA90JwguYmMn2KjA89uNCKF1XoDx4TOqw9kC+JWle6oDiXiElJKaPJMtXIu3dHUgq4IAy90PeVdWlZWNY4em+5GnvuPuspF8O39xdqNZgK6WyIY9PXdB1c3zgOg2P0ke7L0txI3yrno7Sb5daprEWYTsLx3vHlyysOhxHlwrvzI5djaoTPeGkBnd4hweO6nnEY2fUOsUdKaroNqRUxlTcfXvGYK7e7bxi6wrk+kiVRsUDg8q7wuz97RTplPMBaqZOQO5hdAV1hbu8H0kZPtgjBGIbBMxTBpELUwmoK/qrw8nPDixfC+aLc9ok/eem42RtGGXF6xU4if/erQj5NLFXxo3DzwjDc/PTDdXLJaI6UEtt+kdNHZ5RqJedMlcYGKTXjMVjrt/26UWFV2oGpAKViKKRaMdax2p6FHd2wYxh2dK5ZzrvgW6ablY8qW9WW5SSAs8J+CKRdR4krx0vk4XhmWWbGYDGbVrIYYRGLZsXVAiKNUizNgVO0tIuvaeaEVpz8eCGvW7eo5MY3aj9K66aUZULjhNhbMM0ezdY1avR9h7GeUpV5XVjWmWVdCDlhfWjfWzOUTXezfW1VyMVigLyNiSr1Y0iiiGBMc+R0OfBwOm2MojZJUBTNa6O9X1mGOKKXRAg7erennC7IQ8LmlhSel0JcywbKM0jvqUEwzuDFgRVq36HiyKdMXApStvfCWrCOard06U94Prk4qRWsc801IgCGUlp7R6jNjiXa7Ho09HAucTPGtBDATvcM+5XDTeRqrrz+4R1v39w3EZOTbWFWMh5cx7jfMYw7uuFAGJ/hugPT8QM6N+2DMQf64Y6+P+Bc+IjVbz+gtO5Jg600oeunvMiqDZ/Mtrho2QAOoRszw/6ahw/fkuKFfmgdmrhGnKzkdWKdL8RlxYjw8rMb9rdXHHZ7FGXNhf2+58rveXF7S9/1lByZ4oQES5CKZGW+PzOVDGFgd3dHtYkUVypClooPO7wYUonYqnTO0D1/xtU4kONC14/4bkR83z4kRSm5kGLG+lbFPkXWW2OwBLpuYN4cRWtKlKJMeUG1ItmBesrjA9Pb9+SY8A6u95bdKMQUuT+uVDOSjW4C5J/6YxrC2jSd0dgF1rmSc8smKqWQNFNtwR7g6kuLni3LUZkkU5OScmup1o2PURPoWpEgECBTMdogh1aa+LKUiiawvtL1reNRa+LD+wvVFc73UKMldMIwKte7dgudToXzufLuzcr50XN1DbfPLX2fmb2Sq8VjMcWQkjKMgS++ueMhFh4/XDAxYG/6NhZ5iJSp8PyFZfdPrxhuMgwXvv4m4rzym9/EVqCUVudHLdxflPy9spL44stvqRKJubCsrUtinAEXED/gTcCIx1nH0AlrnFtjVpsjwlrI48rb06/5EN/Tz3sIQrILxhrKYvjV//QDl/cLbfgMy6mh5J/08VoyOrfDUivYDCELMhVmm9BkGLSFu5k+cdgrf/oPLX5XSBPcdMqzoIhW3k+ZX//myJ/9xcyvXy/YYOmscv0C9nfNavxTf5Y042rTHcYaKUZxxqClEFP6GFfgrMFaafEAxjVAXd2KRCtUZ1sHQAumNs3GQk80Vwy7G3a9Z/COIELfOYYuEJxv6PrfK0pKLm3PywlqpveG3eDJ2XGMK/f3j9xT8D4w7vaYIZCqoUwLO29b9+2JhVUrJSnVKsZW9EljqVsi+UeIXNNpqZbWySiFssyk44dGIbftwgjNfqxaWj5aaTykahxrSqw5EVMk5YTb5AKUTEG2QNwCT2GKccH60Lo7bOoTqU8qBgBMFWSB8/mMLqlRIfcB69s4RmnC/v9ve2fSI8lVReHvjTFk1tRzGzDICMlC8v9fsIM9CMkCIxDGxu52D9U1ZGZkxBtZ3KhmB72stt+3TCljURkVcd+9557jBgdRMQwD6s5ewjuMLVRdiUuRQ36+K5AqqooVgtIaaw3Wd2gc1axp5mb9zDgRelSpFT6EDy9OSiWGICfqECUtt94ZgIHxWmzsU0bVTInSSlNagq+q9Ecw3jJsOzYbjbOF692Oy5sNbvAoKze2cZZx8PTDgO9H3HCG21wQY2S6ek3eXVJDYnP+kG58gHWjbAppWUlDyw54NWv/2dx1TP6/34A8BNeOz7oippRUzNZ4fD/SD1tu95dod0T7kcPhmp4jJRxZ5onjPGGM5mfPnmL6fk3jPKIHxbNPH4OqjCee/qSnYCAGLBWrKkPwXGxHtjryYHvBg5MLjDYcdzNLECW19yOdAx1uqWVB48GNbM5PsWmDdSNKWQqamBKpFGrKEmmu11GcUeQSyFkU9iiFs44YAyHO3NbCy+MN+32kXGlMCMyHA6cby8mwZdMpnj854+TkhP0Ef/n792T1giePzvjk+dMPva0+WpwTjU2JkaxBdaP87ZaIsVoSfWshBoiLIuWK8pntc8f20cjxoeLmZWD/diHPooinyOhhVpJf47xa9Q7IwzHJcyUXhTLSQXEDdKPm9Yt36yTXQnUssRBT5kFXef6Jxlr49uvI6x8Mh0NmniGlQjdWOgc1wLwkrvZHzh54svKw0Xz+xS/55st/c/3NjrBouk9GthcD75aJb/+5oKY9v/3iEScPNxizxzyeULXy4kXkdldlZFSk0NodE69+mNj2A/3GEpbIdlAsBUodKViq6qgYyeqqEcpRsk8k8VO2NqjYXlFjZlG34CJ+LWpqUPzwt1tefvWGodOEkDG2ogOEvazlFwV+UJSpkA8y8jEolr14t+RQOKrOTeGnAAAFK0lEQVTK1THiiuL8meLTC82TJzDnyuCgN9C7ge/eav7wxxv+/NfE6+tKKIXuXHN+athcJDFr+/FPddgtt4zbU3I/MlVFJmNzpU4zKSxYHF5LYVJKJsaMK7I5dSfmpBoJAXQKrTQ6aULpWdQp4+ZMxKxG4Y1675nivcdqEXVKgvYqTr4rIFIix4DRlbE35Gio0VKy5ep24mo/0YfMRTdSS+V4e83Fk8f0vpPfrUjyODoBDmqWsUSt1FzE+hnW7nKWSAZEy1FSIU0H8nSN6UWugJLr1ZSJMRLCQs6RlDK5wpLk/zbmzBIjNmV0kS0dXVefE+r78LxSNZYq7zlWFcPdIZtCKUnefKt+LaUF2ynGsy1l8ByXI2GZYQbmiraaZDUpRumoeIdKGR0z3hi0cySVxZBwitijIzuoXuQMRosW0lq1rtNrtOuwzpFSIS0z6QNjuj+4OAmLjHGSDrLNQaTmJAFP6ypYrpkaxB1QxjoJCZOLkiKasnyHjPeg1ULJkdvbPZvTLd3Y4Y241/nO47seP57SnTzCDqfM046we4dKAWu2DNuH+G6Dte69LbH8EmuBYjRVq9Xul/9Zm4jWeqUiBUrK0kYzGqUksMlax7DZsrs0lFzoug3zYU+YF3IIhJBIuaL7jn57KvPEnMEmHnUPKRViSZjTnu5koOoRUsHXSlcL3i3YrgdbMbqj0wNaWYoz5Cz6FmctvZfIgDIfWcJMDg7bb7B+xK55ETllakrrCSKJUDlrqjaioC9RouKzjMG876jAEgI3uwPfvL7k+5c3zK9mTgx88mjkV7+44MnjM8bOMfYDqXjmFzsud1e8vbrkcJjRuv/Q2+qjxTmLNRCWwLjZEGbR/JRUyXEhpoD1WgLMjKOSyDVSCOiu4p87nj0Ymd8MXH59ZPdWTvnkSp5gyVA34EZE3Oc0RUbb5KVSwhpJ4A3PPnvKzMSbF1eUKG7BKTkOB0suGasLn/3a8JvPxaPn1UtIEQ630HcKbytTjSwhYvpuPfUZjiVyujU8+/k53Vt492biWA6orcVpIzqkyZG/NkzfJ16VAtuOzabw9AEMTnF9SCxRNgtMBVM15TiyOb2gbq/QLrGkwKt3hZgl+2SNF0Qs6RbKe18ijUJyVpxR77fPqgKMQeM43AT+9aeXawED062Id90gGpmcKuESsi+oqDAJWXlUlZhhOUg4oHWaHGXE26MYzw3aFuy6LzyFypf/SPzu9zNffac4RBlJ+LEwnhW6zZob9N/3xo+a6gzL+Zb5xLJXnqQWdIqYPZhOoRdNnSU1/U7sGlOgKInOUFqjMdhaMZ1Hn3jCoTLtB3x/Jqu9q8NrNTKCtMa+t5LPOcsL++66UdSqNYuGpeQIJeB0orOFwRluauE4T2Sj2GIIS+RwuEbpJ+J6XsQBOCfRe6ErJUdKWQWvrBsyCuqaplxlULR2RQphmkjLRHd29j5VmVrlujmRknRIco5rF6UScyGu/l6xZHTJlKIwtUKKq+uqW0c3hZICCitbUqXIqnSRa+cshm66INuwrtL3lnE7kopnWQJ1rtQglZjeepKXtXhrJepE5YzKBWfAdAZjFFlVVNS4AygHgQpEyGGVV4i3QIgLOWfZfsOwHOcP1pyo+lPI8240Go1Go/HR8BOYhjYajUaj0fiYaMVJo9FoNBqNe0UrThqNRqPRaNwrWnHSaDQajUbjXtGKk0aj0Wg0GveKVpw0Go1Go9G4V7TipNFoNBqNxr2iFSeNRqPRaDTuFa04aTQajUajca/4D/qmVLOtizNPAAAAAElFTkSuQmCC\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# 10. Hyperparameters\n",
-        "Now we are setting the training hyperparameters, we defined some variables which are the batch size, number of training epochs and learning rate."
-      ],
-      "metadata": {
-        "id": "eQWG72KMWXIO"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "LR = 1e-3\n",
-        "BATCH_SIZE = 4\n",
-        "EPOCHS = 15"
-      ],
-      "metadata": {
-        "id": "2xQGFfC1aacO"
-      },
-      "execution_count": 293,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)\n",
-        "test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=True)"
-      ],
-      "metadata": {
-        "id": "e40E1B1AadyX"
-      },
-      "execution_count": 294,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "googlenet_model = models.googlenet(weights='DEFAULT')\n",
-        "for param in googlenet_model.parameters():\n",
-        "  param.requires_grad = True"
-      ],
-      "metadata": {
-        "id": "yVY3ibPua-dN"
-      },
-      "execution_count": 295,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "googlenet_model.fc"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "vYW3nNee74gx",
-        "outputId": "283c8a64-87e9-4d64-e640-c2f489a2e386"
-      },
-      "execution_count": 296,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "Linear(in_features=1024, out_features=1000, bias=True)"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 296
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "num_classes = len(data_df[\"category\"].unique())\n",
-        "googlenet_model.fc = torch.nn.Linear(googlenet_model.fc.in_features, num_classes)\n",
-        "googlenet_model.to(device)"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "niTkCIqgbEAe",
-        "outputId": "cdafd953-e2c8-4270-c045-94a0410ca1fd"
-      },
-      "execution_count": 297,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "GoogLeNet(\n",
-              "  (conv1): BasicConv2d(\n",
-              "    (conv): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
-              "    (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
-              "  )\n",
-              "  (maxpool1): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
-              "  (conv2): BasicConv2d(\n",
-              "    (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-              "    (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
-              "  )\n",
-              "  (conv3): BasicConv2d(\n",
-              "    (conv): Conv2d(64, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-              "    (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
-              "  )\n",
-              "  (maxpool2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
-              "  (inception3a): Inception(\n",
-              "    (branch1): BasicConv2d(\n",
-              "      (conv): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-              "      (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
-              "    )\n",
-              "    (branch2): Sequential(\n",
-              "      (0): BasicConv2d(\n",
-              "        (conv): Conv2d(192, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-              "        (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
-              "      )\n",
-              "      (1): BasicConv2d(\n",
-              "        (conv): Conv2d(96, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-              "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
-              "      )\n",
-              "    )\n",
-              "    (branch3): Sequential(\n",
-              "      (0): BasicConv2d(\n",
-              "        (conv): Conv2d(192, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-              "        (bn): BatchNorm2d(16, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
-              "      )\n",
-              "      (1): BasicConv2d(\n",
-              "        (conv): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-              "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
-              "      )\n",
-              "    )\n",
-              "    (branch4): Sequential(\n",
-              "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
-              "      (1): BasicConv2d(\n",
-              "        (conv): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-              "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
-              "      )\n",
-              "    )\n",
-              "  )\n",
-              "  (inception3b): Inception(\n",
-              "    (branch1): BasicConv2d(\n",
-              "      (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-              "      (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
-              "    )\n",
-              "    (branch2): Sequential(\n",
-              "      (0): BasicConv2d(\n",
-              "        (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-              "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
-              "      )\n",
-              "      (1): BasicConv2d(\n",
-              "        (conv): Conv2d(128, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-              "        (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
-              "      )\n",
-              "    )\n",
-              "    (branch3): Sequential(\n",
-              "      (0): BasicConv2d(\n",
-              "        (conv): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-              "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
-              "      )\n",
-              "      (1): BasicConv2d(\n",
-              "        (conv): Conv2d(32, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-              "        (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
-              "      )\n",
-              "    )\n",
-              "    (branch4): Sequential(\n",
-              "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
-              "      (1): BasicConv2d(\n",
-              "        (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-              "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
-              "      )\n",
-              "    )\n",
-              "  )\n",
-              "  (maxpool3): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
-              "  (inception4a): Inception(\n",
-              "    (branch1): BasicConv2d(\n",
-              "      (conv): Conv2d(480, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-              "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
-              "    )\n",
-              "    (branch2): Sequential(\n",
-              "      (0): BasicConv2d(\n",
-              "        (conv): Conv2d(480, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-              "        (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
-              "      )\n",
-              "      (1): BasicConv2d(\n",
-              "        (conv): Conv2d(96, 208, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-              "        (bn): BatchNorm2d(208, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
-              "      )\n",
-              "    )\n",
-              "    (branch3): Sequential(\n",
-              "      (0): BasicConv2d(\n",
-              "        (conv): Conv2d(480, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-              "        (bn): BatchNorm2d(16, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
-              "      )\n",
-              "      (1): BasicConv2d(\n",
-              "        (conv): Conv2d(16, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-              "        (bn): BatchNorm2d(48, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
-              "      )\n",
-              "    )\n",
-              "    (branch4): Sequential(\n",
-              "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
-              "      (1): BasicConv2d(\n",
-              "        (conv): Conv2d(480, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-              "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
-              "      )\n",
-              "    )\n",
-              "  )\n",
-              "  (inception4b): Inception(\n",
-              "    (branch1): BasicConv2d(\n",
-              "      (conv): Conv2d(512, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-              "      (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
-              "    )\n",
-              "    (branch2): Sequential(\n",
-              "      (0): BasicConv2d(\n",
-              "        (conv): Conv2d(512, 112, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-              "        (bn): BatchNorm2d(112, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
-              "      )\n",
-              "      (1): BasicConv2d(\n",
-              "        (conv): Conv2d(112, 224, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-              "        (bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
-              "      )\n",
-              "    )\n",
-              "    (branch3): Sequential(\n",
-              "      (0): BasicConv2d(\n",
-              "        (conv): Conv2d(512, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-              "        (bn): BatchNorm2d(24, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
-              "      )\n",
-              "      (1): BasicConv2d(\n",
-              "        (conv): Conv2d(24, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-              "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
-              "      )\n",
-              "    )\n",
-              "    (branch4): Sequential(\n",
-              "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
-              "      (1): BasicConv2d(\n",
-              "        (conv): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-              "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
-              "      )\n",
-              "    )\n",
-              "  )\n",
-              "  (inception4c): Inception(\n",
-              "    (branch1): BasicConv2d(\n",
-              "      (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-              "      (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
-              "    )\n",
-              "    (branch2): Sequential(\n",
-              "      (0): BasicConv2d(\n",
-              "        (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-              "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
-              "      )\n",
-              "      (1): BasicConv2d(\n",
-              "        (conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-              "        (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
-              "      )\n",
-              "    )\n",
-              "    (branch3): Sequential(\n",
-              "      (0): BasicConv2d(\n",
-              "        (conv): Conv2d(512, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-              "        (bn): BatchNorm2d(24, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
-              "      )\n",
-              "      (1): BasicConv2d(\n",
-              "        (conv): Conv2d(24, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-              "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
-              "      )\n",
-              "    )\n",
-              "    (branch4): Sequential(\n",
-              "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
-              "      (1): BasicConv2d(\n",
-              "        (conv): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-              "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
-              "      )\n",
-              "    )\n",
-              "  )\n",
-              "  (inception4d): Inception(\n",
-              "    (branch1): BasicConv2d(\n",
-              "      (conv): Conv2d(512, 112, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-              "      (bn): BatchNorm2d(112, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
-              "    )\n",
-              "    (branch2): Sequential(\n",
-              "      (0): BasicConv2d(\n",
-              "        (conv): Conv2d(512, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-              "        (bn): BatchNorm2d(144, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
-              "      )\n",
-              "      (1): BasicConv2d(\n",
-              "        (conv): Conv2d(144, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-              "        (bn): BatchNorm2d(288, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
-              "      )\n",
-              "    )\n",
-              "    (branch3): Sequential(\n",
-              "      (0): BasicConv2d(\n",
-              "        (conv): Conv2d(512, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-              "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
-              "      )\n",
-              "      (1): BasicConv2d(\n",
-              "        (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-              "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
-              "      )\n",
-              "    )\n",
-              "    (branch4): Sequential(\n",
-              "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
-              "      (1): BasicConv2d(\n",
-              "        (conv): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-              "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
-              "      )\n",
-              "    )\n",
-              "  )\n",
-              "  (inception4e): Inception(\n",
-              "    (branch1): BasicConv2d(\n",
-              "      (conv): Conv2d(528, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-              "      (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
-              "    )\n",
-              "    (branch2): Sequential(\n",
-              "      (0): BasicConv2d(\n",
-              "        (conv): Conv2d(528, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-              "        (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
-              "      )\n",
-              "      (1): BasicConv2d(\n",
-              "        (conv): Conv2d(160, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-              "        (bn): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
-              "      )\n",
-              "    )\n",
-              "    (branch3): Sequential(\n",
-              "      (0): BasicConv2d(\n",
-              "        (conv): Conv2d(528, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-              "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
-              "      )\n",
-              "      (1): BasicConv2d(\n",
-              "        (conv): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-              "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
-              "      )\n",
-              "    )\n",
-              "    (branch4): Sequential(\n",
-              "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
-              "      (1): BasicConv2d(\n",
-              "        (conv): Conv2d(528, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-              "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
-              "      )\n",
-              "    )\n",
-              "  )\n",
-              "  (maxpool4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
-              "  (inception5a): Inception(\n",
-              "    (branch1): BasicConv2d(\n",
-              "      (conv): Conv2d(832, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-              "      (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
-              "    )\n",
-              "    (branch2): Sequential(\n",
-              "      (0): BasicConv2d(\n",
-              "        (conv): Conv2d(832, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-              "        (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
-              "      )\n",
-              "      (1): BasicConv2d(\n",
-              "        (conv): Conv2d(160, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-              "        (bn): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
-              "      )\n",
-              "    )\n",
-              "    (branch3): Sequential(\n",
-              "      (0): BasicConv2d(\n",
-              "        (conv): Conv2d(832, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-              "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
-              "      )\n",
-              "      (1): BasicConv2d(\n",
-              "        (conv): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-              "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
-              "      )\n",
-              "    )\n",
-              "    (branch4): Sequential(\n",
-              "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
-              "      (1): BasicConv2d(\n",
-              "        (conv): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-              "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
-              "      )\n",
-              "    )\n",
-              "  )\n",
-              "  (inception5b): Inception(\n",
-              "    (branch1): BasicConv2d(\n",
-              "      (conv): Conv2d(832, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-              "      (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
-              "    )\n",
-              "    (branch2): Sequential(\n",
-              "      (0): BasicConv2d(\n",
-              "        (conv): Conv2d(832, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-              "        (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
-              "      )\n",
-              "      (1): BasicConv2d(\n",
-              "        (conv): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-              "        (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
-              "      )\n",
-              "    )\n",
-              "    (branch3): Sequential(\n",
-              "      (0): BasicConv2d(\n",
-              "        (conv): Conv2d(832, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-              "        (bn): BatchNorm2d(48, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
-              "      )\n",
-              "      (1): BasicConv2d(\n",
-              "        (conv): Conv2d(48, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-              "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
-              "      )\n",
-              "    )\n",
-              "    (branch4): Sequential(\n",
-              "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
-              "      (1): BasicConv2d(\n",
-              "        (conv): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-              "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
-              "      )\n",
-              "    )\n",
-              "  )\n",
-              "  (aux1): None\n",
-              "  (aux2): None\n",
-              "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
-              "  (dropout): Dropout(p=0.2, inplace=False)\n",
-              "  (fc): Linear(in_features=1024, out_features=3, bias=True)\n",
-              ")"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 297
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# Define loss function and optimizer\n",
-        "criterion = nn.CrossEntropyLoss() # Cross Entropy Loss\n",
-        "optimizer = Adam(googlenet_model.parameters(), lr=LR) # Adam optimizer"
-      ],
-      "metadata": {
-        "id": "fZrWxvlIdoGl"
-      },
-      "execution_count": 298,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "total_loss_train_plot = []\n",
-        "total_acc_train_plot = []\n",
-        "\n",
-        "for epoch in range(EPOCHS):\n",
-        "  total_acc_train = 0\n",
-        "  total_loss_train = 0\n",
-        "\n",
-        "  for (inputs, labels) in train_loader:\n",
-        "    optimizer.zero_grad()\n",
-        "    outputs = googlenet_model(inputs)\n",
-        "    train_loss = criterion(outputs, labels)\n",
-        "    total_loss_train += train_loss.item()\n",
-        "    train_loss.backward()\n",
-        "\n",
-        "    train_acc = (torch.argmax(outputs, axis = 1) == labels).sum().item()\n",
-        "    total_acc_train += train_acc\n",
-        "    optimizer.step()\n",
-        "\n",
-        "  total_loss_train_plot.append(round(total_loss_train/1000, 4))\n",
-        "  total_acc_train_plot.append(round(total_acc_train/(train_dataset.__len__())*100, 4))\n",
-        "  print(f'Epoch {epoch+1}/{EPOCHS}, Train Loss: {round(total_loss_train/100, 4)} Train Accuracy {round((total_acc_train)/train_dataset.__len__() * 100, 4)}%')\n",
-        "  print()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "J4UTpUPAdvH0",
-        "outputId": "33d73ea5-3ef7-42e9-fe10-3ad839d2cb41"
-      },
-      "execution_count": 299,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Epoch 1/15, Train Loss: 1.9704 Train Accuracy 54.3452%\n",
-            "\n",
-            "Epoch 2/15, Train Loss: 1.7905 Train Accuracy 60.2203%\n",
-            "\n",
-            "Epoch 3/15, Train Loss: 1.6183 Train Accuracy 65.9731%\n",
-            "\n",
-            "Epoch 4/15, Train Loss: 1.6335 Train Accuracy 64.0147%\n",
-            "\n",
-            "Epoch 5/15, Train Loss: 1.5131 Train Accuracy 71.6034%\n",
-            "\n",
-            "Epoch 6/15, Train Loss: 1.4763 Train Accuracy 70.5018%\n",
-            "\n",
-            "Epoch 7/15, Train Loss: 1.3434 Train Accuracy 72.9498%\n",
-            "\n",
-            "Epoch 8/15, Train Loss: 1.1663 Train Accuracy 78.4578%\n",
-            "\n",
-            "Epoch 9/15, Train Loss: 1.1773 Train Accuracy 78.7026%\n",
-            "\n",
-            "Epoch 10/15, Train Loss: 1.1009 Train Accuracy 78.5802%\n",
-            "\n",
-            "Epoch 11/15, Train Loss: 1.2381 Train Accuracy 77.2338%\n",
-            "\n",
-            "Epoch 12/15, Train Loss: 0.9945 Train Accuracy 81.6401%\n",
-            "\n",
-            "Epoch 13/15, Train Loss: 0.9962 Train Accuracy 80.7834%\n",
-            "\n",
-            "Epoch 14/15, Train Loss: 1.0729 Train Accuracy 80.2938%\n",
-            "\n",
-            "Epoch 15/15, Train Loss: 0.8606 Train Accuracy 85.0673%\n",
-            "\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "with torch.no_grad():\n",
-        "  total_loss_test = 0\n",
-        "  total_acc_test = 0\n",
-        "  for indx, (input, labels) in enumerate(test_loader):\n",
-        "\n",
-        "    prediction = googlenet_model(input)\n",
-        "\n",
-        "    acc = (torch.argmax(prediction, axis = 1) == labels).sum().item()\n",
-        "    total_acc_test += acc\n",
-        "\n",
-        "print(f\"Accuracy Score is: {round((total_acc_test/test_dataset.__len__())*100, 2)}%\")"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "z0q20xXUeZMb",
-        "outputId": "f8922cac-6f76-4725-df80-0d25821f5f46"
-      },
-      "execution_count": 300,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Accuracy Score is: 79.14%\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(15, 5))\n",
-        "\n",
-        "axs[0].plot(total_loss_train_plot, label='Training Loss')\n",
-        "axs[0].set_title('Training Loss over Epochs')\n",
-        "axs[0].set_xlabel('Epochs')\n",
-        "axs[0].set_ylabel('Loss')\n",
-        "axs[1].set_ylim([0, 2])\n",
-        "axs[0].legend()\n",
-        "\n",
-        "axs[1].plot(total_acc_train_plot, label='Training Accuracy')\n",
-        "axs[1].set_title('Training Accuracy over Epochs')\n",
-        "axs[1].set_xlabel('Epochs')\n",
-        "axs[1].set_ylabel('Accuracy')\n",
-        "axs[1].set_ylim([0, 100])\n",
-        "axs[1].legend()\n",
-        "\n",
-        "plt.tight_layout()\n",
-        "\n",
-        "plt.show()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 507
-        },
-        "id": "lttvmDapeeVu",
-        "outputId": "79b9acd9-258f-4bc5-c591-8f776ebfd8e4"
-      },
-      "execution_count": 301,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 1500x500 with 2 Axes>"
-            ],
-            "image/png": 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-          },
-          "metadata": {}
-        }
-      ]
-    }
-  ]
-}
\ No newline at end of file
diff --git a/Ex_3_pre-trained-models-image-classification.ipynb b/Ex_3_pre-trained-models-image-classification.ipynb
new file mode 100644
index 0000000..9b16346
--- /dev/null
+++ b/Ex_3_pre-trained-models-image-classification.ipynb
@@ -0,0 +1,1267 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "WuepB4WsaNWi"
+   },
+   "source": [
+    "# Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-10-26T17:57:17.943770Z",
+     "iopub.status.busy": "2025-10-26T17:57:17.943113Z",
+     "iopub.status.idle": "2025-10-26T17:57:17.949123Z",
+     "shell.execute_reply": "2025-10-26T17:57:17.948531Z",
+     "shell.execute_reply.started": "2025-10-26T17:57:17.943742Z"
+    },
+    "executionInfo": {
+     "elapsed": 19,
+     "status": "ok",
+     "timestamp": 1761350987842,
+     "user": {
+      "displayName": "Ignacio Menna",
+      "userId": "10948974476344730015"
+     },
+     "user_tz": 180
+    },
+    "id": "yKfOHZ7b9h9Z",
+    "outputId": "79820530-e4a9-447f-cc24-80e660961d82",
+    "trusted": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cuda\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "from torch.optim import Adam\n",
+    "from torchvision.transforms import transforms\n",
+    "from torchvision import models\n",
+    "from torch.utils.data import Dataset, DataLoader\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "import matplotlib.pyplot as plt\n",
+    "from PIL import Image\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import os\n",
+    "\n",
+    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
+    "print(device)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Initialize Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-10-26T17:57:17.950310Z",
+     "iopub.status.busy": "2025-10-26T17:57:17.950122Z",
+     "iopub.status.idle": "2025-10-26T17:57:17.996183Z",
+     "shell.execute_reply": "2025-10-26T17:57:17.995661Z",
+     "shell.execute_reply.started": "2025-10-26T17:57:17.950295Z"
+    },
+    "executionInfo": {
+     "elapsed": 46,
+     "status": "ok",
+     "timestamp": 1761350987890,
+     "user": {
+      "displayName": "Ignacio Menna",
+      "userId": "10948974476344730015"
+     },
+     "user_tz": 180
+    },
+    "id": "PXrpTsJ6Wtuj",
+    "outputId": "18409f90-ea5e-4bd6-a4ee-800f1bd18547",
+    "trusted": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Data shape is:  (1167, 2)\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<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>image:FILE</th>\n",
+       "      <th>category</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>/kaggle/input/bean-leaf-lesions-classification...</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>/kaggle/input/bean-leaf-lesions-classification...</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>/kaggle/input/bean-leaf-lesions-classification...</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>/kaggle/input/bean-leaf-lesions-classification...</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>/kaggle/input/bean-leaf-lesions-classification...</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                          image:FILE  category\n",
+       "0  /kaggle/input/bean-leaf-lesions-classification...         0\n",
+       "1  /kaggle/input/bean-leaf-lesions-classification...         0\n",
+       "2  /kaggle/input/bean-leaf-lesions-classification...         0\n",
+       "3  /kaggle/input/bean-leaf-lesions-classification...         0\n",
+       "4  /kaggle/input/bean-leaf-lesions-classification...         0"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#####################\n",
+    "# Reading Data Paths\n",
+    "#####################\n",
+    "train_df = pd.read_csv(\"/kaggle/input/bean-leaf-lesions-classification/train.csv\")\n",
+    "val_df = pd.read_csv(\"/kaggle/input/bean-leaf-lesions-classification/val.csv\")\n",
+    "\n",
+    "data_df = pd.concat([train_df, val_df], ignore_index=True)\n",
+    "\n",
+    "data_df[\"image:FILE\"] = \"/kaggle/input/bean-leaf-lesions-classification/\" + data_df[\"image:FILE\"]\n",
+    "\n",
+    "print(\"Data shape is: \", data_df.shape)\n",
+    "print()\n",
+    "data_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-10-26T17:57:17.997393Z",
+     "iopub.status.busy": "2025-10-26T17:57:17.997188Z",
+     "iopub.status.idle": "2025-10-26T17:57:18.002470Z",
+     "shell.execute_reply": "2025-10-26T17:57:18.001951Z",
+     "shell.execute_reply.started": "2025-10-26T17:57:17.997377Z"
+    },
+    "executionInfo": {
+     "elapsed": 11,
+     "status": "ok",
+     "timestamp": 1761350987903,
+     "user": {
+      "displayName": "Ignacio Menna",
+      "userId": "10948974476344730015"
+     },
+     "user_tz": 180
+    },
+    "id": "jK5Rh6R4ZmAl",
+    "outputId": "ea2c4f0d-33f6-4952-93bc-b405ee52aadb",
+    "trusted": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classes are: \n",
+      "[0 1 2]\n",
+      "\n",
+      "Classes ditrubution are: \n",
+      "category\n",
+      "2    393\n",
+      "1    389\n",
+      "0    385\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Classes are: \")\n",
+    "print(data_df[\"category\"].unique())\n",
+    "print()\n",
+    "print(\"Classes ditrubution are: \")\n",
+    "print(data_df[\"category\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ZxUsTB98cbZO"
+   },
+   "source": [
+    "# Data Split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-10-26T17:57:18.003365Z",
+     "iopub.status.busy": "2025-10-26T17:57:18.003126Z",
+     "iopub.status.idle": "2025-10-26T17:57:18.019073Z",
+     "shell.execute_reply": "2025-10-26T17:57:18.018462Z",
+     "shell.execute_reply.started": "2025-10-26T17:57:18.003344Z"
+    },
+    "executionInfo": {
+     "elapsed": 3,
+     "status": "ok",
+     "timestamp": 1761350987910,
+     "user": {
+      "displayName": "Ignacio Menna",
+      "userId": "10948974476344730015"
+     },
+     "user_tz": 180
+    },
+    "id": "sZnJlQUBcdfM",
+    "trusted": true
+   },
+   "outputs": [],
+   "source": [
+    "train = data_df.sample(frac=0.7, random_state=42)\n",
+    "test = data_df.drop(train.index)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "YacZCxKcdcHL"
+   },
+   "source": [
+    "# Preprocessing Objects"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-10-26T17:57:18.021112Z",
+     "iopub.status.busy": "2025-10-26T17:57:18.020656Z",
+     "iopub.status.idle": "2025-10-26T17:57:18.035118Z",
+     "shell.execute_reply": "2025-10-26T17:57:18.034585Z",
+     "shell.execute_reply.started": "2025-10-26T17:57:18.021095Z"
+    },
+    "executionInfo": {
+     "elapsed": 2,
+     "status": "ok",
+     "timestamp": 1761350987915,
+     "user": {
+      "displayName": "Ignacio Menna",
+      "userId": "10948974476344730015"
+     },
+     "user_tz": 180
+    },
+    "id": "iPf95Fkubanb",
+    "trusted": true
+   },
+   "outputs": [],
+   "source": [
+    "label_encoder = LabelEncoder()\n",
+    "\n",
+    "transform = transforms.Compose([\n",
+    "    transforms.Resize((128, 128)),\n",
+    "    transforms.ToTensor()\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "-stJF61Uel6y"
+   },
+   "source": [
+    "# Custom Dataset Class"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-10-26T17:57:18.035888Z",
+     "iopub.status.busy": "2025-10-26T17:57:18.035730Z",
+     "iopub.status.idle": "2025-10-26T17:57:18.050910Z",
+     "shell.execute_reply": "2025-10-26T17:57:18.050363Z",
+     "shell.execute_reply.started": "2025-10-26T17:57:18.035876Z"
+    },
+    "executionInfo": {
+     "elapsed": 18,
+     "status": "ok",
+     "timestamp": 1761351186955,
+     "user": {
+      "displayName": "Ignacio Menna",
+      "userId": "10948974476344730015"
+     },
+     "user_tz": 180
+    },
+    "id": "-bKGgPYqejQl",
+    "trusted": true
+   },
+   "outputs": [],
+   "source": [
+    "class CustomImageDataset(Dataset):\n",
+    "  def __init__(self, dataframe, transform=None):\n",
+    "     self.dataframe = dataframe\n",
+    "     self.transform = transform\n",
+    "     self.labels = torch.tensor(label_encoder.fit_transform(dataframe['category']))\n",
+    "\n",
+    "  def __len__(self):\n",
+    "    return self.dataframe.shape[0]\n",
+    "\n",
+    "  def __getitem__(self, idx):\n",
+    "    img_path = self.dataframe.iloc[idx, 0]\n",
+    "    label = self.labels[idx]\n",
+    "    image = Image.open(img_path).convert('RGB')\n",
+    "    if self.transform:\n",
+    "      image = self.transform(image)\n",
+    "\n",
+    "    return image, label"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-10-26T17:57:18.051798Z",
+     "iopub.status.busy": "2025-10-26T17:57:18.051576Z",
+     "iopub.status.idle": "2025-10-26T17:57:18.067175Z",
+     "shell.execute_reply": "2025-10-26T17:57:18.066481Z",
+     "shell.execute_reply.started": "2025-10-26T17:57:18.051782Z"
+    },
+    "executionInfo": {
+     "elapsed": 27,
+     "status": "ok",
+     "timestamp": 1761351197352,
+     "user": {
+      "displayName": "Ignacio Menna",
+      "userId": "10948974476344730015"
+     },
+     "user_tz": 180
+    },
+    "id": "CPKYY8jYkEfk",
+    "trusted": true
+   },
+   "outputs": [],
+   "source": [
+    "#########################\n",
+    "# Create Dataset Objects\n",
+    "#########################\n",
+    "train_dataset = CustomImageDataset(dataframe=train, transform=transform)\n",
+    "test_dataset = CustomImageDataset(dataframe=test, transform=transform)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "VgjfN0Knns_c"
+   },
+   "source": [
+    "# Visualize Images"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-10-26T17:57:18.068356Z",
+     "iopub.status.busy": "2025-10-26T17:57:18.068123Z",
+     "iopub.status.idle": "2025-10-26T17:57:18.541446Z",
+     "shell.execute_reply": "2025-10-26T17:57:18.540793Z",
+     "shell.execute_reply.started": "2025-10-26T17:57:18.068336Z"
+    },
+    "executionInfo": {
+     "elapsed": 702,
+     "status": "ok",
+     "timestamp": 1761351275036,
+     "user": {
+      "displayName": "Ignacio Menna",
+      "userId": "10948974476344730015"
+     },
+     "user_tz": 180
+    },
+    "id": "oGhf4lFanuJF",
+    "outputId": "37258fb8-b11c-48bf-d268-9a5bf54cc391",
+    "trusted": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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rrHNUdYUxLdN+xM6gTz9ecDRK8UNJv58QSkVRt1wtar7zySm3r+3yG1+/w97uiIvLNSd7e/zw/mO+9ZU3UM5Rdh1lowmk4FtfuUHXaM7mGZuqpWlaWhxdZ15pXV858FZVx3Q0QgiPourwlIevfLJNy8XVkqPDA8bjPkZ0vHl4g2KdQyA4/uAD3MmUTXtJjWFWPmG5EJi8Jnt+xtm8RFs4uHNMOBBsLi/5yru3eTHfUGjJJxdXTFXM9Rt3md5OWW3miMDnnd/+NVhqrn74mPKyZLGZ0UtjmrqhqhqUUPTDmFGvh9YOTwi8QPHi5QVSKgQtixcLwMdYj8lkAOGCaBMymtymd6iZ15c8f/47+P5DPHPIhz/8CVFjqZo+T68yPquWZNLDFCAfXfBG0mfU7zFJBLvTHRwQRiF+ECG9kGvGMb+64tmLp8wXM3bHY7quIwgDjnYnGKDINTtHU+TYp15u6Kcxo6qH9Rxpz8cLJiA9El9xsrtP03ZkXYuoOmyrqYqGonJIxM930n8hkQRBiCclWAvGboOrUkgHAds0rY4U1rUIbfCEj3CSumvolMDrpVihMQq09vCTGHnQw0XgK4k6q7BCEPkBMgxohcG1GoEmFGL795TAWdBm+5ELhCTQEl04Hj2quZzX1J3AWIkXB/hJSpAMUWFKq2usybGuwgqLUz50GqnB5JoFsGgdQc8ndCGeb+gSR9vlbESNTCxBH/qqJcyWdEbgocgMnK9nxGmPJAzoHe5xtLOHCiRu38cfacoAlO/TtpLuwmGDAjyYHo1QKmdxOmN1taDtUsaTCcoL6FqLN9jBiZQuy5Ba4AmL11ni3oDKwWggqGxNIXP2D4/YG0xZrXJOn539ZW+YvzJMhime7yGdRHkSoSxN09A2mjxryFeOy+eaYRTQFR3COnCgnUAIQIJnLF3tWJuO1QaMhTiRxJHj2UJQ5JrGgFDgBT5FsWI0/ASMQFtwzhJMam684fP5H8346B89IUgEf/9/8Vv87/7D/5LFrCQMPBQ+iQ+qNWSPl9RJS3SUs1jX9KVPZQvC0Of43ZLbVYq5spS15t7dMRfzitmm5b/44/sYYfng1h7ffPcWSgUs5wvm8wYrVvR8w+7+LrbMefs3vsSk38OYmq61tE1HY2E0GmCFJU0PqaoCL4BHD8/pjKCoNX3poVvNaKfPZDJkNBrx3Z98xt7OkNnlnOFgwKAXoJcNNw92+PjjF+zvpKwvC3bGPaQTBJ7HeBpyME25Wresypp5UWHtq80ceuXAuzee0pmO7370gMlgxNH+Dr7SnBzt0NQNddbyaP4M4YPb19R1SdAb8Gd//keogzHjnYDWzVHegmkwRI5HJJ7HJ88u8Y2AacBVccHBZMLb79/l/J99j7qwNMZxWTSkaPqhwPUsh7uSaezT1o56FHH2+JzlumWTl/SSmFGasDvs0+sl27qaFxDGPm1Xszi95NnFjEWeEQY+Nw722R8NEYmjVTF97xqndUabvGBnOqNzmvPZFeXsFourJYFzmLqiKiEvGrrA4pTiyWcvmVeON8bXeevkCKsgTmOk5+GcRlhDGoRUviANY7QROA1p0sdTAq1rRntTdo6nXKzPWRYrpFS4OGQ0Ckl2IrxwwOxshWnnJGnCdHfMLF/TnG7AdNzYm4K1rNuW1tmf96z/wqGkwFcegae2HyOrEErihMAqizYas67Bc/jOYdoO4Sk8T+H3YoLDAbrvYSmRniUwDiUV2tu+WOvQgKkRBuLQQ/kKhETjUxcZtirphKVzmmQ8wCiJrmtk0+GKjvPPMxrdoaXD64d4aYSIfJwUGNsQqAjfObQWdGb74fQCDyM9dNsgW4mxARaFKztYtUQyRg5jPNsBDdZs6DKLKdek/g5l1bEsSrpa44xjU5UcjSfspQOUBCENVB0yMjTCRzcOrEK6kKY2NK2hrlpwMfHgEOtaeiJiujNFO8t80eA8gRrHJP0Q6gp/VRP4ATL1CcYTROQT06GiBZOdmL2xIO4lrJvX/Qk/oz8YU3cNxnQ4Z2grQ91AvmowrUNISzr2GKRDfAvriw2eX2M7EBKkcHieQAO2A+VAWGi1pdwIWmNp9DYjo61Dyg6hJC+vluBACEG2bHncGJ6uHJsHHV+6dY0/+cefcPftI/7d/9Fv8B//7/8Jm7LjaG9AksRkq5wgVQRvOU7rDaPEp+paNCGNEWTZJdPDFCkysicjhklEU9Z875MzVnXDprZ4/oa2+YxNXjIZ94mVRys1vTCgOr1Cej6uq4miPmUJxmnSxGe5bPAjzXRvTNsVOKeRKiaOI6qmYWfiMxmnXM1WGG2IBjH9Yci/svtVnj87oyhaEJKdaR/nYHcnQcoDfnz/GReLnIPdEb1eSlE1PHuy4WB/wNUip2k0OHjV984rB97QC8jKgiTp0U8SqrKi9RSD0ZCry3M2m2bbadu0PDtbsFiuWBVPuFhnaAQ7kx5/8298wNEbU5q4QU7BGw9442DIaXXG548e0qHJFmtePDojXIa8M93hZXHFZ8tT9sJ9dnxH+eKc53PLZbXkeHCXCkHaS7m+dx2HYr1Z0emOumkx2pImIV7oM5gOyQpBVtU8mS0QSjAcjhGehx22ZINLoraHX405PX2IXzQM/IDlxuC6kJ4KCYIAow2buuEw3aW2gss6Q3YGkYNofT7Lz7mYr1lmOQhJL1C8d+2YxJNMp2M86bE3HTPoDyiynLKpUEFCZwUGga4ryrylXmmS2OPam7d59ulDnOqIkoDrt64R+9ummUcvTvn40UNGgzGXm5xlnuOkJIkjUql+vpP+i4jW4BxOOKSSeNLb/t5orGN7MdIG/4tbv5QKT3pItjUyqzWObZ1T0yECg/PBcwJhQXsOkfjIRlOaDl03SClAKyyCpq4wnsKLfPxJAqnE1T5uXWIWFqU8AhTSs/iDHmqQYqTBuo6yWNFUK1xlwAnUIMYJgXISZRUGh+z50AvwPFClRZUChaMNGsTEB7+lKjasCk3QxjgMZau5zDMCJ5BCkNeOMuqhBj5ZVdLoGrGoKVeObneAjBMsHdI5IhliPMAKEIogCfH9EWhF11oMBs/z6JqazjSAREUO/2iA8/vUCkQokF6IIiaWIZfrNZ4zCD/Ai/2/5A3zV4fVpqLVmrbqENaSFQ3zq5ZybVFWcePWiLfeUTz6OGcgdpGF5O13Yj777Dl1oxFK0BpACKQELcS2Hto5GuMwzmEtKH8bqK10eEowGvp0rcZ0EKmAcg5qpnBVR13nNK3hP/k//gH/2//o7/Fv/ne/xj/8B99jtlgQFT7GaJ7mc3rUSCm3PRVGopTP/LKkyi3rYY5rS469IQ8+PuPL75zw7l243DRIWdI1LVam9HsDyqKhlh0qAE8J/KDHg/svuHHQxxpL3XQkvZRBz+Po2oT54orNusTQotuOVVajDUwnMVdXc54+XbE7nRBFIWenF+wd7iNCH0/5DHqSTx+d4UUhSlkGcQ9Dze2bh3x6tubZ5ZKubTjen7AzTHn6Ysl0mIJzaLe9vLwKrxx4A18xHQ0I4hjlJIFU1G3Dg88ec3LtGuVnj7GmwxOglE9ZbusB13f2sM7RiyPiJqT4OMMkiuw44+r5M3qtJRIK6nwrX8gMmxL6TcvtyYidJOHhOudPv/0Zb9494eV3Z9wIJjQi41T8KUXWMG57vFifMxz0MEZjjWWQ9uk6Q9O2VE3Dp48fUbQdT6/mrJuWQZTgjKEUK2bDFSba4F9OSQpDv5GY5zF1M6Suzmnqhpk8ZVZb4kFIXtW48gppFV/bPabnhZiBpawbnpwv8J3H3mBC0bRILJ70CYKAurWYrqCXpOi2IYx8JntDgjihNO1WHlQ3JGGP0fUpm+WKejFn0EvY5GvW1YreoM9op0/XaornBcN4yPlizabTLFuN7/tE2uDLV6s1/HXAkwopBFiwzhAGHkI4tLEIt21WcQKElEgkznUoAdZZmqKjeZIj2wHh8QiiEGMzJOKf/5vSuK18o20wHlhjSXsJAoHRBtVPiCcxqh8iegEaC8ZhY4k3TggGMU1eYbVFRP72ScIXIntjoWyRmw5tJU6ACcBphUAhAwXC4ac+ToAMHUGitrVTZ1FpgHNgspZ111F3gkVTY9ttU00gJaW1OM8jzzKK0Qjp+9i2xlZgJHhhQ9g6bFNjbUuX+pxvMvw0Iopj4jhE9hSXL68o5i1hOiAIfWQQQWOwFqxVmNCnVFv5nKw6oEKqGOMEuhE8frIkTCOseB14f4ZuO/IsZ7noMEaQrzT50hHg8c77tzi+48jrc7xQo/OGJx9fIeyUu/du8NPPnjK9HnF+WtLvbxvXVpkhyw1pPwCtMfW2l8EZkFLgeRCGDtMY0jCmp3xefNowu2r5+lv74FkeXV6xLjX7QcJ/+h/9v/gP/jf/fT798SnPnl5sG5wkMITGNAQqwJQ+YRQzuyxpm+2l7MXjGmEl89lT4k3E/SdL7pyM+btfvc7Fsubp5ZJHLxf82gdvENiKvOzYFDV153j04UuOdnaotSOvDA6FpyTaamyXEcURF7MNVV0xGvUQwlHXDWmcMB1PuJGGKE/x5OkLJpM+xllE034hIRTcvX3IH3/vc/b3huSi48nLOZ4SRJ4gLxs+zUteLjb86vt3uBXtMhkEfH6asL7abC+jr8C/QFdzR5ZvuJhvODk4IAl8kiAgDiPKTc5kPAJr8MX243D4tS9jO4NuWpSSIAxRbCnWLY1RPHk459EnTzi8t89M+kgR4lmLdR3CaYjg06tzqnWHP0oR/Zbnn1/hNT7v33mL+WzGn/7+p4RezNG9A7SpmS82CKUYxgmh5yGU4sXlnE1ZcLHcIDyPqjH0gpjpaMCNe4d4h6cskjWqhY8+uiRGs3cQIRPBl762z3LpOH9Q8XCpaNqGet5QLguWVxXHkxGDMEZpWKxXDNIev/XV9wil5OHLMwySSX/IpJ9S1iXPX57j+wFF2TAYJNy5c8LVfEGHZbi/R1FWGCGIkoi6zOn3UpqyQBctpu7Iq4bFbEWSpJxcO+Fod59+r+Yi22CEod+PMcYRBo7o9Yv3nyO/kAcJa/E8hbMGpENKgdUWrTVSKDpt8BQIKWhMh3EGITxooT0rCOIIbxzilIdyFll2mFVDt6xxhUEKHwIfGSkYxeAgUQHe2IeBRATgLDSzBlM3OG0RsYftKwgCRKZpNzk4jfR9pFKoSKH8EC0cotZ08xIbS3SkkL6H8wQIgef7CCmxtLRKIHxFFCq0FLDUiKWijT1MEtKZbhvMpcBIhRYK5SBrWx5dnRPEKSmKk50DKlcSqoCJjMmpeFQt+PjslLNyTThKGR+MiY/2aYo180cv0csc9nZIJ/vc3j3gWb6iQdA0FVW1BlnhrEQXG+p6Q5gMCJMh0g+Qgx7SC3HN60vjzzg9XZMtDV0DCAOdpBcF3Htzh/F+h3WGbGPZ2e9zvio43Bnw8fcv+einC975zT1232i5XNeQwO6+j7/yWc0LOqMZHyWsLiq6XOMrUEbSzMELY9qiQ5iQG+MxDy6eg4Vl2+IpSVF39MOASAkuHi75B/+3P+Hf/1//Pf7D/+l/TNtlJElIdKCIeyHjnZCmLWnbgiQFpKTIGkznwGp2w4Bo0Od7n74gCizfuHfEyaLg/Tf2+J0fPOHJiyu+9dYJB7uKP//pM84XGT0v5OZ+n/l8w4yM9++csFhsqNsYo+32Ym0dygn6cURRVHgqoGsMk2FKUZRUhUHgmF1kSM9DSJ/HTy6I/IDhwOdLd49Ik4jZYsXt4zHWCW5f2+PZ5Yzv3n9J61rKruKX37vHs2eX7I96PJptCMJXW9dXDryd3t4sxoMRO4MBSaLQTpMEIS/OzljnNabr2B2N8KQkiWJ6cYhREmct0SDhUi/4s48+I6shujai7Qz+ToJ8mWH9AO0MKvIQpqQsNVlmydcd3lyTqID2siAxPp9++pCuabk22WWYDggCSSAjhFTMNxn9KEUKySbPma3WID1CL0Qoxe29fcbTIZM3Dujf8lmaFmk8Vi/PqfOayE842r3BUl4wnIbspfsctgFnm6c415JlOV2p2Y1GDP2Ef/bjTxEI2lqT+B5v1jVHwz6rIkcqxXTQR/keovU43D9ESklV5iihuFou6O336e/22b+9jwwB6Wgyyw9+/0foomG93tDkDUEU0Uv7eELgC4/52SXzzZL5esWN/TFJEXK6WLBzL+LXf/NLzD8qf66D/ouI7lokIDxQwgMc1gpAIAX4vgeIf16jcc5RNzXGWpSn0VgCq3DnBVQd0a6Psxq5aHCLDs9KXM/HOkkwCNB9j1YaAqHwkgCROIzXIhC42iCuSuSqRmMR0x5WaIQz0LTIWmOaFmdbTOyj/D5dp8nWDVQW5TxUEiI9Rd3UaK0JkojA95CejwxDrLNo0aGbBrvS+BkozbaWrVrUMCDuhwQoTN7i6u3lY101lLqjF7dEUcre0QnrXBN1Hknk0+6HVLOC549X5E1NimPQT5k9PmP58BKxKfnK23tY3/ErX36Xjx6fEoQx1hmKfEM2m+P5DmMFXVWDqbCdRmDwkwQVRRhnWVzO/lL3y18lsqWmawTWQlNB6gUc3xgw2utwfk2RexSZZe9A0T+BZw9KfOVRVJpP/3COCsd4QcjVeUncC77QkoPutnHclOByRVtJ9tNdHj06Z6kLrk163DoeEVmf/X6fp9mSRZVjjGU86PNLd2/gjOEnz0/5s9/9Lu/cvcbf/5//D/m//J/+70R7koObGi8JyZuSptM4K7Bya0QhpEB5DlMJTh9lzJ7PGCUJz05n/NrX76CyjMg5/jt/4w3+6EcveHQ+58bRBGMkf/dX3sYzX/RltBFPrxasyoy8bAl6Eb1+hLWOxFPs7fWRCIaDlCg21EWJcRoVxcyuFkwmUybjPuu84v5nz0jSlDRRDAYxSeLjrESMB6S9BN1VWOfRdD1C3yPrDJ8+u+Jw1EdJ6KcRHpKy/Zfc1dwaTVm27I7GCGOoO03W5HStQ3cOpIeTjjCIuXXtmCiQZNkGKxytacmzhk9Wz1nqluVC44qawTRm73Cfa3du8PF3PuTR80ssklYr+knI3Xev8fDDZ1TLlmpmGAwCKl3zUqzxRUBpNNlywePLS6a9HifTKdIZ8qKgKguiKOTO8QlFWXHhVsRhzNH+DuOTCfEbO1RBgavv8OKj71BeeXzl7pvc+9avsnPc48nZU+7fP+WaUKwuDOt5gaMFq3E1TCYDlnnFy4ucKPRJwoB1bfj8dIZtLVL4OON4fHbOZ6fnWGO4sTNmmMYEvo92mqOv3OatX3sTFTm0MKhgGxjyrOJt7y0e/slTlqtsW6MepdjWMB2PWG9qzhYrPn32jDuH+9w+mdB5gkfG44N/7YjIKp796frnPuy/aHjS4Xk+oeej1NbsYnJwnbar2MzOkELQOosMfMIowlmDqyuMcuBJAitJRACdRZYGt7a4oEPGDn/Hx4tixCjeplSFo+0adGfxQg/rO3ynMc6BUHSrAp21hH5EECrwFF3eYNY1ItdgBQEh0hOIaJsyrucVgRGIwMMAwoFygtDzCZNtw5ipG0SkcFLiS4/paMhuf0J2nnFRX9A2GX6n0a1DRD7Sk5hNja5ajBEYYzHagPMIQsHN6S77oz5R4FE3NYsi52G94ePTxzSdRQmF0GAv12Atw9YyPexz4yjg7Q/+Jrfe/yov/+s/4vNHZ5RdyWY1py0aatuBJ0GANPKLINDgGR+cj+4aqldw/vnrwvG1lKrQrNYtSZTw3jtH1O6MxilMY2kySV5ZplgGux5upyVZxHS6oM4bvv+fX9E7ivAHkvlTx+5eTGgb9MZxNa8pLi2mtnTa0ngXeM4iJDhrcc6ihWV3nHC6WTKNBWGsEJHPLFuxXOassoK/8eU3+b3/7J/y6//Wb/Af/K/+dT57+gPK4YrTRU7XeBgNxmps56ErR1tZutZt+yecY5L4XBv32JsMOX25Igl7ZJsS1+RME4/7L1c8OVtzdDDiBx895De+9TZKKvJWcv7pS6ruikZrgmdLpsMhO0Ofa4cTnFQY53AWPE+SJjHf/+kTnJSkSciw12F0g3OOe3cPaasWYwz5aoMQPp11GAefPrpgMh3ghYqq1RhnKRvLxaLl+WzDNAnxlCIJFXn5ak2trxx4y7JjZzTmaH/KYrlgEkzoKoOpLeN+n4lS24alKMaYhqtZzmZdkFc1WVmiAp9JNObavWO+3zzhyWxNJTrO/uAxk2HEDRsxOjzh02fPmK078sbyYPOQsrF01nCxXjPfgDaGx3ZN7Pko5WOdw3OCyE+oq5o4CAikh5SCyAsROHpJiBRDhJAoZ1i8PKc9f0HR5VzaJZebNe0MfuVf/xpf+c1v4nzD8HjM7/6j5/z4kyesLxqWRU20F+JFKWd6zf3LGbp1244Ep2hbyyRN6ScxSoUM4wBrDY/nMz4/u2QUxVzf3aFuNK1xzM4XRI9S3v7bbxL2fETb0nYFRWfJ2gqVtDjXMtkbIhPN9bsHSN/n+aNT1PUex4Nr2B9bzIOaQue88WsHHF97E68Hi08KxmHv5z7sv2h4ysNTHkoplNw2PGnhaCVo38cPJOFOgkk9rFLgLLKLEL7Feg5aTVNb/LXBOUETSgwSPxJ44wARRpjA276qtUXlCtMBziFbgy/YWnK0Frfp0KXG8yXKWYxpQRhUDXG/T5TGDHtD+pMBWb3Ek5Ki38PzIvb3j5jsTwgnKf3xkE21wVM+TVVRbDYURUk/UHzrG1/jxt1bXL92i9PZObPZS7pNhq8tL56f8+mPXvL5j59gi4CTm3v0DwYQeJiiRhcd+67Htf0jROQhnGK9LphvllzUM+q2I/YCjOwYhgnX05TbN4cEoUDrkre+8ssMr73No+WcJoAsX7Bazyg2GU5sX7sBHkEYYVyNkO6LVJ9AKYEfeEyvT/+yt8xfGRbzhuVFR1cJbt3po5IMXWhcvdWdr3ONVBLtNNJJ9u4EfP5xhTYOC+hW8+J+zqAnKB5asn5DU0na1mz10k7gSYETDusMwgl86VhkNReLkunAklcdceBTFJqz5x2xajkza6TweePkiM+evCQKff6f/+k/4t/7n/3btMGIdKfg1sGAaRHRZI5OS168XNJUhv5QUFeSJjMcTFLe2Jvy/rt3GO0O+K/+ybf5ygdv4CuD8iKGI8v80zMuipwXywxrHBflRwziEB/Ftb0xBsFe7DPq9ciznKZ0YBRVUfHidEFvPODw6ACjDHvXrjEaxMRJiGcqik1Gkdc4I1msNhwdDRkNdwhCn6osaVvHPCuYb2qObk3pn7xB+GhN0C6ZrWv+/Cdn/PY37zCdhMSeIpKvpkF/9a7mOCKMPJ6dviTwQtqyZhgn1FoTBT7GtqS9PsYYruYzMJDEPZpOszOZkoYRUkk6Z/i3fvs3efj4IV1refz5KXkcEQRQ6IpqUdGuLUiPzVVH4yzKCboADA5nJMY6sJZAGXppxG6acjIesjtMiaMEayVKWLwvMojGdexO+iRJD+V76E5jOkPd91k1VwzCmLYxhIOA5cVTPvzwE7L1KZ/95AFZ55O3LaO0x+7RAW3osbl6gKxC0B2hL/kiP0kvDunFETJQBIFHXXf4CG7s7DJKEgLl0emOstaM+kPm95f89J/+iLu/9gZOlAjZ0bQFZy+eU1xa5Aje+6U7TA7HTA4HeEFI8DjlUb6iba+YbEKapeXk18cMvzKg1C1VVbE8bfHc6waVn+GcwBqDkQKspdOal0/u4yc9PD9AOw0YXKhoRYc1Guk7jHRYp7F+i58oSDywPiaUOBeg8ZBhQBCHeMagVxmu7PCERHhbZyyKDlNZZC3pao3awDAZ0B8nRL2QyY0Dju6esLuzw8m1A5J+n2FvSmkznl88Zm/3aCvrcJLIT3h5cYYXxBztXmeVX1FWDdPeCGMt/X5K6Gs82ZHXmueLOZfrc87z51sDgcGA68eKu199E389ZtDbo4jOuGjndMZDuRZv3RC1EePRiMbTDHRDsLnBaL3hoCj5JRWQFRteXDyjqyquj/sc7CkG/QGDveu4/jXONzWD/ojNckW2ucDoliD06KTFoVAqwvNjcApkh+9HSOmhEEhriPqv+xN+xrMHLV0B/UQhVcW6aMk3htCD3jiiSQvWc4PuOuIgYjoO+EznWCvojEN6kkACLfgyZCx6WFFSNgWObZew0Y7QUyjhKBsLVjCOfHRrCbyQG3s95nnO/KLCNpZOaoJQIh08Ob+g7jo8TxAHPg8+fkFwIOlFkNc1YWLxJKxmLTiPTrd0ndvqvAO4dbPPO/EhJ0e7/N7v/4AbJ/tcnM751jdu809//6d8/HTGmye7HFc9Hl4uyE1HXTbEKGb5Gi9O0V3O3/76PY53RuRFSK+XYq1jXdTsXrtGNBhylldoA7u336BrGp4u1ux4ls2iYmc3oZ8mVHWJtTBbFjSuw0MxGUYc74/5/MkFDz55xJf+xq/zzr0lHz94yCj22E9DHr28Yn8y4M0b+5gnl6+0rq9e421bni2W7I4n29eDVPT7PZrFnMbWuNCChIuLK7JNzZ1rt0jjgCDytq5AWjNfr9BCUlZr7h7vY7VjlS35bLGV99imQxofZzRF16GEYBgm9PspcT/m6uIKZx1xqJBia37QD2Ju7u1wc3+HUEqiICKIEgQapWCdFawWGTaVDIc+QejjD/rkmw1C9djXJ9T2nM4r+N7vf5fsv9iwWpTsHaTMnpREkxHKKOaXGeu6JByO2AuGJCLgSmdoD5pGI4Xjcr1itl7iKfjSzesM44BBEkNZM+2n2Lah10uJAotB41t4/GfPiHcSeichxpV07YrVbMnl84xv/eqX2T/x6CcSP4CLxRVPZ48obYvpNihfkB47xu/GZHrNfJFx+mRB9UzRFcHPedR/8RBsHaOcsbTC0FmLJzw863DOItIAEfoIX2GExeHoTIdSCqkUTngQeNgAMGBbRxhFhEGM7xSiNnTLjHBVE3kB0aSP6Xsk/Zij4YgkDoj7fZLBmOPDI8JhgJcokiRBeR5VU9HUHU5IrFIs2zWzzTlF17EqCwIlcVayaUqMhLrNuVyeAyGrzRVpEBP5MU2jqTtN5BuquqNqWjaLgiSc4Pt9hOqB7XHRzNjb65NHGQ/PZpS1JgjgaGefeKTQuqH0Ak5nM7zAp06hSxMO00N68YB1vuAkG5LOHbvpPv6JogoELy/XqHXHKJliTcXy4inCNgRBSFVX+MrD60Uo6eOEw/M8TNvi8pZqVdO6HL8y6Py1Bv1nSCcRwhL2JEHPojuB0QrhSQIluXzR4YcS2wickJgzj2mQYqYei2xN2cAw9DjeHRF7grvXd7n/7IIsK9AWbOfQDgLAOhBuKzcKA5+syvj8ZcPdo33qxmA7QHoIaahLi1IObQ2eEAySlCRQbF5c8s4bb5BvZox3JGW9wUYWv98yPOzAC8nWLWWhoXOEvs/edMB3vv0DijLng3embDLD9757n0fPF+xPplyfJvT6O5ycDXh4vuLa7ghPCOax4sNnF/T7ffq9HtLzKQpD22U4C8F4B5cM+eTRC9ZZQRiE3L//mMvLGVeXl/ztb9zlzvEEZ+Hi7IpeP8WGCd/78CGn8w2+r3jjeIdBGDAZJGSN5dMf/JB33rzO09MXHOwNoKz57PGC/b0xVZHz5XvHr7Surxx4jbHsT3foxxGd6SiahmbRIB2IxHIenhOGCfvTHY53e+wdjTg9e0m/P2I8SLg6P2U8TFgWDXne4CcJgyQkDn2ariXyY3w/QEiHQiKkQCDQ1tDZjnbdYK3DV4p+HBEHAanyCOOQz1+ckVcN33rzDQ4OjgiCgE2+oGkKmqYjiXoMkh7ZOme13pAOe+wdHJIvOsI8xC0E7UqwXGa0VcfJ5IBskbFZWjZ5hu9LnAZRSNoy5/0bt1iu1jwva6xQhEohlUN3hmVd4gvH2SZjZ3LM6aPnCOWTRiFJ6OFJgZUOP/AY9PoURcWjP37O4S/vs2lmbM7OaVduW9BnQ1P7CGNYnl/yu7/7fbJhxvi6Tz9NqFOLdxKxaTLWecnsLOf0Jxu6Jz5973W67mc4LGbrH4rFIsRW54rn8EYBYhSgE7AYHBAohei2zlVOsS1RADhN13W0tUO1jqPDPreu7aEUDPo32d2bsC4XOBGhoh5JGuIkVGXL9f03Odo/odYlV+szTCHxS03ZZizXc+4cvUHgS6qmQOuOpu5QLuLqakagAvZ29zk/PcdaSRB6zN0VoUjBOIR2GGnxfJ8kCCmbEm3gcjlDyoBrO4c4oWmamrpuCcOUVVWxPDvjcrliOtylHw/x/ZjGGWoHOq+x+JxezsmyFXsHu5SdouyWNOWG9aplWAw4Hp7w5OoZZ7Lhs4enfOXGAfvDHV6unpMvS+pFQata2k1NbzQmHCRY6xBtge00TanRqxo9b1FCoZyE+i95w/wVoiktvZFgfCixzmEbAW47WOPsRU5XWfYPA3qRz07R5/RJR543tKIi8MA5yTCKqcuc2ln+5Cdz2FqVY1oQwuELQdWarYQOCDwJGIq2ZV12JFFO3WoCT26lN2zlpUJsNb/Guq0Nb6u5/9OX/Na/8Q0+edhx59qIU5ltX491i7CC3kBDJ2lKiec7+nFC3eZEacfbuxYVfMa9L4/wHrS89d77XDwrSHsJm9WaL7+5z3iQ0ksiBpHEyAPu3TwkDH0iKWiqCj+AXurTiphod5+LWcZkukd/2FIvFnzprWOC96+h25bIA89qlC+p245l3vEHf/ADSmNxQkLd8OnTC37763eJg4ixhLX2KTY5UkoePLtg4PnUneZynvNLX36D+0/mr7Surxx4kzAkCgI8paibBiENXejwAx/G0PVSas8y6BLinR6rPc18sSYVAw6uHTKcJvz4uz/GEx51a6gDy+Z8xmxTIj2PeV5iWksgFYEfYIzbeukKKOsWrEVaie/7pGHCOI25tbfH45cvaazi2aIgfXrJteMbeIEk22zQRtPUNXGUkkQRnfZwbsPV1YJ5XjIejyiymuKyIxIpgyhid3efsmr4wctLmk6BtpSlJvI9Jr2IptNcLlcURYUTHtYafN9DOEdrBJGKUMJxMd9wczLh62+/Sactk94AYTuMbenFAYcnu1gpyJ9VqCrhB/+Pzzhdv+C9b57wK7/1NZK9ETLcBvz73/2c0x+sefDdFc10TaR6DG77TKYpVebTfBaQXTXYqz7pWYAXh4yHk5/vpP8CorwA6Sm0rrGIra7XCXRT4tmAUIQEQrG9RUpc1RC3CsIQE2y9Yg8mI4b9Ib1BnzSNCf2OYdrHCMnFPGdZbDg9O2e9WhAnI8ZDwcQ6jg6OuXvthJ3hPlY4mqzAGkHbtayyNYvVnEF/tNXammZbq5MBg2SCkYanz9bE/SGrRcFqsSEMUhAem3zBbt8ySCeESR+lFHVTgYD5KqdqDetyQ+TFKBFs64Ad1E1JGG1f9gSKNE3Znx4T+JJ8XRDFCctFjrCCTbbhYnbJ0dEBg3hAVVVkxZrVes7ias4b033KvGLlNixsS2T7+BZUXRAZR+IP0M0LatFuhzsIRyChrmu62QZdOxortp3azse1FtS2Oew1WzwHo52AMIWmsUSJh6OhKDqOjncZTyI6o5HrkP5iwLPnn6OlpW0NHgJnHWkcYXVAGMHLqznmC+MMiQAJunNYI0A5HII0iEiiiLIucVJwOl+ANDgESmwtKT1PorWh0xYpBAfTMV2l+dbbN9gRAe+O7lL+0YqdOzuUB2u6pEErSOIAb+DzTFnW64rNpkIdBXz1KxPOnj/g6eOKFxcLIml5860p77x7xJ/8QY4mYtiPuQkkaY8w8ukN+9yqNU8evyRrJZezGTvjGCFD4ukeizJH+Qrf80hay+H+kHFP4ExL1IsxWm8bvzSsK8nvffdjMqNxUuFJh3PQOkfnLM26RvYDHl7O+OThC5Z5RZyknC8zOgsvLlacH/Zx4l9yjVcqb/uDVgqlPOJejLsd46aK3k5INjun1IZ6bPFuDbmMM/Rxny6Dy+UcPxRUaPKqptMO09acL1c8OJ8xPDxEmob5bE6U+gjrcO02lVxqjdUW6eTWsABB2Rkoar7cG/CVuykvLmecL9esNznPX7xgf9pns8mpmg6sx6A/Ynd3n6atiZKID+8/4PL8is2moBfF9IMeke/RSyKauqEXB1zb3aG8mtNoTdcKrBUUZceqKLhalgySBKUkQkg6rbmzt8tVlpO3NRLw5TZtc3Kww2DUwxq+kIlYrHU8f35FoRuM1pSnJZNJj/d+4xu8/VtvEvcGdAbWV2tOP7pg8VFNPmsZ+T103kN81sOGPm68pi4a9LNdzJWH0RYT5ly7uU8Qvq7x/owwTiDyaAqNxeLc1nwCY9BXOX5nSff6+L0A0zmU9vnGL32L3XeP2agFm7IkUCF5XnA5v8IrFflmzbh/hG4batORFzWt0SxXK44OE64fjrlz7S6H+3sIB3VXUtZL6q6kzhek6QAXCdI4Igoi6rajrAqscOhOM+iN6GyLFIJVviIkwVMxRVMifMVmteH63hFSSsqqIP7CEGa1rpivSqq2YrXacPNwgjPbeUu+ikiTAXmesSlXvLw6Z9rfIfQkTdvSdS1xEqPbhk5byqqmrlqM3naGat0QqZA0mWJSQXrnkI0Mefm8wxDw7r1bRFGM8AT9fsKtmzf55MUjhOfACbToyIolrrG0lcFVDiEF1jh83yMO0q3hQvNqE17+OuCEY37WYHTAeCdAJILQj0gij+EkYLUytKcd/dmAs5c5s2WFc2z9mrutW9tsOccKgapCWr1NKUshMdaCAym/+KUkQeDTSxN6vT6LTYEwHdppfCVwbDOORju6Tm+bFT1wBp6ez/nqrWNCoXn24Qv23z7mR7/7gN1FCG9rhHQcpxrTdXghvHXTw9oQryp5cX6fF/cTQnsbY3Kunq/YnxbUjaFzDfOrK9K0j+/79Ptbb/Qyz+laTRAmOOfx5PFLJntjlPLwxnu4MCLoGmTa26oEm5zZ5QVdN2DYD2iLEmstvqeoWvjR50/IWk3RWaQ0KM/DVx6LTclnT644Gvf5s5884LLZllTDKEUJw5fef4MHD56xKWr+yZ9/vu0regVeOfAKLEkcYZ1hU2boRLF/6y7+NOTOm9eJLz5l/fISmzoWYYEJfO5985t4K8en3/4xmI69X76HefCC8v4lVsCs3KClZLnKsWhGUcphb0DP8wj9AGMdD07PKYxDWYEQAgcYa1AqJgp9buzukMY+R9MxoyRhMuhzfP0ane6Yz9cIJG3b8OzslKwscGY7J/Vkd/qFJaDg+s6UKAhw1nC5XHG+WJEmMXfvXed0Oefs+RpjHFXrcCKkl0RM+wkX8w1CSDzfZ5yG7PYCzjY50+EAH0HqB/z0kwdoKdidDhklCabV5FXDi9mSq2xD6AmODqfs3BjRf2dI5iqWqw3ZOuOn//ABzTNLm0PZ5cT9EKMMDZrnn7RIUXJwFODtbxB9x3A4Zn9ym73DEUeHBz/XQf9FxCpLVebYziI8D43CCUmYhJimol017CZjvvbBuxy+fQATx0W15Hd++vsEKuGtN96iMQ4rod8PWC+WbDYO6zbouuPy8oqjoxvs7iQMegm7u/vsTHcYD8Y4vZXeBaFPWZasqzWN7nB5TllW24HdsqLwfawFjETiM18suFzOAEfRlPSjIaEKyYsVvcTj9uE14kBuL8F+ROBLlPRZZSuc2XazjgcjJv0hzkg6s00fWm0RKJrCUq9bhgdDpLDbNKKVlM0GY2vKuuX86grTWHYHOyjlUbWaUZzgRRG9XsJZl6NVy2ePzvhbX/s1AuGjlCQrFijf4/bt20Tf/UOCgY8VCmMtTdluG376ESoGDFTzGuEsw4Mh/d6Izfy1FO5nWKAtHbPnDUmSEg8E5abjaG9MoDxO4gnzyzX9OuXF6hLpeSQOIi/gYlPie5IOQxSFtE2HUiFtW+PM1j41DDwCX4EQIBweDtuVLGcFxugvDGUUSm5fyQCev5UyGWNwSJSEQRpx43CPpmk4fXzFvV99jzc+uMZT8xHZ4gIRCnzPbrOYpUAJDa5GBRn20MObxvj1kMgbshse4inL+OQ98ssVd28cUTcN83nGzv4UJcFUNU4bqnrNqlhz794+KoiY3LhGrhVlXTAcDAjjIVdXC374o4+4eTjl80fnHOzvcHAwpK5zKtdyOSs4XxdYoeiMwWhHU7SEqiUKFA9ezmg7wWXWIOII6fkoufWAL8uCySCmqFrWbUfPe7UHzysH3tF4AMqymefUbUfiCbxAI0PHZ0++y5Pz+3ja4/roBhVLTs/mjI4C3n7jA148/Zinl88pkpTkVo/9KGd41Ec/aNGfBFyeVzSFxThJkTdMpjHLxQrhewSBT11rlNp+ZDxf0Uv7JEnMo4tzlNdyuDPlxk5AECqM7dC2Y7ozoc5KPOmxLioePHhAay33bt5gMhiQbTKcA98PCYOQKPCwztBpw4sXG15uNsg0oGrbrQ2j5zNMBoh8CWynzOwPhggcUaDIy4pBGtJ1huU6Y5z0saZj3O8TJX1msznriw1RGnO12XD/xRlaCAahz7GvyKOaR4unjEQfoTWr+YY6a7halyzWG1RkyfSS3gEob4gK+wx7IbO8ZHle4XYVXpmRBn0mLiafrX6OY/6LSd6WOAHOk1s3qDAkCGOcMUipeP+9d/nV/9Y7eAeK02rF54+fkeUN+4fXubZ3k0GaINTWClIAi3GfZBAhhKSpOqyGvb19ZusrusZysHPEeDDZvvK6Ds/3KJucTblhlWc0dYP2JXmjQUPTlkS1Yjod03QaicLk3TYl3Ta0XcWKiv3xEXm+wUzG3Dy6RWdadAdRL6Y1DctsjgpA5AYlJJ6SRGFAFEeUnUG3HcvNEuMMZVmyv7tPpw0Xswv6vR7adKzKnMV6jRAegVREsY91lqoqGY7G1FVBlKaYxnG+WlJljsSbEMVjRKBwShMGMV7sUInAaI1tJXEvpW1a6rrBWIMXCFSi8IUgALKLivXFDJoO3bxurvoZng+mEzjjmJ1mSBmjJNSZ5uVTwcUPnhPVip9kV1yuC0Lp8bW3bvHR43NeLku0tYhAojqNlIrID2nabiuFU1uFSFU3WOMIfUVn4Fc+uEsvEPzujx9glCSOEop8s/X/dw7nDL4vMdZsrVMleN62VHLzZI/7j8/59u/8OeFeS7hRpHJIJxosW3MJh0Jbg5GazkHnNNbLyNKC4Zsb0uYEfWnwh3Dxkyv8/gkGyWa9YH61YDQcEYchUZKyWC7Jy5rVsiLpx/z5pw84uHUHTyiKfo80bXnyyQOGsWI9XzEejDg/u6RpSq69dUgQeFxuGlrdIIRhmAY0ncZTjtjfdnoba5htarKiQTYdceQhlCBMY84XGfk6p8MgpKLTr7iur7oBql5JlTT0bo7ZS09Yz1Z8/ud/Tnqvx1V1yenFJXE85OC9E/Z3D+iaipBgu1jJEg7O2JiOtNen9J7z+Y87ihZO7h7RG/Z49MkLbOWo245BHDMIfB5ezegMJGFM4nv4nofwfe7evc2de9fRTU2z2VDWDcozdFIQ92N2DvdYnl3i+R62M4z6fT545x3atiQOQ+qmYzQcksQxQgg2mw1g0V3HeNgnvQiJZIBtLJNexNu3EnpNSla0PKwDPKkYBAFff+stfM9D1yXPz89Z5DmjKGWYJii79QQ+2BsjUMznjigOiDyffhhz63Cf4WBIgMB30DYdJquo2wwlFVerBWftgudVx6LdcPMg4KtfmnD3zX3SwZRny4zPXp7xMp9x75snWGux1nLtYIR43vHZh0/h7/6LHvNfTCIh0cbSWot2Bt83hFIRRxF/52//Kl/5V97mT+5/m6vPC0wnWa4bvCAg8FJ0a1jrJZ1pt5JtLNpqkjgikCG5rTETy6ZeUZYVsQg4nl4nCSKatiYIPIS1FPUKowt0WZGGPcIgJE2HxCLibH1KXdaEuyEXmwVdB6vNGmcFSZQgPIOMQVvNtcNrOCTWSXTrAZrZeo7Tlq7TdKbBGr2dKlPkOBRFVtHaBqc0nW159vw5+armg/c+YDQe0zQZ6/UMa8EJhXAh8/mcq/MZN2/eIoxDmsowny9o24aoMqAEi82GxKW8+85bZKbEtZq0F9B5UNU5rTCkcZ91VlMWJV1n8VyAH28ndkkp8ZVExg4bhlA7TF5iu1czmv/rwP5BwuV5hdXQlprF/Yq394/47u8/p84tpgVP+kS+QnzRJPXDz19yvtighEAqR9VatIM0Cqm6FoSjaQy1cASewFNsp+o4g0IxjHwGsUckPQprKbJ8Owdaiu08amNxziG+sBoNpI8xDhVGnG42rOqKP/7Pvstv/8rb7A9OcKuOQW9rDNTWmvGkx/2nC4bXO9xoxtrVWGlxztB4GUnwgL23xvzo/Ls0O5o0GLBzeJ11bRAOOqO3yoO2xfd83rx5SJSE/LPvPuTZecZ5dh+jG+6+cYv98ZibuxG+DIilj1CCr75/RBAFfPjwlLpt+ezxS8rW4AcB2lo8ta1lR6HCGEvbaR5fXn4hTbUIa4iiCGss67KkzCukr0h9eO/Oq2UaXznwFlmLOfSpdhRirHDTBPN4gcXSj0N+9Z13aJ0g7XVk2cdIHL3BhIcPP+bi7DPEbk7blYjxPl5vyKMfnXJ6WaOSkr3xkEBIhC9RUtBZw+F0yFmWUSHxvZDDXkoaBezdusGtezdo8ytcXXFysItparLVmnxWMtmZkIxGLK5mZHkJThJFAuO2JvZadwgREAYhUvoUZUEQhoSBj8PSdC37ox6jtEeaeBRJTZxCt/B5/PKKfhzz7skx6Jb1esl0Z8rB4R6xD512SCHQnUGI7UbSumW22rDcbHjz+g3SMGAy6nPUbGt6vvLZNAXlQjO8BioIqMqaxhgu9JrTuuXGtYT/wb/7Nu+/c520P+HR6QXff36fVgpkOiWaxAwCSZ41vHj6hPi0g/Xg5zzqv3h4xuG0xVcK4fn4rWF3P+Bf/fd+m9GtAd979iGl2ZqbLzZL1puCNOmThEMGgz7aVgz8PovVjLppMAb66YDxYIjZcZxfXbHJ1mwM7OztYx2s1ksGgxSjS3Rt0G1OpDSh6tjdneI5TaU78vyKy9mMptBE6ZTZVcZwMEZ3kBcbwFCbhp1gwM5kQuQP6UxLXmZIfOIwoNEVQagomgbnQAoPIQxgqauSfhwSRQFnVxcopaiKhoOdQwI/oGoyyjonjhLSOKHRLY1u2GQefuzjRxLTteAcpuvwrcTWlrPFjJfPX/L+3bdRoYcXS3p+QOBB1lXkeUcU9JmkY+Yvn1EVFV4ckkx6JElMWWbUm4o4jnGt2c7QdgHRK053+evCcFeQF5JqaaAAl/l0WkLmESuxDVp6+8yydtvoNN/kBGEArcPS4iQ462iaFq07kiBAi+3weunA9zyMMSgJQ9/HdBYRW75x7zp//MkjtNwODHFI+GK60XbUr+D6wQ7LTca6qHl6OufhkzP6vT5WSD58dEF7PMHWDcdHu2Qrx94kReSOd3b6+Nqwej4hlA2zboVWDYGv0bXgk8U5VX2JTPf4xi/VuPSS46/fY/F8zXq5YTVfMAhzlrM1450Rn32+4nv3n7GsGnpRzbX9MS4vEInP5XLF3ZM9pDRESYK1hizPWc4KdnZ7KCGJAx9tDUoqlB8Q+h55UdFog2DrkxT6Cqc8OgStk/hCEkSSJB2i/BDXVOyMR6+0rq8ceHeCMaPdXep9ifB9Bgc3Ce68w5/96D/n9HLB0TfeJiXk9MFzTi9e0jqH23xMt1yznvssFpKug/hOilsGTNNd3LBk0za0mWZ3OOHNk2tkWcHVYsnpYkFWt3ROEXmSa+MJb906IN4ZYvIV47CH6vl4rEkHEaJLCFVAFAZUmzkX5y9ZZxnGKvxa8PT8glGvt63lCo/paEDdNFxczQmC7etHYDHWEHs+/TSiP/SoxiHzZcbzqwWdFUS+omxbsqJgfX5J9/kj3j054u3rhxzsjairEqstypMAFE3Gwc6Uw71Dpke79G5NqM9mbJ5mZLlPEHhElU9T1/SVIU4Eg3SIJ3weuHN6Pc29t1Ku3dknSEPOF6f8wz/+DrPWYzK9hucrhHLorsZZg4mgTnM4f8XBkH8NsM6CAE/4jKf7/Prf+YAbX5uwEGvaTqDClKEKqJsK5Xl4StFPUybDHlIZjNW02jIcxqiioW4EUeQxW13iyWgb0AUozwKaB08f0ksitG7Jqg3DqE9ZNVysL9CdJkhWeMJRdS3LdY6SEu06zi5fEKsUqwV105IXBXVVIZRjbzxCmw4VgZMBWZExHozopRGiqWnaBict0mikMChPMAlGxL7HplxhXIcwjidPn7LZrDmZHtG1DabWdG0L2tHommU2Y73OqeqWOI4QQlBX3fbl1JTITlG3JV3Z8fbtN4nCiLQf09gSfEWtO4zbloWkp7i5f42ffu8xTnqAjzASj4A4HKAXNaGyJJHHSkiMcdTdNmX3mi1WClLfx2985qcNoPnR4hld5wh9D6MFYHDWEijFoJcy32S0naazDoHA98CXcjsExBP0kohOQ93pL7rptxI7Zx03j3bopZJGd+RlTuxLNq1GyO33xFqLkhLnzLbxb5OhjUYbA9pwa2/AoD+keVnxkwfP+OjRSwZJiPjpY44GQ371/ZskgWR3f4TvC+oGynXDkdxhuif52i+f8JOPfsqfvSj56HPD9TsT/vC/XqP8jK9/bWfb5X3jJrc/+CrrR5+wO93hv/q9H1FLH18pxr2EN68fYXWFzgvyOCKOElonmPYTVuuGl1dzXs5npGmM7gKMNYDcDg2xditrFSDFdiyokgJfSlA+UT9FYEFspa4OiVIgAx/TdCTRq01JeHUdb+7oPsnxm4De3YST/X3m5Yx0PGCoJenkFgfj2xydtJgP/4Tvf/sPaTd/xu3BTcgShFywv7/Pgw83XB/d4Zd+4w5PfnyfH3/ynLo2nNYzqLvtjFPpsSk7WqeIvICbozHv3jzh+tGUeNjn4vSU1dWMYdLD81I2y4ImN+RNwfhgwtPPH7JZ5ig/ZLlY8dnDU1oEd/yQy+WGJ/M5b1w74d7xwXZoOYqqagh8Sb+fcu14QtO1NLagc5Yiq4g8nzf297AIns7n1J3FOFBOMUqG9KOUssiRcjvTdZOtidOUvekufi+Ga0PsrT42Ffhph3dV0cwrlqsSpEfQU0QogsAg2FpxTtIJ3mFHupPw+cxxmj9jtnhMrUImO3v04j7jgUcqO7rMoBTs7AxQ/ZBnL/N/4UP+i4rWlsD3GY9H/Pf+3t+l97bi208/oioN2BXWwGo1J8tXSBXRdjW6a1kulvT6IXmVoYQi7cfMNxu0tWw2K2azjNFwim5bHj1+SlXW9KMeWhvkdA/XGcIgpD8d05qOplGUdc1607DKroidhw0VseexdprT8xe8ffNLKKVou4bFek2sfI539xj3Rhi7HT+IFWjTUVUb7DBBKUeRb8Btxx1ak5MmIzZFTlZllOuCyXRKfzqlf/mCCyfQVjMaj6mqDYFSLPM1bdXQao1poa0aGt3RtQ2+J9DaoVTEarXm6mzGe9euMfZ9JtdPsGh8qVjmLdZ0YA00ht3BlKMb16G0tE6jtNi6VCUQ9frYIbi6ZOALxolhU4AxIF/fGf856weG5X3NgIhAt7RoxBcyy0219Q1OArHV0zrDpqwoW0tntk5/ngBlt4MRokAinGRd5AS+t80us30NgyMKPHRnWW1qktAnjVJOdg3n64JV2QDbLmhrDVJum6o6ux1iHXqOG0cDdvq7NKUmayY8vlhhMKzLkhs7U7729g3SyONikfH0vGB3t8dysaHrHGWjuVgE/NPvf8J7v3FA8pVDrn76Al7OGQ+HVFXN//Wjf8C3vvk1vv7VXYqmRnmwPM+4djzh+GBE2xU8na15eTlHCXj79gnLRcHBezd5/Pycqu3z4NkpL64y8qrEGsenccCyqLBsbQ6l2Kpnuq7DGIN2DiE9htMhURrix5K2bMg2JZtNRdM6dqYD+v2Aqzbj9777ySut6ysH3qN7b/L217/Mi9Wf8vLFx4i2oRooijZkZzrm/rNPubpccrBzwsHxbd7/+opq+Sl6tWE/3UOvNuSnJWmwSzTp0bu2xxs4Hj4+Y72sto5UuxFHgwFKSnypuCpqTkYD3r9xjHKO9bpgvc5YL1d0WvPhkzOa0KOXRgSNYX86pao72qwljAYsN2es6wqER11XlE3DzYM94jBk1OuTFxV5WXE4nTAe9Al8RdJP6bSBsmUYRNi14YY6YHBvgBOOs9mai/UamcT41jAMPY6mA6zT+J5Pmkacnl6y2pR4QUrWGnbvHXE+sJiqZqQGyE5S1jVJkhBHEVYK/IEC8Zy8esl81vL972sCOyYeWNKpZtM1EJ5gA4/bt1I6rfBlzc29Kb6BBR6lX4HzwHO0vdcuBD9DOEES9/l3/sf/baJ7lj9/+ICL5ZoqMzhTgnWs1kuqqsBTHX4Q4LSjqVuc0ZxfXZLECUWesVhtcM6xnK/w/YS1XbLJMqQT5HlFtinpH45J4xDpwe7OBE1F6zZEqWKxbHh2+gJl9VbD2k/Z2Zlw5CtmyxVn65cM4iFdXZMvMm69c4/Dw0OOD6+xylZ0XUcUJCSxRxL6dG1Dli1Ik4iibqhaix8NCaME1dXMNnP8umWSXmftSsoyZzrawfcDqraic4aWinW+oGxaBIY0SrGDL16dTlCWLVg4f3nB2YsLfuNrv4Jf5Uz7I6wwrMsMn22KTtcGqQ3DeAjCJxmnhEFAl9W4tsF0G/JZgR2ERJMhrZcy0xbja5TstlqX19nmf87Vj1p0Zxn1Fff29/j07ALjIPIVO72I+brGmG2K2TpHWbRYyxfSS4hCDyW3RhkKONwZcrVY0WhDLwgo2xZweEqilGRdVRzv9RgmAZGneHw2Y5WXNHpb8wRQ0sPhGMTbRi0nHHePdtkf9VAKoiBkcJnz2+/f4eNnZwx6EW+fHLDcFCwrxcOnF9w+3ufThxeMRylSWt5764RN1vLps1Pa/phb704I//EZPhF/4xvv02jNbFUQeiHPHz1j9M51hmlKeKipmxKc5v1bR7SNpWhber2I3dtv8evv3CPPMsL+mI9++CGnZ2umoz53DiZIz+fHj8+2Kge3behzCBqt8Tyf/k7CpBfjBx51VZJnJcVVTbZpcM5uvbANbDYlb925xtXZnE39L1nH641DZjyhO/KYhl+hqSTZZkmw9glEx7l+wfcffpck2eHgcJ/16jl9Orx2zWK25PDGm3zjN7/KaDgEKfnhtz+kazveuHaNon6GsQKtNQ8uLwmkYpikvBGnfHD7NvujPp6StGWLbmtSP0bEkov1ho8fPePNt29z/WBInEQ0XY0vfXzlc7gzJQkCjse7IMU2R+8Me8MevTQmiBIi5dGPA3xhieOEOI4oLi4QzhAGPfpWcHn5Ek3Lzt6EYT/m1u6EtTbcGE945/oxO72UxXKBthZjBMPBiCD0CfYG9L9+jUdmSXnVcHRwxNVyTfD4EmEAIQlDD5ko6sFTWu9z8ryicYKdA2BToEKYv1ih/CVKRaTpG/ieo6pm3Nrf4+bhPk3Z4kxJGEnW64a8aJkev67x/owoSfg3/96/xv5Xd/hnn/0hRWMxnaSfpmAVddnQtZam0viJIE1TPF/SmYoir1DWkQYpaZoglSTL1hRBx3qds5xnNE3zRUoKmq5kky+5cfOESAUs5ivwDOuioNiU3Di6hjaGTbYhMw19J3h5OcO5lsY1eInHppsjnWEQx4wGQ1QY0rUQhz3KJqMqN1gtCWUPP/TwfY8wjMnaAt+ThF7KfLmgaEviwEelEaofw7plONyhFC04wzpbo6SjbkvSKKLIClSgqHVFMgjJs4r5vGQyOMRYy+nLGXdObtIfJrSDHlfGMjbQ5B1KKvzYx5OS0c4uuI5Gt6TTPsko3Q5ctw7TOWyjabOG5ipH9WKa8QCXxIgORNPw+sH7/0FZ0A6KquPm4S5HdcOqLGk7TRz5xE3LMte0BjwFzkHgi+1kIQu2275OGyy9SLHYrHAS9sY97h7s8Z1PH4KSHE0mdG0JtuMnD18i79xkFIXb9B2CQMmt3E2AJwXCOY6mI84ulqyalqoWvHiZszfySHo+d28MiX3FW9d2tyNkjeDB8yVFXvHuzUNunexjTUNTa/qDHsNBinAzfv0b79DfP8AfOb76S4cczvf55EefsnPtgLOnZySez9e/+R7rJw+YHu3g9xIGw5i6bhklAb/63nXG4yEu6tHtX2ewd4CfDnj58jnf/Ma7+O9cJ40Vuuv48x8+pShrfE9ihMM4yWAyJohDUI6mrijLgsVFRZ7V2yl8Wz3r1t1LOmQSMxwPUNLHk94/D+D/Tbxy4J09fUrrjzj61beIh4fkecVQlxynB6ye/RBlJfn+Di+fzZmfP8FTFhUn9AW0tuGHH/6UTZcxjGJu3r7Dj/7kR+zrKWM/YBQH1FYghWBvMCYQkndvHLM7GuALidyW9XE4ekmKczBbL7GN5q1rxwx3E8RU8rI+oywyoi4hckOquiYKfTzl0UsSlps133nwOXuTCTd8n14/ZefkEIVDSBhNR8wvZ2AdUvlYIdnZ2WVdtjS6YtIbczAIuHFwSKkNPS8giQMkUNQFq6Li9uQOg8N99m9PeeZecmbOKM4zhtMTlosldbGgnxmMBT/2SY4T5F5O7j/hrK6Y547Ec/ieIo581ouY+x+XzE+fcfPrDUfXBJvlU5p8w+Gb/yp9L0R50ItDRoMUXyypFgWby9fzeH/GN/7mN7n7G8f84MVPOZtvOJhe53B3iDN2e3s3gpNr+1zNlzRVzmqToVTK87OH9HsDJv09RsMBq+IKh0EISFIPpQZcnM2+ePF6aNvRNJpQ9UBLnAAhFKGMKWxGJCt6cUQnDLZuKC8y1s0alXgUzQqtNaJpUJ7jxu3bvHH7DUajKcL3sM4ipWC1WTLtT5iOxlihWWYrhFOUtqEtO3anuzihmMgR5UVOEkUEYUhrWjwvYDAY8+LpJ6TxHrptCcKIrhFo60iSGGstQRSCdPSShKpY0rYFT08v2SzWDO6+Red8gjiiqrbSo0F/jJKSIPZJgxAhLG3dfNHYY9jdGdFkGt1p6lajnMAXCtc69LLEtC3DkwH+MCKuBO3m1V4Nfx3QxiIEzIqKj55eoLQGX6KUz8Vig7GO0AeptsHQKYGEbb3SCXphgu8JrjY51hpaFM5J1kVJoCQfXDvk8dWKQeRx++ZNmk7z7fuP+cnjp9zc3+HurX2u6T0+fvyUVdlsHciAwFO8eW2fMi9Y1A2d7kijkCgOkcIRKEES+CR7IU3V0tUV+/0QtZPStpZAGFTao+4KhqOIx48vOJstePOdE+LpLhdtzt6bexx8oohMwumLM27uDdnfmVBfXXB0NCAv8m0/RN7hHKRJgHGOj++fcvTelzne36fTlsuzc549es7w3hHXdwbQNSTx1rL47/zNX8EFfQa7Ex49fcyDJ49ZrVY0ZU7XtFgNxkEvDLCe4f/N3n/92pYkZp7YLyKWX3tte/y53qTPyvKsojfdzWGP1NJAPQPIQIIEPelJgB70t0gPwgAjQT0ShJHU6p42w64mi80iq1gus9JnXn/82Xb5tcLoYd8i9KZkgQCJ6vpeD87FxVkRK2JFfN/vq9seJbd+BD8MSIcxk9GAOIo5PpxxefHF8kRfeOGdryq6RURSLPkPP/wzVsuS11+5x41DzXJ0glqM2RlNSW4NsOsLnpyfc9JVLCPNa6/f5OQvn/Czv/gEVzl+lDwh6iW/97WboFsu5hvOVhuyMOZ3334TZTRx6JOlMXVZw0totxDbGkBrNJ7vcbC3Q7AfUaiGQMN4NGXVWHRn8YRjmGXkeU5ZlHRtix94jOMBVdOQFyVRHOP7Huv1mqPDfa6vrijyEiG2DOjeOi7mcw5uHKCEoNwUNLYl8D0CYynKgusrw4ura0pjeeNbX2X8m+9wxoZH+Qn5ckW5WnA8uoH1I4zrmMYx44GlDxXR/YRmVHHVPOd0nXO69lgXgkTC6rxHrXLuHR8T7J+zWBXU7/VYuUTbKwLpeHTxJ0jxOqZOGRCys7fDKB0g8pCn15//glP9l09/+L/8Ch/OP+YqXzDKdoijlN3pmK5vWOU5Bgt+B35PKD3kxsM5jyiKidOUuq+5WlyRl5stEk5ogjAgSregizAMwUi6usUYQxx6WGPQUmKEIUsGeH3E8qJgkA5Z5NfMVxs6oRkHA/AMXtejFjWTQYTYjRgPR7TGpzUd88UFYRDiug6JYZCGCE9QFw2elxKEiqvlnNFgguf5OLeNfLS6JYpS4jSh6TRFUVJsSrR29Ga7sSvKnHWxAWe35DO9zdgKJTBGowKP0/kF5WLD1954h0GWMZmNscoggoC6Koljn7wu6LuaVMVYbdDO0tkGR0+W+IhOE3geIvYQ3fbrt7cW3zmSQHE4sySTbe/x5vzvesT8/VHdQuCDwLHICzwBogOJpLcWa8Dztu1oTggk3rZP2jqEEBgsVb69dmp6hzYaAYSB4PHlJV+5c0zZWHyxNWE9PluA9Mhby48/P0dJQRhKiqrbVrBKQeR5eGrrnpZewN444+7xhNlOQJFXDEcp2ThF2q0JKQgkgZ8xGlrqRrO2motNz9FOSF50/PBn5/gKXnn9IVEmsEFCqVuGR7c56HveOA5ZlQVutMvZiyU7B3uEIRijOT29YG82wwmHUJb1uuHOzT2eX57z+b/9Dq+88QaHewf8wT/+75PGHmmgqOucLPL4J1/6TUpt+fzRCz744D0ef/QJy+slTd+hTY9j6+AGgdYG3wMpJdZaeuOw2mKrFmPn6HVOXeQY+7e88FabFTdnR3zv+/+Gn3z/Y24cT0jeGrCppnz0TFFef05iRuy1CaFIedIqSst2QBhJIDxiLyEIPbze55XDQ7oq53qx5P7+Lq8dHTAajBmEEl0blFPEYYyUkr4zWGspqgJeoiOFk6zynN4siaxAxRFqN0L1HhEKT3r4oSL0RzRVRRzFtH3H4WS43e3EIThLW5UkaYjyJPNlhef5KD8kr2om4zGT4Yi+7ei0plxt8H0Pny0YoOk03mDIw7deJXhjnyaCz8WK3jQsywrFgIAClKJ1BkfLnYe3UOMleZuzGNWcLE85vTyj7SWz0U1Mn3MxX0MoYQYXNsf6HkZ4fPhRwdWqYjSG28chJ+mSYvkXRG2Kur7DONnFG6Qc3r/DN/5R/Dee5L+sOqnPeHR+Rq8NoT/YcmjrmlW+QgqJ5yv6ssPqnjrvmIxHrMsVngDTrtiZ7nN2eYmUHvYlb9vhaPuaNA7ZuTfj+nqJoaOsKvIqp3EahwDtuLg459HzTzk9P6ftLG1fs/WkCIqmYJIOyDrJMQfsRfvMY8OmqMHTPDl7xGKx5ubhIbOdCcYqBkmEQ+L5IX3vkM6SOMEgiLdflCqmLFuurhdkwYg936Mt5+jeolSA8hTnF5fM83M8FXB4cIzuaqqm2mYVe8cgGfL06hmnp5dMkiG/+Y2vsuwqbty+hfLB6h4/UjgvwAs0ifSwFoR1SGEoq5yiXDNQPrcf3uHJj17gG1C+wkUKYQVdb7b1nkLgW4GsDGWtqTfm73rI/L2RNtsoC4ASW7d40203eNuci8C9jDFKBVEYoLCMBzHKkzy5Wmw9M85ihSVAIKXAA85XG378uWR/MqXrc54tcs7XFdq5l4UJFt1vGfTabctDfASpJ0l8ycePzml1i/Ilnzy7oG9b7szGLJ5e8/nJGbPZkDs3DjkcD4ljSd9rrAjYCeHqasNPf/acOPbZnWREu3vMHtwg2xlxGhhMJfHijDzIkRuDn00xwxm3vvYqtu84//xdMrHNySfDlCiUPHt6wtHhDG+YUSw0iZcQxAHhMGK0N8UZi/VT4qHl/PmH/OxP/zUfv/8pcSiYxCFfv3PEZjbk8fkVL5YreuuwziGlomk11r0kfVmHZLu57MuWzlrcSKJtj/uCBoUvvPDemYww60uqfMWDO6/wa7/2OuPpXRp8dsfnVM+eIUTEOm/wfUXmB3jAjWHGTTMhF9d0xnJrPOSdBw9p8pqyLIm8gFcf3CPLMppNjrWaOB0gpaJtW6zVOLsNgHt+TN8028aivmeUDtCuQwnB0MsIdYg/9LCyo216mq6hbhpwgk1RMl8t8YTgaHfKdDSkKNbgDOEgQknH4dEhXadpu57lasXp5QUH0ym+hCxKCKYKJ6E3lmhnws7rd3E3dvjw+jGL5WMm4x2W+SnzxQWmN/htxFuHd0mnR7y4fEoYQTxJyG4qrh+vOb1c8PxiweVZjdIKKTpOz3IWmw7fh+lI8XS+QneOemmoNZxdwo1br5KGhjtHR2SB4uLDNatLeO/dguGDHbQzBDcnf/NZ/kuqHz/6jBeXa4aDETce7uBbg+8JkkjRa0PTtSwXc/TL8no/UDTrEk94CF9SVzVS+i/BAVAWPc711E1BFMZIUWOs5vBgl80yB2lZXF0yHI0IpY8SAdNsj6uLBZtVzovz58ymO3iexyALWayu8a3BzkKeigvC6AbDaEyV59R5xd5wyjRJaZo1vXZYA027BjRSRRjr4XeaqqpZlCVhZDBYurZFOEFbNQgJ62rD5XxBYzrCIGIcz5jsTNA09KZCNSUNgt56hK2mrjtiEfHNt76KkY7p4QStehyaznQYY4niGBE4jGnpncAZi9YNXd/RdxYXeEz3Z0yz4fa+URiUlICklx7OgXKC5kRROE1fS4SVf8cj5u+PhHAYw0tn+fZvNwhDjDMgxHY82G2iwfMgkNtNy2yQEPuS0+slnX75c7E9ydiymQXOKZ5fr9ifzui05bMnFyC3RCrtwDmH97IAxvcVkadIfMXbd27wxo0xDsWTswvmmxxnI+aLmr2pYDwe8uXhmE8ePePquiB2IcnRgFEWUNWap5c1Kgz42lu3aZqS83XN0fEOO7sz1m2NRoHy8cMEPc0oKsPObIQKfM4vFzx7+pRb44QqzwmyjNIKUBHH73wVGQ9IJjvsWcnpfEFle77/4+9zebaiWm+4d/MBShpGkUX1Df/42w9Jk4Qqr1itlwy8mGFyi/6jhrOiQcrtWPQk4Oz2w0wpPN/QdD1db9FNw8K2LwEjX8yh8IUX3ukgJo5Tfu/mtxlND5HWYQtHs3zB9KTgRe5oaBjd3OPFekE0HvHw4IC9Tc+NZsRSjTkTObXuOZ1fUq8bJumYURYhPYmMI1TTUi9LrFTE0fa+yRhL27bM1xva3hB4HkkUooRimAxeVr3BaDxmk6+YX66I/BDTNiglCQMfJRXz5YbpcEQUhozGYyZ7U7zMpy0LyrLgcDIj8CMeP3rKcrmmxXB2dcHJ6RW3jw+YjQFPkRxM2X/nNZrZCBPEPL3+HJUMmKiEk4vP6NySMImwlWLiRYTpPr2U3Di8i+0LNmVLOOgYTgbIc83uzpjyYkDSKRI3wuk5vifpOksYDLl5K+DsZIFAU1933Lp9k/uv36MqfsYf/+iv6BpLVMfczb5F4Gd0mw6pLCdXm7/hFP/l1ePnV+R5y+7oEN05SlsjG4110JqeqqxJowgdOEgETd3gK4EfeEhladuCprX4UcjV5ZxhOiJKhign6buOy3xJNsjIRinpOGHdLPDxSbyU0XhEFAVc55cEYYgnBYHnYzpL33SU9YYo8xgmKU9Ewc5oQuSlhF6MPw24FwhCHAEe6+WCV++/jjM9Sgb0BoLAITyLCGOWqw1Ij8X8govrE5bXC7xXxNb8hWK12uB5Ck/6PH70hNmXvkzbNpRtgXKKZVFBFBIEivP5GcLAN995By+S22Pz1rJuVkRRSNM2JGFKQMQ6X9F2Bpyhlz1d11BVNXXd4reKg5s3SIYJ+TxHW0ektgsuEqRUeEqiK4u2Hh6OQP2q4OPnCqTkJbeCINietE2zCXWzQTuHEaA8hRTbY2AhJEb3jAcxt/fHXG/WnKxLrHM4t218E0Kh1HbzozxLqQsulmu0A+y2lWeb/1UEL3t/i6bD+YpWa8q64vjgPkkguXs8ZL0sSAYZz05z/tvvvcsoG/H6rX1+66uvsTMNEU5ibU/XtnSNwNU1w2GA9BzZ4R7p63sk4yknJ+ccPLzDmbskwKBUgh1JDsIp7bomjWJMd4lymiCdEQ5SwmxMMBjixx7tekX+9JT2w48wfcuTkzUbIl5cL3jjzTe4+85XmM/X/Piv/pxvvHWL/VQxv5jjdnum0xFKQtfOmcWK3/3KK3x6cslnpxtWXYvv+QSBT9tvN7jKUwQ/d7MBuG2N7Rflv3zhhffk/IKwDti9ucf6s6dkWUbZb6BtaK4qisIxjA1nn13SZQH79+8xyyZsnn7M4+qU62VO2W4vp9f5GW/duE1nDcVmA+cBh9InDkImwzHYLUGqrTqU5yGkR5am+F2PJyVJFBHFKbA9ZkMpnHNkyQAlFFeLJUkSUjU167wgUj674xGe7xGlKdZaThfX5LpkeXVNcV3gqxG7OwFOC9JBynCoSPZmjI6Omd3fR00ydBJg0oDP6zXtYkVeVlyuL9ibHLBYX5JXc4QSOKFI8Li1f4z1PKRzaGFpdUdTh/Q6QlhB7DqcLHnj4Q1kE7KRjtk4gVVDOg55cPM+2WjA/OK7+DPJ3v4O3/jy2+TNC56cvyDfaLpOsT9OiPwz0APCVUjdWR4/ff43neO/tPKCgDB05HnOs8cn1E1J37QEQUwQCoy2jLIMP9xm95AKYxKW6xwpFUfTAUJJtNEMs4TI97m8PGN/Z4c4iTm/vMJZWC3XBElAU5QcDPcRArQ1XFwvmM83tF2LSEL29ncYhkM2VcPZdc6m2PDOb7zOZDgmHIwxwqFtR1V3WCtZNGtSL+Lu0WsMBkNa29K2Hb7HFsiBwRiD7QuGwxlaa2aTCU1dUNVLDndvY3pHFETUZcloMOD2rUM29ZK6XbN/eIhSkucXp7iixrqKvtV87dW32d+f0ToYDzI629HqhlB5BAiU8mnbjr61+MpDO8smXyOlIC9LXA+T3RkahxoHNFc9TlskW3CDEIJIbReB3jok2xeZ/RWq+a+Vej5WGITa4qIq7VjlGwKfbbMQFs+TCHys7tiZZAQ4Hp+cYbDc3N3lalPSI3BCbhuFpMC9BEQYDY9OX3bIStiyIcS2MtBanNvGlJIoIAl8Om0o6pZBlqBMgxcEmD6ibToOZynfeO0WaZJyc2+A8n2MkUxGAVGSIf0A4WA0WdB3PafXBR8/nnPrjZD6o+dk0wFqZwWZptcNcTCmj8B4A9aXhr07N/itO6+S5xVlWXDx/CnXF2f4V2egSzLfpzeOR8/O+fzxKV/55re4d/MOf3TrNk70VJuK73znu6zOL+nuTcnPr7lebrBSUZYdUkp2j/cIPEtT1dw4mDDJLvjOu5+g5XYjIpXEGoN1DmM0WIuQW2Nw25u//YU39GNkJ9AXWwD7ZrmgkQV10vC4abhx/1Uunj2mO5UwsDyav8+zwuCvtqXC/iDiOq/xe82N0ZD3nj+lM4a66xg+f8HbdcODO7eZiq11PSBACIkMAtZFyaZu6fseT0nSLMM6S1+3hFGALyVtURLG4ctdHyzbiq5riMKA/cmMYTYAISiqhqrZ0DmLjj2EVTTa8vjsnOfXCwaTIfe+/lX8WzukR7cpPcN5fo7npVwtLpHrjkE0Jp56tPYcr1xTtht6UzNOx6B8+qJjGqf4gykoj962mL7HWUsSjGiWBU++d4JtHel+Sna8Ty8VT99/QhYOOLzvk2URfpRQXLfcPJigZgqpDeurz3m+ecLFSlMXjvEkwQ9CLpZP8X2FL0c8PzlHiF9Vq/1cwzRks8oRahv3wSmm2QFRHNF0BXgCi2C9KAlDRZE3GLP9elgu1hTLglvHt5lOJzx9usH0JaG/vWe9ml/jrKbXhiDwMK0BJ1hWC7JkRtd11E2FpzzSZEhjarTWfH75iKbptwYjJfBFgvBiAjxaayjagigIKZY1uzv7jAYxCp91WVO1BXEYIJ3ECkPdaqTc9pP2fYUMFKSSaJTSN4bVZsHi8hI/HuAHPk1ZcHr2gsQPuHt8C9v2aAW+F3F9ccVqvWGaTjjY3WfTNVjPYZoNYRRgnUYax3CQ0OEo6gblKZq22C7Celty0LU902RE6EUgatKdAcliQLdqaDqLNhpPeTjX4cktvKFr9bbC7VeBor/WN1+9w3gY0jYlH7644JKGTluMBWu3bnS0QQrH7iDj1mzM/Ru7/OWHH/Hu58/4+mt3OJiOeLFYgxD4QmCtwQpB6HuEcUDdGRBbMItke8Sc+AoloTYW4xxKWLTiZSYYNBJfhWijSZOYKLAI6XP/1j6PzpdstGWkDEp4NH1P4PxthWbdUZQ9Z3nFTz8/5eOznEUNDyYBTxZXnIcV6f6QsioYCUVuS/5vf/b/4tkfn/C7v/17KFNx+uQxsXA82M8YJR6Rp8jLCp2ETEYZb73xkBuvvcrj0xU7yYDeaMrNmv/y//B/4iD1eefuHpHvs3/zkKMb+3RtyaaXkE7J9naRDtr6GbQVt4/H3Dsb8+H5kqZv6TsNYksE83yJ8j16ve1kt84h3N/yHe8br9/mh3/1Hn1jyNIYLxTc+vVXcYeC5Y8+5eBwzKP3nlBaR+hLmqKhW7bEgY+fetjEEHoBduFYdx2rdUmWJuAsTd/x6eOnjCdj4lGGfemAjGKfTVlzdnGB7i1JtHV7ZllG37Q43wML1hh6qzk9vSLwA7w0pNissZ3lxt4OSejR9T0IReD5xMOAumlopcDzY8pE49044NXf+jWm92/T0LMyHc7zkSqhqq/o+jnOOCbjY64XFzRtxfXqZNssEwxpe4vrNMrvGHopo8Euue1IAuj6jiiKkL2hX/Y8+8GPCIOa1779ZV70czrl07UVba9JdMqD4xv0geH9Hzzh5KMNd740IEodfVOyuTyjWxmqjWA2GfLq/X1U0SLqAdfP5yzWL4gHcHSw/4vO9V86XZxc0dYdbbOFtO9MD5ntjxkkCb1OybIxTZ2zSkMW8yWBP6BzHUp0HB0ckcYh2SBmXczxPI/hYITuO4SUrBcbtDV4nsfObAdjDX26dQIP/RHjwRgnBHXT0nQ9eV4SxT5V25INB9R5zXQ0JIoThPBxKDwHWno0vWYwTIgThxMtjW6ImDKKRzh6LGx7dLVGKkfoC3TfkhcVT09OKa432PGMpjeowGe+Wmy5tKHPw4cPqYuK3hlm4x2WVc5mtaJqa27t3eAPvv4tPv/8Y5yfYn3HYBQR6WCL37SS3llq27Jar/FkiJQCbTRN1dP3mkk2ZpjM8LOUuqgJhCINtmXsGoMmwDmLs9se2Nj38Jyg7y3uV5+8f62+7Uj9jIOxz4vFhpNlTWccvCw28HwPX4HVjns3dhlFHn7g4XsRnVnxw0+ekoQBvufTdZokjbFYqrpmNkhoOkPdFSAU08gjkJANMn797Tv86ONnfHa2wCIRKKzwiDzQzuPx8zU3JwGhb+law2g2JIoiwkHIZDrkO9//gG986T5h6KO7hqrSRHHMxaLmuz/+jHefn1DXPV97/RXe3A8YxCF/8b2f8dv/vVc5X89RnURkAgNM7mQ8lx3/93/2XxFKySxJ+dqrx5SFz4NbN5Bo0liSDGK6rif1OqpFxfe+8x/4wY9+RpoNaMqS24e7fPXujL5quJrnXFU9d+/sIXyfxM+Yl5qnJ9fMxiNuvvYmZrOiaSt+J8hY//FfcrJa/7WhTSLxPEHT9aiXhjXf27Y9fRF94YXX9yN+73d+nU8//Yyr6zXjyZTJ/RGVtKRJSF9WJPjc/vZdXmyusM970h1v+wne9rSLCuFvixAuljWeC7mze8zl/IJVU+G1HU3dkg9i6qYl88BajVIB945uYo1B+T5pliCEIx3EYMPtV8diQV03eL5H63XgtwgM40GKUpKT6zmLdcHuZMLh4RFGd/ihQgU+R195m9fu38JNJzS2YVFuEAikspydPUE7y/X1OdL3GY2GvPfJf+B6cU7XG7StiIIE6QJMv727yzS8sneIGe6iEg9MjxI+l8tL9tI9bGnw85Lf/Kf3+WB+RuEcIoRe92A1Jx9fs7e3g446Tj644PMPcpI9j6+8dYRJIdmZsv/CclgW7D6c4rce3hODqQOuRgXVoqLNG4J++YvN9F9CLectxllW84rZdETXNVxeXVOEEXES4amGOAqZqQFSQl1pyrwGIdmdTggDOL++pCgqMCFFUYKzXM2vWS3WTCZTQj+ka3qyUcqHnz+lqzvywYbHzz/kBz/7gL3dAwQC3fRs2g7dG+Iw4MbeHqPBiNnehLY3lHWJ7jtGWYrVDi8MscZSthoL4M0RvU+cDlhu1nieh0PRN5qy6NiUK8q2RVtDGEbs7u8jA5/NZoPnKxyG2jUs1hvW84rDgz3miwV522C0JV+U/O7bt1g8+Zjx3i5qOAQrcMKgG412jigN8cKIzfWCtusQYUhVVJTVBiG2NZs39m+ipA+eQTtNKhTO+fSBorQNxjp6Z1BBSBgGCGeJIkX4ct7/Slt5UcCnpwuulgtOlwUWQeBJPLllOSi1LaJPk5Cz+Zoui7nc1OxmGceTiotNQdH2RGEIDtZlgRACJUH6Pvk6RylF7Hv8/tdeZeB7vP/ZGTi4Wm4QSmK0Qcjt/XLRwzQF2xnyvGJ4OEL5GiUVQihM1yN0y+2dCe9+8JRvvH2XwE9wznFyteTf/fBD3n96jkDwh9/4EvePpvzwg6fowCc6HNJmmqauGYkhnqewjaNVLZOpor0QfOneTb71xm2ObmSsrko8T6GbHiF9rAZnNL2x7A0i/tf/xT/kT3/4CY/Orzi8eYNgkJCLgPV6zu7uiOzelFY4BrNDnj87ZbVZc7AzxZMgwgiTTSg6w7133uaPnMf/9Z//MY3VL53LkiDw6XqNwdH3/ct79C/4XL/oAPjOf/ghb73+Cu985ct87y+/x86DQypdcH69pi5LqqImqgI+/fET7FRie8vBzUOMaVmeruhKgxMOTwhUFEHncbFesBKWbjig9QJW10v8tmUvG+OFHp0VICVSGJI4+ut8YlVtSOKYyWSMH0a4IKDve/ArHq3fozQd1oQkUcYmL+h6zd50jBcE6MAnvX1IeLSLf3MPnU2oTE9fVWRRSjgdsuxK6rpEhAGir5GuIg53eHb2GC8OGU33aZoc3XoM4zFpMERoTdvlxP4AGyc45YFxFEWJEJJABqTpGFec8uDNGcNZRNbFvFisEMKgTc8oUnx8seZf/rO/IhkHLE9KPBxSS6IgZVnkdLpGC8vbX7rD9aLg+//Px2TLmFtfOiTbGZOdb5ByyL2Hb/xiM/2XUL/3B7/F6fkJ11drmq5meV3z8O4BcRjRVBWCDW0jsK5is1ni+RH7h7tou3UzrhcXdMahrWN3krHZNHRtT1nVxPGA2XSHqs5xzlEUBcIp9mYHdEpztTrjYZwxnuxwVRdsioJys8FYze54h2zsc/P2TTrTYHtD6HsMBjHW9ThjGEVTnJPoSDKMMy7yT6jriKZXrIqKwPeYjDIsPeu8oqw6pFJEQUDZV3R06KYnHWeI2jK/WqACj+EwIiBkudwwHKwpypKr+TXffOsrBHGInkVMdvZQgcCXiqbqCcMU58z2S3+z5PpyQTZI6V+aqZSImExmxGFA14NULVYoVBQRZhGNczjhiDyFAIzysF5IrzXa2K1z1G6POn+lrX744edEUYJzBuVJsiSiakqEAPPynlGgaHXH1aZnVZYMo4T7gylx7CFLAQjargMBSsmff7RxdjVHye29b+hLPn12AU4wz3Ouf1pRaYMSgjTwUFLiIbdoyEnEncMBTV2zWjcEUcAmXzPIHGmSECcpZd5S1S3f/cHHHBzskDc1Hzw64WxTYpUjjRIuNhueX294cr5g0+X8zv/iDyiFYdXmJH5M1TVoZ3GBz7y64u7+Dr/71Qfcvb1D39XUgcfl2Zw09hkME5AO1wt85XF1ueSskdy/d8TNOzfJ65aqKihzQzIb8tn5JfOPXtA4w7e+bmhaw2KZk4YeN/Z3ePbpp7RG8OjzZ3z48b/kG7/2VX7rm1/nz374V5RtixCOzabd1iUqhcMhBDjzt0yuypKEx589YzTL+PX//B+g9gM+O3mXpmjZ35/y9OlHxDJh9eKUgZfQLjtWYcFoEhFoD89k1HVNlg7xfYVWlqvNCp368PIoLvQCdrIRcbB1TcZJip8kW/C6c0jLFgBgLWVfEQUhxmraak3dGeb5KV3ScT2v8Ssfbxji+TDePyC9fczg/m38wwNkFEPoUfQtnXas64LdKKXFUhU5Wjo63RMEEbW2eHFGrQ3GtawXG5I4ozcN49GM1XyOiQzKChJvwPHBHXoVMRqktE2LUiFVnZMmQ5x1ZJOQW9MdetMjNZiqxyaWOPB56/4e/Zf2+Ff/3TOur0o8XzDYCzG2ZrGYM1+1CA17u1NWleb7/+JzXvx0u3Doex731Q67hzPefOeryPALP9pfep1ePmWVF7RdS171HE6Pqdo1zz78lMhLefDgNifX5+TFgqvrJaNsyHRSE6YhnpBcXueowMNay9X8Ct0IdL81Ae0e7uH5PqGLkGoLMrhzfJPNZsPzk1NiL+DueMKmyGlcTdOXdLZjNEjAWnaGMwIr2aw2eMpjmMS0XUGo5LauMgwxpqbTPb2V1IWk1jl50eBJn5uHN2iaJR0dvamJ05jFckWxLlhtVhxN9umReMGWRxvGEXXTEYU+QQqpkpxfneCMw3OK450DjBMcHd6kNTV916OlRnoCY3u6riMIPZQn8WQAQqGtRkrB3myfQG7dn9J7mdvs7LYvdmfMtX0OOJSUSLbowRaLiyK6uqHrDdbYL4zd+49CymeYhLx194gffPgIX3o0CCwOYcDzPARbp7IQDm0N8ypn+UkOAoSQ2ywqIIX8/3HhOqQTTCZD6DpC4VGWHWXT0TpD0Fu+8fD2lp1sLLM04Y1bR4x8ye5eisTgez6+b2iqliD0kPQsrldI4Ti+Naa1jvefXVLbBR+fPAeliEYpZdtzkhdc1Q10PdYIdm7vsv/aHZ41JzRtS68MrbNoa5jEGX/4zbd5XUzZLHMuL2P2dkYMh5Blkr7p2axrfM9itGPdFpyf5hTxiLvHh2zKhmmq8ERM17Scryv+/N1PCYMB3/7WVxkMMt798D1+9ugFyzJnGHtkScJoPOa3f/vr3L15wJMnJ7z15gNMveHPfvoBNw52OLlaUvbty7tdCH1Jbf6Wj5qPZzsIT4ESPPnwc25Vu/zgX3yfnVdeQWaaDz89Qy4VAy9ELDXMNVfrJXZviNQSZyXYgHE8ZBx4tNqyrEq6ssM2GqMFT6+uOBwnTEYZWThE+QojQPoxk+kOutd4yxVtXdHUFZu8Jug0vheyqa8JVUhdDOguLC4IMfv73PzK64wfPGTjS6z0qa3ZmgK6GuG8LQgARasbWru9f7peX+NLyWp5zapYEkUeRaGZrxc4OmxvoQMd9YRRQOJ79Bj2s12CdIwQPkWeEwUxe5ND+mxC0xS0VY2OoZegywXFs6e4CloRoKuSyDfkoiA6ljQbx3AYsHMrJRh5vHg2p5p37NoJ7dzw7nsfcPJBhSdjtOhxHlxfLdnJ9kmGA4rmV8d1P9fTR+cYK4mjEM+zdLbmo598yMCb8M0vPaRvDVXRsl406BZqr6NLLX5gKPsCpQSe8HBW0hqDpzx81NZ931YINFIqum67QYyTmDiJ2Nm/h5CCvmqJhOLjj19grUfg+dy7eZeHD+4xne3R6xpPK6x25OWGIITBYEiWZSAdZdVtF2TTIH1o256mrTmc3mKRX9H3a/JqQ9U3ZGHEbGdC4CkG6QhjLcvlkqZOUN6W4VsVG4QbYoSg7iqM6TkYTHnn197kZHHCO9/4Okp41FWzXQTFtsHG9TVRGLLpa5arbVFJpCWhShiOxkQqZJAOMa59CcuP6F2PMZbZnT1OI4Wot27avK6gcxD4BHFCqz1c6CE7g2t/hYz8uYLAY2c04Hq+YZalnK6WIMBHMZvG7I7H5EVN0ZQUfU9vHL63jWM5Z+m6fmtS9bcbLykEgzBi4Hsc740YZ1sX9OFkQGs6Pnt+zYurFV994yb3buzyydMTJJLf/9ob3Jxl+KGH50mk6xgMQ3TTEwRbqlXTWrQOGWY+Bo9JNuQ3vvSQ773/jP39Q+Z1RSUVTgR4TtC1HUEYEAc+b//OV/D2Atrrals56PsIJbGdYyB9Xrl7RHjR8eSjDSZOmc0GCBEg0Pi+RGtDU3acXKyIBxFCWt7/yfusFwvefOchnhejtSWbpPzlTz9H2ADfD/mT7/4VT58fM1+v6J1lU9WcrQre+/Q5t44OuHPvJjfv3mJ+vWZZldy9s8tytU/dW6ZpyGs7R3z0+Dmb1tD1Dmu+WAb9Cy+8gS+xTpK/WNJ3DT/58IrmzOOFuMRmDc9PKna9mNdvH9FbTdCsuFjXXD3bvris3vZsPrm8Yn+YEfkevvRJEERxhDSGdDxi+todZmlI0nn4YUCnNRjB6YvnNJ2mazokjjgK6HRPvqoZZRlJlCG7AKXG7P7mjFu//22C/dssqhWLIKCoSgaRpOkqRmEArSEKfdbNhkB5xH7EZnNFR0vXF2h8PM+nLDc4q3j67Am97omjACsMx0d3afsNfdezqBfsZ1OOxwdIP8NXEpTDD2JMB21rKMuaYRCw3GzYuCXF/Clnjy4Y3jxkeb7g5Pk5+brg6XKNUY4ohfFsa7rqO0F+brh+XLJc9Bx6U3aYcuEaOrtFGBbzDWVl2P/KIW3fMF+uf7GZ/kuo67OcoxvHWL1tblmuVvhhyGsP3+bg6IBNtcITPtYpwjBiEI+4d+sO2hVcrzpiqWjKntgL6WyPlY4kCwg2HmmSEniKvKhou5bRaEhV1S8ztYKur9nZmVI3S9JAMU72yMYZD+8/5PDGDpu6p9MWX4VIT6JNBy7YUoMMePhgQnrjU9Q1F6s5WMn16prISymvc4p6wSAbbBdH3dPZ7YtL1y2nl+c8PLpP5PsUTUWQ+iznC4qyQgYem6pA1o5v/vpX+eF7P+XWK/dQkU9ZV+SbEk9ukarT6RCrAQt5W6A7SxYOOJjus41/OsbDDCUFnVW0pkGx7YleLzfk1Ro/DumqCosDKdHWEIc+LhGE0wkEAeZ6hV79qtLy5xqECg/DJI1IYp+LfEXsPN68extPW0bDjGjmsarWfHx6xrxuabsW72XE0vfUlqDmtqx7JQVv3z7g9u4YJR2np0vuvXYb5VrG3oBAefzO1+9z43CXv/zpZ6RRAm2Lh9zS1mxP3ziGg4z1ao2SCs/36XuLcS1tq5n3Leu85+bhiDce7hMdHvAnP3wP03T0vSVOYoaDAU3TYHXLrdeOufsbrzF3K5zSpHGwdfErTRAHVHlFH3iEoeL+nT2Ojg/pdYdSW7JWOhpgVE0QedxJh/yb777L7b0Rtw+GVNrx0599yq1bByTRkNzAO2++ys4gJZtN+emnj/n+D96jNgYvCajKiuUyZ3c85PJkzr/54z/n2289IN+0/PCnP+OdO0fsT4d89OKKyXTIG7cPuH845o9/8AHXdYfhi1HXvvDCa50mCmN8T9EqBW2NdB7FaU4tKgY24NWjG+zEIedFTjJIkXlN13TYDkLPx0fSG7gqKgZBwPF4RijBU5K7+wfcff11/EHM5dU5x4MdlFVs1iswEtO2mK7HVwJhBU4bcJYsiTHacnJ9xeThHW7/1jcQe7vUForNhmWzAbckS8fb4m80Zb3CQyB1i+8bAiRF1ZKXOc7viMKYrrecn59QNiXOSpJhiO4DZtNdAulRVx3C2+7+osDn/t4doniKJwM2pqI3hqrVxCqirSowllW1JmgrHn12zYtP1lwDbz8M8M5qHr9/xUXV4M8UaSwZpAHjQQKRwYsC8lVD2UKeb2h6y52do+2L1lqcpymWJdXTjtVBxepwzfnpxS841X/5ZLRmMb/GsSVPdf32xeQFjmW1oqkKrNU0XUOWDMiSEW2rWRdrlJIEiaSpCpIkYDQaggd1XXF4tIMvoOlamrZmNpshxNaJKvFoq54kyxBqe6T6yqs3+O13fovLsmI03SevFjgriQOfQZawWi/wPMEmz4nDmCyJtvE4Kel6x3JR0jZui2MsctrxhlE6xGqLsh5hKOmN2Y7jXuCMwVqDqQrWncVYx6aRzCYHVF3F0xdPwcAffvO3ma9fcP+1e0TZgHpdUlUNroFwECNtQSATnIJIKBJh0MMxnYVxNmG92TD0PRJr0WJLO8q7nq6uoNdMnE+yv09/85rHl59jAREokvGQYHeCmSagJMaAblr6xa/gLz/XvdmEo70dRrHPs+sNgZO8eucm3379BvNVziBKSKKQTeGx3qzZVA3S3zLthQSEwLzkNksh8YCyLhBqxMX1hmw84KMn59ycjejqii+/c5vJONre0VvBb7z9Kn/8/XdBafwQ/DDA9Joir4iihKbrCJRHqhzWgacEnz9dEO7soqOY0c07HBjFHx0e8fnjU77zvR9ydnWJbrfVertHI975h1+DkaJY5ljjcJ3B9pbGa+lsQydbrtAchRE39jKSUJBkI7SxGC3J8xXD8Ziq3NDkFVULIoi4d3PA+5+f8vTZglD43LyhkF5AkIbcvHsDoTy+8s7b7B0e899+58/p7ZbF3LUNl5cVt2cTXruxz/X5NWEQcTweUJQ5wvN5cb5kZ5pxmizZHQUESqGt5Yu6q754ScJiyc6OYLluKMqak/ML4iTj4d5Nqqbi3qstu9Mhq3JD2RrytsPzQsJQYS0EUqGEfFk07NEbGIQR33rlIeM0JvY9pNdz9fyEiT+m3NS0ynBxMUcJRRLFDOIY4wye9BFAFqd0zjBXjoe//4+Jb99m3jeEzrIqVnhasKlXHMyOKYoCegPSUfgNg3SyLTtuKoQKWG5ynG5wMmBVLdiUG6I0Ieoj0sSDcHvvW7VritYQqhQfH2k9BkJRNZpOVQRBB77FaoXAscyv0HRIX1CuKtZ5iX0G64XPI+Z4Hz7h8aNrrhYdOlUkMmAySimuWz59b8VgGuDiDhlEyF4h8Mirns9OL6jbDulL0pGiX/fUl47nP7gkUgGbKv/FZvovoeqmRrcGP1Z0fYMFlPD46OOPScKEMPU5OXtB4MUEWcJwOERrS1MIglhydX2B54VI5bYbMQRt35Iminy5pCosgyxF+Ja2anEG4iSi6UuaTc2mFpRVxXA34/nlc8LBHovlnMiThFEEUtP1DX6o8K1BTWJ8Jbh9cJez+VNaXeLJkMXqkt4Z2r5mPB3hlKPpe+I4QgUKazqc2W5O66pleb3m8vqKsBPsTw8xaluCkBcFebNhJ9vjt7/+VZara7LdGxzu71IVJb7w2bUBerDDSrcc7h4wGU1xFiIc6UpxRY+fJITCoZXgRi8ZLzte7Hm0qseanq5umQYxui8xbc3saMqLnz7BCofYGxDdOkCOhjgMsm+RTuJPh5iLX53W/FyjJGKUBgwjwcOjEVH0OroqWa0r6sry9qs7BIGPEiP29xLGP3nMp+cXXBXttmHt5f2uLyW7g4hvPjzGGM2Hnz4nilLKMkcIQXrL49bxDk3bsFg4kILluuaNBzuksU/ZWJQApx1KGIS0gNoiFaViva7wpOLZ1ZKN8fF6y2xwwGppaNstcOP+g/t8/vlzFufPSQOPLAv5+v/gW4S3h9SmpXct1nSEQuC7AOME1mk603ImDdE8Jyp8DnpLkQtkmKI8nyBM0U1JFidswprf/NpD6qqkrXu+9uY9/ul//tv8l//s32CVQRvJK/du4fqa9z44I0gz5qsF2SDASEng+di2R4ntBn2yN8PEAViLbw3HN6ZkgyE/e/R8G5UyPYNsus2kv8xTfxF98aPmMKYLG4yoKNcds9kuN3f3SQKPaP8YZw2LzZxPnj3jcllR9g4lfLI4whqL1v1fN2ZkYQRYTlYLzpcr9idDkmibNUuSmwirWF0tubw6YVPm7M92yJIB08kE3Xc45+jalnB3TPz6HcwsZV6V7NQ1cRyzrmuE8inaDcNBQixTlHSU1FRVRZxITFVRoPC8CMKUNAtoCkOoFNJJRukUIeA4PmaxOSXyfFwvUIHCSUvX1LiqxzjN7TsP8LyIRloq3aObEj8cABKnBJv1ilGaMs4ySnrSmcOfzylKw3f/8oSr65Le8xBWoAvBvOhYPK3waoVaSzZVCbLewtCtwiegag3GCWKhkJ0g3zjaFp48vsRWHfsHv2I1/1w3btyhXFSsiznG9tscnufTdx2j8ZiibpHa43D3iMEgQSrBer2mqktWRcvV9YYgCFCeIAwSlPQp6wYXxySjjJ1ZzHyxomlqqqpjOtrBoEErri6u8JSk7RvqwR7LTcXdZGvC6I2jMx1atIyymDgKcbZjmo0YpzPW9Zyyt1wvly9rMS1R4BNMd9kUq23tZRCSxgnWaazpabsS5fkkqcd6LRmNxwRewM4o46KvaPIVRVnw7NkJ//j3fo/aVYS7Y8IgI881upF4ukd5PqXr8H2JFZbKlHRNj0li2kjiV4abPeRdgwgV9bJg0IAsJdrUqKYmloo017TLLegmPN7HTwO8UYq9v4scZmgn0G2DF3hIPEzicHHwdz1k/t4okB5aOyBkZxrz+MVnJGFAWWke3DkgiTyk9NjkBabV/PY797lzNOVHnzzl2TyntqA8iS8cv/72Pd66MaJpLaM44oPH5/h+wOHemKdnC3amt6mrml45RKi4zisaJCr0+ODJKU2nCaTkaH/E4c4YjGaU+tR1y3iWslqXfPj4nC9/7asMDg54fnLNi5NT0iRlNJvxs5+9T3lxwtdu7nMyn3PrK7fYfeeI3hrqdguWqZoakDRsj7WN6/FCj43WfG95xs3LIa/dO6brapLBgGJdULVrtBSoYUTsSRLVESSK3HYsL65pq5KHN/d477OnvP/ZOX/6o/fZn4042plRFgVnJxfkqw3KE2glSQKfIAg4uV5TthZfKe4dzPjal+5wsDPk8dM5Skjm65xRHPLBx8+o2x7peWTRF8OdfvF2oqqnvVLMm5o219zcmeIHAZ2DvmkQTlM0FcuypGw0vZV4QiClxdkeX4KTCiUlozjCOs1VXvHp+SWv3r5NJgM8z2c4mnD+4jnrzQYpBcf7B4zSjCiMAEkUJSjfR+9PqV/Z573VGd66I5QeuexZzXN8X+AHAfSaJEgp1xv6tsa0FWMJgT9AYxBeRNMUuG7DumrwwoTKVmjbsTvZpetgtVwQyYSD3QlniyXWOdIwpqqWeHXLUXbIIBrgghjlB2gkUTRiU67o6oowTBmPJtvaLd+n1RX2wKLfFxRPGp5vGhqz3ZGauaYLQXo9unQURU9eWpxxKOUIPQ8pFeblrkoID0/6zKIpdTEnVAZt4exqw0BFf+NJ/suqf/APfoPNuubF6XM8IV5+tUps36FNjjCSQTzg5vERebNmsZqTb3KqpmK5XtDUDbG/xcSFoeP48ADdaYw2XM03ZMmAOI0IVEzfrGnbBmP1lt3aG/K8xElLUbak6Zh4HCFqSVO1+C7EOEiCAUJqpPBxVlG3FZ0zPDt7st1gxTHDzFC1Wwxq23bsDDMksFxfEfnR9ioo9DDWIEPNYFSTDlOauufZ5VNK29ObkqLIee3e68Qq5uNHz7h99wFt1WEiTddpfOWz6Tc438dYMFIjrEAGHhaLlHAnGrDsS3Ln4eHhdgdc7kC1qtCrhryqmMoI2cLYT6lCSTxNGN4c0mYZehBg5dYRa7qaKB1uaz8jC4NfNWv9XLs7GZPxmPE45Ec//ZjbN3aYjGKqPKctl1xfGoTy6LqOqrMUVcNulvIPv/YK//YHH/NoXiKl4v7+lDfvHNI1ObrTzEYDvvXOQ1ZFwcXVmlpb3v3kBfdv7lI0PT/44ac8ulzz9I+/R9l0RGHLi+sFOEEcxfhSMoxD9vdGdO2WwLaYr6ibjq91BVVZ4fkwnUz4/KNP+LPv/Alff/0m/5P/7LfwJDxflyzfHHCpS3w/pG5zmrbFWIFBYKTb1vABbd2S2w2TB0Oqy20bUiJjVtdXWOGI0hRjLJvNAuWFZHFHpx3jYUrXabpe0y06/lf/o3/ManXO//Gf/Qmnp+c8fXaKFg4sRL4E8XP3N1gLnYa67XGh4Mn5Fe+8fsTVpqNHIgRYB0kUczROeH61xr2MG34R/Q3MVQE//NnHvFhv+PrDVxlmQ/JNifA9mr5l4Elwll4bEunjpKPuDU4oAt9n6Pt0VtM7SdX0xJFH6AfUnWZTVBwMBwjp4fs+1jqGown7hwdslkt8IUhGGcNsiPUU4pVDrscpJ8sF8WBEFkc4C5uiQPWW1EYUTYk0GmXhOr8C3VB3JXvxiLaqkJ6H60q06Qk8jaWnzCuKrsD3A0I1RvqSySzkenWKRmCNIYwSrIUomDEcJmTpELRgrVuiwMdYjbaS6eiIPu04u3jKzmRK23Rcz08Q0rJJFFr5DEWK7CoUil5brAPdGcJYESUefddRNxqFACVwTtDrbRG7cw5nBYU1PDfXaLvN+QmxhXff2t37Baf6L5+KJicdDnl19JAkCelMw7Pnz/j84yeUWmCtJQpS6qpgXWzIq5zJaMDnj8/wvYAszgjDgCRKGQ1HjGcDqqbEVwFYw/7BIU23oVis6JuWbDzg5PwSJ0AphRUGX0h6Z/BjDxlIYj9ilE3Zne2w2VzT2I5O11R1iadjctdhOjC6YjJIsaYlzTyu1zWBHxJYy2K1YWeaMhtNkdJRtxVlUdKYnuX1kqppmE3GbJoCLxvg6GjqDbEX8Ntf+SZWdCQ7Q3bGB1ytLglcgLaWtVQQWJTUxHFK1Rik3BpzRgGEniI87+h0g01jGk8gAp+8qyn9hsZoUi9h5AJsKlhbDZFPPBowub3L02VJYxqkCNCuZZClxHFG11tU4AjS5O96yPy9kbECo1vmVxuaruP0coPWmtAPGacR+iXvPgx8qqZFiIBh6OMFEXduHHCaP+buwYx/9K038RSoZICUHZ4RjAKfOAyRKNZlxdUi5+xyw6oqOd1UCOUxCyKccyjPo9cdRijapkFhuFiu+fD5KaFU7I5T7u3v8PW37jGbRPzX/+bfU3kpxWrOH33jVX7j3pS7d3YQpiNKEw7v3+Fi3FKVG1RbkusCIzqEgCwaEIcDjBB0bUvfNmShT3KYIHc0737ygoPpiNFwQFUU6L6n3GwQnsLJLZ1L9ttj3yxLaVrL/uGM5WKJ6Rx/8PU36ZE8O73g/Scn1Bb0S+YyAnzfBwSDJAUM67Kk9j1+/MkJ33rtJlnsYZ1B+Yre9MwmCa/cPuT5quD0cvGFnusXXngHic94mJAMhoziBN31+ErRtC2hktw82OXRac4bd+4SqYim6/nJk2dsOoPnHIGQOCHRxqGtRWtHFibsDTJmwyFBFFKWFel4RBgEdFVFvliiXoa//aaFvQj9+jFP/J7z6wuG2YiqrihcjufJbXRBOGZRhMtLJoFHKH1mowl936L6kGgwZF6s6Lqeqq4xuuKdB/exQcZQTpHKo+pycD6h79ObgiTKOL0+ZzebsixKZrNdNpucTdUyjS3DcMKmW2JQ+HGAc4rNukIEjiRMqUvL2eKaYZogbYfTjsnxjMmLc7x8QW81wgk6Lakbvb1LG/gkoYcSAt8H00m02aLKkjim77utO1QoOrOdGJ6A1Pd5+9ZNHhwd/4JT/ZdP+eaaKmwIfJ/L64rFYs1quUBrTToY4GxL6CReqBjJEedXFyyvr5FWsjveZToZY9BMp1Oy4QBkTTZKqFYNzjouLy5ZbhYoJ0mjDGsN14trlAuI05AoTIiCAGct803OcDTF6ZrpCM7mn9I2Bcof0uqOwAsQ1meeL5nGKU2dEwQBWmuuVyuaxjCbDCj0lnu72RQ4a8mGIVpXCBom2Ygqr4njFKHg4PAAgcG0jpvRa7xy6z5BULN/5w5XeUvXaqbDMa3R1PWCURyzaWsMW5Sj73lb00i+pCpq7P6U9+yaGsMoGKA8R6ML2l7T6Bo/UEgZsOod1nb4YcSqrHBSoLH0VwuMN6JWFVIIgnBIZzpaa5Ce94V5t/8xSOBYLTdEoUccRFyuN5SdwZo1X767j3WWsmzYmwWsFxVRDHrg896jF/zo46ccZiO+cu8GsqsJh2O6pgFrKYuO3NTEScC920f8+Q8/YJCm5HnFw1s3UacXLIqCySijn/c0ZluBh9iSoZRwKCF4/c4xb97c4xvvPMSZjrpomR4d87/5377Fd7/7Hvlpxs2dIT4WhSFvarpJzHI34Lo6o7MWekdRrym7FX3XEA4OkIS0vcYnIh0doHxNu5pTTTv+q//3D/ijb3yZvUQyzQKGmUc2HG+RsJ3GSRiOhlR1jUaSDAKc85hOFJ/94IzdacY4C/jyKwe88uiAf/X9n5Fbi9WGTr/MkgtHr/vtna0v6CPwfcXVxZrlutiaZzvDxXrF2WrMo5MrnAAn/pbjRPfffJ3x/g6L0yXz5YYgTVguF9D2xIHj5OyEdz95Rud8snTI1XJDFobcGCXcPjggDgLm6zXrqkYIRRpvHZtv373P/eMjVvkKHFSbikD5CCtxvWWyu0OQRPSRT/XmMc+lpuhqVvmcLIhxfU9Hz3KdMx0foOKIojfUfc2O51POrxHWUtUNehxxXq/wPcU4S5nMDuiaJWVlWZYLssGY1pQYOqTnaI1lvrxEKEvf9ShfMJ1OaDqBVQJ0wySLuVhcE2YjkAF1UxF5gjSJWRRX7E53uF6u2dmZ4OkebSVeELIcKM6LDTISeMZhGgF62wpiekud92htmBz6vPKtMS8+KTj/uAMDAovne2i9bQ+RQhEKwcE4Y3804fb+HuZXvNu/lis2XK0K+taQFzV9Z7Cmp647osix3mxIfMNqvaIsG4pVwWw2YWdnhzSMCeMAiyGMfJqu4uLihK7TCM/RNg35vNpuhLRAZiFFU+D5Cs/5OOOQShAEwfbnRmEaQZoM6TuHs5rQlwjZ4QcBq/WGOPIQWlK3PbuzXYquQ5t2S9kSkpPzFzw/fcEgzPD2fQI/ZDEvMaIB0eHCmnAY0GxaNovNtkM09MiCmN/89q/x6NFTkoMjrFRkSUYp10RJjGpb0uQIT1k6F5IOUjxlMK1Ea0HjC8TRHjJQBAczdFdT6JqwUhjVo4Uh8H18IbdmmVDQ9IawKYiShFL32M4gznJC6SEmPt4oxTiLMRplJe1mQ3l9+Xc9ZP7eSPmSk3lFZ9i+wyys25q3H9xk93BAWxc466Gt4/hwTBiF5HXPzx6fsjuZ8uuv3yMJIfA9jOlxxoGDOApRUtAbw8XJkrw25Os5v/3OqzSbin/0rbf5l3/2Ex6dXmz5xDg8KQnCbUMRzqA1XF6v+OrdG5TLJVmWEgSSYlMi4iG//4++zrOffEC33KDikKtliYsV7d2Mz9sryrJCkmxJXGxLSXzpkcVDojBCCIPta1pTUeUlVZ2jdh1LteY7P/mA/9kffpvJNCZNYoRSzC81ndbEQNNq3DZiQGd8/GjL63/ttX20kZRljXUtu9MYpSym3RYdtNqgjSEMQ7SDuqoRSgIdP316wvjVhwixfe8KJbhcFPxF+RkWQRIG9PaLZdC/+FFzkhDEEUIKirbhr95/j67TvHXnPrPpgIvLC8bJLoNhxtVqxWw4YjZI2R0O8T0PrQ17wxF3Dg9JwxBfSsIwYJAkVNX2zioKY3whaSwkgyFpkiD9AH9nRv/WDZ60C5b5hqIu8bDUvabsa2LlczS9S9mXrDbXuNYy35yTpXfotGEoYJAOeVEv6eW2Rqs3PUaXrPM593anRH7IZr3mYn3K/tEx+fqKOBmRjSdIaZmOZqw3a/bHMTvZDo9OStq+RQhFrit8OSWLBoS+T121aFOxm06QQtG7Gt8LCAMPXVvWdYHbGVEATnrortsa0TLLTupjtMHW0NaWdEcwfqDY1BGXj1qkUFhnkcrDYhBIlBBkUcit3QMen51wsrjmjdu3f5F5/kup1emcS2MpNy1dbzDGbvPYL016xjgKU2BEiTE9N28dMRikxHGM7TWen6BUSNO2NG2B0TDKRhjXbO88VYS1W0qTH4dcXZfsH+2yutpQVQW91URhQK81hztHRGGCkgpjGvAlzg3BtQS+YDiK6bsK05Vk2SF1tabv1gwCj51pxuV6Q9/U3Du6h7CWLq8xQUdVFajA5/BonyJfgJTEUUQXd6wuVyzXa/6z3/1D2q7m1utv0tKzzktCGeIhKIoVm6ZmN9tHSvB8RdnkoBy2B90E25o358ibFiUlbS8YZiMItouzci1OQdG09EbiRxJlJSL18eKEotzQ6Y6hUcjLBusEhJI6FFRNT36Z081z3K/YL38t3fZb4EUSItSYHsVXf/0rjLOQqjxnNr3Fkw8eYzuJMQajDZ89O8EaeOPODXamA/q6pqk0vu8jhM9oGNP0HW3Tbws8+pai7VgWOVZosumIT54s0dsbTzzpYdFYawCDpxzWSowztFg+Pb/g7dfv0lY5zlrK6wv0coG4f4vj1x/w9N2f8uR0jpEwe/smF5nAE1P24imXyxNca1DSJ4oinOeB8mltTWFLinrb/JbXa6w2qEiQ7Hh0Zy3/9b/+c37n62/yyq19ZsOQ959e8hfvPcYKi+eF7I9T/uAb93FWIyJH3Wj8KMRVhkEcEASKFxfXOCdxtqNqeoT0CH2f2PcJPInTmqppiPyIvtZ8+uiEf/Brr/P+s0uKomQQh7xz75j93SlhmvJv/+QHX+i5fuGF91/983/BqmrZFA2buubBjTtME0EUeuRVQRRnTMeKi9WSqip54/Y9doZj8qrgp48fUWlD029L6185PObN2zeIfY/eGvK6JgnjbYaq7+h0j+97+FEMszHVW7f56fVTqnbFwd6rRFFNUy/BWTwliPyMq+UaITuGcUpDTZylOGFZdhXGSjrVYGIH1mFMz7wp0L1BGUPRB1yXFyxXS4JQsVq9wI8SdLcmjFN8X2FRpGnG+WrJ/tjj5vSAxy+WdK1GKYXRDU1dEkYpUdCReQFlqdHGEQQBfddhNeTlijQccHj3NZ6+/Ql/+f2f4jmP6Uzwn/7Td3jrm8e0uucn3/sZf/UXT5m95ehZglQkqcKUElAIB05DR01AhJ8knHcLmqGkaQ3vnT77hSb6L6Mu5xUrXVDWLc4qdOeoC0WchrSRQAiLVI7r8hmeTJiNbuApRd+1TKdTmqbFtZZNvmSUJcRS0OYV82JJ0/V4XkMc+UjP43p5jfI9/EgRDyVeEFPmmmEyIvUHTJIhCo+27hAqwPMs2I6d6S61aTDSYUy35R3bmsYZ/EARJBGm3RB5luM7RzS15uTZKWngM4gTssmILMqQUvP52SWeFyGFR112xGnMwfSQe4dHrKqanR2wbYvv++TVBqk8yqLG6paT6jGjwYgsGXO1uUT6kMVj6qZDWkmvBUoGlHnJKJ0gvZjOrLGBR9tp6qal7y2BH1BWFVEYA5K8KbGmx5mOJA0ZjBKqQPFiUbJYdTTaYnqQ8YAgHv5dD5m/N3p8sWBvPCYZvORqDwco2xPEIz75+JI7r/4ak8M1f/WnHzJIY/zQRzn4xit32H9JloqTiDgWVEVN1WhCzwNP0hjBs4sVP/zkMZ12/OGvf5m+7TjNSz49uWJV1fSmRygBTuAs9N22cMEKRys01sJnzy94dj7nzl5GXRV0SjHIfNYnL+g2Ba984xVeCT3ORMNZktJRY3rDsrlgXp4SeRFKC/pe44ylN9AZTW86rLVobUAKNALCgOhwwOrxmsuF5pP/5pxhlvLGrUPKvGN/Z4/3P/2MV+/d4u7hhLruGY1SfvbuI3oH603O17/8gGGY0ZkeJQWv3zjkxfWSF6ttkUigBMoZjNYYYwmDkFk2ZRiG/Npbh5w+P6OsehQes3TInaMZozjk8NY+q4s7X+i5fuGFd3e4QyBrprFlka84yAaMsgQRwunJFcIoOtex2KwIwxhfCgJfgHA8ubqkcgrfDxFS8t6LE8I45q1bx0yyGH+c4Xkeq5MLPALSLMNTPuxOUN94hU/KJVfrK/Z3jzBdw2azQgmPwI8o6wVNe4HUgiRJWW2WdH3BKj/jld1bPNidsdYlTV9iTMN0kNF1Hb6n8EIYDUcMXUdZlUzu3CEIB4RBiBMRgg7roDMNve4RwhDEHvNqyeF4xEGW0ZIzygYMggzPDzAYBJa+dXSe4yo/I04HRLFCaEscRCRhgjY9b3z9TT7+6BG/9p98mW982+fh6wdEgwRnAkb7G8K7z2lkhxfs8q0vfY1nr1/xx//NBzTXBqMVVhrSYcC9rx7z4MuvsXPrmChL6RrNp9/9q190rv/SaZ5viTJd1yGED84DB01VUzcVypMkmc8ibxkEkhkC3w/pTM9isaIsS6J466pvOk1ddYRhjLWSdV4S+xr6iF439M4QxQJtDHE2wImC/ThmOpjhjCMvc8ajCdN4ROcsgWfxpEJ4AV0+p3eaTd2yyktGMsIpgTaC+XqJtlD1PdJVNNW25hLPo2paTKsxvUYZg9GCNN3CDUxv6BvLf/EPfo+Ty8955a23UL5C1z62A4OjaypsZzDGYxRHW4CBcvguoes2tNYihMJH0mmLtg1BEBInKcY02yNzT2ypPdLhpMQpSW8tAxHiC59ltST2fEZHe6SDfQZxyJVuGXQG3UKk7fZ6yYGwX+ye7D8GxUFEGHjkeYMfBcyXG7pe86/+1b9jkAo++vFP2dnfwfgRTdNhENy/fUDg+0Q+YA1pNqQscxCK3f2UfJ1jg5Dv/uX7nF7n5L0hUR5+EPDjz5/y+HxJq8FYg68Uzm5rBw2OKAy3hkGzjdlY6+iE4N2PHnPn6B1kqLBtT7WyKBWi2xXPOsvhr7/Nwq9ZmJ6yrcmbnE21QNPROocvtyd9ofJJwwwnFX2zZYM3XUPdVjinSZMRO/d2uP7zOWVn6J3BFCUfPj7h22++wZ1ZyoO9d8gGijs3ZniBT5l3SN9SbRpu7e+RrzaYLENKjzRMGE8swyzmm/EdnO5JkoyPn77g/adXFGVHHIbE/oDl8oLlYsSXHt7joxfXLK8WKM/SVDWRVCxXNVfrL9aD/oUX3k3dMxxkWKtJfMUkGxDGMct8RRhEHM52Ma5lZzxDCp9JOiDyJZEHu8OMi7zbNg1JQWsM7376iP0oZrKTEb+xT+sLnN9yc3jI+mpF5/uYt+/waT1nU5YoX5FvFjwvnhMHKZYO607INzWv3n6Lqq652JyA7fBCQRqGdK2iX1zxtD7jar0giROksQjPxyK3C3fV40cw3btDazou5nOSKEAonySOKcuSdDgmCRKMNviqp2sKlvkVbz+4z8eXL/CUxFU5rTCo0NJ1ehsxUh5hkuCcxTpBiACt6DtDW2zIdsb8wf/497j32h6H9xdIv8RpD5xiUz9leqhQTNDyLjv7U3Z2dkgOZzz5ZMVnP3pKXcCv/w+/xs0vv0GS7iNFgEIQ+IrlyeNfdK7/0qluGuqmwkiL79lt6QYSYcW2as9YGq8jDCS1LsjzBa6VrPM1qO29exr7eJ6iLAuETLlarWjbdmv+6zW6KzAGlOfTOIvvDEm6jcwo6RP5lvHOASoWIDSbekMYxCih8EJJXVZYJ5BW4jCUbYUqQ0LfQ/genZMUdYkxlt40LFYleVmy2Th2hmOkn7DuK6zVBIOU1WKJH0bUfcN0kJJ6iqrpOPRi7KJjoS1a+iw3OaarGcZDnDIUfcs0OaTpOpLAw3UxtoNVueT24QGhEBgC2rbDorcmFPptHKm3YC3GNfTW0LUVNh3hhRFDb4BwGufB06sXEFh0NqBVMdbYl9WDEh9BJMTf8Yj5+yPhC957/BznHINhSjaM+PN3P6KYXzO+PcX3ff4///ZdPn7vCd969RbjOCDyPZqqxUsipPI5O19xfb3k7t19mtqi/JRHpxd8dr7ACzxiT/GNN+/y8adPuFgXtNaSNx1KKHpjiRB4nofVgt5a0ND2FhB4AoQQ5F2LlYKmtnjWZ3cnxQskVdNhdkLe75c8bQs6HIv2ikYX4Bqgp3eOKIgJmi3r35cBi7Kk6/tt6w/g+wFYhycV+w+Oee0fjfiX//zH3D4+YicJGKYR33jzHpPYoEJFHKcEgaJre56urmhvhUzTGL916Kjhel0zaSYc3znAuyP54fffJz/fkCYBwUDyyr0bfHY6J409rNW8uHjKjWnCzYMRcST58sNjnl0XLDclDsXzeUHpxzj1t2yuKquS3ckY3UvSMCJLU6RSvHLvIRfnL3DWIVEEclvO3egOUzRY57izs4u2C66qBqQk8gJ2BgPSJKLPfDaJoA59vDduMt9YzEoSfek+n3Ub5uWKPF8T+gGDYEA8m5LnOXW9YjBImd46YFMUXFyfsru3S1kumV+/4MbsgPXiivJiwVl5SjIYEA/GIA1lWW43AXYAvWAcp2zyFUY4As9hseAMxjn8QCGw1FXOcLADrcX6HudXV6wnHVeLFX5SMElnrIoNI1ISL6W0BYEa4CuxfdFbRdHU1G2JxbEuNkzTlC/93m/RdDnnS4F2HsPIsDve4Uv3/wlHq6c8PVvz4ekKxQUHs3vcfucBowclN96Z0RYN0xu74Pk0dU0Y+ARhSLXc8O6//wT+97/QXP+lkwoEslPbMerk9p7cbmu8fv6K72qNlQ4XWZZ2jopCpFWkWYrW2zv4Tb5gsVpijKWoNjjnCCIP4xzbJgGDM4autnTa0XUVIEgGKeNsxM2jGZHf0vQdWkumOwnCNUTekCiIQHRUXYE1iiwdMM526U3OuirQoqcqK5I4o6o6Il9hAp/51RITJyzWK4Iopakr/NSnLGr0ukCYnq/92td4tviMhw9eIb9uONssaX1J2Recv3jB/t4xm7qlsxWhH4KQeCrCQ3OQKrpeMp3eobMdYeTRlw0KhSckeIq+q9FbZPiWCa1L6qoiTWNiPyQKAzA9Td/Sdg1PPnhCNwQ3yxjt7uNqBzUIJ4njhL6o/w5Hy98v/flHn3K8u8uXXzsmTma89/G7vFic883X3uCtL91iU+f82Z//lFdv3WAyjRlEHmESEQTb+KGzhulkwP7eAGcNWjusVXzw6ITeOEyrGSYJd27u8d3TOXml6XpDFPg4u22SMtbitCb0wVNb45KUHkpZpLMo6bEsKl68WPDxZxd88+1bCOnR1BrGGcWtPT6pLyBQ1KanairavkL3JQoPX2UYqwgCn71shhMa5Qk8AvquprMtXde8jPWFtDheefuQ/6kJeXDjgFES4knLJm8Jk5TxdIzyFev5BadnBasR5DfWdMbj9dmbXJ99RiNhsj+i7kumneb337jBR59d8f2fPcI9OkUKyTAMORgY8qZHAbcmE5LA53q+5rUHN/nJZ6ckaczuOOVP/rsfE51e85/8p3/4hZ7rF154wyjalrUbS/YygqEttGXA7nQfrKGuN+iup++7baBba8qqpqlbZknMMIwpe40QHq8dHzHZGSH2R1TOYGtJiUNHgt0v3+OJqAEPZX1u7hyzKGrmyxW9NvSuZjKeEoUJeV6S5ysmozHShkTRLklWMxvfwtQB4VHMjcpnnA55vHjKZHQDX8X0pmMQZ4RZjDYFTX6FFo4kHRHGEa1uaJo1vvRpmw1d33K9KvC9CZt1wWZ5jdN3Xx5Lw6ZbEgQhRV+jAp9eG4yr8KVHoEK6rqVocoZZxtWzM0LnqHSJbaDHw3h7fHy64f7IsD/NEMQ8efyIDz8+59Nnc97NX3D3zZzBjRnatoShYDwZ0Nse03dkyZjUCwmU4tMPHnHx+fwXnOq/fPLClAE+zmkc0GsD1r4Mym83WMIK6EKCYIh0Aa3Z4PoEqSW+71HUa07PzqibGqkcxjhCP8EZg5BmG/kRGm00uncI3HYtxqK1ZqM3LMsrYuGTTfbwhwMCKem1xvMdRvQEccimyZGeZFl1bFZnNKbB82GVX6MaR5BK1CDB80PSJEOpAOVJZn7KxXKDp3wiP0RmIwZxxO986Svkes7D+1/C+hHrVoNIEF2LazR3jo4YDfeRQmB1zXA6wXcxoe9T6grdLwgDhZHbnLwsIPZShBPkRYFzFmd9QG833MbQ6Y7O9IyyKdIDPxBgQzwFw8MZue2RzsOZntPrUwIVgPEJiJAypA++GP3nPwYlfsDv/uaXGcct08mE3d3X+OP/8C7vffoBnt9S5AZrFQE+unMEYx9hNXEU0RuHNY4k8amrLRlK+ZKPnpxxtiwQKLQzDOKEv/zRI959ekqSxESBTxx6lHWPkh7WWXqjAR9jBJ4EzwNPqZf/hqPoet57dMnBZMRkHONHIbXvsbmR8dPyjD6IUE7hRIMf+jiVID1BJDy0gbZraF+WmJRtxarNqZti2xrXtXgIxoMZKEGt11xnHV9944gREfW6pDQ96SgjSiY8enLBZBjy2ZNL/v17j5j+wz1e/OUj/ujGOxTFGc0mIFyHPOkW7O+k7PgpLpFgfQ73Z2jdEcU+nnI8P5tjZMj+zogmr7mcV9y8OeXzzy/IwgiBwPMChoOUZ9cLbj2494We6xdeeEfZAKUEUgqaJidJE3oNi8Wc8XiMsz3rfM1yU1C1GiVbjvcO8DyfRvfsxRm74ymrPKfqem7t7+BNUrpJTNc0GF2ipUe6e4u181gtlwBsypzIC5jncxJfcLx3m7aryYsNTTdH9x1Hu3s4Ipqm5OzqKctqzuHogOXykvPLx6ybFa/cuofpKpyBy/UlB7M9At/fduUGjtt3H9B3CoujMgXDZEbXaTwZ0JsOqWqatuLFxVN0C6F0nG0uWFQ5Sjpm4wldp7E48jYn8FM2xYphNiVAEXgBUkhUGLN76yZtsSYANqtrdOpQ0nJ1OsdcNAzia9bFkk8/eIq/8Hl7doOr8JzN+pomlhjnMU62/4daB/hRx9g5pLBcn5zxsz95F1t9sZaM/xiUJClaKXrdUjcV6WAATiGcwNoeJx1SKnCSvhbkpkGbjuPphCAIqLuOs/NTet1jjNnmY53EAtpqwmhb5m4xyEBuUXedoe9AeoZaVTx9+pxRkJIev4aTGRZF6Ht4KiUIYozsqPrtMa3WDV1fUW02OG3p6EmihOnhmGKz5nK5RihFGMbEaUy+XkHXcXV9zq1bx1jTcj1fML5xC88zOBnTCoMvLaumoHeaNE7Z3b1D19eEYQLC4KyH7zSbrqTsoe17utoiZYvvNMo5pPBJ0wF+6LHKF6zzkrau6E2D9AKEgMjzSaIYX/lYaam7mkD5DOOMW/fuM7w1pJWaSlk2XQuqIw4GaC+hMM3Lw8VfCWB3knBydsk66tnfn3L/5iEfTD7lez874fn5e3zjtVf5h994m4NRzCgLCaMEpQxd3yKQVFWDMR1VqdnZHbIpSj5+ckajLUr5RB6EnmRZboiiEG2297rWWHSnsVuWBL6UWCO4XNfsTGKcA2stICnLFul5PL+c8+1XjxAOyqamu3/Ip0FOYyAJI4p2RdM32981bgvkeFko07UG3fW0vUUah+625qq+a8EIRqMxvhfQ2gptWk7RTJ3llW6CU5YgGVG3Pf/6332Hv/r0lDiLOJ9v6CXs/OmStnVcDQo++uCKs0cLjnYPkNIR3j/AZTE7OzN2d4eMJ4a6qvB9Rd9rjnfGRHHMyemS3jjKsubqKkcon1oLbu2O+fzpJTuTlLPNiv/H/+X/zD/5n//v/v8+1y+88M6yAUk6YJiETIcZP3n3PaLBhNnOkPnyirxZM18u6WuLcZIsG1A2NafXF9zYPcBHEPoeu8MM4yAOfLzpgDrwaNsW3VeM0glhlHJyfsVmsyGvLsmriqpZ8/j5J9zau8d6+TFeLACzdUw6hdGS+foSz3fsTHZodMF88ZTHL54ReCG7u/usuw2T6Q7PTh/TiYq0Dnh2/jlCw6s3bjGfd2iV0DQVaRzSiRzjHFZ4tLpnkOwRepqAlCo/59bsmMebFdZolPIp8oo4SKn6bcTEdYooTOibmlJZ2qJiEKWYzuCco3GW3mkGTUchCygLdvcEXh/y3/3xnyJdx8XTE776zkOyQ6g+KmjrHNem5CUcZTvEvkQjiH0P1fXYs2vW3/uE7vGKzPsV7/bnunE05eTsDN1KYpngrKBrLEoInAQlJAKFsRbPdzgpqHTH+eqMzOvQbQPagrDbXb6TeP42F+ico21alFJ4nodxGi/wCMKQtu7QRtMbR+A8bh49YH98G2M9Wl1hWomQHrq3tM6n7DoevTjh4nLNerUmjgcM44TOKoaTKYtihS99/CCiq1vsy3w5AqI0ZDhMWOcX3Ng7QvnwzsMv05YNtx7cQ4UCo3vG0YC1KnFC0tsW6QsKvUB5kqPIZwjMm8X/l73/6tUty9IzsWeaZT//bX/8ORFxwqeryqhikawqqYvNdpJa5q4B/QD9LQEtQH1BSJTYajWBYovsIplZLk1kRmaY48/2+3PLTquLdbKuDwsNVCEZA9i3G3tjzrnGnGO843kRIqN3LV46EpEQCPjgEUrSh4p25wnB4nz3RlTFAI13hnk2ZTye06thggCtESrBR8F0fszHv/cdPv/Zz/5mLAkJUQTaYIje47/14/2buK5qzv78V6Ra8Gd/8YpEax7dWWA6jdSBL58854++85gkESDBOYuzg9VkjBGBx1pPlmdsNzs652ns4GGea3jv1l1uqh2XuwqlFdF62rZHZzl395a8Xm9QiSIGT28ts2lO2/eoPEGhQUhGacZyVqCDY7G3x7reMX58nydzeLnesj+/iw+GKAZ1sm091lnqrkYISQiR4AMKhSTBBYf3Dut7rG+Zj5ZMpktsMNTNdoAhJSNeZYZ7XqJ7xZ/9//6arZOsdz3bvuf5dsOd/Tkff/oRf/H1r5gcHPD//BdfkvWB5XTCdx/fYjqaDe530XN1cUOWJSSJYjQqh/91sUAnmqptePTuHVarDc42OGOYTktidNgoEATef3BAmkg+/+LZW63rWyfep69ec3J4gKkTbG8QOsE6y3a3Q0rNxdWKPgo+eOcx0VmyJOXi6poXFzeMsgkPj48o8xKphl5O8IPdXYzglSTPSvaOHvDy6oZNt2LXbZkvDthbDl6Pn77/+9jOoKTmprtiuz1lOp6znN9ivbumNVuEGzZes1tz+PAdDg7v4zpFRsrl9hwtCvKy52i+YDJZMJ3tMStThE+w2y3Pn/6a2WJJMikIMeD6nm53QZoliDjC9pbx+IC8yChGgubsBcZ4gu8QyQxva6z3pHpGHyC6ljxP8cJQjidkSUFdb+l8i1QSpRLwHdvTLylkz/h4xmlt+cnpit36gltLwU+ef475piEvHdPFDJUHtLB0V9e45Ig0zxBeIJOSOBIs3rnFd1c1T359+rc86r99MVumnF5GDmcL+r6jqT3W9m/6vAH/xg5MyOHjFYID6am6mhjB2oAJDmMNWZajEjU4Hrlh1AciWZriGUzHrRnIN2mZkERBPs5IZUrra06rK07mR0zSjNFoTMDjgmNXrWmbitnkkJubnuPDOUEEqu0Vh8sZu6am3fXIPEUJQfBAFAgiWa5p2paiLFgulyymM3I54nc//ISdW5HOFkQaEmFpUihJMMbRuh1lWuBCh7GKV7HlV9tXBLWg1ClCDxg9a3ui1SymKSDRmWTXN1g3DNyGGOitwQbP3nxGkJBPSkLfomRGludIGQnBgk45vn+Ln/30J0OZGiAOPffIACFx9tvE+zfhE3rXUtlA2jseHu5TyowfvHOf11cXrJqWX74444++8w4qAh6mi0HQ1jQN25Wkamp6u8FYQ2UF66olCEVWFGyainU9GGsE6xlnBYlI+D//F3/IuCj47/7lj3m9XtMjmIwGl54szVFy4BlHETmYTfg//tH3+fc/+RVnFxfMPtjnq0nLL1avcUg6X2NcR9031O3gPGWtIUaJQBJFACmQUeB9ZGcqGlfRtx3zyR57y32CNPhoUVoTKWlsz0WR8u+fveQdv+AffvYJZxdr9o73+fWXz3hyes1n3/uAP/3zXzBNZnz/5A5W5bx4+Zp3Hx5TZhnLgz3SFJrtmhAsq5sNewdTIDIaz9FpQiSQZQWbzZpMex49PMRYgbeW/+IPP+Wf/Q9/yWw8Yn9vwWffecgHD2+91bK+deJ9dnFBlIEkwPimoMgLUs1gv9T3FNmYo/mCRCfEINhfHKBEgrOB5WSCloK6rihHA21qeWeP2/dGZNJxXUsWt+5ytr7mvDpns9tytH+LqurY2RVCKuqqZ726Ym9vys3VBSfHJyipOb96yagck+UzrNkNRveTgrqyrHc3CBPpOkPEE1XgqrlkPPuAXb0jEZpGarq6Zr8s+d5Hn2LEcKPr6obR7IDEWLzrODt7TZKmXKxecrl6xvce32e2mKFbi48D6cQay7icsFtvGI0mZGVJ27akaUqaa86u10gk1jsOJ3s42zNOJzxObkHRcRUkv376jE3rSWcH9EnK1YsnxC5w53CfDx5+jC2g3byg22iaUcVstMeo3COIhI1uWI0jX1Y3vDj/1o/3N5HIwN5iSh893hh0KkjSSAy8+fAPKqsYB/FJmikSleBbj4+OUTGjVIK62zGajHHBIBXkOkMIQde3tLHFCzt8RBis0qSKJKkkLRTVdcvL1+fs7R1RuY7xKCFIIEhUokkLRewyertByEiep+y6lhgVy1BydvUKneQgE9r6hrZpqLsdbVMzX87Y1i2phv35MX/ye39Ev9nx6+c/4c67j3Gxx5uASFJiEDhbkyUpNgw9tULldNZTNR0mzBASNs2WLEtIlcQJQ/BgvSBLRux2HdF7pHfIGCjzHGQABZKISHM679A6G4hdBILrKTNBbTY47YlZjvCR2BtiiEOZXnlCHF7P38YQx3tznp23+CCxvufVzRWb7Y5//J0PeO/OAf/yz3/O0/Mb7pzu8d7JnNE0AaVJ05Tr6xt2TctiNsHYlMZ6fvTjX+DjcFncVR3rTY1nAP4XWQE28L/5wx9wb2/Ev/38Cet6S+96EqXxwWKtY1wODxNrHUIItnWNiZb/6j/5PU7NlovbBX+1ecqq23C8uEtvauquwliDH26MxKhQISfgMaEHIknUgyreWUSMHC0PmU326G1P4ypc6CEorDO0dYPUEnMgKa5hVCoWY8Vf/vjnPHp0h0d3Djg8niPVh3zz9IyPb+9h5gU//OA2UhpGuSCYDUWWoGZLOrNjMpsxmY9Js4TtZoMQGtM7Li5rrGt4cPeIXd2QKHjxYs1kPuY/+4cfMJqM+Ob5FU/6luX4f2FV853btylyxTwZMS0Hb9wQItu6Ydf1CKXxXU/rI4vxlGgNs7xg7/33UULgjKFrDcE6pJA4FbC6oWhqpjcF9mRgDhvfsZiNsc7Q9y11W7FtrgkBZosjsizj+OCYtu1I8gSRWSqzxfSWST5hPJoym+Tsje+SyYqm3bFrnuGF4fzmlFt37tK7hrGa4WPg7PyUQhb4Ysp6dcqzy0sSWXJwfMxmt8Ganhg9tblhs9qyv9hHScP1xQ0vdzUo9QZavkWGBJNaEpFQkNI0LVE46l3HtBgzn80RXhBEABIKnbOprzlc3ENNbvjlF0+ZzSd89PF32JsfokLCn/6z/xvXV6cUJw8Z9Q+ZPdpHygmnT14wX86ZTW6j9BQfIqELfPmvf8nzn7/C22/xP7+J3a4DIqvrG7rWoEVG8JEkSYnREYJAiIizhixNiD5g+kEU1JmOVKQkoiS4QAx+8I+VEGPAvSFhCaVASIQQhOBRUqJSgQuOpmtAlDgXOFjsU2Yps8mYptqRqIyAZ7W9YNftWG3PcU2PlR22b4ip4BevnhNFxnhWsG43lGVJmmZs6i3GWs5eXw+erKXiaG/BOHVc9luOHj4i4EhCzmiyHP4uD5M0YWtbckq0Sul7SyIlopygfIOJLf7N/zYb5Vgl2LY72i4hbcVwWRERYx1aSxAS5x1SCMqsRMgM30Zs9DjbIIQnSzO63tD1DVopVD4iVSm5bKku18RoycYFWgt09q246jdxZzplu73m+bohU5LaWJwLfP7yFQ+PDviD73zE519+zVcvz3n/3gFEz9mrC/IyZbOuKIocIRTInFdXp1xtO/bHOUWWcXx0zFevX3O13pDojBjg8a0DPnnnmF3d8sWLV5yudwglyROFQCDV0GJxEQh+6Avj+fyb19y6fwv54W1eFR5JSsF4gLiYHvfGV7e1NVJIUlUQXKRtd9joSLSgTBKmowyfKCaqQCqFCW6gw0VNsD0ueKwzSBReOKpC8ry9ov3Gkiaa3/nkIcY5vLfIEHn37j73D0dY5znfGmSScnhyFxU9pjf44Oj6nsl0ilAC6wwhhkE93Q4XjrKQZPkclSakJqHtHHfvHVLkmq5rCT5yZ6/g+dmGLy7fTpH/1on31sEe88UCWfd4a/HOEqNHqWGMIhOKTGmUkEwnY1KpcEowGhXgLU5KnPdEH5ktZqgs4fXNa4K3lI++z7P1Betmy26zxWUFztXUbct2tyXNUqJQ1N0a70sSFRDKcXp+xny2YDKeIwsNItK2NdubDbE+5eLmmrbdUJSaHkdZFpwsb1HZNU2zI5Elh8v7aBTn1zsOJ1PevT3hurec3lzSu4pEJUzGB1gbkTh+9eVfc/9oSVM58qTAYAl4VAoyeiI9KkmJIuC8x3nD3YNbaFXQ9D3O9YzLKSFGNlWFFIqrSnIwO+HDx0uutpeUxSGTckl1fYWre+48HqMPDC8vThktJ5zMT+hOKra7lmwCaXDY646bH39J94vX7Gc5Vn778fpNNG3Prq5xzpHoFIkmKRXLgzkhGk5fX+AjCB1x3qKjRmiFVR4SWPUbctWSJCVETQgGqTTz2YzLyyuSJEFrTWMrhPDIRCLkQNyJhOFl7aGzHTJVdMZwdXWNFBExUmx3K4JLCL0mzxW9cFxeXCCSlCAC13XF7eWYsYeWlLYbynYq0dx/eJ/tqqY3DcFZ7s6OuTi/5Pjuh2hVoKQjKImxgUQoskQhfESYHpdk4B2TUcrNbkNrWnywCBmGcy0CQkhcsEQMQnnarmNb9yiVIEKg1BmelqADaZqwqRsy5RmXE1SiUAkDlU4rqjbQu0CWl3SmJwSFcgJVdQRj6LqO0ckes8m37kS/iXfu7JOkguqXv6axHoHAS/j1qzPObtb8ow/f5U8++5h/81df8PJiRZocoIVmu27I8xFCwnrT8POvX/PrV6cczaZ89vE7JAKe3zTcrHe8qdGQicD3Ht9lve1Yr1uuNx06TdBq6MN7H0j0sJfzbEwmLPcPF1hn+Or0iu9MJOdTxWl1ynV7hXQSaw29NbS2pQ81UgnKfEQMAlyP1proMlxbk2aDO5WQg2OYCxYTDL3r0UKTyIzOVcN+DJ7OBNpkxA2B40Jy62BGqiPWDdWZpnNkIeLcQFJbzEdsNjteP3/NydEeSgkEivXNlt50HBwf4ExP37copZEiUhQpUS5o6pq+s2RFSd1UOG/ZmB4hIorIfDFmtr/HX/707fgJbz9OFAXbi3NcZyAIylv7ECEVKdOiRCIos3ywrUPQdT1pURIFAw/X9QRvybMxkUgrW55dvSYbLbGbV6w6x3q7oqkrlncPuKqveXX+DePJlN4P4xtpkrBeVzy6+wE3u3OOD45J1Iy2bbC2QcoAria6nlQaNs0NN6sz5nFC3W052jvCNZZlsQejEd517Oqbod9VVxiryJOe52dbooi88+gxnQmcXbxmf3mL+XRJKhNuLwpObyKL6R51XyPFjhg7sjJHOUVRZDS2QkbobUuelFztaoQOSBkwxmCdo+pqltMpIQYuz8+Y7ZXMxnOUHKAbXfeaj3+npHaRb74+4xVrEtNy99E+79y5x89/+Qt+9N//P5DVkhO3RKwMU5EQteDSfDsL+ZtYLDRSL5mMZ8QQWN3sGI0n6CxyfnWF0IHoNKkWRB8JLgygimjQCrRIkZkcPJWlxBqBD3B1fo2xPWlWEIJDaYlMBkVujJFIJIoIYvD6NMbS1BVpoghImqphhqM1FkRCnin6a8Nl2xGtZJyntLZht6to8y0325ownUAQmM6ChxA9aaaRukRYxzu3P+F8c0qa5mQipfOB2nTkOmKcIPcOGxwqJDgCXbR0bUWQQ5/a+oEelSYS19R0xlMbi3cKeEO68j223TEp5wQt8SISrSdLRuRpSZEU5MUIdMT7HqkEPhhc21KkJTZ6qu2OTdVjrrZoY0lSCYlivlgymc3+LrfL36u4c2ufMpXIaPmzXz9l1VkSobHAVdXw42+eM558yPHxAX/+6+e0jeHuwRSdCFrTsq0sddWwP8+5e/Ih9473ydLAeuvYvnhFnmsq65HB8Z/+wx/w+M4C76HLNaVOyJRBpxqFQCpPoiSjyRSFZFJk/Jd/+BneWf6/X/+K8wPJs/aM6/qCTbNhkszRWpNFgVRQyKFnGnDszBYrLPkoJ40C1wmmozm9BesdvTcI3ggaZcC7Ch8jznQY0yE9FGVByBTvfHrEw2pM6D1152mbDq1TcqWRIkGrdCDM5Rn3FxO2mzVN24CQFEXK3bvHWGcgBPquJyKxeBItqXY1+WiC845SZWitmExynn5zhQ2R3jjmiyn7xyfUtSdVb6fIf+vEu1mtSHVCWZZY56nanizTtF2LdY7ldMZsNKE3hrzICLkiVZqmqum6lumoQAg1oPdKT9hXJOM5d+58j9dVz0g4CCnjYsm2rtjszvGmomkixjumkzGJHPHw3e/RNoG6lkzHgcubZ3S95fjoLoUMTNYbNjmcXr/i7PIJ3nd0V1uc6bh9dEzXtSzyCVfNJca27OqK4B1ZMqVc3qaI8N7JnHyypNk1iD5S6oLN+oy6rxGxI5BAmnB5fYajRyWC0EecDoBHygwtJb1rOF7s0/Ud2/U12Sgb+mZ5gkAxGU9oe0OepWxvNtysnjI/ech4NOdydUaDwU4zVi+uCMFjo+VVfU3ziw17qymH5QIz7nn61TkvbzZIA2maYbqO+K1A5W9imkcCCdakXN+s2dubc3C0x/n1GZFIXmQ4IwmhJ9hIYMDlKRFQMUGQ0HUBmTlS0VHVO7K0xHYWIRXRg9ISLRQyAjGAisPso/QkKsFJRV23nF2cM59N6UyL7XqCm9KYDYvpbWprqaotMQyCE+s7ri4uIAgaayiXR7i+pVpvaesGrwO6kCRakicp/+DTz/BtR4iDYttFj/cJCUAvaNoGKz1pPkaqQN/t8MERYqQox7jQEbHsqposyTFdz0yOEC5lNtrD+hYZe0TcMBqNEYgBhhBbCIL53pI0H+GdR0iFdx4lc3rjMb7H2YDSOUUyQwuFtQbvHWhBPp0Q04SsHJHl47/bDfP3KILvKcqETx8/ou46fvbqiqZ3jMoUbSXn6w3/73/zV3x09zYP9vZYbXfcOpwP+mAt2W53FEXJw3szmp1hdbOlHBX86Bcv+Or8is44skSxyDT398e44CnHBUdFwjhX5P1gN/r49iGzccmX37zgZLHgex/e50c/+wWnlzc8enTAZ//17/CF2HF5dYkPkkLMmWZzkmRgmEfr6GzARwvSIqVHSAjR0ruG6AMiKoxTEEELDW/EikhNz47eB1IpETpDa8EoL2hMywtzw+zak5HS1R2zac5m11O1DWXpiDHwz//lX3B4sOSD9064d7Jku6nwziAk7LYtTd2Qlor5bEbXWYz1pKnE+4CUiuXBAc5ZkJokdSyWI4TSSCnQmSbLM7q24t1HJ2+1rm//4s0SiqxA65Tr1SVPX71mOZ8zLnLmk+lAOPHDazcEyEcjorU0tma8nJJlJZMkoRc7uLVFHx5zPD3ibNtS9Q2r9Q0uBKKAtr2hMzW3jz/AOkvUASmh7w3PXn/Jy9fPqW4qfue7v8tiesCLs6e8PP0K7xvMZsXR3j4+mXD3zkPKIqdzLTpR3L/9LqqXJEi0jyg9o5iPiESMD5xfv0L7gge3Dnj+5Oe4SnH36F1MFrg8fcnZ6Snv3d3DMCfJUkYKdm0HIpKlOdJnlGXJZntNlJF2u+PWZI/rqmI0naATgRRvyF6dYddtSNEIUWDCiH/1b/456eRzfu+zP+TLZ7+mcteUo569/QX7WhIay9VFQ9slbNuOe3dGvP/4kM06sPGO7x98yDwb8eriApUWf9uz/lsXv//pd/jLr77i119fYoNmOZtyfXXF5eUVUgikCFjXkUiJEyAEpEqT6JQQBBFJQFL3nijsG5V7RCqFj4EQAzoqZJAE5yiExEtHkBKiJpUJPmpcH2kbR5YavJHMR3OIKftlgQyCy4tr1qsG7wJSJCDh4OCAVbLjer0jFZrzi3OcF6RFhiOw21UkqeDw+C7/qx/+kKdPv+H2e4/pe0+alORS0tQ9SqRkKhvmbYPGiUBd9SzLCTZ4UnKih77tcb1gv5wT0jFFng/CRO/wpkEGj/CRYAQBjxaRkcwYlwv2x0ua4AZhWZDEIOhNh5AOpUYECWVa0u7WZEWBkFvUNGO53GM+X1DVHe12eBV/G0NUdc/NuqXve37/ux9R27/i16c7BIJxliKAw/mczz6+y6RMWW1q2t7St44H9w+YzFJkFGR5yrNnK8ajjIurFRerLevGoRNNoRM+uHvIZJQOBgEIVus1H9w/ZvWzp/zwk3f5wcfvUK1v+ODOAWcXGx6eLFldH/P16Tm3/uBdLiYVtevZmx+CSodRpjBMD9S2p+k3BAYxVqAnxkgIgeADbdMhvcAEiWDQXiTSQ1DUfYsPntZE0qwkahC2R0ePjwbrLFUSyBcl9dMN88UY7+DZywsiCmdec3JywD/4ncf8/PPn/Om/+xX/+T/+hNkkR+uUpt1RjqYEoG1bamW5WW0Yz6fUTU9TOW42p5zcucv1qubqsmIxSzk+OqCva5qupcjGVNstMQT0W2bUt068k/GMUVlycXZOqjXL2YzlfM7x3hIBdL0lyTPKLEcpjekDtlAs/vADQq5xYUDLldpTZ6+og0M4TRccu/qKpr+g6T1CaObjkkQvuFptWG/PybIURYJMEnqzxtmaDz56H6sGQUyZz0mi4eJqw67veHx8h8orrO+pzQBDkGqE3EiU1IgkkjBlU2/ICk3drVFCkescrcZcrwLT/X3CyLETW66qc+Z7hxwe3eZkLNk5z+nFJYEO3pTotFZgPHlZ0PUNfWx459YdlE4RtsarQJmPCFFiOosUIEVgOipJ5AifL/ne9z/iz3/6C/783/0rjDD4xLBa1bxCMinnHM6muOvIq4sNd/QCL0u+fLrhm8trTso9JqOU+WjMYjYlCvW3Oui/jdG+2LFZr4laMMoTzl6dIZF4q97YQ9oBqCH0YCGGRKARaJJU4UNEKYWUmsb05FoySVNuVh1CSaQEaxw+GKSSGDeMvXnt/yaxS+HRUtM0LQ9vHzFdzlgsl6RFxigb8fTsNd88e8HNektZFsg3l9AYPZ1tML6jCz1BRVo7vATSTIEoyKTmf/uH/xne9/zws/8UJyQX16dDr9kJMplT5iXadVQxkogEGeFotEee5rjYkugM11syMspZQZoXbJpzTGtQCKqupVnvyHRKpjQOSNMU2TScuDFH+yeY2iB9xEtFax1KQIyaum5YzHMWkz2sMFjbk+cFi8WCEBx5OUalBctswsXpJdenN3/XW+bvTTx5veHF1YroI8e3j/juO+/Q9V/yetMi34y+7c9LEiWRMnLnZEqWpTRVx4sX1/zi6RX/8PvvExz8zu++w+X5NXmx5MuLG55cr0hSyGXC+w/vkWiJlAnnFzccnexzcHzA9z7+AO8HAw5nFVIn5KXHtA33bu3RFZaX+zkdMElyarNFJJbOVHRtQ6HHBCfQIsXGiPU9PjhiiHjr6buOrjMUKkOS42IkRI/t+6E/7HpiGExJpNS0psXaYT5epQKZarbGcO1q5mVKURSsb2refXTI6XlNB9y5e8jnP/maB/dvsdo0rG+2JCpSVVdoqXG2xjpHWZZcb3f8/KtLVpuv+e7H91iMNXlSsKsqTl9dsd60VNvI4eEMgUKrkhgll+dXCClYHCzfal3fOvF6H7lerciKnJFWCCGZjacQAqPxiCzNIEK73aJQBK3Q92/DoxP6ekf36hpdN0wfCuqrmnIx45vVNS8vrxgVGbeXj5Fyws70vD77kq5fsamv6buGPC8JIeJty3Q84tbxA5q6xbdrnj79hvt33+P2rXcZLw/pmjMmacnFixVdVzNSCV5bslRQiAQnBdduTSsdea4RUjIdz/Hes91WWLOl3nXkecrJXsnnX/wlxWxB1Si6rub43cc8/+Y1rXckmUYXEolGxZxpOSftU0b5nEflISqf8WJ7w6TUSK0wfUeaZQidEBE448nnY4KXKDJm40M+/USSa8Gz0y+ptz0heCrXcbPe8voyZb84wB1OWY/H/NsnW159c07ad+w9HjF9f461ivXLG1LzbeL9Tfz8f3jF7X+wROeWXT04FCUypelqnFFkKiFGCQGkkGRZgvACEEgpQXikiCAMITqaHlIlSUqFR+CcJ8SIiwGNBC3xcag42zj4P6epxHlDqhUHewckCnQecLbjvLvmly9+zmpzTaZyltMJxnYEIel7Q4wRgLY3BBHRacSHlmgT0rTgh+//gL1RSTE9HMZOQqAsplRdhzUNaZESdUQKySQZsesrRsmIrCiJaqhQqVSQBEV0higETQ/eBfp2zWic0ZuGtenIg8U4Q5JLfIworQheEVNFngjMeoOaTLCxRWhJkeaMx3s42dPblqA82+YaGRNmoz0QA3DH2EhZppT5hMsvv/y73TB/j+LFxSWvL294/+ED2u2OXCn+0Xfe57//0U+52BnuHhxzsr9Hmgi0FCglKbKcMs+ZLMa8c39J0wVMb98o0QNfv3jB2dUN01JwZ3/Bp4/uMB/lZFmCCYH9gwV9bxAqwXY9MbYkegpvBHd3b+1xue2YPloyPhK8aLeM5gu0r3HeYF1FH2t6LMqnEBN2fYVlhxKSVJWoTOJ9TVaM0KJiLIeHz8Z6qmqL9RbrDDHCuChJlaTzZhjXCxYthhng4D0733Fud8zkjNV6w/JgycXlDXvLMaM7M6QMtH2HczDKFOV0jDeONMlwIdAYx3J/TrXbIqLmo8fHLBcP2N70ZFrx4sU5D9+9S6ok9+/NODvf8fXTK04Op7w+P2ffzZiMx4TouDi7eqt1fevEO55OSDKNUgnb1Q0iMFBsuo643ZKmBanSZFrTGUtvLFl0ND97wvbz54w7STKG9MGYRbJkJRWvbm64e/s9ZFB0vWNdbTm7eU2eTjF2jXdvXh9WcHLygM61bJsLmstnCCGJPvLOo3cJSP7q5z/C9pZpKdifn5BKjXeRtBhMjZ33nG5fM5lOcanF2C2InBjcwKAFOtdhvSMZJSwXB9Sd4Xe+8w/42ed/SRs6sjSnLKYcHkXWfcWomCITT5akTPoS6RTT6FHBMD5Y8PX6HJUK+uAo5Qwlhua9RLHerNAqhZCghEIJzcXTFSbtOGsaLs87Pnp0l5MHh1zsztlUNb1RWCvZeMl17ZhNpuTThu+cvMN3/tefIQ+OcBbi1wrxvPnbnvXfurj46oa9e3e5/eGIl0lNOhojXaTuakZ5QdcNg/lKCpyPA9LOQvCOth3mBZM0GQzilRpQfL1nMi3o2gbjLCHKN7zjob8bhB+gEELQdgNJSKeSfKIpRhnGrKiqFikVp5sztt0Fi/0JyuekmSKIQN31b3rNkqIoMNaytzdjU69pm5boIIkJf/TZP4ZkTTnO6cINrXtj4xYsItFsupZSKGIItK7hsrpifw55kuKDoXcNmZA09mqYqQwCZ1tWmyuO9g4JBIw1KDRV25EnChkNKtHYoFH7JWtfkZYT7F45CKqQqCLBhY4sm9L2jt52FCLn9PU5nZHYEAkxDiAQCT4Oytk4DCx9G8BnHz1A8BBnIloGIoqHD4/54XbD//TXX3NxfYN6eI+mdiyW06Gvu1mj03Rwu0pKEmc4OF4iE8Xjxw85Oj5gubeA0HOwN2c5GREJjMYz/K7Du462brA2Mp6UzOcHKJ2hM4frYXEw4y+f3LBaFLwkUKRTarNh3V7hGTjdlW1w1oOoSUWBNR1eBsblhDKbs2s3eAI+NHgsSmiCs1RVjYmOJNGU5RiVaMosoTMNfVsPmElrkUqjYsCHgPEQC00RM3QUXJ1fcrOtMV0kf3DE6qri3Xfv0pueECQ//quv+eiDe/z4J1+SSMn3P3kX0xr63rK/v0TpgFSK7Khkt93y8NEJWlg+/vAubdcTvOL09AZ8z9HRkuDA+ZbRbMZ88XbCwLdOvGVZEqTD9haBHGTgMTBf7JFmKd47urqhfyPu6J2l+vULutWapItUEcYLjaPCqj2erK6QUiOF5GZ3TdVUXG1uGBcz8nzC6/NnYCP709s8fuc7XKyveP36FdPFmKgcXddw59Zdri5uCNExnY5BRbQzvDh9wosXlyBhTULfdJSLnP33FnTSYGLH3nKYVXPWcTS/RQyCSCBJhlnMs6tzUqm53AY++OBTXp/+jHE+p3ERlU+Y5zkSQW9a6q5lL59hQ2CE5HB6zMudpcxn9KGm7zryVNB0FYvyiN5GjGt559Z7RBeHedHa4bzi+OQR7+3foq4ams1Tqu0LigTKg7sQMkIU9Pspn//VMzZVhSgSsjtLmkzStQ15NmX64SPCnepvfdh/26JIM17+9Ioffvq7yIMdXil2uzUmzjGN5dWLa7K0gBjJkgSpFW3dDiXmZExkUA+LCBKPALK8JAgQiSRLJU3tEGpIblG/SRwyIuIwkoPQb2Z/LevdNd515Cqn6mp22zVd3RBDpKoqpqMFIIjRMypzfHBUu24QXDlHdJIkzRjnJf/4ox9yOEmgzPCqou0r+l4jRTHYUYZInkqafgURfIjgI127hliQJIoiL/De420FYnC18aJHSInWOevtObtNg1YJWVIiJWRJZNdt0aGk77dUWWTaJciohoQew8DBUG9aPvWOKDyFh+vzhtY4grMEQEQGgIYWrFY31Kb/O9opf/9idV0zGmdIoDeR0STlJ798zmbr2RtNuNzumGSKED3G9ASrUWnOrmq5uliTlxkPHtzCmo4smSCkQmnwbccHH9whUZLdqiIvc87OzslHBc4DQSK14Oj+PdYXK7TrSZKU8SjjvNkw/cEePxY3uDAjFZF1c03j1igNxnSYziHR6EyR6wSZz9FZRpEXtJ3BGo93YeCSux6pRgQRKMYJs2yCUgmdafEYej/oKFKd0NWeYCLpJKH3jv5Ne3IVLXpcMNcJWZ6RjgpePn1N3Rqabc2DB3v0TcJqXbOcFDx5+prvf/gOt45nJGlCcHboh4tkUFMriY+O8ahEEumN5fp6g1RQZpHHDw9Iy4w8SxgvD3ABTl+e4dr1W63rWyfeGCyjssDSIV2KkgoRA01T0fmEelsNlmKJpm1rvnzxjNOf7RAR7iz3mE4y3nn/Hrv+nGZ2l/Wrl8Qo+ebJr2lMT9tVSJkgRMJX3/yMuqq5f/sO7zz4HmcXp3z55c/IizFK5Ni4Yzqb8PzZU0xrmE6nuBBoXcdyqmiqDY2tybMCa3tmyz3uPnyIEZ6duWQ63nsjYBHMy32CkXSuQaWRZlchhaC3HdfbLXSSfrzkg4ff485ixi9f72jaFucMkzwjeot1nhu/orSO8ewe14lgtauQUaEzRZHu41zPZDynSMYoyXAjlZabesXubEMxzbj70UeU8xGT0QGHexmSD2jbKy4ur/jm+ROi35IlCpUk5FnB65dnaOX5y7/4BV9885wsy9lbHPD4k8dMJtO/3Un/LQxBxFWB0z8/59P/w2Ou+4pfX58Sg8MJy/7hCNMFulYSXCCRUJYFSaZxIbLb7egbD1KSpopUpSR5Sh9avPLoEmZpwmZtBqcjBISI1B5vB0uxYD2kit46rDWMiylKSartKzZ1S0DgbaAsSkIn8X1CsIHWtVT1Bqk0aIH3HWWe4GPBP/1Hf8Inj27x/OorHj38mK6WiC5loif0PtDTUCYFfVuhdKTtO4TUFHmC1gkE/WZsqqUNDhsUTbeFmGBdj9KS69UV1bamaS2TSUF0QCo4u77m1sGCo8kBOmp2arBbVCJBIrC2Jc1G2GAwfUeSRLquYddWnL46pa/bgc+rNcEFArAzGzarNV6Ev+Md8/cnJtOCokiHVtiqoSgto0xza3/O/ZMlv/z6GXmmyErFl1+eMZrmXK17Xp5e8uHD21y93lCM5yhlKJqe6XyOipK7945Y3WyYlIrZXDKaLFnttpQnt2muDCG8JE9zthdDdaVuGqrKMlmU1A8kP9VrzlvHu7duIRONNIoQBH3b4IKjUCVFMnnTh47MRnM80PWG3rSIGPBdxLrBGUzlGhvBi0gfO0yzgwhSetrevCHEdTgbyJOCosyJYXj56qAJpDgj2FYtSZIyGWccHS2orCVJNdYEQoAsU7z/aA+iR6cZiZIoOYgAi1FOU9ekiSbYN9zr4AgmIJWkqWr2Dw8HBXPdIiOopKTerHG9JaXHybfbu2+feH2g3XUoGcmzgvgGCXd8Z0lval5WNSFEvnrxjKptud7sWIxGrDdbhICoA+ksoSs+4MW2xlqHlBEhInW9wTrL8eEBq90piB6dSNbrFV89+QVPn35NqScsZvts62tCaFFqzDuPPsbbgE5SEpGTjVLOzn7JO+884u7jkqaqWW0uSLOcpm3J0wwvBLtmA76m7QwnB/fRKmMhUzbtCiXToSRz7yNkyMhkTu8cu805cW9KMc6ZiRrnerbVOevqmjIvqZzlo7vv0+qS1a7BawXWoZMCoSTTYkmiC0RMaJotCIVWI/aWIw4PTzC2pXWK1mxJ055El0idMZlOyItj0mzM01//BRdfPef0+msaEzFNgzeBrpMwrtB5yuv0ivXVhnfu34d/8rc97r9dEaPH9I6f/7tfUhwULD4ZI2PCKNdIAk3wNH1NEIrgFFXl2FssaLY1ItHkWcakzFhvd/ggGE3zweR71ZIkAmM9Ukjms4Sq7un7AfiuUkWSgU5A64iIAt9bpAKvPG2/4np9RdVu6LqGh/cf0HeOdrcbDBu0RijPqFTEaOm7iNAFeVry3v3H/Ff/6D/np1/9Gw5O3sEGCVExHh/Sd5ZEp4x1gTcVMXqkFIwnKRGLFoLeRYQYfIatjVR1w66u2DQNWVKS5SleBtarFVIohJIgBJ5AtasRChKVMRnvgZS4EAleErzDB0kUEhc8o3JE1Ul0NExyxV/95MdcXV7SVBZXG0SiCUKQZBnWeZwLJNlbf5Z+60OlmuADpu24d2/OaFRydblmUWSE6Hn/zgGzcYb3huVyxq+enfLFszM+eHSL0Sjh1tEx6/WKz798yR//4e+w3dYY6/j3P/mSeZ7xu5/epe/hZn1GXmjcxRU6CozUXF6skCIyGRXM9xak0w5zUnJ1Eqhbx15agBY0fk1WKGbpnKbX9Lal63t23W6wzFQZxEjTd7S2AyBEBwTwCtM2yHGKpEAKaNrB61rpwS3OWEPX9ljb44yhHE+xMaK0QAhFqCOV6di1LdOg2G3XTKYJVR+4We8Y5yk+SHpnSHPNdLrg8uICZxy/evqM49v7zCcpWaIR4zFCClbXFV9//ZLvfO8DRBlBBJI0p6pqxtOcclxwfbHmq2cXGBf57oe3UVrwi5+fvdW6vvUOv7y6pswLsmxgMZd5Sp5lzGYl221HmiS8urzm8ydf00fJbFTy6NYtqvGE+XyKSD15fsQL03O9foX0nrq15NmE2XzKZlex2lxiohlud5sV+ewu29UVwQjuvnOf05tX3KyvuHv7PuPxgpvrFXWzoyxGVJs1F1fnlLlg/t1bvD59QdNsCdZQHB4QY+BqtaLIS7KR53p1CcFS5jlN3bI3mtN7Q1nOSGTBZtUyH+UILdlVA3ry7FLw62evabuebXtDnoGPgVlecv/oHV61PderC5wEkBxODsjTFOv9MGrSeSwC6y06JFTbFqkVgmHeM9NjerfFOYO1noFBH5EC9vePyfV3mBvB7uqX2F2NN57+0qJ6OAwpYwSZFxRfryibyX/wIf9tDes8znuCjfz5/+un/MPsdzk8GXN1eUFdNThAScm4zDEqUO06NAkuOoQX6Ewxm+ekKbQtOGtpe0tTd+RFQpokIDWZ9yRJSmeGMZC+Ceh0gGXwZnxiMZuiZUq13dF3a6Z5TiI1s+mCquvJCs3F+fmgmMzB2Z5srLDGE5pI3wdSFfns4+9jm57bJ99BKonHoZKcoCQyibjQkacjok4JKiE6BuJcdIOqOB0RhcCZFmM6fLDDjwv09GgluF5dD+zqNKXMCqL3OO/YNmuyNJCmGVtjSHVGrlOaztOamsl4QiZLXPB0xmF8S289qUp4/uIl3kXoPOaqJiqBWuRDGd04dCII7ltY82/iZ1+/4k9++CHepYggqbYGITTjaY5KU2bL8fD6SjKUVPyj7z/k97/7EBHB2Y4yF0wnezhrefrkJU9eX/DVizP2FjM++YNPAEXbWtJUQQBnDM5blIxMRgnzxYztpubi7Bp3oLiYKZ77Hic8uZTc1Bfsuiusf8MqD8PL0vRDeRjpsbohTRzBCXwYjDGc64kikuQJipwiGwMab93gvBU8Jjh8dBA8trMYa8i1JC/fjEranmgFrvdcbdf86y/XPNIzHj844quvryjHBSdHc2IcHJAOjxfsVjd0TY8Ig63n8nAyJHgZEUoQrGR1XSOF4vD4EKkVl68uUIkizxXjaUmMw4Xg+M4x00XH5dWWb56dMZ5Oefzw9lut61snXmd71GiEFBKdJlhraZqar37dU05LfAjc3NwwyktmOuXB8Qmh6wk+kMqMNEu4bjWfn/+Cbd/QtzsCgkx5qt0W08NiOkc6jy4nnHx4H9tHmrri3fce8eTpr2j7ilsn9ymyKTdnZ2zXW5azJSkJ08kBs+UBqWhxriXYHtsZ5rMR3vW03hKCZTG9i1Apy3sPia5nW51iqTitd1jjuVfmXN28ZlosWW0sq6B5cfUMzJajyYdMxnOE2rBrHEUy5eGtBxwe3OFifcOmq8gyTbAWjUJnBUpJ8myEsYG+r+kirHeX3No7pCgLeuNAaJQe5u0WxTHr9hrv1miVo7UkzyUxBH7+iydcfvGC7z98yPX0hk2ScHFaMTOK7xzd5kCOyVVBZyzdqvsPPuS/rWGcJ0kSUiFoe8eP/8VPeO+fHHAyPyBVLbt2i5ESLwtCU7F3XNKbDbu6ZzweUyqQpkMLQ5oI2n5Izt5JutYh+kheKqwI5EWCVYZJOri9tL3D9wEhU9JSkRWaqtpg32AkEysQZMhMsqs7bq7P2TYbcjHCdR0hWowMWC9wXiB9T5nMeXR0xLq+Zr5/eygT+xYFGFMjoiP4QNf2GNcSqEjTkpAmVGtLlk4QAjqzASmo2h1N02GchRjpuwZvDVJIEq1IZEqiJMY5gh2wm8ezJXvTI1Z9i1DDeFJZjvCEwXIuyXG+o/cVvatRUpIEg1z36CqwPdsRt5ahnijwo4gNFikl0XwLf/lN/OLlOT94/xGzcrCcFBo2Nw0hCtLMc+/RIbv1FdXOsmsa0lRTJBlpnkGeUOaKECW3j2bMF0tGhebjx/fZX4zQQJpK8mKE7Xum0wkhwq6yaJ1RFpr1zrDZDjTBrw9rXlCx2rYEEShHM+q+oesMbdfgMCAgeHA2DijhLGc0mhKFQIiA6Xq6rseFwRIyxEAmJFpIqmbDtl/TGwNRoNN02A/CEFNPDJLpbExQkRAlMWisNUgHSir+/PlTvtxdIfOSH//0Gz59dMKD28csFzmbdUVdp7jekqSKVCuiSvAIvLeYXtPbiqwo2X/wHhdffcF8XuL7mv2TBTFErq/XaA0+1FijESJSlJLJdIxMPVXVwFt6Sb914j2+fYfoA9YMybTrWqwfjOInteGXz55wvt1wZ/+A+XRKCNA6SzkZY30gz1M+v/g1q3rH5fVr3G7LrcN7LKaHFMUJISZc3Jyh5RhdaK5XNxhj2ZsfYXrLdFQwny1YHhzy/Ok3tLuGR4/eAylobEVRFlxcnpLEniKfUW3Ww8HvJEk242B5SJlnFGnOtqnY1S3tzvDug4/Z1ldcV+doZTi/fsmoHNH3ljZWFKMF8/05pZwxKRfsnn3OZrfmd97/IYcHd7mpVnz+/AvGowKdCHSWc7S4gwsCFy2ZLIhCkGU5QmQE11HmmrIYYV1Aq6G8Ujc1SZKikoK2rUjlgALUOgHpePL1V3z5q19hLm5Q14Z/+t98xvjRu7y42WA3O8zrDX/2P/4Kc2V4fPsOx7O9v+VR/+0L50FrMczFhkh1vWP96yV3/iDn2fYFxWjO3nzKxfqa62aLM4EkpoTguLlZUW8jD8Zzbo0KnvYbkAElPbkS9MGjhcL0hpgEMglZqmkaQZZ6pqOEWRHZuEieADhUoqnaDXW9I9OCzvWkcUJ0BtM0uN7QBIWSAi8Uu6ZGajlYairBe3cfUe2u2Dt6n+gD3kZ8EJjgcb4D4cmUpm53lOWcEAVeRG52l6w3FQ+Pj2jMNd4b6rrFuYBQ4PrBHUgqgektSZISxICJ7NoWJGzbLYHAwfwWiZpwOJsOqmSVUTUtWVKAFwgpEEG+UWVrilxx+vOfc/OrS/qLGnYOaQUC8BuDzBKkENA4dP+twcdv4mK74Ue/fMKH947xMYB1LKYF0fd0naFqLPc/esSXP/uGfFRiuw7rA8IYemOpasFolIOSSA0fPn5AVo6I0RNx9J1hu9khBIxFwq7e0XtwXU2iU0xrWJwsON8znE8VG9Pj8UySBZku6awZHNLyERaLcT3WWqKKjMuCtEiRWtC0NVFEdBzwojGAFyCEotAJKgT6zlEmExajA9I8wTFgf5vW4ZylzDNUqnDRIqLGO08IDq0ysnnJB5+9x82fPudf/Osf8V/+4Wdk0WD6GtsnXFxvePqTb/j08V1EW7OYlax3NT/71UuW+3Pu7E3ojCHLO/aTMcY4rm7WjFLNdJ6R5Qmp1jhrmS+npGlBXXecna9AZ3S9Ztf1PH736K3W9a0T79V6w3Q0whhDrjOa3uDxjPIU5z0yCO4dnVCmKeera6wPrKuGs/UNH9x+yB9/9i7P1NcgDCIG3rn3CSf799ndWC6aHZ219K5mUo55ffGSJEkIztP2O7TKmO8dkmSOL778C6rrjh98+vtc7S65ujhltr/kcvWaGCBJ4etvfkogYbk85Oj4kNF4Tmsc0UWMtwgUdXXNbHyLtlPIuOCTex+yqp7x5NUXbHYrttVL0kQx7WuMMAQ1ZnQ45eOHH9ELaH3P8+0lEtjf26PuduTZmHk5BR9RUpLqFJ0UIAVBWJztSdOU4+I2xEjbNygRSGRCluSUZUbXdeR5AQryLCVLMq4uL/jxn/05q/NLzE2D6yyN88yynP2lxuSa60TQHqd8/eUpr9cVH96997c967+FIQgRnPUMrcqEsy/O0aM9ZnsFVlo2mxXVZofrHCJJ0KliJD2Xr3eYVnOleoLw7LqWbejAC2Qc8HYiUWgZEQSsHbjLADIqbG+RY0kUHilTxmOFjzdYt8Vbz6Z16KKgdTteX5+x3lYIIB9n9E1P01icgURIZOKYpAnfe/wJk/FtZJzSm4Z6u6MyDVJqvGtZLveo6hrBoH24WF8OUwZdiwyRdlfRC0fVdhhrSdOMztZE4TDOIKWkzEboJKcx1aAudR5kxATLnb1j3rnzPlk2IgqPMRZjIgkOxJteXt8zK0tGSUFjLVlqOH/yimYFwmhKlePSMMyWegutJU0ksQ3E9ttxor8JKXl2esmDvfkwLjQaKIKTcQZCcffduwhZ8z/+259zMNvj+4+PEUqSqJTRdEKU4G1PqUqM9UyWU2zXoZB4IXHGo7QkLwr++udf8qOffM3J4R6ffnAP72r0JMfdnnKxCFw0V4MVoJLYYFk3K0xsBihGNNhoMKEBApPxPnmZYxlIWsYFxvmMSZR4E2m6Dus8IVgm2ZQsmTLLE4SOhGix9OyaFbvthr43BBsZH8xwIeCtJwY3lLLfjKJq4blzMud3//Eeseq5NRuRpmMkEp0p7t464GA5JVjL1abjy2+uOLm14OPHdzi7HC6To7IgBPjiZ79kMs7Y7Sq+udny4M6Co6M9pATw9J1ls65QSjKfp5jOoYHXrzq2b1lpfOvEa0yPWkw5Xh5QrTtoIMTIrq7oOoMQitl0Bj6wnC0QITLJSvbGE9598B7sKTbbiroxJGLEyd3v4npw9pK6q0jSnGj1cLuXEJyjLMc0zY4Y19yYyJ179znav8vxwvLy8mtePX/FrTu32VYbfOioqh3l0T4ffuc7kIypdw29McTNDushncwQOuCanoP5fablnE21QiI4O39NUUz47uM/omt3vLp8RtVd8vr8KSqX3LrzEUmuScWcECLGrMAMXN3JeI8knSA8eA/ROw5GCzr/RhUXIokexjTatiZIya3jh6SZIziH80N/wTlLDBotc+p+Q2t2SOF59eoJp6en1Fc13VlD7TquVp6Fk2hVEOWOIi04fLTHV//uJRfrjs0X30IIfhNy0AWRKEEIAmc9VkZe/uU58/sTFt8v+OvXT9jUgvl0wWSWk0lYVzucCbhG8nq95aIe3FOsH+YOYwCdJkg9/H4E9D6iECgRMdGjtMJGiXMGZ3LapsOHSDR+sMXL5+T5iBeXr+m7SNsbdKqQ0mCcASGYlCW9GUq/dw8PGeeak8OHbDuHiBnH+0d4F2ijoVCDwGljB4/opulorUCQoxQspykBjbWRq5uK2XjGtt2ybTb0taHI58OIj4i0TTXwyZWkdS1ZknBrecSnDz4huEDDbpiVFyWJEIjQErxFK4k1jtYaxhnMJobzn3/NL//sGUmaUQQ9iLu8BRkhgNlZ0lRjG48K34qrfhNCKoTWvPfeLVzfs911XGxrxqMTHnxwG2vXPPvF5zR9h5BwdDSnqXqiyrHWkOqEYjKMp23XN/R1TQyO9XaHVBneWrIsoalbbh/P+N8d/xDX9qSjlCSTqAe3eLLMqXxLmQry1OF8g/c9fV/ThgYYkrf3DiU8UknyNMcGj40dITrSpMDRQhqHEu6VoetaIJLohMY5dm6H63qM63De0fYNtjPY3jCbzZCZQhJRQdGZnhjBW482EUaBTgeOHh2TX7Ws1jvSYs6rsxvef+8+48kM27VDZbEYUY4LNpuayWhKkadUbSDPBUWmuHNnD60Ue/tTvnlyznZX07RXPHo09HzbtkclEmMsOtXoRKHTnId3F+Tp263rW+/wJNE4EXn26ozqZsdoNCZaS9/2bEPLy6srvrq4oJCa+XiMtYb5aMLjuw/53ve/xy+Tb0BLUJL58i5N77jernGxQyeK2tTcrFcslwukSgjeEXA41xNc5OjoDru6wlQte8t9+s7z0TuPuTFn1JsNKtWU44T9gzneS66uX5PqYbQhyVPQARssWM3to/cRUeFty6ic4qPFdB15usCbSK5nfPDgMzq742T/NS9ef8Hxcp9N09EqIER0lOzPDxBxsJbKlWA0GaNkgo4MRuFKMNISKQuIEpG1JFmGDLCuGrJ0RKIFSgtssLRtT57mUCdcrU7ZdTcUMiWfCj765AF/8eIa2Wl8FGw2/VBpEIJUjZgqxWw+Ix1lNDeGqL8lV/0mJJCIoQ9kEYTQI4MeXKO+3hCTwHw/o1xOuHX7IS5sMKbGe48qJNYHdC9xDnwfQCQEIZEiQSpQvLHIFAoPRDe4aakUhHBIKVAyRVEQXUGoLNN8gSsSlAMvBInW4ALWGZyVeGtwASJiECZpibQJiRjjfWS1XeN1QpSOICJJ4XHOIXRKSmCuBavdCpVNOFzu09odyvvBwLxvkKJjXBRst2uaUBNjwnQyHkY3lKQyO3wXiTHghcW4lkk54rvvfIdb+/do7HA2re2xzpElBdE4hBIkQpNIgWodfhRRveZH/+xz+ptIkuUY0yIAIQRozXi0R1NvUa7DBxhl33LGfxMPljNSIWi6gHYGJSQnh1NsiGw3F6wvzzk8WfB737nP7/7+9zm5NWN9s8YawcU3K1zfY3rL1VXNYpkRgsM7R5YXrDc1WapxPpCmOW3XMp3PqOQO0/Wo/X1WyylmOuZARgqzZdtdYmzE9wmJDEivQQaiHF6pzieMy9lw4TSDt63WAmPNm4eFZJSXLA8XSKkHxkKaUvcNlatx0RK8H4hmwRGiY3++Rzkd40SPZ7CfjHiaqsc2LZnOCYmniYFT23BoDRDo+44f/fRL/uwXX/Hw+BjvWj779BFKeQ6XE7ztaJuWtqtJmXBxccatkyWLxZjO9MwXU95//z67bUWM0Pc9REUkoIC8KLm+3mGDZzrJeb2q+OTof2FxVaIlz79+StcOhsO78wum4zFJmlFtt8gYWYxK5uWUcV6wmM9ZjmccLU/IT0Y8XT2j2daUesGomNE0G/qupu46iiKn2awo0hTbW3ABY1tCNFjvmUxm7Oo1VV1xsHfA5c0lJwf3OX31nPt3HvPxgxmNq+n6S27vTag7z7yc431kthjhIyihMK3h+OQRnQu03YrlZEG12bJrKvYme4Ns3XUooZhJiZYJiZzw3juf8N7tO3zx7JTLuiJGjVIQSMmznFExIs0TjDFDX9k5jHWMxiOUSOl7R5KWOKfY1htm4xHnq3P2F4cDXCMvSdJ8YNaKyKhYEM49Vb/D6owyz/n4k/c5/dkLnrw4JUFy+qvnfPePPsWqgqAkLnakqWYyLtnJagAlfBsA6ESTpSnBB0IY+odd1zEqR0zSEbuvO+4fHXMuNxBqXNex2a5QzqFiwEZPUiYoG+jqnkQlhBDRo4RJIii9pdFhGCvSahApkaDxBGdw1jEqxxzt76FVhrMpqZ6wKBRXqwt2XU30V1yvrnC9x0UQwSFEgpASISSeiI6OO/sPEL6k7Q3GGBCBIsmYbFrSNFClPb0K1M2azntib9BR0quOrmuJQNdZtrsbtB4zns6IDXSdoW5aetcTRMBaR56UJCgaazieHPD+w0+Yj/dofY/zASEUPoAPHYlMmEymxMQNCnLRc3d6GzmHpv6K3WWLcA4lUmQIpEoNzk5lwfF772PanuriJf78JWn6rZf0b+L/9Ee/z66uOD+95P1HR0QxCNU+/Z3H6KLlve8+IHjL7Q8+Ip/NyCclvau5eV3z61fXHM5KZkVGninyYkSIw75y1iJRCCmpm47VuqYsM55+8xzj4fZHd2neOaael/hoOb15ydXuNV5ZetuShpRMl/S+IeKwfUfd1yQqRYiExlaE4AlCYp3Fh0EclSU5Uic4PIuTxUDVmszZ2Bn4kj60dLambja4vmecTjg4OKK2DSJqCA4vOoxtkUpRpCmjNCX4Yf53lTveuXVA/+qGq+st+9MxT1cbzq62fPDoiM26wUdNUVrmizEyaGaLMQG4dzIHOZDpXPRUu46iyFBKgQDn5UAeFJLVdct4WgABH2BTe/6nv/qKDz58uxbfWydepTN875iPR2yrCu8dMUY2NyuKLOfB4TFSSoKPLOcLpuMJi8U+B8dHfM0ZN63l9u0fIlXJ9fo1N5sbXCuZjOds6mvwnrwYsat2OGOZLqZUzZb95R4OgQyaLB1hnAMpuLw5JZ9MyafHeD8Qhx698z5N8yvoa3aVxVhFH7ZkWcpycZt373xC0xpMu2GUlzgXmEynLKZ7EDWr3WpIvs2WV+df0veGJNEE5bg7ybh18pCyGgzubaghGry3dG3HeDwhIOlMhUpH7I8LINK7ns62dGZQbZq+p9y7za3jQ4KNpOMS6wyd6fChw7qOVCum6UAEC9GjgEmZ8unvfMLLvzplTMnrzy/58f/nR7z7e5+Qzgq2/Q27botMxZuX/rcv3t+EEgopJdZYnPNIobHO0DQD/tB7webzmqOPp/S25fryEmJP5gOtc5RlTlMF0kyRJyX1ziKjGrCMrcf1PXqu2IWIlIogARzGOaIX+AgyWspxQlFmhN7S+ob2csumrVCJod1dDgk7pCAsLoISA7syihQhFLcOlvwnP/xjatsTE8U4K/A+IrqWl//mL/AfnKDu7NOYDpmUpLpn06zIkpK2awkhYLxlW225rrYsZ4poGlrTsK36wfBceCJi6OfGjvmooIhL3rv1kPFkQhd7Ej/MJCMkUmt6Z+h9x2SUIxOB6yxkmi6zJK7HdRC7obQ+P7rNKtuwu7rAmY50OmOxOEDupVyHSGzqt9SF/scRSgT6uiWRGoRkNB4xnkxACxZ3HiNHBb5ac/bVr9mtN2jlOH91zv/9n/+E//mvn/Ff//EP+eGHD9g0HTdfVxzsTQeRUPBMJiOUjnjjmO1PyLKE5d6CmCds7i24nudUrqITHTY2+OjorcEHgZTDI8kFR2N2ONshvKJMZ3Sdp3M1QgRcdHhvSdRgBiIEGDdgJa035D7QWMO2WbO1FV6Zgb5meopkxMHykG27xcl+wATHQGcHnU6wgURq8jzFaYlMNOe+5saO0XjSLOGH33+PzxjwxpPJCOsMV9stxajk6vycMh8T4sBoL8djrO0xfaCcjGmqirpqSLOU3pg3ZWWJ7c3gUCYEaZbRmIq6ctxsK/7VX3zFf/MW6/rWiffZ69dIKUjShERK9g4OKbKMUV6ghCAiuFmvWS4W9H3L603F+w8/JJmmvHbn6HTG1e6CemeIIqJURjYpuV5fsdldEkKgFx0hBPb3D+hDQ5JqnHfsqppc52/8a1u01pjW8uidj9hUl9SbDQ/uvcfXz5+jlGE5KtlUFfvzBcu9fWaTI7J8giBjWi5J5Yhts6JyFc1mzcOT92h6RwyOLC8ZTya4fkGWlFxdndPaS8bjGevasVrvBgiBkiRvStnjYgExoekq9vdvY32PjArne5TQJFLy+uKM3u+GhQ6aNFVEBoBDjJ5ddU2wnsV8iRSg/fDaGM1KtNZ43+KChiRht+2wNvCv/q8/5hd/9g13v3OH2XFGVbUDUF9ERum3ifc3EWIcqhHOE6WEGFBS4WOkCx4RoL02iL8OTD4pSULP+c2K1GlEniCzlnExoXdxUFeONK4OWOvZiUiXCDABdIoY0O3AcJiDEIQYCa4nSRXGDWKUTE9Q05S+6Wg3G2zdQ3A0vSNJJalO0FKCUDQ+kmeaxw/eo68rssUIqQOJiqSJxiqN/sMfQB7oXIt1ASF2WNPjvKPq11hj6W2NUoLz9StCkNysb4jO0VnPaDSGEBCqY9f0iBAIWMpkzjsn77JcHLLpO6wfRj3wQ6nYOYsWijzNCBistaS5wGM5r59Q5ordsyf0VUtazlke3WZycIft3gE3l+eQ5yRJRpIWyKQgSQvyb8d4/yaSJOHgYEn0g2+yDzA7mnBzdUG2N2W5PMTu1lSbc6yB6/NTnnxzydMnrxisoQVXV2uUTEiLjK6zxAyUFgQ/VOXStEBrxdPnL3n2as3yeyekewueb17SOENlN3SmQSHRSmJCR+MhkwmSSLABgWJvdsxyekTd7ZAIOjsw8JMkJVE5PvihdegFTd/RmQ4VU+QoYzIu8caxqiq6umGUTrl9fBdUR1u7AUGZgI+G4EFFOXhfE+hVICQCpSLr2PMLcc0ns4JpmhKMZe/ogO1mRwgeP3fc/uGY7ieR6WxKXXXMlkuccTS9pW0Nm03H/r5AaUUMkdV6BV6x3dWMJzl5kZLFiFASLWAyStDa8ehoyb//i1++1bq+deLdrFbMp1PatkMrjYigpaIYZzhrcSGwv7dHmmu8g0/f/z6L6YKqqFk1FUoIejcg4m42V7S7lslkzvXqDGs67t65j4uOWbpku1lTtxt8CIzLMcW4pMiG/kSSa7yx3H/4LuvdBU29Zv/whCcvv0BnGhNrbu/P+P0f/NEbS0CB6QPGrZEqZT5KEaRoWYJrOVreY7fdsW6uudpcspwcYboOAfStYTzeI7Me09fUbUBikanDWItrU6aTCQJB1TToIOk7ixctUmk63yGlRmcZ9+6eUO0KRtmYJ89/SVbkpHqMTgJKabJMsdw7pOsjznv6XvDF11/w3kePmE3G1DfX/Ozf/ZRmM/ipDsVHyfXXW66e/AxVSvbuLulWDlzg8OBbgMZvorWGGBUx07QiYHuDcgrvAlomlMUwytI1kexLxdGjA3Zqw+Yby2RWML0nQDqaRFJLiROBfC7xu56+94REIYIgEQr/GycjCchIIgSd65gXY3QUqNCTjhSdN2zrG66uT7ne7Li4WtF1HhETCIoQh1weY8SFgIye73/4A2SWMV0Mptxd3xC0xNuOkFms929MBjy9aQflqrXEEIeRESXYrM/xPuBCoEyn+CRhPkrojKOtOlRIiBiyIuF4ccQn93849K11Co1BKUnEkiY5ggSEItM5SSKJeri8qlRStQ3W9MxkzoufPkcFSZJoRKop8hH5uGS2f0jbdRAGstZoPufmdULd1n/XW+bvTYQwiAKjGvzOlYDV1Ssm04Td65esz17R1Ru8T4mhIZhAX8H+aMKnHz3i0cmSQmvyUYp3oJNBBeisxcaIVCnFuKAY5UxWG97/dMKTRcVPf/2vIRmj0pJNs8Z5M1hIio7a7EhFgU7HSATzfJ8sS0mSnMpskUqQJwU+eETsB5tU7wkuEKMDIRBKIJHIKOit5aZdUbVr+spwOLrFcrmkizVNv0GkilSkWNcTQoSgsD4Qg6McFbihEoyzQxn9MvecF5bjNQQr2dUtMtGEaY/6tKatJK5NydKEStSsVms2mxrnItY7bt05ZlyOaNsapGSx3KNv2gHdGR0M9Hba1qIShbGC8Tjjjz/7gCevV2+1rm+deB/euo21luAHnmyiNUopettT1S19bxiPx6Qy5dGDB9w5uk2apZz6p5yvrwgyxRjBxeUVVd2xv7hNmuUcHB6Qa0WMit12S3e9wtie6WKKShK61hBEQAlBpnNGZcHGbXh99gwXHCeHR3z19HOapkZGxf2j++RC8+L8a5JsDCFhOT6kM/2blCpSHQABAABJREFUsuIOF4Zh7uV4n83qlLP1GULBOB+zN99ju7ui6iquN89xRjBKBc3JhMubhta26ERy6+g++5MTmrpiu14xzibM905oTIsSmqapECqBAEKCtQqlMyaTQx6N9kEYtBj8KyWgRYqxAhJJohRpknJ+ekHfd9y6c4QwhsXRksWDHauXO5z1aCnxDgKCvrWY9hKCIhEwSr7tk/0mUqkhghMCp+Tgoaskxtph9MJpBJH5ZIKMKfGF4/HJbb5QF8RGIXYBU3QkuqDIFJ3wtG1AlQmjmNF1BhU1pjMDBUdJZB5RQqO1wOPIM8V2e43KFiAsxjuqtqXuPVlSMNtTNP0a44dDbX0gEiBGJIqHhw+4s7iNKiRCZ+AhTQS7dktvKiAik4REJTjTk6gxxjaE6FlvbtjWhoDEesVsMud6W5GoHFxH2+7wEpJCU1ct3gfmi0M+vv8DUGooe1tBmY4RStCaZuApB0eeZ0gRyUqFdZaZKnGupW8a3Jvy3Onn52idDkxmH8lkgtcCqVOS1GO9B2fRWpNmOW3zrbPWb6Jtarx17O3PSbOCrqkIveTw9j5SZogsRR8tuV71/Oqnn2N9yqOPP2C233C4P6dINV1nIAxksMjgtDWZTnBBUztFgqBueyyCdp7ySm5pes3hbEGaFcwmexhjqLs1nZUs0xlFNiYTg5q/94Gq29JVN2+c1gSRQYxkvcF4R/RmoPfZiIsehB8IWVmKtZ7ttiZPco5u3cHQs3VretPihEVlg2o6IcW3gb7tiYCMHhMdMigSIREM7bwaweup4LabUX254vYHH9L0z7CPK1zWkfxyipZjmmpDUeZst4aDwyWp1qw3OxSermmo6gahNEWRM57NqLYbghFIKcjzhBgddd9xvd7yaHGH40PBq4u3M6d568S7v9yn71qkUGRpAhG00iSpHurcTceoHDMuxtzZv0uepkgtCYViPr3F1eYKZzpO9m8RDxN8kNysL3DWIJKU3a6irjuig/vvvsu6uaTaViQqI8kypsWC2WzOq8vntH3Lgzvv0nQNl6tz0iTh3jvf43DvNgpB269xcoNtNcIbLtwzOmtYsI+Qmqppca1BKMWt/Ud4nfH89K+RozlPL37OdLRgOp8iM4kSBceLBJWk7O2XJEn5N6W1ru9IdMnDO/tIIajadnDb8D0qKch0ipIJnbNUbYWUjnVdU45HxJDgvCNVxaDgDhLje4JzSKAYZdy7e4uus+T5mI8+/YDPfv8fcn7xmh//6Z/x83/5c8zaIoB5pt8IGiydj0zKgvfuvJ267j+GEGIoi8bekcaAt24wLRhyHEVWMi2HXvuu2oCz3Brt8fiB4nm1opcOHweVpLMSqYdertCDkcF4NMI3jtpYCBqdSaIL+BCxISB0wNiOi6tTAg8RMsUZjzWe2jkO9hb02xvyPKKcpA9DeTwymIKXKuePf++P2e6uONp/n751g4uKAIEi0SOKNKfrV3QhUFVropTUfcVmt2bXdQShSJIMpTTbag0h4EKP9z2JFm9cYhxBBEZFyfcf/4DJeE5teqT1WGPJ04wsLYgSrOvJUgXa0fkW26VoCZnStHWDkoJilvP1X3/O1asLQlD0dcXm4hQtUzwM/fYkYVSMWa9XvH7+FV2zQYhvhYG/ic45jDWI7Yb5VNF1gw94VUXq3Rm1U6yqissXr9EIptN9Lk83PH7vES9fnNFpyd7eHCEVSEv07eDZOyqpuoBXc64vXrPuWs6SjmZWUDFirgtiBo6OROfoIFhMDgFwwdB2LTfbDRqJwRNSh0QSHNjQDbqVfphpdXYwvtdonHFvLgLD4cuPFJnW3N47Rheand/ShQpiIC8zQtD0rsNKTwwWdAQZCcaTZzlCarwFFQVRBrzpyUclTSFYmcjpuuFf/rf/LX/yf7lPURqW3ZwyHOFTSZ80FEWK9YJd3VBkCXv7CzabLUWRk+cFxg5AkF5G8qLE2h3Ow6ZqkUJwflUzm0xJlGZXVdw6HL/Vur514m2aBu89WSqHj0II1G1LbwzTyZiu60lkzoN3HzIuxiRak+YpVb1is35BsDl3Tt7j7PIaomVXbem6LVJkzOYnJGpNdGtOHt1i1+3o2o7oI3kxYbG3x3ic0bQNSZaRGIfxHca3TEYz7t15F9NZer9ju1mTEVkcKL5+9gV5Puf49jHLvSVd1dF0PXvTY5bjJX0MLCYHdF3PBw8/ozHXPDn9GhM7lAiU2T4+GprOMc1KlEjw1mOiZ1Ndk8kxe9N9dvWOXV8jpMO4jizPEFHT9R4fLZ3tWdfXHO3t44m09dDL9tGBdHjrUEmGTCTSCQSC6XKPf/pP/gneRBCSKEBGwe3Dd5j87xeM51N+9N/9zxxnU/7og/dJk5zPnzzl6fklHz+4y8Pbd//DT/lvaUgx+L5G60kRuCgxSFKtWE7njPKCpqlouhYhIUGxet5w+6ND3N6Kr7cNnQUvUryL6KDIU4XOoEFg+4DSimk6wnWWvneIKAlSELJBIdl70D4QrMB7Q1PV3NysiR7arkULKLOUXJc8X61I0JjgkcCjw7s8vnsfnWpUyAimo/cea3s6b3AECJGqlazbFcZ66rYmyXJm00N27QtGaUofW67XG7SUf8MQ33UNAUdUIFJBFhUf3v2QRGe03iPIyBS0ocG6DhkVWZojVCRRIBXYGPFxEJK9bi/p7Yr9+V22mzU/+9PPMa3HRyi0wt1ccGEdLknIR1NGkwk6SUnTDO8cLjpU8q286jdxfn7D3nKElikX59cUuQJSnnxxwXT/mKM7Jyx9T6bGvPjVM2IHs8mI87NLvDEsjg5I8oSrixuKNGFUllyud1SdZ7Ou6KqvWRyU/HJ3ifz9O5h5Qt6O8FTcbM6wziG0JFM5i9EJnW2o+kvaxiG8QuuCRCkMDiXV0ALpeqSCJFV4H9A+QcSIkAISTR7AmYgCDhcLsjJh1exoG4MJA3nNOzNUQJIMicb2Az414NB6gNdkRYqNgVQOrc9hXAkkmp7AM7nh0ScTxO9P4FaLjztWTyO6ukXfWy5uGm7fHqO0J88iWmt6awlI0iwnywp8tCid0Pc9zvQ4Z0EOMJ7aGJ68PEOmGfPllPG4ZDwt32pd3zrxBmfflEwH+lLddgTv6foeGyJKZrz3zocc7B0Pt4UsR2RDqdhYyXRySNM3eBxaSkQAb+Hkzgm7ZkOMguPjY1bba67WlxRlgrU908mEpusIoScES7Orsa3FWUeelyymR+zqiuAtWVpwcHCb+WSJ75/w4XsfgRiz626od1sUBWUxIomSUqeIMMxYXrdniBC4d/w+y8l9zm++ZLV7RaChMzX3ju/Ru451VdF0hmm5x9H8hEk+oe4qGlPTuw6dR7yIgMDbnigT6nZH3e9woQMkWicUekxEsK1uaJqGsixItcKFgPEeLVMEGUIIykITo8Rg8NZje0ehRnzvDz7j+c+e0fziGhvh4/v3uXt4xNnVNcvFnHHxdjev/xgiSonvLYQ36xMHE3ipBCFaqnaL6Tt66xA+4rXCm55vftZx9J0jLpKeNnTI4MlShbGOIHucdSihEEmCFxJvHWmhyBNNVfeEN5eoqAIhCGzneP3qitlyzM1qTVV1LMf7RGGwpqVtDZU1fwPkyH2CkAl/8gd/BLJHpguafujxi2Doe0NRTgbbs96ytzhBlZGm1Vh3jdaadX3B1f+fvf/q1S1N1/Ow600jfWnmlUPl0N3Vafduc29KpCjIsk0JOtCJABv+Q/4XPpAMCDQEGSBEUrRI7kz27t2xqrq6usLKYc75pRHf6IN3VhOGD7xIEOjGZj1AoYFC9ULVN8Z4w/Pc93VfXnB6dMx69xJRKIiSwhTs+471bkdV5HZaGWa8eecOh+UyxxumAFFk2wnqqkGQMFHTlDMm37Lr9kghMIUG6YjJIphjJ82XP/uEB7+5xANFCiQH7bihb/fUpzfQqmQSgug8RVGxOr7OfnvBlL7mjH9V9+/eY1YLrA0sFoaqkkCJkYrPP/qMn/z1rxnciJGJGycL6koxji7HM84TAkm72zK0Lce3rue5K4HkJpwPFKVgNIHVd6/xoV0Tz0e0LiirgioeYsc9o+hQjaG3G3bDhsmOmcWtEjZOlLHEWpcjIEeHMSbn8gaHEAlTGApdEJLL+dB1RQzQCElpSi66kcnk+Ex6QUIhpAGVcFiEkdSyxllNP7WkENFK4pMnEknCQMr51SkBMmADrCtL/67n9NqeMbQoFYgXh+w2e54+P+fxowt2uz2zZpYRE3OJEYaxH3jx/IKDgyVlpSBGmroiFIZxtFyud0ShuH6yIKbb/G9/9SG/+GjG7WsHvDy/5L97hef6yhvvbLHk8bMnGG24WK/RylCVFVLAi5fn/MEHP+TWtVtURU1hSpQpCFriksSUcza7NZf7HYgSHxMXLy44O73Ovm2xw8TR6gbOjRRCcnp4hE0TRsJyOeNy1+KsI0bLODjODs6YN0uShvPLF5wdXOfk5B6QeP78MY8fPmKxdBwcBB4++4zJjRwcnmJ0xdnqNjEEtrtzjuYn7NYvGNs9t2/cous2bLs1N87e5uz0Nuv9A9p+D2FDVS45XM65fXpIYQ7RsmDfbxlsi0ieQiW8s8h0FeMVAjLmob9RsGyWSKmwtqNWDd4FDmYHCBEJ0REjdH1LbweWsyMqVVBqSQyBYehxKfNK3WRJKd/4i7LkhXf88sFD3rpzl+VszmtVwzRZxq/nZL8t7xMRQRRXD0TkVrG+ih0TCaJQhOSotcZIRYge5SLrjy1vfXAfIR9wOY5M1pGSQsWISpKIxajMJp5SYIiwkCXLecNkHcHnwO/gI0Ipfv3lY+7G69gpcfvm60jgy8dfZASdNggZaWSiqiWFrDidXef1O9dwFCiR0ZO6SKQhcHx0hKlg263ZD3uSanBTy3Z3wcX6GVU9ZztuCDKw7QYShsIo+q4DCra7NSEEVs2Kt27e58bZW1w7vY0bevowIlTB0DuciBhVQgLnIrPSkFIgREgyEWPekL0biaGiKCrsNPDTf/ET+iHnITvv8EngJoepjzg6PsnRl0JmcaYLOO9zd8BOv+M35venfLAoPQMXsNajTcmyanDTxM0b12k2a+yQMKXCaEnfDsQk8MISk2O3bREkZos5pqoAwZExOR98NWNUiodLzxd1h0/55hhSBKk4Oj7l8PCEyXfYMNC6NT44CJI+9ExupBAlppJEGxBJM6tnIALWdQzDSIqJutYM/somFlOm+0WYqYIQwAWTUZPKUzSGGMkJQHECmSMEtS7AC0RSeBcoG02QCa1Mzr8WCRMNSWQrnCewF54nsuVmFIzDiCpquBDQdty7c5PlbMFmu6PdtfgEq9WMcexZLRf45Li8eElSkrIoKbQkEml7y2Y7sh8ts+YMYxT/8O9/D22gEIaz9++/0nN95Y3388cP+c2Tx5Sy4p07t3PyRFFwsFjxzhsfcP/Om8xnc4qyzA9YkH+QpEhJ0DRLRqexPjDFFl0U1PWCl5sLTmYrlrOa9bql0Yr5wZzPnz/mYLWgHc7ZbNYcHByybXd4P1zN4vaUs4qhG2AO24tHWBJNM8eUDZ4Ny3rG4bJgfjSnH1qMrHl6/gXzesUQFzRFgxOBppqRoqTrLxE6MvktEcG8ucZqeci1ucOYFdoeQNIMY0eIG6QAqQLjuEfrEhsCi3qOnSyByHAFLHAhcP/kDvutY7N/wa7doWXDarEgxEDX97mVj2c2m7FqDgm+Y/Qbgvd0Y89oR3ycsNPEaHvs+Rpz0VIkeLre8qe/+Ii3bt3m/tkp81mN/Trh5beVcr4iAokAogAfIkmQea8pEWNAJokympTARwFXGDz/4cTbb9/j+fFTPt9taduEjCELDUuNc44goKoFwQmcg4hHFolVU+Bc3px99Dx9tuPmzfvcvXcTHwOT7UEJqmZOwmP7gUSilImmMfzg/W9QzgpsXOCiR2sYrcfhKAhM48B+uCRIQTcl2sky2Z5133MgDONk8+ImAz4mxGRRWjKGEZccd85u8Yfv/pCTkzMG6xhDD6kj2YGLIdLIFT5EalMjyQuolDDYnJHaDz2mKJACXDchihKSYvP0IS9+fU6KFcFHCqUxVYEwinJW05QlQhq6viPgiCi0Nhxdu/2qAS//UVTTlGz3e3a7PV0b+MY33mRyAyHBL3/+axbzguWsQEqNURpZlRSV5vJiw2zeZB2OMXRdy4vzCw4PVoQUKWYzUiH4XHQ8PpEMVhKmAFIgY+Ll+jlpF9CFoKo1o53ox46+7/BTQghBWcyY1SsQEq00PiZCtMQUCC5h+4DWMqMhRUSic6CGEFmUFR2d9Wz2O6waEUVAlSMkMLJCqQIIWBewsSOlhJSJpixZzefs3UAikrLzHJAIqRidwyaHDoLJN4x9jw17LBX3jm6jSo2fLM2sYL68hpaGv/nZb7jYTdw4O2az27BZrzk7OSEC1jsSgrqsWK4ESoB9/BJrPdtNByReu39GU1Q8fXb5Ss/1lTfeutZctjuWM03dzIhhwofEfHHCvbtvsVocUJgCrdRVL18RVSJERaRg6i1SCfr9jtHmefH5y6e0fUv38iXD4jl23eOVwOoz5rMVRimmsUNgsWPL+ctnmSJyZNn3PaMrWW/PmemI3O6YFnO22z3jMHF8cMIHd/8BUXb86tEn+DQxuYkYPU/WgdcO3uNwXOBUDijY7F6QpINU0HcDnpGyMkz9jkaWVNpixYQSCaESIXRcrNcoramahn7oCQG8zpaOfuozazT0rBYH+NEwr5bU9Zx9v8H5gefnl6wWxyzmM5z3KJNn5YPYk1LHZfuE0fV4FyE6+u0FSEW7bqketNypjrgsOjrn+PGnn7He7liW32FRlWj1Ne/2q0oCUrq67F6pH0GQYsSGCESUyKEWMUmQgeK0orheEWRi3A88+mjPnXduUp81/Dw8Y/IiC7OSQqRIWSS895SVwk8QhKYWmkNXMBiFVoFSK1Jq0KogyZ7gPf3U46+oPvt2oO96hAhU1QFHixlv37mHTwVTDBgviUhCAlPqTAzyPsewpRE7Rja7LY+ePmYKHh8t3b7FDZb5wZyYIlJ6euvRpebb77zLd9/8A6pyxeQDPnraPmCUYUgOgWPvtsQISkJVzCiNZrKOfpjwNiGTyWIpH6nNIdSK7fYhP/rHf0KyCkHE9wPGaHoJXgtIHu8tRVlycf6UkCxHZ7c4Oj5hsTxkdF+3mr+qGAOTcyituX37hL5tqeuSZy8u8Sly7/V7KATOD9iuQxUFXTcxTp6YOm7fuc1+v6GoCvresdn2aC1xQbCuJetrczCSA7Vgxw7re7RSeBy96+ndSDsEIGMf3eQwumLWLCiLrHuZhgkC9H1LlIEQPWEMGEx2D0wjCFBCkMXwuQNU1DNQC1ZHkihnTHHA+hbvPVElEo6Ez2jRKJnGHpEiddUQk6fSRca4hixE9NZRFxUCTwweqUqckEwWtJkwxTIfcss52/6cdtfjUwZAbvoRtc4W1rqQHBwesesGLl9eEoBrZ8fUpYYUmc8rvv2tNzk/v8Q5hzYFl5c7wjLbkV6lXnl13o+O0QWG9YYXF5dokTg+vMb9e+8wa+bUswZdmBxjRyL5QHCW0jQIDM6PbHYXGGmQZUPyLSFZUgrY5Pjs2VOWzQGy1qz3W7abPVjBO++/TX10yrDdc//kDqKQNLOS0Do0iTvX76JEYlgs8JPndHbG7fff4tqNGxSzGr97jhaCMU5sdxfcOXuba0dvcDw7QztNWRiig/O2BSFpyortsGV0A3oAqXtSPGPfjXQ+zwRTBB96kg7EWDNuLwnRcba6wzgO+BCZNwcorbAxcbA45OV6zeoAtBIczI+ZJsvxQYV1E0SBIAP1g0mc715SSceLy5dcds8xhWFZanYXz5i2juI8wDmsL/bYGLK6VSnWXUtnR04P8iHo68rlr/CZUoKUCZXIyEeTPyQfPFqoTIrSkeKsoXxjCbXETT3qcIaqR55fOK7fvs9bt+CjB0+Agmly+CjQYsJIQYqOQpcMAaQ0lK3nVB5yHnpmJ4r12NG1O47PjrHC048D4zgxbkdUUtRmRtUUWO9YlgcUao6npKwyID6kiDaKZC2exL4faIcRVWn24yW73XNGb5EishsuicpTzyTaBPKNoOD04IzXzm7y5s37TDERg0MmnXGOQpCCQlLRmIZRDVjveb59xunhNRTQ9jtsyJtp01RopUgyIWPJNG35+T/+Cx799RMUGhkihTAIIbGDxUugtLxsLynGHhsG6roiAUprhm5g/ezR7+5l+T2rYRhxLtBUDSklAhKhCu6/fpu3C4O1gf1ux3xWoeYzLjdb9p2nLkrKImXLV6kZJ0tdK4rScLlpeeouGO8fspWB0iw5mh9zkM6Yxo5de866f4EN+xwd6CzBeaQsmTcr6mZGEonRdQgvEV5jdIFWGhcifooUumGxXDClHjlKpIDgPQJNkonSwPXZAcFDF/agsnAq+ITEkIAQA1EElDAQElIovE2EQrObPEoLZBKklIgh/15aQVEJlCxJQdCGhG4FpfYILKGxpCnSzJYsDw7Z7Vqcc9y4dsCHnz5h0ew4O1nS92PW8yxmlIXEThNtu6MbPIeHNd5NhBhZLWo+f3zOtWv38D5SFK9Gf3nljffnnz/kvOu5dZzRkDev3eY7H/why/kBTdOgtUIXBQJJShmj55JF+oQMsFod0Xct7X4HWuDiRKELlBQsj4+RRxCCJ8qUTdICDs8O2A5r+tay0EcsVytUo4kpUCh46+7bVOU1Hjz5nPooYSfPopmxOjylmyb8PnBwcEiSPZ88WvPdN/+IWXmDyY48ffEFK7PAhwiFwbqR05ObTNPI8eomhWmwcWTylxgVacoVbi9xUSK1YCYqpjBRmRUkQddu6PuJmBKF1tipZxh6rp+ckWJFWQr23cCsKpjixGQtVfJoKSBEtMhqcVNKylSwKg95/PxXdH3PuOt4Nlr8y4EXP3tBPSjeObsFQuN9xH0VfK4FNgRMVSLl1/ifrypyxbMQipgiKWayFEmhpCQlBSkhDOhTjbsGLT22c4ToUAbOXltx8/g2s9MFxVAxuYln6y1dD1oWV7SqLEZKKTAvNBDYJsds3VOIiE8Jmzyb/Z6jsWLd5vQfEgitqJXExUgEatPw/t1v4WNOdglXNwlFbplv+46maAhExikvztv1Ofu+RQpBSIKERBuFUjC4keAlN1Z3eOfOW9w8OSIqg7WR6CNG5sB7h8PFfFs2ZYmOmphykIfQicIoFAY37LPfd36Q53mTRQXPo5//it/82WcoVTI6RwqBoioJKVutVofHeC24fP6U2azh5OQUUy0ZbeLly2c8ffAZY7v7Xb0qv3cltUGnRNv1GGO499o9Zk2JtyOXFxsePb3k2ulBHu2pmi8f/IaybDBLyfFqBVrixqwqnzcFfTfxaFxzeb9mb3oIgc1uhxbnVGZGsJ5p6pEAURCTxOia0lyx602JTxMh5CxcorlKk3NIEnayVGVFUZaMvmOYeowyKC2RWmbecgJhI4W5slAmzzQMBO9wyWG0QiuDMpqqLEgpMgw93rvcTtcKQSDGgLjiTSeXCDGgjcqjJB+xk8dHjWKONhWT63g0fMnNnUSrkvOLntOTA0KseaupqWrD+fkOowTf/dYbXFxs2NuUxWmuYqVrPvvyU95U1zhazZjPa5z3TC7QW8uiKDg8PHil5/rKG++Li+fsJ48QmoPVCW+9/S3miyOqqkIajSoKhBA5WUKJfPTwirmuKMuCzx5/AUnSLI6YpoFZAbNZQ6gjg51w3lGVBd45hNGkFKkXNT5OaCNZt5cUixPG/QaJIobEZbtl++QxXddRNYrZ/BBZGvpxRxJgk4T5IXW54IP7r/HkcseT508Z+o5Vs0IvriOTJCbFrJjjnWfyPUJahr7Dk+jshmvzJf1+SzsKiqKkKEuSi5A0nd0wWMesOODl+jHXT28zDT1jHJjPDNMUMApWzYz94KhMjZ0GqjIRRUSrCh/tVR80kKJFCUFVFFibLUppGmkvB1yXqJqKbtfxyZMnHM8PUVLigyQhcCGx2beMw8Bs9jW56reVQpYJJ0EWnQuEyAeTmAJaQQwRHwRFlMzrGeWyIBQgC8k07Dg+uc691+7jYk9Ihrfu3cOlX9G7kWADyEhpgCAJSaESCC1IpeTlbGJtHCepQkjJi8sLDrqK0TtilFnoVcEUHEZJDhcr5oXgtaNTvNCkmLURWiuGcYsUkv0woETNZbthmvZc9C0hjGy7LUJqfEgUpSImgRSSopjz9p13eP32PYqqwCqwPtKNPYUySKPQqcAHT0g+j0dkDhInRY4OjtEGptQTfMCoAiUVKeaD3+Q7Xnz0OT/7R39BdBonBBQlCEFR6hz950FIDTEwth3zZs5sfoj1IFWkKmekEBDqax/vV/X4+RqtJIVU3H/tFiJ69pue4B2FKfjGe2+Skse7LOLb20i91CA8wzAyDpayzDfd4IGZYXnvBs+XI4MfCXHCe880XiAp0UnTVE1OGHIanwJJRpLIqUHWTYToAUEIkRgcKY4oFE1VIZVgDI6u35NipvKJlKM4YwwEN+F9oAgJtVqynTq6qSeEiCkFVVlijEEKjVBgXY+bPDEkUoSyMrg4gQ7IAFJnDZEpFGVSVGZGP1mc87gpMdmRqMAgkTFweALFxrBvHU1tCMFT1g2PPz+n3014l/jTjz/iZduxKAsOD5fcfesuv/zoM6wdeeftGwzDdDVVllRlwTuv3aLSBUVZYF4R1fvKG+/79+/iPn/MyXLFm2++x2p1TGEMxkjqWUVZz0nOE6eB5CIhOIJ3zJxhf/kchaCsZkhlULrEjntAsdtfZDFJXYJSKJV/rMViQQqBEAK6MBSzhNQS301IpZBC0fcju3aNGwfu3nyfJCU29Fzs93S7gcXxkmcvnrEoF5wuE33/hN3oWM4O0ZViZMREiQiKUpZcrp/glcfHDM5wKV61+Gqa5hBda3yYMEpTFoqL3Qv204a6mbPvtyilePT8N9TVipgGVrNjnAtIJoZRcjg/pO12TNGyWjTs+z1eKUZnaWYLJpeTbCpjuHz5lJ/81c/oXYtRkkWdIwTLsznTNqtrnfBIJVBSE31knCybfUuIka79Grv3VWmtUOTzYEzgUz4dS5lvvBJIIhIDTC8sVRW4dXxIOjGMcsRVJavlLUJUhDCRZGB5fMQ7vMU0fcbT8wEjNFoGLAKFxjlNaQp8E/AaphSIMbKsYZwcUy/xLvwWhqEERBMoy4JhGnjt2n10U1PP77HuRqISdGPPdr9DRs1yccrDF494vn6BkZ79sCOETHmLyWKqhnG0GFWwnB3x+o17vHb9blYcp0CMiZSg0CWRyG7csigOsH5AqEQSipAmQgoUhUbrhEt7Ru9QSZJCpClnSCFxduDTv/oLnvwvn+BfeLxQTAhEUVEKhYgOU5TYYc/u+RPUvEYQGcaRtu0wRY2SEAtFNZ8jpvi7fmV+b+pnn37Gu2+8xtm1U6qmYb9ZZ2Sv1iQCwXlCSoQQabuOm9cP+fLFhovK8/bKUMkKVdaklBjCSHeoebIK9EnQ95bO7lFXHR9EZIoJKRJFbRBa4u2IsyOISEwJLQwpKKbB4oMjuIiSkqpSoDTJWmw3IKRGm4oQHeM0kGIkxIiWuS19WJUcrpZMIeX4ySCyjU6R0ZhSXkE4PMEFQkzMmxnNrGZn9ygpgXzAJAa8tyzKhhABdJ4npwAi4XViCit23Za96Wl9dhvIsiFJzWgdN2/dwBhNWXWcXCz58tEzbp2c8vT5OemNe/zg++/T7wZ+/eVj5ouK0U5sdiNSJs5OVgiRmKYcCPEq9cob77ffeJv7N1/j/p23OT2+TlU3edYUA67P86k8RBPYbiDFQEyeha05LhboUvH85Tn91HF+vuXk+lGmCSVYLldMYSJJKExFShFtDFVTIqbI6HrmzQpTKLQtkWT4RFWXlL2mMktMoUmiYLlacvHyGSfHN3hy+RBtIt2u4/rpW7x5xzDZz3ixfkLd1Bw0p5SxRJCI3rJpNwQTaGXC6AplIuiIXS2IqUPIIutik+Dp5imTHaibJUPf4p3h5OAWD158xIv1Z3zvve8Rg2Sz27J2a+p6hlYCZE7lsJNCJIkPoJTh6fljkoBSKxbzBV88/DnPn6yJMXGwKJEVHK4a3nn9Nl/cWdK5SLyckHFLuHCARKaESolhHCh0+e/1of9trKKqc5yYcwzTRACEUAgsCJWFaFJDjAQX2T5co6Pk5N1jrr+7YmwkSi9od4l2H5DVksuXT9l+vuXdszcZh48YpkQ/JkTKVAkpDIQ80/dFRDqwyXNQF+xeBjaXHYiAkSVdGPA2YGqBDxZT1Ny79Rq9i5gxB5xroRFJUekF149vcbm7ZL27uIpcs6QIg3dM3iNkwA6RRja8dnKfN+/ep65Lkk65VRcVPiYkDkREkKjLGUI5Zo1k329I0jNMWa0vokPryORHSAmfJHVZY0qNnwY++Rf/mt/8v35K2CRGH5GmzAtLUVApTeED/WgBiQwWFQNNWeDbPc8//5T56oSgJV3fkURAfh0L+Nt6MvQsL/Z8+523mcaBhCCJHJ5hVMEwdEQEui5J1+akWxPVW5p6KXiuNpSpRtmJqRvwZWKcH7MXkll1QFnO2e7X9MPAOHSE6EiALywqkoWdfiKFSBAB6ydII8Kp3NmxEaMLFosFspBZWyAVq/kB+75lHDoSHmMMXoJSmqooIUpqXTB42E4dNlmMKlHS4ILFBc80TZmENQWcc6QEcqkY45B1REnmVngIXJFVkalkcBYXPXaa8lw65dZ0byVGRLbsqeaaqlqAMKgahr6nTIqbN87w7in/h//kBwxDhx1Hivo2dow8eXwOBK5fP+TycouQS37xySNUYbhxMqc2iRQF1v0HFlednVzjlp5zenyHpppjlEKkQNmUFE2FMQ3eejxTntP6iJ88xai5p0/4509/zG5K7NstykAzb9h1awITIbkcuB0CY5io6xpVaCKRZjZHToZ5vcrG7bomek9pBMPUIoXm+PAUax3LVY2zE0jDYFvmszmj3fPeO99ByhUxrXjzjqIqH/JyuOT57gEmGY6X1/Fj4vT4Gi5KTFkSU2L0e+pKc7hY8Wy7ZbPp8V5hpILkUbIgBEtZziiLGV7A7bPXaCqHSQdELbl/64RNu+Y3j39NN+6pi4bZ/AAbUl4kSUxjxsLpwtDMDkkh8tlvfk2KEqME3zy6xVFhSD4w7jt2xrH1HnGQOPo7J9RPBjafdBx6xeu3ryPJyTtfV66YJCBxEVy6CmAHfIzgBTF5ZCKnr5RZxNQ93XFSFdz74BoXtccqw3J2C3PzLVr/kgtRUE4l3dTywf1b/OzzB+zGAi0zNKMsDNZ7CiVxyRKkYNtbFoc1k/Ps1i23796kKAwhvEBWFSRHdI5Z1XDj7BpjyDfyqjD4GBAWqrKmtVseXPyay/YZoxupjUZKwTRNKFUwDD1NU/D6rTf54bvfR5ss2itUSd8HUtQ0psCKjiQSU8z/qwpFYEIWCStGEg7I9DXpDSJEClUgRUVdlNhxy6//15/w2f/0c8xU4uKARmTxmreIUFKoiI4e300II2nmM0Rd4VQgDTt0b5l2LaOCKUV0XSHT1zfer+ro20vaxyPPnj3jYF5c5T0HBhs5Xc2YnKPQihd2zS+ml9x+65CyDPRpR0fMGEWfSAcFUhwy6MDB4pSmnhNlYDFfYMdIcIkUcysZ6YliwjHlzmXwBFwO4AiRZD2FrLKotq5QhcH6iWgdoFDK0JRzgt0QhMSHQEgRAYzeImyiWM3oRpv93TgQIosHo8c7RwgBkQQxRhSC+XJBiJ4Ys0BLCYXznmAjpIgQgqqo6aa8YfrgMKVhUS2Y3IQSBQhP0UTiaHEvBeOwRU8N6gD26y1z03Dn3k0eP35GWZa8/sZrXJxfIg4kSkn6bs80Tby42GCKgtWq5k9+8gk/+VXiv/nP/4CbJw2je7V195U3XhcUi9UhdTNHKYFKMWPARAA34lwiSQWKbIKfesa2R0Q46msOtaFLjrKqWCxmjFOf57jzKreZR9i3uwy/lpowDTmPNiac88iZwPqRWbXAS01dVhilqaqKkBxlVXCxO0fpGiEE3bDDmIZ37n2PxszZ7C/wPmKKI+7eiHRfPMSKgFeKp7snmKhY79ecnd2nrAtGO1CVDQpPpTWL4hpylof3+24D0WUow1XcGzGwWz/n5vVjvJf4KhHjRHIjx4sTytc00zQgVYkPkYhESsPkR1IMHC1PMIVGpMTF+WOebx8hNEgk1SQpdqCWmi/kJZe+ZwyRECw7ESmPCo6/f8DrbkU1q7L342sz5G8regcxXPkAFSmBkjntJSGRUqClQktJY0oKKTIv20DpB45URVjOCHrAhYBUiYM7dzi4c4NgO37z8U+4c2PPLz/b4GOTTfxTxMiCELOwJabEZBMpQKFhUc24cf0mF+05B4dNns23jlIoTg5vUJkGigWOxGgHRtuy6zbU9ZxHz5/y8PlneUGLkXXbElJg9ANN3XCyPOOD17/L3RsnFI1ByJpgQYQKRc7bDZNFloq6KrBjBwQ8gWHoSFKToiSRtRaTjzRCsGgWWEpCSMT+gj//f/wLHv/pl+DA6Ey3InoqpXA+iwbLqsxt/n6kG0eq1YrFySnSjsTtiJIOJRIKQV3XOASu+xr+8lVd+0bNk5dbLvc9J4czkoCPP/+clzvHf/bd9yiMyUCY60veOzyi/+UTqjtn2GUgqQkbJ6bg0aqkmF1D+zo/Z5tHUSHAfLak1A3EyOR6umnHvvO40WOtJcWI93nsJ9HM5ouc+S0yVMLZjpTileo6EIDSGE6ODlhvd4x+IsmEICEkKCWYVSV7NzHZMRPR1AQxopUiuoDzluA9CsXx4RFRJNppAh+ptCSScrQnEe8jy/mCoCdSstSqpCiW6EoRpoiOWXNBiozOciEvuCZuUs8ahJQYV+BXihQ0hDzuefLsnOfrPQdNTUqO+aLGGM00Tbz9+h2kVvzw1gnffv81/uonv+EvfvQxb985Q8r/wDPewsxYNEuMUhiRgPCVHogoJJAXNjvZLGSRicn2DP2ImSKv6WP2PMcqk/vrhaAsKkY/4nyO2UtBcu36dXx0FIWiKBXhCtfV1DVKawKRy81zZtWSyXlKXaALQQhgTIOSmlW14O377xOvRC67/pIhbOmHkYU4pKkO+eDN7/HhF5/wou2wIVCVB6QI690LgrZIbUgUKFUz+MBoMzXq2flzkIlCFghjkCJv+E1Zcf3smKQVMWi27ZqyMNRK492EpOT44DBnahLwLlKoIybv8eWOKAIuWDTw4PEnyHlC1wIzCRZSo2WkLQMbEzk+uk6Qif1+S7vu2bcjPREzVxw0W64PgZX4Wlz1VTkfICVcIIcWxERlCkICHwJJKaQWKKOQUlGVJbMbc/T9gi92z2nm95lXNWN0bNoLVLIs6oree7wSHNy6zWJe8nTzr3l56dCiwjtIKguPggCNIElFURjmMyjqbPcqlcaYgr1QlCYxMyXfeP1tirIklgXOTbiQmEaLd5ZLe8H57hlBeEbvGMcefMAlwXx2QKNK3rn3OtdP5yST2LoBGUCFAqE9KUTG0SNLMDKr78MEpizwbk8IkoIZUsFm2NHUVVbOFw2ahLDnPPv4Eb/4n37Mg1++yB0ipZgJnR0K8zmFMvST5XK3IxpNYzRhpqmMISrJfuxQShOkwMaI0QpT1QQlmcbx36rOvy42056bfzCj3HicdXTTxL6f+PTJC77xxm1un53Qpx5bCoaPn/Ps4ws+/Ms1P/g/36ctniCLnFZWFkuq6oyhHXmxe0xVKmSsMHJGjFsG1eKcZ7At1g05bzkqvHV5c9IzKqNAJIQClyaICkmOiYwxZF2A90RvQSUKLVk2B8T2ApscMUZ8cCQUSmukFjQygsxENokkuEiM2ZObQmKxWIIUjLZDG42WJSIIkgBnsyi1qTMpzskJUwsIgphywtcwDQgH9DHbNqOjnw1EDSplO2Hykqm3xIVhGRUHBwt66/kn/+pf88PvvAsup46VqwVlVePjQPSerpvwzvG9d2/z9r2c4FTqV9tSX3njPVydUhcVKqWsqJMRJRTRBpQuQQpisKRoQQSElhhT0seR0E/obqRcjgjhsU5hJ4tShhgj62lCIBiG/B8SCUzWIVXFbrtjtTzicnOB1oaQIst6hRQaUwpK07DdXlCVgju3X0NEybxYEr1GqIQNPdv+Bf2wZ73fM42OunwNI2/z+k1FOv+UdpoQQVAIw7KaM/Z7PAFVZKD8ySKxaUf6weK8oCxrjC5xMaLLitVsyenBAucjgYK6zJFRPtq8eAaHQNEOe1IKNFXFtl0zLzxSmUz9SZFCCp4+/SUPn35EFJ5maZhvBTfnKw5Paj6Uz0ky0k4dISRKM+fg1g28tXT9jvWw5c8vfsMb1Snfqm/8+37rf+sq33A1wVkgU3dizJtxoRQCCCFSmESKHnkw5/D7r8Gp4PnwhOuzY6QEHxIRyTD2wMSu2yFNwa4dmC4tb967zaZ9igqSstbEBEMfSVIThSWJRDs6pCpYHM2xsYMYGCb3W/tXOZtztlrRu56qXOHchE+ShGSwls24Yzd0dH1PO/QklyhEyczMeOPGPW4en3B4usLUBqEUwakMMQgD55uXFKqkKWpSgv3eYl3+9wkuIqLGiIYYBCJq5voIkmdWJ6oSuk8/46/+5z9j9/mIGQsqNKMdiVpiUszCcRLWOVzwyOBxzrEtJObkkOPTEwSK8+2WbhjxEmRdknTJJMFFT0qRr9kv/7ZKrRj0yCZ1XHcHuMHip5HL3Y7/8X/7S/7Bd9/n7rvHnH92wYd/+hl/+YvPqWcl/8X6TdzNxH7yKG3ofWLqexKCmMCFRGM0pMTzy2fYMDINE9ZOlKagNA2FKZjXS5KQCJ2/GWc93k344BAphxj45HHeIrwkOtDGEIXDSU1RCObzOeM4sNvtiVddJaMKbLAkKXILGU/yCW8zMjR5waKZ01QVgxuJBLTKY5yQP91sBSUxMzVIQRKG5EWeCcdMtPLBoYJCColUGmUSn6y/YC6POZlOicFiXaJShme7l3SxRHfZ5vl/+s9+yNFqxTjscNPEZr0mpITRJlMThwGQ2MmiRQQtaXfDKz3XV37Fm3p2xdqM4BNJgTQGUiI4izQyLxJuyjfYYcT6kXFq8dYiNpFDK7g8jojZkskZ2m7PbrtjsVjggiPE/NBGmyAktKkoijzz7UeH9w4fI3VRI/CkZBmcpWoajg+usV1vKE2OahvsxH48x8WBGH3miqoZr91+k227I0TP7eO73D+yfPjkl2yGDVVylKpGiNw28VPicHWIipHJ9kxBsFqcMg0OPauQMWGHgTs3riOFoHctfb+mVnO0KnPwtxAoIxj7zFtuqhI7tmgF1vcczI7oekelJPb8BQ8/+ikpOJSSnJ4uuV2VyNJTn9ScyUOq/ZZ9mKiKGU2zoNBzUp3QZcF81RDdyMV+z8vxa4DGVyVlInhL8CFn8co8OxKAFFntrASkmBBzRf3eGZcLj8AwP72PKHNkIEi0qrCqYDuuefz0IUI2nJ+/4PLhQ374vTe4fvKCBw8mpDD4BNJIpvFKia8C227ksFSUUlPg2fRbAoFCC7wW3Lh2i7qQlMszBhuJUbLvd0xuZDfusWlgN+wgRdw0ERMsmkP+6Fs/5PhoTio1aImQhhQlEkU2ToI2mt4OODx1oZEqp85MoSc4TzWCayI2DVgXuXZwjHNbXn7+lE9/8hkP/9XH7NcWUxQELQgpsDIFZaEpjKHtWy53W0JM2BgptEa4wDh5dNNgqhllWeOkJsoNw64jWcd6v0eWmubkGOkcRsx+x2/M709FEgHPU33J2bCiNoZFM8dIwfPdmn/8o5/yv7/xXcLG8smXL1jNa+6dnnDQHNKvWqZ+i/USpVaUZp5FqanCBsvet3gbaNsehaZUS5pGUDcVEU+MIzY4pjhBdHn+GiJE0FIjhcGOI5MfEUAhDUVtQAmkTqA8nRvQSnOwWtGYhl3bclwUmCRwLuJiBJly1OTkcZPD+8CyWbFYNuzdHikUShoEEessKWi0VIgrUdVyPmf0lmFyqKRwMUEKpJhthFJpdCkwMTDYgBOGT/xjTqobxO3I4HpmdUVTlLhFgxz2EBLBOrabS8rSUJgC6z11VQPx6iCuEQiiFGz3nvmypixfbd195Y1XSYkUAqUUVVWgjQJtMikzgE8RbyNuCgxtz9h39P2Oodsz2h4/jOwfrzkslmzrEVDZkqQKQgr44Dk5OaRpKqQ27Nodl5tz/OTQuicEQVVIpEiE6DCFwfksPS9Kw6Onv6YsK+pqQZFG9u2WqiioihnCFjTac3JwJ+ewhsjp6jbKFMxLwbs3JT/95N/gUsuzzWcYYzC65vjwDpN1eF9htADn849OYrBbSmk4PVxe8TwFTXPMePmS1nWsFgekkBBKZBoQAaMkSmUJvNEFwTrW7YZCgH32lN/8sz8j9Xumfk15arh59wCtRp6KPS+fO2pT8cbiBp+Gc9pooV8zih6lDbrUOBswU+Lt6gavxeN/n+/8b2WlKPKGqwRw9TxERKavICMZOUeKNK+fYt44I81yV8NHz+X2kqpqqYoDtDTU1YzdsOXLJ495+nBDCoGTlSZawenRisdPdox9jy5LtEkEKfE2QVIMKXEyu4LZW4+IgWpest71lLMZZ4crRjtip8DQe/a7HRSJy/4Zzy+fUjU5oMA5T4pwMl/xxx/8gJtn1+jSBFLnCMSgEBhSEkgtMUlwMKvZxg3lrECXAiHyn2OiZNM5qsMj5oWinRK1ETRW8Nf/w5/z4Z/9DW70yKQwKh9sXfLIGFmWNYuyxApIpmRW5A2/D4H9lHNbhUiEaaJt+9w1CIFCSsbRESdLmCaiUUxSMsaIluF3+br8XpUIJUYKlE58+eKC6/qMJCKDs0w+8nK755/+819w/uSCVV3zD3/4HWoJtIlSrWhmjjBIYiqxPntwQwh4RiZrwWqOV9eY1we4MGBDj0sD/dgirgS6SeT/jx1dfge0wacJ6wdCSmjxFbmqIIqAlDGnBLlACgllNBMeOdMczQ64O1/kpDUf0cqQhKftW8KUEF5ytDxidTCn9XtkIVFXYirvLMl7Ygz4FNHAYrHAY7HBooQGJMnnYA6iQmuNtRONEfSDB1EileKRfcEvdg/5YHUbOTmGqSNMMK0So3dcXyywoyIljy4MEsnU7jFGET1UVfYbT3bEJs+L8y3N4jrLZf1Kz/WVN94UAkhFUZaEaULLMvM2qywWCjbgrGXsBtzoGfuJdrdnHC1d37PpWnbrPf1PWo7/izcJOmJ9ZLE4oJrXtG1HcBPPnz/g8nJPNW9IWKpqwXy2YrvbMEWLc46mXDDZibbvkCJSF3NOjs7yzTp59uOO3bBls88P6aS8y/1b71FIQ5Se5ekttv2O8yd7zo5vcvvaHzArG/7NL/+SrR+J1uHcQEie49UdkqgYpy0pKJwdENowjj3Xrt3CpYSbRgzZBnW8uM6U9oy2vUKZFRgV/y32zef5l3cTy/kcVKJya/7iX/0zLh8+5a17d/DrLa5PvPPaXb548pAf//wz7OeRt2ZnfPeD9ygN/CpcsAsjU+yQQlHqgtIHXueQN9U1ri1P/p0/8r+tZb1HKZW7D5IrT6/MLeeU8CHmVlRdcPDWbdRijk8CUCxmc6T0JEamcU9KBVFMDFNPu9uyu9xgtOL4/usU5Yxbi1t8Mrtk307ECJUySBXRSTONHiEkg/M8f/KYG7dOuXH7DutpS91ISlVzuDgiUhB9wscchmCnMQdjBEsYRtxk6buJeyd3+KNv/oDjk1tEralSgTKafuixNmG0oNDFVftW5HmXCSQx4ULEBccwTDlVpmgIWFTQVINn++Ejfvy//Bsuv3yBdxGBIiIopSbJgKgLhCBnoQpN2+1BJRZFSQgCj6XSCj8GovNo6+i3e8auRygBkwPnSN6hpcAHj0ieZlaTXpF3+x9DRR+o6opiUPzs4ye0h4LvvnuLRv+KZ7uBCExfPkNIgdSCzX5gdXbAsOuQkyIpgVE1gRpvAzFl8EXrt6QAmjn7YcPF+hkhOoJ02DBAFCyrFaZUTF4SXaCUFTEmhrEnCHulODaUVQ1CEEMkhAzXSCnlUY6uEWhQkaQi4+AZnEWrBl1FrB3Z7rfYccDEktOjY8q5oHV7kopIJD7aKxFknim74EgktFLM5jV9nEBljz7RYwpDCgkfAmOfW7/d1FE5g5IFPkS8dvzo0Ue8vbrBcjbDlAbGiZ1smV2f49eOoqxyMpxzCAHmqktkTEEMDjtapM4kQ6k1n3/5gnfferUc9FefpgSPNAahDfPlIe35C6ooGV3E2onRjdiuzScZ69msz9m3l+y6lov9lvPdls45Lp+MDP/yCw6+e520VGAk292Ovuupy4Y7t+9yuJpYby8Y/QZlEk9ffIFUkhg8zo8cLA7RuqSqBWO3pi5qBtsjRMGuvcTJDVFaoCQGTVCOZ+uHzKsZQipC3DJMLVWzZNM+YN1ZmnrGt979Qz78/K8JsqAqa5Kv0bFBJcPx8oSL1tK7DSUVr127hy4z5MP3nslPUEmUCNSNQkvDxXZN63ZIVPayxUBKI/N5w9mqYLItY3gEynPtvQUX0xc8tE+oj1bUlUCqGTfe/AbF4XVe3HjCcl1zqBccyDlzb/goveBCOUY3UQ0j3y1vcVMcUfmS6L8WqHxVEZApZxPJlE/DkYwmFUKDTLgUqa4foY5XxCDxfiJKS2kky8USKUoev/yIJGoWB4b99pxhvUeHiDCJJy+fkSbL3//j73Pt+HP63YQRCneVbBJlomzKfFp3ARv2zBb30JWhpMSFQK00i6ZAyIYoFGPa07qWdtzw8uKC/W6PqQ3Oeu4c3+Q/+eB/x/Xrd/AUhCTwViClIPmsoq60JDqfYSEG9v2aqq6RStJPHdY5Cl2jVYURoMdLHv7sAZ/9v3/O9GST02oEGCFxIeBipFCKUlUMIeIkOAHDNNC6iUYV7K0l+IRPCSkF5ZX9pfZQJMnldksIHpMSR4sZQsxp3cR+Gph2a8rlAeNgf9evzO9NXbt+yNRNPP98S7/3nL7eEJ3lnTvXuex7Wp8QRLSE87bjn/70p3z/e/cZuj03q/ucfHtFijOKak4QXM1wa8RYME0Dg90wDAPRhavbZ8DjKUxBTB6RalQsKaQipYj3lhACLgW0VBRVSUxkP21KKK0y8peEUJIkM6yGmLC9JUwBW8yZ/Ei33zP0HSImrh/cYdbMCHqiY01QkUIaUsgdKxddbneHgJCRYCNFM8OmQBIQbG4Ni5T1GtEHCAkCSCmZdgMueBYVzJaSUUY+3T3l0f6c2+YQLRPSQBcHaDQ34pLSGOqZpt23FEWBkppxmpAyIFLMoSNkT/X779zm5x9+yY/+5tf8X17hub7yxqtVpoH4oSMYw+z0lP3Ll/TbC5LwqMIwDS3tdocdB7qhpx1H1vsdXz55wtPtJRs30vqJ3SdP6S46vvWff8BGDmzHkZQSq8MF55fniFSyWpxwVBwxX6zo9nsuts8QaKLLt5Wha9m2PVI4dvtLbBoYnKXr1pyclPjBce3wtZz4Mw0MsSU4hwsj0zSQbKQcOlSZWCxPKORNZjPF995t+PTxj3Oay3RMra6hVSS4x7iYfWxnhyeMfmJ3fokxhpleUtSazrYE3+LbiWtHtzg6OGU37tCqoTQVQgoO6yXDyxf89K/+KQdv1cyPDpnUjLvf/x6n777F+tkFl5++5PMffYb9m19w9K17FPM5R+/dpn/W89GTx7xRXuOOOubIzPl8fEaUcH1+yAkHlEITYuB8u/33/9r/llWMgXj1gSRizhuNCR8TSl9BL4RifrgiXc1/jSkwpkAIhfOWyW748U//DZWZ8c6bZ3z24cfstx6pFEom2qnFFkfE2YzXbt/m+cuPEUEglWS33SNkQ5KgtUYojUiBsR0obwmUViAF86rEENFmTlKCGCYu9y8ZY8/oB0IMpDHy7o13+Tvv/x2K+ZzWRQTZ5iGTAZ9QXoOUBC+Z7IQuYOx2kBJalLipw4+ZPFUWCoae8198yif/5F/z4vNzimQwVzYVoyRS1WAnrM2aCqMMYbJQKrwSdEOGfCgB3nsG50kIjNGsFg2DczSFwcRIGSwogYo5WGGMgdF7gvPYNOGkomqWv+tX5vemzq7NKcdjLn+54ZvvXufu6QohJDcOVrx3+xo/e/iMKBSFydnS7/3gdZ775zml7aOJ07f/Hk5p9rsXRCcoTIkxJUfzU6ZiYOpHCmEY+gHnAqDy/DRJIP1W+zDazEpOMSKkoNINSqhMaQueEDxCJiQZlCFEVkCnFBFJZOteJOc4+4QkUquS+UFD0zQoVdDFHU52SCmRUeRAhZQ30UTewJPIIqtKlizmKwY/Yp0lOpmzr4MnT40yM79sauI4MlqX+c9KYH2PD57BWx6Ol6y8ZlE2pEIQtGCvHJ0A1/Yoo+l6B0LmEBXIgCelERLGKbDfdxwfLvj2t17j6dPzV3qur7zxiujQRU1ZlXg3oKWkOjxiHAcuH/8GZ3tM1SCMot87LtaZiNJOAw7BEAP7cWQMPqfp9JHvqPt8+vISN3tGOqwYbE/XdhRqTtvveOPtt+n7wKy6zo2z19GmxOgCa9c8P3+MEYnV4RHDtGXbnzNMHUZK7JDo+oFz8ZzJP6SqZkiV6IZIO+zRynDj4A7XD+5xdnCXqpiBVjg7crS4xlzM+fTBn3H94AbYlpkTaAl9mpgfHbE8nmFHw6poCCHR2R3RedbbSw4Xhxwsl7i4QwTNolmRkmYmS0S/4cGvfsRP/vJPGOyW16/dx2KZq4bRCyafCMJSHEtunh7w4Z89pneG4/euo2qJutbwPHW4R0+4ow554+5b3En3abdrpnGgqWtMUbDbdTx4/PTf4zP/21mCqwVAipzDmwKSbEkIMaPyhPM8+ewR6s1D1NmcKCW+3SFS5ORkxedffMinnz/hzddfp5rVV/GQIEXKIdhKc+PmNfS85tb917n15DGPH7b4SSO9xCef6UIoujHH8r180fH2uxUzMzFKya2TGzifmK1KNtOaJxcvGV1LEgPRdTRmwbff+i7feeObFOWCfehzqwsI1hGDJLhAZWaM08QwjSgtCUPEO0Gh5/ghEmRkVs+Yowi/OSc+6dG/bjmIx4hDBcJgvc3JTEVJAJz3aCUZnaMb8+KYxkgoE3VhKEWJFoKkNVI6Ju8oFHkUpBVTt0H4gZWSXHaOdZ/tIRKIk6NezDm7cYbXBfLrZK3fVvsXI//m10/4h//1H7H//BmmL0HA2dGKIUZ+8egZLmWx62vfuMagzumGPYVJtC8HPv6Xj7j+d9+4uukuMGXJ5Ae0UWjVMCuW+DBhvUWgkUmRhCAmi3M9Xbunm1pstHgXUKg88xQy+2+vbpcpRrQ0KDQxRISQSJm/PR+zYMoFRwiOwkjOzg5h6OjsyJQGJDmdiCCJPnt9g0+QyFGwMf85IWbc6bI5ZBwDg/d47wkOvJsAUEpSViVFXWeW+P4SHS3zuiClERcCfe9xU+ThcM5/+sE34dJhfYdNI10amZpjROvZ7baMk2M+q/O+RSLFSDdNlJVGKUXX9sxmFXVdcHp8+ErP9d/hxisJU0fQksXZdVAl0zCyOr1GNZvx/Itf8ezLL5jcgDAFZtaQUmC7fs6m2xMBYwzCZHHL23fuMvXw8JOe6vQY8ZpnYy4omwajBIvmiDBFCqNxk0WpQ7yDGCN977h29hrRC4SMKNNzU9yg73bMmxmXm3O0GnGxY7u/YLNPeJ8wWlIUClMe4IeRXXxMt76kKOuMqfOCo2LJW81t/u5r/y2rukFrgTaClCKX7ZpODDxNO76YsojK+sg4JZQsuX56hJAFkx0wWjArCgpnMS+fs/7Zp1w+eMC4sHzjvRM+fTzQdi3tJKhlQdt7NpsdbtxTNQ3FHUN6Evjwn/yck09ecPePX2N21lDcaBhKy3QpcCmSbMSOjsKUNHVDUdSMY2C++PrW8FVlHEaeOYWYhXwCQGbFr1Y5oHv79JLFR49pzA16cgtruVjw6NljfvyTn7FcnPC97/2Qe6/fZvvignL4OW++eZflUZk94tcOCcEyeMd7r7/OfvdzLl54ZvOG0UL0WVkc04jQjv3+grbrKeoSLRqOZodESryMrPvnrIdzpmnNagFVafjm7R/yw/e+z5AsY5iI5FtESB7nAwWZLx4CILKVY1YvkVIiOjCVJKQR7QX+wZqf/a8/Yni6Z1YvWTRzqrLiQoALDjdN+GlCBEdZNBRSMyaLFpJSK5qiogie3jqqoqDQRbYROYuUgrppiDEz22d1jTIzjEi0/cg49GhToEQWOlaHC5obN3j9rW+zbzs+e/Tx7/R9+X2q8bFFPHccr06x8y0vHmwxWtEPEyFYkoDZSnP6ekUvXxCGgJIJ6TTnX4Kk5fofFKzmC4Q2uJAzwr2f8NZRqRXKGJZNg6IkusgULe0wse8GREykq/xceRV4r6S6mslDzrXOkX2VqXDBk6JE6RyG4YJn8pYUUj7wJggEPjt/whafcahSZaW01hhRgHME665u4Anvs183XPHOK12yms+4XO8Z7ZC1BqakMDVKS3ShEVLhfUCOlqVMxJkAHYBAsJHgICL44sUjNvWOQ1Vh6oKOlq0NXMQtt01DXddopZjGnFOMyHbEaRppW8vy4AAbPJvtHikXvw1f+f9X/w6qZpBEZJyw7RpdLVB5LaMsSk7uvUm1OuLFky94/PBL1tsd+2lk8BZhFASFkgqIaCF4+7U3+fjBC3oL6aFHX0wcvXeMuS8ZNZyenDK5mLF7tSEysh9a9n3H2eqY7W6H1AIjDAf1Ch9GZnVDEoHjozcgSSqjuHvm8Dai9IRPnlld4MZAIRoqPWeaIv10QVxP3JYL/tM3bnDv9BZmvkBUDUknhClIzrMaesLQ8pZruT19yp+//Eue6cTZwT0aNc8YSRep9g6zH7BPPsY/eMm47QmDp0oB0QrkTHF2e86TdodLgbGsmVxEFYLF/IBuGLlII8ffv0XXPuD5L5+zfbjl9rfvcOu7tygWikHC05dPaZzBmHzymqaJEGCcxhza/HUBmeFljEFKgRA5vk9IhQ2BGEEISV1WaAmbT54gao24vULPMwv3k48/Yuw83/+jP+T69dsUuuGde2/xX37nO9y4dQ0vApfbNY8vXtKNniNTEI6W3Lq2YLPb4JxitTrKHnUl6V2eMYs44aaexcFttAAfA2XV8Hx7zpcvP+f5swccHywgSt659QF/8P73maLDhUSUAZkkdnIIdXUDERHvHDJJCl0Qk2XyG4QQaDfRPnrM5cef8uLDB0yXE1iPdZ5NekGIiSlGukKhZ3NkNJhU4qeBLuyZAkxXvmejDQIopfqtJ7RLEzFEjBTIGEkhoKRmtqgQRmBDjmVczhecSY2LkanrKI5XHL/7PsXqBKFrpLRI9TWr+at66B7xR9//DkZoVFHSzLJw6eGDx/QisjirmN+RjOwR5PQtFRXnXwam7sp77iTOBybfYkMPIuDiSPCgpcsENKcJU0c/jvT7nug9pizQhWEWAqWu8dIRAX/FI5ZXgkVtFKWp0VJivSPESJoEwbuMro0Cbz0pJWbaIIF9P+GLhNKQFCAFQUSCn9BKk6IiJou1ORUJEUiASJLjg1PafgATaIr6avPO7hihA1KDcxZvA3Vr8fsdzDwpGnRh8N7R7wLRRXa7js/6B7wv7rLpetw8z4XXqeNGKtHKUFWSojDsdxuC8wglMy9aCKwduHP3GkqUTHbA2v/APt4YHKjMhE12IiIRpriaf44YAk0z5+TmHXTTUL94zheffYYdRrb7Ha3P6DIRPfPVAbP5Ic9/8xtCzA9oejmxu2w5e7bi5nfusjht8HpHqQRaSfpph6k0M1FTzRbEEYpCZtP3lH27IVikDkQJ3mYyipCaUs0YkmXyI5uLC8qyQOuJ/fohtZmR+i1/fPAuf//6tzk4uoaazUh1TTIKUehM5ipmpGaJERLd7nm/uc6d5Zv89Iu/5sXDF6ySoZQ1pZpRjMA44uQM7t5h7Ee2mw0X6xf0tqV96Dg8nbOJA1ZJJm+RusTUDXVZMIU9+4sLNl2HPJboXcJ3ji/+9DM2n57zxz94D3kokUVDrQu894yjJWIpK9j1XfbHfV0A6KLClBVKQTOrEVJgvacfJiab+cgipUzO6T39R8+pB4u73fBwf8H6ouWtt97l7v27eXETgTt33+D44IAuDuy6LRdby+ay52B5xrKes/GR16/f5dnlJZsXkbowFEcz9GzBsxfPIHVoJWjKElPWzBtHJKHLGU+ffcijx88RLlLJA+6dvs237v8hPgVG4fMH7yMyZdKW1gYtNTFNZEBjYF4Fps2eF58/5MM//zkvP3lC3HXoEFFoaq0pjUGmKyuVSJRa47WhszkfdSEFTZGjOl2wFIXGJMGFncBI9BXIYB8cbl4Sg2eORLmsUD67dkzSmpcvL2iKCjObQ4zEccxkIu+YLxquXbuBTYrdbsMXX37C8xcPftevzO9NhcMtn18+5Jvl99h0HUWULOdzgop8tnvB8duGbb9FCwExooRktw7YIadfjcOUbXMJrBvw0ROFY/A9hpqYPN5b2n7D2HumwWOkYb5YZtvS0DIrG6wMdM4RQ3aKiCQQUmUF8RUXfposPoSrWW9AKJ2zoX0i6bw5LqoKKSRCSpSOCBVJMUGSmWzlQ7YGSQkyInXuSrnJo6Pg+OiIkBKtG1FKZK+4kiSfg/oSGeREihRR0CRH6ya0V5nV7i2iUAy7KyGWizy8fM5rx9cpQoNLHh9gGydGLWmmgO0HnPPUszl92+HsRFE3jM5S6poUBFFHirKi78dXeq6v3mrWkqIoMUWJyPf+HIBMQmuFtyMiTagUaJqaw+ND3HSDpAXTkwdMG4uQCqU0105OeXi+w4ZEcnk+4GLAup7Pfr7hy48f89b33uD+330dv8jBxhMdLnrmzQE+TPR+zbq1HC1O2A9bEoFh3HN8vMCnnNgyTo5xtGhZs1wtEQiKsiIlgXOW7f4xwWn+q7t/jz84+yar26+jDo9hOUOUBYmIUCqHQgdPGi1xyD+sLGpWyxv84e0f8Lj/FePFllnTUJVHyFrjkyWl7OO1Y8/U9WzW52z2ax5fPOHlkw3z2wfsIvSDJamUQ6mHkX6MXL9+i+BgONlTry7Zf7hlOk9snu755F9/zg/fe4v6psITCAF2+z3Pzp+zHSbQim+89+6/4yf+t7dsDHjbk2KkLivKMotJovOZ0VxVKCmuhCQwbDv0S4m5nsVJZ9fPeOPNN/OHNY7oNqCrgssBrE+8fNnx4NETRAzE1PL0ciLsJw7MKe8c3+THu3O8E6ikUVpT1RW23bOoNMvlis20QSoJKByeB0+/oN0OnB6c8c273+eNO28SJUzOZvU8Eq0LYsyxflpKikKQrIPukue//pIf/fXHfPHLL5i66co+lRDekaJkiBaKIovMUiLEiNIakUAPA9LnA3KXEgjBoqq4VpZMztH7gK9LeunxfcfYW6IxyFSQrEMJwcwITo4OWN1seLK7wFYDSyMpjcV76Ic91jrqqmCuFL7f8eLFMy4uL7h4+pR2++J3/Mb8/tR33jvkx//0KZOG46MF519uuGz3XLu/4sW45nJ4SWEEOiWk0lxeeKZOohA4AmM70e9birpiZuZMMetuoo2oIpPHJjfgnMXIkuVyhkQQRWTXrXHTSNAVyojMHQ+emCmPCCkRIuHCxOSHLHxVIvvlU0LqrDCmyGCkFANtaJlinW1QNh94tQRlUt543ZUYy2fLZbwaExWF4Ob8FDOr2dgBJAQfsdEhdU4qEiJHXYaQCJNnNoGIHcSMv9RFgfMwtCObizymicCDL56zvXvJarxJ8Dmju3c9e3oWZoYyKlO7vECVJtuHpKRpGiZraduBujEgSrpheqXn+sobr1SCYl4hjUGjIWl8TBnVRZaRY2OG0VsL1mGEYF4VHM3ndNOIJRKioygXfPl8TUqCeDV8d2nKMVRuRDnNL//0Jzz4xSfc/+B1brx/l+VRgVkV7PuOi/4CdKQpG7QRjL6lu6L5dFN+IYbJMlpLSppF0+D8wGA7UgpYPyCF4NAs+G/u/AP+8O7fYXZ8hF4dIeY1qZAIJa6UeblVmUQBtUIWRVbr2dzCKxdH3HzrGzzzv0R2HboskNUMLcjqVQE6KgwVUhxiKkXUid16hO2EVZ5+P2K9QyhNUdWUVYORNbNFhdElZ+/MeTE+59HugmmynG92iCCRLoLMmZwxea5fv0F8+RJZQPl1u+635cMIIcNfxqnHTQIpwGiN1JqyKDBaEr1jnCyTi9x9fY64ueBGUzJfZNV6P3QYpa+wpKfsxpF+t+HJ8+c8fvCIO8vrLDcVJ+2K48ljxsDNsw/4uP4TLtZb7p2cUNcVY1/SbQ2HZ4ecnt5k2JxTzzL44nL7kvNnT1mZA779xh/y/v33GBO0Q49PU84lNat84CUwLyvE2PHkL37OT//5n/Pok4eM3UgIEFBIKdFaUZYFUijsmH2zWinEVVLT6B2DdUQpESR0BJ0CPkSGGCAElrMZdVkglKM4OmZcLOjGnrbv2ezXuH3LEYailFArtn5gPp9x57s3OZ0O8F1CjAq9jVRWobdws15RO8f+/Am1jqyKwMEbt+ntrd/1K/N7U0/Hnr5yWDGhq8gXLx5zcP2YR+kx6/4cIQR5rKhod5GxFQxdQvtEU5ckN7C5eMny4BhiorNbnPccNWeU5Yx22DOEHil1zky2E9EnrJuysC+AFzn+Dy8JHpSWeXSjFM45RpeDXrQoUKoghUQQIQfoyAghJ8+JmAgBpgRRiky/4irQRUYEEakgukD0Pkf9iUSMAakUelaxGXc4QAmRlfOFzornkMWTIiaiFRRTSepfEtSemAIhFOAjPkTGAYZNBtAIBPttz0v7kNm0opEFY+pxwvLCb1mNkXk1YxwmjKlyQt6sYeh6iB5BpK4rqrriFx895N13X+3dfXVVs5HIQmXFYdKQJCqJfLucEkkIlCmy2tEHkr3iZYZInCwaQQCS1Gx7TzQpb7wp4NyIdbk/Pk1ZeGGUor3c8tG/+Am//tNfUMxK5odzhIRioTm4fkR9Blt3gdMDh6slp4e3GbqW5/uXbH1HUVfMyxVp3PH88hkhTlSzmkoXLFTNd5o7fPPgPWbVHFOtYDaDSmTCguD/K+BHCEBdZQ43h4hgiPYc4SYKU7K6fp3nH/0c3UZM9ChVgtbZxxkdeI9KHplChparEl0s0NKzaApiqEjSkIRk2PasX7ygmResVgeosGL/7CEpgVYGlM6/ZRLkpk6iamY8e35OU5fcuXeXOHwNIfiqiqJAKZXVjyEgZUbQkSLGaA5PzjBGs9tv8aLFVILlW8esDehCgpoYx44YDUFE5mqJFgbvI9tuxL+0/EC+zzfcXa6LGXUZmaaX+Ogo98e8fXjKL92GRKAuG4wuEarE+oRgiaFnVRkOjm7x1x/9iDB6/vh7/ykfvPc9nFCMu4Ft26JLhUoKVSsWxhDHkUd//iN+/k/+Bc9+/RgbIlIISlngYha/CEAkMjghj9tYVvXVDDiHmytlcCLgfECJRIiQYkQJQXGlH+jHiUIrqqqkCNBUB5SzM2YHjmJ3yagekLotHR4zq5kK2BWRclHDSpM8jF3ELSYW8xPMM8fRbk7bjrhuzZ333mP1zW+yPLzFj/7sR7/rV+b3pr646OkqxaQ97cmO4luejx79mvPuEiQYA3jFuI/0lzBsE90GGh1JyVNXEeUFpSnYDTu8j8zqA6pihnUjKQWMMcQQGceJyfVAJIms3iXJPM9XAsHVpUP7zKIPKc97IzmtSClizK3jIFxOA4uaYAXRJaqkOK5LRPB5XVQRWeRxRQoyM5hD/G2KWIzZTVNIWM2XtG4iCJmvqTFbA30IBCxCSazz+DFQpYqDOtD3IzFm8MYwjOgoEWgunwTwIJQkhMi0j5gQaBi5qU/YuMckqQh1QfQSbQwmJJzLv5W/ygdW2lA1DZ998YTSOX7xmwe89varMfJfPZ2ortDaoExBCkASGYkoBFIrQsjpEyFyhanTqMIQQsoMXCGysVoqxqiQMZCCx7mJGCaSd7lV533OTDUGUxi4UlnaraXf92gyvvLlh8/QSqCUpKgqLnXBJ+PP0IVifjzn9r27+OSRYU/wcO1qs+raLUfFirsHp/z9/+rvcXbnNSQlzOakUmTJeOT/Z+PFc6XiA1AkWUF9SBwmfLtFlwVqMWf38pIlmlgIRIwQAsE7rO1x3ubWTQikJnLyjUMOy2MYK/aXgXFMxAj79Zpffvw3PHvxIPND94FpM5IQSCFpu55123H/us4vYRK0bYcuJMuDBW6YaMpXQ5f9x1Ai5SSTeDWLEkqTpEaQkEVBEpIJQZ8Sqqw5vruAeUN0e4TZ07uR9lxRaE2RItfmP0DpimmcuLab8z35XW6s5jRCoOOIcBP1wSmuCtTC8l8e/CEPN/8M4sT2ckepCxazhml0KFUjhcS6gTFMPH38BR/ceZ/3XnuTfnT0k+Pl9lmGJKiS1fKISgg+/9Ff89f/8z/j6a8eYfJ4L9OLyPCMFNMVg1qglWb0HmcnSiUyZYicLzbaiVIbSgHFFc1rby2RhJESLTN4BATeB3zX53c4gbl2BylhuVgS4inryqFmGnG0QM8120Zheo8sAtPO4rpI7yKuUMxvKh6MA74YObt/wOxNSSkc7eVTxs3z3+Xr8ntVVVXQGtCl4uHnGz65eMFm36ElGC1JAXbPIu05DPtECJL66n6QEvgh0L9sOXaJuphTVRU+Ws63L/DB5XCbFAiZhksgEuOIjBICVGXFYj6nLBLKenoPg3dIkUhBEkN2DWQilMe7gJAgVSS6CFETXST6hFaSZTVjCIIYVKbJSVAqErxHpiwC8yEQQyL6gEiexWLOvKnovKfShql3OOdRUhCDx7oJowr85Jkz4936FjI9ZntquHw+YYxmu+8oREGwns2zCVIipYQpwZG43FtCY1muG2ZVRRtGRkbU/Bg35oQmN46YosS5SIzZYmd95OhwQZKasjEMw3/oGa8pQUqEFAQfiC4BEh8jEUDmU7IQILREakOMgBCIq5aecA4pNMjitxQq70dCzIHwRipKrentlOerwKwqkCLPlGOIJClJiTwHSAklBGF0+OiAHPO2fbRh/6RFJIG6MpMoIUkxIlPELyLv/9d/j+tnN5FJI8oKTE6pyRncCZTIvE9E/ns2IWRetHAgUoEQiVge4OMFwTlEM2PwLyl6R5EcKuY2iZ0mxrFnDBP90LHvW+KhQq8OMaamWB5gdODpwxe0+x2ub3HrjvDS0mOZPFeLH1feU8+zy0ueXbzk2tERShuKqqRezvGTxceJzn0trvqqJIkYPSFG6rJGpoQkYYoCKQXW7bnsBtbtngMz5/bhDaYAvbeMYaQbW5Sq2fmWMk30YiDaieNHljvdNWqjKMJEITxKCcrZcU67ajyLAn6wOORPj3/CL7d7gkxU8xVSadzgGIc907Dj7PCEbbtGC8kffvePGVAMQ+JXX3zEvFacrq5xsjwltC1/8j/+P/noT/6KyXpcyAIUKa7abCKntiQBIcUrAIGj9zlXVSHppilHuaWEkgmNQovMdI5ApRUx664xypAALQRlYRht1k6sHz9kJiWurPCMOO3xiwqrs4UIL0hTIl14VnOJ6z27dqT1EVkK+jTRpg2nZ8ekM8Na/YakDnjycGK9+3rj/apG66nmDTEEnn+5Y3Peg0gUhQIvOf88sn4aiV6gZIY8XDs65L3bZwRrEQpWqaSaHKNJdGPHbrpktP1VWo8k4Ig+a1l88CgETTOjaBqKoiQQWPf7PEIM+ZAXRPbvypBV7sHH3O5NghgCIWXPb0qJJBK6UvTeExA5DEfnKMDgA4mIdR7hJSkKZBLE6EkiMNc1B6sj2jghpEKLgqgVUniUlPjoaQpNqUsmLDf9Id/0S7CaTxYT08LRe0f04EY4f94z2ZjDDUJe52NKPHnZsb8tKHpBjWQ0BpsS3eSoUv5nhQRtFOMkrn47lVXe3tF2LclbPvr00Ss911feeMuizqeQkAHYSQi8u/Jy+ZTVZDFj+GJKOJ9PP1EkkszqYiUVXpYIIfHeEYPL+YwiIZUgBaiKAikT27777Z+/aGqUTNkjloAk8iYIGfsHOcBB5vinTNnNi5FBUgiJkQKhcnbjdz74Lt/+g+9hhCLZiFiVoBJkzCjory67V1fcq9NgJhXkv1KUCGEQokLqmqF/grUOqw1Dyu0WZfMiNA4Dw9jTTR3bvmXdbUnvHyFESYgVPiXmh3PuNpovPrMMtkX7yEE948bRMb968BgbQ/aIJZDKsB5GxhDZdx0+CUY3MpPQlAUhhKve+NcF2caitclxYVIghEQbmRNUJCSpqBYVKuxQKiBLOL9cczHuSCowOU8hOsqyYtkcUorI7PORO9sTGjQqTZQqUggwukaZikjAaIlYlhxdu8N/d/jf8n/7Z/93OtdjYo7DHHrL+bMvOZxplJ7x5Zef8P7b30TPlrhQ8uEnPyMpODm4w/WDQ9Yf/5Q/+x/+EdsnlzRRMFzNzpxUeW4rBBCJxKvXVGK9x8eJmAJaSlwARCRdyVa01PmbkllvwVV72aeIDZHJB5SEwhRXiu58gE3TwLPPf82gFdXhnDgTuApiiggXGaZAEJoQJXiJSIkJGKPLG7Lw+BJ0WdP7RLIT6+1jnj5p2ex3v8vX5feqRIK6Kfjrn/yUj37xhJgiTS2okuHhJ45+DTJKpFYYLQjJs287mrrh7/7wA6x1fPr4GcXlyPPFgI0RlQyzYkkIgXHsCVj0FSZXFA2VqamrOUII3NX61bYjpVbURcnUWwZviSFR6QIpNN47nHcIke1ORVEC2SIXiQRvET6RZMQnzxRGYnJIL5A2d1SccyQvUMh8G0Vy4+iEhMH6AVNoXHQ54ERLyqKkTDWJiLMTqRd89uPPef/eirO64p3yfZrTFV+6B7R7yzhG+o3NFyYBpc7/rlMPm+eWbmmpg6RKCikT1nl23Y6T2QEpBAKBcRzR2jDYHmIiBEESkqau+cF33ubZ+fqVnusrb7zWjVcoN40QIbeBo8UPI855gnPZInBlqhYi3xfj1SYtZZaQY7KqOFxhxuRVm4yUMFe+sNoYZIJN39L3eQC+amoKedW2SCFD7uGrxDMgIa5QgAKBlAktBZXWzIqKs5NDVqs5s7Lk/W++x3y5BBRCFVCUefgVEsJczXhlvu3mK0RCRJH/YxzgxVXbWSIw+MEz7tu84BYFPimmGBEx4sPEYDNCc9u1XHRbzssefXoDXERVkAK0U482krPb1/jo86f0nSUNnk3Xo5TGCIFLAiENsij58mKP+PAj/vib36DWmmloWc0q6qoiIl5ZXfcfQ7kQkFJcKSsTQ3JQGKrC4IKj322Yn805PqhRSnKhduz7mMO8y5KDwwXNrKIqF5xWS95YX+f2fkmpVP4WRE6eMkqjpCY6CwREWaCXS1K94N2TH/J3X/8r/skvfsH8ZMVYB/ovHLMv1xzcO8auLFIIbt18m3ZIfPSrv0TJgm++8S3maeCjf/Tf8/mf/0uGfmJWrohCsRkHEoEQPQlFkDkEMIoMfMmtodwlIQmkVBkaIhVVUSK0ZrITIkUUeT6HkISYsnZAxJyTimIMGW6fpMzpNjESZWKxWlHMV2yHC6RKYDTSaJQSiBQZJouPCZESo3OM0ROCozKGulgyjC3adpRixeV5x/NHzzku5r/jN+b3p4rCMK9KNptLApZFLbk1O+S14g3W0y/ZuBEfEhpP8BIhFS4GfvLJA96+d5eFitw+OiCNisdxZD+D5XJFWZVZ0Tw2CCVQWqKkuuqCZJ81MdF3LW23y/CL0jD5DLAIMYCSaJUz0q0fiSnTqwRgrSW/fDHfgH2kEhpdakqtWeThCd6RlcRXiukUIkJkjGvTzJikZJgGSAUx5XU9CdBK4aIHLyBB3/a0H/U8/5un/Pc/2/B//T/+MSey4Ww4QS8FQzfy2eYS5yIpiaymVlAWgm4Eu5ccyGOCDKhG4uyIutIgbbd7tNToQuKsw/sJoQQpRqbJoky+SOoEb925/krP9dUBGlrlzUjk00nwuXUXUgZGpyiQugTnIOXbbyDhYsqijnyeBmHwPhBTQgqQQl4F7eVZlJQJKQR6tqLSmk3b0fd7UvQczmYYI3O0lQ9ZYSpAxjyYjTiUUCQhSVi0NDT/H/b+LNayPL3uxH7/YY9nPueOMUdGTpWZNbGKlEollkgWNXQLUrfaTVmWbchu+8Vu2HowYAFtoB/8YMMPhhuG/db9aDQMWN1qSN1Sy2ySEimxyGKp5qzMyIzMiIyIO557z7Tn/R/8sE8EpSeFAbuqQJ0FZFZl3HvP3Sf2Pnvt7/vWWl8/4daNOxzMJmBLIql57cGbhOkAjIMoASG7qhaBVx1xvxzwuu4fDwgL2O5E4+mqX+PwjQEUyfiAxSKjXG7waNo6o24KinLNJsuYb1ZcVkvM6zGGDFt6+kGKkiEeQV5mnUIPCV7QWMuz+RzvJb04Ag/Ode1AESQ8X2U8vjjni/fucpAckvZSvAgoiqK7ie4AQOtbNEF3XakA2NrXNmuE8ISRRgtJrPv4/Qg37pFEgqQXIHRJ0g+RYURfKh6sery2HtFPB+ANsVYEhIRKbVtktrt+xPYzs13KEOiYv/pL/x7vf/oJ6+WKSeIJA0+wLHFHBaLO+dJbn8c4xfs/+QP6qsef/fI3yD/7mO/9F/85+cljaDt/Yl1vGER7pEFAJAMiHeAQlKalaRoCpfBKYZ3tuj8CrBBY57qWmZDYusI2XWSm0JoooPvcek9jLbVpccLjvMM6Tytct9xeqi4xTUpGx8fo0ZAsz9lcrVBWESRDpFTd8oVQ4kyFlwbf1rRFQ1m2BFphgGk6JU1q4n7RKWLzkGZVYfs7fcILDMIp49kYtxHEoWM/HnNkjjjqH/DevRUn3/0Irbq2rbEO4TxSSi5WC/6L3/xn/On33uDNW1OqqwolKvqvJQhpKGxN4xqkCumlg24rUVPhcN3uaaso1hWb9RJwxElEZWowMIgiCB1F21C3NabpcpqDMMQ7qJuy24j1YsmBdXgriXsRznuypsYFBonaNuYavAXlNNY3eOEYpn1msxmbusJ4B06C11jRdfOMMDRtgzIBygoG1vHR957iCsO1dPzmt3/MX/nlL3N4c8L+UcLTkxOWVyedgIvOdpSVHusFOoCUiKhNkVPBlX9Ot5hSEcUJkVXUeY2jW0ISBAE66Oa51hmEk/T6CVVV0VSvtuBD+Bfu5x122GGHHXbY4f/veLVgyR122GGHHXbY4f8n2BHvDjvssMMOO/wUsSPeHXbYYYcddvgpYke8O+ywww477PBTxI54d9hhhx122OGniB3x7rDDDjvssMNPETvi3WGHHXbYYYefInbEu8MOO+ywww4/ReyId4cddthhhx1+itgR7w477LDDDjv8FLEj3h122GGHHXb4KWJHvDvssMMOO+zwU8SOeHfYYYcddtjhp4gd8e6www477LDDTxE74t1hhx122GGHnyJ2xLvDDjvssMMOP0XsiHeHHXbYYYcdforYEe8OO+ywww47/BSxI94ddthhhx12+CliR7w77LDDDjvs8FPEjnh32GGHHXbY4acI/arfePt4hpQKcIDEC4H3HiEdXnm8F9jKorWgHweME83RZMAHT09YN+CQeOuwzuPweAtCCWQi8E2LbT14idYCY1vwHqEDfuk3vsF3fvP3qC8qvOhew7QWKyCaRjRlS2gUWgoQDue3BywlHo9AIPEIwDuHcQ5nwSuPi4FWIAx4AUmqCCJJtmxRQiC371YJgRAe74HulfAevPU0bffehfBIAVIJhADvQSqJweMUYD0Y8E7ghEOnirAXYRqD9Q4Rd7/PN91xCukxVfdmbOkRBlQI8X2Bk3TvyHqEk1hnwYFzgBd0P+VpPvbsAH/nf/JNfKj4/Lv3UN7z5GTOcrFibzbhv/3d77JZrbl7c0Y/HlIWa978wmuEY4kKNPMPV/SjPlVRISPBu790h2pxweOPClaLgrJuiXoRvV5AlTj6PuQ43SOOJSoKePvzb3B1fcHBwRFKh4RRjGlbnn/2nE8+Oee3futbvHH3iHt377BYLen3e8wmewghuFpdY70jSSKE8UQ6JkkC2rZFCkUUhVhrCMMAhMAZR5z0+Oz5CU1bEwchaMk623Cwt0cUhtS2pTCGMOmBVGRZznDQp9dPuV5ccePWDSbTGedPTnn++FOE8OggwtQtB5Mh1ljQGm89pnV4AVfzK4piw80bN7haZXznez9kbzZjna9587UHhJHCWE9W5QhviaKEy+WaRkAgQ5q6Igo1aiIIk5iPvveYv/ePfvizvmx+LnDvOCQJNUJ6mtbSGkdlHM4JauMItCCJNFpAL1R840t3ePeNEcvlgkeP5+zt77PK1hzcuE/eNPzj3/4xvX7IalUw7PV4+94+N44GnJycsFwW/IVf+TJf+sqXkDpCasHDh4/46Ccfs8ky5uuSH390iYw0y6zGIRjGAV56DJKwF3LreMTf+V//TR7cn/HR46dcl5aCmr1jQRKDJCJJY6RP+P3f+Rb/9d9/n2fPCxKp+Mp7+9x9e8jr700JtGLtrlDjgjCuqBeOzSd9/uF/9ZTTJy2z0YhlVbNZrPncnX1+9avv8aOPn1C6DfffvMlZec283WClxRlP6yyELV57wCO8ZW+i+TO/8Bqz6CYajY0tddIgRYV6epOgnHLrXsODd/8+mb7mvIDaxmSPfpnm7EvMF+dcFEv+u/d/i4vVhntHt/kb/9bf4K//xf/jv/a8vjLxeg9BIBECWuPx3hMGCoGgMjVOaFASKSV3j6Z87Z3bKAyPnj+jaSXQ4lxHWs53RBfEAd62WOPAye5rViJQeO+oq4bv/+YfgQiwviRUAq8U1niE8tt63eOdx3pw3uOlQCKwjUHKjgRRILcPCs56hJAEUkCsqKTBtx5hoVobSgdKS6TuflbRnSi8R4gXpOu715WgtcTajvQc4Mwfk52wDoTH1d3vx3W0LRCYjUVQI8Lu7xQLzjhw3QOEVyBTEAJkJBEOhOyOXSiLAIzxSOHxqjvE7eF1xP+qJ/bfANRNRRAmnJ5d8OX33uKTJydcXV1xfDDhvbfu8NGjz+j3E/qxopdMsDajbSLaBsIkJkkjNnnGxdkZ9+obnD6vqKsWh0MrAbbm6PYBYRzz+INnnGTnWNdy8+4+WbnBOMOnjx+zf3jMVId4PFZYiipDJxGfPLvgtXt36fcGKKWomgopJYFQKAd1URHFCca11I2hKEoCFaKkREqBtQ7vPVprzs5OUAKCQFPWFYmIGSQ9Aqmpy4bFeslitWQ2m3W/RwU0eJRpGSUJ11dzjPS8/sU3OL61xw//6PtkVYVtGjwD4jiibFuKPKeqGsbjEcYLLhcrxpMJWkuOD/cIw5AbR/dBO/rTPW7dPGC5WvHw4cfcee02t/CsioY8L/HW8OyzZ5SLkml/zLt/+vWf9SXzc4PWWOJYIYWldYai9kglGA9DNnmDsQ48qEDTOM8/+e5j4uA2x7OYKIr54QfPOJoMWJ094b0vvcOD/+AvMppMefLwIeN+yHQYsdxkrBYBSbrPe196h+ks5vFnT/jt3/0eP/zwHCUEdd2yzFtaJXGNQUhJEEjigwGvvzvli3/2AXtvjAmGgmV/yR+Zp0Tv9fHGEZJhwgAbGLLymgUVkoo3/nLIf/jNr5BvNjSVYLMoKBYhn8zPOZpZoniAQ7GuaoKpZzZc85eOQr7/z+H8fcd4qbg3PaZpK9RY89VffUA6axjsKRaVYpkFNGbJo2dXrHLffU4EBEohkUgcvjnl4O6GUi2Yl4JllaD8kHRSwmRClk45zd/jqszYyE+5yiLMeUS4ueDDZz/mN7/3fVxYYVpB2Hq+8YVvvNJ5fWXiRUjUtjEtpSBQqiM8C1rGKC1RynBnb8I3vvgaplpz98E9Xr+zx/UHcxqv6MpRgdIwuJ1Q5Q1+o5Fa4ISnbVu8AyUlSJDOUc436EmKCgOssaggQGqHUCCtQKMRwuO8BymI4pimqBCiI04BXYUrupsTKISUIB3eelQAhOBr8K57f0p15OusQyDwwuMsCDxCie6bfFf1WtuRsNIC78A50ZGrECgJgVZUtcU58HgQ2+pbAoHEa8CDw4GwiFCgIomMugPvKnjfEbeCcAjxQUhbeerC0+YO0XQlttse1w7/KqI4oWosWVbjnMcLSVUblqsVd2/tkYaKdVayygqydYYcjpkNUvJ1SRj0WFclhJrx/hTnInAxm2yJkoLpdAjC0Is1idJ88XNvUpsWoaCsSp589oz9/SlXV+fMrxfcv/eA/b0Je/t77B2s+eIX3uLhR59wfnHBzaNDvINIKvIiRypFvz9gtVqSra6ZTvfI65b5es1wNEQJR1vWRDoAPL6psMrjpUSrsKtMpSSIIgrnaI2hcSBlQLEpsc4QJyGb64yPLx9x42gfh+HysxM++8lDwkFAuj/k9NGCUEiS8QCNxuUFsi/JizkXF3MCFTAZDTBti0dy4/gQJIRRwsnFBW998YhkEBCEIzblMRaDFArXtkzGY7LrJamKMGVD/nRNE1c/60vm5wbGO5brmiiSKKVIYvDeE2jFZBRzvSxxvrunKKV47eYeX/7iW2hZMZkMCYKnXFwXZNcF4QdPmE5S7h8GzD4/Y39viKSlNjc5vrnPxdU1H3z4ff7wW2s2WcuP3z/h9LpFh5JeEjIaR/QtJPt9bn5uyBd/+Q53PzdGDzSVaHl0+oSmspy1Ck1N2Ew5uXrOYKjpx5JUJGRFQONrJNcY5wiUpK2X6FAiDwWjuz1i3cPZAKXBbix9d4eqPWHtCpKjhm/8dUlxYXn6I0m/OsRpxY0vCCa9AdJ5hLri5q3LrsNZJ0zcTf7Fd69IRuDChly0rA20XvG8XHIs16ghlGJCOLpD3W7I1RVGXrNpnvDxaZ/EHzFS73KgI+wk5MPzj/jt736HyyvD3pFAKwijkMa+GqW+MvEGWhEHGiUFjXFUTQteECjJbDjkaNrneJZw52DEdBxjWsWde2/w7/87KVn53/DBsw1WOBrnmb0+5PjdmIf/5AxBiBHgpUMIj3W2q/JUV2GDwFWWIIko1zlaKIaTPk44pICWFqssYAHwtu0IVwgQXfmntiRsrEXKF1WrRFQerUVXqcruIcL7rlp0rmPh7t8CL7YVJdvqWQic9TjnUQqk8Fi6alaI7gnFe4fzHikF3m0Jd8uLOpDISOCkR3gIAlCBxEtwzmINXaFtAdmRv3fgpedLX5/R34M8U1xfCJqN5On7cxZPi24Q8OJN7ADAk2dnzNcb9mZjvvHLXyVrK2YHh1jjaauCO8d7nF1kLBY5XkN/2COIFaqRxCokX69p3QYhPfP5gr3DA8pszXiYMNpLiEYCR0XQS7m6WBIisbVhPNzj9r3bVFXJeDzj/OKcDx9+wGp5yHg05t7dWxwdHmJsw/XpJXv7U5TQZFkOOCweJ0AiSKOUtqpp6oYASbHMcGVLFEXoCFQQkJclUkuEFEgp0YGmKEq8kERxTBBHjJMIFSdUZUlbORKpaI0jqwzPTs/pJQFRlPDBh8/54MmnvP25u7z7xTdI45haGY6PbvDJw0+4mF9Stw3OgRKgtaKXJkQ6Yp3nrIucvGg5P7/g6ScPuXP3Bkpqjg72qZqaum7wpmExn3P+/JwAGMZ9iiyjzdzP+pL5uUGShhR5S9U4eolGCIsH8qJmOOy6MUpI4lBTtxXHsxFJItlsajyeKA745PmGrLWsy2t+4996HeEEv/NPfsjr9w7pJ55Hn13yz390xmJt2Zv1Ud4SxhF5A0p3HcVQaX71z77HzS+NOP7FGe3Ism5yrurvkV0JrquGeb5hkL5N0awZhJLNco5jjfX7XGdr5maDsZa69lTNFZu6YW86IxBDRnpCICXLsgLnaCkJwgVR0OKaitGwhygVwkLdromPG9682WA3H9LnmFEcMgsMgdV4UeKMZHW+T5P1uSk0n4iPWHLKRrQYJVm0nrKxXD/WfLoy3H4t4vhOgK0zhHBIO6U2CmM8UTQkzwtW5x9h8pIk6KN6hnSgudfTELdY78jKJf/l3/1P+dt/+8//a8/rKxPve7fGvHb3Jt41XC/WGOtxTpAmEQd7Y24djkgij5KKuq0RCFbXlxyMYv7qN77Max+d8nS+ZN6suf/VfVaPryGHxrR477cNXYEU3U3DO4N1BiECAisQkURoSVWU4D3xOEErSRSHeOMo8w1FUeGMR+sAKSXWGbRWBEHXupaWl205azxaSbzxGNPNabUWmO1/4wVSdC1xyZZxhUcqCa576hTKIyTgBB6PkhIvePl+HIJmOwfr2uICqTwiEugR6KED3ZGxEHSVNUAL1oBw3c9427WXvPcUS8d3f/eCr/5aj+O7AeMjhW0SCCLKsqS+doD4V1re/6bj5tEROu5zfnHJ42cXfPcnHxH6kIN+n3ERs/fOlOEopaoqLIbpMGV42CN3KyyGNisIh4I4CKmqFYPDGb0DRWE3lPmaxGrSXkgyWaAGjqZRnD655PyHH7D/wT73X7vPF77wHm++9Sb5ZoP3AfP5JeV8zuHhMaPhkPNnFzTGk4SSss5JkwSx7apEUYLAE8cRUdgSBiFFXSGA8aCPEx6UIpEprbXUTYvyHqEj4vGAJE67a9AJTNOiY08vjGEsCMKQwMFb0yM2qyvWp58RhSleKpyL+PEPHpMtFvzlf/ebTG4csfEV6TBBnAq0kugw2n7eJcvFguFojFcSIQPm55fkeU6oPOvrK27cOMYLSRr3efLsjOWq4PTZBa5pSaMQGUi89aTRqzfi/qSj30tJY0uRlyAExtPd9wJFnjcIpTDG0JpOIzIcpMwvrri6WpBEIYNewK2jhNOFIatqLq4yRuMBeWG4WuT0kz1uHtwEd8GdmxOca1huGpIk4MHxjLgXogeCyRtT3vqVGcPXHJfVR8wXgvOrM7xZE0YBmREooaiqa6xZ4ZwjL0oG/R6SHOcFuXFk2Qq8o60ce7MbIDTOQ16vSLUlUFCZiGVRI0JNHEa09YLrtiVkSS8IGKX7VJUjUi2qN0eaFc1qxmX8nDgKCZUGqejd/pS49fjFmAdJxB8+EbT5EOcEhcmoLVSZpqwCrtee6Wifcb+PacC3Htc02LqgqnLYRKgqJKsbqhuS4R3LL35tzPpS8uj8kso6zss5v/3Df8TffoXz+spX+Dd/6QFpmmCMIXrzFmVZstkUJGmKVpJBP2CTrZmMp1zM50zHY2ybMz8viGLFG/fGBAPHO4czTp8tEAuFROFw2zYuKKWRSnVtVe+JhKJuDNK2xDLGphG1qSnKmjbxON8iclBeIIVHCoUXHfEIIZBSoJTsKlDhkV7gTEfwQnRPct6BFAqAIBAIbEe+zqGU6FrI25muQKCkpDUW6xxRGgENtvUv59d624o2xoOAYCRxQVcWqEigBwIZdcPe1tltVeuxLQjfVelCgA7B5tsK3Hdtai/B1rA+dXzrH2W8/tWQN744JRq0PPgSzG7u8aPf27D6rH4pzNoBolBzuVpQUPCPf/cPyTJLPzIQaNJhn48fnzAYjhlNRhjbEOkY7y1hKHF1gwgN0kIYhmih6CURsUiIoxREy7Cfcu/tETJcMB5WrOZ9wrfuEU6GfPD+Gd/6e/+Yf/p73+ZPf/VdXn/tHtYLwijBWct6ec3BpM/meIYTFislq7wkrypmsylRErMqSiKlqcoapRVhGNAIR+0MV3WBUAJhFdZYQt0JrsrS0JoNUZJQxQ1GStI04eDoEFSIF4rWQd001FVDqDUHd29S3jigWa+oWosUkk+fnnF5XvGb/+B3+fqv1vzC1/8UcdijajxXz07AS2Z7E67n12gFxjo2VcliuaEVHqkUpxdzJuMhN27fIcsrsk3F4irjo0fPePb8nGEcdtVypJmMUvqj6Gd9yfzcYLnMiOOAdNBjs85pGoeMJFGoGQw0RVVTecn1qqAXadZlzuml53A2IdCgCrh3PERHFQ8fL/ju+5/yzntvc+vOMc+ePmexzpDec2M65N7dA771vYfcmA75xc/fJRqAugf7X9lD7UfkLTxaPmGZP+FqYxAi5ObBF1EEjF2ndcibirz1XK+f41zDUN0mKzZYc02WtWilsc6hlMFbi7UtUSAxraSmxJiGuoa69ng3pjQblKhZZyti6VlhqQZrhnGMqyypnGHsjE3dEo8NS3dBWA5QOkG5lFbMMTdy1GCPqZpy+f2cu+N9llkLzuGcJ7TwzvExqWnJVydo12PgDxm5m8yS2/TSKbneMD+5IN5cUa8MYlLw4K2Q+WjG+8/P8MBwHPLlt999pfP6ysSrlaJtWjbZhv1ZwHCYEMcBddXiheXico5WChBM9w+piwJrPUkvobItF+dXHN+eMFQxTlh+fPEx9qVISeCcR25btNa2eGsZDlMm/RTjLIFWSKW4KhuE80itsLZF+u5JC2ER+G0r+V9tVXUtYomzBu9BK4EVCu9cN0umq3hBoAIJdDNZazsS93THqKWgbQ2mdbitgCkZpuTLopv5Ai/UTUKACGB6N6SJGxrbkaq1XavIWw+umyMjQEq2yu7u+1QgsMLjzYsWtXg5VzYZlMrzwbdqpGt456sHjAdw42iP4bjk0b+45KNvP33VU/snHrYvCAeaAT3mzxYEXnDz1ozXHxwx6qcMBgOMMeR5yfPTcx59esrMj2hygbSWOOyh24RJOKFYr2kyQ5T0yfJrju7CnTcUo0NJa4b0q5RhoPngjwryiyXf+JW3MeZzXJ8u+fjjD9kfa45v3ubicgGiRUnPbNCnPpyxWpVURcnBwQGNqbAC8rrAaU/pWkDQ1A21bYjjmFSEXF8vqesa2xpM09JP++Aa4rBHEihSZxlI2VUVVwuen5zTOkNWN5yuMnr9Pu98/j1EMCQaDTi4eUhbF4xOThA/+gl5VTM/v2SzKPjx7/8IJTS/8Ge/zptfeYfFwYzHP/mYtm3olIMSJSQYg9KaosyYTkZssobewFJbz/nlmqqsQSiks9yYjYijiKysUB6Ud0gvftaXzM8NBmlEVTUYFOPhgKwssNbQ1BYtBcNeQhQ46sqQ1Ybf+c7H3N9P+QtfG3FwsIdWklvHik9OPqYyjtOrDacnj8GtKOqMi7WlrjxBGPHk5IIkDkkSid8vGXw9JLyzT9wbsMivyWpBXoaUlSTRfW4c3Kd1FdiCts5xVpImx0gfoUdTjL+mKJcIv0a6htlwSlmW5E2D9CH9KECqhqZpaRqoa4HwiqrOKRqH8Q23DofUDpZliZGglKVcrzlfnzEOJkxkSiDPEGPLNLrBqD8mFDnCAHYfV+1R556wHfPWXkp0d4mqrpjpgIuqQEvBO58b8rlb+8yfrfns45aw1twYDZD9hNyccbhfEyUJSkYMkhFys0aYhEpFrPIVjXEEEXz+K7d5571XEwa+MvHGcUxVN1Q1SKmpq5Krqw23bt0A4SiLCmsdQlnKMidNIso6pzCejavZ399D1DAY9Li5P2FvEHO2WuGc7OaeorP/KCFwCBySq1VOEgVYbzFrC6EGLcF4hOuUyFp1ymrnO9LqKudOKSx8V/l2AitDa2zXQgakdDgpkBZG/ZQsr3DbdrEV9l9SMEu872xQnm1ruZNc0RQ1vUmIikI8Bu8s1rKdMYP0gvV1Q3BEZ8Ow3Rec6IxObB88nAUhBUp6TOsRqms7y0DAtg2+FSx3xFt1Ii8J/Oj3rrh8UvDuVw44urtkbxJw/Ovvsp6v/r/6gP9JRm5Lri42hE7QiyOiSBIHIGnR2jEaxrStpD9coy41lfEMRhOWp+dI5UiShCKrCHxGmeVcLS7QSrI3naKjOSIsMC7BOsso6XN+UiFsyHFyyEEzIOgr3vjihOArN2k3G+riKYGqEd5z6+ZdRBsShQGPH1+TZyWhVjQtWGPYXK9p25Z+kqK1wmnJpqpZZzlJEOKNYRDEVKZG9mNCHRCqmDSJCZTGWouWEu88s8GQjc5ZZRuGcYQ3ljxb893f/T2eXCyZDhO++KXPMT4+ZHpwwC9+8xscv3HB4w8+Zv70KWVV8ZM/+jHL6w1f+/U/x969GxR5ztmTM9LhAOEcZV6yXq0ZjaaIg5DHzx8zkymrdc6H73/EJ4+eAo5hf8y0FxNO+jRWsslrBqMBlprWtD/rS+bnBgKHEp71Omc8SblxNKE1DT2lyLKMKoc4iZmNExbrgk1h+eSs4PsPz7h7a0prSp6dXnA2LwCINGTrFfdu36AX9rheVzw7XRCmfT59fsnkIOTuX0g5/gt7tEHMqjFMR0OcXFPUS6BGqxmzyQRcQyRqvGrZtGuEnlEWc7yTuBaaWjDqjyirDV56smJD0zbkuWdv/wAneyiZIEWFNSsaU2OcpGmgbAyz2ZikNyNoNzRtyDCe4ZGcLy5RgUWHltBWmKbrms6zn3Bj74hpusfhaJ8oDNjTM7TdZ70W+EpxY2h5+vBbPP7shNcPUt64dczFE8N/9fefcPa0pKk9wxF8PKxxTcUwDbh1OGXSC7l1vM+9ew9Y1QHX4jkuUpiwRKpuhFjXBWtz8Urn9ZWJ9+hoxtnZObPpAOcscRLRmhXXixWHh2NmsxGbrKTMK+IwAOlY5jnWOZIoIRKa7338CbMvH3J0tMd7r9/g4ekCR9gJm4wD51+SUtdilVQGEALrFLbyiAhULHGtQxqBkgKJ7EhNdnZZJTtrkhed+Kn1FmvdC1E1SEGgO6MQAv4X/9Ov8nv/7EN+//uXQGcb8sKjtezmta4b0m7NUAgpkbKT8Vd5SZj0aFyFb1wnqHIepRRYT3Fh6fUD1LCbK7u2m936F8fywhdsu9a03La2vQcRCFzTPWDg/QsLNQgw5VbJ2BM8/7jk6vQz3vxywDu/cMxVfsKXf22XjfICZ59taCvLweGEYS8ijhUex9XlNU1VYwykvSFNY2iNY9KfEquQi/M5hzcmSNG1ntvGULUNZdMQqYCbt2+iRglFdY5ILUoAekMUxvSTkGHagwaqeUNbw+HrkvFgwOq85o3Xb+G9wVlBf5ZwfLNHkTdcet+JABFoFaAk9IOYQCucdWgZ0MoYGTuiXopwBmscWnlSHbJcb/BK00tjjDVsNhviKGTY7+NcZ6WIgoBQBzRVxcHtY1Z5yyYz5PM5P/jn3+bwaJ/jO7fQ0zGjg32+8mu/gKm+xNMPP+HTH/2YD7//kEefPOIbf+nP8uDBfRrhuHo+p1mvqJuGtvFURQ4i4PTsmrosEGKMLQx7gz5BGNBajxSaumlJeimv379B2WREfcXB7cnP+pL5uYGzhtZ6WuepyoZskWG8Ybg3oJcE+NJTlDnOOqbDhFVWkdWG7z4653g/oVjNqRp48/4+8+uMt+8eIKzBW88gTcjyimVWcGvU587nUt7+d/c4/PKIRkjmyzWehh8+/31CpfBonN2wKUpU3KOvDaHMMNYgnMMbhY4CsvWCJOjhmhpjAgQzqvI5m3pFWcM4PWbQOySWDutLyqqmqC3CBxgEddOQRCFIx9XmM6QzhHrIpixAJAgievGIullzUedoFRGJPkKkPFuuWeRXLFYnPNj/EofjIdL26aUhhZL044A7+m0uLz4gqqbcbz9P4gvWvQs26inWN2zWFZt1gcczTyIeX6yQdc3n7u/TH/WpfMOzT84YvyWIe4Y3P3/AxemKk+fXvP3u4pXO6ysTr7VbxsAyn19x/7U7HB6NyDc1q1WOUp7hsEfZ1JRNw3jQY39ygBKd4vfpyQVXV2uePj/n6HBEf5CiA4USHrb+qq7b+0I6LJGiqzbxEiUFAodtWvxWiUxjaZxFSoVSnf9VCt9Vnx6klOChaQ1CQBAG3UWiujaz85ZhP+Wv/eX3OJoK/uiHFxgkQaCw1qOUwJhOmSwk4LuK2PmtF9l5XGOJhyH0FJU1XXVtwDiP8B6Jorm0RD0Bgetex3ZBGTj+FfWxEF2wB3b753L7RfHCQ8z2C1213FZgHQQRlNbzw99vOH18yujAcfu1/que2j/xOHlyQRxobt3cR+G5eWPG3VsHSOk4u1ywWJfMrzIW1xnZasMbbx8RVJbXbs9Y5ZZCNEglKZuCaJiCh7wosFHA6/c+x6assWGAMw1aSKT3eNcSRD3qumUwHNKfafrjDbFUXF2smC8uiFPN/v6AtilQOmR2POD52Tk4yd64T2sdpnUgFV5Kkl4PvCNMYuL+EAc8f/oE6yy9NKE3HCDHfVxrqI1nEKcEjaGsaoSQpEkCzhHrAK0DtJDYumU6HPDg9hHV/oD96YhbR/skScx8teFH/+9/yrqu6B+MufX6A9771a9z+viEzz55xG/+N7/H4mtL3nrzAR+9f0kfzWDQp5+VnJw8ZbJ3RBQmnC8LGmc5mBiOx2N62+r9mg3WOQZpwngw5mqpiZOAer2reF/A+048FYaSurVUpnuAPz3PSWJJmEa4RrBelsTSMh6lZEXNxXXJ3/vdR3z19T1+9Rfv8vzkhFQJ7t2acn19zY+XG5q6RmjF9KjH6BcUX/tLr+EnEiliyianMVnXiXOdnsZ7Q0CPgXKEStKYNcgaYxyNE0Ta4tuCJJC0TYGgoW0a1nlJEI4ZJiNGUcZkcIixhqJZU1QLGlOhdESWlWyKBiUEw/6QcZLgRYKz11T5CkNDUddE8RBn+2Bq0DFBlGB8SGsrqqJiMLhH7kp+8OQ7vL2yTO0tlPYsqwDSQ2TsCKYN/rOaerXm7uGY128dcXvW53s//pTzVcE6b3AIqtxgrMU7z/ufXrH8L/87Du9MkAc1t949YnZ0TX+vx2JVICNJHA9f6by+MvGuNxvStEccg2laLi6WDEchSmiEkKjAUhQFi2VOOugz7I/p9VJwNXVTMOynfOmdB1xeX+F9y08efUZROpx0XftXC4zp5rTed0Ir6MhNiM7/qrzvFMSWznsrRBdc4brKthNVgbHdnNVvAw6Uktv+r0MKgWkNUik8AmMtRW742lfvsD9OeL6o6NTHAiG7ZIuO8zxSdbNY1zoEEhkojLGUec5gukdTF/iq6YjyRUiI8gQWVAM+lnjlcGbLqaZTLL/g3hdF8MsAjJeEv1VK/0t/vn2mgA3kzzxhTyBTQeEMd2+NaM93ApUX6CUJaS8giQNGg5hbN2aEIRgjCYOYfqqoq5rpRFC0JclQsFgsOdibcu/eTfKqpily+qOEJ0+esrhckoxSjo+OiQNJ2SqMkSivqEuDNV0Hp6YlCCTOWZyQWBwEihv3DvC+4HJ1wuXjAmE049FNju6/xul5nz/61ofcv32b0WhCrRx5WXO1WLBaL7l781anCm0aEJJQBWRZiaehrzSuqVFRiAklbhBzfPOIVAS06xXFaoGQ4Hz3oFs6x2qTczwYcnw8pW1TFueXuEkPLwyiLjicjFALyeZkyfef/C5vvfcGR/sTjm99hTTpc3l2yg++/0Nef+OYxafXOGsIQxjOUsp2DcLivOKTZ0s+e3bFvf0hn3/nTeI0JQ4Vx7MbKKVQCsbDHkJ0Y60dOlhrCcOA2jjK2iBExaAX0VpHuWmprkr6g4TeeESZ50TWMuyFNI1hkRsWhUcrxaCXMhhOOTgYU+YZJ2crJvtD3EHJn/urdwlv92hcQ1YuWBcVTnoapzEV7I00rWkoS0dbCg5nN1i3S6TUNC0YL0GFJPEEjOH0+lOkrmis4+q6YjieMRiMaSqBFmNMuaRuLEu7AuVwwpPnBZtNg3OCg4Mew8EAV1co1VAaT9l2M7wbsxsIZbm8yun37qCCSzyWWAtM7Wi96Kr4zXPCUJGvvs2B+ZT77V3G6QPaqODh/J9y8PqAORs++/gjDq5vMx4M+PLrE+4dDvnk+YqT64yffPKM+arEeYjCmMK2nOWwfrZhIiuCZEAkM7Tuo5VkNOlCbl4Fr0y8/V7a+WbbltGoz2pTE4VjmnrFYDDAYZHC8NlnZwwGY5y1KOGoTUOeFYRac3w0ZW9/RBQpPn72nCSMqLzAGLNVDnftUSn/OB1KbH1k2/gKhFRI7V7OY4NI4ulmqA6HEy9mvp3yWChFsLX54AVSaVprcAKCIKBuW37rn33Cv/fNPn/uCwf857/1GIskCAOs7Y4LuU298ltls5IY69CysyC1TUmdrVE6wPgage8qfQ1aQ6wlLne4nsfKbaiH3Fb4gNiSqHC8bH93SmaxDd3gZSxV9yPbH2yhWXt8Abb2sPSsLuEPP1ogvIT/06ue3T/ZGKURd1875v5rRxzujZmMpixXGWW5YpAkjAZD6rrl4cNPSUZ90tEeURyTRCF7szFSKk7Prpjujfnghx9z/vyCe2/dZjiIuLh+hIwUQStxtacgY70e4RzUdYm1kqapUf2IPaVQgaS/N8S7gP5BxHK9pCwK1sszypNr7tx9jR/+KOHT5894K+1hhSLsxYwUlKbiKlsxHYxw1jAcjjAWNtUV12fPeVPexRnHaBQwXy84++SE2Y1D3njvHcb3jtBnimpToMIISzcbbF3O2ckJeuvF1WHKH37vfSajFKk1Kkq4deeIujKcX5zj1zVlcU0rWq5CwetvvY4TnkoUDA73uHx6DhjiROIoODgO6K1DjHGsspxHZwsa95BbNw6IA8WgTZhfl0hvuHF0QGss63X2s75kfm6QpjHrLKdt3LbrJ2gby1ZxQi8JaZsW4T3T0ZCmKnCtY9yLWOU1RV5TlY792YTluuT99x/zxv07jMZDNtMLbv+VA9R4QNFUOG8pKgtyQlFtKOoNpi3QYUCkU1rfUhhLZSSKBiUstVVImdKaksXmirqe0/icoFFkWcX+wV0OJkfY9pK131BXCa2eUNoldV0iggDnLE1jKFvPdBTTSwTSGpJ0SFEvaG1IGAsG0RGhLFhurrAMWRWXtM05g6FA2oaBmKCsZrO+JvYRdVOT6RoRnFDbhum1YXP+iGLwDN2fcO9Le3zgLqivRiyXISzX7B/v86eP36RFsv+dHj/6yTPmq4J1VVG1jraxVKUnX0PbCgLZQ/geHoUTNUXzatqaVyZe5+xWTWcwzrLOC27riKPjA0xrOTm55tatY46Opl0mszVk2RpjW4yx2w9ewWAQE2jN67eP+cmTBc9WNVoHnXjJd+5XKWWXO4zbJi13KmUpu9Rlh0MYB9YgtNzOYBVSeBCqqxitQyqFVgolwTq3fZXOi+v8NkLMwX/9Oz/mb/6tt/kbf/2YH3604f3nK5w1XXKVlC9nrs51AR+d11jTtoYg1IRCUG9ydBQSBBonbafMFH7Lpo6gFYgWqhhQHrltVXu5JV77UtCM8F0ogd96cbssDf9yJKxlF01p1uBKgfDbUDAPovU408Uv7NDh+GjKjaMRN45m/NPf+QMG/RGjyYhyk3c3eSGom5bGOA4ejEl7mvy65vmTp9Q3DkniGIWkXhYcjva4Or1G4rFFjm3XDKYx5ZXDO08cBwgXMBgEPDs5J4xC+mmKqR29ZIgTDShNLz0ktwtGY8/eeEA+EqxXJavPzrh5MEVYTU9HCB1R5Dk4z43JHlaAV5LKOqrlEi8kYS+gNx1RCoh7CelwQmpgva7oO8nVh4/41FQcHO8zHPWo59fYxpJIiVeKvb0D6qbi8mqB9RZPQF554ljQ+JoobdisM+bnc/bHQ2ajHg6JHA7ILzcsllccHB9wfnYOTUsvGaKagL5SvP2nelzPof9Q8fwy4vRiyfWmpn5yxjANKeqWTdYgfMN0MqI/6GPDnT7hBVrTopREyc7eGEjxUowaaEUaKbK8pMgrvDUEWiDEi3uTYlM1fHqy5Hgi+OT5NWfXLe++/Rbz+Bm3/+oxPjUsVjlxEmGcRcuIWPZIUk2phkRJi8NytjynLApsnTBxIcYZAq+II0VRZARYcAJrC2pfUJZ9DqYHTKYjcDWr9QrrSoIowWJZbUpGo7ss1mdkpsRbzXQy5Gi2z6jnMKbCY1A6ZKAGmHZDUy8xhaOtDb3YMl9f0tSOONYEOmbZbIjaCG1rUjVFDTytdzQiZKFyNv0f0zd7pHqKY0Ul4O2vpHz8g4cklCRXxzx9cs359SddYeQtv/7VNzm/WPPw2RWfnl+R5xVVCTpV2FKQTFOqWgAarSWo5JXO6ysT7+nZBXGUopTmfL4gr7qZaRA41qsFg2HKcrVAKU+Wl1jXx3tFmPTwqrPqhL0BaRpzOZ8zHff52hfu8a0fP2FVObLabOeYnSVJCLddPqAQopvHvtD2Ku+RThCGMQ6DbS1C6G4eLBwIgVRy6+190XbuhFdSCqIwpG2alwsVzs6X5OUpb30x5D/8H3yd/83/5R+SWdfZnOxW6LRNmPKobcHZWZes6ea2WkmEbQiUwAqJMx2pxj1J0hN4JNVliz6AOujIlnBb/W5FU9sxMrdu98BYTj+oYJvC5V74eV1nRXIt6EQiwq71jRNI2amjveePy+kdePPNuxwd9vnRjx7x7e8/o6k+Jo1DlNbUVY2znijWEAgO3k6xbkNeCZL+kKdXFygEtjSkUUIUBdy6uc/tWzOW68fopKAtI4ajiNOTmrxwnD9fE9HHAVlRIBF4bVgtA9KhRKUOax29eEBZGEybgfJEaUTU8yyvr5kOBkgEYRCih4p8k0FVEylJ2u/jnGOT5xT1Nkc50MzrmkES0wsE0wf3SG7cIMCiZYC4vmKdlST9AWo0wGU1e/0+ZZGhHIS9HmmSUtQVOgi4nF8hIs1sOEF4QVNWHB3uI7xHKM3N+w/49PkpH/74I7Cej37yKcf7M6bDCecXF+gwpNg0BNKxP+7Bg0OUzFhebZgMY/YmI3qx5taNQ+rWI4TB42ka09n8dgBgldVoLRFKghS01hJphRJgTEvpDQiIonDbFPP0+gGbvKGqLIui4snlhijo89lFwXxj+Gj5CW/8xgQbJyAcjcmpN9coWRBpcLbF2Jq6laS9BNNs6EX7xNoTeIElw/gExZqiCsmNQQpL1SzJihwVhkyPbzDtz5AerF0QKo8nxirJyfkFMhqg9SGTfsTIz0FILAHee9b5upsXuxJDjJTdyK5pcsqyoRUB2eaSQI+5eXif1m5YbB5i65Z9E7Mvb6L1hLnN0UHDIlsSC4dlTaNr9oIRomrI3BU+GfHmF/d5El2x9C3J5U32J3tczC8IA8fxwZhJf8idG4d8cnLJdx895vnqgtWy4dH7J3zhm3fJyiWJkvTilGH/ziud11cm3idXc2b9CbcOJowGKVm5psg3tE2XULK3d0BVV4RhDEVBURbkWct0OqHf73E1XyB1QlFkaCVpvOfmwYhvJq/x7YcnPL4sae2L5CiLVC+EVRYhui1DndcXhOuISIjtDFgKAq07a45vadr2Zf/2ZfyjhFAFWO+QUiD1lvAELFc173/s+PU/M2Y61RzPJnx4Nu8i+14sNvDdMSC6ylxKQRhorHVIBM45Ai27zUJb+4+Q3ay4qW2XYOUlaunRE4+R4ISgN4bjGyFxT5NEgtWi4tatkPlnBeuPJV4LytrRGGi3fl+tBCroHgacBdFKJB7TdMfrtrPjHTq88ZXP0VQO/3TNn/nFL9JUNbEOmQ5TLq6XPD2b462noqQ1ls8en/P4Rxl37hxR6TXTGwfUpy1nl1eEWqG1R/iS5TLj1mCf5fMVfuaQoWN1ZbmYF0zTGCkiBr2U4SDByIZ8U3F43CNJHIGFKnNEuk+YgrYNAstnm0tm+3vgwYYCPUiQ1jKMAnrGUted0Euh2Ovts8g2+CRlWZbUTtJ6x5OzSzbLT8jyguF0zP0HD7jx4B57owF1W6H7fW68NSMMIspNzma1QnsPpmEfGCw3yKcJbVNS1pbWGJ6en/P2W/fIm5L9OGJytMfFasPd+/cpi5xgGXafVynIiorUw3g8IxaSNNAc7yXkZY9PH58wSgPuHs8QXtLv9aiulkjhaKoawVZIsQPQdbKKyhEECqUlxhgcnYZAyi4cyHpHgCeONHVTs17neNEJoKJA8fR8wbOzKzaVYTSLGfxiQzkuaJsrGltxviiIoyPSyOF9g7ElXnhCNcI0IdoLpnFL3cR452m9ojElxguEMSAd15sV1jh66Q3292ZYq9GixdqCtt1gfMY6NzSuZG8yZTa6S7H5jGVTkhcGFYeIKKanHZEaIEyL15br5RotIYkFgdIcTMeYYICXKUE4pCw25FnOND1GpwLd9jECxj4mtwaZeSqhKAU4FKYtUcoyNTfQ1tLqGhS8+eAuH/hPkBPB+OpNiirgxq0bCFmhEsfRoeH4jZTgIOG7jyviqSUML0n1OyzFkiRR5KsSacwrnddXz2Z794CV9wRRg1WC49GMs+ycVPcI4u5J2ZgWrTWHe2OEBCs0ddWQZxlChLS2QQqP8po4SBkPhl0sXmNo6xYvNXGa4BCYuqFuDJ2rdzvZfFHECYlU28UCdKQL2yUE/kUQh8Rts5I7kZXuWtXW0TQ1L1hZSIE1nu9+P+Iv/tptklHN5+8f8/DiEun++Jcqqbops+iymJXsxFfWQdNajO8eAJQC6wwOgW8F2arbYCSExGEwc0GUKO7/qR6ib7lxJ2E09vRiQxQ5mlaQzys++E3H4rGnN5IEkaCxIJRDavCqC9cQsjuD1nnaBlTQzZylkli/azW/wPXj5yzLls9/4W2gRSoIkUgDy6zgPedoWotThnjguLrMePC5msPJhKbKKNqa8WQMheTp0xPqpqK2SxZLj7Elcc8y/6xiuXSsn8eYOmZ8c9iluxVrmlrR2Ib5ieXtdyfdOkdvKdY1eWPYOxoyihtwGQc3U/KVxdQRztRsltc0rQHbbR8qigJrHM53lrhBL8KFis+eL/BKE2gFXjAIFJNJD6kF1dUl52VOsxzQLbLyFMuCdDKmPxwyPDrEK4kSEGnNyFpuv/c2tq7J10tsXfHg7SMGwx6np5cc3Dzi9Okzrq+v+PSzJ1ydXREqz439KZvVhrKsmYwGBIGmbgTnZy29kWAy6RElMU3bsF5vGI2GKCXpD/pkm4znJ3P6g5o4Cn7GV8zPD0a9iItFRdUYokgQaQnObWNpBdZt7Y9A03rqphu3pUnEMJVIWtZFhUEgpeK1rylu/pLjdJ0hZUtjK6wN0UGCVl2bWlAQ6pY0CKhMiTEGJRKkKCmsZFNdIlTAer3GmJowNETBiKJ1zMZjYlo2TUYtFHWVUdclZVtjrWN/bx/hLNpdICm5nJ8SJFOa0qOkp7Q1q2yDqRxKa9K4z94kwviMUCYoL9GyQgaSPF/jmpZeaCiqHCs8VWTRQcWlLzEuRzmJN1AZT1s19OOIRV6TVU85iiZ4f8Wqf4YQV9x8s8/TT08gLFFlzPc//ZTrLGc8kfzV//6A6YMK/wXDncuIfh+iqCDwl0QBDPuO52cbyurslc7rKxNv/uklZ03Fx6MevUmPwUgSek0v1Iz7Ka6pkFahXMQoDLEmx2watLFgFToUZFlDVdYcHU+5ur4mjHqEYYCSGik1xjmKbINQIWEcEasebVninUXIbRWJwHrXJeRIhdYa2Aq0nOuqTN8tJVBSbNvXovv+f9mRQ0e6Yjs4/c5315wWz0mPA/7ir3yOP/rkY57OSwwWKRRKKULdZT53wgbQQVdBNw14J6hbS09H2yAMByiaxtPWFtdpWsF5fCDpS0krGh7/KGOzblB4ekPJMBJsPjY8+xcGKz2FdN1mpZiXsme3VViLABCCOIA272xIXrjOidTuWs0vEIiIWaJR6w1COFbrNVXTEuqAKAwIEFRlSTwJKfOcxfmSSX9MVRfcmM0QgaA1MI3H9IcxP/7Jp1xcNLhG0zbn3HyQcnpRo+oxCQe4IKcuNvSSGGcjlqsNYRQzf9ry2cdr7t09YJPD6eOWq+UaLQMOjkIimbI33UfelZw8qZFSk/ZGzC/ntG3n1xXWs1mvQQX0+wOuFyuM99zamxL3+0ymI8JAE2mFcG03JlEROojo9foEQUh/MAAhUEGACjRl3llLVBB0fk0p0GGITUI+uTzn4QcP+ez5BUprPv/uO3z4k0d8/NFHLNc5+0eHNGVDmkY8ezbv9BrWcnJyQS+JEUqgQ8Wojbl5e8q7b93n/PQ5Vd2isg2DYR/vLf1BzIHc5/TkkioKf9aXzM8NAiXpJZq67TICtFZorWhaSxR1I7OybkmimEEYE+mA1hrSOCAKPMZKhJQkSuP7lq/9tSnLcs5i4/A+AiR5bfF5gepLFIYgsHgf0VjRfY8QOCxZXrLJHcNhH60CQvGAqr5mUTymKJcc7h3jKYnDAUXtaI3HSoWKBtgmYzab0hqHNV0C4mZdMRxGNK4lW5ccjSJMZahtTG80JIn7JGKDNS3GO+LgCqkjijbGWbcVnRmM63Iexv0JUkpKV+DlFciWZWYpa6hdy1EyYhjGlHZDbnM+W11yk5TWZUSDhqay3LwbcfrpHHc3IZEr9owhN45/+N0VxxvF7QdDbk330O2CkW/JfMVoOOaNBxlXTzb88Pcfwi/968/rKxPvV/YfcHp5xU/ef85pdcVFGgAthDCY9QkTidARk+GYG5MRoSxxtkG6kNl4xGa9pK7y7il3K35qmwYhFWXbYh2dHxfw1tDkFhFogl4PU5c4YzvVs3dopTohlhdIofHYzuazZValFBa7nXfa7YzUd5UGILZe3C7isttK9PjxiifLDXuve246x//uzSP+o7/zmOVSdSvMvMc5w78cIflic5HWEuE8WiuMtWilUbbBvlj+4OVLcZTSYFee7/7dFepAIPseAvCt4HLlaC48LgOVQjils1uZbr4rFaC7ObdU2xMjuhQrV4Atuk1JIhKYZtdqfoG2rOinEbYsusogr/n08VNUqEE7hqMRk8GQ7GpO/2jC2UenLMIVJyfPuXd3hkaxWFaUTcGtW/sc7M9YrXM2lWf+MOeTRxkSRT+qee2+xXnHYrVilWUY01JVDTbvIlQ3/zDj/D2HywLaTGGd48d/cEb5zoR7r0+JNKSJIgw8besx1jFIe9iwIS9rlIXZbErdtMxmE6xz1E2J0IqsKHny0SMW82vyTYFUil4v6R4uopAkjsjrFi8Es8kYFSgwjqooMU1DEIa0pvOiSymwraUoGtCSe7fuMx71aPKc68trDvdmRHFIVeTEoUKEIU9O55RlzXSYcDgdbbsuMacnF+yNH9BsNnz1c7d5Oh1wcnrNarlAz68YDXuY1tCLYw73p1xf71LXXsA5xyANiIwjL2qE8zhjcMbhjEGrbVZB01I3Nf0k6PQwziDYikidIUpC/tS/fcDkZo/zTcNkOCMOb6G8ZZhkGO9ZbeakUZfXoJSgKDck0T6BDGjaisbk3Dx4A608gpwogKt2xH7vbUy6IYkVWlTMV3OMUwRBj1B7Fus5SkbkrWSxXBEqh7QtUnZrD7MsR4UxSkEoLUIq+rFCkLNeXZPGmiACJ2LKxmKNJS/PkYGiFx8giAi1pSygLmqCSFGLmhCFzQHlSGON7IVc5iVlUxDgsWHLxgDrEWWxIk41l2VNGvaYb5ZM72t+6bZinXn+4Hst3/2u5TvvL7n/Rsn9uzFXraJdrZi99RZabYgLj/vk1cYkrx4ZqSVv3b3B0aQPKiEMFN7Dar0C0S1itk5is4aR7JKi+v0jjDNcXl4Amtfu3SbL19RVSaBDjDFcL9aUZY3wEikVzlmQ3UzVtA3QrfJz3iGlBtHFQXYN6BeRkBKUxnvbKYPx2/CNjuA7wa/rRkdbD6y1Fl5s8JGwWTdcXAakr+ckn9/wjnb8tb815u/+pzlVBa0xWDxKSKQA5z2maXDOEQZy+xu2+c7Od+1v6zDbVX6BECgtOwGU7/bolp96CEEF4KzDlXQkG4AYC3zoEa1Aeo/Q4LriHmUEwnqIunk3mSByEPQEpoWisKh0Nyd7gc1mhRS97pptYdLvcevGIT96+Cmm7cYjja1xgeZmPKaXJLQGgiihPxiQLzNs0zDsj7ieF5xdnuGcZzTqE4YhYZAwHfVJgpDVMqMfR4wGA6xUXF1fM7/eUDQtpvVEVxGffbLkztER09EILwzNosE6g28lvTTi4mRFmTcgNMp0GchIhdQhgdSM9/ZQSnXbqPKavKjINhuKTU4gBbf3D4huxp1n1zkC3d2Mh8MhRDFBEqPwPP30Kacnp51KX0hkUFGWXbJLfzRAC8lkPOzW/7UV+bqlKEr2ZyOsMaw3BWEgGPRS8qohjSPiMMS5miBU6Djk0ek1Z6dX3DyYMRpPyIua46Mpw8mUf/6H3+fRkxNuHc2YTsZdRY8g7e086C8gVSeaNFvNxipvEUIQBRKlFWmokMqxyho2ecl2dRqFbcAHaC1IA8XeVPHVvzTjKqsYD95E6xFFvUL4hlEvoqw1Mu3TmKc429KaCoTC+SWmvcDRJwzGRJFgvVnQWEtlShrr6cWHBI3CtXMKvyEJHTEJWbmgthVVbUjSPlV2CY2ldIpQg7USax239oYMxodkFTiribZrVtuqcxwIIYmjMav8mrquGPQ8o+EAvEQ4SALHer2iKjxCTVitc3SgCGkQ/ZB+3CcMtt1ALQmQeKMoW8eZ25AOp3B+zHefP2NZW6rNBrvxDBLBfBHy5beH/Movz/j+h0/48FnBD75T8fDDhuObAcfjOcHyhN5wwt03b/Bg8LlXOq+vTLxNW219tp6PPn7Eg/sPmAxTjl6/w8OHD4miEcPxACkgijWr1QrXNti24eR0zoMHbyBVF6WYZyVN60BJpBAc701Y1wuMty+VeUhBgMS0bbfPFo/zXTXZfY/r2sjedlnK1r3cw+u9f7kzV4lOlNU6A3SCLceLWMZu1ZpSEuEd85OQ22/NqXxD2aZ88zcU3/5nJU/ed2gtqRpwzhAo1aketiEZsutmY619mZbVPRB0lP/H+dF+u0zBo/B4C9WqUz9r2aVjeOWJpgKf+C4ozHdfJ6BL5zLbPxOCEEGbOdoLaCuPkP6lMtqInTL0BZxUrJYZUSgY9vo4PFGi+dxrt7uNUIHnutpgHZhlze2bx2yyhukoZTpMmJ8scE5iqpaidVSVwxrLZjPHOst4ENFLuyXkxjhMbbharDm9WmEMrNYV18sV0+GAIIxYZhWJviYMFEkSoUTA9UXDD9anKAWbomHQG6IF1Kpitr+PtYZJFLPcZGyyjPX1graqiaKYXhAQDQbMel1a2aA/IAgCpICqbkhHE9J+QlOWXC+uyK4vCYViFIUM79+lrreL57WiKCuqskRoTS+OkAKysqKX9CmrkvV6xSCOWF0vSIKAKI64rE03q+2FKCUJYk2/l7JalGw2zcv7hpaSojLM56c4HP1exN74DrGWzBcrkihm2B8SJ7u1gC/g3XY1pFbYWGOcxXtBEmlCAYGUJFpRa0nrPGVjiIPu7+9wOubO8ZhYew4/FzE66pGvcpSKcC5HywhHgbMVremWL0ipEbLFNA06HON8jZeautow6E1wNieOUtqyIZCe2axHU65otce5IVXVgrekUY2jwjrBZDxgmWVUrcH5bmxjrWcyHtFP+wgkobJU5pqiCpE6JBeO09M1072IkM594H3IZACx7jIYqrICZanrGuM8YWAp6ysCPAd7R6zLNRpJFEZokbPK1uA8vTBgkbXkxmFiz2mz5u39u3wznfAPvvUDLq4cZQPzlWd+VfP9713y4H7Dr/zaDfbuXvFHP1wxXzjOf1Dzcdxw/OwPiIIRlH2+91t/wG/8zX/9eX31ijfQVHWDVIqzyytKE/AL77zOdBoSxSGn51dYL9mbjroNQDpAKElrPIf7U5xrmF81hIFG64hiteiylG3L7cMpp4sNV0W7rRu3TOZBoLql3uGLlCf5Mt1KiE445XlBtOC960RQ0nfe3a01QW6/prXqNgR5j1YaLRTOelrvOb/IibVAGkMhpvTG5/yP/ldD/pP/aEG51Agk1slt+xqkkrRNJ+cXsPXudmRstjJ/4V8Q8zYMg84ShAChumLG+s6z6xREY4kcvNjnC4Rbj27QvV+lBIQCWkE597hV9/cSKIkxDuu2TqLqlT/bf+LRxDAWI9q65bryiDqj34vxMZydX9O0DUY0vPPOG+TrNc+ePePkdM7Noxk39vvsT/f4ZPUEVKcYd63F2IYgCAlQCA+2aFmvN0wmI3ppSn6+wAtNXmToMKR1krxsCbQhCENWWcZBOyTUmqquGY9HCAuSgHG/R5DEeOuIeylOCkxruXj8mLpuiaKIfhSj0n6X66w6vUVZFtvqthPfGO8Y7HWV5uXz57RFCU3D3nCERtG0Bocjkl2Cm0pTZgdHXF6ec3l5iRaeMAyxtmU06NGsuvfctJ2X33nHsDfGjHo4b1FagdJkZYY1hjAMSKMAXERWtDx9fordBuYoCWkgcKbB64TBaMxmseyy13eq5pfIGtvtVpYCpOLgYIDE4m03hqi8p24do8EAHQYIJdislsyGKb/2Cw+4f3NAWWe4dytan7E/vEHZ5gg0oYq4LjKMcQjR4n1BGI1QUjNIx2xqgdKKogqxVhDFEdZsyIqavKxI0gFlaWiqFSBpbZ+mDaiamqIqutzz0QhPjcbhjCOIJP2eZJjsI0Jo64pBrEDEeDSbdc5okkBW8s7915gdjPE02DYjaktCKqzL8DYnDDR51bBaNVxvNkjrQceM+wm+avBWY3yJaVZgGuq2W8Pa1gLvBMNBiooCnHQ8zB/x7791m//tO2P+H/9gzT//YUtpBKvG0zjBt7+74uNPcr75bx/wq78S8eRJwfsPGzarhrPnllCsKBZrlq+44OOVidcLQZwOSHsxb7y+4Pe+/Qlv3buNMS2T0YhHj8+pPrMkYchwEJJnGWl/SBzFRFFIXbe0reueVNoWkNy6eQMlPOerE4RUKOXx3r6cjYot/Vjvt63sFu/ty4hj57oqV0rVXZgCjG0wznVCq84E2ymKXRdm8eJDLZUmDMNthWpASjbrlspoAloiIqy4zTtfPeff+Vsx/6//++U2PAO871rf1hqE7ObHwnfELwSd79N28VNaCpRSnRhqWwk7um1HWgjSWOLddrnD0KHHvls56Luz8zLdSoEUAtEK2rXHLBy2FEjXLU9wskvyar1DeIij3c3rBQ4Pb7E+OScvckazfUSoqUxNnEQcHczIi5LaGZbLJXGckEQhYRQznc0Y9geU/Yb3Pvc6URDw5PkFvUAjtaCXRNuOSsvBZECIIkJinSMd9lCrkvUmJ9SSJIpZ5zlhGOBx9NKA+WJN1Vr2xyP2x2PWWc4mL+n1JcMkwTrPcn7N8uKC0XiEFl16ShKFZFmOAUzbUq/rLhYzignDCONakn5KOhixXi+5fPKEuqgoqpI4jgi0xlpP0zZ44XDWopWiLguc9/SSFDMecbg/oyprGmNYZRuKvKKuWy6bFVm2ZthLyLMCgSOOI8I44enzM1bFhnt372DbijTWVLXm0dNLFusNN/YmDAYpFlBC0Ov3aduWXtqjNX3q1tAUu6fGF2ga043aGk8YarrgUYG1liQKsVvtS5rEaOk5mKZkMaTSMo4NbbGkNjnD4ymVhdJW1DYlCCxNddF1CC20PsB6T5r0wTlMa5GNYVnMycsNNw/vIqip2gWrrKXf2yMNYsr6gmEvpjWap0+umO0dM4hHbKo5OjQ41+Bc3aX8KUkQKiZpimlaQiUQQYgOhszn5zSV5o3XP8f18orPnq4YTvbJyxznCqpiQRTDumnRsqKoG5oaitIjbcws7hGoGCkjVnVF4QucErQuQ7oWb6CuAenRypD0RVfohIpQe4pmzfdOB/ylL/f4D/5nglu/E/CP//Gc0+uWTQ3Ke8oTw//zPzvhc18c8vU/f5tf/+UxP/reBY8ePWVjLFdXDq9fzU3yysRblC2jSchgOOKdt9/kelFgTM587ojimHffusdyXfHs+QmHB9OuMkTANu0J37V9izwjCjVFvma1WBHHIf0k3uYRs53zNngnX5KOc6B91/fvWi8v2rjbXQJSbsvILmZSCYXbCpqkpAvh6D7qL99PFAYoKRFCoIQG6Qg1NBak8hi3oPUzpE557a0RSl7SqaO7+bKSCmtMN09GdtYieKmi1roL2vDbysBY93JNsA4koe6U1950W5PkGII9sKITe3kFQndkHSFQRlCuPfXS4csXrWm/DQHpXgvlCUKB8J7k1QJU/o3AKO4h+z0GgcZLgwgjgmhMsV4T9zVqmGJRSNEQx5o3332D6a2c4XhI0XoGx8ekQYD2BqlAe80q2+CcJY1TrLEMe50dI9I9VnnOMO4TqgVB0D09eUyXj1xV9HspQgZcXm+Ikx7OeVbrFV5obhweYZzl8uQUYyzeWIb9HqY2pMMRy+slRV7gbEtdVvSTmOn+PqZt0FohASMEcZqwurrC1BXDXg/vHVKrrv1cVt2+aWMRWlDUNUkcMhmOWC7XONGtyuzFIYEKuLy6pt8bYL1gk1fcv3+Hjz96hJSKLMvxWyGjkooojHDrJWkSk68LEim5d3RAUVUob4nilGWWEYYx1jvCwHdOAeMpi7pb7F6WP+tL5ucGznUdgPbFooSyYVPWRGFIHASkcQAovKvY6w947/aEb33vkoPb+4TaUDaOVlvUOAJVU5s5i/Wco9kEJw3Sh6Ad2BDlA/A9XLsg0D20gp6PSGcW71vW+QXGNcS9lKtVgZhsqNsVjSlZLT0H+4ckUctitYbggNBvECxepg/2YkkYdR1DhMG2MW2bs2aB0IrZ3h5VM+fxp8/JN5Y831BJg22WpIEky2vqtiSQLUp2r+Vo0cqipcZZx9X1Kc5pRDDC1jVVYxDC0ktDUiVJVUQiIxqZIWNF2kspq4wg9Hy4POP1y7v0e4KvfFlwtD/jt//pNd/7YUtWdXn5poXv/P6aj37ykDfeOqa213hnuLpyzDeO+BXNJK9MvFE0oPEOpCcKJK/fPcDLiEefPOPdd97ixvE+o2HF2fk1xjqGwxFSOHSg8UiytkAHIZvFiv2DMXduHfD89Iy96ZhRGpMEkrTtWl5eqa1GwL/MKhay+2/n/NY43s1pX1iCumUJDutfBh1vFcVslxt0imPntgIpukgwIfU2H1mQJpq+9qA6AtZ42lZzdDvgxv2ITx/W3ev5rbFJdCsJt0XnNmyjI/sultKB8NttRt08WwDGOFosfhsEInsQ74HVAuG2Py9lJ7QqPZTga4HLHJjuYUYE2//1nS8zCCRmG7spBQRyNyd7gaoosd5SO4fUAbI12HqFCgMaC3ldYVxJGDjWCwteMpylOOPZm+7R78VUmw1BW2GHfSQBxwczgkBjGkvbGsqqoG0bTs8u6fX60FoiKRmmCU1jscLS703AWOJAEeqAII4oyoIyCSmrhqZpGLzeRylJFASwva50oPHO09Qtw/GEtq5YLxuqsmIYxV1UYNDliz8/vWC5WZOtc3pRZ+fJvSeKYgaDiKrMKYuiS4jCIbzEStiUBb20D9vhTSRDtNA46alaS08KBuMR+14wGA548OYDzk/PKNcZHksQBDjTMu6nNM2QQMJsNqFYl1hn0TIkigIWqzWNaZlOEzZZwXQ0QmlNVtTY1iN9JyLboYNUkjDo3B6yS6hHa41SCuc68WigIQ0kw0RS5GuKomC9XrMpepTGYw4tI92yLnOWqxWBnlGWGaVtiMKbRKEnkTGL1ZKqnJOGAomjba+wrs8wSGntNaGeEBJRNZfktSXMBqTxCCEco1GM9JJsvcA7T5nNGc7ibt2rUPR64BJBawzeWIpKYO2CQAvi0GM9zBeXZOsKiUIAgQ8wRUlhcuq6Cwfy0jEbxkzHEZvNhkCFqEBSlUva2nejFivJyg374wGj3hi0wNgK6QJ83dCsFH4yoLEWXda0FqwNSEchl01OmlxjrWV2kPJX/nuau6/H/PZvVpydtbTK4x2sVobvfvspyQB6I4XZQD8QtO2ruUle+e782ekp7/6ZXyTLF6yullxezcmqiHvHByAMTVvTNA3ewfHxHkjIsxVCK4QQ9Hs9irJmNBrQNgaB4s7tW6zXK66uN7x164AgDDi9XPDR+Yqidd3cSQoIFdZbVBDgbPtSXCVlFxYhfEfOUvzxur8Xnt1uuUG34UgJQWsseiuo6iI4LC927cZaMdYVToU4J9lsDHFyzOjWOf/j/+UD/q//8Qcs136ranZ4KRCu26yklIbtLlWcx1mPUB6E7DKfRff/ve22bHjXfYx0D9Kjru3hKqCBwAqa0uEbSKRioEPWdYPdCqukANt05imhtw8e1mEMKN1tTLqa71arvcBk2OP5askmy8mzC0ZxTCAhimLqpub6eokTluG+JhAR2bUhcZZykbFKM9KjPexmgw4C2sZwuH/Qtf9si1eeOOoxGo9ZrVbY2OCdQQcBNw72KYuyixlV3Z7o+fmcvckUhyCrcvK8IY8aZnvTThxI51VPggjbtqTDHkkc4dFYuujGoixwW+V80TQIpaA1JHGE8NCLuvV/QkqqqqIsStLU4q1FOof0kCYxWZ7hnCdNUs7OTulFm06RHwY0Td25CkRAoGO+9Yff4/jGMbdu3qCuawSuW90ZBPTTAUWR008TwjAmjROydUEYxoSBpDGOQa9P3dR417I37RPGIYsFFEXZWeKExnlPY7apczsAIH0nmOz1I9q2pTWWUHUhGnlVYY0gDiXDqM+N/SGTHnzh9WOqtuDZ6YLzvOarv/oWubW03jMZTYGAoqhp3T5R3COODHWZI7Gs1xv0eERjnjEYdb72ptGYNoR4idIZaQK9ADApUThkk53jXUFb12yyTonc62m0kKBm6MRg6gWtLXEW8rxik2n29kLaqmaz9JxfOHRY0e8FnWPEGxarM472p/QnRyidIWUAwiO9xzQZ3lniIKZqoCw9+aqlaTqHQC/WRJHCtN1inTR2eGtQPYmcVMggpHUhgewjvEFEBu1bTgrLwSgF0bKuI9a5oB8O+fqfEnz8acaPf3RJiac10DTgFoK6sIhIECSCVw0MfPUlCToimST85AffQbmIg7193HnB22++xtXVM87OlwgiEJLlekkUx1hvOHl6Tq8/YjxK8Yht663LXdZKdbMJofgrf/6rbFYLfucPSh6eXW+X1guEApTAtgYhNEHc3ZC6FnI36+icrYDvqswwDLDWbaMj2S416MhSKUGgVCfGYjufxYMVSJMQCfB+w1D3aFVGY1ICseLr33CM/vf3+D/8x4+Yrzyt6VrHSqrtHHmrZBYCvMS0pmvlbTcbIbo4SfVCUU1XjSsZUF0ZbOtxdbfVSIhu7KyFoD8MiLSGyiC87cRVeIwFpQVSCtrW4WzXbbfGbR8jdj7eF6jrijrL6AcBIo6w21JStg3eGoxpMd6yLipu7vfpDUfcPDrik+9/inOOy9NTZtMpVd1tUHl+fob0jjAMiaKYKIwItSIOA1QUEEcxdWMIooDwwT28F5RViZCCG7Mp0/GE5XrNB59+2j2YeY+zBh2Enb8cT5LE6KC7ni8vrxmOp0ilqIsCLQWLoqKuO694VpRE2jObjDjYm5GXxdZ+J0mTlM1mg1IKIWQXBRiGL0cUOI9WokuRo/OEB0FAYwVZnhPHPR7cvs311ZrTz57T0zAdjbBtixaS0nWaAiUDVqsN6UBytVxzlIQkCsIwJCtK1DaoJo5S6rIbJRnnuV4uuXXjEB1EZGVN0dTdWGYHAPppgHVdEaK16u6f0pPlNVlpCMc9vJBcXi8J1S1eu7OH84bf+84FT65ywllAdCfCesUwTvBedORUV1SlQYqWtmmJhGSc9kmGU5blmijpUxTXOGUxbU4op0ThGCeK7l7lQ9ZZzXDUEmpH03iQLWkaYhpHFNZsyjlRfNAVN8rgHLStJy8Ezinwhrq1NE2niRn1uzCNwlhE6KjbAkmfXhB21jgqjKsARWsbnJdsNgV1rfA2YW80wPqayoVEUiKDGaqtiLyjNS3CdQ7SMDI4FKFUDNMheb2kbASLqw3NQPP9Z4pBIFgsSy7PYf5kg2s9pW3oTSSq9JQbT+Ogqj2qAV2AsZ2l81XwysR79627lIs5URjT1oI4DHj93oCmqbBWUhSd8Xo0GmCtIen1qK8LrBPkeUUQeKJ4iNKditg2Bk9LkkQMeiHSG0ajAW/dPeL5VcbDizW1degooLEW4aGqKoIwwm2tRC8IrCuAuw9rt3vX43w3fxV0lQbSI50jSWJe6Is711LXthayM2Ob6pQkcXijiEVEayJUcAczr/j8e5L/4f/c8X/7Tz6kfWEdkmwdxd0MWkmFMW1nvXQS70VXnXeBzyRRRFk33bFbT75o0cHWR4lDKtEJwzpRNxeLCmurbpatBUp1SxCE8xjjXyq8PeLlOkUpd6lV/zI+e3ZCU2YI280snOvOfygFcRQxHQ9pheJ5/pw7g5TR4ZDz8yWBjhn1U2LpCXVEVlga62nqkjSOOL++ZtQfMB4OKaqS9Wbd5XWLbr+v8xaFI4piQiVobRc4I6Tj6GBKVubkWcbB4R7e2k7LEIR4a9iUXdqVJ6B2sF5vCHSA2iYXjUdjTs/Pcc5R1hXSB+RFRZT08VKyXi3RsSJMom6RgrdEcURrWpq8RQoYj0e41tAaw6jXwzrXzd9wmNYQypAkiUAo3rh3gzhwYBpiKYjjGKlCqrIkK3KqumHQ74IwoiCkyAtMaygaS1VbWmfo9/uMRwNsa0iSmJaA1WKOUgrTGoQz9PopV4vNz/qS+bmB0mDqrsWMEEjRBfloBb1YM+wFRMrT1JKz+TWff/2AmwdTXrtzwO+9f8LX/8JtbK9C6X3S0JHXNVKNGQ5HtO0lsdRYGxP1JPtpyHy1IK9WCCuIE4ktDXEsKPJrKEcESYD1gtZXZJVhfh0yGaRovaFqK6pWMeon5OscrRNsu2AwivE+BtnihXwZPOS9w1uJUNBgabxFKNs5UYTq1m0mFuVLwlhgbIDyLXW7AW/wDpJUE8UaKWMWFyVF1ZDVFYM0oD9qMKom0QHWRgjTVTVNmxPFEutLlsUziqqiqhxZViO05dIL2qrP97+14fm8pa48GkcUQm+giSNBHDiqjSDzltY42hb0tcfWr3ZeX5l4D24krD69oB8muBC8a5F6uyNXCKIgYZNlmNRgTM1qvSAKEu7duY3zhuVyhbE5/f0Zm3VOay3jXg9oOTqasF7m6FDzxuf/DKeLgmfXK4ySKCWQvgtO18K9DDx4QTBKdoEWL5TMUopuheE2TAMExjuwnSJZvvD6iu3Tvu7WCPb7IQ/ejcldi8gecP3BmNnrhoKM67wgXxmSUczXfiPlrL3Pf/Z/foQzW+W14OWuTCkgCkOk6dK4msYi8IRKYL2naeuXm4aEVJ1X11lQshN7bfOg7VY45Vz32lpKamNxsksVEtJj225FoFKdpUgIUF3p36midwDoRDz9hOV8SRqlTIZJtzZSKuIopG5bri4X1LWj8o7DJOYH7z9krPtMBwlhHLGpGuIkYp8htrE4AZdPnpJEEavNiuV6g7WOyAXMV6dESjMY9AGNUkEnOvKWZbbm6npBHEaMB32mkxFSCtaLgjSOKcuiGxu0LXEyQIcR18tHDHoJB7N9giDCe8e4nxLIQ6q6ZpD28NZytdjgFxn7symDfp+2bnDC0R/02ayWHOxNUDqkqWqqstgeE8D2s6MkZVEhhSBS3d7pqiyIe0PCOMThySvDKi+IwwApJVEQ4qUkTRLW6zXLZslkNqVuW9arDb3+kLYKCMKAyXjEcNDrYik7Px5NnmGMJdtU5HmOUxF5VvyMr5ifH1jjt8FBnZPixf5PKaCXKHqhYBxH6Fgx60WcnV2gAkPTlEzGEV/4c2+wLlZIfY1WgsbWFLVE+IQgCdjkGSoYUoaSZXsJwTVJr2SVByg9oG4vaF2JEQ3nV2ums4C6buj1umKrMQV5BXFsiCKYr1pkoshKQ6Itsz2NEDnet9imK5Ks9WRZQ5IKGgODoQK6hMHJQNFud92GytO2BXujlJaCIFLYpiEMNW3riGKNko48M2yWGy6vq+0YzzPrjWhNSelyauOwjUfiiKMI5QRNuSZKLHmxoa0EroQ4VPSTEHSKoabXT2mebvDOYoMu6z9UICNBPwzohzPmWUEW1ixEQWktLn+18/rKxFvPr6izkkAnXUSjkBT/H/b+5FezbE3vw36r2f3++tNFHxnZ3D6rb9mUWKJAS6RJirJsWgI9M0yPDMMjD+yJrT9BMGzAAwLygIAbUaYkEhJJiTRZxWrvrdvmvdlEZDSn/9rdrr0aD/aJKw/TQgFVIPMFAgjkICLy7P1971rv+zzPr2lpU00/9EznMwKCQ9VxfDKlOeyJ8gV93zKZFCyXc7b7mv1+j3MDeZ4ym2Ws1y1usEgRIfBEoePJ/SXzH2lm8xmXlzdEcTqOCiREKJTSBOHB3hF2QxhhCwKcd3jnEF7eZYyOLypB4IRncI5Iqzur0Z3vVwp+4dcesLi3xaPoupy4/hr73+8ojze4BzeoR68w8grnc37jrxV857dn/Mv/9kCsohE9yCiECQEkAaUVwkkG6Sm0ZJ7H3B768SUIARHGsR9Cjju6u70xPuCDQMeSJNX07cA8S1BKc7GufuoF5s4frLX4qbhLqRH4wFsv75cFgPc9z56+i/CfMTSGSCusDygt6UyPRPDs0WOOho55NKO/6nnn7CFFmpDFMfumR0cZtm3QUlIPoy1nsTri4xcv2R8OFGnG8dExURQxDFtiHdHULbNJiZaKLI0x1pHGGZFK8d7T+4Hj4xNM3xOrmJvba5o3b5gVU2azCVII6upApDXGWA77PbPZFB8CeZ6PQS46xlhLnGUIAm1v2Gy2FFlGEscYZ4lVTJ4XvDp/w+nqhCQabShdPyDUqE+QYUxjkzri0HZkWUY/OPp+wMuYuu2w3mOt5fp2M0ZKBkHdtDw8O2I5n3Ob5ZxfXpCkMd550jhBKcWuqjlezFlOJySxRipF23XMpwXrNKWuahCSwXiG0BDpL3e8b6vpxwQzzHCXujeutbRSJFrB0BNngl/98ClF7umbPe+++5Cb2w1qmXH8pKQfOvaNZVe9IUs1Wkfs6po00mTZQNXecujv4YQlOEdvDb2vCc3AJJfsDi1KxizznGV0TOpPaek4eRxTxgqpB0QYMH1PeU+idUI8O2FWKgJ74iglkRlX2xuu96/xhSIXlonOiGYWne05WyryImCHgIoFXjjwilTHRFGFFwO7eozFlFJheoHSjqZx7G4Uh0OHUuN3YKw00yzmcndJUkzxWpGljigqsSIgZYqUBjMYtLMIG4h1SR8Gum4gjgxDvOeDb57wycctjQ0EEeju9DsFMc/yM37+wa/Q3A+YoePKbPmHP/ot1u0Xu/J+cUjCdUsIgiRNsabBeU8Sa/quQ0iFs4F3nj3j6voaKdz4pdZVRHE0CqOk5Pb6lgcPTlktp9zebLm6GgEEk1nJZnfLYrHA1BdEwvGt9++z/OAJf/f//k/QaYSVdgzSUBFejAEd1plRnXz3y9+Nb0MQhLtx69tR8mg3GnevI81j/PPEnc3pl37jHrps2JmGibulXb9CDQXlZEcGDGKJ6R1WFJzkkt/8+Yjf+6ffuVNdv1VW+58SlERgtHeYMXwki2MEBhCj53eECRLdMXSdc3DH0tVqjIETYtyfHVpDcD0KwV1c9d0IXeAcDO7toWP8OSg97rW/rLE2uy2fvXjO4wenDE2P6Szb/RZX13jvmOU5KvKUcYzZdqRxRhkrrOk439Vk+YRESOIso+t6siwjjWMenJ4RkDAYJkWBCA4ZNKm6W0EICXq0tQ2D5WqzvosyDWgl0Ug2l9d4IMpSBusJCPZtQ5omeDvGpT44PcG7gHUDm/2eru24f+9s3O9bQ9v39IPhZLEk1hHbuub8+oaToyVFUbDb7MZ3MUi2mx0CSdt1SAmJTtBSk+QZTdMQKUGIRv9oEkNnLfurKyBiMV9Q7XfMJiVV3bCvGwgO7yyD6UljxZOH97HOUQ0V80lBiHNUnDH4gEPgvGcYDEoqhhDoTEc/jLeUSTGSi3Rc/km/Mn9qyriRj5tnCc46bGA8cAFH84KffTTjZCIx/ZrgAo8fLMjLlDY4yndK+qgbv78Sw34vmObHSGWom4Y+SjheJMSsuVx/xKLM0Sikusc0huNpQZFKMtERJRlFdkrbLrGyZzLbUMie3hmEjug7STl9Q5ICYcIQEsp5SVN7OiPoiVFRzGxyzPHsIXm8ZF6s0OrHPL/5Massp0wnCAouss/J0gVlfiBWnjiGq+qSvjEMPjAMDuciVOKotoGq8ZRzUHLMZyj6FL/RFPOcYiEQMsVZCUKTSI81AefMSHRzMb3v8KJCCkEcIvJ0QpRb5nPJ8jilueiJpSCPApmNeKwe8+sPf43h4MlVx2oy5yQ64vub79Fs/5gb7/XNhjgqmM8UaZHQmYGAwA0e7wW9abi9vcCYHtNDHKWjGtOPd04hBe+/95gszXHOE8Updd1SFMWodOsGZN2xrizbyvCtb3yVzy4uSUTE0UnJ66bGyDGnWYSA9x6l5WipEeIuQ1ngUbi38ZB43nbCcSc8jmnv/kUjlEEJju9lPPsKtH2g9zHl1OBPX3BvNmfyfsW+l5ghQcczvOuYJy0fLFbMdMThrtkK7kbAwRN8wPoxnzmPFJkWDMMwXlHl3c5ZwNEkw1iHCR7nBEIpBjMgwkhWUoypG2YY/0wpBN6No5ox/Sr89OYr9JhcJQA8CPVl431bL55fcC5vGKqGSV6QlQvy+YSugkQpjBuI0sDRoxX7dcM//se/i/SCo+MjFuWEWHfoLCPSKUEIzB1dZT6ZcLpa0LQth6ZHxyllkpAkPbum5Xa74/zqmq4zvPfsHdq+RxFAaZohkCcZwXsWqwXrzQYbAte73RiLCpwsl8QqokgSnHO0Zgxv6emoqpoiL8iyHKE0N+sNG7XHOwdKoCLNy/ML5kXJarlEKYmzlqauEEIyDBZrLXEUj9OVLGWwlr4+kCQxph+QSPIkpbcOIWOsTQjFhCxLsM7Tm54kyTnUNUWWcahqJmWJDJBl6Sh8tANZnnJ2/ww3WHrrabqG2XyCkhlFmZImOTc3a7qux7lAO3zBRdm/BhUp/dMo3bcktdGpIdjuGw5dyvuPFuAVTdOw3bcMquK8EpycTmldjw8diapZlCmHQ02cC+azwPWm4fllwNmK+fQxJ9P3UdLgQ4MXGwbv2NSSi3XMkycTnPBc7j4hSRYsywg7NDirsNZStw6djs2QUOMHc0dT6+mt4er2Y548nBFUDK5CRBkvdx+RJqN6eFUW6GTFzbZjdvQzpEFyvGow/YEwCI6iiNWR4tX6nLVpyaeam3VgGCAvBcVk9JHvrw2CCWZyYLIQxInCuQ6tJdZJrKuJopg0zjk0ijDEJEpjfTMebkWHSDKElvgYjk8111eCOMSUveS98hEfrj4kcgm39RUKT5zl3NzuGUIgpH/MdqKbmw1ts2GxWhISM8IA7oIqTD+gY8Vms2W73TKbzUnigjxPORwa3OABj3ce0x9QkUKI0Ye2qw7cHLbM75/xn/z932IwHhdgtZqwKBJmk4xf/4X3OM9j9puBj6/WNIOndx4VRwRj7hamb7mUY4rUKDCSDHZMEpHiLjlFSxrTjWpmKYm04ud++QFJcQNyRsKSPrQk33xDnr1hYEokT2HI6fsDCEdtU548eMBXH57y7Zfno7tOjqIo7tTYMI6As0Rzupiw3lZ47+5YvpJprvkrf+FDfu97z7na17Tt20izO2GVAy/uwgXu/MojIekupEPeeZhH2QXOj4roKBJYG3D+yyXv2zqaz2mbjqbvQUA9OIoioZhOsG4UfKweHoOy/PhHP+FQdzx48JA4KfDWjT7TtmG72ZJlKdOiGAV9wTErM/q+pZzPmS/mlEWOt5beOh6cnlB3LV4qPJ5JlpBFEb339EjKyQznR/V9pBWP7t9jUk6QUjFJYrwYVZhtb/A4euvI8oKynHA47HFuZJYWSmLKAikFNgiq6kAWJ8yKCZPJGJ7hPSO4Q0qUUmRKUdUOOwzYKMJ3DSqJUP1I7MrzfFRYRwopB9Isww49KozJLNv9niSOEVLS1gdOlkse3H/Ei9evqaoGHSmsBWstRVniBsd6u6PIE4KAOEnxMiKOIoQYYy+RECcJrvtiMPF/HUoQyNMYrQTcxcKOz0Vz6C3f/uwWa3t+7VtnTNMCvOT84po4drz/zQnGWpzIUVKRZZbWbqm7EhsEWrcs0iNOjp4QR8fcNHDYN9xfBqRV6CjiZncgimOKJEMGyWBaJuUcFyTeFwTb0gwHerNBiRWRCgz9iJeEiMYIbjYGZ6ZkybtUzafk2ZzaGS4On3MU3cOFmN7CTf0xZf6MIGr6YLjcF7y+3iCCYDWPkb5nlq1YlQm9t1w3r8kyR1xE7PYGESxJrCimE4ZkIJMRbuhRchzRW29RBPp2R7Pb4JxmGDRt20EQDM5ycrrEEyOFozUHTu7HnH8kkG3MopvxaPmE7a7jypxzcbPm+ZsrqtZQDx3D/QrSL/Zcv7i46niFCxLCQFv3RNHIaWy7hl3V0NQN00nKB++9S8CilabvLSpKSKOE5rBHSmi6Hu1j9k3FDz59xfsfLvjWX50xP874e/9C8vrjmkgnqH3Hmze3mBD4Z//i+zyeTPnq02PuLUt+/+MXnB8sdhjDJrz1dw0KBCN/coyPHG+KQgh0rCmKlOqwH+09CMxgmGQJH/5CjGVPzDXF8BhzcZ9q2vJGlkzSE2Sc0bWBOFmRxYpgD+AH/uzPfsAfvX5FEJrBWqKfMn/F6NsFjA18/OaGrvdYKX6aGR1pzeXNDhdGk7xSEuU89k5QJYUYQw78273OeGMf/+13/02+HSkLbD9SkqQEa8F9qa76adm2Z1aWtG1PtduP641JQZQmiFSSLXJ6N/DZjz+j3Q/83De+ThwXRErguoa6bsYYyVgzmIFe9qRJQtf1SBeI5XjozJIYZwaEVCR5RmcGFosFaZrQNS2h9wyDRcURWZxiNVyc3/Dg+Ij5akUWp5j2BabvCXE0MngHR6wjejewOVS0F9ccL2bjQVIrgoBD1RBJRZEX7JvxJpyn6Yj6izQCgfOWw77Fv0VohsBkOrmbHjlMZxju1hzB+3G64j1ucERK4a1hMclpVc+mbug6Q5pEaKVZbyqauuHr7zwjSlO+98MfUjUNTduwODpDDi1vXr7g9HiB9R7T9rx5dcnDZ88YjKfqK2bTCXEc0xnDdf2luOptdcaTJaObQalRTNT3A+qOylabwJubnkBBkSvapoJQc/quZnp/yk5sub5tUJEnuD0uGBI1497yZ+nbFucDQnmSuCPsPIOVhFBQdXt8ODD4Dh80XT+GGiVxQhJFNP0GRc6uP3DoKpI4RssIKXq8HIOARMjB7Li9veHeyVOaPsZ5RdPdIoQm8VCoCKMFNlici4mTku3mnPnkhDe311TtwGKSc2g6jGlwg2WSek5nZ/zq15ZM0gWfX3+P9TDgdMs8L5hNlgy6og9rutax33SIMC4lg4hGgZoFNwi8l3f+YFhNS8qi4NA0HJqKMrEUk5TSzOivFXUl+K9e/oj1oaXtLd3gCAgG5/BYyrmDL5j98oUb76TIkJHAdBV3QY20ncFaz8XlmvfeecxsJokiiZIJfW85v7jm5P4Tqt2BWAnKIidOYp5fXHDebnnvL614989J0lVH0/+Q/+HfesL/6T/6Hmks+cX3HvL64hoXRxhp+a3vf8Z3k4RnZwvOVks29QXVYJBJRGC8CSJHJSYu/HfgBCVJs4TlYsZmezueFvWYS6q14p13F7zzVYtXFYk6Qg8luzcD05NHrG3Lfh8gWhIpR6574kYQb5Yk773hV35T8p/8I8Wmv7ttv90rw0gQCiC1ZLAS97ZZ+pFNdGgN//zbn9ENlhDAOTuOoO/iMINnzJe+uznjPcHfeX8jgVZi3AmGsSkrJUhSOR521Nsb/5cF8O7jB5R5MYb4BxjswMX1Ndfba9752hOKScHzj9+wft2RyJzpZMZ+e0DGo997X1UcLx9TlgVd3WIHfzeNGD2RUazpd4b1bsfRYk4WZ5TTlILA7e0NdVuTxjG9s2RpiogTBuDy8oK+H8iKkixLUUJRTKbcbl4g7wgzkda0bY2QgrJMx8mOFFRtg72xLJdLemOQAtq+Q0cJbWdoux7pRyUBStBU4x54MikwpqfpO5arnEhKbN+TphlFkhIE1Nstw9CPoTBytKpZF+jaDqUkbduOwkXvWMynnF9lbJsapSWzMuf+yTFvrm84VAemZcGbN5dEkWa/b9jVFbOy4N58QqIlR0dL3FCQTzKs9VzdrIn0/T/ZF+ZPUbm7UJ6fQlMY8XZCQpnnaOW4f7bk3tkpYDCNpSzh/m8eYXSP8Ip5kfNmV5NrxWp6j8X0Z5EMyKHi88sbiryklRGD76jbik3tCENEEDF1Z1BKgSzpzR6tBvCeaX7Gbrembrc0/YCQMY7R0RJ8hpIRN7sLXl9vCWLGYnofJT2xyuisAxxRpNBKkqRT6mbNcjajN9e0Xc2he8O+vWFWLtE6pusrBmsAhVUxa1MxmTymsZ757CFffzzl6upHZEXK2l1we7DUraEfOrwJJPFdpC8K7wSRUKAczgaEBKEcMhpoTI1l1D8gBL53fP5Dx/ba0HR3Wgn53/1cJDA4i8Bj24CYfrHn+oUbb9U0rFYzBjPQdeMH0HtPU9c8OFty77TgcNhDWpCkGV2/4/hkybSMqPYdxWSGikDfDzz8NcWTlUZPr7neS07cEofjw9+M+Mo/nPKTP+p5frWh7TqW8wn7/ZpAYNN0fOf5JZGS4zydgLPujqdqx4i94O7sOgElYbFckGUJTVNhjEXpaCSgjGZYfuk3MpIyJeiCWJeo5YHFz0/YhQ7jBrJygRk8w+AgCQgtOdxk5O/N+MV/8wX/5n9xxH/6T9YMfuQq3bGTEHK8feaJIhF6FF6hUJGiGyzWOgYTxgQsMdqilBTIaBSgIMS4wg0B58aYTCkD1gW8g24Yx9peBASjmM0No5w5hFFw9WWNtTiaId3boAdNnEju52eclIL50YzdzZbzlxeoJvCVb36dqq6pux1FuqCuG27Wa5SQPLx3jyxL2a43ZEVOlEQoB57Aw7MzijwbiVxijBt1diBNY+IoZblc0RnLx5+8IBOKy+0tIcCD0we0VU2WxKP/21tOT46I4xg7WLizlm22W/I0YZYXQGC+WFJVFYe6JghI8oR+cCRasTqa0zbtGLwnJC7A1XrD0WI+ri3EGAR+c3PLtChIIo0ZBrSOuP/kEVWec/PmAmsNSIGOIgSjbS94y3Ra0vQ9ZR7jgxstWWbAuMDtbsfnr16Sl1Pund6ja/Y8PF3cBckAF4JdVZPvK4r1LUoJ+tZiB4OSEYvphPsnqz/pV+ZPTSWxQiqNEKNzAznm7dVdRxABJTy/+4PXnK5mfOPdFW3fkj1JyZ8es3fXWNdghkAsAu+cfQsVBUTw1PsrGlNT1QeadonSGVp6pGvIk3tEWUrbOEQyBVkixBqpW+IkIkkEXd+xbw54EpxvkSGhNxYtY9wdONxRoxKH2e6J8wZjO3xIEHJAyA7toPcej0QScL7Bm544Ai0CKsqYlXPs0DMMFmNHnUIxy0AXtN1rpBhFe99/dUuqM65eX+FEYD9UWKnRKiZZSILvCRUoOeCEByKkjO9WRgNxnCHigBENiR7z/idpTm8zNrs9dT9egkIY3SfejShaADMMxLGmqwMFX+zC84Ub72o5ZzqZIkWgbVukCCzmU9ppjvcO6Q3TskBqhTUGKQRN13J1fY6KPG6+pXvS0Z/sWbsLJnGBshk6s+y7A0NT4cqOv/w/e8p//L/7AR+/ucUHy5vWYfoWH0aVaEDS29Gvq1UEjJGQSEFwHuPHsI2ySMlnOTrNMW1L03Z3PygxhlpIePI458/8+WOiaMZN3xBFlkhEtKEhEgqVZbihw4UKh+P8ZsPJtKD8yhoxuSROPP/T/8k9/sXv7HhzGJNZAoFIaZQKKBmoeoPxA3kR8Wg+pYwVV5sDxo12jKYfaHqDiDTBuTt60cjh5C4AZLQPjFPswY7pLzq6S8sKAu/Gvc9gxluy0uIu7fTLAtj6lihI9BCIIg9aoCYKNU3Y3O4xNy3H+YQol1xfX6DjjNm0ICjB7GjBoev58YuXvL68ompqnty/z73oZPRrR9EI4e4MeZbQDYF/8e1v8/T+KWdHS7Ik59WbVxz2LavTM0Ra0luHVNBta7biHFeOEYrp3SQmy1MirYnKAokihMDpasXt7ZjoVhQFgxtYLZcIIdgf9pjBYYYBlUjmyxmddFgzUKeGXmnsUrMfWsLBkMUJSZwgxUhSst6TRDFSBL7zB3+AEpJpXowWn8FjbI8SI0+6uxNdxUmEC+Phryxz6qbl/M1reucYxGiHKydTrq5vCCEwzaecX93w6MEZD4SiqvY0dY3UESpSKJ0ipWCWjBm8X9ZYkyyhyGKs7ZFKYIbxYuFcYLCOzlmMcfz9f/5Drjdn/MavPGX5KyVNssP3Bm8DQqQ8OnvMzW7Laqrw4YZqaLAhJ0unNM2WxaylTA3TAqRogIFhqJAqRmDRQdMFC9LiXD9GI0oHMrDdG45mZ0RRiR88vatw/UCSgoq2oAyJSomNoulr4swhdWBE0jckMiFNjnACbusbJkXBvl5TTBe4YLHWYH3CobUczeYEX2IGxRBqOtOyPbSIkJClkr4fWCYF9xNJdlTy6mpPs+tQQo+6GTk2atODMQaBYrWYk0/m1GFDqvbk8SjgtTj6Q07XWJwVFFmEln5cpcqR6+78mMDobcAZgXJfzAr3hRvvizdXvB8nEAYWizlNVTGdlJihJ09L5tMcbx2HqqFtDUEIrtZXlPcmFF8b2Dy5xOSGgRXrrURGlmADUiRMhCDLphhXc/9rkt/8Gw/4h3/nJYMBF+zdzlP9VLT0dn0pGE3lwzCgdTJCArwgSyJUrGm8g7bGNu2YIx1GRKBUAJ7f/LcfUyYT+nUgn0tqMxC8xSEp4hzrM/yQYkOHYmA5mRDHBqFaolgRqDl6EPE3/gdf4f/8//ou/XBnIZJvAzIExgYoIkKqcKXn0YNjvvJ0iekc3//sDXIxQ+uIH744x97BIISUDMMY0vDWOvRWYKUUaB2QahylOzea6aUcm3JA4F340gv5/1OpjzCJIhSC2+2G2SxnfrTE4HC1oRAZZ0/nXN1s8SLQdR1FNsG6wHa95fh4yaOnj7i4uqWoK+w8YpsJYilIgFYI8smEdDph29ecfu0dZsfHrI5WnMxXlKtjnv/wI3Y3txwt52R5xkcfbZhPJkzKKXmRo7Sm8xavJUmRM7Q9N5dXLCZTsiQhjjRKK5z1I+lHS9p+9NuiJM4H9lFL/9gxHIF+UtDuU16ZNUYFZk9niMZzZpbUH7eYznC8XNG2Dc55+q5js93ivOd2uyN7GKGiiNMHpxhruD2/ZugMUiq0VEyLCf3QobRmOZ1yOMAPPvqI+WqFimLatqUocqIoomk6rm/W3Kx3yOCYL+ccL+corTi0DXE2euHTeMz3bdSXgI+3peVbElugbgydGfeKzo0cXiUlUSzprEenOZN3zxjOJINfY9zo3zhZziEYbCQ51AM2NGhlCXZNpC7p6inKnxGrnixVeKNo3RUqzojkFIEkjpbkeHJd0pqWEAaUVAQ83dBDiBEs6O2Wqt2jpcccQOuM40lO5xTBt5SpRCU5++6aXWU4zRaApR4CVd1RmZZsKgmy5WT2M2wOCqu2WHqs31KW4+ey7g1N59nvW5xvmJYOHybUjaXa3VDKhPuRItcSa3OcjsgTiBNHZwK18KRSUBQJURYRoprIeaQskMLgfYP3ks3zjlJrQiRYzDSJHp+GXcVYD9v9wGY3IKRmkk2Jv2CCxhd+w3//ey94cO+EWQm/+we/x24/8LUPnlHVO7o+cHq64ux0RZRGHJp6ZOg+bhC/smd7BK3ZcpqdIb2gGTwXO8/p8TGEnr4zOOmRccx+d8uv/LtTPn8+5Q//0QZBjAgKId5SeCHS6i6d6k5QJcYXIEo0wfhRUakl6Bhb1dSHdrToKI2UCoLjyeMpP/+N+xySK9Jccloec/WyRUwnBLvFyYEhWLywDF7gh2vm+YREOop4i7M9184gHlj+w/+V5nvrI37nn+8RYgR9vw2zFEOguR3IZikhC3zvdkOaxUQDxNOCMst5dbG5C7wY1chvozCdHVOxdDTued0d7EGK0Uk0+ocDWaIo0ojNrsd4gbfhLuHmywKIgiRWKTftDrnI6RPF5WbN9ZstL7/3ivdPznj29DGL+YK27UhkTBYnBATTvKTtW/r9hp/95kM663hVNbx8c8nNiwsWWUFn7CjmEwMhlrz35IxkuWR2+oh7j99h/vAR5XLJy+//gL5peHD/jMfHp2M0otLkZQl4oliDgL4zaKVYLhckWuNsYH2zpu17vJA4M1AWOeVkQpIktF2DjRzF0yn1oidEloka0EWJEhGTXKGimF72mCxGdoHho3qEJNiRKd30HcPg0HGEVJKqbpFCUtfPieKIyXRCpyV90xNFikAgiXKEEGSTgjxPQEVESUIIcH1zzXK1YFIWdF1PXhTU/UBZliilaPuWSTxBRwkvPn/J6dESERJkklI1X2IB39YQPMKMwBMlFZMipusNnbF4Y9ES8iTCecsPX1zyK8XARCZ0ZsK+dRTZDC0LXH8gjQ5UdSCNcprhJd1Q46wmL3KutwdkXBFQxKmkb5KR3SwdF7e3rBb3ISR05pJ26FFaY8Iwhqy0jmme0vev6c1hnNq5dIxPDdDbHVYYbGTABGLfksTggiBLTrHDBYiACg2rbE7UBU4mJ0zSI3bVa+rqhjKR+GRBmi65Wt/QtecgS/adpyhjPDl102P6gDWCEDvWVcOiWKFT6NKY3lr2fU0YPGbfsZiUDCrGC0vhHQJLCIKq60m1JB1S5GvHL757htQSpKLtDYeqx3rPIB3JPKIsInov2B8MSfPHbCf68CvPKNKYzXZLmk9wWG7WW05PjhD7HS9efM4Pfvw5j56cMH3gcU8a4uOOg+pouwhBjBkkqIbj0wkvX21IMljmOYfuQFZoUrli371ApZa/9rdX1JXlsz+6W3SHt1EZjA1HjgEUwQdUkDg7ILQmiSLiLMXGinrfoAZHqvRdyLhEK4H1nj/3V2bkTw23Q8+z5D7hZsXs03vsohve/WVNFhtetWvaMO5kjXF0xpKJLc7t6FWMC1OkikmOrvibf/sDfvyj32N7OwqeRs/teOKUQXCvWJAb+Pgna0QEWRYjY0m1relah0tiwjDgBzcKYu6ADozPGz+MF/5EK7z3RFpiHRjrMcZzNEvwJdzsO6QUJPGXt4a39fnVNd4FimVBtpzQe0e9bTlcdUgZs60qrm83vPPwEXVVY3qLQlIWIxmoSGOMi5Bcc/Om41B5Pnz3A+yDd7D9wGAN55fXfPzJc955/JCPfuePuNxvOX30gGdf+SpP33uP1XsPSMqUj/7w27x59RI/GOJEU0wKIilpu5rpasZ0ueDH3/+IxWxCFo8hGrEa1b5SShbzGXYYcM6N7N1h9NP3kwE7Feg4pxpG4lYZxaAFlXUo3yKV5PXQU96LkIcZ7bolVgqhNbbrCUIwy3LyLMN0BuEDGgEOzs+vCH5gXpYIJZFe0vctgzGU5YTBOfIkG5tq07BYLHnz+mK0XoXRJlfmMV3fE6cReTkKLW83FV1nuby+4cHJEWmSU39pJ/ppDdbixZhKEMej1csMIx1Hq0Cs9Kimd4Gtb0keOoJcE8eKprsgiT+k6xOc3dJ3B1yA2/2eKNZUreD+2TOc3/Dm/JbHT75GuXCE4UCiEw51ixcw0HK1fkGcBtr2QD8c6OwOISL6bmC1yAiqB7nHuYAQCYPtUTqlG1o629I0W1ARhA1YiydFaMemqlA6RquOJCro/ICjpoxWqODxdoMMBmManj54Z0zIamuq3tOZK45mMVZIhEo47BpUpAlGIBWsdw7vDkwzzSJKWLsDTd3T7QKJyLDaIpwjaRfs9Q1ZGqF1RAgT8APycsmkjVjOE0DjArh4oCssm6rj+lBRmx7lQQlL33e01Rd7rl/42/lolrHfHbjdNDgEQ2/RCLbbhuPjJWZo2cVb7IfniG8GXlfn6GRKmiwQIWezf8Wu3ZElmjguOL0/o2nXXK0jimxCJxRvLq9JU1BqRjTr+A/+18f8nf/DOW9+aMFrwI43QX/XcIVEMN42kiShNwNIGPqe/a4j1TFJpEBLfIhA3I1ezko+/PUTmiTDtjnNMCGXEbEqmXWCiX3DfHpC6674tK5ohoxUTUj86LcciKk6ixYJEyXZDorlvQm/+Bvv8l/9P36ARRDLUVDlnOPBomSRaw5ty8N5ifaebzy7x+ubDd+5GUU2MtGoSTGmUfUDQ9cjO4c1d9ID5/ECumG0gySJRgSPTsYb8uXmMNqI/KhAtc2XWMC3lb37NartDS6XNEFSVQ5nUo5PFI9WS5rtnl1Vsesq8mlKHI98XCsETtypSTUkk5hsEVHSc/7xCxaLI7IiRRH4+vvvkkhFtR9JJlc3G7avr/jhxY7Pfuc7PPz6e7z3ja/x1V/9FpvnL9meO6YoJpOS/lBxfXXFoas5e3SfJ08eMbQGJQTrzYbpdML2sGe1XBBrTZlmdH1P8GFEExawW3qcCEir0VrilKe2HWhF4IDQEUopDHCIJfnXSybnCemNoK6rO8/mhiJLmE6nZGlK1/X0fU+iBdOiGH3wSt6p6R1pUmCto+1rHJLIGryzrDdbismUwUFV1Ty4f4b3I1Hr5etr3lxe8u6zx5imZz4t6fpj3ly+4Xa9RUcx8zL5E35j/vRU21uSSBGr8fZo3Rg5ewdFQ0pI05jgA1//5XvkR4ptt6brNuSxxvuO1u5wdo/1EhsaBiuZLZ7R9BcY13F1c41KV7y5qVksBBFrBquphhbnPW0zMIn39IeKxlS4YNBSo6QizwJRZNlXV6ROcOgbhmFPwDAEjQwZy/KEvq9QqUCICi9SBidQWrI+/IBpMcNEERZF23b0dcPp9ITBzzEhgTgQjELHc/a715SZ5PZQIVFMFymHxtPV1wz9gEXQd5ahguOzCVXX0ZrAygaWk4y0KAgTQ+8bPJbuENiZgSAcPvbEqcWInEytkJ/OOS1KlBnhFEIogpTY4Lk/cxy85eXNDZ+eX2FszXQyMqm/SH3hxhtpQW8cxaTAGM9iNmW9uUFFkuvhAvP1Dd/8pRTmDa83a1Rk0WIg+IQhFFgvidMlvW1IoimLYsGyTBDNjs/Xr3HCsZhNqfY7jOmJU4VeHPgbf/sd/q//x5/QXY1icCG4C+8Qdx5diQuBzgwMLpCpu1tA71BqDNNAjGBnFwQueH7hz9xjtojZ2Y4oKRAywc3XpF85sPIPmU+/xdD9N3SuRciUXCXc3J6jI01VVURKM4sjpnJF0nc49RTlJvw7f+OrfO93X/L5pwe8HT24J7OSn392j6bq2Q8D33xygukH7i1m3F7dIr1De4U9GEzVEaUaIoUqEkISwHikg0SMY5QQxr1v0ww475ERyEjihcRaR/B3yuovR80/rSj2iFnK977/CQwD9x+cUpQJoTNYB5PZDInm9dUti+WMoAMtHtNVnO8Nru9ZLCNOsgnTJ0viR4Grz644/+g5y+WSk4cP8TjKSclisWS23PHo0RmXlzcMNmDqhs+//X1unn/Og/eecO/eipOnp5x/dkV7GMe9SRxj+o7b3TUox2Q+QTqJsWaEfCiNUJqqbsbM8ij+KXt0O+sQPjALJU2iaLqOICGRmn5wiDRFqBRjGrIiQ7uEja3gLGY5SGSnmM0zDI7r7ZYA7A8jHUkKQYJGy3HSMggQSqKVQmvBYDPavsaLQJEX1LsdTx494vnrSy4ur3h6/wQ7WNI45Wg2YbVcsNlsqaqKxXRCniVMphn+jac3BuMs5kss4E/Le0GiI/JYExggeIpEohX4O41LNxgQjme/eMyuv2VwBiFjpuWU680F08kSgseHjkilBNlzuX7NrHRgI5bz+1SD5eXlS7LpMwY53EXQVkzTOQ/mD1nvLbmOOV08oO1rIhVRdRuybM9M58QqRkeSSHfUTUsqJ8xnC4xPKKOIqt6AaXEYhkjR2RZjK+qDQEcxxtU4a8e1jYbt0NFuv8Pe3KCjBikyGtdS+5p+2CDwHK0WWFdjrcZZj5IRbd2O00GVEoTEDZaoEFzc1jwiYToJbEWDcA5dx1jb4EXAEUiiBOU0ZSg4uXlAsV2RlQWpjO9SwzxBCYhiZJzipOSDZwNfuX7DH/zo+/zk4gW9+2LTmi/eeNOIanPA+cD9+w+J1MDr288p35P0j95wSGpUNEc3mm3TspzOSOLZePX3jovB82ZzzdHsmG3ToPWMRKc0+89xyrGYLDjK5yxih7Ett42ibi33n1n+g//NB/zf/qOP6A9ibCzizpMlR6VlcG/HuxBHmn4wdwitceRgnRtl4D6Ql5qf+40lIVas9DGVaanahkx7Hj1Imes5uMD2k5+j0VfIco0ShoC7s4looCFjwPz4Ee3tV/HHDfpBRXmU8e//L7/Jf/y//y3qvUFKjXGOz67WTGPFr3zzMd96fJ+PX55TVQ1JmqG4C1++Y/WKzuNrR6xBRZrKeUKkiBYp0nicsXjrcP0YFym8xJqASgJxqbHBghntTF/WWIf9lpfPX3H18hV5njAsSpreEA+Gzb7l8fExRaSYlwu8szx4+IDmtqE3A2uzpR06FkXOJE5o31gGM6Bry9nRHOd7tLecPXjAYbOm7jqmyyPq3S33TpbcrveU2Rx/Y3lneUJpJdcfv+Ts/hmrkwX9tkbEKUIK6q7Ftg5iRx8afOex3mGtQ+uI6+tr5tPJmLomambTGdZ5zkOFdYGVTPFOYIIEMzDTIwqw8sNIO9IxwitkHKO9pMYhnwqWKiXeS6Z5jh8sPgSSLKOqG6ZFgfX2pzGRgTGv3XmI4phusFxvNhyfnOKdwzlLpDVHyznWeZIkw3lASpJ4hJwfreaUZY6SYF1Pkkjef/cR87KkahqG/stR89vyQRBFEikdg3XYO2+/VmqErAB111Iep7z3849QYkcsF0gt6cyGPIG+rwkYJtMZdWWIo5ht1bMsI4I+YIeB7a4jy1Kawx6VBpQYmBdPcEFyW11wqGt2csY7s5yh3eBdgUCiI49Qgn19wVRNKTKHbyMWsyU9Dc3Q4L0jCEmsCryX7Js9gxU0xmNsOV6e3IHB9YgwepSvq8/R/jVxkuPoccKy7z4lCIOOe0xw1MMBX4NSAW8lQklclzG0PYtHEa0zzHLJvrKYxrObtHTbAZFotIR5fITjllYaau8JyjJJI55dPIDfVSyKJdPFkjwtiYsp8XSOiCXODePt2jv63rBMF9yf3uPj15/ync+/94We6xduvI8ePmAy2XJoWvb1DWHSc/+vC5qjVzjZ4mrF9daxms/oPbRDwEqB7w5EqSJJU/ywB2sosxUXm2s8W3IZ8/pVTxoLdrIjUxKlYrSYcN5UNM0lx48L/vL//Cl/7//ynGE36obk6KUh3OWY4gMCQRwpms7hgsV5h/KjrcbeYfZ+5pfucfIIVDwHURKp8YNPcAjZEljTnFvCxWPkxYr88ef0pxc0dY1RgdMJ3CsFK6HY3E4J2wIdK/JHLZWEd3/+Pn/pb36d//LvfAcRPGfLAmkc75we82BRggwjvNwErg8VHpjGiqAC1TAeDpZZzJ/95mOazvFPvvcZh8oQlEDFkrSMUZkmBIHtHKbuCa3FdA4VBbJZhOkCtnf/f37E/9WtsrX8zNk9ns5mGBHQUpMGyXZ/4OLyhjJKkGXBKs0QUnJ9ecnDs/tsrnZMsoxIgHUWoQRDZdHOc5oucInHW480nteffkYUR0QqYv/6nKpv2W63fPD+O0SR4mQ1Z5oWY5BG3VPf1BTTgsWD+7RNzUzOEUKy2e745NVzbu2aZTGlSBO2+55JntLJ0c/dti1Flo6Tn1ixlx1ROuG2MyTSI6Smc4I2ePwwWj5iKYmThEjHaDFiJmOlcVJyeCiQn3tSl3ESxQzWsr29GeldgCOMB4A7ZnBvzQiACICCYjpFR+OURSqFsQORFpweTUmUJooijBkQSmHuMt6vbm6xQ8N8NSMwjsHrukEJePjg+E/6lflTU2WmRnasHbBB3oFYBGUekyaeqm3xQvLwgyUqnzB4gTUvCPQ4PyCCwpiAjnICLXHUYVxL33VcbmfkxcDgeubzBet9z6uXa375F76JcxuqusOEin7ocfSYfoO1xyAC3XCL6R0ydfQtDCYac78zB+nA9f6aLAtkkcL6Fi8EXgYOdaDtC6TrmOcLfKxo2j1B1ZRpwt4NSJ0gIsFgHdr1oAaGbsC2A55A8BatFT44Bh+IRIRzimEQ9CaQ5CVxHJBB0LSa9gA4z2a7J0skCyRH9yZc7K4YrCeSlnmiOCkj3rl9iP5vDauTD5g9/gar6SnlZIWKM4g0EBj6FtM0tNWB1lUkhSCXKZNszqOHX/lCz/ULN962DSRxCkkgOunZnN1wI7bYbs9sVnL/bEXTB5r6Fqkc/dCD3BFHGZFyrKY5n77Yk6c9U6VYTM6w3mAOb1guAof2lq43PDl9hB8Sqs5SphGzIiFB87XfHPjs1ZTf/bsbJKNBOzA207eEoOA9SilEACkV3ofxVwh4F4gTxZ/7tx6hsobWnCOkJYkL8AILrG0P8hOC/5DdbU2cZJy+f4LpX/K0uEebbFhlEe/NJUlTEIUZqtAIl1JvPEN2gw4r/uK/+x7ZxZpP/qjhturpreP3P3nJ5e7AvfmUbVXzo9fX+OD4xXfv8cHTd3nz+nM+fn1F5wLvnC54cjLnt3/wAu8DMkicGW/2bdfhfCApFckkIzud4m2gPfR0raHvLUIrIhn99/qg/6tY4dARlzmzNONyvWXf1CwfPyI51UwmC3KtyZQed106QmvJ8dHA7GjKbr9lPp+RTQb8AGawOGeJk5g0zgheEKsIY3u22x0gmJQlWRSz9YGf/PBHPHvnKUkcs96sSaKUSEUoJ+l2Dc4wQj18oO96zi8vOX95Sd8b1tmGs5NjkiQhSRK8G//uIk8pihKlIy7cNYO0TJMCh0AGgfeWgBj1AEHig0XoiLaxRKVGio4n2RQvFG5w3GqDOXZMK0foBoILKB3hvSUrEpzz1E03ksbwxFEEwWNsjRU9+RlkK8NiUlDvC/briv7cMgyWSCqsdTjnkdqhlERHMS5Y0rLAmAAYyjSn7zq0jpiWXzD+51+DCt6N9kzvGAFmksV8yp/9M7/GJPM8f/WaT1+84P1vnlK7HS4MxHKMlhx6gdaalIa67fFokALrIM0znMvI0qdIcUHfHgiuRSqLc90IS7Ad1hkmaY4ZLObgqXYDSZ7idIttDHm0QjpPkg2sdzWreI7DYqwjNDCZD+RxijHQ99CaQNP2PDt7hPd79jtPHC/oDNTScag1R8clXb9DKM+u68hiRZKMkZVdD4fK4howQaCSQOsNIk7xnSAow+IsxQ4NkzRlGHqw44rOdIJRtNGyWCUsysBGGIJTnC6OeHxzRP7Pp0ynp7z/F/4y2dE7xCFFvAWjO09wlghNTESEJhqg9iBFRFTOmURfLDPyCzdeGzxdccv+3jnu/p6DqanrmuPFguAGuq4GMUXrKfvtDWerhElesG8PbLaWEHKS/D6vbq95cloTJNiu5vz2huOjOZN0Tq4DTTvmxV7XG8psQipzJqnG2op1fI1+Ava5RPgRr+Z9QCuFIIyiq+BGfCBjBrIQAXeHAnz/Kye8+80Fjd9zNHtMbSP2jSGPUoKxfG47QhEziT5GLCWRfI/P3efcWMeA4PHJA+7Ht/TXDppnKL+gXKzg7JxwtCezLTd9RDyFD//6KUeh5fWrln/6o5e0LvB6e4GSlxSxYlGk/LlvPOa9h/dZHT9hHgdWhWY2mzLLUr7/6prvP7+mNRbrRu9YdGcQtg76KtDvK6QGmUiiVJPlCuv0CIbQXwZovK16aOgqg/Ng6p66brm4uuTZ08c8vHefarel2W+ph5rL12tkENyub1iuFuRxRtO1hLyl60u2+xY7jDzp6USOcXamZ7/fUTc1vRkI3rNaTvjm++9Q1R23NzuqzhBLzbwcP3JCKoIPxEEwn865PL+gqiuqqubxvTMOdcu+OvDq4gZjBqZ5wtnpyd1+d8wdl1nM602DCBG2GygWc7quYTLJaQePHyShh05blLLkST6uJ+g5khM23tIkHh0lNMGSPdBUf7iliCOmZU431MSZomkCSZxhug6kx/qeyVnKfG5JphFirjG+46g8kIlTSvWY6xeKH37nJVoIbm/XKKWJ4tF10LQti8Ucay2b2wPTaYaSivlsOgaC7Hd/wm/Mn55qO4tzkKZjPkCRJzx7eo+vfe0p65trvvGNr3JykvONXz1m06yR8YwsiUaBk1T03R6QpElH0+RMlwvMcECowPr2mvl0hhYDPvJI1dE7w+5wxaRoaE3F5qCZPlFQj9S4p1oipEZJgceRKcnWDfR9T9PULKxCSotMNIfaMbELlBZIUeGlIVKKKAKRGG4uK4ryIev9hrbVrNKMR0cS4yviSICP6YVntxeUpSLIngFJ04/C2nrjScuUuHQI7UnK8Sbcth22N5iDRShJFMNE5gy2x1mLE3Boeo4WOUY6eguFiCl3EU+++kssjr7C9Cs/j5A5ogeMGUMSrEU4ixIa6QVCe2TmkTIhIZCl8Rc+NH7hxnu4/2MOD15y3lWc5s/YbTeYIGl6Q1EkSDWHEJHGKf5Gchgc4XDLrCzIRI6xCUmaUlUHqranKHNEcOyaAbeuSY9LrHQgNLtqT5FEVE2DNx1dkXB50fPmTU/2OKI+WMI6Qno59pc7+p+WgkQqijhm6HqKNCKNJBtrCRL+nX/vQ3QxMJUL2j7mYDRCKZxL6IeW2ew+zVCRHteEX7jBdI5O7/Am4HXCdz/6GFaBySe/Qnc9GcHN8xccoj11PwOVIYaGEB1z9O57hJ/7BDFE/Fx7hheKf/6jF9TGowUoJGlc0Lc9H330XT4/v+adBysmRcHv/fAF//QHn7Pt7ZjYBXch6RJjHD6MRCKpBQJBaB0yiJH6JAVIQVR8aSd6W9EzhWsdYStp2hGpZ+3AJM0Iy4G8yKibPfNpxmr1hMOh48VnF3z/xy9ZLCesVjkfzKe8uuz4wx+85Hgx5/H9ez9FNXbDgBkM8+kUHUU4NwCCzeaAtZ5Epxg5JkuJOzqQv0tGGUxPWzmiSNC0LZe3O959UvLo4QMub67A+zumNGR5TJpnSCR4x3l9jZ1oMp+QRRLkQJbG2EEgiMcoPqWJtOIoiZhnU2zXYmTgJ6HF4lFek8cxrjfIhynz+oz+sxsEgTRVKC0ZgifRgkEbpitFMQnIs4YqstQEJjJnmnjuFQm7NykvNpZO9Cy/MSMYw/zsMUMLTd1ydX6JMYFqX5NmCUWRIhAY25OlOSEE6uYLejL+NaggBa2xKCVZTFO0Utyub/nu977Hbrflcn9BnDl+pryPdR27dUtxVt5R42qUVCg0Osqwbsd6F0gzhegPRJnidn/BZNJwfrUnTjxZaei6DSpyOOHYH3q6ISWSBud7vKuQytI2nsFptLbIpCEm5fyy5xv5PTbdFYNoUalk0+xZkoH0SG2QsuWovEfwFmQCokOEnkQJrPBcnA8slglSRwgtUNaSp5K+kyxXKxq/J8s8QyewDNxcSe6VE3TUE4SlO0iSLCCHCNsGslKSJpKz/gQ/M7w2l9jgOPQNk0Hx+OiUuqmY6Zijk6/y8MlfB2uRkxXIGFIHvQFjoTNjE/YKoQNaOUIcQMXESYQtc9o/7sjI1/d/RJIFgo04HBxt1xMXmjSLMdbQtzeIaEZZJMwm494zkoFYaAY6rDWkSc7jh/c5v1ijtENiiFLJpBCgeoSe0jbntNZwXN7jdLbEmVv2reMf/9OX2M5jTM/ka3D4tsfuYoT0Y0aylKRSo6WmTAKD9TyeF9ybpfzOJy2PvnLEh7+WUdk9WbYCq0lFRBrLUREXzbFO0KmST/d7ukERR2uC6jDeUE5yZhk8mkuaRYPnloc/J9kkn3O+M7i+IM8WZNmM3gpk3HP666eEiw2fvdqwbg33lxNmecbj4ym32wO//6PPmBUFm0NNpALvPjrl5VXNf/2Hz9m50b8nhECq8XZvzF2KFwI/eLhL4MJDXVsCfszEJSC7L5Whb+vTm5/Qp3C8XCLXCtWOCWDf/9FPePb4jPm0ZLUqKdMcpQXWS7SK2P7RJ3zvx8959s4xP/drZwTjSVPF66sLZrMpURxjvedwONA0NVkcM5kvqesK01mapifLcuJopAu9ubxitRhjHoP3RHFM3/cQLD54Tk8XlJMSbz2H/Y5FOcET8AqOzuasVjNCkLz57Jxt2/ImrelCh47g4DpE3THLCkqVYiMNblyzaKGIkoQ+NGjh8ELSOEkIAuUFwjvKIcJryez9Cb5u2F9syPOMjR/YFRVm1hPlHrmUfPpZg18HkmnKIk9wQ8m+yvnOa8muv2bXHZjFMUPnUEuBUxHRQfPq8wu6tkOnGUEqNust09mcm/Uty3mJt3ZkdE+/zGp+W94HlJRMypQkiXDWs9lU/Mvf+gNkJEiLgnyZ0MeO3aZCq1M2lSTWljgWDJ0nzTW3hz0hcrTdGhMSzNBDkLjhBlslTNSC0/IBIfcsi4JBdKTpjtP3HcgGpVIeH6ckwdIeKg5tRW0Kqm4UhR7sHusjvJrS9TuCsMTK0xnPoQ3M5gv64RaUxoUK8Eymlrq+JlaesszYH3qiEGgOirRQ9J1HqRwvaqbJhEJHWKVJE89gPMY5Ao7Qg4nAVBGmc2SpIviOLIVFHnE/WRBXJ8SnhvXmEh0LvPDUQ8UDeca98ilhrTg9/nWixRlh6EGp8QHEo8uEwUEcQRsBHViPjD3aCYT3hGnOkGUE/8W0NV+48XauZr+PWc5XHNqW3lswA87lFFlGnkqEzghiYBgMWV4SqYTeB1qjEEEjXaDUMe8+eULfbDm/vSRJPEo6do2j7vf0HRgvSRrDPJlS5CW/+0cvuHrV4AZBIgU+7pm/G9P+EBginAikWvFgVrBvWh6dLHhsLH/x595DBMd6X/M//lsfUpYC50oOVUeSJgxuAKMJIUO4gNYHhj4w+JhuKLnZNmg1kIQj3hwu+PrsAUU+Z/4LCdIKbs33+XR/jtNL4iSmGyqcTYiTkkjs0EcwPLzh1eaaq0PgZF6ySDW//I2vkscRf/+f/SG/9aPnzKcp8zzlX/zwFR+/XrM2hqDkXVykRCpxt2IYBWVKjbGQwd9xgKWgMyPzOFIjdzjYL+1EbytRGfu+5rP6NSeLI1KT0+4b0kJR5DlFltK3FafLEwbfk8Qx7733gCzJ+O6PJlysz9lVPaezM77+VU21b+h7Q9M14yFHSqazCdPpdFxzqHG/HsVjvKPUiijW5HnO9nBgUuQE6/Deg1IMQ8/zV+dUTcv7776DcwZjDYMfEJHg8bN3WB3NcbanXjf03cBW9lyoA1aHkYolNNKOCuiOnt53JPGMqc7GONFu4KvH91m7lnW1wxmD0vHoEAieUEA8DPTeoT4okNrTx45WtOxMwzARLGYTdkGxnweiJGaVJGhyPv90oKluCKIiSQKP5hkTl3O5q0mPUoYhodpsWZQlYjqlHSzeeZr6QNO1NF2H3gukjtlud6SN+RN+Y/70lGwFuy0AAQAASURBVHMwm6YUeQTe3WE/Lc4HvPE46/j6k28wSMFsMsHZczYbxdFRSqQdRng2pmLT99jakWYFq3yJLM/QagVuTpnkbKtrltNnbJs9u/6W2ewE1a8Z3A1aLJikMx68U7JtO4K5pHRzjuegrcVrjRkuOV5lNPaWXX9DIqdg9ajj0YoomWD2a+KkYOgNgwGlA4O3pJmjsYagIpJJxs21o2phNc/Ik5y4PCIEzfX2lrqTY365DQgVoXOJsZa8cPTBkMQJkwRWScm9/IjHs3dJQ07x5AEvwj/kSaaYZTMGJ5imE2Y8xL4+RW8TpmdnBGEJth9BN28fguCuAWtIEojicVfcWqTyiFTCdErQGj38MduJTo6XvLg4cKgHqrZBR45pHuODpOkGJsUEhUPqlCQpqZo9hpjJZIETKduqQskrpnlBXjwhSVK64UCWFBxaiQ8V8+weJ6t3ubj9CToquNnfMCkHXn3eIIPAWUEvgB7yleVX/+wpq2pB1Y5fll9/9ID/92//IQLBh0/v8fR4jjGOb31wzPs/KwmVJ2rg6NjRdTviaIb3oAR05kAtxv8nJSKyJDAMCZoZyksmOlCZlh9vLOVE8f7856maV+wcZEVMpAVKRXTCjQAEH5Glnmd//il/9WbKP/x/fsw3nj1g6AYur2959vCMf/vXfoa6OrDxnk8vdxxe7RiCJyg1OozCyO5VscY5j7UjEjAET7hzIY0UBX5KRlJKovQYov5ljXVpawZv8SLQRBXvvfsQWx+DswilWCwWhHCg7Xp21Za6qUninPlyxs9+4x22zZz7Z8dUt4Gzh09otzuCc1xcXBArhXcglUAA3g10ZoxbnBQ5+/pA223pTUsaC/aHA/v9FhECq8WSLM8YrGV3aPnR8+d89NlzvvbuY6SSZMuMd77yDg+e3seZgHaSRo45zVu7J12IEZrhPFmkQWlsUNRWscxyYq2gB2N7dBzzut/ROUEsUgZpKbyknGVs24Yaj5OOzAWqNMATRbXZcLAtfTyQxxOCi6hva3Tux2mWK/n004q27Qmqooh7dJWAPcYlC2xjuf39A+3uBmt68rxkkib0xpApycN7Z+yrAyerFetNxa7eIBUcdl+Omt+WD+C8w9qBSI6rJCFHB4cAhILVwxh0h7Q9xhnSNGJ/cJAFlABnp0yyjCw5IkqmaFGSJQNhgJfnG07efcSb2+fkbs3zq5+QxZpi2tP510g8yMCu65kvJqT0bGvDbddylE85bC3vHD1lkcRjWl5l0H1GOtVcNrckUUTdTqgbjRQlRTJQhRalHXXvmWUF/XCgN4a+UcQTRxc8767u8fL2DeLG8OBoRsgF+3qLCJoiibkZeiYTjQQWheGojFnpiFmf8ez4fRbTJyjpkGKg54Zt+G2EsSziJ1i3xwWH81NarxBRTxgk2jlEs8VXG0KSQX6K0CV3oNe7BixBpYQhIBozrjkjiYgSojihqW+/0HP94otApzld5UiVcrPdoNMIFzRCBpphoNrsCWJPliQoNeHQbSinBUoI4hiCnHBxs2PfbjhyOUXSMtExQUcs5o+52Wwo0xgddhyVCbgdk6Lk+cuGT39SjTdSJfF+NJXXzrOdHvg3Hn2LYTuQTQpOTh+Tf/sjnt/u+Ku/9i2UlLy8fE3I9hT5gfX3IhYnp9ioxpsarzRtL3DCIdUMpcD7FuMCsc4pC4/3nv0u8PpqwNwvGUQgdIqbl6+RuwWzpWTfRjgbgczuyEn2DiSeo4uS47/gOPq9hEPV8Oj0BKkEu8OBeVHyS197h3/wBz+ise5OmS1GtN/dyGLMfvZY6wlIwh1dSdxBE5wLaD3aqN6KzXoTiKMvfbxvSySeqUtY1wN7P1CrPWflGdu6RWUp692Gen/DyemSSEU8OLnPze0Nr159zvsfvMfDfEGeWta7mjc3G+6fnlBOS/I0pTeW3b5ivd2SZTl5EqPVuEsWyhFFltv1ht5KymxGmkDd1uyrA4fqQJmnCK05O14gtWCz3RBFEZtmz2q1wEWWfbthNT3CGwh+3A1LAWdyTksFUaDpWqJEoWTEsdLcqyNMBp2UuA6cVWw6jxCBVAlSJbGDo+sGWjMeQLyKaWXADoY0lcyXC6rriiiFWCrE4KBpmc8ThrbgoxdvaAaDiqAPe2Sekuc5fpoiWsHFH14ytIY8TYnjjJv1FmssEkGSxAzOoaKMm+2Wlzd73lyvmaaaOE3/pF+ZPzWVxIK2N9StYlaOaFBvLTBGV0klmB5J+m4HvmdggNAzDCCLb7KaPmHfd7y5+pSj1RSJZN/UzIoZ2/qabfMCM5yRZx3Od3T9hiwtgAkCRawneHqMMWgR8HYYvdiLGYddh+9B6IjrbUORnHF71bFKzriXTYgNKCmRaUFV7VCiQ9BTJoI4shy6wHq/YT4tQHmq3pCUOUnesWsvcaElSSXr7YFH+ZxolbDeGpRz3J+nlJnieDJlNTlhFk3wvkcKQZzBef1DBmfwcktnPCLEtG5HJBt62yCEQ7kMlXry41v2P15iqx6ZGUSUE84/wkevkMfPENkRQmf89JYjBWKSEuwSURlwPegYEcVUzZ4vsij5wo23HyKSqCQIhRYggkXJmKrZMc1yZtMzGusQMkWnCec3z9mqDYKSFEMqIz54+JR929C2O4ZgWe8hmmiWheXZyQmb7SdEUiCIsLRYaXnxvMH1AeEVAkEUe+zgGZTgB5tLPszPeT97QB4lGNNgTM8qz/jqO0+5evOCy/WB3/hb70ATcfKeYyjXGCRDpMGPcZNagPGWfRtR9S0ygMgDZtB0Q03fZVgiPIqbj/fIzRlu+4DB7in+okVMI4yRdHY8YaY6Raoa7zSdT3DLY379P3zGZ//ZluvNluPFksE5jDcIISjSmDzPaIxFeodQd5ShEIAxok8iiaJRZCO0REhAyjFsIASEH9GBWo2KP6W+VDW/LdFkFHGCjQW3u4rvbT8me5KziHOCNVgJjRlB8UWkOaw3TBKFLTWb3S3H2RyRT5BHMIkihr6HUICIcK5jMslJ8pzLiyvmswlFkRGkYb3bIdHE8ZRDW3PTrwnWoZQeEZZmfP7OmPHQebRgXmTkWYrKxzi8YhJRN1sW0ylJluID1M5yyAz+IInSlNrsSCQUaUxwgUlQzE3MuhwRfqZzTMuYwQqkM+QK/CSlHnrMMJKNZHAgFUpIYpWw3e1QCh4eP2C/vUVGin3n8LMYYeZcX2xoBocRDVZ05DomOI1UGcv5gtsXW+zgefHqNY8f3CO0klev3lC+/w5JnHK7WeNlzCcvr3hzcUMzeOreMpiepVB/0q/Mn5qKlCCKIiaznOk0wQ8WYzq6zhLFEShBunA4caDpt1gPphEcHX8NKY7Ztz1D2OGGmkzfwxpJrCRdn7Pe1Ujh0LIjy+aY4QbvHNttw6MTQRbNMbZj17ZMkgVSCHo3gBwo8mM+f3XO2eI+Ngx0jef9xytuDq84vwl8+PVv8aNXN8TDwMk8cBol9CYn8hBiRWctnTcEpTG9Io5nyGTH9XnH2UnGrtqQSEGuY7TU4Fu+enZEV2hm6RnTZErTGQ4OaruhjGd4lfLx1bcpWTIIhRkMbWeRYg7W4ULGxlbEiWMezwDP8+ufcD8+5rf/wXN+9eQvcRaViPkpgghpW8LFj/C6RJX3YHKC0CkBidACFikkCaFNINN4Bvrt5Rd6rl+48a6bgVku0XHEbK7HEYhpmU2XGDewrdZYUYLoyHLNpEwZQks3dGiZkEaGiI4s0SiV0lfXuMgwz8H6Hft6z2q+YtdsmaYwiJhvf3fPH/3BNdYEBBKBQxCQUuB8oA2eP2qe897pQ0zXIxrHqkiZzxbU1Zr/9J/9IXom+ObPzNhvTrita+Q7hkPvCFIDjkgrlIBemTGOT8vRd+bGRBgfYmZpTLI6woSKKNLcf7zCNgM6ntEergnFHq3KUfk5CKRI8V5gjcbRgYdHP/s+8vYjPv5HO8pCE0cxVWvonUNrTdN1dG5M41JSEPCjv9iN5yytBVIKlBJIOQaICCmIVIL3DmEckZScLHNWRYLWX+bdvq3VfE6sPPPGkU00r82aH1x+xm88+Bay9UihyZKE25tb4qMlWRbjvUUruLw8RxQRp9P3SB4klEdw+YOXVJcvKedTvPJkcUSiYoJdIGUgSI9QitXyPs6CtR1SNtR1x3wyochz0jQlOM/tdst8Nvp+QwdRFDGfzZjEBU/fPSUucrY3e4xxKGkZvGMfG66GCtv1TIsjJjLDeCh0xn7bsikcQR/Y1D1JNCcoTxRB1xsaYzBFhm4cmUrJIoFWPZII1ThEGSM7T+QjVJJSFglcW242N6g0R1Hy5nzNpt6BspAOJHFEkibMJyseTZ5x+EHP9SfXLKYTribliI6zDmMHBmu5ur3EmI44n3F9s6HpDE1vUWr0RCu+XJO8rab3TCJASKSKSeOUvu8RwZBoeHIvYbJIWAfPvnXYkPPOg99gMcvYrj9iu7fM5yXebunbQJpPUbbBhR6hQAqLcRuCDHTOEElN3zoGO0XHCht2HM9OOOxvuT18jg+W6eQU5QPWD+RRTu+H8eAoBWmiUUWgcw4VR1zuahqneXKW4IiZ2yVGD9w0F/igQAxcrw/cu3dGEm0woeFwEziaTSjyhOP5hInOSeIZZSh4PD/F6J7b/pzPDweKPGVjD9jhPjfXrzm0FcQxxuQjJpaYg7nGmgpCBHJgGiZMkhmv16/oOohfHNh+uuW3/tn/h782W6HIYfWIIOy4z7MWt7skXL6GeIrI5oh0hiTGy4iQKIgDh1cv8NubL/Rcv7i4qt8y+Iiz45je7Il0jNKBpu/RcYYLMb3f0nZgGSjTkvNtjxkMTni6oOn2bxiQlEVJW/fUpiNqbtDimKbuqbqcKI4QfY2pFb/939yMEV9OI4QgiiCKBJ5wl9kMP9ye8+P4OQ+HM9IsZTHJKOOEQ3Xgu6/P+Us/97Nk4evsXkqkbKl3n6MmI8N03xk8GZ1pkDrmdDrnuhrwwdDbHULH4B17t+Plmx1dMvDg+AGvFw1nJ4qheU26irk5WAY9JgRJIpwPOK9BCTSKNFpgfcfZr95H7QuG16CE4OX1mk3dI6IUF0BphR38GPcoJMJ74tEndXeLlcg7JqAPYUQDMo6c01QxWM++7ng4y1lMvwzQeFuLaUZWRjgzJScBCa8vN5wPOx7qCVXdcLQ45vWbV/z4k5c8vHfKYlaSJClKtgTp+YM/+F2K4pjFZMXk/RXtsCWaJSS2xF11fPqjz8nzDIRF9Zo8mTAMnixLsfWARIwov6Kk6zryPKeuawjQtT06UkSRoukMPQYiwdHxKZaBy2FLvprR3Oyp6obLqEKXOcJYRN8RFwkhROwHR0/gMATaYYtIU1ItmBRThtogrSRWMcYpFIqAJDItIY1ovEQmchRaxQEbYoz37H1g8mDBZr1HDIrttqGuD2htsZEhiQVZErOYLzgpjyiC5PpmT55oknTCs4ePqOsdpdYURU5velxwFEVBZQZ0FJEmDqSkv7Pa9eZLLODbiiKF95Y3r2+4vdmhCSxLxVceZPzqz5zxwS8/YJ1b6q1Fqikf3P95wuDo6h1CCXq7w/QKFxxe1DjvMENNFK1Iohg7OAISLRVpnDEpK5p6R5ZGXG23TOMC6ww6Hdg2Gx4cv8PVZgR65Jnkpj5nla9YnkzozCWChvl8TlVfozX0/kCp57zYXOH6fhQiIsiDh9ii5IBpQfaGMmjur2Y8PX6PvBj1PEEOFHlGVde83D/na6cTbvZ7Xtx8Tm0dj/IzsnhJVR2QoWRbBZbThH3TocRAUA2HfkeqIjyGVAfKacrB7zn0o8BQhMB7Jwt+/3d/h1/48Gd4JGK0TvGrY4LWKK0JJYTOQLXD1pdIDV4V9Debccy8c3QvPyeWX2xa84Ub74OTOZ0bMOaGwTucl4iQoWRMXW1ZTuZkKma+mNJ7z3z5hG3Tsa5viTUkSrEopzRuQqEVJJ40igkh49AOFFlOHk0oYtB6xn/993/A4aZHBzGmNjEQ/Kg1EzKgIvAE6jbwatjwXvmY9c2OZw/P2O8bOjuGb38wP6X+gcDvBdGJo1jMqH3Evt1jPAivKNMZgYbWH8i1phk0XggkjsF5pCvICkuZWYKzvFjfMBxJfNRxREExWfGTix2ffn7B1z94ymoqRtN4qPF2INGnNG1MMTnl+Ncl/IEjcRnnt1e8erHnB68uCF4SSTDBM3QOKQRKjDfbEb07HjaCH3nDzvkx1eouVMPf0RO6wfNm17Bv+v++n/V/5conHmmBIEmymA/ee4fGGn5w8wnl06/g/cAgEk6WR+ybjM/eXNLamgf3VqRNxKcvnrMZDkzKG54+fZfZyZxsckScaHw3pjrpSPLJix9zvDhmNTliGAxBSIxzdE1DGsekyYQyz4l0RNt35EWG6Qc2hwZzl0p19OCI2Qclhp51vWdRzrD2TmmHoDOGg6sJWWA2L/BW0zSGeJpBFBFlECIB04RMa6QPNFVDoaZ4EeGCRWqJ9Y5UGrwzFMmMJkAvPToEtkMz0m8CoGMGJUnjBRfXW67XG6BDq4EoksQy4rjIeffonZFfOQjSKEVmoxUmVgqfZLgQWCxnEDwnyzlSR/S7lt70gCdWEJSiNWaMwvyygFFEG6xnkioen0U8OYr55vunfO3pknwSMZQaQ0/vAsvJU3Id0btrbKdwvmGSxaRqThJrTN8TJxlexkjpELRIbYiVRAfHTbNFJh06Gzg0W5QwXG7Omc8WtNYRhOZ8c4UQDZGs0XHA06CiFVIabDigIsO23xI3gWY4MJsHDs1r0giS2PPjy3PKfcbX7z3kXupAdNzoW+ZuwtnJL3A8eZcuDFRiQ1XfsukuKLNT1mbPph24rJtxsqonTBPBtq3prOF4+kvsb27J8pJdsyXPYzbVgd12i04kCsk8y5hP5wR6tu0t1muaoef+IqOcR6RbzX/xn//n/M1/r2Ap5JhYNV/hY4XQCSqK8Msc5RVh6JHBkq4KqvML7G1Flk3JJ1/MCveFG6+SCbkWSGFHFXDbEemA9TVJEqFVQPiCWbagsQYfCu6tHjEMG2bpuBfuhx6pKtzgubw9UGYR90+eoATUzQ4lNnRDxOvP4PXzA5IxZkxHAikkzjmGPqA1CA8IgXGBj6tL7tdvqNYBoSMuNzd8tqv48Je/yp/7q1+l+iPwtaZ6IzAP1zQxxMmUKABhR6ITWtsTiFFxTGoEjVHIKJDHEUoJOun48Wff5itf/wCEojUaU02YZTkDPYWOePGTC4osRz8tOVtMSITARQkheGxwmCCIH5Q0V1fkbzTf+OoTLrY9n9xuue5qzGDxDkIAIUfIgxACfEBHCqXk3ZfSqF4e4RAjoclaj3PghOD5ZYUQX47r3tY70Tc53/whg+hZTWc4OZCmml3Xcd5dUaaKVjSU8wnJFOblhO22pXt1SVKM+drDsKW2DRebS3QeE0SgFQq7rrDrLVI6FvMlkchGtbkUxFHMzXYN3rGaLxB3B6U8TbHWoqVkcTSn6Xtutls21Y6rZkuepIgcXptLtlcVSaQ47Hd0uwakwFuJFBDwBCHZNjWzOCHWCdNiQmc6WjugRUxgxAW2kWYIDhkJIiGIEPTG4rKRvGJ8wDuL0hLvHFIokjjCusDBCBb5MZ/s32D9gNWWIpbkeUHqPA9mK6wUCCcYdh4Gz2DdGBICpMkYO+kdXK7XvPO4xA8Wawa8c7x96aMoprUBwZeN920dF4JlkfHLH55xtkpo6oZt1fDb321IsoiTP3+CMT1d03E883T9S6Tscc5hB0GWT2n6mzENKh6IxLgaMaYl0hrvBdvdAaEOWBGTpTOePl7w8uJjokwSgiWojm2zZTV7Qm3WhH5gMvHMckEk4ePnn7KcFwRq0iww+J719hqhFEUZs99v6XrN0TLDiZ7KSl5vL/ng9ClPpk94OtW0pqRuKlx+xNXugn1/S4hntPUt142hGkadkYoyNvsDMpJEUcSu7znsK47nDdNSEbpTds1HlOUDHDOiXNCaPVL0RAYm8ZTGX2OdxCmHUBkmjugjz72TBa+vrvl7/+A/46/9W3+FuQfZD/jpCmRE6A5gHWJ2DHqkH3kD5fFD/HRAtI7wx00nOgyQ6JxEe9JUErwhjgYcGhlgX52zWDzi0F4gGAhyzSTJub94j1e3n3I8z8hTxmZ5gG0PkRTsq1vOZkdM5iu833IwMb/7L3+MawNuEHghsMGN9oV4bEqDAafAB08cK9am4b/85HtU6xjjRm/rrNzzv/1f/I9oVxek35zQfy8ibRNsN0OmPTpogqhAWmwIDEHTtS1pnHM6Kehay/nhDTZW0Aj2/SuevPeQznaoMGfbNwTRcVUNBC/49OUt5zcd9w8DWZoSMwqjhPT0tqP1IEWBtynlBzE/+NH3OVUnnBzl9M4gtRxvtyHgnEPcNVcEeAHOuTsqjcA6fzdyBn/3+xACUkhsCLjgkeFLcdXbmsynRMk3+OjH3+V1+5p4ocjTklQ5Lg8tT77+ITqV9E1LdbFBxgqx0thEMj2e8/XlkskLy/nVjs7syNP3+fi7n9FebDhblbiuJ4sKymjCJJ+QxAlKaZxz3D86oWkO5FlK1xn6rkdHMbPZlL4b/YJZmjDJUxbTkuhMo2KPc4HgBi5NRe5j4mbPYXNgMIbOtrigUCJnkpXoviLREa1pUcqiZESuUzZ1zbFOkErQqZY4UmilcX6g6lqUEsxUhrSOWEiyKEEGi9UJjQ3YIBj6jjB4kkRy7/ERu882xLEijmJ8N7A6OSZd3KP3Er8P9BtDkA4nRha1VoreGPKs4GR1hEBwaGqMGdjt+/G9VRIZJFVvGQY3Cge/LAB+85fv8+Zix48+v+H3ftjf/Xw0D1clq6OcTAjapqEslmgJXR2IU0WceRojeXN5xWSmEMoTJSltv2cwDduqJ4gNUkqyyZRDpRBiAG6JFYio4fZGcHJ2xPObLaaPsM6y3/dk8RQnIE5HslGkLBeXOyJVsjidoZNL/AC31zVpXHJ6cszN7oa2M8xKwTSW/PzpL5EXKTYKtOE+54dLerPnOLTEOqLZD+TKkUWCqn+DCYLaeKQcCGIgjWZUg6czjhB6knjCm9tvE7TACPjseo0bHEjI4oR5qjlOT6iHW7bDnr3xNBYQgrUJJJknhIivvP+E3/nuD/i7TvDv/6W/wrzv0HYgyBTMAaRGyJSgPWQZUmoYLP9f9v4rVrdsTc/DnhFn+tPKO9TelatO6BM78pAtSqQMphYFiZYNCbZgC7Bh+MawYEGGAUO+I+Abw1eGb6wbiaZpA5YhSrRNiSQodp9udT75VN557ZX+NPNIvpirDu0rbgsEToOsDyjsQlVt7FX/HP8cY3zf+76P0BYxE4TN+pWe6ytvvFfbGw7nZ4TUo5QFekICzy3zMEt0wwty5VAyI6VAac5Y3XtA6/Zsd1fMihKttzReM3iPCJr1bosbHMuioio9zx63rF/UuAG00iR5ewW8vcApNYmNQoSUBOOQ6EwgLjy7S0hRcpAVvPf+Hd74muJ5f878dEv8piU9P6ARDb0bcRGUDCRhGILHe09mLFb3bH2DEoJMndKNIwe54UW6otkn8ryAZNGzlswKHILd3vHd33nMMCgG5xiHAa8SwihGL6jdHqmW+DGSaRCzgrM/+xb/4V//ezz+aMMYE1ZOod/SCrRLyPR5m3lqMYaYUElMLyWRSCndfh7T3HeyHXkMAiEmduoXNdXvfvoPeJC9A0Ez7gfKIqO0hlUx53q/pR08S7PEb3qybkFfe679gMIRQkPdOWxmqSrNvhn4oz/6Ae++/pCWmm6zZ2YOeev+61MXJysntWYhUV7ie0dRlGR5QZ6VdE1zu9Y0WgqkUhhrSWmKlHS7gcobhiwRXURYT+MEpm5RSYKE4COFyUhSkXQkChjD9OwlkjEGUpDMy4JC5gxDjTWfe40TgsDge0pTEoPDmIKh75lbg71aszg944nuEUhS9MjMUoee09de46bb0jYbZHQcHR5ycvoW7RgpjKC57rm57CiUpI0tMkmUNgydx9c1ZWY5WMwZho6yyLFZhR8DFzdrfIhYJcmNYvRfkLU+r9/+/g314NDKItD42+CcfmxphOQbp4JgLbgcrSHmAedHolcoqThYFIyu46B6iFR6GouoRJl5xlAhpSRGR4gbxi4SpEAKRww1h6vX6AfPzVXHyWLBZn2Dc4p7Z2fc7M8JY2JRZhwfRVCR7WZkfOI4PFyR5Y6Dk8TFiz39UHD/bkmeBG8fvsk7d36F3md81nyfkgwhB27cFuEdde9xSSD0ETqLVGnO+c0aITKOcsUQPPW4ISVPphVDe82dgzv41HH3cM717hItJEVRQmbZ7h6R5ZEsaZRRXA2wbkHZgpk+pB8mpnW0kKKiWCx4/+F9fvPDP6Lznr/2Z/8sZ32DXp4gdT6FaowdMgmSGxHWklJECEVwnr67oOC9f+JzfeWNNzeJm+1zDhbHLIpTdvsNzoGQjpQkOmlc9MyyAyo7Q1uLEpLRdbx5721eXkkur1reuJeDqFnOSwQF80qhVUTagS7A9//oitAmQkhI4vTvlEArkGLahkJKxDSxeZUBLxL6tEdfFMg2QyvJn/3LX8OzJy8KtsEzVjvKLydSYyD2aN2RkiQGhcJgM0FhBsbY0Y2CXa+w2YITPaNIIHUkn8+QMjL2Oxj2CHlE1w781nefUO8VMXbMK0ORL+nDNKNVcURHixU5yoDRHhFnFAdL/tL/+Jf43/2v/ws21w6bKwyCcZgg1D6BSHGybqeEvAUxf27mFrdeTilub7Zy+mcpQUr/P7krX1QMrOVnyMPELB7x5tFdRP2C6Huc95y/fE5zvie7HjlYHXLd17z5rV+iyiz7/QU67REkohg4e3jMW/fehnZPdjIntZ7kE90woo2hjQNjHrn/9gmujbz84SOUlJgsQyHJyoJJbx6ResKMaaOZzWfs9w3trqX5wHH0C4dcZgNd74giUpaGstHs24gTiYWXOOPowkihLc3YMitK+ghuGCberUoMvqP1Di0FKQQUDpMSd5cHdH6YhDPKgJH00sPC4t2IilNYC1YzjpAEIANvvv4WH/zkj7GZ5s6Dt4hGUWpB7jXXT7aMu45dFnm6uyTThrkqWC5WdLuWEKYXVF03PLh3F21G3nr9DnlmuLzaYGVAkWj6L5KrPq+rZpr9GxmwyiCVRqqpq1XHETWHxIAPGsUKqzcMMaOwdxnDM6R2bOstRI0bMkIc6fuRIgu4oWHfXJPSHRAj1vTcND37WnBUntALR9NuYAwcHdzlo6efklLOsyfPCTFyenqCMg3RdSB7VkeOvtV8+mnDallwdMewOGwJjeeN/H3ePL5HNivYxJo69WhbEURAi0QXRqQ0yGzBfvcpXappt9fM8gOGJOiGS8Le8fA4cVBUXGyeMcaKrduw4m06P9KNipFjPD37/Z65LHh48JCiHCkQNLFn2zd4ZqTgOZivMHog9y2JFikVxWLO2eER7+32fPeD7/Piuubf+Y2/wAPvMPkCbQvoW7Al0s4gWFBm4lw3DaZ5NW3NK2+8rx+f4KImyQVGDeRZjksjMajbN75HyYrL3UhXOfIwZ1UUZCqjVA3y0JFJR30DTT2SYktRdMRoiDKnc4pHn2x49NMdImiMgeg9Ukzt1XTL0/WBqc2KuBUbJYYRpAxUdwM8N8QEr719gucFWswwwiOMxWPQxqNCAWlAKYVUAh8GhJKINND3AyGW5Dan0A4hYN/XnKxO2bBDxJpMj2gFN1vHZx/vePl0ZBwS+dxQ5IrcSBQOkRJFENjcQlhx7bakULLvbpAJ3nr3Nf4X/5vf4K//L/82F5cN1iikkEQBPk4sVXObiCTS51tpRMpJ4fz5rTcBMcbp15RQWpK+SK76Wf3g7z7mm3/+TRarkjQmgjJU2RyxymmGln1dM89nzLKC5TzjoVxyvd5w5733mBeBroaLy5cIn2NEiSoFz6+uWb9suTc/46CckVcZprRsh4Zd2HIcPMOtkKmrW5bzOdoo+mFad8Zogp+g833fT3CLBLNqRgojbesIByC0wjvHTjTEZcXL7Z7yYMGQJt6o94ncFnjfISIkJN04YkM+rW8pUUnQtc0tx7UAoRlFQGhNiJFuGIgpkHSi0wkdHTEFMhQLo5C65Mm2wUuN0pY3X38LISVWFcy0IrcZutXYoBgRuNZxpBcEHamHhrO7r9E1DeF2duxGP63dlCgzycN7xxgtubja4oNDfqFP+Fkpphn56AL71jEvDVWh0SZx940lMpe0G4EfGoZhC3IgATf1IxADQ+fJ84Ku62n7xDDU+BQQscQTaHvJi5eXzBbTwXI1tzx9sWUrC0zRMvqaapVTjy+Yz0uSg+ef3XD/3hn1pmd5IKl7w6xaMPQ9VdHTH0Z2jSe7gS/fvcNrb3yLws5o1DOkUXS9xYtEVZ6ybz5DmJHgBNXMEsIOY2YcLg65Xp8TkrtFv1p0JajDFVG2aBu42u8RGI4Wr1OYObl6iGFLRFGViug3XOw/4s3siFFFxqix+h7EQEoN6+0Vy/kpMiXQDukSJisolwveODnhvK757U9+ysXf2PM/+qt/kS+/8TZGF9i8RDqHb1uk0ghlkLYCpVH61eYkr7zxxghaSga/pXUeLQu6tkMoxTAGhJIMw0BmC9a7DTZLuLHgqCpY5nBS3aE0NYwDza7FlYmoHJ2zaCe4aRr+6Dd3pHYSjiACWTHdGiFNt+sJu4v3t7c9nZAqoTSAQM4c227PN3/hXQ5PSq7aPcbOaZ1HqXy6tYSIVkuEGlAEhGwR0kPKiUmh5QxjZhTaobTnah0gJtx4Q1V65rOcrpfM7AG/+/tP+N4fb8jNhJwSo8SoAuIEK3DBIbMSSGi1w8aAG1vy3FKoA6xMfOObC/7df+9f4a//B/93btqRlEAbPb18JCCm9mBEICd5M4lESAlIKORt68n/7JabUsJ/sfH+rF57uOIkW2C7BWjBftNgq4zLbj9liKsMUSiEMDzbTb7a4fwxzXFFEp7f+Z3foyw6Xn/3AfdP3uT3/+EPeXH9lDt3DmjnHYvFnG4cJlRYplkuTvjgs8cc5DMOyopcaPIsQyTou475coGSiiAC0XuiiyQlaPuOMXo4SujTAwZX4xhIKTAIyUX9EjHXLLIjrporTqVFSIVEEsKeTEGuDKiMum7QC0OWFxhjcDGwrGaIMFFghIjTCy1KrAwczmesdzdIOZGvCqsoskSBIPMekmRfj4iUODs+4bQ0uDiJ+3Z1R3g8kmc54zCQZ0uubm6YVxUzm7PdbfDSsZrPSUGS2Tfo+44YIllmMSrx8N4Ri1nOxdUGH9zPe8n8iamuT+hS0HeO0QuEigyuQ6nIL7/1LptuhCA5XhU07TUeTZ4JqtIQQiAzhmHsuNp4Hr1Y8/B+SY7jxdUF1ewYl/a8vBIUZQZ4XAgIkXN1U/P+O/cgCnaDZxwFVdGw7hz37hyi85YPPlrz7v0T7p2c0aZnNK5DBs1qFjmZlXz99Je5tzqloeO6f8KyCNzUNVqXWKYLgzEzXm4+ZDYrmWUCrVtwYCk4qF5DKMVcaeY2x3mFsnOi99iswPQdegAjDSLN6LsnjEPL0eweN/vnJOmxRcmiuEvMXiNPh6j+gjF+Qu8UUU6C3UpbokkUMsdqjSoLlgcr3t+f8qxt+MHzx/wH/9H/hf/JX/xL/Or7b1OVc7JiDkGglSFqjbANUk3201epV954d63HZKDk5C1bziu2uy3N4NHa0HQ9oswp9SFaRq42j4iLI6yexE5ZCvhYMsQL+lQzjAJlcvAZplxxddnS7yMherwXSDUh15QCqRLagnfTXFcyRSVOmdUCYxJaJ5QQKKX5l/7lX8DMN3Qbz77dEVxED9fITCPNgqHdY0vI8ukmPQ6BRELpjBQgxS3CQPCGWZazqzv23ZZ5lrPbRoI44Ac/uuTRxwNESx885axEyMS+c2wHRYwTe9InT0yWMI6UxlLNJTEkgk8IJizWN7/zOr/x177Nf/wf/zbDEFE6YLVAJEj4qY3BdOqYWsvilqs6bckhRmJkAigIwegjwX2x8X5eZ0czmk88zAQp66nu5dS1Y7seCFJgdc5uaBDes7tcMwbHaVXy5Mcf8Qff+wlXm2t+5V+8S6Tjd3/vR/zhb33MYmYphOAwVzzfbtFeUpiCozt3Ob+65OX6mvmdipimAUHXDRO8Qt4mkqWpS6HUJNGvu4YXN2tGGTjRR3QCIoogFH0asM5yE1pkaTmwM7r9nqg08yIn+EjyEsYRrzsqa9Aip2862qjZttfcW53g60AqBTEIjEzkxjKEwDAMrEUikKG1IUZHHWDoAwfVnN3lNQ0dZW4pipw+SBrvMRIybVBR8OjxS/Ko0WZyAeRZwWa9Y+x7jo4OQcBNtyUQ0FKzafeM3cDxwSGCKXWrOFpw52TF6Pqf95L5E1PD6CBFtLET9m50FKXiV75xyC/8omGvOxY5BDGCGuibQFme4MNuEtUNHicSZib59NNHVOVdpNoQROTZ5RPyaqQb1lgxY5d6+mDoRc/1NiF1jo+ewgaev3jO6/cXHK4idX2D0Iaz+4oXmy2eAasjbx2c4qPleHaKCJq3z77GkBoSkMVDxgitc8xtIoaGMAZKUzIzB/TBctP9hGN3h5gUUo5YO2MYa2y5ZN/fMHhIaDqXE5LAhy3D2HBx9RPuH/8iebYkpMhu9ymV1RAkR4s32IxXpADWdEgZSekAoxPWjiS3RyaJyQ1zvZg0M9qSZTknyyVfOzikGW54vqv53//tv82/uf/T/Pmvf5PZGMkyhQk5DAnZNAipJ7HVK9SrJ1d1L5Bdzt3DAwg11mqk0lTKk/BMiNCBbtgwm99jN1qu6hdsu5J7B0sOypYYRkQKHC8rwiBBFNTRM59lfO/ZJX4MzGZThJ0fJ0GRgOkmIUHrNKmau4SQUzvVu4nWk5hQT7NF5MvfKFn3T1kWR3hARsV8rnC2YzdG9vsRkSx9rKcHkh1ixC2iDXVLmMlwbiCGHTYbqewR3cUeHSRrlfjD31vT7hLOD+RFTpLTZ1C3iSEIrMxRoibTCZck1lQMdcvOdyghqKRCB0PMC66HK/7qf/9X+cM//oQffe8KicCo6VBxcjrHO8/V+cDo+Bk1Q067MiHE6XOSkilMY8pzVfKLjffzuqdfJ2aWo+M7tPs1WS/pbnrcsxYOFXZREMYaoTR9GBmATb9lfbmlUJr3v3nEV799n+eftDz+8Useniw4W60QwnBW3IUo6d30+/e7HbGpOcgL1OgJLv1sTOBHBymx3+2Q1fyWyzvNeGMTcEPL3ZNTTCuogqIoLPvkEEoT1p75omK2XGK05XIvUFqhpCSKxJ3lCTftmpQ8nUzkQnPncI6NFtEq5HVgWG8Rr88YE1Szklx7MqlxvqLve6QVOD8SlGL0PSbC03FLnff4YVKIWgb6LpGyHIWntAXX6w4/RIrKEkOi62uq+Yy6D+w3NXa94ezOMSEJet8hSJRFgUTcRt9OsahFYUlC3I6SviiAJKEdPJVMLCpFpiV/9tfu8Mu/ckjxWokXkX3fYXSLVoLMDrx48dHk8aan6z3aKGTUvPXgbVazu1jTMPSamawpM43vDd1o6XaXhNSzygrMQcQ4T5Vbrvdr3ny95NGne06Oj1gsOzbbjswY+qxl6wLvLN/l4eEd1MxycXVOiHsGtuhiRr85x0sJCLb9miBgXk438d45tFqSG4PvLUPcM/R7qqwiyQOCWSDHhmU+svU1RvSIlCD0zEvNMFge3fyYy/05793/ZZazE4ZwydX6D/nGwy9xvXnEeTfQtRcsygVnh98iyhlRXKNVR4gOIy1GKObLIzJb4l0gyzKyMuNsPufNpuHxeuCy7/kP/4t/xNObPf/Gv/BnOBALykwjYpx0QygU/5RbzUoMjD7x/MZRWMnczrmzOuDJyxdIIwhJ4ZMjyiukCjy4+4DNbs/V9UteXl/goyI3kVwKiIrtvsZmjmUpUOGCOPZEN7VItQWhp3luur3g+XFKaEJASJBuWaNCTJD4ECdYwJe+ep+DU8O1M2iTYVTGfmgZzMjYTC0s6St2jSdEzfFsDjEQ1YYkDInEvps8jVYHhuiRwvD28Tv8+Mc/5fyF4x9e/4Q2gioU1me4fkDKDHTi5eVLxv6UvEiQFGOIuNiiRaQdOqgKMpVDVNOLqA9s/ZaTecVf+7d+haef/T8JXuK9JyI4fqvi7v2C3/zPnzFcRnycbvp5qYkusq89LkwbslT6Z95JJb/wZHxero5smyvmR8ccHp9RzQTdbkSMkc1n17z5C28QyoIgoTiY87TecxNHmutrvv7OGW9+Z0a765DBE286QlGwpuFL33qHLFPUVw1koOYZKsu5H8/QpcVtWoIPIAU6M0QvwQ2TFScEUpwEVkopZtWMu0enKKkJa0e6HFi+mZPJhGo1elkwyjUSSRsiwhiUEHRuQEqwZkbdj8xMxswWkyUtJpwbCT6wK4H5AqVBpUgfEr6fxi1DCDg8lbKE24NsJjPGMNINgjEaROwJY0+dNOtdw8qsOFvO6J+MfP+7n/Dy6UvMG69zOFugk0SJxOFijhsGdvs1i6bDZCWxh4jHKMv1fkPyiWVVYTOLSIIUEil9sXY/Ly2nsCGtBA9OS77zzUPuvZbzWTvyjs0Jt3PGEEZ67xnCiLWRw+oYbe5Qd54sS3SdI3jDQQmDH9m6HdvacXr8Dh+8/JQ71QNElvPis45vf+NLvAxXPP604fTOIRmOKi957XXHj350zsnRkiyPPL9s+cU3vsbBasU8f5N6vIFx4Hl9xaooueqesNDvY/Mlwl0AGqs9AkipQpsK129ohxu0DZzOD4lBY7L7dHHEyhoRGqTwRDFSFhnebymzil1/QKUEYmXpQ0XT9vzk6W9ydvAGi/mMw9P7DP4FnSop8kNE6qibHUJ9ytnBl5DmgBQlLiViHylSRVUdkVmLDiOJkgUHeBd538NlG/lHn9ywawb+5t/7TT588Zx/61/+03z5tbeYZTO0kKQQCOnV3CSvjgVcHFIUhwy+JVMLtDIkn/HsYk3rHUJEtHaAwsoSmyTLcsG8qBjGC5p+zb4dOMgjbQgcziVJl3Tdjq65oa/DJIf3grH/PKVp8rDCZCPySdxy3+NtQIQgBBjHiDYCZQR/6a/eRVc1Yh+IqSamnigidReQsiDPLOLQYwdDlAU+blE60rsOkZZYo+kHx/Wm4bQ6JNnIrt2RyQ7uCn78eMuuTyQRkEpjjESrAjc6cmXwA8RYEYRFyRFjPDFOCuo+JIa9IJtVDHg8DePQofRIHSPf+vUv8ZWvfp/v/dEzQIJMzI9yygPD6Tsl66s9UmoG50n1SIwJnxLaGASJcYy3oueENV8EzX9eIk2bwPnjzxB37xJHA1rw9i++y5cWlsWy5PzmhvPrJ4Tc8uzxMzo1UlSaw3ctYYSf/OFL3n3zDm88OMQUOaHQbOue7fYz3nn4AJ9FeuXBCKQ2qPVAaXMCUz7x2Pd47xjaHjPTeBkAgRIaEuTWcrBcTTN9ldBXEneW8FZQxZwwQLXQU1SrEBhjUVEBnqQ0MTpWZUkYR6TUFLmFOCWdZTYjWTPNlNNIbjNIghA0VhtG1xNlou9HUvQI9OStFYLBBbrBk9kZVanJsoq8gLrfMS4jy+NjquIJy8WK6/WGg2pOWRWEGJhbiT49pi4zjJE4N4CAcXCsliUnJ0e0fTe5FrRCKYsWYkIcflEAWKMROO4dGb7z7SV5EfjPf/tTfvGvfZ02rKm7DUMQ+ODph8S8WjGrIsY6erdl8JaymIFo2dWXHCxKYhqQ0hFTR4jg055uaCiL23eWAmlbzmuP3GmG1tAlmC9K3noYuLlwvHP0Du999YhlcUKnBup0zaZ+xvHsDvPyACEim+EGY8+JypAVOV3fUdgM5wR5NqPpBzIzaXAkW4rsDkEuCX6HEAM+tCwzS2nm1IMnJ6cfoBv2WHOIdwcU9pQDk+gq2LZbrtrPqGzOaqV5tOmZzb/NjAVb9Yxu6Hm+foYUkcPDB/TDOGEGO8dCHZCZAq0kQmmMlmTWkFc5h4sFXzvpeLrp+PC6Y4iO7/70Qz7ZPOdf/VO/xF/4+i9ztriLjkzgnVeoVz5aimTRGHJREYJnP+yog2R1fMK2gSgtY/DsmjUp9AhfMwznyDhwb3WPN0/f5/17b3KmDtjuwYcIoaaqBC4Yrl4MhDHhAox+GrwrPdljRgdjYII/h0SeS3IrKLLJTmSyKUbyzeM7PFBnjJcNlprR9XR9xzB2pKgZI1zt9jS+w2iLFw0+Te3lWTbHyERMA/NSsFwe8tl+zaOrGtKcJ9d7fu/8BU/7G3RuMSrDD4EYA8hICI6UEptNy+VmzRguiaLFCMfKWoyHuu+JUjCERNPv2A1XbIYXCBSFtkgT+I2//B2SgEBEFMAi4qzk9S/PqRYaKRVaK3wQjB60UWSZxJgJnkCSZEZhzBftus+rHQeapudmvUYYTVmteP74guePnnP9yQ0fffdjXv7+c7ofNvjHNfkuckrBl98+4+Rsxo+/e8OTxzuqec7x2YI33n2N1dmKH/7hT7nYrbmMPVkxp8xWjG1ARjWtrxBIUmCzjMQEjz+8c8Lh6fEU9ykE+jaTWwmBURotJbnJUZ1h/LAnGxI6SnyvWF/tebm+ortFsw0i0TGRrJSA1+fHNENiE0Y2Y0/nEkP07LqG0bckH7A6I8aICz3IQDtOft1M5kgSlZZI7+jHaUySpZFFBiCpO0fb9ijcZP0QAp0CR/MVy6pEmozhFtIuAK0kmVIsZzOKoiDLcrIsZ1aUGKkprEWkSY2vtUUrTVYU/P/xWvpnvnIjeO+1Gb/xL53haPlPf+spH2wjh++e4qPEp4H1Zs+yWvHa8RmZigyDZ9f0KBlRdLT1gJIrXIAhaJrOsaoOkamhH3qE1VxuL+n8DpH3rHc3hLDmvXdyknlJrTY8213x+By+fv/P8Od+8ddZnXwNOSt43j6jrHIijqA6rFCcLk4QCs5vLqn0nFzOcV0kU4pMzYh0qOhBRAKRZWEozAIhloixxwBKRxrXEZNCJI1RBQqLURVCam42n6GVIoUBFy390FCZntcO55yt5gw6R5Rvs2kHgmvJ7WucHn2dg+WX6YNiHAVRFCAtpras8rtkJkelCctKjIgUkALKMudsecCvPDjhqFREBDg4v2j4j37zt/k/ffD3+OP2B2xTS5dezQr3ykfLMQwMY0tMkc5JrJ0hdKAqFavSsKl7lNJI4dntn2M5o8wyKguxuyKaxDBe4XeJoCSRhIoOGTI++ijS7iF5MW04CYK8zcxQ00tFREHwn0OgE/o2SEKrKR3qwBS8k47Y/3aOfWrI3zuiP7uiDg3IchJMBUGhcjJzgohgvUCIJQjPGGqsmeMGz+ASQXoOlkcoEYjtDd/9nY/46aMt/RAIfofJLUU+RaD5ELDGMrQePzZ0XU+uC2gTolfYKpKaisPskJftFU9unnMwy1kUJVVhyUWGTCVIzZe+8zYP3jrg8aMb7nxpTr6yXK077pwYTu5Znnw4IqXE+wAkUpz8mXmuCR5GF5iXFSF8EULwefnR8/GjZ4wxUaye8+lnz3j66YuJtSxvpvUlwApFBO6bOaLXpI/hp89vGC8dry3v4FrL5faSi7omtzNKYQn9SAieclFy9eKS9nzHfH7K4vgUZSwiBoa2Zex6YgwMw8jN9Q0pJsbRIcW00I1VJAIxRsYhEfuAbz29HxiTYPt85PT9A3ZZZIie0mqafsSLRBgdNssZUmJRLrjZb2GmsDowzxWLWUESgdxOyVS5tiQUEckgHD55NJrSeh7ajMPsiHPX8nS3RciJ8yyiQ+oMJwIiCqJPDE1g+7xBeijyDPIcqSRaCgIBqyzg6foWIRKzsmJMks12y76uKYocBJgsm2a9EvIsp/bNz3vJ/Ikpk8PhccYPPt3yg892XA6RowclMhtoux1uHDlZvcuyqgjjJVJrmjiy349oWZJSpO46pJkSr67X5wgdebl5SlZmrPefMfqa0WkODkqyquN58yn9lefOSUvwHSImClvx8PBLHJqv0Jln+ABD27Ntz7nHQ/JsxmJ2OmlOUiKkgh5HEAVCZozJoJIiqj0pNdTDS4L35GqBlwLnA9osUaKZuiCmwqqOwECUGSIlbJrhRaQq79K0Oza7S1bzUzIt6UWNMIHMRD68/DFV9g7LxQOsCnTDnhhBqgg4mmEgH16SSOiwRzcrFssFPtQTPzgzJKdACUxmUL3DWM39quKbdw/5rz65ZIiJ5AT7a8d/+Y9+hHn9LtcLw8lNy1de4bm+8sbb9B1JGIiSMl+QUiAFWJQZD88s4UVPFwIyJVxsacdrDvIZRjSovGNMNVWuePIsUjceZ6dIxP0OPv5eS3STT1dpkEngxgmEIMWtuhkxxSH6hE+AmXy82oD0gu/80gl/4RdntL+95cmnirvvZCyWK/q6JM8szbjHKj3lJouBITbYZNm3HTGLlMWcwQ2TXccIvBOT1zL0XO5qPvlsS9eAyDTSRELoGaRDKjkB6iOIpBh7z0cfP+Kb732dWT4w/oEgD/eweY5aPqGTW8rqAKFOyFTGMFwjC4GPHucHbGX46jfv0akd994rplbQELC55Su/uuLZo+f4fiIgiSQIHqIWhFtVsxCCfhgnFfQXBUDbd1OOtU98+MOfogArBbk2FNpQZAVlUU1wdufJTEZKDjc6pICDQ48Lnos/aMlSgTAC3cPC2oldECJdu+fu0ZyzPmd7ueezqzVaG7QxOO8Rt7fAzBiCD/RuOhkXZQkh4tzUepZymtd3TU8Qkd11Q19FeiLt8xvGmeRs9ZC2D4TQUxQL3NjjEwxpIMsMi1Qyqwxx9OybDhhYFBIfO2ISGG9IPlHLhA8ek0lcGJBCk0kok6dUoLRmOwwsspKcgCSx71qCKcAH9mPJ5sWGtm0IzlHajOgD5AYRBVJJ8jyn9yPrmzVGG0w5IyHZ1g2tc2z2NfPDFZGIUoq6bRjGL1TNn9fhScaPP1lzce04vFdRLAcO7hjywnNzted0+TYow812w6wQJJkweca26aZACe9ofM3N9Q2vnbzB4BS7bcObD+6yby4IcaAoPImRR486VrMDbNZSnEqeXe/oa8FXHr7F/eMZgZKXwzNW1Zz65pwgICsXXN885nA5RbD2rqbMl+S2QSRHP3TkxTRqGIYaowWzLCNTM5JoaccaiedgdkKlRupxwGaKdfeScWiYqRUIhVCvse3XSJHhXWRRvcG+W/Po6secHZxwkB2jzI4X/TOasOTqxWO+ZN+kKCe27uDXQMmxvkvbGbRwBOGYqQOW3Zz5aoWVkuhHEAmbTzbQmAQJRRAJ5xxv9RXPThs+eNlOHViXaF50/J2/8Q9Y/M/+u1y9+2rP9ZU33s55Wv8SIzWd2+BCBsKi9cC8fMhSf0qKNZ2HfkzcWx3ix5GN26CzgBYGZSToloNVjrcFIgm214G23iOUuE1imlKakpwsUUpPv7qUJt8ukMS0CSsBwUXOzpa8+Z7maXyJXuZci5bsbIlsF7gkULGjyCTdMBCQCLFGBUURZ9R09K69zb6NWDOdzJQMxPSSMXh+7/sXNE5OIfMBMquxRkLyjC7ig0LpjKEbETbx7MkNL2/2rO461FEkNj3hgZ7CORqB62EsO9rgUbqgCQO2CeRFiYuOX/vz79DMPqU6hnrvGYNAFBlvfk3w8HczPvx+D1KRwvSyEkIwjpGYJkbvGAMxfrHxfl7PXmwgCAph0VKilMIqTZHlzGclVV5RVXOUFPT1nsOjO0gC3nX4oSMEj1CKeEuK6l1HTJHqoKQPHc1PLvjs8YajwwXLYoEQkGszuap9QIVIZiwQ0UojEGRCcLO+wWYZmTa3bbNEiBGbGWw+wTXcGLm4uMA8LOj2gsv6ChcTNpvT9y3WlCip6FwzsaWV4Hg+x7meUmt6LDF0DEPAZhq8xAvFzo9EM3WTrFCMODZDz6fCcSUC7SjoB0duFM71VEVB22yZFzMG5zE6MbqRdruhlBnaZpjJUE+6TVS75WqhpCbLC5q24bgoOFlWiDjy4vIGGQWxHxilRgjFdrvH2i9azZ/X9Ys9620gCZgvPN/45gmHb1QEahKSPF/SDS9o2xZrl3StQ2pFIuKdm8IifAthQMo9OgPR9Xzy6ceslhUmSygxslpIUhCsmzXuWnB8MiP4Pa+dPOCNO7/AVfeU1dKyuakpFwuyXON8x6o6wPkdhV3QDwPGzImhpzCORaZIUrB3N/TjwNyckZueXku09LjBk1uHEn7qlHafkckZQ3QYM6N1Wy62j7l39HX6qAlpjpIeERqKbMVueAI6sd1dsDqNfHD1IcfLLxNMxl484uWzH/DWazO8KYjmGNLIMHSYpKhySxCK1RoOhiOQgiQcOlfEcSQGh0JRFCV+jFibUc1nHDQdXzqquK4HLvZTxyqGRP2s42/9H/4Wf/F/+quv9FxfeeO9s7pP6/zUGtJQItFKY/Qps2JOutOy+dCBHolA4y45Wc1Rqpwg7iLHhZo2BupuRMTpFN5dgwxT4ERMt6CSW97u57aC6BIhTWIPoRLGTPQepSVKCP7Md+6RqkBbHKDe27N54WguA0fLyDyf2i1SZNhMsOsg+AEZ52gzUF+PRNuT65LcRJBbBp8jRA+u5bMfNjz/rMN7j2Cax7nRY41BCIVEoYUmjoFJzxQIbuRys+HeYcXsjUjvr4gK0ighRhQRN9aMZY6Rir4PyGJFEmDMyLvv3+F7ny3YO493EVspvOxZHBl+/ZfOePHBU3ZjJMkpWGMYpzxrcct1EYCLX8x4f1ZJYbXASIVATDcvZbA6w+gCIS0uQDP0bK9vEKaiyHNEMiQiyliUnLKwu66HJMhMSZ5VlL6asl63I7ubHWtu6Mceow1aK8qyoigLog1kmZ3WrFGkrqMqC8ZxJNNm2qKkRCSF95Fx9FijyKzi6HDGhWtYVCWLZUmfJMbDLCbabkeRG/JMMQ4ecgMSlM4R/YiPPUkGfPCUwUA0U7cqdYwukecZafBYk9GMjk3bk4Rku4NmHFBjQOEJ0RFiRLqIlQLve1wq6LqOsrTkeT6NW/qOFBNlmdP1PUYUkAKr5YK2qRn7EWMNpyenNKPD3LagJ5qOnyhO+ou1+3klB0ZCP8JqvmBezAlucnbkxQH7dodgTlGMPHvxiOVihggQg+B6fcXpyRF5CBgN/bhD5QJV9BSzIy6valLqOTo4wiqPWvasR9huO67Pe/7UV/4MVh0Sc42lIskexzX7boFQAuhZ5jPqrsX5LVJ4xmGLFpIYdxzMS+bFgsaNBLOl9Zd4OmLqkbpCyh4X1vggqfLXaHtF658hsxUxeDQSbTKeX/yEcnmHXK8IAkxest4/B1uxiB0PD05pWDNfvMbzq2ccHbxBmSn2uxv25y/wd+Z0w46RSHADztVEsaQUkH8mmHFMCg6Tz0kiEtzU6TLGILTCZo4sG7HWMCtLTuqSN046Nn1gvE3TxwW2H9X8p//b3+T/+N/7Jz/XV954Mz0jUrMoFxMbN6SJuOI8+/YlWVXx9uv3+elnj9nXnky3FPmIlG5KFpEGE0v2HSgtEdKz3xd89kEHn+cLpykhS6Tpr0nJDAZJSpEQJrsBQUwCJBP4+jfvcu/1hPMlF/tArjVbuWUeZog0ouSEP+uGyJAMIlkKUyKUYXB7Tucn1HGBl2tQA5FAChtMyhB/cMj1d2ukUWg1WYM+R/UNAwgkwUWMUogYydZQUjBbKazr8eGQXgq8qBl9wWW9YdPVHC3uk+UTPzdFRZaVON8SsobCeY6KJavCsulGDBmLueBokSF94rg85Gyxob7aEYQgpogU8jaoe/p7bj+vL2qqQmqsypBKIbWiyApkmixXITKlfAUAzWazx8dz5rM5RVlNX6sYiG5EkAh+Ui0KwCg5CT5khjIFMkRQgVB4gg/EFHB1y67pkcYQSWR5RrWYIRBURUWIk6YhpYT3boIKjCNKSWKKU263EPg+MOY9fedZLAuUKZGyYJFJvB9IwSKTIEaPUz1SG8qiZBjXlCljiJbYZUgLyQSMzieqyjBQzkr64CkyzVznpHqy+yyLGVIlKpkotKZXOSF44jCilWXoRo4PDkhhCgMRAvphIPjEECeq2NlsCU1NV+9YLiqEtAQRuWl3iFkgikSfO1RIU4t97CmL1c9xtfzJqjunC46XikePdzz+8Q2ffXzDt/78Q8Z+g5UKZQN9p4lhRArwThBlwMpDenbcbDqqvCSkgaI4IiVHpS2ZBT8b2Lfw7HJPqQ3VIhHDyEm14v03foX8cMX5i2cczo4QyaGZYbOGMdasiopxlPRjfRtfOyOTAVsmvO/wfqQwGZVO+DiClHg5px0GZtkBY9ghVIVgBBIxSrwYENkR/bBhUZ7RDtcM0SIywdXuOQeVIkZDSpGodxRp4LXj+7wYrhD6mLPFKYqn1M0Gm99hO7zgo+4H3EtfRxcZwo8kckq7wEXBfQ553S3IZEmuK9wwIvTt1SVOjlzpJ1GY0QqjJEWZUxQFJ53lzpHmyY2fFMBI8JHmWfdKz/XVfbxoZrlFpUDEEom40BFiQGmNxnP3rCCEQ55c3eCcZ733LOcCrScfWgoRFxLbMYLWPP3EcXMZIUxtqSQmgopGEHwihCmLWamEtQIv0rQxT7Q85oXlO798ShcakoboFbpYcO+Oou1ecrMZ6MaMw2pFUVQQRjIjKbW8NWkXtOxhB1GNKKnxoZuyZ0XJR/6SD9QNvVX4vSeGgBDTAcEIi5ATYDm6SF4IrFOwU8hjTf3iGu7do688WhlG55HCIeTIut9xz1ZUPjEKweEyR1XXKCTFJ/egzzgOc27sjtoItr3natMi5ok3vrLg1779kM3vfkDrAm4IhDC13hESgSAwMXq/qKlyk5OZHJ1ZtNFYNXlGBQIpFKBQQk1Keluw326JPqCzHKUNKEkMTHNzI9EpIaVm6jEIlFRkyiBkQKaAyiTZQiNlmry6UuNSpB0aQvSMmz2DH9gJiS1ysqxAWY0ymhTCFGJfZGgtpuQi79EqY68iulxR947cGCKB0Y8MPpHop58Bzdi7CeaBJDmJiwmnBCaPqEIyekfwEq0U4IjBI4XG+YhIirJz5LPEdQoMUbAspkPL0HuUSGRFhveRTGT03UQWUkISfMAahZWJ/KCiF46+H1BWMbaevh8QRvDZ7jkX9SXIRBIJNVyzWpScHRxj+5K+f7WX1z8PpUXHsljyra8c8r2PL4lR0p63yHSMzgNdvUelChEifQvLbEY/bIkpkmvLdr1ldu+Uto/M56ds9s9wvqdu1hwdLjC24+pm4Kbdgzzhl9/6Dm5wrI4OaAFhLZfXzzk9XDL6Bm1g1z5hVnyVWXmf6DcEp8jkipYNKSV6PzB4gVUHOOHYND+msDNKe4SIR2iZ8HQMwx6lC1zc0w1XSL0iJY8P14wxp8wKJDn7bkQpy8XuY47m98hzyJNiae5w5a7xlNBvwWSsFg/I5EhEQvQM/SUzWxGkok0ZxkZM9OiYUXzSU/gz8mxJHAaEnHLwRZQIzO1dNk6phkIiJcRb4Mhc5JwtenZjYrOLxDT9169633nljbeyM3ZjQ4gj1s6JaWRT78izBf3gyTJNPzpOT1+jsJYXVy8Y24DLEpoRWwicSygVOFgk9o3k+YeROCaU/Mc/sBDTDSTBz7KKRxKFhFxDSFO7OQbBL3zllPnJwMgKEQWIgOtHjvQBuVpTrhQhVbjgmck1WijEds9cCc4OM/p2IHcFZrZgMzTIFMmyBzgJP/30hr/72RN2RkwvXSXx3iGEJDhQt7NoJScq0xh7jh7kuA8jzaeJj3rP/aM9Z186IKmcPM+ZFzkbf4QPhm3ToYqMeemoigEQyA2ERwGXFG8vv8zHw47dEPGlo00KZ1c0M8fX/mLBT24sP/lpSzXLkAoEipgiboj0LsH4xYz381rMFmhtQQistVO2sQ8oqVBK/+yQkpDksxmX9UtS0zEbHblUtzFyhhQnuo5IERcDRk0ELpIgRQFJA4oUQKucoszQavLpunFkXiymZxTG6QXjPYPvGbqB/X5LkpI8z8iKgrap0XpK0jKZZrEoqEU7WYukwgdHISxBOUyKHFQ5123PZt9Q5gWFnhJo5kITliWxm5zjYxuxWqNlxMUIKdG7RGQkOliLAVdamq6nCxFjFLFUdGOi7WuqosJaQwyJwQ/4YWS7r8mMQehIXmgqcq4fP0fOLOfbPUdkCK9Zb7YU88D15obG9ZOSWStScgx+oB5bzrIjlmnx810wf4JqlmlIE+pTCrh/XPH+wwUiGEKQSJXYXE8c8Zn2XJ+vWR5axq6jyitmqxzlJHfnd9ldNWz6DmUU0mheXNbT4c7BWydv8803/0V2fs+T86fMj/dYNswry8ubFm0PICraQRKdIbPHbPcfMMsE3sHgtigxo3U7fEiMfmS1uMOYGpRZ0Piei+2nnB28AaJlGDqEkAi5QIrIdf2Y44Pbm3k2I8sMm7phNZvRbC4m4a0MuOElZ+YeT7prQuM5OL5HTJHoPW1Xk1d3kdpCCJOgkojzNwRyKntIqSUijPhdz+JiQUVFcgPW5vgIySeMWRJSDylipCYIheojNqsoZ4nZAPNu5Hg70BwEuj7S9lPoxKu+dV954z2/vMRpjY8baAaUSYTo6f1m+uFcSVUsKKxjVh6jhONqe0lKgSIXFJnEtxoPOC94+UywPb+1JkyXNdQt186FNM13b+k7Wk2zy5hAy2n2a5TkK79wjIueSGJZzpirCqk0qs04v5YI11CWOUfLDH/jOZk53nqtJBctVy8TN3+coR5qhlViOf8ybbujdT1N3/O7f3zFvtOTZ0tGSEwvai2xKiN48DExpo4st6AUjRhI2pIZxVpE/qsffcKfe/1rVEi0zZFWYZXkoErI0DMQsKOlcyVKaGSMnDyYMTzzHBQVPkludg2vnS2xmeb5dc98lvHg65a/Uj3gw//Vj9ncBGJ0k6dSi+lGZzRKfNFq/rxMVmKURiSBFhqlJC6NKGmQ8hY6ERwhJozNGIJnHGpWXYO83ayVFASmDkwIASEEjojUauIfS42I4RZkIRnHOOWNVxZrDaODcjYj+EBBxGhF8CMxOpKY+KrD2NONPV3TkZSgdiOylNwsHO44Z6lP6ccaoSSLzNI7iQ/TYbBvR6zISHpKx9JSE0dHG2uG1hFIZMkSCJNwRGpkFGQmRyaB9CNhjLTRw0wiywIbHCRPiIGyKMnDiPeOpp0U2kJqghDs24Fd6mn7DaaSCDTdfkAXFozm+CBHZ5qxc5R5xrt33uDpxTnr3RqbG8p5QT3s2d20eOvIzrKf84r5k1OnJ4aPP9lwfJCzLDJODivef+OQAUfbTqlniJ7Lq5cUesnhcol3mvmioDgwNINj3zkG1tw8v0AISZXP0EWFya5hWPKr771DJo9phwtM5ujTBTe7ksN5QYg3LGYVbV+Tq3DbJamJ40u8q0nmDoUV1P012kA3jpSZJoRIMXuLum0wHKBUQ2cczy6+z/07r2O1BUpc7LHGomNBaR3btmX0Ab9/SZFbtvWaw1XG4xcfc1oeYZLiw/VHSJPTXl2z0sfMDs+IWjOaDpgjZM/od+T5ETFuJpiHUNTNFT2aQlWcnC84cifY3CJDwMWEVBVSS8ZujYgRERPeeYiROHj6ek+9uaKrG4QfWSmLIyccCz563jFMDs9Xqle3E7maJ+c75vMjrDYcZYrCNCA9R/NDlJS3jTcBwjI/XOGSoXPnxBToh5Guj5gs0TjBZx9NgRPSTtg7ksC7W4+unW6Zn4MeYkr4KepzYtyOgrPZMQ/vnPKyvsTkOTeNRsiBUmsOiorDky/zbPsRQew5GAXD90/h3oLyT6/Yvux48Yeeg2+MbE4GbMxowg5jl/Sd5umzLU+f1ESR8H5ACIkPkZACWoAbPZksGWKDMBAJFEZNnso3Rigm7NX10z1nH6359tcfMDQtl7uXmDzSuzWZqNBREpVk0/foFEENlA9qFCVlmdH/wCGkoushrHfkpSQkRUySN99f8O3vHPD3/7MNKUrGmBjHQEgBo92r9zz+OShtLUaZ6eQWI0kk8qIixknEB7dYxRCQcrrB9kPLbjMdKqUQcDtHV7eQA63MNNtiErpMhvuEQpCSICRJP3iEiejbEDFlDFU1IwQ/ochsdmtHE1R+ABnph462b2iHhqtmw/PNBVfHhqNySaYN5eKQGEeWXnDjJaPtECFjtqi43A0U+ZxSKAge3wd2wpGkQSLwMZF84N2zA07OjvnDz57S+YbDokSIGecXFwTpMMqRFxXejXjvuY6eXdNRZnNsplE6YYRivBgZh0BEstnvkGnKj75ud3RdR+VzbJbx/fUVR4cHnK2OUJmEEYZhJDeKs7vHnJye8vLpS+rREbWkrr/woH9e/+6//9f4r3/z9/nBHz3m//0PB7Ro+eyzhjf+1Ovc9E/x3TX4xNHB6wzOsDqwXOz2PDu/4Rt33sGnHTEmynnJ9fUN693I88017ZPEr33jW6zOjqb3cObZbJ4wt3MKI+m6AV+d4NOA1QNKGJSZEdwl2ixIMlLlhq59jlYzpDym6/fM8jv4eMWyfJdt1wIjIjXEeAWpRuhA3a7RZk4IkkSHMgKhFG17gdUG50aaZstyeYbSHcpF7leHXI0bpDrAoyiY4/KRi3FPPlQIXZFlC1yAMheEIBnTwCzLsCKji4kkIqgK3ZccXWRU2QFKaowtCX5EZxWha1Eyn9jRKeL2N7T1JcMwBZIcHc6YL2ecjIGud9zveh60Dce64Y+e3rDrX42s9cobb0IioqduLilshpy9xbw8ZkjPcaGhH1tgwehAaEmm7/PaXcXFTWLXvGAxS0Qx0sVIvVPs13ECbd8KqqRISD0RiHQCk3HbvoOpkZrQCPxG0L2Q/MX/wfuUiyVLaVnvG3KlaHxPL9eIIJkvlrxdLTgYV4g/0GSbI1Znb/PityTZfM79rz0hvPkRKRj6XTP9/2m4um74L//+92iaHpmlKaEnKiTTbSY5gdEZ49gTZUQlEDJOPlqZiDl4GwGFiJpHT6758vtvEtot+75BjjnGW/RS0UeHDgXRR7QWLOWcF8MNy7uSjALvLMI2RNGR54aDykDsEDFHCsef+437fPcfrGn34rY5cLuLiMS8+uLW8HkpoQGJEAmhphxspSTKKoiB4MOkCE+38xwp8G6k2W0orYUUiEohBNOvt+H+3rnbG0cihISWEicEQklSEjgXoa5J3X66KaSE0BJrcpL3kx1Ma4zJCKEgEjG2JLcF1ViR5RX9zcBeJ2aZZVZY2n7k3nJFvoFNX+OwVApi9Cg7ILXAmJLKLDnfP8dLT3IjMkSMFSxnggd6xrBvKJJgN7Y0ySJcy8nhHJ8S0TcEPzLPK6IbyHJLZjTOD8ToYQQdBN3zkZvNlrZr0XLqJoTe8/D4FGkiy1mJiob1botX8OT6Jc92l2z2W9zoOF4c8eT8OU9unrDUC1AV5WyG27c/7yXzJ6ZuLhJ/6V/5y3zj2x8yO/hdPvv4CqUEuEjnt4yj4Xj1BilGRrXnpu6ZlnvDsBtJKjL4mrEWnFZvsNt/yriNfOutr/Lg5NskG/jkye9w7+wUXWia3pPnc9abp5wsTshNxtBdo6JhSBKpK6x1iNSjpINKIV2DEjnboWHoP+K0OKGa3ceLQNcMJBzz4hC6EasVXbdmlR1x3XXkJsMGP6EvhxElFhhdMKgdfdeTvCKXC0Z1ha1WtM3A3aMHrLfXLPI5To7MracLa7y7JFOniJizKAzjOBKiBqkxskTrBdprsh+tmdevYRcWLSxCGIzSSGEQpsAHCx6G4Rqd58zNXbLk2XdbQhgJCFxKdF3HanQc1R2n84Y3Dmd87+n1Kz3XV954931gWVo23Q2DVzy+cIzhkNxO8ytUgTEFQXqU0PRji8BwsDqjGCOZXKOywPE8cvFcomVEWPBuouxMlwpBpiV+iJAEWk9j3hRAthK3S4zbKbTjnW+V9EFw5/Aey8UITrLrtpPgRUZ82KIuC/SHd1FXidnsAO8TMirqsWF5/4KbWOPGI6wwGJuILvB73/8RYMgtUyswcYtxUwgsKUi0ERRzTeNu4fMeBh+RCpRVKCDFgBCw2/aMneb68opt2nHv5IwizMlzCMnjk2L0Ap0MmXRkZsa532IdXO56zMpgbSBXgqX2nGSJoyHj+GCJ/orj9fcf8aPf3yGEnH7OW1jgFzDx/69KApAINW2KMqqJb3mrUg4+EKMneI8Qk+ZACMG+3k2xhgSCNmhtUdpg83y6DY4D1mYkeau6l5KgFFFEgncIKfEu4mLPcjYn9DVBg1AapTImzLlAKo3Wk1ozkeGURdkCXVQMEjbuBdrOJ7ABkot9zxurY/A5/cvAoFr6Xk2gEBkZhUMOPcW8ZDEIssLgiMTo6RH8YXuO7BV7ImUqGcYIBuqu47A6JGYGaRQpSVaLijgxvjApoZpAtrCkNnH9ZE0/jhipOD06hDCQGU2WZXRDi0mKxXLBoqrwSfD85iXn9SUpU2TWoGwGPrCud5hCczZfkjpP8F+s3c/r3/v3/8/8lb/wC/zr/9q3+Xf+h3+ZR48/4fd+/8dE19JvRk5P30PkPU8+u+Thm29xffGEg+WKcmV59PhjDg+OWOYrrrnk8vklYq/5yht3OTw6whYzev8pOm2pt5rVwSF12wCJMbaYXKKpyMV7U6aCEcikGdwlyIx9Ayl5rIxoteXOKmc33CDyks4FtIE8t8RQsG+uyI2Y6Fl+YH31lGp2B2tzxv6axBLknl2zZlGckERFPW6o/Q2P6mwiK0VLGq+J3pPkyE23xuiM0hmcLLBUeNHTDFd0rkVbRZ5W7LuOzEpIFfGHa8qPLOZkRooRMj1peMQUbJOEBCJCaXQ+R8oFQ32DDJHVwQHjOBIDBCnIbUfhRorMUpSW1XLO3UX+Ss/1lTfebTdyVi2ZERE64IeO3f4xLpuh9Ip1fUOSzwnCkVlNkZUoqcmt4WS25MQesKl3iPIlH4lASIkItzvu9GdINSlHrRYkLxBxShsKfUSMAgqBrRLvffmIaqmRMZALTdKCLvUs54Y+KSSGKhekmwU6t8zuLdE6IK2m7jqOvlLjlo6hzxHaksuMekz8+NMLPnmyQRpJYTQiGvo+EFxi8P2U7iMVRVGhlKcJfhI1+YgUaVLSjeDwP2shNk3Pp589oh2uiDND1wis1dRdzbxasGsjyhS4rgcFh1lONvO8fLKjdo7DXLKs4GQRee80J3x2QLM7xnw1sj+s+fp37vDxjxr6JkwdAqZNI31hhfzHJUDpKWgEpTDWEJ0juEAcHSFM1p/PRRpSCKKAtu3I93u0kkRrSdYTggYxdT9c30NKKCUJwQOJLMtI3hDkbZjJ0CPHFpU8WZGhYsArhc0rlLKgNFJO45bp1i1QJiPPcnIlkcWCm11g04GqLJmRDGPPo+0lBslxdUibRrzrWJUrYhREH0gMbMY1jkBIkoIJkhCS5clmj1SC0swJfaDTA1VesFoUJN+iR0UXHFJr2kFSWMkYIss8Q4dIHUbGm8T1yyuU1vi+o8okd44PmZUz6maP1gaVWfYicHRyQHO94/R4wfJuxQ8++Yw2Rga/JSmBKGdc+5Fue8Gd6oj5KzJN/3mod17L+OQnH/F3/h97Tu+f8ku/9g3+1f/22/xx/wFqLahmhiGNZHnPxdMnZHPBuj6nnGlEAeubG+orgY6KwirePnuXvkpEs6HvPyOwZrU6YLOtORAP6PrNZE/zCiPm0zNKEp1b+mFNnlW4uGPTLLna3XDn8MtE3yP0DU17w0F1B2uW1P2em80jjqsVxhi2/Z5FPidGwSAUHz16wukouXP3HZpBUeQD7dgiREHb7dmFa1qXKMw9mv4lPglKvYBKYJTgbPU6FyGBGAgCclNQmZJ+FFhbseu3WALV/B5hdBznc8aPR87/7mPeuv8LuHZPrzVJaDJdgFIkHyfdgm/BlLff8S0oi80MMCJNwkdHSCC0RUmFzSzFrKJpWrL8nzKPN4SANjkpaIZBo1VAqIS1GqVari/WVKsjTo/vMw4tmZ6jxcDCrCiiRNQZfrOjpcfq9XRD82kCz4dbrJ+49ewGSRgifgj4MLWbtZ6slloKHr5zgjY5c69RW4+el0Q34FVFTJLBjVhtkWeCm+oxeXZKGxu2F+folWbItliX0wwFKWo6NPsh8nvf+5SQ4m3LUWCNnFiqIeDCgPcjEsHV+oKiNCDU5F+M04s6BYhqwhSO47T5Kml48vgl2cpxuDohBsUwdiiludjsyXXF2NecHB5w/uI5i8UZc214/vxjoq55eO81Mq1JGH74RzcM39/w+rzGU+K+brn/1btk809JHsYU8F4QwmS7+qJuS07+VikURHBdQIqIdyNJTLhJP0y2mnDr140hMMbEpq6p8mzi6XqHMZboPcTp8JmiR0qFVFOXIw4tTlm0UT9TM0s/0FmJ7zpCjCAnVXVUGmkLxjRlpeZFgTX2NhJ1EvYVleErxdf4yfZDQpWocsuNUez6Hq0181SQaolTkUx4VDUjkyXdfs8YRtoxor3CGg2lQqtIuZwx7Hq0E+xdZN/UpBjItSQvLIHEOCZkUmiRkfqEjBahMmIeISlePnrJs8srlkWJVQkfHHXdYI2lDYFr16P6nm+8/jZvv3afF7lhuxc8fOcdZvMTvvfT71MP/cS+BjSSoW75cLdmkX0xJvm83rw7o6wyPvjokr/1n33Ev/DHF/zir73NwbcWLJgRhUPhWR1Ynj19ydivODpaYJTkpr0hlIrRj9TNwOrwXVypsMVA8J7t9ilFZbHFCt2lKWrWrvDtFu86xkESdIEyMPgGkxdcbZ4zmy/QVqPsjK5vMGrEeYc1jqo8ph0SRpbMsp798JzjbMUqP5iStcZrlCyoqo7z7SOUNYgMQtuyKE5o05r96KnM+yg5UOmMfdHhXcDkFVpJ6uYCJXJmhSF6mNtJ5JdShpQ9CMk8P0amkeACS1tyYgsenz/izZPXMMoSomHoHMpERHAI4dBSkobudvMdiTGipSFpTUgjwY8YWyFosUKgvUcnyeDFlJ+QR9Tngo5/Qr360VImrDFYb7lZ3zCrIklqBg/CDczLjGU5Y/3yioPCsSorOqX4v/1f/4D9U8+//Ve+zXK5pn9yyOV/3WK3HSIolE14lVBa4P1EIiIFpAarJZbJQgRTLmZmFPfurUAJdn5P0pphUJi8wvmAD46sqIjCEW1gazb0+RY3zMgfQGkH1h0UbYk0B9RhxMmMxy93vLzak+WaYZxSeprR4/zkiS2KbApP0BBkog+BFBICiRE54+hABLQUSCkJPhLdFBZ/db1llnnmgyPKCTQ+1D0LUxKbBrssqN019+4sOT//lPLeO9RXG77+7n1SM+PJTcPl+pxM9Xz1vTOuL17iz2dk9z1eB77062f83n/yHJNrYh8xKfLm6ey/yff8n8kKPhBFQKtIDJEUJVEmvB8Rcoo3TClMymY/kmKkc44hRmLf0nYFPgaM0njn8HpSMPuhmw5masodF2niJTtt0NaQYsKNA2nssWmgyjJENcNoQ3IepQ3aB2KCoA3GGpKQCGUIYTrMCSEpTMmdcMZ1/ZwuRXwVsRIsAZtZxpCjx4EFhlEIhIcgptQ2KUeCCKA6YjAYbWB02FiyHT2rkxOKrgIZsVoQwnTwrG5vuUSN0IJMKWJMuBQxY8bm0w1FblFKkGeGTGkODlY4LVlHx37oeK2yPDxesO8afvzRByxWc2KMvPXaGYVNfPDR4ykm0/V0rsMmRZHlPN9e/ryXzJ+YUlqjdaTed+z3nh9/8Izf/cGn/Ov/819HH1pG/5I4CoYe8jJHq8SuuYJhhXcFiIQaHW/efQ9RlRRVous9ioy6bTk8vkszNBwcPGC9vUQoiQs7zg4fUpR3eLb9fYzyGDqatqHKVhT5EZv6BXcWh2glQWiu1i946/TrtEEzpB4jImW2oHWC8+uPOC5PMZQMKLp+z3JREVmzrp/wsHyTJrTUoabK73NYvsPF5oYHR1/ixdUfkVsLcU3bS6QqQST6/gapLLv6nJkXdP0aI15i7YKcDJf2aH1CdDfMdMtN95J8IZAIgnN4MRCSmbpcSmF0hrGWYXuBlAaRFNoU0+fhtpPXXVpS8iiTTaJMo6ex1DggnANRQv9qOeOvvPEOvkWi8M6jTUBrRUIxRseiqDg6NmxvRkrreHBnhlCaeuhYj3vOL0cce2ym+Rt/81MuNyNjSEgVyQ+mn8IlCHHC/E1f8FvrkBAkOZFLslwwzzTLeYHOKvbJo6ylGTz0GUYVZMqA6IFIFJFZ9gbb/XM8HpHPGPrEWWmhl2hhsVqhMFw9v5hGbjGilcRkmrZ2KJVIcRpCB5+IxJ9ZnbTUuMGT5JSNbHJJSgHnRgQGqTUySfqN47V7x5ypQywGIRLzhcKFPT46hB84ylYYkXj9zkPqOvHoxQU33Qu29WOycobOI2q24Jn23H9vTxJrCqVR9phv/vopz3685tmPW05PFzw4LFFfXHl/Vs45rNF450kpEVJAxEBw/eTNlYrkPH4Y8G7ExUA99PiY8ONIM3T4lMiUmlKk9O0m69w0n5Xgxa3FRgik94xDBynix4HoB/bSUfcLlFFkQEiSTCiSc6RYI8uScRwnuIBO1PWeutvQjjukEWTznDtuhcsCD6RFaE1KPY3fI1XDeXpMbRq0UzgnOW9ekpmJ3GVRpCgonEQkQ3SBsix4eXUN3Y5FaVABFtKwTYFIIM8lM1XS9ANSaVzs8e6WBf1hT31dIxV45cnmFZSWF/2eIYwsbYHfBZq65kePHvPx+Q2fvviMZV3y6fVj3rl3n3unD3h9eUa/6XCm4spv2ORrDmdLYvqCJf15jc6ztCtW8xlff9eSW2iuBMMoyWYCo2DQCSGmUJesMDijkDbj8vKKYdPzq29/HV/kbIeeecqZmT1j3+BiTwodIraE2GOVoO5rhrHn6N5dom7IrSDGDCEDc2vYtRvyJKmyiEg1CEUzrDmoDtFmNnUFk5v0CkJQ2RlDV/L0+mPeuytYZYLn4ZK667k3X5Bnh/Rx4O7yLW7qp6y3l+SHx1gz4Mdr5uUJZTaCuCLPLClp0IYym5NCQ3YwI7NzopQ07RYXWpRQRHoyeU1Z9jjWKDHDLFb0weOFRxlNZnNS8EijQAgcgqQUITqMLfBdA1IihCWONdoYkppGSyrPEGr6vgtnkW5E6G7iEbxCvXqr2QUSsGscY5QgJEqUhF7wfB1Y147DQ4uzLb1YMDOHPH/8XU7OJNcfSQ6PFB/99AWbZiRpyOcCWwqiTpNdqGPyxk5x0KQ0gRKETAgFKQlkSgQVCW1DGGeYzHK+uSRSgiyQMVHYAkkEOQUUHOgVN8MzGtXgt55MzigxlJWZLEQqm0Lix2uCC4zj9Gd6P4IQaJVw/hZ15W8TSoSaUra0mLJ1w4hQgijipOz0CS0FyQecC2zbjk8/U5zevct79+7xYvMBpTWYVHPn8IDdy5Eidkg9p64j3/3uH/Ojx08oVpLZ8Yqhd8zKGUpHBu9oTUbIBFKVOBdJWeJf+Nde529+8EO8C6yOj/nJjz7+b/hV/2ev/NgjyEgxEkmIBGNXo1K8zbiegPFh7BmGjnbsqceJVCWCpx0dISW8lGghMFohRSSGgPGWKCCEyUsthUJqiRSJ4PwksoqeEC3d0CIUlAmcAZEXUwt7HLFZQWxaBj/hznb7G4QO5IVivlpR2QOK5RKd5ZTlFAiSkqd1NYf9CaXIuVDnSA+DGhmDRwvBLM/xXYBCEp3ChoJR7ClE4J3jA160e1yfCFLgzWSHiikyhESMA8kHopZoZQgpUrqK7/3BDwijoxc9XgSkl1xtWoIfWRgDsyWdH7m42OK04OV2RzUvUHnGVVPTPnnM7/70QypVUoiMQlmMLtldX9H7a9547/7Pe8n8ianT0yM225pxCHzjqw9p2zVXzQjjgAgBTcWgBnwaKSuDG3uGQSFcg4mRu6dv0LiB+VFGxsB+f8PhqkAGy9jtkbFBIZBYGtcTxpGDvGC5NGy2HyGSp7QHjKEneIFRFTe7l5TZiixLdMMFZ4t30Mx4fv2YO6sTqjzSDY7OB8bwjDIzpFjy9PLH3Dm4Q2UclTnk3sHbDKklNQofKg7Ke9BdI0Tk/uqUEBKkEeyCcZxiSmOAgcjoa0Jag4Bh6JhXD28vQpFcalZVhrU7xnFEiQylCvRCMsgOH6bAIiEmq6DvB/Q8x+YlMS1wrkcaixhrBA6lIStyfPAomYGMxCSZYickUmW3scYJKf8pt5q10Qy+YGgzotLstrC+GsAnZiuNzFqy/JD1jWD+xgN+8vGPqF3P8rBgsYwcHc35B3/3E4wVfPlrp9RizdPnAyEKooPbdK5JGMQthWjKrUAKQbgFKIRcQenY9VtkWlEUM/b1Hik6YuzZtxZrMpIyLHOD0oHXTr/Kh5c/ZIhgC0PDDJEarDaE6PFix/nN7jYhRjD2AakiKRi0DiSmExJSTYAEJRFG4MOEBYwpgghT0IJS4CXRBUScPL/d0HH5wnN10/L6Q89ydsIw3HAyqxDXhocXbyD0yGNxxR/85AN+6w9+RHlcMJ9l5GZGaFra1nNvkTN6jw/V9KDFiuvtluVRxf0v5dx9e87zn7T8zh98MA2cvygAUgr0XYtMk+jM9Q0y+SlxKqQJFekdLox0/UA/ugliLwQpetpxstE4qciUmua0eESKyLa+DX6JE8tTKqSfWs/9MEyWo+SpQokPgXEYSGlPKCANhn5wKJkx1DU362u2/QZt04RSyydRR5aVLJenZPkMm+XkJkcKIGkyIyl0hTUZ2fA9LsOebfMUo6aWGNqiM4XZa1wGN7GjKhZcN9eUNuPeTDOojGYcGUNACYnQCpECmZJUeU7rW3wEHQXnv3cOXeBgUZHnc0Yxsk8jfT+wtAW5sWz3e4YA3ifKJHl4cMKjiwt644hA27d0bYuSLSImjFLk2qJLTbNtGKP/Oa+YPzllTEY1c8yXE61KSM8vfu2Qe/cytqXj/GXLcr5Ceo1VM1xs8anG7wNvnL47Ie18pKsbFpViHyLazPHjiBQKo3L6EPAi0fk941hz5/gu0mTEbk1wiSLvKeScm/o5VX6PhKLtnqOoOJx/E0+OS1u64Pj08iNeP30DbUq0gHpc40OP0Dmj1+zCkrI4w7maur2iqo44sKc4bhiiRcucGEeC8EhtkD5Qd9dYo9jun7Ka3WNRrtg3j5nlSzo3OTlMmuhinfqQsnCIpElCYrIZmjkxRdQ8kLKIiOXkVhESITQiBKSY9DwojTEzpABdVAgRiH4kEEheoExJig43jGRZDkRCP0yiYGOJ4dUU+a8+402O4FvC6Om9JOaS+cqw3+6ws5zSHnJ+vmU+D6zrZ4x+YJ5ptLKcnEa0hg8f97z17gn/xr/9Jhf1jH/wWy/5+Kct7SaAFhATIk1c3s9/fMEU43Wr1yWGhDQSYwVBJoTveOvOPZTU9N0NSUnWuzWL2QFGS4ToKcqSr5iv8Ojip3RhgzWSZrimJ0MbQ99K9vse5yQxZgQf8d5DlKSY0EaSVJyCPZJEacBGJAmTgRwd0mlsB1QesNMBIkE3dOhMQkrst1eM+yWrWYXTFd57MivIQkm9TnzoH3G+bnDBkzrF4AT1sEVKT3WYI5SlLEvG6Kj0iifPzynmOTEkkjR86VeOefbjT+mGCaz+RU3lezclkKU0zXLHAZEiIglkgpgibd8xRk/d9+y7njH+4xiaxnuG5NFCkmtNliaus4ie8RboEWMgsxolBSolEOBurUqZEnigj3GaDY89AckYBd5Jun5gCC2mhKLKybIcpacDXXCOMHqEl5hgyKJC+QRM7GUrMiSRu+qELr3Ji93vkqgppMSkjLQzGDnlPutFpN5v8aNHGoPMCg5KydqPaJNN6WkhojLF0DkWxtK6ydtsXKT+4Y6rDy9QBBZlyf27xzgfeHJzSSw9VcpZlgvKk4JdN3B+/RI3CJq2Zn/e4YLjzXdfpxlrZuWSzg30jAxxZGhHiJHDk0NMtvx5Lpc/UXVxsebevSOa+QDC8aVvPOT8coswMHpPvtCs9xu6drLG7feOxfwUfVRCkZOpKQzm5eUL3ilPmBUGEUZmeUnyJcPQUdgjhrqBGDEJFsUZKUGVF9Sp5XLzIbPykJhaBv+Urt+jlaGcf4W6dRwsIkM/cFhmrPfP6IYFhgoXekQMuKA5WHwJ5hn7/TkLUXBQ3aXzn+FpSHbE+YALPdYYEj0+1ihZklmLCwU2z7HJ0bQ3HM7OyFTBOHqW+fvMVMWLyx+z7T5BmRua3jI4gbZQ5NktIEWTl6+h7lb48wBak4RAZQVhdBCnzy8KgTI5KYI0bprlJg1aYGxg8C1RCIzNCA6EiBibkRAEkXhVWeCr+3h3irDaMThQNrJYWsr5AV3fEILHj4Hn1w1fPl6irOT04ABvVjy/3PDu24a+hz7Av/nf+SrlbGRJ4r/1597ka98Y+IM/fMHTT3s2L0fSAOL2piumvXi6iTLNMVzwxDBSWs12BBc8Lu1hHMhoCWqOUJLBg0uCzBhiu8PoFavFA2L9IZZrYGBIlhgqunFHXTvSaAk+kllNCHpSKkeFHyOkMFExRCCoROk1ZkyYPJBEwl8Kkk2I1yablEqSut3jk8cIhbKCLHMYPRBTYJY0UU4nrx0tT+IlbUi8eFHThoYDPUfnk/1Fa8mitORFjkuJsRY8vbpicaSYV5oyl7g+8aVfecDv/p0Lrs87fHi1BJV/HqqrW9J0RSQRCM5DCCASMUXG4Bn9SD8M1H3PTd2w66cQh0Ib1v1ASgmrJEZKjFaQIlZJ1Ohvfd4J3U26hEyb2y9sQpLQ2jCmQDeO+BixOlK3nrq5oh0HikXB8miGLAw+ebZDjQwTt1cEx+ADx2fvkzAEFxHRTWIuraa0LD+gPBzbM15bnTE018iYo01Fygqu2zWp0LhxYGFyfEok0TGSUGmG8yP1EFBKk8Xb2TPwoq3RaFIXaH7Sc/7DG0RKWKE4mq3ou8Dz8ws611MeKIJOPHr0gtVqRkBwujpmVhia+hlv3b9HnuUcHizp+jmHiwW7dvqch+DQ2vLo2RM25xt2uy+6NZ/XTz674roRdHXD6w+WvPn2m/y//v7fRX35iPJuyVi3xDQBzLs0IAyUeYktFmQ20I0JKzVGS+TgmWUKIRwurMnLBUKu2OxrfOy5Pr/iwclrU+hdcqSwpdAGJY7Z7c4pS8t295zF4h6Hy3fJdcnlxQ+QasE8N4iwpLRnCDHHxYIsu0dhGgSgtWSMjv8Pe/8VpFt2pmdiz3Lb/T59HlunTp3yVagqAI0G0B5t2ENyRFFDUq2ZESeo0YVCc6MI6U4RupXXjaRQSBOj0MQEJcWQIocG7GYbNkyjC6YMyrvjT/r8//zttsvoYmdBuptDRguAmvlE1F1mnqz8197fWut7v/cdpAN20utUjCl8g5EdCu8wOqOxBXG0hlQOKSCRrWOcSTdJoggRKqxKUd4j4j55XVIuDhFhiGwq1vQWym1Rlkvy5pTlsmIVr8gSQRQpGjNDPXGL8lGPoi4wukAIhdIRjfX4UBMlBhESlBFY/3lKWNvmbIop2qR4Kc7ToBTBVe08sAitONL8BZ94B71ArANJv8Yph8UCGwidYIxh/yAn6miczwm+Zji4wuFknyI/5Csv3eD0pODXfus5uusNpW3wQeKD4sruJjtbGR9+esDtT854+IlleeYQ54OorgmE8z4cop3rLXODEAm2njDsbyBDSTCBvAlEQpIlPUKwlFVJYzXduIcLJTrrM2guMVvehsjifUwSe9764ICzSUB5iFJwsml7ylK072cpsE0biyQVaBMIc0fwkrIUhEYhE0E80lTS45uGxlqqpsQJh0bhLCzOPK7sYUxEXYP2Gcu6wm2cclTsc/Iop1wVJAlYv8J6MCZhoLsYeggf4+cOu5wRdwW9tIuRCUYrrHakvYgnn19nerSHdxeDvJ+zzJetIE6KNn7St4XXekcTHJW1lFVNWdXMq4LaNmihyOsaAqyCxVtHEunzxOOAEgojJec/lvaQK9AioFUb0YYQSDxKdxnUlsUqRwhFUTbMpguUFGxd3UKmnsYXhLJESIX14EVroydcQ9EUdPc+5mbabXtPQSER58X9/KpLetIoZju9zCQ/Qtae9IrhJG9Yj9YpmiWujPCh9UF3QTDNK3wjyZsGpRJ8cNgmUDtLlHboRBlZ03D47pSz23OMDMSRITaaoi6p89Z0RIsYu6qRA5gXBbYs6fUyzpYLtp6+yitPP0fcySjrms3NDSYnU+pVSSYzqroi1jFZ2uc0TJkWKx493P8Zr5ifH975dMHpW2e8cHONV77YZzw+4+y0xHQiXOOQoUHpmBBbqjJn1NvG9DKK+YTIDBHCUbucbk+TVw3ro8vMyzlKSpaLKT0fUzcr8tWCy6M1ti49wcHZHdbEsNWUNDOs7FELUL7Dld3nsPWizbUNnm4vZpmf0Is2cFaQ+ZuI/RWrkymnpwWz0znDNJAYuD/OcUlEb3iATFrb0G7m0HqKpCGJU25eivDr9yjkESsR2sjOYJjnQNBoHWP9EhsswSVEMqPyZ8RJTGIieicbjO0BVmmGZkTV5HSSSzT1Q5bVQ+roE/qdLmWxgfAz8IruIGa5nJBFKTraIFQrZNZHqB5B1MgswkuFcB4dd7AuxzftnHswmqacYV1FYs5zbB+Dxy68/aRtwCMVjQtEsSaKarwLzOeGB3sTrt7SJEmPyXLBIM45PL3L9gieubzNh7MV157tUboGqQy1C23CD469Rw+wVc5zL6Rcu2n48z+csRzXhHOhVXvfDIjWz3kxawjeobWmsQ1GViipqb2H2tI0FiUNcZQBEbWtibVAhDmdzjrCw8odo1PFg/v3eeeNfZpSYL1FqNYxC3ku6NKtnaUIAiUFTSOwlUOk7VW3txrhJZ3L0EQVvomQSlBVtvXkNZqqaq8y7j8cc+/ais6NEVW5JJEe1Y25lz9gXM5YTB2z5ZhkqJBGQNQQJREDG7M8LVmVNfPJmJtPbUFaEJQj0zE6wCDOOD6c8+wLl3j7W48QXBTez1kVS7RWrZWjD3jb4L2ntDWV8xR1TdNYnAu4IFBKo7QiNOeuZUIQRGgD5pHna1IglcAIifr/5HmgZDuTGkWKlbWMi4bF8YxZ40kjjWs8TW3pZzHXr2whdJvDW9cBayWo0Copz5XTQmmKEPjg0zdY5Quef/mXGSZrKAS2qgm+xjuHEgLjJV3TJek1JFueRjoirRGVQEwsndhTpB6RdJEE4rLLKlh6aYYLln4nwnnNdDkHHGEeqO45igc5SdRGDjrbBjwgz7N3Qyte2+j1sS6wtTlgoCN2tjeZjCdkcUq+rJlOl0RGs7csmc2WSASla4WLwZacjecI2z7fJrpwrvocFxxPXM4Y9gNx6nlwd58Xn10jGXi80aS2z/5iyjR37Gxdb8crbUXSE6AamrIiEhlB1AjTUNgaFzoEJSmZMzv9hCxdRyhNOthkWZzS7ybMV8f00xGRjFE6YqP/ComU1GcFxbKEvGY6yelRkOkhtw9XfPr+EScPZry4vYFwNd9+6zbjwmKM5rVbmxyvCh42CoxCJyNWixmRu00WGk6O5xil+R/93q9z6yvbiN1jFp1jyuDb20cr6KRbBKkIsgHrEFKQpX2WqzEKyH2BkI47753QV10ezPd4ZHO66w2jTUlvtEY/3kIlKbPJnOA8STKkLOZoFwhSUK2m4AXOW5JOnyjqUtYr4u4WQmVoLbHzBhUPWtGkq0BFaB3RVBbFX7Bz1SKv6EUWpUE2CqTC+oKt4QZ37x/RWwt0O4a8qPB1ST8+YWOQMsyyVumbzWikBiyySciSTapywf0Hd5gtc6JIY73h4Wc1q0nTniKUwPsAvnW1aq8KA0enludDRRJ1aUIbL+VpA86VcMg0o7YOIxy91FPVEqMkUWQo64LRaJ36ZMVntx/wnW89YjlXeNfO39Z1g4w1USRpmvOUJNGGOehIEhqLVprGe5Q9fw2r83+DCLyksjUeS9xpm+2eNsmoKhzv3/6M0XrCMEmoQ8XZfMlxOaFYOGanJU5AFHdYWxuA8Ahn+ODOPqum4tLTI4bXE+a+ZD0WpF1B8DDQAxaFI3aal565wnfWPyCfV/8Gj/lfTs5WC1ITnXsoe6xri2ze2PNTbevPHNqPGnxoczlp82KFCCglUOLcjvPcIlKK1mlNivbYK2kNXlIlWR/1IO7w/oN9jhZzzo4skRSkWnFlrcfG9gBkOzsspaeuWh9pGQmkpFWaCtrbHm1wEh4cfErQ8IVXv0Ev6iCaGrxFKg3eIX3ACE1hDeOjGSpxCKUZ6JSOsFRYfAyratEKx7RtQzcqh1EptiopfExiY0YrQTKWVIu2d22dg6CQSoBzRGmMt5ambkiMYGPQRWaGVV7TlBXj+ZTdqzukvRSpLR0MVenwDQx7fc5mS8YnJ2xtbjBeLDk+PSNNYlIdGO6OftZL5ueGv/aNm/S7MZPJnDiLmI+XXL91ndKVNLmn8TWOmjhJkFGE0Y5YF0RG0zSnpFmH2fiEXh+QnrqqENpQrGbExlDJgElasWYnjZBBIKygq26wWghO9mecHp0yPrpHebrg9N6K4/0F0luurHX48s1dbp894mg25fqlERU1+0cL1vsZWSfGKcnJvGIaUi7fusqjD+/jvWxFSLahXs7oZDFZopnPG9546x6Ju0WerdPcmCJuzFGdikhH+HBAXUEUZXgUQRTMVnsIoWkInDzwfPD2+9izmr9x9auUM83bk0MeHJ4hZCCKFE/uwmvNCLTDKUFRTdGkCKkQeogLAZOkCOfwLqcSBtMdEDx4J3BYMAnSa2yxQpoe3hoIrUUw9vHGOB+78B4cw3q/pPECrRPy0rLWW+Pe0YTJKiceJkwXAm8rnryyjsUw6m0TU+ODYukCOo7bsHnr0DpiMh0zW87QqnWAev+NJXffL1uLKkkrftG0gir5eWhv4P2P7/OVr+/S30ho8gmNT9ucUn/utykrtErRIsLIBhX1SEOH4+WYICOM8nSiTX70/R9xctCKaELQKOnauU48ITMoI7E2IFAICTrSyCWtkQYKGRQIT4gcbdvQtq5VrkDp1rNaR4bGthFxjob79x6yu7HBr3z5ixzOPmMhjlrLs2ibupnQTfqIOmFx7CiWM5arCp0JOlsJmEAnS9BG4n1AqdYuk9Dn9PAhwyRh1Ev55S8/jZhdnBo+Z7bKsZFHaoH3ntrbdkTsPGe5zS8INO7cs9l7IiHYXRu0p1pjqKr6J1fLRWWpm4bG+TZOTEukbPu/3gcQnllRspityKsKZDsjKIOgl0ZsD3tI3y5y5xuCtRCatp8vPEoatIxASoxSbcCCUNjGc/fBp5hOlxee+zqJaBXBn48EeGcJCtaibSLRpRIlZVmyZ5dsbkYUpaUuAk3VIEREiAXCesq8JO6kMI3YqDPieaA5K1gUczpZj/XRiNOzM4qiJNUGEyWcTCb0ehm9bockMlgCvm6wtuHmk5e4evkK3X6fQKuU1hL2Hh1xvHdKQFGWNXES01hLWeStOYltSKKI9fXez3jF/Pzwp6/fY2MUkZeOwe4mZ3sLXnn5Gqpfcnq2Yrmo6WQdtO1gy4JuL6a2DUI6goDgZiQ9ydrGVabzMcvVmI10h0T2WZyd8Vxyi+LEsxiXfP/NE+4/WLLdz1jLVnznzY+Y5SscHhsCygtCBXXhSVRADRpW8xO+8PyIr/7KrzLo7vB//s//lH/yrffZXuviPBipGYwi1OYlpiqhso4k0fhQEBvdipqqhl5moLbYZkZZTHGrhumDDHNbM3wGxK6l7o7xBlxTtcH0jGhcjbWK433Lm392wGLeICW8dXCPh2+V2J7Bjtqr06p0fHT2iKe2r3CZPrXLmVWONDEoqSjrAhEpZLBY71EiQnmHz2s8nmg0wC8WhGZGPFhr+5LeYhcWqdtxIv+Y2prHLrwWT12XlHXDxsYaTVkxm1ru3nuAGgZ6/R7bI4NwiswYQKNkTBZVLCtBbgMb8TqwwNspi/mc07MljdP4UvHJuzn79xtEIwkyEIAoEsRGkee23SF5QAQOTiZ8+umcL45G9LvrzJdnYCucL8EbdDQkDY7GLVnWnoDHW4l1FdalaGXYnx0xXxqUdNR1TTseogghUOUgRYRAYBvXzmZKIEikUAgRCIjzIXGPQhNod3HWVQRzbkVGaGfFggbpkdIjneHu3YdcfeISp6sTtndjDh4tONs/4XQ5JfWG+dkUH4ESjuEwxUeO0UZKFEEUS3rdCK0ipEqIzICalCgINnpdumsZW5c2qOv5v8Fj/pcTEUecFSVSgkCeJxBJjGnFSd57HFB7R21rnHfsbvTpdxKa2hMnphVT6RjkuYlEoO2JuhpSj+zXVCee5dSyLCqmsyXOB6QSdHVEJBWDSHFlfQAiUNmAqDw+CIQMSC3a0bTGo2Qr4jvPSkJpgZYCpSKccHx6+32SOOWZp7+ErB04i3cO5yzee6hqsECiiaMh/XQLNVfIwwViWZJYjxYSLQU9qRBixKiXoUTMw/sHiFHC5acvs1oVLE5nVPOK6WKJt46sH1P5QFk2jPqGunEs64ZyueLpW0+w8eQaG5vrbVBKUdAb9jBRgmtqButDTo/PmM8L4jRhfHZKXRRsra8RqRXT6ZRVVSHEhSL/c/4n/9Nv8ODOIz799Ji3fvwR6+trJLd65HLK2bQi627QG66Tr06ZL+bojSvUyzmxNkiXohng8oazD0qqEi6v3+STP7vDpfUrJMshSWzYGm1zf+8e7v4ebm/OrDvi3aNPWOQ1IYAWAecFDaARPLGheenWgN/9jRf4wnPXkKHHjz445p/+y3/G2x8/pAqeg7Mlq6KhtLC2OWAwHNHdXmc6m3C6f0TXRERpgm9KTGjn1V99cosv3rrMU5cvobSmrC8zW624+90HlElg2rNceqlHMzrF9yxerBChT7n0fPjGAYtZQ93qnHjv7BGPTsAsBUlXtAdREWhEwIVAN43QWnO6WnEwDQyzDZTpEOpZm2cuDU3ZoGMDSGTcay2CkejuAGcDdVkRZwYVx0CNqyp8eLxRuMcuvKNeRBTFgGBVzNjs7PLD1z+mlI6O0gjVUDQlXdPFe8V4MePZndfohgWnsyVxlCKEhaBR2nDw8IhVaVkuE95/Y8Fq4cBKZAAVC3rdGCkU03lBQCBVAA1pppBG8oM3bvPF157HmJReJ21Pq25OUa1Y5lOUzoh0SVlInFQ4PWOQJcwqhwsFe6djnJeknRRvA0VVEhRIFbDOIguJFB2UEQRh8ec9vtrbtuUsQNMa6osgQQi8sK2BduORXiM1IC1BttZveE/Sgadf7bEU+yRxwvi4Zn4cMTk+o6qXpPEa/SgmRAIrLNY74khAKOl1hzjncZbWjF8mVEFwdPs9LpUj4lVGnk1Zu9zl9Hj5r/+U/yUlS2Om8yWJidqWw+fXyD7ggsd5T+UtZVOhNVza2WRr2KFpLLW1nExXmNgwnU3J8wrvXWsVaiSdLCYNmu3rhpvb21xOt8mLhpPJGcPhkKduXKaqLB98cJ/DvX1MG/dL5QNVWZGEmDRNSKMEpRQ6ikBrPOAR1M4j6pokkkS6vcUpmpIPP3mb9Y1rbGdruKZqe73aU9Y13gqq1RLSiEbkrReyGcBSUB0WramAFq3BTKeDMZrJomKwOWJWz/jiyy/x1EvPsCoqHj08RD56QN0JzPZngGTv8IBLoxHXN7eYrlY8mpzRG6xx+dJVeoMeJorZe7BHliiiLEMZhTAJMrakwxGPDu9hvSPNuqzmCxargjoIUJJeljLYin7GK+bnh1tPvcZrr32Zqq45Pn7AcVGxt7zPspyQppClHi1qMlORdAL2UcnRW/BwLnjty6+xGo+5detJZnZGUc9Rp4ahHHDy4SNuXr3BYLBDFGt8qHjmmSsM+2MOjpZkukbFMOh0uXa5x/29MYezml4/4W/+lS/xd/7mrzGb5PzTP/ohf/CnH/LunQmND/R6ilhrvHOsDzLOFiX5YsXJvfu89PILXNnZ4Z/9w/8XkQ/0B2ugFal3DLsxd+7tc3z4Kf/D37vF9qjHYjFjenqGXwRUlaCP+qjVBifOoW6sWHvWINIF4w8r9CRGB4sTrSFTDqw/LQklCC2wLrSbZRGoQ4PRio42iA6c5hWny1Oc96x1h1TFsn2+U4GfVkRJgkHgvCTqdPGy00bUInCibA9fyiAtrcvhY/D4quZIsFytWF+7hq0XPLg7ZbzIGV3RrK136SaSWLdRaM5pjKqRosY2EfePDulsPYESiqKo+eEbD/ngoxlV7Vgta8pFaz6QppJ0GJE3Dct5jXUBaaC/oemvR1Rle+pdzCx3Hh7xrW+/y69942Uab1CRIYk0RvdJEkEaGWhWiCCxWJZlw9SBkK3Z/WpVgHI4Demgg595bN2qXKVWVE3VmsvHEmkEwQeCbr/fWY/WqhXSnAcpVHWNDTVBtlmvtgygBE7UxHFEGzUFr331Optb7SxuksZMDwP5ouRkfEBsFAu3YChTIilJ+obFvKSuFFnSIYsisriLLy3JwCN0ygcffMpT2wNqOacoUkZRH7PhaHg8z9B/G4iNxp8/kO39h4MAtWuwvvVXdr6h103Z2RjSMRpbeXLr2T+bcTZfIoVECYECtDTtVZcQlKVjsao52LcYdcao94jd7XXWBj1WixV3PnuA9XB4cNDmNgfRtlKEQygBUuMcmKjTBm6I8zACwnlYh6X2EOoaLSKSKAaTUNZL7t1+m8GzX0M1FUp6rLfk1lJ4genFCK1RkaGuLEQLNp/YYDUv2rlv7wjWgffEUcLpYsrsIOeJG1tsX1pH0s6g9y9vERUTnhzdpL5ZcXz3lK065/ruFt1OQr+XYZKET+7e586d+9y8+QRKFdT1iijOKIoV2kQgJLUVCBlzcDjGeot3HmMMR+MFk1WJFjXPXLvC5qVLP+MV8/PDf/I/+L/w5S9e5eUXb3Dj+asMnp7zwe03ODnLuXG1jwoLRF0gz2Dxnufhmx+zWga+8pu/xcc/eJNRN6HauY6whshscHh4n6IAmaW89/E94mxA5BO6oyuYbo6UGa6+z5OXYhaLJUncJzKGV154ivHSczSLOBh7/uf/u2/y/ke3yYuKsq7pZYFBppEIxnmN9YKeFGSxYZ5XnNy7h5+csXb9OpujDvZ0zKWNTcrhkI/ee4/bhyfMZit86bm7N+Hq9iZrA8VJcorQig/uPmR3a5tBugbzQPlpiVnEmCdKNtIDZqahcZpjWxOAOgTU0CGDwPpwPgHQqiBL16DjGCNiLnV6KJPzYHLKogKjJEoFOB+pI+kQVpbGeeK0jw+WejbDxRHBgdEp1i5At6ZNcfJ4HvmPXXi3ujH3F5AmJcYN+ezO23TWoT8QbPQ1vVjjw4C9wz2u7AyJk0Dl7jE5DQQj0CrBWckf/fEH/PidR+3smW+TdLQWxJFECsnZqqTTlfSMZDZvr/TayDtHWTrKlW2Hm1Xgj//sR6xvDXnxhScxylI5TdGU4GuEt3T1+ZWbbnvKiAgla6yVpLHDuQpXS3CerJexmq7abGHZOnhUboX0CdKpcwGOQSmBdZ4gHO5zf97QvsBs077WgxT4VcCk4LWgChbQPP+FG6xvdtFG45sCV9WYpsf49D41AYnCaE2n02HW5CQNdJIeItYIn4EzaAW2kbgiwjuPSgQiHaCGEhXF2DTCdTI+PDj+13/K/5JilAYRqL1FeWh8wHlPadvCqyRsrfXY3lyjrGvmeUVZO/ZOxxSNJVaGSGuUkpifBPi1WdFSShppsU4TBJzNKsZne/jgSAxc2Rhi9HnRlhBEO8qjpW7HQDBEaQdMF6XVT8aQkihCCEdVL2lca3WpbIHRGiM1zsQcHNzn5tUX6GiFx1G6isqVrGROmg6oQklw7ZW1Nw7bqeitd1jOZwgfWrce0WonFsuCuwcH/MbOa5SuQa2mOFGxzGtWecnNm9eIb0RculEwfeIKclwQC4V1jtRo1gdDxqcTtjbXEEBdlbg0wtYVri7JS8vkdMnBoyOE0pgo4my+Il8uWOYlXkkSIymD5+Dook3yOfcenNCPao5uf8aL9at0ZYW3bQRqpLpkQXMlfZknnn8J+0Tg4KV3mZ4+Yn/vPlE1YVEE3vhuxXOvPU8jPPnkDN/A7vYliu6UD+5/xPRgyeXtIU8/fZ39+ojT44Iv/upNti/3McmI9Z1bZN0B/+Af/iFvffsNJtMVITiEkKx1I375xctsDhNk8FS154efHfDZUcHhZEnjBZ1UU9cl3/xH/4RnXn2RL7/2CsauiHuX0emQ8XTB4cE+oXDM5gu+9cZHPHNtk52NPsZE9DodjqYr3rrzGQ9PVvz6l26x2VknjgTrcge39xb94YpJteDHkwn7VUXjwZ47HrZxmwEh24NbaUuUjlFCIbTmxs4VtEm5f3TIGSdoLTEygiAJQaBNTBxnADjnESJgm5ooiamqGt3pEgDTMbhi9Vif62MX3jS+Qjk7IS4j9h7u09tQJB3FIIHVWcnxkceqBUliGHZ2uTR8glX1HgsXo9M19vZOeffHh7zz7sO2b4VHnCf5gKNsLHjRJuzQNsKdbWdoqyJQFuVP/JG1EqxtRbhg+cff/JecHH2B3/3NX0IoSSJrvHQsK4sIjjUvqaRFq4zGNbimQUlLp+PwtsJ7g9SKgCdKDR5P05QII3BOUFc1kYiIjMbVASWj1j9UeISUNM6CCzg8Opb4JoIAUde0tmcKTCx44cUrvPLKLTyWvFxihGfv3pL3fvQp4+kU7z1JHGHSmGXuqG0A7xn2FJ0kxq8CyWZCrGI6osPbP7zL5s01usM1Kh9QLmapBNPFlChRrB7TuuzfBkIICKUoi9axKpyfJq23dLOYzbUhkVKUlWVa1pyM5xR5hZSSjklIzgVOSkkiqbCuNc1QQqKkJBYR1jv8uVsVgMAx6qUIIWicb9XqstUQCGnw0hBo80CFbK9WnYc46yKEbR9orej31rG2oqhmlLbB1AXduIMWkrxcMJ2dkK7tUHtLyBKqZUl3oFiVBUIZggjEwiCDpJIl6bpB7xsoLVpBpNtTSmIi5ouGf/Ev32C8Kvhv/s3fZri2QXFyRjfrkiY9ut0OaWbpZ31O3r6HnS1xQGRi1gZ9DsenvPPeJ2xvbyGNoJnMCUB3MKIqLXfv3ufg8JTR5gbDjQ2ykxkPH+yxbGZ427DIC87mOUXxs1srP2+s9xSX1yN6mxlbtwQPT+5C6hh0N5h8ktOpLuGHivK65uqNWzx57Rb57AGv/6s/4Kkr18ldw3/+9/+Q8aLmtV/8ClduPs3h/bsMBlvsXrvOR7c/wxYNzz6xzvLsjLt3HrG91eV0UqK7T7ARX2G1sPzw9W/ygz//IfNpQVmD1O07vLSejdGQm5eGVFVN1TQ8uSrZn1XkNa1YkADecnp6xLWTlN0v3WT9ysvs708ZDLf5xjf+Bv/iD/8pJk4J4hFvPHjIf/nHP+K3v/ICiRII4elmKcd7Z/zwswOeurHLjXjE5c0Nrl5+ivl4jCEnjjN0/hFufB830hyVq1bUet73Fa02t/Xd9x6VxGgdIZ3jhRvP0emt8+GddzlZHDNK1wlBIIRCSEO+mBKamjjpEFSEiGKqumo3y2aEL0uQApGmj/W5PnbhPVoF6gW88f4daioGWzFCd5ksPVVdsLExpBMtiJQii9P2TOATsu4m3/nefd5/f49i6VA6oER7knU4rA/gJd631pCNbRChvY8X8nyASAqSTKPNuZlFbQnBYquAEI7J6hN+8J2IJy99iSeeu8zcPsRHBQg4Y46oBYglnShDU0EQ9KoePRGRx4KqKRFKI2NBLGNE2Rpv+7xpbSOpsY1HEyFE6yfdHnksNJK29nqEA60NQXoCDQIwQvDis9d55bUncKHA+YT5KqefCB7dHXM0H9PQMBx0GA56nM7O0I2m38lonCUbRnSTiPX+GlGSkKQdzs5mFC5nuezT7RsCjkVT4nKF8p5r3U3i3kWm6edIFEIo6vPTn8TTTSN2h+sYY4gjw7KsWSxyTiZThJMYY4iUJlaaVBmkkiADRrbjPtY6jDY41wq1pJC44IlihSCQxAopXVvkRXuzo1Rb5ERQKNla1gUR0XiB0ppeb4gLgoBEngdixD6l198gKiPmyxOKuiIzEVpKQDCdT1gfbeCjNlD+5OwzROno6B5FUxHFptVHNB5hBMkQrj+5TT2uEc7ifcA535q7S8Xp6YIP3v6Ir3zxJa5kTyKlIIsUvm7IlyUgqGc5dZHj/HnMYnumYGu0zul0zORkysHpmN2dEVevXkZKTZEXNFVJN4uJZEDUJZGCK1cvkzvPyckJ1tZY2+DcRbLW57z01CYbA033SoJPzuj4Dlm2zv3vnjF774zRE4o/vP8eg/63+O1/53fY3N0i63a4dOkaWsd0Rzt89/vv8eGdRzw8OOPyzhpJFHj7rT/m+s2rvPHGh3z91Rscn5S8/vo7FGXJ2u46D09K/uStHzBb/Ak3rw14/uYaX/uF61y5fMhHd5bsnVgKJ1iVNW98cki/00X5htmyxocYIyWpau19Rx3DoJtw+3DZihT9ETpsMOxo3v/gOwy3dtjeXcPWJZeuXCafj/nBvYcs6oIvP3WVs2XAizaCb1o4fv+HH/HyMxs8/cwltLcM+gMuXbrBwcEZKYb/ztd/k8GVp/hf/pf/KYt4STjXVXgH3gsa76mco3GCyAlUrEBKntx9kjQb8MZ7r+PDMT0zoiwXdDoFJkoQzuKcx6QxobGYKCEo0Vq6xjHlcvbYVr2PXXiXRcrRYUPjC5K1QNKVCFlzdXeD47Fne9Qjz8/YGW3i3RHL0uKd4v6dM97+4T0sAqE4NyM476s5QUC0oe3nOxGBwHuB0gKpIM0kvTVJlXtWK4d3oHRgbb21UhyKDt3ZJv/P/+wtquJNXv3qc/zG3/wi1166jBdH2GCpmiW9KEdVJXoypLwf8eCbDboxhE6OVKa9cpOgI0kU94irhHw5p6kavPREMUTGIKVsr/2ApvbYxrXZjUqDh2A9QgeC8GQm4hdffpXnXnmKSi4QIseFAi0106nDqDUCe8RGkCYabxuGgy7CwSBNEWlE0ukQdxNCVyBEwoefPaAOK/qbCQf7Y/q9EVGs0NIigqTMVyyU4OqrFwYan1M7T1FX2OBItWQ06LM56BMbw7KomeYVB6cTqsYSm5gkNq0n83mhTE0EPrQiOSUR0lKUq3aUzHuU1u3YDwJooyTBEkSbgCJCQOt2JCh4gdIRSkVoExOEwnqJdSCkJBKKvGoQSmOLnOlsSlHkrG+sk/gGW0ypXEWqU1SkqWyJ0w6v4XB6n7kdoyvDuuwSuaZ9xnT70kq9JMsEnY0uRV3S1A1VVeMQLIsKb2ukCHSzhMgYlNJ479jZHDAajZgtlkwenbK4d4xblhRVhRSKqmlN5qUK7G5uMV8umZuIw4MjTscTmhpmkymdJG3nIW1FtbRtyPliiRTQSSLyWhLHinzyeNd1/zYQy5q88PSqNdbs8+x/8iGffHyInTiu7gxxoTVkWa1K7n34DuXZiCYoZJKxuXWJXmeD//jv/l3+D/+n/5T3Phsz6CXcuDKiWO4xPg0U+Yw7nz3inVlFviq5eXnI2XTBqnJ8cHtM0QTuH4758PYhv/Slq/zGr7zE7/x6yY/fu8/33pxw/9Dx8GTKH/7oI0ZZTF2VLMucLzy5g21ymsZT2cA0t6xqz8lsyd7eAd3BCLcsydQZ5crR72ruuJyAZ219DSk9dxcLjt/+AFyDU7B5pcdkUWE7lpe//iLXbj1JLzZEx4Eo6jBf3OfLrzxHb3SDv/+PvsvDt0rkVUl6KRBEG90JMCmXNMFiRSColCA7WKuIBFzefQbiHj9++0+YNzNGSZ+mLpEyogg5TQjopibuZISqQXmFijJs09BbXydfPp6o9bEL79tvfUIdF6xtDAgmp6kXbGz36CYwkQ4jJVujNUI4wfsVd44XbA6v0eknICWh8XgA3cq5w3mfTLh2wEMaINKgPEmqUEpQFBZpAo33FJUlAGmi6fUkzz3ZZ9Pscufbit//5odUdQNC8Offepsfv/UB289tcOvFy/zuX3uJjY11OvGSZKJZfdjhrT+7y/sfH2CeU8Q6wjhJ0QS8E4ggSDqKKIMoWWe1mONsTR0CEkkQAXme0XueLnUeNyfR0rTzxLVl88qAX/vaV7hx+SlOyzmrEjqdDC2nDPua9z5acOfRIxrraWrB2C5JRMxg2GF7ZxPKimSYkHX6gCBfBk72HpKLnN6wT2Tg8NExe3cP6XQuk/QVVZlTVw3OOJ641f/XfMT/8rKoW6HZ2qDHWq+DURIXYOU8+5MzVqsKow3DOCE2EbHWREoTadXaQCJwyp1bQEpKPFVwBKdITIRAos/9mV1oCFgiJUBKPAKjNUrpc8criVQRShm0jhCinf+rypoyL2i93FohWJx2sbaispbJdEaaaEIQlHWFkJIgwdNAFFhWZ3xavEEeKzKZMq2WaJ3gccSFJO0ETNOaw0sBQUsOD+fkeY6QgmWx4MWntrHeM9oZsn+wj43bbFEVGU4OJxzd3yd/OIbG462jrhrS1FDVOSJ4tFEsi5KyLLh2eZfT2SEyWO59eru96uM87agsiJMEodqX3XJ6RpJGDNeHXN7d5MFi/LNbLD9nDLuC2sFHb++xd2dGURQkaUxv0McYyfFpQTbYoFjO2T884fD4lNv3ZzgMa2t9Nnbf5+WXnuPv/d7f4vs/+B7ff/Mudt7jN3/pSxxNjonFggd7Yw5nJdUisLWW8htfu8zB6YwffjgBH/ACTmcF/+xPP+Pe3pJ//7/xBf7Of+tpvvGr9/mDP/qA7701ZTydMlsavv6FS+jQZWs4ZNC7zHh8xvsPT/ng0ZyiClSuDR1579232dwY8dxTm7z+7sf84I3b1Kbh8lOSfl+yvtmhDiXeNhih2FjbRUdruDriWuc6X9x5km7awQuPx/DJ7Qd8eucev/DqV/kv/sGf8gevv8PZzGGXMFpJhtc9IgYCjMspKwq6TUmkLdq3z7X0FlGuuLr7BNr/Gj96849ZiDlKxYimol6t6PYEKINxbSqSK2syGSM8LE7O0H/RV82NrjGJx5sFuzuXWc5PcU2D1oYs7VCWDZc3rrF3+jb3VzmzUvPE9mV0z7cmFBKMhuGaoa4dthbtYH3wDNcEKwKLpad2gSA91nvSXitkclYQpQKPxEjPlWGH3uoa3/vWlDd+cL8VrqQgU4HOwCc1D472uHeyzw/ffI/f/pUv8uvf2CUxS34we59/fv9T7LrE1QFXCkwisdaBb1+2zjvwDhNHDM2I+mxJZSuCAxkEGoNtPL4JcF6Mhfc4b+l2U7709Rd46qWniYxh3EwJSmNzRWka1mI43st5dP+AyWzcuhRpiTKaXtpnNBwwGK7j5zOEj4lCxOKs4PbD24y2Df31BBUCicgY9nqcjhc8uH3A1Se75Lng/Xc+46UbT7C70fk3e9L/ElJby7DTwduGqmoIScRkPGW+KhAB0igm1RGxasOxjZRtPJ48T+mQ4nz+V+KRWF/jhMBLhYpiqjLHRAYpLHiPNq3QSgiJkhpEa0NJAKk1OooQ5xGCAdAmoigrMlujTUISpSADVZWTdbooKamqohXvBU/tGrQ3BBmomyUnx/d4yB4P+JjOYBMrBDaUjOIEJyoq5+mriLW+QaiI2dRhMsVslbOYzdFaIr0jS7soLcFVzMZnWCybW9uE3PPu99+mma8YpV289yijkUpTVhVJErf+zVVFGsckUcTh6YQ0S9ne3mExachXK1xwxCZqr/ulpixLBr2Enc0hdRCsmgMUkis7Gz/rJfNzw87VNe7fn2Cd5+BoxtZGhK3mdAea07FntXCcTBZsDjvcfbRkulqyf1LSSXs8PFqw9+0PiP+rP2Vne5Pnntrh137xNY4O7/P+GzO+8MqLPPs7t/jwzh3eeuc2H3484ZNHY/5q70t84+Xn+PHHC/787WOEEFQVTK3jWz/a4+Rkyr/760/zm7/xIv/Bv3+VX/zSJ/z+v/qUT++WRFEH4VtNTj+J2FobII1mugz84M4ZSQNJMAyvD5g3+yTdnM1fmPAbX/YkHUUn1egIZvMTpBLEiWI2zlk1K3p9yVrSo6ssj9wJZ3VGs6gxvYzO+pNcYsj/4w++wx//+QcUQSAiuHKty2pSYDoZuzcjFs2SZVPy7snHbF7ZZFEsEFoRpSlRPKJeTYl6Gdsbu7z2wi/y9id/TulLlIiw3rIqcoRS6OBBaOIkpSpK4k4Hu/CIv+ir5s66xkSGRDnqZkbSNWys71AUlkF/RFEe8NmDM+4fwXCk6HcaynJBU0WIIHAevJLMZg7vAjGSIB1Ij3OKvHY0AaQJKOVJY00UB0yiEE60LzEBtzY2qO93+Mf/9w9Z1TXdaxqEw8v2Dy1UG0UrfZsYk9uCb/7Jd/numzFpnFE2gXxN4KXD2oBGYQvXysiDxLuKsvLoIBA6INDETYQea5q1gEgE0mlcU7f5wUjCuYjq6vUeX/2lF7n13IvM8gKtA6mGQEaRNyyXEwZZl6ODCUeTM6SEVGmyQcr6+gb5rCIZdKhcw8baFrPJgunJmHuT++hBTHeUkqWGfi+ifB9OPlzBtuDwYU6caCaTOaenJ7zva073O/C3/o2f979UyBBIhMZK8NazXJTkRU0iY2LV9ksjKdvTrjGY81Oh8/4nrREZFD4InJRIbQhIyrohTaByFlXnxAYirQBBEKLN5pUSrTRSKrSSCKEwRrfmGrrN9i2qkiyJ25NvXaK1QQnFqL/eCgqVBCRVPkMFeZ6x5BESqjrnpJpwlz1IFJnpEMvPXYMUQThU5igLj+8IjAlEw5i4VjjXEHz7/5ZFKb0oY5mXjMdzsm6PZ55/knJRcnRwyvRgjJECn3bblKymwUQGJdtn0zuPkhppAmXVnsilVkzmK8qqIcu6JEnCeDKmmyQYBSW+bTM5y8b6GrFskFIQ3IXd6edcefppbLIP9yZs7m5y9OA+vV6P4eYVfvTGbXzjOZ2VLMtWkJd1Y/LGcTqfkBrFxlqKjyMeTI959J2H3N7a4NXnr7BzNebNd96iG2/wwhdf5ubN53nvwx/zznt3+cPX3+D3Lv0u//F/8CsUq9/n00crCgc3tjNkcLimpinmvP5nr9Pr9XjhpWf57/9HN/nxu5/yre/e5v7Dkteeu4o0Cqk0jZOs9TOubeZ0E4GQp3BlRcj2OE3bTebIRzR1hQ2eMm8QscPoEcYERORaAWBHU4kZJ/HHfDxpaGYNWRqTdDqk+h3C5SHqpTE7heDhQ8egMvyt//bXOblzTFV5rl4acWu9z8enj/jk4UPGzRnDMGQ2OyaKDFonSKmpVzlSKi5dexIvGt779F20jvHn+dyzxQLXCSRphm8siT53TNSapnm8tfvYhVdGK7qDHv10BK6i04uIRIWzEHDsH1bktiHLOiRRwqC3y2Q5QVR9mgDCQV15Ii1Y24mQvqFeOaSUTGuHiMR57J7ABzBBcHkzo6kreokhUwOeufYMO2trvL58H7XdkPn2Ksw6SVVafBD4+nPHqFbN6pynEIF6VkCo8F4QJzHKSJSSxE1AJA7hLPawwGxFNEl7/dtUFi0cmZNERQLHNdEOOAGV8HglkCawfWmdp57e5cbNIdsbG+goQVdVez4yjjyfk6QRk3FFUa8R6LYxc7FhY9QlGcZsbW1xWB6jlKJeWR4c73E6OyWKDTo1DNKMSCUEKbGF5+7bdzn4bMx2f4cmafjsozPmqzHOWvLlilX9+FHLf9kJoS1CwrUJU5FoTVoa59BSYJQgjWIiZTCy7dc7AloqpFA0tmwziUR76g1C0TiHkpomBJrQipS0SRBKQgAlFVqZtnBKiVSq7QVHMeY8JIRw/ssFQVWWFIVmOBy2sZdJF3GeUCSNRsUpvszBtS5pyECQEhFJTuOcEDSGDovSUsiCYdLBSkUsFVJ7Gus4mcNIK7xu3djSbkK+zHEEkiiik3TI4g69TkY5qTi+fYQvHKtZRSduNwZ1VRMb3Z7qvUdqQ3Teu63ncybzBXVT0e2m1L7maDxGSUWv1waV72xv4+qGoirRSvPo6ACEY9CRDNd3kFZh4gt9wuf8g3/0FkIo5ouKN975hN/9zVucLUu+98Ye9+6OiZQg1q1gyIl23EVJ6KSCQarZHKaoOOOpFy5zeP8e8/Gch4+OGGw/x1/523+bt77/Z/zDf/DPuXbjJreee5os2+Xew4f8o3/5Lb747BP8jb/6Eslwi9PjKa+89BLz6Zj/6//tn3Myl7z2xRc4OT7mf/t//GO++tUX+JWvfpnnn32Kt998n/2HC6Qy5HlJU1eMlzOSXcn271ji184IGw1Ja0FBVecEV1LmoXUpFBYhI0b9FK8cZVmzvduhac4IBBbFFBcCtZe42tIkFYU6powt/a8GvvYlhbZrFPOGNDriqXSLN773MSLaYae3zbXBZdJ5xNHJPbLR00g6nJ7u42XEsLtBXecoBMFrdnafpkHxyScfEEUxrnCsFjmXd69RVhUqShkJcLYV6EbaPNbn+thv58ubPVZVTSQF66NdmqagLix7JwumxRSEY7jeQ4klPlTE4irB1CzzGU9e67Oqc+rQ0B0p0sxR1pAjcOeqZaMlHR3wXqOEI9WeUZSys3udGzsvsrRgTMYi1Nw/nZI7h3eC4D3OhVYc5QTBnQcZaVpTfAcE0XoWIPCutfoKFSgUUhlMLJARiJ2YSlqwGu8CWmmEVAQT4TcUcZpAM2ejH7O5OWTj8jqj9S5RZugPNjHSYb3AuhwTJ5zNz0hS8Ap8VCCl4t4Hp7z77ifUpcMYzXRWk5QSe7xPHCx3P7lz3vi3DLe6KCPoMWIoB2RFQi7PCDrH1g11XlONS6LdCOE0WdZHeI9RFSK5ODV8jjhPFIp1a3zhzzNyl1VJ8GCUQSvdnkzbFFx+IqunvSb2zrXJRsHjztONRGQRiaWnEjIlkRKkkKAUQkiE1AglEUIQhCJIg9ARPrRfC4HGtiEHQrcFXwjBYDBof2spKesK5x1CKkySEZzH2hIrAlpJVgOJ6ygYV3TWVftzTI1QCcu6pqMdg0ghIqiLmrNZIFWKL7x2hYOjU06Pp61S8/zfjlSE0Zp65th/dMKT166A1RgZsypLqqrEOkekYhrX0NQOR3UeKiEwRgMBFxQP9g65His6MiPLMpy1hKrCKwlCkhcl+ECaGKpywVa6SzkpOD6Z/SyXy88VP3z3mMZ5bO3ZXOuxaiLeuz3m3sN9YhVYGxoGgy5GKx49mrLR72OrBf1exBOXhxAsH98Zs53c4Llf2uL7f/ojysZzcOchq9Lw8i//Epdu3eKP/sV3+cHf/4jNzW1uPn2Fa9cT9s6OWc3mXBdrVMuCz96/Q5kvMFrxg/ce8OYHp/RHfT66e8b3PvgO3/7z2/ytv/5lvv4rv8F8ss9nn37K/YMxR3nD9d80XP7VId0NTV6sOD05YDjUeHtG3VQgA04oZJSQF440jblz/5gQOtx87hqNa52lwvlNZggSoVs/6lXesDZMSbOAkhZvHIYa3ZPMyw9Z5h/xxG9FROUjQmeNQbTDRpxS15bDg4+4euVZmqaDO35IaBq0kAyHG4hGESnF9StPs1wuuH3nfaRMMUnMqpqgVUQUYDbz9HpdgrdtNu9j8PjpRIcdnDDsTRYcRyWzfIkQCY2vuPrEJcrllFhKrmxuE9yCVCfcPy65sr7Bb35NUwXF4fyIR9MjnAtEUqErj0AQGdV6IkvJtUt9hrFhvTvkys4VFqXgaDUhyAFJVFMtjzkZL1FSI7yncZ5zzwE8vnU1CAq8b3tzAYIX5yKoVhTlRTuW1Bq0O1QlMEZi4tDGC556ktQQ91KklSzPSqKOQCU1kalQ3Yzf/Gu/SzaKOD095PD4kOnZKYP+GrrTITKGutKcHVXtolIC11gOH53ywdvvsyxXCHUeBzcP6NRTFxVXrl7iqHrAcGudMq+IlCKNY5T1+KQmD4qiElTLglVvxbXneswmOWorwhiJDCnKSDa3QKuLOd7PMVJhhEQrhZYK/Ln5iZTMVwVIjVQGaQyIgEeCbIfuQ3Ct45UXODy1bqgjRzoYEkUQ15YotNm8nrZYKxmhtGlPwdoQnG2vrKUkeLDBt/O91iKlwzaWOOqCgDRNW5cs3YYGVOUKGcXtZhIIQgEKK0CmmuWWwCgLGEqvUE3DejbE1YpGKyAmE5p+p2E2rSitpbuW0b8U0d/s4KwjkaoVkimFIyARDLIeRZGT1w06UiShNb2o6oSqrBDSteEPrsEtVnQ7Pby3pJHBWcvpbM7ZbMnTJmY+nnEWx+jIcDqZYuuafr+HFNDrZG0/XDmU19Rlg+Zi7X5O+XnajQInPH/47U84OF2Ca3jm2QGbA83ptMHRcDapSGPLM1dG3HxinatXtogiyaOD13n3W6+z/itfIEti7j6YsbvZ48H7b7G9vUFvZ8BXfvHL3LhywAdvfciPv3/K13/pVb7+5S9SOsdnn33CwfyI9z77iH6Uce3SAKkM33vvlHpvSicSaBnYPz7hf/G//yavvvQk/9Hf+Tq//GtX2H72Pd5zn2CelizKHIegbArKKuAbQQgVSkFdCwKB2lVkHUFswCpYlEvuPTrm2o6GIFFStelGwbfPjgs0NZSlwqSWponwocLJFc5HCGOJRp5QOarRHm+y4KnoGutXU8K0x8Hex6TJfaarq7gq4eTskFRpjI5JE4G3Z0RpwnNPvoRrGj679zGNqBECtDBU2lJEKTYIvA2sRY+nT3jswnty11NiSUYR2cAQVMpo0KfMzxChYDjoE8cVa90hihEHJ0eMx0fcNC+RJteY2iXDbsNglLHMzzhbzNFIrFUkSrO2O2CtN6AXb5PFPYIOrKzGywohE/LiEOsHjE/mnE1LvKftzdUSEsHCuVYHgzyPCTw/BZ+P3Aov8KHtiwmpIHiUbj17nXU0eGywrahGSPJljasLVvcKQiFJr3VQUU2WWeaTnIf3Dnh6eK1Np2hmDIdX6fXWMSrl8ONj/uhbb7D3aNKqraVvRTrKUbkCGzxSSdLY8FRnk6QnWBysOP5sSfd6hLU1mzvrnJ1OsYUnyzwyrjGmpioq6nLJpV8YcHljk9tvzFk6SR0szksCmunCXYQk/H8RqwQpRTsiIwXSebyAoAyLyrJqLGksCaI9ndrz3qPE4wPUwlPohioL1Nq3LmbOkS09sRUoJVvlvpQE2RYwASijW9WyilpBhonQUtMEj63beXVEa2npmjanWpsIrSWNrWmsazcFWtE0DU1Tt7PtUoJU+M0UM8gQlcXHhrKGNNXMnKe2DYSIgcnwBaRrQBJo8hW2zFguKvqjLkGAMpI0S38yl1w3TWsUr7ucTebs7mzRlK1ZQCxSrPM/8R6X5yIyITxaaaqmZLoqW2tN0VpvSqVwzpHPK2bzBSI4hoMenSwl7XQ5HU/AS073Jhgd0evJn/GK+flhmMWsjyJOpyWdrmE6nVOUDU/sdLm02UX6ktOTOaNBHyEFRyczbu4krJYr3nv/EVGcsLs14tlbKQcH+ywXjjwvOJ2s2Nka8if/4l/x5I3LnM0m9LIOf/3XXuP+/iHvvfE2dz78mN/67V/ir37j15l/7UvcfXCHe7fvcLY3ozqo0SrwK69s8/z1dfZOTiHJ+MGPj/mXf/4Re8en/N7fe4HNr3lYeE5nCxaLguGgR1N7ep0B1hYEL9AmQXrXinWlR4ZAVdWkiUBpx/h4wkZ/RL/TpWkKrBU0vm3RCCkRRjCZ54xkhg8eo2ybNmZrlNT0up5KO2Z2znE1Zy7GXLrVZe3RDXbUJZAFnkMmy4xtfZngJZPpKb1ORRZ38MGjleHZW69gg+P2vY/JK40IjizRCJ1QWU8SaWzzeO4vj114T+/NoRPobhmyviI/Len3RuAEjRtzbfcVQr1C6w0eHdzjwzsHXNmN0anjZH5MFQAdgxMMog799ZpL/YBRmiTuUVaW0llyEsqyIootmYxI9ZC8qhDK42TJ0cGEahUQWqB8wCw0oZDIkcMGT3Dnuaq09U45hRXt7jw07UlXiPYU7ENo7SHPP8TWKxcwniSOCXlNsG2v2FuLCK0HaFN79h+e8uKrLyI5IIra6EJNzAdvPuD9b/+Ig8UZZR1oaodSAoTHK4c3rWezt4HKWSa6IIsDTadEzhpMCDjZsCpnZL2EMK3ouAzXKGo7RxvFem8dKRWneY3re6pxjhMGIQzewWoRSLPBv9mT/pcQLUAKgZKtkYYyAhk8iVZ0Ogkn4ylp3I69aUHr5SwkFsfcLinjBtlN2pCOEMiEolk5YkybT22itlDr6Dy9SrXrTCoQohVVSYWQiuY8nAGt2h7uuRYhjqK2WCuF9466KkBItBLURYE0EVFsKMoClMf2JXLDtEXQCayySKMpG9DWE6UptlHkDhI5xFQ5natz5CxlPKuYTeakiSbLYrppho7j9nf0lhA8aZYhpKCocqpqSZymOC/xbXMI27Txa0kcEcc9FosVqyLHC8mj/RP6Hc3m+ojRcEDoeyKlMc6jE8Px4Qmz5QrnPLXzHBwc08s6DEc9ev2E09XZz3rJ/NzgPfQ6EUkvAZNwfLKgkxpEgL2TEoRlvHA01Aw2UoaJZm2kWeQL/uyNUy7tbnLrUped7ct8+OnbrPKSa9tdjsZjxoucvf0VzlXc3Z/x/I0tXnhyHd3UfPXV6yyKnD/65u/zvW+9zq/+1tf5wkuv8vILX2T/4BHJd19HxJ/x/FNXuLY9ZGED3/vxQ7Y2E1a2ovtCgvrSkn0/Y77KQUkEGfsPCyoHm7uK2moi3aWpJc7X7UhPrQkNyF5Bv+NZLAJJCuNZTZJ0kSIniDZqNgSPpDV+ya0mtiOCO0IYiCKPMYIqb3PLow74JqBNYF7nLGzB7pUVm+Yqx59EHO+tmCw9GMEo2cLmltLW9NOCrHF0siEKyXNPfQFvHQ8PDmlCw7KY4kLrla5El7L8C44FrOYNoWywK00cpQyG0O0qer0tTmb3KfJDdree4cNPPuLTu0d01wLr/S3K4KhlwDaK4EGHADpBhAzBCiEjpuWCxtasJ5JONKIxMVYGnBQsfUGQSzqRwQUL5O0cogcnBFYIGhtQRhFpia/bIhqCB+tRZwqVaKqOA0C24as461GmPRFLKc8zf9ti7B1UwaIqsBZk5CFu8D7gQ5t1vMynODdHhJS66DIvPJ/+4E3ef+d9vK/bYi7aK/BAa6hhce2JS4EPAesc+6sJkVUMUs3OM5oyizCRoT/q4xclQzqEBwlOV9gNiUpSzqYLtjZHNMvA4cEE7yAddvANSJGh1Lki9wIAnLcYFWN0hDJR6zxVFwjnSbRGK8FstUAK0AqQAQ/kuqLpWSplCT4ndhrTRJA3REEgVdtTdahWT0AbG6m0Rql2Bjic37xI2a5JqRTeno8onQuxhBRESYzUEtfUSN1+p21qojhpr72hVSFTQxQQOx2sbjCIdndPoKNjpOmdbzAEcaQIEuZ1Qy8MqEyDjxp80vDgZEJUDwmq9autbE1dV0TSEAI0zhFJRZYkVEVJZ73PclqzypdUVUnQBqEFxbImqS1FWTEvCoKMma0Kttc2MHHMowcPuXr1KnlRoqQkX5VUtcW5Em004NjdXSPSCUVR4OezdqTpAoA2SlRrYqP5+JMjipVnMDCUNnA0rnG+xmuBVZpuFLGzYeh3I87mS9KeYbG0xGnEO+98RBQZIpWztZ7ywe2cSgXSToLRKeP5CctaMJlDmo3oZjFaar765SFn8yn/4h//E/7kD4Z87Ve+xhe/9iX+u//h83zt65/y0XvvsL8oOHMlZzKnCg1/6+/e5NZf63PSPKQpC5ANsdnApTPKBsIqcHw0J4k3WNuEsplga4cJPVKRMY8XFKUg7UqkASE9wtdQgLUCWyuiTNN4S1MHnGvQjSSsGsqVQMcJKpN4VbY++sJgdMD70Mq5gsdJwaHNKXY+I2XA7KjLnpxSyhOe0c8zKC9RVhnWNVSlxzWWrNfDhIhnbtyiqiwHkxOapiIUDcFDVVV0qr/gOV4fIJSS409XbG6PeOLJq2hRMuhdoSrOiLTh7bfe5nRWEGWBzWHCwKQsFiXCWyKAKCWLTHsVJSVxPyH4iMQJ+r2YgShJowOWfp06DAFLYxdk2qJFRapiepkiFQJfaUIf6pFvd1NCtJaNQhK8bxWfBkSv3cUgPMq0GaoyiPbrpWhFpT60M5u0SlNxbmfZCk8Dpi+QHdv+DYIkBFjmczw1t99/yI/fvkOxqmkKi7PtKVqo9hRstMKGButaAwaagK3O+3W0EcN16ZkWDcXKgbH0h4Z8XrA1iugOe1SHDZ3xiGUWOMxPMUk7m6pkhhYxZbNCaUMIgWBrqka2Ct4LANC6NaxAijYpRwo0UJeuFQRFhsl8hooEMmlV+nnRwFAjtcY5SbCe1Fm0b0V5QSm0NkhlcLQiPylFOxMracMygkArdZ5CJM8jwxzI1nZUn4cFaCUJweFczWo1p9/N2jUcAmVVEqcZUklOTxdYUZJ0PNtbGTpWyChmWjekcY+ujsAIPO0mFzxbWlHtr3h3MeOFX4hwwqKMYzJbkR8LQrbOWVMR+UBoLN0YurFGIPE+EJn2FVHbmlW14mw6I4ti6qZhfHpGlnZwsUMbiUdx98ExJ+Mznr22RbfToSpyVrOc6XxC1smwNtDYitGwR1GURNrga0td1VjX0Is7ROeG9BdAHGtmeQ625uZujN02rIqG6ayklhHrQ0XdwGy2hMYwunmZos4pa00nkUwmc2b5JhtrA26srbF+vM+lSymjq2v84O19mkXFB5/tk2YRdw/O+PAf/oDd9S67axmvvHiZ6mxMf9Dna18ZMp3P+dYf/nNe/853+fqvfp1Xf/ErXL96k9v3PsXGf47oF4QYnv9rV9krP2BVBqK4jbss6zOCtKQa1rd77E2W3H64T+A63UEP76d06FAWAroJVV0gcotWhkh4YhloViUd2aNceJblirzxWC8QHuIosCiPcQ5mxzWRSOhlmm4/wlqJjBq0DijTxgTGqaAoPEXwxFenbP1WyXt/YLHVistbp7hc07FD3MrTJIEgAjoy7UZaKp598lnmixVTG1jUDSqJsJXF2fyxPtfHLrzBeVSkyc9KHn56xPrOFdYuXUYrwahzle//4EPqRpD2BLvb6xTVos3eVQIrYFFYbO44rheUtmJ3Zw2hJFpqkiRGagsiI041Nh/jXcJ4VRHHmmG3NZ73Tc4TG5ofHcY0TuKMxRkQMhCcQiJw3oJUbRKFCIQ1hW9sW1NDQAbZJhbRNsOFlHjv0Ukb1hAs536+rbG+SQXRUBPi1ue3ttBYhzYxD++e8d1/9Sb5qkBq2V57BAkuIDyIoIiCwlcVUoG15yMkohXvgGi/XASQmsY6hGxYLScgYXqsGQ/6pJkgPl6xsd2hMzDgIw7uLRkf7FMtGoKy+NrTKstABNGak14A0I71BHCNw2oL5z1Zj4AgMSbCa08RFeiOQGqBNwEvPcIHlBd0rKJPhBICf650DlKe3ywEZHDt+JhvQxCCbP2YPzfKaHsYbcGVQhBFMVrr9nQsRWvfWJT0koTgA1EcoZwjL0uaIscLi9LnM8WbGS4SyEbigiQvl2ihqa3C+pIojkBKsrhDA8ie5u03xlx68jJmKyVNcpTTnB7kLGtHomKQhvFqjMkrRllMFGVkQtA0Hq0N0+mKe3v79E3K9uYa4+kcLVpf6rIq6UYdZquC0+kZ3jbkdclICvr9Ac4FbO2QXYmnodvptFfVSoAE6y39bpcOMWXZcDY7/Zmul58nkq4gM5Knr62jZNuycCLwaO+MRR7YWu8zWzmWq4amsUSJpt+PUImhtJCYmoODQxI9ZDDo8rd/7z9ksLPFm2+8zUfv/wPE0OAHkrsHSz55tMD6wOl8zoe3YVlZfvnL23z0wR1eeuEW6/1NXn4hQxrLd//0m/zZn36XX/ilr/OFL7/Mjb/+7/Jg7yMWwwdM1SNEUCwXM0QZ0+8meMA2dbsRxYGy9IaCTz4d8+prfUKQ5KsaXceIyhD5iNAYmuBavUEUOMvnNFnEofUMMkldelCCqgqYTsBbj4mgrmGyXJELwSJI4lVAK1BRwOhAN5MkQeGdJa9htvJsD7p86cs99u/sU55IsjTh8OCEzdgTgiIQYBZITEa3NyDJYl586hY/vv0Zzk6pqxlCCJL48Vp8j114bW0RRmJUzHSv4uy0ZLR2wHB4mW+//SmzsmRjs4tWDmMaDDGZ7nKymJE3DY1XoARBdukPO0gpEEGTGkNwS6SIqJAsq5qNTEOxYqGGCFkRgiGTHqFrplWDMZbuE5KzENBNQt3UcD4C4nxAKkE4LzzWnZvSq/aqDhFAC4QNP1E5t18f2vnIEJBCEJwH5REdcJEnyEBw4CwQoN8d8fq33qNatR7JIbRxgCK0eb1KSiSOalIhVoJ4QyFS1xoG+FbsJUJ71YgKBHt+9ShF+wUE8qVltTpBqXYe8+FdTW+wTnkyI7cr8qrC4UlNQlNYgtYEGxABTHIxx/s52sR41yCFaI1PwvkYmm1oXIWTNXKgqDJHEwS28EjT3mwkhWAoYmLbzoYr087mIiThvJcgkWgdoSNNLDVISeM9PniU0Ojz2T6pFFIKjFQopVrHMilwziKlxLlWFKLU57regNGGsi6p7YJaOfRQ0r8yQmqNiKFY5CRJgxaaZanoDLoE4RDSkduCee1IsxHZzjW+917Fr/5Sl24qsF1Fb6NCxprgItJOn6i0lGXJ0dJR2QlPbHTpmzaqsiw808mC7esDtNF0e13yqmI2nZJ2usxXBYv5GVc2Orx042WGwwHeB2pbk0Yxo1G/TWYC4jiGEGiK1oNXRSl3Hu5z/eoltJAslhfjRJ8zSODZqx2oHVJodrc6HJyU7GxtwnjC7YdTjs8aqsax1k9J44i1rXWa4xXPPn2Fzz5+hBQRRhrKVUGVT5hPDR++/Rapkiybgv1Ty8mswTaw1lFc3jCMhilKlPSHm/SGh9x9NCFJY/YPJrz6yrP89/7el/jhm+/y+7//Tf7oj7/DL//qV/jCr15h0fuYajkmjjKSjmNV1oxnijgZokSHRTGmIx1Rx6CrmsbNWJ1oRskQLxR11IAZEJk5RZljMkc+B4MmaAmmfU9KozCxpLa09sNBYnRrniQEaB0gFuTetznwVdsjjEwbxqODoXaO2nkiqdFijUtrPcxZF1NdIc8dnzx4xN1wyGtPv0AIFt/U9LMGjyeEIWmc8srTt3j/04/aPGkJZfUX7NXsrcPXNUIoXBE4vV/yxNU1vvPh24g4oiMVUVQQa8F6x+KKGFd2kaWjrsZ449FRQ5p0aOwK5w3SRDTeMUz6aC2Z13OCFJTENM7RjVJMGmObAywVva5iuK7Zek4z8Q2yAlc07UkTsNaitPqJYEUEyU/yjzk3rFdt3J5XAeEErm5fceI8AsqHNm8x+HbG2HQEXgVUaGeAvWxHjoL07D3Ybwu2D+dqaoGQEBmF957aW7QAVQvC2KN3JE3k2x6fcK33s2rFVtJyXojDeWNYEJzAeUGQHq8F5axmNj1CO4E3HistwQtE4xkkhmnjEVoQbA3+ok/2OU1jCcFhvUcH3QbRE2iahspWrEKBUw5v2xOwdZAJSZ+I1GsSpZGmnc1tzSsUUslzS0iF8wFHe6JuhG/HgZQiMlEbniE4N9BQ4ANCaZQxaCXwrsHbpr15cY6qLinrAqV1e11NgxMNTahB1Dzz5DXodljZNuqytjOWZYmSGq+6NHlD1FN0kwylNYWs8L4kG23x6XuHjKKSL//CBmVRoxPL+tUOn72zANOn8QkueJJYMmtqDhc1vc0eznuMVOxsbhDFEYeTCbNFTlVUJElK0zQE57l5/QrL5ZxOllFUNd4HiqqisxljopjaNhhjqMoKHwJ17VitFvQHHTpJTF1W5GVJP3u8MPF/G+gowdVL69x/eMx8tWCLPncPTtHJiPFKMivPDw0B6iBZ1ZrZWcH4cI/hcIu19SF143l4cEpvZnjzz9/kaLJiPp+wykuuX97g4OSAonZICU9c6bI5jHh0VLF3MiP73od89dVL/MN/9mPGK+hlmt1Le0yOOmxvbvNLX+sxmR7w7e/8IdVTNzgbPkJFhqbJcUGgFORVyWIOWmbUpcHZBploXNFwc32NtXiE7FhmjMkbqOYVu5diGr8kiSLKqEIKiGKNcBGRLBFBEITEKIk3gcZ51Ll2phbgTMCfR6POCkcct1m8wUGUSipboYIgs613em4rnrh+C10OOXtQU1dTVmLJwfKE5mjFc/2X2OluUfvAwEMIAiUlWdbhpede4b1PP+T0bEJly8f6XB/fuUq1cXzgCXXg0funaCPJlSMe1OxsXsZXxwhXEFyOs5rJ4iF60eNq/Bwus6xCTlHX3Ni+ifcTnJvRBEleNETNmBJHOtihsI5OppH5gnw1J8QFaT/CKEdVS+ahZlEJfNAoY3G1xtmG9p0oAd+eQs+9ldGtKb4UEqRvU2aCwIpWFRea8zGjNjIcVHswDhKEEggVcCvxk/GPzijh+PCExtWt7WVoT6yE0M7bBXCiVVnLVMCw7S9b155khWrVrJLzTYEIraFCLfDCt65EraM+wovzX6aNtFJK0IiGJrT2etJJfBHwvl1VnlZoJsTjeYb+20BZtX0XJQXOe6SU2GApyoK8KclF1X4ODajgyYRi5BO6MmrV0Cpue/9Cne+LWqtHrVtRntCfX/G3P1tKjdQG63077+pBSHOuwAytIYY7v5UJltpapGiVv3VjaZzDBwdYrG1ofI6VlqtPrbF7fYOjUNIAy1VJWZY4qRHGIKxvXdOUIlhFpgWdbsbpIse7Y5g94qMfL7hxvU93GzKjuHwzYu+ziFVh6XR71I0ltzUKw7QJ5A5iHQgE1kcDVnXNdLFCSsF0seDyzjaxUqyWS5aLJb1Ogveeg5MZ47MpoSkYdrt42j5ZHMWMl1PaFELNYNCjm0T0kphVXXJ4eMylSzs/u8Xyc0a/Y3j/4/soEYiSiNPlirN5xeL4GOsFnVjha0HTwGRe8q0f3ePXvrCFkYoHj45Zla149NLmBsvFgv29Q/K6HQW6tJmwmC/Z2RzwaDwmD447Bzm3Hy7Ja0cSK67d6DFc73DjiSF7bx3ivWIyzRmfLgjC8+CTj7l65QZ/49/7ZU77D5mPK7L1jDLk1DiCFNjQ+tivZlPKhcb0BaoQdE2Hpk6ZGYerF23robHk1az1bUgEWjgGXYNAt5tcK4m1IlKyjeh0BikdKhJUpSeNwJpAnEmqpnWUkyLQAJ1YEJtAFEuE9KimDbxxquLB+CH9wZBksAV+yen0hLk6Qw4d7392wAenM37nq7/IE9s7BA9SaiKZIpUkiSTPXL+Bd5bj2ePd1ogQwsW0+gUXXHDBBRf8lLi4j7zgggsuuOCCnyIXhfeCCy644IILfopcFN4LLrjgggsu+ClyUXgvuOCCCy644KfIReG94IILLrjggp8iF4X3ggsuuOCCC36KXBTeCy644IILLvgpclF4L7jgggsuuOCnyEXhveCCCy644IKfIheF94ILLrjgggt+ilwU3gsuuOCCCy74KXJReC+44IILLrjgp8hF4b3gggsuuOCCnyIXhfeCCy644IILfopcFN4LLrjgggsu+ClyUXgvuOCCCy644KfIReG94IILLrjggp8iF4X3ggsuuOCCC36KXBTeCy644IILLvgpclF4L7jgggsuuOCniH7cL0xTgQCMAikBBEoGeqkhhMCytCglUAJG3YSbl9dY7xiKMgdl6KcJR6dTjmYL1oddLm+tUeQ1y9wipeTB2QlVLOmjuNzvIYSl1+8RJ5LOsMvLr36Bl176KmVT8um9P2EWv4XbKImSAVp2CXKN4BJMlNLtrbF/NiGJu5xOFoyXx+xsXKGuAlJ2aJqKKxvPY+sjPnr4Fmvr20RxRFUUZEmXrKNZrZZEuosPnoPTT7h66RqjbIe+0fzxt/6IKv+Y7e0XeXAiuXdvwsnxGaVtCFikdAjhkAicEzQNhOCg8SQ9MH1YTQPNStAsBc0qIAJko8CVFwRXn4ZOT+BcoFgKZqeB8REsTgJlCQSJkgFj4JnnRvzaN34HL2KWqzmT2TFRnCJI+F/9zX/y/6t18/9X/I//N/8JcWTwQTGenqG0wJYNLoAPnthIFouCctVw9YkniDONLSpW+QoRwBYlq2KFlJqNrQ1UHBFJAWUFJxWXkjV63R6z6QTnPHiH1gYhJVorlFQUq5IoNpjIoKUmBAh4fAhEUUQURVRVTZ7nKKUQgvZ7jWY6m6NMRNbp0VQ5Qgh0mhBnCVpJTs+mfHj7DicnJ3zpi69wMj/ExYGd3SdozgoGVtFUBUZFLPM5nSxhaacUdonpjtjd2uTe/jFdl/Lk1hbd/pA79++zmM3Jsh5VcPzpGz/i0ckxOMcoivjFL7zM9tYuRVURdzJ8CATrUFrz6OiQo6MjRp2EtNvnB+9/yMOjYyCQxjHeBRpX44PAYYm7EV4KbHCEEBBCcP/dg5/1svm54H/2V74MawVhM2E0fA6JYHbvIcF71OWEvA+hKzDes31q6C4ThtkQozSYBNHrIzo9yGf40xPqxQohJWl3QJQk1E1JVeQ0lUUqjY4N88WM6eSMxgayTpd+khAbRVXWaG2QRiKujJg8GbMvVwitUEZgJgsu3XNkpx4ZAtZbTK/D5s4ORnqaakXwgDDEUZe6tlS9DH/jCssU8nxKmS8wwpN2Mpqi4M6PfsTbb36P/dmE2dgzWMt4+Rdv8oWXN3h6N0XkY7737Y/5/bfnzIVkN3TYqnvELiVLUq788vPop69yvKw4mR3TH3TpDUakaYcQBI8+eJ/9N1/HN8es7QwoIsPMBkrbMEo93Y0E0U0wRtMzXTZ7l+jLAWJiSRYBs9DIyoDVeFsQGcO/97/+z/5rP9fHLrwAAXAenIMQAlrBCkfwAQ8YJchijW0aFsucftxj0O2CFKRJzPZWn6AtAokLnqu7GxilSWPD2iPN0SInlop+KkizjOdevAKygijl+hPX2N7d4eTkIZ2sz6RcYzk/Yag7hCglhIg0GWISyd74mOOTnDSN6GWbvPPBByxXlmGvQ7+r6CRdvKtYrRbM51N0FBM3Gc5WWCdRKiGKYoyWQEaSdDg7O8NWDYsEfuMbX2d2eIusf4kXVJf9gznf/d7r3N97QGMbvLf44AEHTcAHj/cgJAQVsE5QLKCeBqQMiKy9epCxwFvFyX3PEYFVAfNTTzkV2BqCazc/4GksNCie/xsvg1Tsn9xhtapZLS3aFPgg/nWf8b+0KAXOWeK4QzfpcDLZJ0kyEh1hfaAuc6SMePrZJxES8sWMUDkMCoCgNJ20A43DOEFoHKWrOT444spoi8KvCLMGHzyR1oig2+dDG7SWeOdAeOT5c+CcpyhL8rx9UEUIBOdprKWua6RWpEkMUiBCII0iorSDFIKycUSxxtU1nzy4R1mVVEFwdHTE1mDA7OCETqpZ2YL57JjtwS5maQnW4nzAukCcdGlyj9Yd5quSd378KdY70u0dJpNjtrfXMUpgtERJSITm0s4OJ7Mzeibj5VtP0e31ccEzGgyIs4SiLOgMe5RlzfVrl9ndXGcxOWVZlAw7GfLyJWbzBU3ToDQYlVI6S/tWaR+Odj8v8OFnuFh+zogig60Fw+FTrH3xS+RhzjJrqO+f0olitsyITEWMi89Y+CPsoItOHalcI9t+EnPlOkSa5v6nNONDghBoHaOjGBVFJEbjvUBJCxJscPgAUZQyWhugZICqxluB1oYs6yK0oPaOOl9RxEsUAREczhbkFoJ0BB1I4hRSwapa0JwtqPMV/d6AJJHEscKkhtBLaDoxOhbEdKmWC6yr8Z0E7xtqlxOURGPQoiLtGnrrEVEqWdQrVFOSaIVpJGVhObULvGvohi7OORbTCYNyHYlEek1ZCJR2uFAihUP2YuJL2zTLQJ0aml6Kqx1htWAVW6wQyDogSkchSpJmRdyLMP0IF2viRGEWMbqMqVaWthL+1/PYhXe9F1E1lroJWB/wAVyAvPbgQWmIjGJj2MHWJVGUUNWWgG3/AFXFcy8+y47d5UdvfsrJrCCLInbXhgjhyLRgI0moXEOaRexeGRBkTl0XFKsVJ0f7bGxeRkuJCT2WDzSPqhL7zILNa12yLCFKDM7HDLsxV7Y3mC3njPobRPFv8/23vsNcBvJK0k9qIt/DNpa02+V0NqZXB0a9EdY1zBZnWOewVrG2dok7D/boJSl2I6BLR2k9Ws3ppiN60ZD0+hWC+DL1d5aMJ6f4AI11hKAwJqAqiy0rmiaQL0DZgDKKbF0RZEPage6aIEsEYaG48x3JqqoZXBUELzm77fElICSCABq8E7z6ylW2L13ik4e3mc2OOR0XGJMQxzHeXRTezwnBEsUJVb2kqiyD0Tar5bL9b7FkY9TlhWeeAgSTkwmZNBS2huDx3lGtVljvcEXN+mBIWZYcnhxjG0eta8bLnCusoQm4psLoGCEEUoCzHmsbktigpGS5WBCQOGuJjEEpiZDQuPbGKI4NUsn2xCsVgoD3lsVsilKKNE2pypyqaRAI6sbS7XR4+emnGfQ6SCnodTp8Mr7L7c8+Y+e5EViPUAK8Y23QZTw+Jk0SIpOyrGpsDvPVgmGvR1lWRHcNx6djOlmXqi7RccTu5iaPTo+5cWmXJ5+9QWetj5SaTGjKomI+G/Pp/XvEccTlq1eZjhc83HtEJ8l49vp1TKfD62+/zfH4DCE8Wkk6UZfS19TK0dgaQbux9BdL9yfI0RBpK+LeFrLTIUKycespFliUcKhOigiWXkehs4jgAqtyjjJrdLY3aIYpzWKCL5doExN1EwBcU+FtDQRsVYAQ1HVDVTZoNN00Rel2ExSnHSKjUEqhjcELj6gs8bgi6xes4hwlLKYI0FPY7RofBSIviH1G6g2p/3+z92e9tq3nfSf2e7vRz3b1a+3utDyHvUSRoiS7ypYd21VlxHGlkkpylyBAgtzkE+QD5DrXQYAAAVJAUgkKcKrksg1JtmVTpEhJJA9Pv/u9+rlmN9q3y8Xc1GVyTAiQQPF/ua/WnuN9xjPe5/k3c5zISaRCRUG73RCEJI4NPvSIoFBDB4sFjd8SjcMtV4R1TbQRKRVlmZAlkJqGVAqG3nLx6ppPnm1Z3wQGD1sC0XU0UbDqLduPP+e9kxnl8RlqPqMZLENTE3xLxNPZlljlKLOHnBh0nlD0Fq0daRaQaULwmkRnZGJCXKfEJpCnEjMk+K1H2Q43tCip4PXH+v8/fOHG+96jAzyS7bbl+fmCbghoLTFK0A4BoWEyTymnhn4TSFONC5bFbcOmbpnuz1BZBq7DZIb1sueDz15wt9wwKjNEEJRZwqxIOHswwaSOrt7SD47Pntzy+bPfJwrN2ckp3/v+D/gX/+LPONjPSZrIwTwllJGXtxcczI5Jk4zBNZhE0fU9k1HBt77263zvT/6Y3m+oqy1GVyQ4nn3+iqPj+1RlRR/uKLI5601N30UkirW55Jtf+grGZNTdhiyHPJ1ze+v4yYf/mm++/2UeHH2Lw8N9vvrlr/K9P/4jNs0GrST4SBSBJI0MXrDtQQnYm5WcvP8mve25WTwlGw1ED5efBtaf97gtyFzgOsgzCGvo1wKlAzEIdAHVLOXv/Y++wRAaJnnF6mZDHAKPH18iZEAn/0HDjF9qjNKSloFN0zIaz6n7lsnsgOr+FNd1rG/PeXX+gqFz5CREIRBe4IaBssoJPoAQ9KFms1hyW29pho633nyAoqfvLTENiAhKK0yqUQiilDRNg0Tsmim7+x0E0iTBx4gQASEF0UfSNCNLMyBirUXHCGo3crbeko9SYqqxRQZJwUFxzKlOSBODlBIXLYSIbXpik+GCYL1cM6+mCB2JApq+xeQ5ZTVCScGehFk55mKRYoTGVAk3my1BKjpnOTg+IGjJ4V7F33v4d9g73CMtFMiIEJoQIikZJ6OUfDZheX7Dpx98RIiOs5NTtEiIInL16hWJEByOK/ZnI77+/pc4Pj7hz5894V/96I9xeGKAYD3OfrFbw98EZH/7N1levqAuEgoUJikYjGCTQegCru0xmwWVMhQcELYQt5Km7EiPrsB44vU1etsitcbZHhVAeslgLV3XQoSkyPAxEIlkeYEdWrb1BqVS0jLHZAk6kbjoCMqjRGR8F4idoRlXiLQlNT3xtGaVduhhwnQ7oernJFahTUqfw9A2WO8xOiITTexa3OIa3/dwfYu+PEdUkY3xbF5dst7UOKNBaVIE7s7RXN6xTltwjg8/ueMnHw0sryIU4BNBYyON65Cxp7BzqtGc2d4RPjiWqyV32y3WOjb1hqvra+rNHbm2KJmBNAgN48k+ZbarVu8NiS4ZpXPUKlC3DiE0R2JCcA2h6xABAo40zb7Qc/3Cb+emXnP/wQPefrDHvNIsVzXq9Q+ybhwi6fnKrx0Q8Lz4cGDwlnqz5W7T09hAqFu+/6Mfc//khOlkj1cXT7i7bVisLUd7FQezEfeOSo7PcqqJpOssSinqekMMkXrT0XWBZy+f8qc/+JjD0Zz3z054ePQGpyd7hMmUi+0fsx02nJYPuV2+5G6zIk+m6DQjLzL+yX/yT7m53vLZZ0+4evGcB/fnvP2lN8jLEU27ITjL0C1Jk4rp/pT15orBttxuWvIkxUfLYBWu2DCZVMzqN/k3P/xz/u5v7nE6e8h7b5yRm9/iYnmDUgbXO25unvDq2SvWm0C/EqQzwRuPzhjNCj797BXeO5avJIuPPe01CAEmFSRVxDWC9Sbu/lG8nr9F0FLwt/4H7/LgS3O2vqYMax4+OuDo+IAvvfMu1zcdn37y0X94lf+SwtYtZDCbzBgGh3OW3GS0d9c8ff4SguX+g1OyWYlsLW3d0mxbijxFK0WMgc1mS7fecnBwyL37Z6hMEW2Hve1orjasZxkKRZ4kVHkBAqz3JElCdJY0SZDa4LqaCCRpSowAAR8CWhmGfsBZS2IUaaIRRLwQDN6jqwROMmxuGBd7OAGbpiFJCpySDH1HnqekWhJmGV+99w1O37iP9JG8KIgElN81NhwEG4jOUiiIUbBvDui6ljQxRCUZH5QkWYouc2SeUSUGoyQxOpwISCD4QGDHT0iqlOM3TymTFOU8RglCcMQoWSwXHMzGvHH/HkIKyiLh0cNHVEXGBy8eE7xDRIFzjmA90bm/2gPz1wjq9ABTKlb1Fr28YTYbI5XBzA7YbBv6ZILRAbu5wVpHlswZHe0TkoRhvYa+Rl3cINpAL6AfWsQwkKuCrByTVCVtvWEIFu8CUhq00QidEaRACoVWESkgpB5nLC6JDMITVoKxqzjwI3p1x2r8hJUeWG0qinaPSVfR9Rrb9CRhIMaIFAaTpigE3dAzLBaE5g6/2GD6QB4ia9VxfbHikyePQQjS0z3SK8X69o7NleXP+jtujiqUMDy+9Fy8AN+CMOCI2CBQmSdTCl0qTLrjUHgUmS2ZKIMjcLta8vLFOdJ3TE/GGJ3So5FCkqYaFRqiDzgy1mtLbV8xMgUyK9imEdM3hL4jGwbG2uw+rMNf8qh5tR1YffiY08MJ0yrhwUGOj4GbJnDvfonKWuanOefX1/TKEntNM3iKquJr7z3Cu46haZnP99EyUG9aNkNgCANerEhyw0FMuVv3bFuJFIrVak3fed554z5pOULR8cGffcxeNePr7z7i7L0TsndnfLL8nKyuublY0UwU+FcMvmM0nSC9ofcrghV4rxhVFb/z3W/jhsDnj3/I+fUV4nrN8f4xk2JMEIJNXeP9QG83BF8zSefkaU4MGdthSdevGGzgYG+fehjxYvGM6XjM4WFJWbzFA7tH39dcvHrF008azp85tkuB3wQmM0PwLT/78SXLxYYwRK5+DO3lbkceXMSq3e0kF9CuIx4QGuLrHnx0OuK3/8Hb1HbN9fKO1foSrcfcLjaARyYZD948+8Uq/ZcQKtHoVNC5AessmVJE13F++QKtFc5JNpuazt5ioiR2DrxAKcXdYoENDiUExhhC8NT1mlB78lSjtSYtMqIS1HVDIsE7i48R5wNJkiBTjXUtbujxUTKZzeibBq0UIF8X626cpk1EKIGLEes8Qkq8kciDjK4UuDig4oCLAh8CdduQJOmuSRq1m7QowXq7IR2XIDyyTNAS7OBJkhHCC0QA1zb4u5ZiXDFNTulsC97hEXipduM9mSIihOBxtiXgwUh8FGihiFZw8+KS5c0tg+0wKKzrOT+/Yjab4axHoRiVBeOq2G10JZwv7nj54XN+8OFPcMHi3Y4rEmPkC767/kZASIUxmsE2rM/vSOQJWVUymh7Ru2vKquTw0R7DNmd5uUbJ+2zMFKckKvSo7RrTedKoSI3BGkvX92giuY4oI5F9xNUDro8ILA2WICNBKNIyRSYGjCTqASEHbGPpdGQoBaasYFwxpAGbnKBQFMWYTV/zU3vHAs8BkkljyfWOIGhSA86TqhSGjrBuoHGYJGVLz812zeNmwVUzcHR6yvHpMfnkjtXzH9NtF3Rrxfq8ptwrqVVGXkmGsJsaykQyfZRjjuKO25G2XN++IJ1WkBZEITBFSldvWC1ukEPPN999j+PTKbeix1mHyTTeOs5fbggeYmLwFnINpoKpTil9gnFQtx1bF0mVJpUCa+0Xeq5fuPGuN47O9mxWLfvThPfeOuDkeI+tWzK0NWWR4GPKduuQueHZqyWzPOU7332fL79/yvXVgnrj8H7Li+cvaHqPMor9o5zjw4y96e6K/slHV6RZRZkbmtYymexuq873/PD7P6RZD3z53bc4/tYe7i3Jjdxw8aTn7NBSjvZYLVvicMFme4vQkf3pQ7SWVEWJdT2imPDq5oIym2F0xaSYMZ3PccPAEDrSpMDIlL1pSZ5PubxZsK1vSTRU1RRhHdttg7WShycFiRlxsTjHqMjBrGSe7yO2nn/7332PH33/KVcLSzQSbQw67+k7y6cfvGJ1Gyhz6G4kduHRHvwQCQ48oHJIsoiUoFIQFnCQpor/5D//NiIduFzckmRjvFe0veXyZsHqdgkkIH81av45svGETb2gaXvq1hIGh5IRJwPOBopyjB08WZHT1g3eduxP94kGEpmSipzrq1u6vmcMJEbTWctmvaKvWyb5jNr2tNuaSV5i7UBEYHSCQNJ3lrrZEolMpvvM5zOu+44Q/Ou/UCBe7zWNSZESfNiRsaIOJPOCtvIYBFJousERo0AjqaoCYwyDH4hSYxG4wWE9mDQjeb0OIkqca3HOYpQkl5qbizU3N9ec6TP2xvPdWD0MhAh97/F4et/iGktzs0UgqQ7GuxuRlIQu8Oc/+hkfffARfd3inSMET6YNiZDkZUXftjw8ewTRcnl9SVGWGKVYbhZ8fP6MVdcQgicQUFIgFDsW4q8AQGxXhMUF4vkrSAVhbw81mRDsBsIaJT0+ZHjfIZynjktaWRNkSU5G6zui2/KOrNgXGh0HitJQpDlu6Li7WWJ7i/dAAKciYi+ly3cfd0qnJJnCpRK8pG89g4O49PjC4fUafEJMM0z+DnlaMYwdrf2U58sNQ5ISZUbuJSUalWqihkgklSmyDzjb4Dw42xNSaGwk6oyjszEP7j/g8PCENJ9wefyc9vkG6TU46JeWZGZgBDEIhI/MjnL2vzQiThv6Ybd7ffLsA2SqqA7OCMowhIFXLx5z8+opj86OeXjvHjKX0N5htEQLRV87Ej1DpzldiDi7xdeOZC2ZVRYTG4JOqIymA7ogEcER7PCFnusXfjt/+a1jvPOMihTnWnyI3H/nTeZvbPmjP/wTmk7RPV+S92Pe//bb/ED/iNWrlsk0Y7lYcHOx5uzeA26uX3J7t+X0bMbp/RnHRxm+bXEbT1nkzMs5bR+JvSRBY1vPpx+f8+p2jQwZX3n/be7/+imrewsW9YBUOffP3mbTPMGkijce3KfKp4j4Dj/97Me8vHzOdDym6QZu7+7Ym654cHofYzRZkdO0K+JKcLB/hO08y0XN3jxj3d2SZFMe3nsE/pTzm3Pwjv3xmGy/oh92jMzUTLi6u2BbOBbLFwzdJ7x1+i6/+be/QyrP+Jf//ffZth0itYyOBSFEtotAfx1ptoJgA2kqsCFit7ym20PoITrQBtIMhkGAEPyj//Fv8Jv/8F1++uzHXFxtWCyf4YaeuxuHMBlG7aNlTllVv0id/1LierWhrbdIJRmVJS71rO4W9F3L3mSGElBvB+yqpukGpllJUSRsmxUhOppuYPADJpHEGGnbmnrY7Zi0yZjt75HWnrAdcMHT9T0xQpaKHZlEGoqyQhtNqiXXr16gpEQqiVQaYwxKKZzzSCJSSRACGz2d7OgyjyOgiTRty2g0px8sZZYz2AF2RwPvPMWowm63ZDonhghIOhuRQuCjItESYcB7Qbk/Id0vSBJDXXeUZYZzDus9PkZ651FCEJXHjHPyfEQ6MqT5jtwXpOC9r7zPhx8/5qa+3t04ogfjeOuNN+najizJiOxu7hHB3WbD7eKGbdcwTBUHR3vYV55BdHgXEVqg5K/YVT/H5kd/SlIqZmOFmUwoqgm2b7k8/zE3t+cM6xHn2w7TCkb5AXbiaaTEuYymzlnf3NHeXTFND8iGkq5psMHjEkuCwTaOvncYpfHeYb3DzEq6E2ilJBMpwiaILNKsHN2g6PdLfN6i24Z0C0Y5tqanNRIjO3rf4GRPNc0wUeK7wGDACsEgPBLwQ8d6c4t5TRgVUuCCp/U9ITOMpzlJkpGnCUJGlInITKAzSWITcqPJk10DboSgjQPaSEb7GaIMeCGRssCUCcF13G2fQZmALrnb3HD16gnOtayaO37wwQ8xuaE4OtjtnXuH9oqDwzPyqmK12rJyAe0GWAz4uxWIlFspmFYJeZ7RA/iI+YLTmi/ceO8dVpRFjpaevjes255maPjq3/0S2VnC4w8uuXl2xzyd8mA2YfK3v8GnHz9lNK2YZBOEGxF8w/X1FfPZmPfff0CZw/J6SdNIpAUdJMf7c4IHHwNtU+O8Q2nDLNcUVcbBuxXbB0soJ0xcwqpuaGrPq+trxpM9tBpYb56glebeyRmffXrJs2c3NO1Tsmz3hpLKc+/wAVKAVoogBI+fPmaazzg+HFP3S5CK9XbLtm45nO4znVUYqVmtr2haRz/sdGej6gg/CM4v70iNYnCKJ5fnvPPwlN/5T+/zpW/u89/8X3/Mi8vPSKeCfhHQMaKA7U0keDA5iBSkA9cACPCRRAi0ht5ASOBrv/6Q/8n/+tt8tvgZ1zdLtitHlTxgvDfj7Qc51XRGDJJIpOnr/9Aa/6WFj5LBejIdsM2G1lo67xhVY1brFXXTMdSW04MDTg9PmY0mdN0KlRriEBi6lhAsddcxmc0hCkBSjMYcjGdkUSBiYDIaMRqN8SEQrEWIneY9KkiQlFkOCKKAKCSZMWi9a7q7Jgxu2LF7rbcIoRnMjqRolEFrhTEZm+0aREBJKPKKMs8xWYq1lnZbI21ArjvkOANj8DHirUdJgzIKH3tctLTCUaYlSu72zMtVixCCgUAUEi0FiZE4GemipWGLEQVgCEiisOSFwjlLjGKnVReC3lkWy1tGZcX8YI9gJEOwrKTnarXmclXTDy3jIqMcZSijwEqkEqhUEN2v9ER/gWXLya99najWCCcwWcrV5jF+u0WsG66WG7JBkoiUbd5jEwUEcl8z6nOKzrF1GrTfSU9kymJxiVEdB9UUifgL9r61liE43E3DXRVpyhSVWWYKyBRDD5EJTQl3RU2+leyFHJWWBClobcsQLUIKJqMZhcxIWqD1LEcFajTBR4lf17iuRfYDo6Sg7x390BH0TrpTRgjGw7pm8fSa5mDMwm1pVhuMTNBCkBnJONNkZc6qq2m1I08TylGByQ02iajCMC4rSiMIJjCIG+g6NtfXtJslUgZulxsuhy3HJ/skE0sYLEZkTMf7jEdTdKrRKGhbVGpJbU93bRkJhVSC1lts0+GThC4GKv3Fzu4Xbrw/+ug5iZFoEUkSQTc4tsNPiZnk/f/4Kzz48ttcfPCKzfOW1KTcH99j9tUxBwf7EDxtv9upjWeGTCkSb+muW9jAXjlBVpFE7Rih+vU+zY1zEJEQI1qt2PvKEZPfybi0DX47kJo5x/M9vC95ucgIeGKEPJ9T5BYtNd/4+pdxviHVY54+/ZzLm0uuhmtiKzgYj9guWzY3G86Ozjg5mTGdjslsCj5g/QB4ts0VQhuSJCFNpyg5QqlIUUiMLjic3QOpmU0lk3HOZr1i024JQpMdKf5n//tv8dmP7/N7v/dvWTztKKeCbARNKWhudnstmQtkGdEpRB93NRIi0QIBpBY8ejuwqP893SA5mB/z8N4RRTkHadluGga3Zjob07YO63+1KPs5zi8vYOjoa0+32aJHOYiddjovK8bTA2RQuG5DVAPXdxekWrHebrDdQNcObOsNkJCWBaOyZBRaskQhhoDrHcp5FCClJDUJOi/RUhJEJMl3mnCJ2O1toyAxGmOS3Q3TOYTcmc9IdmM46x2dccRRQqKh7mraIVLkE2JsKYqCrrco6cjSHUPVDZaoBEJq1LzCJwKJ2LGyA6RKE4l4Bz4KgtB0PhLt6x2zlkjhdkzrKCl1QpQBoiKGmm3bIoVFqT2Cd4TG8qM//hmb9ZrxpCDLDGmWMZmMGI8LTs6OKKoSqQRSCw41vM9u7N1sez5/+pifffIztNpNBaSO+BgQvzq6f4G9vROmB/sgIu3dls32guevfoLoew5IubMNiclolWNtb9DtBN0Fmu1A0mac2hlnyRwN9M2GECPjfEx0kaHffeQpsVN/LFYrhDakVwm+2u3xl37LeZ6RC02ta/qkpxaC27BGGEVfCPZHOU57Bn+DkZIi2SOXM6LNEPUGL6Eb5/Snc9IkI257wuWC8rYht5JhGHb8lcGTIjhsFO76Ft95zK3l+qfnLOSAWTo0hsQI9kqDCBbXCoSLjJOE8XTCtJqRTip8rlB65yuRyEjjNsS4JdQtm5cX9MstKpFUeYnMCkzM6O5q1EgzOdhjMptSJPmOMJVndOMEFRVJLxF3nr63CLtT7/gQGOoBpUBlf8lyootFQ2IkSkBmNC46pNrw/PMb5OwzHn7jHm/85pe4OX7Jy5895/zPriEIhr5h8DVpoTi9t09zXRNM5HC8RywcF8MFeSopyhyBQBJJjSQxZrdr6gYW2y35aUL27cBaN/gBtk0DZcnNasE4TwjB472nGRrSBJTLGOIaJTd0g0eNFF96722+LL5ElJJE7TGsrvjWe+9T6xa0IKiaTRdZb7ac7h/TrjsmkxHRdbS2RpByebtiPIpoPcLbhm7oGU01SkuEFPR9TySl7jq265ck+Yhte8WDr97jP8//M/5v/6d/SbNYkR1AeRjwLQzd653HFHQO0Ur8NtB0IJMdqUpK+PTjC359XTGejpnOMxIT6NqX1I3FOkVWgA83ICRFkf5ilf5LiEeP3sU3Da8+/whre6IFLQ0hCBw9oetxLiCEp7/rMUISTIYSBhc9m21DXbccnczwwnO7vCFEixmVDFuLdJKyF4QhUm+3jEcjhFZIrdBS4J3FBoH3AeccUkiU2jm+Dc7tbridRcmI0prgPI0aCPsZvR7ACwYH0kg29ZoyjxidoUxFFIrb9Rqtc0KIDHYglQqpDcZonA8QI4O1RCOQQZDKlLptMMpgg8dohXMOlQhilGh+LhPsSKJBIzme79P3PevVms3dHcYYtuuGalbwD/+Hf5dinKH07qWTmgSTGYbgiDESYyBGj1QCIQMGmFYTFss53gbSRNM2HcG73Xg8/urG+3OMTkZIWaNST6MHnjz9kI+ffkA+CGYNuMGhConNAsEHMlliu4GXL5ZshykhL3ljMqNIDO3tHbNkxP58Qtd2eDcQYmSIwABZXqCTlESkaJdQxBTShC4Dq9fYssWXgr7u2bZ3xJgTXQJxTK5KxABCBpSQSBR2gBgVsYSQOqx2DJlC6hTrcvq2Z9IbqvEEqSR906IRaGcwPiBSid+TFDcLstWalhJfOsaVYlwqunbgetlhZMKDR4dMH+xj7pe4SUpMFFpZjBIMQ0/dW5xf0l16nn30gswoZidjdJGikjGShHqzIjRbqiTQ5Qoh5iQqRwRPpkASCa4n9ANx2H3USiFIkgQtxU73/AX5CV+48RaZxvpA5wK9GxASfKpRU8UHf/Yznj2/5O//l7/L5O1jhpFkITralwM6SSm8YqgbBhup1ITxYUFZFgx9R2YSUm3IE411Dtt34GHoBXZwbIeBVnvu/e4+fj5mta2xITCuZqy3PVmW4/B0rqFZbklMghQ7WcX9s33Wm4ZZuY/ttzy7u2JSzFltOsq8JpGWRfOC677FeomUkml5yKuLCxa3K0KIfP7kU05PTjHaoLKBzOTcXDUMYcmo2CMvYF2v0FqjEZjxIXaosd4CHj+sMWbKH/35n/AbX/4W/4f/4/+S//v/5Z/z008+JC08xTGIDZgJmOnuZoBXWCnpFx5hQSpQJnJ+0fNf/Z8/5EvfHXN4f598rMiKCb1PmU6P6IeWrnfEuHOj+RV22Kzv8F0HUqCSlLZrKLIMbweikDtjmBBxbc+9s4eMxxXNZkNoPOvFksH2O5u9MqFxNcFbikyzadbENpKT8+rlDQrJWw8fAuDcwNB39F1PlqUoKRkGixCSoix3Dcl7jBD4EF87ku0ajsPhSoU3Oy25t46uHRilJW6ocV7hgsN5/9q1TTG0uzFxmmckekeBH4bA4CwQcSHgrCMzKW7oCDECgizJEQQSE5Fq57ujhECKnXXjatOyadYEu5NZ2balKhNOzk5JyoSTN/ZR0iDkzmwhxkB0gWAdiVI7fa5UEAQSSRg8XdPzZz/7kO/96Y9p+w2ZSZEEXBRIF/H9r87uzxFONngR8a7l8u4Zj19+wHrTENKczm6JK0tqB1wHQ6OYjfbQaoQUgTqpWM6nbO8dkRQpalrAosduW2IISKl2rmlYvA+kSUpxOME8HNPup6hJxpAG+thj+x6lI2kSoN1i3ZbBRiIJWt6i0eRkuw/NLrC4vWZ1dUXTtCgiRZaD0oh5JOsj9bpjGyJRWEa5JpVjhFC7FVvc2T0iBF3bIzxIFJveIpPIdKaJwjFYz2w0pjiccfD1e0zO9qjTwForVF4isXjfUNcXrOsW6xuWiw1t11PEDN15VBHJcokPkaFuub0+p7265e7ohvHeGYd7p+SDYD5EtFIMztDLBK8Adh/T1jqUVgQfGb7gmuQLN97TgxHnNyt6JwhEJtOcr/2tL5GNAx/+6TW5Gbj67h0Pv3LA/smcyT/8DrGJ+KsFVx+8oNs6UnKKIoFgaV83q7xIqTcNPlgE4IeBrbUEAtZHKA1HvzunHvcwSKKccrN8SaoEzic436BEztBrbBxoNpHZtEDlnk3Tk+iKVzdX7M/P6K1n3bb0waJdzRAanl/UXNdb2nZgcVVzuHfH2ekhOMXD+3O6bgRyoCgKVvUle5M3GZUKiWRU7XG9fMVg10xHZ9ihp+tblDY0bcDS0vY9Q+9Jsox15xDlM/7J//brfPkHD/hn/+z3CXsdahyJBpAQO/DdbvwnYiTRGW+9v4epIsvNmiePGz756RKVLjl4G97/bsXJ/SPubq4IUtP3UJYTsnT2CxX6LyMef/ZTbNvTrBvm+/tk+Yh22CKjIFMF09GUJjis0gzdllpHoh1QMRLcQBQBHzyr1RpjG7I0oa3XJEpzkOxz83TB5eU19/YPaLY1wQ00bcfgInvTCUlMESGipCTPC5SSSBHwvt/t2EQkKI9zHqUkpIJYSBo3YK0lNzlFntM3LUma0nYDPmzI0gLkQGpKEmPYbDekaYJ1A84FnBcUeUbXNTtylXN0zpJISZamCL0rfx13ZCll5I4E5SLeDgy95Uc//ZDlZgveoWXg/tEee4cnYBRCKqIP+AjRB4SQO5vLRKGlorcDCoEYAnfXd9zdbtiuOtZXC55evaJua3xwBAcySnIvqesWN3wxScbfBCSTCTjL7e0VT589xnYtRZpSlDmd36BIcVqzWm+xt5Eq2XL8xhGnJyOGNnL88AGj03tEE7GpZNGfYy/vqEKC0TvNqrWeECJZbnaTi32NLT1t6HE+IcgEIaYoaZGioyok05Hidg2Ds6y3N+RKcTiaUmQT+qCIdAx94Ob6jmgtR9MxbVFw3W+RtafpU5wq2WaaoywybS0ZOcZGnHMk0SC0xqgO7zztYJF6IMkVykSW2xrnU8oy5+G7j5i9dUw6rtA+gNaoNMMNW25Wt9ycX3G3vcOMAoFAmUhi4+huBhJtSeSAHzxu0ZC7Auly1neRZnNNvGo50yMeTg45mu/hTwW3fsH55S1tNxB8wFlPiJ5E7c79F8EXbry//dtf5/f/zZ/QXmzJphnf/o/f4N4bObef3PD20Vt87Zu/xdfOvo3O7rhsGrwO6IkglmP2ioekjxe4F2tEsGhp2GzX9P3AetNyfrHAA1JAmSQoJUiSFJUmPPo793kxfgnDjL5bUGT72CZgRWA2GuFtix9qxtmIT54958G9E6TeEkl5dXHD2eF9ZCi4ulpQLx0vLn5GVJGzkzPeOrrHKBmxWDUc7+3z61/+NaqRRCc9/QB3yytMpun7lt42+BC46l/ioyQ3GZ111K3n5cUd1imadUOWlcwmJ9Rtx83yFikNUia8vLhms/mY6bRgXiScfH3O/+bNf8r/6//5+zy/PKerI+1tZFhHfA9njyZ85x+8y2/9vXcZHdyQj/a4uur54IeXvPx4y3bpKCuDqgdW14Gm9czmhsl4znw+YjY9+UVr/ZcOQzNgm4HEJBRViZCexrZY15HInVH8qtnQbGsoKgSv9+ve0w0ttu+Zz455750vM9iW5fKWYjyiNBmLZ3c8ffqKrre4iwueX14xKQuMELzx8AHj8QhjDEbuNLZKSJz3+OAgCoxUKCGwEbSUxOiJWuJUfD2mjbS2I8tKBmfJ8xShC3jNWB4Gi+23GLNruAAh7MZdXVujxK7hK7nb32olWN8uubq6Zv9gj2I6wkiPUoJgPUYoXL8z/Pjzzz/G+hajHWlmePedNznam4Dc7f8SJXf2lwFQO09yxM5fGiKJVbQXd9S3W9regQO/ddxdX9G1G3AW5SKpjESn6DuH8Lub8a+ww2FecPPiJ9w8fcbR5JgqrXh8fkn0GpXPqaY7z+t13RCiYLP1nMmK+ycVw6bleG/OZDJmcB3rVLIuI7JUqJVjaD1G7fTao/Fk504lFfrWkb5oGZwjHoxgPyHmAUlAxASNQMkWQQ9iYLAtw5CCGqNEoDAJJ6dHTKqScjTl/PwcpxyOnt72tM7iY7kjk5p8RyAUnn3nSKVBKU1wu/CcNM8ofMXMe8Y2EIXnbrOCVpPLgqxKSQ5LdFnsPhCkIDWafmi4u33J+Wef8PQnn9KJltmDglRqCm0Y2kBUBt1ruOvwiy162ZLNJzu5VqYwQTHcdlyt1swPIw9G+xSjKepQstrUdF2/e0ivSQlGJaTZF1vxfeHG+0/+F/8l+/cO+a//q9/j4K0Z989yXn14wVunv87v/NPf5b2vfBMXbvjs6hleeiwDzdAhhWJ0b05xMKF/uILzDd3zW7yHzbonOMHedIbznrrvWTc9PsJskvLwnSOyB4LcPeT7P/0xx3sHlKnk6+/9Oqmp8KFFSk+IkqigtwO3t+d4LEpUjMqKu+WWs6MpShv23nmDr7RfJUkz3ADaNTw8q5jcS/AEYthS9y2Jz2l7x6QydK6hzEoyXbLa1sxmM9peIkLLKJ+Ta0kYDE+fXBEHQPTUc8np4T7vv3mP+WSK9Y7lze/z4skr7MMpVXHKzWrN0Tzlf/W/+8f8t//vf8cf/cFPsavI3mTGf/pf/Ba/9Y8esY7PuV79iL6p2EsnuPSK7/z9Myb/5B2cEyghEELQ9luEHOPCHaP8GKU0m+3iP7jIf1lxcHxIaBtUiKgUluuWqpyxvLtgsb5DG01UmizLUdqQGoVShu16y7bvCFFx/94po1GKEAVVlZNKRbfoePHknHbwaKkYfMD7nXnGvCxJjSbYYWebbiAQCdETYkQJgTL6NbFKEqUkUxqHxSqLTBJUD21w5HlCFA5re6QeyERGlhQkSY6LirrpsH4gxkDb7nZe1ajEOUe9aZDCE6WhrDJSk/Dy/Jyb61v29mYI4RmX+Y5RHSwqCp58/JTFdsnTi1c7gxCj+PI77zCbT0ALIgJiRBmDFILWDWgpdzdgF6H3dFcr+ustJkrGKmVvOsJkOS+vrrk5z2ATSVzkeDzjcHLIy8WSreqRomfwX0wL+TcB2eYZ/tUntC8bZm+dMtk7oe1Tzm9vyfKKrJLYesXJ/YckZk4fUrbtwOnBmHK0hwzDzvjEdcSuZbAdNvaEwZG0gURoirwgMRoChDri+0BYW+x6Rff5DcO+IB464syTVlNkNaLZ3HL+5AlpIpiORlCO6etb4u0l2qdstKZXKTo3lJMx3jf0GowElSuwnmhrkrxgPJlikgbRBrCC8NrffHA7P3KjNdPxCNv1bLY1OJjmBeleTnJYkI0znICAwOgMO7S8evY5L55/yvWTc7af1fSip8w1RalRKJTVZDEnbwTJxuEXA7qLmBm7dDCTQusIG8/qtuez+hXzasx0tmHoPYLwOqUPYOetLrXCR///63H+Bf4DdryKvZHkq1+6x/7ZIV9742scffeE44MTpBQ8/uk/5+LZZ9x1K6pvPERNEnrREqOjcwMYiT6rMEcVtggMzUvUaqc9FDKiCIzLlCJLsT4wf/OA/mHNH/7ZpyTzI957600mszFGJlzevmBczIhYApa2UySm4u2z93j++SsG4cmKnuOjI7K0pOnWBARETQyRTb1LQzFC8+Fn5/zk2TPGk5yqShAE3nj4ACkjnY14G7GyRsmUdbNFmEiezNg2d+TJDaWa8vU3fw1TFIyLhCIf0/sabzuU9iSmR7qBf/C7f4uhUXz28qd0tiEESXfpuNBX/Nbff4NRVfDs85f87j/+BoePctrwnPXdHdvGkiUFzeDJswck5gBEhlABo1PavgWl8d5S14Ft/RKlFNvt5hco819OWLulbjaMQkpIBIGAi4HRaI/t+npnlG4VqTY0fUNiNEUpWDVrOusZj8ekZcK2XaGjRLqAtZIPfvgx7bojERIld6EIRkoSpcgTQ5ntCIPeOVwEozVRgFS7svPWobXGIwkhEmRAKHDa42LDECOD66hEyXZbI9OI9aAtWKUIFkAiidjBoYQkE4beWtbrJYnRtCKQZCkmyei6LcJHHj58xNHJAUcn851rlhQURYZzimgdb775Bh/+wX9PiJ7oJY/efpOjoyOk1vRuR7NXMhJCIMadwxdIEi+x12s2L25JokbZsHsRKYX0Hrtakrie9998kzRRiOB5dHqfcjQhfv6YFxfXbNuetu/+Ck/LXy/88T/7Mbd3Gzo5Q91JDqd7nL6ZUYs/pWsaosoIMuHeG29TVGOevbrg7vaGxXTG+OiA9atnLD97QhIE7fUtw+2Stu7omkhuFalJQaV44YnRoXpPana8AoLENJK0LmiXPb2/wyiFKkqKENHNkr4OmCxFuZ7l5SuSy4ayTXg6NLyQAZWV5FlOYiJ156nyBCkFUgZ0Jjg+rjg5PsG0PZJIfLVFdJHBul1YRtwFZwilUMaQGMN0NCJJEvaOD9H7BW2WMQhDkmQgFIuLO57/9FOePX6CaiUjW5F0CvVCYI4kxhpynTPyKdlaYKKAmBHUbjqm6x6jJXHlEOuA8AnbjednHz5lMr5GSYVUCUYrBreTYpkkpekt0X8xu9Mv3Hi//2//gJPD+/xP/+fvE3yLko6XH/+ID773b3DNQBws0TlkllK9eZ9snhPzQAwD0fXYAbTSJHlK9f4RKktY/+icn/z7j7i5GziYT5mPEybjnOqk4ui3j3iRPyO53cdoQTnKaTuLVx2jskAKS9N3OC9JzZjBLdmb7/Pdb32Hf/cn30NrxyePP+Gdh+/QWcmoHBOcY392gvWOKAX1tqYczzg96dFK8ODhhEhEqcB61WCDJDOaZ68umI8gTROuzm8Y5Q3r1tM0V8zHAzoX9O6awado2zDYjhAiN+stQkQm4wlSReZHh1Sj3+LPP/se26bhZP+QcVkhs5z/6D/7BtF9mVW/YFk3CJVzePAmh4dvIGLJze0FSepIhcVowTDsdJmeiJKGTEm0Tnl1+Zjbu0vEF0zJ+JuAzdUd1g90viVXFVFJbN+SmZTj/Xu8OH+KDZYkNdi6o8syFBoXPFoZ3n7jAUpHvA/IwbG9WPP5569YXK3ItCE3Cf3QI6XeeW1Lyd5kTGoMUiqyLCX6sAtfEIIYAkIIhHztWOU9Ku5kPjF42qFj23iCyDAixVpPmedEOWBMSoiwbWomoxQpDBAhBrLEIIQjTRWX19dooyiqES54Mi0pqxJrPTrX7M3mJKkh+EhE4INAqxTvoXdb2q7BSM3Z0REHh8cMwROG3f8hUYoiTXYJXAJSL1GDpH5+hVvWJGEnWwIQCIL39F3L0O9kI1Wa8qUHjyAEjDLEEDg9OGK5bXl1e4OQvxo1/xy3y5Ls6A3efv/rqInBsiFxW3I82+WWupcEdkzk0Lesr89ZLgU/7QbqxT7hyRXmRUfqFbYfsDYipEInGUKnKJniI/Te4X1AOE+QhmxeMM5HkKWMHj3ETiWr4RUyVfQqMMkq3jyYseosiUggGIJUDFlHKSVFCCjXodOcvb0MFQIxOHpnMWo37p1PK/YOxhRVRisDfpqhbrav9bGOwVryrCAMFh8dSkrSLKUajZBip4BJEo0VBi8Mvres76549sGnvPyzF9Q3NZXO2TcjoihQtSNfCLJgyMsRud45uyXKkJsUIxLWZkC3nkoFwhas0zstr4TeCtabgSLLSZPdOsV5i5A7szWJIAjzhZ7rF268ZTFmlBnOX37AJx98zs1tR9Na5qOdfo8IXdOQpJrJ8wsOTx9g0owQFD62YAe0SnAhEI0kfzQnK1PeSwJP/+xy53CiPfvf2mPvu3O22pIOh5jVK65urri5vaOsxoyLHERDmY9JzAyBIUk8Xeu4XV+TjwsePXzAD3/y55g859nzF0ynM1Iz47ObCz56/ITZbETbBXKTUk0kD2Y7Fl3XBG5ve7RyZHLM7eUN77//DvdPzjjZP2W1vqOsvsbQgdQZ0RpuVs8ZPGTphMXyDlcFknRnYlHlM6RWCDmwrVu6/hUH4/u8fe9tbrdXTKsE7y3bbudTejiZUl9veHH9gqysuFs/Z29+jzQxzPcOSM0MLQzrzYLVZsOoHLDBkZuMUToi0Sn7k1P2p4cs17+68f4cLz56wd7RjNHRlKp8za5UmrIo0VFyenCP5fIabztiCPRtR1oplpsVeZFSjTPwgb7vaVvLellz8eqWIs1QUuBcj1KSwQ9kacrDR4946/4pR/v7tG29iwiUOyKLEIJhGBBCgJCEGLB2IHhPkphd8UawLiClo8gK2r4haEn+WiIWo9h9xEqDTgpCHPCDZeg8Rg7YaHcm7ybbZQIrifeeqkjJUnDe4dxADJI0zYkx4IPBhQEZJTerO4SUlEXOm2+9tRu9SYlGIOXOaSi6XdMkBJrbhhcfPsf0jkSqXfOOUw1sAAEAAElEQVTuWxKlUUrtZEa799RfRFsqAK3o7MDQ1VT5GGMU1vZkv2q8f4HTr3+VbP+A8cEUh2f96ikXn35I2DaM2oSbVyv2Th9ykM5ZLp6zeXxF5zL6bGBzdclxYzixGX3XMQyebrCkSU6RakyiCNHTO48WkeB3Ri3JZMr019+gOJvjAsg8x+UeNXiUlty1S8K1oMgKrNvSbzfEY894/5Swv4+1A8W25KTrEEXByXxOGjWbzZbgLYkyjMuc2cE+1ajcMfqFwI5L+v0eZEObOnofEFFhLxu65ZpEa1KToKTBpAnOOtarNTIXNOsVl1fXvPr8OU8/foVYePbFhFxo9osRaWIwBsrc4HXADxKpFCZRFGlKEJFe9SxCA0FS2JQ+BrrEkZps5+CGI80EWZmglcAOu0tPFBHnIkYL8uIvOZ3oD/8//4p3Hp6w3a7YbB15NmUyMRSpIPiBzGiqVJMYzfDyhuF6BscJLvQgdsbvXb/BBEOQglQnmOOc/b/9iDhKePEnj9n7tYIfZz9j88PIfDrm7Pg+s3HGZPImKuYIkVGmOXV7h/M1RZGxGra8ePWC0+N7r9mdY4psj8+f3HJ194J6M1COR7R9w4OzL/HyxUs+/vhTknTGfhKwZsm1WyCCZnnVk6c5iJ733z3gNx+9w3R0xra+pe1aOjtw8/IpSqZEr8EbruunzGYzpCiYzw7wfkkMFi0rINKFnqF1lNmU1XrDtb3mYHrGx08/Z9tuGOUzEiFZygsG1yGj5o3T9/nxZz9CaigrwXpzzdHem3TdQBQ13oMUmg8+/T5BwLjYQ7jA/v49NvUFb5y+y3xU/GKV/kuIdu35fHFB+vyON94+ZX4yJZ+mBDGwbrvdzTArqesB73v6piY2Bt853v7SW6S5JnhIraduLU8+f0miDYkSVEWK1oHjsxkqEyR5xVe//Gu4esGr4VNu6mts5yhNSamnlOmI1KREoZBaEoQgKwqGviXGAFIhjSFGCxJ83OX2tv0WFxxpWiKkxvmebdtA78nSAiE1TVtjfYdONKvtlnIsMUEjxC45pe26nWmHdbuGPzicbXaMzCSBOFAYw8X5C7705hsc7B1RVRXt0MNrzaJ5HVTvnKeUhvqm4/bTCz79+An9douRakeMFDCtCsqyYnCe1XqNVpLVZkPdthzvzzk6OKBpWy5ubkiTFefn5+TGcDgZ/VUfmb82KFKFiBaGjjj0dJ9e4D/YMtubM5qkiBePUec16l6De3VHfNmQAC4d6FWgVxVtMATn8DYSgiQGgXcR29tdCIeQDC6iiWSjjOL+Ackbx6iDCtv2LDd3hLrBGE+e72FxCJ2yDZphgN4PrLotmY6U4z0Kk1M4j1jdsaxX2AhVkTNNEnxvkTaSJyl5VqFliguBwTlaE6j3NHo8ATXHuIh7dUu4vcO7gI8OdIKSamezGiX9akDbNfbmlutPn9KuGw4pOZiWZMYwKjImZYFKBVliGGUlLkAfBbqc0HYNvmkwMlKIlMQaGACv2ApHmhtGozFN27PedFRFSjUrUBJsE9Da4GJk29R0bY/t2i/0XL9w422bjuVyRV5kpLom1Y5ERUZFhU4KNIE47Pw+6+sVtx++5GD+BjGNBCJOCyIB71u8iyRZQMhAOTOI7zxkON7QVy1Zf0hoIk9frFiuz8mq14k8e6e0zTWDl5zsPeL2dkPvlugkYTI5ZNM2SJFycfUZe9Mz/t7f+o/4vX/x39KJlovzS1arLZtNy3e//h2+882voMWYZnPN7//Jv2Rx1zJsJIXOePPNM+4/POZq+Qm1a9BNhfOebbcgiPAXBgdGKpbbCzZdw8hOaPotTt5ycDBn6BrSymAHi/ABoxQiSA4m9wihRsrAXnHKyl9CVHQu0tYb6kFjRM7JvOL9++/xp5/+jGcvLzBaE8IrBtsiVcfB9E1G5SFnRw+5vr3k+z/4lxwcHPLs5hNcaCjyMSaOf8FS/+VD7xwCRVd3PP3oKaurO2ZHY/JZtkuBSlO6bcvmpgEtsaKnqTvun57w5psn3K2XbFY17W3PJz96TBg8e7OS6dQwmjiibKjZsl3C28ff5iZecnPzkqG95Mmz51jvGJUpeMi0YTbdY1LtMxkfk6cVRho22zvkEJmL18H1MjDEGpUKQoAsSRBKkSRqxw8YICLo2p3uPU8LFjdX5GVKkU2YpwW3iyuGbsuommC03hmCDBbrHForvAggI2WREaKjq1va9YrL6wsm1Yi3H71D8H7npSsERilSrfAB7GBZvryle7VlFA2P7t/n8uKK1XLF3d0SOww8iwGpJDEKBjsgIwwx0rYtl7e37F9dI5Vm2/UUBcyrkq+8/Yj92fyv+sj8tcHqJ5/RBstqMiEREff0mj09o0wqxnlKqLa8vFjw8t/9OYvba/SdRAlBLzw6kaiRpo2OvukRTmJMgknS3cSFnVGR1gLrHFEp9LQiuXeAmhT0wVG7NfVwA26DyAukOCJJCnQxpcv26FJNDB1D8AxDw7jaw+gcJQVl7mjaXQBCkudURbkzh1lsidYShkjXdjRDy3qzpGlW2KHDyBQtcpTz6HWN7h1aaozZTamyNCXVBhcltnX42lH6lEfTQ+zYo7QihkCiFXlekoxKxDgjm4yp9vZxQlAeHeCKgrbpGS7v2Hz2jLtnTygQJEVCRKBkjw+WokqxMZDaFKkMWZKiVCTYnnGSIYTGKGia3e/4RfCFG+/BKKFKFEZGVJGxtzcj4mm6AW8tMlEoGanrltW6ofnZK8Zv7VG9leGE3DVdKVHB79xN6EjagOhTbuOCpV5hh4RJVZHljv29PdwQ2bS3aJ2yXdaUo4rV8oJUnTOfHtH0AyJopOi5uF6zMRG84ZPFE95+8JDf/o33+b0/+D6Lqy3ZuOfudkVb1xzszTg+vk+q4Wx2yM3dgt/4zbfY28/IM7UbQ9oCKUuiD7tQcpWzXN0SY0aiEkQ6UPcbnB9YbxccTA/o2uG1cUVg29wRQk9qRnSdYlFfkCdrjIa2dyRJxsVnS27UmjRP8HSUeY8RBZvulq8+eJdHe2/xhz/6d+wfnrJat8RoOTs+4ermJYd7ktlkn1Gxz7Ta53s//AOuN59yeHDIH/z73+OdR9/4BUv9lw/f+c6Xef7igk3TIHxkddvQbiybriHNFNW45PDwiMP9ewR6nO8oTMbx2SnLxZa67hE2cPXihuAH3np7zmgGvV/SqYCziuuLmtn8HvvHR9zWr1iFLUO9Is0kWqSQqJ2DVdKx5Jzb9Svc7Z8jUBhpCN4TbcL7s69QTE4QWtK1Dd5CojOk7kClbJuAljkhBLquoyom3C4WZGbLaFxRjUeEGCjzguLkAZGdLlJLiYqCosxp+p6s2AUs9ENPaHZ2l8FHLm+vqIeGUo4IMhLDLls3UYoYdiPi6CPuuqZ9tiR0OwLVcTli/60RvXO7IIS7JYu7JU29pa1bBmdRSuOJaGNI8oLbTY+k4f133mK+v89mveL6bsEHn336V31k/tpA3rRcP39KawOTqiKRhnJUEDcDQRhKPWJWwOpmQ7+EebqPkgm17eh7j8kMCIlWGm1SZrMJRZIQB/d6xRAINhASTXVyTPnOA4qzI4qyZNPeMvQrnNvg2g0Ks/MD9wMmG1FMDthYhQ4DQqeAR0SHiI7od6YpWgiE7wm+xXuDDxGnoVs1rD/+nCDOsVHihx4/1AgTQRlCUCR3PdMXG0Y9IHYhG5FdzjXDgJLi9Qg4MhlVJMbgg8ckCapIoUgoTg5JTo8YxhWbtuPZ0CC0pEzBuhqHRx3klHvvIQ/GrH/6EVnYyf1E0NTtwGa5ghCZVilVVRKHiEgVWZKheJ1ZPB4xm4yx7i+Z1RwCtN2AC4E8K/DBUdcNdvDkRUrf78ZXrXNoaciixr3Y4A8NbeJAJRgtIegdFVsIsixSuxWPX/6MZ9cbyvF0J4mpShSCAcXtesV8PEYaSV5U5OkJyEjnPEIqUp0gkoTDWcF2vWI+mzEbH3Kwt8dkVLAZcv74hz/g+vaG/XcOScuUPng+evw5ldQ8Ok5505Ss/Q1XTyJ70xkH+4F6C6vNNUfznG4YuLldohNDmmj6sMa4CdNRye2Lluc3lwhfIE3g/Ools7Lc5al6i7MNWVIwm5Z4B71dYJue2Wifb7//O7gQMUkKoUZpT2om3Kyec357wXwf/sHf+i4/+MljbtaXfPNr32JcTvnk6Y+4WTxnb3pCUcyZTOd8+9d/jRevTvjXf/R9Yow8f34D/8UvVOu/dHCx5a13DnHOc3uxZLnYYpKcN99+m+fPX/Dpx8/59MNXTKqcN94+497pHr4d8FtFs+m5Pb+ljxu07Hjn3RHIO17dNEQJWZKyXnekRcm7771HWZa8XA0M3lH7jPV6Q932+B2LCpUERpVCG1BmV34xOpx7HQR//RkP55owG5FnFU3d7TxpQ6BKDUVa4T0ouyOcNF1DnhUQPEIoooP42rpUyLjbB0uBNop+GEjIiEKybbZolezGjjbgoyU6y0ePPyJER1mUOB+QEYQQhNfjyTZ4+qst7Yslymt86BFx19yjkGRSIYaBREnefvMhSgo2my0X17eslivaod+l43jPEDzRdlxcvMS6gZu64dOnz+j8rww0fg4ZIFUZWklOjh8ynpT07RrpA6ENxJDgg2HoFIqScVUxnYywLnC5uMV6SNKEmAqMMkymIzItaZYNCk14HYgxefMhx7/5DYrTPZL9ijRTeCqaNmPoDduVI4aOpNmw9j1RaMqyYtQHbFtTJCWzfG93PmOk67dsNwtsU+Ncy6vNAhcVQ9yZtPiNY/t0SdtGTFUwShLS4NGVwWeGdjOQ3TToG4+RBYkwaKnwIWCQrx3n7O5MJwaVarLUwDhn+s5Dwt6IbRJoReSi7umbBUPbM0RPlU9Y2x7vAiEGlqsFp7Mjiq+/zfF8xvaHPyFrGxKpkUJA22GMYTqudhJA7xBRkiQJSmqIgfR1VKgQf8nOVVk13un8ggChsNbtnKacRbCTAGltyJNImkGaGYZlj+5SorZ0bkOeZyitiV5jnedOrPnBy+9ztRWIkLFarjmvB6pyxIOzijzJcXaXodp3ls36CXvTHCEHJtUus3TT3OB7yb3TB8xGr2U26cDl4jlC5rz3lXe49/CID3/yY77/wZ9zfb1kVJS8/947PHzwBrdXn/Pp0yWz+RFFZYhR8Onjx+TmkK7VLNbXvPvwHYokx3nDcrthsexZ2wWT6YTf/Z2vMTQbnPfoxCKEwugcoTx1uyIEiR9abjdXaDMhzcb4vqfulkwnBzBsEWlCZUrq+poQLW/cv8fN3RU2ag4PKh6dXHHbJHz82Y/IixSpEq6vbrm8WZLniodn71EVhm9+9VsEZ/jDf/096i8YyPw3AZFdVJ9UntmhZjrfo14Grl89YVwUPLp3j8Xdir7pePLxSxYvL/n1777Dq88+5cmHF+yPC0ZHmvxE0Lstq42jcwoXPdb35JOURw/eZG//FKMz8qzgjjssinqQbJpICDvzithG+sZRlAJlAj7ssppdCAhlOO/uqD/7mHd/7asI75hPR/S9A6VAQD/UGFUgpcEFT71dkUVF1JJypNh0G4qyomk7TKJQUtE7RwiB4AWrzZY03SUZKdQu2EFEvPPcLW+4WVyhpKTMRyiREcIubUZISWYU3e0Kf9WQR0OQfudJLQ39MNAPLSGAQNJtG87PLwhu4MG9U77xpbdxAdZ1TdO3XC3uWGxWVKMpxbggFilFaTjkjKur67/qI/PXBnkx4uREEkPkcD6hKDMaFVARbG9xNpLlI0ZWMso8o1HBaFLQW4cbevoIUmu8lXjrcV1PzFPyLCFLElQmULM5+9/+OuP33oJEgg6IKEhNQVFM6boV3kWu7i6xaYIsRoRgSFXGwdQgRnMORhXH8zOkSri9veTm5hU3dzcoYRCD5/zzJwQhkftTMBp/Z7E3DW1tkRdXdFXBdDIjixpRW8RigzvfEESOLCVGJ/gQ6IcBk2YYKYmA0ilmNiF96wh9PGWjJM+0Y9vf4nqJROJ8pBt2BMTdxKfB9h5rHZk2TMoRWVXRhEh5OmcyvIt6/AIdA3d3d+R5TlHmjMuSEALbtmbwdje5SZKdN7WUuOUdff+XvON98O4Zdy9e4npH39ZIEuzQ4aKn6bZkWUJSGMQohaOSbFyQj0eYaUqeDkRv6V1LGKDMxwgpeXl5zqJuiK7i9PiM28WCqSm5Wt7y7NkTvvT2I9599AjJiDwbk+WaVAckGiUtRMHZyT2GdmAIDc4Z1rdL2iRla7d0nWcyPiZNMt5+9xFtX3N9teTVxS3fW/yQsLWcHAjunY1YLNc8fbZGoXdfe27Lu2+9Rd3f8vzqBYUc0fUDxSijyEvOr2+4axvutj3HkwxrA5lMkcLTuWumxR7Re8ajGRJDno3xtkDEirJYsW4v2NR3pKkh2IHa9+gkZbW54PLuFcf7J6goubq54OBQEu8CF1dbmrbhvbe/SZnv8b3v/4AkiQyD5P69E7J8xrvvnhHCN/jXf/hnv2Cp//LBxx7vBQaJ0pGhazCZopSGxc0S1zpOD2bcLbe4ocMNcZc+VWq2tqOMgf1xhqwGhAO7hLtlzeFZzt48x+gR8+lDQHJzteZkfp/rqyvSIsVkKcbuXKqkEHjvkHKn6U2kZrtxNN4hjEQaB0jquiNaR1ZK2nZBJEepgLOWwQV0mZOkJav1DTiY7R+ytR3PL5+jtSJrSspixGAjeZ4Tg0crTVakdO0W7yVDP6BfpyH1TYeSkSfPPsO6nsSMqPIRRZIxDBIhHCYE1ue3qKuWLKQ0Q4sCUqnY2z/gZnmH0QapDHsm4fT4jOV6RdNs0UognCc3mpBqRtM5+4+OyWcZs/0JWVEQQtjlsfaBjz58+ld6Xv46wSGZTKbIEPBdy+1miVKS1KS7d06SoVNBKgWZNjt9dCJwRLIyI48Ck2qaTUQkCblW4DxKSJJEYipJ8Wif6t4BSZbggmWoa5rBEmJPv97SLbb49YbF1QKfFVSHhuChSMcczKfkOmFaVoxGE24X1zz99GOul9fIsmI6mpJWKcvHt7RXNyiT0hNh6RFeoIUke30+9d6ctBwhrcM1Hp/0DK8lbFIpmnagsw6TlRTjlMm9I9Qbx4jjKRd2w8XmjvW2ZjwekaQ5IgZC2NlamiQnBM2qGTBKMfQWpQzOOvYPDqmdJUkUvbNk+yPEhcGt6x27P893/ucEkjRhmiisc3gEShuUViilyfse7/+yR81vRUaHe9jLniQkuL6lN4H8eIIZJ+hEYvZLksMKMc2RWtNZxyprsdEjVY4igLAoU2D7a87vnjCezphN5iAgn0xI9YxTVyCQXN/e0AwbqqTfZX3GCpFBoiJGwt3qHNkmTMo9omuAhPGooO9b0rQgykDTtfRNT1kavv61R/g2oGUkuozF4panz1/wvZ89J7jdDqEqU956a8a9Nw44nj3g1WXk86cvGJczklTx6asnTMc548mIvu8h1iw2NaVJ6IYGoXZpMHZoMEYzDFsSbUizhGBSUmWITFi+eoolovwEpQLbekAbGJdHJM4hMSQmZbW5ZVTN+dpbe3z88QVeSF68eoJKIu+9/y5//qc/4zxe0vcDbhCkScKDR6f85rD9BUv9lw99b5Ey7iLzBkhMQedbZO45vjcirCPNxqGiReYJ3jnWm5rToyMSE6kONI3aIHqFsJptXXN4NiZJI9EnyJgxLiZkWU70mnGWUeZTlssXZGWGc5YQFMH7XQiGsETpyCqDSvQupk8AaNom0PUtm82GYnyE0QoRJa2z5IWm7Ts22xV7ZUqhU7zxON2T64T5ZI+h74khsNluX6cOdYAnhAHne7wbiAikkrRdjUQwKTOct1xcX0KAcVZxWI5oN0ucEAQ70L5cMO4ScpURokexC3dQBIa+f+2itruY56lhGAL7sxFrI3HOkaUGHwPj4wmMDNmkRKcKoRUDDmQkiEg20nzlG4/+ag/MXyO0dc+4mBOGjrZtuVvfobWizCv6rkcpTYyRUZbuPAFEJCiByEDnOVhLmSTIeSR4jyPQ+4C3DiEiSoLSAW8b7GZJ19asnn1O2jdIHEvfshhuGdqaXBpiZ+naLdqUTEeH7E8PKbKcPMmJCNarZ3z24WNCKpmUMxxQZQlmPKJ9fkVS73TCuIiOES09jh4bOyotINeEShHLA0SZ0J2vWNQNbWexvSfJUqo3Tph85V3c0ZSn6xvqzRWDt6y7LaPphCKbYHu741T4XVTmpmmQQuHjrukpqbhbrTibzpBSEWTA4pHB0wdPoTVCSPIsw0gNktfyowSlJM5bBufxUaJNijEJVRVQ6ou11C/ceKtvHEGU0EVC64g+ME0y0nmBNBFBwIcepXdjM+8tUkfaYYP1Ae8dQkCiJd5bzq8+pesjTrS0/Y6tG1EsN9fkeUWmK8alZ7ldoDON67d4JVl2Ww739ggxkOQJQmYED86ye6EJSTbybLeehIKOjr3pAdXYkCYTtDAYFcjUjFfnz7n4w1sYJOOR4eE7h3zpy/fRwnB+c8XFzTO8H3j73rt8/Nln3CxuOD3bIysDmJ2R96iYEWOLMhkITwwD43KCSXJi35Moj/cCVApiJ/cIdueE0m/vmFeKMhszKSr6PqKVYZbvLAWNdtw7vc92dcvd5oLf+fZD/sW//pCPLxaMRymT2QbvArYtSXTF8+fPKcqKsliyf1j9YpX+S4ld3myIEoJg/+iI+WzEZ48/xNmAGL9uCi5hXXtMmmGD5vDoiL2DETLx+KjoN7C+rNFaoEUgOsOoOqVK97i6uYHFmpOjezutq5AkWU5wMLQDXdfsXKuEQAkDTrK9AyMSxqYgRAhaorQlSom0CiEzBjug4o7g19ZbJvmIu3XL+fo5eVGRVhku9CgZSTMFYvdi8FGwNx6RmpzBdbTdQNe1JGbncuadQxBJ0oTBWl68esZ6sybRhslsn9Z7YvDE1nH78TNYeg7OHrFeb4jOQXzdUCcT6rZnuVyhtCJNEpy11HXN9fKOpy9e0vUdaWZQmeTXfvfLHD88xb8elVofiMRdHqtUSEDqX61Jfo6Hp2doLVgPlsFGnJfUbcsw7ITRVaFJk4RRVe12nBGE1ojoycsCiEhn0SGyWW9QKEZFzraucb3FNFB//oq49SR5xs3NNYsnLzgbzRiPR+wdZnhl6F3JoczoesVq2xAzia4EZV5RpmOkUKw3S86fPOHm2Svy2ZhiNKXXOT4vMdMx8vAIJxNkViBdjVpfI2zPWniCj4i7LX2IjCaGVGvEJIfGcbu4w6+XvPX193n37/4G03fOuPIDd9tr1m1N6yxSSbyL5OmYwQa6vgcB1u6mSDF4UIG2rTFlRdsPSAGz2YTG93R9j5GR0DQoEgqtKIocyAk+EEJAGYVWGgQIIdFGIRGYZKdXL8qcNP1LNtAQMgWpcNJCLjAmhZjiokRF8Hi0NkgFRnkSLRGSHZU8Srq+ee3YI1mvLnj84jNm8306u6GzDXHI2NaBuu3IU0WqBONCcrR/QmIUs2oOHhKzhzFmF4GWpCidYJQmLQ4J1hHlQFHcZ1IMTMtDtsM1pZ5zu7kiS46xveNq1WD0ms4avvnem6T7PaO9PZbbDS8uzkl0zvXimirp6FpBPzG88/aXeFCfYHTDJ49fcHFdYxLDycmc8bji6EAADYIBqRO0LuitR8mMdvCsm0u8HUiSjHE2YpztERJQUeGtIqkyikzz4voxSdqjCBzMjxi6a9quYfCG08NjfvtbOf+P/+aP2NIhTSBLNVJZLq6fMZ9WXN1smY0PyLJfNd6f4/LZiiTVFJUhzQzPXzzGDUccHx7Rto7F3RpZaBKbkMMueKAOlKMZp/eOeXn5CalWtJuIkJ7xpCCREqlzECk2KsqiYvpaBtN2W6QKSB3Jq4K+3iX94IEhMq8mVLIk9ZJJVpGgmO7N8d4zVI5sUuFKjY2BPMvp22ZnnlBv6TuLViUyE6SVIQLLdUORB2IMWO+RQhMDtG1NDDviVJKkgKGp7/AhUmZj0iyn73eB4E+eP8F7S5KUHMwPEQjc7ZbnP/6UuO55ePoQCSRSYfKE0XgCCJq2oR8G0tTs/v6uJ813yoCbuyV112GDo20dwku2XU9bd2gjkGrnPhTizj0rhoCQAuKvYgF/jsmopK43SCUxaUaJxK6XLLcNeV4wlpqqLMlfmxgJIRERkigQUhNEZLVdcXt1w93dlvnePvMqQWYGEcDWHe1iRf1siTGGuq6Z5RUH+R6jrERERRElzaCJnWVpLUNw9MayqD2TZExxVCG0pt6sWLx6QaUV3e2S2y7g3wiU+QhTjSj3D+jb1+lBSU+SKXSS02cayjlKFSinmAyQtRa1iajeoB495NF3vsHsq/fpZM+T/hbvdkE6QqnXtqyW2eyAuu7puo4YLdb6Xb57JimLjBgj81nG0A0EF3hwdMLKDqRJjg+RGB1ZoUlcAkIQQkCKHQdCKIFWOyOYENyu1wmJj6CkRrC7RX/RjvqFG+/ge0qT7nJDZEIIASEGnI/YPkEKicl24fVKgBQOiCSvU0t0XiGFoncdL158QCSh7wR5PmWIluUi4myGDhrpcyKRF68uWfdr9mZzlqsFs9ExdbeiygsSk+OD43BvjkTgrCXPAhc3n5HUZ7jQ7wzpg8WKWxbbDesmMLQ9m7bGmAThHUloeHlzg9y2FEXF5fktQhisAzXzWBf56Ucfg3V85Z37jPcM3/32dxE20jtB06+5Xr3g1VXLqMixruflzee8eeBRSuGCJTqFUSkqGJbLhj6xBAXBSdI8RUi4Wj1nXk2YTafcLl8yriqeX3yOMRIjwYiUq8WGceX5za9+id//wQcMPtBvBMNQMz6QBFrqtWBx3SF0+YvU+S8lLs5bQgjkmaIsUsaVYfG8BikZz+f8xne+wma95Mnj5+TTHBcgiIDUirP7ByzXL+n7HoWlmldcvWwoCgMIZuOU0/uPqKoReZ7vXG6MZ292wPXyDgdEpdnLprwx3yf0geODE4RUfPzxxyy6LXmScbt+hiDwra99Ha8Tnt9d0aZbqtEUJVPa5ooQFKQKoVuyqsRHC9GhTYIdeoo0QWYJSgYQBus8rtviXIuSKUanGFNQZSUxOIxR6KixQ8vF1QWESJnm5Inh7skL1JXlUM9oiwZ8RPhIosyOgXx1RVkWCCA1hiRJGYb+ddygIQD70+mONbrd4OKOGPO9f/Vj5sdPOXvziHtv3sckBiUl+F06UvQBqX7VeH+OTb2haxviz1USzgEKHwQSTZZmFHlGDBEp9S7ZJ3i0Ugjnd3nMfUQIgxeCbVtTdhllmmJkSrMNrDc1NnToQnEw2WNvvsfebA4qEHtPNkRip+g7i6stMinpneXFxUfU6473v+KYHuwz9GvKTHPv7JDVsub86S1t40nKiqQa47UgGImIDlOmzPbepSon7CtoGs+w9hTLHn3VEHrH9PiM9//x36F865Q723JRb/DRopUmNRV9bwkBgo0oldD1nhAESmsSrRmEI0aB9z3d0GJkQpooNn3Dw9NjIpI+OqLrMGbXOMPgqIeGNHgSv/sQjNGTpinitQMchJ23gpRoseNuSKUIIWLdF3Nd+8KNV2v5OnkhwbtdzBhYtApY34HMGVxEBokUEi0jWnu09GgRCGFnoLFeX3Jx84LgDdYGAh6hJbNpRWIKnN8FHmQ64dXLnn41kKSREGBbN9ytlsh9RdN5mm7Di6snvHXvHUL0TMoZ69Yi2ytWzcCrxQIRdxFQTW1ZrFZoDDEIslHFwXhM5ST1nUGPHa6vSWTO0emMhyfvcv7ylvPbJ5R5ysm9Ix6+sc/a1qy6C44mY0woOSxOuOenJNqwXF5jdL4zLm9bzm8uMLmkSCcUWUEfAg9PH3C3WLEeFigt2DQr0nRM2/Qo4RiNxkyKfQSecTlj8FuUcmRJgRAp41LwW79leHa5z+OLa1y729fV27gbRTUCJVpenf8q4eXn0CZBGUmea4o0wXmP8BHvAherC/757ZJ/9A//Hr/5m2f85KOfcfLwEbbrCThUGimKgvXVhrJSNHcDkUhSpthe8/HPPsLIjN/6nb9NWVU474gxcrx3n8cvX9LbLWmVkjiPYaenLLTh9OwM4T1aCIzRbOoOLeG73/gm/+YnP6G5q5H7FW2/pVAVRglUkoC1JEUGevdSTkyya75Ksqm3KLOT6yVJxmADUkN0jhA93keEFEilMVpDtGSJ5tnTc+ptTaIUo7wgXK0o7gJhiBweHtD3PXZw9LbHGI0xhhAC1u1sLn1g95vKnVWqiBEjBQ/OTtk7OOTl5Tnn19d4H+gbx/mTOy7Pb9muar78zS+DtMSgcV0gBk9WfTHbvb8JePHyGTEKBJJN3TA4SFNDOZ8xrkaMRhVpVhDczn/emBQf/M4Jze7cqRKTMt/bZ0OgGzrqtt0lSHlY3N2x2rRURUmeZ0wnY6qqQCqJUHInl/EBlSp0UXA4mpHOJE2/oFsMvHr6OdpJ9s5OkMZRZIa1Meg0YTIpuVgtefyzD5mdnBFJCEhyqdjfP+bh/bcZjaf46Lj4/DHXn39IuWyZHx3y1j/+LuV7D+hTxZKOLlo2bcvJ0RH1tmPT1AQ8UTrSFDZtC34XfSnZkZ+E2N1QRUiQeHwMLNc11vdIAXdtR1LsYmh33hIBlCBNM2TSoUxARkfk9bLKudcjZrPjM0iBByK7BDGNpu6+2Hv3CzdeZy2SFCklPih8iGjN63ikQKRmsJIYFEpqksTvdp4EiAEpDYTIenFNVSQ8v2xJ8hKlBFEk2NAjfMpmu0TrLaWZU5Rz7j79iK7tmYwLyonkq196D6VSxuM5WgQ+f/YxL69fUBRT6saTpjM225p7e/cJvidJdh7GB9UxjY3U2zXW9lgrcN2aZrXh7mZDdxW4/+CA+XFCFPDhZz/j4ekjyvG7+G6BSXsWww0ne/cYXM1ivSVXO+OB2m4ZlTk2DBAthclIkzOk0nzw2U8JbCnSmizP6eot+EiapCAHpMwAi04Gut7T21uSJKHvO0xSI4XHtZKhsOS54cXVLUpd8NvfvceL/3pN0/VEEZCDwOwJXC/p+p1Jya+ww97xBAU0dcOmqdEKqjJnlCZEl1LXln/13/0B77//FmeHU/puydHhAWkiKfKMYC2JAhU1vh3IEkVX9zuDAJ2yv39Ivd2y2dRY6xECdBo4mO3T25ah72Ck6WVkvxiRqwR8ZH8+Z3V7h0FzNJvz5tkJB5MpJ9M5QkVeiZbb7YJ0lOK9g2Cp0hIrHG2zwfndDmtUZLTtQJKkxLiTmoShx7mA9JCZjDwbUY5GXN1ccLe6ZjIekaqEEAMff/IRwgVML0kXgb005f/L3n/8epakZ5rgY+ron7zatYeHzoiMzGSRRVYVq0CWwCx6MMAsZzHoP2x2s2hglrNqdGN6CkQpFlmZTKaMDOXar/7JI03N4ngSs+voBAESWfEtA+ERuH7Njpl93/s+bzEpqZOe1XpFkRXMZzOEEPjgccFjlKbMcpRWNF2H61tm8xmR8RAOYUy5kULy5N590izn8y8/JwrF4MDuI7/+8VMODk44enhEEBCMQESNMd8FfPy2NpsNIiqMSZACJkXKpBpxiEmaQAjECGmWj7EUUuKsxVuHf6sUl8agtaKaTYhe0kZH9I7YW242G7pOkidyzIc2Bm3GEUbwnjB4+q7Hy0h6b0r13gFHlYTXkZdvJF034NodzU4j5EAIY97uvu/Jq5ITo7ltLbv1nqKck2lNKgSTakZVzkmSghAdSQgc5YY//bd/TvnRY1oRGApN3TRvPemag4MlXS9orMO7DuIICOlsTxAR2zUs5zOEzhj6hkggxHH0EoIEEbFu4N7BKfvOovSYlOS9JTImhQ1A7zqKVKLESIcLEYK143gzNSOr3IfxoikEQUQGO1pJvf17Pnj7foz/8gS6wRKjIxeSLE3QSJx3EAec79AqHeHx3hORGKUBxWp1wXp3Q1SK46MlWaEYrCS4ASVzhs5yuDjG+4jWBVOd8f7dO9RETo/ucHlzQed3pFqzXl+yXJ5x/+Rd3PA17bYhmSqMmjOfFSNcvrMIYbm4vmAYOqpySj6pmCiJIWG998Ruy7tPlpw8nHN0fMiz5zu+/M0btuuWX/30Ge9+eMo7d2e8ur7FFBUiXJDolOAdOtXc1tf0vX1rl6ppB0nf3TCrBCYxfPL+H7PfD7x+c8PV/obj2SFFnnC1fkXdexazKZM8RXhD11uSzLNcPmTIPD6sOZiWZGbJT37zU76un6KCZDHXPDib8G/+9H3+l//tV2z3DtdH6jW41iOEwH6XrPZ3lWnB6mJLbjJO5odY57he3eILz7TK+IPP3ieRinrfsLrYs/ctoeuYZu9wMDvg4cN7+Nc9Uirc7QBhYFYtaHuFVIokzUnzAu89eWYIMSCk52R+wvXqklp1uBC58jWxtngFicmYTmc0u5qqqohx3Fe/efacL7/5htVuTfneCWlZYJ0lBk9v9yg55o4GD1IoEIHdviPLUpyvSVUyttD6iA8SlaZkeUlVlqgQuHt4l/Xulq6taZ1ju9+zPV9RdJon1V3KvaGvO8wkI0sT/GDRShFjeNtKswTv0VoDkWGwDIMdIw+9H7GW2mBjwFoPUTApcx5m93hz8YbGWdrVCiIMDi5vbzh+ckqqFH3nsN6z/5avhv8e6uDggETnJGZMvtJvD4voHMF51vs9XddzuFwilaIfWrq2YegHlBlDBbTW9N4zF4ZsOsUbR9J5ZrMl82zGZtORJylpkiKFRmszQi4Gy9C29EOHyAzFvKA6XSBLzaq/otCKpt4ie0uZSjCGRC/ZdR032w4hEg4nM7IkIc6X5PmEzGhib0e+gYsE61DC88HH3+Pev/4zyDNa35MmEmt7tIp0EhCBPE3Z7VoQHqUTnBtZEkZLilzSM14exNvLZqQnakXbDaQ6oe8dp7OKKs14s++pMskwtAQiKoJzlr63VEIT5f9fvlYcyYsgiHG86MQYEYBWit67MQQnSsLfN7kq0SXhbX/buo4YevK8GlnDNmJ0jjYaH2sG1xFJEEIihSJICSHy7OVzbrYdQUgiDdIUCCq874m+JwTLbicwOqfvViRpQTXR9HZg09yyPJjTtntC/G3LpAEnef/slHJyRGMdf/mL/8qu71mWh8ynBVq3nN/sUXqKMSnnN88Y+g1pplndbpj5JU5bfvLrl2RfXXG8PODRvTv8zcWXTDLN/cOMj9//GPlVxdOXL3k9XHFycJe23zH4HUVRUVYaFwaSNKFtYd+19P2KxeSAREtOD4948uA9un1Dv3vBqvf88MmfYIOltR1FClWxIE/n2OAxyjPYLVqVGC0JdHz06AlvbtdMyoSiyJBxzx//wYLN7Za//eKCm6uWbgd2iG/FAN8lvPy20ixBqrfeO2spkgSFZrVqGfaOUp9z9/Ed1CLj1fUbGjvQBctudckff/Z9PBGLYbepyWYZYZAIochJOZye8OTeQ1SRcLNaoRJFjJ48LTCJZjFZ0rY9u66hbXsiFqUEh9UB89mMrm2pkhyTaFwI7NueaTUBITCDJBwtWTercRZra5CO3b5FRIgxkKUpKs2wDrQUCHKMmVLkJSZNqduaED1d3yAIpCZlVmVM8gTpPX/xn/4StYU/ePgJZ/MDri6vaJqWSTlDJ5o8zzBKkRjN7WZDliYQR2CAdQ4hx7HTb1uTMH6UQJLnOUIJkjwjlYpPPvoEj+Bqdct2fTOqQQdBv+mIZcGw70iqnKC+Hf3nv4cq8mScLyJITIpWmsEOIBIW8wOKYs16s6LvB/I8IwYIHkySot++dLUQKCHRlPQJ9MrRNyuQkSovyLMSrRKmZUWepbi3aVnO9jgfyIsKXRjSNBlHjsER+p50NzBpIR8ilVYMqUBlFUd3jmltRA6S6WzGNkr2MiHNRs6ySxzeBSopOJrPmCwKvICbrifzHikVre1ohwbvBT4GFJF919IMNYP1SKAdLFlmMDpS7was7chnS5ohIPx4OPdtiyIg6MBZ5tMzrO1ZTjK8iDjncQFSafCuI9EKEw1gkUkOvkdH3sI6JMSAUuOaF1oTjCZ6ixAgGPfkt6lvffAqE7BDg+3HAGwRR4yc8x5rPda1pNko4Bg6ix0sWmd4FRHR0Xd7rm+u2ezGNJj1qmdfeyaThGHoKbKM8zeXCLkmz2ZkWpPMPLtdy1W7YVpOGYYtRVaSpTlNN/Dq4jmfPv6Qxlmurj4n0Ud8dO8T/tf/+Jd08zegFpwsH7LavOb69lc8unvG2ckJanJIPZzz6OwOt28iz7/uyOcV243l6vUzDpYz/tW/ep80abGq57a+5Ycffco79x7wxbNfobTFmCmpdHRtg+003dAzr0pSmRAiYzSgEPTOYvsVm/UVu64n146b/Z51u+b08C7zyYLgWga7IfgebxVX9hqh6rd/86M37KCaksmarhsYug3LyZSknPAv/9VHeJ3zs+Ep3gu2m57gI0P7Xav5t1VMcybTgdubLS+ub0iVxhHwIbIfLM+vtsTlnKOzGTf7HZ11ECPJAP/tr/8bOxcZEEznB3S7HuMzluqID959l3k1oX9zSagUk/mULC/p2pbedlxf3nC2vMer83OkHLGrXkkOJgdkJmO326G0YrDD27lpRETP4WJJmWbIPMG4gM3B+4GQW/ZdTfCeMsmITmKEITjwQmLSAiErlC7GV4vwlEVG0+wQWKo8R8lxjip85OWz59y+vuZP3v0B7549Gl9UQtP3HUVekOYZvdTE6KnblsurS+6cnJDmBa/fnOODQ0rN0PdMZ1NKX402qiRFKPHWthSpm4YoJenbKMRP33sPZx/Q1Xt0oXn57DXVg2OSKieqt+rM7wqAPKswOgUBWuuRIuYFidHMFzOkjrw+f0nwEedLQJHlxYiJjGGMYBTjpSgTkU5FlPejMLb3qFSRFQVGSIo0JdGGGD3xt3zvJCVGIEiUk7i6ZtutqL+4oHzhyJuUZVTMpWQrPbddhyBwuFgiZYrO8tFO2UVMkiO1IQ2SD+8+4J3H9wkmxQrLrtkwmVRkiWTfdNRtjxMSO3iGwbKYTGk6jxAJWkZCHMiLDCHGzk9VFCSJxTuJUQIfBX3v0CrH9R17u2dRVvjB4ZAIFSgyjRcJgx0vi0YbjBZ0u5akzCnNErvb4Nua1GikktihHwMmlEAajROMmiYhEVoQxbdbu98+ncheI+LoQxyh6fkoQDGjHaDrd2g9QwpHkmnqpsbZQO8lRVS8fvWGZheJLuXO3WPunIA0GWla4O2SaCN3P3yElAVCw8lyQt1fs3u2w21u8KWgGRxdX1MOgvliyiz31O6GRC7Iszlds+Hu6af8X/78X/OffvaX/ObpM375q+dUkxlKlXz98ppffv6Mx/ce8uTRffJKUNVvqEpD3fQkQtHUHgpL19aYOfS7hGcvn6KDoJpVfPLpA0wMhKBwQVD3tyhKjNT0tqNMK1wYF8vlzTP2XcusfAAhReiBwStq2yJtwrq5xTmBwHK7OWc5r8a2cxgI0aO1JDEJ+27gxcVLJuWUfRMp8kCSGOrBkyQpZycB+dmH7BvL7eoGESP1/tuhy/77KEdR5qxv90QJNkZ8jGgpMUoiEwtJw+VtTdP0WB95tb7FLXP2ecdq50FrfvDeY7quZrvZs5hOmWlNXd/yenvBg4d3uHj1NYvpgiydY4lUJmExO+RkdsI3zVdMFhNC61j1LQ+MZpAel0R00ETAx1EP4Z1DKoXvLXOVIucLpgvNxTbQbG6Jg8QPgulkTlHOyCbTsV0mJb0bqLs1oijIZU5EM62mNPWGoe2RSYDgwEl+9be/5qPFQx4cnEGISKU4WCwgMgqqrMV7z/nVFT//zWipmk0mpEVFmhcMtmNSzSiy8QPYD8MYuxsiLthRTGXd6O/NU/JEIVVC6DuCd9T7LTcvbxmCxeiE7EmGTs2olfiuAEiSHGNSQnQIqUhSjYiBdr/n6vKSpmuxXiAGj2kH8qyiLCqSfBQAeWsJwUEqGIYd69s97QAmgDYpOslJTIYMHu89wiSMRlUw2iCEBCGIMRLWPfHpQLjZUL4UHLgFTRzQK89kN4o8b5uG5nZLrA02UwQdyJMSozVFNuHO0QlHx4dMJzPsEKn7hnboSdV4ePW2RWjP4HtC1NjB0TQ9B1NNnmpa29N1LWmWMbgepTUCiXUDto/Uw57FfEJUGtd5PI7OdhgpODk64nqzR1cZIXZ0fY0N4AIkxiB8pNltqYQB7VG5RogCJwM6SYhETGoYIbQDUUWCGy822khUYlB/7+SqENBCgwxoYTEygovYvmFwPS7Afu/RuiMvDxm8gxBHA38/UO/X+DjggmS935JlGc1+x2w2oUinZFnCpExQAtq+Z9dJhgCT4gQTXtJsG4QIzCczqrKgTASzyTHPz7/k/uGMy/UF9w/f5/rqCp0kPLxzxPHSYHvP51+f88WzV3gL2kg+//wNd+495eNPHnFsDMsyY1g1TIqCT/7ZE6ojwXSegpKcLlMSY6iyhPX2KYJAkRcQS7quJ6oe68ALzeV2RZG3eBsxSnF8cMbcW0ycUmUl1pd88+JLnHcsp0t626BVSplVLJZL2m6HY8VydofdvqMdLE2/IU1KDpePSU1GCFdIqbnerrnd7FlOIx++95jFPOd2v+N9fUKwEMN3rebfVnAdKIMQkRDGQ9f5ESARJRSlJEkcbePRaAZv8UFzvWoophWD9eDg6atXPD44YyZyDmdTXOL5yatvqL1jcjyh3e6YSEO39iiVYoqE+cOSJ2dPuH9wh94Gbla3tOsNvQrIVLDe1eRWMoSB9WaDFBFlEm5vbiiLgsyW9G2gvD9hOTtjMj9GCEkInigiJkkYQkeWp0gRSVXKvt7S9hFEJDEJUhpm0wNwA1qB0Qk//dufo+vAR+89IYZAV9cslksm1ch53tdr8jwfFbI+smladAy0fY/1Huc8iUowamytN02DkJI8LfDO497anHI1qsjdMAJ0EJLoIzFGyqrAFDnt0I92mD4gM0kSv/Vn6fe+tvsts+kcIcH2Hd4rhralbTpgh05TZpMZWiryzIwHiARiRGqFlAlJXhKSyOrFBa9evSbJF8zKKVIbysmUIsuQBERkHAPGkbcgtEFJNXZrYoCNJTaRsjdIdYibw1o2zKjIrw1xsCR9h7nuCJc9YqERWUVaphwf3+Ps9AH5ZMa+aeitIMCY9SwcJ0cLjOrpfEfoIkZo9taza/ZIKUa3AJKhbzCJwg0e4kiRM0k6Ht4moxsGNtvxn0k5ip32fc33HtxnW/e0AZJ+AOmJQqGlQpqADy2CAUSPlIK+bjhvLzksjkino8pbaoHWGh89dbvDD3b0EAeHFYpgxwjbb1PfeoX/7c++psgzlIpoLTBGkhhNnhZkSUFVTvBuQAC2G5BSYX1gGBr65pbGX2HKSOk1CE/Xrd/mhI4g96bdgnBYC03dM6sOGbwlTXPuHZ3x8voVdx/eo8gkZT7l5cUz2laCn5ApxXq9QfinHM4f8eb6DdNpwnv3P2IyTfno/RVtrblZ1fzq86es1xteX1zxxa88p598zKfvHPMkaTk4nDGbnFC7yNXqNSFaetmSpzMSWZPIFG1yzm9eUaWeIODN+Q2TyZQqnzEMmtVuw8FkOgI0vKVrWqyM3O6uMFKNocqN4vPPv8SLnkm5YL6YMZtoDqYHiFCyrbcURY4kRcsC6wb23SXdoNk3K56+WlEWOUpLQpwzyR8yf1jx62e/ZNOsSPMj8iz5Hbb572elE0mz68ZXpHcoJdFIEGOg+5tXayYHc5589IQQnvP6zSVDHxiGUUCRaEntPK+urxnswJ1syaP5XV5uXnPdbTicHZFXOem+ogwlL8+v+OblK04e3uVWOba25keffI8oNe+EJyip6W/XuL5H+IwXL1+jNp7QO9I05WbzgkRoZvM5UcLOrinSDHrNvtthkjDOhF2EENg3A8iSkUZtyNKcYWgZgS4RG/rRS5loFJHrN1f87c9+xgfTY2azkq62+CgoswStJHXbsN6s/u7Vs5hW/OH3P8FIxb2zM85vbthtd9w5PUZKwTAMIz5SMCqetUHLHBcD1joSrYkEBu+RKpBlCVGC8IJoe8rDA/LFhJAInr1+QXEw+YdeMv9oqt5tcdaTZznWWaSA4DwhSpwdOwxVWY5eUhHHtKEIIYLv+rd4Q0HXWDY3NTomLKdL5sdHGOcp04SyzNFmJC7ZoQen0EohpQJGC5oIgjB4lB9paySBw9mUqqiYTCdoKwnXDXNdogfwfSDsJSZb8N4P/gRRlKw2Ha4d7TzW+zEdK2qyZESnShrc0COVoShL2rBlMV9S1ztCiLgYEELQD44YRqiFi5bClOQ+0g4d6NFpIMceNLveU+kJqZmyaxqMzgD3FpgR6PuWPM+RUpMIMIVFBYUcAv6mYdfeMpktEFoiHQxEvHT0rkc7gUCNamYXR/fC3/eM9+c/e0Wix9tUjB6pRkyWVpE8y1jOZ9w9OeZgPkNoR+s69m1Nrnt0siNISZoWbK+3mCQhLzSD69nfNkyrgbbbgjhhs+lIzZx+iEznU5bVnEme0rPlzfUlXRM4PgisNpbgW5pmw3J+hyRb8Or6ktbVlEXG6dGHtPueXb0hzw+IVcf7y1M+evcdlNLsdns213vW6y9Y3g88bxrWg6DfGgKWxaykHxxVesTl6hu8C2jpSI1gt/N03R6tBUoWvL66QKsbjKjwTrJqB+phw9E0Q0g435wzDIKD6hCVKj799BN++P0/pOst9b7h5dULbm6vCK5nPilIpGOzHsjzgEm2tF1L5wak0JSZ4b3H75EkGiEliY60tiGTmvce3OfHP19zs11xfv0S/vXvsNN/D0uWkBSCJJN4NxJpIhB9wCiDyAoef/gRyUxzfPeUXgji+Zr6tse6gEklwnusDew7y693r9jXG9Qsoe56ntwpqV3DVOSkpJwdHjP4MKbH3NboHFrlCcIRg8b3Pek0R3hNMUlYmoznv/gcv21YasGsKEi0JvjAdlhx8P6E+vYSt5IcnM1ZtVekJiFVAikl0Qf2uz15XiJwY2ynTOlbR3AdiZHs6w1JVEgX+fFf/jV+6DieTzk4mBIPE/abLQfzkqKYcHm7QkpJ33cQwSjJB/fvc3p8wuADUmvOjo9JtKFuGoIITCcFPowpTM51+ChHbJ+SCBExScZ8MsfZgbZr6V2LSjRFmXF0/4y17fn5r3/J85dPWZ4u/6GXzD+aGqyj6Xcj88B7pJIUaUZZJEBEaUViUohhnHdKPR62ziJiBB/Y72rqviGj4PHdQ8rZjOroAJxHtB0ygPABYRRSS5QeE3fGAz6AD/T9gEBQlflIgfKeNNEUk4ysKHDeU/Qld2TO2uzppg3Lf/IRi3/5A1qZ0buBvFLkZcK+CWz3HUPXoJRkVs5ZbS1aKrI0JxU9QeyRYiAQ2NuapNNEIUl1Qkak6we63uK8w1pPlmT4EDGJQESom4ZEGTSBd05PuF7XRKkJMeD7UQylEjXqLoLEWocTkUW15CCpSNVr5olAS0NoW4RJCEbhbEPrGryAwpSkaYpSAdsOOG9Hw++3qG998Fo/IIVEvAVhKDzOB4YY6bqB1abm6csL8lSTJxneR44PC+49jChtmJVTnq+3BC/oOkOUkSwTzGYZVWkIKNq+5uTsiNTM2O52bOuG3aZDOMU3X+zpQs/h6QH7vWe/9rjuFTfXLZurv+Z7H92l7z0ni7u8vrzkr27/ho/vvk9RLkBI+rqmbwJptsD7msnUcGd6gP/6DX/783P+6+cXBBIQz6mmCSdHFV0/MC9v2O5uSfKEs+M5d46W7GuHMZGu6Xn/8Ts8PrkDQjIpCq5uan7zzUu+uLzh8CDn7p0pRwdL9ltH3+/oBkEUz5gVBwibUJWSPz79J0gUnb/h5eXP2NUdvfds+x1p2qFVhlATmm5PqhWd3RCCJk1TBl+RGYkTPdFZ/ujTP+A//tWPOV7e+R23+u9fxahQRSQpFMJD2w7wVrloUs0HP3iP2fGMm80NLYGyKmmmHc1WUjcDRZ4gWzXiGTc1h0czbvzANGjOjpYsixSxtwgnsdJSZRnvnd0lEmltD4sKYxKcH7DCMYiA7QYSrQgiUhzM+ehf/CF229K+vub5l1+j2xZvJMU7FUme89/+/edMneZ9Pada3uXl7RXlJCfRhjwJDNaNh2+WIdOcTCeIpCA1CdEPMDhSbxjOVyQYTAicTqZsNlc07UCVF/ShADug9AjB8S5ghaMoC4qixPnIYAPBBxDgfUCblIjgzZtzTJKyPJwjtKHvOhwRkygwEpMmTGZzVjdXwPhBU9Gwud7SOU9RTbmfH3L07ozLzc0/8Ir5x1NZURHQxBAJYUzUEdKgTIp3A1qZt9ZNEGK0twHIyAjsj2AUFDqSHsxItMLHiOwH4lslrnduZIgbg5J6dK+EEQ0h5fiqI4IgoqUGJE03IJQZMalC0A4DuVGIwqCPTgiPD2mPFmwZyU9JbmiHmsv1Bmc11luaoaPrWhZFST0MZGakbyWJo+n2JEoTYo+UPUkiiUKBH8NGlMxIlKaoEkIQ7Nqa3vcoRturTiObzZonJ6cUecZNvaYbekTyVn3swEZPnlfjz4/EWcnNrsFUGqkEKknJ0lHYJrVGJgl9YrDNSEp0SpEmEp2MkZ1SiXFvfIv61gfvvYclt5c9dhg/ZN77t207PSa/CEfsoe8TtsKRJwZtNN+83CB1ysnxgugT3ry+5PiORmcKk0RCHJWaeVqgVMbL16/puhfs6wGC5GB+j0RK3jm7xxcXX3F+vkJHy/FyyeIg5eMPC+bVEVUWaewWvOHTRz/gqze/4cvzXxN9wEWFDgnX2x2b/YCSguPTQ5IkYLYdk0nOwydHNHV8+zNFLl9vRmuIXZHqBW9evOb1qy3PD1Y8vn9K0214+OjOOH/xHbXds+8js/khf/Yn79K0mv/wX/6Cn/71M6rD17xz5x5SFrx6+Yr9tqFfNBwv7tHZjq9ePyPLpkyKgpOjd8mTlIvrZ/zs658idMO0WtDZgTLL2CNxwVIVEzJT0jYtm92eJClo9zXLec+f/OFnvL64/j++y39PqxsCpjBMlrAfOnwQNE2HiJEkU9x7ckrT9+zWe6IQeCJpmpFmyZhsNdHEILCDJ/hANziK1NC1NYeVRtmBxBaUSY4QYJ19C5sI48EzKxmiIEpDEHbEyyWw61oKnRDwoCE9nmIWBYdTw1e/+JKtveFP7jzg6598Q+IMu12N2ljOJsfs9y2bTU15lpOLDJNEBuew/UCvBlQQZLnAhIhWGjkI+mcrsgB905BLwyQrCcGxXEw5ObmLSnJubnYorZlUFSBo+4Htdst0OsXWe0CN/lwREYl8C22AalYhc4OdwMbviDpAGdj2a5TQmKHm+vUK0QcMAmXG9pwIknbbsKiWLNIJ0+WUj9/54B92wfwjKik0eVYQvMNJMCZnMl+gJPT12DLFOpRSJElCDJ6haynLkizLiBG0sSRG4ZyjHwak1HTr7ajMRaDTghgjbVMT+W0KT4oSEIKnyEuKasrQ1kgp6FzAZCVZnpEVOXZwZJlBzzL8wwPs0YTXdoezLZlRJBqGBurBYaMjiDGAPjGRtpVYP65RHz2Xqz1SOnzIcIOlyDVaBhIdWW07lMhHwZgwbPYbSmPGUBM3QAiU5YR6GGh2Lb31TLOC6/UepRMS6QnRYsPA4B2ZySAE1FsWs2stQmsu9zWEwHIYMFqjpAAfCN6SL+boYsZ2fUMUjsEN6DQlKXMsCtf/PQM0/s2ff5+/+A8/5tnXHUYnxBiJIRDfilV89MQQISpSI3l0d0KSDLSN4eqyo26uKLKMO/cOqGaKxEDftkShSLOUznm0HlAqQWExBLa7hlh0PHz/Ee0m5+mbK5phw8mdkjt3C9b1iteXW86vb6iSOZ//quFnP/4vvPfuKffvTzkoprx790P6PmBUzmq1I8bkLQAEylyzVt/wky9/ynrbkCQV9bajyif0bcewa2nNQJZHHp4dkE0y7tw7453TBzgr+M+/+gvavoN+YL7MWa3X7Os3aJNwevSAP/2nn7Hbf8iL81cY7anmh/zzk/fZ7m6o92t+9tVPuHu8gKiph1c4v6BIC6Jbs5gt+KNP/xmZmbKtr3hz/Q2IQHQ53SD56vXXlMmYfhNCx3SyJDMpq+0N0dvxv/tdjSU9XkjSecL+pkd2IGVESU2a53g89b6m3tc42+Ol5P79O+RKcf3m+Xj7NXK0VQB915NoRWIjJ9US4yHThjwrCYxzYaIgVSmtGlgrh0ISvBhDAIJDKtDGMHhHmqf4ocPHQBcs07tzfnD2hyg/0G4ayi5jeTrlOr3FR/A20lzsefn1lyzff8JsXtEtFCJJmBVzlFIINXoZ9+2GN1885aDRPJreYeN3bJoty2KCdfDiq+c8fPCQrNqTiWQUT7mO7W5FCIKXby5w1rHb7TBpxnx+gH47K3fWo5RAFinptOJZ/ZKnXzxls9rQdM0o7tGaaVGRJgn3ju4zny0wpkL2ktAHkiTBvm0dlkUGEVz3Hf3ltxU9eDtaBGOU6CQjKSva3Q3WeXI9Ai+M1mgpGZwlxkiapaRZirOWGCSOyNAPby9NjhhBaUWeZehyVOyubm5I04RyNhlV9iKOXuA0I7w90G2M6MQwr0q01igJVgqY5PgPzrgoBNddz+12zcOzhwQC6+0aqTT9EDCmoOlXZElKJJImAa2hs2OEq1QGGSA3CqsTusGTZ4c0g0cISVSBGBxt05FoSV5O2Kxr8jQHBV1viS6iZUaVWjKT0bodIklJtKQbBEmSI4RFRYGQksE6CAEnGDnmzhNKxfbZBtv05LlBJwYGQZ4ZyoNT8mJKu9+gRAfBoZMM4RXR/T2/eCWRjz9+QrP/iu1tJIqxDRFCIDEpgZGkkiWa73+84J0nCUqXuCBpBsGmvuXidY1UAu9HCk6MClRODCm99/TDwH47EAZ4cPcOy0+WTCbFmPUpp3z6yfv8/Nnf0LLjdh/YbhuEtJwuj7h7J+XO0R/w7//9X/H6fEWRG1bbFV98fsnx8ghpFMjAg9P3mRdzXBjoQsvGtQiXcPP6lv1ujwBWqqMqUvKk4NHDOR98eMK9w8d8cfET2n7Fs9dwMD3kdHJAu6+5c2dCqiZ0qiZLFa+vLmn7mv74LkYZqiphs9/yt1/9NYvJlLIsef/+x5R5ho8NLkTSTGNdxOuWuu9RzjIr5nT9liLXfPToD0EI+v2Wb15fs95q5qcJJ6cnGFGSpp5ZMccNe5JUMy3nv9tO/z0s6wQiekojKBc5dl9jtCGGgOt7bs8vaWSk3tTYfUM2T7nqnnG73iNMQpIYTDqyX6UeD+DNds/J8oQsm2NUilYlUY1ipLZu0DoBKYlRYlSGlorBDQipEDKihB4VkUOP7Xu0VHg7yiKN0uOMtYnk0wPOPpmxf33D1FuKsuLNxTnr21tO5guW+RQTE1wNP3n+NRe7FV3X0w89wTsYHN+/+x4H+RzrHOu+phssk7Tkq69ecr26pixzlJHc/uZLbq/XTIqC1WqFc5HgLY8ePiAxhtV2xxffPKXMc+6cnDI/WCCzhL50fPniC7arFR8tH1Mez/jZ81/xzc0LeuexdodRsEwOmcmKF+4pMjecHByTkZJ5zXZ3Q2IO0YnB//aG812Ns1rhiQG0SkkTzW67YrdasZjMSdNkVB4rPeYkK02aGpTWxN9yuY1kGBwwYJ3D2nFe+1vgRtcPWGvp24E0yYg20Aw7kiwlnWTsmx3+LTJRRMAFnA7ENMEeVuwmCbtSsJUDu01H9Jbj+RIfIIgBYwT7tkeISN/3GJ0hhGJwjkQnGO1pOjemzGVxJB7KlH6o2XYNfR9o65Y7xwdE6RFesahSQGKDAB1ohgYtEoZhzLbu7Y57yyX7piMoSBOB8B4p4qiwjwHv5WiBkxE3tCxmUwbnSdE46ShmE7qLG9wwkBcFaZmgO0fcbChO75HP54R2R7ddISWYVFHvd9/q9/qtD971dsvp0QHvf7jlb378iq6WBO8xSYJUAaMkwih+8NEZB0cNQy9o6msCKYGc1EyIvsfaFk9P3UUQGh96JkVkWh6SCHjv47sYJdA6crM7Z3dzjfOQmhM6O9DvA72/5eLFDSd3l9w9nTN0Ld+8uOC9Owf82b/4p/xv//E/882zWx7cWZAkKRfXNedXt8TY8nx5hUoLdvs1aaY4LEtmRUWZGg7nM/LccP/uMcfHC0jWGBOQMdIPHUMnsGHgZrehqdfcOT1A62P++uf/GW1SEjMhTwtmk5LBez7/5hVKeRZFzuW25/j4ABkUYfBc3b5CSUGeZVjnyXSCiwJPixIDQkfOVxtcH2n9npPpY7TR9MMtSdrwzuN7TIox/3RRlhhlKRKD1VMubm7Y15fw3ZgXAKUFrrN4IzATg0oEtvFkRpGrjPqiRkwNbhjQWUm9rgnKk1U53a4l+EBiBFIq8jLFaIMKgaN8hneGycldaKC1Df26IVE5Os3YR8vT6zfoCZThhCRJRjESgBhFLAHwTUs5aFQiuXENQY1KSWlSRKLpjKCc3yWrj0isov5iTb3bcHxyRlWUIEA5z6mc8Gzzgtb1dG1LanI+e/Qxf3T3A7SDdbOiaToGZ1FZhtMJzy5ueHN5zUfvP+Hk6Bhne54+u+F4eczN1Q2TvMBIgVGSNE2YTCvW6w3x/DWrZsW9j5+Q+JR76Sl9NeGd08e4ENmUJ7Rtw3m7wdtA9IIvXj3jYHqI0hl/+9XP+JX6BYvZlLOje1RJSWpTtDcIvrPC/baUFqO2RoEQAdfV7JoaowuEVBDlW4ThKKzK8hSlRj1CFICUhBgRUo85vcqRSQN4pAjYoWe7Ge1DVVliEsPQ9fRDi5SC7XbD4CyL2QyFxAWPnaTYkzn1yZwv1pfM00Nc3xEIbJs9kyylyhesmz1SRYKNI5gjJNT9nrQo2axX5GXKtCjQIkPIDvxA33qsdYikJRBwdmBf13jnUfKUZhgZEm4Y6MKAJJBoQeNh29ZUaYnwHqTi7tEh37xaEY1CjnpvhNRE6elai1EGozRtv8VkCh8swY6te297ZgdTRN3hBk8Ubwlt1uN2e+r4nOmdB/hqgRaKYX9LkBbUt+OMf/t0IiVp2h0nJxPO7ml+9bM1iuTv5r2pMXzy3gH37sG6jqyba6QuyZICgiIKSZIKVOtJZEZaRd682nJwcEwiI7MiZTY55PXlU9JMoiVEOTJvu30gJNfcu39Ks93zy6+/4oMP7rJrr9ls15T5nC9+c0nXpEzyhI+ePOCLr9/wy6+eM/SBJNWkieT9dz5mMTliUhZMphlO1Vy/eEYoI398csp8cYwUgdSUpLrgenVBM3hU0Hh7TiIKfvXFVzw4ucf19Q3Pz885PJzz7sPv8/r8gouLFWlmaRpLNTHorCLXU+RgefHVK7ZtS5alnJ3NGMKeMi2IvkQbQ9/3dM4yWEsIDVVpsF6RZCW0e9btG5I2JVEaZSJ9d4vwc9abARYKpVq2qkYgSZMDvnn5Bfzgd9rrv3dlEoEfFP0QiXJMFoqrMdBgmpc8yA8QEdJCcBM6nCrQPuK9pWt6miwQMRzfvcPJ6SFpELDZ8+zZG262O4Z9zfFyie8bjFMUOtKIyNNhw9dXrzgqBHceP4Q0QepxDtT4gRAiSoIwihA02dpTSkUox9FLmibY2OOlp48OJoaVi0w/vc+PjmcUe0/TD3RDS2ZSDmTFP7nzPmu7p8hzTuanlMEQnac2HrfUXF1u8M7yy/OvEV6Qz1PmyYzffPOGm6sdj+89IDOBfd1STqdkJmE+m+MjTKXm+OBgjKizjmgEs6JEtOC9RiYV27phtVlh9wOfnn1IuX3F1zevsNHThZ5fX3xDSBSbukfhqbuWN5dXVEXB8eKIhzxilnw3JvltlcUU2w8EP+BCByIg4mjdapsGQUFepORFDkRElEilkEYijCESCXE8zGKIpElCogxEj/MdzllUhLwoKcucED27XU1wFpsamts1SVUgs5SwqNgtS25TyTZY6t01je2ooqO1HXbw7OstdxYPCHT44BHS0DZ7VDbB+W4UINlAago22xtUnqNNRXjLSo4xgtTUzYY8yyFoyjTFaY9RAuvCW/6BgGiphz1aZNihBuFoBkeZ5Lx79phUJqSpGalzecZu39J2NVIYonMM3jE/PkQqhzDQ9R06wGq/YyoVTevfdq4gnaQkucECfuhRdeD6xTdM792jOjhgKAy+axDJt0vW+tYHr0AxDAGTJNx/cMjzZyvanWewPYnU/OCzO3zyvTmXK0cUOVEG6nZgWs0YhV6a6AI6tdxu9ixEyXvv3MHi0EZQ2xtKnzOfTrnZPScxOYmZIoJkuQykumLfeQ4PDzjcvub1xSuKcsbtdUecO5od/OoXT/n+p4/48z/5tzy8f8H/4//5/yJ4i1EKlQuub35Ms+95570HqESijOMgS7Btxy/fnOPDOfNpxsmdObOyom8i6+0K51bgAseHx+SZwakN+UTRD4JqkoMYeHTvACEEP/vZLdt9TZJGsvKKs+NT5geP+KNPP+Fq1fDVN6959c2K18kVi8Mph7MFZZFy9+wEER15MiNNToniGq0i+/6W4HsGWloX0GGOyAwH81MSOcG7yO3tis7vKXKNFJEykxjznRfytxWjHuHsPjIA5bwgv9pRtwPPL69o25Z78zlJJlExkJaa2dEBrm3Zbxq29Z6D44I7j+9w9+AB6bqFyY6vU4epDHux5+XLC6QQTJKSd48eMckMm2/OOVYJx2lOP9RkiSJXYgzX2LYkSYZgfE0GJ7A+IGYlvYsIlTDEUYQiGD9G3r8N5C4TeLgg1J6s85h9wfZ6RZ4kPMrPIFUMYkChebm+pBYNQfRstmtenp9DkJQypXUDgxg4ODvl6mbPm/UexBuWkwn37tzl8PAIJeVoE4oRay2L6ZSqLOm9Y9vscduWrrXs6x2LxSFtb3HWc3ZyBlLwvn7IJJvyxfVL1vtbXr56RZIaoos0PqDykTHd2ppNXfP65oIin/A//kMvmn8kdXL/Ea+efUNb78YMWBeRaIbBErp21CkkhjTJCGGE/BuhRjWyjHgVkYlCGo3feYLzxExhkgRsIE/VW5a5QQpB8BEpIK0KFJKoFOb+KeePF1wnEmciaSLwNlLXe4qiYr3fIpAoIVFElAEfarTQBG+JUSIxRN+R5hn7eo9zDufAeUnb9Ag1vkn3bUeiNTIKpDBkxhK1YQiSbbMGL1DaELxj8AP9YIlmnMHqqGh2PX2IzIqCF1d7pKlIQqTvBWmSMisjUqSUSUIU4/9vcI7gLEoK+mDpU2jtQF9HdB8QGmT0aJ0htSFax2AtWgq67Q3VwZJsekZ0lnz399xqnlYzpKjYd7ekOuXkNOebXY3w8Efff58ffW/O3hqGpmO9uyUIw+q2YV9fIIXk9OgOTSNYbXvMW9h30w5E6TEqp3dbdu05aRY5mB5ws1qjGUVR6+aGNBEU+Qk7Hzl/1dC7jmohaHeB0Bn+9E8/4M7JGdvdhqvNS46PDvkf/+//lvX+hoNqwW++es1//atnhD7yy599RVFm3H9wjCkmdKEj9JHlYcF0WvH8myv6/orQS9zQEgO0beD8IOB9Qz9YJpVmsSwhBG63G6oi597dU7737sfcXg/815/8giqPfPzuhxwdPqFzl3z82Yw/+aM/Yrfd8+U3X/PN6y+58tdcrS1Nf8NHT37AJJ+RpYLGLilSRZnm7JqG9W5FqgV13fP6/CVldkuRleRJOb6ahOJnv/4KqTyzySWC9Hfd6793pZC4aMfkEsZLWFTgY+R237BrO1a7kY29tx2iENQnO9I8h6CYTA5Ik8jm4oIXP/2aOZrvffYIpp4X5xesblrsMLaiYrT84ulT7hyccVTMmMuEZVaQCA23HYUw9Ikn0WPEmHcAAnJJ7xxOq7fJKJEoJHYQKKVxfgwekKM1cxSZTBTlQUoaJP4khzbQ3W5RzkIuuB0a2gmIzjPtDcvJHV7KG47ThD/76E/o/cCvXn3FF0+/RinF0Fuu1xuOF0vun90lLwpiiNRdz2qzRmtNkqTs6oZ90zA4i5SaiODs5JS6t0QfRv54cBiTYkzGrFwyny35T7/+K/aup+k6ZIRoA1ZASEb0RxscqJ7ef4eM/G1dnL/i4uKSTBuybPxuCiHph3akU8nR+tXWLcFblDEkyYg1HLoOhEKbBK0N1lui9+ikJCtyvNVvRwgjmtE6T4hxBEpkBn84Y/LuPa5nhi8uXjBXS+azkn2/Yr3fodIERGSz3SJQ3F0esLjzLq116ABDaEl0js4rogjMqwmbviYKh/cWyUhDzJKMXVfTDi0Rz7RYsmkEq3VNWaU0TUM31DhrOZ6esm4c1luMLCg0DHZ8KWudYIzkbD5js2+4rneUVYWIA9Zq+rony1N63+OdY+gtQkNqJC6O817vAvV+II8CnEMrMV5ivB9FbiHg/Ni2DtYR6o729pryLCWZzRDZ3/OL19oW6zp62zDNK/74D75Pype8e/ge//QPjjBZgW4DUjbshobb7Q4hUnZbR64jG1WznM5BO8qJJtHQtPYt57klSSRXqyukFkzzOZPykKFr6bzFOsP560tm08isDDx5eMBPfvqUsgr8s3/xiGZvOb98w5vzKx7dv8+LN99g7iju3bnDvJ0Qh8if/vMZj5/MePNiz9df37CYzzg7PMTbFWHa8/FnJzx/tuHLz69p6x4lNK4Dhyf68eM3KUref/8JJjV07ZrffPOMxYElMSXOtexwnIdzqmzOx987wA016/4NC5ZYu+Pl5QXL6RnFvOD73/+MJ0+eMAw951fPEKbjdvOGJHE40dH3A6uNpywzkiTj4Z3HaJHzk4tf8s3XayZVx3yRUSQFeVYhRSRPM65W1zTNQD98O2bofw8lYMx7jR7vRjHgncf3WVZHJEWCI6Clpq4bgnO4vmN2UBEj7G6+ot12TErFi8+fs76tOf7jd/nNxVecv76l3gXmkyXf/5M/YplXvP7yc3797Gsurm9wS7jz+AOC0iRpgY6RictwXU35FjihtR7DGvCoSpPpBInFOguAMaPYyPqB1Iz/rrcDRWao7UAzOHJtEIVEFqBnM0Jr6ds9Q/SIdctDdczpyQFXbof1nnvVCUflEqSgzCt++tWvefzeA9zgKJKEJ/fvk6cZhPg2gzShKAqkUuybmqbtGKxDqQSBpMpypJD03Y4sTUh1QpQpIXi0VEQtmNmMz+59wC/Pv2YV1gTryVOD0II+eOwAQkr2fiBJvsvj/W29+uZrjM6YzWYk5i3iUAoEDiENRVmRphnBj+slzTISk42dkpCilAEEvR5ITDJe2GIkBE+Igig0w+Dp+vFBlGhDnFfED+7wKg/sXUe32bCpR4pUohXbxjJ4SyEzvI1URcVquxuJcCYdgT9tgMGT5QI81LahTCuUUBgUaanpbcKkKiBacA4TArNyQt3sIAoGNzDsOrytkbIniECVa3Y9DG+teFEm2OAgQr3bUNcN754seXN1hUwkadrhQ48QCV0X2LSb0cTrBIlKcdGNVCw3gH+bNa0FpcqYTFNMN1q2styQJprBj/7mJNEkaUq0lvXLlzg3MH/yBJN/u07jtz54v/r61xRVhUkFs4Mz+n7gs48e8OB4xovVlxwdfIjzkOeBH376kKYXbPaOSMCGNRdvWnbrFp3Btm6piow816ACUnqEUBweLJBCI4Tg6bOXRNnjo+TNmxYayB4pTu/fpZD3WW+3pHPN5eUFdogkZoIxKU9fvuLjd9/hq5dfsNjNKDPDrm04kQue3H2Xjx6l/PE/1WNM3NDz9Nd/w69/uudXz1c4K3BdHCOehMcoxWQx4Z0nZyR5R5opVGoZ/J7ZLOeDdz7hN19/TZJZnI0cLgSDh598/hV3DhcgPDdXG7yXPLl7jLA9F9fPyNMFeTolrxJyD4v5u6S6ZLW94dXl5ySpoyoKinTG0DmscDTtOVmaU0wKpvMERMD6ntu1Y7O+opoZplXFbjNwsJzw+P7h77LPfy+rbXuUMWDGOLsoJYenSx4c3SfNNDrXRC1p3MBuv0U4waya8M3TZ2RlATaghEebyP13DpDZgG0Cx+UBexl5+M57nN29yyyvWA6BWUxovUMXGfcfPqRfpgQF/UTy9HaLjCCTCuEjPji0HsUwYbAMzo0EIQmD7fHWYtIUpQQxjrzaJElHCIJWyMCotGZMCTNGEU2KLARJqjiIBr2XDG7A4fEEplnFvmtZLJac5hntQ8vx5IBZUWGkJM8zJIK26wgCmn7AIenrFiMCbddRlhOs8ygtEVKw29ckWqO1pmnHpDIXx3lku6+x3vPu4UPuHpzys6e/5vn1K/a2xkRFkSa01hG9GAlN34ma/64m1ZQ0SZDCv4VLJBAjVTUlLyvyskIJ8DZgjEEpBSjSpES9Ve5HIqEItOUE5yyEQFe3GJMA4yjB6BQ5KdGf3OPlQvByv6JZ3zApj6j7mtlspKltNlsGP1Akkig8XWcRUuPsAGKEW2g5vpyPB8OJSvlSOoaocd7hnKUbBqSGdb0B66jyBXkxJ9TgI6OyfagxOqKURJoKJUea4eXqBh0LElMgkDRdhwgCR2CwgkVekKQZMo/kytMPe9JUgQ8IYRHRohJJUCAkGBQwoFONThKo9+hJjmss+XyJ2re4wVNkCUmqSILAWk9QgiRRmCzHC02/39O8ecnk9C7qW0gUvn1IgoM0GYlTX3/9EtEK3n205MX1S2zsEfoSIQJSeGwXUWpBmRRcbc6pB4dQhs22J+iIj5Kbmz1lqVBqIMsTtE7ZbjdkSTXmmM4nfPnFDqNyfvjBJzy4c4fpdMSkvTr/FW6QPPvZFdpkzA7HVsvjd+7w5uU1m82G5dHo6+pdQpUVtO3ATjqKfBQWCAaElgy+J1qDHyIiKvJMYRLF/QcnKCmYnxgW85IyP+Yv//qvuXNvDhGk8JycHFMU7/L6teXnP/8FJ/dmLA8l81lGVRQcHt+nuLxhs73iTR6YFSURz77usW7FbFKgYkuICetmxfygpJp+wO32OderS/Jsy6J6ANIhRUWgJssSCJrdvqXeOk4P75CYLdtNQ647PvnoEacnJ+OL5bsCRrWneMuddUMk2sC6u+GXf/VLjBqxhcWs5M7DU6bHUwY867ZjcThhNv0Q33Y07XPu3i84vnPIzZtz5N7w6eOP2QaPzRQMHcKk5FnBolzwcDrlJ1/9iit6ZrMFELHOYwsJrUO2DkdEvg35DgR8jAQfMUrTD5ahc2R5ipGaJAkoLek7P7a7ACIEEfEiIOXbsALvSJKMGDVJWZHOFtBZfBO4eHaBFprDyQKjR6B+mijOjs/otnuGrkdnGf1bCEDvLHXbUbct+2ZgMZsSxeg/ts7ivcN7QI7s9jTJafqO9XbFUXo8+pTtgNKKSZqhoqQg5XsnT3i8POPV7pxX69fUoaNMJERF3QeGwf6DrZV/bHV2doZzA/vdDu9HCqB3jrKsRniFtXgBUiqEUuOho/QIcVGS6WxCkifcvPY0VYkbBmw/EGMcWcwxjDa400P0j97lmej4zetvRqjK9BFVWaJ2G66uV6zqW5ZVSS4td8/mvNns8Ynhal0zLw1KBogdxjiabksvp4Rpxn5zTWpy6nbDer0mTRO6riGGgJAB64YxXjYGnNUI4QhBMKmmYw5xAtfrNd3guVz3PDrK0UqMdKvU4HvPbrUmCsWH9x+zvqmx2hAH0KokKVIQEaU9xBSCo0wFSlnabiAvEjatQxEpjGTfNKQYmt5SCI3Dj1AOLyBCmqRjalME73pMISnKKc46+vUlxd3//d/rt7cT3dT0Q03TRcK25E//6CP27hapRwZxogK9t9ysG6Su6Ls1VaHI0jO23Wbkvr59bQQrqCZL8nxsf3StI/iIThL2raW/2nH/9D7/7s/fR+ucxEiuVpdsLz1GGkyiISiGDqqZYOgDrhP87X/7gvt375KqQ55/fclX7qe8/8FDqjIHO7CYbth1PWU+RYgBmTjqsOZwMeGB6Hjw+A5FsgC1Jc0M1sLL8zdc395wejThs++/x8vXVxRFz/VtQ90MPLjzhE8+OOTk4JT/9F//C13b8eGTu+yHLYW7oJpp5gd3+eb515wcDTw4vI8QBSEE6vaSPM1Jdc5+c0vvbkmMYFIknB38iOv1Ba8vr7BxR5FOqCqNjJGH9x7QDZZ23yKj5JOPPiDPc44ncLW+pekuSPV3vNvflhAjys27AAisHzAaTJFwe77m6mZDohUvvnpFNUs5Op0zP6zIipQ0yVgeLen7U16/esF2tWV34UkHTXtsaaeCN5vX6K7l/p0PKYoKebCksQNPPvuQxTtnb9vcAusiyjmSSU7vAesRQtN1AyoxCKlwzqMCSBFZTEuiiOMLUCb01hKlRskRWBOcGAO6g2Bk+kmQEKMgKg0hEFQkVoJ1pphlj3hnu2a9b0jEmtmkYgiQeY8Po6ozSQy4SGQcr2RZhtIG219jJCDU2/l0glQ5Igps8HgBTVNj43hZ6No9uZiMVhidoNRvbS2CIssp84LF5ID3Tp5w1d/wxcWX1H2DLFK64btW829LiMgwDHSdJcsmpFlOXe/o+gGpFQKBVpqiLMnLgrIqiTHQNR2260lOD8lmObx2aDW+0JSSY/YzElnmiHeOuDzKeLG+oBkaZtOc09ORjrbe7PHRkeaSPnTsO6jyKX5wHOYJWycZhpaTw2Mika7vSDFEm7JSkhh7ZFLQ9BYtYVYusHEgkyXNAJGINgLnxnAHrQURgY8OnGffNRSZIzL+HXS9Y9vuybIMrSRD6LD0RKnR3pOagtduQCYjhTARGdfXDZ7RoyxEj+0dVZahtCAvFGUxsprbrh9Th8Koszi+d8YEw267G4EiztO3DV0Ioz2ps9j9gNztyCcdpIZ6dUXxvf/93+u3Pnh/+KM/5PnrX/HmxQ1//scfIvWocvbBY7dj9u6kust62+JFixQFr65fMsnmHB48IA4XNPUL+tgxmyxItcbaGiMT8rxEmZSb6xsSk/LBO+9yfHTKzfqKfbtitV6x2ji01Nw9PUEoQ5KkpKmi3g8QDPtNx+HJhOk840c/eMjh9Pu8OF/xv/zFfyD6gaLQIJ9jUkNRpJwcnXF0WCDFhMm0xd5aXr+5ZFJ1rDfXHC6npJnh+k3L6Z2K3g3s9hveeXjK7foF+WTG7WrH51/9itwsiSLlR3/4mHpdAwMDks9/8wKlI8fLirOjivW+4SpZ4fsbZjNDYtTb17Mgz1N27S0hKBbVEYPfURZHvP/ohKFz+Bhphi1KBY6OJiiVkpkJSgj27Yqjg5JCCdJ8yk3zNYNvfvfd/vtWb88lrRVRgPUOFxwmV3TDgLURl0hCCNiuZ3ezZV4lqEwhE8n8YMrJQYHdtdxc9pghJ80Tzq+uSMsj8nKCKiZjnN5qR5Km9M7y4HsfjpCBvh+zPLUiCHAxIoQag+djBJXgrB83t/dkWqN1RoiOVCU0gyVGgXUOnCXLc5wLSCkpTeBukdMZwfm2xtuI9RYvNU4I8CPoRmlBUqR89i//mHDdoC/GGD+tNdMy5Wi6pHeOsiwJIdD1HXXXIaLASElRZKM9RAWkHJNwRIxUVcWm3iHdGDzR1C1fPX3KvbMT8qwgTVO6rkUbg9AJbVtjbTciCaUgUylP5g85m5xyUZ/zZnfBut3/Ay+YfzzV7DZsdy1ZVpJmKcH78cAUIKIkSTRVNaGoSqIcRxeE0di73+25fPWayTZnfbvCDmHMr5XjZ18dTeF7D7nJJRfrC15fvebTjz4jiI7r2xXGpDRDizGGMi8Z3Egj2/aOF9fXnFRz+iCQQjGdLek6RVM3HFtJUVbcmkAfPM3QUrcN82qBi45hcGPYjvcMtiNPZtgYcRFkcISwIsZA9GPs5DAMGOFZzAums4yhD4SuQZEQBoF3gbap+eDkjL7bI5Rnu6lJ05Kd6/G2p6gq2sEjSHB2YFtbkixFCuhuWoRQ7LYdQqQURUEeJXawtDHgvEcaEElAR4mUBi0V+FEEKdU4c45Dj7V/z8jIPD3k0d1PeHx2jdHgRKDbDwgZMGqJjJKuF5wcPeCrF9+wnFfcPzmhrnfcXD8lSwsOliV7K3FDh+3HSCcQpEagouDDh++i0xylAy/OvyA1Cev1HucSbOPZDBtur2/4+MkHfPbR97i4vGFV1wy1Zb6c8M7Hh8QAP//1z/nwnftMJzn/t//hf+Bnv/gFXz57ibORZt3yplvz4umKzCS8//CYWZ6xvRm4fNmh9Q7nHdevO07v5hweanzYsq81243j5nZHWQS221u6TvL6xYbJtCevErJk4O70hG9e3bC1gbOTA7LEsG9qZpM52Mj56xWzmWHbKMrUkGU9Js05SOeotaNuV1ytXuODYj6T9L1FyFGAkKdTts0FL26umExzjhfH+JAwuB1XNyN1azlb0nUOmX+H3fu7EhIjxnAPryUhaugdWQpVWdA0A84O+AB5lo7IuWgY9pF927K+anihB+aTEtnlHBQlR4sFd85OySYTrmWPSRKEFTRdy029Qx5PMdOKEAUzrbCp5rIeiEISfCBGS5JIRIiEt0kvzlmSYvReDp1DSIdjzACNAaRQ6JFqjxy7XqQSJtk4Q/Z+h5SSRGqckHgcQgqc87jgwEOSGdTZhHwxwd8O+DbQ9HZk0iYGqeWIHkSjE0NtO4ahg7nASU/TtagBbFeQpIZKThi6gbre8+jOHd59/AjrHM4ODEOLC46268iylCpPsX0LSUoU47ydGOjbAaMT7hzc4/3vf8D+dvsPvWL+0ZRUiuOjExaLQ0L04yVGS5TSxDiGVZjUoLWkbmpcB36wdP3A0Du26w1SMmobokBajzCK8vEZ/odP+OmbN2ifsOkb9u2OKp+y7wX79prY7tDKkJgUGzr6bk9SCJrBse9agoOj6ZRPHjwgzzR1YwloNo3F5Bmb7S2LeaQygWxaIuI4w+0HR29bet+xECVlmiAHwSBq8sTQdBlZkdJ3PZrIftiznC1ZNTVSDmjl2LUDqU6pzJS2HZgkhu8/ep+rpqekYTJZ4IOgazs6H+k6j5KSrm4J0ZMkGd6HUVNBOroGQsq8KnFhvOjeXm+ZIkBEXG/pQ4dQmlznBBuJziMlY7wh46U5Dt9OoPCtD96ujRRpgg+SzhmKbIIbchq7oht62r6H2JHIkjyrKNKMPC2ZVgVJ0rHdtPjYUbdbjDIMfiDPNbc3aw7mc5ZHFVluuVzd0AWJQdN1DdtNTb3fc+fgIVEd8+7D95hNKmJQvHh9wV//5G8pl5p3P1rSdz192yMi/OSXLX/w/Q94/KDi8cN/xXpribbmF7/8kkDGarcjTzVGOKSsmUw1drCj0ZyI9Z6b6x4henyMKGXBR+bzhKvzlmqWsZgXvPN4zuqm4fz5Ne/cP2J+cJc/PHmfH//0b/jqVxec3p3iQ4/RVxwcHBCGQJLDMOyxKicOivb6Emzg3sldyqzg9eUrghDsz1+SpxlJMuC9QwqJ9QIhcpwNrDZbcpOB8sSo0NKw2+2IXvI3P38O/+Z33e6/Z+UCIjHg3KjojBERAybTED2GyHxSkkqNIDJ4hxIKawfs4Glaj5DjnzkoJX/02WfcPTll8vZ1WO1u2dQNdd9wvd1BMraDGx+ILtAONaFVRKkRSkKMI7KSAEqMmdQxQCKIQdB0DbPUUGiFJVAHT+MCfrAYJd++niU+wN5HvljtwSjyNEdGsMEjQqBIxjY2UWBMjtIjsAMZsDqy0YpiEymFolYeMrhMdnSuoRc1m27LzbCmbcYD3TnP0PdkIkF5zVSUPHv9jJmbUGQTbm5v8CFwtFiwbXbcrlZkWc50tgAE6/WKNM/QEfq+p+3a0TYVA4iB2cEBGsVw/V235rdlkhyTZIwSKUgSw3a7wfsxR7YfWiDQFyVt25Lo8QI39APBC5xzSCnJ8nQcS2Qpxfv3aT885Zv1JY1vEG7Prr7lZFHRDx3O7anylG3TUdcWExLSLDAvoJqm7K9rjuczVk3H+fqKu8sjVjtL8AMBz0UW0HbLdHrIut6ML92+x4WaKpswuAJFIDNwOp8jVEQnAnxB7zyRBB3AesswdMTBk68cpki4HPb44CgyQ28dPXsWpeTR3e9xvt5w6wW97XBdTaZ+m961YLAD7bBnWqUMThOjRgRHkeXstj2t7WjqmtwkGKUJImJtz6jCgqHt2NsGWYwiTR0UmpF3jRyTuoKPKG++1e/12x+8TUOZ3tJ7ye2+RupTZrNH3L7YU9ftmLjiA7XrUVnPdveU5eyEIivJUoEUOXmVclYt6Nsd3VCDKHj0YElZVrTDltu9JzVHbG+vaHvJdJLw6YcfMJveIdGWZ29e0bkr/HpPkIbv//B7XFxecbO95Zc/f4VSkjQTVNMMYyT7ruU3L37JcjFnmp9wtHzA0fIA1IwhbOmGl7x4+pyrc0O9GW8vB4cFeZlSVHD/7Jh7906AjKbdEIViWtznf/qf/t9cnW+5mVqyUpJozZPHB2RZxuXuln/xwx/xzr13+fXXX/Hzz3/G1c2abmi5WW05Wc45qubkeUWS5EQkznrKTLOrbyiKBfdPP0JIxdcvf8PNpmPwN6S5JjMp1k3Y11uydELT1fgBut6R5XCymCKjI1U5d44f/i77/Pey6v3u7WhizBlVMuCkIiJIMjPiJMOImYNIiGP4R4yC1CRoE5FyFKNYb9FaoYVkv6/RWrNMK0TnuNjfcLleYfKMqZ9RW0u374g4ilKjlWCwPRGFdYwHsEkQAbSIZImmHSCIgPAR5y1ikiH6SNduSbVCKokyisEPKAFCJJgkwwdHiGHEjkYI3pII8TZsPIAUo7cWSLUexSwGbueCrXC0XU03NLT7hqbeYqSgGwY2ux1t24IEax1aCHaMPPWLzS06FcxFxUeT98iY8/LqkrYfxjlv01JVJYnWrNYbUFCIML68xNhtuLy54sXFGw7fOeZ09oiLz58jevEPvGL+8ZTzkd3NNbvtjul0QoyB1eqWNClJs5x+XxO9p297+q6nmlQoNb5uEZBkGUaPXPB8PqH67F2+rmBwDpOXJH1N8C0Pzo7IsiUIh2RA4Gnamq6FRbWgbyORCc5JbB/IiwxnGxSRdWtpw5je07mGxfQQocaUrjSJxNgx2AHrLArPED0mzxgGjwWGrsOkOdL3NH2Ds4GiSNFCg1AUkyP6GAjGooNBh4TNfgci0oeee7MF06nmN99cIlRKWSiabmDwLdYOKKFIjCJPDRmeVRjDFKzrmckS5yx921BVBSoduzG9HUiKFLnuGQaPQTOhJPQB/XaurohoaSAonHUoObLav019+1Zz0tEOe4JccL3a8ubN1zy69xA3aJ6+OCfJFPP5MTEM+KElTQvenN9QVjsm1QxNymazZ7KAxUxxe51R5jnN0ODpmE8nOD+gpOfxnYcUZolIDE9f/YRmcEyzGSEELrZPyeQEo3O0yPjBp+/zH/7zj1n3NTpXDL1ge9vivef1xRvsQYWLhjfu5zw3v0SbnLI8Y99esRvWbLZveHB8jz/714/xsufOvRMyk7Orb4ghY7W75PjgiEI7Xr664Ha140c/+oS/+i+/4Ppqx9xpqjJH6wWkkn3f8F9//p/58NH3eXD3Hu8+fMTri9+wqq+xfs/z8+dsuw3HywOkMAx9zaQ8QsmU1m7ZXL1kVp0h5MC9kw9QWrPdrWi7c4ielW8JOG7WtxilISmJMWGzXUGMaNUDKU9fn/+OW/33r3Se4KzD7QeyokQoNdpgXORgOUUNUHctte8wWuOCRyqIBIYQEFIQvcNogZCaH//m58zKydj61YY0STlYHFJOZhye3sGLgD+djlmoaYoPkqYdQFqKzCBQWB9Glq4P4CHNE5RKQAzEKBicB6XpW8fNek2qUkRixjaZNthgCcGijcILxxD6MZUoKqTQeCLeOYqspI89PlikAmXGny9KSZQGL0cYgY0WO1iauqfue2KwtF0/ZhejaJsGGQM6MbR9RypyrBizjXerC7xyvF+8zzJfkJQFeM/h0eFoU1GAtjgRsErS1C0hwkG2YHYwIT0sOH7vLqtX1/Srdoxr+66AUf+RGAXB0+z39IMl+EhiDHmaMrQtXW9BjJeyGCLIEfEbIyRZihCC1jkmj894fZhwvV4zzSrWm3PqboXSOc9ePefJ3RkmARsiMkgW+TF6XtEMOwiOKivZbSzCJgRnCDYgVaSzHVEYbOzQRqKzSNdsUVJR5FNuNyts75hUo89GyoALkX3X0XSGqqjoXE1iEgSePM/Z1TusHy96XiguhrHt7KwmmoQk16M62w8kacGLyyvaoSfLJetdC0RU9ETpiMqASNjtNxxWOSKRGBtJsnSEaABHkzmzquK6aeiGhgyBNRrBQPARoiB6cL1HR4vMDVEEpIRAQChBlIyX3G9R/wdYzZdsmoEhNJweLlhUUxKToMw98nTC9fWa2HnquuFmtUGoNUYlHBxX+Cg5mk1xVvD8xZrTwwOm0ynOrxFiVFNe3ViKLGM6h119jc8HpCtJzYJvnj9lMT1gPp2AFZzdPWNSFjgyqsmMz798ypvrNZt1T5LC0dGM5WLB+VXDaj0Q/BWIDpNINpuA0SmPHt7n6GhGUZTU7Z4Xr85BJ5xfPEdpxTB4Ep0hdMfgBva7gfMXLUO/46P3P+Tf/bs/5H/+n/8jMiR8+sFjXlyuECES0RAu+ebl5xzMSxaTKZO55ujke1RZyTt3nvPNm19xebPCDhYtc4z2aNOBHEjSnE39Gut3VOmAEJIsLTlevgPW47tfs6gMVTWDWNI3HVI5CpFRFinOefrOUO+/M0P+tlSaYFKJ7QaaviVJchKliBGywpAag/WewQ50XUuqFYfTKTu7pW1aFKBNZLbIOJ4cI7zkcr9mOTtkmmUE50mTFG0SnPO8Xt+weHKCR9DbgWHox/zRVNP2LRqJUSkuCJqmRSvDZvBI4xjCmJzSDz1aG5rVjkpGplnOdddRiZEZO2iNc6NQBSGxISADIAQxCryPeOdJraNSkkbIsY0XxnluQBCERMSAEAplEqq3EWmdH6gbhwueJNPs6x6lUhIZ0FpixIjh3O124Bx5pXi5ukZ2OR9XGh8Fh/M5kciu3rLhim1ccbW64vqLhn3tyNOMxw8f8fjBOzx65z384AnbgeXsAOu+g7/8tiaTGVVV4G1PU+9pWk1RFGiliESUUaNNLThSnY6/U6UxWoyRrc4htSa/f0rz/ikvtm+43t2QVgk6sUQhaa1DSUlpBPum5dXlioenD9C6xQ6WfugRBJqhom0tSTph17QYAQ+PjkkkdDEgPFRVSqDHhYasOGbfOfb7lqPlEmsDiHFMYrsBoQL7ZsPhfEl0agx6yRV1N2DjKBRr234UG0rBrulp6pqqKtlu11iv8T4wz6dcN2syleNqR1QenQZcD5NpSdt32MGSJRlRa2xXo3RKsB4XPd3QsFge4qIiRoHwAS8lWxmZDo7oPL3t8NG/pfFoRHw7krQDOjWkuUGa0er1bepbH7xJYlDOUMqE1m0YvCfVhsW0ZFqWvPPOEdNyTreHV69WPHv9ivVqy835Cms9ic+Jg8YPJYNVCNWiFaSJJquWNHVDVR2wa8aWWtPWEHu0KplN5ihVcrWpCVbw089/ybQsuNlsIBQ8uveI26uO569eIoTAZJIsE0hnODo6JE9S1reRm9WWNHieP33Fi6e/pCoNZ2cV7xyc8NWvW1A9wxCQRtK3sJhnSOV4arbM5jMODme8fn3L//cv/pKq0Hzvw1MOlhM++vAz7t/f8r/+xX+g945FeZ+0EOz7HcoI3ODouhu8vWQ5lyznn3J5W3N5+wrnPbv6CpUklFlBYhKCH8jTJdc3Vwxxh5CC1OQob7i8WXF+u6MqW6bTOd5Fgh/tJ/vao4QiBo/8Llrt7yp4iSMgE0OmE2zj8DoilUbmCmSkSFLKNKV7e1BGAs47/FuWcvbWW/7ZJ5+xqA75yY9/ymrf8vXTpzw+PaEqK5xz1F3D169ecu/BnDIZhSRKKPquI9oOkSTExKCkIJEJVtRj8lFQFHnOer9BML5UjA8sJwXzZcXNtsfUAyKP7IcdLnRo70Ak+ABGJVgfUNqgwhgPV1uHlyPgIgQIwYGWY2KNizjrxvGFF4QY0UKwnM3YNDustWM0Yd9jtGToR79nPXREIuv1llRlqCShjw6D5LZb8XLIsV3gF7/5NT54qknKWp9zUV8RI7iQ4oJhVsywUbE4PqEbBs5/9YKpqdBaI9W3/iz93pdJM4qyItEViYb9/pLegTGaXOQsDha4GGi6nmYYxpQ0IVEmIfpAsANqWiF+9C7noaPt97TdLZt9wdV2S99airzgaHaGkgLXNBzO72CdYNt1OLvHaE2ZlUid0/Ydu2ZP1zcsy4J5MaWLGbmSTCaW1IAiQ3uDD4ZhaAgyYbNvOSznNG5H12/Z7jqaYU8+qehaz7axuLihHhpSmaL8MEYGFhm3TUeWaaQPzKZzdk2NVjm7/R4RPMJLNq0lSENezEh1JMo9XdQ4C2mS46wd90Tn0EqDixiZ0tuGJElI8oLeQpEXKCVpu47L4Lif5pSJRNse7wek1ig9RgCCGEMdBChlRm60/3t+8TbWkSUp0yrHk7CuG7TRdP1AY5sxKLnPyfOS998/49F7B+zrFoFis9uCbdjvMkIeadqGYfDM5oZ9N9D2PYmsWK89kyrDxkCSpUQHg3Vsd55t+xwfBbarefH1DdpIjo+X/OjTd/joyQ+ZTA/5yX/7a16tX3JzW/Pm5QaTCM7nN9x/cMD9u4/443/yR8wnc3Y7y2++/mYEhocdWfAkaYYPmvmswBSRj773EC33iFDyxRcr+vaGK7dlvpiyvYJ5nnJ4uCCZCWq34c7pKX/2z/8p/59//2O++vIlRZGSpwnr7Zrj5QHOt1g/UA8KIw1aV5wcvENeJvz813/L9eoKdVhwdduRmeMxQ1YZqmzO4Hqc9aRphtJzCJbr6xohE7I0xfaK3a7FzixFrtGyZz6Z/247/fewUl1ijGJaVhwuluA99WZL21uC9cQGdtcN0VmkUiwnBU8e3edNM7BqHVWm+SeffcKn3z9jVi354Yc/5Oe//IKL7ZbUW076GX3XobTmmzev+eriDY+KP6RpWwb31t4RPFmSoYymHTr2dYMQajzgcSiZ0GwajFFopbHWkkrF4XLBEFtWzRanFHaoSYRmUuakznG9r1EyHX2Z2uC9IwRPYkZer42Rzo1eZq1TnBt5szGCRIKIaCNRKkPLhL5vuHN8RlPvqIcBrRV9b3EuEEQg+jGqLTUa69wo4LGWdJLT7Gt+/OxzVqs9fdOSqISzkylhAlunkCriXU+WCI6WC/7P/6f/K65b8+XXn7Opb5iePaAdLN22/4deMv9oauh7ttsNUgT6ekff9/R+nIH7EMH13G42dDaQJfloiRsGcGGkPmUp+SePucgkm+0aP7QU+QQknBzcJ9UTfPC0fccX55csqoxFNWPXR/qu5Xh+TGc9nW8p4kCiFNtYs+vWLKpjBqHoYySVjhD3OB8xxtC5Fi0CWgiwgcRocjyvVmu6YSCTisVswaTIKJTgxlqqyQlK7Sm0QiWO280Kn0Ti0LPft8zygsEFXO8IQhJRvHPnDBHESOESnuBbtk1gXk2RoiHEQN87ZBT44HGuJy8ylNI0TY+1DUezBTYIlIlIDbvakeiMvYiQJ2inCTDqK5RGZ4pyWkGMDF2LyhRRwv+PvT9r2iS5szuxn7vHHvGs755rZWUtQGFtNCluNhQ5Q2pGI5qNTJ9H+jj6AHMpM8pMHFFqrk0QaBQKtef25rs+W+y+6iISfV2CtRlgYB2ztLzKm3zCw8P/fs7v2Nbgzd/xidcKOX1xtRuK/ARvRq7ub1hUR2g94nzg3twzL0eELKbohrekacyDB0vubkaMF2TWE1cRFsn9ZiSKJXE80o6WKE6oh5o0Kqnbe9bzDD1ESCLGRiMix/Fizj/6V/+Qslyg1QHtDoz+lsfPc4b+A+q/rvnm+nqyrvvAeO3Z797y4tt7rm5e8eD0mNPVA957coYThmYbCLua954XlKuCJErZ1Q377SW7zYasWjBbrHnza8tgDkSx4ONnF5yfl5w/ecK3ly9Q92+J1cjFwzP+x//DP+H/8a//LV98+YKT0xmPHhyTxVMh+jhajO1JowoZ1Qx9z2hyPn72I5ruikP7DRDRjA4lHUWWMtqGLFmSlzlODwjZ8+S9c6QM1M2ebtCIUND3Bm1bhNAkccRiNv9D1vmfpf7ZP/6nzBcVZVGRxIquq1Eoorhgd9jQHVruLze8/uJbXn77hubQ8fXbOw5dTxLH/IO/9xf8L//TvyREe25udsRC8LOf/pj/+//6v3KUZVze3/Lw6IT2cCCNIn700cesjtb0TqPMxHb1VtP2A5HpUNkUoxFOgJITgSdKUHGKs5bYO4IR1ELwm7c3GDeSZhlFHL9zNUsOnaXve0ZtWSUTGCSECeHog6DXdkIGBvHub4u2loDAdposjgkRSDnRkKx1CCUQNqIoSi7OTvn28hVj16AHTQgSZz1STB/FY++naEsSkcYxzgf6YeSmHri7PyCFZ5nByXsnHD18yr/+t/+aNJ2K2bMEsrTnN//l/8VyUeLZkZ9JrvUliZyTrb5v1vpbBc8wjOixRwTIioLh0GK0Zf8uKhQcREFie42ONcKDTARJEpM8OqO5WHJT79jUG54/eMrgYq52NyjjyGfROyNUTxRFXG22FHGOChFH8xO0dwxWEyFRZYwaG1QIZHHE8aykrRtclCJCoCwdeVRwvd/TDA2LPGZZHlOk50hlOfQtZ+tTVDKn63t6faDVmiMlKcsK40aGoQMf8Q/yZ7j5mn/TfUqkLHlS8nbXkUlYFSWv7m9wNvD+8QPaesd6BoNOOBjPYEcaPSBFII1ygm+JhESFBBJFlASCAyEND09OSJMlgzNI6TAWsjzDakPrNXVQxKNDCEGS5FO0L5Z4EeHCiFGWvMpwHmTtMea7Ude+88b75rbh7LhAeNjc3ZHFj6i7V8h4R5Gv2R3uyYrff3EPFGlFlJc0XcP2UBOpkvXiiMvPe+Kg8LOe9fqIw7YBEZhVBbEqOD2+mDic55JhuKM4W7IuV4TgUUmEt3uCG8kLS5yf8/L1jvvNDVGcEs/A+8B6nrHb9gQh3gHBBYeD4de/vuTr8o4HF7f89JO/x2KeEMmBcQh8+/k9nd3gXSBIqLKEqszZ3O+I447j84LLFx3HVcp6nXP0YMa+ecuTBxe4QXN9f4VKMqpqzv/xf/yHXF6/YlPf0o87rrcjR7MFWRIRR4o0i7m6uyGLc/R4QywcZTInT3/KaGp+9dlnGCynRwvyXND1G+pGoFzM7f2eMXjiKGboezabHmcgS1NmCqpyycnpMSfH33ea/l4PnjyhbWuMl1M8yFjGvqasBEHFVIuUPDvig4/OeHu54W9++TV3b28xXnB2uuLv/ezH4B3OeZxzeGA+qzhZLDg9OeL50+fcvb4kTxPOq4rHp9Omu6v3hAB6tHjviYKliAW2M+D8dC0QTXfEBIOXCh9AxjFRFBOLKQZURSXWWYQ3aA1JkmMbw+GmJl1kEAJCeEAS3ATBl+IdZEFM9BCj38V3rKbMK0SSIITFOUcUpahIYtyIRxDFMafHJ9xu7hm6biJhOUeaxigF/eA41D2xksxmGQrPOPYIAaezinSAIs+xtme+WnL66JyLi3Pa9p4kVTx59oBNez9Nvl7AaAfaTjMcRtJ0wc9//g//yE/Mn5LCu6gKxHFCnEzGqjiKsUaTJCknx6d0fcvVzQ1CCGZFhYoU8Swj/un7XI8DFjhZPyRJZ5i+g/Cumzqd3PMhWIK3VHFJbxIQe2Z5SasFph9Zrk9phprUjKjBEYRHKdBu8hf0bUsaldweoOk0Ik54fX3No+MjOjMSfEdnBspogTc93nd0usdawaJ999xJgceDh7PTZ3y2+Q06WEQUs+9ryiSm1TC2HbOi4KgUrPOS39y85dD3CJUwGk+aVjhn8M6RxAlpFFHEOftdR5zFGDOddJfzkiye03mHjAISgXbveOfG0Q8DjYtYEU0u5ihC/T69sz8w+I7O1LjUsliskPEE6Pku+s4b7+ZK8Pb1hlm5xDKQJm9QiebQjiRxTwgWSUaxqOhtw9X9BmMSvIuJoggvDUkk6WTNLF+xbwy6bViWcxbHkt1+OoUoKYCBk/kRLD6k6XfEscSYdgpzJzG7XcPL62/wcprT3x8ueXuz5+HxE/7P/5f/iX/zb/89wbxhsB3GQnACGyzL+RwpSn7z63u+/OJf85OfnPHwJOKkWvH8gxXxbE6Vzzj0Ha9f3PPyyz3BBULoyOeK5++v+eFH71H3Pb/97SsuLs7YmBvO1hfoOufrbz5jtbjg9GjFjz74kM+/Hdl1e1SUM9iR03WBGXY4G0iTQKw0iUoxbs+h2yGjhCT2/OSHP+HzL19yfbWlmkvmc4UQGd6kOO25vW+QKJSS5FFBtUi4eHDBerEE2VHkCWn6fS3g72UHC6h3MIceawxFURDHCrwGM1AqT5EoDkv45B9+BOMP+Oy/fsVJNePB+RnBW4qkAL8hAEWW8tGzJ5RlxdHRmt/+5jPOZhVHyxWhiNmakThS9OOIlCCUZOhHNjZQzksWVYL2DvPOLdmNI2LUJHHKaMFEFuMFUntGP6D1SJnlKJVgug7b6Mkfkef0Q0+cQCRSApLgHSoOgCJRit6OYA1Yz6xYIOMYIT1CTFQr5zxBCiIxOTW10ZTZjGeP3+P6/h4rJFGs8HiGfkT3HqstSPAhI3hLJBVWBf7ik4+RTSArSmQsYZHgrOHnP/0ZL779lOUqxuFJswVff33D7lDjrZ4MXg76ccfdZsP/7f/6x31m/lTkvUNIibFmykTP50QqZRgGgrcoJSfDj4R+6Nk0HYuy41iesfp7P2JYrQnbDf2wY3l8yqHVNG2P1naCtrhh2thFwNiRpxcf0/Udt4eabd1xXJScLY8YrOB2f0OWCHZupMxjZJQSpzlDU5NGChUqDv2ettcoY0mjYnI9N3usHdHWkBUS4yPiOJrukesNdRUzLwouN7cMo6X3Mf/b9X/kun+D9xMJVcgUhMIODdZ4YgL/5Oc/56bekcwXnCdz7g8NMRFKpXRu+tgd3nkS7NBhAemg6yxZLCnSilYbokhOBkgJBLDOMWhNFMWMXiJ9DGLaUUOYGrucndaAGy3NXc28WhNnGWr4Ox41/+SHP+TmZuTV61eMTiOiAeIR3TukSji/WLA7tHRdS5ZXvHh1TxzlnJ2eEScxIWSIyND3ki++uWZ5PGd9tCSIkc22xlhBPd4jlaTMI7I0UGYVzrcc+unF0Q2wmp9S5BcoJbjdXfLy9g1tW/Ps4Q9Yrksen77HfXdDu7HEqeF+t2O7PZCuIvI5KAw//OgT7i8dX3x2xXCjiB7mDHaga0Zur/fcXB/YbwND64kIlIuUTz55n+OTwNFFyUfFj/l//pv/N5//9pof/Oh9BBHzvODBaY4QAj3ckRZLfvD+R9ztb3hzd0kQUHfvLuitR+Bp+i2zfAJ4W+8wHegkoSpyfv7DH6FN4Ldf/w2vX96zWgTeXz8kfb/k8VNDmWbkeYZ1HiEdUuYU2Zqr+y3aTCee7zXJDQeESjAWpExwrp8yuXrAGTO9wCwcrjfMTlIGRpIs5S//u/fxTUAHy9l8iRc9dd+yaw5kZcH7z57x1edfsJsvGIzBWTdtgiIlimIOXYM2Ducs46jxfmoXWskF3jls0AwGQnAIYtTo0TcbovMlaSQJXiKVIDjBvFohBCQ6wteAEaRVQpIkeOEAi3OWKIpII0WRq8k0pR0yACqmmmcTDMBqcgUilmgHeTKlmpScxtgqSohUYD2r+ODhM37zxWeMdiCPUgQpfX+gKLPphOIEKkwmLElgtZhxtFoQpCLNc/Zxj8ozyvIE789p2lvyeM4XX71lu2+nTd/JKSalFEEERvs9QOP3iuKY0YxYp6mqitmi5FDvCMiplB3o+uku02jLZt8QRzkX//QX3C4SDtev6caG3faWs6PjabpgWlIhWFZLOtNQdy1ZpHi0rhC+RwpDprIJ3WkVR6uESCq8NshkgXaeZ+sHaJnhbGB0AuMsSWzRztGPDRflGiETuv5AHgl0lDG0I0I5YmIi5XC+Y75cc2gNJ8qRm5zlcklVzHlxd0WSLUlERjfu0aZHG8cUTxZYP02Nvry+QmYpC5mBSEiiCLAo4YnShG4YqLIM7xyjHUEkLMqSWTmbYlhBoLUmRNO1j8BDcHhv0ULQRTHCSdS792kQUyRPWkUiUuJ0KkvoDxoVp2RZ8d1+1+/6AGRF4Gc//oif/fgThs7y288/5Zef/1cGrVnMEq6vNgQLWS4RYoMUOVW5YHvYM+qWNCkoo5SnT47wiUWplDQLqGTO/V1NkkTTf4LxKBHo+4FECtJ4BBmRFiXGOtpxQIUYY6eqpovVU2aPMrqx5f5wIAqXnK2P+Jf/ag4hRrcjv/70U15efc3tmxaFp9/9jr/8xU/5H/7F/4nNy8/pbke+/LTDEAhOogeHFIKyEuRFzpMfLvjH//sfINyOPCmZxYH/5V/99/y7//Q7Pvvdrxjea1jMYHAxZZZTpTnWNbS95mR1QqJyXlz9hmhZksYpsYopsxkRK9quYXu4Jc1LuvFA34xYI/EVlEXM/+7nP6U+ePrtDt1q+uA5Pl+TJ5YindGPnkgKNpt7Qion2oqY7tu+16TtMBInEv+OW5xnBQGJNiPGeqJQcGP3hFQzbHqKNPDefI0Rjk/bO/7dt7/mo6PHPD4/wtipRDsvC05Wa750jq7r8E6zrQ/smz0fPvk5uu5Z+Ij9TccYecp5jvWeXCXc7Q/MYsE6i5FmYNONWJmQRjHRPAMk9aEhDor1as5qvQLrCfuB1ElsEnEIHdYOSEr0OOCdYV4WKOVxztN0Di8UQRvSNMFHMGiNEALJtMkaP9VYos1k1GE6YSkhGXqHlDEfvPced9d33Fxe4q1By5Gj4xltMyDExBo3WuODJ00zmqEnlxllNZsADmmME47/8sv/yPXtKz784Ifk2RnrleL29sDQD+8mAoIgHGkKQn0fJ/q90jRFmxEE9EOHCwb9rioyhDA57/sePQ447yizjKf/8C8ofv4JtR6oh3uarubs5GyKmmGmSr+0pO0OxHFC2zXk1Qmz8pyxaVDOM7iBWEK+yGj8gVQFnjw4YbttOK7mrBdH7EYNwaMiUAE2hyvSKOXZxXsokbMbLpnFHuKC5rBHeEsczRjdQAgQp5ZDt6E/aH68/hHn6xVXtqbZX9LqLVZk0wTGOspYEczIru9xOuK4qOgHTRAgg6LuRoyF0U0ubeMlhZTkccqoHbGQSGBexsyqGV0fcAIEbjIYKgVhmiBEMqWXGikn0hfjtOn+3vkfwtSjjZua4iyO/tCTVwolvtuW+p033rd3t6TxjDx6QJbB3//FX/D06QMub1/z5ZffcnW9wQVIEomSEc7u2Wy3JHnMajlnlWcwOgKW+8OO4ANVkZMkHc5ZrAFnFAaDHUfmZYq2LfPZVEEmiyVRfMyrNy8ICPJkxqPzNcv5SJGv6EfD5d0dr+++xtqeJxdPSNOC6nFBuT7l6YuM//JfL7m+33LXO/7q//srPvvsS94/P+ZhumC5Knl73ePtNONfLUsuHhzhU022hG9efsHxYkHIPKPsQMz5ySdPSKuWXX+LDgnzbE6WlEglGPQBPXru7YbTk8copUkzyyyb09QDTdeCCJRpwYPjD0AIymzBzf2B7abn7c3XEBnW8xXCeUo5ozGam35D4zbM85LVPJAmJYMeSYQguMCiOuHy7RtOjk/+kHX+Z6k4yRFCTXeYoyWNp5iBEBBcwEnFTTsyaoP0gUTFyChCO01nRg7W8h9ef87fvHJUQlC3Na9evuKw2fLkyRN++dvPaJ3msNvxg/eecOhbdJYRO89RtUQsM+rI0g0jwzASup4yU1SHjvwk4xBFBBETRIBC4IWnrErKuEB4zeXlGyIZczxfo5Vg32zRciRSCqM1IkwuZY9A+KmAATl9pY8+MDqDlOCCQQUJKqJzGh8kzgsiGYEIOOPJ4pQ49ogwOZ29M/z0w5/wy/uefqiZPVjRDANKKdJIQhymxiSnkAiatuZktkSPA6MfuGo2/PrFZ7x5e0meFbx8ueXsoqKsZiRxxsEekOlUjhqCJY0lT59/b676vbquRwlJnmb0fccwCCIVgwwkaUzkI5x1yExydnJC/uQhF//zP2LrLf3YEWUK3yv6tqMoMqS0zCpH3Q50A4xNj9GSsjrlvumIlWTf9hhnWM2PSaKANZq6m8hTWRr45OIxOljMOCBVoMpTgo0Zxj3rxZIsWXCzucVbi4kT9ocDWS5Yzk7orWVwA0YP9KMjArIq5s14wI4drRvQcmA0DVIa3s1ycD5h0APSB4yreX7+Ia/v92gUwQdiP+WIrfUYM2JHQ2OncXoQgmHUPDg6oshz7g8jJjikEojgQcIsydC6QVvLoe4YrOFoUTHoEURMCO8mQt7jvUNKQZzE05jeWNwwEsUR4u964718e0cqUx6clSgcs+whR4uK5eKHPD5/wKu31+yansO2JktjrOkZjUMlBdIFurqjyjLMGDE0EKeCy8sbjo/nlFVGXR+oqgU+GIzRONFjjEANOfvdnhvuKLOW984/YHQHTo5OSKTnarPFWwHKk8UDapZQVcccL0/w1qPUPR+//5Rnj3o+ev4er140/NVf/4ZXV7dYE3jv9JSQOqIkJok1R0cls1XJg2czPv74jEMNX375lnbf4q3FrjLW5Qo91mQL+OkPP2Z3OOeLl59ztblDhlsWM8GsiAghxnvHdrdnGA2d3ZEEhaRg0Ftud5esqiVpsiAIhYw8R6slq3XG/W7LZrfn9eUdiyxCJIL9O5NOW490dccwGNI4Yz6b4RwcDlesVzN+8MEH3Gx3f9hK/zNUmqXUdUNQEj06rLWksaLIEwSKZhiIsxLPtKm+3WmSfENnNaN1RFFE0x647xri0dJ0/xrROM6qc17fXPO7r7+m1Y5Z8IxupLm7QeYnhCApFjHatfTdiHGWtm0ovKBXBTs1cn2/4SAlkXJkaUQsI6wf2e5b7o3k4XJFWZVIqRjpwQcG1yBEYDY/wTgPXpKmKcHDaDxSSZzReO9x2jPLZmhnMd7jCShpSKVAKcXo3p2EXSCTMXmUMpiGLIZZEtOPjgcPz/nmyRH2zrNvGgZjUCEQJYp27CemgBEIobnc39Jd9ZxeLHCV47dXX9CNjtlsxWpxhLaWb19+RRRFlFXOMBSMWiMCJHHMJz865oMfPfpjPzJ/MvJ+mtIkaQ5CYLQmjWPiJMEYM5muiphqVtFHUP3Lv89n9S3r+JjabGiahl2953SxwjnJaKFMUuZFyt1mi5Axq9nxdEfvegKe/XggOEvT9QRvKFNHGjmM6bivOy4WczoDxkm8MRjbkKUxTVejx2O67gYbGkajaY3B+Y56N/JglbKra7x3eGdJZcwQOnIVk2SKIkrYXu+5b98gFcyygjxNudy8RZJhUERyxvHRnLPFir/+9muiosSaAWsDkYpIU0FnWgwONxhGDQ/WJ8xWR6BiDn2HjBXSWkRw2AA4GIyh7w2DNlhrWS8m9GYjPGQRiZWoNME6MyFlAwRvgTBNkFRELBUi/jtGRt5cD9xvvubpdscPn39I3ZUImTKOtxytCtbrH9KNjiyJEUHQdHuiKCdSETZYjD2w3d4wNGteXr9GxQknJ+t3P0KMfDfLdzZGD/Dtyzsuzi5wOuX5o58jpMSLjiTrqTc7Pv36Kx4cP2SeH5OnMUJJFhX44BmGHVhFGsd4L+nbCGMX5HPPJ3+x5IefHLO7V/zVL39DMAOkhifvLXj04AiV9pw9i4jTnt694OL8Me8/+hgzKu52tzjRImXNk/MLdoctO73H24znD59RD/fMyhllnrHb3XIwe5qx5mQ+MYIH66hHTaYiimzF2fEExdceXKgZug1ltsa4A2UZcXHyEZGco4eB3c0NX99+Q1ZJoihGiBgZZVgXcbe5ZlauaVoNYkcSmSkk/r0AEGpyqhttmc3m6LGj7Q4g56gYenPA6IDEUyQxwQnu6hGUJ01ytoeW0RmsVDgE9+NIUVo+PXxGt9XMlyXbVzd0UvLl7S2PzlekaUU3GLZ1jXFTO5DWHVYP7K0gzXI2WYw2Kd56RqdJ0xjjJ9NSkihkkmFDYL/bEGcpZZ5wv7lHOsnjiwuCcgzakScJCN7dIQuCFBg73YUlShJcTxHFBC/xCJSwJMLjmdyy0nqUijEEmrElFwFjodcDSSKZVzknD0+4PdxRxjMiUzNLE0Ki6IaWOFFo44kjRQDu7T1B1NSHPevjI9xNS5kteHDxHlJJmrZm1JZGbKeo06AJDLz/ows++vH7vHyx+WM/Mn8yElIilUKIqR9ZqWTq4JURQXqkUuRFQZympD99xivl2fUHxvsGEyzt0LDZ3/Lh448QlAgZOPQjQjhOji9o+57b7SvmSUomIwbXoE1LjOJoVtH1PUoNpElGrw3G6QnUImMEASGnCVHbdYhI4lAcxholA9r0U9uciDiZVxzPZ6AypEzQumNZFARyjNB88/IbzmdzyjiQnHxEWU5FO/fNK2SAtr2fPiqpODt9zL5tUalE2wEQeB/otUF5iGKF1gYVPOdHRyRFyvV+T1mVNLojURlIgVLJVBlYJvRDjYpSXO8o85xYKYy1hFhhvZhQrFFEEJ4kyRjHfmKpy+nDKARwzpDm3w13+p3fzs5rmoNnt/fsmz39YEjiFfv+W3SYkyfHxNES7yJ88CBmbPb3zIojfDB0g6brBUlUIGyKd4puGKY4Q5zivWRsLHmWcn58gXUBN3i2doOUhjzJcYwMw4F5fsTvXn3NoR1571yzmGUksiCORrLMk0Ul3dBiTQxhwTAO6NBxvH5MplKKo4L3LiLOj0p+/av/xPHMkawTxrQnygSjrpGhZLsJDM0ds6Jnka84Xx+zbyOM39EPDXlaMtYNXhnW81Nut19gzY6umREpxbPTJ4w+xhpPFq/48uUtY9IwqwJgCT4wDJoki3F2QEYSFwxNN5CmKd7tkezphxo3Qpy+g/dLiTaw222JZEpAk2eO7balbgNGb1Diu315/bcgOxoEAiGmBTJai/YB1+6J4hjrQMhAWcxpu5bd/oAZGpZHMxz2HXLOgRB03uJ0QlLNEMWGMvFILzkKBeNuxOfxRMTyjkJliDjC7VsOwRNFGXmxpCpLtNPsmwNpkhClvMtr9mg9ATQCAikdSZCkScRoBq6292RJztnxMZJAN/ZIMY2/AlPJvAswdGYyPQkx5TutwhlNEks6rXHCslgUaKdojCZOEqI0QY8jruswo0Wt51igjFMEUKUV1lvSSFGu5hyalq5umM9LukHTDx1FmZKWhsWzkk29IYuW7G8sda356MPzv3Xj2jEQqYSiWDKbt8DI0/dX/OBHj/jdF1f87jdv/7gPzJ+QpFIgFc46QnAkWYqxhnbokP4dftFZqDL0gznOw4Ozh3z58nckUYIxllVVkUcZloDynnbsJ3uvCLTDgb5tGVcWYz2H9kCwnpPT9+hHQ/Cazipq09E0PWbokCGQphFJVGJtSqTAWMeDoyWRCBgN2k2GsEerR6AShr6nHkb0qIlSaIeaIpHYMHC33eKD4OXdnqdHC7yJ2B527JRl126QKmMcO7wd0b6nzJ7z6ctv2XtNVZSM2pClCcM4EmzgUXXGxfKIWAbqceD+sCNKMqLUIAeP9ZpIxGgz3VFb45FEWG3w2mDeraU4irE+QJ4CU+VnN3aoWBAlkrRKGZqBwY7ocST3mqL6btck33njffnlltPzC6zt+ObV5xAK5vMVQg28ubqhLG44XVyQqDnEgSxekSUV2/qe0Q4MXU2RpixWcHq04NvrN5RzRVEmNHXLvFywqGZEKUQR5HE01Qg2PSEYIhUjE8OqOEP6hvfOHvHy+iW//uJvePLgiHkRsZ7P2LeOIl3gfAIipswEioAJGa63DCSYwRFHgWgmWC8T4siz6b+m2Tl2B6i3kqHbAdC3Vzx4tIDwipOjOdZIrvevKNNXPDhe8uhiiQiKvofV7Jir6xt8NY1oWt0yjILl7AhtLLP0lFfXr3hzfUlRBsp8iQgZVmwRwjIax6HfYo3i7VVLCPecnSwo04gsy1mWS168vmMYWrwIE5t53NM0I5fzHRKJVDFHx8ecniz+kHX+ZykpYmIV/jZaIJEsZjOabkffBUSUk5aK+92e4AMmKMxgiDuPF5IsTdDbHmcsURyTL0s6D0bkEwmq33L6pILzAtNpxu09p+89QBUF7dCRLxYUsaR3mnk1w7mO7c0dqYqmk4vS7xCfEusF3nnSJEPKQAgWYRxjvefs6IzFeo33htFY+nEkUtMpYyLzBEIUE4LEW483DpUV2CBQQk3GKSkwxrFrB9KkJM8yPIaurwGFV5L0dI2XgX4YsN5zqA9obQh4+sHQGwcKyjxGG4PTHhVLVsucs7Kia0eaW8Hl/RZRBlYXRyRVQpopiqqi6jN8EKg0Zj6LGE3g/Q+Oefliy69++Zam/t4Y+HsJIabqRDFxgeMsJckyRmsYx2EytxYZxY+f8bKtqaolL27eMtQD8bLCWokQEikkiUoZxhrnHXV7IE8yyrggf/AJ20OPVA4R4NmjT+h6TTMMOK9ZzjPMmCLpWWYzchlz1YwkSUzwAqMN1sX0bSCfpRzN1tPEMT+n14JO75lXAhUMVRqz1w0Ky2A6un4gjSJGpelsy9v7wIPjM+q95a6+JM0Ubd8RpxFeLDmfJazLkjyBPJ0hhCQVkCaK0/KYqlhBiOisZts2KGnxSuOdwusI8S42VeUpTTci4gQ9TtChfhyQTrNarSFSdP2A9mBziSNgvMO9I3oVUU6sYgY74HEkaUSRZgj/d4yMPDk9w3vD2zcdd6khjh0nBtIMkDnfvNqxuR949OCcum9pD78FaTm0PVk65xc//AlpNtK2NcfHM76+dPS9oNk3zKqSR+cls0XF9d1bEgxJiFnM1iyyM25uXjCbRQihuLx7S5GsKdKETz78MUkU6IcbhCr49eevMG4gSUu8hTyv6OuWLIsxRvLh0w+YlZZRj9jOUo83WAd9p/j8K82hjYhUxmqdk8mpXPogOt68OpAl0DQdP/7Bc8ryMbNZyvGq5OXlCyySs7XgdPmMIn3Mr778zwz6QFkUeODQtOjB8+TiB/zgw7/AGMPt/Zcw9hAbrHcomRGJnLTU6FFxdBzz9s2Wr7684cHxMa4fqE5W/OJn5xjdgYwY+qlNRqiMKApEOHptafqWq9v2D1zqf36yHrR1jINmvVjQtgYzBgSCtmtZr4+wWtM2HdV8jqNnGDULpfBhGq2NeBbrCkMA4bBYDJ64WGCc5PXLK6pZzsnRApXaaUyrMpI84/7+jkKVtH1L0x0Yh5pIpQQ8wVkUAREphARlQERqIkqpafy9TCuenh8RpTG7pqbreuI0JU8UxgXapiNXEdWsQiYp1jlIE+wYQAo2zY44kkRS0PUNSoDyJakzDC6QZpIkEmhtSFLJYDsi4fG6R+tArDy75sCu6cA5yiQiEgLrPEY7dNuzmKcsi4RYSrqNYGgl1cOc0+cLslmEE1fYsqEfDXlasExOyJOKo6MjrNS8+GbDbz+9ZRwdUaT+2I/Mn4xGrSdghoroRk3TdZSzOUEotBkISKKnF7zNBZdX95zZBDMEimKOsB5nPdtDzWFoUJFmd9igXcvRckmiKozTNEPLcr5k3x0QIZBhuel2E6Y2yxCkCDFxnqULjN4RqYg4DgQCw1hjtEXGJbtmZHCGw37Lw7XEyYg4lnitydMCF2l2/YDzntvtPSFAnknG/o6+1WRFRBEJCul4dPqcy7vX6DHg2GJsyl8c/Yxd21GuVxP4hZgkzujHnuAiDoNBuxYpBdr25En0rjDEsjcNcRrI8wQZJ+S5oO1GiizFB4dxlqIo8QKqLMIMAg/c9DtIYmzmICjwOW3wNJsNYRwo8oJIKiSK+lBz9B1+1++88T54+Ih////5a/ISlsc5Qmi03rM+TnGuxZkYUQnut5ekxYLz04f0g+XBWYljx679AjU4lrOIZ09y3r49ZvCS42cn5DPJOO4R/YgUHt1Z8pmiaVpSNUV6kiSj7WvqeuRuuGZexWjbc3KUo3AczSQXP/oZ31zecjhojB358otrLt/u6RpNPwxUs98xX0To0WO9YHUS84PzI57OzpkVFcvjjLRMqIqIxTIljUsUgipJ8C7wdv+Sk1WG055Dt0Vrz3p5zuXdhre3l+y2A8fLEz5++mO2+wOff/U1vbljfbSgSk9px5ZZlTFPC9bLH+Cc49/96q9IysA4WmJpKWeBYfTUg+D0Ysl6dUoYFDf9W3731dcsZ0esFjN8aDg7PkcpSTv2uOAp8wIRxXzx9QuS9PtO099LRTGMPd5bvLVEQlLf7pH55PT1xiCTiGGwzBcRzlqCklPGVASMdYgswsUBwjssnIBOjwivyLOKcv2QV19+y9s3W87Paz5an7JMswk2IaC3mrbtiJMYL5PpYwtQADJl9BbpLVIIQphq09rREtuBuFiyPdzj48lN+Xg9JzOay2Fk0wysFyvSJEJKSyQFSr7DQ9uAkhHLMsV4g+5anNYolSHjEis8Wg803QhMMQxpwrsJkWW/25IVGcSeuqnp+xERHEI6shCRqATTDbhOMz+tiKOEq7sDWz3w5KfHxOuYw9BMWX8p0dbipaD2LU1oyU3K6XLOsJO8vTRYHyOkQcrvn93fq+s6XJyQxDHW2SkLG0UkUUySV8RpTv6Tj7nBsNnf43wgLWZIPKMZ0MbiPbT9AWMERTrnODvGeUuna7phzyxf4m2PDz2vb96yKNdUcY4WniSN6YeaTX2PsT0fnVwgEZM73zqKUoDvCGakmi25re9IZEAoR90fWC1OuK8HxkTT6J5tfQ9kqEjQ65YqX3BoGpQq+Ojpc4KEb3b3nOQFt/WGzh7Is5y7Tc+sVJyvz/j25hYrIzo7EseBCE+IQI8dcRzhdYfwAms1OgRUEuNCAJUwr0qM9YzGMYwjeZphg6GuG4a65fTBGi8lepiuQ3rT0QfNZb+ns4a4g1mckkQpZnDMVE6Wl3jj8YHJrPUd9J033o8+/oR60/LVl9+wvTGsTmPGYeTlN1tCkORFRTkXZHnO3d0dfdJzcvKY42WOC45x9ESxxPqAUhFPnz5Cx5YQArvDDQGNkJLFfE1z6Bit59DsWc1S9vWWYjgQRYo0yQkOnNAcuoauH5nNUrbtFVV6YFbOeH7+mFjltINBO3j99p4vv3zLq9sbhgGOZyecH50TKUO9+4pmfkdxZDHWTAxU69geDHE0fekV2YxZeczN5paud2Sy5GY7sCgVi9mahCO++PorytnA3abl9OgRi9mSv//zf8y3r75gsap4fPQjfvv1f2AoD2jlyZKYOI75yx/+Az77+lfsW4csYBwtWZpxfb1nGHqqfEmpZuQLx1F8xGFnGYZbrNPs6wYhFf1owCn2siYvKo6Pj2mb/R+41P/85IzGjAMiTOhErGUWJYxuoIgUCdC2A6lMwXiUD1P3LNP4ybkwjWrdBJ+IIknXDe8aYAS91sR5yqMn7/Py8xd89jfXWPdbfvHP1xy6kbwsGM2UoXXe4zxYHxBBM2jDulphrEF7Q5wohJuQoNJDFitU/C5vqxTSay4vX1NGKfVgeP/8gijP6KzG+UDkBSZIBm0JJiCkp212dOPAopxRVSuKNKH1A95ZIiWJ4wJnh6nLVQREBLu7LdYEvLe0fUfTtbgQUDIwOofyk0nLj5Y8jiagzXaLjiX5k4rat5SuQMqIph0QSiGDJYlj0lhCLAlxxLauKeOCX/zip3z2uzd89cWXOPd9jvf3qtuWUASMtbjgMdYw9B3pYkWSZiw+esob23Lfd1gCe33FaZ7S24a3d68xWlNlFXFwnJ28x76p6YaRQ3tHFAuq/BjjWup+y9AP05QumtHW98ySBExH29QY6+iNpTGWkyQmIUWhqLsNUqXMFgXOtVT5NCWKdMDalpQVZVYymEDb9cRixXq5RJueeTYjCMjyGWPv2PV7ytyRJI6X2wPrIuckL7jpdgQ8P33vF+y2OxrrJigTjl435HHFYBpwAu8VQUzrynmB8ZbFbI4lEEUJbW+IkwQ99ozjgDBggiYYw8nqmCyrOHQtSoapHx5ItMLuevbDAb8d0FHJ2dEZRZwTKYl3EzpMRhFx+DtmNTvb85Of/Zi27bm7u8Ho3xtWkneOroTbqxE7Oo6O1vjg+frVl2h9RJ6VBJGShxwhFZ2xXL75hoM5sDqKUbFDSk2qUkIYGcxAfxgZNbhBoEi4um8JQoPccnR0DCKgZAw+5/LqQFHE3Ng7nj9KSMUGpSTa1pTVOZ98+JQf/eADnHd4KtpWkaoI425483XL3f4rrq869ruO2SwlzSKCsKRZIEsTXt7uSWONeBfTuOkU11cj6+OEN9eXrOfnLKoL6vYWa0Z2u69YzBcoqSiLhDgC5I6nD054s3nN8XqOcQ7jPUoIPnz+Ce7LF1zeXLFYRchSoEdDQFEoQc/AvvG0XUMgQuDwPrA/dCRpRFnMJsNKJN+F4iNOz9Z/2Er/M5TympNZSZ8laO9BCVwBy3JN6jRtMyJjycmiwI2W49maUTTkUUI9COIowg6GPCvxo0NajZQCIQVGW6y1BO2ZL1M+ev8p91d7di+3+EPH+uiErmvQXY/TBhv429pB5zTzrJyiHN6S5CWjHXHWE7oBhaKzGmFrkBrtLdoYIu0I84jF0YKFEiyKlJtesGs7jHOoNGe0Gq8dWZlSViuKKpBlKd5pkhhaLVFSoaTCh4BEIYVHBMdmf8DogWW5At/SDZr9bkMWeVQksQ6C80TKk6aC+WzGaA1Rqhg8uEFjtac7bJgv5iRRgYoisjhDKE+sQHhNcIKgFVdvD4zpJWcnZ2wul9xvt3/sR+ZPRqMeJp9BlgEBFUVYYyYARZaTvf+Ajd7xdnuDDRbdB1iMqNBzulwzK06JRDSV2Oze0htH1++oijlVNafVNfeHHd615HHC8fIDdvUd3dhwu2l5dvqY80XKrr2lVIr1bM1oAm17wNkRydTKNZiOOBjiNEdgiGzDTzkntBY5d9RiTSQtrWnptKFte8o8ojM1PkQUkSKKJoa0DhYjFPu+oCpS7ncty6Lk+fqCT1++IKniqeLSBQhQNw1FHk2nWjw48ERTYiZ4CJ5IxcSRxFqBtYG+H0mjbOqltvDw9IQsW2DenZCbtiWWMVmekI4RiU0orcK4mNgEOHSkpxlKSMyoSZMYIUDK70YM/M4b77//9/+ZRw8e8dEPnxB95Xnx4i3G9KSFIstz8tJT5AW7/QYbehbzM/pu5H6/J+s02gakyLEuMCtPSPKcvr5irSrSTJLHK5SI6AbPanmE9DVHqxlHywuWxTGr+Zqr67fc7d7y9esvsM4wdHccrVc8fnCMMZZDY/jdN695//E5ZZaAsBRZSje0dN4SggfuOHQN+7YnEhblJftNxOZWYFxASsc4OubLEm8qtnVAa0HrNCpSlHNHJAJdb5GHjjROyPOBslrx+uoVserI0yO+eXHF+ighLWc4J2iGa8qi4EI9Zhhe05qBEFIEJVmR8IOPnuG05Ns3r2hnhqwsGYbuHdYsI4Q58zJitz8gpGW5nCNEwvX1Dbo7sFgU3N4d6HtNpCLmy+9Zzb+X05rFyZqx3iMMaCy9HSnSiqFz1Ma+czCOeBuYVUuMG9i0GyIvmCc5WS4p4pTdZqpGKyLFYA3OjBAC1gSst6yPcs7EjDzPWRiFjxWinEGIqGZTZjY4h1QS4zRxnLIfWhIJxhiM8wgpiBIFdlrISZxinac3liydk88T0iLGYHkzdtxtRlyUE5IM4x273Q7rIJcJTgQMniQRCGlRQiCTlNgbnPdEOEzwjM7iXE/XN7SHjvU8pTc1Eo1uAs39lqxUCCkQo0bhiaOImUpRSYQN0IyaNK0IPrCYLUhiMUFiigrvLZJAO4z4GEbvCKMhkxI5SxhGQd9e86NPntP23+3U8N+CtDVs6luSNGNWlhwdrYiUIghHvKw4zGM21zt2+3s+eO9jimJF8I6XVxvef/gxgzV0Q8vb5o6T+YxYJKyXZ6RJTt3vuN9dUuQZs/UHXN+8JTUWHzTtuCWSgbSY8/rmNWk+I/FwOl9z3bZEUuDwSBXo+h3aB/JyhpQKFRxHTUT00lI/3LJ1HhdnNONIq1u8VwxGI5UleXffEicNXvUEHaNdTJTG7Nuevo744PF7fHz+Afpw4KA7zNZR5RXOjwyjZhgthBIpLOPokB5QEZGI8ONI5mKSYo6NQOuaWMYkUYy2ht4a/NiTnp5NcSnfEYRn9B1BRSQhAgsFCUtZ4XPHXCXMZzl5lhKkJBiH8+DDd5/UfOeN9/HDhxyfzCYsVvqEiwcP6ds9X3/7guA8u9sDTjvyCg67nt3mEikl61U23QmlKWcnT7FGsKsPVFXGioTRafpNIIngaHWMsZp+mBBn2np86Ng1rxGq5fT0hIvzU66uWj793WecPz6jHT2ffvqCxXpOcJ56C9vNSx5eZGR5wovLv2JWrbne7hEI9vWBpu+4v9Ysyohnx+c8fFBQPDli0JAkMW2jQRpwjovzBXWT8sVndyAG+m6gKhQypPRti1UBrXecneWkacbd9Z40uefHn3xAmhpQHXGS0A01kXSAIFICtEKphLE3GD+SJZ5f/OwHHK8vuL67orf3HC9nhDGi9xYZafIsppqd0HT36M5z2G8YB8/V/Z79fCCEGGsE19uauv6ed/t7jc7S24C2BucCgkAIAhHAGEsUCbwbMcEy2oGUCKMGmn5AhRQ3GvJIsdttSUXKLC0ZbEvdtzCljGj0SH97ID4/IqJEdRZ9OxKtBzZdzWJ5xKHdgvAMuiPNUozp6fsOYzWlilkuZ/SjJooEfd8RSUUcxTRjz6qqiJIZLjiSRGLddCfsPFgpcFiMn9pYsrRgbHsuFhVxosA6ej3Q9548ikmjGOdGtJn+jXMjxjvqrsZ1PcuqxFvD4HtiYuq7luNyRrGqCAgoRgSOWCaIEHGnt4hYgk/Qo6eYVQgJUS7wIVB3W2IlyNMc5wKdHsjSGIFi2w3kmYBE4bRmSDdcXDz4Yz8yfzIajWF8F/nK0oTZbEY1m5GXOfF7D/hS92z3NVU142T1gHGEV7efc3V7yaPT9wnCoaQlz1P23YEHizWJktztb2i6LadHDxE+MHYt2g20eo+xjro+8PGzn3G7v6U3B1obaPot752e4OxA27TU4+5dg1aMDIYkzTGhYxxbykXC3TPDtnC8bnsenDqUnVz6Wo9UeUyZJ8yyikNXI8IAGBwxs8SyrzuMC9gQU3cZT0+e8V+2v6VaVlhrMNrTdgOWkUWxputGvLAQII1igvcMbiROYopqSZqnE3GNCG00Uk2GLzMOnK/XGBS97hEy4MNInkqcNURZhBaaSkQs8zlOOXIhmFUVQsWEeGKl62EgWPud/QnfeeN9/uExAD5oTk7WPDiXpMkD3vvwlLu3W/7Dv/uMy/qachaRVynzRco4dOwPHdZH1IcD+/3A+ek56/macZOjOzdFj7KS0QZev31DVVZkSYkXlmoe0zcb4iTj/qAJITCbzflH/+AXVHnGZ19/ihwBFN3ba9Ik5oc//BghBmSUgFSEkCB8xbPzC95cb1kVRzw/P2b9iwptOoarO3731d/wutmQ5Yo0U1TlnG++bJB+IE03FLM5kZzx8tstaSaIHnqCjdjuPEkyMvQ1u/uWi/Mz/vIvPmY2yzhap6Qq5tef/xprtpwdrXA+w9meQMKszDHOUzcdTVujbcN67lkdV5ycfsjQPWSoD+x0z11/DdLRHhr6zmC0YeimH7ko50jhOWwtYNGDw2qo77+/J/u9tPCMeiB4N1HVHMSRoO0GnNXksaLpOkQwHJUxmdQ0bhpleaUZGdFWcOgalJcIxGRSMhJCTNM3CD9V7GlncYWkude4y3uOlzEPnp3iVKBrAy54nG4hiyA4RBBkWUESZwyjY17Mcb7HmyleFMt4CvN7RzO0BATDqMAb1nnCsijpDy3O9siqpOsO+DDdW8vgMO2GLIqQaYZWjjRKuW8OGG9x2qAigfcGbw1yb0g6j6oMRjjypGR7V/Ofv/yUk/OK+nCHsoEqilnOZiziNYe6BjUV5GRZgXaGYWxx3mOMQqpplB0c7/p8IwYLw2jeVdxJxqFnVswxo+Nys5ng/N9rUhBkaUGVlyghsNYihCDKS+zZEXfb1+z2W37yyV/Q9x37wy27+pL5PCcowzDsKYuMsdZshh1jX/PehaGuax49fI42hnbYMtoDSk34ybrd8eTRexjnaccDNlja4QBeU7d7olRxdnpBtC+YFRXW9CzLjMEO6FEyDoLDrOQuOtBqQ2davK3J4wS1mHM0X/H27poQSu4Pe9IkJk1WDGOgrhuEaJCRJMkkRo9EScXN9o4v7t/ikkCqMqyLWVbHJAkY7TBSo714N16GoixIQ0ZVztmjUa0mUjFZWk7x1r7GB0sRJ6znx1gXkJGkbod3sSpB3/cUkWSIImwZY5WEfQ9BYJzHdz1EFqmmngERpg/676LvvPFqW6NUxDg6rPMEBXm6ZrVeIKPAX/qfc3e1Y3u4odm1uG6PiDyv7cBsPmIM9ANoo6nSHfqgGVsxOTUDJLmgqx1pBMH3tOM9q8WcIskIhHfg/4YgNIvqnA8+eIzRA59//Q2jHpmVKek85eXlSySCtu4RwfPTn3zMyXoqKsgzycXxI6p8xuhGNvsvOHQKcadYLle0bUu9m1yd8+qIl1/fkOcxw9AQgsIaiRk0L909VVVQbxxJBnmWslqecHbxkNPTCiUi7nYvmZdrHj/4EZvtNZd3V5wsJOv5HGMM3bil6xuKYoG2U9a07gaUcsSyI4kVURLhrOby5R6ExIyOcbQEGwgWXAjooUUAQzedhLIsYbmMyNPkD13qf3bKkhg9jkiZIpEkSuHzBIfHvjtNCOHw3qC8RI4W6z0Q8CHggqWrB6I4wo4jrQnUu5oqrwjGkDiB9OCR2H6AyGB1QCM4vNoiFilb12OEQJuOICYHtbeTk1hIpoiRjEhEIE9TehewYWowEh5yFROGkc5qpJqibi0JZuhJkhgTSUzbIGOFHTVH1TH3Vy1l4lme57zVI95J6vFAa6aaQRXAtI5gR7JBcjTMkItAiCESPYma8799/is2XYe7tyzmBcsyY3m8oMxmhCZitVqSRxmb4cCuaRl0P/2/OtB5ipAShyOOItJIkuWCqsgZBsOh7cizlCrJ6YeRaraASOG/o0HlvwVVWYZQKXmSY0bN9eVbDruah7MZTWh4c/8K63u0Hnlz8y3d0HDo91wcPUSbhjiOqIcdzVjTNjvOH/6AYbA8OlnT9VuasWMc9uR5Toyg7lrWqzkQc7d5RTccSOIEOxrKRFIUJfUYGPoO4zXaGm42tywWHzJ0LYqcZZWRJymjcRyXBYuVBeGZJYpNk3J5c8/t9pbT4xGtBwgR+37g/tCxKtdYk7M93DL6OyDhJH/O/X6PlzFtfaD2HaiE1OZkNsOYgeV8RtN3OO8RMqIbA2kcTz4gCaPROALdqIkkBOcwZuTB6TlCRdTtFoLFWk2R5nR1M3VjRxmhiGnCyKHXZIMmS/LpetlPJ/g0L5BCIqz4zsbA77zxlsUCawxOOaLIMSsyvBtx2rJarCk+lHz40XNUHDhsNkibstnd8+bmLca0CGfY3m2wfcrRsyNmyzmd3rL3U1ejbSfG7OFQM5uXiFhhnaPTmuAd3jmy9RpjAtv9DVVxxv/8L/8ZP7r+iMs3N1y+ecuLy7fIKBA8mNFTFQWffvqCq6sbykVCkeXsDjccLVaEELirX1CYI5rtwP1Q46zg/kbzVg6kWcbYBXa3LUkiUAkksULGKfiAUoof/vg5p6dHPL4453b/lnp4Q7QrSJSi6fYgPHm85sHpAw61pW8bTCVRCUQkhAFGsyVNYw71jjgpkTLG6hEbOnKpSLM1qSpp6hHvQLiAnQ63+OBpa0uaKZ49P+f4ZE6UJIgQWK++L0n4veazkrbt6bQhS2cIDQRPCBaEo7eawUzkng2O3EYIH5BqOqESBEk8gdITlaCtQWUxKks41DUpkEYRSkgSE1DC0fUHsCVZlJDcWqp1wtZrdO9QMsLoEYHHuh7TT/VkIYrRoyePcqxzyAiqLCFXGbGPoChJSLHCERCMztANhlmSUcgMmSdsDzVlnKPyBC8CXgbu+omZq0eD95bBaPABKRTrckWCRJWGcW4Z1ECkFFW64OZm5OpuO0Wb6pHT9Zr1eol1iqo4wtNSZBlLdUxo3pCWMeNYofX0gdgNjjhJGX2HF4LBO4IcSSJACpIoARuw0hOnMaMbSdMU810zGf8N6HixAjGRqw6dRjtLFKWIswXf3L2k0ztkFDB+ZFaWKClY5GuiKEU7jdUWbWr223senj8nzed8+/prTsYFq3lJcJZlNUNIz7a/o9UD8+IJve6JIsn58WOcDSh5YDANXgW8EgSvyRNF119jbMvusKEed/ghIENEdrogjaDpNINpaccNR9kJWX5M3R5I3tHY2qFhPzgiGVHlFYNzaB04Xj1h0Ed0/Yanp4/4zdffchg6RIhIsxg7amLp0e6AkIrBjBg/EMkERCCJPRJP8J5x7IGAFAGhNG0/UsiYRw8fI6Vks9vS6Zqh7zHO4pyhbxrSvCTLKsKgscOAa3u89si0II4zQphOyfiAMQbF5M/4LvrOG69SaopkeEczbhiHmJP1GVVVcmh6osgRQs2sWLAsz3ABVkPJyfkx81nGdrdBa0+SKFZHKYw92pTcvd7QDgMugBKS09M56/WM69tbOgXOW06PLohIGMeBbujIshVZEtAEzs6OSFPFbFbw6vWW+8sdQQQSFZHEhrs7TdcO6G9Gzs/W3N5tsN4w6g5pJX9xviKmYHO1QfdgDERxYGw7vFVkWURVlTx7/oTlesbx6RwlPe24Z7ZYUeQJJ8szWn3DbqiJ4ox5laMNXN9ckiZbknTG0WJOMJaru0sWVcK+PoAEgqHvNHGas93u2dyNFLkgmwWSbMnp8Sn/7J8+w2jLbrOn7lpQoJTBu4AewNiBvEhYH52RZlPmGPs9MvL3kjLBMTBaTdv3WGPJ05SmG8gixegkicyI05hDW+MFJHGKtgZjDOOoMYMmEhEqRAz9QJEVNPsdLlhUkYLRCOspRE4wmmxlkM7S7m/R9wuqWUodOxZ5iTGafT2gpGcYW0QQOAeRGAlZiaoqyjQlxAFhHdgBJwIeD2oC5zvnGe2AlJJKOWLpcMFzXM2RRnJ1fY0zmnmes3U9vdZY68jTDCUj5tUcUJBMd8SjViSFIAoxkYww1vMff/nXjP1AmkiUEOw2O1ZlwYfv/5TlTODnjpvdhiisWMyeUt99iZEj8UxRLUseyJS+M+zrgA2BTreMQeNjgZIxaZSA9DjlsWNPFAuUV4xa/7EfmT8ZFVlBkqY09QGjNUZAXBQM64zXr25ph4Hz4wuyeI7mgLEDoHEi0IxbnOuZZQWfPP8JTkiafo8XlsFLdl1gXZVYZ3i7eUtvGs4WTyniFcbeMIiJdLbXWzbdHTJMHbr9aNk0W/IkIhaO5bxkNC1hhIu0JPOCu3qPlY522NOPPZKSpguksabMVwRZs9nf4L1nMcuQosALhzMdQgVu6x3OxeSiQHqou4FYSfIipR8ayrygzHK2/T2SBDv2xIlASoH3gkRmeOFRkUAKRQgOa6YCnkH3eHrWrmK0koBgXR3RJ1NrEkKTLRccH5/j9EjkPPMhInGKOIlI4wjnLAhBJBR2HME7RCQQfDf4y3feeLumox8PlDkss4oim2F0hzUGz0CUREQUaL0jTWJAgohIsogojXn09BHBQz+OqMhhiVgt32fznz+n7jTzVc7qdDaB3n3Hap0TKTUV36dzqmRBVswYBs1q8Yjd7i27/RW9rlkvHpE8TPgX//wf8/rVLWa07A57vvz2W4Jz3PsJJp6llvmy4r0nTzk5WSOtY/ey5mK14HJzR59IjJ7Yp3pwqAQuHp/y/ONTPvrwPYRX3G3fMl8+5eabNyyPJIiEQcc8PH/I9f1rru/eEqkThJzuGXzwCNFirKTMC/wguby5IU8XSOVQIrBcVpMx5jTl4dmKRElut98ibcLn316TlEvWyxWPH3/AYBriVDGMN2ht8S4lSROM7Rn6jrZtCU4Sy/IPXOp/fhrttEikZ5oaqIy2awGFczCOBhEC1gw4Z+mtnzZTB5GMQQwTfENJrO6IpJhG14CwhmFvkN5TpCkqSqeO0/6O80cniEEQVIfoJetlxmbfo+uaUxHxrDrleF4RRxFCpiRJjFeCTg/c9Xtu+h1aehg05SLDjY6QBAiBoR8wDpLYc/AdWmuMDixmK6I4ZpbM8dqhZGCRFeSZx3tPFCtSBTKS2NGDsfg4xnkQQVIVAq0Dn37xDa+uXk2Eqn6EXNHUHtsqVqrA6reMUrNtGtLUEHTH6eIZr2+/YnA1Pg1EBSyPMoriCGMC3VBw6FqC9shI4CcbBtZoXJju5ub5AuO+P/H+XqPVDHqkaRraoSebLVk9f58mCUQKYpkAEfu2pu2uMc6wa/dEUUJg5Hy5Yj2fXPoBR9vVFEWBCSN3t1fMix9SRDmrtCeLM7Jkyaa9JI4dSeSJE8doDxhTU2UpZhxRMmE5PyOPY6RvOeiGbthOqMbB0mcFVki29QGjO5bVmt54rq7viCNBUCltf2Acdlwc/4A8LulMy6G9I4k9MDCakcFsWZZHXN1ccdAtLo7YNx2xjOg6zWima75UBLogGAbP0TzGBY/1E0zGW4ENhjgSeO/R2mEHw+nxCdopvBQMziCJ8GI6wa5Xa7b7Du9GosiTqZRlJLAkU69vFE+RLjGdbgOBSEpA4Ozf8ag5oDHWcrOpqWYlejSkcY5zI87vaVqJoqMoUryPJ+OKS8jyBOc0+8MOFcdEQqFCTohLknLg/fMnXHW3LI9L0nwah/XdAZkovEjZH24IpWZRzHGuw5nA1d2XzMqUIk6ZVSmJSJBpTvF8yaMHT8mLmC++/i3r05z7m4Gbm3u0aXnzZsP11YbfzV/x/IM1p2cZ417wwcURbvaYXijGPmXQGx49PsGFljw54dsXV9xvLjlZHTGvCgSONFK8fvMtjx88xEQZgohnD57y9Ztvuby6YTErmOUpIgiU8nizR4sOvGRerbi53xBlEoJBipFYKQIOPXacHC04PU7Y3Qnu9neE+oZez2mqE7wPFEVCHE8AdWsst7cbyrlCKcl+v+f0+IQ4/v7l9Xt5JzDWE0KE855h7DHWoaIprjHRowRIRZJkdG3L0Fo8TP2a3iI8GPOuAs85xs4QevPOVFQRxZLRaWrvSZWibzxNfeDR0xOK1GG6A/puj9TwLFvy33/0Cx6uzoiCx1mDSBLivMAiqIeG2/09X9y95MXhkj4YYmuIIomMA8o5SDM2+xbtPaP3OJVRljM6enAdzklykeCxDGNAioQ4jhFRmDY9Z5AikMaKUfjpxRxXtP3Iq9f3/Lu//mtG06GEnOrOrCCYiC9+/ZrhuuEv/9lDTBKo2ykDLWTL7Z3joyc/48XVp2z6O0brOJ5HyEgQp4pFWVHOKiInsCO82FwRFdM0zRqPcYFdfSBL8z/q8/KnJK0HxlHTDj2WQJzkpI+O+d3mCodhc7hFqZQ4ruhMzb7eYJzl8fwpZfkewVsG7elNQ9d3CKWo64ZDc8/jk6eoKKN1Dq9mnM+PuNvtsGPPvtlxtH7Avq+RsWY2m+GNgRDT9iMoQTfckyhP8I5EBgZnuA49GYq+3XHodhRxRd1OSRLrLdu65uI4YHrN/ORDoijldndARB3O9zg7spwL+kEQiTnPL57RbHoEMc5oej0w+ggClLnESSCP0M5RxillXuCcpNdmoqUZg/cdCMGopzveIkuZ53M0CS6MRPEE+wjB4YWmHQXtsEW6ilk5xyUKISTYgIgk9t3mOnlw3MRUVwqIsP7veOM1RuKdYTZfoZB4G/BSkqcx0k3dkEUuAYcPKWkSY13HoFvydEEUp/RjQxIlROkCJSNINO89P8O83RGkQY8d80VCfahZxUvwTMYT3fLm+lOWs0ecHj3ndv+KpjkwRBPOL43WpMmKw6HDy4QnsxN++uOf8oOPfkjbGm7vdvzVX/0VX331LeMQuO1Httu3PHpS8vH5BXVvudrfMjua8eyjOUGsGfuGnJwiT1gtS3aHa9LYIMRUQfjw4phX91s8AmsssfK89+iMk5Mj3l694v7wEu8H5vmCWCZYRpyzOJdgvEHFsL3vCNJQ5iWbYcA6iyKlHXYUKuNo+YAHjxO6focUjqbbMJ8t8H7AmAnBN88vuLnaUjc3nB6dkKaKXu9J89X//6v8z1RRFOM9jNYTpdPdaACapsH0A2WWowNo46YAvzYkKiYXEZ3pwUzdnTLAODoYDEJbUpWQFCV9P2LGjvlyjkWAiuk3nm/evGL37S0Xz9dcPD1jrTJ+sHrKz5/8lJNiMdVWmg4tJeX6jHQ2R8qI5dBMI8Y4Z5bN+PLt19AZRKUwTqOkJ4kCsyLBaEesEvKqRIserKXMM+JI4rXFhUCcJljjAY/zAeenfK1X0LkOkCSRoh9GXn7zgv/017+cXKziHXoSUGOMGwJ91/H5bs/R+4L1wxVtp8nKhCQaUAV8++q3PDr/kOG15m6/pevvWcwLkkiQJSlGQJQpqjTniTpns7+n7UdkqgjSIpD0w/DHfWD+hOSRzBcLZBSx6wZG52krz93mkn6cwEFZEtPUt/S9piqPyJOcWX6KsY59+4Yqz6cJh7PU9Y62bXj/yQ+4mD/kvrv620q//b2iwPP46IT7TmGtpW33SKYNb+gMw9CRyhyZWraHjmAVCMuu21DOllg9EnTMMHYEb1CRo2n3yChBqcDmsOV8mfHg+GNe328Y/S0EEMZSZSXOSur7jqFuwSe8f/YBr+0bLvKcfuyJ4xjcdNJUciqRiCLFcZkzWIt1053rqizwIbDZ7lCRpwd6M9INHWezB1gr0V4z+p5R92RZhrUjcSIYhymn3Oue1fIUo6HXGhWmrL4LgUhNpSOEgFTThLfrOqyz3+l3/e4br9UY35D7iiStEIlgGHbkMiOPS2LZI8RACDM22zvKckHdbhnsyJvbF0SqYFae4+PA5c2vUCIhy04JKqNtOgyaOBKMg2Y+zxEioet74shjbcBGOatlQqBhWaSUxYreTEXHl9e3WHfL80efsNs3vL76mkfna8psgdEdz98/ZX3yL/j1L39FvTdoPxC8wfQDmSowmKkI+arh8vXfcHq0xlro+nvOzx3j2KIiizYdaVxgzY6maxn0wG53T7aG/X5H3cdU+YKTo5g0PeLV2w2X45YqKcliTxRPRelJKjjJKtbVA756+QofUpI0otnsMWNLFknKoxLrEtKsxNgWq0cEgmE8sKgq8BJvBLXbcX6+5u1th4oyUifpmho93P0h6/zPUs6C9JJUKrzzOAPzKqNptgSgN1MMyFqNEoo0jRkaw3jb4iOLVgNZlqObHtdpEhexmp3gReBqc0Mcp6RpQpnlNPUOWZTEPiKmRDUx3Uu41z0/ev6Inzz+mHlcIELA6ZG+75idPKI6eohSEYwaGQzLZA6riCAkuht4s33NkHkcU4uVHwJxiIlkTFZkNL7DocmVoteGWHg8ChsgtiM+hKmYQVjavp6+1L0ijgP9MILJ+Oqrl3z2q7/Beo1MPSHIqXqOiGSI6A96AmgoxbffvsFGhr6zaJOQJQnOO2wYeHP9OY9On9O/+ZK79p5+GImUYp7n5HmOwhOkQ2WS83SN1XBz2E9Y2URRVfM/9iPzJ6NtU3Po2qnaVBtSJWjQrJanzOyCtq+5396xXh3x7PGHDEND004ZdCu21M1bivQpscoI1Ahlef/JE5bzEzb9PW13M/XXes+u2RCvHmHilDheIMaB43w+dYaPhqyImGUlwhVs29cMXUtVLfFOcH7yPmVeYrVHCEWcaNIkoWn2uDiwb7YYYxEkICTaHdBjQ+cOCAJKeogkxngydUxVnHBU5CgLv337Na0IeK+oyhJhPZGSGN7xouOKRSY42HdsagtlViAApMAHTyQnKluVRpyuLhiMmrrYg2IYLVJalJgmiLNqTpYI+n6Ymr6kREiJigVChHexIU+wFiEnX5Bznr6tEX/X5KrbzRVZkXB73zDqHYtZyvFyzuawJc+mUuRgI8Aym5dkaUKev8cwdqwXhu2+5+Xla4IZ8WEgS0oWC4WMPLNyxvX2jr5zVGXFMHoIhnK24urmFtzIaiFR6oYiTwluwOwHvBRIWXF6fE7XHxCy5emDY4ZB8+bmW9L0isWspEpPmGWPWPyTlKZxrJZLgqwRdser333O67trslxinafeeX794i0qjskrCeEG7y1pDk3bIrzn6aMLvHFs73rGTFBlCe24ZyQFkRBcIISUJ4+fo7Vlv3+LxZNEyZQDDWGCnBcJs6LibrMjjgMnqwX7fcssWZBEC243bzg5W1JVc/QwsD/UtJsGKSZmdRInlEVCkZbcbWL2zQacQsiIoir+sJX+Z6ih78EF8iRhtJp5XGLbjlm54PXba6J4MkdgLKkA44FYMAhPIgFrsc1INApOqhXOS24PU75xNpvhR0eRZQxtjdCG0A1I4wkBBBGVmvPji5/xw7MPKJIZ3gV624PuEXFJuXiAMArfaMTQ4fsOaQ1JgEImLGcrrpsbrLnFaEnXe/K0pJzNEUpQFjFCW+4OPU3vcV4jgkcpRSwVXoFBYs2IsRqFI1IxSYBRS5q957e//a9cvXmF7UZEGhAhwvtAKuEsm6FIsZFFO89+PFB3juu3B0Y3GbH29YDwlliW1MOBsH/F84cfE159ymbcEqkYPerp+OwTQpqCcCjpUbHg6GhOLFaMg2Ovv2/W+r2apkEbM+VMZUSWZaRFhtvv2dZ3tN0OLwP3m1tWq4eEMLEI7vdvGfoNp8fnLBfH3O3uuD9cUeYlRTmj1xvqbosQhpkSaOGxRczpyRmbw4bD2OFGxbo8Y5m/x3y+JZEDR6UiDB7NnDJfY03AKU3XQzP2eK9RQdCZnlhMhfTGCvK84oOLc0yoOAwWBsO6Shm2gp3ecbI8ohv2GDNy324ILuHB4ocTQ12lHPoNcT6droNUBJUQBYfMBF46+tBj/QjCI6VgcA3ajmQq5ahact3eUbcNH50/w4kEh0HbEQdU5ZI4FgQskYA4KenbA04YnB8YXYQPASUkQU3ToeCmfmRweGuwzhIncvp4/g76zhuvlQ3tmOFHT5ktybMEJ3oW5Zpu6HHeI6nozQ4TPF4EnLF4ZxEiZp4vGSrJ8fKENI1I0wgXag6HA+frU7aHjvVRgrEdiITb2wNIxdF6hekdp8drrm6vOT4qqJI5joBSGWNXMy/XRHnKp1/9Nct5ySI/Yb04x7gJWdn1L0njOWVacXH8mHE4MJ/N6NUG7Qd043jzqqHrJUYHsjSjazVd7/HOE0XATnByHpHnAusPnD9YkpbnDL3COcG8WnK73TD2W+bVkoBB+B2xElR5xaa5Z+xbsnKGsY6DGchzy3uPl4xNYHPY42zL+VlJao9oR4lKcqSMSRNBEitmiyN22x3OTfcl7TjSDCnBZFSzNYPek+UJd7f3VOV3/mn/7NU7w6BHsiShHXtWccHQOKw1zESG8QYnBL3VFIXAmxGJJ8o9mzc1IhXkZUK1WrNtGt5ub5CJJE9TIgGj81htCaMm1YJBDzgHJgRio3lwdMJHD99jXszABvq+RboR6QNnTz9AihxpFVgN2v3tH6cH0JaYhESW3F5+yfL0iOPVDBLFtp+iPlkXQQgELTDOgQsYN1JGGewG+gLGXOACeOeJw/QRElD88m9+zZu3l1jboQKklcBIiQuBQjn+u7//nFVZcLgMNLeWh+cP+M9ffs7O36GdZH+/Z/1gRogTIuGJU09HQuMPmN3v+PH7P+HV3Wvuu7ckUUIIgqYfpzu6qsJZh/UB7xp8osiqOVJ/zxn/vQQCKRVpFE0lG3Zkv7umGW7Y1G/pdTelOHxO399wv++43V1RZhnnx4+Yzxbcbq653b4kS2JOlg8gWIZhZF7OaIaeq6YljQRPT39A39YYvcOYGqlKsvIR26F7Vz+5Y6EkY29p9IgQ/bumLkkmI9q2JoohjhQyOKSQaN3ghOVBdkShFlx3DdYY6n5PLQLPL56ya2bsxxqFQqQpgpSxsXz08BmfvfiaYl5ylimasceMGu8NAcuimk3l9SHQjyMCCW4yECI8KjhCJNn2I9LB6XzB0WrNvmvpxmGibBmDNoaj5QKpJnNU17T0ZqAfGmxxhPR6ih8KOVG6lMIJifdiImXFkkQorGe6xvkO+s5v57oZiSJHrgKDbpiJyVh11/SkWYkF0lhRbztu7u9ZzErWy2NilXM4NKyqJQ9PzomURyiLwDArKxAS27Yc17eMNHjt0WMgTxcM9UAi4fziGBVDXkW8ufqW9eIUJROQgifnP+B0foyxe5r2hpdvX3DIW9Z2R98NEyZM5XjnKJIZ69lD9odbguzIqxZjR95/viasOqxbIsISLzQqhr7VSDkSxZbTkxXWbYmSgdU8w5iak+WKrdijZMSsWJDEc/rOoIixdLTd1FNZ5jNOVicYO3J32EwLKSmxTc+j04p//k8+5DdfvuSbF9dsXxoenMWA47BrMEPL6fExQimGsUMphwoxsXT0fmAcAS+5uXtNXkpUpFks5txcH/6wlf5nqCSRWA3GTePYzWi5Gw7g5bQAjMTYKWM7JA7vHbqz9I2hyCqOzo9p246vXr2iKHPyRJGUKdII2n1DHKVoq1E6IHrFtq/p7AjB8+y99/jLn/6M5XyOitS00PsOqUdm8xOy6hgZJQjv8XaqKPRuGl3ZYWpSEmZqSqrvPauzmK7rMY0nBEXsJT6RLBdHzGZggkU6SZAB799RqCIY8AgviYXCdD1fffWCL775gmGc4huTwczjRITwjplNkTbw8rM93ZmlqgpuuafdHQizkUQIEGIyRfWaMp+6XU0wJElE3VrSRcLl9htOF+/R9R1Dv0FGMcZadtqgrUUqQSYijuM5G9/RjDty+b256vc6Pjvl/u4OrS1xEhMJx8l6xeevXoE35PGM9dExiyKhazsiLI+On6HiGJU4vnz1Bf24Z1XOOT/+AGs7Dvs9JgQ2+w0BgQst8+Ihvba0w45ExZwuzlDRCut7Gn3Ffn9N3zfw6BTfBqyxeLEhBEvfB3oi0qTEO0NneyIF43AgBEfqY57kZ9yOLaPeY+yI8w3nF0/49uaaWV6xLgSdlty1N2jjKNKKmIgXV19TR4Yky5kXR2QiZrXI2Tb7qZkuqRiMxmOI1FSMkMQlRvcsZ3OazrI/3OPcwF9+/BFEgSSH3jr6rmcYB+azJUEorPVIqdi3Nf3Ycj4/5f/X3p/8epZm57nY83W7+7WnjTgRkRGRbbVkFUVKKomSLq+uLnQte3SnHhow4H/LgAc24IE9MSAYsO/lQC2vSBWrWFWZVZnRZESc9tft7us92JG0Z0oRNEmQ551FTCJwzt57fd9a73reQhY0rkMLCDGgU/H+ppwhJjJTdKIpJNZH4viXnE70+OGHbDd3k/tTBK5urii0Zj5fc+hbRC4Y2wOL5hwluul20Q8URlCaGVJltAhYv6MwBT5mYt8TgsaUK7pbx9YNKK0xdUYEy9gFlssFt3c3lJWgqRSL+oSL0wuacsnd/pJheMtWtNRlye9897t87+NP+NNf/Yqb63eA5t1ly+3NG+xgIQrs+B/Q0jBbVpw9KPjo5IhYjqyOpyDxk+WH/PrVlyS54fjMsGyWbHZ3FOVAkStu9xuMMLTjBMsvTMloPaPbInJBqRtC9ISUQRgqY9Bm+pllkTFFyc3tgcpI5jPDizcbrsotjz44Zr2o6a8k123AykBVLnB+x4sX7yhqjWk0Kks2t3vKEpq6xpiCFCRdN/LucsuDhwuO1mua+znZn2voDhgt6ccBKSJjGskqgkzc7veE3jEzJRrJ0Hq8i4Q2cbw6A6F4222xhwFTalxwSCmZlRWjtRSyJMX30WPekbxj3+7JMfFbH33Mv/jHv8/p0TFSaoKPOB+w1sOYePj0MaZZIYQiB08KkSgESUlSjATniMHjBjsxoaMiOM0HH32I0QUiK8JoGQuPT4roA0briZ6TMoICr0sqJVB9x83NhldfveHF11/R+Y6cE1JMRX2mC46aOeMYKYTgdH7En734mt98sePL1y2myKAjJw+WbHZbdJ0pmpLoBbubgfkiIJSm9wGNwsgamRt0XXJ59yWfXnzG1eY1u3FDloJEpuun1qA3BQSFUx6EJ9078v9c79w7xtEiU8Fi0VDPG3b7W65vL/n04x/ic6bvHbiWfTfSFGfIELhrb8jK0tk7FsWcxw8/Y9seyMmilSSkEZf3+DHy5PwpzfwBfT9SF3NSdiAmP8m+vSEyUpQG6w279892YSRSrgjBU8wTOmsOvSUw4nzHum6otaHWDc/OPuL13Z4RR6MKcqOYUdHZLUFaBma0bWau5pyowEDHs7NnXN3ewqzgfDUnEbHjjqwlvQtorSl0g7WW41JyPR5Q5ggpFAmLMoJ+HOjGHkdCiEBC0Y+WzrW0nUVJxYPTB3if6LqWlDxFNWXwzsoFy8UZKbUskCitSDYyjj1BBnxMGBTaaHyIxBQIMSLEt3t2v3XhXS5PWTcX3Ny85eXbX1OUBavVEbfbPVpFZuVDtKrRpmBuEpKCYWxp2z3L+YLcBUpVoQtN3+/Rukblkll1hIyeDx5/xPbzP0WXhv7g0MKwXC2JQmAHT1HUxDBhI4dhC2lqE7y7e8Hjs0ecqBPoFHVp+Ps/+JSbu88YfODxyUDbWXb7jrGLSGmoKsPRSUXTSNThjpv9S35+eUehPZ9+BMvFnBdvd8xnM7wdsb5l9B2zYoa1mZZAzCWX25GToxotNL2zhDhikOwPdxydnNKUFYrIm+uvySLQzFYYbXh49oA3rw+8fXNLCImTs4bW71lWNagF+wPcjXcoIZg1JWTN1fUty+OKB2cPCFHie4dzHVqNLBYL1qsVMXqkNNzebMnx8Bd91//Wad7UdP2WmCL2PWIxBTeliciEJWAPlrlWaCU4X5/jahiS483NDUpqpJzg6jGFyTFsIY6emDNSaVQWBBtp257BBz57/AH/6vf/G86PTjC6IJEZvafve9p9S13M0Ys1WRnIU+5tEoIoJJ7MGBwhBrzzBOvxIWKKhmV1zlHzkIzHiUDUgooC46CqS3z2XHe3+OhxYzuh8gpFU+j3xd1TKo0Qc1K0SAm1KDiSJQ9np7y0W/wwsjpaUszuWD1Y8cFnHxBT4O7ykrdfv2Y4BJYnE6krDYrrFy2zdcPp6RqfE5GRqlyw2e6RY6ZUguu7L3h4+hmNP+XNzeeYUiKFppCK0fW0DOgEWktKWf11PzJ/Y/RS/hp3UMzyGlUY2JU0o2M1WzKOltZuGMaOy0MHcsHt9hWVLJkvCw62Y14dcX70Cdvdht14zWAnl76pBEaVXJw94vz4nLc3W3rfoqTEhz3r5RHdeMD7AyGO2JTYb+94KSQfP/0Ot/se63pG69CmRArPYqGIqaEwSwgSIQ03+y1f3LyDnHDBUWYoS4PzLZJAXSlGd8PhMHDjJU8fnPDJ+oi/99EP+Hf/+X8hS4hxci4H32NEgZLgvCWkCRJUzjUP5zO6DrwbGAV0ziJSfu8tGDhrGkyaM4RxSiEKjn50ZJGmsJroGMYOhkxOlmfPPiVkOOyvqdIZdSHZOUufe5AZkqQwc4w2xBjp+xHnHGX17Z7db114N7cbzo6PqGvDkwdP2bY79ocNZVnggsLaDabYY2wiOMd68Qgl5pTFyDCMtCGhpeX4+Iz1+hgyE1A6Z7J2rNYnHB0tuNluIGrm6walMv0wYG1kViUKpck588VXX1BWBavlMevZOSJPQ/yUPO9uvkJrRQzHKFVwfjLn4lHJ7S4yWI/3ghAG+nCHcCuqEPj6+hYlJfuD5d//h895+HiBNLCxAykl2tZyejynSz2Dq7m5HlkvS9ouIIWlMIrtrkMZjRE9h/6AKDKlKQFJVS/RanyffiHJ9Hzy8XO2G8/LV+/49S82XL+F508TD8tTsj3Qbge0VGxvD8yXDX1vGcYBmQp8CIz2gBCZWVWhVADRoBQMfUfwAu+Hv9CL/rdRWaQJ2h8z42jf/21iaEeMAD/0hM4zn695+PQD+qHl4DqShKIssYcBZyNVVdEPB3KGdt9ihCbmgbKsGfseYmIMjoujE/7HP/jveXD6gKIsSUxIud57dn3HoW/JZY1XAucdWiSS86SU8TExeM8QIx4x0bOCJ4ZAcg7jEkWbSAWYZUFRamIUZJMIITB2I7qQNNUCIVdopdEyg5QcP3nKb//e36PdHnj14i2Xb15xc31FGBOtD3yx3+ByZnSel5sbfuef/Zj6tGEMA0MvOH58gZCal796gcgJKQSFKtkcDvz65+9QPzSUi5KMR6qBqio5jIcJZZk7Dpd/xocPv8vZ/Bnv9l8iTaAoCgqjUUSk0JDTlLN6LwD6aBmsRDJyc3vHYD3P7I94sDqh9R3ebQljSz1b0Y0BISy5VLgx8tHZA5Q5ZTfsaPcbilnmdrtBC8EHZ58SfENC89Nff8756gLcwM53pDQwDC1dv50gMSmTk+C4WXJ6+gCI9GNHZ6+oywIhMruuRYiGQGLXZYjQVCVFEbjbf81ifgRKEmRiIQOLUtPlkW07kmzJvFmhdEkbIq+3lk/2HV++fYOtRw5DoiqnTY7BR5rCEULAxwPWOhazD3ARtoeWZTHjMO4I7kBTL9nuWlTKfP/pZ4QYsMOAGyzRWZTMjGNLDJlEpDY1Z+sHVPWSGBKH/gbtQYwRow1KRYZhmIJLkEQSKSe8ndKSpC5BfDti4LcuvOcnNU0dKUxDlonl/CFvL39F2/WU5QKlI7v2ilqXLOcnDHaKXKoa0CoTYsJFwcu3L+jHBTkJyqKirMyULVk0VOqI6Lecns1ZLBXOOaqi5Gi5YLQbQqiZN3PEKpGTZOhGVmdHOBvoc8d6uWS1OEMqCaJku+25a3uGmw2zpkIJuGu3bPcji8UKGXuyTdzeJV5eWcoKjM68ebHHR89sWTKbV+xuJPubPetjw2y+5u5mxNmREASbm1sKAy5khMwsFzVVccSb13esjx3GVNTVtJs4DIGM4mg9Z9/eYCN857unPDybs7s9EA+Jo4uaP3j2nOvdyME6bq7vyCSGcaDre4K3lIUiiYoQLd57XDe1/YTIhDjdGtK3RJf9XdDV7YblbIb2EwKylAV1UdKHntrUFA4enT7g6Pycm/5Ab1ucjSgU0QZkEpASImViCPjR0uiaKBJaKQoESmoCI6Up+B//xb/kyflDqqpGKEU/dgw+0o6OfdfR24G4u+V2846yqtE5k91k4PDR0bvphjHmyBA9gx8Z8bT0FOcZTh2R6aPsYkRLQ8wRUUrKomauV0g5zawQcSKTIiaQO5GT5gGrk3N+5+//mP1my5/8x5/y+Rdf4HRm9AM+KcJSsXq+YLvpUVKh9RTKcfb4nPmsYbd9jVaCYqbIgyS0cPVmx+mTI0ytGLOnMiWVWhDDgKlKUg588fZP+OTBb6PNZ3x5+ScMyZKlQSkoS8U4WKJKf92PzN8YdS8D0tZQTMhQkSPdux3u0Y7dsKfvB5RqSCHTd7cM3cCxjBwdf8qsWtN1G7S0WNGyuTnQFCUXDz4kBMnodnTJM/geGweWqyNEC6vZGbf7gWp1QkqAF4QsycLxdtuzLA21NIjqDCFGIExRpwly9O9XHxVZanzo8KJlVp9jrcRImNlE2Hjmj9eE2USWcyEyjHf0zvJ0fYYddixmJfWsJoRESpKYHN5bxjEjdcLFgaPjC3Zdx2HYIrLk3aHjeL0kxYrB9oyj5dNHzzj4xO3hlpgFs+qY2UwQk6Wu6ol9TyYlyb6dkrVijAyh5QKD2434GiQS4xRGl6iqIKWM9SPJR6SU6KJAyr9kZORyfoIdB3KKNFXD6fIRR0drxnGAHDnsX3LXOhCGm+01Plxh5JxdN2Jkpu+BXHJ8smC333LoespiyfHxiqP1Cct1yXc++B6iOoBxCDwpRS7f3TGbzVmsNG3fg4gcnxR03cjdbUvfe3z0FLphPrvh8cMHlIWgKiVNvaAdb6nnMLod05i3QxeetgVRzGhUxcOnCnU8Z7+DHEFiGPaCGDKHuwPBSWzvGPYC5DVguL3cMW8qxiGwOplx8eiE+bIg5ZGmMtSLGbdX73hzd8fpWWa+NO85ngrnItvdLQiNd3vWqxXzeUnpZ9z2Oz785Cf44o6zouSDJ4qua1kta2QWDH3Lvr1EFhX7w4YYLbOyJCXFs6fm/QqLwt5DCP5cbhwYhCZHRxaS69sd58cL3KFHzGo+uHjKmDw7v6cdO1IWdK1jPVvg+hGVBZlI2+0hJWTKaK0gCeZlTQ4BskSj+N/8/j/hkydP0XWJw+PHntE52t6y63pGOxKB0Q787E/+LX235+T8EZWukTFgh57RWcYc6VOgj54xWXrf46JDlyVZa6Qx6JAmDGbKqAJiSlRKYbQmq8w4eoSUCCbnfYoRRMR5O7GSy4LjixP+4H/13/Dhy0/5j//xj3n95SuqVcF3f+dTrE8UZQUhUWhFQJBCYH56QrPWrBuHCB3XNy2/9cnHzI+P+Ord55x/sEAYDSSa2YLrzYGQAk1dYHTi9d3POJ0/58PTH/Lm5ufoJkGUjN2IUFB+u1XIvxMyhwapDEIJqqpC65Lu8gb95EBVCm42Dhk9IUdqPePJs+8wX56xbVuuX73GaMGqkYRgkUny8PxjxjGS0kBVS4YhsKpK6lnD11c37Nstqmi467asZysO7YZZ/RgfR+52r5E58unDJWfrNb+++pqcHbNZSVEo7vZTYIN1Hctmzei2DOMWjcJ58NHjQ8eV8JhGMe5HNr3FeUkMER+mbtTF0Wd0zlMsZtTzOSZr7GjxyXK6qOjsDVIMPDp6SGehdwNGCkbXIlTN7jC8J9Ttebg6oTE1bw93eDzeZ6KoISv6YeT8RDNbFNxe3yJUhU0djHuKskGLyJGvya2njYKiqihLSxIJhMD7NAVIiPz+Rqymg8q30LcuvDKC1g0pWIzR6FJRCAOoqVqFxxyvH3C1uyTGA9kHNIJ3l9cYo1GqQgjHi5eXeBdB1JAvKb9SNHXDan3ESmiG7kA2080iekFRaBCRslhhfceu7YnJcbw64uKTC7788obN4cDHzy84O1rz4uuXNA00VYEpj5BCUpdnNGVCqT2zpsKYOe+uRu4ud8zrksMm0PUW10mCF8zninkz4+sXNwgSy9UM20F/GMgIqlni9GzNcrEgJcn8BIo6sttfE3Oi70EbWJ0YbMhcXXUcOpgvNNeXPcgDSmTKWnGIgt/8as9HH5xzsqj48s0LivrfMV+dkvKMWX3K68tXLFbPmZUrlrOazI7lesWD8xX7doOWBe0howqJKQzRZU6P79OJvpEyiuvbO06OF0R/h3ceBnj25CP2o6MbHEVjGOIOMkSbUQii9WSf8TGQ87T7Gt4jIVWO2BCIokQCKSR++9nH/M73v0/V1Fg7dUUSExGr6zrGYSTmDEqDVNze3rA//Fsunj3jg2ffpVQF1g2kGBi8Y7DT7XeIls1hS1MJTpticjtniVYlsizwzlGUJaN3VEIybxpuuzu01lOcpJ86TkZpjJFYEbEuQLBUCqqy5MGTC/67o2P+9D/9lOubr7l4+oDbtkOSycITU0QbgyxKhnFEqTm1KfjsmWB3O3J9uGLrR8ZuZP8ucvrwBF2Biz2zZkY/HBiFp5wvscHzrn3Ng+VDPnn69/jy5ef04cBytSDEwODvK+83UllN2HtZImWJdZa7l7c8+afnuH5Lf7jjex//Qyya4CT17Ijb3RXtcIsQknbfEbqaxw8fM/rEzc2emA8crRb0Q+J6+5rHZ8/ZHq6oDGxjyziMGKXph1uUksgC2s0lVaXo24G3V5fY8Ut81sgicXOzxadEpeeczlawPON4Oed6e4nIC9azY9o+Mrg9Zam5GXt0FvSHCQWZ0LjskUpTZMGTo1N+8+oFLVvifk+0CiGmTY9l45nN4GR2ineGzWFPiNNqYAwRbSSHYYeI8MmjZ5yun/D6bsfoO2zoMMrQjgfIhuAj5DVdO61BFYVCCAUEJIHSlJT79J7x7Cibkqo2DGKKM8x9xChNNprEtEUQwrervP8VrOYW5zMheHRpCL5HRIFzLbqsoKypZ4YP5gvOTyPJZ0pZ8OGjj8lCIkSiHzou396wnM1oFnNSSFS6pqoLTo8Lhv2Wy92SV7eXBJ+pZxqlAwi4ubmjKAzzxTF1rTg7fkBTVPzD3/1t/uyLV/zxn/wJq1XN6ekaZSSm0FxeXjGrlnz4+Jz1oubxw4/wNpCS5vFpZnex5frLr3n3deJyH8geohNcZ4dSkjBmlBGMvaOZG6qZoV7UnDxcoEuByhWXX2/Y7wOyjxgtidlPp/ch0swTDx9VGG3oh4mN+/Ci4m4zAIoYJDF6Hj085nj1gIunz3i1veE//dmveHh6R9k0PD4LpCT54qvPeXL2ActZQ+9awq5jOV9R6kxhSkKQ7PY3VNFAksRvGU/1d0FCgYuWw1YwV5rjswcYVdIGS9SJ7XZL7RvKuQYB49ATh5F9Ggk+oZSErEhpykFWWpKCRwlB9p6UJB8sz/jJD39MpUt2uy3RO4RUhJTp+wHr3BQxSJ5oOIAqK8ax58UXv2AYB84uPsRoQ/KW0Y50tqd1PQdveXV9h1xE1FIjkIiUcUQSEaMMQihShkMM7LdbpBRURUEWChcdmSmwuyhKfLAkIghJkoo+ZkJO1AvD7/3j36EfPsSmA2Up2dmRlCKm1MQwrZFIITHCEEPJYlnwj/7xD/nVi8/59ZdvCDYxjj1alqxNRZQZoxoW1YrBD2wPLVVVQ/Ic2rc4ecJnH36Hq9uXoAPazEjuvtX8jVIlEV4SUqIfO1LwtF+NrL5Y8E78irOTE7KG5B3WD9jNjpwDkql1G51En33AV9cHahFReiJLDeMUyadkiTZzdu0d0QUMksIUlLlgsCOFVuzbrxn7a3IMQAVFzZO1YZ1OCICfZYYQpqIlI4k9IW/phg4fFbIsUHic3bHrLAI4WhzTLAwpJcbRUylFygoTNZqCu+5AKCK6SnjnMRpqU9AFR9aSV3dbki8ozJxNf0tOI6ZsGPoBoyVj9Mxna17f3jLiWKxq5r5ms9sQ0oiWcLI+RwmFjRmpS7pxjy4Mm23Lw6M5yidmtsBIaOoZShqaeoGMinYcGVxEVQYjFSAIIaDVX3KreRw7tGqQShJ8JAiLzNMpZBwcdbVCac28FIQqcrdtmTUnLI8e0Q4jldFsuyvq+ognF8cYXaELhbeJokxUleXrsGcxP2N8+RahoZ5J9ltLtBayIiZ4/IFmOZ8Ro2V00+L0k0cXuMHw5YvPeWGvmC81hD1K16zWlpxfcn68xoeBDy9+BylH2u6Om/0NGcHD4xWtDXidiUYTAlSFZH2kmJ80zFeS+UIBibYNZDkw9BGjPc0C9ruespJUpUYCiIySmsxIUQiOj2tufvUWrQ31DJarCu89q9Wc5fyIcZu523VU2zs+fP6MP/3Tl3zx60tWRzMKtaIuG9o4oFXAmAIlm2m+GyIxweXNJUqtkDjWsznDMN3g7zVp2HYcmxmlKRDNgiEE7oYWrcB1PeNgMVWNdpmcEq4bKE2D91NrLCNJIUyh97pkDA4lBZWuKFxmpgp+8oMfoYVgc3s9taO1JqOwdgoLjzEghSCkiZmMUAgpyFLTDi27X/2Mq7trHj39hFIVjENL2x/obM/1Ycdt1/HwbMa7/YF6rqh1NQV1R4dSBh/yRM8xmhgTRpsJmYdAIUEqcgI7JkgKrUpcDEil8H7KCJ4VFVYGChFRVjBKz1AEQgZiIpFIIpJSojQludDcDiNHRzO++93v0chjfv2bl4z9wO2rHqEHytOK6EfWiyUpR3o3ErxHFQIfPFnseXV7x8niIVe3bxnj9h4Z+f+jWAuknwJRtJLEIBit5fB5z/LHJRtXYXTg0HZIDdllfBgZXEvf9zx58B0ct/i8ReZj1qLCFImr/TVlseD85EMG22OkBK3IwrA93OHjQNvvKaQEAcerC47Wj9juNvzq1WvOvvMRySX2fkBoRe96lNYk11OVmbu9ByGIWTCS2bYHjhYPkIdrtBYYU3Ozu6IqJIKAcw6RFY+OnnCzueK6vUM1mjIntCwYbc+y0WgN1grsUJJjold7CqMRoWHvHbw35B41K5QpyMoSQ6LrdjifKVTDBxcf0XYHCl0hlEQkGMcBF3p2XUcKGSEVhe9Z5CVFnSnK4n3ogiDYDG463MTgSFERY8QUiqL6djvo37rw1tWS0ffU9RqyYndocTGQEgjpEMKg1ZJxDNOtD7DRYqRDqkCWgboqcH5k8AIlS9wYSXj6ccPl7dXUzisU5yfH2Nxy884SfMaP02D7+HTG0Fv2W1jPDKvTBSEaRhv40fd+jx98+g/4z7/691zdvsJ7i9A9wbds7wq+evmO1apg31nqUlI1YNNA9pIff/cBJ88lew851lOyS60JXjCEjqoSkCxNvWR/uGVoB1IEqsRiVWP0kn7YkcX0IRNSopUkp0wMjvlsTl3VbO5GXJBUtaTvMrdXdxyvRh6cPCQKwa9+80t+/Fvf55/9sx/x1cuvePX1JTd3X3O0PmIYRj7/6gtO1huMaRj7hHcdZanQWlOXM1LsGcZxemjKv8Bb/rdUZ80KoUvGlNkNHapIWDfgc8b1HiU1MXuszSSf3sdXdsQYKc20jpBDRCZBjJ6UIzFn/BjJVvEPf+v7zJWk3d5CyhipySGSxJQtG1MkxKmAZb7JDE3kPLWAs1A4b3n94nMO3YGHTz5GBMthbNmOPW83OzprWaye0NlIFBYxX2CMmZ7z4ClNic0JkRNaZGLw+JCRKpJiRmSFdREpE4gMQqCyJMc4JS4BffCMbmr51bMT5jHRuxGVIykURBvQErJMEyC+0LgQ2e4zx8fHnH+aKY4qbn7+FuEz69iQvGHUI9H3zOqajEKKABl8CiQcoxvR+pbl4pS0U+wO98jIb+TbQB4TNSVZGXRR0eTI5jdbFt8/49oFdhtLWVQ413K9e0tVz1kup1UXbw9k6VA5sNm95S4mPnr4IT98/jGtl9y2d/jU05QlQgju7i5Rx6egFctmwayeMziLEIZ3737D9u6GLAP98AgZBYUyDGmLNJ4ErNcNm80BnwLkxKxucLkjqcCb2x2n8xPmdU3XHjBe0Y0OZ6eM8crUfOf59/jq1RuK2Zq6rJhVsD1skCT6fk+UmeA10SbmZUVIEests9kM17tpzzbA46cPuNzu6UI3YVOTIGeHqSruDu8Yx8CsjrTjhqFvyWLawd13W2ZNw83max7lcxqhESoxOgceQozYPpBipjACIaZYS4REKkGIf8npREbVXO9e46JmNTtBm5Kr7ZZls8Bo0GrEB4P3itGN9MNIxpG7TF2VCGpEzggGtoe3RO8wWjLYPTm3FLrGNIIsbljNzvji1R3RAUhyFkgtqBpFyIG7zUjddGSuKIslOWbU0iO15Ee/9ffw4+9yvFzx8s2v+aM//g/85tUbQhxRJiP1l5w/LDl7kFmvlizMA1q352a/px0yi1lCa03MgqIuaDfTKkVpJF3XooREaEU5kzSlIISW1eIEqTKb3Y6iipgiIpg+coe2R64Mn3z6iF/+8pLCwHwOZSkhBublGl3Oaaodojf85sXnnJ0+5Mmjpwg0N3fXNL6CLAlA71pUSKQoMMrQtT1VMcfTo0TJaCOj26L0Pav5GzXLFTf7O4q6YrhtqUWD1jDsHfvtSGlKYvLkmBgPlmgjKU6nWcjkEBFIQgSZwwRIz5k0Rs6bGefHx7ihn1rPSZCEQIkpvSSRCGSyEMToyEIQ/BT4nnMGMlIrRFTYJHj95gVDcJyePGA/9ly3B95stqScWJ89wKdILRLBdxw3FamaMzpPO7RoYdByKrAhJgQC9365X0o9/R/erwFlkUkpU0iNj56cEmOMRKA0moxgtjzmyDqwPcFlrl2LUxKhMj5FAh5hYHCJcZCsjs8QWrEwC9zrA0/PH3Fg5IvuK1osTS3RRhMjxDQVX6MUKSgG26EqwWq1RHb3p8Y/l1UIrQg50TnLrGgwqsR1FvXzU04+23G9uWK+XHB9+YJytqYsZogoILWE5PDJMgwj3g/UVcP8aIUPM1QeKONI9h4vFKO16OCmg6hIDPaAlAIXPYf2a9KYqGenNLJmeOVo5mBWC3wGVWqkcOy6W3qbUBRoNcVPds6jspg8OrbizU1LU2iqasVSBlKxwOeeJ2fnVKbhl9cviEpivaUbFVIojJYoXeJshx1blFJ4AT54hIoMwaFVRgvNxdGSqlwzWEu2BwZvSUlSasXgO0Y7kKMgxp5haMlZEKJHZZhVFWVR0mTHyQ5yioAAMkFM773Smpw1VVUiZSZlQcwKY8oJI/kt9K0L7267IWXB1fVbBBOk/+HpQ2ZNTeaAswPOJfpRkLKEnAnOsagN/XiJcDNydggxst1vuLx6y9nJY5azE5wzDNaRZOL8/AIRWopXC/buwOim03w1K6c52IM14zjinKAbR253Pc8vfpthfI0sCnzSpNwghOPDp0c8vPjnbHYHbq62bDcdb9+94+72zcTBDY6ySOxaz2gTi1kzIdBGz6wwLJdz9gdFFhFVRBSSo6OKcRgRKqIV5CTZtzvKRvFkfsrl1SVu9JRlQYxxynNF0iwyH330iF/84iusdZyeNTx/dkKyDT5mXBJ03UAKkcLckrNmtViy27VcXd9yvFyTQ2Kz9yyqiqvrS9arGUZJcq3JFWz3A4tF854UVvzFXvS/hRpzQheCrmvR712XrndsLreM3UAqA0pML22tGtq8ZxgOaCmIMZFCRr7f2xMpTzjHkFhh+MkPfgApEJNEaI1Skowg5ESMGR8jNgZyzKSciTGQhSSlRAyTOzKl6dYrtKYfLfbq6yktSQquDh37rmV5VPPo/CFOduQMSaT3HaI5vhso5zNSFBASzgZQgrKYlvtzEu9X7CIySaROFFIRbKBRCpEjQUhyElhnObSewhikEixXJ6RbRXJ7fKXZuDBxmWOcDtJKkHVis9+xmj9iNQts2FPJFb96+Ybbd3csjgzLixVdOFAVgtELAhmhFIFEaSo0fsrbJjIrZ3/dj8zfGCkhKYuKwQ6E4Km0xmiJlobuiy1Pzs4wF1u23Q2LWT3NObueYRgY3UAzzwx9iy4UDy8+YLV4xCHB5nCLbT0XR8+ozJ6b9h2H9g4h38dIhj1SB4SZeNGmrEAbhsNIExJ3XnJ6/hDqBdrM2I+37NuRrrOkkHB+5OT4CDuOWD8SPCzrhpxHtHH0vqMPapr3R0HTrPjg+IIvfvMzbHCQJKqM5CDQwiCE5NBPh9vVbEkS4NxAzBPfwfkD7RA4mZ/xw08+4/Zgp3Q82yGEoB9HrFJkNaULDZ1llBJnB0pdEsKILGoQmTgMPIpHnNiGJBNSTM/p5IsQSClZLldo8z6mk4wRmsT/N6v3v6RvXXiHr0BenJDzFde3b1gvTpg3M8gHSlMgk8Kr6UTrfKIwBTmOSD293FMLVPDw5DHHS01nA6++fk1C4pNlHDpOecz5xTmrozuMVvybP/43FLHk4aNTrB+mB6o7sFjOGYaRuqpZr84oC4spSjaHGwYfKU3D9V4wr1YURWS1hMXiOSlEvmPP2Wy/R8KD32OGgWufGIeIKSxHJ3NCkLTtgd7MKU2FCxsO+5GmWFCWkvPViuvdhlRn5osGYTI5RYJ1zOoZ1jnurkeaBkwhuL1pSbFgdfSQZ8/OeP3qLfvrwPrRnC5nuu6K49MVy+VjxtYR4oC1LafHH/D86XNeff0lw2AxpkKpgpQDQgR6a2mqht4G2nHH7tDR2QOLpkQX94X3G13fbajqjI0BFzIxWJJL2H5kmjAlkov40eKjRghIOZGSIIUEKHywCKWJIeF9xLjMP/7xDzmua2RK7zGIiRwTMQdcSEQhiTERmVpU5IiPCUQgxcktmREg1dT6lQqUYec6/O4SWTXc9R2dd3ywfMST8wte715hQyZ7xdtdxzJP7ceUp/mxBGZNRTt0HPqANgqtFSkGlFJIkVFKoZEYpfE5ToEjQhOyoyo1Ik6t5JQUZTUjnUkOySKSo8iJ6DN12SCVmlapBDgs767fcXq6BqkIq0TzeMX+0PJo+YBGN7w9BGIcMKbAleBTQqqE0QXWRlLOmDqSZP/X+8D8DVIkUmpNjhojCmbNjFlZESOQFJs/2vLRf3dOd9YRbkYux4hPkbP1MX1KKKU5WT8kyETSJX2Euc60bkfbH5Amc3t9xYcPHvDo+TGjHeido+tbjo/X3HVbQsxEG5EKygLGPGKz4lH0KN+x6x2dbWnbgZwyx6sTVBT0ztIPllKXSJPQBqzvqUpBFwcKMyOnlsF6BitIT57x5u6KqqpQQk504pQIwVKokpQ89Ww27bkHj0ITYmI/tkg8Qio+ePiIn71+QUiZWdVQ6iXDcCDqjCRx6DpEhuyn7YLz5RFdb+l9RgmLdSMfmAt+pJ9TlSUxBXKCnDKJjJRMF2CpEKLAuUDGo3TCeodzf8ms5sO/PbD43RM++8GP2LpblMpYvyVkASwIIdK7iFIVPozU9cSgFSJSGUFZTDfU680VlVxNv+Cx4/o3XzOfVzx99F2WsxWCiEs9skj88Ls/4N3dO5zv8GGgrATOj/RjgZKS242lqhNdf4OPMwrT8Pj0Q3bdO+4Od6SUaHyJqRoGu0WSqerI08UHDNbRqMDVb/4Mj6coDV3ryamlLCsW9YLkIWdPinGaoYZM3w3MVhVH61NcbEEmvOvJbtqTrOeastHMw5qrt1vKRpAI9PaG6rDn4dkZdbNGdEu0KBiC5XZzwHlLU2vwgqJY8+LLDd0hsD46QauGthtBTOtVGcmhtayLGqRhdIm6MXjv2O23qItjinxP//lGwzjSzBYIPD4EdFaEbiC6jFEG66cc3ugmjOToLUJIoveQxFR0JeA9wQfiGPjB42c8Pz9HpohIE+oxhQxaT61ooUAqxmDJypAKRRp6QppCGGIMU5FOGaEVCfHevARWJoIfyCGw71qyzBydrokRRKrREpz1JJ1RXU9TzxBTKjg2J2RK1PUC13fE6CnMdNs2ChQJrWviGFBZ4AElNbU0eJUJKmNKQ/QRn8TkaK5L1GLB5u0lVWHQNqGkAAGCjDGS5DM2jrStpaoaUuWxJ47nP35OMygqUbA2R9ztBpq1IcuCIQ6oQhNIZFXRW09JZH7Pav5z2eOezUEyNwsgMjqHkYZ+HElIjCt4/f+64YP/9oQwy7wdB7a3Bx6sTiEnrBvZdS0nJ6c0lSTTER3YoaWqJV13A3lgfXLEzeEKyRyjIs8fLhE54uXA4Bw5AyEQgyeJhFYFx6slshbYYY4pSrRu8N6yG0YO2z3H8xnzaoENFv8eBKOlQMnMvJFIGfE+UuhEIStmZs1m1yMXBVlleuuQOVMWGm8lMUUSHS55atUQQqbvLbPGkGLi+x99j7vDnt55ogy4YWTsMkpKjgQaewAAJbtJREFUmqphVS1Yz495e/kWKaDfD0gnKMqCYzVnmTVP8pLf1Z+ySAtEKVBBMTr75wfjmCLIzDD2DGKcbrg5k7PA+0Dyf8mF91QtEH/mCdrx4Adn7NOGm/2B+Ww9kUVyQIoJKhBDYrO9pjaK5WKFyAEpNPvtG24Ojnltsd6xXqx5cv4hR6uGqjJsD7/hZnfAOkdRrjmqG3729hdY79ElLFcGqSp2m46qUCyXC7puw6qZcbd1LBqN1I71UjN4SNmytz3Z3ZFTQ2WW+Lghpg0hJkYh8SRCChPgPiq2d5HgBrQYaOpANpGudyyXk/lgaCPB7hAmY5TADhP2TsmS3W7PbttRL0pSciAUX/6yZ75SVLOMHzKlvOV4rnlwfsTgBdswMnaJsT1QKFgtFuzChvbgqOuKLDccWsvXr68pajg9XXN0/JjRKjZ3W4hQlzMEiuXihNvbkZvbjqpyf7E3/W+hRIzYvSOFSE5hajN1PWK6zJJGTwgJLSTWjVg7AgKVBSkmck6QBNln4hhYmoIff/wJMnpEnqLBQBCTIElD1pKU39+Wi8mjkMgkbd5DBCJCgI+BEAIp5GlmLDJZRJKQU4QfmW3XQgEnD08RRhGTp2pmmCwRykz7uPTM5nNSjlSlQiRN9JFlVTL4iBKZJBJKZsjT825ToC4KIgnvIlVZkVE4N5IkpDi1pwsNKUZWq2Oa2wVJRKJ02GjRopoOCzkjzfTuD66nljVBSbTWpAZoCk7nFzxSH/Hqqy/o4o4kBnJK+JzwcaQyDRJN3w0gv93H6++CPvnn3+XXf/gL7t46VvWCzo2EkDGmnOiaMXLYtLz8f2ae/9NnZP1LlE40TYWMDpvzlKCjBfhAzD3ORZbzhtEdCM7x4dMPGIJjP2yxPlMWDdf7hPaC87PnzOpATJnSGPCe9XqFNjXKCOz4huArDn2HDQ4fIiGMzBcVezdA6zhaNcToUCoSQ0IiMKXCWkvfB0BwdtyQ3cBqViDKiiF60AqjJLNZRfRTqUoqgB1AKJw9kENEZc0/+v7v8XbXctvtmQZD730VeSQGRe4j29sNqjSklJFBsizX7HZbVsbzT06+x6f6nOPZEk2BEHFCXKLRaXLyQyKlgMjTnnl+f7nJecK15pgwxV8yMjIERQFc/tu3cNVw9PcK3DhwIJMb0FoAHud6lvNTYGQc9+wPW47mZxTacHH2CULdcbO9oylOuTh7DDlTVzC6N1i7mUgo5RInFYe2ZT1f89XXb6iE5nDwiDagVIH3kXHckVJJbTKLxWO6IbDr7jiaa46XR1zdXbEfB3IGYxyH4YALLeRATA4ZFhzlU8gFy3nF9dUWKSTdzhNcYpwLskxYnxnbHi0lulD0xYhOsGsDUSiMGSl1TSbRjwHrBaaEiydrcrR8+esdykSE8Gwu4cFJjX6249HTZ5QnC1bzE169esvVu2te3G7RhWHYJ9ptz9njOc28JAO7mxHJAVPuALi73SFVZt/u6FwJsaQdOoSqmM/m3/ZX+7deWk3JQH034nxg7IYJsSgkznskUChNignv/YSAE4KYpvUi8rSCMw6eOmv+wcefsaoqcrBkqUhSo9VkSDocDlDUHD18QBhG4tDjR0cmE4RAqCmtygbLGCMueFyabtrSKGxKhCzICHo7MjhLVReUTYW3jpAdIRUIDBooSg0iE/w4oSEjuJjJMVNqTVPOCL4nJzh0AVKiKCMxZWxOxBBRUjK6cZpipUxmCvQO0VJpidYKHyOr1Zqv371DG00eJ2OYEBmVM1FAFDCGQBUzKUUqXWHlwD6PvN694UitWB+dclycU27e8Ka9YecOCC1IMpByRhc1Q7gvvN/osx//LqeLC778f/8p9iqDUpAFVVERvCcDlVEUSXD9Hy75+HtPWTw8MISRQidCEWhUjTGTcz5Ei8yR0R9oipoHqweYYk7ftygylRJIwKaOrhs5ujjhantNXc25vHnL8fIpYQz02xe4w57nxwtqU7DUikMMGF2gRCa4gIFp11wKfFA0szleeorC0DQlQnmUlgTveP7gEa7vKcqCvY8EFFlM+8shSoYwILNk7Bw5BupCURcNObU8v/iAdkjctj3BgZCRmCJZGZQ0pBgxStLMZtiYEKZg9AMueJqy4OPimE/TMXOvYQzkYso+niiAYsrfTWnqMEtJJgPTwRLApwgpYYxBfEt+wrcuvFe3N9y1HW/v7tj9+8gPL7/DD//Vd3nbfsHL25fM5guapiLHDN01ZWlYLBbknBmGODm/YqauKj64eM6iOsfZlsHe0XcdWQZmzQpEoikX6HLJ2fmC6Fo2hx2qmtq8KUh8GHjweI0uEoPz3O46fG4RqeSdCZAb6qrg4vyUph252+wIydEODqNmyAyregbeIFtP8oI3lxvaQ8BbO4UuDBE79JhSEFyiz4HZuubsWNEsBH4MHJ2W3G084yDJZaYoNM1sDjIiMmihOHlYcHT+DG817968wzjB+eqCy31L3HzNp88/5vzBB3z00TmX7za8fHnHZnNHPoX9XUe79cyaGd///ie8evmW/X7Hu7eXKKUZRsvd7ZZMxsc5i/kCISzX13vGof2vfsn/tioSiCITJfjR0x8GbGvJUaJERkjBOLTknMlxQkJCxoWJcywAOwZEFDw6O+Hjx48Rcdp9VbogoRBq2vOb6ZKI4urtFcenR5AFQimSd4QMup6BGyjIOOUYe8eYPDFHZJ74zCFHhJbc7feTsc9IhBbEMMErhtGiFSAEQhRTupGUCCEIIREzmLIEIViYijbDOFpSBKUMIYDRBcSMQk1rgSGitZz+j0KiJKj3vO8pZVCxWh3x+s3le3rXBBtRShG8RwqQhSL0Cescs0VD3/eURoJy7FKLCPCgOYUouJg/Ym2OeL1/w+V4DVpjdIIsQN77E77R5R+/oXu7I46G3e6GWCaOZit88EghqMuSs6M1s2oyqPrPRz7MF+yfd7xTe/SsxAXYtlcTiEJMvpTlck1KBa0NqLjFiEBdSUYXGfsWneHD73xK6zuaKlHKjmpV0dSSy6tfM44HgoV3ouYn3zvjXC25OXguN1/j4zVaCHzMaCGn9bsUcUOPf5/udWj3CCTOJ2wfWasVV9eXbK0lmgBJI4UmS0Vre2Zlw9ANpOwJweKHCb362QcX1HXJL999gY2ZJAyF1MybGd5PgJmQB9phR5EbjCjZdxs0hhAjjY98rzrHWIFqJEoplBQIqd4zmwPkjACEVAgg+JGUAzDdbrWeDt1Cyan+fQt968L70xeveLc50AULQvLv//VPWZ3+Pt/9g5+w96/5zZuvsG7g+ZOPKbQhxYwd40QlKQN+PFAZg1EzMhpBoi4FUhqkPsFaj5KGRVOzmBUchg7JwKOnp2x3T/izX35OCIkcJWXZEHwAAjJnDtmz2XwNsaCpn3K731Faz9F8xaI2LKqHOCp8CJCOIEPTVHT7Gzb9DctZw2LhCd4jAF1COYcYArN5AUlhx8BsbRDC4QdBBgZnMeW0fnTztkcXIHVgfVzQ947gJTEG6lnk5MGM1XHNOp/T95pXr9/x4s2G2yvHb/3wRzw4OWI9X3Byuub2bs1oE0ro93CDgrOzcz7+8DMOhx0xBWbNjHdvL7m+vcT6AWUUy0XJk9/9AbudpR/vYwG/0a4dqKqCEDzeWvCJFBKF0Tg3sKhq9kM3kXfet49CmG6HMQtCAGsjD5oZv//972OQZAlSF+iiBqmxPlGWJTkkQFBrybvXb1ifnNPMDf27d3TDgK5hUTaoJKiqiLEDvQvYkEne40Qiiox3lu2+hSJDzoze4lOilBU+Z1IM9L2jKGuMMmQx3eyLQk4GLjIxBYYxYkdHJlKUFUkIsphuxUpkpFRo1GSUEhkpIOaECNMoxfsJGJ+UQGnF0fGad+/esGhqYk6kDAJJZtoPrkrNMI4s4gxdlfSDZV1ViDKyaUf6N+94cHRErSvEGHlWP+Bhc8LL4Ypbu8GUmVLfF95v9O/+z3+IcpIsJmpaG0dmzYqkBCJnkoDBecqqnla0RKZ/sUVsBEff0dxUHZmEtD3CJh48/hiXA4MfSN7R6BKXekQpMFEQs8dJx2JxRhCWwnhU1NTFDBct281vaLdbdNXwyfMfYeo1n7+65ajZcbsf8MoihSUGSwkIpcjOUSiww0BTVyTrGf04IVhDxihNU2g2/ZZmXbOYpyniz0dctqQkaHd35AwhTJealCPLasZ6ccyLy6+pmkS2nv3OEaUhRocxBVpBsAGjCqLLhOjQqqDUBTJFflw/5qE6QqbJOyN1idQTUSumb5zMGSHl+3bzdBPOafpOCKWRQhJjJDg77ch/C33rwvvV1d0U5ff+JpBix//0f/tfOP/ggqPvPOD544Lb/RsG55HMQER80PggySJQlxrrHUbXpAhSOlIeSQJyliwWx8yaOUaWDMOWnCvqoiEdDTy8cHz1m7fcDgckgiwSQkCOgv3ecTcG6kWJyy0//bnl2fOGWTPtYs6ahhACNsj3/3ZPN4xsO4FzdppLq57FiUc3Bd4ZiipjbSDngqI0+FEzTwWbzSVFIZDJIAtBSB5kpjRTApMdE4nMbG5Yrio2dx19lxheDKzWe86OKsyq5/HHT/HVE3795WvevL0h81N++7vf5Xi1YNFUVOUJISeCKxh6S0weqSKCzOpohZKZRTPj42cfcbe/YhhvaO1AzoFFtWK91ujyfsb7jaqyxtmBlDO4iEGRlME5S45grUUhCMFDSgSf8DlDnhjM4xhpsuKf//jHnL/v4oSckbogCQ26oGiqKawgR3RdoY3GxszVzRUnjx5RzpfUObHvWsZBcbxeUGlJ4QbC2DJ4T1aSrCeX82F/IESPjJLJq2yQcmr5Fs2CzHQTJyVS9Ngxk8sGEacPQ8qRWgkKbRBGM+y3+CqhlEEqhY1++mggQElUYbB2xCjJYMfJpR3Bp4zUeqImRc9queTLly/JVUZrg/Dp/Ux6WtNIEoQ2HNqOo+MVY/AcbGY2q/CqI+HQ2lDNmmk70jsKDx/NLmgGw228I3/rr9Lffl1f37GuVggpMKqY1mq6A1pKSmUI3hG7qeORY6IoNCrD+K6FO8npd5eEh5k7ZTAnK6zPaF1gfE9ndwg9R6vJIIQPrGdzmkaRomKxKJEhkvKcu0PL3u7JIXF0eg7FEUnO+eXnf0p2ns8uTnl4VHO5d9M+6xwQk2mw9wdcP7nqQ4iYAkTOyCSQKdJoMMLQuYGhndbf+iFMF7iQiEGQs2T0DpkCECml4nsffMKb6w2DyphRIVOmMXriLFQGLQzt4QBSoZJ4z3POZAHBdjxixU9OPmXhCrp2pO8HUIqiyWhjUFoRkyQlP3kwvklAEBKIpAzRe2KaRis5Td6Nb6Nv/YiXZYMhcxhbYvbEqLh+e8e//j/9If/r/8NPUKcnjO4dYb9Driq8TVirEEoRXUdTzJjVawbr2fdXuDzHyIqqWFNWFW27Zxi3KDnDqDkx70hpoCwWfPLpEcPB8Ud//Mfo2pBwdLsebQrKZoEdRzbXlvWjGabRlEVDP2x5MVxSz2bTD1GUlFqQk2U/bBl9R6EWzEONqQTzMmMKT4jvdzdzIAaFG6dQCCFAZonrJEO2qNJNQA4BovSszxvevR6n+XGIFCV8/NkJfQtffbEj2UytFmwGR7V/y/d/8Bl1s+Bnf/o5bd/z6t1XFMWHGF3jfCTjmFU1XTsS8p5hbNHFmmmc4PFe4qPBu56T9RnlcEtICe8yno4w3s/JvpF1B/xokVmQ3TTrnc0asg30fiQGSXBTqH0MiZwEOQpShuAihMiPPvuUZ+dnxBCRKKSQhDw1Y5ObDjkJhUhQiAm9tz59QLy54urulvX6iCI1pO0doii52R0oRH5vkEnYFCBP86SMYref9nUREi0VjSmIMeDDgIoluqhRQk1AFymmcHDnye/ffFPoaa4cPSFnAuCGjmXTEKIgJUFRG0LwJDJhDEgpiWFaBRQCQpigNEWh0SmBVMQCVAKFfG+m8SQlyUgEAh8DMYONmZwyWoIX0Fkoihlm5VgsGypTIROM0VOXFVlILhqDbDXvDtd/bc/K3zTFhwWHmx4dNLNGURiND5YhDAzeYQpDEBVut0HExHw2n7pkZPxhRP1JyXl7xsmnx/x6fAeqJhJJSZJsxqqI0hFnHZWscIPEIUA63r19Q6ElJhu8jFRlg5nXdDYz+j23w8/A7yCVtKHgARVPjxJD8Rgrd7y9uWbXjRSyRpGx0eFReCdYzI8ZeosykocnR4zjAZtG0DUuCbTWFKpiVtdsDntGP1Iw3YKPZ0s+fPQM66dkurEfSZXGp6lrWYkZw9AiM3StnX5mMZKkxtQF49izFhU/WTxnnWqElpQV9H3P0PfEnKjns2ntTiuc9yQCzjvI04w3xkzCklKefCJCoZWaavK30LcuvALJ4Eas8xS6mBaJReLlF6/5T/+PF/yr//1/SzpR/Or1HxKDZ96cMdiekByr1YIsDIdxRKqafevohh1n6xmYGSEU7LqWw2HP6ZGins8QsmK+XIDQ3O1alscrPri44OX1a3KOeDedMLw/EIOkaeZECyophv7AyekpZX3C1+9eMfgNUmhWiwVKFLT9iDTTfC6OEZ+G96zbCPgp9UUGYo5EL8hpAGEpG4Efe2YLhakMqECOamppzAqefFjx1a9uuXrj0IUik1gs53zynTVVmlOYgnf7Lb/+zTUx1jx8+ISiqPj5L37Bm8srFvM5q8UaHwNKJnLVs5wLrrctWUqcTWTAKE2IDV3XEkPgZrNBaybHKR6lFH13f+P9RkYZvBto7w7IqDCqoB96YvIo8Z5MJZjMKpmJMBUyMYBMcLFc8w++912EC2SR4b1pY7COuiyJMVFmGIYe1TSkoSfGTPCeZjGDceTu6h0+BYq6xnpHEgnrPG0/0Fk/3SxVRmY5uT3HEaRASIGWkspoSJmyUIAjRoFQxVSs0ZDFBGhXU5vc2pEop2QbhaKu5mzvbnEhM29maKUYxxEhpzaxEBBTRGmFUgqR1UTheu+8NlLgXAQyhMlNa4QB6RHv798pZ4QCJQSByObQcnS0ZiN6rLVUZkbScDd2PJuvSBaOlnPubjeMIfP26obb2yuqo3vq2jeaffSQdnyF3guUkmip0WpCIKpCk8ppDqqEIgMhBXyQmKpApYTKgu7rPWdxzY8/ecbPx1fsgqOOgqVcYsdEz/S8FUfLydGeHDGM5BRJ0Uz5yCKCMfjsWcxKctuSVGR2umLRXDCOkX/zx3/CP/3t7yHjnta1mCyolELnAmESLiWUNgTnGPvuzzuXq3KOVjPkfA6FQaqC5WxJd2jxKdONBwoguIHKGJ5+8IztELls75BZcrw4I2XLYDNgkdnh7bTnO68rVCkZe4tSBUIYTNZ8qo75rL4go8g5oY1GlwUxBJy1JCY+s1JgjMaUmpQi7WGcrFWCP2ehS6UBQZKCiSX8X9a3Lrx3uztiigglKYREKTENmqPnj/7nP+b44Tm//T/8mAerLS8vf0ZXvqSpDaYocb7kq6+/fg/BSBwdnfDRg2fURUkMiV27RzHn/HRJWWT248B2f4OSILUjU7A4OmJ1fEL8+kuGcQJq93tHVRSs1ieYGWw3t9xdDxR6idRb6hg4Xp+AeMBvXr6ibfdUumIxmzPXSx4fP6DzN9wMTDDvQRG9mD5u77F2Uku0EnTtiNKS2UJRmOkHH3JGmsw4RFK/o6hK5ivFuxeWd18mxoeJ66sbjuYzqmpFLg3Pnx7z0z/r+c8//RXb3ZZmtmSxbNi3Gz7/zVc8PHvA0dGczLQraZSnKgWD7anKGSEyJWzMYFafIkXAurcs6lN2/UhpakpdIcJ94f1G2Xtw0N6MFGU5rdO4AUiIOJGoRjvNkryL09pRFBBA58xPvv99KiHICZTSCDXNg7SGwTkKXRIyKFMglGEYekLKhJip5jMykkKX7LY9R8cnpL7FOYsNnm3fs+kGdKkREmLKtF1HigEpFUJMnS1tNHVZ0XmFt46sQJYSqSTxfSiDEIKUMlpOt12ERAmJlpFZ0RBzovWODCRvQRZEmYg5UhSGBJAiOUekSGgt0bJisC0hw6yaT5nSxrAPIyuxJguBFFP7LpLQUpJzQmjY9y3r1ZyUAlqVCEAVNdd9R/z6a+ToaTe3+GFg1jQ42/Ls6QeYxb0j/xutTs8xz0fEVwfUe4e9SAUEMVGlYkQbxWqxwgiBMYowOKrCsDo5Zjlfsl6tWa0aZvWCH558j8+7X3B1eIsdAjs5uXgXqxN8lDg3gAiEwVKZEucBGZAykqUAmclyclJjKtrRMnSX3N7eoJPk7m7goyc1Scy4QRCtmHwVBJpCMPQdisw4BHRVURaGk9Upu7GDKlMYKERic3dNCoHQW0SOWBtYmxnPnj3j5e4KG/z7Z8xz2Aycr484Wi1ohx4pBWfLhq7r2NsdSiqUqpBimjd/x5zzB+vfZlEsKcsK7yzDaDGlASZ+eoqR4B05K0ypkFqhiwJdRGII06hzsj2/3wCYognVX7arOb9f0NdKTTFpZGKO5JyxduRf/1/+Ncas+Pv/8r/n+cPvkeQNMTp++eJnfPXyVyhjKFTJoj6lLA3d8I7NViOkwgWFszC4W+YzTW8jXd8jGGnqAnLFcn7C+uSUJw8f8tXr13SHNPFu0WSZWZ8ucbFFGo+znhAi3gWIgSQEp0cL5sUJy1lDzI6iLHDhBp9GXMgkzLQX2Xr6diQlR1EVaBMQwlGUsF5NSL+hi8yMQMqMVoakM9FH+r6nWWoWx4phG9FNRhrJxXzF0ekRX755yeH1Dl0IvAvc3e0RsmC+rGnmBcGNWDdQFmsO3Z5u9MyqOcFpBIYYA0pVVGWDd45g9hTG4wbP9nCYCoIIzGZzjLrH7n2jfj9MYQilIRFxfkCQiN6TQsJaCymToyK4CYCefGYcHN+5uODpgwcE56mLCl2UpKxAKExRIBHYYaBsZtR1jZWSoq6Jw0CylkObJl+EHThaLum6lmbe4NMElQ/vIRqEiNCanDOHdiAzFTP+3GGdsTGRKCiUYoweZ0eyEWg9ndSlVmipUFJTSU1nHTkHQk4QJyfy7WGHKgWl0VR6Rh86lJpizb75kGQmdKQxipgFVV3jXcDnqSukdYEj0OcRweQE11oT4rT/mxEkMUVqunHyd/SDJ6ZpJ7gsKm4Pe05kze2+pb+75Q9+/yf81ne/w4urG1619yEJ36hQivLiIYvynOXY0G63WJeotaGSBSJk4r4nyJIHTx5zvFyxmi9ZLJeUWlPXNbO6oiwLiqJEKHi2fsxNuuNnd3/Kn+1+g1eJ1g2IEFnWM4awBUCGRKU0wziQRaCspoKpsmIce2RQlEqRupYni1OOHq3Ic8GvLt/y8PwBdQ9JlahKkEZHem9KrEuNzIqYNVprzo7O+Xc//SOksJgo+PTkCb+4+TUOTS0rYt4jVeKTZx/xYrthYOLu5ySpjEIrw77d4VNFb0fKouTV5YFZVVJWDUVWxOhwbuDj8ph/sfghC7mgbBrmizXt4cAwWpSUZKPIZJSYOkeTaTARvcM6h9KaoqjwbiSlCSSZ8zRyVEq9f5f+y/qvsDFMrQ4hJjhATJ6cI6DIRIIf+L//H/+vvPvqkn/5v/1H3GpJM1vz0bMfc3z8lJwS6/ka7yJfvfsF+3bCKJaV4m67h1Rjw55wPRADFKphVpYs6iNOjs5pmhoWmkJ+Hzsmvuy+RhtNdVwxhj03Vy1KSparNabwDGNA6syQN4xOcLQ64+goMa86pCxBaba7gRglfSewaqL7TOxcQU4FIgmUFCQiTS2pKwhA106L0konrOsJQZJFoigVQ++o5xJ3EOQgeHR+hlaGsycNn33vt/hPv/iC67sNUgpytviwJyTBclmTtOHVq9fE0HF0vGSwESEbspxzfXmDMiNVNWc1P6EpSowaaZpMVV3w8s0low3UzRLfX3Nx+sF/9Uv+t1VJJEKCHKfAgODd1LGI0+k1hIRIEJwnxUQIk3HPIPnRZ59CCFPMnlbEOAEJpCkwdc0wWIqmngwbADGSxTRj3Wx6qmbO6C3L1YKubVnPZ+zaAy4ENrstzjuUUlNsYISUM+M4UcoygpQF1nr2+5bT9RprHRcPLjDeTzPcYHHeoaVAEChMRSkjRanxSRGim1Z0lEYLqLRG6oRUgT7uiAQqXb9H44lpJ5GMkQIl0vsPUEIIgfXTGlNhJEOIU+CCmtjsQmUEiuQyjakZ4khUiX5sOV4c0/oRH6bkpsEO1FWJS5JPPv6U7vSMX/zmJU8enPH44QVRfctB2d8BmZDRpiLUnt4lUAWzWnC2XCNFplAFFw8f8Pzpc9ZHpzR1TVGUmNJQl8V0wKoms5/UCiEECMEH+gGPV5/yu+6Kz3e/4ueXP+V1umF0LVIKTtfH2AhalOQMp6sTEnFChUrB4Ea0UlgX8JVg70au/B26tcS+o+xmPHvwkMvtnutDoCwK/OgpC0MIIzkEYrRktcT6kT4ekKKm7w987l6wTwPOB3COs+M1Dx9ccNO1ZAVzOaN1G0KwlGpGDh1CSOpyPl1QciAkj1YLIo7ejRA9P6of8U9WP2DpC7RUpAj77Zb2cMBbj5QZKQVSTv6GJDIINbnFpSD66VsR5WQIVkqT/EiICVOUSAnWfbtO47cuvFJKpJSTlTtFchYIMV3NU8pIkYhh5D/8T/8zfXfHP//f/VMO4kBVaWb1MWUxpykNox15cvZ9bBy43W7YtdfM6mNmxQl9d0cSmdP1ESfHZ5TGkFKkrBRVI0A0aL3kn1TnzOo/4vLwFlFkshc4H9AoXnxxxcXjGaZydG1muZpzsnxAWRis32GEInJH7wLOFZRizqxYcnt9xziOGNXgnYcUGVJA6oKiUBRSEh30bSBG2B882oAyUDUCZ8EUifm8QZxnuplGUnB++oC32yv++Kdf8Ds/+pjHjx6iSsVh3zKMe5ztcS0okaiqBecXx9hgCcmRMtztrpnVFU+fXPD28pLN9hqiZNSS+cLQ2cT50YLjoxVfvnqN3xWYRcPXb7/8i77rf+tkmMAUzlsEU3Re8AHvHMQMEVKa8mxjzIg8rQc8OlrxwekxhdRoYwAFSKQymKIgpEw5n9Fu95iyIQgoq3oCFXg4Pj2lPXSUxrDf76mbGjuORGfp+sMEzwgByFjryFIRSZObWU6zW6U1Qki891g3knOgGzvc+5xdJad5s1aZnDzee3pgjInRTitRQhpsHCiVoVBiSgJKhpAUSiu894SckLKANJF4UhYkmUkxY8w3ng6F0QofLSFZcpohkwQVCRG0zKRicp8qKd4nIiWUT6goiDIypoREU6iSUQrsZsvprGaxXHF9d4sLL3n69NFf7wPzN0gyS2pdEJcaO/YUhWZOSaEMZ6cnfPrJJ5ydXzCfr9G6oNQSXUpMoamKAq01WhvQCmEMZDFdmKRCyobTZs3p0cf89uJ7vBre8KJ7zRe3P2cXLYXQeBFQOrKz1xy6kcVsyWgdnghSooViHC3VfI5RiRAtqUi83H6NY8bFco4SmbtBIFVitFu8SEShiXKKhx1sx+A7rIAQBSH0OGfRKrBaNpyfPubrzQ1eJkKIU/tX18x0gZKKrAPeZazbvZ91C6T02DSgSCxl5juzD/m94iOq0ZBJ02rpaEnJY8duItSlPLHMtZzCP8TksUgxIYREK0MMjsNuiw+e+bwhpYj1fsrsTolxGL7V7/XbxwIKSEynJZEj6r3po6yaaQiVE5lpPvTL//xzHv3hEz77F88YugPISFmVSFng/R6lNafLR5wdfchmv2ExX7CsT4nRs2/3HK+nfUTne7b7DUpJ5qWmHaCel5AafvjD77H7j2/Zjx1j6xEpU5YaYxS7u456mVku5szqhvVCY4Pj0FsKWVEUNZBpqiWhi8xmgvlgqJsCHxSqEAgU+zuPEIFUw8F76qqgqWuqueHueoeIAukFq0WmaRRlNcVLlVpy8uGCsZ3TZcuzjy94c3XJl68vefrkEev5CusGFoszttuWG2uxPqHLwHo9I8c5292eqtIoKdn5O0Jds1ysUbon4zCVxjrIaPoxMqvXrFYHxiEgxEhy97eGbzS2Hj+Ok2mNjEoC5zzBBUTMkAUhTLMdosTbgBaCH3z4HJUjRTmbVgfy1IJCSTCS5CfcXVEU0+7q8gyhFDqnKbFnt0FpNTlG6xmjHYgpMNqRtm0ZrSUGT4wRKQUxJXpnpz1BIf48z9kHQTf2FEVF6w/c7O6YL1YYrZBBYjuPWBUoaRCAMYaYBFWhiTFN3oAQsXbkqJwhRxBZEqLHuRE1q9BC0ttEjqBNQQqRIQYKAwKHrgqin1axmrrBDsNkSmFqU4fkpgOAUXTRooKgqeoptk2WVMqSVCRmCaVmHEekajCLmpQktSmYz+Zcb24Yc/jrfWD+BskUBUqXNLM5LiuG/R0yFTx7+owf/tbvsF4fU9cNRVFMHPccqCozhcMXJVJI0OI98UqCkGTvEEmAnv4sUsJYzXeWv8V3L/4x//SjLV/c/Jwvrn7KO3tgKCVeOtbHNWOKlMWcmN3EIyehlGU+U/hxnOBDWaC04arfcH37ju8/ecYZ8NaOyLJm21liBK0m+IvtOwolccmRg2C7azEiM5tXPH3yKV9cviRoicwJYTzBZVQ6QhcNSo2E3BLSBBSRusBEWCwqwtjyzJzwe8XHnKVjShoinhgSMQzE4DGlIckpXnBCv2YW1RypJNZ5YszEBMREfE90izFydXON9XOMMggJIgusG7Cj/Va/V5G/AU7e6173ute97nWv/7/r/lp0r3vd6173utdfoe4L773uda973etef4W6L7z3ute97nWve/0V6r7w3ute97rXve71V6j7wnuve93rXve611+h7gvvve51r3vd615/hbovvPe6173uda97/RXqvvDe6173ute97vVXqPvCe6973ete97rXX6H+P0jshBljf+W4AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "n_rows = 3\n",
+    "n_cols = 3\n",
+    "f, axarr = plt.subplots(n_rows, n_cols)\n",
+    "for row in range(n_rows):\n",
+    "  for col in range(n_cols):\n",
+    "    image = Image.open(data_df.sample(n = 1)['image:FILE'].iloc[0]).convert('RGB')\n",
+    "    axarr[row, col].imshow(image)\n",
+    "    axarr[row, col].axis('off')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "0C8Hs9o9uJX1"
+   },
+   "source": [
+    "# Hyperparameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-10-26T17:57:18.542880Z",
+     "iopub.status.busy": "2025-10-26T17:57:18.542297Z",
+     "iopub.status.idle": "2025-10-26T17:57:18.546297Z",
+     "shell.execute_reply": "2025-10-26T17:57:18.545581Z",
+     "shell.execute_reply.started": "2025-10-26T17:57:18.542845Z"
+    },
+    "executionInfo": {
+     "elapsed": 23,
+     "status": "ok",
+     "timestamp": 1761351367226,
+     "user": {
+      "displayName": "Ignacio Menna",
+      "userId": "10948974476344730015"
+     },
+     "user_tz": 180
+    },
+    "id": "Yej-WSlJuFKR",
+    "trusted": true
+   },
+   "outputs": [],
+   "source": [
+    "LR = 1e-3\n",
+    "BATCH_SIZE = 4\n",
+    "EPOCHS = 15"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# DataLoaders"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-10-26T17:57:18.547280Z",
+     "iopub.status.busy": "2025-10-26T17:57:18.547094Z",
+     "iopub.status.idle": "2025-10-26T17:57:18.564903Z",
+     "shell.execute_reply": "2025-10-26T17:57:18.564318Z",
+     "shell.execute_reply.started": "2025-10-26T17:57:18.547265Z"
+    },
+    "executionInfo": {
+     "elapsed": 46,
+     "status": "ok",
+     "timestamp": 1761352303376,
+     "user": {
+      "displayName": "Ignacio Menna",
+      "userId": "10948974476344730015"
+     },
+     "user_tz": 180
+    },
+    "id": "5_4vj_66uQLf",
+    "trusted": true
+   },
+   "outputs": [],
+   "source": [
+    "train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)\n",
+    "test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Pre-trained Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-10-26T17:57:18.567045Z",
+     "iopub.status.busy": "2025-10-26T17:57:18.566807Z",
+     "iopub.status.idle": "2025-10-26T17:57:18.751126Z",
+     "shell.execute_reply": "2025-10-26T17:57:18.750354Z",
+     "shell.execute_reply.started": "2025-10-26T17:57:18.567030Z"
+    },
+    "executionInfo": {
+     "elapsed": 928,
+     "status": "ok",
+     "timestamp": 1761352390265,
+     "user": {
+      "displayName": "Ignacio Menna",
+      "userId": "10948974476344730015"
+     },
+     "user_tz": 180
+    },
+    "id": "-F86fSgGxz04",
+    "outputId": "d6a591bb-6484-4566-cbfc-5bc3bfb0de7f",
+    "trusted": true
+   },
+   "outputs": [],
+   "source": [
+    "googlenet_model = models.googlenet(weights='DEFAULT')\n",
+    "for param in googlenet_model.parameters():\n",
+    "  param.requires_grad = True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-10-26T17:57:18.752187Z",
+     "iopub.status.busy": "2025-10-26T17:57:18.751918Z",
+     "iopub.status.idle": "2025-10-26T17:57:18.756963Z",
+     "shell.execute_reply": "2025-10-26T17:57:18.756260Z",
+     "shell.execute_reply.started": "2025-10-26T17:57:18.752164Z"
+    },
+    "executionInfo": {
+     "elapsed": 28,
+     "status": "ok",
+     "timestamp": 1761352405132,
+     "user": {
+      "displayName": "Ignacio Menna",
+      "userId": "10948974476344730015"
+     },
+     "user_tz": 180
+    },
+    "id": "FFGCVFxvyJHP",
+    "outputId": "feaf5115-9f8c-4be6-d0ae-9435ee2e8e17",
+    "trusted": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Linear(in_features=1024, out_features=1000, bias=True)"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "googlenet_model.fc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-10-26T17:57:18.757947Z",
+     "iopub.status.busy": "2025-10-26T17:57:18.757684Z",
+     "iopub.status.idle": "2025-10-26T17:57:18.790520Z",
+     "shell.execute_reply": "2025-10-26T17:57:18.789998Z",
+     "shell.execute_reply.started": "2025-10-26T17:57:18.757925Z"
+    },
+    "executionInfo": {
+     "elapsed": 77,
+     "status": "ok",
+     "timestamp": 1761354240989,
+     "user": {
+      "displayName": "Ignacio Menna",
+      "userId": "10948974476344730015"
+     },
+     "user_tz": 180
+    },
+    "id": "fO4No2q-yM9t",
+    "outputId": "68e78a34-fb0b-4489-dd67-3d17bab48253",
+    "trusted": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "GoogLeNet(\n",
+       "  (conv1): BasicConv2d(\n",
+       "    (conv): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
+       "    (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "  )\n",
+       "  (maxpool1): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
+       "  (conv2): BasicConv2d(\n",
+       "    (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "    (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "  )\n",
+       "  (conv3): BasicConv2d(\n",
+       "    (conv): Conv2d(64, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "    (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "  )\n",
+       "  (maxpool2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
+       "  (inception3a): Inception(\n",
+       "    (branch1): BasicConv2d(\n",
+       "      (conv): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "    )\n",
+       "    (branch2): Sequential(\n",
+       "      (0): BasicConv2d(\n",
+       "        (conv): Conv2d(192, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "      (1): BasicConv2d(\n",
+       "        (conv): Conv2d(96, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "    )\n",
+       "    (branch3): Sequential(\n",
+       "      (0): BasicConv2d(\n",
+       "        (conv): Conv2d(192, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (bn): BatchNorm2d(16, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "      (1): BasicConv2d(\n",
+       "        (conv): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "    )\n",
+       "    (branch4): Sequential(\n",
+       "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
+       "      (1): BasicConv2d(\n",
+       "        (conv): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "    )\n",
+       "  )\n",
+       "  (inception3b): Inception(\n",
+       "    (branch1): BasicConv2d(\n",
+       "      (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "    )\n",
+       "    (branch2): Sequential(\n",
+       "      (0): BasicConv2d(\n",
+       "        (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "      (1): BasicConv2d(\n",
+       "        (conv): Conv2d(128, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "        (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "    )\n",
+       "    (branch3): Sequential(\n",
+       "      (0): BasicConv2d(\n",
+       "        (conv): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "      (1): BasicConv2d(\n",
+       "        (conv): Conv2d(32, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "        (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "    )\n",
+       "    (branch4): Sequential(\n",
+       "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
+       "      (1): BasicConv2d(\n",
+       "        (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "    )\n",
+       "  )\n",
+       "  (maxpool3): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
+       "  (inception4a): Inception(\n",
+       "    (branch1): BasicConv2d(\n",
+       "      (conv): Conv2d(480, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "    )\n",
+       "    (branch2): Sequential(\n",
+       "      (0): BasicConv2d(\n",
+       "        (conv): Conv2d(480, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "      (1): BasicConv2d(\n",
+       "        (conv): Conv2d(96, 208, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "        (bn): BatchNorm2d(208, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "    )\n",
+       "    (branch3): Sequential(\n",
+       "      (0): BasicConv2d(\n",
+       "        (conv): Conv2d(480, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (bn): BatchNorm2d(16, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "      (1): BasicConv2d(\n",
+       "        (conv): Conv2d(16, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "        (bn): BatchNorm2d(48, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "    )\n",
+       "    (branch4): Sequential(\n",
+       "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
+       "      (1): BasicConv2d(\n",
+       "        (conv): Conv2d(480, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "    )\n",
+       "  )\n",
+       "  (inception4b): Inception(\n",
+       "    (branch1): BasicConv2d(\n",
+       "      (conv): Conv2d(512, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "    )\n",
+       "    (branch2): Sequential(\n",
+       "      (0): BasicConv2d(\n",
+       "        (conv): Conv2d(512, 112, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (bn): BatchNorm2d(112, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "      (1): BasicConv2d(\n",
+       "        (conv): Conv2d(112, 224, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "        (bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "    )\n",
+       "    (branch3): Sequential(\n",
+       "      (0): BasicConv2d(\n",
+       "        (conv): Conv2d(512, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (bn): BatchNorm2d(24, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "      (1): BasicConv2d(\n",
+       "        (conv): Conv2d(24, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "    )\n",
+       "    (branch4): Sequential(\n",
+       "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
+       "      (1): BasicConv2d(\n",
+       "        (conv): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "    )\n",
+       "  )\n",
+       "  (inception4c): Inception(\n",
+       "    (branch1): BasicConv2d(\n",
+       "      (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "    )\n",
+       "    (branch2): Sequential(\n",
+       "      (0): BasicConv2d(\n",
+       "        (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "      (1): BasicConv2d(\n",
+       "        (conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "        (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "    )\n",
+       "    (branch3): Sequential(\n",
+       "      (0): BasicConv2d(\n",
+       "        (conv): Conv2d(512, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (bn): BatchNorm2d(24, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "      (1): BasicConv2d(\n",
+       "        (conv): Conv2d(24, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "    )\n",
+       "    (branch4): Sequential(\n",
+       "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
+       "      (1): BasicConv2d(\n",
+       "        (conv): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "    )\n",
+       "  )\n",
+       "  (inception4d): Inception(\n",
+       "    (branch1): BasicConv2d(\n",
+       "      (conv): Conv2d(512, 112, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn): BatchNorm2d(112, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "    )\n",
+       "    (branch2): Sequential(\n",
+       "      (0): BasicConv2d(\n",
+       "        (conv): Conv2d(512, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (bn): BatchNorm2d(144, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "      (1): BasicConv2d(\n",
+       "        (conv): Conv2d(144, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "        (bn): BatchNorm2d(288, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "    )\n",
+       "    (branch3): Sequential(\n",
+       "      (0): BasicConv2d(\n",
+       "        (conv): Conv2d(512, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "      (1): BasicConv2d(\n",
+       "        (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "    )\n",
+       "    (branch4): Sequential(\n",
+       "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
+       "      (1): BasicConv2d(\n",
+       "        (conv): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "    )\n",
+       "  )\n",
+       "  (inception4e): Inception(\n",
+       "    (branch1): BasicConv2d(\n",
+       "      (conv): Conv2d(528, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "    )\n",
+       "    (branch2): Sequential(\n",
+       "      (0): BasicConv2d(\n",
+       "        (conv): Conv2d(528, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "      (1): BasicConv2d(\n",
+       "        (conv): Conv2d(160, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "        (bn): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "    )\n",
+       "    (branch3): Sequential(\n",
+       "      (0): BasicConv2d(\n",
+       "        (conv): Conv2d(528, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "      (1): BasicConv2d(\n",
+       "        (conv): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "    )\n",
+       "    (branch4): Sequential(\n",
+       "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
+       "      (1): BasicConv2d(\n",
+       "        (conv): Conv2d(528, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "    )\n",
+       "  )\n",
+       "  (maxpool4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
+       "  (inception5a): Inception(\n",
+       "    (branch1): BasicConv2d(\n",
+       "      (conv): Conv2d(832, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "    )\n",
+       "    (branch2): Sequential(\n",
+       "      (0): BasicConv2d(\n",
+       "        (conv): Conv2d(832, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "      (1): BasicConv2d(\n",
+       "        (conv): Conv2d(160, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "        (bn): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "    )\n",
+       "    (branch3): Sequential(\n",
+       "      (0): BasicConv2d(\n",
+       "        (conv): Conv2d(832, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "      (1): BasicConv2d(\n",
+       "        (conv): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "    )\n",
+       "    (branch4): Sequential(\n",
+       "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
+       "      (1): BasicConv2d(\n",
+       "        (conv): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "    )\n",
+       "  )\n",
+       "  (inception5b): Inception(\n",
+       "    (branch1): BasicConv2d(\n",
+       "      (conv): Conv2d(832, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "    )\n",
+       "    (branch2): Sequential(\n",
+       "      (0): BasicConv2d(\n",
+       "        (conv): Conv2d(832, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "      (1): BasicConv2d(\n",
+       "        (conv): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "        (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "    )\n",
+       "    (branch3): Sequential(\n",
+       "      (0): BasicConv2d(\n",
+       "        (conv): Conv2d(832, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (bn): BatchNorm2d(48, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "      (1): BasicConv2d(\n",
+       "        (conv): Conv2d(48, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "    )\n",
+       "    (branch4): Sequential(\n",
+       "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
+       "      (1): BasicConv2d(\n",
+       "        (conv): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "    )\n",
+       "  )\n",
+       "  (aux1): None\n",
+       "  (aux2): None\n",
+       "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
+       "  (dropout): Dropout(p=0.2, inplace=False)\n",
+       "  (fc): Linear(in_features=1024, out_features=3, bias=True)\n",
+       ")"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "num_classes = len(data_df[\"category\"].unique())\n",
+    "googlenet_model.fc = torch.nn.Linear(googlenet_model.fc.in_features, num_classes)\n",
+    "googlenet_model.to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-10-26T17:57:18.791284Z",
+     "iopub.status.busy": "2025-10-26T17:57:18.791121Z",
+     "iopub.status.idle": "2025-10-26T17:57:18.796664Z",
+     "shell.execute_reply": "2025-10-26T17:57:18.796016Z",
+     "shell.execute_reply.started": "2025-10-26T17:57:18.791271Z"
+    },
+    "executionInfo": {
+     "elapsed": 56,
+     "status": "ok",
+     "timestamp": 1761354245167,
+     "user": {
+      "displayName": "Ignacio Menna",
+      "userId": "10948974476344730015"
+     },
+     "user_tz": 180
+    },
+    "id": "ZbXq518-3eUl",
+    "trusted": true
+   },
+   "outputs": [],
+   "source": [
+    "#####################################\n",
+    "# Define loss function and optimizer\n",
+    "#####################################\n",
+    "criterion = nn.CrossEntropyLoss() # Cross Entropy Loss\n",
+    "optimizer = Adam(googlenet_model.parameters(), lr=LR) # Adam optimizer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Training"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-10-26T17:57:18.797888Z",
+     "iopub.status.busy": "2025-10-26T17:57:18.797625Z",
+     "iopub.status.idle": "2025-10-26T17:59:46.954062Z",
+     "shell.execute_reply": "2025-10-26T17:59:46.953410Z",
+     "shell.execute_reply.started": "2025-10-26T17:57:18.797869Z"
+    },
+    "executionInfo": {
+     "elapsed": 3597,
+     "status": "error",
+     "timestamp": 1761354251428,
+     "user": {
+      "displayName": "Ignacio Menna",
+      "userId": "10948974476344730015"
+     },
+     "user_tz": 180
+    },
+    "id": "xBUB0_HV3l3c",
+    "outputId": "a22160a1-64ae-4dd6-a77f-c8f93615a46e",
+    "trusted": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/15, Train Loss: 1.725 Train Accuracy 64.3819%\n",
+      "\n",
+      "Epoch 2/15, Train Loss: 1.3366 Train Accuracy 74.1738%\n",
+      "\n",
+      "Epoch 3/15, Train Loss: 1.0889 Train Accuracy 81.3953%\n",
+      "\n",
+      "Epoch 4/15, Train Loss: 0.9998 Train Accuracy 82.9865%\n",
+      "\n",
+      "Epoch 5/15, Train Loss: 0.9572 Train Accuracy 83.3537%\n",
+      "\n",
+      "Epoch 6/15, Train Loss: 0.7067 Train Accuracy 88.2497%\n",
+      "\n",
+      "Epoch 7/15, Train Loss: 0.6969 Train Accuracy 88.0049%\n",
+      "\n",
+      "Epoch 8/15, Train Loss: 0.6058 Train Accuracy 89.7185%\n",
+      "\n",
+      "Epoch 9/15, Train Loss: 0.485 Train Accuracy 93.3905%\n",
+      "\n",
+      "Epoch 10/15, Train Loss: 0.5726 Train Accuracy 90.0857%\n",
+      "\n",
+      "Epoch 11/15, Train Loss: 0.3971 Train Accuracy 94.492%\n",
+      "\n",
+      "Epoch 12/15, Train Loss: 0.6815 Train Accuracy 88.0049%\n",
+      "\n",
+      "Epoch 13/15, Train Loss: 0.3241 Train Accuracy 95.9608%\n",
+      "\n",
+      "Epoch 14/15, Train Loss: 0.491 Train Accuracy 91.9217%\n",
+      "\n",
+      "Epoch 15/15, Train Loss: 0.2895 Train Accuracy 96.2056%\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "total_loss_train_plot = []\n",
+    "total_acc_train_plot = []\n",
+    "\n",
+    "for epoch in range(EPOCHS):\n",
+    "  total_acc_train = 0\n",
+    "  total_loss_train = 0\n",
+    "\n",
+    "  for (inputs, labels) in train_loader:\n",
+    "    inputs = inputs.to(device)\n",
+    "    labels = labels.to(device)\n",
+    "    \n",
+    "    optimizer.zero_grad()\n",
+    "    outputs = googlenet_model(inputs)\n",
+    "    train_loss = criterion(outputs, labels)\n",
+    "    total_loss_train += train_loss.item()\n",
+    "    train_loss.backward()\n",
+    "\n",
+    "    train_acc = (torch.argmax(outputs, axis = 1) == labels).sum().item()\n",
+    "    total_acc_train += train_acc\n",
+    "    optimizer.step()\n",
+    "\n",
+    "  total_loss_train_plot.append(round(total_loss_train/1000, 4))\n",
+    "  total_acc_train_plot.append(round(total_acc_train/(train_dataset.__len__())*100, 4))\n",
+    "  print(f'Epoch {epoch+1}/{EPOCHS}, Train Loss: {round(total_loss_train/100, 4)} Train Accuracy {round((total_acc_train)/train_dataset.__len__() * 100, 4)}%')\n",
+    "  print()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Testing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-10-26T17:59:46.955307Z",
+     "iopub.status.busy": "2025-10-26T17:59:46.954831Z",
+     "iopub.status.idle": "2025-10-26T17:59:51.155300Z",
+     "shell.execute_reply": "2025-10-26T17:59:51.154454Z",
+     "shell.execute_reply.started": "2025-10-26T17:59:46.955288Z"
+    },
+    "id": "pM0Kisgz6Zxc",
+    "trusted": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 92.29%\n",
+      "Avg loss: 0.2199\n"
+     ]
+    }
+   ],
+   "source": [
+    "with torch.no_grad():\n",
+    "    total_loss_test = 0\n",
+    "    total_acc_test = 0\n",
+    "    for inputs, labels in test_loader:\n",
+    "        inputs = inputs.to(device)\n",
+    "        labels = labels.to(device)\n",
+    "\n",
+    "        prediction = googlenet_model(inputs)\n",
+    "\n",
+    "        test_loss = criterion(prediction, labels)\n",
+    "        total_loss_test += test_loss.item()\n",
+    "\n",
+    "        test_acc = (torch.argmax(prediction, axis=1) == labels).sum().item()\n",
+    "        total_acc_test += test_acc\n",
+    "\n",
+    "print(f\"Accuracy: {round(total_acc_test / len(test_dataset) * 100, 2)}%\")\n",
+    "print(f\"Avg loss: {round(total_loss_test / len(test_loader), 4)}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Plotting Training Progress"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-10-26T17:59:51.156549Z",
+     "iopub.status.busy": "2025-10-26T17:59:51.156159Z",
+     "iopub.status.idle": "2025-10-26T17:59:51.518307Z",
+     "shell.execute_reply": "2025-10-26T17:59:51.517592Z",
+     "shell.execute_reply.started": "2025-10-26T17:59:51.156524Z"
+    },
+    "id": "fHyZXU_U4qHl",
+    "trusted": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(15, 5))\n",
+    "\n",
+    "axs[0].plot(total_loss_train_plot, label='Training Loss')\n",
+    "axs[0].set_title('Training Loss over Epochs')\n",
+    "axs[0].set_xlabel('Epochs')\n",
+    "axs[0].set_ylabel('Loss')\n",
+    "axs[1].set_ylim([0, 2])\n",
+    "axs[0].legend()\n",
+    "\n",
+    "axs[1].plot(total_acc_train_plot, label='Training Accuracy')\n",
+    "axs[1].set_title('Training Accuracy over Epochs')\n",
+    "axs[1].set_xlabel('Epochs')\n",
+    "axs[1].set_ylabel('Accuracy')\n",
+    "axs[1].set_ylim([0, 100])\n",
+    "axs[1].legend()\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "authorship_tag": "ABX9TyOJiyMyhBGLJDGZKVsl1u0o",
+   "gpuType": "T4",
+   "provenance": []
+  },
+  "kaggle": {
+   "accelerator": "nvidiaTeslaT4",
+   "dataSources": [
+    {
+     "datasetId": 3953171,
+     "sourceId": 6880389,
+     "sourceType": "datasetVersion"
+    }
+   ],
+   "dockerImageVersionId": 31153,
+   "isGpuEnabled": true,
+   "isInternetEnabled": true,
+   "language": "python",
+   "sourceType": "notebook"
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Ex_4_Audio_Classification.ipynb b/Ex_4_Audio_Classification.ipynb
index 85cba68..4dc24e3 100644
--- a/Ex_4_Audio_Classification.ipynb
+++ b/Ex_4_Audio_Classification.ipynb
@@ -1,1171 +1,1557 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "2nDnr4nxmeeq"
-   },
-   "source": [
-    "# **Introduction to this lab:**\n",
-    "In this lab, we will be doing a classification of the Quran Reciters. The dataset being used is an opensource dataset from Kaggle. You can read more about the data [here](https://www.kaggle.com/datasets/mohammedalrajeh/quran-recitations-for-audio-classification/). In this lab we will be doing audio classification. Feel free to change the dataset and try other datasets.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "bxXflDe0pbcR"
-   },
-   "source": [
-    "\n",
-    "# 1. Initializations and Dataset Download\n",
-    "Lets start by downloading the dataset and installing the necessary library for downloading directly with the link from kaggle."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
     "colab": {
-     "base_uri": "https://localhost:8080/"
+      "provenance": [],
+      "gpuType": "T4"
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
     },
-    "id": "UKxK7J8n1dX4",
-    "outputId": "9c75d77f-161b-45e0-de78-f8afe6039d17"
-   },
-   "outputs": [
+    "accelerator": "GPU"
+  },
+  "cells": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Please provide your Kaggle credentials to download this dataset. Learn more: http://bit.ly/kaggle-creds\n",
-      "Your Kaggle username: omaratef3221\n",
-      "Your Kaggle Key: ··········\n",
-      "Downloading quran-recitations-for-audio-classification.zip to ./quran-recitations-for-audio-classification\n"
-     ]
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 125
+        },
+        "id": "I_EAQy-EeDPx",
+        "outputId": "60e4103b-1c69-416e-e1b8-c8c09472d441"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "     <input type=\"file\" id=\"files-a1493d86-85b0-40e5-8150-4df79772447a\" name=\"files[]\" multiple disabled\n",
+              "        style=\"border:none\" />\n",
+              "     <output id=\"result-a1493d86-85b0-40e5-8150-4df79772447a\">\n",
+              "      Upload widget is only available when the cell has been executed in the\n",
+              "      current browser session. Please rerun this cell to enable.\n",
+              "      </output>\n",
+              "      <script>// Copyright 2017 Google LLC\n",
+              "//\n",
+              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+              "// you may not use this file except in compliance with the License.\n",
+              "// You may obtain a copy of the License at\n",
+              "//\n",
+              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
+              "//\n",
+              "// Unless required by applicable law or agreed to in writing, software\n",
+              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+              "// See the License for the specific language governing permissions and\n",
+              "// limitations under the License.\n",
+              "\n",
+              "/**\n",
+              " * @fileoverview Helpers for google.colab Python module.\n",
+              " */\n",
+              "(function(scope) {\n",
+              "function span(text, styleAttributes = {}) {\n",
+              "  const element = document.createElement('span');\n",
+              "  element.textContent = text;\n",
+              "  for (const key of Object.keys(styleAttributes)) {\n",
+              "    element.style[key] = styleAttributes[key];\n",
+              "  }\n",
+              "  return element;\n",
+              "}\n",
+              "\n",
+              "// Max number of bytes which will be uploaded at a time.\n",
+              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
+              "\n",
+              "function _uploadFiles(inputId, outputId) {\n",
+              "  const steps = uploadFilesStep(inputId, outputId);\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  // Cache steps on the outputElement to make it available for the next call\n",
+              "  // to uploadFilesContinue from Python.\n",
+              "  outputElement.steps = steps;\n",
+              "\n",
+              "  return _uploadFilesContinue(outputId);\n",
+              "}\n",
+              "\n",
+              "// This is roughly an async generator (not supported in the browser yet),\n",
+              "// where there are multiple asynchronous steps and the Python side is going\n",
+              "// to poll for completion of each step.\n",
+              "// This uses a Promise to block the python side on completion of each step,\n",
+              "// then passes the result of the previous step as the input to the next step.\n",
+              "function _uploadFilesContinue(outputId) {\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  const steps = outputElement.steps;\n",
+              "\n",
+              "  const next = steps.next(outputElement.lastPromiseValue);\n",
+              "  return Promise.resolve(next.value.promise).then((value) => {\n",
+              "    // Cache the last promise value to make it available to the next\n",
+              "    // step of the generator.\n",
+              "    outputElement.lastPromiseValue = value;\n",
+              "    return next.value.response;\n",
+              "  });\n",
+              "}\n",
+              "\n",
+              "/**\n",
+              " * Generator function which is called between each async step of the upload\n",
+              " * process.\n",
+              " * @param {string} inputId Element ID of the input file picker element.\n",
+              " * @param {string} outputId Element ID of the output display.\n",
+              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
+              " */\n",
+              "function* uploadFilesStep(inputId, outputId) {\n",
+              "  const inputElement = document.getElementById(inputId);\n",
+              "  inputElement.disabled = false;\n",
+              "\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  outputElement.innerHTML = '';\n",
+              "\n",
+              "  const pickedPromise = new Promise((resolve) => {\n",
+              "    inputElement.addEventListener('change', (e) => {\n",
+              "      resolve(e.target.files);\n",
+              "    });\n",
+              "  });\n",
+              "\n",
+              "  const cancel = document.createElement('button');\n",
+              "  inputElement.parentElement.appendChild(cancel);\n",
+              "  cancel.textContent = 'Cancel upload';\n",
+              "  const cancelPromise = new Promise((resolve) => {\n",
+              "    cancel.onclick = () => {\n",
+              "      resolve(null);\n",
+              "    };\n",
+              "  });\n",
+              "\n",
+              "  // Wait for the user to pick the files.\n",
+              "  const files = yield {\n",
+              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
+              "    response: {\n",
+              "      action: 'starting',\n",
+              "    }\n",
+              "  };\n",
+              "\n",
+              "  cancel.remove();\n",
+              "\n",
+              "  // Disable the input element since further picks are not allowed.\n",
+              "  inputElement.disabled = true;\n",
+              "\n",
+              "  if (!files) {\n",
+              "    return {\n",
+              "      response: {\n",
+              "        action: 'complete',\n",
+              "      }\n",
+              "    };\n",
+              "  }\n",
+              "\n",
+              "  for (const file of files) {\n",
+              "    const li = document.createElement('li');\n",
+              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
+              "    li.append(span(\n",
+              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
+              "        `last modified: ${\n",
+              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
+              "                                    'n/a'} - `));\n",
+              "    const percent = span('0% done');\n",
+              "    li.appendChild(percent);\n",
+              "\n",
+              "    outputElement.appendChild(li);\n",
+              "\n",
+              "    const fileDataPromise = new Promise((resolve) => {\n",
+              "      const reader = new FileReader();\n",
+              "      reader.onload = (e) => {\n",
+              "        resolve(e.target.result);\n",
+              "      };\n",
+              "      reader.readAsArrayBuffer(file);\n",
+              "    });\n",
+              "    // Wait for the data to be ready.\n",
+              "    let fileData = yield {\n",
+              "      promise: fileDataPromise,\n",
+              "      response: {\n",
+              "        action: 'continue',\n",
+              "      }\n",
+              "    };\n",
+              "\n",
+              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
+              "    let position = 0;\n",
+              "    do {\n",
+              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
+              "      const chunk = new Uint8Array(fileData, position, length);\n",
+              "      position += length;\n",
+              "\n",
+              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
+              "      yield {\n",
+              "        response: {\n",
+              "          action: 'append',\n",
+              "          file: file.name,\n",
+              "          data: base64,\n",
+              "        },\n",
+              "      };\n",
+              "\n",
+              "      let percentDone = fileData.byteLength === 0 ?\n",
+              "          100 :\n",
+              "          Math.round((position / fileData.byteLength) * 100);\n",
+              "      percent.textContent = `${percentDone}% done`;\n",
+              "\n",
+              "    } while (position < fileData.byteLength);\n",
+              "  }\n",
+              "\n",
+              "  // All done.\n",
+              "  yield {\n",
+              "    response: {\n",
+              "      action: 'complete',\n",
+              "    }\n",
+              "  };\n",
+              "}\n",
+              "\n",
+              "scope.google = scope.google || {};\n",
+              "scope.google.colab = scope.google.colab || {};\n",
+              "scope.google.colab._files = {\n",
+              "  _uploadFiles,\n",
+              "  _uploadFilesContinue,\n",
+              "};\n",
+              "})(self);\n",
+              "</script> "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saving kaggle.json to kaggle (1).json\n",
+            "Dataset URL: https://www.kaggle.com/datasets/mrgabrielblins/speaker-recognition-cmu-arctic\n",
+            "License(s): CC0-1.0\n",
+            "speaker-recognition-cmu-arctic.zip: Skipping, found more recently modified local copy (use --force to force download)\n"
+          ]
+        }
+      ],
+      "source": [
+        "from google.colab import files\n",
+        "files.upload()\n",
+        "\n",
+        "!mkdir -p ~/.kaggle\n",
+        "!cp kaggle.json ~/.kaggle/\n",
+        "!chmod 600 ~/.kaggle/kaggle.json\n",
+        "\n",
+        "!kaggle datasets download -d mrgabrielblins/speaker-recognition-cmu-arctic\n",
+        "import zipfile\n",
+        "\n",
+        "with zipfile.ZipFile(\"speaker-recognition-cmu-arctic.zip\", 'r') as zip_ref:\n",
+        "    zip_ref.extractall(\"speaker-recognition-cmu-arctic-data\")"
+      ]
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 2.12G/2.12G [00:25<00:00, 88.3MB/s]\n"
-     ]
+      "cell_type": "markdown",
+      "source": [
+        "# Imports"
+      ],
+      "metadata": {
+        "id": "ldXyPyimtiA9"
+      }
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "!pip install opendatasets --quiet\n",
-    "import opendatasets as od\n",
-    "od.download(\"https://www.kaggle.com/datasets/mohammedalrajeh/quran-recitations-for-audio-classification\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "Jmog2XrjpE-o"
-   },
-   "source": [
-    "# 2. Imports\n",
-    "Then we will write all our imports, keep in mind that PyTorch is not automatically detects and trains on GPU, you have to tell it to use `cuda`. In case you want to train on Mac Silicon replace cuda with `mps`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "id": "fVzAOYGA1hgJ"
-   },
-   "outputs": [],
-   "source": [
-    "import torch # Main PyTorch Library\n",
-    "from torch import nn # Used for creating the layers and loss function\n",
-    "from torch.optim import Adam # Adam Optimizer\n",
-    "import librosa # Library that is used to read and process audio files\n",
-    "from torch.utils.data import Dataset, DataLoader # Dataset class and DataLoader for creating the objects\n",
-    "from sklearn.preprocessing import LabelEncoder # Label Encoder to encode the classes from strings to numbers\n",
-    "import matplotlib.pyplot as plt # Used for visualizing the images and plotting the training progress\n",
-    "import pandas as pd # Used to read/create dataframes (csv) and process tabular data\n",
-    "import numpy as np # preprocessing and numerical/mathematical operations\n",
-    "import os # Used to read the images path from the directory\n",
-    "import time # Used to calculate time for each epoch or any processing time in seconds\n",
-    "from skimage.transform import resize # Used to resize the images and we will use it to resize audio waves\n",
-    "\n",
-    "# detect the GPU if any, if not use CPU, change \"cuda\" to \"mps\" if you have a mac\n",
-    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "aPxD-DnytC6J"
-   },
-   "source": [
-    "# 3. Data\n",
-    "The dataframe comes with a csv file that contains the path of each audio file and its corresponding label (Quran Reciter). We will read this dataframe.\n",
-    "\n",
-    "Then we will modify the path and add the full path to it so that it allows us to read the audio file with respect to colab structure. Then we visualize the first 5 rows of the dataframe.\n",
-    "\n",
-    "Keep in mind that this step is not the same for every dataset, you may need to read the dataset differently depends on its structure.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 206
+      "cell_type": "code",
+      "source": [
+        "import torch\n",
+        "import torch.nn as nn\n",
+        "from torch.optim import Adam\n",
+        "import librosa\n",
+        "from torch.utils.data import Dataset, DataLoader\n",
+        "from sklearn.preprocessing import LabelEncoder\n",
+        "import matplotlib.pyplot as plt\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import os\n",
+        "import time\n",
+        "from skimage.transform import resize\n",
+        "\n",
+        "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
+        "print(device)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "2xKRRGKbePQr",
+        "outputId": "9aeb4f2e-cc66-4ab8-d548-886319e1f6a7"
+      },
+      "execution_count": 23,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "cuda\n"
+          ]
+        }
+      ]
     },
-    "id": "NkofIz-w2XTD",
-    "outputId": "ae755db4-8bb4-4c4a-f7d8-17dce019e159"
-   },
-   "outputs": [
     {
-     "data": {
-      "text/html": [
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-       "    <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>FilePath</th>\n",
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-       "    [theme=dark] .colab-df-convert {\n",
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-       "\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",
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-       "    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-85ee8706-0493-4725-800a-dd420dee7308 button');\n",
-       "      quickchartButtonEl.style.display =\n",
-       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "    })();\n",
-       "  </script>\n",
-       "</div>\n",
-       "    </div>\n",
-       "  </div>\n"
+      "cell_type": "markdown",
+      "source": [
+        "# Data"
       ],
-      "text/plain": [
-       "                                            FilePath               Class\n",
-       "0  /content/quran-recitations-for-audio-classific...  Mohammed_Aluhaidan\n",
-       "1  /content/quran-recitations-for-audio-classific...  Mohammed_Aluhaidan\n",
-       "2  /content/quran-recitations-for-audio-classific...  Mohammed_Aluhaidan\n",
-       "3  /content/quran-recitations-for-audio-classific...  Mohammed_Aluhaidan\n",
-       "4  /content/quran-recitations-for-audio-classific...  Mohammed_Aluhaidan"
+      "metadata": {
+        "id": "k-Y0ORNsxdDZ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data_df = pd.read_csv('/content/speaker-recognition-cmu-arctic-data/test_full.csv')\n",
+        "\n",
+        "data_df['file_path'] = '/content/speaker-recognition-cmu-arctic-data/' + data_df['file_path'].str[0:]\n",
+        "\n",
+        "data_df.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "hV_ABVH4fFox",
+        "outputId": "b14c0d6a-0549-4065-c737-12b1dfe79e28"
+      },
+      "execution_count": 37,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                   id                                          file_path  \\\n",
+              "0  OELV49874079341496  /content/speaker-recognition-cmu-arctic-data/t...   \n",
+              "1  ELDT67178766030957  /content/speaker-recognition-cmu-arctic-data/t...   \n",
+              "2  GWMS82652863581753  /content/speaker-recognition-cmu-arctic-data/t...   \n",
+              "3  UAPD90278002373588  /content/speaker-recognition-cmu-arctic-data/t...   \n",
+              "4  IHIY50549582963537  /content/speaker-recognition-cmu-arctic-data/t...   \n",
+              "\n",
+              "                                              speech speaker  \n",
+              "0      What was the object of your little sensation.     aew  \n",
+              "1  He had no excuse for the feelings which were a...     eey  \n",
+              "2                           I'll see to poor Hughie.     awb  \n",
+              "3                             Then came my boy code.     jmk  \n",
+              "4    We must give ourselves and not our money alone.     clb  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-a106a1eb-ef6f-486f-9d23-e642b7f1f1ac\" 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",
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+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>id</th>\n",
+              "      <th>file_path</th>\n",
+              "      <th>speech</th>\n",
+              "      <th>speaker</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>OELV49874079341496</td>\n",
+              "      <td>/content/speaker-recognition-cmu-arctic-data/t...</td>\n",
+              "      <td>What was the object of your little sensation.</td>\n",
+              "      <td>aew</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>ELDT67178766030957</td>\n",
+              "      <td>/content/speaker-recognition-cmu-arctic-data/t...</td>\n",
+              "      <td>He had no excuse for the feelings which were a...</td>\n",
+              "      <td>eey</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>GWMS82652863581753</td>\n",
+              "      <td>/content/speaker-recognition-cmu-arctic-data/t...</td>\n",
+              "      <td>I'll see to poor Hughie.</td>\n",
+              "      <td>awb</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>UAPD90278002373588</td>\n",
+              "      <td>/content/speaker-recognition-cmu-arctic-data/t...</td>\n",
+              "      <td>Then came my boy code.</td>\n",
+              "      <td>jmk</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>IHIY50549582963537</td>\n",
+              "      <td>/content/speaker-recognition-cmu-arctic-data/t...</td>\n",
+              "      <td>We must give ourselves and not our money alone.</td>\n",
+              "      <td>clb</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-a106a1eb-ef6f-486f-9d23-e642b7f1f1ac')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\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-a106a1eb-ef6f-486f-9d23-e642b7f1f1ac 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-a106a1eb-ef6f-486f-9d23-e642b7f1f1ac');\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-81765e5a-f783-4e9f-9744-7d4a7f8986c9\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-81765e5a-f783-4e9f-9744-7d4a7f8986c9')\"\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-81765e5a-f783-4e9f-9744-7d4a7f8986c9 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "data_df",
+              "summary": "{\n  \"name\": \"data_df\",\n  \"rows\": 3117,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3117,\n        \"samples\": [\n          \"IOIH24767777996700\",\n          \"AYHR03914040082984\",\n          \"TQZO91358169242198\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"file_path\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3117,\n        \"samples\": [\n          \"/content/speaker-recognition-cmu-arctic-data/test/IOIH24767777996700.wav\",\n          \"/content/speaker-recognition-cmu-arctic-data/test/AYHR03914040082984.wav\",\n          \"/content/speaker-recognition-cmu-arctic-data/test/TQZO91358169242198.wav\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"speech\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1214,\n        \"samples\": [\n          \"Now you're coming down to business, Phil, he exclaimed.\",\n          \"Philip made no effort to follow. \",\n          \"They do not know the length of time of incubation.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"speaker\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 18,\n        \"samples\": [\n          \"aew\",\n          \"eey\",\n          \"ahw\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 37
+        }
       ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_df = pd.read_csv(\"/content/quran-recitations-for-audio-classification/files_paths.csv\") # Read the original csv as a dataframe\n",
-    "\n",
-    "data_df['FilePath'] = '/content/quran-recitations-for-audio-classification/Dataset/' + data_df['FilePath'].str[1:] # Modify the paths in the dataframe\n",
-    "\n",
-    "\n",
-    "data_df.head() # Visualize the first 5 rows"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "JikE6X_dt4WW"
-   },
-   "source": [
-    "# 4. Data investigation\n",
-    "Now lets dig deeper into our dataset. In the cell below we print the shape of the data which is represented by (rows, columns), then we plot the data distribution for each class. we can see below that our dataset is not 100% balanced but its almost balanced."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 744
     },
-    "id": "4_i1dYFY4Lyd",
-    "outputId": "2d75d2f1-9d96-4a85-df73-e5695a5226da"
-   },
-   "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Data Shape is:  (6687, 2)\n",
-      "\n",
-      "\n",
-      "Claases Distrubution is: \n"
-     ]
+      "cell_type": "code",
+      "source": [
+        "# Drop unnecesary columns\n",
+        "data_df = data_df.drop(columns=['id', 'speech'], axis=1)\n",
+        "data_df.head()"
+      ],
+      "metadata": {
+        "id": "pr0UGPw4tfz_",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "outputId": "3ef14d91-a1a3-4cff-f9bf-aef441526c43"
+      },
+      "execution_count": 38,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                                           file_path speaker\n",
+              "0  /content/speaker-recognition-cmu-arctic-data/t...     aew\n",
+              "1  /content/speaker-recognition-cmu-arctic-data/t...     eey\n",
+              "2  /content/speaker-recognition-cmu-arctic-data/t...     awb\n",
+              "3  /content/speaker-recognition-cmu-arctic-data/t...     jmk\n",
+              "4  /content/speaker-recognition-cmu-arctic-data/t...     clb"
+            ],
+            "text/html": [
+              "\n",
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+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
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+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>file_path</th>\n",
+              "      <th>speaker</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
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+              "      <td>aew</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>/content/speaker-recognition-cmu-arctic-data/t...</td>\n",
+              "      <td>eey</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>/content/speaker-recognition-cmu-arctic-data/t...</td>\n",
+              "      <td>awb</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>/content/speaker-recognition-cmu-arctic-data/t...</td>\n",
+              "      <td>jmk</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>/content/speaker-recognition-cmu-arctic-data/t...</td>\n",
+              "      <td>clb</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
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+              "    <div class=\"colab-df-buttons\">\n",
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+              "  </svg>\n",
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+              "\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",
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+              "\n",
+              "    .colab-df-convert:hover {\n",
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+              "      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",
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+              "        document.querySelector('#df-67a1995a-b9bc-4878-8379-b2df2a4e3d96 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-67a1995a-b9bc-4878-8379-b2df2a4e3d96');\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",
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+              "        element.appendChild(docLink);\n",
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+              "    </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",
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+              "    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-dba7cef3-2f14-43fe-a029-2fe0386c301b button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "data_df",
+              "summary": "{\n  \"name\": \"data_df\",\n  \"rows\": 3117,\n  \"fields\": [\n    {\n      \"column\": \"file_path\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3117,\n        \"samples\": [\n          \"/content/speaker-recognition-cmu-arctic-data/test/IOIH24767777996700.wav\",\n          \"/content/speaker-recognition-cmu-arctic-data/test/AYHR03914040082984.wav\",\n          \"/content/speaker-recognition-cmu-arctic-data/test/TQZO91358169242198.wav\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"speaker\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 18,\n        \"samples\": [\n          \"aew\",\n          \"eey\",\n          \"ahw\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 38
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Data investigation"
+      ],
+      "metadata": {
+        "id": "XbrkmEL4eocb"
+      }
     },
     {
-     "data": {
-      "image/png": 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cIJVK0blzZ5w6darUcfLz8/HKK6/AxsYGDRo0wHfffadZFxMTA4lEgoMHD6JHjx6wtLRE586d8eDBA1y/fh2dOnWCVCrFoEGDkJqaqtnv+vXrGDBgAOrUqQM7Ozv06tULt27dKnFeiUSC//u//8PIkSNhZWUFb29vHD169JnP+b927tyJoUOHYu7cudi3bx8KCgqeuf1XX30Fb29vWFhYwMXFBWPGjNGse1rTunbt2mHVqlWax+Hh4ejZsycsLCzQokWLEq/3Y++++y58fHxgZWWFRo0aYcWKFVAoFJr1/21Odu7cOXTp0gXW1tawt7eHn58fYmNjK/U6EBER6SsWMUTV4ODBg2jWrBmaNm2KyZMnY8eOHaWabC1evBjr16/H9evX4ezsjGHDhmm+tAYEBGDmzJl4/fXXERgYiD59+mD16tVazSgIAi5cuIDQ0FCYmZlplufl5WHw4ME4ffo0bt++jYEDB2LYsGGIi4srsf/69evRqVMn3L59G/PmzcPcuXMRFhZWYpuVK1di+fLluHXrFkxMTDBx4kQsWbIEmzdvxoULFxAREYEPPvhAs31ubi6mTZuGixcv4urVq/D29sbgwYORm5tb4rgffvghxo0bh+DgYAwePBiTJk1CRkZGhZ/3zp07MXnyZDRr1gxNmjTBzz//XOb2N27cwIIFC/DRRx8hLCwMf/31F3r27FmhcwGAWq3GqFGjYGZmhoCAAHzzzTd49913S21nY2ODXbt24f79+9i8eTO+//57bNy48anHVCqVGDFiBHr16oXg4GBcuXIFs2fPhkQiqXAuIiIivSYQkdb5+voKmzZtEgRBEBQKhVCnTh3h7NmzgiAIwtmzZwUAwv79+zXbp6enC5aWlsKBAwcEQRCECRMmCIMHDy5xzPHjxwt2dnaax9OmTROGDx9eYps333xT6NWrl+Zxr169hDfffLPEY1NTU8Ha2lowNTUVAAgWFhbCpUuXnvl8WrZsKWzdulXz2NPTU5g8ebLmsVqtFurWrSt8/fXXgiAIQnR0tABA+L//+z/NNvv27RMACKdPn9Ys+/TTT4WmTZuWeV6VSiXY2NgIv//+u2YZAGH58uWax3l5eQIA4fjx4898Do+dOHFCcHZ2FhQKhSAIgrBx48YSr5kglHzdDh8+LNja2go5OTlPPZ6np6ewcePGEsvatm0rrFy5UhAEQfj7778FExMT4dGjR5r1x48fFwAIv/76a5k5P//8c6Fjx46axytXrhTatm0rCELx3wsA4dy5c+U/YSIiIgPEOzFEWhYWFoZr165hwoQJAAATExOMHz8e27dvL7Fd9+7dNf92dHRE06ZNERISAgAICQlB165dy9z+eUyaNAmBgYG4dOkSBg0ahGXLlsHX11ezPi8vD4sWLULz5s1hb28PqVSKkJCQUndi2rRpo/m3RCKBq6srUlJSytzGxcUFANC6desSy57cJzk5Ga+++iq8vb1hZ2cHW1tb5OXlPfPc1tbWsLW1LXXusuzYsQPjx4+HiYkJAGDChAm4dOkSIiMjn7r9gAED4OnpiUaNGmHKlCn48ccfkZ+fX6FzAcW/Sw8PD7i7u2uWPe13eeDAAfj5+cHV1RVSqRTLly8v9bwfc3R0xPTp0+Hv749hw4Zh8+bNSExMrHAmIiIifccihkjLtm/fDqVSCXd3d5iYmMDExARff/01Dh8+jOzsbK2dx8jIqFQTtSf7UJTFzs4OTZo0QefOnXHw4EFs27atRJ+XRYsW4ddff8Unn3yCCxcuIDAwEK1bt4ZcLi9xHFNT0xKPJRIJ1Gp1mds8bur032VP7jNt2jQEBgZi8+bNuHz5MgIDA+Hk5FSlcz9NRkYGfv31V3z11Vea3029evWgVCrL7OBvY2ODW7duYd++fXBzc8MHH3yAtm3baobIrurv4UlXrlzBpEmTMHjwYPzxxx+4ffs2li1bVup5P2nnzp24cuUKfH19ceDAAfj4+ODq1auVOi8REZG+YhFDpEVKpRJ79uzB+vXrERgYqPkJCgqCu7s79u3bp9n2yS+cmZmZePDgAZo3bw4AaN68OQICAkoc+79fUJ2dnUtdfQ8MDKxUXqlUijfffBOLFi3SfBG/dOkSpk+fjpEjR6J169ZwdXVFTExMpY5bVZcuXcKCBQswePBgtGzZEubm5khLS9Pa8X/88UfUr18fQUFBJX4/69evx65du6BSqZ66n4mJCfr3749169YhODgYMTExOHPmDIDSv4ecnBxER0drHjdv3hwPHz4ssc1/f5eXL1+Gp6cnli1bhk6dOsHb27tCnfTbt2+PpUuX4vLly2jVqhV++umnSr0eRERE+opFDJEW/fHHH8jMzMTMmTPRqlWrEj+jR48u0aTso48+wunTp3H37l1Mnz4dderU0Yw2tmDBAvz111/44osvEB4ejm3btuGvv/4qca6+ffvixo0b2LNnD8LDw7Fy5cpSI5xVxJw5c/DgwQMcPnwYAODt7Y1ffvlFU3xNnDixQnc5tMHb2xt79+5FSEgIAgICMGnSJFhaWmrt+Nu3b8eYMWNK/W5mzpyJtLS0Uq8xUPw73bJlCwIDAxEbG4s9e/ZArVajadOmAIp/D3v37sWFCxdw584dTJs2DcbGxpr9+/fvDx8fH0ybNg1BQUG4cOECli1bVup5x8XFYf/+/YiMjMSWLVvw66+/lvk8oqOjsXTpUly5cgWxsbE4ceIEwsPDNUUwERGRoWMRQ6RF27dvR//+/WFnZ1dq3ejRo3Hjxg0EBwcDAD777DO8+eab6NixI5KSkvD7779rRgnr1q0bvv/+e2zevBlt27bFiRMnsHz58hLH8/f3x4oVK7BkyRJ07twZubm5mDp1aqUzOzo6YurUqVi1ahXUajU2bNgABwcH+Pr6YtiwYfD390eHDh2q8GpU3vbt25GZmYkOHTpgypQpWLBgAerWrauVY9+8eRNBQUEYPXp0qXV2dnbo169fqX5LAGBvb49ffvkFffv2RfPmzfHNN99g3759aNmyJQBg6dKl6NWrF4YOHYohQ4ZgxIgRaNy4sWZ/IyMj/PrrrygoKECXLl0wa9YsrFmzpsQ5XnrpJbz11lt4/fXX0a5dO1y+fBkrVqwo87lYWVkhNDQUo0ePho+PD2bPno358+djzpw5VX15iIiI9IpE+G9jbiIiIiIiohqMd2KIiIiIiEivsIghIoPx2muvQSqVPvXntddeEzseERERaQmbkxGRwUhJSUFOTs5T19na2mqtfw0RERGJi0UMERERERHpFTYnIyIiIiIivcIihoiIiIiI9AqLGCIiIiIi0issYoiIiIiISK+wiCEiIiIiIr3CIoaIiIiIiPQKixgiIiIiItIrLGKIiIiIiEivsIghIiIiIiK9wiKGiIiIiIj0CosYIiIiIiLSKyxiiIiIiIhIr7CIISIiIiIivcIihoiIiIiI9IqJ2AGIiKjqBEFAgbIA+cp85CvyNf8tUhVBoVZAoVZAqVZCqVZq/v34v2pBDXNjc1iaWGp+LEwsYGViVWqZhYmF2E+ViIhIg0UMEVENI1fJkSxLRlJ+EpLzk4v/LSv+d0p+CnLluZApZMhX5qNQWQgBQrVnMpIYwcLYosxix9bcFq7WrnCzdoO7tTtcpcX/tjSxrPZsRERU+0gEQaj+Tz8iIgIAFKmKkCxLRnL+v4XJ4/8+Xp5ZmKmTwkQXHMwd4GrtCnepO9ys3Ur8283aDU6WTmJHJCIiPcQihoioGqQXpCM0I1TzE5sTiyRZEjKLMsWOVqOYG5tr7uC4WbvBTeoGb3tvtHBqAXepu9jxiIiohmIRQ0T0HARBQGxOLEIzQxGWEYaQjBCEZYQhrSBN7Gh6z97cHs0cm6GFUws0d2qOlo4t4WHrIXYsIiKqAVjEEBFVUJGqCOGZ4Zq7K2EZYXiQ+QD5ynyxo9UaNmY2xYWNY3Fh08KpBbxsvSCRSMSORkREOsQihojoKRQqBQJTA3Ev7Z7mLktMdgyUglLsaPQf1qbWaOrQVHPHpoVjCzS0awhjI2OxoxERUTVhEUNE9D9hGWG4mngVVxKv4FbyLRQoC8SORFVkaWKJFk4t0NWtK7q7dUerOq1gYsQBOYmIDAWLGCKqtZJlyZqi5WrCVaQXposdiaqJtak1Orl0Qle3rujq1hU+Dj5iRyIioufAIoaIao18RT6uJ13HlcQruJJwBVHZUWJHIpE4WTihi1sXLHHqhjoNfAEbV7EjERFRJbCIISKDpVKrcCftjuZOS3BaMJRq9mmhYpbGFrgYHQszlRxwaQV49weaDAA8ugLGbHpGRFSTsYghIoOSVpCGM3FncPHRRdxIuoFcRa7YkaiGesG+Gb6+faL0CktHoOkgoPkwoHFfwMRc9+GIiOiZeKmJiPReRmEGTsWewt8xf+NG8g2oBbXYkUgP+CmNnr6iIAMI/LH4x8wG8B5QXNB4vwiYS3UbkoiInop3YohIL2UXZeNU7Cn8FfMXriddh0pQiR2J9MxvMjM0Somo+A4mFsV3ZpoPK75TY+lQfeGIiOiZWMQQkd5Q5ckQE3genxf+hoCEAM7ZQlXmZumME/dvVv0AxmbFd2bavgx4+wMmZtoLR0RE5WJzMiKq0QSVCrLLl5H921Hknj4NmJvhxmuFUEp454WqztfiOUcjU8mB0D+KfywdgVajgDYvAx6dtROQiIieqYwGwURE4ioMCUHyZ2sR3rs3Hr46Gzl//AGhoABCVjbGZXGOD3o+fnlaHPChIAO4/n/A9v7A1o7AP58DWXHaOz5VWu/evbFw4UKtHW/Xrl2wt7ev1D7Tp0/HiBEjtJbhef33NfHy8sKmTZtEy0P0vFjEEFGNocrNRcbu3Yh6aTiiR45Cxq5dUKWmldqudyhvIlPVGUuM0fVhUPUcPD0COLsa2NQG2DkECDoAKIuq51x6IDU1FXPnzkWDBg1gbm4OV1dX+Pv749KlS2JHK2Xfvn0wNjbG/PnzxY6iE9evX8fs2bPFjkFUZSxiSG9U5KqWtq++kW4URUUj6aOPEdGrN5I//QxFDx48c3vbgFDYqS10lI4MTWsbL9gWZFfzWQQg9iLw62xgQwvg5EogM7aaz1nzjB49Grdv38bu3bvx4MEDHD16FL1790Z6errY0UrZvn07lixZgn379qGwsFDsONXO2dkZVlZWYscgqjIWMXpKEAT0798f/v7+pdZ99dVXsLe3R3x8vAjJns+cOXNgbGyMQ4cOiR2FqpkgCMj75x/EzXoVUUOGIPOnn6DOz6/YvgUFmJzmXc0JyVD5SnT8xS0/Dbi0CdjSDvhxLPDgb0Bt+MOAZ2Vl4cKFC1i7di369OkDT09PdOnSBUuXLsVLL70EANiwYQNat24Na2treHh4YN68ecjLy9McIz09HRMmTEC9evVgZWWF1q1bY9++fSXOI5PJMHXqVEilUri5uWH9+vWVzhodHY3Lly/jvffeg4+PD3755Zdy91m9ejXq1q0LGxsbzJo1C++99x7atWtXarsvvvgCbm5ucHJywvz586FQKDTr9u7di06dOsHGxgaurq6YOHEiUlJSNOvPnTsHiUSCv//+G+3bt4elpSX69u2LlJQUHD9+HM2bN4etrS0mTpyI/CfePyvymrA5Gek7FjF6SiKRYOfOnQgICMC3336rWR4dHY0lS5Zg69atqF+/vogJ/yWXyyu0XX5+Pvbv348lS5Zgx44d1ZxK9yr6Ohg6VZ4MGXv2ImrgIDyc8xpkFy8CVRgksdtdjkxGVeOXJtIFHkENhJ8AfhoHbGkLXNgAyEo3lzQUUqkUUqkUR44cQVHR05vUGRkZYcuWLbh37x52796NM2fOYMmSJZr1hYWF6NixI44dO4a7d+9i9uzZmDJlCq5du6bZZvHixfjnn3/w22+/4cSJEzh37hxu3bpVqaw7d+7EkCFDYGdnh8mTJ2P79u3P3P7HH3/EmjVrsHbtWty8eRMNGjTA119/XWq7s2fPIjIyEmfPnsXu3buxa9cu7Nq1S7NeoVDg448/RlBQEI4cOYKYmBhMnz691HFWrVqFbdu24fLly3j48CHGjRuHTZs24aeffsKxY8dw4sQJbN26VauvCVFNxyJGj3l4eGDz5s1YtGgRoqOjIQgCZs6ciQEDBuDcuXNo2LAhLC0t0bRpU2zevLnEvufOnUOXLl1gbW0Ne3t7+Pn5ITa2uKlDUFAQ+vTpAxsbG9ja2qJjx464ceOGZt+LFy+iR48esLS0hIeHBxYsWACZTKZZ7+XlhY8//hhTp06Fra1thdvcHjp0CC1atMB7772H8+fP4+HDh8/cviJXmjIzMzF16lQ4ODjAysoKgwYNQnh4uGZ9bGwshg0bBgcHB1hbW6Nly5b4888/NftOmjQJzs7OsLS0hLe3N3bu3KnZ991334WPjw+srKzQqFEjrFixosQVtlWrVqFdu3b4v//7PzRs2BAWFhbYs2cPnJycSn2gjxgxAlOmTKnQ66Sv5DExSFq9BhG9eiH5k08gj32+pjXmN0NQT2WrpXRUW9iZ2aLVoztixyju+H/6w+KmZkfmAymhYifSOhMTE+zatQu7d+/WfM68//77CA4O1myzcOFC9OnTB15eXujbty9Wr16NgwcPatbXq1cPixYtQrt27dCoUSO88cYbGDhwoGabvLw8bN++HV988QX69euH1q1bY/fu3VAqK36RQ61WY9euXZg8eTIA4OWXX8bFixcRHR1d5j5bt27FzJkzMWPGDPj4+OCDDz5A69atS23n4OCAbdu2oVmzZhg6dCiGDBmC06dPa9a/8sorGDRoEBo1aoRu3bphy5YtOH78eIm7UUDxXR8/Pz+0b98eM2fOxD///IOvv/4a7du3R48ePTBmzBicPXtWa68JkT5gEaPnpk2bhn79+uGVV17Btm3bcPfuXXz33XeoX78+Dh06hPv37+ODDz7A+++/r3nTVyqVGDFiBHr16oXg4GBcuXIFs2fPhkQiAQBMmjQJ9evXx/Xr13Hz5k289957MDU1BQBERkZi4MCBGD16NIKDg3HgwAFcvHgRr7/+eolcX3zxBdq2bYvbt29jxYoVFXou27dvx+TJk2FnZ4dBgwaVuFr1NBW50jR9+nTcuHEDR48exZUrVyAIAgYPHqwpNubPn4+ioiKcP38ed+7cwdq1ayGVFs/IvWLFCty/fx/Hjx9HSEgIvv76a9SpU0dzbBsbG+zatQv379/H5s2b8f3332Pjxo0lzh8REYHDhw/jl19+QWBgIMaOHQuVSoWjR49qtklJScGxY8fwyiuvVOh10ieCICDvwkXEzZ6NyEGDkfnDD1A/UfA+F6USk5MaaedYVGt0s/aAkVCDmnKpioDAH4CvugE/jQdiL4udSKtGjx6NhIQEHD16FAMHDsS5c+fQoUMHzfv7qVOn0K9fP9SrVw82NjaYMmUK0tPTNU2jVCoVPv74Y7Ru3RqOjo6QSqX4+++/ERdXPPpbZGQk5HI5unbtqjmno6MjmjZtWuGMJ0+ehEwmw+DBgwEAderUwYABA57ZIiAsLAxdunQpsey/jwGgZcuWMDY21jx2c3Mr0Vzs5s2bGDZsGBo0aAAbGxv06tULADTP77E2bdpo/u3i4qK5ePbkssfH1cZrQqQPOMSPAfjuu+/QsmVLnD9/HocPH4azszM+/PBDzfqGDRviypUrOHjwIMaNG4ecnBxkZ2dj6NChaNy4MQCgefPmmu3j4uKwePFiNGvWDADg7f1v34NPP/0UkyZN0nSe9/b2xpYtW9CrVy98/fXXsLAo7mzdt29fvPPOOxV+DuHh4bh69aqmHfLkyZPx9ttvY/ny5Zri6kmPrzT98MMP6NevHwBg9+7dJZrQhYeH4+jRo7h06RJ8fX0BFDcB8PDwwJEjRzB27FjExcVh9OjRmitoT34oxMXFoX379ujUqROA4jtMT1q+fLnm315eXli0aJGmOdxjcrkce/bsgbOzs2bZxIkTsXPnTowdOxYA8MMPP6BBgwbo3bt3hV+vmk4tkyHryBFk/vgT5FFR1XaedoG5QL1qOzwZIL9CRfkbiUIAHvxV/FO/C+C3AGg2FHjK+5++sbCwwIABAzBgwACsWLECs2bNwsqVK9G7d28MHToUc+fOxZo1a+Do6IiLFy9i5syZkMvlsLKywueff47Nmzdj06ZNmr4zCxcu1Grz3O3btyMjIwOWlpaaZWq1GsHBwfjwww9hZFT1672PLwA+JpFIoP5ffyiZTAZ/f3/4+/vjxx9/hLOzM+Li4uDv71/q+T15HIlE8szjEtUWvBNjAOrWrYs5c+agefPmmtG7vvzyS3Ts2BHOzs6QSqX47rvvNFd2HB0dMX36dPj7+2PYsGHYvHkzEhMTNcd7++23MWvWLPTv3x+fffYZIiMjNeuCgoKwa9cuTVtnqVQKf39/qNXqErfeH3/xr6gdO3bA399fc6dj8ODByM7OxpkzZ566fUWuNIWEhMDExKTENk5OTmjatClCQkIAAAsWLNDcpl+5cmWJZg5z587F/v370a5dOyxZsgSXL5e8QnrgwAH4+fnB1dUVUqkUy5cvL3X1zNPTs0QBAwCvvvoqTpw4gUePHgEonn9g+vTpTy3W9I1aJkPaN98ivG8/JH+8uloLGAAwvvMAzRR1yt+Q6H98H90XO0L54q8BByYD2zoDN3cZ3BDNLVq0gEwmw82bN6FWq7F+/Xp069YNPj4+SEhIKLHtpUuXMHz4cEyePBlt27ZFo0aN8OCJ0QsbN24MU1NTBAQEaJZlZmaW2OZZ0tPT8dtvv2H//v0IDAzU/Ny+fRuZmZk4ceLEU/dr2rQprl+/XmLZfx+XJzQ0FOnp6fjss8/Qo0cPNGvWrMRdmqp63teESF+wiDEQJiYmMDEpvrG2f/9+LFq0CDNnzsSJEycQGBiIGTNmlLiys3PnTly5cgW+vr44cOAAfHx8cPXqVQDFfTnu3buHIUOG4MyZM2jRogV+/fVXAMV3QObMmVPizT4oKAjh4eGauzoAYG1tXeHsKpUKu3fvxrFjxzTPw8rKChkZGdXewX/WrFmIiorClClTcOfOHXTq1EnTOXLQoEGIjY3FW2+9hYSEBPTr1w+LFi0CAFy5cgWTJk3C4MGD8ccff+D27dtYtmxZqatnT3sd2rdvj7Zt22LPnj24efMm7t2799SOnPpEXViI9O07EDHgRaRu2gR1dnUPX/s/goCJ8Q10cy7Se02kHnDJTih/w5oiPRz4/U1gU2vg8lZAUSB2okpJT09H37598cMPPyA4OBjR0dE4dOgQ1q1bh+HDh6NJkyZQKBTYunUroqKisHfvXnzzzTcljuHt7Y2TJ0/i8uXLCAkJwZw5c5CcnKxZL5VKMXPmTCxevBhnzpzB3bt3MX369ArfPdm7dy+cnJwwbtw4tGrVSvPTtm1bDB48uMwO/m+88Qa2b9+O3bt3Izw8HKtXr0ZwcHClLkY1aNAAZmZmmud/9OhRfPzxxxXevyzP+5oQ6Qs2JzNAj5tPzZs3T7Psybspj7Vv3x7t27fH0qVL0b17d/z000/o1q0bAMDHxwc+Pj546623MGHCBOzcuRMjR45Ehw4dcP/+fTRp0kRref/880/k5ubi9u3bJdoO3717FzNmzEBWVlapmZKfvNLUoEHxl9jHV5oetylu3rw5lEolAgICNM3J0tPTERYWhhYtWmiO5eHhgddeew2vvfYali5diu+//x5vvPEGgOJx9KdNm4Zp06ahR48eWLx4Mb744gtcvnwZnp6eWLZsmeY4sZXoqD5r1ixs2rQJjx49Qv/+/eHh4VG5F62GUMvlyDpwEGnfffvUSSl1odmNFKChKKcmPeNr4iB2hKrJSwZOLAcubwNeeAvoNAMwMRc7VbmkUim6du2KjRs3IjIyEgqFAh4eHnj11Vfx/vvvw9LSEhs2bMDatWuxdOlS9OzZE59++immTp2qOcby5csRFRUFf39/WFlZYfbs2RgxYgSyn7hQ8vnnnyMvLw/Dhg2DjY0N3nnnnRLrn2XHjh0YOXLkU4uP0aNHY8qUKUhLK/3eNmnSJERFRWHRokUoLCzEuHHjMH369BKjppXH2dkZu3btwvvvv48tW7agQ4cO+OKLLzTDTz+P53lNiPSFRBCqMLYp1TirVq3CkSNHEBgYiC1btmDFihU4ePAgGjZsiL1792LLli1o2LAhAgMDER0dje+++w4vvfQS3N3dERYWhokTJ+Ljjz/G9OnTsXjxYowZMwYNGzZEfHw8pk2bhtGjR2Pt2rUIDg5Gt27d8Morr2DWrFmwtrbG/fv3cfLkSWzbtg1Acf+QhQsXVnjSyREjRsDCwgL79+8vsVytVqNevXpYvnw55s+fj+nTpyMrKwtHjhwBUNzc6/jx49ixYwfq1q2LZcuW4cyZM5g5c6Zm7PsRI0YgPDwc3377LWxsbPDee+8hIiIC9+/fh6mpKRYuXIhBgwbBx8cHmZmZmDdvHjw9PXHgwAF88MEH6NixI1q2bImioiK89957SElJQUBAAI4ePYrRo0dj79696Ny5M44dO4YPP/wQKpUKWVlZpX4n/5WdnQ13d3colUrs2bMH48ePr8qvXTSCQoGsw78g7dtvoXyiKaJYNr3VEJctnj2aHdG3Enf4Rl0VO8bzs60H9HgH6DAVMDYtf3vSiQEDBsDV1RV79+4VOwpRrcB7iwZozpw5GDVqFMaPH4+uXbsiPT29xF0ZKysrhIaGYvTo0fDx8cHs2bMxf/58zUST6enpmDp1Knx8fDBu3DgMGjRIM1BAmzZt8M8//+DBgwfo0aMH2rdvjw8++ADu7u5VypqcnIxjx45h9OjRpdYZGRlh5MiRZd7O//zzz9GjRw8MGzYM/fv3xwsvvICOHTuW2Gbnzp3o2LEjhg4diu7du0MQBPz555+aTpEqlQrz589H8+bNMXDgQPj4+OCrr74CAJiZmWHp0qVo06YNevbsCWNjY02h9dJLL+Gtt97C66+/jnbt2uHy5csVHoUNAOzs7DB69GhIpVJNPyZ9IKhUyPrlV0QOGoykVatqRAEDAKNinMvfiGo1C2NzdIwLFDuGduQ8Ao69DWzpANzaA6g4dK6u5efnY8OGDbh37x5CQ0OxcuVKnDp1CtOmTRM7GlGtwTsxRCLp168fWrZsiS1btogdpVyCWo2cY38i7csvIY+JETtOKRJ3V4ydZriTBtLz87Vvim9vnxQ7RvVwbAT0ehdoPQ5gv4cSLly4gEGDBpW5/r/zsVRUQUEBhg0bhtu3b6OwsBBNmzbF8uXLMWrUqKpGJaJKYhFDpGOZmZk4d+4cxowZg/v379fosfsFQUDuiZNI27YVReERYsd5pp0LfHDcunpHQyP9tcimJaYFHxc7RvVyaQ34rwYa9RY7SY1RUFCgGQnyabTZv5OIdIsd+6naffLJJ/jkk0+euq5Hjx44ftzAv1j8R/v27ZGZmYm1a9fW6AIm78IFpGzYiKL/DUdd0w2JsMXxtmKnoJrKL7kWFLjJd4A9wwGfgcCLq4E63uXvY+AsLS1ZqBAZKN6JoWqXkZGBjIyMp66ztLREvXqcrbAmUTx6hKRPPkXe6dNiR6kUiaMDJsyWQSnhhG9UkotlHZy6f0vsGLplZAJ0egXovRSwchQ7DRGR1rGIISIAxcMlZ2zfjrRvv4NQWCh2nCr5dV4r7LMLFTsG1TAjHVrjo1vHxI4hDgs7oOdioMscwMRM7DRERFrDHoBEhLwLFxE97CWkbt6itwUMAPQL45c0Ks1XVrXO2wahMLt4jpkvuwAhv4udhohIa3gnhqgWUyQkIPnTT5F78pTYUbRCYm2NV14Hco2KxI5CNYSRxAjnk7Jhl58pdpSaockAYMgXgIOX2EmIiJ4L78QQ1UKCXI60b75F5JChBlPAAIAgk2Fiuo/YMagGaWXjxQLmSREngS+7Aec/B5RysdMQEVUZixiiWibv4iVEvTQcqZs2QSgoEDuO1vneU4kdgWoQX4m12BFqHmUBcGY18M0LQMwlsdMQEVUJixiiWkKRmIj4BW/i4axZNXLCSm2xvBGCump+caVifukJYkeoudLCgF2DgV/nArJ0sdMQEVUKixgiAycoFEj77vvipmMnTogdp/opFJiazPkxCLAxlaJ1fLDYMWq+oJ+AbR2Bm7sBdpMlIj3BIobIgBUEByNqxEikbtgAIT9f7Dg60yFIJnYEqgG6ST1hLLB5YYUUZAK/LwB2DwMyosVOQ0RULhYxRAZIUCiQsmkTYiZMhDwyUuw4OmcSFAZvhZPYMUhkvkVKsSPon5gLwNd+QMC3vCtDRDUaixgiA1MY9gDR48Yj/ZtvAVUtvQqtVmNigqfYKUhkfgmc+LRKFDLg+BJg11DelSGiGotFDJGBEFQqpH37HWLGjEFRSIjYcUTX4maa2BFIRA2t68Et86HYMfRb7MXiEcxu7BA7CRFRKSxiiAyAPCYGsZMmI3XjRggKhdhxagRJWBQ6F7mLHYNE4mfG5oRaIc8D/ngL+GEMkJModhoiIg0WMUR6LvPgQUSNHIWCwECxo9Q4Y2NdxY5AIvHN5pDBWhVxEviqG3D3F7GTEBEBACSCwJ57RPpIlZWFxBUrkHvylNhRaiyJRz2MnZwsdgzSMTMjM1x8mABLee0ZkU+nOkwFBq0DTC3FTkJEtRjvxBDpIdmVK4gaPoIFTDmEh48wQNZQ7BikYx1sG7KAqU639gDf9QaS74udhIhqMRYxRHpEUCiQ/PnniHtlJpTJvMNQEcOi7MWOQDrmpzIRO4LhSw0Fvu/LTv9EJBoWMUR6oig6GjHjX0bG9h2cv6ES3K9GwRgSsWOQDvkmc1hgnVAWFHf6PzgNKMwWOw0R1TIsYoj0QO7Zs4gZOw6F99l8o7LUaekYleMjdgzSEWcLR/gkc34Ynbp/pHgo5vgbYicholqERQxRDSYIAlK/+grx8+ZDnZcndhy9NSDMQuwIpCPdLeuJHaF2yooDdgwELm8VOwkR1RIsYohqKLVMhkcLFiBty1Y2H3tODgFhsFKbih2DdMAvnx36RaNWACeWAz+/AnBgBSKqZixiiGogeWwsYl5+maOPaYmQm4eJmU3FjkHVzEhihO4Pg8WOQXcPA9tfBDJjxE5CRAaMRQxRDZN3/jyix45DUXiE2FEMygv3eTfL0DW38YSDjJNc1gjJd4qHYY48K3YSIjJQLGKIapC0b7/Dw9fmQp2TI3YUg2N9PRR11NZix6Bq5CuRih2BnlSQCfwwGri0WewkRGSAWMQQ1QDq/HzEL3wLqRs3Amq12HEMklBUhMkpTcSOQdXILyNJ7Aj0X4IKOPkB+8kQkdaxiCESmTw+HjETJiL3r7/EjmLwOgcXiB2BqonU1Bpt44PEjkFl0fSTiRU7CREZCBYxRCKSXb6MmNFjUBQWJnaUWsE0MAxeSnuxY1A16CL1hIlaKXYMepbkO8D/9Qce3RQ7CREZABYxRCJJ374Dca/OhiqbM13rjEqFKQkNxU5B1cBPzmaYekGWAuwaCoQeEzsJEek5FjFEOiaoVEh4fxlSPv8cUKnEjlPrtLyVKXYEqga+CaFiR6CKUuQDByYDV78ROwkR6TEWMUQ6pC4qQvyCN5H9yy9iR6m1jEIi0E7uKnYM0iJPa3fUz4gTOwZVhqAG/noXOP4eBzMhoiphEUOkI6q8PDyc9SryTp8WO0qtNz7OXewIpEW+pnXEjkBVFfA1cHAKoOCgG0RUOSxiiHRAmZ6O2KlTkX/9uthRCECT6wliRyAt8stlE0G9FvoHsGsIkJcqdhIi0iMsYoiqmTz+EWInTkLR/RCxo9D/CDHx6F3gKXYM0gJTI1N0jgsUOwY9r0c3ge39gYxosZMQkZ5gEUNUjYrCwxE7cSLksZwboaYZEeUodgTSgvY2DWEll4kdg7QhMwbYOQhI4SANRFQ+FjFE1ST/9m3ETJ4CZUqK2FHoKeoFxMAYErFj0HPyVZuKHYG0KTexuJB5dEvsJERUw7GIIaoGeRcuIO6VmVBzDpgaS0hOxUs5TcSOQc/JL5V3OQ1OQQawZzgQc0nsJERUg7GIIdKy7GPH8HDefAgFHG2npvMPtxI7Aj0HJ3MHNE1kXzODVJQD/DAaCD8pdhIiqqFYxBBpUcaPPyJh8RJAoRA7ClWAU0A4LAQTsWNQFfla1YcEgtgxqLooC4B9E4C7nFeLiEpjEUOkJanbvkTyx6s5cZseEbJz8HJmU7FjUBX55vNup8FTK4DDM4Fbe8ROQkQ1DIsYIi1I/vRTpG3bJnYMqoJeIezcr48kkMD3YbDYMUgXBDVwdAFwY4fYSYioBmERQ/ScUtZvQMZuXiXUV9LroXBQW4odgyqpmU0DOMrSxI5BOiMAf7wN3NordhAiqiFYxBA9h7Rvv0P699+LHYOeg1BQiMlp3mLHoEryNbYVOwLpnAD8vgAI3Cd2ECKqAVjEEFVRxt4fkLpxo9gxSAu63JGLHYEqyS+T8y/VSoIa+G0ecOdnsZMQkchYxBBVQdYvvyL5k0/EjkFaYn4rFA1U9mLHoAqyMrFCu7hAsWOQWAQ18Mts4N6vYichIhGxiCGqpJy//kbiihWAwKFdDYZSiUkJXmKnoArqYuMJUzWHMa/VBBVweBYQ8ofYSYhIJCxiiCoh759/8GjxYkClEjsKaVnbwByxI1AF+bL1HwGAWgn8PAMI+0vsJEQkAhYxRBUkC7iG+AVvciJLA2V0LxytFC5ix6AK8Et6IHYEqilUcuDgVCD6vNhJiEjHWMQQVUBBcDDi582DUFQkdhSqLoKAl+PqiZ2CylHfyhUN0qLFjkE1iaoI2D8JSOS8QUS1CYsYonIUhoUh7tXZUMtkYkehauZzI1nsCFQOP3NnsSNQTVSUA/w4BsiMETsJEekIixiiZyiKjkbcKzOhzs4WOwrpQlQs/Arqi52CnsE3l32XqAx5ycDeUQAnQSWqFVjEEJVB8egR4l6ZCVV6uthRSIdGx9QVOwKVwcTIBF05tDI9S0Zk8R2ZojyxkxBRNWMRQ/QUyvR0xL7yCpSJiWJHIR3zCIiFhKNn10htbRrCuihX7BhU0yXcBg5OAVQchIXIkLGIIfoPdVER4ufNhyI2TuwoJAIhMRlDZU3EjkFP4SeYix2B9EXkGeDIXM7nRWTAWMQQ/Ufi0vdREBQkdgwS0aBwqdgR6Cl8U3lhgSrhziHg5AqxUxBRNWERQ/SE1K3bkPPnn2LHIJE5B0TATDAWOwY9wdHcHi0S7okdg/TN5a3ArT1ipyCiasAihuh/sn//A2lffil2DKoBhMwsjM32ETsGPaGbVX1IwKZBVAV/vA3EXBI7BRFpGYsYIgD5t28jcdkysWNQDdI3xETsCPQEvwJONEtVpFYUd/TnHDJEBoVFDNV6ikePEP/6GxDkcrGjUA1iey0MdoKF2DEIgAQS+MbfFTsG6bP8dOCnlwGObkdkMFjEUK2mKFLh6N5HyGk1QOwoVMMI+fmYmOYtdgwC4GPTAHVyk8WOQfouNQT4eSagVoudhIi0gEUM1Wqnd99HUkwerhn1QOqopWLHoRrG965S7AgEwNfYVuwIZCjC/+aIZUQGgkUM1Vo3/oxB5K1UAMVTCdzJqI+YiRshmFuKnIxqCvMbIXBT2Ygdo9bzy0oROwIZkivbgFt7xU5BRM+JRQzVSjHBaQj4ParU8qgEM9wdsRFKF08RUlGNo1RiSlJjsVPUapYmlugQx3mbSMuOvQ08vC52CiJ6Dhx+h2qdzCQZTu64h7JGa01NViG/6/toF/0DzO9c0G04qnHaB+UC9cROAagKVEj5JQU5t3KgzFHCwtMCbhPdYNXI6qnbx38fj6xLWaWWm7ubw/uT4r4+WZezkPRzEtSFajj0cIDbBDfNdvJUOWK+iEHjVY1hbCnenDmdpJ4wVYWJdn4yUCo5cGgaMOc8YF1H7DREVAW8E0O1SlGBEn9+fQfyQtUzt5PlKBHgNhG5/afpKBnVVMbBD9BUIf6XnEc7HyHvXh7qz66PJqubQNpSipjPY6DIVDx1e7dJbmi6qem/PxuawtjaGLadi/uXKHOVeLTzEdzGu8FrkReyLmchJzBHs3/C3gS4jHURtYABAD8lP6aomuQ8An6eAaif/XlARDUTPx2oVjm18z6ykvMrtK1SrsZ1VRckj/2gmlNRjSYImBjfQNQIarkaOTdy4DrOFdZNrWHuYg6XkS4wq2uGjDMZT93H2MoYpvammp+C6AKo8lVw6OEAoPhOi7GlMey62sGqkRWsm1ujKKF4Lpasq1mQGEtg18lOZ8+xLL5J4WJHIEMWfR44/ZHYKYioCljEUK0RdOYhYoLTKreTANxLdUHkxC1QW0qrJxjVeM1vpop6fkElAGpAYiYpsdzIzAiyB7IKHSPzfCasW1jDrI4ZAMDcxRxquRoFsQVQ5ilREF0ACw8LqGTFzdbcJruVc8Tq525ZFw1TI8WOQQZMkBjhYlwBTt7nEN5E+oZFDNUKafF5uPJL1b8MxSYY487Q9VDWa6LFVKQ3wqPRtUi8jjHGlsawbGKJlN9SoMhUQFALyLqchfyIfCizyx8GWpGpQO6dXDj2cvz3mNbGqP9qfcR/H4+oj6Jg72sPm9Y2SNyfCMd+jlCkKRDxQQTCl4Uj+3p2dT69MvlauIhyXqod1JZ18JnTJ5gc3guLDgXhYUbF7tITUc3AIoYMnlKuwont96BSPt8EZ+mpStzosBiFHfprKRnpkzEx4n6hrj+7PgAg7K0w3Jt1D+kn02HXzQ4SiaScPYGsS1kwtjKGTYeSw0XbdrSF92pv+KzzgctIF8hCZSiKL4Jjb0c8/Poh3Ca6ocHrDfBoxyMoc3Q/Z45fXk75GxFVQV7djhiu/BTf/q+paHaBAq/vuw35c35OEJHusIghg3fx5whkJlasyU158nOVCHAajeyBs7VyPNIfXgEPRT2/eV1zNFraCC2+bYGmG5qi8crGEFQCTJ1Nn7mfIAjIvJAJe197GJmU/ZavVqiRsCcB7tPcIU+RQ1AJsG5mDXM3c5i7miM/UrdXqU0kJugaF6zTc1LtcMdjEjrGv4U7udYllgc9zMInf4aIlIqIKotFDBm0qMBU3Dv/SKvHVCnUuFnYFgnjV0MwEnfkJtId4VEiBsnEnzPGyNwIpvamUMlUyLuTB9sOz57NXhYqgzxZDoeeDs/cLvVoKqStpbD0soSgLu6D85igLPlYF1rbesGmUJxmbGSYBHMbfOOyCsPCh6BI/fSvP7sux7B/DJGeYBFDBkuWVYSze0Or7fihyQ4If3kL1FL7ajsH1SxDIp9dMFSn3Du5yA3OhTxVjry7eYj+LBrmbuZweKG4OEk6lIT47+JL7Zd5PhOWjSxhUd+izGMXPipE9rVsuIwqbjJn7mYOSICMfzKQG5iLosQiWDayrJ4nVgZfoey8RJVV6NgcU4zX4bNYn3K3fe9wMFJzi3SQioieBye7JIMkqAWc3HkfhbKnz6GhLfEJgMx/LVrd3ALTmHvVei4Sn8vVSJi0NoJSovt28+oCNZIOJUGZqSye76WTLVxGu0BiUtwnRpmlhDxdXmIfVb4KOTdz4Dax7JHGBEFAwq4EuE5whZF58XUtIzMj1JtVD4l7EyEoBLhNcYOpw7ObrWmbX3rpgoyoKqLrj8CI2FHIVlTsK0+6TI4lPwdh54wu1ZyMiJ6HRBCEMuYtJ9Jft/6OxZVfdTc0q4W1CdqnHoXltWM6OyeJ4/DcVjhgX313+AiwN7PDPw/uwUhgJ2uqOsHEAj/XXYDFUe2qtP/Hw1tiSncvrWYiIu1hczIyOCmxOQg4GqXTcxbKlAiwGYLMoQt0el7Svf4PzMSOYPC6WddnAUPPRWHnhTcs11W5gAGANX+GICIlT3uhiEirWMSQQZEXKnFi+z2oVbq/wahWCbid1xTxE9ZCMGZLTUNlHxAGqcBCpjr5FlZvM1AybMnu/dEzexX+SK3zXMcpVKjx1oFAKFQsqIlqIhYxZFAuHHiA7JQCUTM8SJQibNwWqBzqipqDqoeQJ8PE9PI7B1PV+T66L3YE0kOCkQlO1X8dXaNeQWKhdi403HmUjY0nH2jlWESkXSxiyGCE30hG6JUksWMAABISBQT2WQ1Fk/ZiR6Fq4HePV2arSxOpB1yyE8SOQXpGZe2KD+w/xawIX60f+5t/InE9JkPrxyWi58MihgxCTnoB/vkpTOwYJWRnKBDgPQf5fqPEjkJaZnUjFHXV1uVvSJXmZ/Ls+WyI/ivLtTsGFa7B3oR61XJ8tQC8dSAQsiJltRyfiKqGRQwZhHM/hKIov+Z9wMgLVAgw74e0EYvFjkJaJMjlmJLsLXYMg+SblSp2BNITAiS44TEDneNexwNZ9c5jFJ9ZgM//rlkXyohqOxYxpPceXE/Cw5BMsWOUSVADwVleiJ24Hmozc7HjkJZ0DJKJHcHgWBibo+PDILFjkB5QWzhgU92PMSZ8ABRqiU7OuedKDG7F1dzPGqLahkUM6bWifAUuHooQO0aFRCZYIGTUZqjqVE+TB9Itk6AwNFY6ih3DoHS0aQhzZaHYMaiGy6/TFmOEz7A5rpFOz6sWgPcOB0OuZJ84opqARQzptStHolCQIy9/wxoiOUmFWy+shLx5N7Gj0PNSqzHpkafYKQyKn5IfSfRsYR7j0DlxMW5l24hy/gfJefjqnH5cOCMydPzEIL2VFJWNexceiR2j0nKzFAhoMA15vSeKHYWeU8tbHLFIm/ySI8WOQDWUYGqNPW7L4R8+AjKVuF9dvjobifDkXFEzEBGLGNJTapUa534MA3Q/p6VWKIrUuC7xQ+roZWJHoecgCY1ER7mb2DEMgqulMxqlhIsdg2oguYM3ZpqtxQfRLcSOAgCQq9RYcjgYarWefgARGQgWMaSXgk7HI/1RntgxnosgAHfS3RE9cRME8+odWYeqz7gYFjHa4GvhKnYEqoHi6w9B9/TlOJNes/qf3Y7Lwu4rMWLHIKrVWMSQ3snNKMS1Y9Fix9Ca6ART3B2+CUo3L7GjUBU0uq5/TRprIl+Zfl+UIO0SjM3we7238ULEJKTLTcWO81Sf/x2G+Mx8sWMQ1VosYkjvnN//AMoildgxtCo1RYmbnZeiqE1PsaNQJQlxj9A/30vsGHrNWGKMbnEcWpmKKW3qY5F0Ld6I7CR2lGfKl6vw0e/3xY5BVGuxiCG9EnU7FTHBaWLHqBayHCUCXCcgZ8AMsaNQJb0UyVnmn0dLGy/YFWTp7HznY5UYti8f7utzIfkwB0dCFSXWC4KAD84Wwm19LizX5KD/HhnC05994eTr63K0+ToPtp/mwPbTHHTfLsPx8JLHffvvQjiuzYHHxlz8GFxy3aF7Cgzbx6v66W690DfvIxxOdhE7SoWcuJ+MC+GcoJVIDCxiSG/IC5W4cPCB2DGqlVKuxg1FJySNWwVBopsJ3HRBJQjYkpaKAVGRaP8gDP5Rkfg6LQ2C8OyOsXK1GptSU9EvMgJtH4Shf2QEDmdnadZflskwKCoSncMf4N3EBMifOF6uSoVBUZF4pFA85cja5RYQDWMYzu9L1/wkVjo9n0wuoK2LEb4cbPHU9esuybElQI5vhlggYJY1rM0k8P8hH4XKsv9e69tK8Fl/c9ycbY0bs63R18sYw/cX4F5KcfHze5gCP91R4MQUa6zrb4FZvxcgLb94vpHsQgHLzhSVmac2ECRGuOgxB51iZiOuQL9eh1VH70Gh4twxRLrGIob0xrWj0cjLLBI7hk7cT3FG5IQtUFvbih1FK/4vIx37s7KwvK4L/mjYEG87O2N7RgZ+yHr27NdvJSbgar4MH7u64c+GDfGFez00NDUDAKgFAYsTEzDe3gE/NfDEvcJCHMrK0uy7ITUV4+0dUM+0+tvTC6lpGJHjU+3nMVS+6brtVzTI2xSr+1pgZPPSfxuCIGBTgBzLe5pjeDNTtHExxp4RlkjIFXAkVFnmMYc1NcVgb1N4OxnDx8kYa/pZQGoGXI0vLmJC0tTo7WWMTu7GmNDaFLbmEkRnFhdFS04WYm4nUzSwq50fyWqrOvjU6RNMDu8FQdC/iwGRqTLsvhwjdgyiWqd2vmOS3kmNy0XwuXixY+hUXIIRggd/DqVHU7GjPLfAggL0lUrRSypFPVMz+NvYws/aCncKyp6d/YIsDzfy8/FNfQ/4WlujnqkZ2llaooNV8VX7TJUKmSoVJtjbw9vcHH2kUkTJi4vc2wX5uFtYiCkOumvm9eID/bp6XFPYmErROv6O2DE0orMEJOUJ6N/IRLPMzkKCrvWNceVhxfriqdQC9t9VQKYAunsYAwDauhjjRoIKmQUCbiaoUKAQ0MTRCBfjlLiVpMKCrmbV8nxquty6nTBM8Sm+i28gdpTnsvlUOFJza8dFNqKagkUM1XiCWsC5H0Mh1MIx+TNSlbje9m0UdnxR7CjPpZ2lJa7KZIiRywEAoYWFuFVQgB5S6zL3OZuXh5YWFtiRkY7ekREYFBWJdSkpKFQXN9twNDaGs7EJLuXLUKBW42Z+AXzMzaEQBHyUnIxVrq4w1mGTPMerD2AhmJS/IZXQTeoJY6HmDNSRlFf89+ViXfJvx8VagiTZs5sM3UlWQfpJDsxX5+K1Pwrw63hLtHAuLmL8m5hgchtTdP4+D9N/K8DuEZawNgPmHivEN0Ms8fUNBZpuy4PfDpmmCZqhC/aYgk7xC3Evt+z3AX2RW6TE2r9CxY5BVKuwiKEaL+RKIlJia+/syAV5SgQ4jEDW4LliR6myVx2dMNjWFkOio9AmLBSjY2MwxcERw2ztytwnXq7ArYIChBcVYYt7PbxX1wUncnPwUXIyAEAikWCDuzu+SU/HSzHRaG5hjlF29vi/9HR0sbKCmUSCSbGxGBwVhR8zn91sTRuE3FxMzGhW7ecxNH5F1d9nSVea1jFC4GtSBMyyxtxOZph2pBD3U/8tSFb1tkDEAhvcmSvFyOam+PSCHP0bmsDUGFh9vggXZ1hhVntTTD1SIOKzqH6CuS2+dvkQL4UPQpHacL6GHL4Vj9tx1f9eQ0TFDOfdgwySUqHC9T8MZ06YqlIpBdzKb4WElz+BYGQsdpxK+ys3F3/k5OBzN3f87OWFT13dsDMjHUeys8vcRw0BEgDr3NzRxtISvaRSvFvXBb/lZGvuxnS0ssJBTy+cbNQYK1xc8UihwG852VhQxxnvJSZgrL099jZogK/T0xBWWHbTNW3peb/23S18Xn6PatbVa1dp8cdisqzk7zJZJsDV+tkfmWbGEjRxNEJHd2N82t8CbV2MsPmq/Knbhqap8MMdBT7ua45zMUr09DSGs7URxrU0xa1ENXKLDPNvqdCpBSYbrcXaWG+xo2idIBR38i9vwBIi0g4WMVSj3Tn7qNZ05q+I0CQ7PHh5C9S2TmJHqZQvUlMw6393Y3zMLfCSnR2mOTri+4z0MvdxNjFBXRMT2Bj/W7Q1MjODACBJ+fQO1quSkrCkbl0IgoCQoiL429jAycQEnSytcL2g+oevtb4eCie1bkfa0meNpPXhmlWz+ro1tJfAVSrB6ah//8ZyigQExKs0/VsqSi0AT5vSShAEzPmjEBteNIfUTAKVGlD8r6Xa4/+qDPB7cFT9keic/B4uZZZ9B1bfBcVn49DNmvU3TWSoWMRQjVVUoMTNv2PEjlHjPEoAAgd8CkWjNmJHqbACtbrUm40RJFA/44ple0srpCqVkKn/7YcQI5fDCICrSem+J4ezsmBnbIS+Uhs8/t6o/N/xlRCgiy5VQlERpqQY3hXm6uJr4ijKefPkAgKTVAhMKv5Lic5UIzBJhbhsNSQSCRZ2NcPqC0U4GqbAnWQVpv5aAHcbCUY0+/fvrt8eGbZd+/cuy9JThTgfq0RMlhp3klVYeqoQ52JUmNS69Aho/3dLAWcrCYY1LV7n18AEZ6KVuBqvxMYrRWjhbAR7C/0bpassgoklDrq/i74RY5GrNPx+YxtPPkChonb0ayISk+G/m5Deun0iFkWysoc0rc2y0hW43mwe2tU9Bqurv4sdp1x9pFJ8m5EON1NTNDE3Q0hhEXZnZmCU3b9XZDekpiBFqcRnbu4AgCG2tvgmPQ3LEhPxep06yFKp8EVqCkbZ2cHCqGRJlK5U4puMdPzYoHiEIztjYzQyM8OezEz4WVvjqiwfcxx1c/eq851CwFUnp9J7fjll34mrTjcSVOiz+987c2+fKAJQhGltTbFrhCWW+JlBphAw+/dCZBUKeKGBMf6abAULk38Li8gMtWaeFwBIkQmY+msBEvME2JlL0MbFCH9PtsKAxiU/ZpPz1FhzoQiXZ/7bmb1LPWO8090cQ34qQF1rCXaPsKy+J69jCruGeFP9Fv6MqiN2FJ1JzC7EnisxmN2zsdhRiAyaRGDjTaqB8nPk2LviCpRPa4tBGkbGErSxfgDHo5vEjvJMMrUKW9LScCo3FxkqFeqamGCwjS3m1qkDs/+NIPZ+YgIeKRTY3cBTs19UURHWpCTjdkEB7I2N4W9jgzfrOJcqYhYlPEJ7SytMemJI5eCCAryflIh0pRJTHBwxr46OvkQZG2Px2w6INcnSzfn0lLmxOS7GxsNCYdid2GuzpHoDMCJ+EpKKat/w0fZWpji/pA9sLap/niqi2qpGFjExMTFo2LAhbt++jXbt2okdp5Tp06cjKysLR44cETsKvLy8sHDhQixcuLBC29f01/ax8/vCcOcf3U6Ap8+83WSod2g5jJRP70RMuhU0uTPWeNwWO0aN1t2+Kb67fVLsGFQNBCMTnHKfh1cjuokdRVTz+zTGYn+OWEhUXbTWJ2b69OmQSCR47bXXSq2bP38+JBIJpk+frq3TVbs5c+bA2NgYhw4dEjvKM12/fh2zZ88WO4ZW5aQV4N7FBLFj6JXwRGuEjd0MlaOL2FEIQOvbWWJHqPH8VPo3yh6VT2XtiuV2n9X6AgYAdlyMQUpO9Y+KSFRbabVjv4eHB/bv34+Cgn+bBxQWFuKnn35Cgwbiz8Yrl1fsKnV+fj7279+PJUuWYMeOHdWc6vk4OzvDysqwRkMKOBoFtSEOzVPNEhPVuN3rY8h9OokdpdYzuheONnIWlM/im8yh0w1NlqsvBhauwY+J7mJHqREKFCpsPh0udgwig6XVIqZDhw7w8PDAL7/8oln2yy+/oEGDBmjfvr1m2V9//YUXXngB9vb2cHJywtChQxEZGVnqeFFRUejTpw+srKzQtm1bXLlypcT6ixcvokePHrC0tISHhwcWLFgAmUymWe/l5YWPP/4YU6dOha2tbYXvWBw6dAgtWrTAe++9h/Pnz+Phw4fP3L5379544403sHDhQjg4OMDFxQXff/89ZDIZZsyYARsbGzRp0gTHjx/X7LNr1y7Y29uXOM6RI0cgeWKG8cjISAwfPhwuLi6QSqXo3LkzTp06VWIfLy8vbNq0SfM4PDwcPXv2hIWFBVq0aIGTJ09CIpE8tembIAho0qQJvvjiixLLAwMDIZFIEBERUc4rpX1p8XkIv56s8/MaipxMBa41mgVZj7FiR6n1Xo6rJ3aEGquuRR14J4eJHYO0RIAE1zxmomPsPITLDGdQAm04cP0hYtJk5W9IRJWm9SGWX3nlFezcuVPzeMeOHZgxY0aJbWQyGd5++23cuHEDp0+fhpGREUaOHAn1E0OpAsCyZcuwaNEiBAYGwsfHBxMmTIDyf/NDREZGYuDAgRg9ejSCg4Nx4MABXLx4Ea+//nqJY3zxxRdo27Ytbt++jRUrVlToOWzfvh2TJ0+GnZ0dBg0ahF27dpW7z+7du1GnTh1cu3YNb7zxBubOnYuxY8fC19cXt27dwosvvogpU6YgP7/ic1Xk5eVh8ODBOH36NG7fvo2BAwdi2LBhiIuLe+r2arUao0aNgpmZGQICAvDNN9/g3XffLfP4Eomk1O8LAHbu3ImePXuiSZMmFc6qLQG/RaLm9dLSL/JCFa6Z9kbayLJ/91T9vK8nih2hxvK1dBM7AmmJ2sIBG+quxrjwflAJnLXhv5RqAV+cYMFOVB20/o4zefJkXLx4EbGxsYiNjcWlS5cwefLkEtuMHj0ao0aNQpMmTdCuXTvs2LEDd+7cwf3790tst2jRIgwZMgQ+Pj748MMPERsbq7k78Omnn2LSpElYuHAhvL294evriy1btmDPnj0ofGJm7r59++Kdd95B48aN0bhx+cMdhoeH4+rVqxg/frzm+ezcubPcGXjbtm2L5cuXw9vbG0uXLoWFhQXq1KmDV199Fd7e3vjggw+Qnp6O4ODgCr2Oj485Z84ctGrVCt7e3vj444/RuHFjHD169Knbnzp1CqGhodizZw/atm2Lnj174pNPPnnmOaZPn46wsDBcu3YNAKBQKPDTTz/hlVdeqXBObUmMyELMHXGGXDU0ghoIzmyAmIkbIJhZiB2nVhJiHqJXgfjNaGsiPxmvTBsCmXM7jBHWYmtcQ7Gj1GjH7iTiXkK22DGIDI7WixhnZ2cMGTIEu3btws6dOzFkyBDU+c/QpuHh4ZgwYQIaNWoEW1tbeHl5AUCpOwxt2vw7mZ+bW/GVu5SUFABAUFAQdu3aBalUqvnx9/eHWq1GdPS/ba07dapc/4AdO3bA399fk3nw4MHIzs7GmTNnnrnfk1mNjY3h5OSE1q1ba5a5uLiUyF8ReXl5WLRoEZo3bw57e3tIpVKEhISUeScmJCQEHh4ecHf/tz1y9+7dn3kOd3d3DBkyRNP35/fff0dRURHGjtV9c6QrR0o3KaTnE5VgjnsjN0HlXF/sKLXSiGjdzE2jT4wkRuj+MEjsGPScQj3Go0vCItzKloodpcYTBODLs7pvnk1k6KplsstXXnlF06zryy+/LLV+2LBh8PT0xPfffw93d3eo1Wq0atWqVMd7U9N/x1d/3FfkcZOzvLw8zJkzBwsWLCh1/CcHEbC2ti61viwqlQq7d+9GUlISTJ6YEVylUmHHjh3o169fmfs+mfVx3mflNzIyKnV3R6FQlHi8aNEinDx5El988QWaNGkCS0tLjBkzpsIDFFTUrFmzMGXKFGzcuBE7d+7E+PHjdT5YQMydNCRG8EpVdUhJViHfdwXaRuyG+b3LYsepVepfjYWkOSAYzuTrz62ljRfsos6JHYOqSDCzxm6nt7EqvLnYUfTKX3eTEJGShyZ1WfQRaUu1FDEDBw6EXC6HRCKBv79/iXXp6ekICwvD999/jx49egAo7qBfWR06dMD9+/e12m/jzz//RG5uLm7fvg1j43+H/7x79y5mzJiBrKysUp3xq8rZ2Rm5ubmQyWSaQiswMLDENpcuXcL06dMxcuRIAMWFW0xMTJnHbN68OR4+fIjExETNnaurV6+Wm2Xw4MGwtrbG119/jb/++gvnz5+v2pOqIkEQcPW3KJ2es7bJy1YioN4UtHdpBJszP4gdp9YQklPwUl4z/GbDq7CPdZdU/MIS1SxFDj6YK38TZ6Idyt+YSlALwFdnI7BhfDuxoxAZjGrphWdsbIyQkBDcv3+/RDEAAA4ODnBycsJ3332HiIgInDlzBm+//Xalz/Huu+/i8uXLeP311xEYGIjw8HD89ttvpTr2V8b27dsxZMgQtG3bFq1atdL8jBs3Dvb29vjxxx+rfOz/6tq1K6ysrPD+++8jMjISP/30U6kBBLy9vfHLL78gMDAQQUFBmDhxYqnBD57Uv39/+Pj4YNq0aQgKCsKFCxewbNmycrMYGxtj+vTpWLp0Kby9vcttgqZt0UFpSI/P0+k5ayOlXI3rQnekjKnYABekHQPDeeX1SX4ZHPBAHz2sPwS+6ctwJp0FTFUdDUrAw4yKD+5DRM9WbUOJ2NrawtbWtvQJjYywf/9+3Lx5E61atcJbb72Fzz//vNLHb9OmDf755x88ePAAPXr0QPv27fHBBx+U6A9SGcnJyTh27BhGjx791MwjR47E9u3bq3Tsp3F0dMQPP/yAP//8E61bt8a+ffuwatWqEtts2LABDg4O8PX1xbBhw+Dv748OHTqUeUwjIyP8+uuvKCgoQJcuXTBr1iysWbOmQnlmzpwJuVxeaiQ5Xbh94ul9fKgaCMDdNFdETdwMtQWviOtCnYBwmAuc2BEApKbWaBNf8cFNSHyCsTl+q/cOekRMQrrctPwdqExKtYCvzrHvJ5G2SITyht0ivSeRSPDrr79ixIgRZW5z4cIF9OvXDw8fPtQMQqALiRFZ+OWLWzo7H/2rTl0TtLi4FiYJbMpX3Y691ga7He6Xv6GB6+fQAptu/SV2DKogpU19LDFahF+S64odxWCYGRvh/JI+cLXjqJFEz4uDutdyRUVFiI+Px6pVqzB27FidFjAAcIt3YUSTlqLEjY7vorBdX7GjGLxeIXyrBQDfIpXYEaiC0tx7o2/eRyxgtEyuUuPb87wbQ6QNteqT9ZNPPikxJPOTP4MGDRI7nij27dsHT09PZGVlYd26dTo9d2aSDDF30nR6TiopP1eJAOexyPGfJXYUg2ZzLRR2al559UvkpH81nSAxxnmPuegc/SriCvg3Wx32X3uI9LwisWMQ6b1a1ZwsIyMDGRkZT11naWmJevXq6ThR7XZmTwhCLrOTb03R3CUdrgdXQlJ73hJ06vzMDthWt/b2B/Gydsfvd8sfLZHEo7ZyxhrLRdj+yEPsKAZvfp/GWOzfTOwYRHqtWoZYrqkcHR3h6OgodgwCIMsuQti1JLFj0BNCkp2QN2ErGh9dDqO8LLHjGJyud+TYVvZUUwbP16xO+RuRaHLqdsaEzNm4l8EBP3Thp4A4vNHXGxamHPSDqKpqVXMyqjmCz8RDreQV/5rmYYIEwQPXQtmAVwi1zfxWKOor7cSOIRq/7KffBSfxBXlMQef4N3EvlwWMrmTmK3Dk9iOxYxDpNRYxpHMKuQr3LvLNu6bKSFPiWuu3UNBlsNhRDItSiclJjcROIQpTI1N0ehgkdgz6D8HcFl+5fIjh4YNQpObXAV3bdTlG7AhEeo3vWqRzDwKSUCRTih2DnqFQpkSAzVBkDpkvdhSD0jYwR+wIouhg0xBWcpnYMegJBU4tMdFoLdbFeosdpdYKTcrF5UgObkNUVSxiSOeCz8aLHYEqQK0ScFvWAo9e/hSCca3qPldtjO8+QEt57Ruy1lfNSRJrksj6o9Al+V1cyay9zRtrip2XYsSOQKS3WMSQTj0MyUBGAq/I6pOwJFs8GLcFajt2zH5ugoCX4+uLnULn/FJixY5AAAQTS+x3X4p+EWOQq+SFiZrgdEgy4tLzxY5BpJdYxJBOBZ95KHYEqoJHiQJu9/sE8sZtxI6i95reSBY7gk7VMXeET1KI2DFqPYVdI8yzXIv3olqLHYWeoBaA3VdixI5BpJdYxJDOZKXkI+ZuutgxqIqyMxS47jMP+b4jxI6i3yJj4VdYe+bh8LWqBwk4EqGYkuq9iB5ZH+B4Ku+m1kQHbzyErIj9RIkqi0UM6cyds/Hgdxn9VlSgwjWLF5E+/G2xo+i1UdHOYkfQGd98NpURi2Bkir/rL0C3yOlIKjITOw6VIbdQiZ9v1r6+otOnT8eIESO0drxz585BIpEgKytLq9tWh127dsHe3l6UcxsSFjGkE0qFCqFXObmlIVCrBQRlN0bcxM+hNuEXo6pocC1O7Ag6IYEE3R/eETtGraSSumGZ3WeYE9FN7ChUAftEfE9ITU3F3Llz0aBBA5ibm8PV1RX+/v64dOmSaJnKcuXKFRgbG2PIkCFiR3ku48ePx4MHD8SOofdYxJBORAelQV7A2+WGJCLBCqFjtkDl5CZ2FL0jJCRhcF5jsWNUu+Y2nnCUcQhZXct09cOL+avxUyL/39QXoUm5CI7PEuXco0ePxu3bt7F79248ePAAR48eRe/evZGeXvOaf2/fvh1vvPEGzp8/j4SEBLHjlCKXyyu0naWlJerWrX0jVWobixjSidArvAtjiJKSVLjV40PIm3UWO4reGRJpK3aEaudnZCN2hFpFgAQBHrPQKXYuIvMtxY5DlXTguu4HvsnKysKFCxewdu1a9OnTB56enujSpQuWLl2Kl156CQCwYcMGtG7dGtbW1vDw8MC8efOQl5enOcaqVavQrl27EsfdtGkTvLy8NI9VKhXefvtt2Nvbw8nJCUuWLIEgVK59eV5eHg4cOIC5c+diyJAh2LVr1zO3j42NxbBhw+Dg4ABra2u0bNkSf/75Z4ltbt68iU6dOsHKygq+vr4ICwvTrHtac7eFCxeid+/emse9e/fG66+/joULF6JOnTrw9/cHUP5r9t/mZI9fwx07dqBBgwaQSqWYN28eVCoV1q1bB1dXV9StWxdr1qyp1Gtm6FjEULXLz5HjYUiG2DGomuRmKXDNayZkvV4WO4peqXslHGaCsdgxqpVvJi9e6Ira0hHrnddgfHhfqAR+tOujo0EJKFSodHpOqVQKqVSKI0eOoKio6KnbGBkZYcuWLbh37x52796NM2fOYMmSJZU6z/r167Fr1y7s2LEDFy9eREZGBn799ddKHePgwYNo1qwZmjZtismTJ2PHjh3PLITmz5+PoqIinD9/Hnfu3MHatWshlUpLbLNs2TKsX78eN27cgImJCV555ZVKZQKA3bt3w8zMDJcuXcI333wDoGqvWWRkJI4fP46//voL+/btw/bt2zFkyBDEx8fjn3/+wdq1a7F8+XIEBARUOqOh4jsdVbsH15IgqNmj35DJC1W4ZtQDqaOWih1FbwiZWRiTbbizpVubWKHtw2CxY9QKMud2GKX6DNseeokdhZ5DbqESx+8m6vScJiYm2LVrF3bv3g17e3v4+fnh/fffR3Dwv//vLly4EH369IGXlxf69u2L1atX4+DBg5U6z6ZNm7B06VKMGjUKzZs3xzfffAM7u8pNtrp9+3ZMnjwZADBw4EBkZ2fjn3/+KXP7uLg4+Pn5oXXr1mjUqBGGDh2Knj17lthmzZo16NWrF1q0aIH33nsPly9fRmFhYaVyeXt7Y926dWjatCmaNm0KoGqvmVqtxo4dO9CiRQsMGzYMffr0QVhYGDZt2oSmTZtixowZaNq0Kc6ePVupfIaMRQxVO3borx0EAbiTUR/REzdCMGdTloroG2q4AyN0lnrCVK0QO4bBC/F4GV0SFiEwR1r+xlTjidGkbPTo0UhISMDRo0cxcOBAnDt3Dh06dNA01zp16hT69euHevXqwcbGBlOmTEF6ejryKzjyYHZ2NhITE9G1a1fNMhMTE3Tq1KnCGcPCwnDt2jVMmDBBs//48eOxffv2MvdZsGABVq9eDT8/P6xcubJEYfZYmzb/zn3m5lbchywlJaXCuQCgY8eOpZZV5TXz8vKCjc2/TXBdXFzQokULGBkZlVhW2XyGjEUMVau0+Fykx+eVvyEZjOgEM9wdsRFKF0+xo9R4dgGhsFGbix2jWvgpePe1Oglm1tjhtgKDwl+CTMWPckMREJ2BuHTdD0tuYWGBAQMGYMWKFbh8+TKmT5+OlStXIiYmBkOHDkWbNm1w+PBh3Lx5E19++SWAfzuxGxkZlWrWpVBo9wLG9u3boVQq4e7uDhMTE5iYmODrr7/G4cOHkZ2d/dR9Zs2ahaioKEyZMgV37txBp06dsHXr1hLbmJqaav4tkUgAFN8RASr+vKytrUs8rshr9jRPZnmc52nLHucjFjFUzXgXpnZKTVbhZtf3UdS6h9hRajQhPx+T0n3EjlEt/BI5fGh1KXJoihkm6/BRdHOxo5CWCULx5Jdia9GiBWQyGW7evAm1Wo3169ejW7du8PHxKTUqmLOzM5KSkkp84Q8MDNT8287ODm5ubiX6ciiVSty8ebNCWZRKJfbs2YP169cjMDBQ8xMUFAR3d3fs27evzH09PDzw2muv4ZdffsE777yD77//voKvQPHzSkws2bzvyedVloq8ZqQdLGKo2qjVAsKvJYsdg0Qiy1EiwG0icvtPEztKjeZ7z/CGHvewcoVHeozYMQxSXP1h6Ja2DOcyHMSOQtXk55vxUOuoH2l6ejr69u2LH374AcHBwYiOjsahQ4ewbt06DB8+HE2aNIFCocDWrVsRFRWFvXv3ajqvP9a7d2+kpqZi3bp1iIyMxJdffonjx4+X2ObNN9/EZ599hiNHjiA0NBTz5s2r8ESTf/zxBzIzMzFz5ky0atWqxM/o0aPLbFK2cOFC/P3334iOjsatW7dw9uxZNG9e8cK/b9++uHHjBvbs2YPw8HCsXLkSd+/eLXe/irxmpB0sYqjaxN1LR35OxcZMJ8OklKtxXdUFyWNXQvjfrXoqyeJGKFxVhtWfwdfcWewIBkcwNseReovQM2ICMhUmYsehapSUU4hLkbqZX0kqlaJr167YuHEjevbsiVatWmHFihV49dVXsW3bNrRt2xYbNmzA2rVr0apVK/z444/49NNPSxyjefPm+Oqrr/Dll1+ibdu2uHbtGhYtWlRim3feeQdTpkzBtGnT0L17d9jY2GDkyJEVyrh9+3b079//qQMBjB49Gjdu3HhqfxeVSoX58+ejefPmGDhwIHx8fPDVV19V+LXx9/fHihUrsGTJEnTu3Bm5ubmYOnVquftV5DUj7ZAIlR2om6iC/v6/u4i4wQ5oVMzTXYWGv74PowL2kfqv69M64XP3QLFjaM0WkwboE35R7BgGQ2nrgUWSd3AkmZPj1RZjO9bH52Pbih2DqEbjnRiqFkUFSkQHcaZu+ldsgjHuDF0PZX3DHVa4qtoHycSOoDUmRiboEhckdgyDkereB71zPmIBU8v8dS8JciU7cBM9C4sYqhaRN1OgUvANmEpKT1XiRrtFKOzQX+woNYpJcBiaKuqIHUMr2tk0hHVRrtgx9J4gMcY5j3noEj0L8YWGOYIdlS23UIlzYbWjJUNcXJxm0s2n/cTFxYkdkWooNqylahF6VbcTdpH+yM9TIsBpNNoNbAS7v74TO07NoFZjYnwDrGyo/3cv/QR+4X5eaitnrLZchB3hHmJHIREdDUrAiy1dxY5R7dzd3Z856pe7u7vuwpBeYRFDWpedWoDEyKeP204EACqFGjcVbdFs/Gq4HVoJiVoldiTRNbuVCjQUO8Xz802NFTuCXstx6YLx6bMRkmEldhQS2ZnQFBQqVLAwNRY7SrUyMTFBkyZNxI5BeojNyUjrHlxLAjhcBFVAaLIDwl/eArXUXuwoopM8iEaXonpix3gujuYOaJ5wX+wYekmABLcbTEPHh28iJI8FDAH5chXOhtaOJmVEVcEihrQuKjBV7AikR+ITgCD/tVB4tRQ7iujGxLqIHeG5dLeqBwmvYFSaYG6HL10+xMgH/lCoORQ5/evYHTbNJioLixjSKllWEdIecghdqpzMdCWut1yAgi5DxI4iqobX4sWO8Fz8CgrFjqB3Cpxa4WXJWnwRy+Y0VNrjJmVEVBqLGNKq2LvpYkcgPVUoUyLAZggyhy4QO4pohIcJeFGmnx1jJJCg+8PyZ7Omf0V4jEaX5CUIyLIVOwrVUPlyVa0ZpYyosljEkFbF3NH/0ZVIPGqVgNt5TRE/YS0E49o57siwSHuxI1RJU5sGqJPHL1sVIZha4Sf3pegfPhq5ytr5d04VdyqE/18RPQ2LGNIalVKN+NBMsWOQAXiQKEXYuC1QOdS+Cf7cAiJhIujfW7OvMe8mVITcvhHmWqzF+1GtxY5CeuJcWAoEgX3NiP5L/z4pqcZKeJAFRRHb7pJ2JCQKCOyzGoom7cWOolPqtAyMyvEWO0al+WXyanF5EuoNxAuZK/FXqpPYUUiPpOXJERTPaQuI/otFDGlNzF02JSPtys5QIMB7DvL9RokdRaf6h+nXhJGWJpZo/zBI7Bg1lmBkir/qvwnfyKlIKTIVOw7poTMcapmoFBYxpDWxd9ipn7RPXqBCgHk/pI1YLHYUnXEICINUMBM7RoV1kXrBVCUXO0aNpJK6Y6ndZ3gtoqvYUUiPcb4YotJYxJBWZCbJkJ1aIHYMMlCCGgjO8kLsxPVQm+nXXYqqEPJkmJDuI3aMCvNVip2gZspwfQED8ldjf6Kb2FFIz91NyEZKDocwJ3oSixjSCg6tTLoQmWCBkFGboaqj3zPbV4Tfff3pyOuXGC52hBpFkBjhiser6Bz7GqLyLcSOQwZAEICzHGqZqAQWMaQVMWxKRjqSnKTCrRdWQt68m9hRqpX1jVDUVUnFjlGuelYu8EyLEjtGjaG2dMIXdVZjQngfqPRwlDmqudgvhqgkDlBPz01eoERiRJbYMQySWq3Cnzf34Hr4KeTkZ8DO2gldffwxsMNkSCSSMvdTqOQ4fnMvroefQm5+JmytHDGo4xR0bzYIABASfwMHL25Bbn4mWnv5YlKvRTAxLu5wXFCUh3W/zsMbQz6Ho42LTp5nZeVmKRDQYBrauzSC9NxPYsepFkJRESantsYG10CxozyTr3ntGwa7LHnO7TE5ey4CH9b84pP0z8XwNMiVapiZsDgmAljEkBY8DMmAWqU/TV/0ycnA/bhw/yim9H4Xbo5eiEsNww/nPoelmTV6ty57xK4dJz9GbkEmJvVaBGe7esiWpUNA8e9ILaix+/QnGNB+AlrU74z/O/khLoUcQ69WIwAAv137P7zQYliNLWAeUxSpcV3ih1ajG8L58Bqx41SLjkEywFXsFM/ml5sjdoQa4Z7HBIyJGowClbHYUchAyeQq3IjJgG+TOmJHIaoRWM7Tc4thf5hqE5V8D208fdHKsxucbFzRvlEvNKvfCbEpoWXucz/uGiISgzB30CdoVr8jnGxc0ci1JRq7tgIAyAqzkVeYjZ4thsPN0QutPbsjKTO2+HxJ9xCbEoY+rfRjSGNBAO6kuyN64iYI5pZix9E608AwNFI6iB2jTCYSE3St5UMrC2ZS/J/rSgwJH8YChqrdlSh+3hI9xiKGnosgCOzUX40aubRE2KPbSM56CACIT49EVNIdtGjQpcx97sReQQPnpjgVdADL9o7Dh/un4pcr30CuLAIASC3sYWvlhJD4G5ArChGZdAf1nBpBpVLiwIVNmNDzLRgZ6deXsegEU9wdvglKNy+xo2iXWo3Jj7zETlGmNrZekBbW3jsxRY5NMd10LVbHNBU7CtUSV1nEEGmwORk9l9S4XBTkcH6I6jKg/QQUKvKx+sAMSIyMIKjVGNrlFXT27l/mPmk5iYhMugMTY1O86v8RZIXZOHBhM2SFOZjSZwkkEglm9l+Bw1e+xuHLX6KFRxd0bzoIJwL3wdu9HUyNzbDhyALkFWajV6uRmmZmNV1qihL5nZeiXcyPMA8+L3YcrWl5OwPwFDvF0/kKtXfkrdj6L2FE7BhkKvgxSroT9DAbhQoVLEz160ITUXXguy89l4TwLLEjGLRbkedwPfw0pvV7H24OXniUHomfL38JOysndGvq/9R9BKghgQTT+74PS/PiDsajus/F9pMfYnyPN2FmYo7Gbq2xZNRXmn2Ssx7i2oOTeG/Mt9h4dCF6txqFlg26YM3BWWji1hr1nBrr5Pk+L1mOEgGuE9DOpTFsT+4UO45WSEIi0X6wB26bJYodpRS/tIdiR9A5wdgcv7ouwNsR7cWOQrWQXKXGzdhM+LFfDBGbk9HzSY6uvU1JdOHI1e8woN3L6NSkL+o5NUIXnwHo22YMTgbuK3MfWytH2FnX0RQwAODq0AACBGTJUp+6z/4LGzGy+2tQC2rEp0WgQ6NesLF0gLd7G4QnBmv9eVUnpVyNG4pOSBq3CsIzRnDTJ+Nja95kifZmdmiRcE/sGDqltG2AhdK1eDuSBQyJh03KiIqxiKHnkhSVLXYEgyZXFsJIUvJ/U4nECGpBXeY+jVxaITs/HUWKAs2ylOx4SCRGsLd2LrX95dA/YWVuizZevhD+d1yVWqn5r6Au+1w12f0UZ0RO2AK1ta3YUZ5b4xsJYkcopbu1B4ye8XdoaFLd+6JXzof4LZlDSpO4WMQQFWMRQ1UmyypCXmaR2DEMWmvP7vj79o+4G3sV6blJCIq+iLPBP6Ot1wuabX4L+D/sOfOZ5nFn736wNrfFD+fWITEzBhEJwfj16rfo3nQgzEzMSxw/tyATf9/6EWP9XgcAWJnbwNW+Ac7e+QVRSfcQ9ug2Grm21M2TrQZxCUYIHvw5lB763fFaiIlH33wvsWOU4FtYO/rCCRJjnPWYhy7RM/Go0Lz8HYiq2eN+MUS1nUQQBE7wQVUSeSsFf313V+wYBq1Qno8/ru9EUMxF5BVkwc7aCR0b98WgjlM0k1PuPbsW6bnJWPjSBs1+SZlxOHRpK6KS78Ha3BYdGvfC0M6vlCpidp5ajUaurUp03o9JCcXes2uRW5CJPq1HYVDHqTp5rtXJUmqC9omHYXHzhNhRqixxWGe82eq22DE0TmcoUDe75vXT0SaVdV18bL4IuxLqix2FqIQfZ3Vlvxiq9VjEUJVdOhyBwJNxYscgqhBjEwnamt2B/Z9fix2lSiR16+DlmdlQQfy3bG9pA/xy56LYMapVjktXjEt/FaF5VmJHISrljb5N8M6L+n2Hmeh5sTkZVVky+8OQHlEpBdzKb4WElz+BoGfz4ACAkJKG4bneYscAAPiZ2IsdodoIkOCWxzR0fLiABQzVWDdiMsWOQCQ6FjFUJSqVGilxuWLHIKq00CQ7PHh5C9S2TmJHqTT/B5ZiRwAA+GaliB2hWgjmdtjm8hFGhftDoTaMke3IMN19lA02pKHajvPEUJWkPcyDSlF7RiYiw/IoAZAN+BStb2+DaZT+DCHteDUcFh1MUChRipbB0tgCHaODRDt/dcmv0xrT8+bjWqz+j2YHAIUP7yIn4DDkyZFQ5WXAeeQyWPl016wXBAHZF39EXtDfUBfJYF6vORxfnAdTx3oVOn721UPI+mc3bDq+BMf+szXLM05/D9nd05CYWsC+1zRIW/bRrJOFXoTs7mnUHbNSe0+0lsotUiIyVYYmdaXlb0xkoHgnhqokOZpNyUi/ZaUrcL3ZPOR3GyZ2lAoTcnLwcqaPqBk62njBTGVYoxKGe4xB56QluJZlGAUMAAjyQpjWbQTHAa89dX1OwGHk3Pwdjv7z4TplPSSmFkg5+AEEZfmjzhUlPkBu4F8wdfYqsTw/IgCykH9Qd9zHcOg9Axl/bYUqv/izQl0kQ9b5PXB8ce5zPzcqFhyfJXYEIlGxiKEqSYriJJek/wrzVbhmPQgZLy0UO0qF9QoR923bT2k4HxuCqRV+dH8fA8JHQabUv35Sz2LZuBMcek6BlY9vqXWCICD3xm+w6z4eVt7dYFa3IeoMfRvKvAzkP7jyzOOq5QVI+/0LOA18A0YWJe8CKNIfwsKjNczdvGHdohckZlZQZicDADLP7oRN+8EwseU8O9oSHM+LiVS7Gc6nEekU78SQoVCrBATmeOPhhHVQm5iJHadc0muhcFKL1+HcNzlCtHNrk9y+MeaYr8OyqFZiR9E5ZXYyVLJMWHq10ywzMreGuXtTFCWEPnPfjJNfw7Jx5xL7Pmbm3BDypAioCvNQlBQBQVkEEwd3FMbfgzw5EjYd9eeupz4I4p0YquVYxFCl5efIkZNWKHYMIq0KT7RG2NjNUDm6iB3lmYTCQkxOEWeUMjdLZzRK0f8iJqHeQLyQ+QFOpDmKHUUUqrzika2MrO1LLDe2sodKllXmfrL7/0CeFAmHXtOeut6yUUdYt+yNpN1vIf3YRtQZ8haMTM2R8fdXcPSfj9zbf+LR93OQ9MNiyFNjtfV0aq37CTlQqNg3lWovFjFUaUkcWpkMVGKiGrd7fQy5TyexozxT57viXETwtXAV5bzaIhiZ4nj9hfCNnIqUIlOx4+gVZU4qMk5/jzrDFkHyjDuW9i9MQr0538N95pew8vFF9pVDsPBqB4mRMbKvHIDrpHWQtnkR6cc2lHkMqpgipRphSRwllGovFjFUaWxKRoYsJ1OBa41mQdZjrNhRymR2OwyeSnudn9cvT3+/MClt6uE9u7WYG9FF7CiiM5Y6AADU/7nrosrPgvF/7s48Jk+KgDo/C4m73kTsupcQu+4lFD28i9ybvyN23UsQ1KpS+yjSH0J2/yzse0xGYdwdWNRvBWMrO1g16wF5ciTURfnafmq1DpuUUW3GIZap0tipnwydvFCFa6a90XpkI9T5da3YcUpTKjEpsSE+8bits1MaS4zR9aF+Dq2c4dYDo5JmIKbAQuwoNYKJnQuMrR1QGBsIM5dGAAB1UT6KEsJg027QU/ex8GwLt1e2lViW/udmmDrVh23X0ZD8ZwJZQRCQ/veXcOg7C0ZmloCghqD+39Dgj/8rsCnU8wp+mI1JXcVOQSQO3omhSkt7qL9XY4kqSlADwZkNEDNxAwSzmvflt83tLJ2er7WNF2wL9OsurCAxwmWP2egcM6fWFTBqeQHkyVGQJ0cBKO7ML0+OgjInBRKJBDadhiP78gHkhwdAnhqDtGMbYCJ1LDGXTPL+95Fz83cAgJG5FcycvUr8SEzNYWRhA7P/DLUMAHlBf8PY0hZWTYq/YZvXa47C2GAUPQpFzvXfYOrUoNToZlR5IUm8qEi1F+/EUKXkZRZBXli62QCRoYpKMEfeyE1ofmY1jFPjxY6jYXQvHK0HuuOOWYpOzucrsdTJebRFbemEz6WL8HW4p9hRRCFPCkfyvvc1jzPP/B8AwLpVP9QZ8hZsu46GoChE+t9boS6UwaJ+C9Qd91GJ/i6KzCSYF1T+S7JKlonsKwfhOvlzzTJz96aw7TISKT9/CCMrO9QZ8tZzPDt6LCIlD4IgQCKRiB2FSOckgiAIYocg/fHwfgaObgkUOwaRzkntTNA2YjfM710WO4pG+LguWNb4lk7O9YPSAW31pDlZnnMHTMx+DcE5vNJPhu/Ckj7wcBRv2HUisbA5GVVKRpJM7AhEosjLViKg3hTk9p0sdhQN7xtJOjmPrZkNWsXf1cm5ntddj0nonPAWCxiqNSJS8sSOQCQKFjFUKZmJLGKo9lLK1bgudEfKmBViRykWFYcehQ2q/TTdrBvAWKjZzUgFMym+d12JoeFDUKAyLn8HIgMRnsJ+qlQ7sYihSslM4pCYVMsJwN00V0RN3Ay1hbXYaTAqyqnaz+FXqKj2czyPQsdmmGayDmtimoodhUjnwpN5J4ZqJxYxVCkZvBNDBACISTDB3Zc2QOneSNQc9QNiIanmno2+CSHVe4LnEFN/OLqmvo/zGfZiRyESRTibk1EtxSKGKqwgV47CvJp9RZZIl9JSlLjR8V0UtusrWgYhKQXD8ryr7fiNpfXhmvWo2o5fVYKJBQ7XW4zeEeORreBAm1R7sU8M1VYsYqjCMhJj4OB8DnXr30Fdj4dwqJsNM0sWNVS75ecqEeA8Fjn+s0TLMCii+pq1+Zo4Vtuxq0ph64kFVmvxTmR7saMQiS6vSImErAKxYxDpHC9fUYVlJsQh8UHp4VzNraWwcXSFudQZxqaOENT2KCywhizHCmol62QyfCqFGjfQHs3HfwTXgysh0fHI9XWuhsO8nTGKJNrvfO+Xnab1Yz6PFPd+GJkwGY8KzcWOQlRjhKfkwd1ev+ZyInpeLGKowrKSEp+6vEiWhyJZBICIEsslEiNYOzrB2s4FZlZ1IDFygFJhiwKZNfJzzSEBJ+ciwxKS7IS8CVvR+OhyGOVl6ey8QlY2xmW1xl4H7fZdMTc2R8fYmjE3jGBkgrP1XsMr4b5iRyGqceLSZQCcxY5BpFMsYqjCspISKrW9IKiRl56KvPTUUutMzMxh4+QCS9u6MDF3giDYQ15kg/wcayiKODwq6a+HCRLIBq5Fq2sbYRIXqrPz9g41wd7u2j1mRxsvWCjCtXvQKlBZu+Aj80XYHV5P7ChENVJ8JpuTUe3DIoYqLCtZexPrKeVFyEyMQ2ZiXKl1ljZ2kDq6wtzaGUYmjlCp7FCUbw1ZjgUENZunUc2XkabEtdZvob3rb7C89qdOzmkbEAq7rhbINirU2jF9VeJ/RGS7dMXYtNl4kM6mMkRliWefGKqFxP+EIr2RlVy5OzFVVZCbjYLcbABhJZYbGRtD6lgXVnZ1YWZZB5A4QCG3QUGeNQplZjrJRlRRhTIlAmyGou2QhnA49mW1n08oKMDktPb4su4drR3TLzlKa8eqLAES3PKYjpcj+0OhZtNTomfhnRiqjVjEUIUU5OagSCbuHDFqlQo5qYnISS3dN8fM0gpSRxdY2tSFsZkj1II95IVSyLKtoFKweRqJQ60ScFvWAk1f/hTuh1ZAolJW6/m63VXiSy2N9uxiWQdNoksP5KELagt7bLVdhI3h4s7BQ6QvHrGIoVqIRQxVSFby0zv11xTygnxkPIoGEF1yhUQCazsHWDu4wtyqDiTGDlAp7VCYbw1ZtgXAwQVIB8KSbJE3bgu8//wARtU42pf5zRDU62WLR8Y5z30sXws3LSSqvPw6rTE193XciLMR5fxE+igtrwiFChUsTHnRjmoPFjFUIdla7A+jU4IAWVYGZFkZpVYZm5rCxtEFlnbFgwtA4gBFkQ3yc6wgLzQVISwZskeJAvL6fYLWQdtgFhlcPSdRKjE5qRHW1gt87kP5ynQ/gV6Yx1iMin4JMiW/iBFVVnxmAZrUlYodg0hnWMRQheRnZ4kdQetUCgWykuORlRxfat3juW8spM4wMnWCWm2HogIpZNmWUKs4uABVTXaGAtd95qG9y1+wunykWs7RLjAXeM5BvIwkRuj+sJoKracQTK3xo/NCLA9vqbNzEhmaR1ksYqh2YRFDFZKf8/zNU/RJuXPf2LvCzLIOJEb2UCrs/jf3jRnnvqFyFRWocM3iRbQe3ghOv23Q+vGN7zxAsxddEGpa9WZrrWy8YBd1TnuhnkFu3wTzlQtxMspRJ+cjMlTxmfliRyDSKRYxVCEFOdliR6gRyp37po4rLG3qwsTMEYLEHvJCG+RnW0Eh5/9q9C+1WkBQdmM0mfg56h9cBiOlXHsHFwRMjG+ADxpWvYjxlVhrL88zPKo3CCMevoxUOZtvEj0vdu6n2obfrKhC8lnElEspL0JmQiwyEVtqnaWtPaSOLjC3enLuGylk2eYQBDZPq60iEqyQN2YLmp78EMbp2hs8o9nNFKBh1ff3S6/e4dQFYzMcc5uP1yM6V+t5iGqTlNwisSMQ6RSLGKqQ4nlbqKoKcrJQkJOFZ819Y2pZBxLOfVPrJCWpIOvxIdqGfg+z0OvaOWh4DHwLG+KyxcNK72pjKkXrmOrrD6O0qYelxu/gUIRrtZ2DqDbKkGnxji6RHmARQxVS2/rE6Ep5c9/YOBUPLmBs5ghBsEdRoQ1k2Zac+8bA5GYpcM1rJtq7NIb1P/u1csxRMc643KzyRUw3qSeMhftayfBf6W49MTppOmIKLKrl+ES1WTqLGKplWMRQhbBPjO7JC/KRHh8F4D+zpksksLZ3hLWDC8ws68DI2BEqlS0KZZz7Rp/JC1W4ZtQDrUY1hPMvnz738TwD4oBmld+ve5H2J+QUJEa4XP9VTI7oCUHg3ydRdciQsTkZ1S4sYqhcapUKhSLMGUFlEATIMtMhy0wvtcrY1BQ2Ti6wtK0LU3MnCBIHKIqkyM+RQl7I/91rOkEA7mTUR8OJG+F1+H1IiqreUVdISMIgmQ+OW0eVv/ET/BLCyt+oEtSWdbDWehG+DW+g1eMSUUkZebwTQ7ULv9VQuQpyc4q/XVGNp1IokJUUj6yk0nPfWEhtIHV0hbm1M4xNHTn3TQ0WnWCGvBEb0fzcpzBJLj1QREUNibDF8bYV397Luh7co69U+Xz/lVe3IyZkvoY78boZ7YyoNpPJVShUqGBhyubGVDuwiKFycWQyw1CYl4vCvFwA4SWWSyRGkDrWgZW9y//mvnGAUmmLgjzOfSOm1GQV8ru+j3bRP8D8zoUqHcPlaiRM2hhBKVFXaHs/U6cqnedp7nhMwpjIQShSs0Am0pUMmRzu9pZixyDSCRYxVC72hzFsgqBGbnoKctNTSq0zNbeA1MmleO4bcycIgj3kRVLOfaMjshwlAtwmor1LI9ic2l3p/YWMTIzNaYV9dqEV2t43J6PS5yh1TnMbfGv/Dj4L93nuYxFR5bCIodqE30KoXLwTU3spigrLnPvGytYe1iXmvrFFUYENZFkW7LytRUq5GtclXdByrBfq/vwRJJVs2tkvzAz7upS/nZmRGTo/DKxayP8pdGyOWYULcDHW7rmOQ0RVwxHKqDZhEUPl4p0Yepr8nCzkP3XuGxNInZxhZecCM4s6AOyhUNgiP88aRTLOzF4lAnAvtS7yJmxGw1/fh1FBxQfasLsaBptO5sg1evbIRe1tvWApj6hyxOj6IzAidhSyFfxYIRILRyij2oSfNlQuzhFDlaFWKZGTkoiclGfMfWNTF8am/5v7pkAKWQ7nvqmI2ARj5A1dj5ZXvoBJfHj5OwAQZDJMTG+Pb53vPHM7P1XVCkzBxAI/112AxRHtqrQ/EWlPbqH2h0gnqqlYxFC5imeaJ3p+z577xgnS/819IzF2gEppi8J8KWTZ5uDcN/9KT1XiRrtFaFf3ECxunarQPr73VPi2dznbpMRUOovCzgtvqd/GH1F1Kr0vEWlfgVwldgQinWERQ+VinxiqdoIAWWYaZJlppVYZm5rBxqkuLG1dYGpRPLiAQm4DWa4VFAW1s3lafp4SAU6j0W5gI9j99V2521veCEHdntZIMZI9db2zhSOaRgdWKkOye3+MSJiMxEKzSu1HRNUnn0UM1SIsYqhc8oKqT7hH9LxUCnm5c99YSIsHF1Cr7VFYIIUs2wKCgc99o1KocVPRFs3Gr4bboZWQqJ/x5UWhwNRkb3zhFvjU1d0t6wF4+rr/EoxMcNr9NcyK8K10ZiKqXoUKFjFUe7CIoXKpVXxTpJqpzLlvjIwgdXSGtX3x3RuJkSMUClsU5FmhMM9cnLDVJDTZAXkvb0GT31fAKLfsIZI7BMkAt6ev88vPr9C5VNauWGX+DvZG1KtKVCKqZrwTQ7UJixgql6Cu2ER5RDWFoFYjNy0ZuWnJpdaZmlvApk7x3RvN3DeFNpDlWEEp18/BBeITANmLn6LVzS0wjbn31G1MgsLg3c8Z4abpJZYbSYzQ/WFwuefIcu2Ocamz8CCdc1AQ1VQFvBNDtQiLGCoX78SQIVEUFSLjUQyAmFLriue+cYW5tTOMjB2gVtmhIF+K/OyaP/dNZroS11suQPu6R2F57VjpDdRqTEzwxIeeJYuY5jaecIj6p8zjCpDgpsd0TIjsD4W6Zr8GRLUdO/ZTbcIihsqlflZbeyID8u/cNyVnuDcyNoGNU11Y2dWFiYUTJBIHKOS2yM+1QlF+zenYXihTIsBmCNoObQyHP7aUWt/iZhrgWXKZr0Ra5vHUFg7YbPsONoc30nZUIqoGvBNDtQmLGCoX78RQbadWKZGdkoDslIRS68ytrCF1coWFtTOMzZygVttB/njum/9n777Do6zSPo5/nylJJjPpvSekk5CQkIRQRBAREFBQAQsiil3sWHbtrrrWXbv76iLY1oq9YEFRQDoEkN5rSO91MjPvH5FApKRN8kyS+3NdXCuZp/wmSzJzzznnPg1dPz3NarGxrjKeuIufIuTj+1Asx/aNULbtJrMunFXOx57HkOIjJ71OtW8q0ypuZO1+t07PLISwj+p62SdG9B5SxIgWyZoYIU6trrqKuupdwK7mDygKJi8fjJ4BOLn6omi9aTC7U1tlpLq88/e+2X7ERMWUF4lb8DDakvymr0/eF8iquMYixqQ3krpv/QnnbgubwgW7z6Oqh3d4E6KnqTHL67XoPaSIES2SkRgh2sFmo7K4kMriU+19E4Cruz9aZx8UPKmvd6O63Eh9rf1+Lefm2qge8RgpG15Dv3MdAFErD0Fc4+NZpgh01i3HIuuNvON7Ow/u6Gu3DEKIrmNukCJG9B5SxIgWWWUkRgi7atz75gClRw6c8JiLyR03n0CcjL5o9d5YGzyprTFSVW5o1943ZcVmVsReR1rgjxiXzMd24BCjqmL50biHIfXHfrbrvWK5vv5Wft7j3aHnJoRQj9VmUzuCEF1GihjRIpss7Beiy9RWllNbWQ5sb/Z1RaPBzdsPg0cATgZfFI0XZrMbNZUmaitP31ygvsbCSqez6DexD76fP8OE3Z782A8GH25sYHAwdBzn759CUb2+s56WEEIIYVdSxIgWWS0yEiOE2mxWK+WFeZSfbO8bFxfcfAIxuPmjdfLBZvM4Ye8bmxU2lEYSfelzRPzyHH0GhhCyfw1fhdzBzTszuvrpCCE6gYzEiN5EihjRImmxLIRjM9eeZu8bDy9MXoE4/bn3TanFHfOoW7i6Yjl3uU3hk10BXR1XCNFJrFLDiF5EihjRIpss7Bei26ouK6G6rARoXMAfHZVBdNhgVoX354KD63i6/gk01QXqhhRC2IdzArBC7RRCdAkpYkSLZGG/EN2bk95AZsoEQrWxUNDAYqe9+JpW8mjANRzZPYQHwjYwoepTnEp3qh1VCNERirRFF72HFDGiRTISI0T3FBwUR3qf0ZhK3LAVW4AGdsaUs/3QLobFLqTa62r6ZIRx5+8aZiup3BG2iyv4Cvf8VWpHF0K0hxQxoheRIka0SNbECNF9aDRa0pLH0MeYgibPCofBRuPPcJlfA4tz1xIQoMFqrSPb6SBvu4VwRlogq9Yd4bn9MTzH7UwOPMIdxu8JPPwjik1GYoXoNqSIEb2IFDGiRTZZKSiEw/P2CiYzcTxelX7YKhqgonnx0eBkY6F2AxaLBT//xseSzD8Dl7PET8OgeF/WbmvcmPPjI4F8zBUM8prIQ76LiD/yFYq5uqufkhCiraSIEb2IFDGiRVonJ8x1tWrHEEKcRN/4YST6DkKXp0CuDRsNJz1uZfgBig+WAODhXgVASPXXeOquoLTBypoIZxKrPNh6sKzpnGUlHowpOZ9Iw2ieCFtOduGnaGoKO/9JCSHaR4oY0YtIESNa5GwwUFtRrnYMIcSfXF09yEo+n0BLOLZiMxwGOPWI6Z4+lWw+uKPp7y4uxdTVg2JrYJBrBd+VG6lTYG+iibAaMweKmo+67K1x4dIdw3HTDeUfkRsYV/Ep+rLdnfTshBDtptGqnUCILiNFjGiRk4tB7QhCCCAqoj8pYWdhyHfGVmDFhrnFc8p9LPxWsLbZ1xTlSNN/92c133EmAKUaMKZ547WsgZKq+hOuVdGg47ad6dyp9OfO8F1Mt32JKX9NB5+VEMJunIxqJxCiy0gRI1rk5OqqdgQhei293oWMfuMJ18VDQQMcAhutW2xv0dv4xekPzFXHih1FgXrz/qa/x1R+jJYzOdq+45DWRuJAf2oWH6bWfPL7WGwant4Xy9PcyaVBudxq+A7/3J+lCYAQanN2UzuBEF1GJk+KFjkZpIgRoqsFBkRz7pBZXBh7B+El0Y0FTButjjhMQUnzNSz+/gpW67E1bi6WXPobm09F26K3ETsoGI3S8j3+lxvEwN1XcYXhZbaHTcamc2lzTiGEnTi7q51AiC4jIzGiRTKdTIiuoSga+iedQ4x7Gpojzdsjt9WBqGo2Htx6wtf9/U8shjJ0O1hDXLOvrTLYGJoVzOoVh1t1v9+KPTmneBLRrmN4Imw5mYXz0dQUtyu7EKKdnExqJxCiy0gRI1ok08mE6FweHgFk9Z2AT3UAtvIGqOrYtKwqLwu/FJ18rYqHR80JX+tb9y38pYgBWOKpcEY/f1ZtzG/1vXdVG5i6YwRe+jN4NGI9Yyrmoy/b2+rzhRAdINPJRC8iRYxokUwnsx+r1cYPm7azZv8hKmrr8HBxISMylLP7xqAop56702Cx8OPmHazZd5iK2jrcXZwZ1TeWrD5hAGw/UsCnazdRUVtHUkgAUzJS0GkbZ4vW1Jt54aelXHtmFt5G+f/SkSTGDSXRbxD6PC0cOXV75Law6Gz87LqZ+qITF+bDsc5kx/OtWUyI8x0cqj+xePo9WMeAam827GrbqEqJWcfNOwegVdK4J2Inl1k+x1iQ06ZrCCHaSIoY0YtIESNaJNPJ7OeXrbv4fdc+Ls5KJdDDjQPFZXy0aj0ueh1nxEWd8rx3lq2joraOKZkp+JpcKa+pw/ZnS12rzcZ7K3I4KyGa+EA/3v59Dct372dobCQA327cyqDocClgHISLwZ2B/SYQZIlqVXvktsqJOkLegdOMnCgnnx42yFDAJ/U+J3zdgsLGaAPRVW7sOlLR5jwWm4Yn9sbxBHdzefAhbnZZgN/hn1Hs+JyFEH9ylulkoveQIka0yFmmk9nN3qISkkMC6BscAIC30ZWc/Yc5UFx6ynO25uazq6CIv587Aldnp6bzjqqqq6eqrp7BMRHotVr6BgeQX17ZeL/CYg4UlzEpLbnznpRolYiwFFIjRuJa4NLq9shtdSiihnUHNp/ycUUBs/nASR9LsSzlE8476WM1CuT18yCgxkxeWfs3vn3ncAjvMJPh3hdwv8/PRB/+GsVS1+7rCSH+Qhb2i15EihjRIr2MxNhNpI8Xy3fvp6CiEj83E4dLy9lTWMx5/fue8pxNh/MI8/Lgl227WbPvIE5aHUnB/oxJjkev02JydsLdxZntRwqJDfBlT2ExGZGhWKxW5q/5g6mZqWha02ZK2J1O58SAfuOIdOoL+W1rj9xW1R4Wfilde9pj/tqZ7HgRlfMxaM6nxnryEZJCjQ23TD/cluRSUduxaW+Lir1YVHwhccZzeTxsKQMKPkNTW9KhawohkOlkoleRIka0SEZi7GdEYjS1DQ08/d2vKIqCzWZjTL940iNCTnlOcVUNewpL0Gm1zBicQVV9PZ+u+YOqejMXZ6WiKAqXD0rni5zNfJ6zicRAf7Kiwvh5yy5i/H3QaTW8vPB3qurrGRIT2TTNTHQeP79IMuPG4V7mia20Aeyw1uV0rFobi9y3Ultw+lESP/9TdzrT2SrJMtbxa4XTKY/Zo7WSkh1I7eJDmC0dnw62vcrA5B1n4+N0Jv8Iz+Gc8k/QlZ98pEgI0QrSnUz0IlLEiBbJwn77WX8gl7X7DnFpdhqB7o0jMV/kbMbd4EJmZOhJz7HZbKDApQP7Y3DSA3Be/0Te/n0tF6Yno9dpifLz5rZRQ5vOKaioZM2+g9w+6gxe/WUZQ+OiSAj049nvf6OPnzfBnjLlwO4UhdS+o4j1SEebZ/uzPXLnFi9HbehTwOEDR1o8zsOj6rSPp2v+4FfST38vZxvZ2cHkLD3UpoynU1Sv58admeg1GdwbsZVLzF/gWrjBbtcXoteQkRjRi0gRI1okC/vt5+v1WzgrIZq08GAAgjzdKamu4ectO09ZxLi5OONhcGkqYAD83U3YgNKaWvzcjCec88nqjUxI7YsNG4dKy0kNDcJJpyXaz5vdBUVSxNiRm7sfA/tOwLc2GFuZGaq7dsH6kfBaVh/Y2KpjDS4lJ3QmO1589afQQhEDsNwEZ6QHsWptbitTto7ZqvCPPYn8g0SuDD7ATS7f4XP4V2kCIERrSREjehGN2gGE45N9YuzHbLGc0Eq5cVrZqc+J8vWmvKaWOvOxT/ULKqpQFPA0nLg7+ord+3F1ciIpJICjyxssVuuf/2vjFEseRBvFxWQzachszg2YiU+eX2MB08Vq3az8XLGu1ccryumLDrf6LSQYWrd+arGfhgGJfq2+d1vNPRxGxu5rudr4EntCJ2LTnnqamxDiT7KwX/QiUsSIFjnLdDK76RscwMItO9l8OI/iqmo2HjzCb9v3kBwS0HTMtxu28v6KnKa/p4UH4+rkxIer1nOkrIJdBUV8vX4rWZFh6HXaZtevqK3jpy07mZieBICrkx5/dxOLd+xhb2EJO/MLifT16pLn2hM5uxg5I+MSpmbeT5rlTJwOa6FBnarQqrHxq/d2qmuqW3mGjfpTdCY73kCn1q9JWRnmRN9wj1Yf3x4Li7wZsXMK5yqvsjZ8Bjbnzr2fEN2WRgeuJ7ZJF6KnkulkokXORlkoaC8T05L4/o9tfLp2E5V1jZtdZvcJZ1Tf2KZjymvrKKk+tqu6s17HdWcO5LN1m3jhpyW4OjmRGhbE2OT4E67/xbrNnBnXB4/jRmguzkzlg5U5LNmxlzPjown39uzU59gThYb0JS1qFMZCI7YiC3RCe+S22hRdxIEDrV+X4uevwWqtafG4JPNC4IpWXdOswM4EN8KrG9hfePr1Nh21pdKVC7afg5/TCB4LX8vIsk/QVdhvXY4Q3Z4pADTy2bToPRSb7XQTWYRo9OIVkzHXtvwGSIieQqvVMSD5XCINySh5p+7qpYaC0Dq+LFpKW359J/ez4eX1bovHWdFyi/4TShpa3wo6yKqgLMunuPI0C27szFlj5W8R25ha/xmGoj+67L5COKzQTLj6J7VTCNFlpGQXrWLyliFq0Tv4+IQxZvANTO57D1HliQ5XwNQZrSysWdemAgZa7kx2lAYLg1zL23TtXI0Ntyx/DHptywfbSZ1Vw8N7Ekk89Hce93mSoqBhXXZvIRySW5DaCYToUlLEiFZxkyJG9GSKQr/EkVw05B7O9rwMj1x3bJVd0x65LWyKjcV+u6isavvULYNL6zeTTLOtbvP1t+lt9BkchBr7qr5xKJwBe67nereX2Bd6HjaNvuWThOhp3IPVTiBEl5IiRrSKm4+v2hGEsDs3kw9nZc/g4v730bc2A+1hcOT2bVtjStl7ZH+7zlU0rW+H3KfqE9ozprLGxUb6QPXeSC0o8OHMnRdznvZVcsKmY5N2s6I3kSJG9DLdpogZPnw4t912m9oxWmXevHl4eno2/f3hhx+mf//+TX+fMWMGEydObPX19u7di6Io5OTk2C1jW5m8pYgRPUdMVCYTh97JucHX4pcXgK1U/YX6LSkKruf3g61vp9ycjfr61hc/LpZc+htbvybmeEs8FDJTA1o+sBNtrDAycccYBtW+xE9hN2MxyZu7kzlUbmXapzX4PF2B4fFy+r1WyerDp58++d4GM6n/qcT18XKCnqvgqi9qKKo+9m/lx10NxL1Uifs/y7n8sxrqLcc+FCirtRH3UiX7Stv3b0u0wE3+nYvepU1FzIwZM1AUpemPj48PY8aMYcOGnrGz8sMPP9zs+Xl4eHDGGWfw66+/dui6s2fPZuHChXZKqQ43H5lOJro3JydXhmRMZWrmfQzgLJwP6cDcPd5M1Rus/FSf0+Z1MEf5+bWuM9nxsnQ72nUvgKUBWlJjvNt9vr0cqXPi6h2DSC55iveC/06td6LakRxGSY2NIW9WodfCd5e5svlGE8+d44KXy6nnAy7d38D0z2uYmaZn040mPp5sYOUhC9d8VQuA1Wbj0k9ruD5Dz7KZRlYftvD6mmMfENz7Uy3XZ+iJ8Ow2n592LzISI3qZNv8mGTNmDLm5ueTm5rJw4UJ0Oh3jx4/vjGx2ZbFYsFpbfsOSlJTU9PyWLVtGbGws48ePp6ysrN33NplM+HTzIkBGYkR3FRIcz/ght3BB9G2EFkVCoeOtdWnJ0qA9VFRWtPv8gIC2NydIrPum3fezKgrr+xiIDXaM6Vw1Fi337U4m4fADPOX3BCWBQ9SOpLqnltYR5qFh7vkGskK0RHlpOCdaR7T3qd8WLDtoIdJT4ZaBzkR5aRgaruO6AU6sPNT476uw2kZhtY0bM51I8tdyXpyOLQWNj/1+oIFVhy3cOlA2Le007rKwX/QubS5inJ2dCQwMJDAwkP79+3Pvvfdy4MABCgoKALjnnnuIi4vD1dWVPn368MADD2A2H/sk5ujUqnfeeYfIyEg8PDy4+OKLqag49gJdVVXF9OnTMZlMBAUF8dxzz52Qo66ujtmzZxMSEoLRaGTgwIEsWrSo6fGjU7q+/PJL+vbti7OzM/v3tzydQqfTNT2/vn378uijj1JZWcn27dubjvnXv/5Fv379MBqNhIWFceONN1JZWXnKa/51OtlfLViwgKFDh+Lp6YmPjw/jx49n165dJxy3e/duRowYgaurK6mpqSxbtqzF52MvsiZGdCcajZaMlPFMHvR3hrpMxHjYgK3WsbqMtdb2mDJ2Hd7boWt4eLZ2Q8xjfGqWEurc/k/MaxU4lORBkKeh3dfoDK8diCRt703McnuR/aHjsWl653ZpX25rICNIy+SPq/F/poK0/6vkjTWnb5E9KFTLgTIb3+4wY7PZyKu08skWM+fGNn4P/VwVgkwKP+xqoNpsY/F+CykBWswWGzd8U8v/jTegVaPzQ28h08lEL9OhMd3KykreffddYmJimkYa3NzcmDdvHps3b+aFF17gjTfe4N///nez83bt2sXnn3/O119/zddff82vv/7Kk08+2fT4XXfdxa+//soXX3zBDz/8wKJFi1i7dm2za8yaNYtly5bxwQcfsGHDBiZPnsyYMWPYsePYFIjq6mqeeuop/vvf/7Jp0yb8/f3b9Pzq6uqYO3cunp6exMcf21hQo9Hw4osvsmnTJt566y1+/vln7r777jZd+3hVVVXccccdrF69moULF6LRaJg0adIJI0f33Xcfs2fPJicnh7i4OC655BIaGrrmU2UPf3XnuAvRGt7eIYwefB1Tku8luiIJzRELOO46/RaVBJhZcnhtywe2oC2dyY43yKWgQ/ct1tiwZfjgbnC8QuHrAl+G7byUSbpX2RA2DZtT79rUd3eJlddW1xPrreH7aa7ckOHELQtqeSvn1IXMkHAd711gYOonNTg9VkHgc5V4OCu8cm7j5rqKovDRZAP/+K2OpFcrSQvUcFWanieX1DMiUoeLDoa8WUX8y5W8vLLr9hTqFQzeoHdp+TghepA2v7J8/fXXmEyNv+yrqqoICgri66+/RvPnLrH3339/07GRkZHMnj2bDz74oNmbfKvVyrx583Bza5xqcPnll7Nw4UIef/xxKisrmTNnDu+++y4jR44E4K233iI0NLTp/P379zN37lz2799PcHDjJw+zZ89mwYIFzJ07lyeeeAIAs9nMq6++Smpqaquf38aNG5ueX3V1NW5ubnz44Ye4u7s3HXN8g4HIyEgee+wxrr/+el599dVW3+d4F154YbO/v/nmm/j5+bF582aSk5Obvj579mzGjRsHwCOPPEJSUhI7d+4kISGhXfdtC2dXIy4mN2o7MKVFiM6SFH8mCb7Z6I4okGvDRvebMvZXZhcrC23rWzUNtiWK5ki7zkuxLOZjJnbo3nu1NpIHBlKz+BBmi+NVlDnlJs4rP5cgl7N5PGwVZxbPR1vVvu9Xd2K1QUawlidGNr7xTQvS8ke+lf+sMXNF/5NP+dpcYOHWBbU8OMyZ0TE6cits3PVjLdd/Xcuc8xtH3IaG61h1zbGCcHuRhbc3mFl3nZFhc6u4daATY2N1JL9axbAILSkBXbe3UI8m62FEL9TmImbEiBG89tprAJSUlPDqq68yduxYVq5cSUREBB9++CEvvvgiu3btorKykoaGhmYFADS+8T9awAAEBQWRn58PNI7S1NfXM3DgwKbHvb29m42EbNy4EYvFQlxcXLPr1tXVNVt74uTkREpKSpueX3x8PF9++SUAFRUVfPjhh0yePJlffvmFjIwMAH766Sf++c9/snXrVsrLy2loaKC2tpbq6mpcXV3bdD+AHTt28OCDD7JixQoKCwub3rTs37+/WRFz/HMJCmqc+5qfn98lRQyAh3+gFDHCYRiNXmQln0eAOQxbiRkOQ7cedvmL5SH7KT3U/rV4x7StM9nxwis/w6CZRE0H207/4Wxj4KAQNiw9SDt7E3S63FonrtoxBKN2EA9EbGJS7ac4F29TO1anCXJT6OvXfDJGoq+G+VtO3anvn0vqGRKu5a4hzgCkBIDRyYUz5lbz2FnOBLmdOLnjuq9ree4cZ6w2WHfEyuQkPa56hTMjtfy61yJFjL1IESN6oTYXMUajkZiYmKa///e//8XDw4M33niDcePGcdlll/HII48wevRoPDw8+OCDD05Y06LXN9+ITFGUNn3aWFlZiVarZc2aNWi1zX8BHh1FATAYDChK2+bfOjk5NXt+aWlpfP755zz//PO8++677N27l/Hjx3PDDTfw+OOP4+3tzZIlS5g5cyb19fXtKmImTJhAREQEb7zxBsHBwVitVpKTk6mvbz7cfvz37ejzssentK3lERBI3u72dywSwh6iItNICR2BId8ZW74VG47fHrmtdkWXs+3Qievi2sPPT4vV2vY1MQA6WyUDjXUsquj4YuwVRhtnDAhi1erW71ejhiqLhnt39+Ne+nFz+B6uVr7CI2+52rHsbkiYlm1FzV8/thdZifA49SzzarMN3V8e1v75Enuy2nTO2nq8DQrnxespqWk8wmwB9I3/a3HUirY78o5WO4EQXa7DE5UVRUGj0VBTU8Pvv/9OREQE9913X9Pj+/bta9P1oqOj0ev1rFixgvDwcKBxxGf79u2ceeaZQGNhYbFYyM/P54wzzujoU2iRVqulpqaxPemaNWuwWq0899xzTVPoPvroo3Zfu6ioiG3btvHGG280PZclS5Z0PHQn8JR1MUIler0Lmf3GE6aLh4IGOAQ2ukd75LYq82tgcV5794M5UUBAx6bWpSkbWcQAu2RZ7KNhSF8/1mzu2FqbrvLS/ihe4hYmBlzMbNMPhBz+HsXWPRtE/NXt2c4MfrOKJxbXMSVJz8pDFl5fW8/r4481YvjbT7UcqrDx9qTGr02I03HNV7W8tqr+z+lkVm77vpasEA3BfxmFya+y8tjiOpZeZQTAy6CQ6Kvh+eX1nBOtZeGeBu47w7nrnnBP5xfX8jFC9DBtLmLq6uo4cqRxvnBJSQkvv/wylZWVTJgwgfLycvbv388HH3xAZmYm33zzDZ999lmbrm8ymZg5cyZ33XUXPj4++Pv7c9999zUVDABxcXFcdtllTJ8+neeee460tDQKCgpYuHAhKSkpTetG2qOhoaHp+R2dTrZ582buueceAGJiYjCbzbz00ktMmDCBpUuX8p///Kfd9/Py8sLHx4fXX3+doKAg9u/fz7333tvu63Umj4BAtSOIXiYwMIYB0WNwK3HHVmKBHrDW5XQanGz8rN1o14YdHh7tG4U5KqHmU7BTEQOwIlRParUnm/aW2u2ane3zPH8+z5tGusf5/MP/V/rmfYFSX6V2rA7JDNHy2VQDf1tYx6O/1hHlpeH50S5clnJsxD+30sb+smMfFszo70RFHby8qp47f6jF00XhrCgdT519YjFy64Ja7hzk3Ky4mTfRwBWf1/DiynruGuxMZohMJbMbv66ZVi6EI2lzEbNgwYKm9Rhubm4kJCTw8ccfM3z4cABuv/12Zs2aRV1dHePGjeOBBx7g4YcfbtM9nnnmmabCyM3NjTvvvPOEfVrmzp3LY489xp133smhQ4fw9fUlOzu7w3vWbNq0qen5ubq6Eh0dzWuvvcb06dMBSE1N5V//+hdPPfUUf/vb3xg2bBj//Oc/mx5vK41GwwcffMAtt9xCcnIy8fHxvPjii03fT0fi4S9FjOh8iqKhf/JoYkz90eRZ4TDY6BmffrdkZfhBig4W2/WaLoYS6jvQCMpUv5UEV4WtNfaZ+tOgKGyLNRJZZWZvQfcqBNaWuTGubDyhLqN4PGwlQ4vno63KVztWu42P0zM+Tn/Kx+dNPLE99s0Dnbi5FXu9vH/hiVOrs0K0bLmpd3WB6zK+8S0fI0QPo9jauwW06HXK8vP4780z1Y4heihPz0AyEyfgU+2Prbxnj7iczN4+lfx0eIXdrzvqnFXU1m7t0DV+9HyeeWVhdkrUKNCqoF9eQEFFnV2v25WMOgsPRfzBxOpPcSqR9YJCJa4+cPdutVMI0eUcr3m/cFjuvn7onV0w19WqHUX0IIlxZ5DoNwh9ngaO9Iz2yG1V4dPArwUd3w/mRO3vTHa8JPNPwJUdj3OcIxobsVn+uC4+THV99xxpq2rQcveuVO5RUrgtbA9XKl/hnmf/QlSI05KpZKKX6tBml92NyWQ65Z/FixerHc/hKRoNfhFRascQPYCrqztnZk3j4gH3k2IejP6wAg64h0hXsOht/Oy8CbPZ/l3WfH017e5MdrzAqu/w/mtbKjvYobMSMSi42+/ibrMp/Ht/H1L23cqdHv/mcMgYbIqs9xBdxFcW9YveqVeNxOTk5JzysZCQkK4L0o35Rfbh8PYtascQ3VREeCqp4WfhWuCCraBntkduq9WRhyk4UNgp1w4ItE8HNw0WBrmW8U25W8sHt9E6FytDBgazZtkhu19bDfPzApifN52BnhN52G8RCUe+RDF3vJAU4pRkJEb0Ur2qiDl+/xfRPv6RfdSOILoZnc6JjH7jiXBKhPye3R65rQ5EVbPxQMfWq5yOp4f9Fs73t63mG0bY7XrHW+oOZ/QPZFXOkU65vhpWlLoztvQ8wg3n8ETYCgYXzUdT3TnFqujlpL2y6KV6VREjOi4gSjbUEq3j7x9FRsy5uJd5YittoKe3R26rKi8LvxSt6dR7uBhKO9SZ7HjRVR+jU0bQ0Emz/pb4axgY50PO9qLOuYFK9te4MG3HmbjphvBIxEbGV32KU6l9NjIVApDOZKLXkiJGtIlPWAQarRarpXsuxBWdS1E0pPY9mxiPAWiPWCGXXrlQvyUWrY1fjFuoL7RThXEKGiXXbtdytuSRZrKyqqpzllLaFIWcSBfiq9zZdqi8U+6hpooGHXfsSuNOpT93hu/iCtuXuOWvVjuW6O6c3cFDpsOL3kmKGNEmOr0en5AwCvbvVTuKcCAe7v5kJk3AtyYIW5kZqmW62Onk9MnjyIG8Tr6LDbO5453Jjpeh284qOm/+fa0C+5PcCKkxc6i4ptPuoyabTeHZfTE8yx1MDTrC7YbvCMhdiGKTnxnRDr6xaicQQjW9qjuZsA8/WRcj/hQXM4hJQ+5iTMBV+BzxbSxgxGkdjqhl3YFNnX4fX18NFjt0JjteYu03dr3eyZQoYE73xdP11Jsw9hQf5gaSvftKprm8wrawKdh0J24uKcRpyaJ+0YtJESPazD9S1sX0Zi4uJoZlXsrFGfeTZhmG02ENnbZQooep9rDyc1nnroM5KiDA/p/s+9T+Tphz579s7Nda8ckOxKkT2jo7oqUlHozeMZGR1ldYFnYtVoOP2pFEdxGYonYCIVQj08lEm/lHyUhMbxQWkkT/yFEYiwzYCqU9cltZNTYWuW+ltqBrNov18LRfZ7LjDXLJ50Cdb6dc+3ib9FayBgWzcclBbL2kRt5d7cIlO4bjoR/Ko+HrObfyU/Rle9SOJRxZSLraCYRQTe/4mEvYlX9kH1C69+Z0onW0Wiey+p/PlOy/M9hpPK6HnbHVydz99tgQXcDhAvsttG+JwVDaKddNsXTdxsArXW0MyAjusvs5ijKzjlt3DSAh/x/8J+BhKv3S1I4kHJFGJyMxoleTIka0mbOrEQ//ALVjiE7k6xvOmME3MDnxLqLKElDypBtdR+SF17L6wMYuvadG0zl7roRXfoqrpus+xFjirZCR7N9l93MkFpuGJ/fFkXzgLu7zepb84JHYkA+QxJ/8+4LeRe0UQqhGihjRLv4RMqWsx1EUUvqezUVD7mWkxyV45Lpjq5L2yB1V62ZlYcW6Lr+vvTuTHaW1VZNl7JopcUctC9HRL8qrS+/paN7LDSZr90yucH2FHWEXYdPJm9deT6aSiV5OihjRLv7SoazHcHPzZWT2lVzc/z4SawagPWwDmTFmF1aNjV+9t1NdY98uYS3x9dVgsXTOmhiAAZquHVWyoLAp1pU+AaYuva8j+q3Yk1E7LuAc2yusCLsaq8Fb7UhCLcHdr4hZtGgRiqJQWlqqdpRONW/ePDw9Pe16TUVR+Pzzz7s8Q0v33bt3L4qikJOT0+F7tZUUMaJd/KOkQ1l3F9sni4lDZ3Nu0DX45vljK5WF+va2ObqYA3mHuvy+AQGdO/0vvmp+p17/ZKoVKErxxN/ducvv7Yh2VBmYuuMsBlQ+z9eht2N2j1A7kuhqIQO65DYzZsxAURSuv/76Ex676aabUBSFGTNmdEmWnqympgZvb298fX2pq6tTO06T3Nxcxo4dq3aMk5IiRrSLjMR0T05OrgzNuJipmfeRbhuB8yEtmGXYpTMUhNSz4uB6Ve7t4dm5Iz9G83b6unbqLU4qT2NDn+mHyVkaax5VYtYxa2cmfQuf4I3AB6ny6692JNEVnN0b18R0kbCwMD744ANqao5tQltbW8v//vc/wsPDuyxHTzZ//nySkpJISEho04hLZwsMDMTZ2TE/PJIiRrSLydsHk5dMY+guQoITGD/kVi6Ivo2QoggolLUunanOaGVh7TpsKvUGNhhKOv0eWfrOWXPTkl06G6HZQei6sLlAd2C2Kjy+N4GkA3fzkPczFASPkCYAPVnIANB03Vu49PR0wsLC+PTTT5u+9umnnxIeHk5a2rHueXV1ddxyyy34+/vj4uLC0KFDWbVq1QnXW7NmDRkZGbi6ujJ48GC2bdvW9NiuXbs4//zzCQgIwGQykZmZyU8//dTs/MjISB577DGmT5+OyWQiIiKCL7/8koKCAs4//3xMJhMpKSmsXr266Zyj06u+/vpr4uPjcXV15aKLLqK6upq33nqLyMhIvLy8uOWWW7BYjo1m19XVMXv2bEJCQjAajQwcOJBFixY1yzNv3jzCw8NxdXVl0qRJFBUVtfl7PGfOHKZNm8a0adOYM2fOaY892bS8nJwcFEVh7969zY79/vvvSUxMxGQyMWbMGHJzj3XJXLVqFaNGjcLX1xcPDw/OPPNM1q5d2+z8v04nW7lyJWlpabi4uJCRkcG6dc3XfFosFmbOnElUVBQGg4H4+HheeOGFZsfMmDGDiRMn8uyzzxIUFISPjw833XQTZnPbZoRIESPaLSw5Ve0I4jS0Wh2ZqROYkv13hrqcj/GwC7Za6TLW2WyKjSV+u6isqlQtg0aT1+n3SDL/1PJBnSTHxUpKdu9rvdxabx0OIXP3Ncw0vszusAuwaR3zU1TRAWEDu/yWV111FXPnzm36+5tvvsmVV17Z7Ji7776b+fPn89Zbb7F27VpiYmIYPXo0xcXFzY677777eO6551i9ejU6nY6rrrqq6bHKykrOPfdcFi5cyLp16xgzZgwTJkxg//7mH5z8+9//ZsiQIaxbt45x48Zx+eWXM336dKZNm8batWuJjo5m+vTpzT5Mqq6u5sUXX+SDDz5gwYIFLFq0iEmTJvHtt9/y7bff8s477/B///d/fPLJJ03nzJo1i2XLlvHBBx+wYcMGJk+ezJgxY9ixYwcAK1asYObMmcyaNYucnBxGjBjBY4891qbv7a5du1i2bBlTpkxhypQpLF68mH379rXpGidTXV3Ns88+yzvvvMNvv/3G/v37mT17dtPjFRUVXHHFFSxZsoTly5cTGxvLueeeS0VFxUmvV1lZyfjx4+nbty9r1qzh4YcfbnY9AKvVSmhoKB9//DGbN2/mwQcf5O9//zsfffRRs+N++eUXdu3axS+//MJbb73FvHnzmDdvXpuenxQxot3CpYhxSN7eIYwefB2T+95Dn/K+je2Re8lmgY5ga0wpe46oM0pxlNnc8Re/lgRWLcBHr95LyO9ukJkWqNr9u4Ofi7w4a8dFjFVeYU3YlVhdPNWOJOwlLKvLbzlt2jSWLFnCvn372LdvH0uXLmXatGlNj1dVVfHaa6/xzDPPMHbsWPr27csbb7yBwWA4YWTh8ccf58wzz6Rv377ce++9/P7779TWNnY9TE1N5brrriM5OZnY2Fj+8Y9/EB0dzZdfftnsGueeey7XXXcdsbGxPPjgg5SXl5OZmcnkyZOJi4vjnnvuYcuWLeTlHftQx2w289prr5GWlsawYcO46KKLWLJkCXPmzKFv376MHz+eESNG8MsvvwCwf/9+5s6dy8cff8wZZ5xBdHQ0s2fPZujQoU0F3QsvvMCYMWO4++67iYuL45ZbbmH06NFt+t6++eabjB07Fi8vL7y9vRk9enSzgrG9zGYz//nPf8jIyCA9PZ1Zs2axcOHCpsfPOusspk2bRkJCAomJibz++utUV1fz66+/nvR6//vf/7BarcyZM4ekpCTGjx/PXXfd1ewYvV7PI488QkZGBlFRUVx22WVceeWVJxQxXl5evPzyyyQkJDB+/HjGjRvXLFtrSBEj2k2KGMeSlDCcC4fcwyivy/HM9cRWKVPGulpRkJllh3JUzeDjo3RqZ7KjNFgYZCjr9PuczhI/Denxvqpm6A62Vrpy4Y5RDKx+ge9Cb6XBPUztSKIjFA2EZnb5bf38/Bg3bhzz5s1j7ty5jBs3Dl/fYz9/u3btwmw2M2TIkKav6fV6srKy2LJlS7NrpaQc26QzKCgIgPz8fKDx0/7Zs2eTmJiIp6cnJpOJLVu2nDASc/w1AgIa967r16/fCV87el0AV1dXoqOjmx0TGRmJyWRq9rWj52zcuBGLxUJcXBwmk6npz6+//squXbsA2LJlCwMHNh8ZGzRo0Cm+iyeyWCy89dZbzQrCadOmMW/ePKzWjq1Z/evzDQoKavb9yMvL45prriE2NhYPDw/c3d2prKw84Xt91JYtW0hJScHF5ViL95M911deeYUBAwbg5+eHyWTi9ddfP+GaSUlJaLXaU2ZrDVkdKdrN3dcPr6BgSnIPqx2l1zIavchKPo8Acxi2EjMcBhl2UUe9wcrChpwOv+h0VGd3Jjtef9sqvuasLrvfX9kUhTURziRWebD1oLoFVXdQUK/nhp0D0Wuy+HvEVi42f4ah8A+1Y4m28u8LLu6q3Pqqq65i1qxZQOMb1fbS6/VN/60ojWu3jv7unD17Nj/++CPPPvssMTExGAwGLrroIurr61u8xumu+9fHjx5zsq8dPaeyshKtVsuaNWuaveEGmhU+HfH9999z6NAhpk6d2uzrFouFhQsXMmrUqBPO0fy5Hur4qXInW09ysud2/DlXXHEFRUVFvPDCC0RERODs7MygQYNO+F63xQcffMDs2bN57rnnGDRoEG5ubjzzzDOsWLGixWxtff2UIkZ0SHhyqhQxKugTOYB+IcNxKXCCfCs2pD2y2n4P2kv54XK1Y+Dp1XUbUUZXfYxOOYsGFevmOgX2JpoIqzFzoKhr9+PprsxWhUf2JPIIicwMOcCNTt/infsbinwA0j1EnanarceMGUN9fT2KopwwZSo6OhonJyeWLl1KRERjy2+z2cyqVau47bbbWn2PpUuXMmPGDCZNmgQ0FhJ/XazeVdLS0rBYLOTn53PGGWec9JjExMQT3qAvX7681feYM2cOF198Mffdd1+zrz/++OPMmTPnpEWMn58f0Nj+2MurcSPg9uzTsnTpUl599VXOPfdcAA4cOEBhYeEpj09MTOSdd96htra2aTTmr8916dKlDB48mBtvvLHpa0dHrexNppOJDpEpZV3HSW9g8IDJTM26j0zlbFwO66Q9soPYHlPKzsN71I4BgMFQ3PJBduJkySfdqH6ziFINVKd542V0UjtKtzPnUBgD9lzHtaaX2BM6EZtWvocOL1q90U+tVsuWLVvYvHnzCSMTRqORG264gbvuuosFCxawefNmrrnmGqqrq5k5c2ar7xEbG8unn35KTk4O69ev59JLL1VthDsuLo7LLruM6dOn8+mnn7Jnzx5WrlzJP//5T7755hsAbrnlFhYsWMCzzz7Ljh07ePnll1mwYEGrrl9QUMBXX33FFVdcQXJycrM/06dP5/PPPz+hKQJATEwMYWFhPPzww+zYsYNvvvmG5557rs3PLzY2lnfeeYctW7awYsUKLrvsMgwGwymPv/TSS1EUhWuuuYbNmzfz7bff8uyzz55wzdWrV/P999+zfft2HnjggZN2qLMHKWJEh4QlpYAibTw7U1BgLOOG3MwFsbcTVtwHCmStiyMpCTCz5PC6lg/sIl3Rmex4mdrtXXq/UzmkteE50B8XFZsNdGc/FnozYucUxmteYW3YDGzO6kxXEi3QOkPkkJaP60Tu7u64u5/838eTTz7JhRdeyOWXX056ejo7d+7k+++/bxotaI1//etfeHl5MXjwYCZMmMDo0aNJT0+3V/w2mzt3LtOnT+fOO+8kPj6eiRMnsmrVqqb9cbKzs3njjTd44YUXSE1N5YcffuD+++9v1bXffvttjEYjI0eOPOGxkSNHYjAYePfdd094TK/X8/7777N161ZSUlJ46qmn2twRDRpHgUpKSkhPT+fyyy9vao99KiaTia+++oqNGzeSlpbGfffdx1NPPdXsmOuuu44LLriAqVOnMnDgQIqKipqNytiTYlNrIwPRY7xzz63k7+2cocLeSqPR0j95NNHGVDR5Vlnm4qDMzja+8FhLaXmp2lGaDB/xGRZL17V3LnbJ5ua6u1o+sItk1ihsWnwQq/zMdIi/s5nHwtZwVtl8dBWH1I4jjoo6E674suXjhOgFZE2M6LDwfqlSxNiJp2cQWYkT8K7yw1bRABUyXcyRLQ/dR+mhUrVjNGnsTNa1+9N41y4nwkXDvjrH+Le6ymBjaFYwq1fIWr2OyK/Tc+3ObJw1WdwfsYWL6j/HULRJ7VhCxalkQjgaGXcXHSbrYjqub/wwLhhyN6N9r8DriFdjASMc2q7ocrYdcqziPSBAnUIi26Vrp7C1ZImnQma/U0+JEK1XZ9XwwJ4kEg/dxz99n6Q4cKjakXo3KWK6naSkpGbtmY//895776kdr1uT6WSiw8y1tbx81cVYLfLGuy1cXT3I6ncegQ0R2Iqlu1h3Uu7XwKc1S2locKx/84MHV6HVfdrl993jdhn3V17Q5fc9HS02BuysZcOurmt00FuM9SvkXo+fCD/8HYpVfnd1GaM/zN4u61C7mX379p20/TE07knj5ubWxYl6DplOJjpM7+JCUGw8h7bKVIPWiAxPJTX8LAwFLtikPXK30+BkY6F2o8MVMAAG11I60N6/3cIrP8OovZAqi+N8JmZBYWO0gegqN3YdqVA7To/yXYEv3xVcTIr7eP4RuJiUvM9R6uR73OmiR0gB0w0dbTct7E+KGGEX4cmpUsSchk7nREa/8UQ4JUJ+AxwCG46xhkC0zcrwgxQddMxP9zWaI6rcV2urZqBrLT9XOKty/1OpUSCvnwcBNWbyyrpu/5zeYkO5ifPLxxLoPJLHw1czvGQ+2spctWP1XDKVTIhmZE2MsIvwfrIu5mQC/KM5d8hNXBQ/m4jS2MYCRnRbe/tUsvmgY7QUPhmz+YBq905XNqh279Mp1NjQZPrh5iKf2XWWI3VOzNwxmOSSp/lf8N+o9U5QO1IPpECfEWqHEMKhyG91YRdBMfHonV0w18mnnYqiITVpFLHu6WiOWOEw2FB/Q0DRMRU+DfxasFbtGKfk7a1gsag3pSeu+hMUMh2yG/gerZWU7EBqFx/C7EBT3nqaGouWv+/ux9/px01he7lG+zWeR35XO1bPEJAEbgFqpxDCochIjLALrU5HWHKK2jFU5eERwNmDrmZq6t+Ir+qPJlf2d+kpLHobvzhvOuXiTEcQGKju9ESjeSd9XVWNcFobnG0kZQerHaPXeOVAJP33zuJm9xc4EDoOm0Y+M+2QaBmFEeKvpIgRdhM3UN1dhNWSEDOEC4bcxRj/K/E54oOtTKaM9TRrIg+TX1yodozT8vSsUTsCWfr9akc4reUmyEwPUjtGr/JVvh9n7LyMC/WvsDHsMmxORrUjdU8xo9ROIITDkY9GhN3EZGaj1emwOGDXJntzMbiTlTyeYFsfbEVmOAwy7NIzHYiqZsOBrWrHaJHBUEK9ygNFyeYfgKvVDdGCxX4ahiT6sWZLgdpRepW1ZW5MKBtHiMvZPB62ijOK56Otcqz9hRyWqy9Eyv48QvyVjMQIu3F2NRKRkqZ2jE4VHprMeUNuY2LETQQVhjUWMKLHqvSy8EvRGrVjtIpGq/4bwoCqBfjqHf9lZWWYE33DPdSO0SsdqnVmxo6hpJQ+x4fB91LnFad2JMeXOB40WrVTCOFwHP/VRnQr8YPOUDuC3Wm1TmT1n8iU7L8zSD8Ow2FnbHXSHrmns2htLDJuoV6NjVfawWxWfyqXgo1BrqVqx2iRWYGdCW6E+8rUJrVUWTTcszuFhCMP8W//xygLyFY7kuPqO1HtBEI4JClihF1FZ2Sj1evVjmEXfr6RjB18A5MT7yKqLB4lTzqM9SY5ffI4Uqj+6EZrqN2Z7Hj9rSvVjtAq5YqNijQvvE1Oakfp1Ww2hRf29yF13y3c4fE8h0LGYlNk1KGJqw9EDVM7hRAOSYoYYVfOrq5EpqarHaP9FIWUvmdz0eB7OctjKu657tiqev4aH9Hc4Yha1h3oPpu3BgQ4zshgVOXH6LvJruK5GhtuWf4Y9PKm2RF8mufPkF2XM9npVTaHXYJNLyNlJMhUMiFORYoYYXdx2d1vAaKbmy8js6/i4v73kVgzAG2uDRznfaHoQtUeVn4u6x7rYI7y8lK/M9lRztZC0o3dp/DfprfRZ3AQmu5Rd/UKq8vcOHfHBM5seInFYddjdfVTO5J6kiaqnUAIhyXdyYTdxWQMRKd3osHs+GsJYqMHkhx0Bk55OsizYUMW6vdmVo2NX923UlvQvTZtdYTOZMfL0G5nBYlqx2i1NS42hg4MZvXyw2pHEcfZX+PC5TuGYdQN4dGIjUyo+hSn0p1qx+o6Bm+IlKlkQpyKjMQIu3MyuBLhwFPKnF2MDM24mIsz7yfdOhynQ1pokPbIAjZEF3CoIFftGG3mCJ3JjpdY+6XaEdpsiYdCZqrsiO6Iqhq03LmrP/F5j/CS/6OU+2eqHalrJI4HrXzWLMSpSBEjOkX8IMebUhYSksiEobcyKepWQooisBU60EfXQnVHwupYfWCj2jHaxWw+oHaEZrxqVxLh3P1eXpYGaEmN8VY7hjgFm03huf0xpOy/nbs8/0VuyGhsSvf7d9Zq0pVMiNOSEl90imgHmVKm1epITx5LlKFfY3exQ2BDuoyJ5mrcrPxcuVbtGO3i5aXBYilXO8YJBrkcYV+dv9ox2sSqKKzvYyC22o0dhx2j25s4uY+PBPIxVzDIayIP+S4i/shXKOZqtWPZj8Ebos5UO4UQDq0Hf4Qh1OTkYiCy/wDV7u/jE8aYwdczue899CnvK+2RxSlZNTYWe2+nuqZ7vgEKDHTMf9spDb+pHaFdahU4lORBkKdB7SiiFZaVeDBmx/mMaHiZ38OuxWrwVTuSfSSMk6lkQrRAfkJEp4kfNJSdq5Z16T2TE0cQ752F7ogCuTZsdJ8uSUIdm6OL2X/gkNox2s3TgTqTHS+06nOM2slUWbrferNijQ33DB/clx6hvKbrf4c0VBRSumgeNbvXYGuoQ+cZhM+5t+EcFHvKcyo3/UL5ivk0lOSicXbFpc8AvEZchdbgDkDNnnUU//galqoSXGOz8Rl7C4q2cU8va10VuW/dTsDUx9B5dK/Rs6P21rhw6Y7huOmG8o/IDYyr+BR92W61Y7WfdCUTokWKzWbrfq8wolsw19by6jWX0VBf16n3MZm8yEo6D39zGLYSWeciWq8gpJ4vi5fQnX8NnjXyMGbzQrVjnNSb7u+xsMJF7RjtllyvsO+3Q5i7sBCz1FaSO/cWXMJTcEsbi8bVg4aSw+g8g9B7BZ30nNqDm8n73714nXU1hpgsLJVFFH//CjrvEPwn3YfNZuXgS9PwyJ6MS1Q6BZ//E7f0cbgPmABA0fevoPcKxj1rUpc9z86mVazcGb6L6bYvMeV3r5bpuAXB7ZtkfxghWiAjMaLT6F1ciOo/gB0rf++U6/eJGkBKyHCc850g3yrtkU/DYrXwryVz+WzzD+RXFRNg8mVy8lhuHTwd5TQbE9Y11PP872/x2aYfKKgqxt/ow61DruDilHEA/LZnFff/+G8Kqoo5J3Yoz4y9B6c/P90tr6tk/FvX8r+p/yLUI7BLnmdb1LnaWFi7rlsXMABabR5mB/2nn66sZyED1Y7Rbn842cgeFML6pQfpqn8m5cs/Qefui++425q+pvc8/c9P3aGt6Dz8cc84r+l4U/+xlC//BABrdTnWmnLc0seh6JxwjR2IueggALUHt1B/ZAfeo67vnCekEotNw9P7YnmaO7kk6DC3Gr4nIHchiq0bbADW/zIpYIRoBSliRKdKPGO4XYsYJ72BzJQJhGpjoaABDoHsStmyV1f8j3dyvuDf4/5OnG8kG3K3ced3/8Td2chVGRed8rwbvniIwqoSnhl7D5FeIeRXFmH9892c1Wbl5q8e5absaZwZlcX1nz/A/3K+ZMaACwH456L/Y1r/8x2ygLEpNpYE7KIyt1LtKB3WYN6vdoRTiq2ej8JAunOZuNxo44wBQaxa3TWtt2t2rmgaLak98Adakw9uaefi1n/MKc9xDkmg9Le3qdm1Cpc+GVirS6nethRDdAYAGlcPtCZvavaswyUylboDmzD2G4nN0kDxD6/gc+5tKD34TfP7ucG8z5UM9b6AB31+Jjb3K5QGR90LSoH0y9UOIUS3IEWM6FTRAwZi9PSiqrSkQ9cJDoojvc9oTCVu2IotIGtd2mTNoT84J2YII6MHARDmEcQXW34iJ3fLKc/5ZfcKVhxYz5LrPsDrz3n1YR7HprMUV5dRXFPG9PSJuOicGRU7hB1F+wBYfXAj649s5bFRt3Xek+qArTGl7DmwT+0YHebpqdDggJ3JjjKad5FkhD+6Z8+EJot9NAzp68eazQWdfi9z6RHM677FPXMiAYOmUJe7g5KFr6No9Zj6jTzpOS6hffGdMJuCL5/G1lAPVguGmCy8R90AgKIo+J5/DyUL/0vxwtcx9MnA1G8UZcs/xiU8BUWr58i7d2GpKcctfXzTNLOeZkmxB+cUTyLadQxPhC0ns3A+mppitWM11+dM8IpUO4UQ3YJ0JxOdSqPVkjT87Padq9EyIGUckwf9nTMMkzAedsVW45idmBzdgJBklu5by+7ixv1ENufvZNXBjYzoc+qpPj/uXEpKYDz/WfE/Ml65gGGvX8o/fn6FGnPjGicfV0/8TT78tmcVNeZaVh7YQKJ/NGZLA3//4V88OXo2Wgf8dLcoyMyyQzlqx7CLoCDHH+PI0nf/YhFgRaiepEjPzr+RzYZzQDReZ16BU0A0bv3HYEodTUXOt6c8pb5wPyULX8dj8MUEXfE8/pMfoaEsn6LvX2k6xiU0iaAr/k3o9XPwOecGGsryqNr0M57DLqfom39hSh1D4KVPUfb7B9Tn7+n856miXdUGpu4YwYDK5/kq9E7MHpFqRzomfbraCYToNmQkRnS6lJGjWfnFJ7R2Urm3VzCZiePxqvTDVtEAFVK4dNRN2ZdRWVfF8DemodVosFit3D3sGiYlnXPKc/aXHmbVwY0465x4Y9JjlNSUcd8P/6akppx/jfsbiqLw2vmP8MjCl3l44YuM6JPN1H7jeGX5ewwOT8NZ68Skd2+kuKaMK9MvaJpmpqZ6g5WFDTlYrT1jCqKnl+MPcSTVfw9cq3aMDmtQFLbFGomsMrO3oKrT7qM1eaH3DW/2Nb1PGNXblp7ynPLlH+MckojHwD9/xvyjUJxcyHvvHjyHXY7OdOIGnkXfv4zXiKvBZqM+bxeuCUPQ6F1wCUum9sAfOPlH2fV5OaISs46bdw5Aq6RxT8ROLrN8jrEgR71Arj6Q0DNHwYToDFLEiE7n4R9IRL/+7Nuw7rTH9Y0fRqLvIHR50h7Z3r7a8gufbf6RlyY8SJxfJJvzdvLwwpcIMPkwud/Yk55jtVlBgRcnPIC7swmAB8+q57rPH+Txc+7AoHcmKzSFb654vemc3cUHmL9pAQtmzOGi/93MVQMuYkSfbM5+8woGhvUn0T+6S57vqfwetJfyw447/aqtXA2l1Dvoov6jAqp/wM/pegrM3b9wrNRAaX8v/JY3UFDROV0XnUP6Yi4+2Oxr5uJD6NxP3frYZq47YSG4cnQn+5N8eFSx/ge0Lm64xg7EUvvnujBr44dFNqsFekiR31oWm4Yn9sbxBHdzefAhbnZZgN/hn1G6ejVXysWgc+raewrRjcl0MtElUs4++aJUV1cPhmddzsUD7qdf/SB0h4FuuK+Eo3t80avcmH0Z5/cdSaJfNBcmj+bqzMm8svy9U54TYPIh0OTXVMAAxPhEYMPGkYr8k55z7/fP8sCIm7DabPyRt4PxCSPwNXqRHdaf5Qdy7P202mR7TBk7D/esaTIabZ7aEVqkYGOQoVTtGHZzRGPDJcsfV6fOmSrpnnk+dYe3UbbsI8wlh6navIjK9QswpY9rOqbk13kUfv1c098NMVlUb/+dinXfYi49Qu3BzRT/9DpOQXHo3HyaXd9SVUrZsg/xOvs6ALQuJvQ+YZSv+oK6Q1uo3bce59DETnlu3cE7h0PI2j2TK11fZmfYhdi0zl138wFXdN29hOgBZCRGdImYjOxmC/yjIvqTEnoWhkJnbAXSHrmz1Zjr0CjNP7PQKtrG0ZZTyAjpx9dbF1FVX43RyRWA3SUH0CgaAt1O/FT4g/Vf4+nixjmxQymtrQDAbG3A8Of/WqzqTQssCTCzNPf0I4HdkSN3Jjteqm0FXzJK7Rh2s0NnJW1QMNsXH8Rite+HLs5BcfhNuo/SX9+idOn76DwC8DrrGkxJI5qOsVSW0FB+rMmAqd/ZWOtrqFjzNSU/z0HjYsQlPAXP4TNOuH7xwtdxz5zYrLjxOfc2Cr/5NxVrvsI96wKcg+Ls+py6o0XFXiwqvpA447k8HraUAQWfoantWIOa0wobCH7xnXd9IXog2exSdJllH72PbU8t4bqExvbIosvc/s0TLNm7hifHzCbON5I/8nZw74JnmJpyLn8f3tjB6Mlf/48jFYU8P/4+AKrqqxnx38tJD07ijqFXUVxTyt3fPU12WH+eHnt3s+sXVpVw3jvX8ellrxLo5gvAWf+9nAkJZzEsKpPLPrqTDy5+nv5BXf8Jr9nZxhceayktL+3ye3cmT0+Ffilvqx2jVeo1vlzH69T3sJebIeWwZtkhtWOILuDjZOYf4TmcUz4fXXknfHhw/iuQNs3+1xWiB5MiRnSZhpJajjyzSrZ1UUFlXTXPLv4vC3YsprC6hACTL+cnjuS2ITOaNqe8/ZsnOFh2hI8vfbHpvJ1F+3jwxxdYdWgjXgZ3xieM4K4zrsGgbz7F4qYvHyEzJLnZ4v11hzdzxzdPUFhdysyMi7htyIwuea5/tSR6H1sP7VTl3p0pIcGGn/+7asdotZfcPmJ5peN1q+uoM/IsrMo5onYM0UX0Ghv3RmzjEvPnuBZusM9Fnd3hzm3w54i3EKJ1pIgRXarw7c3Ubi5SO4boJXZHV/DzoZVqx+gUgwZVo9PPVztGqy3zfJSXy5LUjmF3is3GwL115GyX32u9zZXBB7jJ5Tt8Dv/asSYAA66ECc/bLZcQvYUs7BddyjQ4WO0Iopco92vgt7y1asfoNK6uZWpHaJOEmi/VjtApbIpCTqQL8SHuakcRXWzu4TAydl/L1caX2BM6EZu2nZ3FMmfaN5gQvYQUMaJLucR4oguQIXPRuRqcbCzUbqShoeeuveoOncmO51W3mkiXnvmSU6vA/iQ3QrwNakcRKlhY5M2InVM4V3mVteEzsDl7tP7kqGEQ2K/zwgnRg/XMVxTh0EyDZDRGdK5V4QcpKi1WO0anamjoHp3JjjfIOVftCJ2mRAFzui+ernq1owiVbKl05YLt55BV8yLfh95Kg1tIyycNmtX5wYTooaSIEV3ONd0fxUW6e4vOsa9PJZsOblc7Rqfy8FBoaOhe08kA+jX8pnaETrVfa8UnOxAnnby09mYF9Xqu2zmQpKKnmBf0ADU+ySc/0DcOYs/p2nBC9CDym1Z0OY2TFmNGgNoxRA9U4W1hUUHPXQdzVFBQ9+zHElr1OSatonaMTrVJbyVhUDBKz36aohXqrBoe3pNI4qG/87jPkxQFDWt+QPYNyD8UIdpPihihCtOgIJDf3cKOGnQ2fnH5A7O552+c6ulVo3aEdtHaask2ds/sbbHS1UZGhkybFce8cSicAXuu53q3l9gbej42tyBIvUTtWEJ0a1LECFXofAwYUvzUjiF6kLVRueQXF6odo0u4upaqHaHd0shRO0KXWOytkJHsr3YM4WAWFPgwfOdUXu8/H/TSCEKIjpAiRqjGfWS4jMYIuzgQVc2GA1vUjtFltN2sM9nx4qo/6TU/9stCdPSL8lI7hnAwBr2WKdmxascQotuTIkaoRu/viqGfr9oxRDdX6WVhUXHPXwdzvO7YmewoV/MekntJl3ULCptiXekTYFI7inAgUzPD8DK2c08ZIUQTKWKEqtzPktEY0X4WrY1fjVuoq6tTO0qX6a6dyY6Xpd+jdoQuU61AUYon/u7OakcRDkCnUbj6jCi1YwjRI0gRI1SlDzRiSPJRO4boptb3ySe3sPtOrWqPwECr2hE6LKn+e7UjdKk8jQ19ph8mZ2kt39tNSA0m1KuXDEUK0cmkiBGqcxsZIaMxos0OR9Sy9sAfasfocl7etWpH6DD/6p/w0/eul59dOhuh2UHoNPLLrje77sw+akcQosfoXa8iwiE5BRlxSZTRGNF61R5Wfi5bo3YMVXTnzmRHKdgY7Fqidowul+NiJSVbWi/3VqP6BpAQ6K52DCF6DClihENwHxmudgTRTVg1Nn712EZtbfcfkWgPrTZf7Qh2kWpdrnYEVfzuBplpgWrHEF1Mo8Dsc+LVjiFEjyJFjHAITiEmXBK91Y4huoGN0YUcyj+sdgzVdOfOZMfrU/kJTr10t/IlfhrS46UzY29yXmow8YFuascQokeRIkY4DBmNES3JC6tj1YENasdQjYeHhoaGUrVj2IXeWkyG0ax2DFXYFIU1Ec4khHqoHUV0AZ1G4fZRcWrHEKLHkSJGOAynUDdc4mVjOHFytSYrCyt7134wf9UTOpMdb4B2q9oRVFOnwN5EE2E+0qmqp5ucEUaEj1HtGEL0OFLECIfifnaE2hGEA7IpNn7z2UF1TbXaUVTl5VWjdgS7Sqz5XO0IqirVQHWat2x82IM56zTcOjJW7RhC9EhSxAiH4hTmhnOcjMaI5jbFFLM/76DaMVTnauzem1z+lUfdOvq49O6XoUNaG54D/XHpZS2ne4tp2REEerioHUOIHkl+awqHI2tjxPEKQ+pZcXC92jEcglbb8zb2zHbOVTuC6rbobcQOCka2kOlZTM46bhoRo3YMIXosKWKEw3GOcJfRGAFAnauNhbXrsNlsakdxCA0NB9SOYHfJDYvUjuAQVhlspGfKHjI9yVVDIvGWqYJCdBopYoRD8jw3Sv519nI2xcbSgF1UVFWqHcUhuLsrNDT0vA0iQ6u+xE0rP+wAS7wUMvv5qx1D2IGnq56rh/VRO4YQPZq8cgiHpA80YhwYpHYMoaKtMaXszt2ndgyHERTUM0ejtLZaso1VasdwGL8H60iJlj2zurvrhkXj7qJXO4YQPZoUMcJheYyKQOOqUzuGUEFxkJllh3LUjuFQelpnsuOls07tCA7DgsLGaAPRsjFit+Xn5syMwZFqxxCix5MiRjgsjase93Ok5XJvU+9i46eGHKzWnrUnSkf1tM5kx4upmo+saT+mRoG8fh4ESFerbunu0fEYnLRqxxCix5MiRjg0Y1YQ+kDZJKw3WRa8h/KKcrVjOJye2JnsKNeGvfSTPR+bKdTY0GT64eYio9HdSXq4JxcNCFU7hhC9ghQxwqEpGgXP82RxZG+xI6aMHYf3qB3DIfXEzmTHy9LvVjuCw9mjtRKUHYheK+NU3YFGgUfOS0ZR5P8vIbqCFDHC4Tn38cTQz1ftGKKTlQY0sCRX1kacTE/tTHa8pPrv1Y7gkDY420jKltbL3cHUzHD6hXqoHUOIXkOKGNEteJwbhSI7WvdYZmcbC1mPxWJRO4pDCgzsmZ3JjudXvZAAJ/v9jFsK8il74j7yJw4nb0w2RTMnY9626bTn1Pz0LUVXTyFv7CAKLhpF2dMPYy0rbXq8bvVyCqefT/74oZQ9cT82s7npMWtlBYXTz8dy5LDdnsNRy02QmS7dGh2Zp6ueu0fHqx1DiF5F3hWKbkHn5YJpmMwz7qlWhO6n5Lg3i6I5b+9atSN0OgUbgwzFdrmWtaKc4ltmgFaH1z9fxnfufEzX34Ficj/lOfV/5FD+5AMYzp2I75uf4PHQ05i3/kH5c/8AwGa1Uvb43zBMuAjvl97CvH0zNV/Pbzq/8o0XMUy4CG1g54yaLPbTMCDRr1OuLTruzlFxeMnGlkJ0KSliRLfhdmYoWg9ntWMIO9sdXcHWQzvVjuHQXI2lakfoEqnWZXa5TtX7c9H6B+JxzyPoE5PRBoXgnDkIXUjYKc8xb9qANiAY1wsuRRsUglO/NFzHX4h56x8A2MpKsZWV4nr+FHRR0TgPPpOG/Y3rt+r/yMG8bROuF1xql/ynsjLMib7hMl3J0fQNcueygdJJU4iuJkWM6DY0Tlo8zo1SO4awo3K/Bn7LW6t2DIen1earHaFLRFXOx9kOi6Lrlv2KPq4vpQ/fRf4FZ1F07cVUf/3pac/RJ6VgKThC3fLF2Gw2LMVF1P72E84DhwKgeHqh8fGlbtUybLU1mDesRdcnFluDmYrnn8D9jvtRtJ3bVteswM4EN8J9pWOjI3n0/CQ0GlnML0RXkyJGdCuuqX44RZ16SojoPhqcbPys+4OGhga1ozi8hob9akfoEnprCQOM5pYPbIHl8CGqv/wYbWg4Xk+9iuG8yVS8/DQ13395ynOckvvj8fcnKPvHveSfk0XhRWejGE243XovAIqi4PHg01S9+waFV12ELjYBw9jzqXp/Lk79M1H0zhTfPIPC6ROp/uyDDj+HUylXbFSkeeFtkqlLjmBSWggZkd5qxxCiV5IG9KLb8ZwQTf5L66Dnr3Xu0VaFH6LwYJHaMRyeWy/oTHa8Adot/E6/jl3EZkUf1xe3q28GQB+bQMOendR89QmG0eed9JSGvbuoeOVpjJdfi1PmIKzFhVT+3/OU//txPO56GACnfmn4vPbesXMO7KP2h6/xfv0DSm6dieuFl+CcNZTCmRehT0lHHx3XsedxCrkaG/FZ/tQszqXGLM0w1GJy1vG3sQlqxxCi15IiRnQ7TsEmjFmBVK04onYU0U77+lSx6eC2Trn2888/T1nZibvbZ2RkMG7cuBO+npOTwxdffNHsa1qtlvvvv7/p77///jtLly4FYMiQIQwePLjpsYMHD/Ltt99y9dVXo9HYf3A7qBd0JjteYs0X0MEiRuPtizay+f5SuvAo6n5beMpzqv43F31Sf4wXX9H4heg4FBcDJbdehemqm9D6nLiovvzfj2G6/g6wWmnYuRWXM0ehuBhwShmAef2aTitiALbpbQwYHMSW3w5i7V3/RBzGLSNj8Hd3UTuGEL2WFDGiW/IYHUnNlmKs5fVqRxFtVOljYVHBmk67/jXXXIPNduxdXX5+Pu+88w5JSUmnPMfZ2ZlZs2ad9LG8vDx++eUXLr30Umw2G++//z7R0dEEBARgtVr55ptvGD9+fKcUMABevaAz2fE86tYRbdCwq9ba7ms4JffHcmBfs69ZDu5HG3DqNsW2uhrQ/uUl8ej/p7YTq4Sabz9D4+aOy5DhWCvKGw9raEABsDRgs7Y/f2utcbExdGAwq5fbv62zOL2EQDeuHCJrNIVQk6yJEd2SxlWP1wWxascQbdSgs/Gz8ybM5o6vezgVo9GIyWRq+rN9+3a8vLyIiDh996DjzzGZTE1fLywsJCAggKioKPr06UNAQACFhYUALF26lPDwcEJCQjrv+biWdtq1HdVA5469KXe9aBrmzRupem8ODYf2U7PwO6q/mY9h4tSmYyreeJGyfx4bbXMedCZ1i3+m+ouPaDh8kPo/cqh4+Wl0Ccloff2bXd9aUkzlu//F7eZ7ANC4uaONiKJ6/v+o37Se+rUrcUru36Hn0FpLPBQyUwK65F6ikU6j8OzkVPRaeQslhJpkJEZ0W4YEb1wzAqhenad2FNFK66JyyT9Q0GX3s1gsbNiwgUGDBqGcputVfX09zz//PDabjaCgIM466yz8/RvfuPr7+1NUVERZWRk2m42ioiL8/f0pLi4mJyeHa6+9tlOfg1ZXgLmX9T7o1/ALcEm7z9cnJOH56HNU/vclKt9+HW1QCG433oXh7HObjrEWF2LJPzYl1TDmPGzVVVR//iEV//k3GpMJp7QsTNfcesL1y19+GuPky5sVNx53P0rZUw9S/dn7uE6djj7h1CN/9rY0UEtmtTfrd9pnnx1xejeOiCE5RFpdC6E2xWY7yTi5EN2EtbaBvOfXYimtUzuKaMHBqGoW5NpnH5DW2rRpE/Pnz+f222/Hzc3tpMccOHCA4uJiAgICqK2tZdmyZezbt48bb7wRd/fGTnirV69m+fLlAGRnZ5ORkcHbb79NVlYWVquVRYsWodVqGTNmTIsjPm014qxvaGjoXW9OrYoTN2k/pNzS+VOyegoXG8T+Uc6OwxVqR+nREgLd+OrmoTIKI4QDkJ9C0a1pXHR4XRgL0qLfoVV5WvmluOv3g1m3bh2xsbGnLGAAwsLCSE1NJTAwkMjISKZMmYKrqyurV69uOiYjI4NZs2Yxa9YsMjIyyMnJwdnZmdDQUL788kumTp3KOeecwyeffGLXltEmk9LrChgAja2ebGOl2jG6lVoFDiV5EORpUDtKjyXTyIRwLPKTKLo9l1gvjFmBascQp2DR2lhk2kxdXdeOlpWWlrJ7927S0tLadJ5WqyUoKIiSkpO3Na6urubXX39l7NixHDp0CB8fH3x8fIiKisJqtVJUZL+20UFBvXegPI11akfodoo1NmwZPrgbZKZ4Z5BpZEI4FiliRI/gcW4ftN7S6tIRre+TT25h169bysnJwWg0EhfXtja3VquVvLy8Zov7j/f999+TnZ2Nu7s7VqsV63FdqKxWK/acoevt07s6kx0vtuoTeYFqh71aG/7Zgei1MjxtTwmBbtx8VozaMYQQx5HXCNEjaJy1eF8k08ocTW5ELWsP/NHl97XZbOTk5JCamnpC6+PPPvuMn376qenvv/76K7t27aKkpITc3Fw+++wzysrKSE9PP+G6u3btoqioiKysLABCQkIoLCxkx44drFmzBkVR8PHxsdvzMLqeuN9Nb2Fo2E+KsfeORHXEH042kgaFcJpeFqINZBqZEI5JxpxFj+HcxxPT4GAql8qeCY6gxt3Kz2Vdvw4GYPfu3ZSVlZ10KllZWVmzTmU1NTV89dVXVFZW4uLiQnBwMFdddRV+fs03NzSbzXz33XdcdNFFTee7u7szduxYvvjiC3Q6HRMnTkSv19vteWh1+b2uM9nxMnV7yKFPyweKEyw32jhjQBCrVueqHaXbk2lkQjgm6U4mehSb2ULeC+toKKxRO0qvZtXY+CF0KwfzpaDsiLNGfovZbL81Nt1NvmEkt9feqHaMbm3IgXrWbO66tuY9jXQjE8JxyU+l6FEUvRavyXEyrUxlG6MLpYDpIJNJ6dUFDIB/zUICneRlqiNWhOpJivRUO0a3JNPIhHBsMp1M9DjOEe6YhoVS+etBtaP0Snlhdaw6sEHtGN2ePTqTXXbpfvLyTpyPdt557txyq+8JX//mm3J+/KGSvXvrAYiNc2bmTC8SEo41zfjoo1I++rAUgKlTPZk8xbPpsS1bannxhUJefiUErZ0Wlg8yFPFZvZddrtUbNSgK22KNRFaZ2VtQpXacbuW2s2NlGpkQDkyKGNEjeYyKoHZrMQ151WpH6VVqTVZ+rlJnHUxP4+3d8c5kr7wagtV6rBjas6eee+4+wrAzjSc9fv36WkacZSQpyQcnJ4UPPyjlnruPMGdOKL5+OnbvquOteSU89nggNhvcf98RBmS40qePExaLjeefL+T2233tVsAApFqX8xlj7Xa93qhSA6X9vfBb3kBBhWwM3BqDo324cbh0IxPCkckYqeiRFJ0G78lxoJF5ZV3Fptj4zWcHVdVSONqD0Vje4Wt4emrx9tY1/VmxvJrgYB2pqSdvR/73v/tz/vkexMQ4Ex7uxB13+mGz2Vi7rnGN2f4DZqL6OJGWZiA93UCfPk4cONA4avPhh6Wk9HNpNmpjD5GV83GWNlsddkRjwyXLH1cnrdpRHJ6P0Ynnp/ZHI68fQjg0KWJEj+UU6obHmEi1Y/Qam2KK2Z8nU/jsRauz7946ZrONn36qZMwYt2bd2U6nrs5GQwO4uzW+VERFOXHooJm8vAby8swcPGgmMtKJw4fNfL+gkiuv8rZrZgC9tYQMY73dr9sb7dBZiRgUjFbenJ+SosCzU1Lxd5d9x4RwdFLEiB7NbVgoLn3tt2+HOLmC4HpWHFyvdowexWKxb0G4dGkVlZVWzhnt1upz3nijGB8fLekDDABERDhx1Uxv7rk7l3vuPsLMq72JiHDi3/8u5NprvVm9qpqrZx7guusOsmGD/ToEZmg32+1avd06Fyv9BwarHcNhXT00ihHx/mrHEEK0gqyJET2e95Q48l5ch6W49+5+3pnqXa38XLfOrjvV93aNnckK7XrN776rICvLFV/f1v3af//9Uhb9UslzzwXhdFyHsAkT3Jkwwb3p7z98X4GrQaFvX2dmzDjIK6+GUFjQwOOP5fPOu+E4OXX8U//4mi+A1A5fRzRa6g5n9A9kVc4RtaM4lNRQD+4ek6B2DCFEK8lIjOjxNC46fC5LBJ1MobA3m2JjScBuKqoq1Y7SowQF2fd6eXlm1q2tYey5rRuF+eijUj54v5QnnwqiT7TzKY8rK7Pw9jslzLrZly1b6wgN1RMaqqd/moGGBhsHD5rtkt+jbj0xBvn5tacl/hr6x8oo9VGernpeuSxd2ikL0Y3IT6voFZxCTHiOj1Y7Ro+zLaaM3bn71I7R43h723ez1gULKvD01JKd7drisR9+UMq775bwzycDiY8/dQED8NqrRVx4oQd+fjqsVmiwHBuNs1ho1hmto7KdZN8he7IpCjlRLsSHuLd8cA+nKPCvKamEerX88yGEcBxSxIhew5QdhKG/n9oxeoziIDO/H1qndoweydUOncmOslptfL+gklHnmE5offzkk/n897/FTX//4P1S5s0rZvZsPwIDdRQXN1Bc3EBNjfWE665ZXc3Bg2bOP7/xTXB8vDMH9ptZuaKar78uR6OBsDC93Z5HcsMvdruWaFSrwP4kN0K8DWpHUdV1w6I5KyFA7RhCiDaSNTGiV/G6IBbz4Soa8qUNcEfUu9hY2JCD1Xrim1vRcTpdPg0n7lHZLmvX1pCf38DYMSdOJcvPb2jWhfyrr8oxm+HRR/KbHXf5dE+uuOJY57G6OisvvVTE/Q/4N7Wh9fPTMWuWD888U4Ber3D3Pf44O9vvc7KQqq/w0E6jzCL/5uypRAG3dF88fz9CabV9pv91J1lR3tw1Ol7tGEKIdlBsshpX9DLmwhryX16HrdaidpRu69c+e9hxeLfaMXqss0Z+Z/eF/T3B2x7v8H25TPnpDElmDQd+O0R9Q+8pEn1Nznx7y1BppyxENyXTyUSvo/c14H1xAsg64XbZEVMmBUwnMhrt35msp0izrVU7Qo+1SW8lYVAwvWVfUSedhtenD5ACRohuTIoY0SsZErxxPztC7RjdTmlAA0tyZR1MZwoKksHxU4mpni8vWp1opauNjIzesYfMUxf2Iz3cS+0YQogOkNcD0Wu5nRUmG2G2gdnZxkLWY7HINLzO5O1Tp3YEh2Vo2E+KUYq8zrTYWyEjuWdv9njj8GgmpYWqHUMI0UFSxIheS1EUvKfGofPr3Z15WmtF2H5KykrVjtHjGY1lakdwaFm6XWpH6PGWhejoF9UzRylGJwU43EL+RYsWoSgKpaWlbTpv3rx5eHp6tumc4cOHc9ttt7XpnI6IjIzk+eeft9v1Hn74Yfr373/aY/bu3YuiKOTk5HToXjNmzGDixIkduoa9soiTkyJG9GoaZx0+0/uiOGvVjuLQ9kRXsvXgTrVj9Ao6XX7LB/VifesWqB2hx7OgsCnWlT4BJrWj2FXfIHf+PbU/ikoLf5YtW4ZWq2XcuHGq3P9khg8fjqIoTX8CAgKYPHky+/bZZ/+vVatWce2117bpnH/+859otVqeeeaZFo+1R6HRmcLCwsjNzSU5ORlof8EqTk6KGNHr6f1c8ZmWCNpesqK1jcp9G/g1b43aMXoNi+WA2hEcml/NLwQ5yUtXZ6tWoCjFE3/302942l34uTnz3ysycHVSb2eJOXPmcPPNN/Pbb79x+LDjbN56zTXXkJuby+HDh/niiy84cOAA06ZN69A16+vrAfDz88PVtW0dBd98803uvvtu3nzzzQ5lcARarZbAwEB0OtnRpDPIK4EQgEusF94XxUnHsr+w6G38rP+DBnttWiJOSzqTtc4gg3yPukKexoY+0w+Tc/d+A+as0/B/lw8g2FO9qcOVlZV8+OGH3HDDDYwbN4558+adcMzSpUtJSUnBxcWF7Oxs/vjjj2aPz5s3j/DwcFxdXZk0aRJFRUXNHj/ZqMRtt93G8OHDT5vN1dWVwMBAgoKCyM7OZtasWaxde6wToMViYebMmURFRWEwGIiPj+eFF1446b0ff/xxgoODiY9vnLLX1ulkv/76KzU1NTz66KOUl5fz+++/n/LYhx9+mLfeeosvvviiaSRp0aJFTY/v3r2bESNG4OrqSmpqKsuWLWt27l+npT3//PNERkaecJ9HHnkEPz8/3N3duf7665sKNIAFCxYwdOhQPD098fHxYfz48ezadWzK6/HTyfbu3cuIESMA8PLyQlEUZsyYwdtvv42Pjw91dc3XQ06cOJHLL7+8Nd+2XkuKGDv761Bhe+astkRRFD7//HOgc+db2iv78XkBtm7dSnZ2Ni4uLi3Obe1Krmn+eIyJUjuGQ1kVcYjCkqKWDxR2IZ3JWifVuqzlg4Rd7NLZCM0OQqfpvp/wPH1RiuqdyD766CMSEhKIj49n2rRpvPnmm/x1m7677rqL5557jlWrVuHn58eECRMwmxs3IF2xYgUzZ85k1qxZ5OTkMGLECB577DG75ywuLuajjz5i4MCBTV+zWq2Ehoby8ccfs3nzZh588EH+/ve/89FHHzU7d+HChWzbto0ff/yRr7/+ul33nzNnDpdccgl6vZ5LLrmEOXPmnPLY2bNnM2XKFMaMGUNubi65ubkMHjy46fH77ruP2bNnk5OTQ1xcHJdcckmbP5BbuHAhW7ZsYdGiRbz//vt8+umnPPLII02PV1VVcccdd7B69WoWLlyIRqNh0qRJJ90IOiwsjPnz5wOwbds2cnNzeeGFF5g8eTIWi4Uvv/yy6dj8/Hy++eYbrrrqqjbl7W2kiGmn1s5tnTp1Ktu3b2/TtWtqavD29sbX1/eEyryz2Hvx3fFyc3MZO3Zs098feughjEYj27ZtY+HChZ1yz/ZyOzMU05De0WK0Jfv6VPHHwW1qx+hVvL2lM1lrRFTMx6Ubv6nubnJcrKRkd8/fizeNiOb8/iFqx2DOnDlNU7TGjBlDWVkZv/76a7NjHnroIUaNGkW/fv146623yMvL47PPPgPghRdeYMyYMdx9993ExcVxyy23MHr0aLtke/XVVzGZTBiNRnx8fNi2bVuzqVx6vZ5HHnmEjIwMoqKiuOyyy7jyyitPKGKMRiP//e9/SUpKIikpqc05ysvL+eSTT5q+T9OmTeOjjz6isrLypMebTCYMBgPOzs4EBgYSGBiIk5NT0+OzZ89m3LhxxMXF8cgjj7Bv3z527mzb2k4nJyfefPNNkpKSGDduHI8++igvvvhiU5Fy4YUXcsEFFxATE0P//v1588032bhxI5s3bz7hWlqtFm9vbwD8/f0JDAzEw8MDg8HApZdeyty5c5uOfffddwkPD29xFK23kyKmnVo7t9VgMODv37Z2lfPnzycpKYmEhIRmIxjdVWBgIM7Ox+ZV79q1i6FDhxIREYGPj+O1OPYY3wdDiq/aMVRV6WPh10LZWLCrSWey1tHbysg01rd8oLCb390gMy1Q7RhtMiYpkNnnqN+JbNu2baxcuZJLLrkEAJ1Ox9SpU08YZRg0aFDTf3t7exMfH8+WLVsA2LJlS7PRkb8e3xGXXXYZOTk5rF+/niVLlhATE8M555xDRUVF0zGvvPIKAwYMwM/PD5PJxOuvv87+/fubXadfv37Nioi2ev/994mOjiY1NRWA/v37ExERwYcfftiu66WkpDT9d1BQENA4wtEWqampzdb0DBo0iMrKSg4caFy7uGPHDi655BL69OmDu7t703S0v35vWnLNNdfwww8/cOjQIaBxJsyMGTNUa0LRXUgR0w6tmdt6VHumZB39xGbatGmnHUo93unmfsKxwsjZ2ZnIyEiee+65pseGDx/Ovn37uP3225vmlR7v+++/JzExEZPJ1DRse9SqVasYNWoUvr6+eHh4cOaZZzabSwvNp5MpisKaNWt49NFHURSFu+66C3d3dz755JNm53z++ecYjcZmv0S7iqIoeE+Jx7mPR5ff2xE06Gz87LKp2bxf0TV0+gK1I3Qb6ZpNakfodZb4aUiP7x4f8GRFefP8xep1IjvenDlzaGhoIDg4GJ1Oh06n47XXXmP+/PmUldnvgwuNRnPCFLWj09FOx8PDg5iYGGJiYhgyZAhz5sxhx44dTcXDBx98wOzZs5k5cyY//PADOTk5XHnllSe8RhiNxg7lnzNnDps2bWr6Hul0OjZv3tzuBf56vb7pv4/+Ozg6gtLe79VfTZgwgeLiYt544w1WrFjBihUrANr8+pmWlkZqaipvv/02a9asYdOmTcyYMaPNeXobKWLaoTVzW9tr165dLFu2jClTpjBlyhQWL17cqlaHp5v7uWbNGqZMmcLFF1/Mxo0befjhh3nggQeaiq9PP/2U0NBQHn300aZ5pUdVV1fz7LPP8s477/Dbb7+xf/9+Zs+e3fR4RUUFV1xxBUuWLGH58uXExsZy7rnnnrL4yM3NJSkpiTvvvJPc3FweeughLr744mbDqABz587loosuws3Nra3fQrtQdBp8pvdFH9ixX8rd0bqoXPKL5M20GqQzWesl1HymdoRex6YorIlwJiHUsT/gSQp2Z84VGbjo1W+d39DQwNtvv81zzz1HTk5O05/169cTHBzM+++/33Ts8uXLm/67pKSE7du3k5iYCEBiYmLTG+STHQ+NncCOf/0G2rVeVqtt/L7V1NQAjQ0HBg8ezI033khaWhoxMTHNFq/bw8aNG1m9ejWLFi1q9n1atGgRy5YtY+vWrSc9z8nJqV0bMPv5+XHkyJFm791O9r1av3590/cBGr/nJpOJsLAwioqK2LZtG/fffz8jR44kMTGRkpKS09736EjVyTJfffXVzJs3j7lz53L22WcTFhbW5ufV20gR0w6tmdvaXm+++SZjx47Fy8sLb29vRo8efcIb/JM53dzPf/3rX4wcOZIHHniAuLg4ZsyYwaxZs5p6sHt7e6PVanFzc2uaV3qU2WzmP//5DxkZGaSnpzNr1qxm61jOOusspk2bRkJCAomJibz++utUV1ef8vtxtNWgyWQiMDAQk8nE1Vdfzffff9/0yzc/P59vv/1W9QVtGhcdvlclofXsGS1GW+NgZA3rD2xRO0av1NiZTIrH1nKv+4NYg/qfsvc2dQrsTTQR5tO2trldpY+fkbevysLNRd/ywV3g66+/pqSkhJkzZ5KcnNzsz4UXXthstsWjjz7KwoUL+eOPP5gxYwa+vr5N3cZuueUWFixYwLPPPsuOHTt4+eWXWbCg+Z5JZ511FqtXr+btt99mx44dPPTQQyd0ODuZ6upqjhw5wpEjR1i/fj033HADLi4unHPOOQDExsayevVqvv/+e7Zv384DDzzAqlWr7PdNovF9VVZWFsOGDWv2PRo2bBiZmZmnnJUSGRnJhg0b2LZtG4WFha0eTRk+fDgFBQU8/fTT7Nq1i1deeYXvvvvuhOPq6+uZOXMmmzdv5ttvv+Whhx5i1qxZaDQavLy88PHx4fXXX2fnzp38/PPP3HHHHae9b0REBIqi8PXXX1NQUNBsvc+ll17KwYMHeeONN1R//9NdSBHTRq2d29oeFouFt956q1l/9mnTpjFv3ryTdro43unmfm7ZsoUhQ4Y0O37IkCHs2LGjxU8wXF1diY6Obnbt4+eU5uXlcc011xAbG4uHhwfu7u5UVla2aT5oVlYWSUlJvPXWW0DjgraIiAiGDRvW6mt0Fq27M75XJaNx7d4tRlujytPKLyWyH4xa/vyxFW0w0OmQ2hF6pVINVKd542Vs//qHzhDs4cI7MwfiY3KcD57mzJnD2WefjYfHiaNXF154IatXr2bDhg0APPnkk9x6660MGDCAI0eO8NVXXzV9cp+dnc0bb7zBCy+8QGpqKj/88AP3339/s+uNHj2aBx54gLvvvpvMzEwqKiqYPn16ixnfeOMNgoKCCAoKYsSIERQWFvLtt982tUm+7rrruOCCC5g6dSoDBw6kqKiIG2+8saPfmib19fW8++67XHjhhSd9/MILL+Ttt98+aYFyzTXXEB8fT0ZGBn5+fixdurRV90xMTOTVV1/llVdeITU1lZUrVzabZXLUyJEjiY2NZdiwYUydOpXzzjuPhx9+GGickvbBBx+wZs0akpOTuf3221vcoDMkJIRHHnmEe++9l4CAAGbNmtX0mIeHBxdeeCEmk8mhN/B0JIrNXvOgeom7776bZ555pmm4FcBms+Hs7Exubi7r1q1jxIgRlJSU4Onpybx587jttttatTvrt99+y7hx45pdGxqLmx9++IFRo0YBjXM7P/vsMyZOnMjevXuJiopi3bp1Te2KS0tL8fLy4pdffmH48OGkp6dz/vnn89BDDzVd84svvmDy5MnU1NSg1WqJjIzktttu47bbbms65mTZP//8cyZNmtQ0BDtmzBiKiop45JFHiIiIwNnZmUGDBnHfffc1Xev4vNC4WG/ixIlNvwgAXnrpJV555RW2bt1Kv379uPjii7nvvvta/J51lbq9ZRT89w9oOH0x2V1ZtTa+C9pMbuERtaP0WllZdTi7fNTygaLJQeMk7qnu2KZ8ov0SzQq5iw9Ta1b/96KP0YmPrh9EtJ9J7ShCtNvIkSNJSkrixRdfVDtKtyAjMW3Qlrmt7TFnzhwuvvjiZtfOycnh4osv7tBIT2Ji4gmfTixdupS4uLimgqm980qXLl3KLbfcwrnnntvUOKCwsO0b0U2bNo19+/bx4osvsnnzZq644oo2X6MzOUd64HNJfI/9iVnfJ18KGJUZjeVqR+h2Qqq/wkPXQ38ou4Etehuxg4JRu9u1m7OOt67KkgJGdFslJSV89tlnLFq0iJtuukntON1Gz58jY0fHz23969Dw0bmtLQ0lnkpBQQFfffUVX375JcnJyc0emz59OpMmTaK4uLipx3hb3HnnnWRmZvKPf/yDqVOnsmzZMl5++WVeffXVpmMiIyP57bffuPjii3F2dsbXt3UdaGJjY3nnnXfIyMigvLycu+66C4Oh7bsie3l5ccEFF3DXXXdxzjnnEBoa2uZrdDZDki+e58VQ+nnb+sw7utyIWtYcaHnetOhcOn0+DW3/HKFXU2wNDHKtYEF572vA4ShWGWwMzQxm9cpTbzXQmZx1Gt64IoPkEMduNtDbvffee1x33XUnfSwiIoJNm3p3t8G0tDRKSkp46qmnmqbxiZbJR1ht0Ja5rW319ttvYzQaGTly5AmPjRw5EoPBwLvvvtuua6enp/PRRx/xwQcfkJyczIMPPsijjz7arH3fo48+yt69e4mOjsbPz6/V154zZw4lJSWkp6dz+eWXc8stt7R5X5yjZs6cSX19vUMvaDNlB+E+KkLtGHZT427l5zLZD8YRWCwH1Y7QLaUh/37VtsRLIbNf+37vd4ROo/DKpelk93G8/cZEc+edd94Js0yO/vn222/Vjqe6vXv3UlZWdtJ1OeLUZE2McBjvvPMOt99+O4cPH+7QhlldofyX/ZR/33Lra0dm1dj4IXQrB/PV+QRVHOPqqjAg4221Y3RLtbpQrrW8gAxiqUuLjQE7a9mwq7hL7qco8K8pqUxKc7xReyFE15CRGKG66upqdu3axZNPPsl1113n8AUMgPuIcDzOjVI7Rof8EV0oBYyDCAqSz5Lay6XhIKlG+f6pzYLCxmgD0YGdv7eXosCj5yVJASNELydFTBdKSkrCZDKd9M97772ndjzVPP300yQkJBAYGMjf/vY3teO0mtuwUDwm9FE7RrvkhdWx8kD7pj4K+/P2qVM7QreWqbPvxnuifWoUyOvnQYCHS6fdQ1Hg8Yn9uHxQZKfdQwjRPch0si60b9++U27EFBAQoNru9KJjKpcfpvSLXdBNfpJqTVY+c1pBVXW12lHEn0aMKKDBsqDlA8VJFRrO5NbaW9SOIf4UZdFQuSSXitoGu15Xq1F46sIULhogIzBCCOlO1qUiInrOgnBxjCk7GEWroeTTHQ5fyNgUG4t9d1J1RAoYR6LTF0hnsg7wrfmVYOfbOFyv/n4lAvZoraRkB1K7+BBmi31+Keo0Cv+e2p8JqcF2uZ4QovuT6WRC2IExMxCvi+JA5f0SWrI5pph9Rw6oHUP8hcUi/5901CBD2/enEp1ng7ONpGz7FBxOWg2vXJYuBYwQohkpYoSwE+OAALynOu6GmIXB9Sw/uF7tGOIvXF0VzOZ8tWN0eymW39WOIP5iuQky04M6dA1nnYb/mz6A0UmBdkolhOgpHPTtlhDdk2t/f7wvSUD1Laz/ot7VysL6HGQJnOORzmT2EVn5CS4O9nMnYLGfhgGJrd977HgGvZY3Z2QyIr7r96ARQjg+KWKEsDPXfn74XJYAWsd4Q2VTbCwJ2ENFZYXaUcRJSGcy+9DZKsg01qsdQ5zEyjAn+oafuEn06Zicdbx1VRZDYnw7KZUQoruTIkaITmBI8sXn8r6gU7+Q2RZTyu7cvWrHEKdgNJarHaHHyND8oXYEcRJmBXYmuBHu69qq491ddLwzM4usKO9OTiaE6M6kiBGikxgSvPGdnoSiV+/HrDjIzO+HclS7v2iZXl+gdoQeI776M7UjiFMoV2xUpnnjbTr9ZsbeRif+d002aeFeXZRMCNFdSREjRCdyifPC9+p+aIxd38283sXGwoYcrFZpO+vILJaDakfoMdzqNxFnUH/0U5zcYY0Ntyx/DHrtSR+P8HFl/g2DSQ5p29QzIUTvJEWMEJ3MOcId/xv7o/M3dOl9lwfvpaxCpio5MoOrgtmcp3aMHiXbSYpCR7ZNb6PP4KATep+khnny6Q2DifI1qhNMCNHtSBEjRBfQ+Rjwv6E/zjGeXXK/HTFlbD+8u0vuJdovKFA6k9lbknmh2hFEC9a42EgfeGzPl7MT/fngmmx8TM4qphJCdDdSxAjRRTQGHb5XJmPM7Nz9DkoDGliSu65T7yHsw8dHumnZW0j1N3jq5KXN0S3xUMhMCeDSgeH83+UZGJxOPsVMCCFORX7TC9GFFK2C14WxeIyNhE6Yum92trGQDVgsFvtfXNidySTT/exNsTUwyFXaiTs6BThzSBhPTOqHVvb3EUK0gxQxQqjA7cwwfC5LtHvnspVhBygpK7HrNUXn0Ulnsk7Rn9VqRxCnYdAovJEUyayIALWjCCG6MSlihFCJIdkXv+tS0LidvuVoa+3uU8GWgzvsci3RNazSmaxTxFR+jExOckz+Tjo+TYtlvL+n2lGEEN2cFDFCqMgp1A3/m/qjD+xYR55yXwu/5a+1UyrRFVxcoN58RO0YPZKLJZf+Rmma4GgSjC58OyCONPfWbXophBCnI0WMECrTeTrjd0MKLvHt29zNorfxs34jDQ0Ndk4mOlNwsKwD6EwZOhmVdCTn+LjzVXosoS72GXkWQggpYoRwABpnHT5XJGEcFNTmc1dFHKKwpKgTUonO5O1Tq3aEHq1v3bdqRxCAVoG/RQXxVr8o3HQyyU8IYT9SxAjhIBSNgtf5MXieF93qn8z9far44+C2zg0mOoWbSTpodSbfmsWEOMlLnJp89Do+SInm1sgAFEVGHoUQ9iW/4YVwMKbBwfhd3fKC/0pvC4sKZR1MdyWdyTrfYIN8j9WS5ubKDxlxnOHtpnYUIUQPJUWMEA7IuY8HAbem4RzjedLHG3Q2fjZsor5eNkvsrqxW6UzW2fpZlqodoVe6ItiHL9JjCJH1L0KITiRFjBAOSmtywveqZNzOCjthY8x1UUfIL5JPmbsrFxeor5fOZJ0tonI+BtlIscsYNAovJYbzVHwYThp5eyGE6FzyW0YIB6ZoFDzOicT3ymQ0Rh0AhyJrWH9gs8rJREdIZ7KuobNVkmWsUztGrxBpcOKbAXFMDvRWO4oQopeQIkaIbsAlzgv/W9Jp6OvKzyVr1I4jOsjbR95Yd5V0zR9qR+jxzvFx5/sBcfQ1GdSOIoToRaSIEaKb0Hk4E3ZZf7KysqTTTzdnMpWrHaHXiK/+VO0IPZZeUbivT2P7ZA+9Tu04QoheRooYIboRrVbLyJEjmT59Om5u0vWnu9JLZ7Iu41a/hQSDFP32FufqwrcDYrk5QtonCyHUIUWMEN1QVFQUN9xwA3FxcWpHEe1gtR5SO0KvMtDpgNoRegwFuCbUlx8y4ujn5qp2HCFELyZFjBDdlKurK5deeiljx45Fq5WdsLsLFxdFOpN1sWTzT2pH6BGCnfV8lBrNP2JDcdHK2wchhLrkt5AQ3dzAgQO59tprCQ4OVjuKaIWgIBtgUztGrxJY9S1eOnm564hJ/p78khkvm1cKIRyG/FYXogcICAjg6quv5uyzz0ankwW2jszHVzYo7WoaLAxylWYK7eGp0/Ja3wheS4qUxftCCIciRYwQPYRGo2Ho0KFcf/31hIeHqx1HnIJ0JlNHmm212hG6nWFeJn7OjGdSgJfaUYQQ4gRSxAjRw/j6+nLllVcyduxY9Hq92nHEX+j1hWpH6JX6VH2CrBxrHReNwj9iqUAS+wAAJKtJREFUQvgwNZpgFye14wghxElJESNED6QoCgMHDuTGG2+kT58+ascRx7FaD6odoVdyseTS32hVO4bDaxx9SeCaMD9pnSyEcGhSxAjRg3l5eTF9+nTOO+88XFxc1I7T6zk7I53JVJSl26F2BIfl56Tj1b4RfNQ/hj6uzmrHEUKIFskqPSF6gfT0dGJiYvjmm2/Ytm2b2nF6reBgBelMpp7Eum+AeLVjOBQNcHmwD3/vEyQL94UQ3Ypis9nkFVWIXmTjxo189913VFdXqx2l18nIqMfg+qHaMXq1e1w+42CdTCsDSDYZeDoulHQPo9pRhBCizeRjFyF6mX79+tGnTx9+/vln1q5di3yO0XVMpgos8v5ZVYNcCvi4zkftGKoyajXcFRnINWF+aGXdixCim5I1MUL0QkajkQkTJnD99dcTFRWldpxeQ+8kncnUlmJZrHYEVZ3r68HirASuD/eXAkYI0a3JdDIhBFu3buWHH36guLhY7Sg92sizF1Jff1jtGL1ag2Lies3b1Fh710tfqIueJ2JDOcfXQ+0oQghhFzKdTAhBQkICMTExrFixgt9++426ujq1I/U4jZ3JctWO0evpbJUMNNaxqKJ37H9i0mq4OTyAa8P8MGhl8oUQoueQIkYIAYBOp2PIkCGkpqby888/s27dOlkvY0fSmcxxpCkbWcQAtWN0Kq0ClwX5cFdUIH5OsumtEKLnkelkQoiTOnLkCAsWLGDv3r1qR+kRpDOZ46h0SuA68+Nqx+g0Z3m78WBMMAlGg9pRhBCi00gRI4Q4rS1btvDDDz9QUlKidpRubfjwIizWb9WOIf70D9dP2VrTs17+UkwG7osO5kxvN7WjCCFEp5PpZEKI00pMTCQ2NpaVK1eydOlSqqqq1I7ULen1BVhkqZHDyHbaz9aaMLVj2EWUwYl7ooI4398TRTqOCSF6CSlihBAt0ul0DB48mIyMDFavXi3FTDtYbdKVzJEkmX8CrlQ7Rof4O+m4IzKQy4J80GukeBFC9C4ynUwI0WZms7mpmKmsrFQ7jsNzdlbIGvgOsrDfcVjRcqv+E4obut/uo35OOq4L9ePKUF+MWq3acYQQQhVSxAgh2s1sNrNmzRqWLl1KRUWF2nEcVmSkQlj422rHEH/xP495fFPefdaPhDjruSncn0uDfHCRdslCiF5OihghRIeZzWbWrl3LkiVLpJg5iQED6nE1SmcyR7PZ/SYerzhL7RgtijY4MyvCn4sCvGXamBBC/EmKGCGE3TQ0NDQVM+Xl5WrHcRjDhxdjsX6jdgzxF/Vaf66xvUaDg74KJplcuCUigAl+nmhkwb4QQjQjC/uFEHaj0+nIysoiPT2ddevWsWTJEsrKytSOpTq9UwGWWrVTiL9ysuSTZrKyqsqxpmZluLtya0QAo3w91I4ihBAOS4oYIYTd6XQ6MjMzSU9PZ8uWLSxfvpyDBw+qHUs1VushtSOIU8jQbWcVCWrHAOAMLxO3RgQw1Kv7rNMRQgi1SBEjhOg0Wq2W5ORkkpOTOXjwICtWrGDTpk1Yrd2vI1R7OTsr1Nfnqh1DnEJi7TegYhHjqtVwYYAXM0J8STIZVMshhBDdjayJEUJ0qfLyclatWsWaNWuorq5WO06nk85kju9el884UNe1hXWsqzNXhPgyJdAbd520SRZCiLaSIkYIoYqGhgY2bdrEqlWrevRUswEDzLgaP1A7hjiNLzz/j4/KfDv9PjoFRvt6MCPYlzO8ZcqYEEJ0hBQxQgjV5ebmsmrVKjZu3IjZbFY7jl2dObwIq/VbtWOI09jjNo37Kyd12vX9nXRMC/bh8mAfgpydOu0+QgjRm0gRI4RwGLW1taxfv55169Zx5MgRtePYxahztlNbu0LtGOI0LIor12vepdpq35fDQZ5GZoT4cq6vp+zvIoQQdiZFjBDCIRUUFLBhwwY2btxIaWmp2nHabeTZP1NfL93JHN1/3f/HLxXOHb5OlMGJSQFeXBjgRbSrix2SCSGEOBkpYoQQDs1ms3HgwAE2bNjApk2bqKmpUTtSqzk5wcDs94De042tu1rr+TeeK8to17n+TjrO9/fkggBv0txd7ZxMdLbhw4fTv39/nn/+ebWjtNuMGTMoLS3l888/VzuKEF3GsXb4EkKIv1AUhfDwcMaPH8/s2bO55JJLSE5ORqdz/A7xQUEKUsB0D/FV89t0vJtWw9RAbz5MjWbd4CT+ERvarQuYGTNmoCgKTz75ZLOvf/755yhK958KN3r0aLRaLatWreqS+y1atAhFUbpsFPmFF15g3rx5XXIvIRyF478LEEKIP2m1WuLj44mPj6euro4tW7awceNGdu/ejSMOKvv61qsdQbSS0bydvkbYfJqu306Kwkgfdy4I8GKUjzsu2p71OaCLiwtPPfUU1113HV5eXmrHaZHZbEav17d43P79+/n999+ZNWsWb775JpmZmV2Qrmt5eHioHUGILtezfgMLIXoNZ2dn+vfvz+WXX84dd9zBmDFjiIqKQqt1nD03TG4VakcQbZCl33/C11w0Cmf7uPOv+DA2Dklibr8oJvh79rgCBuDss88mMDCQf/7znyd9vKioiEsuuYSQkBBcXV3p168f77//frNjPvnkE/r164fBYMDHx4ezzz6bqqoqoHF0IisrC6PRiKenJ0OGDGHfvn1N537xxRekp6fj4uJCnz59eOSRR2hoaGh6XFEUXnvtNc477zyMRiOPP/54q57X3LlzGT9+PDfccAPvv/9+i1NS8/PzmTBhAgaDgaioKN577z0iIyObppvt3bsXRVHIyclpOqe0tBRFUVi0aBF79+5lxIgRAHh5eaEoCjNmzABgwYIFDB06FE9PT3x8fBg/fjy7du1qus7Ra3/00UecccYZGAwGMjMz2b59O6tWrSIjIwOTycTYsWMpKChoOm/GjBlMnDixVd8PIXoKGYkRQnR7bm5uZGdnk52dTV1dHbt372b79u3s3LmTigr1Cgknp0Jqa1W7vWijJPNPwFX46nWc7ePOaF93zvR2x7UHFiwno9VqeeKJJ7j00ku55ZZbCA0NbfZ4bW0tAwYM4J577sHd3Z1vvvmGyy+/nOjoaLKyssjNzeWSSy7h6aefZtKkSVRUVLB48WJsNhsNDQ1MnDiRa665hvfff5/6+npWrlzZNFVt8eLFTJ8+nRdffJEzzjiDXbt2ce211wLw0EMPNWV4+OGHefLJJ3n++edbNaXUZrMxd+5cXnnlFRISEoiJieGTTz7h8ssvP+U5M2bM4PDhw/zyyy/o9XpuueUW8vPzW/19DAsLY/78+Vx44YVs27YNd3d3DAYDAFVVVdxxxx2kpKRQWVnJgw8+yKRJk8jJyUGjOfbv7KGHHuL5558nPDycq666iksvvRQ3NzdeeOEFXF1dmTJlCg8++CCvvfZaq3MJ0dNIESOE6FGcnZ1JTEwkMTERaNyDZseOHWzfvp1Dhw516bQzq1W6knUPCm5ufcn2juIb31jS3F3R9IB1IO0xadIk+vfvz0MPPcScOXOaPRYSEsLs2bOb/n7zzTfz/fff89FHHzUVMQ0NDVxwwQVEREQA0K9fPwCKi4spKytj/PjxREdHAzT9jAI88sgj3HvvvVxxxRUA9OnTh3/84x/cfffdzYqYSy+9lCuvvLLVz+enn36iurqa0aNHAzBt2jTmzJlzyiJm+/btfPfdd6xcubJp2tmcOXOaZW2JVqvF29sbAH9/fzw9PZseu/DCC5sd++abb+Ln58fmzZtJTk5u+vrs2bObMt96661ccsklLFy4kCFDhgAwc+ZMWQMjej0pYoQQPVpQUBBBQUEMGzaM6upqdu7cyY4dO9i5c2endjpzcoL6+sOddn3RMTqdJz7eQ/HxGYa3z5k4O/mqHclhPPXUU5x11lnNChYAi8XCE088wUcffcShQ4eor6+nrq4OV9fGhgapqamMHDmSfv36MXr0aM455xwuuugivLy88Pb2ZsaMGYwePZpRo0Zx9tlnM2XKFIKCggBYv349S5cubTZFzGKxUFtbS3V1ddM9MjLa1kHuzTffZOrUqU2jNpdccgl33XUXu3btaiqmjrdlyxZ0Oh0DBgxo+lpCQkKzQqQjduzYwYMPPsiKFSsoLCzEam1s/LF///5mRUxKSkrTfwcEBADHCsKjX2vL6JAQPZEUMUKIXsPV1ZWUlBRSUlKwWq0cPHiQHTt2sH//fg4dOtRs/n1HSWcyx6LTeeLpkY6HZwZenpm4u6eiKI6zfsqRDBs2jNGjR/O3v/2taS0HwDPPPMMLL7zA888/T79+/TAajdx2223U1zc2sNBqtfz444/8/vvv/PDDD7z00kvcd999rFixgqioKObOncstt9zCggUL+PDDD7n//vv58ccfyc7OprKykkceeYQLLrjghDwuLsf22zEaja1+HsXFxXz22WeYzeZm064sFgtvvvlmq9fU/NXRaV/Hj+qazeZWnTthwgQiIiJ44403CA4Oxmq1kpyc3PQ9POr4hgVHp9z99WtHCyAheispYoQQvZJGoyE8PJzw8HCg8Y1Nbm4uBw4caPrTkfU0PtKZTFUuLqF4emTg4TkAT48MjMbYHtEquKs8+eST9O/fn/j4+KavLV26lPPPP59p06YBYLVa2b59O3379m06RlEUhgwZwpAhQ3jwwQeJiIjgs88+44477gAgLS2NtLQ0/va3vzFo0CD+97//kZ2dTXp6Otu2bSMmJsZuz+G9994jNDT0hL1TfvjhB5577jkeffTRExqBJCQk0NDQwJo1a5qmk23btq1Zq2Q/Pz+gcapqWloaQLNF/gBOTk5A4++Vo4qKiti2bRtvvPEGZ5xxBgBLlizp8PMUoreSIkYIIWj8FDk0NJTQ0FAGDRoENHYc2r9/f1NRk5eX1+o1NW5ulcgHpV1DUbSYjAl/FiwD8PDMwMU5UO1Y3Vq/fv247LLLePHFF5u+FhsbyyeffMLvv/+Ol5cX//rXv8jLy2sqYlasWMHChQs555xz8Pf3Z8WKFRQUFJCYmMiePXt4/fXXOe+88wgODmbbtm3s2LGD6dOnA/Dggw8yfvx4wsPDueiii9BoNKxfv54//viDxx57rF3PYc6cOVx00UXNpmlB48L7v/3tbyxYsIBx48Y1eyw+Pp4xY8Zw3XXX8dprr6HT6bjtttuaFuYDGAwGsrOzefLJJ4mKiiI/P5/777+/2XUiIiJQFIX/b+/uo6Kq8z+Av2cAgWF49IGnBFTkQVGRdE3RXyrWEEpAuajH4SGxtYxl2SOyiRCuhz0pgYpunModZ1AxdDf0JCqUnqhdRfIBtjqHzcSDqCGg4QOOwgj39wdxjyM+DEJMY+/XiSP33u+993O//WHv7vd+vyUlJQgLC4O1tTUcHR0xePBgfPTRR3B1dUV9fT3efvvtJ3o2ImKIISJ6KAcHBzg4OIjj09va2nDp0iUx1DQ0NIjTx95v0KBmzkz2C5BIzGBt7QW53BdyGx/Y2QXC3n4izM3lxi7tqbN27Vrs3r1b3E5PT8e5c+egUCggk8nwhz/8AZGRkbh+/ToAwM7ODl999RU2bdqEGzduwNPTE7m5uXjppZfQ2NiI//3vfygoKMDVq1fh6uqKt956C8uWLQPQtRhlSUkJ1q5di/Xr18PCwgJ+fn5YunTpE9V+6tQp/Pe//8XWrVt7HLO3t0dISAhUKlWPEAN0Tcm8dOlSPP/883B2dkZWVhYyMjL02mzbtg0JCQl49tln4evri+zsbLz44ovicXd3d3Gygtdeew2xsbHQaDQoKipCUlISAgIC4Ovri82bN2PmzJlP9IxEv3US4de4QhwRkYlobW1FU1MTGhsbxT+bm5vxf89/jvb2C8Yuz6RZWrpAbuMDG7kv5Da+kMt9YWMzClKppbFLo98YLy8vJCcnIzk52dilENHP+CaGBkR5eTlmzZqFlpaWXs3yotFokJycrDce+XFmzpyJwMBAcWGy3v7l8yT37K26ujqMGDECVVVVCAwMfOL+eRISiQR79+4VF0a7f5t6Ry6XQy6XY+TIkeK+zs5OtLXF4NatWtzS1kJ7qxa3tOeg1Z6DTveTEav9NZJg0KDBsLb27BFYLCy4CjkRET0YQwz1q4qKCkyfPh2hoaE4cOCAscsxqosXL2LkyJHw8fHBd999Z+xyRA0NDXB0dHzoNvWdVCqFtbUHrK09MASz9I7pdC24ffsC2tou405bI9raLqPtzmXcaWvo+r2tEZ2dT884NKnUElZWbrCydIOllZv4u5WVK6ys3GFl5co3K/RE3njjDezcufOBx5RKJT744IMBroiIBhJDDPUrlUqFP/7xj1CpVPjxxx/h5uZm7JKMRqPRIDo6Gl999RUqKysxZcoUY5cEAHBxcXnkNv2yLCwcYWHhCGD8Q9vodC24c+fyz6HmMu78HG7u3r2Ou3db0dFxC3fv3kJHR+vP21oM3HTOUpiby2Fubg8LC3tYmDvA3MIeFhYOsDC3h8Ugp3tCihsGcf0V+oWsXbu2x1o23ezs7Pr1XnV1df16PSLqO4YY6jetra3YvXs3Tp48icuXL0Oj0SAtLU2vzdGjR7Fq1SqcOXMGgYGB+Mc//qE3c4xGo8E777yDK1euQKFQYPr06Xrnx8fH49q1a3pTZiYnJ6O6uhrl5eUG1blhwwao1WqcO3cOTk5OCA8PR3Z2NuRy/Q+Dy8rKkJycjAsXLmD69OlQq9XiwmyPIwgC1Go18vPz8cwzz0ClUhkUYh7VP2vWrMG+ffv0pvLctGkTNm3aJP4Fe+LECaSlpaGqqgo6nQ6BgYHYuHEjgoKCxHMeN5zsL3/5C/bu3YuLFy/CxcUFixcvxjvvvCOuUdBdx4oVK5CRkYGWlha89NJL2Lp1K2xtbQ3qH3q07qBja2vYKuGCIKCz8/Y9AefeP7UABAA/Ty8skUACKSCRAJBAgq4/u/6RAD8fk0AKMzMZzMxtYG4mh5m5HOZmNjAzs35oHUQDadiwYRg2bJixyyAiI2GIoX6zZ88e+Pn5wdfXF0qlEsnJyVi1apXe2gwrV65EXl4eXFxckJaWhvDwcJw5cwYWFhaorKxEQkIC3n33XURGRqK0tBSZmZn9XqdUKsXmzZsxYsQInDt3DsuXL0dqairy8/PFNlqtFjk5OdixYwekUimUSiVSUlJQWFho0D2++OILaLVazJkzB+7u7pg2bRo2btz42IXaHtU/hrh58ybi4uKwZcsWCIKA3NxchIWF4YcffjA4YNja2kKj0cDNzQ3ffvstXn/9ddja2iI1NVVsU1tbi3379qGkpAQtLS2Ijo7GunXrnnjxOOobiUTSFTjMZMYuhYiIaEBIjV0APT1UKpW4CFpoaCiuX7+OL7/8Uq9NZmYmXnjhBYwbNw4FBQVobGzE3r17AQB5eXkIDQ1FamoqfHx8kJSUBIVC0e91JicnY9asWfDy8sLs2bORlZWFPXv26LXR6XT44IMPMGnSJAQFBSExMRFHjhwx+B4qlQoLFy6EmZkZAgICMHLkSPzzn/987HmP6h9DzJ49G0qlEn5+fvD398dHH30ErVbb49/Do6Snp2PatGnw8vJCeHg4UlJSevRPZ2cnNBoNAgICMGPGDMTExPSqf4iIiIj6giGG+sX333+Pr7/+GosWLQIAmJubY8GCBVCpVHrtuhcRBAAnJyf4+vqipqYGAFBTU9NjyNW97fvL4cOHERISAnd3d9ja2iImJgZXr16FVqsV28hkMowaNUrcdnV1RVNTk0HXv3btGoqLi8VAB3R9ZHp/XzzIo/rHEI2NjXj99dcxevRo2Nvbw87ODq2traivrzf4Grt370ZwcDBcXFwgl8uRnp7e43wvLy+9Nzu96R8iIiKivuJwMuoXKpUKd+/e1fuQXxAEWFpa4u9//3u/3UcqlfZYMV2n0xl8fl1dHebNm4c333wTf/vb3+Dk5IT//Oc/SEhIQHt7O2SyruE49w/fkkgkBq/UvmvXLty5c0cvkHV9s9CJM2fOwMfHx+B672XIs8fFxeHq1avIy8uDp6cnLC0tMXXqVLS3txt0j4qKCixevBh//etfoVAoYG9vj6KiIuTm5uq1e1D/dHJ5eiIiIhogDDHUZ3fv3sX27duRm5urt2IxAERGRuLjjz+Gn58fAOD48ePw8PAAALS0tODMmTPw9+/6eNnf3x+VlZV65x8/flxve+jQoT2mK66urjb4m5FTp06hs7MTubm5kEq7XkTeP1Sqr1QqFVasWIH4+Hi9/cuXL8e2bduwbt26h577qP4ZOnQoLl++DEEQxO+M7v3IH+iaGCA/Px9hYWEAgAsXLuDKlSsG137s2DF4enpi9erV4r7z588bfD4RERHRQGCIoT7r/rg7ISEB9vb6i9O9+uqrUKlUeO+99wB0TYk5ePBgODs7Y/Xq1RgyZIg4K1ZSUhKCg4ORk5ODiIgIlJWVobS0VO96s2fPxnvvvYft27dj6tSp2LlzJ7777jtMnDjRoFq9vb2h0+mwZcsWhIeH4+jRo/26lkB1dTVOnz6NwsJCMbh1W7RoEdauXYusrKyHnv+o/pk5cyaam5uRnZ2N+fPno7S0FIcOHdKbSnT06NHYsWMHJk2ahBs3bmDlypWwtjZ8NqnRo0ejvr4eRUVFmDx5Mg4cONCrb3KIiIiIBgK/iaE+U6lUmDNnTo8AA3SFmJMnT+Kbb74BAKxbtw5/+tOf8Oyzz+Ly5cvYv38/Bg0aBAB47rnnsHXrVuTl5WHChAn47LPPkJ6ernc9hUKBjIwMpKamYvLkybh58yZiY2MNrnXChAnYsGED1q9fj4CAABQWFuLdd9/tw9PrU6lUGDNmTI8AAwBRUVFoamrCwYMHH3r+o/rH398f+fn5eP/99zFhwgR8/fXXPdZIUKlUaGlpQVBQEGJiYpCUlNSrKUhffvll/PnPf0ZiYiICAwNx7NgxZGRkGHw+ERER0UCQCIYO9Ceip05bWxusrKzw+eefY86cOcYuh4iIiMggHE5G9Bt148YNFBcXQyqVPvDNEREREdGvFUMMUS/J5fKHHjt06BBmzJgxgNU8uczMTOzatQvr16/HM888Y+xyiIiIiAzG4WREvXT27NmHHnN3d+/Vh/RERERE1HsMMUREREREZFI4OxkREREREZkUhhgiIiIiIjIpDDFERERERGRSGGKIiIiIiMikMMQQEREREZFJYYghIqJfhfLyckgkEly7ds3YpfyiNBoNHBwcjF1Gr6xZswaBgYHGLoOISMQQQ0RETyw+Ph4SiQRvvPFGj2NvvfUWJBIJ4uPjB76wp8zt27fh5OSEIUOGoK2tzdjlEBEZHUMMERH1yfDhw1FUVITbt2+L++7cuYNdu3bBw8PDiJU9PT755BOMHTsWfn5+2Ldvn7HLISIyOoYYIiLqk6CgIAwfPhzFxcXivuLiYnh4eGDixInivra2NiQlJWHYsGGwsrLC9OnTceLEiR7XO3XqFCZNmgSZTIZp06bh+++/F4/V1tYiIiICzs7OkMvlmDx5Mg4fPqx3vpeXF7KyshAbGwu5XA5PT098+umnaG5uRkREBORyOcaPH4+TJ0+K53QP8SopKYGvry9kMhnmz58PrVaLgoICeHl5wdHREUlJSejo6NB7ppSUFLi7u8PGxgZTpkxBeXm5Xj0ajQYeHh6QyWSIiorC1atXe93HKpUKSqUSSqUSKpVK3L9kyRLMmzdPr61Op8OwYcPEdo/r9wcNb9u3bx8kEkmPOj788EMMHz4cMpkM0dHRuH79eq+fhYioPzDEEBFRny1ZsgRqtVrc3rZtG1577TW9Nqmpqfjkk09QUFCA06dPw9vbGwqFAj/99JNeu9WrVyM3NxcnT56Eubk5lixZIh5rbW1FWFgYjhw5gqqqKoSGhiI8PBz19fV619i4cSOCg4NRVVWFuXPnIiYmBrGxsVAqlTh9+jRGjRqF2NhYCIIgnqPVarF582YUFRWhtLQU5eXliIqKwsGDB3Hw4EHs2LEDH374If71r3+J5yQmJqKiogJFRUX45ptv8Pvf/x6hoaH44YcfAACVlZVISEhAYmIiqqurMWvWLGRlZfWqb2tra1FRUYHo6GhER0fj3//+N86fPw8AWLp0KUpLS9HQ0CC2LykpgVarxYIFC3rV749z9uxZ7NmzB/v370dpaSmqqqqwfPnyXl2DiKjfCERERE8oLi5OiIiIEJqamgRLS0uhrq5OqKurE6ysrITm5mYhIiJCiIuLE1pbWwULCwuhsLBQPLe9vV1wc3MTsrOzBUEQhC+++EIAIBw+fFhsc+DAAQGAcPv27YfWMHbsWGHLli3itqenp6BUKsXthoYGAYCQkZEh7quoqBAACA0NDYIgCIJarRYACGfPnhXbLFu2TJDJZMLNmzfFfQqFQli2bJkgCIJw/vx5wczMTLh06ZJePSEhIcKqVasEQRCERYsWCWFhYXrHFyxYINjb2z/0ee6XlpYmREZGitsRERFCZmamuD1mzBhh/fr14nZ4eLgQHx8vCIJgUL+r1eoe9ezdu1e49z8RMjMzBTMzM+HixYvivkOHDglSqVTsQyKigcQ3MURE1GdDhw7F3LlzodFooFarMXfuXAwZMkQ8XltbC51Oh+DgYHGfhYUFfve736GmpkbvWuPHjxd/d3V1BQA0NTUB6HoTk5KSAn9/fzg4OEAul6OmpqbHm5h7r+Hs7AwAGDduXI993dcFAJlMhlGjRum18fLyglwu19vXfc63336Ljo4O+Pj4QC6Xiz9ffvklamtrAQA1NTWYMmWKXm1Tp059SC/21NHRgYKCAiiVSnGfUqmERqNBZ2cngK63Md1vwRobG3Ho0CHx7VVv+v1xPDw84O7urvccnZ2desP9iIgGirmxCyAioqfDkiVLkJiYCAB4//33n/g6FhYW4u/d32V0/wd7SkoKPv/8c+Tk5MDb2xvW1taYP38+2tvbH3uNR133/uPdbR60r/uc1tZWmJmZ4dSpUzAzM9Nrd2/w6YuysjJcunRJHBrWraOjA0eOHMELL7yA2NhYvP3226ioqMCxY8cwYsQIzJgxw+B7SKVSvWF1QNd3NUREv2Z8E0NERP0iNDQU7e3t0Ol0UCgUesdGjRqFQYMG4ejRo+I+nU6HEydOYMyYMQbf4+jRo4iPj0dUVBTGjRsHFxcX1NXV9dcj9MrEiRPR0dGBpqYmeHt76/24uLgAAPz9/VFZWal33vHjxw2+h0qlwsKFC1FdXa33s3DhQvHD/cGDByMyMhJqtRoajUbvWyRD+n3o0KG4efMmbt26Jbaprq7uUUt9fT1+/PFHveeQSqXw9fU1+HmIiPoL38QQEVG/MDMzE4co3f9mwsbGBm+++SZWrlwJJycneHh4IDs7G1qtFgkJCQbfY/To0SguLkZ4eDgkEgkyMjL03qYMJB8fHyxevBixsbHIzc3FxIkT0dzcjCNHjmD8+PGYO3cukpKSEBwcjJycHERERKCsrAylpaUGXb+5uRn79+/Hp59+ioCAAL1jsbGxiIqKwk8//QQnJycsXboU8+bNQ0dHB+Li4sR2hvT7lClTIJPJkJaWhqSkJFRWVkKj0fSox8rKCnFxccjJycGNGzeQlJSE6OhoMbAREQ0kvokhIqJ+Y2dnBzs7uwceW7duHV599VXExMQgKCgIZ8+eRVlZGRwdHQ2+/oYNG+Do6Ihp06YhPDwcCoUCQUFB/VV+r6nVasTGxmLFihXw9fVFZGQkTpw4Ia6P89xzz2Hr1q3Iy8vDhAkT8NlnnyE9Pd2ga2/fvh02NjYICQnpcSwkJATW1tbYuXMnAGDOnDlwdXWFQqGAm5ubXtvH9buTkxN27tyJgwcPYty4cfj444+xZs2aHvf09vbGK6+8grCwMLz44osYP3488vPze9NdRET9RiLcPxCWiIiITEprayvc3d2hVqvxyiuvGLscIqJfHIeTERERmajOzk5cuXIFubm5cHBwwMsvv2zskoiIBgSHkxERERnJ2LFj9aZnvvensLDwsefX19fD2dkZu3btwrZt22Buzv83SUS/DRxORkREZCTnz59/6HTGzs7OsLW1HeCKiIhMA0MMERERERGZFA4nIyIiIiIik8IQQ0REREREJoUhhoiIiIiITApDDBERERERmRSGGCIiIiIiMikMMUREREREZFIYYoiIiIiIyKQwxBARERERkUlhiCEiIiIiIpPCEENERERERCaFIYaIiIiIiEwKQwwREREREZkUhhgiIiIiIjIpDDFERERERGRSGGKIiIiIiMikMMQQEREREZFJYYghIiIiIiKTwhBDREREREQmhSGGiIiIiIhMCkMMERERERGZFIYYIiIiIiIyKQwxRERERERkUhhiiIiIiIjIpDDEEBERERGRSWGIISIiIiIik8IQQ0REREREJoUhhoiIiIiITApDDBERERERmRSGGCIiIiIiMikMMUREREREZFIYYoiIiIiIyKQwxBARERERkUlhiCEiIiIiIpPCEENERERERCaFIYaIiIiIiEwKQwwREREREZkUhhgiIiIiIjIpDDFERERERGRS/h/ylCYlIzGrtgAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
+      "cell_type": "code",
+      "source": [
+        "print('Data Shape is: ', data_df.shape)\n",
+        "print()\n",
+        "\n",
+        "print('Clases Distribution is: ')\n",
+        "class_counts = data_df['speaker'].value_counts()\n",
+        "\n",
+        "plt.figure(figsize=(8, 8))\n",
+        "plt.pie(class_counts, labels=class_counts.index, autopct='%1.1f%%')\n",
+        "plt.title('Class Distribution')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 727
+        },
+        "id": "SuECKlx_epN9",
+        "outputId": "cddb8b47-37e2-4c4b-e684-3608e43b2c20"
+      },
+      "execution_count": 39,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Data Shape is:  (3117, 2)\n",
+            "\n",
+            "Clases Distribution is: \n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x800 with 1 Axes>"
+            ],
+            "image/png": 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Q7k2vdgAhxNBw8cUXk5yczD//+U/i4+NxOBxkZWVhsVhobm7m9NNP55133un2vKioKABmzZrFwoULOXLkCAcOHGD69OkcOnSItWvXUldXx8SJE/H393f1yxJCCI8gBZ8QYtDV1NSQm5vLP//5T8466ywANm7c2Pn4aaedxgcffEB0dDTBwcE9niM7O5uwsDCefPJJxo8fT2BgILNmzeJPf/oTdXV1Mn5PCCF6IV26QohBFxYWRkREBK+//jp5eXmsXr2a++67r/Pxn/zkJ0RGRnLppZeyYcMG8vPzWbt2LXfffTclJSUAaDQaZsyYwTvvvNNZ3I0dOxaz2cyqVauYOXOmGi9NCCE8ghR8QohBp9Vqef/99/n222/Jysri3nvv5bnnnut83N/fn/Xr15OUlMQVV1xBZmYmP/vZz2hvb+/S4jdz5kzsdntnwafVapkxYwYajUbG7wkhRC9klq4QQgghhJeTFj4hhBBCCC8nBZ8QQgghhJeTgk8IIYQQwstJwSeEEEII4eWk4BNCCCGE8HJS8AkhhBBCeDkp+IQQQgghvJwUfEIIIYQQXk4KPiGEEEIILycFnxBCCCGEl5OCTwghhBDCy0nBJ4QQQgjh5aTgE0II4XFmzZrFwoULT+o5KSkpvPTSS523NRoNS5YscWouIdyVFHxCCCGEEF5OCj4hhBBCCC8nBZ8QQgiPZLPZuOuuuwgJCSEyMpJHH30URVEAMJlMXHzxxfj5+ZGamso777yjcloh1KVXO4AQQggxEG+99RY/+9nP2Lp1K9u3b+f2228nKSmJ2267jZtvvpmysjLWrFmDj48Pd999NyaTSe3IQqhGCj4hhBAeKTExkRdffBGNRkNGRgZ79+7lxRdfZObMmSxbtoytW7cyadIkAN544w0yMzNVTiyEeqRLVwghhEeaOnUqGo2m8/YZZ5zBkSNHOHjwIHq9ntNPP73zsVGjRhEaGqpCSiHcg7TwCSE8kuJw4GhtQ2lvw9HejtLW8aejtQ3F3I5itaJYbR1/2mwoNivYbCg2Oxq9Do3B0PHlY/j+7wYftIYf3jag8fHp+NNoRBsQ0KXAEEIITyEFn/BaN998M/X19bLOlgdxtLRgq63FVl2NvbYWW01Nx5/VNdhra7DV1Hb8WV2DvaEBHA7XBtTr0YWFog+PQBce1vFnRDj68HB04d/9GYE+ouO2LijItfmGmJycnC63t2zZwogRIxg1ahQ2m41vv/22s0s3NzeX+vp6FVIK4R6k4BNDxqxZsxg/fnyXhVeFa9mbW7AUFmAtLMRSWIiloBBLURG2ykpsdXUobW1qR+ydzYa9qhp7VXW/DtcYDOgiIvBJiMeQlIwhORlDclLHn0lJaAMCBjmwdysqKuK+++7jjjvuYMeOHbzyyis8//zzZGRkcP7553PHHXfwt7/9Db1ez8KFC/Hz81M7shCqkYJPCOFUjtZWLEVFHcVcYdcve3X/CiVvoVgs2MrLsZWX07b9226P6yIjO4u/7wpBn6QkDMkp6AKlGOzLjTfeSFtbG5MnT0an03HPPfdw++23A/Dmm29y6623MnPmTGJiYnjyySd59NFHVU4shHo0yneLFgnhoRYvXswf/vAH8vLy8Pf3Z8KECSxdupRf/OIXnV26N998M2+99VaX5+Xn55OSkqJOaC9hKSmlfd8+2vfvo33/fsx5R7HJ0hdOoYuMxDcjA9/RmfhmZmIclYkhJRmNVubaCSFOnrTwCY9WXl7Otddey7PPPsvll19OU1MTGzZs4MefY15++WUOHz5MVlYWjz/+OABRUVFqRPZY1ooK2vfto23fPtr37ad9/37sdXVqx/Ja9upqWqqradm0qfM+rb8/xowMSuZPpyYrgezIbFJDUmUiiRCiT1LwCY9WXl6OzWbjiiuuIDk5GYDs7Oxux4WEhGAwGPD39yc2NtbVMT2Orbqatr17Owq7fftoO7C/3+PWxOBxtLbStnMnq8+wsKghF4AgnyBGR4wmKzKL7MhsxkWPI9IvUuWkQgh3IwWf8Gjjxo1j9uzZZGdnc95553Huuedy1VVXERYWpnY0j2I1mWjN2Urr1hxacrZiLSpSO5Loxbqgks6/N1mbyKnIIafi+xmrw0OGMzl2MlPjpjIxdiIhxhA1Ygoh3IgUfMKj6XQ6VqxYwTfffMPy5ct55ZVXeOSRR7ot1yC6amu20LZtG+Z1K2jN2YolP1/tSKKfNMPiMGmrej3mWMMxjjUc4/3c99FqtGSGZzI5bjJTY6cyIWYCfnqZrSrEUCMFn/B4Go2GadOmMW3aNH73u9+RnJzMJ5980u04g8GA3W5XIaH67DYHFUcbKDpQS/HBWqqKmxgbWkTkJx+oHU2cpObhsUDvBd8PORQH+2v2s79mP2/uexMfrQ9jo8YyJW4KU+Omkh2ZjV4rvwqE8HbyUy48Wk5ODqtWreLcc88lOjqanJwcqqqqyMzMZM+ePV2OTUlJIScnh4KCAgIDAwkPD0frxTMem+vaObarmqIDNZQdrsdq7lrs1vgmISO9PE9xvM8pPd/qsPJt5bd8W/ktr+16DX+9P6fHnM6MYTOYnTSbKH+ZzCSEN5KCT3i04OBg1q9fz0svvURjYyPJyck8//zzXHDBBXzwQdfWqwceeICbbrqJ0aNH09bW5pXLsjRWt5G3w8SxnVVUFjRCL4suVdZoGWEworWYXRdQnLLdES1OPV+rrZUNpRvYULqBp3OeZlzUOOYkz2FO8hwSAhOcei0hhHpkHT4hPFxdRQtHd1RxdKeJ6uLmk3ruGa1f4Lf1y0FKJpxOo+GuB0Mw6U7u/3mgMsMzmZs8lznJc0gNSXXJNYUQg0MKPiE8UHVJM0d3drTk1ZYNvMVndFQVsYsec14wMag0ifFcfb06C1unhaQxO3k2c5PnMip8lCoZhBADJwWfEB7CVNjY2ZLXYHLOnrMxMTrGfPBzp5xLDL6WGeP56bR9ascgMSiR2UmzOT/lfMZEjlE7jhCiH6TgE8KNtTVZOLSlgoObyqiraHX6+bU6DTNyfoO2ud7p5xbOd/Dayfw+ZYfaMboYGTaSy9Mv56LhFxHqG6p2HCHECUjBJ4SbURwKRQdrObipjPw91Thsg/sjOllZT+A6WZ7FE7x/12g+DjqsdoweGbQGzk46myvSr2Bq/FS0Gu+dAS+EJ5KCTwg30VTbzsFNZRzcXE5zretmzmbENpLw/sMuu54YII2GO38VTLXWubN0B0N8QDyXpl/KZemXER8Yr3YcIQRS8AmhKrvNQf7uag5sKqPkYC1q/DSGR+oZv/gO119YnBRNYgJXX1+pdoyTotVomRI7hStGXME5Sedg0BnUjiTEkCXr8AmhgtqyFg58U8bhnAramqzqZqmxYY9OQmeS/XPdWdPwKMCzCj6H4mBz+WY2l28m1BjKvOHzuGrEVaSHpasdTYghR1r4hHChov017FxRRMmhOrWjdDHRsIPg5W+oHUP04sC1k3nMzSZsDNTUuKncOPpGpidMR6PRqB1HiCFBWviEGGR2u4O8bZXsXFFMTalrFsw9WXWRowlWO4To1Z4I9/zeGYgt5VvYUr6F1JBUrs+8nkvSLsFX76t2LCG8mrTwCTFILG029m8oY8+aYprr3Hv7ssAQHyYvvV3tGOJEPGjCxkCEGkO5euTVXJd5HZF+ssOzEINBCj4hnKy5zsye1cXs31iGpc2mdpx+O6vwb/jkq7+or+hOk5TA1T/xrPF7A2HUGbkk7RJuHnMzScFJascRwqtIwSeEk9SUNrNrRRGHt1XisHvej9WEwFzCPv+L2jFED5pnTuCWM/eqHcNltBotc5LmcEv2LYyJkJ08hHAGGcMnxCkqza1jx/IiivbXqB3llNQGpxOmdgjRo8IEH7UjuJRDcbC8cDnLC5czJW4Kd4y9g0mxk9SOJYRHk4JPiAEqy6snZ+kxyo7Uqx3FKSobjAzXaNBIo7/b2RPuPRM2TlZOeQ455TmcEXcG95x2j+zdK8QASZeuECepsqCRnE+PUXygVu0oTjet5n2MezeoHUP8kEbD//0qiBqt8/dS9kRzkubwywm/ZHjocLWjCOFRpOATop+qS5rJ+fQYBXuq1Y4yaMaGFxP58TNqxxA/oEkextXXVagdw61oNVouGn4Rd46/k4TABLXjCOERpEtXiD40VrexZekxjmyvBC//eFRjTEIWxXAvjalRgBR8P+RQHHx69FOW5S/jihFX8PNxP5flXITogxR8QpxAW5OFbV8WsH9DKQ6bl1d6x1XWaBhhMKK1uPe6gUNJUby8TZ+I1WHlg9wP+PTopywYtYCfZf2MEGOI2rGEcEvSpSvEj1jNdnatLGLniiKs7Xa147jc1NYv8N/6pdoxxHHv/DKTpYFH1I7hEYJ8grhpzE3cMPoG/H381Y4jhFuRgk+I4xRF4eA35eQsPUZro0XtOKoZHV1F7IePqR1DgEzYGKAI3wjum3gfl6RdonYUIdyGVu0AQriDqqImPn7uW9b879CQLvYAqjWxakcQx2mSEqTYG4Ca9hoe2fgINy27idzaXLXjCOEWpOATQ5q51cr693JZ9Mx2Ko41qh3HLVRXOXAEhasdQwCNw6PUjuDRdph2MP/z+fwx5480WZrUjiOEqqTgE0PSd9237/x+C3vXlaI4ZGTDdxwOhbbTzlU7hgAK42TCxqmyK3bePfQuF39yMUvzliKjmMRQJQWfGHKqS5r45M87WP3fg7Q1WdWO45bq4sapHUEAu4bwDhvOVtNew283/ZYbl93IodpDascRwuXk46MYMsxtNnI+PcY+adHrU5UtgmFqhxjqtFo2BJSoncLr7KraxYLPF3BNxjXcNeEugg3BakcSwiWkhU8MCblbynn391vYu6ZEir1+qKuxYotJVjvGkKZJjKdO26Z2DK9kV+y8d+g9Lv7kYpbkLZFuXjEkSMEnvFpteQsf//lbVv7n4JCffXtSFGgdP1ftFENaw/BotSN4vdr2Wh7d9Cg3fXUThY2FascRYlBJwSe8kuJQ2LWyiA+f3kZ5XoPacTxSXUSm2hGGtKI4ndoRhoydpp1c/dnVvHPwHWntE15LCj7hdRqr21jy4k42Lc7DbnWoHcdjVbYEqh1hSJMJG67VZmvjma3PcOvyWylrLlM7jhBOJwWf8Cr7N5Ty/hNbKTtSr3YUj9fcYMM6fKzaMYYmrZZ1AcVqpxiStlZs5YpPr+Cjwx+pHUUIp5KCT3iFlgYzn7+6m7Xv5GI1D739bwdL05iz1Y4wNCUl0KBtVzvFkNVibeGxzY9x58o7MbWa1I4jhFNIwSc83uFtFbz3eA6F+2rUjuJ16oLS1I4wJMkOG+5hQ+kGLl96OV8c+0LtKEKcMin4hMdqb7by1ev7WPHGAcwtNrXjeKXKegOKRqN2jCFHdthwH42WRn694dfct/Y+6trr1I4jxIBJwSc8UsGeat57PIejO6S7ZTC1t9oxZ09XO8aQsytc9nV2NysKV3DZ0stYVbRK7ShCDIgUfMKjWC12Vv/vIF+8tkfW1XORpnQp+FxKq2W9v+yw4Y5q22tZuGYhv//m97TbZIyl8CxS8AmPUV/Zykd/2s7BTeVqRxlSaoyJakcYUjTJw2TChpv7+MjHXPvFtRxrOKZ2FCH6TQo+4RGO7jCx6I/bqCltUTvKkGOq0aAYfNWOMWQ0pEaqHUH0Q159Hgs+X8BnRz9TO4oQ/SIFn3BrDruDjYuO8NXr+7C0y3IrarBZHLSPn612jCGjMF4mbHiKNlsbv9n4G3636XfSxSvcnhR8wm211JtZ8uJOdq+SBWjV1pAyWe0IQ8bOMNkK0NN8kvcJf17zANQcVTuKECckBZ9wSyW5dXwg++C6jSpNjNoRhgadjg0BpWqnECcpxi+SX+z6El4/Gw7Jmn3CPUnBJ9yKoih8+1UBn768izaZhes2aqocOILC1Y7h/ZITaNBI16An0Wv1/LlJIaylBswN8P5PYOVj4JAhKMK9SMEn3Ia51cqXf9vLliXHUByK2nHEDzgcCq2nn6d2DK/XkCITNjzNvf4jGV+88wf3KLDxRXj7CmiR3X+E+5CCT7iFqqImPnx6GwV7qtWOIk6gPnac2hG8XmG8Tu0I4iTMDRvDjXu/6vnBY2vh9ZlQusOlmYQ4ESn4hOqObKvko+e+pbFaurLcWZU1TO0IXm9nWJPaEUQ/JQfE8/iBTb0f1FAMb14IB2XpFqE+KfiEqrZ9kc/yf+/HbnWoHUX0oa7Ghi0mWe0Y3kunY2OA7LDhCXx1Rp6vbiCwvR9b4Nna4MMb4ZtXBj+YEL2Qgk+owm51sOLf+9n6WT7IcD2P0TL+XLUjeK/kYTJhw0M8Ykgmo+Jg/5+gOGD5b+Hz+2Qyh1CNFHzC5dqaLCx9aSeHt1aqHUWcpPqITLUjeK2G1Ai1I4h+uDwsm8sOrBzYk7e/Ae/OB7N03QvXk4JPuFRtWTmL/7Sd8qOyvp4nqmwJUDuC1yqIkwkb7i4jKJlH9qw6tZPkrYB/XwANst6icC0p+ITLlBzcx3u/vRejcYvaUcQANTfYsKSNVTuGV9oZ3o/xYEI1QT6BvFhajNEZW6hV7oV/zYHy3ad+LiH6SQo+4RK5mzey+KlHaW9ppnjfaqIT89WOJAaoefQ5akfwPjodG/xkwoY7e0KJILGmwHknbCrrmMF7+GvnnVOIXkjBJwbd9s8/4fOX/4Tdau28r3jfUiLjq1RMJQaqNihN7QjeJ2UYTVqz2inECdwUms3sIxucf2JLM7x3LeS87vxzC/EjUvCJQaM4HKz5z+us+98boCjdHqvMW0RIZLNK6cRAmep9ULQy3syZGlJlhw13dVpIOgt3D2IrnGKHZQ/Csl+DQ5anEoNHCj4xKBx2O5//5Tl2LPv0hMdYze00mRbhFyRLUXiS9lY7lqxpasfwKvmx8lbsjsKNYTyXfxC9wzb4F8v5G3xyhyzbIgaNvMsIp7PbrHz6wh85vLnvLpDWhjoUy2f4GFzwhiqcpjFdCj5nkgkb7ker0fKs2UB0Q7nrLrr3Q1h0M9itfR4qxMmSgk84lc1iYelzT3J0e/9n4tZXFOMfsBqNRlZg9hQ1xkS1I3gPvZ6NMmHD7dwZNJop+dtcf+GDn8IH14NNxnQK55KCTziN1dzOJ3/6A/m7vj3p51Ye20dErApvrmJATDUaFIOv2jG8g0zYcDtnhWZy++5l6gU4/FXHAs2WVvUy9NOsWbNYuHBhv44tKChAo9Gwa9euQc0keiYFn3AKS1srHz39e4r2DXxdqZIDG4lOzHViKjFYbBYHbRPmqh3DK9SnyA4b7iTOL4o/5m5Fo/aej8fWwDtXgVkmtgnnkIJPnDJzawuLn3qU0kP7T/lcRXu/JGqYC8fMiAFrTJ6odgSvkB+rUTuCOM5H68PzjVZCWuvUjtKhcBP873Jol52JxKmTgk+ckrbmJhY98QjlR5zUMqcolB1aTFhMvXPOJwZNFTFqR/AKO8NlX1V38YBfGtkle9SO0VXJVnjrEmitVTtJn1JSUnj66ae55ZZbCAoKIikpiddf777G4LFjxzj77LPx9/dn3LhxbN68WYW0Q48UfGLAWhsbWPSHh6k8lufU89qtVupKFhEQ2ubU8wrnqq624wiW7shTotez0b9Y7RQCuCAsi+v2LVc7Rs/Kd8F/LoJm91+s/vnnn2fixIns3LmTO++8k//7v/8jN7drg8AjjzzCAw88wK5duxg5ciTXXnstNpus1DDYpOATA9JcV8sHj/2aqqKCQTl/e3MT1uZPMPjJ8gTuSnFA62nnqR3Ds6UMo1ljUTvFkDc8cBiP7V+vdozemfbDfy6ERvce8nLhhRdy5513kp6ezkMPPURkZCRr1qzpcswDDzzAvHnzGDlyJH/4wx8oLCwkL8+5DQeiOyn4xElrqqnmwz/8mtrSwW2ZaKyqwKD/Cq1eVp93V/VxY9WO4NHqU6WFVG1+ej9eqKzG3xMmR1QfhjcvgAb3XcZn7Njv3xM0Gg2xsbGYTKYTHhMXFwfQ7RjhfFLwiZPS2lDPh48/TF15mUuuV110hNDwTShqz5gTPaqyhKkdwaPJDhvq+70unjTTYbVj9F9dPvz3MmipUTtJj3x8fLrc1mg0OH60ZdwPj9FoOiYt/fgY4XzybiP6zdzayuKnf0d9hWu7FMpytxE9bJ9Lryn6p67Ghi0uRe0YHmtHuMy+VNM1YdnMO7Sm7wPdTc0RePsKMMuEH9F/UvCJfrFZLCx57nGqCo6pcv3ivSuITixS5dqidy3jzlU7gmfS69kkO2yoZkxwKg/tXql2jIEr3wXvXSs7coh+k4JP9MnhsPP5y89SckDdVraS/Z8QEeee3RhDWV34KLUjeKZUmbChlmBDEM8X52Owe3ixVLABFt8CDrvaSYQH0CiKIoOjxAkpisLXf3uZ/evc45Owwc+f0IQbaKwJUDuKOC4gWM+UT+9QO4bHqZ97OrdPHPjONGJgNGh4RTeMmXmb1I7iPBOuh0v/qnYK4eakhU/0av07b7pNsQcdW7i11CzGL8DDP5l7kZZGG9a08WrH8Dj5cfL2q4ZbQrO8q9gD2Pk2LP+t2imEm5N3nEHy402i165di0ajob6+XtVcJ2Pr0sVs/+xjtWN001JXg0b5Ar2PdGO4i6Yxs9SO4HG+Da1XO8KQMylkBL/c9ZXaMQbHN6/AxhfVTiHcmBR8okd7Vy9nw7v/UTvGCdWWFhAYvAY0MiLBHdQGpqkdwbPo9WyUCRsuFeUbzrN5e9EpXvxBceVj8O1baqcQbkoKPtHNka3fsOKfr6odo08VeXuIivtW7RgCqKz3QdHq1I7hOVITadXKLjKuotPoeLZVR2TzEFjc9/N74cBStVMINyQF3ylyOBw8++yzpKenYzQaSUpK4qmnnjrh8Zs2bWLs2LH4+voydepU9u1zr/Xlivbt4Yu/PIfiIYtgFu9fT3SibMmjNnOrHUv2WWrH8Bh1qeFqRxhSfhk4iomFQ+TDoWKHj26DY2vVTiLcjBR8p+jhhx/mmWee4dFHH+XAgQO8++67xMTEnPD4Bx98kOeff55t27YRFRXFxRdfjNXqHp/0q4oKWPrnJ7C7SZ7+Ktr7GZEJlWrHGPIa089UO4LHkB02XGdW6Ghu2eOl4/ZOxG6G96+Hyv1qJxFuRN51TkFTUxMvv/wyzz77LDfddBNpaWlMnz6dW2+99YTP+f3vf8/cuXPJzs7mrbfeorKykk8++cSFqXvW2tjAkmefwNLWpnaUk6coVBxeRGh0o9pJhrRqwzC1I3gMmbDhGsP8Y3nq0GY0Q3FrRksTvLsAmqvUTiLchBR8p+DgwYOYzWZmz57d7+ecccYZnX8PDw8nIyODgwcPDka8frPbbHz24h9prPLcVjKbxUJD2SICQtrVjjJkVVVrUIx+asdwf7LDhksYdUZeqG0muG0Ib1/XUAQf/ER24xCAFHynxM/PO365rfnPP1TfRcMZ2poasLUtweBrUzvKkGSzOmibMFftGO5vuEzYcIWHjClklh9QO4b6inPg01+qnUK4ASn4TsGIESPw8/Nj1apV/X7Oli1bOv9eV1fH4cOHyczMHIx4/bJr+ZfsXrFMtes7W0NlGUbjcjQ6z5h04m0akyeqHcHt1aXIhI3BdklYNlfvX6F2DPex5wNY/2e1UwiV6dUO4Ml8fX156KGH+NWvfoXBYGDatGlUVVWxf//+E3bzPv7440RERBATE8MjjzxCZGQkl112mWuDH1e8fw9r/vO6KtceTFUFh0jIDKWmQiYRuFqVEk2s2iHc3DGZsDGo0gMT+e2+NWrHcD+rn4SoDMi8WO0kQiVS8J2iRx99FL1ez+9+9zvKysqIi4vj5z//+QmPf+aZZ7jnnns4cuQI48eP57PPPsNgMLgwcYcGUwWfvfgMDrt3dn+WHtxC0thQTMWj1Y4ypFRX23GERKJtqFY7itv6NqxO7QheK0Dvz4sVFfhZWtWO4oYU+OTnEJEO0er1Kgn1aBRFGYLTl4Y2S3sb7z36INVFBWpHGXRJ2ddgKpHZo640WfMNgWveUTuGe/Lx4cb7dbRrvPODltqeM6Ryfu46tWO4t7BUuH0N+IWpnUS4mPQtDDGKorDs1eeHRLEHUHLgY8JipUXFlepjxqodwW0pwxOl2Bsk14VmS7HXH3X5sPgWcHjxFnOiR9KlO8R88+Hb5G3b0veBXsJht1Fb+CFBsTfQXOevdpwhwWQNw5ltqnOO5lFm614kXRsayqMx3UcMrmhq4vWaGoqsFmyKQpLBwE/DwrkkJKTzmH/X1vDv2loAfhYezk/DIzof293WxhOVFbyfnIJeo3HiK4G65DCgyKnnFDA2OI0Hdi9XO4bnOLq6Y9/dc59QO4lwISn4hpDczRvY8vEHasdwOXNrCz4NH2EMWIC5xUftOF6vvsaKPS4VXXm+U873YXIKP2yLOGI2c2tJMecFBfV4fIhOyx0REaQaDPhoNKxraeaRinLC9TqmBwSS297Oq9XVvJYwDAW4s7SEaQEBjDT6YlMU/lBZwR9iYp1e7AEci3P+OYe6UEMIzxcewcchS92clG/+AnHjIPsqtZMIF5Eu3SGiuriQr/72ktoxVNNcU4WeL9D5SDeGKzSPc956fOF6PVE/+FrX0kyijw+T/HpusZ3sH8CcoCDSjEaSDAZuCAtnpNHIjuO7yByzWBhpNDI1IIAzAgIYaTRyzGIB4N+1tUz08yd7kNbY/DasflDOO1RpNVqesQYQWy8LWQ/Ip3dD1WG1UwgXkYJvCLCa2/n8pT9hMw/t1dZrSo4RHLoBhuI2Sy5WFz44swAtisJnjY1cERKCph8tcIqisLmlhQKLhYnHC8SRRiMFFgtlViulViuFFgsjDEaKLBY+aajnnqjIQcmOjw+bfKUwcabbgscw7djQGaLidNaWjvF8shPHkCBdukPAqjf+Tk2JjBsCKD+8g8SsUKpKx6sdxatVNvuTMgjnXdXURJPdzuU/GI/Xkya7nVlH87AqClqNhkdjYjgzIACANKORhVFR3FpcDMDCqCjSjEZuKS7i/qhoNra08NfqavQaDb+JjmGiv3PGfnZM2JCfQ2c5IzSDO3d5z6LxqqncC8t/Cxc+p3YSMcik4PNyB9avZv+6lWrHcCvF+1aTNDYEU3Gq2lG8VkujDWv6BHzydjr1vB83NHBWQADR+t7HYgZotXyckkqrw8GW1haeNZlI9PFhsn9H0bcgNIwFod8vS7GkoYEArZbxfn7Myz/GB8kpVNqs3F9WxorhwzFoT70zpDZFJmw4S4xfJH86vAOtIjvqOMXW12H4LBg1T+0kYhBJl64Xqy0rYeW/XlM7hlsq3reUiPgqtWN4tcbRs5x6vlKrlc2tLVwZEtrnsVqNhmSDgUxfX34aHsG5QUH8s6a2x2PrbDZeq6nmkegY9rS3kWIwkGIwMMU/ABsKBVaLU/Ifi5UJG86g1+r5c5NCWEuN2lG8y9JfQEOp2inEIJKCz0vZLBY+f/EZrOZ2taO4JcXhwJS3iJDIZrWjeK26wOFOPd8nDfWE63TMDAw86ec6FLCcoDXomSoTN4aFEevjg0MB6w/WorcrCnYnDfn8NrTeOSca4u71H8n4Yue2HAugrQ4+vk3W5/NiUvB5qXVv/5uqIbK48kBZze00mRbhFyRF8WCorPNB0eqcci6HovBJQwOXhYR0Wy7l1+VlvFBl6rz9ek0N37S0UGyxcNRs5s3aGj5rbODi4O7j/r45PqHjuuPdu1m+vuRbLKxvbubD+nq0Gg2pTtj6UGMw8I2fTNg4VXPDxnDj3q/UjuG9CjfBehnL561kDJ8XOrZjG7u+/lztGB6htaEOg99n+Bgux2qRHwdnMrfZMY+dge+uU9/IfnNrK+U2G1f00J1bbrV2+eTa5nDweGUFlTYbRo2G4QYjf4qL54Lg4C7Pa3c4eLKykufj49EeLyJjfXx4JDqGRyrKMWg0/DE2Dl8njN9zDE+kXVN4yucZypID4nn8wCa1Y3i/dc9CylmQMk3tJMLJZC9dL9NSX8dbD95FW2OD2lE8SszwLBrr56IoMs7KmbIjSon66Gm1Y6iu5ryJ/N9pu9SO4bF8dUbebtaTUXFQ7ShDQ3AC/Hwj+IernUQ4kXTpehFFUfjqby9JsTcAlcf2ERG7Te0YXqfax5mbrHmuY913gBMn4RFDshR7rtRY2jGJQ3gVKfi8yM5ln1Kw61u1Y3iskgMbiU7MVTuGV6mq0aAYB2fXCk+yXXbYGLDLw7K57IAsLeVyuV9CzutqpxBOJF26XqK6qIC3f3MvdqvsJ3lKNBoSsxZQVRKndhKvMdW8HP/NS9WOoRqNwcD192kwa05t9mPu/blYa7r/fIefE078jfHd7m/Y3kDV51VYKi0odgVjjJGI8yMIm/b9+oPVy6qp+rJjeaKoC6OIvOD7XUZaj7ZS9t8y0n6XhkanzlCHjKBk3jmwDaNNJlapQmeEO9ZD9Ci1kwgnkFHqXkBxOPj6H3+RYs8ZFIWyQ4uJTruBuspQtdN4hYakiUO64HOkJWJ2woSNtN+noTi+/3xuLjVT8FwBwZOCezxeF6Aj+uJoDHEGNHoNTbuaKH2jFH2wnqDsINqL26n8pJLkhckAFL5YSGBWIL6Jvih2hbK3yoi/OV61Yi/IJ5AXykqk2FOT3Qyf3gW3LAcnTF4S6pL/QS+wY9mnVOTJBtjOYrdaqStZREBom9pRvEKVEq12BFXVJof1fVA/6IP1+IT6dH417WrCEG0gYFRAj8cHZgYSfHowvvG+GKONRJ4biW+iL62HWwEwl5vxHeZL4OhAAkd3FHrm8o49VauXVROQEYD/cOdsKzcQTygRJFXnq3Z9cVzJNtj6D7VTCCeQgs/DNZgq2PjB/9SO4XXam5uwNn+CwU9aTU9VTbUNR0hk3wd6qaODMGHDYXNQv7me0LNC0Wj6boFTFIXmA82Yy80EZHQUiMZhRiyVFiw1FizVFswVZozDjJhNZuo21BF9hXqF+k2hY5l9ZINq1xc/suoJqJNlhTyddOl6uBX//Cs2s1ntGF6psaqCyKSvsOnn4bCp99nod+9cR21zZbf7zxp9CfPPuqfb/buObeDrne9S3ViK3WEnKiSB2WOvZvLIuZ3HrNz9ISt3fQDA3PHzmT3ums7HCioP8sHGl3ng8r+ic8LCyYoDWk4/n6DVb5/yuTzR9tA6p5+zaUcT9lY7YdN7bz20t9rJvTcXh82BRqMh/sZ4ArM6dirxjfcl5soYCp4rACD2qlh8433Jfzaf2Gtiad7XjGmJCY1OQ9xP4joLxcF2Wkg6C3fL4spuxdoCny+EGz5RO4k4BVLwebB9a1ZQuEe2GBpM1UVHiM/YRI1pOhrUGcv04BWvofxgW7Cy2nxe/eJXTEib2ePx/r5BnH/aT4gJTUSn9WFf0WbeXvssgX6hjE6cRGnNUb7Y/h9+fv5ToCj8/atHGDVsIgkRw7E77Ly/4SWunXGvU4q979THZBPktLN5Do3BwGZf5++wUbe+jqDsIHzCfHo9TuurJe3xNBztDloOtFD+Xjk+UT4EZnYUfeHnhBN+zvdrrdVtrEPrq8U/3Z/Dvz5M2u/TsNZZKf5bMSOfG4nWZ3A/+IQbw3gu/yB6h21QryMG4Ohq2PUujL9O7SRigKRL10O11Nex7n9vqB1jSCjL3UbMsH2qXT/IL5Rg//DOr32FW4gMjmdE3Lgejx8ZP55xqdOJDUsmKiSes7OvJD5iOMcqOl5DZX0xCeHDyUiYQMaw04iPGE5lfTEAK3d/QHpcNslOnpVXZXHOODZP0zFhw7l7k1qqLTTvbyZsZt//phqtBmOMEb9kPyIviCRkUgjVX1T3eKytyYZpqYn46+NpPdaKMdaIMdZIYGYgil3BUmFx6uv4Ma1Gy7NmA9EN5YN6HXEKvv4NNJv6Pk64JSn4PNTqf/+d9pZmtWMMGUV7VxCdWKR2DGx2K9vyVnJGxvn9HruVW7IDU30JaXHZAMSHp2JqKKG2qZLapkpM9SXEh6dQ1VDGltyvuGjSLU7PXV9jxRY/3OnndXc1Tpqw8UN1G+o6ZtqOO/k2U0VRcFgdPT5W/m45kedG4hPuAw5Q7N/PCFbsSpcZwoPhzqDRTMmXxc/dWlsdfPmg2inEAEmXrgc6sm0zh3NkT0lXK9n/CXEZ11NTHqFahj0Fm2gzNzMl47xej2szN/PI2/OxOaxoNVrmT7+HzGETAYgNS+biyT/j1S9+BcAlU24lNiyZVz5/kMum3M7Bkm18uf2/6LR6rjrzF6THj3VK9pZxcwkpG1qz/Zw9YUNxKNRvrCd0Wmi35VJKXi9BH6Yn9uqOi1Z9XoVfih+GaAMOm4Pm3c3Uf1Pf45p9zfuasVRaGHZbx84ofql+mMvNNO1pwlpr7WgpjDM698X8wFmhmdy+c9mgnV840YElcPBzyLxI7STiJEnB52HMrS2sfuNvascYkhx2O6ZjHxI27EYaa1wzgP3Hvjm0jNGJkwkN6H3Wq9Hgz8NXvY7Z2kZu6Q4+3vw3IoLjGBk/HoCzRl/MWaMv7jx+S+7XGH38SI0ZzRMf3MyDV7xGfXMV/171JH+47m18dIZTzl4bNoqQUz6LZ3H2hI3mA81Ya6yEzejecmipsfDDYaYOs4Oy/5VhrbWiNWgxxBlIvD2RkCld/xccFgdlb5eR+H+JaLQdJ/AJ9yHu+jhK/1WKxkfDsFuHoTUMTodQnF8Uf8zdigbZA8BjfHE/pEwHv1C1k4iTIAWfh1n39r9prqtVO8aQZW1vo6VmMX4BC2hrGbwWj57UNlWSW7qD2859rM9jtRotUSEJAAyLTKeivojlO9/rLPh+qLmtgWXf/o+Fl7xIgekQ0SHDOr8cDhum+hISIk69O9bU7E/qKZ/Fc2iMRqdP2AjKCiLrP1k9Pjb84a7/RzFXxhBzZUyf59QatIx8ZmS3+8NnhhM+M7yHZziPj9aH5xuthLQ6fyazGETNFbDiUbjkFbWTiJMgY/g8SPH+PexdvVztGENeS10NOD5H7+Pcwfh92Zz7FUF+oYxJmnrSz1UUBZu95zUFP9r8GmePvZKwwCgUxYHd8f3rsjvsXWYIn4qWRhuWEac55VyewD48EYuTJ2x4mwf80sgu2aN2DDEQO/4Lx9apnUKcBCn4PITdZmXFP18F2frYLdSVFRIYvAY0rvn/cCgOtuR+xZSR53ZbLuW/q59hac6/Om9/vfNdDpZsp7qxjIq6Qlbt/pCtR1YwecTsbuc9WLIdU0MJM8ZcCkBSVAaV9UXsL8ph44HP0Wq1RIcmOu11NI+e5bRzubvalFC1I7i1C8KyuG6ffID1aF/cByf4ICncj3Tpeoidyz6jrrxM7RjiByry9pA4JpSqsomDfq3ckh3UNZuYmnF+t8dqm01dZuxarO18uOEv1LdU4aM3EhOayE1nP8zp6Wd3eZ7FZmbRxle4Zc6jaDUdn/3CAqO4etpdvL32OfQ6H26Y9RAGvfO6rmsChjO4nYTu42jfvalDVmpAAo/tX692DHGqavJg6+twxi/UTiL6QaMo0mTk7lobG/j3Pbdjbm1RO4roQdLYSzAVp6sdwyMY/HRM+/oXaBze39X56sLhrPdTfykfd+On9+O9BoU0k+z/7RV8Q+DuXeA/VD7KeS7p0vUA3yx6V4o9N1a09zOiErpvfSa6s7TZMY/teYcQb6IxGtniW6p2DLf0qD5Bij1v0t4Aa/+odgrRD1Lwubnq4kL2rJT1qdyaolB+eBGh0Y1qJ/EIjWlnqB1h0NnTZMJGT64Jy+big6vVjiGcbfu/oSpX7RSiD1Lwubl1/3sDxeGcWZJi8NgsFhrKFhEQ0qZ2FLdXYximdoRBV5MyNLeS682Y4FQe2r1S7RhiMDhs8PUjaqcQfZCCz43l79xOwe4dascQ/dTW1ICtbSkGX9n4vTemanD4qrNwtascjZGh0T8UbAji+eJ8DHaz2lHEYMlbAUekoHdnUvC5KYfdztr/vaF2DHGSGirLMBqXo9FJq+yJ2K0O2k+bq3aMQbU1tEbtCG5Dg4an7aEk1MoEFq+3/BGwywdedyUFn5vavXIZtaXFascQA1BVcIjwqC1qx3BrDYmnqx1h0Gh8fdlilAkb37klNIuZebL395BQdQi+fVPtFOIEpOBzQ+0tzXyz6F21Y4hTUHpwC9GJB9SO4baqlGi1Iwwae1oiNo208AJMChnBL3d9pXYM4Uprnoa2erVTiB5IweeGtnz0Hu1NMuPT0xXt+YroYc7dS9Vb1FTZsId5Z9FXkxSidgS3EOUbzrN5e9EpMlt5SGmrhfXPqZ1C9EAKPjdTV17Kzq++UDuGcJKSAx8TFisbw/+YokDrhHPVjjEo8mJlwoZOo+PZVh2RzSa1owg15PwDao6qnUL8iBR8bmb9O//BIYNevYbDbqO28EMCw1vVjuJ26mPGqh1hUGwNkQkbvwwcxcTCb9WOIdTisMKK36mdQvyIFHxuxFRwjLxtm9WOIZzM3NpCe91HGANkk/EfMplD1Y7gdBo/X3J8h/ae17NCR3PLHhm3N+Qd+hzKdqmdQvyAFHxuZPPi99SOIAZJc20Ver5A5yPjmb7TUGvFluBdexDbhw/tCRsJ/jE8dWgzGqRbWyBbrrkZKfjcRFVhPnnbZSkPb1ZTcozg0A0gvww7tYydo3YEp6pOHroTNgxaAy/UtRLc1qB2FOEuDn8FpdK17y6k4HMTmz96r2Mku/Bq5Yd3EJWwW+0YbqM2LEPtCE6VN4R32Pi1byqjy/arHUO4mzVPq51AHCcFnxuoLirgyFYZuzdUFO9bTXRSvtox3EJlk7/aEZxqW2it2hFUcUlYNlfvX6F2DOGO8lZC8Va1Uwik4HMLmz/+QFr3hpjiPUuJiK9SO4bqWptsWEZOVDuGU2j8fMkZgjtspAcm8tt9a9SOIdxYy5Z/qx1BIAWf6mpKijmyRbYdGmoUxYEpbxEhkc1qR1FdU+YMtSM4hT0tachN2AjQ+/NiRQV+Fll2SHRnCU3nv3GPMH7nRewskvVI1SYFn8q2fPw+ijK0fkmIDlZzO02mRfgFtasdRVW1AcPVjuAUQ3HCxmPaGFKqZIFd0ZU5bCRvxj1KZuVj/C5/DFaHhldX56kda8jTqx1gKKstKyH3mw1qxxAqam2ow+D3GT6Gy7FahuaPY2WdnnSdHo2HLzieFz20PrhdF5rN+TtlVyDxPXN4Bv8zLOCpwpEoiqbLY6sOmdhf1sCY+KH3wchdSAufirZ8/IG07gnqK4rxD1iFRjs0vxcsbXbMY2eqHeOUbR1CEzbGBqfxwO7lascQbqI9PJPXYx9jVPnveLIgo1ux953X1khrsJqk4FNJXXkphzatUzuGcBOVx/YTEbNN7RiqaRx+htoRTonGz4+tQ2TCRqghhOcLj+DjkJ1jhrq2iCxei/kDmeW/5emC7q16P7ZsXzl5Jhm3rBYp+FSS88mHKI6h2aIjelZyYBPRiblqx1BFjSFB7QinxJ4+NHbY0Gq0PGMNILa+RO0oQkWtkdm8Ev0EmaW/4dnCEX0Wet9xKPDaGhnLpxYp+FTQWF3FwY1r1Y4h3FDR3i+JGlaudgyXM1WDwzdA7RgDVp00NMYl3RY8hmnHZEegoao1chwvRT/J6JKHeb4obUDn+HR3GeUNbU5OJvpDCj4V7Pr6cxx22VNV9EBRKDu0mLCYerWTuJTd6qDttLlqxxiwI0NgwsbU0Azu3L1M7RhCBc1RE/hz1NOMLnmIl4pObVa9zaHwzpYiJyUTJ0MKPhezmtvZu+prtWMIN2a3WqkrWURg2ND6FNyY6LkLMG8NrVE7wqCK9o3kT4d3opVJZkNKc/Tp/CnqabKKH+TV4hSnnfe9rUWYbdLo4WpDcx0IFe1ft5r2Fhm0KnrX3tyEwfdjDH7XYGnzUTuOS1QpUcSpHWIANH5+bPX13gkbeq2e55sVwluqnX7ulJeaKGzovsvQnRN9+Os8v273f3zQytMbzOTVOrA6YES4lvvPMHDDOEPnMX/+xsyzmywAPDTNwP1nGjsfyymxceeX7eTcGoBe279xZ0NRY/QkXnVcyetFSYNy/poWC5/vLufK04cNyvlFz6TgcyFFUdi57FO1YwgP0VhdSWTSV9j083DYvL8xvqbKhj08Bl1tpdpRTootPRE7x9SOMWju9R/J+KNfDcq5t90WgP0H9d4+k4O5/2vl6jE9f8gJ99PwyFlGRkVqMeg0fH7Yyk+XthMdoOW8dD17Ku38bo2Zz6/zR1HgovdaOTdNT3aMDptD4edftPP6RX5S7J1AQ8xUXrZdzr+LEgf9Wm9tLpCCz8W8/7eIGynYvYPaMpndJvqvuugIoeEbUfD+vZYVBVonnKd2jJPmzRM25oaN4ca9g1PsAUQFaIkN/P7r88M20sI0zEzW9Xj8rBQ9l2f6kBmlIy1cyz1TjYyN0bKxqGPR7kPVDsbG6DgnVc/s4XrGxmg5VN3RDf3cJgszkvRMSuj53ENZfewZ/D78OcYV3s2/Swe/2APYU9Ig2625mBR8LtSwpwSdThpVxckpy91OzLB9asdwifroLLUjnLQjMd45Fik5IJ7HD7hun2+LXeHtPVZumWBAo+m7BU5RFFYds5Fb42BGcsf7ana0lsM1dooaHBTWOzhc4yArWsvRWgdv7rLy5DnGPs46tNTFTuPR8OcYX/BL3ipz/dJIb31T4PJrDmVSfbiIraaNyIPhXD36IeqCqsk5sJT6+gq1YwkPUbR3BUljQzAVD86YGndhMofimvYF58nxwgkbvjojz1c3ENje6LJrLjlko75d4ebxvY9ZbWhXSHihCbMddBp4bZ4vc9M6fpVlRul4erYvc//XCsAfZ/uSGaVjzn9beHauka+P2nhsrRkfHbx8vm9noTjU1MadxbPtl/F+gbqjZr/cW8Ej88xEBUkh7goaRVG8v6/IDdR/cYzmDT8Y2K3TYIm1s698PUeO5qgXTHgMrU5HXMb11JRHqB1lUM3Iewl9yRG1Y/SLxt+fBfdYsXtZl/sTfiO57MBKl17zvLdbMOg0fHatf6/HORSFY3UKzZaOFr4n1ptZssCfWSk9F29v7bKwJNfG3+f5kvFqM9tuC6CkUeEnH7eRf08gRv3QGc9XEzeTZ9ouZVFFrNpROt07ZyT3zBmhdowhQbp0XUCxOmj99kcD0e0KhlItpzlmMX/yI5xx2lXo9YaeTyAE4LDbMR37kOCIFrWjDKrmsXPUjtBvtrRhXlfsXR6W7fJir7Dewcpjdm6d0PeMdK1GQ3q4lvGxOu4/08hVo33440Zzj8dWtzr4wzozr1zgS06pnZERWkZE6Dg7VY/VAYdrhsYyM1VxZ3N/yIucnn+HWxV7AO/kFGK1D43/B7UNzfZsF2vdXYWj1XbiA6psJJFG8qgHqQ+uZduhz6iplckdp+Kpz1dT19p9Hbsz05K54vTu48T2lpSz6uBRqptbsDsUooICmDkyldNTvp9FtvbQUdbkdszGPHtUGrMyvl+AtLCmjo937OPu2dPQaQfvc5S1vY2WmsX4Bl5Le7N3fkCoC80gVO0Q/VSd7F0TNjKCknlkzyqXX/fNXRaiAzTMG3nyv5IcCphP8PZ679dm7p1qZFiwlm2ldqw/qCtsDqXLDGFvZIqfzVMtF7M0P1rtKCdkajKzbF8Fl4yLVzuK15OCzwWat5T16zilxUZISzBzwq7HlqlwoGoTBw9vHOR03umeOdNw/GC0QkVjM6+vy2FsYs9jVvwMBmZnphMdHIBOq+VgmYkPtu0h0NdIRmwUZfWNfL3/MLdMnwTAGxu3kRETSVxoMHaHg4++3cdVE7MHtdj7TktdDWF+n6H3uQyb1ftmHFY2+ZGqdoh+OhzjPS0TQT6BvFBWgtHW7tLrOhSFN3dZuWmcT7flUm78pI2EIA1/nOMLwB83mJkY3zFD12xT+PKIjf/tsfK3eb7dzrviqI3DNXbeuqzjsUkJOg5VO1h2xEpxo4JOoyEjwvs6uRQ0VCbM4cnGi/n8WKTacfrlrW8KpOBzASn4BpmlpAlryUkutOxQ0JfBWKYxbuIsSshj6+6lWKxDa+eFUxHo23UQ8JpDR4kI9CctKrzH49Oju46LO2tkKtsLSsivqiUjNgpTUzNxIcGMiOl4A40LCcbU1EJcaDBrc48xPCqcpPDQQXktPakrKyQ2fQ31tbOhnxuXe4rWJhuWjEkYcrepHaVPW0OcvxixWp5QIkiq3uDy6648ZqeoQeGWHrpzixocaDXfF2UtVoU7v2ynpNGBnx5GRep4+3I/5md1fW6bVeGuZe18cJUf2uMzfocFa3nlAl9+urQdox7euswXPx/v+dlR0FCRcC6PN85j2VHPKPS+821hHftKG8hK8K4Wc3cjBd8ga9l2ajNxlRorCSRzxYh7aQxrZNvhz6mqKnBOuCHCZnfwbWEpM0cO7/dyD3mmGkxNLcwb21EgxoUEUdXcQl1LGwoK1U3NxIYEUt3cwrb8EhbOnT7YL6Obirw9JI4JparMc7ckO5GmUTOIcPOCT+Pvz3Zj/1rv3d2NodnM3vmFKtc+N02P8vvgHh9be3NAl9tPnuPLk+f0fU4/Hw25dwV2u//W0wzcepp3DYVQNFrK4s/jsYZ5rDja8wdaT/Du1iKevjxb7RheTQq+QaTYHLTtdU4LgNJmJ6gtgHOC52MfCQdrcth/aK1Tzu3t9pVV0G61MTG191Xd2yxWnvh8FTa7A61GwxWnZTEyNgqAmOAgLsjK4PX1HTOqL8geRUxwEP9Yu4WLxo0it6KK5fsPo9NquXTCaNKiXDOTtnj/epLGhmIqTnfJ9VylNiAVd5+L3LHDxlG1Y5yyCSHp3Ltb9vf2NIpGS0nCBfy+7kJWHw1TO84p+3JvOY9dPAaD3vu62d2FFHyDqD23tvfJGgPhAF0ZZDGF7NOnU67LJ2fPUtrbZX/eE9l6rJiM2ChC/LqP8/kho4+e++aehdlm44iphk93HyA80L+zu/fM9GTOTE/uPH5bQQlGHz3JEWH8adla7pkznYa2Nt7ZvJPfzDsbvc414+uK9n5G4pjrqCqNccn1XKGyVk+6To/G7uSfHyeqSuq5VcqThBvD+HP+QfQO9/13Fl0pGh3FCRfyaN35rMvz/ELvO/WtVtbkmjhvjHvNIvYmUkoPotadpkE9v1JrJbZqGJel3s1F0+8mLlbWMvqx2pZWjpiqmTK87+V8tRoNkUEBJISFMCtjOGOHxbH6YF6Px7aYLazYf5jLJoyhqKaeqKAAooICSI+OxK4oVDW5cOkURaH88CJCoxtcd81BZmm3Yx43S+0YvTrs4TtsaDVa/mQ2Et1QrnYU0Q+KRkfBsEu4we9VZuRdy7oa7yn2vrNkZ2nfB4kBk4JvkDjabbQdqnXJtZR2OwGlfszwv4Krz3yYcWPmQj/Gqg0F2/JLCDQayYw7+WUJFEXB5uh5FubSXQeYMTKVUH8/HIqC3fH9jGCHw9FlhrAr2CwWGsoWERDiPRN7GoefoXaEXuUEe/aEjTuDRjM1f6vaMUQfFK2e/MTLuM73VWblLWBjrfdObFh1yERju1XtGF5LunQHSduearC5eJEnBbTlDkZxGpnjp1BpKCZn7xJaW72n5edkOBSFbQUlTEwZ1m25lPdydhHi58uFY0cBsOpgHolhIUQEBmBz2DlYXsW3haVc2cOafYcrqqhqamHB5HEAJIaHYGpq5mC5iYbWdjQaDdFB3QeMD7a2pkYM/ksx+F6Fpd3zf7Sr9fG46+phmoAAj56wcVZoJrfvXKZ2DNELRevDsYRLeLhqLluPeP7wgf6w2Bx8uaecBZO9ewtJtXj+bwU31TLI3bl9UeqtRBPLJYl30hrVxo785ZSUHlA1k6sdqaymvrWNyT1M1qhrbesyY9dis/Pxjn3Ut7Xjo9MRHRTAdVPGMz6p69pQVpudT3bu5/qpEzqXewj19+PyCWP4cNsedFotCyaPw0evzvp4DZVlRKUsx2o9H8Xu2Q34VdUKDr9AtG3uNz7Vlj4MReOZEzbi/KL4Y+5WNF62Q4i3UHQG8uIv4SHTXHYcCVI7jst9srNUCr5BInvpDgJbvZmKP23Frd5PNeCI0ZHXvJNd+75GUbxnwVjRXcKoKdRUTlM7ximbal2J/6ZP1I7RTcVFk7g7e6faMU6aj9aHt8wBZJfsUTuK+BFFZ+Bw/GU8bJrDjgbX9xC4C40GNj10DvGhfmpH8Tqe3QTgplp3mdyr2IOO7t4KOyObxzJ/3G+YPfUWggLdfeELMVClh3KITvT8Ft36YaerHaFHuR46YeMBvzQp9tyMojNyKHE+l+pe5bwjlw3pYg9AUWDpLs8dLuHOpOAbBIM9O/dUKQ1WIiujuDD+di6dfh/JSePUjiQGQdGer4hK9Ow9mascUWpH6JEn7rBxQVgW1+1brnYMcZyi9+VA4rVcpH2V849cyp7GoV3o/ZDM1h0cUvA5maWsGVtlq9ox+sfqwLfUh6m687lm6m+YNO5itFrv25t1KCvd/zFhsXVqxxiw2mob9nD3Wl9QExjAdoNntUCkBiTw2P71ascQgKL3Y2/iT7iAV7nwyMXsbwro+0lDTG5lEwfKGtWO4XWk4HMyd2/dOxFNpZ3hjaO5JuvXzD3jNkKC3XV+pDgZDruNmoIPCAz3kA8hP6Io0DrhfLVjdGFLS/So7Yv99H68aKrB3+x+k1+GEsXHnz2JN3Ce8ioXH5nHoWZ/tSO5tSW7pJXP2aTgcyLFodC6u0rtGKdEabIRXhHO+TG3cPm0B0hL9b59WocaS1sr7XUfYQzwzPWt6mO6L42jJlOSZ82cfFSfQJrpsNoxhizFJ4CdSTcxx/4Klxy5gMMtMhmhPz7dVYbD4W6D4T2bFHxOZClowNFoUTuGc9gUDGU6JjKb+VMeYeqEK9DpvGvT8aGkubYKPV+g8/G8yQamdvdaaNaTJmxcHZbNxQdXqx1jSFIMAexIupnZ9le4/PB5HG2VQu9kVDS2k5Pvms0Lhgop+Jyo7aCXfnOabCTXj+Dq0Q9y3pl3EB4W3/dzhNupKTlGcMh63G8Kee8aaq3YEjPUjtFpq4fssDE6KIVf716pdowhRzEGsS3xFs62vsIVh8/lWGvve3iLE1t1sFLtCF5FCj4nanfRVmpqUZpthJaHMjfiRq6Y9iAj06eqHUmcpPIjO4lK2KV2jJPWnD1b7QhAx4SNbz1gwkawIYgXSgow2M1qRxkyFGMwOYm3cZb5L1x9ZA4FbVLonarVhzxzTLy7koLPSWzVbdiqvGcf017ZFXzKtEywz2T+pEc48/Sr8fGRNzdPUbxvDdFJ+WrHOCm1oSPVjgCANT3J7SdsaNDwtD2UhNoitaMMCYoxhM2JtzPd/DLzj5xNSbtR7Uhe41h1CwXVLWrH8BpS8DlJm5e37p1QtY3E2uFcOfI+Lpz2CyIjZUscT1C8ZykR8Z4zwcjU5B7jn6oS3X+ttFtCs5iZt0ntGF7P4RvGxsQ7OKP9Za49MotSKfQGxSpp5XMaKficxNu7c/uitNoJKgtkdsh1XDntIUZnzFA7kuiFojgw5S0iJLJJ7Sj90tpkwzJqktox3H7CxqSQEfxy11dqx/BqDr9w1if+H1PbXuL6IzOpMMtktsG0Rgo+p9GrHcAbOMw2zPkNasdwDw4FfRlkcwZjJ86gVHOUnN1LsVg8cx04b2Y1t9NUuQi/0Otoa3L/LvmmjJlEHNqmaoacEPdtFY3yDefZvL3oFPcuSj2Vwy+CdZEL+FXhZKrqfNSOM2Rsza+l2Wwj0CjlyqmSFj4naD9cB3bPmvnoCkqNlfjqJK5IW8i8ab8kJjpN7UjiR1ob61Esn+FjdP8ioSYgVdXra4IC2eFTrmqGE9FpdDzbqiOyWVpDnM3hH8mqxF8yqeUFfnpkGlUWKfZcyWJ3sOGw+37Q8iRS8DlBu7cux+IkSrudwDJ/ZgVexVVn/prszHPUjiR+oL6iGH//lWi0DrWj9MpUq0PRqfcp35ruvjts/DJwFBMLv1U7hldx+EexYtjdnN70Aj87cgY1UuipRmbrOocUfKdIcSgdLXyibwroyhVGt09iwWm/ZebkG/D1C1Y7lQAqj+0nIkbd7tK+WNrtmMedrdr1TYnuucPGrNDR3LJHxu05iz0ghq+G3cOEpue5LW8qdVbpSlTbmtwqFMWze9FmzZrFwoULVc0gBd8pspQ04Wj2zC2r1KTUWYmtiuey5F9w8fR7SIh3n4V1h6qSA5uITsxVO0avGoart/ZjbrT7/Zwn+Mfw1KHNaDxsMW13ZA+M48thCxnf8Bw/z5tCgxR6bqO62cyeEhknf6rkO/oUSXfuqVHMDvxLfZnuexmOM7UcadzBrv3LwcM/zXmqor1fkpgVTFVJnNpRelSjTyBGpWtvCXGvHTYMWgMv1LUS3Ca/CE+FLSiBZSEL+HXBeFqqdWrHESew6pCJcYmhasfwaNLCd4qG+nIsTqOAttxBRst4Fox/hHOm3kxAQJjaqYYeRaHs0GLCYurVTtKjqmoHDj/Xr4WnCQpil5tN2HjIN5XRZfvVjuGxbEEJLEl4gHG1f+KXeafTYpNiz52tPuT6bda++uorpk+fTmhoKBEREVx00UUcPXoUgKuuuoq77rqr89iFCxei0Wg4dOgQABaLhYCAAFau/H57Q5vNxl133UVISAiRkZE8+uijLu2qloLvFNgbzFjLZRVwZ1PqrURVxnDxsJ9zyfR7SRqWpXakIcVutVJb/CGBYe63c4zdptB22rkuv641fZhbTdi4OCyba/avUDuGR7IFJ/JxwoNk1z7DwqOn0WKXX4OeYH9ZI6bGdpdes6Wlhfvuu4/t27ezatUqtFotl19+OQ6Hg5kzZ7J27drOY9etW0dkZGTnfdu2bcNqtXLmmWd2HvPWW2+h1+vZunUrL7/8Mi+88AL/+te/XPZ6NIqnj4RUUetOE7UfuPeYJ2+hxOo42rKbHXuXoSjuPZvUWwRHxoDhGixt7jU7cVRsPfHvP+LSa5ZdOpmFo3e49Jonkh6YyLu5O/GTtS1PijU4mSWB8/ldYTZtdmnN80QvXDOOK04bptr1q6uriYqKYu/evSiKwrhx46isrESv1xMbG8ujjz7Kvn37eP/993nqqaf48ssv2bSpY9ebWbNmYTKZ2L9/PxpNx6fHX//613z66accOHDAJfnlo80pMBfI2BlX0VTYSW/KYv7Yh5lzxs8ICopUO5LXa6yuxKD/Cq3evQrsKrvr/+8PRbnHhI0AvT8vVFRIsXcSrCGpvB//MFnVT/HgsfFS7HmwrfmuHUJ15MgRrr32WoYPH05wcDApKSkAFBUVkZWVRXh4OOvWrWPDhg1MmDCBiy66iHXr1gEdLX6zZs3qcr6pU6d2FnsAZ5xxBkeOHMFud806qDJp4xSYCxrVjjDkKI02IhojuTDuNszjLOwuXkV+4S61Y3mt6qIjxGdspMZ0Fhrco0+zttqGIzwWbW2Fy665xU122HhMG0Nq1Tq1Y3gES+hwFvkv4LGCMVgr3eN7V5yaHBcXfBdffDHJycn885//JD4+HofDQVZWFhaLBY1Gw4wZM1i7di1Go5FZs2YxduxYzGYz+/bt45tvvuGBBx5wad6+SAvfADlardhM8ilbNVYHxlI9k7XnMX/KI0wefyk6FRfl9WZluduJGbZP7RidFAVaTjvPZdfTBAWxy+C64vJErgvN5vxcKfb6YglN579xjzDG9ASPHMvC6pBiz1vkV7dganLNOL6amhpyc3P57W9/y+zZs8nMzKSuruuau9+N41u7di2zZs1Cq9UyY8YMnnvuOcxmM9OmTetyfE5OTpfbW7ZsYcSIEeh0rml1loJvgMyFjcjSV27CZCO1YRRXj36I8868ndDQWLUTeZ2ivSuITixSO0anumjXTeSxpqs3Zug7Y4PTeGD3crVjuDVz2EjejHuUzMrH+F3+GCn0vFTOMde08oWFhREREcHrr79OXl4eq1ev5r777utyzKxZszhw4AD79+9n+vTpnfe98847TJw4kYCAgC7HFxUVcd9995Gbm8t7773HK6+8wj333OOS1wPSpTtgFunOdTtKs43Q5jDOi7oZS5adfWXrOHJsq9qxvEbJ/k+IG/UTasrUHz9pagshyUXXqkxSd4eNUEMIzxcewcfhHuMI3Y05PIP/GRbwVOFIFHeaSi0Gxdb8Wi4eFz/o19Fqtbz//vvcfffdZGVlkZGRwV/+8pcu4/Kys7MJDQ1l5MiRBAZ2LBc1a9Ys7HZ7t/F7ADfeeCNtbW1MnjwZnU7HPffcw+233z7or+U7Mkt3gEx/242lUIo+txelp8h2iG17PsNms6idxuP5+PoRNuxGGmsC+j54kJ117C/4FA3+LPlVt07gH1F7B/06PdFqtLxGLNOObVHl+u6sPTyT/xrm88fCEVLoDSEjYwJZfu9MtWN4JGnhGwDF6sBS0qR2DNEfVTaSSCd51IPUB9ey9dCn1NaWqp3KY1nb22ipWYxv4LW0NxtUzdKSPYdQFxR8m0PU27j9tuAxTNv1hWrXd0dtEVm8qb+a54rSpdAbgo6YmmlotRLi717LRXkCKfgGwFLSBHZpGPUkSouNkJZg5obfgDXTwQHTJg4d2aR2LI/UUldDmN9n6H0uw2ZVb4mL2pCRhA7yNTTBwewxuH6Ff4CpoRncuWuZKtd2R62R2byhvYbni9LUjiJUpCiws7iOWRnRakfxODJpYwBkORYPZlfwKdMwzjad+ZN+y7SJ8zH4+KmdyuPUlRUSGLwGNOp98DE1+g76NSzpCYN+jZ5E+0byp8M70coi47RGjuOl6CcZXfKwFHsCgB1F9WpH8EjSwjcAFllw2TtUWxlGCokj7qUxrIFth7+gqqpA7VQeoyJvD4ljQqkqm6jK9VubbVgyp2A4mNP3wQNUmej6CRt6jZ7nmxXCW6pdfm130hw1gb9zNa8Wp6gdRbiZHYV1fR8kulGt4LNYLBgM6o4BGgjFoWAulPF73kRpsxPUFsg5wfOxj4CDtTnsP7RW7VgeoXj/epLGhmIqTlfl+o0ZM4gcxIIvN9r1M2MXBoxk/N6vXH5dd9EcfTp/Va7kb1LoiRPYVVyPw6Gg1coYzpPhsi7dWbNmcdddd7Fw4UIiIyMxGo1oNBq+/vprJkyYgJ+fH+eccw4mk4lly5aRmZlJcHAw1113Ha2t3y9wvHjxYrKzs/Hz8yMiIoI5c+bQ0tLiqpeBtbIVpd3msusJF3KArhyyzFNYcPpvmTHpOoy+6s9GdXdFez8jMkGdhYlr/VMG9fybg107fm9O2BhuGqLFXmP0JJ6OfIasovul2BO9ajbbOGyShpeT5dIxfG+99RYGg4FNmzbx97//HYDHHnuMV199lW+++Ybi4mKuueYaXnrpJd59912++OILli9fziuvvAJAeXk51157LbfccgsHDx5k7dq1XHHFFbhyZRlLkYzfGwqUWitx1YlcnnoPF027m9hYdVqwPIKiUJ67mNBo1w91MNXqcOgHp6dAExLMHoPrZugmB8TzxIGhN5GoIWYqj0f8ibFF9/J6iatWVxSebkdhvdoRPI5Lu3RHjBjBs88+C3QUbwBPPvlk5/YjP/vZz3j44Yc5evQow4cPB+Cqq65izZo1PPTQQ5SXl2Oz2bjiiitITk4GOhY+dCVrWbNLryfUpbTbCSjzY6b/ldjP1HK4fht7Dq7qmComOtmtFhrKFhEQ8RNaGlw3CcbSbscybha+3zp/FwpL+jDgsNPP2xNfnZHnqxsIbB86HyjrY8/gRcsVvFWozsQY4dn2l8lY+pPl0ha+008/vdt9Y8eO7fx7TEwM/v7+ncXed/eZTB2fsseNG8fs2bPJzs7m6quv5p///Ge3ve0Gm7XMdd3Hwo0ooCt3kNl2OgsmPMKsyTfi7x+sdiq30tbUiK11KQZf1w55aEidOijnrUwMHJTz9uQRQzIZFQdddj011cVO49Hw5xhf8EveKpNiTwxMboV06Z4slxZ8P95XDsDH5/vFEzUaTZfb393ncHQsTaDT6VixYgXLli1j9OjRvPLKK2RkZJCfnz+4wY9THArWCin4hjqlzkpMVRyXJP6CS6YvJCEhU+1IbqPBVIbR+DUaneuWE6nWxw3KeQ9FuWZnlsvDsrnswEqXXEtNtXFn8euw55lQ8Av+J4WeOEWHK6XgO1ketw6fRqNh2rRp/OEPf2Dnzp0YDAY++eQTl1zbVtOGYpV1sUQHxeLAr9TIdOMlXH3Gb5iQdT4ajcf9SDldVUEu4ZGbXXe9KgVHgPNbWzcHD/74vYygZB7Zs2rQr6OmmriZPBj6Aqfl/x/vlw9OcS6GnsZ2G2X1bWrH8CgetQ5fTk4Oq1at4txzzyU6OpqcnByqqqrIzHRNC4t054oeKaCtsDOScWSMm0iVbyk5+5bQ3Dx014oqPZRD0tgwTMWjB/1aDrtC24RzCdi42Gnn1IQEs3eQJ2wE+QTyQlkJRlv7oF5HLVVxZ/NM6yV8lB+jdhThpXIrm4gPlYXz+8ujCr7g4GDWr1/PSy+9RGNjI8nJyTz//PNccMEFLrm+tVwKPtE7pcFKZEM08+J/Tnu0mV2FKyks3qN2LFUU7fmKxLHBVBUPG/RrNQybQADOK/hcMWHjCSWCpOoNg3oNNZjiZ/NUy8UszZetr8Tgyq1o4mzZYq3fNIor1zTxcNX/2U/7oVq1YwgPo8ToyG/by7d7v8ThsKsdx6W0Oj2xGddTWx4+qNeJiNIzbtEdTjtfyWWTuS9zh9PO92M3hmbz4M4vBu38rqagoTJhDk82XsznVZFqxxFDxOUTEnhx/ni1Y3gMj2rhU5u1Ulr4xMnTVNoZzmjSssZSE1DJlv1LaWqsUjuWSzjsNqrzPyQ4/kaaa/0H7To11TbsEXHoasqdcr6Dg7jDxoSQdO7d/fWgnd+VFDRUJJzL443zWHZUCj3hWjJT9+TICPN+cljs2OvNascQHkxpshFeEcGFMT/jsun3Mzyl+zJF3sjS1kp73UcYAwZxmzIFWiac57TTbQ4anB02wo1h/Dn/IHqHZ+/Wo2i0lCZcwO2Br3DG0ZtYJq16QgV5Vc3YHdJJ2V/SwtdPNlMryPeVcAabgrFUzyTNHCZNOZ8CywG27/kCu901y4Coobm2igj/L7D5XIzdqhuUa9RHZeGMubqa0BD2DcKEDa1Gy5/MRqIbnNMKqQZFo6Uk4QJ+X3chq4+GqR1HDHEWm4P86hbSo123ZqYnk4Kvn6yVrX0fJMTJMtlIYSSpo0dTF1RNzoFPqa/33IKgNzUlx4gbsZ666lmA8zc9N7UH44yNuTombOQ64Uxd/V/QaKbu/tLp53UFRaOjOOFCHq07n3V5nl/olfztFuyN3Yv6wAnziDj3/7rd35r7DQ1bPsRaVw4OG/qweIInXU5g1jmdxzTkfEzj1o8ACJlyJcGTr+h8zFyWS+3y14i98QU02sH5wDNU5VY0ScHXT1Lw9ZMUfGIwKc02QptDOS/yJqxj7Oyr2MDho1vUjuV05Ud2kpgVSlXpBKefu7HOii1pFPqiQ6d0noph3ReIP1XTQ0dxx85lTj/vYFM0OgoT5vHb2gvYmBeidhynibvpRXB8v6aqpboQ0we/JWDUtB6P1/oFEnLGNfiEJ4JOT9vRrdR8+RI6/xD8hp+OxZRPw8Z3iLrqd6AoVH30OL6pp2GISkFx2Kn5+q9EnH+XFHuDILeikXljZX3H/pCCr59sJin4hAvYFXzKtExgJhMmz6bYlsu2vZ9jtXrPWm3F+9aQNC4UU1Gq08/dlD2bsFMs+A5GO7drPc4vimdyt6HxoDEhilZPQcJF/Kb6PDZ7UaH3HZ1/19fUtmUR+tA4jIk9783umzS2y22fiZfSsm815pID+A0/HWtNCT5RKfglj+t4PCoFa00JhqgUGnM+wjdxDMa4kYPzYoa4XNlxo99k0kY/2aplRW/hYlU2EuvSuHLk/Vww7U4iIhLVTuQ0xXuWEhHv/JnKdSGn/kt1c5Dzxu/5aH14vtFKSKtnLMKtaH04mngl8w2vcvaRa9hc533F3o8pdistB9YSOHYuGk3fQw0URaGtYBfW2hKMiVkAGKJSsNWVYms0YWswYastxRCZjLWunOa9Kwk964bBfhlD1uHKZrUjeAxp4esHRVGw1XtPC4vwLEqrjeDWIOaE/gTbKIUDVd9w8LBnL9irKA5MeYsIT7qBhuogp523stGX4afwfE1YKPudOGHjAb80so8ud9r5BouiM5AXfwkPmeay44jz/j88QevhLTjamwnImt3rcQ5zCyV/vQnFbgWNlohz/w+/1I6hCT6RiYTOuJHKDx4FIHTmTfhEJlL5/iOEzfopbfk7aNj0Lmj1hM+5Hd/jhaI4daV1bTgcClqt88cFexsp+PrB0WwFm+d0xwgv5VDQl8FYzmTcxJmUcJSte5ZisXjmcAOruZ2mykX4hV5HW5OvU87Z1mzDkjkVw8GBjX80pyfgrAkbF4Rlcd0O956koegMHI6/jIdNc9hxZGgOfG/esxy/4aejD4ro9TiNwY+4n/4FxdJOe+Euale/gT40trO7N2jChQRNuPD78+5dhcbghzFhFKX//DlxN76AvamG6k+fJeGON9DofQb1dQ0VFruDqmYzMcHOeQ/xZtKl2w+y/p5wN0qNlYSaJK5IX8i8aXcRHe388XCu0NpYj2L5FB+j83YgaRo1Y8DPddaEjdSABB7bv94p5xoMis7IocT5XKp7lfOOXMaOhqFZ7NkaTLQX7iZwXN9rOGo0WnzC4jHEDCd48hUEZEyjYfOiHo+1tzbQsOldwuf8HHPZYXzC4/EJT8A3eSyK3Ya1rtTZL2VIK6uXIVf9IS18/SDducJdKW12AtsCODvwGuzpGg7V5bDv4Bq1Y52U+ooSooevwGY9F8Vx6p9Ba/yS6b2t5sScMWHDT+/Hi6Ya/M3uN7ZI0ftyMO5yHqw4h/1HnD8b2dM0713RMdM2bdJJP1dRHB3duz2oW/0vgiZdhj44EkvFYRT7Dz7QOOxdZgiLU1dW384EZ6zJ5OWk4OsHaeFznjP+dg0ljRXd7r9xwmU8de593e5flruOV7a8TWFdKVaHjdSwYdw+aT5XZn3/ifzvOe/x963vAfB/U67jjskLOh/bWXaAR5a/wKc3/h291ou/3RXQlSuMYTJZp0+jXFdAzp4ltLe7X9HRE9OxAwwbHUp1+dRTPldljZZ0vQGt7eSLN2fssPGoPoE00+pTPo8zKXo/9sVdwYPlZ3PoyOBtcedJFMVB896VBGTN7rZcSvXnz6MLiiBs5s0ANGz+EEPsCPRhcWCz0nZsGy371xB+7p3dztuWvxNrbSkR8+4FwBA7ElttCW1Ht2NrqgatDn14wqC/vqGkvEFa+PrDi38DOo8UfM7z+U2vY3d8/2k3tzqf6z64j4tGnd3j8aF+wfzyjBtID0/CR+fDqqPfcP+XzxDhH8as4ZM5aDrK8xv/zX+uegZFgZs/eogZqZPIjErD5rDx8NfP88z5D3h3sfcjSq2VWBK4LOVumiNa2HFsGWXlh9WO1aeSA9+QNDYMU3HGKZ3HanZgHn82fttPbr9aTVgoB3xObebw1WHZXLzji1M6hzMpPv7sjb2SB8pmcfiIn9px3Ep7wS7sjVUEjp3b7TFbYxVovm9tdljN1K54DXtTDRq9AZ/wYURedD8BmV2HDzisZmpX/p2oSx5Cc/z5+uBIwubcQfWyl9DofIiYdy9aH+PgvrghplS6dPtl6PwWPAW2Oin4nCXCP7TL7de2vENyaAJTE8f3ePwZSV0X6P3ZxKtZvO8rtpXsYdbwyeTVFJIZlca05I59aTOj0jhaU0RmVBp/z3mfKYljGR+XORgvxe0pZjsBZb6c5Xc5jjO1HGn4ll0HVoDivhOQivZ+SWJWEFUl8ad0nsbUKSdd8JnThwEDX8NvdFAKv969csDPdybFJ4BdcVfxQMkMjkqh1yO/1NNIfujzHh+Lve6ZLrfDZtxA2Iy+l1bR+hhJuO0f3e4PGnceQf0YJygGplyGXfWLTNroB3uDFHyDwWK38vGBFcwfe2G/17/aWPAtR2uLmZLYscDpqKjhHKsrprSxkpKGCvJri8mITKWgrpQP937Jg2fdNtgvw/0poC13kNE6gQXjH+GcKTfh7++m66spCmUHFxMWU39Kp6nWnXzBWJE48K7OYEMQL5QUYLCr+16hGALYkXQzs+2vcPnh8zjaKsWe8H5l0qXbL9LC1w92+fQwKL4+vIHG9mauzrqg1+Mazc1M+uuVWOwWdBodT557LzNSOwZZj4hM4aEZt3PdBx3j/x6aeQcjIlO49v17+c2s/2Nd/lZe2PQmPlo9j8355QlbEocKpd5KFLFckngnrVHt7MxfTnHpfrVjdWG32agt/pCgmBtorhtYwVJV5cARGIK2uaHfzzkQObBiTYOGp+xhJNSq9++oGIPYHn01D5ZMp+CwLE8hhpYy+R3dL1Lw9UGx2nG02NSO4ZXe3/MFZw+fQmxQZK/HBRr8+eqnb9BqaWNj4bc8sfqvJIfGd3b33jDhUm6YcGnn8Yv2LiPA4M/pCWOY9c/r+fzGf1DeVMUvPv0D39zxAUa9YVBflydQLA78Sg2cabgIZeqlHG3dzY69y1AU95g9aG5pxtj4MQa/a7C0nfx6ZQ67QtuEcwnY0POyGT35JnhgEzZ+GpLFrF3qjNtTjMFsjZ7P/cVnUnJExoWJoammxYzZZseol72KeyMFXx9sMmFjUJQ0VLCx8Ftev/yJPo/VarSkhg0DYEzMCPJqCnl189vdxvcB1LbW89Km/7D4ulfYWXaA1PBhpIYnkhqeiM1u41hdMZlRaU5/PZ5MU2knnSxGjB1PtV85OfuW0tRco3YsGqsriUz6Cpt+Hg7byY8+aUiYQAD9K/g04WEc8qk+6WtMChnB3bu/OunnnSrFGMKW6Pk8UHwGpVLoiSFOUaCioZ3kCFlqqDcyhq8PdpmwMSg+3Pslkf6hzE4746Sf61AULCdY/+oPq1/l1knXEBccjV1xYPvB+ld2hx2HrH91QkqjjYjKKC6Mv51Lp99PStI4tSNRXXSE0PCNKJz8RBOTrf+r8XXssHFyonzDeTZvLzrFeYtG98XhG8bGxDs4o/1lrj0yi9J2KfaEAJmp2x/SwtcHWZLF+RyKgw/3LuOqrPO7LZey8POniA2K5Ncz7wDg1c1vMzY2g+SwBCw2C6uPbeHj/V/z9Ln3dzvv+vxtHKst5sV5vwFgfOwo8moLWXN0C2VNJrRaHcPDZXXOPlkd+JbqmaI7nylTLyK/bS/f7vsSu12doQ1ludtJyg7DVJJ9Us+rrbFhj0xAV933rgblw05uwoZOo+PZVh2Rzc7bd7c3Dr9wNkbO54GiqZiOyJZcQvyYzNTtmxR8fbA3nfrK+6KrDQXbKW2sZP7Yed0eK22s7DJjt9XaxiMrXqC8qQpfvZH08CRevui3XJLZdaPzNquZR1e+xGuXPIb2+PpXccHRPDFnIfcvewaDzocX5/0GP1n/6uRU2kglk+FjsqkNMJFz4FMaGk59ceKTVbR3BUljgzEVJ/f/SQq0TDiX4BVv9nnoyU7Y+GXgKCbuWXZSzxkIh18E6yIX8KvCyVTVSaEnxInI4st90yiKGy/K5QbqvzhG8wbZ91AIAPQaLDE29pauIy9/m0svrdFqic+8npqy3if5/FB6fBtJ7z7Q53GPPhBLbj/H8M0KHc1fdn6NZgDdzP3l8I9kTcS1/KpwIjUWKfSE6Mst01L53cWj1Y7h1qSFrw9Ku+vG5wjh9mwKhlIdp3MOp08+l0LrQbbt+Ry7ffBbwhWHA9PRRYQNu4HGmsB+PcfUFkxfnfia8LB+F3sJ/jE8dWjzoBV7Dv8oVoV3FHp1tfL2LER/NZt7HtctvifvKH1wtMuSLEL0qMpGMiNIyXyQ+uAath76jNrawW0Nt7a30VK9GN+g62hv7nt5ncY6K7bk0egLD5zwmPb0BPqzw4ZBa+CFulaC2/q/tl9/2QNiWBG2gIcKT6dBCj0hTlqT/K7uk7yz9EEKPiF6p7TYCGkJYW74DVgzHeyv3Ehu3jeDdr2W+lrC/D9D73MZNmvf6241ZZ9DWC8FX3932HjIN5XRR1f0O2d/2APj+Dp0Pg8VnEZTjbwdCzFQUvD1Td5h+uBok28iIfrFruBTpmE8ZzF+0tmUOPLYumcpVqvzZ8/VlRUSm76G+trZoPS+LV9t8AjCenl8f2Tf+S4Oy+aaHc5bXNkWlMCykAX8umA8LdWyWKwQp6qpXbp0+yIFXx9kDJ8QA1BtYxgpJI64j8awBrblfkFVdYFTL1GRt4fEMaFUlU3s9ThTgy/DNRo0J5iftqmPHTbSAxN5dN+aAef8IVtQAp8HX8sjBeNpqZJlUIVwliazNM70RQq+PkiXrhADp7TZCWoL5JyQ+dgy4GD1Zg7krnfa+Yv3rydpbCim4vQTHtPWYsOSeQbGA927mTUR4RzRn3hXEX+9Py9UVOBnaT2lnLbgRD4NWsAjBWNpq5IWPSGcTbp0+yYfMfsgBZ8QTuAAfRlkW85gwem/Zcak6zD6OmcbpKK9nxGZUNHrMU0Z03u8vz0tvtfn/UEbS2rV0QFnswYnsyj+V2TXPM19RyfQZpdiT4jBIF26fZMWvl4oVgfYZJlCIZxJqbUSRyKXp95Dc3gT2/O+pKJy4EUVikJ57mKiht9AvSmkx0Nq/JLpafW+8l4mbFwXNpbzd3w+oEjWkFQ+CljA7wvGYDbJ52ohBlu71YHN7kCvk5+3E5GCrxfSuifE4FHa7QSU+TMz4CrsZ2o4XL+NPQdXd+yEfpLsVgsNZYsIiPgJLQ1+3R431WgZoTegtXVdL/BAVM87bIwNTuOBXV+fdA5L6HAW+S/gsYIxWCt7n0wihHCupnYbYQF9L9c0VEnB1wsp+IRwAQV05QqZTGT0hDOo0Bexdd8SWlsbT+o0bU2NGPyW4uN3Jda2rrtTWM0OzBPOwW/bV13u3xTYvSs41BDC84VH8HH0v4vIEprO+37zeaJwNFaHFHpCqEEKvt5JwdcLmaErhGspdVZiiOOSpF/QEtnGjmNfUVrW96LI32kwlRGVshyb5QIUe9euncaUKV0KPm1kOEd8uk7Y0Gq0PGMNILZ+b7+uZw4bybu+83myIAO7Il1JQqipUcbx9UoKvl7IGnxCqEMxO/AvNTLd91IcZ1xOXvNOdu77ul/dvVUFuSSMCqWmclqX+6t1ccT84HZbWgLQtRXxtuAxTNvV93p75vAM3jbM5+nCkVLoCeEmmmVpll5JwdcLxepQO4IQQ5sC2goHIxlHxriJmHxLyNm7lJaWul6fVnooh6TsUEwlYzrvq6py4AgMQdvcsTVa+bCuY/2mhmZw565lvZ63PTyT/xrm88fCESh9LPgshHAtWZqld1Lw9Upm6ArhLpQGK1ENMVyc8HPaotvZWbCCopJ9Jzy+aO/XJI4Noap4GAAOu0LraecRuP5DAPb/YMJGtG8kfzq8E63S84e8togs3tRfzXNF6VLoCeGmzDYZhtUbKfh6I/WeEG5HsTrwLTVwhs88pk69hGOte9ixbxkOR/c3+9L9HxObcT215eEANMSPJ5COgm9TYDkAeo2e55sVwluquz2/NTKbN7TX8HxR2iC+IiGEMzjkd3avpOATQngsTaWdNMaQnj2Oav9KcvYvpamxqvNxh91Gdf6HhMTfQFNtAFW2CBIAbWQEeT61ACwMGMn4vV1n77ZGjuN17dW8VDTclS9HCHEKlAEs6TSUSMHXC/neEcIzKI02IhojuDDmZ5jHWtldspr8gp0AWNpaaav7GN+A+dTWgD1qGOa0MKCBOWFjuGnH9+P2mqMm8Heu5tXiFHVeiBBiwOzSxNcrKfh6Jd88QngUm4KxVM9kzblMnnIhBeb9bN/7Oc21VUT4f4lOfzEt48+lJngfSf5xPHFgEwDN0afzV+VK/iaFnhAeS+q93knB1xv55hHCc5lspJBB6ugx1AVVk3PgU4Kj1lFnmMrh0J28UNOEIziDpwOv5PWiJLXTCiFOkUMqvl5JwSeE8GpKs43Q5lDOi7wJS4ydCqWKpIhYPqydwRuliWrHE0I4iUPGYfVKCj4hhEdwaBVsBrAZFWw+Dqw+Dqx6BZvOgVVrx6q1YdU4sGDDihWLYsNit2GxW7DYrVisFqJCLdgTwknMO42zAoMZPaKJ0qZKaut7X9dPCOH+RuhiAWmtPxEp+HojHxaEOCWKRsHm812RpmD1cXT8qbNj09mxaO3YjhdpFmxYFRsWh7WjQDtepJmtFixWC1br8W2TzMe/TkJoqJbTJxVTo63iQNXPiGlZRZt5EuGOBqYYJtOus1AdZabSp57SlipMtVV9n1QI4VY0GlkjszdS8PVKKj4xNNn0CnajgtVwvDVNr2DTO7Dq7Mdb0xxYf1ykfVeo2aydRZrFYulYKsFKx5eLGY0aJk+pR6ddSbvVzJuB73JO9U62JVVzwVf7OBQ3hYP5f2VMyjTSGycwrNaP04nD4qtQE22m0thIaVsVlbUmHA7ZeUcId6bVyjaHvZGCrzdS7wkP4tAqnS1pNqOCVa9g1dux6h3YtA6sGjsWjQ2rxtbR3enoKNSsditm2/ECzWrFbDF3FDd2oO34l4fRaOD0iRaCg9dgtZqwO2BT6NO0mnyIctSA3oHVWEycZiLW8Os4UrmEb03LmDz+UlIZg6HeSlyRL3H4Mp5obIbR1MRYMPk2UWaupqymArtdVvUXwp1IC1/vpOATQkWK5vi4NIMDq0HB7qNg1Tuw6o5/aW1YNfbvizTFhtVhw2y3YLFZsRwv1MwWCzabreNDiuX41xA1ahQkJm6h3XyE73qBTX7n8O/GUUwymUnQ1eNrNrMk08EN/3gC06V/ocl8FZHJX5Oz8xO2aT/ljAlXkWhLR2ns2JtTb9EQU2wkBiPZRGLXZ1AXbcUU0EyZtYay2gosliH8jy6EG5CCr3dS8PVGWvjECdh9jnd3fjc27Xh3p03vwKK1dxZp1h8WaQ5r1yLteJcnoFqXpzeJj9cyJusg7e1baf/BGD+bJpDXNHehdSgcPlZHTGIt/q0N/D6wkpsT4snc9Q+2pN5Gc/MFJIwKpfRQDpu+/QCdzsD0068mri0Fpbnrpuw6m4bIMgORhDOacBzadOqjbFQFtVDuqKWktpz29nYX/wsIMbRJwdc7Kfh6o5VvHm/i0CnYDApWI52zPG16R8d4NN13RZr9+3Fpyg8mD9itnQWa2WLuGJdmB1rVflUiOFjDxIll2OxraW+3dXv8y5DnONKgMKlNw952G6H2WkKbyiFMy94zYsh6dxtZmbnsahxBdeWZJGWHULR3OXa7hXVb38Hg48f0064hunkYSmv38wNoHRrCK30Irwwlg1AUTSpNkXZMIa2UK7WU1FXQ0toy2P8UQgxpMoavd1Lw9UJjkG8etSkaBatRwW4Aq4/S0fWpdxyf4enAprVj0di7TiD4cZFmNWO2WDrGXClA+/Ev4dF8fGDKlGb0Piuw2pp7PCY/6Cd82BANgKasozoPslbh11CGIWI4bww7yksGA+GfvsSw6/5KSZkGU0kWSWODKdn/CQ67HYu1jdU5b+HrG8j0CQuIbIhGaet9/J5G0RBcpSe4Kph0goEUmsPtVIW1Ua6po6ShgsamRqf+ewgx1EnB1zsp+HqhMejUjuCxbAbH8bFp3y3F4cCqUzpmeOp+0JLGd12eVsz2rhMIzJYfLMXx3bg0aSQRKJx2mp2w8LVYLOWcaO5Eu34YfzFfhYKDUAccLOhYa8+3rRKt4iDBL4p8RylNZ2QTuG4naSv+SM2UR2lrtmEqTiI243qqjn2Itb1j1kp7ezMrN/8Lf/8QZoy/ltDaMBRz/2fuBtbqCKwNJJVAIJHWEAdVEW1U6OplLUAhnMDX11ftCG5NCr5eaIdYwWfXH+/yNByf6enj+H5sms7RMcMTO1bsHd2diq2jQLN3jEvrthSHjY4vIZxkxAgNKanbaW8/SF9zJN4L+BOmxo6CbHQz7LAr+GgVtC0mABJ9gsmnlC+yLMxfB7qqErIbV7FVOxOA2vIIwobdSEv1IlrqazvP29rawFff/J2goEjOGjufoKpgsJ78ki3+DVqSGwJIJgBIoD3AIWsBCnEKjEaj2hHcmhR8vfCELt0T7z5wfK00re14l6e9Y/cBx/HlOH4wy/O7sWl2ux0cSJencDuxsVqysg9jNm+mP3Mhdoc8wMrG7z/ttxQ3ATDCvxWNraNJMBEfAD4OymXBsHiUkjIC13/IiGsncqQ8AIDGmgB8g64jzP8z6soKu1yjqamaLzf9ldDQOKaPuYYAkx/YBj7Ty7dFy7AWP4bRdS3ACmMjZbIWoBB9kha+3knB14vB6tLtefeBjmU4vtt9wHq8SLPQMXnA6jg+Ns1m6VzY1mwxdyzFAQPafUAIdxcYqGHSZBN2+yrM5v5NY24wjuO1ljP5bpp9gl3D4dKO8XKjAlqgoeO4xOM/O4oGcqcNY+QHZR3HL3mcqgtfoL6m43rtzQb0hsuISVtN5dG93a5XX1/O55teJjIyiWmjrsS3wgj2U5/ib2jXdK4FOEHWAhSiT9LC1zsp+Hrx44LPpu9YhsN+fFyaVX+8VU3b++4DZtvxsWnHizSr1arq7gNCuDu9HiZPacVoWIHN1v/JDYpGz798HqXZ8n3BNbzeTs3xm8N9GzsLvmGt35/330n5PKPXg82Gtq2ZMYf/x+boa3EcL9xsFh2NdXMYNjqEkgMbe7x2dXURSze+SGxMGlPTL8NYoe9oMXcSWQvQ+bZt28b27dupr68HIDo6mhkzZjBixIgTPmfLli1s376dhoYG/P39yczMZM6cOej1Hb9O9+zZw6pVq7BYLIwfP57zzjuv87n19fX873//4/bbb5fiZBBIC1/vpODrjVHLoujtmC1mLBaLx+8+IIQnGD/eTkTkOiyWUmwn2YC1LuRpdjR0XU6psqCh8+/JPt//PbGpCjp6bjmmr6Ntyhj8Nu0GwLh3A2OumM7e2mGdxyuKhuryySSNDaVo7xeg9NyKV1F5lCWVz5MQP4opqZfgU64ZlDU9ZS3AUxccHMycOXMIDw8HYNeuXbz//vvccccdREdHdzt+7969rFy5kksvvZTExERqampYsmQJGo2G8847j9bWVj777DMuvfRSwsLCePfdd0lNTWXkyJEAfPHFF8yZM0eKvUHg4+ODTje0xt2fLCn4eqHT62hpbfm+21QIMWjS0jQMT/uW9vb9fU7I6EmF/3m82ZjW5b6RVg1F1d8vlhin/X4mbEJtMZqAGJTj1djysQ4u3fT9cyOXPEvsNa9SUdG1mc5UPJLErCDKDi3Gbj1xE31p2SE+LjtEctI4Jg07H135yb+mk9HTWoCNkXZMIS1UKHWyFmAPMjIyutyePXs227dvp6SkpMeCr7i4mKSkJLKzswEIDQ0lKyuL0tJSAOrq6jAajWRlZQGQmppKVVUVI0eOZO/eveh0OjIzMwf5VQ1NUkT3zf1nJahMmoiFGFxRUVrmzM0nPuG/tLfvH9A5rJoQ/qr8vNuciZiargVZpKOm8+9GWzvRfhGdt98PPYQm9vtf8hqHnZEbX8Do173VoKokjui0GzAGBPaZrbBoN4u/+RPbWYUjznVvuRpFQ0iVnhF5IZx1NIVra6eywH8WsxOmMHrYSIKDgl2WxRM4HA727duH1WolMTGxx2MSExMpKyvrUuDl5eV1dgGHh4djtVopLy+nra2N0tJSYmJiaGtrY82aNVxwwQUuez1Djfyu7pu08PXB19eX5uaeF3UVQgycv7+GyVOqcThWYjaf2tizz0L+zLGGri1xOhSOHe26tl2IrbrL7URDGJVtHffZUTg2PYXUxabOx/UlR8jOzGE7E7tds64ylKDoGzA0fUxTdWWfGY/mb+do/nYy0s9kXOQsNJWun3AhawF2V1lZyRtvvIHNZsNgMDB//nyioqJ6PDY7O5vW1lb+/e9/Ax1F4umnn85ZZ50FgJ+fH5dddhlLlizBarUybtw40tPTWbp0KZMnT6a+vp73338fu93OrFmzGD16tMtep7eTgq9vUvD1Qb6JhHAurRYmT2nDz28VNtupFxh5QTfzUUN4t/uz23Ucau5aSPqbu65tl6j1ZfsPbr+ZUsTjWi38YPmT4BVvknrdWPLLDN2u0Vzvh9H/aiISl1FTfLRfeXPzviE37xvGjJpFVsg0MKk3ZKTbWoCBDqoj26nwaaC0xURVbXWf5/B0kZGR/PznP6e9vZ0DBw6wZMkSbr755h6LvoKCAjZs2MC8efNISEigtraWr776inXr1jFzZsf6jZmZmV26bQsKCjCZTFx44YX85S9/4corryQwMJB//etfJCcnExAQ4LLX6s2kS7dvUvD1QQo+IZwne6yDmOgNmC1FOGNobKs+hVfMl9LTdFh/U/eZVYbWii63E3+0fMohn2rME0dj3Lqvy/3Jnz1B9exnaKrvPmbP3GrApr+IuJEbKD+8o9/Z9x9ay37WMn7MuWT4T4Jq9ccK+zZrGdbszzD8mThE1gLU6XSdkzbi4+MpKytjy5YtXHzxxd2OXbNmDWPHjuW0004DICYmBqvVymeffcaMGTPQaLpOGLLZbHz55Zdcfvnl1NbW4nA4SElJASAiIoKSkpJu4wjFwMjv6r5JwdcH+SYS4tSlpGgYMXI37e27OcXe2y7e8f8j1U3dCxBfBQ79qDs3wmBFY+66xEtieys/tma8jvO3dr1P21RLVslHbAm+BKWHesdu01FXNZPErFCK960+qdewa/9ydrGciWMvIt1nPEqt+6zVNBTXAlQU5YSvyWq1divqvrutKEq3x9avX09aWhpxcXGUl5d3KZbtdnvH8lzCKaSFr29S8PVBmtuFGLiICC0TJhRgtqynvd25v9y+DX2YtQ3du1kBxrXCbkvXX9qjA1vgR/VdYnMN/OgUb4cf4sLIcBzVtV3u99v+NZnXnMkBU+QJEmmoKh1P0thgivd+itJTZdiL7Xs+51vNl0wZfxkpjEbpoTVRbX2tBVhaU/79/tceYOXKlYwYMYKQkBDMZjN79+6loKCA66+/HoBPPvmEoKAg5syZA8DIkSPZvHkzcXFxnV26a9asISMjA62264Scqqoq9u/fzx133AF0dB1rNBp27NhBYGAg1dXVxMfHu/YFezFpnOmbFHx9CA6WmWxCnCxfXw1TptSCZiVmi/PXgqszTuTvTZM40QJ3jrLuLXcj/Jq6FXzDaoshtuvPuEVjp2h6OsOW/KiZD4j++CmqrniFql7G3ZmKh5Mw5joqDi/CZjm57W8UxcGWnR+zVbuUMyZcRaItHaVR/a7eE+lxLcBoG6agZspttZTWVtBudt+1AFtaWvjkk09obm7GaDQSExPD9ddfT1pax/I+DQ0NXVrtZsyYAcDq1atpamrC39+fkSNHMnv27C7nVRSFzz77jPPOOw+DoeMThY+PD5dddhlffvklNpuNCy+8UH6/OJEUfH3TKNKm3Ku9e/fy0UcfqR1DCI+g0ShMnmwmIHA1VmtN308YAIfGwHP+77Gne00HQLhDg2VVKTZH17e2J1L3c0P5U92On5YxhkZLU5f7xlti/5+9+45vus4fOP76ZqdpmnTPtKV7Q5myBFlO3Av3nXqOc+DWO8+7n+Pce57iAPfCCeIEVBQEBJG96d57ZH9/f9Qio0BHkm+Sfp+Ph4+7Nt/xTmmTdz7j/eYfj5X3WFzZnl7EioyrcNgOP4Jnim6hpeoDOluaDv+EDkOp1DBhxFnEd6Yitvlv4ncooiDKtQBlPjFz5kxGjBghdRh+TR7hOwL5E5hM1jv5+SLxCT9hs+3Em7N635keZF3zoR/PbXWz2n1wopaoaurxeIsuig0HJHxrNVU4h+WgWrPpoOM1O9ZRkLuBNbbDF9Btrg0jJPw8NPqPaa6uOOyxh+Jy2Vn6y5to1HomDD+HmLZExI7ASfy6awGaak1kYgJSaYtwURveSaXQSFlzFS2tvW+dJ5MdivxefWRywncE8i+RTHZ4lmQFOTm/Y7X+iq1vM5h9VmE4iXnNyYc9pmVPa4/fj6Ohx+8nKUPoqdzzjyP0TF7T8z3CP38Gy3nPUFoh9HzAHzpadGh0ZxKT+jU1uw9OHnvL7ujkuxWvodOFMrH4XCKbYxA7A3OzRI+1ACP2qQXYPPhqAcoGTn6vPjK508YRGI1GqUOQyfyS2SwwdVoZqamvY7X2vhxJfzkUETzjupTDpTkWl8C2yp4TvnB3z1PMFrHnl8HXIjchmE2HvFfal/cRYjzyZ2a7VUVr27Ek5ow54rFHYrW28fXPc/h097M0xzcjaAP/JTykWUHKLgNjtidyevVwLlAdw3FxYxlmySM64lAbZGSy/flLwpeamsoTTzwhdRg9kkf4jkClUmEwGGhvl9edyGQAWq3AmDFNCIpvsNsPsZDOCz4Ke4Q9zYdfN5fa6KT2EI8Z7T0XEbYcYliyQ+GgckIBcZ+v7PFxZX0lhY1fsUI95VB7R/YSXQrqq8eTXGim5PcvD39wL3R0NLPopxcwGqOYWHQOxtowcARHfbzBWAtQNjBqtRq9Xi91GH5PTvh6ISwsTE74ZIOeIMCIkXbCwr7D4ajtsR6dt2wJ+xufNB96tK1bxa5DL+7TWXtuf2bpaIZDzMy+mVXDTYe5n+HHD8maNYqtlb2bCagpyye5KIyyDfNxe6B+XWtrHQuXPYvZHM+E/LMx1Og5qKFwgBuMtQBlfeMvo3v+LvDnA3zAZDryG41MFsxyckSmTfsZvf5dHI5DjaF5R7s6g6c7j9x0PschUN5wcHcN6No9rGyv6fExS1PlIa+5QluOuzDrsPdN+OgewiN7/9m5ptRCXPYFaPQhvT7nSJqaKvl82ZN82/gW1kQ7KA+/tjCQddcCLNwWxbElOVxom8QpERMZaykmNc6CWq2WOkSZj/ky4WttbeX888/HYDAQHx/P448/zuTJk5k9e3aPx8+ZMwez2cy3334LwGOPPUZhYSEGgwGLxcLVV19NW1ubT2KXE75ekD89yAarxEQF02dsJjrmDay27ZLEME9/L43OIw8nRtceuoVHZogVwd3z1uGY5go0ip4LOAMsH3n40TuFtZ38Ta+hVPU+yWqojMSceAGG8Mhen9MbdXUlfPLj4yxtfR9bomtQvMKrnALRFRryt0UwbXcWF7ZP5HTz0UywDCc9PhWdVq7PFux8OShz4403smzZMj799FO+/vprfvjhB379tec1zA899BC33347X3311d5ajQqFgqeeeooNGzYwd+5cvvvuO2699VafxC5P6faCnPDJBhuTScGIkeU4HYuxWqWbLvvFfBc/Nh95xEYlimzf2XTIx3MMrXCI6h8K0U2iPppd7eU9Pv5q7BbGG0MRWw/9KVyz8WfysyeyrjHliLF2a6kPRW84F43+cxor9vT6vN6oqt7Bx9WPkJiYy5jUmagrhSOuMwwWCrdARJWaiKpwcghHFNLkWoBBzmw2++Q+ra2tzJ07l7feemtvAvfqq6/22DHltttu4/XXX2fp0qXk5+fv/f6+I4Gpqance++9XHnllTz33HNej19O+HpBTvhkg4VaDWPGtKJSf4PD4ZtphkOp143jf63D6E2mUmhVsKn90CN86bq2QyZ8ABZ1GLvoOeFrFqzUjh9G1KJVh40h8uOHiT/3WSore59ZdbZrUWlOJTb9O6p3/N7r83qrvHwT88s3kZI8lFFJx6E89Ox10OqxFmCkixpzB5VCI+XN1XItwAAXHh7uk/vs3LkTh8PB6NGj937PZDKRnZ2933GPPvoo7e3trFq1irS0tP0e++abb7j//vvZvHkzLS0tOJ1OrFYrHR0dhIR4bplHTwbBgP/AyQmfLPiJDB/h4OhJXyMoPsblkjbZcwk6XlDehLWHAso90VUfvn1XiqbpsI9bOPwo4vs5hz8fQBBFMpc+gi5EecRj9+W0K2lpnEZS/sQ+ndcXe0p+44OfHmQ13+GOk1/2Q+uVpO0wMn57MmfXjuI8zWSmxx9FYVIOESbfJA8yz/HVCF9vTZw4EZfLxXvvvbff93fv3s1JJ51EUVERH374IatXr+bZZ58FwG4/9AdWT5FH+HpBTvhkwSwrC1JSV2G1bsYHrzm98rXpITYeppvGvgwibNrZc1HlbvFC02EftzgP371isX43V2anIWzZedjjVBU7Kcz9iZX0reaeKArUVYwiuchEye8Lemzp5gnbd61k+66V5GSOpyhyEkK1vLsVIKRFSUqLgRQMQCLWUDd1UVaq1M2Ut9dQ29BzSR+Zf/DVCF9aWhpqtZqVK1eSnNxVAL65uZmtW7fu7bMMMHr0aK655hqOO+44VCoVN998MwCrV6/G7Xbz6KOPolB0ffA6MCn0Jjnh6wWTyYRCoZBrP8mCSlycQGHhVqy25Vj9qL99WejpvNmc2OvjCztg7RFq0EWLh+/ra+k48pTer2MiGXGEhA/A+O080s8bxo4K7RGPPVBNaRaWAiMVmz/A5cX+dJu3LWPztmUU5E4mP2w81AROuzZfOKgWoN5NfbRdrgXoh5RKpc8aJBiNRi6++GJuueUWIiIiiImJ4d///jcKhQJB2H/T1rhx41i4cCHHH388KpWK2bNnk5GRgcPh4Omnn2bmzJksW7aMF154wSexg5zw9YpSqSQyMpLaWt+WoxgMnnjiCZqbDx7KGTlyJCeeeOJB39+0aRM//PADDQ0NuN1uIiIiGDt2LEOHDt17zE8//cSyZcsAGD9+POPGjdv7WFlZGQsXLuSyyy7b+wlrsAkNFRg1uhqX6zusNi82ve0HmyKKpxwX4qb3b6aOsiMvwDc7Dz9Ck9RaA4bDX2NO/BZG6vWInT2XftmX5dO7qZ3+EC2Nff/51pbFE5N+IQ2l72Fr9+7U+vpNS1jPEoblzyA7ZBTUyYlfTzSdioNrAcbYqNZ31QKsrK+WawFKxGw2H5RsedNjjz3GlVdeyUknnURYWBi33norpaWl6HQH7wafMGECCxYs4IQTTkCpVHLttdfy2GOP8eCDD3LHHXdw9NFHc//993PRRRf5JHZBFL00dxBk3n//fTZs6Knjpmwg2tvb2fdXsKamhtdff52LL76Y1NTUg47fvXs3nZ2dREVFoVQq2bp1K1999RXnnXceGRkZVFdXM2fOHM477zxEUeTtt9/msssuIzY2FrfbzUsvvcRJJ51EYmLvR5CChUoFo8e0odF8i8vln4vU3zLNZUFLaK+Pj3ZDx7cVuI6w1m9zwt3oGjYf8nG7UsvI5DjEI2wQeXHVUMxfr+5VbNbh01huPh13L9chHig0vBNby3xa63ouGO0NI4tOIkM9DLHBvz4I+DunSqQxxkGNoY1KRx3l9VU4vDhCK/tTRkYGF1xwgWT3b29vJzExkUcffZRLL71Usjh6Qx7h66WYmBg54fMCg2H/YZUff/yR8PBwUlJ6Lm9xYBJ41FFH8dtvv1FSUkJGRgZ1dXXExsYyZMgQAGJjY/d+b9myZSQnJw/KZK+42EVE5FLs9nL8dSBiY9g1fUr2ALKbRVb1IqHSdBw+adK4bMToI6nuPPxI4Ee5bfzl697Fpvv1G3LPGs+G2pjenXCAtkY92pCzibJ8QV2pb2ogrlr3OauFhYwZdiqp5CE2yUlLb3TXAowmgnwicCsyaYpxUmNso9LZQHlDFVabH62bCCKxsbE+vd+aNWvYvHkzo0ePprm5mbvvvhuAU045xadx9MfgnNPqh+joaKlDCHoul4t169ZRXFzcqyF6URTZuXMn9fX1exPEmJgY6uvraW5upqmpifr6emJiYmhoaGDt2rVMmTLF20/Dr6Snw/QZvxJqfAu7veeyI/6gTZPDM51T+3xeY8mRRyqNKicKa+MRj7Nojrzw+wvDDkjrfa29mPn3ERPbt127+7J1qOm0nkh81oh+X6OvRNHN8jXzee+3+ymL2I0QJo8L9FV3LcCcbeEcsyud81vGcZZxEpOSRpKVmI7By+U3BhNfJ3wAjzzyCEOHDmXatGm0t7fzww8/EBUV5fM4+kr+S+6lmJj+fUqX9d7mzZuxWq0MGzbssMdZrVYee+wxXC4XgiBw4oknkp6eDnQl5lOnTuX1118HYOrUqURHRzNv3jymT5/Ojh07WLJkCUqlkuOOO+6QI4mBLjpaYOiwHdhsy/xqQ0ZPRARe0d5Nc1vfFsGnOgV2VB15jVuBsR2OvOwOi0LH4Svtdfl9bAyFO3tXKFlwOcle8TSN2dfgsPVvkb/LqaSx9mgsBSZK13/Xr2v0h9vtYtnqd1EqNUwYcRbxnamIbfIav/7oqRZga4SL2nC5FuBA+TrhKy4uZvXq3i3r8DdywtdLERERKJVKeWGuF61Zs4bMzMwj7rjSarVceeWV2O12du7cyZdffkl4ePje6d6RI0cycuTIvcevXbsWrVZLUlISzzzzDJdffjktLS188MEHXH/99ahUwfNnYDAIjBpdi9v9LTabn9RYOYKfzXezornvo2CWBidVvTguS9/Wu4TP1bu1dnMSt/OURoPYyxo26t0bKMxbx68U9Or4ngnUlg8juchE6e+fIIq+2yHqctlZ+subaNR6Jgw/h5i2RMQOOfEbKGODEmODkTSMQDIdYS5qI61UKZsob62mofnIo9KDnVKpDIiRNX8RPO90XqZQKIiKiqK62ncLqAeTpqYmdu7cydlnn33EYwVBICIiAoC4uDjq6ur48ccfe9zk0dHRwdKlS/nLX/5CeXk5kZGRe/9zu93U19dLMiXgaUoljB7TiU73DU5nk9Th9Fqd/mjmtOTT175fgihSuqupV8emans3cmKxdvTquEplKy3jCjEuWdOr4wHMC58n5byn2FPR/+ldgJrSISTknU/1tvdw2m0DulZf2R2dfLfiNXS6UCYWn0tkUwyihG33go1cC7DvujfvyXpHXsPXB/K0rvesXbsWg8FAVlZWn88VRRHnIQrnfvnllxx11FGEhYXhdrv3q53ldrsJhk3qRUVujjlmCSrVBwGV7LmEEJ4TZmPrx79BnkNJVVPv5qotqqbeHdd2+Fp9+1pQ0PdkK3XRfRg8sB6uviKayNSL0IeZB3yt/rBa2/j65zl8VvI8zfHNCFr5bcQbdG0KknaHMHJbPKdUDOUi4RhOiBnHCEsB8VGxPi1F4q+C4cO6L8kjfH0gb9zwDlEUWbt2LUOHDj2oNt5HH32E0Whk2rRpAPzwww8kJCQQERGB0+lk27ZtrFu3rseafTt27KC+vp5TTz0VgMTEROrq6ti2bRstLS0IgkBkZKTXn5+3pKYKZGatxWpdR4DM3u7nC9MjbGnuX8IdXtv7hCuWw3fh6JbUUApxveuqM9+4lVmWRMTS3m+EUTZUU1j3Bcu10/s6oHmQ5lojIeHnodV/SlN12cAu1k/t7Y0s+ukFjGHRTCw8B2OtEY5QAFvWf5pOBQklehLQU0wsTo046GsByglf38gJXx/II3zesXPnTpqbmykuLj7osebm5v0+yTocDhYuXEhLSwsqlYqoqChOO+00Cgr2Xx/lcDj44osvOPPMM/eeHxYWxvHHH88nn3yCSqXi1FNPRa0+fA9VfxQZKVBcvBub/Qes1sAcodwTei7vNPfvxVojwrYdvV/fFOnu3cidqbOJME0iLfbWXh2/aXwiOe/0bedzyE8fk3PuaDZXmfp0Xk86WnRodKcTk/o1Nbs3Dfh6/dXaUsvCZc9gNsczIf9sDDV6cAbm72UgUdkFYst0xKKjiGicqpxBVwtQTvj6Ri683Af19fU8/fTTUochG6R0OhhzVCPwNW63b9dveZJVGc+dqueotPdvNGhEp8CG73s/qvV78mMYa3qz/xbOHTqJDS27enVsitPMw483wRH68B5I1Oj47ZQnaaj1zMYHQekmIno55ZuWe+R6AxUVlcz4nDPQVWmhlxthZJ7nVog0xzipNrZT6awPylqAN910k8/aqgUDeYSvD8LDw1GpVIdcLyaTeYMgiIweYyMk5Fuczt5NT/qzd0MforKl/1N/6spebLndh95a0+tjLcoQeltefY+qiY6j8gn58bc+xSPYreT9Pofllr/i9MAUqOhSUF81juQiMyXrFg34egNVV1fCJz8+TlxsOmMzTkNTpaQPnfI8at6aj3l9zceUNXft586KGsLscRdzTPpRhzxnzsr3eH3tJ5S3VBOhN3FC9mRun/Q3dKqu3sgfbfiK+5f+jw57J2cVnsC/p16z99zS5krOf/cmFlz8EkbtEXr1eZnCLRBepSa8ykwOZkQhjZYoFzWmdirFRsobK2nv6N1GJX9kMBjkZK+P5ISvDxQKBdHR0VRWVkodimyQKChwExe/DJttd18HkvzS72E38FVL/4vOGkXYtKtv5SqU7b3fWW9x920DwpdFbk77sU+nAKDZvJL87KP5rTmt7ycfQk1pHpaiMMo3zMftkv6Xpap6Bx9VP0JiYi5jUmeirhQGvHaxr+KN0dwx6QqGhCchAu+vX8Sl8//BF5e8THb0kIOO/2jj1zyw9EUePuE2RiYWsLOhlBsX3o+AwL+nXkNDRxO3LHqIx064g2RzApd8cBvjU4YzLaOrX/c/v3qMOyZdIXmy15NgqwUoT+f2nZzw9VFCQoKc8Mm8zpIskJPzO1brGmyBO3u7nxZtAc92HM1AhnsK2mCNs/fnp4VYEVy9/wFa+ljq5D3TZk6Pj0Ws7Hu5pshPHiVx1nOUV3ouC6otTSIu6wLqdr+HvdM/Rm/Kyzcxv3wTKclDGZV0HEofvnxOzxi/39e3HX05r6/5mDUVG3pM+FaXr2dkUgGn5U0HwGKK55Tcqayt6FojuaepgjBtKCfndnWFGZtczPb6PUzLGMfHG79BpVBxfPYkLz8rzwnkWoBywtd38n76PrJYLFKHIAti4eEKpk4rIzX1DazW3td583dulMxR/5tW18Dm9qxlR+6ssa8cQ9+OT2pv6tPxLkR2jO9/t5b0JQ+hM3j2c3dDVQSmhAsxhPtXQdo9Jb/xwU8PsprvcMf5/q3H5XbxycZv6XRYGZ7YcxHsEYkF/F61lTUVG4GuBG/xjuV7p4CHRFjodFhZX72Vxs4W1lVuJic6nSZrK4/88DL3Tp/tq6fjFSEtSlJ2GRizPZHTq4dzgeoYjo0fyzBLHtER/vX7JCd8fSeP8PWRnPDJvEGrhTFjmhEU32C3+8fIjCf9aL6X1c0De5OPcwtsLm3u0zkZ+hbo3aZbACzNVRDetzhfTS3hHqUS+lESQ1W5m6KO7/mFcX0+93BaGwzoDOcSEfI5DeW7PXrtgdq+ayXbd60kJ3M8RZGTEKq9W0pkU+0OTn39amxOOwaNnpdOu5esqNQejz0tbzqNHc2c8eY1iIg43S4uGHYK1469EACzzshjJ/6D2Z/fh9Vp54yCY5mcNpqbFz7AJcNPo6S5kr9++A8cbic3jv8LJ+ZM9upz8zZdmwJLWwgWQhhJPHa9m7poG9XaFio6a6mqr5Gslqmc8PWdnPD1UWRkJAaDgfb2dqlDkQUBQYCRo2wYjYtxOGrxYccsn6nRT+WVlmwGuoArs9nNyj5eIkXdtzVJMc0VaCLTsLt7X9hwi7oO28g8tCt+71twfwhd/CYZ5xWzvULfr/MPxdquQaU+hbiMxVRtX+fRa3vC5m3L2LxtGQW5x5AfNg5qvLPuMD0imUV/eZlWWzsLtyzhhgX/5f3znu4x6fu5ZA3PLH+D+2bcyLCEXHY3lvOfb57iiWVzmT3+YgCOzzqa47OO3uectWyq3ck902cz4cVZPDPz38QYIpg57wrGWIYSZQj3yvOSwoG1AB1akYZo39cCVKvVcpm0fpCndPshKSlJ6hBkQSA3V2TqtJ/R6d7D4aiVOhyvcApGnuXvODwwClC7u2+jewAJiqY+Ha8Q3STq+15g/bthA3spTfrkbkwRnq8J6XQoaWqYiiX/6CMfLJH1mxbz7or72GJYC1GeH4PQKNUMCU+iKC6b2yddQV5MBq+ser/HYx/+4WVOz5/BrKEnkRudzvFZR3PbpL/x7PI3cPfwaczmtHPnV4/xwLE3sbuxHJfbxdjkYaRHJjMkwsKayo0efz7+RG3rqgVYtC2a40pyudA2iVMiJjLWMoyUOIvX6pwmJibKLdX6QU74+iE5OVnqEGQBLDFRwfQZm4mKfgObbbvU4XjV56ZH2O6B4tDpToHdNX0fVY+m9+3SulnUveu2sa83wzejiOp/1xZFewv5O99GofRCuyxRoLZiJMlFM7uGlP3U2vVf8u6q/7LDuAHBC8lvN7foxubquSCx1WFFccDPSCF0vU32NHX51E/zmJQ2msK4bFyiC6f7z9Etp9uJyx2EQ/aHoXIKRFdoyN8WyfTdWVzYMZHTzUczwTKC9PhUdFqdR+4jL63qH3lKtx/kXzZZf5hMCkaOLMfhWIx1EDSd32W8kPebPbPQO6HeQd96WnQJd/a94byFvicbdsHFnonpWD7qe4LZTbd2MXlnjmd9XXy/r3E4NaWZWApmUbH5fVz+2oFBFFm17nNWCwsZM+xUUslDbOp/rA8s/R+T08aQGBZLm72DTzZ+w88la3nj7EcAmP35fcQZo7h90hUATMsYx0sr3yM/JoviP6Z0H/nhZaZljEOp2H9EaWvdbj7b/B2LLnkZgPSIFBSCgnd++5zo0Eh21JcwND6n37EHA4VLIKJKTYSHawHKgy79Iyd8/ZCQkIBSqRx0fQtl/aPRwJgxLShV32B3DI61n52qZJ6ynY4nKu4qRJE9O5v6dW6Ive9T5ZZ+Fjycm1bOnYIAA5i+jp5/P7FnP0t1lXdeW2rL4ohJu4jG8vewtvVhN4uPiaKb5Wvm84viE8YOPxOLIwOxpe//LnXtjdzw+X+paa/HqDWQG53OG2c/wtFDRgFQ3lK9X+vG68ZdhIDAwz/Moaqtlki9mWkZ47j16MsPiE/k9kUPc9eUawjRdK291Ku1PHbCHdz59ePYXQ7umT6beKPcf31fnqgFKAiCPOjST3JrtX6aM2cOZWXSNC2XBQqRESOcmM1LsDuqpA7Gp14xvcm3LZ6Zvim0Kdi2pLRf5+4Mvw5FZ99G+b5PH8ff3f37237zqyzUqwe2bsuZnMPyvNnYvTgKbDB34midT0td3+sHSkGl0jB++NnEd6YgtklfVFrmPR0mF7WRnVQpmilrrabxgFqAMTExXH311RJFF9jkEb5+slgscsInO6SsLEhJXYnVugW7n86eecsa020eS/YAwmr61//ToHQjdPZ9ijWptQb62Sjhh+Fapqzu37ndVCWbKcz7ldUMHdiFDqO9SY9GfzZRyYuoK9nmtft4itNpZ+kvb6BR65kw/Bxi2hIRO+TELxiFNCtJaQ4lhVDGkIg11E1ttJVqVTPl7TWkpPS/7uVgJ2/a6Cd5DYGsJ/HxCqbP2EZs3OtYrVukDsfnmrXFPN8+xmPX04mwdWf/qv3nhrYj9KMUTFJ96d6F+n31WuQmhHBzv87dl2nRi6QmeDehsXeq6eg8kYTskV69jyfZHZ18t+I1Pt7xJA1x9Qg6eadmsNO1KbDsCmHktnhOqRjK5MRRUocUsOSEr5/kNQSyfRnDBKZMqSYz602s1uVShyMJUVDxovqftLs8t0qksFOg1dq/xCc7pH9r1DQuGzG6/u24tQpOKiZk9uvcA6UuuIdQk3cnYdxOBfU1E7EUTvXqfTzNam3j65/n8FnJ8zTHNyNo5LeywUKXbpY6hIAl/5X0U2hoKOHhwVNQU9Y/KhWMH9/GiBEf4XB+hSgO3mmmxaYHWNvu2bIfior+dx1J0/a/EbxFY+73uW9m1vT73H0pmusorPzU65VUBARqy4aSXHQagiKw3hLa2xtZ9NMLLKicQ2tCO6gDK35Z36ii9SiNGqnDCFjyX8cAyNO6g1txsZNJk79FofwIl8t/dzz6QpXheOa2HNyMfiDMbti4u//N25NUfS/UvPdcRf/XIP6iLcdVmN3v8/el/2UBOTG+aWBfUzqExNzzUXuoVpovtbbUsnDZM3xZ8xodCVZQ+W+9QVn/aeXRvQGRE74BSEtLkzoEmQQyMgSmz1hNqPFt7PYKqcORnEMRztPuv+H08H7/vDZwDGB6OE7R0O9zLQOcll4+KnRA5+8r9qN7iIz2zf66uopoIpIvJCTM7JP7eVpTUyWfLXuSbxvfwproAG8UspZJRptmkjqEgCYnfAOQnp4udQgyH4qJUTBt+g7iE+ZhtQZ3y6S++CTsYXZbPd9RoL10YKOmke4BJHzW/k8lA7wSvRnBaBzQNbop7DZy1/4PlY/WqTXXGdGZz8ccG7gtJOvqSvjkx8dY2vo+9gSX/E4XJOSEb2DkP4MBCA0NJT7eO1XxZf7DYBA45pg6cnLfwmb7Sepw/Mr2sL/yUbPn17ImuAW2lvd/DR5A2AD6E1va+t8xA6BVYaNmoue6LGi2/UqBzne7vjtatbgVpxMzJNdn9/SGquodfLTsEX60foojQQR5wC9gqWJDUIbK6/cGQk74Bkge5QteSiWMHdfB6DGf4HR9gSgOsoJ6R9ChHsJT1pleuXZ6o2sgDSsA0Fv7v3kiqaF/hZ739V5W/0cYexLx6RMkJXj0kodlt6pobTmWxNyxvrupl5SXb2L+sodY7lyEK17O+gKRLsMsdQgBT074BigjI0PqEGReUDTUxTHHLEGl+hCns/+L/4PZ6/r/Uu/wTnP46t0D/5mr2vvfRcLU2USYZmBTskv1exBzPfuBMP3r+9GH+q5evuhWUF81luSi43x2T2/aU/IbH/z0AKv5Dnec/PYXSHR5/SuVJPuT/Bs/QBaLBY1GHmYOFkOGCEyfsRaT6S1s9oGP8gSrVeY7+b7VO7/3WQ6BkrqBraFL0tkQnJ0DuoZFFzWg8wFWjfbsdLeytozClm89es3eqCnNw1J0NgplcDRn2r5rJe//fD+/qX5EjJWLN/s7RYgKbaq8fm+g5IRvgJRKpbxbNwhERSmYNn0PSZbXsVp/lzocv9aoG8MLrcO9dv3Y+oFPneeFtg/4GhZlyICvMSduC0LIwK+zr9Dv3yMzfuDPr69qS5OIy74Ajd6zz0dKm7ct473l/2WD7heICY5kNhjpssIR5B3XA+ZXCd/kyZOZPXu21GH0WVZWltQhyPpJHyIwaXID+QXvYLN9D/1oxTWYuAQdLyhvpdPtnZ+TEpFdO5sGfJ1M/cA2fABY3AN/eWxUdNIw3vMbHxI/vhtzpNrj1z2ShsoITAkXEhox8NFPf7J+02LeXXEfWwxrIcr3P1fZ4cnTuZ7hVwlfoMrM9EwrJZnvKBQw5qhOjjrqM9zuBbjdNqlDCgjfme5n/cBmWw+rwKqkrnXg/xYp6oGvAbTYPfM78VFum0eusy9FZxv5W19HIcGoR2uDAVXIuUQkerbQtj9Yu/5L3l11HzvDNiJEyImfX1AK6LLlrlaeICd8HmA0GklI8OH2OdmAFBSKTJn6AxrNBzidvuliEAzKDafwerN3u8sYaga27q5bgrJpwNdIah/4NQAWGXZAeopHrrUv7e8/kG+SZp2ptV2DzXEycRlDJbm/V4kiK3/7jHd//S97zNsQzHLiJyVtuhmFVp5u9wS/TvgWLFiAyWTizTffZMmSJYwePRqDwYDZbGb8+PHs2bMHgP/85z8MGzaM//3vf1gsFkJCQjj77LNpbvbd7kp5Wtf/JacITJ/xO+Hhb2Cz7ZY6nIBiV0TxtOsSXF68h06EzTs8k4DHMPCSKJbmSg9E0mXd2BiPXWtfUR8/RFycNJsOXA4lTQ1TsOQfLcn9vU0U3SxfM5/3frufssjdCGFy0iEFfW6E1CEEDb9N+N566y1mzZrFm2++yTnnnMOpp57KpEmTWLduHT///DN/+9vfEPbpKr59+3bee+89PvvsMxYtWsSaNWu4+uqrfRZvdrZnemfKPC8iQmDqtFJSUt7Aal0rdTgBab7xYUpt3inB0q2oQ6DD7pmUMtw1sMLJALFNFWgUntmJPCdhG4JW65Fr7Utwu8ha9jgavUQ7TUWB2oqRJBedDEJwLqp3u10sW/UuH2x4mKrocgQflsWRyev3PMkvE75nn32Wq6++ms8++4yTTjqJlpYWmpubOemkk0hPTyc3N5eLL76Y5OQ/p5esVivz5s1j2LBhHH300Tz99NO88847VFVV+STm+Ph4jB5qpSTzDK0Wjp7URGHR+9jtSwDvJizBanPYlXzWEub1+4jlntt5Gmrvf9HlbgIiifpoD0QDVco2msfle+RaB1KVbqHI9YtXrt1bNaUZJOXPQqkO3hJVTqedpb+8wfzNj1EXW40QIpdz8TZ1Yigqk+c/KA1WfpfwffDBB9xwww18/fXXTJo0CYCIiAguueQSjj32WGbOnMmTTz5JZeX+0y3JyckkJibu/Xrs2LG43W62bPFdO6Lc3MBuQxQsBAFGj7YzfsIXiOJnuN2eWRc2GLWrs3im81iv3yfCLbBpT5PHrqfp6H/R5X1Z1J5LdD8r8N7vYdhXrzAkQdpOMHXlcUSnXYgu1PsfDqRkd3Ty7fLX+HjHUzTENSDo5MTPW+TpXM/yu4SvuLiY6OhoXnnlFcR9eiu9+uqr/Pzzz4wbN453332XrKwsli9fLmGkByssLJQ6hEEvL09k6rSf0OrexeGokzqcgCYi8JruHhqd3h8ZzW0VcXqo1ItW4UbROfApXQALnluw/0noNoSUJI9d70Apn99DqEnaDQZNNSYMUecTFh0naRy+YLW28fXPL/FZyfM0xzcjaPzu7TTgydO5nuV3ixHS09N59NFHmTx5MkqlkmeeeWbvY8XFxRQXF3PHHXcwduxY3nrrLY466igASkpKqKio2Ltbdvny5SgUCp+urbNYLJjNZpqamnx2T1mXpCQFuXnrsVpXY5MrrHjECvO/+anZNy8RLSUDr5vXLTe0A8HumSTV4nR65DrdNo5LIHdPmUev2U3RUk9hxXyWG2ciSrh6ob1Zj0Z/FlHJi6gr2Taga/20fQ8/79hDQ3vX6GicKZRpeZnkxh96E8z3W3fx8449NHZ0YtBoKEqK54SibNTKrpG4X/eUs2DdZuxOJ6OGWDh5WN7ecxvaO3hx6S/Mnj4enbp3yXN7eyOLfnoBY1g0EwvPwVhrBC+1HBxMlGYtmoRQqcMIKn75kSQrK4vFixfz4YcfMnv2bHbt2sUdd9zBzz//zJ49e/jqq6/Ytm3bflOoOp2Oiy++mN9++40ffviB6667jrPPPpu4ON9+0pRH+XzLZBKYOrWCtLQ3sFpXSx1O0KjXj+el1iKf3MviEthW0eqx62UbPFf3ztLhuUQU4GXLTuhlItEf+pWLyI2SfmTb3qmmo/NEErJHDug6phAdJxTlMHv6BGZPH09GTCSvLVtFVXPPvy+/7iln4brNTM/L5NbjJnH2qCJ+K63gi9+7lva02+y8t2odM4fmcvmkMazeU87Gij+n/+evXs+JRdm9Tvb21dpSy8Jlz/BlzWt0JFhBFZybWHxFJ0/nepzfjfB1y87O5rvvvmPy5Mk0NzfT1NTE3Llzqa+vJz4+nr///e9cccUVe4/PyMjg9NNP54QTTqChoYGTTjqJ5557zudxFxYW8sMPP/j8voONRgNjjmpBqfgau8OLlYAHIZeg5znhJqxe6qZxoNRGJ7UevF661nNJWlJrDRg8djlKlE10jMkn5MffPHfRA8TMv4+a05+mrsazo5N95XYqqK+ZiKXQTOnv3/TrGvkJsft9fXxhDj/tKGFPfSNxpoM3ye2ubyQ1KpzhKV3ruSMMIQxLTqCkoQmA+rYO9Go1w5K7ZoIyYiKpbmkjLyGWNSXlKBUKCpPi+xVrt6amSj5b9iRRUcmMzz4TXbUGXHIHn77SFwRXNxd/4FcJ35IlS/b7Ojc3l+rq3i++vuqqq7jqqqs8HFXfxMTEEBsb26e4ZX0hMnKkkzDTYhyOalzyzInHfWV6iM3NvnuDqtjl2XqZFlWTx66VVF+KIjQetwfnSBcVuTj9R49d7iAKp53c1c/zc/oVOD00td1fAgK1ZUUkF5koXf8Rorv/8bjdIr+VVWJ3ukiJ7LnzQmpkOL/uKaekvonkSDP1bR1srqxhxB9rJ6OMBuxOF+WNzYSH6CltaGL0EAsddgeL1m/lqslH9Tu+A9XVlfBJ3WPExWUwNuNUNJVKuVBALylNWrRpJqnDCDp+lfAFi8LCQjnh84LsbEhO/gWrbSsOaTckBq2S0DN5q9l3XWNyHAp2N3h292qs0OSxa2lcNmJ0kVR1em4M8t2wzZyREIdY4b2SUeodaynM3cgae47X7tEXNaUpJOSeT83293HYrH06t7Kphae/+wmny41GpeSS8SN6HN0DGJ6SSLvNzrOLf0IUwS2KjE1PZmpeBgAhGjXnjh7K27/8hsPlYkRKEtlx0by38jfGZ6RS397BKz+uwuV2MyM/i6GWgY32AVRVbeejqkdISsxjdOpJqCsFuWX3EYQUR+9XZ1fmGXLC5wUFBQV8803/pjBkB4uPFygo3ILVugKrvCHDa2zKWJ52nI/bh8MQUbU2dnv6mqJnduh2s2jMHk34RAG2j08m/X3v1ggN//xpLOc9Q2mFf7xx1ldEE5F8Ea0179PR3PuOKtHGUG6cPhGrw8m6skre+eU3rpp8VI9J3/aaer7bvIPThxeQHGGmrq2DT9Zu4OsN25ie39XzvDApjsKkP9d276ipp6KplVOLC3hg4WLOP6oYo07LU98uIy06AqPOM3Xgyso3Ula+kdTkoYxMOh5lpZz1HUpIsXc60wx2frlpo6/+85//sHbtWqnD2MtsNu9XFFrWP8YwgSlTq8nMegurdYXU4QS990IfpsKHU4AqUWTHziaPX9fk8OSKQLAodB69HsArqXtA6f36bWlf3keI0X8+1zfXhaI1zcIc1/vyNCqlgiijgaQIEycU5ZBgMvLjtt09Hvvl+i0MT0lkTFoy8eYwCpPiOL4wm+82b8ctHpxgOV0u5v+6njNHFlLX1o5LFEmPiSQmLJSoUMPetX+etLvkNz746QF+FRbjjguKt2CPUicYUMd6cOGsbC/5t81L5N26/adSwfgJbYwY8REOx1eIorSLzweDDabrWNTi2xfZQquCxna7x6+rtw28y8a+LF5YcL9NVY9tVN6RDxwgZX0lhY1fgX8M8gHQ2arDxRnEpPXv+bsB5yHWAtpdroOeqqJ7arCHf8ZvNm4nOy6apHAToijulxS6RXG/WrCetm3nL7z/8/2sUy9DjJWLN3eTR/e8R074vCQ/Px+FQv7x9o1I8XAnkyZ/g0LxES6X50p1yA6tVZPLs+3H+Py++uq+reXqLbWHumx0S7J6ruXbvr4Z5pvXB8OPH5IV519/Sw6bktbmGSTljj3scQvXbWZHbT0N7R1UNrWwcN1mdtbUM/yPXbZvr1jLwnWb9x6fFx/LzztKWFNSQX1bB1uralm0fit5CbEoFPunglXNrawtreTYgiwAYoyhCMCKnSVsrKimpqUNS7jZo8+7J5u2/sh7y//LBt0vEO0/o7GSUEDIMDnh85ZB/tvlPSEhIaSnp7Nt28AKjw4WGRkwJG01Vusm7J4f9JEdghslr2j/j+Y2324fDBFh087er+PqrTitHcHu2QTN0tYAXmgR+6Z5EyfFRCHWeL9uXsJH91B74mM01vvPaLnoVlBXNZbkIjMl677o8Zg2m413VvxGi9WGTq0iwWTk8qNHkxXX1eO4saNzv8X90/IyEARYtH4LzZ1WQrUa8uJjOb5w/wL8oijywerfOXlYLlpV19ugWqXk3NFDmf/rBlxuN6cNz8cU4vnp/ENZv2kx61nMsIJjydaPgrrBtzNNlxmO0hi8/ZilJojeHLMe5NatW8f8+fOlDsOvxcYKFBVtx2r7SepQBqUfzffxfLPvd3Ie1QZrl5V7/LpTIht5pf3vHr1ms97MhDjv9Id9eMtwUub/4pVrH8ieN5afEy7E5fS/l/xoSznlGz7E7fKfhFRSgsCoopNIVw1FbBg8iV/E+TmEFEZLHUbQkuccvSgnJwet1jM7vIKNwSBwzDG1ZOe8LSd7EqnVH8PLLblHPtALHOXemSbN1Ht+6tLU2USYpucyIAM1N60cfFR+QrPxZ/KNu31yr76qLU0kNutCNPoQqUPxD6LIyt8+491f/8se8zYEs7Q9kn1BYVChz5V753qTnPB5kUajYejQoVKH4VdUKhg3roPRoz/G6VqEKA6eT6/+xCmE8qxwLXYJBvij3AKbSzxbbLlbqsY717XovFP1f726Gsdw3yXdkR8/THy8H+3g2EdjVTimhAsJjZBHeLqJopvla+bz/roHKI/cgxAWvKuwQobFIKjklMSb5J+ul40aNUrqEPzG0KEuJk1ejFL1IU6XZ3uUyvrmC9PDbOuUZmovp8WNy0tt2xKVTV65rkXpvZGn74f7bhZAEEUylz6CLsQ/d4W2NhhQhZxDROIQqUPxKy6Xkx9XvcMHGx6mKroCITT4Ej/DKN/2vR+M5ITPy6KjoxkyZHC/eKWlCUyfsZYw01vY7WVShzPo7Q49j3ebpdsJ17jHe8l+DA1eua7F7b2XyrmRmxB8sBu0m6piJ4V+vIzC2q7B5jiZuAx5duRATqedpb+8zvzNj1EXW43gp4l7X6mTQlHH+a4slNvt5v7772fIkCHo9XqGDh3KBx98sPfx9evXc/zxxxMaGkpsbCwXXnghdXVdm6vmzZtHZGQkNtv+XQBOPfVULrzwQp89h/6QEz4fGKyjfFFRAtOm7yYx6XWs1t+lDkcGWJWJPOU4S7LOTqlOgR1VbV67frjLs102ulns3mvxYhWclE/M8tr1e2L8dh7pCf7btsblUNLUMAVL/iSpQ/FLdkcn3y5/jY93PkNDXAOCLrATP8NI347u3X///cybN48XXniBDRs2cMMNN3DBBRewdOlSmpqamDJlCsXFxaxatYpFixZRXV3N2WefDcBZZ52Fy+Xi008/3Xu9mpoaFixYwF//+lefPo++knfp+oDb7eaJJ56gpWVwTGOGhAiMHl2HW/wGUfTvGivnn1dCdfXBOwNPPjmM664/eN3WggUtfP1VG7t3dz2vzCwtl14aTk7On+Ub3nuviffebQLgnHPMnHW2ee9jmzZZeerJOp55NhGl0vdrqV41vck3Lb4rNXGgo2td/PKr91qKbUx6kJC63zx+3ZUpI/mrwrMFnfc1ypbALY+VeO36PXGHmvl1+kO0NPr3OtoYy3ZKfv8M5LeqQzIYwpk49FzM9WZEH3bL8QRBpyT+jjEotL5JWm02GxEREXzzzTeMHftnHcjLLruMjo4O8vLy+OGHH/jyyy/3PlZWVobFYmHLli1kZWVx9dVXs3v3bhYuXAjAY489xrPPPsv27dv9ugdw8C0E8EMKhYIRI0awePFiqUPxKoUCRo/uRB/yLU6n52usecOzzyXi3mc92a5ddm67tYqjJ/U8vfDbb1aOmWIgPz8SjUbg3XeauO3WKl5+OYmoaBU7d9iY+1oj994XhyjCnf+sYsTIENLSNLhcIk88UccNN0RJkuytM90kabIniCKlO72zqaKbttOzRZe7WZorIdx7b0grtRW4hmaj/G2L1+5xIEVbEwV73mO5+fT9/gb8TU1pBkn5s6jc8gEuh39/gJRKe3sji356HmNYNBMLz8FYawRHYCR+htFxPkv2ALZv305HRwfTp0/f7/t2u53i4mIcDgeLFy8mNDT0oHN37NhBVlYWl19+OaNGjaK8vJzExERee+01LrnkEr9O9kBO+HxmxIgRLF26FPchWgIFusJCF7Fxy7DZ9uAMoFJaZvP+LzTvvN1EQoKKoUN7Toz+8Y/9177deFM0P/ywm1/XdDJjhpGSUgdD0jQUF+sBSEvTUFpqJy1Nw7vvNlFUqNtvNNBXmrVDebZ9PD32l/KRPIeSHU2dXru+WiGi6PBsH91usU0VaKMysLm8Nw3608hQJnp+cPKwdL9+Q+5Z49lQ69/dDerK44hOu5Cm8vextg2OmZL+aG2pZeGyZzCb45mYfzYhNXrww7qLeykgdFyCT2/Z1ta1pGTBggUkJibu95hWq+Vvf/sbM2fO5MEHHzzo3Pj4eACKi4sZOnQo8+bNY8aMGWzYsIEFCxZ4P/gBkhM+HwkNDSUvL4/169dLHYpHpaQIZGWvw2pdi81/lwT1isMh8s03bZx5pqnXn9RsNhGnE8KMXcthhwzRUF7m+GOaWKSszEFqqoaKCgdfLmrj+RcSD39BLxAFFS+r/0WbXdoX/vBa7/6CZBk6EBwur1xbQCRRH83ONu9tOno1ehNHh4Uh+njpR8z8+6g58xlqq73zs/OUphoThqjz0eg/pqW2Uupw/FpTUyWfLXuSqKhkxmefia5aA17oCT1Q+oIoVGbffgDOy8tDq9VSUlLCpEkHrxEdPnw4H374IampqahUh06RLrvsMp544gnKy8uZNm0aFovFm2F7hLxpw4eCafNGZKSCadNKSU55Hat1rdTheMSyZe20tbmZcWzvi+y+9FIDkZFKho/oGtFLSdHw10sjuO3WSm67tYpLL4sgJUXD44/X8be/RbBqZQeXXVrKFVeUsW6d90a79vW96T5Wt0s71aARYdsO707z54R4bzMIgEXlneLL3doEO9UTs498oIcJLic5vzyDWuv/bwftzXpQnUV0im83uQSquroSPln2GEvbPsCe6PK7d/zQCb7/AGw0Grn55pu54YYbmDt3Ljt27ODXX3/l6aefZu7cufz973+noaGBWbNmsXLlSnbs2MGXX37JX/7yF1yuPz8UnXfeeZSVlfHSSy/5/WaNbvIInw+lpKQQGxtLdbV31hn5gk4nMHpMA4LwDTa7VepwPOqLL1oZPTqEqKje/Vm8/XYTSxa38eij8Wg0f76SzpwZxsyZf7bi+urLVkL0Anl5Wi65pIxnn0ukrtbJfffW8PobyWg03kvGqkKO5dWWTKScygUosgqs7/Tu5oB0XQt4cYlgkg9eLt/JquM6CWaG1LvWU5j7O7+S7/ub95HdqsLpPIGEbBMVW1ZKHU5AqKrazkdVj5CUmMfo1JNQVwpSvySgSTaiTfZOy8Ijueeee4iOjub+++9n586dmM1mhg8fzj/+8Q8SEhJYtmwZt912GzNmzMBms5GSksJxxx2HQvHn67zJZOKMM85gwYIFnHrqqZI8j76SEz4fGzVqFJ9//rnUYfSZIIiMGmUj1LgYh6Mu6DbMVVc7WPNrJ//+T2yvjn/vvSbeebuJhx6OJy390IVzm5tdzHu9kccfT2DTZhtJSeq9/zmdXVO+aWneaRbuEEw8K16JQ5R+3aiq0vujmclq724IsTi9P+X5o66Ua3IzUGza7vV7Hci88DmSz3uakgo/GwbqgdupoL5mAsmFZkp+/1rqcAJGWflGyso3kpo8lJFJx6OslO6FXIrRvW6CIHD99ddz/fXX9/h4ZmYm8+fPP+J1ysvLOf/88wOmhar//2UHmaKiooD55eiWl+dm6rSf0Orex+Gokzocr1i0qBWzWclRRx25o8K77zTxxhuN3P9AHNnZh/+3fP65es44w0R0tAq3G5z7rKNxufDq7sjPTQ+z0yp9smcUYdMu7+/ajhe8ew9Lh2/W1q0eY/bJfXqStug+DAHSvktAoKaskOSiMxAU8ltZX+wu+Y0PfnqAX4XFuON8/7NTmrXoC7zTrtAXGhsb+eijj1iyZAl///vfpQ6n1+S/Eh/TaDQMHz5c6jB6JckiMH3GRiKj3sRm2yl1OF7jdot8uaiN6TNCDyqX8sADNcyZ82f3hnfebuK11xq4+eZo4uJUNDQ4aWhw0tl5cGK1elUHZWUOTjmla9oiO1tLaYmDX1Z08PnnLSgUYLF4pyn6DuPFfNDsH43IC9rA5vR+4hkpeqfLRrekVu/V4dvXnNgtCCHea+V2OIqGKgrrvgD/ri6xn5rSFBJyL0Ctk67kUKDatvMX3v/5ftaplyHG+q40Sui4BARFAP2SHaC4uJhLLrmEBx98kOxs36+77a/A+CgXZMaNG8fKlStx+mn9ErNZwYgRpTgcS7Fa/Xvnnif8+msnNTVOjj/u4EX5NTVO9n1d+uyzFhwOuPv/9n/zv/AiMxdfHLH3a5vNzdNP13Pnv2JQ/HGB6GgV11wTycMP16JWC9x6WwxaLyyU71Ql87TtVED60T0AW5l3N1N0Mzm9O/qcVF+KIjQet5enyBsVndRPGEbEV6u8ep9DCfnpY7LPHc2WKpMk9++P+ooowi0X0lbzAR3NgVED1J9s2vojm/iRwtwp5BnHQq333psEjRLD6MDum7t7926pQ+gXudOGRBYsWMDKlf614FirFRg9pgml4htc7g6pw5H100thb7Gk1T+WDcS5BVq+KcMXdX23xv8bTeM2r95jet4Iqjq9U+tvXzPa07jsqa1ev8+hiBodv538JA11/vmh9FD0Rhui/VOaqkqlDiWgFRccR5Z+JNR5fqNV6LgEzCene/y6siOTR/gkMn78eFavXu0XhZgFAUaMtBMWthiHowaX9CHJ+ulX8x0safaPZA8go8nNKh99pFR3eK9lWzeLxuyThO8rw04uy0yFbbu9fq+eCHYreevn8LPlr7iO0LHhhw2f8sPGT2lo7ao+EBeewvEjLiQ/ecwhz1m87kN+2PgpjW01GHQmitOO5uTRl6FWdW1gWrntGz5ZMQebo5Ojso/ljHFX7z23vrWKZxbcyq2nP49es39HnM5WLWrNacSmfUv1zg39ffqD3pr1i1gjfMmoopNIVw1FbPBQ4idA6HjfFlqW/UlewycRs9nM0KFDpQ6DnByYNm0Fev27OBy+WaMk844m7Qieb/WvWo91e7y7c7ZbtMaBYGv1+n0sCt+tE1s7Jtpn9+qJZvNKCgw7jnic2RDFKWMu59YznueW058jK7GYF7+8i8qG3T0ev3Lbt3zyy0scP+Ii7jznVc6fdDOrdyzh01/mANDW2cxbSx/ltKOu4JoTH2Tltm/5fc/Pe89/94cnOWX05Qcle90cdhUtzdNJyhvX9yct+5MosvK3z3j31/+yx7wNwTzw9ca63EhUkXoPBCfrDznhk9CECRMk672XkKBg+owtRMe8jtUm3dSRzDPcgoYXVf+gw496oqY7BXbXtPvkXrmhvrmPxYfdCuYkbkWQeCNC5MePkBh/+NeowtRx5CePIcaURKzZwsmjL0Wr1rOrZmOPx++q3kBabAGjMqcSaYwj1zKSkRnHsKe2q49wXWslOo2BERnHkBKTQ1bCMKobSwBYtf07lAoVw9ImHjYm0a2grvIokotO6Mezlu1LFN0sXzOf99c9QHnkHoQB7OI2TkryYGSyvpITPglFRkZSUFDg03uGhQlMmVJJRuabWK2/+PTeMu9ZbHqA3/xs2WVCve/Wf2XqvT+6B5Bk9U1iCVCjaKd5fJ7P7nco6UseQmfo3Zu82+1i1fbvsDusDIntOfYhsfmU1m1ld81mAOpaKthQ8gv5ltEAxJgScThtlNZto93awp7aLSREptFha+Xzla9y9oRrex17TWkOlsJzUB6mRZasd1wuJz+ueocPNjxMVXQFQmjffqbaTDPaFGkKLcu6yH8FEjv66KP5/fffvX4ftRrGjGlDpf4ah9M3uyZlvlFhOIm5zSlSh7EfQRTZs9N3uyWHaH1TI8/SWg8+XCL5aV4nF3zru/v1RFW5m6KO7/mFQ0+Rltfv5NGPr8XpsqNV67n82P8jPjy1x2NHZU6l3drM459cj4iI2+1iQt5Mjh1+PgAhWiMXHnMb8xY/iMNpY3TWdPIso3hzycNMyj+V+pYq/rfoX7jcTk4YeRHFaQf3Q91XbVkisZkXUL/nfWwdvkvYg5XTaWfpL6+jUeuZOOIcolsTEDuOXM0hbJp/vUYNRvIuXT/w7rvvsmnTJi9dXWT4cBfhEUuw2+WG48HGoYjg35qX2WPzr502hTYF25b4bqfkq5nLOKb0Wa/fp0VvYnycb8uVvP92HOLuMp/esycl5z3C9oqe1185XQ4a22rotLezZuf3/Lx5Idef/FiPSd/WirW8+s29zBz1F1JicqlrqeCDn55lXM4JHD/iwh6vv63iNz5a/j9mz3yM/7xzEX+Z+k/CQiJ4+KO/8+9z52LUhx8x/tCIDqyNH9LW4P1NN4OJTh/GxGHnENkUjXiIMl7arHCi/+rb2SzZweQpXT9w9NFHe+W6mZkC02esxhD6tpzsBamPwh72u2QPIKzGt32WY/Fu0eVuYZ3NhKkPrtfoTRvG+seuxqRP7sYU0fPCfZVSTbQpkeToLE4ZcxmJkeks+b3n1lQLVr7K6MzpjMs9kcTINIYOmcDMUX/lq7Vv91jj0OGy8+6PTzJr4mxqW8pxu11kJgwl1mwhxpTE7urefVhuawhBGXIukUlpvX/SsiOydrbw9c8v8VnJ8zTHtyBoDk4rwqYlSxCZ7EBywucH4uPjycrK8tj14uIUTJu+nbj4eVit3ho5lElta9jlfNJsljqMg2hF2OrD6VyACHe9z+5l0fu2JdScpB1dazIkpmhvIX/n2yiUR95oJopunK6eS3nYnbaDNqspFMruEw86/stf3yTPMgpLdBZu0Y1b/HMUyeV29qkQtq1djdU+k7jMYb0+R9Y77e2NLPrpeRZUzqEtoR3UXemFNiscbbK8ds8fyAmfn/DEKF9oqMAxU2rJyn4Lm+3nI58gC1jt6nSe7vTPHYiFnQKtVt8W7A21+26azqL0bduzMlUz7WPzfXrPQ9GtXUxeeMV+3/tkxRy2V6yjvrWK8vqdfLJiDtsqfmNk5lQA5n33AJ+smLP3+IKUsfy48TNWbf+OupZKNpWt4vOVr1KYPPbPxO8PlY27Wb1jMSeOvASAWHMygiDw0+aFrN+znOqmElJi+tbayuVQ0lR3DJaCyX3/AciOqLWllgXLnuHr2rl0JFoxTZfX7vkLedOGn0hKSiItLY2dO/ves1algtFjOtBqvsbp9M3idZm05unvo6HV/6ZyAZQVvt8urLP6roakxe37z8lfFDo583uf37ZH0fPvJ/bsZ6mu6hppa+tsZN7iB2jpaECnMZAYmcbVJz5AbtJIABraavYb0Ttu+AUICHy+8lWa2+sI1ZspSD6KmaMv3e8+oijy9vePc/rYq9Cqu9YOalRaLph8K+/9+BROl4Ozx1+L2dCfeoUCteXDSS4yUfr7Z4hebpc3GDU0VrDRupwsy3SpQ5H9Qd604UfKysqYM2fOkQ/cx7BhLiKjvsdul35Rt8w3Vprv4olm6Yt298TsBtd3FTh8WK9OKbjZrr8Ewe2bUcWP8qZxV6dva1cKIrz3ejRiuX+sxXUm57A8bzb2IOi1HZVYTdXW93Ha7VKHElQEQcFFDz1FVHKq1KHI/iBP6fqRpKQk8vJ6V3crPV1g+oxfMYa9JSd7g0iD7iheaB0mdRiHlN+GT5M9gIwQq8+SPYCk9iaf3aubKMDW8Raf3/dQVCWbKeRXqcPwiLryWKKGXIjeKK8z86ScCZPkZM/PyAmfn5k2bRoKxaH/WaKjFUybvouExHlYrXKvyMHEJeh4QXkLVj/qpnGgtlLfFEDeV06ob+tKWpqlGWV7JWV31/oNP2Fa9CKpCb5dq+ktTTUm9JEXYIqJlzqUoKBQqhh/9vlShyE7gJzw+ZmIiAhGjhx50PdDQgQmH1NPbt7b2Gw/ShCZTGrfmB5ig59109hXgltga7nv15Bm6HybZMY2VaBV+rD68h92qBqwjpK+88a+UhfcQ6jJf5LQgeho1iEqzyI6xXMVEwaromnHYoqJkzoM2QHkhM8PTZo0Ca226w1FoYCjxnYy5qjPcLkWIoryOpPBqMxwGm80J0odxmGlN7p6qqrhdcmqZp/eT0AkUd+fjQID9/UwSW57SIrmOgqrPkOiluAeZ7eqaG8/gYScUVKHErBUWi1HnX6u1GHIeiAnfH7IYDAwfvx4CovcTJmyFLX6A5xO39Y1k/kPmyKKp5wX4e/7CKt3+zbx6hav8P3fhkXl2+LL3d42bUaIlSbZPBT9is/Jjgme1ye3S0F99QSSC+Xdpf0x8sRTMZiP3PlE5ntywuenxo0bTWzsd9jsJVKHIpPYh8ZHKLf7d7qX6RAoqZNmvjla9F3R5W4WQZpCyE7Bze4JQyS59+HEfXQPkdHBMbULICBQU1ZIctEZCIdZUy3bnzEymtGnniV1GLJDkH+T/ZRKpSMj/Vapw5BJbFPY31nQIs1oUl/E1ffcVcEXTC7fJ3xJDume72upZV1rPfyIwm4j97eXUKn9K66BqilNISHnAtQ6ndShBIRJF16KWiv/rPxVcP11Bpm4uJMxhRVLHYZMIm2abJ7unCZ1GEekRGTXzibJ7m+w+a7ocjdLh+93I3fboKnBPiJXsvsfimbrKgr0vq1P6Av1lVGEWy4ixBwhdSh+LblgKNljJ0gdhuww5ITPz2Vm3gkEyYpoWa+JCLyqvYdmp39P5QIU2pTUtdoku7+6o9rn90xq9X2Sua+lxRpJ738oEZ8+TlKC1FF4XktdKNqwWYTHJ0sdil9SKFVM+cuVUochOwI54fNzJtMw4mJPljoMmY8tN9/N8jblkQ/0AyHVVsnubVI7UVibfH7fpPpSFIJ0L5/zIjYiRBy8MP6dxkZO3bWLUdu2MmrbVmbt2c33bYevUzivoYETdu6keOsWpuzYzgM11djcf37Q+KylmSk7tnPUtq08WLN/cl3usHP8zh20uf7suJH+zYPoDcGznq9bZ6sWp/tUYtMKpA7F7xQfdxKRSf5TGFzWMznhCwDp6begUOilDkPmI3X6ibzUki91GL2iE2HLzgbJ7p8f2i7JfTUuGzG6SEnuDWATXJRNzDzo+7FqFTdER/N+Sirvp6QyJsTANeVlbLP1PAL7eUszj9XVcnVUJJ8PGcI9cfF80dLKE3W1ADQ6ndxVVcUt0TG8lGThs5YWluyTQN5dXc2N0TGEKv/8cKKsKaGobbGHn7F/cNhVtDRNJylvnNSh+A2DOZxxZ50ndRiyXpATvgCg08UzJPUaqcOQ+YBLCOF54QZshylo1/7WK1RPKab1mYcPeYz1+2+pv/I8amZOpPqEsdRffg6dX32+/3XenUfN6VOoOX0K7e/N2+8xx6bfqb/iPETX4TspFHUItNuk66eaqfdtl419WTRmye4N8EbGwR0/jgk1Mik0lFSNhlSNhtnR0YQoFKzr7OzxGms7OynW6zkpzESiWsN4g4ETwoz8bu0atS11OAhVKDg+LIxCvZ7RISHssHcljwtaWlAJAtONB28qMix9h8x4aZJxbxNFgbrKo0guOlHqUPzC0ef/BY0+ROowZL0gJ3wBIjn5MkIN2VKHIfOyRaaH2dx56GTPsXkDHZ9/iCrt4NGdfSnCTBjOv4yIZ+YS+dJ76I87hZaH/oNt5U9d19mxlbbXnsd85wOY7ryftleew7FzGwCiy0nL4/cRdsM/EZSHn5oTy6V9Ux+i9X1nj24WhbS7EVdrKnEOO/TmDZcosrClhU5RZKi+5xmCYXo9G63WvQlhqd3OD+3tTDQYAEjRaLCKIhutVppcLtZbrWRrtTS7XDxVV8udMbGHvH/ix3djjpSmfI0v1JRmYyk8F6UftbvztcScPPKOniJ1GLJeGry/qQFGoVCRk3s/q1adCX5fglfWH3tCz+bt5kO3I3J3dtD8338QdtO/aH9jzmGvpRm2f3s+1Rnn0fnlZzh+X4N21DhcpbtRp2WiGT666/G0TFwlXd/reHcu6qLhqHMOP60c4RbYtKepd0/OSywq6e5vcUnf03jZCD2T1u7/va02K7P27MEuioQoFDyVkEiGtudWcCeFmWh0ubigZA8ATuAck5krIqMAMCmV3B8Xzx2VlVhFNyeHhTHBEMqdVZWcbw6n3OHg7+VlOEWRv0dFcawxbO+1FZ1t5G17neXRs3D7wc/KG2rLEojNvJD6Pe9h6wjOEc1DERQKeaNGgJFH+AKIKWwoSUkXSB2GzAusyniedszicG+LrU/ej3bMRLQjjurTtUVRxPbrCpxlu1EXjQBANSQDZ9keXNWVuKoqcJXtQTUkHWd5KZ2LPiX0r38/4nVzW0WcbmnfyGORbv1gklX6N/hXozYhmML2+16qRsv81CG8k5LKOWYz/6iqZPsh1vD90tHOi/X13BUbxwepqTyVkMjS9jaer6vbe8w0o5FPhgzhy7R0romKZmVHB1ttNs4ym7mpooI7YmJ5MjGRf1VVUe/cfwmAbt0P5JvLPP/E/UhDVTjGuAsJjfSvDijeVjTteGJS06QOQ9YHcsIXYNLTbkarjZc6DJmHvRf6EJWH6aZh/W4Rzm2bCb382l5f093WSs0J46iZMZqmO64j7Nrb0I7sShZVKWmEXnoNjbdcReOtVxN62bWoUtJoffxeQv82G/vKn6j765nU/+1c7L+t7vH6LSXSTad2i3D7vuhyN0urdPfu1qFwUD1h/6UeGkEgRaMhX6fjxugYsrVaXm/sufXZU3V1nBxm4kyzmSytjmlGI7OjonmpoR53D+tI7W43d1dX8Z/YOErsdlyIjAoJYYhGS6pGwzrrwWsFoz56kLi4wNhx3l9tjSEo9ecSmTQ4EiC9MYwJ51wodRiyPpITvgCjUhnIzv4/qcOQedDvptl82XLoRc+umipan32YsH/ch6DpeWquJ0KIgYiX3iHi+TcIvfTvtD73KPa1q/Y+HnLyWUTN+5ioeR8TcvJZdH75KYLegDqviJZH7sZ896MYr7qJ5ntvR7Tb97u2xSWwrUK64sPdjI66Ix/kJZbGUsnuva+3sw7/MxABh9jzhwmr243igDKfSkHYe96BXmioZ4LBQJ5Ohwtw7pMUOkSRnmZuBbeLrGWPo9EHd9Jna1djtc8kPnOY1KF43YRZF6ELDZU6DFkfyQlfAIqOmkpM9PFShyHzgBZtAc+1TzrsMY6tm3A3NtBwxXlUTxtJ9bSROH5bTcdHb1M9bSSiq+ddsoJCgSoxGXVGNoazL0I7aRrtb73S47Hu5kba572I8brbcGz6HWVSCqqkFDTFoxCdTpxle/Y7PrVRup25+9JbfV90uVtYZzMmTdiRD/SyZbpS3Pldm3geq61hVUcH5Q47W21WHqut4ZeODk4KMwFwe2UFj9X+WTR6cmgo7zQ1sbClhTK7nZ/a23mqrpbJoaF7E79u2202vmhp4dqorqnLNI0GhSDwYVMTS9va2GW3U3iIFmSq0i0UuX7xxtP3Ky6Hksa6Y7AUTJY6FK9JzMmj8JgZUoch6wd500aAysr6Nw2Ny3A6pZ9Wk/WPGyUvq/9Ny2GmcgE0w0cT+fL7+32v+aF/o7IMwTDrEgRlL0dO3CKiw97jQ63PPkrIGeejjI7FsXkD7FuOxeUC9/4xVu5q7t09vUgQRJTt0iV8ABZdJM126f8GV44yMWYDNLhc3F5ZQa3LhVGhIEur5aUkC+P+2HVb6XDs9yn/ysgoBASerKulxukkXKnkmNBQro/afz2aKIr8p7qK22JiCfmjj69OoeC/cfHcU12FXRS5MyaWWPWhd+WGffUKQ84rYldF8O7c7SJQWz6c5CIzpb9/iniI0dVApNJoOfaq2Qh+1stZ1jtywhegtNpoMtJvZfOWO6UORdZPy8z3sqr5yC+cihADiiEZ+31P0OlRhJlQ/fH95vvvRBEVg/Hy6wBof+tlVFn5KBOSwGHHtuJHrF8vwDj7joOub1u1HGfZHsJuvxsAdU4+zpLd2Fb8iKu2GhRKVJaUvcfn2BXsbujo9/P2lDS9FcHVcwLrK0nKENZLGkGXObGbOcpg4N64w6/vnZucst/XKkHg71FR/D0q6rDnCYLAGwecC10jhJNDM3o4o2cpn99D7TH309bs6PU5gaqmNI3E/POo2voeTru0v6eeMv6cCwiPC8LeeYOEnPAFsISEc6mq+oSm5pVShyLroxr9VF5pyabnlVJ956qpgn0+dYudVlqf/C+u2hoErRaVJRXTP+5Fd8yx+50n2qy0PvUAprse3PupXRkdi/HaW2l56D+gVmO6/W4E7Z9TdVF1NnZ7JOqByTG0g8TLCC1u/xjpaFZYqZswjMgvVx35YAkpWuoprJjPcuNMgmjg65DqymOIGnIRzRXv0dkq/UjwQCRk5TLihFOkDkM2AIIoHqakv8zvtbfvYMUvJyGKwfEJcjBwCkbu0c1luzXw/vRUokj0sjoa26X/fbs+eSc31Eg7wv1R3jTu6twqaQzdpnamcsUT26UOo1eqzv4/NtYcflQxmBhMnTg7PqG5pkLqUPpFpdZw4UNPEZGQJHUosgGQR/gCnMGQTmrq1eza9YTUoch6aYHpEbY3B16yB1BoU7DJD5I9gBS19OsIk9qb9m59q/+unobvGnDUdU1XahO1xJwSg7Ho4NZj3eq+rKNhcQOOegdKoxLTSBOxZ8ai0HRdtOmnJqo+qMJtdRM+MZz4WX9O2dpr7ex+ZDfp/0lHqVfyrX43V2QNga27vPZ8PSVm/n3UnP40dTWHb90XLNqb9aj1ZxCd8hW1e7ZIHU6fjTv7fDnZCwJywhcEUlOuor5+MS0tv0kdiuwIdhkv5P3mwB3Z0FVZaV2zkNY1C3E2d22YUEclYx43C336yEOe17LyE1rXLsTVUotCH0ZI9njCJ12MoNIA0LZhMU1L5yLaOzEUTiNi6uV7z3U2V1P97r+Iv/gJFNo/y9ckKJq88yT7wNJcCeFdm2bU4WrizopDE9v1nJp+bKLkyRLS705Hl3jw7tWmn5uofr+axEsTCckIwVZto3xOOQgQPyseZ6uT8lfLSbosCXW0mj2P78GQayBsWNfO4IrXK4g9KxblPuVO1hwVRXEAJHwKp53c1c/zc/oVOI+waSlYODrVuOzHk5hjonxz4OxYjs/KYcRJp0odhswD/GMBimxAFAoV+XmPo1TKdZH8mVWVxFO20z20as/3QkTYvLMRpTGS8EkXE3/xE8Rf/AS6lKHUzL8Xe+2eHs9r37iExqWvYR4/i4TLnify+Ovo2PwDjUvnAuDqaKZh0dOEH/NXYs65h/aNS+jY/ucbYv1XzxE+6ZL9kj2AaAm7bHSLbapAq+yqjRhWHIZxqBFtnBZtnLZrpE6noGN7zxtcOrZ3EJIZgnmsGU20BmOBEdMYE507u4oX22vtKPVKTGNMhKSFYMg1YKvo6pjRtLwJQSlgGmna75ovx21D0Evb47e31DvWUqjZKHUYPuV2KairHk9yUWCUNVFrdZzw95tQKIK7huJgISd8QSIkJIXsrLukDkN2GG8ZHqTGEbijGUXt0OlwEZIxBn36KNQRiagjEgk/+iIUGh22ip6nqmzlm9Al5WLIm4zKFIt+yHBCco/GXrkNAGdTFYI2BEPu0Wjjs9AlF+Go7ypq3L5xKYJCRUj2uIOua3ZKV3S5m4BIov7gllqiW6RpeRNum5uQjJ6LaodkhNC5u5OOnV0Job3GTtu6NkKLuj64aWO1uO1uOvd04mxz0rmrE51Fh6vdRc38GuIvOHhHbo2yjaZxeR58ht4V/vnTWBIC9SNQ/wgI1JQWkFx0JorellSSyKQLL8V8hJ3fssAhT+kGkfj4M6irX0pNzQKpQ5EdYK3pVr5tCYyRl0NxlB/cO1Z0u+jY/CNuhxVtYk6P52kTc2nbsARbxRa0Cdk4mqro3LGK0IIpAKgiEhEdNuzVO1CGxWCv3Epo4TRc1jaafniD2Fn/7fG6Bnut557cAFhURnb+8f+tpVZ23rsTt8ONQqsg+drkHqdzAcxjzbjaXOy6bxciIrgg4pgIYmbGAKA0KEm6PImyl8oQ7SLmcWaMhUbKXi4jYmoEjjoHJU+WILpEYk6NwTSqa7Tvk7wOLvrWF8/cM9K+vI/6cf+ho3VwrOfrVlOaTHzO+dTseB9HDy3ppJY2fBRDp8sF/oOJnPAFmZzse2lpWYvVWi51KLI/NGuLea79KDxVgkUKUW6BzSV/bpKw1+6m6vWbEZ12BI2emNP+iSYqucdzDXmTcXW0UPXmbYAIbhehw47HNPZsAJS6UKJOvIG6zx9DdNoxFExBnzaCuoVPYhx+Es7mamo+vAfcTkzjz8OQMwEATUc1z6+08/wqO7ubukZO82OU3HW0huMzD13c94nlNp5f5aCk2U1UiMCZuWrun6ZFp+rqLPHmOge3f2ulzS7yl2EaHjv2z4Rtd5ObGa93sOpvBsK0XcdbhD/vpYnXkH53Ou5ON80rmymbU8aQ24f0mPS1bWqj9rNa4i+KJyQtBHuNnco3K6n5pIaYU7qSvrARYYSN+LObR/vmdmxlNhIuSGDrbVuxXGlBZVKx4+4dGLINqMJUfB66nYuHJCPuKjn0P6gfUdZXUtj4FSvUUwL5T6Rf6iuiCE+6kPa6D2hvkn6JQje9MYwZV1wndRgyD5MTviCjVoeRn/cYv645D1H0j/ZXg5koqHhJ/U/a7YH9TpbT4maV+8/noI5IJP4vT+G2ddCx5UfqFjxO7HkP9Jj0WUvW0bz8PSJmXIU2IRtnYwUN37xE07K3MY+fBUBI1jhCssbtc87vOGp3EzH9Cipe/BtRM29BaQinct6N6CwFhJmMKDrrSQoTeGCalswIBSIwd62DU97pZM0VCvJjDp4ue+t3B7d/Y+OVU/SMsyjZWu/mko87EQR47FgddR1uLvusk9dO0ZMWruDEtzqYMkTJSVldSd3VC6w8ME27N9kDSHL8WURYoVKgje1a06dP1dO5q5P6r+tJvCTxoFhqPqrBPM5MxKQIAHQWHW6bm/LXyomeGY1wQJNbt8NNxbwKkv6WhL3GjugSMeR0ddDQxmnp2NFBWHFXcrh+bBz5AZLwARh+/JCsWaPZWjn41iG31IeiD5uFRv8ZjZX+8W82/fJrMJjDpQ5D5mHyGr4gZDaPJCXlKqnDkAFLTPezpl048oF+rnHP/kVjBaUadXgC2rgMwiddgiZmCK2rPu3x3KYf3iA0fwrGoceiiU4lJGsc5kkX0bL8gx7bTolOBw1fPU/EsX/H2ViJ6HahSy5EHZmEOiIRW+UW8kO7ppdnZqs5IVNNZqSSrEgl903VEaqB5WU9f9j5qdTJ+GQl5xWqSTUrmJGuYlaBml/Ku47f2Shi0gqcU6BmVKKSY4Yo2VTbFePbvztQK+H03P1HDy0dh6n+LILo6DnZd9vcB78CH+YVufbTWkILQ9Gn6hHdIuzzoxOd+389J2k7gkZz6Iv5oYSP7iY8cnCOQXS2anG4TyM2vUDqUCg4ZjqZYw5eMysLfHLCF6SGpF6LKaxY6jAGtaqQ43itJU3qMAYs1SWwo6rtsMeIoojo6rldluiwgbB/0isIiu4TDzq++ad30KUNRxuXAaIb3H8mb6LbCW43WfqDkyyXW+Sd9Q7aHTDW0vNi+HEWFasrXPskeG4WbndyQmZXopEZoaDDIbKm0kVDp8jKchdFsUoaO0X+tdjKM8cfPDVraekqT1P1fhXtW9qx19qxllq7vt7cjnmsGYCyF8uoer9q73nGYUYavmugaXkT9lo7bevbqJlfg3GY8aDRPWu5leZfmok9PRYAbbwWBGhY2kDr2lZslTb0afq9x5crW2gdm9/jz8BfKazt5G2ei1IV+B+Q+sNpV9LSOJ2kvAmSxRCdMoQpf71SsvvLvGtwfpwaBBQKFfn5j7Pil5NwuQ7/Zi3zPIcinKfFK3AGQf8oS4OTqn2+blz6Gvq0kajConHbO2nfuARbye+Yzu7qxVv3+aN/lG65BAB9xmhaVn6MJiYNTUI2zsZKmn54A33GaIQDyj3Y60po3/wD8Zc8BYAqIgkEBa2/fYUyNBxHfRma+EyG6P7coft7tYuxL7djdUKoBj46R09edM8J33mFauo63Ex4pR0RcLrhyhFq/jGxaxo2XC8w91Q9F33cSadD5KKhao7NUHHpJ51cM1rDriY3J7/TgcMF/5ms5cw8NUkNpSiMCThbnJS9WIaz2YlCr0Bn0ZF6UyqhBV3TlPZ6O+yTy8ScHIMgCNTMr8HR6EBlVGEcZiT2jNj9YhZFkYrXKoibFYdC25UoKzQKEi9LpPL1SkSHSPyF8ajD9x95XFhg5+ylR/739SfaDT+RnzWRdY09rwcNdqIoUFc5muQiEyXrfLv5Thti4OQb/4Fao/XpfWW+I7dWC3KVVR+zceNNUocx6HxgnsNHzYG/BkYQRVJ/aaSy6c9dhHULn8S65zdc7Q0otAY00amEjTkT/ZCuEeWqt25HZYol6sQbgK6dvM0/vUv7hsW42upR6E3oM0YTfvSFKHR/rtkSRZHqN28j7KgzCckYvff7Hdt/oeHr5xFdDswTL8Q49FhezFjOjLKupNDuEilpFmm2inyw0cGcNQ6WXhLSY9K3ZLeTcz/o5N4pWsYkKtne4Ob6RVYuH67hX5N6fqNbutvJzV9bWXqJgYyn2nj7DD1xoQKj57Sz7dpQYgwKpueNoKrTP3YNdxNEeO+NGMSywGrnJQoCm899jsrKwP+wNBDRSRVUbPoAl9M3u5dPvvmfZI4a65N7yaQhJ3yDwPoNN1Bd3fP6KpnnbQ/7K/9uPVHqMDwi365gx+JSqcM4yKeZX1BU+nqPj02b1056uIL/zdQf9NjEV9s5KlHJwzP+nJp9Y52dv31mpe0fRhQHTD3bnCLDX2zn9dP0qBQwbV4HNbd0tUob9VIbdx2tZWa2mr8Om8rK5m0efIaece/O4WS9GzhdHbo5EzNYMfRmbB2De+NZeGwjDaXvY2v37izNyJmnM+mCv3r1Hj1ZtGgR9957L+vXr0epVDJ27FiefPJJ0tPTWbJkCccccwyNjY2YzWYA1q5dS3FxMbt27SI1NZXXXnuN2bNn89prr3HLLbdQWlrKpEmTmDNnDhaLxefPx9/Ja/gGgZzsezAYMqUOY1DoUKXytHWm1GF4THitTeoQehQh1h/yMbcItkPkCR0OkQOWx6H8I8nr6aPvvd/bOC5dxfB4JS43OPfZqexwgeuPLy0K/6yx+EryLlAF3sodVfl2iuw/Sx2G5BqrwwmLvQBjZIzX7pGUW8DE8y722vUPp729nRtvvJFVq1bx7bffolAoOO2003C7ez+629HRwX333ce8efNYtmwZTU1NnHvuuV6MOnAF3iuBrM9UqlCKCp9n5arTcTpbjnyCrN/eCLmfutbgmIpSi7BtR6PUYfQo7I+iy3d8Y+X4TBXJJgWtNpG3fnewZLeLLy/omp696KNOEo0C90/rSshmZql47Gc7xfHKvVO6/1psZWa2CuUBmeDGWhfvbnCy5oqu0ic5UQoUgsDLv9qJCxXYXOdmVELXtLHF5Z8TJTtVjXSOzkP/0zqpQ+kz4zdzSTtvKDsrBveastbGELQhZxOZtJD6sp1HPqEPDOZwTpp9m2St084444z9vn7llVeIjo5m48bet9xzOBw888wzjBkzBoC5c+eSm5vLL7/8wujRo49w9uAij/ANEiEhQ8jPfxz5n9x7Vpn+ydLWwCqFcThFVoHmzp533kpNb60BoKZd5KKPOsl+po2p8zpYWeHiywtCmJ7e9Vm2pNlNZdufydidR2u5aayWO7+zkvdcG5d+2smx6Sr+d9L+07+iKPK3z6w8dqwWg6YrEdSrBV47Vcfd39u49FMrz5ygIzGs6+8pyXpwFxJ/8fVQqSPov+RP7yYs/NBFtAcLW4cGq30m8VnDPXZNQaHgxOtvlbTe3rZt25g1axZpaWmEhYWRmpoKQElJ7+sRqlQqRo0atffrnJwczGYzmzZt8nS4AU8e4RtEoiInk552Izt2PiJ1KEGnUTeKF9pGEEytAtSV/tfuqZuqo6sUysunHLxOb19LLjHsf55C4N+Ttfx78uFHjQRB4Me/Gg76/klZ6r1FmPdlaa0HPx2Ietu8iVNiYxCra6QOpc8UbU0U7Hmf5ebTcLuD52+rP1wOJY21k7AUmChdv3jA15tw7kVY8go9EFn/zZw5k5SUFF566SUSEhJwu90UFBRgt9sJDe3a0LXvNgOHwz8/gAYKebhnkElNvYqYmBOkDiOouAUN/1PeTmcQvSGFumHTLv+czk3WWxGcVqnD2I+l0f82tnRzIbJrQqrUYfSb7tevyY2sljoMPyFQW15MctGpf9ay7If0kUcx+pQzPRhX39XX17NlyxbuvPNOpk6dSm5uLo2Nf77mREdHA1BZWbn3e2vXrj3oOk6nk1WrVu39esuWLTQ1NZGbm+u94AOUnPANQnm5DxIa2nOje1nffWt6kN87pI7Cswrbweb0z7WIeaH+N30a1tmMSRN25AMl8tqQUlAE7st9zPz7iI6VZp2ZP6opTSMx7zxU/eimYoqN4/i/3+CFqPomPDycyMhIXnzxRbZv3853333HjTfeuPfxjIwMLBYL//nPf9i2bRsLFizg0UcfPeg6arWaa6+9lhUrVrB69WouueQSjjrqKHn9Xg8C9xVA1m9KZQhFhS+gUpmlDiXgVRhm8npz8BWJtZX5b7HuDN1hWplJyKKLlDqEQ9qorsU+Mk/qMPpNcDnJ+eUZ1Fr5LatbXUUMkakXoTeaen2OSq3h5Bv/gTbk4OUKvqZQKHjnnXdYvXo1BQUF3HDDDTz88MN7H1er1bz99tts3ryZoqIiHnzwQe69996DrhMSEsJtt93Geeedx/jx4wkNDeXdd9/15VMJGPIavkFKr7dQWPAUa3/7C6I4uGtd9ZddEcXTrr/iwj9Hwvorzi2wubRZ6jAOKUXdJHUIPbIoDayXOojDWFys4tjAK8m3l3rXegpzf+dXAqtlnDc114ZhiDwfTcjHNFcfucD2jCuvIybVf9o9Tps27aAdufuu2Rs/fjzr1q075OPdTj/9dE4//XTvBBlE5I9Lg1hExHjS02+VOoyANd/4MCW24Er2ADKb3fjzcsQEpX8mo0lu/+4B+3r4JhRREVKHMSDmhc+RnBB8f3MD0d6sQ1ScSXRq9mGPG3vmeeROmOyboGR+SU74BrmU5MuIiz1F6jACzpawK/isxX/XbA1E3W7/rtUYQ4PUIfTIYvfPItXd7IKLkgkZUocxYGmL7sMQJk9O7ctuVdHWdjyJOWN6fDx3wmTGnXWej6OS+Rs54ZORk/NfjMYCqcMIGO3qDJ7uPE7qMLwi3Smwq8Z/1+8BmF11UofQo6R2/9zVvK/X0ytB8O+RyCNRNFRRWPcFBPbT8DjRpaCuehzJhTP2+35iTh4zrrxeoqi865JLLqGpqUnqMAKGnPDJUCp1DC16CZ02QepQ/J6IwGu6+2j00x2sA5VQ75tG7QMRaquVOoQeWZqrpA7hiNZoKnEOC/wd+iE/fUx2rH9O7UtJQKCmrIDkojNRKJWYY+M5+aZ/olLLxatlcsIn+4NWG8OwYa+iUvV+x9dgtML8b35qC87pJEEU2bPT/0eptJ3+mVjFNlWgVfpp9eV9/Dji8MWqA0XC/LuJiArOv8WBqilNJnnoxZx2+78JCZNf02Vd5IRPtpfBkEFR0f9QKIKnPZgnNejH81JrkdRheE2BQ0lNi3+vQ9MrXQgd9VKH0SMBkUR9tNRhHNFrkZsQzIGfBAh2K3nr56BUy29jB1KqFYw9YwoRCUlShyLzI/Jfimw/4eZR5OU9irxAZn8uQc9zwk1Y/Xn76gCFVftX94qe5IZ2Ivhx+zqLyih1CEfUoXBQNSFL6jA8QrN5JQWGHVKH4VcEAab/JY+EDLPUocj8jJzwyQ4SG3MCmRn/kDoMv/KV6SE2dfpvojFQWhG2BsB0blaIfxZd7mYRAmOt1FuZ/rkOsj8iP36ExHj5A2q38Wdlkj48RuowZH5ITvhkPUpO/ivJyZdLHYZfKA09k7eag3tDS2GnQKvV/zdspGv9u2RMUoA0d/9ZV4a7IDhG+QDSlzyEziCv5xs2PZmhUyxShyHzU3LCJzukzIzbiY+XtsG21OzKGJ52nB9kvTQOpqwMjGbASaomqUM4LEuHf49A7mvFKP+ffu4tVeVuCjt/kDoMSWWOimXc6elShyHzY3LCJzus3Jz/EhU1TeowJPNe6COU24M73TOJAht3+f90LkCcoknqEA7L0lItdQi99krsFgRjqNRheIzxuzdIT/D/dajekJwfwdSLcxECvMaizLvkhE92WIKgpCD/Kczmniu4B7ONYdfyRYv0Tca9Lb9VxOEKjPWJUW7/3KHbLamhFIUQGC+rzYKVuvGBX5NvX5ZP7yYsPDDWUXqKJTec468sRKkKjN87mXTk3xDZESmVWoYW/Y/Q0DypQ/GZVk0uz3RMkToMn2gvDZxpyDCHf3bZ6KZ22YnVRUodRq+9n90kdQgepWhrpmDXOygUg2OkKzHbzAlXFaFSK6UORRYA5IRP1isqlZHiYa9hMATPQu9DERF4Vft/NLuCeyoXIN4tsLXcvzdC7CvEWiN1CEdk0YRLHUKvfReyGzE7TeowPEq39jvyIiqlDsPr4jNMnHj1UFQaOdmT9Y6c8Ml6TaOJZHjxm4SGBtc00IF+Mt/DirbB8SKa0ehCDIzZXABUHf6/Ri5J4f/dNva1ZkzgjEj2VvT8/xIbG7x/w3FpJk66ZihqbfA+R5nnyQmfrE80mgiGF79BaGiu1KF4RV3IJOa0DJ6p6+rdgdOPNF5nR3C0Sx3GEVkCZD1ktznxWxH0wdFurZvgdpG9/Ck0uuBLiGKHhDHz2qFodHIZGlnfyAmfrM/U6nCGF7+BMTRf6lA8yiWE8CzXYw+kIa8ByHQIlNQFRjkWgDxDm9Qh9EqS1f+T0n3VKdppHB98H3JUezZSyK9Sh+FR0clGZl43DI1eTvZkfScnfLJ+UavNFBe/jtEYPEnfQtMjbA3ibhoHiqsPjCLB3fy9y0Y3S6t/7yTuycd5gZFM95Vp0YukJPh/QfHeiLKEcvL1w9DKyZ6sn+SETwKXXHIJp556ar/PX7JkCYIg0NTU5LGY+kOtNlE87HWMxkJJ4/CEPaGzeLc5VuowfEYhiuza2SR1GH2Sog6MzSWWxlKpQ+izhYYdkJYsdRhekfrFfYSaAjtJikw0cMr1xegMg6vkjMyz5IRPAk8++SSvvfaa1GF4RFfSN48wY5HUofSbVZnIU46zGTxje1BoV1DXapM6jD5JVAZGceiwzmZMmjCpw+iz38fGSR2CVygbayis+oxArUkcHm/glNnF6ELlZE82MHLCJwGTyYTZbJY6DI9Rq8MoLp5HWNgwqUPpl3dCH6QqyLtpHMhQHVjJHkAMgZHwAVgCqBZftzmJ2xA0GqnD8Ar9is/Jjgmc359u4XEhnDJ7GHpjcP67yHxLTvgksO+U7uTJk7n22muZPXs24eHhxMbG8tJLL9He3s5f/vIXjEYjGRkZfPHFF4e8XkdHB8cffzzjx4+XbJq3u05fWFixJPfvr3WmG/m6Jbh2KB6JToQtOxukDqPPwl3+XXR5XxZl4HVoqVS20jo2eNbkHijuo3uIjA6cqd2YFCOn3TwcgymwyvzI/Jec8PmBuXPnEhUVxS+//MK1117LVVddxVlnncW4ceP49ddfmTFjBhdeeCEdHQfvqGxqamL69Om43W6+/vprSUcOu5M+k2mEZDH0RYu2iOfaJ0odhs8N7RBot7mkDqPPQu21UofQa0nuwJw/XFBolzoEr1HYbeT+9hIqtf+/7SVmh3PKDcXoQ+WRPZnn+P9v/iAwdOhQ7rzzTjIzM7njjjvQ6XRERUVx+eWXk5mZyV133UV9fT3r1q3b77yqqiomTZpEfHw8n332GSEhIRI9gz+pVKEUD5tLVNRUqUM5LDdK5qjvonUQdNM4kLsisMqGdNN2+n/R5W4We+BNmQPMD92CYEmQOgyv0WxdRYF+q9RhHFbasGhmXiPX2ZN5npzw+YGioj83PCiVSiIjIyks/HPna2xs1+7Rmpr920pNnz6djIwM3n33XTR+tPZGqdRTVPg8iYnnSR3KIf1g/i+r2wNzFGYgwkXYtLtJ6jD6TK0QUXQEzgifpT3w1osBiAJsHp8kdRheFfHp4yT5aU6bOy6eY/9WgDIARiFlgUf+rfIDavX+u68EQdjve8If28vc7v1Ho0488US+//57Nm7c6P0g+0gQlORk30N62k1Sh3KQ6pDpvNqSKXUYkshrAac78PYj5xg6EMTAGY21NAVuL9eXLTtBFdyjS+nfPIje4F/PsXh6MlMuykWhGHwfRGW+ISd8AeyBBx7g4osvZurUqX6Z9AGkpl5NXu7DCIJ/lBRwCkaeFa/GMUi6aRyotSQwihcfKDtAumx0i2muRKsMzMX2u1VNdI4J3s0bAMqaEoraFksdBgCCABPOymTcGRlShyILcnLCF+AeeeQRzj//fKZMmcLmzZulDqdH8fGnM3ToHJTKUKlD4TPTo+ywBs5IkScluQW2VgRG8eIDpWsDK24BkUR9tNRh9NuXQ4P/b8Sw9B0y46Vdz6pUK5hxWQFDp1okjUM2OMgJXxB4/PHHOfvss5kyZQpbt/rnguTIiAmMGP42Gk2MZDHsNF7EB82BVx/NU4Y0BN7O3G7J6iapQ+gzi8oodQj99q5pM0J88HeeSfz4bsyR0sw+aENUnHzdMDJGSPeaKBtc/GsRwyBhs9kIDe0a7VqyZMlBj+/evfug74n7TEFOnjx5v68BnnrqKZ566imPxulpRmMeI0d8wNrf/kpHx3af3rtTlcxTttOA4B+5OJTKXc1Sh9BvcULgbYKw+Mkyhv5wIbJjQgpp7wfOzuj+UHS2kbftdZZHz8Lt8t0yj9AILTOvGUZEQuDVa5QFLnmEz4ecTicbN27k559/Jj8/uNfIHIpen8jIEe9hMo306X3fDHmAWsfgTfZyHArKGg6u4xgoosTAKxSd5HBIHcKAvJpSAkql1GF4nW7dD+Sby3x2vyhLKGfeOlJO9mQ+Jyd8PrR+/XpGjhxJfn4+V155pdThSKa7/2509HE+ud8a0+0sbg3MBfSeEl0X2AV1Tc7A6bLRzdIRmBtkum1R12EbkSt1GD4R9dGDxMV5P7nNHBXLGbeMwGAe3K9HMmnICZ8PDRs2jI6ODhYsWEB4eLjU4UhKqdRSWPA0Q1KvBbxXhqBJO4Ln2kZ77fqBQCWK7NgReFOi+wqx1hz5ID9jaQn86dDFwwbHqh/B7SJr2eNo9N5J+gSFwPgzM5hxaT4qTfCPmsr8k5zwySQjCArS0mZTVPQ/VF5Y4O4WNLyo+gcdAVh3zpMKbQoa2gN7hE/dUSV1CH2W1FCKQgjsl9g3IjahiIqQOgyfUJVuoci10uPX1RnUzLxuKMOmJXv82jJZXwT2q5EsKERHTWXUyI8wGDxbDHmx6X5+C9xlax6jq7JKHcKARGvsCPbAqsMHoHbZidUF9q5wu+Biz8TBUx8u7KuXGZLgubWXUZZQzrpjJJacwZE0y/ybnPDJ/EJIyBBGjviQmJgTPHK9CsNJzGtJ9ci1AlmICJt3BvZ0bp4xMHv/Alg0gb90Y25aeVd14EEi5fN7CDUNfId15qhYTr9lBGFReg9EJZMNnJzwyfyGSmWgsOBpMjJuRxD6v87FoQjnGdelOAf3TC4ARe3Q6Qjc+nsAmfrAG93rZlEE/uL8dZpqnMWDY/MGgKKlnsLKj+nvbLygEBh3Rtd6PbW8Xk/mR+SET+Z3UpIvZ9jQ11Cr+zcN8rHxEfbYBm8Jln05ygN3dKzbEE3g1g9M8mFtN2/6YbhO6hB8Sv/LQnKj6/t8ns6gZua1QymeHtjr9VJTU3niiSekDkPmYXLCJ/NLERHjGD3qE4zGwj6dtzXsMj5uMXsnqAAT5RbYXBK4yVK3RGWT1CH0m8Ua+Ak3wKtRGxHCzVKH4VMxH95LVEzvdylHJv2xXi9XXq8n809ywifzWzpdAiNHvEt8/Fm9Or5dnc7TnSd6OarAkdPixhUEO5RjCbyiy92SWgOvfmBPrIKTigme3VTl7xROO7mrn0elOfLbZObIGM64VV6vJ/NvcsIn82sKhZa83AfIzr4HxRHWQ72uv48GpzyV261xT2AX/u0W7u771Jq/sDT6roODt72ZGXi1EAdKvWMthZpNh3xcpVEw+fxsZlxW4Ffr9RYtWsSECRMwm81ERkZy0kknsWPHDgDmzZtHaGgo27Zt23v81VdfTU5ODh0df5Y1aG1tZdasWRgMBhITE3n22Wd9/jxkniUnfLKAkJR4HqNGfkSoIbvHx1ea/8UPrYHbu9TTUl0CO6qCI+Ez2mulDqHfwjqbMWnCpA7DI37RluMqzJI6DJ8L//wpLAkHj5R3TeGOIn9iogRRHV57ezs33ngjq1at4ttvv0WhUHDaaafhdru56KKLOOGEEzj//PNxOp0sWLCAOXPm8OabbxISErL3Gg8//DBDhw5lzZo13H777Vx//fV8/fXXEj4r2UAJoigG/pyPbNBwuWxs3/EAZWXz9n6vQXcUtzhuxRoE05eeMrHWxcpfA69YcU92RN2Msq1C6jD6bdbQSaxv2SV1GB4xu2oo415dLXUYPueKSmTl2LvoaHUCUDQliXGnZaBUB8aYSV1dHdHR0fz+++8UFBTQ2NhIUVERM2fOZP78+Vx33XX84x//2Ht8amoqubm5fPHFF3u/d+6559LS0sLChQuleAoyDwiM31aZ7A9KpZbsrH8ztGgOanUkLkHH/5S3yMneAcp2Bv5mDQCl4EbREdhTiRalQeoQPObV2C0IxlCpw/A5ZV05hU1fow/TcOLfi5h4dpZfJ3vbtm1j1qxZpKWlERYWRmpqKgAlJSUAhIeH8/LLL/P888+Tnp7O7bffftA1xo4de9DXmzYdenpb5oUS51QAABiZSURBVP/89zdWJjuMqKhjGDNmIbsSnmG93E1jP3l2BZVNnVKH4RGZBiuC2yl1GAOS5A6eosXNgpXaCTlShyGJWEUt599eRGphlNShHNHMmTNpaGjgpZdeYsWKFaxYsQIAu/3PFovff/89SqWSyspK2tuDYze57PDkhE8WsLSaKK7IPoaHspIIUcq/yt0iam1Sh+AxOYbALbrczWIPnn8PgPeyA7tzS18pQkKIu/v/sPzvBbQR/r8es76+ni1btnDnnXcydepUcnNzaWzc/9/sp59+4sEHH+Szzz4jNDSUa6655qDrLF++/KCvc3MHTwHuYNT7IkMymZ+6KDGKSRFGrttUwormwf1JVS3Cth3B84acrg38jSeW9sag+mi9RL+Hq3LSETbvkDoUr9MXF5Pw4ANokgOnkHJ4eDiRkZG8+OKLxMfHU1JSst+UbWtrKxdeeCHXXXcdxx9/PElJSYwaNYqZM2dy5pln7j1u2bJlPPTQQ5x66ql8/fXXvP/++yxYsECKpyTzkCB6GZINZil6LR8VZ/Cv9AS0iuCZQuurIptAc6fnmr9LLUXTJHUIA2ZpqpQ6BI9bPSbwewQfllpN9OzZpLzxekAlewAKhYJ33nmH1atXU1BQwA033MDDDz+89/Hrr78eg8HAf//7XwAKCwv573//yxVXXEF5efne42666SZWrVpFcXEx9957L4899hjHHnusz5+PzHPkXbqyoLOprZNrN5Wwvi041rH1xVG7rKzdGrh16w70fuY3jCp9ReowBkREYFRGBjZX8EztRrpDeOEpB2Jn8P2N6fLyiL/3HnR5eVKHIpN5lDzCJws6uaF6vhiRxa1D4tANotG+UDds2hk807kAUWLgdtnoJiCSpI+WOgyPqld00DghuBIihcFA7D/uIPX99+RkTxaU5IRPFpTUCoEbU+NYOjqHaZH+v9DaEwrbwRZknUZMzuBoTWZRG6UOwePm5wb+hppuxhkzSFu4gIiLLkJQ+k/HDJnMk+SETxbUUvRa3ihK49WCVBK1wd2Jw1YWPG/A3Qy2wO2ysa8kMfj2xy0y7ID0FKnDGBB1QgJJLzxP0lNPoo6NlTocmcyr5IRPNigcH23mhzG5XJccg0YIvmneWLfA5tLgKLa8L01HcHQLSXIGdi3BQ/l9bIAmSSoVkZddStqCzzFOnix1NDKZT8gJn2zQCFEq+Ed6At+NzmZieHB1C8hsdhNszUbC1U4EW3AksZaOwC8v05OXErYiaLVSh9En+uJihsz/kJibb0ah10sdjkzmM3LCJxt0MkJ0vD8sgxfyUojTBMc0b/3uFqlD8Li80OCZora0VEsdgldUKdtoGRcYGxwUJhNxd/8fKW+9iS4rS+pwZDKfkxM+2aB1amw4P47J4YqkaFQBPMub7hTYVRM8yVG3TH3wPKekhlIUQnC+3H6e7//lZsJOnkn6wgWEn302QhAu6ZDJeiM4X4Fksl4KVSn5v8xEvhqZzWhTYDa5T2gIzvVhadrgGbVUu+zE6iKlDsMrPjJuRUhOlDqMHuny8kieO5fEhx5CFRmcP3+ZrLfkhE8mA/JC9XxSnMETORYi1YGzo1IQRUp2NEkdhlckqZqkDsGjLJrg7U6xaZx/JXyqhHgSHnqQ1A8/wDBmtNThyGR+QU74ZLI/CILAufGRrDgqlxtTYzEo/f/PI9+hoLrFKnUYXhFL4Bdd3pdFEVibG/riZctOUEn/QUlhNBJ9042kf/EFppNPlqdvZbJ9+P87mkzmY6EqJbcOiWfFUXlclhTl12VcTNX+v36qvyLcwdMiDiDJFWTbqPexR9VEx1EF0gWgVhN+/vmkf/UlUZdfjiLAdg7LZL4gJ3wy2SFEaVTcm5nEj2NyODM23O/+WLQibA2yVmr7MjqCo8tGN4u1XeoQvGpRkQRrSRWKvRsy4v51J6rw4J02l8kGyt/ew2Qyv5Os1/JMXgrfjspmhh+1aSuyCrRag3PDBoCuM7hKmVhagyuBPdC7YZsREuJ8dr/QKVMY8vFHJD70EBqLxWf3lckClZzwyWS9lBuqZ15RGl+MyGJqhPSJn6KiQ+oQvEYQRJTtQZbwNZRJHYJXiQJsH5/s9fuEjB5N6jtvY3nuWbmenkzWB3LCJ5P1UXFYCG8OTWPh8EyOiTBKEoNJFNi4K3inczNCrAhuh9RheJTR2oxJI/0HBW96JXUPKJVeubZh3FiSX32FlHlz0Q8b5pV7yGTBTE74ZLJ+Gm4y8PbQdD4fnsnkcN8mfvmtIo4g3gSQHRI8RZf3ZQnSWnzdtqnqsY3yYOcNhQLj8ceR+sEHJL/yCoaxYz13bZlskJETPplsgEaaDLwzLJ1PizOYHhmGL/b0dpQGZ0LULUMfnL1nLcrALO7dF98OG/jbiqDVYj7nHNK/WEjS44+jL8j3QGQy2eAmfeEkmSxIjDaH8ro5lJ0dNl4uq+WdqgbaXW6P3yfeLbClvNnj1/UnyergfH5Jbv8t8eMpb5g3cWJ0FGJt3zepKIxGwmfNIuKiC1FFRXkhOpls8JJH+GQyD0sL0XJfVhJrxuXzn/QEknUaj14/o9GFGLyzuQAkKpqkDsErLPbgrZvYzSm42TMxrU/nqGJiiLnlFjIWLybmxhvkZE8m8wJ5hE8m85IwlZIrk2P4myWaRXXNvFhay/Lmgddiq94dnKNf+4omuIoud7O0Nw6Kj9lzh5TxL0HgSJ9MNEOGEHnpX7u6Ymg8+8FIJpPtT074ZDIvUwgCJ0SbOSHazPrWDl4sq+WTmiZs7r4P02U6FZTUBW85lm4mZ3DWrLM0VUJE8L/s/q6pwTE8F/XqjT0+HjJmDOEXnI9x6lQExSDIgGUyPyD/pclkPlRgDOGp3BRWjc3j5tQ4ojV9e/OPq7N7KTL/YrDVSh2CV8Q0V6JVDo62X98P3/95Kk0mIi65hLSFC0mZ+xph06fLyZ5M5kOCKAb7aiCZzH/Z3W4+rmnipdJafm/rPOyxClEk4ed66lqDfx3YzvBrUHQ2SB2GV5xaOI4dbcFdhBlAJ6p4/X86dKlDCD/3HIzHHSf3uJXJJBT8cwsymR/TKBScHRfB2XER/NrczrtVDXxS00ST03XQsYV2BVsGQbJnVDmDNtkDsKiN7JA6CC8L14ZzYtqJRHx4BjEJGVKHI5PJkBM+mcxvDDcZGG4ycHdmIl/VtfBeVQOLG1pw/jEGH1od/MkeQF5oB1iljsJ7kkS11CF4hVJQMjZhLKdlnMYxlmNQK4PzecpkgUpO+GQyP6NVKJgZY2ZmjJlau4P51Y18VNnA5p1VUofmE1khrUGd8FmcwdUyLiUshVPST+Hk9JOJNcRKHY5MJjsEOeGTyfxYtEbNFZYYrrDEsCU5kc9+q+DT3yooaQjenbpDtC1Sh+BVSR2B//zSTGlMS5nGjJQZZEdkSx2O1+zevZshQ4awZs0ahsn9e2UBTk74ZLIAkR1nJDsum5uPzWZtaROfrq1gwe8VVLcE11SvRRncdQYtLTUQKnUUfZcVnsX0lOlMT5lOujld6nBkMlkfyQmfTBaAhlnMDLOYufPEXFbsauCrjVUs3lzD7vrAH/mLUwTvhg2ApIZSFMYE3KLn2+55Wn5k/t6RvOSwZKnDkclkAyAXQZLJAphCITA2PZJ/z8xnyS3H8N1Nk/jXSXlMyIhCowzMP+8IV3B22eimdtmJ1UVKHUaPBASGRg/l5pE3s+iMRbxz0jtcVnhZUCd7ixYtYsKECZjNZiIjIznppJPYsWP/fdQ7d+7kmGOOISQkhKFDh/Lzzz8DIIoi0dHRfPDBB3uPHTZsGPHx8Xu//vHHH9FqtXR0BP6HMVlgk0f4ZEHDbrej6WN7JofDgVodPLsJ06JDSYsO5dIJQ2izOflxWx2LN9eweEsNNQFS0iXMEZxFl/dl0YRT2ekfz1Ov0jMqbhTjE8YzNXnqoNt40d7ezo033khRURFtbW3cddddnHbaaaxdu3bvMf/85z955JFHyMzM5J///CezZs1i+/btqFQqjj76aJYsWcKZZ55JY2MjmzZtQq/Xs3nzZnJycli6dCmjRo0iJCREuicpkyEnfLIANnnyZAoKClCpVLzxxhsUFhZSX1/PypUr0Wq12O12xowZQ2FhIfPmzdu7APudd97hueeeY8WKFbzwwgtccsklUj8VrwjVqjiuII7jCuIQRZENFS0s3lzDd1tq+K20iX50dvMJndU/EiFvsii0/CLRvRWCgtyIXMYljGNswliGRQ8b1CVUzjjjjP2+fuWVV4iOjmbjxo2EhnYttrz55ps58cQTAfi///s/8vPz2b59Ozk5OUyePJn//e9/AHz//fcUFxcTFxfHkiVLyMnJYcmSJUyaNMm3T0om64Gc8MkC2ty5c7nqqqtYtmwZVquV008/ndtvv53HH3+cf/7znzQ1NfHMM8/sd87tt9/Oo48+SnFxMTqdTqLIfUsQBAoSTRQkmrh2aib1bTaWbq3lx+11rNzdQGnD4bt8+JKqo1rqELwuyeXbbDs1LJVRcaMYHT+ao+KOwqwz+/T+/mzbtm3cddddrFixgrq6OtzurrWVJSUl5OXlAVBUVLT3+O7p2pqaGnJycpg0aRLXX389tbW1LF26lMmTJ+9N+C699FJ++uknbr31Vt8/MZnsAHLCJwtomZmZPPTQQ3u/fuONN5g0aRJGo5EnnniCxYsXExYWtt85s2fP5vTTT/d1qH4lMlTL6cOTOH14EgBVzVZ+2d3Ayl0NrNzdwJbqVqRoupiqtyI4g7gI3x8s1navXr87wev+L0of5dX7BbKZM2eSkpLCSy+9REJCAm63m4KCAuz2P/tW77vsQxAEgL2JYWFhIRERESxdupSlS5dy3333ERcXx4MPPsjKlStxOByMGzfOt09KJuuBnPDJAtqIESP2+3rs2LHcfPPN3HPPPdx2221MmDDhoHNGjhzpq/ACRpxJx8lDEzh5aAIAzR0OVpc0sLakiTWlTawtbaLV6vR6HLmhbdDq9dtIztJaBx5qKxupiyQvMo/cyFzyIvMYGj1UTvB6qb6+ni1btvDSSy8xceJEoGuTRV8IgsDEiRP55JNP2LBhAxMmTCAkJASbzcb//vc/Ro4cicFg8Eb4MlmfyAmfLKAd+ELqdrtZtmwZSqWS7du39+oc2cFMIWqm5MQyJadrAb8oimyvaWNNaRNrSppYV9bE9po2bE7PlhbJ0LUOjoSvoQziTX0+L1ofTV5k3t7/ciNyB90mC08KDw8nMjKSF198kfj4eEpKSrj99tv7fJ3Jkydz0003MXLkyL3r/o4++mjefPNNbrnlFk+HLZP1i5zwyYLKww8/zObNm1m6dCnHHnssr776Kn/5y1+kDivgCYJAZqyRzFgjZ4+0AOB2i5Q0dLC1upVtNW1s++N/d9S2YXX0LxFMUQd+F4reMFqbMWuSabIfush0bEjs3pG7/Mh88iLz5JE7D1MoFLzzzjtcd911FBQUkJ2dzVNPPcXkyZP7dJ1Jkybhcrn2O2/y5Ml88sknfb6WTOYtcsInCxpr1qzhrrvu4oMPPmD8+PE89thjXH/99UyaNIm0tDSpwws6CoVAapSB1CgDM/L//L7bLVLa2MHW6ja21bSy7Y//3V5z5EQwQdno5aj9h0UXic1tx2K0kBKWQmpYKslhyaSGpZISlkK4LlzqEAeFadOmsXHjxv2+J+6zgFU8YDGr2Ww+6HvDhg076HuzZ89m9uzZng1WJhsAOeGTBQWr1coFF1zAJZdcwsyZMwH429/+xoIFC7jwwgv5/vvvJY5w8FAoBFIiDaREGpie9+d0o9stUtbYye76diqbOylvslLZ1ElFcyeVTVYqmjuJJsi6bChUEJYA5pQ//kvu+i88hRci0zAaYvduApDJZDJvEsQDP5bIZDKZRFwdTSjbKqG1CtpqoK36j//++P+2VrC3g6Oj63/t7eDyVUFpAXQmCImAkEjQR+zz/8N7/r4hGhRKH8Unk8lkhyYnfDKZLLC5XQcngY4OsLeBvaPr/zv+aGslKAABBGH//69Qg0oDyj/+U2n//P/qkK4ETh8uJ28ymSxgyQmfTCaTyWQyWZALzO7qMplMJpPJZLJekxM+mUwmk8lksiAnJ3wymUwmk8lkQU5O+GQymUwmk8mCnJzwyWQymUwmkwU5OeGTyWQy2f+3d28hUXUNGMefzSSi6CimZIRpmoXFKBMU2XQQKyKDxFChmKYByS4ytAiEDiIkCkFYGNgBoiDqRiyC0OoiM4q0oYIIig7E3Eh6UZEZSul78b3KO2ofBeZ2Vv8fCHuvWRueLQgPa8kaAIaj8AEAABiOwgcAAGA4Ch8AQJI0MjKi8vJyJSQkyLIsPXv2zO5IAKYI37QBAJAktbW1qbCwUB0dHUpPT1diYqJmzZpldywAU4C/ZMBAfr9fnz590vXr1+2OgjDy9u1bzZ07V6tWrbI7CoApxpYuAEB+v1/79u1TMBiUZVlKS0vT8PCwGhoatGDBAkVFRSknJ0ctLS1jz3R0dMiyLN26dUtut1tRUVHKz89Xb2+v2tralJWVJafTqR07dmhgYMDGtwPACh8AQKdOnVJGRobOnTunx48fy+FwqKGhQZcvX9aZM2eUmZmpzs5Oeb1eJSUlad26dWPP1tbW6vTp04qOjlZpaalKS0sVGRmpK1euqL+/X0VFRWpqalJ1dbWNbwj83VjhA8JYS0uLXC6XoqKiNHv2bG3YsEFfv36dMC8vL08VFRWqqKhQXFycEhMTdfToUfEvvBgVFxen2NhYORwOJScny+l0qr6+XhcuXNCmTZuUnp4uv98vr9ers2fPhjxbV1cnj8cjt9utsrIy3bt3T83NzXK73VqzZo2Ki4t19+5dm94MgMQKHxC2enp6tH37dh0/flxFRUX68uWL7t+//9MSd+nSJZWVlam7u1uBQEDl5eWaP3++du/ePc3JEQ7evHmjgYEBbdy4MWR8aGhIbrc7ZCw7O3vses6cOYqOjlZ6enrIWHd3958NDOD/ovABYaqnp0ffv3/Xtm3blJqaKklyuVw/nZ+SkqLGxkZZlqXFixfr+fPnamxspPBhUv39/ZKkmzdvat68eSGfRUZGhtxHRESMXVuWFXI/OjY8PPyHkgL4FWzpAmEqJydH69evl8vlUklJic6fP6+PHz/+dP7KlStlWdbYfW5url6/fq0fP35MR9wZLy8vT1VVVROu/1ZLlixRZGSkgsGgFi5cGPKTkpJidzwAv4kVPiBMORwO3blzRw8fPtTt27fV1NSkw4cPq6ury+5oYa+1tXXCKtXfJjY2VgcPHtT+/fs1PDys1atX6/Pnz3rw4IGcTqd27dpld0QAv4HCB4Qxy7Lk8Xjk8XhUU1Oj1NRUXbt2bdK544vgo0ePlJmZKYfDMR1Rw0pCQoLdEWaEY8eOKSkpSQ0NDXr37p3i4+O1bNkyHTp0yO5oAH4TW7pAmOrq6lJ9fb0CgYCCwaBaW1vV19enrKysSecHg0EdOHBAr1690tWrV9XU1KTKysppTh0exm/ppqWlqa6uTj6fTzExMUpNTdWNGzfU19enwsJCxcTEKDs7W4FAwL7QU6Cqqkrv378fu7csS5WVlXr58qWGhobU29ur9vZ2rV27VtL/fk8jIyOKj48fe2b00O//qq2t5WvaAJtR+IAw5XQ61dnZqYKCAi1atEhHjhzRiRMntHnz5knn+3w+ffv2TStWrNDevXtVWVmp8vLyaU4dvhobG+XxePT06VNt2bJFO3fulM/nk9fr1ZMnT5SRkSGfz8dRNwBmJLZ0gTCVlZWl9vb2ST+7ePHihLGIiAidPHlSzc3NfziZmQoKCrRnzx5JUk1NjZqbm7V8+XKVlJRIkqqrq5Wbm6sPHz4oOTnZzqgAMAErfADwC8afNSeFHoMzOtbb2zu9wQDgF1D4AOAXjD9r7mdjnDcHYCZiSxf4C3R0dNgdAQBgI1b4AAAADEfhAwAAMBxbugCg0G3vwcFBxcTEjN3/92y6UeOPX0lLS+NIFgAzFit8APCvwcFBBQIBvXjxQkuXLrU7DgBMGQofAPyrra1N+fn52rp1q4qLi+2OAwBTxhphDwIAAMBorPABAAAYjsIHAABgOAofAACA4Sh8AAAAhqPwAQAAGI7CBwAAYDgKHwAAgOEofAAAAIaj8AEAABiOwgcAAGA4Ch8AAIDhKHwAAACGo/ABAAAYjsIHAABgOAofAACA4Sh8AAAAhqPwAQAAGI7CBwAAYDgKHwAAgOEofAAAAIaj8AEAABiOwgcAAGA4Ch8AAIDhKHwAAACGo/ABAAAYjsIHAABgOAofAACA4Sh8AAAAhqPwAQAAGI7CBwAAYDgKHwAAgOEofAAAAIaj8AEAABiOwgcAAGA4Ch8AAIDhKHwAAACGo/ABAAAYjsIHAABgOAofAACA4f4BUKfHsFb/PuQAAAAASUVORK5CYII=\n"
+          },
+          "metadata": {}
+        }
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "print(\"Data Shape is: \", data_df.shape) # Print the data shape (rows, columns)\n",
-    "print() # Empty line for better viewing\n",
-    "\n",
-    "print() # Empty line for better viewing\n",
-    "print(\"Claases Distrubution is: \")\n",
-    "\n",
-    "class_counts = data_df['Class'].value_counts() # Get how many data sample for each class\n",
-    "plt.figure(figsize=(8, 8)) # Plot a figure of 8 by 8 pixels\n",
-    "\n",
-    "# we use this for plotting the how many data sample for each class\n",
-    "# autopct='%1.1f%%' automatically adds a label to each slice of\n",
-    "# the pie with its percentage of the whole, formatted to one decimal place.\n",
-    "plt.pie(class_counts, labels=class_counts.index, autopct='%1.1f%%')\n",
-    "\n",
-    "\n",
-    "plt.title('Class Distribution') # Add title to the whole figure\n",
-    "\n",
-    "plt.show() # Visualize/show the figure - Like print()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "SUp_V5ZavZw1"
-   },
-   "source": [
-    "# 5. Data Split\n",
-    "Now lets split the dataset into training, validation and testing. Here we will be using it in a bit different way since we will be reading the image paths from the dataframe so better we split the dataframe directly and then read the images. We will do that by getting a random sample of 70% from our data using data_df.sample(frac=0.7,random_state=7) then we will create a new variable of test dataset which we will drop from it the 70% of the train which means that it will be 30% of the data which is the rest of the data using data_df.drop(train.index). Then we will do the same between testing to create validation which will be 50% of the data which means the final split we have is the following:\n",
-    "\n",
-    "* Training: 70%\n",
-    "* Validation: 15%\n",
-    "* Testing: 15%"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
     },
-    "id": "q5O1RkPeZ2D-",
-    "outputId": "6cdada79-12a6-42bc-8c56-9bc6935d4f3e"
-   },
-   "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training Shape:  (4681, 2)\n",
-      "Validation Shape:  (1003, 2)\n",
-      "Testing Shape:  (1003, 2)\n"
-     ]
-    }
-   ],
-   "source": [
-    "label_encoder = LabelEncoder() # Defining our label encoder\n",
-    "data_df[\"Class\"] = label_encoder.fit_transform(data_df[\"Class\"]) # Class encoding\n",
-    "\n",
-    "train=data_df.sample(frac=0.7,random_state=7) # Create training of 70% of the data\n",
-    "test=data_df.drop(train.index) # Create testing by removing the 70% of the train data which will result in 30%\n",
-    "\n",
-    "val=test.sample(frac=0.5,random_state=7) # Create validation of 50% of the testing data\n",
-    "test=test.drop(val.index) # Create testing by removing the 50% of the validation data which will result in 50%\n",
-    "\n",
-    "\n",
-    "print(\"Training Shape: \", train.shape) # Print the Trainnig shape (rows, columns)\n",
-    "print(\"Validation Shape: \", val.shape) # Print the Validation shape (rows, columns)\n",
-    "print(\"Testing Shape: \", test.shape) # Print the Testing shape (rows, columns)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "nHDdSpYGvqd5"
-   },
-   "source": [
-    "# 6. Custom Dataset Class\n",
-    "\n",
-    "Now lets create some preprocessing objects, first we start by LabelEncoder which converts string classes to numeric for example instead of having the classes as (x, y) it will be (0,1) because machines understands numbers only.\n",
-    "\n",
-    "\n",
-    "* `__init__(self, dataframe):`\n",
-    "    When you create an instance of CustomAudioDataset, you need to provide a dataframe (a kind of table with data that we read before).\n",
-    "    Inside this function:\n",
-    "    * self.dataframe: stores the provided dataframe.\n",
-    "    * self.labels: converts the 'Class' column of the dataframe into numeric values with the label encoder we created before. Then moving them to the device (like a GPU `cuda` we defined before) for faster computation.\n",
-    "    * self.audios: loads the audio files listed in the 'FilePath' column of the dataframe and converts each audio file into a spectrogram (a visual representation of the spectrum of frequencies in the audio file).\n",
-    "\n",
-    "\n",
-    "* `__len__(self):`\n",
-    "    This function tells you how many items are in the dataset. Keep in mind that this function is available in the original `Dataset` class in PyTorch but with the concept of inheritance, we are taking this function and overwriting it.\n",
-    "\n",
-    "* `__getitem__(self, idx)`:\n",
-    "    This function is used to get a specific item from the dataset by its index (idx).\n",
-    "    It retrieves the audio and its corresponding label for the given index and prepares them in the required format for the machine learning model. Keep in mind that this function is available in the original `Dataset` class in PyTorch but we also are taking this function and overwriting it.\n",
-    "\n",
-    "* `get_spectrogram(self, file_path)`:\n",
-    "    This is a helper function to convert an audio file into a spectrogram.\n",
-    "    It involves several steps:\n",
-    "    * Loading the audio file.\n",
-    "    * Computing its spectrogram, which is a visual way to represent the different frequencies present in the audio.\n",
-    "    * Converting the spectrogram to a decibel (dB) scale, which makes it easier to visualize.\n",
-    "    * Resizing the spectrogram to a fixed size, which is necessary because the machine learning model expects inputs of a consistent size."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "M8XqYh0uVdDe"
-   },
-   "outputs": [],
-   "source": [
-    "class CustomAudioDataset(Dataset):\n",
-    "  def __init__(self, dataframe):\n",
-    "    self.dataframe = dataframe\n",
-    "    self.labels = torch.Tensor(list(dataframe[\"Class\"])).type(torch.LongTensor).to(device)\n",
-    "    self.audios = [torch.Tensor(self.get_spectrogram(path)).type(torch.FloatTensor) for path in dataframe['FilePath']]\n",
-    "\n",
-    "  def __len__(self):\n",
-    "    return self.dataframe.shape[0]\n",
-    "\n",
-    "  def __getitem__(self, idx):\n",
-    "    img_path = self.dataframe.iloc[idx, 0]\n",
-    "    label = torch.Tensor(self.labels[idx]).to(device)\n",
-    "    audio = (self.audios[idx]).unsqueeze(0).to(device)\n",
-    "    return audio, label\n",
-    "\n",
-    "  def get_spectrogram(self, file_path):\n",
-    "    sr = 22050 # Set the sample rate of the audio file\n",
-    "    duration = 5 # Set the duration of the audio files\n",
-    "\n",
-    "    # Set the size of the spectrogram images\n",
-    "    img_height = 128\n",
-    "    img_width = 256\n",
-    "\n",
-    "    # Load the audio file\n",
-    "    signal, sr = librosa.load(file_path, sr=22050, duration=duration)\n",
-    "\n",
-    "    # Compute the spectrogram\n",
-    "    spec = librosa.feature.melspectrogram(y=signal, sr=sr, n_fft=2048, hop_length=512, n_mels=128)\n",
-    "\n",
-    "    # Convert the spectrogram to dB scale\n",
-    "    spec_db = librosa.power_to_db(spec, ref=np.max)\n",
-    "\n",
-    "    # Resize the spectrogram to the desired shape\n",
-    "    spec_resized = librosa.util.fix_length(spec_db, size = duration * sr // 512 + 1)\n",
-    "    spec_resized = resize(spec_resized, (img_height, img_width), anti_aliasing=True)\n",
-    "    return spec_resized"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "xhODo1Go31aX"
-   },
-   "source": [
-    "# 7. Create Dataset Objects\n",
-    "In this cell we will create 3 objects from the dataset class we created before. Those will be for training, validation and testing."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "id": "oGIgkQamZlj_"
-   },
-   "outputs": [],
-   "source": [
-    "train_dataset = CustomAudioDataset(dataframe=train)\n",
-    "val_dataset = CustomAudioDataset(dataframe=val)\n",
-    "test_dataset = CustomAudioDataset(dataframe=test)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "COfQ-SS-_VuO"
-   },
-   "source": [
-    "# 8. Hyperparameters\n",
-    "Now we are setting the training hyperparameters, we defined some variables which are the learning rate, batch size and training epoch."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {
-    "id": "hVyPxzTdaNLH"
-   },
-   "outputs": [],
-   "source": [
-    "LR = 1e-4\n",
-    "BATCH_SIZE = 16\n",
-    "EPOCHS = 25"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "WIOyWAak_qHy"
-   },
-   "source": [
-    "# 9. DataLoaders\n",
-    "PyTorch provides a class called DataLoader which allows us to create objects of it to simplify the training. Dataloader is an object that we can loop through it to train according to batches. When we start training, we loop through epochs, if you skip the batch size it means that the amount of training data in one batch is equal to the complete amount of training data, this method is not efficient and in most of the cases you need to train through using batches. Dataloader allows you to loop through the batches easily during the training. When you create a dataloader. You define the batch size and enable the `shuffle` to randomize the data and then you can loop through it in each epoch to train normally."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {
-    "id": "dpx6JAIkaHbY"
-   },
-   "outputs": [],
-   "source": [
-    "train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)\n",
-    "val_loader = DataLoader(val_dataset, batch_size=BATCH_SIZE, shuffle=True)\n",
-    "test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "sWbNHGn3_3TL"
-   },
-   "source": [
-    "# 10. Model\n",
-    "\n",
-    "This code defines a neural network class named Net which inherits from the class `nn.module` in PyTorch.\n",
-    "\n",
-    "* `__init__(self):`\n",
-    "  This is where the neural network is set up. Each layer of the network is defined here.\n",
-    "  self.conv1, self.conv2, self.conv3: These are convolutional layers. They are particularly good at processing data that has a grid-like topology, like images (or in this case, spectrograms from audio data). They work by sliding a small window over the input and transforming the section of data within this window at each step.\n",
-    "  self.pooling: This is a pooling layer, which reduces the size of the input it's given, helping the network to focus on the most important information.\n",
-    "  self.relu: This is an activation function that introduces non-linearity into the network (helping it learn complex patterns).\n",
-    "  self.flatten: This layer flattens the output of the previous layers into a one-dimensional array, which can then be fed into traditional dense (fully connected) layers.\n",
-    "  self.linear1, self.linear2, self.linear4: These are dense layers, where each neuron is connected to all neurons in the previous layer. They are good at learning complex relationships in data.\n",
-    "  self.output: This is the final layer that outputs predictions. The number of neurons here is equal to the number of unique classes in your data.\n",
-    "  self.dropout: This is a dropout layer, used to prevent overfitting (where the model learns the training data too well and performs poorly on new, unseen data).\n",
-    "  \n",
-    "  The firsr linear layer is taking the shape from the flatten, how we calculate the input shape? We know well from the function `get_spectrogram` in `CustomAudioDataset` that the input height is 128, and width is 256. So we keep reducing them by the following (1 is the input channel which is only 1 channel):\n",
-    "  * Starting by (1,128,256)\n",
-    "  * After conv1 will be (16,128,256)\n",
-    "  * After first pooling will be (16,64,128)\n",
-    "  * After conv2 will be (32,64,128)\n",
-    "  * After second pooling will be (32,32,64)\n",
-    "  * After conv3 will be (64,32,64)\n",
-    "  * After third pooling will be (64,16,32)\n",
-    "  So as per this flow, we know that the firsr linear layer input size is\n",
-    "  (64 x 16 x 32)\n",
-    "\n",
-    "* `forward(self, x)`:\n",
-    "  This function defines how the data flows through the network (from input to output).\n",
-    "  The data (x) is passed through each layer in the order they were defined in __init__. After each convolutional and pooling layer, the data's shape changes.\n",
-    "  After passing through all the layers, the network returns its final output."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
+      "cell_type": "markdown",
+      "source": [
+        "# Data Split"
+      ],
+      "metadata": {
+        "id": "KuNMzItF2r4_"
+      }
     },
-    "id": "Mjs684BmaOH9",
-    "outputId": "71666dc8-7f16-4bf5-aecc-e79fafe68639"
-   },
-   "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Net(\n",
-      "  (conv1): Conv2d(1, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-      "  (conv2): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-      "  (conv3): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-      "  (pooling): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
-      "  (relu): ReLU()\n",
-      "  (flatten): Flatten(start_dim=1, end_dim=-1)\n",
-      "  (linear1): Linear(in_features=32768, out_features=4096, bias=True)\n",
-      "  (linear2): Linear(in_features=4096, out_features=1024, bias=True)\n",
-      "  (linear4): Linear(in_features=1024, out_features=512, bias=True)\n",
-      "  (output): Linear(in_features=512, out_features=12, bias=True)\n",
-      "  (dropout): Dropout(p=0.5, inplace=False)\n",
-      ")\n"
-     ]
-    }
-   ],
-   "source": [
-    "class Net(nn.Module):\n",
-    "  def __init__(self):\n",
-    "    super().__init__()\n",
-    "\n",
-    "    self.conv1 = nn.Conv2d(1, 16, kernel_size = 3, padding = 1) # First Convolution layer\n",
-    "    self.conv2 = nn.Conv2d(16, 32, kernel_size = 3, stride = 1, padding = 1) # Second Convolution layer\n",
-    "    self.conv3 = nn.Conv2d(32, 64, kernel_size = 3, stride = 1, padding = 1) # Third Convolution layer\n",
-    "    self.pooling = nn.MaxPool2d(2,2) # The pooling layer, we will be using the same layer after each conv2d.\n",
-    "    self.relu = nn.ReLU() # ReLU Activation function\n",
-    "\n",
-    "    self.flatten = nn.Flatten() # Flatten and vectorize the output feature maps that somes from the final convolution layer.\n",
-    "    self.linear1 = nn.Linear((64 * 16 * 32), 4096) # Traditional Dense (Linear)\n",
-    "    self.linear2 = nn.Linear(4096, 1024) # Traditional Dense (Linear)\n",
-    "    self.linear4 = nn.Linear(1024, 512) # Traditional Dense (Linear)\n",
-    "    self.output = nn.Linear(512, len(data_df['Class'].unique())) # Output Linear Layer\n",
-    "\n",
-    "    self.dropout = nn.Dropout(0.5)\n",
-    "\n",
-    "  def forward(self, x):\n",
-    "    x = self.conv1(x) # -> Outputs: (16, (128, 256))\n",
-    "    x = self.pooling(x)# -> Outputs: (16, (64, 128))\n",
-    "    x = self.relu(x)\n",
-    "    x = self.dropout(x)\n",
-    "    x = self.conv2(x) # -> Outputs: (32, (64, 128))\n",
-    "    x = self.pooling(x) # -> Outputs: (32, (32, 64))\n",
-    "    x = self.relu(x)\n",
-    "    x = self.dropout(x)\n",
-    "    x = self.conv3(x) # -> Outputs: (64, (32, 64))\n",
-    "    x = self.pooling(x) # -> Outputs: (64, (16, 32))\n",
-    "    x = self.relu(x)\n",
-    "    x = self.dropout(x)\n",
-    "    x = x.view(x.size(0), -1)\n",
-    "\n",
-    "    x = self.flatten(x)\n",
-    "\n",
-    "    x = self.linear1(x)\n",
-    "    x = self.dropout(x)\n",
-    "\n",
-    "    x = self.linear2(x)\n",
-    "    x = self.dropout(x)\n",
-    "\n",
-    "\n",
-    "    x = self.linear4(x)\n",
-    "    x = self.dropout(x)\n",
-    "\n",
-    "    x = self.output(x)\n",
-    "\n",
-    "    return x\n",
-    "\n",
-    "model = Net().to(device) # Create an instance of the model and move it to the GPU Device\n",
-    "\n",
-    "\n",
-    "print(model)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "gMLpr3SlKRhs"
-   },
-   "source": [
-    "# 11. Model Summary\n",
-    "This cell is optional, here we show only the summary of the model and what is the output size for each layer. keep in mind that -1 here will be replaced by the batch size."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
+      "cell_type": "code",
+      "source": [
+        "label_encoder = LabelEncoder()\n",
+        "data_df['speaker'] = label_encoder.fit_transform(data_df['speaker'])\n",
+        "\n",
+        "train = data_df.sample(frac=0.7, random_state=42)\n",
+        "test = data_df.drop(train.index)\n",
+        "\n",
+        "val = test.sample(frac=0.5, random_state=42)\n",
+        "test = test.drop(val.index)\n",
+        "\n",
+        "print(\"Training Shape: \", train.shape)\n",
+        "print(\"Validation Shape: \", val.shape)\n",
+        "print(\"Testing Shape: \", test.shape)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "h1czW4eX2nwO",
+        "outputId": "047f436b-0f45-48d7-df7a-7a9ca622d98a"
+      },
+      "execution_count": 40,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Training Shape:  (2182, 2)\n",
+            "Validation Shape:  (468, 2)\n",
+            "Testing Shape:  (467, 2)\n"
+          ]
+        }
+      ]
     },
-    "id": "Ii9mJSYAorqR",
-    "outputId": "c17bedaf-1425-4eb3-bbe3-64b478199b40"
-   },
-   "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "----------------------------------------------------------------\n",
-      "        Layer (type)               Output Shape         Param #\n",
-      "================================================================\n",
-      "            Conv2d-1         [-1, 16, 128, 256]             160\n",
-      "         MaxPool2d-2          [-1, 16, 64, 128]               0\n",
-      "              ReLU-3          [-1, 16, 64, 128]               0\n",
-      "           Dropout-4          [-1, 16, 64, 128]               0\n",
-      "            Conv2d-5          [-1, 32, 64, 128]           4,640\n",
-      "         MaxPool2d-6           [-1, 32, 32, 64]               0\n",
-      "              ReLU-7           [-1, 32, 32, 64]               0\n",
-      "           Dropout-8           [-1, 32, 32, 64]               0\n",
-      "            Conv2d-9           [-1, 64, 32, 64]          18,496\n",
-      "        MaxPool2d-10           [-1, 64, 16, 32]               0\n",
-      "             ReLU-11           [-1, 64, 16, 32]               0\n",
-      "          Dropout-12           [-1, 64, 16, 32]               0\n",
-      "          Flatten-13                [-1, 32768]               0\n",
-      "           Linear-14                 [-1, 4096]     134,221,824\n",
-      "          Dropout-15                 [-1, 4096]               0\n",
-      "           Linear-16                 [-1, 1024]       4,195,328\n",
-      "          Dropout-17                 [-1, 1024]               0\n",
-      "           Linear-18                  [-1, 512]         524,800\n",
-      "          Dropout-19                  [-1, 512]               0\n",
-      "           Linear-20                   [-1, 12]           6,156\n",
-      "================================================================\n",
-      "Total params: 138,971,404\n",
-      "Trainable params: 138,971,404\n",
-      "Non-trainable params: 0\n",
-      "----------------------------------------------------------------\n",
-      "Input size (MB): 0.12\n",
-      "Forward/backward pass size (MB): 12.59\n",
-      "Params size (MB): 530.13\n",
-      "Estimated Total Size (MB): 542.84\n",
-      "----------------------------------------------------------------\n"
-     ]
-    }
-   ],
-   "source": [
-    "from torchsummary import summary # Visualize the model layers and number of parameters\n",
-    "summary(model, (1, 128, 256)) # Visualize the model layers by taking the input size expected (-1 in the output will be replaced by the batch size in training)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "zVqfUtXHKm8S"
-   },
-   "source": [
-    "# 12. Loss and Optimizer\n",
-    "In the cell below, we will create the loss function which will be the Cross Entropy loss. Also we will have create the Adam optimizer and we will provide to it the parameters of the model we created together with the learning rate."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {
-    "id": "Rl-DZMsycuTQ"
-   },
-   "outputs": [],
-   "source": [
-    "# Define loss function and optimizer\n",
-    "criterion = nn.CrossEntropyLoss() # Cross Entropy Loss\n",
-    "optimizer = Adam(model.parameters(), lr=LR) # Adam optimizer"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "YcyloTU8K4MZ"
-   },
-   "source": [
-    "# 13. Training\n",
-    "Now comes the training part. If you know well the training process of deep learning. You will just need to know PyTorch syntax. We start by initializing the for loop with the specified number of epochs which we defined before. But before that we define 4 lists and inside the loop we define another 4 variables.\n",
-    "\n",
-    "## Variables:\n",
-    "* `total_acc_train`: here we will keep tracking of the training accuracy progress during each epoch, we calculate the accuracy during the batch size and we print it in the end for tracking the accuracy on each epoch.\n",
-    "\n",
-    "* `total_loss_train`: here we will keep tracking of the training loss progress during each epoch, we calculate the accuracy during the batch size and we use the loss value to optimize and modify the model parameters.\n",
-    "\n",
-    "* `total_acc_val`: here we will keep tracking of the validation accuracy progress during each epoch, we calculate the accuracy during the batch size and we print it in the end for tracking the accuracy on each epoch and help us know if there is any overfitting.\n",
-    "\n",
-    "* `total_loss_val`: here we will keep tracking of the validation loss progress during each epoch, we calculate the accuracy during the batch size.\n",
-    "\n",
-    "## Lists:\n",
-    "* `total_acc_train_plot`: We append the losses of the training accuracy to visualize them at the end.\n",
-    "\n",
-    "* `total_loss_train_plot`: We append the losses of the training to visualize them at the end.\n",
-    "\n",
-    "* `total_acc_validation_plot`: We append the losses of the validation accuracy to visualize them at the end.\n",
-    "\n",
-    "* `total_loss_validation_plot`: We append the losses of the validation to visualize them at the end.\n",
-    "\n",
-    "Then, we start to loop through the training dataloaders, we use the enumerate functionality to loop through data and indices at the same time. We are not using the indices here, but lets kept it just if you want to try different stuff with the loop or debug. In the second loop, we start by getting our data from the data loader, then we move the inputs and labels to the cuda device. We allow the model to make a prediction or what is called forward propagation, then we get the output of the model and compare it with our original output using the loss criteration. We add the loss amount to total_loss_train. Additionally, we get the accuracy by comparing the correct batch with the predicted batch and we add it to the total_acc_train. Then we do the `batch_loss.backward()` which makes the backpropagation and we use the optimizer to do a step on the weights using `optimizer.step()` and then we reset the optimizer gradients using `optimizer.zero_grad()` which is a very important step that has to be done before proceeding.\n",
-    "\n",
-    "After that we exit the batch loop (train dataloader loop) and we start with the validation. Don't forget that we are still in the same epoch. In side that we start by with `torch.no_grad()` so that we freeze the weights, which means that we need the model to do predicitons without being trained. We just need to see the validation preformance. Then we do the same steps which are predicting and calculating loss and accuracy and storing these values.\n",
-    "\n",
-    "At the end we print after each epoch the epoch number, training loss, training accuracy, validation loss and validation accuracy. Accuracies are calculated by dividing the right predictions by the complete number of samples and multiplying by 100. Losses are being divided by 1000 just to normalize the values and avoid large values.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
+      "cell_type": "markdown",
+      "source": [
+        "# Custom Dataset Class"
+      ],
+      "metadata": {
+        "id": "V0EVkqAJ4KeL"
+      }
     },
-    "id": "vd3UUvybc8Tl",
-    "outputId": "ce0ba3ba-53e4-4ac0-a562-ead41cd6332a"
-   },
-   "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1/25, Train Loss: 29.3048 Train Accuracy 10.7669 Validation Loss: 1.5507 Validation Accuracy: 12.9611 -- Time/Epoch: 18.0 Seconds.\n",
-      "\n",
-      "Epoch 2/25, Train Loss: 7.1539 Train Accuracy 12.4119 Validation Loss: 1.54 Validation Accuracy: 11.2662 -- Time/Epoch: 17.92 Seconds.\n",
-      "\n",
-      "Epoch 3/25, Train Loss: 7.0109 Train Accuracy 14.5482 Validation Loss: 1.4718 Validation Accuracy: 18.0459 -- Time/Epoch: 17.87 Seconds.\n",
-      "\n",
-      "Epoch 4/25, Train Loss: 6.5764 Train Accuracy 21.9825 Validation Loss: 1.3249 Validation Accuracy: 28.4148 -- Time/Epoch: 17.92 Seconds.\n",
-      "\n",
-      "Epoch 5/25, Train Loss: 5.9859 Train Accuracy 30.2072 Validation Loss: 1.2505 Validation Accuracy: 32.004 -- Time/Epoch: 17.89 Seconds.\n",
-      "\n",
-      "Epoch 6/25, Train Loss: 5.5383 Train Accuracy 34.7362 Validation Loss: 1.1395 Validation Accuracy: 37.7866 -- Time/Epoch: 17.95 Seconds.\n",
-      "\n",
-      "Epoch 7/25, Train Loss: 5.1693 Train Accuracy 40.2905 Validation Loss: 1.0353 Validation Accuracy: 42.8714 -- Time/Epoch: 17.95 Seconds.\n",
-      "\n",
-      "Epoch 8/25, Train Loss: 4.8427 Train Accuracy 44.1359 Validation Loss: 1.0389 Validation Accuracy: 44.666 -- Time/Epoch: 17.91 Seconds.\n",
-      "\n",
-      "Epoch 9/25, Train Loss: 4.5464 Train Accuracy 47.1694 Validation Loss: 0.8757 Validation Accuracy: 51.8445 -- Time/Epoch: 17.95 Seconds.\n",
-      "\n",
-      "Epoch 10/25, Train Loss: 4.0763 Train Accuracy 53.1724 Validation Loss: 0.8132 Validation Accuracy: 57.0289 -- Time/Epoch: 17.89 Seconds.\n",
-      "\n",
-      "Epoch 11/25, Train Loss: 3.5389 Train Accuracy 59.389 Validation Loss: 0.6858 Validation Accuracy: 64.2074 -- Time/Epoch: 17.92 Seconds.\n",
-      "\n",
-      "Epoch 12/25, Train Loss: 3.0501 Train Accuracy 64.922 Validation Loss: 0.6231 Validation Accuracy: 67.996 -- Time/Epoch: 17.9 Seconds.\n",
-      "\n",
-      "Epoch 13/25, Train Loss: 2.6789 Train Accuracy 70.0278 Validation Loss: 0.5693 Validation Accuracy: 72.2832 -- Time/Epoch: 17.96 Seconds.\n",
-      "\n",
-      "Epoch 14/25, Train Loss: 2.2801 Train Accuracy 74.3431 Validation Loss: 0.4422 Validation Accuracy: 77.1685 -- Time/Epoch: 17.95 Seconds.\n",
-      "\n",
-      "Epoch 15/25, Train Loss: 2.0211 Train Accuracy 76.8426 Validation Loss: 0.4331 Validation Accuracy: 76.3709 -- Time/Epoch: 17.94 Seconds.\n",
-      "\n",
-      "Epoch 16/25, Train Loss: 1.8071 Train Accuracy 79.342 Validation Loss: 0.3972 Validation Accuracy: 79.5613 -- Time/Epoch: 17.95 Seconds.\n",
-      "\n",
-      "Epoch 17/25, Train Loss: 1.6137 Train Accuracy 82.0765 Validation Loss: 0.3519 Validation Accuracy: 80.3589 -- Time/Epoch: 17.89 Seconds.\n",
-      "\n",
-      "Epoch 18/25, Train Loss: 1.488 Train Accuracy 83.3583 Validation Loss: 0.3506 Validation Accuracy: 82.3529 -- Time/Epoch: 17.97 Seconds.\n",
-      "\n",
-      "Epoch 19/25, Train Loss: 1.2418 Train Accuracy 86.2423 Validation Loss: 0.2147 Validation Accuracy: 88.9332 -- Time/Epoch: 17.9 Seconds.\n",
-      "\n",
-      "Epoch 20/25, Train Loss: 1.1849 Train Accuracy 86.6054 Validation Loss: 0.3177 Validation Accuracy: 85.344 -- Time/Epoch: 17.91 Seconds.\n",
-      "\n",
-      "Epoch 21/25, Train Loss: 1.0964 Train Accuracy 88.7631 Validation Loss: 0.3015 Validation Accuracy: 85.1446 -- Time/Epoch: 17.95 Seconds.\n",
-      "\n",
-      "Epoch 22/25, Train Loss: 1.02 Train Accuracy 88.9981 Validation Loss: 0.2627 Validation Accuracy: 87.338 -- Time/Epoch: 17.96 Seconds.\n",
-      "\n",
-      "Epoch 23/25, Train Loss: 0.8852 Train Accuracy 90.9848 Validation Loss: 0.1911 Validation Accuracy: 91.0269 -- Time/Epoch: 17.96 Seconds.\n",
-      "\n",
-      "Epoch 24/25, Train Loss: 0.8473 Train Accuracy 90.7498 Validation Loss: 0.2121 Validation Accuracy: 90.1296 -- Time/Epoch: 17.92 Seconds.\n",
-      "\n",
-      "Epoch 25/25, Train Loss: 0.8072 Train Accuracy 90.9635 Validation Loss: 0.2174 Validation Accuracy: 90.4287 -- Time/Epoch: 17.97 Seconds.\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "total_loss_train_plot = [] # Empty list to be filled with train loss after each epoch\n",
-    "total_loss_validation_plot = [] # Empty list to be filled with validation loss after each epoch\n",
-    "total_acc_train_plot = [] # Empty list to be filled with train accuracy after each epoch\n",
-    "total_acc_validation_plot = [] # Empty list to be filled with validation accuracy after each epoch\n",
-    "\n",
-    "\n",
-    "for epoch in range(EPOCHS):\n",
-    "  start_time = time.time() # We use this to calculate the time of each epoch, it starts a counter once called\n",
-    "  total_acc_train = 0\n",
-    "  total_loss_train = 0\n",
-    "  total_loss_val = 0\n",
-    "  total_acc_val = 0\n",
-    "\n",
-    "  for inputs, labels in train_loader:\n",
-    "    outputs = model(inputs)\n",
-    "    train_loss = criterion(outputs, labels)\n",
-    "    total_loss_train += train_loss.item()\n",
-    "    train_loss.backward()\n",
-    "\n",
-    "    train_acc = (torch.argmax(outputs, axis = 1) == labels).sum().item()\n",
-    "    total_acc_train += train_acc\n",
-    "    optimizer.step()\n",
-    "    optimizer.zero_grad()\n",
-    "\n",
-    "  with torch.no_grad():\n",
-    "    for inputs, labels in val_loader:\n",
-    "      outputs = model(inputs)\n",
-    "      val_loss = criterion(outputs, labels)\n",
-    "      total_loss_val += val_loss.item()\n",
-    "\n",
-    "      val_acc = (torch.argmax(outputs, axis = 1) == labels).sum().item()\n",
-    "      total_acc_val += val_acc\n",
-    "\n",
-    "  total_loss_train_plot.append(round(total_loss_train/1000, 4))\n",
-    "  total_loss_validation_plot.append(round(total_loss_val/1000, 4))\n",
-    "  total_acc_train_plot.append(round(total_acc_train/(train_dataset.__len__())*100, 4))\n",
-    "  total_acc_validation_plot.append(round(total_acc_val/(val_dataset.__len__())*100, 4))\n",
-    "  epoch_string = f\"\"\"\n",
-    "                  Epoch: {epoch+1}/{EPOCHS}, \n",
-    "                  Train Loss: {round(total_loss_train/100, 4)}, \n",
-    "                  Train Accuracy: {round((total_acc_train/train_dataset.__len__() * 100), 4)}, \n",
-    "                  Validation Loss: {round(total_loss_val/100, 4)}, \n",
-    "                  Validation Accuracy: {round((total_acc_val/val_dataset.__len__() * 100), 4)}\n",
-    "                  \"\"\"\n",
-    "  print(epoch_string)\n",
-    "  print(\"=\"*30)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "pJf4dDRBT8Ms"
-   },
-   "source": [
-    "# 14. Testing\n",
-    "Now in this section, we will be testing our model, we will start the code by with `torch.no_grad():` which means that we are telling PyTorch that we don't want to train the model we will be using it only for testing. Then we will declare initial loss and accuracy as zeros, we will start by looping through the testing dataloader like we did before during training. Inside the loop, we got our data and we moved it to our GPU (`cuda`) and then we ran our model on the data and we got the predictions. After that we get the loss and then we add it to our overall loss, we do the same for accuracy, and finally we print the accuracy."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
+      "cell_type": "code",
+      "source": [
+        "class CustomAudioDataset(Dataset):\n",
+        "    def __init__(self, dataframe):\n",
+        "        self.dataframe = dataframe\n",
+        "        self.speakers = sorted(self.dataframe['speaker'].unique())\n",
+        "        self.speaker_to_idx = {spk: idx for idx, spk in enumerate(self.speakers)}\n",
+        "\n",
+        "    def __len__(self):\n",
+        "        return len(self.dataframe)\n",
+        "\n",
+        "    def __getitem__(self, idx):\n",
+        "        row = self.dataframe.iloc[idx]\n",
+        "        file_path = row['file_path']\n",
+        "        label = self.speaker_to_idx[row['speaker']]\n",
+        "        audio = torch.tensor(self.get_spectrogram(file_path), dtype=torch.float32).unsqueeze(0)\n",
+        "        return audio, torch.tensor(label, dtype=torch.long)\n",
+        "\n",
+        "    def get_spectrogram(self, file_path):\n",
+        "        sr = 22050\n",
+        "        duration = 5\n",
+        "        img_height = 128\n",
+        "        img_width = 256\n",
+        "\n",
+        "        signal, sr = librosa.load(file_path, sr=sr)\n",
+        "\n",
+        "        # Pad or trim to 5 seconds\n",
+        "        target_length = sr * duration\n",
+        "        if len(signal) < target_length:\n",
+        "            signal = np.pad(signal, (0, target_length - len(signal)), mode='constant')\n",
+        "        else:\n",
+        "            signal = signal[:target_length]\n",
+        "\n",
+        "        spec = librosa.feature.melspectrogram(\n",
+        "            y=signal, sr=sr, n_fft=2048, hop_length=512, n_mels=128\n",
+        "        )\n",
+        "        spec_db = librosa.power_to_db(spec, ref=np.max)\n",
+        "        spec_resized = resize(spec_db, (img_height, img_width), anti_aliasing=True)\n",
+        "        return spec_resized\n"
+      ],
+      "metadata": {
+        "id": "vRO05VCC4HEM"
+      },
+      "execution_count": 41,
+      "outputs": []
     },
-    "id": "4lOb5Xa9eFc6",
-    "outputId": "2c6b8f43-2629-4799-8fbf-64cd3c125e0f"
-   },
-   "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accuracy Score is: 88.24%\n"
-     ]
-    }
-   ],
-   "source": [
-    "with torch.no_grad():\n",
-    "  total_loss_test = 0\n",
-    "  total_acc_test = 0\n",
-    "  for indx, (input, labels) in enumerate(test_loader):\n",
-    "\n",
-    "    prediction = model(input)\n",
-    "\n",
-    "    acc = (torch.argmax(prediction, axis = 1) == labels).sum().item()\n",
-    "    total_acc_test += acc\n",
-    "\n",
-    "print(f\"Accuracy Score is: {round((total_acc_test/test_dataset.__len__())*100, 2)}%\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "47uUczlbUGMi"
-   },
-   "source": [
-    "# 15. Plotting Training Progress\n",
-    "Let's plot the training and validation progress through out the EPOCHS. The results may not be very good, feel free to play with the layers, hyperparameters and text filteration to achieve better performance!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 507
+      "cell_type": "markdown",
+      "source": [
+        "# Create Dataset Objects"
+      ],
+      "metadata": {
+        "id": "SMAjHoKaBu4O"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "train_dataset = CustomAudioDataset(dataframe=train)\n",
+        "val_dataset = CustomAudioDataset(dataframe=val)\n",
+        "test_dataset = CustomAudioDataset(dataframe=test)"
+      ],
+      "metadata": {
+        "id": "zlvM7XVj9uwE"
+      },
+      "execution_count": 42,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Hyperparameters"
+      ],
+      "metadata": {
+        "id": "FF0oL6P0Bxoa"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "LR = 1e-4\n",
+        "BATCH_SIZE = 16\n",
+        "EPOCHS = 25"
+      ],
+      "metadata": {
+        "id": "dSC_m-lzBybB"
+      },
+      "execution_count": 43,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# DataLoaders"
+      ],
+      "metadata": {
+        "id": "xUKrgf7RB2MZ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)\n",
+        "val_loader = DataLoader(val_dataset, batch_size=BATCH_SIZE, shuffle=True)\n",
+        "test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=True)"
+      ],
+      "metadata": {
+        "id": "MQVfq-3RBsjM"
+      },
+      "execution_count": 44,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Model"
+      ],
+      "metadata": {
+        "id": "bE0X0iWuHJUt"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "class Net(nn.Module):\n",
+        "  def __init__(self):\n",
+        "    super().__init__()\n",
+        "\n",
+        "    self.conv1 = nn.Conv2d(1, 16, kernel_size=3, padding=1)\n",
+        "    self.bn1 = nn.BatchNorm2d(16)\n",
+        "\n",
+        "    self.conv2 = nn.Conv2d(16, 32, kernel_size=3, padding=1)\n",
+        "    self.bn2 = nn.BatchNorm2d(32)\n",
+        "\n",
+        "    self.conv3 = nn.Conv2d(32, 64, kernel_size=3, padding=1)\n",
+        "    self.bn3 = nn.BatchNorm2d(64)\n",
+        "\n",
+        "    self.pooling = nn.MaxPool2d(2, 2)\n",
+        "    self.relu = nn.ReLU()\n",
+        "    self.dropout = nn.Dropout(0.25)\n",
+        "\n",
+        "    self.flatten = nn.Flatten()\n",
+        "    self.linear1 = nn.Linear((64*16*32), 512)\n",
+        "    self.linear2 = nn.Linear(512, 256)\n",
+        "    self.linear3 = nn.Linear(256, 128)\n",
+        "    self.output = nn.Linear(128, len(data_df['speaker'].unique()))\n",
+        "\n",
+        "  def forward(self, x):\n",
+        "    x = self.conv1(x)\n",
+        "    x = self.bn1(x)\n",
+        "    x = self.relu(x)\n",
+        "    x = self.pooling(x)\n",
+        "    x = self.dropout(x)\n",
+        "\n",
+        "    x = self.conv2(x)\n",
+        "    x = self.bn2(x)\n",
+        "    x = self.relu(x)\n",
+        "    x = self.pooling(x)\n",
+        "    x = self.dropout(x)\n",
+        "\n",
+        "    x = self.conv3(x)\n",
+        "    x = self.bn3(x)\n",
+        "    x = self.relu(x)\n",
+        "    x = self.pooling(x)\n",
+        "    x = self.dropout(x)\n",
+        "\n",
+        "    x = self.flatten(x)\n",
+        "    x = self.linear1(x)\n",
+        "    x = self.dropout(x)\n",
+        "    x = self.linear2(x)\n",
+        "    x = self.dropout(x)\n",
+        "    x = self.linear3(x)\n",
+        "    x = self.dropout(x)\n",
+        "    x = self.output(x)\n",
+        "\n",
+        "    return x\n",
+        "\n",
+        "model = Net().to(device)"
+      ],
+      "metadata": {
+        "id": "Pxh2jji4HKJJ"
+      },
+      "execution_count": 49,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from torchsummary import summary\n",
+        "summary(model, (1, 128, 256))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "5Jw0uU0tHKZM",
+        "outputId": "557db003-2d62-4d1d-db26-a32e338f186b"
+      },
+      "execution_count": 50,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "----------------------------------------------------------------\n",
+            "        Layer (type)               Output Shape         Param #\n",
+            "================================================================\n",
+            "            Conv2d-1         [-1, 16, 128, 256]             160\n",
+            "       BatchNorm2d-2         [-1, 16, 128, 256]              32\n",
+            "              ReLU-3         [-1, 16, 128, 256]               0\n",
+            "         MaxPool2d-4          [-1, 16, 64, 128]               0\n",
+            "           Dropout-5          [-1, 16, 64, 128]               0\n",
+            "            Conv2d-6          [-1, 32, 64, 128]           4,640\n",
+            "       BatchNorm2d-7          [-1, 32, 64, 128]              64\n",
+            "              ReLU-8          [-1, 32, 64, 128]               0\n",
+            "         MaxPool2d-9           [-1, 32, 32, 64]               0\n",
+            "          Dropout-10           [-1, 32, 32, 64]               0\n",
+            "           Conv2d-11           [-1, 64, 32, 64]          18,496\n",
+            "      BatchNorm2d-12           [-1, 64, 32, 64]             128\n",
+            "             ReLU-13           [-1, 64, 32, 64]               0\n",
+            "        MaxPool2d-14           [-1, 64, 16, 32]               0\n",
+            "          Dropout-15           [-1, 64, 16, 32]               0\n",
+            "          Flatten-16                [-1, 32768]               0\n",
+            "           Linear-17                  [-1, 512]      16,777,728\n",
+            "          Dropout-18                  [-1, 512]               0\n",
+            "           Linear-19                  [-1, 256]         131,328\n",
+            "          Dropout-20                  [-1, 256]               0\n",
+            "           Linear-21                  [-1, 128]          32,896\n",
+            "          Dropout-22                  [-1, 128]               0\n",
+            "           Linear-23                   [-1, 18]           2,322\n",
+            "================================================================\n",
+            "Total params: 16,967,794\n",
+            "Trainable params: 16,967,794\n",
+            "Non-trainable params: 0\n",
+            "----------------------------------------------------------------\n",
+            "Input size (MB): 0.12\n",
+            "Forward/backward pass size (MB): 24.76\n",
+            "Params size (MB): 64.73\n",
+            "Estimated Total Size (MB): 89.62\n",
+            "----------------------------------------------------------------\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Loss and Optimizer"
+      ],
+      "metadata": {
+        "id": "MT95LO8bIoB0"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "criterion = nn.CrossEntropyLoss() # Cross Entropy Loss\n",
+        "optimizer = Adam(model.parameters(), lr=LR) # Adam optimizer"
+      ],
+      "metadata": {
+        "id": "spdEMfmOHKNa"
+      },
+      "execution_count": 51,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Training"
+      ],
+      "metadata": {
+        "id": "W7jvF48_IuDy"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "total_loss_train_plot = [] # Empty list to be filled with train loss after each epoch\n",
+        "total_loss_validation_plot = [] # Empty list to be filled with validation loss after each epoch\n",
+        "total_acc_train_plot = [] # Empty list to be filled with train accuracy after each epoch\n",
+        "total_acc_validation_plot = [] # Empty list to be filled with validation accuracy after each epoch\n",
+        "\n",
+        "for epoch in range(EPOCHS):\n",
+        "  total_acc_train = 0\n",
+        "  total_loss_train = 0\n",
+        "  total_loss_val = 0\n",
+        "  total_acc_val = 0\n",
+        "\n",
+        "  for inputs, labels in train_loader:\n",
+        "    inputs = inputs.to(device)\n",
+        "    labels = labels.to(device)\n",
+        "\n",
+        "    outputs = model(inputs)\n",
+        "    train_loss = criterion(outputs, labels)\n",
+        "    total_loss_train += train_loss.item()\n",
+        "    train_loss.backward()\n",
+        "\n",
+        "    train_acc = (torch.argmax(outputs, axis=1) == labels).sum().item()\n",
+        "    total_acc_train += train_acc\n",
+        "    optimizer.step()\n",
+        "    optimizer.zero_grad()\n",
+        "\n",
+        "  with torch.no_grad():\n",
+        "    for inputs, labels in val_loader:\n",
+        "      inputs = inputs.to(device)\n",
+        "      labels = labels.to(device)\n",
+        "\n",
+        "      outputs = model(inputs)\n",
+        "      val_loss = criterion(outputs, labels)\n",
+        "      total_loss_val += val_loss.item()\n",
+        "      val_acc = (torch.argmax(outputs, axis = 1) == labels).sum().item()\n",
+        "      total_acc_val += val_acc\n",
+        "\n",
+        "  total_loss_train_plot.append(round(total_loss_train/1000, 4))\n",
+        "  total_loss_validation_plot.append(round(total_loss_val/1000, 4))\n",
+        "  total_acc_train_plot.append(round(total_acc_train/(train_dataset.__len__())*100, 4))\n",
+        "  total_acc_validation_plot.append(round(total_acc_val/(val_dataset.__len__())*100, 4))\n",
+        "  print(f'''Epoch {epoch+1}/{EPOCHS}, Train Loss: {round(total_loss_train/100, 4)} Train Accuracy {round((total_acc_train)/train_dataset.__len__() * 100, 4)}%\n",
+        "              Validation Loss: {round(total_loss_val/100, 4)} Validation Accuracy: {round((total_acc_val)/val_dataset.__len__() * 100, 4)}%''')\n",
+        "  print(\"=\"*60)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "5igfcuTdIsQe",
+        "outputId": "dbc2e2f8-b1a9-4263-f6ce-a837dfb2a6ba"
+      },
+      "execution_count": 52,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/25, Train Loss: 3.77 Train Accuracy 23.0064%\n",
+            "              Validation Loss: 0.6509 Validation Accuracy: 36.7521%\n",
+            "============================================================\n",
+            "Epoch 2/25, Train Loss: 2.0741 Train Accuracy 52.6123%\n",
+            "              Validation Loss: 0.3695 Validation Accuracy: 61.7521%\n",
+            "============================================================\n",
+            "Epoch 3/25, Train Loss: 1.2152 Train Accuracy 69.9817%\n",
+            "              Validation Loss: 0.2426 Validation Accuracy: 73.0769%\n",
+            "============================================================\n",
+            "Epoch 4/25, Train Loss: 0.7799 Train Accuracy 80.8891%\n",
+            "              Validation Loss: 0.2138 Validation Accuracy: 75.4274%\n",
+            "============================================================\n",
+            "Epoch 5/25, Train Loss: 0.5558 Train Accuracy 86.8011%\n",
+            "              Validation Loss: 0.155 Validation Accuracy: 81.6239%\n",
+            "============================================================\n",
+            "Epoch 6/25, Train Loss: 0.3243 Train Accuracy 92.3923%\n",
+            "              Validation Loss: 0.1255 Validation Accuracy: 86.3248%\n",
+            "============================================================\n",
+            "Epoch 7/25, Train Loss: 0.2479 Train Accuracy 94.1797%\n",
+            "              Validation Loss: 0.106 Validation Accuracy: 88.4615%\n",
+            "============================================================\n",
+            "Epoch 8/25, Train Loss: 0.2613 Train Accuracy 93.8588%\n",
+            "              Validation Loss: 0.1139 Validation Accuracy: 88.0342%\n",
+            "============================================================\n",
+            "Epoch 9/25, Train Loss: 0.2395 Train Accuracy 93.7672%\n",
+            "              Validation Loss: 0.106 Validation Accuracy: 89.5299%\n",
+            "============================================================\n",
+            "Epoch 10/25, Train Loss: 0.1595 Train Accuracy 96.0128%\n",
+            "              Validation Loss: 0.0745 Validation Accuracy: 92.094%\n",
+            "============================================================\n",
+            "Epoch 11/25, Train Loss: 0.1294 Train Accuracy 96.7003%\n",
+            "              Validation Loss: 0.0927 Validation Accuracy: 91.8803%\n",
+            "============================================================\n",
+            "Epoch 12/25, Train Loss: 0.1395 Train Accuracy 96.517%\n",
+            "              Validation Loss: 0.1027 Validation Accuracy: 91.8803%\n",
+            "============================================================\n",
+            "Epoch 13/25, Train Loss: 0.1332 Train Accuracy 96.4253%\n",
+            "              Validation Loss: 0.0557 Validation Accuracy: 94.2308%\n",
+            "============================================================\n",
+            "Epoch 14/25, Train Loss: 0.1134 Train Accuracy 97.2044%\n",
+            "              Validation Loss: 0.1043 Validation Accuracy: 89.9573%\n",
+            "============================================================\n",
+            "Epoch 15/25, Train Loss: 0.1384 Train Accuracy 96.8836%\n",
+            "              Validation Loss: 0.074 Validation Accuracy: 91.453%\n",
+            "============================================================\n",
+            "Epoch 16/25, Train Loss: 0.1143 Train Accuracy 96.9753%\n",
+            "              Validation Loss: 0.0623 Validation Accuracy: 92.9487%\n",
+            "============================================================\n",
+            "Epoch 17/25, Train Loss: 0.1579 Train Accuracy 96.6544%\n",
+            "              Validation Loss: 0.086 Validation Accuracy: 92.3077%\n",
+            "============================================================\n",
+            "Epoch 18/25, Train Loss: 0.1027 Train Accuracy 97.3419%\n",
+            "              Validation Loss: 0.0716 Validation Accuracy: 93.5897%\n",
+            "============================================================\n",
+            "Epoch 19/25, Train Loss: 0.1321 Train Accuracy 97.1127%\n",
+            "              Validation Loss: 0.0874 Validation Accuracy: 92.5214%\n",
+            "============================================================\n",
+            "Epoch 20/25, Train Loss: 0.0987 Train Accuracy 97.6169%\n",
+            "              Validation Loss: 0.0564 Validation Accuracy: 94.2308%\n",
+            "============================================================\n",
+            "Epoch 21/25, Train Loss: 0.1802 Train Accuracy 96.6086%\n",
+            "              Validation Loss: 0.0998 Validation Accuracy: 91.6667%\n",
+            "============================================================\n",
+            "Epoch 22/25, Train Loss: 0.1461 Train Accuracy 96.8836%\n",
+            "              Validation Loss: 0.0872 Validation Accuracy: 92.9487%\n",
+            "============================================================\n",
+            "Epoch 23/25, Train Loss: 0.0708 Train Accuracy 98.121%\n",
+            "              Validation Loss: 0.0644 Validation Accuracy: 93.3761%\n",
+            "============================================================\n",
+            "Epoch 24/25, Train Loss: 0.0497 Train Accuracy 98.5335%\n",
+            "              Validation Loss: 0.0389 Validation Accuracy: 96.1538%\n",
+            "============================================================\n",
+            "Epoch 25/25, Train Loss: 0.0135 Train Accuracy 99.6792%\n",
+            "              Validation Loss: 0.0265 Validation Accuracy: 97.2222%\n",
+            "============================================================\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Testing"
+      ],
+      "metadata": {
+        "id": "A3BXLOrqKbjp"
+      }
     },
-    "id": "z9A8soPovvOj",
-    "outputId": "5e01b9cd-d439-4b69-dc00-1f3e04b68acf"
-   },
-   "outputs": [
     {
-     "data": {
-      "image/png": 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ixvnz5x1iuvJ1kdX3lKx8fhNxBg3hInKHMZvN9OzZM0P5lT1I4+PjiY6OpkGDBly4cIHdu3ffcL8dO3YkX7589uX0nhMHDhy44bZNmzYlPDzcvlylShX8/f3t21osFpYuXUqbNm0cJiwsXbo0LVq0uOH+wfH8EhMTiY6Opl69ehiGwaZNmzLUf+655xyWGzRo4HAu8+fPx83Nzf5NN9jGR+zfv3+W4gHbGJtHjx5l5cqV9rIpU6bg4eFh71Xs6upqH5faarVy7tw50tLSqFmzZqY/Vb2epUuXkpKSQv/+/R1+ojtw4MAMdc1mMy4utrd4i8XC2bNn8fX1pVy5cjd93HTz58/H1dWVAQMGOJS/9NJLGIbBggULHMpv9Lq4HfPnz6dgwYJ07tzZXubu7s6AAQNISEhgxYoVAAQGBpKYmHjdIT4CAwPZsWMHe/fuvekYateuzQMPPGAv8/X1pU+fPhw8eNA+pMrjjz+Om5sbv/zyi73e9u3b2blzJx07drSXTZ8+nQYNGpAvXz6io6Ptj6ZNm2KxWBxeZwDt2rXLcm8zsI0zumTJkgyPK69hun79+tn/Tv/pcEpKCkuXLrWfe1ZeC7Nnz8ZqtTJ06FD76/HK/V6pYsWK9vcdgJCQEMqVK+fwernV50pERORqalOrTX0vtKkXLFjA2bNnHdp7nTt3ZsuWLQ5D1vz666+YTKYMv5iEy222m2nX3Yxnnnkmwxj1V75OU1NTOXv2LKVLlyYwMNDhuv/6669UrVqVtm3bXjPupk2bEhYWxk8//WRft337drZu3XrD8cKz+lyVLVuWatWqObT3LRYLM2bMoHXr1vbzmT59OgEBATRr1syhvV+jRg18fX0zDElUsmRJIiMjrxvjlTw9PTNt72c2j0CfPn0cers///zzuLm5MX/+fODyfTJw4ECH5/uZZ57B39/fPmzlpk2biIqKYuDAgQQGBjocI7PXxY3eU7Ly+U3EGZRAF7nDFC5cONOJAnfs2EHbtm0JCAjA39+fkJAQe4MgNjb2hvstVqyYw3J6wz+zcdButG369unbnj59mosXL1K6dOkM9TIry8zhw4fp0aMH+fPnt4+X1qhRIyDj+aWP2XeteMA2rl6hQoXw9fV1qJeVn8Wl69SpE66urkyZMgWApKQkZs2aRYsWLRw+OE2aNIkqVarYx2wOCQlh3rx5WXpernTo0CEAypQp41AeEhLicDywfbD49NNPKVOmDGazmeDgYEJCQti6detNH/fK44eFheHn5+dQXqFCBYf40t3odXE7Dh06RJkyZTI03q+O5YUXXqBs2bK0aNGCIkWK8PTTT2cYM3LEiBHExMRQtmxZIiIiePnll9m6dWuWYsjs9XJ1DMHBwTRp0oRp06bZ6/zyyy+4ubnx+OOP28v27t3LwoULCQkJcXg0bdoUsN1HVypZsuQNY7xScHAwTZs2zfAoXry4Qz0XFxdKlSrlUFa2bFkA+7iTWX0t7N+/HxcXFypWrHjD+LLyernV50pERORqalOrTX0vtKknT55MyZIlMZvN7Nu3j3379hEeHo63t7dDQnn//v2EhYVdd0jAm2nX3YzM2rQXL15k6NCh9nHH0697TEyMw3Xfv38/lStXvu7+XVxc6NKlC7Nnz+bChQuAbVgbT09P+xc013Izz1XHjh1ZvXo1x44dA2xj458+fdqhw8zevXuJjY2lQIECGdr8CQkJt93ed3V1zbS9n9kQR1e//n19fSlUqJBDex8y3sseHh6UKlXKob0P3PB5gKy9p2Tl85uIMyiBLnKHyWzW7ZiYGBo1asSWLVsYMWIEv/32G0uWLLGPGXblBITXcq2Z6Y1MJgTMzm2zwmKx0KxZM+bNm8err77K7NmzWbJkiX1inqvP71rxZLcCBQrQrFkzfv31V1JTU/ntt9+Ij493GEdv8uTJ9OjRg/DwcMaNG8fChQtZsmQJDz30UJael1v1/vvvM2jQIBo2bMjkyZNZtGgRS5YsoVKlSjl63Cvl9OsiKwoUKMDmzZuZO3euffzCFi1aOIzL2bBhQ/bv38/48eOpXLky33//Pffddx/ff/99tsXRqVMn/vvvPzZv3gzAtGnTaNKkCcHBwfY6VquVZs2aZdprZMmSJbRr185hn5m9F9zJsvJ6yY3nSkRE7g1qU6tNnRV3cps6Li6O3377jaioKMqUKWN/VKxYkQsXLjBlypRcbZdfPVFkuszuxf79+/Pee+/xxBNPMG3aNBYvXsySJUsICgq6peverVs3EhISmD17NoZhMGXKFFq1akVAQMBN7+taOnbsiGEY9rkEpk2bRkBAAM2bN7fXsVqtFChQ4Jrt/fT5htLdK+39K2Xl85uIM2gSUZG7wPLlyzl79iwzZ86kYcOG9vKoqCgnRnVZgQIF8PT0ZN++fRnWZVZ2tW3btvHff/8xadIkunXrZi+/nZ91FS9enGXLlpGQkODQY2bPnj03tZ8uXbqwcOFCFixYwJQpU/D396d169b29TNmzKBUqVLMnDnT4Sdsmf08Misxg63nwpU9hM+cOZOhB8qMGTN48MEHGTdunEN5TEyMQ9L2Zn5uWbx4cZYuXUp8fLxDL4z0nzNf3ZM5JxUvXpytW7ditVodeqFnFouHhwetW7emdevWWK1WXnjhBb755hveeuste2+t/Pnz07NnT3r27ElCQgINGzZk2LBh9O7d+7oxZPZ6ySyGNm3a8Oyzz9p/1vnff/8xZMgQh+3Cw8NJSEiw9zh3FqvVyoEDB+y9zsEWL0CJEiWArL8WwsPDsVqt7Ny586Yn97qWW3muREREskJt6punNrVNXmxTz5w5k6SkJMaOHesQK9ienzfffJPVq1fzwAMPEB4ezqJFizh37tw1e6FntV2XL18+YmJiHMpSUlI4ceJElmOfMWMG3bt35+OPP7aXJSUlZdhveHg427dvv+H+KleuTPXq1fnpp58oUqQIhw8f5osvvrjhdjfzXJUsWZLatWvzyy+/0K9fP2bOnEmbNm0wm80O8S5dupT69es7PTm+d+9eHnzwQftyQkICJ06coGXLlsDlc9uzZ4/DfZKSkkJUVJT9M0v68ELbt2/Pts8xWfn8JpLb1ANd5C6Q/k3ulT0IUlJS+Oqrr5wVkoP0n5LNnj2b48eP28v37duXYYy/a20PjudnGAafffbZLcfUsmVL0tLSGDt2rL3MYrFkqSF1pTZt2uDt7c1XX33FggULePzxx/H09Lxu7GvXrmXNmjU3HXPTpk1xd3fniy++cNjfmDFjMtR1dXXN0KNk+vTp9p8UpvPx8QHI0BjNTMuWLbFYLHz55ZcO5Z9++ikmkynLY29mh5YtW3Ly5EmHcQbT0tL44osv8PX1tf8U+ezZsw7bubi4UKVKFQCSk5MzrePr60vp0qXt668Xw7p16xyey8TERL799ltKlCjh8PPWwMBAIiMjmTZtGj///DMeHh60adPGYX9PPPEEa9asYdGiRRmOFRMTQ1pa2nXjyU5XPseGYfDll1/i7u5OkyZNgKy/Ftq0aYOLiwsjRozI0FvoVno83epzJSIikhVqU988talt8mKbevLkyZQqVYrnnnuO9u3bOzwGDx6Mr6+vfRiXdu3aYRgGw4cPz7Cf9PPParsuPDw8w9w933777TV7oGcms+v+xRdfZNhHu3bt2LJlC7Nmzbpm3Om6du3K4sWLGTNmDEFBQVm6zjf7XHXs2JF//vmH8ePHEx0d7TB8C9ja+xaLhXfeeSfDsdLS0rL0+sku3377LampqfblsWPHkpaWZj+npk2b4uHhweeff+5wLceNG0dsbCyPPPIIAPfddx8lS5ZkzJgxGeLPjvZ+Zp/fRJxBPdBF7gL16tUjX758dO/enQEDBmAymfjxxx9z9Sd5NzJs2DAWL15M/fr1ef755+0NkcqVK9uHtbiW8uXLEx4ezuDBgzl27Bj+/v78+uuvtzWWduvWralfvz6vvfYaBw8epGLFisycOfOmxzL09fWlTZs29jEbr/ypKUCrVq2YOXMmbdu25ZFHHiEqKoqvv/6aihUrkpCQcFPHCgkJYfDgwYwcOZJWrVrRsmVLNm3axIIFCzL0KmnVqhUjRoygZ8+e1KtXj23btvHTTz9lGNs6PDycwMBAvv76a/z8/PDx8aFOnTqZjrfXunVrHnzwQd544w0OHjxI1apVWbx4MXPmzGHgwIEOkxtlh2XLlpGUlJShvE2bNvTp04dvvvmGHj168O+//1KiRAlmzJjB6tWrGTNmjL2HSO/evTl37hwPPfQQRYoU4dChQ3zxxRdUq1bNPnZhxYoVady4MTVq1CB//vxs2LCBGTNmOEykmZnXXnuNqVOn0qJFCwYMGED+/PmZNGkSUVFR/PrrrxnGZ+/YsSNPPfUUX331FZGRkRkm2Xn55ZeZO3curVq1okePHtSoUYPExES2bdvGjBkzOHjwYIbn+WYcO3aMyZMnZyhPfw2n8/T0ZOHChXTv3p06deqwYMEC5s2bx+uvv24fszCrr4XSpUvzxhtv8M4779CgQQMef/xxzGYz69evJywsjJEjR97UOdzqcyUiIpIValPfPLWpbfJam/r48eP8+eefGSa/TGc2m4mMjGT69Ol8/vnnPPjgg3Tt2pXPP/+cvXv30rx5c6xWK3/99RcPPvgg/fr1y3K7rnfv3jz33HO0a9eOZs2asWXLFhYtWnRT7dhWrVrx448/EhAQQMWKFVmzZg1Lly4lKCjIod7LL7/MjBkz6NChA08//TQ1atTg3LlzzJ07l6+//pqqVava6z755JO88sorzJo1i+eff95hAs1rudnn6oknnmDw4MEMHjyY/PnzZ+iR3ahRI5599llGjhzJ5s2befjhh3F3d2fv3r1Mnz6dzz77jPbt22f5Ol0tLS0t0/Y+QNu2be1f9IDty8EmTZrwxBNPsGfPHr766iseeOABHn30UcB2nwwZMoThw4fTvHlzHn30UXu9WrVq2eeGcHFxYezYsbRu3Zpq1arRs2dPChUqxO7du9mxY0emnYOuJyuf30ScwhCRPKlv377G1bdoo0aNjEqVKmVaf/Xq1cb9999veHl5GWFhYcYrr7xiLFq0yACMP//8016ve/fuRvHixe3LUVFRBmCMGjUqwz4B4+2337Yvv/322xliAoy+fftm2LZ48eJG9+7dHcqWLVtmVK9e3fDw8DDCw8ON77//3njppZcMT0/Pa1yFy3bu3Gk0bdrU8PX1NYKDg41nnnnG2LJliwEYEyZMcDg/Hx+fDNtnFvvZs2eNrl27Gv7+/kZAQIDRtWtXY9OmTRn2eSPz5s0zAKNQoUKGxWJxWGe1Wo3333/fKF68uGE2m43q1asbv//+e4bnwTAyXu8JEyYYgBEVFWUvs1gsxvDhw41ChQoZXl5eRuPGjY3t27dnuN5JSUnGSy+9ZK9Xv359Y82aNUajRo2MRo0aORx3zpw5RsWKFQ03NzeHc88sxvj4eON///ufERYWZri7uxtlypQxRo0aZVit1gznktXXxdXSX5PXevz444+GYRjGqVOnjJ49exrBwcGGh4eHERERkeF5mzFjhvHwww8bBQoUMDw8PIxixYoZzz77rHHixAl7nXfffdeoXbu2ERgYaHh5eRnly5c33nvvPSMlJeW6cRqGYezfv99o3769ERgYaHh6ehq1a9c2fv/990zrxsXFGV5eXgZgTJ48OdM68fHxxpAhQ4zSpUsbHh4eRnBwsFGvXj1j9OjR9niud89eS/Hixa95Pa98jtPvn/379xsPP/yw4e3tbYSGhhpvv/12htd2Vl8LhmEY48ePN6pXr26YzWYjX758RqNGjYwlS5Y4xPfII49k2O7q1+vtPFciInJvUpvakdrUNnd7m/rjjz82AGPZsmXXrDNx4kQDMObMmWMYhmGkpaUZo0aNMsqXL294eHgYISEhRosWLYx///3XYbsbtessFovx6quvGsHBwYa3t7cRGRlp7Nu3L0PM6c/L+vXrM8R2/vx5ezvf19fXiIyMNHbv3p3peZ89e9bo16+fUbhwYcPDw8MoUqSI0b17dyM6OjrDflu2bGkAxt9//33N63K1m2nzGoZh1K9f3wCM3r17X3Of3377rVGjRg3Dy8vL8PPzMyIiIoxXXnnFOH78uL3OtdrH19K9e/frfoZKf/2nX/cVK1YYffr0MfLly2f4+voaXbp0Mc6ePZthv19++aVRvnx5w93d3QgNDTWef/554/z58xnqrVq1ymjWrJnh5+dn+Pj4GFWqVDG++OILh/iy8p6Slc9vIs5gMow89HW6iNxz2rRpw44dO9i7d6+zQxG55/Xo0YMZM2bcdE8uERERcS61qUVurG3btmzbti1LcwbcrSZOnEjPnj1Zv349NWvWdHY4IncMjYEuIrnm4sWLDst79+5l/vz5NG7c2DkBiYiIiIjcYdSmFrl5J06cYN68eXTt2tXZoYjIHUhjoItIrilVqhQ9evSgVKlSHDp0iLFjx+Lh4cErr7zi7NBERERERO4IalOLZF1UVBSrV6/m+++/x93dnWeffdbZIYnIHUgJdBHJNc2bN2fq1KmcPHkSs9lM3bp1ef/99ylTpoyzQxMRERERuSOoTS2SdStWrKBnz54UK1aMSZMmUbBgQWeHJCJ3II2BLiIiIiJyj1q5ciWjRo3i33//5cSJE8yaNYs2bdrY1xuGwdtvv813331HTEwM9evXZ+zYsQ6JunPnztG/f39+++03XFxcaNeuHZ999hm+vr5OOCMRERERkeylMdBFRERERO5RiYmJVK1alf/7v//LdP1HH33E559/ztdff83atWvx8fEhMjKSpKQke50uXbqwY8cOlixZwu+//87KlSvp06dPbp2CiIiIiEiOUg90ERERERHBZDI59EA3DIOwsDBeeuklBg8eDEBsbCyhoaFMnDiRTp06sWvXLipWrMj69eupWbMmAAsXLqRly5YcPXqUsLAwZ52OiIiIiEi2uOfGQLdarRw/fhw/Pz9MJpOzwxERERGRu5RhGMTHxxMWFoaLy533w8+oqChOnjxJ06ZN7WUBAQHUqVOHNWvW0KlTJ9asWUNgYKA9eQ7QtGlTXFxcWLt2LW3bts1038nJySQnJ9uXrVYr586dIygoSG10EREREckRt9o+v+cS6MePH6do0aLODkNERERE7hFHjhyhSJEizg7jpp08eRKA0NBQh/LQ0FD7upMnT1KgQAGH9W5ubuTPn99eJzMjR45k+PDh2RyxiIiIiMiN3Wz7/J5LoPv5+QG2C+Xv7+/kaERERETkbhUXF0fRokXt7U+5bMiQIQwaNMi+HBsbS7FixdRGFxEREZEcc6vt83sugZ7+k1B/f381zkVEREQkx92pQ5IULFgQgFOnTlGoUCF7+alTp6hWrZq9zunTpx22S0tL49y5c/btM2M2mzGbzRnK1UYXERERkZx2s+3zO28wRhERERERyXElS5akYMGCLFu2zF4WFxfH2rVrqVu3LgB169YlJiaGf//9117njz/+wGq1UqdOnVyPWUREREQku91zPdBFRERERMQmISGBffv22ZejoqLYvHkz+fPnp1ixYgwcOJB3332XMmXKULJkSd566y3CwsJo06YNABUqVKB58+Y888wzfP3116SmptKvXz86depEWFiYk85KRERERCT7OLUH+tixY6lSpYr9p5p169ZlwYIF191m+vTplC9fHk9PTyIiIpg/f34uRSsiIiIicnfZsGED1atXp3r16gAMGjSI6tWrM3ToUABeeeUV+vfvT58+fahVqxYJCQksXLgQT09P+z5++uknypcvT5MmTWjZsiUPPPAA3377rVPOR0REREQku5kMwzCcdfDffvsNV1dXypQpg2EYTJo0iVGjRrFp0yYqVaqUof7ff/9Nw4YNGTlyJK1atWLKlCl8+OGHbNy4kcqVK2fpmHFxcQQEBBAbG6vxFUVERO4RFouF1NRUZ4chdxl3d3dcXV2vuV7tzqzL6rXSvSx3qxu9n4iIiMjtu9X2uVMT6JnJnz8/o0aNolevXhnWdezYkcTERH7//Xd72f3330+1atX4+uuvs7R/fZARERG5dxiGwcmTJ4mJiXF2KHKXCgwMpGDBgplORKR2Z9bd6FrpXpZ7wfXeT0REROT23Wr7PM+MgW6xWJg+fTqJiYn2SYmutmbNGgYNGuRQFhkZyezZs3MhQhEREbnTpCfcChQogLe3t5ISkm0Mw+DChQucPn0agEKFCjk5orub7mW5m+n9REREJG9zegJ927Zt1K1bl6SkJHx9fZk1axYVK1bMtO7JkycJDQ11KAsNDeXkyZPX3H9ycjLJycn25bi4uOwJXERERPI0i8ViT7gFBQU5Oxy5C3l5eQFw+vRpChQooOEXcojuZbkX6P1EREQk73LqJKIA5cqVY/Pmzaxdu5bnn3+e7t27s3Pnzmzb/8iRIwkICLA/ihYtmm37FhERkbwrfZxkb29vJ0cid7P015fG5c45upflXqH3ExERkbzJ6Ql0Dw8PSpcuTY0aNRg5ciRVq1bls88+y7RuwYIFOXXqlEPZqVOnKFiw4DX3P2TIEGJjY+2PI0eOZGv8IiIikrdpqAfJSXp95R5da7nb6TUuIiKSNzk9gX41q9XqMOTKlerWrcuyZcscypYsWXLNMdMBzGYz/v7+Dg8RERERERERERERkRtxagJ9yJAhrFy5koMHD7Jt2zaGDBnC8uXL6dKlCwDdunVjyJAh9vovvvgiCxcu5OOPP2b37t0MGzaMDRs20K9fP2edgoiIiMgdoUSJEowZMybL9ZcvX47JZCImJibHYhKRm6P7WERERCT3OTWBfvr0abp160a5cuVo0qQJ69evZ9GiRTRr1gyAw4cPc+LECXv9evXqMWXKFL799luqVq3KjBkzmD17NpUrV3bWKYiIiIhkK5PJdN3HsGHDbmm/69evp0+fPlmuX69ePU6cOEFAQMAtHS+rlOCTu9G9dh9fqXz58pjNZk6ePJlrxxQRERHJSW7OPPi4ceOuu3758uUZyjp06ECHDh1yKCIRERER57qy88Avv/zC0KFD2bNnj73M19fX/rdhGFgsFtzcbtykCwkJuak4PDw8rjvPjIhc2716H69atYqLFy/Svn17Jk2axKuvvpprx85Mamoq7u7uTo1BRERE7nx5bgx0ERERkXtZwYIF7Y+AgABMJpN9effu3fj5+bFgwQJq1KiB2Wxm1apV7N+/n8cee4zQ0FB8fX2pVasWS5cuddjv1UM/mEwmvv/+e9q2bYu3tzdlypRh7ty59vVX9wyfOHEigYGBLFq0iAoVKuDr60vz5s0dEoVpaWkMGDCAwMBAgoKCePXVV+nevTtt2rS55etx/vx5unXrRr58+fD29qZFixbs3bvXvv7QoUO0bt2afPny4ePjQ6VKlZg/f7592y5duhASEoKXlxdlypRhwoQJtxyLSFbdq/fxuHHjePLJJ+natSvjx4/PsP7o0aN07tyZ/Pnz4+PjQ82aNVm7dq19/W+//UatWrXw9PQkODiYtm3bOpzr7NmzHfYXGBjIxIkTATh48CAmk4lffvmFRo0a4enpyU8//cTZs2fp3LkzhQsXxtvbm4iICKZOneqwH6vVykcffUTp0qUxm80UK1aM9957D4CHHnoow5ChZ86cwcPDI8P8XCIiInJ3UgI9l5yMTWLh9pOs3hft7FBERETuWYZhcCElLdcfhmFk63m89tprfPDBB+zatYsqVaqQkJBAy5YtWbZsGZs2baJ58+a0bt2aw4cPX3c/w4cP54knnmDr1q20bNmSLl26cO7cuWvWv3DhAqNHj+bHH39k5cqVHD58mMGDB9vXf/jhh/z0009MmDCB1atXExcXlyHhdbN69OjBhg0bmDt3LmvWrMEwDFq2bElqaioAffv2JTk5mZUrV7Jt2zY+/PBDe+/et956i507d7JgwQJ27drF2LFjCQ4Ovq14xPmcdR9n9718t93H8fHxTJ8+naeeeopmzZoRGxvLX3/9ZV+fkJBAo0aNOHbsGHPnzmXLli288sorWK1WAObNm0fbtm1p2bIlmzZtYtmyZdSuXfuGx73aa6+9xosvvsiuXbuIjIwkKSmJGjVqMG/ePLZv306fPn3o2rUr69ats28zZMgQPvjgA/t7xpQpUwgNDQWgd+/eTJkyheTkZHv9yZMnU7hwYR566KGbjk9ERETuPE4dwuVesnpfNC9N30KDMsHUL60PbiIiIs5wMdVCxaGLcv24O0dE4u2Rfc2uESNG2OeMAcifPz9Vq1a1L7/zzjvMmjWLuXPnXney9R49etC5c2cA3n//fT7//HPWrVtH8+bNM62fmprK119/TXh4OAD9+vVjxIgR9vVffPEFQ4YMsfca/fLLL+29wW/F3r17mTt3LqtXr6ZevXoA/PTTTxQtWpTZs2fToUMHDh8+TLt27YiIiACgVKlS9u0PHz5M9erVqVmzJmDrvSt3Pmfdx5C99/Lddh///PPPlClThkqVKgHQqVMnxo0bR4MGDQCYMmUKZ86cYf369eTPnx+A0qVL27d/77336NSpE8OHD7eXXXk9smrgwIE8/vjjDmVXfkHQv39/Fi1axLRp06hduzbx8fF89tlnfPnll3Tv3h2A8PBwHnjgAQAef/xx+vXrx5w5c3jiiScAW0/+Hj16YDKZbjo+ERERufOoB3ouCfYzA3AmPvkGNUVERESuLz0hnC4hIYHBgwdToUIFAgMD8fX1ZdeuXTfsuVqlShX73z4+Pvj7+3P69Olr1vf29rYn3QAKFSpkrx8bG8upU6cceoy6urpSo0aNmzq3K+3atQs3Nzfq1KljLwsKCqJcuXLs2rULgAEDBvDuu+9Sv3593n77bbZu3Wqv+/zzz/Pzzz9TrVo1XnnlFf7+++9bjkUku91t9/H48eN56qmn7MtPPfUU06dPJz4+HoDNmzdTvXp1e/L8aps3b6ZJkyY3PM6NXH1dLRYL77zzDhEREeTPnx9fX18WLVpkv667du0iOTn5msf29PR0GJJm48aNbN++nR49etx2rCIiInJnUA/0XBLs6wFAdIIS6CIiIs7i5e7KzhGRTjludvLx8XFYHjx4MEuWLGH06NGULl0aLy8v2rdvT0pKynX3c/XkeiaTyT6cQlbrZ/fwNDerd+/eREZGMm/ePBYvXszIkSP5+OOP6d+/Py1atODQoUPMnz+fJUuW0KRJE/r27cvo0aOdGrPcHmfdx+nHzi530328c+dO/vnnH9atW+cwcajFYuHnn3/mmWeewcvL67r7uNH6zOJMH8rpSldf11GjRvHZZ58xZswYIiIi8PHxYeDAgfbreqPjgu19plq1ahw9epQJEybw0EMPUbx48RtuJyIiIncH9UDPJSGXeqCfS0zBYnXuB00REZF7lclkwtvDLdcfOf0z/9WrV9OjRw/atm1LREQEBQsW5ODBgzl6zKsFBAQQGhrK+vXr7WUWi4WNGzfe8j4rVKhAWlqawySDZ8+eZc+ePVSsWNFeVrRoUZ577jlmzpzJSy+9xHfffWdfFxISQvfu3Zk8eTJjxozh22+/veV4JG9w1n2c0/fynXwfjxs3joYNG7JlyxY2b95sfwwaNIhx48YBtp7ymzdvvub47FWqVLnupJwhISEOk53u3buXCxcu3PCcVq9ezWOPPcZTTz1F1apVKVWqFP/99599fZkyZfDy8rrusSMiIqhZsybfffcdU6ZM4emnn77hcUVEROTuoR7ouSS/twcmE1gNWxI9PaEuIiIicrvKlCnDzJkzad26NSaTibfeeuu6PVBzSv/+/Rk5ciSlS5emfPnyfPHFF5w/fz5LScdt27bh5+dnXzaZTFStWpXHHnuMZ555hm+++QY/Pz9ee+01ChcuzGOPPQbYxjtu0aIFZcuW5fz58/z5559UqFABgKFDh1KjRg0qVapEcnIyv//+u32dSF5zp97Hqamp/Pjjj4wYMYLKlSs7rOvduzeffPIJO3bsoHPnzrz//vu0adOGkSNHUqhQITZt2kRYWBh169bl7bffpkmTJoSHh9OpUyfS0tKYP3++vUf7Qw89xJdffkndunWxWCy8+uqrGXrTZ6ZMmTLMmDGDv//+m3z58vHJJ59w6tQp+5dwnp6evPrqq7zyyit4eHhQv359zpw5w44dO+jVq5fDufTr1w8fHx/7+PAiIiJyb1AP9Fzi5upCfm8N4yIiIiLZ75NPPiFfvnzUq1eP1q1bExkZyX333Zfrcbz66qt07tyZbt26UbduXXx9fYmMjMTT0/OG2zZs2JDq1avbH+ljLk+YMIEaNWrQqlUr6tati2EYzJ8/3544s1gs9O3blwoVKtC8eXPKli3LV199BYCHhwdDhgyhSpUqNGzYEFdXV37++eecuwAit+FOvY/nzp3L2bNnM00qV6hQgQoVKjBu3Dg8PDxYvHgxBQoUoGXLlkRERPDBBx/g6mobFqdx48ZMnz6duXPnUq1aNR566CHWrVtn39fHH39M0aJFadCgAU8++SSDBw/G29v7hufz5ptvct999xEZGUnjxo0pWLAgbdq0cajz1ltv8dJLLzF06FAqVKhAx44dM4wj37lzZ9zc3OjcuXOW3tNERETk7mEynD1wZS6Li4sjICCA2NhY/P39c/XYkZ+uZM+peH7sVZsGZUJy9dgiIiL3mqSkJKKioihZsqSSHU5itVqpUKECTzzxBO+8846zw8kR13udObPdeae53rXSvexc98J9nBUHDx4kPDyc9evX59gXG3qti4iI5KxbbZ9rCJdcFOznwZ5TcCZePdBFRETk7nPo0CEWL15Mo0aNSE5O5ssvvyQqKoonn3zS2aGJSBbpPnaUmprK2bNnefPNN7n//vud8qsAERERcS4N4ZKLQnxt455rCBcRERG5G7m4uDBx4kRq1apF/fr12bZtG0uXLtW44yJ3EN3HjlavXk2hQoVYv349X3/9tbPDERERESdQD/RcFGxPoKc4ORIRERGR7Fe0aFFWr17t7DBE5DboPnbUuHFj7rFRT0VEROQq6oGei4L9LiXQNYSLiIiIiIiIiIiISJ6nBHouSu+BfkZDuIiIiIiIiIiIiIjkeUqg56JgXw9Ak4iKiIiIiIiIiIiI3AmUQM9FIX4aA11ERERERERERETkTqEEei4KuTSEy7nEZCxWTUQjIiIiIiIiIiIikpcpgZ6L8vt4YDKB1YDzF9QLXURERERERERERCQvUwI9F7m5upDP2zYOerQmEhUREZEc1LhxYwYOHGhfLlGiBGPGjLnuNiaTidmzZ9/2sbNrPyL3Ot3HIiIiIs6nBHou00SiIiIicj2tW7emefPmma7766+/MJlMbN269ab3u379evr06XO74TkYNmwY1apVy1B+4sQJWrRoka3HutrEiRMJDAzM0WOI3Crdxzfn4sWL5M+fn+DgYJKT9TlJREQkM2f+W8v+pd+xb8NiDkTt58jZRM7EJ5OQnEaaxers8K7JMAxSLVbik1I5E5/M0fMX2Hc6gX2nE5wdWpa5OTuAe02In5n/TiWoB7qIiIhkqlevXrRr146jR49SpEgRh3UTJkygZs2aVKlS5ab3GxISkl0h3lDBggVz7VgieZHu45vz66+/UqlSJQzDYPbs2XTs2DHXjn01wzCwWCy4uemjsoiION++0wn8tXEbJTZ9yIPJf3JlSyDB8OSQEUqUUZBDRihHKMRx18Kcdg/jgnsQnh6ueLm7Yna3/evl7oqnuwteHq6Y3Vzx8nDF080VLw8Xh3oebi6kpFlJTrOSlGohKdVCcpqV5FQLSVeV2f62OixnVjezqSCLB3mz4uUHc+1a3g71QM9lwZcmEo2O1xjoIiIiklGrVq0ICQlh4sSJDuUJCQlMnz6dXr16cfbsWTp37kzhwoXx9vYmIiKCqVOnXne/Vw/9sHfvXho2bIinpycVK1ZkyZIlGbZ59dVXKVu2LN7e3pQqVYq33nqL1NRUwNYDfPjw4WzZsgWTyYTJZLLHfPXQD9u2beOhhx7Cy8uLoKAg+vTpQ0LC5R4nPXr0oE2bNowePZpChQoRFBRE37597ce6FYcPH+axxx7D19cXf39/nnjiCU6dOmVfv2XLFh588EH8/Pzw9/enRo0abNiwAYBDhw7RunVr8uXLh4+PD5UqVWL+/Pm3HIvce3Qf39x9PG7cOJ566imeeuopxo0bl2H9jh07aNWqFf7+/vj5+dGgQQP2799vXz9+/HgqVaqE2WymUKFC9OvXD4CDBw9iMpnYvHmzvW5MTAwmk4nly5cDsHz5ckwmEwsWLKBGjRqYzWZWrVrF/v37eeyxxwgNDcXX15datWqxdOlSh7iSk5N59dVXKVq0KGazmdKlSzNu3DgMw6B06dKMHj3aof7mzZsxmUzs27fvhtdERETuTYZhsOVIDB8t3E3k6CVM+2wwHda04cHkP7EaJra5lOcYoVhwwdeURCWXQ7RyXUtft7l84PYNP5iGsjCtN/MvdObT8/155tQIGh4ZS4H904neuZxVm3cxdd1hJv59kLHL9/Pp0v94f/5u3pqzg1dmbKX/1E08++O/9J+6icHTt/Dm7O28O28Xoxbt4fM/9vHtygP8sOYQ0zYcZc7m4yzacYoV/51hbdQ5Nh+JYffJeKKiEzkem8S5xBQupGRMnpvdXAjwdCOfOZOseh6lr9VzmT2Brh7oIiIiuc8wIPVC7h/X3RtMpixVdXNzo1u3bkycOJE33ngD06Xtpk+fjsVioXPnziQkJFCjRg1effVV/P39mTdvHl27diU8PJzatWvf8BhWq5XHH3+c0NBQ1q5dS2xsrMM4y+n8/PyYOHEiYWFhbNu2jWeeeQY/Pz9eeeUVOnbsyPbt21m4cKE9qRQQEJBhH4mJiURGRlK3bl3Wr1/P6dOn6d27N/369XNILv75558UKlSIP//8k3379tGxY0eqVavGM888k6XrdvX5pSfPV6xYQVpaGn379qVjx472pFmXLl2oXr06Y8eOxdXVlc2bN+Pu7g5A3759SUlJYeXKlfj4+LBz5058fX1vOg7JIc66jyHL97Lu46zfx/v372fNmjXMnDkTwzD43//+x6FDhyhevDgAx44do2HDhjRu3Jg//vgDf39/Vq9eTVpaGgBjx45l0KBBfPDBB7Ro0YLY2FhWr159w+t3tddee43Ro0dTqlQp8uXLx5EjR2jZsiXvvfceZrOZH374gdatW7Nnzx6KFSsGQLdu3VizZg2ff/45VatWJSoqiujoaEwmE08//TQTJkxg8ODB9mNMmDCBhg0bUrp06ZuOT0RE7l5pFivros6xaMdJFu88xYnYJBq5bOErtx8Idz8BQHRgFVwfGUVEmfsvbZQM5w9hnNtP2pl9WM/uh7MHcDl/ALeEY/iSRCXTISpxKMPxUlx9OO9ZhLMehTnlXpiTroU57lqIwxTitMWfZIsVDzcXPN1dMbu54Onmgq+rhQCXJHxdkvE1JeFjSsabJLy5iDdJeBoX8bQmYTYu4mG5gIflAm7Wi7inXcA17QKuqQmYUi9gSk3ElJIIKQlgKgX8m4tX+tYpgZ7L0hPoZ5RAFxERyX2pF+D9sNw/7uvHwcMny9WffvppRo0axYoVK2jcuDFgS7y0a9eOgIAAAgICHJIy/fv3Z9GiRUybNi1LibelS5eye/duFi1aRFiY7Xq8//77GcY7fvPNN+1/lyhRgsGDB/Pzzz/zyiuv4OXlha+vL25ubtcd6mHKlCkkJSXxww8/4ONjuwZffvklrVu35sMPPyQ0NBSAfPny8eWXX+Lq6kr58uV55JFHWLZs2S0l0JctW8a2bduIioqiaNGiAPzwww9UqlSJ9evXU6tWLQ4fPszLL79M+fLlAShTpox9+8OHD9OuXTsiIiIAKFWq1E3HIDnIWfcx3NS9rPs4a/fx+PHjadGiBfny5QMgMjKSCRMmMGzYMAD+7//+j4CAAH7++Wf7l1xly5a1b//uu+/y0ksv8eKLL9rLatWqdcPrd7URI0bQrFkz+3L+/PmpWrWqffmdd95h1qxZzJ07l379+vHff/8xbdo0lixZQtOmTQHH94oePXowdOhQ1q1bR+3atUlNTWXKlCkZeqWLiMi9KSnVwl97o1m04yTLdp3i/AXbL7aKmU4x3vwTD5lsv4y0+hTApdlwgqt0ApcrBhJxM0NIWUwhZXEvd9XOLyXXOXcAzu2Hs/sv/x1zBA9LIqGJewhN3EPFqwPz8IPAopCcCvEJkJ7sNnJgjPWUxOzfZw5RAj2XhfhdSqBrElERERG5hvLly1OvXj3Gjx9P48aN2bdvH3/99RcjRowAwGKx8P777zNt2jSOHTtGSkoKycnJeHt7Z2n/u3btomjRovakG0DdunUz1Pvll1/4/PPP2b9/PwkJCaSlpeHv739T57Jr1y6qVq1qT7oB1K9fH6vVyp49e+yJt0qVKuHq6mqvU6hQIbZt23ZTx7rymEWLFrUnzwEqVqxIYGAgu3btolatWgwaNIjevXvz448/0rRpUzp06EB4eDgAAwYM4Pnnn2fx4sU0bdqUdu3a3dJ41XJv03184/vYYrEwadIkPvvsM3vZU089xeDBgxk6dCguLi5s3ryZBg0a2JPnVzp9+jTHjx+nSZMmN3U+malZs6bDckJCAsOGDWPevHmcOHGCtLQ0Ll68yOHDhwHbcCyurq40atQo0/2FhYXxyCOPMH78eGrXrs1vv/1GcnIyHTp0uO1YRUTkzhR7MZU/d59m0Y6TrPjvDBdSLPZ1hbwsvBu8mAfP/oyLNRVc3KDOc7g0egU8M/467LouJdcJKZtxnT25fimpfnb/5b9jjkBKPJzeee19u/vYOhN4+ICH7+W/zb6Oyx4+tmT81XWvrneHUAI9lwX7egAQnaAx0EVERHKdu7etB6kzjnuTevXqRf/+/fm///s/JkyYQHh4uD1RM2rUKD777DPGjBlDREQEPj4+DBw4kJSU7GtfrFmzhi5dujB8+HAiIyPtPUA//vjjbDvGla5OjplMJqzWHOjpcsmwYcN48sknmTdvHgsWLODtt9/m559/pm3btvTu3ZvIyEjmzZvH4sWLGTlyJB9//DH9+/fPsXjkJjjrPk4/9k3QfXz9+3jRokUcO3Ysw6ShFouFZcuW0axZM7y8vK65/fXWAbhc6qlnGJfHWL3WmOxXfjkAMHjwYJYsWcLo0aMpXbo0Xl5etG/f3v783OjYAL1796Zr1658+umnTJgwgY4dO2b5CxIREbk7nI5PYsnOUyzcfpJ/Dpwl1XL5/6SwAE8erhjKk77/UmbLB5jOXGrflHoQWnwIIVd3Lc8GWUmuxx4BN89LCe8rkuDu3uDimnG7e4AS6LlMY6CLiIg4kcl0x/R0eOKJJ3jxxReZMmUKP/zwA88//7x9HOXVq1fz2GOP8dRTTwG2sZD/++8/KlbM8CPMTFWoUIEjR45w4sQJChUqBMA///zjUOfvv/+mePHivPHGG/ayQ4ccx1D08PDAYrFwPRUqVGDixIkkJibaE1SrV6/GxcWFcuVy4EMBl8/vyJEj9l7oO3fuJCYmxuEalS1blrJly/K///2Pzp07M2HCBNq2bQtA0aJFee6553juuecYMmQI3333nRLoeYXuY+DuuI/HjRtHp06dHOIDeO+99xg3bhzNmjWjSpUqTJo0idTU1AwJej8/P0qUKMGyZct48MEHM+w/JCQEgBMnTlC9enUAhwlFr2f16tX06NHD/p6QkJDAwYMH7esjIiKwWq2sWLHCPoTL1Vq2bImPjw9jx45l4cKFrFy5MkvHFhGRO9uhs4ks2nGSRTtOsfHwea74HpcyBXyJrFSQyEoFqex2GNOCV2HTKtvKwGIQORLKP5Ll+ZOy1fWS6/c4JdBzWfoQLucSU7BaDVxcnHBDiIiISJ7n6+tLx44dGTJkCHFxcfTo0cO+rkyZMsyYMYO///6bfPny8cknn3Dq1KksJ96aNm1K2bJl6d69O6NGjSIuLi5DAqtMmTIcPnyYn3/+mVq1ajFv3jxmzZrlUKdEiRJERUWxefNmihQpgp+fH2az2aFOly5dePvtt+nevTvDhg3jzJkz9O/fn65du9qHfbhVFoslQzLMbDbTtGlTIiIi6NKlC2PGjCEtLY0XXniBRo0aUbNmTS5evMjLL79M+/btKVmyJEePHmX9+vW0a9cOgIEDB9KiRQvKli3L+fPn+fPPP6lQocJtxSr3Jt3H13bmzBl+++035s6dS+XKlR3WdevWjbZt23Lu3Dn69evHF198QadOnRgyZAgBAQH8888/1K5dm3LlyjFs2DCee+45ChQoQIsWLYiPj2f16tX0798fLy8v7r//fj744ANKlizJ6dOnHcaEv54yZcowc+ZMWrdujclk4q233nLoTV+iRAm6d+/O008/bZ9E9NChQ5w+fZonnngCAFdXV3r06MGQIUMoU6ZMpkPsiIjIncUwDFItBklpFpJSLFxMtZCUaiUuKZVVl8Y0330y3mGbqkUDaV6pIJGVQikV4gsXzsHyd2H997axxd084YFBUH8AuN/4F06S+5RAz2X5fWxDuFisBucvpBDka77BFiIiInKv6tWrF+PGjaNly5YO4xy/+eabHDhwgMjISLy9venTpw9t2rQhNjY2S/t1cXFh1qxZ9OrVi9q1a1OiRAk+//xzmjdvbq/z6KOP8r///Y9+/fqRnJzMI488wltvvWWf2A+gXbt2zJw5kwcffJCYmBgmTJjgkCAE8Pb2ZtGiRbz44ovUqlULb29v2rVrxyeffHJb1wZsPULTe5WmCw8PZ9++fcyZM4f+/fvTsGFDXFxcaN68OV988QVgS2qdPXuWbt26cerUKYKDg3n88ccZPnw4YEvM9+3bl6NHj+Lv70/z5s359NNPbzteuTfpPs5c+oSkmY1f3qRJE7y8vJg8eTIDBgzgjz/+4OWXX6ZRo0a4urpSrVo16tevD0D37t1JSkri008/ZfDgwQQHB9O+fXv7vsaPH0+vXr2oUaMG5cqV46OPPuLhhx++YXyffPIJTz/9NPXq1SM4OJhXX32VuLg4hzpjx47l9ddf54UXXuDs2bMUK1aM119/3aFOr169eP/99+nZs+etXCYREec4ugFmPQsFI6DpMMhXwtkR3bKE5DS2HIkhMTntUrLblvBO//tiqi0RnqHsqnpJqRYuplhISrNisRrXPaari4n7S+UnslJBmlUMpVDApaS41QIbJsCyEXDxnK2s4mPw8Lu23ueSZ5mMKweEuwfExcUREBBAbGzsTU+ek13ue2cJ5xJTWDiwAeULOicGERGRu11SUhJRUVGULFkST09PZ4cjd6nrvc7yQrvzTnG9a6V7We5kf/31F02aNOHIkSM37K2v17qI5AkntsKkVpB06QtdVzPU7QsNBtnGw74DJKVa+HP3aX7bepxlu06TnJYz8+q4mMDL3RUvD1fMbq5UDPOneaWCNKlQgEBvD8fKh9fCgpfhxBbbckh52zjnpRrnSGySuVttn6sHuhME+3pwLjGF6PgUKOjsaERERERERCQ7JScnc+bMGYYNG0aHDh1ue8gqEZFccWYP/NjWljwvUts2nEjUClj1CWz+CZoMhapPwqVJmvOSlDQrq/ad4bctJ1i84ySJKZfn9yiSz4sCfmY83V3xcnfF08MVTzdXvDxcLv3riqe76+X17i5X1btc5uXuivnSv+6uJvvcJtcUfxKWvA1bf7YtmwPgwSFQqze4ul9/W8kzlEB3gmBfM/+dStBEoiIiIiIiInehqVOn0qtXL6pVq8YPP/zg7HBERG7sXBT88BhciIZCVeGpGWD2hz0LYPEbcO4AzOkL676F5h9A8XrOjhiL1WDtgbP8tvU4C7afJOZCqn1d4UAvWlUtROsqYVQK879xoju7paXA2rGw4iNISQBMUP0paPI2+Ibkbixy25RAd4LgS+OeK4EuIiIiIiJy9+nRo0eGseRFRPKs2GO25Hn8CQipAE/NAs8A27ryLaF0E1vifMVHtiFIJrSASm2h6XDIVzxXQ7VaDTYdOc9vW07w+9YTDrm1ED8zj0QUonXVMO4rFpj7SfN0+5bCgtfg7F7bcuGa0PIjKFzDOfHIbVMC3QnSE+hnlEAXERERERERERFnSThjS57HHIL8paDbbPAJcqzjZoZ6/aFKJ/jzPdg4CXbMgt3zbeUP/A/MvjkWomEY7Dgex29bjvP71hMci7loXxfo7U6LygVpXSWMOqWCcHVxUtIcbL34F70Oe+bbln1CbF8yVO2cJ4e9kaxTAt0JQvwuJdDjlUAXEREREREREREnuHAOfmxj6ykdUBS6zQW/60zW5xsCrcfYxu9e+Boc/Av+Gg2bJkPTt20J9mxMFO87Hc/cLSf4fctxDkQnXg7D7MbDFUNpXTWM+qWD8XBzcnL6/EH4dyKs+QosyeDiBnWeg0avXO7JL3c0JdCdINjXNhNvdEKKkyMRERG5+1mtVmeHIHcxvb5yj6613O30GheRXJUcDz+1h1PbwTcUus2BwKJZ27ZgZej+G+yeB4vfhPNRMPv5y+OjF7v/lsM6fPYCv209zm9bjrP7ZLy93OzmQtMKobSuWojG5Qrg6e56y8fIFgmnbb3wt82Ao+sul5dqDC0+gpByTgtNsp8S6E4QfKkHerR6oIuIiOQYDw8PXFxcOH78OCEhIXh4eDhvHES56xiGQUpKCmfOnMHFxQUPDw9nh3TX0r0sdzu9n4hIrku5AFM6wrF/wSs/dJ0NQeE3tw+TCSq0gjLNYO3XsGIUHN8E4yOhcjvb0CVZTMifjE3i963H+W3rCbYcibGXu7uaaFQ2hNZVw2hSIRRfs5PTmEmxsOt32DYdolaAkf7FpwlKNoTafaD8I7ZrI3cVJdCdIESTiIqIiOQ4FxcXSpYsyYkTJzh+/Lizw5G7lLe3N8WKFcNF41rmGN3Lcq/Q+4mI5Iq0ZJjWFQ6tBrM/dJ0JoRVvfX9uZqj/om2c7z/ehY0/wPZfMXbPI7VOP85Vf554i5m4pDTik1KJT0q79LD9vf7gOdYdPIdh2HbnYoJ64cG0rlqIyEoFCfR28peKqRdh72Jb0vy/xbYhWtIVrgERHWwTql5v6Bu54ymB7gTpk4ieTUzBajVwceYEByIiIncxDw8PihUrRlpaGhaLxdnhyF3G1dUVNzc39YbOBbqX5W6n9xMRyRWWNJjxNOxbCu7e8OQ0CKueadWLKRbOJiZnSHjHJ6USn3x1Wfrf7SnoUZW+Kd9TK20nHqtHw6pxfJXaidnW+hhc+wvCWiXy0bpqGC0qF7LPHeg0ljSIWg7bfoVdv0HK5aFkCC5nS5pXfvzme+3LHUsJdCcIujQGusVqcP5CCkG+Tn5jEBERuYuZTCbc3d1xd3d3digicht0L4uIiNwGq9U2Tvnu38HVAzpNgeJ1M1Tbeyqeb1ceYPbmY6RajJs+zG4Kspw3iHRZzxtuP1HM5QyfeozlGZelfO/Th2O+Efh5uuPv6YafpxtF83vTMqIQYYFe2XGWt84w4Mg6W0/znbMh8czldQFFbQnziA4QWllDtNyDlEB3AndXF/J5u3P+QirRCUqgi4iIiIiIiIhIDjEMmDcItk0DFzd44gcIf/CK1QbrD57nmxX7Wbb7tL3c7OZiT3b7Xkp4+5ndbf96uuPr6WZPhPt5XlFudsPfsyl+bq9g/Pstpr9GUzFlL5/EvwzF20Oz4RBQxBlXIqNTO2xJ8+2/Qszhy+XeQVCxjS1pXrQOaHite5oS6E4S7Gu+lEBPphx+zg5HRERERERERETuNoYBi9+EfycAJnj8WyjXArCNjLBk50m+WXmATYdjAFvn6uaVCtKnYSmqF8t3+8dv8D+o9iT88Q5smgzbZ8DueVB/gG3sdA+f2z/GzTp/ELbNsD3O7Lpc7uEL5VtBRHso1Rhc9as3sVEC3UmCfc3sPZ2giURFRERERERERCRnLP8A1nxp+/vRL6ByO5JSLczceIzv/jpAVHQiAB5uLrSvUYRnGpSiZHA2J7X9QuGxL6FWb1g4BA7/DSs+hI0/Qu1nwCsfuHnaJiS98l93z0vLV6/zAtebTGkmnIYds2y9zY+uv1zu6gFlHrYlzctEgod39p673BWUQHeS4EsTIpyJVwJdRERERERERESy2erPYMUHtr9bfERM+Y5M/mMvE/8+SHRCCgABXu50vb843euVyPnJO8OqQc/5sHMOLHnLNmTKsuG3ti+T6w2S7leUXzgHh1aDYb20rQuUaGAbnqVCa/AKzK4zlLuUEuhOEnJp3PMz6oEuIiIiIiIiIiLZad13sGQoALH13+Cz0w34+YM/uJBiAaBwoBe9HihJx1pF8THnYnrQZIJKbaBsc1j/PRz7F9KSIS0pk3+THJctKZf3Y1ggNdH2yKrCNW09zSu1Bb+C2X5qcvdSAt1Jgv08AIiOT7lBTRERERERERERkSzaPAXmDwZgcdBTPP9nZSzWKAAqFPLn2YaleKRKIdxdnTgxprsn1Ot3c9tYrWBJhtSLWU+6p13quFq6CeQvlf3nIfcEJdCdJPhSD3SNgS4iIiIiIiIiItnB2DEb5vTFBExIi2T4sRaAQf3SQTzbMJwGZYIxmUxOjvIWubiAixe4ezk7ErnHKIHuJCFKoIuIiIiIiIiISDZIs1j5d+nP1FjTDzes/JzWmHctXWlVJYxnG4YTUSTA2SGK3LGUQHcS9UAXEREREREREZHbcSEljekbjrJxxWw+SnoXN5OFedZ67K45gj8blKFYkLezQxS54ymB7iTpMxtHJ6RgtRq4uNyhP58REREREREREZFcdTYhmR/WHOKHNQcpeXEHP3qMxGxKZX/+htTt8TOP+Ps4O0SRu4YS6E4S5GubRNRiNYi5mEp+Hw8nRyQiIiIiIiIiInlV7MVUdhyLZcH2k0z/9whJqVYqmQ4yyfwRPiRjKdmY8Cd/sU3QKSLZRgl0J3F3dSHQ252YC6lEJyQrgS4iIiIiIiIiIoBtaJbtx+LYejSGbcdi2Xo0lqjoRIc6LUJjGXNxFOaUC1CsLq6dpyh5LpIDlEB3omBfsy2BHp9M2VA/Z4cjIiIiIiIiIiK5LCnVwq4TcWw7FsuWI7FsOxbDvtMJWI2MdYvk86Ja0UB6VjS4b+kgTCnnIaw6PPkLeGjYFpGc4NQE+siRI5k5cya7d+/Gy8uLevXq8eGHH1KuXLlrbjNx4kR69uzpUGY2m0lKSsrpcLNdsK8H+07DGU0kKiIiIiIiIiJy10u1WNlzMv5Sr/IYth6NZc/JeNIyyZaH+pupUiSQKoUDiCgSQJUigbYRDGKPwvgWkHASClSEp2aCZ4ATzkbk3uDUBPqKFSvo27cvtWrVIi0tjddff52HH36YnTt34uNz7W/N/P392bNnj33ZZLozJ+AM8bP9rOZMvBLoIiIiIiIiIiJ3E4vVYP+ZBLYcuTwMy84TcaSkWTPUze/jQZUiAVQpbEuURxQJINQ/k+FYzu6HKU9A7GHIHw5dZ4N3/pw/GZF7mFMT6AsXLnRYnjhxIgUKFODff/+lYcOG19zOZDJRsGDBnA4vxwVfmkg0OiHFyZGIiIiIiIiIiMjtOBmbxNqos2w9autdvv1YHBdTLRnq+Xm62ZLlV/QuLxzolXkHUcOAM7th12+2x8mttvKAYtB9LviF5vBZiUieGgM9NjYWgPz5r//NWUJCAsWLF8dqtXLffffx/vvvU6lSpUzrJicnk5x8uYd3XFxc9gV8m4J9zQBEawgXEREREREREZE7jmEYbDx8nvGrDrJwx0ksVw3F4u3hSuXCAfZEedUigRQP8r7+aApWKxzfCLvmwq7f4dz+y+tMLlCyIbT6FAKK5NBZiciV8kwC3Wq1MnDgQOrXr0/lypWvWa9cuXKMHz+eKlWqEBsby+jRo6lXrx47duygSJGMbxwjR45k+PDhORn6LQtRAl1ERERERERE5I6TkmZl/rYTjF8dxdajsfbyKkUCqF400Na7vEgApUJ8cXXJwtDDllQ4tNqWMN/9O8SfuLzO1QPCH4IKraFsC/AJyoEzEpFryTMJ9L59+7J9+3ZWrVp13Xp169albt269uV69epRoUIFvvnmG955550M9YcMGcKgQYPsy3FxcRQtWjT7Ar8NwX7pQ7gogS4iIiIiIiIiktedTUhmytrD/PjPIU5fmtPOw82FttUK0/OBEpQv6J/1naVehP1/2JLme+ZDUszldR5+UPZhKN8KyjQDs1/2noiIZFmeSKD369eP33//nZUrV2bai/x63N3dqV69Ovv27ct0vdlsxmw2Z0eY2S7EV5OIioiIiIiIiIjkdbtPxjFh1UFmbT5mnwS0gJ+ZbnWL07l2MYJ8s5h7SoqF/xbbhmfZtxRSL1xe5x0E5VpChUehVCNwy5v5LJF7jVMT6IZh0L9/f2bNmsXy5cspWbLkTe/DYrGwbds2WrZsmQMR5qz0HuhnE1KwWg1csvKTHhERERERERERyXFWq8Efu08zfnUUf+8/ay+vUiSAp+uXpGVEITzcXG68o4TTsHuebRLQqJVgTb28LqCorZd5hdZQ7H5wcc2BMxGR2+HUBHrfvn2ZMmUKc+bMwc/Pj5MnTwIQEBCAl5cXAN26daNw4cKMHDkSgBEjRnD//fdTunRpYmJiGDVqFIcOHaJ3795OO49bFeRj+yYxzWoQezGVfD4eTo5IRERERMSRxWJh2LBhTJ48mZMnTxIWFkaPHj1488037ROgGYbB22+/zXfffUdMTAz169dn7NixlClTxsnRi4iI3LyE5DSmbzjCxL8PcuisrYe4q4uJ5pUK8vQDJbivWL7rTwIKcP7g5fHMD/8DXDG5aHA5W8K8QisoVA1utC8RcSqnJtDHjh0LQOPGjR3KJ0yYQI8ePQA4fPgwLi6Xv807f/48zzzzDCdPniRfvnzUqFGDv//+m4oVK+ZW2NnGw82FAC93Yi+mEp2QrAS6iIiIiOQ5H374IWPHjmXSpElUqlSJDRs20LNnTwICAhgwYAAAH330EZ9//jmTJk2iZMmSvPXWW0RGRrJz5048PT2dfAYiIiJZc/jsBSb+fZDpG44Qn5wGgL+nG53rFKNb3RIUDvTKfEOrFRJO2ZLmUSth929wcptjnbD7bAnz8q0hpGzOnoiIZCuTYRjGjavdPeLi4ggICCA2NhZ//5uY2CGHNPl4OfvPJDLlmTrUCw92djgiIiIikk3yWrvzVrVq1YrQ0FDGjRtnL2vXrh1eXl5MnjwZwzAICwvjpZdeYvDgwQDExsYSGhrKxIkT6dSp0w2PcbdcKxERufMYhsE/B84xfnUUS3edIj1LFh7iQ8/6JXn8vsJ4u7vChbNw/hDEXHqcPwQxhy8tHwHLVfPbmVygeH1bT/Pyj0DAzc35JyLZ71bbnHliEtF7WYifmf1nEjWRqIiIiIjkSfXq1ePbb7/lv//+o2zZsmzZsoVVq1bxySefABAVFcXJkydp2rSpfZuAgADq1KnDmjVrMk2gJycnk5x8uf0bFxeX8yciIiJyhaRUC3O3HGfC6oPsOhGHP4lUNJ0hskgSLYumEu4WjenAEfj3UqI8NfH6OzS5gH8RCK1kS5iXawk+QblzMiKSo5RAd7LgS7M0RyekODkSEREREZGMXnvtNeLi4ihfvjyurq5YLBbee+89unTpAmCfxyg0NNRhu9DQUPu6q40cOZLhw4fnbOAiIiJXSkmE84eIObGXTVu2cOzgHkLSTjLadIai5jP4m2xjnRN96ZGBCfwKQWAxyFfc9m9g8cvL/oXB1T0XT0hEcosS6E52OYGuHugiIiIikvdMmzaNn376iSlTplCpUiU2b97MwIEDCQsLo3v37re0zyFDhjBo0CD7clxcHEWLFs2ukEVERC6LPQorR2PdNBkXayqBwIPp61yvqusT4pgUtyfJi0NgUXAz52roIpI3KIHuZCF+lxLoGsJFRERERPKgl19+mddee80+FEtERASHDh1i5MiRdO/enYIFCwJw6tQpChUqZN/u1KlTVKtWLdN9ms1mzGYlIUREJAfFnSB1xWhcNk3C1ZqKCxBj+HDECCHRqzAFipWleHgFXPOXuJwg9/BxdtQikgcpge5kwb4egHqgi4iIiEjedOHCBVxcXBzKXF1dsVqtAJQsWZKCBQuybNkye8I8Li6OtWvX8vzzz+d2uCIicq9LOE30wg8I2PEj7oZtuNx/rBX43NqBkMoP8XT9ktxfNNC5MYrIHUUJdCdL74F+Rgl0EREREcmDWrduzXvvvUexYsWoVKkSmzZt4pNPPuHpp58GwGQyMXDgQN59913KlClDyZIleeuttwgLC6NNmzbODV5ERO4ZCedPcuS3Dyh1YArB2HIs661lmeLdlQr1HuGL+4oQ5KtfP4nIzVMC3cnsY6DHaxJREREREcl7vvjiC9566y1eeOEFTp8+TVhYGM8++yxDhw6113nllVdITEykT58+xMTE8MADD7Bw4UI8PT2dGLmIiNwLdu47yKlFo6lzehoVTLbE+WZraVYXe477Grflk/AgTCaTk6MUkTuZyTAMw9lB5Ka4uDgCAgKIjY3F39/f2eFwPOYi9T74A3dXE/+920Jv6iIiIiJ3ibzW7szLdK1ERORmxCelsmDDbtJW/x+tL8zGz3QRgD0u4URFvEjtZp3Ir97mInKVW21zqge6kwVdGgM91WIQezGVQG8PJ0ckIiIiIiIiIpK3GIbB1qOxzFyzi6Dt4+lh+h1/0wUwwVFzOIl1X6Fswycod9W8HSIit0sJdCczu7ni7+lGXFIa0QnJSqCLiIiIiIiIiFwSl5TKnM3HmfXPHuqc+ZWBbr+TzyUBgHM+4Xg0eYMi1dqCEucikkOUQM8DQvzMxCWlcTo+mdIF/JwdjoiIiIiIiIiI0xiGweYjMUxdd5glWw7S3rqQ79x+I8g9HoCL/qXwbPYG+Ss9rsS5iOQ4JdDzgGBfM/vPJBKdoIlERURERERERCSPOLEFzh+CwGKQrzh4BkIOzt0WezGVOZuPMWXtYaJOnuVJ12UsdptLiGssAJbAkrg+OASviPbg4ppjcYiIXEkJ9Dwg2M82sUV0fLKTIxERERERERGRe54lDf58F1Z96lhu9ofA4pcT6oHFbMvpf5tv/lf1hmGw6UgMU9ce5retx7GmJtPR9U8mmecQajpvqxNYDFOjV3Gt0glclcoSkdyld508IOTSzNDRCUqgi4iIiIiIiIgTJUbDjKchaoVtOTQCEk5B4mlIjoNT22yPzHjlz5hcvzLB7u5lrxp7MZXZm44xdd1hdp+Mx5002ruu4H9ecyhgRNsq+ReBRi9jqvokuGnOOBFxDiXQ84BgX9t/Akqgi4iIiIiIiIjTHN0A07pB3DFw94HHvoDK7WzrUi5AzOFLj0O2x/lDl5cvnoeL52yPE5sz3b3hU4BE78LsTc7PuhhfDlqCCTYK0MX9LIM85xKUehIMwK8QNHgJ7usGbuZcO30RkcwogZ4HhFwawuWMhnARERERERERkdxmGLBhPCx4FaypEFQGOv4IBSpcruPhDQXK2x6ZSYq7Irl+2CG5bpw/iCklAVPiaXwTT1MdqO4CXDn/ZyrgUwAaDIIaPcHdM+fOV0TkJiiBngcE24dw0SSiIiIiIiIiIpKLUi7AvEGwZaptuUJreOwr8PS/uf14+kPByrYHtrHNNxw6z9S1h5l37DieafEUNZ2mlNtZHg5Lok6+BIItpzCdP2RL2td8Gmr2siXqRUTyECXQ84BgjYEuIiIiIiIiIrnt3AH4pSuc2g4mF2g6DOoNAJPplncZcyGFmRttY5vvPZ1gLy9ZsBAd69Th0WqFCfByz4bgRURyhxLoeUDwpSFcziakYBgGptv4j0pERERERERE5Ib2LISZfSA5FnxCoP14KNnwlnZlGAbrD55n6rrDzNt2gpQ0KwBe7q60rlqIzrWLUa1ooPIdInJHUgI9DwjysU0immKxEncxjQBvfRMrIiIiIiIiIjnAaoHlI2HlKNtykdrwxCTwD7vpXZ1PTOHXjUeZuu4w+88k2ssrFPLnyTrFeKxaGP6eynGIyJ1NCfQ8wNPdFX9PN+KS0jiTkKQEuoiIiIiIiIhkv8SzMLM37P/Dtlz7WXj4XXDzyPIuDMNgbdQ5pq47zIJtJ0mx2Hqbe3u48mjVMDrXLkaVIgHqbS4idw0l0POIYD+zLYEen0LpAs6ORkRERERERETuKsc2wrRuEHsE3L2h9WdQ5Yksb34uMYVf/z3K1PWHOXBFb/NKYbbe5o9WDcNPvc1F5C6kBHoeEexr5sCZRE0kKiIiIiIiIiLZxzBg4ySY/zJYUiB/Keg4GUIrZWFTg38O2HqbL9x+ube5j4crj1YrzJO1ixFRJCCnz0BExKmUQM8jQnxtE4kqgS4iIiIiIiIi2SL1IswfDJsm25bLPQJtx4LnjZPe24/FMnj6FnafjLeXRRQOoHPtYjxaLQxfs1JKInJv0LtdHhHsaxtvTAl0EREREREREblt5w/CL13h5FYwucBDb0H9geDict3N0ixWxi7fz2fL9pJmNfDxcOWx6oXpXEu9zUXk3qQEeh4R4mfrgX4mXgl0EREREREREbkNe5fAr70hKQa8g6D9eCjV+Iab7T+TwKBpW9hyJAaAFpUL8l7bCPL7ZH2SURGRu40S6HlEsH0IlxQnRyIiIiIiIiIidySrFVZ8aHtgQOGa8MQkCChyg80MfvznECMX7CIp1YqfpxvvPFaZx6qFYTKZcid2EZE8Sgn0PCJYY6CLiIiIiIiIyK26cA5m9oF9S2zLtXpD5PvgZr7uZsdjLvLKjK2s2hcNwAOlg/mofRXCAr1yOmIRkTuCEuh5RPClIVyiNYSLiIiIiIiIiNyM45thWleIOQxuXtB6DFTtdN1NDMNgzubjvDVnO/FJaXi6uzCkRQW63l8cFxf1OhcRSacEeh6RPgZ6dEIKhmHoJ1IiIiIiIiIicmObJsPvg8CSDPlKQMfJUDDiupucS0zhzdnbmL/tJABViwbyyRNVCQ/xzYWARUTuLEqg5xFBlybkSLFYibuYRoC3u5MjEhEREREREZE8KzUJFrwCGyfZlss2h7Zfg1e+6272x+5TvPrrNs7EJ+PmYuLFJmV4vnE4bq4uuRC0iMidRwn0PMLT3RU/Tzfik9I4k5CsBLqIiIiIiIiIZO7cAZjxNBzfBJjgoTfggZfA5dpJ8ITkNN6bt5Op644AUKaAL592rEblwgG5FLSIyJ1JCfQ8JMTXTHxSGtEJyZQuoJ9NiYiIiIiIiMgVDAM2jIfFb0FqInjlh3bfQ+km191sXdQ5Xpq+mSPnLmIyQa/6JRkcWQ5Pd9dcClxE5M6lBHoeEuxr5kB0ItEJmkhURERERERERK4Qdxzm9IP9y2zLJRpAm7EQWPSamySnWfhk8X98+9cBDAMKB3oxukNV6oYH5VLQIiJ3PiXQ8xD7RKLxSqCLiIiIiIiICLZe59umw/zBkBQLbp7QdBjUfva6Q7bsOB7LoF+2sOdUPABP1CzCW60q4uepIWNFRG6GEuh5SLCvbSLRM+qBLiIiIiIiIiKJ0fD7/2DXXNty2H3Q9hsIKXvNTdIsVr5ZeYAxS/8j1WIQ7OvByMer0KxiaC4FLSJyd1ECPQ8J9k3vgZ7i5EhERERERERExKl2z4ffBkDiGXBxg0avwQP/A9drp3IORicyaNpmNh6OASCyUijvt40g6FK+QUREbp4S6HlIcPoQLuqBLiIiIiIiInJvSoqFhUNg80+25ZAK0PZrCKt2zU0Mw+CntYd5b94uLqZa8DO7MezRSjx+X2FMJlPuxC0icpdSAj0PsfdAVwJdRERERERE5N5zYAXM6QuxRwAT1B8AD74BbtfuQX4yNolXft3Kyv/OAFAvPIhRHapSONArl4IWEbm7KYGeh9gnEU3QEC4iIiIiIiIi94yUC7BsOKz92racrwS0+RqK173uZnO3HOet2duJvZiK2c2F11qUp3vdEri4qNe5iEh2UQI9D7FPIhqfjGEY+pmViIiIiIiIyN3u6AaY9Syc3Wdbrvk0NHsHzL7X3CT2YipvzNrG71tPAFClSACfPFGV0gX8ciNiEZF7ihLoeUj6EC4pFitxSWkEeLk7OSIRERERERERyRFpKbDiQ1j1CRhW8CsEj30JpZted7P9ZxJ4ZtIGDkQn4upiov9Dpen7YGncXV1yKXARkXuLEuh5iKe7K35mN+KT04hOSFYCXURERERERORudGqHrdf5yW225YgnoOVH4JXvupv9ufs0A6ZuIj45jbAAT756qgbVigbmfLwiIvcwJdDzmGA/sy2BHp9MeMi1f64lIiIiIiIiIncYqwX+/hz+fB8sKeCVH1qPgYqPXXczwzD4esUBPlq0G8OAWiXyMfapGvZfsouISM5RAj2PCfE1ExWdqIlERURERERERO4mZ/fD7OfhyFrbcrmW0Poz8C1w3c0uplh49detzN1yHIDOtYsx/NFKeLhpyBYRkdygBHoeE+yXPpFokpMjEREREREREZHbZhiw/ntYMhRSL4DZH5p/ANWeBJPpupsej7lInx83sP1YHG4uJt5+tBJd7y+eS4GLiAgogZ7npP/8Sj3QRURERERERO5wscdgbj/Y/4dtuWRDeOwrCCx6w003HDzHc5P/JTohhfw+HnzV5T7uLxWUwwGLiMjVlEDPYy4n0JOdHImIiIiIiIiI3BLDgK3TYP7LkBwLbp7QbATUegZcbjz0ys/rDvPWnO2kWgwqFPLn2641KJrfOxcCFxGRqymBnscogS4iIiIiIiJyB0uMht8Hwq7fbMuFa0LbryG4zA03TbVYeef3nfyw5hAAj0QUYlSHKnh7KH0jIuIsegfOY0L8bAn0M/FKoIuIiIiIiIjcMawW2DgJlg6HpBhwcYfGr0H9geB64/TLucQUXvjpX/45cA6Al5qVpd9DpTHdYJx0ERHJWUqg5zHBvrZJRDUGuoiIiIiIiMgd4thGmPcSHN9oWy4YYRvrvFCVLG2+60Qcz/ywgaPnL+Lj4cqnHavxcKWCORiwiIhklRLoeUz6EC5nEpIxDEPfNIuIiIiIiIjkVRfOwR/vwIYJgAFmf3joTajZK0u9zgEWbDvBoGlbuJhqoXiQN991q0nZUL+cjVtERLLsxjNX5KCRI0dSq1Yt/Pz8KFCgAG3atGHPnj033G769OmUL18eT09PIiIimD9/fi5EmzvSh3BJSbMSn5zm5GhEREREREREJAOrFTb+CF/WhA3jAQOqdIJ+G6DOs1lKnlutBp8s3sPzP23kYqqFB0oHM6dvfSXPRUTyGKcm0FesWEHfvn35559/WLJkCampqTz88MMkJiZec5u///6bzp0706tXLzZt2kSbNm1o06YN27dvz8XIc46nuyu+Ztt/tNEaB11EREREREQkbzmxFSY0h7n94MJZCKkAPebD49+AX2iWdpGQnMZzk//l8z/2AdDrgZJM7FmLQG+PnIxcRERugckwDMPZQaQ7c+YMBQoUYMWKFTRs2DDTOh07diQxMZHff//dXnb//fdTrVo1vv766xseIy4ujoCAAGJjY/H398+22LPTg6OXExWdyC997qdOqSBnhyMiIiIit+BOaHfmFbpWInJHSIqFP96D9d+BYQUPX9skoXWeA1f3LO/m0NlEnvlhA/+dSsDD1YX3H4+gfY0iORi4iIjArbc589QY6LGxsQDkz5//mnXWrFnDoEGDHMoiIyOZPXt2pvWTk5NJTr7ckzsuLu72A81hwb4eREUnaiJREREREREREWczDNg6DRa/CYmnbWWVHofI98A/7KZ2tWpvNH2nbCT2YioF/Mx807UG1Yvly4GgRUQku+SZBLrVamXgwIHUr1+fypUrX7PeyZMnCQ11/ElUaGgoJ0+ezLT+yJEjGT58eLbGmtPSJxKNTtAQLiIiIiIiIiJOc2onzB8Mh1bbloPKQMtREP7gTe3GMAwmrD7Ie/N3YbEaVC0ayLddaxDq75kDQYuISHbKMwn0vn37sn37dlatWpWt+x0yZIhDj/W4uDiKFi2arcfIbkqgi4iIiIiIiDhRcjws/wD+GQuGBdy9oeHLULcfuN3cOOXJaRbenLWd6f8eBeDx+wrzftsIPN1dcyJyERHJZnkigd6vXz9+//13Vq5cSZEi1x/3q2DBgpw6dcqh7NSpUxQsWDDT+mazGbPZnG2x5gYl0EVEREREREScwDBgx0xY9AbEn7CVlW8FzT+AwJvvjHc6LolnJ//LpsMxuJjg9ZYV6PVASUwmUzYHLiIiOcWpCXTDMOjfvz+zZs1i+fLllCxZ8obb1K1bl2XLljFw4EB72ZIlS6hbt24ORpq7QvxsCfQz8Uqgi4iIiIiIiOSKM//BgpfhwHLbcr6StuFayjS7pd1tORJDnx83cCoumQAvd758sjoNyoRkX7wiIpIrnJpA79u3L1OmTGHOnDn4+fnZxzEPCAjAy8sLgG7dulG4cGFGjhwJwIsvvkijRo34+OOPeeSRR/j555/ZsGED3377rdPOI7sF+9p+DnZGk4iKiIiIiIiI5KyURFg5Cv7+Eqyp4OYJDwyC+i+C+62NUT5z41Fem7mNlDQrpQv48n23mpQI9snmwEVEJDc4NYE+duxYABo3buxQPmHCBHr06AHA4cOHcXFxsa+rV68eU6ZM4c033+T111+nTJkyzJ49+7oTj95pgi/1QI9WD3QRERERERGRnGEYsPt3WDgEYo/YyspEQosPIf+NfyGfGavV4MNFu/lmxQEAmlYowKcdq+Hn6Z5dUYuISC5z+hAuN7J8+fIMZR06dKBDhw45EFHeEHLFGOiGYWhsNBEREREREZHsdHY/LHgV9i2xLQcUsyXOy7e85V0mp1kYPH0rv205DkC/B0szqFlZXFz0mV5E5E6WJyYRFUfpk4gmp1lJSE7TN9UiIiIiIiIi2SH1Iqz6FFaNAUsyuHrYhmp5YBB4eN/ybmMvptLnhw2sjTqHm4uJj9pX4fH7imRf3CIi4jRKoOdBXh6u+JrdSEhO40x8shLoIiIiIiIiIrcr7gRMagVn99mWwx+CFqMguPRt7fZYzEV6TljHf6cS8DW78fVTNXigTHA2BCwiInmBEuh5VLCvBwnJaUQnpFBKk3SLiIiIiIiI3LrkBJjyhC157lcImn8AFR+D2xwydefxOHpOXMepuGRC/c1M6FGbimH+2RS0iIjkBUqg51HBvmYOnr1AdIImEhURERERERG5ZZY0mN4DTm4F72DoueCWJwm90l97z/D85I0kJKdRNtSXiT1rExbodfvxiohInqIEeh4VfMVEoiIiIiIiIiJyCwwD5r9kmyzUzQuenJYtyfNf/z3Kq79uJc1qcH+p/HzTtSYBXhp+VUTkbqQEeh4V7OcBQHS8EugiIiIiIiIit2TVp/DvRMAE7cdBkRq3tTvDMPi/P/cxevF/ADxaNYxRHapgdnO9/VhFRCRPUgI9jwrx9QTgjHqgi4iIiIiIiNy8rdNh2XDb3y0+gvKP3Nbu0ixW3pqzg6nrDgPwXKNwXoksh4vL7Y2jLiIieZsS6HlUeg/0M/EpTo5ERERERERE5A5zcBXMecH2d91+UKfPbe3uQkoa/aZs4o/dpzGZYPijlehWt8TtxykiInmeEuh5lMZAFxEREREREbkFZ/bAz0+CJQUqPArN3rm93cUn02vSerYejcXs5sLnnasTWalgNgUrIiJ5nRLoeZQS6CIiIiIiIiI3Kf4UTG4PSbFQtA48/i24uNzy7g6cSaDHhPUcPneBfN7ufN+9FjWK58vGgEVEJK9TAj2PCrkigW4YBiaTxlQTERERERERuaaURJjyBMQehvzh0GkquHvd8u7+PXSe3pPWc/5CKsXyezOxZy1KhfhmY8AiInInUAI9j0ofAz0p1UpCchp+nu5OjkhEREREREQkj7KkwYyn4cRm8A6CLtPBJ+iWd7dox0kGTN1EcpqVqkUCGNejlv2X4iIicm+59d8xSY7y9nDDx8MVgOgETSQqIiIiIiIikinDgAWvwH8Lwc0TOv8CQeG3vLsf1hzkucn/kpxmpUn5Akztc7+S5yIi9zAl0POwYD+Ngy4iIiIiIiJyXX9/DhvGASZ4/DsoWuuWdmO1GoxcsIuhc3ZgGNC5djG+6VoDbw/9eF9E5F6m/wXysGBfM4fOXiA6Xgl0ERERERERkQy2/wpLhtr+jnwfKj56S7tJTrPw8vStzN1yHICXI8vxQuNwzUcmIiLqgZ6XBfvaxkFXD3QRERERcaZjx47x1FNPERQUhJeXFxEREWzYsMG+3jAMhg4dSqFChfDy8qJp06bs3bvXiRGLyD3h0N8w6znb33Weh7ov3NJuYi+m0n38OuZuOY6bi4mPO1Sl74OllTwXERFACfQ8LeTSEC5n1ANdRERERJzk/Pnz1K9fH3d3dxYsWMDOnTv5+OOPyZcvn73ORx99xOeff87XX3/N2rVr8fHxITIykqSkJCdGLiJ3tei9MLUzWFKgfCuIfO+WdnM85iIdvv6bfw6cw9fsxoSetWhXo0g2BysiIncyDeGSh6VPUnJGk4iKiIiIiJN8+OGHFC1alAkTJtjLSpYsaf/bMAzGjBnDm2++yWOPPQbADz/8QGhoKLNnz6ZTp065HrOI3OUSTsPkdpAUA4Vr2sY9d3G96d3sOhFHjwnrOBWXTKi/mQk9alMxzD/74xURkTuaeqDnYekJdA3hIiIiIiLOMnfuXGrWrEmHDh0oUKAA1atX57vvvrOvj4qK4uTJkzRt2tReFhAQQJ06dVizZk2m+0xOTiYuLs7hISKSJSmJMKUjxByCfCXhyV/Aw/umd7N6XzQdvl7DqbhkyhTwZeYL9ZU8FxGRTCmBnocpgS4iIiIiznbgwAHGjh1LmTJlWLRoEc8//zwDBgxg0qRJAJw8eRKA0NBQh+1CQ0Pt6642cuRIAgIC7I+iRYvm7EmIyN3BaoFfe8PxjeCVD7rMAJ/gm97NzI1H6T5+HQnJadQpmZ8Zz9WjcKBXDgQsIiJ3AyXQ87AQP00iKiIiIiLOZbVaue+++3j//fepXr06ffr04ZlnnuHrr7++5X0OGTKE2NhY++PIkSPZGLGI3JUMAxa+Bnvmg6sZOv8MwaVvchcG//fnPgZN20Ka1aB11TB+6FWbAG/3HApaRETuBkqg52Ehvp6AbRJRwzCcHI2IiIiI3IsKFSpExYoVHcoqVKjA4cOHAShYsCAAp06dcqhz6tQp+7qrmc1m/P39HR4iIte15v9g3be2vx//Bordf1ObG4bBx4v/Y9SiPQA826gUn3Wshtnt5sdOFxGRe4sS6HlY8KUe6EmpVhJTLE6ORkRERETuRfXr12fPnj0OZf/99x/FixcHbBOKFixYkGXLltnXx8XFsXbtWurWrZursYrIXWrHbFj8pu3vh9+FSm1vanPDMBi1aA9f/rkPgDcfqcCQFhVwcTFlc6AiInI3UgI9D/P2cMPbw/ZteHS8hnERERERkdz3v//9j3/++Yf333+fffv2MWXKFL799lv69u0LgMlkYuDAgbz77rvMnTuXbdu20a1bN8LCwmjTpo1zgxeRO9/htTCzD2BArWegbr+b2twwDD5cuIevlu8H4O3WFendoFQOBCoiIncrN2cHINcX7Gvm8LkLRCckUyLYx9nhiIiIiMg9platWsyaNYshQ4YwYsQISpYsyZgxY+jSpYu9ziuvvEJiYiJ9+vQhJiaGBx54gIULF+Lp6enEyEXkjnd2P0ztBJZkKNsCWnwIpqz3GjcMg5ELdvPtygMADH+0Et3rlcihYEVE5G6lBHoeF+zrYU+gi4iIiIg4Q6tWrWjVqtU115tMJkaMGMGIESNyMSoRuaslRsPkdnDxHIRVh/bjwCXr45UbhsF783bx/aooAEY8VoludUvkULAiInI3UwI9jwvxMwO2iURFRERERERE7nqpF209z89HQWAxeHIaeGT9F9mGYfDO77sYv9qWPH+3TWWeur94TkUrIiJ3OSXQ87hg30sJ9IQUJ0ciIiIiIiIiksOsFvi1NxxdD56B0OVX8C2Q5c0Nw2D4bzuZ+PdBAN5vG8GTdYrlTKwiInJPUAI9j0tPoGsIFxEREREREbnrLX4Tdv8Orh7QaQqElM3ypoZhMGzuDiatOQTAB49H0Km2kuciInJ7lEDP44IvDeESrSFcRERERERE5G72z1j45yvb323GQon6Wd7UMAyGztnBj/8cwmSCDx+vwhO1iuZQoCIici9xcXYAcn0hvh6AeqCLiIiIiE2JEiUYMWIEhw8fdnYoIiLZZ+MPsHCI7e+mwyCifZY3tVoN3pqz/XLyvJ2S5yIikn2UQM/j7JOIKoEuIiIiIsDAgQOZOXMmpUqVolmzZvz8888kJ6utKCJ3KEsaLHwd5vYHDKj5NNQfmOXNrVaDN2ZvZ/I/hzGZYFT7qjxRU8lzERHJPkqg53H2MdDjNYmoiIiIiNgS6Js3b2bdunVUqFCB/v37U6hQIfr168fGjRudHZ6ISNYlxcLUjvDP/9mWGw+Blh+DyZSlza1Wg9dnbWPqOlvy/OMOVWlfo0gOBiwiIvciJdDzuPQE+sVUC4nJaU6ORkRERETyivvuu4/PP/+c48eP8/bbb/P9999Tq1YtqlWrxvjx4zEMw9khiohc29n98H1T2LcU3Lygw0Ro/Bq4ZC1NYbUavDZzKz+vP4KLCT59ohqP36fkuYiIZD9NIprH+Zjd8HJ35WKqheiEZHzMespEREREBFJTU5k1axYTJkxgyZIl3H///fTq1YujR4/y+uuvs3TpUqZMmeLsMEVEMtr/J0zvAUkx4F8YOk2BsGpZ3txiNXj1163M+PeoLXnesRqPVSucU9GKiMg9TtnYO0CwnwdHzl0kOiGZ4kE+zg5HRERERJxo48aNTJgwgalTp+Li4kK3bt349NNPKV++vL1O27ZtqVWrlhOjFBHJhGHA+u9hwatgWKBILej4E/iFZnkXFqvBKzO28uvGo7i6mBjTsRqtq4blYNAiInKvUwL9DhDia+bIuYucidfkUCIiIiL3ulq1atGsWTPGjh1LmzZtcHd3z1CnZMmSdOrUyQnRiYhcgyUVFrwCG8bblqt0gtafgbtn1ndhNXh5+hZmbjqGq4uJzzpVo1UVJc9FRCRnKYF+B0gfB/1MgiYSFREREbnXHThwgOLFi1+3jo+PDxMmTMiliEREbuDCOZjWDQ7+BZig2XCoNyDLk4WCLXn+0rTNzN58HFcXE190rk7LiEI5F7OIiMglSqDfAYL9bAn0aPVAFxEREbnnnT59mpMnT1KnTh2H8rVr1+Lq6krNmjWdFJmISCZO74KpneD8QfDwhXbjoFzzm9pFmsXKoGlbmLvlOG6XkuctlDwXEZFckrXprcWp0nugRycogS4iIiJyr+vbty9HjhzJUH7s2DH69u3rhIhERK5hz0L4vpkteR5YHHovvaXk+cBfNtuT518+eZ+S5yIikqvUA/0OEOKnBLqIiIiI2OzcuZP77rsvQ3n16tXZuXOnEyISEbmKYcDfn8OStwEDij8AT/wAPkE3tZtUi5WBP29m3rYTuLua+L8n7+PhSgVzJmYREZFrUA/0O0CIrweAJhEVEREREcxmM6dOncpQfuLECdzc1D9GRJwsNQlmPw9LhgIG1OgJXWfdUvL8xZ832ZPnY7vUUPJcREScQgn0O8DlIVw0iaiIiIjIve7hhx9myJAhxMbG2stiYmJ4/fXXadasmRMjE5F7XvwpmNQKtkwFkyu0HA2tPgU3j5vaTarFSv8pm5i/7SQeri58/VQNmlYMzaGgRURErk9dVO4AGgNdRERERNKNHj2ahg0bUrx4capXrw7A5s2bCQ0N5ccff3RydCJyzzqxBaZ2hrhj4BkAHSZB+IM3vZuUNCv9p25k0Y5TeLi68E3XGjxYvkAOBCwiIpI1SqDfAYIvjYF+IcXChZQ0vD30tImIiIjcqwoXLszWrVv56aef2LJlC15eXvTs2ZPOnTvj7u7u7PBE5F60Y7Zt2JbUCxBUBp78BYLCb3o3KWlW+k7ZyJKdp/Bwc+HbrjVoXE7JcxERcS5lYu8APh6ueLm7cjHVQnR8CsWC9LSJiIiI3Mt8fHzo06ePs8MQkXudYcCKj2D5+7bl8CbQfjx4Bd70rpLTLPT9aSNLd53Gw82F77rVpFHZkOyNV0RE5BYoE3sHMJlMBPt5cOTcRc4kJFEsyNvZIYmIiIiIk+3cuZPDhw+TkuI4T86jjz7qpIhE5J6ScsHW63znbNvy/X2h2Qhwvfk0Q1KqhWd//JcV/53B7ObC991r0qCMkuciIpI3KIF+hwj2NdsS6PGaSFRERETkXnbgwAHatm3Ltm3bMJlMGIYB2DpdAFgsFmeGJyL3gtijtvHOT24FF3fbRKH3db2lXSUkp9F70nr+OXAOL3dXvutWkwfKBGdzwCIiIrfO5VY2OnLkCEePHrUvr1u3joEDB/Ltt99mW2DiSBOJioiIiAjAiy++SMmSJTl9+jTe3t7s2LGDlStXUrNmTZYvX+7s8ETkbndkPXz7oC157h0M3X+75eR57MVUuo5by/+3d9/hUVb5+8fvmcnMpPcOoSO9S7chKNgRdG2r6Lq6q+Cq6K6Lvf3E1XXlq6KsFRsWdGVdO6KiCDaaIkV6aEkIKZM2JTPP749JhgQChJBkksz7dV1zzfOcp+QzGYMnd86c892WAkXbw/TK1cMIzwEALU6DAvRLL71UX375pSQpJydHp512mn744Qfdcccduv/++xu1QPgRoAMAAECSli1bpvvvv1/Jyckym80ym8064YQTNHPmTP3lL38JdnkA2rLVb0pzz5LK8qS0vtK1X0odRzboVgVlbl363HdamV2kuAirXv/jcA3tlNjIBQMAcOwaFKCvWbNGw4YNkyS9/fbb6tu3r5YuXarXX39dc+fObcz6UCUlhgAdAAAA/ilaYmJiJEnJycnavXu3JKljx47asGFDMEsD0Fb5vNLCe6T3/iR5XVLPs6U/fCrFd2jQ7fIcTl387DL9utuh5Gib3rx2hAZkxTduzQAANJIGzYHu8Xhkt/sD3c8//zywUFHPnj21Z8+exqsOASnRNknS3hICdAAAgFDWt29frV69Wp07d9bw4cP1yCOPyGaz6dlnn1WXLl2CXR6Atsbnld69Wvr1Pf/+ibdKY+6QzA0aj6ddRRW67LnvtG1fudJi7Xr9jyPULTW6EQsGAKBxNej/eH369NGcOXP0zTffaOHChZowYYIkaffu3UpKSqr3fb7++mudc845yszMlMlk0oIFCw57/ldffSWTyXTQIycnpyEvo1XZP4ULi4gCAACEsjvvvFM+n0+SdP/992vr1q068cQT9dFHH+mJJ54IcnUA2pzP7vSH5xabNPkFaexdDQ7Pt+8r0+/mLNO2feVqnxCh+X8aRXgOAGjxGjQC/R//+IfOP/98Pfroo5oyZYoGDBggSXr//fcDU7vUR1lZmQYMGKA//OEPmjRpUr2v27Bhg2JjYwP7qamp9S++lUpmChcAAABIGj9+fGC7W7duWr9+vQoKCpSQkCCTyRTEygC0Ocuelr572r99/hyp7+QG32pjbokue/575ZW41CU5Sq9fM1wZcRGNVCgAAE2nQQH6Kaecovz8fDkcDiUkJATar732WkVGRtb7PmeccYbOOOOMo/76qampio+PP+rrWrPACHSmcAEAAAhZHo9HERERWrVqlfr27RtoT0xk4T0AjWzt+9Knt/u3T7v/mMLzX3cX6/IXflBBmVs90mL02h+HB9b5AgCgpWvQ564qKirkcrkC4fn27ds1a9YsbdiwoVlGgw8cOFAZGRk67bTT9O233x72XJfLJYfDUevRGlV3LsrcXpW7K4NcDQAAAILBarWqQ4cO8nq9wS4FQFu240fpP9dIMqTjr5ZG/aXBt1qZXahLnv1OBWVu9WsXpzevHUF4DgBoVRoUoJ933nl65ZVXJElFRUUaPny4HnvsMU2cOFHPPPNMoxZYU0ZGhubMmaN3331X7777rrKysnTKKadoxYoVh7xm5syZiouLCzyysrKarL6mFGWzKNzqf7vyS5gHHQAAIFTdcccduv3221VQUBDsUgC0Rfs2S29cJFU6peMmSGc8IjVweqjvt+zT75//Xg5npY7vmKDXrxmuhChbIxcMAEDTatAULitWrNDjjz8uSXrnnXeUlpamlStX6t1339Xdd9+t6667rlGLrNajRw/16NEjsD9q1Cht3rxZjz/+uF599dU6r5kxY4amT58e2Hc4HK0yRDeZTEqOtmtnYYX2lrrUIan+U+UAAACg7Xjqqae0adMmZWZmqmPHjoqKiqp1/HCDSwDgsMr2Sa9fKJXvkzIGShe8KFkaFBto8W979adXf5LT49Oorkl6fsrxirQ17F4AAARTg/7vVV5erpiYGEnSZ599pkmTJslsNmvEiBHavn17oxZ4JMOGDdOSJUsOedxut8tubxsfD6sO0FlIFAAAIHRNnDgx2CUAaIs8FdKbl0gFm6W4DtKlb0u2qCNfV4fPfs3RtHkr5fb6dGrPVD192WCFWy2NXDAAAM2jQQF6t27dtGDBAp1//vn69NNPdfPNN0uS8vLyFBsb26gFHsmqVauUkZHRrF8zWAILiRKgAwAAhKx77rkn2CUAaGt8Pum9P0k7vpfC46TfvyPFpDXoVu+v3q2b31olr8/Qmf3SNeuiQbKFNWj2WAAAWoQGBeh33323Lr30Ut1888069dRTNXLkSEn+0eiDBg2q931KS0u1adOmwP7WrVu1atUqJSYmqkOHDpoxY4Z27doVmG991qxZ6ty5s/r06SOn06nnn39eX3zxhT777LOGvIxWp3qhFeZABwAAAAA0moV3SWv/K1ls0sXzpJQeR76mDm//tEO3vfuzDEOaNKidHrmgv8IshOcAgNatQQH6BRdcoBNOOEF79uzRgAEDAu1jx47V+eefX+/7/PTTTxozZkxgv3qu8ilTpmju3Lnas2ePsrOzA8fdbrduueUW7dq1S5GRkerfv78+//zzWvdoy1Ki/Yut7C11BrkSAAAABIvZbJbpMAv6eb3eZqwGQKv3/bPSsqf82+c9LXU6oUG3eWXZNt39318lSZcO76AHz+srs7lhi48CANCSNHgFj/T0dKWnp2vnzp2SpPbt22vYsGFHdY9TTjlFhmEc8vjcuXNr7f/tb3/T3/72t6Outa1IZgQ6AABAyHvvvfdq7Xs8Hq1cuVIvv/yy7rvvviBVBaBVWv+h9Mlt/u2xd0v9L2zQbeYs3qyHP14vSbr6hM6686xeh/1DHwAArUmDAnSfz6cHH3xQjz32mEpLSyVJMTExuuWWW3THHXfIbOYjWk2BOdABAABw3nnnHdR2wQUXqE+fPnrrrbd09dVXB6EqAK3OzuXSO1dLhk8acqV0wvSjvoVhGJr1+Ub936KNkqQbTu2m6acdR3gOAGhTGhSg33HHHXrhhRf08MMPa/To0ZKkJUuW6N5775XT6dT/+3//r1GLhB8BOgAAAA5lxIgRuvbaa4NdBoDWoGCr9MZFUmWF1P106czHpKMMvQ3D0MyP1+vZr7dIkv42oYeuP6VbU1QLAEBQNShAf/nll/X888/r3HPPDbT1799f7dq10/XXX0+A3kQCi4iWMoULAAAA9quoqNATTzyhdu3aBbsUAC1deYH0+oVS2V4pvb90wUuS5eiiAZ/P0N3vr9Fr3/nXLLvnnN66anTnpqgWAICga1CAXlBQoJ49ex7U3rNnTxUUFBxzUahbctUioqWuSlW4vYqwWYJcEQAAAJpbQkJCrekRDMNQSUmJIiMj9dprrwWxMgAtnscpvXmptG+jFJclXfq2ZI8+qlt4fYZue/dnvbN8p0wmaeb5/XTxsA5NVDAAAMHXoAB9wIABeuqpp/TEE0/Uan/qqafUv3//RikMB4u2h8keZpar0qf8UpeyEiODXRIAAACa2eOPP14rQDebzUpJSdHw4cOVkJAQxMoAtGg+n7TgOil7mWSPky6bL8VmHNUtPF6fbnprlT78eY8sZpP+9bsBOm8gn3wBALRtDQrQH3nkEZ111ln6/PPPNXLkSEnSsmXLtGPHDn300UeNWiD2M5lMSo62a1dRhfYSoAMAAISkK6+8MtglAGiNFt0r/fofyWyVLnpVSu11VJc7PV5Nm7dCn6/Lk9Vi0pOXDNaEvulNUysAAC2IuSEXnXzyyfrtt990/vnnq6ioSEVFRZo0aZJ+/fVXvfrqq41dI2pIrp4HvYSFRAEAAELRSy+9pPnz5x/UPn/+fL388stBqAhAi/fj89K3/+ffPu8pqcvJR3V5hdura175SZ+vy5M9zKznrjie8BwAEDIaNAJdkjIzMw9aLHT16tV64YUX9Oyzzx5zYahbSjQLiQIAAISymTNn6t///vdB7ampqbr22ms1ZcqUIFQFoMXa8In00V/922PulAZcXO9LK70+/e/n3Xryi03asrdMkTaLXpgyVCO7JjVRsQAAtDwNDtARHCkx/oVE9zICHQAAICRlZ2erc+fOB7V37NhR2dnZQagIQIu1a4X0zlWS4ZMGXS6ddGu9LnNVevWfFbv0zFeblV1QLklKiLTq+SlDNaQjay0AAEILAXorkxwYgU6ADgAAEIpSU1P1888/q1OnTrXaV69eraQkRoUCqFK4XZp3keQpl7qOlc5+XKqxAHFdnB6v3vwhW//+eov2FDslSUlRNl19YmddPqKjYsKtzVE5AAAtCgF6K0OADgAAENouueQS/eUvf1FMTIxOOukkSdLixYt144036uKL6z81A4A2rKJQev1CqSxPSusnXThXshw6/C51Ver177bruW+2Bn7XTIu1608nddUlwzoowmZppsIBAGh5jipAnzRp0mGPFxUVHUstqAcCdAAAgND2wAMPaNu2bRo7dqzCwvzdeZ/PpyuuuEIPPfRQkKsDEHSVLunN30v5G6TYdtJlb0vhsXWeWlzh0ctLt+nFb7eqqNwjSWqfEKHrTumqC4a0lz2M4BwAgKMK0OPi4o54/IorrjimgnB4KTEsIgoAABDKbDab3nrrLT344INatWqVIiIi1K9fP3Xs2DHYpQEINsOQ/jtV2r5EssdKl82XYjMPOm1fqUsvLNmqV5ZtV6mrUpLUJTlK14/ppvMGZspqMTd35QAAtFhHFaC/9NJLTVUH6ik5mkVEAQAAIHXv3l3du3cPdhkAWpIvHpB+mS+Zw6TfvSKl9al1ONfh1LNfb9G877NV4fFKknqmx2jqmG46s1+GLObDz5EOAEAoYg70Via5agR6qatSTo9X4VY+UgcAABBKJk+erGHDhum2226r1f7II4/oxx9/1Pz584NUGYCg+ukl6ZvH/NvnPil1HRM4tKOgXHMWb9b8n3bK7fVJkvq3j9MNp3bX2J6pMhOcAwBwSATorUyMPUy2MLPclT7tLXEpKzEy2CUBAACgGX399de69957D2o/44wz9NhjjzV/QQCCb+NC6cNb/NunzJAGXipJ2rK3VE9/tVkLVu5Spc+QJA3tlKAbTu2uE7sny2QiOAcA4EgI0FsZk8mklGi7dhVVKL+UAB0AACDUlJaWymazHdRutVrlcDiCUBGAoNqzWnp7imR4pYGXSSffpvU5Ds3+crM+/Hm3qnJzndg9WdPGdNPwLknBrRcAgFaGAL0VSo6pDtBZSBQAACDU9OvXT2+99ZbuvvvuWu1vvvmmevfuHaSqADQrp0Paulja+Jm09n3JUyZ1OUW/DLpXT766XJ+tzQ2cOq5XqqaO6aZBHRKCWDAAAK0XAXorlMJCogAAACHrrrvu0qRJk7R582adeuqpkqRFixZp3rx5euedd4JcHYAmYRhS3jpp00L/dC3ZyyRfZeBwWWIf3eK+SZ8886MkyWSSzuyXoamndFPvzNhgVQ0AQJtAgN4KJUf7FxLNLyVABwAACDXnnHOOFixYoIceekjvvPOOIiIiNGDAAH3xxRdKTEwMdnkAGourRNqyuCo0/1xy7Kx9PLGrCjJP1rO7u+jF3Vlyq1wWs0nnDczU9ad0U7fU6ODUDQBAG0OA3goRoAMAAIS2s846S2eddZYkyeFw6I033tCtt96q5cuXy+v1Brk6AA1iGNLe9f4R5hs/k7K/k3ye/cfDwqVOJ0rdT1N5hzGatdKrF5ZslddnyGox6ZIhWbru5K7qkMQ6WQAANCYC9FYouWoKFwJ0AACA0PX111/rhRde0LvvvqvMzExNmjRJs2fPDnZZAI6Gq7RqLvOF0qbPpeIdtY8ndpG6nSZ1P13qNFpGWLg+/TVX98/9VbuLnZKkCX3SdefZvdQ+geAcAICmQIDeCqXEhEuS8ktYRBQAACCU5OTkaO7cuXrhhRfkcDj0u9/9Ti6XSwsWLGABUaA1MAxp74b9c5lvX1rHKPMTqkLz06SkroFDOwrKdc/7P+mL9XmSpKzECN1/bl+N6Zna3K8CAICQQoDeClWPQN/LCHQAAICQcc455+jrr7/WWWedpVmzZmnChAmyWCyaM2dOsEsDcDiuUmnr1/vnMi/Orn08obM/LO9+utRxtGSrPZLcXenTc99s0ZNfbJTT45PVYtK1J3XRtDHdFWGzNOMLAQAgNBGgt0LJMVVzoJcQoAMAAISKjz/+WH/5y1903XXXqXv37sEuB8CR7FouffGgtG2J5K3x6WGL3T/KvDo0rzHK/EBLN+frrgVrtHlvmSRpZJckPTCxj7qlxjR19QAAoAoBeitUvYhoiatSTo9X4VZGHQAAALR1S5Ys0QsvvKAhQ4aoV69euvzyy3XxxRcHuywAdVn1hvS/GyVv1aCnhE415jI/4aBR5gfaW+LSQx+t03srd0nyfwr5zrN667yBmTKZTE1cPAAAqIkAvRWKDQ+TzWKW2+tTfqmLxWIAAABCwIgRIzRixAjNmjVLb731ll588UVNnz5dPp9PCxcuVFZWlmJiGJUKBJW3Ulp4l/Td0/79HmdJp93vH2Vej+Db6zM074dsPfrJejmclTKZpMuGd9BfT++puEhrExcPAADqYg52ATh6JpNJKdXTuJSykCgAAEAoiYqK0h/+8ActWbJEv/zyi2655RY9/PDDSk1N1bnnnhvs8oDQVV4gvTZpf3h+8m3SRa9Jyd3qFZ6v2VWsSU9/q7sWrJHDWam+7WL13vWj9eDEfoTnAAAEEQF6KxVYSJR50AEAAEJWjx499Mgjj2jnzp164403gl0OELpy10rPjZG2LpasUdLvXpXG3C6Zj/wrt8Pp0b3v/6pzn1qi1TuLFWMP073n9NZ/p56ggVnxTV87AAA4LKZwaaWq50HPLyVABwAACHUWi0UTJ07UxIkTg10KEHrWvi+992fJUybFd5QueUNK63PEywzD0P9+3qMHPlgbGBh1zoBM3XVWL6XGhjd11QAAoJ4I0FupQIDOCHQAAAAAaH4+n7T4YWnxP/z7nU+WLpwrRSYe8dKt+WW6a8EaLdmU7780OUr3n9dHJ3ZPacKCAQBAQxCgt1LJMf4pXBiBDgAAAADNzFUi/edP0oYP/fsjrpdOe0CyHP5XbKfHq6e/2qw5X22W2+uTLcysqad0059O7qJwq6UZCgcAAEeLOdBbqZRoFhEFAABA83r44YdlMpl00003BdqcTqemTp2qpKQkRUdHa/LkycrNzQ1ekUBT27dZev40f3husUsTn5EmzDxieL74t70aP+trPbFoo9xen046LkWf3XSSbhzXnfAcAIAWjBHorVRyjD9AZxFRAAAANIcff/xR//73v9W/f/9a7TfffLM+/PBDzZ8/X3FxcZo2bZomTZqkb7/9NkiVAk1o0yLpnaskZ7EUnS5d/LrU/vjDXpJT7NQDH6zVh7/skSSlxdp199l9dGa/dJlMpuaoGgAAHAMC9FaKRUQBAADQXEpLS3XZZZfpueee04MPPhhoLy4u1gsvvKB58+bp1FNPlSS99NJL6tWrl7777juNGDEiWCUDjcswpGWzpYV3SYZPaj9U+t2rUmzGIS+p9Pr08rLt+tdnG1Tm9spskq4c1Vk3n9ZdMeHWZiweAAAcC6ZwaaWqA/S9BOgAAABoYlOnTtVZZ52lcePG1Wpfvny5PB5PrfaePXuqQ4cOWrZsWXOXCTQNT4X03p+kz+7wh+eDfi9d+eFhw/Nyd6WufXW5HvhgrcrcXg3Mitf/bjhBd5/Tm/AcAIBWhhHorVT1HOglzko5PV7mzAMAAECTePPNN7VixQr9+OOPBx3LycmRzWZTfHx8rfa0tDTl5OQc8p4ul0su1/6BIA6Ho9HqBRpV8S7prcuk3Sslk0Wa8LA07BrpMFOv5Je69Ie5P+rnncWyh5l19zm9dcnQDjKbma4FAIDWiAC9lYqNCJPNYpbb69O+MrfaxUcEuyQAAAC0MTt27NCNN96ohQsXKjw8vNHuO3PmTN13332Ndj+gSWR/L731e6ksT4pIlC6cK3U5+bCXbM0v05QXf1B2QbkSIq164cqhGtwhoXnqBQAATYIpXFopk8mk5GibJBYSBQAAQNNYvny58vLyNHjwYIWFhSksLEyLFy/WE088obCwMKWlpcntdquoqKjWdbm5uUpPTz/kfWfMmKHi4uLAY8eOHU38SoCjtHyuNPcsf3ie1le69ssjhucrsws1+Zmlyi4oV1ZihN69bhThOQAAbQAj0Fux5Bi7dhc7lU+ADgAAgCYwduxY/fLLL7XarrrqKvXs2VO33XabsrKyZLVatWjRIk2ePFmStGHDBmVnZ2vkyJGHvK/dbpfdbm/S2oEG8XqkT2ZIPz7n3+99nnTe05I9+rCXLVybqxveWCGnx6f+7eP0wpShSonhv3EAANoCAvRWrHoh0XwWEgUAAEATiImJUd++fWu1RUVFKSkpKdB+9dVXa/r06UpMTFRsbKxuuOEGjRw5UiNGjAhGyUDDleVLb0+Rti/x7596p3TirYed71ySXvtuu+7+7xr5DGlMjxQ9delgRdn5VRsAgLaC/6u3YtVTuBCgAwAAIFgef/xxmc1mTZ48WS6XS+PHj9fTTz8d7LKAo7PnZ+nNS6XiHZItRpr0rNTzzMNeYhiG/vnZBs3+crMk6aLjs/T/zu+rMAszpQIA0JYQoLdi1R8JzC91B7kSAAAAhIqvvvqq1n54eLhmz56t2bNnB6cg4FiteVdaMFWqrJASu0qXvCGl9DjsJe5Kn/7+n5/1nxW7JEk3jeuuG8d2l+kIo9UBAEDrQ4DeilVP4cIiogAAAABwlHxe6YsHpSX/8u93HStd8IIUcfiFP0ucHl3/+gp9szFfFrNJM8/vp98NzWqGggEAQDAQoLdigQCdKVwAAAAAoP6cxdK7f5Q2fubfH/UXady9ktly2MtyHU5d9dKPWrvHoUibRbMvG6wxPVKbvl4AABA0BOitGIuIAgAAAMBRKt4lvTZJ2rteCguXzn1K6n/hES/blFeiKS/+qF1FFUqOtunFK4eqf/v4pq8XAAAEFQF6KxaYA50pXAAAAADgyPI3Sa9O9C8WGpPhn+88c9ARL/txW4H++PJPKq7wqHNylF6+apg6JEU2fb0AACDoCNBbsZSqEegOZ6VclV7Zww7/cUMAAAAACFm7V0mvTZbK86WkbtLl70nxHY542ce/7NGNb62Su9KnwR3i9fyUoUqMsjV9vQAAoEUwB7sANFxsRJhsFv9bmF/qDnI1AAAAANBCbf1Gmnu2PzzPGCBd9Um9wvOXvt2q6+etkLvSp9N7p+n1P44gPAcAIMQQoLdiJpNJSdH+zhvTuAAAAABAHdZ94B957i6ROp0oTflAik457CU+n6GHPlqn+/63VoYhXT6io575/RBF2PjULwAAoYYpXFq55Gi79hQ7WUgUAAAAAA608jXp/Rskwyf1PFua/IJkDT/sJa5Kr26d/7P+t3q3JOm2CT3155O7yGQyNUfFAACghSFAb+UCC4kSoAMAAADAfkuflD6707898PfSOf8nWQ7/K3BxhUd/evUnfbelQFaLSY9c0F/nD2rfDMUCAICWKqhTuHz99dc655xzlJmZKZPJpAULFhzxmq+++kqDBw+W3W5Xt27dNHfu3CavsyVLrp7ChTnQAQAAAEAyDOnze/eH56NukM576ojh+e6iCv1uzjJ9t6VA0fYwzb1qGOE5AAAIboBeVlamAQMGaPbs2fU6f+vWrTrrrLM0ZswYrVq1SjfddJP++Mc/6tNPP23iSluu5Gj/CPS9zIEOAAAAINT5vNL//iItedy/P+4+6fQHpSNMv7I+x6FJTy/VhtwSpcXa9fafRmp0t+RmKBgAALR0QZ3C5YwzztAZZ5xR7/PnzJmjzp0767HHHpMk9erVS0uWLNHjjz+u8ePHN1WZLVogQGcKFwAAAAChrNIlvftHad37ksksnT1LGjLliJct3ZyvP72yXCWuSnVLjdbLfximdvERTV8vAABoFVrVHOjLli3TuHHjarWNHz9eN9100yGvcblccrn2h8sOh6OpyguK5Oo50BmBDgAAACBUuUqkNy+Tti6WLDZp8vNS7/OOeNl/V+3SrfNXy+M1NKxTop674njFRVqboWAAANBaBHUKl6OVk5OjtLS0Wm1paWlyOByqqKio85qZM2cqLi4u8MjKymqOUptNSjSLiAIAAAAIYWX7pJfP9YfntmjpsneOGJ4bhqF/L96sG99cJY/X0Fn9MvTK1cMIzwEAwEFaVYDeEDNmzFBxcXHgsWPHjmCX1KhSYlhEFAAAAECIKt4pvTRB2r1CikiUprwvdTn5sJc4PV7duWCNZn68XpL0h9Gd9eQlgxRutTRHxQAAoJVpVVO4pKenKzc3t1Zbbm6uYmNjFRFR9xx1drtddru9OcoLiuo50IsrPHJVemUPo9MHAAAAIATs/U169XzJsVOKbSddvkBKOe6wl2zMLdENb6zU+pwSSdKdZ/XSH0/s0gzFAgCA1qpVBegjR47URx99VKtt4cKFGjlyZJAqCr64CKusFpM8XkP7St3KZLEbAAAAAG3drhXS6xdI5fukpO7S5e9J8YeertMwDM37IVsPfLBWTo9PSVE2/fPCARrTM7UZiwYAAK1RUAP00tJSbdq0KbC/detWrVq1SomJierQoYNmzJihXbt26ZVXXpEk/fnPf9ZTTz2lv/3tb/rDH/6gL774Qm+//bY+/PDDYL2EoDOZTEqKsivH4VR+qYsAHQAAAEDbtmWx9OalkrtUyhzkn/M8KvmQpxeVu3Xbuz/r01/9n2Y+sXuyHvvdAKXGhDdXxQAAoBULaoD+008/acyYMYH96dOnS5KmTJmiuXPnas+ePcrOzg4c79y5sz788EPdfPPN+r//+z+1b99ezz//vMaPH9/stbckKTH7A3QAAAAAaLPWvi+9e7XkdUudT5IunifZYw55+ndb9unmt1ZpT7FTVotJfxvfU1ef0Flms6kZiwYAAK1ZUAP0U045RYZhHPL43Llz67xm5cqVTVhV65McXbWQaAkLiQIAAABoo1a8Iv3vRsnwSb3OkSY9L1nrHkXu8fr0xKKNeurLTTIMqXNylJ64eJD6tY9r5qIBAEBr16rmQEfdqhcS3csIdAAAAABt0ZJZ0uf3+LcHXyGdPUsyW+o8dUdBuf7y5kqtzC6SJF04pL3uPbePouz8+gsAAI4ePYg2IDmmKkAvIUAHAAAA0IYYhrTwbmnpE/790TdJ4+6VTHVPwfLfVbt053trVOKqVIw9TA9N6qdzBmQ2W7kAAKDtIUBvA6pHoDMHOgAAAIA2w1spfXCTtPJV//5pD0ij/1LnqaWuSt3z31/17oqdkqQhHRM066KBykqMbKZiAQBAW0WA3gakxBCgAwAAAGhDPE7/YqHrP5BMZuncJ6VBv6/z1J93Fukvb6zUtn3lMpukaad2119O7aYwi7mZiwYAAG0RAXobEFhEtJRFRAEAAAC0ck6H9Oal0rZvJItduuBFqdfZB53m8xl67pst+udnG+TxGsqIC9esiwZqeJekIBQNAADaKgL0NiAlmjnQAQAAALQBub9KC66T9qyWbDHSJfOkzicddFqew6lb5q/WNxvzJUkT+qTr4cn9FB9pa+6KAQBAG0eA3gZUz4FeXOGRu9InWxgfVQQAAADQipTlS18+JC1/STJ8UmSS9Pt3pcxBB536xfpc3Tr/ZxWUuRVuNeuec/ro4qFZMh1iYVEAAIBjQYDeBsRFWBVmNqnSZ2hfmUsZcRHBLgkAAAAAjqzSLf3wrLT4EclV7G/rda40/v9J8R1qner0ePXwx+s1d+k2/2kZsXrykoHqlhrTzEUDAIBQQoDeBpjNJiVH25XjcCq/xE2ADgAAAKBlMwzpt0+lz+6Q9m3yt6X3kyY8LHU64aDTN+WVaNq8lVqfUyJJump0J902oafCrZbmrBoAAIQgAvQ2IjnG5g/QS5kHHQAAAEALlrdO+mSGtOVL/35UijT2bmngZZK5diBuGIbe+GGH7v/gVzk9PiVF2fTPCwdoTM/UIBQOAABCEQF6G5HMQqIAAAAAWrKyfdJXD0k/vSQZXslik0ZcL514ixQee9DpReVu/f3dX/TJrzmSpBO7J+uxCwcoNTa8uSsHAAAhjAC9jQgE6IxABwAAANCSeD3SD89Jix+WnNXznJ8jnXa/lNilzku+27JPN7+1SnuKnbJaTPrr+B764wldZDazUCgAAGheBOhtRHWAzhQuAAAAAFoEw5A2fiZ9eoe0b6O/La2fNOEhqfNJdV7i8xmatWijnvpio3yG1Dk5Sk9cPEj92sc1Y+EAAAD7EaC3ESkx1QG6O8iVAAAAAAh5eeulT2+XNi/y70cmS2PvkgZdftA85zXN+XqznljkD9svHNJe957bR1F2fm0FAADBQ0+kjUiOtkmS8pkDHQAAAECwlBdIX82UfnzBP8+52SqNuE466VYp/PCjyL/bsk///HSDJOnec3rrytGdm6NiAACAwyJAbyNSmAMdAAAAQLB4Pf7Q/KuZkrPI39bzbP8850ldj3h5XolTN7yxUj5DmjSonaaM6tSk5QIAANQXAXobkRzDHOgAAAAAgmDjQv90Lfm/+fdT+0gTZkpdTq7X5V6foRvfWKW9JS4dlxatB8/vK5OJxUIBAEDLQIDeRlQvIlpU7pHH65PVYg5yRQAAAADatL0b/AuEblro349Mkk69Uxo85bDznB/o8YW/admWfYq0WfT0ZUMUaePXVAAA0HLQM2kj4iOsCjObVOkztK/UrfS48GCXBAAAAKAtKi+QFv9D+uG5/fOcD/+TdNJfpYj4o7rVlxvy9NSXmyRJD0/ur26p0U1QMAAAQMMRoLcRZrNJSdE25Tpcyi91EaADAAAAaFzeSumnF6WvHpIqCv1tPc6UTn+wXvOcH2hXUYVufmuVJOnyER117oDMRiwWAACgcRCgtyHJ0XblOlzaW8I86AAAAAAa2ae3Sz/827+d0kua8JDU9dQG3cpd6dPU11eoqNyj/u3jdOfZvRqxUAAAgMZDgN6GVM+DvpeFRAEAAAA0pl0rpB+e9W9PeFgaeo1kafivkw99tE6rdhQpNjxMsy8dLHtY/edMBwAAaE4E6G1IdYCeT4AOAAAAoLH4vNKHt0gypH6/k0Zcd0y3+/DnPZq7dJsk6V+/G6isxMhjrxEAAKCJmINdABpPSkxVgF7iDnIlAAAAANqMFS9Lu1dI9ljp9AeO6VZb9pbqtnd/liT96eQuGtc7rTEqBAAAaDIE6G1IcrRNEiPQAQAAADSSsn3S5/f5t8fcLsWkN/hWTo9X17++QqWuSg3rnKi/nt6jkYoEAABoOgTobUj1CHQWEQUAAADQKD6/R3IWSWn9/POeH4O7/7tG63NKlBxt01OXDFKYhV9HAQBAy0ePpQ1hDnQAAAAAjWbHD9LKV/3bZ/3zmBYNffunHXr7p50ym6QnLh6k1NjwRioSAACgaRGgtyEE6AAAAAAahbdS+nC6f3vgZVKHEQ2+1bo9Dt21YI0k6eZxx2lUt+TGqBAAAKBZNHwIAVqc6ilcCss9+scn65UQaVV8hE1xkVbFR1iVEGVTfIRVcZFW2cMsQa4WAAAAQIv10wtSzi9SeJw07r4G36bE6dH1r6+Qq9Knk49L0dQx3RqxSAAAgKZHgN6GxEdYFRMephJnpZ75avNhz42wWhQfaVV8pD9U92/X3o+LsPlD+Ehb1b5V4VaCdwAAAKBNK82TvnjQvz32bik6pUG3MQxDf3/3F23NL1NGXLgev2igzGZTIxYKAADQ9AjQ2xCz2aSX/zBM3/yWr6IKt4rKPSoqd6uowqPico8Ky90qrvDIZ0gVHq8qir3aU+w8qq8RbjUrIdKmpGib+mbGaWBWvAZ1SFC31GhZ6AwDAAAArd9nd0kuh5Q5SBpyVYNv8/LSbfrwlz0KM5v01KWDlRhla8QiAQAAmgcBehszuEOCBndIOORxn89QiasyEKgXVfhD9uIKjwrLPCqqcKu43BNoL6qx7TMkp8enPcVO7Sl2as0uh978cYckKcpm0YCs+ECgPjArPjClDAAAAIBWYtu30s9vSjJJZz0mmRv2CdSV2YX6fx+tkyTdfmYvDel46N9RAAAAWjIC9BBjNpsUF+GfjqVDUmS9r/P5DJW6K1VUFbLvLnJq9c4ircou0uqdRSpze7V08z4t3bwvcE37hAgNrBGq98mMZQoYAAAAoKXyeqQPb/FvD5kitRvSoNsUlrk1bd5KebyGzuibrqtGd2q8GgEAAJoZATrqxWw2KTbcqthwqzooUv3bSxP6pkuSvD5DG/NKtDLbH6iv3FGojXml2llYoZ2FFfrg5z2SJKvFpN4ZsbVGqXdMipTJxNQvAAAAQNB9P0fau06KSJTG3tOgW/h8hqa/vUq7iirUKSlS/7igP/19AADQqhGg45hZzCb1TI9Vz/RYXTKsgySpxOnRzzuLtWpHkVZmF2rVjiLll7q1emexVu8s1svLtkuSEiKttQL1AVnxiouwBvPlAAAAAKHHsVv66mH/9mn3SZGJDbrNM4s368sNe2UPM+vpy4YoNpy+PQAAaN0I0NEkYsKtGt0tWaO7JUuSDMPQzsIKrdyxf5T6r7scKiz36MsNe/Xlhr2Ba7umRGlgVoIGdYjXyK5J6pIcxagVAAAAoCl9eofkLpXaD5UG/r5Bt1i6OV+PfbZBknT/eX3UOzO2MSsEAAAICgJ0NAuTyaSsxEhlJUbq3AGZkiRXpVfr9pRoVXahP1jfUaTt+8q1eW+ZNu8t07srdkqSOiZFakyPVI3tlaphnRNlD2MedQAAAKDRbPlK+vU/kslctXCo+ahvkedw6i9vrJLPkCYPbq/fHZ/V+HUCAAAEAQE6gsYeZgksMnplVdu+UpdW7yzSyuwiLd9eqJ+2FWr7vnLNXbpNc5duU6TNohO6JevUnqka0zNVabHhwXwJAAAAQOtW6ZI+vNW/PfSPUsaAo7+F16cb3lip/FKXeqTF6MGJffkEKQAAaDMI0NGiJEXbdWrPNJ3aM02SVOqq1JKN+fpyfZ6+3JCnvBKXPlubq8/W5kqS+raL1ak9/GH6gPbxMpvpqAMAAAD1tmy2tG+jFJUqjbmjQbf418Lf9P3WAkXZLHr694MVYeMTowAAoO0gQEeLFm0P04S+6ZrQN10+n6G1exxatC5PX2zI0887i7Rml0Nrdjn0xBeblBRl0yk9UnVqz1SdeFwyCxYBAAAAh1O0Q/r6Uf/26Q9IEfFHfYsv1ufq6a82S5L+cUF/dU2JbsQCAQAAgo8AHa2G2WxS33Zx6tsuTjeO6678Upe+2rBXX6zP1Te/5WtfmVvvrtipd1fsVJjZpOM7JejUnv5AvWtKNB8jBQAAAGr65O+Sp1zqMErqf9FRX76zsFw3v7VakjRlZEed3T+zsSsEAAAIOgJ0tFrJ0XZdMKS9LhjSXh6vTz9uK9CX6/P0xfo8bd5bpu+2FOi7LQV66KP16pAYGZg3fXjnRIVb+VgpAAAAQtjGhdL6DySTRTrrn9JRDjZxVXo19fUVKq7waED7ON1+Vq8mKhQAACC4CNDRJlgtZo3qmqxRXZN1x1m9tX1fmb6oCtO/31Kg7IL9C5FGWC06oXvVQqQ9UpUex0KkAAAACCEep/TRX/3bI66T0voc9S0e+nCdVu8sVlyEVbMvGyx7GANUAABA20SAjjapY1KUrhrdWVeN7qwyV6WWbNq/EGmuw6WFa3O1sGoh0p7pMRrcMUGDOyRocId4dU6OYroXAAAAtF3f/p9UuFWKyZBO+ftRX/6/1bv18rLtkqTHLxqg9gmRjV0hAABAi0GAjjYvyh6m8X3SNb5PugzD0K+7HfpyfZ4Wrc/T6p1FWp9TovU5JZr3fbYkKSHSqkFVYfrgjgka0D5eUXZ+VAAAANAGFGyVlvzLvz3+/0n2mKO6fPPeUv393Z8lSded0lWn9kxr7AoBAABaFFJBhBSTaf9CpDeM7a59pS79uK1AK7KLtHx7oX7ZVazCck9g+hdJMpuknumxGtwxvmqUeoI6JkUySh0AAACti2FIH98mVTqlzidJfSYd1eUVbq+uf22FytxeDe+cqFtOO66JCgUAAGg5CNAR0pKi7ZrQN0MT+mZIktyVPv26u1grsou0IrtQK7cXanexU2v3OLR2j0OvfecfpZ4UZdOgDvEa1CFBQzomqH/7OEXa+HECAABAC7bhI2njp5LZKp352FEtHGoYhv7+n5+1IbdEydF2PXnJIIVZzE1YLAAAQMtA4gfUYAsza1CHBA3qkKCr1VmSlFPs1IrsQi3fXqgV2YX6dZdD+8rc+nxdnj5f5x+lbjGb1CsjJjBCfXCHBGUlRjBKHQAAAC2Du1z6uGq+81HTpJSjGz3+5Beb9N9VuxVmNunJSwYpNTa8CYoEAABoeQjQgSNIjwvXmf0ydGY//yh1V6VXa3Y5tDLbH6iv2F6kHIdTa3Y5tGaXQ69ULaiUHG2rmkvdP59678xYxYRbg/lSAAAAEKq+eUwqzpbisqST/npUl/5v9W79a+FvkqQHJ/bVyK5JTVEhAABAi0SADhwle5hFQzr6p26ptruoIjBCfUV2kdbuLlZ+qVsL1+Zq4drcwHlZiRHqlR6r3pmx6pURq94ZsWqfwEh1AAAANKH8TdLSJ/zbE2ZKtqh6X7oyu1C3zl8tSfrjCZ118bAOTVEhAABAi0WADjSCzPgIZcZH6JwBmZIkp8erNbuKAyPUV+3wj1LfUVChHQUV+qxGqB5jD1PPjBj1yogNPHqkxSjCZgnWywEAAAiYOXOm/vOf/2j9+vWKiIjQqFGj9I9//EM9evQInON0OnXLLbfozTfflMvl0vjx4/X0008rLS0tiJVDkn/h0I9ulbxuqds4qefZ9b50V1GFrnlluVyVPo3tmaoZZ/ZqwkIBAABaJgJ0oAmEWy06vlOiju+UGGgrLHNrXdVipOv2lGjdHoc25ZWqxFWpH7cV6sdthYFzzSapU3JUYJR6r4wY9c6IU1qsndHqAACgWS1evFhTp07V0KFDVVlZqdtvv12nn3661q5dq6go/0jmm2++WR9++KHmz5+vuLg4TZs2TZMmTdK3334b5OqhtQukLV9KFrt0xiP1Xji01FWpq+f+qPxSl3qmx+j/Lhkki5l+KAAACD0mwzCMYBcxe/ZsPfroo8rJydGAAQP05JNPatiwYXWeO3fuXF111VW12ux2u5xOZ72+lsPhUFxcnIqLixUbG3vMtQPHwuP1afPeUq2rEaqv2+NQfqm7zvMTIq21Rqr3yohR99QY2cLMzVw5AAA4krba79y7d69SU1O1ePFinXTSSSouLlZKSormzZunCy64QJK0fv169erVS8uWLdOIESOOeM+2+r0KOlep9NRQqWS3dPJt0pjb63WZ12fo2ld+0qL1eUqOtuu/00arXXxEExcLAADQtBra5wz6CPS33npL06dP15w5czR8+HDNmjVL48eP14YNG5SamlrnNbGxsdqwYUNgnxG5aK2sFrN6pseqZ3qszh+0vz2vxFkrUF+3x6HNe8tUWO7R0s37tHTzvsC5YWaTuqVG+6d+SY9Rt5RodU+LVvuESEYJAQCARldcXCxJSkz0f9Ju+fLl8ng8GjduXOCcnj17qkOHDvUO0NFEFv/DH57Hd5ROuLnel838aJ0Wrc+TPcys564YQngOAABCWtAD9H/961+65pprAqPK58yZow8//FAvvvii/v73v9d5jclkUnp6enOWCTSr1JhwpcaE6+TjUgJtTo9XG3NLa0wD4384nJVan1Oi9Tklte5hDzOrS0q0uqVGq3vq/ueOSVGMWAcAAA3i8/l00003afTo0erbt68kKScnRzabTfHx8bXOTUtLU05OTp33cblccrlcgX2Hw9FkNYesvHXSd0/7t894RLLWLwR/44dsPb9kqyTpsd8N0KAOCU1VIQAAQKsQ1ADd7XZr+fLlmjFjRqDNbDZr3LhxWrZs2SGvKy0tVceOHeXz+TR48GA99NBD6tOnT3OUDARNuNWifu3j1K99XKDNMAztLnZq3W5/mL4xr1Sb8kq1eW+pXJW+QMheU5jZpI5JkeqeGuMP1dP84XrXlGiFW1m4FAAAHNrUqVO1Zs0aLVmy5JjuM3PmTN13332NVBUOYhjSh7dKvkqpx5lSjwn1umzppnzdtWCNJGn6acfp7P6ZTVklAABAqxDUAD0/P19er1dpaWm12tPS0rR+/fo6r+nRo4defPFF9e/fX8XFxfrnP/+pUaNG6ddff1X79u0POp/RLWjLTCaT2sVHqF18hMb13v9z5PUZ2llYrk15pYFQfWNeqTbllqjM7dXmvWXavLdM+rXmvaSshMjASPWuNUaux4Rbg/DqAABASzJt2jR98MEH+vrrr2v1u9PT0+V2u1VUVFRrFHpubu4hPzU6Y8YMTZ8+PbDvcDiUlZXVZLWHnF/ekbYvkcIipAkP1+uSLXtL9efXlqvSZ+i8gZm64dRuTVwkAABA6xD0KVyO1siRIzVy5MjA/qhRo9SrVy/9+9//1gMPPHDQ+YxuQSiymE3qmBSljklRGttrf7BuGIZyHE5tzN0fqm/OK9VveSUqKvcou6Bc2QXl+mJ9Xq37pceGq3uaf5R6t9RodUmOUueUKKXFhMvMPOsAALRphmHohhtu0HvvvaevvvpKnTt3rnV8yJAhslqtWrRokSZPnixJ2rBhg7Kzs2v122uy2+2y2+1NXntIchZLn93h3z7pFimh4xEvKSxz6w9zf5TDWanBHeL1j8n9WWcKAACgSlAD9OTkZFksFuXm5tZqP9xolQNZrVYNGjRImzZtqvM4o1uA/UwmkzLiIpQRF6GTasyvbhiG9pW5a4XqG/NKtDG3VHklLuU4nMpxOPXNxvxa94uwWtQpOUqdkyPVOTlKnZOj1Tk5Sl2So5QQZWvulwcAAJrA1KlTNW/ePP33v/9VTExMYF7zuLg4RUREKC4uTldffbWmT5+uxMRExcbG6oYbbtDIkSNZQDQYvpwpleZKiV2lUX854unuSp+ue325tu0rV7v4CP378uOZ1g8AAKCGoAboNptNQ4YM0aJFizRx4kRJ/oWJFi1apGnTptXrHl6vV7/88ovOPPPMOo8zugU4MpPJpORou5Kj7RrRJanWseIKj39e9apQffPeMm3LL1N2QbkqPN4651mXpPhIqz9UT4ryP6dUPSdHKdLW6j78AgBAyHrmmWckSaecckqt9pdeeklXXnmlJOnxxx+X2WzW5MmT5XK5NH78eD399NPNXCmU84v0w7/922c+KoUd/vcgwzB054Jf9N2WAkXbw/TilUOVEsPvTgAAADUFPcWaPn26pkyZouOPP17Dhg3TrFmzVFZWpquuukqSdMUVV6hdu3aaOXOmJOn+++/XiBEj1K1bNxUVFenRRx/V9u3b9cc//jGYLwNos+IirBrSMUFDOibUavd4fdpZWKGt+aXasrdMW/P9j235Zdpd7FRRuUcrs4u0MrvooHumx4arc3KUOlWNVq8O2LMSImULMzfTKwMAAPVhGMYRzwkPD9fs2bM1e/bsZqgIdfL5pA9vkQyf1Ps8qdvYI17y3Ddb9PZPO2U2SU9eMkg90mOaoVAAAIDWJegB+kUXXaS9e/fq7rvvVk5OjgYOHKhPPvkksLBodna2zOb9gVphYaGuueYa5eTkKCEhQUOGDNHSpUvVu3fvYL0EICRZLebAiPJTe9Y+VuH2atu+/aG6P2Av1db8MhWWewJTwizbsq/WdRazSVkJEeqcHKWBWQka2ilBAzvEM2IdAADgSFa/Ie34XrJGSeNnHvH0hWtzNfPj9ZKku87urTE9U5u6QgAAgFbJZNRnSEkb4nA4FBcXp+LiYsXGxga7HCDkFJW7A8H61vwybckv09aqEewVHu9B51vMJvXNjNXxnRI1tFOChnRM5KPFAIBWgX5n/fG9Oka5a6WXJvgXED3tfmn0jYc9/dfdxbpwzjKVu736/YgOeuC8viwaCgAA2ryG9jkZ1gmgWcVH2jSog02DOtSeEsYwDOU6XNqSX6qNuaVavr1QP24r0J5ip1bvLNbqncV6YclWSVKnpMhAoH58p0R1SY7ilz4AABCaCrZKr57vD8+zhkvDrzvs6XkOp/748k8qd3t1Yvdk3XNOH/pRAAAAh0GADqBFMJlMSo8LV3pcuEZ1TdaUUZ0kSbuKKvTTtgL9uK1AP20r1IbcEm3bV65t+8r1zvKdkqSkKJuGdEzQ0E6JOr5TgvpkxjGXOgAAaPtKcv3heWmOlNpHuvQtKcx2yNMr3F5d88pP2lPsVNeUKD116WBZLfSZAAAADocAHUCL1i4+Qu0GttN5A9tJkorLPVqRXRgI1FftLNK+Mrc+W5urz9bmSpLCrWYNzIqvCtQTNbhDvGLCrcF8GQAAAI2rokh6bZJUuFWK7yhd/h8pIuGQp/t8hm6Zv0qrdxYrIdKqF68cqrgI+kcAAABHQoAOoFWJi7RqTM/UwEJXrkqv1uwq1o/bCvXTtgL9tL1QReUefbelQN9tKZAkmU1Sz/RYHV815cvQTgnKiIsI5ssAAABoOHe5NO8iKXeNFJ0mXbFAikk/7CWPf/6bPvolR1aLSXN+P0Qdk6Kap1YAAIBWjgAdQKtmD7NoSMdEDemYKJ3cVT6foS35pfpx2/5R6tkF5Vq7x6G1exx6Zdl2Sf6R7cO7JOrE7ska3TVZqbHhQX4lAAAA9eD1SPOnSDu+k8LjpN//R0rscthL3lu5U09+sUmS9ND5/TS8S1JzVAoAANAmEKADaFPMZpO6pcaoW2qMLhnWQZKU63Dqp+pAfXuB1u52aFdRhf6zYpf+s2KXJKlHWoxGd0vWid2TNaxzoqLs/PMIAABaGJ9PWnCdtPEzKSxCuvRtKb3vYS/5aVuBbnvnF0nSdad01YXHZzVHpQAAAG0GCRGANi8tNlxn9c/QWf0zJEmlrkqt2F6opZv3acmmvfp1t0Mbcku0IbdEL367VVaLSYM6JOiEbsk6oXuy+reLUxgLbAEAgGAyDOnjv0m/zJfMYdJFr0odRhz2kh0F5frTq8vl9vo0vk+a/np6j2YqFgAAoO0gQAcQcqLtYTrpuBSddFyKpJ4qKHNr6eZ8fbspX99szNfOwgr9sLVAP2wt0L8W/qaY8DCN7JKkE7ona3S3ZHVJjpLJZAr2ywAAAKHkq4elH5+TZJLO/7fU/bTDnu5wevSHuT9qX5lbfdvF6vGLBspspv8CAABwtAjQAYS8xCibzu6fqbP7Z8owDGUXlOubjf5AfenmfSqu8Oiztbn6bG2uJCkzLlyjq0anj+6WrORoe5BfAQAAaNO+myMtfti/feajUr8LDnt6pdenG+at1Ma8UqXF2vX8FUMVaeNXPwAAgIagFwUANZhMJnVMilLHpCj9fkRHeX2G1uwq1pJN+VqyMV/Ltxdqd7FT85fv1PzlOyVJvTJidUK3JJ3QPUXDOiUqwmYJ8qsAAABtxuq3pE9u82+PuUMads0RL3nww3Va/NteRVgtemHKUKXHsVg6AABAQxGgA8BhWMwmDciK14CseE0d000Vbq9+2FYQmO5l3R5H4PHcN1tls5g1pGOCTuierBO6JatvuzhZ+Lg0AABoiN8+9S8aKknD/yyd9NcjXvLKsm2au3SbJOnxiwaob7u4JiwQAACg7SNAB4CjEGGz6OTjUnTycSmSpPxSl77d5J/uZcnGfO0udmrZln1atmWfHv10g2LDwzS0U6KGd0nUsM5J6pMZKysLkgIAgCPZvlR6+wrJ8Er9L5LGz5SOsAbL4t/26r7/rZUk/W1CD03om9EclQIAALRpBOgAcAySo+06b2A7nTewnQzD0Nb8ssDo9GWb98nhrNSi9XlatD5PkhRps2hIxwQN75yo4V2S1L99nOxhTPkCAABq2POzNO8iqdIpHTdBOm+2ZD78H+A35pZo2usr5PUZmjy4va47uWszFQsAANC2EaADQCMxmUzqkhKtLinRunxkJ1V6fVq7x6HvtxTo+60F+nFbgYorPPpmoz9glyRbmFmDsuI1vEuShndO1KAO8SzyBQBAKNu3WXptkuRySB1GSRfOlSzWOk+tcHv15YY8ffDzbi1alydXpU/DOiXqoUl9ZTrCaHUAAADUDykNADSRMItZ/dvHq3/7eF1zUhf5fIY25Jboh60F+mFrgb7fuk/5pW59v9UfsEtSmNmk/u3jNKxzkoZ3SdTxHRMUE173L80AAKCNceyWXpkole2V0vtJl74pWSNqneKq9Grxhr364Oc9+nxdrsrd3sCx/u3jNOfyIXy6DQAAoBGZDMMwgl1Ec3I4HIqLi1NxcbFiY2ODXQ6AEGYYhrbkl/nD9C379P3WAu0pdtY6x2ySemfGanjnJA3rnKhhnRKVEGULUsUAgKNBv7P++F5JKi+QXjpT2rtOSuwi/eFTKTpVkuTx+rRkU74+WL1Hn63NUYmzMnBZ+4QInd0/U2f3z1CfzFhGngMAABxCQ/ucjEAHgCAxmUzqmhKtrinRumRYBxmGoZ2FFfp+a4F+2OoP1LfvK9eaXQ6t2eXQC0u2SpJ6pMVoWOeqhUk7JSo1NjzIrwQAABwTV6n0+oX+8DwmQ7p8gSojkvX9pnz9b/VuffJrjorKPYHT02PDdXb/DJ09IFMD2scRmgMAADQhAnQAaCFMJpOyEiOVlRipC4a0lyTlFDv1wzb/CPUfthZoY16pNuSWaENuiV79brskqXNylPq1i1OvjFj1yohR78xYpcYQqgMA0CpUuqS3fi/t+klGRIJ+GfOS5i8u0cdrFim/1B04LTnarrP6pevsAZka0iFBZjOhOQAAQHMgQAeAFiw9LlznDsjUuQMyJUn7Sl36cVtB1Sj1Aq3d49DW/DJtzS/T+6t3B65LjrapV0asemfEVgXrseqSEiWrxRyslwIAAA7k88r4z7UybflSbnOE/uz5m754u0CSf22UhEirJvTN0Dn9MzS8S5IshOYAAADNjgAdAFqRpGi7JvTN0IS+GZKk4gqPVmYXau0eh9budmhdVaCeX+rWNxvz9c3G/MC1tjCzjkuLVq90f6DeO9P/HBfBIqUAgBC19Enpt0+lrmOkLmOkjIGSuen/2GwYhtbsLFbl+zdq0N4FchsW/cF5k5b4OiomPEzj+6TrnAGZGtU1iT9+AwAABBmLiAJAG1Ph9mpDbonW7fEH6mt3O7Q+p0Slrso6z28XH1E1Wj0mEKxnJUTy0XAAOEb0O+svaN+ruWdL277Zvx+RIHU5xR+mdx0jxXdotC9lGIY25Jbog9V79MHPu3Vh8YuaGva+fIZJtxg3ytd7os7pn6kTj0uWPczSaF8XAAAAfg3tcxKgA0AI8Pn8C5Su3VOstXtKAsH6rqKKOs+PslnUs3pO9Yw49cqIUc/0WEXY+IUeAOqLfmf9Be17tW+ztPkLafOX0tavJXdJ7eOJXaWup/rD9E4nSuFHV5vH69PPO4v09W/5+vCXPdqUVypJ+qPlQ91pfV2S9Mvg+9X9jGkKt/L/WAAAgKZEgF5P/CIDAPsVV3i0fo9Da6tGq6/b41+g1F3pO+hcs0nqnhqjPu1i1a9dnPq1i1PvzFhF2pgNDADqQr+z/lrE98rrkXYt94fpW76Udv4kGd79x00Wqf3xVaPTT5XaDZEstf8fWD3K/NtN+/Ttpnx9v2Wfytz772GzmDUjc7mu2vuov2HsPdKJ05vj1QEAAIQ8AvR6ahGdcwBowSq9Pm3JLwuMUq8O1/NL3QedazZJXVOi1a9dnPpWPfpkxirKTqgOAPQ7669Ffq+cxdLWb/xh+uYvpYLNtY/bY6VOJ6owfbSWGv30SU60lm3Zd9D/L+MjrRrVNUmn9kzTmdblinzvSsnwSSOnSac/KJmYMg0AAKA5EKDXU4vsnANAK5DrcOqXncX6ZVex1uzyP+eVuA46z2SSuiRHBUL1fu3i1KddnKIJ1QGEGPqd9dcqvldF2dLmL+Xa8LlMWxfL5imudXinkaxvvP30g3mAXFknamCPzhrVNVm9M2L964ps/Vp6bbLkdUsDfy+d9xThOQAAQDMiQK+nVtE5B4BWIs/hrArUHYFgPcfhPOg8k0nqnBQVCNT7totTn3axig23BqFqAGge9DvrryV/r8pclfpha4GWbMrXt5vytT6nRGb51Me0TSeaf9GJll90vPk3WVVzsW6TlDHAP3d611Mli116bZLkLpV6ni1d+PJB078AAACgaRGg11NL7pwDQFuwt8QVGKFeHarvKT44VJekzslR6pO5f071PplxioskVAfQNtDvrL+W9L1yV/q0MrtQ327ep6Wb8rVqR5EqfbV/ZeqZHqPR3ZI1uluShnVOUrTJJW1fun/+9Ly1dd+804nSZe9I1vBmeCUAAACoiQC9nlpS5xwAQkV+qT9UXxMI1R3aVVRR57mx4WHKSoxUVkKkshIjam23T4hUuNXSzNUDQMPQ76y/YH6vfD5Da/c4tHRzvr7dtE8/bC1Qhcdb65ysxAiN7pqs0d2SNbJrkpKj7Ye/qWOPtOWr/fOnl+VJmYOlK/4rhfPfAgAAQDAQoNcTv8gAQMuwr9SlNbsdtYL1nYV1h+o1pcTYlZUQUWfInhEXrjCLuRmqB4Ajo99Zf8H6Xt33v1+1YOUuFZZ7arUnRdk0qluyRndN0uhuycpKjGz4FzEMqXCrFNtOCjtC8A4AAIAm09A+JxPvAQCCIinarpOPS9HJx6UE2spcldpZWKEdBeXaUViuHQUVVc/l2llYoVJXpfaWuLS3xKUV2UUH3dNiNikzPtwfrNcI19tXbadE22ViwTYAQJVyl1eF5R5F2Swa3iVJo6oC8x5pMf6FPxuDySQldmmcewEAAKDZEaADAFqMKHuYeqTHqEd6zEHHDMNQUbnnoGA9uypc31VYIbfX5z9WUCFp30H3CLea1b5qpHp6bLjS46oeseFKq9pPjLQ1XmgCAGjR/nBCZ/1uaHv1bx8vK59gAgAAQB0I0AEArYLJZFJClE0JUTb1bx9/0HGfz1BuibMqQK89gn1nQbn2OJxyenzalFeqTXmlh/w6NotZqbF2f6geF66MqmA9LTZcGVXPabHhsoURtABAa1fXH2wBAACAmgjQAQBtgtlsUkZchDLiIjSsc+JBx92VPu0uqtDOwgrtKa5QrsOpPcVO5TqcynE4lVPsVH6pW26vTzsLK444H3tSlG3/6PWaI9qrA/eYcMVGhDFlDAAAAAAArRgBOgAgJNjCzOqUHKVOyVGHPMdd6VNeiTMQrucEAnaXcoorlONwKrfYJbfXp31lbu0rc+vX3Y5D3s8eZlZKjF0pMXalxtiVGhMe2E6p2k+NtSspysbipwAAAAAAtEAE6AAAVLGF+edIb58QechzDMNQQZnbH6Y7nMop3h+u5zhcyi12ak9xhRzOSrkq6zea3WTyj2hPjrYrNTa8RsC+P2iv3o+y879uAAAAAACaC7+FAwBwFEwmk5Ki7UqKtqtPZtwhz3N6vNpb4lJeibPq2eV/dri0t9TfnudwKb/UJZ8h5Ze6lV/q1vqcksN+/SibJRCqJ8fYFBdhVWy4VbERVsWGh1U9+/fjIsIC2/YwM9PJAAAAAABwlAjQm8uGj6WP/uofZiiTZDIfvG0yV+0fuF2f86rvJ8lilcIipDC7ZI2QwsL9D2v4sbWbLUH65gFA6xNutSgrMVJZiYcezS5JXp9/RHt12F4dtFc/qtvyHC5VeLwqc3tVtq9c2/aVH1U9NotZsVWBesxBYXvYYYL4MCVF2WUxE74DAAAAAEIPAXpzcZdJxTuCXcWxMVv3B+vWCCkioeqRKEUm+p8jEvZvRybuPyc8XjIzvy8AHMhiNgXmSe+t2MOeW+qqrBrF7tTeUpfyS1xyOCvlqPDI4fTIUVHpf665XeGRz5DcXl9glPvRCjOblBEfrvbxkWqXEKF28RFqnxChdgkRykqIVHpcuKzM4Q4AAAAAaIMI0JtL11Ola76QDEmGT5LhfzaM2tuBYwdu1+e8qmO+SslTIVW6pMoKyeOUKqseB7VX7XsqapxT43xvjaDF55HcHsldNb1AUfZRfANMUkT8YcL2Q4Txtqj9I+sBIMRF28MUbQ9T58MshHogwzBU5vbWCtmLKzwHh+6HCeErfYZ2FFRoR0Hdc7mbTVJabLg/VI+PUPuE2kF7ZnyEwq18igkAAAAA0PoQoDeXyKpguLXxeavC9ANCdneZVFEkVRRI5QX+54rC/dvlBfuPu0slGf7jFYVSweajKMBUNeo9QrJG7h/9HhZxQFtkHedVt9XYr3VtdVukZItmhDyANslkMgWC90xFHPX1Xp+hXIdTu4oqtKuwQjsLy7WryL8w6q7CCu0sqpC70qc9xU7tKXbqRxXWeZ/kaHtg1Hr7hAi1j6/ejlS7+AgWRwUAAAAAtEj8torDM1v8o8Bt9R/teJBK1/7wvFbAXnjA9gHHfR5JRtUo+Qp/e5MxSfaYQzxiqx4HtIfX0W6LZq54AG2KxWxSZrx/FPnQTgcf9/kM5Ze5qsL1ijqD9nK3V/ml/gVTV+0oqvPrhFvNslnMslst/ucws2xh+59tYVXHwyz792sct9e49sBj1W32MIsibRZF2S2KsIUp0mpRpN1/DQusAgAAAADqQoCOphdml2LS/Y/6Mgz/KHd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-      "text/plain": [
-       "<Figure size 1500x500 with 2 Axes>"
+      "cell_type": "code",
+      "source": [
+        "with torch.no_grad():\n",
+        "  total_loss_test = 0\n",
+        "  total_acc_test = 0\n",
+        "  for inputs, labels in test_loader:\n",
+        "    inputs = inputs.to(device)\n",
+        "    labels = labels.to(device)\n",
+        "\n",
+        "    predictions = model(inputs)\n",
+        "\n",
+        "    test_loss = criterion(predictions, labels)\n",
+        "    total_loss_test += test_loss.item()\n",
+        "    test_acc = (torch.argmax(predictions, axis = 1) == labels).sum().item()\n",
+        "    total_acc_test += test_acc\n",
+        "print(f'Loss Score is: {round(total_loss_test/1000, 4)} and Accuracy is: {round(total_acc_test/test_dataset.__len__()*100, 4)}')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "V1E_dwEIKdY3",
+        "outputId": "d694663e-e6e0-41d4-a2eb-ac982cb0eee6"
+      },
+      "execution_count": 56,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Loss Score is: 0.0041 and Accuracy is: 97.0021\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Plotting Training Progress"
+      ],
+      "metadata": {
+        "id": "zzkuTBcUL1vQ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(15, 5))\n",
+        "\n",
+        "axs[0].plot(total_loss_train_plot, label='Training Loss')\n",
+        "axs[0].plot(total_loss_validation_plot, label='Validation Loss')\n",
+        "axs[0].set_title('Training and Validation Loss over Epochs')\n",
+        "axs[0].set_xlabel('Epochs')\n",
+        "axs[0].set_ylabel('Loss')\n",
+        "axs[1].set_ylim([0, 2])\n",
+        "axs[0].legend()\n",
+        "\n",
+        "axs[1].plot(total_acc_train_plot, label='Training Accuracy')\n",
+        "axs[1].plot(total_acc_validation_plot, label='Validation Accuracy')\n",
+        "axs[1].set_title('Training and Validation Accuracy over Epochs')\n",
+        "axs[1].set_xlabel('Epochs')\n",
+        "axs[1].set_ylabel('Accuracy')\n",
+        "axs[1].set_ylim([0, 100])\n",
+        "axs[1].legend()\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 418
+        },
+        "id": "AUV9WQ-bL1Gc",
+        "outputId": "55731ef7-af7d-40f3-e265-ec999df8d41e"
+      },
+      "execution_count": 57,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1500x500 with 2 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        }
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
-   ],
-   "source": [
-    "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(15, 5))\n",
-    "\n",
-    "axs[0].plot(total_loss_train_plot, label='Training Loss')\n",
-    "axs[0].plot(total_loss_validation_plot, label='Validation Loss')\n",
-    "axs[0].set_title('Training and Validation Loss over Epochs')\n",
-    "axs[0].set_xlabel('Epochs')\n",
-    "axs[0].set_ylabel('Loss')\n",
-    "axs[1].set_ylim([0, 2])\n",
-    "axs[0].legend()\n",
-    "\n",
-    "axs[1].plot(total_acc_train_plot, label='Training Accuracy')\n",
-    "axs[1].plot(total_acc_validation_plot, label='Validation Accuracy')\n",
-    "axs[1].set_title('Training and Validation Accuracy over Epochs')\n",
-    "axs[1].set_xlabel('Epochs')\n",
-    "axs[1].set_ylabel('Accuracy')\n",
-    "axs[1].set_ylim([0, 100])\n",
-    "axs[1].legend()\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "gYG69QqrYJan"
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "accelerator": "GPU",
-  "colab": {
-   "gpuType": "T4",
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "name": "python3"
-  },
-  "language_info": {
-   "name": "python"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+  ]
+}
\ No newline at end of file
diff --git a/Ex_5_Text_Classification.ipynb b/Ex_5_Text_Classification.ipynb
new file mode 100644
index 0000000..c68c634
--- /dev/null
+++ b/Ex_5_Text_Classification.ipynb
@@ -0,0 +1,665 @@
+{
+  "metadata": {
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3",
+      "language": "python"
+    },
+    "language_info": {
+      "name": "python",
+      "version": "3.11.13",
+      "mimetype": "text/x-python",
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "pygments_lexer": "ipython3",
+      "nbconvert_exporter": "python",
+      "file_extension": ".py"
+    },
+    "colab": {
+      "provenance": [],
+      "gpuType": "T4",
+      "authorship_tag": "ABX9TyM9L6zeH+oPU1MjvFSIQQPd"
+    },
+    "accelerator": "GPU",
+    "kaggle": {
+      "accelerator": "nvidiaTeslaT4",
+      "dataSources": [
+        {
+          "sourceId": 533474,
+          "sourceType": "datasetVersion",
+          "datasetId": 30764
+        }
+      ],
+      "dockerImageVersionId": 31154,
+      "isInternetEnabled": true,
+      "language": "python",
+      "sourceType": "notebook",
+      "isGpuEnabled": true
+    }
+  },
+  "nbformat_minor": 4,
+  "nbformat": 4,
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": "# Imports",
+      "metadata": {
+        "id": "apWIPRTGNhDH"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": "import torch\nimport torch.nn as nn\nfrom torch.optim import Adam\nfrom transformers import AutoTokenizer, AutoModel\nfrom torch.utils.data import Dataset, DataLoader\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport numpy as np\n\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\ndevice = 'cuda' if torch.cuda.is_available() else 'cpu'\nprint(device)",
+      "metadata": {
+        "id": "1Gxfz-0QNg0G",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "executionInfo": {
+          "status": "ok",
+          "timestamp": 1761839966416,
+          "user_tz": 180,
+          "elapsed": 20300,
+          "user": {
+            "displayName": "Ignacio Menna",
+            "userId": "10948974476344730015"
+          }
+        },
+        "outputId": "8242fc0f-3b35-4243-b55e-66bc4a1c4898",
+        "trusted": true,
+        "execution": {
+          "iopub.status.busy": "2025-10-31T01:48:04.986173Z",
+          "iopub.execute_input": "2025-10-31T01:48:04.986446Z",
+          "iopub.status.idle": "2025-10-31T01:48:09.581682Z",
+          "shell.execute_reply.started": "2025-10-31T01:48:04.986425Z",
+          "shell.execute_reply": "2025-10-31T01:48:09.580778Z"
+        }
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "text": "The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.\n",
+          "output_type": "stream"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": "0it [00:00, ?it/s]",
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "2e4210bbf8644f22b38bac4c55413586"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "name": "stdout",
+          "text": "cuda\n",
+          "output_type": "stream"
+        }
+      ],
+      "execution_count": 1
+    },
+    {
+      "cell_type": "markdown",
+      "source": "# Data Loading",
+      "metadata": {
+        "id": "fChHdOsEOemV"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": "data_df = pd.read_json('/kaggle/input/news-headlines-dataset-for-sarcasm-detection/Sarcasm_Headlines_Dataset.json', lines=True)\ndata_df.dropna(inplace=True)\ndata_df.drop_duplicates(inplace=True)\ndata_df.drop(columns=['article_link'], inplace=True)\ndata_df.shape",
+      "metadata": {
+        "id": "ewj8Yh-kQxSx",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "executionInfo": {
+          "status": "ok",
+          "timestamp": 1761770506616,
+          "user_tz": 180,
+          "elapsed": 215,
+          "user": {
+            "displayName": "Ignacio Menna",
+            "userId": "10948974476344730015"
+          }
+        },
+        "outputId": "795ce607-890f-4910-f09a-48cfb689cd00",
+        "trusted": true,
+        "execution": {
+          "iopub.status.busy": "2025-10-31T01:48:09.583097Z",
+          "iopub.execute_input": "2025-10-31T01:48:09.583813Z",
+          "iopub.status.idle": "2025-10-31T01:48:09.719405Z",
+          "shell.execute_reply.started": "2025-10-31T01:48:09.583790Z",
+          "shell.execute_reply": "2025-10-31T01:48:09.718557Z"
+        }
+      },
+      "outputs": [
+        {
+          "execution_count": 2,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "(26708, 2)"
+          },
+          "metadata": {}
+        }
+      ],
+      "execution_count": 2
+    },
+    {
+      "cell_type": "code",
+      "source": "data_df.head()",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "-AGEEz_nOIV0",
+        "executionInfo": {
+          "status": "ok",
+          "timestamp": 1761771760546,
+          "user_tz": 180,
+          "elapsed": 190,
+          "user": {
+            "displayName": "Ignacio Menna",
+            "userId": "10948974476344730015"
+          }
+        },
+        "outputId": "5ed38281-14a9-4a62-be5c-2595be167766",
+        "trusted": true,
+        "execution": {
+          "iopub.status.busy": "2025-10-31T01:48:09.720395Z",
+          "iopub.execute_input": "2025-10-31T01:48:09.720742Z",
+          "iopub.status.idle": "2025-10-31T01:48:09.769763Z",
+          "shell.execute_reply.started": "2025-10-31T01:48:09.720716Z",
+          "shell.execute_reply": "2025-10-31T01:48:09.769035Z"
+        }
+      },
+      "outputs": [
+        {
+          "execution_count": 3,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "                                            headline  is_sarcastic\n0  former versace store clerk sues over secret 'b...             0\n1  the 'roseanne' revival catches up to our thorn...             0\n2  mom starting to fear son's web series closest ...             1\n3  boehner just wants wife to listen, not come up...             1\n4  j.k. rowling wishes snape happy birthday in th...             0",
+            "text/html": "<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>headline</th>\n      <th>is_sarcastic</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>former versace store clerk sues over secret 'b...</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>the 'roseanne' revival catches up to our thorn...</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>mom starting to fear son's web series closest ...</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>boehner just wants wife to listen, not come up...</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>j.k. rowling wishes snape happy birthday in th...</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+          },
+          "metadata": {}
+        }
+      ],
+      "execution_count": 3
+    },
+    {
+      "cell_type": "markdown",
+      "source": "# Data Split",
+      "metadata": {
+        "id": "HX4DMlAzzBde"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": "X_train, X_test, y_train, y_test = train_test_split(np.array(data_df['headline']), np.array(data_df['is_sarcastic']), test_size=0.3)\nX_val, X_test, y_val, y_test = train_test_split(X_test, y_test, test_size=0.5)\n\nprint('Training Size: ', X_train.shape[0], 'Which is: ', round(X_train.shape[0]/data_df.shape[0], 4) * 100, '%')\nprint('Validation Size: ', X_val.shape[0], 'Which is: ', round(X_val.shape[0]/data_df.shape[0], 4) * 100, '%')\nprint('Training Size: ', X_test.shape[0], 'Which is: ', round(X_test.shape[0]/data_df.shape[0], 4) * 100, '%')",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "kaes61FBOoo0",
+        "executionInfo": {
+          "status": "ok",
+          "timestamp": 1761784433356,
+          "user_tz": 180,
+          "elapsed": 82,
+          "user": {
+            "displayName": "Ignacio Menna",
+            "userId": "10948974476344730015"
+          }
+        },
+        "outputId": "8c6991f3-36a1-4f96-d9d9-60d296263ca9",
+        "trusted": true,
+        "execution": {
+          "iopub.status.busy": "2025-10-31T01:48:09.771739Z",
+          "iopub.execute_input": "2025-10-31T01:48:09.771997Z",
+          "iopub.status.idle": "2025-10-31T01:48:09.782958Z",
+          "shell.execute_reply.started": "2025-10-31T01:48:09.771978Z",
+          "shell.execute_reply": "2025-10-31T01:48:09.782205Z"
+        }
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "text": "Training Size:  18695 Which is:  70.0 %\nValidation Size:  4006 Which is:  15.0 %\nTraining Size:  4007 Which is:  15.0 %\n",
+          "output_type": "stream"
+        }
+      ],
+      "execution_count": 4
+    },
+    {
+      "cell_type": "code",
+      "source": "tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')\nbert_model = AutoModel.from_pretrained('bert-base-uncased')",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 301,
+          "referenced_widgets": [
+            "916b653c1d4640968de431d751e255ae",
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+            "d4a90348885d438da77ec42d1fd4ba1b",
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+            "b03ab11e51644b0c801d772727ad38af",
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+            "d5f20c75b5fc454daaa4f2ef1eef8c39",
+            "00223b3230a6476793a2786a6ec4ca78",
+            "fa12d2a50a4d48cca6f2fcfc498fee76",
+            "7aff2c6ae89a470cbfcd2f858945b7d3",
+            "4c09149a5d9941789a8f83fcd143bb97",
+            "4aeee76f662745969d68e04dcf53ea5d",
+            "7acaf34fdbd34f3db4f3164472d464b3",
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+            "f57b80853f104e7886466144af6cfcf6",
+            "d3c298099f5d4d4fa03269f08418cd17",
+            "939b58b4500a436d9cebb506470f7094",
+            "e4e906a4b5a3482b84b26cffd2716585",
+            "4fe3dd7aa9b947dfa8e72d383ba0ae79",
+            "46cf55add5554ad28fcbcaaa600637c2",
+            "16f98a02410b4ec98539b32b812a41b5",
+            "32c2236645894dc388cab046e881096c",
+            "06373a727cc24fc698aa36be4f779d56",
+            "fad0854d41424d319641fd5c2d8e52ef",
+            "2a3ca08a126a40a482f0a284c8d0f1cf",
+            "a531bd58ddf54a3baac710915a296e9b",
+            "7b9dd60565d540f893198ec9d4def393"
+          ]
+        },
+        "id": "noFLk8rczAiG",
+        "executionInfo": {
+          "status": "ok",
+          "timestamp": 1761778331290,
+          "user_tz": 180,
+          "elapsed": 28050,
+          "user": {
+            "displayName": "Ignacio Menna",
+            "userId": "10948974476344730015"
+          }
+        },
+        "outputId": "776c4112-5250-4a00-eff4-0f795d92661b",
+        "trusted": true,
+        "execution": {
+          "iopub.status.busy": "2025-10-31T01:48:09.783560Z",
+          "iopub.execute_input": "2025-10-31T01:48:09.783809Z",
+          "iopub.status.idle": "2025-10-31T01:48:24.831974Z",
+          "shell.execute_reply.started": "2025-10-31T01:48:09.783765Z",
+          "shell.execute_reply": "2025-10-31T01:48:24.831303Z"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]",
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "c698bc489ebb40389455ff811d6d73db"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]",
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "3b0670b3246e4e6b91d40effb8d3a331"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "name": "stderr",
+          "text": "2025-10-31 01:48:12.653299: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:477] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\nWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nE0000 00:00:1761875292.854166     100 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\nE0000 00:00:1761875292.912340     100 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+          "output_type": "stream"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": "model.safetensors:   0%|          | 0.00/440M [00:00<?, ?B/s]",
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "93cc7da87b7a43f8bbd94c1db6885900"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "execution_count": 5
+    },
+    {
+      "cell_type": "markdown",
+      "source": "# Custom Dataset Class",
+      "metadata": {
+        "id": "-vXkurpKkLwH"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": "class dataset(Dataset):\n  def __init__(self, X, Y):\n    self.X = [tokenizer(x,\n                        max_length = 100,\n                        truncation = True,\n                        padding = 'max_length',\n                        return_tensors = 'pt')\n                        for x in X\n    ]\n    self.Y = torch.tensor(Y, dtype=torch.float32)\n\n  def __len__(self):\n    return len(self.X)\n\n  def __getitem__(self, idx):\n    return self.X[idx], self.Y[idx]",
+      "metadata": {
+        "id": "wubgma29LBbC",
+        "trusted": true,
+        "execution": {
+          "iopub.status.busy": "2025-10-31T01:50:16.654009Z",
+          "iopub.execute_input": "2025-10-31T01:50:16.654571Z",
+          "iopub.status.idle": "2025-10-31T01:50:16.659352Z",
+          "shell.execute_reply.started": "2025-10-31T01:50:16.654544Z",
+          "shell.execute_reply": "2025-10-31T01:50:16.658563Z"
+        }
+      },
+      "outputs": [],
+      "execution_count": 17
+    },
+    {
+      "cell_type": "markdown",
+      "source": "# Create Dataset Objects",
+      "metadata": {
+        "id": "fbqv27HVkO9I"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": "training_dataset = dataset(X_train, y_train)\nvalidation_dataset = dataset(X_val, y_val)\ntest_dataset = dataset(X_test, y_test)",
+      "metadata": {
+        "id": "-INWfr2ke1ag",
+        "trusted": true,
+        "execution": {
+          "iopub.status.busy": "2025-10-31T01:50:19.534130Z",
+          "iopub.execute_input": "2025-10-31T01:50:19.534949Z",
+          "iopub.status.idle": "2025-10-31T01:50:24.801808Z",
+          "shell.execute_reply.started": "2025-10-31T01:50:19.534921Z",
+          "shell.execute_reply": "2025-10-31T01:50:24.801190Z"
+        }
+      },
+      "outputs": [],
+      "execution_count": 18
+    },
+    {
+      "cell_type": "markdown",
+      "source": "# Hyperparameters",
+      "metadata": {
+        "id": "Jj-Q_Pweif_3"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": "BATCH_SIZE = 32\nEPOCHS = 5\nLR = 1e-4",
+      "metadata": {
+        "id": "Ve8GRTs-iWM5",
+        "trusted": true,
+        "execution": {
+          "iopub.status.busy": "2025-10-31T02:03:29.554065Z",
+          "iopub.execute_input": "2025-10-31T02:03:29.554633Z",
+          "iopub.status.idle": "2025-10-31T02:03:29.558305Z",
+          "shell.execute_reply.started": "2025-10-31T02:03:29.554607Z",
+          "shell.execute_reply": "2025-10-31T02:03:29.557470Z"
+        }
+      },
+      "outputs": [],
+      "execution_count": 34
+    },
+    {
+      "cell_type": "markdown",
+      "source": "# Dataloaders",
+      "metadata": {
+        "id": "FxC1vWJAjrSt"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": "train_loader = DataLoader(training_dataset, batch_size=BATCH_SIZE, shuffle=True)\nvalidation_loader = DataLoader(validation_dataset, batch_size=BATCH_SIZE, shuffle=True)\ntest_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=True)",
+      "metadata": {
+        "id": "UgkV3e-MiqdX",
+        "trusted": true,
+        "execution": {
+          "iopub.status.busy": "2025-10-31T01:58:32.261708Z",
+          "iopub.execute_input": "2025-10-31T01:58:32.262347Z",
+          "iopub.status.idle": "2025-10-31T01:58:32.266209Z",
+          "shell.execute_reply.started": "2025-10-31T01:58:32.262321Z",
+          "shell.execute_reply": "2025-10-31T01:58:32.265582Z"
+        }
+      },
+      "outputs": [],
+      "execution_count": 29
+    },
+    {
+      "cell_type": "markdown",
+      "source": "# Model",
+      "metadata": {
+        "id": "rsl4HteOkIvx"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": "class MyModel(nn.Module):\n  def __init__(self, bert):\n    super(MyModel, self).__init__()\n\n    self.bert = bert\n    self.linear1 = nn.Linear(768, 384)\n    self.bn1 = nn.BatchNorm1d(384)\n    self.dropout= nn.Dropout(0.5)\n    self.linear2 = nn.Linear(384, 128)\n    self.bn2 = nn.BatchNorm1d(128)\n    self.dropout2 = nn.Dropout(0.25)\n    self.linear3 = nn.Linear(128, 1)\n    self.sigmoid = nn.Sigmoid()\n    self.relu = nn.ReLU()\n\n  def forward(self, input_ids, attention_mask):\n    pooled_output = self.bert(input_ids, attention_mask, return_dict=False) [0][:,0]\n    output = self.linear1(pooled_output)\n    output = self.bn1(output)\n    output = self.relu(output)\n    output = self.dropout(output)\n\n    output = self.linear2(output)\n    output = self.bn2(output)\n    output = self.relu(output)\n    output = self.dropout(output)\n\n    output = self.linear3(output)\n    output = self.sigmoid(output)\n    return output",
+      "metadata": {
+        "id": "aSxYScvojtWh",
+        "trusted": true,
+        "execution": {
+          "iopub.status.busy": "2025-10-31T02:03:32.825219Z",
+          "iopub.execute_input": "2025-10-31T02:03:32.825510Z",
+          "iopub.status.idle": "2025-10-31T02:03:32.832306Z",
+          "shell.execute_reply.started": "2025-10-31T02:03:32.825487Z",
+          "shell.execute_reply": "2025-10-31T02:03:32.831686Z"
+        }
+      },
+      "outputs": [],
+      "execution_count": 35
+    },
+    {
+      "cell_type": "code",
+      "source": "for param in bert_model.parameters():\n  param.requires_grad = False\nmodel = MyModel(bert_model).to(device)",
+      "metadata": {
+        "id": "cwuxE3wDmtL3",
+        "trusted": true,
+        "execution": {
+          "iopub.status.busy": "2025-10-31T02:03:35.293360Z",
+          "iopub.execute_input": "2025-10-31T02:03:35.293680Z",
+          "iopub.status.idle": "2025-10-31T02:03:35.305882Z",
+          "shell.execute_reply.started": "2025-10-31T02:03:35.293657Z",
+          "shell.execute_reply": "2025-10-31T02:03:35.305026Z"
+        }
+      },
+      "outputs": [],
+      "execution_count": 36
+    },
+    {
+      "cell_type": "code",
+      "source": "# Model Architecture\nmodel",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "nR843-absSwv",
+        "executionInfo": {
+          "status": "ok",
+          "timestamp": 1761787073072,
+          "user_tz": 180,
+          "elapsed": 52,
+          "user": {
+            "displayName": "Ignacio Menna",
+            "userId": "10948974476344730015"
+          }
+        },
+        "outputId": "688fbb44-1559-4282-c8b2-9977cd0d3bb3",
+        "trusted": true,
+        "execution": {
+          "iopub.status.busy": "2025-10-31T02:03:37.284926Z",
+          "iopub.execute_input": "2025-10-31T02:03:37.285220Z",
+          "iopub.status.idle": "2025-10-31T02:03:37.291508Z",
+          "shell.execute_reply.started": "2025-10-31T02:03:37.285199Z",
+          "shell.execute_reply": "2025-10-31T02:03:37.290880Z"
+        }
+      },
+      "outputs": [
+        {
+          "execution_count": 37,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "MyModel(\n  (bert): BertModel(\n    (embeddings): BertEmbeddings(\n      (word_embeddings): Embedding(30522, 768, padding_idx=0)\n      (position_embeddings): Embedding(512, 768)\n      (token_type_embeddings): Embedding(2, 768)\n      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n      (dropout): Dropout(p=0.1, inplace=False)\n    )\n    (encoder): BertEncoder(\n      (layer): ModuleList(\n        (0-11): 12 x BertLayer(\n          (attention): BertAttention(\n            (self): BertSelfAttention(\n              (query): Linear(in_features=768, out_features=768, bias=True)\n              (key): Linear(in_features=768, out_features=768, bias=True)\n              (value): Linear(in_features=768, out_features=768, bias=True)\n              (dropout): Dropout(p=0.1, inplace=False)\n            )\n            (output): BertSelfOutput(\n              (dense): Linear(in_features=768, out_features=768, bias=True)\n              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n              (dropout): Dropout(p=0.1, inplace=False)\n            )\n          )\n          (intermediate): BertIntermediate(\n            (dense): Linear(in_features=768, out_features=3072, bias=True)\n            (intermediate_act_fn): GELUActivation()\n          )\n          (output): BertOutput(\n            (dense): Linear(in_features=3072, out_features=768, bias=True)\n            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n            (dropout): Dropout(p=0.1, inplace=False)\n          )\n        )\n      )\n    )\n    (pooler): BertPooler(\n      (dense): Linear(in_features=768, out_features=768, bias=True)\n      (activation): Tanh()\n    )\n  )\n  (linear1): Linear(in_features=768, out_features=384, bias=True)\n  (bn1): BatchNorm1d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n  (dropout): Dropout(p=0.5, inplace=False)\n  (linear2): Linear(in_features=384, out_features=128, bias=True)\n  (bn2): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n  (dropout2): Dropout(p=0.25, inplace=False)\n  (linear3): Linear(in_features=128, out_features=1, bias=True)\n  (sigmoid): Sigmoid()\n  (relu): ReLU()\n)"
+          },
+          "metadata": {}
+        }
+      ],
+      "execution_count": 37
+    },
+    {
+      "cell_type": "markdown",
+      "source": "# Loss Function and Optimizer",
+      "metadata": {
+        "id": "6HlVoGxns5JA"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": "criterion = nn.BCELoss()\noptimizer = Adam(model.parameters(), lr=LR, weight_decay=0.01)",
+      "metadata": {
+        "id": "CK-ar7TQsZEy",
+        "trusted": true,
+        "execution": {
+          "iopub.status.busy": "2025-10-31T02:03:39.861386Z",
+          "iopub.execute_input": "2025-10-31T02:03:39.862029Z",
+          "iopub.status.idle": "2025-10-31T02:03:39.866727Z",
+          "shell.execute_reply.started": "2025-10-31T02:03:39.862006Z",
+          "shell.execute_reply": "2025-10-31T02:03:39.865887Z"
+        }
+      },
+      "outputs": [],
+      "execution_count": 38
+    },
+    {
+      "cell_type": "markdown",
+      "source": "# Training",
+      "metadata": {
+        "id": "07Qyiv9ttgQ2"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": "total_loss_train_plot = []\ntotal_acc_train_plot = []\ntotal_loss_val_plot = []\ntotal_acc_val_plot = []\n\nbest_val_score = 0.0\npatience = 3\npatience_counter = 0\n\nfor epoch in range(EPOCHS):\n  total_acc_train = 0\n  total_loss_train = 0\n  total_acc_val = 0\n  total_loss_val = 0\n    \n  if epoch == 3:\n      for name, param in model.named_parameters():\n          if any(f'layer.{i}' in name for i in range(10, 12)):\n              param.requires_grad = True\n\n  for inputs, labels in train_loader:\n    input_ids = inputs['input_ids'].squeeze(1).to(device)\n    attention_mask = inputs['attention_mask'].squeeze(1).to(device)\n    labels = labels.to(device)\n\n    outputs = model(input_ids, attention_mask).squeeze(1)\n\n    train_loss = criterion(outputs, labels)\n    total_loss_train += train_loss.item()\n\n    acc = (outputs.round() == labels).sum().item()\n    total_acc_train += acc\n\n    train_loss.backward()\n    optimizer.step()\n    optimizer.zero_grad()\n\n  with torch.no_grad():\n    for inputs, labels in validation_loader:\n      input_ids = inputs['input_ids'].squeeze(1).to(device)\n      attention_mask = inputs['attention_mask'].squeeze(1).to(device)\n      labels = labels.to(device)\n\n      outputs = model(input_ids, attention_mask).squeeze(1)\n\n      val_loss = criterion(outputs, labels)\n      total_loss_val += val_loss.item()\n      acc = (outputs.round() == labels).sum().item()\n      total_acc_val += acc\n\n  # Saving for plots\n  train_loss = total_loss_train / len(train_loader)\n  val_loss = total_loss_val / len(validation_loader)\n  acc_train = (total_acc_train / len(training_dataset)) * 100\n  acc_val = (total_acc_val / len(validation_dataset)) * 100\n\n  total_loss_train_plot.append(round(train_loss, 4))\n  total_loss_val_plot.append(round(val_loss, 4))\n  total_acc_train_plot.append(round(acc_train, 4))\n  total_acc_val_plot.append(round(acc_val, 4))\n\n  print(f\"Epoch {epoch+1}/{EPOCHS}, Train Loss: {round(train_loss,4)} Train Accuracy: {round(acc_train,4)}%\"\n        f\"Validation Loss: {round(val_loss,4)} Validation Accuracy: {round(acc_val,4)}%\")\n  print(\"=\"*70)",
+      "metadata": {
+        "id": "AfumTUAytdAw",
+        "trusted": true,
+        "execution": {
+          "iopub.status.busy": "2025-10-31T02:22:14.303677Z",
+          "iopub.execute_input": "2025-10-31T02:22:14.304391Z",
+          "iopub.status.idle": "2025-10-31T02:34:42.725614Z",
+          "shell.execute_reply.started": "2025-10-31T02:22:14.304369Z",
+          "shell.execute_reply": "2025-10-31T02:34:42.724815Z"
+        }
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "text": "Epoch 1/5, Train Loss: 0.2967 Train Accuracy: 87.3496%Validation Loss: 0.3049 Validation Accuracy: 86.4953%\n======================================================================\nEpoch 2/5, Train Loss: 0.2937 Train Accuracy: 87.6812%Validation Loss: 0.3016 Validation Accuracy: 87.2691%\n======================================================================\nEpoch 3/5, Train Loss: 0.2749 Train Accuracy: 88.4568%Validation Loss: 0.3032 Validation Accuracy: 86.1707%\n======================================================================\nEpoch 4/5, Train Loss: 0.2881 Train Accuracy: 87.8256%Validation Loss: 0.2963 Validation Accuracy: 87.7434%\n======================================================================\nEpoch 5/5, Train Loss: 0.2561 Train Accuracy: 89.5266%Validation Loss: 0.2799 Validation Accuracy: 88.3674%\n======================================================================\n",
+          "output_type": "stream"
+        }
+      ],
+      "execution_count": 42
+    },
+    {
+      "cell_type": "markdown",
+      "source": "# Testing",
+      "metadata": {
+        "id": "-ESz6AKDy90N"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": "with torch.no_grad():\n  total_loss_test = 0\n  total_acc_test = 0\n\n  for inputs, labels in test_loader:\n    input_ids = inputs['input_ids'].squeeze(1).to(device)\n    attention_mask = inputs['attention_mask'].squeeze(1).to(device)\n    labels = labels.to(device)\n\n    predictions = model(input_ids, attention_mask).squeeze(1)\n\n    val_loss = criterion(predictions, labels)\n    total_loss_test += val_loss.item()\n    acc = (predictions.round() == labels).sum().item()\n    total_acc_test += acc\n\nprint(f'Loss Score is: {round(total_loss_test/len(test_loader), 4)} and Accuracy is: {round(total_acc_test/test_dataset.__len__()*100, 4)}')",
+      "metadata": {
+        "id": "wJ5H4H1wy_wR",
+        "trusted": true,
+        "execution": {
+          "iopub.status.busy": "2025-10-31T02:38:26.343938Z",
+          "iopub.execute_input": "2025-10-31T02:38:26.344239Z",
+          "iopub.status.idle": "2025-10-31T02:38:49.722486Z",
+          "shell.execute_reply.started": "2025-10-31T02:38:26.344216Z",
+          "shell.execute_reply": "2025-10-31T02:38:49.721773Z"
+        }
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "text": "Loss Score is: 0.2782 and Accuracy is: 88.6948\n",
+          "output_type": "stream"
+        }
+      ],
+      "execution_count": 43
+    },
+    {
+      "cell_type": "markdown",
+      "source": "# Plotting Training Progress",
+      "metadata": {
+        "id": "cKHuOLfvzirl"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(15, 5))\n\naxs[0].plot(total_loss_train_plot, label='Training Loss')\naxs[0].plot(total_loss_val_plot, label='Validation Loss')\naxs[0].set_title('Training and Validation Loss over Epochs')\naxs[0].set_xlabel('Epochs')\naxs[0].set_ylabel('Loss')\naxs[1].set_ylim([0, 2])\naxs[0].legend()\n\naxs[1].plot(total_acc_train_plot, label='Training Accuracy')\naxs[1].plot(total_acc_val_plot, label='Validation Accuracy')\naxs[1].set_title('Training and Validation Accuracy over Epochs')\naxs[1].set_xlabel('Epochs')\naxs[1].set_ylabel('Accuracy')\naxs[1].set_ylim([0, 100])\naxs[1].legend()\n\nplt.tight_layout()\n\nplt.show()",
+      "metadata": {
+        "id": "57bXQ3whzw3V",
+        "trusted": true,
+        "execution": {
+          "iopub.status.busy": "2025-10-31T02:39:38.085387Z",
+          "iopub.execute_input": "2025-10-31T02:39:38.086030Z",
+          "iopub.status.idle": "2025-10-31T02:39:38.542519Z",
+          "shell.execute_reply.started": "2025-10-31T02:39:38.086003Z",
+          "shell.execute_reply": "2025-10-31T02:39:38.541737Z"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": "<Figure size 1500x500 with 2 Axes>",
+            "image/png": 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+          },
+          "metadata": {}
+        }
+      ],
+      "execution_count": 44
+    }
+  ]
+}
diff --git a/Ex_5_Text_Classification_Transformers.ipynb b/Ex_5_Text_Classification_Transformers.ipynb
deleted file mode 100644
index d8cd508..0000000
--- a/Ex_5_Text_Classification_Transformers.ipynb
+++ /dev/null
@@ -1,2835 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "JUHknvycKcVO"
-      },
-      "source": [
-        "# 1. Initializations and Dataset Download"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "MzY0AYH67qSO",
-        "outputId": "61fe5020-8352-4246-fe17-fb67ed2fcab1"
-      },
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "\r100%|██████████| 3.30M/3.30M [00:00<00:00, 171MB/s]"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "\n"
-          ]
-        }
-      ],
-      "source": [
-        "!pip install transformers --quiet\n",
-        "!pip install opendatasets --quiet\n",
-        "import opendatasets as od\n",
-        "od.download(\"https://www.kaggle.com/datasets/rmisra/news-headlines-dataset-for-sarcasm-detection\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "LPkdrUo2JdQQ"
-      },
-      "source": [
-        "# 2. Imports\n",
-        "Lets start by getting all our imports, keep in mind that PyTorch is not automatically detects and trains on GPU, you have to tell it to use cuda. In case you want to train on Mac Silicon replace cuda with ```mps```.\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "bAN6P0pW6j3v",
-        "outputId": "281feafc-e820-48ea-87e7-60e62f2b3e72"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Available Device is:  cuda\n"
-          ]
-        }
-      ],
-      "source": [
-        "import torch\n",
-        "import torch.nn as nn\n",
-        "from torch.optim import Adam\n",
-        "from transformers import AutoTokenizer, AutoModel\n",
-        "from torch.utils.data import Dataset, DataLoader\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "from sklearn.metrics import accuracy_score\n",
-        "import matplotlib.pyplot as plt\n",
-        "import pandas as pd\n",
-        "import numpy as np\n",
-        "\n",
-        "\n",
-        "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
-        "\n",
-        "print(\"Available Device is: \", device)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "DhGF7tTcKoO6"
-      },
-      "source": [
-        "# 2. Dataset\n",
-        "Now lets read the dataset, this lab was developed by Google Colab, so dataset downloaded and read from the path shown below. We will be reading the dataset using pandas `read_json` function, then we will remove the nulls/missing data from our dataframe as a filteration process, keep in mind that this process is essential as missing data will stop the code from training. You can skip dropping the missing values if you are 100% sure that there are no missing values in your data. After that we printed the dataset shape and we used the `head()` function to visualize the first 5 rows, this step is optional as it just allows us to see the first 5 rows and will not affect the training process."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 224
-        },
-        "id": "5y9givr27601",
-        "outputId": "4d1a0735-c929-4a4d-f319-0cc4a1d4c839"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "(26709, 3)\n"
-          ]
-        },
-        {
-          "data": {
-            "text/html": [
-              "\n",
-              "  <div id=\"df-0f39440f-8fa9-440b-9b9b-dc7ea3226da0\" 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>article_link</th>\n",
-              "      <th>headline</th>\n",
-              "      <th>is_sarcastic</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
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-              "    </tr>\n",
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-              "      <td>https://www.huffingtonpost.com/entry/roseanne-...</td>\n",
-              "      <td>the 'roseanne' revival catches up to our thorn...</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>https://local.theonion.com/mom-starting-to-fea...</td>\n",
-              "      <td>mom starting to fear son's web series closest ...</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>https://politics.theonion.com/boehner-just-wan...</td>\n",
-              "      <td>boehner just wants wife to listen, not come up...</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>https://www.huffingtonpost.com/entry/jk-rowlin...</td>\n",
-              "      <td>j.k. rowling wishes snape happy birthday in th...</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
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-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
-              "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0f39440f-8fa9-440b-9b9b-dc7ea3226da0')\"\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",
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-              "\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-0f39440f-8fa9-440b-9b9b-dc7ea3226da0 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-0f39440f-8fa9-440b-9b9b-dc7ea3226da0');\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-b8de57b3-cdd9-439b-8cda-768ffb1e13b9\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b8de57b3-cdd9-439b-8cda-768ffb1e13b9')\"\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-b8de57b3-cdd9-439b-8cda-768ffb1e13b9 button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "text/plain": [
-              "                                        article_link  \\\n",
-              "0  https://www.huffingtonpost.com/entry/versace-b...   \n",
-              "1  https://www.huffingtonpost.com/entry/roseanne-...   \n",
-              "2  https://local.theonion.com/mom-starting-to-fea...   \n",
-              "3  https://politics.theonion.com/boehner-just-wan...   \n",
-              "4  https://www.huffingtonpost.com/entry/jk-rowlin...   \n",
-              "\n",
-              "                                            headline  is_sarcastic  \n",
-              "0  former versace store clerk sues over secret 'b...             0  \n",
-              "1  the 'roseanne' revival catches up to our thorn...             0  \n",
-              "2  mom starting to fear son's web series closest ...             1  \n",
-              "3  boehner just wants wife to listen, not come up...             1  \n",
-              "4  j.k. rowling wishes snape happy birthday in th...             0  "
-            ]
-          },
-          "execution_count": 3,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "data_df = pd.read_json(\"/content/news-headlines-dataset-for-sarcasm-detection/Sarcasm_Headlines_Dataset.json\", lines=True)\n",
-        "data_df.dropna(inplace=True)\n",
-        "print(data_df.shape)\n",
-        "data_df.head()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "zaFElfPjL9V3"
-      },
-      "source": [
-        "# 3. Data Splitting\n",
-        "Now we will split our data into the following:\n",
-        "\n",
-        "*   Training Size 70%\n",
-        "*   Validation Size 15%\n",
-        "*   Testing Size 15%\n",
-        "\n",
-        "We will do this by splitting our data twice using the `train_test_split` function in `sklearn` the function takes inputs, outputs and the testing size. After that we will print the training, validation and testing shapes and sizes."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "hokW9q5H84jb"
-      },
-      "outputs": [],
-      "source": [
-        "X_train, X_test, y_train, y_test = train_test_split(np.array(data_df[\"headline\"]), np.array(data_df[\"is_sarcastic\"]), test_size=0.3)\n",
-        "X_val, X_test, y_val, y_test = train_test_split(X_test, y_test, test_size=0.5)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "Pl2jXW2s-Cz9",
-        "outputId": "45aae6ef-fb2f-4042-ea33-8eec8f954a8b"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Training set is:  18696  rows which is  70.0 %\n",
-            "Validation set is:  4006  rows which is  15.0 %\n",
-            "Testing set is:  4007  rows which is  15.0 %\n"
-          ]
-        }
-      ],
-      "source": [
-        "print(\"Training set is: \", X_train.shape[0], \" rows which is \", round(X_train.shape[0]/data_df.shape[0],4)*100, \"%\")\n",
-        "print(\"Validation set is: \",X_val.shape[0], \" rows which is \", round(X_val.shape[0]/data_df.shape[0],4)*100, \"%\")\n",
-        "print(\"Testing set is: \",X_test.shape[0], \" rows which is \", round(X_test.shape[0]/data_df.shape[0],4)*100, \"%\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "5tghc-3yM5cP"
-      },
-      "source": [
-        "# 4. Download Model and tokenizer\n",
-        "In the next cell, we will download the model `bert-base-uncased` and its tokenizer using the function `from_pretrained`. Make sure that if you use any model on huggingface or any transformer or pretrained model, use its own tokenizer, if you use any tokenizer with any model the results will be very bad as it wasn't designed for that model.\n",
-        "\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 304,
-          "referenced_widgets": [
-            "5419e79091ae4d9a9e5a1c3ae70e0738",
-            "1b9489600e4a418d93c6d5fbbbc4032d",
-            "47044fbc983b4d979ddcdc5fe84b0e6f",
-            "4414ecbfbcab4a798ca689f53216f176",
-            "20cf2252cb354f3c8f814176dc606fe3",
-            "ddb4b5e7175e4285b079c1b59b743ad7",
-            "be2d2cf247eb4a9990e4c64dab595f87",
-            "97192fa0e6f54c2d918be288809c7094",
-            "b2b6b5edd65a4f29be5ced6f7a135a7e",
-            "49ed66975902491d9b0f80731ec9bf34",
-            "79992db289d6497f991c022b9203fd0b",
-            "4032848db554458891ebbde2686c189f",
-            "4c62707eaa314744831b460a166fdc3f",
-            "772e18240c8543c4a198cc529dcba319",
-            "a104eddd6d1d4b48a36c4a98b83531df",
-            "b02a48f948f44ccfa0e357e6ae74d212",
-            "53aae7279b0d481a9fb3a758d0e5225d",
-            "30a4ba11f941426d837df3d5f9db488e",
-            "77a32ec7dc874b928bc07ddd4417fc1e",
-            "840adca8cda44ad3a884eceb10171464",
-            "a409d59a69044ee79f2628aacb51a484",
-            "841551f177d84072a8bc07308877e72c",
-            "4cda98ea83c14d0db77cb4633372ee58",
-            "2915e7953e1b475fa7eebf283aad29e7",
-            "ff4a6251374e4b898542380e1537f8d5",
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-            "888afbef9f724fad8e559a15bc44f8cb",
-            "28f135e71c17466da0c97622d3fea9dc",
-            "ac8f63bd2240479390a16f84d3cb2cfd",
-            "0c6a411498d444b9b0035a02fdbed596",
-            "f04d121bbc194cba8b0e8c7024bdf5f5",
-            "a2cf2d62f102460086d016913b3db797",
-            "3f9b30a407424ac4bfbb50bd19832064",
-            "2185c3db607149cdbda9b057d89f56ff",
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-            "f54645e8e4364fdfb17a2868cfe6a239",
-            "d4921d447edb468e9d71ef6382d344e2",
-            "801469164c4e4d649dcd065d215388d9",
-            "b89aa1cdb0244496844425aaafd33926",
-            "38cd5bee924247769a7f2aa1a1ebbb0d",
-            "c9c77357201c4f899fabafba8b332a50",
-            "383bb38b8d8a4d6ca5141c752f183872",
-            "cbc523d80d5c4138b8a2117c89386f95",
-            "b766ce69e83448358e1444a7af1afcb3",
-            "8f1ae163be9d4301b9ac188c7dc25c74",
-            "0246157bbf064bfdb63cf60ebc056814",
-            "a8f7f5e48d9246b8b5adbef88a74a83d",
-            "4c9e6c073279434aad437ae1ab24ed8c",
-            "ef225c3d085240bf9ba0075da6744004",
-            "98bb77d2547d4dffbb337758f1a901d0",
-            "e49c28d0f9be437795a86dabe43a4648",
-            "f26942b33a56445493349ae4bf15aa68"
-          ]
-        },
-        "id": "NQdfbj61_EWd",
-        "outputId": "b53f92fa-47fe-4a52-b864-59518794670b"
-      },
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
-            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
-            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
-            "You will be able to reuse this secret in all of your notebooks.\n",
-            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
-            "  warnings.warn(\n"
-          ]
-        },
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "5419e79091ae4d9a9e5a1c3ae70e0738",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "tokenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "4032848db554458891ebbde2686c189f",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
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-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
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-              "model_id": "2185c3db607149cdbda9b057d89f56ff",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "383bb38b8d8a4d6ca5141c752f183872",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "model.safetensors:   0%|          | 0.00/440M [00:00<?, ?B/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-uncased\")\n",
-        "bert_model = AutoModel.from_pretrained(\"bert-base-uncased\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "xi0ijes0NfOb"
-      },
-      "source": [
-        "# 5. Dataset Object\n",
-        "Now we will create the dataset object. This part is not complex but its a bit tricky. We need to convert our dataset to PyTorch `Dataset` object as it will be more efficient during training, you can use the dataset as its, but lets keep things professional and efficient. First we define our class that will be taking the main `Dataset` class with the concept of inheritance. Let's make the concept simpler. There is a big class that PyTorch, this class has several functions inside it, we will recreate that class and modify some functions to match our needs.\n",
-        "\n",
-        "In the cell below, we rebuilt the `constructor` function which is `__init__`. We put `X` and `Y` as a parameters to this function which are the inputs and outputs respectively, then inside the function we define the tokenized inputs using the tokenizer function and converting the outputs to tensors. Additionally, we moved all our data to cuda device. Keep in mind that we set `return_tensors` as `pt` which means return pytorch tensors, other option would be `tf` which is tensorflow tensors. Most of the existing transformers now don't support tensorflow. Then we modified the `__len__` and the `__getitem__` to match our needs which gets the specific length/shape of the data, and the data of specific row in our data respectively."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "ety1c6kFAnXO"
-      },
-      "outputs": [],
-      "source": [
-        "class dataset(Dataset):\n",
-        "    def __init__(self, X, Y):\n",
-        "        self.X = [tokenizer(x,\n",
-        "                            max_length = 100,\n",
-        "                            truncation = True,\n",
-        "                            padding = 'max_length',\n",
-        "                            return_tensors='pt').to(device)\n",
-        "                  for x in X\n",
-        "                 ]\n",
-        "\n",
-        "        self.Y = torch.tensor(Y, dtype = torch.float32).to(device)\n",
-        "    def __len__(self):\n",
-        "        return len(self.X)\n",
-        "    def __getitem__(self, index):\n",
-        "        return self.X[index], self.Y[index]\n",
-        "\n",
-        "training_data = dataset(X_train, y_train)\n",
-        "validation_data = dataset(X_val, y_val)\n",
-        "testing_data = dataset(X_test, y_test)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Y8ILFmPPQct4"
-      },
-      "source": [
-        "# 6. Training Hyper Parameters\n",
-        "Now we are setting the training hyperparameters, we defined some variables which are the batch size, number of training epochs and learning rate."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "aOCXB0guB56U"
-      },
-      "outputs": [],
-      "source": [
-        "BATCH_SIZE = 32\n",
-        "EPOCHS = 25\n",
-        "LR = 1e-4"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "H6lnYwBHQsts"
-      },
-      "source": [
-        "# 7. Data Loaders\n",
-        "This concept may seem complicated, but its very easy, PyTorch provides a class called `DataLoader` which allows us to create objects of it to simplify the training.\n",
-        "\n",
-        "`Dataloader` is an object that we can loop through it to train according to batches. When we start training, we loop through epochs, if you skip the batch size it means that the amount of training data in one batch is equal to the complete amount of training data, this method is not efficient and in most of the cases you need to train through using batches. `Dataloader` allows you to loop through the batches easily during the training. When you create a dataloader. You define the batch size and enable the shuffle to randomize the data and then you can loop through it in each epoch to train normally.\n",
-        "\n",
-        "In the next cell, we defined a dataloader for each of our data (training, validation and testing)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "gqr4FlauCHC4"
-      },
-      "outputs": [],
-      "source": [
-        "train_dataloader = DataLoader(training_data, batch_size=BATCH_SIZE, shuffle= True)\n",
-        "validation_dataloader = DataLoader(validation_data, batch_size=BATCH_SIZE, shuffle= True)\n",
-        "testing_dataloader = DataLoader(testing_data, batch_size=BATCH_SIZE, shuffle= True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "vO2N0d9CR8wD"
-      },
-      "source": [
-        "# 8. Model Class\n",
-        "Creating a model in PyTorch seems not very straight forward in the beggining, but if you understand how machine learning and deep learning works, it will be easy for you to understand PyTorch structure easily.\n",
-        "\n",
-        "In the next cell we define a new class `MyModel` that inherits from `nn.Module` like we did for the dataset. Remember, in simple terms, we want to redefine some functions in the class to match our needs. In the constructor which is `__init__` and we give it the bert model. Then `super(MyModel, self).__init__()` This line calls the constructor of the parent class `nn.Module` to ensure it's properly initialized. Which means we have the original constructor together with our part of it!\n",
-        "\n",
-        "Then, we create our layers, we will have in addition to bert layers a dropout layer, 2 linear (dense) layers and a sigmoid activation function since we are dealing with Binary Classification Problem. Keep in mind that we have to mention for each layer the number of inputs and outputs except for the dropout layer. Linear 2 in this case is the output layer without the activation.\n",
-        "\n",
-        "In the function forward, this function is the forward propagation of the model, how is the data flowing inside the model from the input to the output. This means we can control this completely. That's how PyTorch is so customizable! In the below cell, we define the flow as follows, starting by Bert followed by a linear layer then dropout, another linear and finally the sigmoid."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "UHca8UXrCPNi"
-      },
-      "outputs": [],
-      "source": [
-        "class MyModel(nn.Module):\n",
-        "    def __init__(self, bert):\n",
-        "\n",
-        "        super(MyModel, self).__init__()\n",
-        "\n",
-        "        self.bert = bert\n",
-        "        self.dropout = nn.Dropout(0.25)\n",
-        "        self.linear1 = nn.Linear(768, 384)\n",
-        "        self.linear2 = nn.Linear(384, 1)\n",
-        "        self.sigmoid = nn.Sigmoid()\n",
-        "\n",
-        "    def forward(self, input_ids, attention_mask):\n",
-        "        pooled_output = self.bert(input_ids, attention_mask, return_dict = False)[0][:,0]\n",
-        "        output = self.linear1(pooled_output)\n",
-        "        output = self.dropout(output)\n",
-        "        output = self.linear2(output)\n",
-        "        output = self.sigmoid(output)\n",
-        "        return output"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Z2YllOwpUwa0"
-      },
-      "source": [
-        "# 9. Pretraining\n",
-        "This part is very small, the first 2 lines in the next cell we are freezing the bert model weight. Remember that Bert is a pretrained transformer that has been already trained on a huge amount of data. We want to fine-tune it on our task and not training from scratch. The model already has good amount of \"Knowledge\". Then we create an object of our model that we defined previously giving to it Bert and we mode it to Cuda."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "rHgTaK23Blcw"
-      },
-      "outputs": [],
-      "source": [
-        "for param in bert_model.parameters():\n",
-        "    param.requires_grad = False\n",
-        "model = MyModel(bert_model).to(device)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "psJRZJgaVTTv"
-      },
-      "source": [
-        "# 10. Loss and Optimizer\n",
-        "In this cell, we define our loss function as Binary Cross Entropy which is `nn.BCELoss` in PyTorch. Then, we will define the optimizer and we will give it the model parameters/weights which will be the parameters of the new layers we added as those are the parameters we will want to train."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "dq8PygIFCgTv"
-      },
-      "outputs": [],
-      "source": [
-        "criterion = nn.BCELoss()\n",
-        "optimizer = Adam(model.parameters(), lr= LR)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "TwWZJ02TVyJN"
-      },
-      "source": [
-        "# 11. Training Loop\n",
-        "Now comes the exciting part. As we mentioned several times, nothing is complex here if you know how deep learning works. You just need to know PyTorch syntax. We start by initializing the for loop with the specified number of epochs. But before that we define 4 lists and inside the loop we define another 4 variables.\n",
-        "\n",
-        "\n",
-        "### Variables:\n",
-        "*   `total_acc_train`: here we will keep tracking of the training accuracy progress during each epoch, we calculate the accuracy during the batch size and we print it in the end for tracking the accuracy on each epoch.\n",
-        "\n",
-        "*   `total_loss_train`: here we will keep tracking of the training loss progress during each epoch, we calculate the accuracy during the batch size and we use the loss value to optimize and modify the model parameters.\n",
-        "\n",
-        "*   `total_acc_val`: here we will keep tracking of the validation accuracy progress during each epoch, we calculate the accuracy during the batch size and we print it in the end for tracking the accuracy on each epoch and help us know if there is any overfitting.\n",
-        "\n",
-        "*   `total_loss_val`: here we will keep tracking of the validation loss progress during each epoch, we calculate the accuracy during the batch size.\n",
-        "\n",
-        "### Lists:\n",
-        "*    `total_acc_train_plot`: We append the losses of the training accuracy to visualize them at the end.\n",
-        "\n",
-        "*    `total_loss_train_plot`: We append the losses of the training to visualize them at the end.\n",
-        "\n",
-        "*    `total_acc_validation_plot`: We append the losses of the validation accuracy to visualize them at the end.\n",
-        "\n",
-        "*    `total_loss_validation_plot`: We append the losses of the validation to visualize them at the end.\n",
-        "\n",
-        "## - Training\n",
-        "Then, we start to loop through the training dataloaders, we use the `enumerate` functionality to loop through data and indices at the same time. We are not using the indices here, but lets kept it just if you want to try different stuff with the loop or debug. In the second loop, we start by getting our data from the data loader, then we move the inputs and labels to the cuda device. We allow the model to make a prediction or what is called forward propagation, then we get the output of the model and compare it with our original output using the loss criteration, we add the loss amount to `total_loss_train`. Additionally, we get the accuracy by comparing the correct batch with the predicted batch and we add it to the `total_acc_train`. Then we do the `batch_loss.backward()` which makes the backpropagation and we use the optimizer to do a step on the weights using `optimizer.step()` and then we reset the optimizer gradients using `optimizer.zero_grad()` which is a very important step that has to be done before proceeding.\n",
-        "\n",
-        "## - Validation\n",
-        "After that we exit the batch loop (train dataloader loop) and we start with the validation. Don't forget that we are still in the same epoch. In side that we start by `with torch.no_grad()` which means that we need the model to do predicitons without being trained. We just need to see the validation preformance. Then we do the same steps which are predicting and calculating loss and accuracy and storing these values.\n",
-        "\n",
-        "At the end we print after each epoch the epoch number, training loss, training accuracy, validation loss and validation accuracy. We use the printing of \"=\" signs just for making the printing output looks clean.\n",
-        "\n",
-        "\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "background_save": true,
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "2uoLgdllCSFh",
-        "outputId": "0b5adc49-5066-4da6-c499-b177b20a9650"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Epoch no. 1 Train Loss: 0.2620 Train Accuracy: 80.1241 Validation Loss: 0.4801 Validation Accuracy: 83.6495\n",
-            "==================================================\n",
-            "Epoch no. 2 Train Loss: 0.2143 Train Accuracy: 84.3442 Validation Loss: 0.4439 Validation Accuracy: 84.6231\n",
-            "==================================================\n",
-            "Epoch no. 3 Train Loss: 0.2014 Train Accuracy: 85.1466 Validation Loss: 0.4202 Validation Accuracy: 85.8962\n",
-            "==================================================\n",
-            "Epoch no. 4 Train Loss: 0.1957 Train Accuracy: 85.5424 Validation Loss: 0.4077 Validation Accuracy: 86.2207\n",
-            "==================================================\n",
-            "Epoch no. 5 Train Loss: 0.1910 Train Accuracy: 85.9489 Validation Loss: 0.4052 Validation Accuracy: 86.3455\n",
-            "==================================================\n",
-            "Epoch no. 6 Train Loss: 0.1870 Train Accuracy: 86.0077 Validation Loss: 0.3973 Validation Accuracy: 86.5701\n",
-            "==================================================\n",
-            "Epoch no. 7 Train Loss: 0.1853 Train Accuracy: 86.5158 Validation Loss: 0.3958 Validation Accuracy: 86.8447\n",
-            "==================================================\n",
-            "Epoch no. 8 Train Loss: 0.1833 Train Accuracy: 86.5051 Validation Loss: 0.3922 Validation Accuracy: 86.7948\n",
-            "==================================================\n",
-            "Epoch no. 9 Train Loss: 0.1830 Train Accuracy: 86.4409 Validation Loss: 0.3912 Validation Accuracy: 86.4953\n",
-            "==================================================\n",
-            "Epoch no. 10 Train Loss: 0.1804 Train Accuracy: 86.7940 Validation Loss: 0.3951 Validation Accuracy: 86.2207\n",
-            "==================================================\n",
-            "Epoch no. 11 Train Loss: 0.1805 Train Accuracy: 86.6816 Validation Loss: 0.3948 Validation Accuracy: 87.0944\n",
-            "==================================================\n",
-            "Epoch no. 12 Train Loss: 0.1793 Train Accuracy: 86.7940 Validation Loss: 0.3992 Validation Accuracy: 86.8198\n",
-            "==================================================\n",
-            "Epoch no. 13 Train Loss: 0.1785 Train Accuracy: 86.8261 Validation Loss: 0.3926 Validation Accuracy: 87.0195\n",
-            "==================================================\n",
-            "Epoch no. 14 Train Loss: 0.1781 Train Accuracy: 86.9598 Validation Loss: 0.3874 Validation Accuracy: 86.5452\n",
-            "==================================================\n",
-            "Epoch no. 15 Train Loss: 0.1772 Train Accuracy: 86.9812 Validation Loss: 0.3987 Validation Accuracy: 86.6201\n",
-            "==================================================\n",
-            "Epoch no. 16 Train Loss: 0.1767 Train Accuracy: 87.2005 Validation Loss: 0.3905 Validation Accuracy: 87.0195\n",
-            "==================================================\n",
-            "Epoch no. 17 Train Loss: 0.1773 Train Accuracy: 86.9544 Validation Loss: 0.3898 Validation Accuracy: 86.5452\n",
-            "==================================================\n",
-            "Epoch no. 18 Train Loss: 0.1761 Train Accuracy: 87.0774 Validation Loss: 0.4249 Validation Accuracy: 85.4219\n",
-            "==================================================\n",
-            "Epoch no. 19 Train Loss: 0.1764 Train Accuracy: 87.1630 Validation Loss: 0.3842 Validation Accuracy: 87.0195\n",
-            "==================================================\n",
-            "Epoch no. 20 Train Loss: 0.1755 Train Accuracy: 87.0881 Validation Loss: 0.4035 Validation Accuracy: 86.1957\n",
-            "==================================================\n",
-            "Epoch no. 21 Train Loss: 0.1750 Train Accuracy: 87.1095 Validation Loss: 0.3990 Validation Accuracy: 86.9695\n",
-            "==================================================\n",
-            "Epoch no. 22 Train Loss: 0.1755 Train Accuracy: 86.9865 Validation Loss: 0.3868 Validation Accuracy: 86.9196\n",
-            "==================================================\n",
-            "Epoch no. 23 Train Loss: 0.1740 Train Accuracy: 87.2379 Validation Loss: 0.3879 Validation Accuracy: 87.0444\n",
-            "==================================================\n",
-            "Epoch no. 24 Train Loss: 0.1744 Train Accuracy: 87.1630 Validation Loss: 0.3948 Validation Accuracy: 86.8947\n",
-            "==================================================\n",
-            "Epoch no. 25 Train Loss: 0.1747 Train Accuracy: 87.2647 Validation Loss: 0.3862 Validation Accuracy: 87.1692\n",
-            "==================================================\n"
-          ]
-        }
-      ],
-      "source": [
-        "total_loss_train_plot = []\n",
-        "total_loss_validation_plot = []\n",
-        "total_acc_train_plot = []\n",
-        "total_acc_validation_plot = []\n",
-        "\n",
-        "for epoch in range(EPOCHS):\n",
-        "    total_acc_train = 0\n",
-        "    total_loss_train = 0\n",
-        "    total_acc_val = 0\n",
-        "    total_loss_val = 0\n",
-        "    ## Training and Validation\n",
-        "    for indx, data in enumerate(train_dataloader):\n",
-        "        input, label = data\n",
-        "\n",
-        "        input.to(device)\n",
-        "        label.to(device)\n",
-        "\n",
-        "        prediction = model(input['input_ids'].squeeze(1),\n",
-        "                            input['attention_mask'].squeeze(1)).squeeze(1)\n",
-        "\n",
-        "\n",
-        "        batch_loss = criterion(prediction, label)\n",
-        "\n",
-        "        total_loss_train += batch_loss.item()\n",
-        "\n",
-        "        acc = ((prediction).round() == label).sum().item()\n",
-        "        total_acc_train += acc\n",
-        "\n",
-        "        batch_loss.backward()\n",
-        "        optimizer.step()\n",
-        "        optimizer.zero_grad()\n",
-        "\n",
-        "    ## Validation\n",
-        "    with torch.no_grad():\n",
-        "        for indx, data in enumerate(validation_dataloader):\n",
-        "            input, label = data\n",
-        "            input.to(device)\n",
-        "            label.to(device)\n",
-        "\n",
-        "            prediction = model(input['input_ids'].squeeze(1),\n",
-        "                            input['attention_mask'].squeeze(1)).squeeze(1)\n",
-        "\n",
-        "            batch_loss_val = criterion(prediction, label)\n",
-        "            total_loss_val += batch_loss_val.item()\n",
-        "\n",
-        "\n",
-        "            acc = ((prediction).round() == label).sum().item()\n",
-        "\n",
-        "            total_acc_val += acc\n",
-        "\n",
-        "\n",
-        "    total_loss_train_plot.append(round(total_loss_train/1000, 4))\n",
-        "    total_loss_validation_plot.append(round(total_loss_val/100, 4))\n",
-        "    total_acc_train_plot.append(round(total_acc_train/(training_data.__len__())*100, 4))\n",
-        "    total_acc_validation_plot.append(round(total_acc_val/(validation_data.__len__())*100, 4))\n",
-        "\n",
-        "    print(f'''Epoch no. {epoch + 1} Train Loss: {total_loss_train/1000:.4f} Train Accuracy: {(total_acc_train/(training_data.__len__())*100):.4f} Validation Loss: {total_loss_val/100:.4f} Validation Accuracy: {(total_acc_val/(validation_data.__len__())*100):.4f}''')\n",
-        "    print(\"=\"*50)\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "WwCEX_58Hh0A"
-      },
-      "source": [
-        "# 12. Testing\n",
-        "Now in this section, we will be testing our model, we will start the code by ```with torch.no_grad():``` which means that we are telling PyTorch that we don't want to train the model we will be using it only for testing. Then we will declare initial loss and accuracy as zeros, we will start by looping through the testing dataloader like we did before during training. Inside the loop, we got our data and we moved it to our GPU (cuda) and then we ran our model on the data and we got the predictions. After that we get the loss and then we add it to our overall loss, we do the same for accuracy, and finally we print the accuracy.\n",
-        "\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "background_save": true
-        },
-        "id": "sbn4vDHMC0DK",
-        "outputId": "0468a52a-be81-49ec-cda0-65d6b7c3bb54"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Accuracy Score is: 86.6%\n"
-          ]
-        }
-      ],
-      "source": [
-        "with torch.no_grad():\n",
-        "  total_loss_test = 0\n",
-        "  total_acc_test = 0\n",
-        "  for indx, data in enumerate(testing_dataloader):\n",
-        "    input, label = data\n",
-        "    input.to(device)\n",
-        "    label.to(device)\n",
-        "\n",
-        "    prediction = model(input['input_ids'].squeeze(1), input['attention_mask'].squeeze(1)).squeeze(1)\n",
-        "\n",
-        "    batch_loss_val = criterion(prediction, label)\n",
-        "    total_loss_test += batch_loss_val.item()\n",
-        "    acc = ((prediction).round() == label).sum().item()\n",
-        "    total_acc_test += acc\n",
-        "\n",
-        "print(f\"Accuracy Score is: {round((total_acc_test/X_test.shape[0])*100, 2)}%\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "MYBHs7dCXNGq"
-      },
-      "source": [
-        "# 13. Plotting and Visualizations (Optional)\n",
-        "The results may not be very good, feel free to play with the layers, hyperparameters and text filteration to achieve better performance!"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "background_save": true
-        },
-        "id": "RwynCNZ3Gusa",
-        "outputId": "d5413eeb-b9d9-4a6e-c5ca-1b8ab709f889"
-      },
-      "outputs": [
-        {
-          "data": {
-            "image/png": 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-        "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(15, 5))\n",
-        "\n",
-        "axs[0].plot(total_loss_train_plot, label='Training Loss')\n",
-        "axs[0].plot(total_loss_validation_plot, label='Validation Loss')\n",
-        "axs[0].set_title('Training and Validation Loss over Epochs')\n",
-        "axs[0].set_xlabel('Epochs')\n",
-        "axs[0].set_ylabel('Loss')\n",
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-        "axs[0].legend()\n",
-        "\n",
-        "axs[1].plot(total_acc_train_plot, label='Training Accuracy')\n",
-        "axs[1].plot(total_acc_validation_plot, label='Validation Accuracy')\n",
-        "axs[1].set_title('Training and Validation Accuracy over Epochs')\n",
-        "axs[1].set_xlabel('Epochs')\n",
-        "axs[1].set_ylabel('Accuracy')\n",
-        "axs[1].set_ylim([0, 100])\n",
-        "axs[1].legend()\n",
-        "\n",
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-        "\n",
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